diff --git a/translated_images/zh/.co-op-translator.json b/translated_images/zh-CN/.co-op-translator.json
similarity index 89%
rename from translated_images/zh/.co-op-translator.json
rename to translated_images/zh-CN/.co-op-translator.json
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diff --git a/translations/fa/1-Introduction/01-defining-data-science/README.md b/translations/fa/1-Introduction/01-defining-data-science/README.md
index 303f24f5..85e08700 100644
--- a/translations/fa/1-Introduction/01-defining-data-science/README.md
+++ b/translations/fa/1-Introduction/01-defining-data-science/README.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "43212cc1ac137b7bb1dcfb37ca06b0f4",
-  "translation_date": "2025-10-25T18:36:48+00:00",
-  "source_file": "1-Introduction/01-defining-data-science/README.md",
-  "language_code": "fa"
-}
--->
 # تعریف علم داده
 
 | ![طرح دستی توسط [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/01-Definitions.png) |
diff --git a/translations/fa/1-Introduction/01-defining-data-science/assignment.md b/translations/fa/1-Introduction/01-defining-data-science/assignment.md
index 58d19ed0..38a6cc98 100644
--- a/translations/fa/1-Introduction/01-defining-data-science/assignment.md
+++ b/translations/fa/1-Introduction/01-defining-data-science/assignment.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "4e0f1773b9bee1be3b28f9fe2c71b3de",
-  "translation_date": "2025-08-24T21:31:09+00:00",
-  "source_file": "1-Introduction/01-defining-data-science/assignment.md",
-  "language_code": "fa"
-}
--->
 # تکلیف: سناریوهای علم داده
 
 در این تکلیف اول، از شما خواسته می‌شود که به یک فرآیند یا مشکل واقعی در حوزه‌های مختلف فکر کنید و بررسی کنید که چگونه می‌توانید با استفاده از فرآیند علم داده آن را بهبود دهید. به موارد زیر فکر کنید:
diff --git a/translations/fa/1-Introduction/01-defining-data-science/solution/assignment.md b/translations/fa/1-Introduction/01-defining-data-science/solution/assignment.md
index 44845de0..37106956 100644
--- a/translations/fa/1-Introduction/01-defining-data-science/solution/assignment.md
+++ b/translations/fa/1-Introduction/01-defining-data-science/solution/assignment.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "a8f79b9c0484c35b4f26e8aec7fc4d56",
-  "translation_date": "2025-08-24T21:32:10+00:00",
-  "source_file": "1-Introduction/01-defining-data-science/solution/assignment.md",
-  "language_code": "fa"
-}
--->
 # تکلیف: سناریوهای علم داده
 
 در این اولین تکلیف، از شما خواسته می‌شود که به یک فرآیند یا مشکل واقعی در حوزه‌های مختلف فکر کنید و بررسی کنید که چگونه می‌توانید با استفاده از فرآیند علم داده آن را بهبود دهید. به موارد زیر فکر کنید:
diff --git a/translations/fa/1-Introduction/02-ethics/README.md b/translations/fa/1-Introduction/02-ethics/README.md
index 7f660d27..b191141c 100644
--- a/translations/fa/1-Introduction/02-ethics/README.md
+++ b/translations/fa/1-Introduction/02-ethics/README.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "58860ce9a4b8a564003d2752f7c72851",
-  "translation_date": "2025-10-03T16:03:35+00:00",
-  "source_file": "1-Introduction/02-ethics/README.md",
-  "language_code": "fa"
-}
--->
 # مقدمه‌ای بر اخلاق داده
 
 |![طرح‌نگاری توسط [(@sketchthedocs)](https://sketchthedocs.dev)](../../sketchnotes/02-Ethics.png)|
diff --git a/translations/fa/1-Introduction/02-ethics/assignment.md b/translations/fa/1-Introduction/02-ethics/assignment.md
index 5d79f8c1..96321a00 100644
--- a/translations/fa/1-Introduction/02-ethics/assignment.md
+++ b/translations/fa/1-Introduction/02-ethics/assignment.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "b588c0fc73014f52520c666efc3e0cc3",
-  "translation_date": "2025-08-24T21:25:17+00:00",
-  "source_file": "1-Introduction/02-ethics/assignment.md",
-  "language_code": "fa"
-}
--->
 ## نوشتن مطالعه موردی اخلاق داده‌ها
 
 ## دستورالعمل‌ها
diff --git a/translations/fa/1-Introduction/03-defining-data/README.md b/translations/fa/1-Introduction/03-defining-data/README.md
index 92caf942..d2bf34a9 100644
--- a/translations/fa/1-Introduction/03-defining-data/README.md
+++ b/translations/fa/1-Introduction/03-defining-data/README.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "12339119c0165da569a93ddba05f9339",
-  "translation_date": "2025-09-05T14:24:17+00:00",
-  "source_file": "1-Introduction/03-defining-data/README.md",
-  "language_code": "fa"
-}
--->
 # تعریف داده‌ها
 
 |![طرح‌نگاری توسط [(@sketchthedocs)](https://sketchthedocs.dev)](../../sketchnotes/03-DefiningData.png)|
diff --git a/translations/fa/1-Introduction/03-defining-data/assignment.md b/translations/fa/1-Introduction/03-defining-data/assignment.md
index 9b5d92c7..c463fce8 100644
--- a/translations/fa/1-Introduction/03-defining-data/assignment.md
+++ b/translations/fa/1-Introduction/03-defining-data/assignment.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "2e5cacb967c1e9dfd07809bfc441a0b4",
-  "translation_date": "2025-08-24T21:35:43+00:00",
-  "source_file": "1-Introduction/03-defining-data/assignment.md",
-  "language_code": "fa"
-}
--->
 # طبقه‌بندی مجموعه داده‌ها
 
 ## دستورالعمل‌ها
diff --git a/translations/fa/1-Introduction/04-stats-and-probability/README.md b/translations/fa/1-Introduction/04-stats-and-probability/README.md
index 5ba9c25b..653653f5 100644
--- a/translations/fa/1-Introduction/04-stats-and-probability/README.md
+++ b/translations/fa/1-Introduction/04-stats-and-probability/README.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "ce95884566a74db72572cd51f0cb25ad",
-  "translation_date": "2025-09-06T12:56:39+00:00",
-  "source_file": "1-Introduction/04-stats-and-probability/README.md",
-  "language_code": "fa"
-}
--->
 # مقدمه‌ای کوتاه بر آمار و احتمال
 
 |![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/04-Statistics-Probability.png)|
@@ -64,7 +55,7 @@ CO_OP_TRANSLATOR_METADATA:
 
 به صورت گرافیکی می‌توان رابطه بین میانه و چارک‌ها را در نموداری به نام **جعبه‌نمودار** نشان داد:
 
-<img src="images/boxplot_explanation.png" alt="توضیح جعبه‌نمودار" width="50%">
+<img src="../../../../translated_images/fa/boxplot_explanation.4039b7de08780fd4.webp" alt="توضیح جعبه‌نمودار" width="50%">
 
 در اینجا همچنین **دامنه بین چارکی** IQR=Q3-Q1 و مقادیر **دورافتاده** - مقادیری که خارج از مرزهای [Q1-1.5*IQR,Q3+1.5*IQR] قرار دارند - محاسبه می‌شوند.
 
diff --git a/translations/fa/1-Introduction/04-stats-and-probability/assignment.md b/translations/fa/1-Introduction/04-stats-and-probability/assignment.md
index e740770f..d3dfdea8 100644
--- a/translations/fa/1-Introduction/04-stats-and-probability/assignment.md
+++ b/translations/fa/1-Introduction/04-stats-and-probability/assignment.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "01d1b493e8b51a6ebb42524f6b1bcfff",
-  "translation_date": "2025-08-24T21:44:27+00:00",
-  "source_file": "1-Introduction/04-stats-and-probability/assignment.md",
-  "language_code": "fa"
-}
--->
 # مطالعه کوچک دیابت
 
 در این تکلیف، با یک مجموعه داده کوچک از بیماران دیابتی که از [اینجا](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html) گرفته شده است، کار خواهیم کرد.
diff --git a/translations/fa/1-Introduction/README.md b/translations/fa/1-Introduction/README.md
index 2863e268..5a860372 100644
--- a/translations/fa/1-Introduction/README.md
+++ b/translations/fa/1-Introduction/README.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "696a8474a01054281704cbfb09148949",
-  "translation_date": "2025-08-24T21:15:11+00:00",
-  "source_file": "1-Introduction/README.md",
-  "language_code": "fa"
-}
--->
 # مقدمه‌ای بر علم داده
 
 ![داده در عمل](../../../translated_images/fa/data.48e22bb7617d8d92188afbc4c48effb920ba79f5cebdc0652cd9f34bbbd90c18.jpg)
diff --git a/translations/fa/2-Working-With-Data/05-relational-databases/README.md b/translations/fa/2-Working-With-Data/05-relational-databases/README.md
index 54414d96..fcfe7145 100644
--- a/translations/fa/2-Working-With-Data/05-relational-databases/README.md
+++ b/translations/fa/2-Working-With-Data/05-relational-databases/README.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "11739c7b40e7c6b16ad29e3df4e65862",
-  "translation_date": "2025-12-19T10:34:34+00:00",
-  "source_file": "2-Working-With-Data/05-relational-databases/README.md",
-  "language_code": "fa"
-}
--->
 # کار با داده‌ها: پایگاه‌های داده رابطه‌ای
 
 |![ یادداشت تصویری توسط [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/05-RelationalData.png)|
diff --git a/translations/fa/2-Working-With-Data/05-relational-databases/assignment.md b/translations/fa/2-Working-With-Data/05-relational-databases/assignment.md
index 2a4454c9..7acc03b0 100644
--- a/translations/fa/2-Working-With-Data/05-relational-databases/assignment.md
+++ b/translations/fa/2-Working-With-Data/05-relational-databases/assignment.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "25b37acdfb2452917c1aa2e2ca44317a",
-  "translation_date": "2025-10-24T09:52:38+00:00",
-  "source_file": "2-Working-With-Data/05-relational-databases/assignment.md",
-  "language_code": "fa"
-}
--->
 # نمایش داده‌های فرودگاه
 
 یک [پایگاه داده](https://raw.githubusercontent.com/Microsoft/Data-Science-For-Beginners/main/2-Working-With-Data/05-relational-databases/airports.db) مبتنی بر [SQLite](https://sqlite.org/index.html) که شامل اطلاعاتی درباره فرودگاه‌ها است، در اختیار شما قرار گرفته است. طرح پایگاه داده در زیر نمایش داده شده است. شما از [افزونه SQLite](https://marketplace.visualstudio.com/items?itemName=alexcvzz.vscode-sqlite&WT.mc_id=academic-77958-bethanycheum) در [Visual Studio Code](https://code.visualstudio.com?WT.mc_id=academic-77958-bethanycheum) برای نمایش اطلاعات فرودگاه‌های شهرهای مختلف استفاده خواهید کرد.
diff --git a/translations/fa/2-Working-With-Data/06-non-relational/README.md b/translations/fa/2-Working-With-Data/06-non-relational/README.md
index e58fc79a..b330f77f 100644
--- a/translations/fa/2-Working-With-Data/06-non-relational/README.md
+++ b/translations/fa/2-Working-With-Data/06-non-relational/README.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "c182e87f9f80be7e7cdffc7b40bbfccf",
-  "translation_date": "2025-09-05T14:10:08+00:00",
-  "source_file": "2-Working-With-Data/06-non-relational/README.md",
-  "language_code": "fa"
-}
--->
 # کار با داده‌ها: داده‌های غیررابطه‌ای
 
 |![طرح دستی توسط [(@sketchthedocs)](https://sketchthedocs.dev)](../../sketchnotes/06-NoSQL.png)|
diff --git a/translations/fa/2-Working-With-Data/06-non-relational/assignment.md b/translations/fa/2-Working-With-Data/06-non-relational/assignment.md
index f388d96d..edde9f19 100644
--- a/translations/fa/2-Working-With-Data/06-non-relational/assignment.md
+++ b/translations/fa/2-Working-With-Data/06-non-relational/assignment.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "f824bfdb8b12d33293913f76f5c787c5",
-  "translation_date": "2025-08-24T21:13:33+00:00",
-  "source_file": "2-Working-With-Data/06-non-relational/assignment.md",
-  "language_code": "fa"
-}
--->
 # سود سودا
 
 ## دستورالعمل‌ها
diff --git a/translations/fa/2-Working-With-Data/07-python/README.md b/translations/fa/2-Working-With-Data/07-python/README.md
index 3f02885a..aa9850d1 100644
--- a/translations/fa/2-Working-With-Data/07-python/README.md
+++ b/translations/fa/2-Working-With-Data/07-python/README.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "7bfec050f4717dcc2dfd028aca9d21f3",
-  "translation_date": "2025-09-06T15:24:30+00:00",
-  "source_file": "2-Working-With-Data/07-python/README.md",
-  "language_code": "fa"
-}
--->
 # کار با داده‌ها: پایتون و کتابخانه Pandas
 
 | ![طرح دستی توسط [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/07-WorkWithPython.png) |
diff --git a/translations/fa/2-Working-With-Data/07-python/assignment.md b/translations/fa/2-Working-With-Data/07-python/assignment.md
index 394ef639..6089f8dd 100644
--- a/translations/fa/2-Working-With-Data/07-python/assignment.md
+++ b/translations/fa/2-Working-With-Data/07-python/assignment.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "dc8f035ce92e4eaa078ab19caa68267a",
-  "translation_date": "2025-08-24T21:09:18+00:00",
-  "source_file": "2-Working-With-Data/07-python/assignment.md",
-  "language_code": "fa"
-}
--->
 # تکلیف پردازش داده در پایتون
 
 در این تکلیف، از شما خواسته می‌شود کدی را که در چالش‌های قبلی شروع به توسعه آن کرده‌ایم، گسترش دهید. این تکلیف شامل دو بخش است:
diff --git a/translations/fa/2-Working-With-Data/08-data-preparation/README.md b/translations/fa/2-Working-With-Data/08-data-preparation/README.md
index 0c77a7d2..328d76d1 100644
--- a/translations/fa/2-Working-With-Data/08-data-preparation/README.md
+++ b/translations/fa/2-Working-With-Data/08-data-preparation/README.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "1b560955ff39a2bcf2a049fce474a951",
-  "translation_date": "2025-09-05T14:12:37+00:00",
-  "source_file": "2-Working-With-Data/08-data-preparation/README.md",
-  "language_code": "fa"
-}
--->
 # کار با داده‌ها: آماده‌سازی داده‌ها
 
 |![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/08-DataPreparation.png)|
diff --git a/translations/fa/2-Working-With-Data/08-data-preparation/assignment.md b/translations/fa/2-Working-With-Data/08-data-preparation/assignment.md
index 661447f6..a3c98cd6 100644
--- a/translations/fa/2-Working-With-Data/08-data-preparation/assignment.md
+++ b/translations/fa/2-Working-With-Data/08-data-preparation/assignment.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "f9d5a7275e046223fa6474477674b810",
-  "translation_date": "2025-08-24T21:02:14+00:00",
-  "source_file": "2-Working-With-Data/08-data-preparation/assignment.md",
-  "language_code": "fa"
-}
--->
 # ارزیابی داده‌های یک فرم
 
 یک مشتری در حال آزمایش یک [فرم کوچک](../../../../2-Working-With-Data/08-data-preparation/index.html) برای جمع‌آوری برخی داده‌های پایه درباره پایگاه مشتریان خود بوده است. آن‌ها یافته‌های خود را برای شما آورده‌اند تا داده‌هایی که جمع‌آوری کرده‌اند را اعتبارسنجی کنید. می‌توانید صفحه `index.html` را در مرورگر باز کنید تا فرم را مشاهده کنید.
diff --git a/translations/fa/2-Working-With-Data/README.md b/translations/fa/2-Working-With-Data/README.md
index f4d15e3e..bfcde018 100644
--- a/translations/fa/2-Working-With-Data/README.md
+++ b/translations/fa/2-Working-With-Data/README.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "abc3309ab41bc5a7846f70ee1a055838",
-  "translation_date": "2025-08-24T20:52:22+00:00",
-  "source_file": "2-Working-With-Data/README.md",
-  "language_code": "fa"
-}
--->
 # کار با داده‌ها
 
 ![عشق به داده](../../../translated_images/fa/data-love.a22ef29e6742c852505ada062920956d3d7604870b281a8ca7c7ac6f37381d5a.jpg)
diff --git a/translations/fa/3-Data-Visualization/09-visualization-quantities/README.md b/translations/fa/3-Data-Visualization/09-visualization-quantities/README.md
index aca809bb..1ffba1c0 100644
--- a/translations/fa/3-Data-Visualization/09-visualization-quantities/README.md
+++ b/translations/fa/3-Data-Visualization/09-visualization-quantities/README.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "a49d78e32e280c410f04e5f2a2068e77",
-  "translation_date": "2025-09-05T14:17:41+00:00",
-  "source_file": "3-Data-Visualization/09-visualization-quantities/README.md",
-  "language_code": "fa"
-}
--->
 # تجسم مقادیر
 
 |![طرح دستی توسط [(@sketchthedocs)](https://sketchthedocs.dev)](../../sketchnotes/09-Visualizing-Quantities.png)|
diff --git a/translations/fa/3-Data-Visualization/09-visualization-quantities/assignment.md b/translations/fa/3-Data-Visualization/09-visualization-quantities/assignment.md
index 682fcb88..bf86f579 100644
--- a/translations/fa/3-Data-Visualization/09-visualization-quantities/assignment.md
+++ b/translations/fa/3-Data-Visualization/09-visualization-quantities/assignment.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "ad163c4fda72c8278280b61cad317ff4",
-  "translation_date": "2025-08-24T23:03:49+00:00",
-  "source_file": "3-Data-Visualization/09-visualization-quantities/assignment.md",
-  "language_code": "fa"
-}
--->
 # خطوط، نمودارهای پراکندگی و نمودارهای میله‌ای
 
 ## دستورالعمل‌ها
diff --git a/translations/fa/3-Data-Visualization/10-visualization-distributions/README.md b/translations/fa/3-Data-Visualization/10-visualization-distributions/README.md
index 0e882a4e..f29cc6a8 100644
--- a/translations/fa/3-Data-Visualization/10-visualization-distributions/README.md
+++ b/translations/fa/3-Data-Visualization/10-visualization-distributions/README.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "80a20467e046d312809d008395051fc7",
-  "translation_date": "2025-09-05T14:19:33+00:00",
-  "source_file": "3-Data-Visualization/10-visualization-distributions/README.md",
-  "language_code": "fa"
-}
--->
 # نمایش توزیع‌ها
 
 |![طرح دستی توسط [(@sketchthedocs)](https://sketchthedocs.dev)](../../sketchnotes/10-Visualizing-Distributions.png)|
diff --git a/translations/fa/3-Data-Visualization/10-visualization-distributions/assignment.md b/translations/fa/3-Data-Visualization/10-visualization-distributions/assignment.md
index 1666e45f..954bbb0c 100644
--- a/translations/fa/3-Data-Visualization/10-visualization-distributions/assignment.md
+++ b/translations/fa/3-Data-Visualization/10-visualization-distributions/assignment.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "40eeb9b9f94009c537c7811f9f27f037",
-  "translation_date": "2025-08-24T22:38:08+00:00",
-  "source_file": "3-Data-Visualization/10-visualization-distributions/assignment.md",
-  "language_code": "fa"
-}
--->
 # مهارت‌های خود را به کار بگیرید
 
 ## دستورالعمل‌ها
diff --git a/translations/fa/3-Data-Visualization/11-visualization-proportions/README.md b/translations/fa/3-Data-Visualization/11-visualization-proportions/README.md
index 4a25db7e..e8a97fa7 100644
--- a/translations/fa/3-Data-Visualization/11-visualization-proportions/README.md
+++ b/translations/fa/3-Data-Visualization/11-visualization-proportions/README.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "42119bcc97bee88254e381156d770f3c",
-  "translation_date": "2025-09-05T14:16:34+00:00",
-  "source_file": "3-Data-Visualization/11-visualization-proportions/README.md",
-  "language_code": "fa"
-}
--->
 # تجسم نسبت‌ها
 
 |![طرح دستی توسط [(@sketchthedocs)](https://sketchthedocs.dev)](../../sketchnotes/11-Visualizing-Proportions.png)|
diff --git a/translations/fa/3-Data-Visualization/11-visualization-proportions/assignment.md b/translations/fa/3-Data-Visualization/11-visualization-proportions/assignment.md
index f7e12677..4743d269 100644
--- a/translations/fa/3-Data-Visualization/11-visualization-proportions/assignment.md
+++ b/translations/fa/3-Data-Visualization/11-visualization-proportions/assignment.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "1e00fe6a244c2f8f9a794c862661dd4f",
-  "translation_date": "2025-08-24T23:07:22+00:00",
-  "source_file": "3-Data-Visualization/11-visualization-proportions/assignment.md",
-  "language_code": "fa"
-}
--->
 # امتحان کنید در اکسل
 
 ## دستورالعمل‌ها
diff --git a/translations/fa/3-Data-Visualization/12-visualization-relationships/README.md b/translations/fa/3-Data-Visualization/12-visualization-relationships/README.md
index 123f19d1..200ab921 100644
--- a/translations/fa/3-Data-Visualization/12-visualization-relationships/README.md
+++ b/translations/fa/3-Data-Visualization/12-visualization-relationships/README.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "0764fd4077f3f04a1d968ec371227744",
-  "translation_date": "2025-09-06T11:28:02+00:00",
-  "source_file": "3-Data-Visualization/12-visualization-relationships/README.md",
-  "language_code": "fa"
-}
--->
 # تجسم روابط: همه چیز درباره عسل 🍯
 
 |![ اسکتچ‌نوت توسط [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/12-Visualizing-Relationships.png)|
diff --git a/translations/fa/3-Data-Visualization/12-visualization-relationships/assignment.md b/translations/fa/3-Data-Visualization/12-visualization-relationships/assignment.md
index 45e80ff9..5f78f99b 100644
--- a/translations/fa/3-Data-Visualization/12-visualization-relationships/assignment.md
+++ b/translations/fa/3-Data-Visualization/12-visualization-relationships/assignment.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "680419753c086eef51be86607c623945",
-  "translation_date": "2025-08-24T22:42:10+00:00",
-  "source_file": "3-Data-Visualization/12-visualization-relationships/assignment.md",
-  "language_code": "fa"
-}
--->
 # شیرجه به کندوی زنبور
 
 ## دستورالعمل‌ها
diff --git a/translations/fa/3-Data-Visualization/13-meaningful-visualizations/README.md b/translations/fa/3-Data-Visualization/13-meaningful-visualizations/README.md
index 4537db7a..a8a77f16 100644
--- a/translations/fa/3-Data-Visualization/13-meaningful-visualizations/README.md
+++ b/translations/fa/3-Data-Visualization/13-meaningful-visualizations/README.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
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-  "source_file": "3-Data-Visualization/13-meaningful-visualizations/README.md",
-  "language_code": "fa"
-}
--->
 # ایجاد مصورسازی‌های معنادار
 
 |![اسکچ‌نوت توسط [(@sketchthedocs)](https://sketchthedocs.dev)](../../sketchnotes/13-MeaningfulViz.png)|
diff --git a/translations/fa/3-Data-Visualization/13-meaningful-visualizations/assignment.md b/translations/fa/3-Data-Visualization/13-meaningful-visualizations/assignment.md
index 519be2c4..eae6f808 100644
--- a/translations/fa/3-Data-Visualization/13-meaningful-visualizations/assignment.md
+++ b/translations/fa/3-Data-Visualization/13-meaningful-visualizations/assignment.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
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-  "source_file": "3-Data-Visualization/13-meaningful-visualizations/assignment.md",
-  "language_code": "fa"
-}
--->
 # ساخت ویژوالایزیشن سفارشی خودتان
 
 ## دستورالعمل‌ها
diff --git a/translations/fa/3-Data-Visualization/13-meaningful-visualizations/solution/README.md b/translations/fa/3-Data-Visualization/13-meaningful-visualizations/solution/README.md
index 085e9910..15f64e1c 100644
--- a/translations/fa/3-Data-Visualization/13-meaningful-visualizations/solution/README.md
+++ b/translations/fa/3-Data-Visualization/13-meaningful-visualizations/solution/README.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
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-  "source_file": "3-Data-Visualization/13-meaningful-visualizations/solution/README.md",
-  "language_code": "fa"
-}
--->
 # پروژه تجسم داده‌های Dangerous Liaisons
 
 برای شروع، باید مطمئن شوید که NPM و Node روی دستگاه شما اجرا می‌شوند. وابستگی‌ها را نصب کنید (npm install) و سپس پروژه را به صورت محلی اجرا کنید (npm run serve):
diff --git a/translations/fa/3-Data-Visualization/13-meaningful-visualizations/starter/README.md b/translations/fa/3-Data-Visualization/13-meaningful-visualizations/starter/README.md
index ac4a7d44..bb5b1918 100644
--- a/translations/fa/3-Data-Visualization/13-meaningful-visualizations/starter/README.md
+++ b/translations/fa/3-Data-Visualization/13-meaningful-visualizations/starter/README.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
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-  "translation_date": "2025-08-24T22:34:08+00:00",
-  "source_file": "3-Data-Visualization/13-meaningful-visualizations/starter/README.md",
-  "language_code": "fa"
-}
--->
 # پروژه تجسم داده‌های Dangerous Liaisons
 
 برای شروع، باید مطمئن شوید که NPM و Node روی دستگاه شما اجرا می‌شوند. وابستگی‌ها را نصب کنید (npm install) و سپس پروژه را به صورت محلی اجرا کنید (npm run serve):
diff --git a/translations/fa/3-Data-Visualization/R/09-visualization-quantities/README.md b/translations/fa/3-Data-Visualization/R/09-visualization-quantities/README.md
index 6033f06d..5498d1b5 100644
--- a/translations/fa/3-Data-Visualization/R/09-visualization-quantities/README.md
+++ b/translations/fa/3-Data-Visualization/R/09-visualization-quantities/README.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "22acf28f518a4769ea14fa42f4734b9f",
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-  "source_file": "3-Data-Visualization/R/09-visualization-quantities/README.md",
-  "language_code": "fa"
-}
--->
 # تجسم مقادیر
 
 |![طرح دستی توسط [(@sketchthedocs)](https://sketchthedocs.dev)](https://github.com/microsoft/Data-Science-For-Beginners/blob/main/sketchnotes/09-Visualizing-Quantities.png)|
diff --git a/translations/fa/3-Data-Visualization/R/09-visualization-quantities/assignment.md b/translations/fa/3-Data-Visualization/R/09-visualization-quantities/assignment.md
index 285a1ebb..b3f5eaa3 100644
--- a/translations/fa/3-Data-Visualization/R/09-visualization-quantities/assignment.md
+++ b/translations/fa/3-Data-Visualization/R/09-visualization-quantities/assignment.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
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-  "source_file": "3-Data-Visualization/R/09-visualization-quantities/assignment.md",
-  "language_code": "fa"
-}
--->
 # خطوط، نمودارهای پراکندگی و نمودارهای میله‌ای
 
 ## دستورالعمل‌ها
diff --git a/translations/fa/3-Data-Visualization/R/10-visualization-distributions/README.md b/translations/fa/3-Data-Visualization/R/10-visualization-distributions/README.md
index 1037770c..9f71c39e 100644
--- a/translations/fa/3-Data-Visualization/R/10-visualization-distributions/README.md
+++ b/translations/fa/3-Data-Visualization/R/10-visualization-distributions/README.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
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-  "source_file": "3-Data-Visualization/R/10-visualization-distributions/README.md",
-  "language_code": "fa"
-}
--->
 # تجسم توزیع‌ها
 
 |![طرح دستی توسط [(@sketchthedocs)](https://sketchthedocs.dev)](https://github.com/microsoft/Data-Science-For-Beginners/blob/main/sketchnotes/10-Visualizing-Distributions.png)|
diff --git a/translations/fa/3-Data-Visualization/R/10-visualization-distributions/assignment.md b/translations/fa/3-Data-Visualization/R/10-visualization-distributions/assignment.md
index 9b51bf3e..d511ccad 100644
--- a/translations/fa/3-Data-Visualization/R/10-visualization-distributions/assignment.md
+++ b/translations/fa/3-Data-Visualization/R/10-visualization-distributions/assignment.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
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-  "source_file": "3-Data-Visualization/R/10-visualization-distributions/assignment.md",
-  "language_code": "fa"
-}
--->
 # مهارت‌های خود را به کار بگیرید
 
 ## دستورالعمل‌ها
diff --git a/translations/fa/3-Data-Visualization/R/11-visualization-proportions/README.md b/translations/fa/3-Data-Visualization/R/11-visualization-proportions/README.md
index 7277cb15..908b648b 100644
--- a/translations/fa/3-Data-Visualization/R/11-visualization-proportions/README.md
+++ b/translations/fa/3-Data-Visualization/R/11-visualization-proportions/README.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
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-  "language_code": "fa"
-}
--->
 # تجسم نسبت‌ها
 
 |![طرح دستی توسط [(@sketchthedocs)](https://sketchthedocs.dev)](../../../sketchnotes/11-Visualizing-Proportions.png)|
diff --git a/translations/fa/3-Data-Visualization/R/12-visualization-relationships/README.md b/translations/fa/3-Data-Visualization/R/12-visualization-relationships/README.md
index 75eb579d..359f90cb 100644
--- a/translations/fa/3-Data-Visualization/R/12-visualization-relationships/README.md
+++ b/translations/fa/3-Data-Visualization/R/12-visualization-relationships/README.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
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-  "source_file": "3-Data-Visualization/R/12-visualization-relationships/README.md",
-  "language_code": "fa"
-}
--->
 # تجسم روابط: همه چیز درباره عسل 🍯
 
 |![طرح دستی توسط [(@sketchthedocs)](https://sketchthedocs.dev)](../../../sketchnotes/12-Visualizing-Relationships.png)|
diff --git a/translations/fa/3-Data-Visualization/R/13-meaningful-vizualizations/README.md b/translations/fa/3-Data-Visualization/R/13-meaningful-vizualizations/README.md
index 04fba3cb..e8228e3d 100644
--- a/translations/fa/3-Data-Visualization/R/13-meaningful-vizualizations/README.md
+++ b/translations/fa/3-Data-Visualization/R/13-meaningful-vizualizations/README.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
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-  "source_file": "3-Data-Visualization/R/13-meaningful-vizualizations/README.md",
-  "language_code": "fa"
-}
--->
 # ایجاد مصورسازی‌های معنادار
 
 |![اسکچ‌نوت توسط [(@sketchthedocs)](https://sketchthedocs.dev)](../../../sketchnotes/13-MeaningfulViz.png)|
diff --git a/translations/fa/3-Data-Visualization/README.md b/translations/fa/3-Data-Visualization/README.md
index dbec1a51..a7df24f5 100644
--- a/translations/fa/3-Data-Visualization/README.md
+++ b/translations/fa/3-Data-Visualization/README.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
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-  "source_file": "3-Data-Visualization/README.md",
-  "language_code": "fa"
-}
--->
 # مصورسازی‌ها
 
 ![یک زنبور روی گل اسطوخودوس](../../../translated_images/fa/bee.0aa1d91132b12e3a8994b9ca12816d05ce1642010d9b8be37f8d37365ba845cf.jpg)  
diff --git a/translations/fa/4-Data-Science-Lifecycle/14-Introduction/README.md b/translations/fa/4-Data-Science-Lifecycle/14-Introduction/README.md
index 2387d9d1..eeef3e08 100644
--- a/translations/fa/4-Data-Science-Lifecycle/14-Introduction/README.md
+++ b/translations/fa/4-Data-Science-Lifecycle/14-Introduction/README.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
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-  "source_file": "4-Data-Science-Lifecycle/14-Introduction/README.md",
-  "language_code": "fa"
-}
--->
 # معرفی چرخه عمر علم داده
 
 |![طرح توسط [(@sketchthedocs)](https://sketchthedocs.dev)](../../sketchnotes/14-DataScience-Lifecycle.png)|
diff --git a/translations/fa/4-Data-Science-Lifecycle/14-Introduction/assignment.md b/translations/fa/4-Data-Science-Lifecycle/14-Introduction/assignment.md
index 98f8bf36..2a618da8 100644
--- a/translations/fa/4-Data-Science-Lifecycle/14-Introduction/assignment.md
+++ b/translations/fa/4-Data-Science-Lifecycle/14-Introduction/assignment.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
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-  "source_file": "4-Data-Science-Lifecycle/14-Introduction/assignment.md",
-  "language_code": "fa"
-}
--->
 # ارزیابی یک مجموعه داده
 
 یک مشتری از تیم شما درخواست کمک کرده است تا عادات فصلی هزینه‌کرد مشتریان تاکسی در شهر نیویورک را بررسی کند.
diff --git a/translations/fa/4-Data-Science-Lifecycle/15-analyzing/README.md b/translations/fa/4-Data-Science-Lifecycle/15-analyzing/README.md
index 6a51b2b9..3836f714 100644
--- a/translations/fa/4-Data-Science-Lifecycle/15-analyzing/README.md
+++ b/translations/fa/4-Data-Science-Lifecycle/15-analyzing/README.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
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-  "source_file": "4-Data-Science-Lifecycle/15-analyzing/README.md",
-  "language_code": "fa"
-}
--->
 # چرخه زندگی علم داده: تحلیل
 
 |![طرح دستی توسط [(@sketchthedocs)](https://sketchthedocs.dev)](../../sketchnotes/15-Analyzing.png)|
diff --git a/translations/fa/4-Data-Science-Lifecycle/15-analyzing/assignment.md b/translations/fa/4-Data-Science-Lifecycle/15-analyzing/assignment.md
index f975df88..7dba5590 100644
--- a/translations/fa/4-Data-Science-Lifecycle/15-analyzing/assignment.md
+++ b/translations/fa/4-Data-Science-Lifecycle/15-analyzing/assignment.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
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-  "source_file": "4-Data-Science-Lifecycle/15-analyzing/assignment.md",
-  "language_code": "fa"
-}
--->
 # جستجو برای پاسخ‌ها
 
 این ادامه‌ی [تکلیف](../14-Introduction/assignment.md) درس قبلی است، جایی که به طور مختصر به مجموعه داده‌ها نگاه کردیم. اکنون قصد داریم نگاه عمیق‌تری به داده‌ها داشته باشیم.
diff --git a/translations/fa/4-Data-Science-Lifecycle/16-communication/README.md b/translations/fa/4-Data-Science-Lifecycle/16-communication/README.md
index 5b6a891b..7e2c59c9 100644
--- a/translations/fa/4-Data-Science-Lifecycle/16-communication/README.md
+++ b/translations/fa/4-Data-Science-Lifecycle/16-communication/README.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
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-  "source_file": "4-Data-Science-Lifecycle/16-communication/README.md",
-  "language_code": "fa"
-}
--->
 # چرخه حیات علم داده: ارتباطات
 
 |![طرح دستی از [(@sketchthedocs)](https://sketchthedocs.dev)](../../sketchnotes/16-Communicating.png)|
diff --git a/translations/fa/4-Data-Science-Lifecycle/16-communication/assignment.md b/translations/fa/4-Data-Science-Lifecycle/16-communication/assignment.md
index 3b666677..e421afeb 100644
--- a/translations/fa/4-Data-Science-Lifecycle/16-communication/assignment.md
+++ b/translations/fa/4-Data-Science-Lifecycle/16-communication/assignment.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "8980d7efd101c82d6d6ffc3458214120",
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-  "source_file": "4-Data-Science-Lifecycle/16-communication/assignment.md",
-  "language_code": "fa"
-}
--->
 # تعریف یک داستان
 
 ## دستورالعمل‌ها
diff --git a/translations/fa/4-Data-Science-Lifecycle/README.md b/translations/fa/4-Data-Science-Lifecycle/README.md
index 04c7b40a..534c8b88 100644
--- a/translations/fa/4-Data-Science-Lifecycle/README.md
+++ b/translations/fa/4-Data-Science-Lifecycle/README.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
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-  "source_file": "4-Data-Science-Lifecycle/README.md",
-  "language_code": "fa"
-}
--->
 # چرخه حیات علم داده
 
 ![ارتباط](../../../translated_images/fa/communication.06d8e2a88d30d168d661ad9f9f0a4f947ebff3719719cfdaf9ed00a406a01ead.jpg)  
diff --git a/translations/fa/5-Data-Science-In-Cloud/17-Introduction/README.md b/translations/fa/5-Data-Science-In-Cloud/17-Introduction/README.md
index 96676dd8..6afee1af 100644
--- a/translations/fa/5-Data-Science-In-Cloud/17-Introduction/README.md
+++ b/translations/fa/5-Data-Science-In-Cloud/17-Introduction/README.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
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-  "language_code": "fa"
-}
--->
 # مقدمه‌ای بر علم داده در فضای ابری
 
 |![طرح‌نگاری توسط [(@sketchthedocs)](https://sketchthedocs.dev)](../../sketchnotes/17-DataScience-Cloud.png)|
diff --git a/translations/fa/5-Data-Science-In-Cloud/17-Introduction/assignment.md b/translations/fa/5-Data-Science-In-Cloud/17-Introduction/assignment.md
index d9140e6a..203888dc 100644
--- a/translations/fa/5-Data-Science-In-Cloud/17-Introduction/assignment.md
+++ b/translations/fa/5-Data-Science-In-Cloud/17-Introduction/assignment.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
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-  "language_code": "fa"
-}
--->
 # تحقیقات بازار
 
 ## دستورالعمل‌ها
diff --git a/translations/fa/5-Data-Science-In-Cloud/18-Low-Code/README.md b/translations/fa/5-Data-Science-In-Cloud/18-Low-Code/README.md
index e5f5aa30..8301ba33 100644
--- a/translations/fa/5-Data-Science-In-Cloud/18-Low-Code/README.md
+++ b/translations/fa/5-Data-Science-In-Cloud/18-Low-Code/README.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
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-  "source_file": "5-Data-Science-In-Cloud/18-Low-Code/README.md",
-  "language_code": "fa"
-}
--->
 # علم داده در فضای ابری: روش "کدنویسی کم/بدون کدنویسی"
 
 |![طرح‌نگاری توسط [(@sketchthedocs)](https://sketchthedocs.dev)](../../sketchnotes/18-DataScience-Cloud.png)|
diff --git a/translations/fa/5-Data-Science-In-Cloud/18-Low-Code/assignment.md b/translations/fa/5-Data-Science-In-Cloud/18-Low-Code/assignment.md
index 9b411d33..26235eec 100644
--- a/translations/fa/5-Data-Science-In-Cloud/18-Low-Code/assignment.md
+++ b/translations/fa/5-Data-Science-In-Cloud/18-Low-Code/assignment.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
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-  "source_file": "5-Data-Science-In-Cloud/18-Low-Code/assignment.md",
-  "language_code": "fa"
-}
--->
 # پروژه علم داده با کدنویسی کم/بدون کدنویسی در Azure ML
 
 ## دستورالعمل‌ها
diff --git a/translations/fa/5-Data-Science-In-Cloud/19-Azure/README.md b/translations/fa/5-Data-Science-In-Cloud/19-Azure/README.md
index ce9f2150..cc730d3b 100644
--- a/translations/fa/5-Data-Science-In-Cloud/19-Azure/README.md
+++ b/translations/fa/5-Data-Science-In-Cloud/19-Azure/README.md
@@ -1,12 +1,3 @@
-<!--
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-  "source_file": "5-Data-Science-In-Cloud/19-Azure/README.md",
-  "language_code": "fa"
-}
--->
 # علم داده در فضای ابری: روش "Azure ML SDK"
 
 |![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/19-DataScience-Cloud.png)|
diff --git a/translations/fa/5-Data-Science-In-Cloud/19-Azure/assignment.md b/translations/fa/5-Data-Science-In-Cloud/19-Azure/assignment.md
index 4181b7dd..92a292eb 100644
--- a/translations/fa/5-Data-Science-In-Cloud/19-Azure/assignment.md
+++ b/translations/fa/5-Data-Science-In-Cloud/19-Azure/assignment.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "386efdbc19786951341f6956247ee990",
-  "translation_date": "2025-08-24T22:11:38+00:00",
-  "source_file": "5-Data-Science-In-Cloud/19-Azure/assignment.md",
-  "language_code": "fa"
-}
--->
 # پروژه علم داده با استفاده از Azure ML SDK
 
 ## دستورالعمل‌ها
diff --git a/translations/fa/5-Data-Science-In-Cloud/README.md b/translations/fa/5-Data-Science-In-Cloud/README.md
index 7dabbebd..c44ecdaf 100644
--- a/translations/fa/5-Data-Science-In-Cloud/README.md
+++ b/translations/fa/5-Data-Science-In-Cloud/README.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "8dfe141a0f46f7d253e07f74913c7f44",
-  "translation_date": "2025-08-24T21:52:46+00:00",
-  "source_file": "5-Data-Science-In-Cloud/README.md",
-  "language_code": "fa"
-}
--->
 # علم داده در فضای ابری
 
 ![cloud-picture](../../../translated_images/fa/cloud-picture.f5526de3c6c6387b2d656ba94f019b3352e5e3854a78440e4fb00c93e2dea675.jpg)
diff --git a/translations/fa/6-Data-Science-In-Wild/20-Real-World-Examples/README.md b/translations/fa/6-Data-Science-In-Wild/20-Real-World-Examples/README.md
index 1aedf452..bdcca4fe 100644
--- a/translations/fa/6-Data-Science-In-Wild/20-Real-World-Examples/README.md
+++ b/translations/fa/6-Data-Science-In-Wild/20-Real-World-Examples/README.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "0f67a4139454816631526779a456b734",
-  "translation_date": "2025-09-06T18:14:54+00:00",
-  "source_file": "6-Data-Science-In-Wild/20-Real-World-Examples/README.md",
-  "language_code": "fa"
-}
--->
 # علم داده در دنیای واقعی
 
 | ![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/20-DataScience-RealWorld.png) |
diff --git a/translations/fa/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md b/translations/fa/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md
index ed0d8037..7f3db3cc 100644
--- a/translations/fa/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md
+++ b/translations/fa/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "d1e05715f9d97de6c4f1fb0c5a4702c0",
-  "translation_date": "2025-08-24T21:51:48+00:00",
-  "source_file": "6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md",
-  "language_code": "fa"
-}
--->
 # بررسی یک مجموعه داده از کامپیوتر سیاره‌ای
 
 ## دستورالعمل‌ها
diff --git a/translations/fa/6-Data-Science-In-Wild/README.md b/translations/fa/6-Data-Science-In-Wild/README.md
index d9ea6824..4425b8b7 100644
--- a/translations/fa/6-Data-Science-In-Wild/README.md
+++ b/translations/fa/6-Data-Science-In-Wild/README.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "07faf02ff163e609edf0b0308dc5d4e6",
-  "translation_date": "2025-08-24T21:45:38+00:00",
-  "source_file": "6-Data-Science-In-Wild/README.md",
-  "language_code": "fa"
-}
--->
 # علم داده در دنیای واقعی
 
 کاربردهای عملی علم داده در صنایع مختلف.
diff --git a/translations/fa/AGENTS.md b/translations/fa/AGENTS.md
index 459aca93..93ef6395 100644
--- a/translations/fa/AGENTS.md
+++ b/translations/fa/AGENTS.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "cc2e18ab65df63e75d3619c4752e9b22",
-  "translation_date": "2025-10-03T11:03:39+00:00",
-  "source_file": "AGENTS.md",
-  "language_code": "fa"
-}
--->
 # AGENTS.md
 
 ## نمای کلی پروژه
diff --git a/translations/fa/CODE_OF_CONDUCT.md b/translations/fa/CODE_OF_CONDUCT.md
index f242d6cf..fb3c8b9d 100644
--- a/translations/fa/CODE_OF_CONDUCT.md
+++ b/translations/fa/CODE_OF_CONDUCT.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "c06b12caf3c901eb3156e3dd5b0aea56",
-  "translation_date": "2025-08-24T20:49:43+00:00",
-  "source_file": "CODE_OF_CONDUCT.md",
-  "language_code": "fa"
-}
--->
 # منشور رفتار کد متن‌باز مایکروسافت
 
 این پروژه منشور رفتار کد متن‌باز مایکروسافت را پذیرفته است. [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/)
diff --git a/translations/fa/CONTRIBUTING.md b/translations/fa/CONTRIBUTING.md
index 84fbdb1b..8695e83e 100644
--- a/translations/fa/CONTRIBUTING.md
+++ b/translations/fa/CONTRIBUTING.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "10f86fb29b5407088445ac803b3d0ed1",
-  "translation_date": "2025-10-03T13:26:29+00:00",
-  "source_file": "CONTRIBUTING.md",
-  "language_code": "fa"
-}
--->
 # مشارکت در آموزش مقدماتی علم داده
 
 از علاقه شما به مشارکت در برنامه آموزشی مقدماتی علم داده سپاسگزاریم! ما از مشارکت‌های جامعه استقبال می‌کنیم.
diff --git a/translations/fa/INSTALLATION.md b/translations/fa/INSTALLATION.md
index b140384a..71a26af7 100644
--- a/translations/fa/INSTALLATION.md
+++ b/translations/fa/INSTALLATION.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "a64d8afa22ffcc2016bb239188d6acb1",
-  "translation_date": "2025-10-03T15:15:16+00:00",
-  "source_file": "INSTALLATION.md",
-  "language_code": "fa"
-}
--->
 # راهنمای نصب
 
 این راهنما به شما کمک می‌کند محیط خود را برای کار با دوره آموزشی «علم داده برای مبتدیان» آماده کنید.
diff --git a/translations/fa/README.md b/translations/fa/README.md
index dbb5bee2..ff2a7759 100644
--- a/translations/fa/README.md
+++ b/translations/fa/README.md
@@ -1,256 +1,248 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "33d252f7491b696d85df7f680e7e7b90",
-  "translation_date": "2026-01-16T09:17:01+00:00",
-  "source_file": "README.md",
-  "language_code": "fa"
-}
--->
-# علم داده برای مبتدیان - یک برنامه درسی
+# علم داده برای مبتدیان - یک برنامه آموزشی
 
 [![باز کردن در GitHub Codespaces](https://github.com/codespaces/badge.svg)](https://github.com/codespaces/new?hide_repo_select=true&ref=main&repo=344191198)
 
 [![مجوز GitHub](https://img.shields.io/github/license/microsoft/Data-Science-For-Beginners.svg)](https://github.com/microsoft/Data-Science-For-Beginners/blob/master/LICENSE)
-[![همکاران GitHub](https://img.shields.io/github/contributors/microsoft/Data-Science-For-Beginners.svg)](https://GitHub.com/microsoft/Data-Science-For-Beginners/graphs/contributors/)
+[![مشارکت‌کنندگان GitHub](https://img.shields.io/github/contributors/microsoft/Data-Science-For-Beginners.svg)](https://GitHub.com/microsoft/Data-Science-For-Beginners/graphs/contributors/)
 [![مسائل GitHub](https://img.shields.io/github/issues/microsoft/Data-Science-For-Beginners.svg)](https://GitHub.com/microsoft/Data-Science-For-Beginners/issues/)
 [![درخواست‌های کشش GitHub](https://img.shields.io/github/issues-pr/microsoft/Data-Science-For-Beginners.svg)](https://GitHub.com/microsoft/Data-Science-For-Beginners/pulls/)
-[![درخواست‌های کشش خوش آمدید](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)](http://makeapullrequest.com)
+[![خوش‌آمدید PRs](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)](http://makeapullrequest.com)
 
 [![ناظرین GitHub](https://img.shields.io/github/watchers/microsoft/Data-Science-For-Beginners.svg?style=social&label=Watch)](https://GitHub.com/microsoft/Data-Science-For-Beginners/watchers/)
-[![شاخه‌ها GitHub](https://img.shields.io/github/forks/microsoft/Data-Science-For-Beginners.svg?style=social&label=Fork)](https://GitHub.com/microsoft/Data-Science-For-Beginners/network/)
-[![ستاره‌ها GitHub](https://img.shields.io/github/stars/microsoft/Data-Science-For-Beginners.svg?style=social&label=Star)](https://GitHub.com/microsoft/Data-Science-For-Beginners/stargazers/)
+[![شاخه‌های GitHub](https://img.shields.io/github/forks/microsoft/Data-Science-For-Beginners.svg?style=social&label=Fork)](https://GitHub.com/microsoft/Data-Science-For-Beginners/network/)
+[![ستاره‌های GitHub](https://img.shields.io/github/stars/microsoft/Data-Science-For-Beginners.svg?style=social&label=Star)](https://GitHub.com/microsoft/Data-Science-For-Beginners/stargazers/)
 
 
-[![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG)
+[![دیسکورد Microsoft Foundry](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG)
 
 [![انجمن توسعه‌دهندگان Microsoft Foundry](https://img.shields.io/badge/GitHub-Microsoft_Foundry_Developer_Forum-blue?style=for-the-badge&logo=github&color=000000&logoColor=fff)](https://aka.ms/foundry/forum)
 
-مدافعان ابر آزور در مایکروسافت مفتخرند که یک برنامه درسی ۱۰ هفته‌ای و شامل ۲۰ درس درباره علم داده ارائه دهند. هر درس شامل آزمون‌های قبل و بعد از درس، دستورالعمل‌های مکتوب برای انجام درس، یک راه‌حل و یک تکلیف است. روش آموزشی ما مبتنی بر پروژه به شما امکان می‌دهد همزمان با ساخت پروژه‌ها یاد بگیرید؛ روشی اثبات‌شده برای تثبیت مهارت‌های جدید.
+مدافعان ابری Azure در مایکروسافت مفتخرند که برنامه‌ای ده هفته‌ای، شامل ۲۰ درس، با موضوع علم داده ارائه دهند. هر درس شامل آزمون‌های قبل و بعد از درس، دستورالعمل‌های مکتوب برای تکمیل درس، راه‌حل و تمرین است. روش آموزش مبتنی بر پروژه ما به شما اجازه می‌دهد هنگام ساختن یاد بگیرید، که روشی اثبات شده برای تثبیت مهارت‌های جدید است.
 
-**از نویسندگان عزیزمان صمیمانه تشکر می‌کنیم:** [Jasmine Greenaway](https://www.twitter.com/paladique)، [Dmitry Soshnikov](http://soshnikov.com)، [Nitya Narasimhan](https://twitter.com/nitya)، [Jalen McGee](https://twitter.com/JalenMcG)، [Jen Looper](https://twitter.com/jenlooper)، [Maud Levy](https://twitter.com/maudstweets)، [Tiffany Souterre](https://twitter.com/TiffanySouterre)، [Christopher Harrison](https://www.twitter.com/geektrainer).
+**تشکر فراوان از نویسندگان ما:** [Jasmine Greenaway](https://www.twitter.com/paladique)، [Dmitry Soshnikov](http://soshnikov.com)، [Nitya Narasimhan](https://twitter.com/nitya)، [Jalen McGee](https://twitter.com/JalenMcG)، [Jen Looper](https://twitter.com/jenlooper)، [Maud Levy](https://twitter.com/maudstweets)، [Tiffany Souterre](https://twitter.com/TiffanySouterre)، [Christopher Harrison](https://www.twitter.com/geektrainer).
 
-**🙏 تشکر ویژه 🙏 از نویسندگان، بازبینان و مشارکت‌کنندگان محتوا از [سفیران دانشجویی مایکروسافت](https://studentambassadors.microsoft.com/)،** به ویژه آریان آرورا، [Aditya Garg](https://github.com/AdityaGarg00)، [Alondra Sanchez](https://www.linkedin.com/in/alondra-sanchez-molina/)، [Ankita Singh](https://www.linkedin.com/in/ankitasingh007)، [Anupam Mishra](https://www.linkedin.com/in/anupam--mishra/)، [Arpita Das](https://www.linkedin.com/in/arpitadas01/)، ChhailBihari Dubey، [Dibri Nsofor](https://www.linkedin.com/in/dibrinsofor)، [Dishita Bhasin](https://www.linkedin.com/in/dishita-bhasin-7065281bb)، [Majd Safi](https://www.linkedin.com/in/majd-s/)، [Max Blum](https://www.linkedin.com/in/max-blum-6036a1186/)، [Miguel Correa](https://www.linkedin.com/in/miguelmque/)، [Mohamma Iftekher (Iftu) Ebne Jalal](https://twitter.com/iftu119)، [Nawrin Tabassum](https://www.linkedin.com/in/nawrin-tabassum)، [Raymond Wangsa Putra](https://www.linkedin.com/in/raymond-wp/)، [Rohit Yadav](https://www.linkedin.com/in/rty2423)، Samridhi Sharma، [Sanya Sinha](https://www.linkedin.com/mwlite/in/sanya-sinha-13aab1200)،
-[Sheena Narula](https://www.linkedin.com/in/sheena-narua-n/)، [Tauqeer Ahmad](https://www.linkedin.com/in/tauqeerahmad5201/)، Yogendrasingh Pawar، [Vidushi Gupta](https://www.linkedin.com/in/vidushi-gupta07/)، [Jasleen Sondhi](https://www.linkedin.com/in/jasleen-sondhi/)
+**🙏 سپاس ویژه 🙏 از نویسندگان، بازبینان و مشارکت‌کنندگان محتوا از [سفیران دانشجویی مایکروسافت](https://studentambassadors.microsoft.com/)،** به ویژه آریان آرورا، [Aditya Garg](https://github.com/AdityaGarg00)، [Alondra Sanchez](https://www.linkedin.com/in/alondra-sanchez-molina/)، [Ankita Singh](https://www.linkedin.com/in/ankitasingh007)، [Anupam Mishra](https://www.linkedin.com/in/anupam--mishra/)، [Arpita Das](https://www.linkedin.com/in/arpitadas01/)، ChhailBihari Dubey، [Dibri Nsofor](https://www.linkedin.com/in/dibrinsofor)، [Dishita Bhasin](https://www.linkedin.com/in/dishita-bhasin-7065281bb)، [Majd Safi](https://www.linkedin.com/in/majd-s/)، [Max Blum](https://www.linkedin.com/in/max-blum-6036a1186/)، [Miguel Correa](https://www.linkedin.com/in/miguelmque/)، [Mohamma Iftekher (Iftu) Ebne Jalal](https://twitter.com/iftu119)، [Nawrin Tabassum](https://www.linkedin.com/in/nawrin-tabassum)، [Raymond Wangsa Putra](https://www.linkedin.com/in/raymond-wp/)، [Rohit Yadav](https://www.linkedin.com/in/rty2423)، Samridhi Sharma، [Sanya Sinha](https://www.linkedin.com/mwlite/in/sanya-sinha-13aab1200)،
+[Sheena Narula](https://www.linkedin.com/in/sheena-narua-n/)، [Tauqeer Ahmad](https://www.linkedin.com/in/tauqeerahmad5201/)، Yogendrasingh Pawar ، [Vidushi Gupta](https://www.linkedin.com/in/vidushi-gupta07/)، [Jasleen Sondhi](https://www.linkedin.com/in/jasleen-sondhi/)
 
-|![تصویر یادداشت دستی ساخته شده توسط @sketchthedocs https://sketchthedocs.dev](../../../../translated_images/fa/00-Title.8af36cd35da1ac55.webp)|
+|![یادداشت تصویری توسط @sketchthedocs https://sketchthedocs.dev](../../translated_images/fa/00-Title.8af36cd35da1ac55.webp)|
 |:---:|
-| علم داده برای مبتدیان - _یادداشت دستی توسط [@nitya](https://twitter.com/nitya)_ |
+| علم داده برای مبتدیان - _یادداشت تصویری توسط [@nitya](https://twitter.com/nitya)_ |
 
-### 🌐 پشتیبانی چندزبانه
+### 🌐 پشتیبانی چندزبان
 
-#### پشتیبانی شده توسط GitHub Action (خودکار و همیشه به‌روز)
+#### پشتیبانی شده از طریق GitHub Action (خودکار و همیشه به‌روز)
 
 <!-- CO-OP TRANSLATOR LANGUAGES TABLE START -->
-[Arabic](../ar/README.md) | [Bengali](../bn/README.md) | [Bulgarian](../bg/README.md) | [Burmese (Myanmar)](../my/README.md) | [Chinese (Simplified)](../zh/README.md) | [Chinese (Traditional, Hong Kong)](../hk/README.md) | [Chinese (Traditional, Macau)](../mo/README.md) | [Chinese (Traditional, Taiwan)](../tw/README.md) | [Croatian](../hr/README.md) | [Czech](../cs/README.md) | [Danish](../da/README.md) | [Dutch](../nl/README.md) | [Estonian](../et/README.md) | [Finnish](../fi/README.md) | [French](../fr/README.md) | [German](../de/README.md) | [Greek](../el/README.md) | [Hebrew](../he/README.md) | [Hindi](../hi/README.md) | [Hungarian](../hu/README.md) | [Indonesian](../id/README.md) | [Italian](../it/README.md) | [Japanese](../ja/README.md) | [Kannada](../kn/README.md) | [Korean](../ko/README.md) | [Lithuanian](../lt/README.md) | [Malay](../ms/README.md) | [Malayalam](../ml/README.md) | [Marathi](../mr/README.md) | [Nepali](../ne/README.md) | [Nigerian Pidgin](../pcm/README.md) | [Norwegian](../no/README.md) | [Persian (Farsi)](./README.md) | [Polish](../pl/README.md) | [Portuguese (Brazil)](../br/README.md) | [Portuguese (Portugal)](../pt/README.md) | [Punjabi (Gurmukhi)](../pa/README.md) | [Romanian](../ro/README.md) | [Russian](../ru/README.md) | [Serbian (Cyrillic)](../sr/README.md) | [Slovak](../sk/README.md) | [Slovenian](../sl/README.md) | [Spanish](../es/README.md) | [Swahili](../sw/README.md) | [Swedish](../sv/README.md) | [Tagalog (Filipino)](../tl/README.md) | [Tamil](../ta/README.md) | [Telugu](../te/README.md) | [Thai](../th/README.md) | [Turkish](../tr/README.md) | [Ukrainian](../uk/README.md) | [Urdu](../ur/README.md) | [Vietnamese](../vi/README.md)
+[عربی](../ar/README.md) | [بنگالی](../bn/README.md) | [بلغاری](../bg/README.md) | [برمه‌ای (میانمار)](../my/README.md) | [چینی (ساده)](../zh-CN/README.md) | [چینی (سنتی، هنگ‌کنگ)](../zh-HK/README.md) | [چینی (سنتی، ماکائو)](../zh-MO/README.md) | [چینی (سنتی، تایوان)](../zh-TW/README.md) | [کرواتی](../hr/README.md) | [چکی](../cs/README.md) | [دانمارکی](../da/README.md) | [هلندی](../nl/README.md) | [استونیایی](../et/README.md) | [فنلاندی](../fi/README.md) | [فرانسوی](../fr/README.md) | [آلمانی](../de/README.md) | [یونانی](../el/README.md) | [عبری](../he/README.md) | [هندی](../hi/README.md) | [مجارستانی](../hu/README.md) | [اندونزیایی](../id/README.md) | [ایتالیایی](../it/README.md) | [ژاپنی](../ja/README.md) | [کانادا](../kn/README.md) | [کره‌ای](../ko/README.md) | [لیتوانیایی](../lt/README.md) | [مالایی](../ms/README.md) | [مالایالام](../ml/README.md) | [مراتی](../mr/README.md) | [نپالی](../ne/README.md) | [زبان پیجین نیجریه‌ای](../pcm/README.md) | [نروژی](../no/README.md) | [فارسی (Farsi)](./README.md) | [لهستانی](../pl/README.md) | [پرتغالی (برزیل)](../pt-BR/README.md) | [پرتغالی (پرتغال)](../pt-PT/README.md) | [پنجابی (گورموخی)](../pa/README.md) | [رومانیایی](../ro/README.md) | [روسی](../ru/README.md) | [صربی (سیریلیک)](../sr/README.md) | [اسلواکی](../sk/README.md) | [اسلوونیایی](../sl/README.md) | [اسپانیایی](../es/README.md) | [سواحیلی](../sw/README.md) | [سوئدی](../sv/README.md) | [تاگالوگ (فیلیپینی)](../tl/README.md) | [تامیل](../ta/README.md) | [تلوگو](../te/README.md) | [تایلندی](../th/README.md) | [ترکی](../tr/README.md) | [اوکراینی](../uk/README.md) | [اردو](../ur/README.md) | [ویتنامی](../vi/README.md)
 
-> **ترجیح می‌دهید محلی کلون کنید؟**
+> **ترجیح می‌دهید به‌صورت محلی کلون کنید؟**
 
-> این مخزن شامل بیش از ۵۰ ترجمه زبان است که اندازه دانلود را به طور قابل توجهی افزایش می‌دهد. برای کلون کردن بدون ترجمه‌ها، از sparse checkout استفاده کنید:
+> این مخزن بیش از ۵۰ ترجمه زبان دارد که به طور قابل توجهی حجم دانلود را افزایش می‌دهد. برای کلون بدون ترجمه، از sparse checkout استفاده کنید:
 > ```bash
 > git clone --filter=blob:none --sparse https://github.com/microsoft/Data-Science-For-Beginners.git
 > cd Data-Science-For-Beginners
 > git sparse-checkout set --no-cone '/*' '!translations' '!translated_images'
 > ```
-> این به شما همه چیز را می‌دهد تا دوره را کامل کنید با سرعت دانلود بسیار سریع‌تر.
+> این به شما همه چیز لازم برای تکمیل دوره را با سرعت دانلود بسیار بالاتر می‌دهد.
 <!-- CO-OP TRANSLATOR LANGUAGES TABLE END -->
 
-**اگر مایلید زبان‌های ترجمه بیشتری پشتیبانی شوند، فهرست آن‌ها در [اینجا](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md) موجود است**
+**اگر مایل هستید زبان‌های بیشتری پشتیبانی شوند، فهرست آنها را در [اینجا](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md) ببینید**
 
-#### به جامعه ما بپیوندید
-[![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG)
+#### به جامعه ما بپیوندید  
+[![دیسکورد Microsoft Foundry](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG)
 
-ما یک سری آموزش Discord با موضوع یادگیری با هوش مصنوعی داریم، برای کسب اطلاعات بیشتر و پیوستن به ما به [سری آموزش با هوش مصنوعی](https://aka.ms/learnwithai/discord) از ۱۸ تا ۳۰ سپتامبر ۲۰۲۵ مراجعه کنید. نکات و ترفندهایی برای استفاده از GitHub Copilot در علم داده دریافت خواهید کرد.
+ما سری آموزش یادگیری با هوش مصنوعی در دیسکورد داریم، بیشتر بدانید و از ۱۸ تا ۳۰ سپتامبر ۲۰۲۵ به ما بپیوندید در [سری یادگیری با هوش مصنوعی](https://aka.ms/learnwithai/discord). در این سری نکات و ترفندهای استفاده از GitHub Copilot برای علم داده به شما ارائه می‌شود.
 
-![سری آموزش با هوش مصنوعی](../../../../translated_images/fa/1.2b28cdc6205e26fe.webp)
+![سری یادگیری با هوش مصنوعی](../../translated_images/fa/1.2b28cdc6205e26fe.webp)
 
 # آیا دانشجو هستید؟
 
-برای شروع از منابع زیر استفاده کنید:
+با منابع زیر شروع کنید:
 
-- [صفحه مرکز دانشجویی](https://docs.microsoft.com/en-gb/learn/student-hub?WT.mc_id=academic-77958-bethanycheum) در این صفحه منابع مبتدی، بسته‌های دانشجویی و حتی روش‌هایی برای دریافت کوپن رایگان گواهینامه را خواهید یافت. این صفحه‌ای است که می‌خواهید در نشانه‌گذاری خود داشته باشید و هر از چندگاهی بررسی کنید زیرا حداقل ماهانه محتوا را به‌روزرسانی می‌کنیم.
-- [سفیران دانشجویی مایکروسافت](https://studentambassadors.microsoft.com?WT.mc_id=academic-77958-bethanycheum) به یک جامعه جهانی از سفیران دانشجویی بپیوندید؛ این می‌تواند راه ورود شما به مایکروسافت باشد.
+- [صفحه مرکز دانشجو](https://docs.microsoft.com/en-gb/learn/student-hub?WT.mc_id=academic-77958-bethanycheum) در این صفحه منابع مبتدی، بسته‌های دانشجویی و حتی راه‌هایی برای دریافت کوپن رایگان گواهینامه را خواهید یافت. این صفحه‌ای است که می‌خواهید در مرورگرتان ذخیره کنید و هر از گاهی آن را بررسی کنید چون حداقل ماهی یک بار محتوا به‌روزرسانی می‌شود.
+- [سفیران دانشجویی مایکروسافت](https://studentambassadors.microsoft.com?WT.mc_id=academic-77958-bethanycheum) به یک جامعه جهانی از سفیران دانشجویی بپیوندید، این می‌تواند راه شما برای ورود به مایکروسافت باشد.
 
 # شروع به کار
 
 ## 📚 مستندات
 
-- **[راهنمای نصب](INSTALLATION.md)** - دستورالعمل‌های گام به گام برای راه‌اندازی برای مبتدیان
-- **[راهنمای استفاده](USAGE.md)** - مثال‌ها و جریان‌های کاری رایج
+- **[راهنمای نصب](INSTALLATION.md)** - دستورالعمل‌های گام به گام نصب برای مبتدیان
+- **[راهنمای استفاده](USAGE.md)** - مثال‌ها و روش‌های کاری رایج
 - **[عیب‌یابی](TROUBLESHOOTING.md)** - راه‌حل‌های مشکلات رایج
-- **[راهنمای مشارکت](CONTRIBUTING.md)** - چگونه در این پروژه مشارکت کنید
-- **[برای معلمان](for-teachers.md)** - راهنمای تدریس و منابع کلاسی
+- **[راهنمای مشارکت](CONTRIBUTING.md)** - چگونگی مشارکت در این پروژه
+- **[برای معلمان](for-teachers.md)** - راهنمایی برای تدریس و منابع کلاس درس
 
 ## 👨‍🎓 برای دانشجویان
-> **کاملاً مبتدی:** تازه با علم داده آشنا شده‌اید؟ با [مثال‌های دوستانه برای مبتدیان](examples/README.md) ما شروع کنید! این مثال‌های ساده و دارای توضیح به شما کمک می‌کند مبانی را درک کنید قبل از اینکه به کل برنامه درسی بپردازید.
-> **[دانشجویان](https://aka.ms/student-page):** برای استفاده از این برنامه درسی به تنهایی، کل مخزن را فورک کنید و تمرین‌ها را به ترتیب انجام دهید، با یک آزمون قبل از درس شروع کنید. سپس درس را بخوانید و بقیه فعالیت‌ها را انجام دهید. تلاش کنید پروژه‌ها را با درک درس‌ها بسازید نه صرفاً کپی کردن کد راه‌حل؛ هرچند کد راه‌حل در پوشه /solutions در هر درس مبتنی بر پروژه موجود است. ایده دیگر تشکیل گروه مطالعه با دوستان و طی کردن محتوا با هم است. برای مطالعه بیشتر، [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/qprpajyoy3x0g7?WT.mc_id=academic-77958-bethanycheum) را توصیه می‌کنیم.
+> **مبتدیان کامل**: تازه‌کار در علم داده هستید؟ با [مثال‌های مناسب مبتدیان](examples/README.md) ما شروع کنید! این مثال‌های ساده و خوب توضیح داده شده به شما کمک می‌کنند قبل از ورود کامل به برنامه، مباحث پایه را درک کنید.
+> **[دانشجویان](https://aka.ms/student-page)**: برای استفاده مستقل از این برنامه، کل مخزن را فورک کنید و تمرین‌ها را خودتان انجام دهید، ابتدا با آزمون قبل از درس شروع کنید. سپس درس را بخوانید و بقیه فعالیت‌ها را انجام دهید. سعی کنید پروژه‌ها را با فهم درس‌ها بسازید نه کپی کردن کد راه‌حل؛ البته کدهای آن در پوشه /solutions هر درس پروژه‌محور موجود است. ایده دیگر تشکیل گروه مطالعه با دوستان و مرور همزمان محتواست. برای مطالعه بیشتر، ما [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/qprpajyoy3x0g7?WT.mc_id=academic-77958-bethanycheum) را توصیه می‌کنیم.
 
 **شروع سریع:**
-1. برای راه‌اندازی محیط خود، [راهنمای نصب](INSTALLATION.md) را بررسی کنید
-2. برای یادگیری نحوه کار با برنامه درسی، [راهنمای استفاده](USAGE.md) را مرور کنید
-3. با درس ۱ شروع کرده و به ترتیب ادامه دهید
-4. برای پشتیبانی به [جامعه Discord ما](https://aka.ms/ds4beginners/discord) بپیوندید
+1. راهنمای [نصب](INSTALLATION.md) را برای تنظیم محیط بررسی کنید
+2. راهنمای [استفاده](USAGE.md) را مطالعه کنید تا نحوه کار با برنامه را یاد بگیرید
+3. از درس ۱ شروع کنید و به ترتیب پیش بروید
+4. به [جامعه دیسکورد ما](https://aka.ms/ds4beginners/discord) برای پشتیبانی بپیوندید
 
 ## 👩‍🏫 برای معلمان
 
-> **معلمان:** ما [برخی پیشنهادها](for-teachers.md) را درباره چگونگی استفاده از این برنامه درسی ارائه داده‌ایم. مشتاقانه منتظر بازخورد شما در [انجمن بحث ما](https://github.com/microsoft/Data-Science-For-Beginners/discussions) هستیم!
+> **معلمان**: ما [برخی پیشنهادات](for-teachers.md) برای نحوه استفاده از این برنامه را ارائه داده‌ایم. خوشحال می‌شویم بازخورد شما را در [انجمن بحث ما](https://github.com/microsoft/Data-Science-For-Beginners/discussions) بشنویم!
+## ملاقات با تیم
 
-## تیم را ملاقات کنید
-[![ویدئوی تبلیغاتی](../../ds-for-beginners.gif)](https://youtu.be/8mzavjQSMM4 "ویدئوی تبلیغاتی")
+[![ویدیو تبلیغاتی](../../ds-for-beginners.gif)](https://youtu.be/8mzavjQSMM4 "ویدیو تبلیغاتی")
 
-**گیف توسط** [Mohit Jaisal](https://www.linkedin.com/in/mohitjaisal)
+**گیف از** [Mohit Jaisal](https://www.linkedin.com/in/mohitjaisal)
 
-> 🎥 برای مشاهده ویدئویی درباره پروژه و افرادی که آن را ایجاد کرده‌اند، روی تصویر بالا کلیک کنید!
+> 🎥 برای دیدن ویدیو درباره پروژه و افرادی که آن را ساخته‌اند، روی تصویر بالا کلیک کنید!
 
 ## روش آموزشی
 
-در ساخت این برنامه درسی دو اصل آموزشی را انتخاب کرده‌ایم: اطمینان از اینکه مبتنی بر پروژه باشد و شامل آزمون‌های مکرر باشد. تا پایان این مجموعه، دانش‌آموزان اصول پایه‌ای علم داده را خواهند آموخت، از جمله مفاهیم اخلاقی، آماده‌سازی داده‌ها، روش‌های مختلف کار با داده، تجسم داده، تحلیل داده، موارد کاربرد واقعی علم داده و موارد بیشتر.
+ما در حین ساخت این برنامه درسی، دو اصل آموزش را انتخاب کردیم: اطمینان از پروژه‌محور بودن آن و گنجاندن آزمون‌های مکرر. تا پایان این سری، دانش‌آموزان اصول پایه‌ای علم داده، از جمله مفاهیم اخلاقی، آماده‌سازی داده‌ها، روش‌های مختلف کار با داده‌ها، مصورسازی داده، تحلیل داده، موارد کاربرد واقعی علم داده و موارد بیشتر را خواهند آموخت.
 
-علاوه بر این، یک آزمون کم‌ریسک قبل از کلاس قصد دانش‌آموز برای یادگیری یک موضوع را تنظیم می‌کند، در حالی که پس از کلاس آزمون دوم به حفظ بهتر مطالب کمک می‌کند. این برنامه درسی برای انعطاف‌پذیری و سرگرمی طراحی شده است و می‌توان آن را به‌صورت کامل یا جزئی گذراند. پروژه‌ها از ساده شروع شده و تا پایان دوره ۱۰ هفته‌ای پیچیده‌تر می‌شوند.
+علاوه بر این، یک آزمون با ریسک پایین قبل از کلاس، نیت دانش‌آموز را برای یادگیری موضوعی مشخص می‌کند، در حالی که آزمون دوم پس از کلاس، حفظ بیشتر اطلاعات را تضمین می‌کند. این برنامه درسی به گونه‌ای طراحی شده است که انعطاف‌پذیر و سرگرم‌کننده باشد و می‌توان آن را کامل یا بخشی از آن را گذراند. پروژه‌ها از کوچک شروع شده و تا پایان چرخه ۱۰ هفته‌ای به تدریج پیچیده‌تر می‌شوند.
 
-> راهنمای [قوانین رفتار](CODE_OF_CONDUCT.md)، [مشارکت](CONTRIBUTING.md)، [ترجمه](TRANSLATIONS.md) ما را بیابید. ما از بازخورد سازنده شما استقبال می‌کنیم!
+> راهنمای [رفتار ما](CODE_OF_CONDUCT.md)، [مشارکت](CONTRIBUTING.md) و [ترجمه](TRANSLATIONS.md) را بیابید. ما بازخورد سازنده شما را خوش‌آمد می‌گوییم!
 
-## هر درس شامل موارد زیر است:
+## هر درس شامل:
 
-- یادداشت اسکیچ اختیاری
-- ویدئوی تکمیلی اختیاری
-- آزمون گرم‌کردن قبل از درس
+- خلاصه‌نویسی اختیاری
+- ویدیوی تکمیلی اختیاری
+- آزمون گرم‌کننده قبل از درس
 - درس مکتوب
-- در دروس مبتنی بر پروژه، راهنمای مرحله به مرحله برای ساخت پروژه
+- برای درس‌های پروژه‌محور، راهنماهای گام‌به‌گام برای ساخت پروژه
 - بررسی دانش
 - یک چالش
-- مطالعات تکمیلی
-- تمرین
+- مطالعه تکمیلی
+- تکلیف
 - [آزمون پس از درس](https://ff-quizzes.netlify.app/en/)
 
-> **یک نکته درباره آزمون‌ها**: همه آزمون‌ها در پوشه Quiz-App قرار دارند، مجموعاً ۴۰ آزمون با سه سوال هر کدام. این آزمون‌ها در داخل دروس لینک شده‌اند، اما می‌توان برنامه آزمون را به صورت محلی اجرا کرد یا در Azure مستقر نمود؛ دستورالعمل‌ها در پوشه `quiz-app` موجود است. این آزمون‌ها به تدریج بومی‌سازی می‌شوند.
+> **نکته‌ای درباره آزمون‌ها**: تمام آزمون‌ها در پوشه Quiz-App قرار دارند، مجموعاً ۴۰ آزمون هرکدام با سه سؤال. آنها از داخل درس‌ها لینک شده‌اند، اما اپلیکیشن آزمون را می‌توان به صورت محلی اجرا یا روی Azure مستقر کرد؛ دستورالعمل‌های آن در پوشه `quiz-app` است. آنها به تدریج به زبان‌های مختلف بومی‌سازی می‌شوند.
 
-## 🎓 مثال‌های مناسب مبتدیان
+## 🎓 نمونه‌های مناسب مبتدیان
 
-**تازه‌کار در علم داده؟** ما یک [دایرکتوری مثال‌ها](examples/README.md) ویژه با کدهای ساده و خوب توضیح داده شده ایجاد کرده‌ایم تا به شما کمک کند شروع کنید:
+**تازه‌کار در علم داده هستید؟** ما دایرکتوری ویژه‌ای از نمونه‌ها ایجاد کرده‌ایم [examples directory](examples/README.md) با کد ساده و کامنت‌گذاری شده برای کمک به شروع شما:
 
 - 🌟 **سلام دنیا** - اولین برنامه علم داده شما
-- 📂 **بارگذاری داده‌ها** - یادگیری خواندن و بررسی مجموعه داده‌ها
+- 📂 **بارگذاری داده** - یاد بگیرید چگونه داده‌ها را بخوانید و کاوش کنید
 - 📊 **تحلیل ساده** - محاسبه آمار و یافتن الگوها
-- 📈 **تجسم پایه‌ای** - ساخت نمودارها و گراف‌ها
-- 🔬 **پروژه واقعی** - جریان کاری کامل از ابتدا تا پایان
+- 📈 **مصورسازی پایه‌ای** - ساخت نمودارها و گراف‌ها
+- 🔬 **پروژه واقعی** - جریان کاری کامل از شروع تا پایان
 
-هر مثال شامل توضیحات دقیق برای هر مرحله است، که آن را برای مبتدیان مطلق ایده‌آل می‌کند!
+هر نمونه شامل توضیحات دقیق در مورد هر مرحله است، بنابراین برای مبتدیان مطلق بسیار مناسب است!
 
-👉 **[شروع با مثال‌ها](examples/README.md)** 👈
+👉 **[شروع با نمونه‌ها](examples/README.md)** 👈
 
-## دروس
+## درس‌ها
 
 
-|![ یادداشت اسکیچ توسط @sketchthedocs https://sketchthedocs.dev](../../../../translated_images/fa/00-Roadmap.4905d6567dff4753.webp)|
+|![خلاصه‌نویسی توسط @sketchthedocs https://sketchthedocs.dev](../../translated_images/fa/00-Roadmap.4905d6567dff4753.webp)|
 |:---:|
-| علم داده برای مبتدیان: نقشه راه - _یادداشت اسکیچ توسط [@nitya](https://twitter.com/nitya)_ |
+| نقشه راه علم داده برای مبتدیان - _خلاصه‌نویسی توسط [@nitya](https://twitter.com/nitya)_ |
 
 
 | شماره درس | موضوع | گروه درس | اهداف یادگیری | درس مرتبط | نویسنده |
 | :-----------: | :----------------------------------------: | :--------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------: | :----: |
-| ۰۱ | تعریف علم داده | [مقدمه](1-Introduction/README.md) | آشنایی با مفاهیم پایه علم داده و ارتباط آن با هوش مصنوعی، یادگیری ماشین و داده‌های بزرگ. | [درس](1-Introduction/01-defining-data-science/README.md) [ویدئو](https://youtu.be/beZ7Mb_oz9I) | [دیمیترای](http://soshnikov.com) |
-| ۰۲ | اخلاق علم داده | [مقدمه](1-Introduction/README.md) | مفاهیم، چالش‌ها و چارچوب‌های اخلاق داده. | [درس](1-Introduction/02-ethics/README.md) | [نیتیا](https://twitter.com/nitya) |
-| ۰۳ | تعریف داده | [مقدمه](1-Introduction/README.md) | چگونه داده‌ها دسته‌بندی می‌شوند و منابع رایج آن‌ها. | [درس](1-Introduction/03-defining-data/README.md) | [جاسمین](https://www.twitter.com/paladique) |
-| ۰۴ | مقدمه‌ای بر آمار و احتمال | [مقدمه](1-Introduction/README.md) | تکنیک‌های ریاضی احتمال و آمار برای درک داده‌ها. | [درس](1-Introduction/04-stats-and-probability/README.md) [ویدئو](https://youtu.be/Z5Zy85g4Yjw) | [دیمیترای](http://soshnikov.com) |
-| ۰۵ | کار با داده‌های رابطه‌ای | [کار با داده](2-Working-With-Data/README.md) | مقدمه‌ای بر داده‌های رابطه‌ای و اصول کاوش و تحلیل این داده‌ها با زبان ساخت‌یافته پرس‌وجو، معروف به SQL ("سی‌کوئل"). | [درس](2-Working-With-Data/05-relational-databases/README.md) | [کریستوفر](https://www.twitter.com/geektrainer) | | |
-| ۰۶ | کار با داده‌های NoSQL | [کار با داده](2-Working-With-Data/README.md) | مقدمه‌ای بر داده‌های غیررابطه‌ای، انواع مختلف آن و مبانی کاوش و تحلیل پایگاه داده‌های اسنادی. | [درس](2-Working-With-Data/06-non-relational/README.md) | [جاسمین](https://twitter.com/paladique)|
-| ۰۷ | کار با پایتون | [کار با داده](2-Working-With-Data/README.md) | اصول استفاده از پایتون برای کاوش داده‌ها با کتابخانه‌هایی مانند Pandas. آشنایی پایه با برنامه‌نویسی پایتون توصیه می‌شود. | [درس](2-Working-With-Data/07-python/README.md) [ویدئو](https://youtu.be/dZjWOGbsN4Y) | [دیمیترای](http://soshnikov.com) |
-| ۰۸ | آماده‌سازی داده‌ها | [کار با داده](2-Working-With-Data/README.md) | موضوعات مربوط به تکنیک‌های پاک‌سازی و تبدیل داده برای مواجهه با چالش‌های داده‌های ناقص، نادرست یا کمبود اطلاعات. | [درس](2-Working-With-Data/08-data-preparation/README.md) | [جاسمین](https://www.twitter.com/paladique) |
-| ۰۹ | تجسم مقادیر | [تجسم داده](3-Data-Visualization/README.md) | یادگیری استفاده از Matplotlib برای تجسم داده‌های پرندگان 🦆 | [درس](3-Data-Visualization/09-visualization-quantities/README.md) | [جن](https://twitter.com/jenlooper) |
-| ۱۰ | تجسم توزیع داده‌ها | [تجسم داده](3-Data-Visualization/README.md) | تجسم مشاهدات و روندها در بازه‌ای مشخص. | [درس](3-Data-Visualization/10-visualization-distributions/README.md) | [جن](https://twitter.com/jenlooper) |
-| ۱۱ | تجسم نسبت‌ها | [تجسم داده](3-Data-Visualization/README.md) | تجسم درصدهای گسسته و گروه‌بندی شده. | [درس](3-Data-Visualization/11-visualization-proportions/README.md) | [جن](https://twitter.com/jenlooper) |
-| ۱۲ | تجسم روابط | [تجسم داده](3-Data-Visualization/README.md) | تجسم ارتباطات و همبستگی‌ها بین مجموعه‌های داده و متغیرهای آن‌ها. | [درس](3-Data-Visualization/12-visualization-relationships/README.md) | [جن](https://twitter.com/jenlooper) |
-| ۱۳ | تجسم‌های معنادار | [تجسم داده](3-Data-Visualization/README.md) | تکنیک‌ها و راهنمایی برای ارزشمند کردن تجسم‌ها جهت حل مؤثر مسائل و کسب بینش‌ها. | [درس](3-Data-Visualization/13-meaningful-visualizations/README.md) | [جن](https://twitter.com/jenlooper) |
-| ۱۴ | مقدمه‌ای بر چرخه زندگی علم داده | [چرخه زندگی](4-Data-Science-Lifecycle/README.md) | معرفی چرخه زندگی علم داده و اولین گام آن یعنی کسب و استخراج داده. | [درس](4-Data-Science-Lifecycle/14-Introduction/README.md) | [جاسمین](https://twitter.com/paladique) |
-| ۱۵ | تحلیل | [چرخه زندگی](4-Data-Science-Lifecycle/README.md) | این مرحله از چرخه زندگی علم داده بر تکنیک‌های تحلیل داده متمرکز است. | [درس](4-Data-Science-Lifecycle/15-analyzing/README.md) | [جاسمین](https://twitter.com/paladique) | | |
-| ۱۶ | ارتباطات | [چرخه زندگی](4-Data-Science-Lifecycle/README.md) | این مرحله از چرخه زندگی علم داده بر ارائه بینش‌های داده به طریقی که تصمیم‌گیرندگان به راحتی بفهمند، متمرکز است. | [درس](4-Data-Science-Lifecycle/16-communication/README.md) | [جالن](https://twitter.com/JalenMcG) | | |
-| ۱۷ | علم داده در فضای ابری | [داده ابری](5-Data-Science-In-Cloud/README.md) | این مجموعه درس‌ها علم داده در فضای ابری و مزایای آن را معرفی می‌کند. | [درس](5-Data-Science-In-Cloud/17-Introduction/README.md) | [تیفانی](https://twitter.com/TiffanySouterre) و [ماود](https://twitter.com/maudstweets) |
-| ۱۸ | علم داده در فضای ابری | [داده ابری](5-Data-Science-In-Cloud/README.md) | آموزش مدل‌ها با استفاده از ابزارهای کد پایین. |[درس](5-Data-Science-In-Cloud/18-Low-Code/README.md) | [تیفانی](https://twitter.com/TiffanySouterre) و [ماود](https://twitter.com/maudstweets) |
-| ۱۹ | علم داده در فضای ابری | [داده ابری](5-Data-Science-In-Cloud/README.md) | استقرار مدل‌ها با استفاده از Azure Machine Learning Studio. | [درس](5-Data-Science-In-Cloud/19-Azure/README.md)| [تیفانی](https://twitter.com/TiffanySouterre) و [ماود](https://twitter.com/maudstweets) |
-| ۲۰ | علم داده در دنیای واقعی | [در دنیای واقعی](6-Data-Science-In-Wild/README.md) | پروژه‌های مبتنی بر علم داده در دنیای واقعی. | [درس](6-Data-Science-In-Wild/20-Real-World-Examples/README.md) | [نیتیا](https://twitter.com/nitya) |
-
-## گیت‌هاب کدسپیس
-
-برای باز کردن این نمونه در یک Codespace مراحل زیر را دنبال کنید:
-۱. منوی کشویی Code را کلیک کنید و گزینه Open with Codespaces را انتخاب کنید.
-۲. در پایین پنل گزینه + New codespace را انتخاب کنید.
-برای اطلاعات بیشتر، مستندات [GitHub](https://docs.github.com/en/codespaces/developing-in-codespaces/creating-a-codespace-for-a-repository#creating-a-codespace) را بررسی کنید.
+| 01 | تعریف علم داده | [مقدمه](1-Introduction/README.md) | یادگیری مفاهیم پایه علم داده و ارتباط آن با هوش مصنوعی، یادگیری ماشین و داده‌های بزرگ. | [درس](1-Introduction/01-defining-data-science/README.md) [ویدیو](https://youtu.be/beZ7Mb_oz9I) | [دیمیتری](http://soshnikov.com) |
+| 02 | اخلاق علم داده | [مقدمه](1-Introduction/README.md) | مفاهیم اخلاق داده، چالش‌ها و چارچوب‌ها. | [درس](1-Introduction/02-ethics/README.md) | [نیتیا](https://twitter.com/nitya) |
+| 03 | تعریف داده | [مقدمه](1-Introduction/README.md) | نحوه طبقه‌بندی داده‌ها و منابع رایج آن. | [درس](1-Introduction/03-defining-data/README.md) | [جازمین](https://www.twitter.com/paladique) |
+| 04 | مقدمه‌ای بر آمار و احتمال | [مقدمه](1-Introduction/README.md) | تکنیک‌های ریاضی احتمال و آمار برای درک داده‌ها. | [درس](1-Introduction/04-stats-and-probability/README.md) [ویدیو](https://youtu.be/Z5Zy85g4Yjw) | [دیمیتری](http://soshnikov.com) |
+| 05 | کار با داده‌های رابطه‌ای | [کار با داده](2-Working-With-Data/README.md) | معرفی داده‌های رابطه‌ای و اصول کاوش و تحلیل آن با زبان ساخت‌یافته پرس‌وجو، یا همان SQL (خوانده شده "سی‌کول"). | [درس](2-Working-With-Data/05-relational-databases/README.md) | [کریستوفر](https://www.twitter.com/geektrainer) | | |
+| 06 | کار با داده‌های NoSQL | [کار با داده](2-Working-With-Data/README.md) | معرفی داده‌های غیررابطه‌ای، انواع مختلف آن و اصول کاوش و تحلیل پایگاه‌های داده سندی. | [درس](2-Working-With-Data/06-non-relational/README.md) | [جازمین](https://twitter.com/paladique)|
+| 07 | کار با پایتون | [کار با داده](2-Working-With-Data/README.md) | اصول استفاده از پایتون برای کاوش داده‌ها با کتابخانه‌هایی مانند Pandas. دانش پایه‌ای برنامه‌نویسی پایتون توصیه می‌شود. | [درس](2-Working-With-Data/07-python/README.md) [ویدیو](https://youtu.be/dZjWOGbsN4Y) | [دیمیتری](http://soshnikov.com) |
+| 08 | آماده‌سازی داده | [کار با داده](2-Working-With-Data/README.md) | موضوعات مربوط به تکنیک‌های داده برای پاک‌سازی و تبدیل داده‌ها به منظور مقابله با چالش‌های داده‌های گمشده، نادرست یا ناقص. | [درس](2-Working-With-Data/08-data-preparation/README.md) | [جازمین](https://www.twitter.com/paladique) |
+| 09 | مصورسازی مقادیر | [مصورسازی داده](3-Data-Visualization/README.md) | یادگیری استفاده از Matplotlib برای مصورسازی داده‌های پرندگان 🦆 | [درس](3-Data-Visualization/09-visualization-quantities/README.md) | [جن](https://twitter.com/jenlooper) |
+| 10 | مصورسازی توزیع داده‌ها | [مصورسازی داده](3-Data-Visualization/README.md) | مصورسازی مشاهدات و روندها در یک بازه. | [درس](3-Data-Visualization/10-visualization-distributions/README.md) | [جن](https://twitter.com/jenlooper) |
+| 11 | مصورسازی نسبت‌ها | [مصورسازی داده](3-Data-Visualization/README.md) | مصورسازی درصدهای گسسته و گروه‌بندی شده. | [درس](3-Data-Visualization/11-visualization-proportions/README.md) | [جن](https://twitter.com/jenlooper) |
+| 12 | مصورسازی روابط | [مصورسازی داده](3-Data-Visualization/README.md) | مصورسازی ارتباطات و همبستگی بین مجموعه‌های داده و متغیرهای آن‌ها. | [درس](3-Data-Visualization/12-visualization-relationships/README.md) | [جن](https://twitter.com/jenlooper) |
+| 13 | مصورسازی‌های معنادار | [مصورسازی داده](3-Data-Visualization/README.md) | تکنیک‌ها و راهنمایی‌هایی برای ارزشمند کردن مصورسازی‌ها جهت حل مؤثر مسئله و کسب بینش. | [درس](3-Data-Visualization/13-meaningful-visualizations/README.md) | [جن](https://twitter.com/jenlooper) |
+| 14 | مقدمه‌ای بر چرخه عمر علم داده | [چرخه عمر](4-Data-Science-Lifecycle/README.md) | مقدمه‌ای بر چرخه عمر علم داده و اولین گام آن یعنی کسب و استخراج داده. | [درس](4-Data-Science-Lifecycle/14-Introduction/README.md) | [جازمین](https://twitter.com/paladique) |
+| 15 | تحلیل | [چرخه عمر](4-Data-Science-Lifecycle/README.md) | این مرحله از چرخه عمر علم داده بر تکنیک‌های تحلیل داده متمرکز است. | [درس](4-Data-Science-Lifecycle/15-analyzing/README.md) | [جازمین](https://twitter.com/paladique) | | |
+| 16 | ارتباطات | [چرخه عمر](4-Data-Science-Lifecycle/README.md) | این مرحله از چرخه عمر علم داده بر ارائه بینش‌های حاصل از داده‌ها به گونه‌ای که تصمیم‌گیرندگان راحت‌تر درک کنند، تمرکز دارد. | [درس](4-Data-Science-Lifecycle/16-communication/README.md) | [جالن](https://twitter.com/JalenMcG) | | |
+| 17 | علم داده در فضای ابری | [داده ابری](5-Data-Science-In-Cloud/README.md) | این سری از درس‌ها علم داده در فضای ابری و فواید آن را معرفی می‌کند. | [درس](5-Data-Science-In-Cloud/17-Introduction/README.md) | [تیفانی](https://twitter.com/TiffanySouterre) و [ماود](https://twitter.com/maudstweets) |
+| 18 | علم داده در فضای ابری | [داده ابری](5-Data-Science-In-Cloud/README.md) | آموزش مدل‌ها با استفاده از ابزارهای کد پایین (Low Code). |[درس](5-Data-Science-In-Cloud/18-Low-Code/README.md) | [تیفانی](https://twitter.com/TiffanySouterre) و [ماود](https://twitter.com/maudstweets) |
+| 19 | علم داده در فضای ابری | [داده ابری](5-Data-Science-In-Cloud/README.md) | استقرار مدل‌ها با Azure Machine Learning Studio. | [درس](5-Data-Science-In-Cloud/19-Azure/README.md)| [تیفانی](https://twitter.com/TiffanySouterre) و [ماود](https://twitter.com/maudstweets) |
+| 20 | علم داده در زندگی واقعی | [در محیط واقعی](6-Data-Science-In-Wild/README.md) | پروژه‌های مبتنی بر علم داده در دنیای واقعی. | [درس](6-Data-Science-In-Wild/20-Real-World-Examples/README.md) | [نیتیا](https://twitter.com/nitya) |
+
+## کد اسپیس‌های گیت‌هاب
+
+برای باز کردن این نمونه در یک کد اسپیس این مراحل را دنبال کنید:  
+1. منوی کشویی Code را بزنید و گزینه Open with Codespaces را انتخاب کنید.  
+2. در پایین پنل، گزینه + New codespace را انتخاب کنید.  
+برای اطلاعات بیشتر به [مستندات گیت‌هاب](https://docs.github.com/en/codespaces/developing-in-codespaces/creating-a-codespace-for-a-repository#creating-a-codespace) مراجعه کنید.
 
 ## VSCode Remote - Containers
-برای باز کردن این مخزن در یک کانتینر با استفاده از دستگاه محلی و VSCode با افزونه VS Code Remote - Containers مراحل زیر را دنبال کنید:
 
-۱. اگر برای اولین بار است که از یک کانتینر توسعه استفاده می‌کنید، لطفاً اطمینان حاصل کنید که سیستم شما پیش‌نیازها را دارد (مثلاً Docker نصب شده باشد) در [مستندات شروع](https://code.visualstudio.com/docs/devcontainers/containers#_getting-started).
+برای باز کردن این مخزن در کانتینر با استفاده از ماشین محلی و VSCode از افزونه VS Code Remote - Containers این مراحل را دنبال کنید:
+
+1. اگر برای اولین بار است که از کانتینر توسعه استفاده می‌کنید، لطفاً اطمینان حاصل کنید که سیستم شما پیش‌نیازها را دارد (مثلاً Docker نصب شده است) در [مستندات شروع کار](https://code.visualstudio.com/docs/devcontainers/containers#_getting-started).
 
-برای استفاده از این مخزن، می‌توانید مخزن را در یک volume ایزوله Docker باز کنید:
+برای استفاده از این مخزن، می‌توانید یا مخزن را در یک Volume جداگانه Docker باز کنید:
 
-**توجه**: در پشت صحنه، این از فرمان Remote-Containers: **Clone Repository in Container Volume...** استفاده می‌کند تا کد منبع را در یک volume داکر کپی کند نه در سیستم فایل محلی. [Volumeها](https://docs.docker.com/storage/volumes/) مکانیزم ترجیحی برای حفظ داده‌های کانتینر هستند.
+**توجه**: در پس‌زمینه این کار از فرمان Remote-Containers: **Clone Repository in Container Volume...** استفاده می‌کند تا کد منبع را در Volume داکر به جای فایل‌سیستم محلی کلون کند. [Volume‌ها](https://docs.docker.com/storage/volumes/) مکانیزم توصیه شده برای حفظ داده کانتینر هستند.
 
-یا نسخه‌ای که به‌صورت محلی کلون یا دانلود شده است را باز کنید:
+یا نسخه کلون شده یا دانلود شده محلی مخزن را باز کنید:
 
-- این مخزن را در سیستم فایل محلی خود کلون کنید.
-- کلید F1 را فشار دهید و فرمان **Remote-Containers: Open Folder in Container...** را انتخاب کنید.
-- نسخه کلون شده این پوشه را انتخاب کنید، منتظر شروع کانتینر بمانید و همه چیز را امتحان کنید.
+- این مخزن را در فایل‌سیستم محلی کلون کنید.  
+- کلید F1 را بزنید و فرمان **Remote-Containers: Open Folder in Container...** را انتخاب کنید.  
+- نسخه کلون شده این پوشه را انتخاب کنید، منتظر شروع کانتینر باشید، و شروع به کار کنید.
 
 ## دسترسی آفلاین
 
-می‌توانید این مستندات را به صورت آفلاین با استفاده از [Docsify](https://docsify.js.org/#/) اجرا کنید. این مخزن را فورک کنید، [Docsify را نصب کنید](https://docsify.js.org/#/quickstart) در رایانه محلی خود، سپس در پوشه ریشه این مخزن تایپ کنید `docsify serve`. وب‌سایت بر روی پورت ۳۰۰۰ در localhost شما سرو خواهد شد: `localhost:3000`.
+می‌توانید این مستندات را آفلاین با استفاده از [Docsify](https://docsify.js.org/#/) اجرا کنید. این مخزن را فورک کنید، [Docsify](https://docsify.js.org/#/quickstart) را روی ماشین محلی نصب کنید، سپس در پوشه ریشه این مخزن دستور `docsify serve` را اجرا کنید. وب‌سایت بر روی پورت ۳۰۰۰ در localhost شما: `localhost:3000` ارائه خواهد شد.
 
-> توجه، دفترچه‌ها از طریق Docsify رندر نمی‌شوند، بنابراین هر زمان نیاز به اجرای یک دفترچه یادداشت داشتید، آن را جداگانه در VS Code با اجرای هسته Python انجام دهید.
+> توجه داشته باشید، دفترچه‌های نوت‌بوک توسط Docsify رندر نمی‌شوند، بنابراین هنگام نیاز به اجرای نوت‌بوک، آن را جداگانه در VS Code با استفاده از کرنل پایتون اجرا کنید.
 
 ## برنامه‌های درسی دیگر
 
-تیم ما برنامه‌های درسی دیگری تولید می‌کند! نگاهی بیندازید به:
+تیم ما برنامه‌های درسی دیگری نیز تولید می‌کند! ببینید:
 
 <!-- CO-OP TRANSLATOR OTHER COURSES START -->
 ### LangChain
 [![LangChain4j برای مبتدیان](https://img.shields.io/badge/LangChain4j%20for%20Beginners-22C55E?style=for-the-badge&&labelColor=E5E7EB&color=0553D6)](https://aka.ms/langchain4j-for-beginners)
-[![LangChain.js برای مبتدیان](https://img.shields.io/badge/LangChain.js%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=0553D6)](https://aka.ms/langchainjs-for-beginners?WT.mc_id=m365-94501-dwahlin)
+[![LangChain.js for Beginners](https://img.shields.io/badge/LangChain.js%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=0553D6)](https://aka.ms/langchainjs-for-beginners?WT.mc_id=m365-94501-dwahlin)
 
 ---
 
-### Azure / Edge / MCP / Agents
-[![AZD برای مبتدیان](https://img.shields.io/badge/AZD%20for%20Beginners-0078D4?style=for-the-badge&labelColor=E5E7EB&color=0078D4)](https://github.com/microsoft/AZD-for-beginners?WT.mc_id=academic-105485-koreyst)
-[![Edge AI برای مبتدیان](https://img.shields.io/badge/Edge%20AI%20for%20Beginners-00B8E4?style=for-the-badge&labelColor=E5E7EB&color=00B8E4)](https://github.com/microsoft/edgeai-for-beginners?WT.mc_id=academic-105485-koreyst)
-[![MCP برای مبتدیان](https://img.shields.io/badge/MCP%20for%20Beginners-009688?style=for-the-badge&labelColor=E5E7EB&color=009688)](https://github.com/microsoft/mcp-for-beginners?WT.mc_id=academic-105485-koreyst)
-[![AI Agents برای مبتدیان](https://img.shields.io/badge/AI%20Agents%20for%20Beginners-00C49A?style=for-the-badge&labelColor=E5E7EB&color=00C49A)](https://github.com/microsoft/ai-agents-for-beginners?WT.mc_id=academic-105485-koreyst)
+### Azure / Edge / MCP / عوامل
+[![AZD for Beginners](https://img.shields.io/badge/AZD%20for%20Beginners-0078D4?style=for-the-badge&labelColor=E5E7EB&color=0078D4)](https://github.com/microsoft/AZD-for-beginners?WT.mc_id=academic-105485-koreyst)
+[![Edge AI for Beginners](https://img.shields.io/badge/Edge%20AI%20for%20Beginners-00B8E4?style=for-the-badge&labelColor=E5E7EB&color=00B8E4)](https://github.com/microsoft/edgeai-for-beginners?WT.mc_id=academic-105485-koreyst)
+[![MCP for Beginners](https://img.shields.io/badge/MCP%20for%20Beginners-009688?style=for-the-badge&labelColor=E5E7EB&color=009688)](https://github.com/microsoft/mcp-for-beginners?WT.mc_id=academic-105485-koreyst)
+[![AI Agents for Beginners](https://img.shields.io/badge/AI%20Agents%20for%20Beginners-00C49A?style=for-the-badge&labelColor=E5E7EB&color=00C49A)](https://github.com/microsoft/ai-agents-for-beginners?WT.mc_id=academic-105485-koreyst)
 
 ---
  
-### سری هوش مصنوعی مولد
-[![هوش مصنوعی مولد برای مبتدیان](https://img.shields.io/badge/Generative%20AI%20for%20Beginners-8B5CF6?style=for-the-badge&labelColor=E5E7EB&color=8B5CF6)](https://github.com/microsoft/generative-ai-for-beginners?WT.mc_id=academic-105485-koreyst)
-[![هوش مصنوعی مولد (.NET)](https://img.shields.io/badge/Generative%20AI%20(.NET)-9333EA?style=for-the-badge&labelColor=E5E7EB&color=9333EA)](https://github.com/microsoft/Generative-AI-for-beginners-dotnet?WT.mc_id=academic-105485-koreyst)
-[![هوش مصنوعی مولد (جاوا)](https://img.shields.io/badge/Generative%20AI%20(Java)-C084FC?style=for-the-badge&labelColor=E5E7EB&color=C084FC)](https://github.com/microsoft/generative-ai-for-beginners-java?WT.mc_id=academic-105485-koreyst)
-[![هوش مصنوعی مولد (جاوااسکریپت)](https://img.shields.io/badge/Generative%20AI%20(JavaScript)-E879F9?style=for-the-badge&labelColor=E5E7EB&color=E879F9)](https://github.com/microsoft/generative-ai-with-javascript?WT.mc_id=academic-105485-koreyst)
+### سری هوش مصنوعی تولیدی
+[![Generative AI for Beginners](https://img.shields.io/badge/Generative%20AI%20for%20Beginners-8B5CF6?style=for-the-badge&labelColor=E5E7EB&color=8B5CF6)](https://github.com/microsoft/generative-ai-for-beginners?WT.mc_id=academic-105485-koreyst)
+[![Generative AI (.NET)](https://img.shields.io/badge/Generative%20AI%20(.NET)-9333EA?style=for-the-badge&labelColor=E5E7EB&color=9333EA)](https://github.com/microsoft/Generative-AI-for-beginners-dotnet?WT.mc_id=academic-105485-koreyst)
+[![Generative AI (Java)](https://img.shields.io/badge/Generative%20AI%20(Java)-C084FC?style=for-the-badge&labelColor=E5E7EB&color=C084FC)](https://github.com/microsoft/generative-ai-for-beginners-java?WT.mc_id=academic-105485-koreyst)
+[![Generative AI (JavaScript)](https://img.shields.io/badge/Generative%20AI%20(JavaScript)-E879F9?style=for-the-badge&labelColor=E5E7EB&color=E879F9)](https://github.com/microsoft/generative-ai-with-javascript?WT.mc_id=academic-105485-koreyst)
 
 ---
  
-### یادگیری پایه
-[![یادگیری ماشین برای مبتدیان](https://img.shields.io/badge/ML%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=22C55E)](https://aka.ms/ml-beginners?WT.mc_id=academic-105485-koreyst)
-[![علم داده برای مبتدیان](https://img.shields.io/badge/Data%20Science%20for%20Beginners-84CC16?style=for-the-badge&labelColor=E5E7EB&color=84CC16)](https://aka.ms/datascience-beginners?WT.mc_id=academic-105485-koreyst)
-[![هوش مصنوعی برای مبتدیان](https://img.shields.io/badge/AI%20for%20Beginners-A3E635?style=for-the-badge&labelColor=E5E7EB&color=A3E635)](https://aka.ms/ai-beginners?WT.mc_id=academic-105485-koreyst)
-[![امنیت سایبری برای مبتدیان](https://img.shields.io/badge/Cybersecurity%20for%20Beginners-F97316?style=for-the-badge&labelColor=E5E7EB&color=F97316)](https://github.com/microsoft/Security-101?WT.mc_id=academic-96948-sayoung)
-[![توسعه وب برای مبتدیان](https://img.shields.io/badge/Web%20Dev%20for%20Beginners-EC4899?style=for-the-badge&labelColor=E5E7EB&color=EC4899)](https://aka.ms/webdev-beginners?WT.mc_id=academic-105485-koreyst)
-[![اینترنت اشیاء برای مبتدیان](https://img.shields.io/badge/IoT%20for%20Beginners-14B8A6?style=for-the-badge&labelColor=E5E7EB&color=14B8A6)](https://aka.ms/iot-beginners?WT.mc_id=academic-105485-koreyst)
-[![توسعه XR برای مبتدیان](https://img.shields.io/badge/XR%20Development%20for%20Beginners-38BDF8?style=for-the-badge&labelColor=E5E7EB&color=38BDF8)](https://github.com/microsoft/xr-development-for-beginners?WT.mc_id=academic-105485-koreyst)
+### یادگیری اصلی
+[![ML for Beginners](https://img.shields.io/badge/ML%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=22C55E)](https://aka.ms/ml-beginners?WT.mc_id=academic-105485-koreyst)
+[![Data Science for Beginners](https://img.shields.io/badge/Data%20Science%20for%20Beginners-84CC16?style=for-the-badge&labelColor=E5E7EB&color=84CC16)](https://aka.ms/datascience-beginners?WT.mc_id=academic-105485-koreyst)
+[![AI for Beginners](https://img.shields.io/badge/AI%20for%20Beginners-A3E635?style=for-the-badge&labelColor=E5E7EB&color=A3E635)](https://aka.ms/ai-beginners?WT.mc_id=academic-105485-koreyst)
+[![Cybersecurity for Beginners](https://img.shields.io/badge/Cybersecurity%20for%20Beginners-F97316?style=for-the-badge&labelColor=E5E7EB&color=F97316)](https://github.com/microsoft/Security-101?WT.mc_id=academic-96948-sayoung)
+[![Web Dev for Beginners](https://img.shields.io/badge/Web%20Dev%20for%20Beginners-EC4899?style=for-the-badge&labelColor=E5E7EB&color=EC4899)](https://aka.ms/webdev-beginners?WT.mc_id=academic-105485-koreyst)
+[![IoT for Beginners](https://img.shields.io/badge/IoT%20for%20Beginners-14B8A6?style=for-the-badge&labelColor=E5E7EB&color=14B8A6)](https://aka.ms/iot-beginners?WT.mc_id=academic-105485-koreyst)
+[![XR Development for Beginners](https://img.shields.io/badge/XR%20Development%20for%20Beginners-38BDF8?style=for-the-badge&labelColor=E5E7EB&color=38BDF8)](https://github.com/microsoft/xr-development-for-beginners?WT.mc_id=academic-105485-koreyst)
 
 ---
  
 ### سری کاپیلوت
-[![کاپیلوت برای برنامه‌نویسی جفتی هوش مصنوعی](https://img.shields.io/badge/Copilot%20for%20AI%20Paired%20Programming-FACC15?style=for-the-badge&labelColor=E5E7EB&color=FACC15)](https://aka.ms/GitHubCopilotAI?WT.mc_id=academic-105485-koreyst)
-[![کاپیلوت برای C#/.NET](https://img.shields.io/badge/Copilot%20for%20C%23/.NET-FBBF24?style=for-the-badge&labelColor=E5E7EB&color=FBBF24)](https://github.com/microsoft/mastering-github-copilot-for-dotnet-csharp-developers?WT.mc_id=academic-105485-koreyst)
-[![ماجراجویی کاپیلوت](https://img.shields.io/badge/Copilot%20Adventure-FDE68A?style=for-the-badge&labelColor=E5E7EB&color=FDE68A)](https://github.com/microsoft/CopilotAdventures?WT.mc_id=academic-105485-koreyst)
+[![Copilot for AI Paired Programming](https://img.shields.io/badge/Copilot%20for%20AI%20Paired%20Programming-FACC15?style=for-the-badge&labelColor=E5E7EB&color=FACC15)](https://aka.ms/GitHubCopilotAI?WT.mc_id=academic-105485-koreyst)
+[![Copilot for C#/.NET](https://img.shields.io/badge/Copilot%20for%20C%23/.NET-FBBF24?style=for-the-badge&labelColor=E5E7EB&color=FBBF24)](https://github.com/microsoft/mastering-github-copilot-for-dotnet-csharp-developers?WT.mc_id=academic-105485-koreyst)
+[![Copilot Adventure](https://img.shields.io/badge/Copilot%20Adventure-FDE68A?style=for-the-badge&labelColor=E5E7EB&color=FDE68A)](https://github.com/microsoft/CopilotAdventures?WT.mc_id=academic-105485-koreyst)
 <!-- CO-OP TRANSLATOR OTHER COURSES END -->
 
 ## دریافت کمک
 
-**با مشکلات مواجه شده‌اید؟** راهنمای [عیب‌یابی](TROUBLESHOOTING.md) را برای راه‌حل مشکلات رایج بررسی کنید.
+**با مشکلی مواجه شده‌اید؟** راهنمای [رفع اشکال](TROUBLESHOOTING.md) ما را برای یافتن راه‌حل مشکلات رایج بررسی کنید.
 
-اگر گیر کردید یا سوالی درباره ساخت برنامه‌های هوش مصنوعی دارید، به همراه سایر یادگیرندگان و توسعه‌دهندگان با تجربه در بحث‌های مربوط به MCP شرکت کنید. این یک جامعه حمایتی است که سوالات در آن استقبال می‌شود و دانش به طور آزاد به اشتراک گذاشته می‌شود.
+اگر گیر کردید یا سوالی درباره ساخت برنامه‌های هوش مصنوعی دارید، به جمع یادگیرندگان و توسعه‌دهندگان باتجربه ملحق شوید و در بحث‌ها درباره MCP شرکت کنید. این یک جامعه پشتیبان است که سوالات در آن خوش‌آمد گفته می‌شوند و دانش آزادانه به اشتراک گذاشته می‌شود.
 
 [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG)
 
-اگر بازخورد درباره محصول دارید یا هنگام ساخت با خطا مواجه شدید، به آدرس زیر مراجعه کنید:
+اگر بازخورد محصول دارید یا هنگام ساخت با خطا مواجه شدید به آدرس زیر مراجعه کنید:
 
 [![Microsoft Foundry Developer Forum](https://img.shields.io/badge/GitHub-Microsoft_Foundry_Developer_Forum-blue?style=for-the-badge&logo=github&color=000000&logoColor=fff)](https://aka.ms/foundry/forum)
 
@@ -258,5 +250,5 @@ CO_OP_TRANSLATOR_METADATA:
 
 <!-- CO-OP TRANSLATOR DISCLAIMER START -->
 **سلب مسئولیت**:  
-این سند با استفاده از سرویس ترجمه هوش مصنوعی [Co-op Translator](https://github.com/Azure/co-op-translator) ترجمه شده است. در حالی که ما برای دقت تلاش می‌کنیم، لطفاً توجه داشته باشید که ترجمه‌های خودکار ممکن است دارای خطاها یا نواقص باشند. سند اصلی به زبان بومی خود باید به عنوان منبع معتبر در نظر گرفته شود. برای اطلاعات حیاتی، ترجمه حرفه‌ای انسانی توصیه می‌شود. ما مسئول هیچ گونه سوءتفاهم یا برداشت نادرستی ناشی از استفاده از این ترجمه نیستیم.
+این سند با استفاده از خدمات ترجمه ماشینی هوش مصنوعی [Co-op Translator](https://github.com/Azure/co-op-translator) ترجمه شده است. اگرچه ما در تلاش برای دقت هستیم، لطفاً توجه داشته باشید که ترجمه‌های خودکار ممکن است دارای خطا یا نواقصی باشند. سند اصلی به زبان مادری آن منبع معتبر تلقی می‌شود. برای اطلاعات حیاتی، استفاده از ترجمه حرفه‌ای انسانی توصیه می‌شود. ما مسئول هیچ گونه سوءتفاهم یا برداشت نادرست ناشی از استفاده از این ترجمه نیستیم.
 <!-- CO-OP TRANSLATOR DISCLAIMER END -->
\ No newline at end of file
diff --git a/translations/fa/SECURITY.md b/translations/fa/SECURITY.md
index fa952e25..25a75292 100644
--- a/translations/fa/SECURITY.md
+++ b/translations/fa/SECURITY.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "0d575483100c332b2dbaefef915bb3c4",
-  "translation_date": "2025-08-24T20:50:34+00:00",
-  "source_file": "SECURITY.md",
-  "language_code": "fa"
-}
--->
 ## امنیت
 
 مایکروسافت امنیت محصولات و خدمات نرم‌افزاری خود را جدی می‌گیرد، که شامل تمامی مخازن کد منبع مدیریت‌شده از طریق سازمان‌های GitHub ما می‌شود، از جمله [Microsoft](https://github.com/Microsoft)، [Azure](https://github.com/Azure)، [DotNet](https://github.com/dotnet)، [AspNet](https://github.com/aspnet)، [Xamarin](https://github.com/xamarin) و [سازمان‌های GitHub ما](https://opensource.microsoft.com/).
diff --git a/translations/fa/SUPPORT.md b/translations/fa/SUPPORT.md
index 46b2bef6..30abeb2f 100644
--- a/translations/fa/SUPPORT.md
+++ b/translations/fa/SUPPORT.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "872be8bc1b93ef1dd9ac3d6e8f99f6ab",
-  "translation_date": "2025-08-24T20:47:37+00:00",
-  "source_file": "SUPPORT.md",
-  "language_code": "fa"
-}
--->
 # پشتیبانی
 ## چگونه مشکلات را گزارش دهیم و کمک بگیریم  
 
diff --git a/translations/fa/TROUBLESHOOTING.md b/translations/fa/TROUBLESHOOTING.md
index 04e27cf6..26a37f8c 100644
--- a/translations/fa/TROUBLESHOOTING.md
+++ b/translations/fa/TROUBLESHOOTING.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "93a6a8a8a209128cbfedcbc076ee21b0",
-  "translation_date": "2025-10-03T15:30:55+00:00",
-  "source_file": "TROUBLESHOOTING.md",
-  "language_code": "fa"
-}
--->
 # راهنمای رفع مشکلات
 
 این راهنما راه‌حل‌هایی برای مشکلات رایج که ممکن است هنگام کار با دوره آموزشی «علم داده برای مبتدیان» با آن مواجه شوید ارائه می‌دهد.
diff --git a/translations/fa/USAGE.md b/translations/fa/USAGE.md
index 31e7fcb9..a6edea64 100644
--- a/translations/fa/USAGE.md
+++ b/translations/fa/USAGE.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "f546349678757508d69ce9e1d2688446",
-  "translation_date": "2025-10-03T14:53:38+00:00",
-  "source_file": "USAGE.md",
-  "language_code": "fa"
-}
--->
 # راهنمای استفاده
 
 این راهنما مثال‌ها و جریان‌های کاری رایج برای استفاده از برنامه درسی «علم داده برای مبتدیان» را ارائه می‌دهد.
diff --git a/translations/fa/docs/_sidebar.md b/translations/fa/docs/_sidebar.md
index b31f3cdf..b5f5068d 100644
--- a/translations/fa/docs/_sidebar.md
+++ b/translations/fa/docs/_sidebar.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "3767555b3cc28a2865c79202f4374204",
-  "translation_date": "2025-08-24T21:14:19+00:00",
-  "source_file": "docs/_sidebar.md",
-  "language_code": "fa"
-}
--->
 - مقدمه
   - [تعریف علم داده](../1-Introduction/01-defining-data-science/README.md)
   - [اخلاق در علم داده](../1-Introduction/02-ethics/README.md)
diff --git a/translations/fa/examples/README.md b/translations/fa/examples/README.md
index 955c5cb8..38d87171 100644
--- a/translations/fa/examples/README.md
+++ b/translations/fa/examples/README.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "9bef7fd96c8f262339933117d9b3e342",
-  "translation_date": "2025-10-03T12:56:42+00:00",
-  "source_file": "examples/README.md",
-  "language_code": "fa"
-}
--->
 # مثال‌های مقدماتی علم داده
 
 به دایرکتوری مثال‌ها خوش آمدید! این مجموعه از مثال‌های ساده و با توضیحات کامل طراحی شده است تا به شما کمک کند حتی اگر کاملاً مبتدی هستید، با علم داده شروع کنید.
diff --git a/translations/fa/for-teachers.md b/translations/fa/for-teachers.md
index 81a63b10..e7fb15bf 100644
--- a/translations/fa/for-teachers.md
+++ b/translations/fa/for-teachers.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "f7440be10c17a8a9262713af3d2818a9",
-  "translation_date": "2025-09-06T19:52:26+00:00",
-  "source_file": "for-teachers.md",
-  "language_code": "fa"
-}
--->
 ## برای آموزگاران
 
 آیا مایلید از این برنامه آموزشی در کلاس خود استفاده کنید؟ لطفاً با خیال راحت این کار را انجام دهید!
diff --git a/translations/fa/quiz-app/README.md b/translations/fa/quiz-app/README.md
index 1619c1fc..6cc9df81 100644
--- a/translations/fa/quiz-app/README.md
+++ b/translations/fa/quiz-app/README.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "e92c33ea498915a13c9aec162616db18",
-  "translation_date": "2025-08-24T22:12:46+00:00",
-  "source_file": "quiz-app/README.md",
-  "language_code": "fa"
-}
--->
 # آزمون‌ها
 
 این آزمون‌ها شامل آزمون‌های قبل و بعد از جلسات آموزشی برای برنامه درسی علم داده در https://aka.ms/datascience-beginners هستند.
diff --git a/translations/fa/sketchnotes/README.md b/translations/fa/sketchnotes/README.md
index 32b42713..04e0402d 100644
--- a/translations/fa/sketchnotes/README.md
+++ b/translations/fa/sketchnotes/README.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "3a848466cb63aff1a93411affb152c2a",
-  "translation_date": "2025-08-24T21:45:08+00:00",
-  "source_file": "sketchnotes/README.md",
-  "language_code": "fa"
-}
--->
 تمام اسکچ‌نوت‌ها را اینجا پیدا کنید!
 
 ## اعتبارها
diff --git a/translations/ur/1-Introduction/01-defining-data-science/README.md b/translations/ur/1-Introduction/01-defining-data-science/README.md
index 6f32b535..384d5f08 100644
--- a/translations/ur/1-Introduction/01-defining-data-science/README.md
+++ b/translations/ur/1-Introduction/01-defining-data-science/README.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "43212cc1ac137b7bb1dcfb37ca06b0f4",
-  "translation_date": "2025-10-25T18:37:45+00:00",
-  "source_file": "1-Introduction/01-defining-data-science/README.md",
-  "language_code": "ur"
-}
--->
 # ڈیٹا سائنس کی تعریف
 
 | ![ [(@sketchthedocs)](https://sketchthedocs.dev) کی طرف سے اسکیچ نوٹ ](../../sketchnotes/01-Definitions.png) |
diff --git a/translations/ur/1-Introduction/01-defining-data-science/assignment.md b/translations/ur/1-Introduction/01-defining-data-science/assignment.md
index 15751c22..49478adb 100644
--- a/translations/ur/1-Introduction/01-defining-data-science/assignment.md
+++ b/translations/ur/1-Introduction/01-defining-data-science/assignment.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "4e0f1773b9bee1be3b28f9fe2c71b3de",
-  "translation_date": "2025-08-27T09:03:35+00:00",
-  "source_file": "1-Introduction/01-defining-data-science/assignment.md",
-  "language_code": "ur"
-}
--->
 # اسائنمنٹ: ڈیٹا سائنس کے منظرنامے
 
 اس پہلے اسائنمنٹ میں، ہم آپ سے درخواست کرتے ہیں کہ آپ مختلف مسئلہ کے شعبوں میں کسی حقیقی زندگی کے عمل یا مسئلے کے بارے میں سوچیں، اور ڈیٹا سائنس کے عمل کو استعمال کرتے ہوئے اسے کیسے بہتر بنایا جا سکتا ہے۔ درج ذیل پر غور کریں:
diff --git a/translations/ur/1-Introduction/01-defining-data-science/solution/assignment.md b/translations/ur/1-Introduction/01-defining-data-science/solution/assignment.md
index 632f1842..8e06d30e 100644
--- a/translations/ur/1-Introduction/01-defining-data-science/solution/assignment.md
+++ b/translations/ur/1-Introduction/01-defining-data-science/solution/assignment.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "a8f79b9c0484c35b4f26e8aec7fc4d56",
-  "translation_date": "2025-08-27T09:04:34+00:00",
-  "source_file": "1-Introduction/01-defining-data-science/solution/assignment.md",
-  "language_code": "ur"
-}
--->
 # ڈیٹا سائنس کے منظرنامے: اسائنمنٹ
 
 اس پہلے اسائنمنٹ میں، ہم آپ سے درخواست کرتے ہیں کہ آپ مختلف مسئلہ کے شعبوں میں کسی حقیقی زندگی کے عمل یا مسئلے کے بارے میں سوچیں، اور یہ کہ آپ ڈیٹا سائنس کے عمل کو استعمال کرتے ہوئے اسے کیسے بہتر بنا سکتے ہیں۔ درج ذیل پر غور کریں:
diff --git a/translations/ur/1-Introduction/02-ethics/README.md b/translations/ur/1-Introduction/02-ethics/README.md
index b373bbdf..996f9de2 100644
--- a/translations/ur/1-Introduction/02-ethics/README.md
+++ b/translations/ur/1-Introduction/02-ethics/README.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "58860ce9a4b8a564003d2752f7c72851",
-  "translation_date": "2025-10-03T16:04:50+00:00",
-  "source_file": "1-Introduction/02-ethics/README.md",
-  "language_code": "ur"
-}
--->
 # ڈیٹا اخلاقیات کا تعارف
 
 |![ اسکیچ نوٹ [(@sketchthedocs)](https://sketchthedocs.dev) کی طرف سے ](../../sketchnotes/02-Ethics.png)|
diff --git a/translations/ur/1-Introduction/02-ethics/assignment.md b/translations/ur/1-Introduction/02-ethics/assignment.md
index 1b174027..f2efa6cd 100644
--- a/translations/ur/1-Introduction/02-ethics/assignment.md
+++ b/translations/ur/1-Introduction/02-ethics/assignment.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "b588c0fc73014f52520c666efc3e0cc3",
-  "translation_date": "2025-08-27T08:56:17+00:00",
-  "source_file": "1-Introduction/02-ethics/assignment.md",
-  "language_code": "ur"
-}
--->
 ## ڈیٹا اخلاقیات کا کیس اسٹڈی لکھیں
 
 ## ہدایات
diff --git a/translations/ur/1-Introduction/03-defining-data/README.md b/translations/ur/1-Introduction/03-defining-data/README.md
index 0dfc6fb6..580596b7 100644
--- a/translations/ur/1-Introduction/03-defining-data/README.md
+++ b/translations/ur/1-Introduction/03-defining-data/README.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "12339119c0165da569a93ddba05f9339",
-  "translation_date": "2025-09-06T06:48:52+00:00",
-  "source_file": "1-Introduction/03-defining-data/README.md",
-  "language_code": "ur"
-}
--->
 # ڈیٹا کی تعریف
 
 |![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/03-DefiningData.png)|
diff --git a/translations/ur/1-Introduction/03-defining-data/assignment.md b/translations/ur/1-Introduction/03-defining-data/assignment.md
index 69815c5c..10b2fff4 100644
--- a/translations/ur/1-Introduction/03-defining-data/assignment.md
+++ b/translations/ur/1-Introduction/03-defining-data/assignment.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "2e5cacb967c1e9dfd07809bfc441a0b4",
-  "translation_date": "2025-08-27T09:08:19+00:00",
-  "source_file": "1-Introduction/03-defining-data/assignment.md",
-  "language_code": "ur"
-}
--->
 # ڈیٹا سیٹس کی درجہ بندی
 
 ## ہدایات
diff --git a/translations/ur/1-Introduction/04-stats-and-probability/README.md b/translations/ur/1-Introduction/04-stats-and-probability/README.md
index 8f1e3911..6a925ba8 100644
--- a/translations/ur/1-Introduction/04-stats-and-probability/README.md
+++ b/translations/ur/1-Introduction/04-stats-and-probability/README.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "ce95884566a74db72572cd51f0cb25ad",
-  "translation_date": "2025-09-06T12:59:24+00:00",
-  "source_file": "1-Introduction/04-stats-and-probability/README.md",
-  "language_code": "ur"
-}
--->
 # شماریات اور احتمال کا مختصر تعارف
 
 |![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/04-Statistics-Probability.png)|
@@ -64,7 +55,7 @@ CO_OP_TRANSLATOR_METADATA:
 
 گرافک طور پر، ہم میڈین اور چارٹائلز کے تعلق کو ایک ڈایاگرام میں ظاہر کر سکتے ہیں جسے **باکس پلاٹ** کہا جاتا ہے:
 
-<img src="images/boxplot_explanation.png" alt="باکس پلاٹ کی وضاحت" width="50%">
+<img src="../../../../translated_images/ur/boxplot_explanation.4039b7de08780fd4.webp" alt="باکس پلاٹ کی وضاحت" width="50%">
 
 یہاں ہم **انٹر-چارٹائل رینج** IQR=Q3-Q1 اور نام نہاد **آؤٹ لائرز** کا بھی حساب لگاتے ہیں - وہ قدریں جو حدود [Q1-1.5*IQR,Q3+1.5*IQR] سے باہر ہوتی ہیں۔
 
diff --git a/translations/ur/1-Introduction/04-stats-and-probability/assignment.md b/translations/ur/1-Introduction/04-stats-and-probability/assignment.md
index 5226beae..62219452 100644
--- a/translations/ur/1-Introduction/04-stats-and-probability/assignment.md
+++ b/translations/ur/1-Introduction/04-stats-and-probability/assignment.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "01d1b493e8b51a6ebb42524f6b1bcfff",
-  "translation_date": "2025-08-27T09:17:09+00:00",
-  "source_file": "1-Introduction/04-stats-and-probability/assignment.md",
-  "language_code": "ur"
-}
--->
 # چھوٹا ذیابیطس مطالعہ
 
 اس اسائنمنٹ میں، ہم ذیابیطس کے مریضوں کے ایک چھوٹے ڈیٹا سیٹ کے ساتھ کام کریں گے جو [یہاں](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html) سے لیا گیا ہے۔
diff --git a/translations/ur/1-Introduction/README.md b/translations/ur/1-Introduction/README.md
index 7c7cae5e..3992c2b2 100644
--- a/translations/ur/1-Introduction/README.md
+++ b/translations/ur/1-Introduction/README.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "696a8474a01054281704cbfb09148949",
-  "translation_date": "2025-08-27T08:43:45+00:00",
-  "source_file": "1-Introduction/README.md",
-  "language_code": "ur"
-}
--->
 # ڈیٹا سائنس کا تعارف
 
 ![عملی ڈیٹا](../../../translated_images/ur/data.48e22bb7617d8d92188afbc4c48effb920ba79f5cebdc0652cd9f34bbbd90c18.jpg)
diff --git a/translations/ur/2-Working-With-Data/05-relational-databases/README.md b/translations/ur/2-Working-With-Data/05-relational-databases/README.md
index 0aebd31d..7e4b9a51 100644
--- a/translations/ur/2-Working-With-Data/05-relational-databases/README.md
+++ b/translations/ur/2-Working-With-Data/05-relational-databases/README.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "11739c7b40e7c6b16ad29e3df4e65862",
-  "translation_date": "2025-12-19T10:37:04+00:00",
-  "source_file": "2-Working-With-Data/05-relational-databases/README.md",
-  "language_code": "ur"
-}
--->
 # ڈیٹا کے ساتھ کام کرنا: رلیشنل ڈیٹا بیسز
 
 |![ اسکیچنوٹ بذریعہ [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/05-RelationalData.png)|
diff --git a/translations/ur/2-Working-With-Data/05-relational-databases/assignment.md b/translations/ur/2-Working-With-Data/05-relational-databases/assignment.md
index ac193bb9..f747fcef 100644
--- a/translations/ur/2-Working-With-Data/05-relational-databases/assignment.md
+++ b/translations/ur/2-Working-With-Data/05-relational-databases/assignment.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "25b37acdfb2452917c1aa2e2ca44317a",
-  "translation_date": "2025-10-24T09:52:48+00:00",
-  "source_file": "2-Working-With-Data/05-relational-databases/assignment.md",
-  "language_code": "ur"
-}
--->
 # ہوائی اڈے کے ڈیٹا کو دکھانا
 
 آپ کو ایک [ڈیٹا بیس](https://raw.githubusercontent.com/Microsoft/Data-Science-For-Beginners/main/2-Working-With-Data/05-relational-databases/airports.db) فراہم کیا گیا ہے جو [SQLite](https://sqlite.org/index.html) پر مبنی ہے اور ہوائی اڈوں کے بارے میں معلومات پر مشتمل ہے۔ اس کا اسکیما نیچے دکھایا گیا ہے۔ آپ [Visual Studio Code](https://code.visualstudio.com?WT.mc_id=academic-77958-bethanycheum) میں [SQLite extension](https://marketplace.visualstudio.com/items?itemName=alexcvzz.vscode-sqlite&WT.mc_id=academic-77958-bethanycheum) استعمال کریں گے تاکہ مختلف شہروں کے ہوائی اڈوں کی معلومات دکھا سکیں۔
diff --git a/translations/ur/2-Working-With-Data/06-non-relational/README.md b/translations/ur/2-Working-With-Data/06-non-relational/README.md
index 47ed3f6b..b9728e83 100644
--- a/translations/ur/2-Working-With-Data/06-non-relational/README.md
+++ b/translations/ur/2-Working-With-Data/06-non-relational/README.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "c182e87f9f80be7e7cdffc7b40bbfccf",
-  "translation_date": "2025-09-06T06:36:28+00:00",
-  "source_file": "2-Working-With-Data/06-non-relational/README.md",
-  "language_code": "ur"
-}
--->
 # غیر تعلقاتی ڈیٹا کے ساتھ کام کرنا
 
 |![ [(@sketchthedocs)](https://sketchthedocs.dev) کی طرف سے اسکیچ نوٹ ](../../sketchnotes/06-NoSQL.png)|
diff --git a/translations/ur/2-Working-With-Data/06-non-relational/assignment.md b/translations/ur/2-Working-With-Data/06-non-relational/assignment.md
index 6162dd2d..aeb489ad 100644
--- a/translations/ur/2-Working-With-Data/06-non-relational/assignment.md
+++ b/translations/ur/2-Working-With-Data/06-non-relational/assignment.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
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-  "translation_date": "2025-08-27T08:41:42+00:00",
-  "source_file": "2-Working-With-Data/06-non-relational/assignment.md",
-  "language_code": "ur"
-}
--->
 # سوڈا منافع
 
 ## ہدایات
diff --git a/translations/ur/2-Working-With-Data/07-python/README.md b/translations/ur/2-Working-With-Data/07-python/README.md
index 2bd3525e..b3d1362b 100644
--- a/translations/ur/2-Working-With-Data/07-python/README.md
+++ b/translations/ur/2-Working-With-Data/07-python/README.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
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-  "source_file": "2-Working-With-Data/07-python/README.md",
-  "language_code": "ur"
-}
--->
 # ڈیٹا کے ساتھ کام کرنا: پائتھون اور پانڈاز لائبریری
 
 | ![ [(@sketchthedocs)](https://sketchthedocs.dev) کی طرف سے اسکیچ نوٹ ](../../sketchnotes/07-WorkWithPython.png) |
diff --git a/translations/ur/2-Working-With-Data/07-python/assignment.md b/translations/ur/2-Working-With-Data/07-python/assignment.md
index 67e7f74b..9485a77c 100644
--- a/translations/ur/2-Working-With-Data/07-python/assignment.md
+++ b/translations/ur/2-Working-With-Data/07-python/assignment.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
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-  "source_file": "2-Working-With-Data/07-python/assignment.md",
-  "language_code": "ur"
-}
--->
 # ڈیٹا پروسیسنگ کے لیے پائتھون میں اسائنمنٹ
 
 اس اسائنمنٹ میں، ہم آپ سے درخواست کریں گے کہ آپ ان کوڈز پر تفصیل سے کام کریں جو ہم نے اپنے چیلنجز میں شروع کیے ہیں۔ اس اسائنمنٹ کے دو حصے ہیں:
diff --git a/translations/ur/2-Working-With-Data/08-data-preparation/README.md b/translations/ur/2-Working-With-Data/08-data-preparation/README.md
index 5edb3e4a..a9f0c39d 100644
--- a/translations/ur/2-Working-With-Data/08-data-preparation/README.md
+++ b/translations/ur/2-Working-With-Data/08-data-preparation/README.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "1b560955ff39a2bcf2a049fce474a951",
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-  "source_file": "2-Working-With-Data/08-data-preparation/README.md",
-  "language_code": "ur"
-}
--->
 # ڈیٹا کے ساتھ کام کرنا: ڈیٹا کی تیاری
 
 |![ [(@sketchthedocs)](https://sketchthedocs.dev) کی طرف سے اسکیچ نوٹ ](../../sketchnotes/08-DataPreparation.png)|
diff --git a/translations/ur/2-Working-With-Data/08-data-preparation/assignment.md b/translations/ur/2-Working-With-Data/08-data-preparation/assignment.md
index 67a963c5..a59b68a6 100644
--- a/translations/ur/2-Working-With-Data/08-data-preparation/assignment.md
+++ b/translations/ur/2-Working-With-Data/08-data-preparation/assignment.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "f9d5a7275e046223fa6474477674b810",
-  "translation_date": "2025-08-27T08:29:45+00:00",
-  "source_file": "2-Working-With-Data/08-data-preparation/assignment.md",
-  "language_code": "ur"
-}
--->
 # فارم سے ڈیٹا کا جائزہ لینا
 
 ایک کلائنٹ نے اپنے صارفین کے بارے میں بنیادی معلومات جمع کرنے کے لیے ایک [چھوٹا فارم](../../../../2-Working-With-Data/08-data-preparation/index.html) کا تجربہ کیا ہے۔ وہ اپنے نتائج آپ کے پاس لائے ہیں تاکہ آپ ان کے جمع کردہ ڈیٹا کی تصدیق کریں۔ آپ براؤزر میں `index.html` صفحہ کھول کر فارم دیکھ سکتے ہیں۔
diff --git a/translations/ur/2-Working-With-Data/README.md b/translations/ur/2-Working-With-Data/README.md
index aff15a9b..425791dd 100644
--- a/translations/ur/2-Working-With-Data/README.md
+++ b/translations/ur/2-Working-With-Data/README.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
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-  "source_file": "2-Working-With-Data/README.md",
-  "language_code": "ur"
-}
--->
 # ڈیٹا کے ساتھ کام کرنا
 
 ![data love](../../../translated_images/ur/data-love.a22ef29e6742c852505ada062920956d3d7604870b281a8ca7c7ac6f37381d5a.jpg)
diff --git a/translations/ur/3-Data-Visualization/09-visualization-quantities/README.md b/translations/ur/3-Data-Visualization/09-visualization-quantities/README.md
index d0d567c3..e1778821 100644
--- a/translations/ur/3-Data-Visualization/09-visualization-quantities/README.md
+++ b/translations/ur/3-Data-Visualization/09-visualization-quantities/README.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
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-  "source_file": "3-Data-Visualization/09-visualization-quantities/README.md",
-  "language_code": "ur"
-}
--->
 # مقداروں کی بصری نمائندگی
 
 |![ [(@sketchthedocs)](https://sketchthedocs.dev) کی اسکیچ نوٹ ](../../sketchnotes/09-Visualizing-Quantities.png)|
diff --git a/translations/ur/3-Data-Visualization/09-visualization-quantities/assignment.md b/translations/ur/3-Data-Visualization/09-visualization-quantities/assignment.md
index c5c8298f..acf42b3d 100644
--- a/translations/ur/3-Data-Visualization/09-visualization-quantities/assignment.md
+++ b/translations/ur/3-Data-Visualization/09-visualization-quantities/assignment.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "ad163c4fda72c8278280b61cad317ff4",
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-  "source_file": "3-Data-Visualization/09-visualization-quantities/assignment.md",
-  "language_code": "ur"
-}
--->
 # لکیریں، اسکیٹرز اور بارز
 
 ## ہدایات
diff --git a/translations/ur/3-Data-Visualization/10-visualization-distributions/README.md b/translations/ur/3-Data-Visualization/10-visualization-distributions/README.md
index 42e9ded8..1846c390 100644
--- a/translations/ur/3-Data-Visualization/10-visualization-distributions/README.md
+++ b/translations/ur/3-Data-Visualization/10-visualization-distributions/README.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "80a20467e046d312809d008395051fc7",
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-  "source_file": "3-Data-Visualization/10-visualization-distributions/README.md",
-  "language_code": "ur"
-}
--->
 # تقسیمات کو بصری بنانا
 
 |![ [(@sketchthedocs)](https://sketchthedocs.dev) کی طرف سے اسکیچ نوٹ ](../../sketchnotes/10-Visualizing-Distributions.png)|
diff --git a/translations/ur/3-Data-Visualization/10-visualization-distributions/assignment.md b/translations/ur/3-Data-Visualization/10-visualization-distributions/assignment.md
index 00f63dc3..50b71ad4 100644
--- a/translations/ur/3-Data-Visualization/10-visualization-distributions/assignment.md
+++ b/translations/ur/3-Data-Visualization/10-visualization-distributions/assignment.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "40eeb9b9f94009c537c7811f9f27f037",
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-  "source_file": "3-Data-Visualization/10-visualization-distributions/assignment.md",
-  "language_code": "ur"
-}
--->
 # اپنی مہارتوں کا اطلاق کریں
 
 ## ہدایات
diff --git a/translations/ur/3-Data-Visualization/11-visualization-proportions/README.md b/translations/ur/3-Data-Visualization/11-visualization-proportions/README.md
index 4d3851d2..07803e8a 100644
--- a/translations/ur/3-Data-Visualization/11-visualization-proportions/README.md
+++ b/translations/ur/3-Data-Visualization/11-visualization-proportions/README.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
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-  "source_file": "3-Data-Visualization/11-visualization-proportions/README.md",
-  "language_code": "ur"
-}
--->
 # تناسبات کی بصری نمائندگی
 
 |![ [(@sketchthedocs)](https://sketchthedocs.dev) کی اسکیچ نوٹ ](../../sketchnotes/11-Visualizing-Proportions.png)|
diff --git a/translations/ur/3-Data-Visualization/11-visualization-proportions/assignment.md b/translations/ur/3-Data-Visualization/11-visualization-proportions/assignment.md
index c7bc24ec..4eaa7729 100644
--- a/translations/ur/3-Data-Visualization/11-visualization-proportions/assignment.md
+++ b/translations/ur/3-Data-Visualization/11-visualization-proportions/assignment.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
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-  "source_file": "3-Data-Visualization/11-visualization-proportions/assignment.md",
-  "language_code": "ur"
-}
--->
 # ایکسل میں آزمائیں
 
 ## ہدایات
diff --git a/translations/ur/3-Data-Visualization/12-visualization-relationships/README.md b/translations/ur/3-Data-Visualization/12-visualization-relationships/README.md
index 937adf9e..4b2c16ea 100644
--- a/translations/ur/3-Data-Visualization/12-visualization-relationships/README.md
+++ b/translations/ur/3-Data-Visualization/12-visualization-relationships/README.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
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-  "source_file": "3-Data-Visualization/12-visualization-relationships/README.md",
-  "language_code": "ur"
-}
--->
 # شہد کے تعلقات کی بصری نمائندگی: شہد کے بارے میں سب کچھ 🍯
 
 |![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/12-Visualizing-Relationships.png)|
diff --git a/translations/ur/3-Data-Visualization/12-visualization-relationships/assignment.md b/translations/ur/3-Data-Visualization/12-visualization-relationships/assignment.md
index c98045a8..6aa90dd1 100644
--- a/translations/ur/3-Data-Visualization/12-visualization-relationships/assignment.md
+++ b/translations/ur/3-Data-Visualization/12-visualization-relationships/assignment.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
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-  "language_code": "ur"
-}
--->
 # شہد کی مکھیوں کے چھتے میں غوطہ لگائیں
 
 ## ہدایات
diff --git a/translations/ur/3-Data-Visualization/13-meaningful-visualizations/README.md b/translations/ur/3-Data-Visualization/13-meaningful-visualizations/README.md
index 0240201c..d1c534aa 100644
--- a/translations/ur/3-Data-Visualization/13-meaningful-visualizations/README.md
+++ b/translations/ur/3-Data-Visualization/13-meaningful-visualizations/README.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "cfb068050337a36e348debaa502a24fa",
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-  "source_file": "3-Data-Visualization/13-meaningful-visualizations/README.md",
-  "language_code": "ur"
-}
--->
 # بامعنی بصری نمائیاں بنانا
 
 |![ اسکیچ نوٹ از [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/13-MeaningfulViz.png)|
diff --git a/translations/ur/3-Data-Visualization/13-meaningful-visualizations/assignment.md b/translations/ur/3-Data-Visualization/13-meaningful-visualizations/assignment.md
index 01ac602c..260a9b6e 100644
--- a/translations/ur/3-Data-Visualization/13-meaningful-visualizations/assignment.md
+++ b/translations/ur/3-Data-Visualization/13-meaningful-visualizations/assignment.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "e56df4c0f49357e30ac8fc77aa439dd4",
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-  "source_file": "3-Data-Visualization/13-meaningful-visualizations/assignment.md",
-  "language_code": "ur"
-}
--->
 # اپنی مرضی کی وِز بنائیں
 
 ## ہدایات
diff --git a/translations/ur/3-Data-Visualization/13-meaningful-visualizations/solution/README.md b/translations/ur/3-Data-Visualization/13-meaningful-visualizations/solution/README.md
index dba775bb..fca65294 100644
--- a/translations/ur/3-Data-Visualization/13-meaningful-visualizations/solution/README.md
+++ b/translations/ur/3-Data-Visualization/13-meaningful-visualizations/solution/README.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
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-  "source_file": "3-Data-Visualization/13-meaningful-visualizations/solution/README.md",
-  "language_code": "ur"
-}
--->
 # خطرناک تعلقات ڈیٹا ویژولائزیشن پروجیکٹ
 
 شروع کرنے کے لیے، یہ یقینی بنائیں کہ آپ کے سسٹم پر NPM اور Node انسٹال اور چل رہے ہیں۔ ڈپینڈنسیز انسٹال کریں (npm install) اور پھر پروجیکٹ کو لوکل طور پر چلائیں (npm run serve):
diff --git a/translations/ur/3-Data-Visualization/13-meaningful-visualizations/starter/README.md b/translations/ur/3-Data-Visualization/13-meaningful-visualizations/starter/README.md
index c3224558..06f4115c 100644
--- a/translations/ur/3-Data-Visualization/13-meaningful-visualizations/starter/README.md
+++ b/translations/ur/3-Data-Visualization/13-meaningful-visualizations/starter/README.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "5c51a54dd89075a7a362890117b7ed9e",
-  "translation_date": "2025-08-27T10:11:29+00:00",
-  "source_file": "3-Data-Visualization/13-meaningful-visualizations/starter/README.md",
-  "language_code": "ur"
-}
--->
 # خطرناک تعلقات ڈیٹا ویژولائزیشن پروجیکٹ
 
 شروع کرنے کے لیے، یہ یقینی بنائیں کہ آپ کے سسٹم پر NPM اور Node انسٹال اور چل رہے ہیں۔ ڈپینڈنسیز انسٹال کریں (npm install) اور پھر پروجیکٹ کو لوکل طور پر چلائیں (npm run serve):
diff --git a/translations/ur/3-Data-Visualization/R/09-visualization-quantities/README.md b/translations/ur/3-Data-Visualization/R/09-visualization-quantities/README.md
index b10092cf..c6435272 100644
--- a/translations/ur/3-Data-Visualization/R/09-visualization-quantities/README.md
+++ b/translations/ur/3-Data-Visualization/R/09-visualization-quantities/README.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
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-  "source_file": "3-Data-Visualization/R/09-visualization-quantities/README.md",
-  "language_code": "ur"
-}
--->
 # مقداروں کی بصری نمائندگی
 |![ [(@sketchthedocs)](https://sketchthedocs.dev) کی طرف سے اسکیچ نوٹ ](https://github.com/microsoft/Data-Science-For-Beginners/blob/main/sketchnotes/09-Visualizing-Quantities.png)|
 |:---:|
diff --git a/translations/ur/3-Data-Visualization/R/09-visualization-quantities/assignment.md b/translations/ur/3-Data-Visualization/R/09-visualization-quantities/assignment.md
index ce0d3e1c..5afce419 100644
--- a/translations/ur/3-Data-Visualization/R/09-visualization-quantities/assignment.md
+++ b/translations/ur/3-Data-Visualization/R/09-visualization-quantities/assignment.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
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-  "source_file": "3-Data-Visualization/R/09-visualization-quantities/assignment.md",
-  "language_code": "ur"
-}
--->
 # لکیریں، بکھراؤ اور بارز
 
 ## ہدایات
diff --git a/translations/ur/3-Data-Visualization/R/10-visualization-distributions/README.md b/translations/ur/3-Data-Visualization/R/10-visualization-distributions/README.md
index 290b386f..73396a95 100644
--- a/translations/ur/3-Data-Visualization/R/10-visualization-distributions/README.md
+++ b/translations/ur/3-Data-Visualization/R/10-visualization-distributions/README.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "ea67c0c40808fd723594de6896c37ccf",
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-  "source_file": "3-Data-Visualization/R/10-visualization-distributions/README.md",
-  "language_code": "ur"
-}
--->
 # تقسیمات کو بصری بنانا
 
 |![ [(@sketchthedocs)](https://sketchthedocs.dev) کی طرف سے اسکیچ نوٹ ](https://github.com/microsoft/Data-Science-For-Beginners/blob/main/sketchnotes/10-Visualizing-Distributions.png)|
diff --git a/translations/ur/3-Data-Visualization/R/10-visualization-distributions/assignment.md b/translations/ur/3-Data-Visualization/R/10-visualization-distributions/assignment.md
index ada2f1cb..6b206ba3 100644
--- a/translations/ur/3-Data-Visualization/R/10-visualization-distributions/assignment.md
+++ b/translations/ur/3-Data-Visualization/R/10-visualization-distributions/assignment.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
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-  "source_file": "3-Data-Visualization/R/10-visualization-distributions/assignment.md",
-  "language_code": "ur"
-}
--->
 # اپنی مہارتوں کا اطلاق کریں
 
 ## ہدایات
diff --git a/translations/ur/3-Data-Visualization/R/11-visualization-proportions/README.md b/translations/ur/3-Data-Visualization/R/11-visualization-proportions/README.md
index 4e93d9f1..fd8c1c0f 100644
--- a/translations/ur/3-Data-Visualization/R/11-visualization-proportions/README.md
+++ b/translations/ur/3-Data-Visualization/R/11-visualization-proportions/README.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
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-  "language_code": "ur"
-}
--->
 # تناسبات کی بصری نمائندگی
 
 |![ [(@sketchthedocs)](https://sketchthedocs.dev) کی اسکیچ نوٹ ](../../../sketchnotes/11-Visualizing-Proportions.png)|
diff --git a/translations/ur/3-Data-Visualization/R/12-visualization-relationships/README.md b/translations/ur/3-Data-Visualization/R/12-visualization-relationships/README.md
index d964c254..acf2d69e 100644
--- a/translations/ur/3-Data-Visualization/R/12-visualization-relationships/README.md
+++ b/translations/ur/3-Data-Visualization/R/12-visualization-relationships/README.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
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-  "translation_date": "2025-08-27T10:26:09+00:00",
-  "source_file": "3-Data-Visualization/R/12-visualization-relationships/README.md",
-  "language_code": "ur"
-}
--->
 # شہد کے تعلقات کی بصری نمائندگی: شہد کے بارے میں سب کچھ 🍯
 
 |![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../../sketchnotes/12-Visualizing-Relationships.png)|
diff --git a/translations/ur/3-Data-Visualization/R/13-meaningful-vizualizations/README.md b/translations/ur/3-Data-Visualization/R/13-meaningful-vizualizations/README.md
index c5e985ef..0f2b3abd 100644
--- a/translations/ur/3-Data-Visualization/R/13-meaningful-vizualizations/README.md
+++ b/translations/ur/3-Data-Visualization/R/13-meaningful-vizualizations/README.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "b4039f1c76548d144a0aee0bf28304ec",
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-  "source_file": "3-Data-Visualization/R/13-meaningful-vizualizations/README.md",
-  "language_code": "ur"
-}
--->
 # بامعنی بصری نمائیاں بنانا
 
 |![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../../sketchnotes/13-MeaningfulViz.png)|
diff --git a/translations/ur/3-Data-Visualization/README.md b/translations/ur/3-Data-Visualization/README.md
index 791522ce..16cfac43 100644
--- a/translations/ur/3-Data-Visualization/README.md
+++ b/translations/ur/3-Data-Visualization/README.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
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-  "source_file": "3-Data-Visualization/README.md",
-  "language_code": "ur"
-}
--->
 # تصورات
 
 ![ایک شہد کی مکھی لیونڈر کے پھول پر](../../../translated_images/ur/bee.0aa1d91132b12e3a8994b9ca12816d05ce1642010d9b8be37f8d37365ba845cf.jpg)
diff --git a/translations/ur/4-Data-Science-Lifecycle/14-Introduction/README.md b/translations/ur/4-Data-Science-Lifecycle/14-Introduction/README.md
index c728ecc3..ba80d27d 100644
--- a/translations/ur/4-Data-Science-Lifecycle/14-Introduction/README.md
+++ b/translations/ur/4-Data-Science-Lifecycle/14-Introduction/README.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "07e12a25d20b8f191e3cb651c27fdb2b",
-  "translation_date": "2025-09-06T20:24:33+00:00",
-  "source_file": "4-Data-Science-Lifecycle/14-Introduction/README.md",
-  "language_code": "ur"
-}
--->
 # ڈیٹا سائنس کے لائف سائیکل کا تعارف
 
 |![ [(@sketchthedocs)](https://sketchthedocs.dev) کی اسکیچ نوٹ ](../../sketchnotes/14-DataScience-Lifecycle.png)|
diff --git a/translations/ur/4-Data-Science-Lifecycle/14-Introduction/assignment.md b/translations/ur/4-Data-Science-Lifecycle/14-Introduction/assignment.md
index 9cb725d4..f797bc0b 100644
--- a/translations/ur/4-Data-Science-Lifecycle/14-Introduction/assignment.md
+++ b/translations/ur/4-Data-Science-Lifecycle/14-Introduction/assignment.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "564445c39ad29a491abcb9356fc4d47d",
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-  "source_file": "4-Data-Science-Lifecycle/14-Introduction/assignment.md",
-  "language_code": "ur"
-}
--->
 # ڈیٹا سیٹ کا جائزہ لینا
 
 ایک کلائنٹ نے آپ کی ٹیم سے نیویارک سٹی میں ٹیکسی کے صارفین کے موسمی خرچ کرنے کی عادات کی تحقیق میں مدد مانگی ہے۔
diff --git a/translations/ur/4-Data-Science-Lifecycle/15-analyzing/README.md b/translations/ur/4-Data-Science-Lifecycle/15-analyzing/README.md
index fa03f0be..70af8906 100644
--- a/translations/ur/4-Data-Science-Lifecycle/15-analyzing/README.md
+++ b/translations/ur/4-Data-Science-Lifecycle/15-analyzing/README.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "661dad02c3ac239644d34c1eb51e76f8",
-  "translation_date": "2025-09-06T20:23:49+00:00",
-  "source_file": "4-Data-Science-Lifecycle/15-analyzing/README.md",
-  "language_code": "ur"
-}
--->
 # ڈیٹا سائنس لائف سائیکل: تجزیہ
 
 |![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/15-Analyzing.png)|
diff --git a/translations/ur/4-Data-Science-Lifecycle/15-analyzing/assignment.md b/translations/ur/4-Data-Science-Lifecycle/15-analyzing/assignment.md
index 479cf3c3..79e547b1 100644
--- a/translations/ur/4-Data-Science-Lifecycle/15-analyzing/assignment.md
+++ b/translations/ur/4-Data-Science-Lifecycle/15-analyzing/assignment.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "fcc7547171f4530f159676dd73ed772e",
-  "translation_date": "2025-08-27T09:57:30+00:00",
-  "source_file": "4-Data-Science-Lifecycle/15-analyzing/assignment.md",
-  "language_code": "ur"
-}
--->
 # جوابات کی تلاش
 
 یہ پچھلے سبق کے [assignment](../14-Introduction/assignment.md) کا تسلسل ہے، جہاں ہم نے ڈیٹا سیٹ پر مختصر نظر ڈالی تھی۔ اب ہم ڈیٹا کو مزید گہرائی سے دیکھیں گے۔
diff --git a/translations/ur/4-Data-Science-Lifecycle/16-communication/README.md b/translations/ur/4-Data-Science-Lifecycle/16-communication/README.md
index 02e01b0b..d972c83c 100644
--- a/translations/ur/4-Data-Science-Lifecycle/16-communication/README.md
+++ b/translations/ur/4-Data-Science-Lifecycle/16-communication/README.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "215a3254ba5a222a57c5bb0192cea8e3",
-  "translation_date": "2025-09-06T20:25:43+00:00",
-  "source_file": "4-Data-Science-Lifecycle/16-communication/README.md",
-  "language_code": "ur"
-}
--->
 # ڈیٹا سائنس لائف سائیکل: مواصلات
 
 |![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev)](../../sketchnotes/16-Communicating.png)|
diff --git a/translations/ur/4-Data-Science-Lifecycle/16-communication/assignment.md b/translations/ur/4-Data-Science-Lifecycle/16-communication/assignment.md
index c598fb20..844f0456 100644
--- a/translations/ur/4-Data-Science-Lifecycle/16-communication/assignment.md
+++ b/translations/ur/4-Data-Science-Lifecycle/16-communication/assignment.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "8980d7efd101c82d6d6ffc3458214120",
-  "translation_date": "2025-08-27T10:05:03+00:00",
-  "source_file": "4-Data-Science-Lifecycle/16-communication/assignment.md",
-  "language_code": "ur"
-}
--->
 # کہانی سنائیں
 
 ## ہدایات
diff --git a/translations/ur/4-Data-Science-Lifecycle/README.md b/translations/ur/4-Data-Science-Lifecycle/README.md
index cb1abe21..97be8e41 100644
--- a/translations/ur/4-Data-Science-Lifecycle/README.md
+++ b/translations/ur/4-Data-Science-Lifecycle/README.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "dd173fd30fc039a7a299898920680723",
-  "translation_date": "2025-08-27T09:47:58+00:00",
-  "source_file": "4-Data-Science-Lifecycle/README.md",
-  "language_code": "ur"
-}
--->
 # ڈیٹا سائنس کا لائف سائیکل
 
 ![communication](../../../translated_images/ur/communication.06d8e2a88d30d168d661ad9f9f0a4f947ebff3719719cfdaf9ed00a406a01ead.jpg)
diff --git a/translations/ur/5-Data-Science-In-Cloud/17-Introduction/README.md b/translations/ur/5-Data-Science-In-Cloud/17-Introduction/README.md
index 280a729b..39a289de 100644
--- a/translations/ur/5-Data-Science-In-Cloud/17-Introduction/README.md
+++ b/translations/ur/5-Data-Science-In-Cloud/17-Introduction/README.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "5f8e7cdefa096664ae86f795be571580",
-  "translation_date": "2025-09-06T06:34:25+00:00",
-  "source_file": "5-Data-Science-In-Cloud/17-Introduction/README.md",
-  "language_code": "ur"
-}
--->
 # کلاؤڈ میں ڈیٹا سائنس کا تعارف
 
 |![ [(@sketchthedocs)](https://sketchthedocs.dev) کی اسکیچ نوٹ ](../../sketchnotes/17-DataScience-Cloud.png)|
diff --git a/translations/ur/5-Data-Science-In-Cloud/17-Introduction/assignment.md b/translations/ur/5-Data-Science-In-Cloud/17-Introduction/assignment.md
index 9877420d..66bd26ac 100644
--- a/translations/ur/5-Data-Science-In-Cloud/17-Introduction/assignment.md
+++ b/translations/ur/5-Data-Science-In-Cloud/17-Introduction/assignment.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "96f3696153d9ed54b19a1bb65438c104",
-  "translation_date": "2025-08-27T09:38:49+00:00",
-  "source_file": "5-Data-Science-In-Cloud/17-Introduction/assignment.md",
-  "language_code": "ur"
-}
--->
 # مارکیٹ ریسرچ
 
 ## ہدایات
diff --git a/translations/ur/5-Data-Science-In-Cloud/18-Low-Code/README.md b/translations/ur/5-Data-Science-In-Cloud/18-Low-Code/README.md
index bca73862..ae386eaf 100644
--- a/translations/ur/5-Data-Science-In-Cloud/18-Low-Code/README.md
+++ b/translations/ur/5-Data-Science-In-Cloud/18-Low-Code/README.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "bd4da10766c64fce4294a98f6479dfb0",
-  "translation_date": "2025-09-06T06:33:13+00:00",
-  "source_file": "5-Data-Science-In-Cloud/18-Low-Code/README.md",
-  "language_code": "ur"
-}
--->
 # کلاؤڈ میں ڈیٹا سائنس: "کم کوڈ/بغیر کوڈ" طریقہ
 
 |![ [(@sketchthedocs)](https://sketchthedocs.dev) کی اسکیچ نوٹ ](../../sketchnotes/18-DataScience-Cloud.png)|
diff --git a/translations/ur/5-Data-Science-In-Cloud/18-Low-Code/assignment.md b/translations/ur/5-Data-Science-In-Cloud/18-Low-Code/assignment.md
index ef412720..f5a65200 100644
--- a/translations/ur/5-Data-Science-In-Cloud/18-Low-Code/assignment.md
+++ b/translations/ur/5-Data-Science-In-Cloud/18-Low-Code/assignment.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "8fdc4a5fd9bc27a8d2ebef995dfbf73f",
-  "translation_date": "2025-08-27T09:33:49+00:00",
-  "source_file": "5-Data-Science-In-Cloud/18-Low-Code/assignment.md",
-  "language_code": "ur"
-}
--->
 # ایزور ایم ایل پر لو کوڈ/نو کوڈ ڈیٹا سائنس پروجیکٹ
 
 ## ہدایات
diff --git a/translations/ur/5-Data-Science-In-Cloud/19-Azure/README.md b/translations/ur/5-Data-Science-In-Cloud/19-Azure/README.md
index 441e5c1e..e2cdc661 100644
--- a/translations/ur/5-Data-Science-In-Cloud/19-Azure/README.md
+++ b/translations/ur/5-Data-Science-In-Cloud/19-Azure/README.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "472d3fab1c5be50f387336e7a686dbe1",
-  "translation_date": "2025-09-06T06:35:39+00:00",
-  "source_file": "5-Data-Science-In-Cloud/19-Azure/README.md",
-  "language_code": "ur"
-}
--->
 # کلاؤڈ میں ڈیٹا سائنس: "Azure ML SDK" کا طریقہ
 
 |![ [(@sketchthedocs)](https://sketchthedocs.dev) کی اسکیچ نوٹ ](../../sketchnotes/19-DataScience-Cloud.png)|
diff --git a/translations/ur/5-Data-Science-In-Cloud/19-Azure/assignment.md b/translations/ur/5-Data-Science-In-Cloud/19-Azure/assignment.md
index 1b19445e..7b1904da 100644
--- a/translations/ur/5-Data-Science-In-Cloud/19-Azure/assignment.md
+++ b/translations/ur/5-Data-Science-In-Cloud/19-Azure/assignment.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "386efdbc19786951341f6956247ee990",
-  "translation_date": "2025-08-27T09:45:38+00:00",
-  "source_file": "5-Data-Science-In-Cloud/19-Azure/assignment.md",
-  "language_code": "ur"
-}
--->
 # Azure ML SDK کا استعمال کرتے ہوئے ڈیٹا سائنس پروجیکٹ
 
 ## ہدایات
diff --git a/translations/ur/5-Data-Science-In-Cloud/README.md b/translations/ur/5-Data-Science-In-Cloud/README.md
index 582ebcb8..4b691a7f 100644
--- a/translations/ur/5-Data-Science-In-Cloud/README.md
+++ b/translations/ur/5-Data-Science-In-Cloud/README.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "8dfe141a0f46f7d253e07f74913c7f44",
-  "translation_date": "2025-08-27T09:25:51+00:00",
-  "source_file": "5-Data-Science-In-Cloud/README.md",
-  "language_code": "ur"
-}
--->
 # کلاؤڈ میں ڈیٹا سائنس
 
 ![cloud-picture](../../../translated_images/ur/cloud-picture.f5526de3c6c6387b2d656ba94f019b3352e5e3854a78440e4fb00c93e2dea675.jpg)
diff --git a/translations/ur/6-Data-Science-In-Wild/20-Real-World-Examples/README.md b/translations/ur/6-Data-Science-In-Wild/20-Real-World-Examples/README.md
index 93be0c6f..9ce281e4 100644
--- a/translations/ur/6-Data-Science-In-Wild/20-Real-World-Examples/README.md
+++ b/translations/ur/6-Data-Science-In-Wild/20-Real-World-Examples/README.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "0f67a4139454816631526779a456b734",
-  "translation_date": "2025-09-06T18:15:38+00:00",
-  "source_file": "6-Data-Science-In-Wild/20-Real-World-Examples/README.md",
-  "language_code": "ur"
-}
--->
 # ڈیٹا سائنس حقیقی دنیا میں
 
 | ![ [(@sketchthedocs)](https://sketchthedocs.dev) کی اسکیچ نوٹ ](../../sketchnotes/20-DataScience-RealWorld.png) |
diff --git a/translations/ur/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md b/translations/ur/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md
index 9f3ed44f..ed7dc98d 100644
--- a/translations/ur/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md
+++ b/translations/ur/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "d1e05715f9d97de6c4f1fb0c5a4702c0",
-  "translation_date": "2025-08-27T09:24:49+00:00",
-  "source_file": "6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md",
-  "language_code": "ur"
-}
--->
 # سیاروی کمپیوٹر ڈیٹا سیٹ کا جائزہ لیں
 
 ## ہدایات
diff --git a/translations/ur/6-Data-Science-In-Wild/README.md b/translations/ur/6-Data-Science-In-Wild/README.md
index d4d5e3e1..81587ead 100644
--- a/translations/ur/6-Data-Science-In-Wild/README.md
+++ b/translations/ur/6-Data-Science-In-Wild/README.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "07faf02ff163e609edf0b0308dc5d4e6",
-  "translation_date": "2025-08-27T09:18:20+00:00",
-  "source_file": "6-Data-Science-In-Wild/README.md",
-  "language_code": "ur"
-}
--->
 # جنگلی دنیا میں ڈیٹا سائنس
 
 مختلف صنعتوں میں ڈیٹا سائنس کے حقیقی دنیا کے اطلاقات۔
diff --git a/translations/ur/AGENTS.md b/translations/ur/AGENTS.md
index b6704932..5f6709b3 100644
--- a/translations/ur/AGENTS.md
+++ b/translations/ur/AGENTS.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "cc2e18ab65df63e75d3619c4752e9b22",
-  "translation_date": "2025-10-03T11:04:09+00:00",
-  "source_file": "AGENTS.md",
-  "language_code": "ur"
-}
--->
 # اے جی ای این ٹی ایس۔ایم ڈی
 
 ## پروجیکٹ کا جائزہ
diff --git a/translations/ur/CODE_OF_CONDUCT.md b/translations/ur/CODE_OF_CONDUCT.md
index 7190b417..11363e44 100644
--- a/translations/ur/CODE_OF_CONDUCT.md
+++ b/translations/ur/CODE_OF_CONDUCT.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "c06b12caf3c901eb3156e3dd5b0aea56",
-  "translation_date": "2025-08-27T08:16:06+00:00",
-  "source_file": "CODE_OF_CONDUCT.md",
-  "language_code": "ur"
-}
--->
 # مائیکروسافٹ اوپن سورس ضابطہ اخلاق
 
 اس پروجیکٹ نے [مائیکروسافٹ اوپن سورس ضابطہ اخلاق](https://opensource.microsoft.com/codeofconduct/) کو اپنایا ہے۔
diff --git a/translations/ur/CONTRIBUTING.md b/translations/ur/CONTRIBUTING.md
index 98a1c6e0..3954a905 100644
--- a/translations/ur/CONTRIBUTING.md
+++ b/translations/ur/CONTRIBUTING.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "10f86fb29b5407088445ac803b3d0ed1",
-  "translation_date": "2025-10-03T13:28:16+00:00",
-  "source_file": "CONTRIBUTING.md",
-  "language_code": "ur"
-}
--->
 # ڈیٹا سائنس فار بیگنرز میں تعاون کرنا
 
 ڈیٹا سائنس فار بیگنرز کے نصاب میں تعاون کرنے میں دلچسپی لینے کا شکریہ! ہم کمیونٹی سے تعاون کا خیر مقدم کرتے ہیں۔
diff --git a/translations/ur/INSTALLATION.md b/translations/ur/INSTALLATION.md
index 56ccb332..37f87e6a 100644
--- a/translations/ur/INSTALLATION.md
+++ b/translations/ur/INSTALLATION.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "a64d8afa22ffcc2016bb239188d6acb1",
-  "translation_date": "2025-10-03T15:15:36+00:00",
-  "source_file": "INSTALLATION.md",
-  "language_code": "ur"
-}
--->
 # انسٹالیشن گائیڈ
 
 یہ گائیڈ آپ کو ڈیٹا سائنس فار بیگنرز نصاب کے ساتھ کام کرنے کے لیے اپنا ماحول ترتیب دینے میں مدد دے گی۔
diff --git a/translations/ur/README.md b/translations/ur/README.md
index fdc218e1..b27cbb7a 100644
--- a/translations/ur/README.md
+++ b/translations/ur/README.md
@@ -1,204 +1,195 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "33d252f7491b696d85df7f680e7e7b90",
-  "translation_date": "2026-01-16T09:19:10+00:00",
-  "source_file": "README.md",
-  "language_code": "ur"
-}
--->
-# Data Science for Beginners - ایک نصاب
-
-[![Open in GitHub Codespaces](https://github.com/codespaces/badge.svg)](https://github.com/codespaces/new?hide_repo_select=true&ref=main&repo=344191198)
-
-[![GitHub license](https://img.shields.io/github/license/microsoft/Data-Science-For-Beginners.svg)](https://github.com/microsoft/Data-Science-For-Beginners/blob/master/LICENSE)
-[![GitHub contributors](https://img.shields.io/github/contributors/microsoft/Data-Science-For-Beginners.svg)](https://GitHub.com/microsoft/Data-Science-For-Beginners/graphs/contributors/)
-[![GitHub issues](https://img.shields.io/github/issues/microsoft/Data-Science-For-Beginners.svg)](https://GitHub.com/microsoft/Data-Science-For-Beginners/issues/)
-[![GitHub pull-requests](https://img.shields.io/github/issues-pr/microsoft/Data-Science-For-Beginners.svg)](https://GitHub.com/microsoft/Data-Science-For-Beginners/pulls/)
-[![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)](http://makeapullrequest.com)
-
-[![GitHub watchers](https://img.shields.io/github/watchers/microsoft/Data-Science-For-Beginners.svg?style=social&label=Watch)](https://GitHub.com/microsoft/Data-Science-For-Beginners/watchers/)
-[![GitHub forks](https://img.shields.io/github/forks/microsoft/Data-Science-For-Beginners.svg?style=social&label=Fork)](https://GitHub.com/microsoft/Data-Science-For-Beginners/network/)
-[![GitHub stars](https://img.shields.io/github/stars/microsoft/Data-Science-For-Beginners.svg?style=social&label=Star)](https://GitHub.com/microsoft/Data-Science-For-Beginners/stargazers/)
+# ابتدائی افراد کے لیے ڈیٹا سائنس - نصاب
+
+[![GitHub Codespaces میں کھولیں](https://github.com/codespaces/badge.svg)](https://github.com/codespaces/new?hide_repo_select=true&ref=main&repo=344191198)
+
+[![GitHub لائسنس](https://img.shields.io/github/license/microsoft/Data-Science-For-Beginners.svg)](https://github.com/microsoft/Data-Science-For-Beginners/blob/master/LICENSE)
+[![GitHub شراکت دار](https://img.shields.io/github/contributors/microsoft/Data-Science-For-Beginners.svg)](https://GitHub.com/microsoft/Data-Science-For-Beginners/graphs/contributors/)
+[![GitHub مسائل](https://img.shields.io/github/issues/microsoft/Data-Science-For-Beginners.svg)](https://GitHub.com/microsoft/Data-Science-For-Beginners/issues/)
+[![GitHub پل درخواست](https://img.shields.io/github/issues-pr/microsoft/Data-Science-For-Beginners.svg)](https://GitHub.com/microsoft/Data-Science-For-Beginners/pulls/)
+[![PRs خوش آمدید](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)](http://makeapullrequest.com)
+
+[![GitHub دیکھنے والے](https://img.shields.io/github/watchers/microsoft/Data-Science-For-Beginners.svg?style=social&label=Watch)](https://GitHub.com/microsoft/Data-Science-For-Beginners/watchers/)
+[![GitHub فورک](https://img.shields.io/github/forks/microsoft/Data-Science-For-Beginners.svg?style=social&label=Fork)](https://GitHub.com/microsoft/Data-Science-For-Beginners/network/)
+[![GitHub اسٹار](https://img.shields.io/github/stars/microsoft/Data-Science-For-Beginners.svg?style=social&label=Star)](https://GitHub.com/microsoft/Data-Science-For-Beginners/stargazers/)
 
 
 [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG)
 
 [![Microsoft Foundry Developer Forum](https://img.shields.io/badge/GitHub-Microsoft_Foundry_Developer_Forum-blue?style=for-the-badge&logo=github&color=000000&logoColor=fff)](https://aka.ms/foundry/forum)
 
-مائیکروسافٹ کے Azure Cloud Advocates خوش ہیں کہ وہ ڈیٹا سائنس کے بارے میں دس ہفتوں پر مشتمل، بیس اسباق پر مشتمل نصاب پیش کر رہے ہیں۔ ہر سبق میں پری-سبق اور پوسٹ-سبق کوئز، سبق مکمل کرنے کی تحریری ہدایات، ایک حل، اور ایک اسائنمنٹ شامل ہے۔ ہمارا پراجیکٹ پر مبنی طریقہ تدریس آپ کو سیکھنے کے دوران تعمیر کرنے کی اجازت دیتا ہے، جو نئی مہارتوں کے موثر طور پر ’لگ جانے‘ کا ثابت شدہ طریقہ ہے۔
+مائیکروسافٹ میں Azure کلاؤڈ ایڈووکیٹس خوش ہیں کہ وہ 10 ہفتے، 20 سبقوں پر مشتمل نصاب پیش کر رہے ہیں جو مکمل طور پر ڈیٹا سائنس کے بارے میں ہے۔ ہر سبق میں پری-سبق اور پوسٹ-سبق کوئزز، سبق کو مکمل کرنے کی تحریری ہدایات، ایک حل، اور ایک اسائنمنٹ شامل ہے۔ ہمارا پروجیکٹ-بنیاد تدریسی طریقہ آپ کو سیکھنے کے دوران تعمیر کرنے کی اجازت دیتا ہے، جو نئے ہنر سیکھنے کا ایک مؤثر طریقہ ہے۔
 
 **ہمارے مصنفین کا دلی شکریہ:** [Jasmine Greenaway](https://www.twitter.com/paladique)، [Dmitry Soshnikov](http://soshnikov.com)، [Nitya Narasimhan](https://twitter.com/nitya)، [Jalen McGee](https://twitter.com/JalenMcG)، [Jen Looper](https://twitter.com/jenlooper)، [Maud Levy](https://twitter.com/maudstweets)، [Tiffany Souterre](https://twitter.com/TiffanySouterre)، [Christopher Harrison](https://www.twitter.com/geektrainer)۔
 
-**🙏 خصوصی شکریہ ہمارے [Microsoft Student Ambassador](https://studentambassadors.microsoft.com/) مصنفین، جائزہ لینے والوں، اور مواد فراہم کرنے والوں کو،** خاص طور پر آریان آرورا، [Aditya Garg](https://github.com/AdityaGarg00)، [Alondra Sanchez](https://www.linkedin.com/in/alondra-sanchez-molina/)، [Ankita Singh](https://www.linkedin.com/in/ankitasingh007)، [Anupam Mishra](https://www.linkedin.com/in/anupam--mishra/)، [Arpita Das](https://www.linkedin.com/in/arpitadas01/)، ChhailBihari Dubey، [Dibri Nsofor](https://www.linkedin.com/in/dibrinsofor)، [Dishita Bhasin](https://www.linkedin.com/in/dishita-bhasin-7065281bb)، [Majd Safi](https://www.linkedin.com/in/majd-s/)، [Max Blum](https://www.linkedin.com/in/max-blum-6036a1186/)، [Miguel Correa](https://www.linkedin.com/in/miguelmque/)، [Mohamma Iftekher (Iftu) Ebne Jalal](https://twitter.com/iftu119)، [Nawrin Tabassum](https://www.linkedin.com/in/nawrin-tabassum)، [Raymond Wangsa Putra](https://www.linkedin.com/in/raymond-wp/)، [Rohit Yadav](https://www.linkedin.com/in/rty2423)، Samridhi Sharma، [Sanya Sinha](https://www.linkedin.com/mwlite/in/sanya-sinha-13aab1200),
-[Sheena Narula](https://www.linkedin.com/in/sheena-narua-n/)، [Tauqeer Ahmad](https://www.linkedin.com/in/tauqeerahmad5201/)، Yogendrasingh Pawar ، [Vidushi Gupta](https://www.linkedin.com/in/vidushi-gupta07/)، [Jasleen Sondhi](https://www.linkedin.com/in/jasleen-sondhi/)
+**🙏 خصوصی شکریہ 🙏 ہمارے [Microsoft Student Ambassador](https://studentambassadors.microsoft.com/) مصنفین، جائزہ لینے والوں اور مواد کے تعاون کرنے والوں کو،** جن میں خاص طور پر شامل ہیں: Aaryan Arora، [Aditya Garg](https://github.com/AdityaGarg00)، [Alondra Sanchez](https://www.linkedin.com/in/alondra-sanchez-molina/)، [Ankita Singh](https://www.linkedin.com/in/ankitasingh007)، [Anupam Mishra](https://www.linkedin.com/in/anupam--mishra/)، [Arpita Das](https://www.linkedin.com/in/arpitadas01/)، ChhailBihari Dubey، [Dibri Nsofor](https://www.linkedin.com/in/dibrinsofor)، [Dishita Bhasin](https://www.linkedin.com/in/dishita-bhasin-7065281bb)، [Majd Safi](https://www.linkedin.com/in/majd-s/)، [Max Blum](https://www.linkedin.com/in/max-blum-6036a1186/)، [Miguel Correa](https://www.linkedin.com/in/miguelmque/)، [Mohamma Iftekher (Iftu) Ebne Jalal](https://twitter.com/iftu119)، [Nawrin Tabassum](https://www.linkedin.com/in/nawrin-tabassum)، [Raymond Wangsa Putra](https://www.linkedin.com/in/raymond-wp/)، [Rohit Yadav](https://www.linkedin.com/in/rty2423)، Samridhi Sharma، [Sanya Sinha](https://www.linkedin.com/mwlite/in/sanya-sinha-13aab1200)۔
+[Sheena Narula](https://www.linkedin.com/in/sheena-narua-n/)، [Tauqeer Ahmad](https://www.linkedin.com/in/tauqeerahmad5201/)، Yogendrasingh Pawar، [Vidushi Gupta](https://www.linkedin.com/in/vidushi-gupta07/)، [Jasleen Sondhi](https://www.linkedin.com/in/jasleen-sondhi/)
 
-|![Sketchnote by @sketchthedocs https://sketchthedocs.dev](../../../../translated_images/ur/00-Title.8af36cd35da1ac55.webp)|
+|![@sketchthedocs کی اسکچنوٹ https://sketchthedocs.dev](../../translated_images/ur/00-Title.8af36cd35da1ac55.webp)|
 |:---:|
-| Data Science For Beginners - _اسکیچ نوٹ از [@nitya](https://twitter.com/nitya)_ |
+| ابتدائی افراد کے لیے ڈیٹا سائنس - _اسکچنوٹ از [@nitya](https://twitter.com/nitya)_ |
 
-### 🌐 کثیر اللسانی مدد
+### 🌐 کثیراللسانی معاونت
 
-#### GitHub Action کے ذریعے تعاون یافتہ (خودکار اور ہمیشہ تازہ ترین)
+#### GitHub ایکشن کے ذریعے مدد یافتہ (خودکار اور ہمیشہ تازہ ترین)
 
 <!-- CO-OP TRANSLATOR LANGUAGES TABLE START -->
-[Arabic](../ar/README.md) | [Bengali](../bn/README.md) | [Bulgarian](../bg/README.md) | [Burmese (Myanmar)](../my/README.md) | [Chinese (Simplified)](../zh/README.md) | [Chinese (Traditional, Hong Kong)](../hk/README.md) | [Chinese (Traditional, Macau)](../mo/README.md) | [Chinese (Traditional, Taiwan)](../tw/README.md) | [Croatian](../hr/README.md) | [Czech](../cs/README.md) | [Danish](../da/README.md) | [Dutch](../nl/README.md) | [Estonian](../et/README.md) | [Finnish](../fi/README.md) | [French](../fr/README.md) | [German](../de/README.md) | [Greek](../el/README.md) | [Hebrew](../he/README.md) | [Hindi](../hi/README.md) | [Hungarian](../hu/README.md) | [Indonesian](../id/README.md) | [Italian](../it/README.md) | [Japanese](../ja/README.md) | [Kannada](../kn/README.md) | [Korean](../ko/README.md) | [Lithuanian](../lt/README.md) | [Malay](../ms/README.md) | [Malayalam](../ml/README.md) | [Marathi](../mr/README.md) | [Nepali](../ne/README.md) | [Nigerian Pidgin](../pcm/README.md) | [Norwegian](../no/README.md) | [Persian (Farsi)](../fa/README.md) | [Polish](../pl/README.md) | [Portuguese (Brazil)](../br/README.md) | [Portuguese (Portugal)](../pt/README.md) | [Punjabi (Gurmukhi)](../pa/README.md) | [Romanian](../ro/README.md) | [Russian](../ru/README.md) | [Serbian (Cyrillic)](../sr/README.md) | [Slovak](../sk/README.md) | [Slovenian](../sl/README.md) | [Spanish](../es/README.md) | [Swahili](../sw/README.md) | [Swedish](../sv/README.md) | [Tagalog (Filipino)](../tl/README.md) | [Tamil](../ta/README.md) | [Telugu](../te/README.md) | [Thai](../th/README.md) | [Turkish](../tr/README.md) | [Ukrainian](../uk/README.md) | [Urdu](./README.md) | [Vietnamese](../vi/README.md)
+[Arabic](../ar/README.md) | [Bengali](../bn/README.md) | [Bulgarian](../bg/README.md) | [Burmese (Myanmar)](../my/README.md) | [Chinese (Simplified)](../zh-CN/README.md) | [Chinese (Traditional, Hong Kong)](../zh-HK/README.md) | [Chinese (Traditional, Macau)](../zh-MO/README.md) | [Chinese (Traditional, Taiwan)](../zh-TW/README.md) | [Croatian](../hr/README.md) | [Czech](../cs/README.md) | [Danish](../da/README.md) | [Dutch](../nl/README.md) | [Estonian](../et/README.md) | [Finnish](../fi/README.md) | [French](../fr/README.md) | [German](../de/README.md) | [Greek](../el/README.md) | [Hebrew](../he/README.md) | [Hindi](../hi/README.md) | [Hungarian](../hu/README.md) | [Indonesian](../id/README.md) | [Italian](../it/README.md) | [Japanese](../ja/README.md) | [Kannada](../kn/README.md) | [Korean](../ko/README.md) | [Lithuanian](../lt/README.md) | [Malay](../ms/README.md) | [Malayalam](../ml/README.md) | [Marathi](../mr/README.md) | [Nepali](../ne/README.md) | [Nigerian Pidgin](../pcm/README.md) | [Norwegian](../no/README.md) | [Persian (Farsi)](../fa/README.md) | [Polish](../pl/README.md) | [Portuguese (Brazil)](../pt-BR/README.md) | [Portuguese (Portugal)](../pt-PT/README.md) | [Punjabi (Gurmukhi)](../pa/README.md) | [Romanian](../ro/README.md) | [Russian](../ru/README.md) | [Serbian (Cyrillic)](../sr/README.md) | [Slovak](../sk/README.md) | [Slovenian](../sl/README.md) | [Spanish](../es/README.md) | [Swahili](../sw/README.md) | [Swedish](../sv/README.md) | [Tagalog (Filipino)](../tl/README.md) | [Tamil](../ta/README.md) | [Telugu](../te/README.md) | [Thai](../th/README.md) | [Turkish](../tr/README.md) | [Ukrainian](../uk/README.md) | [Urdu](./README.md) | [Vietnamese](../vi/README.md)
 
-> **مقامی طور پر کلون کرنا پسند کریں؟**
+> **کیا آپ لوکل کلون کرنا پسند کریں گے؟**
 
-> یہ مخزن 50+ زبانوں کے ترجمے شامل کرتا ہے جو ڈاؤن لوڈ سائز کو نمایاں طور پر بڑھاتے ہیں۔ بغیر ترجموں کے کلون کرنے کے لیے sparse checkout استعمال کریں:
+> اس ذخیرے میں 50+ زبانوں کے تراجم شامل ہیں جس سے ڈاؤن لوڈ کا سائز کافی بڑھ جاتا ہے۔ بغیر تراجم کے کلون کرنے کے لیے اسپرز چیک آؤٹ استعمال کریں:
 > ```bash
 > git clone --filter=blob:none --sparse https://github.com/microsoft/Data-Science-For-Beginners.git
 > cd Data-Science-For-Beginners
 > git sparse-checkout set --no-cone '/*' '!translations' '!translated_images'
 > ```
-> یہ آپ کو کورس مکمل کرنے کے لیے ہر وہ چیز فراہم کرتا ہے جس کی آپ کو ضرورت ہے، وہ بھی بہت تیز ڈاؤن لوڈ کے ساتھ۔
+> اس سے آپ کو نصاب مکمل کرنے کے لیے درکار تمام مواد تیزی سے مل جائے گا۔
 <!-- CO-OP TRANSLATOR LANGUAGES TABLE END -->
 
-**اگر آپ اضافی زبانوں کے لیے تعاون چاہتے ہیں تو ان کی فہرست یہاں دستیاب ہے [here](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md)**
+**اگر آپ اضافی ترجمانی زبانیں چاہتے ہیں تو یہاں دیکھیں [here](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md)**
 
-#### ہماری کمیونٹی میں شامل ہوں  
+#### ہماری کمیونٹی میں شامل ہوں
 [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG)
 
-ہمارے پاس ایک Discord میں AI کے ساتھ سیکھنے کی سیریز جاری ہے، مزید جاننے اور شامل ہونے کے لیے [Learn with AI Series](https://aka.ms/learnwithai/discord) پر تشریف لائیں 18 سے 30 ستمبر، 2025۔ آپ کو ڈیٹا سائنس کے لیے GitHub Copilot کے استعمال کے ٹپس اور تراکیب ملیں گی۔
+ہمارے پاس ڈسکارڈ پر AI کے ساتھ سیکھنے کی سیریز جاری ہے، مزید سیکھنے اور شامل ہونے کے لیے [Learn with AI Series](https://aka.ms/learnwithai/discord) پر آئیں، یہ سلسلہ 18 سے 30 ستمبر، 2025 تک ہے۔ آپ کو GitHub Copilot کے ذریعے ڈیٹا سائنس کے ٹپس اور ٹرکس ملیں گے۔
 
-![Learn with AI series](../../../../translated_images/ur/1.2b28cdc6205e26fe.webp)
+![AI کے ساتھ سیکھیں](../../translated_images/ur/1.2b28cdc6205e26fe.webp)
 
-# کیا آپ طالبعلم ہیں؟
+# کیا آپ طالب علم ہیں؟
 
 مندرجہ ذیل وسائل کے ساتھ شروع کریں:
 
-- [Student Hub صفحہ](https://docs.microsoft.com/en-gb/learn/student-hub?WT.mc_id=academic-77958-bethanycheum) اس صفحے پر آپ کو مبتدی وسائل، طالبعلم پیکس، اور یہاں تک کہ مفت سرٹیفکیٹ واؤچر حاصل کرنے کے طریقے ملیں گے۔ یہ ایک ایسا صفحہ ہے جسے آپ کو بُک مارک کرنا چاہیے اور وقتاً فوقتاً چیک کرنا چاہیے کیونکہ ہم کم از کم ماہانہ بنیاد پر مواد تبدیل کرتے رہتے ہیں۔
-- [Microsoft Learn Student Ambassadors](https://studentambassadors.microsoft.com?WT.mc_id=academic-77958-bethanycheum) ایک عالمی طلباء کے سفیر کمیونٹی میں شامل ہوں، یہ آپ کا مائیکروسافٹ میں شامل ہونے کا طریقہ ہو سکتا ہے۔
+- [طالب علم مرکز صفحہ](https://docs.microsoft.com/en-gb/learn/student-hub?WT.mc_id=academic-77958-bethanycheum) اس صفحے پر آپ کو ابتدائی وسائل، طالب علم پیک اور حتیٰ کہ مفت سرٹیفکیٹ واؤچر حاصل کرنے کے طریقے ملیں گے۔ یہ وہ صفحہ ہے جسے آپ کو بُک مارک کرنا چاہیے اور وقتاً فوقتاً چیک کرنا چاہیے کیونکہ ہم ماہانہ مواد کو اپ ڈیٹ کرتے رہتے ہیں۔
+- [Microsoft Learn Student Ambassadors](https://studentambassadors.microsoft.com?WT.mc_id=academic-77958-bethanycheum) عالمی طالب علم سفیروں کی کمیونٹی میں شامل ہوں، یہ آپ کا مائیکروسافٹ میں داخلہ ہو سکتا ہے۔
 
 # شروعات کیسے کریں
 
 ## 📚 دستاویزات
 
-- **[انسٹالیشن گائیڈ](INSTALLATION.md)** - مبتدئين کے لیے قدم بہ قدم ترتیب کی ہدایات
-- **[استعمال کا گائیڈ](USAGE.md)** - مثالیں اور عام ورک فلو
-- **[مسائل کا حل](TROUBLESHOOTING.md)** - عام مسائل کے حل
-- **[شراکت کا گائیڈ](CONTRIBUTING.md)** - اس پروجیکٹ میں تعاون کیسے کریں
-- **[اساتذہ کے لیے](for-teachers.md)** - تدریس کے رہنما اصول اور کلاس روم کے وسائل
+- **[انسٹالیشن گائیڈ](INSTALLATION.md)** - ابتدائی افراد کے لیے قدم بہ قدم سیٹ اپ کی ہدایات
+- **[استعمال کی گائیڈ](USAGE.md)** - مثالیں اور عام طریقہ کار
+- **[مسائل کے حل](TROUBLESHOOTING.md)** - عام مسائل کے حل
+- **[تعاون کی گائیڈ](CONTRIBUTING.md)** - اس پروجیکٹ میں تعاون کرنے کا طریقہ
+- **[اساتذہ کے لیے](for-teachers.md)** - تدریسی رہنمائی اور کلاس روم کے وسائل
 
 ## 👨‍🎓 طلباء کے لیے
-> **مکمل مبتدئین:** ڈیٹا سائنس میں نئے ہیں؟ ہمارے [آسان اور مبتدئین دوستانہ مثالوں](examples/README.md) سے شروع کریں! یہ آسان، اچھی طرح سے تبصرہ شدہ مثالیں آپ کو بنیادی باتیں سمجھنے میں مدد دیں گی اس سے پہلے کہ آپ پورے نصاب میں داخل ہوں۔
-> **[طلباء](https://aka.ms/student-page)**: اس نصاب کو خود استعمال کرنے کے لیے، پورے ریپو کو فورک کریں اور خود مشقیں مکمل کریں، پری لیکچر کوئز سے شروع کریں۔ پھر لیکچر پڑھیں اور باقی سرگرمیاں مکمل کریں۔ کوشش کریں کہ پروجیکٹس کو حل کی کاپی کرنے کی بجائے اسباق کو سمجھ کر بنائیں؛ البتہ، وہ کوڈ ہر پراجیکٹ-مرکوز سبق میں /solutions فولڈر میں دستیاب ہے۔ ایک اور خیال یہ ہوگا کہ دوستوں کے ساتھ اسٹڈی گروپ بنائیں اور مل کر مواد کا جائزہ لیں۔ مزید مطالعہ کے لیے ہم [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/qprpajyoy3x0g7?WT.mc_id=academic-77958-bethanycheum) کی سفارش کرتے ہیں۔
+> **مکمل نوآموز:** کیا آپ ڈیٹا سائنس میں نئے ہیں؟ ہمارا [ابتدائی دوست مثالیں](examples/README.md) سے شروع کریں! یہ آسان، اچھی طرح سے تبصرہ کی گئی مثالیں آپ کو بنیادیں سمجھنے میں مدد دیں گی اس سے پہلے کہ آپ مکمل نصاب میں غوطہ لگائیں۔
+> **[طلباء](https://aka.ms/student-page)**: اس نصاب کو اپنے لیے استعمال کرنے کے لیے، پورے ریپو کو فورک کریں اور مشقیں خود مکمل کریں، پری لیکچر کوئز سے شروع کریں۔ پھر لیکچر پڑھیں اور باقی سرگرمیاں مکمل کریں۔ کوشش کریں کہ پروجیکٹس سبق کو سمجھ کر بنائیں، بجائے حل کی کوڈ کاپی کرنے کے؛ تاہم، وہ کوڈ ہر پروجیکٹ سے متعلق سبق کے /solutions فولڈر میں دستیاب ہے۔ ایک اور خیال یہ ہے کہ دوستوں کے ساتھ اسٹڈی گروپ بنائیں اور مواد کو ساتھ ساتھ دیکھیں۔ مزید مطالعے کے لیے، ہم [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/qprpajyoy3x0g7?WT.mc_id=academic-77958-bethanycheum) کا مشورہ دیتے ہیں۔
 
-**فوری آغاز:**
-1. اپنے ماحول کی ترتیب کے لیے [انسٹالیشن گائیڈ](INSTALLATION.md) دیکھیں
-2. نصاب کے ساتھ کام کرنے کا طریقہ جاننے کے لیے [استعمال کا گائیڈ](USAGE.md) کا جائزہ لیں
-3. سبق ۱ سے شروع کریں اور تسلسل کے ساتھ آگے بڑھیں
-4. تعاون کے لیے ہماری [Discord کمیونٹی](https://aka.ms/ds4beginners/discord) میں شامل ہوں
+**جلدی شروع کریں:**
+1. اپنے ماحول کو سیٹ اپ کرنے کے لیے [انسٹالیشن گائیڈ](INSTALLATION.md) چیک کریں
+2. نصاب کے ساتھ کام کرنے کے طریقے سیکھنے کے لیے [استعمال کی گائیڈ](USAGE.md) کا جائزہ لیں
+3. سبق 1 سے شروع کریں اور ترتیب سے آگے بڑھیں
+4. معاونت کے لیے ہماری [Discord کمیونٹی](https://aka.ms/ds4beginners/discord) میں شامل ہوں
 
 ## 👩‍🏫 اساتذہ کے لیے
 
-> **اساتذہ:** ہم نے [کچھ تجاویز شامل کی ہیں](for-teachers.md) کہ اس نصاب کو کیسے استعمال کیا جائے۔ ہم آپ کی رائے کا خیرمقدم کرتے ہیں [ہمارے مباحثے کے فورم](https://github.com/microsoft/Data-Science-For-Beginners/discussions) میں!
+> **اساتذہ:** ہم نے [کچھ تجاویز شامل کی ہیں](for-teachers.md) کہ اس نصاب کو کیسے استعمال کیا جائے۔ ہمیں آپ کی رائے کا انتظار ہے [ہماری بحث فورم](https://github.com/microsoft/Data-Science-For-Beginners/discussions) میں!
+## ٹیم سے ملاقات
 
-## ٹیم سے ملیں
 [![پرومو ویڈیو](../../ds-for-beginners.gif)](https://youtu.be/8mzavjQSMM4 "پرومو ویڈیو")
 
-**گیف بنانے والے** [موہت جیسال](https://www.linkedin.com/in/mohitjaisal)
+**گف بذریعہ** [محیّت جیسال](https://www.linkedin.com/in/mohitjaisal)
 
-> 🎥 اوپر تصویر پر کلک کریں تاکہ پراجیکٹ اور اسے بنانے والوں کے بارے میں ویڈیو دیکھ سکیں!
+> 🎥 پروجیکٹ اور اسے بنانے والے لوگوں کے بارے میں ویڈیو کے لیے اوپر تصویر پر کلک کریں!
 
 ## تدریسی اصول
 
-ہم نے اس نصاب کو تیار کرتے وقت دو تدریسی اصول منتخب کیے ہیں: اسے پراجیکٹ کی بنیاد پر بنانا اور بار بار کوئزز شامل کرنا۔ اس سیریز کے آخر تک، طلباء نے ڈیٹا سائنس کے بنیادی اصول سیکھ لیے ہوں گے، بشمول اخلاقی تصورات، ڈیٹا کی تیاری، ڈیٹا کے مختلف طریقے، ڈیٹا کا بصری اظہار، ڈیٹا کا تجزیہ، ڈیٹا سائنس کے حقیقی دنیا کے استعمالات، اور بہت کچھ۔
+ہم نے اس نصاب کی تیاری کے دوران دو تدریسی اصول منتخب کیے ہیں: اس بات کو یقینی بنانا کہ یہ پروجیکٹ پر مبنی ہو اور اس میں کثرت سے کوئزز شامل ہوں۔ اس سیریز کے اختتام پر، طلباء نے ڈیٹا سائنس کے بنیادی اصول سیکھ لیے ہوں گے، جس میں اخلاقی تصورات، ڈیٹا کی تیاری، ڈیٹا کے مختلف طریقوں سے کام کرنا، ڈیٹا کی تصویری نمائندگی، ڈیٹا کا تجزیہ، ڈیٹا سائنس کے حقیقی دنیا میں استعمال کے کیسز، اور مزید شامل ہیں۔
 
-اس کے علاوہ، کلاس سے پہلے کم دباؤ والی کوئز طلباء کا ایک موضوع سیکھنے کی نیت مرتب کرتی ہے، جبکہ کلاس کے بعد دوسری کوئز مزید یادداشت کو یقینی بناتی ہے۔ اس نصاب کو لچکدار اور دلچسپ بنانے کے لیے ڈیزائن کیا گیا ہے اور یہ مکمل یا جزوی طور پر لیا جا سکتا ہے۔ پراجیکٹس چھوٹے شروع ہوتے ہیں اور 10 ہفتوں کے دورانیے کے آخر تک زیادہ پیچیدہ ہو جاتے ہیں۔
+اس کے علاوہ، کلاس سے پہلے ایک کم زور کوئز طالب علم کے تعلیمی ارادے کو متعین کرتا ہے، جبکہ کلاس کے بعد دوسرا کوئز بہتر یادداشت کو یقینی بناتا ہے۔ یہ نصاب لچکدار اور دلچسپ بنانے کے لیے ڈیزائن کیا گیا ہے اور اسے مکمل یا جزوی طور پر لیا جا سکتا ہے۔ پروجیکٹس ابتدا میں آسان ہوتے ہیں اور 10 ہفتوں کے دورانیے کے اختتام تک پیچیدہ ہوتے جاتے ہیں۔
 
-> ہمارے [کوڈ آف کنڈکٹ](CODE_OF_CONDUCT.md)، [شرکت](CONTRIBUTING.md)، اور [ترجمہ](TRANSLATIONS.md) کے رہنما اصول دریافت کریں۔ ہم آپ کی تعمیری آرا کے خواہاں ہیں!
+> ہمارے [کوڈ آف کنڈکٹ](CODE_OF_CONDUCT.md)، [کنٹری بیوشن](CONTRIBUTING.md)، [ترجمہ](TRANSLATIONS.md) کے رہنما اصول دریافت کریں۔ ہم آپ کی تعمیری آراء کا خیرمقدم کرتے ہیں!
 
-## ہر سبق میں شامل ہے:
+## ہر سبق میں شامل ہیں:
 
-- اختیاری سکچنوٹ
+- اختیاری خاکہ نوٹ
 - اختیاری اضافی ویڈیو
-- سبق سے پہلے وارم اپ کوئز
+- سبق سے پہلے گرمائی کوئز
 - تحریری سبق
-- پراجیکٹ پر مبنی اسباق کے لیے پراجیکٹ بنانے کے مرحلہ وار رہنمائی
-- علمی جانچ پڑتال
+- پروجیکٹ پر مبنی اسباق کے لیے، پروجیکٹ بنانے کے مرحلہ وار رہنما
+- علم کی جانچ
 - ایک چیلنج
-- اضافی پڑھائی
-- اسائنمنٹ
-- [سبق کے بعد کی کوئز](https://ff-quizzes.netlify.app/en/)
+- اضافی مطالعہ
+- اسباق کے بعد کا [کوئز](https://ff-quizzes.netlify.app/en/)
 
-> **کوئزز کے بارے میں ایک نوٹ**: تمام کوئزز Quiz-App فولڈر میں موجود ہیں، کل 40 کوئزز تین سوالات پر مشتمل ہر ایک۔ یہ اسباق سے منسلک ہیں، لیکن کوئز ایپ کو مقامی طور پر چلایا یا Azure پر تعینات کیا جا سکتا ہے؛ `quiz-app` فولڈر میں ہدایت نامہ دیکھیں۔ یہ تدریجاً مقامی زبانوں میں ترجمہ ہو رہے ہیں۔
+> **کوئزز کے بارے میں ایک نوٹ**: تمام کوئزز Quiz-App فولڈر میں موجود ہیں، کل 40 کوئزز ہر ایک میں تین سوالات۔ یہ اسباق میں لنک کیے گئے ہیں، لیکن کوئز ایپ کو مقامی طور پر چلایا جا سکتا ہے یا Azure پر تعینات کیا جا سکتا ہے؛ `quiz-app` فولڈر میں دی گئی ہدایات پر عمل کریں۔ یہ آہستہ آہستہ مقامی زبانوں میں منتقل کیے جا رہے ہیں۔
 
-## 🎓 ابتدائی افراد کے لیے مثالیں
+## 🎓 ابتدائیوں کے لیے آسان مثالیں
 
-**ڈیٹا سائنس میں نئے ہیں؟** ہم نے ایک خاص [مثالوں کی ڈائریکٹری](examples/README.md) بنائی ہے جس میں آسان، اچھی طرح وضاحتی کوڈ ہے تاکہ آپ شروع کر سکیں:
+**ڈیٹا سائنس میں نئے ہیں؟** ہم نے ایک خاص [مثالوں کا ڈائریکٹری](examples/README.md) بنایا ہے جس میں سادہ، وضاحتی کوڈ شامل ہے تاکہ آپ کی شروعات ہو سکے:
 
 - 🌟 **ہیلو ورلڈ** - آپ کا پہلا ڈیٹا سائنس پروگرام
-- 📂 **ڈیٹا لوڈ کرنا** - ڈیٹاسیٹس کو پڑھنا اور دریافت کرنا سیکھیں
+- 📂 **ڈیٹا لوڈ کرنا** - ڈیٹا سیٹس کو پڑھنے اور دریافت کرنے کا طریقہ سیکھیں
 - 📊 **سادہ تجزیہ** - شماریات کا حساب لگائیں اور پیٹرن تلاش کریں
-- 📈 **بنیادی بصری اظہار** - چارٹ اور گراف بنائیں
-- 🔬 **حقیقی دنیا کا پراجیکٹ** - شروع سے آخر تک مکمل ورک فلو
+- 📈 **بنیادی بصری نمائندگی** - چارٹس اور گراف بنائیں
+- 🔬 **حقیقی دنیا کا پروجیکٹ** - شروع سے اختتام تک مکمل ورک فلو
 
-ہر مثال میں تفصیلی تبصرے شامل ہیں جو ہر قدم کی وضاحت کرتے ہیں، جو بالکل نو آموزوں کے لیے بہترین ہے!
+ہر مثال میں تفصیلی تبصروں کے ذریعے ہر قدم کی وضاحت کی گئی ہے، جو بالکل ابتدائیوں کے لیے مثالی ہے!
 
 👉 **[مثالوں کے ساتھ شروع کریں](examples/README.md)** 👈
 
-## اسباق
+## دروس
 
-|![ سکچنوٹ بذریعہ @sketchthedocs https://sketchthedocs.dev](../../../../translated_images/ur/00-Roadmap.4905d6567dff4753.webp)|
+|![ خاکہ نوٹ بذریعہ @sketchthedocs https://sketchthedocs.dev](../../translated_images/ur/00-Roadmap.4905d6567dff4753.webp)|
 |:---:|
-| ابتدائی افراد کے لیے ڈیٹا سائنس: روڈ میپ - _سکچنوٹ بذریعہ [@nitya](https://twitter.com/nitya)_ |
+| ڈیٹا سائنس برائے ابتدائی: روڈ میپ - _خاکہ نوٹ بذریعہ [@nitya](https://twitter.com/nitya)_ |
+
 
-| سبق نمبر | عنوان | سبق کا گروہ | سیکھنے کے مقاصد | مربوط سبق | مصنف |
-| :-------: | :----------------------------------------: | :--------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------: | :----: |
-| 01 | ڈیٹا سائنس کی تعریف | [تعارف](1-Introduction/README.md) | ڈیٹا سائنس کی بنیادی تصورات جانیں اور یہ کہ یہ مصنوعی ذہانت، مشین لرننگ اور بڑے ڈیٹا سے کیسے تعلق رکھتی ہے۔ | [سبق](1-Introduction/01-defining-data-science/README.md) [ویڈیو](https://youtu.be/beZ7Mb_oz9I) | [دمیتری](http://soshnikov.com) |
-| 02 | ڈیٹا سائنس کے اخلاقی اصول | [تعارف](1-Introduction/README.md) | ڈیٹا کے اخلاقی تصورات، چیلنجز اور فریم ورکس۔ | [سبق](1-Introduction/02-ethics/README.md) | [نیتیا](https://twitter.com/nitya) |
-| 03 | ڈیٹا کی تعریف | [تعارف](1-Introduction/README.md) | ڈیٹا کی درجہ بندی اور اس کے عام ذرائع۔ | [سبق](1-Introduction/03-defining-data/README.md) | [جازمین](https://www.twitter.com/paladique) |
-| 04 | شماریات اور امکانات کا تعارف | [تعارف](1-Introduction/README.md) | ڈیٹا کو سمجھنے کے لیے امکانات اور شماریات کی ریاضیاتی تکنیکس۔ | [سبق](1-Introduction/04-stats-and-probability/README.md) [ویڈیو](https://youtu.be/Z5Zy85g4Yjw) | [دمیتری](http://soshnikov.com) |
-| 05 | رشتہ دار ڈیٹا کے ساتھ کام کرنا | [ڈیٹا کے ساتھ کام کرنا](2-Working-With-Data/README.md) | رشتہ دار ڈیٹا کا تعارف اور ساختی کوئری زبان (SQL) کے ذریعے رشتہ دار ڈیٹا کی دریافت اور تجزیہ کی بنیادیات۔ | [سبق](2-Working-With-Data/05-relational-databases/README.md) | [کرسٹوفر](https://www.twitter.com/geektrainer) |
-| 06 | NoSQL ڈیٹا کے ساتھ کام کرنا | [ڈیٹا کے ساتھ کام کرنا](2-Working-With-Data/README.md) | غیر رشتہ دار ڈیٹا کا تعارف، اس کی مختلف اقسام اور دستاویزی ڈیٹابیس کی دریافت و تجزیہ کی بنیادی باتیں۔ | [سبق](2-Working-With-Data/06-non-relational/README.md) | [جازمین](https://twitter.com/paladique) |
-| 07 | پائتھن کے ساتھ کام کرنا | [ڈیٹا کے ساتھ کام کرنا](2-Working-With-Data/README.md) | پینڈاز جیسی لائبریریز کے ذریعے ڈیٹا کی دریافت کے لیے پائتھن کا بنیادی استعمال۔ پائتھن پروگرامنگ کی بنیادی سمجھ تجویز کی جاتی ہے۔ | [سبق](2-Working-With-Data/07-python/README.md) [ویڈیو](https://youtu.be/dZjWOGbsN4Y) | [دمیتری](http://soshnikov.com) |
-| 08 | ڈیٹا کی تیاری | [ڈیٹا کے ساتھ کام کرنا](2-Working-With-Data/README.md) | صاف اور تبدیل کرنے کی تکنیکس تاکہ گم شدہ، غلط یا نامکمل ڈیٹا کے چیلنجز کا مقابلہ کیا جا سکے۔ | [سبق](2-Working-With-Data/08-data-preparation/README.md) | [جازمین](https://www.twitter.com/paladique) |
-| 09 | مقدار کو بصری انداز میں دکھانا | [ڈیٹا کی بصری نمائندگی](3-Data-Visualization/README.md) | میٹ پلوٹ لائبریری استعمال کر کے پرندوں کے ڈیٹا کو بصری شکل دینا سیکھیں 🦆 | [سبق](3-Data-Visualization/09-visualization-quantities/README.md) | [جین](https://twitter.com/jenlooper) |
-| 10 | ڈیٹا کی تقسیمات کو بصری انداز میں دکھانا | [ڈیٹا کی بصری نمائندگی](3-Data-Visualization/README.md) | ایک وقفہ کے اندر مشاہدات اور رجحانات کی بصری نمائندگی۔ | [سبق](3-Data-Visualization/10-visualization-distributions/README.md) | [جین](https://twitter.com/jenlooper) |
-| 11 | تناسب کو بصری انداز میں دکھانا | [ڈیٹا کی بصری نمائندگی](3-Data-Visualization/README.md) | متفرق اور گروہ بند فیصدی حصص کی بصری نمائندگی۔ | [سبق](3-Data-Visualization/11-visualization-proportions/README.md) | [جین](https://twitter.com/jenlooper) |
-| 12 | تعلقات کو بصری انداز میں دکھانا | [ڈیٹا کی بصری نمائندگی](3-Data-Visualization/README.md) | ڈیٹا کے سیٹوں اور ان کے متغیرات کے درمیان رابطے اور تعلقات کی بصری نمائندگی۔ | [سبق](3-Data-Visualization/12-visualization-relationships/README.md) | [جین](https://twitter.com/jenlooper) |
-| 13 | معنادار بصری نمائندگی | [ڈیٹا کی بصری نمائندگی](3-Data-Visualization/README.md) | مؤثر مسائل کے حل اور بصیرت کے لیے آپ کی بصری نمائندگی کو قیمتی بنانے کی تکنیکس اور رہنمائی۔ | [سبق](3-Data-Visualization/13-meaningful-visualizations/README.md) | [جین](https://twitter.com/jenlooper) |
-| 14 | ڈیٹا سائنس کے زندگی کے چکر کا تعارف | [زندگی کا چکر](4-Data-Science-Lifecycle/README.md) | ڈیٹا سائنس کے زندگی کے چکر اور اس کے پہلے مرحلہ یعنی ڈیٹا حاصل کرنے اور نکالنے کا تعارف۔ | [سبق](4-Data-Science-Lifecycle/14-Introduction/README.md) | [جازمین](https://twitter.com/paladique) |
-| 15 | تجزیہ کرنا | [زندگی کا چکر](4-Data-Science-Lifecycle/README.md) | ڈیٹا سائنس کے زندگی کے چکر کا یہ مرحلہ ڈیٹا کے تجزیہ کی تکنیکس پر مرکوز ہے۔ | [سبق](4-Data-Science-Lifecycle/15-analyzing/README.md) | [جازمین](https://twitter.com/paladique) |
-| 16 | مواصلات | [زندگی کا چکر](4-Data-Science-Lifecycle/README.md) | ڈیٹا سے حاصل شدہ بصیرت کو اس انداز میں پیش کرنا کہ فیصلہ سازوں کے لیے اسے سمجھنا آسان ہو۔ | [سبق](4-Data-Science-Lifecycle/16-communication/README.md) | [جیلن](https://twitter.com/JalenMcG) |
-| 17 | بادل میں ڈیٹا سائنس | [بادل ڈیٹا](5-Data-Science-In-Cloud/README.md) | اس سیریز میں بادل میں ڈیٹا سائنس اور اس کے فوائد کا تعارف کروایا گیا ہے۔ | [سبق](5-Data-Science-In-Cloud/17-Introduction/README.md) | [ٹیفنی](https://twitter.com/TiffanySouterre) اور [ماڈ](https://twitter.com/maudstweets) |
-| 18 | بادل میں ڈیٹا سائنس | [بادل ڈیٹا](5-Data-Science-In-Cloud/README.md) | لو کوڈ ٹولز کے ذریعے ماڈلز کو ٹرین کرنا۔ | [سبق](5-Data-Science-In-Cloud/18-Low-Code/README.md) | [ٹیفنی](https://twitter.com/TiffanySouterre) اور [ماڈ](https://twitter.com/maudstweets) |
-| 19 | بادل میں ڈیٹا سائنس | [بادل ڈیٹا](5-Data-Science-In-Cloud/README.md) | Azure مشین لرننگ اسٹوڈیو کے ذریعے ماڈلز کی تعیناتی۔ | [سبق](5-Data-Science-In-Cloud/19-Azure/README.md) | [ٹیفنی](https://twitter.com/TiffanySouterre) اور [ماڈ](https://twitter.com/maudstweets) |
-| 20 | جنگل میں ڈیٹا سائنس | [جنگل میں](6-Data-Science-In-Wild/README.md) | حقیقی دنیا میں ڈیٹا سائنس پر مبنی پراجیکٹس۔ | [سبق](6-Data-Science-In-Wild/20-Real-World-Examples/README.md) | [نیتیا](https://twitter.com/nitya) |
+| سبق نمبر | موضوع | سبق کا گروہ بندی | تعلّمی مقاصد | منسلک سبق | مصنف |
+| :-----------: | :----------------------------------------: | :--------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------: | :----: |
+| 01 | ڈیٹا سائنس کی تعریف | [تعارف](1-Introduction/README.md) | ڈیٹا سائنس کے بنیادی تصورات کو سیکھیں اور یہ کہ یہ کس طرح مصنوعی ذہانت، مشین لرننگ، اور بڑے ڈیٹا سے متعلق ہے۔ | [سبق](1-Introduction/01-defining-data-science/README.md) [ویڈیو](https://youtu.be/beZ7Mb_oz9I) | [ڈمٹری](http://soshnikov.com) |
+| 02 | ڈیٹا سائنس کی اخلاقیات | [تعارف](1-Introduction/README.md) | ڈیٹا اخلاقیات کے تصورات، چیلنجز اور فریم ورکس۔ | [سبق](1-Introduction/02-ethics/README.md) | [نيتیا](https://twitter.com/nitya) |
+| 03 | ڈیٹا کی تعریف | [تعارف](1-Introduction/README.md) | ڈیٹا کی درجہ بندی اور اس کے عام ذرائع۔ | [سبق](1-Introduction/03-defining-data/README.md) | [جیسمن](https://www.twitter.com/paladique) |
+| 04 | شماریات اور احتمال کا تعارف | [تعارف](1-Introduction/README.md) | احتمال اور شماریات کی ریاضیاتی تکنیکیں تاکہ ڈیٹا کو سمجھا جا سکے۔ | [سبق](1-Introduction/04-stats-and-probability/README.md) [ویڈیو](https://youtu.be/Z5Zy85g4Yjw) | [ڈمٹری](http://soshnikov.com) |
+| 05 | رشتہ دار ڈیٹا کے ساتھ کام کرنا | [ڈیٹا کے ساتھ کام](2-Working-With-Data/README.md) | رشتہ دار ڈیٹا کا تعارف اور ایس کیو ایل (کہا جاتا ہے "سی کویل") کے ساتھ رشتہ دار ڈیٹا کو دریافت اور تجزیہ کرنے کی بنیادی باتیں۔ | [سبق](2-Working-With-Data/05-relational-databases/README.md) | [کرسٹوفر](https://www.twitter.com/geektrainer) |
+| 06 | نان ایس کیو ایل ڈیٹا کے ساتھ کام کرنا | [ڈیٹا کے ساتھ کام](2-Working-With-Data/README.md) | غیر رشتہ دار ڈیٹا کا تعارف، اس کی مختلف اقسام اور دستاویزی ڈیٹا بیسز کو دریافت اور تجزیہ کرنے کی بنیادی باتیں۔ | [سبق](2-Working-With-Data/06-non-relational/README.md) | [جیسمن](https://twitter.com/paladique) |
+| 07 | پایتھون کے ساتھ کام کرنا | [ڈیٹا کے ساتھ کام](2-Working-With-Data/README.md) | پینڈاز جیسی لائبریریز کے ساتھ ڈیٹا دریافت کے لیے پایتھون کے استعمال کے بنیادی اصول۔ پایتھون پروگرامنگ کی بنیادی سمجھ سفارش کی جاتی ہے۔ | [سبق](2-Working-With-Data/07-python/README.md) [ویڈیو](https://youtu.be/dZjWOGbsN4Y) | [ڈمٹری](http://soshnikov.com) |
+| 08 | ڈیٹا کی تیاری | [ڈیٹا کے ساتھ کام](2-Working-With-Data/README.md) | ڈیٹا کی صفائی اور تبدیلی کی تکنیکیں تاکہ گمشدہ، غلط یا نامکمل ڈیٹا کے چیلنجز سے نمٹا جا سکے۔ | [سبق](2-Working-With-Data/08-data-preparation/README.md) | [جیسمن](https://www.twitter.com/paladique) |
+| 09 | مقدار کی بصری نمائندگی | [ڈیٹا کی بصری نمائندگی](3-Data-Visualization/README.md) | میٹپلاٹ لائبریری کا استعمال کرتے ہوئے پرندوں کے ڈیٹا کی تصویری نمائندگی سیکھیں 🦆 | [سبق](3-Data-Visualization/09-visualization-quantities/README.md) | [جن](https://twitter.com/jenlooper) |
+| 10 | ڈیٹا کی تقسیم کی بصری نمائندگی | [ڈیٹا کی بصری نمائندگی](3-Data-Visualization/README.md) | وقفے کے اندر مشاہدات اور رجحانات کی بصری نمائندگی۔ | [سبق](3-Data-Visualization/10-visualization-distributions/README.md) | [جن](https://twitter.com/jenlooper) |
+| 11 | تناسب کی بصری نمائندگی | [ڈیٹا کی بصری نمائندگی](3-Data-Visualization/README.md) | الگ تھلگ اور گروپ کردہ فیصدات کی تصویری نمائندگی۔ | [سبق](3-Data-Visualization/11-visualization-proportions/README.md) | [جن](https://twitter.com/jenlooper) |
+| 12 | تعلقات کی بصری نمائندگی | [ڈیٹا کی بصری نمائندگی](3-Data-Visualization/README.md) | ڈیٹا کے مجموعوں اور ان کے متغیرات کے درمیان تعلقات اور ارتباطات کی بصری نمائندگی۔ | [سبق](3-Data-Visualization/12-visualization-relationships/README.md) | [جن](https://twitter.com/jenlooper) |
+| 13 | بامعنی بصری نمائندگی | [ڈیٹا کی بصری نمائندگی](3-Data-Visualization/README.md) | مؤثر مسئلہ حل کرنے اور بصیرت کے لیے اپنی بصری نمائندگیوں کو قیمتی بنانے کی تکنیکیں اور رہنمائی۔ | [سبق](3-Data-Visualization/13-meaningful-visualizations/README.md) | [جن](https://twitter.com/jenlooper) |
+| 14 | ڈیٹا سائنس کے لائف سائیکل کا تعارف | [لائف سائیکل](4-Data-Science-Lifecycle/README.md) | ڈیٹا سائنس لائف سائیکل اور ڈیٹا حاصل کرنے اور نکالنے کے پہلے مرحلے کا تعارف۔ | [سبق](4-Data-Science-Lifecycle/14-Introduction/README.md) | [جیسمن](https://twitter.com/paladique) |
+| 15 | تجزیہ کرنا | [لائف سائیکل](4-Data-Science-Lifecycle/README.md) | ڈیٹا سائنس لائف سائیکل کا یہ مرحلہ ڈیٹا کے تجزیہ کی تکنیکوں پر مرکوز ہے۔ | [سبق](4-Data-Science-Lifecycle/15-analyzing/README.md) | [جیسمن](https://twitter.com/paladique) |
+| 16 | مواصلات | [لائف سائیکل](4-Data-Science-Lifecycle/README.md) | ڈیٹا سائنس لائف سائیکل کا یہ مرحلہ ڈیٹا سے حاصل شدہ بصیرت کو اس طرح پیش کرنے پر توجہ دیتا ہے کہ فیصلہ سازوں کے لیے اسے سمجھنا آسان ہو۔ | [سبق](4-Data-Science-Lifecycle/16-communication/README.md) | [جیلین](https://twitter.com/JalenMcG) |
+| 17 | کلاؤڈ میں ڈیٹا سائنس | [کلاؤڈ ڈیٹا](5-Data-Science-In-Cloud/README.md) | اس سلسلے کے اسباق کلاؤڈ میں ڈیٹا سائنس اور اس کے فوائد کا تعارف کرواتے ہیں۔ | [سبق](5-Data-Science-In-Cloud/17-Introduction/README.md) | [ٹیفنی](https://twitter.com/TiffanySouterre) اور [ماڈ](https://twitter.com/maudstweets) |
+| 18 | کلاؤڈ میں ڈیٹا سائنس | [کلاؤڈ ڈیٹا](5-Data-Science-In-Cloud/README.md) | لو کوڈ ٹولز کے استعمال سے ماڈلز کی تربیت۔ | [سبق](5-Data-Science-In-Cloud/18-Low-Code/README.md) | [ٹیفنی](https://twitter.com/TiffanySouterre) اور [ماڈ](https://twitter.com/maudstweets) |
+| 19 | کلاؤڈ میں ڈیٹا سائنس | [کلاؤڈ ڈیٹا](5-Data-Science-In-Cloud/README.md) | Azure مشین لرننگ اسٹوڈیو کے ذریعے ماڈلز کی تعیناتی۔ | [سبق](5-Data-Science-In-Cloud/19-Azure/README.md) | [ٹیفنی](https://twitter.com/TiffanySouterre) اور [ماڈ](https://twitter.com/maudstweets) |
+| 20 | جنگل میں ڈیٹا سائنس | [جنگل میں](6-Data-Science-In-Wild/README.md) | حقیقی دنیا میں ڈیٹا سائنس پر مبنی پروجیکٹس۔ | [سبق](6-Data-Science-In-Wild/20-Real-World-Examples/README.md) | [نيتیا](https://twitter.com/nitya) |
 
-## GitHub Codespaces
+## گٹ ہب کوڈ اسپیسز
 
-اس نمونے کو Codespace میں کھولنے کے لیے یہ اقدامات کریں:
-1. کوڈ ڈراپ ڈاؤن مینو پر کلک کریں اور Open with Codespaces کا انتخاب کریں۔
-2. نیچے والے پین میں + New codespace منتخب کریں۔
-مزید معلومات کے لیے، [GitHub دستاویزات](https://docs.github.com/en/codespaces/developing-in-codespaces/creating-a-codespace-for-a-repository#creating-a-codespace) دیکھیں۔
+اس نمونے کو ایک کوڈ اسپیس میں کھولنے کے لیے ان مراحل پر عمل کریں:  
+1. کوڈ ڈراپ ڈاؤن مینو پر کلک کریں اور Open with Codespaces آپشن منتخب کریں۔  
+2. پین کے نیچے + New codespace منتخب کریں۔  
+مزید معلومات کے لیے [گٹ ہب ڈاکیومنٹیشن](https://docs.github.com/en/codespaces/developing-in-codespaces/creating-a-codespace-for-a-repository#creating-a-codespace) ملاحظہ کریں۔  
 
-## VSCode Remote - Containers
-اپنے مقامی کمپیوٹر اور VSCode کا استعمال کرتے ہوئے اس ریپو کو کنٹینر میں کھولنے کے لیے VS Code Remote - Containers ایکسٹینشن کے ذریعے:
+## VSCode ریموٹ - کنٹینرز  
+اپنے مقامی کمپیوٹر اور VSCode کے ذریعے اس ریپو کو کنٹینر میں کھولنے کے لیے VS Code Remote - Containers ایکسٹینشن استعمال کریں:  
 
-1. اگر آپ پہلی بار development container استعمال کر رہے ہیں، تو براہ کرم یقینی بنائیں کہ آپ کا نظام [ابتدائی دستاویزات](https://code.visualstudio.com/docs/devcontainers/containers#_getting-started) میں دی گئی ضروریات پر پورا اترتا ہے (جیسے Docker انسٹال ہو)۔
+1. اگر یہ پہلی بار ہے کہ آپ ڈیولپمنٹ کنٹینر استعمال کر رہے ہیں، تو براہ کرم یہ یقینی بنائیں کہ آپ کا سسٹم [گِٹ ہب ڈاکیومنٹیشن](https://code.visualstudio.com/docs/devcontainers/containers#_getting-started) میں دی گئی ضروریات کو پورا کرتا ہے (یعنی آپ کے سسٹم پر ڈوکر انسٹال ہو)۔  
 
-اس ریپوزٹری کو استعمال کرنے کے لیے، آپ یا تو اسے علیحدہ Docker والیوم میں کھول سکتے ہیں:
+اس ریپو کو استعمال کرنے کے لیے، آپ یا تو اسے ایک علیحدہ ڈوکر والیوم میں کھول سکتے ہیں:  
 
-**نوٹ**: یہ Remote-Containers: **Clone Repository in Container Volume...** کمانڈ کا استعمال کرے گا تاکہ سورس کوڈ کو مقامی فائل سسٹم کی بجائے Docker والیوم میں کلون کیا جائے۔ [Volumes](https://docs.docker.com/storage/volumes/) کنٹینر ڈیٹا کو مستقل رکھنے کے لیے ترجیحی ذریعہ ہیں۔
+**نوٹ**: اندرونی طور پر، یہ Remote-Containers کی کمانڈ: **Clone Repository in Container Volume...** استعمال کرے گا تاکہ سورس کوڈ کو لوکل فائل سسٹم کے بجائے ڈوکر والیوم میں کلون کرے۔ [والیومز](https://docs.docker.com/storage/volumes/) ڈیٹا کو برقرار رکھنے کے لیے ترجیح دی جاتی ہے۔  
 
-یا پھر ریپوزٹری کا مقامی کلون یا ڈاؤن لوڈ ورژن کھولیں:
+یا پھر اس ریپو کا لوکل کلون شدہ یا ڈاؤن لوڈ شدہ ورژن کھولیں:  
 
-- اس ریپوزٹری کو اپنے مقامی فائل سسٹم پر کلون کریں۔
-- F1 دبائیں اور **Remote-Containers: Open Folder in Container...** کمانڈ منتخب کریں۔
-- اس فولڈر کی کلون کی ہوئی کاپی منتخب کریں، کنٹینر شروع ہونے کا انتظار کریں، اور تجربہ کریں۔
+- اس ریپو کو اپنے لوکل فائل سسٹم پر کلون کریں۔  
+- F1 دبائیں اور **Remote-Containers: Open Folder in Container...** کمانڈ منتخب کریں۔  
+- اس فولڈر کی کلون کی گئی کاپی منتخب کریں، کنٹینر کے شروع ہونے کا انتظار کریں، اور تجربہ کریں۔  
 
-## آف لائن رسائی
+## آف لائن رسائی  
 
-آپ [Docsify](https://docsify.js.org/#/) استعمال کرکے یہ دستاویزات آف لائن چلا سکتے ہیں۔ اس ریپو کو فورک کریں، اپنے مقامی کمپیوٹر پر [Docsify انسٹال کریں](https://docsify.js.org/#/quickstart)، پھر اس ریپو کے روٹ فولڈر میں `docsify serve` ٹائپ کریں۔ ویب سائٹ مقامی ہوسٹ پر پورٹ 3000 پر دستیاب ہوگی: `localhost:3000`۔
+آپ [Docsify](https://docsify.js.org/#/) استعمال کرتے ہوئے اس دستاویز کو آف لائن بھی چلا سکتے ہیں۔ اس ریپو کو فورک کریں، اپنے لوکل کمپیوٹر پر [Docsify انسٹال کریں](https://docsify.js.org/#/quickstart)، پھر اس ریپو کے روٹ فولڈر میں `docsify serve` ٹائپ کریں۔ ویب سائٹ لوکل ہوسٹ پر پورٹ 3000 پر دستیاب ہوگی: `localhost:3000`۔  
 
-> نوٹ کریں، نوٹ بکس Docsify کے ذریعے رینڈر نہیں ہوں گے، اس لیے جب نوٹ بک چلانی ہو، تو اسے VS Code میں Python کرنل کے ساتھ علیحدہ چلائیں۔
+> نوٹ کریں، نوٹ بکس Docsify کے ذریعے رینڈر نہیں ہوں گی، اس لیے جب آپ کو نوٹ بک چلانے کی ضرورت ہو، تو اسے VS Code میں Python کرنل چلا کر علیحدہ کریں۔  
 
-## دیگر نصاب
+## دیگر نصاب  
 
-ہماری ٹیم دیگر نصاب بھی تیار کرتی ہے! ملاحظہ کریں:
+ہماری ٹیم دیگر نصاب بھی تیار کرتی ہے! ملاحظہ کریں:  
 
 <!-- CO-OP TRANSLATOR OTHER COURSES START -->
 ### LangChain
@@ -207,7 +198,7 @@ CO_OP_TRANSLATOR_METADATA:
 
 ---
 
-### ایزور / ایج / ایم سی پی / ایجنٹس
+### Azure / Edge / MCP / Agents
 [![AZD for Beginners](https://img.shields.io/badge/AZD%20for%20Beginners-0078D4?style=for-the-badge&labelColor=E5E7EB&color=0078D4)](https://github.com/microsoft/AZD-for-beginners?WT.mc_id=academic-105485-koreyst)
 [![Edge AI for Beginners](https://img.shields.io/badge/Edge%20AI%20for%20Beginners-00B8E4?style=for-the-badge&labelColor=E5E7EB&color=00B8E4)](https://github.com/microsoft/edgeai-for-beginners?WT.mc_id=academic-105485-koreyst)
 [![MCP for Beginners](https://img.shields.io/badge/MCP%20for%20Beginners-009688?style=for-the-badge&labelColor=E5E7EB&color=009688)](https://github.com/microsoft/mcp-for-beginners?WT.mc_id=academic-105485-koreyst)
@@ -215,7 +206,7 @@ CO_OP_TRANSLATOR_METADATA:
 
 ---
  
-### جنریٹو AI سیریز
+### Generative AI Series
 [![Generative AI for Beginners](https://img.shields.io/badge/Generative%20AI%20for%20Beginners-8B5CF6?style=for-the-badge&labelColor=E5E7EB&color=8B5CF6)](https://github.com/microsoft/generative-ai-for-beginners?WT.mc_id=academic-105485-koreyst)
 [![Generative AI (.NET)](https://img.shields.io/badge/Generative%20AI%20(.NET)-9333EA?style=for-the-badge&labelColor=E5E7EB&color=9333EA)](https://github.com/microsoft/Generative-AI-for-beginners-dotnet?WT.mc_id=academic-105485-koreyst)
 [![Generative AI (Java)](https://img.shields.io/badge/Generative%20AI%20(Java)-C084FC?style=for-the-badge&labelColor=E5E7EB&color=C084FC)](https://github.com/microsoft/generative-ai-for-beginners-java?WT.mc_id=academic-105485-koreyst)
@@ -223,7 +214,7 @@ CO_OP_TRANSLATOR_METADATA:
 
 ---
  
-### بنیادی تعلیم
+### Core Learning
 [![ML for Beginners](https://img.shields.io/badge/ML%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=22C55E)](https://aka.ms/ml-beginners?WT.mc_id=academic-105485-koreyst)
 [![Data Science for Beginners](https://img.shields.io/badge/Data%20Science%20for%20Beginners-84CC16?style=for-the-badge&labelColor=E5E7EB&color=84CC16)](https://aka.ms/datascience-beginners?WT.mc_id=academic-105485-koreyst)
 [![AI for Beginners](https://img.shields.io/badge/AI%20for%20Beginners-A3E635?style=for-the-badge&labelColor=E5E7EB&color=A3E635)](https://aka.ms/ai-beginners?WT.mc_id=academic-105485-koreyst)
@@ -234,7 +225,7 @@ CO_OP_TRANSLATOR_METADATA:
 
 ---
  
-### کوپائلٹ سیریز
+### Copilot Series
 [![Copilot for AI Paired Programming](https://img.shields.io/badge/Copilot%20for%20AI%20Paired%20Programming-FACC15?style=for-the-badge&labelColor=E5E7EB&color=FACC15)](https://aka.ms/GitHubCopilotAI?WT.mc_id=academic-105485-koreyst)
 [![Copilot for C#/.NET](https://img.shields.io/badge/Copilot%20for%20C%23/.NET-FBBF24?style=for-the-badge&labelColor=E5E7EB&color=FBBF24)](https://github.com/microsoft/mastering-github-copilot-for-dotnet-csharp-developers?WT.mc_id=academic-105485-koreyst)
 [![Copilot Adventure](https://img.shields.io/badge/Copilot%20Adventure-FDE68A?style=for-the-badge&labelColor=E5E7EB&color=FDE68A)](https://github.com/microsoft/CopilotAdventures?WT.mc_id=academic-105485-koreyst)
@@ -242,19 +233,19 @@ CO_OP_TRANSLATOR_METADATA:
 
 ## مدد حاصل کرنا
 
-**مسائل کا سامنا ہے؟** ہمارے [Troubleshooting Guide](TROUBLESHOOTING.md) میں عام مسائل کے حل تلاش کریں۔
+**کیا آپ مسائل کا سامنا کر رہے ہیں؟** ہمارے [مسائل کا حل گائیڈ](TROUBLESHOOTING.md) میں عام مسائل کے حل دیکھیں۔
 
-اگر آپ پھنس جائیں یا AI ایپس بنانے کے بارے میں کوئی سوال ہو۔ MCP پر دوسرے سیکھنے والوں اور تجربہ کار ڈویلپرز کے ساتھ تبادلہ خیال میں شامل ہوں۔ یہ ایک معاون کمیونٹی ہے جہاں سوالات خوش آمدید ہیں اور علم آزادانہ طور پر شیئر کیا جاتا ہے۔
+اگر آپ پھنس جائیں یا AI ایپس بنانے کے بارے میں کوئی سوال ہو تو MCP کے بارے میں fellow learners اور تجربہ کار developers کے ساتھ بحث میں شامل ہوں۔ یہ ایک معاون کمیونٹی ہے جہاں سوالات کا خیرمقدم کیا جاتا ہے اور علم بلا جھجک شیئر کیا جاتا ہے۔
 
 [![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG)
 
-اگر آپ کے پاس پروڈکٹ فیڈ بیک ہو یا تعمیر کے دوران کوئی غلطیاں ہوں تو یہاں وزٹ کریں:
+اگر آپ کے پاس پروڈکٹ فیڈبیک ہو یا آپ کو تعمیری غلطیاں ملیں تو یہاں وزٹ کریں:
 
 [![Microsoft Foundry Developer Forum](https://img.shields.io/badge/GitHub-Microsoft_Foundry_Developer_Forum-blue?style=for-the-badge&logo=github&color=000000&logoColor=fff)](https://aka.ms/foundry/forum)
 
 ---
 
 <!-- CO-OP TRANSLATOR DISCLAIMER START -->
-**دستخطی تحریر:**
-یہ دستاویز AI ترجمہ سروس [Co-op Translator](https://github.com/Azure/co-op-translator) کے ذریعے ترجمہ کی گئی ہے۔ جبکہ ہم درستگی کے لیے کوشاں ہیں، براہ کرم آگاہ رہیں کہ خودکار تراجم میں غلطیاں یا ناواقفیاں ہو سکتی ہیں۔ اصل دستاویز اپنی مادری زبان میں ہی مستند ماخذ سمجھی جانی چاہیے۔ اہم معلومات کے لیے پیشہ ورانہ انسانی ترجمہ تجویز کیا جاتا ہے۔ اس ترجمے کے استعمال سے پیدا ہونے والی کسی بھی غلط فہمی یا غلط تعبیر کی ذمہ داری ہم پر نہیں ہوگی۔
+**اہتمامِ خیال**:  
+یہ دستاویز [Co-op Translator](https://github.com/Azure/co-op-translator) نامی AI ترجمہ سروس کی مدد سے ترجمہ کی گئی ہے۔ اگرچہ ہم درستگی کی کوشش کرتے ہیں، براہ کرم اس بات کا ادراک رکھیں کہ خودکار ترجمے میں غلطیاں یا نقصانات ہوسکتے ہیں۔ اصل دستاویز اپنی مادری زبان میں معتبر اور مستند ذریعہ سمجھا جانا چاہیے۔ اہم معلومات کے لیے پیشہ ور انسانی ترجمہ تجویز کیا جاتا ہے۔ ہم اس ترجمے کے استعمال سے پیدا ہونے والی کسی بھی غلط فہمی یا تشریحی اختلافات کے ذمہ دار نہیں ہیں۔
 <!-- CO-OP TRANSLATOR DISCLAIMER END -->
\ No newline at end of file
diff --git a/translations/ur/SECURITY.md b/translations/ur/SECURITY.md
index 7fac96e4..4288048b 100644
--- a/translations/ur/SECURITY.md
+++ b/translations/ur/SECURITY.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "0d575483100c332b2dbaefef915bb3c4",
-  "translation_date": "2025-08-27T08:17:03+00:00",
-  "source_file": "SECURITY.md",
-  "language_code": "ur"
-}
--->
 ## سیکیورٹی
 
 مائیکروسافٹ اپنی سافٹ ویئر مصنوعات اور خدمات کی سیکیورٹی کو سنجیدگی سے لیتا ہے، جس میں ہمارے GitHub تنظیموں کے ذریعے منظم کردہ تمام سورس کوڈ ریپوزٹریز شامل ہیں، جن میں [Microsoft](https://github.com/Microsoft)، [Azure](https://github.com/Azure)، [DotNet](https://github.com/dotnet)، [AspNet](https://github.com/aspnet)، [Xamarin](https://github.com/xamarin)، اور [ہمارے GitHub تنظیمیں](https://opensource.microsoft.com/) شامل ہیں۔
diff --git a/translations/ur/SUPPORT.md b/translations/ur/SUPPORT.md
index 507a70c8..072ec68d 100644
--- a/translations/ur/SUPPORT.md
+++ b/translations/ur/SUPPORT.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "872be8bc1b93ef1dd9ac3d6e8f99f6ab",
-  "translation_date": "2025-08-27T08:14:04+00:00",
-  "source_file": "SUPPORT.md",
-  "language_code": "ur"
-}
--->
 # معاونت  
 ## مسائل درج کرنے اور مدد حاصل کرنے کا طریقہ  
 
diff --git a/translations/ur/TROUBLESHOOTING.md b/translations/ur/TROUBLESHOOTING.md
index 2f3a7dd7..55e42dac 100644
--- a/translations/ur/TROUBLESHOOTING.md
+++ b/translations/ur/TROUBLESHOOTING.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "93a6a8a8a209128cbfedcbc076ee21b0",
-  "translation_date": "2025-10-03T15:31:26+00:00",
-  "source_file": "TROUBLESHOOTING.md",
-  "language_code": "ur"
-}
--->
 # خرابیوں کو دور کرنے کی رہنما
 
 یہ رہنما ڈیٹا سائنس فار بیگنرز نصاب کے دوران پیش آنے والے عام مسائل کے حل فراہم کرتی ہے۔
diff --git a/translations/ur/USAGE.md b/translations/ur/USAGE.md
index c863d8d4..c6d8b380 100644
--- a/translations/ur/USAGE.md
+++ b/translations/ur/USAGE.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "f546349678757508d69ce9e1d2688446",
-  "translation_date": "2025-10-03T14:54:00+00:00",
-  "source_file": "USAGE.md",
-  "language_code": "ur"
-}
--->
 # استعمال کی رہنمائی
 
 یہ رہنمائی ڈیٹا سائنس فار بیگنرز نصاب کے استعمال کے لیے مثالیں اور عام ورک فلو فراہم کرتی ہے۔
diff --git a/translations/ur/docs/_sidebar.md b/translations/ur/docs/_sidebar.md
index d838f69b..3fd84ece 100644
--- a/translations/ur/docs/_sidebar.md
+++ b/translations/ur/docs/_sidebar.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "3767555b3cc28a2865c79202f4374204",
-  "translation_date": "2025-08-27T08:42:49+00:00",
-  "source_file": "docs/_sidebar.md",
-  "language_code": "ur"
-}
--->
 - تعارف  
   - [ڈیٹا سائنس کی تعریف](../1-Introduction/01-defining-data-science/README.md)  
   - [ڈیٹا سائنس کے اخلاقیات](../1-Introduction/02-ethics/README.md)  
diff --git a/translations/ur/examples/README.md b/translations/ur/examples/README.md
index 164b1f1c..5fccbf6d 100644
--- a/translations/ur/examples/README.md
+++ b/translations/ur/examples/README.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "9bef7fd96c8f262339933117d9b3e342",
-  "translation_date": "2025-10-03T12:56:59+00:00",
-  "source_file": "examples/README.md",
-  "language_code": "ur"
-}
--->
 # ڈیٹا سائنس کے ابتدائی دوستانہ مثالیں
 
 مثالوں کی ڈائریکٹری میں خوش آمدید! یہ سادہ اور واضح تبصرے والی مثالوں کا مجموعہ آپ کو ڈیٹا سائنس کے ساتھ شروعات کرنے میں مدد دینے کے لیے بنایا گیا ہے، چاہے آپ بالکل نئے ہوں۔
diff --git a/translations/ur/for-teachers.md b/translations/ur/for-teachers.md
index edb8f767..56aa2bd8 100644
--- a/translations/ur/for-teachers.md
+++ b/translations/ur/for-teachers.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "f7440be10c17a8a9262713af3d2818a9",
-  "translation_date": "2025-09-06T19:52:40+00:00",
-  "source_file": "for-teachers.md",
-  "language_code": "ur"
-}
--->
 ## اساتذہ کے لیے
 
 کیا آپ اپنی کلاس میں اس نصاب کو استعمال کرنا چاہتے ہیں؟ بلا جھجک استعمال کریں!
diff --git a/translations/ur/quiz-app/README.md b/translations/ur/quiz-app/README.md
index e3e27fa2..b0ff5915 100644
--- a/translations/ur/quiz-app/README.md
+++ b/translations/ur/quiz-app/README.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "e92c33ea498915a13c9aec162616db18",
-  "translation_date": "2025-08-27T09:46:42+00:00",
-  "source_file": "quiz-app/README.md",
-  "language_code": "ur"
-}
--->
 # کوئزز
 
 یہ کوئزز ڈیٹا سائنس کے نصاب کے لیے لیکچر سے پہلے اور بعد کے کوئزز ہیں، جو یہاں دستیاب ہیں: https://aka.ms/datascience-beginners  
diff --git a/translations/ur/sketchnotes/README.md b/translations/ur/sketchnotes/README.md
index 36987338..591eb834 100644
--- a/translations/ur/sketchnotes/README.md
+++ b/translations/ur/sketchnotes/README.md
@@ -1,12 +1,3 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "3a848466cb63aff1a93411affb152c2a",
-  "translation_date": "2025-08-27T09:17:53+00:00",
-  "source_file": "sketchnotes/README.md",
-  "language_code": "ur"
-}
--->
 تمام اسکیچ نوٹس یہاں دیکھیں!
 
 ## کریڈٹس
diff --git a/translations/zh/1-Introduction/01-defining-data-science/README.md b/translations/zh/1-Introduction/01-defining-data-science/README.md
deleted file mode 100644
index 8436f7c1..00000000
--- a/translations/zh/1-Introduction/01-defining-data-science/README.md
+++ /dev/null
@@ -1,176 +0,0 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "43212cc1ac137b7bb1dcfb37ca06b0f4",
-  "translation_date": "2025-10-25T18:38:39+00:00",
-  "source_file": "1-Introduction/01-defining-data-science/README.md",
-  "language_code": "zh"
-}
--->
-# 数据科学定义
-
-| ![由 [(@sketchthedocs)](https://sketchthedocs.dev) 绘制的速写笔记](../../sketchnotes/01-Definitions.png) |
-| :----------------------------------------------------------------------------------------------------: |
-|              数据科学定义 - _由 [@nitya](https://twitter.com/nitya) 绘制的速写笔记_                     |
-
----
-
-[![数据科学定义视频](../../../../translated_images/zh-CN/video-def-ds.6623ee2392ef1abf6d7faf3fad10a4163642811749da75f44e35a5bb121de15c.png)](https://youtu.be/beZ7Mb_oz9I)
-
-## [课前测验](https://ff-quizzes.netlify.app/en/ds/quiz/0)
-
-## 什么是数据？
-在我们的日常生活中，我们总是被数据包围着。你现在正在阅读的文字就是数据。你智能手机里朋友的电话号码列表是数据，你手表上显示的当前时间也是数据。作为人类，我们天生就会处理数据，比如数钱或者给朋友写信。
-
-然而，随着计算机的诞生，数据变得更加重要。计算机的主要功能是进行计算，但它们需要数据来操作。因此，我们需要了解计算机如何存储和处理数据。
-
-随着互联网的出现，计算机作为数据处理设备的角色变得更加重要。仔细想想，我们现在使用计算机更多的是进行数据处理和通信，而不是实际的计算。当我们给朋友写电子邮件或在互联网上搜索信息时，我们实际上是在创建、存储、传输和操作数据。
-> 你能记得上一次真正用计算机进行计算是什么时候吗？
-
-## 什么是数据科学？
-
-根据 [维基百科](https://en.wikipedia.org/wiki/Data_science)，**数据科学**被定义为*一个科学领域，使用科学方法从结构化和非结构化数据中提取知识和洞察力，并将从数据中获得的知识和可操作的洞察力应用于广泛的应用领域*。
-
-这个定义突出了数据科学的以下重要方面：
-
-* 数据科学的主要目标是从数据中**提取知识**，换句话说，就是**理解**数据，发现一些隐藏的关系并建立**模型**。
-* 数据科学使用**科学方法**，例如概率和统计。事实上，当*数据科学*这个术语首次被提出时，有些人认为数据科学只是统计学的新潮名称。然而，现在显而易见，这个领域要广泛得多。
-* 获得的知识应该被应用于产生一些**可操作的洞察力**，即可以应用于实际业务场景的实用洞察。
-* 我们应该能够处理**结构化**和**非结构化**数据。我们将在课程后面讨论不同类型的数据。
-* **应用领域**是一个重要概念，数据科学家通常需要对问题领域有一定程度的专业知识，例如：金融、医学、市场营销等。
-
-> 数据科学的另一个重要方面是研究如何使用计算机收集、存储和操作数据。虽然统计学为我们提供了数学基础，但数据科学将数学概念应用于实际从数据中获取洞察力。
-
-一种看待数据科学的方法（归因于 [Jim Gray](https://en.wikipedia.org/wiki/Jim_Gray_(computer_scientist)))是将其视为科学的一个独立范式：
-* **经验科学**，主要依赖观察和实验结果
-* **理论科学**，从现有的科学知识中产生新概念
-* **计算科学**，通过一些计算实验发现新原理
-* **数据驱动科学**，基于发现数据中的关系和模式
-
-## 其他相关领域
-
-由于数据无处不在，数据科学本身也是一个广泛的领域，涉及许多其他学科。
-
-<dl>
-<dt>数据库</dt>
-<dd>
-一个关键的考虑是<b>如何存储</b>数据，即如何以一种允许更快处理的方式来组织数据。有不同类型的数据库可以存储结构化和非结构化数据，这些内容将在<a href="../../2-Working-With-Data/README.md">我们的课程中讨论</a>。
-</dd>
-<dt>大数据</dt>
-<dd>
-我们经常需要存储和处理结构相对简单但数量非常庞大的数据。有一些特殊的方法和工具可以将这些数据分布式存储在计算机集群上，并高效地处理它。
-</dd>
-<dt>机器学习</dt>
-<dd>
-理解数据的一种方法是<b>建立一个模型</b>，能够预测所需的结果。从数据中开发模型被称为<b>机器学习</b>。你可以查看我们的<a href="https://aka.ms/ml-beginners">机器学习入门</a>课程以了解更多信息。
-</dd>
-<dt>人工智能</dt>
-<dd>
-机器学习的一个领域被称为人工智能（AI），它也依赖于数据，并涉及构建模仿人类思维过程的高复杂性模型。AI方法通常允许我们将非结构化数据（例如自然语言）转化为结构化的洞察力。
-</dd>
-<dt>可视化</dt>
-<dd>
-大量数据对人类来说是难以理解的，但一旦我们使用这些数据创建了有用的可视化，我们就可以更好地理解数据，并得出一些结论。因此，了解多种可视化信息的方法非常重要——这是我们将在<a href="../../3-Data-Visualization/README.md">课程第三部分</a>中讨论的内容。相关领域还包括<b>信息图表</b>和<b>人机交互</b>。
-</dd>
-</dl>
-
-## 数据的类型
-
-正如我们已经提到的，数据无处不在。我们只需要以正确的方式捕获它！区分**结构化**和**非结构化**数据是很有用的。前者通常以某种结构化形式表示，通常是表格或多个表格，而后者只是文件的集合。有时我们也可以谈论**半结构化**数据，这些数据具有某种结构，但可能差异很大。
-
-| 结构化数据                                                                   | 半结构化数据                                                                                   | 非结构化数据                            |
-| ---------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------- | --------------------------------------- |
-| 包含电话号码的人员列表                                                       | 带有链接的维基百科页面                                                                        | 《大英百科全书》的文本                  |
-| 过去20年中每分钟建筑物所有房间的温度数据                                     | 以JSON格式存储的科学论文集合，包括作者、出版日期和摘要                                         | 企业文件的文件共享                      |
-| 进入建筑物的所有人的年龄和性别数据                                           | 互联网页面                                                                                     | 监控摄像头的原始视频流                  |
-
-## 数据的来源
-
-数据的来源有很多种，几乎无法一一列举！然而，我们可以提到一些典型的数据来源：
-
-* **结构化数据**
-  - **物联网**（IoT），包括来自不同传感器的数据，例如温度或压力传感器，提供了许多有用的数据。例如，如果办公楼配备了物联网传感器，我们可以自动控制供暖和照明，以尽量减少成本。
-  - **调查问卷**，例如我们在用户购买后或访问网站后要求他们填写的问卷。
-  - **行为分析**，例如可以帮助我们了解用户在网站上的浏览深度，以及离开网站的典型原因。
-* **非结构化数据**
-  - **文本**可以是丰富的洞察力来源，例如整体**情感评分**，或提取关键词和语义意义。
-  - **图像**或**视频**。监控摄像头的视频可以用来估算道路上的交通流量，并向人们提供潜在交通拥堵的通知。
-  - 网站服务器的**日志**可以用来了解我们网站的哪些页面被访问得最多，以及访问时长。
-* **半结构化数据**
-  - **社交网络**图可以是关于用户个性和传播信息潜在效果的绝佳数据来源。
-  - 当我们有一堆聚会照片时，我们可以尝试通过构建人与人之间拍照的关系图来提取**群体动态**数据。
-
-通过了解不同的数据来源，你可以尝试思考不同的场景，数据科学技术可以应用于更好地了解情况，并改善业务流程。
-
-## 数据的用途
-
-在数据科学中，我们专注于数据旅程的以下步骤：
-
-<dl>
-<dt>1) 数据获取</dt>
-<dd>
-第一步是收集数据。在许多情况下，这可能是一个简单的过程，比如数据从网络应用程序进入数据库，但有时我们需要使用特殊技术。例如，来自物联网传感器的数据可能会非常庞大，使用像IoT Hub这样的缓冲端点来收集所有数据是一个好习惯，以便进一步处理。
-</dd>
-<dt>2) 数据存储</dt>
-<dd>
-存储数据可能是一个挑战，特别是当我们谈论大数据时。在决定如何存储数据时，最好预测未来查询数据的方式。数据可以通过几种方式存储：
-<ul>
-<li>关系型数据库存储表的集合，并使用一种称为SQL的特殊语言来查询它们。通常，表被组织成不同的组，称为模式。在许多情况下，我们需要将数据从原始形式转换为适合模式的形式。</li>
-<li><a href="https://en.wikipedia.org/wiki/NoSQL">NoSQL</a>数据库，例如<a href="https://azure.microsoft.com/services/cosmos-db/?WT.mc_id=academic-77958-bethanycheum">CosmosDB</a>，不对数据强制执行模式，并允许存储更复杂的数据，例如层次化的JSON文档或图表。然而，NoSQL数据库没有SQL的丰富查询功能，也无法强制执行引用完整性，即关于表结构和表之间关系的规则。</li>
-<li><a href="https://en.wikipedia.org/wiki/Data_lake">数据湖</a>存储用于以原始、非结构化形式存储大量数据。数据湖通常用于大数据，其中所有数据无法放入一台机器，必须由服务器集群存储和处理。<a href="https://en.wikipedia.org/wiki/Apache_Parquet">Parquet</a>是通常与大数据一起使用的数据格式。</li> 
-</ul>
-</dd>
-<dt>3) 数据处理</dt>
-<dd>
-这是数据旅程中最令人兴奋的部分，涉及将数据从原始形式转换为可用于可视化/模型训练的形式。当处理非结构化数据（如文本或图像）时，我们可能需要使用一些AI技术从数据中提取<b>特征</b>，从而将其转换为结构化形式。
-</dd>
-<dt>4) 可视化/人工洞察</dt>
-<dd>
-为了理解数据，我们通常需要对其进行可视化。拥有许多不同的可视化技术，我们可以找到合适的视图来获得洞察力。通常，数据科学家需要“玩转数据”，多次进行可视化并寻找一些关系。此外，我们可以使用统计技术来测试假设或证明数据之间的相关性。
-</dd>
-<dt>5) 训练预测模型</dt>
-<dd>
-由于数据科学的最终目标是能够基于数据做出决策，我们可能希望使用<a href="http://github.com/microsoft/ml-for-beginners">机器学习</a>技术来构建预测模型。然后，我们可以使用具有类似结构的新数据集进行预测。
-</dd>
-</dl>
-
-当然，根据实际数据，有些步骤可能会缺失（例如，当我们已经在数据库中拥有数据，或者当我们不需要模型训练时），或者有些步骤可能会重复多次（例如数据处理）。
-
-## 数字化与数字化转型
-
-在过去的十年中，许多企业开始意识到数据在做出业务决策时的重要性。要将数据科学原则应用于企业运营，首先需要收集一些数据，即将业务流程转化为数字形式。这被称为**数字化**。将数据科学技术应用于这些数据以指导决策，可以显著提高生产力（甚至实现业务转型），这被称为**数字化转型**。
-
-让我们考虑一个例子。假设我们有一个数据科学课程（比如这个课程），我们在线向学生提供，并希望利用数据科学来改进它。我们该怎么做？
-
-我们可以从问“什么可以数字化？”开始。最简单的方法是测量每个学生完成每个模块所需的时间，并通过在每个模块结束时进行选择题测试来测量获得的知识。通过计算所有学生的平均完成时间，我们可以找出哪些模块对学生来说最具挑战性，并努力简化它们。
-> 你可能会认为这种方法并不理想，因为模块的长度可能不同。更公平的做法可能是将时间除以模块的长度（以字符数计算），然后比较这些值。
-
-当我们开始分析多项选择测试的结果时，可以尝试确定学生难以理解的概念，并利用这些信息来改进内容。为此，我们需要设计测试，使每个问题都能映射到某个特定的概念或知识点。
-
-如果我们想更复杂一些，可以将每个模块所花费的时间与学生的年龄类别进行对比分析。我们可能会发现，对于某些年龄段来说，完成模块所需的时间过长，或者学生在完成之前就退出了。这可以帮助我们为模块提供年龄建议，并减少因错误预期而导致的不满。
-
-## 🚀 挑战
-
-在这个挑战中，我们将尝试通过分析文本来找到与数据科学领域相关的概念。我们将选取一篇关于数据科学的维基百科文章，下载并处理文本，然后构建一个像这样的词云：
-
-![数据科学词云](../../../../translated_images/zh-CN/ds_wordcloud.664a7c07dca57de017c22bf0498cb40f898d48aa85b3c36a80620fea12fadd42.png)
-
-访问 [`notebook.ipynb`](../../../../1-Introduction/01-defining-data-science/notebook.ipynb ':ignore') 阅读代码。你也可以运行代码，实时查看它如何执行所有数据转换。
-
-> 如果你不知道如何在 Jupyter Notebook 中运行代码，可以查看 [这篇文章](https://soshnikov.com/education/how-to-execute-notebooks-from-github/)。
-
-## [课后测验](https://ff-quizzes.netlify.app/en/ds/quiz/1)
-
-## 作业
-
-* **任务 1**：修改上述代码，找出与 **大数据** 和 **机器学习** 领域相关的概念
-* **任务 2**：[思考数据科学场景](assignment.md)
-
-## 致谢
-
-本课程由 [Dmitry Soshnikov](http://soshnikov.com) 倾情创作 ♥️
-
----
-
-**免责声明**：  
-本文档使用AI翻译服务[Co-op Translator](https://github.com/Azure/co-op-translator)进行翻译。尽管我们努力确保翻译的准确性，但请注意，自动翻译可能包含错误或不准确之处。原始语言的文档应被视为权威来源。对于重要信息，建议使用专业人工翻译。我们不对因使用此翻译而产生的任何误解或误读承担责任。
\ No newline at end of file
diff --git a/translations/zh/1-Introduction/01-defining-data-science/assignment.md b/translations/zh/1-Introduction/01-defining-data-science/assignment.md
deleted file mode 100644
index b6e67339..00000000
--- a/translations/zh/1-Introduction/01-defining-data-science/assignment.md
+++ /dev/null
@@ -1,44 +0,0 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "4e0f1773b9bee1be3b28f9fe2c71b3de",
-  "translation_date": "2025-08-25T16:56:01+00:00",
-  "source_file": "1-Introduction/01-defining-data-science/assignment.md",
-  "language_code": "zh"
-}
--->
-# 作业：数据科学场景
-
-在本次作业中，我们希望你思考一些现实生活中的流程或问题，涉及不同的问题领域，并考虑如何通过数据科学流程来改进它们。请思考以下问题：
-
-1. 你可以收集哪些数据？
-1. 你将如何收集这些数据？
-1. 你将如何存储这些数据？数据的规模可能有多大？
-1. 你可能从这些数据中获得哪些洞察？基于这些数据，我们可以做出哪些决策？
-
-尝试思考三个不同的问题/流程，并针对每个问题领域描述上述每个要点。
-
-以下是一些可以帮助你开始思考的问题领域和问题：
-
-1. 如何利用数据改进学校中儿童的教育过程？
-1. 如何利用数据在疫情期间控制疫苗接种？
-1. 如何利用数据确保自己在工作中保持高效？
-
-## 指导
-
-填写下表（如果需要，可以用你自己的问题领域替换建议的问题领域）：
-
-| 问题领域 | 问题 | 收集哪些数据 | 如何存储数据 | 我们可以做出的洞察/决策 | 
-|----------|------|--------------|--------------|--------------------------|
-| 教育     |      |              |              |                          |
-| 疫苗接种 |      |              |              |                          |
-| 工作效率 |      |              |              |                          |
-
-## 评分标准
-
-优秀 | 合格 | 需要改进
---- | --- | -- |
-能够为所有问题领域识别出合理的数据来源、数据存储方式以及可能的决策/洞察 | 解决方案的某些方面缺乏细节，未讨论数据存储，至少描述了两个问题领域 | 仅描述了部分数据解决方案，仅考虑了一个问题领域。
-
-**免责声明**：  
-本文档使用AI翻译服务 [Co-op Translator](https://github.com/Azure/co-op-translator) 进行翻译。尽管我们努力确保翻译的准确性，但请注意，自动翻译可能包含错误或不准确之处。原始语言的文档应被视为权威来源。对于关键信息，建议使用专业人工翻译。我们不对因使用此翻译而产生的任何误解或误读承担责任。
\ No newline at end of file
diff --git a/translations/zh/1-Introduction/01-defining-data-science/notebook.ipynb b/translations/zh/1-Introduction/01-defining-data-science/notebook.ipynb
deleted file mode 100644
index b64542f0..00000000
--- a/translations/zh/1-Introduction/01-defining-data-science/notebook.ipynb
+++ /dev/null
@@ -1,431 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "source": [
-    "# 挑战：分析关于数据科学的文本\n",
-    "\n",
-    "在这个例子中，我们将进行一个简单的练习，涵盖传统数据科学流程的所有步骤。你不需要编写任何代码，只需点击下面的单元格来执行它们并观察结果。作为一个挑战，鼓励你尝试用不同的数据运行这些代码。\n",
-    "\n",
-    "## 目标\n",
-    "\n",
-    "在本节课中，我们讨论了与数据科学相关的不同概念。现在让我们通过**文本挖掘**来探索更多相关概念。我们将从一段关于数据科学的文本开始，从中提取关键词，然后尝试可视化结果。\n",
-    "\n",
-    "作为文本素材，我们将使用维基百科上关于数据科学的页面：\n"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 62,
-   "source": [
-    "url = 'https://en.wikipedia.org/wiki/Data_science'"
-   ],
-   "outputs": [],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "## 第一步：获取数据\n",
-    "\n",
-    "数据科学流程的第一步是获取数据。我们将使用 `requests` 库来完成这一任务：\n"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 63,
-   "source": [
-    "import requests\r\n",
-    "\r\n",
-    "text = requests.get(url).content.decode('utf-8')\r\n",
-    "print(text[:1000])"
-   ],
-   "outputs": [
-    {
-     "output_type": "stream",
-     "name": "stdout",
-     "text": [
-      "<!DOCTYPE html>\n",
-      "<html class=\"client-nojs\" lang=\"en\" dir=\"ltr\">\n",
-      "<head>\n",
-      "<meta charset=\"UTF-8\"/>\n",
-      "<title>Data science - Wikipedia</title>\n",
-      "<script>document.documentElement.className=\"client-js\";RLCONF={\"wgBreakFrames\":!1,\"wgSeparatorTransformTable\":[\"\",\"\"],\"wgDigitTransformTable\":[\"\",\"\"],\"wgDefaultDateFormat\":\"dmy\",\"wgMonthNames\":[\"\",\"January\",\"February\",\"March\",\"April\",\"May\",\"June\",\"July\",\"August\",\"September\",\"October\",\"November\",\"December\"],\"wgRequestId\":\"1a104647-90de-485a-b88a-1406e889a5d1\",\"wgCSPNonce\":!1,\"wgCanonicalNamespace\":\"\",\"wgCanonicalSpecialPageName\":!1,\"wgNamespaceNumber\":0,\"wgPageName\":\"Data_science\",\"wgTitle\":\"Data science\",\"wgCurRevisionId\":1038046078,\"wgRevisionId\":1038046078,\"wgArticleId\":35458904,\"wgIsArticle\":!0,\"wgIsRedirect\":!1,\"wgAction\":\"view\",\"wgUserName\":null,\"wgUserGroups\":[\"*\"],\"wgCategories\":[\"CS1 maint: others\",\"Articles with short description\",\"Short description matches Wikidata\",\"Use dmy dates from December 2012\",\"Information science\",\"Computer occupations\"\n"
-     ]
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "## 第2步：转换数据\n",
-    "\n",
-    "下一步是将数据转换为适合处理的形式。在我们的案例中，我们已经从页面下载了HTML源代码，现在需要将其转换为纯文本。\n",
-    "\n",
-    "有很多方法可以实现这一点。我们将使用Python中最简单的内置对象[HTMLParser](https://docs.python.org/3/library/html.parser.html)。我们需要继承`HTMLParser`类，并定义代码以收集HTML标签内的所有文本，除了`<script>`和`<style>`标签中的内容。\n"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 64,
-   "source": [
-    "from html.parser import HTMLParser\r\n",
-    "\r\n",
-    "class MyHTMLParser(HTMLParser):\r\n",
-    "    script = False\r\n",
-    "    res = \"\"\r\n",
-    "    def handle_starttag(self, tag, attrs):\r\n",
-    "        if tag.lower() in [\"script\",\"style\"]:\r\n",
-    "            self.script = True\r\n",
-    "    def handle_endtag(self, tag):\r\n",
-    "        if tag.lower() in [\"script\",\"style\"]:\r\n",
-    "            self.script = False\r\n",
-    "    def handle_data(self, data):\r\n",
-    "        if str.strip(data)==\"\" or self.script:\r\n",
-    "            return\r\n",
-    "        self.res += ' '+data.replace('[ edit ]','')\r\n",
-    "\r\n",
-    "parser = MyHTMLParser()\r\n",
-    "parser.feed(text)\r\n",
-    "text = parser.res\r\n",
-    "print(text[:1000])"
-   ],
-   "outputs": [
-    {
-     "output_type": "stream",
-     "name": "stdout",
-     "text": [
-      " Data science - Wikipedia Data science From Wikipedia, the free encyclopedia Jump to navigation Jump to search Interdisciplinary field of study focused on deriving knowledge and insights from data Not to be confused with  information science . The existence of  Comet NEOWISE  (here depicted as a series of red dots) was discovered by analyzing  astronomical survey  data acquired by a  space telescope , the  Wide-field Infrared Survey Explorer . Part of a series on Machine learning and  data mining Problems Classification Clustering Regression Anomaly detection AutoML Association rules Reinforcement learning Structured prediction Feature engineering Feature learning Online learning Semi-supervised learning Unsupervised learning Learning to rank Grammar induction Supervised learning ( classification  •  regression ) Decision trees Ensembles Bagging Boosting Random forest k -NN Linear regression Naive Bayes Artificial neural networks Logistic regression Perceptron Relevance vector machine \n"
-     ]
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "## 第三步：获取洞察\n",
-    "\n",
-    "最重要的一步是将数据转化为可以提取洞察的形式。在我们的案例中，我们希望从文本中提取关键词，并查看哪些关键词更有意义。\n",
-    "\n",
-    "我们将使用一个名为 [RAKE](https://github.com/aneesha/RAKE) 的 Python 库来进行关键词提取。首先，如果尚未安装该库，我们需要安装它：\n"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 65,
-   "source": [
-    "import sys\r\n",
-    "!{sys.executable} -m pip install nlp_rake"
-   ],
-   "outputs": [
-    {
-     "output_type": "stream",
-     "name": "stdout",
-     "text": [
-      "Requirement already satisfied: nlp_rake in c:\\winapp\\miniconda3\\lib\\site-packages (0.0.2)\n",
-      "Requirement already satisfied: numpy>=1.14.4 in c:\\winapp\\miniconda3\\lib\\site-packages (from nlp_rake) (1.19.5)\n",
-      "Requirement already satisfied: pyrsistent>=0.14.2 in c:\\winapp\\miniconda3\\lib\\site-packages (from nlp_rake) (0.17.3)\n",
-      "Requirement already satisfied: regex>=2018.6.6 in c:\\winapp\\miniconda3\\lib\\site-packages (from nlp_rake) (2021.8.3)\n",
-      "Requirement already satisfied: langdetect>=1.0.8 in c:\\winapp\\miniconda3\\lib\\site-packages (from nlp_rake) (1.0.9)\n",
-      "Requirement already satisfied: six in c:\\winapp\\miniconda3\\lib\\site-packages (from langdetect>=1.0.8->nlp_rake) (1.16.0)\n"
-     ]
-    },
-    {
-     "output_type": "stream",
-     "name": "stderr",
-     "text": [
-      "C:\\winapp\\Miniconda3\\lib\\site-packages\\secretstorage\\dhcrypto.py:16: CryptographyDeprecationWarning: int_from_bytes is deprecated, use int.from_bytes instead\n",
-      "  from cryptography.utils import int_from_bytes\n",
-      "C:\\winapp\\Miniconda3\\lib\\site-packages\\secretstorage\\util.py:25: CryptographyDeprecationWarning: int_from_bytes is deprecated, use int.from_bytes instead\n",
-      "  from cryptography.utils import int_from_bytes\n",
-      "WARNING: Ignoring invalid distribution -umpy (c:\\winapp\\miniconda3\\lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution -umpy (c:\\winapp\\miniconda3\\lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution -umpy (c:\\winapp\\miniconda3\\lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution -umpy (c:\\winapp\\miniconda3\\lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution -umpy (c:\\winapp\\miniconda3\\lib\\site-packages)\n"
-     ]
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "主要功能可通过 `Rake` 对象实现，我们可以使用一些参数进行自定义。在我们的例子中，我们将关键词的最小长度设置为5个字符，关键词在文档中的最小频率设置为3，关键词的最大单词数设置为2。可以随意尝试其他值并观察结果。\n"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 66,
-   "source": [
-    "import nlp_rake\r\n",
-    "extractor = nlp_rake.Rake(max_words=2,min_freq=3,min_chars=5)\r\n",
-    "res = extractor.apply(text)\r\n",
-    "res"
-   ],
-   "outputs": [
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "[('machine learning', 4.0),\n",
-       " ('big data', 4.0),\n",
-       " ('data scientist', 4.0),\n",
-       " ('21st century', 4.0),\n",
-       " ('data science', 3.909090909090909),\n",
-       " ('computer science', 3.909090909090909),\n",
-       " ('information science', 3.797979797979798),\n",
-       " ('data analysis', 3.666666666666667),\n",
-       " ('application domains', 3.6),\n",
-       " ('science', 1.9090909090909092),\n",
-       " ('field', 1.25),\n",
-       " ('statistics', 1.2272727272727273),\n",
-       " ('classification', 1.2),\n",
-       " ('techniques', 1.1666666666666667),\n",
-       " ('datasets', 1.0),\n",
-       " ('education', 1.0),\n",
-       " ('archived', 1.0),\n",
-       " ('original', 1.0),\n",
-       " ('chikio', 1.0),\n",
-       " ('forbes', 1.0)]"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 66
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "我们获得了一份术语列表，并附有相关的重要性程度。如您所见，最相关的领域，例如机器学习和大数据，位于列表的顶部位置。\n",
-    "\n",
-    "## 第四步：结果可视化\n",
-    "\n",
-    "人们通常通过视觉形式来最好地理解数据。因此，将数据进行可视化以获取一些洞察通常是有意义的。我们可以使用 Python 中的 `matplotlib` 库来绘制关键词及其相关性的简单分布图：\n"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 67,
-   "source": [
-    "import matplotlib.pyplot as plt\r\n",
-    "\r\n",
-    "def plot(pair_list):\r\n",
-    "    k,v = zip(*pair_list)\r\n",
-    "    plt.bar(range(len(k)),v)\r\n",
-    "    plt.xticks(range(len(k)),k,rotation='vertical')\r\n",
-    "    plt.show()\r\n",
-    "\r\n",
-    "plot(res)"
-   ],
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ],
-      "image/png": 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"
-     },
-     "metadata": {
-      "needs_background": "light"
-     }
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "然而，还有一种更好的方式来可视化词频——使用**词云**。我们需要安装另一个库来根据我们的关键词列表绘制词云。\n"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 71,
-   "source": [
-    "!{sys.executable} -m pip install wordcloud"
-   ],
-   "outputs": [],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "`WordCloud` 对象负责接收原始文本或预先计算的单词及其频率列表，并返回一张图像，然后可以使用 `matplotlib` 显示：\n"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 69,
-   "source": [
-    "from wordcloud import WordCloud\r\n",
-    "import matplotlib.pyplot as plt\r\n",
-    "\r\n",
-    "wc = WordCloud(background_color='white',width=800,height=600)\r\n",
-    "plt.figure(figsize=(15,7))\r\n",
-    "plt.imshow(wc.generate_from_frequencies({ k:v for k,v in res }))"
-   ],
-   "outputs": [
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "<matplotlib.image.AxesImage at 0x224b8677400>"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 69
-    },
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "<Figure size 1080x504 with 1 Axes>"
-      ],
-      "image/png": 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"
-     },
-     "metadata": {
-      "needs_background": "light"
-     }
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "我们也可以将原始文本传递给 `WordCloud` - 让我们看看是否能够得到类似的结果：\n"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 70,
-   "source": [
-    "plt.figure(figsize=(15,7))\r\n",
-    "plt.imshow(wc.generate(text))"
-   ],
-   "outputs": [
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "<matplotlib.image.AxesImage at 0x224b9e5fbb0>"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 70
-    },
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "<Figure size 1080x504 with 1 Axes>"
-      ],
-      "image/png": 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"
-     },
-     "metadata": {
-      "needs_background": "light"
-     }
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 61,
-   "source": [
-    "wc.generate(text).to_file('images/ds_wordcloud.png')"
-   ],
-   "outputs": [
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "<wordcloud.wordcloud.WordCloud at 0x224b99d76a0>"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 61
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "你可以看到词云现在看起来更吸引人了，但它也包含了很多噪音（例如，像 `Retrieved on` 这样的无关词）。此外，我们得到的双词关键词（例如 *data scientist* 或 *computer science*）也更少了。这是因为 RAKE 算法在从文本中选择优质关键词方面表现得更好。这个例子说明了数据预处理和清理的重要性，因为最终清晰的结果能够帮助我们做出更好的决策。\n",
-    "\n",
-    "在这个练习中，我们通过一个简单的过程，从维基百科文本中提取了一些意义，以关键词和词云的形式呈现。这个例子虽然很简单，但很好地展示了数据科学家在处理数据时通常会经历的所有步骤，从数据获取到可视化。\n",
-    "\n",
-    "在我们的课程中，我们将详细讨论所有这些步骤。\n"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n---\n\n**免责声明**：  \n本文档使用AI翻译服务[Co-op Translator](https://github.com/Azure/co-op-translator)进行翻译。尽管我们努力确保准确性，但请注意，自动翻译可能包含错误或不准确之处。应以原始语言的文档作为权威来源。对于关键信息，建议使用专业人工翻译。对于因使用本翻译而引起的任何误解或误读，我们概不负责。\n"
-   ]
-  }
- ],
- "metadata": {
-  "orig_nbformat": 4,
-  "language_info": {
-   "name": "python",
-   "version": "3.8.11",
-   "mimetype": "text/x-python",
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "pygments_lexer": "ipython3",
-   "nbconvert_exporter": "python",
-   "file_extension": ".py"
-  },
-  "kernelspec": {
-   "name": "python3",
-   "display_name": "Python 3.8.11 64-bit ('base': conda)"
-  },
-  "interpreter": {
-   "hash": "c28e7b6bf4e5b397b8288a85bf0a94ea8d3585ce2b01919feb195678ec71581b"
-  },
-  "coopTranslator": {
-   "original_hash": "50c0f0a5204a18405611cbe7e0fec56b",
-   "translation_date": "2025-09-02T10:11:59+00:00",
-   "source_file": "1-Introduction/01-defining-data-science/notebook.ipynb",
-   "language_code": "zh"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
\ No newline at end of file
diff --git a/translations/zh/1-Introduction/01-defining-data-science/solution/assignment.md b/translations/zh/1-Introduction/01-defining-data-science/solution/assignment.md
deleted file mode 100644
index 16426830..00000000
--- a/translations/zh/1-Introduction/01-defining-data-science/solution/assignment.md
+++ /dev/null
@@ -1,46 +0,0 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "a8f79b9c0484c35b4f26e8aec7fc4d56",
-  "translation_date": "2025-08-25T16:57:01+00:00",
-  "source_file": "1-Introduction/01-defining-data-science/solution/assignment.md",
-  "language_code": "zh"
-}
--->
-# 作业：数据科学场景
-
-在这个第一次作业中，我们希望你思考一些现实生活中的过程或问题，涉及不同的领域，并考虑如何通过数据科学流程来改进它。请思考以下问题：
-
-1. 可以收集哪些数据？
-1. 如何收集这些数据？
-1. 如何存储这些数据？数据的规模可能有多大？
-1. 可以从这些数据中获得哪些洞察？基于数据可以做出哪些决策？
-
-尝试思考三个不同的问题/过程，并针对每个问题领域描述上述每个点。
-
-以下是一些问题领域和问题，可以帮助你开始思考：
-
-1. 如何利用数据改善学校中儿童的教育过程？
-1. 如何利用数据在疫情期间控制疫苗接种？
-1. 如何利用数据确保自己在工作中保持高效？
-
-## 说明
-
-填写以下表格（如果需要，可以用自己的问题领域替换建议的领域）：
-
-| 问题领域 | 问题 | 收集哪些数据 | 如何存储数据 | 可以做出的洞察/决策 | 
-|----------|------|--------------|--------------|----------------------|
-| 教育 | 在大学中，通常课堂出勤率较低，我们假设出勤率较高的学生在考试中表现更好。我们希望提高出勤率并验证这一假设。 | 我们可以通过课堂上的安全摄像头拍摄的照片，或者通过追踪学生手机的蓝牙/WiFi地址来记录出勤情况。考试数据已经存储在大学数据库中。 | 如果我们追踪安全摄像头的图像——我们需要存储课堂期间拍摄的几张（5-10张）照片（非结构化数据），然后使用AI识别学生的面部（将数据转换为结构化形式）。 | 我们可以计算每个学生的平均出勤率，并查看是否与考试成绩存在相关性。我们将在[概率与统计](../../04-stats-and-probability/README.md)部分中进一步讨论相关性。为了提高学生的出勤率，我们可以在学校门户网站上发布每周出勤排名，并在出勤率最高的学生中抽奖。 |
-| 疫苗接种 | | | | |
-| 工作效率 | | | | |
-
-> *我们仅提供一个答案作为示例，以便你了解作业的预期内容。*
-
-## 评分标准
-
-优秀 | 合格 | 需要改进
---- | --- | --- |
-能够为所有问题领域识别合理的数据来源、数据存储方式以及可能的决策/洞察 | 部分解决方案细节不足，未讨论数据存储，至少描述了两个问题领域 | 仅描述了部分数据解决方案，仅考虑了一个问题领域。
-
-**免责声明**：  
-本文档使用AI翻译服务 [Co-op Translator](https://github.com/Azure/co-op-translator) 进行翻译。尽管我们努力确保翻译的准确性，但请注意，自动翻译可能包含错误或不准确之处。应以原始语言的文档作为权威来源。对于关键信息，建议使用专业人工翻译。我们对因使用本翻译而引起的任何误解或误读不承担责任。
\ No newline at end of file
diff --git a/translations/zh/1-Introduction/01-defining-data-science/solution/notebook.ipynb b/translations/zh/1-Introduction/01-defining-data-science/solution/notebook.ipynb
deleted file mode 100644
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--- a/translations/zh/1-Introduction/01-defining-data-science/solution/notebook.ipynb
+++ /dev/null
@@ -1,539 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "source": [
-    "# 挑战：分析关于数据科学的文本\n",
-    "\n",
-    "> *在本笔记本中，我们尝试使用不同的URL——维基百科上关于机器学习的文章。你会发现，与数据科学不同，这篇文章包含了许多术语，这使得分析变得更加复杂。我们需要在完成关键词提取后，想出另一种方法来清理数据，以去除一些频繁但无意义的词组。*\n",
-    "\n",
-    "在这个例子中，让我们做一个简单的练习，涵盖传统数据科学流程的所有步骤。你不需要编写任何代码，只需点击下面的单元格来执行它们并观察结果。作为一个挑战，我们鼓励你尝试用不同的数据运行这些代码。\n",
-    "\n",
-    "## 目标\n",
-    "\n",
-    "在本节课中，我们讨论了与数据科学相关的不同概念。现在让我们通过**文本挖掘**来发现更多相关概念。我们将从一段关于数据科学的文本开始，从中提取关键词，然后尝试可视化结果。\n",
-    "\n",
-    "作为文本来源，我将使用维基百科上关于数据科学的页面：\n"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "source": [
-    "url = 'https://en.wikipedia.org/wiki/Data_science'\r\n",
-    "url = 'https://en.wikipedia.org/wiki/Machine_learning'"
-   ],
-   "outputs": [],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "## 第一步：获取数据\n",
-    "\n",
-    "数据科学流程的第一步是获取数据。我们将使用 `requests` 库来完成这一任务：\n"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "source": [
-    "import requests\r\n",
-    "\r\n",
-    "text = requests.get(url).content.decode('utf-8')\r\n",
-    "print(text[:1000])"
-   ],
-   "outputs": [
-    {
-     "output_type": "stream",
-     "name": "stdout",
-     "text": [
-      "<!DOCTYPE html>\n",
-      "<html class=\"client-nojs\" lang=\"en\" dir=\"ltr\">\n",
-      "<head>\n",
-      "<meta charset=\"UTF-8\"/>\n",
-      "<title>Machine learning - Wikipedia</title>\n",
-      "<script>document.documentElement.className=\"client-js\";RLCONF={\"wgBreakFrames\":!1,\"wgSeparatorTransformTable\":[\"\",\"\"],\"wgDigitTransformTable\":[\"\",\"\"],\"wgDefaultDateFormat\":\"dmy\",\"wgMonthNames\":[\"\",\"January\",\"February\",\"March\",\"April\",\"May\",\"June\",\"July\",\"August\",\"September\",\"October\",\"November\",\"December\"],\"wgRequestId\":\"77162785-16e9-4d7f-a175-7f3fcf502a66\",\"wgCSPNonce\":!1,\"wgCanonicalNamespace\":\"\",\"wgCanonicalSpecialPageName\":!1,\"wgNamespaceNumber\":0,\"wgPageName\":\"Machine_learning\",\"wgTitle\":\"Machine learning\",\"wgCurRevisionId\":1041247229,\"wgRevisionId\":1041247229,\"wgArticleId\":233488,\"wgIsArticle\":!0,\"wgIsRedirect\":!1,\"wgAction\":\"view\",\"wgUserName\":null,\"wgUserGroups\":[\"*\"],\"wgCategories\":[\"CS1 errors: missing periodical\",\"Harv and Sfn no-target errors\",\"CS1 maint: uses authors parameter\",\"Articles with short description\",\"Short description is dif\n"
-     ]
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "## 第2步：转换数据\n",
-    "\n",
-    "下一步是将数据转换为适合处理的形式。在我们的案例中，我们已经从页面下载了HTML源代码，现在需要将其转换为纯文本。\n",
-    "\n",
-    "实现这一目标的方法有很多。我们将使用Python中最简单的内置对象 [HTMLParser](https://docs.python.org/3/library/html.parser.html)。我们需要继承`HTMLParser`类，并定义代码以收集HTML标签内的所有文本，排除`<script>`和`<style>`标签中的内容。\n"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "source": [
-    "from html.parser import HTMLParser\r\n",
-    "\r\n",
-    "class MyHTMLParser(HTMLParser):\r\n",
-    "    script = False\r\n",
-    "    res = \"\"\r\n",
-    "    def handle_starttag(self, tag, attrs):\r\n",
-    "        if tag.lower() in [\"script\",\"style\"]:\r\n",
-    "            self.script = True\r\n",
-    "    def handle_endtag(self, tag):\r\n",
-    "        if tag.lower() in [\"script\",\"style\"]:\r\n",
-    "            self.script = False\r\n",
-    "    def handle_data(self, data):\r\n",
-    "        if str.strip(data)==\"\" or self.script:\r\n",
-    "            return\r\n",
-    "        self.res += ' '+data.replace('[ edit ]','')\r\n",
-    "\r\n",
-    "parser = MyHTMLParser()\r\n",
-    "parser.feed(text)\r\n",
-    "text = parser.res\r\n",
-    "print(text[:1000])"
-   ],
-   "outputs": [
-    {
-     "output_type": "stream",
-     "name": "stdout",
-     "text": [
-      " Machine learning - Wikipedia Machine learning From Wikipedia, the free encyclopedia Jump to navigation Jump to search Study of algorithms that improve automatically through experience For the journal, see  Machine Learning (journal) . \"Statistical learning\" redirects here. For statistical learning in linguistics, see  statistical learning in language acquisition . Part of a series on Artificial intelligence Major goals Artificial general intelligence Planning Computer vision General game playing Knowledge reasoning Machine learning Natural language processing Robotics Approaches Symbolic Deep learning Bayesian networks Evolutionary algorithms Philosophy Ethics Existential risk Turing test Chinese room Control problem Friendly AI History Timeline Progress AI winter Technology Applications Projects Programming languages Glossary Glossary v t e Part of a series on Machine learning and  data mining Problems Classification Clustering Regression Anomaly detection Data Cleaning AutoML Associ\n"
-     ]
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "## 第三步：获取洞察\n",
-    "\n",
-    "最重要的一步是将数据转化为可以提取洞察的形式。在我们的案例中，我们希望从文本中提取关键词，并查看哪些关键词更有意义。\n",
-    "\n",
-    "我们将使用一个名为 [RAKE](https://github.com/aneesha/RAKE) 的 Python 库来进行关键词提取。首先，如果尚未安装该库，我们需要安装它：\n"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "source": [
-    "import sys\r\n",
-    "!{sys.executable} -m pip install nlp_rake"
-   ],
-   "outputs": [
-    {
-     "output_type": "stream",
-     "name": "stdout",
-     "text": [
-      "Requirement already satisfied: nlp_rake in c:\\winapp\\miniconda3\\lib\\site-packages (0.0.2)\n",
-      "Requirement already satisfied: langdetect>=1.0.8 in c:\\winapp\\miniconda3\\lib\\site-packages (from nlp_rake) (1.0.9)\n",
-      "Requirement already satisfied: pyrsistent>=0.14.2 in c:\\winapp\\miniconda3\\lib\\site-packages (from nlp_rake) (0.17.3)\n",
-      "Requirement already satisfied: numpy>=1.14.4 in c:\\winapp\\miniconda3\\lib\\site-packages (from nlp_rake) (1.19.5)\n",
-      "Requirement already satisfied: regex>=2018.6.6 in c:\\winapp\\miniconda3\\lib\\site-packages (from nlp_rake) (2021.8.3)\n"
-     ]
-    },
-    {
-     "output_type": "stream",
-     "name": "stderr",
-     "text": [
-      "C:\\winapp\\Miniconda3\\lib\\site-packages\\secretstorage\\dhcrypto.py:16: CryptographyDeprecationWarning: int_from_bytes is deprecated, use int.from_bytes instead\n",
-      "  from cryptography.utils import int_from_bytes\n",
-      "C:\\winapp\\Miniconda3\\lib\\site-packages\\secretstorage\\util.py:25: CryptographyDeprecationWarning: int_from_bytes is deprecated, use int.from_bytes instead\n",
-      "  from cryptography.utils import int_from_bytes\n",
-      "WARNING: Ignoring invalid distribution -umpy (c:\\winapp\\miniconda3\\lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution -umpy (c:\\winapp\\miniconda3\\lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution -umpy (c:\\winapp\\miniconda3\\lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution -umpy (c:\\winapp\\miniconda3\\lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution -umpy (c:\\winapp\\miniconda3\\lib\\site-packages)\n",
-      "WARNING: Ignoring invalid distribution -umpy (c:\\winapp\\miniconda3\\lib\\site-packages)\n"
-     ]
-    },
-    {
-     "output_type": "stream",
-     "name": "stdout",
-     "text": [
-      "Requirement already satisfied: six in c:\\winapp\\miniconda3\\lib\\site-packages (from langdetect>=1.0.8->nlp_rake) (1.16.0)\n"
-     ]
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "主要功能可通过 `Rake` 对象实现，我们可以使用一些参数进行自定义。在我们的例子中，我们将关键词的最小长度设置为5个字符，关键词在文档中的最小频率设置为3，关键词的最大单词数设置为2。可以随意尝试其他值并观察结果。\n"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "source": [
-    "import nlp_rake\r\n",
-    "extractor = nlp_rake.Rake(max_words=2,min_freq=3,min_chars=5)\r\n",
-    "res = extractor.apply(text)\r\n",
-    "res"
-   ],
-   "outputs": [
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "[('data mining', 4.0),\n",
-       " ('polynomial time', 4.0),\n",
-       " ('dimensionality reduction', 4.0),\n",
-       " ('anomaly detection', 4.0),\n",
-       " ('data set', 4.0),\n",
-       " ('bayesian networks', 4.0),\n",
-       " ('language corpora', 4.0),\n",
-       " ('mcgraw hill', 4.0),\n",
-       " ('mit press', 4.0),\n",
-       " ('retrieved 2018-08-20', 4.0),\n",
-       " ('artificial neuron', 3.9642857142857144),\n",
-       " ('statistical learning', 3.9470198675496686),\n",
-       " ('feature learning', 3.9470198675496686),\n",
-       " ('reinforcement learning', 3.9470198675496686),\n",
-       " ('deep learning', 3.9470198675496686),\n",
-       " ('main article', 3.9411764705882355),\n",
-       " ('machine learning', 3.9144111718974948),\n",
-       " ('pattern recognition', 3.9),\n",
-       " ('neural networks', 3.875),\n",
-       " ('artificial intelligence', 3.864285714285714),\n",
-       " ('supervised learning', 3.835908756438558),\n",
-       " ('speech recognition', 3.833333333333333),\n",
-       " ('bayesian network', 3.833333333333333),\n",
-       " ('explicitly programmed', 3.8),\n",
-       " ('biological brain', 3.8),\n",
-       " ('unsupervised learning', 3.780353200883002),\n",
-       " ('outlier detection', 3.75),\n",
-       " ('ieee transactions', 3.75),\n",
-       " ('isbn 978-0-262-01243-0', 3.7391304347826084),\n",
-       " ('training data', 3.7222222222222223),\n",
-       " ('training set', 3.7222222222222223),\n",
-       " ('artificial neurons', 3.7142857142857144),\n",
-       " ('make predictions', 3.666666666666667),\n",
-       " ('international conference', 3.666666666666667),\n",
-       " ('computer vision', 3.645833333333333),\n",
-       " ('mathematical model', 3.642857142857143),\n",
-       " ('genetic algorithms', 3.6233766233766236),\n",
-       " ('modern approach', 3.5555555555555554),\n",
-       " ('knowledge discovery', 3.5128205128205128),\n",
-       " ('genetic algorithm', 3.5090909090909093),\n",
-       " ('information theory', 3.4272727272727272),\n",
-       " ('training examples', 3.3222222222222224),\n",
-       " ('machine', 1.9673913043478262),\n",
-       " ('learning', 1.9470198675496688),\n",
-       " ('computer', 1.8125),\n",
-       " ('research', 1.7692307692307692),\n",
-       " ('regression', 1.75),\n",
-       " ('theory', 1.7272727272727273),\n",
-       " ('training', 1.7222222222222223),\n",
-       " ('algorithms', 1.7142857142857142),\n",
-       " ('related', 1.7),\n",
-       " ('information', 1.7),\n",
-       " ('representation', 1.6666666666666667),\n",
-       " ('discovery', 1.6666666666666667),\n",
-       " ('predictions', 1.6666666666666667),\n",
-       " ('model', 1.6428571428571428),\n",
-       " ('based', 1.625),\n",
-       " ('methods', 1.6111111111111112),\n",
-       " ('algorithm', 1.6),\n",
-       " ('examples', 1.6),\n",
-       " ('systems', 1.5625),\n",
-       " ('approach', 1.5555555555555556),\n",
-       " ('biases', 1.5454545454545454),\n",
-       " ('classification', 1.5333333333333334),\n",
-       " ('application', 1.5),\n",
-       " ('tasks', 1.5),\n",
-       " ('models', 1.5),\n",
-       " ('environment', 1.5),\n",
-       " ('compute', 1.5),\n",
-       " ('input', 1.4545454545454546),\n",
-       " ('racist', 1.4285714285714286),\n",
-       " ('perform', 1.4166666666666667),\n",
-       " ('journal', 1.4),\n",
-       " ('decisions', 1.4),\n",
-       " ('computers', 1.4),\n",
-       " ('improve', 1.4),\n",
-       " ('researchers', 1.4),\n",
-       " ('program', 1.4),\n",
-       " ('represented', 1.4),\n",
-       " ('accuracy', 1.4),\n",
-       " ('typically', 1.4),\n",
-       " ('system', 1.375),\n",
-       " ('optimization', 1.375),\n",
-       " ('field', 1.3529411764705883),\n",
-       " ('databases', 1.3333333333333333),\n",
-       " ('terms', 1.3333333333333333),\n",
-       " ('learned', 1.3333333333333333),\n",
-       " ('outputs', 1.3333333333333333),\n",
-       " ('called', 1.3333333333333333),\n",
-       " ('observations', 1.3333333333333333),\n",
-       " ('applied', 1.3333333333333333),\n",
-       " ('target', 1.3333333333333333),\n",
-       " ('original', 1.3333333333333333),\n",
-       " ('study', 1.2857142857142858),\n",
-       " ('trained', 1.2857142857142858),\n",
-       " ('approaches', 1.25),\n",
-       " ('complexity', 1.25),\n",
-       " ('signal', 1.25),\n",
-       " ('inputs', 1.25),\n",
-       " ('output', 1.25),\n",
-       " ('process', 1.25),\n",
-       " ('people', 1.25),\n",
-       " ('peter', 1.25),\n",
-       " ('christopher', 1.25),\n",
-       " ('problem', 1.2),\n",
-       " ('performance', 1.2),\n",
-       " ('difference', 1.2),\n",
-       " ('michael', 1.2),\n",
-       " ('predict', 1.2),\n",
-       " ('norvig', 1.2),\n",
-       " ('learn', 1.1666666666666667),\n",
-       " ('class', 1.1666666666666667),\n",
-       " ('applications', 1.125),\n",
-       " ('found', 1.125),\n",
-       " ('features', 1.1111111111111112),\n",
-       " ('introduction', 1.0909090909090908),\n",
-       " ('statistics', 1.0714285714285714),\n",
-       " ('experience', 1.0),\n",
-       " ('series', 1.0),\n",
-       " ('order', 1.0),\n",
-       " ('medicine', 1.0),\n",
-       " ('respect', 1.0),\n",
-       " ('question', 1.0),\n",
-       " ('subfield', 1.0),\n",
-       " ('leading', 1.0),\n",
-       " ('actions', 1.0),\n",
-       " ('maximize', 1.0),\n",
-       " ('evaluated', 1.0),\n",
-       " ('instances', 1.0),\n",
-       " ('generalization', 1.0),\n",
-       " ('context', 1.0),\n",
-       " ('hypothesis', 1.0),\n",
-       " ('addition', 1.0),\n",
-       " ('types', 1.0),\n",
-       " ('members', 1.0),\n",
-       " ('bioinformatics', 1.0),\n",
-       " ('connection', 1.0),\n",
-       " ('layers', 1.0),\n",
-       " ('ethics', 1.0),\n",
-       " ('david', 1.0),\n",
-       " ('martin', 1.0),\n",
-       " ('springer', 1.0),\n",
-       " ('citeseerx 10', 1.0),\n",
-       " ('github', 1.0),\n",
-       " ('andrew', 1.0),\n",
-       " ('cybernetics', 1.0),\n",
-       " ('proceedings', 1.0),\n",
-       " ('arxiv', 1.0),\n",
-       " ('archived', 1.0),\n",
-       " ('survey', 1.0),\n",
-       " ('bibcode', 1.0),\n",
-       " ('cambridge', 1.0)]"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 6
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "我们获得了一份术语列表，并附有其相关的重要性程度。正如你所看到的，最相关的领域，例如机器学习和大数据，位于列表的顶部位置。\n",
-    "\n",
-    "## 第四步：结果可视化\n",
-    "\n",
-    "人们通常通过视觉形式来最好地理解数据。因此，将数据可视化以获取一些洞察通常是有意义的。我们可以使用 Python 中的 `matplotlib` 库来绘制关键词及其相关性的简单分布图：\n"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "source": [
-    "import matplotlib.pyplot as plt\r\n",
-    "\r\n",
-    "def plot(pair_list):\r\n",
-    "    k,v = zip(*pair_list)\r\n",
-    "    plt.bar(range(len(k)),v)\r\n",
-    "    plt.xticks(range(len(k)),k,rotation='vertical')\r\n",
-    "    plt.show()\r\n",
-    "\r\n",
-    "plot(res[:30])"
-   ],
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {}
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "然而，还有一种更好的方法来可视化词频——使用**词云**。我们需要安装另一个库来从我们的关键词列表中绘制词云。\n"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 71,
-   "source": [
-    "!{sys.executable} -m pip install wordcloud"
-   ],
-   "outputs": [],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "`WordCloud`对象负责接收原始文本或预先计算的单词及其频率列表，并返回一个图像，该图像可以使用`matplotlib`显示：\n"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "source": [
-    "from wordcloud import WordCloud\r\n",
-    "import matplotlib.pyplot as plt\r\n",
-    "\r\n",
-    "wc = WordCloud(background_color='white',width=800,height=600)\r\n",
-    "plt.figure(figsize=(15,7))\r\n",
-    "plt.imshow(wc.generate_from_frequencies({ k:v for k,v in res }))\r\n",
-    "plt.show()"
-   ],
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1500x700 with 1 Axes>"
-      ]
-     },
-     "metadata": {}
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "我们也可以将原始文本传递给 `WordCloud` - 让我们看看是否能够得到类似的结果：\n"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "source": [
-    "plt.figure(figsize=(15,7))\r\n",
-    "plt.imshow(wc.generate(text))\r\n",
-    "plt.show()"
-   ],
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
-      "image/png": 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HYqFee6iVP/9uAxMmFbHmjpmEI1FqD7ahatopvY1+5/RTPtKBnm4vf/rtmyy+bhwLltzE0do2Xnp2D/GYxoc/eR2yLBGNxvjtI6+x8616Vt44mYKiDI4dbeeFp3YRDES578GFKAY52Wd7Wz8/++GLFJVm8tHPrKSvx0dBUTpGk4LZYmD67DI2vXGYD/3TcsyWxILH5wlRV9tBeVUuTrdlUBkS+NinV3Lre2bx4jO7aGroGeF6EgvNn3zvBdxpNj708HUIAtQf7UCShlo+Guq6eOS/15KR5eDBh68jFIrwwlO7+PqXH+fbP74Pm8OMqmps3XiEv/5hE1NnlTF5WgnH67v47c9fS+52XwqikRj/99NX2LvzOKtWTyG/MI2jte08+4+dhIMx7npgHopBRpJEava3UFSSybJVk5Blka0bj/DEX7eRnmln5tzyIdd8vK6TP/76Tcoqs1l83XjaWvooKEpPxrnous7hA638JbiReYuryMh0sPH1Gn7901coKExnTGUiNsJqM/HpL9xI812z+NsfNw/OEcPvRyQcY+fWY4ybWMBtd89GFEWe/ccOvvXv/+Dnj34Mq82Ipuls23yUF5/ezQ1rpjJhShF7dh7nd79Yzz3vX8Dt984e1u/bIahGeaW9hlfba9jZe5wXWvexPKealXnj8cUjvNCyjx29x1E1jWW5Y7kudxwmSeHgQBuPHttCUI2RbrRyZ/F08i1u4prKH49txR8P44uF+WT1cqocObzcdpB1bYewKUa6wz7uKpnJ0pwqBARe6TjEc837yLe4+cqkmwDQdJ21bQd5rmUvTsVMjtlJSI3xqbHL+UP9FqocOSzLHUuNt52fHV7P/86+j6Aa5cWW/WztqUfVNBZmV7IqbzxWJeXileLtcU0pB5FYnHaPj99t3InTbMSkKFTlZBCIROnw+BFFgWAkSnGGG5vRQH13P8FoImNEtsNGlsOGNxRG03UmFmSjyFIyQ0vbgI8TZu08lwOXxUyPP4AsSUwvSeyAqJpGrz9Irz+IqutIgkBZZhoGWeJ47wDhaIxwPI5JlinOcGExDHdDiKsaLf0efOEIsiiS53bgNJvo9gXo8vrRdJ00q5kcp52+QIi1+4/gspiZWpTLmOz0c7J2pLhwfKEoP/zoGkKRGN/7++us3VHL3UumYDJMxmG5jXBsf7KtrqvEtW5kKRtZygNA04LE1OaEpUCPIYpWjEpF8hxNjxCLNyJLWYiC85J+l7IoIgoC1blZLK8egzyC6d5uNpE+mLGo2xfgC39/keM9/SysLGFeeTEZNisWo8KP123mUFvniOMMX+yPjpR0ITk/bC4Ly++ec97nnUAxyMy47vIo+COhaTout5U73zuXqTPLAHC6LHz3P5+i9mArM+aOARKBsaIoMm12GTPnlqMoEituOM8I2VOIRVUWXTeOu+6fj8VqZO7CSmIxlZ1bj9HS1EtJWRY1+1vY+VY973toCQuXVSNJItNnl6HrsGdHAwuWVVNeeXJHtOFYF7fcOZNVN08dccylKyfy2tr9bN9ylMXLxwPQdLyHlqZebrx1GnZHIp5DEATMFgM5+S7SMmwjKgcA0Wicgb4AS1aMZ86CCswWA0tXDv1uo5EYb7x6EINR5sOfuI7c/ERxu6xsJ9/86hNserOWVaun4OkPsn3TUarG53P/hxaRle1M9vHEX7Ze8H0+Gwf2NbFrez0PfeI65i+uQhRFps4sRVU1du9sYN6SsZSOyQLg019aPeTc/MI0fvK9F2hv7R9ixdN1aGnu4+73LWDR8nHDxkwqOwKsuXMm190wCYC8Ajf/863nOXK4jZKyTORBa6nVbiIn343daU66LJ2OpulkZjl4z/3zklmdbHYT3/vPp6g91Mq0WWWEglGaGnooKslg3uIqMrOdlFXk8Ow/3qK1uReT6ezueueDVTZyS+EUaj0dzM0cw9zMxNyn6Tr1vm629zRwb+ksOkJeNnXVMTWtmHyLi+8fXMuHKxexIKsCTdfQdPDGQnhjYW4unMKi7Aqea9nHM817+ML46/HHI2SYbHy6+joOedp4sfUA87PKscgGVuVNwKlYeL2jNimXPx7msePb+cbU28kxO/h57RtnvA4dnaZAH6931vK+MfPoCfvY1FXH5LRCxiiZF/WepXj3cU0pB+F4nCMdPbQNeNnb3IHVoFCZk0FTn4e/bt9HVU4GMVXFbbVgMxqoae+i1xfAG45ikEU+sng2A8Ewrx46RiAS4+Hlc7AaFA61dfHo5t3Mryimpc/DpMJcVowvp9Pj56ldB3GYTXxm5XwicZU3ahvY19xBRXY6h9u7+cCC6ZRlpfGz17YwvTifY119+CMRPrdq4YjKgT8c4bcbdpDttJNmNWMxKhgkiSd3HUSREspKfyDEBxZMp8Pjo767D6vRgCBAQboLg5TKdHIpWTy5DKvRgFGRmVqeT01z1xlaa0SiNQQir2M1LcNuXkVMbaPX+wNMynQgTiR2mCz3NxKttTChyHYi0YPYzCsRZQcXslA+V3KddswGBbNB4b45UzEPugyMxhu1Dexv6WD1pLH8xy3LMQ260oWiMQyymIqlvcikZdgor8pN/p2T50KSRHp7/UBisZyX7yYzy8GLT++is32A8qpcyitzkm4nF0JlVW7SHUWURKbPGsObrx5KKgfHjnYS8IVpae7lxad3AYlFYE+XF68nRG+3b4hyIMsSiwYX/SMxbmI+hcUZvPriPhYsqUbTdOqPdSLLIiVlWSjKuc9pgiBgtRqZOquMTa8fJhyOUT2+gPLKHJxuazKuIRZTObiviWgkzo5tx5K/Ms9AEE3TOHq4nVWrpxAMRmht7mP67DIyMk9aZ6fNKuXJx7ads1znS11tB8FghJbGHl54KgCAqmr0dPsY6A/Q1+NLKgdeT5CGY1309wWIhGN0d3oI+CNEBt0BT8ViMTBrfsWw46diNMpMm1WW/NudbsNkVhjoH906eCaycpxJWQHyCtIQRIG+wedYkkTMFgNeb4jODg9mi5GuDg+RSIzsXNd5j3ehaLpGZ8hDV9jHrr6EJXWcKxerbEDVNep93czNTCjloiByIkTGoZgps2UgIpBndrGx8ygARlEm3+LGaTCTYbQTikeTVtiR6I0EsMlGMow2JEGk2pnDjt7GYe3ig1ZBTYeOkIeesJ/dg/JWOnNwKO+ca2qKa5drSjlwmk0sHzeG323ayV0zJw5xCbKbDCyoLKEozQUwGFMgkjEYFLm7qQ0dnaJ0F7PKCtle3zykb4fZxHtnT+H1w/W0e3yEY3EmFGTTOuDhcHv3kLaVOencN3cq//vqFjq9PkoyXERiKiZFpjDdidVgINM+cvo9SRIpSndhNijkuRy4LWZa+j1sqWti+bhyJEFgb18HPf4A4/OzmViQQ77bwYrxZ57wr2YiaoTNvVtZmrX4nNpHtSi+mB+H4kARL+4jLpz232d6VQqCgsU0j7g2VIHQdXBY70QSHbT1/hNxtRN0CEf3oOshHNa7UOQShAsMkjtXyjLTqM7NZHNdI6/W1HH9xEqUQeVS03X6/EFEUcBlNiesbtEYogAZdktSMYirGpvqGmn3+C+prNcKgiAQV7Uhx3Q9sfBTTotbMhjkIRlgJDER9Kmdcn5BcQb3f2gR2zYdZce2Y7z5Wg3lldncfPtMisvOvHs42kLPZDaeEqRKwvVD1YiEE4vNYCCCpukcq23HdFoQ9uz5FbhPSy1qMimYzKO7ZyqKzHU3TOLPv91AS3MvVouRwwdbKS3PJjfPdcZrGFl+A/c/uIitG49wYG8Tu7Y3kF+Yxg1rplI9oQBJEtE0HZ83TDymcmjf0Ll+4dJqyioS7jOqqhGLqhiNypCAaZvt0i7Agv4IuqZztLYdo/HkvROA2fMqcLkT97izfYC//2kLHe395OanYTYp+HwhYtH4iJOTyWwYEocwEoIo4nSdrG9ywvKnqRem/huMctJdDBLPMZB0fTOaFKbPKku4M/12I1m5TgK+MBOnFLNs1cQLGvNckEWRqBZPLtgFQcBttFJodXN3yUycBjMRNY5BTMyJ2WYnhwfaGe/OH7Qc6Ml+TtXDT9wlcTBu4PTjo2GXTYTiMbyxEOmijbaQByCZ/S2kRtF0jTpf12DcA6QZLORaXNxVPAOX0UJUjSOL79wG4QvHD7O/t4M0k4UPj5/1jo2b4tJzTSkHkJg846o2mOHhpEnVrBiwGU9OUHWdPaw/3MC9sycR1zSicZXRUlGIgkCaNbFAkqTERDDai9UoS8hSwl3DIEmomo4oCNiMBmwmIxXZGeS67COeC2A1GFgztZrmPg9bjzUjiSIFaQ5kUaQ43YXZIFOVk0GO044AowaBXktIgkSeOffsDQcZiHo44DnA7PRZF1052LC/gZmVhYSiMfbUtzGlLO+8+5DENARBBgREwYRODNCR5TwkKZ1o7CgGuQxBsJytq2H0B0LUdfUSjEQ50tVDOBajy+dn3cGjWAwGnBYTJekuHGYTVqOB982fRnOfh5+8uoWt9U0UpbkQRZFuX4Cm3n5unlLNinEVGESJWWWFmBSF12qOYTcZcJrNNPUOcKSzB7vJSLcvcN7yvtuwWI0M9AWGTDXBQJhwKEZGpn2I9ikIjJhK9VQURaKyOo/C4nQ62j3UHengz795k/7eAF/91nsG+xEQhMSC+NR5wjMQHLHPE4t/SUoEoHs9ISRZwjyoCFisRiRJ5PZ755CT5x6iMIuSOCy96OlZiEZi0bJxPPb7TWx5o5YpM0ppberjptumY3OcPUXs6YiiQG6+m9W3TWf2/Arq6zp5+m9v8affvMm/feNOrDYToijgcJix2k188GPLhsloHFxAS5KIwSgRicTQND3ZzusNnbdc54PFlrjHd753HlnZQ+PJJEnEOqicvP7KQdav3c/nv3oL5VU5WCxGmht7qKvtGN6pcG7fhTA4xsVCFIUzZhsSxYS7mNliwJ1mZdykQixWI/kFaWTnOkc97+0yxV3E652H2dvXzMLsSmakFzPGnkmZLZOfHV6PIkpUOXNYllONVTbw0cpF/L1xJy+07sdhMLM0p4oM4+jv8tGIayp/atjGYU87x/29/Kl+G4uyK8izuFiaO5ZHal8n3WglGE8o4wZRpsKRxZudRznsaccwGAgtIlBsy2C8K49Hal9HESXKHVksz6nGYTj/3w0k1hO+aARRELAqhrMmOtnQdpzHj+2n1JGWUg6uMa495UAQmFqUx38+/RqFaU4+sXzu4HE49c3rspjp9vl5fl8toiCQ5bChAxuONPDMnsO09nkIRmPcPTvhd3n6T0TTdf68dS+b6o7T6wtikmWun1Q12HZo63AsTiga47VDxzDIMkXpTu6cMXFYjndd1xkIhfjRus1IoogAWI0G8lwOVk6o4LWahPnbbjYyoSBhti9wO3nlUB27m9r4+NLZuCwXNilcTHb07UTVVer8x/DFfUxxTWZO+my6Iz282f0mneFujJKRG3JWkWtKXEd3pJuXO1/BE/OSacxgUcZCcszZ1Ppq2db7FgMxD1X2yuQYPZFeNvVspj3cjqpr3JR7A0WWQtpC7bzQ/hJNwSaO+OsYYytjTtos7Mr5T+KnY1RkirLcfO2P6/AEQlTmZ7JyeiW6HicQXo838DhxrRNNG8BlewhRtNHr+RGR2EFE0Y6AhCzljOwoJAgoUiFO6z30+36DKDqxmhYNKhHnzt6Wdr753HrimkYkpuILRwl19fGt519HEkVyXQ4+sWwOc8Yk/H/H5WbxjdtX8vSeGjYePc6GI8cRBQGXxczY3EwK3c7kTl95Vhpfu2U5f9yym79u348siZSku7lj+nia+gb45Rtvvc07fG0jCFBelcOhAy3UHWmnYtBlaPeO43j6Ayy/fuJ5Kfinpok0W4yUjsmisCidvTsb2LrhSLKdwahgtRnp6/ERCkaThaq2bTw6Yr+HD7YybVYpVpsJVdXZsaUOq9VIYXEiK1BFVQ4Wq5HDB9uonlAwpGjbhcboutOticDk1w9jtZkwmmTKK3POaTF7KqfeE8Ugk1eQRnaui6aGHp5+fDvxeGK3WjHITJxSxI5txxgYCFA5Nm9YH5BQhPIL02lu7KW705N0c9m57dglDUiuHJuLyaxw+GALVeNmDVmsnzpsZ9sAhkE3IKNRTtTo8IRob+2/ZLJdCro6vfT2+Fi0fByz51dcVOVkNOZmllFqz0DXddKMCQ8Ct8HKnSUz6I8kXKjsigmzlEimsDi7ikpHDjFNRRElMkw2jKLMp8YuJ9OUeL+Mc+Xy6erlKKLE4pyqpHWk0Orm09XLMUkKAgLLcsayIKucuKZhlY2kG61IgsjtRdPoDHsREDji6WBPfzOiIDAvcwwV9mziuoZdNrJGTSgODsXMe4qn0zsor00xYpEvPEajI+jj5/u3UmB38d6KydgMqcDmdyvXpHLwqRXziMTjyQDLiuz0xK67ctKcmuWw8b27bkzszg2aTUVBYGZpARPyc9B0HVkUsRoN5DjsTMhPmJnnjClE1QowyTJrplRz/cRKdF1HkSQsBoVshzU5ed8/byqyKPLC/lqmleSzevJYun0BXqs5Rmu/B4c5a5j8TrOJz69aCCTMr2aDgiJJrJlSzYrx5YkdA0FAkRLp1RaPLWVWWaJqrGOU/O/vNL64j32eA9xZcDt22YY8uMB1yHaWZC5GERXe6tvBAc9BMozpiIg80fo04x3VTHYllDGTlNgZK7OW4ZAd/M/Rnw4Z46j/KEbJyB0FtyEiYZOtCAjkmLJZlLmATb2buTH3BlyKE6N4ce7L9z+8GqNBZk1kHJquYzYoWAZN/hbjAkyG6YCGIMiIgg0QSXd8Cp04ICIKZgRBJsP1r4OfC2S5v44oWNDlMZgNMxAEK2mOTwymPT1/8/CcsiL++OG7R/1cFAXsxlNdVUTKMtP4+JLZfGD+NNTBwEJJTFi+TIqS3D2SRZHrxpUzd0wRsUHXFkWSsBoV4qrGbdMmDHHlMysyH1wwnXtmT8JhOrMbRlGai5/dfwuCALbTAhAL0px8+45VANiukGf8QhAEgTXvmcW2TUf5/tefZt6iscSicTa/WYvVZmLB0urzWgxHIzE2rK9hz44GqicUYHOYaGvuZ9vGo0N8xtMzbZRX5vDqS/sRJZG8gjT27jpOzYGWYX1Ksshra/cRj6tUjc/naE0br718gFWrp5BXmMjjXj2xkLmLqnj8T5vpaOtn/KRC4nGVhmNdGAwy97xvPmbL+X1PgiBw3Q2T+Nq//I11L+yjsjqXwuL0IW3icY3+Xj/9/X56unwEgmFamnpRFAmny5J0Xdm/p4l//GUrU6aX4E6z0d/n541XDjKmIgd5MH7BaJRZsmIiu3Y08LMfvsSiZeNxuS30dHvZseUYX/yPW8nIcuB0WZk9v4L/++kr/P6X65kyo4ymhm52bas/r+s7gapq+H0hvJ4QbS196JpOa3MfmdlO7DYTdqcZWZaYMKWYuQvH8tjvN9LW2s/4CQVE4yr1Rzux203cce9czBYD5VU5rF93gD//5k2mzCylqb6HV9fuu6RVtnVdJxZTGegL0NkxgHcgiKbpNDf2EoupOF2WpOXlXMnMcmAyKfzg/z2DYkgEPLvTbCxaVs2d9829JNdjlg2U2oamwRUFAbfBgtsw3GprkGSKbenDjhdY3cn/tspGrHLi2T+1D5OkDKmDMFpNBLtiwq6YUHWNtuBA8rhFNlI8QsE/AXAaLDhHkPdCaPF72NHdilFWUC+h8pviyueaUw4gsdtuPcWFSJGkpC/1CURBwG0dvstuUhRMyvCJzTA4OZ2oCgvDFzEA8ik7HidkmFaUx7eef51dja3og3+XZQ6fHBK5jUeWy6jII9ZSMMryEJmuFMptZWQY0jGIiXug6zq+uI/1XW/QEe7EE/dSailG13XiJKwMD5V+EFmUh+zIKaKCRR4+8RWYC9gz8AI9kR6WZi3BLboAkEUZs2TCIBiwSVbM0sWzpLhsib5GSm8rCGZEho8lSa7hx06pdyAJCbN5wrBlHDx24VYOkyJjUmxnb3gKgiAkA5PP1k6RpBGtU4okDTtfEIRhv8XRkCVxxOceEkrJlWARuxhUjs3lq996D3/53UaeefwtFEVi4tRi7ntwYTITTsKtYwRXDGHQrXFQgZBkEXealfa2AbZvPoqqaqRl2Fl9xwzec9/c5Glms4Ebb51GOBJj3Qt7iUbjTJtVxte+dzdffPgPyf4SQwjc96HF7N/dyM9/+BIGk8Lq26dz7/sXJHdzZVnigx9dSmFxOi8+vYv1Lx/AaFQoLEln1eopQ2InRFE8a7Gv5L2pziM718lAv5/xkwqTC/kT7N/TyH984bHBGgUaug7f+uo/EkrXnTO5/0OLMJoU0tJtaKrG3/+4mVAoitNtYcacct5z3zwsSd93geLSDP75K2t44q/beOKxrUTCMdIybEyZXpJ025EkgTkLKomEYzz52Dbe2nqMCZOL+Oq33sPnPvr783blbGns5ZH/Wcuhfc1ouo6m6fzyx+sQBYGC4gw+8YUbGDdojXnon5ZTXJrBi8/s5rWX9mM0KRSVZnDjmmkohsS9ue7GKfT1BXjlhb08/9QuSsuzuOU9s6g70jHke4WEwn+2HXlRFIZ9X4Jw4nk8WYNiy4ZafvD1Z9A5GQOz660GBOC9Dy7i7gfmJccc6TmWZQlxsG5Kf2+AZ/6+HVXV+MBHl2Czm1FVlebGXh77w0YsViNr7hw9DfS1ikDCdeidQtd1Wvwemn0eOHcv3hTXKIJ+KW2jlwiv14vT6cTj8eBwXPn5/Ue7xddqjMD6rteJ63EWZy5KKgcxLcaW3m30RHpYk7earX3b6Qh3cnPujejo/PvB/+T/jf9PDJIheb+SvtExD9+s+S7fmfSN5BiJQjZR9nsO8ELHS9yWfwsTHOMRBIHGQBNvdm/glvybcShX/vPxdtF1HTWuIkpS8gWuaRrhQBSTxXBWv/W3ixpXkxVuT6+iG4vGgYRry4VyIhg2HlMxW43DFo1XG2eack8Umjv175HOO73daH2dy5gn2m7bfJRf/uhl/umfb2DqjNK33d+p7c51rhvt2s9lzBPnnK9sF6vduXCu13Cu415If2eSd6Q2I30n5yvbmZ6dvbsa+cWPXuahT1zHtJknnztN0/n0Q7+hpCyTf/7qLWcc71rkfH87FzpGSI2jahreaJjf1uzkV4fe4oGqqXxo3Ezsp7gViQhYFAWjdFL5//Lml5IxB2vXPEhMSxSKO5FRSRREjJKEafCc0a5F1TTCapyYpg4J9DZKMgbxzOnZVU0josaJnse5J4rd+WJRjJKETTEOFrlTiarxRMA3QsKCLsqYBjdgI2qcYDyGrus4DaYzpukOxqKE1cT7z2U0XxGFas9n7XzlbTlfg1yrSsD5oKMjDlaa7I700OA/nszkIAsylbZKNvduZayjChEBh+LAJJkYiA7QF+1H1eN0R3owiyZsig1PzIM/HqDAUsAEx3h6I73JsRRRQRQk2kLt6LqOTbEhCVf3gvJMaKrO1nUHmThnDM60hNXA2xfgZ//+Dx788s3kFA43hV9MDu9uxO62kl+aOaTQlKpq7N54JOGut2x4XvVzpb/HxyuPv8X2Vw/y/i/exKQ55Wc/6QrmbPPBaJ+ffvx85pXznYMuVMZLNW6nx4/dZDxrut1LIdvFnL8v9nf2TvR3rsfOtc3pioI+WIkv6A8T8EcQRYFYTKWjfYCuDg9zFly7mfjOxDuxbohpGt946zWODvRQ5+mlL5IItH+0djeP1u4e0jbdZOHL05dwZ/nwDFKKKNIdCvBS0xGeqj/I0YEeoppGntXB4rxS7qmcTKUrY0Q7iDcaYW9PG/84dpDtnU30hkMYRJEyRzo3FFeyqriSQptrWB0eXdcJxmPs6m7l2YbDbO1opCPkxyhKlDnSWFFUyc0lY8m3OUes4bO+tZ6HX3+KG0vG8v35N1LT18UTxw7yRls97UEfZkmh2OHi1tJxPDguYbl6vbWeb+xYT7vfyz9uvJ+J6TnD+oVETOq3d73BX47sJc1k4bVbH8KqXNx6HZealHKQ4qKTbkhH1VVETv4gFVGh3FZGV7iLN7s3kGfOwSSZEAURSZBYk7eaDT0bebljHemGdGalzcAkmdjnOUBHuIMiSxGbejZRai1lsmsSnZEudvXvJq7FscgWprmnJcdyG9yU2Up4q38HpdZSprqmYB3BNelKw+cJ0t/lxe8N4Uyz4esPIMkSFZMKiUZitNZ34/cGkSSJ/LJMHG4rkXCMhkNtvPjnzSBAblE62acoAy11XfS2e0CAsnH5mK1GYtE4Xa39DPT4EESBovJsrA4zrQ3dRMMxopEYFpuJoD9MXklinI6mXrrbBxLBr1YjxYPBtD3tA7z+9C7ySjLw9PrJzneTme9GVTW6Wvqw2Exk57uHXGckHKOxtp1IKIpilMktzsCZZqO3w0NnS18iradBonxCwrUkPdvJTffPx9ufyob0buWX67Zxw9RKpg/GV6W4NhAEgdw8N6Vjsnn5+b3UHelAlkW83jBHatqoGpfH8sFibCkuPpqu0RrwYlEMVKdl0ejtpyXgpdDmpNjuGpIW1WEwkmMZ2eVV1XV+sGcDLxw/TIHNRZU7i6gap9nv4feHd7Kpo5FHltxKuXPoRlV/JMQfa3fzq4OJZBYFNid5VidxTaU7FOAHezawtbOZz09dyDh3VjKtK4A/FuXxY/v5xYHtBGJR8m0OplhyUXWdnnCAH+/dxOstx/jC9MVMz8wfUUFQdZ2eUIAt7Y38ZN8W2oM+3EYzY10mQvE4HQE/7cGTabqnZeZT4Uynxefh8WMHGJeWjTyCEtcd8rO9s5mYpnJLafVVpxhASjlIcQmY5Bq+s5AIFs7h9oJbRzwny5TJHQW3DTu+KHPBiO2r7JVDshedilkyMTd9DnPTL7xi7uWgpa6Lrev2EwnFiISj5JdmsXtjLV/7zYeJhGPUHWimr8tLb4eXcdNLmLtqItFQjPqaVjqa+6g/1Eo4EMXusmAwKoT8EfZvP4bNYabxSAdzVk5g/vWTaDnWxVvrDyEIAp4+Pw1FGay8ezavP72LoD9MLBLHYJIJ+iPMWzmR6UvG0nysk/pDreg69LQNsPr9C8grzqC7dYC2493EonE0TUMUhYRyENdoO97Dq0+8xYRZY7jp/vnJ69z4/F6O7mvCnWnHbDNisZlwptnoau2jZlcDalyjsbaDNR9YSNXU4sv4jby7yMlxsfz6SWRmXTpXPH84wqGWLjzBMGk2C9X5WViMChtqGki3W+j2BrAaDUwtzaPLE+DIYA0ZTzCc8G/XdWpbu2jr82I2KFTmZZLpsLLjWAtmg0KfP0hc01g4tgQ5VRDyqiArx8n9Dy1m9/Z6OtoGCIQj2GwmVt82nWmzykjPePuZ5lKMjFGS+b9ltwPgiYT56b7N/O7wLlYVVfLxiXNwnOJWJJBwExqJJt8AXUE/H6iezvLCcrLMNnzRCG+2NfCLA9s4OtDDLw9u47vzbkyeE9NUnj9+mD8c3oXbZObeisnMzy0hzWgmqMY42NfJn2v3sKGtAZfRxL/PXI7baEYQBGKayqb2Rn5+YBvocF/VFFYWVZBncRDTVA73d/OPYwd4taWO/923hX+buZRK18h1X+q9ffz+8C6yLTbeN3YaY5zpKKJIfyREvaeP8enZybaZZitzcop4q6uFlxpreXjiHLLNtmFWnk3tjXSHApgkmdvKRi8AeSWTUg5SpLiCsDktzFhSzD9+uZ4Hv3wz2187RMAbxGAyYHNaiEbi9HV5qTvYwtRFVTjTbKy4cxYbnt/DqrvnkF2QCHQf6PGhxlVmLx/P2GnFbHpxHztfP8ysZeOo2XWcw7uOM3ZaCbGoyvbXDrH8jpkIAhSOycLustBQ00Z+aSYBfxg1rmF1mLHaExNz/aFW6g+1UlKVy8Q5Y9j+Wj4TZpUxfUl1MquI0aQweV4F7Y09Q65PUzUe//mrfPFHD1A6Lg9d05O+tSarEavDDDqEghEO7WxIKQfvIMVlmWctnPZ2iKkqB5s72XGshfKcDLYcaUQUBSYV5fDEtgPMKi8kx21H03XCsTgbDzcQiMQoznDR7UvUhuj0+Hl+12EmF+dR29aNJxRm2fgxvLLvKNkuO6VZ7kuaYjTFxUcUE5W+826bfrlFedchCALKoMutJIpJv3hBEJAFEeUcC6qF43HuGDOBj0+ci0UejC+zQonDzf7eDp4/Xsv6lnqiahzDYPxBo2+AtU1HGAiH+PyUhdxRPmHIeKV2N+jQ7Pewruko91ZOZmZWIQIJV6THju6lLxzk7orJPDRuJhnmk8UX821OCu0u2gJedna18lrzMYrt7iHxEifoCQWQ0kU+N3UhYxxpQxb6c3KKhrVfVlDO347u46inl5caj/D+sdOGfB5R42xqb8QbDTMvp5hy16V1671UXPpkwilSpDhnjGYDZltikSyKIgajTCQcp6Gmjd0baimuyCGnKP2cghEtDjNpWQ4EQUi4IIWiqHGNcDBKdmE6Y6eWMP/6SdzziesS2U8EAZvDgtlqxGQxIskSuq7j6fXzzG/fJKcwneKqXJxpVuIx9YKuT9N1+rq8FFUmfDUFUUCURALeEK89sQOjyUDJ2DzSsx1EI7ELGiPFlUk0ptLYPUBpVhorJ1dSkObkWEdvogAlUJWXydLxY5hVXkggHKU/EGJqaR5LxpeR5Ui8+I+09bCzvpXatm4aewboGPATiiWC/sqy01g0roxlE8pTVoMUKS6AC1WrRUHgvVVTTioGgxglmckZuciiiCcSxhuNJD+r7e+mbqCXAruTublFyKdZJSRRpNyVTp7VTiAeY29PB6quoes63UE/2zqbyTLbmJNTRLppqNuwKAgU2ZysLKogEI+yr7eD7tDIbqkuo5kl+WWUnaYYjEaJ3cWsnEIUUeIvR/YS04a+C48O9HJkoJuYpvGe8olDqmRfTaQsBylSXEEM7tsMmaQ0TaOvy4soiYydXsKRvU1EQtEhJxkMMkF/eEhfoigMq95nMMq40m30d3kpGZuL3WXB7w0hnPDHPK1YIDp4+4P0dngYP6uMWCwRr1AxqWhIn+FgFF07+6tFkkSKK3N4a/0h5qyYgKpqaKpG0B+hr9PLdXfOJK8kkz/9dx8ZgwWnUlwbJOq2yHiDied0IBgmy2FNZPwAnKeksjXIEnFVIxKLowPeUGJR4baZyXbaWDOjGgCLUcE+WPvCaTG/g4kfU6S49rjQ34/DYKTCOfIOudNgQgRUSGbv0XWd9oCP7lAAWRT50Kv/GNFKEVHjdAR9QKJAm6braOjUefoIxWOku9IpsDpGXNSbZJlqd6KWVIvfQ284QIFteMVtl9FEuTP9nLMJSaLILaXjeK6hhnpvH5vaG1laMCb5+a7uVlr8XortLmZmF161c1JKObhGeSfSoJ2rHD3hvezs/g4l9psY677/ovYfiLWxrfNrGKU05uV8E+Eq1dLPhCRLlIzN5fWndvJfD/2a9GwnDrdlyOdzV03ix1/6K2MmFLD6gfm40kf20xUlkSkLKulo7uVb//R71LjKopunseqe2aOOn1ucTsnYXP7fR36DxW6icEz2kM+nL6nmb//7Ci//bRsr757NotVT6e/28bvvPsexAy2IokhHYy+3PbSYtGwnH/+v23n0By/x5K9ex51h56b3zWfcjDJKxubyq68/jWJSKKo4OcaxAy387WevUHeghbr9zRyYW8EdH12K0Xz1BXldi4xkwRpp3jEqMpOKc/nzxt186rdPk5/mYMWk8mQNmVPPcFhMjCvI4m+b9/HsjkMYJAlBgIqcDKaU5PHfz21AEARumjaWpRPGDDv/Suf0e3a55+krDV3XU/fkKsJlNA0JXj4VURASxTJ0HX3QNhHXNPyxCHFdI65q1Hl6Rzz3VKInFAugN5ywApglZdRgXwEBm2LEIEr4YhFC8fiI7QyihO08A4anZeZT5c5iR2cLf63bx5L8MgRBwBsNs7+3g4FIiDvHTMBhMF61z3FKObjGSKaFi3dilNzIwpkr074zMqnEtdAl6lsjpoUwyleGn7Gm6ZyYC853Uhg7tZiqKUUIosBnv3cPoiTy1V8+iGGw2ui/PvIBdBKTHgLJmgaiKLDqntmseM8sdF1HHHTV+ZcfP5CscTBxTjkTZiUmMHemnXs+sYK7Hr4ueb4gCtzziRWDsgtMXVCZXG0JgsDH/vOOxAs7cYBTL616Wglf/eWDierdgzK5M+18+tt3J6uFC+LJIkolY/P4ys8/gK4n3hmCKCIIcMdHlnLbh5ckxzwxRNm4fL744wfQND1ZcMlwnhVYU1wadF0nHIzyzYd/R9ORDm57aAm3fHDRiCt1QRAoynDxxVuWJL/7E3nCv/vATUinFO0SBYFF48pYMPZk3ntRTDwT71s8DX3RtGQ7QYAv3LL4qnoJH69t5z8+8Ev6u31MmlfBNx792OUW6YpB13Xau73kZDjOq2J4irfPhb5FR1MMzjSONvjfs7ML+fzUhaSZzpxR0GkwoYhSspbBxUM47xoEsihyV/lEdna1sLWjiUbfACUON0cHeqnt70YWRVYUVYwY43C1cPVKnmJUwmofr7V+lDnZ/0WmecpllUUQBLIs01ld8tQl6d9mKGBV0aOXpO8LYd+bNeSWZZF1AbUFBFHgxJJYkiX8AwH+++Ff89U/fyp5bMTzBhdYuqay67WDeHt8XHffgiHtBQEYnMAFQUCQBE6fz0+tUXD66m7oZ8PHH0m2M8k70meCJIwYBCWIApIoUbeznhd+9RqH3zrG91/+Cva086sEfS5EwzECvhCnv38Sm18JpUtWJAwmJVGt+B1ekGqahm8giKxIWO1XRtXougMt7FhfA8DujbUsXD2F9Ozh5ns4WQX+dOQRCvWJgoA4wnM30vkjHbui0XXiMZV4TE0UEbzK0XUIR6LEVQ1d07FYjIiCQDAURdMT1awtZgOqqhGPq2g6WEwKqqYTjcVBT7gcGhSJnv4AT67dw3vXzMRqMWBQUsuUaw1FFLHKCqIgEFHjFNqc5Fgd52T903U9qUiEB4uSjdgOCMajRDUVi2y46Av1G4vH8r1dbzIQDfNcQw0PT5rLkYEejnl6mZlVQL7VeUUUPrtQUr+6a5Du0E40PXr2hikuGmpcZaDbyyt/2sDsG6YSDcdwZthxpNmIhKL4B4KocZVIMILZZiItx4UgCvS2DxD0BhEEAZvbijPDjiiKBDxBPL1+Hvi325NjaKqGt89PaDCDkKZqZBWmY7QY0DUdb5+f3JJMKqaUJM+JReN0NfUgKxKRcAyDUSYjLw3ZIBMJRujv8hKPxgkHI9hcFrKLMhFG2a3TdZ2QP0xfx0DiZW834cpMpL3sbOohLduF0WKgv8ODjk56rptwMEJ/p4dYJI4oCWQVZaAYZPo6BohHEwsjxaQQDUdJz3EjySLdrX1Ikkg0HMNoMZCel4YkiVRNLyMjz833P/LLIXJpqoZvIIC3148ggDvLicVuHvU6zsT2Vw/yg8//mXBw6O9HkkUsdhMZuS4qJhQyY8lYxk4vJS3TgSS/c65sXW0D/Os9/8uEOWP43Pff+46NeyZMFgMWm5FoJI7NYUExpF4r70bWbzlCY1s/7d0e7rpxOtkZNn71182oqobXH+a9a2ZS39xLZ7eHQCjKwpnleP1htuxuwGEz4rCZmT25hB37GzlwpI2/v7CLWZOLmTKu8HJf2jmj6zqxSBxvr4+M/DSCvjCapmGxmxBFEV3X6esYIBaJY7abcKbbCfrD+Pr8iJJIWrZz1E2VS40Ayc2pS53xSxAEsi020k0W6j199IZDiRoK57CYFgWBSlcGJkmmLxykPeBlUnrOsI2aiBrnyEAiW16e1Y7beHE3UyyKwp3lE/jZ/q281nqMNWPGcaCvg1A8zuL8MtzGy++18XZIzeLXCBHVgz/WQlQdoNG3FlWP0h3aRUTtS7bJNE/DIDqTmW7Cah+e6FFchgpEQcEXaySsDgCgiFbcxioUMZElRNNVImo/wXgHUdWDqscQBQmD6MSmFGCS04bIo+kxOoLbkkqKgIRNKcBpHDOkXUKOXvoiNbiNVQiI+KKNxHQ/AjJmOR2HUoYoKMkfv65reKPH8cWOJ/sxiE4yzdOGTRBR1U9f5BBWOQeTnIE3Wk9E7UdHwyDasSlFmKS0IbEKuq4TUfvxxRqJaj50XWWowVUg1zofSTjppxgJRtm/4TBH9xxH16FubyPTl09g0qJq2us7efnRDRjNBqLhGMXj8lnynrnIisSOl/fRcKCJWCROTkkmK9+3CFemg9ZjHWx6eidvPr6V3x78QWKMUJSX//AmDQebychLo72hiwW3zGDxnXOIx1VqttWx/rHNVEwt4a5/vhmAvo4BvvW+nzHrhskEPCEioQg3PriUyull7Hz1ADXb6gB47bHNLL93Hu//9zuRRjERa6rGq3/eRGdTD7FIHLvbyuL3zCEj381zv3iVkgkFzFw1mT99+ynGz61k2T3z6GntY8OTb+Ht8dHX6eGmDy9j/JwKnv+/1/APBAgFIzjcNvo6BrjxwaVkFKTxvQ/9gpkrJ+Ht86PGVVZ/5DqKq/NHlEnXdTw9Pl59bBP9nV7CgTBlE4tYevdcLG9zZ93mtGCyKICApmnEInGajnTQcKiN15/Zxazl47nr48spG5+PNMLO96Vg95uH6Wrrx9N75RSEK67M4Y6PLaOn3cPCmybjcFvPflKKawpN1xhfmUdFaRb7alvZdaCJxbPLMRhkpo8vpN8bIhpXaW7vZ+7UEiLROK9uqmXWlGJKCtK4fdUUXn6zhmgszqKZ5QRCUT5w55yr0mrQ0djNi79/g49+8146m3qIhmOUTShANCbmiD9++xnyx2RTOqGQ6cvGs39jLYe21ZFXlsXCW2disV8e5UAWRExy4n73hoNEtUtr0apyZTLGmc7WjiYeP7affNt8XAbTsHe4DkTicQySlHRDzBisN7C5vZEdXa3MzSnGecpiPBHw7OWV5jqMksy4tGyyLBff0nzHmIn84fBu2gM+Xmo8wr6eDnKtdqZk5mGWr27X16vvl5diRPyxFo57n8UXb6Y/fJi4FqLR/yKycPJFbVXyMRhOmPs1+iOH2dX9Xca6HyCq+ekMbiOqeohpQQRBZEne/yaVg1C8m3rvk7QHtxDTAgiIaHoUSTCQa11AhfNurMrJUuKaHqfR9xJhtYew2kcg1kGl824mGf9pmOx9kRq2dvwbla73ElUH6IvUENeCxHQ/FimbMsetlDrWJNvraPRHajjue4Go5sMTrcNtqGJ5wa853R0mGO9gT88PyTRPxyi56AhsQdUjxDQfgqCQZ1lAhesubEpi8Zkoyd7B4f4/0Bs5gCyYielB/NFm4nqQNOM4LHIO2ZaZQ5QDi8PMkrvmsuX5Xaz+8DLGziwfsnMdDkaYtnwi05aPBz3hKoMOhZU5WOwm+rs8NNe209XciyvTQeW0MrIKMtjwxLYh1xPyhykdX8gtD69k34bDvPLHDSy6YzYGo8KsVZMJeIL0dwwMOae3vZ8V9y/EneVk3R83cHDLUSqnl7H3jRpmXj+ZaUvH03qskxkrJyEpo7+YejsGePqRddz+yeuJx+Ic3XOc+n2NFFbmsubjK/jjN5/k+MEW3FlOFt2RCHC2OiyUTy4mEoqy69UD7HujhupZ5QiSQPG4AgxmAz0tfaTnuvAPBEjLddHf5WHV+xdjc1t58Tfr2fr87lGVA03TaTjYwq5XD7Do9ln0d3nZ9doBZq6a/LaVgwU3Tmb8zFIkWUKNqwR9YTpb+qjZeZy6Ay1sfGEPalzlk998D64M+zviYrTrzdpzygr1TiEIAgajwns/tepyi5LiMtLd5+eN7UfJyXDQ0Z0oUGe3molE4oSjcSpLsxAAfyBMS8cAFrOBJXMriETjpDktCRc9UUjEbIkCqqqddcwrjT1v1hAORAh4ggD4+gN0t/bhzkxk0/F7ghzb20jNtjoW3jKDrKJ02o51svfNGsw2E2UTC1GMMoe21SUtCdWzywl6QzQfbUcURawOM6UTCjm6+3iijSwxZlIRvW399HUOEI3EKazMpbAih9ZjnTTVtmM0KRRW5mIwKRw/1Eo4GKFsYiFZBenJmDRIZPfJtdgxSTK7ulvZ3N7IrOwCTLJCVFWJqipuownbKYXR3g5jnOmsKqzg6EAPfz+6D6Moszi/lFyrHUWUCKtx+sMhWvweoprKjcVV2AfHdihG7iqfSE1/Fy81HSHdZGFZwRgyzFbimsZxXz9P1R9kd3cbEzNyWJxfOizN6sWgyO5iWcEYXmmpY23TEeo9fawoLKfI5ryqYqBGIqUcXCNY5TxKHWvQiHOg9+f0R45Q6bwXl7Ei2camFAwLFIxqXpr863AoJZTab8YouVH1EP5YKybppDVAEEQMooNC23JsSgGyaCGm+jnue55679OYpPQhmYgkwcjE9I8R10L0hPewp+cnZ5Rf1cM0+1/GZayiynUfimjBH2ujpv/3HOj7BVmW6Qn5SVghcq3zcRur8cea2dH9rbPen7bAG5ikDMY4bsOsZBFT/bQGXue47zkchmLM9puQRCOgc8z7D5r8L1Ppupccyxw0Pc5x3ws0+daSa5lHiWM1snDm4KnTcWU4SMtxIp5Swr23o59nf/kqC26ZmQiwFTir/7HRYiCjIA2j2YAr00EoED5jewBnhp3s4gziURWry0J3UyIzxJhJRex+7QCtR9sxWw3klWWfsZ+BTg+gY3Ulrn32DVMom5BIaZpTkonVbuaNv2/lq3/5NLIiEYvEObCplqbDbYyZXITBbCASjoGuIwoCjnQ7siwRsAcRBxcGuqZjcZhxZthBgMyCdA5uPjKqTLqq4e31IcsSZrsZs91MybgC7Bdh97pqShGL10xLusmccBk4sreJx366jp1vHmbHa4c4vLuR2cvHI5whLuNiMNDj4+i+5lSRr2uUuKZS62ti38AxDJLC0qyppBkuTbXqsBrlkLcBh2yl3F7wtvvTVA2fP4zdasRoUBL+3uEosbhKOBKjtWOAMcUZVJRkousQjcaxWYxEonGE015KbqcFfzDCC+sPMrm6gNILiN96p+lq6WXzs7uYtXIS/kHlQBAEWus66WsfoKAi4fYiG2QEUUAxyEmFSJIlZEVCkiW6W/rY+sIeJi0cS09bH9te3IMjzca+jbVMXTIOSUm02fLCbiYvrKanrY/WY51ocY14PE56joutL+zG/t4FPPt/rzLjuknIBplYNM7xQ630dgxgc1nY+PRObvzg4iEbKLIoMTUzjymZeWzraOJHezcxxpmGSVKIaSomSeaBsVOZnvX2nxcAgyRxc2k1/liU39Xs5NeH3uKNtnqyzDYUUSSiqgxEQrQHfVS6MliaX5ZUDhRJYn5uCR8aN5PfHtrB/x3azhttDaQbzai6TrPfw9GBHsY40/jwuFlMSMs5izQXhiQI3FM5mbVNRzjY24kkikzLyh9SkO1q5ZpVDlTNi6r5kUUXonh+C7mrEZPsxiQnqoMaJCeiIOMwlJFumnjG82JaAHSdUsfNuI3ViIPVElUtiiic1LTNUgaljjVIggFRMCRdk6xKHq+2foi+yMEh/QqCiE0pSLronA0dDQSJce4HcRrGIAgCqhbFE62j3vsMPeH9J5UDQcAouTBKLiRBGSLnaATjXUzP/BK5lnkIgoiuayiilf7IYfojteTbliCRmHia/C9jEJ2UO+7EKLsS8ulxOoPbCak9WOSsUVOmKkaFoC+Mjj7kpZfI1jP0nN62frqae5m7ehqHttVxcPPRs16HIIpDM3icwzpxiDXglPZWpwWj2UDZpGImLxpHep77jP1kFqYjDe5U5ZfnEIvGORG5e3DLEXx9AT74X3fx9CMvUzTug6DpdDR2k57rYsaKSex67SC6dnJHUBCBU0oxnFjzerp9dDb1kJ7npuVoO5mFQ13WTkWURNJyXFgcZibOr8Kd7SQUiFySNKeCIGAwKVRPL2HW8vEc2duEbyDIge31zFxaPWQX7lTCgQi7Nx3hwPZ6etsHCIeiONxWiqtymbm0moKyrBHPDfnDHN3fQn1NK6313TTXddLb6UHXoXZPI//2wM9HHO8LP7ofh9s6bOdqz6YjvPy3bWiqzqe/czcmi4FYNM7ezUfZs/Eo3W39aJqG022jZGwu0xZVkVeaOayfw7uO8/wfN9Hf7Rs29tQFldzx0WXneksHrzPCjtdrOLSzgb5OD9FIHFe6jbLx+cxaNp7MfPewrDV9XR5e/NMWanYdp7gyhzs/tgx35pkX0vU1bfzpv19KuPZV5fLQV9aM2C7oC7N9/SEO7zpOX6eXeEzFlWFjzIQCZi8fT3q286zxLLquc2BbPTvfqKG9sYdYNLFwmzCrjKkLqxAladh99cWDHPDUY5IMVDuKMUsXZ4d2JGJanGP+NnJMaRdFOchMt3PDkvFomp6otCvC7oPNLJgxhsJcF7sONON2Wlg6pxKPL4ym67gdZvJzXKiqhkGRmDetFJNRwW418Z4bp6FpOm7nlRF0fzZajnTgynQwdek47GlWOhq7sbks5I/JIuBJZOqzOsyMm12O1Wlh4oKq5Ln55dm4MhyUTShk+9q9HNvXhMVuIhyI4My0Y3NZyMhzM3nRWCRZYttLe6nf14TVbiYUiCArEum5bnKK8ymbVMRj33uW9uPdhPwRZq2ahCAI9HV6OLrnOH2dHtJynAQ8ITR1+Muj0pXB56Ys4G9H97G5o4n1rfWJGiQGExPSsxEvcqrwDLOVB8ZOZUJ6Ni81HWFnVys7u1oIq3EsskKe1cGS/DKuL67EcZoPv8Ng5O7ySRTbXDzfWMuOzhZ2hlsxiCIFdif3V01ldclYxqVlY7iERRGr3ZlMy8xnc0cjk1w5jE/LPufK0lcy15xyEI7V0eH9OYHITgRBJt/1JRymJQSje4jEW3GYFyGLrsst5hWDgIDDUIrbODapGABI4tDFlSCIGKThufNtSh6KYCWuhdB17YLrDAhIuAzlOA2lyZemJBpwGsYgChKhePcF9XsCu1I4mLnpRHl4EZOcjklKJ6p50fSTGQ+iqheznIVBOrnYUCQHoiATUQfOuB6fv2YGT/7kJV5+9E1W3L+QGSsmjdq2oDIXV5aT73zwEcwOE470kz6R//jxi9TtSiyWfvzJ3zJhfhVzV08btS//QIBH/9+T1O1pIBqOEQ5Guf6DS854T8LBCDXb6ji2txE1rrHigYUsvmP0egfOdDv3fukWfvPVv6HrOrmlWVz/gcXY3Tae/cUr3PjQMqqml9Hb1s8//ucF3vvlWymsymPdoxvYt+EwFocZ6RwmaXeWg2ceWUd3Sx+uLAf3fimxiNvw5HY2P7OTxoMt/PwLf6R6TgU3PbSM0vEFVM0Yw08/83t0XWfSompWPrAIq+PSLCwkWSIz14XNacE3EKR3MEB7JI7ua+LPP36ZI3uaCHhDxGIquqYhyRJGs8Irf9/O7R9ZysLVUzCdptB0NPfx5x+t5diBFiKR2KAylvjM0+tn5xuHRxxztMrSvZ0e9m2pw9sX4OGv34Ea13jk3x9nz6ajCdmiiYJj8qBsFruJvNLMYf0M9PrZv/UYnS19wz5zZYxcX2M0Dmw/xh9/+BKNRzoI+sPEo3F0XUeSJUwv7mXtY9u477OrmLl0HPIpSq7ZaiIeV9n5xmFqdh1n0c1Tz+ratWXtPt5afwg1rjFhVtmIbfZuPsqjP3yRlmNdhAKRQXlAVkQ2vrCXtX/Zyvu/eBNTFlSOGmcSCUd59AcvsuG5PXj7A0TDsUQfBokNz+9h8twKbrx/3skYKqAp2Mmfj6+jIdCOU7ESUiNkm9IwS0b2e+p5suVNRAQmucq5PncWUTXG31teZ0X2TAosmRz1NfNyxw4+OmYNewfq2NizD03X8MYC3FqwiAnOMgQEtvUe5MX2rbgMdjRdI8c0uuJ9PiiyRFlhRvJvVdNo6/Tw5vY6Dh/rwGo2kJ1hJzM98W8ksjNOzrflxcOfuysZi92Erz+ArpNUBi4Ee5oNd46D5ffMQxAFjGYDNdvrUIxyMlDZkWbDne1k+b2JZygSinJwy9FEG0lA03WsdhOeHl+yXoRikHFlOcgbk82kBVXIsoTFPjxg1iDJTMvMp9yZji8aIaqpCCQKf5klBbdp6Jz6mSkLeHDcjDMuvq8rLGdSei6gJ4KOT8NlNLMov5TJGbkEYonsQtqgddkgyVhlBbtiRD5tc00QBJxGE8sKxzAjqwBfLEJc0xCERGVmu2LEbjCOmjFofm4xL9/yIYySTM4FxiMIgoAiSmSarQhAdVoWla6Ms553NXBNKQeReBNNfV8iFDuMWRlLMLofTQsAIjG1h77AExjlImTjlMst6hWDJBgxSemIwuiPgo4OOvhjzTQHXqMntIdgvJO4FiSuh4lqw3cQzxdBkDBLmQjC0EkmKZf+9oKjzHI2AvKQxYOAmBhP14fsqDsN5QTjHXiix3AaygHwRRuIawEchpJhZvBTmblqMpMWjgXAYEos9gqr8rjr86uHLG4AzDYTX/i/j6CpWsKyIIlJF5abHlqGGlf5+A/fh6xIyIqMYpS59eGVyV3UsomFfOn3DyOIAhaHhff/x52DdQB0JEXCODj+d1/8cmISM8osvG0mmqbTUtdBW30nD379LnLLsokEInzl1u+fUTkQRIEFt8xk5spJ6CdSD5oUBFHgUz/5IEaLEVEUuO2T16OpGqIoMGPFRCYtGIuOnlAMBJANMnd+9qbkbnnCmpCotdDb2ofBpHD/v92eqNcgi5gsiR3U2TdMZdryiWiqiiglTPEnsjzd9OFlrHxgITqgKFLy3l8KdF1H0/Wke89I2Xl0XefovmZ++V9PUbOrAU3VKSjLZPysMZhMCi0N3dTsbOB4bTu/+NoTSLLIotVTkOSTO8ruLDvXv3cu3r5E8HFXax/P/WEjkVCMkqpcbrhv3ojy2RxntpTGonE8vX5+/c1n2PF6DbIiU1yVi8NtwdsfoKmui4AvTNXU4hHPn7qwim/86eP0d3vx9gfobhvg5//xxDnfvxP3Z9+WOh7593/QVNeJrukUV+VQPa0URZE4XtvOkX3NHDvUwg8//xe++OMHmL54bPLZN1kMlE8oICPHSU+Hhz2bj1A6Nm/U+hfRcIwd62uIReMoBpnFt0wfJs/ONw7zs6/+g46mXkCnZGweVVOLkSWR+pq2RBG+A81855N/4F8f+QCT51UMD57UdP78o5d58c9bCPrCSJLIpLnlFIzJJuANUbPrOJvX7sPT58fvTSwiBSDfnMm9xdexvms31Y5iJrvKMYgy3liQR+qe5CvV70cWRV7p2MHmngNMdVfSG/EQ0xKKYFiN0h0ZQEenO9JPf9THh8pWczzQwcbu/ZTb8tGB3x9/kS9XP0BIjfBEy5vn9Z2dD6IgMHNSMZOrE1YJURQwnCGe6WqnYmoJr/5tCz/57B9wptvRNZ1D2+t4+Y8bCQejDPR4WXn/gkQWtWFnnzxSObWEPWXZPPrNpzCYFObdPI0TeYSSbaaVsPv1bB79xpMYzUYmzK8c5i7synIw98apfOdDv8CRbmf29VOomFLC+r9tZe8bh6icXsZ1985Lzq2nIosiaSbLWesOAGRbbGSfZWHtMJhwGM6cuUcSzn3M01FEiXSzhXTzuZ8rCIkCaZWut2edS8QoxtjUfpx0k4UpGbnnXVDtSuWaUg76g88T1/qpyPorJqWCfS0JlxpBEJClTHTixLWzV+J7dyEgnEExAEDXaQ9uYU/P/xBR+8kyT6PAugyj7EZAZ+9Z4gnOlZEVlIvjxy0K8hkX9aeON879IJs6v8S2rq9RZFuFqoVo8q9DFEyU2m9GYPSXnKxIyM6hk5QkSyPn9RcEzLaRJ82RJm1giLuMJEtJn1FBYMSdIEi4D51AMSYWT9FQFE3VMBiVRKDawWacaWf2kxQEYVBRGT4Jn+q7euoCTVZk5BEyjozq9pOI08ZsMw47z2BSRlz8nQiKNRjfmewQuqbT0dTLQE9CKS4blz/M7cXTF+D5P27i0I4GFKPMg/++mhvvm59Me6qpOvu31fGrbzxD/cFWfv2NZxg7tYTc4pP+1c40G4tvnppUXA/vaWTtY9uIhmNkFbi5+X0LRhbwHB7zX379KfZuOsrSW6fzwOdvJD3HiUBiKL8nyJG9TeSM4s5lMMrklWSQV5LYIfMNBM9bOejp8PCPX7xG09EODCaFh79+J8vvmJnMZKjGNTav3c/vv/sc7Y29/PhLj/GTF76AM82arOtRXJlL8dhcejo8bF17gBvunYdilEe0Hhza0UBPhwd0mDS3nOyCoS50nS19PPaTdbQ39mAyG/jkt+9i8ZppQ+R5/Zld/OH7L9DTNsAPP/9nfrb2X7Cd5vayZ8tRNr6wl6AvjNlm5Ku//BCT55YnvxNPr59Hf/Airz2xIxF/M4gkiJglIwZRxiQZMEmJ30edr5k8cwZ55nQ0NEptudR4G5nqrhz13sqiTJElmwJzJpqusblnH5qu0xrqxqnYKLJk44n5KbaeOcbo7SAIArIsIV+mtJxnQhus1CsiXLSgUUmW+Ph33stglUogcQ/+9XcfH/zjZFHM7zz3xSHnrrx/QfInK8kSd3/+puRv/kTiilMNk5Iscc8/n9ImWXUz8X8f+/a9CILADR9YzPXvX3SyjZBQPgYPnEvW0BRnQUNnbVMtPeEg0zLzmJ1TdNUHIp/gnUvQ/Q4QidVjM87AIBcOW8BJghnQ0fTI5RHuHSXpxX1RegurvbT4XyWm+ZiZ9RXm5nyTCekfptxxB/nWpcmS6NcKmebpVLveTyDWSp3n7zT7XyXDNImFud/DphRcEz/+oup8sgsz+MP/e4JvPfC/PPPIOj7xow9ebrGQJCkRjHyRlMKLgT5oJVDjKuFghEM7Gtiydj/hYBR3pp2Zy8YNiRnQdZ1DOxoG3X50bntoMSvvmoNikBFFEVFMFFKbMq+SVXfPwe6y0Nfl5eW/bU1WgIaTxe2EwQrWQ5+7U46f/k84+6Jnx/oalt85k899/71k5buRJBFREpEkEWeabdCNZ+SFdlIuQUj+fT5oms62Vw5Qu6cJELjvs9ez/PYZCRkG749ikFl00xQWrZ6KyWKgp93D2r9sGdJPTlE6pdX5yIpE7d6mwR3/4eiazu5NR5KBoivunDVEZk3TePPZPRyvbUcQBN7/Lzex5Jbpw+RZfvsM5q+ahMGo0N02wNrHhsqjqhrbXzlId2s/CPDgv6xmyvwKxFP6cWc6uPfTqygZm3dO90oWZVRdQ0NH1yGuaUiCmPx1JI7reOPB5DkSIop48rvTB/+Jgoim64lzYFRXuGsZXdfZ093GGy0NRNSLm6pTFMUh3/WJooknjp34zZweX3SiQv2wfgaLLJ5aXX7ENqf97pNjicLQNkPkuXiK0bsJVdeIaSoxTSUcj9Po7edn+7dikRXm5BRR6rg4bnpXAteU5QCEQVP/6bOejqoH0fU4wjkEr17tKKINHe2iuPsAxLUwYbUfm5KPVclP7vDrqPSE96DqZ8+YczXhix3nmOdJKl3vZZz7g+cU8Hy1IcsS139wyVnjEt5pMgvT+drfPnu5xQCgs7mPI3ubEpmXonG8fQFq9zSxee0+Wuq7cGXYuPdTK8nMcw950Qb9YY7sbaS3w4M708786ydjsgy3lAiiQPW0EnKK0vENBNn04j7e++lVyUXBpcThtvL+L950QYXi3i7efj+1e5rw9gfIyHWyZM20EQOyBVFg2qKxvP7MLsLBPl5/djfveXh58t7IikT11GI25rnoaOzljWd2MWZCwbBq3p4+P7W7GwkHo7gy7cxcOm7I5wM9Pmr3HCfgDZGZ72bprdNH3FUVRZFZy8fz5nO7iXbHeOO53dz24aXJhVt3Wz9NRzuJReO4MmwsvmXkGKHMXBcTZ5fRcLiNaHjk+JATlNvyiapxDnoakAWJxmAHVfYizJIJo2SgIdCGiMi+gWNn7AcSrks6OvsHjqHqGh3h3ktqPbgSiagqW9qb8UbCzMwuwHStLYFSXFJa/B7293agajpdIT9/PbqPjoCfGVn53FMx+Qra0nr7XFO/DLOhiv7Ac4SiBzAbJgCJBWws3oU/vAVBkFHEa38yTDdNpMm3lmb/qyiiFREFHRWXoQJFOv/AG0WyYVPyaA3U0R3aha7H0dHwx1qo8zyOIg4PMgrH+4nrQTQ9RiDWDmhEtAG80eOIgowi2jCIdi7EeBXXwkTUATQ9ij/WgqbHiethvNEGRNGAJBgwiE5k8cIqFIbjfeioBGMdNPtfGYxVkJAFE1YlF7tSPLhAuZamghSn89hP1/HYT9cNO26xmxg/o4xlt89g8c1TMZ7m6uTrD9J4uAOA0uo87G7LqIv9jMHAZoDWhm4C3tB5B/VeCFPmV+JwXp4sbj1tA3Q0JiqXVk0pxmQxjnp/coszkoHaLcc6CfkjQwLNK6cUkVecSWdTL5vX7ufeT61M3s8T1Ow6TldrInh68aAl4lQ6mvrobhsAYNz0UowmZVR58ssyk+5rLXVdhAJhrIMudT3tA3h6ExsyY6eWYDCO3k/5xEIMRmWIcmCUFIos2biUk3O0RTbxUNlqXu3aiQCMseUzPa0KRZRYkT2TzT37aQp0McFZRoaxFwGBLJM7WcjQIpsYay9GESVMooEHSlaxpecgGUYn4xwlZBhdI8p3KTnc101/OER1eiYHe7vwRSPYFAPlrnSyLLZkAKmm6zT7Bmj1e/HHookMR0YzFe503EZz8t7GNY293e3YDUbSTGaO9PfgjUYwSjJTMnNxGU2ouk5NXxd1A71samvELMusbTyKSUosga4vqRwW8JoixenUDfTyk72bqfP0ATpOg4l5ucU8PHEORXbX5RbvonJNKQdO03L8kR20eX6I1TAJXY8xEHwJb3gDoehh0qxrMCkll1vMS06eZQHdtl30hPfQHz6MLBqRBTPTMv8F5wUoBwbRQb51Cb5oE8c8TyT875ES2aCsSwaVg6HWmgbf0/SFD6PqIULxXjQ9RmdwG6F4F5JgJMM8hVL7zSji+cvjjTZw1PNXopqXqOolqnlR9RC7e/4bSTRiljIosd9Ehnn0TEGjoelxopoHUZDoCu2kN3IISKgBqhbFZihgnPsDZJimpnw2r3HSsh1Y7WYEIbFQaWvoQVM1iity+OCXVlM1pXhYkDlAKBChe7AQXX+3j5f+vGXUeJBoJJ5wQyHh/jLQ439HlIOC8iwu1wPs84QY6PEDidiDp3/7JgbjyK+iSCiGtz8RkK2pOgO9/iHKQXq2k6opRdTsbKCjqZf9244xZ8WE5MIxFo1zeHcjvZ1eDEaFRTdPHWYt8fQF8PYn3HK62/p54v9eH/F7BQgHowT9CUupqmp4+gJJ5cA3ECToT7itZhemIZxhsZmW7UzGn5zAqdhYkDl8ziq15fKQbfWw42MdRYx1FA07Pt5ZmvzvTKOLWwsWJv+e7Cpnsqt8VLneCZ4+VsO6pqNcX1zJcd8A/mgEbzTClMxcHhw/nQJ7olCnpuv86sAOWv1eREEgqqoEYlGuKyrn3rGTcBkT9z2ixvnF/u3YDUbKnGkc7usmEIsSiEX5f/NXDioHCQViZ1cbDZ4+zLLCq03HUAa/oxXF5Uh6ytUmxZmpcGXwgeoZtAd9nMi+NCe7iDLnteNOdIJrSjkwKiXkOj5Nf+hFApHtGOUSwvFjGOUiMmz34rSsRBIvTVGZK4UTNQAmpT9Mf+QwEdUDgEG0YZZPTbEl4jKMYVrmP+Myjh7cBiAKEhnmyUyWPoUv2khMDyIJRmxKPi5DBemmCcS1EKfupDsNFRhEF6PFPViVvKS7jstQzvTMf8FpGDOsXaZ5CtMyv4jbeDIvtFFykWWejqbHASix3zTkHFk0Y5QSAYdmOZPxaR9GFszDAq/NchZVrvsSlobBNK3N/nUcHvgDRbbryTJPRRISizodlUC8nb09P+bwwKMsyJl6xnuW4upn6S3TmbookfYvFovz+COvsXfzUbwDAfze0LDF3QnicZVQILFIbKhpo6Gm7ZzHjISiF0X2s2EeJeD9nSAaiREJJ66zdncjtbsbz/FMfdj9EQSBGUuqee3JHYQCEV7+6zbmrJiQ/LyrtZ/6g61EwzEmzh5DUcVwy3E0HE2mfz20o4FDOxrOURyd6CnyxCJx4rHEnGSxm86oe5kHM3u9W2nxe+mPhPjwhBmYZJkt7U38tXY/Fe50bi8fj1GSkQSBhfkl2A1G0k0W4prG43UHeP74YZYWliWVA0goCA09/ZQ503howgwsikJPKEiBLfG+V0SJG0qrmJCRzUAkRJ7VwYcmzMA6mFnGIA6vO5EixekU2V3XnIVgNK4p5QDAbBiLQc4nZlmDpgcBAUlwoEiZiOLVUVDl7SIIAmY5E7M8eq5oQRCwKDmUKjefU5+SYMBlrBhScfkEifoBQ8mzzj9nea1KLmXKyAWJHIZSHIbSYe3PVW6j5KTQtnzUz/KsQzO+1HufJhTvZqz7foySa8hnqh6lpv93DETOXqwsxdVPwZgsJs0pRzHIiUBhHY4MBr5ufH4PZdV5ZI5QOE7gZJBuQVkWpePyRkx3OhL2s2SMulhcjliD5NiQtFqUVudRVJkzas2AUxFFcVh2IIAxEwooHJNNd2s/+7fW0dnSR85gVd3jh9toPNIOwMLVUzCaDcMXgcLJPGZjJhRQWJ59Tgt3WZGGWDES3Q6ed7Zo33f5OjSuadw7djIT0hPKmk0x8lZHK3u62riuqJxMcyKgemXx0PfNLF8B65vr8ceiyRz+AKqmY5UN3DKmmgJbwvJQ4Tp5ngCkmywEYzGMkoxFMZBptiYr7qZIkWIo15xyACCJdqQR/OBTpDgbmh5DQyWs9g5RDnQ02gJvElW9pJvO310pxdWNIApUTi5i0c1TWfvYVra9cpBpi8YmaxOciqRImK2JRUf5xALu/dRKnOnn5j53qQq3XUkYTAqmwToU42eUcufHl48YsH06AgJWx3D3LKNJYdbycRzYfoxQIMLmtfu5/aElhIMR6mvaEoHhWQ4mzBozoruQ8ZQUuZPmjOH2jyw9J2VOEASsp7iLKSYFxZDo3zcQStbBGIlwMIKuvQvTBQ0iAOXOk2l7XUYTWRYr9Z4+wvGEFUfXdQ72dfFU3SFq+7sZiITpCwXpDPnRdG1ofwKkm83kWa9tz4B3DbqGFt2IHjuAaLkHQbz23HaudK5J5WAkVM1Hp/cRrMaZOM1L35Exz/RyOB8EQRixr5QZ9OJT4bqLrZ3/wZttnyHbPBOTnE5cCzEQOcJA9CiyYGZi2sMX1Pf5PA+p7/bKQhAE7E4LK+6cyY71h+jt9PLK428xdmoxOUXpQ74vi81EdkEadfubGejxYTAqONzW1Hc6iCPNijvbQfOxTno6PVhsRmzO0YO2z4V5qybx5x+tJRyMsuHZPdz2ocX0dnqp29+CpulMmVeBK3PkCsruTDvOdBsdTb10t3uwOsyYRrIwnAWn25pU7toau8+4+O9pHyAev7ipNK82lFOq6gqDNQcSxQUTxza3N/JP659lYnoOd5SPp9Du4kBvJ7/cv31YX6IgYBDlUavhXkuceI9c2/OJDroftH4uVkr2Kw1dC5AogGG+Ir/Ld41yoOkRovE2zErgHRtzR0crfzi4m75IkJ0dbdxWMY7/mn8djd4BfrF3O8cG+jDJMv86Zwnj0rP4ya7NdAT9tPl9jE/LoqaviwcnzmBBfjH7ejr4ya4teCMRcq12vjBrYTJwK8XFo8i2EqPk4ujA3+kMvUVM8w9mKcqj0nUvZY7bMEsZF/Rjru/t5yvPvczc0iI+Om8msiTxuSdfpKmvnz++7z0c6erliX2HuHfaJMbnZl2Cq0vxthCgqDKXZbfP4PGfv8buDYfZvfEIK+6chWw46bPscFspq85j00t7qd3bRHf7AFkFaW87/jdRETrxqgyHIkPcKq4mMnPd5Jdksm/zUfZvPcZAr39YhqHzJS3bwaS5FWx4bjetDV001LTj9wSpP9iCIAhMW1SFfZQxcorSyS5Io3Z3I3s21uLrDyQzJJ3XdeW5cQ8Gk9fsaCDojyQqiJ9eRVnXqdvfQjQSTx5TNQ1N14csmM8HXdeJqHFM8tWRdlkH2vxeCgffYYF4FE8kTJrRgmHwHvzm4C4kBH6+/BYsg9fV6vegvY1Nt0SOuRPl/q5Ojh/ppK2xh3krxl+Vv/9zQ0Qw3oBgvJ5r0wdPRwv9CUHIRDDfBFx5VZWv6txdcbXvnP/F1G5U3f+OyveXw/t4cOJ0Hr3xLuYXFHNH5QQUSSLPZuezM+bzy1W3cl1xOW+2NBAdLMhS4nBxW3k1gXiUlSWV9IQC+GMR/mfHJj4zfT7/s+wmJmfl8vuDu97Ra3m3IAgiOZY5LMz7ATeXPMvtZetZU/oiywt+zYS0j2CRM9/GhKzjMJk40tVLTNVQNY1jPb1k2KyDnzJKnY4UVwKCIGB3WZi1bDxFlbnoOjzxf+vpau8f0s5sNTJuRimFZVmE/BGe/NXrdDb3oqraMOuRruvEonF8A0FikfgZrUvONGsiCFqHjqZe/J7QJbnOS43dZWHS3DFk5rkIeEP85UcvM9DjG1IA7gS6rhONJDIWxaKj3x9BEFh223RESSQaibHtlQM01XXS0+GhqDKbooqcUTMQOdNsTJlXgTvTjt8T4tEfvIi3P3BWeU4EH58gPcdJ6bg8jGaFUCDKs7/fkAx0PoGm6bQd72Hv5qPJNKaqrrO+pZ5H9m87p/s3Eqquc/vzf7rg899pBOCpY4fwRML4oxEO9XZR29fNuPQsnMaEq5YoCJhlhbimEVbjdIcC7O3poDccPHPnZ8AgSTiMRnpCQXrDQYKxKP5o5KJZ+S8lmqbhGwiyed0Bjh1qo7fLS3+Pj2gkhm8giKYlsnkF/WF0Xaev24ca14iEY/R2eenp8ODzBFFHeK7PB12PoWv96GobutqBrnmT90/XY+hqb+KY2ploo/Wi6+pgMclQ4rgeP6W/eKIfPTLYJo6utYPWBvoAoJ46eKKvE+3VnlPGiA820dE1X6KN2paQRz+ZOEBXe9C1ALrWN/h5J7rmH+w7mri2E59pfnRtYPA6A6eIEUZXu9DV1sHzA5x4b+uaL3H92gC62p74pw3AoCucrgfR1Q6I7gS1FdS2RD8XqS7VxeKqthw09n0RW+zcdko0PUIkdow0y62XVqhTcBlNtPq9WBQFsyRjMxjQdJ0GTz/PHKtB1XWavB6K7A50EruAeTYHbqOZdLMFkyyh69Ds89Ie8POrfTuQRBFJEBifkdpZvhrJddrZ09JOVFUZCIdwmk1vaycsxTvPmAkFzF05gfbGHlrru3n+Dxt5/xdXYzCerEpbMbmQRWum8dSv32DL2v1EglFuet98cgrTUQwyOhCPqYT8YZrrOtm1oZY1H1zE+Omlo26UZeS4KByTzUCPn4FuH3/933WsuHMWxsFd7nhcRY1rFJRljZpJ6e2iaTqaqqHGVVRVQ41ryVSjkMjY4+0PnKy4LItIkoQoDU0TOW3RWA5sr+fVx99i/VM7CfrD3Hj/PDJyXIn7o+vEYyoBX5jGw+3s3FDLfZ9ZxZjx+aPKNmVBFRm5brrb+tj+6kFyixPZ2cZNLyUzz3XG65q7aiIHdzSw4bndvPL4WwR9Ya5/71zSc5woijyoxKkEfCGO17Sza8Nh3v+FmyitPlnpWBAE5q6cyK43aqk70MLjv3gNR5qVqfMrMVoMaKpGX6eXZ/+wkf5uH6IooJ3ieuSLRjjS34MsiuRY7Vhkhc6gn/5ICAFIM1nINFsJxKJ0Bv1EVRWbwUCu5WR8nabr9EdCROJxsiy2KzZ3v1GS2NzWSFvAi0U2sK+nnXSzhfl5xZgHrQQ3llRxsLeTr297jXJXOo3eAdoCXtzGC4/LcRlNTM3M47cHd/K/e7aSb3Og6TqfnjoP6QrfhQ8Forzy1E42vLQfSRZpb+7FnWFnwaoJbHjpAHc8uJBffecFSqtyWPO+eXz9n/7Av/7ofra9XsO+rcfQNJ2cwjRW3DadwjFZF5QtS9djENuFFnoCXW1EwAByGaL1QyAVgtqI5v06yJXo6nHQPCBakWyfAXkiemwvmvcbSM4fgDKYJTF+GHXgs0jOH4IyATQPqudfQW1BUMYh2r8E0ikVxdVu1L5bEe3/hh55DV1tQVAmIFo/AlIOaN1ogV9ArAZdjyFIeQjmm8G4AEEwoXk+B8pE0HrR4w0giAiGeYiW+yDehBb8LQhO9NhbCIaFgI4e24tgXIlofT/oMfTwU+iR19G1fgTBAYZpg7ER6ejBP6GrjSAo6LE6ICGDaPscglyMHt2NHvwjenQHerwWoq8DcuJ8861v9zG5aFzVyoE/shObbRaSNDxjyOkIWghBeGdNN7NzC3jp+FFafB4WF5ZSlZZJJB5jb3cHdoORj06exaMHd9MdPPlyFRERhBOmT9DRscgKLqOJz82YT6HdmVhYaO9uf9WrFZMsYzUY6PYHqO3qYWpBLgfaOi+3WCnOA7PVyNyVE9m76Sg1u4/z0l+2Mmv5eCbPO5lZxWo3s+ru2YQDEdY/tZPdG2vZs+kIWQVpONwWdB2CvjB9XV5CgQiCIHD9vXPRGd2ILogCt39kKe1NvfS0D/D0b95k5xuHycx1oZOozByLxPnmnz7+tt10RqO9sYcD2+vx9vmJhKJEQoldyxPU17Ty5x+txWQxYDIbMJoNuLMcLL55aOpfh9vK7Q8tQY2rbH5pP9teOciO12vIKkjD7jSja+DzBOnv9hIJxRAEgXs+cd0ZZTOaFJbeOo3HfrKO+po2ejoS8QNVU4txpp05INyVYefuf7oOdNj26gE2r93P9tcOkVXgxuawJHdt+7u8RCNxRFHggc/fOKyfiklF3HDfPP7+yCu0N/Xyf//1FEWVOWTmuYmGY7TUd6FrOjfcN4+X/rKF3o5Equm4plHv7WN9Sz2dQR8riyuodGXw6OHdGEQJHZAFgXurJrOvp4PtnS2YZIVALML9VVPJtdoQgFa/lzdaG3AbzSwrLLtilQMd+NaCVTxZd4j2gJc5OYWsLK6gOv1khr2by8YCOru62jju7afSncG9YyexsbWRdNPJ51sWReblFmGQzr6cMUgyK4vLUUSR3d3ttAV8ZFnemQxhbxer3cRtH1hIT6cPd7qVOz60CEEQaKrrxOowsf+tBvKK0+ls66e304ukSAR8IdY/vZsvfP9u3Bl2nvzdRnZvPkpmrhOL7QKKhKptaKFnEKQSRNs/gRZC9X0Hzf8zJOe3ANDVNgQxE8n2aRDMaL7voQX+gOj8LoJchSDloUc3IiiJ+VKPvIogV4BclthAkNKR3b9BCz2JHn1jZDk0P3p0O4L1A4iCG4iD6Ep8FPgFerwWyf4vILrRI+vQQ/9AELPAkEgmooeeRXR8FdE2Hj26Cz30d3R5IoLoQo8fRbR9BsEwE837H4j2f0FQqtEjbyYUith+9PDahEIkjwW1EdX/Y3TBgmD9YKL/6BZEywcRLR8B3YPq/Q/00JMI9s8gGuaAMhV14OMIxkUI5rsRBIUrbTl+ZUlznsiikzzXFzEr1WdtG9M6aR349jsg1Ul6wyFMkoxZMRCOx6np6aLcnU66ycLh3m6eOHKQBk9/cqdkNDItVhYVlPCPIwfJsliRRZFJmTlUp6esB1cbug4TcrM42NHFnpZ2lpSXppSDq5DScXnMXjGB5mOd+D0h/viDFymrzsNxyiI0M8/Nez6+nOKqXHa+UcPx2g562gfobhtA1zWMZgOuDBtVU4opGZtLfknGWeMSZi0bR+CLN/Hmc7tpONxOa0M3TUc7UAwyNqeF3KL0S1rc7PDuRv7wvefp6/KO+HnLsS5ajnUNOVYwJmuYcgCQV5rJA5+/kfIJhezeeISmox30dnroaukDHUxWIxk5LnKK0ikZm0vWCGljT2fJLdN58ldvEAlF6WkfYNz0Ukqr8xDPIVVqYXk2H/zSaiqnFLF30xGa67ro7fTQ2Zyormy2GMkqSCOnMJ3S6lwycobHfImiwHV3zsRiM7Lh+T3U7W+h/XgPzXVdONwWiqtymbtyIktumUbNzuPJ+ygIUGR38uEJM3ni2AGOe/oJxmJ4ImH+bdYyVE3juzvfYGdXK80+D7OyC1hcUMaP92xmb0872ZYKQvEYf67dQ7bFxsqi8guOX3gniGsaRXYXn502esprWRS5rXw8t5WPH3J8UkbukL+NksxHJ80+57HTTBbuqJjAHRUTzt74KsDqMONwWji48ziTZpWxbX0NtfuaqZpYSHf7ABa7iZyCRLafnII0Wuq7iIRjF6Qc6GobemwvoEHoaQAEVPTIq4ljgCC6EIwLEZTE/RUMs9DDLyQ6ECwIxoXo0a3o2m2gq2jRXYjmW0E4Pa3sGeYxwYRgXIConJY5UA+jh59HtH8ZwTB58ODSxC69egxBHz8o01QE43UJZUSJokdeA60PRBcCZgRlysnrMSwArRUdEfQQemQjuh5Ai76FENubsKboQfTYbuD9iXPkcgTTdQhSPrqeg6BMSVgTAAQJUAavTwbBkPh3hXFVKwc202wMUh6iePaHXNRtiMI7lyaw1e+lJxRkZk4BdqORQCzKL/Zu53+Wr2ZmTj5WJeFLWVmRgSJJGESJFcXl2A1GDJJEmsmKVVHQAZMkc9+4Kezr7iCixpEFcUgBmBRXFxPzctjV0sbhrm4+On/m5RYnxSlUTC7iU9++GzWuUj29dNT8+4ois+y26eSXZhIORhBFEWkEn3Znuo3r7pjJ5HkVdLX0MdDrJxaJoeugGGVsDjPuLAfZBW5MFuNZ41lESWTZbTOomlo8GHcQRFP1ZPpUZ5oV8yhpQcdNL+Wj/3EbkVCUikmFF6RDjJtewke/dlvSX/5cGK06NEBaloMb75vH9CVj6Wrpx9vvTwbqGowKNqeZtCwH2QVpIwb3nk7BmCw++/17iQ36+mfmuSkcM7zw2Wik5zhZ/b4FzFxaTVdbP96+RKwDJCwTNqclKY8ySlVnxSCz6OapVE0porWhZ/A70rDYTWQXppNbnI7RZOCeT65g2W3Tsadb6QPigy5GqpZwMTVIEjFNJWE/1olpGvJgsS510BUxpqlJdxhBECh3pdPs89AR9FH4LinW9G7DaJTxnmKtsznM2F0WOpp7mb9iPFl5LnZtOsrc5ePJzHMR9IfpaOnDnWGno6UPq8OM0XQhges66CHQIyA4QUhshAjGJQjGZSebCZbkLn7ibwMM+vwLGECZknDJiR1M9CdICMoUBM5DmRVkBGmE37XmAz2IIJ5S40kwD8oQJhnPJxWecpI4+G/QG0Mwkly4IyCIJnRNGDymgtaHIFgQBDsISsLTw7QapAKSCo2YSUIBSFw1ghH0c58zrwSuauUg1/FpZCn97A0BSbSRZrkdgzy6z+rFpDsYIBiPMT+/mCyLlQM9nfw1tC/hO2q2sKCgZNg5p1oCsixDzeBOo4mFI5yT4uqjKiuD327bRZrFjEUZOknruk5UVQkPBjuKopDM3pHi0pNdkEZ2wbnl1M7Mc49YBO10BFEgK99NVv7Z254LgihQUJZFQdn5WQ5zizOSfvgXysXo43QEUSCnMD1ZuOztIEniiFaK80EUhbd9nYIgkFOUQU7R6H1MnD0GZo8hrmmsbzlGXyTIt3e8TjAe47Yx46lyZ7Knu51vvvU6kLAszMjKxyzJrGuu483WBoySxNTMPERBwCjJ3FhSxfqWep4/Xsud5RPIMF8dLjMpzp0Zi6t49Efr+NZn/kRucQYf+OwqDEY5oby6LJSPz+f15/fygc+uwuG2smzNVH77g5fQVJ3MPCcrbpt+TnVFRkIQzCBlIRjnJdxjhnBiI0Vg1Fw3ggBiDkjF6LFdoPkR5HEgXojFc4QxRAcIDvR4A4JxXuKY5ksoBqILkgrImd6pp8shDP1vKR1UAcF8B8KQelrCKW2lEfo5XXaRE9aJK5GrWjkwyHkIwrldgoABm2k2wjuUoKnCnc4bzQ18bfOrid8DAp+bseDsJ14G/DEPW/vWUWqtpsw2DuGUh1of1LSF0x70X9d/kznpKxjnmHHR06mNNua1gllRyLRZmZA7fOfjWG8fn3z8uaRLwIKyIr5+04p3WsR3NTHVT5PvcdoD69BQKXXcS4FtzTWcNjDF5UQSBObnlTAlMw9d1xEFAYfBhEGSeH/1NIKDRcGssoLNYGRaVh4VrgziuopRknEYTAjAr6+7A5Mks6xgDKF47Iqt/vvRiTN579hJ1+jsfumpnFjA57/zHjRVQ1YSSRCmL6hk3LRibA4zRWOyKB+fT3qWA0EUuO7WacxeWo2m6ZjMCla7GfGCYlEEkEsQpAL00JNoghVBdIPaBHocwXTmmKAkojvhZhN8DJ04ou3hpBXiJDqgDVYaP4+EHYIBwXIfWvB3CFIBSNnooWcSx+XKi+JyKZquR/X8J3rob2BaBagQbwQxHcFwjhsTgghiBnp0Oxjmg2hBECxwBRV7u6qVg9M5NR3ZSC9y4R28XIus8PEps1FPqeRoPIeAqcuBhkY4HkTVh6bm03WdvmgXG7qfZU3+g4jCyQnFE+slooUvuiy6rrOr/03+P3tvHV7Heebv30OHUcxMlplkxjiJw4xt06TMvLvtdqH9/srdbrvFbZu26baBtknTtGHbMSRmZsuSLLAYD/PM/P44sixZkiFxYojuK1cSzbw0c86ZeZ/3fZ7PowgKU10LrqoJWXFqCl9cuQhFkvifO29CFAUUUeR/77sNkywzIzeLx95zF+owBZPLNaDwaqY/spv+yB4q3B/HIuejSBOrrxO8fQiCgEVWhrT8h2M3GEdN8g2STKp59Lsk25pcxTTJMib58nzXALhMZlymCbfYN4ssS6Rljox3MVkMI3YDMoftUhoHhQEuCmImovWjaKE/JFV/9CiClIdg+eAwY2/4CvrovwVBBLlkUKHRiiAVDL3ndT2OFvhv9PAzoIUAFbX3DZDykdy/RBCzTg1knAEKiNYPoiGj+r8Bmh/BMAvB+iGQiscZH4we75n/L5w2LJS5SI5/Qws+mlQ2QkFQpiJaP3y2O3eGYSIgWh9GC/wAbeB9ILqRbJ9HMF1/9jbeQQT9ShD4PQOfz4fT6cTr9eJwnE6X7g2vIxw7Rrr9fUjiyDTq/shWApFtpNjuwigXvNNDviRouoqqJ5AFAxoqCS2OQUz608X1GJIgAzpxLQ7oKKJh8NhgUh0tQmPwCK/3PM/7Cr+IIIgoogERiR8e/xLLMm5jiqMGHQ1RkJAFBVEQ0XUdVU+g6gl0dERBQhEUBEEkoSXQ0ZAFZSjzc0yLIIsGBARCaoCN3c9hl13MTV2JgIBRTL5IriZDYYK3RkILIQoKmh5Pfv+QEQVD8sUDaHocTY8lX0BISIIRQRDRdQ1NjyEIMrqeQCOBgIgkmAAdVY/Q6n8eT/QwJa6HMMtZyIKZ5MtIR9NjaKgICIiCgoA8+D1WUfUoomBE06OD33sFATGpy40GaIPnY4CANBiAl7yGxLCxGhAECU1PDGp3C+gMupkJhmF96ugkhl2nONjm4D0gngyWAwRBQeTcPvsTTDDBBGfj9JTx1H9Px7yMtUB7rmNjHz9zWnpmH0ldt7GeZyPb1kfUHX5+vLGc0RoCQnIfQ9eIqRq6ntztMwzKRauaPpSnShYlFElE03XiqoquJ0dgVuTBRIdgkCXiQ+UFookEoiCi6sm+jLKEOqy+NMy1eGRfIpIooGo6ymBsXExNxiDJ47gijzd3HovLd3nhTRCIbicYPUCq7b4xPMp0AtGdWI2z3zXGQUuoju19a7kp533U+vexsfs53l/0z7gNGTzW+F1WZNyOpmu82PFH/AkPd+Z9hCnOGgASepwnm/+HjkgzQdXPT+v/FYBbcz5AhT2pAtARbmTvwOt4Yj2kGbNZkn4zJdZqImqIN3pf5KhvN1EtTI65mOUZt5FjKmJn/2t0Rlq4JvNOHEpyC+17xz7DvfmfwGVI52+tj9IRaUYUJLb3rwHgk2XfwjyxejvBMDa13Uex40G6QhsIJ7pwGadS5noEm1KMqkdo8T1De/BVEloAq6GIEsd7SDHNJKYNcKz/J7iMUxiI7mcgchBFcjI383+IqX0c6v02gXgTqhamL7ITAZm5WT/GphQSiDdRN/BrfLHjyKKFPNst5NpuQBZt+ON17O76MtUpX+CE749E1V4K7fdgVQroDK0npg7gi9ZS4f4orf7nEUUjk1I+hyxaaPL9md7QdlTCGMUUyt0fIc1cQ194Fyd8f8Qi5zAQ2Y+ORq7tBkqc7wNdJqYN0OJ7lq7Qa8S1IAbJxZTUL+MwVBDX/NR7fkNveAcA6eb5FDruxiznThgIE0wwwZvm9PNjDO+MsTw2zvPYudoeWeZc54f+OmffZ38eCkP/PtzWw7P7jzAQDOG2mvniqsWYFQPPHzrGxuONSKLA3KI87poxhX2tHTy3/wjRRIKEpvH1m1exo6mVxr4BPrRoDs/uO4LFoLCqqpSvPreWyTkZNPd7sBmNfGb5Ag62d/H8wWMMhMKkWs3803VLUUSJvx84yhv1TQiCwNLyIopS3WxuaOb+OdNwW8z8ZP1W5hbmsayi+CzXdH5cVcZBQh3ApBSPmc9AEh0gCGj6m8+ueKVhFM2YJAuBuBdPrJd0Yy798R4cipv+WDfZ5iJssoMMUy4vdz45oq4iGnio+J846NnKrv4NfKDkX0e4FQEc9+/nnvxP4JDdbO57mb0Dm8gxF7Fz4DXawye4K+8jOJVUdg2s56WOx3mg4LNnHW+mKY/3FX2RV7v+TIYxlwWp109MZCYYl7bAy8xI/08QRBo8j9Hq/wcV7o/REVxDX2Q3U9O+ilnOoi3wIscGfsLsjO8jCCJxzU9X6HXKXI8wOfWfCcXbMEoujJKLBTmP0uL/G57IIcpcH8Ci5AzuhEWoG/glBjmVmpSfEkq00uB5DLOSTbp5IboO4UQn/dG9TEv796FdhUC8EU/0MFNS/4WOwBpqB37BzIxv0xF8lf7IXnKs15FpWUqh/S5k0c4J72Mc6/8xi3P/AIA/WofLOIW5WT/GF61lf89/kG1ZhVnJpiu4kZ7wZqpSPofdUEYk0YlVKQAEGn2PE050MifzB2h6jHrvY7QHX6XI8cDgTsgEFwNN0+jqDxCKxCjKSUG6DNwAfcEIDa29qJqO02qiMNuNQXnrr3pV0zhY30F+hotU18RizQTvHnRdJz/FyXvmJhdG//P5tURiCXQd/rh9H7953504zUlVtnhC5dl9h7l71hSm52UjCsLQTsF4ZNhtfGDhnKG+StLcvLdmBgBf+dsrxBIqUV3l6b2H+N8HbsdhTu46q5rG2qP19AVDKJJEc/8An1258KJc81VlHMBgBr8xA1g00NVxzl2dmCQLZsmGJ95LQo+TbSqkO9KKU0nBJJmxyWffVjoXU50LyDOXggAl1kns7H8NX3yAhsBhpjrnk2UuQERijnsFR7y7aQkdv0hXNsGVQlekgxPBOrJNueSaC1DENyOhNzY5tmuxGUrQdY1080I6gmuIqF10h95AEox4ogfxxo6i6fHkyn2sDqexEl1XybAsxmmoRhINOI1V5+wrpnroj+5jrvsnWJRszHIW3YbX6Y/sIcU0k1Pbz3m2W7Aqp2XyAvFGTFI6Vjkfp7EaT+wQdkMp/ZE0EpofSTSjiE788XpULYIoGAkl2oa2uq2GAjLMCzHLmZjkdKQ+E4F4MwbJTU94KxmWJaSZk3K4xsFkkLqucdL3HJnW5fRGdgKg6TF8sXriqgdZnDAOAFQ9jifWiDfahChIWOR0UoyVSIMujudDLK7y6tZjHGns5D8/shqL6dLrlfd6Ary4+ShHGjvJcNv4l/dfQ1bqW3vWQ/Jav/Sj5/jcA8u4ecnkc1d4F6LrScnZxv4BTnq8BOMxBMAsK6TZrBQ4naRYzBOLXlcY0USCbY0t1Hf3k26z0BcMoeoakXgCgyRhUk65Y0NcVREEAbOiIAweAxAFgYSqous6oVhsyBUIoDzjtKpZOJ7g9fpmWge8pFrN9IfCaJpGNKEO9iUN9SUKIkvKi9nT0o7VaOS6SeUXLb/JVWUcGOQ8vOG1xBLtSIpjRJBLJN6AqocRxbf+kLxSMIpmLJKVjnAzBtFEtqmQo75d2GRnclL/FnEbklrCQtIDG4CIGiKmRbHJDgREBEFAFCTMkpVQIjBmO6o+ke35aqUucIwnW37HiozrSDdmXlTjQBGTQXmCICKLFnR0VC1MXPMiCibCic6hGIQC+12Y5dPqUCYpDfE8lc4A4loyk61xUE1CEAQUyUEk0YV+SnRAEDCNob0tCUYQBARBRhKSmV0FBHRdYyBygPbgKxgkN4poJ6b5BttLvlFkwYYsDuqJIyII8mBMg0Zc82OSRktl6iSIqv2oWphwoh0Aq5KPTS5CEt+ezMlXIqoepT20g/7IcdJMVXSF9xFRPeRZF563Ct7lSHFOGv/y/mv4/fM7ON7cfe4KE1wUdF0noWk8sfcA6+obaPEknxlxLTkhzLLbuHvqFB6cMe0cLU1wuZHQdLzhKHFVxWo0kGKxIAoCLrOJqbmZ/HnXQcwGhaJUN9Nzs5iak8nr9U0cau9CEODGyZWk2ay8VtvAs/uOcKJ3gBl5p5P5DTcVVU3DG46gahpWo5EUqwVBEHBbzFRlpfOn3QcxKwpl6alMzc1kel4W2060EIoP8MllZ8rLvnmu3CfgGDhMy/CEXqTN801c5hsxKvmgq4TjtQyE/oFRLsUkF13qYb5jGCUzZsnGydB+SmzVZJny2d63hq7ISfIs52ccJINxxpYTE4XRFqpRNGEUTfjjHjRdQ0BA0xOE1QAW2UYintzZ0QbN6ZDqJzGYIOV0j8P7nFhhmWBsookeILlSntBCCAhIogVFdGA3lFHqfAhxhIuhSEzrH/r/C8EguREQiaq9mOS0wXwUXmTBNsLdThDO3u5ImWCN3vBWdDQKbLdhkrPoCK6lzf/8iPbGkl8WEJFFG1G1b4xzEkY5lVTzXPJsN444M/F7GoksGMmyzKLccTNd4X0cGXiKLPMsDNKZ0opXDoIAsiQiS+JF/bgnvjnn5lBnFz/ftp1Mm43PLJpPqtWCqmn0BkMc7OwiwzbhjnUlYjEoLC4tpHXAi1GR+fSKBTjMJmRJ5JEFsznR2z84gTchigKrJ1dQ1913OvBYEilNd3Pr9KSkbEGKk2ynHUWSeE/NDLKcp/MlWA0KyyqK6fD4MCoyn79mEVajAVkUeWj+TJp6BxAGDRMBMMoyCU1jUlY6NuPF27m8qowDs6GaLMdn6fD9gA7vfyGKZtBB0yOYDdWk29+HMlZWvasUERGTZMGfGMAsWbHKDhTRSEekhUmOOeesLyBgU1xEtCCdkZO4DekoggH5LKu/sqhQYZ/BId8Oss2FuAxp7Oh/DVk0UGipoDF4FG+8n+5oK6Ig8kbPiyPMDkU0YJas9ETb8SUGkAUFi2Sf2IadYBTtwVfJsC5BRKY7vBmrUoBJziTLupJG75O4jFNxm6YRUz2EE+2D7j9vDoPoJNVUQ4vvr5S6HiaUaMUfq6PE+R6kN+nDLwxO1jUtCoJIMNFCs/8v51VXEo1kWBbQ6n8el2kqdqWUcKITk5yJQXRR6LiLZt/TOAylmOVcQvFWZMmGVc5nYpo3GlGQyTBPZ3fvz4lqXhTR+qafOUcbu3j0uW3ctKiaFXPK8AYi/PGlXRTnpNDY3k9tczduu5k7VkxjenkuopjsJxiOsnFPAxt21xOOxplcksW9187AbbcQjsb51u/W8vDNcynKSeXptftoaOvlQ7cvIDPFzv8+s5mcNAe3Lpt6XmMMR+NsPdDImu21+IJRKgszePD6WaS6Tl/3gD/EK1uOsfVgEzazgVuWTUHTRyZt8vjDrN1xnC0HGvEFI0PucFNKs/n8g8sBCEVivLHvBOt2HicYilFVnMmDq2fjtl997jV1vX0EojFurMzhtsmThqSoE6rGqrJSLIaLt3M6wTuHKAjkuZ3kuZ2jzmU57SMm9wBOs4k5hSMT7iqSxNzCvFH1ZxXkjOxLFClMcVGY4hpVNsfpIMd52vultquXFw4dIxiLsaSs6AKu6NxcVcaBKBhwWlZhNc0kFD1ANNGCIMiY5FLMhmpk0XVFbxdfKIIgYJFs6IAkyMiCQoYpl219a8gy5RFM+FjT+WfqAofwxftoCBxmXdczrMy4k8nOuQgIZJryKLRU8UTzjzCKZm7OeYhi26Tx+0RgtnspETXE062/JKqGyDEXc1PO+zBLNoqsVbSHm3m+/Q8IwFTXfLJNhUP1ZUGhyjGLDd3P8auG/4dddvFI8ZcxShN+0hOMJM1cw+He7xJWO3EbZ5JnuwVRUMiyrCShBTnu+SXRRDeK6CTDsvhNGweCICBipNz9IY4P/JLtnR9HFq3k22/HbZrBhe5CDGuZLOtK6jyPsrXjg5ikTIoc93Eo9t3zqKmQaVlONNHLod7voeoBTFIGk1P/BYPBSZHjATQtwb6er6PqQcxSFqWuh7HKo19OEySRBAXQT7uJvQkaWnv5n6c2MqUkm9mTkrEn8YTKnmMn2bz/BDWTC7l5cTXrd9XzncfW8r9fuReX3UwsrvLKtmP8bcMhVswpI81p5ZVtx6g72cM3P34zug7eQJgTbf247GYa2vrYdqCZe1fNJDPFzhv7TvD+m2rOa4zxhMrr+xp44uXdLJpeQnaqgxc2H+E7/7eW737qViRJIBpLsGFXPS9sPsLKueW47Wb+8MIuItHTuXBi8QQb99SzaW8Dd65IJjX7weMbWDa7lIdumjtUZt3O4zy7/gCLZ5SQ7rbzj02HaO7s53ufvvVN3+fLlfK0NBKaxtbmFl5vbGJRUSEGSUKWRFKtEy59E1xcilNdPDx/FpIo4jAZL6qxfdXNlEXBgCJl4TRncNoVRmQ8TdxLxakVFm1YNmCB0zq+p1fT9VOKviPW+/RhZ8eqmzwOBdYKPlD8FaRBF6Bl6bexJC05iTKJCjflPIR+hsuQJMjJNgUBq+Tgpuz3sjr7AQROvUDh0+XfRhSkoXtabp9GqW3yUJ6EZRm3siT9RnSSOxiSIA8aK3ZWZt7BsoxbB/uSWJZ+61A9QRDIM5fyYMFn0Ui6JcnC+a+2JK//zAzLZ14hQ9c3Vv3xyp8a33j9jt3T2euOV08Y/u8LqHc+/Z15f8br+8L7Hb/e6fraeX8W58JlnEyl+5OAhiBICCiDrkVG8u13kGe7+fTvQ5AQkDGIKczK/B7iWdLb59luJs92IyOSJgpgkfOYnv4fQ5NHQZARSP4GHIZKritYhyiYRrSVapqF2zQdEYVs6yqyrMsRMVDsfO9gsyIzM741KJaQzJ2QaVkKiKSZ55JinoU4bBzLcp9GGMwRYhDdlLo+QInzoaHrTLpRCciClXL3hylzPQKnfoWCzNkMmXMlkRyvzul7LIz4+0LaudTouk4g3gGIGKXRq4PnQhAFOnt9/O/TmynPT+e9N87BYT39so4nNMry0/nw7QswmxTKCzL41Pee5lhTF/OnFuHxh3ll6zGWzy7jfTfORQCmlGXzye8+zaa99SydWUphtpu2Hg8FWS6cVhNmk0yvJ0B2upO2Hg9VRRnnNdZ+X4g1244zt7qAh26aiyyJFOem8tn/+iu7j7VSM7mAfl+IN/adYMnMEt57wxxEQcDtsHKooWOoHX8oyuETnUwqymTJzBIEYOuhJtq6vLjtyYlw90CA9bvqWTwj2Y4kiRRlp/CZ7z/DnmOtQwbU1cKUrAw+XDOH3+7aw2f+/gJV6encPXUy11aU4TKZht7TE1x8vL0+Pr/86/S09jPvhhn825NnV0e8GjDIMilvU8LDq844gMFJzxn+8Lquo2lhEGTEC5hsvl1EtQTPnzzIX5v3MRALMS+tiC9MWYVDMeGNhXm2ZR+90SDtIQ/7+9tYnVfNZyetRBJE/nHyALv7WrArRta11zIzNZ/PVq8kx+KkPeTlvw+v5Yi3kzSjlfuL53Bd7qRk3oCeRv7aso8Z7jweP5FUMflo5WJuyR8/QEoQBCRBRjrjq6KII33bREEc4XstIQ0ZJKPaQx4yBgCkM56VZ7Z1IQQSfh5r+gWqrnJ/wcOoeoKNPWs57NuPP+7FKtuptE3ippy7SDeOdDHTdI3+WC+vdb/CYd9+vPEBjKKJMlsly9KvpdhahjyYgOrMet2RTrb1b+Kgdy99sR50wKm4yDBmMd01m2nOWTgV94h6p5LFtYZbWN/zKnX+o4TUIHbZyRTHdFZkrCbNmD5KOUXXdXqjXezx7OSwbz8d4VYiWgSjaCLXnM+8lEVMd83BIo12jeiOdvKH5l+Takjj1px78Se8rO9+ldrAYUKJAA7FxRTHDK7LupkUQ9qofqNahOP+I2zp20RLqJFgIoBBNOI2uCmzVbEs/VoyjFmjtaQR6Il2s61/I4e8+/DFvZglC5X2apYO3tuxvi9nJ2kIjIUoSKOeAaeQOLtf5liByslnCggYxrQpBEEc071IECSkwawrAhIM/f+w7/8ZbZ6+D6frDp0TTxsfgiAk2zljvLqu0xbuIs+SNerc2TjiqyeqxpjsLMconZ/v6oudm1jbtYWvTPoIGcZUWsOd/Pnky0x1lrM8Yx6Gy+BZezaSQexRAolODnn+QJFtBbJguuAJnDcQ4fFX9mA2Kbz3hjk4bSPbsJgUSnJTsZoNCIJAVooNSRTp9QQBCISj9HqCVBZmJGMFgKLsFDJTHew6epIVc8opzErhWHMXvZ4goihQWZTJyS4PRoOC2WggN9N1XmMNhWN09vlYMK2IaCxBFMhKcyCKcKypi5rJBUSicQLhKIXZKchSUlhiSmnW0NhgMAA3oTHoFZU0QQUBadgD3R+M0ucJkpFiJxJL7jrkpDtAgGPN3VeVcSAIArIo8vnFC1lWUsRvd+5hZ2srX1vzGj/bup2PzZvL7ZOrMSuj3yETvHV0HaLhGNFQlFg0fqmHc8VzVRoHY6FqXtq838FhWoLbctMlHYuu67zR1cCevhb+ffoN5FtT+I+9/+CXta/zT1OuRQf6okFO+Hv59KTllNjSCCSiKKKEputE1QSHB9r5TPUKPlu9knAijlU2kNBUvrrnORZmlPCNWbdx1NvBUyd24TSYWZxZBsCBvlbK7Ok8veLDqJpGTLs6lYIiWpjD3n3s9+6hN9qNIiq4DakktDhtkVaUM3JhaLrGUf9B/tD0a6JaGLvsJMOYSVyPU+s/TH3gGLfl3MfclIUjDARd1zkZauLp1j/SGm7BJttJN2ah6RpxLUZrqBlv3INbScXpHGkcqLrKzoGtPN36RwQE7LIDm2wjokXY2r+R/d7dPFL0ccpsVSNeJhE1zF/a/siJQB1G0YhNtpMipRJOhGkNN9N4sp6+WC/XZNyARR4rAE4noPrZ69nB9v43CCUCmEQTRoORuBanO9o56v4A+BM+nmn9I7sGtmGSzJglC25DCpqu4U/42dG/mcVpK8f8PPpivTzW9AtiWgSTZCHTZCGUCLLXs4v2cCt35b2HKvvkcwb0nsKq5CGLE8F9ZxLXEnSEu3mxYxP3FdyIUVTQ0DGICoog4437UUSZqJbM0qzrOk4l6XqYbU5HERQUMflaCCUihNQwoiBglkyIiATVEJquY5XNmEQjN2YtpcHfPNR/viWb+anThwzaYCIZKG6WTKi6SiARwmW4PBTjBJLB3if8L3MyuJlsy2wqnLcivQlFrbqWHrr7/ZTmpREIR0nHNsKGlCURi3FYhmohaWxqgxlUI7EEgiBgNJw2BgVBwGpSCIZjiKJAQbabTXsb6Or3o8giU8tyaO/2oGk6lYUZ5x1Jouk67T1efvnMZn7//I6h42ajgcRgAKWqacTiKgb59O6wySCPMGJtFhPVJVm8vreBzfsaMSgSRxq7uGvldE5dpqpptHZ7+OmfXufXz24dquu0mob6upoQBAFZEqjJz2NWbg7He3r56+EjrKs7wdfXrscbjfKxeXMv9TAnmOCcvGuMA50YmhY6LTp7CVF1nXpfNzrQFfbjiYUpsqWyruMY/zTlWiC5UjkjJY9JzuQqrElOvrA0XUcQIMviZFlWBaIgYJKS51qDAxzxdvDzBQ9glhXK7BnkWd0c83axKCOpTiSKIg+W1GAUZQRJ4GqdXvVFe9na9waFlmJuzbmHXHMBoNEb7aEv1ovLMHKi3hFp488n/0BUi7AyYzVL0q7BaXATSgTZNbCVNV0v8PeOv5BrLqDAUjRUT9VV6gLHOBGsY2Hqcq7LvJk0Yzog4E/4aAk14k/4KLGWjxpjY7Cev5z8AxbZynWZtzDbPQ+LbGUg1sdrXS+zqXcdj7f8hs+V/ysuQ8pQPbNsYV7KEiY7plNiLSfLlIssyPgTXl7vfY313a+wZ2A7012zKZDHzpTYEW7FExug0j6ZeSmLyTLloOpxuqPdhNQAdmXkBC6uxflb21PsGthGlimHxWnXMNkxHafiJKpF6Yy00xPtItOUPeaq2AHPbsrtk1iddRtltgpAoC3cwvPtz3DIt5/Dvv0UWkrGMWZGMy/rF+dV7t2GN+7njd7dnAx3sLl3D4XWHHqi/WQaUym25vFCxwayTRkc8NZSaMlhIOZlQdpM0o0pPN++nkxTGssz5qEIMmu6NhNMhDBJRiY7yzFLRnb2H6Q/5qXKUcK8lGko59gZOOJroDPSw03Zy2kNd7G1dy8PFN78Dt2Ns6OIFia57maS6+633FZlYQYP3TSH5zYe4m8bDvL+m+eS6hy+cze+65wA2CxGFFmk33s6SWckGqffF6KqKANREMhOdRBPqDR39FOYnUJFQQZ7j53EH4oyqfj8hTZkWaI4J5Vls0tZMWfkc8k2mFxJkSXMRgV/KIqu6wiCQM9AEF07/f40GWRmVOSw43Azf3hxJ5mpDu5YPpXr51dxyoowKDIluamsqqlk0YyRzyK7Zexdv6sFWRSpzsxgUmYG8/Pz+bdX1vLYrr18pGYO0sTOwQSXOVe0ceCP7EA2ZWExTAEgFDtMXO0as2xC6yemtr2TwxsXTddI6BpNgT42ddUhDa6WrsiuHCpjkCTMsjLmC0VEwK4YEc84F9NUJEEcak8UBERBQB3m5+1UTEjC5RV/8XYQUgNU2CdxQ/ZtpBlP++LmW6zkD5vcQ3L1f0vvBgZifUxxTufG7DuG3J6sso35KUtoDjWys38zOwc2k2cpGHL30FCJazF0dNKM6TgUZ1LTHgGH4mSKc8aY49N1nZe7niOhx5nlqmFx2vIhadgUQxo3ZN/OUf9BuiId7BrYxqrMG0fUn+2eN6pNh+JilnseJ4J1HPcfIayGhl7sZ+KNe5jmnM0NWbcNMwSMFMljSzi2hBrZ7dmOUTRyc/ZdzHSfDn40SiYcipMK+/iB6nbZwY1Zd1BqqxhyGcszF7IwbTl1gWN0RzoJJPznbRxMMDZpRjfzUqcR0WLcnLMcTdc45j9BZ6QXHSiy5iELEjbZwq25K2kMtvJG7x4eLrqDyc5ywmoEAE/cS63/BF+q/CCiIKLrOv0xD5X2ErojvXSEe4ipcRT57MbBNGcl2/r2EVGjNARamOmufgfuwjuPzWxgalkOZqOB/31mM89tPMSDq2cnV9vPA4fVSHVxFtsONlGal4bFpLDzyEnC0TiLpycXdkwGGavJQGefn5rJhRRkufEEIrT1+EZM8nVdJ6FqJFQNTdOJJ1Q0TUu6oQkCTquJKWVZNHcMEAzHSHVaiMZVuvp8pJckf/82i5G8TBd7a1uZXJKFyaDw2q464qo2op/2Xh+SKPLlh1dRkpvGmY+aFIeFisIMmjv7mRXNw223EI3F6ezzk+6+cuVix0LXdRr6+lEkiRyHfSgZlabpuM1mJFEkpibO0coEE1weXNHGQZfvF2jyNCwpSeOgL/gXvOF1Y26v6nqChDbwzg5wHBRRIt/qZoo7l/eUzKXAmoI/HiGUiJ278iBjXWOW2UGOxcWGjuOsyKmkPeylO+JnfnrxKEPiascomii1VYzymx+LkBqkKdRAQo8z271gMFh1WFuSiWxTLkbRRJ3/GJquDfmFy4JCtjkXt5LK9v43iGgRKu3VFFlKMYrj+y77El4aAsdRRANTnDNH5YywyjbyLcV0RjqoCxwdZRyMh0Wy4pTdqLpKQhv/RWSRrFTaq7HJ9nHLDOewbz+qppJrzWeqc9Z51RlOgbWENGP6iFgSURCHuVKFSegTfqIXAxFx6LMXBZFUg4v+qJeGQDNTnZX44oHB4ONTUgajd1M1dMRhwcsRLcpRXwP+RAhVV4mp8fPKNW+UDExylFLrb6Qn2s+S9HNLKF/JTCnL5v7rZvHoc1txWE3cdc3086pnt5i4ddlUnl1/gF//bSuKJBEIR7lv1Uwml2YBYDTIpDqtdPT6yEyx47AacdnM1J/soaIgmZAyFImxcXc9R5q6ONLQSZ8vxG//vh2nzcyNiyZRnp+O3Wpk9YJJ/H3TIf7vhZ0osoQAiKJAWX4GRoOM02bm2ppK/rRmL794ejNuuxm3w4LLZh7x7pFEge4BP0+9uge7xYTRIFGQ6Wbl3AqMBhm33cwNCyfxwhuH+f3zO5Clwb6kZMyE8fIOSblg/nbkKMd7+ihyu0ixmDHKMr5IlN1t7fijUd47a/pVvzA3wdXBFW0cpNseJsV6Wks2ofZhM8zBYV5+RvIjiKv99AfPT0P87UYQBOanl+CJhfnt8S1EtDiKKHFNdhXZlvNTyhjrxWyRDXxx8jU8f/IQ67tqERCocGRSk1Z0Ucd/JWCSzNhlx3kFNvsTPqKDK6abetawe2DbqDK90W6iWhRPfIDhd18URMptk7g+61Y29axhY/erHPDsJt2YSbltElOdM8kwZY1qry/aQ0KLowoJXu58jg09r44q0xpqRkdjIDbaqNV1jbbwSZpCDfRGewipQWJajKgapjXckixzlmu2yjYs8vlrufdEuwCdHFM+snjhjw23kjIqiB2S908S5KTKzWXg8nc1kGp0MxD38UL7Bqa7qsg0paGI7cS1BA7Fhj8RpDc2wAvtG/DEfcx2T+FkqIN9nqNE1RiyIDHNVUWhNZdnWl/FLJkotGQT1xN0RnqwyRYQIKxG2NV/kLZIF5t79jDFWYEiyhzw1KKjIwkiM93V1KRM46mTL1JtL8VwETNkXw4ossTyOWXMmpSHQZERBYEF04qwmA1Jf33AYTXxodvnk+46vVJuUGS+8v5VFOUk3QVlSaS6OBO3fR6tXR4SqobbYaYsP30oINhqMnDPtTMIhWPkZTgRBIGHb67hhoWTSHVah9rJz3JjNMjMrBips+60mQcFJgTK89N5341zOdk1QCgSR5El3A4zFpMy1M608hycdhNdfX4UWaIkL5W51QXkZbrQdegZCLL9UDNleWnkpLsQRYFoLM5Ta/ai6XDT4mokSaSqKBO3w0Jrl4dwNNlXisOC+Ry7KrquE07EeabhEIf7u1FEkSU5xSzLLcYoXZ5Tl8q0NHa3tfP8sVoi8QSiIKBIEgUuJ59eOJ9bJ1e9I1lGtr2wh3WPv4Eki9z1+Zsonzm2e2lf+wA/+/zvMZgU5t88i+X3LBhVRtd1fH0Bdq85wIkDzfR3eojHEihGBXeGg5zSLCpml1A6vRBRGv99q2kaR7bWcfD1Y3Sc6CIaiWFxmCmoyqVm9QyySzIQxbO/r709Pjb/fRf1+5oIeUOY7WZKphYw78aZmKzGiQwuF5HL8xd2njjMS7GcEdxmNc3CZVmNKIz0Z4yrXQSioyd9l4oMk43bC6bTGw0QVRPIgkiGObmKa1dM3Fs0G3mMia0kCFyTU8WCjJIx261JKybfmkIwEUURJNJMNhxKUuFksiuHb8y6DcObmNxdaSQVj85P/UbVEkOSsifDzeOWU0QFZYzJjUW2Mi9lEeW2So75D7NnYAeHvPuoD9Syc2ALS9JWMj91yQiFplOr5Kqu0hRqGLdPs2RGOUNxJpDws7brBQ549xJI+FAEA06DG9Ogks2ZiYrGQhKkESvD5yKuJcdrGGOCfz7IojxKdelMJkyDi4NdtvK+wtuQBBGHYkMWJBJ6gixzOi7FTlu4i1xzJjUpU0GAVIOLhK5yU/ZydHQskgmzZGJ11mL8iRCSIGKRzBRZ86iylyS/O4NtT3VVUmIrwCQZsclmBARuy70GHR2rZEYWJCySmYGYhxnu8d3OrlQkSaQwO2XEMUWWmDNMhcdokJlZOTK/hCyJLJxePKpeQZabgqyR8VBDdWSJ8vz0EccqizKoHCZhalBkppRmQ2n2OcedneYgO2384HCjQaaiIIOKgtPtZ7iT7yhV1aht7qaxvZ/P3r+U3AzX0PEDde3sr2vjpsXVQ9eam+4kN/3CJWJ/fWQnHQEf91VMRwBcRvNQYrHLkWvKSpmRk00oHh/KjiuJIlaDQprF+o4pFbXXd/LGcztRFJmVDywat1w4EOGNZ3dgtpnIKR07buXQG7X8+l+foLu5l6AvRDyaQNM0RFFEMciYrEbsKTbu+NT13PKx68ZsIzAQ5Nf/+gR7XzuMv89PJBRD0zQkWcJsM/Hio69xx6eu54YPrhz3/hzZdpxHv/IkJ4+3E/SGURMqkixhsZvZ9Mx23vcfd134jZpgXK7oWaJwhu+803wdJqVkpD75qbIYRhkMkAzwrff18HLbUT5TvextHe+I8QgCToMZp2G0/KEsimSax35oC4JAitFKinFs32xZFMm3jv1ysSlGbMrVHQT2ZjBKpiE3oY+WfI4sU+64ZcfKuyAgYJRMZJlySTNmUpOyiO5oZzIw2LOddd0vYZedTHOddsexSFZAwCJZ+XT5l3EpY39mwAiJTx2dzb0beKN3PWbJzP35D1Nqq0QSJAQEAgk/L3Y8y86BLW/yboyNWUrqlvvjvova7gQXH0EQyDEnJ3SBRIgXOzYyEPeyMmP+0HfJJBrJMKUO7awpKJjNI3M02BUbdsV2xrGRz500o5s048jvrkU+/UzbO3CEDT07WJQ6C+tEIsOrBkEUsFoMdPX7OVDXjiJLROMJdhxu4dCJDm5aMvkt9zEQDfN03UGevP4Bcm2OocUDQRA42NfJ/x7cjjcWodKVzhdmLmYgGuZvDYep8/ZR4kihNeBF1TW+vXA1vz+6m85QgP5IiCKHmwZvPx+ZUsPU1Cx2drfy8/3bUNGYnZ7LR6bUkNA0njq+nxO+fnrCQcJqnK/VrKLcNb6bqiAIWAwKFsOFG0GXK30dA3z3A7+gp6UHk81EzQ0zKJlagMFowNPjpX5/M0e21hHwdlM6o2jMNsKBCN987084sPEImqZRNrOYmSunYHNZ6GzsYdsLe2g+2sov/+VxdE3npo+sGtXGydp2fvyp39F4KLkrXj2/nFnXTEVWJOr2NHLg9WP8/PP/h38g+HbejncVV7RxcCZuy40kvfFHW56S6CDb+XlE8fTL7pQbQ7E9lY9WnrautcFEProO4qDcnCCANBiYpw1LUjZcdUAfrMtg2rLh59TBvgQhGVA84Xd4+eA2pGKT7IBAX7SXClv1m/p8BEEYlIJUKJJKeaDgEaySjTf6XqMxWMdU18yh1fMsUy4myYymq/jjXgrMRefVpz/uoyV0gpAa5K689zDNOTtpGAgCOjph9e3x3c+3FLJrYAsnQnVE1QhGyXTuShNcMnRdRxtcvV+dvQRIxiIIgsBUZwVTnOVj7uToQ8+pi/N8mu6qYqozGYQ+8cy7ehCAyoIMHrhuFn/bcIBfPLN5MMFZKv/0vmtYNbfiLfdxwttPutlKrs0xmNMjiT8W5Sf7tvCp6QupcKXxh9o9/OrQDu4snUxcU1mWW8y6kw18ZEoN39u9EW8sQjAeI8NspcjhpjMUoDolg66Qnxyrg2/seI2fLLsVl9HE47X7eLr+ILcVV1M70EOpM5Uvz17OS821/O7ILr61cPVbvq4ric3P7aK3tQ+T1cSnf/wIy+9dkPwdCwLoOrqmE/JHOLDpCFU1oxX5dF3niW89y4FNR9E0nfd89U7u+9ItSfcjQUDXdO763I1848Ef07C/iUe/+hQzVk4htyxrRBtP//AFTta2oes6D375Nt7zr3cOuTDpus6Wv+/m119+nHAg8o7dm6udq8o4EM7qRiKiSDkjjujAP04e4q9N+0gz2fhBzR0AvNR6hH39rRwa6GBldgU7+5rJNbv4jxmrOezp4P/qd9Ae8qGIIl+dfj2l9jSiWoK/NO5lU1cDbSEvZknhP2fcwBR3Nrt6W/ht3VYiiQT5NhcfrVxMntX1tt2HCS4MEZHprlm0hBtZ2/0Ckx3Tscq2QdUnAQ0NTVdJ6AkMonFokgWnlKeSk3FJkEecExBwGJxJ9RB9ZHCwJEgsSF3Chp5Xeb33NYqspZglCyIig6bpYFBxHJNkHlrhVXUVVU9uVxtFw4h8C6qu0h45SXOo8aLfo+nO2bzU8Td8cS+vdj3PdZk3I4vK0Hg1XUPVE0PHJiaClxZV13msYTO35E0ftQs5XhyOrut0R3zYFRMW+eLsML6VhIYTXL4IgoDFpHD78qncvnzqmOcvQi9jGrCN/gHSLEmjwShJXJdfzj9vfok7SydjU4zkWZ3kWh3YFAN2g5FALIYkiOTbXRhEiYSmIYsiCU3jSH83RQ43OVY7oiAyNyOPP9Ud4LbiauwGI1PTsnAYjFS60nm1pe4iXNOVRXBQWleSRfIrcpCG5b04hT3FyqLb5475mXc19fD6szuIR+PMXjWV9/xrco41VFaCnNJMHvjnW/n+h/6XSDDC879ay0e/996hNk4ea6N2VwPxaIKSqQW879/vQZRGLrAuubOGvesP8+rvNxCPTihCXQyuKuNA1XyAjCiYx/iiqmh6GFEwAUm3EFEQuCV/CvlWF4837BpR2mUw86Wp1/Cfe17g/5Y+xL/vfYFgIkaBNYVPTlqKLIg823yAdR3HKbanEkzEeK3jOD+efzdbuhtpDXrIt7oIJWL896H1fHXGdRhFmXUdtfzj5CE+XrX4HbknkHzpBxM+jJJ5zKDQdzuCIDAvZQnH/Ic56jvIT+u/y5L0a8gx5SMIyXwFHeFWjvoOcnve/RRbyobqeuMeXu9dR0yLUWarJM2QjiTIxPUY9f5aNnavxW1IpcBSPOJFJwgC12beTH3gGMf9R3is8RfUpC4izZBBQo/ji/toDTdR6z/CB4o/RfqgHKtNtpFqSEMSZDb1voZTScEm20noCZqC9Wzt20RMi170e5RqTOfazJt4pesfrOt+kYFYL9Ncc3AqLqJqhN5YDw2BWlakX0e+ZezgtyuBeEKl1x8kEB5UDhsu5iMkdwrdNjNum2VEPU3T8AQjBCKxEb7GBlnCYlSwmYxIooAODARC9PvDp9tO5sMaKm8zG7GbT0sVh2NxerwBonGVTJcNu9k44vmWUFV6fEGicZWcFAeKJOKPh7k+ZzIpRgsJTcUXjxDTEqi6hkMxY5ON9EYDJHQNSRBwKGa8sRAvtR1iiiuXCmcmdtlEV8SHruuIgojTYCaUiI1qxx+PEEhEkQQR+2B8kz8eQdU13AYLJmlsSeYJrmze7s+0yOGiLxqiNeAl13baVccoyqiaRkJLxlb54zHMg5K6yUVt4Qx1Ph2G7dgLp49ikmTiqkpC01BEgVAiPhTsrIgS4jDX5XejYELZzCIUo0w4GOXpH77Ag/96B2m5KVgcZqTBlfuzfQ/2bThMYDAL+PWPrBizrCAIzFgxGVmRiYZjHNx0DF3TEQZTbzccaMHXHwBgxf2LRhkGp9qYc+1UXn9mG/Fo4KJc+7udq8o46As8g0HOxmFeicDISXBM7cAXfg2baT5mpXKcFk6TZrKRYrCQZXYgCQJmSSGUiNHo72NTVz1GSabW202hLRmMJiFgV0wcHOigNxLEoZhQRImOkJfeaIBnmvYN+quLzEjNO0fvF5eEHmdr3ytMdS4gy3z1pKu/mJhlC/cXPMwL7c/SEKzlufY/E1WjgI4sypgkC07ZNSrpkyRIJLQEuwe28UbvOjRdQxREEpqKSTKRYkhlpqtmzHwHdtnBQ0Uf5YX2Z2kJNfJky++IaTFEIRnXYJEspBjSRvSpiAZmuefTF+ujMVjHT+u/j0WykNDjWCQrM91zqbBVs677pYt+j67JTLrt7fFs47DvALsGtqHqKqIgYRAN2GUHy9Ovvej9vpP0+YP86pXtrD/UQDyhEorG0XQdoyJhVGTcVgvvWTaT+xaflqiMxBPsqj/JS7tr2d/UTo83+TK0mgxkuexMLsjkoRWzyUt1Ek+oPLFpH4+u2YEsiUPBlZIoIEsSWW47s0pyWT2rkqkFWUiSyNGT3Xzr6deo6+jlI9fN42Or549wWez3h/n+sxs53t7Lzz96O/lpLnb2NfFM827+bdrNmCSFv5/chyeWzG5cYEvl9vyZ/PeRNRRaU3AazCxML+O4r5P9AyfxxEIkdI3ZqYW80naYsBonpEaZn1bKIU8rgUSUhKZSZEvj5rzpPNW0g7im4jRYmJlSQHfExzFvJzEtQbk9g+typiBPGAcTXCApJgu3FVfzq0M7WJlfiixK5NucFNid2BUj2ztbyLTY2dLRxPWFb86NqdyVilGS2dTeiFUxsKenjUXZhRf5Sq5cZq6YzOLba9j2wh42/XU7tbtPsPDW2UxfWk1eRTYZ+akYzIZxDYSWY21EQ8mFFl+fn+0v7R23L11Ppgvv7/YQCUUx25ILDT2tfUQG3YXKZ42/8JRbmoV8nnlFJjg3V9WdDMb2oOkVOExL4QwpU1UL4I1sQJGyz8s4EAeX84ZviYfVOPv62yi0pXBn4XR+dvR1EoMuHgZJpsKZwbr2WqpdWcxKzceqGLEpJhyKiY9WLibL4kAbdP+4WPTHuvHF+onpUSRBRtUTlNom0xA4jKarmCUb6cbT7lSd4RYSepxscxEnQ3VE1DBW2U6WqeCq2FVQRAPTXLPwxb1kGM8/ayiAS0nhvvz30xA8zslQE764Fx0Nk2TBbUgh31xItjlvREiLVbaxKG05OeY8+mO9hNQQmq4iCwoug5sCSzFFlhLkMVSOBEEgy5jLgwUfoD5QS3uklWDCjyiImCULqYZ0CizFOA2uEfVKbRU4FCfH/Ifpj/YS02JYZSsFlmLKbVV0RNrQ0Uk1pI7q0yxZmOmqIabFcJ4lCHosJEFiVeaNTHZO40Sgjv54H3EthiIoWGU72abcUcHc2aZclqWvotRWOeY9cMhOalIWYRRN2MZJwPZO4rCYuGF2FVV5GYSiMZ58fR9dngCrZ1ZSkZOO02qiOm/k92rDoQa+99cNCMCs0lwWT7Ikd5zCMbo8fvY2tnPvomkj6hhkiZklucwsSf42NU3HF47S2NXHnzfv50BTB/95/yoqczNG1Ht22yFurakmP801zhUkV9VWZlWxs/e0e5lBlFmaWUmBNYVfHt9IIk/FZTBTbEsj2+Ii1+LCLCl0hn0sziij2J5OXE1Q4cwEHQ552mgK9GIUFeZmFZNjdvGL4+uZlVJIra+L78++B1EQ6Ap7eaO7DpfBjMtgYVdfM9dkVyNfgDLWBBNA8jH7wclzeaHxKDu6WjFKEiZJptDu4pHq2aw9WU9b0EeB3cWNRVWEE3GqUzLJMFuZlZGL02BieW4JDoOJaanZZFisSIKITTEgCgI2xYBNMfLZGYt4teU4mq5T4UpjRV4pOjqzM3LJsiTVmdLMVq7JLzv7gK9CZEXmo99/L0WT89i99iAnDrTw1/95iVd/v5GK2SVMX17N1MWTqJpbOubE3NcXQE0k5zs//czvzqtPXdOJBE8bB2F/hEQ82YY7c/xgb5vbelYp1QkujKvKODg7CXQ9ApyWedR0nb+3HOSYt4umQD/PNR9k5hir+qfmgoookWG2c3ignccbdtETCeAymofa6osE0dHpiwY55u0ixWghw2Rjdd4kHj+xE5fBgkmSqUkrpMKZMaqfN0NX5CT90W46oy0UWCo47ttHiW0ymq4RUUN0R9qRhKSMZEekCV98gEJrJX3RTo759pJnKeFkqB5d1yiwvvUgskuNSTKx7C2sXsuiTKW9mkr7+WVylQSJTFM2maazSweOhyAIWGQr01yzmMb5JxdLN2aSPo7xU2QtpchaOuY5h+Lkmswb3tRYIWks55oLyDUXnFf5UlsFpbbxv1epxnRuzL7jTY/nYmMxGqgpz6emPLnDtu5APd3eIDfMqmJB1egVxbiq8vjGvQwEwvzLncu5dkY5KTYLup50B+oY8DMQCFGQ7h6xumaQJeZXFPCBVXOHjqmaRmNXP//vT2tp6OzjjSNNI4yDwnQXrX1ennp9H1+6fdk4q3Vjuz5YZANmScEgSqiDUrcPly5k/0Aru/qaMIgSqUYbGvrQE/KYr5Mt3Q3UpBURUmOYZAWLbMAkKSiDvts6OiIMZcbVgcSgy4dVNnJD7pQxpSePHW1n27Z6QsGxXeAKCtJYvLQSl8sy5vkJ3hy6ruPzhVm//iheT4gZMwqors5FuQxWXA8fbmX71noikTgOp4X77p+Pw2DkgcoZo8rm2py8f9LsEcfMssLCwVX/Qkdy4eP+iuQO3/K8saW/AYocbj4yZXTG+eG7Efl2J++tmnnB13S5k4id2z/fle7g7i/czLybZnFsRz1Ht9dzePMxDmw6yv5NRymeUsDSO2u4/dOrMVlGxirpw8RbltxVg2I4d54Tm8uKYjz9fRzxRDvLBqQgnUsse4IL4dI/Ed4iquYnFDuAqvmJqR2AgCe8dkQSNF1PEIjtIKF6kMTTGWEFoNSRRrbFwcKMYlKMFuyKkdlp+aiahtto4RNVizHLBh4um0ea0cqSzFIKbW40XWd+RtFg0Coc8rRjlhUWZ5ai6TobO+vIsTiZnpLLAyWzqfV2k9A0DFIy98DFxG1MxxPvpcBSxjHfHmJqhN5oOzrgTwwQTHgJqQHq+tayNP1mCixl7Bt4g45IM0bJTFgNkGZ8c5PbCSZ4NxOOxuny+AFYPasSlzW5WCAISbeisuxUYPQOzlhIokhJZgpzyvI41tZDfyA84vy0ohxMisKLu2u5c8FUSrPGbjeYiPJi60HqfN282n6YSueg8ocwvEyM507uQxREfPEwiihhV0xous7zrftZlFGGRTLQFhqgKWBHHxZ3MfwFnGlyUGRL45e1G3EZLEx151LtyqbW10m9v5ty+9iLIM1Nvbzwj730948tPThnbgkzZhVOGAcXGVXV2L2rkd//bhORcJyG+i5SP2YnLy/l3JXfZhpP9PD35/bg90fIznZx9z01KMr55aqZYHzOFirh6Tk/aWpJEimqziO/Mod5N8yko7GbI9uO8/LvNtCwv4nOxi6sTgu3fGzkwpzNZUWSRNS4yh2fWk1mYfo4PZxGFEUs9tOyx2arEUlOfg98Zxlv2B9B0959cSFvF1eBcRDEG16HN7yBmNpKmCP4I1tGrKqdCiRKsd6OaZhLkSAITHXnjGpzOKfiA6a4k5PnNEkmzTRS61vVNU4GPdgVIwsziumLBNnYWUdcS26F2RUTc9LOb6X1zSAiDmWaBTgRPEJcj1Num0atfy86OgbRxOyUZdT691Jsq8IqO3Ab0pnuWoiIiFkaO2/CBJcWTdep6+ilMufcD9W3iqpp7Kg/SULVWDLp7EHF3lCEvY1t5LgdVLwDY7tcMRsULAYDOkEOtXSy+Bz37VzoQKfHj1GWyEkZqTIkSyL3L5nBN59exxOb9vLv947WAwcBk6iwJLOc2amFmCUFi2xkikvHKMoooszHKpbjMpi5IXcqoiAgCSKpRhuyIHJr/nTCiTh2xYRZVvjspFUYxGTmX0VMKpWYJQVZEPlE5QpsiokHS+YTiEeQRQmHYqLIlkaVIwsNHYtkSLponkFBUSorV02mq9OL3x/G74vQ2+vH6w2PcU0TXCzUhMaJE934Bu9zc1MvPt/EPb/aUIxKUu9A1/H3jx+ge+JgywW1K0ki7kwnrgwHJVMLmLKokq/d/d/0d3h46XfrRxkHBZNyMVqMxCJxek72M2VR1QVfS1peKmabkaA3RP2+ZqYtG3tXv+NE93nthExwflzxxoEipZPt/DxptvfROvA1ZNGF03wtwrCEZ4KgoEhZGOU8ROHir0SJCCxIL+a7B9fwkc1PIQCLMksod1yaSVOqIZNtfa/ij3uGVBskQSLfXIpbSeOl9ie5Mec9nAgcYV3nMziVFKa7F5EpvbOB0hOcG1XV+N1ru/jOe9+8K9D5IgoCs4rHTwA3HLvJyPyKQiTx3b2RK0si180s5zdrdvLNv7zG529ZwopppSjSha14xhIqvb4AL+8+zrr99UwpzGL1rJGxUdF4ghVTS/nz5v2sP9jAA0tmUJZ9ZlImHUkUyTKP75ubbUmey7eOXi1ONdpgmGdAoW38XY8ciwsAt8GC2zDyuWo9hxRqeXk2RUXpaFpSK13VNNauOcTPf7r2rPUmeGsIooDTcfqzstlNGC+CS9Gxo+38+altGE0Kn/38akymc7uPTPD24cpwgCCgqhpNh0+OWUZNqKz/05tLlikIAiarkYrZJRRU5tDf6WGg2ztCZQiSAc12txV/f4Bnf/oSi26fi+ECvxslUwuwp9jpbRvgtac2c/unViPJo987e9cfIjyOm+IEF84VbxwIgoQk2BEFG2alCkVKx2FegSicmY1TGPzn7ZnM5Fqc/LDmzlPKhEk5tXfAA67KnvSDrLBPR0DkwcLPISLySPGXOXW1AsLQeYAS22RERK7Pvv/0eCcCBs8LXdfZ0dDKb1/biSQIFGek8JkbF7GnsZ2/7TiEJxRhTkkeDy2fxc9e3oovFKHXH8JtNZOT4qCmLI8X99QSjsXp9gVZWFnA/Quns/5wA6qmc+ucan7z2k7Ks1LJSXHy2IbdvF7bxBce+wdTC7N5//LZ7G1s59kdh+j1BanOy+STqxfgC0X4/t83kZvioLa9h1vnVrNicinSGf7eff4gr+w/TmlmKjVl+Tyz7RCCALfOqebp7Yd47WA9q2dUcNf8pHb5+sMNvLjnGP5IjLwUB1+9cyWBSIw/vL6X+o5eHlg8g7mleclkN2/s441jTYiCwLXTylk5tZRX9h5ne/1JwrE4FpOBr96xEqfFeNVIWwqCwAdX1dDW5+OlPbV89fGXKVqTwh3zJ7NyahnpTusIOcRTBCIxfvriZn7+0tbBI0nf3LxUJx9bPZ+7FkzFbh6cYA9WjSc0FEnkg6vm8q9/fJm/bDnAl+9cceaI3s7LvWjIsogsD3f91DGbr3xBhMsdRZFYtKSC7dsb6O7ycuNNM8jLf2suRbquc+xYO/v2t5CR7kDTtHNXmuBtpaqmDFmRiQQjbHxmO8vvW0jxlPzBZJlJw+Dxbz5Lw76ms7bz8mMbKJ9VTGFVLoIojPLIaDrcStORVgDyK3JGPX6ySzJZfu8Cnv7hCxzf3ch/ffAXfOy/HsKZbh9RTtd1fL1+tj6/h3k3zSQt5/R3srA6l7IZRZysbad+bxNPfOdZHviX2xHF0xKz+zYcYes/dhM7JUE9wVvmijcOTiEIAnbTIhAkBEFBeAcT75z6wchnTcL2dvU9qDU8+LeENPjfkR/t8N/sqTIC5z9eTdWIRWLIBhlZOfvXRk2oREMxzDbTiFWEU+cSsQSyQUaSJXRdJxqKEYvGMZoNGIdNEBKxBIm4itEyvlTaKeKxBLIyMkGLmlCJxxIYjMpFUzFQNZ3v/HU9j378LlIGte57fEH2NbXzwKIZTC/K5t+eeoX9TR2IgsCCikI6PD5sRiNxTaMvEKLT4+f/u/86bCYDn/7t31lcWYSm6YPZtZOa+boOReluvnrncho6+/jB+28GwBeOsLm2iZtnTWJSXgb//tSrHGntJjfFga7rzCnN4xPXLwDG1p92W81kOG3Ud/YxrTCbPU1tfGzVfGRJ5O75UzDKEuqwl/vR1m6WVhezsKIQqyn52djNRu6ZP5VX9x8fctk71tbDgeYOfvTwrcRVle/8bT2FGW48oTA1ZfncPX8q/98z6zjR3ceMopwrZAp7fhhkiW+853qumVbGHzfu4Xh7L//93Ov8du1O7lgwhTvnTyHTZR+hvS4KAukOG+nOpDtfLKEyEAgzEAiz8dAJclIcLJ9SilGRhyLyTt3r5VNKKc1KYfPRZuo7+3Cah2ernvC5nWB8BEEgO9vF93/wwIhjbwVV1Whq7MHvC5NxxqRvgktDWm4KK+9fwCu/30hXUw//duv3mH/zbLKK0wl6Q+x+9QDNR9uYfd109qw9MG47a/7wOj/86K/JKclk0oIyckqzsNjNRENRmg6fZOcr+wl4QlidFu78zOox8w88+JXbOVnbzvaX9rHhL9vYve4QUxZXkVmQCgh4un20HGul6XArskFmyuIq0nJGtnHPF27m+O4GWo6288dv/JV9rx1i1jVTkY0KJw60sG/9IbKK0gmkO/D2nl8cxQRn56oxDgAc5qWXeghXLQPdXtb8cRMzlk+mau7ZJd1a6zr4+y9e5QP/3/1YnSPdDfo7PRzbUU9VTRnpeUmXhTf+toMNf9rC4jtqWP3I6ZXQltp2Gg82s/Su+SjGs29Fbv3HbpbcWTPiWG9bP7W7GpiyqIqULNcFXO34+MIRzCYFp8WUXIXRIRSLDyW7AoGcFCcdAz4EAZwWI/6IEbvJSH8ghKbppNotyGIyTiTdYcMTioAgoKkauq4TiSeGDAUQhiaFgiAQiMTp8gT409YDuK1mUu2WIcvPYjRQkOY+68teFEXyU1209/t4aW8tJRkpOK2mcevct3A6aw7U8ZOXtlCZm879C6ePWa7XHyTLbUcUkpr96XYrvb4gTouJNIcFURSwm43E4lefT+ipe7dyWhlLqos50trNy3tq2VbbzGPrdnGwqYP/uO9ast2nJ04Wo8J9i6ePVCtSNbYdb+GH/3idb/7lNQaCYe4d435Lksj7V87lW0+v48Xdx7h34TSuko2YCd4BLvauXXe3j85O70Qw6GWEIAh88FsP4usPcmRbHUFPkJd++xqQjEewuSwsvGU2n/yfh/mX679JZ1PPmO3klGTQ2djFQI+XDX/ehppQh1yHFKOM2WaiaEo+d332RubfNHvMNgwmA//y2Cd48tt/Y9MzO/D2+dj1yj4ScRVd15FlCYPJgDvTSVpuCmbbSJdEQRAompzHp3/8AR796pO013dxdHsDhzYfR5JFLA4zJdML+eA3HuCp7/yNrS/subg3813KVWUcxBIdaHoIo1yAIEz4PA4nHAjT3tBNIhYnGo6TkuUkryKHgDdIZ2M3oigS8ocpqs7H6rLQVtfBQLcXSZbIKEhDEATUuEprbQfxSBx7io3CSXloqsaJgy1EghEUo0JOWVIZJRFLUL+3EUEUSMl2k1eeTSQYxdfnJ7MwbchoEASBFfctJOQLjRivt9dHNBSleEoB4qBSQSKu0nq8ncBAEEmRyC3LwmAy0N7QyV9+8A9Ss10YzAbKZhQRCUbxDwTILs4YUj7QdR1Pt4/Wug4AnKl28iqzEceQWhwPp8VEjtvBG8eacJhNWE0GMp02LAaFo209+MJRerwBrptaTlPPwJheHn3+IIdOdiEKAtF4gsI0N4FIjMPdXexraqe938+kQQlLURAwGxV2NbSS4UyuNE/Oz8BpNpHtTubNqMpJxx+ODt7Pc19DQZqLfU0drDtUz0NLZ2E1GtB0naOt3bT2edF0neaeAbLdDgaCYcqzU8lLdfLb13Zy/8LpROJxatt7aB/wIUsiReluSjJS2Hj4BHsa21E1jXA8TlG6m4FA+G1z5bscUWSJ6UXZTCnIpLath5+/tJVttc2s2V/He5aeXQpRkkQWVhWSUDU+8+hzvHaggdUzR+dkEYCFlYWUZqWy/XgLc8vzR+RjmWCCdwpd12k92U9Xp/dSD2WCM7C5LPzz7z7Ozpf3c3R7HX3tAwC4s5xMXlBBzeoZKCaFZffMp/lIG/lVo8VZPvZf7+Pg60c5cbCFntZ+gt4QalxFNsq4M5wUTs5j9qqpQwt942EwGXjoP+/hmvcsYc+6g7QcbcPXH0BTNSxOCxn5qRRPKWDqkkocKWPvPk1bOomvPf0Ftjy3i/q9TQR9ISwOM6XTi5h/00zS81KZd9MsRFmkbOZbE4aY4CozDgZCfyeWaCXb9UVkwXWph3NZ0ds2wDM/ep6qeeWocZXXnmzlw995D12NPbzwq7WUzSxGU3UyC9KIRWP8/RevkFueQyKW4NiOeuZeP4OgN0RbfQdBb5C6vY186FsPYnFYOFnbhn8gSFdzD9OWTiK7OBNPl5eTte3EInFa/7yVD37zAdSESu3OBpqPtHLzR1ZhqRo/+DXoDbPrlf2EgxHe/7V7kMxGQr4Qf/2fF8kpy8Kd4cSVngy6aqvvpK+9n+ajrVjsZspmFBENxziytY72+g5u+9QNZBdnoGs6z/7kRWwuGyarkeySDHLLs7iQcAtJFPnU9QvZcryZLk+ANIeVkswUFlYWsr+pg7qOXq6dVk5JVgozi3PJdjkwGwyYZJl0p5V4QsVltdDW7yWWULl7/hQynDY0XccTDHGiq5955fkUZ6QgCIMKNYumc7yjF4DCdDfLq0vZ09hGXWfy2LTCbEyKzPyKAizGc/ttmw0KU/IzEQQoyUxBlkRUTaOl1zO4IwJdHj/pDhvd3gAtvR4APnRNcmcmnlDp9QVJt1tBB08wTElmCjfOruJ4ey+CADfMqCQ/zUUoGhtyR5pdkku22/GuMBUkUaQ6P5PVMys52trN3hNtPLBkxnnVLcpMarQHIzE8w+RMh/aSBAGLSeGehdP4779vYvPRphGuYBcTXdeJxRK0t3vo6vTi8QQJh+NomoYsS1jMBlwpVvJy3WRkOpEukyREmqbj9YZoPNFDT6+PUDBGInHu5JNFRWnMnFWELI/vcqlpGu3tHtpa++nvDxKNxtF1sFgMpKXZKSxKIyXFet6LDomEyuubaunt9ZOZ6WTmrCLs9qSrWCAQobGxh64OL6FwFFXVMJkMuJwWsnJc5OS4MRikcXcDmhp7OHDgJNFofMzzOTlups8owGYzjXn+TFRVo6fHz8BAkIH+AP39Qfbtbaa7O+nKMTAQ4m9/3TVu3oS8/BRmzSrCeI6d4KG8GbqO3z94Dzq9hEJRdF3HaFRISbVRWJBGVrZz3Ov3+yO8+soBREFg5qwiCgrT6On2cfRoO15vCKvVSGVVNnl5KckFMFWjvW2A2toOAoEIVquJioos8vJTLpvv9vkiCAImi5Eld9aM2lUfzoNfGT/PjNVpYf7Ns5l/89i7Ahc0HlEgryKbvIo3L5uekuni5o+MpdKWZPUjy1n9yPI33f4Ep7mqjINIvG4wrO/dMP24cERRZPHtNbjSHXzrvT+mrb4TSRKxOi3MWjWN7OLkavX2l/agqjq3fvw6+jsG+PsvXqWjsQuDUWHWNVOZsriK//rgL2g51k75rGI0VcNsMxH0hug52UdWcQZGq5Gl9yzAYjPxvUd+TkdjN2Uzipi8qJLAQPCcH1FOaSZTFldycNPRoWOyQaagOg9RELC7rdjcVuxuGwtvncMfv/E0qx9eMRRb4Ep3MGVR5YgdiYAnyN7XDvH9tf8xKlnLhVCU4aYoY2R24cJ0N4XpI48tHZS1HJ46q6GrD4tRYfWMytMBp0C2286d86aO2d/100cmEctJcYySuTTIEtdOKz/va5icn8nk/NNJ1CRR5ObZk0aVW1hZyMLKkcm/7GYTt9dMHlV2VnHuKLWj6UWnV6OWVY+fiOhq5dSc5czA8LPR0NkHgCKLmI0KBJMGwvCfjCyKzCzJpSw7jT31rajDExFcJPr7A+ze1cihg620tvbT0+3D6w0RDscH3QHE5ETVZSE3z83MWUUsX1FNauqlzXQdj6vs2N7AhvVHOHGih74+P+FQjETi3AbUdaunMnVawbjGQVtbP5s21nJgXzPt7R4GBoJEowkgGVCdkmqjoCCV+fPLWLp8Emazcu54qbjKc8/u4uDBVqZNLyC/IBWr1ci+vU2sf+0IdXVddHf5CIdjg8aBgsNhJjvHxSc+dS1FRWcqVp3mWG0Hv//dJjye0JjnFywsp6g4/byNg64uL7/42VoGBoJ4BkJ4PCHCw4JAe3v9/ObRjePWX7Z8EtXVuec0Dk4ZF/v3tfDKywdoaOimpzt5DzRNx2iUcbut5OenUjO/lOuunzpmQLvPF+LXv1yPIAi8/5ElyIrEk49vZefOBnzeMBaLkfKKTD7wweWUlWdyvLaDP/zfGxw72kEwFMVqMVBSlslDDy1m6rT8q0ZIYYIJzsVVZRyIogVdH3uFZAKIhKJDfu3hQASjxUAimsBoMQ6lKgew2MyEvMmXiapqRMMxFKNCPBZPpjHXkwlHjGaF2l0NHN9zgmvfu5TmI62oCQ10iIai6JqGDoSDybIjeBPuqSarkWseWEx3Sy+7Xt2HYlKYe/0MBEEgHksM+eaPh2yQiQ8GOQMjfPnfKbJddu5fOB3zeWSKnODyp6l7gF31J6nISacsO3Vo5yaWUDna2sWLu4/hC0VYUl2EJAok1PG/o5qmsbuhjd+s2YFRkajKTSfVbqGtb3SAnSAIpDmsrJ5ZyQ///jrhWJxMl52LGZC8e3cTv/n1Bnp7A2P+tmIxlVgsjM8XpqWljyOH2+jp8XP/AwsuWfIyTdNY8+pBnnh8C50dHnQdXC4LZeVZyJJIZ6eX3l7/UHlRFEjPcJCfn0p6up2aeaXI8tiG3NEjbTz5xFb27W0mOIZkot8fwe+P0NzUy5HDbRw+3MqHP7ISh/NM5bzxiURiBANR3thUy5NPbqW+rnOUL38wGE32L3DOZ15erpvFSyrp7vYSCEQJBCJ4PaE3nU8iFIpxvPb0mMzmpFhEKJS8H5Ik4nCYx32m2u3jxzcNx2IxsmNHA4/9ZhMnT/aNugehUIxQKEbb4Cp/b48/Ofkfx6hTVY1jxzqIROKsf+3woEEHXm+IXTsbicVV/v3fb+c3j25k396mocRhXm+YvbubiEYTfP+/HpiQaJ3gXcNVZRzYjQvpCfweVfMhCY4JK/8MRFHgye/8jYAnSOn0QnJKs2g50opwRtbTijml7Ft/iO9/8BdIssiUhZXklGZitpnZ+OctrPm/jaTlpVBYnUdnUw8tx9pZ84dN+Pr8FE/JB8CRaufxb/4Vb6+P0ulFZBVn0HiwhWf+50Vaj7dz8ng7t378OvIqcnj8W3/l2PY6ZEVGUzWW3j2ffesP89pTm+lp6SUUiHD7p1Zjtpr45T//AUmWUAwys1ZNA5LbldOXVvOdh35K0eR8HvzXO6jb08hzP3uZtrpOOhq7ue0T11NYncdtH7+en3zyURSTQsXsUm74wIpzBjtfTCxGw7iZbSe49PhCETYdbmR/UzveUISm7gF0XecXr2zjhd3HyHBaWTypmFmlyR2Sbm+Ax17bRVzVkgnRjApWk4FAOEa/P4QnGOaWmmqWVpeMUCsKx+L8bfthdjUkZQBVVaM/EMIbjNAXCDG9KIcHl85EPku+BEUSmVaUTVVeOtuPj61l/laYOjWfQDCKIEBefiqzZxdTWZVNZpYTo1HG5w1z6FArz/99D15vGK83zPrXjlBensnKa6ZckiDpxhM9PPrr9Xg9YUwmhRXXVHPLrbNwuSwIgkAslmD71np+85uNRCNxRFFg3rxS7ntgPmazAZNJGdN9pLmplyce38KO7Q0kEhqSLLJiRTXz5peSkZHcxWtrHWDjxqPs29tMX1+AtWsOEQhE+cpXb8NgOD9luEg4zvbt9ezd00x9XSdZWU7m1pRSXJKO1WIkFIrR2NTDvr3NlJdnnnUiDlBRmU1efiqJhJpUnIur7Np5gp/8z6tv6v4WFKTygx+9Z4QNum7dYf705Dai0Th5eSn867/disk0tnuj2WLAaj33rm1vr4+f/3QNPd1+srKcrL5xOlWTcrBZjXi9YXbvbuTlF/cTCsUYGAiyds0hqifnsmDh2Lunqqqxf28z7a0D3HnXXObOLWHv3mb+9uwu/P4Ihw+28uMfvUJ9XSe33DqLpcuqaGrq5S9/2k5Xl5djR9o4criNWbOL3sxtm2CCK44LNg42bdrE97//fXbv3k1HRwfPPvsst99++9B5Xdf5+te/zq9+9SsGBgaYN28eP/vZz5g8+bQbQjQa5Utf+hJPPvkk4XCYa665hp///Ofk5b21JFwO8zXE1U5O9n+VNNsDmJSKUYHJspiGJF6aVa1LjSvDyZ2fvRG724asJCfYRVPyyavIRhm2ImI0G7j3n25LZhsUwGBUkBSJe75wM6qqoWs6kixhtBgoqMrl35/8XFKyVAfFKCNKIp/44cPoejLB0Sn50/yqXD7+3w8NqR0YzQYkSeLBr9yBpia3/BWDjGIyUHPDTGaunIKu6YiyiNFiRBDg0z/+AACiJA4lUxEEgY9+/yES8cRQmvWSaQV84kcPJ+uLIgazAVESWfW+pSy9ez4AkiIhnyMB0JkrcxMG59WNPxxl05ETrDtQj67rqIMrlgebOjjc3InDYsJpNQ8ZB2VZqdxaU83W2haauwdo6/Oi6+C0mpiUl87Nc6pZUFWIwzwyt4Oq6bT0DHByMJ4DwKBIFKS5uGfxNO6cPxW3bXDF+VS1M756giBQkOZi8aRi9p5oH7vQWyAjw8HHPn4N6el2qifnYVAkJFlEFEWEwVXr6TMKWbKkki//81N4PCH6ev0cPdLOvPll5+2qcrHQdZ2//GUHXk8YQYApU/N5+JGlpKbaRtz79FtnEk+o/PqX60kkNNrbB4hGE2SNo2gWCsV47bXD7NxxgkRCw2o18pWv3sqMmYUoijQUW1A1KYclSyt57m+7eeLxLYRCMbZtreeZv2zn/gcXnNezo7vbx/N/30MkEueee+Zx5z1zsdlMSNKpe56UlY7GEuiajs1+9ntsMMgYhj3jVFXDnfLm3b4MBpm8vJF5EdwuC6e85gwGiZxcN5a34LYJ0NvjR9dh8pRcvvrvt+N2W5FlEUEQ0DSdmbMKmTIlj2994+8kEip9fQE2bjjK/AVl497nUChGSWkG731oEQaDQtWkHOrqOtmxvSGpFLatnjk1JXzkYysxGmWmTM2nvr6Tta8eJpFQ2b+/+aoxDt7qe00ftA7fitjE8DFMvFcvPy7YOAgGg0yfPp1HHnmEu+66a9T5733ve/z3f/83jz32GBUVFXzjG9/g2muvpba2Frs9GYX+uc99jn/84x889dRTpKam8sUvfpGbb76Z3bt3I11gZtHhtHu+TV/wL2haEH9kM2O9KIvSfozbcuOb7uNKRRAF5EHpseHyopIsDU2oh3NmzgEA4xgPfEESRsmVAphto9uUFQlZGV32lJrQcAwmZcxMimP1BUmXo+GpXWVl7HwMikEeN1huLHa0tPKNVzfw0NyZ3DG1GlmaeIhdzeSkOPj2+27gbN4aw3cA3DYzH1o1jw+uqgF9WNDw4L9OlT318jPIEp+8YSEfX71gzLYFIVlWGFZnZnEOf/qn94zqG5IB6+9dPosHl85EEEaffysIAtx404zBHQBh1E6AIAgYDAJFxenceddcfvubjeg6dHZ48AyE3nHjQNN0tm+rB8BqMzFnbvEowwCSE9xV107hyce3EghEaG/z0N3ppbBwtO++rus0N/WwfVsD8UF3xA99eAVz5pYMTVZPIYoSkiRy3wMLONHYw4bXjhCLJXjh+X1ce/1U0tLOnQMgEokTjca56+4a7ntw/tg7A4qEwXhVbfqPQteTLkj//p93kJZmH3EPJElAkkSWLqti7ZpDbNlch6pqdLR76O8PjhvzYrUamTU7GQwtCAIGo8ys2UXs29tMJBJHliVWrqzGZEqeF0UoLctk8+vH8ftV2loH3qnLf9vxJ3p4oe0bDERP4lCyeG/JLy6ovqarQ4lT3+zEfv/A3/HEOqhJuw+L7D53hQneUS74CXPDDTdwww03jHlO13V+9KMf8dWvfpU777wTgN///vdkZmbyxBNP8NGPfhSv18tvfvMb/vCHP7BqVTLq/I9//CP5+fmsXbuW66+//k1fjM1YgyQ6z1rGpJxdo/9qJa88m498572XehhXHLoOqqad07d3gqsDQRCQLuBlJwjC0OT5QspfSPb0s43plCEhvg1G6+lrO8fYJJFpMwqGjoXCMWKxdz6fRXe3j2Ag6ftuMirkDyrQnEnSqJHJL0jl6JE2fL4w/kBkzDZPJfhqqO8CkmpG8xaUoihjL2Kdumf33jePbVvqCIVieL0hNm44yl13j68YM5yionSWLKvCOc5CyKl+rnZuumXmKMNgOIIgMG9+KVs21wEQjsTo7w+MaxwYjDK5eafzwJxKCHfKjUyWRcorskb053JahuIYvN6xg7qvRBxKBg8U/Zij3nXs7P3TBdXVdZ2dvU+RbZ5EgXXWmx6Djo6OdtmnbdR1nXgsQTQSxzK4i/dO9u3tDybjJAVwplhRzpGE9mJxUXtpbGyks7OT6667buiY0Whk2bJlbNmyhY9+9KPs3r2beDw+okxOTg5Tpkxhy5YtYxoH0WiUaPR0AJjPN3YGPJdlNS5WX8QrmmCCCSaY4GwIAiMmsom4SkI9t2zoxSY0LEhYlITRIgjDEAQB8+B5VVVR1bGVjEKhGI2NPUMBsdNnFJ6Xy0xZWRZZ2U5ONPQQjSY4drQdTdMRxXNP6isnZZOT4zpnuaudRYsqzlkmI/P0YqCa0MaVbIXk5P/MQHmz2XhaUUwSST8jw7PBICddZoFY9PJK4KjrOjEtSEj1YhStBBN96Gg4lRwMg67TwUQfIdUDgFlyYJFTkISzT/tULU5Q7Sei+tF1DVk04VAykQUDMS1EX7SZrkgdsmjEIFkRgExTBYIgouoJAvFeopofAQmbnIpJsgMCOjq+eCdRNYAsmohp4StCV1LX4dDORja+eID3feZa0rLOvgB9MYnHEjz63RdorO2k9UQ333/iY1RMzX9H+r6oxkFnZycAmZmZI45nZmbS3Nw8VMZgMOB2u0eVOVX/TL797W/z9a9//WIOdYIJJphggvNA13Wi0QT9/QEC/giRSJxYLEEioaFp2pD2/VD5SzROi+X0RE9VNULB2LhlNU3D50sq9hiNygi//OGEwzHa20+7k+Tnp5xXcLEgJA2EEw09qKpGZ6eXcDh2XsG46ekOHI7zVzi6GlEMEvkF5xZuMA773DRdRz2LGpgkiljMI++/JIuc2vVTFGlUILUgnvaqH8+AvFToaLSHDrOr/y+U2RfRFaknroWpSb2fDFMZvngXu/r+QlQLoKNhllxUOa8h21SJcJakiWHVyzHva/RGm9B1jbgeotKxkirHSnzxbg55XqYncoKI6qMrfByA1bn/gqDrdEfqOOR5mZgWAnTchnymuW/GKqXQEz3B1p7fIyJhll0EEr24lNGJ1y43BMCVaqdyWt5ZFxzeDgxGhS997z56Ojx8ePV/vaN9vy37E2duA+q6fs5t0LOV+cpXvsIXvvCFob99Ph/5+WNbT6oWIpZoRdU9Y7qCmJQyFGlCLWaCCSaY4Gzous5Af5ADB1qor+/iZEs/fb1+fP4w4dCggaCqqAntvBKMvd2kpdlIS7fT2eElHIpRd7yTBQvKhnKfnELXdbq6fLS3JSf9qak2UtzWMdtMJDQCgdM7Ek6X5bzdCobHGMTj6mBSrbMbB5IkYjYbzpqE7d2A3WZGUcZP7vZmEIRk3Nt4nO3c5YqGRiDei1lysSzjI6h6HJPkQBBEdvc/gyiILMv8GKCzt/856n2v4zbkYpYc47ZplGyU2hcx2bUaRTBxyPMSu/ueYZJzJemmYhZlPEy8M0SlYwWl9oVDn1FUDXLI8zIphnyqndcRUgdY3/lzUo1FlNsXsav3z1gkF4syHiGc8LKl9/eoV4D0vCAKlEzKpmTSm0/edjHG8E5zUY2DrKwsILk7kJ19+kZ2d3cP7SZkZWURi8UYGBgYsXvQ3d3NwoULx2zXaDRiNJ57xSWudtMT+APByE4Smo8z17Ak0UGO60sTxsEEE0wwwTk4eOAkf31mJ8eOttPbm1SPEUUBm82E02Uh3WLHYJBRZIlYPMGhg62XdLyKQWb1DdN57LebCIdjbNtWz/QZBUybXjA0odd1nZ4eP089sZVQKIYkiVRUZZNfOPY7Qdf1obwowKBq0Pm9qIfnS9A0bSig+WyIooB0CSYClxvnK/16oZwtF+GVGsdhkhzkW2dglk+7u2i6Sr1/M6nGArb0/B4AT6wNo2glovrOahwIiIQSAzT4txDVAgTj/UmXpbMs4Oq6TlyL0OjfTsg8QH8sKa3si3fQHamj2FZDa2g/q7I/h1VOwSw5yTRVEIj3XsQ7ceFEwjF2bazl2P4W4rEErjQ7C1dNJr8kHVESiYRi/O4HLxGLJiiqyGLZzdNxDVP70jSdbesOc2RvM+FAFE3TkRWJRddNYfLsIp77v81UzSigpb6L5roubE4zsxZVUDU9H0mW8A0E2bHxGM3Hu4iEY6RlOpi9tJKy6tyzjPqd4aIaB8XFxWRlZbFmzRpmzpwJQCwWY+PGjXz3u98FYPbs2SiKwpo1a7j33nsB6Ojo4NChQ3zve997S/17w+voD/4Vp/kazMpkWgf+k0zHJ9H1EP7IdqzGmRjl4rd2kVcxXWEff2vZy2FvB+hQ5sjgoxVLMEoTiV8mmODdRN3xTn720zXU1yUDce0OE4sXVzJrVhHpGQ6MJhlZTqrziIJAa2s//3bwL5d41HDTTTM4dPAku3Y20niih5/9ZA3TZxRQWJiGJIt0dXrZt7eZ48eTLqxFxelce93UcYN/RVEYoQwUj6vnLU4QHRaULYriuK5LE4zmSp2oXwokQcF4hjy7hkZMDVFgmUWqqWjouFlyYj3H4mhjYDv1/s0UWGeRY6imN9pMU3AXOvpZYwRUEiAIFFrn4DAkF4NL7QtxyJmIyMS1MEYpuUMnChKKYBxSPLoUqKrGoZ2NvPTn7SxYNRlFkenp8KBp2pC+hKxILLh2Mod3NbFrUy01y6tGGAf7ttTz9KMbuf3hJSRiKq88vZPc4jQy81KQJJHt64+y6cX9zFxUTnFVNieOtPOPP27Bal9JUUUWqqpxsr6bzFw3BpNC3aFWnv3d6zz8hdWkZ7suzY0Z5IKfVoFAgPr6+qG/Gxsb2bdvHykpKRQUFPC5z32Ob33rW5SXl1NeXs63vvUtLBYLDz74IABOp5MPfvCDfPGLXyQ1NZWUlBS+9KUvMXXq1CH1ojdLKHYIszKZDPtHUKQ02j3fxWFehkkuxqRU4o9sIaH1o0jjp5t/N/Nk4w4MksInKpej6hptIQ+KOPFCm2CCdxt/+tM2TjR0A0lJyU9/5nrm1pRgsRhGrZ7ruk44PL5//zuJO8XKpz59HY/+aj2bN9fR2NhDW9tAMrBUSE7uI5E4giBQXp7FRz++kurq3HEno4oijTAc+vuCJBLaebn9dHV4h/7fYJCwnyMnwQRvM1ervTGGWpqEjF1JxyCZKbaen0oWJHccOsLHsMlplNjmYZIc+OM9ozsUBM70zFAEAzY5FbuSPmafFtmNL9YFlmTQc1QLonHp3BF1Taen00s0HGfWonLSslzEonFMg1m/IWkcTKspJRyMcWxfy6g29m6pw+G2sXDV5KSc7sk+gv4Idqd5yJ1RNsjc9cGlmMxGCkoz+OvvXqfjZB9FFVk43Fbu/OBSTCYFURLJL03niZ+to7O1/8ozDnbt2sWKFSuG/j4VC/D+97+fxx57jH/+538mHA7ziU98YigJ2quvvjqU4wDghz/8IbIsc++99w4lQXvsscfeUo4DAE0PYpCzkUUnAgqSaEXXwkiiHbNShSf0MrHESczKuVUQ3m1ousbJ4AB3Fs6k3JGBiEClIxNREC5YxvNqWvXRdR1dH7ymq+eyJphgXHp7fdQOU9eZPaeYZcsnjXCTGY6u63i84Xd4lOOTk+vmjrvncuJEN+3tHgAi0WQ2ZLvdxMxZRSxcVM7iJZVYrcazxhBYLMYRgbEnW3qJxRKYxsjBMhxd06kblD+VZZGcHPc560zwNnO5a2ZeRARBYHbq3ezse4oUYyGphkIGYq3IgpF0Uwnoo+NwIOlSpIhGvPEO4nqUYKyJ3X3PjChrFC2YRDvtkaNkmasQELDIKSiihRLbfA56XsSuZGCWnHSFj5NlrsQqp1DpWMEBz/NkmisIJPpoDuwmzVTyjt2TM5FkkdmLy9n+2hH+48O/Y+G1k7n+nhosBaON+PFe/WnZTravP4rPE0JTNbrbB8gpTMNgPP1bnzS9AIcruWNicyRjaaLhZKxFPKay4e972bL2CL2dHmKRBFanmVjk0itjXbBxsHz58rNOFgVB4Gtf+xpf+9rXxi1jMpn4yU9+wk9+8pML7f6sSKKbhDqArqsgCMhiBuF4HVbjXARBRiOGpkfP3dC7EAGB6Sl5PNG4E1EQmeHOxyhJ6LpOSI3xctthnm3ZRygRZUVWFR8oW4hFNnDQ08ZPj62nJxygzJHBI2ULmeTMuiINhKEH5ODYdV3HE44QjMWwGw0XpH8/wQRXKgMDoSH/eEEQKK/IOqthoKo6hw+efCeHeFaOH+/gG19/Fr8/wsprqvnAh5aTmTncx/p0/oZzPafMZgOlpRkYjTLRaIK9e5vw+yPY7aaz1j10qJXeQQUno1Fh0ll2J650RFEYurZIJI6uM7igcokHdiaX23jeMgIiIrJgGPPSJjuvI6HFWN/5M0IJDy5DDnNS7yGdEnb1/5kjnlcJqh40LcGj9e8h2zyJxRkfpMq5gm09j/NU06exK5kszfgw67p+PNSuJBiodl3Htt4/8kTTp7DJ6dxf9ENkwcA09y0k+mM83/r/UPU46cZS0gbdmuam3Yu/s4dnWv6FdGMpWeZKzJLzLWVZfqukZ7v4yo8e5OjeFv7x+Fb+8yO/45Nfu4Np80qQziN3zOp7a9i/tYGP3vADXKk25iyrZPnNM1CGxcyY7cPiZc9o8rEfvMTBnSf4xH/cTll1Du0tffzmey9yOViyV5XPiEkuoS/6NJoeRsKBzTib3sATmJRiwvE6Emo3kvDmU8dfzQiCwAPFNbgNVn56dD0Og5n3lMxlfloJhwfa2d9/kq9OvQGzrPD/9r/Azr4catKK+daBl/h89SryLC5ebDvEmo4j5FicuAzjJ/C5XOkLhUHXkSURXYcOn5+Xjh4n024j1+m8al/uE0wwHIMiD1PHSCoWjRuMOJgR+cUX97+jYxwPNaHx85+uZWAgRFl5JnffM4/st7A9L4oChUVpTJ2Wz66djXR0eFm39hB331ODeZj7wSl0XSccivHnP20jHk+u/qWm2Vi8uPKtXNZljclswGhUCIVidHX5CAajWCwGLrvZ+KWfb11UREGk2D6PYvu8Mc8LgsiMlFuZkXLrqHNzU+9jbup947Z9Q+6XR/z9iO13w9oVyDZXcUf+N0bVs8hOFmd8gMUZHxh1ziBYWJ3zT+P2eSmIRpIr+FPmFFMxLZ9f/H/PsXfzcaqm52Mepiymn/HfU2iJpCvRt3//Ycomjx1EfLZfwdF9LSxcNZnKaflomk5ftw9vf2B0wcGO38lcrFeVcWA3LSKu9g4+sAVSbffii7zOid5PIAoKTvN1mJTSSz3MyxZFlLglfxqrcyezvrOWbx54iZ/U3E972MuO3iY8saTrgE02EtdUusI+6vzdPH5i+1AbM1LyUa/QbMK/3LyDI109pFrNJFSNkx4vug6PzJtFRcalVbjSdJ2+aICTwf5R53ItbjLN46tPTHD1cNzXSTARxa6YKbNnvC19pGfYcTjMdHf5UFWdPXuaaG7qJSfXjaIkffeTEp9h2k4O8L//uw7PQBBRFIaShV0q4gmVY0fbgaSyEALEYonBjMbnzvg8Frm5KSxZWkV9fReegRBPPbENh91MzfxSXC7LUKBxNJqgvy/AC8/vZc/uJjRNx2IxcPMtM0lNu3oXpdLS7aSkWBkYCBKLJfjLn7dz9z01uFwWFEVC15Pfl3g8gSSJmEzKxELLBJccNaGxbd0R+rq8pGQ4iIRiePsCzJhfhqwkvSa6Wgfo6/LSfLwTvzdE3cFWAt4whWUZGM0GvP0BvH1BOk/2E43EkCQJV6qN1CzHeWUyrppRwNG9LezYcIxoOMbWdUdGGCX93T56Ojx0d3jQVJ2Gw+3omk5KpoOMtzkm4aoyDkxKCTmuzw/9bZSLKUz9LwLRHUiiA7txPgb50ktEXY6omkYwEcUsK8iCxJzUQlIMFnzxMFbZwJy0Ir5QvYoUo5VAPIpJkumLBskw2fnC5FUU2dKIqgl0dIxXaBDz9VXlmA0KfcEQmqZTUZHGwqICZuRmY7jEuuOqrvJGdx3fOfQCMU1F1U8n5Pn8pOt4pGzxJRzdBO8U3zj4PPv7T7Ioo4yfz3vf29KHxWJk6bIqWlr6iEUTNDf18uMfvcKiJZWkptoQRPD7ItTXd7HljeP4fGFmzymm9WQ/HR2es7ataRoeT4j+viDxRIJEXCOeUInHEjQM+ugDeDxB9u9rprPDg6JIyIqELEsYDDKZmc7BlenRJLPcOujq8tLS3Mcf/+8Npk8vwO4wIw7TsRQlAYNBxukwk5XlJCXVNm6QsaJILFpcQXvbAC88v49AIMLPfrqGLVvrmFydizvFhiBAT4+f/XubOX68g1hMxWw2cO31U7nlllkX/iG8RXRdp68vgGcgSDyuDk7OVWKxBMdrO4bK9fcH2L+vmY4OD4osIsvJe20yKqSm2bDZzh1EXVSURnlFFs0tfSTiKs89u5uebh+TqnNw2M2oWnI3xecPU1aexfz5pRiNlyD+YsIemWAYgiiQmuGg/nAbLQ09GE0yS2+azvxrJqEYZHRN5+i+ZvZurieRUMkpTGPnhmM43Bbu+uAyFKNC/ZF2SiZls+65PYiigKpq2F0W7vrAUgrLM5m1qJyC8tNJga02E9Wzi8jMTcr43/2hZaz96y62vXYEZ4qVVXfMIuiPkpKRXOxrruti69rDBP0RFq+eyuHdjTQcbWP2ksoJ4+CtIAgSFsNkLIbJl3oolz0RLc6GruN0hLzIokhYjVPlzKbMkUFaNMxhTztPNO7ALptI6Cp3Fs4k1Wjj1vxp/KlpF1lmJ5qmMTO1gKnuXKQr8Ek8pyCXOQWXp/EoCiJTXbl8quoaQokY3REfL7UdJJB4d8bQ6LpOa2gAm2zEbRw7gdUEb54bbpxOZ6eXtWsOEYsm2L+/hf37WzAYZERRIBpNoOs6VpuRm2+Zyeobp/Pnp7ad0ziIRhNs3VLHmlcPEY0miMXixKIq0VicYPD0d7mpsYff/HoDJpMBo1FO5lMwyNjtJh56/2ImjaMDLssiD753IU/8cTNdXT62bK5jy+a6UeVEScBsMpCWZqe0LINFiyuZW1MybpIyt9vK7XfOwWRSePXVg3R2eNi9s5HdOxvHLJ9fkMo1qyZz2+2zR0ihvlOoqsamjcfYtOEY0VicWEwlFk0QiyUIhU7f54b6Lh791QZM5mSWaINBwmBQSEu1cdsds5k5q+icfblcVq5fPY2uLi8HDpxETWi8vqmW1zfVjip7x51zmD276NIYB1fmhvYEbxOSJDK1poSpNWMHRQuiwIpbZrLilpljnu/r9vHrbz/PZ795F+WT8xBFga52D7//4Ss013WRX5rBA5+4ZkSd1Ewnt7zndD6v9CznqDLDmbmonJmLyt/E1b11rmrjYILzxyDKFNvSMIgymq5hlg1McmbhUMxYJSN3Fc6iwd9LRI1jkRVMooIkCDxYPI/Dnnb88QiyKJFmtF3SAKOrFUkQKXNkUubIREen0d/D5u76d61x4ImF+NbBF1iRVcW9RXMv9XCuOlJSbLz/4SVUVGSxY3sDDQ3dDPQH0TQNo9FAdo6L8vIs5swtYc7cYkxGhbLyLNatPXzWdlVVo71tgIMHzh7AnEhoeL1hvGeoIJnNBjye0Lj1WlsH6O3xk0ho45YB0FSdYDBKMBilpaWXo0fbiccTLF1WNe7ENSPDwZ13z6V6ci47d57g2NF22loHCAaj6Og47Gby8lOYPDmPGTMLmTwlD+MlMAxg0Hg+2c/BcwSKJ+9zCK935PGUFCtLllWdd3+Tp+Tx4Y+sZMvmOvbtb6b1ZD/BQARN0zGZFOx2ExmZDsoqMt/12Z8nuDrQNI2AP0IiriJKAvGYSntTL7FIHIvNeMW7zgn6hepUXgb4fD6cTiderxeHY8LXeoJ3H02BXj627f9oD3velW5Fr3Ue5Z92/ZkHi+fxxcmrL/Vw3jEe2vzo2+5WdApd10kkNPr6/Ph9EaLRpBKNLIsYTQpOhxmX24ooCug6eAaCnGztx2oxkpPrHtP1R1U1urt9dHf73tSYJFGgoDANh8M86tyG9Ud48omtdHZ40HWYW1NCeUUWNrsJcdiLWlN1wpEYXZ1e9u9r4cSJZD6H8oosvv7/7iIzyzmq7TPvi98fwesJEQxGSSSSyk6KImO1GnG5LVgsRsTzzHSsqhqNjT0Eg1FEQSAr20l6+lt7r2maTkf7AL19YwQ3ngeKLJGd48LtPv9dOV2HYDDCwECQYCBKPKGCDpIkoCgyJrOCy2XFah373vT1Bejs8JBQNYwGmYrKrBGuYGMRCERoGMzHYTIq5Oa5R7hCRaNxams76ArXkWkrZHJVyQjp2mAgQlNT71CfVZNykscT/SS0KFrISkebn3hCxWoxUFqWQX+sldbQQaa7bzqv++KNdSIKElY5BVE4bRhpukpb+DBRNUiZfcF5tTUW4YSXWv/rdIaPM821mhxL9Ztua4LzJ5FQ+cfjW9n7xnGikeROqs1hZsaCMpbdNB1nyuW3o30hc+eJnYMJJpjgimNLdz3ahJ/A24ogCCiKRFaWi6ysc5WFlFQbKalnD7yVJJHsbNdbUhAai9pjHTz6qw10dHiwWg188Z9uYubMIgxGeVBq83TZpNSmTiKuUjfoVnP0SBsN9V20tw+QnmE/66RUEATsdiOHo38gpPShiBYqHNeSZT6/SVmjfzNWOYU0UzmiICNJImVlmeeueAGIokBuXgq5eSkXtd2zIQhgs5nOK05hLFJTbaSe4/tzJjabienTC8Y9bzQqTJmaS2fnXylMmzIqp4XVZmLylLxR9bojJwirXsocC6l2nXZh03Udp5KFxeE67zG2BPdhU1KxyC7gtHEgIJJpqkDXz77LdS6Mko0S21w8sXbCqv8ttTXB+SPLEjfeW8OS66eOXCBwmDBeBTlNJoyDCd5WdF0fmsINXys6c1p36tzZtuKGb3JdSP0zxzBeH9pQIpjRZca7jjPHMvzcWG2MNfYR9QQuS7esU9d/Ptd+ru3U8e7D2e7B8DpxTWVHb+PgmPShz23Mtob1c+bnerbvxVif9/D8F2cb/3jXf2a9t/p7GK+PMz+TK317+3xYs+Ygvb1+BAHm1JSyeEnlWZObQXLiWFSUTll5JkePtCWlBPsCg8nfzt5fd+QYmp5gWebnAQFRkM47WWSBrQYBEQHxHHXG+sWNz7vhcx6PmBbmkOdVan0b0VCZ6lrNJMcKwgkfb/T+nhOB7fRHT+I0ZDPdfRM55kkc97/BgYEXiWsRTJKd1TlfxCCYaQ7tZVvvE0RUP4c8r1Jqn890102IgswR71oOe9eRaixgVdYnh/o/GTzA9r4niWkRDKKJlVmfwCanUed7g90DzyIgYpGcVDqWMdW1moQe5aDnFWp9m6iwL2Z26h1DbQUSvWzo+jXeWBcmycbslDv+f/bOOj6O807cz9AyaVfMsthMMbOd2A5T06Zt0iTlNuXeFa5419/1ynSla5NS2qSBNhw75NiOmZkkixlWq+Xdgd8fK8tWJFNiJ7arp58m0cw7874zOzPv+2WKHdOoDW1mt/9ZEloEs2hnZd4XsUpuREHCLDpQxCuvAnd37DAH/I9jlb1M8r4fs3RpeYqYrSbM1pGTI1zujAoHo1xUNEPni9v/zi5/E/dXLeHqnHG80naQp5t2UxPsJGFoZFlczM8o590lV1Fg9yEJw2dmwzBI6CqHA+082rCVHT0N9CbCWCUTVa5srsufyLKcsVil4bnHf3v0Nf5Yu4GYlmT7dd9AFkb2eb32lZ/QFg1wc8Fkvj35liH7/IkIH9v8Z470t7F62RfwmR0c6+/gkfotbOk+Tn8yikO2MM6Tx11jZjPFWzRkWld1nbZoH+s6jrKh8xiHg20EElEUQSLT4mKar4gbCyYz3pOPLIiX3ET/P/uf4/GGHbx3zEzur1zK7t4m/l6/ld3+RkJqHI/JxjRvEe8fM5tKVw7yaVZXqq7RFg3wcN1mNnbV0hELICBQ4khnWc44rsufSLrZCQzNq78/0MLTTbs52NdCTbCTqJbKT/3n45v4y/FNw/r5+4KPU+nKJqolWfrSD/CYbHxj4g3MzigbbPO3ui389NCL6IbBRysW8pGKRYP79vib+Naep+iI9fPisi/gkM2DwkhnLMjLbQdZ3bqfumA3SUMjw+JkTkYZdxRfRZHdhyKO/Iwd7e/gjnW/4ipfCT+96r2ousZL7Qd4umk3daEuVEMny+JiTkYZd5bMpNB+7il0dUPncKCdz29/hI5YPxWubH4w7Q4K7W+f9vidoqG+G1XVEQSBsrKsswoGJzAMA107qbkdqXbBSNhkHz3xOiKaH7vkI6L2crh/Nf3JNuJaiAlpN5NrncSqlm+QYa0kqUUocc5FRGZH79+odC2j2DGH5vBODvevQhHMCILExLTb8CcaaQpvoyN6iAxLBQuyPsP2nr8QUXuxyWmM99xMc3gHvYl6dEPDZy6hyr0SgX9dX/6YFqQzdpxrcj6HS8lAQEQSFJxKBstzPsejjf/Oipwv4lZOmsAKrBMpsk1GFCQ2df2No/3rmOi5jhL7VcS0IBGtj4melSjCSRe2se6luJQsDgVeHdL/vsBqpntvI882AcMwkEUZAYmx7mUEkh14zQWUOWYjCSmNsiyYmeS5FpNoJaL2DZ7HMAxeavsFpY7ZTMhdjoGOPmBZyLWMIy93PJIgsbn7EQ4H1jJlhBoGFxJNT2Cgc0LlIAkmBAQ0I4mBPnCfTQPjTJ64CkRBQRQkdENDN1JZDEUkJNGEYehoA20N9IFzimhGqi8gZVETFHzmSirc19IZO3hRr3OU4YwKB28R3YijaX4M4gOFghQk0YMkXnr+Zu8UBgYxLcFefzNHAu0837IXRZSwyAqKLtERDfDX+s1s7K7haxNuYLqveJgWN6zGebhuC787tg4DA5tsxilb0AydXf5GNnXX8kzTHr4+6UYKbGlDjz/lf2cb55landjXHPGzu7eJ7+5/jqSuYhJlTKJMWI2zruMINxVMHnbsbn8j39v/PDXBDiySCbMok2ayDdYv+EfjTtZ3HOVT1ctYmTcBk3BpvZoGoGNwONDO0827+enBlxAEMIsKdtlMfyLKcy17eaX9EN+edDMrcscPW2TFtSQvtOzj+wdWEdMSOGQLVimldTke6uZnh17ihZZ9fH3iDYzz5A0Rro4Hu9jYWYNm6HhMduLRADoGDtmMSxnuf35icS4LIhXOLOrD3TRH/CevxzDY39eMbhhohs6+vpYhhb5aIn5CapxKVxaymBLWNF1nX18zPzn0Ent6G7HJJiySghmZ/mSUxxu2saplH5+oXMxNBVOwyiNrlAwgqiU53N/Gk407WdN+GFEQUEQJURdpjwZY33mU24umn/Pvo+oa+/ta+Naep+iM9TPek8f/TL2dPFvaOZ/jcuZETYMTsQ+6rp/VX13XdJqbejl2rB0As1kmM9N5TrECDjmTad73sbnrd6SZiiiwX4WITLVrJTbZx+7eR8iyjCWi9lDtWolDyRg8tsg+A0U88cwaKKKVhVmfozm8nabwdmTBTIljHunmUmyyj47oYVQ9xsz0+2iL7qcpvI2EHibDXEGpcyGSePm7MLxVLKKDTEsJm7v/RqlzFgW2idgkN4IgIhip31MQTlpXdEOjObKP4+FtCAh0xxswS/Y3fLNS9ZKGzkUCI1lzxrmXsb3nCdpiRyl1zMRnKjz5HAknLXgjnf9UDHTaooe5Me9rqfZIA4tsndboQWpDmwGD7ngjJY5pb/W2nRHDMNjV8wBJI0ow2Yqqx5iV+Vk0I8nx4EtEVT8WycNk3z0EEo3s6f0zdjmDhBYi3z6LMa6raQptoDmyGVWP41CymOK9j5Dazt7ev2KXMwgl26n23Eq6pYoD/kfoSzSgGyqZ1vGMS3vX4P26tFRl/xpcWiuQywzDUAlEnqEn+Ac0IwCGgCLnku78EC7r1Re9f92Ip6o+i24k8dIyt72RuKbySttBnIqFpTljWZxVRbbVTV8iwvrOozzbvIeGUA8P12+hciBL0gl0w+Dv9dv45dFXSTc7WZpdzZyMUrxmO8FknE3dtaxq2ce2njq+u+85vjv1VjymiyecrWk/zIut+6l0ZzMvs5xiuw/NMGgK93Aw0MqktMJhH7Ncq4cp3kJybR6meIuocGbhM9sJqwkOB9p4unkXhwJtPNO0mwmePMZcpAJXbw2Dff5maoIdjPPksSJvPGXOLBKayraeOp5s2klbNMBPDr7IdF8xGRbn4JG6ofNi60F+cOAFDGBpzliWZY8ly+pCM3T2+pt5oWUfx4IdfHPPU/xq5vvIspysSr0idwKLsioxgLiucvtrvySYjHF9/iQ+Ublk2EidimVwMh7ryeNQfxvNYT8GBgICMU3laH876WYHYS1ObbCTiJrArqRSWTZH/ITVONXuXKQB94/GSA8/PriaA32tjPPksTJvAhM8eSiiRHPEz6rW/WzsquF3x9ZhkmRuyJ+EcpqaH73xEA8d30RDuJuV+ROZ6i0kzWQjkIhSE+wkpMYotqef06+S0FX29Dbxk0Mv0hLxMzu9lG9MuulfqjDemDGZ7N7dQDKhsWHDUebNr6S4JAO73TzEimAYBvG4SjAYpbmpl6f+uYOjR1LCwbTpJfh8znOz2gmQa59ElrWamuAajgfX4VAykAQFs+QkoUcBA0k0DxEMRjqRW8lDQEAc0KAqoo3WyG5k0UyxYy7t0YP0xOs4FHgBSVDIsY6nM34Um5w2KhgMoIhWJnpWUmifwm7/M4SS3UzwrMAspeaBlGZaG2wf1fo5ElzPdO9tZFpKWdf5uyHnExDBMM7ZVSzfNoEcSyW14S281vF/zMu8h1xr9eC5dPTUuc7h0TKJNiJaH04xY6B/g5jWz7HQBsa7l5NrrWJ91x9Oc/SFi8My0OhNHGea7yPEtQAN4fU4lByOBp7FLmVS7lrJvt6H6Y0dRRRNqHqU2ZmfpzN6gLboLgKJBprCmyhxLsImp7O967f0JeqQRSuSoFDkWIDPUpHqy9ApciyiCIOo5me//2HGet71pooWjnJhGBUO3gJJrZ2OwA+QpQwyHZ9BlJxgGJjltycvbUJtoDf0F1zWlTgsc85+wDuIjoFq6CzJrubD5QvxnpKbfkZ6CaIg8Jfjm2gM91IT7GSqt2hwf22wk9/XrMMiKtw9Zg7vLZmJfIrbxpzMMgrtXr6z9xn2+pt4tf0wtxZePK3Kw3VbuKlgMvdXLcVnHhpApxs6Am/UEEG21c3HKhZhlUzDNMoT0/LJsDr5zt5nONzfRnc8dIkKBymNd6U7h/+YeD1Fp7i8TE8vxi6b+Mmhl+hLRtjSXcv1+ZMH97dGAvy1bhMhNc6dJbP4wtjlQ1xvpvmKKXVm8F97n6Em2METjTv5RMXiwf1mScYspT5XUTU5GJNgFmU8JttpxysKAuM8ufytLklbtI+krmESZY6HOgkkY0xJKySgRjkaaOdYsIPJ3kKSukZrtI+olmCsJxdJEFENjWeadrPb30SVK4fPVl/NNF/xYBacancuczLK+MmhF3msYTvPNO9mvCePCtfIkbyt0T4USeZjFYtYkl2NRTq5yDvhviSO4F53AmXANS6hqezsbeDXR9ZQF+rm6txxfLpqGZmnCGb/CixaMpYtW2qpr+uioz3AD773HHPmllNY5BvMHKTrBrFYEr8/RH1dN/v2NtHZ2Y8gCBQVp3PjTdNwuYdboUYirPbQG69DIKXttSvpxLUQnbGj+BONZFjKh7n56IZOSO2kP9mGqsewy+kDrhmnWjl1NCOBU8kmzVREVOsjw1JGhqWcXNtEJMGEW8mlM36MUX3qSZJ6lPrwDkRBwqlkIItmTo0ayjAXczy4lbClB5+5GBEJm+ShM1ZDWOshkOzAKp3MUmWX0+iO13M8tIUMyxi8pnwEBJoj+2iPHSWodtMc2YfXVIBN9tAU3kPSiCELJnzmwkG3GQC3kk1vvInjbCXDXIzHlIdmJGmJ7qc7XkdcC9MSOUCGuQSTZGO8+2r2+p8ny1qOKCh4TQVYJDs2yZNqrwfpS7SRaUnl549pQVoiB+hLtCIiYZfTyLCMGXRherOIgoxLKaApvBGL5KbCdR26oZLQQoTUNnQ00sxjMEtukkYMp5KLKMiD/0/qERJ6iPbIbsySixzbVOQBi5lJdGCVT84fEbWbA32P4jOXE9f7UfXYoCLnUmRD1zHybV4Kbd5LzgX4QjEqHLwF4motmt6Pz3kvaY53va19Gxgk1WaiiX04Lcve1r7fLHlWD4uyq4YIBpDK4X9N7jj+cnwTYTVGT3xo+r1nWvYQUeMU2n3cVDB5iGBwguvzJvHjg6uJaAk2d9VeVOHAa7LxsYpFwwQD4LQLOlEQ8I7QHkAWJXKtaeRYPRwKtBLVkkNcXC4lTKLEDfmThggGJ7g6dxw/P/wKmqHTGO4dsm9zdy1t0QAWSeEDpXORR7hPM9NLybK46Ij181Lr/iHCwZtFREi5BgkS/kSEmBaSzQABAABJREFUnliIHJuHQ4E2YlqC8Wl5hNQ4+/zNHO5vY7K3kL6BdhIi5c4sREEgrCZ5rmUfZlHmqvRiJnkLhqTHFAQBu2zmXUVX8Wr7YY71d7LP30y5M2vE31EWJGb6SliYWTlEMDhxrjNNigJgk00kdY0dvQ385ugaakNd3JA/iXvL5pFlcV2Sz87FpLw8m3vunc8Tj2/jwP5mWlv9PP7YVkQxVQlZkkQ0TR9Mx3oCm93MpEmFrLx2EhMm5p9zDn5dTxJV+xAFCZvkJdNSTUNoMzoqoiCl3H0EmWr3taccZaDqcdJMRYiCjKrH8ZjyMYsOBETcplwSWpC4HkIRLcS0AMeCrzA/8zMUO+YQ0wLIghlNTpJtGYtV9lzQe3g5Y2CgGnGSWhy75CXPNhazmFIaCAhMSrue5sh+olo/upHEofioci2kN9FETAsxOe1G5FMW05mWMpJ6jLDqJ6lHB/qAmB7EIjoosU8jqgXRDBUAHY2I2oeAQKF9MjmWkzUiiuxTEBGJ6SESRmzgXDoxLYzHlIth6MS0EDopy8YU700cD20lrPpRRAsGGhbJQYVzHt3x+oHxXo804HqqGxoJPUqOtQpZMBHTw+dspTgTmpEkqYexyT4kQaE/0YRDySbTOg45bsYieQAdpykXf7wuZW05BZucTo51CoIgIAsWDMCh5BBKtg3rSzViRDU/kmjBhIFJdGIYOv5EPW2RnQSSzTSG1lNgn4tZcr/j37c/1K7nutxJKRfmS1SAeauMCgfnSVLroD/6ErHkQeLJo+hGlEDkOaKJVPEfi1yO13EXsnTS1zeerKM/+jxxtQ4QMMulOK1LMStlgw+WYRgk1HpC8Q3Ek7Xoej+iaMWijMdlvRpZ8gIChqERiq0hHN9CJLGbWPIIXcFf4o88DqQ+hPneHyMIMqrmpy/yFKDjsd2ELPkG+wrHNxGIPEOa/d3YzJMBiCYO4g8/Qobz4xgk6Is8RUJtQkDBbp6J07psSCxFLFlDf/R5EmojIGFRKnBal2GSC0d8YXwWB2McI5vYM8wpFwhV10lq6pB9W7uPY5AKCv7u/udP+9uouo5m6HTGgqi6NqIQcSGYkJZPpuX8XTYMw6A9GuBQfxuN4V4CiQhhNU5S1+iJh2iL9mGQCuI+vzwlbx+yKDLdVzzivgyzM6VJNSCqJobsOxJoJ6ymJsafHFx9WiGqJdqX+nekD9XQBzXkbxoBPCY7OTYPgUSUjlj/KcJBknGePBKayoPaeg4FUpNWR6wffyJCns0zaJVojfTRHu3DZ3ZQ5szCNIK7kCAIA/sz2dp9nKawn5iWHDH2wKlYqHBlD7oxnS9WWeFQoJVfHnmFhlAPNxdM4f1jZpP5LygYQMqffN78SrJzPOzb28SB/c001HfT0xMiEomTSKjIsoTDYcGTZicjw0lJSQaVVTlUVOSQneNBUc79WXOZcnCZcgb/DiU7MUt2sizVpFtOWo6r3MsH/1sUJLzmIrzmoiHn8pgKUudUchAQqQttQNeTCIRxKbkIgkiebfKQYxzKpWlZfKcwS3bGukdWkgmCQLq5mHRz8ZDtubZqcm3VIx5jkRyUOmcN217hHLmezBjHjNOOzS6nUeVeNGSbIliodM0fsb1ZslPtHq4YybZWkG2tGLbdJntGbP9W6Yrux6Fk4zOXoxsqrZEdOEx5ZFrGp6wFehgAERmXqYAK9w0AuEx5KKINq+Sj2LmY/kQTOiqp1YmIVfJR4lyKSTypLHMoOYz13IaAMBCIXIEgpFzssqyTyLCMRRFtiG/RGjLKuXOFCQcGofgO+iIvEFcbME4x7Z0g2/VJHJbTv8hnR0AQTEhiGpLoTT3MogdFShVPkSQfwikLmnB8O219/4VhRLEolRio9EX+QSi+gSzX5wcW5qnJvCf0IMHYWsxyGZLoIpY8SjD6Kgm1iQzXx5BE58BVagiCDVFILVxk0TfY/4kxAuhGlEh8GwbaQAzESW1vQm0gEH0eh2U+MBkAVe+gP7oaqzKe3vDDA9foJqE1I4o2XLZrBo8PxTfT5v8WAmBWytGNOL3hhwnHN5Hl/ncsSjlvXN5aRAX3CMGjgiAMyVB0qtekbui0R1MFk/qTUZ5v2XvmnwdQDQ3V0JEvUvaObOuZiySNRF8iwrPNu1ndeoCOWD8RNU5C1xAAy0BQ7olqx5dy9n4B4bSC0anC2Klh3YZh0JsIk9BTmrFVrfvP2o9m6CQ09bRZf85nvCZRpsKVxcG+Vjpi/YSSMRpC3SiiPCisSoJIQ6iHiJqgM9qPPxGm2p2DeUAI6Ij1Y5Byb/KaT+/GdKJKuAH0JyNETyMcmCUF9xncoc6GPx7hBwdWsd/fgttkpdyVRbrZMcSa8a/GiUxFRUXpzF9QSTgUJx5X0TQNwwBBFJBEEZNJwmxRcNgt2B3mc85sdCascholjnmnBBq/OeyyjzGO+ahGDMMwsEguxH/hLESjvHOYJNdAIHIEHR1JNOGQMzFJDjKkoUKVjAWr1QOARfIMWBVAFs3YZN8bzmvHJw11vZYEhVzbcGu/Q8nGoZylyMoF4nCgjaead1If6ibH6uZ9JXMG54fGcC8P12+iKdLLVb6SYcqvK5ErSjgIxbfT7P828WQ9JjkXURiuldN5az+qLPrwWG/AQCcYe4VQbC1O6xLSbHcAqeAjYaBfTQ/REfgBGBpF6b9HFF1gGEQSO+kIfI9A9BlMctGAVQC8jg/gc9yHJLoRBAldj9Ae+B6ByD/xOu4cEA5EHJaF2M1z6Iv8k1jyEG7bzTgsp2o03tpk1x74H3I8X8NhWYggyBhGEkGQEbAMXFeQNv+3EQULBem/RBTsYBiEYmvp7P8p/ZEXUJy5SOJQNxpJEE+b4vJ0GKQW+wIwKa2AD5TOPesxbsV63otKTdfO3miA87VIhNU4TzTs4MGa9US0BPMyy7k6ZywVrlTgtSgI1AY7+dnhlzgcaD+vc7/dCAiYz/P6DVIZgQCK7T7ur1o6YrraUxEFYTDG4K1iEiUqXFls7qqlKxakKeLHn4hQ7szCIimIgsAYZwaBRITmcC+d8SB9iQhlzizMAy4/J8af0n2doUAWJ13LdOP0ua9EQHqTC3kdg9c7jwHgMVnpS0R5vGE7Fa5sqlzZ/5KWgxMIQsqNKCPDRcaZ4oAvMJKgYJPfespYUZDPEsA8yihvDx5TMVelf2Lgr1R6UpN4fkXqLheawr38o2k7+TYvN+RNZlN3DV/Z/Ri/n3UfumHwRNNWolqCe0sXsK2ndsDKfymr8d46V5RwEIi+gm5EKMt6CKtcyUih7gJvzSwlCBLCQN5jUbAwsFwaMXVpOL6FeLKGLPcXBjT7qZxmVqUai1JNNL4H1d49KByY5VJO6o0NRMmBRakgGHsZw4inAnQEAQELBiZEzICIKFguaOpUq2kyLtu1CJgHFhpDyzaFYutIqPXkpf03ipg9uMtqmoRJLiYc30aa/V3DhIM3g4iAQ7YQSsYwiTKLsqvOwd3m9F6Ap3udI1pixGJaI5/9/KgPdfNC616Caox7S+fxgdK5uE1WxFMCl3vj4TMuOi8pznPxKSBglVOLcN0wWJBViekcBIwL5cupiDJlzizCaoKeeIimcA8hNcasjDFIgogoCIz35LGu4wjHQ130xkOE1TilzsxB9yG3ktLyJ3WN8ICFZyQ0QyeUTLlPWWTTiO5Hp17hm0Eg5Zb0b+NWkmtz8739L7DX38zvjq3lK+OvI93s+JcWEEYZZZS3jihIWOUrPxWyYRgcCrRiGLA4q4ocaxqlziwerd/K5q5aKt05HAq0cV/pAqZ6iyi2+3i57eAVLhpcYcJBUuvCYZ6FRR6DIFje8QkyrtaiG1Fa/V+jte8bw/ZblKohrk+q3kFv6GHC8c0ktVZ0PYRmBDGM2GBxkLcDq2k8AvIp92/ofYwlD2OQpLn3izT7/22E4ydicO6a+DMhCAJj3Tm0RftoCPfQn4iSZj4/QUgWpcGFZlCN4R0hzWljuJfkeVgPzgd/IkJ9qJs0k405GWWkmWzD6jhEtASBRPSi9P9OIwgCRTYfVslEU6SXnniIXKvnTb+fwuA/OCftjSQI5Fo92GUT/kSEw4F2wmqcCWmpgnMIqTiS51v2ciDQSkSN41KsZFldg9r9UmcGVslEWI3TFOkdMWDcMAxiWpK6UDeyIJJlcWGX31xMwZkRqHLnsCJ3PAZwX9l8fnxwNS+3HaTA5uUj5QuxyedWzGuUUS4WumEQTSZRteFzlygIOMyjz+jFwDAMYkmVpKYhSxJWRR69z2fAAPqSEZ5q3sELrXsHdV8iAt3xIMVaOhE1SabFhTgQV2aXTVdsIPIJrijhQBZdaHo/hqFdEtGchh4FdHzO+zDLJcP2p2IVUkFtSa2Fuq47MYwYHtvNWE13I4s+AtHn8Icfu/BjO4OwIYluznQDdSMKhkG666OYpLwRjvchiRdO47A0ZyxrO44QTMZ4unk37ymeMejucSqGYZDUNWRRHBLw6lFsg24sB/tamZc51N8xqWu83nmMqHZx/AgNw0AzjFRlZmH4vpiW5FCgjbaBYNwrkSm+Ip5u3k1YjfPQ8U18qmoZttMUCUtoKpIontb1SBnYZwA98TC6YZzR114QBGyyiUK7D38iTHc8iGYYg5mIAKrdOSR0jSOB9oG2XhyyeXBStUoKCzLLWd12gD29TXTG+ocE/xpGKlXv7t4m6sPdFNl9lDozLmoMQMqKCAuzKmmN+Hmw5nX+VLuBHKubWwqnXTC3rFFGeTMEojG+8uRqXjtaN2xfjtvJK5/94KUwTV9xJDSNrz3zEq8ermX2mEJ+fPt1WJTRb8GZEIAVuRP5aMViMswn00BLgsjxYCeiwGClap1LM5PgheaKemKclvl09j9AKL4Nm2kikmiDNwRziYIZ4W2qPitJaQiCjFUZi9t2M8IZ/Kz94UdIqi3kpH0Ln+NuIKUVDcU3wBmtBqfXnAoIIIhgJN7QzkDTe87rWk5FFr0giNiUiTityy/6i7IsZyxPNu1iW/dxHq7bgkM2M9lbiEO2IAoCSV0jqiXwJyK0RPwszKockvu+2p2DXTYRVGP8seZ10s0O0s1OZFEkoiY4GGhhdev+i2Y5cCgWsiwuOmP97PU3UWT34RmojhxIRNjZ28DjDduQRBH9HMZgGMYQFygd46wL5Heaqd4iZmeU8lTTbh5t2Ea62cG8zHLcppTgphk6US1JfyLCsWAnC7IqUtmPRrgmSZQocaTTGw+x19/EPn8zBXbvwHk0krqGz+wYEhtil82UONI50t9OQlcptvuGLP7TTHZyrG4awz3YZTMVriwcsmXweEWUuKN4Brv8jez2N/FI/Vauz59EmsmOJAiE1QTHgh38+uiaVLpTXwkTPPkX/8YOjO2Wwml0xoI80bidnxx6CY/JxrKcsRctY9coo5wNWRSpyEqnPxonqWlEk0ma/f3EVPXsB4/ypmnp62dLXRPRpMqWumaa/QHKMoennh4lhSgIZFs9bO+tpzXsJ92USigRSEZJNzuwyibcipWaYCf5Ni8tUT99ichozMHlhIAJ3YjS5P8WTstMFDETQRiqnUyzXY/VVPm2jMdmmoQkuumLPofTuhRJOJnlxjBUUot+BUEQUPU+EAQsysmxqVonseRR9IHcyEMRQDBhoGEQH9HNQRBMyKKbaKIFTe8f3K4bYULxTW/+uswzEAU7fZGnsVvmIQknYwtOukldOFOmWZT50riV/PjQarZ11/PDg6updGWTZ/MgCRIRNU5XPEhdsAuTJDPdVzxEOKh057Aou5p/NO5gW089X9v9TyZ48rBICp2xfvb3tVDhysIA6oJdF2TMp5Jn8zA3s4ynmnbzt7otNIR7KLT5UA2N+lA3e/zNFA8IDLt6G4cdrxs6rZE+9vW1kNBVkrpGWzQw6Pu+u7eRR+WtmEQZkyiTZrIx1p173u5XFxNFlPhg2QJimsrLbQf5xZFXWNW6nzGODCySQkxP0hMPUR/qpisW5NGFnxiiwXkjtxVO40h/Oy2RPv5z79NM8RamzqMlietJPlW1bEhWJZtsotiRPuAranBVfslgligAkyhT6cpmTftheoQQV+eOxXFKmlFREBnvyeODZQv4y/GNPFy3hT3+JsqcmSiiREe0n609dUTUOPMyy7m9aNrbev+dioUPlM4hkIzyXPMefnBgFWZJYWFW5SUtNI5y5eIwm/jkwlmEZiYIJRI0+wN8d9Vr1HT1nv3gUd40LouZPLeLvkiMYp8Ht9Vy9oP+xRnrzuVQoJUX2/ZzMNCKKAiE1Dj3ls4jzWRnVkYZazoO0RELEFETSIKEeIXbva4o4SCa2I8oWDDLhSTUNhIML7bhfBsrCVuUaty2W+gN/YmOwI+wm2cjijY0PUBCrceqTMRumYOACbt5Jv7Q3+gLP4FhJDGMOKH4RhLq8RGDqAVBQJEykAQH/ZEXAANBsKDrUVwD2nxRdGBRquiPvow//ChJrRXDUInEt5FUh9+bc8VqmkCa/TZ6Q4/QGfgxdvMMBNGCpvURV4/jsMzHZprChXq8BEGgzJnJl8Zdy+rW/Wzrqac+1MW+vmY0Q8ciKnjNdsZ68pjiLRyWLjW1MJ2Hx2Tl9c5jNIZ7qQ12YhIlcqweFmZVclvhNB5r2EF9qPuCjPlU0s1O7iiagSiIbO6qZVXLfjRDxyal3Feuz5/Espxq1rQfZvcIwoFm6OzsbeB7+18goaskdG2I1mJtxxHWdhxBEkRMokyVO5vPVV9zSQkHALk2D5+pXsZYTy6bu2qpDXaxpuMwCV1FESTSTDYKbT6uzhl3RsEAUq5m/kSEl9sOUhfq4onGHQgIOBQzebY0VH2otc0iKeTb0tBJuZ5VuXOGFB8zSzLV7hxebDuAWZDJtaZhfYPrmlU2cUP+JNJMNl5uP8R+fzN7/c3ohoFDNlPsSGdmegnLcsZR4c66cDfuHMmwuPhI+UL6k1Feaz/CLw6/jEVSmJ1R+raPZZRRBEHALMuYHTI+bFhkGasymqf+YuOz27l/8WyOd/VSlukjw3lpzQOXIj6zg1sKprLH30R7NIAkilS5cpAFCbMosjJ3Il6Tg75EmEpvDuWubIrt6Vd03IFgGOeYouUSor+/H7fbTSAQwOU6qR1MqK1oevCMx5rk3MF6AW95HNEXaer5NNmeb+BzvHfENqrWTSD6HIHIcwPaew1BMCOLPryOu3BaFiIICpreT1f/r+iPrkYULIiiDbMyFkXKpDf0N4oz/jykaFrq3L34I48TiDyDYUQQMCFLmRSl/3Ewy1BCbaE39BdCsXXoJJEEG2alArt5Dm193yYv7bu4bakqnsHYGlp6v0y68yP4HPcMqdfwRpJaF4HIU/RHVqEZQVLCiRlZTCfd+THs5umpdKyGwV5/E52xIJkWJ5PSCka0KMS0JOs6jmKRZMqdWeTYPMPaGIZBTFdpifjpjgWJqAl0DBRBwi6b8JodZFvdp/Vlj6hx6kM99MRDJHQVSRBxm2wU2b14THaOB7uoD3eRY/UwzjM0liKhqezobSCYjFHs8J228u3p0A1jIFNOL33JCLphYBJlfGY7BXYfTtlMQ7iHY/0dTPIWDHGp0Q2dlkjfYJGus+FQzFS5coZVon4rHA600RTuRRZFFmVVnfbaXxnI4lBg81LpHjk/tWboNEf8dMWChJIxVENHEkRskok0s41sqwfnKS4/I2EYBklDoy7UTXcsSFRLpmpuSKlaGhWurGFxKZ2xfvb7W1ANnYkDhezEU+5xayTAwUAriigy1p1HlnXkeg5JXaMz1k9rpI+gGsMwUsJFutlBgd172iBkwzAIqXE2ddVilRQqXdlknqaP07G1u45AIoLP7GCqr2jENnWhLmr6OxEFgRJHBmOcby01Zjye5LVXD7FlU01qgwAfuHc+RcVnPu+aVw6yZ1cDH7hvPp40+yXtp7tzex2bNhzjhpunUlDou6THernS0R/i/keeZl9rx2DMwahVa5RR3j5Ot3YeiStKOHi7SVkAGlGk3MHqwyOhGzGSWge6HsRAR0BBEu3IYjqCYEUQhAE/8n6SWjuGEQcUZMmHKJhJqm2YlGIEwTxEOEgdE0TVOtGNCKkCbRbMctkpgZI6mh5A1bsxjCggI0vpSKKLeLIGRc5HFj0D19NPQm1OWSTE9LNOkLoeTV2XEYKB6xJFB7KYkRrr6Id/lFEue1RV49iRdg4dbGHPnkZ2bKvj/33vDiZNHlk4OcEfHljLmpcP8v0f30lWtvtt/R5EInG2bKrBZjczc1bZWds/98wu/vnEdj7/byupqs5DFEe/XReaUeFglFHeWc5n7XxFuRWdCU0P0t7/Sxzmq3Bbl16Qc0qiG6tpwlnbpVydzjyRpqoEuwcyBb2hH9PIFXlTx7iQxNP/yIIgIktpyNLw7EFW03ggJWQASKILq2nskDbxaIL1T26jvzfMLZ+4esgELwgW2moVHvjmq7TVdVI1fQx3fHYFhRUpH8dju+r543f+yXce/+x5LQx62vp48jcvM+/GqVRMLTmnY9vqu/jOB35NuD/KxHmVfP4X95xzf6OMMsrpkSSRiqocSsuzkBWJfXubzum4O94zi5tvmY7L/daqBr8ZgsEYG14/xoSJBefUfumyccyZV4HDYTnfMh6jvE0kVJVdTa08v/8oB9u76I1EcJhNlKZ7WVZVxrKqUkzymZc0oXiCrXVNbDjewKH2LrqCYQwDHBYTZRk+5owpZGlV6Rn99Nv7g3xv9ToOtXfyuaVzWT62AsOAzXWNPLnnELVdPfTHYnisVsqz0rm6qowZxfnYTAMFFXWdJ/cc5GevbmRWSSFfXr4QiyKz5uhxVh84SkNvH6F4gjSblUn52Vw/oYpJeTmnFVjbAkH+97VNrK+pH7bPaTHz3Cc/cOb7qmk8sXM/v163haWVpXx26VxkUeTlwzW8eKiGpt4AkUQSr8PKlPxcbphQxdiczDMK0LphcLi9i+f3H2FnYytdoTDxMwSiTyvM479vugaradTt7FLhX0Y40I0YSbUdXbkyc8m/WQzDYMMzOwn6w6y4az7CG154wzBIxFUS8eSwY3XN4Imfr8aT7uBTP3wfgijg8p4MTlZVjUhopGDqM6PrOrFIHE07d6NWdmE6P3z+S7zwx7Xs23TsvPscZZRLFcMwMIyT/+ZEMURBQBA46X6mGxi6gSgJQ9oLAgMTuTC48NV1A13XkSQx1W4gREMQGPgGnGwrCAKSJCBJIrIsndHL1jCM1DgMA7NZxmyWz7iIGHptxmB/b7y2E/tPtBUGCkqeOPeQe2AY9AeiHD3SxthxeajqyQxgkiQOtj9xTl03kBUJpyIN7j/ztZ38DURRGKK8ONFGFIUh1zXSNY1ybhiGQVcozG/Xb+PJ3QeIJlOLTINUCspjHT28cvg4z4wp5MvLF1DoHV5DRdV1Xq+p539Wr6PZHxjM9jY4wwQMjnR0s/rgUZ7ac5Avr1hIVVbGiL+Vrhv0RaI09gbwh2NE4gm+u3odz+w7RELVUuc0DJoIsL+1A0kQGJeTOSgcGEAkkaQzGKbZH2BfazvP7TvCS4eOER94Vg0MWvv6OdjWyYsHa/jo/Kt419QJmEdISaobBsFYnJ5QZPB6TlxfLHkOmaGMk+Np9AfY09zGYzv3s+5YPUnt5Hha+gLsb+lg9cGjfHrxbK6fUI1JHup6bBgGcVXjyT0H+fW6LXQHw6d2k3ofTmkvDrwTSU27wnP/XH5c1sKBpofQdQVRTGmmdD2Kwcgvg6r3oRmRt3N4bxuappOIJkgmUtcuySIWmwVJFtF1g0Q0gSiJqKqGmlARBAG724phQH93kPqDzaiqTqA3hCCA02NHEAV0TScRSzL3hmmYLMqQD2W4P0oilqS1ros510/BbDOhmBVMZgVdN4gGo+SOyeLbD39q2Ad2yHgNEGURm9Oa+kjEVcxWE3d9+SZsLsuQxYGa1IhHE+iangp2syoo5tS4BFFAMctI8vmlboxHExgDCyU1qSEIAhabGcWcejU0NdWnmtQQRRGL3ZTqw4BwfwSL3Yw88MGOBGNIsojJoqBrOtFQHIvdRDQcx9ANzFYTJosy2G8ilhK4FJOM2WZCFEVikfhAvzpaUkOQBsZjemde1YSqEVfVgQBoAec7ULhIH1hgSeLFryD9dvZ1rrS29vHy6n1s23qc9rY+NF0nNzeNFddOYvmKiZgGntUNrx/lFz9ZxXf+5w42bTjGq68coL8/RmlZFu+6YwbTripBHng/nnpyB7//zav88rf38szTO9m08RhqUmfqtGLe8945FBa9Ob/7zo5+/vDAWja+fpREQkVVdR55/H586cPjvAzDwO+P8Pyzu9mw/jAd7f0IAmRkuFh69TiuvX4KdkcqfuPggRZWPb+Hgwdb8PeEURSJqrG53Hb7DMZNyEeSUmN94bndvLhqLw313YRCcX73m1f5w+9fG+zzT3/7OGlpqVicUCjOo49s5ql/bCeZ1EgmNX7+6w9QXZ07bKyqqrF3TyNP/XMHhw60kFQ1SkoyufX2q5gxqxTTwPu5Y1sdP//JKj73b9dy+FArL63eh98fJivLzdKrx7Hi2kk4nW+/JeVyxTAM/JEov1izmcd27kORJMZkeJlemEem005vJMrG2gaa/AFeO1aHAHxlxSLy01xDnl9REHBbLfSEIphliQyng3E5mRR43SiSRF13L6/XNhCIxNha38wfNu7gS9csxOewnXZsumHQHQ7z/ZfW88KBI5hkiQm5WeS6XcRUlYbePtoCQaqzM0izjWyJaA308/M1Gzne1YvLamFmcT5FXg8xVWVbfQuH2jvpCoX50+ZdFPu8zC0tHPZe5rid/PfNy+kNR+gNR+kNR/iPp1/CHzl/RWhjbx8/euV1Gnr68NqtzCwpoNDjJpxMsK2+mcPt3bT3h/jd69sp9nmZUpDzBuEYttQ18au1m+kKhRmT7uXO6ZMYn5tJLKmytaGZh7ftxR+JkuN28tH5M1heXY7bZrmCQ3svTy5r4aDR/00yGUuO53MAdAR/SzD2OiMV8NKNOHG1Aa/txrdvgIaGofvBiDKkzoBgQ5DSL1g3XU09PP37Vzm87TiJhIrH5+Rdn1nBpPlVhPrCPPrTF3Cm2elp6+PQtloUk8w3/3Y/sXCc//3iX6nd25h6qVftAeA7j30Wb7ab9oZufnL/H+ls6WHeDdP4yP9792Cfv/vGYxzf20jjkVY6Grt5+eGNzFoxiXd9diVaUuP/vvYoh7bVEOwN88ixnwx+QDRN5/D246z683rqDjSniqnlpvHFX38Qi93EjlcP8LcfPkt3Sy9f+r8PM3lhdepWGgYHNh/jqd+8Qk97H5IiMWvlZFbcNR+3zzH8ppwjT/3fK7Qd7yQRV2k43IIoiSx7zxyuu28Rhm5wcEsNz/9hLa11ndjdVpbcMZtFt80gFonzpRt+wMe++x4mzqsC4H+/+BDlk4u47t5FNBxp5Ycfe4C7v3ozL/xpHYGeINd/cBFL7phNqC/CYz9fxcEtNSTjKmWTC7npI0spGZfPwz98lmgoTtAfovFoG7JJ5pr3zmPF3fPPqNW8GGi6zvMHjrChroGEqiEJIv9z0zVnNd1faLqCYXojEaqzMy96X22BIOFEgorMC/d+vlXqajs5dLCFMaWZLFxSjaHrbN18nD//cT2SJHLt9ZOHtP/pj1Zht5lYvmIi8YTKlk01PPD715AViSlTi1PadgOSqsaPf/A8Ho+NG26YSldXkC2baggEXuZzX7iWjMyR60ycCV+6g7vvnc+Kayfxz8e3sWVzzWnbdnX28+MfvsDxmg4mTSni6uUTUVWNuuNdqJqO2XLyOdu6pZb6ui6umlFKRoaTPn+YjRuO8ccH13H/Z65hTGnq2Sgrz8LpnElLSy9/+8tGFi6pZsbMk9maHA4LJ+YHm83EjTdP46oZY3j15QO88vKBEcepaTpbNtXwhwfXYbOZuO6GKZjMMnt2NfKzH6/ivXfN4Yabpg4KXgC//sXLGBjMW1CJw2Fhz65GHvv7FhRF5oabpr7t7/LliqYbvHK4lid27cdmUrhl8lg+Mm8GWa7UN98wDELxBD97dSN/37GXjccbeW7/Ye6ZPRXLKZmRREGgxJfGZ5fOIT/NzfTCPOzmk4krDMPgUHsXX3zieY53+9nb0sGBtk4WlBefcXzP7TtCNJlkccUYPrFwJsW+tMF3JqGqNPYGcFrMyNLISqv2/hDdoQgLyov5/NJ5jMnwDsZhhOMJHty4g9+9vo0mf4BtDc1Mys/GaRma8OBEtWmH2USh14Om62+66FmTP4AsiiytKuULy+ZRkHYyVigQjfF/67fx5y07qevxs62hmarsdGymk/exLxrllSO1dAbDFPk8/NcNVw8RIKYX5VGRmc6Xn1xNR3+IcDyBzaSMxp5cglzWwoEk2BDFk5J9LFlLUuvFLBcgMPTjK6CMmBL0YmIkD0DsKQy9h0G7PYBpMoL9vgvWj91jY94N07j1E9dgtpl5/Gcv8MiPn2PivFTNhGgoRv3BFu747Aru/urNdDb34PI6cPucfPn3H+bvP3kBm8PCHZ+7dogLQF5pFv/52Gd46revEOwNDenzsz+9G03V+OotP2bR7TNY+YGFQ/Z/4Zf3smPNQX748d8P2d7d4ufZ368hqzCd9/7b9djdNlpq2nF4Ur/j7GsnU1Sdy++/8fiQ4wQEcoozeNdnV5Bfls2xXQ3841cvMnFuBS7vW8uEsnfDEd7/5Zv46H+/m72vH+EXX3iI2Ssnk0yorP3HVibOr+Tj37uTmr2N/O8XH2LMuHwyC89SVMaArpZe6g4286kfvx9RElHMMrIs8c9fvUh7Qzf/9uv7MNvNrP7z6zzxi9V87H/uBGDnmgPc+83bmDivkh2vHOCBbz7OzOUTSc+9cFWnz4VQPMGRzi7eM20iU/JzSagqiiTRE44QjMcp9qbREQxhGAZWRaE10I9BytqQ7rCR5XTQGQzTF42iajp2s4liXxqSIFDf20cgGkWRJHLdLtJsVhp6/RgGRJJJEqrG+JxMgvEE62vrqenuRdV1irxpI1ovoskk3aEIbqsFl8VMkz+AJApku5y0BvrpDkVQJHGwr1A8TkNvH6qmYwDjc7Loj8V47VgdnaEQcVWlxJeG3WSiqS9AbziaKpbjcpDpdNARDBGKJwaLO43LyUIRxYtiVblq5hiumjEGk0lGEFPuMIsWj+Vzn36IvXsaWXndpDfEAsE3/+s2HE4Luq4zcVIhP//xKnZsr6OyMgeHM6XFFACPx8ZXv3EzFotCMqmRkenk0Uc2c2B/MwsWVQ1q5M8VWZbIzU0jNzeNTRuPIZ7GAqPrBqtf2EvNsXbuuW8BK6+bPLhgTrk5GYinLKDveM9M3nfXXBRFQhAENE0nNy+NPz64jq7OfkrGpFxAKqtyqajM4fChVh57ZAtlZVksWFg14hgkSSQjw0lGhpPamg6U01gde7qDvLbmEA67mU99bgVjxqSyNN148zR++L1nefbpXUyYWEBZ+cnsXP39Uf7nh++hqDiV2GH2nHJ++L3nOHK4jfkLI/jegkLjXwXDMOiLRnlw4w4Mw2BqQS7vnzF5UDCAlIuWw2zivjnT2Nfazp7mdjYeb2RJZSkVWUMFfI/NyvtmTB6xL0EQKM/w8YFZ0/jmsy/T0R+kIxgase2p1Pf4uW5CFV9avgCf3TbkPTTJ8jkVHyvN8HLvnGmUZniHHG83m7huQiUbahvY3dzGofYuAtHYMOHgQlOVncGH5k4fIhgAuK0Wrh1fwea6Rg60dbK/pYNQPDFEOAjGE+xraQdgTkkhRW9w8ZIlibE5mYzNyWRnYyvHOnsIRGNkKqPvw6XGZS0cFKR9C5dzaDBuhvP9pDveiygMNeMl1Haa+/7r7RweRvQJBCkdwbwcTi3GNpAd6EJhsZkxWU0c3FKTcrsxoLOpd9CHV9MMxs+toGJqCRabeXAh/k5Qf6gZTdOZd9M0cgZSIbquOoc87ALY3TZaj3ey/eV9BHpCBHqCxCKJtzymquljGD+7HJfXwexrJ/OLz/+F9sZudF3n6K56XF4Ha/+5DUi5A+3dcIRlhWevlyFKIotum0FW4clJKhKMsuPVg9z91ZvIGZOFIMDcG6fywDcfp/5gMwDjZ1cwbmYZDreN2ddO5ndff5TO5t63XTiwmRTKMnzsaW6noz/M+NxMct0u9rV1sL+1g/sXzGJrfTNJXaPI6+GBTdtZWDaGQDRGXzTKXVdN4cXDx+gIhsh1uajp7uGjc69CFiUe2rabgjQ3saRKtsvB8qpynjtwBIB0u51oMklVVjp9kSg1XT3U9/axv60Dr82G0zw8VW00kWTN0eOUZniZXVLIX7btYmFpCRZF5uEde8l0OAjGYhSkebimuozVh2roCAZxmMwYGFRlZdAbiVLb3UN3OML+tg4yHQ7iqsZD2/aQ63Ki6jqiIPCuKePZUt/E0c5u8j1uYskkFZnpKBfJFUlRZDo6AjQ39RLoi5BIqIRDMTAMIuH4YFzBCWbOLsNmT90jURTJy/dSVpFN/fEuAoHIoHAgiiIzZpVhGXB1UxSJCRMLeGn1fvbuaWDOvPKLpuEOh2McPdpORoaLufMrh/QjCALCG4QSi8VEQ0M37a19hEJxkkmVY0fbicdVEomLW3G3o6Of2poOFi0eS27uycWO1aqwfOUkvv/fz7Bvb/MQ4WD6jDHk5p3UItvtZgoKffT1hYlGEjBasPac2N3URl2PH7tJYXJBDkW+4d9AYcBlaM6YIvY0t3Oko4vWQD/lmefnGidLImNzMpBFkVhSJZpIjFhc9FQ8Nis3TarGa7O9KcWAAEwtzKUyc+T4BpfFTHmmj93NbXSHwhe9urQgwOwxhZSke0ccj9duoyTdy4G2TjqCIRKnxPMAqJpGXzQVZ5hmt2IZwR3WJEv47Kk1SDAeJ6Fpw9qM8s5zWQsHgiANycVvNY3DopQjjHBZgqAgChdX4h6GEQLTbQjKeBAunhl555oDbHp+N/mlWdjdNjRVQ9d1Tk1S6/Y63jG/9VOJheOYLQpm68i1CE6Hpuo8/X+v0tXSy5jxBSmffE3H0N96GJPL5xyMMRAEAUmRSMaTaKpOLJJA03Xi0ZQQcsvHr6ZiSvHIY9T0QYEMUosvb7ZnSJtoKE4insST6RoMTlRMMiaLQjiY+qh6MpzIijwYxCjJIsnE8IDwcyGk9nOk/yDtsVYSehyLZCXLkkuZoxKXMnIWrBMoksTy6goOtHXQ0Bvgj5t38tF5M4a0OTW8TEDg5onVJDWd76xaQ2sg5UM+MTeblWMr+O5L66jt9hNNJJFFkQ/MnMqRji5ePXqczlAqcC3P7WLl2MrBQLdiXxpzxhTitJi5c9qk047VY7OS6bTTGghytLObUDzB+NwsDnd0sa2hmZsnjiWh6TT6++gKRdhS38RH5l5F6SmTYGm6l5nFBbQG+gf7Wl9TT1JVuXvmFPyRKH/YtIOG3j4AMhx2Vo6tuKgVSDVNZ8f2Ola/sJdoJIHHY8NsUVJxOwltMDj2VFdKr9cx5G+rVcHtttLRHiB+6kJaAK93aC0MT5odi0Wmuzs05PtxoenvjxIJx8nNSxvijjMSiYTKs0/vYtOGY5jNMp40OyaTTFdnP5qqX/RAxmg0QbA/RprXjtl80vosCAK5eWkkEio9PUPr6+TmDdWYiqKAokiD361Rzo4BbKlPZcZyWSyUpntP635ikiXyPSlFYSAapzsUQdMN5POwfJ0o2maSJSIJHVU3zioclKZ7yXE533TqW6tJoTDNg9My8nwoi9Kg+1NcVdEvwHx3JuwmE4VeN/bTZA1SJHEwsDqmqoOBzycQBRHrgEtTLKmO+KxrukE0mZrPLLJ8ScV3jXKSd361eAHx2W9FFGzA8MlGEh147bdikvKGH3iREMzzMeIvAEmQsk7WKBCsIHovSB+aprNl1V5caXaWvXcObp+TV/6+iU3P7xo6FlEcql48uWfAVYFUNNFFDguyOizEowniA4G354JhGMSjcV762wY++O3bmbViEq31XWx7ad8FGZMkCcMnAEHAbDORU5zB/BunUzqh4JRdAuH+KLIik0yktB6RUIxoMDZMWHmjW4XdbcNqN9HV0kv55GLAIBFLEovEBy06oiRekHSK7bFWXmh7ktZoE3m2QqySjb6Yn65EJxnmzLMKB6qmY5ElZhTlMyk/hxcfPUpnMIwiikQTSXTDoC8aG5ywo8kkunEyY4VJllA1naSeWsDFkipmWUIQIJxICVuarqPpOvLAffLZ7cMWAMZAuzNN1KIgMDY7k9WHj/HYrv3MG1OEzaRgVWQsskxFZjqVmem4rBY8VjO6oaMOTFyn/mK6YaCdsiiwmhTCAxOZbhjENW3Q/cRttWA6jS/xhaK1xc+zT+8i0Bfh3XfOoqg4HYvFRDSW4OCBlhGPOd3iUxSFoRU9DQbvwQlO7L3YE7ZA6p1LJtQhAvVI7NrZwGOPbKasIpv33TUXj8eGySyzbUstx462X9RxwokMTiczIZ36jdQ0fSBr0tD7ZTYroylRLwANPX1Aypf9j5t38uy+IyO20zHo7D/pBhSMxVF1DfkNli/DMOgNRznQ1kFtdy/doQjBWJy4qpLUNPyR2CkpN42zCp5eu/VN+/cD2BQFt9Vy2u+aIHBKocazj+etYjebcJpPX6NIEAROyEEjvbd2s0JlVgbHu/3sbm6jvT+E6xTlia7rtPgDHGrrQiCl/HFdIDepltoOHB4bLq9jyPj7e0NEQjF82Z5LQkF6uXBF3SlFOn3AooAJp3km8PZJqUZiK8Rewoi9BJhOvtjmeYiur16QPkQxtbiPhOIICDQebeO5P6495+NNZhlXmoP6Q810NvdidZhxeuxD/H0vJCXj8pFkiTWPb8HmsuJw22g43Er5pCIs9tN/JERJSmVBCsWIhmNsfXEvDUdaL8oYT5BV4CMty8XrT+/A7XMgKxJHd9UzaUEVopSyCuxae5DisXnseu0gTcfamDR/ZP/mE5itCnNvnM7zf1hLQVk2NpeVVx/djCfdxZhxBWx7ce8FGXtMi7Kq/Snqw8d5d+Hd5FoLkAUZVVfRDA2ncvYq4b2RKH/fuZf6AU15eUY6peleesIRHg/s59vPv0I4kWRGcT6QWvD99LUN9EWiVGT6yHW7QBBYe6yObQ3NKJJEWYYPsyyztaGJ/3jmRUySxJwxhWQ6T1/N2We30egP8O0XXuWuGVMoTR9ZsM71uJBFkfoeP3fNmIwsipRlpDOvtJgn9x5M+X6XFFLiS+OGCdX8dftuILWw+/dl87EpCplOOy8fqeU/V63h3llTGZ+TyeZ6N19/9iVEQWBcThYl3jSOdHQNXWhfJLo6+2lu6mXBoiomTSnCbjenMrj0Qm9vmJwcz7Bj2lr9nGpNiEQS+P0RPGk2LKcE+RoGtLf1DTm2pydELJokK9t1UQuBuT02bHYzDfXdJAYEhNMtSA4faqW/P8rK6yZTWZVzsmikbhA5jVuhIAgYGGcVPM4Fh8OC1+ugq7OfaDQxENQMhm7QUN+NxayQlTXUvXVULrgwBGIpa2o0qbKn+dwFQVXXeaOSPRiL89jOfTy//yjdoTCRRJKkpg20NQYVG+eDLElIb+E9kSVxWDrQdxJFkt7SeNJsVhZXjuH1mgb2t3Twk1c28J7pE6nI8pFQdfa2tPHHTTvpDUeoyErnquL8C1bb4PVndjJlYTXON1RjbzzaRltdF3NvmDoqHJwH/zJ3KvWwvL0ByYLzS+D4/Ag7zs+l5ox9CAJX3zmXv/3gGe5f/F9kF6Vz68eW8efvPnVOx8smmamLqzm6q44v3/RDPBkuvvW3+3H5HDz47SfY/MIe/J39GLrO9pf3s+IDC1hx1zxsZ0jH98qjm3jugddore8i0B3kvmlfJb88h3u/fgvFY/O45eNX8+RvX+Yrt/wYdIPCyhz+/XcfJtQa4c///SR7Xz9Cb2c/h7fV4vI5uedrNzN92QTe8/nrePRnq3j0Z6uYfe1kyicVDtZlSI11N70dAZJxlQ/N+BrzbpjKzR9bhifjzVXRzsjzcv19i/jnr1/mSzf+EFESKRmXz4S5FVjsFm79xDL++J1/svGZnYybVU7J2HzMtjP/toIgcOOHFxOPxPnOPb8mEUsycV4Vd37hOqyOC+f2dix0mOOhYyzPvpEKx1gkQTr/zDN2K/fMmjqoiVZkCasik+t28u3rlg76upskmbqeXjKdDj4y5yokUcQsSyRUDZuisKyqjLklhUhiyiQtAB+fN5OEpiMKYJEVFEninplTkcWhk60gCFRlZvCNlUswDOO05m4AWRR5z9SJ3DppHI6BoGWrInPXjMmDvrEmWUISBOaUFDIlP2fQdcaqpFLiTszNpnylD8NIadFkUeSDs6YT1zQEGHQ7uKaqHADzRZ7YFZOMySQN0bAbusFzz+6mv3/k1Mzr1h7mXe+Zhc/nQNcNmhp7OHSwhZXXTcaTdlII03WddWsPs/K6yTidFlRVY+eOerp7gkydXjJM63ohsdlMjB2bx769TTz/zG7ee9ecQfciXTfQNR1RFBAlEZst9VvGYydd69rbA2zdUkssNrK7ndksI0siff4Iuq6fNjD6XMjNS6N6bB5bNtcwd34FVVW5IKSqMD/1z+2kee1MnFz4ps8/yukxSaklisdqYV5Z0RmVCKcyIS8L5ZTnN5JI8oMX1/P0vkPEkyppdivzSouYmJ9NjtuFy2LGZlJo6w/yb0+sGszvfzaEU/55JfBWr0QWRRaUFfPeGZP4y5ZdrKs5oRgSMYxUPYNIMkmux8V9c6YzrTB3iKVY1w2O7W7gif9djcmiEPSHue6ehUxdMg5ZkXjqt6/w+jM7UUwSZZOKuPOL16MlNV58eAOr//I6W1bvwemxs+SOWcy9fioHNh/jbz96Dn9HgLX/3Ma0JeNYedd8zDYTB7fW8tjPVqGqKpMXVLPy7gXYXVaeeWANFpuJbS/tJ9wfZckds5h3w1Ref3oHu9YdIhqKEQ5Euefrt1B5jkVaL0euKOHAMAySegeh6EbiWj2GMfwFT7PdiNVU+fYMKPJn0KOgTABlPEjZpF6/CzvpVk4t5hsPfXJA+5ZyS5l93RREUcCZZudTP3rfYC2ANyIIAsVj8/n3334IXU8df6JWwL1fv5UP/MctQ9qLkjioURQlkf/3j88P0zAuvm0mC2+5aojPcqqQkoggCpRPKeILv7x30H/yhF+9w23j0z+9e5hrjiSnssAsv2suV985G4OUttfAGJz0P/AfN3PXV24aOtaBxcWZuO3+5WCczIwiiAJ/3vM9RFlCFAXKJo08VoAJcyr5/jP/jmEYQ4oxCaJA2cRC/nroh8jK8MWjxWbmfV+6kfd+8fqBaxEGXIkE7v7qLUOKOylmmQe3/zfieS5CW6KNCIJIhbP6TQkGkHItcZqHCyySIOCyDPWzFwUBRRJxWsyDrjZJTUsJBAOm81OxmUy8MSz+1KwXpyJLIi7p3AQnq0nBylC/cKuiYFWGChWKJKGM4BI00vY3nhN4S64E50NBoY+SMZm88NweEgmV9AwXB/Y1cfhwG2VlWSPO5na7hc9+8k/MmFUGAmzeWENGhpOrZowZDD4+QTKh8cXPPMSkqUX0dIfYvbOeGTNLqazMGcyMFA7HOXqkjXAozsEDLSQSGtu31hEIRHE4zJSUZJDmdWAYBj09IerrugiH4jTUp4L61752hKwsFy63lcIiHx5PSrN37fWTOXCgmb8/vImdO+qoqs5DFAXq67pIz3TywQ8vwu22MXNWGY8/uoXf/OplWpp70XWDXTvr8fvDpI9QP0EQBJxOK9Xj8lm9ai89PSHSMxyEgjHu+/BibDZT6rpCcY4ebSMUinPkcBuJpMrWzbX0dAaxOy0UFfvw+Zy4XFauvmY8tcc6+O//eoqp00owm2V2bK+jzx/mwx9bQn7+hXETHWUoXntKCeW2Wrhl0jhmjTm3iteCcNKuZxgGr9fU89iufRgGzCop4MvLF1KW6UN4Q1uzLMNFd965vDnT3TkRHH7PrCl0BkM8s/cwumEQSSRTcSFeN/NKi7ll8ljGpHtH+HwZRMMx2hq6+MafP0FLbQfbXt5PXlkWeaVZzLtxKsvvmkc4EOXZB9dwYMsxpi0ex00fWcrxfc0su3M242aUpdYMosCEuRXc0Beh8Ugb19+3ELvbhigKxKMJ/vT/nuQrv/8IiViClx/ZxK7XDjLvxmnEIgm2rN7Lv/36g9hdFgwdNE2jvaEbq83CB795O3tfP8Lqv7xO+aSi866tdLlwRQkH0eQhGnv/nXiyDgQZTe9DEpyAgW4kMMl5OC3z3r4BWa6BxF5IbMWI/AGMfpDGIFiuAevNF6wbQRSQxaEP6KmasrM9vKkFrzQsUmOkbW88bqTFryiJZ1yUn64/4IzBiYIgIJpGPu/Zxno63piNRRAE5FNMj2caKwLD7vup+05nwjxRcZYR7tEJweN04zlXImoEh+RAFuS3RbNRkZnOt69dOqQvl8XCHVPGX/S+r2Q8Hhvvu3suVpuJbdvqMHSD6rG5/M8P3sOObXUcP9457Jg73jOTvr4Ir760n1A4TnV1LrfcfhXVY/OG+MHLssT77p5LzdF2Nm44hq7rXL18Are+awZujx1BSGny6o538d3/enrwOItFYdXze1j1/B4Q4MMfXcw1KyaiaTrbtx7ngf97bbCt02nlkb9uBMDhtHDPfQtYuDhVu8TpsvCVr93Iquf3sH7tEdatPTSYCnXixEKsA0kLCgq9fO1bt/DwXzbywgt7sNvMzJ1fwdRpxTz1jx2YRvgGeX0O7v3QQp54dAsH9jehaQZ5eWknrS8G1NV1Drkuq8XEc0+nYrUkSeT9d8/l+pumIggC1ePy+Ny/XcvTT+5g16561KRGaVkWn/zUNUyZVjT43MuKhGsgaPxUBFHAZjPhcFpGaxycIwIwMS+b1QePEY4naAn0D1TUPb/vmW4YrDpwFMOALKeDWyePozIrfdh5DMMgmkiSHA0YPyNnu/vRZJJHduxj9cFjzBlTyDevW0quZ2QhfiQkSSSrMJ303DRESWTjc7sJ90fRNJ3Nq/aya+1BZEUm0BNizPjCwflUEFMKSEkRB9c/giAMKjSlUyqgNx5po+5AM//z4d8BYLaaKJ140vo3dVE1DrcVSZYwDAMtqmGxmSke6yIt00V+WTav/WPrRU3aoGo6SVXDrJy5yvzF4ooSDvoiq9D0EAXe/4fdPI3DbddSkvFLMHT8keexKBXYTBPevgEJPjBNBbkSQZuKodVBYj+GWn8FGSJHuRQRBQGdt2+SO1MA2yhvjfx8L5/+7PJh208U/nojmqZz27tm8K53zzzLmQ3MJpn7PryI+z68aMQWoigwYWIBjz35mbOOU5YlVlw7iRXXnj6r1KkIgoDNZubW22dw6+0zzthuwoQCJnz/3cP2TZg4sjuPKAqUlGTwxS9df9r9EyYWntN1nRhDyZgMPvP5FWdsN3lKEf/763uGbXe7bXz4Y0vOqa9RTjJnTCFWRcYfjbKrqZUV48qHWS3Pha5wygXPYTGR7R5e3M8wDBKqxraG5gsy7n9ljnX28MKBo0iCyN2zppDrOb9iioYBiWgCNamRjCcHF/0djd08/btX+elLXyERU3nsZ6uGHCeKwojJGAQhFVp+qkeCyaKQOyaTbz98P2araVisicky1PoMKQXkqcrQi21fqm/pYdu+BlbOH4vH9fann7+ihIOk1ordPB2XdTGS4EAUrUiiB7tpIoJgpjv0V6LJyTjM09+eAUUfxoi9AoIDlMkI5ukI1jvhAlZHHmWUkXDKbiJamIgWxmOMnLN6lCuT89FmjTpQjHKpIggCJeleVoyr4Mk9B9lS18RTew5x3fhKPDbrEF/1ExnSuoIhRFEky+kYEjPjtaXck0LxBG2BfnTDGHK8puvsamrlH7tGrpI9yrkTjMXp7A9hkiWCsTjRpDqQ3lQ4pwxehmHQ2x5g55oDdDb34slw4s5woiY1vNlujuysJ9IfpaO5h8ppJYPH5Y7J5NiulOI1tzSTjNyUq58zzU4sHGfv60coqsoluyid3DGZZBels/Yf28gq9KGYZfJKs/Ckn398oqrpHG/qJqlqOGxmctJd9PZH6OuPoGo6xXk+zCaZmsYuNM3AaTeTm+mmpaMPXU8JpW6HhUyvk7aufvqCEawWE5FognA0wbHGLhRZoijXS9rbKCRcUcIBgoSIRMrwJSCJHpJqO5gmokgZ6EYcVet5+8YjV4DVBCip2gtaB4YeAKkYwXRu2rVRRnkzFNpKkASJnf6tpGdlYRKHVxUeZZRRRrkYnNDEJzSNuKqSUDVaA0HiA4kBVF2nvsefCvAfyJBjliVMsjxk0W6SJe6eOYWG3j52Nbby4IYd1Hb1Mj43C4/NgiSIJDSN/miM9v4QRzq6mZifzXumTxyMUxIEgQVlxbx0qIaeUIRn9h7GoijkeVyIgkAgGuNIexdP7z1MJJkkzWbFH4m+I/ftfDEMY+AeayQG7nM0qQ6mKNYNg7ruXkxnuc8XEpfVQrbLyeGOLv6yZTeHO7pwW6yDgsGJRBGZTgeV2elkOR1DUidLkojL56CvK4iu6cy6ZhLeTDeCKLDyrvnU7W/CYjdz9Z1zSD8lW9vCW65i86o91B1oxu62DQoHxVV59HX201bfhd1lJbPAh2KSuedrt7D1xb3U7G3Cm+UiuyhVlLVichHKQDX6wTHJEmWTCgddfN0ZDmavnIwoCoRjSR5+bjuTq/LJznBhNSts2HkcSRKJxhLUNnWzfO5YGlv99IeiqJrBwqvKeGbNfvKy3DhsZvKyPJhNMs+u3U9uphuvy4bZJNPTF6apzU9fMEpNQxe3L59yUX6zkbiihAOTlEsovhXDSIAAFqWMvuhq7OYpJLQOVN0PCAR6Q2x79SB2l5Wrloyl4Wg7pWMvfP0DwbwAtDJI7sNI7oXkETDCCJYVcIUJB/U1Hbz49G4sVgV3mp15S6vxpp+fOfFUkgmVDWsOM3XWGFzud66i8+VKoa2YyZ7pbOvdSFJPUOEci02yE9EiGOiU2MtwyGdPZzrKKKOMcr4kdZ2fvrqB7lCEhKaRUDXCiQStgX4AAtEY/++F1zBJEookYpJlsl0Obpk8ltKMk+WjRUGgLNPHZ5fM4Y+bdrLxeCOPbN+Ly2LGaTEjCQIJTSMYTxCOJxCA/DTXELddAVhYUcLyseWsPniMTXVN1PX4yXQ6EAXoj8Vp7A1QluHlkwtn8cqRWtYdq387b9ebpj8W5y9bdtPQ4x+8zwlNG5IC9jsD99kkp5It+OxW7p0zjWzXxfn+F3o9LK4cQ3NfgJ1NrexsGppyXBjIUJfhsFGdk8n7ZkxmSkHOkEQQbp+T5e8fHh+64JarTttvdlE6N3906bDtDo+NeTdOG7Y9pziDmz4yvP1I6cgVk8z42eWDf6fnpLH8fXMxjFTCjOI8H0lVw2pR6O2PUNPYRWFOqkJ6TUMXi2eo6LqOSZHpDaSsA7FEkvHluZQXZWAAh2rbUVWdGxaNTx3X2IXPY2fhVeXEEyq/+fvro8LBm8VunkYksRfdiAICXttNNPZ+mYTagKaHkEUPJjmXF/+5A1+2m62vHGTyvAq2vLj/oggHevgB0FoQxCwEuQxM80B0g5hxwft6p+lo7aO9xc9N75nJrq3HWffiAa69bfqwwLxzRZRESsoyh1QkHeXcMUsWFmZcg1N2sbtvB0dCB8EwMIlmyhxVFFiL3+khjnKBGTc+j698/Sby871nNd/PmVtOfoGX8vKst2dwo/xLoek6zx04OqQw2akkVI0NtQ1DthV63cwpLRoiHEAqg9jUgjyynA421zXxem0Dh9o66Q5HSKgaVkUmw2FnZnE+UwpyWVhePCR3viAIeO02PrtkDpVZGbxypIa6bj+dwRA2k4nCNDd3z5rMynGV5LqdtASCl41wEE0mefVILQfbhicmgNTv8Mb7nOm0c+uU8RdcODCMlND32M59vHb0OAIpQSHdYRsscmkYENdUOvtDNPf109zXT2cwxA9uXUme582lHH8nEYRU2uTrF43naH0nR+s6yUp3YjErlOT78LrtzJ5cwt6jLXT3haksyaLLH8IwDCRRxG4dsOgbBiZFIp5IcmrIt81qQhIFFFlEU9/eQPkrSjhwmKdjkUuRpdTHxWmZR477C/RFV2FR8vDZb8OiVODvfpEFN0xhyysHMHSDYGDkfOFvFcF6GxgqCBZAQRDtIFy5i930TBcTpxeTSCTZ+OphVFXDZMgc2tfMs49uRdN0Js8Yw6Ll46k71snOLbXcftcczBaFjtY+HnlwPfd/5To62wK8+vweDu9r4dP/cT2ZA6ZDNamxa9txXn1uDwALrh7PtDll/Pr7z/ORz69g/84GVj25g8994yZ2b6vDl+GkeuK5pb67EnErHub4FjPJM52EnsAwdERBwirZsMuOd3p4o1xgvF4HXu+5/a5Z2W6yss9cIXuUUd4sZlnmT3ffhqqf+4JGkaTT1jGQJZFCr4dsl5MlVaUDWYU0dCOVWlmRRCyKgt1swqIMd5kRBYFCr4e7Zk7mxolVxFQV3TCQBAGzLOMcsETousE9s6Zww4RK0h3Dq7UDZDjtfOemq4kkkjjNZnyO87NsS4LADROqmFVSgCxKpJ/heIfZxH1zpnHL5LGYJIls99AFfbrdxk/fdd0pVZ3PjiyK5J6yEFckkVunjGNBeTGKJJHuOH0tCbfVwicWzuJ9MyanrD2njCeaTPLAhm08sn0v2W4n/7FyMZMLcjDL8kllhcFgatOn9h7ir1t3s7e5nR2NLWS7nEiiQNmkQu771m3nfD3vJIZh0NUb4i9PbQEgK91FRXEW/aEYW/c1YOgwqSqPDK+DZ47tJxSJD6RFH/5cZae78Hns/OiPr5CT4SI/y/OOJq65rIUDwxhaAVPAjCJlD/4tCjZ8jjvw2m8GRETBBEj4stw8+cBammo6+PMPnien+CIFCIuZEP0HRuQh0NoxpFwE+/vAcjOCcFnf+hExgFgkwaE9zWTleZBliVg0ySMPrOPT/3EDkXCctav3c2B3E2MnFfCH/32ZFTdNQTHJ7NhUQ0aWK5XGLNfDtbdNp/ZIO2oy5aNqGAa9PSHWvXiAD312Od2d/by2ah85+WmoSQ1/d5A92+tSRZ/quunu6Ce/aDTw2yyZMZ9jjYBRRhlllAuBOBBMfCERBAGzIpPxJmuMCIKA3WzCbj59oUpRTFkZvPbTL9gVSSLP8+YFa0EQ8NiseGynLyR6AkkUSXfYT7tglyWJQq/nTY/lxHjSbFbSzmE8siiS6XSQ6RyqhDAMg87+EA9t3YMowPXjq7huQiWyKI7oWmwYBgvKi9lQ28DBtk7quv1oho4syFhsZiy2y2fOSk+z8/E75wOplKomRebqOVUsVnUMQJFFJFHk6x9fgSgKCAgoisSY/HRMppQrlSAIOGxm7rxuGqqWKtooiSKGYaAMpHf/6seGZ6y7mFzWK1TdCNEbXn1exzgss7n2/XM4frCFwvIsCsqyKB2Xf3EGGHsKI/4yOO5HkAow1DqM2FMIKGC96ezHX2a89sI+Nq05zIRpRXzos1djMssc2d/Cwd2NfOffHgXAZJapHJ+H1WZi7KRCtm2sYcnKiWxed4T7PnX1YM5is0UZUqvBMAzqazrZ9vpRmuq6wTCw2lOanpKyLOqPd1Ff08GcJdUc3NtEIp4kPWvUp/5CoKoamqajKKliaqOBzaOMMsooo5zAAGq7e4kmk+S6nRT63MiidEb3RpMkDbobmWQJ4TJM8C4IApIgYLcOFWZMiozpDU4ijjcIPG+sQC8IAmaTwunEIrv19ELtxeCyFg6SWg/N/V8EJERBASQEQUFAGVjADH/YCsT/Zu+rPVy1uJqKiYUkYknWPr2TpbedPtDlzWLENyHYP4agTAJBRJArMCQvRvRZhCtQOFi0cgLLb5zM03/fSrA/hjfdiSQJFJVm8r3f3jOkwJcgCMxbVs1ffr2G8qocNNWg5Cz+z4oiMnF6Cf/+nVuRBsqxCwL090U4sLsJs8XEmPJsnv77VnILvDicZ9eEvBPoup76EAqXRx2APz6wln8+vo1vfed2ps8Y804PZ5RRRrnCiMWShMKxVNFHScThsKS0rKf5PmqaTjAYw+22ntc3VNd1NN1AlkbWaI/y5jnhw5HUdMLxJLqhIwnDC/6dSDt7oLWTRn8foiAwJt2L9A4U+hrl9FzWwoEomLEq1RhGEllKxywXYpaLsSiVKJIPUXAgiXZEwT7oxiMKFo7tfZ7Jc8qx2EyoqsaxfU0XRThAUMAIY6AhGAIGKujhKzruoLQqh4KSdNat3s9td88hvzgdq93MlvVHyS3wIooCvgwnDpeVssockgmVZx7dxtLrJg6eIxSM0t7sJxqJ09biRzbJZGS5yB9w/9q5qZaMbDeiJJCV46G4LIuHfvsaM+ZVkJHtoq83RMW43HfqFpwRwzA4sK+ZzGw3mZmXXwDWKKOMMsqFZu3rR/jr3zdRWOBDkSVuv2U61ZW5p9U8d3UH+e4Pn+P733nXeSWt6OkN09TcS3VlzmAF7lHeOgJQkenDa7fSHQ6z6uBR8tPcFHrdmKVUzIE+IBQEY3G21jfx0NY9BCIxxuVmMTEvB3EEQWKUd47LWjhQpCzKMh4iptYSSx4nrtYSSeyjP7YeUbCjSD4UKROTXIBJykWWfJjlIhweG4d21pOW6aS/J4zdef4VF88FwTQfYqtAa8MQPaD3QvIAgnnRiO0Nw0DHGFHavtTxpjspq8xGEASWXjeJpx7ZQjyWJM3n4EOfuYbXVu3lwO5GsvM8zFlUjcOV0vhcd/tVbF1/lBnzKgbP1dbsZ/uGGtIzXezb2YC/J8zS6ybi9Tm45b2z2Lz2CMmkRkl5Ft50Jx6vnZLyLKbMHIPZrDB5xhgqLkL2qQtBKBjjlz9/kVtuv4qrl088p6Iwo7wzxJIqW481EU+qLJtYds6aRn8oyq66liHBmBW5GRSmey5abvFRRrncWbKwmg+8by5PPbeLV147RHlZFjISXV39NLX6kSWRnGwPGekn3UWPHGsnkdRw2M2UjclEliUikTgNTb2Ew3GsVoXy0iwURaK/P8q2HXXsP9hCNJogN8dDfl4aypuMYRjlJIIgkOm0c/fMKfxly25er2ngWGcP1dkZpDvsyKJIXFXpCUeo6/bT3BfAJEmMy83ikwtnkeG0jc6FlxiX9VshCCKKnIEiZ+C0zAJA06MktXYSWgvxZB3hxG76oi+h6f2YpBxyPV9mzvIJ7Fp/hI6mHnTdYP51ky/O+CxLUmW7k9vBiIFgQzAvQDAPz98LoBk6D9fupNKTSbkrA4/Zdtl44ZWPzaV8bEpb7013cu/9ywb3FZdlcs8pf5/KvKVjmbd07NBzVedSXj1c8y/JEtUTC0bMQPTJL183+N933DPy/b0UOHq0nf5AdLQ07WVAIBzlO4+/Qld/iB0/+Mw5v4td/SEe3bgXfyhCeyBEXzjK52+Yz/vmT0WULpc3+sonEIiwfs0hwqE4kiwyf1EVWdmed3pY50RXZz8H9jUzcXIhaV77FeMio6oaGKTimxAIhWI88dQOPB4bmqZTe7yLBXNTiqRgMMbRY+1oukF7ewBtyVgqK3LYvque2uNd2O1menpC9PSGmTe7nGg0SWtbHx2dAZpa/JhMMjnZHpQr15D/tmKWZd4zfRJOi5nXaxo43t3L9oYWoskk+oArl1VRSLNZmVVSyLicTJZUljI+N3NIjYNRLg0ua+HgVAxDJal3k1AbiatNJNQGEmrrgFCQhyAXYZbzkUUPbq+D7AIfvZ39WO1m+npGzsX8lhHMCNZrBywFEcAO4pn94EVBYGtXI2vbasmxubgqo5AShw/T6MtzUWlr9fP0kzuYPbeCvLw0Xl93hJYWPxgGufleZs4qJSvbg3iKX6ShG3R3B9m2tZbGhh40VSc908n0q8YwpjRzcMKOROLs39fMkUOt7NvbRF9fhBdX7eXAgebBIKzSsiyuv2kqr687TN3xLm66ZRqeNDu6rrN2zSEO7G9myrQS5g5YWPbtaeT19Ue49faryMxyYxgGbW0Btm2ppa3Vj2FAdrabGbPKyM1LGzLueDzJxtePEgnHWbComtYWPzu219HTE8RkkikZk8mcuRU4zmBRMwyDxoYeXnv1AGazwjUrJl5Ri5S3Qr7Pw+eun0ckkeTxTft4cc+xd3pIF5VEQuVPv19LOJTyGbc7zBSVZDDtqjGX9DORSn4g0tTYw67tdZSWZV02wkFHe4AXntlFVo6bNO/pU09eTuzc3UAgEEHTDFZcMwFJEqit76K7J8iH7llAOBznH0/vpKGph/y8NAzDYPGCalwuKy++coDNW4+Tm+2hpraTCePymTalmGM17fz6968xf0452dluZl41hqSqce3yCbgu0Zi0y5VUFiYL75o6gXllxTT5++iLxIglU2ljZVHErMi4LRayXHZy3a4zZo66lHl99T72bK7F0A1uvW8BuUW+M7bXdYOW+i5eeXIn6Vlu5lwzDm/Gpe1WfFkLB5oepjf8MtHkMeLJWpJaF4JgSrkSSVlYTROwyMVIYhqy6EIS3Uiik2cfWYsrzc7BHXUUVeTQ3tTD5LkVZ+/wfNFDGPEXMdSjYCQHNwtyNYLt9mHNJUHk9pLJdMVCtEf6ORLo5FcHX8cqKSzOLWdJTvlpU4ON8tbo80d4afU+QsE43V1Burr6cbmt+HvDhF89yL49jXzk40sHc8MbukFjYze//80a6uu7BgPoetaFWPPyAe6+bwGz55QjCAKJhEZHe4DOjgDB/ii6rhOLq4RD8UFTaiyWqu7Z2trHqhf2sGhJNW6PDU0zWPXCHvbsbCAaTTB7TjmiKLB7VwOvvLif2989C8OAQwdb+f1vXqW7K4jHa0cANqw/wppXD/LBDy9m0pTCwedGVXUO7GumtdVPLJZk/dojIKSyJ3T3BKmr7WT23PKRbxQpwaCttY+H/vQ69XWd3H7HTKy2y/MjfzGwmRUq8zIB2Hik4YoPtNNUnXWvHiQjy83EyYX0+cM8+88dbFx/lA9/Yik5uZ5L8pvldFq4euVEvOlOams63unhnBfFJRl88GNLyM1Pe6eHcsHIy00jP8/L4aNt5GS7EQSBYH8Ml8uKySQPJqCIxVNzqdms4HCYEUWBNI+NmuMdxBMqgiBgs5oQRYHsLA9d3cF3+Mr+tTDJEkVeD0VvMcXqpczh3Y2sfmwbuqaz+MYpZxUOkgmV9S/s459/WI8300VGjpuZS8ae8Zh3mstaOFD1Xtr8/4ko2DArRVhMlSiiF0EwIwoprWdCawOtffAYp2U2wb4IC2+cSv2RNhbeOIXnH9p4UcZnRB6B5E5QJoJoJhW2Y4A48oMkCAKKKCEJInt6W9nZ3cwYl48Sp5cNHcfZ2d3MlyYtvWxcjS5HXltzkMlTivj6f96KzWYiHIrz6MObWffaIebMq8DncyArErF4kj8+sI66uk7ueM9sZs4uRRAE6uu7+MF3n+WXP11NRUUO6RlOnE4Li5eOZf6CSp55aietrX6WXT2ehUuqB33QFUUCIaXtFwWBrq4gBYU+NFWjtqaT0rIsuruC9PnDeH0Ompp6yMxyY7OZiEQS/OLHqwhH4nz440uoqs4DARqOd/Hzn67ipz98nh/94v34fENTux470k5Pd4j33jWXyuocZEkiHk8SiyWw24dbDU4s8Pz+MA/+7jVqjrXz7jtns3DJWMxm+ZJcAF4IrtTrupBIksTYcXnc8d7ZJJMajQ3d/OZnL/LH373GF796AybTpTfVCIKALEsoinTZxYI4nBYqqnLe6WFcULIyXVy3YhLH67vYtqOepYuqKSny8dRzOwmFYvj7IqiqjtuV0vj7+0I0t/rJz/NSU9tJXo5ncF9XdxBV1dixq56qipP3SRRT1WgTcRVGM12P8jah6zqB3hCJuEokGCMaSbypcxzY0cC21w6x6PrJjBnB9fpCcul9sc8Hw0DTo+hCHDXeTyR+AM4SzFvk/T4ms4LVbiYajvPS37fQ2dx7cYaX3Itg/yCCMp6haVVHHmNCU/najudpCvVxfeFY/mPy1fgsqSqNK/Kr+cDav16UcY5ykmRC5f7PXENmVkpzlZ5uMGN2KXv3NFBb08GceRVIskh9XRdbN9cwb2EVK66bNFgDwJfuZOnV43nyiW2seeUA73rPLKSB1HwAloFy6TabCY/HPsTdByA7x4PdbqapoYdJk4uor+tCS2osXFzNqy8foL29D7NFobc7RHFJOpIksnbNIZqae7nx5mnMnV+JNJA/2edzcP2NU/nTg+t4/tnd3PWB+UP68vvD3P7umcyYVYrFkkr/axgGGCCMoO1WTKmidr/6+Usc2NfEne+fw7LlE5Dlt2bNCsXi/Py5DTT3BPja7UvZebyFxzfvpakrQJrDwrKJFdw2ezzpzqEuKglV4/Yf/oXqvEy+897lbK1p4uH1uzna2o0iiZTlpPOplXMpy0kJ46miibC7vpV/bN7P7vpWIvEEuV4XV0+q4LZZE7CblWHXokgSiaTKQ+t38eLuo/hDUQrSPdwxZyILx43Borx1wcgwDHbXt/L31/eyt7GNWFKlwOdm5ZQqVkypwG2zDPZx8/f+xKyKIpZPruBHT69F0w0+vGwGk0ty+fXqzazdf5zCDDdfvW0JY7LOrNG6UAgCyIqEzZ7K0j3Olc/SFRN46A/raW7qpWRMBrpmcOhgC488tJHjx9rxpNm56farWLx0HMpAMSBN0zl0INWmrqaDNK+Dm981g4VLqlEUiWgkwf/+eBW5+V50XefVlw6gyBLX3zyVlTdMQTFJF0yYMwwDVdXZu6uBxx7ZTMPxLnzpTm67cxbzF1YiyxK6btDe1sfT/9jOru31BPoieH12rrtpKsuvm4Q8UMCo9lgHj/5tEwsWV3P0UBtrXz2AphksWzGeuz+4EH9vmAd/u4a8Ah+h/iivrz2MLIvMnl/B+++Zj3ng/aw52s5Pf/A8LU2pOev7P3sf5ZUnF79dnf386fdrKShKx98bYuO6IygmmTnzK7jr3vkoA0JaJBzn1ZcOsOrZ3XS2B1C1VPC8LIv8+9dv4qqZpRfkHp4vZrPCrTdO4wc/XcXUKUVkZ3u4esk4/uM//4FJkVmysJry0iz6AhGqKnL5x1M7aG7xk5vj4aMfXITForB4QRWPP7mdfz6zA4/bxsc+tHjw/NlZbiRR5NvffZo5s8q4fsUknBcpIckoo5zAZFaYsaiaLa8eomx8HmXjzj9hiq4ZrHtuD9vXH2HctJJR4eBMmOQCJhccPM+jRO78tI6uGdz1xWvZu/EYV98x46KMD9EJ6KSEAemsGn9RELk6r5IF2aWYxJOV8wAUUeLe8pmjVoOLTGlZ9qBgAKn773bbsFhMRMIJdH1ggbmzHlmRKBmTiabpaNrJzDQFhakF2dEj7SP2cSayc9w4nRYaG7vRNJ29exuxWBQmTS7itVcP0d4WwGo1EQ7HKS3LQpIEdu2ow6RITJpUOCgYAEiSyPQZY3joT+vZvuX4MOHAZJYZNyF/UDA4cb2ne8hMJpmf/uh5tm89zl33zOP6m6ZekKJohgHRRJKa9h5++PQ6th5rxGExk5Pmoqmnj9+9vIXajh4+d8N8cjzOU/ozCIRj1HX2snr3UX727OuYFJkMl52OviAbjzTwpZsXDfaT1HSe2X6QX6/eRCASo9CXhtdho7Hbz/8+v4ENh+r55h3LyPW6hlyTIkt86aEX2FbTRL7Xjcdu5XBLJ197eDWfuW4e75o9AfNbEBA0Xeev63fxm9Wb0XSdLI+TTKuDuk4/P3hqLfsb27l/5RyyPI6Uq0U0xraaJo62dtIXjtHVH+bBV7fjsVmo6+rFbJLYcbyF/3zsZR785B3viFZcFEUmTi5Cljeyf08jxSUZ1Bxr5/vfeZqqsXl89P6rOV7TwYO/WYOhG1y9ciKCIHDsSDs//H/PUD0uj49+6mpqjrbzf798GUNPLaQNoKcnxN7djUyZXsJ9H1nEgX3N/OmBdbg8NhYuGXvBsp4YhsH+PY38/EcvMHFyEdfeMIXDB1r4xY9eAN1g0bJUX9FoAn9vmBtumYonzc76NYf5zS9eoqDQx4TJKXc+TdNpaerl0b9uorI6lw99chkBf3gwrscwDDrbA2zfUpu6ro8toamxmyce3oLZbOL996aSLIwpy+S/f3Qnm9Yf5aE/rEfXh2Y2MAaEle1bjjNtxhg+9ImlNNR18fjDm7FaTdx591w0TWfrplqee2ont9wxg7LyLP72pw3s29PIb/744XckhuGapeOAlJA5piSDX/30/YPv0/Jl47lm6fjB/ZCyMnzzq0NrBQkDdWOKCn18/lPLh20H8KbZ+dC9C4ec618ZwzAwSAWCm0azNl00RFFg6rxyHnzl3wHhvJ89wzDQVI09m2vQVD2lxLvIXNZPQ2phcv6Bug1H23nliW10NPvJzEujoDyL9JwL77spmJdihH4FlmUIUiHGiVWX6ENQqoe1lwSBid6UNKgaOrIoEddU+hMxdAyW5V2EuIhRhuD1DZ8YRUFAFAWMU1IMdXcHCYfi/OmBtTz0x/XDjlEUmWQiOWz72XC77Xh9Dlqa/WhaKjagqDgDl9uKy22lva0Pm81MJJKgpDQTSRLp6QkhSiKeESZ170BAaFfXcL9bt9uGyXTui9rH/76FTRuP4fHYmDCpkNRH7sLNsO19QXTD4P+9dwXzx5YgAC29/Xz/yddYf6iOqSW53D574mA5+RQG9Z1+ntx6gM9eP49lE8sxKzIJTWNvXRs5aSnfAcMw2NfYzl/X7cJuNvGtO65mVkUhkigSiSf51epNPLFpH79/ZStfvHEhdsvJGIr+SIzGbj9/+fR7KM32oek6rx+q5yfPruf/Xt7CrIpCyrLfnIbeMAw2Hm7gobW7yE1z85/vuZrq/FS8QmtvPz9/fgOrdh9hXGEWt84cj1lJ+V439fRxy8x53DJzHA+8so0HX91GebaPX37oZnK9bt77k79R1+mntTdAvs/zJn+Rt4Y7zYYkifh7wyQTKs8+uZOsLDf3f+4aXG4bC5aMpbs7yD8e3criq8dhGPDcUzvJzvXwic8ux+W2smDxWLo6+3n8kc0sWpby0dU1nZy8ND708SW4PTbmL6qmo6OfJx/bxvxFVYjihUneEAknWP3cHsaUZvGR+5fhdFqYv6iatlY/j/xlAwuWVCNJIqVlWXzlmzcPHjdhUiEH9jexf28TEyYXDm4P9EWYv6iK9983H5tteB1UVdUor8zhvo8uJmOgBsqxw21s31o7KByIoojLZcXrc6RcEUdATWpUj8vj3o8sIj3DCYurOXKolW1barnz7rnEokmOHW2jrCKbeQsrsdst3HjbdHZsPU53VxCvz3FB7t/5cOp3ZCSFw0ifmdN9elLHn/++kQj2R5FlCYt1uEXxzWAYBrpu0NsTGvyN32m6eoP87bkdfO7uxWdvPMqbYojy7U3S2dpH0/EuMnI8F2hUZ+ayFg7eLC8+uoVFN02lqCKHlrpOnn9oI5/+7h0XvB8jsR2MOESfG5q50jRzROEgoWt8edsz3FQ4njSzjYnePLZ2NfB800GSusYNheNZkV91wcc5yklE8dxqTMiShNksM31GKRWV2SO2yck9f4FTEFKWh+NrDpFMqBw72s7ylZOw2kxkZbvp7OhHUSRkWRy0cCiKBIZBMqkNO19STVk0RlpInKkC6UjU1HRw/Y1TWLvmEA/832t86nPLyctLu6ACwrvnTmRWReGgtjvX6+Ldcyexp76NnXWtLBxfSm7a0ElVEkWmj8ln5dSqweNMksT0svzBNtFEkl11LTR29/HZ6+ZxVVkB8kAGMLvFxAeXXMWBxg5217Wyt6GN2ZVFQ/r48LKZlA4IAJIoMq+6mG01Tfx9417W7K+lJDNt8HznQ1LTWL3nKL2hCN+8YxlV+SezXOX53KycWsWehjbWH6pj8fhSsj0pYceiyCwePwa72URxRhpOi5k5VcX4nHYUSWRCUTYv762hMxB+x4SDZELDMAzMZhlN0zl6uJXsHA+NDT1AD5ByI2lt7iUaTaDIEseOtJGbl0ZjQ/fgeSwWEy1NPUSjCWRZQpJF0rwO3B4bkHKBq6rO5fFHNg3TpL+l8SdVjtd2MqYsk4a6rsHtdkfKshePJbHazKiqhr8nRDAYI5FQ0fWUGigUig85n8NpoaDIN6JgACDJItk5niGLxswsNw113SO2Px2yLJKb50kJBifOk+1m17b61B9CyqoYjyWJhBOYFJn+QARZkbBYRvN6nspDD65nTHkWS64Zf1phDCAeSxKPJ3G6zl6xubsryBc+/ice+uenL/RwR0TTdfyBCH3BKImkhiKLFGSnYTbJ9AWjBMNxblw8YbB9JJqgLxglPc2OSZFRVY3a5m7KizLRdQN/IEIgFEUQIMvnwm41EU+qtHQEkCWRRFLFabOQ7nUgS29/zaZYNIG/K0g4GEMdmBPNVgWXx4bb50A6h+rYuqbT2xWk3x9OxacIYLWZSUt34DjHqtxdbX10tweGfZPMFoXsAi8O15mzZcWiCYKBCNFQnGgkweur9gEp1+fG2k6cA9+/U8nIcZP5JtYdI/EvKRzYnVYiwRit9d0E+yIIAtQdasWZZiP9AqayE11fJOWjccqDZOjA8EUcpCoI1gd76YiFqA32EFYTHO7r4K6y6WRZXXxr5wssz68adS16hxEEKCpJR5RESssyee9dc895gSwKqeSlZ1rEFBWlEw7HaWrsoa8vwtjxeVitJgqL0tmzs4FIJE5GpguzOfX6lpRmcmBfMy3NvUx4Qw2IxoZudN2gpCTjTV/vCe65bwHzFlSRk+Phsb9v4dGHN3PXPfPIuIAp2SYUZqOcMqGIgkCGy0FJppem7j76I7FhwoHHbmFaad4Z3WeCsQSHW7rIdDsoyfJifoMJ3WpWmFlRwJ9f20ldZ+8Q4UAAppfmD2kviSKVeRm4rGb2N7ajv0kzb0dfiJaeAACN3X30haND9jd09SEATd19xBLq4HZREEl3pixFFkXGrEikDwgGADaTCcMwiCdV3ika67tIJjWKSlKVzaPRJDXH2vnzA+uGtBs7oQBdMzDkVJtjR4a3GTexEF0zQB5I3CAPXajJsoiq6gNuf8YF0vKmXIYO7m+mtyc8ZN/ESUWoqo6qauzcXsf6NYdSwhCp/gN9Ud5YzEQxSZjPsPiWZWnErF/n60KQ0nQPPU8qFUbqPFaricnTivn7oU088cgW8grS2L2zgTnzK8jJu3KyH10IzvUxOnyghc6OfpaumHDJuSv1h2I88dJuBEHg0PF27FYzn3zPfLIzXDS1+Xlt2zGONnTyv/+RUpD2BML8/YWdvGv5FApz0qht6uaXD6/jx1+6lfbuIGu2HiUSTRKOxikrzGDFvGrau4N865fPs3hGOX3BKDaLwvULJ5D/NqYGNgyD+qPt7Fh/lJ2vH6XhWAeh/mgqFjDLReXEAmYsrmba/EocLstpvxHRcJxdm2pY99weDu9uxN8TQhQFsvLSmDqvggXXThwQMM48nhef2Majv30tJVycQl5JBh//+o1Mm3dmT5AD2+t55amdNNZ00N7USzgYA6CvJ8QffvjCiMfc+Ykl3P3Z5SPuO1/+JYWDjNw0ju5pxO60EuyL4PY6OLi9jsKK7AsqHBiJHSBXIohOQABDx9BqQa1HsFw9rL0oCFR5sri7bDpbuhrZ39tGIBFDFiUKHB60t8HPbJRzY9LkIjweO7t3NTB3fiUlYzIHg4t1Tae1tY/MLNewLC0OpwVJEmlu7h2Y9Id/YQqL05FliXVrD2NSJCoqclIFe3I8bAgdob29j/ET8jGbUwuNefOrWPXcHjZtOPr/2XvvODuu8v7/Pe32vnd7X61WZVe9d8mW5N4LxrRgm9ATAgQChPwgX1oIEEgIIaGZYmOwMe6WbVmWbHWr192Vtvd+e5+Z3x93daXVrrpsS/Z+eJmX9s45Z86cmTnz1M/DnLnlGcuj3x/h1ZcPoek6q9ZUX/I122wmZFlkzXXTGRwM89qrR8jOsXPbHXNxnMMKcr5wWEZv2iZFwm4x0tsdGlPYVSQJt+3s50+mVIZCUewm45jc2pIoku20EUkk8EdiIwUyATxjjO+0mFFkib5AmIt9NX3hGJFEElXT+NX6nWMmggO4rZYRyo8okFFwBEFAQMCgSKe4r9Pt3o7Y1LEwNBhi06tHyPLamTSlEFEUKC1LJ9A/+MlrRgn3TqeZREKltMyLokh89OOrRrVxOMzE4klUVWNoKEw0msBsTitB7e2DeLPtl5wcfypkWaSkJAun28L9H1k60jMkgNVmJBiI8uRjO3C6LNz3ocVk5ziIx5McPdTBGZN33mqc47SSJJKX5yLLa2NwIITdYWbBokrmLpiALL/9lt4rCamkSmNDL7WHO7DajPgGTyqFx+u6OHKwHVXVcHusTJtVis1u4vD+NtY9u5dEQiUSiWO2GFhzw3TCoRiHD7TR1eEDASZNKaBqOIlUTWns2tFAR9sg2TkOps0swe4w09LUx8F9rSQSKcxmAzNml1JQ5CESjnP4QBud7UOoqkpxqZe5CyekaaU7hji4v414LElhkYfq6cUjlMxQJE5L5yBf/8T1rN9eRzKl4bCn99lpVQU4bGZ+8PD6TPuCbCdmk0Jz5yCFOU5efOMIa5dMSVNmN3Rz6FgXC6aVAjqb9zSwav5EQCccTXsgdF3n2Y2HaO8ZeluVgwM7Gvnz/21k37ZjyLJEUUU2RRU5qCmVno4hNr2wnz1bjnHdPfO57xOrMFkMo/aKRDzFy0/u4vH/3chAbwCTxUDpxFzMFiOhQIRX/rKL44facWbZzrnn18yrIJVUCQfjRMNxWo73cOxge/rgeWzLyUQKRZEoLPNSWOalraGPproujCaFyppCsk4PSxMEyiZdPgaz96RyMKG6AJuzkmQixZFdTUyoKcLpsWG2ju3uvWgk3oTkQXTL+wEjJPeiR/+EoIydAC0KAtWuPL63/1UEAeyKiZSusbOvhcbAACbpPXm7rjgIgkBOrpP77l/IY49u4+c/Xc/UmiJcbguxaJK+Xj8dHUN8+Wu34vGMjN+dPCUfl9vKpg1HMCgyWV4biWSKnBwnS5dPAtLhSDabkS1v1FFckoXDmS4t73JbUVWNrs4hbrx5ZsZzUDkxlzvumsszT+3h5/+9nslTChDENLPJju3HWXv9dObNv3zsI1abkZtvm43PF+GVdQfJyXGwctXUs1pFzxdjWeB1gIw1eKzgY84r6faE9VQfc2fWObHbjzXSmJ6BE8xOl0EYdVnNfP7W5djNY+9BBlnC6zjpRh7rnALvXJLliTjqI4fa6e3xs2tHI/W13bz/I0sy4T/XXlfDnx/Zxq4dDUypLkQQBHq7/QiiQHauA8Ugcc2aap54bAe7dzYyZZjRo6fbjygK5OQNfwx1aGsZ4Pmn9lA9vZiOtkH27W5m9XXTzjv5+kTsdyyWIBSMkUqpBANRQsEYRpOCLIuYzAaWrZrCi8/uY9+uZqom56Pp0NPtQ5IkcvPSxQcTiRTicKhCV+cQ27YcI5G4/B4bXdfR1OE5h2KoqobfFxkx5/PFQH+aFvme9y9i1tyycbpe0us70B/khaf3UFLmzeRqpVJpT38slsRiNSKKIk0NfUQiCdbcMB2rzYSq6ZjNBjxZtkxolqqm9wyny0IkkuDxR7bxxa/fCqRD1no6fdhsJva82YiqaixaVkU8nsRsNmCzm+jp8vPsk7v5+N+t4VhdN/v3tFBU4sFgNGcIMAK+KC8/f4CiEg8Op5ltb9QhSSLTZ5UgDSvXFpMBr8vGH57dRSyRZF5NyVmTjyVJZG51CTsONFNVms2h4138zR0LUVWNYCSO1Wwg22Mj22NjxuQiTEYFEHA7rXjdNnzBKIoiZUJa3w60HOvhT//7Gvu3N5BfksVdDyynpDIXi9WIqmr09/jZ8PQedr5WyzO/24Lba+O2Dy8ZNc6BHQ08+/utDPQGyClw8f5PXUtZVR5Gk0IkHOfYoXae/+N2mut7SCXVUWyDp2LGgglUzy4jkUgRCcV45cndJ5WD83jdpi+YQGVNIfpwlMGzj2yjqa4Lm9PMmjvnMnuMWkQW2+Vj3npPSpv7tx7j+vcv4vnfb8FoNrB7Uy33fWa0Jf+SYb4Fwr+EyG9BLECP/hXMd4Jx+ZjNZUHkfRWzaI/4MEsKDoOJpKZyxNdDXzTEp6YuHQ8pukIgyyKrVlfjdFl59ZVDbNxwmFgsidGo4HJZqJlejNk82kJdWOThgY+t5MkndvLyugOoqorNbmbFqikZ5cBoVCgqzmLz67WsWDUlI/A5neZMYbSCQjeGYYYcWRa59fY5eLJsvPrKIf76lzdBhyyvnQ98aCkrr52K2Xz5YokFQSA728Edd81joD/IY49sIzfXybTT2JIuBgPBCJquI50irMQSKXyRGA6zEdNFMmoYFYlsp5X2AT+h2GiO6ZSq0eULYjUacFpPiynVodcfGhW7PxiKEk+lyHHYLlood9lMWAwKkXiCGWX5FLgdV6Wglkym2PpGPbVHOjCZDBQWe3jwU9cwe05Z5gM6b2EliUSKzRvrePWlQ0BacFq+Kp1/JUki8xdVkkiqbNlUx/p1B0EAl9PC8mumZKxtsiKRl++is2OI1187SiKRYt6CCq6/acYZPS+nI5FIse65/Wx4+RDBYJS+ngAP/2ITTzy2g5xcB1/95h0oisTiZVWoqsa2zcd48bl9iKKA02Vl1XBytNVm4uY7ZvPcX/fwo+89h8NhZu6CCUypLkSSLu99jMeSvLLuIOvXHSQQiNLfF+T/froei9WIy23hm987/7w5s8VAMqnyvz9dj81mRJYlvDl2rrtpBtNnlp57gHchdF2ns2MIvy/CDbfMQpJE3tx2PPM+JhMqtYc7QID21kGSSRVZkZg0tYDCIg8Wq5ElKyaf9CBrOn09AVqa+kmlVA7saSGZSCsaikFmwdKJuD02IpE4rc39zJhdSiqpUX+0E03X8Q9FGBwIoWk6NpsRvy+MIAgsWlZFRWUOug6d7YO88uJ+JkzMQ5ZFOtuHyCtwMWVaUUY5kEQBh81EYa6LyRW55Hjs58yPmlZVwF/X7+fV7XVMqyrAaTOhaToepwWr2TD8m4VwNJ7Z8+XzzNe73NBUjU0v7Kd2bwtGo8yn/+V2ps2vQD4lR6RiSgGFZV66Wgc5friDJ365iZW3zMR1ivEuHkvyxosH6WkfQhAFHvzyTSxeXT1inAlTC9A1nT/+z4Yzev5PhaxIyEq6lorVdmHGZ4vNiGW4j67rWIeZzURRxOm2vuWJye9J5SDojxLwRWg82sHffv0Onvi/DW/JeQSpCN32GQj+AD3xJILze6BUIwhjPySCIJBttpFtHmltzjU7SGkqFnm8Cu1bhQkTc/npzz86ZrGmyVML+Na/vQ+DQcoI/IIgYDYbWLi4kmnTi4nFkmiahiCm46FNZsOYyX2yLDF/wQQmTy0gEU8nL0qSiOUUN7AgwGf+fi0PfGzlCA7u3Dwnn/viDcRjSTxZtowgJAgCdoeZ1WunsXDRROKJFOhgMEjYbCYMpxUoM5sNfOhvlnHPfQvPi7bw3vsWcdMts3CfspGeCBP5x6/cQiyawOUeXbPhgjDcdXdDB/MrizEb0h8aTdfp8Qdp6hlk6ZRynGMUZzsfOMwmqovyeHlfPQ1dA8yrLB6haEQTSbbVtpDnslOeMzLuWge21LbwviWuzG8pVeNoRy+BaJwZZfmI56ivcibkuewUZ7vY39LFhoPHuX/ZrBGK0dUAo0nhRz/7CJqqgSAgSSImk4LVZhyR/GexGFh5bTVz5k0gMczklf5omjLJ8RarkVWrq5k7v2LY+i6gKCJWm2mE4J9X4OQjD60gGkkgCGkh3WoznrdipSgyK6+dyrwFFaOOSbKUYcyxO8ysvn4aC5dMPGU+Erbhj7aiSCxbMYUZs0pJJlUkScRuN7NqTfUIcoOyimy+/v/uytQ7OR0ut5UvfvUWlNOU3w9+dBl3v38hkOZKX3HNFObMKx/VXxwW0DxeG//4tVtH7WMffnAF7/vAYgRBYKA/yNN/2UVOroNr107DYJRJJlLs3tnID779LP/9qwexX6YwwasJup5WGkVRyITlmM0GREEgFIzx0x++yBe+dgtZ2Q42bzxKX08gU7X5dKiqxv49zTQ39XHrXfMA2L2jMRPmJ4oCdrs5864MDYYJBWP88r/X84GPLqOkPJvmhl4efXgzoFNakc1H/nYVtYc7ePrxnTjdVj7xd2uIJ1Lk5bv59Oevz8zDYjWOuP+artPZ52f3kVZe2qIwY1Ihd6yegctu5md/fIPmzkGa2gf4wW9eZcXcSuZNK8VsNGQUhO99/rbMnKdNLKC1a4hv/+9LaJrOklkV3Lyy5q27KeeBztYBju5pIRpJsPzG6UydXYp0mhdNFAUKy7KpmVtO6/EeBnuDHNzZyLLrp2fadDT10drQQyqlMmlGMfNWTBo1jslsYNkN01j3+JsEfZG35freKbwnlQOHy8KPPv8IH/jc9ZhtRpLxy+cC1lNNEHvplF8EkMqAHRB7GRI70eUKBNPa0X11nY6Inx8f2sSBwU4KLU4emLSAhTllmJTLHPJ0leEEH/MJpEMoLp8QZTDI5J1BEzcaFXJyRgv6JyqsOl0WnBdwLlmRRoUbnQ6X24rLPVJwl+Uz9xMEAYNBPi8awrT18/znfIJG9XRIkojbbQX3ZeBFH765T24/yKyyApZXlyMg0Dbg45HX95LSNGZXFGSScC8UBlliVkUBU4py+NPW/ZRku1k6pQxZEgnHE/zspW3Ud/Vz85wpTC8dHbf5q1ffZEpRDjUleWiazmuHGthS24zNZGBlTQXSsOA6KsZfH/nHqccFQUAWRW6ZO4U3j7Xxs5e247JauHH2pMyzHYjE2FrbQmGWg+ri3ItiRHqrIYrCGd+dU3HiGc3ynvkZPdnm7OVrRWH4GR6DseN8kHkHztFfEASMRiWT3zMWDEYZ72lJ+acnFhsM8lmpKyVJHPOaT31PBQEcTgsO55nnLMvSGceBdJXV1uZ+mhp7+cznr2diVV6m+KEny8a2zfWZPIT3GkRRwJvtIBZN0HCsB4fDTFtrP+WVuUQjcQK+CCVl2cTjSY7Vdo3Yn80WAwP9gUyhRU3VCAViGIe/K1s21RKNnvRYxqJJ9uxsZNrsdKHLLK8dURTxD0UoKvXidFnYu6s5YzTx+6JIksiCJRMpKHTzz1/8I5/83Fry8l0oikR76wBz5lfQ1xtAEE/StSZTKrsOtVJR5OUfP3otyZTGTx/ZRN9gCJfdzEN3L0bTdXRNR5SEzP4iCHDv9bO549oZmE4pDOlxWnj/jXO457pZoOvIUpo5ryTPzY+/chcATpuJ+66fc95evEtFV+sA/T1pUoeps8oQZTEd8TlGKGhukRtJlkjEUzQe6RqhHPR0DOEfzjGpmVeOcgaa76xcJzkFLtoaey9+0ldB+uh7Ujm4829XccfHVg5vivA3X7rp8g2uR0FtG/WzYFgCehDUIIhji2VJTeXHhzaxMn8iX5mxmuPBfl5sO0qOyc4kV87lm+NVBl3X+d6vXuHZDWkqr+J8N5/9wAqWzn5nqniO462Bw2xidkUB//zYSzjNJmxmA239flKqynUzq1g5dUJGCL9QCILA5MJsPrxyLj99YQv/+LvnyHPbsRmNtA34SKka8yqL+Nia+ZgNIwXB6aX55Ltt/O3//IUcpw0d6PEFEUWBz9+8bAR7UiiW4NldR+kY9BOMxjnY2k08meLZXUep7+zHYTaRZbeworqCifnpuOa5FUV89sYl/PCZ1/nGn17mh8+8Tq7Lij8SZygUQdV0vnLnKqYU5V7K8o5jHAiCgMttJRpO8Oq6g4QCMURRoL11gGef2sOEylyKS73v9DTfMRQWuVmyYjI/+s4z5Oa58GY7sDtMZOc6ufH22Xzu4w+TnWOntMw7QglbtbaaH3/vBT7x4f+jqDiLf/nu3VTPKGLvrib+/m9/zZz5EygpTSfkS6JARWUuhw+08btfbmJyTRE3315Fbr6TG26bxb988THMFgPzFk6gYJg9qul4D3/6/RYGB0KYzAY+9unVAGTnOnjfhxbz+KPb+J8fv4TLbeWTn7uOCRNzM4pFNJ5Mh7gJAkOBCIIoZPJTjGN4yk9AkaVRpAAnmMJG/55mfMu0OQvt6+XGUH+IkD/N8vbzbz/D/37nmTO2PVVf8A+FRhwL+CLEImn64YIS7xmNj4IgkJ3vQpLETD7AuxHvSeXg1CIrggC2s1hiLnhsZSo4v31RfTV0BuMRbiiejCSIuI0Wtvc0E9PeOTrCKwaneA704UTQcbyLIEAsmeRT1y2iqXeQZ3cdpXMoQFWBl5U1E7h17hSyTvMaCAgUeJw4zEaU87CoS6LImumVFHkcPP3mEQ62dBFJJJlWms/KqRXcPHfKiIRgSRIp8DhYNrmMD66YzV+2H2T9geP4wlEWVJVw+/xqlkwqHUGLGk0kee1QAx2D/sxv+W4HkXiSvU2dADgtJipyPUzMTwthgiBw4+zJVBfn8tTOw+xt6sQfjuKxWphWksfcCUUsm1KeoSk9vYKz2aCQ73FgMxk5IRG4rGYKPU5MhncXb70oCHiybDhdb38V33cDBEGgsNjDJ/5uDeue28cvfvYqqqqRlWVjxTVTuPWuuZcWHvg2Qtf8IFgQ0vy26HoStCCgg2hHEAyj2pwNgiBgMhu48bbZ3Hjb7FHHP/qJa/joJ64Zs29evpvv/eQDI34rn5DL179z96i2doeZf//vDwHw0GnH7r5/EXffv2hUn7kLJzB34WhjmCxLzJ5fwez5o8PjAGRJZMH0Mh5+ajvf+vk6JFHk2oVVFGRfiK/7ykYinszU+DFbjeenmAgCymnewGQ8hTqcRG22Gs+aTWA0KwjCJYghV8Er9p5UDq5UCAh4jVZ29LbgNdkIJmOkdA3zOEvRON7t0CGpaiDADbMnc8Pscxf7U2SJP/7D/Rd0GkEQmFqcy9Tic1vhvXYrD3/mZJLn/ctmcf+yWWftk+O08YtP3nVBczqB0mw3f3/T0nO2+8Pfv3/E30unlLF0StmI3x5aPZ+HVo/NinY1w2wx8MWv3vJOT+OqhsEgM39RJfMXVb7TU7k0xDeAYT6IaXpQUi1pNkAxD0xrQS6G+KtgWAji5aN4vJogCAJ5Xgf/9NDoMOZ3C4Th2kEA1987n8kzis8ZciwIArlFI3PLBFHIKJC6dnamJSHz/+9eK+W41HkFQREl7pswm+daD2OUZVRNY7a3mCKr652e2jjOA5qmc7Sxm3AkTkGui8Ic51XJPPPO4d270Y5jHOMAPXUctEHQVVCqQAuD1p2O9xDdCHI5utoG2gDoKVCqQTBDqgm0fhAd6Rw+bQgEJwhpr7+uDkByTzqs17gYRA96qnVkGz0xPM5AOrRXKkMQxz1QVzssNiNGs0IoECW3yM2StTUZpqYLgdliwDBMD+4fDJ/1axQKRNHOoUBc7RhXDq4giILAzKxCJjiyGIxHsMlG3EbLO0YRNo4Lw2AgzC//spW2riHuvm4W77t+tGt6HFc++uM+DviOUe2sIMfouWgFL64mqA020xjqIKrGMEsmrstbhEW+fFzU72U0hzt5c+AwMS2BIsjcVrQSs3R1EDe0hLs46D/GmtyFGETlPWJE0NGjTyGI+SC6QI9BYh+kDoBSA6mj6Ho4LeSLOaAHINUIplXosecR5ImkCT5U0KPo0ScRpFyQnUAC9FB6TD0EnGjzFwQpP91G7UGPvYggV4IgptuM46pHdr4Lp8fGQE+Aw7uaueHe+RelHLiz7VjtJvq7/TQc7UznE4wxTGq4RoWm6hcfgncRdrBT9wjtbch1GFcOrjCIgoDTYMZpSLNFHBrsIt/iIMs0buE4gSvVvlzf1Etj2wC9g0EC4dg7PZ33BBJaks19e9k9dBQQUAQJr9HNXM8UJtpLkS6CYnQg7mdT3268RhfZRs9Fh4ce8jfw144NZBlcOBUboVT0oubzboSma7RFesg2ui9aWTKKBlwGO7sGj3DI38B1+YuuGuWgLdLDuq6tLMuejUF8d+WFnA2CXA1qC+gCCPa0kC6XIpiuQ48+iZ7cAakGBEkFtLSXQe0EZATzTWSCtUUnSIXDfwsIUj66Mgv0OIJh3nAbxyltNFA7EQTjyHHGcdWjZEIOJROyaa7vYu/WY9QfaKf6Igr7FVfkkJXjoOVYD/u2HmewL0h2/mjvf+PRLvo6fedV5+CMuIhupmEa9XTV5ejFnfcCMP6lusKxb7CD/nj43A3fQ7hSt/WjjT2EhtkOxvH2QNU1joXaOB5sI9/kxanYOeQ/zn/WP8YRf8OYdHbnQokllwfLb2eCreiinzVd16kLNiMhcUvBcu4pXs09xavfU4Lg2eBLBFnXvZW++NBFj5FnymJVzjymuSZilK6udZ3uquQfJn0Qq/ReogwVEIwrwXR92sIf30BaAYiAnq57IQgOBMGRziUwrQXrR0Awgn4ZOOUFA/rlGGccVxTsLgtL1taQleMg5I/y3//6FLtfrx8z7CceTXDwzUY2Prdv1LfBnW2nem4ZFpuRgd4Av/nBC0RCI7/ng70BnvvjdgZ6/LzdKKlMM1YG/REO724mEnprDZDjnoMrBB/f/GfCydGCZWckQJXzvUtjerUgnkhxvK2PaHx09d1xnB1Wo4HP37qcT163iGzHxXnIck1Z3FywFKNoYDAZ4F8P/YLnOzdT7ZyAcIEivlk2USLnXdQ8TiCppQgmIzgUKx6jA7sy7vk7Fe3RXprDnST1i2diEwQBWZCQBZEr12QwNhyKDYdy7pok7ypoEfTIw6D2gj4Ilo9B6jgk96EH20GuANPNgAixp0lXclwChnkg2tH9X0vnGxhXQXIXemI3gh5EN6xAMF83+lzRp9ATexD0MLphJRjnQMKI7v/n9Djmm9MhR+O4qiEIAvNXTaHleC9P/HITzfXd/OBLf6KoIpsJ1QVYrCZikQR9XT5ajvcQ9EWYvqCCFTfNGDGOJImsuWseu9+o5+jeVja/dIj2xn7mr5qMy2tnsDfAvu3HaTveS/nkfBqOdJ6VyjQRTxIJxYmEYvgHw3S1DgBpBaX1eA8erx2L3TRcCdmUqTR9JkysKaKo3Et7Uz9vvHAA/0CImnkVGEwy0XCCob4A81dOZtaSqktfVMaVgysGCS3F12etHeXC+kvTAcxXmVXsvQZd12nv8dE3GOIiDNXveYiigNtqxm29eCuqKAgYRANm2USBZKTA7KUr1g+k709KVzngO8YT7a/SGukiy+DkzsJrWJo9C0lIV/FtDXfzH3V/oC3ag67rfHPaJ6lxnqQPHEoE+XXjUxSYs4moMTb370MSRBZ6pvGBshsxiQYCyTC/anyKvb46AskQOjpb+/cjCAILPNP44uQ0hWFUjbO5fy/Pd26mLz5EoTmHe0rWMNc9JVNt+Yi/kee7NrMseyYHfMfZ2r8fXddZm7eI+0uvzxSvagx38EzHRg74jhPXEngUB2vzF3Fr4Qp0XSehJdkxcJinOzfSFe0j15TFPcWrWZg1HVVXeb1vD9sHDuIxODjoO85Hym7moP84b/TvZYGnhk9U3o2AQFxLsq3/AM92vk53rJ88Uxb3Fq9lQVYNgiDQFunh8dZXKLMV0BbpYffgEUySkSXemdxfej2KKNMe6eE3Tc9wJNBEKBnmS/t+gigICAi8r2QtdxevvoQnaSR0XSemJtjYu5sXu7fQHx+iwJzNfSXXMcc9BUEQUHWV2kALL3ZtpjbQTESNkWN0c3vRNazInp3Zj1/t2UldoJnF3hm80rOD/UN1GEWFmwtXcEfRKo74G3mpextV9hIO+I5zOHAcq2xhZfYc7i5ejSKmP7Ube3bxSMsLDCaD6LrOo4u+jemUUKgDvmO82rOTSfYy9vpqOeJvxK5YWZUzl7uKrkUWJXRdZyDu45nOTWwbOIgvEUTT01bSbJObv6+6nymO0VWU33EIZrA+xMnAUDFdE8i4Csy3ASICMrr5bk7mA0iAhGD9GKCl+yCBXIZgvoO0UjgcGK5MR1BqTp7Lcg+C5c5T2kgI1o+PHGcc7woYjArv+/gq3F47v/3ROgK+MEf2RDi6r/Ukp5CuZ0KBlDNQPHtzHXz2X+/k+5//I83Hemg42kFTfVdmr1UMMnd8dBlF5dn89zefIh4Z2xj4i+89zytP7iIRS2YKuGrDNKn9PX5+/e8vpvMVhpmWZIPEV378AeYsHVuwFwQBs9XAJ//ldv7t848S8EXYsbGWN1+vy8wNoLAsm1lLLmkpMxhXDq4Q3F02Y0wPQaUjC7P87rxNuq6TUjWSKRVNO1k9VhRFZElEkaUMNbVwkbHamqaTUlVUVUfVtMyLeqLCsigKSKKIIosj6l+ca96qpqGqOpqmkVI1Dh/rpG8wmGmTSKQIhs8eYmRQJExnqbw66pxq+lyqpg8nJJ34yAqIYprHX5ZEJEl8jyQ4joam6yS0BEOJILmmrPRv6Bz0H+d/Gh5npmsStxQsoz7Yws8bnkAHVubMAaDIksO3pn+aNwcP80jLi+j66W5pncFEgH2+ema6JvFQxe10Rvt4ou1VTJKBD5TeiFU28/7S67mlcDlPd2wknIpxe+FKHIoN63BsfUJLsaH3TZ5q38DynDlU2oo54DvGD2p/x2cr72Np9kwEQUBDpzPax+Ot65nmquRTlffgSwQzXghd19PXcfwJzLKJ+0rW4jY4aYl0km1M0/Spusa2gQM82rKOhVk13FN0LQf8x/hh7e/54uQPM9czlYSa5HiwjevyF1FozuZnDY+zwDONe4vX8KvGp1mZM5eJ9hJe79vDE23rWeydzr2ONewbquP7tb/ly5M/wgLvNHRdpyvWz35fPYu9M/jUxHtpDLXzRNt6rLKJu4tXk2108zdlt7Br6AivdG/n/SU3UGTJRQDchrNXRb5QJPUU67q3sq57Kyuz51BhK2TX4FG+e+TXfGXKA8zNmoqAyFDCj0228MGyGzFJRtZ1beEn9Y9SYsmlwlaUWceGUAfHQ20syprONTnz6IsPZdZZQ6cl3MV+Xz3X5Mxjde58jgabebxtPTbZzC2FKwBYljOLWZ5JvNi1lSfa1o/Kn9J0neZwJ/t99azOXcDavIUc8Tfx57ZXsMlmbipYRlxL8lTHRhrD7Xxm4n3YZQu/aPgL/XE/P5j5D1duqJIgIHCycrSup0BQADOCcFJBEpAZLZqcvk+Ko34ThLQCcPJcyhj9xg1t71bIisQN75vP0uunsf7J3ezdeoyO5j6i4QRGk0J2gYuqaUXMWzGZafMrxvxGCoJA6cRcfvinT/Hin3awdf0RejuGkGSRCZMLWHv3XGYsqqS/24/TbSUiS0jyaNlEENLzGeFZOEs6lKxI50ymFASBmQsn8P0/fILn/7idQ7ua8PWHkBURp8dG6cRcqqYVne9ynRPvTqnzPDHQ7SMeiSOIAlaHBYfHRjQcIzQUQVXTloucoiwQwN8fJD5c/tzutiLJEr6+AN4CD5IsEhgIYTQbMFkvLiHuppLqMX+/p+LsvOpXK+KJFD39Abbub2Lr3iZaOgfxh6IYFIn8bCezpxazYm4llSXZWM0GjBdQcVHXdeKJFIFwjPZuH3uPtlHb1ENTxwD+QIxoPIlBkbBbTeTnOJhSnsuimRVMKPbidlgQBM4qXB9v7aO2qZem9n4a2vppbB9gwBceEcP4yHO7eOS5XWed5/03zeUz9y8/47nSSohOMBxj0B/m0LEu9td10NDaT+9gkFAkjiSKmE0KXreNCcVe5kwtZuaUInI8dgyK9J5REqJqgvZoDylNZWv/fmJqnJsLliEgEElFWd+9gwprEX9Tfis22cwi7/S0YN++nuU5s5EQEAURq2zGbXBgEMfeGlO6SoWtkA+X30y20QVAfbCVvUN1fKD0RmRRIt/sJaElcShWREGk2JqHx3CyirI/GWRj7y6WZs/mA6U3Igkii70zCCYj/Lb5WRZ5pyML6efdlwiyJm8BdxZdM8LCnL7mGHuH6lBEhQcr0jkSAPOzTu4lQ4kAb/TtZZZ7Eu8vuQGzbGSBdxrN4S4ea32JOZ4pAOSbvVyTMw+PwUFrpJvbCpdTYM7h0ZYX6Yr24TE42Nq/nwVZNdxXch0myciCrBqaw538qe1l5melLbZJLcUcz1TuK70Op2JjYdY0Dvkb2DNUy93FqzFKBoqtebREujFIBvItXsptBZfnITgNndE+dg0e5tqcedxWuAqjpDDfU0NjuJ0n2l9ljmcKoiCwNHsWS7NP7rOT7KU8sPOb1AWbM8oBQG98gAfLb2dp9ixkcfR+FNeSXJs7nzuLrsUqm5jrmcpB3zEO+I5llANJkHAqdlyKDfEMoVBxNcF1eYu5vWgFZsnEHPcUDvqPcdB3nJsKljGY8NMR7WWJdxZTHeUoosztRav4Ye0f8CWC2CyXr6jnWwlBkMG05p2exjguA3Rdp9MfRAAKXI4xjxlkiWzbWxteKQgCDpeFOx9Yxp0PLLvoMcxWI3c+sJw7H1gOpK8hFE8QiidAFigs8/Lr9V864xgPffkmHvryTRd1/rPNS5DSysun/uW2yzr2WHhPKwcbHtvKjhf3YbaZWPPBpSy+eQ471+3nwOa6TJ3tj33nPqKhGH/47nAMpA6Lb52Dosj88p//xKd++EEqZ5TxzP+tp3pRFbNXjS3knw90XSeupWgKDhJMxjBJCkVWFy6DGfFdJOQFwjFef/MYjzy3i5auwRGhOPFEimC4l/rmXl54/TB3rp7BbddMx2E7P2uYrusEQjFe3lrL+m211Db1kEiOpqyLxjWi8SS9g0H213bw+Ev7WDqngg/cNI+aiWePQ/3eL1/hSEP3BV3zxWLL3gZe2VbHmwdbCIyRgJRSNeLJFL5glOOtfby8tZYJxV7uu2E2K+ZNxGo2vCcUhIZQOz+pfxR/MoxBkPn4hLuodqatQwk9RUukk2JLPvXBlkwfg2igPdJDTI1jlc/v+ZIEkVyjJ6MYAHgNLprDnefV/0Soy2AiQIklbwR70Ux3FVsH9tEbH6TAnA2AXbFSaM4ZpRhAWiFqjnRRbMml0Dx2XlJUjdEV7cNtcHA02JT53SabqQ00E1fTBg+DqGCWTBhFAx6DA3lYOTJJRpK6SliN0RMbIM+UxZHAqeNY2O+rJ66lx1FEmUJzNs5T4ulzjG6OhdrOa30uJ4LJMIOJAFE1zuFAQ+Z3u2zleLCNhJbEICnE1Dh9cR+hZISknkLTNSRBJJwa+b5lGVwUWLLHVAwADKJMiSUv4yEC8BpdDCQuLHnRICqUWvMwSyPHCSTTxBSSICILEsFkmJgaR9N1BuIBTJIBo2Q407DjGMdbhpSm8cUn06Eyj/zNvSOOqbrO3z/xPFNyvfy/W95aZXBPWyfRRJIKr4d8Z9oTqWkaLYM+Ov1Bit1OCl0OpAukh9eBDfWNPH3gKJ+/Zgk1Becuonm14z2tHNz1d+m4XbvHxvI75hMNxWg/1sWca6pZcMNMNFVDNsh0NfXi7w/wDz99IOMZOLLjOIUT83j5929QPrX4ssxH03Ve6zzGvsFOkpqKiECFPYvriia/a6hMQ5E4L75xhD88u5P+oZMsTDaLkWy3DYMiEU+qDPnDBMIxfvvUDnoHQ3gc5+8q7xsK8eQr+2juHMz8ZlAkXHYzdqsJRZZIqRqBUIyhQJhkSkPVNDa9eZz2bh/f+PSNVJZkn3H8aVUFOE9TVnoHg7R2DZFMpRWRolwXxXnusbpnUFbgOetxXYfH1+1l95GTgpUkithtRpw2MyZD+vWNxBL0D4WJxtPxjcdb+/jfx7cgSxKrFkzEoLz7X/MKawF/U3ErdYEWNvTsHPbQDitFuk5MTdIYaufZjk0j+s12T7kgRiNZkEYJ6umY1vMfQ0MD9FG0picE8qR2MknXIMhnZDhKe5ZSSLJ0RopUDZ2YluCov4mBuG/EsTnuKWj6iZUShg0QApIgjUri1nWNmJrgkL+BntjAiGOz3ZMz4yhnmO+FrM/lgqbrRNU4e4fqRihvAmlFTNM1QskIb/Tt5WigKX3dw5d96j04AZNkQBLO7ME0ikomt+DUc10ojJIh4zkaaxyPwcls9xTe6N9DQktiUywcGKrn2twFZBmcF3HGcYzjrYMArJk8gVzH2RPwj/cNYFJkilwX/wx//dn1NA4M8unlC/nMioUAhOIJfrN9D3/ac5BPL1/AQ4vnYTFcmHIgAKUeFysmluG2XKFhe5cZ736p4QJgMClUzS6n4UArfR2DVC+qYsK0Erz5bsqri3jxt69TOCGH6kXppJHSyYX0tvZTu6vhHCOfH1K6xrOth/mHmpUUWV0MxiP84fguuqOBd4VyoGoae4+28eQr+zKKgSyJrJhXyfyaMvKyHRgViVgiRd9giH217WzcdYx1bxyhMM91XucQBIEcj52lcybQPRCkNN/NpPJcyouyyHbbcNrNGIaVg6FAhLqmXjbsrKezx4cONLT18/Bfd/Cvn73pjAVOPnDTvIwScAIbdtTz6PO7GAqkqfIWzSznvhvmnHWuNsvZrXyCAPfdOIf99R14HBamTsinssRLQY4Tt8OK2aSg6zrhaIL2bh/b9jey61ArqqbTNxji8Zf2MnNKITke+7vee2CRTZRZCigy59Ic7uSpjo2U2wrJMbqRRYliSy4O2cr7S68bJXhdKM/+paykIAiYRCMO2UpPbABN1zIJyMeCrVglM95TvBJng0FUyDF56Ir2MxD3kWf2jmpjFA0UmHIos+Zzc8Gy05QIAdN5WppNkpECczZV9hKuy1s0YhzhtHHOZ31EQUzn/7yFGfwW2USuKYt5nqks884a4X0VBRGjZKA52MKrvW8yzz2VlblzcMg2Aqkwr/WcPSRwbFy+d+xsDFuKKJNv9iILMqquIgkia/MXMcNVlXmWxjGOKwWSKPLxpfPP2e7X23Yzp6SQopmXpuDmOewc6Dzp2R8IRxmMRDFIFx9mKwgCM4vymVn03mG3GlcOToEkS0xfNgVvYRbdTb08/T+v8IGv3EZusZcbP7qKltoO6nY1EoskcHisIOjc8NEVPP6TFymeeGnUh5C2rsXUFAUWB2ZZIUuwoogi6ruEAqdvMMSGHfW09/gyv73v+tncuXYmBdkjNwRd11kwvYyywix+8cQW2rrOnw/dZjFy/dKpVBR7Kclzk5/txGU3jxL2dR0WTC+jKM/FL5/YSt9QCIBdR1rp7g9QkDP2JpXtGW0BcTtGjm+zGsnPdlyyUD63uoTPfWgVxXluinJdZLmsGA0jX1tdT7tOp1UVoMjb2LynEYDa5h6aOwbJdtve9crBCTgUK7cULOcHdb/jyfYNfHzCnZglE8uyZ/F85xu8OXCEKkcpOjpd0X5kUWKZcRY6OtqwdTyYjJDSVPzJMMFkGJNkHKVQXOoc52dNY+fgYdwGB0WWXJpCHWzrP8ANBUuwSOenrFhkI9NdVRz0H+evHa+xMGsaVtnMYCKAruss8k7HZbAzL2sq2/oPsM9XT4WtkJSm0hnrwywaM7kC54LH4GSWexJ7hmrJN3kpsxWQ1FS6Yn1YJBOLTTPOPcgpcBtsiILAzoFDQHrvcxscmQTfc+EEA1VMjRNOxVB1laFEEIOgYJQUZFEmz+SlxjmBg77jZBmcFFlySWhJOqP9OBQLC7KmDXtfVDQ04mqSxng7G3t3vyX1KHR0Ulp6zqFUFA2dwbgfl8GGUTSeMVxpLDSFOzFJRq7PX0KeKes9836P4wrGJTyCvkiMzQ0tVOWMNnBcKCbnejnS3UsipWKQJQYiEVRNw2G6OgokXikYVw5OgZpSESWBsimFFE/M49XHtjDU48ed48DptTN92WT8A0EGOn2YrGlLWWFlHvll2RzcUk/14kmXdH5ZkFieN4F/3PkspXY3g7EI+RYHeebLy+LxTkDXddq6h9h1uDVT+nt6VQH3XD+bnDGEbUEQyPbYuGP1DA7Ud/DG7vP3zoiiQFmBh9ICN5J4ZuYeQUgrEmsWTeKN3Q0M+MNomk4snqS+pfeMysHbBUEQMBpkbl5Rc9bkYkFIczRXleWwZvEU9tV2EIrEUVWNuuZeZk8t5gJDLK9qlNsKuSF/CX9qfZmZrioWZk1jQVYNuq7zRv9eXu7ZhoCIx+jgmpx0NdWEmuTVnp282LWFsBpjMO7n101PY5FM2BUr35n26cs2P7NkZE3eAkRBYF3XViJqDJts4eaCZazJW3je40iCxHTXROLqajb27uKXjU+ho2OTzawavi6TaGBF9hwkJDb37+PZjk1IgoTH6GTtBZzLLBm5Jnc+iiizqW8PT3VsRBYksowurss//3FOoNxayMqcubzet4fN/fswS0buLV573srBYCLAUx2vsXcoTRnrT4b4t9qHMQgK5bYCPj/pg9hkM9fnLWajtJuXe7bjTwRRRAWv0c31+YsAKLbksixnNlv69rKt/wBZRicrcubSG7v44mxnwkDcz1Mdr7FvqJ5AMkRMTfCtI79EFmTKbAV8YdIHz3ssr9FJZ7SPbx35JYogZ3I9bsxfSpWj9LLPfRzvDbQN+Xn6wFH2tHWSVFUqsz28f84MJuacVECTqsq2pjaeOXCUrkCQApeD++ZMQ5FE1FPYefa1d/Hrbbvp9AcAmF9axJfWLB9xvn3tXTx7sJaDnd30hcI8vH0Pzx2qBaDY7eSjC+cwvfDCDK8TvB52NLfRMjjExBwvA+EIgiDgNJsynkpd19ne3MZrx5po6h8knEiSa7dxw9SJLJtQhnmY6jSeSvHorv08f6gOgEKXg08tW8Ck3JNhx7Fkit/t3EsoHmflxHL+svcwrUN+XBYTaydP5MaaSUhXofL+nlYOfvHVx9iz4TAmq5FUIsWSW+fw3C9e5eibDQiCQMnkQspriql9s5E//vszKAYFu8fG9R9ZnklYFkSBWz52La8/ufOS5yMJAveUz2SWt5C2kA+vyUqVMweX4eqPcYvFU9Q19TDgO5lncNOKGjxOy1mtXlazgduvnc7mPQ0XVEPgXAVFToXZZGByeS67DrcSG47bH/RdGVWpTygI5wNZEsnPdpDvdXCstQ+AwdNYlN5tMIkGPlh6I6quYR7OBZAEkevzFrPUOwurbEYQBKySmRU5c5ifVU1KS4eESaKU6WMQFVbmzB3Tkn4iHMWp2Pji5A+Piu//QNmN3FMyMtFOEWQ+UHojuq6PClsSBAGvwcXthau4Pm8x6nACrFkyYRDlzPswyV7K16s/lpnjma5/kXc6s9yTSGop9OHrP5EXIQgCTsXG2ryFLMuZRUpT06zvw9cuCxKrcueyJHsmZsnEwqwaZrmrsMppxpsfzPwHzJIRQRBwK3auz1vCyuy5pPT0OLJ4MgejwJzNl6f+DcbTrO4PVdxBSh8ZhmeUDFyft5gV2XNQdW34Hp1/eJfLYOfe4jXcVrhy5NpCxssjCAJeo4tbCpazJndB+jykcztOrKlNtnBz/lKuzZmXuQ9W2cxc95QRlvzl2bNZkFVzRprQSfZSvlb9ICZx5L36ZOW9wzkmaarW9xWv5fbCVaPmfCKXodpZwT9PfQizfCq1p8CnKu9FG87bOOJvZGPvbua4J1NhK0ISRGJagq19+/m/xif5Zs0nzjvBfhzjOIGmgSG+/8rrdPqDzCkpwChLvH6smdeONfHTe26hpiAXXdfZ3NDMv6/fjNVoYG5JIeF4gi89uQ5BFMi1nzT0lWe5+eD8mfQEgnzj+Q0jjp2Aw2RkdlE+dpOBoz19zCzKZ1F5CQBOs5GcMfqcCzaTgTyHnaM9fZRluekNhrAYlBG5AjrpMCZ/NMaMonyMksSu1k6+/dJGvnvrWhaUFacpzkWJVRMrKHI5ebWugQMd3YQTyRHn03SdtiE/r9Y1sKetkxyblRmFeexoaef/vfgagiBwc82lGY7fCbynlYMH/9+9fPSbOgIgSiKiJHLfP96KPlx2WxBFJFlk2pJJTF3w+fRvgoA4zGtbvagKURIxWYz875vfQZYvPfTAIitUu/KZ6kpry+IF13cdDe1EbK8wPN4YwvjJxMT0NWq6jqbrSOfJ/X8uRONJmjuHMgK+zWykprIA+TyE+HnVpZgMCtF48pxtLxZOuxlpOCxI19NJvlcjjIqMxXwy/jsSS7yrC7MJgjCmIGQ8jblFEIRhC+uZPzYW2XTW/AMBAccYlY6tshkrI+dwpnmdetwkGc4a86+IMk7D2T+OJ6/rzFu5IAgYJAXDGYopmiRjRsA/fd3cp1CwCoKAUUqH7YwFWZRwjrG+NmU0taaAMOpcFwJJELErVs7lUz3XOqevafQ8Tl/P87pXp127IAjYT7l2SZDOOeex7nl6nPRzl9RSHA00YhQN3FCwNONp0XUNAYHHWl4imIyMKwfjuCBEE0leq2+gKxDkn9YuZ15JmsL3bxbO4baf/4EfbdjMrz5wJwPhCBvqG7EZDfzLDdcwOS8bdJ3XG5r59J+eHaEAOExG5pYUgq7z3Zc2jXne8iw3pR4XJV29/G7HXqYX5nH3rDTjo4DAxYgeui5QnZ/D0e4+lk0oo90XIN9hJ3qKUC8AP7jjBsyKkjb+CFDb3cc3X9zAzpZ2ZhUVYDaICEI6EbnE46IvGOZgx9gshbquk1RVlleW8ZEFs5FEEV80xt2/eITH9x68KpWDCwo2+O53v8u8efOw2+3k5ORw++23U1dXN6KNrut84xvfoKCgALPZzMqVKzl8+PCINvF4nM9+9rN4vV6sViu33nor7e3tl341FwhZkTEYFRSjgiSnwzZkRUIZ/k0eDuUQJfHkbwYZURQRRTHTRxAEDEYF8QKs1WNBR2dLTxNfefNZPrP1CT699Qk+ufVx6vy9lzTuGx3NfG3rK/zH7s0Ek2MX5trS2cK2rlYSw1bVh4/s4fZn/kBnOHBJ5z6BWDxJe/dJV31hrhOzSeZ8AhVlWRzB/HOhsq6qaSSSKaKxJOFonFA4TiAcIxCK4g9G8QWjxOPJEUL0lShQ67pOMqUSiycJRxOEInGC4RiBUAz/8LWcCCc62ecdnPA4xjGOywpZkPAYXLRFenitZxd7h2rZNXiEv7a/xmOtL1FlLyXHdH6hWeMYxwkE43G2N7VR4nZR6HQQiMcJxOMoksSkXC9Huvvwx+L0hyMc6x1kTnEhJR4XsigiSxJLJ5SNqmEgCGkGNPEsMa2CkC5CesI7Kw7/LYkionjxhsnq/Fxqe/qGaxPEyXfaR9GX2owGkppKOJEgFItjMxqwGhR6g2FSwwUwT8h3onBuRaXI5WRxeSkGSUIWRbIsZsq9HtoGL4zK+ErBBXkONm3axKc//WnmzZtHKpXia1/7GmvXruXIkSNYrekH4/vf/z4/+tGPePjhh6mqquJb3/oWa9asoa6uDrs9bTP53Oc+x7PPPstjjz1GVlYWX/jCF7j55pvZvXs3kvTeLWme0FT++8hmHpy0gHzLyXj3IqvrosbTdJ1wMsHhgR7m5xWxqqgCm2KkOxxE1dMek2yzFU3XmeD0YFEUlGFX+kemzuLIQM9luKo0UqpGMHySNzzLbUM+pQLyuZDndVDfklaSzqeLrutEY0l8wSht3UMca+mjuXOAnv4ggXCMcCROPJEinkyRSKgkUqkrUpA+URl5KBilfyhEfUsfDa19dPT48QUjBMOx9HUkUiSSKomkiqqdXtl3HOMYx7sBgiAwP6saFZVt/QfYPnAAgGyjm1sLVnBt7vxxxqLToOs6rWEfAlBodZ6R9ve9jKSq0RuK0DwwyJ62zlHf5SyrhXA8QTyVIhiP4TSbMJ1CkS0AOfYrg1FREGBaYR6PvrmfrkCQSDxJgWNkYbZYKsXWxlbWHTlGVyBAKJYgmkzSEwyR73RcuAUSMCsybos5o9CkPbYSCXV0naWrARekHKxbt27E37/5zW/Iyclh9+7dLF++HF3X+fGPf8zXvvY17rzzTgB++9vfkpuby6OPPsrHP/5x/H4/v/rVr/j973/P6tWrAfjDH/5AcXEx69ev57rrrht13ng8Tjx+0uIdCFwea/aVBgGBLJOFhTll2JRLz6xPaip1Q/0cGeilNejHrhhYVljOC831RJIJ+mMR7p1Yg91g4leH3mRqVi63VEzGLF9+pg5N04jGTrr1zEblggq7WUwn53Su9zaZUmntHOSNPQ1s2FFPU/sAKXVsgVkQSHuCBOGKY4XSdZ1+X5i9R9p4eWst++vSicZnwglLiyCMewzGMY53K6yymdW5C1idu+CdnspVgZim8uCmP2OTDfzf8rvJeRcQfFxuCAIYJJFZxQXcP3cGymlGWkUS8VjNDEaiyKJIStPSxCKnNIunUhjlKyNSvSIrTUayp7WDhKZS4Bp5zzfUNfLPz73C2smVPLhoLvnOdNjRv7+6+aLPKYniqHW7mnFJd9LvT7tLPJ50Maempia6u7tZu3Ztpo3RaGTFihVs3bqVj3/84+zevZtkMjmiTUFBATU1NWzdunVM5eC73/0u3/zmNy9lqlcNBAR+UbuNMrsnY+FYnFuG13ThiTlGSWZubiG1g30U2RysLK4gqapMdmcDOvv6ujg62M9dE6uZl1d0GbIbzgwdRgjfaUH2/PsrysmX7mzdkimVfbXt/O7pnew92p6xolvNBvKznXicFmwWIyajgkGRUOT0f/XNPeyr6yCVunKs7m3dPv7w7E5e2nI0U+XZoEhke2zkuO047GbMRhmjIiPLEooiEQhF2XmwZUSBuXGMYxzjeK8ipakEEjGMokT8KrXivtUwKwqTcry0Dvkpy3JTmZ2V+c6m6X51ZEnEbkwn+zYPDDEUiZLvtKPrOj3BEF3+EA7ThdWMOYF0oU6BSOLy5BUaZZkit5NNx5sp9bhGJTZvqG9EFAT+ftViCpxpr8K+9i5iybcur/Fqw0UrB7qu8/nPf56lS5dSU5Nm+OjuTidr5OaOLC2dm5tLS0tLpo3BYMDtdo9qc6L/6fjKV77C5z//+czfgUCA4uLLU5X4SoIkCMzxFjEYj9ATDWaUg8vplqob6uO19gaWFpQRTMTxmt8eV6AoCJmKvgDxRArtAqzbscToiqWnQ9d1uvoC/OKJrRysT1dENSgSC6aXsWhGOSUFHrwuK3arCbNRxqCk80cEAf74wi6ONHRfMcpBOBLnl3/Zyvptteh6ev1KCtxcu2ASkytyyc2y47JbsJgUjAYZSUqHaNU2dtPSOTSuHIxjHOMYB2CVDfzz7NUogkSuZdxrMBbsRgOrJ1fy003b+fXWXayYWI7bYiacSNI26CPPYee6qRPJsVuZV1rEE3sP8ac9B5hbUkQ8leKV2uMYTiFk0XSdUDxOMJYglkqhajqheIKmgSGMsozdaMB+St2BXLsNj9XM68ebmJqXjdVoQJYkyj0uXBdRkVgSBSqzPfx1/xGWTijFdJpHo9TjIqVqvH68mSl5OQyEI2xtbKEvGGbyME2pruukNI3BSJRYMkVfKEJcVen0B8i2WTEpMl7raLKFdwsuWjn4zGc+w4EDB9i8ebQb5vQkEl3Xz5lYcrY2RqMRo/HdX8BCEkTurZg16nejdPlcdYok0REK0OgfBEDVNQ4P9LC5owUdHUGA+blFrG9toME3yHONdSwrLGNqVs4lnVeWROyWk1YFXyBCKnX+So9vuPLw2aBqGpvePMahY11A+jlcu3gK779pDqX5nrPSm6ZS2sWEGb5lePNQC1v3NWbCg/KyHXz6/cuZPaV4BBvR6dCGcxTGMY5xjGMcacPKraXV7/Q0rmjIksTckkIeWjKXFw/X8cutu1A1DYMs4zKbuHNmev3MisINU6vwRaNsOtbEa/WNeK1W5pcVkdI0uvxBAMLxBH/YuZ+tTS0kVY1QIsGR7l6+8vTLKJLIkopSPrHsZNVku9HA51Yt5rfb9/DjjVuRRZEpedl8ZMHsi1QORKrzc3jxcD0VWZ5Rx++aWU3rkI9H39yPeZjmdGFZMbOK80eEBjUNDPGtda+RUjV6giH6QhH+d/NOHKZ0zsXP77vtgud2teCipM7PfvazPPPMM7z++usUFRVlfs/LS9Nvdnd3k59/ssx0b29vxpuQl5dHIpFgaGhohPegt7eXxYsXX9RFvFugAy2hIZ5vPUwgGcsIqx+duIAKR9ZFj3tjeVUm0bjC4eEf5yxLlxJHwCTLGESJj0+fj66D3WDAbjCxvKicWTkFWBUDTsOlK2ZGg0x+toPDDWnBvaPXTyJ5bm/ACTR3DJ6zjarqbN3XlOH1L8hxsGrBRMoKskZVRz4dvmAUTb1y1IMdB1uIx9Pro8gSi2aUs2B6Gco56HLjidRVS8N6NUPXdaJqmGc7H+aa3LvINuaP2Wb74Csc9u0goSeY7lzEIu91GY77lJbk0dafsDr3bgrMZRc8B1VL8Uznb5jhXkq5ZfJ5M31oukZ9cD/rex4npadYkX0L05wLkd+CKsHvFJJagr+0/x89sVZEQeKe4k+SZyp5B+ej8ok3nmCCw8uXZ6ziwGAXf27YR2NwEEWUmO0t5EMT55BtPhkOoQMDsTDPtRxhS08zQ/EIWSYry3LLuatiOiZJHnXP/YkYz7Yc5vWuRnqjIVR9pOGg0pnNZ6qXMMGRha7rRFJJPvzao9xcWs09FdPZ2NnAi2219EZD2A1GluWV85GqeSPyxXSgdqiXZ1oOcWCwi1gqRZHNyZrCKq4rnpT59pyKSCrBcy1HeaOrkf542suZa7YzI6uANUVVFFgcI86hahoNgQGebTnC4aFugskYNsVIkdXF4rwyludVYFVGGk2+uvMFDg12ow9/SQssTr634CbcxrGFTV3X6Y2G+EvTQXb1tRFMxsm3OLiueBJrCqswnCI0hpJx/nh8L21hHx+dNI+9/R280l5PdzSEXTGyMKeUO8unkXcVeSqsRgOrJpYzozCPQCyOqmlIoohFUfAMW8gFQSDfaeehxfO4Y3o1CTWdZ5BjtxGKx4kNf9PNBoVbp01mxcSyMc4k4DSPlCkEQWDN5EqmF+QRSaaZA60G5YLrHPzknptwmk1IgsCKynIm5XjJc9gxyBL/uHoZRlnCJMsUOO18ec1yhiJRVE3PeAGunzoRXQfLcBG0IpeDL59WuO3kVQwzKwkin1q+gERKxW0ZGVb1tetWZtbkasMFKQe6rvPZz36Wv/71r2zcuJHy8vIRx8vLy8nLy+OVV15h1qy0BTyRSLBp0yb+7d/+DYA5c+agKAqvvPIK9957LwBdXV0cOnSI73//+5fjmq5aJDWVfz+wgTWFVcyznKxy6TZemuvKYzrZX5Ekyp2jNWnbaQpAvtVOvvXybWxmk0JJwcnzDvrDNLUPUJjjOmfBspbOIQb9J8NkziTCp8OKTtKG5XjsZLtt51QMVFXjeGsfidTFv8QnEoFPQLtE6313fyBTSVqRRSaWZp9TMdA0nQFfhO7+d2fC/qUgnAqysfcpgikfa3Pfh8d4YZ4wXdeJa1EkQUE5g9Cs6in64p1o+tjPUWuknvXdj3Nj/gdxG7IxS1bEU9ikdTR6Y+1n7H/OOaLTH+8mpZ05aX0sCAhMsFXjNebxXOfvCaUC6Ly7vE+yoHBLwUcYSvTxP8e/TvIC1+hyQ9d16n19BJJxtvW28KXtz2GRFRwGE62hIWRRJKamRrTviYb4+q517OxtxW004zSY2NPXwdbuZl7pqOd/lt6F5RQBOZCI8d29r/JiWy1TXbnMyS6iMxJgQ8cxTJLC8vwKri2oJOuU74umaxwa6qbQ5kJH51e1O/AYLQgI1PX1kW2ykt6Bhcy8NnU18sMDG2kN+SiwODBIEhs7G9jc1cTu/nb+aeY1I7zfkVSCj7/xBPv6O7EpBgqtTiLJJLW+XjZ0HmeCI4t8iz1zDk3X2TPQwVd3vkB/LEy+xY5ZUmgIDLCtp4XXuxpZfP3oqtCLcktxGcwMxCM81XSQcCo5Sjk6dX1bQkN8buvTNAYH8ZqsuA0mtve2sKmrgR1lrfzL7DXIw3SYqq7RFQmyo7eV1qCPw75uso02zLLCwcEu9vS3U+/v5euz15BlujJYfM4HBlkmz2Enz3Hmb78oCLjMJlzmkYKwzXjy2ZNFkSK3kyKcp3c/I4yyTInHdcFzPhWV2SeNqHaTcUToUrF75FyybdZR9KuntgewGAxU548Mkx8FgUzewum41Ot5J3FBysGnP/1pHn30UZ5++mnsdnsmR8DpdGI2pymcPve5z/Gd73yHiRMnMnHiRL7zne9gsVi4//77M20ffPBBvvCFL5CVlYXH4+GLX/wi06ZNy7AXvVehoyMKAveWzxphAbr6Cm+PhsVkYEp5LhaTQiSWtgy8+MYR5laXYDEbzmjl1HWdF14/PKIs+9nWYzSn8rnD2XYcaKate+iSGH6MBhn5FMtSz0Dw4geDC6Yj1HWdAV+YLXsbMsnL40hD13XCqQAt4TpSepL+RDduQ/Z5W9Z1dFJ6khe6/sBs13LKbJPHbGeTnXyy8l8RGVuJaw0fw6l4mOVelkn+P3UOsmDgHyb94Iz9zwVJkHmg4quIF7hjpAupGbDJTgzixRUlu9KRLkpnR9WvrITDpsAg39z9Mv808xquL5mMAERTSYLJON5hoTKtmKr8qnYHe/s7+OTURXxg4hxMkkwwEeef31zH+o56fnzoDb4y85rMM7Wlp5mtPc1cUziR/2/2GuyKkYSm8lTzIb61Zz0TnV5uLJky6mlJaTo7elrxJ6L8ZPHtzMgqANLKRjiVzDy7uq7TEBjgt/W7SGoaP1t6J3OzixEFgZ5IiM9tfZoXWo8yxZ3LPeXTM/Na317P7r52VuRP4EeLbs0I3L54lDpfH1PduSOe4VAyzrq2WiKpJP/f7LXcWHLSK9YSHKI5OIhDGZ0Ie1PJVG4snkJK13i5vW7U8RHXrGv8y651tISG+NTURXy4ai6KKOGLR/n6m+v4U8M+Jtg9fLhq7oh39pi/n75omO/Mv5GVBROQBIHOsJ/PbXuGXX3tbO1u5pay8ZCmcVxe9AfDvLCnjoMtXaSGSVe+c//1mA2Xz9t7QRLI//zP/+D3+1m5ciX5+fmZ//70pz9l2nzpS1/ic5/7HJ/61KeYO3cuHR0dvPzyy5kaBwD/8R//we233869997LkiVLsFgsPPvss+/pGge+eBR/Ikauyc7O/lZ8iWjmv9QZrB1XEwRBoCjPxbSqgsxvr+86zms7jxEfI9lY19PMQwfrO3nq1f0jj53lHHnZJ5+zzl7/CAv8yPHTsfktnYM8/tJeuvsvTZj3OCxYT8kF2HGgmaFANBPidKHI9dozHo9ESuXw8W6SY+Ro6MPVr0OROK/trOfV7Wf/CL4XoaPjS/YjCCJeYwH98U400mup6zqarhFKBVD1FJFUiFDST1QNo+npNtFUGH9ykIbQYcJqkGDSRyQVQtVP3o+wGiCUGu7HyPsUU6OEUn4Gk73IgkI4FSCsBkidIqhGUiFCKf/wuKPfB03XMuMEkz7CqQBJ7WT4WHz4WCQVJHVaf13XSWoJwqkgwaSfUCpAXI1d9LN5OtLhKEESWjyzfieu48Q5dF0nocUJpwKZ9VV1NXM8nAqSUE/WQVH1FFE1TEpLZvoHk340XSWqhkec4+T6RAilAsPrExyxPlcqBuMRrims5JbSqSiihCJK2BUjBRbHCGv7YCzCnxv3M92Tz4cmzsUmG1BECbfRzJdnrkQUBJ5qPkQ4dfKZag0NMRSPsiI/HXIjiSJmWWGOtwhZFOmKBImrqdE5guiousaNxVOY4y3KzMtjtFBsdWbaa7rOkaEedve3c3/lLObnlGCUZBRRosDq4PMzltMfC7O5q4m4dvKd8CdiaLrOZFcOkiAgCyKKKOE1WVmSV4bHZBkxJ03XCSbjuAwmss1WJEHM9JngyOLawoljKvqZglrnYWjZN9DJ1u4WpnnyeWDyfMySkpnTV2dfi10x8j9HtxFVR7+b76+cyZLcMkwnrt3i5AOVs+mPhWkJ+8557nGM40JxvGuAlKryD7cs46t3XsNX77wGo3J5aWQvOKzoXBAEgW984xt84xvfOGMbk8nEf/3Xf/Ff//VfF3L6dzW+vvsFIqkESU3j23tfxm20YBiO1fzi9GuY4jqHa+sqQGGuixVzJ3K0sYdAKJ1T8YOHX6XfF2blvEosJgOiKKBpOrF4igPHOvj1k9sJxxK47GZ8wShwZl+AJAksnF7GvqPt6Hraev/8pkO47GYKc50oiowApFIqkViC4639/GndHvbXtuOwGglG4mMqEueD4gI3uVl2Gtv70XXwBaL82y9f5sG7FuO0m5ElER0dTU0zIKRSGi67GYdtbOq3eTUlvLzlKKloglRKY9v+Rl7eUsDcmtJ0jQhRQNU04vEUQ4EIL7x+mL++egBRFLGYJCKxK8tC+k4iqcVpCddRbKnEa8ynJ9ZGKBXAqaTD3IIpHz+p/zJ3FD3Eft9WBuJdFFsquSbnTlwGLy92PUp7tIGBeDfPdf4Wg2ii0FLOquzbyTYVAvDHlv8kmPIRSQX5UNkXKbVWZc6/beAl6gJ76I13ktDi/LLxW1hlO4u9N1DjTCflvdD1B9ojjXTHWnmg4itU2Wdk+mu6Rnu0gR396+mOtZDSU1hlBwuz1jLdtRCAnYMb2Dv0Ot2xNu4p/iQzXUtHXP+hwE72DW0hmPIhCRKF5nKuz78fs3TpIQ8JLc6vmr7DRNt0emLt+JL9mCUrK7JvpdJeg6hLRNQgW/pf5HjoMKqWxGvMZ57nGsqsk5EFmV81fptpzgWsyr0DgM5oC6/1PMlC71qq7DOIaVG+V/tpPlL2Jfb5NtMVbSbLkMeKnNsotkwgkBxiY99TdEabSagxLLKNac6FzPWsQrmCvSECcHvZtJGe4jEE3T397cTVFE6jiebQyPwrHVBEibiaoikwwLSsdL6LLKTrtyRUNWNRSXshUghC+tzaGb7pbqOZxbllZ51XOJWg1teLUZRI6Rr1/r4Rx+OqiiSIDMUj9EdDFNlcAMzyFpFlsvL7Y7txG80syS0jy2TFbjCOKcibZJkZngJeaa/nt/W70kU7HV6yTOlv5MVW0T0Vb3Q1YpAkFuSUYBBPKe4lCJTY3Exy5rCrv5UDA50szD0ZwiQJArO9RSPyHSRRpMDiIKGpRFNXvoI6jqsPJkXGYTZeUK2oC8WVUbFiHPx/s68740btMlx4tv6VCEWWWDyrgmOtfazbfIRoLEk8keJ//7yZv67fR2VJNmaTgWg8SXv3EJ29flRNY9bkYhbOLONnf3zjrONLosiq+VWs31bHsZb0h2rTruM0tPUzbWIBXk86uSkQitHU3s+xlj5iiSSl+R5uu3Y6v316B75A9KKuzeuysXT2BGqbehj0R9CB13c3cOh4N5PKcrBbjWiaTjSeJBiO4QtG+chtC7hh2dQxP24LZ5Qxc3IRW/c2ogP9Q2F+/PuN1EzMp6zAg8EgE40l6ejxceh4F4FQDJvFyMr5E4nFk6zfNu5BgBPCUIz2SCPzs67FKjtoCh0hkBzCIZ8kRIirEeoC+1jqvRGb7ETVU1jktBfqtsKPEkr5+c9j/8RdRR+n1DopnYwmnNw+H6r4Z4IpH/9Z/+VRc1juvZml3ht5pedxWsP1PFjxNQSEEaFjdxd/gqSW4BuHHhjV35foZ333EzgVD7cXPYRDduNPDmKTT8a5Lsu+iUVZa/nJsdHnl0SZbGMhK3JuJcuQy1Cinz+2/phy21RmupZc3MKehpgapTF8hDW59+JUPGzpf4E3BzdQZKnAItnZNbiR5nA9txR8GLNkZffQ62wbeAm3IZss4/kZPnRdY8fAehZlrcGTezdJLY59+B6aJAtl1skszFqLSTRTH9zP7qHXKbZMpMhScVmu8a2BQJHl7HHZJ4gqkprKK+3H2NLdPKqNWVawygYS2knLdpUzh3yLg5fa6yizu8k12winEqxrrUUSRCa7crCcoeClYdhqfjbE1RQ90RC+RJT/OrSZn4+ReOwymrEqhhHe7xpPHn9Xs5THG/fzn4c283Ddm6wqrGRZXjnTPPm4jZYRQo9JUri2cCItoSE2dBznH7Y9TY0nn2sLJjLLW8AkV86YSc8XgtaQD1EQKLCMHTtebHOyqz99H05VDmyKEZtiGDFfAVCGQ6XGi1GO462AUZFo7B2kPxjBYlQQBIH3LZ4xgk72UjGuHFwh8JpsadYQfx+VjmxkUUTVNTrCfhKaiuEy0pm+k8jNsnPfDbORRJFXt9cxNExR2jsYoncwNKKtxWRgwfRSHrhzEaIgnFM5EASBPK+Dj929hN/8dRv1zX2omkZ7j4/2Ht+o9gZFYnpVIffdMId500p5acvRi1YOAFYvmkTfYJBnNh5iwJdOoB70h9m2v2nM9rH4ma37ZqOBT923DFGEHQdaSCRVQpE42/c3s31/86j2uVl2blhWzR3XTufVHfW8sbthzHCt9x50emMddMaaaAnXIYkyXbFWemPt5JtKM0xBsmhgkn0GZdZJo0aQBBlZNGQUgjNZos9kwZREGQkQEREQLrh/e7SBuBZjsfd68kwlCIKA0zCavexM/UVE7LKLzlgzdcH9aHoKWVToj49dV+ZioAgKlbZpTLRPA6DcOpWdg+vToUXo7BzcQLYxn6ZwLQDhVICOaBPhVACP4fySwwUEquwzqBw+x4jziwa8hny6oi3E1AjBlA9NVwmnrvzkfPN5VJVNaSoGUWJpXhkr8yvHbKOIIkVWV+bv2d5C7qmYwf8d3ca/7nmFQquDaCrFYCzCvRUzWV1YdeZnRhBGsPOMBZ10SJ7HaOHW0momOLxjtss2W/GcRqpxb8UM5mUXs6mrgT39HWzoOM7L7fXcUDyZBybNJ9dsGzG3PIudv6tZytK8crZ0N7F/oJPv73+NSmcW76+czV3l084rfOhs15JW2M+8Hro+2tOiiNIIUoFxjOPtQK7LTk1JHo09g0QTSaaX5iOdg3jlQvHukDjfJUhqGr8/vot/nXND5rdN3cdZkF1KlfPS6gxcSSjJ9/CR2+ZTXZnHm4daqWvqoW8wRDSeRJZFPA4LE0qymVNdzLLZE8j1OohEE9gsRkKRs7OMKLLE/GmlOGwmNu9u4EB9B23dPkKROClVxaDIuOxmivPcTJ9UwJJZFVSWZGNQZKZU5FLf3HvR12W3mrjvxrlUFGfz5qEW6pp76OkPEo4m0DQNo0HBYlLIclnJ9dopLzw7PW15URZ/98FVbN7dwO4jrTS1DzDgj5BIppBEEbvFSK7XzuTyPOZPL2VudQk2i5E8rx2P00JX35UvGL3V0NCoC+4jx1iUoeb0GvNojzYw2TEbi5T2JkmClAkzutIQUyMYJRMG0XRRIRSDiT62D7yMqqfIMuQhiwoCwkWzIo0FUZBHrJ8kSKj6idohOv5EP3mmYpJaOq/AqbiZ51mJQ3GPOZ6ua2ijGJOEMyoSh/072e/bSqG5HLNkGxYUdXSu/uR8AcizOEAQKLQ6ua9y5nn1syoGCq0OrLKBxbllFFmdGCWZAouDanfeCBa7i8GJnAdREFmQU8LqoqpzdxqGIAhUOLIos3u4qWQK+we6eLLpIH9pPEClI4vbympG1fexKUaW51cwL7uYpuAAu/ra+fmRbfznwTeocecx1X3xobcFFieqrtEXG7t4ZHckiCAwzKI0jnG8NUglUrQcaaNuVyOJWCITDnjjx1ZjMJ308g2FowQiccqz3Wg61Hf2M6eiEGkUIcvFY1w5uIKgo9Me8mUsIJIgMhiLEE29++LHvW4baxZPZs7UEvp9ISLRBMmUhigKmE0KHqeFbLcNRU7HlJpNCt/93K0kkilMRoWywjMLckaDzPSqAsoLs+gbChIIxYgnVDQ9zdtsMio4bSa8bhvWU5iS7rthDstmV4IAJXljCy3ngsNm4tqFVcyaUsSAL0QokiCRVNHRkUURRZEwmxRsZiNZLutZhT1BECjMcXLXmpksnTOBoUCEaCyJqmoIgoDRIGGzGMly2XA7zJmxZk4u4uufuJ5oLElOlh1Ffm9attIVLlMcDx1kZc5tTLHPAaA5UsvG3qeJquGMcgAgnMXymGFoeQdK5ZklK7FUhLgWPa+CkqejP95JZ7SZlTm3U25NM7280ffcZZ2jIHAWC6qAy5BFtrGA5dm3jjgiC2lufkU0kDiFXjSmRYiqowW1M7F47Rp8DY8hhwVZqzFLNrpjLRwJ7L7o67nSMNtbiCQI1Pn66I2GyDGfm/89mIjxQmstuRY7H500jzyz/bLE55+AVTFS5crhz437qff3sTivDIt8YfkdoiCQY7ZzTaEVVdc46uvhyFAPNxRPOWPxT7OsMNWdx0RHNrW+Xp5qPsSRoe5LUg6W5pXxy9rt7Opr4yPDTEUn0B0JUufvw6YYqfGMrl8yjnFcLvS1D/DCLzdQODEfi8PMCe3g9NfWH4lhNRq4buYkNF3j3595fQSj4+XAuHJwBUFAwGEw8UZ3A4tzy2kMDOBLRC85nvJKhSSKZHtsZHvO/aGTRJG5NedftEgQBBw20xkTfsdCSb6HkvxLtx4LgkCWy0qW69KTPQVBQFEkinJdFOW6zquP22HB7Xj3lnW/ELRGjhFRA0ywVWOQ0hzWReYJaLpKe+Q4HkP2eY0jCzImycLx0EFchmwkQcIi2UaECJ1gN7rYGgEn+mv6SGt3iWUiJsnCa71/Zan3RhyKB39yEBGREuvEU/pr6bPrKqdy0QuCSFJPENfSjEa7hzYRTPk5Hfrw/zT98lYLFxBY5L2eLf0vkm8updBcTjgVJKIGKbVMwiLbKDSXcySwi6nOuQgIHPRtx5ccOO9ziIJMIOVD1VX64h280ff8mGxFJ3juz8R3f6Wi0OrklpKpvNhexx+O7eaBSfNxDRfz0oHDg10EknEW55Zl+sRUlcF4hMF4lEAiRq45bfW+XOqBIojM8ORT48nniaYDVDq9rMyvxCBJ6EBCTXFkqAezrDDZddLjs6W7GYfBSLU7LxPGk9Q0OiMBAok4HpN1RIhEJJVgV187ExxZFFidmfkHU3GOBfoRBAGv6cKKZZ2OWd5CFuSUsre/g780HuSuimkookQgEeOnhzbTHwvz4OT5o8KjxnH1IpFSeWTHPjbUNaKqGvcvmMENNVWX1fp+oYhHE0iyyHV/sxLplPwB2TBSVFckiea+IV45cIxwLIEoCJdV8Ydx5eCKgiJKPDhpIf9+cAP/9OZzZBktfHjifMrsV2a4wziuPJzKKHa5N4urEYf8O8g1lmCVTiYamiUrOaYijocOUu2cd84xBEFAFg2szr2bDb1/ZdfgRiY7ZrEy+3Y8xhy29q9jU9+zxNQIMTXMb5q+h012cm3u3cx2Lzvn+Af929nQ8yRDiX6SepxHW36MWbIxw72EG/M/gEPxcFPBB3m97zn+2PoTEloCtyGbFdm3UcJE2iLHeLn7z7RHG4mkQvy141e82PUoJdZJfLjsCxSYy6i0TeP5zt8jIjDdvZiZrsUZb4gv0c/zXb+nIXiYqBbmaGA3m/qepsgygY+WffmsHpXTVuoM/4a57lWISGzsfYZAchCrbGemawkllnQoysqc2/EnB/h9y48wieax8z/O8jhfm3snL3Q+wn/WfxmH4qbauQCTeJLIYdfga2zqfZZAaoiknuDXjd/BKtuZl3Ut1+TccZ7X985AEATMksInq5fQGQnwi6M7WNdWS6nNTUxN0hEJMhSPcHtZzQjlIMtkYWFOKb+q28H71v8+I/SYJIWJDi93VUxndeHEMSsrn++8qpzZPDBpPt/f9xpf2PYsVU4vuWY7g/EInZEAAgKfmLpohHLwQttRXmytxWk0UWhxYpUVOiMBmoJDFFmdXFNQiVk6GUIRTMb52eEtNAQHyTPbyTPbSGgq9f5+BmJhVhVUsjivbMTcDg12cXCwG18iTRGe9r5H+NnhLWSbbdhlI0vzyymxuRGH1/df5qzhs5v/ynf3vcqfGveRZbLSFBigI+xnWX4Fn5yy+C1lhxnH2wdd13mzuZ0/7tzHP6xZSrbNRq7D9o7fX6PZgM1tJeyPkF185rDjKYU5KJLEgZYurEYDf7t6AcbLmIwMIOiXi+z6bUQgEMDpdOL3+3E4xmYXuBpx4laouoaqpxOkZDFdDuZyC3q6rpMatjBKgjTCXa/qKqquoQgX99EYxzsDXdfZ19rFo9v3cfe8aSyoKH6np/SOQtf14TAgHVEYuXFqw5bjEwKyhpZOGD5LMb4T7U70E0hba9KW9tHbqIgwQrA+Ydk/fS7p+PrR/U9lNDpxLSfOIwDC8HzTx0aPIACiII3R9+Q1ioJ41v7CWdbk1LXR0YbXQzztt5NzZMQsRq7hmeZ46vGz3aPTr2F0/3Ov8duJhKpyw4u/oDsSYP/dX0A+h3dYB9B1BuIRnm4+xLMtR2gN+TBKErkWO0tyy7izbDoTnGmBQtN19vS3858HN9MUGqTM7hkWuHWCyQRNgQHiWoovz1zF3eUzkMX0cxBKJpj/1x8z1Z3H42s+dM610Rmu9uzv47Hj+3i9u5H+WAinwUyx1cnqwipuKJlM/iksQLv62vjj8b0cGOykLxpGQyfbZGNpXhkfnDiHCQ5vuuL8cPtoKsmLbUd5qvkwDf5+Ask4BkmiwpHFLSVTua9yJoooj9Adf3zwdf5wbA+RYSrR9PN3soq9gMB35t/ATSVTM9cO0BkJ8uu6HWzoOE4gGaPY6uL2shrunTAT8ylKVCAR46eHt/BC61F+vPg25maf3Gv14bX/4IZHeWjKQr4wfcVZ13Acbz80Xed/N+1kT2sH//uhOzLPzjst77TWdvDt+/6DwW4frmwHkpLeF3648ZtYHZYRxj9dPxnm+vL+Y6yeVolyDgXhQmTnceXgCkJaYNcIJeMYRAmzbCCpqcjnWcjlQhBVY3zn6A8JpkJ8qPR9zHbNyLwY67pf5bHWJ/jBjG/jNY57La4WqJrGc/tq+dmG7Xz15pWsmHwlUziOYxzjeKtQ7+vjX/e8gigIfGve9RRbXZn9Xdd19g108k87nqfGk8c/zliZTngex3lD0zU0XUcSzq08nw2qrhJVE6S0FFbZjCK+t4M5dF1nKBLFJMvEUilSmoZRlrEZDYiCiCCk20STKSKJBJquY5RlLAYlbUgVBDRdpz8Uxm0xE44nSKgqiiRhMxpQJAl/NEYsmeK/X9tGTyDMv962GkkUsBuNmUJi0USScCKBpunIkojVaMA4zCqm6zqD4SgOs5FoIkkslUIUBKzD48cSSVRdJ5FSsRgUdCCaSGBSFOwm41mvPxlPMtA5NOr3nBIvoiSSTKl0DAVwmI209PkyhVKf2HaAr955zTkrJF+I7PzefhKvMGjobOo6zrOth6ly5vBg1QI2dTdQ7c4bQVF3uSAI4E8G2Oc7SLVjMibp/OPzx3HlIRiLU9vVS0q9uuKpxzGOcVxeNAUHaQj087dTFpFzGi2oIKRz2/ItDsKpJAltfL+4UPTH/XTFBqiyF2OWzi7wnQ2DiSCv9uxix8BhPj7hdiY7Ss/d6V2MeCrFx373JMsmltPUP0SHz0+By8FDS+dRXZCDiIAvEuP3O/axq7mdhJqiyOXkjlnVzCsrwiBLhGJxbvjJw/zg7ht56XA9jf2DVGZn8eCyeUzI9vDw1j3sbmmnsW+IhKryqUeeJt9p528Wz2ZuWRH+SIxHduxje1MboXgcr83KmqmV3FgzCavRQCyZ4sHf/oVPrVzIzqZ2DnZ2YzEo3D9/JlW5Xh7ZsY9gLE59Tz+zSwuwGg1sPtZMTUEeX71x5VlrEcgGGYfXzkDHIIlYEqPVSHZRFsKwEhBPqdR29OI0m3hm1xFKsl0AtA/4L1vF+8xcLuto47gkpDSNPzfu46aSqdT5ezHKCkd9PeSZ7W+JciAJMh6Di45oF43hZqY4Jo0IObjSoOs6gWictkE/Q+FoRmM3yhJOs4kch5Vc52iquURKpX3QT3cgSCSRBB1sJgMFLgf5LjvKKXzeuq6zvaENXdeZVVpAQ+8gfcEQTouZyXlejIrM0c4+egJBDLJMuddNoduZcVe/2dTOYDjC8qpyRFFI9w+ESaoqFqNCkdtJgcuOfBqHeH8wzP62LpwWM1Pys7EaR7J+JFWVpr4hmvoGqcrzUprlQhTTloSGvkH80RitAz7ebOoglkyyp6WTWPIkVaUgCEzMzaI8e7QnSNU0OoYCdPmChOIJdF3HajRQ6HaQ57SPuZlF4glqu/rQdJ3J+dkYZJnWAR/d/iCxYapVh9lIicdFtuPSE7MvFK0+H60+H0tKS0dYTFt8PlKaRqnLNeK+Xw7ouk44kaC2r58pOdlYDW9/Zd5gPM6OtjZ0PX3PLYpMhcdDnv3tpWBsGBjEajCQY7O+43G8bxc6AgFiyRTFLuc5awS81TgRoOWLR0mqGkYxzXKlk95LGgL9tId9LMkrx3qBDENXI3RdJ5SK0hzuIqElsclmSix5mGUjvkSQpnAXOjouxUapNQ9dh45oHxE1RlxNoqFRZS/BKpnwJ8O8OXiE46F2oqkY+WYvhWYvkiDRGO7EJBroT6QrkReZs3EZ7ASTEVoiXSS0FFbJxARbEbIokW10cVP+Yvrjvnd6ia4YRBJJDnX08HfXLsakyPx6y26e2H2QYs9SXGYTT+47zL7WTr503XKcZiPPHqjlz7sOUuhyUOZNswwmUirrDtdzz9wa8hx2EimVHHv6O/SplQtQtfn86JXNdPqD/PDuG4bzytLRGU/sOcSWhhY+e80iSrPc7G3t5PHdB7EZDNww7WQe1J93H+RDC2fx0LK5hONJbEaFWEqlwxdgVkk+K6vK+ebzG3hgyRy+euMqvvXcBtqH/FSM8Q3OXHswyvZnd3F4ax3xcByTzcTMa6ax6JY5yIqMzWTg+pmT6PGF+NvVCyjPTY9V6nWPkikuFePKwRUEnbQ1v9yeRZ2/l3AyTlJT37KPq4hAvikPg6hwJFBHubUM8xm8B8FkiIZwEz2xPjRdxWVwMtlehUtxEkqF2OM7QJWtknxzLr6En91D+zBLZhZmpdlHeuN91AcbmOGqxqFcuAtb03T2tnby0sFj7GvtpH3ITyiWQBIFLEYD+U47106dwCevWTii30AowiuHj7HhSAN13f34Imme9SybmerCPG6YVsXSqjIc5rT1R9fhRy+9QSAa5x+uW8qv39hFQ88g+S47n7xmASZF4Rcbd1Lb1YfVaGBNTSUfX7mAfFda+PrZq9vY2djO7//2Xg629/DSoXoaegeJJVO4LCamFeVxx5ypLJ9UPkI4PdrVx1cef4kZJfl89eZVlGeP/GBHE0me3XeUX72+iy9ct5QPLZmFQRQZjET5j5c20+0P0hMIE4rF0XWd323ZM4J1QZZEPrlq4SjlIBxP8OqRBl45fIyjnb30hyLouo7HaqGmKJcbp09i+aTyUcpKfyjC/216k2giyd+tWUxz/xAvHKijtrOPYCyOIkuUZrl4YNlcbp45+YLv96Vic3MLj+zbzzMf/iDS8Puj6TobG5oIJxN8aNasC1YOdF0noQ6H+Z2B0aIrGOLn23fy9WtXviPKQXcwxP+3fgN31VQjCgKBeAxHRwcPzJ6Nw/T2eQZ7w2GydI1sq2U0D987hKFoFF8sRpnL9ZbEFr/Z1k5XKMj7pk/HY35nq9qX2z1Mduawrq0WgyQxwZGFUZSJqEnahisNC6QpPJ2Gd7/HOKmn2NC7m/64D5diw2N0kGvyIGkiT3e8gVFSUASZUCpKXEtSbMnlxa5tqLpGgdlLU7iTvpiPNXnziKpxumOD9MaGaI/2YpAU8kweJEHipa7tKKJCrsmNUTTgNtixamZe691NKBXBJBkZiPsJpiLM9Uy5rNd4LFiHIsoUW8oyxR1Px0C8n6HkAEXm0is2UsAoyyyoKGZ6UR4AK6vKeXjrbiLxBC6zib/sPkyxx8n2xlYAegIhjnT1MhiOUJrlAkAWRZZUljK3tGjU+IokIYk6kigiCUImlAjSSsVrdY2smVrJvLIiJFHEUlnKgfYudrV0sHrqyQKE88qKWFpZOuJb0DLgwyhLTMnLYWp+NuiwsqoCj9WM1WggEI2d9doHu3zU7jzOnX9/E1kFHjobuvnjd59kzprpyKfM02oyYFBO3uMpRTnjRdDezZAFgQXZZTzetI/j/j5+fGgTNsV4yTRtZ4KOjk22UWkrZ9fQXrpjPZRbR7s1hxI+Xul5jfrgcSyyBRGBvvgAb5r2cl/xnai6xsbezei6Tr45l8ZwM891vQTAfM9sAI4G6tjcv52pjtEVaM85T13nzaY2frBuM/XdfZRkubiupooch5WUptEfjFDX3TdKgA1E4/x55wEe2bYPoyxzXU0VpV4Xuq7T2DfI63XN1HX3EU+luH5a1Yh4vb5gmKf3HGHRhBIm5np57UgDv3p9FwCLK0tYWzORJ3cfYmNtIzNLCrh99tQR5/75azvo8gVZMrGUG6dPIqlq7G5uZ3tDK039gzgtZuaUFlyykGIxKNwyczK6nhbY//zmAfyRGLfPrmZK/kmaTkEQmJw/krYzqar8ZdchHt68m1A8wdrqiZRne5BEgbrufrbUN3Ospx9REFg1ZcKYHoSBUITH3zxIbVcfZV437184HUWSGAhF8EViOM1XzgdIEAQWlZaQ0jRMyoVvfaFEglePNzCnsJBil3PMNjk2Kx+aPZMsyztDeagDVoOBv1ucrip+oLubX+/aTWcw+LYqB4tKrrxk+F0dHfSHI5S6XFewf/TyoNzu4cHJC3i8cT9PNx8ipWlpJVkQUASJKlc2D0yez8LcsozF9N0KXdeJqgne6NvHFyfdT545C01PJ9A3R7o4FmrjK1M+jFE0sG3gIFv693NfyVoSWpJprgks885kr6+el7q3c13+AvLNWcx2T0ISRK7PW4hNsWTOA2CSDNxUsARx+Cnrjg1yPNTOvcXXUmjO5qC/gb92bLrsysGRwAEskpUCc/EZlQNREFEEwxUdISAKwojYfLvJSDylouo6mq7TEwgyKdeboRcocju5d+408k6JGhAEgYIxogjOhWgySTiewGuzZlZIFkUcZhPtQ37UU0LwilyOMb/faeVDyCgN1mG5QhQE1HOE/qgpFVESySnxYjAZKKzMI5lIjQoZ6hwKMBSKsGBimt79jSNNvG/JjPEiaO9WSILI7aU1HBrqptqVh8toZoorj+y3UDkQBYEqeyV7hvZTHzxOgWlkkZekluSg/wgH/IdZmb2UascUREGgK9rDfzf8Eotk5s6iW3AbXAwkBgFoibRRZilhv/8g/YkB3IqLjmg3LoMTpzK2UHU2RJMpfrhuM7VdvcwrL+KzqxdTkuXCYlDQdJ1oIslgODoqfGVXUzvP7D2KKAj8080rmFtWhNNsTLvbIzEm52fz89d28Nste6guzGVi7kjqsFKvi4dWzKM3EKZ1wMfelk5WV1fywPK5mBWFlKrxs9e209Q3OGrOO5va+f69NzC/ogi7yYim6VwztYIfrdvMa7WN/OaNXcwsvgVZurRN2m4ycvPM9EempX+I9UeOk0iqLKgoOmdC8oG2Lp7cfZj+YISv37qKlZMn4LSYEIR0BcY8p41Ht+3jN5t3M7usEK/NMmoz7Bjyk0ip/O3KeSycUILHakYUBaKJdMKY8x22oJ4KURCYlO29qL66rhOIxXmuto7KrCyKGf0cC4KA02RiRUX5pU71onHq3VE1DV80hiJJuE1m6vv7efpILZ3BAJIocufUKcwtKmJLcwsD0Sh311QD8Mqx4wTicW6ZMpm+cJjf791HfzhCodPBrVMmM8HjoTMQ4NH9B+gMBtB1WF5ezh1TpzAYifDSseNsbW3ljqlTWFZWhiJJpFSNl48f57XGRlRNpyYvh1smT8ZuNPL9199gdkEBbzQ3A3DzpEksKy8b8/p0XafZ5+Ox/QfIsdk43NNDldfL+6ZPw2020xUM8rs9e+kLR8i127htymQKHQ6eq63jL4cPk1BV9nV14jCa+OCsmbx87DgfnDmDP+zdT67dytqJE/mPzVv42/nz6A4G+fPBQ4QTSWbk53HjpCqyLBY2NTUxEInSHQxS199PidPJpxeO9Fg2DAzy2z17eWjeXErOoEi+lTBIMotyS6hweOiNhoikkulCjIKIVTbgNVnxmq3v2ho6p0PVVSJqnBxTOuzkBAtTKBnFJlswigZEQcBtcOBPpovvmSQjbsWOLErYZDNx9dzFSAUECszeEQQiUTWGUVQwSWnjVa4pi8H46DojbwfcBg9uw5VNMqLqGn3BkwUQB8IRzAYFaTjhOM9hp8jt4MOLZo3op0hS5vskCFyUoGxWFBwmI21DfjRAJM0sNhCK4LKYMcgy8eFw3TMxeZ3+Rb8QA6DTa8diN/Pzz/8Wd56LnpY+apZMRhlWMFRNo7FnkGfePEIwFqexZ4CEqrGvuZN7Fk+/4Os9G8aVgysESU1FFkTcRgtLcstJ6mlLj3yJbAjngq7rFJjyqLRXsN9/iFmukQ9YRI2y33cQryGLue5ZuAzpD1220UuptZh9voPcXngTOUYvQwkfUTVGe7STyY6JtERbOR5sZJZ7Br3xPootheek7BsL2xtaONrVi91k5BOrFjCjJH9EqJXVaMBrH6kYRBJJ9rZ00jbo5/0LZrCgohi7yZhZyyybhVWTK9jd3MHz++vY3dxOadbJOHSTIjO1IAe7yYhJUciyWdB0nYUTijPW8Mq8LJIplVBsdLGlqQU5rJpSkRlPEqHE4+L6aZPY29rFlvoWBkJhchy2t/T+ng2b61toG/CxoKKY66dNwmY6WS06x2Fj1eQJbD3WwuGOHo5195NVWTJq44unVFZOKeeaqRNwW05WaTYpCm5rWjH484GDhBIJArE4bzQ3I4sid9VUc8/0aQikPRg/37GTyqwsuoNBXqyrJ6lp3DdjGu+bnn4e23x+HjtwgO2tbVgUA9dVTeR9M6ahiCI60Drk4ze7drO/u5vKrCxybbZ0jNgw6vv7+cfn1xFLJbl16hQ+OGsmzlMs6SlNY19nF4/u20/D4CBGSebaygo+MHMmVoPC559/kdq+Ptp8fj7d/yxGWWJRSQmfWriAXLsNTde555E/EorHKfd4+NKKZVR4Rn6Enz5yhL8cOsJQNEpNbi4fmDmDmrx0Vddvb9hITV4udX39bG1pxW40cNPkSdxRPTXDknE+0EmHz9z1yB/RdB2zIvPR2bPxWi3o6Nw4qQqLotAZDPLdTZt4+kMfxGU28/Lx46wsL8NlNvNCfT23T5mCAHzrtY18cOYM8u12tra28tzRWj4yZzYv1h/DKMt8esFCNP0kiajDaGJNZSVHensZiEbR9JMVPss9bircc1B1nScOHeJQTw/zi4pYf7wBl8nEpxYs4HBvL48dOEh1bi4ey2jF8sT1bWxq4u8XL2ZxSQk/3bad6pwcFpYU8/82vMb9M2ZQ6HCwu6ODp44c5YE5s1ldOYGjfb2YZIX7pk9DkSQSKZXuYJDDPb10BYN0h4IsLC5me1s7D82by3c3vs5H5sym2Ongxbp6Xjl+nNunTqUnFOLXu/bw9WtWcUNVFaquZbxqoiBytLeXxw8d5n3Tasi1vf35NicgixKFVieF1rdfObnSYBBksgwODvkbme6qJKmlEAWRIksOPbFB/MkQNtnCsWAbxZb0OykMe1rGgiSIaOgk9NSoY8JplcKzjW5CqSiDiSAeg5M9Q7VMtL81nrWOWDsPN/2coeQQk+1TuTH/dhRRIabG2DW4ne2Dm8kzFXBz/h24DGlFSdVTHPTtY8vAJoLJALmmPNbk3kShuRhBEAilgjzd8Tht0VZkQWKGaw6Ls1Zgla0ktSSv9b7EQf9+REFknmcRi7KWIQkSCS3O5r6N7PPvJqZGyTbmclvhPeQYz17JOp5UeeNYM7OKC7CZDLx0uJ6aglzsRgMC8KGFM/nVll1MzsumpjCXoUiMoUiUWcX5uMbYMy4EiiRx4/RJ/H7bXiZke5iUm832xlbqevr55IoFb3n+lNPr4OaPr6HxQAv+/iAzVlZTXlOCYkx/A0RBIN9tZ0pRNm39fkq9bkRRYNnk8gv6TpwPxpWDKwQfeu0PPHLNh5CEdBycxNvn6pVEiWrHFPYOHaAp3DrChZXUkvTE+2iNtHP4wNER7siYFscsmUhqSfJNeewa2ktzuBVfwkexuYgJ1grqQseZ7qqhPz7AfM+ci5rfrqYONF0n12lnTlnheb2gQ+EIbYN+tOHEYovBMEoI99qtlHnTRXAOtfdw04zJGWFeFkXcVsvwvwVMSprjOs9pH16DNIWaTlqw1HR9xLyqC3NHuevToT1erEaF/mCY+p4BchxvjVfoXAjHE7QM+IgmU8wszc/EXZ5673PsVpxmE6qmU9/bz/yKYsTTPB2SKDBjeFM+k5Lji0b53Z593Fkzla+uWsmRnl5+smUbOTYrKysq0IH+cIR19ce4vqqKL61cTjiewG5Mu5YHIxH+sHcfwUSCf1y+lGA8wQ/f2ExCTfHA3Dn4o1H+uH8/HYEAn128iIFIhJ9v3zniuz7B4+Hhe+7il2/uoj8cGVFqXtd16vv6+NpLr7C4rISvrlpBOJFEEcXMPfzqqhU0DAzy9ZfX88/XrKTK68UkyziG3d8C8Ou776S+r59vvrqBeGpkleNnj9bywze28HeLF1HqcvNqQwP/uXUbn1+6hMk52fSEQjzzWi33TKvhX65dxeGeHv584BA5NhvXTDh/SloBcJlM/OauOxEEONDdzVNHjlLiciGLIo8dOEhXMIiuazQNDaHrOkVOB4UOB3u7uiiw2zFIEpXeLHrDYTY1NXO8fwBRFEiqGrMLCwjEYszMz+P7r2+mPxzm9qlTmTGs5MiSiNdqwW40ZsIqTqzxsb4BnqmtRdd1moYGmZWfn3l/7qqeSoHDAQKsq6/HF4uOqRycQI7VxvLyMqyKQr7DTn80QlcwyOvNLRwbnm9K1ajOzSEQj1Ph8WA3GLEYDJS4XIiCQE8oRLbVyq6ODio8bg5093C0t48Kt5v6/n5UXWdVRTmSINDq83O4p4ehaBSAiVke5hcVZp6PE89+0+AQL9bV8/VrVzEjLy+T7zKOdw6CIGCWTdxdtIo/t23g4abnqbAVclfRSvJMWXyg9Dr+vfYPqOhMsBZyX8maMwx08p8FZi+pAZVvH3mYxVk1XJ+/CIuU9rqefsdtsplbCpbyWOsrBJJhso0uHqy4FYCjgWYeb9tAQ6id1nA3092VvK949UVTmnZHO7i76H6cBje/bf4Fb/Rv4Jqc6zCKRhZkLcYomagPHkU9RakREMg25nJz/h04FCev9r7E7qEduA0erLKNTX2voqHx6QmfJ/r/s/fecXad1b3+s8vZp/fpvY9671225d5tOgYDARKSSwi5gVzCLwk3IY2QXFJogQChN2Mb3KssS1bv0jRN7/WcOb3tvX9/nNGRRlPURnLRefjwsWbXd79nl7Xed63vUiMk9ARG0Yiu67w09Cw9kS4+UvlJImqEJ3t/hUWyssK9mqZgAz2xbt5d8kGcBhc90U4c8sUdVbNiYGlpId/dfYC24TGWlRbxntVLMqFG9y9fgCSKfG/PYQYDQVwWM3curmNZSeFFjnxxBAEeXL4QdPjGq/sYCoao9Hr44LplU+oGzfhoX8UzL0oinkI3rjwnuqYjSmJGqSh96LRk6oa6cqKVqYnBRa6J05J1Dt4iBJNx4mpq2h9ZEaWLFqO5GgQEaqyVlFtK2TO6lxpbFWdfcTrpRM4aWyUbc9ZOGRUxiAZcBhdeoxtNV2kNtWEUFVyKgzp7NS8N7SSYDBFVoxSZC66ofUOBcHq61mm/5KnCWDJFOJEe0XdbzdMm6wiCgM2U1jYeDUcnGYyCIGQUR843eo2yzIRvMGvU5kwjGE6zKTN7MhaOXNK1ZJhDpbJQPJFWbgK+/tJevvHyvmm3OzvyG4jEpy30ZTYYsCiGi0awVns9PLRoEWUuJ0sLCzjW389vTjWwreqc4Vvl8fDuJYvIs04ebW0dHaMnMM4Hli1jSUEBOtA0XM9PjhzjIytXMBKJcGpwiPcuXcL2iZCeE/2D7O/pzhxDEkUcJiNmg0wkOTk8IKVp/ObUaUpdTj6/Zcu0lSZzLBb80SiyKOK1WCiwT3bqBEHAYTRmnIXz0XWd7xw4yHuWLOahidCdPJuVr+56nYO9vZlQp0q3m/+1YR2KJFHsdHB8YJD2MR9UX6RzL0AQBBwmI5IgUOv14jGnQ4oO9PayvKiQv9i+FX80yo7//j4AXouFWq+XhqFhzoyMsig/H5fJhC8axWow8OSHH0E577kTBYESh5MfvOshdrV38P9276HK4+avbr5p2vbouk5fMMh/7N3L9x5+EJfJxD/ven3S3eSxpEPWxExhudmvUZEkrAZDeh9BQNfS96pRknjyQx+cpBZ09p2aKcaWroeF1WAgz2bj5dZW7qqvx6IYODE4ODGbk37IM8WRmPz4OUymTJjD+YzHY5S6nPz6xEnqvd43JSk9y1REBJa4aljsOvswCZnioivd9axw101aDvCxynsy/55nL+dLC38vczyv4uT3qu4lfY+cu78+Uf3AlHehIAjMd1TwxQWPTjnHPHv5ecsnr7sS5jsWUWwpxSSa2ejdzO6RndyUdxuCIGAQFBRx6iCZgEiBuQhVTw9oFJtL6Y10kdLS70mn7OKY/xAd4TZq7fUYRAUREQ2NQ759PFD8HqySDUU0UmmtpiXYwAr3asyShfGEj+ZgA6s866m1zZ8xF+J8RAEWFOTyf27fio5+XkG8dBE7oyzz8MpFPLRiUXo9wHlF8+wmI/u/8KlZDWYB+LPbpq9eb5Ak3r1qMe9auXji+OnznpsVl3nsDz44rQ9Q5nHydw/cmmnLrs99ItOO7z368CX5DYIgIJ33DfrVvzzJPb9/G0aLMbM+GEsQiMQocqfFXZ461MiOpbWzyqReLlnn4C3CWCLCH+z+5bSvhs8tuYkF7iszrC8VSZRY5V7O/3T+DLtszxiCBkEmz5iDjs5y11LciivTRj0TMiBgla2YZQudkW48Rg8myUStrYqfdP2KplALJslEwUWmE2fibI9o+qVbx2cro57dT2d6Y17XdHRdRxKm6fmreEsnVXXa5dpEpVjgsgrb6Xo69GyuEDh3eS6LGavRMOv12k3TJ7GlX9zClA/OhRTYbRhlacIAhCqvh1da2ydtU+l2Y5kw+M6i6zrBRILXO7o42NOH4Twj1SBJqLpOPKUSTaXIs1kz+5a7XRzq7Zm1TWfRdJ0On58ar3fGROULE8IuJxQsEIszEo4wPy8vs6/dqOCxWBgOh0lM3Cvz83MzBqckiBgliXhqatjCpVxPfyCIIMDpoSFGwhEK7fb0jKQg4ItEeaKhIfPREgWBGq+HhuFh3uju5n+tX4fFYMAkyyzIz+NHR45y97x6QokEBlGk1OmkaXgEi2JgUUE+H125gm/s3w+kdcqD8TjhZIJAPMZIOEKO1UJcVTHLMglV5fjAAE0jI6woKrrsazufC3+DArudJQUF/M/ho9y7YF7GCaxwuZAEAa/FQvPICP2hIEZJwm02p52+WAyzwUCt18vTTU18ev166vNy0YHXOjqocLk5MThIntWK6yJJ3UsK8nlo4UL+Y+9evrF/P59auzbrILwFyMSiz/IOm7J8lm3ON1jPZyaDdMZzzLD8SjGKRkSkiW+ynbAamnX7dOHVFAfG3uDE+BESWoLxpJ8ic2nGGd6QswWzbGbn8Iu8NPQsW3NvZrFzGTEtTlJL8pPO7yGLE0m3CCx0LgWgxlbH3UUPsGv4Ffa0vMYS13Juzb8Lk3Tx0B9dYKLA1/R9lv4vM66/2IydMN33/sL1sxx/psNfeNzz2zFbk3RdZ3w4gNVlxT80OR+l6UArd358ci2S4UCIAX+IhaXpgYxjnf3ctLgayDoH7zicBhNfXHbrtC+XIsv1iRld4KinxFLEId9RND19M1plC8tcS3hm4Hn2jx1iuWsJBtGQTvBKRZEFmWJLIVbJgkUy0xBsZoN3LSbRiN1cjEk0cth3jHxTHmb5ytRSClxpZ6V/QkP/UpRmrEYF10RuwOB4kJSqTfGqU2o6YTOeTE/PSdLczc4Mjk//Uh4NRUhOhJycH1J0dhRL1fRpR+gTqoovHL3k81/MjbIaFawTMZyf2L6G969bOue6/+cTS6Uyzp0+kURuvOB8BmmG/BpdZ35uLn+0YR3zz0soPqtNLU5MqybPK/6m6tplTbQYZZn4RBtn/MBf4UdcJz1zcXamJt0+nZSmYpINmdkwszx7dctLwSTLlDodfPGFFwHwmM3ct2A+a0rTkn6PNzTwWnsHmyrKuaXm3JREidOJLIrkWa0UOxwZJ+7vbt3BDw4d4UsvvYxRlrmrvp5Sp5M23xjPNreQ0nTsRoU/27wJgHafj1+fOkXveIDBYJgzo2N8cNlSqj0e7ppXz1dee50Kt4uVRcUU2G1IgsCSgvxMnxtliUq3G/MM8bMCaTWmGu+5fI4ShxOPxYwkCPzdbTv4/qHDfOmll1EkiVtrayl3uQC4va6W1tEx/vaVVymy2/n/btqOy2xiaUEhJU4HOVYLe7q6qM3x4jAa+eL2bfzo6DGC8TjLCgu5ta4Ws8FAjsWaOeb5eCwWdNL5Nn++dStffuVVDvf1s7nixi5sleX6MZYYI6bFkEWZ3mgPeaaLh9qEUkGeGXiSD1d8nApLNbtGXqY/1pdZH9diLHWuZIlzBYd8+znsP0C+qZACUxFOg5vteTtY5FiKJEgk9XPvuISWoNhcxiPlv8dwfIhvtn2NQlMJqz3rpmvGDY2aVPn5Pz3Ojg9t458+/B94i9yZda3HO9EuKGwqixLBaJz2IR/axDTrXAcWZZ2Dtwj1rjyqHd5rGj50MRRJYXPOBpqCLZw1Lw2igaWuhQzFh9g7epCT4w1YJAspPUkwFWKle1naOZCtWGULo/FR8ow5GCUjIiLVtkrOhNq4veCWK27X+uoyfrD7MAPjQXY3d7B9fnWmbPhMuK0mqnI9GGWZPWe62DavCrf1XFy8rkOfP0DzwDCiKLCktGBOE3qOdfcTjMVxnCflqes6R7v7CcYSOMxGavLPGTgGScJskBkJhQnH0oXIzi/eNRaK0Ng/POs5zxrLCVUlqWqTjnEhVqNCXUEOrzW3s7ulk3uWzpvUP+dz/gzRldI+5uPMRGEsXyTKkb5+lhZefDZMEATy7DacJiNto2PU5+ZglCSCiXQfeUgbi16zmRMDA9Tl5JBQUxzvH5gkO5e5lmn+JYkiy4uKeL6lhVODg5S5XKiaRkJVybFaM3HlaTUMGAgGqXCni9CZZHmSM3Fuduvc8Z1mE8uLCnmptZVlRQXYjUYah4YZi0TZVD63UpKlLiffe/ihadetKytl3XkSow8vWpRpaSSZJJZKsbqkhJyJsC5BECh2OPjC9q1TjnX3vHncPW9q/Yp5ubn8xbZt057/E2tWT7v8m/ffl/l3ns3GZzZumHa7s22qz8nh/2w716YPrViW+Xeh3T5p3fnk22z83x03T1q2pKCAJQXn7sP/vPeezL8X5efzD7fdOuU4N1VXTZsHsuUChaUv3zpD7HqWLNcAURAZTYxwcvwoAgItoQa25qa/u1E1Qnekk45wKyPxIRoCp8gzFVBjq0MQRNwGD92RLnyJMfqiPZMGVk4FThBXYyiigj85hlfJxSSZEQWRrbk3cWL8KDE1iiRIqLpKibmUEks5/dEeemM9GAQDqq6Ro+RmkqBnu4bNtZWUum+sJHrJIPGhL72H8aEAt35kO7d+6Nw77Jt/+gPECwYuS3OctA+P8fyxZpKqxqZ5FdkiaO9UPjlvw3XVHpYFiS05G7HJk+O7Fzvnc2/RnUTVaKYgmltxcWfhrdTZa+mN9hHX4hhFBa/iodaWHn00SyaWOhdhFI1U2yqRBAld17klfxvVtkqWuRZfcVuXlxexvrqM3S2d/NfOA8RSKRaVFOCymFA1jVAsTo8vgKbpbKqrAECRZdbVlLKzqY3XWzr49cFT3La4liKXAx2dnrEATxw+zeHOPpaVFbKsrBCDJHIZkUuz0j3m5we7D3PvsvkUuRykNI1TvYM8fayJQCzGu1Yvxmk2ZQxup8VEicfJ6b4hXm5oxW0zU+Cwk9I02ofH+PXBk3SO+Gc9p9Egk2u3crSrj6NdfSwuySfPbkPVNaKJZNoBOa+Ww7Z5Vexqbudgew/fee0AOxbWUuJxYpJlYskk4xPVqBOqypa6ikn7Xi5GWeblM63s7uxkKBTGbDDwwKIFF9+RdLjRrXW17OnspM03hkGU0HSdFcVFlDid5Fit3FRTzfPNZ+gNBHEYFYznGe3xVIrXOzppGBpmX1cPkWSS/z54mEKHnXctXoRBFLm9rpb2sTG+s/8gOdZ0eFKF28X9Cxdgm0hmd5iMLC8q4rFTpznS18/C/Hw2V5ZjNxrZ393Dod5e+oNBRsIRfnbsBBVuNzfXVFHmcvGJNav45r4DfGPvfkyyTCAeZ0FeHqtKri605mpJqirHBgbY2d6BpuusLC5606v7Zrk+qLpKX7SPjnAHwVQQTddQJIVcJZdl7mVvaS38642u6zQFm+iKdLHUtZR805WFyF5L6u0LqLbVMZ70M570syX3JhZNhPiktBS+xBgmyUy1tY6oFsWfTEtw22U7txfeQ3ekk4gqsNS1AlkwZIqkOWQH7fFhVF3FKllZ7FyG25Ae2FrqWolVttEd6SShJXAaXJiktJCHSTKjain8KR+SILE5dzvV1tpZr0GRJT5zy8Zr1UVvWQRBwGw1IeQLbLx3FTbXObts27s3YDBO/vbGEun8VLvZiEES08VP5zIpkaxz8JbhWucUXIhBNLA9b2pCjkkycVfh1NEym2xlmWsRy1yLpj2eKIjMc9Qxz1GXWSYIAstci6/KMYB0AtBnbt2IDrxxppN/efZ1KnLc2EwKqpYOURkOhanNz8k4BwDzC/N4ZMNy/uOlvXzv9YPsa+vCa0u/uIYCYZoHRih02vnwppUUuR3nEhbngK3zqnjySANHOvvIsVlRdZ2O4TFah8dYXlbE+9ctmzTiXOJ2sKW+kpbBUX5x4AQnewfxWC2omsZgIEQgGmPrvEpeOHVmxnO6zCbWVpXyeksHvz3aQOvQKC6LOR1Xqmo8sHLhpP6pzvPw8a1r+PcX9/DTvcc40NZDrsOGIkskUilC8QRDgTAVOW5WV5ZclXNQ6/WypbKCcDKBkC9Qm+Ol2puuKyEJAvcvnI9JlqeEGkF6ZuD2ulqqPR56xsdRdR2bojAvN13UzSzL3FpbQ5HdzkgkgsdspsjhoN3nQ5xIWHWZTBQ7HRmHRABsE2FVgiBQ6LDzh+vX0TA8TCAWQ5YkKlyuSe1xGI18fM0qGoeGSWoaxU5HJhTLpigU2u3k2WwsLSzMtMsopV+xC/Pz+ZNNG2geHiGWSuG1WJiXl5sZpf/AsqVYFCVzT9iMCg8uWojFcPWhRrMhCAI2RWFBXi5VbjcV7tlH9rJcGxJagmP+YxzzH5t2vSiI1Nhq2JK7ZU7Op+s6Z4JneGrgKTrDneniWKIBTdeosdWwzL1sTs7zViSuxhmOD+NSXNjkS1eLe2HwBRqCDUiC9JZ0DursMxdVsxscrPXObHQvcCxmgWP673Sdff6Mx5YEiXr7AurtUwd68k2F5F9CWFOWc5gsRkwVeZOWrbpt2ZTtBCE9GKjIMuORKEc7+rhpcQ2GORzXuWGcg5bgPo76nkURTTgMeaz23odVcl80VKI/2oJbKcQkXdpLRNVTtIcOo4hmyqxXZxRfjJgapiW4lxxjKYXmuhm3CySHOeF/icFYG05DLutz3o1Zmr6631l0XWcgdgaPUoRRml2rW9VTdIVPEE75WOSaXrHkahAEgfrCXL5w9zZ2NbXzckMbzQPDBGJxFFnCZTFTX5DDjoU1k/YzGmS2z68m32nnd0cbeaO1i4MdvWmtYJedu5bVc8fieuYX5c65RvDiknweXrWIXx44we6WTiKJBHkOG+9Zs4T7ViygMnfyvWc1KjywciFmg4FnTjRxtKufeCqFx2JmdVUJf3TzekaCYXY2ts94TkWWuHlBNQlV5XdHGzjU0Us8qWJWDJR6nFMcH0kUWV9TRoHTzisNrexsaudIZx+RRBKLYiDPYWVFWRG3LqrFZrw6I1UQBBYV5JNnm/ocnQ3rmQ2rorCsqJBlRVM/NmeLj228ILa7eiIu3SBJrCwpZmVJ8YzHFycchELHzFU1JVGkYgYDekF+Hgvy86bZ6xxVHs+U2gdnWXtBRWGTLLOi+NrPKsiiyLzc3IyjleXNQdd1YmqMwfggkVSEseQYMTWGTbLhNDixyTaKzTPfv5dLRI1wbPwYjYFGbs6/meWu5SiigqqrGEXjO3rWoD/Wz/ODz7M1dyv19vpL3s+tuNPqfIrr2jUuyw1NyB+mr3WAupXn8sFO7m5k3poa5PNyLfOcNrYvrEbXdRIplW+/uI85C3uY4IZxDsKpMXKMZazw3MlR37Mc8z3P+px3IVwku/uI7ynWe99zyc6BiESpZdF1KWyl6SkCySGssmvGbVJ6klPjrwA6W/I+SGNgN3tHf8X2vI/OemwdnaO+Z9iQ856LOge6rhFO+QgkZ4+JvxpEQaDc66JgzRLuWFJPIqWi6hoC6TLlRoOE1ThVFcSsGFheVkRtvpdPJtaQVDUE0nrsVqOCRTFMkkcVBPj3D96Lqml4zytg9Gd3bOFTN60jz2HNSKetKC/i2T/9SGYE+nw0TWdzXQVLSguIJtKJrrIoYjOlE4EvTHoVBIEcm4WHVy/i9iV1xJMpdHQkUcSiGLCbjIxFI/zfD95CnTd32uRhQRBwW808tGohty6qIZ5U0XUdURQwSNKk/IezGCSJ2nwvJR4nD65aROK8pFxZlDAZZCxGA7IocmZ8hMFIiI2FFRS67PzNAzuIp1KZ2ZgsWbJcGYqosNqzmsXOxWho/LLnl+wf3c89Rfew0r0SWZRRxLlTPYqqUUbiI7gUF8tcy6i11c7pzOlbmaH4EK2hVtZ711/yPoIgcH/x/dxecDsOg+Mati7LjYx/aJyDzx2b5Bw8971XqFpSPsk56Brxs7uxA4BYUqVndHyOg4puIOcAwCRZcch5FJnraQzsRkdD06E1dIhDY0+gozPfsZUFzq3E1RC7h39GU3APY/FebAYPy1x3UGFbRnPwDQLJYfqizYwnB6izb2Ct90GiapBjvudoDr7BSs/dLHRuB9KjQr3RRl4f/gmanqTYsoA1nvvxJfppCu5mted+rLKbcMrH471/z/vK/46EFuWY/wXaggeRRAObct5PkbkOHZ3T4zs55PsdDjkXURAvOmsQVYNU2VbhVUpZ4b6L77T+AdvyPoKua3RHTrF/9DHCKT+lloWsy3mYlJbkteEfcia0n9F4Dy6lgOXuOyky1zMUb+eNkZ8TTI3iMZRwc8HHkYX0qHJH5Ch9XY1oqGzN+zB5xkpAoDNynL0jv0TTVartq1nmug2jZGXf6K8xSTaaA3tJ6jGWuG5l0USfTYcgpIuRXYpa0fmIooDDbJrWOJ7uHAXOqaPHXptlihFsUgyUeV3THkcn7dB4rBa4xCKpgiBgVgxTwnfOfrD9yRg9iXFqRG/6RaBPTa8VSMf3m+yGSR96/YJjnb8MwGyQMZ8thHbesc7nhe4zWGQD6wvKkUWRHLvl0pzgi8jGZclyoyMIAibJlInzNommjES0W3FPEaq4HCP+fGGDs6T0FAktgUk0ZZyOmY55sXPNJmIw12SuBf2ikmzTXXdCSzAQGyCYCqLr+rTtvFBK+SxWyYr1ogNlF7/u6dp1qftkeWei6zq+wXH6WgcY6Rmlu6kXgPB4hP62AXTtnNIfpAc9izxpJ1WRJO5bPf+y7aKLcUM5B6qeIpQapS10iGJzPQIiEXWcY75nubvofxNR/Zzwv8hQrI1i83xuK/xDBuNt3Fn0x7iVc1O6mq7SGT7GltwP4TWWok8UDzGJNlZ57kVAyBQUAUjpcV4e/A4PlPwFOion/S/TEtzLAuc29o89Rjjlwyq7aQy+TpllCQIS3ZHTaJrKAyVfoD/WxIGx33Bn0Z+Q1KLsH/sN7yr9a0KpMfaNPjbrNSe1KCIiJjGdZGmW7IiiRET1o+kqR/3Pssb7IGWWxah6ElGQESSBu4o+w486Ps9dRX+CS0nnQyS0KAdGH2eF+y5KLAs56n+GXUM/ZHv+R0lqMexyDrfkf4L28GGO+19kS+4j6OjsGf4p9xZ/joQW5cT4i3RHTlFjX4OqJ2kMvM79xf8HRTShM1Vd5uK/qUZSS6HpGiZJeVPVnq4Vmq5zbKSffziyk7bAGI+1ncIsyfz71vsot7n4ResJftBwmKSWosbp5Q8WrWNJTiFPdzbR4BtiKBri8HAfXqOF79z0EHE1xU+aj/LGYDftgTEE4PcXreODdcs4MNzDfx5/g5FYmEXeAj6xYA0eo5mvHN3FC90tKJLEr86cYGlOEf937Y5LMvo/vnrVte6iLG8i+kQdkXgqNSkRPMu1I6JG+Fbbt0hoCT5Z9UkO+Q6xa2QXQ7EhDKKBOnsdt+bfSrWtGmlidnw0Mcp327+LL+HDn/ST0BLo6Pzt6b/NGJ+yIPP5eZ+n0loJnNPBP+Y/xs7hnXREOkhqSfKMeazzrmNzzmZssm2KQf3ZY58lR8nhc/M+R2OgkecHn6c93I4gCBSZi3hPyXuotFYymhjlK01fYXPOZkRB5MXBF/EoHh6tfBRFVPjv9v+mK9LFIsciHip5KBPrr+kaJ8dPsn9sP83BZgKpABLpXIC13rWs967HIZ8LnT0TPsPzA8/TFeliJDGCpmt8reVrk9rtkB3805J/QhbOmUU/7/45u0Z2kdASqLqKSTLxvtL3sTl3ar7eWcMtkAzw8vDLHBo7xGhiFJNkYr5jPrcX3E6xuRhRT0s2p/QUrw6/ystDL/O+svcRV+O8PPQyXZEuNF2j2FLM9tztrPGswSBc29yjLG8uuqbTtL+FJ7/5PJ2numk90QWAyaxwx8duxmhJO/D+cJS/+fXLaJqOpmuZqAddh7//wO1XlRd4ITeUc3DC/yKnx3dSYlnAAud2BERGYt30x1p4uv9rACiiCUkwXOCpTy3yVGiuwyq70sbohEEqCEJmNOP8rUfjvfgSfTzT/28AyIKB+c4tyKJCiWUhnZHjuI1FNAX2cGvh75PS4ozFezkdeJWe6GkAzJINAYGxeD922Yvd4EUQBDzKxeKSz460nDW8ddB1BEQSaohYKpQJg5KFc9PW+kTVsPMrAwaSw5gkG3ZDLqIgUWffwM/Hvpi+JlHBZSjALNspMNVyenwnGipj8V6G4508PXHtBlGh3Lo0c54q6woU0YQgiFOqL18KTYEeftOzh13DJ/n3lZ+i1v7mqr9cCyRRZEVeMV9cuZ0nOxp4sGoRCzzp+HZd11mRW8SK3CIUUeLJ9gZe6W1jsTddSfjF7jP844Y7+If1dxBKJrArRg719hJKJfmXjXfROj7Kj5qP8O6axQSTcb56ZBefXbaJAoudn7Uc48WeFj5Yt4J/WH87AlDvyuXR+Ssvq/3ZUa93Ph1+H3/92sv89ZabqHJPn1eRZY7RYTA2yE+6fkJzsJkcYw519owwJrYAAQAASURBVDpGE6OcHD/JYGyQD5V/iBpbzURhKIkiUxFFpiKiapSWUAsxLcZ8+3zscnq2VBTEzL8BYlqMZ/qf4ZXhVzAIBsosZSiiwmBskKf6n6Il2MJ7y95LnjFvsoOAzlhyjMO+wzze9zhmyUy1rZpAMsBQbAiLfG4WNqbGOOg7iFWyYpftdEW6+FX3r4hrceJqnHxjPsfHj+M1enlXybsQBZGuSBc/6foJcS2OVbJSa6tF0zUGYgP8qudX+BN+7i66O5NwrOoqDtnBPPs82sPt9Mf6WeRYhEc5d6+aJNOUd9Uq9yq8Ri+BZIADYwcYT04uUHUhQ/EhvtH6DQZiA5nfI5wKc2r8FCfHT/JoxaMsc52nBKWnnYnnBp5jNDGKUTBSZ6/Dn/TTH+3nZ90/I5QKsSN/xzty4CtLGlESWX/vaioXl3PguaPc8/tTRWEAHBYTX3zoJpr7R+gY8rG+vox4QuWFEy1zPihzQzkHy9y3U2lbwf6RxwilRjEqZhTRSJ6xgvtL/hxFNKJNxLGfRUBE06dWpp3qQMyMQTRiN3i5r/hzGCXzxEhb2ouosq3k1aHvk2eqQhYM5BorSGpxjJKFhc5trPE+gCTIqLqKiIRBVFD1JKqeRNNTqOcVHZkOk2RDRyeU8qHpGr5EH4powSw5iKlBREEkoYUxSXZ0XeN8R0i84NplUUFHQ9WT6LpOJOXHKKZfvrqukdTjqHqKhBZBFtMVdQ2iEbdSxP3Fn0eRTFOqHMui8ZL6cCYWOMtY4Czj0b1fvarjvF1J6RovdrfSFfKhiBKt46Ms9hZmZtwXewuoc+UgCgIOJd3XZklG1VRaJnIIcs1WBAT6w0EafEP8T+ORzPGLrU4ur5xYlhsRkywzPycPiyFbDfh64k/6ORM6w4fKP8QK9woQwJ/w82Tfk+wa2UVjsJFSSykmyYRbcfNIxSNA2qn4WffPGI2PcmfhnVRYK6YcW9M1jvuPs3t0NyXmEt5T+h7KLGUICITVME/3P81rw6/xytAr3Ft07ySDX0dnPDnOzuGd3Fd0HyvcK9IJz5pKd7SbXGNu5juT1JNousa9xfdSaCrk38/8OyfGT7DEtYQ/rf9TxhJj/EvTvzAYGySYCuI0OKmwVnBP0T0UmgqptFYiCiKqrtIYaOQXPb/goO8gm3I2ZWY15tnnMc8+j3AqzG/7fstIYoSb8m9isXN20ZAaew019hp0XWcoPsRx//EZt03qSX7c9WP6Y/3cnHczdxbeiU22oeoqR/1H+Xn3z/mfzv+h0FRIofmcsEJEjdAZ7uSuwrvYnrcdo2RE1VV2Du3k590/5+T4yYyTkuWdjbfYw9Z3zZwLI4kiHpsFo0FGEEASREyKgD8cnWJbXS03lHMAUGCqpsBcyxHfM2zN+zAeYwkOQy6nxl/BKrswilbyTVWZBOQCUw2toQMEU6PkmsqxyTOPiiW0KD2RBkYTPYRTPnojDeSZqnApBeSbajgx/iIOQx6yYCDHWI7DkINHKUIRLRwae5Il7rS3KAsKOcYyWoJ7aQi8jiKYsMpuCs21uJQCTJKd0+M7AYFQyjfr9dpkD15jKb3RBuJqiMFYG6s89yAgYJYc5JmqOOF/CaeSj1myk2+qQRFMgECusYIzoQPkp8bINZZjl3NwyLl0ho8zluilP9rMEvfZ4mYC48lBzgT3MRhrp8SyEEkw4FaK8BpLOTn+MjaDB0Uwk2uqmDWJei4Ip2K0hfoZjQdBgAprPmWWXIZi4/RFR1nhSSsbDcX8+BIhyqxptZZT412EUlEUUabaVkS+ycVYIshYPEhEjTOWSMeqbspdiEGc+vhsrK2gyOVgXsHcq78IgoA2UVn3LP54jG+f2sczd38Em0Hhv04fIHHe+umkQUtsLgQEXuhuocBi54P1y1EkCatBocTm5PMrtlLhcBNLJREQMrr3BlEkkprdGc1y4yEIAkV2B/9n49zIbGa5PLbkbknXJZgwtt2Km0XORRz1H2UgNkBci2dyGS6HcCpMU7CJSCrCzaU3U2opzYxe22Qb23K30RpqpTHYyMroSmrtkzXsJUGi2lbNWs/aTNskUZrWEck15lJiLsEqW8k35tMebmetZy0SEibRRIGpgLgWJ6pGcRqcCAhsytk05XyV1koqLBWZUKDZCkHONZ3hTk6Nn6LIXJRxDM62a6V7JW2hNp4ffJ5Xh1/lfWXvm7TvQudC1njWZH4nWZBZ6V7Jq8OvEkwFGUuMZZ2DGwBJTtdaajveiZo69x2vWlKOJJ/7lufYLTRoOq+cbEXVNOoKc+e0mCbcQM5BgamWpB4HBBY5b6Ix8DroOkbRwlrvQ3RHThJK+hAvSOpY6bmbjvARIuo4KS1tGOUYy7DLXmRh8qi3pqvE1RB5pkpERKJqCB0NEYXNuR+kPXyYUGoMs2g7b3ZCYLnrDrojp6i0rkgvEQQKTGnjdTTeTUKPYpkwpo2ihXXeh+mLNmGRncxzbMQ9S2iRJMjU2tbSFTnJeHKQPFMVdfa0Z2qS7Cxx7aAncopQcgwJmbNxUQICK9x30RU5QVQdR9WTyKKBRa6b6AwfJ5zykWeqosa2BlGQKLLUI4tGkloMl5JPlXVlJkxpY8776AwfI5TyYZPIXHupZdFEH879yzumJuiP+fDFg0TVBK8Pn+LP5j1MMBXlp52vUm0vxCFb2D/axHgyQoHJzUtDR4mpCRRRJpyK0xjo5l2lm+mODPNU335KzLmYJIW4lpjRS/+9rdNXgZ0LPCYzFtnAkx0NHBnp4/7KhRgliQXuPB5rO4lJMtAV8lNhn12rPqYmiapJDKKELIocGuqlwGIn32LjjrJ6ftR8hDyzDU3X2FRYyQJPHgKwKq+Epzoa+c7pA1TY3dxSWjPrebK89UmoKgf6ejg9MkxcTeEymllXUkKl040kimlJ41CIPT1dDIRDiIJAjdvDuuJS7EYjuq7zzcMHiKWSeM0W7qiuI9c6OWmzaXSEvb3dBONx8m02VhcWU+50IQgCz5xppsBmZzAcotU3hkmWWZSbx8rCImQx/TEMxGPs7emmze8joal4zRbWFpVQ40kbS72BAHt7u+kLBbErCssLClmcV3DD5D4sdCxEvCAk0yyZsUgW4locTb/8XC6AQCrAYHyQXGMuucZcJGHyQINVtlJlq2Ln8E7GEmNT9ldEhfmO+ZdknCuikjGMzZIZERGvkv59BQQUUSGemnwtCS1Bd6SbofgQoVSIhJYgqSXpifago0/K+7seNAWbAKi0Vk5bP2GBcwEvDb3EyfGTU9aVmksnhXNBOsTLZXDhS/pIXiRCIMs7g/GRIC//5HX62wZAgFg4jsNjo2x+ySTnIM9hozLPTVPfMKtrSpElEVHMhhVdEQXmc4aMRXaywnNX5m+PsRiPcXoNaa+xBK+xZNKyHGPZtNuaJBvzndOPnjkMOSx1TR9HVmyZR7Fl3qRlBtFIqWUhpZaF017L+ddzMcyyg3rHhinLBQTcSiFuZXrt+FxTObmmydrxVtnNAufWKdvnGMtm7BeXko9LmXrtpZbpC6rNBaIgpCsDqwkSWpJ9o00k9RRlllwqbHkcGG1mpaeWgZiPeY5SdODJ3r1UWvNxGWwEUlHG4kE256bbmNRUVnpqmO8oJaVrGITrX0U2z2zj3soFdAR8E1KjIhbZwJ+v3EZvKIDFYGB5bhE2Q1padVlOISVWBwbxXFujqSQNvmG8Jgv1rlxEQeDV3jYcipE7y+fxSP1yTo8NEUzGUSQJr8mScd22F1djNSikNA2vKStf+nZH03VODA3yX0cOsrqoBEWS6AmOE4znopPOZxmJRvj7PTvRdJ35OXmousZYLEpSO2eklTqcdAf8/PjkMVYVFk9yDlp9Y/zjnteYn5OLy2SiYWSYk0ODfHjJcqrcHp5tbWEgHGRhbj4FNju9wQBv9HRhkNayoqCISDLBNw8doHF0mCX5BVgNCr5olEAiDsBgOMR/HzuEpusU2e34YjG+tv8NPr58NetLSi+85HckLoNryjJhIjz0aqqmJrUkkVQEi2zBIE5NdJQFGZtsI6bGiKmxKaP0IuIlFxkTBTHjfJw9xnTnPHs9A7EBXhh8gbZQG3EtjtPgxCgZEREJpUKXfa1zgT/pRxCEaX8PAJecnrH1JXxTHDabbEO+YCZaQMj0yVxXv83y1iQwEqSnuZfi2kJMFhOFVfns/OUeNHXy/dI57KNtcIxQLIFZMfDi8RYqct2TZNmvlhvGOchy46DqGkd9bbQG+9ievxRN13mm/xC6DgZJZlveUh7vfQOnYkUHyq15JPUUkVSce4vWYZLSMx6KZKDA5OZMqA+3YsNlsCEKIsqblBhmECXqXDnUuXImLV/sLWCxd2qF7RKbkxKbc9KymJqiK+ij0OrgjvJ6EmqK57tbiCSTCIDHZGFTUcW057crRm4uyc4WvFPQdZ2ucT/DkQi3VdVQZHcQTiawGRSkCQPtqZYmGkdH+Pfb7qbY7gB0NF3P5BYIgsBdNXWc8Y3yeFPDlHP88MQRvGYLH122EotsoHlslG8e2s+h/r5M4nI4keSRxcvIt9roCwb4l317OD40wIqCIg729fFKZxt/vmELywoKUUSJuJrKVJ7e3d2JLxbjY8tWUuVyE0kl+Yc9r/Hz0yduGOfgwhH9uUJAQBRENF2b1jjV0TM5etPODghwqSLG0+0/04xDXI3zZN+THBw7yGLnYm4ruC1tXAsyGhq/6/8dQ/GhSzrvXHL2d5hpxkIlvVwUxCn9Mt2yLDcemqZhNBvJKfIgKzJLty3k6e+8iK5Ndg5CsQQOiwlRTD+fo8FINucgS5aLoeoqY4kgimigzl7MruFTJLUUkA5gKjZ78Sp2Xhk8Tpkll0JT2kgpNHtoDfXzUOkmUrrKSDyAaWL0Kv0BfLOuaO5wGIwszSniB42HeLL9NAICCz353FY2c62MLO9MREFgXUkpz7S28L+ee4qbKqp4aN4CvGZLpiDW/r5eFuTkUufxzhoeMpNhc6Cvl+7AOG/0dgOQ0jQC8RgL8/IyH7PFefmU2B0YJAm32YzLZCIYT88MNI4Nk2uxsjgvH4diTMsxG86NKLf5fbzY3sobPV0To2Y64/E4FU4XqqbN6UjajYZZMuM0OOmN9hJX41PWJ7QEY4kxrLIVi3SJNU/mgL5YHx3hDkRB5APlH8BtOFdtPpwKTwmxul4UmgrRdZ3B+OC064diQ2hoFJmKJikbzjVHRrv5Ues+GsYH+I9176XKnnPxna4zh0a62DfSzsPlK8gzz1yV/nJ5fbCVX3YcoWl8CEWUWJNbwXsrV1DjOJcD+FTPKf7t1E5+uOURftd9kmd7G4mqCeqd+XygahWL3UWT4vdjapJX+lv4dcdReiJ+3IqFm4vqeF/VKizSpQvTXAo2p4XyBSV4Ct08/V8v8sR/PovNZUW44D1mNhpoGxyjfWiUxt4hPFZzVq0oS5bz+XHHy+wfa6Y/Osbfnf4ZFdZ8Pl59Owuc5Xy/7QU+eeDfWOWpI9/kyjzEdoOZRc5yftK5k5vylyJNzAT82byH+E7r8zzR98+gw8bcBXys6jbgnSPHKQoC6wpKWZlXnNHllgQRgyi+Y64xy6VTYLXxLzvu4OhgPz85eYxPPP0EX9y4jU1l5UiCQFJVsV6hdram64QSCT64aBnvmj85PNJtNmfcCYdxcu6WIJyr75dSNeQZ7k1d14mlUmwpK+djy1biMp5LulWy9RauGqfBSYm5hGP+Y/REeyi1lE6qARBMBWkKNlFgKiDHeP0M0JgaI6WnsMv2dNiScK4o2nB8mIHYwIz7ng3VSWpJUlpqThOWlziXIAoiraFWRuIjeBXvpKrTh3yHSGkplruWz8n5ZmyHp5i/c93Hg698a5J4xVuJuJZkPBFFvcJ8mAvRdZ1ftB/m/53eiVMxsSGvkmgqyUt9TZzy9fPnS3awxJ12yiKpBD0RP3928HE6QmNszqtB1VX2DnfQ6B/ga2sfyjhUSU3lJ22H+Fbj6yx0F7KtoJbO0BjfbNzN4ZEe/t+6hzAI0pzN+eQUe7n1Q9sQRIGCijxGesconVeEyTr5HVlfmIPdZORk9wA2k8KyiiIUeW5nELPOwXmkpTyT6HoSOPtQCYCMIJhnfInouoauhwFxYrtrM3KhaRF0JicmCYIRAeMVv+B0XUXXI6QlTM0Ib0Is/dXw/vLtvL98alXlQpOHf1j6kak7CIAuICBSaPJQby/J9F2xOYe/WvT+KbsscVayxFk5101/UxAEAQkBScqOqGZJj+QbJYn1xaWsLCziH/fs4rXuDpYVFOI0GpmXk8MrHe0EEnFsE6FEmq4jieIk43u6QVABmJ+TS7vfR6HdgUEU0c/uL8wQinIBZU4nTzQ3MhKJYFeMCBkZaAFJECi2O2jzjWFXjJQ5XQCouo7A28uh1/V0Am1KT89wpvQUOjpJLUlMjSEJEpIgTYlLv5YoosJC50JOjJ/gyb4nsct26ux1iILIeGKc3/T+Bl/CxzrPOorN0+fsXQs8igezZKYn0kNDoIF6ez2QLvL23OBztIfbZ9zXKBlxK250dA77D1NiKZmUF2GWzJl/a7qGqqvopCspnw2vSukp4mocQRAQSedKCIKAW3GzI38HLw6+yI86f8R7S9+LU3Giaiq7RnalazUoXrblbrtmfQPpwR5JmhqmpOs6CU0lqamAjkGUUSby0dLLU+iALEgYpbRznVBTaBOhhKquIQkiZslAfGImXtU1NF3HIEqYJMNE0TyNhJq+f2VRwjhxz6q6TkxN2y9JTZ3T57M1OMqPWg9Saffy9fXvxmkwoQPHx3r5wqHf8njnccptHlzKud+3NTDCj7Z8iHJbOnLgN53H+JdTr3BwpJtSqxuDKNEV8vHtpt28p3IFf7xwG+JEn/57w2t8p3kPL/Q2cmfJAuYqrOBsqJ4syuSWeskt9ZJKpKZs1z06TiAa47al52b85/p9l3UOJtB1jZTaSSj8cyLRp0mp6WlwUXBiMq4jx/NvwFQNb13XSaU6GBh5CFkqJ9f7DWRpaoLvXOAL/B2h8C+A1IQDk8Jh/xQux+cQuLJ6AYnkaUbG/ghBsOJ1/z1GZenFd3oLcTkPhK7rhFMxeiMjHPadYZW3Brvh3Mvi7WRMZMlytWi6znNtZxiLRih1OokkU/QEAmwqK8tI4L57/iJe7mjny6+/yi2VNYBOXFXZUFKG12zBF43ii0Xp9PuIqypdgXGsikKOxYLVoPDx5av5sxef4T8O7GVFYRHxVAp/PMaKgiLqvRcfbd5cWsFTLc18+fVXeXDeQlwmE2PRCMV2B6uLSthWXsnenm6+e/QQ2yqqkAWRvlCAEruD7RVV17gH546knuSg7yCvD79OSA0xEk9X8f1N7294ZegVHAYHy1zLuDn/5uvWJkEQqLXVcmvBrTzT/wzfavsWToMTWZTxJXwoosIG7wa25m2dNnn4WpFvymeleyWhZIivt36dYnMxAgL+pJ9iczFrPGs46j867b6SIFFjq2G+fT4Hxg5wcvwkNtlGSkthlsz85cK/zGzbEe7giP8IvoSPmBbjTOgMSS3JK8Ov0BhsxCyZKTGXsNqzGqchndt1X/F9BJNBjo0f48sNX8ZusBNVoyS0BF7Fy6MVj2KVrdO27Vqi6zrD8RC/7TrOgZFO4lqKZZ4Sfq9uIwZB4rHOIzzf14CqaVQ7cvlwzToqbF5+3XmEttAISU3lTGCYha5C/nDeNv6r+XXCqThjiQgDkXEWuYv4/OLbSGgpXuhr4MW+RsKpBPWOPB6t3UCeyc6L/Q38tO0AFtmITTZOMtSvlgMjnQzHQ/zhgi3YDenBUgGosuewvbCON4ba6QyN4fKknVgBuKNkARX2c/KwVfYcck02+qPjpHQNGYnnehuQBJF6Zz6doXOKXBU2D7Ig8fpQW9o5mCNGekbZ9et9bHpwLTnFHiRZ4nfffJ47P7EDo/mc/TkeiTHgD7KwpGDOVYrOknUOJtC0UQLBbxEM/xRZLsGorEYQFHQ9hiA6EISZi/uk1D40zY8mOFHV0WvmHFjMdyCL+ajaGPHkUeLxvdfkPO9kTo138Uz/AertJWzNXfJmNydLljcNQRBwm0zs7GpnZ1cHJklmQ0kp99TMy8T1FzucfPWW2/llw0l+fvoEBlFiXXFJZgTt1c52ftvSSEJVKbE7+PHJY7iMJj66bCXLCwpZWVjEv956J79uPM2PThzFbDCwLL8Q+0RBvnpvTjrEKKNQI1Hj9uAypg0Hp8nEX2+5iV81nuJ3LY0kNY1Sh5MKV1qut9Ll5vMbNvNEcyO/PH0SQYByp4vlBW+vSum6rpPSUgiCgF22T5G1hHSM/1kkQaLMWoaKOq1hbpWtVNuqcRvc0842KKJCmaUMl8E1aw0EURBZ511HoamQw77D9EZ7SepJ6mx1LHAsYIlzCUZp6sBUvb0edC5aX8EgGphnn0eR6dzvVWAqYL5jPibRlLnWEksJETWCcaJo5l0Fd1FsLubE+ImMo7LCvYJV7lX4k3509EmzAOdTbinnXSXv4oDvAD3RHlRNxSJbKLdMVuYLpUIMxgYJq2GA9OyI+dy6UCqEUTSSnJA4FwQBRVB4pOIRFvsXc2r8FOPJcYyikXJrOWs9a/Eonsy9LiDgMXpY4FgwrcKRJEiUWcpwGpxYpatzKDR09gy20REe4/1VqxEFgX8++QKrcspZm1PJUk8JtY48VF3nJ20HaA0OU25Nj6jvG+rgG+vfS4HFSUJNIQkiwWQMoyTzpWV3A/DwK9/mPZWriKspnuk5zcMVy7HKRr5/5g32D3ewraCOr556ka+sepB5zgJ+2XGY42O9V3VN59MfCRBLJamx52TeTQAWWaHI4mA4FiKQiE3ap86ZN+lvoyQjixIRNZlWa0OnKTCELx7hn0++lAk/PovHaEGZ40iLkD/CKz/bTcgXZu3dK6laUk7D/jPc9pGbMvcegEGSGPAHOdDajXki7HNhaX5WrehakEp1E43tRJZLcDu/gMV0C4KgoOkJdC0y676KYREO28eRxDwM8rUbrTIbN2I2bkTTY4TCP846B5eJIAis9daz1lv/ZjclS5Y3HVEQ2FhazsbS8lm3mZeTy/+3eWroHsAD8xbwwLzZR86W5heyNH/6AZNPrVo76W+H0chHlq6ctCzfZuMPL9jufKrcHv5k7VSp5rcTRsnIltwtbMm9tEJyJsnEwyUPz7i+zFLGRyqmCaucwK24eaD4gUs6l4BAhbVi2uJl024vCHyq+lOXtK3T4OTTtZ+etOymvJu4Ke+mzN8W2cK9RfdO2kYS04XFVron3yuQnlk4G2o0U/vKrGWUWaeX3j7LEtcSlrgubwDpbE2GNZ41rPGsmXVbWZRnvAZIX/eDJQ9e1vlnIqmpDMYCdIVG2TfSgQBsyq/BIimEUjGe7D6OTTaiSDLDscCkXIVF7kKKLOmcPbOskFDTYS4rvGXYDSYEINdkxxePEE7F6Q6PcXSsGwGBOkceXqOVsUSEcDLBMk9aQazc6qEtODIn1wagocFExeDzEYW0k6Wio10Q/OiQL14YMKmpuI0WPjV/M3mmqQ671zi3s0CyQWLJtoUs3rqAQy8cY6zfRzQUm7KdWTGQVDUOt/dlwjvnFedmnYNrgaaHSKn9WMy3oBgWI0wUOBMFI0wzMnIWQRCQJBdu5xeuV1OzZMmSJUuWLFkuCUkQcSkW5rsK+WjtBrxGK6OxMA7FREdolDeG2vj+pg8TTsU5NtYzaV/DDHkuiighnpdwDeBUzFTac3h3xSrKbR78iShmyUBMTWKUZFqDw5RZPYzEQ5n8g7kg12hDEWV6wn7Kbe5MvkVUTTEcC+IymLDKM0d/TIcAlNrcnPT1U2JxsT7v2ucdipKI3W1l2faFuHIdHHn5BANtg2gXSJlW5Ln52E3XruAqZJ0DID2lq+sJII4gWGYNIcqSJUuWLFmyZHkr8nTPSRrHBxiJh/jemTeod+Rzd+li1uRUMBQL8rXTLwNgkgx8ev523IqFcpuHr51+GadiJqmpKFeQ+C4KAlW2HFZ5y/lO8+uIgohBlHi0dj1FZiePVK/lPxt3kme0k9RVnIaLj9xfKiu8peQYrTzedZzl3pKMI9Ab9rN3uIN5rnyKLc6LHGUyAgI3F9bxWMcxnuttYLG7CJvh3EBxMBnHJMozqqldCd4iD5sfWocoilQtKceZ66C4ugCj5cpySq+GG9g50Bjz/y2qOoCqDZNS+wCIxl4imWwAJvTtBRGv659QlMlT55Ho8wRC30PTxjPLZLkUj+tvkKXJsWwXoutJ4snjRCK/JZFsRNfjyFIBJtNWrJb7EIW5S9Q5SzLZQTj6G+KJA2haBINchtl8J7JUBML1vw2eOt3EL46cIJxITFpulGQeWrqQB5dOrQyd5erQdZ0ef4Cv795HPJniI2tXsKgwP5uIPQf81xsHeK7xDBfq9jiMJv6/27ZT6XW/OQ17C6LrOrvaOvn5kePkWK38ybaNOE1Xrrh2Oezr7Oarr+xGEgXunF/PI6uXXfNzZslyPVnhLaPWkceOovkoooxJkrEbTLiNFt5XuYqxeJiUrmEUZcySAYts4HOLbiWUjGOSDTwAeCbCZW4tms/m/JpJz6YsSvxe3UYcE8a9IAj83+X3UGh2YJYVHixfxnAsSEJTMYgSOUYrAvDuipVsjFQjACbZgCLKeIyWObnmemceD1Us479b9vJnB59gjbeMmJZk92A7gWSMR2vXkjtNWNDFWOQu4r1VK/h1x1HGEzGWe0vQdJ3eiJ9jY73869oHKTJfntMxG2abiZLacyGY3kI3nrunDzu71tzAzoFOPLEfTYsCKrqeLvKi6yk0PYIw0TW6LqEzVStYFJ3IUhEpVFR1kGSqGV0PTcxAzIymRQlHfo0/+P/Q1JG0KJ8A8YRKJPYykegz5Li/gnQRB+NyiCeO4hv/MvHEIXQ9joCZeOII0dhrGI1rEKZRYbrWjIQinBoYIhCbXFzHJMtsrq647u25EUhpGoe6e3nyRAM6UJ+XQ3WOB4uSnSm7Wnr8AY73TdVXd5vNRJNzN33+TmA8FuOl5lZebm5DFEV21NewobLsutSHDcTiHO8bQBZFlhe/vZKWs2S5FArMDgrMjmnX5Zhs5JhsU5aXWKcfvPBOs60oCFO2P7/ImFMx45xGichmMFLvzJ+17VeKIsm8v2oVBWYHP249yLeadiNLIkvcxfzBvE2szimbVNjsUhAEAYtk4PdqN1Bp8/LrjmN8q2k3IgL5Zhsrc8pxGOZ+IPdCnv7Oi+z44FYU8/X9Tt/AzoFIQe6vM39FYzsZGv0wFvOtuByfu2D0f6oahFFZjVFZAegkk+30DW276Bl1XSWeOIBv/MuIoh2366+xmu9BEC0kEscY9f850dir+Ma/jNf9VYQ5GNFX1RGC4R8Ri+/BqKzF4/orDHIduh4iGPkZ44GvoetRFMP1lTB9z/JF3FRXxVg4wmg4ypHefr61Z/91bcONhg6kdJ3kRPyiOsfl1m9kPnfTZj68ZgW+SISBQIhnGpp5vunMm92styS6DklVQ9V1NFU9V/HsMkikVB47fornG8/wJ9s3srjw2hgdWbJkeXtglg3cXjKfHUX1aOgICIiCgCRMrsnyYNlS7ildhEGcrDRU78znx1s+hCAIyBOJzYIg4FRM3F++hHtKF2WOK0Dm2HMx46nrOpqmI4oCmjo5v6BxXwvb37tp0hBuuu6GnrmuazHregM7B0Im6Tj9pzyxVEQQDJPXTbe3IALiRIXFS9F41tH0IIHQd9HRsNs+it36COnCaWBU1pLn+Ta9gzuIxfcRS+zFbNx0xVeXPqNOInmKWHw3klSEx/VXKIYlE+034bA+iq6NMx78z6s6z5VglGVKnA5KnA500tn3Wefg2mIQRVaWFLGpqhxN19lYWZ6RrMxydVgUA5UeF5UeF5FkkrYxX9Y5mAGX2cSWmnIah4ao8npYXFRw2bMGY5EIh7p7aR/zEcvOzGTJkoVzBeBm3UYUkZi6jSgIKNJUk/h6FA5NJVL880e/zrs/dx9feugrKKZzrsBw7xif+MqHJm1/sLWH54+1cOvSOuqLcjApMoaJ2jRz5SjcwM7B9UXXQdP8xOKvYZDrMBnXTqpGLAggy+UYlSUkk80kEkeu2jlA10ip3aRSXdgs70KWCi64cayYjJsIhL5zdee5As5vh54dwb4uCIJApdfNf79vbuTxspzj/Pv5wsqkWSYjCAK3z6vj9nl1F994BkYjUVqGR+ewVVmyZMny5iArMp/++sfxD41z66Pbuef3b82s+8affB9JnjzLsbK6hGKvkzeaO3mtoZ3yXCerqkvIsduwmeYm/CjrHFw3dJLJM+h6DB2VZPIMqjp2wTYpQETTY6RSg1d/Rj2GmhoCNGSpBEGYrMkrCCKi6EAUL16pNEuWLFneCmiazkAgSMeYH7fl2sf8ZsmSJcu1RBAErE4LggAb71+DM+dczsjSbQuRDZOdg0QyRSKlUlOQg91kpHt0nCcOnCbPaeN9G5fNSZuyzsF1Q0fTAgAkkw2M+P43zDDCmA5TmpoEfflnVNFIF9AQRAswXTU/+ZqoI11v4qkULcOj9PjHCcYTaJqG1ahQ6LBTl5uDzahcdLrNF4nS5fMzFAozHouRVDUkQcCqKOTZrVTneHGZTZPiFy/kRN8ApwaGMBlk7ls0HwSBWDJJ68gY3f5xgrE4oiBgURQK7Daqczw4zeck3UbDEfZ1duOLRNlRX0OuzUokkaRpaITeQIBIIokAOM0myt0uanI8SLNIqTUPjXC4pw/1Ap1kgPWVZVR5PbP2yUgozBsd3QRiMW6fX4fHYiYUT9A0NEJ/IEgkmUQUBJwmI+UeNzU5HkRBmLWvNU2jfcxPx5gPXzRKIqXOOHtkkCQWFOSx6BrFlOu6zmg4QpdvnOFQmEA8TkrTkEURu9FIgcNGTY4Xq2K4Lmo6uq6jahptYz46x/yMR2Mk1Jn7x2QwsKy4gOoc75R1mqYzGArROjLGSDhMPKViEEU8VgsVHhflbtes16TrOns7u2kf9VHicrCluhId8IUjtIyMMhgMEUkkMUgSdqOREpeDco8L6wUJ7rquc7xvgJP90w943L9kwZR9LmQkHKZ/PMhoJMpwKMzutk4iySRKQuKFpjM0D00tqFTsdLClpnLa51WY+L+qaQyHwrQMjzISjhBPpTBIIh6LhUqvmzK3a8r+LcOjHOrupcjpYFNlGX2BIMd6B4ilUpS7XSwoyMOiGIgmkxzs7mUgEESRZOpyvdTkejPT/1myZMlyPhaHhcpFk4vz3fGxm6dsNxQIs6+lC4MkMb84j+2LqhEFgf/31Otz1pasc3DdEBDFtBFukGuwWd89ZST//G0VuXYuzogwkUydVlGaaiCmC6urb+tAiOahEZ482cDB7l66fGkDXNV1bEaFYqeDFSVFvHv5Impzc6Y1FPzRKC81t3Ggq4fW0TEGAyH8E0aZJIrYFIUCh40FBXm8a9kilhUXzuggvNbawbf2HMBkkLl30XxGQ2F+ceQku9o66PT5CURjiGLa4ShxOvizmzaztqI0s39/IMi39hygYXCYHJuV+rwcfnLoGAe6eukZDxBJJBAQcJpNVHrdbK2u4P0rl85oXB3o6uXvX9xJQp3qbP7jPbdd1DnoGQ/wH6/vpX3UR5HLSbnLyY8PHeNQ99n2pJ0Dl9lEldfDttpK3rd8Saak+4WEEwl+dfQkO1s7aBsdYywcJZ5KMVNgmcNo5A82rbkmzsFwKMzTp5s41jtA+5iPoWDaKUxpGgZJxGE0Uuh0sLgwn/euWEJ93rWfYQvFE/zsyHFeb+ukffQ852mG7fPtVv5026ZJzoGu66Q0jd+dauLlllaah0cZCoYnDF+JHKuFKq+brTWV3Ld4PnbjzPlVT5xo4DfHT7OxqpyNleWc7B/iNydOc7Crl4FgkHA8gSKnnYPlJUX80eZ10/bTyy1tfGP39DlF22urLuocPHWqiWcaWhgOhRkNR4hM5Bn4ozG+v//ItPtsq6lkU1U54nTG+MTzu7+rh18fO8XxvgGGQmHiyXN9VJeXw50L6rhtXi1G+dyn8lB3H19+4VUWFxZQk+PhX17Zze72TmKpFJUeNx/fsJptNZX86OBRHjt2mt7xAEZZYllxIZ9Yv5o15SVZ+eAsWbJcEidfb2Te2hpkw7l3kCgILCjJZ0FJHpIo0jXio9jj5KG1i+fsvFnn4LohIMuVCBgQRRcW020YDDXX9oyCCUnKAURSah+6HgXOOSQ6OpoeQVNHEWXXNW3LteJY7wBf27mH/V09JFWVUpeTRYX5iIJAp89P8/AIzcMjnBoY4q9uv4l5eTlTPsyarvPEyQb2d/ag6ToFDhtLiwtwmIxEEymah0doGBymaWiEpqER/vX+OylzO2f9wIfjCYZDYf5z116eOt2EpusUOR0UOez4YzH6xgOE4nHy7VOl4s7yRkcXvzx6gr2dPdgUhYX5uVgUhf5AkDMjo+zv7KFhcJh4SuWPNq+b9hgbq8r40h03E4jFCMYTtI2M8XRD8xX19e62Dn44MsaB7l4cJhOLC/MxGWR6/QHaRn3s7eymYXCIpKryyQ1rpuyv6Trf2nOAnx0+zngsTrXXze0r63CYjAwGQ7zW2kHfeAAdmJ+fy0NLF6Znfq6RUa5pOt/ff4Te8QCiIFDsclCXV4xFMTAejdMwOMTxvgFO9Q/SOjrGP997O3mz/F5X3R5d52uvvcFvjp8imkxRn5fDPYvmYTEY6AsEebm5jdFIBIClRYXct3geRU4H8/NzJx1H13X+8/V9PHbsFAPBEDajQl2uF6/VQjAep2FwmJ2tHRzvG+DMyCif2boBl3nm2UOdtNNytLefb7y+n32d3dhNRkrdTkQEhsMRBgNB+scDuM3TFza6Y34dRQ4HwXicYDzO7vauaaVfZ8JjsbCoMA99Qm2rcXCEo7392BSFDZVl5NunDrTU5E591s/vo/ax9D3bMjxKsdPB2rISEKB1ZIwu3zi94wE6xvwYJIk75k/Nj2gZHuUXR05ycmCQ+QV5nBke5fTgMP+99xChWILv7z9CqctBvqOIo739vNHRTanLSc3Eb5HlzaG3z0dz6+xhuyVFHmqr505OPEuWK+X1x/dRvaxiknMQisUJRuNIE/Kszx9r4ZEtK6jIm7t6Olnn4DoiiXkYjetIJBuIxfeknQXhWk4xS8hSGbJUSDy+F1UbQpK8nA1n0rUo8cQRND14Ddtw7RgKhfnWnv3s7ewG4DPbNnJbfQ1mxYAAhBIJnj7dzI8OHuVITx9/8+zL/Pf7H8R0gUKP02RiU2U55W4XW6orqPS6MRsMyKKIqmn0B4J8/fX97Ono5GT/IL84eoL/vX32ZPGUpvHvu97gxaZWbqmr5uFli8i1WREFgaSqMRQM0TQ0Qql75gIqvz3ZRCyZZEd9DR9bvxKvxYIoCMRTKq+0tPGfr+9jPBbjxwePcs/CeZR7XFOOUe52Uex0oGoaKU3jQFfvFTsHj59oIJ5Mcfv8Oh5duxy32TzRnhTPN57h67v3MR6L8+ODx7hjfj1lF1zbkZ5+fnP8NP5ojPn5ufzzfXeQZ7MiSyLxlMqO+hr+8ukX6RkPMBKKcNfC+sw5rgVem4Vb6qpJahrbayspdToxGWQkUSSpqnSM+fnb51+lbXSMw919/PZUIx9bt+qatAVgX0c3jx0/RSSRZHVZMV+642ZyrFYkQSCWSnFzbRWf++1zBGJxAvEYdy2sx2maGub26+On+eGBI4QSCRYW5vGZLRuoyvFgECVUTWMoHOLrr+9j55kOnjzZiN1k5DNbNmQ+NNPROx7gm3v20+Hz8Yeb17K5qgK7KT3jEE+p9PjHiSSS5Finnw2ty8uhKseDqmkkVZVoMnlZzsHNdVVsri4HIJJI8vMjJzja24/dZOTBpQtZUVI4ZR9ZlGa8d5Kqyp72LmxGhU9uWM2t82qxG5XM8X925ATf33+YjjEfT51uYk1ZyRSDPhCLsbO1nX+693bybFaebmjmv/Yc5PTgEP/22h5uqq3ikxtWo2o6vzx6gu/sPcTpwWH6AoGsc/Am0t45wpNPH8v83dM7RkG+E/m8pM8tG+uyzkGW60YykeLL7/3Xade1HG7jg198GEiHQTb3j/DYvpOEonEaegZJqBqNvUNocyzsknUOrhOCICCKDhy232Nk7NOMB/8DTRvHYr4bScpH0wOo6gCx+F5SqW5cjs8gSbkXP/BFzqkYFmBU1hKOPo5//J9wO/8CWa5B12NEok8QDH6bmXIf3uo819DCoe4+UprGH2xcw6Nrlk+S5tR1nd9bt4qOMT/PNjRzpLefZ0638MDSydWuJVHkkdXL0HQdoywjXRAzX2C389G1K2gfHaNnPMBLzW0XdQ500qEQdy+cx+du3oxVUSYZKpUeNytKi2Y1yILxOMuLC/m/d96M3Ti5guz7Vi7heP8AzzQ0Mx5Lj8RO5xwIgoBBkjBIErquY50h3OdSCMTirC0r4S9v2z4lh+OR1cs50tvPSy2t+KMx9nV2T3EOdrV14I/GEAWBj6xdSU2OJ3MMqwLrK0rZXF3Br46dxBeNcqp/iC3XsCCeLIr8ybYN6KSL7134WxQ67PzR5rX8+W+fJ6mp7GrrvKbOwQvNZ4gkkpgMMh9YuYxKj/tc/xgVttRUsrmqgqdPNzEajtA8NMLa8tJJx/BHo3xj9z6C8QQei5kv3X4zCwvyJl1bodPOV+65nQ/95Nc0Dg7zfOMZtlZXsqq0eMa2jYTCtAyL/P6GNdy/eD5GWc60Tdd1qrxuVD2t030hgpDWb1IkCSQJoyxjnEYycDYsisJZc9ogSZgmwnxEQcBuVGad+ZgOnfTM6R3z6/joupWYLrieP9y0lkPdvZzoH6RzzE/ryNgUg14UBFaUFLGkqABREFhfXsrzjWc42tuPPxbnE+tXUepKPwNbair5nwNHGQqF8Udil9XWLHPL6pWVLF5Ykvn7Q5/8Lp/6ve0UFboyyxQlmxeS5Tqi68SjCR790numrPrh3/wy814VBYGKXDerq0voHPaxuLwQSRC4f/VCjIa5NeezzsEVkEi2EI48RjLVjq4FULV0MlxK7WZ49BNIkhdRcKAoS7BZ34sknjWSJEzGTbidf4Ev8BV8gX/CF/jKlOMrhoVTqjJHos8TiT2PpvlQ1XFUtQuAcPjXJOJHEUUngujAYtqBxXxn5kMnSQXYrR8gmWohEnuRSOxlRNGGrsUQBAWTaQuaFkTTxq9dh10DfJEo+7u68UWjuEwmPrR6WcZgOIsgCJgNMrfPr+X1CcP08ZOnuX/J/CnhBrPp/YuiwIrSItwWMz3jAXr9l9ZXVkXhM1s3YFOmJkOLooBRvPjj94eb105xDCBdJ2JrdSUvNrWSUFVahoYvqU1XgyDApzavnTa522SQuam2ilfPtKNqGmemkZnsHPOjahoCsLAgb8oxJFGkOseDLKZnEjrH/FB9DS8IZq0OLYki22urUCSRhKrSPx6cqGtybZzp1pExdF1HFkXqpwl/k0WRutwcnhGaSakaPf4Aa8snH+PZhjOMhiOIgsCt9TUsLsyfeu8J6ZyVP9y4lv/12O/oHw/yUnMrK0uKZr225cWF3L1w3iTHACZ0wAVhWrmDtzJ5NhsPLl0wyTGACeUQo8L2mipO9A8SiMUZDIam7C+KAgsL8jJOf67NmgmrqvK6ybVZzx4Qq0HBZTYRjieIpVLX/uLehpwcP86ven7OYKyfP6r5LPMdCxCFq9eW13SN77V/m9OBU2zM2cz9xQ9jPK+CrygK2G1GHHbT2zIXJKYm+VnHXvqifkRB5JWB09xauIj3Vqzji8d+zVAswPsr1vPeirVIgsgpfw9P9BzhyFgnA7FxLJLCSk8lj1ZvotaeT0JL8VfHf8NoPMw/r3gvDsO5fhmJBfl680uEUnH+eN6tFFvmLozlRkaURHY8spW61VM/eMW1hQjiuaJsJoPM2toyllUUkeOYeMfo+pwP8WadgwkEFEQxF0F0cLHPnKoOEovvJpnqyCwTxfQof0rtIaX2AKATw2q5H0g7B4IgIAgWbNb3oyhLCUV+RTy+H00bQRBtSGI+RuMarObbkcTJU5qJZCPR2MvoemrSOXU0EqmWzDJZKsBivvPcdQkCRuN6cjz/Tij8Q6Lx19G0MEbjYqzW92AybiQQ/AbJZAvC2+h26PL56Q+kP9hrykuwTGOAQ/r663JzMgmFpwaGiKdSU0KLLoZJlrEZjQikQ4aSqooiz9xfArCxqhyPxXzFHxybUWFtRdmM++fZrRnDZDyeuKJzXA52o5HVZTMnUxY47AhCuqZHMB6fsl6f+B+AYYaCMukS90Jm+zcbi8GAzWgklEiSmgjNulZqM2enhQVAnql/pPPqg0zTP4e6e0mqGqIgsL22atZ7b1N1ORbFQCSRpG1kjGAsjmOGnAGn2cTS4sLrptp0rRFIPz/1ebnTvzeAfEc6vyShqtMa9ALCOQeAtMN+9t4odDgmqXaJooAiS4QTCVTtzb+v34ro6Kh6ClVX5/TZ19A44j9EUkvSGe4glAxhEc/LvdNBVTXU8yrTCqIw66zuWw1V13lp4DQ3FyxguaecX3Tu55ivm5XeCvoiPp7oOcwqbwX1jkL2j7bTFhpmY24dJRYXvVE/v+05gj8R5m+XPYxbsbA2p5r/aHqBM6FBVrjTIxC6rtMb9XHC38M9xcvJMzku0qosl4okS2x/38Zp30W//9UPT5rV1HQdu9mI1aigTbxLnjnSyG3L6lHkufs2vX2swWuM2bSJsqLjl7yt2XTlBcoEQcKoLMGoLLnkfVyOT+NyfPoKzyegGOrxuP522vUe119f0XHfTIZDYXyRKABmg0zz0MiMRlsgFkebkPJMqRpjkShFzqnOga7rhBNJwokE8VSKpKqh6trEAwnR5DkD/GKfLkEQqJ8lIfJSqPC4kGfZ3yBKmYiw1DRqRHNNldeNNEt75ImPqU5aLedCSl1OZFEkqWqc7B+kwjN51CmlaZwZGSWlpY3bcrdrTts/HbqeNkPC8cTE766S0jRUTUPT9QmDRZu0/bWiyuvhQFcvKU2jaXA4E5JylqSq0jQ0kp5dkERKnJPX67pO2+hYRm2pJmdmJSpBEJBFkQqPm9MDQ/ijMQaCoRmdA4vBQInT8Y5wDCA9K1Tqcs6az3LWgT0rL3shAmAznVN6Es5zBtL5C8KkbQUEdP2t4fTeSIiI3JR3K+3hM6xwr8I/lGRfS1NmfSKZYu/BNjzuc2IDZSUe6msL3ozmXjGlFg8PlK7EpVg4ONqB12jj0/U7aAr082eHf05/dJx6RyEfqFjPh6s2ZmZlklqKPJODX3YeoD08jMdYwabcOn7ZuZ/Hug6yxFWKQZBIaCrNgUF0oN5ZkP7+ZJkzBEFAnwgvigSik7417nwn4sS3s3PYh8Ni4kz/CPKEzbOnqZNbltQyvVz9lZF1DrK8LYkkk5nRvCdONvLEycZL2k8nLad5IUPBEM3DIxzpHaBxcJi+8QD+aIxwPEFcVUmo6rQGwmzYTTNLRF4KlxtHfa252vZsrankiRMNDAZD/PDgUcrdbkrdDhRJIpJIcmpgiNdaO0iqKnV5OSwvKZqjls9MXyBI0+AwR3oHaB4apj8QxB+NEUkmSaTSv/tcJ3rNxG3zavndqSYiiQQ/PXycYqeDIqcDWRIJxxMc6xvg9dZOdKA218uiwgtmF88b4RYQcF2kQJhAWoL27L5n5UGnQxLFWUPv3m6IgnDVzycCGGYYXZZFkXeIH/W2RxREHix5V+bvPfvO8MIrpzJ/L5pfxKGjnZP22bSu9m3nHLgUC1bZiFtJixhU29PvB5OkIAkiCTX9btCB7vAYo4kQ0VSSpK7iT0RIailCyXQ+TK7Jzua8en7ZuZ++iI8yqxd/MsLu4WZq7fmZY2eZW8LjEQ48c4QzR9tRVY1kPIXZZuSRv3w3RrNCSlXp8wXwhaK8cqqVirz0AJAvHJ3zgausc5DlbYmm65mHodBhJ89mvaSYO6NBnqRZDmlJwp8eOsbTDc2MRaJ4LGbK3E7qcr2YFQPKRELvq2faGQ6FL7mN0jTJmZfDW21k5mrDaZYVFfLu5Yv5/v7DHOsd4ItPv8DK0mKsimEih6SHHn+AmhwPn922MaMec6042tPP/xw8wistbUSTKXJtFkpcTopdDsyyAYMsYRBFnjzZeF3ixFeXFfOe5Yv46eET7O3s5i+ffZllRQWYDDIjoQi72zvxRaPMz8/ljzavw3ZBfQJd1zn/+yBcwhNxduRcR5/14yIITJts/HZmLp6vGWdS3lld9Y5izcpKli8tm3Ub+W1YqE4WJCRBnHimBUxS2pnPzFqhE07FeWngNLuGmhAFEcOEWuJQLDCRpH+O24sW82T3YX7TfYhPz9tBT3iM1uAQ76tYh1e5dpLONzKj/T4OvXicnGIPNosRu8vKmWMd6BMDkxajwtYFVYwGIzzqWUWJNz17XOJxYpjDkCLIOgdZ3qYYZTmtfgJsrirngSULZhzFOx9RECfFCfsiUX58KF2sKJZKcdeCem6pq6bE7cBlNmMxpJ0DWRRpHx1j5DKcgyyTkSWRD69ejlVR+Oorr9MwOEzL8AiaDhbFQJHDwbuWLeLWeTVsmCXXYi7oDwT5t9feYE9HFwLw0NKFbK2uoNjpwGk2YTIYMEgiBlHkpebW6+IcGCSJT2xYjc1o5Ouv7+N4bz8nJ+Q+rRMF/W6pr+a2ebWsKSuZsr8iy5gnFCt0dMZjMWyzOFg6OuPR9EihIsnvqJmBLFlmQpalSbKl71Sme3ue8vfyo7Y9LHWXcVfxUvJMdmRR4vWhZv67ddekbcutOWzMq+W5/hO8q3wNe4ZbsBlMrPBUvGPCC99q6KqGzWWlcnEZBqOBNXes4B8/9G9o6uSBG699snKa12a+pMGgy+GGdg50Xae5e5iSXBdW8+WNUg6OBXl2byPvumkpFtO1HeHMMhWv1YLTbKIvECSlaSwsSBfkulwaB4c50NVLLJVieXEhf7JtA6WuqQXOztYJyEYLXx06Oif6BhAm1HQeWroQcUJu1W4yUuiw4Zmo53At2dPeyYn+QTRdZ3NVOX+8dQN5tqka/SlNm7a69LVC1+Fk3wCKJHH3wnpum1eLJKb7x2EyUeSw4zJPr6oiCgIVHhenB4fQdWgbHaPYOX3SoK7rpFSNLl9aectuNM5Yo+BakH2Orp4T/mPsHdvDSvdqljiXsmtkJy2hZuyynZXu1dTY6gimArwxspuOSDtmycxK92oWOafPdQunQpwJNdMZ7mAkPkJMiyIKEg6DgzJzOYudS3EYZi/+COl7K67FODl+nK5IJ6OJUVJaEkVUsMp2ikxFVNgqKTWXz3gsURBQdZVj/sM0B5vwJ/1ouoZbcVNnn8cCx0JM0sxhc68MvcTpwAlU/dyzKyBQbavlzsJ7Zm3/SHyYV4deIqpGuSX/NvJM+TQETtMQOIUvMUZKT2KXHVTZaljkXILTMHOtGkirJXWGO2gInGQg3k80FcUkmSk0F7LQsZhSS/mcqDJdCkOxAP5khC15dSx2lyAJIlE1wanxXmCyQyEKAu8qX8szvSd4ceAUu4abWeAootJ2dRLrWWbGZDNRWJmP3WNn92/2se+pw6SSKsLErK2qaYwGI1P2e62xndqi3BmFLK6EG9o50HSd3+w8zofvWHPZzkE0nqS5a4iUenlx6FnmhjKXkwK7jYbBYd7o6CKaTGCUpcse0RgMhvBPjJ6uqyil0GGf9hjd/nFC8ZljsrNcGj85dJwXms+Qa7PyhR1bZ+zva037qD8Tg7ulunJaxwCgZXhk2uTqa8X39h1mZ2sH9fk5/Mm2jelwucvon1WlxbzY3Eo8pbKrtZPNVRUzbnuop5dALIZBkih1O3Fbpk9GvhZczS8ucE5JJp08fmO6GqOJERoCp7BJdgZj/Tw/8AwRNYosSPRH+7i98C6O+g+zf3QvUTWCKIicCTZjqDBQb58/6ViHfPt5tv9p/EkfMTVGUkugoaX7WpAxSUZeG36FD5Q/SpllZqMe4HTgJE/0/pqxxCgxLU5KS6KjTxxLQhGNlFnK+Ezd52Y8hqppfL/jv2gKNhJNRUhNqPTJgszBsf0sdS/noeL3YJGnLyY3Fh+hOdhEVD1nSAkIGMSLz47F1Bht4Vb6Y30scS3jteFXOOI/RCgVJKml0NGQBZkj/kOcHD/G/cUPk2+aPj8hmorw6vBLvDG6G3/CT0KLo6EhIqKIRvaNvsGW3O1szNmCSbr2z1+RxY3LYOEXnfuJqAk0dPYNt9IcHJx25LnGnsfanCp+3XWQmJpkXW7NZdcoyXLp5BR7uPkDm5EVGbvHxnDXCGXzizFa0vapPxzjS796kZILahod6+zj0W1zW4PnHf8rR2IJnn6jgRcPNKGqOvdsWsiONfWMh6J8+4k3eO1oKx39Y5Tkurh/y2JK8118+4k3ePdNyygv9HCitY/Hdp7gLz9yK75glF+9fJQjzb2U5bsJRdNyjS8eaKahY5A/engTqqbxxGsnMRll7lg3H/FtJIf2diLPbmNFSREHu/sYCIT47t7DfHb7xrTe7wUfrgtjqS/UND/713ShI2fVbF5oOsNQaKrWeZbL49Uz7SRSKqUuF4UOO5AeRb7e7sH54fPRZHJK/YKzv/uPDx27rjMHLzafQUOn1OXKOCyX0z+3z6/jG3v2MxAI8WxDM+9bsYQKj2vKtWm6zjd3H0AHcqwWNleVv22kGyVRwD4xW+uPxgjF49e0/sRbncbgKWJqlFvybyepJ3ll6EXOhJp5sjdOSk+xLe9mzKKZ3/Y/zkhimD0ju6izzZvUXxbJylB8AE3XmGdfSJWtCofBSTgV5rj/CK3hVjoi7fyg4zt8Yf5fIwtTTQdd1znkO8BPu35IMBVARKTYXEqdvQ6LbCWcCtMT6aI93Eatfd6s1/Rk32P0RrvJNxVwU+4OHIqDodggR/2HGYj1s2dkF8WmErbm3YQkTA0Rurvofm4rvIuEFieQDPDt1v9kNDFyWf0aToX4bd/jDMeHcMh2bs2/kxxjDr7kGCf8x2gLt3LEd4hcYx53FNyN+QJHJaHF2TnyCs8OPE1MjVJnn89i51Lssp3xpI8j/kO0h9t4qv8JzJKZNZ71yJdQ++ZSmOlJWOIq4aM1m/lJ+xv8/anfYTeY2JY3nz9bcAdfb35pyvaSIPLeinV8ct/3qXXksz63Zk7al2V6JFnC6krfR9VLy6laXIZ43myAIktsXVDFXcsnPz/fffnAnL+/3/HOwXg4RkP7AH/+yC3ke+yIgoBikDB57PyfD91C54CPL3x4B8W5aVm78VCMSOycFnVK1QhHE+np/tZ+AuEY//CH9/DKwRb6RwMArFtUwQ+fPUAklmQ8HMUfirKssPiG/VhdjLPG+lmTXZswVs5fr2papv/O9uL5/SkKAvcsmseutk4OdPfwX3sPEk4k+OTGNXjM5oxSiK5DLJXkeN8gzzQ085G1K6jJ8WaOk2+34baYGQqFebahhUdXryDXZk3r9ZOWj3y2oYWfHj5B8DrUEphLLuzns5KsZ9EuoZ/nmrN5Iif7B3n8RANbqiuwGpVJHzMBAVEUplSqnkuqcjwYZZloMsWTJxt47/LF2EzpOhbpeybF9/cf5pnTzRdVK7qwn8/K357lcvrZKMuoms6R3n6ebWhhXUUpZsVwyf3jMpv4w43r+NJzLzMUCvMXT7/Al267mQqvK5OUGIwn+PsXd3KouxeDKLKqtIjNVRdUU5tDpn3eubL+gfQ9VOJyZmo0PHGykfkFeROiBEJGKvSsXOs7nf5YH+8p/QDb824hpkbRdJVnB56iLdzKu0vfz+acbRhEAxE1zDMDv6M70kVST6II52bL6+3zeaT8o5PCdc725dbcm/hJ1w/YP7aXnmg3LaEm5jsWTmnHcHyQ3/T+imAqgEky877SR1jjXXfBiLROMBVEEWefqe+MtLPBu4kPlD+KJEiZtqxwr+KnXT+kPdzGzuGX2ZS7FRFxapFIyYgRI7puxWlwYZCuLPS3K9LBctdKHin/CBb53P21xrOex3p+ySHffg759rM5Zxsm6VxNG13XaQu1smv4FeJajHuKHuDmvB0YJVPmGGu9G/hN76/YO7qbV4dfpsJaSaHp8u0GoyjzkepN6IA40de/2/7ZzABImdXLL7f8EaIgpIsjFi5mR8GizHMiCgICAt9c++i0swdOxYxBFFmfU4NDvn6zizciY/0+Tu1pYuMDa5EkES54fdlMCg+vW4zAWVGW9PL3bVw2pzUO4AZwDhwWE1XFXr71+B5uWV3HynmlGAxS5kGB9EjUTF7X2c5PpFL4Q1EKvA6cVhMLKvPZdzotf2ZWZDYtqWLXsVby3HaSKZWKQk/WOZiBsUiUgWCQYCxBMB4nGItzamAISIcJHJ8wHG1GBbvRiM2okGu1Uui0TzpOkdPBZ7dv5K+feYkzI2P8+NAxfnXsFLW5XrxWM6qmMxwK0+UbJ5pMIgAfWLl00jEWFeazqDCfttEx+gNBHvzeT7hzfh25NivBeJzDPf2cHhiiJsdDldfN3o7uy5Y0fbMYDUcZDIUIxOKE4nFC8QSnB89VUt7X2Z3RarcpBqxGhRyrheIL9PPnkoeWLuRYXz/BeJzP//a5KevNBgPFTgcbq8p4z7LF6VoP10A5ZEt1JT8+eIxAbJAzI2M8+L2fsqO+GpfZxGg4wp6Obrp946woLkSWJXa1dsx4rN7xAKPhCKFEgmA8wVg4wom+QQDiqRTPN53hzMgoNkXBZjRiNxkpsNvwWqeGRLxn+WL+5rlXGAgE+ePfPDVlvcVgoMztZEtNJQ8vWUiJ2znJABYEgXcvX0Tj0DCPHT/Nwa5e3v/Dn7OspIgih53xaIwD3T0MhyIYJJElRQV8dvumWQv6XS0DgRDD4TDBeILQxPPeMHDuPvztqUYK7XbsJiNWRcFuUiiw26ftn7PXWO52samynBebW3musYXmoRGWFhdgMxqJJJKMRSIsyM/lM9s2XrPreiux1LUcAQGTZKbCWgmAy+CmxFyKUUqrWp1dntSThFJBPMq5QRJREFnlWTPluAICRsnIWs8GTgdOMZ700xPtmuIc6LrO3tE9BJJ+BAQeKnk363Om73unwXXR6/EqObyr9H3Ignye0yhQbq2kxlZHT7Sb/lgfkVRk1ph/4ewozxXiNLi4p+h+rLJtUjtyjLksci6mIXCS4fgwwVSQHOO5WPyYFqUx2MBwfJgFjkWs9qydNLMgIOBWPKz1rKcxcIrOcDs9kW7yjQVI08zKzIYwYdyfz4X1ceQLBtam09mVpnEMNF3nmd5jWGQjOwoWZm2aa0zQF6ZxXwubH1o37fqzkQ6+UJR9LV2cGRxN107RdT65Yy3GK8i7nIl3vHNgMRl4+KZlrF3o55cvH6V/NMD9mxdjsxhBSDsG6nmZ4KKYLlSjaunRvxF/OpQkHecqkEil9e4TqXP656IocNPKWr739D7WLqjAaTXjcVy/5L63Gz88cJTv7jtEfJownpSm8VJzKy81t05aflNtJd989/1Ttl9RUsS/3H8n//H6Pk70DTAei9E0OJyOExfScoUmg4zbbCfHZsVpmjzyYTMqfHTtSkLxBId7+ghEY/zo4FEEQcAgiRNVgYv51KY19I2HONLTT2SaOglvRb679yA/PnRsRqWdx0808PiJhszfggDrK8r4/vsfmvO26IAvEqHY6aDa66FxaASTLCOdpwev6+niaW2jY5wZGeXZhhb+9s5b2FxdMecJyh6Lmb+4dRv/+OJrtI/5GAqG+P6+w+mK4rKE02RiR301f7p9EzvPdMzqHPzvJ57lSE/ftDZIJJnkm7v3T1pmNhj48OplfHb7uUKKOjAaClPucVPpddM26sNkmOifiQ000knETUMjNA2N8HJzG399+02sLps62viFHVspdtp57HgDI+Ewe9o6Sek6kiBgNsiUuBysKi3ms9s2UeC4trKEf/nMS+xsbZ9x/b++umfS32aDzMfWreLTW9bPuE+Jy8kjq5cRjMdpHByhZ3yc9jFfZrbAYjBQYLfPuP87CUVUcBpcGSPRKJqQBRmHwTEpadciT4Sp6ToJ7fLeYV5jDgYhHa8fSUWnrE/oCVrDZ4hrcTyKlw3ezVdxRbDCvRKjODXxXkDAo3hRRCNJLUkwGbhoQvDVMM8+f9okbAEBu+zAKtuIqBHCqWAmrwIglApxJtgMQLWtFtcMDpHT4CLPVIA/6acz0sFi11LMb7JZpus6wWSMhJZiKBbgd73HWOOtYqG7+E1t142AwWhAMSv0tQ5gdVgyTpzDa58kJd3cP0zb0BiiIOB1WOga8c+5yMM73jmIxpMcbupBlkTK8t0YZAl1wqgXgMpCD/sbOhkPRynLd2M1K7gdFo409+ALRjjdkR4BNBgkinKdNHYOsed4O70j48QTEwWHBIF8rx2zonC6vZ87NyzMFsCZhdpcL7fW15C8SCz32ZtdID3CPxM1uV6+et/tHOrp43jvAF0+/0ShMwGHyUix00F9Xg7LSgqxKVOnl2tzvXzpjpvZ1drBif4BxiIxJAHcFjPz8/NYX1FKgcNOrnWc2+bVEEkkZzRWq3M87KivIaVpMyrFzIbDZGRDZRnlbhcLC/NnHalxW8zsqKshoaosLZqaEFefn8OOS+jnswgC1OXmTFrmNJvYXFVOfW4OS6Y5x/l4rWZura9FR2dx4eRt+8YD/GD/YX559BS5Ngv3LZ5PidOBzahMVIZMh+MEY3HaRn3sau1gKBjiG7v3sbgwH88Mo8hXw7LiQv7twbt5qaWVpqERxmNxDKJIrt3K0qIC1lWUYjcaWVNWzO3zavFap1dRWlteQp7deskjlIosUXtBP3eN+fn2Gwd48mQjpS4HDyxZQLHTgVUxnOsfTSMQi3FmZIxdbZ20j47xw4NHqM/LwXleZWNhQv3pI2tXsr22it3tXZwZGSMcT2AyyJS6HCwvKWZ1adFFc6IWF+YTSSTJsVnwWK7sN1hZWpSRWL0UFFmatbozpEc/15SVUHCHnT3tnZnfTxIFbEYjhXbbtEX08uy2jAJUfV7ONEdOIwgCRQ4Ht8+rxWZUJlWrLnU72FGfjr12nFdIzSCJLC8uRCDdb+ffKw6TkS01FQRjcQrsc+uMWaTJv4soiMiCnHESzi1Pz8DpTF/pW9d1ImoEf9JHVI2Q0BKktBSarhFIBTIOhaZPnTn1JXyEU2mZ5xpbPfI0eQCXQ7G5dMZ3rEkyI01Ugk3q11YkIs+UP2MIlEE0ZBKck3pqUuXruBpnKJ62HfwJHyfGj0+bB+BP+ompscx256srvVmkNJWn+o7RON5HS3CQUouXT9Zun3OpzCxTMVuNpBIqv/zqbymoyEuHFgH3/dHtKOepYiqyTFW+h0RKxWu30NQ3nC2Cdrloms6wP0QknsSkyKycV4p9QplIEAQe2r6M/ac76RkaJ9dlw2kzc/fGBRxq7Kajf4wNSyoZ8YcQBYG60jzC0QQ9w36KcpzcvKoOxZB+SSmyxLzyPE609lNd7J2tSTc8dy2s566F9XN6TEkUWVNWMq3++6XgsZi5b/F87ls8f8ZtSt1O/u7uW2c9zu3z67h9ft0VtQGgzO3i8zdvuaRtq3M8fOW+22dcf//iBdy/eMEVtwWg0uPmi7duv6Rta3Nz+NcH7pyyPKWq/PLoSX559BQGSeRzN29hW03ljPHg0WSSzz35LM81nmEgEOLMyBhrroFzAJBrt/LeFdNLO56lJtfLvz1094zr/+QqQ1eSqsp39x3k8RMNOE1GvrBjG+sqSmfsn2Aszmcff5qdrR30+AN0+8cnOQdnkUSR6hwv1TlX9j4SBIEPrFrGB1Ytu6L9z/L7G6eGq8wFgiBQ7nFRfoFyx2wsLSrgaw/eddHtREFgTXkJa8qnvk82VpazsXJqjobZYOCj61ZOe7wSl5O/uu2mS27n5SAL0yvwiMLUWPw0F5a7gkgqwunASVpCzfRHe/An/UTUSFq1SNfR0UnOMtsQUyOoE4pCHsXD1UoM2GT7VR9jLjBLloxTdTmoeoqImnaWdo28yq6RVy+6T0JPzLmBdyUIgoBHsZJnclBtz2ddTjVVWfnS64LBZGD++topyy8cxMl32rAaFSKJBI29wxR7nFdddPVC3vHOgc1i5IGt03/8BUGgriyXurLJN351cQ7VxVNHlaxmhU1Lq6Y9VkrVGBwLsmpeabbuQZYs5+GPxjjW2084kWBjZRlbqytmVVYwGwwsLizgucYzaLpONPnOlpAdCYU53N1PcmIGaGPl7AXgLIqBhQV57GztIKVpxFNv/mhjljeP2e6VSzEXomqU5waeYu/YHnwJHyXmEmpt9XgULxbZgiykk5mfH3iaYCo47THOdzfmIi79Sgzya4EoiFfsoqQThCXm2efPKHV6PsXmEgwXSdS+HsiixG1Fi7mNxW92U2447G4bG++7+GCKw2KkY8RHx5CPQX8Q6yzFLq+Ud7xzcK3RdZ39p7t4YX8TNouRTcumdx6yZLlRiSSSGQPWOhHWNdsHV9N1jk1UBlYkaVJF63cioXgiE/p1KS95Tdc50Z9O4DcbDNe1PkGWdx4n/EfTOvxJH2u9G9iRfzsugwuTZEYWZERBZDA2wM6hlwkyvXNgEk2ZUCJffIw3R6D4WnBl1yAJEhbJTFALsti1hDWe9RcNy5FEGeNbwDnI8uaRSqRo2NfCweeOsuG+1ZTNK6HlcBsLNtQhnxea2To4xunuIeqLcqjIdQPMeSG9rHNwlQiCwMLKAkryXFhMBpzW7Ic6S5bzcVvN2CaM3n2d3ZzsH2JZccG0I4zj0Rj/ve8Qr7d1IAoC5R73FYfFvF3ItdswG9KhIbvbOmkYHGFBwfTT+L5IlG+8vo99nd0YRJHqHM+kePgsWS6X5lAToVQQEZH7ix7Ea5x674VSoVnj4T2KN5P70BJqQtM1pOtU9fetiFEykW8sYDw5TjAVRBYNmGep6JwlC8BQ9yi7HtuLmlQZ6h6lZnklT3/nRWpWVE5yDlRNo9BlY11dWcbpzIYVvQWxWYxp9aMsWeaA8+NO50o67uwx3wwpOpuisL22iuN9A4xFonzsp4+xtaaCpUWFOM0mVF3DF4nSPDTCga5eRsIREqpKns3Gp7esm3P95rcaTpOR2+fX0j42xmgkyqM//hVbaypYXFiA3WQkpWmMhMM0Dab7xx+NktQ0anO9fHj18msi9fpOY7pCiG/mM/FWIqpG0HQNWTDgNLinrNd1neZgI1FtqkrRWRRRocJaRVu4lbHEKPvG3mC9d9O0ffv26vcrywGwSTZq7fU0h5o4NX6SVe41FJtLganXfS3e91nmhkv9babLFbmS3zIRSyAbZCoWlqFrOqIkEg3Fph5fh9dOt9PUP4LDnK7P88jWFXMqSZ11DrJkuUr0iYQ9gbkp2vWTrmdZ4Khiiasmo8pxte37/LGv8UjF3Sx2Xf8Kl4Ig8K5liwjG4nznjYOEEgmeaWjh6YaWdCERgczox9n6I+vLS/nirduoyfW+I4ITZkMQBD62biXhRIIfHjhKMJHgt6eaePJUYyY64/z+kUWRzVUVfP6WzVR43O/4/pkLIrGn8Qe/QY7ryyiGJRPPbBxN9SHLhW92895UnLILSZBJ6HFawy3U2Ooy95uma7SEmtg3uoeYOrNzIAgC67wbOeQ7wHB8iJ93/xhFNGbqL5x1xnR0knqSkfgwpebZc2uuloxBdb5hpV+uMX5l7TNJZhY4FnHId4DOSDu/63+Sh0vejcvgRpiobHW2P6JqhIHYAEXmoolE7CxvFWLxFD976hD371iCy25Of64mbonzC96lVI2eAT9nOodxOczUV+Zjtxov+/6WZBHJIBGPxDFZjbQcakU2SFOOU+J1cvvy+nTNJQEMkpQNK8qS5a2GPxnkTKibSmsROcapI2+XywfK75iDVk1Gm3MV5MtDFkU+sWE1O+preK6xhaO9/fSOB4gk07KwdqORIoedefm5bKoqZ0lRwZzXNngrY5Ak/nT7Ju5dNJ9nG5o50TdIXyBANJlCFkXsE5K88/Jz2VJdzoL8vBtilFHTAmmJSF1FEIwIghlND0z8bUAQrOh6DF2PAxqiaEfXNUCdWBcBRKzmu4glDnP2k6frMeKJwySSp7Fb34MgWBAus/jUO4X5zoUc9R9mJDHMd1q/wU35t1JoKiShJTkTaua4/yhuxY1DdhBIBWY8TpG5mLsK7+U3vb9iPOnnu23fpMxSTqWtGpNkIpKKMBIfpi3cSoGpgM/P+//m/Fp0XSeqRolp6UrRMTVORA2TUOMAhNQQ7eFWTJIZRTQiCiI22YZBMMzp8yQIApXWKm7Ou5Wn+p/giO8gLcFG5jsWkmtMS1SHUyEGYv10RTrxGLx8tPITWefgLUY8meLxF49RW5FLYZ6T8UAUh81ErseG3WZCFARUVePVfS386/dfJpXSSKVUVi8p548/vJ3CXMdl3Ve5JTmUzSvmya8/h5pSkQ0yH/qrd2E0T85FUWSJoUCIPU2dGA0y96+ee/n8G/NtmCXLHKDrOmE1yonxM5wab0XVVMaTYcotBUiCxFhiHFXXiKkJ4loCr+LErTgIpSKMxP2kdBVJkMgzurEZLCS0JGPxcQKpMPlGLw5DOhE3qaXojQ1jFo2EUhFMkkKeyYMiGkhoSQaioyS0JAZRwmt0YZPTxxqK+Yipccyy6S2jUV3pdV8zact3ArW5XmpzZy7+daMxHvwvECRARzEswKisYDz4bQQBRNGN1Xw3kehzpNQ+QMRufZBkqhNVHcFmfTfh6FOIogur+XwJYp1kqo1Q5Neo2hiiaMds2oYsXVxR5p3IAsciNuVuTSclJ3w83vsrACRBxiJZKLdWcm/RA7wy9AL7xt6Y9VgbcjZjkS08N/AMo/ER+mK9dEY60NEn6i8YsEhmco151+x6Xhx8lucHn5m20FtzsJF/aPwbAEREFFHho1WfZKlz+Zy3QxYNrM/ZhCwa2D2yk6H4EEd8h0hN1ESQBAlFVDBLFgrNRShSNhn5rYg/EOVrP3iFQCiGgICOztqllXzyvRspynMSjSf59fNHqSj2cvf2RbT3jPLcrtO8tKeJd9+5AqNy6Wa2yWrk5vdvZtVtywiOBnEXuLE5LQgX5BO0Do4iCgL/8IE7iCdTfO3p3SyvLMKszN3sQdY5yJLlCtHQGYr5OOxrpDPcR1xNYJMtvK/8NsySiZcHD9ATHcJrdBFIBlnjWcRKz3w6wv3sHT1BSk+R0JKUmPN5qPRmIqkYJ8bP8Nu+13ig+CY25y5HEkSG4z6+0vADNucux5cMEPr/2TvP8Diu82zfU3a290XvHSRAgr2LTaIq1YtVLLlbtuM4jq3Pjh3HcUkcx3HiJG6Ji2K5SbZ6l6hKib13EgABAkTvWGwvM/P9WBAkCIBFYpVxX5ctYuqZ2d2Z857zvs+TCHNjzjIq7AVs6N1NY7CNhJZEQ6PAksXKjHns9zewoXc3sijhNqQCkkkmudxQdT9W03WYlHmAQDj6KpoewGZaTSyxh0SyAQQRxVCFQS5AlkpIJJtPc1QBxVCG1XI9SbUTu/XuC3Ep55x0YyazXLMxiMqolAKHwclszzx8ShrmEwzS7LKduZ4FOA3OUc7JkiBxbeYNlFhLqQ0eoi/WTSReh0PJoMi+gKmOalyKmxnu2ejo5FryxrQlFNuFLLpR5DxqnLMos1VwYGg/7ZFWBuLdhOK1CHo3biWbUte1lNrG+q+4DC6mO2cwaBnEZXBOOKCRZkxjpns2YTWMTR5rKJdhyqTGNeuM76NDHm1WaZEtTHFU4VY8ZBgzRtKAxuxncDLNWUOOOQ+3wTNuexVRYbHvCirsldQHa2kNtxBKhlBRMYkm3IqHHHMuRdZi7PLZm2ZOcv5RNQ1Zkli9Yho+t5W+wRDrtzfw55d38IX7l6OqGp29Q9x57UyuX1ZFNJZgwB9m0+4j3LJq+lkFB2pSpa9jgK6mHpKJ1L8Bpl0xFdlwPMVYFiVUTafbHyShahgN0jkf/psMDiY5JaFwjC37mnE7LIQicZrb+zHIEtPLsykv/MtIbZgISRAptuWwIn0OOwYOcXXmAnLMqRExTddQ0YioUW7NWYFZMg7vI2EzmCmwZiIJIl3Rftb37uK23JW4FDurMhdQFzg6SulDQyehJ5nnrSbfkslvm16kOdRJpsnHo82vMt9XTYbZQ3e0nx2Dh6h0FLGpby9TncUsTZuFPxHg5Y71F+UejYeu6+zsa+VoaIAqdxZljkmDnUnGR0BGEr0IgoSua+h6HE3zo2p9GORiDHIZRsN0ovEthKNvoelRQEbX46DH0fQQIq5xjwwCXAKOtO+Vqc4qpjqrxizPMefykcJPjFmeacriU8WfHfdYoiBS4ZhChWMKmh4nFNuGLLoxK8dNIWe4ZjFjgk53b/BRrMZZeOQsREHBKtuY65kPzEfVQgRjW+kP/RlRSJDjnIosjXXAzrMUkGcZay53MuX2SsrtleOuEwSB+d5FzPcuOu1xJsKjeLkx+5bTbpdjzuX23A+d0TF9xjR8xjT4YAuvfSCxmBVuunIad103C0kS0TQNn9vGU2t28fkPL0PXIR5LjojSmIwGyovS2VPbhqqOdRM/FYPdfl55+E00VUM6QYhj6sKKUcFBpstOS98gm+qPouswtzjvnAtTTAYHk5ySYDjGc2/uRdV18jJcGGSJpvZ+3txcx0MfW0lxru8vOkA4FZIgkmPOwG44PnoXUxO81L6eQmsWbqOHuJIkoalo6Jwq9jdLRgotWUiihE02o+pJgskwQ4kgRZZsJFHCoziZI1dhkhQiyShpRjcGUcaruLAbLi2vAEWSOeTvwiBKk8HBJBMz6tkiYFRmEU/sR9UGkEQ3oBKNb0ZVu9G0QUDFIBcSjrxKIPwUiWQzkugjEn2HRKKWsLAGXQ9hVGYjih5iiVcIRV7BpMxDGqfD+peIKCjYTe+9c30ykmjFaV5OQu0kkqg9Z8edZJLzjUDK/DYnw4UkpQbsRFEkL8tNMBxD10HVUvV8J9bIOW0mYrEk2lmW+gUGQvR3DnL3V27BaDmeZmYwju6qpzttLJtaTG8ghEGS6BkKvbcLPAWTwcEkpyUUiWO3GrlxxTQyPHYGgxG+/p/P8dhL2/n6p6+52M276MiCRFxLkNCSJ60Rxmh9R9UYtYEmbsy+ghxLBs+2vX1G5xAQkMQTRgZ0cBqspJncCILA0rRZxLUEwWQYq2TBbrDSHOqgwl5AX9xPf9z//i7yDPjfQ+vJtTipD/QgCxIfLp3Ljt4WXIqZWb48HmvcQbU7i6muTEodPnIsrlH77+1v582OOgKJGFNdGdxaUDMZeP6F47DejySmZuMEQUCWsrFb70XXowiCEUn0oBiq0eUwRmUeBrkQQZBx2j8BGDAblyCKTtATuB0PIQgmRNEDiChyBQ7bxxAFG4JoOWU7LiUSag/9wSexmeZhNc4EBBLJLloG/oF8778DGv7wGoaib6PpUcyGKWTYP50q1iZJIPousUQTOgmC0S1YjTPw2e5HEp3EkvV0Dv0MTUvgsd6Cy7Jq5Lzh+AH6gn8mpjYhYsFpXoHHehvCsPlZNFFHS//XSWoD2E2L8NkeQBQMp7wWXdeIqy30Bh8jlmjEZCjFZ7sPg5Q1+duf5KIjigJuh4Vt+5qZVZWHzWIkEo2zadcRwpE4r7x7AJNRRtU0IrHEyH7ReBJJPnt3bYPRgDvdiWIy4M5wjVmvahr+cBSTwcBQJDoi8/32/gam5KYjS5M1B5NcQCRJpCDbQ3lBGqIo4nKYmVOVzzvbGi520y4JcszpGEWFn9b/GYts4qGK+7HK4xve2AwWFnin8ZPDf8YkKpTb83EbUwoV+/wNrOncyN7Bw9QGmljfu4u78q9GEcd/wSqigc+V3sUzbW/xUvs6BEFknream7KXcmXGXJ5ufYv1vbsotuVSahubJ3yuaQz0YpYN3Fs8B0FIZeB2RQMptRngaHCAPNv4ak7+eITX2g+xIqucLLOD7+56lemeHEonZxX+ojHIhaP+FgRpzDJRHKvwohjG5rTLZJ90LAtGcex2lzqS6ELVA4TiOzEZKpFEC4ORNcNFrhY0PYLRUEy6oRRBkGkb/FcGI6/isd4OaMQSTQyEn8dnv59s91dA1xEFE4IgoMj5ZNg/S3fglyTUrlHnFQUzDvMyjPL9xJLNdAd+hclQOhygQDi+lyznl5BEC60D30UUHPhsd57yWlTNT3fgNxhED1nOL9Ifeobe4B/JcDyIJEwq90xycTEqMqsWV/DrJzayp7Ydr9PKwFCYo+0DXLNkCg8/sZFEUsViUth9sI2rF0/BIIvsOtiKz2UdmW04U9SkypaXd/Luk5twZzhHUou++cRDWOxmwrEEb+8/QrbbxhOb9pLmTNXc7DrSzqdXzT+n1z4ZHExyWgyyhNVsRBRTX3RBEPC6bPiDEZJJDYPhL9uEyWmwcW/BtaiaCoKASUxNB96Ss3ykY3wMSRC5M28Vt+au4NjMgqbriAhMcRRSastD07VURrQgYBANCMD3a74wcozbclcCqRmLcnsBXyy/b1i+ESRRQhYkim25fKH8HjQ0xOGCOlk8/5/TDE8OaSYbgiAwlIiOLNd1HU3XJvQU6okGqfV3s6e/HZNkIK6p+OPR8TeeZJK/YARBxmKcQSDyLkmtH1Ew0x9+hgz7pxAECRErVmXGsa2xKNOIJZs4/uPTMMhZuC2rETAwYqYBiIIJRc5GFMbOpBjlAoxyHiAiiS4MUjZxtQ0rqeDAYVqGRZmGKJhxW1YzGHn5lMGBrusktUHCsZ0U+n6MIuXgMC+na+inqFoI6YSgbzAe5snmHWzpbeLq7Klck12FzWBkffdhWkID3F009z3dy4ODHZQ60jFcgGfj+WZHfz27Bxu4NmsemcMzyqeiLdzL9oE6im3ZVDsLL0wjLzMMssQNy6eRSOo8tWYXDc09eFxWPnzLXG6/egY3tQ9w4HAHpQVp/PSP7/Kxr/0OURDoHQjx4N1LMBnProudke/jy7/6LLqmpz6/4Y/wmJSpzaSwenYlLb2D3LlwOjWFqQGPX7+xBUmc9DmY5AKTTKpEYwl0XR8xs/EHItgsRmT53H4hL0cEQUARDHDSCL9hAt10gyhjGOenJyEhTVBUdKyg+dj+I+cGTONI4AkIGC+CNJ58wgNKFlLTqoFEjHAyQXOon4W6io5OTE2S1FUSWup/XqOVfKubWwtrKLR5UDUNizwp7TfJJCcjIGBVZtIffJKk2oOmRUgku3CarxzucA/QH3qKoeib6HqMeLINl/n64/sLMrLoTflEHCvMPg26rhOK76Qn8DBxtQN0FVUL4zAtHdlGklzDXhECspROUu053VFJqr2E43up7byF4wGKkWOBjK7raOi0hQcJJmN8bdp15FpcSIJIUlOZ5clnjrdw5IhJTUMdHijRdB2jJCMACU1FRUcg9VwSBZGkrvK7xk38zZQrcSpmjKJ8Wacy7Rw8zO+bXmemu4xM0+n9djqj/fy0/lnme6dQPe2jwHDApicQENBIFevLgoKqJ9F0DUmQU+mtOmhoqHpyZGBKEMTU+uE0s2P7iIKIqqvouoYgCEjIiMPbJPXE8DmO+0ykBpJUVFRkwXDOzb3OBkEQsFuN3H/zXO69cQ7JpIosS0jD0qJTSzOZWpqJqun81b1X8JunNtE7EOLDN8/jhuVVGOSzCzoVs0L+lFzURBJZkRFFEVVVEaXjA7OKLFGU4aEo3YMgpFKN7rti1kiK0bnirIKDn//85/z85z+nqakJgKqqKr75zW9y3XUp0yZd1/n2t7/NL37xCwYGBpg/fz4//elPqao6rqgQi8V46KGHePTRR4lEIlx55ZX87Gc/Izc399xd1STnlKSqcbRjgNauQXwuK5Fogu0HjlJTkXOxmzbJJYTHaB0VuJhlhTJHOo8f2cnW3mbSTDZsspFIMsGPD7xDc7AfoySjo3N1zhSuz5vKnxt3EEzGERH4zuwbkCaQEZxkkr9kDJIPk6GCUHwPCbUTj/UWUq9znVBsO4HoOvLc30WR82kb+O64Bm9n432ik6Cl/xuk2T+C13oXCbWbtsFvj9omkexE1+MIgkws2YxBzp7gaMdbYJDSsBpnUeD9IYqUB+joJBFQhs8Lh4e6eaRhI42BHlpDg9xVOJtZ3nze7qzlz03bqfHk8VeVywHY2NPAuu7DGESJzsgQD1WtIs1k58eH3qI1PIBVNrIis4KZnjxebN3L1t5m/mP/a5hlhW/WrL5E3GDeI2dZ/Ooy2NB1aAl1jywLJAd5tfNRLJKdlvBhAkk/t+V+io29r9IebWaWeynL028GXWf34Hp2Da6nL95JUkvikN0s9F3DDPdiJEFmW//b1Af2UGCt4IB/K33xbiyylWnO+Sz2XY+AwPPtj6DpGjdkfxjrsJRrQo+zofdV6gK7uD3303iNF9d/5FjQIkvChDn9siQwc2oeM6e+v/TdaCjGuqc38+4Tm7jh01dRvWQK7zyxkSvvuwLFdHywrG8oRDiewGZSWHeoiQynnbmluScJOLw/zio4yM3N5fvf/z6lpaUAPPLII9x8883s3LmTqqoqfvCDH/Af//Ef/OY3v6G8vJx/+qd/YtWqVdTW1mK3p6YIv/jFL/L888/z2GOP4fV6+fKXv8zq1avZvn37hKOmk1xcZFniaOcAv39+K1lpDg41dpFMaty7es7FbtoklxBfnX7VqL8FYLYvj9m+sQ/Mb8wYW8g+05vHTO/5r424nAgMhOhs7iEtx4PTax9jhjPJXy4O8zL6Qo8Rju2lyPvfI8sFQUYUzcSSTcSSzUSTDZgNU05xpBS6rhNLNpFQO0moPYiCmXB8P4qUgyiYkEUPmh4lFN9BNNFAXD3eqRQEkXB8D4HoJgRBIhBdi9d6HwCqFiCWbCGePEpS7U4dU87GKBcjSW5sxgX0hZ7Eblw47HQtYTPOTRWPCwIVzkw+WbqE1zoOcFfhHNJMqb7EVdlTiWkqTcG+UddhkRTuLJxN9rDggaprdEWGuL1gFjlmFxlmBxZZ4d7ieazvaeCr067FY7y01NwuBAYp1f0LJiOjlse1GIGknyVpN/B61xP86ehPWJ5+M3nWMvYObmSacx5pxhwSWoJKxywyTKln9ube13mj60lyzEVkmvMBaIscYTDRyyLftbiVdBoD+9ja/zZuJZ1Z7qWU2Kp5p+c5BuO9WKTU5xpTI9QFdpNrKcGjnD+zvEuRntY+Dmyso2h6AbFIAovDzMbnt7H0zoWjgoOjfYO09w8RiScRBXhm635qCrMungnajTfeOOrvf/7nf+bnP/85mzZtYurUqfznf/4nf//3f89tt90GpIKHjIwM/vjHP/Lggw/i9/v59a9/ze9+9zuuuirVkfj9739PXl4er7/+OtdcM6l8cymiyBLzphVQkuvj0JEustOd3HnNTKYUZZ52GlbXdXqiIVyKCUUa/XXri4awyAom6fKezj2XJDWNff2dlDq92AzHU4kSmkogHsOuGEflx+q6TlRNsqu3nYWZp9cIn+TyonZbI7/57pPc/tfXcsUtcy9I3cj5IplUaWroJhiIMqU6F4NBYtumBmwOE5VTczh0oI2eDn+qwM9ipKQ8E1mROLS3lWg0gaZqCKKIokgUlWWQV+Ab9zyxWIJ1bx5MqRvJIpnZbgqK0zCaTq2cc7lhUaoJxYqRRR9GQ9HwM1TAokwnnmxjKLoWg5SBz3bf8B4CAiJGuXTcIm6AUGwbkUQdkuhE1UP4I2/gMl+NWakk0/kF/JE1xJJNmOQSMuwPYpBSggE241xEk41o4hAJtQuP9XZclmsBSKi9DEXeIqkFEAU7Q9F3MBmKU8GB4MBn/zD+8KsMhl8GJOymxe9rBDTNZMduMI38LSLwhSkrWNfdwK7+FqY6s1iZVYlAKvVIRx9Jmf1Loi3ci46OLI7tBhZbp1Jur+FI8AD7h7aywHs13dFW9g5uIpQMkGESWeA7rmSl6zp22cWvGv+J3ljHSHCQ0OKsTL+NSsdMREEizZhNXXAPnZGj4IYSWxXb+t/iwNA2Mkx5SIJEZ7SFoUQfFfbbEC5iStExkqpGR7efox39hMJxNH38KZo0j43ZVfnv61y6pmN1mMkoSP2ugoMhBHHsHJ8sSrT0+TEZZFZNL6O2o/d9nXc83nPNgaqqPP7444RCIRYuXMiRI0fo7Ozk6quP29QbjUaWLVvGhg0bePDBB9m+fTuJRGLUNtnZ2VRXV7Nhw4YJg4NYLEYsFhv5e2ho6L02e5L3gK7rOO0mblo5jZuYdlb7arrOM0f2cXNRFRnm0S+kV1rqWJCRT7F9Ul/8GLquMxiPoGqjzVP6o2HWdTZxVW4pTmW0EpKqa/RFz73O8SQXn+ySdFZ/YgXF1XmIl/msQTyWZOPaWg7XduD7m6uxWI387hdvU1KZSVlFFoHBCIfrOln/5kGuu2UWOfkeFN1Ab0+AUDDKn36zjhtum4M3zU5m9sQ51eFgjId/8jqrVs9AEAS2bWxg1eoapk7PO2v1kEsZUVDIcHxmzHKD5CXN/uGJ9sJhXjLuGkEQ8J6igNhumo/dNL4iisd62/C/rh6zzmQoItP5VxMeV5EySLM/MOH68dB0nfXdh9nV30JPNMg7XfXM9Iw/66jpOht6GlFEGRGBnmgASIVK2WYnL7buJd3k4JrsqZdNgKDrOnEtSUfk+KyJP5F6B3RG+3GGrBOmSCV1jZ7YIE+3rkPTNYpsWWO2MUlmREQU0YRNdiEgpOoFBBGNVN1YX6yLtkgjQ4l+4lqMiBoGdBJ6/ITjWPAZs0ZqDEREzJKVmJYSm3AY3JTZp7N7cAPzvFdhlezsHlyPz5hFrrnk3Nys94GqaRyo7+D3z23hUGMX4UgCYYIynQU1Re87OLC5LNhcVna/vQ+jxUj99gZKZxQhGUZ31bPcdgrT3WS5HKQ7bcwpzh1V73cuOOvgYO/evSxcuJBoNIrNZuPpp59m6tSpbNiwAYCMjIxR22dkZNDcnLKz7+zsRFEU3G73mG06OzsnPOe//Mu/8O1vf3vC9ZOcfyLJBGta6mgPD+GPRVEkifvKZmI3GAkm4rzccoimoQGMkszVeeWUu3wMxqI827SPxxv20B0JUmB3c0VWMT6ThbXtjTzesJsDA11kme0szCxgdlouoUScTV3N7O3vRBJFbsivpNDuQdN1Xmg+gN1gZP9AFy7FzJW5pdhkhaeP7MMgynRGAlhkmbtKavCaJp4mDiZivN3WgCAI1Pt7ybY4uDqvHJfRTHNggOeaDpDQVArsLq7Jq8BmMBJKxHmttY7D/j6SusaSzEKWZBURTMRY39nE3r5OHIqR1QVTyLKkcid39razrvMIoUScpK5xc2EVBTYXzzUfQESgKxJEkSTuLpmBz2ylKxzgnY4j1Pt7mOLOwEnqJdAW8vPo4V1s7m7hsL+XKe50lmYV4zKa2d3XzrsdTSRPcnrtCA2xprWO7kiQNLONu0qmYxRl6vw9vNNxBAGBoXiUVbllVHkyz/mDZZJzQ3ZxBtnFGaff8DLBaDIgiiJ93UMMSCIWqxFJElPOtleUk53nof5gO6tWz8DlSf2Gb75rHgDP/mkzN945l6yc0xdbOlwWbv7QPEwmhUcffof6gx2UVmRithhPu+8klx6ZZgdXZU0ZmREQALfRwtKMMpKahluxIAsiFY4Mci1uTNLxWSJBEMi2uBARyLe6ybd6RpZ/uHg+nZEhzLLhsgkMjtEVG+BHtU+O/N0Z7Qfg8aNrscrmCYMDFQ1/PERHpA9ZlFmdPTbgExBH7sfJXj0AbeEjvNv7ApIgk27MwSLZkAUlpUh3AgZROW1B8TTnfLb1v0Xt0E7K7NNpCO5nRfotGCXTKfe7EMTiSf700g5qj3Rz/bIqivN8yPL4dqVe9/tPTXOmOVl0yzycaU5C/hBOn4Oa5VUoxtGznmbFgM1kZN/RTnY3t5NIamOUEd8vZx0cVFRUsGvXLgYHB3nyySf5yEc+wtq1a0fWn/wDO5PputNt87WvfY0vfelLI38PDQ2RlzeZm3whSWgq6zqPkGG2sSKnhFeO1vKnht18snIeBlGkyp3JVHcGu3rb2dJ9lGyLHZtBYXFmIU8f2c+SrCLybS58JgtGSabGm43dYGReeh4VzjTSzFY0XefgQBf1/l5W5pTSMNTHH+t38eWapUiCyLaeVoyizC1FVZhlAy7FTCAR47HDu3mwagGrvGW81lrPqy113Fs2c8JriasqG7qaybTYuTKnFKtBwSwbSKgq/7V3HfcN77ulu4U32w5zU2EVLx49SGvQz4qcEhRJxqWYUDWNvX2dbO9p5dq8Smr9PTx1ZB8fq5iLUZL5yb71fK5qES2hQTZ3HcUiG4ipSZ5o2Mt95bNYlVvGmtZ6Xjx6iI9UzMahmKj2ZPLy0UME4zEyzCkNY7fRwixfDo1D/SzLLibH6sQipx4W+TY3M7xx/n3PWv52eko5JKYmeerIPnwmK9fmVfBuxxF+V7eDT1TO5cjQABs6m3hw6kKOBgdY01pPgd2Nyzi+L8NE9Hf5ef6Xb5Ce6yWrOJ13ntxMe2M3Lp+DJbfOYfGNs0f9pg9uaeCJ/36ZT3/vbmLROC/88k1a6ztRjAbmXjOd5XcswOo43obtb+zj3We20tXci8lqYsayKSy5ZQ7eTNfINrquE48meOfprWx7fS8DXX7UxAlBkgD/9tLfIUoigYEgLz78NmabiUWrZ7H2yc3sXVdLIpYgqzidux+6kbScVKfB3xtg59sH2LPuEF3NvSSTGpn5XhatnsW8a2oQRIFYJM5jP3yB0poC2hq72PX2AabOL+WmT1/Fq79/lwOb6llw/UyuuGUOug4//MyvuPvLN7DxpZ007mlh0epZTJ1fysu/WUtvxwCrP7mC6oXlSLJEMp7k3We28cKv3hy5lHv/7iZmLps6oloBqSnnFx9+G0kSmbqgjHee3krzoTasdjOVc4u5/qPLsZxwT3Vd5+CWBtY+uZnW+k6ioeOzsQAr717IDR9fcVbfg7PFbFEwKBJ9vUHi8STpGc7zJoUsDJ/P7jSjqhqaprPurYME/GG6Ovw0N3RTOiWLOz+8mHgswesv7eHQvlY0TWf5NdUsWlZJLJpg+6YG3n3jANFonKLSDG69ZwE2u4kjh7t5+tFNhIJRHC4LV6+ewdTpk++l84FDMeM4YcZUEASqXWMFMczjKJyJgsCS9NJxj1vqSKfUcXnmtTtkC9NcRezzN3E40EZITY3GN4Y6zmj/LJOHuwtWMNtdftbnPhI6SE+0ndXZD5BtLkIWZJrD9Wd9HAC3kk6ZvYbtA2tRh5WSpjgujXrGZFJjX307S+aU8OGb5mG1KOc1iJQkkeySTLKK0knEkygmA5IsjTlnQ1cfde29VOdnjEiYnmtVp7MODhRFGSlInjNnDlu3buW//uu/+OpXvwqkZgeyso5PU3V3d4/MJmRmZhKPxxkYGBg1e9Dd3c2iRRPbtRuNRozGyRGfi4HPbeMf/+o6InqS3zRsY4o7g2meTFRN4992r+WTlfOIaSqbu4+yv7+T/liEbKudmKbilMwUO7xYZAMlDi95NtfIcXNtTmwGI4V2NxWuNARBIJJMsK+/iyca97Kxq5m4pmIQJTRdRxLAIEpUeTKo9hxXLwgkYhhlOTWSrpjoCAfY2t1y2uuyygrVnsxRx2oY6mNdRxO9wyk6CU3l6txyNF1nS3cLtxdPY6YvB4GUMEQkmaApMECO1ckMXzYFdhcPbXyBe0pnYJQkWoKDVLjSMIgiDf6+YWlOAaMkc0VWIRlmO93REGvbU2ZyZtlArtU5SsJTEASsBoU8mwuX0UyZ0zdqVsRtNFPkGJ2W1RUOEIjHWJZdTJU7A4/JwqfWPsEnKuciCgJ5Nhfz0vNIN1vZ1dtOXBs963AmJGIJWuo62PD8Dsw2I2m5XjIL09izrpY96w8x1Bfk+o8tP/45DYao23GEHW/u58kfv0J+ZTbZJRl0NffS1dw7yh7+mZ+/xmP//gLudAdlM4sI9Id46iev0LD3KB/+2s2k53oB0FSNP/zrc6x9YjPzrplOxawitr2+l73ra1l17xIWrp45UsCbjKu0NXQR8oc5sOkw/t4hcsoyiQSj7F1Xx8e/fbzjcXDLYZ7871cw203klmWCrrNr7UF2v3uIeCzBFbfMRVM1Wmrb2f7GXjIL0tBUnad/toae1n46m3sRJZEnf/wKmYVpFE3NpW57I//3rSdweO0EBkL8+UcvUjI9HzWp0Xa4k//79pN8589fxOGxIcoi5bMLuU5dxoFN9bzz9Fb8vYEx40LJhEp7Qxd7N9Sx9skt2D1WcorTaT7YzqM/eJ7mg+18+eefGNl+22t7eeS7T+HJcDHvmhqCgyFe+PVbiKLAnV+8npnLzr8hmCiJSKJIIp6ks7Wf9CwnsXji9Du+B1RVp7mhh8OHOli8YgpGk4H+3gDP/WkLf/sPN3HdzbPQAdkg8uJTuzGZFD7ymZUkEkn+7dvPkJ3rwWgysGdHE8uvrqawJJ1wOD5Su/D841som5LFgisqiEUTWG2T76hJLhwOg5X7Cq4kpiUYiAf5Y/MbvN61gwcKV1FgmXi2URJF3IqdTJMHl2KdUHL7VBhFEzEtTFe0BUU00hPrYGPvKyji2f8GREFkrmcF/9uwls39UaY4ZmEbVi662OjD9QWFOV5s1vP/++5s7uGNP7zDzJXTyCpOx2wzsfHZrTz+H8/j8Nr59L89QE5pJrquk+22M6c4d6Q8RzzHQcv79jnQdZ1YLEZRURGZmZm89tprzJyZGnmNx+OsXbuWf/3XfwVg9uzZGAwGXnvtNe666y4AOjo62LdvHz/4wQ/eb1MmOQ9IkojHaR0xpJKGv4CKJJPQNTRdZ3dvO/v6O/n2nFVs6W5hV1/7ezpXqjhK5PbiaXy0Yk5q6k4QMIoSCU1DRMBuMI2Joq2yglGSQEjp7J88tTkesihilUePAiQ1jQyLjf9devvItKEkisPa4VpKB3t4+bH/6ugYxNQ0o0GUSGoaOqkH3t2lM3jgzcfIt7tZkVNCpsVObySExWDAOFwEJgsi6gQFTidy/HynR0NHFFLHBjCKMnE1FQBIgoBFVpBFEUkQ3/dUZF/HIJ/513tZctNsJIOEvzfAQ9d8j6d/toYF18/Ek+Ectf0vv/EnvvrLTzNzRRWCKAybvYCspO5H474WHvvhC+RXZvHN3/81RqsRNamy9onN/P77z1I5u4ir7l2CwSgTDcV48ddvMXfVND79L/cgigJzrprGvz34SxLxBNWLxo6IbV2zl5s/cxUP/svd2D020HVUVRsxmQGYtqSS7zxRjNlmGnGo7G3v54tX/hPvPruNJTcfH9WKRxN88p/uwmK3cP+UL7FnXS3ff/7/0dHUwy++9ih9bf0UTske2fah//kkzQfaeOja71E+u4i/+vf7WfvUFh79wfOE/WHsbmsqDaIog8zCdCw2E9te33fKz6CruYc7vnAtt33+GiwOC7qm8e17fszWNXtoOtBK4dTclPPmmj1EQlE+9b0PkV2cjqZq5JRm8r9fexSby0JW8fkfQRUAm8NEIqESCETJzHGTSCTP+XnajvbxhY/+El+6gyuvr2HmvOKReoOSiiymTMsbeamGQzHqDnWwZ+sRnvj9egBUVaO9pZ/pswtxuqw8++ctrLimmvlXlI/MdMxZVMYffvk2/v4wy6+uxuWxnfPrmGSS8TjmQG+WjZgx4jRYKbXnsK53HzXuEmpcE+frC8P/f8xoc+x64YT3XKqI/cR1IFDtnE9/vJt3ep4nqoXJMhVyVeZdbOt/a9Te4jgJOMfqF04ky5RPgaWCxtB+7sr77CWT4iVJIllpDrp6hoYDBeFcqoWOYagvwIu/eI267Y3Y3VY+9P9u5qVfv8FXH/k8bYe7+P13H+erj/w1ug5v7W/gQGsXDosJAfjI8tko8rmzLjurI33961/nuuuuIy8vj0AgwGOPPcbbb7/NK6+8giAIfPGLX+R73/seZWVllJWV8b3vfQ+LxcK9994LgNPp5BOf+ARf/vKX8Xq9eDweHnroIaZNmzaiXjTJpctQIkrtYC/Vnizeam9gri8XHZ2olsQiG9CBOn8v/dHwyD4C4FBMdIYDeE0WjKI8Mg3mNKaWlzi8GCUZk2Qgy+qgtq2BrkgAr8lKOBEnzWQbdbzzRZHDg67r7Ohto8abRVLTEDRwGExUutLY0NlEns2JWTaQ1DTsBiM5VifrOo7QGw2xraeVMmcaRjGl3d8TDfHxyrkszylBGjaPS13D+FeR0FQiyQSqrhFRE0STiZSJjyAMOx+L9EZD2GQFRZIRBYGomhzeRyecjGMQJDLNdjTgSKCfLKuDF5oPsjSreOQOnst7WFKTT2lNPsbh6VZPppOrH1jKc//zGvs31nHFLaOdS2evrGL2VdXIwwVWI/dk+Im77tnthAMR7v3KTVhdlpTijEFi5ooqNr60i00v7WL+dTPwZLro7x4iGU+SW56FYTi4yCjwYbGb6WkbAH3syy+nJJ1510zHneE8rl99UlqjxW7CaFZIJpIkoinzP8WkkJHvY7DLPypCyyvPxmwzY7GbyChMw+G2kp7nJTgYxmo3Ew5GUZOpYLW0phCDUcbmtpBe4CMj34fTZ8eb4UKSRYKDYTKO3QsBJASEMyigzS3NZMbyKhxe+4hJ4fxrZ3BoWyOdzb0UTs0lGo4RHAyRkZ+6P5IsIUoiRdW5JGIJ/D2BC/ZCdnls9HYPIQgCFotCwB8+/U5nSU6+l+/9+MO4TsgDPnZ9VrtxTHG3JAr89ddWM29xKYIgoGn6yDZ33r+IRcsrefnp7Wx4u5a/+up1eH12FlxRTs2sQt5+bR+/+K81XHn9dJZfXX3Or2WSSU6HIAikGZ04ZAsiIuI4HfAzwS67uCf/b0b+XpV518jx0425PFhyvPbzmsy7uSbz7lH7l9mOC5bM86xknmflqPVmycoDhf9vzHn14XPkmkvIMhWedbvPF0ZF5vplVfz5pR3MmJLLrKo8JGn8imRRFM7a9OxkTFYjq+5fxke/ezfP/fRVwoEogihi99qZlunmhf9dA0Cu18n1MytRdQ2jLKNq2sWdOejq6uL++++no6MDp9PJ9OnTeeWVV1i1KiVp9ZWvfIVIJMLnPve5ERO0NWvWjHgcAPzoRz9ClmXuuuuuERO03/zmN5MeB5cBXpOF2sFuvrVtDUZJ5uszVyIJIjWeLN5tP8Lfb3mFfJuLEod3pIgpNYJew8/2b6TE6eWu4umUu1IyXfeUzuBn+zfyYvNB7iieztLsYual5dEbCfKvO99OpfXklXNrUTWCkDq/xTA6p1QWRLIsjpEfhkU2nLIYOdUmAY/RglkaXeRjlGT+Y9GN/GTfBn5Tu400k5V7y2ZQ7cni7tIZ/LZuO9/Y8iqqrrG6YAq3FFUz05dNa2iQb2x9Fbdi5hOV87AaFEKJOIFEjMcb9/BM0z5cioUPl88k3+Ym02IfCZAssoG04fbu7Gnj6SP76I4E+d8Dm6n2ZPCpKfMRgHSzlUp3Gj/cvZb5afncVlyN12Tlf/ZvpDHQj67D93e+xfLsElbmlHLPcHufObKfTIuNL01fBghYDQY8JguQmulIM9lGZoPeC06vDbPt+GyOIAjkV2SRiCfpbu0bs31Rdd6ol9bJL7DW+g7UpEbJ9PxR27jSHLjS7OxdX0sskkpDcafbsdhNtNZ3MNQfxKDIqdShoTClMwpH5ecfw53hSvkFTNAGXdfx9wbY8uputr22j66WXiLBKPFonL72QSrmlowENABmmzGVuiSAyaxgdaYCGlEUEGUJTdVGtre5UqlLgihgthoxWYypwM+QyilNqmef3gXgTHPg9I2+JpvTjK7pRIKpGT+jxYjTZ6dxXwv9nYNYHWbUpEr9jiaMZgVPlus9nfu94HJbOHK4G7NZwTI8Va8D/X1Burv8xKJJerr8KEZ5ZP17YaKR0ZOxWI2UVmSxc0sjXp8Nq81EV6ef6pp84vEETY09OF0Wll1dzaMPv0Minvqc9u5sxuuzM21mPgF/hP6+4Htu6ySTvF8yTR5KbNkYpfcu2Xuq38vZBhvjzkqc9KxN6gk0XaUjepSjoTpuy/30JTNrAKDroGk6BkXmWz9+kbLCdPKy3BgN0hi53ZI8H7esqnlf55NlCU3VObzjCN0tvfj7hggPhRns8iMb5ZE0WUkU6RgM0NwzwH1LZrC7uQPt3NYjn11w8Otf//qU6wVB4Fvf+hbf+ta3JtzGZDLx4x//mB//+Mdnc+qLTlLV6OjyE08kyc/2IEkiBw93YLMYyc/xnNEXOpFUaWrtIyfDhcU8tnAKUtJZvX1BuvoCOGwmcjNdyCdEo5qm0djSR26mC5PxQut2C9xaVM2SrKJRSzMsdr47b3wZWlEQuDKnjCtzysasq/Fm879Lbx+1zK4Y+VDpDD5UOmPM9p+rHluX4jNb+fdFq0f+XphRwMKMU+v9u4xmPjV1fEm+clca/73k5nH3+cK0sRKADsXEfWWzuK9s1qjl73YewShJ/Hbl3cTVJH88vIt6fy8zfTn8y/zrRrabm57H3PRUEeO8jHzmZYwvhWYzGMc9z9/WLB13+wK7m3+YPXY2blFmIYsyC4FU3cdDM5aNu/+ZIghjR6jMNjO6xuji4GFsw53niUjGk6kRGONo9RDZICEbJBLx5IiAgdlq4sYHr2TNb9/l//7xcTKL0jm8qwnFrLDq3sUjswknopgM4y4/Rsgf5umfreGtP29i9spqbvv8NXizXIiSyI//5pEx24vi6InzU12bKI6enj9XL0GjSTmthr8sSyy4bgYHtzTwyHefYtqSChKxBFvX7GHWldXMXH5h6g28aXZMZgXZIOFwmnG6rMSiCQTg7TX7OLD7KBarwvNPbGXVDTOYNuv4b3nazAKMxtO/smSDxJTqXORxCp3TMhxo6ui0Q0EQWLW6hnde28+zf9pCLJYgM8dNVU0esWiSrevraTvaj2KUWHHtNNze1ExmY10XLz6xDckgkV/oZdHSivd3gyaZ5H0wxZHPd6Z9FEkQL6kO9kQk9Dj1gT10RJqpD+6lzF7D1EukEPkYsXiCx1/ZCUCGz8FQMMr++gkKvs9B59yT6aKgKpfH//05piwox+a24k538vLDb4IOVYtSz5gj3f1Ioki2x0lcVdl3tIurppcB526Q/dwlKH3AiUTjvPjmXtq6Bvnc/cswGQ384H9eY1plNn/7iSuHp5pOTTAU49ePrecTH1pEWdH4BUOaqtPSMcBLb+3DZjHyibsX47QfL5bUNJ19h9rwOC0XITg49+i6TiDppy3SyhTHqafkNV0lkAxglWzjGrecb4YSfoyiEUU0nvbh6zNZiCaTvNZSR1RNEohHmeK6PFUxTkU4kBpVH0GH/s5BJFnE7j77HGxvlgsd6GntJ7fsuMleaChMyB/BleYY6fSJksj8q2vY+MJOBnsCGEwKpTMK+dDKKgqr8sadOTgd7Ue62f3OIaoXlXPf127Gl+1G13UiwRjRcByr6/J1Ui2Zlk9+ZTZdTT10NvVgcZi5/mMrmH9dDa60818AaDIZmL8kVQdSM7twZPkxhZ/b7lnAbfcsmHD/b3z/rjM6j91h5gtfWz3uuoVLK8ddbrWZuO7W2Vx36+xRyz0+Gx/97Mpx97nt3gVw78TtnWSSC4kgCMjC5ZWBEVFDBJNDTHXMYqZ76YgfwqWC2aTw9c+cmTmv/RwULJvtZlbdv4xV948etGvc00wsHKNsdio92GhIpRINBMMcaO1Glsar7nh/TAYHZ4HZpKBrOn0DIVRVw2pWzrkxkcEgMWd6AdFYgl0HWsesl2WJW66ZcU7PeSaYJJllWcXkWM99J6In1sW7PW+cNjiIaTF2Dmxltns+dvHCqxns8e+g2FpGlmmshN7JzPTl0B+LEEzEEQRYkFEwShnpg0JrfSedTb1kF2cgSiKJRJJNL+3EZDNSUnP2hjCzVlbz5p828vof13P/39+KbJDQNJ3Gfa201HUwbUnFiDynpulse2MfkWCU+//+ljEpS+8FLamRTCRxpTlGjcbvWXeI3rZ+vKcw37rUaTrYRu22Rm7//DVcefeikXSmSS5/dF1nMJpKIbMpCobJNN2/OKJqnJh29spfAgIOg+U8tOjUKKKROZ7lzPEsv+DnPlNkSaSqbKxJ3IWmeProbIhst4Nuf5D+YJi+QJgrpha973qHk5kMDs6CVDAg0j8YYjAQISvDiSyJ6MCRo728+s4BItE4pYXpLJlbittpoaPbzwtv7CEYipOV7hzJjY9EE2zeeYS9tW2oqsa8mkIWzi4+5cu6/kgXr6+rJRyNc98t88hMc5BMqryy9gCyJNLc1oc/EGVuTQErFlakAoz9Lew51MaAPwK6zoJZRSyZW3rWTqFGSWb+BCkv74WuaAfbBjaiaiom+fjMyEC8n52DWxmM92EQFRZ5l+FRfPgTA6zrfYs9/h30xDvJNuVS5ZiBS3Gz37+b2sCB4eJnN0u8KzBKE0fxCS1BXWA/dcGDaLpGpb2acvsUDKJCd7STLf3rSepJ0o0ZzHTPQ0DgwNAeNvSupTFUj9vgodJeRZl9yoTnMIgS1+Z98NMMwoEIz/3iDY7WtuPOdHJoSyN73j3EstvnUzDl9EHUydQsrWTZHfNZ84d1xGMJiqvzCA6G2bJmN4rRwKIbZ2EZnkkTgIx8L0F/mP/79hOYLCZEScDutjJtcQWLbpyNfJYdYG+Wi9zSTHa8uZ+MAh/peV5aajtY9+w2fGdgvnUuSMSTdDR2Ew3HaDnUTjyWoLWug7rtjZitJrxZLmzvwXDH4bVhd1l49ffvsvW1vQiigMEok1mQxsIbZlJaUzAZLFymRBIJHnr9FZxGIw/OmkuFL+1iN+mi88R/vog308WyOxeechYxEU/y66/9kdWfWUXuJdARfK+s7d7Dm907z3o/q2Tim9X3n4cW/WWQKinT0XUumIt9IBLlcGcvQ+Eo4WicRFJlVlH2SC3juWAyODgLJFlMGSDFk7S2D5CT6SIaTRCJxPnJI29z3YpqMtMdbNpxhHVbD7Pqiin85vGN5Od4WDa/nMPNPXR0D6HrsHVPE+3dflYsrEDXdX756Doy050U5/smPH9WupMVi8r5j1++QTAUg7TU6OmOfUcxGQ3csLKacCTOz367luqKbIKhGFt2NzN/RiF9gyHWbqonL9tzwb7AE6HqSd7peY08SxF5lkI29L09ss4kmii3TUEQBPYP7eJgYC8LPFdgkW1MdUzjSOgwNc45eBUfVjnVQfIZ03EqbnRdY0v/ehpCh5jqmLgwKKyG2OPfwQzXXGyyHZtsRxQkklqSFzqeZGX6tQDUBQ6wd3Ansz0LKLaW4TCsp9JeRZ65ALvBOeHx/5KYMq+Ewqm5rH9+O73tA8iyxLUfXcYtn12F0TR+Xc2psNjN3P/1W8gqTOfdp7ew/rntKEYDpTMLufZLV1AxKyVJqes6PW39vPX4ZtzpDjwZLgxGA8lkkpa6Dra9to9YJM6qe5eclcSVJ9PF6k+u5Omfvspz//M6oiSSUZDGbX91NY37WjiwpeGsr+ms0KGvfZDvfvgnaKpGOBgl0Bfkpf97m7ef2Iwoidz8matY/cnxU10mIhKMsu31fURCMXJKMnD47AhAOBhl00u7aNjTzEf/4XaKqidNvC5Hdnd38mZTI1k2Ox+qmnb6Hf4COLCpntzSTJbecerUL01V2bpmDyvuGVtTdjnRGu5hS9+hs97PaTi7gYbWxh7efGY7ZquRaz80H7vr3Mw66LrOvi2N+PtDLLlu+sjyrtZ+7C4rFpsRXddpPNDOjnV13PngmRk2DvQEOLSrmaLKLDLzvOekrSeiqiprtx5mf30HX3hg+Tk//ngEo3FsJiMLygowDAe+5zIwgMng4KxxWE1EYwkCoRhet5VYLEF9UzehSJxl88uQZZGBwRCHGrpoaumjrrGbT9y9mDSPDafDzMtv7ycWT1J7uItX1u7n9XcPAjA4FOFIS+8pgwOb1URhrneMo6iqasybUUhlSSY6YDDI9PQFU8Zi0Th52W5sVhMWczMuh/mijw4OJYYIq2GKrCWkG7Mos01lS2wdABEtwh7/drpjnQzGB8i1FKCho4gKXmM6JslEujEDl3Lc+Ks71sm2/o1IgkRvvIdMU/Ypz6+ICnbZyYa+tSz2LifDlI2ISGesnbrAQYLJIQBUXaXGNQdJkHApHkyiGa/iI9OUc9Hv4aWC2WZi1b2LueWzq0gmVERJxGxLKfGcyIylU/ivN/8hpWx0muDUne7kpgevZNV9i1PHFAUUk4LZZhzxHQD41Tf+RFdzL99+/IvDhc6pUZx4NME37/oRb/5pI1fdsxgBAafPzqf/+W50TcPqnPhlJskSU+aVUDD148QjCUBHVmSsdjPzrp1BPBZHEAVMFiNf+O+PppSH7Cm1pm/96W9GRijzKrL4+m8+i2IyYDQr/HjtP47ck7QcD99+/Isow2lLs1ZW8d9vfzPluyCAL8fNvzz70IRttNhTv2GHx8Yn/+lDaJqG7aRrWnTTbGqWTRm51l3vHOSNRzdw59/ewJwrq0buo6bpHNrayM8e+h11O5smg4PLlLVHmy52Ey5bFJPCD1//Brb3UCN1KTHTXTqiEngyOjoJLUlvfIgD/mY6Iv04FQtfrLidMlvuWZ0nM89D1Zwi6ve1kky+N4W1iSivyUfTRgsGbFizjxmLSimqTL3XI6EY3a0DZ3xMh8fKzMXlyMr5SbXTdJ2Obj8tHWfepvdLPKmyu7mDuo7ekaDgKzcvm5w5uJh43VY6e4YwGERMwy93TdUQEJDl4QhOklIyXWrKJEwZ7swbZAl52FRL03XuvWUeq1dWp7TJSeW3vVecdjOimDqOJAmoqkZBjgePy8o3//157DYTN141/YK4/J0OHW3YWuWYSVeqTQktQW1gP5qucU/ex9gxuJWeWOcpjzUY72dN1wt8tOCzWGUbr3W9eFpjL5No5urMG2kJN/FOz+u0RVpY7FuOhopLcfGZki+PuAGcbEl+jtXCLjlCiTi/PbSD7kiIr89Zzhsthwknk1xXUI5ZHlsAr+upl+vpCloVkwFPpuuM22E0K6OMycZj59qDzF5ZTXruaLUwi82EyazgP0FaUpRE7GeYiiPJEnaXFU5qrqzIWOym1B8COE4yvTrxHsgGGafvuITzidcuydKobU++Vtkg4csZ7Xo9HhNdkyCkgpcTA7Su5l40TSOnOB2byzpyv3Rdx+m1oaoa0VDstOec5NJD13XeaW662M24JBFEcZQ6lSCKCMJxVTFV1UDXcXjto9TEIHVfdV1HP0EjUhCFUdvpuo6m6cdyS2BYxvhiDB7NcJdQ4y6ecP2xJiY1lZc6NvNw4ys807qeH9R8esJ9fvsfr9Cwv41kQmXRtdO4/p4FyAYJk0UZlZqsJjX+799eorttgO7Wfkqm5fKhz6zk4M5m3nlxN9FInLnLKrn5o0tob+rlyV+txeY001zXyV0PrmDK7ELeeHo7rz2xjXkrp3DHp5YTCkRZ98oeXv3zFja/vh+7y8KX/u1udF2npaGL7/3174hHE6y6Yy5qQuXQrqMc3tfK9IWl7N5Qzye+tpq0LBfP/N+79PcEuP1TyyiuzEbXdfZuaeTPP38TQRDIK03no//vehTluPdOyu5s9H2bCFXTCUXip97oHGMxKiypKKQ6P3Ok1kA5x3VGk8HBWeJxWdlX247XbcU8rBZUXpxBMplk255mCnK91DZ04rCbKcrzkplm562NdaxYWMH+ug76BkOYjAYKcj3sPtDK0Y4BMnwOWtoHKCtMGxntj8YSJBIqoXAci0lBlkWisQThSBxV1QhH4kSi8eP68jBigHSMaDyJLEvcd8s8Zk/LRxSFEcffizny7ZCd6Oh0xNqxyDbqggcA0HQNVU9iEBUSeoLmUAPyCdbuIiKKaGQwMYBZsmAQFeJaDEU0Igoi4WSIhmAtczynnkZWdZVAwk+WKYdZ7nl0RtuJaTHSjVnoukBDsJYCSzEqKiIiVjnVCbTIVvyJAcJqCKNoRBYvf7Wo8RAQaA/62d3bgarraLp2SQZFLq+NtsOdDPUFMQ0HvZqqsWfdIdobu1lw3czz65p3GWGyKISHIvS2D5BXkYUkiWiaTjgQZf3z2zEoMpmFk3nqlxs60B4I0DDQf7GbckkSHAzyw0/+L7vfOYjTZ+fmz13N1Q8sHTFh/Pad/8HBTYcJDob4p2e/wuxVx1OyhvoCvPCLN1j/zFb6Ogdxem1cee8Sbv/iDSmhBFVj++t7eOanr3JkbwuyQaKwOo/P/vsDZBVdeGW6kweyxjD8LJQEkVtzl9AW6eW5tg282L6Jm3MXj9lc13Vu++QyRFHA3x/ix3//BNePoyam6zqJeJK6PS18838+yq4N9fR1D5FIqBzY3sSn//5G0rJdfO/zv6NqdiHm4fSgRVdP42P/7/qR41x12xxEUaC3yw+AxWZk1e1zOFrfxZJrp1E5s2DkfKqm85X/uJfdGw/TeLANs9VE8ZRssvK9BPxhPvS5K9m7pZE7PrWc6+5ZwMbX9o0EeWpS42f/+DT/8ttP40qzj7meNzbWEgjGuOnKaSRVjSdfPXUdRzKpsftQK1bzhRt41dGp6+iltqMXSMnkf+nGKzCdQxXHyeDgDBEFAYfNhMNuJhCKkpvpwmoxMuAPY7UY+dKnV/Hoc1sJBmNUV2SzclEFVouRz96/jN88vpF1WxqYUpbB0nllGBWZK+aVkUio/Pqx9QTDcTJ9dh56cBWhcIxfPrqOts5BItEEP//9Wm68cjozq/N47LltHG7uJZFU+cMzW6iuyObqpVNI99pHZE0FIDvDhaLIRKIJunuH2LLzCE+9sgtBgE/dcwVV5Vnn1QL8dMiigZXp1/Jm9ytsH9hMqbWChBpHkRSKrKWs7XmdJ1p/T4YxC5NkHhnFN0lmpjqm8UrnsxRYipnvXUKaKZNK+1SeavsjRtHIdNcsLNKpR4gjapiXO58hlAxiksws8C7FJtuRBImPFD7Imq7nebfnDewGJ4t9K0aCgwXeK3it80V2D25nvncJVaeoa7ic8ZjM+MxWDvX3YDFcugHQHV+8nj98/1m+++GfUDmnGASBzqYeDu9uJi3Py+1fuHYy/WuYyrkllNYU8Mh3nmTrmj24fHZCQxEa97bQ3drH8jsXMH3JpV1AH00mCcRjRBNJ4pqKNjwQIgoCBlHEKMlYDAYsBsOE0+tJTePI4MDIAInDaCTdajvjGDKpabQM+UkMG9aZDQbyHKeuP9J1nXAiQTAeJ5pMkhhuuyAISIKAIkkjbTcbDBM6nR6bcY4mk6n/qUkiySSvHK5DHU7FSGoqLUN+3CbzuMcQBAGf2YLbPP768c557L5HEqm26zDSbpuiYFOMyKdIZ9B1nb5ImP5IBJNsINtuRxIEAvEY/ZEISS3l8uoymbAalJHrDyfi9IUjxNQkoiBgV4y4TKazUmJa/8w2PvX9+3jgm7ez/fW9vPKbt8kqSmfmypQy3neeeohYOMYd2Q+O2VcxKeSWZ/GZH95PRoGPuu1H+NmXHqFiTjEzVlTT29bPy79+i+pFFXzuRx8hFopTt70RT4brjNt3MTj2TFyaXsPzbRt5u3v3uMFBKBDlD/+1BtkgkUyoREJxNE0fI9suCAIGRSKnKI1HfvgyngwHC1dVERgM43BZMBhlBEEgK99HV9sAhRWZOFwW0rNdY0zRTj6urh8fyT+GKIrklaYjGyQUo4wkSahJFYvdhI6OKIkYFJlkQh33+R8cCmO2Ktg91jEDqgCvrT/EkZY+blhRRSSW4Kd/eBerWRnJADkZXU9J3c+uPneCLROh6zpJTaM8y0fxdcd9n17acejiOiT/JWO1GLlycUoje+4JslJzhv89pTST73zpxjH7FeZ6+dbfjq+5feNV07nxqumjlpmMBr70qbHmVQAfu2usCRjAX31k+ci/BUHgH75wPdFYgjXvHCTD5+ChB1MO1o88von9dW1MKc1AFC+u1F2epZCPFH5mzPIccz735n983H0MooG5nsXM9Yx+kK3KGP/+ToTd4ODDBZ8ad122OZePFn523HX5liI+Ufz5szrX5UqJ00tS12jw91HkGJviopgVSqcXIMriSO78hUQQBK7+8BI8GU62vraHtoauVIqMz85tn7+GRTfOwpvpmgwOhimcmsvHvnUHG57fTsPeo/R2DGAyK1TMKebOv72OGcumnjaN62IRSyY5PNDPto42Nre2cHign65QkEgiJdtoNhhIs1jJtTuYmpbOtPQMSj1eStyeMS/McCLOx557krZAAFkUWV1WwT+tuAqr4cyuvWlwgI8+9xRtgSFEQWB1WQX/dc0N4257LCg40NvN5rZWtnW00TgwQF8kTCyZRBZFLAaFTJuNfIeT6vQMqtMymOJLI91qHfe72zLk57m6QxwZHKBxcICmwQGGYsfTwXrCYf7f669O2H6TLPPlBYv55MzTm01FEgkaBvrZ3NbKxtaj1PX30R9JddYdipEsu51ZmdkszstnZmYWPot13A5KUtP4v907+dm2zVSlpfOr1bei6hq/3b2Tlw7X0R+JkOd0cn1pOXdMqSLLZqcnHObpQ/t58tABWob8WA0Kc7NzuLtqGvNzcjGNk+I4HtVLUupnsiJjspnYv7GOAxvrRoIDSKUajYfRolC1qJze1n7aG7swWY3Y3VbaG7qZsWI4dTDNwVB/kJ6jfWQVZ3D1A0vfk7/KxcAxrBLYERl/1qm7rZ+BngC3fOwKAv4wjYfaAZ2B3gBtTb30dvppruvEZE6lGMUiceavrsHltaEYDdgcZlRN4/C+VuxOCwO9QxRNyUbXNEAY5TCs6zrNdV10tQ4w2BekpaGbjFwPBkXCbFFoPdyNwSBTPDVVdzBebcV4j/pwKEbL4W56O/20NvaQluXC5rTgy3Cya109NpcFs1Uhtzh9JOi598a5BELRkbQpm0XhlqtqqCwZ35sqkVB5Y2NtKr3sPBNPqtR39uGymNjf0oVxeAZsfW0T186ogHMoZzoZHHxAEUUBh91E49EeXn/3EJqm0e8PMaOqYkxu5SSTnEy21U5cU9nW3UrWON4W7jQHdz90dkHZuUYUReZdU8O8az6YMzjnmrzyLD705Yv7mZ0toXicF+vr+N3eXezv6Ro3vS0RizEUi9Ew0M/ao00oksRtlVP57vIrx5gq2RUjd02dxo82byCpaRzs7WFfdxfzc86sEPu1xgb8sZSfgEGSuGNK1YTb+mMx/rB3N48f3Eezf3DMelVViakRBqIRDvb28GrjYTKsNr6yaAm3VEwZmTE9hqbrHOjp5kebN5xRW98ruq4TjMd5+XAdj+7fw76ebpInFYn2RyP0RyPs7+nmpcO1XFdazoerayj1eE9ZFNky5KcvEubXu7bz9KEDI8tr+3ppHfKDDndOreZXu7bzyO4dqMOjutFkklca6mkPBPjHpSuYkZl1RiOl3izXSK/RoMjYXBZCQ5Ezug/N+1t5/Q/riEXiGIaduSPBKMlkEgB3ppPrPr6CtY9v4uWH38RkNVG9uIKldyy4ZAPtEzka7k7l1k9wH3OK0qieV8zhfW2YrQpX3TYXgMHeAOFAFJfXRkdzH/mlGXS1DeD0Wuk82kfdnhYUo4FVd8xl4apqancdpbWxh5W3zCan0Mdgb4DKmfkYzccDPF2HptoOZIOE22enpaEbT7oDxSgzf+VUDuxopn5vC4UVmXjSHVTPS9VWeDIcFCezESUBm9OCO5ogmVRx+eyUVecSCUbp7RzE4bIy1B/E3x/C5jDzwJevY+tbB1Ou7ekOsgt8I8FATeVoCW6T0cC86QUTzgzE4klaOgc51HDq+shzgabrRGJxYvEEB1u7KMn0ogMJ9dyn/k4GBx9QDLLEzKqUKVQgGEUQBFYsrKC6IvuiS5lOcukjCgKz03J47Wg9wcSFLbaaZJJjvN7UwH9v3UhbIKUglmmzMS09gzSLFZMsk9A0BqNROoMBGgf66YtEEAWBQpcbeYLZ0VsrpvDbPTvpi0RoGfKzua2VmZnZpy3oG4hG2NzWQnh4xqLC42NO9sReHo/s2cn/bNtCVE1iEEUKXW4qvD48ZjOyKBJLqvRFwrQHAxwZ6CcQj+MwGkmzWMfNHxcEgUpfGl9bvHTU8u0d7bx+pAFN17ErRm4uryTPOX6qk0EUmZN1av+RmKry5wP7+PWu7XQEA0iCQKnHQ4XHh9dsQRJFhuIxGvr7ONjbQ18kwp8P7KMnFObrS5aR53BM2OEMxGL8Ye8u3mhs4NqSMtIsVur6e9na3kYokeDZuoNE1SRPHdpPqcfL7Kxs4qrK+pZmOoJB9vd0sbG1hQqvD6ty+g64v2eIYzISyYRKJBDF5TszA83NL+2kvaGTe752C0VV+aiqyuaXjuefC4JA+exiCqbk0FLbzsHNh/nj95/Bl+th5opTG3peTFRdoyHQzgttm9B0jQLr+CPiRpPC6g+PzVYoqsweUQ6CVDDZeLAdNalhtppIJjXM5lSdZFl1LmXVo9WQXD47C1eNvj+iKLD8ppnjtqO8Jp/yEww1swt9ZBemVB1zCtPImaBWKrcotfyG+8ZeQ15JOnklp68LMRpkbrpyGjkZE6cOCkJqduFCTFKbFQNzS/MYDEXI9TpJd9oQBIEMpx1l0gRtkjNBEAScdjPL5pdd7KZMchlhlGQWZRVgEEXSzDYerJ6PRVYwXuQ0tEn+8hiIRHj5cD0dwQAAMzIy+fLCJeQ5nCkXYFFE1XUiiQT+WIzecIj6vj4O9nWzumz8+glBEMiyO7iutJzf791NOJFgZ2cHrUN+it2nVoja1dlBk39gpNbhzqlV4yp4AbQFhnh03x6iahJFklhVVMLHZswm02ZL1UQIAklNI5RIMBiN0hUKsr+7C0WWmJY+fmdNFASK3R4+fVI7f7tnJ28cSflv2BSF1eUVZzwTcjKarrO+pZn/3bGVnnAIu2Lkloop3FBWTo7dgVVJ1QREk0m6QyHWNh/h93t30xUK8mZTA9l2O19ZdAUmefyuhQ48U3uQT8yczZ1TpmFXFGr7evmPzevZ2t5Gs3+QR/fvIdfh5JtXLKfY7SGpapR7fPxg47skNY1dXR0MRqecUXBwYPNhtr26m+KaAva8c4jOpp4xvgfH1IxOltBMxJOomoZiMjDUF+D1P7xLcDA8sr6vY4C2+k6cPjvuTDfF0wsIDUUI+c9sZuJcs7H3AFv6ak+5jaqrBJJhOiJ9NAQ7EASB67Lmvu9zT51diMmiIEoigiDgTXekFN8uc0xGmQ9dP3tEfGY8ZEliyewSqkrPv4Gerut0DgZ4eWct7QOpAZOidA9XTy9HOseDvpPBwSSTTDKCLIrk2o6PklR7My9iayb5S6YrFKQrGBzpjN9TXcPi3Pwxo9Juk5lsO0Aac7NzCcRjeM2n8LIQBO6aOo3HD+wjpqrs7e7kYG8PRS73hCPeqqaxua2VzmBw+JwmbpggAAGo7+8bST+yKQp3Tp3GrMysMcf3mCHP4aRKT2dBTh6qruFQLo7ctK7rxNUk/7V5Iz3hEAZR5PrScj43Zz7p1tH1BE4jpFus5DucRJNJfrVzGzFV5fED+7itcipVaekT3kubYuTD02aQbknVVdRkZLKysJit7W2ous5gNMrfLVrKzMzsEenv+Tm55NodNPkHaR3yE04mzuiarvnIMjY8t53ffvdJDIrMFbfNp2bZVAC2vrKLZ3++hr72AeKRBD/94m9wZzhZdOMc7vzyahbdNJvW+g7+7eP/g9lmYubKKuZcfbxGMBaOseG5bRzYVE8ynsTiMHPtR5aPqme4kNQOtfBi+6ZTbqOjo+oaOjqyIHJ/4VXM9055X+cVBAGLzUT13IllVC9XBEHAZjn171EUBXIyXORcgEL0YDTOs9sOUJTm4YopRQDsPdrJs9v288CyWSgTBOXvhcngYJJJJpnkEkHTdf5v0w4e3rANSI0W201GqrMzeGDeDKZkpiOJIv5IlAcffZaWgUFEUcBpMlGdncE9s6dTnZ0xKu97c1ML337pTfyR6KhzSaLIJxfP4YF540/nX2ySmjYSGAAk1dMbLplkecJR6xPJdzpZWlDIa40N9EcibO9oY1Fu/oQqPkeH/Bzo7SY23IYbyipwGk0THj+hqsf10fVUcHGq4nhRELCdwUj4+ebtpib293YDUOB0cUNZORkTFEcfU3tanJfPm02NHOztIZiI80pDHVPT0idUgKpKS8ehGEeOaZJlch1OLLKBcDKBWZaZn5OHdEyme/jeZNhsNPkH6Y9GiJ/Bd+FL//MpDEYDiViCRCyBIIqYbaYRAYXpy6ZSNrt4tA+CIKAM1wsUVufz1z/+OIloAkEAk9WIpuqIw35GGQVp3P8PtxOPJdB1HVEUU/4iF8lLyGd0Uuk49YyRKAiYJROF1gyuSJ9GsTUL4wdUkvuDSEJVGQrHWFFdMuKMnO9z8c9PvYl6jguiP7DBQUr+SkVABC6MKckxSawLrZCSOq8GiONIg41dfqlxzEjm5Gp/Ubx4hjIXAlXVRl4qZ1MHkjLnYfh+pf59zNhHGDbhmWg/dfhFKEmX9nfi/XD8vgoj9+SyQYdwPI6m6zwwfxY2o8LR/kHeqmtk3eEmfvPAHZSledF0nUAsRkmal2umltHpD/B2fSMf//1TfP2a5dxSM2UkQEioKrGkytLSQsozjjuwi4LAzJzzPxX+Xsmy2XGZjne0frxtE9XpGVSlpSO+j89VEASsBoVbKqbyWmMDOrCh9Si3VVbhMpnGHFcfLgQ+3J9SdRGAu6ZOO6X8aYXXl6orUMEfi/Lw7u0Uu93kOZzvq+3nm1ca6kYCshy7g5qMzFO2VRAE8hxOMq02Dvb2ALDu6FG+dAqrmQKXa9QshCAIWGQDVkUhnEyQ43BgNsij5GdkURxRlIolk2i6Nua4J2MbTmuZqDj4dEaLkiSmnMcnSDeXZAmr08KlkjxzffY8rj3DFCFBEBA5HnxNMj4nypyefJ+0YZM8AWGUsd75RNU0Dnf28vTmfaOWH+0ZHDWQci74wAYHoLOh8+/Isi4hy7IQRXQgCgrCeeoopzpsETStH1k+Ozvy90sisZ94bB0W690Igmtkuaq2EI9vwWRahSCcWov7YqJqOr/49ds89dyO4UBBQ9fhltUz+cRHlmK3TzxCd7mi6zpf/9aT1NZ18vnPXslVy6ee8b7RaIK33z3EK2v2crixh2gsgd1mpLwsi3vvms+M6WNVFXRdZ2AgxDe+8zSCIPC5B1dSdUJR2QcFXdd5+Lfv8vxLu/nQnfO45475F9XT471iMyrcUFVBnjtVPLm8rIi/f/41XthXy98sXwik0mPK0rzcOzuV6vCpxXP45gtv8M+vvsWUzDSmZh0vuDMbZFaUF7OqsvTCX8x7xGM2sySvkL3d3QxEI3QGg9z55GPcUFbB3VOnUenzYZINGMSzf6bLoshUXxrT0jPY291FXV8f+3u6Kff6xhQmR5JJ9vV00Tlc+zAnO4fiU6QgQSpV6JriUp6tO4iq66xvOcq9Tz/OnVOrubGsgkybHZMsI11igcLW9jYg9d2yKUbiqkpvOHTKfWKqOkoBr2XIn+pUTXBdLuPYAEwShRGvBI/JjCSIo4IvQTi+PjWjdLZX9sFHFEQmtUbOLbqu09kTQJQE0j32kYG3aCzBjgMt7KtrJ81jZ+ncUjxOy3n/LVuNRm6YNTYN7Nb51ZMOyWeOwMKMf6YnuptG/7MIgoTPVINTKUKRXGMk7o6haSEgjiDYARFVbUaSCtC0bnQ9gSCYEEU3uh5D0wYADVGwg2AgHt9CInEQi+UuRNGJrgXR9DCgIUkZgISqdiAIBkBAFF0IwvGRMV3X0LQuQEDXk6mIVHSh62F0PQqoSFIWYEDT+tH18PD+cXQ9iqp2oGkDiGI6gmBCEBSMyiIEwTocuAyg6xqQGN7GiKb1oOtJRMGCILoQJrgv5xNRgJk1+SQSSYYCUQ43dtN8tO+Ct+NCoqoah2o7iMYSHGnqOeP9EgmVp5/bwaOPbwbA57MhSxKappFMqhiViX/SkWiCxqYe3C4LvT0BqHzfl3HJoWk6TUf7CIVj1NWdf2m5882xl01JmgezYqAvFJ5wG4fJxF8vW8DmphYe2byDf73l2gm3vRwQBIEPVU2jNTDEKw119IRCxFWVpw8d4KX6OqZnZHBDaQVzs3PItNlTo/6c+TX6LBauLCphf083mq6zprGe5YVFI7nwxzjqH2RXZ2dK9hG4uXzKaV/EgiDwtSXLGIhF2NHRjj8WoyMY4L+3bOS3e3ayJK+Aa0vKmZqWhtdswa4oF/2zCcbjdA8HAqqu8+LhWl48fOoC1/EYjEVPud5qMIw763JsmUmWx3RyL59v7aWDpmvEtAQJLZky3UNAFiWMoiEVfF1Gz4KLRSyu8vBTG+ntD/LtL9yA024mnkjyu2e38OeXdmC1GInHk7y1qZZvfv56fG7beW2PxWjg5rlnPpD4fvgABwcQVQcwiBa85umEEu30RnfSH9tHoX01Ztk37j7JZCPx+GbM5pvQ9SRDQ9/H6fxHQsHfIAhWBMGEybSSRGIfyeQREAwohumIUiax6Buoah9xKReDoZpY7C00PYyuRzAYZqAocxjyfwej6QoEwYJRWYQknzh6myQY+BmilIaabEUQrJhMV6Lpg6jJFpLJBsyW25DlcsKh34IgIonZiJKXpHqUaPRVNLUHgzITk+k6IpEXUJOt2O2fJ6l2EA7/EUnKQlO7MCgzUZTZhEN/QhCMiKITk+nak9pzYRBFkYXzS1k4PzWq+dgTm/mfX719wdtxIZFlibtum0tLWz9XLCo/4/1a2/vZuuMIkWiCj9y3iNXXzcDpMBOJxhkaiuD1jP9wEgQBp8PM7TfPRhRFysvGV0S5VKg73IUkCpQUn15u7kQkSWTl0kqsFoUrV0y9LGcNTkTVNMLxBDtbOgjH45Sle0/ZUSpJ81Lkc7OpqWXEBRhS5jl727tG1SKIgsDc/BxspouTI30mOIxGvrZ4KTMyMnm27hCH+3vpDAaJqUm2trextb2NHLud5QVFrCgsoTo9nQzrmb2grQaFedm5ZNvstAaG2NTWypGBAdIs1pF7rGoah/v7ODCch59td7AwN++UjsDH8JrN/OfVN/D4wX282nCYxoF++iNhBqNRXqiv5YX6Wso9Xq4qLuWK/AIqvL4JnY0vBAPRKOciM0HTdVRNm9Dv4HT3LrX+Mv/hXkTiWoLWcC9t4V6OhrvpjfuJq0lkQcSl2Mix+Mg1p1FozcAsGSeDhFOQVFUONXQyrTwHhy312zxwuJNX3jlAdVkWd6+eQ8PRXp54dSfvbD3MbVfPuLgNPod8oIODzvBGQMcsZ5BnW4kiOjkSeB5Nn1jpwGAoS3WytX5isXcxm28kHt+BpvVhNM0kmagjnthHMnEAi/U+ZLmA1Eh/AqNpJaragdlyM/H4dkDEavkwCCID/X+FoswGwGy+FVG0j3t+QVAwGGaAriNJaejEkKTs1GwFOvH4VgTBjK6HcDi+AQjE4/uQRB8Wy/3o2hCBwL9jsdyB0biMmP76Cce2YbV+GE0dJBD4N0BE1wMYlOkkEgdQtW4kPnipJpcq99298Kz36eoaoqc3QEa6gyuXT8XlNKdyqC1GrKdRVbDZTHzqY8vea3MvGJqm8d8/e42sTBd//5WzN+26csVUrlxxYUZXzheBaIyndu/HZlToC4XZdrSNKRnpXFlectqXebbTwb72LuKqinG4ODecSPBmXQM7W9tHtjOIEiU+zyUdHEBqJPnWyqmsKCxiXUszW9ra2N/TxeH+fobiMdoCAf6wbw8v1NexqriEWyumMj8n95RmXJAKmIvdbhbk5vHEwf2EEwleqK+lJiMTsyFVpOmPRdnS3jbiQnxVUTFey5mlDxwr2P1ozUxWFZXw7tFmtnW0sb+nm6bBARKaRl1/H/X9fTxbe5DVZRXcUjGFcq/vjAy+zjVJTeWYJ4AkCBS53FRPIKt6KgQ4pdHmeB4OY48wyXthMB7knZ69vNG5nYOBFhJacsw2kiCSb0lnsa+a67PnkWnyTAYIE6BpOsFwjNwsF4IAiaTK25vrSSRVPnHnIqrKsphamsXGnY3UNnZd7OaeUz7QwYEgiBTYr0MSjhcdHas/mHgfE7JcQjJxkHh8Oy7X94jFNqTSfAQTinE2oughmTjA2IeYALo68m8dfWQq+sTjTxQYHNvvWEoQGFCTLST0/RgMVeh6aMQ1Uz/p3KLoTqUEiSZg/OBHEt2ABIIJfThA0nUVQTBjNC5EksZ3AJzk0iEciRONJsjOcmEwSB/Ih3p7xyD1DV24nBPLUX7Qiakqh7p6sBkVbEaF1dUVLC4uINtpP+1nfqxI7kRsRoU7ZlSzoOi4mokgCKTbz+80+LnEZTJzQ2kFKwtLqO/v40BPN9s72tnQepTOYBB/LMoTB/dzsLeHLy9YzIrC00sr+swW5mXn8mZTI/2RCGsa6/nUzDnkD5uIdYdCrGtpBlKzGFfkF2IznJ2qkCSI5Dtd3FPt5JqSUmr7etnf082WtlY2tbUSSsRpCwzxyJ6dtAWG+PzcBVR4x5/ZPp84jtUC6DqSKDI7K4evLrriPR1L+gA+ly51omqclzu28OejaxlMBMkyeSixZeNWbBhEA6quMpQIczTczdFQF48efZOWSA9fqrgdh+FSKau+tBAEkCRpJFhv6RjgQEMH0ytzKcr1jgiAOB1mhkKxi9zac8sHODjQCSZaRwUGAGZ5fDe9EzEaFzPk/w6yXIwgOFCU2STie0km9iGKPiRjHrJhCuHw4wiCEYOhCqNxOaLoIxJ/FiFsR1FS8oDh0O/Q9TBW60eGj352D01BUNCSvSQ4AHoCBBOSlIcgmAgM/WdqVkHyjjmuqnYSiTxLMrEfQbQjSZmjthEEGUWZRTJRl7ouKQtZPvP0lkkuPLquk0iqqKqWUhu62A06T2zZdoRk8vRqJB9k3GYzn1o0hxyXA0WSsJuMyGdQeKsDzf2DZNito/LiZVEkx+VgSubZpWldagiCgMVgoCYjk6q0dFYWFtMw0M9z9Yd4+tAB4qrKwd4efr1rOzMysiaUJj2GJIrMyMyi0utjQ2sLPeEwrzYe5pMzZ5PUNPZ0d3LUPwikTNhK3J7TzkhMhCgI+CxWfBYrc7JyuLq4lMP9/Tx2YA9vHGkkmkzyZlMjlV4f+Q7nyOzFhcJlMmFVFPzRKAlVJRCPYVWUc17o+EFF13VaQoO82LKfSmcG0zxZBBNxCu2nNtc7V9QGWnizaxfBZIQ78payNG0aXqMTs6QgCSKarhPTEgzGQ+waPMwfm95gXc9epjkLuT1v6elP8BeIJIpk+uwcbOiktXOAtzbX0dkT4P6b52EaNkbTdZ1gOI7NcvGliM8lH+DgACRBIZLsOaOA4ERE0Yfd8RUEwQZIiGIGVtsn0PU4gmBAFF2YTNegaYOAjihaAQGDoQK748sIghlRdGIx35YqJkZDktIBCYfj705xZgNW26cRBBuyXDJcHCxgNK0AXQNBGm6PG6v1YyMFyYJgwmCYiSDYEQQrdsc/IIoerJZ70ImP1EqktrEhSRYcjn9AlDKw2j42XGitIIrnRtFoKBBlw6Z6du5upr1jkGgsic1iJC3dQVVlNgvmlZCRfmYW9qcjGk2wc3cz23Y20dTcRyAQQZYl0nx2Zs0o4IpF5Xg844+KRKIJdu85yrqN9Rxt6SMSTWC3GnF7bFSUZTJ3ViEF+b4x0qC6rtPeMchrb+znUF0nA/4QBlnC5bJQmO9j9swCqqfmIsujO3P1DV387tENtLYOjCxTjDJ33DKHqyZIg0kmVf7312/T2eWnfyBEd28A/1CEg7UdfOlrjyEPv7hlWWTxwjI+ct/iUftv3trAw79bRyJ+XBc8O9vFPXfOp2pKzmnvbyAYZfOWBrbtbKK9Y5BIJIHVqpDmczC1Mpv5c4vJznKNujepz/8we/a10NY+QDSaxGJRyMt1s2RROXNnFY26p4lEkh27jrJ5WyNNTb0cOdqLqmrs2N3Mxz/z8Kj2zJlVyH13L8TpON7pGwpEePq57ax9t27UtqtWVvGhO+adUiZW13X6B0K88tpe9u1vY9AfxmiUKS1OZ9XKKkpLMpCk0Z3BrduP8H+/W8ctN85kyaJy1m+sZ/3Gerp6AghAXq6HZUsqmD2r8JQF4qdCEgXSbDYyHaeaZRzLvvYuWgb8XF9V8YGcVToRWRTJsNlIs1qp9PmQBZE/7tuNpuscGRhgf283S/IKTnucQqeLGZnZ7OzsIJJM8ucDe/lYzUwiiQSvH2lE1XVEQWBhbj6ZtnMz02KUZQpdbvKdLsq9XtDhjaZGwokEe3u66AmHyHe6zsm5zhRJEJiVkcVbzUfQSZnQtQz5KTmNc/Qkx/l17SZWZpexq68Nr8lKW8h/wYKDw4F2OiJ9LPJN5c68paQZXeM+AzJMbvKtaQSSER5rfpO3undPBgcToCgyqxZX8tPfv8Ohxi6GglHm1xRSWZyJKAqpAbuESmvHAFfMKbnYzT2nfKCDg4HYIdZ3fhVJMAECxY6bKbBffdr9BEFElk9MsRGGO/cnbmMcDgpOxDhqP0FSAPeoLU5V8CsIJ57nxJSKsZ12SfIC3nGOIo1IqUryRJ0/aWRdSkXp3KDrOgcOtfPLh9dSW99JPKGOyJIKQioP9VBtB5mZznMSHCQSKj/4z5fZvLWRWCw5om8PKY+ETVsb2Lm7mc98cgUZ6Y6RB6WuQzAY5YlntvHUs9uJROPD+x5v57oNdezY1cw/fPVGrNYTFaVgx65mvveDFwgEoyRVDU3TR3SON29t5NkXdvKrn32M9LTRHTuT0YDXY6OvP4TfH6a9YxDFIOMfikx4jZqus21HE8FQDE3TicUS6DqoSY2hoehIx9dgkIhExqaTWa0m0nx2evuCDAyE6O4ZQtP0cbc9EV3Xqavv4lePvMP+A23E4skxn+WBQ+14vdZRwUEkkuDBLzzC4GCYxEmf/74Drbzx1kHuuHUOH7t/ycjnEYsl2b6rmfUb61FVjUg4nvp84yqD/tHqPOFIHP0kHUNJFHE6rTgcZgb9YTq7/ESjCXr7g6e9xh27mvnBj15mYDBMMqmOfJb7D7Tz2psH+Oj9S7jxuhpk+fjoaSgU40hzL4fqOtm0pYEt248QCcdHTGhq6zvZsq2Re+9awE2rZ77nAOFMOfad7woE+dGb69F0nfvnzziv57yUEAUBj9nCLRVTeGz/XlRdI66p9IZOLcF5DIMksTg3n1cO19M42E/jwAC7ujrIsNrY2HIUSAUQVWkZGKVz+1mKgkC23cH1peW80dQIgD8aI5w4MwdgYHhkXwBShcDBePw9t+fqklLeaj4CQNPgIFvaWk8r2zrJccLJONXuLLb1tpDUNOLj5PyfLwLJMBE1xmxPBS7l1OmHZsnI0rRp/Ln5LTqj/ResjZcbsiSyckEFiYTK5j1NFOX6uHFFNV5XStFM13XqmroxyBLTyj9Y9Zof4OBAYFHmv6Lrx0dMBeEDfLkXGV3XOdLUy8OPvMvufS2kpzm46YYZzJ5ZiMNuorcvxMHaduKxJIV54wU1Z48si1SWZRKPJZk/t5iaaXmk+Rz4/WFee3M/Tz67nbXralkwv4Qrl03FYDjWwdNpbunj6ee2Y7eb+PTHl7FoQSlWi5HevgCHG3vYsq2RosI0LCdNFeq6zo9+sobBoQgrllZy74cWkJnuJBKJ09zSx8YtDUQiCbyesa6iuTluPv+ZK0FPpX888Ilf0D8wVpryRAyyxC9/9rFjdYK8ufYgP//VWxTmefi7h27A6zkegIyX7VA1JZtv//0tAHT3BPjcF3972vuq6zotrf385vfr2LbjCD6vnbvvnM/c2YU4HWb6+sPU1nUQCEYpKRodNJtMMnNmFBCJJll2RQVTyrMwWxSOHu3l/363nh27m3n08U1cuWIqBcPfA6vVyIOfWManh4ul//vnr/HCy7uZN6eIb3/j1lHHTwUmo++rxaJw0/UzuPG6GgD+5Ycv8tY7h057nYcbu/nOvzxHMBRj/pxiHrh3EQX5XoYCEV55bR/PPL+Dn/z8dVxOM8uvqBzzeb7w8i4kSeS2m2Zz/TXT8XltNB/t49HHN/P2u4d47a39VFflMKXi/Lw0VF2nvqeP323dRVPfIGvrjxCMxfjnG1dR6HGf/gCXOKqmoZPqQJ9OolTXdfqjEY79UGRBxGY880Lr2VnZlLg9NPkH0HSdFw/XMdWXRjCR6mhXpWdQ7vGecSc5qQ2bDZ7Gx0DXdTRdZyB6XP7TbJDPKpXHa7akVLl0iKpJmgcHRylVnQ03lVXys22baRkaoi8S5tm6gyPGc6f6DI5dh87pr/mDTI7VxX/vf5e6oW4iaoKVWWUX7NxG0YAsSpgkZUzN0UTbg4BV/OD5CJ0rBEHAbjVy+zUzue3qGSODgCd+v+dOL+B3P/zIRREROJ98oHvLfdG99ER3kVTDCIJIhmU+6eZZF7wdp3JOTmoqgUQMp2JOffEu00zyRFJly7ZGdu9roSDfxz989UaKi9JGrjk7y8306nNrDicIAnfeNo87b5s3SrLSbHZy682zGRgM89Rz26mt62TJwvKR4EDXdQKBCEOBKPPmFrNgXgked6ozn5vjITfHw/IrKsY9Zzgco6c3gKLIrL6uhqICXyoP2qLg9dqYNWPiNAZBEEYX6p2h4oksHd9upGMsCEiiiCyfXpFFGt5fks6wY5NMpfRs3dFEXq6Hv/vyDVSWZ42cOzvLzbSq8WelBEHky39z3ZhLmzolh7/9wtX81d/+Hr8/zJ49LSPBwch9Gb6UYw9ZQRBOe33HtkvtcuaOn6qm8ds/biAQjDKtOo9vf+NmDIbU49BsVvjIfYuRZZHfP7aJn/7vmyyYW4L5JDfVRELlgXsXccctczGZUvmn5WWZ3H/PIto7BjnS3EtPb4DK8uMdNV3XiYbjxGMJDIqMxXbSi1kAq6LgtVqQTpEOJQoCTpORI739/N/GQbxWCyvLi/jQ7OkU+zyjXlSKJOOxWkaUiy4X1jQeZldnB8sKiij1eDHJMgZRRBKP19touk5cU+kIBPjJ1k2ow89ap8nEFO+Zp5MaZZmrikvY1tHGQDTCuuYm+sOpwN2mKNSkZ5J+FilF/7l5AwDXlZaRZbOjSBKyKCIOm3vppHTo46pKbV8fD+/aDqQ+1yKXB5/lzAtEq9MyMEoSSU1jKBbjpYY6luQXkOdwjsiG6uiowx14RZQwjBN8CIKAyWDgywuW8HdvriGaTLK5rZXvvvsWfz13IVVpaRgleeS7paOjajoJTaMnHGLd0SYaBvr5xhUrsFzgeolLhQcrF3FosIueaJAyZzp5VtcFO3e22YtHcdAS6iappaRLTxXM1Qfa0NGZ5j594f5fMie/X8asA8QzfLdeTlxeb4uzQufQwO8octxEd3I7NjkbVb841eQNgV6K7b5xu/0NgR7+Ze+r/Hj+h7AZLm1JwVPR3x/iYG0Hqqpz0/UzyM25MFPRE53CZjWSleXEaJQJBqNomnbCPiJul5U0n40DB9vZuKWB2TMKcLutmE2GU7bbajVRmO/jcGM3z7+8C6vVSJrXjs1mHJV6cjkz6A+z/0AbqqpxzVXTKCocW3cxEaf6yLMzXaT5bPj9YfyBU8+YnG86O/3U1XeiaTp33jpnJDA4katXVvH8S7vp7PKzZfsRli0ZHTD6fHbmzykeCQyO4XCYKCjwUVvfSTCYSgc7Fpglkyrr1+xl3ct7KKnK4f6/uWbUvqIg8PGFs/n4wtmnbL/TbOKPH/vQGV3rvMJcflt4xxlteykxGI3w5KH9/GLnNnIdTmoyMilxucmw2TDLBjRdpz8Sobavh7ebm+iLpL5TDsXI9aXl5DjOLnVxZWERv929g4FohCb/IP5h+dIil5tZWdlnNWzTHhziudpD/GzbZko9XmoyMil0ufGZU0FaXE3SHQqxt7uLtc1HiKkqAlDi9rAkLx/HWcx6ZNntXFlUwvN1h9CBnZ0d/M2rL3JlUQkFTheyKBJKxOmPRAjG41xbWsaszPFns0RBYFVxKQ0D/fxuzy4GY1G2trfxqReeYVp6OlN96bjMZgQgmIjTFQxyeKCfpsEBoskk09Mz0M6FWcJlyiutB7m1MOVY3hoa5NBgF9WerAty7mpnEdNcRazr3cccTzml9hwsJ/kY6LpOVIvTHR3kmbZ1eBQ7N+csuiDtm+Ty4gMcHIBR8uA1TWMofgRFdKJqFz44iKlJvrztcf687NPj5qvaDSbm+QowiNJlO2sAqcLVrm4/NpuR/HwvRuOFGznyD0Xo7QsQDEaJxZIkkxqqpnG0pX84L5CRtBxIdWCzs13cdvMcXnhpF//zy7coLUlnzqwiKsuzyM1xkZHuHFOIemzfT35sKQ8/8i4bNzewd38b82YXUTMtj6LCNHKz3RiN8mU9rR6OxGnv9GO1KuTneTCbzk6FQdN0+geC9PWHCIVixOOpehBN00dUiLSLLEbU0tpPPJ7KB55SMf7LOy3NgdttoavbT21955jgIDfbjXkcbwlZkrAMBwwn1sEAGAwyy26YgWKUaa4frYvddqSH7vYBEASsNhPFU7NBh/bmXsLBKPFo6j6WTcvF5jCTTKh0tfbT1z1ENBzHaDJQPj0PQYCDu44yc1EZalKlp8OPjk5Wnpfu9gE6jvahqTqKUaa8Jg+DQSYWTdBxtI+h/hDhYBSrw0zljHxkg8Rgb4CWhh50dHyZTjJyPRckEDbJhpH0mtYhP61D/tPuk+twcG1xGR+tOfsZYp/FylXFpdQP9BNXVXojYQyiSKXXR+VZSos6FCOiIKRSv4a9DE6FLIpUeH3cV13Dwtyzl5T+m3kLaQ8E2NXVQVLTONTXy6G+3jHbWQ0GpqVnTBgcAJgNBj4+YzYWg8LTh/bT5B8kpibZ1tHOto72CfcziCJpVusHLr3iTIgkExwNDvBGex0VznR0oM6fMs67UMGBIsrM81RycKiZHx56nBUZMyixZmGWjYiCiKZrRNU4reEe1vbsoSnUyQ3ZCwgmI2zvrxv3mNNdxRjED3Q38ZQkEiobdjae0bZel5XqD1DdwQf4UxfIti5CEoyYJDdRtR+fcm7TWk6Fpmts6jnC3sE2WkID/LZhE7Ig4jPZWJ07jYSusq23mcOBHuwG00hgkNBU6oe66Y4G6I+FKLJ5GUpE6Yj4WZZRTqbZQUJT2dnfQkOgB4MoMc2dQ4k9DYN48Uau1aRGNJbEYlYwXKAR9Fg8ye49R9m8tZHDjd309wdheApQEAQCgVSwMB4Ou5kbr6shP9fD1u1HOFTXwR/+tBGLWaF6ai7z5xazaH4JnnEch+fMLMTjsrJh82H2H2xn09ZG3lp7iOKiNObMKmT5FZXk512+xjJqUiMaTWA2KShnWUwbCsV4Z30tu/a00NzSRygUJeXdkfpcOrv858SF9f0SjSVGRjhttolHaS1mIwICoXE0rM0mA/I4AeSJnM2ldhztp/lwJ7qm090+wPXGBaRluXj18S0kYkmyC3wcqeugp2OQVbfPYbAvwKuPb8Gd5mDvlkYcbgu5xenE4wl+/f0X+Mlzf0s8lmTn+jp0HbLu8dLb4aeptgNN1Wk82I4ki1TOKKCloYv1r+7D6bGyYc0+SqtzKa3KQdN0Xn18C0azkVgklX+/5Nrp5BafnQLce6EmI5P7qmewp7uT1iE//dEIgVicmJpE1TREQcAky7hMZjJtNsrcXpbkF3BlUQmm95hCdfNwUXNXKFXM7jabWZxXcNayoteXliOJIg39/bQHAwxGowTjceJqEk3XkUURs2zAbTaTY3cwxZfGysJi5mTnvCfp0CKXm+8sv5LHD+xjV1cHrUND+GNRkpqGQRQxyjJuk5lch4N066lTlgRSsqYPTJ/BtPR03m4+wv6eblr8fgZjUaLJJKquY5QkbAYFn8VCjt1BmdfLotz8v0jp04Sm0hDoZSAWYUdf60hd2bQLFBgAbOmv5ZWOrYSTMQbiAX7X9BoiIjaDCUmQUHWVSDJOQk+9ExVBZltfLet79qHq2rjPql/N+xJu5ewU0z5IhKNxvvuzV8YsPyYrnhKwEHA7zCyfXz4ZHFwu2JUCWoJriGl+klqYC+u8KGCUZDQtNWxtlRVkQcQkGYbXptYPxMI817Kbm/NqMEoyCU1lXfdhDgx2kGay8WbHISqdmRwO9DAUj/LxssVs6jnCi217KXdk0BsNsqu/lQ+XzKPCkXnRRm1EScBgkBgcTKCqF2ZYeMu2Rh75/XraOga5YnEZK5ZW4nSaMSoysiyxcXMDL6/ZO+H+NpuJxQvLmF6dx9HWPo4097JnbwsbNjewZ18LXd1+7v3QgjEj54IgUFKcTkG+l44uP0eaejlwqJ0Nmw7z6OObOVTbwVf+9jpcrjNzUr3UECUBxSARiydJJtTT73ACf356K88+vwNJElm5bApFhWnYrEYMiowsifzsF2/S3HLqUdQLgclkGPmthEJxjMr4nb9IJI6OPqYwHc6stuGs2mQxIEoiggQDvUGa6jtJy3IRiyaYOquQpdfXsGtjPWse38qq2+cQHIrS2+nn3r9ehUGRkGUJi81Ion9ipRvFZECSJSQZgoEIh3YdpXJGAb0dfkRR4Pp7FjLYG6S0OgeDUabtSC9vPL2DZatnEI8n6TzaR/n0vAsSHBS7PXxq1hy6gkE6QgH8Ix1sdVheFIySjMNoJN1qo8DpxK4Y39fnku904TaZ6AoFEYBcu5NF72Ekf15OHtMzsmgbGqIrHGQoGiOUiJPQVDQdZDH1/HeZzGRabeTY7BBTR3Wsd719gOySDNJyTz/QIAgClV4fX1qwmIaBPjqCQQKxGElNQx4ODpxGI16LhULnmRWrWwwGFucVUJORSevQEO3BAEOxGLFkKsAxSCJWg4LbbCbLaifTZkORxhozioLAqqISsm2pTuac7JwxXhFlHi8PLVxCOJEgx+7AelIw5jKZua96OkvzC5FFkVz7uZHCPlc4FBMrssqIqkmWZqYkLY2SjFW+cNr3fbEhOiJ9mESFLNP4oh82abTvh6pryIKELIwf0F3O2QznArPJwJc+tnLMcl2HeCJJe9cg72w7TJrHzjVLplyEFp4/PsDBgU7d4GNkW6/AIqSiOZN0blRyzgRREJjtLcAqG/nN4Q3cXjATo3T8gWcQJGZ7C5AFidc6DozaV9N10k12rs6eyq/q1zHLm0++zcMrbftJaEn+1LSNlVkVXJ9TTVRN8v19r7C55wiFVi/mC/gwOhGrxUiaz05jYzftHYNMq8oZN4/7XBGOxNmw6TCNTT1cc1U1H7lvMelpdsRjBXi6TmNTD+oZ5K/Y7SaqpuQwpSKL+XOKmTOrkH/70Sts2NzA4gVlVE6QdiLLEnk5HnKz3cysyWf+nCJ+9OM1bNnWyLadTRN6F1zqmE0KGekOaus7ae8cJBZPnpEc58BAiOde2Ek4HOeB+xZz0/UzsNtNI50F7Zim6Rmin9W4+9mRn+sZmRU5VNvBogWlY7bp6Q0wMBhC16Gs+NxJ/o7HYG+Apx9+l5seWIxilOk42jcSmJnMCm6fDdkgYXdYiA7L0NqdFpweGw//4EV0HZbdUINiMoy6x5qmk0yoSLJE0B/mtSe3Mm1+CZm5bo4e7h7xv8jI9bBjXR2/+feXiIbi5JdmIIkiQX8Yq8PMrCtSBomSJJKVf+Geo4okked0kuc8Nx4sp+Nwfx89w4XIRllmcV4+Pst7c+o2yTIlHg8lnlPr3OuaTndLL4e2NbLs9vkjy7OL07GdhUu4IAip4umMLGrO4dfVphip9KVR6XtvAeExo7kZmROPomfbHdxaOfHz0qYoLC0oek/nv1CYJJlDg100B1LSoIIAc335LM68MAW/K9JrmOY8t/fIJp/aRPBs0XWdrsEgT23cR31HL7quM60gk7uWTMduvvRUkxSDzPXLqsZdlzI/i1EzJZdf/XkDXb0Bqsou3EzR+eYDHByArqtkWhYgDMugHPvvpY4sSrgUMy7FjNdow2Ewo+oaoUScuKaysaeRg/4OHq5PKWL0xoKYRAMJTeXc/pTPHK/XRmV5Fpu2NPDSq3uYXp1L3hmMeL1XYtEEgWAUTdMpKkzD67WNBAaQ6tjVH+4iMc7Itz4suXfyLIsoivi8NubPLcFkNhCNxsd4EGi6PkbSTxAE7DYT06vz8PnstLT109U9dE6v90LiclmYOiWHdzfU8/qbB5hZk09JUfppP8uhQJRgKIYkiZSWpONwjP427tnbQl9/8LRpRcdUgXp7g2jDBlTnmox0J1VTcujpDfDEM9uYM7sIxXB89EzX4fW3DuD//+ydd4AV5bn/PzNzet/eewd26b2DiGLX2GOqiYnm3hRTfjfJzU29KTe9aGKaSTSxd1QUpUiRzsKy7C7L9t5PrzPz++MsB5ZdYEFAJPv5Q5c5M/O+09/nfZ7n+zj9OBwmZs3MPe99OBHXkI+hfg9l03PweYN0tgxQVB4NgxRFYWyjSgCz1UBReTQEyOowIYoCVrsR16AXnyeIzxPg6OEOisuz8HqCDPV7yC9JIzHdQXtjL0kn1KiIS7QyZU4+6bmJOOLNIEBadgKKLGO1m8jMTyIYCI87Of2DyMt1NbhD0RAys1bHNUVnV0xu95sHOLC5Bq/Lx3WfvoKtL+1Cp9cx0D2EwWxgyoJiAr4g1e/WE/QHuemB1QR9IV546A16WvsZ7HYyb810elr6ePUvG7jxgdUUz8wnFAjy5E9fwevyk1OWQfGMPPa+VcVQrwtnr4s1965g0ryiD6Sn8nLi+pzJqMOSsrXOHgLyxatzkGRwkGRwXLT2zgV/KMwT7+xn06EGFpXlYjboSbSZEYWRY7N+t5fDrT3MLMjEeBHzF88GQRCwmPRUlGQQZzex40ATK+YXv9/dOm9c1sZBRPWzt/cnmLXRj2yycSaJhvL3uVdnRoDYwyIdi6FHGJajUzBLOr477XpKbMenhwwaLWbN+6d2pNdpmD0zjx27Gzhc08H//OAFbrx2BlPLMzEa9Qw5fTS39NPQ2MPC+UWUTz6e/6GeZrSooo76XRAELFYDdpsRQRBYv6GaObPyycmKR1ZUjjb08OQzO9mx6+iYScXBYJg31h+itr6LBfOKKClKJc5hIhyO0No2yNPP78bjDpCR5iArc+Ss3959TTz7wh7mzyukfFImaWl2NJJIb5+btzYe5mhjD6rKiOM70zGijv37e/3Qn7bNMX4/1p5OKzFjWg7lUzKpPNDK9370MjdcO53pFdmYzXqGnH5aWvupb+hm9ow8Zk7PBSAl2YbBoMXnC7HuzSqKC6Pn1esNsmX7ER5/8l3CoTN/LIuLUhGAlrZ+nn5uF2tWV2DQa3G7AyiqgsNhjsX6j32M6gn/U0cZI1F5V5F77lzAvgMtHKhq5Qc/fpl77pxPdlYCbneAdW8d4rkXd+Pzh7j/08uxniw5eo74fUF+881naWvsxe8N4XMHueau+WTkJpJbksq37v0LZpuBnKIUxgyDPGGRIiu0N/Wyc+NhJElk+sJibvr4Yix2I6tvm8tX736Y1Mz4mAGQmGonrySN3/7Pc+gMWvJK00CInkNZlqk72MqODYcBlUVXT+WGjyzEnmDhzs+t4qHvvEA4FKFgUgY3fnQRaTkXz3twsej3+3iz8SghWUZAYE5GBkXx4z/Ovo5BWmo7WH77fERJ4Kmfr8VkMTL/uhn0vTZIRmEKh3fUY7IZueqjS0AQ+OePXuTzv/0E86+bQXtdJ1d/fBlanYbEjDj2b6xGjiigqux9qwqzzcQNn72SXesq2ftWFYPdTq78yBLksMzLj7zFpHkXT1N/grGZ5EgFiE0+dfrev0miM73/T+RiGZXBcIR9jR3MLsric9csjI1tTszdUlSVhq4BntteRVlWyiVrHBxDEoVoMVP3qYuZfhC5jI0DgVzrGhQ1PPwvCb3ouOi90IgiCAIhRUY3nPX/Xh5Eg6QlzWSnN+BhQXI+AkJ0Nlt4f6MDBUGgtDiVj394Eb//0waaWvr41UNvjlonJdlGRXlWbJmqqmzcXMObG6rxeoO4PQH6+6PJgG+8dYi9lS1YLXrMZj2pKXYeuG9lNKdgOKb9UHUHR49284nP/BmzWUc4pBCRFQryk7jlxtm8s612VF9VFZxuP+vWV/H6GwdHBa8IgkBCgoVbb55DWurIcAZZVtl/sIWdexrHHFyLosCH75jP5EkZI65zb5+bV18/wKHD7Xi8QTzeYDQ5V1H5+z+38uq6A1gseixmPTddP4NpFdnvWRHmyNFunntxDz29bjzeAG53gCGnD7c7wI9//irxcWbM5mibn7l3GWmpjliBl4K8JD7+4YX84U8bqTvazW9//9aoY01MsFBcmBpbptdr+PiHF/HwnzawaUstW7YfQa/TEApHUFW4ZvVUzGY9Tz6747T9XjS/iCmTM6mqbucPf97IH/68MdbmmivLufdjS7APh1uEIzLvbKlj/YZq3N4AHk+Q7h4nsqzw2hsH2LOvCYtFj8mkp2JKBjddNzNW8To3J4HvfvNGvvu/L7J1+xHe2XZcsUMQBLQaiXs/uoTVV5y/CQWDUceXf3rH8UzlmIY2PPCdm6M357Cm9rHlH/vymtjfxRVZfPuRjxEORdi/rZ7SaTl85ad3Eg7J/OobTzPY78HqMHHXA1dw5/0rj+8r+j9u/8wKbvvM8mONRyUpnX4O721mxY0zWXjlFELBCN/8xJ+4+va5mCx65q2cxNwVZdE+n9DfywlZUXjy0EE63NHBnE6SuKd8+ll5rZx9Lo7saaR62xEMZj1JGQn43H4ciVaMZgNmu5mhvqPEpzmwxluwJVjpaOhGlER0ei0anQb9sNdMlEQkrYQgRG+J9qM9ZBQkY0uwoDVocQ96SMqMxxpnRhAF3EPjqwY9wYXl94e3xWpK9AU8TE+4eCIoMFyMDoWIouCTA7jDPiLq6UNrJUEkx3zucWiqqhKRlRFFCzWSGC1eOPz8RGSZiKziC4Zx+QJYDfpomKkK2hMmeiKygi8YYu/RdvyhMIFQGH8oOobTazUxr/0x739EVmIy5aIoxvZ1bJ2wrAxvE1332G9ajXTGwoon9ul0v3f2ujh8tJMFMy6vehGXsXEAWtHKUeezhBUvicZpxBsufgx4msFOot7CC62VLEjMjxa5sSYSlMO4w0H6g14iikJ3wI1fDscMiFOhFSVuy53JC637iNMbSdRbaPT0MTU+k0xT/PtuIMyakcuPv38rGzfXsHtvE53dTsLhCFarkfQ0B3Nn5zOpdGRGf1ePk/qjPbF/6/VakpOiswWBQJhAIExvnyc6cywfLyg3c3ou//P163n51Upqj3Qy5PRhtxmZWpHN1avKkSSR1vZ+bDYjwgmhEHq9liuWT0IURaqq2+nuduL3h9BoJBISzEyZlMmqFZPJGSO2emp5Fp+//0r27muitX0Al8uPqka17Qvyk1m5bBIVUzJHqdgEAmFa2gZobj1eqj4x4bgKhNsTwO2JVkkdGPCeUivcYNCSlGglzmFCPINSjtsd4GhjL07n8RmNpMTjiXyDQz4Gh6Lx1V5vaMS2giAwtTybH3z7FjZtqWXXnkY6OocIDV/L1BQ7c2bmMa0ie8Q2N10/g9ycRF5+rZK2tn5CoQhpaXGsWFrGogVF1B3pYuM7NZjHkAA9hlYr8aPvfohnX9zDzj2NDPR7kDQiSYlWJk/KQKc//oyoikp3r4ujjb2xZVaLEaslGtLk84fw+UOAG4fdSDhyYsV0gSmTMnjkdx/n5Vf3sWdfMwODXowGLUWFKVy1qpyykrRRRpreoCU5yYrDYRrTMyWKAlarkeQkG8aTaiCcXF1z5G8wdqGdsbaXUWQFRVZwDnjp7RxCb9Sh00X7KojC2MmEYxRaVNWodyUcjOAc8NLe3Et8khVRPN7XyyVcRVVVAnIEcVhBS1FVgnKE7W2tPF1dhScUfQ6W5+YxPzPrDHsbiSPJTv7UHMoXlpCWl4xWr+Fv33mG4ZMOAsSnOAj5QvR3DjLQNUROWQaCAFq9Fq/Th2fIi96kJxKKEPSH8Ln8BLwBckrTaTrURt+0AUL+EI5EW/SdJlxcmY0JTs+UuDRc4eh7fElqAZPiUs+wxflDVVXcET9vdO1mbccOmr1d48rasmlNvLj4e+fcZlPPEH9Zv5N9DR2EwhHyUuP50IIKFk/KwzCc1/Xstipe3HmIniEPAx4fj2/ax3PbqwB48MYlXDm9GK0k8qc3d7LpUANN3YNEZJm7fvavmIH+6BduIzvRgapGc6mq27p5bMNeDrZ0gQqTc1K594o5FGUkgqoy4PHzf89vwmLQkWw3s25vHd5gmMxEG59ZPZ+ZhWMX8jyRQDDCI09sGfvYgf4hLzsPNGEx6ZlTkXtO5/BSRVDPxvd0ieByubDb7TidTmynKHSjqgo7uv+HaYlfQiua6fBuRhBEMi2jM88vNFu663myaTfeSIgZ8Vl8rmw5VYPtPN28l2ZPPyFFRi9qsGuN3FMwlw6/k4iqMDshh9faD3FFWimucIAXWyv5ZsUagnKYV9oOsrGrjoAcJsccz0cK55FjTrhsPuITTDDBqVFVld6OIV76+1Z83gCSJDJ/1RTK5+SjPUv5WUVRaahu560X9hIMhJA0EqtunkXhlIwReTyXA6qq8tfKvRgkDVpJwh0McrC3m80tTQz4o0Z0rsPBo9fdTI5jfKo+J3JwSw0H3qkhFAhTOiufzqZe5l49jcpNh8kuyyDoC6LICjW7jhIKhLj6EytIy0tioHOIZ371KhkFqUxfOZnm6nbeXbsPW4KFSXMLmbmqgpcefgOP009WcRr5Fdm01nYwbdkkBEHg2V+/xse+fevE+/99RFVVNnTWs6OnCX8kTJYljpUZxeRbL04Inifi52+N63ilfQdBJYxNa0Ivas/o/bJoTPxxzpfOqc2OARdf+OOL6LQabllQjsWg493aFqpauvjkFXNYUVGARpLoGnQz6PXj9Pr5/pNvMbckmw8tjBaLS42zYjcZEIDm3kEG3H6e2lJJ56Cbz149H7s5GtKZnxKPTqMBomFH337iDVIcVlZWFBKRFV7cWY3LF+DXn76BFLuFAY+f7z65noNNncwuymLRpFwE4Ml3Kul1eXnqqx/GZjp9uKjbG+CTX3/8lL9LoojNomfVwjJuXFWB5hKX8R3P2PkYl7VxsLv3hxTb70AUdPQHDqKTrKSbF1/k3k4wwQQTTHApoKoq8//6B7q9o8NwBKA4IZGvzl/M8ty8iYH2ZYCiqmxqaSTRZKY86cIqjqmqyue2P8vXp67CptOzrbsJTyTILblTL2i7x9jZX8PvjrxIV2CAWfElzI4vIUFvO2M0gkaQmBl/bom0D7+2nSe3VPKnz32IwrRoocCeIQ+/fmULiqLyn9ctIjXuuIe8z+Xlvt89yxXTivjs1fPH3Kc3EOK3a7fS0jfEd+9aTYJ1pGJXOCLzyBs7eOdQI//38WvISnREj7+uha/97VU+vXoudyyexoDHz7efeJNBt4//+tByJmdHvTjr9x/h64+9xq8/dSPzSk4vUxyJyByq7zrl73qdhvRkO7bzlJN2oTkb4+AyDisSSDctpMe/F0EQ0AgGrNrc97tTE0wwwQQTvI9k2uz4wxF8kTCqqmLUakmzWJmaksoNxWXMy8yaMAwuE2RV4UfvbmZeetYFNw4AzBodNUPdGDRa2n1OApEwO3ubKbEnY9ddWC3BZm83vUEnUx0F/GfxTSTrHRf8Pj7Q1EFmgp3c5OPCHYk2E0Vpiby+r45Br3+EcXA+kBWVPfVtePxBXttTw7HAOrc/SDAiU9PeO2L99HjbiP5lJNgREBhw+87YlkYjMbX0zOFHlyOXrXEgCJBuXoI30omiRgsZScKlnfU+wQQTTDDBheWr8xfT5/MSkGVUVcWg0ZBgMlEQF0+C0fS+FZK8nGlxDtHj81KakIRF995r8ezv7gSgIvn0hT8lQeBLsxeSZDp9VejzRakjhVbvEHpJg6womDQ6mt2DZFviudCVOrxygKAcYm5CGXFay0UxcD2BEOaTioSKoohOqyEckYnIZ1dEc3youP1BVAF6XSMH+NfMKmNK9kgjUK/VYDyhyKU0nH84nhpI/85clsaBP9KHQYrHG4m+QERBx1CwDknQY9JevAShCSaYYIIJLh0EQWBOxsVVkJkAdne1c7i/l0yr7bwYB8/VHiLdamNKUjLiKar7QlQSfHX+xZN47fQ68UaOizvMTcphcVoBVu2FlxnXCRokUcKk0V00z1ecxUTXoBtOSH0+pjhk0GmGcwTOntP2XxCIs5gwR2Q+feXc2GD/GLqTiq8eUxGc4Oy4LI2DVs96Cmw3s6vne9h10Vg6f6SHNNO897lnE0wwwQQTTPDvgz8Spm6gjy6P+5QqbGfDUMDPgd5ubHr9Bayhfm4owB0F07EMGwN2nZF4/blV2D5bkg1xOLRmmj09hJNktGfINTgfzCvO5nevbqOqpZtpeVEVwq5BF1UtXRSlJY7KFxgPgiBg1usIhWX8wRCctA+NKLCgNIdntx+kuXeAWYUjVcU+eFm0lyaXpXFQYL8ZEQ3p5iUU2G4GoD9QhapevGqFE0wwwQQTfPCp6e/lN7u3s6awBJNGy58qd1Pb34fdoOfuSdP4xNSZQDQhdVdnO48e3Mverg4EBGalZfDAzLmUxCciCALb21p4aO8O7p85l21tLbxUX0MgEmZeejafmzmPwrjjVeUfP7SfJ6oP8uSNd/BMTRX/OnyAfp+PSYnJfG7mPGalZcTafab2EP86dIAWl5MEo5Hrikq5vaycRKMptj9PKMifK/fwRmM9fX4vZq2OyYkp3DNlGtNT0tBKEqqq4o9E+EfVfl6sq6bP7yPH5uCjFdO5IrcgGi6jKvx0x1aODPRx/4y5PLR3B9X9vTj0Bm4umcw95dPQixKecIg/7NvFG41HaHYOIasqbzc3Ig5L9t4/Yy6fnTEHgC6PmxeOHGZDcwNNQ0OIosCkhGQemDWX6clpCIJA3UAff6rczda2Zro8Hqr7evjbwX0AOPRGvjx3ETcUlwHwVtNRfrh9E91eDyBwdX4RP1lx1YjrqqoqIVnm8epKnq+tpt3tJtls5oaiUm4vqyDOYEAQBP55qJK/VO7hiRtv5xe7trG5tQkBWJiZwyemzqTQcfya9QY8/KVuRywJeHlaIVdnXRwJ9XJ7LjnmVNZ172ZZylSKrVnj0vJ/L9w4bzJvHajnK39Zyx1LpmI3GXjncCMtPUN86YbyczIOdBqJ0sxkntl+kEff3sPMggwC4QhXTC3CatQjiSJXzyjlneoGfvjMBq6aUUKyw0LvkIcth5v43t2ryUy40EFclz+XpXEgCTpUVaXIfhuiED3ERMMU1EtunuHioKoq4bCMHJbRaCU0WumsXxiqqqIoKpGwjKIosVpNgihEi4/oTr1PVVVRFZVgMIIoCmh10kWXSFRVlWAgjKqoaHUaNNoLKzl27JgDgfCo3zQa6bTna4LzQ1S/XyXgH+saiGh1mjNeA0VRCfpDo94ckiSe9X18rJ5AKBAGAbQ6zZh1Eia4tFBUlT6/n2drDhGIhClLSGZlbgH1g/2YhmOZVVXl7eYGfrR9M4kmMx+ZMh1ZVXi+9jCfXPs8D191PRXJqURUhW6vh//evJ5cexx3TZrKQMDHMzWHaHU5+cUVa8ixOwAIywqtLie/3LWNbe3NXJVXhEYUcYWC6IYlE2VF4SfvbuHvVftYlVfI1QXFNDkH+cfB/XR7PHxh9gISTNEB2lc3rGNrWwu3l00h1WKjy+Nid1cHrlAgFrcfUmS+selNNrc2cVV+EVlWO7u72vn6xjfxLghzc8kkEKLegO3trfT5/eQ74vjIlOns6Gjlpzu2IIkCHy+fgU6SWJyZQ67dwfN1hwlGItxSOpk4QzQxtzDueJLo0aEBNjQ1kGmzsyq3kKFggKdrDvLZ11/kxVs+TKrFSrzBxHWFpUxOTOEP+3YyLTmNawpLkEQRnShRkpAY29/c9Cx+s+paen0+Pv3aCwQioycGw8PH+lpDHVfkFXJ9URmNQ4P89cBempxDfHXeYhKMJsKKTLvHxSdefR6TRssdZRX0+ry83lCHPxLma/OWkGaJJt3mW+NZlVES8xzYtBdPxSbR4OC2rKX8pu55vlb5J+7IXsaS5AritdbTFsMQEDBI5xbqZdRp+dW91/PHN3fy3LtV+INhpmSn8v8+tJzZhaMT+wVBwKDXoj1NgU9JFJhdlMnHV8zi+R1VbDhYT2qcjQWlOViNekAg2WHmf++5msc37uOVXYfxBEIkWE3MKcrCYTIOtwUGrQb9SW2JgoBJrxtVi2iCkVyWxsEx2jwbyLauAsAf6UUmjF13eVWxGw8Bf5i//uIN3np5H6tvmcUnvnDlWQ2OVVXF4wqwY1MNG16ppL66A4/Lj86gIS7ByrT5BXz0cyuxx1tOuX11ZSs/+epTpGQ4+NRXrqZ4ysWN+3UN+fnGp/5KZ2s/H/38Kq6/a2wZtfNJQ20Xn7/zYRRZHVFN+fq75/GJL6zGYHrvsbcTnJ725n4+c+OvR12D1bfM5N4Hr8JqP/3M1kCvi8/c+Gt8niCKcnz76fMLuO9r15BbdHYKKJ1tA3zz04+i1Unc++WrmbOk5OwOaIL3BUVVaHEN8X/Lr2JqStqoJNgen5e19bXkOeL41qLlZFrtqKrKLSWTuf6Zx/jrgb38bOXVsfVz7A5+svwq4o1GZEVhdlomX9uwjtcbjvDpabNig6qwonCor5tnb7oL/Rjx2/t7unjy8AE+PW0WX5i9AEEQCMsyRXEJ/KlyN1fmF7LImIMgCOzv7mRueib/OWsBJm1U//7YM3GsouyW1mbeaDzCNxcu485JUQlOXzjEF9e/yu/37WR1fiFm7XAVZ0FgRU4en5s5D0kUuWfKVG5/4UlerDvMx8pnoJc0zM3IotCfwK7OdjyhEEuzckm3jpZQnJuexWPXZ6I9QSe+IimV/1z/Cjs727i+qIxEk4nFplzSLTYeP1RJXlwcV+YXohVHf8ssOh1licmUEU1KHov1TQ2809rMAzPncd+02UiiSFiWmZOewc92bOWd1iauLyqLre/QG/jzmpvQShJhWSbVbOGpmoN0etwx40BWVB6r34OiKhgkLSvSi1iaVjhm++ebysGjvNN7EAUVd9jHI0fX8sjRtVg1JkwaPSJjD4YtWiOPzP7iObUpCAIWo54vXr+YL15/Zpn4BKuJx7905xn3aTMZ+MiKmXxkxcwxfgcQSHFY+dKNS/jSjUvG3E+8xcT/feyaUcsL0xPZ8IP7ztjXf3cuS+NAVkL4It20ezcTpy8FVPqDVWgEw1kbB6qq0trYh3PAg1anIS0zHnv8xVE+OIbPE4xawaZzSzTyuP30dA4R8IdorO0cMcgZD35fiNee2cUTf9iILCvEJ1mJT7aiDnsS+rtcmE6j86vIKj0dgwwNeNBoJYYGRmuMX2j6e1z0dA0RCkVorOu+KG1aHSaWXFWOx+XH7fTT0dKPa/DM8mnni672Qfq7nWe83la7ieyCpAvuzfF6AoiigMF48RLmzBYDS6+qwOOOXoOutgEG+zwA4/IjGow6Fl05haF+Dx5XgP4eF50nVLk+W3o6hhjodWMy6+npGDrn/Uxw8VmUmUOOPW5MdZx2t4vagT6mJqfSODRIu9sFQEiWSTCaaHIO4g4FY+svyMjBpI16HSRRZFJCEolGEw1D/ciqguaEJNuPTJke8xSczNbWZoJyhCybnZ2dbbHlvnCIQCRCh8dNRFHQShKr84t4o7Geh/buYGl2LplWO0kmEzrp+DBgXcMRJEHEpjewo6M1ttyo1dLhcdHj85JnjxoHCSYTi7NykYbfGxadnmybg73dHcNe+vE/4wIwFAzS7/fij0SIKAqBYanZPt+FeWfu6+4gpMjcWFQWu6ZaSaIkPolks5m6gT784eNex9vLymPGi1aSSDFbkIcrbB9jVnIWmzsb8Msh0k12sixnX0jvXDnkamJDz34AzJrj32MVFW8kcNH6McHlwWVpHERUH72Bvfjlbjq8mwEVhQjJxtFW6JmQIwq/+e6LHNzViCPezD2fu4I1t82+aIObUDDM+pf2YjTrWb5m6jmFw8TFW1i2ppykVDuzFhchac5uENjZOsDm1w8SCIRYsrqcK2+aSVKanUhExuMMDIdYnPpWkjQiZVOzuf6u+cQnWckvSTvrY3ivZOQkcMNd8xka9LLi2mkXvD1BEEhJd/C1H9+GqqoM9Lr5/Y/W8s66qgve9jHefnk/Lz2+HbfLjxwZLdsmigIarYZ5y0t58H9vQa+/cMZBMBDm9Wd240iwsHxNBYJ04Z8fQRCIT7Ly1R/fiqqqOAd9/P03b/LqU7uiv49jHxabkS9856ZofHIwwuvP7ebhH7xyzn0qKE3jhg/PR6vTMG1ewTnvZ4KLT5zBiOEU6iv+SBh3KMg7bc1U9/eOuLdMWi0FcfFETpBOtOsNI4wMSRSx6fX4IxFCsozmhNnwjDFm2o/R5/ciqyp/P7h/lGpLls2ORauLGcGfn7WAVLOVd9tb2NrWTLbNwdLsXFbkFBBniA4mO71ufJEwf9y3a9Q3blJi8oiEYp0kkWAc6XnTSmL0OM/CNgjLMlV9Pbx05DCdHjcC0VAuTzgazqdcoHDgoUAAk1aLXjMyvNCg0WDTGXAFgwROGPiffB0kUQCVEefkhaaD/M/0q7BodWztbuRAf8dFq5A81VEwwqgcLzpxQuJ9gtFclsaBXnKQZVmFqsqkmRcC0SJoWnHssJfT0d7ST+2B6AyKa8hHfXU7Xnc5FtuFLWhyjM7WAV5/djfp2YksWV1+TsaBRiuxbM1Ulq05+0qNqqriHPDQ2tCLI8HCqhtnMH1+wVkZR4IgkJYVz71fvurMK18g9AYtd9+/4n1pWxAERElEvMgxjjMWFGK1m/B5AgT8Iar3NVO5s5HEFBuzFxeTnB6H3qAlMy8RzWliQM8H7U19vPrUTgonZ7D06vJTOLgvHIIgIEnCiGtwNkMOQRAQROE9nSdBELA5THzii6vPeR8TvH+IgnjK8a5OlDBoNCzKzOGawpJRoS4mrRab7ricpS8cGhHmJqsq3nAYnSSNMAyAUf8+EYtOh4DAAzPnxkJbTiTdYkUzPLPvMBj41LRZXFdYSmVPJ++0NvHnyj3IqsoNRWXoJQm73oBZq+NLcxbiMIz+xmXZjid6ighoz8M7rc/v428H99LhdnPPlGmxWghHBvvZ3fn8e97/qTBrdQQjEcLD9S6OfdNCsowvEsKo1Y64jqfy3pyISdJR4+zGotXT4/fEcg8uBhWOfCoc/35h0xNcGC5L4wBAIxjwRXpo9ayPLUsyTCfecHbKAVveqCISlklJj8Pj9tN0pIf25n5Kyi9OzHxDTSeuQR/pp6/yfcFQVRW/L0QwECYrPwmr3TiRSPsBobQii9KKLFQ1Gv713N+3UrmzkaRUO9fcPpeCsrSLdi3rqtrwuC8t1/bEXTzB+SLFYiHfEU+Hx02K2UqG1Ra7v1RVJTwc2nOMPV0dXF9Uhk6jQVVVmp2D9Pm8ZFkL0Z5FeN+stEweO1RJ7UAfV+YVjnieI4qCQDQ3QFYUFFQ0gki61UqaxUJJfCLf2fI2+7s7WZVbgF4ysjQ7jw3NDXR5PSzOzhvxjIRkGa0oElHPrniURhTRSRK+cGiE9+QYnlCQhqEBZqdmsjK3IBZu9Xxt9ZgGvEGjQRIEnIHg2Vn4JzE1OZW1R2vZ1NrEraVTEIies2bXIN1eL3n2uFhfxsuH8qdR5+whrMgk6s1UJPx7Vted4IPPZWscgECKaTYAshLAGWogpLjOag+yLLPx1QNotBLTFxTQ1tRHU20XzfXdFE/JuOADK1lWqK/pxDV08eLUT0ZRjqu9aDTSRZ/9nuC9c2zW+5gyjiAKiJJw0QwDWVaorWrHe4kZBxNMcL5IM1tZU1DMQ3t38t0tb7M4Kxe7Xk+/z0f9YD+z0jO5qfj4xFRlTyc/fnczs1LTcYVCPFd7iCSTmZW5Z+eVnZeeyfVFpfyjah89Xg9TU9KQBIFm5xABOcLtZeXkO+IZCgZ48K1XmZ6STobVhlaUqOrrpmFokMVZuRg00UHwqtwC3mo6ykN7d9AwNEBxfCIhWaZuoB+bXsdnps+J5RiMF7NWR6bVzltNR3m8upLypBRCskJJQiKTE5MxD+cq7Ops58Ujh0kwGtnX3Ulldydj+WoSTSbSrTbebj5KnsNBmsWGoihMTk4hzx6HrCr0+3wMBgJ4wyFkVaU/4ONAdxcmnZYEo4k4g5HlOflsaGngt7vfpcU1RFFcAu1uF681HKE0IZEFGdkxr8t4cYb8rMooIazIeMMhvOHgmTd6H+nyDxBSI2Sbkt/vrkxwiXEZGweQaJg2/JeCIEjI6mhJw9NRs7+VrrYBdDot85aXcXh/M1W7mzh6uJN5y/3YHGMrnfi9Qf7+27fYtv4Q3/jFXRRNSsc55OX1Z3azb/tRhgY86A1asguTWb5mKhWz82Ix+35fkDee28vRmg6ajnTT1tRHKBhh1+ZaPnXtLzn5u3H9XfO56SMLRoZLqCqdbYP81yf+PKpv81ZM4t4vX4X2NOFJb7+8j91b6xnocTHQ544lEB893MG3Pvv3EdsaLXq+8J2bKK0YWYhElhW+cOfDoxJws/KTuPv+lZRNHbn+6ehqG+DtVyqpqWylr9tJOCxjsRlJTrMzaXo2c5aUkJIRP6IKoqqqvPCPbbzwj20j9iVpJa65bQ63fGzRGdsN+EMc3t/Crs111B9ujyWyWh0mCkrTWLRqChVz8i5LT0rNgVb+/pv1OBIsfOw/V2Ew6lj33G72bD1Cf68bjVYiOz+JpVdXMH1+IcaTlJc8Lj9vvrCXozWdNB3ppr2pj0hYZseGw3zi6p+P+uTf9qmlXP2hWWMmRYfDMjUHWtnwyn6OHu7E4w5gthoompTO0qvLKZ916V4DRVH43uf/SUNN54jl8UlWbr13KQtWlJ1iy+OoqkpNZSsbXz1A3cE2nINeZHns2dsV103jjk8vQ28YOeMZDITZv+Mom18/SEt9Dz5vEGucidLyLJZdXUFJReaoc6iqKg/94GUO7Grk5o8sZPm1Uzm4u4m3X95P05FugqEI8QlmKubks/rmWSSlnl5bPBKWqatqY/O6KhpqOhnq9yBpJGwOE2nZ8Uybm8/0+YXY48YWfHAOenn92d0c2NlAX7cLQRBIz0lg3rJSFl4xGZNF/77dB1pJ4sq8Imw6A0/WHOQP+3YSlCPEGUyUJ6WQOyxPeoy7J0+l0TnEr3Zvxx+OMDkpmc/OmENpQtJZtWvS6vjq3MWUxSfx/JHDrG86Gj0vFiur8gqxDocyGTUa4gxGXj5Sw1AwgEYUybDa+Uj5NK4vKsU4HHdv1xv4zuKVPH24inWNR3iurhqDpCHXHsctpZORhLOfHJIEgWsLS+j1eVnfeJTnaqpxGAx8ac5CJicmk2Iy8/GKmfxh305+s/td9JLEzNR0vr14JZ9/c+2o/elEiQfnLORXu7bx1wP7CMoR8uxxPDh3IXn2OIYCAb6/bSPb21ujUsZyhF2d7Xzi1efQiCK3lZXzpTkLsev1fGvhCv5RtZ/XG+p4ovogcXoDS7PzuHvy1BEhVONlXVsNy4bViSp97bT5nBTZz+6aXkx+e+QF2vx9PDr3q+93VyY4gXa3C0kQSbVEQ+HbXS4O9fawODsH41l6s86Vy9o4qBn8OwAKEfyRHlKNc8e9raqqbFl/CDmi4EgzM2NBIaqioNVJ1FW10dflPGWIjaKouAa9dHcM0dsVLejyw688QXf7EIqsxCr41R/uICc/hckzcjh2uT2uAI/++k1Cwagm/zGlmWAgTG+Xc1RbHndgTM+qOKyj7hz04vMEY4MJ56D3jCUE924/yjvrqpBlJRoXO7x+OCwz2OfmxIAMs1VPODRaQ1ogqvTS1+3G4/QRicioKpgshjHXPxlVVZEjCk//9R2e/esW/L4gqqIe77oAtQda2bq+GoNBx8obHIgnxuaq0VwLFQHXoJfg8PnUaCQ8Lv8Z2x/s9/Db777Ezs210boOI9rup+ZAGxtfPch1d8zlrvuXX/CY/YtNKBimr9tFf4+LmoNtPPXHTTTXdw/fE9F1mo90s2NTLbd+YjE3fnjBiMHZUL+Hv/9mPaFQZMR9HPCHCQZG38c+z+gQAVVV8boDvPDYNp772zaC/lA0+W842fFodQcb1lay6sYZfOzzV2Iwai9JI0Fv0BKJKLiGfETCEVQ1ajwHfKEzbhuJyLz+9C6e+OMmBvs9qIqKTq8hGIhEFWHU422kpDtwxJtHnINjyfD/emQjb724j2AwMvxMA60DHDnYzsZXD3DD3fO5+aML0elHJmcODXjobB2gurIVt8vPE49swu8Nxq5De2Mfh/a1sOm1g3zlRx+iaNJoj6qqqvg8AX7/41fZ9OpB5Ih8/DoSlSY8uKeJreur+Z9f3419pnnU9nu2HuEX33oeZ78H5YRnseVoDzs31bL+xX186Qe3kJLuOO/3QGlCEv+47kNIgnjamWSDRsPi7BzmZ2QhqyqgIiBEa7uclDeQbDJz9+SpwDJARRJEtJI0Ikn57slTua1sSmxW/1TY9AZun1TBLaWTh5NjBQQBNCf016jR8qPlq6PnbvjEi4KARhSRBDF2zgRBINlk5r4Zs/nktJnDeRHCCetG1/vGgqXIqopeGjmE+L/lVyGr6ojjOGasfHXeYh6cszCmZHQshl8SRaanpPGbVdcOn7dosq9OlHjulrvQnGSQCIJAaUISv46tH80X0A2f43iDkZ8svwr5FOFPx8K2BEEgwWjkczPn8pnps1GHr5ckimjF4+fkrklTubV09HVYU1DCqtzCEbkI7nCAiKIgCgJBJUIgcnYTkhebTv8AgyH3+92NCU7inZYmHq86wNUFRdxTPo1vbXqLssQk9nS281+Lll6UPlzWxkGcPqohLggiRk0yVu348wTCoQg7NtWiAnOWlqLVSiSnOcgvSeNoTSdd7YPkFqeOmsk/mSNV7fzpp6/jcwe58uaZlEzJQKORaG3s4+CeJorLM0bM8iUkWfnLa18CVSUSkXnkJ6/xzroq5i4r5f6vX4tOP/KSGUy6ETPmx0jJiOOPr3wBiBocP/vGs7y74fC4jv2z/3Utn/xSNGlSURR2bKrj199+gaLJ6Xzii6tHaLsLgoDFNlrGVBAFfvLovdF9yCq73qnj25/7x7jah+jg6R+/e4sXH9tOMBjG5jCxYOUkSqZkojdq6e9xUb2/hY7mfipm540uJiXAtXfM5do7ogZhX5eLT133CyLh8cXL6vUasguTqD3YypRZuUybk09adgLhcITmum5eemIH3W2DbHytktJpWZetXn1X2yAP/eBlgsEwK2+YztylJZjMBuprOnj7pf001HbxzF+3UDE7j8kzcmPPQ1p2An95/cHYffzLbz3Pnq31LFg5ifu/ce2o62U06xFO8vyEghFee2Y3jz+8AYvNyBU3TGfGgujMcl+3i/WjdiDTAAEAAElEQVQv7ePg7kbeeG4PRpOOD9+/8oIXtztbBEHgaz+5LfoPFY7WdvK5D/1uXNuqqkrVnmZeeXInA71uVl4/nTvvW0ZqZjxeT4D1L+zjn79/G0VW+PTX1nDFDTNGec88Lj/P/30ra5/ciSPewlUfms3kGTlYbEa62gZ488W91Oxv5YV/bMNqM3D1bXOQTlKSCodktq0/hCCKZOUnsvzaaeTkJzM06GHTqwfZsamGtqY+/vHbt/jmL+4a5bUIBSP84EtPsHdbPRqtRG5xCnOXlpKZl0gkLNPVPsi+7fWoCkyaNjK5SlVV9r97lB9/7Wm8bj+5halc9aFZpGXFEwnL7N1ez9Y3D3FgdyM//sqT/OCRj0XvpfNoIIiCcMYB+jEkQRyXGpxK1GDQnibJVSsd/11VVRQ1hKIGiZatkhAFHYIgAtGB++kMF0EQogP5E+dPVBWVENGFmhHragVpzPoB0X740Ulje83HqsVwbJ86SYJTHK8oCLFtVVXm2ASU8RTn/cT1x2rrVKpSY6174nkei1P9PtY5vzKzlE9veZIUoxVBgLsLzl4h8WLiiZx5omyCi49OkvjktBk0DEYlkDWiwAOz5vLfG9efeePzxGVpHChqBFBjOQcArlATQ8EG4g2l49pH5Y4GnANeJFFgwcqy6CxDip2iyRnUHmyjel8zFbPzzqha9PRf3yGvKJXvPfxRMvMSR/ymKEo0HvyED5koicQlRF1J4bCMTh99OWp1GhwJllEf3rE4+cOo1UlozkK+1Gw1YLZGB/yyrGAZrmEgaSSsDhNxiaNVMU7XB0GMehHGi6qq7NtWzzvrqgj4Q1TMyuXLP7yV5HTHqHVDwTAa7ehKtyf/O1pwbPwDBqNZzy0fXcSaD80hKW2ke3nmgiJmLirmKx/9E/29bmoPtl22xkEwEEYQBD7/nRtZclV5bFA/dW4+JVMyeegHL9NQ28WerfUUTcqIFXaTTryPQ5FY2JxWH72Px+NpaW/u55+/fxt7nJl7HljBVR+aPcKoWH5NBQ/94BXWPrWTnZtqmbOkhEnTc873KXhPjJjFR8VkGr96iSwrNNR20tLQQ2FZOnd9ZjlpWdGqstZhY6m3c4jn/r6VQ3tbmLWomITk43KLqqJSd6idV57YQUq6g/u+dg1zl5UcD92am8+iVZP53fdfZsPaSra9fZjy2XnkFI4s7HbMgzN7cQn3/b81pGcfl2Zcsrqcn33jWda/uI+Oln7qqtoon5U3YtsXH9vO3m316I1aVt80k3seWIn1pJDMex5YSTAQHpXT5Hb5+cdDb+Nx+pizpIRv/OKuERMk81eUUTw5gz//7HUOV7by5ov7uP6ueeM+xx8UVCJ0e16keeh3GLSZGKQ00q13YtVXIAjn9hlXCdPleQ6rrgKrfvK4tpFVD/X936c06cfn1OZ4GArswKDJwqDJ4IMkGyAIAtdlTWZRSj6DQR+pJitmzYVRKworEYJyGIOki6lZRRT5lN6SsVHxysFTFon7IKOqKqGIzKDXT6rjzOOVSw2b3kAgEkFRVQ739RJRVNyh4EX1jF+WxkGX710ExBEn0hk8ikmTOi7jQFFUdmyqJRQIk5Rmp3RqdDbL5jBSUJqG0axj/44G1tw2B7PVcNoLptFIfObr15CRO1rreHxFpy6MxvOlTCQis2dbPT2dQzgSzPzH/9wwaoB+jGPG0/km6hExjmn8HathUDwlgz3bjjA04BkhhXe5MWdpCbMWFY2a7c8pTKF4SiYNtV20NfUSicjnrU1FUVn/4l783hCTp+ew+Mopo9oXJZEb71nA68/tprtjiLqq9kvOOHgvBANhBvvcKLJKdmHyqIraBpOW7IJoPPNgnxv/SWFKgUCYd16vIhiIMGNBIRVz8ka9c0wWA1fePJNNrx+ksbaL9ub+UcYBQFyihaVrykcYBse49o65rH9xH35viO6OIcpPPAZ/mLVP7UAQIK84lbvvXzHKMIDh2d4xJhD2bqunq3UASZL45JevQqsbbVQuunIKL/3zXYYGvLz98v7L0jgAEAU9yeZryI37PH2+N+nyvohOk4JBk0FEceMPN6OioBGtGDSZiIIWRQ0TiLQRUVygqug1qeg1qShqkGCkC4u2bHgQHkVVVUJyH0G5A1DRiA4MmgxEQUtIHiAY6SLNevuIfslKgGCknYjqQRT0mLT5CGiRVQ/+cAuCIKGoQfRSCjopCeEUWvyKGiYY6abb8yJ2w2wsumJ0Uip6TTKy4ick96IiE1HcaEQbRk0mIBFRXAQiraioaEUHek0aoqBBVnz4I80oahhR0GHS5iEKF1ZaVBAE4vUm4vWnr7z+Xtnce5DDrmZmx5cwNyGat1TrauWwq+Us9qISkIMjCqZdTtR39/N/r27m0U/f+n535axJt1h5qa4Gs1ZLdW8PkxKTWVtfR4r57OX4z5XL0jho9awnQT95xEsoIA9g0CSeZqvjDPV7OLy/hXBYZuEVk2Oz7qIokl2QRGZuIo110WThlIy4UW74E8kvSSWvKPWyHTheCAb7PHS1DhAJy8xcUERy2vmPIx4PqqoSDITp6XQy0OvG5wkQCkaQIzIBfziau6CCHFZQFPW098EHmalz8tGPMXAzWfQx48nnDZ515e3ToSoqe7fVI4gCXk+QbW+NHRLn9QTQajX4PNHqxYqijhlmN2Yb5623FwZJFNENe1yC/jDKSUnIiqwS8EcNAlESRz0joWCEqj1NiKLAQK+Hd14fuwBfT+cQGq2Ec9CLa9A3pqFrc5gpKR9bRCA1I1oFVpaVWH+O0Xikm8G+aOLx3KWlp0w2PhVHDkVVrrQ6DQd2NlK9d+zBTzAQBgGa63su6Wcx1WzhqoJicmyOMSstjwdR0JFgXEaf9w2CkR70UgpdnueQFc+w11wmwbQSm34qrmAlA763EQQdAmA3zEavSUVWAriDVXR5niHL/inijAuG9y7T6f4XEdWLRjBh0hagkxKjxkGkmwH/Jro9zzEn800gGgLkCu5jKLAdAQ0R1U2cYQHxxqV4Q3U0DP6EeOMyQnIfeimZVOst6KSxv8OqGsYXPoIvfBRR0BOW+4f7m0xQ7qLD9U80oi0aJqzNRS9Fw3q7PM+iqH4UNYQgaEkyrcakLaDPt55ApA0VlYg8iMO4gETT+1Pr5nzzl4bX6PT3c8TdFjMO3u0/zGPNFy/sZIILR1lSMjl2B4FIBBUIyREahwYpKx5f5Mv54LI0DorstxOnLxmOx4ziCXegjFOtqGpPU0yZpq2xj0d/+WbsN7fLh8cVIBKW2bW5lopZeUimU4fMpGbGv0f5z0vzI3chcQ/58bijsZA5RSnvi3xqJCLTUNPJ1vXV1B/uoKdjCI/LTzgUQRCjAnt+7wkydZf6SPM9kJxmHzMsTZLE2Gy+eh4NA4iG3HU096MqKof3t3B4/5lnxEKhCIqsjExMPw2X+pOlM2jIzE3CkWDhcGULB3Y1suCKSeh0GhRFpbGuix0ba5E0IrlFKdjjjs9WqqqKHJbp7hhElhXe3XB4XDlHoVB4zMG13qAhPmn0rJUgCMfDfFR11H3Q2dI/vD9xRK7SeFBVlf4uV1ScQYXffu+lM24TCcuEgpFR6lmXCkXxiXxpzvgmqU6HJJoRBA2K6ico99Dhepw0622Igh5v6AjuYBVWXQU93ldw6OeSaL4CAQ0Q9e5pJTsJpuW4QwdG7TuiuNFKDuKNSzFp8xGF6ASARV+GTpNEt+f5E9b14A4ewKwrI8l0Je5QFU2DvyLeuBhVVRDQkG69k7DcT6fnacLywCmNA0k0kWBaTq/vdZIt12HXzxzxDVeJYNNXEGdcPLxEIBBpp9P9BGnW2xCQ8Ibr8YXr0UoJdHtewKafhkayE4p0MeDbeNkYB8uSp1LramV6XNGo32bEFWHXjscIV9nUM/r6nwp/KMyepnbqOvtQgbL0JGbkZqCqKk/vPEhRagK1nX04TAbmF+YQZzbydvVRLAYdTb2DRGSZhSW5FKUkMuD1U9/VhzsQpGPQRSAc4ba5FViNelr7h9ha10wgHCE/OZ6ZuRlYjXrquvrYUd9KMBLGpNexoDCH3KQ4ZEXhcEcPlS1d+IIhdBoNt80tRwC8wRDrq+pp7h8kzW5lUUkuNqOBIa+ft6qP4vQHsBsNzCnIIivezpDXz5a6ZnpcbmRFpSwjmUXFueM+R+eLowP9bG1twR8JIyAgAPfOmHXOEwrnwmVpHMQbyqJxsuFOWtzriKg+HPoSUoxzzritLCsc2NWA2xWV4Hx3Yw3vbqwZc92dm2q5877l6E+jkqLVaS75QcilRiQiE4lEZ0kNRt1F9xooikJ9dQd/+NFaGmq7SEq1M31+ITmFyVjtRrQ6DbKs8Pzft1K972zcuB9MdIaLrwKkqiqhUASNVqJsWjYz5hWMXoeRA/zi8swRSc0fdERRpHx2LvNXlPH2y/t5/KG32fZWNXEJFnyeIG1NfTTWdTFlZi4LVk7CZBkZHiArCuGQjN6gZfq8gnEVbiwpzxpTZEEURXS6sw/hCwaOT8gYjGe3vaqqhIfVnZJS7ay+eeZo4QFG3geiKJ5VftUHlYjsQlEjSKKZiOxEECTMumjek0VXhkGTCSiE5QGM2iwEjuVlnemTL5Fuuxt3sJI+33r0mlSSzWvQCLYx11bUwHAokw1BkDBqcghGorK9giCikxLQSg5k1Q8IqJx76KFGtKOVEkcYDBFlEEHQxo7dqq/AqM1DVjyoKJh0BWhEGyZtAVrx7KVJL1Vuy17GQNBNon7kddEIEjdlLqLIOr7iazv6xx7bnAqNKJKVYCcQjvDi3mqy4u1YDXoe2bCT/7xyAVnxdg61d+P0Bbh+xiRe2lvNpIwUpmSm0Dnk4ndvbud7t1yJ0+fnlf01JFhMTM5IIRiJoJFEvMEQD7/1LtNy0smMt7GvqYNQRGZZWT4RWSHZZkarkWjtd/Kvdyv5r+uWUd3ew2uVteQlx5NmT8AbCiENfwfc/iD9Xh+ZcXbWH6on3mJiVl4mf9iwg5K0JPKS4mjtd/LKvsPctWAa2+tbqO3spTwrBWE40f/9oLa/j7AiMzM1PZqXysWfzLosjYMoKkecT5JhXookGHCGjjAQrCbVdHo50662AZqO9BAOycxeUkxOQcqoj6Vz0Muud+ro6XRyeH8L81dOOqVq0UQ00dmj1UqxhFWvJzAsp3fxCAUjPP/3rRw+0ErJlEzu+dxKiiZnYDLr0WglBEHA7wvy9sv7L2q/3i/ej5AuURTRG7UoskpOfhI33rPgjNtotNK4Q4o+KMQn2bjz08vwe4NsXX+I7o4hhGGZ4pT0OG66ZwFL11SQlZc06tglSURn0CBJIqVTs7jh7vlnbE+rG53cD4AQFRY4WwymaDiLCng9Z1cQKuqV0CKIAnqDhuvvnn/a+izH+nmpKVadb2TFT6/vNXRSIjopGY0Q9RjppWTMumIUNYyqRgARgyZjeGa/CFQdihpEEk8fY64VHSSZr8IZ2MNQYAfBSA8a3djGgUa0IQo6QnI3ihrGGdw9PFCPDmdG5heo4/KwioIeWfEwemUBgZE34bF8CYOUjkmXj6KGhr8XMqKgRSvG4TBEc1Bk1XPmxj8g2LXmMb0DJo2eJL2dFEPcuPZj1hgIKuOLqFBUlR6nh73NHSiqwt7GdjzBEFaDnkAozLKyfBKtZvQaDZtrG+n3eAmGI8zMy2BufhaeYIg3Dh7hSHcfcWYjeo3ElMwUlpXlo6pR+dp9zZ20D7r51o1l6LUaQhGZ+u4+ZuSmE4pE2NnQiqKqDHkDDHh9KKrKwdYubEY9qyYXYjcZCMsKWklEBRxmIysmFRBvNnKgtZMup5sel4dX9tVQ29GHQafB7Q+SZDMz4PaRZLPQXFmLTiOxYlIBRamjc6wuBpIgkB8Xz8z0jIvqLTiRy9g4gJDsIsFQgYBISHESUc4s23XkUAfdHYOIosCH719Jdn7SKJPN7w3R1+1i77Z6Nq87yLwVZVw4u+4yjlc5BbZ4M1ZH1JXdUNOJLCtcnLIfUeSIwu6tR9DpNJTPzmP6vAKkk9R1VEWNhZ5NcP4RRIGcgmTqDrXT1+NClMRxKXWdDR+EJ0sUBQ5XtlJ7sI2KOfnc++BVWGzR+ioajYjeqENv0I4yDARBQKOVyMhOpOlIN0MDHiStFMthuFhk5iUhiAKKrHD0cAeLVo1PFQeGE/8zHOj1WjrbB4mE5VPWlvl3QFGDdHtfZCi4E5O2gAzrPeilZECkIO7rNAz+FFWNoNOkkGG9G6u+nHTrXbS5/kJVz2cBlWTTdaRab8YTqqPN9Rdcwb14QodxBnaTZf8UgqClfuB7hOQ+REFLnGExek0aAM1Dv8cTOkhQ7qGm72s4DPNIMd9IvHEpne4n6PG+gkawkuN4gPfyPUw0XUmr8090up8k1fIhEs1XnHJdjeggz/Eljg7+CFWNYNTmkm69G5O2gGz7/bS7H6XV+ScEQUO69S4STMvOuV/jRVFUatp72FHfyseXz4ot31zdyEu7qvEEg/z+0zfHltd39vP8jirqOnv5/p2rSXkP6jomyRBTLxoPZo2RYOjMxoGiqhzp6uONqiN88apF6LUaWvqdxyfuBAGLXheVmNVG3zEROfqbRa9DEMCg1aDVaAiGo3WOTDodNqMhVnFbVVUCoTAaUcCkj4YFGnVaIoqC0xfg/9Zu5j+uXEBhSgJ1XX08/Na7oKoEIxEMWi26YdVCnUaK9cug1ZBoMQ0v1yAr0fVlReEHt62OeRi0koTFoCMzwc43b1jBnuZ2Hn5rB3lJcTy4ZjEXH4E/7t3N8zXVWHXRCZbvLV81EVZ0vtCIRmoG/4FBisMTbiX5BGnTsYhEZOoPd9Df46Z4ciZpWfHRma+TZTGNOoqnZFC9v4Wdm2rx+0JYrOc/41+ShJi6iH94Bv1yVsU5RnyChfSsBDRaiR2baulqGyCnMGXM4z7Rq3C+zouqqvi9IfQGLWazflTOg6qq9HU7OXpS1dsJxkaUxFi4j88dGOfsocDsJSXUVbXT3tzPob3NTJ9fcF7vgQ/CU9Tf42Lb+kN0tQ/y4PdvIacweZT88anQ6TVMnZtPY10XRw510Hykm8JJ6RftOQLIL04hPslKV9sg294+zA0fno89zjyqjVO1X1qRhcW2n75uJ68/s4s7P7P8lG0d28fl+H4U0JBquYlk83XDxyecECokEGdciCMWNisM5xeAQZNJQfzXAWX4l+jA0awtpDjhu8PfEwAxtk1J4g859pBG149uk2X/JKBGcwkEMaYIaNIWkB//teE2BIThqRybfhpW/RQA9FIqeXEPxto/HXGGBTgMs2PHDWDU5JDjuH+M7QUSTMuJNy2K/fvYebHpp2LV/98Jx3J+hjvR73A0KkA5oeDbsWUIUJSeSGHayFnnhaU5ZCXY+X+PvzpieX5qPB9ZNoMfv7AxVgTuXLg5axFXpc0myeAY9zZWjZGh0JknuVRVxR2Iev5SHVb2NXXQ5/bGflcUlTer6llVXkR9dz8CAgmWqEdrU00DhSkJNPUN0D7gpDAlEU8wOOYLuDQ9CZc/SGVLJ/lJ8VR39OAwGdBqJFyBIJnxdiwGPVvqmmPbZCc4eOvQUVr6hyhMTqDP4yXOdFxl8OT3QYrdSk6Cg7eqjnDngmn4gmHcgSA2o56OQRc2o4GVkwrJirfzlX+99r4YB1NTU/miaUEs3+DkENqLwWVsHAhMjr+Xbt9OwoqPHOvV2HT5p92iq22Q5iPdKLLCzMVF6E8Ray0IAuWz8lj/0j76ulzs2FDDyuunnf8jEATs8Wa0Og11hzro63KSkhEXHWip0QdWFIVRs9onEquGGlsQfYldLCNDHa5geawTKpzRyBFEgdlLitm9pY7m+h5++OUn+frP7iA5PQ5JEqMvZUVFkRX8vhAarYTVfup6EydWeY5+3E5//KIokJxmp6djiI6WfjyuACZLVAJPkRXcLj+//e5LyOOU7hwVFqVGNe8v7jUYcQpi98/FaF8UBRzxFjRaidqDbfR1O0lItsXuY0VVRyQ3Q/QeuPKmmax7ZjdtjX288Ng2HAlm0rMTYuspioqiKPg8QQZ63WTlJ52ynsaxD/rxBWf3HJz8HEX3dfbXUD2pE2faPhgIEwhEq3tv23AYW7wJR/zxxGBBiBpfOp0mFvJ2DINRx7I1U9n4aiXV+1tY++QObrt3KQnJtpin4dhz5Bz0EvCHSc9OGFVo8b0gSiI3f2wRf/jRWjqa+vjDj9by8S+sxh5vRhSF2DWQIzI+bxCTSY/phImWGQuKKChNZaDPxb8e2URWfhIzFxWj0UQHpqoarb4tRxQa67rIykvCFme67AyEY/kC0ilqGkQH66NlOqPxyqM9btH1dWOOOATGfoZEQXtshXG1ER2o686wzhhbjXEsx4yR0eseM5TGOnbxlMfyXmjoHuCp7Qe4fcFUfvPqVm5bMBVVVTnQ0sknV87m1b01vLSrmtykOP771uNeD+kU+TCiIKCRpPd8zzq0Fhzas5O6/FrZHeMKKxIFgdL0ZBIsJj76h6eYlpNOXlI8Bm30muq1Ep1ONx975Cmy4x3cu3wOJr0WrUYiGJb51F+eIxyR+co1S0i0mvCFwhi0GjQnvvMFAYfJyH9dv4xfrduKyx9gfmEOV84uJsVu4bY55XzhsZcx6rQsKskjNyla82VRSS4uf5D/fWkDTl80wfg3H7keSRSx6I9ff4NWg04jIYkCP737Gn7x+lae+dXjaDUi18+YxJ3zKtjX3MG/tlfiC4WxGHR87dqLU434ZFLNFlJPkC2t7e+76H24jI0Dlb19PyPRUEGiYSo6yY6ihpBOoXOsqiptjb00H+1Bq9cwbW5+rHDTWJRUZBKXYKWvy8Wm1w6w/Nqp5z3eWRAEJk3LZtv6ajpa+vnuf/6TK2+eQVyCJSalWTo1i6lzjhs90Q+tQk/nUOyj6XX7cTujIVWuQS9HazoxW/Qxw8JqH1vP/1xRZIW+HhfhYARFUQmHIrQ09AIQ9Idob+rDHmdG0oiIYlTf/MTiTYIgUDE7n5XXT+fFx7bTXN/Df97xMHOWlJBbmIJGI+Ec8tLW2MfhA618+itXs/zaqbE8BVVV8XmCDA14URQFOaJE9eIVBVWFgR4XDTWdaPWaaKKlXkNcogXtsDtUo9WweHU5z/9tK+9urEFr0DBrYTGSRqL5SDdvPL8HnzdI6dSsUyYkBwPhmLSmLCsM9LpwD0WT3Af63DTUdmG1GWPXwOYwxQrPnS/cQz7cLj9yRCEYDNPbOQSA3xektbEXSSshSSIms564BMsFS+YVBIGpc/LYvaWO3k4n3/7cY6y6cQb2eDNBfxi300fFnPwR1XEFQSAhycqnv7aGP/xoLbveqaO5vpvp8wpjhcBcTh9tDb3UHmwjrySVr/zo1hHGQSgYpq/bFRsADw14GRoOBRsa8NJY14UjwYIkCkgaEavdNOI5CIciDPS6CYdlFCVqiHa1DQLgdQdoOdoLgoAkiYiiiMVmGCHVqSgqQ/0e/L4gihytFN3WGH3Jh8MyXa2DNNV1x3Il9EZt1Gg6YZCQmGKnsCydyp0NPPfoFp57dMuIc2s06cjITWTJVeUsvaqcpDR7zNsoCAK5hcnc9ZkVPP7w26x7bg+H9rUwbV4Byal2FCV6TprrezhyqJ2FV0zmkw9edV6NA0EQWHPrbA7tbWbHxho2vnqQ2oNtTJtXSEq6g3AowlC/h4a6Llob+vjOQ/eMuA90eg0f/9JqBvs8HK3t5EdfeYry2bmUTc3GZNbj9wXpbh+i9mAb7S39/PbpB7DFXViN+Qn+vdFpJawGHXUdvei1Em5/gJa+IUrSk9CIItfOLCPFbuHNyiMXtV/nYlxkmMannCUIAklWM9+5ZdWo34a8fsKywmdWzOUzK47ndHqDIcIRmetnlI2afc9JdPDZlaPrkYiiwKy8TP7yqQ+N+u2eRTO4Z9GMUcv1oshNsyZz06yRIYt2k4HffvSG2L/vO6FvGXF2fnrnmlH7un7GJK6fMWnU8otFRFEIRCKIgoAvfFwW+vd7dvLzK9dcVO/BZWwcCMxO+ia9/n00u18jovjItq4m2Th2OfNQMELjkW56u5yUTc0iKdV+2sG+xWpk8owcmuq6OLC7kb5uJ8lpjvN+FDMWFrGyrovNrx2kt8vJX36+DlVV0WglLFYjd5qXjzAOAHq6nHzt43/G7wsR8IVGFKfau72evdvrEUUBo0lHfLKNmz+ykKtvPX3I1dng8wb5xX8/R0NNF35fiFDw+MxEZ+sAv/7OiwiCgN6gxWDSMmNBEV/90chCJaIocNM9CzBbDWx4pZLu9kF2ba5l6/pDoEaTDg1GHY54M7aTwhQiEYWtb1Xz6C/fiJ2DE2ds33hhL2+8sBetVsJg0pFfksp/fOsGMvOiBaW0Oolrb59DV9sA1ftaePP5vbz29G4kScRiNZCaFc89n1uJTq/lO//52JjnoKGmkx986V8E/OFR12DLG4fY8sYhREnEaIoaRrd+YjGrbhz94nsvrHt+D688sYPBPs8I1ZjGum5++OUn0WgljGYd85dP4nP/fd0FKygHMG95GY1Hutn2ZjXdHYP8+WevD9/HGqx2IwnJthGDQohe47nLShElkbVP7KCtuY+Nr1YSDEZgeFujWYc9zkxeceqoQW1zfQ/f+/w/8XmDBPwhIuHj12DHxhp2bKyJXQN7nImbPrKQ6+48/sHqbB3gp19/hq62Qfy+EOFQJPbbkUPt/O+DTwwP6nUYTTpW3zyLj/7n8ZnCgD/E3379Jjs21uD3hUZcA+eAl7//dj3/+N16dHotBpOOyTNy+PpP74gl1IbDEdqaepFlhcRkG7KijFDvUpWoolNX6wCP/vIN2pv6+PgXrxwRtmMw6Vh5/TS0Og1vvbSPztYB1j23h3AwWkVeq9NgMutJSLaRmZd4QZJ5JUnkP751A2lZ8ezaXEt/j4s3X9hLJCwjSgJarYTJbCAzLxGTefTkTW5hCl/+0a386w8bqK/u4OjhTqp2NyPLciwXxWo3UVqRdd4N7BPxhAMMhDykGh1oBYlmby82rYl4/bkVJmr09BCvs2DT/vvmUXwQMWq1xFtMHOnsIycpDqcvQE17L6unF7/fXZvgA85QwE9Vbw8CsL2tFbMuOtlVN9AfHcNM5BycH3yRThTC2HWF+CN9IwaIBzu6yY6zYzPoEQSBSFgmOdXOmltnM2l6DraTivUEwmEOdHSjEUWmpKWg00gsvTpaC1SOKHic/phxIEgC+jwb868rp2JOPtJ7kNYzGLTc+sklTJqWTV1VO4N9HhRFwTA8qKyYnTd6G6OWOUtLxrV/vUFLSuaplQ0EQSAtO55rbp9Damb8uIoYSRqRKTNzx6ymOhZZ+UljLtfqNFxz2xxmLy7m0N5mOpr78bj9qIqKcXi2O7cohcLJGSMMOVGAlHQH81eUjav9uEQrxhMGJYIgkJzu4LNfv5Z92+ppPtpD0B9Gb9CSmhnH1Ln5pGcn0N/t4ro75lE2NWuUkovVbmLusvEVLDEYdSSnO8a17tmQkZPIvOVlIwa1Y5FblDKqcm5Cso2lV5cz0OsmIfnUCXKlUzO55vY5ZOUnnTbZ1WDUcfdnVlAxK4+6qnacw14do1lPQoqN0qljF9jSG7QsWDkpluPT1tiH2+mL3QMJyTZyi1LIL0nFYh3p/TJbDcxZWjIutSutTkNaZvyIZUaznmnzCvC6A2fcXhCiCdQnIkkiRZMzxj3gTs2Mj3lvFEWl9Wgvf/7Z67Q09LJ49RSmzs0nLsGCKEaVOBRZweMOsG9bPW+9tI+Nrx3gihumY5sxMqzGbDGw+uYZlM/MpeZAazRUzh0t4GeyGkhKtZNfkkpuYcqo+gAzFhRhc5iHvTVjf5g0Wolrbp+D0awfs5aBIAhYrAY+fP8KlqyeQk1lKz2dQ/i9oZiBmpTqoLAsjcy8sWcys/OT+NL3b6F6bzMNdV30d0frH2h0GhzxZjJyEiiekkliiu2CDbRbvL1s7j3MbdkLiNOZeb5tJ7Pi81mcfG4zjc+2vMvylMnMiD99uOsElxYmvRa7yUBtRy8LS3Np6BrA6QsQZx428i6yut77jU6r4UOzp4xarpVElk0qwG66PCswXwjMWh1FcQnU9PeSHxdHcUL0fVjZ3XnRJxAE9WLrRJ4HXC4Xdrsdp9OJzTa2xJqqqtQ7n0EjGjFI8dj1hRhP0Eje19ZBXkI89mHj4Ez4w2Fera6jy+XmntnTsRlOXYbdFwrzrz2VzMnJojz97Ar/TDDBBBME/CHWPrmTv/x8HcuvncrHvnAlicljv+uaj/bw8P++wv53j/LgD25h+TVTL3s5z/eD6qHWEcbBL2vXMis+nwpHDu/2HcEd8ePQmZmTUIhNa+K1jn0k6+10BgYwSXqWJE+iKzBE5WAziqqwra+WW7Lm0u4bYFXaVHb115NicBBRZexaEz1BJ63efkJKhDxLMrMTCukJOKkaakESRPpDbhYllZGkt3HI2UqdqwONKFFsS6fUNj6d+wnOHllR2HSogXX76/jUFXN4efdh+tw+vnHLCvRaiVf31lDd1sPhth5uX1DBtLwM0uKsvH2wnvqufp7efoAHVs+nIC2R8uxU6jp6efdIC6/sPszqacWUZiSzoCRnwpv0b0yfz4soiMQboxNem5sbWZyd+57vifGMnY9xWXoOOr3bSDHNIcU0m5DsJNE4FU+4A3e4FZsuh10tbbxVe5S7Z03Dotext6Wdg51dRGSF7DgHiwpyaB4YYntTKwIwNT2V2TmZlCQn4g5E48CaBwap6+1nSUEunmCIV6pquG5KKWura4koKi2DTubkZLHhSANNA0OEZZnry8tItZ6bC3qCCSb49yEUCMdCilIz47CdJuFe4Li3WavTfDBkmD6gNHi6+fPRtzBIWg4ONTMrPh+tqCHDFE9IifB2dxVZpgSsWiNbeg4zPS6PEns6OlFLSImws78eRVUotqbxeuc+FFXl3f4jzEosZFNPNZPtWWhFiSxTIvE6K5IgEVYiPN70DrMSChgIedjVf5R5iUXkW1IwSjqGQl5eaNvJNekzcIX9bOo+RIrBTpxu4ltzIZBEkYqcNJLtFjIS7Fw3qwxFVdEPCwLkJseTbLewoCSHeIsJ03C4ZkaCHZvJQFFaIvEWY0zNx242MiUrldykOEx6HXHm85f/N8EHk0TTyAiNxdm5F70Pl6Vx0O7dTLJxFiZNCg2uF4eNgzYiig+bLoeipEReOHAYdyhEqqrS6XLh9AdYXVbMjqZWjvT0s7a6lpsqJmE3GLCO4SVwBoI09Q+yMC+bQDjCvvZOCpMSCEZkZmdn0O320Dbk5EhfP2vKiunxeHl8134eXLFojB5PMMG/N66wl6db13PI2YBBo+e7k+87K73uyw1BEmPJ1d3tQ7idfhKSR+eEhIJhDu5upLG2C7PVQGZu4phVhCc4P6QaHKxMLceuNTEY8qKqMBjy8m5fHYqqUuNsZ2VKeUzZqtSewRRHNJemL+DCGwkwxZ5NuSOHVKMDURDItyRT5+pAEkScYR8JegsmjZ6DQy10+AeQEGjy9KAMO/kdOjOT7FkkGaIzf/sHGzk41IxWlAgrMlpRwh0OTBgHF5BEm5lEW3QAV5g2MhSuPDt1zG1K0scOn02xW0ixT1yrCY6zu6MdnSSh00j8btcO5qRncnf51ImE5PdKRPUzLBZJSHEDoKoRFDWaEOgwGjDptLETrZUkcuLjyI1zsKu5jdYhJzqNRHacHav+zGFHiqoiKwrdbg9ZDjv5CfHEm4y0DjqpbOuky+lBECDDfu7FTSaY4HLGojFye/Yqdg5U83jz63wwSpRdOEwmPZOn5/Da07t5Z10Vqqqy4tqppGVFpUY9rgDNR7t5d0MNuzbX4nb6ueHu+aRkON7vro8LWfHR6XmKXu+rKEqQNOttpFnvOq+hFMFIFx3up3AF95Dr+Dx2w3tP+Ddp9GSZEonTmbFqjYSUCAeHmjFJelanT6Pd3496wr1r1ByfWNJLWmRFwSeHUIGBoAcVKLFmsqnnEBWOHLoDQ7jDAZwhH3Wudu7MXYwAvNS+K7YfrSiNMJyT9DbidRbuK1wVlfsUwCydOux1ggkmuLTp9LgxabXs7+rknvJp/Gn/bu4un3pR+3BZGgeuYAObO78Aqoo73MbG9gcIyk7ybNdFdbGHNc/l4b8FBDSiGPswOYwGBjx+ghEZk05FPKHgSVRXW0EvSfhDESKKQtPAIKIgYjMY6HZ7cAeD+EIhcuPjKExK4L6FczDrtO9bGewJJjhfHEtRUlBiWv8QTToVh3XIlWH9fojqYwsIxzXpT9ju2DaCICAKIhaNCYtmQrkFQJQEKubmc8M983nxse28/UolG9ZWRkviDFfFUaP/QavXsOa22dzy8UWx6smXMqqq0uN9FX+4mdLEn6KV4onIrthvw3cQwLC2/bH7R0GNFfMSADG2PLoNgDr8m4ROSiHTdg8tTi8q4RHtR8/e6H2djuh9eoLwAQI6UUOi1sqrHfs44unEGwlilKIeH0kUh/cdxaIxMNmRxQutO3m9Yx96SYskiJTY0vlD/Rt8omAFG7qriKgyJbZ01ncd4Ne1a0nQWyiwpsX6Gn2mjpNujOfazNl85+DTiIJAsTWdTxSsPKtrMsEE/46oqoqsqqNEK8ThZ/39fJe2u11IokiuwzEuUY3zzWWZkBxRop6DkxEFLYEwPFtZxbrDR8iOdzAvNwuLTkdIlllSkMfLVTVMSk1iwOfnteo6NKLEvNxMSlOTeHTHXrrdXubmZHL1pGL+ubsSVyBAksWMMxDk3vmzeGTbLvSSRFhRuHXaFA51dbO/rQsVlWWFeVxRUngRztAEE1wYVFWlzd/D35vW0uLrYjDkRlFVFiVN41P5NyCrCn9rWkvlUB0iIkuSpnNN+kIcWistvm6eaH2DI64WZBSKrFncnX0VOea02P53DhzikYYX+P2Mr6ERL8u5i7MiHIpQtbeJza8d5MihdoYGfKCqGEw6ElPtFE/JYP7ysrNSRXq/CctOWpwPY9fPIN60DFmJVlrViA5k1U2n+xkG/BuRRDPp1jtwGBYgINHrW0en+wkEROyGWaRbP4xWsjMU2EGn+xkiipOhwLukWG6gMP5biIKWiOyi2fkQCablOAxzY8ZHv/9tOt1Po6oRHIa5pFlvQyudWrUNRldgPt2n85gxfOL6Z9pmPJzNfgVBIBAJ45NDOHRGxJMl1S5RZEXBGfZjlHQYJM0lb+xO8MFlwOfnR29v5pXqmmgEyPBz9IXF87lv/hw04vvzzBzo6eKv+/dyVUExi7Nz+Npb6/jV6mve8wTz2SQkX5bGwQQTTHBhCMph/t68FkVR+Hj+9bzTu489gzXck3M1qYYEflv/NP1BJ58quIGAHOKF9k3kmzO4Jn0RnoiPo5428s2ZKMj8s3kdkiDyQNGtsRnWCePg8icQaafD9U/ijIuRRCMtQw/jC9czO2MdzuAeOt3PUJzwP/jCDXR7XyDD+lFEQcPBnvuZmvIoINDleRqt6CDV+iFq+75BquVGbPqZHOq5nyz7p7AbovVsxjIOwsoAh3oeoDD+v1FUH32+N3EYFpBgWvZ+npYLwuNHd/JQzSZeWPlZkgwfjLj2Zk8/H3nnb9xbvJAP58+ZMA4muGD4wmHePnKUna3tDPn87GnvoMfjfd+Ng5OJRricW6G7EzmbsfOlceQTTPABoafXxbu7G+jpc7/fXaG3z82OPQ1097ouWpsqKs6Qh0S9HY0gYdeaUVGRVRm/HGRz715uzVpJhjGZfHMGM+JKaPJ20B8cwqYxkWJIoM3fTbO3E6vWTF9w6N89veDfDgEJBBWQsekrKEz4NoKgRUUhGOnAqM1GEs2YtAVEZDey6sMbbsCkyUMj2tGIZoyabPyRVgB0UhK+cCPuYCWSYEQj2k/bvj/cQlDuotvzPL3edQho0Unjq8kywQQTXD6YtFqunVTKd1ev5Nc3XcvszEtDArjP52XA78cZCPBuWysNgwMXvQ8TxsEElzQut58XXt03osLw+SIUirBnfzNNLX3jdvfvPdDC93+2lgOH2s5rX8LhCJWHWqk72j3uvlQeauUHP3+VfQdaz2tfTodGkJhiL6Da1chrnVvZP3SEfHM6Nq0ZV8RDWJFJ1DuA6CyHUTKgoBBUwuwaPMzzbRvYP1jLYVcTHf7e4ejyCevg3wmNaEcSzPjCjciKH06I/deIDiKyE0UNEZL7EQUdIlp0UiJhZRBFDaCoIcLKYMwIsOom4w4ewB2qJtG8CpO24LTtayUHOimJbMdnKUz4BrlxX8CsG1/BxAkmmGCCC83ezg6qert5u6mBjc2N/GX/3ov+lZzw209wSVNd18nDf93EVSsmo9Gc35jq/kEvL7y2jwWzC8jOTBhXZfKy4jQ+87EllBaNLVd3rgw5/bzyxkGK85MpzEsal/uwuDCV+z66hEklaWdc93whCgJZpmQ29uxBBUqtOZRYc7BozIiChE7UMBBykWKIR1VVAnIIARFJEFnbsYVSay43ZS5DJ2pZ27mFvYO1F63vE1waSKKROMMCen2v0zT0a1QiGDU5gIRZV4IruI9W55+QVT8WfTk6KQlR1GPTT6fV9ScEJFRVJtG8CoCwMoSiBokoTiKKE3eoEpt+GoFIB33eN/CEqlGRiShu4o1LMGjSiTcspt31VyTRik5KJM6wEL3m/D7TlwqiINIXcLOu/RDtviGsWgNzE/OYlpCJNJyHoKgq1UOdVA600RNwE1IiJBmszEvKo8SeEltPVVW8kRAbu+o46u4lKEewaQ0U21OYnZiDVWuIreeTQ2zuqqfW1UVEUci1JLA0tYhEvSX2flNUlVbvIBs6a+kNuEkx2ii2p7wvCZgTTHCpEJAjeMIhjg4McteUCr67ecNF78OEcTDBJc2eyuYL4jUA6O510dI2wILZp59pPJGcrARyss5/CEL/oIfG5l6K85PHvU12RjzZGfHnvS9nIqSE6Qr0827/QXSilhp3M9emLSLZEMfCxKm81LGZeJ2VkBLmoPMIGcYkEnUOwoqMIIBW1NDi7WJLbyUmjeGi93+C9x+LfgqSaCUk9wIqWkv0mdJLyaRZbyMY6URAg0GbjSRaEASRDNs9+MMNgIBWiseoySYQaSMk95JgugKtFI+seGkZ+gNTUh5GI1qwGaZj1pUgClq0Ylw0pInovnzhBlQUNKIFjXj5ykwLwM8PvUWK0YZJo2NPXwvbexr4TMkSFqYUxNb5V+MuXCE/KUYbKrChs5bKgTYeKF1KsT0ltr/f1GxkX38LFXEZaASJI+5eBkI+SuwpWLUGVCCsKjxcs5ldfc2U2FMQEXiueT/7+lt5cMoVxOmiBcB6A25+cvANegJuJjnSGHB2saOvCXckcNHP0wQTXCokGs28UHeYGSnpOAwGZFUhGn978fJvLnvjICSH6Q0OkGFKOfPKY7ChZxe7B6qZnzCVRUnTzm/n/s3w+oLs2NPIlneP0NnjRCNJJMZbmF6RzfJFJVgt0YFiR9cQ6zcdprIqGmYTiSjc9+BjsUz99DQHd90yh8mlx+MDe/vdvLP9CJVVrfQNeFFVlZzMeK5aOYVJpeloh70OEVlhy/YjbNpWR31jDx1dQ/z5sS089cJuIBoKc+M107j+qmmxfQ8MevnL41s4VNMRXUcU+PhdC1k8r+iUx9rY3McrbxygobkXvz+M3WakqCCFFYtLyMlMQJJEFEVl6456Nm6t5WhjD20dQ/zr2Z28+ubB2H6uv2oqN117XJ/d6fLxj6feZc/+5tiyj965gGULS07ZF1VV2b6rgU3b6mhtH0BRVOLjzEydnMmVKyYTZzfF1g2HZQ4camP95sO0tg+gqpAQZ6Z8UgZXLJuEYJR5rn0jd+asJteURliVeat7Fxt793BDxlI+nHM1/2x+nf+tfhRRECh3FLE0eQYGSceNGUt5vn0D2/uqSDMmMC9hCodcDQDsHqjmtc7ttPi66AkM8OXKXxOvs/HR3GvJMV+eM7r/zoiCFrOuEDMnq7dJGDQZGDSjY391Ujw6aaQxHJYHUVQ/Vn05Bk067uBBIsO1bTSiDZt+bG1wrRSHXZp5Xo7lUieiyhTZk/lowTz0kpYW7wA/q1rPGx2HmRKXjl0Xlb/9bMkSREHAKOkQgB19Tfy+djNHXL0x4yCsyLzYvJ/bcmfx8aL5iIJAUI4AEKcffo+oKtt7jrK2rYovTl7JwuQCBOCd7np+X/sOW3sauDpjMqDyVmctR929/Oek5cxLyiOiKPz5yNb35TxdrqiqSqfLzcuHa9nd2k6/z4dZq2NSahI3l0+iODFxhKfaFQjw6y3vsrOljQW52Xxl2SKkkxJzqzq7eWj7DloGnXx1+WIW5eXEvs8RWeZIfz8b6xs50NVNt9uDqkYl4ienJHFVaTFTUkeOx3709mb2tLXz2F238sKhw7xcVUO82cTd0yuYnpHO81XVvFJdi6KqXF1axN0zp8WGyc8cqOKxvZV8bflipqal8XxVNZuONtLn8+EwGFiQm82asmLSL4B4TSAc5pXDdWxpbKLd6Y4WNUyIY01ZMXOyMtFrzm2IPT01jWy7HZtej0Wn59tLVyJcRMMALnPjQFVVnGEP67t38JHc66ILhzXCT+REebiTl89PqGAo5MYT8V74Dl/G+P0hXl53gH8+s4PM9Diy0+Px+kNU13aw/2ALyxYWx9YVBAG7zcjk0gw6u524PQHmzy5Ao4m+oOLsZhz2keXFX3qtkqdf2kN2ZjwZqQ58/hDbdzewv6qN/37wGspK0hCG9cE1WpHsrHgUVaW7x0VRQQoFuUlEJeQFsjNGegbMJj3LF5eSn5vE7v1NvLu7Ebfn1DNb1bWdfPN/nwegYlImifFW2joGefm1SvKzE8nJPL5/SSOSlRGHIAh0drvIy0mktDgtFuJ0spfCaNCxaF4RGWlx7K1sZuvOo7jc/lP2RVVVfvfnjbz65gFMRh1TyjKQJJHG5j60GpElC4phOH9TlhW27KjnFw+/SXycmcK8ZMIRmYbGXvYeaGHJgmJa5XY8YR8rk2chCRIqUO9uoTswgKIqxOts3FtwI2ElOmDQiVp0ohZBEJgdP4lyewEyCpIgohO1XJ22AAGBCkcRJdZcFJTYBImIiPF9Lub06roDvP7mQX71f+evQJeiqhw42MrmLbXcdfs8EhPObtZaVVX6Bjz84tfr+My9y8m+AJ6sDwombRGSuJUj/d8mWuNAQ0H8V9/vbl1SqMBVGZNJNlgRBAGtmMT85Hy2dR+l0+fErjMCkGFyjNguxxKPSaPHGfajqurwthLxejNbeupZklrEjIQsbMOFu098Pl5rO4RB1LI6fRK64YJtcxJzeezoTnb2NnJlehmgsq3nKAkGM8tTSzBqojtamlrEy60HmeC9o6gq+9s7+e9162kedBKW5ZjyzZ72Dp47UM1XVyzmQ+WTY9fPotdzRWEBb9bV8489+ylPS2FNaXFsnDTkD/Cv/QfYUN/ItZNKmJqWGhu2KqrKpoYm/vv19biCQSKyEpMHFQWBnS1trKut54tLFnJ1aVGszU6Xi6quHp7cf5CfbtpCIBxBFAR8oRDFSYm8WlNHh8sFKtT09JJoNnFVaXTMMODzU9fbx/72Lv64Yw97WtsJRCIoqoooCOxqbWdnSxtfWb6YwoT48/IeV1WVLreHL770KtXdPYRkGUWJHueBzi5erznCR2dN5775szFqR1e2PxMGjQZREDgy0E9ZYhLJZvOZNzrPXLbGgaqqNHjaeLL1TerczbT7+1iWPJN5CRWs797Bu30HiKgRZidM4Zq0xXQF+nihfQOd/j7yzBnclLkch9aKTtSiFc/+4k4wkkGnj3e21TFrWi4PfHIZjuHZakWFzq6hmNcAIDXZxnWrpwIqNXWddHY7+cht8zAYjl0HYVR+wI1rpnHFsjIy06Ja5aqq8tpbh/jzY++wc18TeTmJGI06RFFgwZxCFswu4J3t9eytbGbxvCKuXDEZcXifJ788dDqJGRXZTC/PRpREdu5tOu2xvvrmAZwuPw/934cpzEsa7g8MOX2YhvsQbQfmzcpn3sw89uxvYdfeRubOzOPma2ecsM7Ivmi1ElMnZ1IxKROdTmLHnsbT9mX9pmpefn0/k0rS+fbXrsdijg62ZVkhFJZPOKcQici8tv4gyYlWvvP/biA12Rbre1ePk6QECwF/HM6wlxpXM3mWdJp9XewYOMTipGloRc1wErJ+zEG9RpSwiKYRy3TDz5ZO0Mb+vpQIhsKnNQTPCTV6/iMR5Zw/VIqiEgxFEMSLO5t0qSEKerJsnybLpgwviRY0m2Akqcbjs6Y6USJJb8ETCeKJBIHooO7QYAdPNe3l0FAHA0EvvkgIbyTEmszJI/b1s9m38K19r/CZ7Y9Tak/l7vw5LE4pxKzRxe7nBk8fzd5+5q39cWw7VY22k26yR2UIVOjxu0kyWDFIx4ciyQbbJSMh+UEmOoB18+DLr9Hr8bK0II+Pz55BQWI8vR4vf925l+cPHeaHb20izWJlYV52rNDf3JxMPjJzOr/esp1vv/E2k1OSyYlzEFEU1tUeYV3tEYqTErh/wVxsBn3sugtAqs3KvJxsshx2lhXkUZgYj6wo7Gnr4Edvb6ZpcIhnD1YxJyuDRMuxQW/U8Pjdtp385sZrSTSb+cFbG9ne3EplZxcPLl3IqqJC/vftTaytruW1miMx4+AYD23bQbLVzP+uWTXsyRDZ1tTMb7e8y8ajjeQlxPPZ+XNwGN97KKs3FOYLL65lf0cXeXFxfHHpAqanpxFRFV45VMvf9+zj4e07ybDZuGXq5LOuT7C7s50nDx2k1+fl+8tW8YMtG3lozfUX1Xdw2RoHgiCQb8nkI7nX8kTL63y59CMAtHg7qXE38vmSu7BojPyk5u/kmzPoDPSRY0rjs4W38lTLm1S7GpgTPyWWiDXBe0MQBERJJByR8QfC2G0qkiQiCQLZmfGj1o0+SydUIxUFxNN8MBLiLSTEj9TxLi1MISPVQXevi4isHN939I+YgSEIwxURTzHQir34BMaVtCxJ0X76fEHCYRmdToMkCSQmjOzfiL6Ix/oknPZYR/ZFOG0IoqKorH2zCllWefCBK7HbjCP6qNONfvwlSURWVHz+ELKsoNVKCIJAZnrU6MowJnFXzlX8pellnCE3cTobi5OmsSRp+iU5uH/vnP/XsSgKzJyey8zpuee0vSAIpCTZ+NkP7zi/HfsAEn0eJgyCM6Gc5BlXhiuUH3ufbO9t4MGdzzI1PoMHypaSbY6n2+/i14c3jthOEATKHGk8vuTjbOk5ynPN+/he5atcmVHGA6VLSTJEvWACAjmWeL46ZfWoJyjRYEErSoRkecx32LHK3+8Hiqrgl71E1GOeTz0G0YAwznGAqqp4Iq6oB3ScSIKEQTSd97ouKvDnHXvocLlZkJvNd1avIHF4BjrOaOSH11xJn8/H5oYm/rRzN3NzMtFKUS+PIAh8bPZ0qrq7ea3mCF9/bT0P3XwtDf2DPL6vEq0k8R+L5pPlsI+Y4BAEgUkpyfzs+qtHXfflhfmYdTo+8q9n6PX4aBoaOsE4iF7wK4ryWVqQRzASYVVRAbta21mSmcui3BwSzCbumFbBy9W1NPQPjjpeRVX54dVXMisrIzYYX1VciCgIfPuNt3m9po7rJ5ViP8GYOVeeOVDF4Z5ebAY9j9x6A9lxjthvn54/G38kzF937eU3W99lzaRizDrdWe2/w+PmqsJidne0I4rC+6Lod9kaByeiEn3hCAi4wl4cGiuSICEgkKyPozswQCASwqGLvtjidDZcIS+KqkwYB+cJm9XA/Nn5PPPSXn7x8HpWr5hESWEqSQlWjEbte35YVVXF5Q7g8QYIhiLIskJnj4uwrBCJyBdV/WL54lJ27Wviez9by1UrJjN/dj7pqQ7sNuN5V1w6HU6Xn65uJ1kZcaSnOs64vkYjsWJxKX94dDP/95vXuWrFFComZ5KSbMNs1iMKAhpR4oqU2VyRMvvCH8BFRlWjnoK+Pjd+fxiDQUs4PDIZPhKRGRzy4XRFQ7lsVgMJ8RZEUYi53b2+EP0DHoLBMKIgYLMZSUiwIIkisqzQ0NiLoiiYzXqSk2wjjDRVVRkY9DLk9EWNM42Ew2HGbjPEDMb6o93DhpuG9DTHCO8PRD1UAwNeIpGoZygh3oJ52GPU1e1Ep9MQDIbxeIKI4nD/ho9hgsuTJk8/KcMD94Acpt07hE1rwDasLrSuvRoVlZ/OviWaVKyquEJ+vOHgiP0ce4vqJA0r0kpYmJzPU417+cfRHVybWR4zDkpsKWzpOUpFXAYJBvOY+xAFhQyTgy6/E3ckGOtLu2+IsHphRChOh4rKUKiffzT/jgZvVEVtQcJKrkm/HYtmfKF/ETXCr458m95g17jbzTLl8aHMj5NrPnUO27kQlmXWHq7FotOxojA/ZhicyK0VU9h4tJHmwSGaBgYpSkqM/SaJIt9cuYyWQScHOjr50dvvEJZlWged3Dt3FnOzM8f08JzqLRKNx48n3mQiJMv4QuFRWxUlRkMkdZJEgtmMAGQ77Fj00cF1ssUMqoo3HIqFuh1jckoyefFxI2bpRUGgKDGBKakpvF3fQMvQECXJiWjew3gjoihsqG8gEIlwS/lkshyj66osyc/j+arDtDtdVHV1Mzc766zaMEoaXMEgzmCApqEhTNqzMy7OB+/JOPjhD3/I17/+dT7/+c/zy1/+Eoh+3L7zne/wyCOPMDg4yNy5c/nd737H5MnHXZPBYJAvf/nL/Otf/8Lv97Ny5UoeeughMjMz39PBjEKIhjIoqkLVUD3pxmTSjUnsHDjEYVcDOlGLN+KnxJpNR6Cfo55WDg7V0xXoY7qjBEEQOOJupTvQjySItPq6SDUkop2o3HrWmE16rl5Zjk6rYevOev7wt83E2U0snl/E4vlF5OcknfO+FUWl6nA7m7bV0tw6gKyoKIqC3x+irWOQnMyLq+gzbUoWX7r/Sl598wBbd9bz5sZqKiZnsmJJKdOmZGE2XZw4eo83gKIoxMWNL15RkkQWzy9CVeHtdw7z1Iu7efG1/cybnc+yhSWUFKZe1gPIcDjCrt2NvPbGAWRZIT7eQjAYicWSyrLC0YYe1r5+gP5BD4IgYDHruf6a6ZQWpwEqg0M+1r5eSXVNB6qiIogC0yuyuW7NNIxGHaFQhKee20lH5xBms4HP338FGcNeGYChIR9//ts7eDwBwhEZjUZiycJiFi8sxqCPfoiffXEP/QMeWtsG+J+v3zDcdpSeXhdPPruLjs5BFEVFr9dQMTmLVSsnY7cZeeyJ7aiqiiSK9A14iERk4uPMfPiO+WS+D8pXE1x4JEHkheb9MWPgqLuXHX2NTI3LIM04XCtCa0AjSrR4Bkg2WhkK+dnW20BvcGSxR1fIT/VQJ0kGC0ZJR0RVkFUFk0Y7YlB2bVY5O/oaebR+O9dllWPS6AgpMr0BDyX2FOxaA5IgsjilkN/Xbubl1gMsTC4gKEfY0FWHPxLmYqOqKgPhXtp8TbFl9Z5qvBEXZskyrgksAYEMYy560YishomoEYJKAG/EjTxs8Ni1cWgFPRpRg0bQkGbIQi+ef9W2Dpebfp8fq15Pj8fD6zVHRq3TNDgEQEiW6XC7RxgHAAlmE/9z5XK+8OKrPHfwEAathpWFBVw/uRSr/tTfMX84TLfbw4Dfjy8UJiTLyIqCMxBEHJ5EGTlhF/07zhj1bguCgEYUkUQRs14X82gcM0ZUVUVRVaQTrkmmwx5b70TiTEaShj0ULUPRvIv3ErY24PPT4/WiqtEE/XW19aPW6fV6CQ2rLDYODJ61cTApKZl1DfW4Q0Feq6/j5tJJFzkd+T0YB7t27eKRRx6hoqJixPKf/OQn/PznP+fRRx+luLiY73//+6xatYra2lqs1qj1/YUvfIGXX36ZJ554goSEBB588EGuvfZa9uzZgzTGxT1XBATsGjPzEspp9XVj01rIMaexOGk6Rz1tyKrCipQ5ZJhSsGothJUwrb4uiq05FFiyEBDoDvQTr7MjCgL9QSfJ+okP6LkS5zBx87XTmT87nwOH2thf1cYzL+2h8lAbX//CmlFhN+Olua2fnz/8JqFQhJuumU5udiJWi4GePhf/eOrd83wU42Pm1GymlKZzqLaDyqpWdu1r4pFHN/Opjyxh7qy8mHrShUSnjeYA+P2hcW9j0Gu5YmkZM6dmc6A6eo02vFND1eF2/usLa8hIc5y3xNxLjb5+Dxs2H6YgP5mrr6zA4wnwm9+/hTpsHHh9Qd546xBGo5YHbl0JwBPP7OCJZ3byza9eiygKvLvzKFu3H+HDd8ynpDiNUGg4PGHYO2A06vivL1/L9h31vLh2/6g+1Df0sG1HPd//1s1kpDkYGPRiNOrQaY/fL1/94tV0dTv50v97YtT2z7+8j5bWfu792GISE6wcrung5dcqcThMXLF8EgD7Klv47L3LmVSWTne3kz/97R127G6YMA4uUzJMDnSShr8e2YaCOhznb+HqzClYtNEB3pXpZVQNdvDLw2+TZrRHpRPVqAfgRAZDPv5Q+w5mrQ7DcBihOxxgeVoJWeZhI1cQmJGQxccK5/Nmx2EaPL0YJR2yqhKQwzw4eeWwcSCwJKWIyoE2XmypZEdvE3pJQ4rBiklz8WdKI2qYGtdBQupxb0lvsIuOQCuJ+lQkzvzOlgSJa9Jvwx/xElKChJQgnYFW3ul9k6FwPwBXpNxAvC4RnahHJ+oxaSw4tOdfVMAViOZKuYNB/rB91ynXE4gOtoPhyJi/lyUnUZqUROuQE40oUpqcRLp9bOUfWVFoHhzijbp69rV30uZ0Eo7IMJzLICsKg34/Vv3J1zf6TdFIowftkiCOS6lHJ0ljhv1qRBHd8LgyEI7wXoMIfKEQ8vA34cn9VTy1v+qU6wpwkodkfKRZrNw+aQpX5OZj0umw6i6+MMc5GQcej4e7776bP/7xj3z/+9+PLVdVlV/+8pd84xvf4Oabbwbgb3/7GykpKfzzn//kvvvuw+l08uc//5l//OMfXHHFFQA89thjZGVlsX79elavXn0eDus4Ro2BpckjJeuKrNkUWbNHLLNpzSxIHC17tyR5xqhlE5w7oiiSkRZHRloc82blY7cZee7lvVQeamXlktFVSqVhhaJjD+NY7NnfTHvHIHfeMpcPXT8zNngNhSKxgd3YfYnG9yvKcJzrebTNBUHAYNAyc2oOU8oyKC5I4S+Pb2VPZRPlkzJGxP8fWz/aF/U9v7yO4XCYsNuMtHUM4nT7R0iWng5RFEiIt7B8USmzpuWSnGDhH0+/y57KZjLSHOenc5cgLpefgUEv1149jfTh46yYksnWd6MzQz5fiHd3HiUt1cETT+8AoLm1n6ONPYQjMpIksnd/M8VFqcyfWxjLPTmZWL7IGGSkx1GQm8RLa/dTMSWTaRVZpKbYRuWgjLV9MBhmz95Grr16KkUFUS9P+eRM9h1oofZIF0sXReVuiwtTmDUjF6NRh16vISsjjr5+zzmdM4gOCA739RJRFaalXLyCfBOcmTlJueRaEyi2pVA12E5/0ItR0lJsT6HAenyWuDwug69MWUWDu5egIhOvM1FoS6Yv4MamO/6uSjXY+FTJIvoCHoJyBK0okWK0UmpPjdUuEIgO6G7Jmc5kRzot3n4CcgS9pCXFYCV9WBVJEATi9CbuL11K5WAbrlAAh87I1PhMJjnSKLSeuzf5bFFRCSshDjn3AmDVRD0q7oiTQ0N7mWSdNq6JS0EQSNanwQljOYvGxu6BrTHjYLJtOgn68dezOVeOxbnb9HrumF5Oln10+MsxDFoNZSlj9+mN2nr2tndg1mrRiBJbm5pZmJfN5NSUUV/Mfp+P323bwcb6RkqSE7luUinpNitGrRadJOEJhfjem2MV9HrvHz1/ODxm+HBEUWKz+EatZlx5g6fjmJIQwF3TKyhLPv19Oi3j7N+J+7u70EsSU5JTorkj+3bziWkzL/2E5AceeIBrrrmGK664YoRx0NjYSFdXF1deeWVsmV6vZ+nSpWzbto377ruPPXv2EA6HR6yTnp7OlClT2LZt25jGQTAYJBg8bs27XK5z6fYE7yPBUIQhp4/kRGtsYBPnMGMZDrE5ljB8MglxZkRBoKGpj/JJo7XP4fhAyaDXxP72+UPsO9hCe+cQeTmJY25nMGgxm/R097oIBMLnLdynpX2AjFRHLA5dr9PgsJvQaKRhI2f0C0yv02Ax6+nt9+D1h7BZ3rubWa/TsGheEX9/cjuPPbWdT390KfoT4tt9/hA6rRTLg1AUhfYuZ6zvAFaLAavFiCgIp7xGlwuyoiAPh+Icw2YzIgjRKxaRFYKhCMXFqbF7qnxKNBRSr9MQjsh4fUFSU+ynNAzORGqqnfvvW8Hhmk6qqtvZuv0Ia1ZXMH9uwRnzVQKBMIFgGKvVGPsAShoRo0GHxxuMfTgTEo7nFwgI0ST093BtRUEgxWKZqGp7CVJkS6bIFh30LU87dS0UURCYEpfOlLj0EctzLCO9SQaNloXJZy4aKQgCOklDRXwGFfFjv7ePtZtstLLKOHJiaE3mlDO2cV5RocPfQlegPZpMbSokokaocVdS467EL3vRie89kfVikm6zYTdGPTSTU1JYU1Z85o1O4kBnFz/bvBVfOMx/LJzHoN/HP/cd4Mn9B/ncQjMp1uPefllRqO/r543aegoT4/nM/DnDWv/S/2fvvOOjus70/71lep9R710CJIHoHYwN7r3FiVOcnmw2PdnNbvLbZHeTbE92k03d9GbHjnvHDTA2vUuAUO9d0/u99/fHiAFZAoQNbujxJ/mgmXPOPffOzDnv+573fZ50PdZgMERi2rXmjT/Xbq9v2rG9kQjDoRQVfb7Djk58Y6f2bosZj8VM2+gYeQ47dy6ou+Dfi16/D7NeTy0pB2xnbw8fXvDm6rKct3Nw7733snfvXvbs2TPlvYGBVBFOdvbko8js7Gw6OzvTbfR6PS6Xa0qbk/1fi+9+97t861vfOt+pzuJthIFBH//8n0+QmWGlqMCDQS/T2z/O7v2d5OY4WLqwZNp+a1dUsXnLUf7th0+zbkUVOp2Eyahn5dLyNIPO4oZiTPfpefCJfYSjCSxmPceaBxgZC2KzntngL8h1UVGaxWPPHGRsPEhWpp1INMHShhIa6k+dLPUP+ujpGyMYitN0tC9V49DUi2nCucjNdlKQ50obZN//8XNEYwkqy7JwOcyEInGONPUyOhZkYX3RtE5IdpaduVW5bH6piWAoSl6Og0g0yaL5RSxecOrZDI0E6OweJRSOcaSpF0VRaTzah9VixGLWk53poLjQk57LTdc0cLS5nyeePUxL2zC1c/IQJZHePi8AH37vynQ6SSye5Jv/+ihOu4nS4gwsZgPDIwH2HuzCZjWycknZDD/tdyZ0OhlZltLFxgDDw/5U7QCgkyUyM2zkZDm4bG3NpL4nDWy320LfgJfkRL3A+UIUBMpKMinMd7NsSRnPbD7Cq7taqazIJif7zJE/AIvFiMNmoqt7NF2sF4slGRsPkZVpQzeRmnQh60Y0TePBY0d5ubuDy0vLua6ymj19vezo7aZ1fIy5GVnsHejjEwsXE00meba1hbiqYNMbuGNuLWUuN//+yjaKHE6OjgwhixJfWbGaLp+XB483MRgKUZeZzXvm1bGls53xaJS7auvp9vnY3tPJsvxCRsNhnmg5ji8WY35WDh+oX/COMuJm8dZDQ+OgdzcqChbJRqVtHuPxEZoDRwgk/ZwINrHYtYo3U6H2jcKok7mmppL7Dzby9PFmVpYU4jSZprTTNA0VJuXvA4yEQnz7uS30+vzcuaCO9y6sp98f4MTIGI81HaM6M4Nb6+elefw1DcYjUaLJJJkWC0UuB0bdKRNT1TS2tXcQiMXIsMzsFPt8cHx4hOaRETwWczqyr2oaJ0bGaBwcItduo9jlRHqD659ekrisvJTDfQPcf/AIN86tIcc+fcG6ommkiAjP75qaAMF4nLiiEEkkSCgKb2uF5O7ubj73uc/x7LPPYjSeObL52gfx2qry6XC2Nl/72tf44he/mP7b7/dTWHh+BR6zeGthtRqYV5PHvoOdHDjcjSSJuJ0Wrtwwj1uuW4jTPv1isXBBMV/9zJX86aFdPPLUAURRYGF9EUtOo4EszHPzza9ez6/+sJ3HnjqALEvMry3gkx9ax1PPH0E9Q1Q0K9PO+25fxn0P7eaV3W3EYgkyPFZqayZHz55+/gh/fngPqqqRVBQUReWZF5t4fusxBAGuuaKOz3xsQ/r7u2JJGS++fJyXth8nFktisxqpLM/mA+9ZQUN90bRRZY/Lyp03LwFBYM/+DiLRBG6XhTmVOZN+G1tfbeYXv3sZVdXSXPnPbT3KS9ubEQRYt7KKv/nc1en2NquBr3/pWp567jCbXzrKo08fBCDTY2PNikosFkM64itLIssWlrJzbxtPP38kpWrpMLN0UQm3XLeQ7MyzG6dnw+lR5bOtBTNtdzHgcVnIzXbw3ItNOOwmxsdD7NxzSkfCZjOybEk5Tz93GI/bQn6+i4FBH5FIgrWrUo7rZWvn8J//8wx//PNO1qysJBpN4A9EWFBfhMGgSxfiKaqa/vfJexYEgVd3thCOxCkryUJVVUZGA0iikK5ROdlWUVI54apyqr8kiVx9VT2//f0rFBa4KSvNZMfOVnp6x7h604WPbp3EVeUVRJIJ/LFUjnM4kcBhMFKXlU0gFuPm6jls7eqk2p1BKJHgG2vWc//RIxwYHCDPZmcoFCLLauUrK9YAEFOS7OjrocaTyacXL+O/dmznwGA/i/MK+MzTj3HH3Fp6A376g0HMOh33trdw+5xaCh0OvvjsUyzPL6A6481LSZnFOx8qCod8uwCw65wUmkswSiassg1/0suB8Z0scq18B7kGKTPyY8uWsL29ixdb2vlq4hnuWFBHVWYGqqoyHArTPjbO1tYOarIz+Myq5em+SVXlP7ds5/DAINVZGXx5/Wosej1lHjd3zq+jbXSMH726i7nZWSzIz02lkokCuXYbOkmiaWiYPV295FitGGSZ4WCIPx04zG/37H/dqsHnQlJR+PpTz/OldStZV16KLEns6OjiR9t3MhQI8oHFC8i329Pr4ExOOl/b5mTfO+bX8dyJVvb39vOh+x7kniUNLCzIxyTLjIUjdHq9bGvrRC9LfGvThimO17nQkJ3Lj/bs4g+HDxJKxPnUoqVvb4XkvXv3MjQ0xKJFp443FEVh69at/PCHP+T48RT918DAALm5pzFoDA2lTxNycnKIx+OMj49POj0YGhpi5cqV017XYDBgOEtl/Cze/nA7LXz245dP5rAWUmkNZ8vBliWRDWtruGxNdToX/7XtRVGgoa6IBd8tPK1Natx5E4b+dMOLokBVeTZf/9J1pxaBCc2D0/HB96zkA3euOOO9ndJlSOHWGxZx6/ULJ9UOnD7n0+d+unFYWpzB33z2qkkL0rf++RGKCzwUF6UK1m65diE3X9Mw47kIgoDZpOfW6xdxy3WnzUmAEycG+eGPnmfXnja++0+3M29uHh99/xo+8v7V035Gr537+eAP9+6gsjybRQuL0xH1U/d+SmMiEknw/g//jHt/96l0pPvNgttt4eYbFvLr373M//vHh6ipzuX2m5fw5LOHADAZddx8w0IMBpmf/WoLfn+ErCw7t9yQqksSBIH6eQX81cc3cN8Du3jwkb2YzXquuqKW+tpCVFXjl7/dymNPHkzVw2jwic/+hqxMO9/8+5smTnwE7n9wD0PDfiRZZH5tIXfeuhS3O8W28cBDu/nTn3cRicVRVY0v//19OB1mPvHh9axbU83VG+sQBYHf3/sqo6NBysuyeN97VlA7kZIny9LkEw1h4rXXSQSRSh+R0EsSSfUU/aTDYEDVNAySjNNgJBSPoxNFcq1WrHo9OVYr45EoMSVVBLkwOxezLkVpPBAMEE8myXVnYNUbKHW6aPeOsyg3n7rMbDa3tTAejVLtycAfi9E6NsZ3tm/BOGF0BBMzL8CfxSwAWoPH8CVS3PkOnYs8UxF60Yhd58Sf9HIscJCwEsIqT1+I+3ZFvsPO9268hq8/9Rwvt3eyta3j9KU9lfYqS5S4nek+qqZx7/7DPH+iDaMs8R/XXYVton5BFATWlpXQODjEL3ft5dvPv8RPb7sRjzkV3Ct2Orm5dg4PHm7i608/x7df2AIaxJJJ9LLER5ctpnOCGnUy3nhK4q3182gaGuZvn3iWpKam9zpREFhZUsRt9bWTBNCSqsrDR46yubkFfyyGPxqj15dKWf/lrn08eawZm96AzaBnQX4eH1u+OF3YbDPo+Z+bruXLjz3Nnp5e/uHZF0E7rXJREBAFuKKy4nXdWaHdwT9ddjmj4TBWvQHz61BZfqM4L+fg8ssv5/DhybLm99xzDzU1NfzN3/wNZWVl5OTksHnzZhoaUgZMPB5ny5Yt/Ou/ppQSFy1ahE6nY/Pmzdxxxx0A9Pf3c+TIEf7t3/7tQtzTLN6GmE7Y7Pz6nr3fmdqcy5adybxShuvM5y2mPIEZtU0qKo2NvUiSSF1twZS5/OM/3PyG5gLTOyUANdW5fOPvbuCzX/xDerqvZ/yZ4O67pjpXsViSxqO92KxGqipz0q+ryluTuy4IApXl2Xz7m7dOev2Gaxek/+2wm7jr9mXcdfuyacfQ62VWr6hk9YrpOcs/+qF1fPRD6844h+VLy1m+9Mw53bffspTbb1l61nu4amMdV22sm/b9z//Vxkl/W8wGPv3xDWcc71zQNA1vLEooESepqgTisQnBrZMCf6faJlSF7oCPkUiYvkCATLMlrYx7eqqTVa/HrNPTE/BTGg7ROj7O1RWVyKLAjdVz+O9dr1LucvPRssXIokiF28Om8gqK7E4UTSXDdOFTFs5075FkEm8kgiAI2AwGrOcpdvR2gC8aJTxRzOk2mTC+BYbIWwkNjX3jrwIp0bN8cwkmyUKOMR+3PpPeSCdJLclh7x5WZLz+38qbjZMim3U52fzqzlt46lgzW9o66Pb6AHCbTRQ5nawvL2VV6ak02paRUba2tWOUZT67ZjUVHs+kvUMvS7xnQR1tY2Mc6O3n3gOH+dSKpUiiiMdi5svrVzM3O4vnTrTQ6wuglySqszK4a0Eddbk5/OVwE/1+/6QTBKfJRO5E4fJJGHUyuXYbVoP+VA3VxOlElnUqPbdOkvjJrTfweNNxnj/RynAojMtkYm1ZCbfUzSX/NQxLqqbR5fVxbGgk/ZrDaMQxkRUTiMYJRFOBBqvBkKK0lk492yyrlV/eeQsvnGjjmeYTnBgeJZpMYjcaKHDYWV5cxOWVZed9agCQUFVaxkY5OjKEw2hicW4eDsOFp7s9G87LObDZbNTWTi4UslgseDye9Ouf//zn+c53vkNlZSWVlZV85zvfwWw28973vhcAh8PBRz7yEb70pS/h8Xhwu918+ctfpq6uLs1eNItZvNVQVQ1/IML4WIhEUkHVNFxOCxkZVpJJlf5+b0r9WCeRkWHDZNIzMOBFkiVCwSgJRcXtspCZYUNVVXy+FBOOqmo4HCYyPDaSSYX2zhFe3HKUgnw3JpOOjAwbDruJcDjO8HCASCxBSZEHkylldKialmLVmRC5EkWBkpLMaSngTsLrDTMyGkBVNfR6maxMG+azFF8riorPF8bnixCfEAFzuy1keKxEInEGBv0oiorFrCcjw4ZeL+P1hYnHk8TjCuFwDJ1OoqjQg6KqjI6G8PvDZGXZcTjMCKSYpJpbBti6rZmK8ixEUSAn24EoiggCjI4GCQSjCIJARoYVg17GH4gSCsUwGmQQBELBKPn5bszmd55B9m6Bqmn8/vABev1+NASea2vFYzLjMhnRNDAmZax6PXk2G5IgEoon+One3Vj1ejaWVmCQZYocTszyKaPAqjewLL+Ax08c5/s7X6XGk8H8rBxEQaTU5SLfZqfQ7iBjIlp5bWU1T7U0E07EEQSBv121FulNUExWNY3NJ1r44pNPYdXr+cKqlXxo0TuP3e7721/hvkOHiSkKP73pRq6oOHfB8bsJMSWaZikySWbKLKnCbZ2op9hSQXOgkagaZt/4qyzzrEd8hwmjCoKAx2Lm7kULuHvRgnO2r8rM4Ge333TWNtk2K/9947XTvucymXjfwvm8b+FU9kdIMfy8t2EyBf63rrx8SrsNFWVsqJhc55Zjs7Hl0x+ddtyEqmLW6fjossV8dNnis84fwCDLfGndKr60btU5254JsiiyqbqCTdUVr3uM6XBgoJ/n2lvJslhoHhtjd18Pf7Ny7dufrehs+OpXv0okEuHTn/50WgTt2WefTWscAHzve99DlmXuuOOOtAjar3/96wuqcTCLWbwRhMMxnn7mMEPDAfz+CJ1dI9xwXQObrqhly7ZjtLYNk0gk0TSNmupcVi6v5Fe/eRmXO8WuNO4LI4oCn/30FQSCUV7aeozh4QDJCc7n99y2FL1B5pVXWzjS2MvISJChYT9rVlVRV1vAyGiQLduO8diTB/mnf7iZORPpUSMjAV548Sj9/d4Jgmr4xMcvQzad2UA+fmKAV3e2ok3UTCyoL2Lj5fPO2H7cG+apZw7h80UYHg7Q0zfGe+9cweqVlTz+5EG6e8aQJBG9TmLZ0nLm1xeye087e/a2k5frYmw8hNVi4AN3ryQSTXDwUBcPP7qPm65fyOUb5iLLIv5AlFd3tNJ0tBevN0xX9yhXb6onJ8eBIAo890IjXl8Evz9CUZGH+XWFbNveTCyWwOsNU1Tk4UTLIJuuqGXD+qkUuLN4cyCJIl9Ydu7NtTojky2d7dRmZvHXSyefIv310uVT2pe53Hx26dTTJotOzzfXTY7e1mVlU5eVPaXtLGYxExwPHCKUTIm92WQHxeZTzlGFpYbt8mai8TAd4ROMxofINOScaahZzOKCoD8YYEl+AVeUpr6LH3nsId7WBcnT4aWXXpr0tyAIfPOb3+Sb3/zmGfsYjUZ+8IMf8IMf/OCNXv5NRUwZJ5IcwaYrQhJTkde4EkAnzkw9cRbvHARDMZpPDPD+961EVTUefmwf9XWFJBJJfv277Vy2robMTBc9vePs2dtBdVUuoXCMOXPzuO7q+cRiST73pT/S1++lv9/Ljh2tLFpUgixLPPdCE43z+li/tobbb13C+HiIpYvLWL3qVCpKcZGHD9y9ild2TFZfPNLYy+hokDvvWEpujpNwOI7JePY0AKfDREVZFrIscqJlkH37O7hiw9wzfmfHxoIMDvq5685l+HwRHnp0Lw0Lihge9vP0s0f47j/disdjZcu24xw42EXJBK1n/6CPez64huwsO/G4gsEgYzDouGpTHSdaBtOaFYIgkJlh4+YbFxKNJli9spJFE2xV4XAcVdGoKM9m2dIyjjUPcO+fd1Jc5EGSRK6/dgE/+NHz3Di/mNKSDI4c6Zl1Dt4BEAWBEofrLcmdnQl2dHVzcGCAm+fOIdNimV3PLxGkUop2oKIhCRKF5lJsulPEC3mmYly6TMbjoyTUBAe9u7gi+4a3cMazuBRgNxhoHhtlmyQRiMXJtlh4s5myLk7Z+LsUAlLKKZjYOJJqmN7QS5TYruWtpjjrG/fz5IHj9Hv9rKwq4fJ5p6If46EwLzS2cax/mPrCHK5eUP265MP7xv1sO97BhrllZNpfn5rxOwVmk57cHCePPLYfs1lPbrYTu81IKBTD7wtTWpKJKAi4nRYcThO2CV2CmqpcdDoJSRKxWQ0EgzH8/ig6vUxOtgMBgbtuX0Z5WdbrEmPx+yM4nWacjpQBY7GcvVA/Gk3wp/t2snRJGXa7CbvNiM8XOSs7mMNuwmLR8/Cj+xBFkcrybIwGHd3dYxj0MtkTlJput4XWtqG0AnNBvhuPO+Uon64V8Howv74wdX9mPQa9TDyWxGI2YDTqMBplMjNtqTSmhHLuwWbxlkMUBMrdbsrdbz8V5nA8waNHj3H/kSOsLCok0zI1n3kW706Mx0foCJ0ANGRBR41tciqMXjJQYZtDZ7iFpJbgsG8367OuRhbenk7uLM4fHcFhdo60siFnLpnG8ys4H4r6+W3by8TVBHMdBdxUeGG0COqysokmk7SMjSKLEu+tnf+mW5izzsEMEVcCdAaeQhBEim1XE1f8dAWfpi+0lZgySqZpEVZdId3BZ4gp47gMtSTVIEktTCQ5gl6ygQaVzrsuyvwcZiMrq4r48XM7Odo7NMk5MOn1LCjO5XD3AE29Q1xVX8XrSckdD0fY1drNkrICMt9ZpA3nDVESsdmNmM16liwuxe22YrebSCYVMjPtyLLE+rXVRGMJIuE4RmMqrUeSxAmjWwNBQJIEPBnWFJ1pRTZFhR6Ghv04J9SKBSHVJxiKzmhebreVtvZhhob9FBd5CASimM36M3LqB0MxDh3u5nOf2YjBoOPIkZ5zXkPWSVgtxhT97Nx8MjNtGI06CvJdRKJx2tqGKChw09vnRRRFbLaUY3S2uofpIE4Ug4fDU9llprufVKrv9MXVs5jF60VfwE+XzzdRSD2LSwnH/YeJKCmBLJNkpsI2d0qbufYFbB1+mqSSYDg6SE+4nRLL+QuKzeLtidFYkF2jrSz1lJ23c2CTjazLquG5gSPsH+u4YM6B02BkTVEJkWQCvSRh17/5AnyzzsEMIYtmnIYKxmJNqFoSg+TEbZhHKNFNse1aJMHIeKwRECi0XkV/eBuBeBf51nUMRfZQaXoPrf6/XLT5WQx65uZn47GZpxxiGHUy5dkecpw2AtHY9APMYhI0VWNsNMSO3a28uqsVu9XIB+9eRWVlNl/63JXc/9AeHnp4D6Iksn5tzRlz+EVBYE51Lv39Xv73x88TjsRxOMx84a834XZbMOhlFswv5P4H9vDcC03cdvNili8r5/6/7OZwYw89veP8+OcvUlWRw203L6ZhfhGjo0F+9NMXiETimIx6vvF312OzTRW3AXA6zaxfV8P/+8eHcDrMZGfZsTtSxvzm5xt5dUcLHZ0j/OTnL5Kf5+ID71uFTi8xOOTnhZd6ef6loymqzI+uJy/XyYc/tIaf/XIr0UicgkI311+z4IzXBmg62stjTxzk4KEuDh3uZvsrJ3jve5ZTWZGN1Wqkojyb+x7YxWNPHOBDH1idTlGaxSzeLHR5ffT5/W/1NGbxJkNDo9G/n4SaCk74Ez6+1/yNKXzySS1JVEkJI8bVKI2+/bPOwdsId86vY1N1BVa9HsubzBZmkvXUuwpp8vXSFhy6YOO+0NGOWadjdVHxBRvzfDHrHMwQoiChE62IE49MFGR0ohVJNGKQ3KhagvF4M4PhVxiPNSELZkRBxih5kAQDFl0u5+Ly1TSNSDzBn3Yc5In9xwhE4qyuLuYTly8jx2FjR0sXP3l+Jz1jPtwWE3eumM9Ni+YivY4UoZNQVJV9Hb388qW9dI15WVJWwF9vWonbYkIDjvcP859PbGXQH6K2IJtwLPG6r/VOgaKoHDzcRSKp8L/fvxtREnnu+Ub27u+ksNDD3Ln5fK0iG1VNiavJsoROJ/H//v7GSSq0//LPt6HXy4iiwLVXz+fKjbWomoYoCBgMqWNpSRJZtaKKJYtSrAx6far/jdc3cN0181HVVPqPKAro9SkZ+uuumc+Vm2rTqUFnqzmQJZFPfuwykkllYpxTef/r19awemXlxDVAEAUEBLa9fByP28L/fO99iKLAI4/u45VXW7j15kWsWlHJkkWlaJqGOFGULIoCl62rYe3qKmR58nexuiqX0tJMVEVLaz3o9XI69WjTFfNYP6E2bDCkntXvf/Px9DgF+S6++PkrkSURVdWQZYlvfeNm9HqZnGw7DQuKmMXbDzMRGILU92G6tjMVyjvbuDPpr2ga7ePj9AdSBanaxP84wzVmIub5RuZ2oceZ6ViXIoai/QxG+1BJreMqCiOxwbP2SWgJmoONbFLj6MQ3nyXtfH4rZxKUPJOw10z7XyycLgZ5PnCYjDhMb4zmM5yM8/uO7ewb60QSRD5Ytppr8hcgIuBLRPhV6xZeHDyKTWfifSUr2JAzD4Mon3Wuf737t1ydV89jPftpCw6x0F3KZ6o3kmdynnsdQUM9TQPpJN7M04NZ52AGSH1AGqqWQENB1ZJomooo6EiqUVQthoCEQ1eKaNZRZr8ZQRBp8/2FU2H8c3+oiqrxl92N7DzRzX/dfR0eq5lAJIbTnIrM5jntfPXatZRne9jZ2s2fdx5iXn4WNXlZr/u+2ofGeXB3IzcvmcfyikL+66mX+eWW3Xz+qtXEEwo/2ryD5ZXF3Lqklt+9vI/m/pFzD/wOh6ZpBANRRFFAkkSSisrgUICsTBuSlDLUjdMY5Ke/JghCmn4UQKeTzijqJcsCsjx5o9Hrz/zTPNtY00Gvl6cdb7pxIpE4wVAMSRKRJJFEPEn/gJ95c/MQhNTzME3DjDRFXGsCkiRikqbfRAVBmLaf5TSaVVEUMRomOxwnn7MoTn/NWbz12N7Zxf977nlEQeDRD9xNj8/Hd1/aysGBAcrdLj66ZDGXlZUhAA8caeQXe/YyGAwyLyuLjy9dwrrSkmmDHpqmkVBV2sfGeLCxiVc6u+j2+dCAbKuVlUVF3F43j6qMDGRRnLKZaprG/v5+trS10zg0xNGhYQZDofQmfMvv/zjt/SzIy+Ubl13G/NzpmWpOCkrFFIWt7R081NjEkaEhRsIhLDo9lR43V1dXcXVVFR6zeYrQYnp+gKqqDAVDPNnczHMtLbSNjeOPxfCYTMzJzuLGOTWsKSnBZjCccZyT95pUVfb29fHA4SPs7OlhLBzBaTLSkJvLHXV1LC0sQCdKl1yanoZGa/BomqVIREQWz15HEFdTp+6+xDgdoRYqp0lBuphQNY37WvfzVPdxYkoCm97Ij1bdgl6afq+4t3U/D3ce4cbiWt5bcYpi99BYPz8/toOYovCxOctYklE47ef/VPdRft+yj9XZpXxq7sqL/h3ZPthBW2CUD1Sem4b0QqMlMECJNZPvL7qbw95u/vv4Myz2lOHWW/hL125GY0F+uOSDdIdG+XnrS2Qa7Sxyl5xVtTiixPnJiRf4p/pbcRus/KzlRX7ZuoWvzbsB+RzPMttiZe9AH5IopPUN5mW+Pjvv9WLWOZgRNIKJHjoDTxFRhtGAQusmrLp8BAQOj/2QQutGXIZ5jMeaOTT6fWz6IhQtjijoMEguBEHCILnOehVFVdl6rI3bltZRkpFqazOeMpSynVbMER3ecBSLQY/LYsIXfv1pQhrQ5/XjC0cpdDsIRGIsKM7l9y8fQFU1wvE4rUOj/O0N63FZTCyvKLoknANJElm6JMWU8/VvPogoilRX5bB2TXU64v9uhdGoY+niMv543w6+8Q8PIogC9bUFrF1dhXSeNQWzuHShkTJmevx+ev1+Pv3IYwwEAyiqxr6+fv5ly1aMskzr6Bg/eHUHSVUlrijs7u1ldEsYoyyzsnjyqZCmafhjMR44coQf7diFNxpFJ4roJiiwO71eWsfGeODIEe5ZtIiPLlmUFjQ6HY80HeXxY8fT8zRIEtFkSqnZotdPS9Zg0emRxDNv6LIo4o9G+e5LW3iwsYm4oqCXJGRBJBiPs6unlz29fWxt7+DLa1ZTlZExrWEfSyZ5obWN/3nlVVpGR1MnbJKEThQZj0Z5sbWNre0drC8r5a9XLGdOZuYZT46D8Ti/3LOPX+3bRyAWQxIEDLKMPxrjudY2tnd28aFFDcRV5XUJNb2ToWoKbcHjRJQwAA2u5dxZ9PGz9vnPY3/PYKyXcDJIa/AoFdY5b6pTNRoLsX+0j+8svZp8s4OokkQvySiqSiARI64qyKKIXWdEFkXuLG+YiD6n+quaRjgZZ0t/K+tyy1mVXUq26czEItcUzSWqJBmMBNOvJVWV4MS1dBPXkkSR8ViYxMRJulWnxyzrCSZiJDUVRVVJaiqZRgtxRSGYjKXnZNMbMEg6wok4lY4M6tw5k+YaVZLp03aX3oQoCPjiUZKaSlJVkEUJh96ITnxjQaIiawaX58yj2JpBocXDT068QFtgCLOzgBcHm7i7dBWSIFBizSBDb2XfaAd1zkKM59gTr8ytp9ZViIjAbUVL+eHxzXSFRyiznt3Q90ajNI+O0Dx6yt767oZN72ydg3cjBEHEpi+iIfMrU95bmPm3k/6e6/7IlDb1ns8CMD/jC2e9jkZKlc8+zRGZqmk8sf8Ye9t7AYFQLI4/MrMi1jNB1TTC8QSNvYP82xNb05tfjjPFOBOOJxAAsz5lEBt0cnojfjdDEATcbiuf/+tNb/VU3nQIgkB+vouvfPHqt3oqs3gXQFFVvvviFpxGI9dVV9EXCPJcSwtdXh9/PnyEY8Mj1OfksKQgn5bRUTa3tNLt9fFiWxtLC/KRT1tvoskkv967j//dsRNJFKn0eKjLyabE6QQEWsfGODQwQMf4OL/Zt4+EqvCZ5cuwGiYzet0ybx7LCgsm5qexuaWFJ443A/CpZUspdDimMIk5jSYK7A7OhKSm8fjxZsbCYXJsVupzcihxupAlkc5xL3t6e+j0+niprZ1ip5PPrFiOyzS5VieuKDzf2sp/bHuZLq+PHKuVmsxM5mZlYTXoGQ2HOTI4SPPIKM+1tBKOx/m79eupzpzqaMSSSR440sjvDxwgEIuRZbEwPzeXuVmZyKJIt8/Hgf5+/nDgIE6jieSEYXepYDDaz1CsHxUFAYFFrtXoBf1ZU2wWuVby5MD9xNQoXeFWIkoIs/zmsfbZdEZcBhM7h7qocWaRb3ZgkGS6guM82HGYYCKOANxV0UC5fWrtVlRJ8MpgB7uGu7HrDLT4Rvj0vJXYdTNLyVE1jbbAKI91Nqav9YGqxRRbXfyl/RC9IT8xNck8Vw7vrVjIwx1HGIwEkASRsViYr86/jH2jvfzhxF5KrG7GYmGuLqphXW45+0d7ubd1P2U2D1+sX0c4Geexzkb2jvTg0JsYiYb4Uv068i0O/u3gC7gNZloDo1hlA1+oX0ee+Y0xpNhlE0596vcoCgIGUSasxImrCq2BQX7duhXTaaf79fLMUsqyjLb0qaJFNqATJQKJc9tt60tKWVlYRDSZQBYlTPLZU5guBmadg7cRREGgLMvNoe5+6otyMOpkIvEkRp1MUlX43tMv80+3bWJNdQkHOvv57cv73tD1JEEg02ZheXkRH71sCWVZHuLJJNFEEp0k4jAbsRoNnBgYoaEkj75xP/7ZguZZzOKSgKZp7DvSRXVZNlbL68/pPTI4yL133UmZ281gMIiGxoONTTzTfILlhYX8y1WbyLFaCcRiDIfC7OjuptvnYzQSIdt6yvja2t7BT3ftRhAElhTk89mVK1iQmzsp0r+7p5dvv/gShwcHeep4M/U5OVxdVZneWAVBYH5uTjo9KKEodHq9aedgVXERtdnZ570RB2IxQvE4q4qL+MyK5ZPmlVQUXu3u5l+2bOPY8DDPtbRyy7y5OI3G9HU0TaNjfJzf7T9Al9dHqcvFp5cv5ZqqagyylK7NGAtH+NOhQ/z+wEFe6ermN/v28/eXrcOqn2zYNo+M8tTxZsYiETItFr64ehU3z5uLJAgIgkBSVTkyMMj3X3mFV7u6LynnQEOjO9zKeDwVlXXpMyi31pyzX71rKZsHHyGhxRmND9MT6aDKVnuxp5uGUZL5WM1ynutt5smuY9h0Bu4oq+fQWH/K0C6cw/O9JzjuHabY6kJ+TTTdLOvZVFBNe2CMcruHdbnl5xVxjylJDo32EUzEuTy/kqe7j3HcO0yu2U6Dp4D5Ho2eoJdXhzp4b3kDAG6DmdvK5mPTGVC1VB69Rdbz1QWX8XT3MQbCASLJBKtzSvHFIzT7TkXK46pCvTuPuysX8a8HX6A76CXbZKM/7Of64lpKbB6iSuINOwaQsr2mU1UXBYFck5Mvzrmahe6S9OuCICDOII4fSKbspVQ6pIKqqehn8MxHwmH2D/QxGAoiiyLzMrOozTz/demNYDZP4G0EWRS5adFcOke8/HLLHn7+4m42HzlBIBpDFEQaivPY0dLFb1/ez6snOjHqTvl2u9t6+NOrB2nuH+FgZx/3vXqQ/R19ALQMjvLArsMc6OyjsWeQP716kG3H2hEEgZJMF7WFOTy27yi/eGk3v9m6l30dvaiahkmv45oFNTy8t4lfbdnDkZ7B2eK2WcziXQRFVRkaDTA0Gpj2/dHxEInkGzMcF+XnU+pKpUl6zGbqcnIQBQENuLKqErfJlEqfkWUW5qeUwAOxOL7oqQhbOJHgRzt3EVMUPGYTH1m8iMX5+VNSgBbl5/GZFcuRRZH+QIDtnZ34Y29OQKPQ4eADDQ00vMZhkSWJhXl5XF2VEjns9fvpDwQmUafGFIWd3T3s7+vHJMvcNb+eq6qqMOrkSY6Nx2Lmzvo61peVIosiTxw/zpGBwUlUFwlF4eBAP0eHhwG4Zd5crqupnlSDIYsi83NzuK12HlnWS0vXIaZE6Q53EEymGKrmO5eiF898agCAAJmGHIotKYpwb3yUrnAbqvbmOVVJVcVlMHFn2QI+MWcFrwy20xf2k1AVAokYg5EAta4cyuxuROHCm3YaGnFVwZ+IMhgJ0ODJp9jmoifo5c9tBxiNhhiKBokpCtrENzLHbJ9kDEuCQLbZhiSIacfkTBTCZlmP05BKJTJJOhKagiSIZJlstAdGcRmMXJZXccHv83SYJB11zkK2DzczFg8RU5N0h8eIJKfSb0+H3aOt9Ee8+BIR9o61Y9MZyTE5z9nv4GA/jcNDOI0mBAR+fXD/OehsLjxmTw7eRhAEgYaSPGxGAz1jPpKqSq7ThlEno5clPn/Vak4MjCCKAhlWC2aDDrc1xZdvNxnIcVh536oFE6lAemym1HG6Wa8j227lxkVzUTUNo07GaUkdobktJq5ZUM2JgRHGQxF0kkhZljvFKiNJ3LhoDoe7XEQSSbIdVtbPKSPTdmltJrOYxbsV8XiSnfvbycm0k+WxTXpPEAQ2rX3jRZcVGZ70v2VRxGEwYJRlIokE5W53OlVREATcE6k2MSVJbKIOAKBpaIjmkREEQaDU5WZZQcG01xKABbk55NqsdPv8tI6N0evzT1t7cKFRn5PD3KysaWsAzDodxU4nJlkmkkwyHAqTVNV022AszssdnSRVlaoMDwvz8jCdQU06w2xmWUEBW9s7GAwGebalhSWFBenUokAsxomRUcKJBGadjg3lZRjkqVu9IAgsys8nz2anzz+9c/huxGh8iP5oFyoqkiAz37kU4RxxUgEBEZEG5wpagkeJqhF6I50Ekj4culO1hJqmMRDtIaQEiSlRYmqE/mgPYeVU3v6+8VdwGzIxiEYMohGrbE//fdZ5x0I80XUUfzyKBlQ7s8i3ONCLMj0hH0ORIJoGS7OKUDSVB9oO8+pgJ4IgkGE0szqnDItuZukwCVVhc08zLw+0E0jEeKD9EBvzq5jvyWMoEpy4lsZKfQkxJclQNEhvyEdUSeDSn0qXe627JUz8dzo0NB7vbGLLQCvDkRAPtB9ieVbRtP1japJQMk5f2I8vHiWcTODUm2Z8X+cLg6Tj9uJlPNi1m+8ffRpJEDHJOu4uXYVFNvBw9x4avb0c8/cRSET57pFHWewpZVVmiurWrjPzm9Zt+JIRFFXlhoIGnDrzOa8bVxTmZ+ewvrgUFdjW1XFR7u9smHUO3kYQBNBJEnPys5iTP7VgpSzLTVnW9Aqj1bmZVOdmTvtenstOnmv6ozdBEPBYzXgqpqeEdJpNrKkpneEdzOLtBE3T+MN/PUXHsT4+9+93YXO+c5w6TdMYHwnw7Y/9YtLrH/ra9dQuLX/Xs6vEE0n2Hu7i5V0tjPvCJBWVNUsr2LR2LuP+MPc9soeR8SDZGXauvmwehXku/vTIbpx2M43NfQRCMdavqOLKtXOJxZPsOtDBtl0niMaTrGgo5Yo1c/D5I/zmLzs4cKQbh93EEy8c4for6llYW8iJ9iEee+4wvYNePnfPZRQXpAz8ZFJh18FOtuw8gT8QwWjQ8eWPXzGJYeq1yDRP3gxlUUQWRQyyjEWvn8TnppswllVVQzktori3pw9FVZEFgeoMD8YzGM6CIKCTJHJtdrp9fsbDEcYjkdf/QcwQgiBQ6LCTYZl+40+djEgYJpyDaDI5KRIYTSZoGk7xpBc7nWRazGdMWhAEgRKXE4/ZzGAwyO6e3knRV38sRu+EsZ9vt+MxnZkdKctiIWOCPelSEIHT0BiM9jIQTYlBFppKyTLkzqivgECNvR6LZCWkBOmPdDMU7Z/kHCiawiO9f2AkPoiiKSiaQkKNE50ofAZ4cegJZFGHKEhISOSbi7ky5xbyTWfntHfojazNKSOuplThXQYTLoMZq87A7WXzCSZSJ2RugxlZFFmWXUydJxcBcOhNGCac8BuK52GSdEhnOV2QBJF6Ty4lNjeqpqWLjCvtGThKjYQmIudOvQmdKPI38zcgCQKSIKIXJQQENhVUpwgDJk4IBKDOnUuJLfW8lmQWkVQVLLKeBZ48yu0eFE3FpjPiMVi4LK8i7UjcXjYfq07Plr5W6ly5rM8rZzga5MBoH/1hPxWO16+PU23P5TPVG8k9LaL/zwtuJ8fkQESgxp7LRyvWMxYPoWoqJklP1oRY2kJ3KeXWbK7VFqSfm8dgRS+mTOtF7hLqnYXE1CQ22Ui+2TWjvUsUBB461sQr3V2MxyKMR6N8f+cr2A0GPtrw5rA5zToHs7ggCPrC7H6hiVefOURv6xCqouLIsFFclcOa6xqYs6TsXct2E/CFGR/yk5HrxGy9OBHKWDTBYPcodpcFZ4bt3B2AWCTOtsf209s2xE0fW0/t0ot7BHuhYTDoWLC6Cv9YiOP7OzhxqJvAeAgtJT79rsbYeIitO0+wac0covEkz25poqY8B1kW+d7Pn+fO6xaRl+1gf2M3z2xp4tZrGjh8rI+CXBcfvG0FXn+Yf//JZhrmFdI/5ONoywDvuX4xsizxP796kfxcF3MrcrjtmgbC4TiL6opYMr84XVtQXODhg7ct58v//BfC0VPaJgeaeti2q4XLV1VTlOfG6w+fVWcDmJT+eDr0kpTOgT+F0/59mq3aF/CjAQlV5cHGJp5rbTvj9TRNYzScMsYiySSR004gLhYMkoTNYDgH689pXPOveSepqoyEUnN2GI2Yz+D8nITTaMQ88Vz7A4FJ6Z6xpEJgIpXKbTahO8u6K4liet6XgnMQVSL0RboIJlOR/HmOBvTizNRnU0WlNipt8zjg3clwrJ/BWC/l1mpEIWUAa2iMxAcZjg2ccZyQEgTl1N8m2ZwWYjsbjJJuWiNYJ0rkW6YWy5fapg8k5s4gR18UBAosTpgmnlRgdU55rcY5NZiZ9RomJEEQcOiNOPSpNcZlOHXCMN2YJvnUb+Dk/ZXZPTzccYSO4BiKpjLPlUOe5Y3VHFh1RqyvKcqe48hL/1sWJPLMLvLMU9kmiywesHimvH4SBklHlT33rJTD06EmIxNJEBGFlBaLgIAkChjPQFt7MTDrHJwFj/9uO8/86VW+/rMPk10w/Q/tUoemaQx0jfKjv7+fg9ub0TQNiyOVJ9jTNkTjrlYO72jhe49+Ccn85ovGXGxomsbhV0/wwoN7uOuzV1JeO326wxu9xlDPKD//x4e46q4VrLpmwYz66Y06SubkkUwqFJRPz8/+dobZZuSuz12Foqg8/PMXOXGo+62e0puGpKISjsZxOS3EEwoGgw6TUcfIWJC9hzrp7BlFFAUURWVOZS7+QBRV1Vi/opLcLAeZHisWi4HBYT8d3aM88swBXnzlOIIAsXiSzp5R6qrzcNnNmIw6HDYTmaelFRn0MnqXZYpD39Y1QkGOk3mVuZhNerJn4KieKUIppP/v3PDHYmmDOhCPE4jPLOdXmyiCvNiQxFQa5us50UrpEWjElZTFqJOkcwpb6mU5Xdfw2mehaGp6LL0kn9MwMcrnbvNugVE0cVXOrWzMvgkAWZQRmXlRrlE0cXfxX/Heok+m+5+ekiQLMl+t+Zfzqs0TBAFZeHdTZF8oVDky+Z9VN3Fy4ZAEAfki1FdcCLyR0+1Cu4MC2xsvtH4juCSdg0Q8FUmSdRJoqaisziCnXtc0ZJ2EJEtce/dKju3rQFNTSnUnVWYlSSQRSyLrJZIJBVVJLQR6o5xWoL0UoGka/rEQ//XFP3BsbzvzV1bxns9dSXVDMbJOIhyKsvOZIxjNevSmd+fil0wo9HeMMNI3ftGKtTVNY6TPS3fLIKp6fpvO3/7oQxdlThcb6cLJCQVm8V166nQmuJ0Waspy+Nb3nyDDZWHT2rlkuq2M+yMYDDK//q8PYtDLE3zhGvFEyhi0WYwpJWpSYn0n1a+v3lDLJ9+3BmlCaVp8DWe/dl7lbqfazuQU50KYnWadDoGU8viSggJqs2YmCGQ3GilxOS/ADM6Os4khzQSymEo7iiUVEqoyKaVqOiQUheTEWmB9zSmDJIjp04KEopxJ7DkNRVPf9GLHtwonDXGZ17cfCYKATtDBGfoLgoBeOHOK3SzeGERBwCi9M2yJny398OvuKwrCW348fkk6Bzufa2R8xM81713JyICXP3zvGW7/1OU8/acd+MaCLFxTzcqr6jGeFukOB2PseO4IdqeFOQuLeeAnL7Dh1sW8/MRB/N4wSjzJxjuXUVU/fe7+uxGaqvHE77bRtLuNhjXV/NV37iC3+NSxp8VqYsOtS6b20zQiwRghf4R4PIlAKsptdZgnPXOAwe5RDCY9JouB8eFUHq3dbUGnl/GPh4hF4pgsBpweG4KYovsLB6IEfWEcHiuKohL0RUgmkkiSiNlmwuY0T/LqkwmF0UEfmqqSXeBBOM1wSjlAQQLeCFn5LvRGHZqmEYvECfoijPR7Ob6/g3g0wVDPGMaTudcC2F0W7C7LpLFikTjhYJRYJIGqqKnNxiBjsZkwWScfb8eiCYLeECF/lN0vNpGMJxkb9NHTOpRuY3WacLitk/oFvGH8Y8FJRkFOkSflDJ8BqqoR9IYJh6IoSRVJEjBZjFjspkn9NE0jGorhGwtin7hu0BcmHpv4HE06HC4rOsPkpUXTNKLh1L3HoxP3LgroDTosdhNG8znYQi4xJJIK8USS6zbUsXxhaUqDRBDIyrAxryqP+5/Yx4ZV1USjCSRRJOu0CP7pz1Gnkygu8HD4WB97DnVSmOdmaDRATVk2RqMOURKxWgx094/TP+TDYTNhNunx+sNEY0kSSYXR8RCj40HsNhNlRRk8v/04R5r7KMhx4Q9GKSvKwHAWRe8LgSyrFYGU4bskP5/Pr1p5Ua/3ZkIQBGRRxGMy0xcI4I/GiMQT8BodhNPhi8aIJFLpXtm2yb9/nSRh0esn2kVJqMq0Y0DqdxmIxVEuISrTWczizcA7fT+7JJ2DBaur+N+v38/lNy9h1wtHWXr5PNxZdq58zzIGukbpOD5AOBidYqi+Fq2NvQz3jXPdB9Yw0D3KK08dvqScg3AoxpO/ewVnhpVr3r96kmNwJqiKSm/7MC8+uJsdm4/gGwmkaOLyXKy7cRFrrmvAk+NI/7C+ec/PqJpfRHVDCQ/97AVi0QQ3fGgtJXPyePSXWzi+v5OqBcX89b/cSWa+i0Q8yZZH9vHQz1/gxo+sZ2zQx67nGxkd9CFJIosvm8v196ylbG5++hojfeP840d+TiQU4ycv/B0G06nPXVU1/vyj53jwJy/wvce+RM3CEgAad7by+G9fpqd1kKGeMVRV43++ei+SLhWxE0WR2z59BTd+eF16rMB4mC2P7uWVp1N1GbFoHEkScWbaWXllPVfcsYzsQnd6Xq2Hu3n4/16iu2WQga4R4tEEv//PJ/nz/25Oj3nN3au56/NXTQoyvPr0IR748XNEI3HGh/wkEwq/evWb5BRNnxuZTCgc29fO0398labdbUQjcfRGHVULith4+zLqVlRiMOoQBAFV0di75Rg//Ye/cNPH1qMqKtufPMhQ7zjJeJKC8myuuXsVq69vwHjac/SOBHju/l3sev4IA52jxGMJJFkiM9fJ6usa2HDLEtzZ9nf8gnohoGoa/mCUUW+IYy0DHGjqJhpLcMtVDSyZX8xXPrGJB5/ex8/+sA1ZlliztIKcLDuVpVnp/H9BgOqybCwmPTlZDiKRONt2t+APRnE7LJQXZ2IETAYdq5dU8Ojmg7R2jnDDFXXUzylg284WmtuHyPLYeGlHM+3dI1y9fh7z5xYQiyfZ8uoJguEYFrOez3zoMi52rHRhXipvN6mqHB0aTisQv3FM/r6dK2J/sWCUdczNzqIvEKDb62MkHCbPYZ/2PELTNDq9XsYmCq0X5OZO4tqxGfTk2VLOYq/fjy8aQ9O0aX9b/liM0XDoLbvvWcziYiKhRhiLdRJXU/U8Tn0+VjkT4W2aivR2wiXpHFjtJnIKPTQf6uLonnY+9o0badzTTsuRbmRJIugPT0kRSR3Vp4zbWDRBMqEQDcUZGw5wdF8HkiSyYHXlW3NDbxFaDnUxOuilen4xC1ZVzahPX8cIv/+PJzi+v5N5y8opqclDSSq0HunhLz95nsGuUe787CZcmafy7Y7uaSfsj7Lm+oXsebGJx3+7jfJ5BWQXebA4zOx45hAvPbyX2//qilQHAcaHAzz1++1kF3lYvqkOg0lPx7E+tj22n/7OEb7wH+8lZwbOzJngznGy8ur5+MeDbH1kH/7xEFfcvoysidoUQYCK2sJJffzeEMf3d2IyG1h7QwNOj41IOMbRPe08/IuXSCYV7vzsJkwTpw92t5WlV8xjwZpqXnnqICcOdbH2hoVUzT/FalFcnTvl9LFhbTWeXAchf4Rff/cxBrpGOBv2bz3Gj75xP5IkseTyeWTmOfGPhTj0ygl++e1H+cBXr2XJhrnIpxWWBn1hnv3TDuweK3OXlLHqahujAz5eefogP/mHv2BxmlixqT7d3jsS4OjedhxuG3MWlmJ3WQgFIhze0cr9P3oOnV7mmrtXoT9HceulAEVRaWzuRydJ/PNXbgTg3sf20N49SkNtIZkeK59439op/T71/lOvybLEZz64Pv338oVlLF9YNqWPJAnUVudRW5036fXrN9ZPaXsSqxaXs2px+fne1hvCgtxcSlwuToyO0jg0yO6eHlYWFb1hZzJdK0AqWWokFEbVtHMUFl94WA16VhcXsaWtndaxMQ4ODJyRlckbjbKnt5eRUAi9JHF5efmkdFa7wUC5x4NBlvDHYmzv7KQ6MwPjNHSmhwYG6A8Ep7w+i0sDmqaR1GK0BraSaazEY3h3MRPGlCBtwZcZjBxjOHqCJRl3U+u8AXk29eucuCSdA4C11zfwx/9+huLKHExWI+FAhGRcwZphJhqJEYvE2bvlGEO94+zf3kxVfRE2h5mj+zrwjgYI+MKU1OQy1DuG2WpAkiXcWW9tAcmbjY5j/UiSSF5ZJmbbuVl64tEEu54/wsFXTnDN3au46WOXYXOmqP/Gh/z88ftPs+2JA5TXFXD5bcsQxVQmb3fLIF/83t2U1xaQmefif/7mT8xfVcXdX7yagDdC465Wmna3omlXpK8V8kewOsy857ObqKgtRJREgv4Ikiyx7fH9vPjwHu763FWv674FQaB0Th6lc/IY7Bnj+N4OEASWbayloq7wjP3yijO44zMbsTnMODJsEznhKs0Huvjel/5Ay+FuRvq8FFZkA1BQnkVBeRaRUIzelkF6WgapX1HJmusazjq/zDwXmXkpZoUHf/ICA12jZ2zrGwty3w834xsN8aXv383yjbVIcqqW5tArJ/jFPz/Mgz99keqG4kkOWzQcJ5lUuO4Dq1lxZT16o45kQiEjz8WvvvsoLz24Z5JzUFSVy91fugZPjgO7y4IgCChJhf3bjvOjr9/P8QOdrLpmfnrelzJEQSDLY+XAkS7+/PheNGBoNMD65VXo5AsRLX/nwWYw8ImlS/j65ucYDoX50Y5dxBWFJfn5WA2nNnpN04gpCl1eL11eH/l2G3POUp8gCgJuswmbwYA/FuPp5hMszMvFNSHM9mbBIEmsKCyiIS+XXT29/PnQYTLMZi4vL0sXOmuahi8W45Gmo7zU1k5CVbm8vIxF+XmTThgMskx9TjYVHg+Ng0P8+fARytxuNlaUI06wQ6kTiswPNR6lL3DpaBzMYiriapBtgz9kScYH33XOgUX2sNjzPoaizWwf+slbPZ13FC5Z56CoIodNty+jqCoHvUGmblk5nhwnBpMOWZZwuK3EIglu++QG7C4LDreFjFwHZpsRvUFHUWUOJTW5WO0mAt4wgijMyEB+N8E/HkIQBRxu67kbA+MjAY7t68AxERE/PfffmWnnspsXs+3xAzTtbmf5xjpsE/n6JouB8nkFyDqJgrJM9HqZ3OIM7G4rBpMBWSfhHQlyeqGkKArULCqhpCYvXcxqsRm54UNree7+nRzZ2Uo8mnhTI9WiJFJUOZk1SBRFMvNdFFXlMNLvJRyMnqH3xcHx/Z30tAxSs7CYZROOAaQKgWuXlVOzqIQX/rKHjqP9UyhUy+YV0LC2Jv0MZZ3EmusW8ItvP0xv+/CktpIkUjY3f/JrskROkYe8kkwC4yGi4Zkx0LzbIYoCcytzkWWJUCiGIMDC2kJKCzPetXTAM8GVlRV0jHv5+e7d7OntZSgYpDozk3yHDateT1LR8MeiDAZDDIdCxBWFuxfMP6tzAFCdkUmFx82+vn6ebWkhmkxQn5OD3WggllQIxmK4zWY2lJeRbZ3ZWne+EASBQqeD9zc0MBwKc3R4mO+9vJ2X2tqpzsjArNfhi0ZpHBpif18//YEA87Ky+OjixdgNU6k452RmcmVFJT1eH90+H/+x7WW2dXRSleFGFiUGg0H29/XT6fVS4nLSOe4l+iZQvs5iFm8mBEFEJ5gwSnakWUao88Il6xxIssiSDafUPz05Tjw5zkltSu0mSudMPm5/rSF8Msp7KUKURNBSaRAzQdAbYqh3nJwiD7aJ6HF6LFHA4bGRketkpH8c72gQm8uCBljsRiQ5ZRTJehmdQYfRnNoQJVlEEASSyclFd3qjDmeGDd1rCiVzij2YLAYC4yF8Y8E3PVLtHw+xf+txju1rZ7hvnHAgSjgUo79jmNyiDLQZPssLha7mfuLxBNULSqYYnnqjjtziDGSdSFtTL/NXVXIyR1vWSWTkOLA5Jos+2VwW0CAem2po+EYC7H7xKM0HOxnt9xIORQn5o/R3jFBZX3jR2J7eaRAEAaNBR31N/rkbX0Iw6XR8aFEDWVYLP925i7bxcTq8XvSShCyKaJpGQj1F45lrs02rDPxaVGV4uK22ll5/gMFgkGdOtLC9swudJKKoGklVZXF+Povy8y6acwCpiP/60lI0TeNHO3dxfGSELp8Pi16PNFFvEZqgLV1WUMBfrVjGgrzcaWlPLXo9d9bX4o1GuO/QYdrHx+mZGEsUBKLJJDpJ4n3z5+M2m/jxzl2XlHPwwKuH0DToHPFytHuQXJeNj29aRlGGi3AszrMHTiDLIiP+EK8c78Sgk/jmHZtwW02MBsL86eUDHO4cwG0zc8fKOuqLc5ElCVXT2N/Wx192HKbf68duMnLbijpWVBUhSxLPHDjOE3uPEU0kaSjN49bldWQ5rGiaxotHWnly33FGAiFcFhNX1Fdy7aIaNE3jSNcgD+48QufwOBaDjlU1pdy5qv6inm7F1QjHfM/QFdpFXAnh1BdQZb+CfMuCNDtXk/cpjvme4aai/0AUUr+1uBKmyfcEMSVEnetGzLILTdPYMfJL9IKJSvsGDo0/xEisFZ1opN51C/nmegQk9o3dS0TxUm5bS+P4YwSTI9h02cx1XEO2qRpRkCd+5xFag9voDR8gmBhCEGRyjNXUuW7CJM1MaGwWZ8cl6xzM4o3D6bGiqhreYf+M2ifiCrFwDEO+a1L++klIsojJaiAWSRCPpZg4BECa2OCFicIPQRSmUDG+lq9PkiVknTxpkThJQ2uyGFCSKtHImxupbj3Sw6+++yjH93fgznJQXltAdqEnlQoRjqMxVRzpYiMUiKIqGlbn9MquRnMqZS7gnSw+JsliimHoNZ9D+u/XfB6Nu9r4xT8/TNeJATLznJTXFpBTkkk8miDsf3NPS2bxzoQgCDiNRm6rncfSggKeb21lS3sHbWNjeKNRZFEkw2ymzO1mSWE+a0tKqPScWaDoJAyyzI1z51DicvFgYyM7u7sZDIaIJMFhMFDidLK4IB+n8eKfDJv1OjZWVlCbk83TzSd47kQLbePj+GMxXCYT9Tk5XFVVybrSErKs1rTWwXTwmM18buVKlhUW8lBjE3v7+vBGoziNBlYUFXLrvHmsLC6ieWR02nqEdzMGvEGeOXCcey5bwtUNVdy3/RD//MAL/PjjN6OoGp0jXva0dHPz8nl84bo19I75cJiNROIJfrdlHyOBEJ+8chnHe0f42eZdfO2WyyjKcNI17OUf7nuWW5bV8t41C/CFo+S57EiiyNamdv647QCf3LQci9HAY3sa+eXzu/j8dWtoHRzjwZ1HuHlZLcUZTvrG/el6k35vgMf2NFGS5eSu1fMZ9odQzoPS+vUgqcbZNvhDukK7qbCtw2Ly0B9pZOvgD1mW8UHK7an6plByhMHosUlUyCoKgcQgEcWHop0SUPTGe4gkxxmJtWKQrBRaFuGN96ATjWmtCH+8j9bgywyEG8k0VpFvrqczuIstg9/nmvx/xKZLnbwn1AjHfZux63IotCwhlBjmqO9ZomqA1VmfRnqdVLWzOIVLa0WYxQVF2bz8NPtQJBjFdA51YEkW0enlCW2IqRFyVdVIxBJYbCZk+aTi5FSe9JnEBFRFRUkqk1g6NC21hMWiCURRQHcWas+TUJIKSuLMVIAzRcgf4cnfvsz+bce58zMbueUTGzCYUvSdY0N+hnrGGO7zvuHrnC8MRh2CKBCPJKZ9PxFPoirqOZm7zgb/eIgHfrSZ4/s7+NDXbuD6D61BklN51L1tQ/S1D6Fd5M1uFm8uVhYV8sw9H0TVtCmsQldVVXJ5Raqg+fT3ZFHkPfPruLV2LqIgoJuGjUgQBAyyTIXHTanbxYcXLUJFm3BGhRRxhCAgCsI06stnhlGWWVKQz8K8XFRNO+XbThBRSKI4pUhZFASuq6lmU2VFmo70bLi8vIztn/w4mqalhM7OMDe9JFHkcPCRxYu4Z2EDKqQFJU7elziDexMEAatBz4byMtaWlqTE4CYWVFEQkCfGaMjLZfOHPzTtZ/VuRm1RDqvmlJBlt/DZa1Zx+3/8nsbuQUqz3CQVhZr8TNbNLcdtNVGdl4EgCPjCUbYebeebd1xBXXEudUW57G/vZUtjG+9ZvYA/vbyfytwMPrB+EZIoTAqo3Lf9INcunMOi8gJEARJKDT99dgcdw+OEY3EisQQZNjMlWW5Ks93p6HwiqRCMxbGZDBRlOCnPObfD+0bREXyFrtBu1mR9mlLbKgREqpKX8+rw/3Hc/zxZpmpsuvPLmtA0lZFYCxX2dcxzXo+AgIaGiMjJXV1DQ9ESlNvWUe++GQGBLGM1Wwd/yEDkGFZdFqIgYZbdXF/wHRAEBES0if86gztYmflxREF+w9ojlzpmnYN3ITRNIxiPo5MkEkpKUEcniph0KTpKNI1API5h4v2kpiGLIiY5JeI2059URW0hGbkOxof87H6hidXXNUyN6J8Gi82EJ8eRSikJRKcY7tFQjJE+L3mrMrG5po9kzxSxSDwV7VY1BOnUnPxjIYLeMIaqXBwTirCiLKYM5Fgi5UCcNq9wIErQGz77xQQA7axpMUO9Y/S0D5FbnMGGW5disZvSRYaqoqQ1HM52DQ0ueOpNXkkmOr1E5/G+KYJWqqoy0jdOPJqgoCIbcWKzO1/0tAwy1DtOUWUOm+5cnnaKNE0jHk/iHwulC9Nn8e6AJIpnVPk903vChMF6LiP79LZcwBIMURAQz8M4FiYMddMMhS/P9kymGzt1f2/8BkVBOKvRL8CM0q/ebchyWDFOnC47LEYcFiMD435Ks1KMc9lOG3ZTKn315Ho14E2t04UeZ8pBk0Ty3A76xv0oqkrb4BjLq4qQJ1I0T66nSUWhZ9THfz2+lR88tT09h7JsN+FYgvkleSypKOSrv3uSuqIcbl85nwUluRhEmXy3gzVzSvnF87vYfLCF25bXsaK6CJP+4kXH+yJHMIhW8s0NiKSekVn2UGJdzu6R3+GN95y3cwCgE01U2zciCVO/byf3Np1goty2Nt3Goc9HL5oJJofQUOGkorUgoKgJNBQ0NKy6DCKKHxVl+qjiLM4Ll96KcAkgnEhw918eYH1JKU3DQ3T7fFS4PfzVsmVUZ2QAGnf++T6urqzk2MgIbWNjFDocfHzxEhpyc2e0OUMq7/zaD6zhj997msd/u22CXScbnUE+ZfzFEmgaGE163Nl2KuoKeWjHS7Q19VJYkZ0Wy4qG4xzacYJIKEZJTV7acH9dv28tdQrRcayPga5RcktSUZ9kQmHLI3vRGWTK5uVjsqRYTix2EwaTHu9IgIHuMYqrUkeXqqLSdWKArhMDZ7yUJIvoTXrCgSjRcPyMfOKamhKNO11DQdM0lKRKV/MgHcf6KSifvnBSEAWMViPxWIKQP4KqqhdMiXve0jJcmXb2bT1Gf+cIecUZCBPKur1tw7Qc6cHutlJZX5QSmVPO3ztQJxwug9mQ3gA0TSMRS9JxtI/etqG0fsSbjRSVX5Lh2CgevQuD9OZT3PkTAXSiDqM4tbD0fDEe92KWzOhF3dsi7zaiRAkkgiS0JFkGDzpx9rh/Fm8PxJNK6jSF1H6hqBrSaU6UJIpTfkMnT7MSyqnT5ISipNpO9IlPc9KsAbIk8PVbL2fjgsr0viYIApIoIgoCn7xyOVcvrObJfcf4x/s3c+OSeXx84zJkSeTqhmoWluWx+eAJfvj0K+xu6+Zvblx/IR/HJEQUL0bZjiCcegaiIKIXLagkiauhs/afbpcQBAG9aMEgnb1uRxRELLL7tL8lEATUiRQlTdOIKF7aAtvoCu0hnBwjoUUJJ8dJqJGTh4izeIOYdQ7eKDQFNdmKqJsZz//MxtTQNB9oCQQp83UNEYzFODDQz+dXrEAvyfxk9y7+dOgQX169GqteT0JReKm9nb9duxaHwcjvDh7gz0cOU+hwkDPDojtBFLjm7lUc29fBoVdb+OHX/sxltywmvywLSRaJRRP0tw/jznGy5toFGM0GFq2fw8FXTvDw/70EpAq6NU2jrbGXR37+EvOWlrH08nlvjJVFSNUcNB/s4tn7drBoXYpRp7dtmId//iI5hR7W3bAo3dxiM1GzsITWwz386juPcOsnNmAwGfCNBVICXz1jU3LrT/U1kl+ayStPHmTn5sMIAugNOpJJhewCd7rI3ZVpI7vQzfanDrHnxUYioXLQoL9zhEd/tRXDWViTZJ1EaU0u8UiCPS8eJafQg8VuIplU8eQ4yMo/VYAVjybwjQVJxJIoSYVYNAFo9HUMk0wqKZVoqxG7O1UQ7slxct0H1/CH/3qKH37tPm79xAZszpQGwZaH99LXPswNH16bUqAWTrLBnx+y8l14cpw07m5j1/ONlM8rIJlU6Goe4Knfb5+ipgypDWB0wEciniQRT+IdSUXshvu89LYNIutkZJ10QQrKW4Id/Ljlt9xdfAtLPWenib0YOOhtIseYSZm1GIlzR64jyQhxLYlDZ5vy3quje6lzzKHAlHsxpnre6I30s314N6+M7uEbc79AgfntMa9ZzKJ9cIwRXwizXsex3iFiiSSlWWdfT3JdNlwWIwc7+lkz14g/HKV7xMvldRVIksjCsnz2tPYw4A3gtpqIJxVEQcCk11FXnMue1h5WVBdjNxuIJZIkkioOi5FgNEZSUcl12vjI5UtwmIw8sqeRj29cRiyRJBxPYDcZuXPlfFwWE997fNsFcw6SahwQUkb4BCRBP1EvcGq91zQtFZVHQJxYp06m7mialjbINU1JG/KvhSDM5GROQDpLEEFFodH7BI3ex6hz3UShZSEmycVR39PsH7tvBuPPYiaYdQ7eIDSlGyX4Y0TX9y7cmMRQY68ASSTTDa9rDKMss6qomIbcFNvSFeXl/Hr/fsKJBFa9DkkUWVVUzNL8AgAuLyvjp3v2MB6JzNw5EARsTgsf/+YtPPrLrRze0cLv/+NJVFVFkiQURcXhsXLdB9ek+1TWF3Hbp67gqd9v5w//9SR6gw4NSMQSVDUUc83dqyipyTvzRWcIs83I3CVltB/tY/fzjcTjSXyjQfJLM7nqvSuZs3gyn/PV71vJ+JCfwzta+JdP/xq7y4qkE6msL2LZpjpefuLAtNcxmg0svmwurUd6eOmhvbzy1CEMJh2SLHHnZzay5vqFADgzbKy9fiFjg34e/NmLPP/A7pTOgZaK3s9ZXMrhV05Mew1JEpmzqJR1Ny5k75ZjfP/Lf8RkNSCKEtffs4ar7lqZXph724d5/NdbGen3EovGGeweRdPgt//2OBa7GYNRR92KCq774Jo0k9OVd60gHk2w7fH9/M9X78VoMZBMJDFZDGy8cxlX3LYMg+n1R3wzc11cfttSouEYv/v3J1J6CULqvupWVFBUlcNQz9ikPqqi8qf/fpqxQT+xSJz+zpSQ25O/e5mdzx7GYNJjcZj48vff/7rndRI7x/azxD2fI/7jLHEveNMj7msyl51X+yO+48S1JKsyFk9575rcyy/UtC4IKqyllFmKOeRrequnMotZTII/HOXpA8exmgwc6ujnqgXVlGa5CUanJ6oQBAGr0cCNS+bxYmMrXSNexoJhMu1WllUVIYsiNyyeS1PPIL96YQ+5LhuKqtJQmk99cQ53rV7AT57Zwe+37sNlNZFIKuS67GycX0nH0Di7W3oQxVTk/HjvMJfXpURVRwNhtja1EY4n0EkSrQOjXLngwgUje8MHkQQZi3yqlsGtL6YvfIhgYhiDmAoMJbUoY7FOjJIds5wSENWJZgQEwsoYdjF14h5V/PgTgxilqcGLCwFNU+gK7cKlL2aB+3YkQUbRkoSTY6japcO4dbFxyTsHmqaBOowaex5NGUaQ8hANayci9gJJ/38iWT6IIGWgqX7U+C4EKQ9BLkeNPosa24KWPEgy8N8IghnBsBZRV40SfRYBHZrSiaaOI8g1SMaNIMioiWNoyTZE42UIggk1cRwtvhvR/F5Qx1AiD6DGX0HAiJZsR5BrEI1rEQTTjO9LEkUcp4kDOQxGIokEqpoqBBZhEvuGVW8gqSgk1fOj0hREgdziDN7/5Wtoa+ploGuEkD+KIIDBpMedZaf0NH57SZZYfNkc8koyaDncg3ckgCgKuLMdVNSl2HtON85u+fgGEgkl/VpWvpv3f/natHEvSiJ3/NVGZN1rjoA1jdql5dQtq6DlSDcBbxij2UDJnDyq5hdNqY0orMjhnq/dwLF9HYwP+wEBT46dyvnFhP0RKuuLyMqfGlUSBIGyeQV84KvX0XKoG99YEEEUsNpNVM4vmvScFq6rwZ3joONoH/7xEHqDTE5hBtULSxgb9FE2Jy+tsPzaa7iyHLz3C1fTsLaGsUEfmqZhtpqYs7hk0hGq2WqkvLaAvNLUidOSDfOmjJdbnDHpWekNOm762GXMW1pOZ3M/kWAMg0lHXmkm5bWFWGzGdHtBTAnAfehvb6Cyfqrgm04n8dFv3JTWqDjZZ/W1C8gt9tDZPEDIH8Fg1JFXmkVlfSG9bUMM9oxN0lEQBIHSufnklZz55Ey6AIJgUTXGcX8rHy9/H4/1bcab8OHSOwkkghzwNjISH0PVVErMhSxy15NQE7QGO2kOtpFQE2QZMljqbkAv6hiOjbJn/BAxJUa2MZM6Rw02nZWkmuSI7xgdoR4UFJw6B2szlxNTYxz1n6Aj1E2Dq5ZSSxGSIKFoCkf9LbQE2lHRyDflUOuoRhRE9owdZMvQqxhlIyOxUXKMWSx1L8CX8HPYd5y+SD+rMpeSb8xJbehqko5wD0f9J1A1lSpbGRXWUjRN5amBFym1FNEW6sQoGqh3ziXPdOY8Ym/cT3e4j7ASZig2QkxNcFXOZQjAswNbuLngagCaA6lnU2krRS9OX8geU+I0+ZvpDvehoVJtq6DaVv62SIWaxaWB+SV5lGS58IVjbKgrZ3VNSargXSezbm4ZRr08ZZ/QyxJX1FfisproGfWTabewoDSPTHvqJDbbaeXz167hYEcfgUgMm8mA02JCFESq8zL59FUrONw1QCgax2U1U5zhRBJEMu0W8tx2RvwhBAEuqy1nSUUqcGc16inwOOke9aKqGsurilhScWaxzbNhKHocb7wXg2RD0DTG4900+Z4iw1hBjukUtXuJdTntwe3sHf0jNY5NGCQbI9EW2gIvU2BpwKlPzS3bVIMsGNk3eh9V9g2omkJPeB/j8U5yTbWv85M5FwRsumzGY130hw9jlGwMRo8xHG1B1U7ZL5qWKm5OqGHCyXEULU5UCRBOjqGXLOhFc5p+dRZTMftkiKKEfgHICLp5aIkjKOo4kvkOBNGBEv4NkvkmIAO0IFp8J+jqEeQqBKkEhIMg2BB19SDoEUQnAGpsOyh9iKZrEUQ3SuQ+BEFGNG5ES7ajxrYiGlaCYEJTOlAjjyOa7wLBiCCVIghHEKSs1LhSNuf7USVVlZHIqULasUgYi04/URCXUsgcCgXT7/uiUXSShO515LILgoDFbqJueQV1yyvO2V4URQrKsykoP3dB0xV3TI6qerIdXH/P2vTfkiRy5V0rJnfSSOf+l8zJo2TOzE4isgrc0xrnwCQH57WQJsTNXitwNqWdLFE+r4DyeQVT3rM6TBRVnbm/KApk5DrPqY6cXejmmvevPmubaecmidQsLDln7r8oCuSXZZFfNn19hKyTufWTU6PXsk6iuqGE6oap41ctKKZqQfHk60gi131gzZS2FxrNgVYyDW6yDBnkGLNoCXSwxLOARn8zXeE+Kq0lE7m2KSN3PO5j3/gRMgwu8o05SIKEIEBcTfDHroeYa68iy5xHR6ibqBJldeZS9o4f5pC3iQXOWnRiiqdbFAR0go4sQwZbh3eSZcyk2FyIlOILQBIk8s05KJrKYd9RHDob5dZico1ZSKKER++i1FKIfSK1SC/qyTVlsXlwC1X2cvKNOakCyugwLwxtp8Zajl7Ss2XoVQyinixDBo/0PsPNBVdTbC6gOdDGztF9XJO74Yx1F8FkiJdHduHRuyi3FhPXEsiCRDAZ4oWh7WnnoDPUQ0SJUGIpnNY50NA45GtiMDpCvikHVVN5uPdp7im9g2zj2QXLZjGLCwWzQce6uWXYzZNZ9vSyxOKKqWs0TOx1Rj1r55ad8f2SLBclZ0hPqszNoDI3Y8rr2U4bVy6YPtJuNxtZPafkLHcyc4SSYxz3bU5H2QVBIsc0l3mO6ybl+bv0hazI/CiHxx9l9+jvUTUFWTRQYGlgnuMaDFIq+JNhqGBxxvs47t/MSPQEkmgg01hBvnkBmnZxNHskQabWeQN7R//Aq8O/QC8aselyqLCvwZfoSbdTtDgtgS0c920mrobwxnuIKkH6I0eQBB0LPXdRYF5wweYVDsWIRBJ4Ms6deTHY76WzY4R59YVYLG9+ndtMcEk7B5qmoSl9qLFt6Nz/B2I2mpSHEvwJmuEyBNFxxr6CICPo69ASR1GVVkTjZa8dHUGuRDRcBoIFTelDiTyEaNx41jkJohVR34CWOIAgl04z7swQTSbZ2tHBwtw8TLLM0ydaWJCbg1mnA1LCPts6O1nV0YHbbOaZlhaKHU7cppmfTrxtMRt8nMUMsWN0H82BNr599L8JJcMk3AkWe+bj1jt5JbobAVjqbqDYkjIWDKIeSRA5EWjD41lEha0UnaCjLdzJQHSIT5a/H72oRxIkjvpP4I372Tm6j6XuBSxy1SMJIoqmIAkSkiRRbCnAY3AhToqYa/gSfg6MH0EDWoLt1DvmIgsyFbZS3Hon+aYc6p2nIn1m2USFtSTtLAAomkJXuBdBg+UZi5AEkc5QD8cDrXj0LpJakmXuBjINHlQ0Gn3HiSjRsxZl60UdFbYSFjjnpWkIg5y9OPG1iClxjvpbOORtwqVPrbF9kUE6w32zzsEsZnERkW+aj0tfSEKNAhqSoMMoOTFJDk7fOAVBJNdUi12XR1Txo6EgCwZMsguDeMr4lUU985zXUmxZSlKLIwoSJsmRoiRVE5jlU07SysyPo2hn1xZakvF+5rtumfSaRc5gY+7XMEhWxAmTNcc0h3XZnyeuBgEBg2TDIFooNC9GL6ZsGFHQUWheiNtQMu217LqzB/POFz3dY7S3DnHltfPP2dbpsmAw6jAa3r4EDZe0cwCgJfsBGUHMTfGOSXloWgi00BQhp1SHmRdkClIeCEZAQpDLUCNPnGESF57j3azTUZORwY927aTD62V+dg531c/HotcDKZ7tpQUF/ObAfppHRqjOyOQzy5eRYbGcc+xZzOLdgJgSo9F3nG/Vfhm9qKcn3MeOsX2Mx32UW4v5cOldHA+08FDvk2QZM/hgyR3YdFauz9tIZ7iHV0Z2s2tsP3cX30pMjSMKIkYpFYXUi7oJzm6FiBLFprOluenlsxxla5rGSHyMx/o284nyu7FIZu7rfnSSyNBMoQEJLYEkSugnCvyMkoG4GkdDQ0Ag05BK49MJEgKkNAPOAqNkwCyl0iROzjdNRzwxZlJLopwlaqhpKoqW5Krc9Sx2LUiZJAKYpIsvMjaLWQDcc1mqXsf0NjbOLgb0khm9NDPaaEEQseoysOqmnnScDp1owmUoOmsbQRBw6M9+gi8IqXSh1+qXSYIOt2HyybKAjF0/1bjPkMpPayNg0WVgOcf8AcZGgzzzxEGaj/WTSCh89stXkZFp44E/7aTxcA8Oh4krrq4nN8/JM08cZKDfS8AfJTPLzic/u5Fjjb38+Y+vMjTgZf+edm69cxkV1Tn84dcv035iiKSqsHptDRuvqaejbZjHH9qLTi9x590rcTjNHD/az2MP7kHTIJFIsv7yeaxcW81bmWV5yTsHqdqCCBph0Mxomh9B0IEwET0TdKClKCrRoqC9Rg345Kd3uoF/crNUfaAlQdCjKSMgTRzbTWysaGq65gFO30yFs447E0iCwPycXL6+bn2a8lcWRZigGBUQqHB7+Pu169Kb+nTUbbOYxbsVjf5m8k25ZBo8iIJIUk1ikcz0hPtIGrMwSgYWuurIMmTwn80/5YMldxBT4vgTAcqtJbh1Dn7c+ltiapxSSyFRJUaTv5kScxEdoR50og6Hzk61tZytQzsoNRdikAwEkkHssg1JEFFJrQGqpqFqCpogElGiKFoSj95FX2SQ/ujgpHmbZRMjsTGSaooJRRREVE1NCwGpmjpxOiGSZ8xm1+gBesL9WGQzzYF2NuasQZpgDZn6ez+7cyBM/Hc6TJIRWZDoDPZg19voCHXj1NsBUDQ1nQecmpeKUTKSY8yiI9TDAmctTp2D3kg/ZtObo3WREkNMPfOTz+9SRYqB5mQd2oVd/08+55P7y9vpOVuMr1/UcRbvLmiaxoG9HYiiwJf//nr0ehlZFmk63MOJ4/18/R9voaV5gN07WtCtqKCjbZjb37ucsopsvvGV++jvG2duXT7X3bSQo0d6eN89a9JU47feuQxREhgfC/GD/3iaK66uo7gkgw1X1rJvd3ua2juRSNLVOcK//+D9NB3u4WhjL/ULi7DZ3rpMjkvaORAEAaQCBHkeSugPSMarUaPPglSKIHpS6nvyXJTok0hmJ2psD2qiCclwKhdakHLRlB40pQtEO2BMFw6rsRcRDatAtKNGHkIy3zxxXQdoQdTkEQQxByW6GU6n/hL0gBEt2YWmDoFgAsF63op/AkyrMqqdNs5077+TIQgCeoMOu9t6VnrQ80U0GkcSRWSd9IY30HA4hl4vI0mvfzNOJhWi0QTJpILVakKWz77xnhQci8WSiKKA2Ww4q2DdxUIoFMNgeGP3fqFwItDOfOfc9O/KIptx6R0MxUYJJsM8M/ASESWKUTJwT8mdAPiTAf7c/Rhd4T4Mko41GcuxyVb0oo6/qvgQ93U9ij8ZpMpWxrW5l2OSjFyTdzmP9W3m20f/m6SmUGTO52Nl76Ul2MPTAy/SFe7lWKCFV0Z2c0fh9RSYcpljr+Kfmr5PkTmfHGMWFvmU4bwhazU/b/sD3zjyryxw1nJn0Q3sHN3PyyO76I708kD347wwuJ07i66n2FzAyozF/F/bH0loSdZmLqPGVo6qadh1p9IDJEHGKBkQzqIqJgoiBsmQdixgQrFY0HNd3hX8uPW3uA1OPHonOcYsBATu7XqEtlAnYSXK/7X9kXJrCTfkb2Rt5nKeH3yZ/27+PyJKlBxjFn9ddQ9vxpakovJQ72O8OPQydxfdwYqMpW9ovJNGsMDMFZnfLhhPePl522+IqXG+VPWZSd+zNwpFU9gxtoe/9DzKLfnXsyZzxbk7zWIWbzISiRTlt9Npxmw+KdAJvb3jFJdkIOskrHYjOr2M3xcmO8eB3W5Cp5NwOExEwvGUKruY+v2f3NsikTi/+fkWYrEEiqIRDEZRVQ1JEtNtT0IQBEpKszAYZIxGHbIskkxcnJqNmeKSdg4ABMGMbPsCSuj/SPr+FnRVSJb3I4ipoyjZ/ncowf8hMf55BP08RNN1CIL9VH/9CgTjJhLeLyNIuUiWexD0qaJRUdeAEv4tmtKHaFiLZLot1Uc3D8GwAiX4MxBMSKbr0OJ7OJnzJ4h2ROPlKMGfkxj/PKLpqlRf4UKl/Fz4NKa3C3R6mStuX8pltywmHk+SSCjodDNzgFLtk5jNU8WontvcSEGBm9q6AuQZsOTEYgmSSXXaYqO/3L+bZSsqqKw8f4XJk2hrG+LJxw7wwgtN/Of330flOYqhVVVj185WHnt0P0ajjs994So8nplR1l5I/P63L3Pl1fUUF5/7qPdi486iyTTBZtnE1bkb0n+vnIYqNNuYyWerPjLteKXmYj6S9SGyM1LrQzSWIBiKYTbpudx2GTfmXIVOlognFAL+KDWuCmrspwr4x7whrAYDOlHigyW3n3He+aYcvjnvS+m/VVWjRj+HhtJ6jAYdo94QNosBvS61vK/KWMKqjCVTxvnBwm+n/z3PUcU8x9npEfNM2dxacM2U1yVR4vLsNVyePbWA/H3FN59xvBvyN3FD/qazXvPtDk3TCCthBqJD5Jty02ll7xRIgkSWIRMNDemCR/YFLJKZAlP+pFqYWczi7QSdTsJmN9HdOUpXxwg6nURGlp2Kymz2726nr2eM3u4xNE3D5bacMagmSSKRSIL+Pi8ej5WujhHGxoJ88KPrGBsN8OufbwVSAbLx0RDBQJThoQDmCRshldjx9gkuXPLOQep0oBjZ8U/Tvi3q5iC6fnyW7hI621dgmrVPkMuQzF9BECYfYQqiDdn8PjC/79SL5jtOb4Gom4fo+v553MgpSKLIkvwCcmxnWpAFlhUUUGC3n+H9dz7Gx0M0NfZSUZlN3gyFspoaexkZCXD5FVPpP6+7/vzEsQ4d7CYUjrF+/Zwp773/g+fPJvRaVFXlUvWlXBqbemfUXpJE1qytISvLzkMP7n3D13+9+MSn3l48/BcSsXiC/7vvFf7+r64CoLl9iKHRAMsWlLCvsZu66nyyPFZ6B7w8s7WJT929dlL/vUe6mD+ngEz3+TltsXiSp7c0MX9OAXMrcth7uJOGeYVkumcNsosNDY3mQAtPDjzHh0ruIt/0xjVa3kw4dHbuKX3fuRu+DsiiRIOrngZX/UUZfxazuBAQBIG6BUWEQjGee+YwaHDT7UsoKcti6YpynnrsABaLgSXLy8nMslNYnJHOSqiozsFiTQUT8wrc2B19PPvkQTZeVU9RcQaFRR5eePYIFquBlWtSwZeerlGOHe0jFkuwZ2crVpsRm81IWUUqWGi1G8nLd804qHmxMOscvAthlGW+vn79Gd8XBYFvbthwxvffKQiFYjQ19jA6GkJVVQoL3cyrLWR0JMBLLx2l8UgPPT1jZGTYWLW6CrNZT3vbMCdaBlAVDZfLwpy5eZiMepqaenniiQNoqoaqalgsBlauqiQSidNyYpDu7lHmziugqMiDJImoqsbAgJejjb3EE0nsdhO1tYUYjDoOHeziiccPYDLriceSZGbaWNBQTCAQ5fixfgb6vSxeUkpOrnPiCFNjfCzE0WN9+H0RRFFg6bJynE4zHe0jnDjRj6JoOBxm5szNw+W6sEXjoVCMQwe78Pki6A0yNTW55OW5GB8PcfxYH97xMIIgUD0nj+JiD4mEQnv7MLFogrGxEOFwjCVLy3A4zGx56ShFRR46O0eQZYmaOXnpsY4d7WNoyM/KVZVkTGgaDPR76egYQRQFxsZC5OQ4qKsvRJJEhocDNDX2kIgrRGMJ8vJc1MzJe9tSv4GALIsMjQboG/Qx5g1hMuroH/KjaRr61yz2474wA8N+8nOcjPvCaBroZAlV0+juHWNg2I+qaWS5rRQXeOjoGWPUG0RRVOqq85ElkSPNfSSSCmPeFG1xR88oIKCTJTQNQpEYjc39KcGlLAfFea4ZnXzNYmaIq3FOBNtQNeWtnsosZjGL1wmH0zwty9BlG2u57DUEk5uuOeXs3v7eU6ly2TkO7vrAqklt3/+RyQEggOo5eVRPQ69ePKFNVFScQdHb4GR91jm4SJCM14DoAmY34ouFI4e72be3g/wCF4Ig4PdH0+8lEgqx2FS1RJ8vQnzi9QP7O4lGE6xcmVKiDAWjmM3TG57bX25GkkQK8l1IkkgikeT55xqJTRitkXCCRFLBMEG1EAhEMJmnL3p7+qmDZGbZyM5xIggQCER5/vlGfL4wublO4jEFVUmlfvn8YaKxJOLE/QYCEa686sJF4jRN44nH9yPLEmazntGRAI89OsB77lpOLJogFIqjahpBf4Rf/3Ir//Ctm4lGE2zdcgy/P8KcOXkk4klUNVXT8NMfv8BtdyzF6TTT0jJIb+84t5+mVfHwQ3spKckgI8OGqmqcODHIIw/vZd36GhRF4/77d5GRaSMvz8UjD+/FYjFgMurYs6edhoUl1MxQs+KtgCCA3WqiuX2InQc6sFkNLK4tRqeT2LqrheI8N057qh4pGI6x90gXoiCQn+MEYMuuE5QWeLCYDTS1DNDePUJ1WTb7GruJxJLsb+ympMCDLxjh6S1NzKnI4eDRXsqKMojFT9UsvbijmbKiDBy2U/nj4/4ww2NBnDYTngvsXL6dEVWiNAdaaQm2EVVjOHUO6h1zEYWp63JcidMR7qIj1MVYYpykmsQoGSkw5VFjq8Spd6bb9kUGaPIfYzA6zH7vQaJKjEf6nsJ6GhPMnYW3ohd1CIKAqqmMxsY4EWxlIDpERIkgCRJuvYs59moKzWfWUTkbVE3jLz2PMs9RgyRIHPQexqV3sdBZjy/h55DvCKIgsdyzhEy9Jz2XjlAXL4+8mh7HpXexKXsDBmnymhVIBNk1theHzk65tZQm/3F6I30k1AQOnZ0aexWlluJJdShRJcb9PQ+liy0NkpGFznoqbeWTxlY1lbZgB0f8R1niaiCkRDgWaCaQCGKQ9BSbC1ngrEcnTjZTgskQTf5jdE3oaaSucipVts45jzrHPORpPuOLCU3T6Pb62NXVw23zUwJgCUXh1Y5uyjPc5DtmdlLfPjpOltWCxfDWFU03+4Z5qqeJ1dllNHgK0lTLgUSUF/pOEFcVrsirwmVIfd9VTePAaA/7RnsYj0UQBMgwWJjrymG+Ox+DdOozjClJjoz3s2ekm/FYGKtOT60rj5XZpeiEt74WbRYpzDoHFwmiYflbPYV3PYwmPWNjIRxOM0uXllFQ6EEUBTKz7MyfX0QgEGHlykpKJjzyFCuMSm/vOIIg0NY2hNVqQKevYUFDMa++egK328rGTbXpBcpsNlA/v4hXtp9Akk7l5AqCgMGgo7NzhKqqXObOy8flsiBJIkuXlbNt2zEqKnLYdGVduo/dbmLJ0jKeePzApAXQOxFVv+t9KykvyyKpqOlraRr09Y4jCgIdHcPp+7hQC2jAH+WZpw+TM1FkFQ7HCQQiDA74sNlNjI+HGB8LEYsl2L2rFVU9tQnn5jq5YmNtusAqHI4RicRZuqyc0tJMDhzo5KUXjxIMRsnIsLFiZSX3/3nXpOsriorTaWbtuhqsViP79rbT1+clJ8dB05EePvaJy8jKcuD1phynt++pQUogzmEz0jMwjttppnfAiyyLlOR7yPTY0mxjiqLSeKIfq9nALVc3YLcasVuNqTSgiY9VEkUKc12sWlzOw88e5NCxXpo7hlBUFUVRCUcSOGwmSgrcLKkvpnfAC0BJgYdMt5WT5KKBYJTOvjGi0QShSIxINA5cGs5BVImyfWQnzww+T1JN4tQ5UTSFXWN78einih0eC5zg4b7HGYmNYZEtGCU9vrifpKaw3LOYq3M24ppwEMbjXrojPfgSAcLJMCoa/oSfuHqKx107jYHOnwjw7OCL7BnfjyxIWGQLYSWMPxEgb3Q3dxXfTqV1emGts0FDY8vwywzFhvAnAgSTIcYTPtpDnaBpdEd6GY2P0RHq5q8qPoLEKZYqDfAmfBz1N+PWO9mQtQYDkw3SiBJln/cgAgLbR3fSHxnAKlsJK2HG4+Ps9x7mhryrme88pYYrCCAiEVRC9EX6GIwNk2XImOIcaGj0RvrZPPgig9EhBmPDKJqChsZwbASjaKAr3MOtBadqgwKJIE8MPMuesX3kGLOxyzZ6Ir20hjpw6hwsdjegF/RvmdTNUDDElpaOtHMgCgKFTge28zD07z94hPc01L2lzkFPyMv97QfINFpp8ORzcmEKJxNsH2wjmIyzPLMYl8GMpmk823uMHx99GbOsJ9dsJ5pM8nK4jY7gGBX2zLRzEFeSPN1zlN+17MEoybgNZkZjIZ7sPsqVBTX89dy1szJFbxO865yDJ/v2szZzDlbdqcIwRVNpDw5xYLyDPJOLBlcpJnmWyuydjjlz8vC4rRw/3se9f9pBXr6LD39k3RnbB4NRfvrjF/j0Zzbidlt44blGFFVD086LJRZIFTFddXU9c+bksW9fO3v3tHPTzYspLcs87/tIJlWSyZSRLIgCOlFKz/fHP9zMpz+zEY/HyvaXj+P1hl/XfM+EeCJJLJbg7vevxjxx0iHrJOx2I088fgBV0di4qZZ4PMlTTx5MRwP1ehm324JeP3kJEQQoLs5IOU96GVEQUJJnZ12wO0w4HGYEQcBo1JGIJxFFkQ1XzONnP32R3BwnWVl2ysvf3gJZoiDgsJo4eKyHVYvL6Rv0TWgbvKadKFBRnEl2ho1jLQNkOC2THE8AVVUJR+LE40kEwGEz4bKbWbe0ErNJjygINLX04wtGSSSSJJJT01riCYUjzf2YDDpKCzwcPt77LqYimIruSB/PDD6PQ2fnxrxryDZmoWgK20d28mT/s1OKh/NMuaz2rKDYUohVtiAKImPxcR7te4odo3uYY6tOOwfl1hLyTTkEkyF+nUgxQd2Ud+0kETe9qE87eybJyHznPKps5eSZctGLOhRN4cWhrTzZ/xzPDbxEZcX5OwcnccB7hM9UfBSLbOG+7gfZM7aPq3Ku4Lq8K/lL72McGD+EPxHAqXMgIFBoyufWghsYjY8xFh8joU49ZT0dTf5jFJuL+HDp3bj0TpKqwmFfE/d1/4W94wcot5ZglVO1MnpBz0351xBXE+wc28vDvY+fdWx/IsCxwAk2ZK1lsasBSZAYjo3wg5af8vzQFlZ4lpJnShEuHAs0s3N0Dwud9WzMuQyzZCaQDPK7znsZjY2xyLmAKls54llYt4KxGE80Had9zMtoKIxRlvni+lU4TUZGw2GebGomqao0DgxRmenhPQ11OIxGjg4O89s9+4krCrU52dw2fx52o5Fjg8P8Zvd+EopCvvPU6cBQIMjjTcfZ093Lp1ctozY39X3TNI3OcS+/2rWPkVCYYpeTOxfUYTXo+eO+QzzWeIyOsXEyrRaunVvF0qLCGX0H3kr8oWUPwWSc/15+CxbZgIJKOJHSfrHpUgEdTdPoDfv4ybHtLMss5sNVyzFKMnFV4YdN2/h9yx6WZ5WwLLP4HFebxZuBd51zsLn/EIe9XQxH/dTY8rirdDUDES8/O/EcSU1lKOrjhoLF3FiwGIN0aQmgvNvg9aZODdasrSE/38U/fuvhtHMgyyKJuEIkmkhH2kPBGGOjASorswkFo3R1jVBccsqYN5v0jIwEgFPR+ZPG8OnQtFRdwvh4iJo5eeTkOvjtb7YxOOhNOwdmk4Hh4UC6/9nGMpv1GI06Dh7oYv1lc9JUp+FQjJGRABWV2cRjSdrbR3C5zOl+rx3n5HXOhOn6uJwW8nKdHDrUxR13LiOZVBkeDiAIKW7m4pJUUdWTjx+YdGpwtmudzuZw8l/T3fupNlMpIDUtRdfa0FDCLbctQZZFDAbdBT01udAQRIFMjxVF1SjKdeF2monGEzzw1D4ONPXg80dYsbCUssIM3E4LKxaWsXn7MWzHjLR0DnPwaA/BUJQl9cUkFZX9Td20dA4ztyKXNUvKicUT3P/kPjQNViwspbIki988uIP2nlHC4TiRaJx7H9/DoWN9RCIJljeU4LSb+MvTx8jJtKO7hGoN4mqc1mA7Y3EvV+Zczhx7NZIgoWkaV+dsZOfYHkLJ8KQ+Hr2LtZkrJ2lAePRulrkXc9jXiC/hT3//jJIRo2REFCRkUUZTNWw6a9p5eC30op459mpgsp7ADXnX8dTAc3SGO9/Qd9sim6h1zCWmxCi3ljIaG6fEUkyuMYdSSzH7xg8yHvemnIMJIT6rKBNX40iCTIKzOwdxNcHN+ddRbi1DPMmqJwjsHNvDUGwYX8Kfdg4EQcAiWzCoSczSuXnaVVSKzYVclrUGi5QKEmQY3Cx2NfDy6A46wp1p56AvMsB43MtC13yyDVkIgoBZMtHgrOfx/mcIJAPoxLPv64qmcbh/kIoMDx9Ztog/HzjMr3bt4wvrVpJUVJoGh6jKzODvrliHTpKw6HUkFIX/ePFl/vmaK1A1jSePNvPCiTaunVvNL3ft5cqaShbm5/G/23emr+OxmLmhtob2sXHCiVNpf3FF4Qdbd7C2vIR1FSVoGpj1OnSiyD1LF7K1tZ1PrlxKiduFUX5nmGiSKBFOxompCsXGiZPJ1xB3qZrG873NxBWF6wrnUWhxpt+7rnAeT3U38Vzv8Rk7Bz3hYbYNH+GK7AYyjU6a/F2c8Pdwbd4y5IkAmz8R5un+3ewfb2VNZi3rs+oxy0b2jjXzp84t5Bid3Fa0lhLL62cOPBs0TeOov4u5jgvv8Pi8If7vP59heMDHV75zG55MG7Fogn/47O9Bg698+1Y8Wa+fdOad8c07DwQSUWLKCMsyKmnydfN/Lc+zKrMaq87I52uupTUwwJ86trMxp27WOXiLoWmnovavZ1M8cqiHBx7YRSgYxWgy8PFPnCqyzi9w4/ZY+c9/ewK9QeYf//k2srIdbLiils986tfk5Kai0Q7Hqc3rio21/Pu/Ps6HP/Qzysuz+fr/u4kjh7u5/8+7ONE8wKuvnODppw7x4Y+up7wskz/fu4NDB7vQ62UWLymjvPzUAnPVNfP59395jFe2N7NsWTmf+NTlbHnpKE8/fYiW5gE6O0d48C+7+dgnNlBamsl1NzRw7x9f5be/2YbRqOPLX7mW8oosrry6ns98+tdkZTkoLHDjdKYW3j/fu5OdO1sYHPTxb//yOIWFHj768fVnZGaKx5P82788TlfXCONjIXp7x7j2ugbWr6/hi1++ll/+4iXu+eDPAI1lyyv5yEfXsXhJGffd+//ZO+/4OM46cT9Ttheteu/Nttx7jZ3m9B4CCQmhHeGAoxy53I+DO8pxXAeOUI5ADhISSEglPXGK7bj3XtR7b9vrzPz+WEn2emVbkuWm7POJYml2yjsz777v++1befml3axaVUleXtQVQxAYcSWKRUCnOzGkDOd8RoDXXt3Lxo3HaKjv4mc/fYfcXAcPfnY1oiggnVSjQR46r6ZpeL0hXn1lD++9ewijUcddH1vMlVfNiLNWXCqIgsDC2YUsmFmAKAp84d6VCILA/Kp8bl8brQQsDFkSHrpvJaIgcP9ti0AQmDs9lzuvi+4TUVQ27qjlyqWVXLty2sgxd6ydGyMECgL8/UMnUoEKosC8GXncfcP8kWshwPyqIc2jwIjvcDQ3f9S1QzzJkH+pCl7jJaAEafN3kKJ3kGlIHynAJQgCBklPubWUfYMHY44RBAGRE4Xpoo9awygZ0Il6IucQdCwIAqImog5VzR4+t06UsMpWwloEFQ1pgk4VDp0DERFRkDBLJsyyCYscXWibRCMCAsGTXJ7GS6YhnSxjBuJJgrxe1OPQ2RkIO89qeTgTRtFAnjlnRDCAaE2NDEM6aOAN+0f2HS7YJsVUGRfQiTpUTR1zO6wGA2VpqaRZzFxZVsIP31k/8lmK2czMrExSzCfmhpq+AQ52dvH1l94AQCeJ3Dl7Bj0eL2hQnpaKw2RkVXEhz+8/HG2jKGLW6aLFR4fQNI2wonCos4t/v2UtsiTFfKetBj2SKGI16LEbL10XylP5yvSVfHHzn7l//R+4OqeCjxXPpSo5G4N4QiGhonFgoIMW7wAPbnw6pm5TtJq8Rk/Ac8brRIv2RQsYhtUIASU09LdKuTWHcmsOkiCOjG/dgUHcYT9fLr+VLGMykiASURXe6tjFA0VXM92ej06U0TQNRVNHCsYOp/VVNHVEsRcdH4SRAooa0fH05G3DiEJ0VFU0lf+rf5t/mxNNfy1NYkxFNHmKSt2xDgL+IJpmJRyOcGR/CyUVmXHKvPFyac6y54BR0vH3M24jz5yCK+znH/b/iSVpZdFS2rKBGUl5uCN+ItrFLTCRAGqbemjtGGT1kvIJuclcdU0VV42SdhTAajVy3yeXc98nl8ds/9KXr4EvXzPqMbl5Kfz00U/FbJs1u4BZs0cvDf93f3/zadtWXJzOL3/92Zhta66cwZorZ4y6/6xZ+cz613jz8UNfvJqHvhif/vPj9y7l4/eOPa5Fr5f5zj/dPupnWSY9//Cd2+K2L1pcwqLFJ1wdhlOw2mwm7n9gRdz+FouBl175xsjf02fkMn1GNNDyllvnc8ut8+OOKS3NYPVJ6V7//h+i/sV79zRy+GALzzz3FSRJZPeuBg4eaKGvz0N2tuPsN3wREISh6U6KdmZpuFML8aXFhj8TRtlH1TQMehlVU0+JcwFOWTxK0ilfnFGuxan7AAElwvaOVnr9Xu4on4EnFC3kY9dfPguSM6FqKr6ID4NowCAa4gpI2k7Ju6+hEVSCHHYeY+fAXtr9HXgiHkJqmIASIKAGmCjDVavb/Z1s799NtbuWgfAAQSVISI3gjrgxnWN9BH2Mtjwq5Ein9IRzWSpYdVYkIbYAZFQAPffaCLIoY5EscYumExWVT7Q83ZCGVbZw3F1DjikLg6jHrwQ44jqOVbbEuHWdCUVViahRYS+sxH7PJFFAL8XeqygK5CfZefqBjyFLEgwtDrvcHjQYWRiO5RlrQ+cLq2r0XKcgIMQsNC8louX+YhEEgQVp+bx0zed4vGYbH7TX8G7bcRZnFPLl6auY5shAIhrj4o0ESTdaua90AXZdfJ8vsJ457biiqTxe/xbN3m5Mkh6bLmpJPzjYyEutm0kz2Hmo7CYkQaTO08HTje/T7OumydfF7bnLyTen82LrZg4MNuIO+ymwZPCZkrW0+fp4s2Mn3YEBMozJfKroGqw6E9899CQz7AU0eruYZs/nltxl7OqrZkP3AXxKgAJLBg8UXcOuvmre7tyFXtQR1iLcW7gGh2zl5bYtHHU28+9HnyXTmMxnS66f1JgKg0mP2Wqgu91Jdm4KTbXdJDnMI7UTzoUpJxyk6q20+fvRixJdASfusJ96TzcRTSGoRE17l6tYoGkaA04fiqoSDEYIhCI47CZSHBYURaWnz0MgGEYQBFIcZuxWIy5PAK8viKpqBMMKKUlmHHYTmhbNYKIoKoFgmFBYISPVis1iJBJR6ep1EQwryJJIqsOC1WIgHFHoG/Di80cz2NgsBjLT7KiahtcXpH8wej6rxUCKw4IgQFNbP0lWE063H0kSSE22Yhoq1LR+WzWBYISC3GR0skR+dvKU0VwmODcUVcVgjAZ8AzQ39WG2GDCbJjdWSNM02vqcKKpGYcbY6mGcb3SyxBWLy86+4znw+0O72drewmDQz80l03ivuQ5vOMQDM8ZXz+NSRhTEocXMaAut2G0hJcQLra+woWcz2aZMZibNIMOQhlk20eht5o2OdybcDk3TqPXU87uGpwmoQars01iYPBebzope1POr2scnfO4TnN9xUzyP5xcQRixaZ6PKPo2ZSdN5o3Md7YEO0g2ptPk6qfc2sjxtCWXW4jGdxx8Oc6Szmyybjfdq6rii5MxuH0UpySQZjbxXXU9ZegogkGI2kmYxY9HrONDeSURV2dPWPnKMLxSmZdCFOxik0+Whw+Um02bFIEkszMvllcPHWJCXgySK0exE+ujYlu+wc6SzB1kUSbWYsRkuvMAuDik6hrXpw4QVBV8kHLe/IAjkW5P5p7nX84XKFbzSdJDnG/fznwff498W3kKW2Y4ApOjNWGQ9V2aXMyP5zIU7R6PF10Otu51/n/t5atytvNq2DYA5ySWE1DAHBxuG2i9SbsvlU8XXsL57P7flLiPVkATAQ2U30Rno576CKym35+GPBDnkbCTTmMztecv5U9MHVLtbmZdchohAoSWLTxZFFXT9QTfb+49xTfY8sowp/Lz6L9R5OohoCg69la9X3slfWrfQ7uujJD2HL5TdxLbeo3y76r5x3+tYCAXD5Ben09LYw8z5hdQcbadyZi7OAe85n3vKCQdrsqp4pXUXNtlIf8jDHEchBwaascgG3u7Yj18JkW10oBcvz1t/c8MRGlv7SE220D/oZfGcIlYvKWfv4VZ2H2rG4wuiqRppKVbuvn4uG3bUsGNfE7lZDnr7PditRj53z3J0OolX3ztIV48Lq8XAoMvPjWuqmD0tly176jlwrI1AMIwGlOSncfNVM2lq6+eN9YfwByJoaFQUZXD3DfPweIO8u+kYTe39hMMKsixy53VzSXVYePhfXuSO6+bS1evCPeRPvXpxObsPNrPzQDOCEA2ctFkMfP7jKyYt0DbB5c3cuYW0NPfx+qv7EEWBtHQbq66YRpLDfPaDx8lPXvoQlz/Ab756+qrEU4036qv53fV38dm3X8Aoy9h0Bjo87ovdrElDFmUcOjs1njo8ER8a2oj1QNOgN9gXs3+jr5n9zsNkGNP4Wtlfk6x3jKT9DKth1NPohAWii1tt6LyjEVSD7B7YjzPs4u6821idvgJ5aP7xK36Uy1ZddeGx62zMTppJk68FT8RLSA1j01n5eP4dLEiee9Z4g2EcJiODgSCvHTmOXpL4+NxoVjmDTqY8LZUkU6xWWyeKfPf6q3jhwBH2tLWTZjGztrKcFLOZBxbO5ZXDx6jp7WdGVjr2ocV8t8fDB7X1GGSZo109hJUIt8ycjk6S+MKyhbx86Ch/3nuQnCQ7ayvLRoSDBxfP44X9R2gaGODqilJmZF74RAxJeiOyKNHiGWTY10bVNLoCHpo8/TEafk3TCCgRTLIOURDIMdv5fOUyPJEQf27YgzcSdWcTBYHFGYVs6q5nZ28z5UnpI8k3IJrJSBLFM1bqdod9JOksCIBOlDHL516RPKIpOMNe6j0deCN+0gxJWGTTUJtFCi0nnr9fCeEMednScwS7zsw0ewEGUYdOlMkcclsySnpUjRGlhIp23mLllIhKYWkGLfW9KIpKzaE2KmfmsXX90XM+9+W5Qj4DV2RMJ0VvpdXXR6rBxtzkQryRII3eHtZ3HSakKtyYMxeLPLnSuKqF6fYfJKIFyDLNRxbPvdOOeh1Vo2/Qy5cfuAKbxYiqaUQUlRfe2kt2hp2ygjS8/hDvbz3OwlkFBIMRrBYD996yAFEUeOTfXqa2qZvpZdmEwwqKqvGpO5dgNES/2IFQhCde2M7iuYXkZSfT0+9m6556FszMx+UOoKqwaHYh08sysVmMaEBLxwBvbTzCFYvLMBpk1m+r4cCxNq5YVMag28+00kzuu20hm3fVsetgMysWlHLDmiqaOwYwG/V88rZFpy1JnuCjiSxL3HHnoovdjCmLRacf0QB6QiE6vW6s+qmTwc0oGig057Oxdwt13nqm2cowy9G0i23+dqrdtTH7B5UQiqaQqs/EPOSrr2kaAyEnR1zHUU4TbyAIIlbZwqDfhTsyur+0qmkEFD8myYRDnzQiGETUCFt7d8akP01wZlxhN9v6d5KiT+azRQ+QMiTEjRcBgSvLillWFOsymmwycevM+Kr2giBQlJLMN9fEu1OWpqXyjdXx24tSkvni8sWjXj8/2cHfrFo26mdVWZlUZZ2fANmxUmBJJsecxPqOGmamZJNtsjMQ8rGxs47egCdWOAAeO7aFEnsqqQYLBkmmP+jjuLOLfItjJI2piMCqrBLebD3Ky40HMEoyxbZUVE3DGfJT6+rlvtIFI7UTRiPfko6Kyrude/FGAoTVCJoGte52DjubaPZ1s733GHOSS0Zcjs6GSTIwzZ6PXpTJMiajolFgzhjpVye7JKYabMxyFGMQZSyyCQ2NPFMaXYGBONfF4WMzDA7Wde4hw+hgbnJp3D7nSlF5Ju+9uo9IWKGuupPr7ljAlg8SwkEckiBSYcvGoTcTVCI0e6MaIotk4O78pSQbrFhlw0n+jJOFQFj10enfQ5pxBvKpofqThCgKFOWl4rBHO74EeLxBBl0+5k7Pw2TUYzLquf/2xaQlWzgOQ25BxqiLULKV7j4P08ui6TgLcpKxWU601ecLMuDykZPhQK+TKMhOYUZZNkl2E2kpViKKSkvnAH9Zd4DC3BRuWFOFy+0noqikJlsQELj5qlmUFKQhiiKiIDB3Rl40yMpsQBLFUdMuJkiQ4MLxiemzefzgLto9bn647QMMksQ9lbPOfuBlgizKlFpLqLCWsaV3BwElSI4xC6/i5ZDzGHadnYHQ4Mj+2cZMkvUO6jwNvNX5LlnGDDwRL/XeJpp9rVjl0WtD6EU9FbZyDjgP807X+7T62xARCathrs5cg06U0Ys6ii1FbO/fzebe7XgiUZN/q6+Nem8T6YYLWw01pIZo83cQUAL0hfqHNPAhDjqPYpOtGCUD6YY07KfEZYyVVl8b3ogPvxqgyddMRIvQ7GvloPMIBlFPij551DoTY0EURIySkQZvE8+2vIhB0iMgoBf1ZBjSmJk0g2xT5qgLtZPROLcYjKlOitHC5yuX8kT1Dn59bAsmScYi66lIymBlZik+JVagPTDQzvsdNQDoxWi8RprRwkPTVpA2lL1IEATSDVa+UbWa5xr28ULjftQhjbosiCTrz76Yd+is3J2/ir6gm2S9lUp7HnadCb8SpMyWQ745HaOkH1nYpxmSWJ5WNWIJGOamnCVkGB0AyKJElb0Qs2TAHfbFBBrfnLMUh+7Ed98o6VmdMYsGTydhTYkGL4sSpdbskfPNTCpCFARkQUIUBD5dspaBkCeumN9kkZ5pJxxWqDvWEbWyZ008Q9HJTDnhoNrVzu/rN9DpH4wzT92Zv5gbcifHp7Y/WEObdxuqFibNOINcy1IchhL6gtVDe2g0uT/AGW4hrHiodNyFQUqi1vUaIdWLXZdHjnkJ3YH9DAYbULQgxbZrSdKfPeWVfEpOdItZT3qqDYNe4pqVlYiCSE+fG0dS9MvW2jlI34AXnSzS3jVI/lBAp4AQl1/dZjOSkWrFoJdYu2o6EUXF6fKTZDXh9gaYWZnD9LIsDh5r43//tImbrpxJSrKF5CQTJflpVJZk0jfgxWLSE1HUUds7bH436GWcbh8jdssECS4CgiCgqhrrD9Tx7r4aAqEI0/IzuGXJdDKSoukZH3trO8FwhK/eunLkuI2H6tlwsJ6v3LKCfreX36/bxdr5lby+8yiLKvLJSLLy3r4aMpKtPHDVAqxGPd5AiHf31bC/oYMBtx+zUcfSaYVcNacUq9GAqqo8/s5OkixGki0mPjhYRziiUFWQyS1LZ5BiNU+Kefq6onJyrXZmpWVhkGUqk9MoTro0Yi4mi3RDGrfl3si6rvXsHtjHTk3FoUtiUco8UvQp/LH5uZF9UwzJ3Jp9A293vsfGns0ggEWyUGEr44asq1nXtX7Ua+gEmfmO2fQF+9g7eIA6TwOyIJNmSOHKjFWAjCzIzEmaSU+wlz0D+2nwNmEQ9eSYsrkj9ya29+3miPvYhXkoRAu4/aHpGdxhD2EtzGDIhYbKMy0voBNkjJKR67KuZmXaxAp5Ptf6FzoCXShaBF/ET0AJsq1vJwedh5EEicUpC7g158YxxxkMo2oqbf52eoO9aJpKf2ggmpkGjaAaYlf/Hmo89dyecyO55tNXUrfodNw9pwqH6fwo8KYCArAqs4RCSzJ9QR8hNYJVNpBjTsKvhPEMBRYP7/uduWvpD/oIKBGiGb50pBksZJvt6E+qjiyJIjOSs/gb8xW0e514I6Gh1MAyqQYLdv2Z34kgCKOmBS22ZlFsjY9hsOvM2EexICxMqYj526ozMcsRH6+yKLUibluaIYm0ofiFYbJMKQxfvcAS6wY22nknE0mWKChN5+2X91A5M6qInQymnHDwQvN2UgxWPlOyJqZTAjES4LkQUQM0uT8gx7IEq5yNThw9l3OWeQFp6iw6/bto9W6myHolXf79zEv9KwxSEjrRRLt3B3mWFSTpCzFKE5ucdbLEg3cu4cW39vG3P3wBRdEoK0rnoXtXIooCbm+Ax57ZRFePi1WLyijOP72myqCT+eqDV/Lcm3t47f1DACyYVcAnb1vEkdpOXll3AI8viCyJ3Hp1tApkfnYy16yYxuN/3oLXH8JmMfDXn7yCVMeZn/eKBSX8/MkNfO0Hz5OT6eBbf732jPsnSHA+0DSN5p5B/rRhH0unFeAPhlm3txpvIMTn1i7CYtRzvLUHXzBWW9Y54GZ/QwehSASXL8i6vTWYDHrS7GaeeG8X0/IyyEmx8/buaqoKslhVVYyiamw4WE9JVioVOekcbe3it29tx2bSs6qqBEGAw01d1HX2kZ/mYEllPi5fkFd3HCUQjvDpaxZi1J97CmajJLMwM5e56dnR1LSTmGLvUkEWJYothXyy4GP4FD+qpqITZWyyDQGBUksRdl1UyyYJEjPs0ygw5xFQA9FMUYKMRTajF3VU2spHzSgkCALJege35tzAVRlXEBnSJupEHTpRP7KPQ+/g5uzrWZO+iogWQRQEjKIRu85GvjmPgHLdhIJ+RQS+M+Nh5KHUnnpRx5Xpq1iWuoikoXtbmrqQ6fZKUvXRhA8p+mT+qvjBkbSgcfeEOGI1SNE7+GzR/aioOPSxCyK7zsZ9BXejaOrItQDuK/gYES26SBwNs2RGHqolsTBlHhW20rjsUQCr01cwP3n2yDtq9rXyl/Y3yTXl8KnC1dh1thELQViNsHtgL6+0v8lsR9UZhQNZkihKmVqC8PlAFiVK7GmcrTSfIAgU21IptqWO6bySIJJutI4IFwnOnYoZufzf/7zDQ39346Sdc8oJBy2+Pr5SeT2ltqwTub4nmaDiRBRlLHIWZl36qCbMiBqi1vUmfqUXf6QXmy4fnWilwHIFB/ufJNeylALrGsrsN1LtfAUEgQWpXwTOHAtx9w3z4gpKCYJAaUE6X/30lUSUqMuOLInIsoSqacwoy+K+Wxehqip6nYxOFx2YP3HLgrjzC4LA9LIsHvnCtShqdPLQyRKyJLJodiGzp+VGg2uIVsAVBAGTQcc1K6ZxxaIyVE2LTnwGHaIo8OL/fmHkHcyelsuM8mz0Q7nwSwvS+NHDt0aPmSRpN0GC8aJpoKgaX7hhCbOKsqIF2FSVvbVt+IJzsRjH5osvCALXL6jEbNBxsKmTaXkZ3LdmLgcbO6nv6GPFjCJsZgM//NT10XoOgsCgt4x//MPb7KtrZ2llIXpd9DurKCpfuGEJMwoy0TQNXzDE9uMt3Lt67qQIB8PfSd0oqRQngzMVvRuN0xUJnAgnj/mSIGHX2UZ1kckyxfp1y6I0tABOits3zXD6hY8oiJhlM2b59G4RoiBglqP1B04lSWePWVyPB0EQyDKeuA8BAZvOio0TCy+LbMFykluUTtSRbRpbphhZlEk1jO4CJAkSKfr4RXamcexV4i2yGctpnptNZ8WmO3Ef3cEemrwtrEpbRoE5/0TNDjTQINecQ0SL4I9MPO1sggSXI2XTc4iEFWbMyQdhKE7iHJe+U044KLKkU+/potSaOaSdODHhjCdl2pnQSzbCioeI6kXVwkS900RULYKGgqYpDIbr0IgwPeljtPm244v0IAoyhdbVpBjLafftwBlqwmEoZVH616h3v0md+y2mO06fMUUQogvy0RBFAZNRB5xYOEQiCmjRglWjpYA807lG218nS6NWWRUE4bSf2a0ntG2yLCGftI8oiljMUyOveoLLF0GAZKuJ+aW5IwvLrGQb3mB4REAeC6IokJNiJxCOkJFkJdlqxKCTsRh1BMLR4kwCUeucoqooqobNZMBmMuDyBUdymwtAqt3MnOLskfZkp9jZUd2Cco6FbS4UDZ4+/vf4Jrb3NIKmMTMll/9aeAd+JcQzDbt5u+0oelHii5WruCKrDBmRgBLmmYY9PN+4F1fYz5zkXL4240q29NTzXONeGtx9ZJuSWJNVxteqruSx45t4pmEPJlnPTEc2X52+hoqkjISD4hRFFmRkQaLd34kn4hmpvqyh4Yq4+bBnKzbZRtpphJkECaYSSckW/u5f7hqZI57f9O2RxC7/9bvPIZxjkpcpJxwsTSvnJ8feYP9AI8WWrBjXolmOfCrtpzc3jhWdaCbfegXHnC+haQoZptlkmuZS7XwJV6iZWuF1ss2LCCleDg/8EVk0YZAceCOd7O//HaIgk2qoxCg5ODLwRzyRblQtyKzkT59z22IQwGzSnzbFXoLx4xz0EQzE5nk2GHXY7MZL3vqhqhp+X4iAP4Smacg6CYvViCxPPZeS8ZJsMcUWeBrSZA9/dUZ7Oqqqxmm7ZVlECIMsikjSiec6XCKpz+Xn+c0H2FvXTp/Liz8Uoc/l5boFlTHXdliMp7Rn/Nr48aAoKh53IK5vnw2r3YjJpI9pq6ZpPFW3g8qkTL4790ZUTaXa2Y1J1uEOB5iRlM19JYvY39/GMw27mJOSS4rBwhO129nQVcu/L7yNIlsqfQEvKQYzD5Yt5cGypSx7/b/43cr7ybcm4w4HMEg6/m7m1dyUN5MXm/bzd7te5LcrPkmG0faR789TkUJzPrMdVbzX/QFHXcfINGYiCRKusIsmXwuqprI6fQXT7ZVnPxkQCkbweYOEwwqiKGAwypgtxguaOU9RVEKBMDq9hKw783IsElYQJeGs80zAF8LvDaA36DDbouOIElHweQJoGpitRuQh7wFN0wgFwqiqhmkSCmclOD/4g2FcngA6nUTKUDIaQRBiraQnFbsURil8OV6mnHCwd6CRQksaPQE3PYHYvN0OnXlShAOATNNcMk1zY7YtSv9azN9pxviUaCsyvx3z96yUByelPaMhSxI3XTnzvJ3/o8hPf/QqWzZUxyzUVl01nb955EYcKZMT03I+0DSNni4nz/x+M1s/PE4gEKaoJJ277lvGkpXl6E9jRfqocLbkZbIkEoooI25zqqrR7/YTGkfmLVWDn/5lE4caO/jKLSuYUZCBSa/ju0+vi20L58cd8kz0drv4zc/eZeN7R8Z13JcfvoFbP7YwZpsGRDSVZL0JRVOxyQYWpEVTRmaYbGSYoi4++RYHASWCqmn4IlGLwn8svIOZydEx2mod22LFojNwf9litvY08GrLIT5XPnqKyASXN6mGFD6WdztF5gKOuavpCfagahoW2cyS1IXMTqpimq18TLUOgoEwG949wivP7aCpoReTWc+8RcXc95lVFJaM3S3qXHEPeDm+t5H8sixyis983eP7msgtTseRduYsUkd31/PaE5sonZnHvV+7DgBXv5c3ntpMX5eT2z+/hoLyE25lx/c14XMHWLr28s5W5g2GaOt3UpqZOpSNUsPpC+D0B8hLSZq0QN2LwYbdtXz/sbdYMD2fn//93RfkmlNuRfC1aaMHZPiV0HnVvCVIcCmjKCrvvL6f11/aPbLtyIFW3K4PqJyRQ3qmPaFtPQMlWakcbOzkYGMHmQ4b3YNujrR0ERxyFxoTmkZTdz/56Q5mFWchCgKHm7vodXlJsU1+cbeLhSgIXJVVybsdx+gLeqlyZFPlyCZJf8LfPqKq7OlrodSWhkGSafb2E1IV5qXmTeiaAjAnJZdjg52TdBcJLkWS9Q7WZl3F2qyrzuk8Ncc6eOGPW2mo7QaiwsLGd48gCAKPfO/2C2I9CAbC9HUOYnVYsA0Vd/Q4fTRVd6JpGpFQBEe6jdziDHraBlj37DZmLCwmuyidGQuLEUSBtvoe+joH0Rt05AwJDvNWTaO/x01P28DItZIz7Ky8eS67Tsl/397YSySskFt6IsNOb+cgve0D+D1BQsEwpTPzSc6w01bfTX+XE71RT15JOrJOpqW2i0g4QigYwWo3UTY7n3AoQkdTLwPdbjRVpXxuIRabkY7GXrrb+pFkicz8FDJyJ9f9q8fl4dt/foefPnALOck2ghGFdw7WUNfVx9/etGpMwkE4otDSNUhmig2zUfeRnhMvX1FqnFS7Ojgw2Hyxm5EgwUVBUVQO7G6K297S2MtA3+jFmxKc4Oq5ZRRmJvOr17fyqze28sLmQ5gNeqzGsZviBUHgpkXT6ff4+NXrW3n8nZ3sqG4h2WLCcBaXggvCJM6Dq7JKuadoPpqm8U7bUR6r3ownHASiVqxDA+3s62/lmuxKLLIBURCQhHOLCfvoTuMJxktLUy+D/d6YbYqiUl/dic8bvCBtUCIKLbVdbHptLx3N0XpMXa39PPHvr9HZ1EvD0XbeeWYbXpcf14CXvk4n3W0DdLX0oakag70e3v7TFjqb+9i/pYa9Hx4n6B9fQT33gIetbx1g55DFUNM0dn9wlEPb6miu6eQP//UGHqeP/i4n6/68jc7mPvZ+eIwDW2vpbOnjz79YR1N1B+0NPbz0mw/wOH201nWz8dU9tNZ30dHUSzgYxuvy89oTH9LZ3EfNgRY2vbYPn3tyA8cL05IpzUhh/dF6ADyBIIdbu1hUkodBHtv42to1yG9e3EJTR/+ktu1y5BKYkc6dYc/gMxU+afX14Q77WZpWfqGalSDBJYMAiSrUp+GeK+bEFeZbWJZHqs084vtfkpXKN25fRUNnP2FFJdNhJdlqoq3Phd1kRE4X+dY9V2I1GjDqZO5cMYtMRzTTyr1r5uGwmJBEkVsWTyc/3cGA24dOlijJSiGsRIOT9XK0eNDHV89FOyUN5LJphWSn2MecOWm82JPM3HDbPKZV5eLzBqM/vtDQ7yFcg15am/vwece2+JAEkdkpuVQ5smnw9PH3u17mupxpzE7Jo97Tx1vtR1iUVsjslFxkUSTb5ACg3t1LuT3jzCcfBVXTODjQzqzkyXEbTTC1EYTTZHO5gEOk2Wpk2vxi+rtdMdtlncSVdyzE1e/l6Z++ScAXomJuIbklGSy+uoqKuYVomkbj0XYOba/DkWbH2e9BAPzeIIZRkomcjoq5RbTUduMe9J3YKIDfF0LWS8y7Yhp5pZns2XiMIzsbSEqx4ezz0NncR0pmEpIssvKmeZitRg5sraG3w0lHUy96vY7r71s+koDk6O4G9m2qJiUzCZ/bj9cdwDXgxWybvFoTgiBw64IZ/GrdNu5ZOouOATf9Hh/zinPHfI7DdZ3sO97GHVfN/shXX5oSwsG+gSZea93NHfmL+aDrEA2enrh9ugJOrsuefRFalyDBxUeUROYvKWHvzoaY7RXTc0g5iw/rVEYQBJZUFsRtL85KoTjrhNlbFAUqctOpyI31Cy7LidYMMRl03Lx4xtBWmUUV+SP7LJt2omiPxWRgxYyiM7Zp6bT49pTlpI1c63wQ9bkuYfa8QsIRhUhYIRJRh/5VaG8d4Ilfr+f44bazniukROgOuEnWmzHLemRRJKBEMMsGuv1uXm85RI7JwZVZFZjl6ELGqtNzdc40fnVsE1+bsYYskx1fJIRJ1mGUzuxD7g4HeK3lEK2+Qf5u5jWT8jwSTG2KSjNISbMx0HfCeiDLElVzCjBbzo8APlaSUizRoGNBQJZlVFVFAFRFRVW1Efdos82INcnM3BUV0aJ9NhMW++g1l8aKIAjodDIpmXYq5hSSnGFDlEQsNiN2h4W5KyrQBLAlmQn4gtgc5mghVQF0ehlNVRElgXAwjKqoIEtomobZZsRg1jNnRTmCIKA36khOn/x5Z35RDhFV4UBzJwebO5mRl0mKZWzPRFVVjjZ24vZdGMvRpc6UEA4yDUksS6sgzWDjiLONZekVJOliO8T+gaaPtP9Ygo82kiRy7Y2zGez3suHdwwQDYWbOLeCeB1aQkmpNfDcuE/q9fyLZfA+CMPn1CURRQNTL6EYJTtc0MJrGVl8hpCq80nyQ9Z01RDQVh97EZ8qXUmBJZltPI3+q30Wq0cJbbYcB+Mr01SxNL+KvK1fx6+Ob+PqO51FUlUVphXymfClbehp4veUQzlCAb+x8gVWZpXy2fBkBJcwvjm3kmYbdFFlT+c7sG8izOBJ9OcFZKa3I4t5Pr+TFP26jvrYLm93EldfN5M5PLL0gWec0TaOzqY9nHn2b1touqvc1cfODV6A3yKP3XwGKZ+Twwv++R1KqlS98704Kp2UzZ0U5L/3mAwQBlq6dRXKmndd/+wE73j2MzxMk4A1y62dXM9jn5sX/fY/2hl4ajrRx++dWUzgthz/81+tU72siEopmbLri1vkEA2H2fXicIzvrCQcjfPofbqVoeg7TFxaNXOuKW+eTVZAW560hiCKlM/KoO9DKT/72aRAE7vv6deQUpbPmtvm89NgHiJJI2cw8bv70FZP+XI16HdfPqeTNfcfpGHTz1euWn3E8iEQUDtV18NaWY9S19lLf1kc4ovCDx97GeNI4mJFi49O3LGbxzNjqzNXNPWzYXcuRug66BqLWm/RkK4tmFHDjyhk4bKa462sa1LX2sm7bMQ7UtNPv8mE06EhzWKgszGDl3BIqCjOQpbH1wz3HWvnVnzfh9gX42n2rWTZ7cioyC9plGKXrcrlISkrC6XRit9tRtag0LQgCj+x5ir+dfjMZxtiiMm917McbCfDxwuUXqdUJpgLff+TZyzJbEUQnpHA4qhWGqMCg00txKdESXFzONCS39H+V/JSfIAgXVq/T1tLPT//1NfbvaozZPpyt6NRUpiFVIaxG+5kggEGUkQQRRVPxK+GYRYVRin4GjBynoSELEgZJIqKqhFUFlWjxRVkUMYgyIVUhpEYQEJAEEYMkX5RMTwkuT5SISjgcQVE0BAF0OglZJ12Q/qNpGpqqEQqG0VQNQRSiQrkgEAlF0Bt1oEEoFEGnlxBFkUg4QiSkgCBgGBLUI2EFJayAAJIcbX84FEGJqKBpiJKIzqCLjv0x19IhSsJIGlM0DUkn0VbfzcGttcxeUU5mXiovPvY+OUVprLl9Ycy1ZJ2MKAqEwxH0hmhboilZo8JNODzUBqKpvgVRiFohwwqCELVkD+872fS7fdz782coz0rlZw/eekZ32mAowvs7a3h140Eiikpjez9OT4CSvFRsJsOIX1G6w8rHrp3L3MoTCRNqmrv56x89RyAUQdM0TEYdkiji9QVBEJg/PY9/+fLNWE9K9axpGjsPN/PoMxtpaO9DJ0vYLSbcvgD+YBhZFLliQSnf/txazCe5kL615WhctiJN09h7vJUfP/UBLZ2DfOuz13L14opR600Nc+ra+UxMCcuBKAjRGQj41szbcegtI5PNMEWWdALK+IJ1EiSYSgiCgF4vf+TTll7qKKqT+u47EYR494ZwpAkN9ZL2hRUEAYMkY5Di+5ksSNjE009eox2nl8SYejVn2jdBgrEiySKSfHFciARBQJAEjKMUAZWGYwaE6MJ6GFknx9VC0I1i6dMbdBB3WgFplGudGp+QkZtCOBjmqf96AwQBi83IbZ9ZfdprGST9qOcarQ2jHX8+SLaaePP/fQbgrAkO9DqJ65dP47rl0wiHFf7xV2+wYXct37hvDQtm5MfWmjnl2LL8dNYum0ZFQTor55WSYjejaip7jrbykz+uZ/vBJnYdbmb1wrKRYz3+EFsONFLf2sdDdy/nrqvmYjLq0NDo6few83AzDrsJk2F0K+3w/aiqxv7qNn72p41093v5/hdvZOW8EqRJjCucciNrqsGGqqkElTDqSUF9pbZMdOfBFJ8gQYIEk4kgiJgM88i0fyPus/aBb58x8UKCBAkSTBSL3cRdf315x+0IwthHyOHFv0B04T1ynBD97EzChSAIPPLg1THbRCQWzyxk+exi2rudHG3s5IoFpSPK60hEIRiKYDTqKMhOQacTo5YUQSQrzc4tq09fl0oADHqZiKJyqLaDXz2/Gafbz7c+ew0r5haP2Q1prEw54cAd9nPE2UaTp5uAGlvtc35KMTMd8cF+CRIkSHCpIAhGki0fQy/H5/y3GBbzEcpAnSBBggSXLIqqMuDy4fYGCYQiRBQFVdUIBMOIooA/EFsHx2o2UFmYwQc7q3ni1R04XX6ml2SS6rDgsJnOXItBEDDqZY7UdfCr5zYx6PbxN/euZsWckjO6Ek2UKSccvNNxgHWdB8gyOjCcUimx3JZ9kVqVIEGCBGNDFPRYDUtH/Szd/uUL3JoECRIkSHAqg24/Ww82svNQE43t/YQiSjSWRIMBl49AMBKXklonS6ycV4LbF2DD7lp+9sxGUh1m5lTkMn9aHtOLs8jPcowuJGgafU4fj720hf3VbVy7tJIZJZnodefHI2bKCQebeo5xR/5irs6ciSSIieC0BAkSXJZoWphA+DjBcDUWw1IkMZlQpBGDrhJBSFgPEiRIkOBiEFFU/vTWbp57dx+pDgtXLSwnNyMJm9mIXifx/s4a3t1RPeqxaQ4LH187n4UzCjhY087Rxi4OVLfz7vbjLJhewF1Xz2bJrKI4lyZVg9rmHorzUpldkcueY62s23ac29fMwmaZvHoRw0w54cAiGUYqbl6ugoGqavR2u9i5tZbGum6CgQg2u5Hi8kwWLSsjaajU+sloGtRVd7J3ZwMdrf1EFJUkh5mCojRmLygiI9N+Ts9DVTWcA9FCSB1tA3R3OPF6gwT8IUKhCJIkYjDqsNlNpKXbyMlPpaQ8E5vdeMGyP3S0DtDU0ENH+wADfV68ngChYGQod7OE3qDD7jCRkmolPTOJ/KJUUlNtSPIkLLROucVwSKHmeAcNtV10dzpxDfqJhCPIehmTWY/DYSavKI2iknSyc1O4TLvqhNA0jWAwQs3RdhrreujucuJ2+YmEFfQGGbPZQFKyhcLiNApL0snIcpy356OpGs2NPTTUdtPeOsBAv4eAPwxo6A06khxmMrOSKCrNoKQ884IE1A0TijTj8r9NKNKITsrBZEil1/M7cpN/xEfRtUjTNFRFpaGuh4baLjrbBhkc9BIMhKNB0EYdjmQzWTnJlJRlUlCchnyetGqKotLW0kdH6yCd7QP09Xrw+4IE/GE0TUPWSUP92ExGVhIFRWnkFaTGBJheSNwuP9VH22lq6KW3y4XXG0CJqOh0MlabkZQ0Kzl5KRSWpJOZPbnft+H5rOZYB63NffT3uPH7Q6iqhsmkx2I1kp5lp6gkg6LSDMwWw0dqPFQUla72QWqOd9De0k9/3/AYBCaTHqvNSGaOg6LSDApL0jEYdJP+fIbn9/raLloa++jrdePzBgkFwhiMemxJRnJyk5k+O5/s3ORobYOPMK1dg7y+6QiBYJgvfWwlK+YUoz8pWPxgbccZ4x70OonpxZlMK8qgz+mlqX2ALQcaeOG9/WhoFGankJOeFHOMAGSm2vjb+6/E6fbz6xe38Py7+0hJMnPN4koMkzw3TTnhYE3mDP7YsIkWby/55nT0J2XGyLOkkmNKvoitO8H7bx/ktRd2MZy18DNfuopZcwtQVY3jR9p58tfrqa/pwuP2oygaOr2E1WbknVf38eBDV1I150SRpVAowusv7Oad1/fT1TGIzxdC0zT0OgmLzUhRSQZ3fXIpcxcWoxvHZKlpGl5PkL07G9izo5766k5cTj8+X4iAP0QkoqBEooVZBEFAkqIFVAxGGbPFgC3JxMw5BVx70xxKyjMn+xEC4HEH2L65hs0fHKW9dQCPO4DfFyIUDBOJqChDBWREUUAUo+nTDAYZo0mPxWogM9vB7PmFLFxWRk7exAY9URJHjguHI3z4/lHee/MgHUPtCfhDhMKRkTRysiSh08tYrAbsSSaq5hSw9uY5lFVmTViQUlWVo4fa+O2j747ruPJp2TzwV6uxnWPxnGF++d9vUXOsY+Tv+YtLuOu+pZgt0dQV4VCE9946yPp3DtPVMTjyfMLhqElWFEUkWUSvl7FYjThSzMxdWMy1N84hvyh10gTNSFhh59Za3nvzIM2NPbhdJ/qNokRT8IlitB1Gkw6rzURWjoM111axbHUl1kms7HnaNqr9CEjo5VI0VERBTyjSBFx22afPmXAowqYPjvHBO4dob+3H4wrgH1JMKMrQd1yKvi+TWY/NbqKwOJ2rb5jFvEXFGMdRNfZ0KIpKfU0Xe3fWs393Ez1dLvy+IH5fiGAwghJRUJRoGgxREJBlCZ1eGhprjOTkJbPyymksXVWBPSlewTMeWpv6eO6pLTQ39gLR4Mi1t8zl+lvmxuznGvTx/tuH2PjeEXp7XHjdQYKBMJEhF4gRxYlRh9kcXYgWFqdz6z2LmTErPublZDrbB/ndr96nu9M5su2LX19L+fQcRFFA06JCwRsv72X39jr6e9x4vcGhZ6UCGpIkIsvRZ2S1GcnJT2HNtVWsvHJaNAXmBL/vPl+QX/7XW7S19I/5GFknccXVM7jlroUTuuap1Bzr4Jnfb6K/zzOy7W+/fQt5hdFxTFU12pr7eOMveziwp4mBPg8+b+wYJEkSOp2E0azDajVSUJzOVdfNZNkVlUjyuXtGaJpGd6eTda/vZ+fWOgb7PXg90XcUCSuoqhpNO3qSsGsyj014s9lNPPjQGkorss6pjRcKUTyhUPb5Q5ypRHLvoGcorkBk2eyiGMHA7Q1Q19pLMBwZ/eCTEASBNIeV1CQLORlJbD/UREePi85eV5xwgABJNhOVhRmEwhE+ecNCfv3CZn770jbSHFYWzsg/c8zCOJlywsHG7qM0eLpp9w+gE+SYTnxf0Upuz1908Rp3Er3dbg7taxn5+8j+FmbOKaCrw8nP/v11Gmu7o/mHhwgFI/QHPQz0efnZv7/O3//gDkrKMlEVlVef38XzT22lr9cdc41gMEIw6GGw30tfr5uH//HWkYH7bPT3eXj7lX2sX3eI3h53dAEXUk67v6ZpRCIakUgIvz/E4IAPWgdoqO1m0wdHufnOBdxx79JJS6MZiSjs3FLLc09toam+F683gKqMvmjSAEXRUBSFcFjB5w0C0cqY9bVd7N3ZQHeXkwc+v3pkETsehvNj93S7ePzn77J7Wz0ulx9NjW+PpmiElAihUASvJ0B3p5Pmhl72727k7vuXceXameMS4EbOq0W1g4f3t5x955OQdRKRyOnf63hpqO2OaYPRpOeqfi9mi4GOtgF++d9vcXh/C15PcNR8/oqioigqoWAEjztAV8cgjXU97NvVwP2fX83CJaXnbOlpaerl6cc/ZO+OelxO/8hEPFpb/P5ofx7o99LW0sexw21s+uAo933uCiqm55xXDaeAGE1nqkUnmUC4GkG4OJrni0nNsQ4e/8V71BztwOsJxIyLw2hE89b7IyH8vhD9vR5am/o4sLeJZVdUcu+nV05YIx4KRjh8oIXXX9rN0YOtUYE2EB71+z2MqmmEQsPf8yB9PW5am3o5tK+ZzeuP8dkvXU1BURrCBFMP+v0h6qq7qD7aDkSToRSVpscIB3XVnfzhNxs4sKcJjycwqkw5bMULBiO4nX66Opz0dLm49ua58TufQjAYjloDmvpGth0/0j60GBQ5fKCZX/3327Q2941owk8lElGJRFQCgTCDA17aW/s5cqCF7Zuq+cojN2JPii8iNRYURaW2upP66q4xH6PTS1RMzxn3tU6H1xPg+JH2GOGp+mgHeYWpUeXEtlp+98sP6GjtJxgcfSEZiUQrlPv9IQb6vLS3DnBgbxNbP6zmK393w4Tmq5PZvOE4v310Hf29ntO+IyWiokRUAv5wjKBzNhwpFrzey6fasCgKpDks6GSJ1z48zPxp+dgshqggN1SXYlgJmJliRxQFVFVl4+5a1i6bDkDvgIdn3t7L/uq2Uceprj43Ow43kZFiY2ZpNuYhS2I4onKgpp2OHidFOanYrWdW1ul1MivnFtPv9PJ/f9nGfz7xHv/1jdspzE6eNAXalBMOvj3zTtTTFBGSz5Bf+2JTe7yTcDjCU7+JWgxOpxzUNI3mhl7+9H+b+H8/uJ39e5p459V9cYLByaiqRlN9D889tYWv/r+bx+Tq84NH/kz10XYikdEXTmMlFIzQ1eHk2Se3EAop3PeZVee0uBu2Zrzy3E5efnY7zkEfp3ndY0JVNPy+IFWz8zGcJrfw2ZB1EoP9Xn71k7fZuaV2pPjLWAmFIjTWdfP4o++CpnHV9bOQz0P2gYvBYL+X/l4Pmqbxn99/meOH20cdNM9EMBCm+kg7j/7b63zj27cwb3HxhKqYaprGkYOt/OZn6zh2aPTB+0yoqobb5Wfb5ho6OwZ56Otrmbuw6LwVkdPrSvAGtzPgfZZB38uASrbjn4Cp0TfOhqZpbP7gGL95dB2d7c4zFocbDUVRGez38vare2lp7OUb376FvIIUYHyF0g7ubeLH//IqfT3ucfeZk1FVDY87wLZNNQz2+/jOj+4i7RzdPYfRNOjpcg39rlF7rIPHfraOg3ubx91mvUFm+szcCbWjvqYLRVE5fLCVHzzyLG5XYFzHDz+jD98/SltLP//5q09hsZ5/K92FouZYB2uurWLH1lr+83sv4/WMb/GsKCpup5/33zpIZ/sg//LTezGNUr/gbGiaxp//sIWnH994WqEAIao41+AjYawUBIE1i8rZcqCBzfsbuOvv/o+0ZAuhkEKS1ciX71nJghnRbJe5mUlctaiCv2w4yL88vo4nXtuJQS/T0evCYtRz3bLpvDdKzIEvEOK97dXsOdaKXieRkmRGAAY9AXyBECa9jisXlVOcm3rW9up1MretmUVnr4vn39vPt3/xKr/81j3YLZPjyj3lhIPBsA+HzoxePD/V984XNcc7qD3WwXtvH0IUBHQGGZ1eQlO1ITPwiQWnoqhUH2tn84bj7NvVQGN991CBq6gGW5JFgoFItPriSV/qzRuOc8+nVmKzn93Md/3t8zl6qDVuuygKSJKIKIlDrjrRyo6RUARF1VAiKpGIEjchedwB3nplL7n5KVx53cwJvRtNiy7OXvjjNp57autIpd/TtvEkM6FG1L9cVVUURYtZaJRPz6GgOB1RGn+bBAFCwTAv/HEbu04WDASQZQlZFhFFEVkWh6wr6lBlTiVusTPQ7+XFP26noDidyhk543pGwtD1zBY9mnaiAufI71rU9ehcFjcTYXDIj3XfnxpiBIPh9krDz0cnoirR/qMoDOKEPQAA3Y1JREFU0cqlJz8eTYPuLhdP/mYDOXkpZOU6xvV8VFWj+kg7j/3POo4ejO/Xsk5CkkQkUUDWSWgaI/1YiSgx3z9N1Wio7eYX//kWD//TrVRW5Z4XC4IkOEizfYEk860o6gA6OR9JSDr7gVMAVVHZvOEYv/35e3S0DcZ8Ftt3oq6MwxYnVYlqok+2BqmKxqF9zfzbP77Id//jHtIzz1wZ9FRy8lOorMpl0/tH4z6TZHFkrBFFAVkno2nRfqwqKhFFjRunNFXj6KFWfvWTt3nk+3dgMEzOXNXd6ULTNNpa+nn6/z5k/+6mkc+ibp8ioiQMCbPR75SmaiiqOjJuCQJUzS7AZJ6YG1bUZ72XHzzy5xjBQJSiblbDc4deJxIMRt1WIpH4Z6SqGrXHO/nZv7/BI9+9fdwKJVEQMZn0mMz6kfFP0zQY+ldVtdNaDM8n1cfaaajrjhMMou5VQ/PqyPOJoCrakPUgtq2qqnH0YCu/efQ9vvzw9eNyh9U0jQ/eOcwzv98UJxgYDDLF5ZksXFpKfmEaFqsBrydIc1Mvu7fVUV/dRSgUb+UQxej7FcVocTer1UhZZRb2SXJXvVAsmJbHd79wPc++s5dDte30DfqwWwyU5KWRkmQZ2U8UBB7+1FVML87ktQ8P09HrQq+TWDKziI9dO5ckq4kDNW0YTilal5Vm546rZ2O1GKhp6mHQ40fTNBw2E8tmF3HTyqpo8bVT/JlkWcJqMsQVR5Mlkb+6czld/W62HWziX3/3Lv/81zdOSmrTKScc/OeRV7gpdz5XZExHuoyKBXW0DfDY/7yLJArMXVTMvZ9eybSqXLzeIC8/u4NXnt+J56TBdrDfyyvP7aS/z4MoilRW5fDxT61g3uISdDqJ/bsb+c3P3qWuunPkGCWismNzDSXlGWfVTK+5too//d+HdLQNIEnikH+8mfLp2cyYlUdJRSb5hWlYbUZkOeqa0tvt5vCBZta/c5gjB1pxu/wx5+zpcrHtw2rmLCgiNd02ruejaVEhad3r+3nuqS1EwvEDu8msJznVSllFFlVz8snOdWBLMiPLIgF/mJ5uFy2NvdQe66C5sXckdmLhklJSUq0TnqD3bG8YWfgDmC0G8gtTWXnVdOYtLiE3LwWzRT8SmHfkQAtvvbqf6iNtcZqj+tou1r9ziNz8lHHFAYiiyPzFJfzx9W/gGvThcvpxDfpxOX24XX6cgz6OHGxlz/b6Cd3jRHEOeHnuqS14XCfcQSxWA0WlGay6ajpzFxaTlZuMyaRDUVS6O50c3tfCm6/spa66E78vtqr50YOtfPDOIe7+5DL0hrENX5qm0dk+wDNPbIoTDIxGHZk5DlZfM4O5C4tHAiJBw+MOUlfTye5tdWx89zC93Z4YF6yWpl7+75fv8/A/3UpGVtJ5UUZoWghBkJHEFDQtDEKEKThsxxCNn2nluae20tE6EPOZ2aInvzCN1ddWMWd+EXmFqRhNuhGrTvXRdnZsrmXLhmMM9HljFoA1xzr4xX+9ySPfuwOLdeza1szsJOYuLGLP9np83iBGkw6LxUByqo05C4uomJZNcVkGGVlJGE36aH5zX4imhm727Gjgg7cP0dE6ELeo2rT+GDfvb2He4uJze2BDDPR5GOj38sZLu9my4TgQFXrtdhMFxWnMnFtASXkm9iQTRpMevz9EV7uTuupODu9voa/Hhd8fZsnK8gn35ca6bv79ey+PjP2iJJCaZmPWvEJWrJlGZVUOKalWJEkkFIrQ0tjLjs210ViSln7CpwgJm9cfY+tVx1l55fRxtcNiNfAfv/wUXrc/OhY6/bidw7/7aGvpZ9uH1eNyk5kM6o538u/ffWlk3JdlkbQMO/MWl7B8dSVllVk4ki2IokAgEKaxrpstG46z6f2jdHYMxlilFUVl8wdHWb66koVLS8fchs72QZ59YlPM3CMIkJ2bzN33L+fq62eNKhx+4lMr+PD9ozz9+EbaW/tjlDcl5Znc8Ykl5OankJWTjCPZjHgZBi0LgsCcilzmVJzdciZLIretmcVta2aN+vkTP7g/bpvJoGP1/DJWzy8bV7uuWVzBNYsrRm2vQS/zz1+6aVznGwtTbpZRNJVMYxLiZSQYAKDBscOtVFbl8o1/uGVEu+XQy9z/+Svo7nTy/tsHR/zq/b4QB/ZENUPTZ+XxN4/cSEl55sigPmdBEZ/6wmr+4xQNxcF9TdyjLD/rm9frZe765FJeeW4nJWUZLFlVwdwFxdFF9Ch+srIskZXjICvHwcorp/Pmy3t55olN9PfGDr5HD7VSc7SdlLSKcU1AmqZxcG8zT/32wzjBQJJEikrTWX1NFdfePIfUtDMLHqqq0d/rZt+uRo4caGHBstJxLRZi2xXVjg+TkZXELXcv5OY7F2K26GPuUZIEMrMdZGY7WHXVDF5+djvPPrkF56Av5pxbNhznulvmYrWNzzwoigJmswGz2UBWTmzgvaKorHt93wUXDiIRle6OEz632bkO7vjEEq6/dT5G0ylaEFkiJy+FnLwUVl9bxZ//sJmXntkRJ2S++8YB1t48h9R021mfj6ZpBPxRoXLrxuMxnyWnWll70xw+dv8y7HEZwATsSSbmLSxm7oIibrx9Pr/71fts+7CGYOCEtm3/7kZef2kPD/zVauRJCBA8GUUdxBV4B19wF6rmRxaTsZmuwWpYxWkj5S5zNE2jv9fDG3/Zy/HDbTGfpWfaufmuhdxy18K4gHBJEnAkW1i8vJyFS0q59qbZPPnr9ezb3RgTK7V1YzVvvLyHuz+5dMzvShRFZs4pYMHSEvp7PcxbVMyiZaWUT885rZLFZNYzrSqPyhm5XHvjHJ749Xo2vHs4pu+gwZ//sJl5i4on5XUGg2HefWM/r7+0BwC7w8zCJSXcdOcCZszKH137Ph9gDuGwQs3RdnZurYu2Z4IE/GGa6nuA6LhcNSefex5YPqK0OhmDQUdZZTal5VksXVXBH36zgZ1bamOEqHBY4e1X9rF4efmYlQHD6HQSjhQrjhRr3GdN9dGMVxdaOPD7QiPPR2+QmbeomHseWM70WfnIp7wfk0nP9JnRPrRkRTlPPLaeQ/uaYwQEtzvAhnWHmTO/cEyZ1FRV5YO3D8XEQQAkOSx8/MGVXH/r3NN+LwxGHWvWViFKAo///L0RNzaAro5BSsozL5vg4wRnZ8oJByvTp3F4sIVUvZVkgzVGSBAFAfESzg8uSRK337M4zuwtyxK33L2ITR8cjTMDGo06rrlxNvlFaacsREXyi9Iom5bN/l2NI9sbartRVXUkU8XpEAS46vpZlJZnUj49Z1yBxAaDjtvuWUQgEOL3//tBTKBwb7ebtpZ+IhF1zIG3mqYxOODjyd+sx+uJ9WHV6SWWrKzgU3+1msKS9DFN+KIokJZh55obZ3P1DbOG7vfcZ+f0TDuf+sIarlxbddaBWtZJ3HnvUsIRlSd+HfuMOtsHqT7SQX7h+UvFeDHIzk3mr756LctXV541KF5vkLn7/uUEgxGe+8OWGHeo1uY+qo+0s2x15VmvqWlRjfFrL+6O0XTZk0zc++kV3HTnwrNa0QRBIDs3mS9+/TpEUWT9O4djXMJee3EXV1w9g9KKyc3IFYzUEwgdIsXySWQplWC4jl73b7AaljNVU5lGwgr7djeydcPxmPeVnGLhs1+6iquun3XW76ooiZRPy+GzX76ax362jn07G2LO9cIft7L8ikpyh+IPxkJBURqf/OwVJDnMJKdax5TUAYaykWTY+OTnVuF2+di6MdYP+cjBqIU1XjgdPwF/iD/9bhN+X4jUdBs337WQm26fjyPFctZjdTqJGbPzmTE7/6z7jgVBiCqtvvC1a88a5CuIAiXlmXz8wRX097k5evCEUKipGnXVnbQ09U6phackRy29n/3SVRSVZpxxX1EUmDm3gE88uJKfd79BW/OJDEyRsELt8U462wfJL0o763Vdg34O7W+OURiKosCM2XlcNQZ3X1mWWLCklH27Gln32v4Ry5zHHeCFp7fx8HdvG/N3I8GlzZSbYURB4NW23Tx6/C1+Uf02v6h5Z+RnT3/DxW7eGTFb9CxcPrq5qawya1SNeF5hKqXlmaMu3q1WI/mFsYEtzgEvPm8obt9TEYSo32DVnIIJZRgSBIGb71xIXkHsgKUoKu1tA3GL/LOx8d3DHD/cHrd9yYpyPvulq8YsGIzWzskQDHQ6ietumcuKNZVjzoUviAJrb54z6uRw9FDrpGYRutjoDTJ33ruExSvKxjx5GAwyV18/i9yC+OCsA3ubzhqgOhw38PKz23EOxFpn1qydydU3zB7S9p+9LYIgkJxq4aY7FpCdF2uV8bgCvPbCrrOfZJwISOikXIy6SvRyPib9HCTBxtS1GoBz0M+61/bjcceOD7d+bBFrrq0a87lEUYimMr1+FsmnaI4H+7289crecbVN1kmUlGeSmm4b9+JHEATSM+1ccXUVtqRYV8FgIEzt8Y7THDk+NA28niAWq4FrbpjNzXcuGJNgcD6wJZn41BdWjyv7T3llNguXlmG2xLq0DGdFmkqkZ9j5xIMrzioYnMycBYXRdOT6WGWGx+2nuaF3TOdoaeqlvyc2eYlOL7NideWYa3AkOcxUzsiJ6cuaFrWiDvZfWEtMgvPHlBMOknRm1mbPodyeTYrOikM2j/wYpEs7DWBeYeppA3h0OomC4vS47Tl5Kaf139cb5TiT6qluMOcTi9XAwmXxvpDOQV+sef0sBIMRXnluZ9z2/KI0bv3YIrJzJy9910QpKc9kycrycaWWEwQBi9U4qr9oQ233RQmYO19Uzc5n0fKycaVpFQSB5BQLs+YWxH1Wc6xzDFmqolm6dm6ti9malevgimtmYLGO121LpKAojfmLS+I+27z+2Lgzj5wOVfXSMfjP9Hufxh14n7aBb9E+8I+0DTyMqo1PqL6cUFU16v9+IDYdb1FpBlffMBtRGp/blqyTmDErP67GiqpqbN14fCil8YVBliXyClPJzY+1VmhAZ7tz9IMmgCBEx6Lrb5s3arHMC8UVV81g1tzCcR0jyVE3pCRHrEATCkZG6jlMBSRJZNXV06msGl9GqKjWvgTTKTU7vN4gHe0Dpzkqlt5uN+5TBG9ZFqkcZ3aq3PwU7KcIuj5fkIba7nGdJ8Gly5RzK1qRXnnaVKa6SziVKRCnZT+VjKz4TCWp6bbTFtTRyRKWURarHrd/lL3PDyVl8a4WAV8oLvDsTBze30J7W/zgt3RVOaWVWZdEtcaqOfnk5o+/SJcsi5RXZsdtH+j3XPDMQueTRctKSUk7e4zAqRjNegpL4oXi/l73mNLrbXz3SJwgOmNWtMrnRMzftiQTZZVZSJKAcpIrmMvp4+ihNhaNIgyPG0GHRb8YAA2F6I1KaFoISbQyBXU6QNRFYtuHxwmdkvN9yYpyHMnmCSkAMnMc5OSnIOyoj6lLMNDvoa6ma1TB83xhs0ers8egRfvOZGG2GFi8opzs3ItX7FMQ4Mbb508oZXV+YSrmU+K/FEWNs/xdzsiyyLU3zpnQvDValfZwSImLyzodXm8w7vsligKpaePL4GW1meJSf6uqRl/P6VOqJ7i8mHLCwbB1QNM01FNWD6emh7rUSMs4cyDtqUGzggBWmxHjacyBoiiOqqk9dXA4nzhS4gWXUCiCOg6t+M4tNXEFzlLTbVTNzsd6CeTAttmNFJdlTCioWRJFUtLiA+ZOVyTsciQ51UpxWSaGcQYUQlRblpQc34c87gDaGKSDrR8ej9tWVpmFY4Ja1ehEaiUp2RITbK9pcOxgy6QIBwI6bKarzrjHVCQSUdizIz5gfs6CwnEHow4jyyKZWUmYzfoYy044rFBX3XlBhQP9UGX2U/H5Js+CYbWbWLis9KL6fecXpY3LXeZk7Elm9KfMWaoarUUzVSgszRhTfMBoJKdY44QKJaKcvlbBKYSCYcKnuqsKAgbj+IO9T039rWkageDYPQISXNpMOeGg2dvL/xx7g/0DzYTUMIIgoKEhIvLF8mv4RNGKi93E03KqOfVUTp0g9QZd1I3lNBo1QWTUdGLKBdRIjxbsGc05PbbjNU2LczMAKChMvSTciQCSki0T0ooDIESrCJ+oNhMl4A+dU3G3S4n0TDv2pIlpfkVRwGiMfz5jcQkZ7PfGBO9BNItMapp1woHegiBgMOowmw30E+tf29Y6NtP+WK4BOlQthD+0H19wB6oWJPoABDLsX2OqFULTNI2+Hk9cTQNbkonktImnGRYEAYvNiF4v4+VEn1GU2CxaF4LhWginok2i96DNZqSwKN7SdiEpq8yecBpLvUFGkuOFg7Eufi8HyiuzJ2ztNpp08cKBoo1Z4TdckyOGoTThsnXsY0ooGIkr9ikIwjlXbE5w6TDlhIOnGzeRbLDwXwvu54eHXuQ7M+9kX38jHYEBVmWML1fyheZsmmfxlAlSlkX0evmMAZWjfXYuGunh4lrKUEGxaKGtk4rMMLzwj247NU/9eAkGI7S19MdtT82wX7Rgu1OxJ5lxJE+sLcMFiU5Z+04ZqwFAapoNi21ik8bwojDu+YzBalBX0xXnmjUcZzDeyq0nEwpFRnWZmGyTeiB0iD7Pk2haEJ2URUhpQy9PTjaZS5H62s64bQ6HZchtYuLvS1W1uPTLqqoxcI6xV8PfUVWJFhJTVe3EeBjdYUTA1zQNrzswLnfK8SJJIvmFqedUgX4yKCrNmHBRQEEY3b4/lu/75UJR2cSsKjA0HsY9IG3Mz8dqN2E06mNqJqmqRleHk5LysVvhBwe9+P2xc7soCiRPcB5McOkx5YSDNl8/X6m8jnJbNnpBZlZSARW2bF5p3cWuvjpyzWNPX3ehGa/pXBTFCVX1HS+aFtVMeD2BaPBT2yCtjb10tA8w0OfF7fLj84YIBEKEghFCocjQv2GC5+jC1NvtindBEiAlzYrdfvEC7k7GbNYnNCZnwGY3jjkTxmQSLaAVO2n2drv40XdePC/XC/jPTRA+FY0wRl0lspiKUVeBST+L5r4vASpTz3IA7S3xlpeWpl6++pnHz8v1QuNIinAyiqLi8wbxeYN43AGaG3ppbe6ju9PJ4IAXrzuA3x8mGAwTDkYIhiJD/4ZHLd44WQiCMGEL3WQy3grUHzUyLuLzycp2kOQw0dt9okZBOKxwaG8TxWWZYxLqVFWluaEX1yn1eYwm/TkJPgkuLaaccGCRDThDPjRNI9Vg48BgE/nmVAJKOC4G4VLj1CIoZ0MUBSTx/GmJNE2Llk5v7OXooVb27mjg+OE2XE7fBXN5Gez3xpnddbKE2WK46BqyYWSdNK4sPB81dHr5ogSN9/de2OC4yCRnlxLQIQoGBEEmED6ORgRNm1wB5FLigr4vTRv3+wqHI3R3umis62bfrkYO7WuiqaGXyHm0BowHQSAumPdiYB6lum6CE1xMRVJBcRrZuSlD9Y6ik3g4pLDhvaOsvHpGfMD8KWgadHe6OLCnMcaaJwgwrSr3rAVIE1w+TDnhYGlaOQElqhG6OquKJxs2kmlMwh0OcGPO3IvbuLMwoSCy86Qk0jSN40fa2fzBUbZsOE5rc99F8YH3+UJxLjayTorLlHAxkWVxShUrm2x0OumiCAceT+DC9tlJvpheLkAQdIiCGaf/Dfy+I9hM1zJVsxV5JikV7JgZ4/vSNBjs97Brex0b1h1h/+7GcaVivmAIAjrdxZ/ST021mSCWi/l8bHYTi5aVcmhfE4NDGaA0TaOuuoMX/riN2z62iPRM+6jWJ03T6O/z8OZf9nJoX3PMZ2azgVvuXnhB7iHBhWFcI8n3vvc9vv/978dsy8zMpLMz6iuqaRrf//73eeyxxxgYGGDJkiX84he/oKrqRPGaYDDIww8/zJ/+9Cf8fj9XX301v/zlL8nLy5uE24ErM6sQBQFJELkmaxY6UWYg5KXQks6c5KJJucZUR9M01r9zmJf/vINjh9tiUgCejMmkJzU9Wp7eZNZjMusxGHTo9RI6vYxOL9Pe0s/m9ccm3BYlosTZeyRJHLeV5XwiCEJcPEiCE4ji5BSaGy+jaXQFIZpPffLflzDm4ndjRZbSkKVoVpNU6bOomg9ZTEG4hKu8TxyNSDjeBVEQBWRpbIXqxoMojZ7JLa5VWtQf+5Xnd/L+WwdjMlSdjCSJ2B0m0tJtWG0mTGY9RpMenV5Cr4uOhaFQhAO7G6dUzv7REC+hsflS5EK4Ap+JpVdUsnt7PZs3HBsJKvZ5Q7z1lz30dbtYuKyM4rIMklOs6A3Rfusc8NJY18POrbXs3FobYzUQRYEbbpvHnAVFF+mOEpwPxj2bVVVV8e677478LUknBtj/+I//4Mc//jG///3vqaio4Ic//CHXXnstx48fx2aLmpu+/vWv8+qrr/LMM8+QmprKN7/5TW6++WZ2794dc66J4tBb8EWC7B1oxBX2c33O3GhaU007ry44U4kN7x7hd796n872wbjPkpLNzJ5XyKx5hWTnOrDYjJjNBnR6Gb1eQpYlJFlEkqI/WzYcPyfhQJTEOOOIqqpTqkBYgvPDaBlTsnOTuemOBeQVxVddPhcEwGw5f2l1JdGChAWX/21sxmsvul/5+WC091VWkcUtdy8cNZ3tuSAQrXh9NpyDPl7+8w7efmVvfJE7AfIKUlmwpJSK6dmkptuwWqPxNTq9hE4nx4yF/b1uBvo8U144SHBpk5xi4ROfXkl3p5NjR9pGwrLcrgAb3j3CgT1NpKbbRlx3FUXF7wvR3+uJxgCerCwU4Lpb53H3/csviXpDCSaPcQsHsiyTlZUVt13TNH7605/y7W9/mzvvvBOAJ554gszMTP74xz/y0EMP4XQ6efzxx/nDH/7ANddcA8BTTz1Ffn4+7777Ltddd9053g50+Af48dHXafP3E1EVVqRX4Ar7eb/zMMXWdBamTkKRoilMW3MfTz++MU4wECWBRcvKuP3jSygsScduN6HTS2dcpGiahk53bgOGwSDHuU6Fw+oFrdWQ4PLEZNIjCLHeI3qDTMWMnMtWy+Xyv4PNeDVTz7VIwDSKr7rRpGfOgqKLUtQrElbYt6uBt/6yNy51rsEgc+9nr2DlldNwJJsxWwyjpm0+Ga8ncFHrDyRIMExJeSZf/4ebeex/1sXUFlEUld4eN71jyLxmthi4674l3Hj7gjEJ2gkuL8YtHNTU1JCTk4PBYGDJkiX86Ec/oqSkhIaGBjo7O1m7du3IvgaDgdWrV7NlyxYeeughdu/eTTgcjtknJyeHmTNnsmXLltMKB8FgkGDwxODscrlG3Q/guaZt5FtS+dvpN3H/5kfRAJNkIKiGafD0JISDs/DmK/voOKUasSAILF1Zwef/5pqhyrJjN/Ofa0kFR7IlTgCJhCP4fSFUVUVMWIMSnIbRissF/NFsWpcyiuqksefTo34WjNSSk/yvU7IMWmpqfDCjzxu8aFZCrzfI6y/siRMMdDqJR75/B4uXl6E36MY8FmramIp6J0hw3hFFgeKyDL79L3fx/tsHefKx9WNOF6zTS8yeX8Rd9y2lanYeRpN+SloyP+qMSzhYsmQJTz75JBUVFXR1dfHDH/6Q5cuXc/jw4ZG4g8zMzJhjMjMzaWpqAqCzsxO9Xk9ycnLcPsPHj8a//uu/xsU6nI5qdwdfqbyeDEMSwypnvSihEyQCytTN9DEZuN1+9u6oi9PK5+RFXTFy81PGPQj4zjHIMC3DHicAaBr093nwuALYJ1jpNsHUJzvHwakVElxOX7S6sqZd0hOaTs4i0/7NuO3tA9+95Cu9TwRBgKwcR9z23m4XoWDkgr8vTdPo7nRyYG9j3Ge33L1oSDA4c42ZU4lEFMIJi2eCSwhREjl6qC1GMLDajOgNMn5fkEhExWIxYk8ykpWbwsy5BcxfXExhSTo6XbT/X8rjaIKJMy7h4IYbbhj5fdasWSxbtozS0lKeeOIJli5dCsR3lLEM6mfb51vf+hZ/+7d/O/K3y+UiP3/0YkBJOjPtvn4KzKmARlhVcIZ8dAddFFomVrL8o0JTfQ8ed+xiXhAgtyCVOQuKJjQIDPafW6Ehi9VAeqYNrydWq9Hb42JgwJsQDhKclpLyLERRQFFOCAc+b5CeTifhkDLuuiIXCkEw4jDfhV6Ot3IadeWctxRlF5mSisw4NzDnoI/uTieFJelIFzCQU1U1jhxojUtopNNLXHPj7CHBYHztCQUjk14LI0GCiaBp0Nvj5gd//xzHD7cBUdfhqjn5fPZLVzN9Zt5ZBd+EUDC1OSefDIvFwqxZs6ipqRmJQzjVAtDd3T1iTcjKyiIUCjEwMHDafUbDYDBgt9tjfk7HjbnzeKVtN8+3bCesKbzaupvH696nL+hmtqNworf6kaCvxx3ncqE36CgqTZ/wQqr2eMc5tUkQBKbPjM9k1dbcT1f74JSqJJxgcknPSiIz2xG3vaGuG5fTF3/AJYIoGLCbrhmqnh37k538PQTh0hRqzg2BrJxk0jLix/YjB1sIX2BXME3T6GiLr8xeUJSG3WGa0MLI5fTRd4FrbyRIMBp+X5Cf/dvrVB+JCgaCABXTc/jCV9dSNTt/JMPcmX4STG3OSTgIBoMcPXqU7OxsiouLycrKYt26dSOfh0IhNmzYwPLlywFYsGABOp0uZp+Ojg4OHTo0ss+5siK9kk8Xr6bTP8jClFJ299eRYUzi06VrKLKmT8o1piqhUCQ2EwHRHP72JNOEzudxBzhysPWc27VkZQXCKYF8XR2DHD3Uit+X0MQlGB1BgJVXTYvbfnBfMx1tA3F9PcHFQxCivvyLlpXFfbZ5/XFcTv+FVQRo4PfH1zKwJZknlJUlElHoaBuku+v08XIJElwotmw4zsG9zSOWMbPFwMorp1M5I+fiNizBJcO4VFAPP/wwt9xyCwUFBXR3d/PDH/4Ql8vFgw8+iCAIfP3rX+dHP/oR5eXllJeX86Mf/Qiz2cx9990HQFJSEp/73Of45je/SWpqKikpKTz88MPMmjVrJHvRZDA3pYg5yYWomgYCSIKIPxLCGwlikS9+BclLFb1ejsumoWmgKhOblDesO4xz8Nw1tLPnF5KRmURXx2BMu7ZsOM6CJaVMn5WXSKOWYFTWrJ3Jq8/vihEiuzucbNl4nOKyTCxW46Tn0E8wMWSdxPI103j/rYMETioy1tLYy4Z3D3PnvUuHNJoXoDECGI3xhRZVdWJRxQN9XvburE9kWUtwSbBrWx3B4InvmNGkp7wy+yK2KMG5Mhhy0eBtZV7yjEk537hWVK2trdx7771UVlZy5513otfr2bZtG4WFUXedRx55hK9//et86UtfYuHChbS1tfHOO++M1DgA+MlPfsLtt9/OPffcw4oVKzCbzbz66quTUuPgZARBQBJFpKGCQUddbewbaJzUa0w1bHZTXHGxcChCV6dz3MVf62u6eOX5XZOinbVYDdx4+7y47Y113bzy3E56u90J96IEoyBQWJzOijXx1oN33zjAjs01qIl6GZcMoihSVpHF3IVFcZ+9+Kft7N3ZwIXK9yMIo9dB6O4YJBxWxjUehkIR9uyoY/e2+rPvnCDBBcDl9MXMzdF5fvDCVpRPMKn0BPvZ0LNj0s43LsvBM888c8bPBUHge9/7Ht/73vdOu4/RaOTRRx/l0UcfHc+lz5kO/wCusJ8V6ZUX9LqXE7l5KXGl3cNhhab6bjrbB8aca7yjbYAnf7Oelkks9nPtTXP44J3DNNZ1j2zTNNj6YTWKqvGFr15DRlbShHwhw6FIVJiUxYQv5RRCEKJucbd/fDH79zTS03nCpcM54OOJx9YTDiusvXnOhN/7QL+H40faWbSsLGG9OkcEAewOE2tvmcuxI+0xyQz6etz870/e4dMPrWHlVdMnfI3ebhfVRztYvvrM84AgCJRVxGtSe7pcHD/SRnqmfUzvW1VVDu5t4tknt+B2+Sfc7gQJJpMkhzmarGFIQPB4Arzy3E6CgTBzFxaRnpk0kqI0MSXGs7l3N03edkJqmEpbMQtSZnLMVU+Np5GgEiZZb2dV+kKcYTfb+vYhIBBQQ8xJqqQqqZzd/YdxRzw4wx4Gwy4+nn8jAgKbenfT7u8iWZ/EopRZZBrT2Nizk1ZfJyE1wnR7CUtS5+CN+NgzcIQmbzsKCjdnrwHAFfbyfMtbBNUQlfYSFiRXTTi73ZSIbNvaW81TDR+ecZ/eoJubcuK1zwlOkJZpJzsvhebG3hitQn1NN+te3889DyzHaIovVDSMqmocP9LGU7/dyL5dDZOWn1wQBFLSrHz6oTX86B9fjDHNBwNhtm44RmtTL9ffNp+1N8/BYjm761g4HKG1qY89OxrYubWWez+9klnzChLCwRSkqCSdjz+wgv/9ydtEIif6ZEfrAI///F0O7W/mzk8sobjs9EkRhtE0DY8rwLEjbezZXs+BPY1EIirzFhYnhINJQJJEZs8r5OrrZ/HSs9tjXBqbG3r41Y/f5tCBFm65ayG5+SlnPZ+mafT3ujlysI3d2+o4fKAFg0E3BuEACorTSE2z0tfrGdmuKCpPP/4h5dOyyc5NPuN4oSgqb72ylz/9bhPdnc4x3H2CBBeG+YtL2LqxGl8kmp1QVTTqa7p48rENvPin7egN8S7Gw8iyhMmsx5ZkIr8glZLyLBYsLcVmP38V4i81pttLKbUWEFLCPFb/LHOTp9Ph7yaohrkycwlvtK+nyduGJEjs7D/ENyo+TWegh539Bykw59Ad7GMw7GZ56jz0og5ZkKl2N9DgbeWm7NUcdtVy2FmLXWelyl5Gha2IgBLiicYXWZQyixp3E63+Tq7IWIhO0GHXWekPOekPOVmeNp/OQC9HXLVUWoux6SZWoG5KCAf9QQ8W2ciS1PhgtmEODDZPyfzgk4kkiVx1/UwO7G3C6z6ROtTnDfLysztwDvq4696lZOUmx2gTlIhKY1036944wKYPjtLX4x4RLopLM+hsHxg1uG88CILA3MXFfOLTK/nDY+tjzJ+RiEpDbTf/94v3eOGprVTNzWfmnAIysuzY7NHMIv5ACNegn7aWPlqa+qg91kF/r4dwKEIorHDbxxZdliZVTdNQFBWfJ4jHE8TrCQz9BId+or973H5qjsXXEmlp7OV3v3yf5FQrZosBi9WA2WLEajVgsRqxWA1DP0ZM5suv2I0gCOj0Mquunk5LUy9/+fPOmM8HB3y8+8YBtm2sprQyi7kLisgvSiPJYUaWRQKBMH5/iN5uN61NvTTV99Lc2IPPGyIcihAOKySnWC7b4laaphEMhvG6g3g8AbzuAB5PEN9w3/FG/+3pdtE6iiVw/TsH6WjrP6mvnPqvAbMl2qcMY8h4JggCtiQTN94xn9bmPrZvqon5vKfbxavP7WTjusNUVuUye34h2bnJJDlMIAgEfGF8viA9XS5aGntpqOuivWUAvz/6viIRddR6CqO2w25kzXUzeeHpbTGfNdV38w9fe5pPfeFKVq6pRKePvS/ngJe9Oxt5+c87aKjtIjA09lltRswWA329bpRIwp3tfKCqw/05EDP+eb3RPu3xRvt4V4eTjrbBmGOViMru7XUoijLUf41YLCf68vD4OLxNp5cuu/FwmFVXzWDz+mNs3Vg94pKrqhpul39MFq7h+gY7ZBFZlrBYDaxZO5OPf2oF9qSJZfO6XIioET7s2U1noAdREOkJDqBpoBd1pOuTyTKkkWZIoT80SKo+mTS9gwxDCgJwUJRwhqMZy/JMmWQZ0zBIehRNpdXfSY4pnSxTOn0hJ/WeFtxhL5t6d9MT7EcQBHqDg4TVCK6IB7vOSr4pG0EQ0IZmoFxzJtnGdFRN5RgCITUEfISFA4MoMz+5mDvyF592H5Okxx1JmHXPxoo103jzL3vZu6Mhxo/f4w7w2gu7eOuVveTmp5CemYQoirid0TzkA/1eVFWNWWDnFabygx/fy8//8424SX68CIKAyaTnpjsW4Oz38tqLu+MsE8FAmO6Ak553nGxYd3jowKEPh9oVbZ92WQoCp+M3P3uXV56LLnqjtxd7c9rIvcffdH+vh7df3QcIw/+dQCBGoP6f//ss5dOyL7uBXxAEHMkW7rpvGW5ngPXvHorRSCsRFeegjz3b69m7syF6x6f0G4g+v6nUbwAG+r184/O/G9Fsa8QG3Gon/W+0ez98oJUjB6PpEOP7z4nek5ufwm///KUxtUkQBPIKUvnUF9ZEM54daI3pu+GwQm+Pm94Nx9i68fjItU80eOjX072vMb5Do0nPVdfN4sP3jsZo/jUN2lsG+I/vvsSjZj2FJRnY7CbCoQh9PS66Op2EgrGZ34wmHWtvmUtRSTpP/XZjwpJwnjh2uI3//fHb1ByLptAeT39WVY366i4aaoZcV8/Qn+ctKubh795GSmp8FfbLAYNR5pv/eCu/ffRd3vzL3nEfr2nR75caUgiHFPy+EC/+cRt7d9Tz5b+7garZ+ZfdPDFWmn0ddPi7+XjBTShahJ39BwGNgBrCHfYS0SL0hQYotOQgCiIdgR7CWgS/EsAb8Y9o8mXhhHApAJnGVHb2HySiRhgIOZFFiY5ALx3+Hh4oug2/EuDAwHEkQcIg6ugIe/ErAfSiDnEotlYnTJ7AOiWEg2XpFSiainiGh5JvTk1USB4Dsizxze/cyj989WlammLdizQNwiGFxroeGut6znAOkeLyTP7fD+4gLcPGgsWl5ywcwFCQYIqF+z63CoNRx9uv7hs1xeHwwPVRQVXVc3LhGhaYTplHhz89ab/L95kKgkBWjoPPfeUqjGYdH753ZKhScux+mqqN2wogy9Lla5PUIKIo59h/hqXPU/vPSQLYOM8vCALl07L5xj/czGP/s46D+5rj0xZrRDPSjRNZN7bkF4IgUFiSzqe+sIbf/GwdLqcvpr+oqobXE+TIgZYznsdiNbDm2ioe+PwVdLYNkpZhSwgH54lhS+oF6c+X4XioaRqhUIT+Xg+b1x+jubE3rvDgRFFVjbrqLn79k3f4m7+/8bJUJI2FbGM6kiDx+4YXSNLbmWYrBQRkQaLW28z/1DxJhiGVYnMeXcE+zLKJx+r/TEAJsCptIUk6GwZRj07UjSjfREGk0lZCtbuJ/z7+O9IMyVybuYJ0QwpbenfzeP1zJOltVNiLEQSBSlspbf5uHq15Cg2NB4puQxIkLFI07bwkiJgkI4IwcVfXKSEcWOSz+7rNSi64AC2ZGqRn2vmn//gYv/rx2xw/3I7H7R/T4CGKAvYkM/MXF/PAX60mZ8gnePaCQmRZjPH3PheSU6zc//nVFJdn8vqLu2lt6sXl9E8oM5LRpCPJYcZkNiQCrz4CpGcm8dDX11IxPYe3/rKH9tYB3K7AuAUfWRaxWI04ki0sXF6KmIg3OC8UFKfzrR/eyWsv7ua9Nw/Q2+3G64kX6s6GTi9htUXf12jZq06HXi+z8spphIJh/vLcTtpb+gmHlTFfMyMziWtvmsOd9y3FaNSRnZdMRlYSRw/GV19OkOB8oqoafb1u3n/zIK88t5Oe7hMJGvQGGavNiMGgQ5LE6Fx46nyoDaU2V1UiEZVgIIzPG4z7PtQe7+TNl/eS9aXkCddIupQxyUb+qvSemG0BJYhO1LE0ZQ5XZS4d2d4V7CNN7+ArZZ+M2f/kfYYxSgY+ln993Pa/LrsvbptDb+POvLVx20usHwcg25TBHXnXju2GTsOUEA4uR/IKUlh55bSYCSI1zXb6A4i66Zx8jMVqGLUC7Ami2tJTJ8OxmELzClL51j/fxbtv7Gf3tjo6OwZxDvjw+0NEwgqapiFKInq9jNliIMlhJicvhSUry1m+uhKb/cSgkJXj4Nqb5+IaqnlQVJqB2XL6wOaxYDTpuPr6WcxdUMTOLbXs3dVAZ/sgrkEfHneAQCBMOBw17YuCgCgJyLKE0ajHZNZhtZlISbNSUpbJgqUlVM7IRRTPvsCbNjMPQRBjFpPTZuai0088Fa/FamTFmti+IMkiujFqOEvKM8e14JkoVtvZB/qZ8wqw2mKF9eKyzLgUueMhNc066vOZqFbKZNJz4+3zWby8jK0bj7N/dyNdHU6cgz68nmjfiYRVQEMURSR5uJ/rMVuMJDlMZOcmUzkzjwWLS8jIso+p71yK6PTRwmPOgfNbMTo1beLuFxarkY9/agWrr6li8wdHObS/ma5OF66h9xUMRlAi0QWKKEV9oIfHJavNQJLDQl5hCtNn5jN/SQmOZPO4rm+2GLj+tnnkF6Wx/p1D1FV30d/nwe3yEw4pKIqCIAjIOgmjUYfVbiI5xUJJeSbX3Dg7psK72WJg0bIyNA0iYQVBgMLS8RfntNqMzFlYRHrmiYrSsk4ivyh13OeaKGazgQWLSygsjm1/UtL4nu+pzJpXgN1x4hyCACUVZ08WAGC3m5i7sIiMrKRzasPZKCnPRK8/8/IpyWFm0bJSBk/5bp06Po6XhUtK6S87ESQvCFBWmXXGY6IVvwf44/99yMb3jhAcqiMiSSJllVnMmJ3PjFl5ZGQ7sFgNJwSEU86hRFSCwQhup5/OjkFqjnZwcF8TzQ0n4pEURWX/7kZqjrazYGnpOd3r5YIoiKQbkuMsmVbZTKm18CK16twQtMvQV8DlcpGUlITT6cRut5/9gAQTRlU1nINeGmq7aW/txzngIxAIo6kasi4aiORItpCTl0J+UdpF0xREIgptzf10tA3Q2+PG7fTh94dRFBVRjE7cBoOM3W4mKdlMeqad7NxkrDbjlDR9JhgbmqYRCSs0N/ZG+063G4/bTzAQQdO06ELTIGMy60lOsZCSZiMrx0Fqui2RnegioGkaAX945H3197rxuAMjGcyGv+dmq5GUFAup6Tay85KHUjee+/sKhSK0NPbS2tRHd6cTvy9EKBRBFAUMRh22JBPpmUkUFKWRnZt82owvCRJcSJyDPp77wxb+MpSudJirrp/FXfctpawye0KWc0VR2buzgZ/88NUYS4Ssk/jCV6/h5jsXjtmNL8H5Zzxr54TlIMEZEQSw2ZqpqHiSioroNpPlC4hy8UVdVGtahKD/BSKhE8FUqSnRn5MxmG5H1i9CEBID1FSiy+vmtwd3U5acwscrZ0/4PMPZjEorsiitOLP2bSz4wmHeaaqhze3ippJKipLGVhvkZFRN43h/L08dPdG386xJfHzaLFKM56aVvVTRNI0m1yB/OnaABZk5rC0qH3U/QRAwmfVUzsihckbOBW5l1M1osvrKxab6QAsDvW5mLSnFbDGgaRqvPrWFFdfNIjXj3JRukbDCOy/sZPm1M3Fc4kG7Dcc62LnhGO5BH3d+7gqSz2LBPx/s3lRNUrKF4sosJHly5ypV1Wis6+aDdw7FCAYV07P59F9fSWZW0oRdaiVJZMGSElasmcbLfz5RgCsSVujpchHwh7Dqpp5r0UeBhOorwVnQUCKtBH1PjfyoahcXqlLp6VEIBzfHtGu0HyVSfQm0NcFkMxgM8ELNIXZ1tl3spsQQUhW2d7TyRsNxun3esx9wGnSiiFGScQYDvN9cz1uN1XhCUzuhQrfPw0u1hznS1332nROcM+1NvVQfbCV00oJx+vxCzGOoEwMQCoZprO6ks7U/7jNREqicU4DRfG7uoxeC5HQbVQuK2Pb+EbwXqVBdblEaqZl2hPNgaQoGwxw91EbvSZp9iBYWTc+wT4qSb8bsvLhtHneAUGhs8TkJLj0SloMElykCkpSNKOaiagOgnV+f6QSXFkVJyTx/631YdZf+4mO8CEBRkoOvzl9Om8fFf+78kP7A1O/fs9KzePbme0kyjG1xmmASOGldKAgC5VXxi7zT4XH62bOpmso5BWTlxZpsRVGkdPqFt+xMBEeqFavdFFev4kJy6vObTEKBMI21XXEB8FVzCibN7W20+AvtpP8nuPxICAcJLlN0mGxfx2j9EtGc1SE0tR8lfAjP4NcvduMSnGcMkkxJ0vmbUC8mghBNi5dkkPCEQujFqe8SJwgCJllH8QTcsBKMDU3V2L+9jid/+jYGgw6bw0zBUFXwI3sa+fOvP6DhWAf/+IsHKZuZC0TdQ7asO8Sbz26nv9uNyWLgB7/5LAO9Ln77b69Tf7Qdi92E2WLgi9+5lcq5BezfVsczv3oPV7+Pv//xfRRWZKJpGj0dgzz32HqO7GnCZNHzyb+5ljlLSzmyq5FX/rAZa5KZY/uamL2klPu/thaLzcg7L+zk9T9uIxJSKKvK5e6/Wk1BWSab3jpAe1MfXa39HN7dRHp2El/+/h18+OZBMnMdrLoh6mr4zC/fQ9bL3P351ePSkGuahnvQx8tPbGLvphpUVeXG+5ax9q6FeJx+XvnDZra+dwRN1Vh90xxuum8ZZquB157eisfpp7m2i/pjHVx56zxu/uQyHv+318nMT2b3h9V4nD5u+MRSbrl/OQM9bt54Zjsb39jPnZ+9gqtum4dOLzPY6+ZHX32KOUvL2P7+UXIKU/nEl6+mqCKLppounvjxW/h9IVrruymqyObjf30VsxYVj3ovESVaxyUGgWjhwEmiu8sVt81k0ifiDS5jpqRwoGkaKhqapiGdkuc1EXw6NYi+RxOCEB3gNE0DMZ34/GuXJ5qmEVFVVE0bqn4oIAjR/MWSIIz0Y0VTCSsKkiCik2IHYkVVCasqkiggD30PFE1D0VRkURy6hgZoQwtSEWmUoE1N06LHqepIWyRBQBLFuNoiqqYRUiLIooQkCERUFWVIZSUIoBNP1AQIKgri8LZTzqNpGkFFQQD0UvRzTdMIq8rI+SD6PPTS6BOQpkVbq4y0Idp2UYi2XzzpOZ68//BzP/m5nLrvye9o+NyiIKBq2kXpgSPvSFOHMmlF708e5R3F7gvDz0USBSQhdv/hsTSsqOjEaIaoE8/nxDsVh95PRFXRAFkUUTQVZbh/nXT+U595UFFGKnwKMGpfhuH+rKAbEpYip9yrbqjvnnr+k/vuqXpMSRBG7X9TkVAwzP/+8BX+6ls3U16VyzO/eh9nfzTrzfR5hfzTLx/ka3c9GlMjYP+2OnZsOMaD37iesqpc+nvc2BwmbA4TX/zH2/jLk5tYuXYWVYuKR7TQc5aWUjk7n3/6/OMj54qEFV747UbyStL5zN/dyECPm+899Du+9+tP4/cFaW/u46HvrODTD1/Pj776NHVH2pm1uJiqBSUsvGIasizx5rPb2bXxOPmlGYRDCm8/v5NH/usT/PV3b8fnDmBzmMnIcdBc24XH6cdg0vHhmwf59s8fGP/D0uCt53aQmmHne499hkhY4VsPPkbp9GwKyrK48tb53Hz/cvyeII/+04ssu7YKszWdgC/Ihtf38Z1ffIr0rKRotjxRoKOlD5PNwD//9nP097j43kO/Z+nVM0jPdnDfV64mEomOdcNDm6KoNNZ0cf0nlvIff/oiz/7vB+zbWktOYRpPP7qOq+9YwKLV03jn+Z10tw0wc2HRaW9FQIhPnqCBzzs5boqaprHpg6Nx21PTbZguA7cyAE1T0VAREMdcG0DTVFQt6pInCBKiMLWW01PrboCIqtDq62NHXy19QS9fKL8aVVNxhf0YJT0WOWGynooMLx6nAoqqcrS/h6eP7mdHRwv9AT+yKJJjtXFlfil3V1SRZ4um6tvW1sKDbz/PPRWz+NGq2LzHr9cf57tb3uOT0+fwzYUrCSgRnji8h7caa/jinMXs6+7gzYZqBgMB8ux2PjNjPjeVTsMk60bOoWoanV4PTx/dyzuNtXT6PFhkHUtzCnhgxlxmp2XFLOTqBvu55aUn+faSNSzKzuP3h/ewqa0RbzhMttnKP6+8lnkZOfQHfNz44hOUO1J59KpbSDHFBtq6QiHW/Pk35FpsPHXjPTiMJvyRCN9Y/zpb25uJqCr+SJhrCsr4zXV3jP4cNZXdXe08dWQfB3o7cQWDGOWoxeHm0mncWjoNy5BbkqZp+CMRtrY389TRvRzq7SKiqhQnpfDAjLlcV1SO+SQXJkVVOT7Qy+MHd7GxrZGIojI9NZ27yqu40KZ0VdPo9/t4ue4If6k9SrN7EFkQmZWezWeq5rM0Ox+DfGKo7/R6ePb4QTa01NPkHiSsKiQbTKzKK+L+6XOpSE4bERI1YFt7C49sfItvL1lDksHIk0f2sre7nYiqkm9L4r/X3EiZIxVnMMAv9m2j3tnP/TPm8kFzA+tbGugLeEkxmrmppJLPzVxAuvlEgKonHOKuV56m0+shrCpoGnxy+hz+cdlVcff5YWsjX3rvFb6/4mpkUeQPh/dR7+xHJ0osyc7jb+YtoyI5Lea5tLqd/OnYAd5trqXH5yUQiRBQIgiATW9gdV4x/3bFdSP9YCrT3TGI3xtgwcpoZoniadm0NkSLWQqCACJxgamdLX3kFaZTUJaBrJPIyHGMfCaKUYFZEOMXn9G6HydO1t/jIuALMm1OAWaLIRpUPqeAw7saSE6zkVeczrQ5Bej0Mpm5ybgGvCgRlUM769m5/hiCKNDV2s+ya6tGFtDT5xZSMj0HWZawJ0erzpbNyKHxeAetjT04ez0UTcsmMzd53MKfzxuktb6Hw7saeXuo+rzZaqS/2016toMP39xP9cFWBKD+aDuRoVz/oigya1EJeSeldw36Q2garLl5HgaTjtTMJFIz7Az2ecnIibZttLoCZquRFWtnYjTpychx4HUFCIciSJJIKBjG44zWHzKYdGe8P0kWcaRY4rbv391IXmEqojhxpWkkrLBl43EO7WuO2Z6caiU7Nxmd7tJYYkaVBH5EQT/qIt4dqqPbv40s8yqs+qIxnXMweJSj/f+DO9xImeNBSpM+efaDLiMujTc3iRwYbOJX1evQizLHXG18ruxKXGE/L7XsoMiSzrXZE89skiDBhaBmsI+vf/A6gUiYFbmFpJssDAQDNDoHqHP24QoFz+n8rW4nv9i7DZvewJX5JQBs72jhHzatYzAY4DMzF4wsDhtdA/zT5veoGehlbno2VxeW0uP3squzjeqBXv5hyWqW5RTEWOgUTWNPdxtbOpoJKhFuLK7EFw5zfKCHNJMFAbDIeq4vKmdDayN7utu5prAspo3vNdfiC4dYmVeEwxi1DhlkiS/PXcKtpdOpGezl0T1bz3if+7o7+OzbL5JtsbEqtwib3kC3z0uDq5+6wT7C6gkNaUhVeK3+GD/ZvZkMs5XriysQBIEDPZ08svEtGp2DfHne0hErRc1gH/+89QNqB/tYlpNPgc1Bu8fFL/ZtQxalES32haDH5+UnuzezvqWeaSnpLM8pwBUKsqurjUc2vskji67gtrIZyENt2tnVypuNx0kzmrmxuBKzTsfx/l5eqTtGn9/Ht5deSb4tNk+8PxJmXXMtzkAAs07PXeVV9Af8HO/vJd0Uu/CoHujj37ZvJMlgZG1ROaqmsqernScO78EZDPC95VdjkKJTj0nW8YPl19Dj93Gwt5MnD+/lTIRUhWePHcQVClKVmsHCzFxqB/tY39JA3WA/z9z8CZIM0TzyvX4vv9q/nQ9a6rmppJIZKRk0uAZ45tgB/JEIDy9axfKcghhheCqj08uIoojX5cdoMRAKhFGVMwuysk4iFAwRCkYwWTSUiIIoSUOCQXQfdQzViGWdjCCK+LzBaBGtkILfG8A0FPws6aQRn39BAA0N54CX3//3m/zspa9ic5h58fGNMec0mOLfW3ZBKjaHmdb6HvZtreWK62chSuNf+A6npr3vK9ew+qY5yDqJgD+ETidxcGc9R/Y08s1//zjhUIQfffWpU9o1uqBpMutPLMIFOJsSQRDAOHQuAWFEeLj+niX85t9e4+jeJowmPbfev+KM5zEYZYpK0uMqIb/y/E7mzC8kvzh93NmKVFXF5fRzYHcTv/zvt2IKkAoCVEzLpmgC9TvOFxHNS6PrebItV2LVxdcdsBvKsRtGz5B2OpKNVSzN/hX7er8/Wc28pJhywsFfWnZxY+48bsqdzw3v/ysANtmIVTbSG3Rf5NYlSHB29nS10ev38qW5S/j8rEUjbh6+cBhPODiy+JkofX4fCzNz+X+LV1NodyAIAtUDvXzmred58sheri4sozgpmaAS4dljBznS18XX5i/nvmlzkEWRiKryXnMd393yHq/VHaMyJT1mgRhRFbZ2tHDftDl8buZCLProBOcOBbHoohOkXpK4prCMV+uPs7urnSvyikcW3oqq8kL1YSw6PTcUV4ycVxJEZqdnMzs9m+P9Kfx877Yz3uf7zfWEFYUvz13CHeVVI9sHAn5UTcOmP2E1aHO7+PX+HZQkpfCdpVcyLSUNQRBo97h4aN3L/PbQTq7IL2JBZi4hJcK7TbUc6e/mS3OW8KmqeZhkHRFV5fX6Y/zDpncosl8Y3/mIqvBBSz3rW+u5o7yKL85ZjF0fXXAd7O3iy++9wrPHDzI/M3fEn/+aglKKk5KZnpKOPOSi0+l18x87P+SD5jo6ve444cAZDHCot4svzl7MzSXTRiwRzmBg5HrDtLid3Fhcwf9bvHrkPK1uJ59683n2dXfQ4BxgWkp04SCLIktzCtA0jQyz5azCQURVaXIN8p2la7i5ZBqSKOIOBXl4w5u821TLrs5WriooRRAEGp0DbGxt5OaSafzNvGUkGYxRV1NEfntoJwZRosxx4YqGXWxSM+xMm1PA63/aRn5pBo01XRiM0QV2w/EOBnrdeN0Bju1rQlFViiqyKJ2RS3NtN5vfPkhKhp1gIMSSq2ZgMhswmPRYbSYO724kFIpQOTsfe7KFmkOt9He7cDt9HN3bhKqq5BSmUjojh8O7GvC6A/i9QURBYMaCYuqPjJ5xTBAEMnKTOTaklW6q6aRs5pkDpkVJZNbiEj54ZS99nU4q5xacVStefbAFZ58XnyfI4d2N+H0hCkozmLW4hKN7mpBkEYNRh9cTYNV1s9AZdOgNOg7ubMDZ58HnDU5Y8+4a8NJU00Vncz8BX5gD2+oon3Xme+ztdJKZm8z8FRUIosBAn5vkdNtp/fsNBh3TZ+WTnplEd6dzZHtzQy8//dfXuOv+5ZSUZpCabhvpD6eiaRqqquH1BOjv9dDe2s+ubfWsf+cQHncgZl9HsoWlq8rJmeQg66DSjytUQ1j1ICJj1uVh0xUjCCIhxYkv0oYsWvCEm1G1EHZ9GRY5n4DSTbdvC73+nUiCAbdcj1VXgFVXAqj4Ip24Q7VIookkfSV66cTYp2kKnnAz3kgrqhbCKKVi15cji/GWmNjnpeAON+ILt6GhYtHlYtOVjtll6VJgygkHzrCPEmsm4kk2OkVTCSphjNLUNx0nuPzJsdoxSjI7OtqoTE5nVnoWKUYTZp0Os+7ctZwWnZ4l2fkjggFAuSOVtYXlvFhzmJ0drRQnJdPmcXGgp5Ncq50biiuQh3zOdZLEjNQMZqVlcqSvh3aPK0Y40ACrTs8nps0eEQwg6sYxjCSKlDlSqUhO43BfF61uJyWO6GRSO9jH3u52FmTmUpWaMeH7LHEko6Gxub2ZXFsS01PSsekNJBtjA/FUTWN3Vzvdfi93VVSNCAYQfRfXF1Xw0z2bea+pjgWZufT6fRzv7yXNaOaKvGKMQ1pwWRRZmp3PtJR0ApHIhNs9HgYCAfZ1d2CWdVxbWIZdbxhpe0lSCitzC/mwtZEjfd0jwoFZp2dWWmye/kyz9f+z995xcl3l/f/73Da97Oxs75JWvUtWsS3L3ca4gAvGYHoNEAIJvwQSAkn4phfSKIEEEwjNxg13Gze5S7J616ps7236zC3n98fsrna1u9JKlixZ7Fsv2ZqZc885986de85zzvN8HuZGinj00D4yVj4B3OgJjyMls8KFXFUza4yL0kSGql83uK62ngr/MZnEIq+fVaWVvNHRTFcqMWIcnCoKglnhCNfVzh7Z3QoYLlaXVrKh5SiHBvq4sjqflTVpmfRlUlT4g2PuvUVFJdiOpDVxLIiyP/MmEontJEmaRxBohFyLCbsXAQIpbbrTL+HVKsnaPcRzeYnkAtcyQu78bnTG6mIgu4W01Y4q3IRciwka80euQdbupT/zJhmrbchXWaIIF2W+GzDUKCmriUSuAa9eyWB2B5aTIGDMIeJeDShk7S76s1vIWt0IBF69hgL3CjTFRzJ3hITZgERiOQn8+ixSVhMgKfKsR1eDaLrKB794DZte2EdvxyALV9YRKQrg8uj098RpO9rD+huXYlkOXa39VNZGqZtThrxJsndrI00NeWNi+HwCYS9rrp7Pzo2HaT7cRW19KRTkJ6/dbQNcct0istkcPZ2DlFVHuOLmZWx99SDtTb1ousqHv3w9kaIA6epCVl8xb+S7uGj9XMproxRE/XzwC1fT2NBJMOzjshuW5uU+BdTUl+DyGEPuS2OpmllMb2eM5ZfOxus/eZLL7vZBejsGufKWZaQSWXo6BqmojbLqinn4Am4aD3SSMy0KCv2omkptfSmXXLeI9qYeSisLuflDlxAeygA+Z0k16dTYnV1VU7ni5qUEhrI+q5rC5TcuJVIUJJPO0dHcR83sUhRF0NnWT9XMYgJhDzd+YO1IHTPmlZPLmiTjGZoaOqmpL6GpoYtc1iQ+kOLGDxojweXHI4SgsqaQy69ZwMP3biKbPSZdu3tHC03/7xEWLq2mZkYR0aIAPr8r7w4k8rEPlmmTTuWIx9L09SRoaerl6KEuBvqT4xSQPF6DK65byMXr557xJJFpq4vezJa8i5CTxEw9x5zwZ/HqZSStZg4O3EPQmIVAw5FZVOHBp1Vi2nHi5hGydi9JsxnLSaIpfvz6DCQS0xmkK/0aGbub2eFPjBgHUkoGcvs4GrsXlxpBoJHVivFqFSc1Dvqzu2lJPIGuBABJe/J56kLvo8C14ITHnU9ccMbB4oIanmjbSj68xOFQvJP9sTYOJzp5d8XyU6orFft7HKcHoQTwBb6eHzysQ5jZ53HsFoTwohnL0V1XIkR+0iZlBiu3DTP7MtLpRyghNGMZuuvykTJTwXES2NYebHMPtt0BThyEQIgQilqBZixG1eoR4vRiKKSUSKcPK7cZy9qHdPoAByGCqFodmrESRa0hHwh7+j/ykXbM7djWfhynd0h21EAoBajaTDR9CapWddptXGgsLy7n7vnL+OW+7Xzr9eeZG4myrLicdZW1zAoXjgswPVX8hkHE7Rk3aM6NFGE6Di2J/OpSZzJBfzZNLJvhb994cUzgb9oyOTjQiyMlSdMcU48iBKW+AMXeEyc/Crs8XFk1gx/v3sK+vm7qwhEE8PChvZiOzW31C0ZWtk+HK6pm8sF5S3niyAH29naxoLCYVWVVrKuoodjrHzl/R0r29nVh2vlV+MbYwJh6Dg/24chjk8l4LktvJkVFIIRPH+vvq6saVYEwB/t7Trvfp0Isl6UtGSOWy/JfOzYSHjVZtxyHPb1dJC2T/uOkUBv6e9na3U5bYpB4LkfWttnT24UtjwUbj0ZXVcp9gTH1T0bE7SHq8Y0JbhciP4l3pBzjznWqKIqgJliAWxs7dPkNFwr5IPdh3KpG0HDRm06RtsyRuIIjg/0oQlA4Ks6lN/0afZmNhFyL0ZUgaeso3akXmFP4/xEw5iCx6Ew+hZQWXr0GRbhwZJqcndf4z1o9tMR/TdpqxqNVkpPddKeeoy70aSKei7CcBJ3Jp4jnDhA05pIym2lPPMqsgs8jhAZIkuZhDg38J0XeK1CFC0fmyFrD95EkZ/czmNmBroawnQw96ZeQ2EQ960iYB2no/w4lvmuI5/bTw0t49SoGMttxqUVE3PlEkJV1RWP84YdZfslsll8ye9z7APULK6mfYMVe09QJP1t79eQToHXXj3frrZxRTOWMY4sAl1y3aOTfq6+cz+or5487Zsa8cmZMIJUqpaS9sRdVU1iydtaU5EkvuXbhpJ+tWDeHFevmjHlP01Uuv3HphOUXTqAYpOkq179v9bHXmsp1d6waeX3NbSsnrOv2T10+8u85S/Lj456tRzl6sIOvfvsDeHwuOlr6+MV3niV2vBrRcQRDHq69cSldHYO8umH/SEZxgHgszWsb9vP6S/txufUhlSENwZCYg+WQzZik0ycOYPYFXNx020re/d4VFJyFxHc+vYq64PsxlBApq409ff9G3GzAq5cBkLY6qfLfSIn3EkDgSAshVIKuesrlNeTsAWqCtxE0Zo7UKdAIu+ZjS5OWxOPHtShpjD2AS4lQH/4YqvDiyByKOPEis5QOzYlH8GlV1AZvQ6Cwr/+/aI4/Mm0cnEtuqVzJ421b+fGhF8jaJn+7+0GK3SGuLl3EwnD1KdWVzTyGYx1GKAV4fJ/EsTtJxf8BK7cDKROAhqIWY7jfxBf6KlJmyaUfJ534LrbdBDILwoWSLsJwX48v+Kdwkoh2KU2s3BtkUj/HNvfgOH1IJwXkAAHCQAg/ilKMZqzE7fsgqjb3lCbwUkpymcfJpH6KYzUinX6kzAByqP5Q/rw8N+L2fhgwAB0wT1zxuHbSZFMPkk0/gGO3j2rHAtT8tVFCKEoxuvtq3N4PoKjnj5/iuSLocvPh+Uu5pLyaDa1Hea7pEK+2NfH4kf28b84ibpoxb0o7CM4kPq0KAmWC+8WlaUMrKfnJW862R5RpOlKJEZ/1YaqDYQrd3nFuJQKmlH/Ao2ksKSrFUFR29nSyrrIWgeDpow1EPT6urJl50jpORMTt4YvL1nJNzSyebz7MhpYjbGg5yqOH9/G5JatZXlKRV20ib+xI8oZOW3Ks+6Fb07m0opaZQy4o9pAqj1fXxxlqCoz4078dWI5DzrZxpKQ3nSJljf2NRr0+akIFI4aaIyU/27uNXx/YRcLMUR8uJOrx4dG0oR2QiQ1PTShT9s03VHXcvQLjg11Ph/yK+fh+iKH/jNYjqgmGWVtew2NH9uPWNOZFimlODPDTPVupCoS5smrs/SWlRanvWnz6LEx7gD29f0lb4lHmRI5NDi0nSbH3arx6NVJaI8/deG4fsdxuqgLvp8C9DFtmaOj/DocGvkfEcxE5e4BYdjdB1yIqAreTtbvpTj1P0LUQXQkz7H/uyBx+fRZRzyUglKG382149WpqQh9CV4I40uJA3z8zmNlOxL16ZMegInAbXcnf0pt+jXL/zWSsTlJmEwXu5QgubFnJ2ECKFx7ZypaXD7DysrlUzSg6Yzr+5wuVdUWUVhXynb94MJ9JWcKMeWXMmFt2wuOEEFRWR/jwpy8nXOjjmce2k4yP3eGQEjJpk0z61MZ5IWDpRXXccscqFi6pIhg+O1ncs1YPLYnHydjdONIkY3dhO8dcmjTFR8g1DyF0BALlFBZjJ0IiGcjuYV7B59GVfMbwqSgSWTJN0myhP7Ob7nQ+a3TG7satRk9y5PnFBWccFLoC3Fq1mqtKF5G2cggBfs1NSPfiUk/zZpEWZm4zufSTmNlXgeHVKRvHbiab+kl+JV+tJBX/Jxy7edSxaRy7iWzq52j6QlzeWydvRlpkU/9HKv6fSKeX/CT6+EJppExjO93YVgOWuR1v4CvorkuHVqBOTjr5X2QS3xvaLThuAjlSfwcZ6yi2uReX+yaE4kM6A1OqH8Cx+0gO/ml+B0VOdJwD0sSxEzh2K7Z1ECu3BV/w66j6qQUGXYgEXW6WFpcxOxLl9tkL2drZxve3b+RHO9+k0h/ikoqhoKrhoMAJDIF4Lostx6/S5hybjDV+AOhNp1CEGPHF9+k6blWjMhLk62uuIDjBqrEqxBiXjWGm4oMrhKA6GGZJcRlbu9poT8Rpig/SmUpw5+yFBN6igowQggK3h7Xl1SwqKuUDc5fw1NGD/N/ebfzb1tf49uU3DO0gQMTtxa+7uH32Qm6cMXd8XTCyc2IoKm5NI5nLYR23Cu5IScp8+zIZu1QVv25QHQjzRysvHTFgRqOIfAA4wM7uDn68awspy+R7V99MuT+IMSRF+tM923ijo3nc8cNMfXJ/didkU629xOvnowuW87cbX+SHOzbhUjV8hsHykgo+u+QiKgLBMeV9ei1urRxN8aIIg6BrPoPZ3WPKBFxz8eqVaMqxCZAjrSFXIg8BYxaa4kfDT9Szjl3Jx8nZ/UOTCgXT7kdKi7TZAoChjPXLdqlFBIw56OrYmI+8K0WCtsRD9Ge2YTmDpM12iryXAw4Cga6GcKlF6GoBmhrErZWiKX5smc4ruV1Y8+Rx+PxuLrthCauumEewwPeOkdE8FQIhLx/8wtWkU1kEIBSBP+jB6z/5jp6iKlRUR/jwp9az9rI5PPHgFt545SDp1Ok9ryJRPytWz+Tyaxcyo76YcNiHqp0dn3pHmuzo+VtKfOuoDtxCzhnk4MA9Y0Y9RWiIoT/jmUAO6iQIQFO8mPLUYlVV4UIROrXB2yh0rxhZQFBP08vjXHHBGQeDuRR+3Y1XDY+891Y1rKXMkk09gJnbiGZcjG5chGW+iZl9HcgiZZJM4juo+gIcuxPNWIVuXIJtNZDLPgsylS+T+gmG570T9kdKSSb536Ri/wgMW/QKijYL3ViDqlYgMXHsZszMBhynHchim9tJDv4Z/vC30YyVJ9xBkFKSS99POv5tkMnhq4NQyjFcl6JqdUiZw7YOYmZfRMoYucyT2NbBU1BnlDjOIImBL2NmNzCy2yA86MZqNH0RQgkgnXje/Sr3OmAiZQIz+wLJuIIv+DcoaunvhPb4RIyWZPXpBl5Np6SuHls6fPWlp2iOD475HJlXrBn2E89L10v29nZP6Ps+kMnQmohhOc5IzgQJbOpoxVA1Zg75/lcHwxR5fRzo78Gj6RR5vCPfyZmSjS32+llRUs6/vvkqLYlBnms6RNYyuWPuopMffBKG+6gIQdBwEdANPrZwBY2xfh4+tHfEHUoVCitLK/ifnZvpSMYpdHvG5DU4/lwL3B5KfX5eaDpCLJc9dt2lJGNbNAz0vmXXr6kS8XipC0XY3t2OIyVFQ64yk0n7Ngz20pdN86662SyKlqIO5btIWSaDucw4Y+edjC0l27raydkW/3HVTSwpKhvJ/eBWtfF5K46bQAg0pBz7+9GEd4IVeAdHZocmJceev4rQkdJBETq6EqTUdz0HB75Nc/xeDLWQmQVfwKNXjKkpL7U4fhJhyzR7+/4WJNQXfAmPVs6h/u8gOfZ9CaHCyPRIGerL8PlcGDLPJ0LVFMJnwZ3lfEIIQSjiIzSBNOlUj/cHPCxZXsP8hZWkUzm2bjrMgb3tHD3URUfbAIlEhnQyh2nZqKqC260TDHlG5ElrZxYzb2EFVbVRPF4DXVeH5Fjf6jxLsm3TEXZvb+buT60f85kjTVJWGwFjBm6tmFi6gXjuMEzxMmiKF8uJk7P7kdIeymkwevorR/4ee24KSn2X0xh7gLBrAR61mIzdi6GG0BX/UDk5cvzwcQKVIvcqejNbKfauxaOWkLLasZwEMHFcyPnIBWcc/M2uB7mmfDFXlCwYlwDt9MlhZjfg8tyCL/QtED4gQ7zvs5jZFwAby9yJZe7H8LwLX+ivESIEOCQG/pBc+mHAwraO4tjNqNrx7k0SK/c6qdjfMzyZVpRyPIHP4fLcDsLLmId8MEk6+SMyie8jZRzHbiQV/3v8Bd9BUUom/ZFKp51U/F9HGQYuDM+78QW+hlBLxrQhnQGSsW+RSz+Abe6a8pWS0iGT+P7IpB8UNGMVvtDfoGrDcpWC4R+VbR0gMfAH2OZuwMbMvERW+z88gS8C7yxL+0yxsaOFsMtDZSCYTxoFJK0cR2P9GKo2xq2ivqAQt6axrauNzR2tLCoqRSLZ0HKE19qayDn2uPot6fDbxkNcVFrJ8pJyBILX25p4trGBWQWFrC3P70oUeXysr6xjR3cHf7/pRb6+Or97IMgH+TfHBzEdhzkF0QmTVk0FVQjmRooo9QV4ubWJl1uPsri4jBmhyLj72JEynxRuyK0HGErQZYMYdpc6Nkg93XiQuZFiir2+kdWkzlSczlSSsMsz4voigMXRUpYVl/NMYwOLoqVcWTVjyOUon6TrtbYmLqmoIWC4iLg9LI6W8tTRBu7dv4Ny/yUEdBdZx+bBhj0c6O857YBbOJa4K+8qYg8ldMy7EJmOPbRlnv8b0A3WlFfxTGMDP9ixiSKPj6pgCAWBg6Qvk6I5NsjS4nLcmkbU40NXFBoH+8naFprMq0+93NrI00cbTrvPp8twcjJn6Pwg/z1P9p2eCgPZNPv6u0aMgeFrlk+8NpSoTzkmXZGxWrGdFFIpQEqTpHkI73GT94lWHwU6bq2EnnSCrN2DS43mpTizO/DoFajCB0gsJ06B6yJqiz+GS42O1DWVHC1SWsSyu5gT+WOCxlwcmSPn9KMqJ3Lh+N1cXJnm5CiKgsutYLg0Lr92IZdfu3BUcPFE9+Ioo3nUbXWmF/C2v3mUZx/fMc44UIWH2QWfYGfPPyJxKHQvpTJwA4pQh3qnDK3MT9yfgF5HkWctO3r+FkfmqC/4ONX+95Bz+tjd+236s7swnRg96Y0EjFksjf45uhKkLnA7jpNlY8eXMZ0kxZ61zC74BLriZ3ffP9Od3kzG6qQn/QadyQ3UBu+g3H8NtaH30Ri7n40df0TW7sGrVzI7/CkCxltzlX07ueCMg5SdpdJbOEat6EygKIUY3lsRyvDKhAeX59b8JHhosq2oUdzeO1GU8FAZFcPzLnKZJ0FaIE1s6/A440DKNMnBbzJsGAgliifwOdy+DzN6JSqPABHA4/skQrhJx/8ZKVNYuTfIpX6N2/8Z8vEBY8nvTPwCx+kcPiM0YwW+0F+iKMfLLgqEGsEf+msSWOTSD07xKsl8PzJPj1wTTV9CMHIPQgmMPw9A1eYQiPwvgz3vQdotQAYz+xK6+2p0Yym/iwPcc82HuXf/TiJuDyVePy5Voy0Zpz0R45qaWSwrPhaI59V0PrtkFT/YsYlPP/MgcyJF5GybrlSC5SUV9GbGB6oVeXwIIfizl5+h1OdHStjV00GR18dXVq4biSEQQnDn3EW0JgZ5+NBebnrop9SFCnCpGp2pBF2pBB+ev4yZ4Qj6afozCyGoDxcyv7CIRw7tYTCb5bNLVk1o2G/vbufNzjYGM2maEzEcR3Kgv4d/3PwSAcNFTSDM2vJqirz55aRf7d/FG+1NVAXCRIdW1BtjAyRyWT6zeBWRIdWiYfejP1l1GX/52rP8xavP8n3fRqIeL/Fclub4ILqq8tDNHyQwpAZ0TU09W7vaefjQXjZ2tFLpD9KZSpCyTK6pqac5PnBa1wMga1v8ct9OBnMZOlNx9vd1k7RM/mvHRkp9AYKGi0srapkzpKp0aUUNH1mwjB/v3sLdT9xHdSBMwDDoSSdpS8RZU1bF/MJi3JrGiuJyFkVLeb29ibufuI8ZoQidqQR96RQzQgWkJ3A3O5u82HKEg/29xHIZDg/0Yzo2Gzta+Jc3X8ZvuJhbEGVNefUpJymTUuLXDWYXRPlt4yE+8uSvRwxEj6ZT5g9w55xF3Fa/cMTYTplNtMR/TcSzmnh2L7HsPuZHv3HStoQQBIy5GGqElvh9FHnXk7W6aE8+yuyCP0QIge3kyNhdWE4C0xlAShMhVDQliIpnCm2ouLUK+jJv4FKj9Ge2ksg1EHYvOaXrMs00oxk9uT/2z3M35iaPk0UdRghBdeAWqgO3TPh52DWfVSX/Mmm9QijUhd5HXeh9Y953qYUsL/5/kx6XN0o+yeyCT477bGHhV05wnMGM0F3MCN01aZnznQvOOFgeqeNwvIMydxif5hp78w+tQp0OQgmi6WNVDTRjCQJ9xM4WIoKqLxtbRp2JQB1eJx81OT9GLvNbbPvI0CsVzViB4bmD8YbB6P74MNzXYmVfJ5d9BoBM6ue4vHeCEh2/ZS4HMbOvgBz68QkPHv9nJjAMRjfixu37JGbmBaTsn7zcSBsmZvYlbGs4W6ILb+ibICbf6hVCoChFeHyfIBXLJxOxrN3Y5m40ffHQVvnvFrfMnIdb1djf101/NkPaMqkPR/jgvCVcVzOLEt9YQ+szi1dR6gvwQvNhejNpKgNBPrFwBfMKizEde5xvtVvTuHv+UgTwfNNhYrkMN82ayx2zF7GiZOxKqUfT+ZNV61lbXsNzTYc4GuvHkg5zI/lswFdVzxwTgOvRNFaWVDArPHWN64jHy6UVNXSmEjhSclX1zAmHp3193TzbdCi/qgwsL8kbSVs62wCYGS7M51wYMg4+s/giZoQKODrYT9zMoisqa8urubJqBpdW1I6ZcCpCsLiolH+74kYeP3KArV1txHNZijw+VpSUc3F5zZgsziU+P3+86jLmRKK83t5M1rJZXVbFbfUL6EoleeLI/pHYjVMlZ9s8fmT/iGrQcDDxoYE+Dg30oQhBmS/AnEg+wM2lanx4/jIWFJbwdONBDvb3krEtaoJhrq6exeVVdSOSsn7DxV9feg2/3LeTXT0dtCdiVAZCfGLhCgrdXn66ZytB19gdu6DhYnlJORX+sffR8WiKQlUgxMJoybg4FAVBVSDEoqLSMYpHGzta2NLZNnKuw4bvpo689n08l2VJcTk+3SDkcrOypJyqQHhc21GPj+XF5ZQP9TFr2zzffISnjzawoqSC+oJCXGpegSVpmmztaudf33wVQ9G4c8iFLerNx201Dt6DIgxmFXyeQs+wyoyCV6/FpZUw0XPZq1cxI/RJWhMP0hT7GZriZ1b4ixR7r85rxJNDCJWE2cCu7q/nXX+ETtRzKTPCnwZAV0L49VkT+iarwk19wRdpiv2cQwPfI+xeRnXwA+SdoVR0JUzQmIsAdDWCT69FoODVq3Bpxe8offVp3tmYOYuB/iRCCMIFPjRdpbc7NiZR2mT09yXehh5OMxWEPFPOw28jsViMUCjE4OAgweDYAeuFzt38svFVFoQqKfdExsjpzQ9WMjt44qj+0fR3XYZjHQYEmrGaUPQ+RlvV0knT37VmKHhYQXddRbDwnjF1OHY3A11X5INyhQ9v4Kt4/B8bVUIS7/8sufSTgI0QQXyhv8Llvf2k/ZPSIpP8Ien4t5Eyv0LsD38Hw3PzOOPAzL5KYuD/w7EbAVC0esJFT55UCtWxu0gM/AnmkAECECy8D81YPW7Asa0jJAe/gZl9HgBNX0kwet9JJVyllNjWAQa7r2M4CNvt+wSewJdObLxMWs9VI+/5Qn+Dy/uBKQdrX8ikLZP/3b2Fn+7Zxp+uvpx3z5hz8oOmmeYdSFNsgD94/lGqAmG+sfYKoqPzcEjJ4cF+bnrwJ7y3fgF/fek1NPT/Jzm7n1kFn8dQz2zyJiltetKv0hL/FXWhT+LVa5BS0p/ZyO6eb7C++jk05cL2lZ/md4fDBzr45Y9fRtdV3v+xdVTVRvmXb/1mXP6HidizsyUvjvDIl85+R38HOdHc+XguuBlTXzbBLH8pSSvLwXj7mM9KXCFg6sbBMRQUpZDjt9uE4ubYKpKKok6QsGnM5FuSlyQ9huMMYpsHGVFAEm40YxVTQQgNVZuJUIqR9lEAzNwGDM/N48ra1hHkSKwB6MaKqeVIEB5Uff4Y42AybLsFx24ZeZ0/j5OvWOV3DwoQShjp9AzV1Yp04nAKxsE000wzDeQVubpSSWYXREeS1MFQpleZd0dTFeU4Rayzs04mcbCcOI400dUQinBhyThZuxtNDfG76Do5zYVLKpWjpSkvypBK5g2C117ch207RItPPCFNJTL4Ayd3s5vm7HPBGQfXlS/BnkRx47SlTBGjYg3Gvn/swT5xGTFucnyc9KHdPpQzIY+ihFDUqRswQilBKAUwZBxYuT0TlnOcTpDpkV6p2sQJb8bVLwzUKfZH2j049rHkT2b2JRIDHUxp8JOZkd0PAOnEON6QmmaaaaaZCkHDxfLicl5ta+Jft7zK7IIohqqSyOU4ONDLa21NVAdCXFt79mWTBRpB13wGMlto6P8Ox56HDvUFX0QVZ0cXfpppzgW1M4v53FfehRBQVXtM23/Rshpu/9DFkx8o4ec/2kBLY+/b0MtpTsYFZxy0pfpJWhNvX5V5CijxhCb87MQIJgryPb6MOGmZ8UhnACmHFWUEQgmdUiZlIXwIccyHVzrd5FfAjos5cBJjZPnyOyFTQZ0gmHhipEyNmeDb1k5sa+cU2zme7DgZwWnOLVJKUnYfPm2q9847k33NXTz82m66BxNcv3IuVyyeiaq+PT7btuPwnd+8yh2XLaYscuJVtmkmJ+L28ntLV/Or/Tt4tbWRJ48cwEHiUjTCbjeXVdZy04y5zC/M7/ZWBG7FkRbaFJ91p4IQAo9WSV34k2SHEjjl4wSC46RMp5nmnY4/4Gbh0vEJZ6vqili0rGbS46SUlFcUTBsH5wkXnHFwz+EX2DfYOvI661jY0qHA8PGJmVdS4jk9/fSpBcae+vawlGnG7CaIU9tSy2cDPNY3KQcnLiizY9oRU25HAU6eYAVAkuOMrfa/80JhzntcqsYH5i7h5pnzxgSFTpWMPcjewSdYWXj3Wejd+UEmZ7Jh52EqoiHuvnI5Qa/rjGdZzZkWDW29uHSVmeVjs2YqQvDBK5cR9J769zPNMTRFYU4kyldWriNp5pPVSfKB0bqaz/bs040RgQqPVn7iCt8iitBwa6W4tdKz2s4005yPVFQXUlVz8kUlb2D6uXe+cMEZB99YdDvOqKywlnTY0neEV7v3U+YJv4Waz5JfqHAxxi9fnjxoZywWklFa9mKSrCBCZ0ww9UTZl98ieSNFZTio2OW9C01fwOlcO6EUoajvnIQh7wQUIQi63OMyHbemtrN/8EmEEJhOhuWRD2DJDHsHHifrJJgXfjd+rZjtfffSkz1I1o5T4VlK1kkQ0ssIu6p5s/f/WF74QV7u/A8KXLVkrAFq/GtwqQH2Dz5Nxh5gbuhdVHiXTikF/dvNsC5DMmOSypjMLCukLBIckfdzHDliJIzWcBjeo5NDL4QYq1svR/5zTCowkcnx6t6jLKgppa60cIxuuAQK/N6x7w3XNdTY6F/TGGnyoc9+V5MHHo8qFAKGa8IM3tNMM83bx99/98NTyp5cP7eMgf7kSctNc/Y5/0bpt4j7uLgCKSVrovV0pPvZPdjCgnDVOerZxChKECGGpU7lkPuPnPIAL2WGkSzEMKTuM0GiHuEBoYHMr+xLZ6opwW2QU5QXE+7836HymrEMl+dOphKUPM25Q0obG5OrSr5KR3oPRxIvs6LwbgpLZtGTPUhnei+l7gUsCN9EQ3wDFxd9how9yNHEazhDu1G2NEFKBnLNrI5+goBeStZJcCTxMtW+iygwqtnefx9F7tm41fPPXUYC+5q6+PYDG2js6ue57Qe556lN/NkHrqK8MMRN3/gRG/75cwC8tOsI2w618d5LFvIvD2xgTmURr+5tJJHKctOa+XzkmpWYps3z2xt44NVddA8mcBs6//G595DM5PjHe19gb3MXAY+B3+3i999zKavmVLH5YAv//cQbHG7v498/fwvzqkqQUtLaM8hPnn2T7YfaMDSND1+zgiuXzmJ3Yyf/8fDLVBcXsP1QG3Orivjie9ZRFPJNGwjTTDNFsnYWZ/QCGwJd6GjK+Tc9cqRNzskhUDAU4x3zO3e5T+4qLYRg3VXzWXfV/LehR9OcjPPv7n+LZOwc9nEuKSkrS9zKENTOvyh4RSlltNuOdPqQTh9CnZpft2P3IJ1jrkSKWjthOaGEx+RkGJY0PTkWjnPyHAcAQgRRlCCOnTcObPMoeKZXMt8JhPUqBAq64iHrJGlKbqI1tRVb5tCEi9Fr5Pnv81g2bUdaI25gmmIQ0EvziZ9kjoFcM025jfi0QrxaFPUU4mneThQhmF9Twt9/8t385LebWVJXzhVL8xm9Y6nMpBlse2NJBMX86Mt30NEX549++CjXrZhDa88gr+9r4gs3XcLC2lL6E2kiAQ/RoI9v3n0N//3URq5YPJM184754F40u4plMyv4+D//auS9nGVz/yu7qC4u4EvvXUc8leVz//EgNSUFWLbNQCLD7904jz+69TK+8ZOn2NPUyboFdajq9G9ummmmwk8af8ie2E5saWM5Jl7Vx43lt3J58dXnumvjOJQ4yI+P/oBidwlfnPXH57o7b4nhZ+rYRGzTz63zhQvOOHimfSedmdF+95L+XJKebJzbqlZPety5QlGLULVaHPso4CBlFsvciaFeftJjpXRw7KYxCkG6a82EZVW1BqH4kfYgIDFzO5DSOWlyHCmz2NbhKZ2LqpajqBU4dj4plZV7nXwMwvlnlE0zFjFqt8l00iStHqLuegQKA7l8UjtD8ZN1YvRnG9EVL6qiEzPbMZ0M5nByvVH1GIqXqKueiFFH1D0Ll+KbMMHTOwU55DY0LIc5zDXL61EVhYKAl8KAl8FUmu5YksKgl7rSAhRFUBg8PUWawWSGVCbHmrnVeF0GXpfBwroSdh5pp640QlkkwKLaUnRNpSQcIJnJ4Uh5mvmqf/eQ0kLKJEK4xgg7nF5duaG6/KckKjHNueXiwsuo880kbsbYHdvBQG5qi2Hngt5cD1knQ0e67Vx35ZSRUmJZNt2dMWIDKTKZvMeDy60TCnspKg6h6cq0gXCecMEZB0krS8xMj7xWgALDz/ri+SwpmDxS/lxiuK/OZy8mi5QpzMzT6K61J81DIJ0+LHP7SPZiIfzorksnLKvqsxEiDOSDtW1zF7Z1GE2fNXn9UiKdQazclimdh6JVoWr1WLmtgIVl7sDMvoHuWj/9gz+P8WqFRN35+8CtBqn0Liegl9CV2Y9L8VHomoEidIJGGYXGDDozeynzLCJi1NGe3okiYpR45qMqBjW+Y8aprngo8cyjPbWD9vQOQnoFHq2Ad5qbma6qqIpCfyJNwGPQE0uSzh4LvHcb+tj7W4LH0EhnTWKpLH6Pi6xpoWtqPgB2KDYgZ9njGzsOl66iawoDiTSmZZOzbHoGk0SD+dgiVVHQtSFTYCT4YZqpIp1e7NxGFH0+qjbzLdXl2M3Y5k4042LERDlvpjkvWRBazILQYrJ2hpSdZGtu87nu0qTM8s/mkujlFLneWfeXlJJ4LM3Lz+3l1Rf3c2h/OwN9+diCcMTHzNmlrL1sDpdeNZ9QeFra93zggjMO7qxZC7yztqcM9/Vkkv+Lbe0HsuSyG9Czr2C4LodJVvalzGHmXsXMvjzynu66Mp8UbYJTV9RqNH0htnUAMJEySTb5Y9TgnyKUyX6MFmb2xaF+nRxFCaO7LsXMvohjtwIW6fg/oahFQ4HJJ0dKh3xAszpFhagzj5QSS5ooQkE9D4NnzzQRVw0RV95wDuglBPR8IHipZ/x3tiRyx5jXUffYCdXywg+MeR3USwmG3tkKLS5d49KFdfzoqTcoCQdo6RnEpZ/4vphdUURDWy8PvLITn9uFdBzuuGwJAa8Lj6FTXhjihR2HaOoaYP3iGVQVhXlp12FaewbpiSV5+s0DtPfGWV5fwUWzq9h2uI2WngEs26EyGmbJjHIOd0xL/p0uUjpIuw0zfT927k0UbQ6WWoLuvRUhCvILL5lnkHIQoUTRjDUoWgWO3YuVfX4oWaNE1RejGqtxrEOYqftwrIM45mGEEsTw3T20wPPOGYumOb+Juop5b8X7znU3TplczuIXP3qJZx7dRjjiZ+GyGoJBDxJJbCDN4QMd/Pd/PENzUy8f/ewVuD3GySud5qxywc18ftjwLHfUrKXAOKbaYzk22/qPIhCsKJxxDns3MYpajCfwRRL9fwhkcewW0on/BBx016XjtrulzJLLPEU68b2hSTgoahVu/8cAg4kDkjVc3jsws8/jOF2ASTbzKIpaicv3fhQlfFwbObLph0knvs/ogOcTI9Bd69Bdl5NN3Q9ksMztJAe/gdv7AXT3lUMB02ORUoIcxDJ3Y+Y2I4QPl+e9U467ONPY0mJL33PU+RdQ7D6/AtinObv43AbvumguYd8xVzgh4LM3ruFQWy+6prJkZjk+t0Fh0MvnbryYAn++rEvX+Ph1qygvDBHwuLhx1TyOdvWTzVloqoIxZFB4XTrXLK+noa0XkHhdeReUaNCHqih8+dbLcBs6BX4PLk1j9dxqoiEfvYMpFEVQXxEl5HNTWxLhQ1evGOnnjavnE/K5UJV31s7MuUGA8CCUQhAuFLUCoVUjMABJLvEfKPoCFKUOx2rAzDyG4b0LK/scjnkA1VgOQ+Ko+fw0gXw+GOFD0ary9U47d00zDQCvvrCPJx7awsq1s7jlzlUUl4bweA2QkE7l6Gwf4MFfvM5TD29h7oIKLr924bnu8u88F5xxsLnvMDdVrgDGSno2pnrI2dZ5aRwAGO7r8AQ+Tzr+bcDCym0hOfjnaMYyNH15XtZTWjhOB1ZuI1ZuF47TQV4/0Y/H/3k0fdEJd0w0Yzku7/uGJvwW0uklnfguZu41dGM1iloJ2Nh2a74NcxfSGUTV5uHYbZPnUBiFooTx+H8P2z6KlX0NcLByG0laR1CTP0XVZqGo5QhhIGUWKQew7WYcuxPpDCKdfnTX5Ried5/C1bORThop4zh2y5hPHKcHx25HUYIgfHm51RNcI4nEkjm2DWyg1HN+uqFNc/Zw6RpzKsdu2QshqIyGqYyGx5UfHVCsqQorZ1eOvC4rDFJWOF6ZSQhBWSQ4LsnZvOrJpXsX1IzffYkEvEQCx3b95lW/s1wNziVCCIRaiKovxrGOoLpWoep5lRTH7iSXuh/V2A+4kXIAVa1DOv0oailW+lFQfOjua1C0eoRQEWo5ij4PKbNorktQ1LObN+F0cByHJx7ZxtEj3dz90XUj7huvvXyAsvIw1bVRlBMYlolEhvt+8Tof+cT6Kef+yDlZDicOsie2m45MKyk7ha7oFBpRFgaXsCi8DHXUDrElLX7Z9L+k7RQfrf0MLalm3uh7mc5MBxKHYncpKwvWUOebha6MjevI2hkOJQ6wN76bjkwbaTuNruhEjSIWhpayKLQU5SQxdicjZaX4RfOPkVJyVfH11PnHu6Jl7QzPdj3JwcQB1kWvYHnBRSOfSSnZE9vJzsGtdGU7MR0Tr+ql0FXELP9s5gYW4FG9Y8bxptRRnu54jN7csdjCqCvKx2s/d8Lx3pEOu2M72DW4na5s50iwddRVxCz/HOYGF+BW3G+Ll8Uzj25HVRU+/oWrKa8cu0AYDHspKQ9TWBzk8x/6L154ate0cXAecMEYB46UDP9xpMQeynUgpaQvl6Ap0UOFN3KOe3kiXLh9n0IIN6nYPwAWjt1MLt1OLvPsSKIziTWUCyHvryyUAjz+P8Dw3AKceAtbCAOP/ws4TjfZ1K8AiZR9mNkXMHOvIYZuh3wb+QBTzbgYj//TpGJ/h22d3DgAgaJW4w//M6nBvyKXeSLfjtOF5XRjmdvzkqrDDtLSIb8zMSoR3Ekcpy1zN+nEf+FYjUgZR8okSBvJcF3HyCR+QCb5f4jhFT7hQSh+hAiiu6/D4//YmPL3Nv0rrakG+nNd/KzxH9CETrV3DleV3EmROz/x2xvbyCvdj5Kw+il117K68HpqffMRQpCzM/zw8Ne5q+YrvNr9KA2JHYT0Qi4rei8BvYAdA6+QcZIcSexiTeENpOw4uwdfY2XkGlYVXjuF6/vOQyI5GD/EKz2vcTh5lKSVRFd0CvQwS8OLubz4MtzqOzdQeZoLDJkDAZ7wPzMSHyNcCBFAVUtxh2Zh5d4gG/tnVH0BruAfndPuThUhBKvWzuJwQxeWdex5u3RFLZp28kBQy7Jpbe7jmHLZibEci639b3Jv80/JOfn8PW7VS9ZOc5B9bBt4k7XJddxWedfIMVI6tKVbaE418mb/Ru5v+QU5J4dbdZO20zQkDrBjYCt3VX+EhaGlI4aF6Zhs6n+dB1t+NdKWR/WSGdXWpdHLuaXijvEdPQVUoVJoRHm+62kqPFXU+OrGGBwSScbJ8HLPC3hVH0F97ALAb9p+zYbu58g5WXTFQFM00lYaiWRj3yv8/qw/ptpbO+YYt+KmyFWMJU26M120Z9rI2KmT9vWh1nt5pecFco6JoRqoqKTtFBJ4o+8Vvlz/Nco9lSet50zQdKSb8qoIZRXhScuUVxRQXl5Ac2PPpGWmefu4IIwDKSX3N73BPYefpy+b4K5X/m2M+ooAFoareXfFsnPXyZOQfzD7cfs+jqrNIBX7F2xrL2CBtI6bLgtARdXn4w18Gd11GeCa0gqAUPz4Qn+DqtaQTnw3P7HGBpka1YYAFFyeO/GGvoZ0BlDUUmxrzxTPRUFRKvAX/AfZ1P2kE98dJZ1qgpzITWnonLQZ6O4rUJTQpPU7Ti9W7nUcu52TGRJSJkAmxpayARQUbXyK95vKP0ncGuBHh7/Jeys/T5V3FqrQMJS8a9eRxB5+0/JDriv7EEWuCg7Gt/Fi1wNopQZV3vq8D6XZx/3N/8mS8DqWRa4ga6cIG0Vk7TSHEzuoDyxjcXgdz3T8jKUF67mo8Fp2DbzK3OBKgvqZM2CllMStBK/0vMa2gR20pzvIOBl0xaBAD1Prq2ZRaAGLw4twK1O7f06nD5v6tnBv8/10Z4cf+vnJRVemm5Ae5FrlqkmPlUPfnEC8o+KIpnmHIDTARDpxpLQBBaGWoSjVmJlnMbwfQMoYOGlQgzh2C4pShO65AVWrJD3w9RHjQODKP2tk+lhdZ+U3BS+9sJc3XmsgNpjmAx+6hIHBJC8+uxddV/EH3Ky7fC5bNh+hqyPG4GCK9999MXPnl+P1GmijklHt2NbEvT97jZtvXcmKVXVI6fDTH71EV2eM0tIQN926gu1bGnlz02HcHoNcburJM1WhUuOr46LCtcwLLGSmrx5DdWFLi5e6n+fB1l+xqe91VkcuodI79llsSpOfNf6IlZE13FR+G34tQNyM8WTHb3i992We63qamb7Z+PUAAJrQqPXOYHXhxcwPLqLONxNdcWFJiw1dz/JQ271s6nudlQVrqfCe/oTYUAyWhlfyYvezHEk20Jvrpsh1bMdPSsm+2G4Gcv3MKKin2ls38llHuo0Xu5/FkTafm/VHzPDPQiDIOVmOJA/Rnm4l6ioa12aRq4Qby29FSoedg9v4v8YfnbSf7elWXuz+LarQ+MKsP6LWPxOBIOtkOJw4SEemnYjr7XPbVVVljEE6IQJMy0ZVp90izwcuCOMA4Pbq1VxfvoSvbPkpH6q7jIjLD+QnFR7VoMQTwq2cmrycEIG8wo/Q80nEJiwTApEbWlmaSApPIJQQOBIhvORX9ydrTyClC911HcHoGszsS+Qyz2Bb+3GcHgQ6ihJF1eeju69GN9YihH/k2Kmfl4Hb/wUMz83k0g+Ry74yFLvgoCjFaMYKXN73omqLhg6wUbU6rNzwhP3kt02+PwYu7124vO/FzL6EmX0Jy9yJY3cjZQohDIRSgKrVoWkL0F2XoOoLgBN/TwJt6Ls5vUyKw65DLsYHPXlUPzY2AgWP6sV7XMKul7sfZmF4LbMDeUOzPrCU1nQDbenDVHqOKT/V+eZzUWTsTkCHfRRFqMwLXoRXC/J8532siOQnxvtim0jZ8TNmHDjSYV/8APcc/imd2S5UoaIKFUE+6U+b1U5zqoUN3a/w2ZmfYHXhRahnwUc6YSXYMrCNzmwX9f6Z3Fb5Hqq8lViOyYAVQxPqGEN+NBkny0+O/h8JK8UX6z+H/jsQHD7N24ui1qBoM8jGvgXoeAr+HaFW4on8J9nYP5BM/gSEG8P3QXTP7djZV0knfw6YCCWIK/jVkbpUYzFW7jUy/V8A4cUb+SmIs6G8IllzST2r1sxk545mtm9tpLyqAK/fxWc+fxUbX2tg4+uH0DSFG9+zHI9H575fvsHsOWXjalq0pIqD+9sxjPxvf8+uVkzL5ve+eDVbNh/hyce2o+sqt79/Ddmsxa9+9uqUeymEoMRVyp2VHxrznpQ615TcwJaBjXRlOmlNN48zDgCqvDV8qOaTIwsDEaOQiwvX05Q6ypHkIXIyN5IwVAhBhaeKOyrvHtOWLnWuKc231Z/roz3T8paMA4CIEWFRcCn7E3tpTjURNYpHsqLb0uaNvlfwaX7mBhegj5K0TdpJbGkT0sPM9M9CFzpCKOhCZ2FwCQuDS0b6ffx1VFFBqHmBjCkM9Qk7gSMdIkaYOv/MMW0tDi1ncehY3W8HM+pL2Pz6IY4e7qamrmicW5rjOBw91E1bc990ErTzhAtitB2+wQO6hyUFNSwIVVIwZBy8FcJFj520TEHJ8yfum+KjoOSVKbc5fC5ChHF5bsLluWnKx06d/MNU1WrxBL6EJ/ClE5dWo/hCf4kv9Jen0Q6AB8N9LYb7zLjN6K6LCRY9jSVzaMJAOUVVo7jZzcud3+di992c7C4Z/fCUSHqyrTSl9rM/tpnRT+nZgeVjjiv3zBx3PIChuBFCRRsaNLxqgJSdQKDgyJNLW06V7mwP9zY/QGe2izJPCWsLV1PrrcajeUlbadoyHeyPHyRhxpkbnPOWfXEnoz83QF+2D4Aby9/F3ODsEVeAiGtyQ0hKSc7JcSB+iHLP+EnNNNOcCYTiw/B9FMP30THvq1od3sj3xpU3fHdj+O4e936+rgLcwa8CX53w8zNFJmPy4rN7aG3tJzaYJhB0I4Bw2IumqXi9LlLJLCVlIQxDpSDiJzaYHtmFG9Pn455PnR2D7Nvdys9/8ioul0ZxSQjTtDFcGj6/CzHFWIPRWNIiY6fJOTlsaeEgkdLBp/qRdIy4AR3PmsJ1Y3YMhRCEjDBe1UfOyWI6uXHHWNIibacxnSy2tHGQONLGp/rpo5fcBMecCkIIfFqAReGlbBvczOHEAeYE5uHT8iNJb7ab/bE9lHsqmRdYMOb6lnkqKHGV0Jpp4edN/8u66OUUGkV4Nd/Q5P3MTdQrPFUUuUroynbwy+afcmnhegpdRXhU7xlvaypcc+NStm46wre/9Rs+8ftXU1ZRgKapQD73QWtTHz/416fx+lxc/e6lY46VUpKIZ4jH88koFVWhqCgwdPypY1k2PT1xpAOGS6Ow8K3PFS9ELgjjYDSfnnU1qlAmzWg67ZpwYRAzu+jMNFDpXYBPO/XV9slWq/OfTS4ZL4TC8sgVXFb03jHvG8e55WjKZFJso1oWnDQJ3emyN7afnmwPXs3L7838NDXeqjH9W8piri+9hoSVwKd5T3g93gqmNMk6ORQUCozwKRkhnZlOurLd08bBNNOMIjaYJpHIMn9BBX29Cbq7Y0igraWPhgMddHYMUl5RQH9/ksajPbS29DGrvhTLcmhp7iMWS9PRPoBhqORyFv19STo7BintijGzvoQ588u5+tpF6LqKpqu88WoDjUe6EULgOFNPpOFIhwGzn32x3eyJ7aQ720VuaNIupcOgNYAqtEkdQ0vd43/3mtBGniHOqDg1Rzr053rZG9/N3tguerJdQ8aIjTPUlkvJK1G9VVShUuGpos43i73x3ayMrMWr5gVQXul9ESEEs/yzKTzORcirermz+sM81v4wB2J72DHwJjXemSwKLWGGv55SdzmuM+Te6VW93FX9ER5v/w17B3eyvX8ztb5Z+bZ8syjxlGMI422bD61eN5vrb1nOC0/t5M+/9HOq66JECgOApKc7TtPRHsJhL++5czUrVo8VjenuivHUEztobOxBURXcLp2Pf2o94bBv4sZOQuPRHv77B89TVhamsirCrbevOgNneOFxwRkHGdtkW/9RurOxkaBkyE/JFoarmBM8/1Qk3i76c61k7SRZJ0nC7MVQPczw538Yvdlm+nMtONKm0FVNkauOnJOmMbkVnxYmZnYT0KMUu2dhKB56so2YToasnSBp9ePTI1R5FyOlQ1emgZjZhURS5K4j6qrFkRb9uTa6s0ewpYlPLaDCuwBNuMg6SVpTu8g5KVyqnxJ3PR41SHNqB4biJWZ2YskcRa46oq46YmYn+2Iv0J05Qsrqw6OGqA9egoJG2h6gLb0X08ni0woods/CrfpJWYO0pHZiSxP1JNlLFVRURac/10mpuxqBgq7kdyjmBlfSmNzLYLiHsF6MIy0yTmqozPkTVBu34uQckzJ3CaXu4gkHAUUIgkM+uxORD+bvpy3TzmBuEFNaGIpOxCigwlMx4bFSyvyOhJUgY2doSbcxaOYD2Tf1beFw4uhIWY/qZmFowUg9jnRoTbfTn+snZsbZPrADgJ5sLy90bRhnWKwvuhRVUYmbCd7s30qRK0q9fyauoeBmRzps6H4ZB0mRK8qi0LG8DT3ZXvbG9uNVPSwvWDriFpC2M3Rmu+jN9pKyU9jSRhUafs1HibuYUnfJGHWV0efdkm7lUOII1d5K6vy1xM0EzakW+s0BLMfMx3oYYWq8Vfi0iQe2nmwvrek2YmYMW9q4FBeFrkIqPRV41LdHWWSa85uCiI+a2ijdXTE8HoP6OWU4jkRVFQ41dOL1uZgzr5xXXtpPR/sALpfO1dcvxDJtWpr7qKiM0NMVIxT2koinCYY8ZLMmnR2DLFxcxaLF1ezb24bPazB3QQVz55dz9Eg3Pp+LFSvrmIpfi5SSlJ3ksbYH2TqwmTJ3BXMC8ygwCvGoHjSh8dvOJ+jMdkxahz7pAsv4tuJWjEfaHmDn4DbKPZXMDS6gQI/gVj2oQuWZzsfpy525QNdCo4g5gfk81fEoR5KHKPdUknNyvNn/Bh7Vw4qCNRMeN8NXzyfrPsee2E4OJvbRkmrm8Y6HCesFrClcxyXRy3ArnjPyO5/ln8MnZ3yO3bEdNMT305Ju4pG2Byh0RVlbuI6LCy8bt6h1tlAUwUd/70qqa6O8+cYhujoGaWvJ7yj7/G4uuXwuqy+p57KrF4zZnbIth2ef2Y1p2nzuC9dQEPGRSGTw+z3kchaNR3tob+vH5dKZM7eMUNhLKpVj29ZGPB6dRDxDpNBP/exSFCE4sL+D557djaoorFw1g4rK/MJiNmty9Eg3XV0xVEVh/sIKQiEvtu1wYF87mq7S3RXD63NRP7sUv9/NwECKvbtbsSwbf8DNvPkVqKrC4cNdJGJpMhkTy3ZYtqyGYMiL4zh0d8c51NBJLmsRLQoye3YpuqHS35dk7942HMehsDDArPoSDOPcTs8vOOPg2Y5dvNK9jyJ3kGc6dnJlyUJaUr2krCxV3nOjm3++0JE+wN7Y88zwXQRCYNv5QOeezBEak1txqT6kdNjU+2suKfoQEslL3T9iReRWHGkPTa4tqryLaUnupCHxGvWBS3CkjWbn/f+bUtvoyTbiUrw40ub1nl+wvvhTKEJlX+xFQOLTCkgh8m40Arb0PYRL9aEJg75cC4O5DuYE17Gl70EiRhUFRhUpe4CG+GtcW/pFpLTJ2klMJ40lc5gyg0RiY7Gt/1HcagBF6HQmGsjYCWb4L2Jr/8Pk7DSFripidhdpe2LlJSEEuuJifnA12wdeojl1gDJ3HfNCq/BrIVYXXk/WTvNy98PoIv9gDeqFLApdjFc7f7YnhwfgAXOQhJk45RUp0zHZE9vHKz2vczh5lIFcP6aTNw6irihzAvVcWrSWGROodfym7THa0u1k7AxZJ4cl80GMj7c9NWZeUWREKfeUjRgHOcfkifanOJw8ymAuRnLonmpNt/Gzpl+N6+Ml0TWoqAyagzzQ8jC1vhqKaqKUDGWn7cn28OOjP8ORDnODc5gbmD0if7gvtp+fN/2KBcF5LCvI+/r25fp4ouMZDieO0p3tJmHlfYQ1oRHQ/VR6KlhTeBFrCleNk1GE/G7Nr5p/zZXFl+NW3TzftYEdg7vpzfZiShOXYlDiLubTMz4+zjiwpc3mvi283ruJplQzA7kBLGnjVt0Uu4tYGJzHlSWXEzUKpw2E33EMQ+OiNWMlNF99eT/VtVFuuCkfC9Xe1k8o7GXZilqqa6Ij5a6aQCJy7vyKMa8vXjd7zOvSsjDzFowtczIkkuZUI2/0vUKlp5pbK++k1jdzxLCWUvJyz0lccqe4mymRNKaOsLn/dWp9M7mt8v1UeWtH2nKkzUvdz51S/0+GS3Ux0z+bIlcxW/s3sTS8ggPxvQzk+pkbXECNr27C44QQeDUfKwpWszi8nI5MGw3x/bzau4EnOx6myFXMotDSM7KTm3eB8nNRwVqWhFbQkWnjYGIfr/S8yGPtD1HsKmFecNFZ2zU+vi9uj87171nO2vVz6GgbIJnIIITAH3BTVllAIDjeKIon0rS19XPVNQspiPgQQhAIeHAcSXNTL88/t4fKyggtzX10d8e46uqFDAyk+P53fsudd63BshwO7O9A11XqZhRjmhamaaNpKtmshW05SClpONjJnl0thMI+uroGaW7u5fb3rcY0bX77zC78fjdV1YWomopjS2zb4dGHtxAtCiAEHDrURTKRZenyGp55aieqIpgxq4QD+9sZ6E9xy3tXMDiY5tlndqEIQbjARyaTyytr2g4P3r+JyqpCLMvmyOFuLMtm8ZLxcThvJxeccfBy9z6uK1vMssgMNvcd5jP1V9ORHuCV7n2YztSVFi5UpHSoD16KVw3jkL8ebek9HIi/RKFRgxCC9vQ+OjMNFLtnIKVkpn8NXi3M5t5f0505TKl7Tr4uJHOD6zEULw42jrQ4mniTruwhQnoZUkra0vvozg4fI0lZg1R4FozsQKTtAbb1P0KldzGG4iVh9eLTwlR5l2A6Gcq885jlX0vOSXN/85+RsPooctdR6V2IIlTmBNcT1IuRUhKzOtje/ziV3kXoipv+XBuKUIm6ajkYf4WbK/6cAqOC7uxhjibfnPQaaULn0qKb6cw0YUmToBbBGNoVKDCKubLkfXRnW8jYaVShEtALCOp5w1NTDG6t/Dzl7vGDQ4FRzOXFtxHUC9GFzp1VX8ateNF0ncuK3kuBMbnO/akyw1dLSA/Smm7jvpaHeE/FjZR5ppap2JEO+2IH+HXzg7RnOpkXnMOVxZfhU30MWjF2DexmQ/crdGd7ubXyZmb6j52rQHBD2XUjvr3t6Q6e795AT6aXO6tvp8R9bKvdUAyKR229a0Lj0ujFrIwsx5GSN3o38nrfJmp9NdxY9i40ZeyKva7oCARuxU2Zu5RBM0bMjFPizhsHB+INONLBr/mImTF6sr0j16At04HpmMz0zxgZHLNOjqZUM6aT46LICkrdJRiKzqAZY1Pfm+wc3E2/OUDEiLAgNG/Ca2dLh6ZUM091/JaGxGFm+2dRHF2DJS26Mj30ZHuIGOPd4F7teZ2HWx+jJ9fLsvASrixZj1tx05XtYmv/Dn7b+TwDZowP1bwfj3pmVhanuXCYM7d8jBFQUODjotUzCRecjYDok5OPz+rCkhaFriJqvHVjdtxi1gAt6ZYT1DB1HOnQm+3BkhbFrhIqPTVj2ho0B2jNtJzxKXC1t4Za3ww2979BR6aNV3s3IIRgTeTSCRcPRiOEwBAG1d5aytzlJOw4z3U+xeHkQRYEF53RGDAhBC7VRY2vjjJPBQNmPxu6n+NQ8iBzg29vPgFFERQU+imYop9/NmOhKAoez9g4CcuyOXKkG6/X4Lp3Laa7K8Z//9fzrFlbD4BjS9ZfMR/pODz15E7a2waYM7ecpctraWnpp6c7xuVXzEMIQSZjsmdXC5s3HaGmLkpsMEVsMDPibqQogrLyMFdds3AkRVJXV4znn9vDkqXVaJpCd3ccIWDp8hpsy2HRshouvWwO0WiARx/eys3vWU5fb4Ijh7v5/BevJRzO7yQIIWhvG+DZZ3Zz8aX1SKC3J05RUWDaODjTZB2TMm8BYcOLRzFQENQHStkz2EJjqodLznUHzzEBPYpL8Q8pIOiYThbTyVLpXcjC0LWAYGXkNvxahIwTRxEqbjWQl/NUvWTtFHIox0JQL8ZQvEN1aVhOFtPJMDtwKVXexYDgIm4joEcxFC9Lwu8emZgfTmxkdfT9WE4WW5qsib5/KBcB6IoH95APZ8SoQqCgCR1duLHl5AFlOTuLKjRWR+8cqcut+nGkgy1N/Hokv4KhBtDEiVWjgnpkUuWggF5AQB+f6Rny/qhzgism/Myt+qj2zRl5PS+Uf/ho6GPePxNUeStZX3QpD7b+ho19mzmaamRxaCGrIiup89WgKZP/9Pty/bzY/RLNqRauKLmca0uuJGJE0ISKKS2WhRfzSNvjbOrbQllvCcWuKIGh1X8hBAtDx9QmGvTDbOx7k17Rx7zgbGp9kyeW0xSV+aG5QH7Ab0w1ARDSgywrWII+SZ/dqosyTxnbBrYTt2Ij7++LH0QRChdFVrBtYAdtmXbKPKVIJO2ZDkzHYpY/798qEERdhXy09m4UoeBTfbhUFwoCS1osDS/m3w9+j65MF3ti+05gHNg0JA6jCpW7qu+g2luFoRhI8gHWWTuLVxurfNaabueZzufpynbz3oqbWV98KT7ViyJUck6WWf6Z3Nf8IG/0bmJRaD5rCle9Lat907xzKIyOdfFzewzKK6bmlnM2EAgKhozggVwfHZn2EUWilnQTv2n9NUkrjludWAXwVFCEoGDoedyb66E720mZJ7/T0Zxq5OHW+0hZyUld+U4Xt+JhTmABu2M7eb33ZY4mDxPWC1gcnlgyfU9sF/25Xur9cyhylyDIuzIO5AbozHRgSpOQHj4jhv+uwR0Mmv3UB+ZS5Coeaas/10dXpgPLsfJtnYXnSGtzH2+8vP+0j/d6XVx/S17gw+dzYVk2yWQWx5EjKkeOI8llLbxeF6qqEAh6yGRMHCfvSh4MeQgE3CQSGTRdwbInl1G1bQfbkSxeUs2ll+XHYVVV0DQFy8rvMkQK/WMUltKpHC63zo03L0NVVYTI9xXA7dYJhjyoqoLXa5DN5aXbLcvBcSTBYP6eH044mEpl8fnd3HRLft4gBCMJCs8lF5xxUOWNciDWzuxAOXOC5Xz/4DOsLZrNroEmFobPrSV2PjA8aR5GEwZBvYS+XDOa4qLAqKA/24KueMg48ZF4gFLPHPqyzRS5ZoxMrI+vS1c8BI0SBs1O5mqFeNQgfblmdMWNLS0smaPau5SQXs5znd8hY8cJG+UE9RI6MwdZFL6erJ3CdNIoQ7fmiFrFUBzZcDiZKnRyThrbOZYzIagX4dXC9OfamBNcR8oaQCDQVQ9uNciRxGbqA5fQkz1K0uo7Oxf4PEFXdK4sWU/YCHFv8wO0pdvpynTzSs/rVHkruKJ4PUvCC/EcNzhLKenJ9rB9cBdlnjJWDa2gDw9YKioVnnLWF13KrsG97Brcw6rIihHj4FzgUl2UuUvYYMaImQkkEoHgQLyBsB5mWcESXuvdSGuqjRUFy0iYCQZzMQxVp2xUsLMudMrcpflA8VGDpipVyj1lLAjO4/nuDfTnBkbamAiP6mZlZDnzgnPGrGB6VPeEKr3bB3bQmemi1F3MdaVXjdkZ0BWNZeElbOx7k85MF6/2vsHqwovGVzLNNOcRAkGVt5Z6/1wOJw/y/UP/SqGriIydoS/XQ8Qo5JqSG3jpJK5FU2tLocpby0xfPQ2J/Xy34V8odBWRtlP05nqIGkVcVXI9r/e+NO7YfbHdHEocIGEnSFoJDicPknbSvNa7gZZ0Ix7Vg0f1cmXxtbiOiwUQQjAvsIBiVwlb+jdiSpMrItfiUieSNIeOTBuPtz8E5N0+fZqfnJMjYSVIWQlqvTNYHFqOMkpSOmUl2Tm4jdZ0M2k7RUemnaydod+xuOfo9/GoXjyqhxrfDBYGl4zsWLRnWnmi/WEUoRxry86RsOIk7SQz/fUsDi0/K8ZB0+Fu/vd7p/+9FpWEjhkHfhczZhbz/LN7qayMUFQcJJnM4XbrFBUHeeO1BtLpHAf2txMtDmAYGqaVOyVVLbdbJxTy0NraT0GBl0DQSzyePlZAjBeyKSkN49gOqVSOBQsrsW1JNmsek9ad4Lp6vQa6ptJwsIPZc8rIZi10XaW8IoJpWmRzJvX1ZZimhX0CY+bt4oIzDt5bdRGaUFGFwt11l/KNHffydPsOLi6azerorJNXcEEjxqvjCJjhX0naHuCxtr8nY8cIaiXcXPl18o9dlcOJN3ih6wdUehZR41uOOiSFdvzWpxCCJeEb2NL/EPc1fxXTThMxqrip8s/I2HE2dP+IrkwDmnAxJ7gOnxZBQeXGiq+yofseNvbehyZ0lhTcyLzgFYgxGvhizOti9yyOJDfzUOtfYige7qj+OwzFw1Wln+OVnp/xcvc9uBQfF0fvptZ/EVeWfJbnO7/P6z2/oMwzlzLPHKYkGP0OxlAMVkVWsjS0mFd73+DF7pc5mmxkb2w/++MHmRuYzfurb6faWzVihJnSoiXVRsbOUOEpp8gVnVB3O2JEqPZWsi9+gL5c/8hD8VygCz3fTxT6cwPk7ByDZoxBc4D5oXlUeipwpEN7pjNv/OT6SNtpqjyV+TwLo6QSpZQg80ooo6UfHekQNvLi4MMKKBMFJgOE9RBzAvWTfj4aRzocTTaRttMsL1iKpmh5IYUxoiqCiBFGUzSOJhrzfbywb91p3uEIIQhqIT5a+2me63qaHYNbaU41EtSDXBxdzzUlN9CRbs274oy5mfPjioI66S1+7PNjv9uIUchHaj/N811Ps3NwO02po4T0EOuiV3JN6btoSjaysffVcePfrsHtvNzzAtZQYk6JRMr87kZrunmkT2sLL8OljN/l8Gl+5gcW0Zg8go3NpdH1k064l4SWM2D2cyC+h55sNwPmALrQiLqKWR+9ioujlxHWC8b8thN2gjf6XuFAfN9Q7/ILOCYmW/o3jvRvecFFzA3MRx9afVgWXsmg2U9DYv+otnSKXSVcGb6OtdF1BLXQWXmOFJeFuObdS8a8p+ka3Z2DvPL8PoIhN7PnV1BYFEAIQV9vgv27WxnsT/Heu1ZzyeVzR44TQnD9DUt44L5N/Mkf/QIEFEQC/MM/38XcuWUcOdzFV7/yS/wBN3fdvZZA0EMylRtJpCbI7ywdb9QNr9pD3m1ozdp6HvnNFr7x9V9jWQ7XXreIdw/F7yjK+GSGbrfGH/5/N/A/P3yRdCqDP+DhjjtXUz+7FKEMS7gP5ahQ88eXlIZYd/kc/ucHL5BMZlm8tIb337WGQNDNV/7k3fzge8+Ty+YoLSvg9vetmjAvyduJkJNpfp7HxGIxQqEQg4ODBINjk1Tls6qC5dg4OBiKji0dtKGHwu+yr+7wV338NZjsFhgwW3mo+S/56IwfjHl/ZBJ1CnVNxui6TlRmdN0THXN8mVNp/0Jn+Jo45Ceiz3Y9z7b+HSSsJEWuIr48+wtUeMoQQpC2Mzza9ji/aXucq0qu4I7K90y4Hd+b7ePe5vt5tfcNPlRzF1cUXzahn21D4jA/PfoLmlLNfHPB107oVjQaRzo82PoID7U+wtLw4nwStEnciiSSA7EG/vvIj5kTqOf2yvewL36QHx6+h1vKb2R98aX8075/w6/5+PTMj9GQOMzPG+9lSXgxH6h5H4aij2RjHjRj7IvtZ+fgblrSrcTNBDknR84xyTk5HBwuLlzNp2d+fMzkX0rJM53P8dPGX1Dvn8kfzv59/PrJ/WqTVpLvNvyQHYO7pnRdVKHy3yu/c0K3sGmmOR84lWfxZM/vk40vp/PcH5O75jSPG338052P8UTHb5jtn8tnZ35p0niBKbV13K7l6Y5np9PWmWKitlub+vjKp+9h9brZfPKL1+IPjHXrTadyfPcfn2D7m0f5h+9/hLKKY267pztFPX6eMNE84kRtnKj8VPp1/DzpRPL6J2r/THKiufPxXHAjjCltGmIdbO47RH8uxRfmXIeUkvbMAD7NRcg4975c54rJbrbJHnon+vxU6jrdfp2o3Jls/0JntEvQTH8dtb5qdhTs4r7mB2hOt/Jc14vcVX37UDZPiTWUjE1h4u1RyK/eDU+OTWninMM1BoHAr/uIugrpzfWRtjMcSRzBkQ71gZnoQqPaV0ljsonubC892R7SdppaXzXqKM30hsQR7m36NUeSjfg1H37NT6mnFL/qRVN0WtItHE02nbw/QplyQGE+UVN+Czmg+QnogRMO18e78k0zzfnK2RgLzvRz/62OFyk7xf74HkzH5OIT7Bqcblvn6rzeChO1/Yt7XsKR8OkvXYvPP97tyutz8dmvXM+Hb/pX7vvJK3zxazeesL630pdTmc+c6LOp9Gsqc5ap1vV2c8EZB2/2HuanRzYQMrxs7Gng92ZfQ8LK8HjbVqq8hVxXvuTklUwDgC7cVHoXn+tuTHOGUYXKkvAiDsYP0ZXt5kD8IM5QThAFBZ+aN6BzTg5TTqzwZUmLzFBmU4/qGZlknyv8mp+oUciBRANpO01jqgVd0an1VaMJjRpvDbsG99Cd7aYn24cpLSq9FSjkEyYmrCQPtDzE4eRRFobmc23pVfmcCUMSsJZjcX/Lw1MyDk4FXTGGjDK4OLqGWytvRjmhASCm5K40zTTTnF1ydpatA5toS7dS661jtn9ikYJpYM+OZkrKQhMaBsP4fC6ixUH27GietMw0bx8XnHHwWNsW3l2xnGvLFnP9c38LgF9z49Nc9OYS57h37xyEEPj1KNeU/f657so0ZwFFKAR0P5rQR/IQAGiKRslQoq/ebB9xM05IP851D0nKStOV6cKregnrobMyYR1eS8k7/Jw4QMuneom6CtnUv4WebC+9uV5qvFW4hiRoKz3lZO0srak2Bs0YIT1ISA+ObOkO5gbZHz9IxChgfdGlYxKmDfeh3xw44+foUgwiRgGa0GhOtaALHU1o5+VK0jTT/K5jOjl2Dm4jY6cZMAd4s/8NAK4puQGX+vYkFHsnIgQM9ieJD6YJhCZWqIrHMiQTGQKh313vjvOJC26POmllqfBGxmzvmdIiY+dG4g6mmeZCxpEOKTuNdYK8HjEzTnOqlayTpcpTMTKoKSiUe0qp9FTQlGrmcPIo5ihFKMgnSNsfP0B7ppNaXzXFrqKzMii6h1Q/cnaOpJU8YVlN0YgYERQEDYlDZOwMc4KzEUMBjkE9SFAP0phqpj/XT6WnAmNUBlZTWtjSxlCMcWojUko6Mp3si52+PN9kKEJhQWgeAc3PocQRtvRvn7SsIx1sxz5tH9xpppnmrZFzcmzoeY4HWn/JM52PowmNd5XexNzggukdvROwaFkNPd1xfnHPSzQe6ca2ji322LZD4+EufvGjDfT3JVmwpOoc9nSaYS64nYMVkRk82LyJrG3lkzkNtrJ7sIWD8Q5urVp9rrs3zTRnHVva/LbzeboyXdT5ainzlBLSQ+hCI+fk6Mx0sm1gJ9sGdmAoOpcWXYwmhqRjhaDIFeXS6FoebP0NT7Y/g+mYLA4txKd5GTRjbO3fzm+7nseluFhRsIziUYnNzhQCQa23GoGgPdPByz2vsbZwFR7VQ9bJGwuVo4wagAIjTEgPsju2l5SdZk6gfuQzj+qmwlPGkWQjlrS5LFiPMSqAOqgHCOkhenN97BzYRYWnjLAeIm2n2Rvbz1MdvyVppc74eQIsCM5jaXgxL/W8yr3N99OabmNBaC4hPTSU3C9OW7qdhsQhriu9mipP5VnpxzTTTHNiXKqbm8tvJ2UlECgE9CAlrtKRJJnTTMx771rD7u3NPPbAZnZvbyJaHCQY8gCC2GCK7s4YRw91UVldyM13rDrX3Z2GC9A4uLlyJU+2beOew8+TcUz+auf9VPui3Fy5goXhaYt0mgsfB0lrqo2NfZt4s38bLsUYcVVxpEPOyZG0Ung1D7dW3MqcQP2YnTaX4mJtdBUD5iDPd23g/paHeLLjGVShYjkWCSvvnndVyRWsLVw9ZgX+TFLtrWJlwXI292/hsfaneKn7FRSh4kgHRSj89aJvoo7SBI8YBYT0EPviBzAUg2rvsUm0R/VQ4SlnS/92JJJKTwW6yPc7L7sY4Kriy3mo7RFe7H5pyHAysKRF0koRMQr4QM37+NGRn5zx8/RqXt5beTNCCF7oeokn2p9mQ/cr6IqGRGI7NlknS8pOsy568Rlvf5ppppkamtCY4ftdl0Q/darrivjaX9/GfT95ldc27GP/7jZULe/JYVs2LrfOJVfM5X0fvoSq2uhJapvm7eCCMQ6Gt9qDuodbKldyXdkSMrYJAryqgUc10JTpbb9pLnwMoXNt6ZWEjVA+6DjTxaAdw5Y2btVNsauIiwtXs6ZwFZXeinE+7vnJcpD3VNxIvX8GG3pepSF+iKSdIqD5WRRawLqii5kTmH3WDAMhBD7Nx901d1LhLeON3s305HpQUAjqIWq9FRwv0h3W8zsHtrSp9lZiKMd8gN2qizJ3PjuyR/UQMSJjgqgNxeDqkisI6gFe6H6JtnQ7AkGRq4jLi9dxTckVZOwsAe3sJHsL6yHuqr6D1ZGVvND9MgcTDfTm8on6CvQw84NzWVawlDpf7Vlp/53KZDKDk5cH07b5w//6DV5D5xt3X4PfM73qO800ZxMhYEZ9CV/82rv58Gev4GhDJ709cYQQFEYD1M0qJhj24vEa03Eb5wkXTJ6DnQNNuBSdmYESVKFwONFJlbdwTJKjC4mTSY1O87uLJJ/ISw7/mUAbXIySKZ3s/hmdH+H4+01hfGKYyeoYPl49jd/icP6B4f8P9QAhGNeH0W0d38fh44dVmSbqy0hbo67Z8dfKlvaE5z+6/lO5PhOdb/4cgOPOd6Qn07/3ETr74/Qn0tSWFOA2Jkg/fRxSSvriKa792g+pKg7zT5+6kVnl0yuV00zzdjD8XB09JImR/0zPZc42p5Ln4IKJ0H25ax9b+o5gO/nB/+OvfY/ebPyCvNmklKQsk6PxPrL25EGn0/xuMjyBHM5FoCnamL+qUFFEfvJ6Mu1lIcS4Ok5lkj/6+NPV+VaEgqqM7oM6YR9Gt3X858P1DNcxmdb18dfs+Gs12fmPrv90jKDx5zD+fIf7MU0eKSVPbt7PD594g/5EeopHCQJeN5csqGVBTSnlhaGz2sdpppnmGMPPUUU59jefUXh60eN844JxK/pdQiJ5s6eZ/97/Ot9cfh0zg9MrX9NMM83vFqmsSWNnP8l0bsrHCAGGpvLvn3vP2evYNNNMM807nGnj4B1IxrbY1tuK6ZxY+32aaaa5sHEch754mtbeQWLJDJYj0TUFv9ugpCBAWWTs1rHtOLT3xWjvjZPM5FAUQYHfQ3VxmJBvrP74S7uOUBYJEPZ7ONDSja6pzCwrRFcVDrb1Ek9lKYsEqK+I5hPF2Q5NXf0MJNLMLI8ymMzQ1jtIOmfiMXQqoiEqCkMoyrEVwmQmx5aGVnRVYfXc6jGrh46U7DraQfdAgksX1uHSNRwp6RlM0tIzyJGOPvY2d2HbDq/sPkqBP99/VVWYVVZIZVF4zPlsPtBCLJUecWnwe1wsm1mOoY8dBqWUDCYzbD/cxoyyQoJeN42dfQwkM0gpCXrdzCiLEPS6x612mpZNS88gHf1xMtnxmcP9HhdzKosI+yfWep9mmmmmOR+4oIyDrf1HcHBQhYIlHR5t3UJAP/YQXhyuYV6o4qz3I2tbHIn3cmCwm4FcfjDy6QZl3iD1wSKKPf4x5U3HZldfOw2xHhJmFreqUxuIsKCglKBxTHO9JTnAzr52WpOD/LbtALFcll8f2U7E5Rsp8+H6lRjKhRlnMc000xzDtGz2NHbyyBt72HaojXTWRAiB7Th4DJ3b1i3ig1cuHymfs2xe33uUR17fy76mLhD5ibDf42LdwjpuXDOf6qIChh8df/nTp7lkQS1hv4cXdxwiZznccdlidFXh6S0HaOzsZ2ZZlL/40LVUF4fJ5kye3Lyfl3cd4erl9exv6eZoRx+JdI6cbbN0Rhl3rFvCRXOqRp5PXf0J/vKnTxP0urj/Gx8Zc36O4/Djpzfx/PZDPPU3n6I47Md2HHYeaeexN/bS0jtIS/cgCPjZc1vQ1LyXrEvXufuq5eOMg+e2NbCvuYtUNsfB1h5mlEX4rz+4nYg+fhg82tnPV3/0OO+5eCEBj4tX9xwllsqQzOQIePJuSZ941yoi/mMJm3KWxUs7j/DQq7voiSUxdI1EOsfRjj4URVAZDXPR7EqKwr4L2jjo6Rhky6sN5LIm85bVMHNu2Rlvo6OlnwM7m7nkmoUjqjfnG7GBJC1HeiirilAQPTtCBu9kHOsI0m5E0ZcglIK3UM8hpN2Coi9FKOePm6CUJlbqF6jGahR9zrnuzilzwRgH80KVDJopWpJ5hY93lS+jN5ugL3sseVKN78zrsR9P1rZ4ru0gP2/YQn8uhUvRyDk2CTNL0HDzqblruLH6WPbVlJXjFw1beLx5L4NmBq9qkLFNDFXl3VXzuX3GEorceWOiKdHPho5DdKYTtCZjSCl5s6cFj3YsEO8DM5djnIeqTFl7gLbkBgpccwi7Tv+HkrF6aU+9QoFrHmFX/ckPOMukrE66UhuJuBcRNGrPdXem+R3BkZLGrn7+4+FXaO+LccXSWSysLcGt6yQzOY529VNbEhlzzJaDLXz/sdexbIf3X7GUimiInGmx40gHj2/aRypr8tFrV1IUyj9vJLD5YAtXLJnFh69ZyWNv7OW+DduZX13CLWsW0NQ9wC+e38pvtx7k49ddNNJOc/cAj2/cx/rFM7hmWT2qorC/pZtfvriNZMakKOynrnRs36aKKhTmVBYR9Llp6R7gly9sQ1UUPnLNSiJB71AZMc4wAPjAlcuIpzIk0jm+9P2HT9pWzrR5YfshKqMhblg1j9KCALFUhgdf2cV9G7ZTGgnwoatWjJQ/1NbLz5/fSiZn8aGrVlBVFCaZyfH0mwd4YvM+Vs6u5CPXrCQa8p2g1dPDth1+9p1nqZ1dymXXLzrj9Z8KQhGomsIrj+4GIc6KcdBytJvHfrWRNVfOO6lxIKUklciyc9Nh1lw5/4z1wbYd9u9opqS8gMKS8cGdQgg0XUUo74yFOiltkHFARyhn/h49Hsfaj515Dl2temvGgbkHO/syulp7XhkHYJFLfBsj8LVTNg6kNEHGQPgR4tyoqV0wxsGqwpksOkkeA6969i9yeyrGQ0d3YSP5woJ1VPvC2FKSNLMcTfSPiw94pHEPPzqwkSWFFfx/s64kYLgYzKV5pGk3Pzm4mWKPn5uqF2KoKvPDpZR6gwzm0vzttmcxHZvPz7+UKn94pD639vZ+pVJKTCeOI03cWuGk5UwnQVd6I4YafEvGQb6eTbjVyFk1DqR0yDlxQOJSw5OWy9oDdKU34dXLCFJ71vozzTSjyZkWT2zaR0N7D3esW8wHr1xOyJd3c5FSksqaIyvpALFUhme2HKS1Z5Cvf+Aa1i+egT70+dKZFVi2zTNbDrKivpLLl8xEGVrZ742luOvypUSCXgaTGf7joZe5c/1S3r16Ht2DKe7bsIO9TZ1j+pbI5Lh2Rhl3XraEonDe0Fgxu5JYKsPjG/fxyu6jp20cKEp+4l9ZFCbodRHwulAVlcUzysa5UB1PZTQE5A0iVTn5arMjJbbj8J6LF3DN8tkYuobtOIR8bv70nid4YtO+McbBwdYejnT28cErlnPFkll4XDpSSgr8Xp7ecoBMziQa8uGaYKfirdJ0qIvNLx2gYU8ba66Yh+E6d0N7YXGQy65fxI6Nh89ZH45n/85mNr988IwaB/09cTa/dIC1V82f0DgIhLwEQt4Jjjw/kU4vdvZZFG0uqrHsrLen6CtQ1DqE8taMR8VYjaLNQajFZ6hn5x5pN2FnX0Z1XYHQqs9JHy4Y48CrufBq516vOmObDOTSlHuDLImUU+bNPzSklCyLVo7RVu/Lpni8eQ8AX1l8BXX+gqFEVRJVKOzsa+eF9kOsLa6l3Bci7PIQdnnoy6TwqDqqUKj0hZgRmHxSfrZxZI7mxDOowkVt8MZJy3m1EhYVfhH9La5IePUyFhf+PpriP3nht4AlUzQnnsKtRqn0XzlpuaBey6LCL6Ar09vG07x9ZE2LF3cepiwSYP3imWPcVIQQ+Nxj80+09AxyqL2HWeVRFtWVoqvHlI+iIR+r5lTz5Ob97G3qZNWcqhHt/4DXlZ90CygMevG6dUojAVy6RlHIixAwmMqMaUtTFJbOrCAa8o+04fe4uGb5bH714jYa2nrImtZZmSSfaaIhH+sWzRiJS1AVhWWzKtAUhZaewTFlMzkL07QpDHhRhwwvIQRBrwtdVUhlzbN23q88vYvll9TT3TbAoT1tzFuWn1D81989Rs2sYja+uI/erjhzl1Rx9+euIhD2ksuavPbcXp5/ZBvpZI6Vl83mPR++BMd2ePHx7Rw50EFbUx9LVs3g0N42XG6dT3/13RgujTdfPsgT926kvzdBSUUBt3zoYuYuqjrhKnnr0R5+9cMX+dAXriJaGgIJh/a18eBPXuHzf34LXt/k47dl2mx7/RCP/vJ1LNOmbnYptmUD+bG1tzPGr3/0Eof3t1NRE+WWD62lZmYJqWSGH/z94+zZ2kgilqHxYCeVdVFuuftiameXksuaPPLz19m0YT+KorD+XYu4+j3LUbX87vuerY08ed8mmg53oxsqN75/DZdet5A9W47yyx+8SOPBDja+sA+Pz8X6dy3ihvevwXEctr7WwAM/foVQ2MNtH7+M+gUVI30d6E3yyx88z+G97RSXF3DD+1cxf2k+3uabv/cTrr11BY//6g0yaZMVl9Zzy90X4wu4J702ZwrpdGNn30Cob08GdkUtAvWte3MoajGct4bB6e0aOdYhbHMHqnHukl6e/0/ndxjFngBzw8Xcf2QHtpR8uH4lSwsrUIUYFwvQMNhNRzrO4kg5pUNxCFJKxFA9Zd4QR+J9xMws5We4n5OltxitCz8Zo8vYMkdn+nWKPSsn1NMf0YtHwzPBg2AqaTZG16Og457kgXKyukbXM9nnw/WYToru9JuU+9af8LwUYZxWf07Wl9H9mWaa0UgpMS2Hox19rJxdRVXRybfS++MpBhIZls4sx62PlXJVRD4oORLw0t6XD1QeNg78bgNFyd+rmqLg0nUMTR2Sbs3XYdljhRF8HoOAxzWmDQEUBn14XQaDqQzxVBZX6PwefhQhiAZ9BI5LkuY1dBBgDU1OhykMegn53Oxu6uTKZbPQVQUpJYc7+khlcxQGffjdZ34BSzqSF5/Yydf+5f3s2HiEra83MHdpPq6ju32Afdua+Nyf30SwwMd3v/UbnnpgM7d9bB1bXm1g04b93PaxdRSWBPnh3z8OAm66aw2drQNkMyYrL63ntWf3cvsn1vF///ks7Y291Mwuoag0zB2fupxoSZBnHniTl5/cRWlFwQl960srCzCzJjs3H2X9uxbhSMnmDQeIFAXxeCdPpiilpLWxh2cf3sq6axcyd0k1//PPT5LNmABkMya//MELBAt8fOXv7mDD4zt48tebueMTlxGJBvjUn9zAb372Gq1Hevjsn96Eqgpc7vyuzsP/9xqH9rbx5f93G8lEhp/8+zN4A27WXbeIQ3vb+NUPX2TdtQv52JevIx5Lo2kKiiKYt7SGuz5zOU/8ehPXvGcFM+eVY7g0hMgHxC9dPROPx+D5R7eP9HOY//yrh6iqK+KP//FOdm46wjMPvInX56JudimtjT088vPX+P2/eA/ZtMnPvvssb7ywjytvWnpG7pUJr6+TJDv4Rzi5rUO7B8+QHZoaeoueQqglgMAxD5KL/yOa9/3Ymaewcy8jlACu0L8itDmARFqHMJP/g21uBplD0edj+L+I0OYihhZGHXM/2f7fw3F6UfUFGMG/QNFnH+uP3Usu9lco+kIcax92biNCKUL3fx7VdTlCqEP17CbT/wWk04dqLMIIfgtFqxupx7E7MeN/h6LNx7Z24+TeRCjF6P7fR3WtQwg1PwbLAczUr7DS9yOdHhjOhWOswlXwgynn85FOD1bqJ1jpRxBKAN33WZD2qDIWjrkDK/UT7Nx2QKIai9H9f4RQ88ahYzWRjX0Dae5AOjHSmacAFRB4SzYihDFUz1as5E+xzR1DfV2C7v8KQq08Y/OG8/vp/A6kwPDwyTlrsByHJ1r28nTrPmYGo3xw5gpuqVmIV9NHvry+bIqMbfLb1gMsf/BfxtiYkvy2dpUvjOXYE7Z1ujjSpi31Ivv67yFltuE3apgT+hClvktQ0BEIXmr/PEWeFWTtQVoSv0VXfNSHP0i1/zo0kV+l3Nj153RlNufjAJIvsaP3PwBYU/I3lHnXDZ2HxTMtHyBptuFSC1gY+Rw1gRtG+tKd3szuvu9T4lvL0fhvqPG/GyFUGuOPMjv0QWoCN6EJN47M8duWD5K02nGrhSwq/AJV/muPXS8paUk+x6aub466hjZSOqwv+w5RzwqklGTsLvb2/4iu9CZMJ4Ffr2J26G5KvZegCgMpHV7v/Bo9mW1k7T7aUht4s/tvAFhf/j0iroVA3r3pudaPkrI68GolLC78MuW+dWP648gcTcmnOTjwMzJ2L2FjDvMLPknEvQhQkNi83PEloq4lpK0u2lOvoCle6kPvpyZwAyqeaQNhmgnJWha2I9FVBfcUVqJzlo1p27gNbWRSPxpVVTA0lUzOHMkVk38/PxAP34dCnNxoVZX8BGp0sWFtc0PXsCwH0zqzz7SzgRDgNibPz3G8ab+ivpKlMyv4zWu76YklWVxXRs9gksc37aO6uIB3r5rL2fg57956FJdbp7yqkMRgmvvveZlMKodnaCV+/Q2LqZlViqYpLFs7i8P7OshlLQ7ubqVmVgnzllWjqgq3fOhi/u3PH+Cm96/BcGtU1hZRVhVh1vxyyqoKiRQFGOhPUisEVTOLsC0HkNTUl7Br8xEyqRNLyiqqwsVXL+DVZ/dw8dXzkY5ky2sNfOzL1530nurtjpMzLZZfUk+40M/FV83nyfs3IyWkUzm2vtrAX3zvw4QKfMxfUct9//0i8YEUhcVBvD43LpeOpqsEQsd22CzL5qWndvKRP7iWUMSH1+9m3tJqdmw8zLrrFrHrzaNUzyhm5WWzCRX4CEfzC3jDsQRurwtd1/D6XGPqBdANDY/PNbKDNEx3xyDbXjvEH/3NHXh8BktWz2Dv1kYaD3ZSW18CwLvet4ry6kLSySwz55bT2do/pfvgtBFujMDXccxtmKl70L0fQNGHYoiU0QtfNo61Byv1M1RjBZrnZhzr4JidBilAKH6MwJ8AEjP5Q7Kxb+Eu+CGIoeunzcZd9BhW6tfY2d8Cxz8LHBz7CLa5Fc19A0bor7HTD5Mb/FPc0YdAKc3nRdDm44k+jpn6OU7upYnrsRqwc1vQPDeihW7FTj9AbvCPcUcfHTo3EyvzDHb2GVzBb4Lwkx38GprrUvTAV05h/M1ipx/FyjyJ7vs4Qo1iJn8MMjH6QgMWQq3ACN4CMo2Z+Feyg3+OO3IPoCLUUlzB/4edeSQfR+H/DEKtHTpeP1aPNBFaLYbnVpApzMQ/kx38+lA908bBeYkQgip/mG+tfBd3zVzGQ427+G3rAf5m22+578g2/nHVTcwK5X9ww1lX68NFrI5WT+gHW2B4KHSf2eCg9tRLbO78K2aHP0iRZwXd6S3sG/gxQmiUeS8BIZDSpmHgV9QE3s3qkm/Rld7MocF78WollHhWI4TC4sIvkXUG2NT5DSp8V464FbmU8MiPShEa11b+iv7cPnb0/CvHD6kSyUDuICW+tcwI3Mb+gZ9QF7yZSt81tCafp8y7Dk0pRVUMrq36Fb3Z3ezq/c6obLnHKPdewvXVvwbAkRbNiafoSm/G0ApGVuoV4UIVLhYXfgld8XI0/hh7B+7Bb1QTMmYCgqXRr5C02tjW80/U+G+gwn9F/rzUwpHzMlQ/11XdS1fmTfb1/2jceYGkOfkMu3u/S334gxQYc2lNPs+23n9hRdGfETbyKyWWk+Jw/EEqfFewquSv6E5vZv/ATwi56il0LeJM/dCnubAwNBVVycuHZkwb10myA7t0DUNTSU8grwlg2TZZ08Lj0tHUtyZoYFo2ln0sUzUMGcuOQzZnomsKuj7UxtDtPdEeWs60x+1KnM+E/R4+9a5VNHcPsPtoB/uauvB5DK5eVs/t6xYzp/LMC2JIKdnw5C76umN88oZ/QUpJtCTEgV2tLFk9A4BIURBFFSBA0zUs2yaXtchlLAoK/ahDLmYFUT+DAyls20ZRFPQhw8hwaahq3rhzHAfbdnjpyZ28/PQuMmmTWH+S0srIhN/haIQQXHTZHO7/8Ut0tvYT608hBMxZdGI3FseRpBMZDF3D7c3vSAVC3hFJ3Fh/kq62Ab752f9FGRpDdZd20kDgVCKLmbX4928+iD5kYCuKYMW6/LM5Gc/g9Rno+uklcJyIgd4khkfH7TUQIr+DoagKmXRu5EdQXBoGhhIiagqmeXYNaSFUhFaJtNsANyjFKJP5ucsMqrEC3f9ZQEF1XTK6JlStHjX49WPFnSRm/B8Bk/wJDic78yAUP5Pm4ZUSxViOHvgDhPCgqLXY2ddwzJ2ortKhfgsQHhTFj3OCelRjNbr/9xHCjaLWYGdfGarnSpCp/L+1hUPnIlBdl+FY+ziVsVc6vdi5DWjuG9F9Hxo6zxCZ/s8cuzpCRTVWoRqrRh3Xgxn/NsNfvhAGQqvEViIgPAilbNx3IYSK6roY1XXxqHo6MeP/xsRP0tNj2jg4SyhCsDBSxsJIGZ+bdzE/3P8GPz6wkf/a9xr/uPpmAKIeHx7VoNwT5CuLrxizq3AyRgbdU+yXlJJ9/fdQ6r2Y+ZFPARB2zSZj99CReoVC90Jcal45wKUVsrDw8yhCw6/XEDcb6clsp9C9GF348GhRFFtHCA1DDeLVSifqKUIIdOEb2Q4c1ydsZgZvJ21105Z8kah7KboSpDe7AwdzVD0quuJDEeMfBPnMsm68SilS2vRkdtCf3Utd4BYCevVIGZcaZkn0D0e1DTt6/w3LSY5MZjxaEY7MoQgdQw2d4LxUdOFFMP68JDYHB35Ohe8K5oTvBiDkmsUbnV+nLfkCAb0GReR/fj6tgkWRL6ApHoJ6LV3pzQxmG4i45iMunCTmp4XlOPRkEihCGScBfKbJ2RZ92RQ+3UVAP/fxS5MhhEDXVGpKChhIZmjpGSDkm+gePUY06CMS8NLY1U86ZxKSxzT6HUfSF0/Tl0hTFgnidZ3Y0DgZyUyOgUQa25Fo6rBxAF0DCVI5i5DPPeKqo2sqCEhmzKFYq2PPv554ikRm8tVoMfTHkZKTeOi9LVi2wzNbDxJLZfnHT97IorqyMTkdzgapRJYdbxziL7/7YapmlZBKZHjq15vZv6OJRRfV5gtN8Ahxe3RCES8DvQlSiSwuj86hfe1U1UVH/O0nbTOe5X/+6Um+8nd3sGhlLS88toPdW45Oqb9ur8G6axfx4uM7iA+muPLmZSjqiZ9xiiIIhL2YpsVATxy9PExnWz+25SCAgqif2tklfOn/3UZdfUne7c60MYxj0xtNVzFzFo7tjBgNvoCbgmiAD/3+NaxaPyc/Ec9ZI/dSpCjI4X1txAZSeP0ubNvBthxc7vzvQ1EFjiOxLHvIPQU4yc5aSXk+z0fr0R4qagoZ6Eti5iyCYe8xY+Y8VjcSSgShzWTiSb1ESgvpdIMTQ8ocyAGkTAPWKbbjRVFnIIa8FIQSAaEinb5TrMeHotUhhHvodSEwuh6BQEOSQTppEArIJIgAp7QwJ7NIGUPRakaOU7S5jJ1iS6TMIu0ekPGh65NEyhT56zPV6fhwPd0gE0iZQzrpoXpsmGA+cjpMGwdnmIxlkrZNArobbWgVo8Dl5cP1K/nfAxtpS8VGys4MRqkLRNja28r2vjZWFVWjjVppS1r5gdGrGWNcARQhCOgu+rMp4rmxwYAnw5ZZYrnD1ATePfKervjx6uX0pLdgOqm8cSDywbbDE1i3GsGjFpG2OrGdzFsOLB6NQGCoQTJWL4YaRFP8KEJHSntKMQmjkVKSsjppTjxJ0JhJqXctitBHPpM4JMwm0lYnlkwTzzUOqS2d2sPrZFhOhoTZTH3oAyPvudQwXr2MWO4IcuhhoAiNgF6LpuQfgpriRxUGphOfcHfkbHA41svBWPeQ+5og5HKzqKCMkHFyLXZHOjQmBij2+PFpk/sNny4JK8u9R7bh0ww+MWfNW6rLkZLOdBxDUSfcjUtaObb2tjIzGCUQOvuyx28Fl66xftEMHnxlFy9sP0R5YYjwKLWinGWTzpojgcqV0RBzqop47I29vHmwJa++MzQJ7BxI8Nqeo7h1jXnVxXjdb+17dKRk04FmVs2tpqIwL8gQS2d4fNM+Qj43cyqLR4Jywz43PrdBW2+MPU1dLKwpQQhB1rR480ALbb2xSdtx6RouQ6OtN0Yikx2zU3EuSKSz7GnsJOxzUzgUrC0lZ8WVaJhtrx8iXOindnYpuqGh6yo19SW8/tweBvuSkx6nGxoLV9Ty4hM7ef6RbXiDbt54fh/v/cilJ21TCCipCNN8qIv4YJqDe1rHrG4f2NlCb1eMrvYBdENj2+uHqKyLEi3Jx8Zc9q5F/MvX7yebNvng56+eQnuC4rICisrCPPvINirrohze154PSBbg9rq44salPPebrcxZVIltO3h8LhZdVIfP70YIqPn/27vzKKuqO9Hj333OufNUc92aqJGpGAooRJmcQAyCQ0zQ2DHRNunXpMXgS9ZrM7x+ul53gq+7X3cn6xnTIYmRaEI6oobEESOiRIGIIMVoYQ2MRVHzcOtO5+z3xy0uXKrAAoqh4v6sVWvBOfvuu++vTp179lyRw+b1e3j71Rqycv2UjA7iDbhYeNdV/PntvUTCMQybRixqUjI6l7JxeUyaXsL+3YfZ8MoO8osyiJsW2cEAk6YnxrWnZXhwex1s3/QxHa09FJVlU1iaTSxmsn/XYWp3HqLpcBt7th9AWpIxkwrxp3u45a4Z/OHXm6icWkzToTb86R5Kxw7/cq8Xh5F8YD+dlHGsyEbi4VeBeGJYr3kUZOg83kdHiFNXejrxR3SuPYk6nC0f4UZzXEO89xnioV+A8CPNgxiuuzljr8agLJCnPeALW8ofv5R9mOE3MSPvkIhPHBk/BMROz+yspBXCjPwRM7IRMBMVMvMg51oB+ySqcjDMGnvaeaGhhmyXl2ynF6du0BuPsrXlIJoQXJ1dnEybZndxd/kUGnra+Jcdb7JoVCV5Lj+aEHRGw9R3tzEju4i5wXLsp3T1O3SDqox8Njc3sqahhqN93ehCEDVNFhaNwzjLPgcSK9G6cdqFnxjiJDm1L2JgS79IvH7YiWR5BNoFtJZL4jJEY88rxGWYMb7F2PXUyZpNoY0c6H4Vp5GFLhyEzVbi1rlVsIZalkQr0ulx1hIx7G9lAoFxys1LJF8tT0lzcbVFevmos5nfNe6kyJPGvIIxjPZnM5QVo9siIdY21nBb8URKL+OqWUPRG4/w5pFaSnwZzHaWDjif7nCzsGj8ZSjZuXPYDBZeNY6a+iZe2rKH7r4I44pysBsGfZEoTe3dZPk93H39FADcTjufqR7LR4eO89Rr73OktYui7ADRmMmO+ibe29PIguoxTCrJG3ROwrlwO2zsajzGqjfeZ0JxEEPX2HuwmT9uq2VyaT6zKkuSab0uB9dNKudX6z/gBy+8zYLqsXicdprauvmw7gimaZ2xPJkBDyW5GWz/+Ai/eWs7U8oLMHQN07SYWBKk5JTlUo+1d3O8q5dINE4oEiVuWoTCMbbWHiLT58ZuMxiVkzZg8vG5cNgM8jL87Go8xtPr3ic3zY/WP0E1zetiTEE2FfmZid6SYeLxObjjS7OTY9t1XaN8fB7xmIllSWbdNIGi0uzEHBAEFZX5+NMT95vREwoQmsbubY0cP9rJjbdOoXrOaKSEcZOLMOw6/oAbTddwe53MnDeBYGEGHp+Te5fNZ9+Og5imRdXVZThddrz+xEPj8aZOmg61UTmlGN3QONLYSnqWN1k5yM5LA6ByajFpGUNrZMrOC7Dgs9Vs37Sf1mNdXHfLZMZOLkTTNXRd4+Y7q/ngvf0cbmxFABUTClLG+4+fUsy826Zy5EArpmlRUJJYUnzWvEr8ATe1uw8T6YuRnu1LztXIL87k9ntnUvPn+v7VigwKS04uRZ6e5ePGW6ckzn/cnJyMbZkWx450EA7HGD2xEMu0OHqojbLxedgdBp+9bw6b1+/h6MFW/GkeZt80gWBBorf+lrtmkJmTyMew6YyfMgrzUg2tEwKSzwHnQfYS612JMMqwef4Ooedihl8lGq893wKd5+uGnocQNjS9DDQfVvxjhFGK4f4yumPOgO/us7OD8CZ6Tfq/uKV5MHVCstlMPPQsmr0aw30PQssiHvot0djOQfI78d4DfxfSPEI89Gs0+zUY7iUILZN4aPUZ8jl/qnIwzDQhOBxK7GAcs0w0oaEh8Noc/M24mSwpq0pJPzu3lG9VzeP3jbt4ru5DoqaJJgQ2XSfo8jErt2TAl6NDN5hfMIa67lY2Nzfy3rF67LpBptPD/IIxZ60cGMKJ1zaK9sju5LG41UdfvBmHnonR3/2GhJ7YASwZRxMGUauLiNmGy8hBEydbFgWgoQ97y/v5sKTFkd4NtPRtozLjb/DYTh/LKtnXsQq3EWR04K9w6hm0hLfRFt41SG4CgZ7YGOY86JoLj5FHZ/TkjTFqdtMXb8ZvL0mpeF3uTuTp2aOozipiV3sTs3PLuLdievLcb+u3k+Fws7v9GG2RXkp9mdxZMhmXbmNXexPPN+zg/ZYDHA51kmZ3MSu3hOuCFUSsONtaD9MeCdEZDVPbeZxJGXncUlSJXdNpj/bxhwO7ONTbQa7Lx7z8MRT3L+XbHYvwfMOHNPa0k2530RntS/ZKrKrdQnXWKMan5QCCzccbOdzbwedLpwDQ2NPGW0f3c6CnHYHg+rwKZuWWcrC3ndUfb+PPxw+Q7fKy4eh+pmUVMj9/DJrQqO08ztoDO7FrOgsKxzE+LTcZg95YhLUHdlLX3Uaazcm1eRVMTE9Milu59z0mpgd5t7mB3liUsWk5LC6agMc2/L0op9KEoCQ3g2W3z+blLXvZWnuIDTvqkhOGAx4nn501MeU1E4qD/N3iWazdtJuXNu9J3mvSvC7umDWRhVeNI9N/4euye10OFlSP4UhrF798Y2uiVd+SzJlQypJrqwasrnTPDVMwLYu3a+r48R/ew+O0k5PmZd6Uiv49AvYN+j4eh51FM8bTHQrz7u5G3tlZj8NmkOFzk+F3p1QO1n+4n3Uf1NIbjhKNJ+ZXHO/q5Ue/fxeHzcBm6Dx0+2ymjz7/1T46evuIxk1My2LLvoOJVZ0QifkccZOCzABfmlfN7AklwzbcaMo1FQOO5eank5ufeNi8cfGUlHPjqooYV5XYD0g3dMZOKhx0zP+kq05Wnk88SN+w+OR319SZFUydOfC9AWbfNGHQ4yd6gev3NdHVHuLmz00fNN1gdF2jdGyQ0rGDD5/z+F3MvXnwzd9OjO2fd/u0Aed0Q6fqmnKqrikf9HVFZTkUleVwMNTIjo7tfGi+Q27sFgK2AEIIxkwsZMzEQt5teYdMb6JS4XDauP6WyWf8LG5PoqdjMHfef7LnxmY3mDyj7Iz5DDvR39sVq8HSi0BaCKMYIYb6mGghZQ+acAI6VmwPZng90jq9B0siZSwxNIY4UvYhZbz/O/Fc/i5O5sP55iMl0moGGcLwPohuqzzHMiQILYBum4gZ2YBmjAXNTzz0HMjIKalMpOxL9LxIiRXdjhl5k8F6DoSWhpRdWLGdIBz9E5DL++9N8dPy2YYZfhPVc3CFG+VN539MvqF/JaI4lpTYNA2fzUmBx4/PlrpesU3TmRssY2wgm+ZwD6F44kJx6gYBu4tclzdlHC4kHgyKvGk8PPFajoa66TNjaAg8NntKD8NghNAYG7iXHa0/5OPO58hwTKA1UkNbZBcV/rtSWtp740fY17GKXNfVtIQ/pDvWSKF3HjbtlJZuYeA0smkJbyc7MhUhDNx6ELueaP1IDOUxiVkhLBknboUxrQiasJ/Tl/CJfOJWCFPGMU/LR0pJR3QvH3X8khLfbbiNIGGzNRFj4UPvr9BImSiLKSN0RD/iUO+b9JnHBryfJuzYNR/Hw9tIc4wBBF5bUXL4j5QSi3jyc5kyjGkl5ikIIdDQKfcvobZzNX57GX5bGU2hdwmbLYx1fwldOC5SL8zw2tLcSHO4h7vKpjLZyOOZ/e/j0HWWlE4l3+OnMj2XA73tzMotpcAdSO7rEbcstrceZmvLQRYVVXJ9fgUuPREbC8kTu9/BrhnMDZazu7+S8YXyqeS7Azxdu4X9nce5s7SKo6EuNhz9mHx34rrc2FRH0BVgbCAHIST7u45T03aUz5dO4XBvJ7+sfZ90h4vr8yoIm3F8dgcCyLC7mZJZwMHedqoyCpiSWUCOy4smtP6lg71Myshj3aF9HAl1plQOntizkVAsyvyCsRzq7eD5hg8xNI3xabmsP1rL+y0HWVKaeHB6vmEHHsOesgv6xWIzdCaWBCnKTqO5o4fecBRLSgxdw+u0k5OWuqykrmtMqSigsD99XzSG1r8Of266D5/LmTIE5l/+ZlHK5OQZ40bxz19dTGlu4sHT0HV+8LXbBwxDsizJpJIgn5szieaOHqIxE4dNJyfNR066d8DffV6Gn68uvJpbZoynLxLt3xvASV6mj46eMLdeM540z8B13oWAMYVZfO3WWXxubg/haBxNgMtho+i0HZJnVZZQnp+FZZ25VbQ8LytZtvK8TH700J1keAdWlgxd5z+W3p4y5LGls4dfrd/Gx0daWH7HXMrzMtH751uYpqT2SAs/fWUzb35Yy7hROWRfhF2Sr3SHG1p48Zl3OXaonYVLZlBQcmX3NJ4qzZbBOH8lqxp+ztys6wnYUiu4pZ4yvMbI3utG04vQHfMxwy9jht8CLQtH2v8BcfbNBZOEB8N1D/G+5zBjOxBaLrp9GiK6KZlEyj7iod8SD7+ENJuRVivRzn8AzYfNfR+68zNDeitphYj3/YZ4+FWkeQxptSE7vw3Ci83zALrjpqF/cOFCWi1EO7+bmAQs3Gi2idg8XyWxQ/EQnlOEF911O7J3JdHuFaClo9uvRRgFJ5No2ejOW4iHXyMeeRtNz0d3zsM8JT4naLYqdNt0Yn2/QoRWg56DI+2HgEDoeejOm/vz+SOaXoDuuhkztnnon3kIVOVgmDl0g1HedEZ504f8Gk0Igm4/QfcQ/wgBXWjkuHzkuM79hlTguQFTRqnv/h0fdT6DU8+ixLeYoHs24sQlISDbWU0ofow/Nz+GJgyKfYvJck5NafXWhZOKwN3sbvsJW479A3Y9wOTMh8nUJyGlpKH79+zt+DmmjBIxO+iO1fNRxy8p9i2mIvCFIZVXSkl914vs63wa04oStTrojjWwt/0pSvy3UxG4C0O4Odizjs7ofvZ2/ILazl8lX1+V9Q3y3HMR6EzI+Dt2tf2YPzV9E5eeRb7nWtLs4wa8p0MPUBr4LPvan2bzse/i0NOZlv1d/PYSpLT4uGsNtZ3PYloRolYn3S317BQ/ojywhHL/59GEnSLvZ7CkycedzxG1uvAYeYxL/2syHOfXOnG5TMko4NpgGW7Dzt7OZra2HOLusmlkOb2MDmTz55YDTEzPo+K03b/jlkmey8/NheNw6rbkhNOjfV28emgvz837a3JdPvLdfv5z759o6G4j1+XjhYYd/O/qW5iZU0JTXxfbWg4NqZw7248Ss0wWFIyj3J+VmKhKYhy6z+5kbCCbza5Ema/KTl0BIt3hpjItyObmxpTjLeEe1tTv4Ll591PgSaMl3MOB3nbeP36A8WmJyY9XZ49ibrAciWRv5zF2th29JJUDSCwbmuFzk+EbWou/JgQ5aV5y0j55cve0itQW5Sy/hyz/yYdaTRNMHzNwV3qJRNMEeRn+T9y1+IR0r4t078CxzF6no39n48HpmkYw3Ucw/ez3wVE56YzKGfo92ed2MGPs4Cu2aJqgenRqbBqbO9i4s57rqyq4bnJZyj4PUkoCXifrP9zPsY4eukPhT2XlICc/jbu+ch2WZZGeOcSHriuEz+bDpbswztCKnucqGPT4iCJ8GK4l6I75JCfIipPXqTDKEuv+a2fa3dyO4boD3TE38XphR4hAIj9x4m/PgeFchO4YOLclMVkY0NKxp/3f1DkHwoUz41mEltb/fyeG81Z0x3VnzEdo2djT/nVgPpn/hdAS5bGsZmKhp9HsM9Bt1SAMsLqJhX4OwoHN87VPDBskhmBrxhjsvv+JlN2AjtDSMVwLEScqV8KHzf1XGM6FgAnCgdDS0O2zgdQGFqFlYfP+LYZ1TyItNpLPDMKPzf0lDOfi/nyc/fnM4uRypxdOVQ4+hTThoNi3iELvfKS0EEJDF3YEpy7ZJrBpXqoy/3v/5FmRTHMqgUa2cwpz8v4dKS0QAl0kuleFEIzyfYYCzw0DyqALG5pwkO2axuLiVwDw20u4Ovd7yWFLc/N+iN4/zOlEeQfmY0+mn5C+lPFpXxmQxtCcyRWFclzVZOT/P6Q0EUJDE3bK/HcmJy2f/FwGQdcscpzVyc+VHHKFoNR3G6O8A1s5TpRHiET8ygOfo8R/K0jZ/36OxLwKIUDqzAn+e8pqR5pwMCv4rwiMAWW6XEZ503FoNjQEfpuDPnNoE6hsmk62y5vsMTgxPO5oqAtDaARdPoQQZDjcSAldsTBd0TDdsQjlvkwE4NJt5LrP/OB36nz19kgIl2Ej3eFCO+X9LkRzXw8CKPAE0ERieKDXcNAWCWH1b5ZT5svCpunEpYXHcNASPp8JeMpIFgpH6QpF8LsdyYnecPL6jMctevoi+N3OYZ1zMJLYHTZy8tMudzGG1ZG+w6w79iq7O3dyX+lXGO+bgERS31vHq00vEbAF2N9Ty2jvGG7PvxOP4aUpcpQ1B39De6yNXEceC4ILKXaXsKd7J682vUxXrBO75mBx3m1MTpvK0b4j7Oj8kI5oO3W9++kzQ3xjzN8TsKUPawUrsTSoB3GGxUZOLLN59te7EFpqmlPzE0KAnongbL1GBkJPnaAthIZIWdJTgJ6FILVB6lzykdLEiu1Dxg9gT/sGQi9I5ItFPPJH5DnOlRDCGPDZBN5TzotET4SW2jAjjJJB8tJABBDawIaRxLmh5XMhVOXgU0gI0f/w+Um/fomhnX3b9sTNSU+ZVHsqXdjR9TOPvxYYaPqJzWX05AZrANopeerCjs7Zx3EnHt4/aZv5xPKjqYUY+BCeiJGeUoZTz+nCgc7ZJy8m8rCd8SFfCDEgboMdu9w0oZ1xxRUB/XOm5CA7SSfOn/4FFnT7CJsxOmNhAjYnPbHEuEyPYcdvd+LQdI6Fu8l1+YhbFl3RMP7+4XiGphOz4oDEknCsrzuZr9fmIGLGCcVjybJIZHLp28Sk+8QQtFPLerYv2ByXl6hl0h7pI8PhJmzG6TNjpDvcyX1KBtufRPl08XucZAc8vLenkdkTSijOSU9O7ewORXhuYw07G5q476arhtRro4wMec58vlz8AE/s/4+Ue0rUitISOc783AXcWbCEn9b/Jw2hesb5K/lF/U+5e9QXCTqCbOvYypbW98i0ZxJ05nN/8VdwG14O9R3k141PMykwBYmkrqeWCu8Ybi/4e+IyhkdX19CF09D0HLA6iIfXotuvBRnFjL6LGdmEPfBPl7uAl5WqHCjKp1x3f4t9nxmjI9pHc183aXYXdj1xezhb21TA7iJimuzvbMGl2/DYHARsZ6+gBZ1+rs0r5+f7NnNb8UQ2NTfgtTko8qSjC42bC8ezqvZ9Hhw/h/ruVna0HaHQkwYklv99r7mBMYEc2qMhNh6rY2wgB4DxablsaNrPm0dquTavnJhlEjHjTM7IBxJLAutCo767jQp/B16bgzS7Cykl7dE+WsI99MajtEf6ON7XQ5rDRZbTyy1F43li90burZjOR13NNIW6uSFvdLJSMXIGRygXy+iCLBZUj+HX67fz9SdepDiYgddpp6cvQl1TG/G4yQ1VFSyaMT65jKsy8p3cOfz0u4Ak25FDmacCm2Yj055FKB6iNdLKwVAjvznwDCfuHOXeCiJWhKPhI/y5bRMxK44p44TMvuS8tExHFiWeUpz6UBrAlKEQQoBRis33LeJ9vyYeehGEhqaX4gisQHfeNKKGvg03dZdSBhWwl+PUr+y13pXhseHoxzzfsIOwGedPx+po6uviK2OvocyXSYkvg4xTWsmznV7KfCe7cou9GSwsGs8fDu7ihcYdLCmdyrz80ehCEHT5iVkDJ11rQvD3k+fxi4+28M8f/pEibzpLSqck5+ksmzCXH+/5E/+0/XVKfRl8rrQq2XNwT9k0nt6/he9tf52g289/GzuLxt7EhjZl/izuGz2D3zXU8I/bXsOu6dxZUpWsHGQ5PSwoGMt/1W1jU3MDi0dVcktRJRL49ccfsKejia5ohHWH9rKno4m/HnM1hZ40Hqmaz1MfbebxD98g0+HhsyWTmJKZyHNsWk6ybBqQ6/Jdot0prjyappGX4WNiSRC/+9P1AON22Lnn+qmU5mawcVcDh1s76ejpw+2wc2NVBdWjC7hmfPGnLi6fZrrQsGmJXuMTW5YaQsdreFla/nXS7emY0kRKi5gV4/Wml1mcfwejvWNp6K1jZd2TybwMYaCdYRNR5fwJ4cBw3YzhuvlyF+WKI+S57jJ1Bejq6iIQCNDZ2YnfP/RJvIqiKIqiKOeirmc/XfEu/uvAr/hM3iKK3MUUuArpjHVwPNzM746sYVKgijJPOaM8JRwMHeDt4+v52/JlAPyqcRUV3tFMS7+K5w6txqE5KPaUErdiZDlyCDqDrD74LCWeMjJsGTSGGtjW/j7/a8I/0hRuYlPrn6hKm0a5d/DlYxVlKM7l2VkNllUURVEURTmDrlg3ndEOZmfNxZQmPbFuLGkRNsO0x9qZkjYNp+6iI9ZB3IqT5cimOn1G8vWTAlUUuArRhMaivNvJcxXQGmmhJ96DJjScuosbc25CAO3RNsb7K1kQXIhA4DN8jPdPIN1+plWCFGX4qZ4DRVEURVEURfkLpnoOFEVRFEVRFEU5Z6pyoCiKoiiKoigKMEJXKzoxEqqrq+syl0RRFEVRFEVRrmwnnpmHMptgRFYOursTGx8VFRVd5pIoiqIoiqIoysjQ3d1NIDBw9+VTjcgJyZZlsW/fPiorKzl48KCalHwRdXV1UVRUpOJ8EakYXxoqzpeGivOloeJ8aag4XxoqzheflJLu7m7y8/PRtLPPKhiRPQeaplFQUACA3+9XF9IloOJ88akYXxoqzpeGivOloeJ8aag4XxoqzhfXJ/UYnKAmJCuKoiiKoiiKAqjKgaIoiqIoiqIo/UZs5cDhcPDoo4/icDgud1H+oqk4X3wqxpeGivOloeJ8aag4XxoqzpeGivOVZUROSFYURVEURVEUZfiN2J4DRVEURVEURVGGl6ocKIqiKIqiKIoCqMqBoiiKoiiKoij9VOVAURRFURRFURRAVQ4URVEURVEURek3IisHP/rRjygtLcXpdFJdXc0777xzuYs0orz99tvceuut5OfnI4TgxRdfTDkvpeSxxx4jPz8fl8vF9ddfz65du1LSRCIRHnroIbKysvB4PNx2220cOnToEn6KK9uKFSu46qqr8Pl85OTkcMcdd7Bv376UNCrOF+7JJ59k8uTJyV01Z86cySuvvJI8r2J8caxYsQIhBA8//HDymIr1hXvssccQQqT8BIPB5HkV4+Fx+PBh7r33XjIzM3G73UyZMoWtW7cmz6s4X7iSkpIB17IQggcffBBQMb7iyRFm9erV0mazyZUrV8rdu3fL5cuXS4/HIxsbGy930UaMl19+WX73u9+Va9askYB84YUXUs4//vjj0ufzyTVr1siamhp59913y7y8PNnV1ZVMs3TpUllQUCDXrVsnP/jgA3nDDTfIqqoqGY/HL/GnuTLdfPPN8qmnnpI7d+6U27dvl4sWLZKjRo2SPT09yTQqzhdu7dq18qWXXpL79u2T+/btk9/5znekzWaTO3fulFKqGF8MW7ZskSUlJXLy5Mly+fLlyeMq1hfu0UcflRMmTJBHjx5N/jQ3NyfPqxhfuLa2NllcXCzvv/9+uXnzZllfXy/feOMNuX///mQaFecL19zcnHIdr1u3TgJy/fr1UkoV4yvdiKsczJgxQy5dujTl2Lhx4+S3vvWty1Sike30yoFlWTIYDMrHH388eSwcDstAICB//OMfSyml7OjokDabTa5evTqZ5vDhw1LTNPnqq69esrKPJM3NzRKQGzZskFKqOF9M6enp8qc//amK8UXQ3d0tR48eLdetWyevu+66ZOVAxXp4PProo7KqqmrQcyrGw+ORRx6Rc+bMOeN5FeeLY/ny5bK8vFxalqViPAKMqGFF0WiUrVu3smDBgpTjCxYs4N13371MpfrLUl9fT1NTU0qMHQ4H1113XTLGW7duJRaLpaTJz89n4sSJ6vdwBp2dnQBkZGQAKs4Xg2marF69mt7eXmbOnKlifBE8+OCDLFq0iPnz56ccV7EePrW1teTn51NaWsoXvvAF6urqABXj4bJ27VqmT5/OkiVLyMnJYerUqaxcuTJ5XsV5+EWjUZ555hkeeOABhBAqxiPAiKoctLS0YJomubm5Kcdzc3Npamq6TKX6y3IijmeLcVNTE3a7nfT09DOmUU6SUvKNb3yDOXPmMHHiREDFeTjV1NTg9XpxOBwsXbqUF154gcrKShXjYbZ69Wq2bt3KihUrBpxTsR4eV199NatWreK1115j5cqVNDU1MWvWLFpbW1WMh0ldXR1PPvkko0eP5rXXXmPp0qV8/etfZ9WqVYC6li+GF198kY6ODu6//35AxXgkMC53Ac6HECLl/1LKAceUC3M+MVa/h8EtW7aMHTt2sHHjxgHnVJwv3NixY9m+fTsdHR2sWbOG++67jw0bNiTPqxhfuIMHD7J8+XJef/11nE7nGdOpWF+YhQsXJv89adIkZs6cSXl5OU8//TTXXHMNoGJ8oSzLYvr06Xz/+98HYOrUqezatYsnn3ySL3/5y8l0Ks7D52c/+xkLFy4kPz8/5biK8ZVrRPUcZGVloev6gFpjc3PzgBqocn5OrIxxthgHg0Gi0Sjt7e1nTKMkPPTQQ6xdu5b169dTWFiYPK7iPHzsdjsVFRVMnz6dFStWUFVVxQ9+8AMV42G0detWmpubqa6uxjAMDMNgw4YN/PCHP8QwjGSsVKyHl8fjYdKkSdTW1qrreZjk5eVRWVmZcmz8+PEcOHAAUPfm4dbY2Mgbb7zBV7/61eQxFeMr34iqHNjtdqqrq1m3bl3K8XXr1jFr1qzLVKq/LKWlpQSDwZQYR6NRNmzYkIxxdXU1NpstJc3Ro0fZuXOn+j30k1KybNkynn/+ed58801KS0tTzqs4XzxSSiKRiIrxMJo3bx41NTVs3749+TN9+nS++MUvsn37dsrKylSsL4JIJMKePXvIy8tT1/MwmT179oBlpT/66COKi4sBdW8ebk899RQ5OTksWrQoeUzFeAS41DOgL9SJpUx/9rOfyd27d8uHH35Yejwe2dDQcLmLNmJ0d3fLbdu2yW3btklA/tu//Zvctm1bcjnYxx9/XAYCAfn888/Lmpoaec899wy6xFhhYaF844035AcffCBvvPFGtcTYKb72ta/JQCAg33rrrZTl3EKhUDKNivOF+/a3vy3ffvttWV9fL3fs2CG/853vSE3T5Ouvvy6lVDG+mE5drUhKFevh8M1vflO+9dZbsq6uTm7atEkuXrxY+ny+5PebivGF27JlizQMQ37ve9+TtbW18tlnn5Vut1s+88wzyTQqzsPDNE05atQo+cgjjww4p2J8ZRtxlQMppXziiSdkcXGxtNvtctq0acnlIZWhWb9+vQQG/Nx3331SysRSbo8++qgMBoPS4XDIa6+9VtbU1KTk0dfXJ5ctWyYzMjKky+WSixcvlgcOHLgMn+bKNFh8AfnUU08l06g4X7gHHnggeS/Izs6W8+bNS1YMpFQxvphOrxyoWF+4E2u922w2mZ+fL++88065a9eu5HkV4+Hx+9//Xk6cOFE6HA45btw4+ZOf/CTlvIrz8HjttdckIPft2zfgnIrxlU1IKeVl6bJQFEVRFEVRFOWKMqLmHCiKoiiKoiiKcvGoyoGiKIqiKIqiKICqHCiKoiiKoiiK0k9VDhRFURRFURRFAVTlQFEURVEURVGUfqpyoCiKoiiKoigKoCoHiqIoiqIoiqL0U5UDRVEURVEURVEAVTlQFEVRFEVRFKWfqhwoiqIoiqIoigKoyoGiKIqiKIqiKP3+PyS4qljFzZdGAAAAAElFTkSuQmCC",
-      "text/plain": [
-       "<Figure size 1500x700 with 1 Axes>"
-      ]
-     },
-     "metadata": {}
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 61,
-   "source": [
-    "wc.generate(text).to_file('images/ds_wordcloud.png')"
-   ],
-   "outputs": [
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "<wordcloud.wordcloud.WordCloud at 0x224b99d76a0>"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 61
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "你可以看到现在的词云看起来更吸引人，但它也包含了很多噪音（例如，像 `Retrieved on` 这样的无关词）。此外，我们得到的由两个词组成的关键词更少，比如 *数据科学家* 或 *计算机科学*。这是因为 RAKE 算法在从文本中选择优质关键词方面表现得更好。这个例子说明了数据预处理和清理的重要性，因为最终清晰的结果将帮助我们做出更好的决策。\n",
-    "\n",
-    "在这个练习中，我们通过一个简单的过程，从维基百科文本中提取了一些意义，以关键词和词云的形式呈现。这个例子虽然很简单，但很好地展示了数据科学家在处理数据时通常会经历的所有步骤，从数据获取到可视化。\n",
-    "\n",
-    "在我们的课程中，我们将详细讨论所有这些步骤。\n"
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--->
-# 数据伦理简介
-
-|![ 由 [(@sketchthedocs)](https://sketchthedocs.dev) 绘制的速写笔记 ](../../sketchnotes/02-Ethics.png)|
-|:---:|
-| 数据科学伦理 - _由 [@nitya](https://twitter.com/nitya) 绘制的速写笔记_ |
-
----
-
-我们都是生活在数据化世界中的数据公民。
-
-市场趋势表明，到2022年，三分之一的大型组织将通过在线[市场和交易所](https://www.gartner.com/smarterwithgartner/gartner-top-10-trends-in-data-and-analytics-for-2020/)买卖数据。作为**应用开发者**，我们将更容易、更便宜地将数据驱动的洞察和算法驱动的自动化集成到日常用户体验中。但随着人工智能的普及，我们也需要了解这种算法在大规模应用时可能带来的[武器化](https://www.youtube.com/watch?v=TQHs8SA1qpk)危害。
-
-趋势表明，到2025年，我们将生成和消费超过[180泽字节](https://www.statista.com/statistics/871513/worldwide-data-created/)的数据。对于**数据科学家**来说，这种信息爆炸提供了前所未有的访问个人和行为数据的机会。随之而来的是构建详细用户画像的能力，并在潜移默化中影响决策——通常以一种营造[自由选择幻觉](https://www.datasciencecentral.com/the-pareto-set-and-the-paradox-of-choice/)的方式进行。虽然这可以用来引导用户朝着偏好的结果发展，但也引发了关于数据隐私、自主权以及算法影响的伦理界限的关键问题。
-
-数据伦理现在是数据科学和工程的_必要护栏_，帮助我们尽量减少数据驱动行为可能带来的潜在危害和意外后果。[Gartner人工智能技术成熟度曲线](https://www.gartner.com/smarterwithgartner/2-megatrends-dominate-the-gartner-hype-cycle-for-artificial-intelligence-2020/)将数字伦理、负责任的人工智能和人工智能治理列为推动人工智能_民主化_和_工业化_等更大趋势的关键因素。
-
-![Gartner 2020人工智能技术成熟度曲线](https://images-cdn.newscred.com/Zz1mOWJhNzlkNDA2ZTMxMWViYjRiOGFiM2IyMjQ1YmMwZQ==)
-
-在本课程中，我们将探索数据伦理这一迷人的领域——从核心概念和挑战，到案例研究和应用人工智能概念（如治理），帮助团队和组织建立数据和人工智能的伦理文化。
-
-## [课前测验](https://ff-quizzes.netlify.app/en/ds/quiz/2) 🎯
-
-## 基本定义
-
-让我们从理解基本术语开始。
-
-“伦理”一词来源于[希腊词“ethikos”](https://en.wikipedia.org/wiki/Ethics)（及其词根“ethos”），意为_品格或道德本质_。
-
-**伦理**是关于在社会中规范我们行为的共同价值观和道德原则。伦理不是基于法律，而是基于广泛接受的“对与错”的规范。然而，伦理考虑可以影响公司治理举措和政府法规，从而创造更多的合规激励。
-
-**数据伦理**是一个[新的伦理分支](https://royalsocietypublishing.org/doi/full/10.1098/rsta.2016.0360#sec-1)，它“研究和评估与_数据、算法及相关实践_相关的道德问题”。其中，**“数据”**关注与生成、记录、管理、处理、传播、共享和使用相关的行为，**“算法”**关注人工智能、代理、机器学习和机器人，**“实践”**关注负责任的创新、编程、黑客行为和伦理规范等主题。
-
-**应用伦理**是[道德考虑的实际应用](https://en.wikipedia.org/wiki/Applied_ethics)。它是积极调查_现实世界行为、产品和流程_中的伦理问题，并采取纠正措施以确保这些行为与我们定义的伦理价值保持一致的过程。
-
-**伦理文化**是关于[将应用伦理_付诸实践_](https://hbr.org/2019/05/how-to-design-an-ethical-organization)，以确保我们的伦理原则和实践在整个组织中以一致且可扩展的方式被采纳。成功的伦理文化定义了全组织范围的伦理原则，提供有意义的合规激励，并通过鼓励和放大组织各级的期望行为来强化伦理规范。
-
-## 伦理概念
-
-在本节中，我们将讨论数据伦理中的**共同价值观**（原则）和**伦理挑战**（问题），并通过**案例研究**来帮助您在现实世界的背景中理解这些概念。
-
-### 1. 伦理原则
-
-每个数据伦理战略都从定义_伦理原则_开始——即描述可接受行为并指导合规行动的“共同价值观”，用于我们的数据和人工智能项目。您可以在个人或团队层面定义这些原则。然而，大多数大型组织会在公司层面定义这些原则，并通过_伦理人工智能_使命声明或框架在所有团队中一致执行。
-
-**示例：**微软的[负责任的人工智能](https://www.microsoft.com/en-us/ai/responsible-ai)使命声明写道：_“我们致力于推动以伦理原则为驱动的人工智能发展，将人放在首位”_，并在以下框架中定义了6个伦理原则：
-
-![微软的负责任人工智能](https://docs.microsoft.com/en-gb/azure/cognitive-services/personalizer/media/ethics-and-responsible-use/ai-values-future-computed.png)
-
-让我们简要探讨这些原则。_透明性_和_问责性_是其他原则的基础价值观，因此我们从这里开始：
-
-* [**问责性**](https://www.microsoft.com/en-us/ai/responsible-ai?activetab=pivot1:primaryr6)使实践者对其数据和人工智能操作以及遵守这些伦理原则负责。
-* [**透明性**](https://www.microsoft.com/en-us/ai/responsible-ai?activetab=pivot1:primaryr6)确保数据和人工智能行为对用户是_可理解的_（可解释的），并解释决策背后的内容和原因。
-* [**公平性**](https://www.microsoft.com/en-us/ai/responsible-ai?activetab=pivot1%3aprimaryr6)——关注确保人工智能对_所有人_公平对待，解决数据和系统中的任何系统性或隐性社会技术偏见。
-* [**可靠性与安全性**](https://www.microsoft.com/en-us/ai/responsible-ai?activetab=pivot1:primaryr6)——确保人工智能行为与定义的价值观_一致_，尽量减少潜在危害或意外后果。
-* [**隐私与安全**](https://www.microsoft.com/en-us/ai/responsible-ai?activetab=pivot1:primaryr6)——关注理解数据来源，并为用户提供_数据隐私及相关保护_。
-* [**包容性**](https://www.microsoft.com/en-us/ai/responsible-ai?activetab=pivot1:primaryr6)——关注有意设计人工智能解决方案，使其适应_广泛的人类需求_和能力。
-
-> 🚨 想一想您的数据伦理使命声明可能是什么。探索其他组织的伦理人工智能框架——以下是[IBM](https://www.ibm.com/cloud/learn/ai-ethics)、[Google](https://ai.google/principles)和[Facebook](https://ai.facebook.com/blog/facebooks-five-pillars-of-responsible-ai/)的示例。它们有哪些共同的价值观？这些原则与它们所处的人工智能产品或行业有何关系？
-
-### 2. 伦理挑战
-
-一旦我们定义了伦理原则，下一步就是评估我们的数据和人工智能行为是否与这些共同价值观一致。思考您的行为可以分为两个类别：_数据收集_和_算法设计_。
-
-在数据收集方面，行为可能涉及**个人数据**或可识别的个人信息（PII），这些信息可以识别特定的活体个体。这包括[各种非个人数据](https://ec.europa.eu/info/law/law-topic/data-protection/reform/what-personal-data_en)，这些数据_共同_可以识别一个个体。伦理挑战可能与_数据隐私_、_数据所有权_以及相关主题（如_知情同意_和用户的_知识产权_）有关。
-
-在算法设计方面，行为将涉及收集和管理**数据集**，然后使用它们训练和部署**数据模型**，以在现实世界中预测结果或自动化决策。伦理挑战可能来自_数据集偏差_、_数据质量_问题、_不公平性_以及算法中的_误导性表现_——包括一些系统性问题。
-
-在这两种情况下，伦理挑战都突出了我们的行为可能与共同价值观发生冲突的领域。为了检测、缓解、最小化或消除这些问题，我们需要针对我们的行为提出道德的“是/否”问题，然后根据需要采取纠正措施。让我们看看一些伦理挑战及其引发的道德问题：
-
-#### 2.1 数据所有权
-
-数据收集通常涉及可以识别数据主体的个人数据。[数据所有权](https://permission.io/blog/data-ownership)是关于与数据的创建、处理和传播相关的_控制_和[用户权利](https://permission.io/blog/data-ownership)。
-
-需要提出的道德问题包括：
- * 谁拥有数据？（用户还是组织）
- * 数据主体拥有哪些权利？（例如：访问、删除、可移植性）
- * 组织拥有哪些权利？（例如：纠正恶意用户评论）
-
-#### 2.2 知情同意
-
-[知情同意](https://legaldictionary.net/informed-consent/)定义了用户在充分了解相关事实（包括目的、潜在风险和替代方案）的情况下同意某一行为（如数据收集）的行为。
-
-需要探讨的问题包括：
- * 用户（数据主体）是否允许数据捕获和使用？
- * 用户是否理解数据捕获的目的？
- * 用户是否理解参与可能带来的潜在风险？
-
-#### 2.3 知识产权
-
-[知识产权](https://en.wikipedia.org/wiki/Intellectual_property)指的是人类创造的无形成果，这些成果可能对个人或企业具有_经济价值_。
-
-需要探讨的问题包括：
- * 收集的数据是否对用户或企业具有经济价值？
- * **用户**是否拥有知识产权？
- * **组织**是否拥有知识产权？
- * 如果这些权利存在，我们如何保护它们？
-
-#### 2.4 数据隐私
-
-[数据隐私](https://www.northeastern.edu/graduate/blog/what-is-data-privacy/)或信息隐私指的是保护用户隐私和用户身份不被泄露的行为，特别是涉及个人可识别信息时。
-
-需要探讨的问题包括：
- * 用户的（个人）数据是否安全，防止黑客攻击和泄漏？
- * 用户的数据是否仅对授权用户和场景可访问？
- * 数据共享或传播时是否保留了用户的匿名性？
- * 用户是否可以从匿名数据集中被去识别？
-
-#### 2.5 被遗忘权
-
-[被遗忘权](https://en.wikipedia.org/wiki/Right_to_be_forgotten)或[删除权](https://www.gdpreu.org/right-to-be-forgotten/)为用户提供了额外的个人数据保护。具体来说，它赋予用户在特定情况下请求从互联网搜索和其他位置删除或移除个人数据的权利——允许他们在网上重新开始，而不受过去行为的影响。
-
-需要探讨的问题包括：
- * 系统是否允许数据主体请求删除？
- * 用户撤回同意是否应触发自动删除？
- * 数据是否未经同意或通过非法手段收集？
- * 我们是否符合政府关于数据隐私的规定？
-
-#### 2.6 数据集偏差
-
-数据集或[收集偏差](http://researcharticles.com/index.php/bias-in-data-collection-in-research/)是指选择一个_非代表性_的数据子集用于算法开发，从而可能导致对不同群体结果的不公平性。偏差类型包括选择或抽样偏差、志愿者偏差和工具偏差。
-
-需要探讨的问题包括：
- * 我们是否招募了一个具有代表性的数据主体集合？
- * 我们是否测试了收集或管理的数据集以发现各种偏差？
- * 我们是否可以缓解或消除发现的偏差？
-
-#### 2.7 数据质量
-
-[数据质量](https://lakefs.io/data-quality-testing/)关注用于开发算法的管理数据集的有效性，检查特征和记录是否满足人工智能目的所需的准确性和一致性要求。
-
-需要探讨的问题包括：
- * 我们是否捕获了适合我们用例的有效_特征_？
- * 数据是否在不同数据源之间_一致_地捕获？
- * 数据集是否在不同条件或场景下_完整_？
-* 信息是否被_准确地_捕捉并反映了现实？
-
-#### 2.8 算法公平性
-
-[算法公平性](https://towardsdatascience.com/what-is-algorithm-fairness-3182e161cf9f)检查算法设计是否系统性地对特定数据主体的子群体存在歧视，从而导致在_资源分配_（资源被拒绝或剥夺）和_服务质量_（AI对某些子群体的准确性不如对其他群体）方面的[潜在危害](https://docs.microsoft.com/en-us/azure/machine-learning/concept-fairness-ml)。
-
-需要探讨的问题包括：
-* 我们是否评估了模型在不同子群体和条件下的准确性？
-* 我们是否仔细检查了系统可能带来的危害（例如，刻板印象）？
-* 我们是否可以通过修订数据或重新训练模型来减轻已识别的危害？
-
-探索类似[AI公平性检查清单](https://query.prod.cms.rt.microsoft.com/cms/api/am/binary/RE4t6dA)的资源以了解更多信息。
-
-#### 2.9 数据误导
-
-[数据误导](https://www.sciencedirect.com/topics/computer-science/misrepresentation)是指我们是否在传达从诚实报告的数据中得出的洞察时，以欺骗性的方式支持某种期望的叙述。
-
-需要探讨的问题包括：
-* 我们是否报告了不完整或不准确的数据？
-* 我们是否以误导性结论为目的可视化数据？
-* 我们是否使用选择性的统计技术来操纵结果？
-* 是否存在可能提供不同结论的替代解释？
-
-#### 2.10 自由选择的假象
-
-[自由选择的假象](https://www.datasciencecentral.com/profiles/blogs/the-illusion-of-choice)发生在系统的“选择架构”使用决策算法引导人们采取某种偏好的结果，同时看似给了他们选择和控制权。这些[暗黑模式](https://www.darkpatterns.org/)可能对用户造成社会和经济上的伤害。由于用户的决策会影响行为画像，这些行为可能会推动未来的选择，从而放大或延续这些伤害的影响。
-
-需要探讨的问题包括：
-* 用户是否理解做出该选择的影响？
-* 用户是否了解（替代）选择及其各自的优缺点？
-* 用户是否可以在之后撤销自动化或被影响的选择？
-
-### 3. 案例研究
-
-为了将这些伦理挑战置于现实世界的背景中，可以查看一些案例研究，这些案例突出了当忽视伦理问题时，对个人和社会可能造成的危害和后果。
-
-以下是一些示例：
-
-| 伦理挑战 | 案例研究 | 
-|--- |--- |
-| **知情同意** | 1972年 - [塔斯基吉梅毒研究](https://en.wikipedia.org/wiki/Tuskegee_Syphilis_Study) - 参与研究的非裔美国男性被承诺提供免费医疗服务，但研究人员_欺骗了他们_，未告知其诊断结果或治疗的可用性。许多受试者因此死亡，其伴侣或子女也受到影响；该研究持续了40年。 | 
-| **数据隐私** | 2007年 - [Netflix数据奖](https://www.wired.com/2007/12/why-anonymous-data-sometimes-isnt/)向研究人员提供了_50000名客户的1000万条匿名电影评分_，以帮助改进推荐算法。然而，研究人员能够将匿名数据与_外部数据集_（如IMDb评论）中的个人身份数据相关联，从而有效地“去匿名化”了一些Netflix用户。|
-| **数据收集偏差** | 2013年 - 波士顿市[开发了Street Bump](https://www.boston.gov/transportation/street-bump)，一款让市民报告路面坑洞的应用，为城市提供更好的道路数据以发现和修复问题。然而，[低收入群体的人们较少拥有汽车和手机](https://hbr.org/2013/04/the-hidden-biases-in-big-data)，使得他们的道路问题在该应用中不可见。开发者与学术界合作，解决_公平性_问题，如数字鸿沟和公平访问。 |
-| **算法公平性** | 2018年 - 麻省理工学院[Gender Shades研究](http://gendershades.org/overview.html)评估了性别分类AI产品的准确性，揭示了女性和有色人种的准确性差距。[2019年Apple Card](https://www.wired.com/story/the-apple-card-didnt-see-genderand-thats-the-problem/)似乎为女性提供的信用额度低于男性。这两者都说明了算法偏差导致的社会经济危害问题。|
-| **数据误导** | 2020年 - [乔治亚州公共卫生部发布的COVID-19图表](https://www.vox.com/covid-19-coronavirus-us-response-trump/2020/5/18/21262265/georgia-covid-19-cases-declining-reopening)似乎通过非时间顺序的x轴排序误导了公民对确诊病例趋势的理解。这说明了通过可视化技巧进行误导的情况。 |
-| **自由选择的假象** | 2020年 - 学习应用[ABCmouse支付了1000万美元以解决FTC投诉](https://www.washingtonpost.com/business/2020/09/04/abcmouse-10-million-ftc-settlement/)，家长被困于无法取消的订阅中。这说明了选择架构中的暗黑模式，用户被引导做出可能有害的选择。 |
-| **数据隐私与用户权利** | 2021年 - Facebook [数据泄露](https://www.npr.org/2021/04/09/986005820/after-data-breach-exposes-530-million-facebook-says-it-will-not-notify-users)暴露了5.3亿用户的数据，导致向FTC支付了50亿美元的和解金。然而，它拒绝通知用户数据泄露的情况，侵犯了用户关于数据透明性和访问的权利。 |
-
-想了解更多案例研究？查看以下资源：
-* [Ethics Unwrapped](https://ethicsunwrapped.utexas.edu/case-studies) - 涉及各行业的伦理困境。
-* [数据科学伦理课程](https://www.coursera.org/learn/data-science-ethics#syllabus) - 探讨标志性案例研究。
-* [哪里出了问题](https://deon.drivendata.org/examples/) - Deon清单中的示例。
-
-> 🚨 想想你见过的案例研究——你是否经历过或受到过类似伦理挑战的影响？你能想到至少一个其他案例来说明我们在本节中讨论的伦理挑战之一吗？
-
-## 应用伦理
-
-我们已经讨论了伦理概念、挑战以及现实世界中的案例研究。但我们如何开始在项目中_应用_伦理原则和实践？我们又如何_实现_这些实践以改进治理？让我们探索一些现实世界的解决方案：
-
-### 1. 职业准则
-
-职业准则为组织提供了一种选择，用以“激励”成员支持其伦理原则和使命声明。准则是专业行为的_道德指南_，帮助员工或成员做出符合其组织原则的决策。它们的有效性取决于成员的自愿遵守；然而，许多组织提供额外的奖励和惩罚以激励成员遵守。
-
-示例包括：
-
-* [Oxford Munich](http://www.code-of-ethics.org/code-of-conduct/)伦理准则
-* [数据科学协会](http://datascienceassn.org/code-of-conduct.html)行为准则（创建于2013年）
-* [ACM伦理与职业行为准则](https://www.acm.org/code-of-ethics)（自1993年起）
-
-> 🚨 你是否属于某个专业工程或数据科学组织？浏览他们的网站，看看是否定义了职业伦理准则。这些准则反映了哪些伦理原则？他们如何“激励”成员遵守准则？
-
-### 2. 伦理清单
-
-虽然职业准则定义了从业者的_伦理行为_要求，但它们在执行方面[存在已知的局限性](https://resources.oreilly.com/examples/0636920203964/blob/master/of_oaths_and_checklists.md)，特别是在大规模项目中。因此，许多数据科学专家[提倡使用清单](https://resources.oreilly.com/examples/0636920203964/blob/master/of_oaths_and_checklists.md)，以更确定和可操作的方式将原则与实践**联系起来**。
-
-清单将问题转化为“是/否”任务，可以被操作化，允许它们作为标准产品发布工作流程的一部分进行跟踪。
-
-示例包括：
-* [Deon](https://deon.drivendata.org/) - 一个通用的数据伦理清单，由[行业建议](https://deon.drivendata.org/#checklist-citations)创建，并配有命令行工具以便于集成。
-* [隐私审计清单](https://cyber.harvard.edu/ecommerce/privacyaudit.html) - 从法律和社会曝光的角度提供信息处理实践的一般指导。
-* [AI公平性清单](https://www.microsoft.com/en-us/research/project/ai-fairness-checklist/) - 由AI从业者创建，以支持在AI开发周期中采用和集成公平性检查。
-* [数据与AI伦理的22个问题](https://medium.com/the-organization/22-questions-for-ethics-in-data-and-ai-efb68fd19429) - 一个更开放的框架，结构化用于设计、实施和组织背景下伦理问题的初步探索。
-
-### 3. 伦理法规
-
-伦理是关于定义共同价值观并_自愿_做正确的事情。**合规**是关于_遵守法律_（如果有定义的话）。**治理**则涵盖了组织为执行伦理原则和遵守既定法律所采取的所有方式。
-
-今天，治理在组织中有两种形式。首先，它是关于定义**伦理AI**原则并建立实践，以在组织的所有AI相关项目中实现采用。其次，它是关于遵守其运营地区所有政府规定的**数据保护法规**。
-
-数据保护和隐私法规的示例：
-
-* `1974年`，[美国隐私法案](https://www.justice.gov/opcl/privacy-act-1974) - 规范_联邦政府_对个人信息的收集、使用和披露。
-* `1996年`，[美国健康保险便携性与责任法案（HIPAA）](https://www.cdc.gov/phlp/publications/topic/hipaa.html) - 保护个人健康数据。
-* `1998年`，[美国儿童在线隐私保护法案（COPPA）](https://www.ftc.gov/enforcement/rules/rulemaking-regulatory-reform-proceedings/childrens-online-privacy-protection-rule) - 保护13岁以下儿童的数据隐私。
-* `2018年`，[通用数据保护条例（GDPR）](https://gdpr-info.eu/) - 提供用户权利、数据保护和隐私。
-* `2018年`，[加州消费者隐私法案（CCPA）](https://www.oag.ca.gov/privacy/ccpa) - 赋予消费者对其（个人）数据的更多_权利_。
-* `2021年`，中国[个人信息保护法](https://www.reuters.com/world/china/china-passes-new-personal-data-privacy-law-take-effect-nov-1-2021-08-20/)刚刚通过，成为全球最强的在线数据隐私法规之一。
-
-> 🚨 欧盟定义的GDPR（通用数据保护条例）仍然是当今最具影响力的数据隐私法规之一。你知道它还定义了[8项用户权利](https://www.freeprivacypolicy.com/blog/8-user-rights-gdpr)以保护公民的数字隐私和个人数据吗？了解这些权利是什么以及它们为何重要。
-
-### 4. 伦理文化
-
-需要注意的是，在_合规_（做到“法律条文”要求的最低限度）和解决[系统性问题](https://www.coursera.org/learn/data-science-ethics/home/week/4)（如僵化、信息不对称和分配不公平）之间仍然存在无形的差距，这些问题可能加速AI的武器化。
-
-后者需要[协作定义伦理文化的方法](https://towardsdatascience.com/why-ai-ethics-requires-a-culture-driven-approach-26f451afa29f)，在行业内跨组织建立情感联系和一致的共同价值观。这需要在组织中[正式化数据伦理文化](https://www.codeforamerica.org/news/formalizing-an-ethical-data-culture/)，允许_任何人_ [拉下安灯绳](https://en.wikipedia.org/wiki/Andon_(manufacturing))（在过程中早期提出伦理问题），并将_伦理评估_（例如，在招聘中）作为AI项目团队组建的核心标准。
-
----
-## [课后测验](https://ff-quizzes.netlify.app/en/ds/quiz/3) 🎯
-## 复习与自学
-
-课程和书籍有助于理解核心伦理概念和挑战，而案例研究和工具则有助于在现实世界中实践应用伦理。以下是一些入门资源。
-* [机器学习入门](https://github.com/microsoft/ML-For-Beginners/blob/main/1-Introduction/3-fairness/README.md) - 来自微软的公平性课程。
-* [负责任的人工智能原则](https://docs.microsoft.com/en-us/learn/modules/responsible-ai-principles/) - 微软学习平台提供的免费学习路径。
-* [伦理与数据科学](https://resources.oreilly.com/examples/0636920203964) - O'Reilly 电子书 (M. Loukides, H. Mason 等人著)
-* [数据科学伦理](https://www.coursera.org/learn/data-science-ethics#syllabus) - 密歇根大学提供的在线课程。
-* [伦理解读](https://ethicsunwrapped.utexas.edu/case-studies) - 来自德克萨斯大学的案例研究。
-
-# 作业
-
-[撰写数据伦理案例研究](assignment.md)
-
----
-
-**免责声明**：  
-本文档使用AI翻译服务 [Co-op Translator](https://github.com/Azure/co-op-translator) 进行翻译。尽管我们努力确保翻译的准确性，但请注意，自动翻译可能包含错误或不准确之处。原始语言的文档应被视为权威来源。对于关键信息，建议使用专业人工翻译。我们对因使用此翻译而产生的任何误解或误读不承担责任。
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-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
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-  "source_file": "1-Introduction/02-ethics/assignment.md",
-  "language_code": "zh"
-}
--->
-## 撰写数据伦理案例研究
-
-## 说明
-
-你已经学习了各种[数据伦理挑战](README.md#2-ethics-challenges)，并且看过一些反映现实世界中数据伦理挑战的[案例研究](README.md#3-case-studies)示例。
-
-在这个任务中，你将撰写一个反映数据伦理挑战的案例研究。这个案例可以来自你的个人经历，或者是你熟悉的相关现实世界情境。只需按照以下步骤操作：
-
-1. `选择一个数据伦理挑战`。查看[课程中的示例](README.md#2-ethics-challenges)或在线探索类似[Deon清单](https://deon.drivendata.org/examples/)的案例以获取灵感。
-
-2. `描述一个真实世界的例子`。思考你听说过的某个情境（新闻头条、研究报告等）或你经历过的情境（本地社区），其中发生了这一具体挑战。思考与该挑战相关的数据伦理问题，并讨论因这一问题而产生的潜在危害或意外后果。加分项：思考可以在此处应用的潜在解决方案或流程，以帮助消除或减轻这一挑战的不利影响。
-
-3. `提供相关资源列表`。分享一个或多个资源（例如文章链接、个人博客文章或图片、在线研究论文等），以证明这是一个真实发生的事件。加分项：分享能够展示该事件潜在危害和后果的资源，或突出防止其再次发生的积极措施。
-
-
-
-## 评分标准
-
-优秀 | 合格 | 需要改进
---- | --- | -- |
-识别出一个或多个数据伦理挑战。<br/><br/>案例研究清晰地描述了一个反映该挑战的真实事件，并突出了其造成的不良后果或危害。<br/><br/>至少提供了一个链接资源以证明事件的发生。 | 识别出一个数据伦理挑战。<br/><br/>至少简要讨论了一个相关的危害或后果。<br/><br/>然而，讨论较为有限或缺乏真实事件的证明。 | 识别出一个数据挑战。<br/><br/>然而，描述或资源未能充分反映该挑战或证明其真实发生。 |
-
-**免责声明**：  
-本文档使用AI翻译服务 [Co-op Translator](https://github.com/Azure/co-op-translator) 进行翻译。尽管我们努力确保翻译的准确性，但请注意，自动翻译可能包含错误或不准确之处。原始语言的文档应被视为权威来源。对于重要信息，建议使用专业人工翻译。我们不对因使用此翻译而产生的任何误解或误读承担责任。
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-<!--
-CO_OP_TRANSLATOR_METADATA:
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-  "source_file": "1-Introduction/03-defining-data/README.md",
-  "language_code": "zh"
-}
--->
-# 定义数据
-
-|![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/03-DefiningData.png)|
-|:---:|
-|定义数据 - _Sketchnote by [@nitya](https://twitter.com/nitya)_ |
-
-数据是用于发现和支持明智决策的事实、信息、观察和测量。数据点是数据集中的单个数据单位，而数据集是数据点的集合。数据集可能有不同的格式和结构，通常基于其来源或数据的来源。例如，一家公司每月的收入可能存储在电子表格中，而智能手表的每小时心率数据可能是 [JSON](https://stackoverflow.com/a/383699) 格式。数据科学家通常需要处理数据集中不同类型的数据。
-
-本课程重点是根据数据的特性和来源来识别和分类数据。
-
-## [课前测验](https://ff-quizzes.netlify.app/en/ds/quiz/4)
-
-## 数据的描述方式
-
-### 原始数据
-原始数据是从其来源获得的初始状态数据，尚未经过分析或组织。为了理解数据集中的情况，需要将其组织成一种人类和技术都能理解的格式。数据集的结构描述了它的组织方式，可以分为结构化、非结构化和半结构化。这些结构类型会因来源而异，但最终会归入这三类。
-
-### 定量数据
-定量数据是数据集中的数值观察，通常可以进行分析、测量并用于数学计算。一些定量数据的例子包括：一个国家的人口、一个人的身高或一家公司季度收入。通过进一步分析，定量数据可以用于发现空气质量指数（AQI）的季节性趋势或估算典型工作日的高峰时段交通概率。
-
-### 定性数据
-定性数据，也称为分类数据，是无法像定量数据那样客观测量的观察数据。它通常是各种主观数据格式，用于捕捉某物的质量，例如产品或过程。有时，定性数据是数值形式，但通常不会用于数学计算，例如电话号码或时间戳。一些定性数据的例子包括：视频评论、汽车的品牌和型号或你最亲密朋友最喜欢的颜色。定性数据可以用于了解消费者最喜欢哪些产品或识别求职简历中的热门关键词。
-
-### 结构化数据
-结构化数据是以行和列组织的数据，其中每一行都有相同的一组列。列表示特定类型的值，并通过名称描述值的含义，而行包含实际的值。列通常会有一组特定的规则或限制，以确保值准确地表示列。例如，想象一个客户的电子表格，其中每一行必须有一个电话号码，并且电话号码不能包含字母字符。可能会对电话号码列应用规则，确保它永远不为空且只包含数字。
-
-结构化数据的一个优势是可以以某种方式组织，使其与其他结构化数据相关联。然而，由于数据设计为以特定方式组织，改变其整体结构可能需要大量努力。例如，向客户电子表格添加一个不能为空的电子邮件列意味着需要弄清楚如何为数据集中现有的客户行添加这些值。
-
-结构化数据的例子：电子表格、关系数据库、电话号码、银行对账单
-
-### 非结构化数据
-非结构化数据通常无法分类为行或列，也没有格式或规则可遵循。由于非结构化数据对其结构的限制较少，与结构化数据相比，添加新信息更容易。如果一个传感器每两分钟捕获一次气压数据，并收到更新后可以测量和记录温度，那么如果数据是非结构化的，就不需要更改现有数据。然而，这可能会使分析或调查这种类型的数据变得更耗时。例如，一位科学家想从传感器数据中找到上个月的平均温度，但发现传感器在某些记录数据中标记了一个“e”，表示它坏了，而不是典型的数字，这意味着数据不完整。
-
-非结构化数据的例子：文本文件、短信、视频文件
-
-### 半结构化数据
-半结构化数据具有使其成为结构化和非结构化数据结合的特征。它通常不符合行和列的格式，但以一种被认为是结构化的方式组织，并可能遵循固定格式或规则。结构会因来源而异，例如从定义明确的层次结构到更灵活的结构，允许轻松集成新信息。元数据是帮助决定数据如何组织和存储的指标，并根据数据类型有各种名称。一些常见的元数据名称包括标签、元素、实体和属性。例如，一封典型的电子邮件消息会有主题、正文和一组收件人，并可以根据发送者或发送时间进行组织。
-
-半结构化数据的例子：HTML、CSV 文件、JavaScript Object Notation (JSON)
-
-## 数据来源
-
-数据来源是数据生成的初始位置或数据“存储”的地方，会因数据的收集方式和时间而异。由用户生成的数据称为原始数据，而二手数据来自为一般用途收集数据的来源。例如，一组科学家在雨林中收集观察数据被认为是原始数据，而如果他们决定与其他科学家分享，则对使用这些数据的人来说是二手数据。
-
-数据库是常见的数据来源，依赖数据库管理系统来托管和维护数据，用户通过称为查询的命令来探索数据。作为数据来源的文件可以是音频、图像和视频文件，也可以是像 Excel 这样的电子表格。互联网来源是托管数据的常见位置，可以找到数据库和文件。应用程序编程接口（API）允许程序员通过互联网创建与外部用户共享数据的方法，而网页抓取则从网页中提取数据。[《处理数据》课程](../../../../../../../../../2-Working-With-Data)重点介绍如何使用各种数据来源。
-
-## 总结
-
-在本课程中，我们学习了：
-
-- 什么是数据
-- 数据的描述方式
-- 数据的分类和归类方式
-- 数据的来源
-
-## 🚀 挑战
-
-Kaggle 是一个优秀的开放数据集来源。使用 [数据集搜索工具](https://www.kaggle.com/datasets) 找到一些有趣的数据集，并根据以下标准分类 3-5 个数据集：
-
-- 数据是定量还是定性？
-- 数据是结构化、非结构化还是半结构化？
-
-## [课后测验](https://ff-quizzes.netlify.app/en/ds/quiz/5)
-
-## 复习与自学
-
-- Microsoft Learn 单元 [分类你的数据](https://docs.microsoft.com/en-us/learn/modules/choose-storage-approach-in-azure/2-classify-data) 对结构化、半结构化和非结构化数据进行了详细的分类。
-
-## 作业
-
-[分类数据集](assignment.md)
-
----
-
-**免责声明**：  
-本文档使用AI翻译服务[Co-op Translator](https://github.com/Azure/co-op-translator)进行翻译。尽管我们努力确保准确性，但请注意，自动翻译可能包含错误或不准确之处。应以原始语言的文档作为权威来源。对于关键信息，建议使用专业人工翻译。因使用本翻译而导致的任何误解或误读，我们概不负责。
\ No newline at end of file
diff --git a/translations/zh/1-Introduction/03-defining-data/assignment.md b/translations/zh/1-Introduction/03-defining-data/assignment.md
deleted file mode 100644
index 21ce3500..00000000
--- a/translations/zh/1-Introduction/03-defining-data/assignment.md
+++ /dev/null
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-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "2e5cacb967c1e9dfd07809bfc441a0b4",
-  "translation_date": "2025-08-25T17:01:00+00:00",
-  "source_file": "1-Introduction/03-defining-data/assignment.md",
-  "language_code": "zh"
-}
--->
-# 数据集分类
-
-## 说明
-
-按照本作业中的提示，识别并分类数据为以下每种数据类型之一：
-
-**结构类型**：结构化、半结构化或非结构化
-
-**数值类型**：定性或定量
-
-**来源类型**：原始数据或二手数据
-
-1. 一家公司被收购，现在有了母公司。数据科学家从母公司收到了一份包含客户电话号码的电子表格。
-
-结构类型：
-
-数值类型：
-
-来源类型：
-
----
-
-2. 一款智能手表正在收集佩戴者的心率数据，原始数据以 JSON 格式存储。
-
-结构类型：
-
-数值类型：
-
-来源类型：
-
----
-
-3. 一份关于员工士气的职场调查，数据存储在一个 CSV 文件中。
-
-结构类型：
-
-数值类型：
-
-来源类型：
-
----
-
-4. 天体物理学家正在访问由太空探测器收集的星系数据库。数据包含每个星系中的行星数量。
-
-结构类型：
-
-数值类型：
-
-来源类型：
-
----
-
-5. 一款个人理财应用通过 API 连接用户的金融账户，以计算其净资产。用户可以看到所有交易记录，格式为行和列，类似于电子表格。
-
-结构类型：
-
-数值类型：
-
-来源类型：
-
-## 评分标准
-
-优秀 | 合格 | 需要改进
---- | --- | --- |
-正确识别所有结构、数值和来源类型 | 正确识别 3 个结构、数值和来源类型 | 正确识别 2 个或更少的结构、数值和来源类型 |
-
-**免责声明**：  
-本文档使用AI翻译服务[Co-op Translator](https://github.com/Azure/co-op-translator)进行翻译。尽管我们努力确保翻译的准确性，但请注意，自动翻译可能包含错误或不准确之处。原始语言的文档应被视为权威来源。对于关键信息，建议使用专业人工翻译。我们对因使用此翻译而产生的任何误解或误读不承担责任。
\ No newline at end of file
diff --git a/translations/zh/1-Introduction/04-stats-and-probability/README.md b/translations/zh/1-Introduction/04-stats-and-probability/README.md
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+++ /dev/null
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-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "ce95884566a74db72572cd51f0cb25ad",
-  "translation_date": "2025-09-06T13:01:37+00:00",
-  "source_file": "1-Introduction/04-stats-and-probability/README.md",
-  "language_code": "zh"
-}
--->
-# 统计与概率简要介绍
-
-|![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/04-Statistics-Probability.png)|
-|:---:|
-| 统计与概率 - _由 [@nitya](https://twitter.com/nitya) 绘制的速记图_ |
-
-统计学和概率论是数学中两个密切相关的领域，与数据科学高度相关。虽然在没有深厚数学知识的情况下也可以处理数据，但了解一些基本概念仍然是有益的。在这里，我们将提供一个简短的介绍，帮助您入门。
-
-[![介绍视频](../../../../translated_images/zh-CN/video-prob-and-stats.e4282e5efa2f2543400843ed98b1057065c9600cebfc8a728e8931b5702b2ae4.png)](https://youtu.be/Z5Zy85g4Yjw)
-
-## [课前测验](https://ff-quizzes.netlify.app/en/ds/quiz/6)
-
-## 概率与随机变量
-
-**概率** 是一个介于 0 和 1 之间的数字，用来表示某个 **事件** 的可能性。它定义为正结果（导致事件发生的结果）的数量除以总结果数量，前提是所有结果的可能性相等。例如，当我们掷骰子时，得到偶数的概率是 3/6 = 0.5。
-
-谈到事件时，我们使用 **随机变量**。例如，表示掷骰子时得到的数字的随机变量可以取值 1 到 6。1 到 6 的数字集合称为 **样本空间**。我们可以讨论随机变量取某个值的概率，例如 P(X=3)=1/6。
-
-上述例子中的随机变量称为 **离散型**，因为它有一个可计数的样本空间，即可以枚举的单独值。有些情况下，样本空间是实数范围或整个实数集。这类变量称为 **连续型**。一个很好的例子是公交车到达的时间。
-
-## 概率分布
-
-对于离散随机变量，可以通过函数 P(X) 简单地描述每个事件的概率。对于样本空间 *S* 中的每个值 *s*，它会给出一个介于 0 和 1 之间的数字，并且所有事件的 P(X=s) 值的总和为 1。
-
-最著名的离散分布是 **均匀分布**，其中样本空间有 N 个元素，每个元素的概率相等，为 1/N。
-
-描述连续变量的概率分布更为复杂，这些变量的值来自某个区间 [a,b] 或整个实数集 ℝ。考虑公交车到达时间的情况。实际上，对于每个确切的到达时间 *t*，公交车恰好在该时间到达的概率是 0！
-
-> 现在您知道了概率为 0 的事件是会发生的，而且非常频繁！至少每次公交车到达时都是如此！
-
-我们只能讨论变量落入某个值区间的概率，例如 P(t<sub>1</sub>≤X<t<sub>2</sub>)。在这种情况下，概率分布由 **概率密度函数** p(x) 描述，其满足：
-
-![P(t_1\le X<t_2)=\int_{t_1}^{t_2}p(x)dx](../../../../translated_images/zh-CN/probability-density.a8aad29f17a14afb519b407c7b6edeb9f3f9aa5f69c9e6d9445f604e5f8a2bf7.png)
-
-连续型均匀分布是均匀分布的连续版本，定义在有限区间内。值 X 落入长度为 l 的区间的概率与 l 成正比，最大为 1。
-
-另一个重要的分布是 **正态分布**，我们将在下面详细讨论。
-
-## 均值、方差与标准差
-
-假设我们从随机变量 X 中抽取了 n 个样本：x<sub>1</sub>, x<sub>2</sub>, ..., x<sub>n</sub>。我们可以用传统方式定义序列的 **均值**（或 **算术平均值**）为 (x<sub>1</sub>+x<sub>2</sub>+...+x<sub>n</sub>)/n。当样本量增加（即 n→∞），我们将得到分布的均值（也称为 **期望值**）。我们用 **E**(x) 表示期望值。
-
-> 可以证明，对于任何离散分布，其值为 {x<sub>1</sub>, x<sub>2</sub>, ..., x<sub>N</sub>}，对应的概率为 p<sub>1</sub>, p<sub>2</sub>, ..., p<sub>N</sub>，期望值为 E(X)=x<sub>1</sub>p<sub>1</sub>+x<sub>2</sub>p<sub>2</sub>+...+x<sub>N</sub>p<sub>N</sub>。
-
-为了确定值的分布范围，我们可以计算方差 σ<sup>2</sup> = ∑(x<sub>i</sub> - μ)<sup>2</sup>/n，其中 μ 是序列的均值。值 σ 称为 **标准差**，而 σ<sup>2</sup> 称为 **方差**。
-
-## 众数、中位数与四分位数
-
-有时，均值并不能充分代表数据的“典型”值。例如，当存在一些极端值完全超出范围时，它们可能会影响均值。另一个很好的指标是 **中位数**，即一半数据点低于该值，另一半高于该值。
-
-为了帮助我们理解数据分布，可以讨论 **四分位数**：
-
-* 第一四分位数（Q1）：25% 的数据低于该值
-* 第三四分位数（Q3）：75% 的数据低于该值
-
-我们可以用一个称为 **箱形图** 的图表来直观表示中位数和四分位数之间的关系：
-
-<img src="images/boxplot_explanation.png" alt="箱形图解释" width="50%">
-
-在这里，我们还计算了 **四分位距** IQR=Q3-Q1，以及所谓的 **异常值**——位于区间 [Q1-1.5*IQR,Q3+1.5*IQR] 之外的值。
-
-对于包含少量可能值的有限分布，一个好的“典型”值是出现频率最高的值，称为 **众数**。它通常应用于分类数据，例如颜色。考虑一种情况，我们有两组人——一组强烈偏爱红色，另一组偏爱蓝色。如果我们用数字编码颜色，最喜欢的颜色的均值可能会落在橙色-绿色光谱之间，这并不能反映任何一组的实际偏好。然而，众数可能是其中一种颜色，或者两种颜色（如果投票人数相等，在这种情况下我们称样本为 **多众型**）。
-
-## 真实世界数据
-
-当我们分析真实世界的数据时，它们通常不是严格意义上的随机变量，因为我们并未进行未知结果的实验。例如，考虑一组棒球运动员及其身体数据，如身高、体重和年龄。这些数字并不完全随机，但我们仍然可以应用相同的数学概念。例如，一组人的体重可以被视为从某个随机变量中抽取的一组值。以下是来自 [美国职业棒球大联盟](http://mlb.mlb.com/index.jsp) 的实际棒球运动员体重序列，数据来源于 [此数据集](http://wiki.stat.ucla.edu/socr/index.php/SOCR_Data_MLB_HeightsWeights)（为方便起见，仅显示前 20 个值）：
-
-```
-[180.0, 215.0, 210.0, 210.0, 188.0, 176.0, 209.0, 200.0, 231.0, 180.0, 188.0, 180.0, 185.0, 160.0, 180.0, 185.0, 197.0, 189.0, 185.0, 219.0]
-```
-
-> **注意**：要查看使用此数据集的示例，请查看 [配套笔记本](notebook.ipynb)。本课程中还有许多挑战，您可以通过向该笔记本添加代码来完成它们。如果您不确定如何操作数据，请不要担心——我们稍后会回到使用 Python 处理数据的部分。如果您不知道如何在 Jupyter Notebook 中运行代码，请查看 [这篇文章](https://soshnikov.com/education/how-to-execute-notebooks-from-github/)。
-
-以下是显示我们数据的均值、中位数和四分位数的箱形图：
-
-![体重箱形图](../../../../translated_images/zh-CN/weight-boxplot.1dbab1c03af26f8a008fff4e17680082c8ab147d6df646cbac440bbf8f5b9c42.png)
-
-由于我们的数据包含关于不同球员 **角色** 的信息，我们还可以按角色绘制箱形图——这将帮助我们了解参数值在不同角色之间的差异。这次我们考虑身高：
-
-![按角色的箱形图](../../../../translated_images/zh-CN/boxplot_byrole.036b27a1c3f52d42f66fba2324ec5cde0a1bca6a01a619eeb0ce7cd054b2527b.png)
-
-此图表表明，平均而言，一垒手的身高高于二垒手的身高。在本课程后面，我们将学习如何更正式地验证这一假设，以及如何证明我们的数据在统计上显著。
-
-> 在处理真实世界数据时，我们假设所有数据点是从某个概率分布中抽取的样本。这个假设使我们能够应用机器学习技术并构建有效的预测模型。
-
-为了查看我们数据的分布，我们可以绘制一个称为 **直方图** 的图表。X 轴包含多个不同的体重区间（即 **箱**），而 Y 轴显示我们的随机变量样本落入某个区间的次数。
-
-![真实世界数据的直方图](../../../../translated_images/zh-CN/weight-histogram.bfd00caf7fc30b145b21e862dba7def41c75635d5280de25d840dd7f0b00545e.png)
-
-从这个直方图可以看出，所有值都集中在某个平均体重附近，离平均体重越远，出现该体重值的次数越少。也就是说，棒球运动员的体重与平均体重差异很大的可能性非常小。体重的方差显示了体重与平均体重可能的差异程度。
-
-> 如果我们取其他人（非棒球联盟成员）的体重，分布可能会有所不同。然而，分布的形状会保持一致，但均值和方差会发生变化。因此，如果我们在棒球运动员上训练模型，当应用于大学学生时可能会产生错误结果，因为底层分布不同。
-
-## 正态分布
-
-我们上面看到的体重分布非常典型，许多真实世界的测量值遵循相同类型的分布，但均值和方差不同。这种分布称为 **正态分布**，在统计学中具有非常重要的作用。
-
-使用正态分布是生成潜在棒球运动员随机体重的正确方法。一旦我们知道均值 `mean` 和标准差 `std`，我们可以通过以下方式生成 1000 个体重样本：
-
-```python
-samples = np.random.normal(mean,std,1000)
-```
-
-如果我们绘制生成样本的直方图，我们会看到与上图非常相似的图像。如果我们增加样本数量和箱数量，我们可以生成更接近理想的正态分布图像：
-
-![均值=0，标准差=1 的正态分布](../../../../translated_images/zh-CN/normal-histogram.dfae0d67c202137d552d0015fb87581eca263925e512404f3c12d8885315432e.png)
-
-*均值=0，标准差=1 的正态分布*
-
-## 置信区间
-
-当我们谈论棒球运动员的体重时，我们假设存在某个 **随机变量 W**，它对应于所有棒球运动员体重的理想概率分布（即 **总体**）。我们的体重序列对应于所有棒球运动员的一个子集，我们称之为 **样本**。一个有趣的问题是，我们能否知道 W 的分布参数，即总体的均值和方差？
-
-最简单的答案是计算样本的均值和方差。然而，可能发生的情况是我们的随机样本不能准确代表完整总体。因此，谈论 **置信区间** 是有意义的。
-
-> **置信区间** 是根据我们的样本估计总体真实均值的范围，其准确性具有一定的概率（或 **置信水平**）。
-
-假设我们有一个样本 X
-
-1</sub>, ..., X<sub>n</sub> 来自我们的分布。每次从分布中抽取样本时，我们都会得到不同的均值 μ。因此，μ 可以被视为一个随机变量。一个置信度为 p 的 **置信区间** 是一对值 (L<sub>p</sub>, R<sub>p</sub>)，使得 **P**(L<sub>p</sub>≤μ≤R<sub>p</sub>) = p，即测得的均值落在该区间内的概率等于 p。
-
-详细讨论如何计算这些置信区间超出了我们的简短介绍范围。更多细节可以参考 [维基百科](https://en.wikipedia.org/wiki/Confidence_interval)。简而言之，我们定义了计算样本均值相对于总体真实均值的分布，这被称为 **学生分布**。
-
-> **有趣的事实**：学生分布以数学家 William Sealy Gosset 的笔名 "Student" 命名。他在 Guinness 啤酒厂工作，根据一种说法，他的雇主不希望公众知道他们使用统计测试来确定原材料的质量。
-
-如果我们想以置信度 p 估计总体均值 μ，我们需要取学生分布 A 的 *(1-p)/2 分位数*，这可以从表中查得，或者通过统计软件（如 Python、R 等）的内置函数计算。然后 μ 的区间为 X±A*D/√n，其中 X 是样本的均值，D 是标准差。
-
-> **注意**：我们还省略了一个重要概念 [自由度](https://en.wikipedia.org/wiki/Degrees_of_freedom_(statistics)) 的讨论，这在学生分布中非常重要。您可以参考更完整的统计学书籍以更深入地理解这一概念。
-
-关于体重和身高的置信区间计算示例可以在 [配套笔记本](notebook.ipynb) 中找到。
-
-| p   | 体重均值       |
-|-----|---------------|
-| 0.85 | 201.73±0.94  |
-| 0.90 | 201.73±1.08  |
-| 0.95 | 201.73±1.28  |
-
-请注意，置信概率越高，置信区间越宽。
-
-## 假设检验
-
-在我们的棒球运动员数据集中，有不同的球员角色，可以总结如下（查看 [配套笔记本](notebook.ipynb) 以了解如何计算此表）：
-
-| 角色              | 身高       | 体重       | 数量 |
-|-------------------|------------|------------|------|
-| 捕手              | 72.723684  | 204.328947 | 76   |
-| 指定击球手        | 74.222222  | 220.888889 | 18   |
-| 一垒手            | 74.000000  | 213.109091 | 55   |
-| 外野手            | 73.010309  | 199.113402 | 194  |
-| 救援投手          | 74.374603  | 203.517460 | 315  |
-| 二垒手            | 71.362069  | 184.344828 | 58   |
-| 游击手            | 71.903846  | 182.923077 | 52   |
-| 先发投手          | 74.719457  | 205.163636 | 221  |
-| 三垒手            | 73.044444  | 200.955556 | 45   |
-
-我们可以注意到，一垒手的平均身高高于二垒手。因此，我们可能会得出结论：**一垒手比二垒手高**。
-
-> 这个陈述被称为 **假设**，因为我们并不知道这一事实是否确实为真。
-
-然而，我们并不总是能轻易得出这个结论。从上面的讨论中我们知道，每个均值都有一个相关的置信区间，因此这种差异可能只是统计误差。我们需要更正式的方法来检验我们的假设。
-
-让我们分别计算一垒手和二垒手身高的置信区间：
-
-| 置信度 | 一垒手         | 二垒手         |
-|--------|---------------|---------------|
-| 0.85   | 73.62..74.38  | 71.04..71.69  |
-| 0.90   | 73.56..74.44  | 70.99..71.73  |
-| 0.95   | 73.47..74.53  | 70.92..71.81  |
-
-我们可以看到，在任何置信度下，这些区间都没有重叠。这证明了一垒手比二垒手高的假设。
-
-更正式地说，我们要解决的问题是确定 **两个概率分布是否相同**，或者至少它们的参数是否相同。根据分布的不同，我们需要使用不同的检验方法。如果我们知道分布是正态分布，我们可以应用 **[学生 t 检验](https://en.wikipedia.org/wiki/Student%27s_t-test)**。
-
-在学生 t 检验中，我们计算所谓的 **t 值**，它表示均值之间的差异，同时考虑方差。研究表明，t 值服从 **学生分布**，这使我们能够为给定的置信水平 **p** 找到阈值（可以通过计算或查阅数值表获得）。然后我们将 t 值与该阈值进行比较，以接受或拒绝假设。
-
-在 Python 中，我们可以使用 **SciPy** 包，其中包括 `ttest_ind` 函数（以及许多其他有用的统计函数！）。它为我们计算 t 值，并反向查找置信 p 值，这样我们只需查看置信度即可得出结论。
-
-例如，我们比较一垒手和二垒手身高的结果如下：
-```python
-from scipy.stats import ttest_ind
-
-tval, pval = ttest_ind(df.loc[df['Role']=='First_Baseman',['Height']], df.loc[df['Role']=='Designated_Hitter',['Height']],equal_var=False)
-print(f"T-value = {tval[0]:.2f}\nP-value: {pval[0]}")
-```
-```
-T-value = 7.65
-P-value: 9.137321189738925e-12
-```
-在我们的例子中，p 值非常低，这意味着有强有力的证据支持一垒手更高。
-
-此外，还有其他类型的假设我们可能想要检验，例如：
-* 验证某个样本是否服从某种分布。在我们的例子中，我们假设身高服从正态分布，但这需要正式的统计验证。
-* 验证样本的均值是否与某个预定义值相符。
-* 比较多个样本的均值（例如，不同年龄组的幸福感水平差异）。
-
-## 大数法则和中心极限定理
-
-正态分布如此重要的原因之一是 **中心极限定理**。假设我们有一个独立的 N 个值 X<sub>1</sub>, ..., X<sub>N</sub> 的大样本，这些值来自任何分布，均值为 μ，方差为 σ<sup>2</sup>。那么，当 N 足够大时（换句话说，当 N→∞），均值 Σ<sub>i</sub>X<sub>i</sub> 将服从正态分布，均值为 μ，方差为 σ<sup>2</sup>/N。
-
-> 另一种解释中心极限定理的方法是，无论分布如何，当你计算任意随机变量值之和的均值时，最终都会得到正态分布。
-
-从中心极限定理还可以得出，当 N→∞ 时，样本均值等于 μ 的概率趋于 1。这被称为 **大数法则**。
-
-## 协方差与相关性
-
-数据科学的任务之一是发现数据之间的关系。当两个序列在同一时间表现出相似的行为时，我们称它们 **相关**，即它们要么同时上升/下降，要么一个序列上升时另一个下降，反之亦然。换句话说，这两个序列之间似乎存在某种关系。
-
-> 相关性并不一定表示两个序列之间存在因果关系；有时两个变量可能依赖于某个外部原因，或者它们的相关性纯属偶然。然而，强烈的数学相关性是两个变量某种程度上相关的良好指示。
-
-从数学上讲，显示两个随机变量之间关系的主要概念是 **协方差**，其计算公式为：Cov(X,Y) = **E**\[(X-**E**(X))(Y-**E**(Y))\]。我们计算两个变量偏离其均值的偏差，然后将这些偏差相乘。如果两个变量一起偏离，乘积将始终为正值，最终得到正协方差。如果两个变量不同步偏离（即一个低于平均值时另一个高于平均值），我们将始终得到负值，最终得到负协方差。如果偏差无关，它们将大致相加为零。
-
-协方差的绝对值并不能告诉我们相关性的强弱，因为它取决于实际值的大小。为了归一化，我们可以将协方差除以两个变量的标准差，得到 **相关性**。相关性的好处是它始终在 [-1,1] 范围内，其中 1 表示强正相关，-1 表示强负相关，0 表示完全无相关（变量独立）。
-
-**示例**：我们可以计算棒球运动员数据集中体重和身高之间的相关性：
-```python
-print(np.corrcoef(weights,heights))
-```
-结果，我们得到如下 **相关性矩阵**：
-```
-array([[1.        , 0.52959196],
-       [0.52959196, 1.        ]])
-```
-
-> 相关性矩阵 C 可以为任意数量的输入序列 S<sub>1</sub>, ..., S<sub>n</sub> 计算。C<sub>ij</sub> 的值是 S<sub>i</sub> 和 S<sub>j</sub> 之间的相关性，对角线元素始终为 1（即 S<sub>i</sub> 的自相关性）。
-
-在我们的例子中，值 0.53 表明一个人的体重和身高之间存在一定的相关性。我们还可以绘制一个散点图，将一个值与另一个值进行比较，以直观地观察关系：
-
-![体重与身高的关系](../../../../translated_images/zh-CN/weight-height-relationship.3f06bde4ca2aba9974182c4ef037ed602acd0fbbbbe2ca91cefd838a9e66bcf9.png)
-
-> 更多关于相关性和协方差的示例可以在 [配套笔记本](notebook.ipynb) 中找到。
-
-## 总结
-
-在本节中，我们学习了：
-
-* 数据的基本统计属性，例如均值、方差、众数和四分位数
-* 随机变量的不同分布，包括正态分布
-* 如何找到不同属性之间的相关性
-* 如何使用数学和统计学的严谨工具来验证一些假设
-* 如何根据数据样本计算随机变量的置信区间
-
-虽然这绝不是概率和统计学中所有主题的详尽列表，但它应该足以为您进入本课程打下良好的基础。
-
-## 🚀 挑战
-
-使用笔记本中的示例代码测试以下假设：
-1. 一垒手比二垒手年龄更大
-2. 一垒手比三垒手更高
-3. 游击手比二垒手更高
-
-## [课后测验](https://ff-quizzes.netlify.app/en/ds/quiz/7)
-
-## 复习与自学
-
-概率和统计学是一个非常广泛的主题，它值得开设一门独立的课程。如果您有兴趣深入学习理论，可以继续阅读以下书籍：
-
-1. [纽约大学 Carlos Fernandez-Granda](https://cims.nyu.edu/~cfgranda/) 的优秀讲义 [数据科学的概率与统计](https://cims.nyu.edu/~cfgranda/pages/stuff/probability_stats_for_DS.pdf)（在线可用）
-2. [Peter 和 Andrew Bruce. 数据科学实用统计](https://www.oreilly.com/library/view/practical-statistics-for/9781491952955/) [[R 示例代码](https://github.com/andrewgbruce/statistics-for-data-scientists)]。
-3. [James D. Miller. 数据科学统计学](https://www.packtpub.com/product/statistics-for-data-science/9781788290678) [[R 示例代码](https://github.com/PacktPublishing/Statistics-for-Data-Science)]
-
-## 作业
-
-[小型糖尿病研究](assignment.md)
-
-## 致谢
-
-本课程由 [Dmitry Soshnikov](http://soshnikov.com) 倾情创作。
-
----
-
-**免责声明**：  
-本文档使用AI翻译服务[Co-op Translator](https://github.com/Azure/co-op-translator)进行翻译。尽管我们努力确保准确性，但请注意，自动翻译可能包含错误或不准确之处。应以原始语言的文档作为权威来源。对于关键信息，建议使用专业人工翻译。对于因使用本翻译而引起的任何误解或误读，我们概不负责。
\ No newline at end of file
diff --git a/translations/zh/1-Introduction/04-stats-and-probability/assignment.ipynb b/translations/zh/1-Introduction/04-stats-and-probability/assignment.ipynb
deleted file mode 100644
index 90e8436d..00000000
--- a/translations/zh/1-Introduction/04-stats-and-probability/assignment.ipynb
+++ /dev/null
@@ -1,264 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "source": [
-    "## 概率与统计简介\n",
-    "## 作业\n",
-    "\n",
-    "在本次作业中，我们将使用[此处](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html)提供的糖尿病患者数据集。\n"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "source": [
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "\n",
-    "df = pd.read_csv(\"../../data/diabetes.tsv\",sep='\\t')\n",
-    "df.head()"
-   ],
-   "outputs": [
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "   AGE  SEX   BMI     BP   S1     S2    S3   S4      S5  S6    Y\n",
-       "0   59    2  32.1  101.0  157   93.2  38.0  4.0  4.8598  87  151\n",
-       "1   48    1  21.6   87.0  183  103.2  70.0  3.0  3.8918  69   75\n",
-       "2   72    2  30.5   93.0  156   93.6  41.0  4.0  4.6728  85  141\n",
-       "3   24    1  25.3   84.0  198  131.4  40.0  5.0  4.8903  89  206\n",
-       "4   50    1  23.0  101.0  192  125.4  52.0  4.0  4.2905  80  135"
-      ],
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>AGE</th>\n",
-       "      <th>SEX</th>\n",
-       "      <th>BMI</th>\n",
-       "      <th>BP</th>\n",
-       "      <th>S1</th>\n",
-       "      <th>S2</th>\n",
-       "      <th>S3</th>\n",
-       "      <th>S4</th>\n",
-       "      <th>S5</th>\n",
-       "      <th>S6</th>\n",
-       "      <th>Y</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>59</td>\n",
-       "      <td>2</td>\n",
-       "      <td>32.1</td>\n",
-       "      <td>101.0</td>\n",
-       "      <td>157</td>\n",
-       "      <td>93.2</td>\n",
-       "      <td>38.0</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>4.8598</td>\n",
-       "      <td>87</td>\n",
-       "      <td>151</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>48</td>\n",
-       "      <td>1</td>\n",
-       "      <td>21.6</td>\n",
-       "      <td>87.0</td>\n",
-       "      <td>183</td>\n",
-       "      <td>103.2</td>\n",
-       "      <td>70.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>3.8918</td>\n",
-       "      <td>69</td>\n",
-       "      <td>75</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>72</td>\n",
-       "      <td>2</td>\n",
-       "      <td>30.5</td>\n",
-       "      <td>93.0</td>\n",
-       "      <td>156</td>\n",
-       "      <td>93.6</td>\n",
-       "      <td>41.0</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>4.6728</td>\n",
-       "      <td>85</td>\n",
-       "      <td>141</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>24</td>\n",
-       "      <td>1</td>\n",
-       "      <td>25.3</td>\n",
-       "      <td>84.0</td>\n",
-       "      <td>198</td>\n",
-       "      <td>131.4</td>\n",
-       "      <td>40.0</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>4.8903</td>\n",
-       "      <td>89</td>\n",
-       "      <td>206</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>50</td>\n",
-       "      <td>1</td>\n",
-       "      <td>23.0</td>\n",
-       "      <td>101.0</td>\n",
-       "      <td>192</td>\n",
-       "      <td>125.4</td>\n",
-       "      <td>52.0</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>4.2905</td>\n",
-       "      <td>80</td>\n",
-       "      <td>135</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 13
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "在这个数据集中，列的含义如下：\n",
-    "* 年龄和性别不言自明\n",
-    "* BMI是身体质量指数\n",
-    "* BP是平均血压\n",
-    "* S1到S6是不同的血液测量值\n",
-    "* Y是疾病在一年内进展的定性指标\n",
-    "\n",
-    "让我们使用概率和统计方法来研究这个数据集。\n",
-    "\n",
-    "### 任务 1：计算所有值的均值和方差\n"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [],
-   "outputs": [],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "### 任务2：根据性别绘制BMI、BP和Y的箱线图\n"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [],
-   "outputs": [],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "### 任务3：年龄、性别、BMI 和 Y 变量的分布是什么？\n"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [],
-   "outputs": [],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "### 任务 4：测试不同变量与疾病进展（Y）之间的相关性\n",
-    "\n",
-    "> **提示** 相关性矩阵可以为您提供最有用的信息，帮助判断哪些值是相关的。\n"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "### 任务5：检验糖尿病进展程度在男性和女性之间是否存在差异\n"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n---\n\n**免责声明**：  \n本文档使用AI翻译服务[Co-op Translator](https://github.com/Azure/co-op-translator)进行翻译。尽管我们努力确保准确性，但请注意，自动翻译可能包含错误或不准确之处。应以原始语言的文档作为权威来源。对于关键信息，建议使用专业人工翻译。因使用本翻译而导致的任何误解或误读，我们概不负责。\n"
-   ]
-  }
- ],
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-  "orig_nbformat": 4,
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-   "name": "python",
-   "version": "3.8.8",
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-  },
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-   "language_code": "zh"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
\ No newline at end of file
diff --git a/translations/zh/1-Introduction/04-stats-and-probability/assignment.md b/translations/zh/1-Introduction/04-stats-and-probability/assignment.md
deleted file mode 100644
index 0325d181..00000000
--- a/translations/zh/1-Introduction/04-stats-and-probability/assignment.md
+++ /dev/null
@@ -1,38 +0,0 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
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-  "source_file": "1-Introduction/04-stats-and-probability/assignment.md",
-  "language_code": "zh"
-}
--->
-# 小型糖尿病研究
-
-在本次作业中，我们将使用一个小型糖尿病患者数据集，数据来源于[这里](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html)。
-
-|   | 年龄 (AGE) | 性别 (SEX) | 体重指数 (BMI) | 血压 (BP) | S1 | S2 | S3 | S4 | S5 | S6 | Y（疾病进展） |
-|---|-----|-----|-----|----|----|----|----|----|----|----|----|
-| 0 | 59 | 2 | 32.1 | 101. | 157 | 93.2 | 38.0 | 4. | 4.8598 | 87 | 151 |
-| 1 | 48 | 1 | 21.6 | 87.0 | 183 | 103.2 | 70. | 3. | 3.8918 | 69 | 75 |
-| 2 | 72 | 2 | 30.5 | 93.0 | 156 | 93.6 | 41.0 | 4.0 | 4. | 85 | 141 |
-| ... | ... | ... | ... | ...| ...| ...| ...| ...| ...| ...| ... |
-
-## 说明
-
-* 在 jupyter notebook 环境中打开[作业笔记本](../../../../1-Introduction/04-stats-and-probability/assignment.ipynb)
-* 完成笔记本中列出的所有任务，包括：
-   * [ ] 计算所有值的均值和方差
-   * [ ] 根据性别绘制 BMI、BP 和 Y 的箱线图
-   * [ ] 分析年龄、性别、BMI 和 Y 变量的分布情况
-   * [ ] 测试不同变量与疾病进展 (Y) 之间的相关性
-   * [ ] 检验糖尿病进展程度在男性和女性之间是否存在显著差异
-
-## 评分标准
-
-优秀 | 合格 | 需要改进
---- | --- | -- |
-所有要求的任务均已完成，且有图表展示和详细解释 | 大部分任务已完成，但缺少图表或计算结果的解释 | 仅完成了基本任务，如均值/方差计算和基础图表绘制，未对数据得出任何结论
-
-**免责声明**：  
-本文档使用AI翻译服务 [Co-op Translator](https://github.com/Azure/co-op-translator) 进行翻译。尽管我们努力确保翻译的准确性，但请注意，自动翻译可能包含错误或不准确之处。原始语言的文档应被视为权威来源。对于关键信息，建议使用专业人工翻译。我们不对因使用此翻译而产生的任何误解或误读承担责任。
\ No newline at end of file
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@@ -1,581 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 概率与统计简介\n",
-    "在本笔记本中，我们将练习一些之前讨论过的概念。许多概率与统计的概念在用于数据处理的主要Python库中都有良好的体现，比如 `numpy` 和 `pandas`。\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import random\n",
-    "import matplotlib.pyplot as plt"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 随机变量和分布\n",
-    "让我们从 0 到 9 的均匀分布中抽取一个包含 30 个值的样本。我们还将计算均值和方差。\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sample = [ random.randint(0,10) for _ in range(30) ]\n",
-    "print(f\"Sample: {sample}\")\n",
-    "print(f\"Mean = {np.mean(sample)}\")\n",
-    "print(f\"Variance = {np.var(sample)}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "为了直观估计样本中有多少不同的值，我们可以绘制**直方图**：\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plt.hist(sample)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 分析真实数据\n",
-    "\n",
-    "均值和方差在分析现实世界数据时非常重要。让我们加载关于棒球运动员的数据，来自 [SOCR MLB Height/Weight Data](http://wiki.stat.ucla.edu/socr/index.php/SOCR_Data_MLB_HeightsWeights)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df = pd.read_csv(\"../../data/SOCR_MLB.tsv\",sep='\\t', header=None, names=['Name','Team','Role','Weight','Height','Age'])\n",
-    "df\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "> 我们这里使用了一个名为[**Pandas**](https://pandas.pydata.org/)的软件包来进行数据分析。我们将在本课程后面讨论更多关于Pandas和在Python中处理数据的内容。\n",
-    "\n",
-    "让我们计算年龄、身高和体重的平均值：\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df[['Age','Height','Weight']].mean()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "现在让我们关注身高，并计算标准差和方差：\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(list(df['Height'])[:20])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "mean = df['Height'].mean()\n",
-    "var = df['Height'].var()\n",
-    "std = df['Height'].std()\n",
-    "print(f\"Mean = {mean}\\nVariance = {var}\\nStandard Deviation = {std}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "除了平均值外，查看中位数和四分位数也是有意义的。它们可以通过**箱线图**来可视化：\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plt.figure(figsize=(10,2))\n",
-    "plt.boxplot(df['Height'].ffill(), vert=False, showmeans=True)\n",
-    "plt.grid(color='gray', linestyle='dotted')\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "我们还可以制作数据集子集的箱线图，例如按球员角色分组。\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df.boxplot(column='Height', by='Role', figsize=(10,8))\n",
-    "plt.xticks(rotation='vertical')\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "> **注意**：该图表表明，平均来看，一垒手的身高高于二垒手的身高。稍后我们将学习如何更正式地检验这个假设，以及如何证明我们的数据在统计上有显著性以支持该结论。\n",
-    "\n",
-    "年龄、身高和体重都是连续随机变量。你认为它们的分布是什么样的？发现答案的一个好方法是绘制数值的直方图：\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df['Weight'].hist(bins=15, figsize=(10,6))\n",
-    "plt.suptitle('Weight distribution of MLB Players')\n",
-    "plt.xlabel('Weight')\n",
-    "plt.ylabel('Count')\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 正态分布\n",
-    "\n",
-    "让我们创建一个人工权重样本，该样本服从与我们的真实数据相同均值和方差的正态分布：\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "generated = np.random.normal(mean, std, 1000)\n",
-    "generated[:20]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plt.figure(figsize=(10,6))\n",
-    "plt.hist(generated, bins=15)\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plt.figure(figsize=(10,6))\n",
-    "plt.hist(np.random.normal(0,1,50000), bins=300)\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "由于现实生活中的大多数数值服从正态分布，因此我们不应该使用均匀随机数生成器来生成样本数据。以下是尝试使用均匀分布（由 `np.random.rand` 生成）生成体重时的情况：\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wrong_sample = np.random.rand(1000)*2*std+mean-std\n",
-    "plt.figure(figsize=(10,6))\n",
-    "plt.hist(wrong_sample)\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 置信区间\n",
-    "\n",
-    "现在让我们计算棒球运动员的体重和身高的置信区间。我们将使用[这个stackoverflow讨论](https://stackoverflow.com/questions/15033511/compute-a-confidence-interval-from-sample-data)中的代码：\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import scipy.stats\n",
-    "\n",
-    "def mean_confidence_interval(data, confidence=0.95):\n",
-    "    a = 1.0 * np.array(data)\n",
-    "    n = len(a)\n",
-    "    m, se = np.mean(a), scipy.stats.sem(a)\n",
-    "    h = se * scipy.stats.t.ppf((1 + confidence) / 2., n-1)\n",
-    "    return m, h\n",
-    "\n",
-    "for p in [0.85, 0.9, 0.95]:\n",
-    "    m, h = mean_confidence_interval(df['Weight'].fillna(method='pad'),p)\n",
-    "    print(f\"p={p:.2f}, mean = {m:.2f} ± {h:.2f}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 假设检验\n",
-    "\n",
-    "让我们探索棒球运动员数据集中不同的角色：\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df.groupby('Role').agg({ 'Weight' : 'mean', 'Height' : 'mean', 'Age' : 'count'}).rename(columns={ 'Age' : 'Count'})"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "让我们来检验一项假设：一垒手比二垒手更高。最简单的方法是检验置信区间：\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for p in [0.85,0.9,0.95]:\n",
-    "    m1, h1 = mean_confidence_interval(df.loc[df['Role']=='First_Baseman',['Height']],p)\n",
-    "    m2, h2 = mean_confidence_interval(df.loc[df['Role']=='Second_Baseman',['Height']],p)\n",
-    "    print(f'Conf={p:.2f}, 1st basemen height: {m1-h1[0]:.2f}..{m1+h1[0]:.2f}, 2nd basemen height: {m2-h2[0]:.2f}..{m2+h2[0]:.2f}')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "我们可以看到这些区间没有重叠。\n",
-    "\n",
-    "一种统计上更正确的验证假设的方法是使用**Student t检验**：\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from scipy.stats import ttest_ind\n",
-    "\n",
-    "tval, pval = ttest_ind(df.loc[df['Role']=='First_Baseman',['Height']], df.loc[df['Role']=='Second_Baseman',['Height']],equal_var=False)\n",
-    "print(f\"T-value = {tval[0]:.2f}\\nP-value: {pval[0]}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`ttest_ind` 函数返回的两个值是：\n",
-    "* p 值可以被视为两个分布具有相同均值的概率。在我们的例子中，它非常低，这意味着有强有力的证据支持一垒手更高。\n",
-    "* t 值是用于 t 检验的标准化均值差的中间值，并且它会与给定置信度的阈值进行比较。\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 使用中心极限定理模拟正态分布\n",
-    "\n",
-    "Python 中的伪随机生成器设计为产生均匀分布。如果我们想创建一个正态分布的生成器，可以使用中心极限定理。为了获得一个正态分布的值，我们只需计算一个均匀生成样本的平均值。\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def normal_random(sample_size=100):\n",
-    "    sample = [random.uniform(0,1) for _ in range(sample_size) ]\n",
-    "    return sum(sample)/sample_size\n",
-    "\n",
-    "sample = [normal_random() for _ in range(100)]\n",
-    "plt.figure(figsize=(10,6))\n",
-    "plt.hist(sample)\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 相关性与邪恶棒球公司\n",
-    "\n",
-    "相关性使我们能够发现数据序列之间的关系。在我们的玩具示例中，假设有一家邪恶的棒球公司根据球员的身高支付薪水——球员越高，拿到的钱越多。假设有一个基本工资为1000美元，并根据身高额外获得0到100美元的奖金。我们将采用MLB的真实球员数据，计算他们的虚拟薪水：\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "heights = df['Height'].fillna(method='pad')\n",
-    "salaries = 1000+(heights-heights.min())/(heights.max()-heights.mean())*100\n",
-    "print(list(zip(heights, salaries))[:10])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "现在让我们计算这些序列的协方差和相关系数。`np.cov` 将给我们所谓的**协方差矩阵**，这是协方差在多变量上的扩展。协方差矩阵 $M$ 的元素 $M_{ij}$ 是输入变量 $X_i$ 和 $X_j$ 之间的协方差，主对角线上的值 $M_{ii}$ 是 $X_i$ 的方差。类似地，`np.corrcoef` 会给我们**相关矩阵**。\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(f\"Covariance matrix:\\n{np.cov(heights, salaries)}\")\n",
-    "print(f\"Covariance = {np.cov(heights, salaries)[0,1]}\")\n",
-    "print(f\"Correlation = {np.corrcoef(heights, salaries)[0,1]}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "相关系数等于1表示两个变量之间存在强烈的**线性关系**。我们可以通过将一个值与另一个值绘制在图上直观地看到线性关系：\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plt.figure(figsize=(10,6))\n",
-    "plt.scatter(heights,salaries)\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "让我们看看如果关系不是线性的会发生什么。假设我们的公司决定隐藏身高和薪水之间明显的线性依赖关系，并在公式中引入一些非线性，比如 `sin`：\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "salaries = 1000+np.sin((heights-heights.min())/(heights.max()-heights.mean()))*100\n",
-    "print(f\"Correlation = {np.corrcoef(heights, salaries)[0,1]}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "在这种情况下，相关性略小，但仍然相当高。现在，为了让关系更不明显，我们可能想通过向工资中添加一些随机变量来增加额外的随机性。让我们看看会发生什么：\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "salaries = 1000+np.sin((heights-heights.min())/(heights.max()-heights.mean()))*100+np.random.random(size=len(heights))*20-10\n",
-    "print(f\"Correlation = {np.corrcoef(heights, salaries)[0,1]}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plt.figure(figsize=(10,6))\n",
-    "plt.scatter(heights, salaries)\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "> 你能猜到为什么这些点会排列成这样的垂直线吗？\n",
-    "\n",
-    "我们已经观察到像工资这样的人为设计的概念与观察变量*身高*之间的相关性。让我们也看看两个观察变量，例如身高和体重，是否也相关：\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "np.corrcoef(df['Height'].ffill(),df['Weight'])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "不幸的是，我们没有得到任何结果——只有一些奇怪的 `nan` 值。这是因为我们的序列中有些值未定义，表示为 `nan`，这导致运算结果也未定义。通过查看矩阵我们可以看到，`Weight` 是有问题的列，因为计算了 `Height` 值的自相关。\n",
-    "\n",
-    "> 这个例子展示了**数据准备**和**清理**的重要性。没有适当的数据，我们无法计算任何东西。\n",
-    "\n",
-    "让我们使用 `fillna` 方法填充缺失值，并计算相关性：\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "np.corrcoef(df['Height'].fillna(method='pad'), df['Weight'])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "确实存在相关性，但没有我们人工示例中那么强烈。实际上，如果我们查看一个值与另一个值的散点图，关系会明显不那么明显：\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plt.figure(figsize=(10,6))\n",
-    "plt.scatter(df['Weight'],df['Height'])\n",
-    "plt.xlabel('Weight')\n",
-    "plt.ylabel('Height')\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Conclusion\n",
-    "\n",
-    "在本笔记本中，我们学习了如何对数据执行基本操作以计算统计函数。我们现在知道如何使用完善的数学和统计工具来验证某些假设，以及如何根据数据样本计算任意变量的置信区间。\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "---\n\n<!-- CO-OP TRANSLATOR DISCLAIMER START -->\n**免责声明**：  \n本文件由AI翻译服务[Co-op Translator](https://github.com/Azure/co-op-translator)翻译。我们尽力确保译文的准确性，但请注意自动翻译可能存在错误或不准确之处。原始文件的母语版本应被视为权威来源。对于关键信息，建议采用专业人工翻译。因使用本翻译而产生的任何误解或误释，我们概不负责。\n<!-- CO-OP TRANSLATOR DISCLAIMER END -->\n"
-   ]
-  }
- ],
- "metadata": {
-  "interpreter": {
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-   "source_file": "1-Introduction/04-stats-and-probability/notebook.ipynb",
-   "language_code": "zh"
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-}
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diff --git a/translations/zh/1-Introduction/04-stats-and-probability/solution/assignment.ipynb b/translations/zh/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
deleted file mode 100644
index 37ed8e9b..00000000
--- a/translations/zh/1-Introduction/04-stats-and-probability/solution/assignment.ipynb
+++ /dev/null
@@ -1,955 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "source": [
-    "## 概率与统计简介\n",
-    "## 作业\n",
-    "\n",
-    "在本次作业中，我们将使用[此处](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html)提供的糖尿病患者数据集。\n"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "source": [
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "df = pd.read_csv(\"../../../data/diabetes.tsv\",sep='\\t')\n",
-    "df.head()"
-   ],
-   "outputs": [
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "   AGE  SEX   BMI     BP   S1     S2    S3   S4      S5  S6    Y\n",
-       "0   59    2  32.1  101.0  157   93.2  38.0  4.0  4.8598  87  151\n",
-       "1   48    1  21.6   87.0  183  103.2  70.0  3.0  3.8918  69   75\n",
-       "2   72    2  30.5   93.0  156   93.6  41.0  4.0  4.6728  85  141\n",
-       "3   24    1  25.3   84.0  198  131.4  40.0  5.0  4.8903  89  206\n",
-       "4   50    1  23.0  101.0  192  125.4  52.0  4.0  4.2905  80  135"
-      ],
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>AGE</th>\n",
-       "      <th>SEX</th>\n",
-       "      <th>BMI</th>\n",
-       "      <th>BP</th>\n",
-       "      <th>S1</th>\n",
-       "      <th>S2</th>\n",
-       "      <th>S3</th>\n",
-       "      <th>S4</th>\n",
-       "      <th>S5</th>\n",
-       "      <th>S6</th>\n",
-       "      <th>Y</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>59</td>\n",
-       "      <td>2</td>\n",
-       "      <td>32.1</td>\n",
-       "      <td>101.0</td>\n",
-       "      <td>157</td>\n",
-       "      <td>93.2</td>\n",
-       "      <td>38.0</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>4.8598</td>\n",
-       "      <td>87</td>\n",
-       "      <td>151</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>48</td>\n",
-       "      <td>1</td>\n",
-       "      <td>21.6</td>\n",
-       "      <td>87.0</td>\n",
-       "      <td>183</td>\n",
-       "      <td>103.2</td>\n",
-       "      <td>70.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>3.8918</td>\n",
-       "      <td>69</td>\n",
-       "      <td>75</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>72</td>\n",
-       "      <td>2</td>\n",
-       "      <td>30.5</td>\n",
-       "      <td>93.0</td>\n",
-       "      <td>156</td>\n",
-       "      <td>93.6</td>\n",
-       "      <td>41.0</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>4.6728</td>\n",
-       "      <td>85</td>\n",
-       "      <td>141</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>24</td>\n",
-       "      <td>1</td>\n",
-       "      <td>25.3</td>\n",
-       "      <td>84.0</td>\n",
-       "      <td>198</td>\n",
-       "      <td>131.4</td>\n",
-       "      <td>40.0</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>4.8903</td>\n",
-       "      <td>89</td>\n",
-       "      <td>206</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>50</td>\n",
-       "      <td>1</td>\n",
-       "      <td>23.0</td>\n",
-       "      <td>101.0</td>\n",
-       "      <td>192</td>\n",
-       "      <td>125.4</td>\n",
-       "      <td>52.0</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>4.2905</td>\n",
-       "      <td>80</td>\n",
-       "      <td>135</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 13
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "在此数据集中，列的含义如下：  \n",
-    "* Age 和 sex 不言自明  \n",
-    "* BMI 是身体质量指数  \n",
-    "* BP 是平均血压  \n",
-    "* S1 到 S6 是不同的血液测量值  \n",
-    "* Y 是疾病在一年内进展的定性指标  \n",
-    "\n",
-    "让我们使用概率和统计的方法来研究这个数据集。\n",
-    "\n",
-    "### 任务 1：计算所有值的均值和方差\n"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "source": [
-    "df.describe()"
-   ],
-   "outputs": [
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "              AGE         SEX         BMI          BP          S1          S2  \\\n",
-       "count  442.000000  442.000000  442.000000  442.000000  442.000000  442.000000   \n",
-       "mean    48.518100    1.468326   26.375792   94.647014  189.140271  115.439140   \n",
-       "std     13.109028    0.499561    4.418122   13.831283   34.608052   30.413081   \n",
-       "min     19.000000    1.000000   18.000000   62.000000   97.000000   41.600000   \n",
-       "25%     38.250000    1.000000   23.200000   84.000000  164.250000   96.050000   \n",
-       "50%     50.000000    1.000000   25.700000   93.000000  186.000000  113.000000   \n",
-       "75%     59.000000    2.000000   29.275000  105.000000  209.750000  134.500000   \n",
-       "max     79.000000    2.000000   42.200000  133.000000  301.000000  242.400000   \n",
-       "\n",
-       "               S3          S4          S5          S6           Y  \n",
-       "count  442.000000  442.000000  442.000000  442.000000  442.000000  \n",
-       "mean    49.788462    4.070249    4.641411   91.260181  152.133484  \n",
-       "std     12.934202    1.290450    0.522391   11.496335   77.093005  \n",
-       "min     22.000000    2.000000    3.258100   58.000000   25.000000  \n",
-       "25%     40.250000    3.000000    4.276700   83.250000   87.000000  \n",
-       "50%     48.000000    4.000000    4.620050   91.000000  140.500000  \n",
-       "75%     57.750000    5.000000    4.997200   98.000000  211.500000  \n",
-       "max     99.000000    9.090000    6.107000  124.000000  346.000000  "
-      ],
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>AGE</th>\n",
-       "      <th>SEX</th>\n",
-       "      <th>BMI</th>\n",
-       "      <th>BP</th>\n",
-       "      <th>S1</th>\n",
-       "      <th>S2</th>\n",
-       "      <th>S3</th>\n",
-       "      <th>S4</th>\n",
-       "      <th>S5</th>\n",
-       "      <th>S6</th>\n",
-       "      <th>Y</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>count</th>\n",
-       "      <td>442.000000</td>\n",
-       "      <td>442.000000</td>\n",
-       "      <td>442.000000</td>\n",
-       "      <td>442.000000</td>\n",
-       "      <td>442.000000</td>\n",
-       "      <td>442.000000</td>\n",
-       "      <td>442.000000</td>\n",
-       "      <td>442.000000</td>\n",
-       "      <td>442.000000</td>\n",
-       "      <td>442.000000</td>\n",
-       "      <td>442.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mean</th>\n",
-       "      <td>48.518100</td>\n",
-       "      <td>1.468326</td>\n",
-       "      <td>26.375792</td>\n",
-       "      <td>94.647014</td>\n",
-       "      <td>189.140271</td>\n",
-       "      <td>115.439140</td>\n",
-       "      <td>49.788462</td>\n",
-       "      <td>4.070249</td>\n",
-       "      <td>4.641411</td>\n",
-       "      <td>91.260181</td>\n",
-       "      <td>152.133484</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>std</th>\n",
-       "      <td>13.109028</td>\n",
-       "      <td>0.499561</td>\n",
-       "      <td>4.418122</td>\n",
-       "      <td>13.831283</td>\n",
-       "      <td>34.608052</td>\n",
-       "      <td>30.413081</td>\n",
-       "      <td>12.934202</td>\n",
-       "      <td>1.290450</td>\n",
-       "      <td>0.522391</td>\n",
-       "      <td>11.496335</td>\n",
-       "      <td>77.093005</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>min</th>\n",
-       "      <td>19.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>18.000000</td>\n",
-       "      <td>62.000000</td>\n",
-       "      <td>97.000000</td>\n",
-       "      <td>41.600000</td>\n",
-       "      <td>22.000000</td>\n",
-       "      <td>2.000000</td>\n",
-       "      <td>3.258100</td>\n",
-       "      <td>58.000000</td>\n",
-       "      <td>25.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25%</th>\n",
-       "      <td>38.250000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>23.200000</td>\n",
-       "      <td>84.000000</td>\n",
-       "      <td>164.250000</td>\n",
-       "      <td>96.050000</td>\n",
-       "      <td>40.250000</td>\n",
-       "      <td>3.000000</td>\n",
-       "      <td>4.276700</td>\n",
-       "      <td>83.250000</td>\n",
-       "      <td>87.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50%</th>\n",
-       "      <td>50.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>25.700000</td>\n",
-       "      <td>93.000000</td>\n",
-       "      <td>186.000000</td>\n",
-       "      <td>113.000000</td>\n",
-       "      <td>48.000000</td>\n",
-       "      <td>4.000000</td>\n",
-       "      <td>4.620050</td>\n",
-       "      <td>91.000000</td>\n",
-       "      <td>140.500000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>75%</th>\n",
-       "      <td>59.000000</td>\n",
-       "      <td>2.000000</td>\n",
-       "      <td>29.275000</td>\n",
-       "      <td>105.000000</td>\n",
-       "      <td>209.750000</td>\n",
-       "      <td>134.500000</td>\n",
-       "      <td>57.750000</td>\n",
-       "      <td>5.000000</td>\n",
-       "      <td>4.997200</td>\n",
-       "      <td>98.000000</td>\n",
-       "      <td>211.500000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>max</th>\n",
-       "      <td>79.000000</td>\n",
-       "      <td>2.000000</td>\n",
-       "      <td>42.200000</td>\n",
-       "      <td>133.000000</td>\n",
-       "      <td>301.000000</td>\n",
-       "      <td>242.400000</td>\n",
-       "      <td>99.000000</td>\n",
-       "      <td>9.090000</td>\n",
-       "      <td>6.107000</td>\n",
-       "      <td>124.000000</td>\n",
-       "      <td>346.000000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 5
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "source": [
-    "# Another way\n",
-    "pd.DataFrame([df.mean(),df.var()],index=['Mean','Variance']).head()"
-   ],
-   "outputs": [
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "                AGE       SEX        BMI          BP           S1          S2  \\\n",
-       "Mean       48.51810  1.468326  26.375792   94.647014   189.140271  115.439140   \n",
-       "Variance  171.84661  0.249561  19.519798  191.304401  1197.717241  924.955494   \n",
-       "\n",
-       "                  S3        S4        S5          S6            Y  \n",
-       "Mean       49.788462  4.070249  4.641411   91.260181   152.133484  \n",
-       "Variance  167.293585  1.665261  0.272892  132.165712  5943.331348  "
-      ],
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
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-       "\n",
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-       "\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>AGE</th>\n",
-       "      <th>SEX</th>\n",
-       "      <th>BMI</th>\n",
-       "      <th>BP</th>\n",
-       "      <th>S1</th>\n",
-       "      <th>S2</th>\n",
-       "      <th>S3</th>\n",
-       "      <th>S4</th>\n",
-       "      <th>S5</th>\n",
-       "      <th>S6</th>\n",
-       "      <th>Y</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Mean</th>\n",
-       "      <td>48.51810</td>\n",
-       "      <td>1.468326</td>\n",
-       "      <td>26.375792</td>\n",
-       "      <td>94.647014</td>\n",
-       "      <td>189.140271</td>\n",
-       "      <td>115.439140</td>\n",
-       "      <td>49.788462</td>\n",
-       "      <td>4.070249</td>\n",
-       "      <td>4.641411</td>\n",
-       "      <td>91.260181</td>\n",
-       "      <td>152.133484</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Variance</th>\n",
-       "      <td>171.84661</td>\n",
-       "      <td>0.249561</td>\n",
-       "      <td>19.519798</td>\n",
-       "      <td>191.304401</td>\n",
-       "      <td>1197.717241</td>\n",
-       "      <td>924.955494</td>\n",
-       "      <td>167.293585</td>\n",
-       "      <td>1.665261</td>\n",
-       "      <td>0.272892</td>\n",
-       "      <td>132.165712</td>\n",
-       "      <td>5943.331348</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 8
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "source": [
-    "# Or, more simply, for the mean (variance can be done similarly)\n",
-    "df.mean()"
-   ],
-   "outputs": [
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "AGE     48.518100\n",
-       "SEX      1.468326\n",
-       "BMI     26.375792\n",
-       "BP      94.647014\n",
-       "S1     189.140271\n",
-       "S2     115.439140\n",
-       "S3      49.788462\n",
-       "S4       4.070249\n",
-       "S5       4.641411\n",
-       "S6      91.260181\n",
-       "Y      152.133484\n",
-       "dtype: float64"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 9
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "### 任务2：根据性别绘制BMI、BP和Y的箱线图\n"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "source": [
-    "for col in ['BMI','BP','Y']:\n",
-    "    df.boxplot(column=col,by='SEX')\n",
-    "plt.show()"
-   ],
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ],
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-     },
-     "metadata": {}
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "### 任务3：年龄、性别、BMI 和 Y 变量的分布是什么？\n"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "source": [
-    "for col in ['AGE','SEX','BMI','Y']:\n",
-    "    df[col].hist()\n",
-    "    plt.show()"
-   ],
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ],
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"
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-      "text/plain": [
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"
-     },
-     "metadata": {}
-    },
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
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"
-     },
-     "metadata": {}
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "结论：\n",
-    "* 年龄 - 正常  \n",
-    "* 性别 - 一致  \n",
-    "* BMI，Y - 难以判断  \n"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "### 任务 4：测试不同变量与疾病进展 (Y) 之间的相关性\n",
-    "\n",
-    "> **提示** 相关性矩阵可以为您提供最有用的信息，帮助判断哪些值是相关联的。\n"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "source": [
-    "df.corr()"
-   ],
-   "outputs": [
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "          AGE       SEX       BMI        BP        S1        S2        S3  \\\n",
-       "AGE  1.000000  0.173737  0.185085  0.335428  0.260061  0.219243 -0.075181   \n",
-       "SEX  0.173737  1.000000  0.088161  0.241010  0.035277  0.142637 -0.379090   \n",
-       "BMI  0.185085  0.088161  1.000000  0.395411  0.249777  0.261170 -0.366811   \n",
-       "BP   0.335428  0.241010  0.395411  1.000000  0.242464  0.185548 -0.178762   \n",
-       "S1   0.260061  0.035277  0.249777  0.242464  1.000000  0.896663  0.051519   \n",
-       "S2   0.219243  0.142637  0.261170  0.185548  0.896663  1.000000 -0.196455   \n",
-       "S3  -0.075181 -0.379090 -0.366811 -0.178762  0.051519 -0.196455  1.000000   \n",
-       "S4   0.203841  0.332115  0.413807  0.257650  0.542207  0.659817 -0.738493   \n",
-       "S5   0.270774  0.149916  0.446157  0.393480  0.515503  0.318357 -0.398577   \n",
-       "S6   0.301731  0.208133  0.388680  0.390430  0.325717  0.290600 -0.273697   \n",
-       "Y    0.187889  0.043062  0.586450  0.441482  0.212022  0.174054 -0.394789   \n",
-       "\n",
-       "           S4        S5        S6         Y  \n",
-       "AGE  0.203841  0.270774  0.301731  0.187889  \n",
-       "SEX  0.332115  0.149916  0.208133  0.043062  \n",
-       "BMI  0.413807  0.446157  0.388680  0.586450  \n",
-       "BP   0.257650  0.393480  0.390430  0.441482  \n",
-       "S1   0.542207  0.515503  0.325717  0.212022  \n",
-       "S2   0.659817  0.318357  0.290600  0.174054  \n",
-       "S3  -0.738493 -0.398577 -0.273697 -0.394789  \n",
-       "S4   1.000000  0.617859  0.417212  0.430453  \n",
-       "S5   0.617859  1.000000  0.464669  0.565883  \n",
-       "S6   0.417212  0.464669  1.000000  0.382483  \n",
-       "Y    0.430453  0.565883  0.382483  1.000000  "
-      ],
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>AGE</th>\n",
-       "      <th>SEX</th>\n",
-       "      <th>BMI</th>\n",
-       "      <th>BP</th>\n",
-       "      <th>S1</th>\n",
-       "      <th>S2</th>\n",
-       "      <th>S3</th>\n",
-       "      <th>S4</th>\n",
-       "      <th>S5</th>\n",
-       "      <th>S6</th>\n",
-       "      <th>Y</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>AGE</th>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.173737</td>\n",
-       "      <td>0.185085</td>\n",
-       "      <td>0.335428</td>\n",
-       "      <td>0.260061</td>\n",
-       "      <td>0.219243</td>\n",
-       "      <td>-0.075181</td>\n",
-       "      <td>0.203841</td>\n",
-       "      <td>0.270774</td>\n",
-       "      <td>0.301731</td>\n",
-       "      <td>0.187889</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>SEX</th>\n",
-       "      <td>0.173737</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.088161</td>\n",
-       "      <td>0.241010</td>\n",
-       "      <td>0.035277</td>\n",
-       "      <td>0.142637</td>\n",
-       "      <td>-0.379090</td>\n",
-       "      <td>0.332115</td>\n",
-       "      <td>0.149916</td>\n",
-       "      <td>0.208133</td>\n",
-       "      <td>0.043062</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>BMI</th>\n",
-       "      <td>0.185085</td>\n",
-       "      <td>0.088161</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.395411</td>\n",
-       "      <td>0.249777</td>\n",
-       "      <td>0.261170</td>\n",
-       "      <td>-0.366811</td>\n",
-       "      <td>0.413807</td>\n",
-       "      <td>0.446157</td>\n",
-       "      <td>0.388680</td>\n",
-       "      <td>0.586450</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>BP</th>\n",
-       "      <td>0.335428</td>\n",
-       "      <td>0.241010</td>\n",
-       "      <td>0.395411</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.242464</td>\n",
-       "      <td>0.185548</td>\n",
-       "      <td>-0.178762</td>\n",
-       "      <td>0.257650</td>\n",
-       "      <td>0.393480</td>\n",
-       "      <td>0.390430</td>\n",
-       "      <td>0.441482</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>S1</th>\n",
-       "      <td>0.260061</td>\n",
-       "      <td>0.035277</td>\n",
-       "      <td>0.249777</td>\n",
-       "      <td>0.242464</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.896663</td>\n",
-       "      <td>0.051519</td>\n",
-       "      <td>0.542207</td>\n",
-       "      <td>0.515503</td>\n",
-       "      <td>0.325717</td>\n",
-       "      <td>0.212022</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>S2</th>\n",
-       "      <td>0.219243</td>\n",
-       "      <td>0.142637</td>\n",
-       "      <td>0.261170</td>\n",
-       "      <td>0.185548</td>\n",
-       "      <td>0.896663</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>-0.196455</td>\n",
-       "      <td>0.659817</td>\n",
-       "      <td>0.318357</td>\n",
-       "      <td>0.290600</td>\n",
-       "      <td>0.174054</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>S3</th>\n",
-       "      <td>-0.075181</td>\n",
-       "      <td>-0.379090</td>\n",
-       "      <td>-0.366811</td>\n",
-       "      <td>-0.178762</td>\n",
-       "      <td>0.051519</td>\n",
-       "      <td>-0.196455</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>-0.738493</td>\n",
-       "      <td>-0.398577</td>\n",
-       "      <td>-0.273697</td>\n",
-       "      <td>-0.394789</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>S4</th>\n",
-       "      <td>0.203841</td>\n",
-       "      <td>0.332115</td>\n",
-       "      <td>0.413807</td>\n",
-       "      <td>0.257650</td>\n",
-       "      <td>0.542207</td>\n",
-       "      <td>0.659817</td>\n",
-       "      <td>-0.738493</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.617859</td>\n",
-       "      <td>0.417212</td>\n",
-       "      <td>0.430453</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>S5</th>\n",
-       "      <td>0.270774</td>\n",
-       "      <td>0.149916</td>\n",
-       "      <td>0.446157</td>\n",
-       "      <td>0.393480</td>\n",
-       "      <td>0.515503</td>\n",
-       "      <td>0.318357</td>\n",
-       "      <td>-0.398577</td>\n",
-       "      <td>0.617859</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.464669</td>\n",
-       "      <td>0.565883</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>S6</th>\n",
-       "      <td>0.301731</td>\n",
-       "      <td>0.208133</td>\n",
-       "      <td>0.388680</td>\n",
-       "      <td>0.390430</td>\n",
-       "      <td>0.325717</td>\n",
-       "      <td>0.290600</td>\n",
-       "      <td>-0.273697</td>\n",
-       "      <td>0.417212</td>\n",
-       "      <td>0.464669</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.382483</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Y</th>\n",
-       "      <td>0.187889</td>\n",
-       "      <td>0.043062</td>\n",
-       "      <td>0.586450</td>\n",
-       "      <td>0.441482</td>\n",
-       "      <td>0.212022</td>\n",
-       "      <td>0.174054</td>\n",
-       "      <td>-0.394789</td>\n",
-       "      <td>0.430453</td>\n",
-       "      <td>0.565883</td>\n",
-       "      <td>0.382483</td>\n",
-       "      <td>1.000000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 20
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "结论：  \n",
-    "* Y 与 BMI 和 S5（血糖）的相关性最强。这听起来很合理。\n"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "source": [
-    "fig, ax = plt.subplots(1,3,figsize=(10,5))\n",
-    "for i,n in enumerate(['BMI','S5','BP']):\n",
-    "    ax[i].scatter(df['Y'],df[n])\n",
-    "    ax[i].set_title(n)\n",
-    "plt.show()"
-   ],
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "<Figure size 1000x500 with 3 Axes>"
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"
-     },
-     "metadata": {}
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "### 任务5：检验糖尿病进展程度在男性和女性之间是否存在差异的假设\n"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "source": [
-    "from scipy.stats import ttest_ind\n",
-    "\n",
-    "tval, pval = ttest_ind(df.loc[df['SEX']==1,['Y']], df.loc[df['SEX']==2,['Y']],equal_var=False)\n",
-    "print(f\"T-value = {tval[0]:.2f}\\nP-value: {pval[0]}\")"
-   ],
-   "outputs": [
-    {
-     "output_type": "stream",
-     "name": "stdout",
-     "text": [
-      "T-value = -0.90\n",
-      "P-value: 0.3674449793083975\n"
-     ]
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n---\n\n**免责声明**：  \n本文档使用AI翻译服务[Co-op Translator](https://github.com/Azure/co-op-translator)进行翻译。尽管我们努力确保准确性，但请注意，自动翻译可能包含错误或不准确之处。应以原始语言的文档作为权威来源。对于关键信息，建议使用专业人工翻译。对于因使用本翻译而引起的任何误解或误读，我们概不负责。\n"
-   ]
-  }
- ],
- "metadata": {
-  "orig_nbformat": 4,
-  "language_info": {
-   "name": "python",
-   "version": "3.8.8",
-   "mimetype": "text/x-python",
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "pygments_lexer": "ipython3",
-   "nbconvert_exporter": "python",
-   "file_extension": ".py"
-  },
-  "kernelspec": {
-   "name": "python3",
-   "display_name": "Python 3.8.8 64-bit (conda)"
-  },
-  "interpreter": {
-   "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
-  },
-  "coopTranslator": {
-   "original_hash": "ebf5783d7ab3f7ab30a437492a30b229",
-   "translation_date": "2025-09-06T17:10:25+00:00",
-   "source_file": "1-Introduction/04-stats-and-probability/solution/assignment.ipynb",
-   "language_code": "zh"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
\ No newline at end of file
diff --git a/translations/zh/1-Introduction/README.md b/translations/zh/1-Introduction/README.md
deleted file mode 100644
index 759d02ef..00000000
--- a/translations/zh/1-Introduction/README.md
+++ /dev/null
@@ -1,29 +0,0 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "696a8474a01054281704cbfb09148949",
-  "translation_date": "2025-08-25T16:37:59+00:00",
-  "source_file": "1-Introduction/README.md",
-  "language_code": "zh"
-}
--->
-# 数据科学简介
-
-![数据的实际应用](../../../translated_images/zh-CN/data.48e22bb7617d8d92188afbc4c48effb920ba79f5cebdc0652cd9f34bbbd90c18.jpg)  
-> 图片由 <a href="https://unsplash.com/@dawson2406?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Stephen Dawson</a> 提供，来自 <a href="https://unsplash.com/s/photos/data?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>  
-
-在这些课程中，您将了解数据科学的定义，并学习数据科学家必须考虑的伦理问题。您还将学习数据的定义，并对统计学和概率论有一些初步了解，这些是数据科学的核心学术领域。
-
-### 主题
-
-1. [定义数据科学](01-defining-data-science/README.md)  
-2. [数据科学伦理](02-ethics/README.md)  
-3. [定义数据](03-defining-data/README.md)  
-4. [统计与概率简介](04-stats-and-probability/README.md)  
-
-### 致谢
-
-这些课程由 [Nitya Narasimhan](https://twitter.com/nitya) 和 [Dmitry Soshnikov](https://twitter.com/shwars) 倾注 ❤️ 编写完成。
-
-**免责声明**：  
-本文档使用AI翻译服务[Co-op Translator](https://github.com/Azure/co-op-translator)进行翻译。尽管我们努力确保翻译的准确性，但请注意，自动翻译可能包含错误或不准确之处。原始语言的文档应被视为权威来源。对于重要信息，建议使用专业人工翻译。我们不对因使用此翻译而产生的任何误解或误读承担责任。
\ No newline at end of file
diff --git a/translations/zh/2-Working-With-Data/05-relational-databases/README.md b/translations/zh/2-Working-With-Data/05-relational-databases/README.md
deleted file mode 100644
index d11177ba..00000000
--- a/translations/zh/2-Working-With-Data/05-relational-databases/README.md
+++ /dev/null
@@ -1,199 +0,0 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "11739c7b40e7c6b16ad29e3df4e65862",
-  "translation_date": "2025-12-19T10:41:14+00:00",
-  "source_file": "2-Working-With-Data/05-relational-databases/README.md",
-  "language_code": "zh"
-}
--->
-# 使用数据：关系型数据库
-
-|![ 由 [(@sketchthedocs)](https://sketchthedocs.dev) 绘制的草图笔记 ](../../sketchnotes/05-RelationalData.png)|
-|:---:|
-| 使用数据：关系型数据库 - _由 [@nitya](https://twitter.com/nitya) 绘制的草图笔记_ |
-
-你很可能过去使用过电子表格来存储信息。你有一组行和列，其中行包含信息（或数据），列描述信息（有时称为元数据）。关系型数据库建立在表中列和行的核心原则之上，允许你将信息分布在多个表中。这使你能够处理更复杂的数据，避免重复，并在探索数据的方式上具有灵活性。让我们来探索关系型数据库的概念。
-
-## [课前测验](https://ff-quizzes.netlify.app/en/ds/quiz/8)
-
-## 一切从表开始
-
-关系型数据库的核心是表。就像电子表格一样，表是列和行的集合。行包含我们希望处理的数据或信息，例如城市名称或降雨量。列描述它们存储的数据。
-
-让我们开始探索，创建一个表来存储城市信息。我们可能从它们的名称和国家开始。你可以将其存储在如下表格中：
-
-| City     | Country       |
-| -------- | ------------- |
-| Tokyo    | Japan         |
-| Atlanta  | United States |
-| Auckland | New Zealand   |
-
-注意，**city**、**country** 和 **population** 这些列名描述了所存储的数据，每一行包含一个城市的信息。
-
-## 单表方法的不足
-
-你可能觉得上面的表格相当熟悉。让我们开始向我们不断增长的数据库添加一些额外数据——年降雨量（以毫米为单位）。我们将关注2018、2019和2020年。如果我们为东京添加数据，可能看起来像这样：
-
-| City  | Country | Year | Amount |
-| ----- | ------- | ---- | ------ |
-| Tokyo | Japan   | 2020 | 1690   |
-| Tokyo | Japan   | 2019 | 1874   |
-| Tokyo | Japan   | 2018 | 1445   |
-
-你注意到表格有什么吗？你可能注意到我们不断重复城市的名称和国家。这可能占用相当多的存储空间，而且多份拷贝基本上是没必要的。毕竟，东京只有一个我们感兴趣的名称。
-
-好吧，我们试试别的方法。为每一年添加新列：
-
-| City     | Country       | 2018 | 2019 | 2020 |
-| -------- | ------------- | ---- | ---- | ---- |
-| Tokyo    | Japan         | 1445 | 1874 | 1690 |
-| Atlanta  | United States | 1779 | 1111 | 1683 |
-| Auckland | New Zealand   | 1386 | 942  | 1176 |
-
-虽然这避免了行的重复，但带来了其他几个挑战。每当有新的一年时，我们需要修改表结构。此外，随着数据增长，将年份作为列会使检索和计算值变得更复杂。
-
-这就是为什么我们需要多个表和关系。通过拆分数据，我们可以避免重复，并在处理数据时拥有更大的灵活性。
-
-## 关系的概念
-
-让我们回到数据，确定如何拆分。我们知道想存储城市的名称和国家，所以这可能最好放在一个表中。
-
-| City     | Country       |
-| -------- | ------------- |
-| Tokyo    | Japan         |
-| Atlanta  | United States |
-| Auckland | New Zealand   |
-
-但在创建下一个表之前，我们需要弄清楚如何引用每个城市。我们需要某种形式的标识符、ID或（在技术数据库术语中）主键。主键是用于标识表中某一特定行的值。虽然这可以基于值本身（例如我们可以使用城市名称），但它几乎总是应该是数字或其他标识符。我们不希望ID发生变化，因为这会破坏关系。你会发现大多数情况下主键或ID是自动生成的数字。
-
-> ✅ 主键通常缩写为 PK
-
-### cities
-
-| city_id | City     | Country       |
-| ------- | -------- | ------------- |
-| 1       | Tokyo    | Japan         |
-| 2       | Atlanta  | United States |
-| 3       | Auckland | New Zealand   |
-
-> ✅ 你会注意到在本课中我们交替使用“id”和“主键”这两个术语。这里的概念也适用于你稍后会探索的DataFrame。DataFrame不使用“主键”这个术语，但你会发现它们的行为非常相似。
-
-创建了cities表后，让我们存储降雨量。我们不再重复城市的完整信息，而是使用id。我们还应确保新创建的表也有一个*id*列，因为所有表都应该有id或主键。
-
-### rainfall
-
-| rainfall_id | city_id | Year | Amount |
-| ----------- | ------- | ---- | ------ |
-| 1           | 1       | 2018 | 1445   |
-| 2           | 1       | 2019 | 1874   |
-| 3           | 1       | 2020 | 1690   |
-| 4           | 2       | 2018 | 1779   |
-| 5           | 2       | 2019 | 1111   |
-| 6           | 2       | 2020 | 1683   |
-| 7           | 3       | 2018 | 1386   |
-| 8           | 3       | 2019 | 942    |
-| 9           | 3       | 2020 | 1176   |
-
-注意新创建的**rainfall**表中的**city_id**列。该列包含引用**cities**表中ID的值。在技术关系数据术语中，这称为**外键**；它是另一个表的主键。你可以把它看作是引用或指针。**city_id** 1引用东京。
-
-> [!NOTE] 
-> 外键通常缩写为 FK
-
-## 检索数据
-
-将数据分成两个表后，你可能想知道如何检索它。如果我们使用关系型数据库，如MySQL、SQL Server或Oracle，我们可以使用一种称为结构化查询语言（SQL）的语言。SQL（有时发音为sequel）是用于检索和修改关系型数据库中数据的标准语言。
-
-要检索数据，你使用命令`SELECT`。其核心是你**选择**想要查看的列，**从**它们所在的表中。如果你只想显示城市名称，可以使用以下语句：
-
-```sql
-SELECT city
-FROM cities;
-
--- Output:
--- Tokyo
--- Atlanta
--- Auckland
-```
-
-`SELECT` 是列出列的地方，`FROM` 是列出表的地方。
-
-> [!NOTE] 
-> SQL语法不区分大小写，意味着`select`和`SELECT`是相同的。然而，根据你使用的数据库类型，列和表可能区分大小写。因此，最佳实践是始终将编程中的所有内容视为区分大小写。编写SQL查询时，常见约定是将关键字全部大写。
-
-上面的查询将显示所有城市。假设我们只想显示新西兰的城市。我们需要某种形式的过滤。SQL关键字是`WHERE`，意思是“在某条件为真时”。
-
-```sql
-SELECT city
-FROM cities
-WHERE country = 'New Zealand';
-
--- Output:
--- Auckland
-```
-
-## 连接数据
-
-到目前为止，我们只从单个表中检索数据。现在我们想将**cities**和**rainfall**的数据合并。这通过*连接*它们来完成。你实际上是在两个表之间创建一个连接点，并匹配每个表中某列的值。
-
-在我们的例子中，我们将匹配**rainfall**中的**city_id**列和**cities**中的**city_id**列。这将把降雨量值与其对应的城市匹配。我们执行的连接类型称为*内连接*，意味着如果有任何行与另一个表中的内容不匹配，则不会显示。在我们的例子中，每个城市都有降雨量数据，因此所有内容都会显示。
-
-让我们检索所有城市2019年的降雨量。
-
-我们将分步骤进行。第一步是通过指定连接的列——之前强调的**city_id**——将数据连接起来。
-
-```sql
-SELECT cities.city
-    rainfall.amount
-FROM cities
-    INNER JOIN rainfall ON cities.city_id = rainfall.city_id
-```
-
-我们已经突出显示了想要的两列，以及我们希望通过**city_id**连接表。现在我们可以添加`WHERE`语句，只筛选2019年。
-
-```sql
-SELECT cities.city
-    rainfall.amount
-FROM cities
-    INNER JOIN rainfall ON cities.city_id = rainfall.city_id
-WHERE rainfall.year = 2019
-
--- Output
-
--- city     | amount
--- -------- | ------
--- Tokyo    | 1874
--- Atlanta  | 1111
--- Auckland |  942
-```
-
-## 总结
-
-关系型数据库围绕将信息划分到多个表中，然后再将它们组合起来进行显示和分析。这提供了高度的灵活性来执行计算和其他数据操作。你已经了解了关系型数据库的核心概念，以及如何在两个表之间执行连接。
-
-## 🚀 挑战
-
-互联网上有许多关系型数据库可用。你可以使用上面学到的技能来探索数据。
-
-## 课后测验
-
-## [课后测验](https://ff-quizzes.netlify.app/en/ds/quiz/9)
-
-## 复习与自学
-
-在[Microsoft Learn](https://docs.microsoft.com/learn?WT.mc_id=academic-77958-bethanycheum)上有多个资源供你继续探索SQL和关系型数据库概念
-
-- [描述关系数据的概念](https://docs.microsoft.com//learn/modules/describe-concepts-of-relational-data?WT.mc_id=academic-77958-bethanycheum)
-- [开始使用Transact-SQL查询](https://docs.microsoft.com//learn/paths/get-started-querying-with-transact-sql?WT.mc_id=academic-77958-bethanycheum)（Transact-SQL是SQL的一个版本）
-- [Microsoft Learn上的SQL内容](https://docs.microsoft.com/learn/browse/?products=azure-sql-database%2Csql-server&expanded=azure&WT.mc_id=academic-77958-bethanycheum)
-
-## 作业
-
-[显示机场数据](assignment.md)
-
----
-
-<!-- CO-OP TRANSLATOR DISCLAIMER START -->
-**免责声明**：  
-本文件由人工智能翻译服务 [Co-op Translator](https://github.com/Azure/co-op-translator) 翻译而成。虽然我们力求准确，但请注意自动翻译可能存在错误或不准确之处。原始文件的母语版本应被视为权威来源。对于重要信息，建议使用专业人工翻译。我们不对因使用本翻译而产生的任何误解或误释承担责任。
-<!-- CO-OP TRANSLATOR DISCLAIMER END -->
\ No newline at end of file
diff --git a/translations/zh/2-Working-With-Data/05-relational-databases/assignment.md b/translations/zh/2-Working-With-Data/05-relational-databases/assignment.md
deleted file mode 100644
index f04a3725..00000000
--- a/translations/zh/2-Working-With-Data/05-relational-databases/assignment.md
+++ /dev/null
@@ -1,74 +0,0 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "25b37acdfb2452917c1aa2e2ca44317a",
-  "translation_date": "2025-10-24T09:52:59+00:00",
-  "source_file": "2-Working-With-Data/05-relational-databases/assignment.md",
-  "language_code": "zh"
-}
--->
-# 显示机场数据
-
-您已获得一个基于 [SQLite](https://sqlite.org/index.html) 的 [数据库](https://raw.githubusercontent.com/Microsoft/Data-Science-For-Beginners/main/2-Working-With-Data/05-relational-databases/airports.db)，其中包含有关机场的信息。以下是数据库的模式。您将使用 [SQLite 扩展](https://marketplace.visualstudio.com/items?itemName=alexcvzz.vscode-sqlite&WT.mc_id=academic-77958-bethanycheum) 在 [Visual Studio Code](https://code.visualstudio.com?WT.mc_id=academic-77958-bethanycheum) 中显示不同城市机场的信息。
-
-## 指导说明
-
-要开始完成任务，您需要执行几个步骤。您需要安装一些工具并下载示例数据库。
-
-### 设置您的系统
-
-您可以使用 Visual Studio Code 和 SQLite 扩展与数据库进行交互。
-
-1. 访问 [code.visualstudio.com](https://code.visualstudio.com?WT.mc_id=academic-77958-bethanycheum)，按照说明安装 Visual Studio Code
-1. 按照 Marketplace 页面上的说明安装 [SQLite 扩展](https://marketplace.visualstudio.com/items?itemName=alexcvzz.vscode-sqlite&WT.mc_id=academic-77958-bethanycheum)
-
-### 下载并打开数据库
-
-接下来，您将下载并打开数据库。
-
-1. 从 [GitHub 下载数据库文件](https://raw.githubusercontent.com/Microsoft/Data-Science-For-Beginners/main/2-Working-With-Data/05-relational-databases/airports.db)，并将其保存到一个目录中
-1. 打开 Visual Studio Code
-1. 在 SQLite 扩展中打开数据库，按下 **Ctl-Shift-P**（Mac 上为 **Cmd-Shift-P**），然后输入 `SQLite: Open database`
-1. 选择 **Choose database from file** 并打开您之前下载的 **airports.db** 文件
-1. 打开数据库后（屏幕上不会显示更新），通过按下 **Ctl-Shift-P**（Mac 上为 **Cmd-Shift-P**），然后输入 `SQLite: New query` 来创建一个新的查询窗口
-
-打开后，新的查询窗口可用于对数据库运行 SQL 语句。您可以使用命令 **Ctl-Shift-Q**（Mac 上为 **Cmd-Shift-Q**）对数据库运行查询。
-
-> [!NOTE] 
-> 有关 SQLite 扩展的更多信息，您可以参考 [文档](https://marketplace.visualstudio.com/items?itemName=alexcvzz.vscode-sqlite&WT.mc_id=academic-77958-bethanycheum)
-
-## 数据库模式
-
-数据库的模式是其表的设计和结构。**airports** 数据库有两个表，`cities` 表包含英国和爱尔兰的城市列表，`airports` 表包含所有机场的列表。由于某些城市可能有多个机场，因此创建了两个表来存储信息。在本练习中，您将使用连接来显示不同城市的信息。
-
-| Cities           |
-| ---------------- |
-| id (PK, integer) |
-| city (text)      |
-| country (text)   |
-
-| Airports                         |
-| -------------------------------- |
-| id (PK, integer)                 |
-| name (text)                      |
-| code (text)                      |
-| city_id (FK to id in **Cities**) |
-
-## 任务
-
-创建查询以返回以下信息：
-
-1. `Cities` 表中的所有城市名称
-1. `Cities` 表中爱尔兰的所有城市
-1. 所有机场名称及其所在城市和国家
-1. 英国伦敦的所有机场
-
-## 评分标准
-
-| 卓越表现 | 合格表现 | 需要改进 |
-| -------- | -------- | -------- |
-
----
-
-**免责声明**：  
-本文档使用AI翻译服务[Co-op Translator](https://github.com/Azure/co-op-translator)进行翻译。尽管我们努力确保翻译的准确性，但请注意，自动翻译可能包含错误或不准确之处。原始语言的文档应被视为权威来源。对于重要信息，建议使用专业人工翻译。我们对因使用此翻译而产生的任何误解或误读不承担责任。
\ No newline at end of file
diff --git a/translations/zh/2-Working-With-Data/06-non-relational/README.md b/translations/zh/2-Working-With-Data/06-non-relational/README.md
deleted file mode 100644
index 2c7a87cf..00000000
--- a/translations/zh/2-Working-With-Data/06-non-relational/README.md
+++ /dev/null
@@ -1,156 +0,0 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "c182e87f9f80be7e7cdffc7b40bbfccf",
-  "translation_date": "2025-09-05T11:30:08+00:00",
-  "source_file": "2-Working-With-Data/06-non-relational/README.md",
-  "language_code": "zh"
-}
--->
-# 使用数据：非关系型数据
-
-|![由 [(@sketchthedocs)](https://sketchthedocs.dev) 绘制的草图笔记](../../sketchnotes/06-NoSQL.png)|
-|:---:|
-|使用 NoSQL 数据 - _草图笔记由 [@nitya](https://twitter.com/nitya) 绘制_ |
-
-## [课前测验](https://ff-quizzes.netlify.app/en/ds/quiz/10)
-
-数据并不限于关系型数据库。本课重点介绍非关系型数据，并将涵盖电子表格和 NoSQL 的基础知识。
-
-## 电子表格
-
-电子表格是一种流行的数据存储和探索方式，因为它设置和使用起来相对简单。在本课中，您将学习电子表格的基本组成部分，以及公式和函数。示例将以 Microsoft Excel 为例，但大多数部分和主题在其他电子表格软件中具有类似的名称和操作步骤。
-
-![一个空的 Microsoft Excel 工作簿，包含两个工作表](../../../../2-Working-With-Data/06-non-relational/images/parts-of-spreadsheet.png)
-
-电子表格是一个文件，可以通过计算机、设备或基于云的文件系统访问。软件本身可能是基于浏览器的，也可能是需要安装在计算机上的应用程序，或者是需要下载的应用程序。在 Excel 中，这些文件被定义为 **工作簿**，本课将使用这一术语。
-
-一个工作簿包含一个或多个 **工作表**，每个工作表通过标签标识。在工作表中，有称为 **单元格** 的矩形区域，这些单元格包含实际数据。单元格是行和列的交叉点，其中列用字母标识，行用数字标识。一些电子表格会在前几行中包含标题，用于描述单元格中的数据。
-
-了解了 Excel 工作簿的这些基本元素后，我们将使用 [Microsoft 模板](https://templates.office.com/) 中的一个库存管理示例来进一步讲解电子表格的其他部分。
-
-### 管理库存
-
-名为 "InventoryExample" 的电子表格文件是一个格式化的库存项目表，包含三个工作表，标签分别为 "Inventory List"、"Inventory Pick List" 和 "Bin Lookup"。Inventory List 工作表的第 4 行是标题行，用于描述标题列中每个单元格的值。
-
-![Microsoft Excel 中库存清单示例中的一个公式被高亮显示](../../../../2-Working-With-Data/06-non-relational/images/formula-excel.png)
-
-有时，一个单元格的值依赖于其他单元格的值来生成。在 Inventory List 电子表格中，我们记录了库存中每个项目的成本，但如果我们需要知道整个库存的总价值呢？[**公式**](https://support.microsoft.com/en-us/office/overview-of-formulas-34519a4e-1e8d-4f4b-84d4-d642c4f63263) 用于对单元格数据执行操作，在本例中用于计算库存的成本。该电子表格在 Inventory Value 列中使用了一个公式，通过将 QTY 标题下的数量与 COST 标题下的成本相乘来计算每个项目的价值。双击或高亮显示一个单元格即可查看公式。您会注意到，公式以等号开头，后面是计算或操作。
-
-![Microsoft Excel 中库存清单示例中的一个函数被高亮显示](../../../../2-Working-With-Data/06-non-relational/images/function-excel.png)
-
-我们还可以使用另一个公式将所有 Inventory Value 的值相加，得到总价值。虽然可以通过逐个相加每个单元格的值来计算总和，但这会非常繁琐。Excel 提供了 [**函数**](https://support.microsoft.com/en-us/office/sum-function-043e1c7d-7726-4e80-8f32-07b23e057f89)，即预定义的公式，用于对单元格值执行计算。函数需要参数，即执行这些计算所需的值。当函数需要多个参数时，这些参数必须按特定顺序列出，否则函数可能无法正确计算值。本例使用了 SUM 函数，并将 Inventory Value 的值作为参数，生成了 B3 单元格中的总和。
-
-## NoSQL
-
-NoSQL 是一个总称，涵盖了存储非关系型数据的不同方式，可以解释为 "非 SQL"、"非关系型" 或 "不仅仅是 SQL"。这类数据库系统可以分为四种类型。
-
-![一个键值数据存储的图示，显示了 4 个唯一的数字键与 4 个不同值的关联](../../../../2-Working-With-Data/06-non-relational/images/kv-db.png)
-> 来源：[Michał Białecki Blog](https://www.michalbialecki.com/2018/03/18/azure-cosmos-db-key-value-database-cloud/)
-
-[键值](https://docs.microsoft.com/en-us/azure/architecture/data-guide/big-data/non-relational-data#keyvalue-data-stores) 数据库将唯一键（唯一标识符）与值配对。这些键值对通过 [哈希表](https://www.hackerearth.com/practice/data-structures/hash-tables/basics-of-hash-tables/tutorial/) 和适当的哈希函数存储。
-
-![一个图形数据存储的图示，显示了人与兴趣和地点之间的关系](../../../../2-Working-With-Data/06-non-relational/images/graph-db.png)
-> 来源：[Microsoft](https://docs.microsoft.com/en-us/azure/cosmos-db/graph/graph-introduction#graph-database-by-example)
-
-[图形](https://docs.microsoft.com/en-us/azure/architecture/data-guide/big-data/non-relational-data#graph-data-stores) 数据库描述数据中的关系，并以节点和边的集合表示。节点表示实体，即现实世界中存在的事物，例如学生或银行对账单。边表示两个实体之间的关系。每个节点和边都有属性，用于提供关于节点和边的附加信息。
-
-![一个列式数据存储的图示，显示了一个客户数据库，其中包含两个列族，分别为 Identity 和 Contact Info](../../../../2-Working-With-Data/06-non-relational/images/columnar-db.png)
-
-[列式](https://docs.microsoft.com/en-us/azure/architecture/data-guide/big-data/non-relational-data#columnar-data-stores) 数据存储将数据组织为列和行，类似于关系型数据结构，但每列被划分为称为列族的组，其中一列下的所有数据是相关的，可以作为一个单元进行检索和更改。
-
-### 使用 Azure Cosmos DB 的文档数据存储
-
-[文档](https://docs.microsoft.com/en-us/azure/architecture/data-guide/big-data/non-relational-data#document-data-stores) 数据存储基于键值数据存储的概念，由一系列字段和对象组成。本节将通过 Cosmos DB 模拟器探索文档数据库。
-
-Cosmos DB 数据库符合 "不仅仅是 SQL" 的定义，其中 Cosmos DB 的文档数据库依赖 SQL 来查询数据。[上一课](../05-relational-databases/README.md) 已介绍了 SQL 的基础知识，我们可以在这里将一些相同的查询应用于文档数据库。我们将使用 Cosmos DB 模拟器，它允许我们在本地计算机上创建和探索文档数据库。有关模拟器的更多信息，请阅读[此处](https://docs.microsoft.com/en-us/azure/cosmos-db/local-emulator?tabs=ssl-netstd21)。
-
-文档是字段和对象值的集合，其中字段描述对象值的含义。以下是一个文档示例：
-
-```json
-{
-    "firstname": "Eva",
-    "age": 44,
-    "id": "8c74a315-aebf-4a16-bb38-2430a9896ce5",
-    "_rid": "bHwDAPQz8s0BAAAAAAAAAA==",
-    "_self": "dbs/bHwDAA==/colls/bHwDAPQz8s0=/docs/bHwDAPQz8s0BAAAAAAAAAA==/",
-    "_etag": "\"00000000-0000-0000-9f95-010a691e01d7\"",
-    "_attachments": "attachments/",
-    "_ts": 1630544034
-}
-```
-
-此文档中感兴趣的字段包括：`firstname`、`id` 和 `age`。其余带下划线的字段是由 Cosmos DB 自动生成的。
-
-#### 使用 Cosmos DB 模拟器探索数据
-
-您可以[在此处下载并安装 Windows 版模拟器](https://aka.ms/cosmosdb-emulator)。有关在 macOS 和 Linux 上运行模拟器的选项，请参考[此文档](https://docs.microsoft.com/en-us/azure/cosmos-db/local-emulator?tabs=ssl-netstd21#run-on-linux-macos)。
-
-模拟器会启动一个浏览器窗口，其中的 Explorer 视图允许您探索文档。
-
-![Cosmos DB 模拟器的 Explorer 视图](../../../../2-Working-With-Data/06-non-relational/images/cosmosdb-emulator-explorer.png)
-
-如果您正在跟随操作，请点击 "Start with Sample" 以生成一个名为 SampleDB 的示例数据库。展开 SampleDB 后，您会看到一个名为 `Persons` 的容器。容器包含一组项目，这些项目就是容器中的文档。您可以探索 `Items` 下的四个单独文档。
-
-![在 Cosmos DB 模拟器中探索示例数据](../../../../2-Working-With-Data/06-non-relational/images/cosmosdb-emulator-persons.png)
-
-#### 使用 Cosmos DB 模拟器查询文档数据
-
-我们还可以通过点击 "New SQL Query" 按钮（从左数第二个按钮）来查询示例数据。
-
-`SELECT * FROM c` 返回容器中的所有文档。让我们添加一个 where 子句，查找年龄小于 40 的人。
-
-`SELECT * FROM c where c.age < 40`
-
-![在 Cosmos DB 模拟器中运行 SELECT 查询以查找年龄字段值小于 40 的文档](../../../../2-Working-With-Data/06-non-relational/images/cosmosdb-emulator-persons-query.png)
-
-查询返回了两个文档，注意每个文档的年龄值都小于 40。
-
-#### JSON 与文档
-
-如果您熟悉 JavaScript 对象表示法（JSON），您会发现文档看起来与 JSON 类似。此目录中有一个 `PersonsData.json` 文件，您可以将其上传到模拟器中的 Persons 容器，方法是使用 `Upload Item` 按钮。
-
-在大多数情况下，返回 JSON 数据的 API 可以直接传输并存储到文档数据库中。以下是另一个文档示例，它表示从 Microsoft Twitter 账户获取的推文数据，这些数据通过 Twitter API 获取后被插入到 Cosmos DB 中。
-
-```json
-{
-    "created_at": "2021-08-31T19:03:01.000Z",
-    "id": "1432780985872142341",
-    "text": "Blank slate. Like this tweet if you’ve ever painted in Microsoft Paint before. https://t.co/cFeEs8eOPK",
-    "_rid": "dhAmAIUsA4oHAAAAAAAAAA==",
-    "_self": "dbs/dhAmAA==/colls/dhAmAIUsA4o=/docs/dhAmAIUsA4oHAAAAAAAAAA==/",
-    "_etag": "\"00000000-0000-0000-9f84-a0958ad901d7\"",
-    "_attachments": "attachments/",
-    "_ts": 1630537000
-```
-
-此文档中感兴趣的字段包括：`created_at`、`id` 和 `text`。
-
-## 🚀 挑战
-
-目录中有一个 `TwitterData.json` 文件，您可以将其上传到 SampleDB 数据库。建议将其添加到一个单独的容器中。操作步骤如下：
-
-1. 点击右上角的 "New Container" 按钮
-2. 选择现有数据库（SampleDB），为容器创建一个 ID
-3. 将分区键设置为 `/id`
-4. 点击 OK（可以忽略此视图中的其他信息，因为这是一个小型数据集，运行在您的本地机器上）
-5. 打开新容器并使用 `Upload Item` 按钮上传 Twitter 数据文件
-
-尝试运行一些 SELECT 查询，查找 `text` 字段中包含 "Microsoft" 的文档。提示：尝试使用 [LIKE 关键字](https://docs.microsoft.com/en-us/azure/cosmos-db/sql/sql-query-keywords#using-like-with-the--wildcard-character)。
-
-## [课后测验](https://ff-quizzes.netlify.app/en/ds/quiz/11)
-
-## 复习与自学
-
-- 本课未涵盖电子表格中的一些额外格式和功能。如果您有兴趣了解更多，Microsoft 提供了一个 [庞大的文档和视频库](https://support.microsoft.com/excel)。
-- 这篇架构文档详细介绍了非关系型数据的不同特性：[非关系型数据和 NoSQL](https://docs.microsoft.com/en-us/azure/architecture/data-guide/big-data/non-relational-data)。
-- Cosmos DB 是一个基于云的非关系型数据库，也可以存储本课中提到的不同类型的 NoSQL 数据。您可以通过此 [Cosmos DB Microsoft Learn 模块](https://docs.microsoft.com/en-us/learn/paths/work-with-nosql-data-in-azure-cosmos-db/) 了解更多。
-
-## 作业
-
-[Soda Profits](assignment.md)
-
----
-
-**免责声明**：  
-本文档使用AI翻译服务[Co-op Translator](https://github.com/Azure/co-op-translator)进行翻译。尽管我们努力确保准确性，但请注意，自动翻译可能包含错误或不准确之处。应以原始语言的文档作为权威来源。对于关键信息，建议使用专业人工翻译。对于因使用本翻译而引起的任何误解或误读，我们概不负责。
\ No newline at end of file
diff --git a/translations/zh/2-Working-With-Data/06-non-relational/assignment.md b/translations/zh/2-Working-With-Data/06-non-relational/assignment.md
deleted file mode 100644
index 773c47a5..00000000
--- a/translations/zh/2-Working-With-Data/06-non-relational/assignment.md
+++ /dev/null
@@ -1,31 +0,0 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "f824bfdb8b12d33293913f76f5c787c5",
-  "translation_date": "2025-08-25T16:36:11+00:00",
-  "source_file": "2-Working-With-Data/06-non-relational/assignment.md",
-  "language_code": "zh"
-}
--->
-# 苏打利润
-
-## 说明
-
-[Coca Cola Co 电子表格](../../../../2-Working-With-Data/06-non-relational/CocaColaCo.xlsx)缺少了一些计算。你的任务是：
-
-1. 计算 2015、2016、2017 和 2018 财年的毛利润
-   - 毛利润 = 营业净收入 - 销售成本
-1. 计算所有毛利润的平均值。尝试使用函数完成此操作。
-   - 平均值 = 毛利润总和除以财年数（10）
-   - 关于 [AVERAGE 函数](https://support.microsoft.com/en-us/office/average-function-047bac88-d466-426c-a32b-8f33eb960cf6) 的文档
-1. 这是一个 Excel 文件，但应该可以在任何电子表格平台中编辑
-
-[数据来源归功于 Yiyi Wang](https://www.kaggle.com/yiyiwang0826/cocacola-excel)
-
-## 评分标准
-
-优秀 | 合格 | 需要改进
---- | --- | ---
-
-**免责声明**：  
-本文档使用AI翻译服务 [Co-op Translator](https://github.com/Azure/co-op-translator) 进行翻译。尽管我们努力确保翻译的准确性，但请注意，自动翻译可能包含错误或不准确之处。应以原始语言的文档作为权威来源。对于重要信息，建议使用专业人工翻译。我们不对因使用此翻译而产生的任何误解或误读承担责任。
\ No newline at end of file
diff --git a/translations/zh/2-Working-With-Data/07-python/R/notebook.ipynb b/translations/zh/2-Working-With-Data/07-python/R/notebook.ipynb
deleted file mode 100644
index 98d47a40..00000000
--- a/translations/zh/2-Working-With-Data/07-python/R/notebook.ipynb
+++ /dev/null
@@ -1,2142 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "9f9b980c",
-   "metadata": {},
-   "source": [
-    "##   Pandas 在 R 中的用例\n",
-    "我们需要使用 dplyr 库来解决 R 中的 Pandas 用例。我们将从导入常见的数据科学库开始。\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "id": "625abf4a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "options(warn=-1)\n",
-    "library(dplyr)\n",
-    "library(tidyverse)\n",
-    "library(lubridate)\n",
-    "library(zoo)\n",
-    "library(xts)\n",
-    "library('ggplot2')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d786e051",
-   "metadata": {},
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0f47587a",
-   "metadata": {},
-   "source": [
-    "Series 类似于列表或一维数组，但带有索引。所有操作都与索引对齐。在 R 中对行进行索引时，我们需要使用 row.names。\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "id": "f659f553",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a<- 1:9"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "id": "9acc193d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "b = c(\"I\",\"like\",\"to\",\"use\",\"Python\",\"and\",\"Pandas\",\"very\",\"much\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "id": "f577ec14",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a1 = length(a)\n",
-    "b1 = length(b)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "id": "31e069a0",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "  a\n",
-      "1 1\n",
-      "2 2\n",
-      "3 3\n",
-      "4 4\n",
-      "5 5\n",
-      "6 6\n",
-      "7 7\n",
-      "8 8\n",
-      "9 9\n"
-     ]
-    }
-   ],
-   "source": [
-    "a = data.frame(a,row.names = c(1:a1))\n",
-    "print(a)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "id": "29ce166e",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "       b\n",
-      "1      I\n",
-      "2   like\n",
-      "3     to\n",
-      "4    use\n",
-      "5 Python\n",
-      "6    and\n",
-      "7 Pandas\n",
-      "8   very\n",
-      "9   much\n"
-     ]
-    }
-   ],
-   "source": [
-    "b = data.frame(b,row.names = c(1:b1))\n",
-    "print(b)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a83abe74",
-   "metadata": {},
-   "source": [
-    "时间序列是序列的常见用途之一。在时间序列中，索引具有特殊结构——通常是日期或日期时间范围。创建时间序列最简单的方法是使用 ts 函数。但我们将尝试另一种方式来实现时间序列。我们需要使用 lubridate 库，通过 seq 函数创建日期索引。\n",
-    "\n",
-    "假设我们有一个序列，显示每天购买的产品数量，并且我们知道每个星期天还需要额外拿一个物品给自己。以下是如何使用序列进行建模：\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "id": "eeb683c7",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# We will use ggplot2 for visualizing the data\n",
-    "# If you want to change the plot size repr library will be used\n",
-    "library(repr)\n",
-    "options(repr.plot.width = 12,repr.plot.height=6)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "id": "e7788ca1",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[1] \"length of index is  366\"\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "plot without title"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 360,
-       "width": 720
-      }
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "start_date <- mdy(\"Jan 1, 2020\")\n",
-    "end_date <- mdy(\"Dec 31, 2020\")\n",
-    "idx = seq(start_date,end_date,by ='day')\n",
-    "print(paste(\"length of index is \",length(idx)))\n",
-    "size = length(idx)\n",
-    "sales = runif(366,min=25,max=50)\n",
-    "sold_items <- data.frame(row.names=idx[0:size],sales)\n",
-    "ggplot(sold_items,aes(x=idx,y=sales)) + geom_point(color = \"firebrick\", shape = \"diamond\", size = 2) +\n",
-    "    geom_line(color = \"firebrick\", size = .3)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3f199e43",
-   "metadata": {},
-   "source": [
-    "我们正在合并 additional_items 和 sold_items，以便计算产品的总数量。  \n",
-    "如你所见，我们在这里遇到了问题，无法找到总数，因为在每周的时间序列中，未提及的日期会被视为缺失值（NaN）。如果我们将 NaN 与一个数字相加，结果仍然是 NaN。  \n",
-    "为了进行加法运算，我们需要将 NaN 替换为 0。\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "id": "abe41544",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<table class=\"dataframe\">\n",
-       "<caption>A data.frame: 53 × 1</caption>\n",
-       "<thead>\n",
-       "\t<tr><th></th><th scope=col>additional_product</th></tr>\n",
-       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th></tr>\n",
-       "</thead>\n",
-       "<tbody>\n",
-       "\t<tr><th scope=row>2020-01-01</th><td>10</td></tr>\n",
-       "\t<tr><th scope=row>2020-01-08</th><td>10</td></tr>\n",
-       "\t<tr><th scope=row>2020-01-15</th><td>10</td></tr>\n",
-       "\t<tr><th scope=row>2020-01-22</th><td>10</td></tr>\n",
-       "\t<tr><th scope=row>2020-01-29</th><td>10</td></tr>\n",
-       "\t<tr><th scope=row>2020-02-05</th><td>10</td></tr>\n",
-       "\t<tr><th scope=row>2020-02-12</th><td>10</td></tr>\n",
-       "\t<tr><th scope=row>2020-02-19</th><td>10</td></tr>\n",
-       "\t<tr><th scope=row>2020-02-26</th><td>10</td></tr>\n",
-       "\t<tr><th scope=row>2020-03-04</th><td>10</td></tr>\n",
-       "\t<tr><th scope=row>2020-03-11</th><td>10</td></tr>\n",
-       "\t<tr><th scope=row>2020-03-18</th><td>10</td></tr>\n",
-       "\t<tr><th scope=row>2020-03-25</th><td>10</td></tr>\n",
-       "\t<tr><th scope=row>2020-04-01</th><td>10</td></tr>\n",
-       "\t<tr><th scope=row>2020-04-08</th><td>10</td></tr>\n",
-       "\t<tr><th scope=row>2020-04-15</th><td>10</td></tr>\n",
-       "\t<tr><th scope=row>2020-04-22</th><td>10</td></tr>\n",
-       "\t<tr><th scope=row>2020-04-29</th><td>10</td></tr>\n",
-       "\t<tr><th scope=row>2020-05-06</th><td>10</td></tr>\n",
-       "\t<tr><th scope=row>2020-05-13</th><td>10</td></tr>\n",
-       "\t<tr><th scope=row>2020-05-20</th><td>10</td></tr>\n",
-       "\t<tr><th scope=row>2020-05-27</th><td>10</td></tr>\n",
-       "\t<tr><th scope=row>2020-06-03</th><td>10</td></tr>\n",
-       "\t<tr><th scope=row>2020-06-10</th><td>10</td></tr>\n",
-       "\t<tr><th scope=row>2020-06-17</th><td>10</td></tr>\n",
-       "\t<tr><th scope=row>2020-06-24</th><td>10</td></tr>\n",
-       "\t<tr><th scope=row>2020-07-01</th><td>10</td></tr>\n",
-       "\t<tr><th scope=row>2020-07-08</th><td>10</td></tr>\n",
-       "\t<tr><th scope=row>2020-07-15</th><td>10</td></tr>\n",
-       "\t<tr><th scope=row>2020-07-22</th><td>10</td></tr>\n",
-       "\t<tr><th scope=row>2020-07-29</th><td>10</td></tr>\n",
-       "\t<tr><th scope=row>2020-08-05</th><td>10</td></tr>\n",
-       "\t<tr><th scope=row>2020-08-12</th><td>10</td></tr>\n",
-       "\t<tr><th scope=row>2020-08-19</th><td>10</td></tr>\n",
-       "\t<tr><th scope=row>2020-08-26</th><td>10</td></tr>\n",
-       "\t<tr><th scope=row>2020-09-02</th><td>10</td></tr>\n",
-       "\t<tr><th scope=row>2020-09-09</th><td>10</td></tr>\n",
-       "\t<tr><th scope=row>2020-09-16</th><td>10</td></tr>\n",
-       "\t<tr><th scope=row>2020-09-23</th><td>10</td></tr>\n",
-       "\t<tr><th scope=row>2020-09-30</th><td>10</td></tr>\n",
-       "\t<tr><th scope=row>2020-10-07</th><td>10</td></tr>\n",
-       "\t<tr><th scope=row>2020-10-14</th><td>10</td></tr>\n",
-       "\t<tr><th scope=row>2020-10-21</th><td>10</td></tr>\n",
-       "\t<tr><th scope=row>2020-10-28</th><td>10</td></tr>\n",
-       "\t<tr><th scope=row>2020-11-04</th><td>10</td></tr>\n",
-       "\t<tr><th scope=row>2020-11-11</th><td>10</td></tr>\n",
-       "\t<tr><th scope=row>2020-11-18</th><td>10</td></tr>\n",
-       "\t<tr><th scope=row>2020-11-25</th><td>10</td></tr>\n",
-       "\t<tr><th scope=row>2020-12-02</th><td>10</td></tr>\n",
-       "\t<tr><th scope=row>2020-12-09</th><td>10</td></tr>\n",
-       "\t<tr><th scope=row>2020-12-16</th><td>10</td></tr>\n",
-       "\t<tr><th scope=row>2020-12-23</th><td>10</td></tr>\n",
-       "\t<tr><th scope=row>2020-12-30</th><td>10</td></tr>\n",
-       "</tbody>\n",
-       "</table>\n"
-      ],
-      "text/latex": [
-       "A data.frame: 53 × 1\n",
-       "\\begin{tabular}{r|l}\n",
-       "  & additional\\_product\\\\\n",
-       "  & <dbl>\\\\\n",
-       "\\hline\n",
-       "\t2020-01-01 & 10\\\\\n",
-       "\t2020-01-08 & 10\\\\\n",
-       "\t2020-01-15 & 10\\\\\n",
-       "\t2020-01-22 & 10\\\\\n",
-       "\t2020-01-29 & 10\\\\\n",
-       "\t2020-02-05 & 10\\\\\n",
-       "\t2020-02-12 & 10\\\\\n",
-       "\t2020-02-19 & 10\\\\\n",
-       "\t2020-02-26 & 10\\\\\n",
-       "\t2020-03-04 & 10\\\\\n",
-       "\t2020-03-11 & 10\\\\\n",
-       "\t2020-03-18 & 10\\\\\n",
-       "\t2020-03-25 & 10\\\\\n",
-       "\t2020-04-01 & 10\\\\\n",
-       "\t2020-04-08 & 10\\\\\n",
-       "\t2020-04-15 & 10\\\\\n",
-       "\t2020-04-22 & 10\\\\\n",
-       "\t2020-04-29 & 10\\\\\n",
-       "\t2020-05-06 & 10\\\\\n",
-       "\t2020-05-13 & 10\\\\\n",
-       "\t2020-05-20 & 10\\\\\n",
-       "\t2020-05-27 & 10\\\\\n",
-       "\t2020-06-03 & 10\\\\\n",
-       "\t2020-06-10 & 10\\\\\n",
-       "\t2020-06-17 & 10\\\\\n",
-       "\t2020-06-24 & 10\\\\\n",
-       "\t2020-07-01 & 10\\\\\n",
-       "\t2020-07-08 & 10\\\\\n",
-       "\t2020-07-15 & 10\\\\\n",
-       "\t2020-07-22 & 10\\\\\n",
-       "\t2020-07-29 & 10\\\\\n",
-       "\t2020-08-05 & 10\\\\\n",
-       "\t2020-08-12 & 10\\\\\n",
-       "\t2020-08-19 & 10\\\\\n",
-       "\t2020-08-26 & 10\\\\\n",
-       "\t2020-09-02 & 10\\\\\n",
-       "\t2020-09-09 & 10\\\\\n",
-       "\t2020-09-16 & 10\\\\\n",
-       "\t2020-09-23 & 10\\\\\n",
-       "\t2020-09-30 & 10\\\\\n",
-       "\t2020-10-07 & 10\\\\\n",
-       "\t2020-10-14 & 10\\\\\n",
-       "\t2020-10-21 & 10\\\\\n",
-       "\t2020-10-28 & 10\\\\\n",
-       "\t2020-11-04 & 10\\\\\n",
-       "\t2020-11-11 & 10\\\\\n",
-       "\t2020-11-18 & 10\\\\\n",
-       "\t2020-11-25 & 10\\\\\n",
-       "\t2020-12-02 & 10\\\\\n",
-       "\t2020-12-09 & 10\\\\\n",
-       "\t2020-12-16 & 10\\\\\n",
-       "\t2020-12-23 & 10\\\\\n",
-       "\t2020-12-30 & 10\\\\\n",
-       "\\end{tabular}\n"
-      ],
-      "text/markdown": [
-       "\n",
-       "A data.frame: 53 × 1\n",
-       "\n",
-       "| <!--/--> | additional_product &lt;dbl&gt; |\n",
-       "|---|---|\n",
-       "| 2020-01-01 | 10 |\n",
-       "| 2020-01-08 | 10 |\n",
-       "| 2020-01-15 | 10 |\n",
-       "| 2020-01-22 | 10 |\n",
-       "| 2020-01-29 | 10 |\n",
-       "| 2020-02-05 | 10 |\n",
-       "| 2020-02-12 | 10 |\n",
-       "| 2020-02-19 | 10 |\n",
-       "| 2020-02-26 | 10 |\n",
-       "| 2020-03-04 | 10 |\n",
-       "| 2020-03-11 | 10 |\n",
-       "| 2020-03-18 | 10 |\n",
-       "| 2020-03-25 | 10 |\n",
-       "| 2020-04-01 | 10 |\n",
-       "| 2020-04-08 | 10 |\n",
-       "| 2020-04-15 | 10 |\n",
-       "| 2020-04-22 | 10 |\n",
-       "| 2020-04-29 | 10 |\n",
-       "| 2020-05-06 | 10 |\n",
-       "| 2020-05-13 | 10 |\n",
-       "| 2020-05-20 | 10 |\n",
-       "| 2020-05-27 | 10 |\n",
-       "| 2020-06-03 | 10 |\n",
-       "| 2020-06-10 | 10 |\n",
-       "| 2020-06-17 | 10 |\n",
-       "| 2020-06-24 | 10 |\n",
-       "| 2020-07-01 | 10 |\n",
-       "| 2020-07-08 | 10 |\n",
-       "| 2020-07-15 | 10 |\n",
-       "| 2020-07-22 | 10 |\n",
-       "| 2020-07-29 | 10 |\n",
-       "| 2020-08-05 | 10 |\n",
-       "| 2020-08-12 | 10 |\n",
-       "| 2020-08-19 | 10 |\n",
-       "| 2020-08-26 | 10 |\n",
-       "| 2020-09-02 | 10 |\n",
-       "| 2020-09-09 | 10 |\n",
-       "| 2020-09-16 | 10 |\n",
-       "| 2020-09-23 | 10 |\n",
-       "| 2020-09-30 | 10 |\n",
-       "| 2020-10-07 | 10 |\n",
-       "| 2020-10-14 | 10 |\n",
-       "| 2020-10-21 | 10 |\n",
-       "| 2020-10-28 | 10 |\n",
-       "| 2020-11-04 | 10 |\n",
-       "| 2020-11-11 | 10 |\n",
-       "| 2020-11-18 | 10 |\n",
-       "| 2020-11-25 | 10 |\n",
-       "| 2020-12-02 | 10 |\n",
-       "| 2020-12-09 | 10 |\n",
-       "| 2020-12-16 | 10 |\n",
-       "| 2020-12-23 | 10 |\n",
-       "| 2020-12-30 | 10 |\n",
-       "\n"
-      ],
-      "text/plain": [
-       "           additional_product\n",
-       "2020-01-01 10                \n",
-       "2020-01-08 10                \n",
-       "2020-01-15 10                \n",
-       "2020-01-22 10                \n",
-       "2020-01-29 10                \n",
-       "2020-02-05 10                \n",
-       "2020-02-12 10                \n",
-       "2020-02-19 10                \n",
-       "2020-02-26 10                \n",
-       "2020-03-04 10                \n",
-       "2020-03-11 10                \n",
-       "2020-03-18 10                \n",
-       "2020-03-25 10                \n",
-       "2020-04-01 10                \n",
-       "2020-04-08 10                \n",
-       "2020-04-15 10                \n",
-       "2020-04-22 10                \n",
-       "2020-04-29 10                \n",
-       "2020-05-06 10                \n",
-       "2020-05-13 10                \n",
-       "2020-05-20 10                \n",
-       "2020-05-27 10                \n",
-       "2020-06-03 10                \n",
-       "2020-06-10 10                \n",
-       "2020-06-17 10                \n",
-       "2020-06-24 10                \n",
-       "2020-07-01 10                \n",
-       "2020-07-08 10                \n",
-       "2020-07-15 10                \n",
-       "2020-07-22 10                \n",
-       "2020-07-29 10                \n",
-       "2020-08-05 10                \n",
-       "2020-08-12 10                \n",
-       "2020-08-19 10                \n",
-       "2020-08-26 10                \n",
-       "2020-09-02 10                \n",
-       "2020-09-09 10                \n",
-       "2020-09-16 10                \n",
-       "2020-09-23 10                \n",
-       "2020-09-30 10                \n",
-       "2020-10-07 10                \n",
-       "2020-10-14 10                \n",
-       "2020-10-21 10                \n",
-       "2020-10-28 10                \n",
-       "2020-11-04 10                \n",
-       "2020-11-11 10                \n",
-       "2020-11-18 10                \n",
-       "2020-11-25 10                \n",
-       "2020-12-02 10                \n",
-       "2020-12-09 10                \n",
-       "2020-12-16 10                \n",
-       "2020-12-23 10                \n",
-       "2020-12-30 10                "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "text/html": [
-       "<table class=\"dataframe\">\n",
-       "<caption>A data.frame: 366 × 1</caption>\n",
-       "<thead>\n",
-       "\t<tr><th></th><th scope=col>total</th></tr>\n",
-       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th></tr>\n",
-       "</thead>\n",
-       "<tbody>\n",
-       "\t<tr><th scope=row>2020-01-01</th><td>53.59979</td></tr>\n",
-       "\t<tr><th scope=row>2020-01-02</th><td>      NA</td></tr>\n",
-       "\t<tr><th scope=row>2020-01-03</th><td>      NA</td></tr>\n",
-       "\t<tr><th scope=row>2020-01-04</th><td>      NA</td></tr>\n",
-       "\t<tr><th scope=row>2020-01-05</th><td>      NA</td></tr>\n",
-       "\t<tr><th scope=row>2020-01-06</th><td>      NA</td></tr>\n",
-       "\t<tr><th scope=row>2020-01-07</th><td>      NA</td></tr>\n",
-       "\t<tr><th scope=row>2020-01-08</th><td>40.93455</td></tr>\n",
-       "\t<tr><th scope=row>2020-01-09</th><td>      NA</td></tr>\n",
-       "\t<tr><th scope=row>2020-01-10</th><td>      NA</td></tr>\n",
-       "\t<tr><th scope=row>2020-01-11</th><td>      NA</td></tr>\n",
-       "\t<tr><th scope=row>2020-01-12</th><td>      NA</td></tr>\n",
-       "\t<tr><th scope=row>2020-01-13</th><td>      NA</td></tr>\n",
-       "\t<tr><th scope=row>2020-01-14</th><td>      NA</td></tr>\n",
-       "\t<tr><th scope=row>2020-01-15</th><td>59.24704</td></tr>\n",
-       "\t<tr><th scope=row>2020-01-16</th><td>      NA</td></tr>\n",
-       "\t<tr><th scope=row>2020-01-17</th><td>      NA</td></tr>\n",
-       "\t<tr><th scope=row>2020-01-18</th><td>      NA</td></tr>\n",
-       "\t<tr><th scope=row>2020-01-19</th><td>      NA</td></tr>\n",
-       "\t<tr><th scope=row>2020-01-20</th><td>      NA</td></tr>\n",
-       "\t<tr><th scope=row>2020-01-21</th><td>      NA</td></tr>\n",
-       "\t<tr><th scope=row>2020-01-22</th><td>38.26416</td></tr>\n",
-       "\t<tr><th scope=row>2020-01-23</th><td>      NA</td></tr>\n",
-       "\t<tr><th scope=row>2020-01-24</th><td>      NA</td></tr>\n",
-       "\t<tr><th scope=row>2020-01-25</th><td>      NA</td></tr>\n",
-       "\t<tr><th scope=row>2020-01-26</th><td>      NA</td></tr>\n",
-       "\t<tr><th scope=row>2020-01-27</th><td>      NA</td></tr>\n",
-       "\t<tr><th scope=row>2020-01-28</th><td>      NA</td></tr>\n",
-       "\t<tr><th scope=row>2020-01-29</th><td>44.58327</td></tr>\n",
-       "\t<tr><th scope=row>2020-01-30</th><td>      NA</td></tr>\n",
-       "\t<tr><th scope=row>...</th><td>...</td></tr>\n",
-       "\t<tr><th scope=row>2020-12-02</th><td>41.74811</td></tr>\n",
-       "\t<tr><th scope=row>2020-12-03</th><td>      NA</td></tr>\n",
-       "\t<tr><th scope=row>2020-12-04</th><td>      NA</td></tr>\n",
-       "\t<tr><th scope=row>2020-12-05</th><td>      NA</td></tr>\n",
-       "\t<tr><th scope=row>2020-12-06</th><td>      NA</td></tr>\n",
-       "\t<tr><th scope=row>2020-12-07</th><td>      NA</td></tr>\n",
-       "\t<tr><th scope=row>2020-12-08</th><td>      NA</td></tr>\n",
-       "\t<tr><th scope=row>2020-12-09</th><td>37.85650</td></tr>\n",
-       "\t<tr><th scope=row>2020-12-10</th><td>      NA</td></tr>\n",
-       "\t<tr><th scope=row>2020-12-11</th><td>      NA</td></tr>\n",
-       "\t<tr><th scope=row>2020-12-12</th><td>      NA</td></tr>\n",
-       "\t<tr><th scope=row>2020-12-13</th><td>      NA</td></tr>\n",
-       "\t<tr><th scope=row>2020-12-14</th><td>      NA</td></tr>\n",
-       "\t<tr><th scope=row>2020-12-15</th><td>      NA</td></tr>\n",
-       "\t<tr><th scope=row>2020-12-16</th><td>46.73560</td></tr>\n",
-       "\t<tr><th scope=row>2020-12-17</th><td>      NA</td></tr>\n",
-       "\t<tr><th scope=row>2020-12-18</th><td>      NA</td></tr>\n",
-       "\t<tr><th scope=row>2020-12-19</th><td>      NA</td></tr>\n",
-       "\t<tr><th scope=row>2020-12-20</th><td>      NA</td></tr>\n",
-       "\t<tr><th scope=row>2020-12-21</th><td>      NA</td></tr>\n",
-       "\t<tr><th scope=row>2020-12-22</th><td>      NA</td></tr>\n",
-       "\t<tr><th scope=row>2020-12-23</th><td>40.42143</td></tr>\n",
-       "\t<tr><th scope=row>2020-12-24</th><td>      NA</td></tr>\n",
-       "\t<tr><th scope=row>2020-12-25</th><td>      NA</td></tr>\n",
-       "\t<tr><th scope=row>2020-12-26</th><td>      NA</td></tr>\n",
-       "\t<tr><th scope=row>2020-12-27</th><td>      NA</td></tr>\n",
-       "\t<tr><th scope=row>2020-12-28</th><td>      NA</td></tr>\n",
-       "\t<tr><th scope=row>2020-12-29</th><td>      NA</td></tr>\n",
-       "\t<tr><th scope=row>2020-12-30</th><td>41.20298</td></tr>\n",
-       "\t<tr><th scope=row>2020-12-31</th><td>      NA</td></tr>\n",
-       "</tbody>\n",
-       "</table>\n"
-      ],
-      "text/latex": [
-       "A data.frame: 366 × 1\n",
-       "\\begin{tabular}{r|l}\n",
-       "  & total\\\\\n",
-       "  & <dbl>\\\\\n",
-       "\\hline\n",
-       "\t2020-01-01 & 53.59979\\\\\n",
-       "\t2020-01-02 &       NA\\\\\n",
-       "\t2020-01-03 &       NA\\\\\n",
-       "\t2020-01-04 &       NA\\\\\n",
-       "\t2020-01-05 &       NA\\\\\n",
-       "\t2020-01-06 &       NA\\\\\n",
-       "\t2020-01-07 &       NA\\\\\n",
-       "\t2020-01-08 & 40.93455\\\\\n",
-       "\t2020-01-09 &       NA\\\\\n",
-       "\t2020-01-10 &       NA\\\\\n",
-       "\t2020-01-11 &       NA\\\\\n",
-       "\t2020-01-12 &       NA\\\\\n",
-       "\t2020-01-13 &       NA\\\\\n",
-       "\t2020-01-14 &       NA\\\\\n",
-       "\t2020-01-15 & 59.24704\\\\\n",
-       "\t2020-01-16 &       NA\\\\\n",
-       "\t2020-01-17 &       NA\\\\\n",
-       "\t2020-01-18 &       NA\\\\\n",
-       "\t2020-01-19 &       NA\\\\\n",
-       "\t2020-01-20 &       NA\\\\\n",
-       "\t2020-01-21 &       NA\\\\\n",
-       "\t2020-01-22 & 38.26416\\\\\n",
-       "\t2020-01-23 &       NA\\\\\n",
-       "\t2020-01-24 &       NA\\\\\n",
-       "\t2020-01-25 &       NA\\\\\n",
-       "\t2020-01-26 &       NA\\\\\n",
-       "\t2020-01-27 &       NA\\\\\n",
-       "\t2020-01-28 &       NA\\\\\n",
-       "\t2020-01-29 & 44.58327\\\\\n",
-       "\t2020-01-30 &       NA\\\\\n",
-       "\t... & ...\\\\\n",
-       "\t2020-12-02 & 41.74811\\\\\n",
-       "\t2020-12-03 &       NA\\\\\n",
-       "\t2020-12-04 &       NA\\\\\n",
-       "\t2020-12-05 &       NA\\\\\n",
-       "\t2020-12-06 &       NA\\\\\n",
-       "\t2020-12-07 &       NA\\\\\n",
-       "\t2020-12-08 &       NA\\\\\n",
-       "\t2020-12-09 & 37.85650\\\\\n",
-       "\t2020-12-10 &       NA\\\\\n",
-       "\t2020-12-11 &       NA\\\\\n",
-       "\t2020-12-12 &       NA\\\\\n",
-       "\t2020-12-13 &       NA\\\\\n",
-       "\t2020-12-14 &       NA\\\\\n",
-       "\t2020-12-15 &       NA\\\\\n",
-       "\t2020-12-16 & 46.73560\\\\\n",
-       "\t2020-12-17 &       NA\\\\\n",
-       "\t2020-12-18 &       NA\\\\\n",
-       "\t2020-12-19 &       NA\\\\\n",
-       "\t2020-12-20 &       NA\\\\\n",
-       "\t2020-12-21 &       NA\\\\\n",
-       "\t2020-12-22 &       NA\\\\\n",
-       "\t2020-12-23 & 40.42143\\\\\n",
-       "\t2020-12-24 &       NA\\\\\n",
-       "\t2020-12-25 &       NA\\\\\n",
-       "\t2020-12-26 &       NA\\\\\n",
-       "\t2020-12-27 &       NA\\\\\n",
-       "\t2020-12-28 &       NA\\\\\n",
-       "\t2020-12-29 &       NA\\\\\n",
-       "\t2020-12-30 & 41.20298\\\\\n",
-       "\t2020-12-31 &       NA\\\\\n",
-       "\\end{tabular}\n"
-      ],
-      "text/markdown": [
-       "\n",
-       "A data.frame: 366 × 1\n",
-       "\n",
-       "| <!--/--> | total &lt;dbl&gt; |\n",
-       "|---|---|\n",
-       "| 2020-01-01 | 53.59979 |\n",
-       "| 2020-01-02 |       NA |\n",
-       "| 2020-01-03 |       NA |\n",
-       "| 2020-01-04 |       NA |\n",
-       "| 2020-01-05 |       NA |\n",
-       "| 2020-01-06 |       NA |\n",
-       "| 2020-01-07 |       NA |\n",
-       "| 2020-01-08 | 40.93455 |\n",
-       "| 2020-01-09 |       NA |\n",
-       "| 2020-01-10 |       NA |\n",
-       "| 2020-01-11 |       NA |\n",
-       "| 2020-01-12 |       NA |\n",
-       "| 2020-01-13 |       NA |\n",
-       "| 2020-01-14 |       NA |\n",
-       "| 2020-01-15 | 59.24704 |\n",
-       "| 2020-01-16 |       NA |\n",
-       "| 2020-01-17 |       NA |\n",
-       "| 2020-01-18 |       NA |\n",
-       "| 2020-01-19 |       NA |\n",
-       "| 2020-01-20 |       NA |\n",
-       "| 2020-01-21 |       NA |\n",
-       "| 2020-01-22 | 38.26416 |\n",
-       "| 2020-01-23 |       NA |\n",
-       "| 2020-01-24 |       NA |\n",
-       "| 2020-01-25 |       NA |\n",
-       "| 2020-01-26 |       NA |\n",
-       "| 2020-01-27 |       NA |\n",
-       "| 2020-01-28 |       NA |\n",
-       "| 2020-01-29 | 44.58327 |\n",
-       "| 2020-01-30 |       NA |\n",
-       "| ... | ... |\n",
-       "| 2020-12-02 | 41.74811 |\n",
-       "| 2020-12-03 |       NA |\n",
-       "| 2020-12-04 |       NA |\n",
-       "| 2020-12-05 |       NA |\n",
-       "| 2020-12-06 |       NA |\n",
-       "| 2020-12-07 |       NA |\n",
-       "| 2020-12-08 |       NA |\n",
-       "| 2020-12-09 | 37.85650 |\n",
-       "| 2020-12-10 |       NA |\n",
-       "| 2020-12-11 |       NA |\n",
-       "| 2020-12-12 |       NA |\n",
-       "| 2020-12-13 |       NA |\n",
-       "| 2020-12-14 |       NA |\n",
-       "| 2020-12-15 |       NA |\n",
-       "| 2020-12-16 | 46.73560 |\n",
-       "| 2020-12-17 |       NA |\n",
-       "| 2020-12-18 |       NA |\n",
-       "| 2020-12-19 |       NA |\n",
-       "| 2020-12-20 |       NA |\n",
-       "| 2020-12-21 |       NA |\n",
-       "| 2020-12-22 |       NA |\n",
-       "| 2020-12-23 | 40.42143 |\n",
-       "| 2020-12-24 |       NA |\n",
-       "| 2020-12-25 |       NA |\n",
-       "| 2020-12-26 |       NA |\n",
-       "| 2020-12-27 |       NA |\n",
-       "| 2020-12-28 |       NA |\n",
-       "| 2020-12-29 |       NA |\n",
-       "| 2020-12-30 | 41.20298 |\n",
-       "| 2020-12-31 |       NA |\n",
-       "\n"
-      ],
-      "text/plain": [
-       "           total   \n",
-       "2020-01-01 53.59979\n",
-       "2020-01-02       NA\n",
-       "2020-01-03       NA\n",
-       "2020-01-04       NA\n",
-       "2020-01-05       NA\n",
-       "2020-01-06       NA\n",
-       "2020-01-07       NA\n",
-       "2020-01-08 40.93455\n",
-       "2020-01-09       NA\n",
-       "2020-01-10       NA\n",
-       "2020-01-11       NA\n",
-       "2020-01-12       NA\n",
-       "2020-01-13       NA\n",
-       "2020-01-14       NA\n",
-       "2020-01-15 59.24704\n",
-       "2020-01-16       NA\n",
-       "2020-01-17       NA\n",
-       "2020-01-18       NA\n",
-       "2020-01-19       NA\n",
-       "2020-01-20       NA\n",
-       "2020-01-21       NA\n",
-       "2020-01-22 38.26416\n",
-       "2020-01-23       NA\n",
-       "2020-01-24       NA\n",
-       "2020-01-25       NA\n",
-       "2020-01-26       NA\n",
-       "2020-01-27       NA\n",
-       "2020-01-28       NA\n",
-       "2020-01-29 44.58327\n",
-       "2020-01-30       NA\n",
-       "...        ...     \n",
-       "2020-12-02 41.74811\n",
-       "2020-12-03       NA\n",
-       "2020-12-04       NA\n",
-       "2020-12-05       NA\n",
-       "2020-12-06       NA\n",
-       "2020-12-07       NA\n",
-       "2020-12-08       NA\n",
-       "2020-12-09 37.85650\n",
-       "2020-12-10       NA\n",
-       "2020-12-11       NA\n",
-       "2020-12-12       NA\n",
-       "2020-12-13       NA\n",
-       "2020-12-14       NA\n",
-       "2020-12-15       NA\n",
-       "2020-12-16 46.73560\n",
-       "2020-12-17       NA\n",
-       "2020-12-18       NA\n",
-       "2020-12-19       NA\n",
-       "2020-12-20       NA\n",
-       "2020-12-21       NA\n",
-       "2020-12-22       NA\n",
-       "2020-12-23 40.42143\n",
-       "2020-12-24       NA\n",
-       "2020-12-25       NA\n",
-       "2020-12-26       NA\n",
-       "2020-12-27       NA\n",
-       "2020-12-28       NA\n",
-       "2020-12-29       NA\n",
-       "2020-12-30 41.20298\n",
-       "2020-12-31       NA"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "index = seq(start_date,end_date,by = 'week')\n",
-    "sz = length(index)\n",
-    "additional_product <- rep(10,53)\n",
-    "additional_items  <- data.frame(row.names = index[0:sz],additional_product)\n",
-    "additional_items\n",
-    "# we are merging two dataframe so that we can add\n",
-    "additional_item = merge(additional_items,sold_items, by = 0, all = TRUE)[-1] \n",
-    "total  = data.frame(row.names=idx[0:size],additional_item$additional_product + additional_item$sales)\n",
-    "colnames(total) =  c('total')\n",
-    "total"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "id": "387cb4c2",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<table class=\"dataframe\">\n",
-       "<caption>A data.frame: 366 × 1</caption>\n",
-       "<thead>\n",
-       "\t<tr><th></th><th scope=col>total</th></tr>\n",
-       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th></tr>\n",
-       "</thead>\n",
-       "<tbody>\n",
-       "\t<tr><th scope=row>2020-01-01</th><td>53.59979</td></tr>\n",
-       "\t<tr><th scope=row>2020-01-02</th><td>30.41127</td></tr>\n",
-       "\t<tr><th scope=row>2020-01-03</th><td>48.54839</td></tr>\n",
-       "\t<tr><th scope=row>2020-01-04</th><td>39.20897</td></tr>\n",
-       "\t<tr><th scope=row>2020-01-05</th><td>39.09894</td></tr>\n",
-       "\t<tr><th scope=row>2020-01-06</th><td>47.53019</td></tr>\n",
-       "\t<tr><th scope=row>2020-01-07</th><td>44.94766</td></tr>\n",
-       "\t<tr><th scope=row>2020-01-08</th><td>40.93455</td></tr>\n",
-       "\t<tr><th scope=row>2020-01-09</th><td>37.66561</td></tr>\n",
-       "\t<tr><th scope=row>2020-01-10</th><td>31.68825</td></tr>\n",
-       "\t<tr><th scope=row>2020-01-11</th><td>45.30576</td></tr>\n",
-       "\t<tr><th scope=row>2020-01-12</th><td>26.45509</td></tr>\n",
-       "\t<tr><th scope=row>2020-01-13</th><td>45.81249</td></tr>\n",
-       "\t<tr><th scope=row>2020-01-14</th><td>46.84547</td></tr>\n",
-       "\t<tr><th scope=row>2020-01-15</th><td>59.24704</td></tr>\n",
-       "\t<tr><th scope=row>2020-01-16</th><td>29.28688</td></tr>\n",
-       "\t<tr><th scope=row>2020-01-17</th><td>32.41731</td></tr>\n",
-       "\t<tr><th scope=row>2020-01-18</th><td>45.23295</td></tr>\n",
-       "\t<tr><th scope=row>2020-01-19</th><td>48.54330</td></tr>\n",
-       "\t<tr><th scope=row>2020-01-20</th><td>36.69353</td></tr>\n",
-       "\t<tr><th scope=row>2020-01-21</th><td>43.09588</td></tr>\n",
-       "\t<tr><th scope=row>2020-01-22</th><td>38.26416</td></tr>\n",
-       "\t<tr><th scope=row>2020-01-23</th><td>45.56863</td></tr>\n",
-       "\t<tr><th scope=row>2020-01-24</th><td>25.70944</td></tr>\n",
-       "\t<tr><th scope=row>2020-01-25</th><td>37.38721</td></tr>\n",
-       "\t<tr><th scope=row>2020-01-26</th><td>44.53955</td></tr>\n",
-       "\t<tr><th scope=row>2020-01-27</th><td>46.88427</td></tr>\n",
-       "\t<tr><th scope=row>2020-01-28</th><td>48.05540</td></tr>\n",
-       "\t<tr><th scope=row>2020-01-29</th><td>44.58327</td></tr>\n",
-       "\t<tr><th scope=row>2020-01-30</th><td>26.19490</td></tr>\n",
-       "\t<tr><th scope=row>...</th><td>...</td></tr>\n",
-       "\t<tr><th scope=row>2020-12-02</th><td>41.74811</td></tr>\n",
-       "\t<tr><th scope=row>2020-12-03</th><td>35.03915</td></tr>\n",
-       "\t<tr><th scope=row>2020-12-04</th><td>25.84637</td></tr>\n",
-       "\t<tr><th scope=row>2020-12-05</th><td>27.73147</td></tr>\n",
-       "\t<tr><th scope=row>2020-12-06</th><td>39.00993</td></tr>\n",
-       "\t<tr><th scope=row>2020-12-07</th><td>41.03187</td></tr>\n",
-       "\t<tr><th scope=row>2020-12-08</th><td>26.33862</td></tr>\n",
-       "\t<tr><th scope=row>2020-12-09</th><td>37.85650</td></tr>\n",
-       "\t<tr><th scope=row>2020-12-10</th><td>41.98943</td></tr>\n",
-       "\t<tr><th scope=row>2020-12-11</th><td>36.68901</td></tr>\n",
-       "\t<tr><th scope=row>2020-12-12</th><td>46.96883</td></tr>\n",
-       "\t<tr><th scope=row>2020-12-13</th><td>39.70374</td></tr>\n",
-       "\t<tr><th scope=row>2020-12-14</th><td>46.59464</td></tr>\n",
-       "\t<tr><th scope=row>2020-12-15</th><td>41.24742</td></tr>\n",
-       "\t<tr><th scope=row>2020-12-16</th><td>46.73560</td></tr>\n",
-       "\t<tr><th scope=row>2020-12-17</th><td>32.68275</td></tr>\n",
-       "\t<tr><th scope=row>2020-12-18</th><td>46.64238</td></tr>\n",
-       "\t<tr><th scope=row>2020-12-19</th><td>25.22163</td></tr>\n",
-       "\t<tr><th scope=row>2020-12-20</th><td>39.79997</td></tr>\n",
-       "\t<tr><th scope=row>2020-12-21</th><td>34.45013</td></tr>\n",
-       "\t<tr><th scope=row>2020-12-22</th><td>48.71183</td></tr>\n",
-       "\t<tr><th scope=row>2020-12-23</th><td>40.42143</td></tr>\n",
-       "\t<tr><th scope=row>2020-12-24</th><td>32.41991</td></tr>\n",
-       "\t<tr><th scope=row>2020-12-25</th><td>39.12296</td></tr>\n",
-       "\t<tr><th scope=row>2020-12-26</th><td>29.43616</td></tr>\n",
-       "\t<tr><th scope=row>2020-12-27</th><td>39.09337</td></tr>\n",
-       "\t<tr><th scope=row>2020-12-28</th><td>38.09288</td></tr>\n",
-       "\t<tr><th scope=row>2020-12-29</th><td>41.00681</td></tr>\n",
-       "\t<tr><th scope=row>2020-12-30</th><td>41.20298</td></tr>\n",
-       "\t<tr><th scope=row>2020-12-31</th><td>43.25232</td></tr>\n",
-       "</tbody>\n",
-       "</table>\n"
-      ],
-      "text/latex": [
-       "A data.frame: 366 × 1\n",
-       "\\begin{tabular}{r|l}\n",
-       "  & total\\\\\n",
-       "  & <dbl>\\\\\n",
-       "\\hline\n",
-       "\t2020-01-01 & 53.59979\\\\\n",
-       "\t2020-01-02 & 30.41127\\\\\n",
-       "\t2020-01-03 & 48.54839\\\\\n",
-       "\t2020-01-04 & 39.20897\\\\\n",
-       "\t2020-01-05 & 39.09894\\\\\n",
-       "\t2020-01-06 & 47.53019\\\\\n",
-       "\t2020-01-07 & 44.94766\\\\\n",
-       "\t2020-01-08 & 40.93455\\\\\n",
-       "\t2020-01-09 & 37.66561\\\\\n",
-       "\t2020-01-10 & 31.68825\\\\\n",
-       "\t2020-01-11 & 45.30576\\\\\n",
-       "\t2020-01-12 & 26.45509\\\\\n",
-       "\t2020-01-13 & 45.81249\\\\\n",
-       "\t2020-01-14 & 46.84547\\\\\n",
-       "\t2020-01-15 & 59.24704\\\\\n",
-       "\t2020-01-16 & 29.28688\\\\\n",
-       "\t2020-01-17 & 32.41731\\\\\n",
-       "\t2020-01-18 & 45.23295\\\\\n",
-       "\t2020-01-19 & 48.54330\\\\\n",
-       "\t2020-01-20 & 36.69353\\\\\n",
-       "\t2020-01-21 & 43.09588\\\\\n",
-       "\t2020-01-22 & 38.26416\\\\\n",
-       "\t2020-01-23 & 45.56863\\\\\n",
-       "\t2020-01-24 & 25.70944\\\\\n",
-       "\t2020-01-25 & 37.38721\\\\\n",
-       "\t2020-01-26 & 44.53955\\\\\n",
-       "\t2020-01-27 & 46.88427\\\\\n",
-       "\t2020-01-28 & 48.05540\\\\\n",
-       "\t2020-01-29 & 44.58327\\\\\n",
-       "\t2020-01-30 & 26.19490\\\\\n",
-       "\t... & ...\\\\\n",
-       "\t2020-12-02 & 41.74811\\\\\n",
-       "\t2020-12-03 & 35.03915\\\\\n",
-       "\t2020-12-04 & 25.84637\\\\\n",
-       "\t2020-12-05 & 27.73147\\\\\n",
-       "\t2020-12-06 & 39.00993\\\\\n",
-       "\t2020-12-07 & 41.03187\\\\\n",
-       "\t2020-12-08 & 26.33862\\\\\n",
-       "\t2020-12-09 & 37.85650\\\\\n",
-       "\t2020-12-10 & 41.98943\\\\\n",
-       "\t2020-12-11 & 36.68901\\\\\n",
-       "\t2020-12-12 & 46.96883\\\\\n",
-       "\t2020-12-13 & 39.70374\\\\\n",
-       "\t2020-12-14 & 46.59464\\\\\n",
-       "\t2020-12-15 & 41.24742\\\\\n",
-       "\t2020-12-16 & 46.73560\\\\\n",
-       "\t2020-12-17 & 32.68275\\\\\n",
-       "\t2020-12-18 & 46.64238\\\\\n",
-       "\t2020-12-19 & 25.22163\\\\\n",
-       "\t2020-12-20 & 39.79997\\\\\n",
-       "\t2020-12-21 & 34.45013\\\\\n",
-       "\t2020-12-22 & 48.71183\\\\\n",
-       "\t2020-12-23 & 40.42143\\\\\n",
-       "\t2020-12-24 & 32.41991\\\\\n",
-       "\t2020-12-25 & 39.12296\\\\\n",
-       "\t2020-12-26 & 29.43616\\\\\n",
-       "\t2020-12-27 & 39.09337\\\\\n",
-       "\t2020-12-28 & 38.09288\\\\\n",
-       "\t2020-12-29 & 41.00681\\\\\n",
-       "\t2020-12-30 & 41.20298\\\\\n",
-       "\t2020-12-31 & 43.25232\\\\\n",
-       "\\end{tabular}\n"
-      ],
-      "text/markdown": [
-       "\n",
-       "A data.frame: 366 × 1\n",
-       "\n",
-       "| <!--/--> | total &lt;dbl&gt; |\n",
-       "|---|---|\n",
-       "| 2020-01-01 | 53.59979 |\n",
-       "| 2020-01-02 | 30.41127 |\n",
-       "| 2020-01-03 | 48.54839 |\n",
-       "| 2020-01-04 | 39.20897 |\n",
-       "| 2020-01-05 | 39.09894 |\n",
-       "| 2020-01-06 | 47.53019 |\n",
-       "| 2020-01-07 | 44.94766 |\n",
-       "| 2020-01-08 | 40.93455 |\n",
-       "| 2020-01-09 | 37.66561 |\n",
-       "| 2020-01-10 | 31.68825 |\n",
-       "| 2020-01-11 | 45.30576 |\n",
-       "| 2020-01-12 | 26.45509 |\n",
-       "| 2020-01-13 | 45.81249 |\n",
-       "| 2020-01-14 | 46.84547 |\n",
-       "| 2020-01-15 | 59.24704 |\n",
-       "| 2020-01-16 | 29.28688 |\n",
-       "| 2020-01-17 | 32.41731 |\n",
-       "| 2020-01-18 | 45.23295 |\n",
-       "| 2020-01-19 | 48.54330 |\n",
-       "| 2020-01-20 | 36.69353 |\n",
-       "| 2020-01-21 | 43.09588 |\n",
-       "| 2020-01-22 | 38.26416 |\n",
-       "| 2020-01-23 | 45.56863 |\n",
-       "| 2020-01-24 | 25.70944 |\n",
-       "| 2020-01-25 | 37.38721 |\n",
-       "| 2020-01-26 | 44.53955 |\n",
-       "| 2020-01-27 | 46.88427 |\n",
-       "| 2020-01-28 | 48.05540 |\n",
-       "| 2020-01-29 | 44.58327 |\n",
-       "| 2020-01-30 | 26.19490 |\n",
-       "| ... | ... |\n",
-       "| 2020-12-02 | 41.74811 |\n",
-       "| 2020-12-03 | 35.03915 |\n",
-       "| 2020-12-04 | 25.84637 |\n",
-       "| 2020-12-05 | 27.73147 |\n",
-       "| 2020-12-06 | 39.00993 |\n",
-       "| 2020-12-07 | 41.03187 |\n",
-       "| 2020-12-08 | 26.33862 |\n",
-       "| 2020-12-09 | 37.85650 |\n",
-       "| 2020-12-10 | 41.98943 |\n",
-       "| 2020-12-11 | 36.68901 |\n",
-       "| 2020-12-12 | 46.96883 |\n",
-       "| 2020-12-13 | 39.70374 |\n",
-       "| 2020-12-14 | 46.59464 |\n",
-       "| 2020-12-15 | 41.24742 |\n",
-       "| 2020-12-16 | 46.73560 |\n",
-       "| 2020-12-17 | 32.68275 |\n",
-       "| 2020-12-18 | 46.64238 |\n",
-       "| 2020-12-19 | 25.22163 |\n",
-       "| 2020-12-20 | 39.79997 |\n",
-       "| 2020-12-21 | 34.45013 |\n",
-       "| 2020-12-22 | 48.71183 |\n",
-       "| 2020-12-23 | 40.42143 |\n",
-       "| 2020-12-24 | 32.41991 |\n",
-       "| 2020-12-25 | 39.12296 |\n",
-       "| 2020-12-26 | 29.43616 |\n",
-       "| 2020-12-27 | 39.09337 |\n",
-       "| 2020-12-28 | 38.09288 |\n",
-       "| 2020-12-29 | 41.00681 |\n",
-       "| 2020-12-30 | 41.20298 |\n",
-       "| 2020-12-31 | 43.25232 |\n",
-       "\n"
-      ],
-      "text/plain": [
-       "           total   \n",
-       "2020-01-01 53.59979\n",
-       "2020-01-02 30.41127\n",
-       "2020-01-03 48.54839\n",
-       "2020-01-04 39.20897\n",
-       "2020-01-05 39.09894\n",
-       "2020-01-06 47.53019\n",
-       "2020-01-07 44.94766\n",
-       "2020-01-08 40.93455\n",
-       "2020-01-09 37.66561\n",
-       "2020-01-10 31.68825\n",
-       "2020-01-11 45.30576\n",
-       "2020-01-12 26.45509\n",
-       "2020-01-13 45.81249\n",
-       "2020-01-14 46.84547\n",
-       "2020-01-15 59.24704\n",
-       "2020-01-16 29.28688\n",
-       "2020-01-17 32.41731\n",
-       "2020-01-18 45.23295\n",
-       "2020-01-19 48.54330\n",
-       "2020-01-20 36.69353\n",
-       "2020-01-21 43.09588\n",
-       "2020-01-22 38.26416\n",
-       "2020-01-23 45.56863\n",
-       "2020-01-24 25.70944\n",
-       "2020-01-25 37.38721\n",
-       "2020-01-26 44.53955\n",
-       "2020-01-27 46.88427\n",
-       "2020-01-28 48.05540\n",
-       "2020-01-29 44.58327\n",
-       "2020-01-30 26.19490\n",
-       "...        ...     \n",
-       "2020-12-02 41.74811\n",
-       "2020-12-03 35.03915\n",
-       "2020-12-04 25.84637\n",
-       "2020-12-05 27.73147\n",
-       "2020-12-06 39.00993\n",
-       "2020-12-07 41.03187\n",
-       "2020-12-08 26.33862\n",
-       "2020-12-09 37.85650\n",
-       "2020-12-10 41.98943\n",
-       "2020-12-11 36.68901\n",
-       "2020-12-12 46.96883\n",
-       "2020-12-13 39.70374\n",
-       "2020-12-14 46.59464\n",
-       "2020-12-15 41.24742\n",
-       "2020-12-16 46.73560\n",
-       "2020-12-17 32.68275\n",
-       "2020-12-18 46.64238\n",
-       "2020-12-19 25.22163\n",
-       "2020-12-20 39.79997\n",
-       "2020-12-21 34.45013\n",
-       "2020-12-22 48.71183\n",
-       "2020-12-23 40.42143\n",
-       "2020-12-24 32.41991\n",
-       "2020-12-25 39.12296\n",
-       "2020-12-26 29.43616\n",
-       "2020-12-27 39.09337\n",
-       "2020-12-28 38.09288\n",
-       "2020-12-29 41.00681\n",
-       "2020-12-30 41.20298\n",
-       "2020-12-31 43.25232"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "additional_item[is.na(additional_item)] = 0\n",
-    "total  = data.frame(row.names=idx[0:size],additional_item$additional_product + additional_item$sales)\n",
-    "colnames(total) =  c('total')\n",
-    "total"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "id": "bdb60236",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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w4ufUoM9EatB+SA06AkE/gmnQW+6DBpAatBG7cREFrNSgzViYoAMOxA/CatDE8QhIDdqIj3EhbzGkBm3GwgQdrV2PIyKuBu1pTmrQJozUoGPeBE4N2ozbEPTw1i/mBa9Bnzca8l0cNoSyDdGgZ2yjNCBU0zVIDToCQT8Gd0E5LyLsg8bKjywIpgZtxIU8aDekBn0uQYcahpXnEliD3gbb1tUtqUEbcSENevrEToIO4kEPRB1apgZtQ6Q+rbGEBn2BXRw+9itKSQ06AkGP3tOGShypQbMndtyCoEcg8Mb7R/CmSw06BEE/JmjQ+7cba9A926BTgxaiudhIxrVIDdqMhQk62JJXhJYRNGgsceTx2GXb4AGxhgYdEicEuwaJIyQWJuho7RpsH3SP0tBZtR3R+vSIRTToaI7LN3IftBm3Ieho7+K4rgbd8wjjLQjaAZTEEZBnIjVdi9SgIxD0I9i7OB6pQQMYoUF7NXNolgltXGrQdqxM0MHWvPM0aHAoon5s5PE4QIN22xecGrQZqUGbsTJB92X3B7aLI/dBi+Hfp35P1qUGbUZq0GbchqBTg37jVhq047spIjkEqUE7IjXoCAT9SA36hTtp0J4vDwrNMqGNSw3ajpUJOtaaV1gTV4PeQo9Hdw3a8eVBqUGbkRq0GSsTdF92fyyiQfstbKto0D4lpQZtRmrQZkwi6N/feP0/h6BTg37jNA1acM0DFl23lwdFcghSg3ZEatB/E/Ph387Qk01PDfoHd9KgPRGaZUIb12mduWfHD7rxWJmgY615qUE7YMS7OIJ50KlBc6haaOQLrn+KDt12cwj69/H/aR70aKQG3Y0RffqxsnOipwZtRo8GbbvLK8vxLDo16F2C/hD0P77BZfPENrMyQcWn2QNi05uD50DPnHvN37PxVAMcQV3IZS7yB699kgPLfn8ZUkUMyDzo32d70I6P0HFFLaZBe/5OUlQNunvDXaCIrb2OQMYBsFvXtZGdyfUuO3TbTdxm507Qp4Uk4IhZWIOO80uj4zTo/kdWfPrUo5mB6whNMj0atLHXRDleZYduu5UJui+7HeiY2Y+tpEFvZu5aSoPuZugwGvTnOq6pQVcY6TukBr1z89kE7alv8DN9rX3QJuZaZR/0XnsnQ0dxtMDruA5BN+aPjO0OuzjEtcyNNecRdHmT8AwP2qdhRQ5natAyW2oM3QfdydBRCBpcT8MYByLuPmj9NrvJauDEJwmP/10I+ixXAZ7mC2vQs70CHEP3Qdu1nB+kBm3G/GBXU4ZuE+Dk+zX5Lg4DsC4yatB9He6zD3rIbXI9XPq0bM7teOJ8gn54tNrrEi6pQc83X6FBd67xBtyGoF2bVLPNjtWgZ2+hOONdHM8LnKNBE815DYnjAbTkdQi6xcgLUmvQ6UGPMz1asxb7vwYYIdegR9cqvkAHDZpszivcJAQR2rg1NWhkpKzjTp1O0Oe5CqxIIL80l6BJLlpsiG1D21J+gf0adNuc1bZafbGvLHE0aABXIugYGjQ6ZK+4iyOGB+0IbquCQoPu5ef4GrRiCXLoU4KfTVT9KS2QBt0WdB2JI4gGTQzZJOgRps/t96ga9HFffm9R0jN7jHC6Bv0olRBpac+UJzkEzCNRL1yHoFucoEHPvxmI4TYE/YiqQffu4uDLPxzZxj/ZCt4inCBxHFc8QWqhSYeJeg5Bzws9BuJcDZpbr2HNLwY/L03QoTTo8lDMfdDPQTdfg55yk/BwDRt8uDJIw9DdtpHWiKqnEZ2gNWTnMBaPRbANqNSgU+IYb3o3uK7TvYvD/QYhnJCK20Zq0A+54DugTxE5WuwhnapB0wvJOhq0wh31Np/vaGQfNL4VqN8oOW5D0HE16Gm2+epqYg1akuWN8YvuUYOW3uN8/tftxnIC0mMTNGjP6+obeH3+C103okH31+uF2xD0Y1yDA+XGfBcHIXEMrJU42sDnXRzSu3/Pf9IKtJGwD87SoD2vq++Wm92M7VA5kSS0PLQyQZ+2wlUaZ/3qx8f8PmdF8dcx9CZhCG9hngb9OS6hDcqDVhOPuqEnbCAHa31dlwtNs/xcnOuvsSxOKnGIS9cbZMfKBN2XvQu1mlkf/sJSc6V1mSJIvN3gXRwlUM/6ST8CfiUI2uQaau8TSs44Sxyf6/JxpDkN+njW4xbhVjM0mRx7F0eHVCeEoG3vQtDDVj14hobUoF84410cXJY35mnQP59U/BppNxahQfsYsx3RXRqzi4OpRVt/UxxT9GkatKRt70LQ3xjR4MgYjqlBP5Ea9CGlgoDO0aCFGKJBuzH0F9klTC02fgZ0R6zws0JxUduuTNDKmzwF3GbVu5WX0KB/jqYGfTzZqUGrR5K+oU/RoF/CgJsHzVV1rKW3Pq3RJ2nQstVvZYL+/mMcP13DrlybQf0sqgb9jdSgj98E44Am6C6L+pKP06A/5Thp0FxVQzVorsizNOjLe9APxWJZzU1P5RAqKDVoXZY3JmvQ5QEGmG1nBB/NUuOtQX8KcpkmX5xZZC2GoEMaQ6UGPZagdRrZvkh73fxAIbu0wSMgNWhXRCLo0oAxL1jRLmAEaA1aZseYrGdq0KLVb2WCtmtkffwMhkKnatDQtaQGDX6zlyu1jR5Y/hr0z1gO/bCFoOmcA0pVEcq285wq1ybo7z9naNA/BYBfPoXO1aCV90RSgz5+89ag+c7w1aCbaDDIWnuEhqAH8F9QDVqE1QlaqpE1jT+ikfeJMlWDVsYDqUEXn5w16NHiWV1nI9eJ6paY6CpxVB6NqlB7qPuqJ6oGLcHyBK0A7PX64TBTJmrQWzNFmeLP0KClV+qiQQ+aWCKCHn97o6neIHGITPQk6HYRk2fu4WfRU6InykPXlji45bED1HNNzaljpLl5vjtYCJgSUoMGv9nLFS66DC34N/Smvkk4fRGpFD+gdl8NGtZ+MKQGPdSDHqtVYKcgDkA96NSgpRi7sC2pQfPPQG+bRFM7ljiboYumA2v3j38O1aQGfSZByyB0MF/n8OFLjuyTNGilpncvDRqfdWM06EG3N+gqlfugZfw8TIPWLg9GC/ZaUoOOT9DfkGrQhIPB+B7ILg6RQf5IDdoV4n3Q0yc2plvhGRQEOUiDHs7PssAyNeiBBD1Mg1Z60EAUdZN3ccSSOEJo0MLCPDMYSEZ+j9AFtXVt9QM0aHEskxr0UA86ggb9+Qp70KM1aB3Ax7pMJvTLmHUBYyWOKRo0D2WjibQk7bs4pnr5gjvYQ+Of1KDPJGgZxI3/Ok3MCkn3kPOl0cXmxsRuGjSqA+FZ6gN1Aevvg9aUaIZFg1Yvpm4a9E8vdxTjFQJhZ76QNKlBzzZdQYqdnUPZtpOS/e6HLB182IsEETleoUG3BaQGbYFEg1bv2nC7it4dI0OzpgY9kKCHadA0wAkpD9W7NzhBBXRr0EYzuq4FKMBHg3YWoa+gQdctLTaj332FhwmiETrVqUqdGvRQD3q/NTT1+S3kKziZC8+d3AQqqbkqwEWDtoSgXvzsRtBwLcjnK2jQh1Nf4NH9UPeosUM04sdaRpeJadAhROj1CfoNmi2UGrQWxM2uamT2EnS/4xpHg3aVOKSo19J7aNCGMeOqQZ+kcXAFUBp0CAn6OgQtoS2xBq11KBU3u15mbqwNdAGSrIM1aDhmQZUVpABpu/HGcJX1IhBBtxUO06A9LuY0ek4N+lSCPmrQ/Y6lGO2EVHmC3UaO0KDP8hZct9ndVIPevUA6ma/MqwIYZ+H2pAa9Y2WCfv57r/Sz+Lmo9Vj10Smm9kFL32uE112U4KJBW715SyaqhPkatOASLqBB99rQiabpttpPSQ0awfoE/YanBq1nTNyDFsT82v7uGx/3ehcHYYSYoZ8IJHHoNWjqBJ3B42q+mmI07lRq0MassQhaoHRpHE9V9yi11K36b4Pqcg+Y+cY4+miL1KBNEL1u1Oa4u1xM60F330kRQRYepQY9iKA9h5AC0ITUaKlO9nK7ppbQoF0ljtSg1aVO6fd2+WjvGLlq0Mr8qUEP9aCHe04gsLoEGrS8MCrHZ4CnBi2uIDXoKs2smzaYB13bU37yQ2rQJxL0DqrZVBp0f/sP16A7Nq2kBl186tag8QqGwaJBQ0EWPYJGatCazUypQRuzRiNoFsM06AbftuED0EODVm77PiA1aBsQ27o35fThFUVZNGh6jfcTQWDrZtwiPFeDllwhm2Rlgh6ko6l5D0j+pXIRTMBCRPzI62Bq0LZyQ2rQ71Fg0KAnPTmwYRSId9I1NGhJ295Lg6ZCGXmfCQctkuQzmY+Dn9MzDAPy+2FEYwjqqEG7R58hNehXmoga9GeUWTToSfwM/aStSFd3NII8O0aDdmrd9Ql6B8XPcsHLpWG/dvdkiAbdY+JwDVojg1ZYUIM+Q+F4L/z7KDNr0GwtfZfztO+LK8d+UmIDc2aIBu0Vn1yJoFG0XIm+bGPbXFr2i15Ae8UZmYWpQbsi4D7ofbDa9kHztwj7LsZjMq2qQVcXjjRCatAP5WLGp+3UoB2mL3BBqUHfUoMGJY4hZhixe9CMCRfXoN/9pH6P5coE/fwn0KBVq7gwLadBH/uCK04/aQ9GpgYtrmCwBj1qXwIVor/HgfVdHMOJWqZBCyZxlxHkWb0GLTPyfZNo290+fTSxPkHvUGjQpIfsK+2nBi3J8sLKGvS7T+Zp0M9af/6ZNGiR9tvtzr5EBHs5S2rQezENVNmvRNAE2qnj7tUWQPq8Kryjjg4n/wQNupcEdRVP1qDfRLZPvumCUeh3cXSqvKtq0K/qW37WvYtyZYJ2G0I6CDVo2DDPbU2pQWNlnaBBS90jXw1aaJyDGV24pQb9jnQPxKzn56UJ+vnPs2cVoCNJ7F0c8BTuMjs1aHH51usWadDy8NVTg37j67D2q4yYMmXuqUE/U76GxfseYWrQIpAatMGWGogGvU/iTg26B6lBv/5fQoN+4ctW/QQN+ht9GnSvCf4atIZlS05WR9BXImgckBgiCx2NgPt8D4OnaJU31aBHtSqjQWtd2CpPF7b3k1FUktSgkSRq63ShEnljIjVof3Ro0Ia7uEJDUoM+WYOWVlSZxg4H4WntDoEra9BjnVRASWYM2dovcqxM0M9/F9CgR3sJJVKDPn6RT7QB7+Lova+4nzl60ArvLjXoh1qDNu2Wg8sUlLE+Qe+QDGQ26VANeg9puypJDboDtZ/tpkGjB1BLzFPdoEEbImyufeSGT9WgJT95X5zRatBdBK3FlQiahWr16uoAZh90X9QjRWrQrmA1aL1X6jXTt2IXB5pIVySXSxHnz9Sgj2YJ8m5GDdoqccBJcKxM0IP5DYFoqcUJ2tFaRNyhAl1stkxuQ7Ti1KCVZb5Pz98HrVlcJmrQhjVPq0E/pA8bQwR9P4nDq2eVoMN4TINWFmaxg5W5PTVoL/J742YatEwmkEh3RxFBQx72jtcE+vM0aNgsusyh+6B7c65P0B8QmlF7iCjXg+VV7+JQV0haD0+bw7HUoMtP0uZ316ANqdFM7Ls40GHI/TIb2pntQEOLmqhBQ2YxBY99F0cnLkTQGPb+UoUXXf3DadDPcwOGwNGD2KrDR9uGoU/XWVuDHn1TAcVTg2YTgUcRL5i/BoCfscdkXV6xIs3AaNAtew99F8fnH++ogViZoGXTwfuOq2ip5TRor+cT2o8ffi4D6ddRbLac5y2UNd9Ng+5Nbw6LPj4LMz3oYK36ihY1dR80PbcAG/UatC7p1n6Rl7UyQT//MVc8ak8MHcYzGnRtkWf4B1xtKczdV4OGzshJdYgGLamfvYaHWYP23NNLFBVnHzRkY2rQQwn6A9hhBcfNcIeR1qAri/RRHH0WW6Va23pgcLv4Cx0qv9ShplKQiK5B05eDCxwoQVOhOVoWeCrKuzhqNjisvKLA+ARciKBhHHsEdV2K15kA5222oTz1tmnAENhf9FG6Jf43CTEDxAchpAZtyw//qJSkVIxVDZcSQ4OmMzeryDZyQmzNXzgFgZUJWjaGBCGcKsgTeUy0Bu2lurTLSuMdfM7voyQ1aFu50zToDfIX4ISfj/Z90B0jsZXSkIRzNWj67DwNuuAncFoKylqZoJ//2CsW8bN6lKLpt4NtWNpRGrToSm6rQYOMIB+oO3kAACAASURBVCfVRlKzXjvT9U0fUpHY+7N9H7Snniom6Iba+TK6UAaT1cnUoIcS9Ac0ZeJHQF2qE9w+aOXPKtS5seOEomh3tlgjxC8/4C90oX3Q74ZWTGvanEO53Cq77Ql348jLQcojq3ESbB5xNOj662HlZUjiNFyIoFuQDxQVQR4wI7r8I3YfdFcVeIH4UuNO0EWlUD1g5Qzstnl4hDSqqAi6teFAaeRtOzShXYNmUkMOvqqcUzVoQe5tvAb9gBfyB370gJUJmpsO+O3pmrgdNehnUVPexaHRoA8HnTXoTkG9zBlRgz44qYeDEI8SFckDtKpcIsyuPGgVzG1CGieWONrEXSGlyAgUqUGP9KCJbkZvUDeHXd74uFd64rs4BFcCP3RmNGEDmUqRv/y6jgatu2qc1iAXVcj7Z2jQtBqoImgk45AAiGxQgwbdscAp0y9P0DsaT/Y9mAB/mRA/ugbIu072XRxI5bJKlOmrTM4atMKTFC0eQDbXOWsWJIQaNOYSyJ+o5q/3Ey8djSMvB/MnJLUUppmW41qDNqgkHeDmemrQcwi6RjuUtsc+xNhBZumfT9EnadBVsfCcWE2DlhGC0SNUAN3FwVG+OsowxHOzNGhbuJQatHLh3LEyQXMeEDyUOmNyOhB6F40SNFOtziySF1GmdN8H3eXhlnkB2+S9VRGl176cd3bNXrH6zH4Ng8KlsBr0z6GZGrRkMWcHXV/5Zcqy3xuPkcHKBP3ztxY28G/7UVLikAJZEbf9JqG89KNc0GmGSEbAJpIevtEnMBzN6+lPJmI6KPjyEAkbodGgkeppzNsHTd3AxIpid8RyIVgvKlooh1Nq0CMJeseG+sz19zaV3wDZapdBFGXthkk2WJns+nxa7F0cdaMIuRptS06Q0NgGZccWblnuw0HiQtsz+9NHmlCavnRM5SAzgYi1D7oOZ1KDnoVn0x8PVOc/6dpzQGnd5igTvJ1Ft3qJpvAE1eQOdZdNgjXQVn7yakvSMLjq/jb4XIHalN40Terxk2BofiB3NTJmjJANr2ho9R+cq0EXAoHgjlUnRIGQTYPWBvNBNOguCOTA0n/Gm6jwjTtkX6TYDg1am+ix2z9Gg3bqb2kLhNKgmyGUGvQggv75W3Ss+fafmwb9gO6KXFiD7pzqvAZdpZd3cSgNGigO+nI4fLjQWBq0JWdq0OaMyxP0jrfb1CQUHLEPVxjWfdAy6tHbM1SDFg9skwb9qBpIwdBMYTM1aNXh74OyUOFgnIqeZWnhNQzPhJ6JpUF/RsbrX2rQcwj6G9WYbroGPweUZbDpDfM+aI9RsTUfis/+2z658FR+USxBowztvcS2QCWOxv3ra4NXWlU0xe2DFnviZQ6HxgQCSn3+gbm3Wfug8bWYxNIEDQwiKIzVYJ8U2PwQ+YvWfdA6kIEhOh6W06CPqTfCh8aI0skBm6tBC6OpN1KDtudNDXqkB005yXJ8mu8z89lAmozjT3sXh0xGcHTdfT0czhE8QlI4MR0ItwYoiLdNDs0c3fgkP5ioQUNlMCXF0qAxr0Cjcpit1GZcnqB3oJfON/yRn7dt/2TuBvO7OCQ1mqxylziIGU6uXSRoiUPHzw84CmrsljbnXA0azwKeYDRonVHl6Ybc9OMvmAZdfUoNeg5BA6AWTsiL/Mx9ngVohgC9e848SRYJtuZD8fkLVW+6K3zAFyCvjSLojeRnsN6eNZa3DaBE3C3X2KGXZhgNWr4bpDKhfzwCAaU6e2ftXIrUoEcRNDuQtd1be9B4mYezUCrUZXBlRpIY0eq/gEs7z1uAo00k7b584kXtXPSmcqdJPluDViUnG25Yf0tbQKlBT/a2VTykWTWP/0C2EBS2NEF/w2fkHYPo+hOYHnfOAJeBNcpvQApKgl1Qqwl9U73OLdCg5WXXzjbECApSPUWDFiYhNWimv+1LhngMpQZtzrg8Qe9AL51v+MNo3dpPQImcAhJYg1aJuKIC8VPiE29w2+xUVnMrkVJJCK5BoyD6m7l0ZA3Tj5wVNejzgsoCFyJoANTC2eXWchR3qgYNulJHkvPVZk3XiYDfB83WcXTG1tagFXm25r0Hx5NvZWioCVRBqUGjtnAWLk3QrAs0YhV8tXZq0KxJhgzCIcGO9ffI+LlOW8TRML6LBq21pdO9fxUyrL+Fxfpp0B0rLppzpgZNXDSMpQn6G5ZZ0kKfC+1uwGXoctZ1WUVdDo1yqwl9U73OzQ4JixcIf6fcmorTnp+W1aDBwdoE+lK0yyBfkpsGzcZEyMmt7NG3k1Vap5kU9mVCmX55gt6BXjrf8NLxIUY4DfqQxzuiU4xr1nK7xCFGw0ygIAEwQWwNmtYkdEaVp2WxGQkvDZpV53Cl/ZOzKOxjncdVDsGFCBoA5V6OdGtDa9A+BI2sKhThCOCrQZsB3leLrUErih1jAlWOkwbN3t9uztXUvpVpn3swU4MeRdCcpzFkFRQttedr0PhwWPldHGAm4KhWRWiKOkznd/Z1Neh+M4TlYMV6adACfkbfAAjyM/D7dAwULcev04LClibob1hmSQvLHKaOx9agXV33rrleZ/7i7gFZvED4O+XWPEqxs9cNpOwQXJDkmo9huqiNNuCTCES4hJZk1qCbm5s8P2MqR92hLUG3GSkxBT8FJTQ39/IEvQPvOPYIF6OrcX0NmjJC0RN1AnB+jQo7yNCzMaRLgyaXc+SgphmvqUHXsjHULVVyIMG7OavL+aRNDXoOQQOg3MuRbq1dg3YZF9Bk3T87S24bv77JvQ3YA1KtWoOid5kG/fzc26802ZJZetNUabsbsyv4gIYDaVGTnkv9k4K3ztoMRUfqVu43liZodhSNWAVFS21q0FIU18DepQcygUe1KgJV2vtDatBsOVixAg0aGMzi4XDIqPop9I2wDitVvWiWfvsGpCCxNEF/wzJLWhhykUHrfTTovkf1auvZGck3pXSGUvpGdabLDWTsEDSfpIUvo0G3C7bGOFLLwS6Z06Ap3UQPZcblCXoHeuUqj8zHt7Bq0Mq5Jcchj6sGrZMkBPTaKUIf8pMr6OGTs5OqimTJxJpmvIsG3e9KtbON0aD1frwnLkTQABD3cgPOsZlViKdB7188Neh2dyllD48vMz8fo2JxdSq07fYiDyA4b3OrAnV9HnkTqcrrbsrO4KPXcxV5R7R1BqGlqZ4gHK7cpQna4Gn0QzRkVtOgrQb0exd1tNkRQxx+r1CrIlB1vD+kBs2WgxUref6o003mc4oFmDofsOVSYUu5ZOPXbDOOzHo6QX/DMktadAdO1fHFNOg6jpTX3snPddbed3Ec7WFWUk7faMKCa2nQjdIjBZFPTIEWGu1C7cIWtXyBR/cD5p1FuClyLE/QO+SONHXAZ4DstukmYIf/KM4TWYMW7jXGIVgvaobpc1KFLIVUQibWNCPj4amqr053qgzf4DRokR3WynGv9fWR3Qd9lgD9Xa87Qf86YipBA6Dcy5Fu7W006NfgtXpjDWCJQ6MdjJtMsAb9gPiDYlABDPG0t7yicsbZcpzexQEe0GWHUkTdB/3jbCxN0AZPox+iITNHg6Z8b3w4AM0+1kMAORdcRHo1aDiB7eoaloysQTvtRlOD8oAOiKBB42fGvoujXLLxa26z/2B5icMySw6p7Z4CuSwspkF32qDHW4moK+3VoMmk0OQQTeheN5CyQ3BBAjdwez8VJyyySNXrH/IFcWok4Wg4oYzzylo4DRot0GyJKF33DfgYBL1fEJoQb/eWJ5zGx200aN25XYdAhl63Bi1A3dnSwmUaNFKa6rA2MjxOZVUgzVw6VOLWHBHAug+6sKP9bMgOzTb+XRxTnZdPpeMI+l+LaNB0C/T0yn006Koi0SXhzkG/Bj1uMoXVoDe8PXtN6G7M3uCj0wBB8DFSgy6aUbV0/pwaJXH8axEN2hZEiOIgzGVwZg/C98Zrmq5BA/VhHp9Qg4ZTYa1hu7qGJeNq0Ppx7NbhlAu0Q6lBq16oIT2vmhCWCpCU5TBHrxksYcRNwr/x+9f//fnrP3/9+ev/TfCgLbPkk/YzrrsDp+pEUA3652xq0Idvsgn9bjcfqCapLMU8DZrwCfCCdBr0iIdBt6aWHTE16FfaEQT9t+f837/+/fXXrz8nEPTxYhDg7Z4atBmkEdjJQzvDSVKD3o9qnMEN2RVjs4qzQ90dsMqL1dqEA885qlp+muzAtyI0Isl5doR5wCCC/vev//n5P5GgIVDuZdHrgswqyDRoKIHHWAAn6+fLuRo07RylBl0klmeSdKrSd9NmIYrRDLlCeNyKw3qDROFJVA36ByMI+p+//vc/v/74+n/RNWhhEi4HVERQDRoL1ed5CM3UK6tODdqY/Kx3cWA/0lpCpUGDgrpUZbdwQVwN+jGGoL+Z+c/ve4T/NcGDFvElC0MmcigE1aB/MFKDZn9ScCs9oTp5atBsehhO7+JQbQXZqrfD4lm7NejuTcGV910UFFeDfgx6F8e///j6+q9fv/7F8PP5GnT73cm1uKkGzd1zZedZatD7QcWApkhGU33VOVj7bMfkEtZUadDtMv/ZR1jRrLA07GsRGvk4ev5Y/klCEpR7yc7Onh66pwYNz9f2x+7xgkCJQ7dqDZtYIg36gS5QGrvggJiCiwbdsiCZRbD/epNbR+XfB46yd0XuER+29brLhNfCFb00QXPDSOe9IglYFwkq4o4aNDJfK6esuf1TRZva+Bw+qFURqNIKR4sodkT0I07uoUELCBfMgJf7PufwLg6hXWga0itQQBEHHP/BVMUU9n160C6OH/z+PcGD9glN8H5FxwU5FM7SoCUFjdOgMX4uGfpYW10pNyQsbihygPBqmtTYwgbkUjsFHl6EkwYt4ediFWQ06M9Zj3dx9P/4JVbLvTTo32e9blSxdsrHh/XWRGrQhyN7E7KWr65Bo6NFs8qzjQWcOUWDftA3FfeeV2rQcH2q5afKDS4jRWhEkbPr8FPCnaD/58DP/zOToCGQQajAhTLfPL6jBr1t0HxtPGiyoMU1aEopNTn/4kxO+6BrAaon9jtMnn4NWlAfkZ+aw5KnqXvdZYJquKJHShws7HU/TeeGUX/0qAz6PrihBo21U328ZrUq2tTG5/BBYCk2o3C0iFI3yXpuIxgeXvugtU1FuUCVB83k3dAzHfbUhoBQPUajTkqteAJiWfom4ecqjjB1Lj5gEX5hxvj9NGh8EoBHkXg1jgbd3vsS3OyXBVzqLhMkcdoHrbKF9QlcNegON/bdLcglmzRosznKfEMI+q9//fHr1x//+msGQX+gcAo048MkQd9Pg5Zpzazli2vQxCrF2dIeVFGVhwYN6FNwXnF3FLs4et1i1fJT5C66pWXpe2nQf+M/rxuFv/8zk6AhUAEYPz1187fA/TRowndUBK+gbbpVa9hsQjXo45zf0IcpLM6/3Dlw0KCP/QdH5HocCbojP3FAlD816AL/9evPv6n5P38Of9SbG0Z8u+rjS5k/MV+D5u18H5ysQSOVwYT6pSoDP6pWEQRlieJ09ll3lQ3y8K1fgz6GQIqt0Gy535inQSMgL8hdgz5uZRL4iGRdI28STtlm17vGsrnU7P99QvwYuuy8OKukIGTxaBtS660o0mNLaxwNuiWM6e/i+DCm4Kq7NeijRkU7nNBHpt5pGrSA8dp03ho00HyiCwSxPEF/IBlSyBF782G4gAat346AObBkphKLa9CSSsVp8VuOwKF+Dbrk54MTKIsZSZy+Dxr8WoRGFDnrqhTdKBZjaYmDB+WssNOzo5HX16BVw0zquLCX9wWd161a7uHxGxINmrBBc2WvoanoAkcN+rAwdLflq4CzNWg6ia8GXayrxxGiWjo/WPomITpBmOOaFLSIhJWxvAYt3DFmQbUwVtGmNj6Hj1ILswyIo0WU6h/9TNWgH4/3IzaanpeMuokatCmjSYPGW+jdfMdxjrgPpLmb2rgKKEFP3WbXu8ayudTs/31ifQ16HEEfK6vLTw26PMdGeW+47oMmOp6iUbzec/dBAyVUXgFWA2EM1UbkvFFexvIPqnxAEiZ9RDQ+VLiIBq0bZ8SUZ45/cDMNmjyqoiqfd3F8jh9N4BdzFi4atLK76tzt1yI0osgZDTCxGh3H63UIGgTlrLD93dHMdg3ag4xA//TzRdrsCv+ZTqhYgPo16FE+/xANGoP+IlzexdFwV3djvgq4mgb9FgBZR+Q4QpSL5Asjd3EMf90oNyn57tH5t3AOqIzlNWj8aDeqhbGONjuCCMyF7ws5CkeLKHVY9CNBxHdx7LicBi0RALfm70M1MDe1cRVggp79utHeNZbNpWb/7xPra9DCwozAltZbaNBIZCNMD2PWuzgoGsXLWVWDBlznzwfrDRplLneCPu11owqnwDQ+VLiABq03gp3y/JhODXo/qvJyaQ3aFl3ji5i2P66iQR9Y2fNGIIWREgeL8aZT/iXb3x3NfAkNWl8hYw7vdQTQoFETU4PW41XARTRo8camRtHYcFu4GSGoDsuKEbQY9rqfpnOTkm9LfXwpUwIuoUE746PblX5ZHW12BBFYKKLixuNDdEXu1KD59Ej2a2jQymcDtubvQzMue9e2CAR9uI4DTJ2EZ1KP5e/j99ag4eeToSfU6nSna9DVY87HtDfUoJExgWzPxsu5iAZd8LNu1ceKJLJspXF6xCDoDxREahofKsTRoNvJ5CNxEBSPuBkS/+NsDZpykno0aFV4y/EsFrLhuWzR9QN53lt/fyyQBg1MNizcbfNoFQ5dtjpLr1sQjKBBUP4l298dtDBfg25/AeTn3z42Puf9PMG6vrclFccxxH3E6Ro0sYjcToMmF1qRVUcKNEHUspr8UBLQOjjK0b4Nd2/HjVmOq4OfRl6aoLlJybemPr7k/fFvTNegmzmzfQ7Xv2g8WIMmNvEfxYNyDL9RatCCJwGwo/D8EsBbgwa7xR8DNGikG6vuFV3eNTRopoK6qd4OYNFgsnF5mENLE/TPtTTHTZ2EZ1JFmu/jszVoZJEGuXK0Bk2oBM2srlMV7QYU0+WGii760UQc789fehOQ6Fa7egiS+GvQ8Dq7HRZ92Qr1sY5Iia2swuKlQC5ZsQ+aOIWHFWU7Si7j0PbLE/QHn8nPjxvT+FDBqEFrp9bnCEaK0GQ6S4OW4Ggb6nh0Yi8RLhur6+ttlSj1452aiG7pQEDlGQzQoFHb9d0bUoP+fAI9Fq1Df5yAcu6myrqoBl02BjWZ2P7uYIXZGnRF0PtAAUbNaA26ITHFFR006D3ME+VvnHN3fH2sYusslqqt3RXCQX8RozRoOJ3Uvnc665CTBEG6AqAkAuu4cratnIHVf2oEMO7W0gQNTBCwmXDo40tZGCSWEfpQ0Rl0qr15eNY+6LI2OOQ7atCEVMKPdesKAeQ5sgzl1YOrbX0No1raS4MuPUf19EAyaDVoczOZ8nlo0EAMC44SwVWWRS1N0D+XUxxDY30OshErOrE9UPlFXzuLg7MmLCjCuziwKhw1aOZ68PgJWvZftumHF5hcy0WCJAHexUEU46FBO4xG5JJdNGjxwBB4jceilifoD14zrm0nttkHODguGrTZfWyPH08O16DZi8ZPl7ZpYiE5ajOlPLN70KLk7/PUwkn2pIqqxryLA0ul7Q5PDbpT5gAGKatBy6rxG63HMXYdgn7hOhq0LRQ4llFws0eXM/WZzhYo90FrZuQIKi/x0aD5OiE/0+qnyjDmXRy96HUDu2lT5B4JHl8VucYQ7xARB3H0UNTSBA2OfWAC4dDHl7IwyEGDFkXTuPOPe1Lna9CFT1lHmyo6Ro+qvFy6rJJl8P1SIjd1VEsP0aD1JSG5r69Bo0mpaSnphaUJuryUN0ydZBix2Inv4/0adH1fWJRVdOlna9DQPog3HN/FwVwPHj8By/5xYXNw4rR9JkhSiAjKNc7q20tcgifCadBFabU8JHTx+qJbKZYn6A8UPYuPLC9OctCgUX5W2rjVeYTNLvfdgUNEZnLZwfrUc62ozZTyzP3exYGm0nbHUhr0c3jqScG24nG4DkGDoAIwtr872jk1aOwsHRiE16CFdXaSmv4iXDToHgPIYlbSoMHhqYxJYM8CXjttrw+T4HyC5ialiSCZNDIlwGUftICf8f7HPamTNej3BAA7L74GjXv1rE1WUyTw0KCheEJZErbuSoxguUxtjRDt3k585skr2MD09bhkvbClCfobDgEYnUcVab6P++yDVq8foiuXLx5j2ITaj5QatLCIGnoNGl9ppJbI156VNOiCn6lK+6Lb1xd4NTgcWJ6g31AwrGV4KOHzLg6RTyYqHRyPgnyq09J2JFyGMzRomROYGrQ64w4fDZpYVCW562+fT94atMLL5ncCXIagYVCMx82DHlbweReH2YDjpTXOwGwNWnMVhQbNuTHQK7CHaQg30KD9HZV3MStp0MdxpXv0BGo/aIgWVXEax9IEza1ZJoJk0ogm46x3cRD9j3tSp++DLuss6tZo0OSD4FQsLkLradWLLhGI4UdHtfT6+6A5LjNYI0Rj3WdoGu8WHlKS+bcHokEfjswj6N8/f/6GrwftRIOGEYs6dg+1Bu0Q/smLOVuDpipQaNCc78GspBuc5nCwpvvUoEnLNXE6QVvq6aYAUg0mDzEjzGhQWwmZfBpB/xDz7w9TuxH0G4qGtAwPJW6pQcszo5Br0MLneAA03S3kS7DdhPWrenJjisZCNjyXNcLEUiHBOlrEUhr0++xnhGmbydWvmUXQv78GEzQMivG4edDT0HfUoKmIQgqFBl3xs857tACXhhhnHT2EgkwLMqGvBq1vRHo3wmkatCAD8C6Od7fu1ySqF5q50HhWWTiJoH9/jSBoJkiTdA4P1qvFHJrOSSoC3v+4J3UFDfo71U01aDhuOFeD/uxGQHKnBg3ml8RIZxH0P77BZhNgEx2yFMOdwU5YLLKYDGQUFyOz0WxVhxmKDBuZvT25NV/AArbqf5Wuz2bEEHkJLyIk08lM3JoPfBbUcsIqqur2u3q6KcBV0xwkr8ho0NaT2QiWoH9/DfGgj1D4BMSq5uVK+mjQXHIRGvnBR+KgIwlrOx5sKzajerr4TXcLve0QGjTmqU7RoPeKgd6/nAaNFMBXI5ny4psnUzzoDy+nBs3XcJwP1uqPl9ZICbJmV9VNa6aKgnYNWvI4F7QkjFNrYmjQiJIwQ4Om78pS7/VaU4PW1gtwOrhVv5SpmKLnEPQT99KgafJ1Au614p7UEhq0aBOdNBSyXF7LVowGLRuD/S0Nt8sEDRp23gWjblkNuquC75RIc7HbQw4n5u6DHuBBd/oqfB6R/1Yfn7YPWjQ/SqywDxqL5BUWMSspyPBFsprujfugRYZoSgDXrRn7oOsuEa1QP2D2QYuo2WEwIrYT8hBVqcD/g8bwptshujxBv6FgWKnj1QGNBo1Wj2RV2tgwkY/E0U87EPa5UvoYrktF7QQLCw+hQWMnvohznSOpjMw1OT/o1KDLFtGWg02qzycXDRpIjxKxZgf/8k8S0qAWeXasdYwoobdFenAOfvWRnIvxyJegqptMrCnpdA1a4QY+iCG0tS0IzVODFQjm7IPW3hV5J19MgzbW23A0slW/GNxc0Vd+F4fKeZUmkfXd6hq0fIU3A2TUQ7vRBoDOFOb1WC6lnVc6DfrVglS05tnG+S4Oe76JGnQ5uCWFLU3QGAzdZOgTyv9ZXIPmbmJ4oalA8S4OddkAaZMRTE33Kg2afAptO6YwOAkAitVDaKKieDApGyJ8wEwHETU7DEZkAR+mQYNehrK/VydowShjfV7M8epAHA26JQm+2SV3MST8pwd2N8l1qaidYKHrptGg6xYEWgtvY4zXqSoHatDcai7APTVouVEEVidoBtQiz7obHSMqoAZdhep0fsVdjKJs7bkaX9A4F1HVECovoNKg2xZs3AKikfUXIbuxID3t1YiKIUdlp46oi2hTDNOgiULEbb00QX8mpXWYG8JLWd+doUELXNifg6KF7XwNWpIbX3+73fl2Bo3QoN0aOTVoez5mE6CxApaj8cJ08S6K8wn6dS2NXmroJiKLpBnrE2tp0ADbgezijqaCCBo04BcfFzapCcQmlHcF0kYWJOnRoK2sQ69QRyyrQVt4QW7E8SjWdasT9OPBKqasz8uFJgasrUFLapHwnx4X0aCZ5+0+qeCKwOiAKSs1aC5z/W0/7KFBazkEjreAd3QsT9DbDiANNU9Yd6NjRC2uQZsr1J6rMVeD1ikMcg269yFIQ8ffQ4PW2yXxjxw06CYW4sioHOefHT3twFmaoF8rKsbQLsOMLRRzaATrRT9wrxWvSjAeZwDk36kaNKkBt2xVu4Fo5b63/2RIDdqer1+DFi/IcGttJQzxLojzCfrxvMaGoKXdJItLsFOUYxdTg36eRvZBc6LFCDQVzNSgubCrJgxaRTikPYzH2rECDWEhSJIatADIfDdq0AcXQ3O/FxxxJUEfsTpB/4AWobm1zWPlrpEatKiSBhM16JJFBRBr0II9dtixo330acQ4ld8uvHAlbwGoN8BoUSw8Tj7+fpjToNkKyg4X5aw5aFc4qjOXIGg8WqUWebZBO1jBR4PujvS25ssjtgYNXrtk5VVHT/B0oHL7atCkx6Xv95Aa9KeYpTVoOAoqUrTKBLsiV439uUdYZ1yboA9XaVnmJf1NhWN4IbCW6uoHtiUKfbbUoN+HibgUKEqsQT/2m/F4+fKYWATdXCKqdvJPS6QGDR4EotCtPr42QStDOgKGIUtN7g4NuuM9crKcqUG/v7S3lY9342vCYFSEtieQcr7/Hrx3wSUJkqQGLQF8yd0atGrSYt4Cknx1ghaADTaptEbYNWhezNLZ2LJAatDYd/peD6lB0x4xsBAQdYmJGzBO5bAIq+iPBZ00aFPLIEUdSmE0aFF1eGTFpi8OqF0WAisQNLXIs5O/YyCYNWjbdhSs3K358ujqcrZC7TkiLT9DwJW1o9Poe82kBi24j1+Omn43AAAAIABJREFUPTq9vjmvqEH37SV/5+Dn4IB3cfAhKtbYdca1CVoxJUwJgDSyvrNq0LRrxZTIurDb2za1XQMAUuqZ7+IotjkBRREaNNptpLMuskqI0zRoUUubNGhLE5naVKFBK8oHk9JkDB1fm6CVIR0BQ6dQnrdZg9bxs4mIFtagjc44dARaP8u78TVhUCoC3G1YRP6kH6I4sBwSYg0aCs9s/oDipEWDrpvUYzDCl+ygQduMkBS2OkELwPm8zoPgBzYNuvIbkKw6I1uSmKJBG5vyJho0dux4RucXSDXowk5hqyK8pegTgwZdBCXlMLBMgPbrfjw16PMImoiieH+jgxU69kG7eA3HS6tJLjVovHqKZel90GzY0+f9cxBq0GSAZnCpufpesAw56TZ17e9KtCnwjlUUU6Vil2S0sauvaxM03W5sqz75S1UI5zi+MGsfNB624yPkQho04bVzbSE3qljY8DkIVmKb6AYIwyL+HofaQnQEHuCkQYMPO/SL+dE06OLw2gTNh3QstmLvq6YwyvM+510cslJuokEzufkOrwlD+i6OuiCmXSXF8WlEGvQG6gaKKyINlwcfVM7jClhVB8r8ql1P8CWnBj2QoAVoO/X42WVbWwO7Bg0dx9JL0JLEihp0p2NHn2Ii0zfE7+KQpcJzYrxO5RNq0Kbh3h8L0ho07wK/Qt3GmRJEBOiw3D/5atDGEYGtrRcnaDIM3R4PsoM76Prkd3EU3b2Vx66tQQ90+uXv4gBhbAYhpPugKTYzuNRsfU+Q1uEmbXUySPXoVWyiadDVerU2QdPtJlmYlQuwVAdIDZoF6MdN0aAlNQDzh9agubVWXrUJ4rnE/syW1kR0BB5AadCAY4yMXngro2FbCmNdF9/TSSVjprzItQmaDel4pAZN5B7N3NqArm9+vEld2OE1Yeg16DKCQWzTLRcY5O/iABZG8RVRhlOFENOhdoyJWQW7UranBkAOFM4KwSmZEdDRehlanaAFYNwY783wPwA1aMRVxyaKfmHAX+mnX5O5puBCCWdnxm+paP0w4QDQa9CfinQzv+0yPt+67+JgY9g9J/x6KVne5tv+CfJYtKSgXe0QUmpF9YsTNNmXeg4SA9Kghfs6Ta5NXf6RGKpjy2nQVKTSfOZjFQkhgCCkSoJ62sQC6M1b+F0cQg260wQiiasGvbVvDAULQd2yS3nQdLvxk1VQC0upYCGAbXZmoHDoZz5U3N62oYVMBGjpcA1acNu/yoKyDLfaQzUNa2n5XNoYM7QmSi6Q3gcNOMbGdnIO2wwVfLpcEsqAaW6hQet7ytAplGfXim70jiDzxN1pTko9aLv1R7JqFMz5/Xe8Bv1uJL7Da8J4R8JCI/b+QMJZ3FDMIgLLatDi+kWBlLgMkANZt5c9JZmDHGVdaRcHhq2eXOVZPF+PPUcAGjTad2iwg5SNdTByl5uwjYAh+ujyFV+mT9KgkUpULMNmOPSHbubjefDDFg1a2qb9K7f8XRx6x5MrFUu9f/LToOVeElZfk/OSBL03EbnK67pWhWAadBF0BdOgn3sqWmeT52ZoFRa0mFVmMmnQQGIJ1CYurEEL8juYQCRx1KDxqKkuRdrYaxM0PjGYKPaVUFALS6lgIaBt/gp01efcLannsVga9E9XfRyPs9/FAWQpWAYvFWxvRfDWiZU1aOCrsZ1s2WJp0BXWJmg4/LLcjjN0CuXZnbMPWoZQGvSrq949NlKDFk8OgPy/Pxv2QSMZNvKspIQGS2vQklN4HtugOHx03QfNEw9yHjm8OkFDqPhZ4ca4EVKwd3EUWeZo0DIzP4vpPA2ajF8VLMNnoFNxVGSbxxLHjbWAT6fsDuZdHEoLdG4olnr/FGEf9IYcvyRBmzVomUQsQc+7OCyuDVJEwc7v/6E06M9iWgeGgkaAYuKBPr9ag0YSe6f9gc+qa66eKcY25DysEJThuw+aygEHIHTZaxM0uia3fqMAoC7CcjZYyznv4pAlDqhBf4A9ho7mhg62Z/QXCKwPpAYtrGFYS+vmEhycE+fEZcG5RRq02mvlS5RhyLs4JEGLpLS1CVobCOKJtketjLBlUZ7d6Ro0USDSbppLl0CSd3edX+mDatCFPq5ulDZSExqiSCPWoCHtz+ZZK8JNejpwjidTvG1QVF4BWpI5NtTmQg6vTtAC0PPjc9B0bxHDLA1a+ipd2LYinXJPGEuA2pYECVrPIdKa4O+I3/PeYcKXJauRzEmzLDOPJY4ba4Egna4/5PugJRbo3FAkNRO2ab15bBJLgXb6xQmaXOXrkw0/20eUUIMmY01BfnRBOfb3Vh4r2+3jv/YtTu0iiN4HEJYjaARoQfEk8k/ZT1xdg9ZykqK+1KCrY5t4lq9N0OIRJzo/WoN2Jw+CVQnmPzb7e8O4a/jwsYwsDnSOxRo06HKgE0R/XYfpc4ytemtgBmy3RyC0wDNul/izS2nQPo0jmZaS0tYmaG0giOM5bhmHVljHNkmDNrLqod32/HBRVsKQWtbwXUgNuvKgbWNLYAneYnyVhn3Q8BJJglqTqEJSg+ZzIYdXJ2gBFKzj5kHO0aAlLNh6TAfbtoqh+cqJBMU8068cMEFv1X9xaYpeZi97iAYN17LHvorI6HFjDVo5UKFFidGglZD1NXKgzXxxgiZX+TF+7Q8W0aCPPNolcLQDbi9OUyx06SKnqqrMU655PMpdHHitTCHEt0cVdaiNTw3aXAakQdfDj52ITWXsqiac5WsTtHjEmc5DiWjH8YM5GjThKBLMD2nQaBYz5E+8HhM6adC1A6+/NGD60O/i0BYLnOu5C3AxDdpXPWCQGvRADxq5RnVPKSJiNkvrbQ301fUo2o3+xc3RZjVT0kWD3gpnFMnMd3jN/ZSKQBqGxbP1N4EGjaZIDVoCeM1ODXogQQtALdTyUzrkuzhsnpiTBk07o/iA0AebKnefy1g8AAsnwC4rNWhl3oqgjWMCSKRNXxxoM1+coCnS4ONJ+4jq0qANrg1SwtZ+qdvNZ03y8jOgeSEi2orInTXoH5y/D5rYGxNRg95Lcdegxb0rSMdo0NJiylSggcjKoncLZAhA0LrIsjotm8TVAsw6jk/M0qBRgHU9D7YEPdOwouLyw8NxH3TvE0fA9KFe2eDDF5IRi4zae2nQ2NS1DeQBGrTIQFFhaxM0epH8tVdjnVqty0x8HdtCGjRtw2izGqfNbx80l5nv8HoyvRddtRFYPNt+w4o+xAWkB40GymB9+kCN4heqEGcNumoGeYfATU1q0EJmqE5Q3p+OslYnaAHAhRr2RkQujM42wVIBpxUNVR4tp9xAgxbUBH5XB5tCs1RrH8GyqUH/fCv27yvzAoeBsE1L/yXzE5EOVija6RcnaJw02DYEckvyFLYZPWe9a4OUsLVf7qFBjwBB0NpJbE7L7OLoskDgyeuwl+KsQVNaj7CMY/DcWqf13OpUiHWIA6Z2C4QIQNDkpUncYb4WIrQiqkkNmgW4Cvm9iwOuSwxg+lR9KqtZZUhHP6yoQZukjudZhKHFtheZGwEGZ39xP6cGTWvQkosv2lAWjlxagz5hSWk6IKwG/dgYFUFcMWWJfTQvrEFb1lidytEUUtJ7rUGX7C9jhuYUfg450xMaIVkjELQAimWWJ1LhYEgN2sZjqUFDLCu4l02uHjpKEKXT9YevBv2M+mwix3M6lBTchG1F0SKrZa4ekQhdDS5O0H2kYR9R62jQPtTnFTNDXClyK0cQeYXTNGgJEblq0E6NuBcz4l0cTxLlTEUmWOEikxq0xkvXGSOb5WsTNHlpPsOM9WrBapbRoJUxoiNAPoitQet+c0ZtCOboSlzFe2nQzyRto4htpzXoQ8xStX3vTJEN1yPWJmiheyUAkV5dxZYatBDN0A+tQb/mqkqvKM+C7hgz/2U7FgwatOghADAnmIsqxF+D/vmjiV+qlMcGJfZB021vG47YOgwfXp2gBaiv3MDFz5OK/higQZte3wl5TLfXoHFLmf5v56rK2y/cMTxnTTuHevFMeg1auGEUya7rjwEatNgOfl3Gwzbxfj6tk435/m3mixN0H2mUhCke0I8RGrSq+rK/q86frEGrytdwAbicjfP5wbn6amDZpBTf1zKMPPWq29oiXKfkAHxUYXo/MwRlgO/ieJ97N5ONdsnzsgBmbYKmPZ6+4qtS0MkFVgPZpvR8q94Uz22isufBJTRo8dK2AceIuqQArKu6oAiXJRWw7hh+gi9eq0HjtmDuNl0imVumQTuIvbZclMeSGnRH/d9/RO6VAJwLCA1oYqZ1a9BVXRrvi0Nq0ExRcLi5YRo02zPlGt+4Y5IlRuhBo4FyW9bRan54cnYzhUg06GcdstVrg/4JwCwf4KTQ72g2GYEdXp2geXR6sp9cKoI0adBHuqpr2w8oHfHWCG00DFeoX3MkGSJr0F+AJ7uJ1s6CodmqCNrBMw3VoDXOCQiBBk22Ir4WKKYXml6+dUhYiSE92ukXJ2jFKg+cboIdQY1PdGrQnymhjqPLcrf2i1qDFtXbuFfy8uHEgjkIEsZAnx/bBy1dujdhW77LVcFw5xe/4VmfsMVv0D4JIrFfiFiUK0hiIBI8kWShrr4xboEZAQiaXpP7im9KQUYPeLRXgwZGq27wEp6UToMeMm3KSg/Fq/dBiwQCw1AArPuClwTV2imfvlACIsXQfdA0eQpaRaBBV1UQKz4B20BVeCy9M0EfhaxN0E5+G51eP+39NWhRIUKoNOhBjk1bI7B4kOnfR+SmySfHBiXZHS2gsxQ1N9/fAQ7tfzH9YHkXB3EEqhz1AOGvR3ulGrRsstUbuI1D4ANSgzaUZ8uEHF6doHkoPFk/HurVoIlJrzMSGMKaaBiPPNlmNY12VIMmxCBtPQS1qINNnbdPHatv2YF8h1+s5V0cUmyPVpvhiiztFe2DFm8UqTxtzhLi2xOuGrRtREDx4BNXJ2i+e8SZNejToIXyFF/u1n5RatBCB9oSgEBpIWeGWKnaW6ejfP1H18uS+LRthA+cJrpi9Ls49Mst4UELSxAUbC2kSOKqQcvPyyb52gQtCFR7wTIFeLR/H7SwIlXi50G9Bq2pWA5ogL6dGdGzWw1pSeJjrXH7p6+uxZ7ORNPvdgBSgO6xWrLDhSXBGQ7tptOgq694V1LtYBupqUEP9KB9hAAyg37aO2jQxjQSKDToJw061UtXWCwe2AyEHUtDbZKlHQtD+8cWwPRShsaqGKtBg+kIen0e2I/IpwM/2Y7twC83kuJTgx5I0DwUnqwfFTlo0GxyEYAhPOhdHN0Rw3f6F8/sc5BhxYkatPwxUlEqcK05nNnas9S1DtageTomQWnQat9EFDNpyk8N+kSC7gpL7b12tgb9AOb6u6wR3r3TqveUAys2IoOk+mG4Ye4+TI+qi2MrINL+VE+UEE6DLrJ4atDHpVL1FiOszNSgxxG0IFDtBTVr8KPX0aDHcR40QH+cGdZdPGbagGN4VWrbPotcY5FneCsokGmOeBr0EY4adJGhHiS2oZoa9EAPmnSvNDC4gMSJ+tJ0a6s9jQRKDXog2hnZSByO5sgnR8tixZrRPbZwVjFfYEAN+gDydUSyUqDB+eoSRaNh3hRTuao4aybV2iZBCILmofBk/egoNWh9a36nb24SyuJAQ03gV2JowGuGzCrdWmNaBchO7Ww7KMrSFSnaB81Z0ByUBFqSWZUa9IkE3RUC2XstNWhd9nfaz72uRmNgCsZHeFWaFcM1aHPaH4gGnPSsSyh1zOGrQX8SKJQwKolZg1Y/vYN8UwQfGgQgaEGg2gu2GemoiUtoMcSc+igjjKrYUO6hgiHv4jBs9QDWB2g2agumUvc1M6bywhziOm8Egd9ADVoWdpJQTIiytq5xJbR2bYLWCjoEDC4gcaJ6sY5ybbWnkeDYbkX06uI4adDSoPpdHJJaIKlSQFF1klaqFO7CZt6qouYlCIUHTRvY1iGtlRzTCjeQ6AndZNuYF5jISik7VlyaahM+ZwN6fHWC5iEYoPwZLfo1aD65InWRZwENGijE3DeAVIlSC1lJa5tsimrFEYGT2wKUON4GesRuymi+hFTxIy1QnUCSoAStNw25b2ybxLiPdCpBT8BGfFNm7q9efH57nzBbsDV/icokRZkTaS5hAz6hBWzQFyDxcx4prBDisx1Qkgw2Dc/UY1hZ88FAulSw8T3tUcKnTr4U6/huO185k4T94oDUoGvUEoeDOf1qJyAjCIK6YSoH5BzrNWhILq6TumjQbZwu8qDFG/T62hmcS4CBAvfcR1eVBG2EV25rDVuu0jp52+jHlU73/MHqEodO0CFgED6IE5Z90Nq3CluBt5tdnbShpcHOIYHV065DkmlYpTFq0ChLQt/M7d2lQUurJfmFKmOUBs3XLElq3gdt2x2UGnSJVTToci55Gbm6Bl2oc179gymASg36Ifw5MOUOaj8N+qG/i4YahLaZBMM0aG1ejKD9wttLadD2usWmG4IKc/IDyHfXHCo4dOxxHssYgyy5+Yt6qRLvXlifOfs7LdRuT35gKA52el1hHq6b/td+1deBGScPG0iS0ber3ifor9NWylnv4pDN8rUJWq4XmcGujciiLIosD5TMCpoWzxk8tqIG3TYNlNsSH7MZPh+1D/gASak8fe0sIujPd9d5Ay6UZSEyDVrUlQNg1aD1MLiLaxO045w0uIDEidZLhcTA490jwQ0np0G7oAbtsl1M7kzC7rj9lQ2kJW20YAGsQcNugegQBJJfisuohvIkDVrgLUMHT3oXh+w5xNUJmofElWXPaNFo0C39fjZBHVL4Gwm4yD4RHd+sliUS1qD58EJfE/idrMH3BibXwcqLJVVeF79PRCevc3VvnahBC/j/HA26aKKt+r/j6gRtCCrMyQ+otVSIYWragfi5c0iXUwvxUmurkLIk9enOAGkRDRrkZzC0VlSmRS9BqxpCW7jDnV+KZFT2tOtpatDN+cI3I/KsTdAj9aKmFKQ8YDRvW6VBwy6gzi00eM7wMVKDJsReb0B+6zU1aEmezmYOpUFjHrTMOJMR9kynaNAbzAgA1iZoxzlpcAExxoa8PXhHKrBvg4ATVVIadG3nSI8UmpDokHDYLgZ53GTKKk1q0FQ42jgkgHVQWqE7QFmoaDRM4lAXZMywZ2oZAS5sdYLm0dKiPKkFtXCxH4XSHmuVEaPBky6zEO32WdcFgaH+gKBEeh+0F1DPjawkNWh5mfAuDrMdHRdALSovpAZ9IkEL+keaWZinCF4OXYD4rEghHRYcCihdQcRLLfwZVuzFKjScAdKi+6C5gmGn1xUX16BJ96AzHk0NGkgg++nbtQlaOuLs4L1aWM0QPWGLVgq526r5Dfvrz3/hNOjDJ8WLxYA1DM3ZE7t+Pq6oQSOj0HXe2ON0whPoEA/UOGsftAhrE7TjnDS4gKj7a3BCWTdWUghXwBP0PmiPN6BL0U5Iekj0WSO/MNgdn6FBm8vGNGiZN6xcE/lvFcZq0JqJAR3VvJfBWLEkk2ptkyAEQfNQuLJOdPSzi0NeZh3pMDd4BQUeCmhc5Lj7oL8zpAYtcnJbBNOgK+DWybYxdVwAHR7+IM67OIDMVydoxTLPZdbkE76LAxmyFb/q63/nL/sd8VLZmdc3hTSXoNGgwZ4d6fGnBi0HFnw06XBPxMtbqioDru0kDVqWb22CHqkXNYVoWrd9F4fCGlLDlnkcZg3awRGWAvI6UoM2Yk0NGt7xZDFCkAmNTFODHuhBu81JgwtIZGG8VKYsxK3Y8FNNUiLRgu/iaNP31Sabhpgb2N0oRFhnLbsUEWimc+pmRXDKELSAmD1oE1MOU4MeSNA8FK6sHx31aNAPgl/p239Y4WWG1KDRIWFhGScb6jPK6x2vQffcPsaWD/pui7EyPGtq0PEIWrHMc5k1kGrQXILqNHP/EMtfdv8CGrSECsCeHenxr69Bk8WSbmznxImhQSOOT2rQ4wh6pF7UFKJp3S4NmqhG7UEDJjTNPr4VmZL3T30a9BDB6/NxqAbd28y6ncauPS5YTXHrMH3DVTxgkBr0QA96xJSUnlJk6fWkP2k6+PmNr73C1nlm3WlPtBMyNWhjkTM0aHJsKNQh6oINc627Q1KDHkjQPBRc6UdHnRo0lVpnJMDDbhEdk0Vdxna2Bk3X4apBs6NQeb2NNuT5yivIIlWZYd7FASI16BMJumNU9XTbIA1ajWJqySQOmX8lX/Y0l5AatCHtD5obC+pnUUlCd/NR5XDqSoGD5OexcKlEQ7nE2gQ9MvBpCoHLwwITtcghN6SziK/6yNamca2XNqf4ZNCg+S6Slwml/3zs16AHBm+1ca97FXJPzW5Ks5q2S0O+i8OcfW2CJuAoJxl0MTU9S8x14sqDBs3WMNIjhUpPDdpYZKVB77t9RH60xT/csJOA835VDdpUs+4SFydoQQMpuNKPjkx97hNGAanLLC4RnWCSq0P6jdKgPdcKbEjQVayjQdcPgPhEbyQ/H04AG40ur0Gr6mtTER21OEGzEI4qSWYNFBr0UJmmmFqtxAFVYvOv8DQqngZmsszhkMTunegmaPe1+gBE4lAU26svFDXDL+zXFdRhhKoYBw1auhIa4oPFCXq4O7wBnwTVNBq0c1jcUwSlQVuj3w5zik/01kk4u4RZtFcBlbmSBv1ABA7JOiYSRbCz7a+m7daxL+GVrMtSQxSQa9A+/pIKlyXojikpPUWte1p+7vNSRXjPj1qDBn55x6tKzqLmU2rQxiKbfdDEL0ZbV2Ey0jqOJ1iD/hynLtgw17o7ZLIGrXX1zIhA0AJwy7bslBKKyey4kiCpX87Ue34A0bC+EkEWy2hPDRoIayQY+S4O0CLqAppzP75Ux0OwHRcgyNqvQUuCOCzD8SuQ+eoELR5VfGYNLPugobQeU+s5Mfb5UUV09byx+Vc+oUxq0PqkT7QigmI4bRuTtXfefD1gbVpViA2CYvyiVy6VJRYXVQxnDUDQw91hIvynDgfSoLcCj0qDZu/3j6M8qGnvqkF3t3IpIqDlgutY754PfjndPWhK3hCty1JDFEgN+gyCdlQOLOuelp/9VvE6U83PlQZ9Gj8Xhb8/yTVofbRMcgOUskoTXoPeu3Ijo+Z6bNa78jCQ0SiZ+bN8bE1SYTGS/rIiiAYNH1+coAWglmnpGTW+iHs0WK3C6nVWfnxkiQat8KIEVG4Z7WIN2q5nNkWRZHZAdA0ap1ma3TaWoaGxrLLvLcDYw0BRIiiVIGdq0CcSdJ/S0KUriArQeS16Ox6751Lu4jikKZxpI9P6LHvQllQwP6acj3T4B2rQLXVpLwS4DSdl6pag4REgptc2neVBFf36rl+c5DWZeLc5bYnFRRXDWQMQtEGY0IEc7+hhXm7rMESftTSl2Qe9JwPG+DjKg5r2FQuzWY+2StwX7VVAZY7ToCX6AoNGg4b9A9A7r/gZG+iojfxA17+Lw8bPtlYsrBuopZjsuyxBd0xJ6SmcDhqu6/akpWkEQN7FAce5I13SDg1aJppiuesvSMoqzTANGlltVCh3cdAjD+t6sgIsZiHqqa0DkwqLYfuLUWkoTNKgP/aprnFxghZA7hL6kdE+Vvgyt+aDML01MdZuKp5oE5iUkTqDSoO29xfCCnSBozRoWGBQXhxpnKQ3qdbcAP5T2df7Lg6RUAEOCckomaNBU+swMQCvTtCadZ7Nrcgo5Wcugd/I+QBtt9f4MTGtz7K3Ad49HnEDiUY6/MM0aI/IRSbey0oFF1+Ff9qmmvIuDoSfBYKZmzW0n483IZFvcYLWCxPmuBgrDzz61eHeaeqxANOgYXdjHOVBTXvPfdBeGnRTF7LWMmIGcji4Bg1NbZnsMVODhvk5CXo/ivWXnuo1697EVZwFokEPrZMr/P1piXdxdDdK4ywfBqWxbFzldVtzaR+fLFRunWGusaEJx9DT9kEjYTUZbi9O0AKU85LqL0cysryLwyGKEqWWtJthQekngm3WuzhiadDwGeXl9mnQDKD8qjJnaNBgNqkH3dlCkiDueBLjJCDz1Qm6WvWVt3rN/WZ5F4evBXh2rt1MTMsSjgQb5N2L8ssotg8z38URT4NWoE09RYMGCwmkQWN5aE5anKC1woR6K04bhUuqaZ/WU1TZ1LDhtVugiugGMh7UtAoNGsi9qgbt0MzwXGqKRR21DlsEG/6FGrTYE8UsAQ7JdnEI6x0yG2hOuhlBn6NBCxYFYu7WG7B6kRo0VxSsMgzUoNFvYtQqL+VY+PTpZ1BuvAdxkgYtxNnv4kgNuvg60I3ZUWjQ9BK5NR/K01Xm3mhzNQ16iHpRC1+iKqZq0A4Sh6QyGeBg9PVPsU/CbsdQP2H+uzgwTgIyX52g66bQVWHut4MGzdxKZqrQquaC0lODrvIKmzc1aChtOz59WGYoJeursrZcNxYnaIMwoQMVKhKHS1mri2KfmUd49xfRoPk+oscJ0Dsg3yylQZN+OOcUaCoWRXjnadASnKxB00iC5pMbdLHUoHWFvz/N16DBEAWkt6U0aOqzr+tyLQ1a6UmZalZd4+IELYB0CLiSUbUPmnnRAV19z2vxLBLHMZ9iz7hJGakznKBB09tQD0gNGj4o3ydht2Oon/B1HO+mWFcQwyHp2eNXJ2gidtXnlkO8D1oWbprtsBD0Mc4UeEd4RfQJMG1q0Ia034CMsy6ZnUutD8tMVBMO9IqqkX3E24HFCZpSBvpKfxcDfEISHOHqbRG1W7B796zu0iOes4AExxM0aKgdwCC8W4MeGbvJNOhNVJ29/bHMkzRoIw4TgrklP9GoN65K0GpmkYgQ8izqS+PmzCZIJMTbS+WaiB2u3WiZNcY+6Mtq0F1V0KWQhYqtM66wgjQEDh2rHfCmKlV8sjhBY8CFRQMRG+D6Lo7NfqOQkDjIsbhxSSRmqNvTpkEbVhBkTNAFpQZtLlSu+Gks8MJX86tvWghiOCQ9e/yaBL37fpp1HihHb9UTvhq03ZHFCRp3j49xplyJ6XsNAAAXrElEQVSDNoQZQFpA4pDoMF0atBSpQcvgwzIT1YTjPOhxSIYYvThBo7TgFZyz/i183Nfb8pUaCg9aJK2MASQ46jXo4iq8xEMwCF9Kg4bLlWkBWmt47WiOBm1tRdkQ6qmhAxclaH2sYpA+iCy+GjQSDhhx1KBPGHEHtKufWoNWLF48j1RnqzQradAk0/l0uiY21WvQ8M50mSlKHIaQQZSzVKg5vjhB4/j0sHwgObKVpwa9PYZo0Hg4J6mFcn7EhbQZqCeWkdjTEl2YPMvUoM2FqjXouk+HOhL98ekGfJKlZ49flqDhgd45kcVw3gdtlhpIgsYoT1SKNI3G8CdBK3+dTx6gduK+GnR3KKptutHbh4Da2ESikvqNqbE4QQ/vQ4l/C8B9H7Qj/EL1PkBh+FcRLQjLkfxelD3wwOQXfeN5WQcBNA5jWplTIAavHSk16LfoMMmdOuziEElfU5EE/YJB+iCyeO+DViRioXoXx1C00+KLjDd1h8lkkolYpVlJg4Y/i55ol0ITm2o16JafRxJnR8eaKlbxyeIELYB8IDlylrMGjX4TF36AoN0MyoyTBt34Tt0/pwpXBH6/uAZtVg3AXJqiYmvQ/R2bGjRYvyTRRTRoKywEfa4G3fCzZofGpHlspLmTNWjbdhfMGNXG98I6mQ0TNxjZGsWcSIfFCTo1aBLg0FtJg5aRisDJtgceR9s+RqlLpJea/o7gNWj5bjIhgcKtzsbp1C3dvnaw5i6sG1JDB5KgXzBIH0SWGBo0zAmxNehiVakED8f5K5mIVZpPVGSSCo4XQoV1xmsUqLy7Bf193y6cZJkH6+oIqSoXq48xhz7NQCtQ9FasusjFCVoAucjhyFkhNGjE/YynQb95o90HTdBaD+qiRDVonsBsa6yycb6+h8RR1+WmQat3WeyKn21/xlB3wkFN0VK8osKrE7ScngW55QigQaOzKJwG/bLy+29jWzgN2sgw2qchXAi6JVZR1fziSzUCraoNfoWtAUJ7hoS4guyLE/Twnt69DyZBiRAaNOdBnzxNjo4d7pOpJg/nl2pNK1C9BdBHbyLqU0FE0KLqRHyFhwNsnE5JVo4aljiTNLIx19CHJOgXaOkD5g48S2rQYmwlqJSdFcnLOsgMe8KjBm2qH2MBhWEoIA0a3fZMNrLMo2wSkVnwXRzCCHd4nGksxzYQNMcXJ2gB5OOI8L+0cVkIDRpxP8No0O9ZMYefyaIkwu9hF8cAC1SmtIBeYrKhDI0D6gfwek3Twdxw49wJF8llAz7J0rOHr07QwhWazq3XHRUjcnpQtZQGLYTVA9JgqmzVT9BG5oH4uZvAQih+CISXN0ODhrA4QU/SoIlbIoE1aARRNOhD7IDu4tAWxCfRFQhJHJYC5fUZ0YgIlFOh0qAFRK/SoKm0J2jQ5RQeJqWYkQT9gk2DxsZuDA0aRhgNGtXH5enN1Yk16B2OD/hQqpWt8PY23IdYtRERxM+skylUh5q0Z2vQ38h3cQwjaP3+GKvIifnPyNgNokGDCKNBA5Bs53UAo0E7LboqC8ozalUB2Mhm0qABexxE2sAa9CPQuzgup0HrV3abBo2eRr2L1KA1eXeE0KC5RXc89Jx48AK3naHdjNEkBo5GVvxsN7M6EumwMkG73IBlQPu3vAcdEPE06B3Gdtuq/6K60KRFp07RoEELVDWoHgXRuilyL4jxVzg7TtGgi7YbU0MHFiZo/d4KsjTDqc1Dg1aEoalBe1VH9fbugZYn5PNYZUoj+uqfpH6gj4K0lcFHurF9aobsTg1alOlqEofl9oVVg0bzwJnkXup+ATJhKjVoB8DWv3USnRvoja3Xg24G5bS1GF1ZBmvQnU5aatCDCFoi1Q3VoHGICZq7hDNULVPkmRq0qgregjkN5wuZBz2i1p78qUF/ff3+G8f/PgTt8SIqBrQGjUJ6aa4qjRCpQbPFHVLP0KABEaJjF4elvj6cq0F/plBq0FVWOUH/fv35/fniQ9COLWbToLFz0sfQMb1TaYgKqUHbmnucBk3VKgTRcBM1aGxl+YKSQqYY4rD+W1E9G9xNsqjmxOIELcApepzUSxWMLXf7U4OmNWgMkyQOd4IOsBYP1aC7g9DUoD8sPZ+gFRq0D8U8odKgqZQhNGiJFRfSoBHMkXmNVBNDg8aQGrQZZxH0P74hynYutuo/dt5eQXcJ9qpn1EFU4lc/10sddQ3reLisb7JxLBmuzrsGa3ltPltJ0+bQeXO1DwKCft4cPEPiEMKoQSMY8y4OnxV6pgZNOjepQTffzOG6QoMecleaLhHXoIXFSG7e9Ye799Wgv17EHFqD9hy1hp0SsrG5lgbd9byF3igxYBHndA3a8oTKC/KGO2HfEK5BS+0Irl2lBg3WL0mk0KD53HL4bWXzH5rzNGg93/Rp0GN5Z4bMO8KDhmo4a2dnSKQGfeoujj5s1X/sfIXII3LIPmjl2/6w+rsIWlmXouQfTNkHbeVOecMNIGi2MOk+6K5WdfCm2Fv61irsSIJ+ITXoHmBTfqYGrSGeTg3aUw6vvvnv4oA0aHemMWrQjW1I6RIN2oyPAGNol6to0IOeJBRALuGmBq2oRaJr/ui6OoM7CJqL3el7hCE06A5oxPvpEnTvPujBkszXXovjrxMaUp2sQbew1y02PTXoBv4atJ+uaX3Uu/gJkUGILFud+6g3i76mm9OxzCAWuXZ9Rnq/AWYJgu4Fp0EjiDyZux/raoGPbWXIYmu3V+VX0KCtOHXAjdWgpct/rzclqGboygZXfjOCVgmi7KkNPRVfg/YdbCYfx0uD/sws+XsB42rQRqjexTEAIzVonjn9NGhtSX4aNHKRlyfo6g6MhYcNuJkGDbeswVYLQb89LE11sF5O57mMBj0fDhr0DO2KisCE64PZ0cIG8a0IWt3RqUGLjVDc9yBgGhIDNo5BiCxbxTau27opNwkfVKwss6DHyrt60AdYJjKjQbtJHBOhd2cGzQ9vDVpZl7Dsz6fUoDFYNWg4kDHVIEwDgm+7j4ZmrUKC22vQdCRs06AxXEqDHuWfemnQshXkthr0FIljpAbdXT0DPqS03QRHi0OrAQ5enqCP+hDNNEE1aEsqEloNGhydFoYTwLoPuv/eDu8fpQZtxoCNQ46QetASR819Ft+JoPUb3lODtt2DM4PkmZOnd2TZKrZx61snnAADRujlCbqAfSvYjTXoHn4mc+kkjs41gtOoN+x1Ip9PqUFjcNOgR/YwDknbbV2/etiBexE0gdSg8VojaNDdXjypQUtkHFRI7bQlNejPN3MPj9WgiRo8NWgQlydobxFYBj8N2qL90tDvgwbmzWwN2llnaS+QLz41aDNk02Geklaiu2NTg+4wvavDU4PutEKbb6AHjWM7gEgWWbaKbZzQR514r6NAjwdtSKTD5Qm6D1vzQYbI88V8S9177sTRoAl+nqBBj5Y4hsNJg+7k56EadGcVdiRBv2HYf4ePpktp0O/Kvb0bHUH3rg96DbpKlRq0uQ50C/k6GrRjxalBH7G4Bm3af0zD+C6OksJma9DOaDV1bBfHjtSgzZBOh3N2UqYGvQZBe2b20qBHaHIWDdq6G9pPgz4fkW2jtaF5ZiAI3XSeRHLCHSMi6xIE3Yet+o+cbuBk25C7JkYN2v0WjvObB7jauvMtqkFPuO/mpEGPtgJDatBLELRFg0az+GjQjdd6ugbtys++7+5i60O/yLIsqkFP2hnhokGPqp5BatBnEvTSGnQ7u87ToB/H7dCyKU8lgonjNA1agCU16NP2rhXw23U6AqlBr0HQnrkvp0E/irctSs0iohKYOCKLlZFtO92DphG66VKDPpWg+3CyBj3kDo9Jgy6VFodJP9uDjqtBu3TxqRo02xipQZuRBP3GuRr0xAXaokEf6dS4oQMqErNtBHw1aL/dQT4EevYujtSgB+W5PEEvrUFbU5EwaNAFJcv5mU4ydxdHVbchzxgN2kmCWEREOFtrAeGpQbtnSIIekjvyiDS9i2MnZY3/rL76yDxT7uJw6livm3iRG+4a1qUGPdF0MRgNGsVH512RoCF8WGTstoBVNGi/FriEBx1Bg7aXlRr0EgQ9QoNWzL3YGvRnj93gbVuLaNC9bTBVg56C1KAH5bk8QZ+pQavmcSANGt5n9/onvyYD8ayhQfevUiVD28v54GyCphFZ8XPVoIXXlxr0jhM16BgPCTSw7YP+nFHws/7SJ/GMqVN2lgnYsYsQdEikBn0mQfeB1KCJKWrwoKfB+qj3Gwp+Vl/8HA3aFtg4Shz+SA16ggaNVDF0JCRBv0G0MnyKmqTX06C1sHmZUzRo4zZBx5uEoRwtF9xYg9aPBU3yyxP0MA2anOaGXRzTGNqoQWttCOZB77AqFJ7b7NwZ+myCpnFlDboYS6lBq00fNZHoaR55vvQQtKY1w2rQRgU5cp/GNu4a1hFx9Gc0hQqN1iDoPhAro8iDjog5Escj2C4OVw06HlKDPkuDHnw/Ign6jREatE/16lQ0phG0AZP2QQtnFK5BO9rig7NXj5M16K6CUoM+k6AH7oNmd3HoSnNLxOC9w8RaS9B71sMx5l0cTojccNfWoIup5O9mJUEPyR15vnz2aKMpfDRoC8K3W1SENu4a1knGfqjQaA2C7oPy7uwHkUfkt23xNvI+Ee5dHIdGitynqUGfuA96KJKg3zCH/BCCa9ABH4V7Yta7OKRZjo2UGjQBsQZdJryCBj020+UJeqAGTSC6Bk0TNFnLUFY/m2cqFK2UGrQZF9egwY+yDCySoIfkjjxf9veEYClcgwkVQrVbtdU9lG01Qht3DetSg55nuhiXJWjb+4KGI5gGPcyDdkdq0KlBN1mvRdCU2zjhqbPcB/2N1KCNOHv1sGnQfu1wlgZtqjgljgPEjeE6aaJr0B213EmDHraLIzXoQEgNegmCds0deb50EfSdNOgKkW2Lbdw1rEsNep7pYmzFPzkij8h72mbdB71/jNxuqUGnBt1mvRZB32kfdFhE06CPSA0aB3NBqUGb81yeoFfRoGUSdAANeihusnhcjqAZXFqD3pDPshwckqCH5B4zX3we/usj6LFTLDLPRLYttnEXsU4w9EOtvGsQdB9CadBOj2eztpEvuluWoLsfyg3NMqlBpwbdZL0WQYfXoL1eoMHZRj5jOPj5llgEnRq0FKlBj8pzdYKW80l0Ddr6U3oNGNuoSoa/Yimyl5oEbUZq0GgODhcn6F4+CaVBT5E4qGVg/EvwIvNMZNtiG3cR61KDdje9m09CadBzbhJe1YMeLg2ditSgI2jQA+bGpQla5fGF16Afk7bZpQb9yVLkSYkDR2rQP59Nv3VJ49IErfL4oGRmNhr1oIoHbrqLoxtJ0Gbs1kUUofvabr8iMdskQX/Q5/HZc0eeL2mbDZFti23cRaxjuGDMHZqLE3SXx7e3dxAN2gc3ta17P1TkdksNuqMoLw16yB2aqxO0HE3L7itiGA3aBZGJJhZB5z5oKVKD/vmcGrST6RDqVuvad5wEbUMo27YkaCfcQ4MWx+tJ0D7oCFkiz5e0TYa68yPZ1iC0cRex7pTFJQmawGeKpgY9CYEkjmZ5jtxuZxsnlziKXBfQoEezdhL0G8Q2u9SgZyEOQbcCV0ocOEwE7XpLbQZBQ9VaLiElDguiv4vDDTclaC1GetCXI2gGoAY9/LFUKTraznYJSdAG+A6VyPMlbZMhNWg3QNaNf7GLFHYNesIlJEE/4dzOkefLPW2zODrDdnH4Y0GJw5fcztCgZ6wxSdA/oBs6NehZGGabwzxKiQOHmKAt24Y9LKBgbzsjP6fEoYX7UpgEbcMo2zy6NwnaDGQfdAR949GtQRtyKdImQf/AO1RR2zZxrEaey4Nsc1mAI7dbbOOuYh0wfobP2yToJ8jpO17imHm3JPJsuYkH7Y9VJI5hBnT0rte7OIYgCfoF8h2b6tKUtk29nx2ZaFKDNmIVgh40yLvmj3gXh5fxKXGcDp1tc3ccXafdNBj/Y7sKXI6gGYwm6L75I2w7vxmaBH060oO24R62xfplpAn4WDdmkG99Ho6s7fzmqKqUJGgBUoOehVvYNqKz15A4Rg3zCR60X5SrKyUJWoAJ+6BzF8cP7mDbkHBpCYIeFyhO0KA9+VlRThI0D0O/3IFoRuAGto254XBuw3FX8/VKNZShzXmnatDaRkiCZmEZVDcgmiG4g23XI2j2coZ70F2Yu4sjPWhnmObTHYhmBG5h29UkDn6CjNag+zC57VKDdoXtDvEtiGYA7mHbtW4SCibI4F0cnZjdduvs4lgCr+F3thmJC+FaoyknSHRc2YNODXom0jYjltCggyK0dSlx8MhdHNOQthmxwi6OqAhtXRL0EKRtNqRtRoQ2Lq2zIwl6CNI2G9I2I0Ibl9bZkQQ9BGmbDWmbEaGNS+vsSIIegrTNhrTNiNDGpXV2JEEPQdpmQ9pmRGjj0jo7kqCHIG2zIW0zIrRxaZ0dSdBDkLbZkLYZEdq4tM6OJOghSNtsSNuMCG1cWmdHEvQQpG02pG1GhDYurbMjCXoI0jYb0jYjQhuX1tmRBD0EaZsNaZsRoY1L6+xIgh6CtM2GtM2I0MaldXYkQQ9B2mZD2mZEaOPSOjuSoIcgbbMhbTMitHFpnR1J0EOQttmQthkR2ri0zo4k6CFI22xI24wIbVxaZ0cS9BCkbTakbUaENi6tsyMJegjSNhvSNiNCG5fW2ZEEPQRpmw1pmxGhjUvr7EiCHoK0zYa0zYjQxqV1diRBD0HaZkPaZkRo49I6O5KghyBtsyFtMyK0cWmdHUnQQ5C22ZC2GRHauLTOjiToIUjbbEjbjAhtXFpnRxL0EKRtNqRtRoQ2Lq2zIwl6CNI2G9I2I0Ibl9bZcSpB9+Efp9S6PrLdbMh2MyObzgyXpkuCXgnZbjZku5mRTWdGEvTtkO1mQ7abGdl0ZiRB3w7ZbjZku5mRTWfGwgSdSCQSCRZJ0IlEIhEUSdCJRCIRFEnQiUQiERRJ0IlEIhEUSdCJRCIRFEMJ+jd2/G9Q/++ObDczfrcfs9V44G2RrcdBO1vxHBDOIOjfrz/Y/7vjN9II2W48WoLOVmPxJpDjoeP/bD0KSparGppDEnQ8JEHbkQRtAODYJUGLoSVobHrDGE7QL8f+91cVEtWdnJ3+we93S+xNdzyZ7UagnhHtYfD7rfH7+OE5X0sBI1uPgpLlvnTtNpqgP7PlaT94Dvx+X7xb6t1kubApkAStx5GgocbL1iOhZLkvXbtNkTiADv1cyW/4+43x+6tsjnquZLsRgDkmW41CQ9BfZcNk65FQstyXrt3GE/QrZvp8309lp0P4LLJJ0AYkQeuRBN0FJct96dptvAb9Ba0tv8s/zfcb4/cTMEFnuzEA2y1bjQRD0Nl6NJQs96Vrt1EEXcySxnS4p7PTv0F50NluBMAh9zmRrYaj9oy/jg2TrYfDwnJfunabQdCN8/+7TNN8vzP2NvrcJKzPZbuBAIfcV7Yaj8M+aGR1y9YDYWG5L127DZM4Ds/P1GvLO4b/Ovw7fr81DgT92Wb3PpTtRgIactlqIhyecvs0zudrth4KC8sFIehEL3L8JxJ3RxJ0WCRBJxJ3RxJ0WCRBJxJ3RxJ0IpFIBEUSdCKRSARFEnQikUgERRJ0IpFIBEUSdCKRSARFEnQikUgERRJ04rr49av9lEgshBy3iesiCTqxOHLcJu6AJOjEkshxm7gufmj5P3/++uf3p3/++r+vr//79efZRiUSciRBJ66Lb4L+6/evX7/++fenv3798fX15zdLJxKrIAk6cV18E/S//vaZ//rz+9N///r3//7619k2JRIKJEEnrotvWv7j13++vv7zI3bkO4wTqyEJOnFdfNPy8/bgz9///fXrf0+2KJFQIQk6cV0kQScWRxJ04rqoJY4//kiJI7EUkqAT18Xz1uCff329bxL++9d/n21TIqFAEnTiumi32f3x66+zjUok5EiCTlwXzwdV/lk8qPLPs41KJORIgk4kEomgSIJOJBKJoEiCTiQSiaBIgk4kEomgSIJOJBKJoEiCTiQSiaBIgk4kEomgSIJOJBKJoEiCTiQSiaBIgk4kEomgSIJOJBKJoEiCTiQSiaD4/87RXKHCQFeFAAAAAElFTkSuQmCC",
-      "text/plain": [
-       "plot without title"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 360,
-       "width": 720
-      }
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ggplot(total,aes(x=idx,y=total)) + geom_point(color = \"firebrick\", shape = \"diamond\", size = 2) +\n",
-    "    geom_line(color = \"firebrick\", linetype = \"dotted\", size = .3)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "38e65fd5",
-   "metadata": {},
-   "source": [
-    "我们想要按月分析产品总数。因此，我们计算每月产品总数的平均值，并绘制柱状图。\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "id": "294dde87",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "index = seq(start_date,end_date,by ='month')\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "id": "7542d95e",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "              total\n",
-       "2020-01-31 41.03847\n",
-       "2020-02-29 40.91568\n",
-       "2020-03-31 39.27424\n",
-       "2020-04-30 37.63589\n",
-       "2020-05-31 38.75129\n",
-       "2020-06-30 38.75744\n",
-       "2020-07-31 38.35212\n",
-       "2020-08-31 40.43712\n",
-       "2020-09-30 38.90043\n",
-       "2020-10-31 37.99855\n",
-       "2020-11-30 41.20759\n",
-       "2020-12-31 38.46355"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "plot without title"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 360,
-       "width": 720
-      }
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "x<- as.xts(total, dateFormat =\"Date\")\n",
-    "(monthly<-apply.monthly(x,mean))\n",
-    "ggplot(monthly, aes(x=index, y=total)) + \n",
-    "  geom_bar(stat = \"identity\", width=5) "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "945feffd",
-   "metadata": {},
-   "source": [
-    "## 数据框\n",
-    "数据框本质上是具有相同索引的多个序列的集合。我们可以将多个序列组合在一起形成一个数据框。  \n",
-    "例如，我们正在创建一个由 a 和 b 序列组成的数据框\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "id": "88a435ec",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a = data.frame(a,row.names = c(1:a1))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "id": "c4e2a6c1",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "b = data.frame(b,row.names = c(1:b1))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "id": "2bb5177c",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<table class=\"dataframe\">\n",
-       "<caption>A data.frame: 9 × 2</caption>\n",
-       "<thead>\n",
-       "\t<tr><th></th><th scope=col>a</th><th scope=col>b</th></tr>\n",
-       "\t<tr><th></th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
-       "</thead>\n",
-       "<tbody>\n",
-       "\t<tr><th scope=row>1</th><td>1</td><td>I     </td></tr>\n",
-       "\t<tr><th scope=row>2</th><td>2</td><td>like  </td></tr>\n",
-       "\t<tr><th scope=row>3</th><td>3</td><td>to    </td></tr>\n",
-       "\t<tr><th scope=row>4</th><td>4</td><td>use   </td></tr>\n",
-       "\t<tr><th scope=row>5</th><td>5</td><td>Python</td></tr>\n",
-       "\t<tr><th scope=row>6</th><td>6</td><td>and   </td></tr>\n",
-       "\t<tr><th scope=row>7</th><td>7</td><td>Pandas</td></tr>\n",
-       "\t<tr><th scope=row>8</th><td>8</td><td>very  </td></tr>\n",
-       "\t<tr><th scope=row>9</th><td>9</td><td>much  </td></tr>\n",
-       "</tbody>\n",
-       "</table>\n"
-      ],
-      "text/latex": [
-       "A data.frame: 9 × 2\n",
-       "\\begin{tabular}{r|ll}\n",
-       "  & a & b\\\\\n",
-       "  & <int> & <chr>\\\\\n",
-       "\\hline\n",
-       "\t1 & 1 & I     \\\\\n",
-       "\t2 & 2 & like  \\\\\n",
-       "\t3 & 3 & to    \\\\\n",
-       "\t4 & 4 & use   \\\\\n",
-       "\t5 & 5 & Python\\\\\n",
-       "\t6 & 6 & and   \\\\\n",
-       "\t7 & 7 & Pandas\\\\\n",
-       "\t8 & 8 & very  \\\\\n",
-       "\t9 & 9 & much  \\\\\n",
-       "\\end{tabular}\n"
-      ],
-      "text/markdown": [
-       "\n",
-       "A data.frame: 9 × 2\n",
-       "\n",
-       "| <!--/--> | a &lt;int&gt; | b &lt;chr&gt; |\n",
-       "|---|---|---|\n",
-       "| 1 | 1 | I      |\n",
-       "| 2 | 2 | like   |\n",
-       "| 3 | 3 | to     |\n",
-       "| 4 | 4 | use    |\n",
-       "| 5 | 5 | Python |\n",
-       "| 6 | 6 | and    |\n",
-       "| 7 | 7 | Pandas |\n",
-       "| 8 | 8 | very   |\n",
-       "| 9 | 9 | much   |\n",
-       "\n"
-      ],
-      "text/plain": [
-       "  a b     \n",
-       "1 1 I     \n",
-       "2 2 like  \n",
-       "3 3 to    \n",
-       "4 4 use   \n",
-       "5 5 Python\n",
-       "6 6 and   \n",
-       "7 7 Pandas\n",
-       "8 8 very  \n",
-       "9 9 much  "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "df<- data.frame(a,b)\n",
-    "df"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6531fe0e",
-   "metadata": {},
-   "source": [
-    "我们还可以使用重命名函数来重命名列名\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "id": "8f45d3a5",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df = \n",
-    "  rename(df,\n",
-    "    A = a,\n",
-    "    B = b,\n",
-    "  )"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "id": "0efbf2d4",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<table class=\"dataframe\">\n",
-       "<caption>A data.frame: 9 × 2</caption>\n",
-       "<thead>\n",
-       "\t<tr><th></th><th scope=col>A</th><th scope=col>B</th></tr>\n",
-       "\t<tr><th></th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
-       "</thead>\n",
-       "<tbody>\n",
-       "\t<tr><th scope=row>1</th><td>1</td><td>I     </td></tr>\n",
-       "\t<tr><th scope=row>2</th><td>2</td><td>like  </td></tr>\n",
-       "\t<tr><th scope=row>3</th><td>3</td><td>to    </td></tr>\n",
-       "\t<tr><th scope=row>4</th><td>4</td><td>use   </td></tr>\n",
-       "\t<tr><th scope=row>5</th><td>5</td><td>Python</td></tr>\n",
-       "\t<tr><th scope=row>6</th><td>6</td><td>and   </td></tr>\n",
-       "\t<tr><th scope=row>7</th><td>7</td><td>Pandas</td></tr>\n",
-       "\t<tr><th scope=row>8</th><td>8</td><td>very  </td></tr>\n",
-       "\t<tr><th scope=row>9</th><td>9</td><td>much  </td></tr>\n",
-       "</tbody>\n",
-       "</table>\n"
-      ],
-      "text/latex": [
-       "A data.frame: 9 × 2\n",
-       "\\begin{tabular}{r|ll}\n",
-       "  & A & B\\\\\n",
-       "  & <int> & <chr>\\\\\n",
-       "\\hline\n",
-       "\t1 & 1 & I     \\\\\n",
-       "\t2 & 2 & like  \\\\\n",
-       "\t3 & 3 & to    \\\\\n",
-       "\t4 & 4 & use   \\\\\n",
-       "\t5 & 5 & Python\\\\\n",
-       "\t6 & 6 & and   \\\\\n",
-       "\t7 & 7 & Pandas\\\\\n",
-       "\t8 & 8 & very  \\\\\n",
-       "\t9 & 9 & much  \\\\\n",
-       "\\end{tabular}\n"
-      ],
-      "text/markdown": [
-       "\n",
-       "A data.frame: 9 × 2\n",
-       "\n",
-       "| <!--/--> | A &lt;int&gt; | B &lt;chr&gt; |\n",
-       "|---|---|---|\n",
-       "| 1 | 1 | I      |\n",
-       "| 2 | 2 | like   |\n",
-       "| 3 | 3 | to     |\n",
-       "| 4 | 4 | use    |\n",
-       "| 5 | 5 | Python |\n",
-       "| 6 | 6 | and    |\n",
-       "| 7 | 7 | Pandas |\n",
-       "| 8 | 8 | very   |\n",
-       "| 9 | 9 | much   |\n",
-       "\n"
-      ],
-      "text/plain": [
-       "  A B     \n",
-       "1 1 I     \n",
-       "2 2 like  \n",
-       "3 3 to    \n",
-       "4 4 use   \n",
-       "5 5 Python\n",
-       "6 6 and   \n",
-       "7 7 Pandas\n",
-       "8 8 very  \n",
-       "9 9 much  "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "df"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8ac0204f",
-   "metadata": {},
-   "source": [
-    "我们也可以使用 select 函数选择数据框中的一列\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "id": "88b51fdc",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Column A (series):\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<table class=\"dataframe\">\n",
-       "<caption>A data.frame: 9 × 1</caption>\n",
-       "<thead>\n",
-       "\t<tr><th></th><th scope=col>A</th></tr>\n",
-       "\t<tr><th></th><th scope=col>&lt;int&gt;</th></tr>\n",
-       "</thead>\n",
-       "<tbody>\n",
-       "\t<tr><th scope=row>1</th><td>1</td></tr>\n",
-       "\t<tr><th scope=row>2</th><td>2</td></tr>\n",
-       "\t<tr><th scope=row>3</th><td>3</td></tr>\n",
-       "\t<tr><th scope=row>4</th><td>4</td></tr>\n",
-       "\t<tr><th scope=row>5</th><td>5</td></tr>\n",
-       "\t<tr><th scope=row>6</th><td>6</td></tr>\n",
-       "\t<tr><th scope=row>7</th><td>7</td></tr>\n",
-       "\t<tr><th scope=row>8</th><td>8</td></tr>\n",
-       "\t<tr><th scope=row>9</th><td>9</td></tr>\n",
-       "</tbody>\n",
-       "</table>\n"
-      ],
-      "text/latex": [
-       "A data.frame: 9 × 1\n",
-       "\\begin{tabular}{r|l}\n",
-       "  & A\\\\\n",
-       "  & <int>\\\\\n",
-       "\\hline\n",
-       "\t1 & 1\\\\\n",
-       "\t2 & 2\\\\\n",
-       "\t3 & 3\\\\\n",
-       "\t4 & 4\\\\\n",
-       "\t5 & 5\\\\\n",
-       "\t6 & 6\\\\\n",
-       "\t7 & 7\\\\\n",
-       "\t8 & 8\\\\\n",
-       "\t9 & 9\\\\\n",
-       "\\end{tabular}\n"
-      ],
-      "text/markdown": [
-       "\n",
-       "A data.frame: 9 × 1\n",
-       "\n",
-       "| <!--/--> | A &lt;int&gt; |\n",
-       "|---|---|\n",
-       "| 1 | 1 |\n",
-       "| 2 | 2 |\n",
-       "| 3 | 3 |\n",
-       "| 4 | 4 |\n",
-       "| 5 | 5 |\n",
-       "| 6 | 6 |\n",
-       "| 7 | 7 |\n",
-       "| 8 | 8 |\n",
-       "| 9 | 9 |\n",
-       "\n"
-      ],
-      "text/plain": [
-       "  A\n",
-       "1 1\n",
-       "2 2\n",
-       "3 3\n",
-       "4 4\n",
-       "5 5\n",
-       "6 6\n",
-       "7 7\n",
-       "8 8\n",
-       "9 9"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "cat(\"Column A (series):\\n\")\n",
-    "select(df,'A')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "45397ec4",
-   "metadata": {},
-   "source": [
-    "我们将提取满足系列中某些逻辑条件的行\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "id": "010bcba8",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<table class=\"dataframe\">\n",
-       "<caption>A data.frame: 4 × 2</caption>\n",
-       "<thead>\n",
-       "\t<tr><th></th><th scope=col>A</th><th scope=col>B</th></tr>\n",
-       "\t<tr><th></th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
-       "</thead>\n",
-       "<tbody>\n",
-       "\t<tr><th scope=row>1</th><td>1</td><td>I   </td></tr>\n",
-       "\t<tr><th scope=row>2</th><td>2</td><td>like</td></tr>\n",
-       "\t<tr><th scope=row>3</th><td>3</td><td>to  </td></tr>\n",
-       "\t<tr><th scope=row>4</th><td>4</td><td>use </td></tr>\n",
-       "</tbody>\n",
-       "</table>\n"
-      ],
-      "text/latex": [
-       "A data.frame: 4 × 2\n",
-       "\\begin{tabular}{r|ll}\n",
-       "  & A & B\\\\\n",
-       "  & <int> & <chr>\\\\\n",
-       "\\hline\n",
-       "\t1 & 1 & I   \\\\\n",
-       "\t2 & 2 & like\\\\\n",
-       "\t3 & 3 & to  \\\\\n",
-       "\t4 & 4 & use \\\\\n",
-       "\\end{tabular}\n"
-      ],
-      "text/markdown": [
-       "\n",
-       "A data.frame: 4 × 2\n",
-       "\n",
-       "| <!--/--> | A &lt;int&gt; | B &lt;chr&gt; |\n",
-       "|---|---|---|\n",
-       "| 1 | 1 | I    |\n",
-       "| 2 | 2 | like |\n",
-       "| 3 | 3 | to   |\n",
-       "| 4 | 4 | use  |\n",
-       "\n"
-      ],
-      "text/plain": [
-       "  A B   \n",
-       "1 1 I   \n",
-       "2 2 like\n",
-       "3 3 to  \n",
-       "4 4 use "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "df[df$A<5,]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "id": "082277db",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<table class=\"dataframe\">\n",
-       "<caption>A data.frame: 1 × 2</caption>\n",
-       "<thead>\n",
-       "\t<tr><th></th><th scope=col>A</th><th scope=col>B</th></tr>\n",
-       "\t<tr><th></th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
-       "</thead>\n",
-       "<tbody>\n",
-       "\t<tr><th scope=row>6</th><td>6</td><td>and</td></tr>\n",
-       "</tbody>\n",
-       "</table>\n"
-      ],
-      "text/latex": [
-       "A data.frame: 1 × 2\n",
-       "\\begin{tabular}{r|ll}\n",
-       "  & A & B\\\\\n",
-       "  & <int> & <chr>\\\\\n",
-       "\\hline\n",
-       "\t6 & 6 & and\\\\\n",
-       "\\end{tabular}\n"
-      ],
-      "text/markdown": [
-       "\n",
-       "A data.frame: 1 × 2\n",
-       "\n",
-       "| <!--/--> | A &lt;int&gt; | B &lt;chr&gt; |\n",
-       "|---|---|---|\n",
-       "| 6 | 6 | and |\n",
-       "\n"
-      ],
-      "text/plain": [
-       "  A B  \n",
-       "6 6 and"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "df[df$A>5 & df$A<7,]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "bf537050",
-   "metadata": {},
-   "source": [
-    "创建新列。\n",
-    "\n",
-    "以下代码创建了一个序列，用于计算 a 与其平均值的偏差，然后将其合并到现有的 DataFrame 中。\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "id": "0bbd19f8",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df$DivA <- df$A - mean(df$A)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "id": "f36d96af",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<table class=\"dataframe\">\n",
-       "<caption>A data.frame: 9 × 3</caption>\n",
-       "<thead>\n",
-       "\t<tr><th></th><th scope=col>A</th><th scope=col>B</th><th scope=col>DivA</th></tr>\n",
-       "\t<tr><th></th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
-       "</thead>\n",
-       "<tbody>\n",
-       "\t<tr><th scope=row>1</th><td>1</td><td>I     </td><td>-4</td></tr>\n",
-       "\t<tr><th scope=row>2</th><td>2</td><td>like  </td><td>-3</td></tr>\n",
-       "\t<tr><th scope=row>3</th><td>3</td><td>to    </td><td>-2</td></tr>\n",
-       "\t<tr><th scope=row>4</th><td>4</td><td>use   </td><td>-1</td></tr>\n",
-       "\t<tr><th scope=row>5</th><td>5</td><td>Python</td><td> 0</td></tr>\n",
-       "\t<tr><th scope=row>6</th><td>6</td><td>and   </td><td> 1</td></tr>\n",
-       "\t<tr><th scope=row>7</th><td>7</td><td>Pandas</td><td> 2</td></tr>\n",
-       "\t<tr><th scope=row>8</th><td>8</td><td>very  </td><td> 3</td></tr>\n",
-       "\t<tr><th scope=row>9</th><td>9</td><td>much  </td><td> 4</td></tr>\n",
-       "</tbody>\n",
-       "</table>\n"
-      ],
-      "text/latex": [
-       "A data.frame: 9 × 3\n",
-       "\\begin{tabular}{r|lll}\n",
-       "  & A & B & DivA\\\\\n",
-       "  & <int> & <chr> & <dbl>\\\\\n",
-       "\\hline\n",
-       "\t1 & 1 & I      & -4\\\\\n",
-       "\t2 & 2 & like   & -3\\\\\n",
-       "\t3 & 3 & to     & -2\\\\\n",
-       "\t4 & 4 & use    & -1\\\\\n",
-       "\t5 & 5 & Python &  0\\\\\n",
-       "\t6 & 6 & and    &  1\\\\\n",
-       "\t7 & 7 & Pandas &  2\\\\\n",
-       "\t8 & 8 & very   &  3\\\\\n",
-       "\t9 & 9 & much   &  4\\\\\n",
-       "\\end{tabular}\n"
-      ],
-      "text/markdown": [
-       "\n",
-       "A data.frame: 9 × 3\n",
-       "\n",
-       "| <!--/--> | A &lt;int&gt; | B &lt;chr&gt; | DivA &lt;dbl&gt; |\n",
-       "|---|---|---|---|\n",
-       "| 1 | 1 | I      | -4 |\n",
-       "| 2 | 2 | like   | -3 |\n",
-       "| 3 | 3 | to     | -2 |\n",
-       "| 4 | 4 | use    | -1 |\n",
-       "| 5 | 5 | Python |  0 |\n",
-       "| 6 | 6 | and    |  1 |\n",
-       "| 7 | 7 | Pandas |  2 |\n",
-       "| 8 | 8 | very   |  3 |\n",
-       "| 9 | 9 | much   |  4 |\n",
-       "\n"
-      ],
-      "text/plain": [
-       "  A B      DivA\n",
-       "1 1 I      -4  \n",
-       "2 2 like   -3  \n",
-       "3 3 to     -2  \n",
-       "4 4 use    -1  \n",
-       "5 5 Python  0  \n",
-       "6 6 and     1  \n",
-       "7 7 Pandas  2  \n",
-       "8 8 very    3  \n",
-       "9 9 much    4  "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "df"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2be67ef7",
-   "metadata": {},
-   "source": [
-    "我们正在创建一个系列，计算A列字符串的长度，然后合并到现有的数据框中\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "id": "c67f2bd0",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df$LenB <- str_length(df$B)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "id": "cef214b2",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<table class=\"dataframe\">\n",
-       "<caption>A data.frame: 9 × 4</caption>\n",
-       "<thead>\n",
-       "\t<tr><th></th><th scope=col>A</th><th scope=col>B</th><th scope=col>DivA</th><th scope=col>LenB</th></tr>\n",
-       "\t<tr><th></th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
-       "</thead>\n",
-       "<tbody>\n",
-       "\t<tr><th scope=row>1</th><td>1</td><td>I     </td><td>-4</td><td>1</td></tr>\n",
-       "\t<tr><th scope=row>2</th><td>2</td><td>like  </td><td>-3</td><td>4</td></tr>\n",
-       "\t<tr><th scope=row>3</th><td>3</td><td>to    </td><td>-2</td><td>2</td></tr>\n",
-       "\t<tr><th scope=row>4</th><td>4</td><td>use   </td><td>-1</td><td>3</td></tr>\n",
-       "\t<tr><th scope=row>5</th><td>5</td><td>Python</td><td> 0</td><td>6</td></tr>\n",
-       "\t<tr><th scope=row>6</th><td>6</td><td>and   </td><td> 1</td><td>3</td></tr>\n",
-       "\t<tr><th scope=row>7</th><td>7</td><td>Pandas</td><td> 2</td><td>6</td></tr>\n",
-       "\t<tr><th scope=row>8</th><td>8</td><td>very  </td><td> 3</td><td>4</td></tr>\n",
-       "\t<tr><th scope=row>9</th><td>9</td><td>much  </td><td> 4</td><td>4</td></tr>\n",
-       "</tbody>\n",
-       "</table>\n"
-      ],
-      "text/latex": [
-       "A data.frame: 9 × 4\n",
-       "\\begin{tabular}{r|llll}\n",
-       "  & A & B & DivA & LenB\\\\\n",
-       "  & <int> & <chr> & <dbl> & <int>\\\\\n",
-       "\\hline\n",
-       "\t1 & 1 & I      & -4 & 1\\\\\n",
-       "\t2 & 2 & like   & -3 & 4\\\\\n",
-       "\t3 & 3 & to     & -2 & 2\\\\\n",
-       "\t4 & 4 & use    & -1 & 3\\\\\n",
-       "\t5 & 5 & Python &  0 & 6\\\\\n",
-       "\t6 & 6 & and    &  1 & 3\\\\\n",
-       "\t7 & 7 & Pandas &  2 & 6\\\\\n",
-       "\t8 & 8 & very   &  3 & 4\\\\\n",
-       "\t9 & 9 & much   &  4 & 4\\\\\n",
-       "\\end{tabular}\n"
-      ],
-      "text/markdown": [
-       "\n",
-       "A data.frame: 9 × 4\n",
-       "\n",
-       "| <!--/--> | A &lt;int&gt; | B &lt;chr&gt; | DivA &lt;dbl&gt; | LenB &lt;int&gt; |\n",
-       "|---|---|---|---|---|\n",
-       "| 1 | 1 | I      | -4 | 1 |\n",
-       "| 2 | 2 | like   | -3 | 4 |\n",
-       "| 3 | 3 | to     | -2 | 2 |\n",
-       "| 4 | 4 | use    | -1 | 3 |\n",
-       "| 5 | 5 | Python |  0 | 6 |\n",
-       "| 6 | 6 | and    |  1 | 3 |\n",
-       "| 7 | 7 | Pandas |  2 | 6 |\n",
-       "| 8 | 8 | very   |  3 | 4 |\n",
-       "| 9 | 9 | much   |  4 | 4 |\n",
-       "\n"
-      ],
-      "text/plain": [
-       "  A B      DivA LenB\n",
-       "1 1 I      -4   1   \n",
-       "2 2 like   -3   4   \n",
-       "3 3 to     -2   2   \n",
-       "4 4 use    -1   3   \n",
-       "5 5 Python  0   6   \n",
-       "6 6 and     1   3   \n",
-       "7 7 Pandas  2   6   \n",
-       "8 8 very    3   4   \n",
-       "9 9 much    4   4   "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "df"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e37d50de",
-   "metadata": {},
-   "source": [
-    "根据数字选择行\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "id": "59fe5316",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<table class=\"dataframe\">\n",
-       "<caption>A data.frame: 5 × 4</caption>\n",
-       "<thead>\n",
-       "\t<tr><th></th><th scope=col>A</th><th scope=col>B</th><th scope=col>DivA</th><th scope=col>LenB</th></tr>\n",
-       "\t<tr><th></th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
-       "</thead>\n",
-       "<tbody>\n",
-       "\t<tr><th scope=row>1</th><td>1</td><td>I     </td><td>-4</td><td>1</td></tr>\n",
-       "\t<tr><th scope=row>2</th><td>2</td><td>like  </td><td>-3</td><td>4</td></tr>\n",
-       "\t<tr><th scope=row>3</th><td>3</td><td>to    </td><td>-2</td><td>2</td></tr>\n",
-       "\t<tr><th scope=row>4</th><td>4</td><td>use   </td><td>-1</td><td>3</td></tr>\n",
-       "\t<tr><th scope=row>5</th><td>5</td><td>Python</td><td> 0</td><td>6</td></tr>\n",
-       "</tbody>\n",
-       "</table>\n"
-      ],
-      "text/latex": [
-       "A data.frame: 5 × 4\n",
-       "\\begin{tabular}{r|llll}\n",
-       "  & A & B & DivA & LenB\\\\\n",
-       "  & <int> & <chr> & <dbl> & <int>\\\\\n",
-       "\\hline\n",
-       "\t1 & 1 & I      & -4 & 1\\\\\n",
-       "\t2 & 2 & like   & -3 & 4\\\\\n",
-       "\t3 & 3 & to     & -2 & 2\\\\\n",
-       "\t4 & 4 & use    & -1 & 3\\\\\n",
-       "\t5 & 5 & Python &  0 & 6\\\\\n",
-       "\\end{tabular}\n"
-      ],
-      "text/markdown": [
-       "\n",
-       "A data.frame: 5 × 4\n",
-       "\n",
-       "| <!--/--> | A &lt;int&gt; | B &lt;chr&gt; | DivA &lt;dbl&gt; | LenB &lt;int&gt; |\n",
-       "|---|---|---|---|---|\n",
-       "| 1 | 1 | I      | -4 | 1 |\n",
-       "| 2 | 2 | like   | -3 | 4 |\n",
-       "| 3 | 3 | to     | -2 | 2 |\n",
-       "| 4 | 4 | use    | -1 | 3 |\n",
-       "| 5 | 5 | Python |  0 | 6 |\n",
-       "\n"
-      ],
-      "text/plain": [
-       "  A B      DivA LenB\n",
-       "1 1 I      -4   1   \n",
-       "2 2 like   -3   4   \n",
-       "3 3 to     -2   2   \n",
-       "4 4 use    -1   3   \n",
-       "5 5 Python  0   6   "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "df[0:5,]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6abec1b7",
-   "metadata": {},
-   "source": [
-    "***分组是指根据某些条件对多列进行分组，我们将使用 summarise 函数来查看差异***\n",
-    "\n",
-    "假设我们想计算列 A 在每个给定 LenB 值下的平均值。然后我们可以按 LenB 对我们的 DataFrame 进行分组，并计算平均值，将其命名为 a。\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "id": "f944a949",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df1 = df %>% group_by(LenB) %>% summarise(a = mean(A))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 55,
-   "id": "8ffd39cd",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<table class=\"dataframe\">\n",
-       "<caption>A tibble: 5 × 2</caption>\n",
-       "<thead>\n",
-       "\t<tr><th scope=col>LenB</th><th scope=col>a</th></tr>\n",
-       "\t<tr><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
-       "</thead>\n",
-       "<tbody>\n",
-       "\t<tr><td>1</td><td>1.000000</td></tr>\n",
-       "\t<tr><td>2</td><td>3.000000</td></tr>\n",
-       "\t<tr><td>3</td><td>5.000000</td></tr>\n",
-       "\t<tr><td>4</td><td>6.333333</td></tr>\n",
-       "\t<tr><td>6</td><td>6.000000</td></tr>\n",
-       "</tbody>\n",
-       "</table>\n"
-      ],
-      "text/latex": [
-       "A tibble: 5 × 2\n",
-       "\\begin{tabular}{ll}\n",
-       " LenB & a\\\\\n",
-       " <int> & <dbl>\\\\\n",
-       "\\hline\n",
-       "\t 1 & 1.000000\\\\\n",
-       "\t 2 & 3.000000\\\\\n",
-       "\t 3 & 5.000000\\\\\n",
-       "\t 4 & 6.333333\\\\\n",
-       "\t 6 & 6.000000\\\\\n",
-       "\\end{tabular}\n"
-      ],
-      "text/markdown": [
-       "\n",
-       "A tibble: 5 × 2\n",
-       "\n",
-       "| LenB &lt;int&gt; | a &lt;dbl&gt; |\n",
-       "|---|---|\n",
-       "| 1 | 1.000000 |\n",
-       "| 2 | 3.000000 |\n",
-       "| 3 | 5.000000 |\n",
-       "| 4 | 6.333333 |\n",
-       "| 6 | 6.000000 |\n",
-       "\n"
-      ],
-      "text/plain": [
-       "  LenB a       \n",
-       "1 1    1.000000\n",
-       "2 2    3.000000\n",
-       "3 3    5.000000\n",
-       "4 4    6.333333\n",
-       "5 6    6.000000"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "df1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 56,
-   "id": "3b859950",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df2 = df %>% group_by(LenB) %>%\n",
-    "summarise(MEAN = mean(A),count =length(DivA))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5d3f0287",
-   "metadata": {},
-   "source": [
-    "## 打印和绘图\n",
-    "当我们调用 head(df) 时，它会以表格形式打印出数据框。\n",
-    "\n",
-    "任何数据科学项目的第一步是数据清洗和可视化，因此可视化数据集并提取一些有用的信息是非常重要的。\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 57,
-   "id": "69946dc7",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#dataset = read.csv(\"file name\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 58,
-   "id": "4976f190",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<table class=\"dataframe\">\n",
-       "<caption>A data.frame: 6 × 4</caption>\n",
-       "<thead>\n",
-       "\t<tr><th></th><th scope=col>A</th><th scope=col>B</th><th scope=col>DivA</th><th scope=col>LenB</th></tr>\n",
-       "\t<tr><th></th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
-       "</thead>\n",
-       "<tbody>\n",
-       "\t<tr><th scope=row>1</th><td>1</td><td>I     </td><td>-4</td><td>1</td></tr>\n",
-       "\t<tr><th scope=row>2</th><td>2</td><td>like  </td><td>-3</td><td>4</td></tr>\n",
-       "\t<tr><th scope=row>3</th><td>3</td><td>to    </td><td>-2</td><td>2</td></tr>\n",
-       "\t<tr><th scope=row>4</th><td>4</td><td>use   </td><td>-1</td><td>3</td></tr>\n",
-       "\t<tr><th scope=row>5</th><td>5</td><td>Python</td><td> 0</td><td>6</td></tr>\n",
-       "\t<tr><th scope=row>6</th><td>6</td><td>and   </td><td> 1</td><td>3</td></tr>\n",
-       "</tbody>\n",
-       "</table>\n"
-      ],
-      "text/latex": [
-       "A data.frame: 6 × 4\n",
-       "\\begin{tabular}{r|llll}\n",
-       "  & A & B & DivA & LenB\\\\\n",
-       "  & <int> & <chr> & <dbl> & <int>\\\\\n",
-       "\\hline\n",
-       "\t1 & 1 & I      & -4 & 1\\\\\n",
-       "\t2 & 2 & like   & -3 & 4\\\\\n",
-       "\t3 & 3 & to     & -2 & 2\\\\\n",
-       "\t4 & 4 & use    & -1 & 3\\\\\n",
-       "\t5 & 5 & Python &  0 & 6\\\\\n",
-       "\t6 & 6 & and    &  1 & 3\\\\\n",
-       "\\end{tabular}\n"
-      ],
-      "text/markdown": [
-       "\n",
-       "A data.frame: 6 × 4\n",
-       "\n",
-       "| <!--/--> | A &lt;int&gt; | B &lt;chr&gt; | DivA &lt;dbl&gt; | LenB &lt;int&gt; |\n",
-       "|---|---|---|---|---|\n",
-       "| 1 | 1 | I      | -4 | 1 |\n",
-       "| 2 | 2 | like   | -3 | 4 |\n",
-       "| 3 | 3 | to     | -2 | 2 |\n",
-       "| 4 | 4 | use    | -1 | 3 |\n",
-       "| 5 | 5 | Python |  0 | 6 |\n",
-       "| 6 | 6 | and    |  1 | 3 |\n",
-       "\n"
-      ],
-      "text/plain": [
-       "  A B      DivA LenB\n",
-       "1 1 I      -4   1   \n",
-       "2 2 like   -3   4   \n",
-       "3 3 to     -2   2   \n",
-       "4 4 use    -1   3   \n",
-       "5 5 Python  0   6   \n",
-       "6 6 and     1   3   "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "head(df)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "dcca35a8",
-   "metadata": {},
-   "source": [
-    "ggplot2 是一个非常优秀的库，因为它可以简单地从数据框中的数据创建复杂的图表。\n",
-    "\n",
-    "它提供了一种更具编程性的接口，用于指定要绘制的变量、显示方式以及整体的视觉属性。\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 59,
-   "id": "515c95b2",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAABaAAAALQCAMAAABR+ye1AAAAMFBMVEUAAABNTU1oaGh8fHyMjIyampqnp6eysrK9vb3Hx8fQ0NDZ2dnh4eHp6enw8PD////QFLu4AAAACXBIWXMAABJ0AAASdAHeZh94AAAVU0lEQVR4nO3c61bbuhaAUYU7bIjf/203OEBoC0RObGlJmvNHNmeckchR449VY5omAEJKtQ8AgO8JNEBQAg0QlEADBCXQAEEJNEBQAg0QlEADBCXQAEEJNEBQAg0QlEADBCXQAEEJNEBQAg0QlEADBCXQAEEJNEBQAg0QlEADBCXQAEEJNEBQAg0QlEADBCXQAEEJNEBQAg0QlEADBCXQAEEJNEBQAg0QlEADBCXQAEEJNEBQAg0QlEADBCXQAEEJNEBQAg0QlEADBCXQAEEJNEBQAg0QlEADBCXQAEEJNEBQAg0QlEADBCXQAEEJNEBQAg0QlEADBCXQAEEJNEBQAg0QlEADBCXQAEEJNEBQAg0QlEADBCXQAEEJNEBQAg0QlEADBCXQAEEJNEBQAg0QlEADBCXQAEEJNEBQAg0QlEADBCXQAEEJNEBQAg0QlEADBCXQAEEJNEBQAg0QlEADBCXQAEEJNEBQAg0QlEADBCXQAEEJNEBQAg0QlEADBCXQAEEJNEBQAg0QlEADBCXQAEEJNEBQAg0QlEADBCXQAEEVCHQC4Izalgj09ksARCfQAEEJNEBQAg0QlEADBCXQAEEJNEBQAg0QlEADBCXQAEEJNEBQAg0QlEADBCXQAGUs/ufpBBqghLnOyxIt0AAlpC+Pi55yxiqbEmigM+mv/y55zrZPCbgEQEnp4yLHsuecsczWBBrozMdPCAUaIJR0Tp8FGmBrxxs43MUBEMfx7mf3QQPEsbjJfz67yFMCLgGwtYvqPAk0wDYuG54PL1HkKQGXANjQ5XWeBBpgdSsMz4fXKfKUgEsAbGOlOk8CDbCmtYbnw4sVeUrAJQDWtmqdJ4EGWMnKdZ4EGmANaw/Phxct8pSASwCsZos6TwINcKFNhufDKxd5SsAlANawWZ0ngQY433bD8+Hlizwl4BIAl9m4zpNAA5xl8zpPAg2w3PbD82GZIk8JuATAmcrUeRJogEUKDc+HtYo8JeASAMsVrPMk0AC5Sg7PhwWLPCXgEgBLFK/zJNAAGSrUeRJogFNqDM+HhYs8JeASAFlq1XkSaIBfVBueD6sXeUrAJQBOqVrnSaABvld3eD4cQpGnBFwC4GcB6jwJNMA/QtR5EmiAP8UYnmcCDXAUp86TQAN8CjQ8zwQaYBaszpNAA7yJNjzPBBoYXsg6TwINDC9onSeBBsYWdXieCTQwrsh1ngQaGFbo4Xkm0MCQwtd5EmhgRPGH55lAA4NppM6TQAODaabOk0ADI2lneJ4JNDCKtuo8CTQwiMaG55lAAwNosM6TQAP9a3F4npUM9Mtt2t1P08NV2t1ttATAm2OTm63zVDTQ+93rRqWH+7fHdL3JEgDT+wWN40OzCgb6Lr3OzXe7dLuf9vPX6y8BMH0kJLU8PM8KBno3PzGl/fyf3RZLAHzpc+XjuFjBQKd0fPzm7x3pqzOXAJgjNWek+ZJUmKDfHvcmaGArH0Ne8yWpcA36bv/+9fpLAKRu+uwuDqAnqZcbOGbugwa6cWxyHz/K8puEQB/6aPIfBBroQX91ngQa6ECHw/NMoIHGdVrnSaCBtvU6PM8EGmhW13WeBBpoVud1ngQaaFPvw/NMoIH2jFDnSaCB5gwxPM8EGmjJOHWeBBpoyUh1ngQaaMZQw/NMoIEmDFfnSaCBFow3PM8EGghu0DpPAg0EN2ydJ4EGIht3eJ4JNBDV2HWeBBoIavDheSbQQEDq/EaggWgMz+8EGghFnY8EGghEnb8SaCAKw/NfBBqIQZ3/IdBAAIbn7wg0UJs6/0CggbrU+UcCDVRkeP6NQAPVqPPvBBqow/B8kkADNahzBoEGijM85xFooCx1zibQQEnqvIBAA8UYnpcRaKAQdV5KoIESDM9nEGhgc+p8HoEGNqbO5xJoYEuG5wsINLAddb6IQAMbMTxfSqCBLajzCgQaWJ86r0KggZUZntci0MCq1Hk9Ag2sx/C8KoEGLvKlyeq8MoEGLjA3+f3Bibs2gQYucDhZkzpvQqCB8332ufJxdEqggfOl9zo7Zzch0MD5PmZn5+wmBBo4V9LnbQk0cJ6vN3DUPpZOCTRwhuPPBf2EcDsCDSylyYUINLCMOhcj0MAChueSBBrIps5lCTSQx/BcnEADOdS5AoEGTjI81yHQwO/UuRqBBn6jzhUJNPAjw3NdAg38QJ1rE2jgO4bnAAQa+Jc6hyDQwF8Mz1EINPCVOgci0MCROoci0MA7w3M0Ag3M1DkegQYMz0EJNKDOQQk0DM7wHJdAw8jUOTSBhnGpc3ACDYMyPMcn0DAkdW6BQMN4DM+NEGgYjTo3Q6BhKIbnlgg0jEOdGyPQMAp1bo5AwxAMzy0SaBiAOrdJoKF3hudmCTT0TZ0bJtDQMcNz2wQaeqXOzRNo6JM6d0CgoUOG5z4INHRHnXsh0NAXw3NHBBp6os5dEWjohuG5NwINfVDnDgk09ECduyTQ0DzDc68EGhqnzv0SaGiZ4blrAg3tUufOCTQ0yvDcv5KB3t/tXh/vr1K6ftxoCejascnqPISCgX7ZvX6k9q8Pb643WQJ6Njf5+ED/Cgb6Nt3sXx9uX15bfZvutlgCenY4L5LheRwFA53S/v1hmvZpt8US0LHPPlc+DsopGujXh1368j/++r+/OHMJ6Fh6r7PTYxxFL3E8T9P928PbBP3rRWifQPjHx+Ti9BhHwUA/p93d83Szey3001V62mIJ6FbS5wGVvM3uaXe8hnG/zRLQpeQGjjGV/UWVx9urtzrf3L9stgR059hkP58ZjN8khNA0eWQCDXGp8+AEGqJS5+EJNIRkeEagISJ1ZibQEI06806gIRTDM0cCDYGoM18JNERheOYvAg0hqDP/EmgIQJ35jkBDbYZnfiDQUJU68zOBhorUmd8INNRieOYEgYY61JmTBBoqMDyTQ6ChNHUmk0BDWepMNoGGggzPLCHQUIo6s5BAQxnqzGICDQUYnjmHQMPW1JkzCTRsS505m0DDhgzPXEKgYTPqzGUEGrZheOZiAg0bUGfWINCwOnVmHQIN6zI8sxqBhhWpM2sSaFiNOrMugYZ1GJ5ZnUDDGtSZDQg0XMzwzDYEGi6jzmxGoOES6syGBBrOZnhmWwIN51FnNifQcA51pgCBhsUMz5Qh0LCMOlOMQMMS6kxBAg3ZDM+UJdCQSZ0pbaVAP9/tLj6UE0tATYZnKlgj0C/3VykJNP1SZ+q4OND7x9c6p+unlY7nuyWgvC9NVmdquTDQj9fpzctqx/PvElDe3OT3B59Gqrkk0E+3rx/e3d3z+p9gpwR1HT6BSZ2p64JA797q/N+0xd8AnRVU9dnnysfB6C4IdEp3H1+sdjh/LQFVpPc6+yBSlwka/vExO/sgUtcK16D/E2i6kvSZINzFAX/4egNH7WNhdCvdB33jPmh6cPy5oJ8QEoDfJIR3mkw0/i0OmKkz8fjX7MDwTFACDepMUALN4AzPxCXQDE2diUygGZfhmeAEmkGpM/EJNENSZ1og0IzH8EwjBJrRqDPNEGiGYnimJQLNQNSZtgg0ozA80xyBZgjqTIsEmgGoM20SaHpneKZZAk3f1JmGCTQdMzzTNoGmW+pM6wSaPhme6YBA0yF1pg8CTXfUmV4INH0xPNMRgaYn6kxXBJpuGJ7pjUDTCXWmPwJNDwzPdEmgaZ460yuBpnHqTL8EmpYZnumaQNMudaZzAk2jDM/0T6BpkjozAoGmPYZnBiHQNEadGYdA0xR1ZiQCTTsMzwxGoGmFOjMcgaYJhmdGJNA0QJ0Zk0ATneGZYQk0oakzIxNoAlNnxibQRGV4ZngCTUzqDAJNRIZneCPQhKPOcCDQxGJ4hk9VAn3yFHSKDuXYZHWGrwSayuZPw/EB+FQw0OlPWyxBgw5/2Kc+EjCigoH+byfQ/OOzz5WPAwIqeYljf5OuX+ZX+O4lsutNV9J7nf2Zwz/KXoN+TOlxcg2aLz6+Hfszh38U/iHhy3W62Qs0H5I+w8+K38Vxn3ZPAs3MDRzwq/K32T1fnb7G7GwdgLuf4ZQa90HfCvTwNBky+FVvylNnyCLQFGZ4hlwCTVHqDPkEmnIMz7CIQFOKOsNCAk0RhmdYTqDZnjrDWQSarakznEmg2ZThGc4n0GxIneESAs1WDM9wIYFmG+oMFxNoNmB4hjUINGtTZ1iJQLMudYbVCDQrMjzDmgSa1agzrEugWYfhGVYn0KxBnWEDAs3FDM+wDYHmMuoMmxFoLqHOsCGB5myGZ9iWQHMmdYatCTTnMDxDAQLNcuoMRQg0CxmeoRSBZgl1hoIEmnzqDEUJNJkMz1CaQJNFnaE8geY0wzNUIdCcos5QiUDzK8Mz1CPQ/EydoSqB5ifqDJUJNN8yPEN9As031BkiEGj+ZniGIASaP6kzhCHQfGF4hkgEmg/qDMEINAfqDOEINJPhGWISaAzPEJRAj87wDGEJ9NjUGQIT6IEZniE2gR7LscnqDOEJ9EjmJh8fgNgEeiSHfU2GZ2iDQA/ks8+VjwPII9ADSe91tr3QBoEeyMfsbHuhDQI9jKTP0BiBHkNyAwe0R6BHcGyynxBCQwS6e5oMrRLozqkztEuge2Z4hqYJdL/UGRon0J0yPEP7BLpH6gxdEOj+qDN0QqA7Y3iGfgh0V9QZeiLQ/TA8Q2cEuhPqDP0R6C6oM/RIoNtneIZOCXTr1Bm6JdBNMzxDzwS6XeoMnRPoVqkzdE+gm2R4hhEIdIPUGcYg0K0xPMMwBLot6gwDEeiGGJ5hLALdCnWG4Qh0G9QZBiTQDTA8w5gEOjx1hlEJdGyGZxiYQAemzjA2gQ5LnWF0Ah2T4RkQ6JDUGZgEOiDDM3Ag0MGoM/BBoCMxPANfCHQY6gz8SaCDUGfgbwIdgeEZ+IZA16fOwLcEujLDM/ATga5JnYFfCHQ96gz8SqArMTwDpwh0FeoMnCbQ5RmegSwCXZg6A7kEuih1BvIJdDmGZ2ARgS5FnYGFBLoIwzOwXMlA729Tun56f5FfX6WHmn1psjoD5ygY6P0uvbk5vEjngZ7f3/tD++8GqKJgoO/Sw2ulH3bX84v0HujDozoD5ysY6N3hiS+7q5fuA/3Z58rHAbSsYKA/arW/vv4u0OmrM5cII73Xufk3AlRUMNBXaf/x1XX3E/T7+2v+jQAVFQz0Q7p9/+olXXcd6KTPwApK3mZ391nlpxNXMZoO29cbOGofC9Cyor+o8nzz8dXLbaeBPn7n6eBKOlCX3yRckSYDaxLo1agzsC6BXofhGVidQK9BnYENCPTFDM/ANgT6QuoMbEWgL2F4BjYk0GdTZ2BbAn0mdQa2JtDnMDwDBQj0cuoMFCHQCxmegVIEehF1BsoR6HyGZ6Aogc6kzkBpAp1FnYHyBPo0wzNQhUCfos5AJQL9K8MzUI9A/0KdgZoE+ieGZ6Aygf7+ANQZqE6gv1tenYEABPqfteUZiEGg/1pZnYEoBPrrsvIMBCLQx0XVGQhFoN9XlGcgGoGe1BmISaBd2gCCGj3QhmcgrLEDrc5AYAMH2vAMxDZsoNUZiG7MQBuegQYMGGh1BtowXKDVGWjFWIE2PAMNGSnQ6gw0ZZhAG56B1gwSaHUG2jNCoA3PQJO6D7Q6A63qPNDqDLSr50AbnoGm9RtodQYa12mgDc9A+7oMtDoDPegv0IZnoBNdBPrYZHUG+tFBoOcmHx8AOtFDoA+PhmegM+0H+rPP2xwKQC19BPqtzgINdKaPQC99DkAD2g/0xzWOTQ4EoJ4eAu0GDqBLHQTaTwiBPnURaIAeCTRAUAINEJRAAwQl0ABBCTRAUAINEJRAAwQl0ABBCTRAUAINEJRAAwQl0ABBBQ00ADED3QX7lM1WZbNV2UbdqlHf91L2KZutymarso26VaO+76XsUzZblc1WZRt1q0Z930vZp2y2KputyjbqVo36vpeyT9lsVTZblW3UrRr1fS9ln7LZqmy2KtuoWzXq+17KPmWzVdlsVbZRt2rU972Ufcpmq7LZqmyjbtWo73sp+5TNVmWzVdlG3apR3/dS9imbrcpmq7KNulWjvu+l7FM2W5XNVmUbdatGfd9L2adstiqbrco26laN+r4BwhNogKAEGiAogQYISqABghJogKAEGiAogQYISqABghJogKAEGiAogQYISqABghJogKAEGiAogQYISqAzPFyl3d2+9lG04j+fqSzPtyndvtQ+ihbs73bDnoBOptPu0pvdmB+QxfY7n6kcTz5VmV52h60a8puZk+mk53T7ehY9pNvaB9KGm+QzlWO3e572N+mu9nHEdztv0t2YJ6CT6aSbwx7pTpbHZKNyPM7V2add7QOJL418Ag75ps8y5udjqZd0baNy3Kbn2ofQivdrZmN+L3MyZdqn69qH0ILr9CLQOa7SdL+bL55xwv37JY772gdSg5Mp00N6qn0IDbhPj/6qkSWlm/knX7WPowUPbz8l3D3UPowqnEx5XnY3tQ+hAc/pxrWgPK/FeZ72t2OOhQvdz3dxjLlTTqYs+50LHBmu3u4aE+gcab4G/ZKuah9IfA9vlzhev5cNOUI7mbJcO48y3M6XgQQ6x9C3Jixzld6u1O/H/F7m85Hh5ep6yJvkl0qfah9JfG7ezDb097Ih3/RCT27gyCPQ+e7nv228+GiddrjNbtBbxp1KJzmJlpHnHC/pav92YfWx9oHEd5fe/h2OuzF/6dLJdNKtsXARG5XlcGuC7/0ZrgfeKifTSf7evoyNyvN0nXZDDoXLzf+aXe2DqMPJBBCUQAMEJdAAQQk0QFACDRCUQAMEJdAAQQk0QFACDRCUQAMEJdAAQQk0QFACDRCUQAMEJdAAQQk0QFACDRCUQAMEJdAAQQk0QFACDRCUQAMEJdAAQQk0QFACDRCUQAMEJdAAQQk0QFACDRCUQAMEJdAAQQk0QFACDRCUQAMEJdAAQQk0QFACDRCUQNOvlF5u0u5+/vrhKl09VD4eWEig6VdKu/TqrdDXb1+k69pHBIsINP16LfJ+ekhX0/SYds/T8y491j4kWEKg6VdK/82P03STnl6/ejJC0xaBpl8pfTwevvr4DzTCB5Z+CTSN84GlXwJN43xg6dcx0B/XoG8qHxEsItD06xhod3HQJIGmX8dAuw+aJgk0/foS6Olh5zcJaY5AAwQl0ABBCTRAUAINEJRAAwQl0ABBCTRAUAINEJRAAwQl0ABBCTRAUAINEJRAAwQl0ABBCTRAUAINEJRAAwQl0ABBCTRAUAINEJRAAwQl0ABBCTRAUAINEJRAAwQl0ABBCTRAUAINEJRAAwQl0ABBCTRAUP8Dr9AKPKfUMOkAAAAASUVORK5CYII=",
-      "text/plain": [
-       "plot without title"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 360,
-       "width": 720
-      }
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot(df$A,type = 'o',xlab = \"no\",ylab = \"A\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 60,
-   "id": "41b872c9",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-       "plot without title"
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-    "\n---\n\n**免责声明**：  \n本文档使用AI翻译服务[Co-op Translator](https://github.com/Azure/co-op-translator)进行翻译。尽管我们努力确保准确性，但请注意，自动翻译可能包含错误或不准确之处。应以原始语言的文档作为权威来源。对于关键信息，建议使用专业人工翻译。因使用本翻译而引起的任何误解或误读，我们概不负责。\n"
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\ No newline at end of file
diff --git a/translations/zh/2-Working-With-Data/07-python/README.md b/translations/zh/2-Working-With-Data/07-python/README.md
deleted file mode 100644
index 30c41ee6..00000000
--- a/translations/zh/2-Working-With-Data/07-python/README.md
+++ /dev/null
@@ -1,289 +0,0 @@
-<!--
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-{
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--->
-# 使用数据：Python和Pandas库
-
-| ![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/07-WorkWithPython.png) |
-| :-------------------------------------------------------------------------------------------------------: |
-|                 使用Python - _Sketchnote by [@nitya](https://twitter.com/nitya)_                          |
-
-[![介绍视频](../../../../translated_images/zh-CN/video-ds-python.245247dc811db8e4d5ac420246de8a118c63fd28f6a56578d08b630ae549f260.png)](https://youtu.be/dZjWOGbsN4Y)
-
-虽然数据库提供了非常高效的方式来存储数据并通过查询语言进行查询，但最灵活的数据处理方式是编写自己的程序来操作数据。在许多情况下，使用数据库查询可能更有效。然而，当需要更复杂的数据处理时，SQL可能无法轻松完成。  
-数据处理可以用任何编程语言编写，但有些语言在处理数据方面更高级。数据科学家通常偏好以下语言之一：
-
-* **[Python](https://www.python.org/)** 是一种通用编程语言，由于其简单性，通常被认为是初学者的最佳选择之一。Python有许多额外的库，可以帮助解决许多实际问题，例如从ZIP压缩文件中提取数据或将图片转换为灰度图像。除了数据科学，Python还常用于Web开发。
-* **[R](https://www.r-project.org/)** 是一个传统工具箱，专为统计数据处理而开发。它包含大量的库资源（CRAN），使其成为数据处理的良好选择。然而，R不是通用编程语言，很少在数据科学领域之外使用。
-* **[Julia](https://julialang.org/)** 是另一种专为数据科学开发的语言。它旨在提供比Python更好的性能，是科学实验的绝佳工具。
-
-在本课程中，我们将重点使用Python进行简单的数据处理。我们假设您对该语言有基本的了解。如果您想深入学习Python，可以参考以下资源：
-
-* [通过Turtle Graphics和分形趣味学习Python](https://github.com/shwars/pycourse) - 基于GitHub的Python编程快速入门课程
-* [迈出Python的第一步](https://docs.microsoft.com/en-us/learn/paths/python-first-steps/?WT.mc_id=academic-77958-bethanycheum) [Microsoft Learn](http://learn.microsoft.com/?WT.mc_id=academic-77958-bethanycheum)上的学习路径
-
-数据可以有多种形式。在本课程中，我们将讨论三种数据形式——**表格数据**、**文本**和**图像**。
-
-我们将专注于一些数据处理的示例，而不是全面介绍所有相关库。这将帮助您了解主要的可能性，并让您知道在需要时可以在哪里找到解决问题的方法。
-
-> **最有用的建议**：当您需要对数据执行某些操作但不知道如何操作时，请尝试在互联网上搜索。[Stackoverflow](https://stackoverflow.com/) 通常包含许多关于Python的典型任务的有用代码示例。
-
-## [课前测验](https://ff-quizzes.netlify.app/en/ds/quiz/12)
-
-## 表格数据和数据框
-
-在我们讨论关系型数据库时，您已经接触过表格数据。当您拥有大量数据，并且数据存储在许多不同的关联表中时，使用SQL来处理它是非常合理的。然而，在许多情况下，我们有一个数据表，并需要对这些数据进行一些**理解**或**洞察**，例如分布、值之间的相关性等。在数据科学中，我们经常需要对原始数据进行一些转换，然后进行可视化。这两个步骤都可以轻松使用Python完成。
-
-Python中有两个最有用的库可以帮助您处理表格数据：
-* **[Pandas](https://pandas.pydata.org/)** 允许您操作所谓的**数据框（Dataframes）**，它类似于关系表。您可以拥有命名的列，并对行、列以及整个数据框执行不同的操作。
-* **[Numpy](https://numpy.org/)** 是一个用于处理**张量（Tensors）**的库，即多维**数组（Arrays）**。数组的值具有相同的底层类型，它比数据框更简单，但提供了更多的数学操作，并减少了开销。
-
-此外，还有几个您应该了解的库：
-* **[Matplotlib](https://matplotlib.org/)** 是一个用于数据可视化和绘制图表的库
-* **[SciPy](https://www.scipy.org/)** 是一个包含一些额外科学函数的库。我们在讨论概率和统计时已经接触过这个库
-
-以下是您通常在Python程序开头导入这些库的代码：
-```python
-import numpy as np
-import pandas as pd
-import matplotlib.pyplot as plt
-from scipy import ... # you need to specify exact sub-packages that you need
-``` 
-
-Pandas围绕几个基本概念构建。
-
-### Series（序列）
-
-**Series** 是一组值，类似于列表或numpy数组。主要区别在于Series还有一个**索引**，当我们对Series进行操作（例如相加）时，会考虑索引。索引可以简单到整数行号（从列表或数组创建Series时默认使用的索引），也可以具有复杂结构，例如日期区间。
-
-> **注意**：在配套的笔记本文件 [`notebook.ipynb`](notebook.ipynb) 中有一些Pandas的入门代码。我们在这里仅概述一些示例，您可以查看完整的笔记本文件。
-
-举个例子：我们想分析冰淇淋店的销售情况。让我们生成一段时间内的销售数据（每天售出的商品数量）：
-```python
-start_date = "Jan 1, 2020"
-end_date = "Mar 31, 2020"
-idx = pd.date_range(start_date,end_date)
-print(f"Length of index is {len(idx)}")
-items_sold = pd.Series(np.random.randint(25,50,size=len(idx)),index=idx)
-items_sold.plot()
-```
-![时间序列图](../../../../translated_images/zh-CN/timeseries-1.80de678ab1cf727e50e00bcf24009fa2b0a8b90ebc43e34b99a345227d28e467.png)
-
-假设每周我们都会举办一个朋友聚会，并额外拿出10盒冰淇淋用于聚会。我们可以创建另一个以周为索引的Series来展示这一点：
-```python
-additional_items = pd.Series(10,index=pd.date_range(start_date,end_date,freq="W"))
-```
-当我们将两个Series相加时，就得到了总数：
-```python
-total_items = items_sold.add(additional_items,fill_value=0)
-total_items.plot()
-```
-![时间序列图](../../../../translated_images/zh-CN/timeseries-2.aae51d575c55181ceda81ade8c546a2fc2024f9136934386d57b8a189d7570ff.png)
-
-> **注意** 我们没有使用简单的语法 `total_items+additional_items`。如果使用这种方法，我们会在结果Series中得到许多`NaN`（*Not a Number*）值。这是因为在`additional_items`的某些索引点上缺少值，而将`NaN`与任何值相加都会得到`NaN`。因此，我们需要在相加时指定`fill_value`参数。
-
-对于时间序列，我们还可以使用不同的时间间隔对序列进行**重采样**。例如，假设我们想计算每月的平均销售量，可以使用以下代码：
-```python
-monthly = total_items.resample("1M").mean()
-ax = monthly.plot(kind='bar')
-```
-![每月时间序列平均值](../../../../translated_images/zh-CN/timeseries-3.f3147cbc8c624881008564bc0b5d9fcc15e7374d339da91766bd0e1c6bd9e3af.png)
-
-### DataFrame（数据框）
-
-数据框本质上是具有相同索引的多个Series的集合。我们可以将几个Series组合成一个数据框：
-```python
-a = pd.Series(range(1,10))
-b = pd.Series(["I","like","to","play","games","and","will","not","change"],index=range(0,9))
-df = pd.DataFrame([a,b])
-```
-这将创建如下的水平表格：
-|     | 0   | 1    | 2   | 3   | 4      | 5   | 6      | 7    | 8    |
-| --- | --- | ---- | --- | --- | ------ | --- | ------ | ---- | ---- |
-| 0   | 1   | 2    | 3   | 4   | 5      | 6   | 7      | 8    | 9    |
-| 1   | I   | like | to  | use | Python | and | Pandas | very | much |
-
-我们还可以将Series作为列，并使用字典指定列名：
-```python
-df = pd.DataFrame({ 'A' : a, 'B' : b })
-```
-这将生成如下表格：
-
-|     | A   | B      |
-| --- | --- | ------ |
-| 0   | 1   | I      |
-| 1   | 2   | like   |
-| 2   | 3   | to     |
-| 3   | 4   | use    |
-| 4   | 5   | Python |
-| 5   | 6   | and    |
-| 6   | 7   | Pandas |
-| 7   | 8   | very   |
-| 8   | 9   | much   |
-
-**注意** 我们还可以通过转置前一个表格来获得这种表格布局，例如：
-```python
-df = pd.DataFrame([a,b]).T..rename(columns={ 0 : 'A', 1 : 'B' })
-```
-这里`.T`表示转置数据框的操作，即交换行和列，而`rename`操作允许我们重命名列以匹配前面的示例。
-
-以下是我们可以对数据框执行的一些最重要操作：
-
-**列选择**。我们可以通过写`df['A']`选择单个列——此操作返回一个Series。我们还可以通过写`df[['B','A']]`选择列的子集到另一个数据框——此操作返回另一个数据框。
-
-**按条件过滤**某些行。例如，要仅保留列`A`大于5的行，可以写`df[df['A']>5]`。
-
-> **注意**：过滤的工作原理如下。表达式`df['A']<5`返回一个布尔Series，指示原始Series`df['A']`中每个元素的表达式是否为`True`或`False`。当布尔Series用作索引时，它返回数据框中的行子集。因此，不能使用任意的Python布尔表达式，例如写`df[df['A']>5 and df['A']<7]`是错误的。相反，您应该使用布尔Series上的特殊`&`操作，写`df[(df['A']>5) & (df['A']<7)]`（*括号在这里很重要*）。
-
-**创建新的可计算列**。我们可以通过使用直观的表达式轻松为数据框创建新的可计算列：
-```python
-df['DivA'] = df['A']-df['A'].mean() 
-``` 
-此示例计算列A与其平均值的偏差。这里实际发生的是我们计算了一个Series，然后将其分配给左侧，创建了另一个列。因此，我们不能使用与Series不兼容的任何操作，例如以下代码是错误的：
-```python
-# Wrong code -> df['ADescr'] = "Low" if df['A'] < 5 else "Hi"
-df['LenB'] = len(df['B']) # <- Wrong result
-``` 
-后一个示例虽然语法正确，但会给出错误结果，因为它将Series`B`的长度分配给列中的所有值，而不是分配给每个元素的长度。
-
-如果我们需要计算类似这样的复杂表达式，可以使用`apply`函数。最后一个示例可以写成如下：
-```python
-df['LenB'] = df['B'].apply(lambda x : len(x))
-# or 
-df['LenB'] = df['B'].apply(len)
-```
-
-经过上述操作后，我们将得到以下数据框：
-
-|     | A   | B      | DivA | LenB |
-| --- | --- | ------ | ---- | ---- |
-| 0   | 1   | I      | -4.0 | 1    |
-| 1   | 2   | like   | -3.0 | 4    |
-| 2   | 3   | to     | -2.0 | 2    |
-| 3   | 4   | use    | -1.0 | 3    |
-| 4   | 5   | Python | 0.0  | 6    |
-| 5   | 6   | and    | 1.0  | 3    |
-| 6   | 7   | Pandas | 2.0  | 6    |
-| 7   | 8   | very   | 3.0  | 4    |
-| 8   | 9   | much   | 4.0  | 4    |
-
-**基于数字选择行**可以使用`iloc`构造。例如，要选择数据框的前5行：
-```python
-df.iloc[:5]
-```
-
-**分组**通常用于获得类似Excel中*数据透视表*的结果。假设我们想计算列`A`的平均值，按`LenB`的不同值分组。我们可以按`LenB`分组数据框，然后调用`mean`：
-```python
-df.groupby(by='LenB')[['A','DivA']].mean()
-```
-如果我们需要计算平均值和组中的元素数量，可以使用更复杂的`aggregate`函数：
-```python
-df.groupby(by='LenB') \
- .aggregate({ 'DivA' : len, 'A' : lambda x: x.mean() }) \
- .rename(columns={ 'DivA' : 'Count', 'A' : 'Mean'})
-```
-这将生成以下表格：
-
-| LenB | Count | Mean     |
-| ---- | ----- | -------- |
-| 1    | 1     | 1.000000 |
-| 2    | 1     | 3.000000 |
-| 3    | 2     | 5.000000 |
-| 4    | 3     | 6.333333 |
-| 6    | 2     | 6.000000 |
-
-### 获取数据
-我们已经看到，从 Python 对象构建 Series 和 DataFrame 是多么简单。然而，数据通常以文本文件或 Excel 表格的形式出现。幸运的是，Pandas 为我们提供了一种简单的方法来从磁盘加载数据。例如，读取 CSV 文件就像这样简单：
-```python
-df = pd.read_csv('file.csv')
-```
-我们将在“挑战”部分中看到更多加载数据的示例，包括从外部网站获取数据。
-
-### 打印和绘图
-
-数据科学家经常需要探索数据，因此能够可视化数据非常重要。当 DataFrame 很大时，我们通常只需要打印出前几行以确保操作正确。这可以通过调用 `df.head()` 来完成。如果你在 Jupyter Notebook 中运行，它会以漂亮的表格形式打印出 DataFrame。
-
-我们还见过使用 `plot` 函数来可视化某些列的用法。虽然 `plot` 对许多任务非常有用，并通过 `kind=` 参数支持多种不同的图表类型，但你也可以使用原始的 `matplotlib` 库来绘制更复杂的内容。我们将在单独的课程中详细讲解数据可视化。
-
-这个概述涵盖了 Pandas 的重要概念，但这个库非常丰富，你可以用它做无限多的事情！现在让我们应用这些知识来解决具体问题。
-
-## 🚀 挑战 1：分析 COVID 传播
-
-我们将关注的第一个问题是 COVID-19 的流行病传播建模。为此，我们将使用由 [约翰霍普金斯大学](https://jhu.edu/) [系统科学与工程中心](https://systems.jhu.edu/) (CSSE) 提供的不同国家感染人数数据。数据集可以在 [这个 GitHub 仓库](https://github.com/CSSEGISandData/COVID-19) 中找到。
-
-由于我们想演示如何处理数据，我们邀请你打开 [`notebook-covidspread.ipynb`](notebook-covidspread.ipynb) 并从头到尾阅读。你也可以执行单元格，并完成我们在最后留下的一些挑战。
-
-![COVID 传播](../../../../translated_images/zh-CN/covidspread.f3d131c4f1d260ab0344d79bac0abe7924598dd754859b165955772e1bd5e8a2.png)
-
-> 如果你不知道如何在 Jupyter Notebook 中运行代码，可以查看 [这篇文章](https://soshnikov.com/education/how-to-execute-notebooks-from-github/)。
-
-## 处理非结构化数据
-
-虽然数据通常以表格形式出现，但在某些情况下我们需要处理较少结构化的数据，例如文本或图像。在这种情况下，为了应用我们上面看到的数据处理技术，我们需要以某种方式**提取**结构化数据。以下是一些示例：
-
-* 从文本中提取关键词，并查看这些关键词出现的频率
-* 使用神经网络从图片中提取有关对象的信息
-* 获取视频摄像头画面中人物的情绪信息
-
-## 🚀 挑战 2：分析 COVID 论文
-
-在这个挑战中，我们将继续讨论 COVID 疫情的主题，重点处理关于该主题的科学论文。有一个 [CORD-19 数据集](https://www.kaggle.com/allen-institute-for-ai/CORD-19-research-challenge)，其中包含超过 7000 篇（撰写时）关于 COVID 的论文，提供了元数据和摘要（其中约一半还提供了全文）。
-
-使用 [Text Analytics for Health](https://docs.microsoft.com/azure/cognitive-services/text-analytics/how-tos/text-analytics-for-health/?WT.mc_id=academic-77958-bethanycheum) 认知服务分析此数据集的完整示例已在 [这篇博客文章](https://soshnikov.com/science/analyzing-medical-papers-with-azure-and-text-analytics-for-health/) 中描述。我们将讨论此分析的简化版本。
-
-> **NOTE**: 我们没有在此仓库中提供数据集副本。你可能需要先从 [Kaggle 上的这个数据集](https://www.kaggle.com/allen-institute-for-ai/CORD-19-research-challenge?select=metadata.csv) 下载 [`metadata.csv`](https://www.kaggle.com/allen-institute-for-ai/CORD-19-research-challenge?select=metadata.csv) 文件。可能需要注册 Kaggle。你也可以从 [这里](https://ai2-semanticscholar-cord-19.s3-us-west-2.amazonaws.com/historical_releases.html) 下载数据集，无需注册，但它将包括所有全文以及元数据文件。
-
-打开 [`notebook-papers.ipynb`](notebook-papers.ipynb) 并从头到尾阅读。你也可以执行单元格，并完成我们在最后留下的一些挑战。
-
-![COVID 医疗处理](../../../../translated_images/zh-CN/covidtreat.b2ba59f57ca45fbcda36e0ddca3f8cfdddeeed6ca879ea7f866d93fa6ec65791.png)
-
-## 处理图像数据
-
-最近，开发了非常强大的 AI 模型，可以帮助我们理解图像。通过预训练的神经网络或云服务，可以解决许多任务。一些示例包括：
-
-* **图像分类**，可以帮助你将图像分类到预定义的类别中。你可以使用诸如 [Custom Vision](https://azure.microsoft.com/services/cognitive-services/custom-vision-service/?WT.mc_id=academic-77958-bethanycheum) 的服务轻松训练自己的图像分类器。
-* **对象检测**，用于检测图像中的不同对象。诸如 [计算机视觉](https://azure.microsoft.com/services/cognitive-services/computer-vision/?WT.mc_id=academic-77958-bethanycheum) 的服务可以检测许多常见对象，你也可以训练 [Custom Vision](https://azure.microsoft.com/services/cognitive-services/custom-vision-service/?WT.mc_id=academic-77958-bethanycheum) 模型来检测一些特定的兴趣对象。
-* **人脸检测**，包括年龄、性别和情绪检测。这可以通过 [Face API](https://azure.microsoft.com/services/cognitive-services/face/?WT.mc_id=academic-77958-bethanycheum) 完成。
-
-所有这些云服务都可以通过 [Python SDKs](https://docs.microsoft.com/samples/azure-samples/cognitive-services-python-sdk-samples/cognitive-services-python-sdk-samples/?WT.mc_id=academic-77958-bethanycheum) 调用，因此可以轻松地集成到你的数据探索工作流中。
-
-以下是一些从图像数据源探索数据的示例：
-* 在博客文章 [如何在没有编码的情况下学习数据科学](https://soshnikov.com/azure/how-to-learn-data-science-without-coding/) 中，我们探索 Instagram 照片，试图了解是什么让人们对照片点赞更多。我们首先使用 [计算机视觉](https://azure.microsoft.com/services/cognitive-services/computer-vision/?WT.mc_id=academic-77958-bethanycheum) 从图片中提取尽可能多的信息，然后使用 [Azure Machine Learning AutoML](https://docs.microsoft.com/azure/machine-learning/concept-automated-ml/?WT.mc_id=academic-77958-bethanycheum) 构建可解释的模型。
-* 在 [面部研究工作坊](https://github.com/CloudAdvocacy/FaceStudies) 中，我们使用 [Face API](https://azure.microsoft.com/services/cognitive-services/face/?WT.mc_id=academic-77958-bethanycheum) 提取活动照片中人物的情绪，以试图了解是什么让人们感到快乐。
-
-## 结论
-
-无论你已经拥有结构化数据还是非结构化数据，使用 Python 都可以完成与数据处理和理解相关的所有步骤。这可能是数据处理最灵活的方式，这也是为什么大多数数据科学家将 Python 作为主要工具的原因。如果你对数据科学之旅很认真，深入学习 Python 可能是一个好主意！
-
-## [课后测验](https://ff-quizzes.netlify.app/en/ds/quiz/13)
-
-## 复习与自学
-
-**书籍**
-* [Wes McKinney. Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython](https://www.amazon.com/gp/product/1491957662)
-
-**在线资源**
-* 官方 [10 分钟 Pandas](https://pandas.pydata.org/pandas-docs/stable/user_guide/10min.html) 教程
-* [Pandas 可视化文档](https://pandas.pydata.org/pandas-docs/stable/user_guide/visualization.html)
-
-**学习 Python**
-* [通过 Turtle Graphics 和分形以有趣的方式学习 Python](https://github.com/shwars/pycourse)
-* [迈出 Python 的第一步](https://docs.microsoft.com/learn/paths/python-first-steps/?WT.mc_id=academic-77958-bethanycheum) 在 [Microsoft Learn](http://learn.microsoft.com/?WT.mc_id=academic-77958-bethanycheum) 上的学习路径
-
-## 作业
-
-[对上述挑战进行更详细的数据研究](assignment.md)
-
-## 致谢
-
-本课程由 [Dmitry Soshnikov](http://soshnikov.com) 倾情创作。
-
----
-
-**免责声明**：  
-本文档使用AI翻译服务 [Co-op Translator](https://github.com/Azure/co-op-translator) 进行翻译。尽管我们努力确保翻译的准确性，但请注意，自动翻译可能包含错误或不准确之处。应以原始语言的文档作为权威来源。对于重要信息，建议使用专业人工翻译。我们对因使用此翻译而产生的任何误解或误读不承担责任。
\ No newline at end of file
diff --git a/translations/zh/2-Working-With-Data/07-python/assignment.md b/translations/zh/2-Working-With-Data/07-python/assignment.md
deleted file mode 100644
index f5a72ff1..00000000
--- a/translations/zh/2-Working-With-Data/07-python/assignment.md
+++ /dev/null
@@ -1,35 +0,0 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "dc8f035ce92e4eaa078ab19caa68267a",
-  "translation_date": "2025-08-25T16:31:03+00:00",
-  "source_file": "2-Working-With-Data/07-python/assignment.md",
-  "language_code": "zh"
-}
--->
-# Python数据处理作业
-
-在本次作业中，我们将要求你详细阐述我们在挑战中开始开发的代码。作业分为两个部分：
-
-## COVID-19传播建模
-
- - [ ] 在一个图中绘制5-6个不同国家的 *R* 图进行比较，或者使用多个图并排展示。
- - [ ] 查看死亡人数和康复人数与感染病例数量之间的相关性。
- - [ ] 通过视觉上关联感染率和死亡率并寻找一些异常情况，找出一种典型疾病的持续时间。你可能需要查看不同国家的数据来得出结论。
- - [ ] 计算病死率及其随时间的变化。*你可能需要考虑疾病持续的天数，将一个时间序列进行偏移后再进行计算。*
-
-## COVID-19论文分析
-
-- [ ] 构建不同药物的共现矩阵，查看哪些药物经常一起出现（即在一个摘要中被提到）。你可以修改用于构建药物和诊断共现矩阵的代码。
-- [ ] 使用热图可视化该矩阵。
-- [ ] 作为一个扩展目标，使用[弦图](https://en.wikipedia.org/wiki/Chord_diagram)可视化药物的共现情况。[这个库](https://pypi.org/project/chord/)可能会帮助你绘制弦图。
-- [ ] 作为另一个扩展目标，使用正则表达式提取不同药物的剂量（例如 *每天服用400mg氯喹中的**400mg***），并构建一个数据框，显示不同药物的不同剂量。**注意**：考虑药物名称附近的数值。
-
-## 评分标准
-
-优秀 | 合格 | 需改进
---- | --- | -- |
-所有任务完成，图形化展示并解释清楚，包括至少一个扩展目标 | 完成超过5个任务，但未尝试扩展目标，或结果不够清晰 | 完成少于5个（但超过3个）任务，图形化展示未能有效说明问题
-
-**免责声明**：  
-本文档使用AI翻译服务 [Co-op Translator](https://github.com/Azure/co-op-translator) 进行翻译。尽管我们努力确保翻译的准确性，但请注意，自动翻译可能包含错误或不准确之处。原始语言的文档应被视为权威来源。对于关键信息，建议使用专业人工翻译。我们不对因使用此翻译而产生的任何误解或误读承担责任。
\ No newline at end of file
diff --git a/translations/zh/2-Working-With-Data/07-python/notebook-covidspread.ipynb b/translations/zh/2-Working-With-Data/07-python/notebook-covidspread.ipynb
deleted file mode 100644
index b8f57d5c..00000000
--- a/translations/zh/2-Working-With-Data/07-python/notebook-covidspread.ipynb
+++ /dev/null
@@ -1,2456 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# COVID-19疫情估算\n",
-    "\n",
-    "## 数据加载\n",
-    "\n",
-    "我们将使用由[约翰霍普金斯大学](https://jhu.edu/)的[系统科学与工程中心](https://systems.jhu.edu/)（CSSE）提供的COVID-19感染者数据。数据集可在[这个GitHub仓库](https://github.com/CSSEGISandData/COVID-19)中找到。\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 124,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import matplotlib.pyplot as plt\n",
-    "plt.rcParams[\"figure.figsize\"] = (10,3) # make figures larger"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "我们可以使用 `pd.read_csv` 直接从 GitHub 加载最新数据。如果由于某种原因数据不可用，您始终可以使用 `data` 文件夹中本地可用的副本——只需取消注释下面定义 `base_url` 的那一行：\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 125,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "base_url = \"https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/\" # loading from Internet\n",
-    "# base_url = \"../../data/COVID/\" # loading from disk\n",
-    "infected_dataset_url = base_url + \"time_series_covid19_confirmed_global.csv\"\n",
-    "recovered_dataset_url = base_url + \"time_series_covid19_recovered_global.csv\"\n",
-    "deaths_dataset_url = base_url + \"time_series_covid19_deaths_global.csv\"\n",
-    "countries_dataset_url = base_url + \"../UID_ISO_FIPS_LookUp_Table.csv\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 126,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Province/State</th>\n",
-       "      <th>Country/Region</th>\n",
-       "      <th>Lat</th>\n",
-       "      <th>Long</th>\n",
-       "      <th>1/22/20</th>\n",
-       "      <th>1/23/20</th>\n",
-       "      <th>1/24/20</th>\n",
-       "      <th>1/25/20</th>\n",
-       "      <th>1/26/20</th>\n",
-       "      <th>1/27/20</th>\n",
-       "      <th>...</th>\n",
-       "      <th>8/20/21</th>\n",
-       "      <th>8/21/21</th>\n",
-       "      <th>8/22/21</th>\n",
-       "      <th>8/23/21</th>\n",
-       "      <th>8/24/21</th>\n",
-       "      <th>8/25/21</th>\n",
-       "      <th>8/26/21</th>\n",
-       "      <th>8/27/21</th>\n",
-       "      <th>8/28/21</th>\n",
-       "      <th>8/29/21</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Afghanistan</td>\n",
-       "      <td>33.93911</td>\n",
-       "      <td>67.709953</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>152448</td>\n",
-       "      <td>152448</td>\n",
-       "      <td>152448</td>\n",
-       "      <td>152583</td>\n",
-       "      <td>152660</td>\n",
-       "      <td>152722</td>\n",
-       "      <td>152822</td>\n",
-       "      <td>152960</td>\n",
-       "      <td>152960</td>\n",
-       "      <td>152960</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Albania</td>\n",
-       "      <td>41.15330</td>\n",
-       "      <td>20.168300</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>138132</td>\n",
-       "      <td>138790</td>\n",
-       "      <td>139324</td>\n",
-       "      <td>139721</td>\n",
-       "      <td>140521</td>\n",
-       "      <td>141365</td>\n",
-       "      <td>142253</td>\n",
-       "      <td>143174</td>\n",
-       "      <td>144079</td>\n",
-       "      <td>144847</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Algeria</td>\n",
-       "      <td>28.03390</td>\n",
-       "      <td>1.659600</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>190656</td>\n",
-       "      <td>191171</td>\n",
-       "      <td>191583</td>\n",
-       "      <td>192089</td>\n",
-       "      <td>192626</td>\n",
-       "      <td>193171</td>\n",
-       "      <td>193674</td>\n",
-       "      <td>194186</td>\n",
-       "      <td>194671</td>\n",
-       "      <td>195162</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Andorra</td>\n",
-       "      <td>42.50630</td>\n",
-       "      <td>1.521800</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>14988</td>\n",
-       "      <td>14988</td>\n",
-       "      <td>14988</td>\n",
-       "      <td>15002</td>\n",
-       "      <td>15003</td>\n",
-       "      <td>15014</td>\n",
-       "      <td>15016</td>\n",
-       "      <td>15025</td>\n",
-       "      <td>15025</td>\n",
-       "      <td>15025</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Angola</td>\n",
-       "      <td>-11.20270</td>\n",
-       "      <td>17.873900</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>45583</td>\n",
-       "      <td>45817</td>\n",
-       "      <td>45945</td>\n",
-       "      <td>46076</td>\n",
-       "      <td>46340</td>\n",
-       "      <td>46539</td>\n",
-       "      <td>46726</td>\n",
-       "      <td>46929</td>\n",
-       "      <td>47079</td>\n",
-       "      <td>47168</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 590 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "  Province/State Country/Region       Lat       Long  1/22/20  1/23/20  \\\n",
-       "0            NaN    Afghanistan  33.93911  67.709953        0        0   \n",
-       "1            NaN        Albania  41.15330  20.168300        0        0   \n",
-       "2            NaN        Algeria  28.03390   1.659600        0        0   \n",
-       "3            NaN        Andorra  42.50630   1.521800        0        0   \n",
-       "4            NaN         Angola -11.20270  17.873900        0        0   \n",
-       "\n",
-       "   1/24/20  1/25/20  1/26/20  1/27/20  ...  8/20/21  8/21/21  8/22/21  \\\n",
-       "0        0        0        0        0  ...   152448   152448   152448   \n",
-       "1        0        0        0        0  ...   138132   138790   139324   \n",
-       "2        0        0        0        0  ...   190656   191171   191583   \n",
-       "3        0        0        0        0  ...    14988    14988    14988   \n",
-       "4        0        0        0        0  ...    45583    45817    45945   \n",
-       "\n",
-       "   8/23/21  8/24/21  8/25/21  8/26/21  8/27/21  8/28/21  8/29/21  \n",
-       "0   152583   152660   152722   152822   152960   152960   152960  \n",
-       "1   139721   140521   141365   142253   143174   144079   144847  \n",
-       "2   192089   192626   193171   193674   194186   194671   195162  \n",
-       "3    15002    15003    15014    15016    15025    15025    15025  \n",
-       "4    46076    46340    46539    46726    46929    47079    47168  \n",
-       "\n",
-       "[5 rows x 590 columns]"
-      ]
-     },
-     "execution_count": 126,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "infected = pd.read_csv(infected_dataset_url)\n",
-    "infected.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "我们可以看到，表格的每一行定义了每个国家和/或省份的感染人数，列对应于日期。类似的表格可以用于加载其他数据，例如康复人数和死亡人数。\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 127,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "recovered = pd.read_csv(recovered_dataset_url)\n",
-    "deaths = pd.read_csv(deaths_dataset_url)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 理解数据\n",
-    "\n",
-    "从上表来看，“省份”列的作用并不明确。让我们看看 `Province/State` 列中存在的不同值：\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 128,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Australian Capital Territory    1\n",
-       "Xinjiang                        1\n",
-       "Martinique                      1\n",
-       "Guadeloupe                      1\n",
-       "French Polynesia                1\n",
-       "                               ..\n",
-       "Fujian                          1\n",
-       "Chongqing                       1\n",
-       "Beijing                         1\n",
-       "Anhui                           1\n",
-       "Turks and Caicos Islands        1\n",
-       "Name: Province/State, Length: 87, dtype: int64"
-      ]
-     },
-     "execution_count": 128,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "infected['Province/State'].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 129,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "<style scoped>\n",
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-       "      <th>Long</th>\n",
-       "      <th>1/22/20</th>\n",
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-       "      <th>1/24/20</th>\n",
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-       "      <th>1/26/20</th>\n",
-       "      <th>1/27/20</th>\n",
-       "      <th>...</th>\n",
-       "      <th>8/20/21</th>\n",
-       "      <th>8/21/21</th>\n",
-       "      <th>8/22/21</th>\n",
-       "      <th>8/23/21</th>\n",
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-       "      <th>8/29/21</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>58</th>\n",
-       "      <td>Anhui</td>\n",
-       "      <td>China</td>\n",
-       "      <td>31.8257</td>\n",
-       "      <td>117.2264</td>\n",
-       "      <td>1</td>\n",
-       "      <td>9</td>\n",
-       "      <td>15</td>\n",
-       "      <td>39</td>\n",
-       "      <td>60</td>\n",
-       "      <td>70</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1008</td>\n",
-       "      <td>1008</td>\n",
-       "      <td>1008</td>\n",
-       "      <td>1008</td>\n",
-       "      <td>1008</td>\n",
-       "      <td>1008</td>\n",
-       "      <td>1008</td>\n",
-       "      <td>1008</td>\n",
-       "      <td>1008</td>\n",
-       "      <td>1008</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>59</th>\n",
-       "      <td>Beijing</td>\n",
-       "      <td>China</td>\n",
-       "      <td>40.1824</td>\n",
-       "      <td>116.4142</td>\n",
-       "      <td>14</td>\n",
-       "      <td>22</td>\n",
-       "      <td>36</td>\n",
-       "      <td>41</td>\n",
-       "      <td>68</td>\n",
-       "      <td>80</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1112</td>\n",
-       "      <td>1113</td>\n",
-       "      <td>1115</td>\n",
-       "      <td>1115</td>\n",
-       "      <td>1115</td>\n",
-       "      <td>1115</td>\n",
-       "      <td>1115</td>\n",
-       "      <td>1115</td>\n",
-       "      <td>1115</td>\n",
-       "      <td>1115</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>60</th>\n",
-       "      <td>Chongqing</td>\n",
-       "      <td>China</td>\n",
-       "      <td>30.0572</td>\n",
-       "      <td>107.8740</td>\n",
-       "      <td>6</td>\n",
-       "      <td>9</td>\n",
-       "      <td>27</td>\n",
-       "      <td>57</td>\n",
-       "      <td>75</td>\n",
-       "      <td>110</td>\n",
-       "      <td>...</td>\n",
-       "      <td>603</td>\n",
-       "      <td>603</td>\n",
-       "      <td>603</td>\n",
-       "      <td>603</td>\n",
-       "      <td>603</td>\n",
-       "      <td>603</td>\n",
-       "      <td>603</td>\n",
-       "      <td>603</td>\n",
-       "      <td>603</td>\n",
-       "      <td>603</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>61</th>\n",
-       "      <td>Fujian</td>\n",
-       "      <td>China</td>\n",
-       "      <td>26.0789</td>\n",
-       "      <td>117.9874</td>\n",
-       "      <td>1</td>\n",
-       "      <td>5</td>\n",
-       "      <td>10</td>\n",
-       "      <td>18</td>\n",
-       "      <td>35</td>\n",
-       "      <td>59</td>\n",
-       "      <td>...</td>\n",
-       "      <td>780</td>\n",
-       "      <td>780</td>\n",
-       "      <td>780</td>\n",
-       "      <td>782</td>\n",
-       "      <td>782</td>\n",
-       "      <td>783</td>\n",
-       "      <td>783</td>\n",
-       "      <td>784</td>\n",
-       "      <td>785</td>\n",
-       "      <td>786</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>62</th>\n",
-       "      <td>Gansu</td>\n",
-       "      <td>China</td>\n",
-       "      <td>35.7518</td>\n",
-       "      <td>104.2861</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>4</td>\n",
-       "      <td>7</td>\n",
-       "      <td>14</td>\n",
-       "      <td>...</td>\n",
-       "      <td>199</td>\n",
-       "      <td>199</td>\n",
-       "      <td>199</td>\n",
-       "      <td>199</td>\n",
-       "      <td>199</td>\n",
-       "      <td>199</td>\n",
-       "      <td>199</td>\n",
-       "      <td>199</td>\n",
-       "      <td>199</td>\n",
-       "      <td>199</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>63</th>\n",
-       "      <td>Guangdong</td>\n",
-       "      <td>China</td>\n",
-       "      <td>23.3417</td>\n",
-       "      <td>113.4244</td>\n",
-       "      <td>26</td>\n",
-       "      <td>32</td>\n",
-       "      <td>53</td>\n",
-       "      <td>78</td>\n",
-       "      <td>111</td>\n",
-       "      <td>151</td>\n",
-       "      <td>...</td>\n",
-       "      <td>3001</td>\n",
-       "      <td>3007</td>\n",
-       "      <td>3012</td>\n",
-       "      <td>3020</td>\n",
-       "      <td>3023</td>\n",
-       "      <td>3032</td>\n",
-       "      <td>3040</td>\n",
-       "      <td>3043</td>\n",
-       "      <td>3046</td>\n",
-       "      <td>3055</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>64</th>\n",
-       "      <td>Guangxi</td>\n",
-       "      <td>China</td>\n",
-       "      <td>23.8298</td>\n",
-       "      <td>108.7881</td>\n",
-       "      <td>2</td>\n",
-       "      <td>5</td>\n",
-       "      <td>23</td>\n",
-       "      <td>23</td>\n",
-       "      <td>36</td>\n",
-       "      <td>46</td>\n",
-       "      <td>...</td>\n",
-       "      <td>289</td>\n",
-       "      <td>289</td>\n",
-       "      <td>289</td>\n",
-       "      <td>289</td>\n",
-       "      <td>289</td>\n",
-       "      <td>289</td>\n",
-       "      <td>289</td>\n",
-       "      <td>289</td>\n",
-       "      <td>289</td>\n",
-       "      <td>289</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>65</th>\n",
-       "      <td>Guizhou</td>\n",
-       "      <td>China</td>\n",
-       "      <td>26.8154</td>\n",
-       "      <td>106.8748</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>3</td>\n",
-       "      <td>4</td>\n",
-       "      <td>5</td>\n",
-       "      <td>7</td>\n",
-       "      <td>...</td>\n",
-       "      <td>147</td>\n",
-       "      <td>147</td>\n",
-       "      <td>147</td>\n",
-       "      <td>147</td>\n",
-       "      <td>147</td>\n",
-       "      <td>147</td>\n",
-       "      <td>147</td>\n",
-       "      <td>147</td>\n",
-       "      <td>147</td>\n",
-       "      <td>147</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>66</th>\n",
-       "      <td>Hainan</td>\n",
-       "      <td>China</td>\n",
-       "      <td>19.1959</td>\n",
-       "      <td>109.7453</td>\n",
-       "      <td>4</td>\n",
-       "      <td>5</td>\n",
-       "      <td>8</td>\n",
-       "      <td>19</td>\n",
-       "      <td>22</td>\n",
-       "      <td>33</td>\n",
-       "      <td>...</td>\n",
-       "      <td>190</td>\n",
-       "      <td>190</td>\n",
-       "      <td>190</td>\n",
-       "      <td>190</td>\n",
-       "      <td>190</td>\n",
-       "      <td>190</td>\n",
-       "      <td>190</td>\n",
-       "      <td>190</td>\n",
-       "      <td>190</td>\n",
-       "      <td>190</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>67</th>\n",
-       "      <td>Hebei</td>\n",
-       "      <td>China</td>\n",
-       "      <td>39.5490</td>\n",
-       "      <td>116.1306</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>8</td>\n",
-       "      <td>13</td>\n",
-       "      <td>18</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1317</td>\n",
-       "      <td>1317</td>\n",
-       "      <td>1317</td>\n",
-       "      <td>1317</td>\n",
-       "      <td>1317</td>\n",
-       "      <td>1317</td>\n",
-       "      <td>1317</td>\n",
-       "      <td>1317</td>\n",
-       "      <td>1317</td>\n",
-       "      <td>1317</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>68</th>\n",
-       "      <td>Heilongjiang</td>\n",
-       "      <td>China</td>\n",
-       "      <td>47.8620</td>\n",
-       "      <td>127.7615</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>4</td>\n",
-       "      <td>9</td>\n",
-       "      <td>15</td>\n",
-       "      <td>21</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1614</td>\n",
-       "      <td>1614</td>\n",
-       "      <td>1614</td>\n",
-       "      <td>1614</td>\n",
-       "      <td>1614</td>\n",
-       "      <td>1614</td>\n",
-       "      <td>1615</td>\n",
-       "      <td>1615</td>\n",
-       "      <td>1615</td>\n",
-       "      <td>1615</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>69</th>\n",
-       "      <td>Henan</td>\n",
-       "      <td>China</td>\n",
-       "      <td>37.8957</td>\n",
-       "      <td>114.9042</td>\n",
-       "      <td>5</td>\n",
-       "      <td>5</td>\n",
-       "      <td>9</td>\n",
-       "      <td>32</td>\n",
-       "      <td>83</td>\n",
-       "      <td>128</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1521</td>\n",
-       "      <td>1522</td>\n",
-       "      <td>1523</td>\n",
-       "      <td>1524</td>\n",
-       "      <td>1525</td>\n",
-       "      <td>1525</td>\n",
-       "      <td>1527</td>\n",
-       "      <td>1528</td>\n",
-       "      <td>1528</td>\n",
-       "      <td>1528</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>70</th>\n",
-       "      <td>Hong Kong</td>\n",
-       "      <td>China</td>\n",
-       "      <td>22.3000</td>\n",
-       "      <td>114.2000</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>5</td>\n",
-       "      <td>8</td>\n",
-       "      <td>8</td>\n",
-       "      <td>...</td>\n",
-       "      <td>12049</td>\n",
-       "      <td>12052</td>\n",
-       "      <td>12057</td>\n",
-       "      <td>12062</td>\n",
-       "      <td>12069</td>\n",
-       "      <td>12074</td>\n",
-       "      <td>12077</td>\n",
-       "      <td>12094</td>\n",
-       "      <td>12100</td>\n",
-       "      <td>12107</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>71</th>\n",
-       "      <td>Hubei</td>\n",
-       "      <td>China</td>\n",
-       "      <td>30.9756</td>\n",
-       "      <td>112.2707</td>\n",
-       "      <td>444</td>\n",
-       "      <td>444</td>\n",
-       "      <td>549</td>\n",
-       "      <td>761</td>\n",
-       "      <td>1058</td>\n",
-       "      <td>1423</td>\n",
-       "      <td>...</td>\n",
-       "      <td>68287</td>\n",
-       "      <td>68289</td>\n",
-       "      <td>68289</td>\n",
-       "      <td>68289</td>\n",
-       "      <td>68289</td>\n",
-       "      <td>68289</td>\n",
-       "      <td>68290</td>\n",
-       "      <td>68290</td>\n",
-       "      <td>68290</td>\n",
-       "      <td>68290</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>72</th>\n",
-       "      <td>Hunan</td>\n",
-       "      <td>China</td>\n",
-       "      <td>27.6104</td>\n",
-       "      <td>111.7088</td>\n",
-       "      <td>4</td>\n",
-       "      <td>9</td>\n",
-       "      <td>24</td>\n",
-       "      <td>43</td>\n",
-       "      <td>69</td>\n",
-       "      <td>100</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1181</td>\n",
-       "      <td>1181</td>\n",
-       "      <td>1181</td>\n",
-       "      <td>1181</td>\n",
-       "      <td>1181</td>\n",
-       "      <td>1181</td>\n",
-       "      <td>1181</td>\n",
-       "      <td>1181</td>\n",
-       "      <td>1181</td>\n",
-       "      <td>1181</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>73</th>\n",
-       "      <td>Inner Mongolia</td>\n",
-       "      <td>China</td>\n",
-       "      <td>44.0935</td>\n",
-       "      <td>113.9448</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>7</td>\n",
-       "      <td>7</td>\n",
-       "      <td>11</td>\n",
-       "      <td>...</td>\n",
-       "      <td>412</td>\n",
-       "      <td>412</td>\n",
-       "      <td>412</td>\n",
-       "      <td>412</td>\n",
-       "      <td>412</td>\n",
-       "      <td>412</td>\n",
-       "      <td>412</td>\n",
-       "      <td>412</td>\n",
-       "      <td>412</td>\n",
-       "      <td>412</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>74</th>\n",
-       "      <td>Jiangsu</td>\n",
-       "      <td>China</td>\n",
-       "      <td>32.9711</td>\n",
-       "      <td>119.4550</td>\n",
-       "      <td>1</td>\n",
-       "      <td>5</td>\n",
-       "      <td>9</td>\n",
-       "      <td>18</td>\n",
-       "      <td>33</td>\n",
-       "      <td>47</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1583</td>\n",
-       "      <td>1584</td>\n",
-       "      <td>1584</td>\n",
-       "      <td>1584</td>\n",
-       "      <td>1586</td>\n",
-       "      <td>1586</td>\n",
-       "      <td>1587</td>\n",
-       "      <td>1587</td>\n",
-       "      <td>1589</td>\n",
-       "      <td>1589</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>75</th>\n",
-       "      <td>Jiangxi</td>\n",
-       "      <td>China</td>\n",
-       "      <td>27.6140</td>\n",
-       "      <td>115.7221</td>\n",
-       "      <td>2</td>\n",
-       "      <td>7</td>\n",
-       "      <td>18</td>\n",
-       "      <td>18</td>\n",
-       "      <td>36</td>\n",
-       "      <td>72</td>\n",
-       "      <td>...</td>\n",
-       "      <td>937</td>\n",
-       "      <td>937</td>\n",
-       "      <td>937</td>\n",
-       "      <td>937</td>\n",
-       "      <td>937</td>\n",
-       "      <td>937</td>\n",
-       "      <td>937</td>\n",
-       "      <td>937</td>\n",
-       "      <td>937</td>\n",
-       "      <td>937</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>76</th>\n",
-       "      <td>Jilin</td>\n",
-       "      <td>China</td>\n",
-       "      <td>43.6661</td>\n",
-       "      <td>126.1923</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>4</td>\n",
-       "      <td>4</td>\n",
-       "      <td>6</td>\n",
-       "      <td>...</td>\n",
-       "      <td>574</td>\n",
-       "      <td>574</td>\n",
-       "      <td>574</td>\n",
-       "      <td>574</td>\n",
-       "      <td>574</td>\n",
-       "      <td>574</td>\n",
-       "      <td>574</td>\n",
-       "      <td>574</td>\n",
-       "      <td>574</td>\n",
-       "      <td>574</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>77</th>\n",
-       "      <td>Liaoning</td>\n",
-       "      <td>China</td>\n",
-       "      <td>41.2956</td>\n",
-       "      <td>122.6085</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "      <td>4</td>\n",
-       "      <td>17</td>\n",
-       "      <td>21</td>\n",
-       "      <td>27</td>\n",
-       "      <td>...</td>\n",
-       "      <td>443</td>\n",
-       "      <td>443</td>\n",
-       "      <td>443</td>\n",
-       "      <td>443</td>\n",
-       "      <td>443</td>\n",
-       "      <td>444</td>\n",
-       "      <td>445</td>\n",
-       "      <td>446</td>\n",
-       "      <td>446</td>\n",
-       "      <td>446</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>78</th>\n",
-       "      <td>Macau</td>\n",
-       "      <td>China</td>\n",
-       "      <td>22.1667</td>\n",
-       "      <td>113.5500</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>5</td>\n",
-       "      <td>6</td>\n",
-       "      <td>...</td>\n",
-       "      <td>63</td>\n",
-       "      <td>63</td>\n",
-       "      <td>63</td>\n",
-       "      <td>63</td>\n",
-       "      <td>63</td>\n",
-       "      <td>63</td>\n",
-       "      <td>63</td>\n",
-       "      <td>63</td>\n",
-       "      <td>63</td>\n",
-       "      <td>63</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>79</th>\n",
-       "      <td>Ningxia</td>\n",
-       "      <td>China</td>\n",
-       "      <td>37.2692</td>\n",
-       "      <td>106.1655</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "      <td>4</td>\n",
-       "      <td>7</td>\n",
-       "      <td>...</td>\n",
-       "      <td>77</td>\n",
-       "      <td>77</td>\n",
-       "      <td>77</td>\n",
-       "      <td>77</td>\n",
-       "      <td>77</td>\n",
-       "      <td>77</td>\n",
-       "      <td>77</td>\n",
-       "      <td>77</td>\n",
-       "      <td>77</td>\n",
-       "      <td>77</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>80</th>\n",
-       "      <td>Qinghai</td>\n",
-       "      <td>China</td>\n",
-       "      <td>35.7452</td>\n",
-       "      <td>95.9956</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>6</td>\n",
-       "      <td>...</td>\n",
-       "      <td>18</td>\n",
-       "      <td>18</td>\n",
-       "      <td>18</td>\n",
-       "      <td>18</td>\n",
-       "      <td>18</td>\n",
-       "      <td>18</td>\n",
-       "      <td>18</td>\n",
-       "      <td>18</td>\n",
-       "      <td>18</td>\n",
-       "      <td>18</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>81</th>\n",
-       "      <td>Shaanxi</td>\n",
-       "      <td>China</td>\n",
-       "      <td>35.1917</td>\n",
-       "      <td>108.8701</td>\n",
-       "      <td>0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>5</td>\n",
-       "      <td>15</td>\n",
-       "      <td>22</td>\n",
-       "      <td>35</td>\n",
-       "      <td>...</td>\n",
-       "      <td>668</td>\n",
-       "      <td>668</td>\n",
-       "      <td>668</td>\n",
-       "      <td>669</td>\n",
-       "      <td>669</td>\n",
-       "      <td>669</td>\n",
-       "      <td>669</td>\n",
-       "      <td>669</td>\n",
-       "      <td>669</td>\n",
-       "      <td>669</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>82</th>\n",
-       "      <td>Shandong</td>\n",
-       "      <td>China</td>\n",
-       "      <td>36.3427</td>\n",
-       "      <td>118.1498</td>\n",
-       "      <td>2</td>\n",
-       "      <td>6</td>\n",
-       "      <td>15</td>\n",
-       "      <td>27</td>\n",
-       "      <td>46</td>\n",
-       "      <td>75</td>\n",
-       "      <td>...</td>\n",
-       "      <td>923</td>\n",
-       "      <td>923</td>\n",
-       "      <td>923</td>\n",
-       "      <td>923</td>\n",
-       "      <td>923</td>\n",
-       "      <td>923</td>\n",
-       "      <td>923</td>\n",
-       "      <td>923</td>\n",
-       "      <td>923</td>\n",
-       "      <td>924</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>83</th>\n",
-       "      <td>Shanghai</td>\n",
-       "      <td>China</td>\n",
-       "      <td>31.2020</td>\n",
-       "      <td>121.4491</td>\n",
-       "      <td>9</td>\n",
-       "      <td>16</td>\n",
-       "      <td>20</td>\n",
-       "      <td>33</td>\n",
-       "      <td>40</td>\n",
-       "      <td>53</td>\n",
-       "      <td>...</td>\n",
-       "      <td>2420</td>\n",
-       "      <td>2432</td>\n",
-       "      <td>2436</td>\n",
-       "      <td>2445</td>\n",
-       "      <td>2451</td>\n",
-       "      <td>2454</td>\n",
-       "      <td>2462</td>\n",
-       "      <td>2466</td>\n",
-       "      <td>2471</td>\n",
-       "      <td>2476</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>84</th>\n",
-       "      <td>Shanxi</td>\n",
-       "      <td>China</td>\n",
-       "      <td>37.5777</td>\n",
-       "      <td>112.2922</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>6</td>\n",
-       "      <td>9</td>\n",
-       "      <td>13</td>\n",
-       "      <td>...</td>\n",
-       "      <td>255</td>\n",
-       "      <td>255</td>\n",
-       "      <td>256</td>\n",
-       "      <td>256</td>\n",
-       "      <td>256</td>\n",
-       "      <td>256</td>\n",
-       "      <td>256</td>\n",
-       "      <td>258</td>\n",
-       "      <td>258</td>\n",
-       "      <td>259</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>85</th>\n",
-       "      <td>Sichuan</td>\n",
-       "      <td>China</td>\n",
-       "      <td>30.6171</td>\n",
-       "      <td>102.7103</td>\n",
-       "      <td>5</td>\n",
-       "      <td>8</td>\n",
-       "      <td>15</td>\n",
-       "      <td>28</td>\n",
-       "      <td>44</td>\n",
-       "      <td>69</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1181</td>\n",
-       "      <td>1182</td>\n",
-       "      <td>1183</td>\n",
-       "      <td>1185</td>\n",
-       "      <td>1185</td>\n",
-       "      <td>1185</td>\n",
-       "      <td>1186</td>\n",
-       "      <td>1187</td>\n",
-       "      <td>1188</td>\n",
-       "      <td>1188</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>86</th>\n",
-       "      <td>Tianjin</td>\n",
-       "      <td>China</td>\n",
-       "      <td>39.3054</td>\n",
-       "      <td>117.3230</td>\n",
-       "      <td>4</td>\n",
-       "      <td>4</td>\n",
-       "      <td>8</td>\n",
-       "      <td>10</td>\n",
-       "      <td>14</td>\n",
-       "      <td>23</td>\n",
-       "      <td>...</td>\n",
-       "      <td>458</td>\n",
-       "      <td>459</td>\n",
-       "      <td>462</td>\n",
-       "      <td>463</td>\n",
-       "      <td>464</td>\n",
-       "      <td>465</td>\n",
-       "      <td>466</td>\n",
-       "      <td>466</td>\n",
-       "      <td>470</td>\n",
-       "      <td>472</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>87</th>\n",
-       "      <td>Tibet</td>\n",
-       "      <td>China</td>\n",
-       "      <td>31.6927</td>\n",
-       "      <td>88.0924</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>88</th>\n",
-       "      <td>Unknown</td>\n",
-       "      <td>China</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>89</th>\n",
-       "      <td>Xinjiang</td>\n",
-       "      <td>China</td>\n",
-       "      <td>41.1129</td>\n",
-       "      <td>85.2401</td>\n",
-       "      <td>0</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "      <td>4</td>\n",
-       "      <td>5</td>\n",
-       "      <td>...</td>\n",
-       "      <td>980</td>\n",
-       "      <td>980</td>\n",
-       "      <td>980</td>\n",
-       "      <td>980</td>\n",
-       "      <td>980</td>\n",
-       "      <td>980</td>\n",
-       "      <td>980</td>\n",
-       "      <td>980</td>\n",
-       "      <td>980</td>\n",
-       "      <td>980</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>90</th>\n",
-       "      <td>Yunnan</td>\n",
-       "      <td>China</td>\n",
-       "      <td>24.9740</td>\n",
-       "      <td>101.4870</td>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>5</td>\n",
-       "      <td>11</td>\n",
-       "      <td>16</td>\n",
-       "      <td>26</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1000</td>\n",
-       "      <td>1007</td>\n",
-       "      <td>1010</td>\n",
-       "      <td>1014</td>\n",
-       "      <td>1021</td>\n",
-       "      <td>1031</td>\n",
-       "      <td>1039</td>\n",
-       "      <td>1047</td>\n",
-       "      <td>1064</td>\n",
-       "      <td>1067</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>91</th>\n",
-       "      <td>Zhejiang</td>\n",
-       "      <td>China</td>\n",
-       "      <td>29.1832</td>\n",
-       "      <td>120.0934</td>\n",
-       "      <td>10</td>\n",
-       "      <td>27</td>\n",
-       "      <td>43</td>\n",
-       "      <td>62</td>\n",
-       "      <td>104</td>\n",
-       "      <td>128</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1420</td>\n",
-       "      <td>1420</td>\n",
-       "      <td>1421</td>\n",
-       "      <td>1428</td>\n",
-       "      <td>1428</td>\n",
-       "      <td>1429</td>\n",
-       "      <td>1429</td>\n",
-       "      <td>1429</td>\n",
-       "      <td>1429</td>\n",
-       "      <td>1430</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>34 rows × 590 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    Province/State Country/Region      Lat      Long  1/22/20  1/23/20  \\\n",
-       "58           Anhui          China  31.8257  117.2264        1        9   \n",
-       "59         Beijing          China  40.1824  116.4142       14       22   \n",
-       "60       Chongqing          China  30.0572  107.8740        6        9   \n",
-       "61          Fujian          China  26.0789  117.9874        1        5   \n",
-       "62           Gansu          China  35.7518  104.2861        0        2   \n",
-       "63       Guangdong          China  23.3417  113.4244       26       32   \n",
-       "64         Guangxi          China  23.8298  108.7881        2        5   \n",
-       "65         Guizhou          China  26.8154  106.8748        1        3   \n",
-       "66          Hainan          China  19.1959  109.7453        4        5   \n",
-       "67           Hebei          China  39.5490  116.1306        1        1   \n",
-       "68    Heilongjiang          China  47.8620  127.7615        0        2   \n",
-       "69           Henan          China  37.8957  114.9042        5        5   \n",
-       "70       Hong Kong          China  22.3000  114.2000        0        2   \n",
-       "71           Hubei          China  30.9756  112.2707      444      444   \n",
-       "72           Hunan          China  27.6104  111.7088        4        9   \n",
-       "73  Inner Mongolia          China  44.0935  113.9448        0        0   \n",
-       "74         Jiangsu          China  32.9711  119.4550        1        5   \n",
-       "75         Jiangxi          China  27.6140  115.7221        2        7   \n",
-       "76           Jilin          China  43.6661  126.1923        0        1   \n",
-       "77        Liaoning          China  41.2956  122.6085        2        3   \n",
-       "78           Macau          China  22.1667  113.5500        1        2   \n",
-       "79         Ningxia          China  37.2692  106.1655        1        1   \n",
-       "80         Qinghai          China  35.7452   95.9956        0        0   \n",
-       "81         Shaanxi          China  35.1917  108.8701        0        3   \n",
-       "82        Shandong          China  36.3427  118.1498        2        6   \n",
-       "83        Shanghai          China  31.2020  121.4491        9       16   \n",
-       "84          Shanxi          China  37.5777  112.2922        1        1   \n",
-       "85         Sichuan          China  30.6171  102.7103        5        8   \n",
-       "86         Tianjin          China  39.3054  117.3230        4        4   \n",
-       "87           Tibet          China  31.6927   88.0924        0        0   \n",
-       "88         Unknown          China      NaN       NaN        0        0   \n",
-       "89        Xinjiang          China  41.1129   85.2401        0        2   \n",
-       "90          Yunnan          China  24.9740  101.4870        1        2   \n",
-       "91        Zhejiang          China  29.1832  120.0934       10       27   \n",
-       "\n",
-       "    1/24/20  1/25/20  1/26/20  1/27/20  ...  8/20/21  8/21/21  8/22/21  \\\n",
-       "58       15       39       60       70  ...     1008     1008     1008   \n",
-       "59       36       41       68       80  ...     1112     1113     1115   \n",
-       "60       27       57       75      110  ...      603      603      603   \n",
-       "61       10       18       35       59  ...      780      780      780   \n",
-       "62        2        4        7       14  ...      199      199      199   \n",
-       "63       53       78      111      151  ...     3001     3007     3012   \n",
-       "64       23       23       36       46  ...      289      289      289   \n",
-       "65        3        4        5        7  ...      147      147      147   \n",
-       "66        8       19       22       33  ...      190      190      190   \n",
-       "67        2        8       13       18  ...     1317     1317     1317   \n",
-       "68        4        9       15       21  ...     1614     1614     1614   \n",
-       "69        9       32       83      128  ...     1521     1522     1523   \n",
-       "70        2        5        8        8  ...    12049    12052    12057   \n",
-       "71      549      761     1058     1423  ...    68287    68289    68289   \n",
-       "72       24       43       69      100  ...     1181     1181     1181   \n",
-       "73        1        7        7       11  ...      412      412      412   \n",
-       "74        9       18       33       47  ...     1583     1584     1584   \n",
-       "75       18       18       36       72  ...      937      937      937   \n",
-       "76        3        4        4        6  ...      574      574      574   \n",
-       "77        4       17       21       27  ...      443      443      443   \n",
-       "78        2        2        5        6  ...       63       63       63   \n",
-       "79        2        3        4        7  ...       77       77       77   \n",
-       "80        0        1        1        6  ...       18       18       18   \n",
-       "81        5       15       22       35  ...      668      668      668   \n",
-       "82       15       27       46       75  ...      923      923      923   \n",
-       "83       20       33       40       53  ...     2420     2432     2436   \n",
-       "84        1        6        9       13  ...      255      255      256   \n",
-       "85       15       28       44       69  ...     1181     1182     1183   \n",
-       "86        8       10       14       23  ...      458      459      462   \n",
-       "87        0        0        0        0  ...        1        1        1   \n",
-       "88        0        0        0        0  ...        0        0        0   \n",
-       "89        2        3        4        5  ...      980      980      980   \n",
-       "90        5       11       16       26  ...     1000     1007     1010   \n",
-       "91       43       62      104      128  ...     1420     1420     1421   \n",
-       "\n",
-       "    8/23/21  8/24/21  8/25/21  8/26/21  8/27/21  8/28/21  8/29/21  \n",
-       "58     1008     1008     1008     1008     1008     1008     1008  \n",
-       "59     1115     1115     1115     1115     1115     1115     1115  \n",
-       "60      603      603      603      603      603      603      603  \n",
-       "61      782      782      783      783      784      785      786  \n",
-       "62      199      199      199      199      199      199      199  \n",
-       "63     3020     3023     3032     3040     3043     3046     3055  \n",
-       "64      289      289      289      289      289      289      289  \n",
-       "65      147      147      147      147      147      147      147  \n",
-       "66      190      190      190      190      190      190      190  \n",
-       "67     1317     1317     1317     1317     1317     1317     1317  \n",
-       "68     1614     1614     1614     1615     1615     1615     1615  \n",
-       "69     1524     1525     1525     1527     1528     1528     1528  \n",
-       "70    12062    12069    12074    12077    12094    12100    12107  \n",
-       "71    68289    68289    68289    68290    68290    68290    68290  \n",
-       "72     1181     1181     1181     1181     1181     1181     1181  \n",
-       "73      412      412      412      412      412      412      412  \n",
-       "74     1584     1586     1586     1587     1587     1589     1589  \n",
-       "75      937      937      937      937      937      937      937  \n",
-       "76      574      574      574      574      574      574      574  \n",
-       "77      443      443      444      445      446      446      446  \n",
-       "78       63       63       63       63       63       63       63  \n",
-       "79       77       77       77       77       77       77       77  \n",
-       "80       18       18       18       18       18       18       18  \n",
-       "81      669      669      669      669      669      669      669  \n",
-       "82      923      923      923      923      923      923      924  \n",
-       "83     2445     2451     2454     2462     2466     2471     2476  \n",
-       "84      256      256      256      256      258      258      259  \n",
-       "85     1185     1185     1185     1186     1187     1188     1188  \n",
-       "86      463      464      465      466      466      470      472  \n",
-       "87        1        1        1        1        1        1        1  \n",
-       "88        0        0        0        0        0        0        0  \n",
-       "89      980      980      980      980      980      980      980  \n",
-       "90     1014     1021     1031     1039     1047     1064     1067  \n",
-       "91     1428     1428     1429     1429     1429     1429     1430  \n",
-       "\n",
-       "[34 rows x 590 columns]"
-      ]
-     },
-     "execution_count": 129,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "infected[infected['Country/Region']=='China']"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 数据预处理\n",
-    "\n",
-    "我们不需要将国家进一步划分为地区，因此我们首先会去掉这些细分，并将所有地区的信息合并在一起，以获取整个国家的数据。这可以通过使用 `groupby` 来实现：\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 130,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
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-       "\n",
-       "    .dataframe thead th {\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Lat</th>\n",
-       "      <th>Long</th>\n",
-       "      <th>1/22/20</th>\n",
-       "      <th>1/23/20</th>\n",
-       "      <th>1/24/20</th>\n",
-       "      <th>1/25/20</th>\n",
-       "      <th>1/26/20</th>\n",
-       "      <th>1/27/20</th>\n",
-       "      <th>1/28/20</th>\n",
-       "      <th>1/29/20</th>\n",
-       "      <th>...</th>\n",
-       "      <th>8/20/21</th>\n",
-       "      <th>8/21/21</th>\n",
-       "      <th>8/22/21</th>\n",
-       "      <th>8/23/21</th>\n",
-       "      <th>8/24/21</th>\n",
-       "      <th>8/25/21</th>\n",
-       "      <th>8/26/21</th>\n",
-       "      <th>8/27/21</th>\n",
-       "      <th>8/28/21</th>\n",
-       "      <th>8/29/21</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Country/Region</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Afghanistan</th>\n",
-       "      <td>33.93911</td>\n",
-       "      <td>67.709953</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>152448</td>\n",
-       "      <td>152448</td>\n",
-       "      <td>152448</td>\n",
-       "      <td>152583</td>\n",
-       "      <td>152660</td>\n",
-       "      <td>152722</td>\n",
-       "      <td>152822</td>\n",
-       "      <td>152960</td>\n",
-       "      <td>152960</td>\n",
-       "      <td>152960</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Albania</th>\n",
-       "      <td>41.15330</td>\n",
-       "      <td>20.168300</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>138132</td>\n",
-       "      <td>138790</td>\n",
-       "      <td>139324</td>\n",
-       "      <td>139721</td>\n",
-       "      <td>140521</td>\n",
-       "      <td>141365</td>\n",
-       "      <td>142253</td>\n",
-       "      <td>143174</td>\n",
-       "      <td>144079</td>\n",
-       "      <td>144847</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Algeria</th>\n",
-       "      <td>28.03390</td>\n",
-       "      <td>1.659600</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>190656</td>\n",
-       "      <td>191171</td>\n",
-       "      <td>191583</td>\n",
-       "      <td>192089</td>\n",
-       "      <td>192626</td>\n",
-       "      <td>193171</td>\n",
-       "      <td>193674</td>\n",
-       "      <td>194186</td>\n",
-       "      <td>194671</td>\n",
-       "      <td>195162</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Andorra</th>\n",
-       "      <td>42.50630</td>\n",
-       "      <td>1.521800</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>14988</td>\n",
-       "      <td>14988</td>\n",
-       "      <td>14988</td>\n",
-       "      <td>15002</td>\n",
-       "      <td>15003</td>\n",
-       "      <td>15014</td>\n",
-       "      <td>15016</td>\n",
-       "      <td>15025</td>\n",
-       "      <td>15025</td>\n",
-       "      <td>15025</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Angola</th>\n",
-       "      <td>-11.20270</td>\n",
-       "      <td>17.873900</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>45583</td>\n",
-       "      <td>45817</td>\n",
-       "      <td>45945</td>\n",
-       "      <td>46076</td>\n",
-       "      <td>46340</td>\n",
-       "      <td>46539</td>\n",
-       "      <td>46726</td>\n",
-       "      <td>46929</td>\n",
-       "      <td>47079</td>\n",
-       "      <td>47168</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 588 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                     Lat       Long  1/22/20  1/23/20  1/24/20  1/25/20  \\\n",
-       "Country/Region                                                            \n",
-       "Afghanistan     33.93911  67.709953        0        0        0        0   \n",
-       "Albania         41.15330  20.168300        0        0        0        0   \n",
-       "Algeria         28.03390   1.659600        0        0        0        0   \n",
-       "Andorra         42.50630   1.521800        0        0        0        0   \n",
-       "Angola         -11.20270  17.873900        0        0        0        0   \n",
-       "\n",
-       "                1/26/20  1/27/20  1/28/20  1/29/20  ...  8/20/21  8/21/21  \\\n",
-       "Country/Region                                      ...                     \n",
-       "Afghanistan           0        0        0        0  ...   152448   152448   \n",
-       "Albania               0        0        0        0  ...   138132   138790   \n",
-       "Algeria               0        0        0        0  ...   190656   191171   \n",
-       "Andorra               0        0        0        0  ...    14988    14988   \n",
-       "Angola                0        0        0        0  ...    45583    45817   \n",
-       "\n",
-       "                8/22/21  8/23/21  8/24/21  8/25/21  8/26/21  8/27/21  8/28/21  \\\n",
-       "Country/Region                                                                  \n",
-       "Afghanistan      152448   152583   152660   152722   152822   152960   152960   \n",
-       "Albania          139324   139721   140521   141365   142253   143174   144079   \n",
-       "Algeria          191583   192089   192626   193171   193674   194186   194671   \n",
-       "Andorra           14988    15002    15003    15014    15016    15025    15025   \n",
-       "Angola            45945    46076    46340    46539    46726    46929    47079   \n",
-       "\n",
-       "                8/29/21  \n",
-       "Country/Region           \n",
-       "Afghanistan      152960  \n",
-       "Albania          144847  \n",
-       "Algeria          195162  \n",
-       "Andorra           15025  \n",
-       "Angola            47168  \n",
-       "\n",
-       "[5 rows x 588 columns]"
-      ]
-     },
-     "execution_count": 130,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "infected = infected.groupby('Country/Region').sum()\n",
-    "recovered = recovered.groupby('Country/Region').sum()\n",
-    "deaths = deaths.groupby('Country/Region').sum()\n",
-    "\n",
-    "infected.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "您可以看到，由于使用了 `groupby`，所有数据框现在都按国家/地区进行了索引。因此，我们可以使用 `.loc` 来访问特定国家的数据：\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 131,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 1000x300 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "infected.loc['US'][2:].plot()\n",
-    "recovered.loc['US'][2:].plot()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "> **注意** 我们使用 `[2:]` 来移除序列中包含国家地理位置的前两个元素。我们也可以完全删除这两列：\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 132,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "infected.drop(columns=['Lat','Long'],inplace=True)\n",
-    "recovered.drop(columns=['Lat','Long'],inplace=True)\n",
-    "deaths.drop(columns=['Lat','Long'],inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 调查数据\n",
-    "\n",
-    "现在让我们转向调查一个特定的国家。我们来创建一个数据框，其中包含按日期索引的感染数据：\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 133,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>infected</th>\n",
-       "      <th>recovered</th>\n",
-       "      <th>deaths</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>2020-01-22</th>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2020-01-23</th>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2020-01-24</th>\n",
-       "      <td>2</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2020-01-25</th>\n",
-       "      <td>2</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2020-01-26</th>\n",
-       "      <td>5</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2021-08-25</th>\n",
-       "      <td>38223029</td>\n",
-       "      <td>0</td>\n",
-       "      <td>632272</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2021-08-26</th>\n",
-       "      <td>38384360</td>\n",
-       "      <td>0</td>\n",
-       "      <td>633564</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2021-08-27</th>\n",
-       "      <td>38707294</td>\n",
-       "      <td>0</td>\n",
-       "      <td>636720</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2021-08-28</th>\n",
-       "      <td>38760363</td>\n",
-       "      <td>0</td>\n",
-       "      <td>637254</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2021-08-29</th>\n",
-       "      <td>38796746</td>\n",
-       "      <td>0</td>\n",
-       "      <td>637531</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>586 rows × 3 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "            infected  recovered  deaths\n",
-       "2020-01-22         1          0       0\n",
-       "2020-01-23         1          0       0\n",
-       "2020-01-24         2          0       0\n",
-       "2020-01-25         2          0       0\n",
-       "2020-01-26         5          0       0\n",
-       "...              ...        ...     ...\n",
-       "2021-08-25  38223029          0  632272\n",
-       "2021-08-26  38384360          0  633564\n",
-       "2021-08-27  38707294          0  636720\n",
-       "2021-08-28  38760363          0  637254\n",
-       "2021-08-29  38796746          0  637531\n",
-       "\n",
-       "[586 rows x 3 columns]"
-      ]
-     },
-     "execution_count": 133,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "def mkframe(country):\n",
-    "    df = pd.DataFrame({ 'infected' : infected.loc[country] ,\n",
-    "                        'recovered' : recovered.loc[country],\n",
-    "                        'deaths' : deaths.loc[country]})\n",
-    "    df.index = pd.to_datetime(df.index)\n",
-    "    return df\n",
-    "\n",
-    "df = mkframe('US')\n",
-    "df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 134,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-      "text/plain": [
-       "<Figure size 1000x300 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "df.plot()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "现在让我们计算每天新增感染者的人数。这将使我们能够看到疫情发展的速度。最简单的方法是使用 `diff`：\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 135,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 1000x300 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "df['ninfected'] = df['infected'].diff()\n",
-    "df['ninfected'].plot()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "我们可以看到数据中有很大的波动。让我们更仔细地看看其中一个月：\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 136,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 1000x300 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "df[(df.index.year==2020) & (df.index.month==7)]['ninfected'].plot()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "看起来数据中明显存在每周波动。因为我们希望能够看到趋势，通过计算移动平均值（即，对于每一天，我们将计算前几天的平均值）来平滑曲线是有意义的：\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 137,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 1000x300 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "df['ninfav'] = df['ninfected'].rolling(window=7).mean()\n",
-    "df['ninfav'].plot()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "为了能够比较多个国家，我们可能需要将国家的人口考虑在内，并比较感染者占国家人口的百分比。为了获取国家的人口数据，让我们加载国家数据集：\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 138,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "      <th></th>\n",
-       "      <th>UID</th>\n",
-       "      <th>iso2</th>\n",
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-       "      <th>code3</th>\n",
-       "      <th>FIPS</th>\n",
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-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>8</td>\n",
-       "      <td>AL</td>\n",
-       "      <td>ALB</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Albania</td>\n",
-       "      <td>41.153300</td>\n",
-       "      <td>20.168300</td>\n",
-       "      <td>Albania</td>\n",
-       "      <td>2877800.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>12</td>\n",
-       "      <td>DZ</td>\n",
-       "      <td>DZA</td>\n",
-       "      <td>12.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Algeria</td>\n",
-       "      <td>28.033900</td>\n",
-       "      <td>1.659600</td>\n",
-       "      <td>Algeria</td>\n",
-       "      <td>43851043.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>20</td>\n",
-       "      <td>AD</td>\n",
-       "      <td>AND</td>\n",
-       "      <td>20.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Andorra</td>\n",
-       "      <td>42.506300</td>\n",
-       "      <td>1.521800</td>\n",
-       "      <td>Andorra</td>\n",
-       "      <td>77265.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>24</td>\n",
-       "      <td>AO</td>\n",
-       "      <td>AGO</td>\n",
-       "      <td>24.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>Angola</td>\n",
-       "      <td>-11.202700</td>\n",
-       "      <td>17.873900</td>\n",
-       "      <td>Angola</td>\n",
-       "      <td>32866268.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4191</th>\n",
-       "      <td>84056037</td>\n",
-       "      <td>US</td>\n",
-       "      <td>USA</td>\n",
-       "      <td>840.0</td>\n",
-       "      <td>56037.0</td>\n",
-       "      <td>Sweetwater</td>\n",
-       "      <td>Wyoming</td>\n",
-       "      <td>US</td>\n",
-       "      <td>41.659439</td>\n",
-       "      <td>-108.882788</td>\n",
-       "      <td>Sweetwater, Wyoming, US</td>\n",
-       "      <td>42343.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4192</th>\n",
-       "      <td>84056039</td>\n",
-       "      <td>US</td>\n",
-       "      <td>USA</td>\n",
-       "      <td>840.0</td>\n",
-       "      <td>56039.0</td>\n",
-       "      <td>Teton</td>\n",
-       "      <td>Wyoming</td>\n",
-       "      <td>US</td>\n",
-       "      <td>43.935225</td>\n",
-       "      <td>-110.589080</td>\n",
-       "      <td>Teton, Wyoming, US</td>\n",
-       "      <td>23464.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4193</th>\n",
-       "      <td>84056041</td>\n",
-       "      <td>US</td>\n",
-       "      <td>USA</td>\n",
-       "      <td>840.0</td>\n",
-       "      <td>56041.0</td>\n",
-       "      <td>Uinta</td>\n",
-       "      <td>Wyoming</td>\n",
-       "      <td>US</td>\n",
-       "      <td>41.287818</td>\n",
-       "      <td>-110.547578</td>\n",
-       "      <td>Uinta, Wyoming, US</td>\n",
-       "      <td>20226.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4194</th>\n",
-       "      <td>84056043</td>\n",
-       "      <td>US</td>\n",
-       "      <td>USA</td>\n",
-       "      <td>840.0</td>\n",
-       "      <td>56043.0</td>\n",
-       "      <td>Washakie</td>\n",
-       "      <td>Wyoming</td>\n",
-       "      <td>US</td>\n",
-       "      <td>43.904516</td>\n",
-       "      <td>-107.680187</td>\n",
-       "      <td>Washakie, Wyoming, US</td>\n",
-       "      <td>7805.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4195</th>\n",
-       "      <td>84056045</td>\n",
-       "      <td>US</td>\n",
-       "      <td>USA</td>\n",
-       "      <td>840.0</td>\n",
-       "      <td>56045.0</td>\n",
-       "      <td>Weston</td>\n",
-       "      <td>Wyoming</td>\n",
-       "      <td>US</td>\n",
-       "      <td>43.839612</td>\n",
-       "      <td>-104.567488</td>\n",
-       "      <td>Weston, Wyoming, US</td>\n",
-       "      <td>6927.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>4196 rows × 12 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "           UID iso2 iso3  code3     FIPS      Admin2 Province_State  \\\n",
-       "0            4   AF  AFG    4.0      NaN         NaN            NaN   \n",
-       "1            8   AL  ALB    8.0      NaN         NaN            NaN   \n",
-       "2           12   DZ  DZA   12.0      NaN         NaN            NaN   \n",
-       "3           20   AD  AND   20.0      NaN         NaN            NaN   \n",
-       "4           24   AO  AGO   24.0      NaN         NaN            NaN   \n",
-       "...        ...  ...  ...    ...      ...         ...            ...   \n",
-       "4191  84056037   US  USA  840.0  56037.0  Sweetwater        Wyoming   \n",
-       "4192  84056039   US  USA  840.0  56039.0       Teton        Wyoming   \n",
-       "4193  84056041   US  USA  840.0  56041.0       Uinta        Wyoming   \n",
-       "4194  84056043   US  USA  840.0  56043.0    Washakie        Wyoming   \n",
-       "4195  84056045   US  USA  840.0  56045.0      Weston        Wyoming   \n",
-       "\n",
-       "     Country_Region        Lat       Long_             Combined_Key  \\\n",
-       "0       Afghanistan  33.939110   67.709953              Afghanistan   \n",
-       "1           Albania  41.153300   20.168300                  Albania   \n",
-       "2           Algeria  28.033900    1.659600                  Algeria   \n",
-       "3           Andorra  42.506300    1.521800                  Andorra   \n",
-       "4            Angola -11.202700   17.873900                   Angola   \n",
-       "...             ...        ...         ...                      ...   \n",
-       "4191             US  41.659439 -108.882788  Sweetwater, Wyoming, US   \n",
-       "4192             US  43.935225 -110.589080       Teton, Wyoming, US   \n",
-       "4193             US  41.287818 -110.547578       Uinta, Wyoming, US   \n",
-       "4194             US  43.904516 -107.680187    Washakie, Wyoming, US   \n",
-       "4195             US  43.839612 -104.567488      Weston, Wyoming, US   \n",
-       "\n",
-       "      Population  \n",
-       "0     38928341.0  \n",
-       "1      2877800.0  \n",
-       "2     43851043.0  \n",
-       "3        77265.0  \n",
-       "4     32866268.0  \n",
-       "...          ...  \n",
-       "4191     42343.0  \n",
-       "4192     23464.0  \n",
-       "4193     20226.0  \n",
-       "4194      7805.0  \n",
-       "4195      6927.0  \n",
-       "\n",
-       "[4196 rows x 12 columns]"
-      ]
-     },
-     "execution_count": 138,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "countries = pd.read_csv(countries_dataset_url)\n",
-    "countries"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "因为该数据集包含有关国家和省份的信息，要获取整个国家的人口，我们需要稍微聪明一点：\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 139,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>UID</th>\n",
-       "      <th>iso2</th>\n",
-       "      <th>iso3</th>\n",
-       "      <th>code3</th>\n",
-       "      <th>FIPS</th>\n",
-       "      <th>Admin2</th>\n",
-       "      <th>Province_State</th>\n",
-       "      <th>Country_Region</th>\n",
-       "      <th>Lat</th>\n",
-       "      <th>Long_</th>\n",
-       "      <th>Combined_Key</th>\n",
-       "      <th>Population</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>790</th>\n",
-       "      <td>840</td>\n",
-       "      <td>US</td>\n",
-       "      <td>USA</td>\n",
-       "      <td>840.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>US</td>\n",
-       "      <td>40.0</td>\n",
-       "      <td>-100.0</td>\n",
-       "      <td>US</td>\n",
-       "      <td>329466283.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     UID iso2 iso3  code3  FIPS Admin2 Province_State Country_Region   Lat  \\\n",
-       "790  840   US  USA  840.0   NaN    NaN            NaN             US  40.0   \n",
-       "\n",
-       "     Long_ Combined_Key   Population  \n",
-       "790 -100.0           US  329466283.0  "
-      ]
-     },
-     "execution_count": 139,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "countries[(countries['Country_Region']=='US') & countries['Province_State'].isna()]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 140,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 1000x300 with 1 Axes>"
-      ]
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-     "output_type": "display_data"
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-   ],
-   "source": [
-    "pop = countries[(countries['Country_Region']=='US') & countries['Province_State'].isna()]['Population'].iloc[0]\n",
-    "df['pinfected'] = df['infected']*100 / pop\n",
-    "df['pinfected'].plot(figsize=(10,3))\n",
-    "plt.show()"
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-   "source": [
-    "## 计算 $R_t$\n",
-    "\n",
-    "为了了解疾病的传染性，我们关注**基本再生数** $R_0$，它表示一个感染者平均会传染给多少人。当 $R_0$ 大于 1 时，疫情可能会扩散。\n",
-    "\n",
-    "$R_0$ 是疾病本身的一个特性，不考虑人们可能采取的一些减缓疫情传播的防护措施。在疫情发展过程中，我们可以在任意时间 $t$ 估算再生数 $R_t$。研究表明，这个数值可以通过取一个 8 天的窗口进行粗略估算，计算公式为：  \n",
-    "$$R_t=\\frac{I_{t-7}+I_{t-6}+I_{t-5}+I_{t-4}}{I_{t-3}+I_{t-2}+I_{t-1}+I_t}$$  \n",
-    "其中 $I_t$ 是第 $t$ 天新增感染人数。\n",
-    "\n",
-    "接下来，我们将为我们的疫情数据计算 $R_t$。为此，我们将取连续 8 天的 `ninfected` 数据，并应用上述公式计算比值：\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 141,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-      "text/plain": [
-       "<Figure size 1000x300 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "df['Rt'] = df['ninfected'].rolling(8).apply(lambda x: x[4:].sum()/x[:4].sum())\n",
-    "df['Rt'].plot()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "你可以看到图表中有一些空白。这些可能是由于数据集中存在 `NaN` 或 `inf` 值引起的。`inf` 可能是由于除以0导致的，而 `NaN` 则可能表示数据缺失，或者没有可用数据来计算结果（例如在我们帧的最开始，宽度为8的滚动窗口尚未可用）。为了让图表更美观，我们需要使用 `replace` 和 `fillna` 函数来填充这些值。\n",
-    "\n",
-    "接下来，我们来看看疫情初期的情况。我们还会限制 y 轴的值，仅显示低于6的值，以便更清晰地观察，并在1的位置绘制一条水平线。\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 142,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 1000x300 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ax = df[df.index<\"2020-05-01\"]['Rt'].replace(np.inf,np.nan).fillna(method='pad').plot(figsize=(10,3))\n",
-    "ax.set_ylim([0,6])\n",
-    "ax.axhline(1,linestyle='--',color='red')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 143,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 1000x300 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "df['ninfected'].diff().plot()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "鉴于报告导致的数据波动较大，通过运行滚动平均来平滑曲线以获得整体情况是有意义的。让我们再次关注疫情的最初几个月：\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 150,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 1000x300 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ax=df[df.index<\"2020-06-01\"]['ninfected'].diff().rolling(7).mean().plot()\n",
-    "ax.axhline(0,linestyle='-.',color='red')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 挑战\n",
-    "\n",
-    "现在是时候让你更多地尝试代码和数据了！以下是一些你可以实验的建议：\n",
-    "* 查看疫情在不同国家的传播情况。\n",
-    "* 在一个图上绘制多个国家的 $R_t$ 图进行比较，或者将多个图并排展示。\n",
-    "* 观察死亡人数和康复人数与感染病例数量之间的关联。\n",
-    "* 尝试通过视觉上关联感染率和死亡率并寻找一些异常情况，来了解一种典型疾病的持续时间。你可能需要查看不同国家的数据来得出结论。\n",
-    "* 计算病死率以及它随时间的变化。你可能需要考虑疾病持续的天数，在进行计算之前对时间序列进行一定的偏移调整。\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 参考文献\n",
-    "\n",
-    "您可以在以下出版物中进一步研究COVID疫情传播：\n",
-    "* [用于COVID疫情期间Rt估算的滑动SIR模型](https://soshnikov.com/science/sliding-sir-model-for-rt-estimation/)，博客文章作者：[Dmitry Soshnikov](http://soshnikov.com)\n",
-    "* T.Petrova, D.Soshnikov, A.Grunin. [全球COVID-19疫情的时间依赖性再生数估算](https://www.preprints.org/manuscript/202006.0289/v1)。*Preprints* **2020**, 2020060289 (doi: 10.20944/preprints202006.0289.v1)\n",
-    "* [上述论文的GitHub代码](https://github.com/shwars/SlidingSIR)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n---\n\n**免责声明**：  \n本文档使用AI翻译服务[Co-op Translator](https://github.com/Azure/co-op-translator)进行翻译。尽管我们努力确保准确性，但请注意，自动翻译可能包含错误或不准确之处。应以原始语言的文档作为权威来源。对于关键信息，建议使用专业人工翻译。我们对因使用本翻译而引起的任何误解或误读不承担责任。\n"
-   ]
-  }
- ],
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-   "source_file": "2-Working-With-Data/07-python/notebook-covidspread.ipynb",
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diff --git a/translations/zh/2-Working-With-Data/07-python/notebook-papers.ipynb b/translations/zh/2-Working-With-Data/07-python/notebook-papers.ipynb
deleted file mode 100644
index d6451d3e..00000000
--- a/translations/zh/2-Working-With-Data/07-python/notebook-papers.ipynb
+++ /dev/null
@@ -1,2343 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 分析 COVID-19 论文\n",
-    "\n",
-    "在本次挑战中，我们将继续探讨 COVID 大流行这一主题，重点处理与该主题相关的科学论文。这里有一个 [CORD-19 数据集](https://www.kaggle.com/allen-institute-for-ai/CORD-19-research-challenge)，其中包含了超过 7000 篇（撰写本文时的数据）关于 COVID 的论文，并附有元数据和摘要（其中约一半的论文还提供了全文）。\n",
-    "\n",
-    "使用 [Text Analytics for Health](https://docs.microsoft.com/azure/cognitive-services/text-analytics/how-tos/text-analytics-for-health/?WT.mc_id=academic-77958-bethanycheum) 认知服务分析该数据集的完整示例已在[这篇博客文章](https://soshnikov.com/science/analyzing-medical-papers-with-azure-and-text-analytics-for-health/)中进行了描述。我们将讨论这一分析的简化版本。\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 145,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 获取数据\n",
-    "\n",
-    "首先，我们需要获取将要处理的 CORD 论文的元数据。\n",
-    "\n",
-    "**注意**：本仓库不提供数据集的副本。您可能需要先从 [Kaggle 上的这个数据集](https://www.kaggle.com/allen-institute-for-ai/CORD-19-research-challenge?select=metadata.csv) 下载 [`metadata.csv`](https://www.kaggle.com/allen-institute-for-ai/CORD-19-research-challenge?select=metadata.csv) 文件。可能需要注册 Kaggle。您也可以从[这里](https://ai2-semanticscholar-cord-19.s3-us-west-2.amazonaws.com/historical_releases.html)下载数据集，无需注册，但这将包括所有的全文数据以及元数据文件。\n",
-    "\n",
-    "我们将尝试直接从在线来源获取数据，但如果失败，您需要按照上述说明下载数据。此外，如果您计划进一步实验，下载数据是有意义的，这样可以节省等待时间。\n",
-    "\n",
-    "> **注意** 数据集相当大，大约 1 GB，以下代码行可能需要较长时间才能完成！（大约 5 分钟）\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 146,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\winapp\\Miniconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py:3441: DtypeWarning:\n",
-      "\n",
-      "Columns (1,4,5,6,13,14,15,16) have mixed types.Specify dtype option on import or set low_memory=False.\n",
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-       "      <td>NaN</td>\n",
-       "      <td>document_parses/pdf_json/5f48792a5fa08bed9f560...</td>\n",
-       "      <td>document_parses/pmc_json/PMC59580.xml.json</td>\n",
-       "      <td>https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5...</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   cord_uid                                       sha source_x  \\\n",
-       "0  ug7v899j  d1aafb70c066a2068b02786f8929fd9c900897fb      PMC   \n",
-       "1  02tnwd4m  6b0567729c2143a66d737eb0a2f63f2dce2e5a7d      PMC   \n",
-       "2  ejv2xln0  06ced00a5fc04215949aa72528f2eeaae1d58927      PMC   \n",
-       "3  2b73a28n  348055649b6b8cf2b9a376498df9bf41f7123605      PMC   \n",
-       "4  9785vg6d  5f48792a5fa08bed9f56016f4981ae2ca6031b32      PMC   \n",
-       "\n",
-       "                                               title                    doi  \\\n",
-       "0  Clinical features of culture-proven Mycoplasma...  10.1186/1471-2334-1-6   \n",
-       "1  Nitric oxide: a pro-inflammatory mediator in l...           10.1186/rr14   \n",
-       "2    Surfactant protein-D and pulmonary host defense           10.1186/rr19   \n",
-       "3               Role of endothelin-1 in lung disease           10.1186/rr44   \n",
-       "4  Gene expression in epithelial cells in respons...           10.1186/rr61   \n",
-       "\n",
-       "      pmcid pubmed_id license  \\\n",
-       "0  PMC35282  11472636   no-cc   \n",
-       "1  PMC59543  11667967   no-cc   \n",
-       "2  PMC59549  11667972   no-cc   \n",
-       "3  PMC59574  11686871   no-cc   \n",
-       "4  PMC59580  11686888   no-cc   \n",
-       "\n",
-       "                                            abstract publish_time  \\\n",
-       "0  OBJECTIVE: This retrospective chart review des...   2001-07-04   \n",
-       "1  Inflammatory diseases of the respiratory tract...   2000-08-15   \n",
-       "2  Surfactant protein-D (SP-D) participates in th...   2000-08-25   \n",
-       "3  Endothelin-1 (ET-1) is a 21 amino acid peptide...   2001-02-22   \n",
-       "4  Respiratory syncytial virus (RSV) and pneumoni...   2001-05-11   \n",
-       "\n",
-       "                                             authors         journal  mag_id  \\\n",
-       "0                Madani, Tariq A; Al-Ghamdi, Aisha A  BMC Infect Dis     NaN   \n",
-       "1  Vliet, Albert van der; Eiserich, Jason P; Cros...      Respir Res     NaN   \n",
-       "2                                    Crouch, Erika C      Respir Res     NaN   \n",
-       "3  Fagan, Karen A; McMurtry, Ivan F; Rodman, David M      Respir Res     NaN   \n",
-       "4  Domachowske, Joseph B; Bonville, Cynthia A; Ro...      Respir Res     NaN   \n",
-       "\n",
-       "  who_covidence_id arxiv_id  \\\n",
-       "0              NaN      NaN   \n",
-       "1              NaN      NaN   \n",
-       "2              NaN      NaN   \n",
-       "3              NaN      NaN   \n",
-       "4              NaN      NaN   \n",
-       "\n",
-       "                                      pdf_json_files  \\\n",
-       "0  document_parses/pdf_json/d1aafb70c066a2068b027...   \n",
-       "1  document_parses/pdf_json/6b0567729c2143a66d737...   \n",
-       "2  document_parses/pdf_json/06ced00a5fc04215949aa...   \n",
-       "3  document_parses/pdf_json/348055649b6b8cf2b9a37...   \n",
-       "4  document_parses/pdf_json/5f48792a5fa08bed9f560...   \n",
-       "\n",
-       "                               pmc_json_files  \\\n",
-       "0  document_parses/pmc_json/PMC35282.xml.json   \n",
-       "1  document_parses/pmc_json/PMC59543.xml.json   \n",
-       "2  document_parses/pmc_json/PMC59549.xml.json   \n",
-       "3  document_parses/pmc_json/PMC59574.xml.json   \n",
-       "4  document_parses/pmc_json/PMC59580.xml.json   \n",
-       "\n",
-       "                                                 url  s2_id  \n",
-       "0  https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3...    NaN  \n",
-       "1  https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5...    NaN  \n",
-       "2  https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5...    NaN  \n",
-       "3  https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5...    NaN  \n",
-       "4  https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5...    NaN  "
-      ]
-     },
-     "execution_count": 146,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df = pd.read_csv(\"https://datascience4beginners.blob.core.windows.net/cord/metadata.csv.zip\",compression='zip')\n",
-    "# df = pd.read_csv(\"metadata.csv\")\n",
-    "df.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "我们现在将把出版日期列转换为`datetime`，并绘制直方图以查看出版日期的范围。\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 147,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "df['publish_time'] = pd.to_datetime(df['publish_time'])\n",
-    "df['publish_time'].hist()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 结构化数据提取\n",
-    "\n",
-    "让我们看看可以从摘要中轻松提取哪些信息。我们可能感兴趣的一件事是了解有哪些治疗策略，以及它们是如何随着时间演变的。首先，我们可以手动整理出用于治疗 COVID 的可能药物清单，以及诊断清单。然后，我们逐一检查这些清单，并在论文摘要中搜索对应的术语。\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 148,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      " + Processing medication: hydroxychloroquine\n",
-      " + Processing medication: chloroquine\n",
-      " + Processing medication: tocilizumab\n",
-      " + Processing medication: remdesivir\n",
-      " + Processing medication: azithromycin\n",
-      " + Processing medication: lopinavir\n",
-      " + Processing medication: ritonavir\n",
-      " + Processing medication: dexamethasone\n",
-      " + Processing medication: heparin\n",
-      " + Processing medication: favipiravir\n",
-      " + Processing medication: methylprednisolone\n",
-      " + Processing diagnosis: covid\n",
-      " + Processing diagnosis: sars\n",
-      " + Processing diagnosis: pneumonia\n",
-      " + Processing diagnosis: infection\n",
-      " + Processing diagnosis: diabetes\n",
-      " + Processing diagnosis: coronavirus\n",
-      " + Processing diagnosis: death\n"
-     ]
-    }
-   ],
-   "source": [
-    "medications = [\n",
-    "    'hydroxychloroquine', 'chloroquine', 'tocilizumab', 'remdesivir', 'azithromycin', \n",
-    "    'lopinavir', 'ritonavir', 'dexamethasone', 'heparin', 'favipiravir', 'methylprednisolone']\n",
-    "diagnosis = [\n",
-    "    'covid','sars','pneumonia','infection','diabetes','coronavirus','death'\n",
-    "]\n",
-    "\n",
-    "for m in medications:\n",
-    "    print(f\" + Processing medication: {m}\")\n",
-    "    df[m] = df['abstract'].apply(lambda x: str(x).lower().count(' '+m))\n",
-    "    \n",
-    "for m in diagnosis:\n",
-    "    print(f\" + Processing diagnosis: {m}\")\n",
-    "    df[m] = df['abstract'].apply(lambda x: str(x).lower().count(' '+m))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "我们在数据框中添加了多个列，这些列记录了某种药物或诊断在摘要中出现的次数。\n",
-    "\n",
-    "> **注意** 在查找子字符串时，我们会在单词前添加一个空格。如果不这样做，可能会得到错误的结果，因为*chloroquine*也会在子字符串*hydroxychloroquine*中被找到。此外，我们强制将摘要列转换为`str`类型，以避免错误——试着去掉`str`看看会发生什么。\n",
-    "\n",
-    "为了更方便地处理数据，我们可以提取仅包含药物计数的子数据框，并计算累计出现次数。这可以帮助我们找到最受欢迎的药物：\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 149,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Name</th>\n",
-       "      <th>Count</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>hydroxychloroquine</td>\n",
-       "      <td>9806</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>remdesivir</td>\n",
-       "      <td>7861</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>tocilizumab</td>\n",
-       "      <td>6118</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>chloroquine</td>\n",
-       "      <td>4578</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>heparin</td>\n",
-       "      <td>4161</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>lopinavir</td>\n",
-       "      <td>3811</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>azithromycin</td>\n",
-       "      <td>3585</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>dexamethasone</td>\n",
-       "      <td>3340</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>favipiravir</td>\n",
-       "      <td>2439</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>methylprednisolone</td>\n",
-       "      <td>1600</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>ritonavir</td>\n",
-       "      <td>948</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                  Name  Count\n",
-       "0   hydroxychloroquine   9806\n",
-       "3           remdesivir   7861\n",
-       "2          tocilizumab   6118\n",
-       "1          chloroquine   4578\n",
-       "8              heparin   4161\n",
-       "5            lopinavir   3811\n",
-       "4         azithromycin   3585\n",
-       "7        dexamethasone   3340\n",
-       "9          favipiravir   2439\n",
-       "10  methylprednisolone   1600\n",
-       "6            ritonavir    948"
-      ]
-     },
-     "execution_count": 149,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dfm = df[medications]\n",
-    "dfm = dfm.sum().reset_index().rename(columns={ 'index' : 'Name', 0 : 'Count'})\n",
-    "dfm.sort_values('Count',ascending=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 150,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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xlink:href=\"#m2263dfa67e\" y=\"273.312\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_7\">\r\n      <!-- ritonavir -->\r\n      <g transform=\"translate(259.79733 322.513562)rotate(-90)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#DejaVuSans-72\"/>\r\n       <use x=\"41.113281\" xlink:href=\"#DejaVuSans-69\"/>\r\n       <use x=\"68.896484\" xlink:href=\"#DejaVuSans-74\"/>\r\n       <use x=\"108.105469\" xlink:href=\"#DejaVuSans-6f\"/>\r\n       <use x=\"169.287109\" xlink:href=\"#DejaVuSans-6e\"/>\r\n       <use x=\"232.666016\" xlink:href=\"#DejaVuSans-61\"/>\r\n       <use x=\"293.945312\" xlink:href=\"#DejaVuSans-76\"/>\r\n       <use x=\"353.125\" xlink:href=\"#DejaVuSans-69\"/>\r\n       <use x=\"380.908203\" xlink:href=\"#DejaVuSans-72\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"xtick_8\">\r\n     <g id=\"line2d_8\">\r\n      <g>\r\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"289.503409\" xlink:href=\"#m2263dfa67e\" y=\"273.312\"/>\r\n      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</g>\r\n    </g>\r\n    <g id=\"xtick_9\">\r\n     <g id=\"line2d_9\">\r\n      <g>\r\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"321.968864\" xlink:href=\"#m2263dfa67e\" y=\"273.312\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_9\">\r\n      <!-- heparin -->\r\n      <g transform=\"translate(324.728239 318.50575)rotate(-90)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#DejaVuSans-68\"/>\r\n       <use x=\"63.378906\" xlink:href=\"#DejaVuSans-65\"/>\r\n       <use x=\"124.902344\" xlink:href=\"#DejaVuSans-70\"/>\r\n       <use x=\"188.378906\" xlink:href=\"#DejaVuSans-61\"/>\r\n       <use x=\"249.658203\" xlink:href=\"#DejaVuSans-72\"/>\r\n       <use x=\"290.771484\" xlink:href=\"#DejaVuSans-69\"/>\r\n       <use x=\"318.554688\" xlink:href=\"#DejaVuSans-6e\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"xtick_10\">\r\n     <g id=\"line2d_10\">\r\n      <g>\r\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"354.434318\" xlink:href=\"#m2263dfa67e\" y=\"273.312\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_10\">\r\n      <!-- favipiravir -->\r\n      <g transform=\"translate(357.193693 330.83075)rotate(-90)scale(0.1 -0.1)\">\r\n       <defs>\r\n        <path d=\"M 2375 4863 \r\nL 2375 4384 \r\nL 1825 4384 \r\nQ 1516 4384 1395 4259 \r\nQ 1275 4134 1275 3809 \r\nL 1275 3500 \r\nL 2222 3500 \r\nL 2222 3053 \r\nL 1275 3053 \r\nL 1275 0 \r\nL 697 0 \r\nL 697 3053 \r\nL 147 3053 \r\nL 147 3500 \r\nL 697 3500 \r\nL 697 3744 \r\nQ 697 4328 969 4595 \r\nQ 1241 4863 1831 4863 \r\nL 2375 4863 \r\nz\r\n\" id=\"DejaVuSans-66\" transform=\"scale(0.015625)\"/>\r\n       </defs>\r\n       <use xlink:href=\"#DejaVuSans-66\"/>\r\n       <use x=\"35.205078\" xlink:href=\"#DejaVuSans-61\"/>\r\n       <use x=\"96.484375\" xlink:href=\"#DejaVuSans-76\"/>\r\n       <use x=\"155.664062\" xlink:href=\"#DejaVuSans-69\"/>\r\n       <use x=\"183.447266\" xlink:href=\"#DejaVuSans-70\"/>\r\n       <use x=\"246.923828\" xlink:href=\"#DejaVuSans-69\"/>\r\n       <use x=\"274.707031\" xlink:href=\"#DejaVuSans-72\"/>\r\n       <use x=\"315.820312\" xlink:href=\"#DejaVuSans-61\"/>\r\n       <use x=\"377.099609\" xlink:href=\"#DejaVuSans-76\"/>\r\n       <use x=\"436.279297\" xlink:href=\"#DejaVuSans-69\"/>\r\n       <use x=\"464.0625\" xlink:href=\"#DejaVuSans-72\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"xtick_11\">\r\n     <g id=\"line2d_11\">\r\n      <g>\r\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"386.899773\" xlink:href=\"#m2263dfa67e\" y=\"273.312\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_11\">\r\n      <!-- methylprednisolone -->\r\n      <g transform=\"translate(389.659148 379.727625)rotate(-90)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#DejaVuSans-6d\"/>\r\n       <use x=\"97.412109\" xlink:href=\"#DejaVuSans-65\"/>\r\n       <use x=\"158.935547\" xlink:href=\"#DejaVuSans-74\"/>\r\n       <use x=\"198.144531\" xlink:href=\"#DejaVuSans-68\"/>\r\n      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3103 4656 3366 4563 \r\nz\r\n\" id=\"DejaVuSans-36\" transform=\"scale(0.015625)\"/>\r\n       </defs>\r\n       <use xlink:href=\"#DejaVuSans-36\"/>\r\n       <use x=\"63.623047\" xlink:href=\"#DejaVuSans-30\"/>\r\n       <use x=\"127.246094\" xlink:href=\"#DejaVuSans-30\"/>\r\n       <use x=\"190.869141\" xlink:href=\"#DejaVuSans-30\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"ytick_5\">\r\n     <g id=\"line2d_16\">\r\n      <g>\r\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"46.0125\" xlink:href=\"#me5fd5945af\" y=\"66.548794\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_17\">\r\n      <!-- 8000 -->\r\n      <g transform=\"translate(13.5625 70.348013)scale(0.1 -0.1)\">\r\n       <defs>\r\n        <path d=\"M 2034 2216 \r\nQ 1584 2216 1326 1975 \r\nQ 1069 1734 1069 1313 \r\nQ 1069 891 1326 650 \r\nQ 1584 409 2034 409 \r\nQ 2484 409 2743 651 \r\nQ 3003 894 3003 1313 \r\nQ 3003 1734 2745 1975 \r\nQ 2488 2216 2034 2216 \r\nz\r\nM 1403 2484 \r\nQ 997 2584 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-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
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-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "dfm.set_index('Name').plot(kind='bar')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 寻找治疗策略中的趋势\n",
-    "\n",
-    "在上面的例子中，我们对所有值进行了 `sum` 操作，但我们也可以按月进行相同的操作：\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 151,
-   "metadata": {},
-   "outputs": [
-    {
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-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th>hydroxychloroquine</th>\n",
-       "      <th>chloroquine</th>\n",
-       "      <th>tocilizumab</th>\n",
-       "      <th>remdesivir</th>\n",
-       "      <th>azithromycin</th>\n",
-       "      <th>lopinavir</th>\n",
-       "      <th>ritonavir</th>\n",
-       "      <th>dexamethasone</th>\n",
-       "      <th>heparin</th>\n",
-       "      <th>favipiravir</th>\n",
-       "      <th>methylprednisolone</th>\n",
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-       "    <tr>\n",
-       "      <th>publish_time</th>\n",
-       "      <th>publish_time</th>\n",
-       "      <th></th>\n",
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-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"12\" valign=\"top\">2020</th>\n",
-       "      <th>1</th>\n",
-       "      <td>3672</td>\n",
-       "      <td>1773</td>\n",
-       "      <td>1779</td>\n",
-       "      <td>2134</td>\n",
-       "      <td>1173</td>\n",
-       "      <td>1430</td>\n",
-       "      <td>370</td>\n",
-       "      <td>561</td>\n",
-       "      <td>984</td>\n",
-       "      <td>666</td>\n",
-       "      <td>331</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>0</td>\n",
-       "      <td>19</td>\n",
-       "      <td>0</td>\n",
-       "      <td>3</td>\n",
-       "      <td>3</td>\n",
-       "      <td>18</td>\n",
-       "      <td>11</td>\n",
-       "      <td>1</td>\n",
-       "      <td>3</td>\n",
-       "      <td>12</td>\n",
-       "      <td>19</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>45</td>\n",
-       "      <td>72</td>\n",
-       "      <td>5</td>\n",
-       "      <td>27</td>\n",
-       "      <td>12</td>\n",
-       "      <td>52</td>\n",
-       "      <td>16</td>\n",
-       "      <td>3</td>\n",
-       "      <td>21</td>\n",
-       "      <td>11</td>\n",
-       "      <td>14</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>188</td>\n",
-       "      <td>238</td>\n",
-       "      <td>50</td>\n",
-       "      <td>124</td>\n",
-       "      <td>68</td>\n",
-       "      <td>113</td>\n",
-       "      <td>13</td>\n",
-       "      <td>14</td>\n",
-       "      <td>77</td>\n",
-       "      <td>48</td>\n",
-       "      <td>14</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>459</td>\n",
-       "      <td>191</td>\n",
-       "      <td>158</td>\n",
-       "      <td>209</td>\n",
-       "      <td>132</td>\n",
-       "      <td>135</td>\n",
-       "      <td>41</td>\n",
-       "      <td>12</td>\n",
-       "      <td>92</td>\n",
-       "      <td>48</td>\n",
-       "      <td>21</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>381</td>\n",
-       "      <td>149</td>\n",
-       "      <td>243</td>\n",
-       "      <td>186</td>\n",
-       "      <td>110</td>\n",
-       "      <td>132</td>\n",
-       "      <td>18</td>\n",
-       "      <td>48</td>\n",
-       "      <td>84</td>\n",
-       "      <td>30</td>\n",
-       "      <td>29</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>381</td>\n",
-       "      <td>178</td>\n",
-       "      <td>202</td>\n",
-       "      <td>165</td>\n",
-       "      <td>108</td>\n",
-       "      <td>138</td>\n",
-       "      <td>29</td>\n",
-       "      <td>58</td>\n",
-       "      <td>117</td>\n",
-       "      <td>56</td>\n",
-       "      <td>27</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>307</td>\n",
-       "      <td>115</td>\n",
-       "      <td>172</td>\n",
-       "      <td>165</td>\n",
-       "      <td>145</td>\n",
-       "      <td>91</td>\n",
-       "      <td>24</td>\n",
-       "      <td>56</td>\n",
-       "      <td>95</td>\n",
-       "      <td>45</td>\n",
-       "      <td>35</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>319</td>\n",
-       "      <td>123</td>\n",
-       "      <td>185</td>\n",
-       "      <td>190</td>\n",
-       "      <td>91</td>\n",
-       "      <td>98</td>\n",
-       "      <td>28</td>\n",
-       "      <td>90</td>\n",
-       "      <td>111</td>\n",
-       "      <td>46</td>\n",
-       "      <td>26</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>10</th>\n",
-       "      <td>319</td>\n",
-       "      <td>96</td>\n",
-       "      <td>212</td>\n",
-       "      <td>227</td>\n",
-       "      <td>72</td>\n",
-       "      <td>127</td>\n",
-       "      <td>39</td>\n",
-       "      <td>97</td>\n",
-       "      <td>117</td>\n",
-       "      <td>81</td>\n",
-       "      <td>37</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>11</th>\n",
-       "      <td>272</td>\n",
-       "      <td>66</td>\n",
-       "      <td>170</td>\n",
-       "      <td>197</td>\n",
-       "      <td>79</td>\n",
-       "      <td>104</td>\n",
-       "      <td>27</td>\n",
-       "      <td>77</td>\n",
-       "      <td>124</td>\n",
-       "      <td>77</td>\n",
-       "      <td>44</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>12</th>\n",
-       "      <td>255</td>\n",
-       "      <td>102</td>\n",
-       "      <td>229</td>\n",
-       "      <td>271</td>\n",
-       "      <td>98</td>\n",
-       "      <td>76</td>\n",
-       "      <td>31</td>\n",
-       "      <td>76</td>\n",
-       "      <td>87</td>\n",
-       "      <td>56</td>\n",
-       "      <td>59</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th rowspan=\"7\" valign=\"top\">2021</th>\n",
-       "      <th>1</th>\n",
-       "      <td>2191</td>\n",
-       "      <td>780</td>\n",
-       "      <td>1787</td>\n",
-       "      <td>2523</td>\n",
-       "      <td>892</td>\n",
-       "      <td>841</td>\n",
-       "      <td>198</td>\n",
-       "      <td>1208</td>\n",
-       "      <td>1096</td>\n",
-       "      <td>805</td>\n",
-       "      <td>474</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>163</td>\n",
-       "      <td>66</td>\n",
-       "      <td>184</td>\n",
-       "      <td>173</td>\n",
-       "      <td>85</td>\n",
-       "      <td>76</td>\n",
-       "      <td>9</td>\n",
-       "      <td>86</td>\n",
-       "      <td>61</td>\n",
-       "      <td>52</td>\n",
-       "      <td>63</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>172</td>\n",
-       "      <td>85</td>\n",
-       "      <td>190</td>\n",
-       "      <td>295</td>\n",
-       "      <td>87</td>\n",
-       "      <td>100</td>\n",
-       "      <td>17</td>\n",
-       "      <td>150</td>\n",
-       "      <td>82</td>\n",
-       "      <td>85</td>\n",
-       "      <td>36</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>198</td>\n",
-       "      <td>70</td>\n",
-       "      <td>125</td>\n",
-       "      <td>161</td>\n",
-       "      <td>83</td>\n",
-       "      <td>60</td>\n",
-       "      <td>13</td>\n",
-       "      <td>130</td>\n",
-       "      <td>144</td>\n",
-       "      <td>60</td>\n",
-       "      <td>37</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>141</td>\n",
-       "      <td>55</td>\n",
-       "      <td>138</td>\n",
-       "      <td>179</td>\n",
-       "      <td>69</td>\n",
-       "      <td>55</td>\n",
-       "      <td>21</td>\n",
-       "      <td>108</td>\n",
-       "      <td>141</td>\n",
-       "      <td>106</td>\n",
-       "      <td>44</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>144</td>\n",
-       "      <td>29</td>\n",
-       "      <td>138</td>\n",
-       "      <td>182</td>\n",
-       "      <td>75</td>\n",
-       "      <td>41</td>\n",
-       "      <td>12</td>\n",
-       "      <td>128</td>\n",
-       "      <td>116</td>\n",
-       "      <td>66</td>\n",
-       "      <td>42</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>112</td>\n",
-       "      <td>49</td>\n",
-       "      <td>96</td>\n",
-       "      <td>270</td>\n",
-       "      <td>64</td>\n",
-       "      <td>59</td>\n",
-       "      <td>5</td>\n",
-       "      <td>169</td>\n",
-       "      <td>106</td>\n",
-       "      <td>44</td>\n",
-       "      <td>50</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                           hydroxychloroquine  chloroquine  tocilizumab  \\\n",
-       "publish_time publish_time                                                 \n",
-       "2020         1                           3672         1773         1779   \n",
-       "             2                              0           19            0   \n",
-       "             3                             45           72            5   \n",
-       "             4                            188          238           50   \n",
-       "             5                            459          191          158   \n",
-       "             6                            381          149          243   \n",
-       "             7                            381          178          202   \n",
-       "             8                            307          115          172   \n",
-       "             9                            319          123          185   \n",
-       "             10                           319           96          212   \n",
-       "             11                           272           66          170   \n",
-       "             12                           255          102          229   \n",
-       "2021         1                           2191          780         1787   \n",
-       "             2                            163           66          184   \n",
-       "             3                            172           85          190   \n",
-       "             4                            198           70          125   \n",
-       "             5                            141           55          138   \n",
-       "             6                            144           29          138   \n",
-       "             7                            112           49           96   \n",
-       "\n",
-       "                           remdesivir  azithromycin  lopinavir  ritonavir  \\\n",
-       "publish_time publish_time                                                   \n",
-       "2020         1                   2134          1173       1430        370   \n",
-       "             2                      3             3         18         11   \n",
-       "             3                     27            12         52         16   \n",
-       "             4                    124            68        113         13   \n",
-       "             5                    209           132        135         41   \n",
-       "             6                    186           110        132         18   \n",
-       "             7                    165           108        138         29   \n",
-       "             8                    165           145         91         24   \n",
-       "             9                    190            91         98         28   \n",
-       "             10                   227            72        127         39   \n",
-       "             11                   197            79        104         27   \n",
-       "             12                   271            98         76         31   \n",
-       "2021         1                   2523           892        841        198   \n",
-       "             2                    173            85         76          9   \n",
-       "             3                    295            87        100         17   \n",
-       "             4                    161            83         60         13   \n",
-       "             5                    179            69         55         21   \n",
-       "             6                    182            75         41         12   \n",
-       "             7                    270            64         59          5   \n",
-       "\n",
-       "                           dexamethasone  heparin  favipiravir  \\\n",
-       "publish_time publish_time                                        \n",
-       "2020         1                       561      984          666   \n",
-       "             2                         1        3           12   \n",
-       "             3                         3       21           11   \n",
-       "             4                        14       77           48   \n",
-       "             5                        12       92           48   \n",
-       "             6                        48       84           30   \n",
-       "             7                        58      117           56   \n",
-       "             8                        56       95           45   \n",
-       "             9                        90      111           46   \n",
-       "             10                       97      117           81   \n",
-       "             11                       77      124           77   \n",
-       "             12                       76       87           56   \n",
-       "2021         1                      1208     1096          805   \n",
-       "             2                        86       61           52   \n",
-       "             3                       150       82           85   \n",
-       "             4                       130      144           60   \n",
-       "             5                       108      141          106   \n",
-       "             6                       128      116           66   \n",
-       "             7                       169      106           44   \n",
-       "\n",
-       "                           methylprednisolone  \n",
-       "publish_time publish_time                      \n",
-       "2020         1                            331  \n",
-       "             2                             19  \n",
-       "             3                             14  \n",
-       "             4                             14  \n",
-       "             5                             21  \n",
-       "             6                             29  \n",
-       "             7                             27  \n",
-       "             8                             35  \n",
-       "             9                             26  \n",
-       "             10                            37  \n",
-       "             11                            44  \n",
-       "             12                            59  \n",
-       "2021         1                            474  \n",
-       "             2                             63  \n",
-       "             3                             36  \n",
-       "             4                             37  \n",
-       "             5                             44  \n",
-       "             6                             42  \n",
-       "             7                             50  "
-      ]
-     },
-     "execution_count": 151,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dfm = df[['publish_time']+medications].set_index('publish_time')\n",
-    "dfm = dfm[(dfm.index>=\"2020-01-01\") & (dfm.index<=\"2021-07-31\")]\n",
-    "dfmt = dfm.groupby([dfm.index.year,dfm.index.month]).sum()\n",
-    "dfmt"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 152,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "dfmt.plot()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "一个有趣的现象是，我们在两个时间点观察到巨大的峰值：2020年1月和2021年1月。这是由于一些论文没有明确标注出版日期，而被默认标注为当年1月所导致的。\n",
-    "\n",
-    "为了更好地理解数据，让我们只可视化几种药物的数据。我们还将“清除”1月份的数据，并用某个中间值填充，以便生成更美观的图表：\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 153,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-      "text/plain": [
-       "<Figure size 1000x300 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "meds = ['hydroxychloroquine','tocilizumab','favipiravir']\n",
-    "dfmt.loc[(2020,1)] = np.nan\n",
-    "dfmt.loc[(2021,1)] = np.nan\n",
-    "dfmt.fillna(method='pad',inplace=True)\n",
-    "fig, ax = plt.subplots(1,len(meds),figsize=(10,3))\n",
-    "for i,m in enumerate(meds):\n",
-    "    dfmt[m].plot(ax=ax[i])\n",
-    "    ax[i].set_title(m)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "观察到羟氯喹的受欢迎程度在最初几个月有所上升，然后开始下降，而法匹拉韦的提及次数则显示出稳定的增长。另一种可视化相对受欢迎程度的好方法是使用**堆叠图**（或在 Pandas 术语中称为**面积图**）：\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 154,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "dfmt.plot.area()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "甚至更进一步，我们可以以百分比计算相对受欢迎程度：\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 155,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "dfmtp = dfmt.iloc[:,:].apply(lambda x: x/x.sum(), axis=1)\n",
-    "dfmtp.plot.area()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 计算药物与诊断的对应关系\n",
-    "\n",
-    "我们可以研究的一个最有趣的关系是不同的诊断如何通过不同的药物进行治疗。为了将其可视化，我们需要计算**共现频率图**，它将显示两个术语在同一篇论文中被提及的次数。\n",
-    "\n",
-    "这样的图本质上是一个二维矩阵，最适合用**numpy数组**来表示。我们将通过遍历所有摘要并标记其中出现的实体来计算这个图：\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 156,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "m = np.zeros((len(medications),len(diagnosis)))\n",
-    "for a in df['abstract']:\n",
-    "    x = str(a).lower()\n",
-    "    for i,d in enumerate(diagnosis):\n",
-    "        if ' '+d in x:\n",
-    "            for j,me in enumerate(medications):\n",
-    "                if ' '+me in x:\n",
-    "                    m[j,i] += 1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 157,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[4788., 2264.,  741., 2109.,  348., 2730.,  975.],\n",
-       "       [2111., 1238.,  231.,  998.,   79., 1394.,  364.],\n",
-       "       [2186.,  821.,  691., 1063.,  185., 1136.,  573.],\n",
-       "       [3210., 2191.,  522., 1538.,  160., 2191.,  622.],\n",
-       "       [1803.,  773.,  406.,  880.,  133.,  909.,  410.],\n",
-       "       [1982., 1102.,  379.,  885.,  113., 1366.,  370.],\n",
-       "       [ 504.,  356.,   83.,  259.,   23.,  354.,  106.],\n",
-       "       [1419.,  640.,  345.,  742.,  108.,  760.,  314.],\n",
-       "       [1537.,  678.,  330.,  782.,   93.,  826.,  301.],\n",
-       "       [ 967.,  634.,  201.,  431.,   44.,  656.,  136.],\n",
-       "       [ 660.,  336.,  293.,  385.,   53.,  452.,  148.]])"
-      ]
-     },
-     "execution_count": 157,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "m"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "可视化此矩阵的方法之一是绘制一个**热图**：\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 158,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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transform=\"scale(0.015625)\"/>\r\n       </defs>\r\n       <use xlink:href=\"#DejaVuSans-64\"/>\r\n       <use x=\"63.476562\" xlink:href=\"#DejaVuSans-69\"/>\r\n       <use x=\"91.259766\" xlink:href=\"#DejaVuSans-61\"/>\r\n       <use x=\"152.539062\" xlink:href=\"#DejaVuSans-62\"/>\r\n       <use x=\"216.015625\" xlink:href=\"#DejaVuSans-65\"/>\r\n       <use x=\"277.539062\" xlink:href=\"#DejaVuSans-74\"/>\r\n       <use x=\"316.748047\" xlink:href=\"#DejaVuSans-65\"/>\r\n       <use x=\"378.271484\" xlink:href=\"#DejaVuSans-73\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"xtick_6\">\r\n     <g id=\"line2d_6\">\r\n      <g>\r\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"246.671625\" xlink:href=\"#mf4b223099c\" y=\"273.312\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_6\">\r\n      <!-- coronavirus -->\r\n      <g transform=\"translate(249.431 338.754187)rotate(-90)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#DejaVuSans-63\"/>\r\n       <use 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id=\"ytick_2\">\r\n     <g id=\"line2d_9\">\r\n      <g>\r\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"113.615625\" xlink:href=\"#m8f88a8ba07\" y=\"43.488\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_9\">\r\n      <!-- chloroquine -->\r\n      <g transform=\"translate(47.923438 47.287219)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#DejaVuSans-63\"/>\r\n       <use x=\"54.980469\" xlink:href=\"#DejaVuSans-68\"/>\r\n       <use x=\"118.359375\" xlink:href=\"#DejaVuSans-6c\"/>\r\n       <use x=\"146.142578\" xlink:href=\"#DejaVuSans-6f\"/>\r\n       <use x=\"207.324219\" xlink:href=\"#DejaVuSans-72\"/>\r\n       <use x=\"246.1875\" xlink:href=\"#DejaVuSans-6f\"/>\r\n       <use x=\"307.369141\" xlink:href=\"#DejaVuSans-71\"/>\r\n       <use x=\"370.845703\" xlink:href=\"#DejaVuSans-75\"/>\r\n       <use x=\"434.224609\" xlink:href=\"#DejaVuSans-69\"/>\r\n       <use x=\"462.007812\" xlink:href=\"#DejaVuSans-6e\"/>\r\n       <use x=\"525.386719\" 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  <use x=\"210.9375\" xlink:href=\"#DejaVuSans-69\"/>\r\n       <use x=\"238.720703\" xlink:href=\"#DejaVuSans-7a\"/>\r\n       <use x=\"291.210938\" xlink:href=\"#DejaVuSans-75\"/>\r\n       <use x=\"354.589844\" xlink:href=\"#DejaVuSans-6d\"/>\r\n       <use x=\"452.001953\" xlink:href=\"#DejaVuSans-61\"/>\r\n       <use x=\"513.28125\" xlink:href=\"#DejaVuSans-62\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"ytick_4\">\r\n     <g id=\"line2d_11\">\r\n      <g>\r\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"113.615625\" xlink:href=\"#m8f88a8ba07\" y=\"91.872\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_11\">\r\n      <!-- remdesivir -->\r\n      <g transform=\"translate(53.539063 95.671219)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#DejaVuSans-72\"/>\r\n       <use x=\"38.863281\" xlink:href=\"#DejaVuSans-65\"/>\r\n       <use x=\"100.386719\" xlink:href=\"#DejaVuSans-6d\"/>\r\n       <use x=\"197.798828\" xlink:href=\"#DejaVuSans-64\"/>\r\n       <use x=\"261.275391\" xlink:href=\"#DejaVuSans-65\"/>\r\n       <use x=\"322.798828\" xlink:href=\"#DejaVuSans-73\"/>\r\n       <use x=\"374.898438\" xlink:href=\"#DejaVuSans-69\"/>\r\n       <use x=\"402.681641\" xlink:href=\"#DejaVuSans-76\"/>\r\n       <use x=\"461.861328\" xlink:href=\"#DejaVuSans-69\"/>\r\n       <use x=\"489.644531\" xlink:href=\"#DejaVuSans-72\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"ytick_5\">\r\n     <g id=\"line2d_12\">\r\n      <g>\r\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"113.615625\" xlink:href=\"#m8f88a8ba07\" y=\"116.064\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_12\">\r\n      <!-- azithromycin -->\r\n      <g transform=\"translate(41.925 119.863219)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#DejaVuSans-61\"/>\r\n       <use x=\"61.279297\" xlink:href=\"#DejaVuSans-7a\"/>\r\n       <use x=\"113.769531\" xlink:href=\"#DejaVuSans-69\"/>\r\n       <use x=\"141.552734\" xlink:href=\"#DejaVuSans-74\"/>\r\n       <use x=\"180.761719\" xlink:href=\"#DejaVuSans-68\"/>\r\n       <use x=\"244.140625\" xlink:href=\"#DejaVuSans-72\"/>\r\n       <use x=\"283.003906\" xlink:href=\"#DejaVuSans-6f\"/>\r\n       <use x=\"344.185547\" xlink:href=\"#DejaVuSans-6d\"/>\r\n       <use x=\"441.597656\" xlink:href=\"#DejaVuSans-79\"/>\r\n       <use x=\"500.777344\" xlink:href=\"#DejaVuSans-63\"/>\r\n       <use x=\"555.757812\" xlink:href=\"#DejaVuSans-69\"/>\r\n       <use x=\"583.541016\" xlink:href=\"#DejaVuSans-6e\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"ytick_6\">\r\n     <g id=\"line2d_13\">\r\n      <g>\r\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"113.615625\" xlink:href=\"#m8f88a8ba07\" y=\"140.256\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_13\">\r\n      <!-- lopinavir -->\r\n      <g transform=\"translate(63.31875 144.055219)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#DejaVuSans-6c\"/>\r\n       <use x=\"27.783203\" xlink:href=\"#DejaVuSans-6f\"/>\r\n       <use x=\"88.964844\" xlink:href=\"#DejaVuSans-70\"/>\r\n       <use x=\"152.441406\" xlink:href=\"#DejaVuSans-69\"/>\r\n       <use x=\"180.224609\" xlink:href=\"#DejaVuSans-6e\"/>\r\n       <use x=\"243.603516\" xlink:href=\"#DejaVuSans-61\"/>\r\n       <use x=\"304.882812\" xlink:href=\"#DejaVuSans-76\"/>\r\n       <use x=\"364.0625\" xlink:href=\"#DejaVuSans-69\"/>\r\n       <use x=\"391.845703\" xlink:href=\"#DejaVuSans-72\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"ytick_7\">\r\n     <g id=\"line2d_14\">\r\n      <g>\r\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"113.615625\" xlink:href=\"#m8f88a8ba07\" y=\"164.448\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_14\">\r\n      <!-- ritonavir -->\r\n      <g transform=\"translate(64.414062 168.247219)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#DejaVuSans-72\"/>\r\n       <use x=\"41.113281\" xlink:href=\"#DejaVuSans-69\"/>\r\n       <use x=\"68.896484\" xlink:href=\"#DejaVuSans-74\"/>\r\n  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     <use x=\"63.378906\" xlink:href=\"#DejaVuSans-65\"/>\r\n       <use x=\"124.902344\" xlink:href=\"#DejaVuSans-70\"/>\r\n       <use x=\"188.378906\" xlink:href=\"#DejaVuSans-61\"/>\r\n       <use x=\"249.658203\" xlink:href=\"#DejaVuSans-72\"/>\r\n       <use x=\"290.771484\" xlink:href=\"#DejaVuSans-69\"/>\r\n       <use x=\"318.554688\" xlink:href=\"#DejaVuSans-6e\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"ytick_10\">\r\n     <g id=\"line2d_17\">\r\n      <g>\r\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"113.615625\" xlink:href=\"#m8f88a8ba07\" y=\"237.024\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_17\">\r\n      <!-- favipiravir -->\r\n      <g transform=\"translate(56.096875 240.823219)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#DejaVuSans-66\"/>\r\n       <use x=\"35.205078\" xlink:href=\"#DejaVuSans-61\"/>\r\n       <use x=\"96.484375\" xlink:href=\"#DejaVuSans-76\"/>\r\n       <use x=\"155.664062\" xlink:href=\"#DejaVuSans-69\"/>\r\n       <use x=\"183.447266\" xlink:href=\"#DejaVuSans-70\"/>\r\n       <use x=\"246.923828\" xlink:href=\"#DejaVuSans-69\"/>\r\n       <use x=\"274.707031\" xlink:href=\"#DejaVuSans-72\"/>\r\n       <use x=\"315.820312\" xlink:href=\"#DejaVuSans-61\"/>\r\n       <use x=\"377.099609\" xlink:href=\"#DejaVuSans-76\"/>\r\n       <use x=\"436.279297\" xlink:href=\"#DejaVuSans-69\"/>\r\n       <use x=\"464.0625\" xlink:href=\"#DejaVuSans-72\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n    <g id=\"ytick_11\">\r\n     <g id=\"line2d_18\">\r\n      <g>\r\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"113.615625\" xlink:href=\"#m8f88a8ba07\" y=\"261.216\"/>\r\n      </g>\r\n     </g>\r\n     <g id=\"text_18\">\r\n      <!-- methylprednisolone -->\r\n      <g transform=\"translate(7.2 265.015219)scale(0.1 -0.1)\">\r\n       <use xlink:href=\"#DejaVuSans-6d\"/>\r\n       <use x=\"97.412109\" xlink:href=\"#DejaVuSans-65\"/>\r\n       <use 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xlink:href=\"#DejaVuSans-65\"/>\r\n      </g>\r\n     </g>\r\n    </g>\r\n   </g>\r\n   <g id=\"patch_3\">\r\n    <path d=\"M 113.615625 273.312 \r\nL 113.615625 7.2 \r\n\" style=\"fill:none;stroke:#000000;stroke-linecap:square;stroke-linejoin:miter;stroke-width:0.8;\"/>\r\n   </g>\r\n   <g id=\"patch_4\">\r\n    <path d=\"M 282.959625 273.312 \r\nL 282.959625 7.2 \r\n\" style=\"fill:none;stroke:#000000;stroke-linecap:square;stroke-linejoin:miter;stroke-width:0.8;\"/>\r\n   </g>\r\n   <g id=\"patch_5\">\r\n    <path d=\"M 113.615625 273.312 \r\nL 282.959625 273.312 \r\n\" style=\"fill:none;stroke:#000000;stroke-linecap:square;stroke-linejoin:miter;stroke-width:0.8;\"/>\r\n   </g>\r\n   <g id=\"patch_6\">\r\n    <path d=\"M 113.615625 7.2 \r\nL 282.959625 7.2 \r\n\" style=\"fill:none;stroke:#000000;stroke-linecap:square;stroke-linejoin:miter;stroke-width:0.8;\"/>\r\n   </g>\r\n  </g>\r\n </g>\r\n <defs>\r\n  <clipPath id=\"pc1bf481d13\">\r\n   <rect height=\"266.112\" width=\"169.344\" 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-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.imshow(m,interpolation='nearest',cmap='hot')\n",
-    "ax = plt.gca()\n",
-    "ax.set_yticks(range(len(medications))) \n",
-    "ax.set_yticklabels(medications)\n",
-    "ax.set_xticks(range(len(diagnosis)))\n",
-    "ax.set_xticklabels(diagnosis,rotation=90)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "然而，使用所谓的 **Sankey** 图可以实现更好的可视化效果！`matplotlib` 并不内置支持这种图表类型，因此我们需要使用 [Plotly](https://plotly.com/python/)，具体可以参考[这个教程](https://plotly.com/python/sankey-diagram/)。\n",
-    "\n",
-    "要制作 Plotly 的 Sankey 图，我们需要构建以下列表：\n",
-    "* 图中所有节点的列表 `all_nodes`，包括药物和诊断\n",
-    "* 源节点和目标节点的索引列表——这些列表将显示哪些节点位于图的左侧，哪些位于右侧\n",
-    "* 所有链接的列表，每个链接包括：\n",
-    "   - 在 `all_nodes` 数组中的源节点索引\n",
-    "   - 目标节点索引\n",
-    "   - 表示链接强度的值。这正是我们共现矩阵中的值。\n",
-    "   - （可选）链接的颜色。我们可以选择突出显示某些术语以提高清晰度\n",
-    "\n",
-    "绘制 Sankey 图的通用代码被组织为一个独立的 `sankey` 函数，该函数接收两个列表（源类别和目标类别）以及共现矩阵。它还允许我们指定一个阈值，忽略所有低于该阈值的链接——这可以使图表稍微简化一些。\n"
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-            "gridcolor": "white",
-            "linecolor": "white",
-            "ticks": ""
-           },
-           "baxis": {
-            "gridcolor": "white",
-            "linecolor": "white",
-            "ticks": ""
-           },
-           "bgcolor": "#E5ECF6",
-           "caxis": {
-            "gridcolor": "white",
-            "linecolor": "white",
-            "ticks": ""
-           }
-          },
-          "title": {
-           "x": 0.05
-          },
-          "xaxis": {
-           "automargin": true,
-           "gridcolor": "white",
-           "linecolor": "white",
-           "ticks": "",
-           "title": {
-            "standoff": 15
-           },
-           "zerolinecolor": "white",
-           "zerolinewidth": 2
-          },
-          "yaxis": {
-           "automargin": true,
-           "gridcolor": "white",
-           "linecolor": "white",
-           "ticks": "",
-           "title": {
-            "standoff": 15
-           },
-           "zerolinecolor": "white",
-           "zerolinewidth": 2
-          }
-         }
-        }
-       }
-      }
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import plotly.graph_objects as go\n",
-    "\n",
-    "def sankey(cat1, cat2, m, treshold=0, h1=[], h2=[]):\n",
-    "    all_nodes = cat1 + cat2\n",
-    "    source_indices = list(range(len(cat1)))\n",
-    "    target_indices = list(range(len(cat1),len(cat1)+len(cat2)))\n",
-    "\n",
-    "    s, t, v, c = [], [], [], []\n",
-    "    for i in range(len(cat1)):\n",
-    "        for j in range(len(cat2)):\n",
-    "            if m[i,j]>treshold:\n",
-    "                s.append(i)\n",
-    "                t.append(len(cat1)+j)\n",
-    "                v.append(m[i,j])\n",
-    "                c.append('pink' if i in h1 or j in h2 else 'lightgray')\n",
-    "\n",
-    "    fig = go.Figure(data=[go.Sankey(\n",
-    "        # Define nodes\n",
-    "        node = dict(\n",
-    "        pad = 40,\n",
-    "        thickness = 40,\n",
-    "        line = dict(color = \"black\", width = 1.0),\n",
-    "        label =  all_nodes),\n",
-    "\n",
-    "        # Add links\n",
-    "        link = dict(\n",
-    "        source =  s,\n",
-    "        target =  t,\n",
-    "        value =  v,\n",
-    "        color = c\n",
-    "    ))])\n",
-    "    fig.show()\n",
-    "\n",
-    "sankey(medications,diagnosis,m,500,h2=[0])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 结论\n",
-    "\n",
-    "您已经看到，我们可以使用相当简单的方法从非结构化数据源（如文本）中提取信息。在这个例子中，我们使用了现有的药物列表，但如果使用自然语言处理（NLP）技术从文本中进行实体提取，将会更加强大。在[这篇博客文章](https://soshnikov.com/science/analyzing-medical-papers-with-azure-and-text-analytics-for-health/)中，我们描述了如何使用云服务进行实体提取。另一种选择是使用 Python 的 NLP 库，例如 [NLTK](https://www.nltk.org/)——关于如何使用 NLTK 从文本中提取信息的方法可以参考[这里](https://www.nltk.org/book/ch07.html)。\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 挑战\n",
-    "\n",
-    "继续围绕以下方向研究 COVID 论文数据：\n",
-    "\n",
-    "1. 构建不同药物的共现矩阵，查看哪些药物经常一起出现（即在同一篇摘要中被提到）。你可以修改用于构建药物和诊断共现矩阵的代码。\n",
-    "2. 使用热图可视化该矩阵。\n",
-    "3. 作为一个进阶目标，你可以尝试使用 [弦图](https://en.wikipedia.org/wiki/Chord_diagram) 可视化药物的共现关系。[这个库](https://pypi.org/project/chord/) 可能会帮助你绘制弦图。\n",
-    "4. 另一个进阶目标是尝试使用正则表达式提取不同药物的剂量（例如，在 *每天服用 400mg 的氯喹* 中提取 **400mg**），并构建一个数据框，显示不同药物的不同剂量。**注意**：考虑药物名称附近文本中出现的数值。\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n---\n\n**免责声明**：  \n本文档使用AI翻译服务[Co-op Translator](https://github.com/Azure/co-op-translator)进行翻译。尽管我们努力确保准确性，但请注意，自动翻译可能包含错误或不准确之处。应以原始语言的文档作为权威来源。对于关键信息，建议使用专业人工翻译。对于因使用本翻译而引起的任何误解或误读，我们概不负责。\n"
-   ]
-  }
- ],
- "metadata": {
-  "interpreter": {
-   "hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
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-  "kernelspec": {
-   "display_name": "Python 3.8.8 64-bit (conda)",
-   "name": "python3"
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-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.8"
-  },
-  "orig_nbformat": 4,
-  "coopTranslator": {
-   "original_hash": "563424d708ece56f0ef9b78b58755442",
-   "translation_date": "2025-09-02T06:04:52+00:00",
-   "source_file": "2-Working-With-Data/07-python/notebook-papers.ipynb",
-   "language_code": "zh"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
\ No newline at end of file
diff --git a/translations/zh/2-Working-With-Data/07-python/notebook.ipynb b/translations/zh/2-Working-With-Data/07-python/notebook.ipynb
deleted file mode 100644
index c0855ae6..00000000
--- a/translations/zh/2-Working-With-Data/07-python/notebook.ipynb
+++ /dev/null
@@ -1,1510 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Pandas 基础示例\n",
-    "\n",
-    "本笔记将带您了解一些非常基础的 Pandas 概念。我们将从导入常见的数据科学库开始：\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 系列\n",
-    "\n",
-    "系列（Series）类似于列表或一维数组，但带有索引。所有操作都基于索引对齐。\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0    1\n",
-      "1    2\n",
-      "2    3\n",
-      "3    4\n",
-      "4    5\n",
-      "5    6\n",
-      "6    7\n",
-      "7    8\n",
-      "8    9\n",
-      "dtype: int64 0         I\n",
-      "1      like\n",
-      "2        to\n",
-      "3       use\n",
-      "4    Python\n",
-      "5       and\n",
-      "6    Pandas\n",
-      "7      very\n",
-      "8      much\n",
-      "dtype: object\n"
-     ]
-    }
-   ],
-   "source": [
-    "a = pd.Series(range(1,10))\n",
-    "b = pd.Series([\"I\",\"like\",\"to\",\"use\",\"Python\",\"and\",\"Pandas\",\"very\",\"much\"],index=range(0,9))\n",
-    "print(a,b)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "系列的一个常见用途是**时间序列**。在时间序列中，索引具有特殊的结构——通常是一系列日期或日期时间。我们可以使用 `pd.date_range` 创建这样的索引。\n",
-    "\n",
-    "假设我们有一个系列，显示每天购买的产品数量，并且我们知道每个星期天我们还需要为自己拿一个物品。以下是使用系列来建模的方法：\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Length of index is 366\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 1000x300 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "start_date = \"Jan 1, 2020\"\n",
-    "end_date = \"Dec 31, 2020\"\n",
-    "idx = pd.date_range(start_date,end_date)\n",
-    "print(f\"Length of index is {len(idx)}\")\n",
-    "items_sold = pd.Series(np.random.randint(25,50,size=len(idx)),index=idx)\n",
-    "items_sold.plot(figsize=(10,3))\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Additional items (10 item each week):\n",
-      "2020-01-05    10\n",
-      "2020-01-12    10\n",
-      "2020-01-19    10\n",
-      "2020-01-26    10\n",
-      "2020-02-02    10\n",
-      "2020-02-09    10\n",
-      "2020-02-16    10\n",
-      "2020-02-23    10\n",
-      "2020-03-01    10\n",
-      "2020-03-08    10\n",
-      "2020-03-15    10\n",
-      "2020-03-22    10\n",
-      "2020-03-29    10\n",
-      "2020-04-05    10\n",
-      "2020-04-12    10\n",
-      "2020-04-19    10\n",
-      "2020-04-26    10\n",
-      "2020-05-03    10\n",
-      "2020-05-10    10\n",
-      "2020-05-17    10\n",
-      "2020-05-24    10\n",
-      "2020-05-31    10\n",
-      "2020-06-07    10\n",
-      "2020-06-14    10\n",
-      "2020-06-21    10\n",
-      "2020-06-28    10\n",
-      "2020-07-05    10\n",
-      "2020-07-12    10\n",
-      "2020-07-19    10\n",
-      "2020-07-26    10\n",
-      "2020-08-02    10\n",
-      "2020-08-09    10\n",
-      "2020-08-16    10\n",
-      "2020-08-23    10\n",
-      "2020-08-30    10\n",
-      "2020-09-06    10\n",
-      "2020-09-13    10\n",
-      "2020-09-20    10\n",
-      "2020-09-27    10\n",
-      "2020-10-04    10\n",
-      "2020-10-11    10\n",
-      "2020-10-18    10\n",
-      "2020-10-25    10\n",
-      "2020-11-01    10\n",
-      "2020-11-08    10\n",
-      "2020-11-15    10\n",
-      "2020-11-22    10\n",
-      "2020-11-29    10\n",
-      "2020-12-06    10\n",
-      "2020-12-13    10\n",
-      "2020-12-20    10\n",
-      "2020-12-27    10\n",
-      "Freq: W-SUN, dtype: int64\n",
-      "Total items (sum of two series):\n",
-      "2020-01-01     NaN\n",
-      "2020-01-02     NaN\n",
-      "2020-01-03     NaN\n",
-      "2020-01-04     NaN\n",
-      "2020-01-05    54.0\n",
-      "              ... \n",
-      "2020-12-27    43.0\n",
-      "2020-12-28     NaN\n",
-      "2020-12-29     NaN\n",
-      "2020-12-30     NaN\n",
-      "2020-12-31     NaN\n",
-      "Length: 366, dtype: float64\n"
-     ]
-    }
-   ],
-   "source": [
-    "additional_items = pd.Series(10,index=pd.date_range(start_date,end_date,freq=\"W\"))\n",
-    "print(f\"Additional items (10 item each week):\\n{additional_items}\")\n",
-    "total_items = items_sold+additional_items\n",
-    "print(f\"Total items (sum of two series):\\n{total_items}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "正如您所看到的，我们在这里遇到了问题，因为在每周的序列中，未提及的日期被视为缺失值（`NaN`），而将 `NaN` 加到一个数字上会得到 `NaN`。为了获得正确的结果，我们在相加序列时需要指定 `fill_value`：\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2020-01-01    26.0\n",
-      "2020-01-02    25.0\n",
-      "2020-01-03    37.0\n",
-      "2020-01-04    30.0\n",
-      "2020-01-05    54.0\n",
-      "              ... \n",
-      "2020-12-27    43.0\n",
-      "2020-12-28    44.0\n",
-      "2020-12-29    36.0\n",
-      "2020-12-30    38.0\n",
-      "2020-12-31    34.0\n",
-      "Length: 366, dtype: float64\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 1000x300 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "total_items = items_sold.add(additional_items,fill_value=0)\n",
-    "print(total_items)\n",
-    "total_items.plot(figsize=(10,3))\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 1000x300 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "monthly = total_items.resample(\"1M\").mean()\n",
-    "ax = monthly.plot(kind='bar',figsize=(10,3))\n",
-    "ax.set_xticklabels([x.strftime(\"%b-%Y\") for x in monthly.index], rotation=45)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 数据框\n",
-    "\n",
-    "数据框本质上是具有相同索引的一组序列。我们可以将多个序列组合成一个数据框。假设上面定义了 `a` 和 `b` 序列：\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
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-       "\n",
-       "    .dataframe thead th {\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>0</th>\n",
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-       "      <th>3</th>\n",
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-       "      <th>6</th>\n",
-       "      <th>7</th>\n",
-       "      <th>8</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1</td>\n",
-       "      <td>2</td>\n",
-       "      <td>3</td>\n",
-       "      <td>4</td>\n",
-       "      <td>5</td>\n",
-       "      <td>6</td>\n",
-       "      <td>7</td>\n",
-       "      <td>8</td>\n",
-       "      <td>9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>I</td>\n",
-       "      <td>like</td>\n",
-       "      <td>to</td>\n",
-       "      <td>use</td>\n",
-       "      <td>Python</td>\n",
-       "      <td>and</td>\n",
-       "      <td>Pandas</td>\n",
-       "      <td>very</td>\n",
-       "      <td>much</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   0     1   2    3       4    5       6     7     8\n",
-       "0  1     2   3    4       5    6       7     8     9\n",
-       "1  I  like  to  use  Python  and  Pandas  very  much"
-      ]
-     },
-     "execution_count": 42,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df = pd.DataFrame([a,b])\n",
-    "df"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "我们也可以使用Series作为列，并使用字典指定列名：\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>A</th>\n",
-       "      <th>B</th>\n",
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-       "      <th>2</th>\n",
-       "      <td>3</td>\n",
-       "      <td>to</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>4</td>\n",
-       "      <td>use</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>5</td>\n",
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-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>6</td>\n",
-       "      <td>and</td>\n",
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-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>7</td>\n",
-       "      <td>Pandas</td>\n",
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-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>8</td>\n",
-       "      <td>very</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>9</td>\n",
-       "      <td>much</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   A       B\n",
-       "0  1       I\n",
-       "1  2    like\n",
-       "2  3      to\n",
-       "3  4     use\n",
-       "4  5  Python\n",
-       "5  6     and\n",
-       "6  7  Pandas\n",
-       "7  8    very\n",
-       "8  9    much"
-      ]
-     },
-     "execution_count": 43,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df = pd.DataFrame({ 'A' : a, 'B' : b })\n",
-    "df"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "通过转置（然后重命名列以匹配前面的示例）可以实现相同的结果：\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
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-       "\n",
-       "    .dataframe thead th {\n",
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-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>A</th>\n",
-       "      <th>B</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1</td>\n",
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-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2</td>\n",
-       "      <td>like</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>3</td>\n",
-       "      <td>to</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>4</td>\n",
-       "      <td>use</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>5</td>\n",
-       "      <td>Python</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>6</td>\n",
-       "      <td>and</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>7</td>\n",
-       "      <td>Pandas</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>8</td>\n",
-       "      <td>very</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>9</td>\n",
-       "      <td>much</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   A       B\n",
-       "0  1       I\n",
-       "1  2    like\n",
-       "2  3      to\n",
-       "3  4     use\n",
-       "4  5  Python\n",
-       "5  6     and\n",
-       "6  7  Pandas\n",
-       "7  8    very\n",
-       "8  9    much"
-      ]
-     },
-     "execution_count": 44,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pd.DataFrame([a,b]).T.rename(columns={ 0 : 'A', 1 : 'B' })"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "从DataFrame中**选择列**可以这样完成：\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Column A (series):\n",
-      "0    1\n",
-      "1    2\n",
-      "2    3\n",
-      "3    4\n",
-      "4    5\n",
-      "5    6\n",
-      "6    7\n",
-      "7    8\n",
-      "8    9\n",
-      "Name: A, dtype: int64\n",
-      "Columns B and A (DataFrame):\n",
-      "        B  A\n",
-      "0       I  1\n",
-      "1    like  2\n",
-      "2      to  3\n",
-      "3     use  4\n",
-      "4  Python  5\n",
-      "5     and  6\n",
-      "6  Pandas  7\n",
-      "7    very  8\n",
-      "8    much  9\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(f\"Column A (series):\\n{df['A']}\")\n",
-    "print(f\"Columns B and A (DataFrame):\\n{df[['B','A']]}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "根据过滤表达式**选择行**：\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>A</th>\n",
-       "      <th>B</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1</td>\n",
-       "      <td>I</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2</td>\n",
-       "      <td>like</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>3</td>\n",
-       "      <td>to</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>4</td>\n",
-       "      <td>use</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   A     B\n",
-       "0  1     I\n",
-       "1  2  like\n",
-       "2  3    to\n",
-       "3  4   use"
-      ]
-     },
-     "execution_count": 46,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df[df['A']<5]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "它的工作原理是，表达式 `df['A']<5` 返回一个布尔序列，该序列指示序列中每个元素的表达式是 `True` 还是 `False`。当序列用作索引时，它会返回 DataFrame 中的行子集。因此，不可能使用任意的 Python 布尔表达式，例如，编写 `df[df['A']>5 and df['A']<7]` 是错误的。相反，你应该在布尔序列上使用特殊的 `&` 操作：\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>A</th>\n",
-       "      <th>B</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>6</td>\n",
-       "      <td>and</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   A    B\n",
-       "5  6  and"
-      ]
-     },
-     "execution_count": 47,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df[(df['A']>5) & (df['A']<7)]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "创建新的可计算列。我们可以通过使用直观的表达式轻松地为我们的DataFrame创建新的可计算列。以下代码计算A相对于其平均值的偏差。\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>A</th>\n",
-       "      <th>B</th>\n",
-       "      <th>DivA</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1</td>\n",
-       "      <td>I</td>\n",
-       "      <td>-4.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2</td>\n",
-       "      <td>like</td>\n",
-       "      <td>-3.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>3</td>\n",
-       "      <td>to</td>\n",
-       "      <td>-2.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>4</td>\n",
-       "      <td>use</td>\n",
-       "      <td>-1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>5</td>\n",
-       "      <td>Python</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>6</td>\n",
-       "      <td>and</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>7</td>\n",
-       "      <td>Pandas</td>\n",
-       "      <td>2.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>8</td>\n",
-       "      <td>very</td>\n",
-       "      <td>3.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>9</td>\n",
-       "      <td>much</td>\n",
-       "      <td>4.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   A       B  DivA\n",
-       "0  1       I  -4.0\n",
-       "1  2    like  -3.0\n",
-       "2  3      to  -2.0\n",
-       "3  4     use  -1.0\n",
-       "4  5  Python   0.0\n",
-       "5  6     and   1.0\n",
-       "6  7  Pandas   2.0\n",
-       "7  8    very   3.0\n",
-       "8  9    much   4.0"
-      ]
-     },
-     "execution_count": 48,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['DivA'] = df['A']-df['A'].mean()\n",
-    "df"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "实际发生的是我们在计算一个序列，然后将这个序列赋值给左侧，从而创建另一个列。\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# WRONG: df['ADescr'] = \"Low\" if df['A'] < 5 else \"Hi\"\n",
-    "df['LenB'] = len(df['B']) # Wrong result"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>A</th>\n",
-       "      <th>B</th>\n",
-       "      <th>DivA</th>\n",
-       "      <th>LenB</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1</td>\n",
-       "      <td>I</td>\n",
-       "      <td>-4.0</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2</td>\n",
-       "      <td>like</td>\n",
-       "      <td>-3.0</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>3</td>\n",
-       "      <td>to</td>\n",
-       "      <td>-2.0</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>4</td>\n",
-       "      <td>use</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>5</td>\n",
-       "      <td>Python</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>6</td>\n",
-       "      <td>and</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>7</td>\n",
-       "      <td>Pandas</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>8</td>\n",
-       "      <td>very</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>9</td>\n",
-       "      <td>much</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   A       B  DivA  LenB\n",
-       "0  1       I  -4.0     1\n",
-       "1  2    like  -3.0     4\n",
-       "2  3      to  -2.0     2\n",
-       "3  4     use  -1.0     3\n",
-       "4  5  Python   0.0     6\n",
-       "5  6     and   1.0     3\n",
-       "6  7  Pandas   2.0     6\n",
-       "7  8    very   3.0     4\n",
-       "8  9    much   4.0     4"
-      ]
-     },
-     "execution_count": 50,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df['LenB'] = df['B'].apply(lambda x: len(x))\n",
-    "# or\n",
-    "df['LenB'] = df['B'].apply(len)\n",
-    "df"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "根据数字选择行可以使用`iloc`结构。例如，要从DataFrame中选择前5行：\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>A</th>\n",
-       "      <th>B</th>\n",
-       "      <th>DivA</th>\n",
-       "      <th>LenB</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1</td>\n",
-       "      <td>I</td>\n",
-       "      <td>-4.0</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2</td>\n",
-       "      <td>like</td>\n",
-       "      <td>-3.0</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>3</td>\n",
-       "      <td>to</td>\n",
-       "      <td>-2.0</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>4</td>\n",
-       "      <td>use</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>5</td>\n",
-       "      <td>Python</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   A       B  DivA  LenB\n",
-       "0  1       I  -4.0     1\n",
-       "1  2    like  -3.0     4\n",
-       "2  3      to  -2.0     2\n",
-       "3  4     use  -1.0     3\n",
-       "4  5  Python   0.0     6"
-      ]
-     },
-     "execution_count": 52,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.iloc[:5]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**分组**通常用于获得类似于 Excel 中*数据透视表*的结果。假设我们想计算每个给定的 `LenB` 值对应的列 `A` 的平均值。那么我们可以按 `LenB` 对我们的 DataFrame 进行分组，然后调用 `mean`：\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>A</th>\n",
-       "      <th>DivA</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>LenB</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>-4.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>3.000000</td>\n",
-       "      <td>-2.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>5.000000</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>6.333333</td>\n",
-       "      <td>1.333333</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>6.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "             A      DivA\n",
-       "LenB                    \n",
-       "1     1.000000 -4.000000\n",
-       "2     3.000000 -2.000000\n",
-       "3     5.000000  0.000000\n",
-       "4     6.333333  1.333333\n",
-       "6     6.000000  1.000000"
-      ]
-     },
-     "execution_count": 53,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.groupby(by='LenB').mean()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "如果我们需要计算组内的平均值和元素数量，那么我们可以使用更复杂的 `aggregate` 函数：\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 58,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Count</th>\n",
-       "      <th>Mean</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>LenB</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1</td>\n",
-       "      <td>1.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1</td>\n",
-       "      <td>3.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>2</td>\n",
-       "      <td>5.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>3</td>\n",
-       "      <td>6.333333</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>2</td>\n",
-       "      <td>6.000000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      Count      Mean\n",
-       "LenB                 \n",
-       "1         1  1.000000\n",
-       "2         1  3.000000\n",
-       "3         2  5.000000\n",
-       "4         3  6.333333\n",
-       "6         2  6.000000"
-      ]
-     },
-     "execution_count": 58,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.groupby(by='LenB') \\\n",
-    " .aggregate({ 'DivA' : len, 'A' : lambda x: x.mean() }) \\\n",
-    " .rename(columns={ 'DivA' : 'Count', 'A' : 'Mean'})"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 打印和绘图\n",
-    "\n",
-    "数据科学家经常需要探索数据，因此能够可视化数据非常重要。当 DataFrame 很大时，我们通常只需要通过打印前几行来确保操作正确。这可以通过调用 `df.head()` 来实现。如果你在 Jupyter Notebook 中运行，它会以漂亮的表格形式打印出 DataFrame。\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 59,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>A</th>\n",
-       "      <th>B</th>\n",
-       "      <th>DivA</th>\n",
-       "      <th>LenB</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1</td>\n",
-       "      <td>I</td>\n",
-       "      <td>-4.0</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2</td>\n",
-       "      <td>like</td>\n",
-       "      <td>-3.0</td>\n",
-       "      <td>4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>3</td>\n",
-       "      <td>to</td>\n",
-       "      <td>-2.0</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>4</td>\n",
-       "      <td>use</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>5</td>\n",
-       "      <td>Python</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   A       B  DivA  LenB\n",
-       "0  1       I  -4.0     1\n",
-       "1  2    like  -3.0     4\n",
-       "2  3      to  -2.0     2\n",
-       "3  4     use  -1.0     3\n",
-       "4  5  Python   0.0     6"
-      ]
-     },
-     "execution_count": 59,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "df.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "我们还了解了如何使用 `plot` 函数来可视化一些列的数据。虽然 `plot` 在许多任务中非常有用，并且通过 `kind=` 参数支持多种不同的图表类型，但你始终可以使用原生的 `matplotlib` 库来绘制更复杂的图表。我们将在单独的课程中详细讲解数据可视化。\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 61,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "df['A'].plot()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 63,
-   "metadata": {},
-   "outputs": [
-    {
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-       "<Figure size 640x480 with 1 Axes>"
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-    "本概述涵盖了 Pandas 的大部分重要概念，但这个库功能非常强大，你可以用它实现无限可能！现在，让我们运用这些知识来解决具体问题吧。\n"
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-    "\n---\n\n**免责声明**：  \n本文档使用AI翻译服务[Co-op Translator](https://github.com/Azure/co-op-translator)进行翻译。尽管我们努力确保准确性，但请注意，自动翻译可能包含错误或不准确之处。应以原始语言的文档作为权威来源。对于关键信息，建议使用专业人工翻译。对于因使用本翻译而引起的任何误解或误读，我们概不负责。\n"
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-   "language_code": "zh"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
\ No newline at end of file
diff --git a/translations/zh/2-Working-With-Data/08-data-preparation/README.md b/translations/zh/2-Working-With-Data/08-data-preparation/README.md
deleted file mode 100644
index 20b66d27..00000000
--- a/translations/zh/2-Working-With-Data/08-data-preparation/README.md
+++ /dev/null
@@ -1,345 +0,0 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "1b560955ff39a2bcf2a049fce474a951",
-  "translation_date": "2025-09-05T11:32:04+00:00",
-  "source_file": "2-Working-With-Data/08-data-preparation/README.md",
-  "language_code": "zh"
-}
--->
-# 数据处理：数据准备
-
-|![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/08-DataPreparation.png)|
-|:---:|
-|数据准备 - _由 [@nitya](https://twitter.com/nitya) 绘制的速记图_ |
-
-## [课前测验](https://ff-quizzes.netlify.app/en/ds/quiz/14)
-
-根据数据来源，原始数据可能存在一些不一致性，这会在分析和建模时带来挑战。换句话说，这些数据可以被归类为“脏数据”，需要进行清理。本课程重点介绍清理和转换数据的技术，以解决数据缺失、不准确或不完整的问题。本课程中涉及的主题将使用 Python 和 Pandas 库，并在本目录中的[笔记本](../../../../2-Working-With-Data/08-data-preparation/notebook.ipynb)中进行演示。
-
-## 清理数据的重要性
-
-- **易于使用和复用**：当数据被正确组织和规范化时，更容易搜索、使用和与他人共享。
-
-- **一致性**：数据科学通常需要处理多个数据集，不同来源的数据集需要合并在一起。确保每个单独的数据集具有统一的标准化，可以保证在合并为一个数据集时数据仍然有用。
-
-- **模型准确性**：清理过的数据可以提高依赖于这些数据的模型的准确性。
-
-## 常见的清理目标和策略
-
-- **探索数据集**：数据探索（将在[后续课程](https://github.com/microsoft/Data-Science-For-Beginners/tree/main/4-Data-Science-Lifecycle/15-analyzing)中介绍）可以帮助发现需要清理的数据。通过可视化观察数据集中的值，可以设定对其余数据的期望，或者提供解决问题的思路。探索可以包括基本查询、可视化和采样。
-
-- **格式化**：根据数据来源，数据在呈现方式上可能存在不一致性。这会导致在搜索和表示值时出现问题，数据虽然在数据集中存在，但在可视化或查询结果中未正确表示。常见的格式化问题包括解决空白、日期和数据类型问题。解决格式化问题通常由使用数据的人来决定。例如，不同国家对日期和数字的表示标准可能不同。
-
-- **重复数据**：重复数据可能会产生不准确的结果，通常需要删除。这在合并两个或多个数据集时很常见。然而，有些情况下，合并数据集中的重复数据可能包含额外信息，需要保留。
-
-- **缺失数据**：缺失数据可能导致结果不准确或偏差。有时可以通过重新加载数据、使用 Python 等代码填充缺失值，或者简单地删除缺失值及其相关数据来解决。数据缺失的原因有很多，解决缺失值的方式可能取决于数据缺失的原因和方式。
-
-## 探索 DataFrame 信息
-> **学习目标**：在本小节结束时，您应该能够熟练地查找存储在 pandas DataFrame 中的数据的一般信息。
-
-将数据加载到 pandas 后，通常会以 DataFrame 的形式存在（详细概述请参考之前的[课程](https://github.com/microsoft/Data-Science-For-Beginners/tree/main/2-Working-With-Data/07-python#dataframe)）。然而，如果您的 DataFrame 数据集有 60,000 行和 400 列，如何开始了解您正在处理的数据？幸运的是，[pandas](https://pandas.pydata.org/) 提供了一些方便的工具，可以快速查看 DataFrame 的整体信息以及前几行和后几行。
-
-为了探索这些功能，我们将导入 Python 的 scikit-learn 库，并使用一个经典数据集：**Iris 数据集**。
-
-```python
-import pandas as pd
-from sklearn.datasets import load_iris
-
-iris = load_iris()
-iris_df = pd.DataFrame(data=iris['data'], columns=iris['feature_names'])
-```
-|                                        |sepal length (cm)|sepal width (cm)|petal length (cm)|petal width (cm)|
-|----------------------------------------|-----------------|----------------|-----------------|----------------|
-|0                                       |5.1              |3.5             |1.4              |0.2             |
-|1                                       |4.9              |3.0             |1.4              |0.2             |
-|2                                       |4.7              |3.2             |1.3              |0.2             |
-|3                                       |4.6              |3.1             |1.5              |0.2             |
-|4                                       |5.0              |3.6             |1.4              |0.2             |
-
-- **DataFrame.info**：首先，`info()` 方法用于打印 `DataFrame` 中内容的摘要。让我们看看这个数据集包含哪些内容：
-```python
-iris_df.info()
-```
-```
-RangeIndex: 150 entries, 0 to 149
-Data columns (total 4 columns):
- #   Column             Non-Null Count  Dtype  
----  ------             --------------  -----  
- 0   sepal length (cm)  150 non-null    float64
- 1   sepal width (cm)   150 non-null    float64
- 2   petal length (cm)  150 non-null    float64
- 3   petal width (cm)   150 non-null    float64
-dtypes: float64(4)
-memory usage: 4.8 KB
-```
-从中我们可以知道，*Iris* 数据集有 150 条记录，分布在四列中，没有空值。所有数据都存储为 64 位浮点数。
-
-- **DataFrame.head()**：接下来，为了检查 `DataFrame` 的实际内容，我们使用 `head()` 方法。让我们看看 `iris_df` 的前几行：
-```python
-iris_df.head()
-```
-```
-   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)
-0                5.1               3.5                1.4               0.2
-1                4.9               3.0                1.4               0.2
-2                4.7               3.2                1.3               0.2
-3                4.6               3.1                1.5               0.2
-4                5.0               3.6                1.4               0.2
-```
-- **DataFrame.tail()**：相反，为了检查 `DataFrame` 的最后几行，我们使用 `tail()` 方法：
-```python
-iris_df.tail()
-```
-```
-     sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)
-145                6.7               3.0                5.2               2.3
-146                6.3               2.5                5.0               1.9
-147                6.5               3.0                5.2               2.0
-148                6.2               3.4                5.4               2.3
-149                5.9               3.0                5.1               1.8
-```
-> **总结**：仅通过查看 DataFrame 的元数据或前几行和后几行的值，您就可以立即了解您正在处理的数据的大小、形状和内容。
-
-## 处理缺失数据
-> **学习目标**：在本小节结束时，您应该知道如何替换或删除 DataFrame 中的空值。
-
-大多数时候，您想使用（或必须使用）的数据集中会有缺失值。处理缺失数据时需要权衡，这可能会影响最终分析和实际结果。
-
-Pandas 处理缺失值有两种方式。第一种您在之前的章节中已经见过：`NaN`，即非数字。这实际上是 IEEE 浮点规范的一部分，用于表示缺失的浮点值。
-
-对于浮点值以外的缺失值，pandas 使用 Python 的 `None` 对象。虽然同时遇到两种表示缺失值的方式可能会让人困惑，但这种设计选择有其合理的编程原因，实际上，这种方式为绝大多数情况提供了良好的折中方案。尽管如此，`None` 和 `NaN` 都有一些限制，您需要注意它们的使用方式。
-
-在[笔记本](https://github.com/microsoft/Data-Science-For-Beginners/blob/main/4-Data-Science-Lifecycle/15-analyzing/notebook.ipynb)中了解更多关于 `NaN` 和 `None` 的信息！
-
-- **检测空值**：在 `pandas` 中，`isnull()` 和 `notnull()` 方法是检测空数据的主要方法。两者都会返回数据的布尔掩码。我们将使用 `numpy` 来处理 `NaN` 值：
-```python
-import numpy as np
-
-example1 = pd.Series([0, np.nan, '', None])
-example1.isnull()
-```
-```
-0    False
-1     True
-2    False
-3     True
-dtype: bool
-```
-仔细观察输出。是否有任何结果让您感到惊讶？虽然 `0` 是一个算术上的空值，但它仍然是一个有效的整数，pandas 将其视为有效值。`''` 则稍微复杂一些。虽然我们在第 1 节中用它表示空字符串值，但它仍然是一个字符串对象，而不是 pandas 所认为的空值。
-
-现在，让我们反过来以更实际的方式使用这些方法。您可以直接将布尔掩码用作 `Series` 或 `DataFrame` 的索引，这在处理孤立的缺失值（或存在值）时非常有用。
-
-> **总结**：在 `DataFrame` 中使用 `isnull()` 和 `notnull()` 方法时，它们会显示结果及其索引，这在处理数据时非常有帮助。
-
-- **删除空值**：除了识别缺失值，pandas 还提供了一种方便的方法来从 `Series` 和 `DataFrame` 中删除空值。（特别是在处理大型数据集时，通常建议直接删除缺失值，而不是以其他方式处理它们。）让我们回到 `example1` 来看看实际效果：
-```python
-example1 = example1.dropna()
-example1
-```
-```
-0    0
-2     
-dtype: object
-```
-注意，这应该与您的 `example3[example3.notnull()]` 输出类似。不同之处在于，`dropna` 删除了 `Series` `example1` 中的缺失值，而不是仅仅索引掩码值。
-
-由于 `DataFrame` 是二维的，它提供了更多删除数据的选项。
-
-```python
-example2 = pd.DataFrame([[1,      np.nan, 7], 
-                         [2,      5,      8], 
-                         [np.nan, 6,      9]])
-example2
-```
-|      | 0 | 1 | 2 |
-|------|---|---|---|
-|0     |1.0|NaN|7  |
-|1     |2.0|5.0|8  |
-|2     |NaN|6.0|9  |
-
-（您是否注意到 pandas 将两列数据提升为浮点类型以容纳 `NaN`？）
-
-您无法从 `DataFrame` 中删除单个值，因此必须删除整行或整列。根据您的需求，您可能需要删除其中之一，pandas 提供了两者的选项。由于在数据科学中，列通常表示变量，行表示观察值，您更可能删除数据行；`dropna()` 的默认设置是删除所有包含任何空值的行：
-
-```python
-example2.dropna()
-```
-```
-	0	1	2
-1	2.0	5.0	8
-```
-如果需要，您可以从列中删除 NA 值。使用 `axis=1` 来实现：
-```python
-example2.dropna(axis='columns')
-```
-```
-	2
-0	7
-1	8
-2	9
-```
-注意，这可能会删除您可能想保留的大量数据，特别是在较小的数据集中。如果您只想删除包含多个或所有空值的行或列，可以在 `dropna` 中使用 `how` 和 `thresh` 参数指定这些设置。
-
-默认情况下，`how='any'`（如果您想自己检查或查看方法的其他参数，请在代码单元中运行 `example4.dropna?`）。您也可以指定 `how='all'`，以便仅删除包含所有空值的行或列。让我们扩展示例 `DataFrame` 来看看实际效果。
-
-```python
-example2[3] = np.nan
-example2
-```
-|      |0  |1  |2  |3  |
-|------|---|---|---|---|
-|0     |1.0|NaN|7  |NaN|
-|1     |2.0|5.0|8  |NaN|
-|2     |NaN|6.0|9  |NaN|
-
-`thresh` 参数提供了更细粒度的控制：您可以设置行或列需要具有的*非空值*数量，以便保留：
-```python
-example2.dropna(axis='rows', thresh=3)
-```
-```
-	0	1	2	3
-1	2.0	5.0	8	NaN
-```
-这里，第一行和最后一行被删除，因为它们只有两个非空值。
-
-- **填充空值**：根据您的数据集，有时填充空值比删除它们更合理。您可以使用 `isnull` 来就地填充，但这可能很费力，特别是当您有很多值需要填充时。由于这是数据科学中的常见任务，pandas 提供了 `fillna` 方法，它返回一个 `Series` 或 `DataFrame` 的副本，其中的缺失值被替换为您选择的值。让我们创建另一个示例 `Series` 来看看实际效果。
-```python
-example3 = pd.Series([1, np.nan, 2, None, 3], index=list('abcde'))
-example3
-```
-```
-a    1.0
-b    NaN
-c    2.0
-d    NaN
-e    3.0
-dtype: float64
-```
-您可以用单个值（例如 `0`）填充所有空条目：
-```python
-example3.fillna(0)
-```
-```
-a    1.0
-b    0.0
-c    2.0
-d    0.0
-e    3.0
-dtype: float64
-```
-您可以**向前填充**空值，即使用最后一个有效值填充空值：
-```python
-example3.fillna(method='ffill')
-```
-```
-a    1.0
-b    1.0
-c    2.0
-d    2.0
-e    3.0
-dtype: float64
-```
-您也可以**向后填充**，即将下一个有效值向后传播以填充空值：
-```python
-example3.fillna(method='bfill')
-```
-```
-a    1.0
-b    2.0
-c    2.0
-d    3.0
-e    3.0
-dtype: float64
-```
-正如您可能猜到的，这对 `DataFrame` 同样适用，但您还可以指定填充空值的 `axis`。再次使用之前的 `example2`：
-```python
-example2.fillna(method='ffill', axis=1)
-```
-```
-	0	1	2	3
-0	1.0	1.0	7.0	7.0
-1	2.0	5.0	8.0	8.0
-2	NaN	6.0	9.0	9.0
-```
-注意，当没有可用的前一个值进行向前填充时，空值仍然保留。
-> **要点：** 处理数据集中缺失值的方法有很多种。具体采用哪种策略（删除、替换，甚至是如何替换）应根据数据的具体情况来决定。随着你处理和分析数据集的经验不断增加，你会逐渐形成更好的应对缺失值的直觉和方法。
-## 删除重复数据
-
-> **学习目标：** 在本小节结束时，您应该能够熟练识别并删除 DataFrame 中的重复值。
-
-除了缺失数据之外，您在实际数据集中还会经常遇到重复数据。幸运的是，`pandas` 提供了一种简单的方法来检测和删除重复条目。
-
-- **识别重复项：`duplicated`**：您可以使用 pandas 中的 `duplicated` 方法轻松找到重复值，该方法返回一个布尔掩码，指示 `DataFrame` 中某个条目是否与之前的条目重复。让我们创建另一个示例 `DataFrame` 来实际操作一下。
-```python
-example4 = pd.DataFrame({'letters': ['A','B'] * 2 + ['B'],
-                         'numbers': [1, 2, 1, 3, 3]})
-example4
-```
-|      |letters|numbers|
-|------|-------|-------|
-|0     |A      |1      |
-|1     |B      |2      |
-|2     |A      |1      |
-|3     |B      |3      |
-|4     |B      |3      |
-
-```python
-example4.duplicated()
-```
-```
-0    False
-1    False
-2     True
-3    False
-4     True
-dtype: bool
-```
-- **删除重复项：`drop_duplicates`**：简单地返回一个数据副本，其中所有 `duplicated` 值均为 `False`：
-```python
-example4.drop_duplicates()
-```
-```
-	letters	numbers
-0	A	1
-1	B	2
-3	B	3
-```
-`duplicated` 和 `drop_duplicates` 默认会考虑所有列，但您可以指定它们仅检查 `DataFrame` 中的某些列：
-```python
-example4.drop_duplicates(['letters'])
-```
-```
-letters	numbers
-0	A	1
-1	B	2
-```
-
-> **要点：** 删除重复数据是几乎每个数据科学项目中的重要步骤。重复数据可能会改变您的分析结果并导致不准确的结论！
-
-
-## 🚀 挑战
-
-所有讨论的材料都提供为 [Jupyter Notebook](https://github.com/microsoft/Data-Science-For-Beginners/blob/main/2-Working-With-Data/08-data-preparation/notebook.ipynb)。此外，每个部分后面都有练习，试着完成它们吧！
-
-## [课后测验](https://ff-quizzes.netlify.app/en/ds/quiz/15)
-
-
-
-## 复习与自学
-
-有很多方法可以发现和处理数据准备工作，用于分析和建模，而清理数据是一个需要“动手实践”的重要步骤。试试 Kaggle 上的这些挑战，探索本课未涉及的技术。
-
-- [数据清理挑战：解析日期](https://www.kaggle.com/rtatman/data-cleaning-challenge-parsing-dates/)
-
-- [数据清理挑战：数据缩放与归一化](https://www.kaggle.com/rtatman/data-cleaning-challenge-scale-and-normalize-data)
-
-
-## 作业
-
-[评估表单数据](assignment.md)
-
----
-
-**免责声明**：  
-本文档使用AI翻译服务[Co-op Translator](https://github.com/Azure/co-op-translator)进行翻译。尽管我们努力确保准确性，但请注意，自动翻译可能包含错误或不准确之处。应以原始语言的文档作为权威来源。对于关键信息，建议使用专业人工翻译。对于因使用本翻译而引起的任何误解或误读，我们概不负责。
\ No newline at end of file
diff --git a/translations/zh/2-Working-With-Data/08-data-preparation/assignment.ipynb b/translations/zh/2-Working-With-Data/08-data-preparation/assignment.ipynb
deleted file mode 100644
index 5af741ee..00000000
--- a/translations/zh/2-Working-With-Data/08-data-preparation/assignment.ipynb
+++ /dev/null
@@ -1,155 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "source": [],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "# 作业：评估表单数据\n",
-    "\n",
-    "一位客户正在测试一个[小型表单](../../../../2-Working-With-Data/08-data-preparation/index.html)，用于收集其客户群的一些基本数据。他们将测试结果交给你，以验证所收集的数据。你可以在浏览器中打开`index.html`页面查看该表单。\n",
-    "\n",
-    "你还获得了一个[包含表单记录的CSV数据集](../../../../data/form.csv)，其中包括表单的输入数据以及一些基本的可视化图表。客户指出，其中一些可视化图表看起来不正确，但他们不确定如何解决这些问题。你可以在[作业笔记本](assignment.ipynb)中进行探索。\n",
-    "\n",
-    "## 说明\n",
-    "\n",
-    "使用本课程中的技术提出建议，以确保表单能够收集准确且一致的信息。\n"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "!pip install pandas\r\n",
-    "!pip install matplotlib"
-   ],
-   "outputs": [],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "source": [
-    "import pandas as pd\r\n",
-    "import matplotlib.pyplot as plt\r\n",
-    "\r\n",
-    "#Loading the dataset\r\n",
-    "path = '../../data/form.csv'\r\n",
-    "form_df = pd.read_csv(path)\r\n",
-    "print(form_df)"
-   ],
-   "outputs": [
-    {
-     "output_type": "stream",
-     "name": "stdout",
-     "text": [
-      "  birth_month       state   pet\n",
-      "0     January         NaN  Cats\n",
-      "1         JAN          CA  Cats\n",
-      "2        Sept      Hawaii   Dog\n",
-      "3     january          AK   Dog\n",
-      "4        July          RI  Cats\n",
-      "5   September  California  Cats\n",
-      "6       April          CA   Dog\n",
-      "7     January  California  Cats\n",
-      "8    November          FL   Dog\n",
-      "9    December     Florida  Cats\n"
-     ]
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "source": [
-    "form_df['state'].value_counts().plot(kind='bar');\r\n",
-    "plt.show()"
-   ],
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     }
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "source": [
-    "form_df['birth_month'].value_counts().plot(kind='bar');\r\n",
-    "plt.show()"
-   ],
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
-      "image/png": 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REdGFcYNe0hTgbOAlwH7ASZL2G7XaKcA9tp8BfAr41/q5+1FdTPw5wGzgc/XrRUREn3SzR38osMz27bYfBi4A5oxaZw7w5fr+RcALVF0lfA5wge2HbN8BLKtfLyIi+kS2N7yCdAIw2/ab6sevBw6zfWpjnZvqdVbUj38LHAZ8ELja9r/X7ecCl9i+qMP7zAPm1Q+fBdy6eT8aANOBu3rwOr00iDXBYNaVmrqTmro3iHX1qqa9bA91WrB1D168J2zPB+b38jUlLbE93MvX3FyDWBMMZl2pqTupqXuDWFc/auqm62YlsGfj8R51W8d1JG0NPAm4u8vnRkTEBOom6BcD+0jaW9I2VAdXF45aZyFwcn3/BOCHrvqEFgJz61E5ewP7AL/sTekREdGNcbtubK+VdCpwKTAFWGD7ZklnAktsLwTOBf5N0jJgDdWHAfV6FwK3AGuBt9peN0E/Syc97QrqkUGsCQazrtTUndTUvUGsa8JrGvdgbERETG45MzYionAJ+oiIwiXoIyIKV1TQS3qbpGlt1wEgacaGbgNQ31HdtPXTIG0/AEmfkPSctusYTdIUSV9tu47JoP5dfbztOpraqGlgTpjqkV2BxZKuARYAl7q9o83fAwyo0WZgCNiFagRTm84CDumirZ8GafsB/AqYX58b8iXgfNv3tlgPALbXSdpL0jb1tCStkbTzhpbbXtOvWsZ4/3WSjm6zhtHaqKm4UTf1HDuzgDcCw8CFwLm2f9tyXTOB9wIvBD5j+6yW6jgCOBJ4J9UEdCN2Al5l+8A26hoxiNtP0rPqek4Cfgb8H9s/aqueuqavAPtSnavywEi77U/2uY47ePwOTaMcP62f9XQi6fPA7sA3WP939a0tpabS9uixbUl/BP5INXZ/GnCRpMtt/69+1yNpH+ADVHP/fAJ4u+2/9LuOhm2AHai2/Y6N9vuoTnZr1QBuvynAs+vbXcD1wLslvdn23H7X0/Db+rYV62/HvrK9d1vvvRG2ozpT//mNNgOtBT19rqmoPXpJ7wDeQPUf8ovAt23/RdJWwG22n97HWp5LFfDPAT5K9bW/nyeLbZCkvWz/TtJOVPl6/wDUNDDbr67nU8ArgB9Qfav4ZWPZrbaf1c96OpG0ve0HW3z/Z9v+taSOXX62r+l3TfF4pe3RTwNebft3zUbbj0h6eZ9ruR5YTtVXfyhwaNUr8WhNb+9zPaMNSfoP6r1BSfcCf297aYs1DdL2A7gB+GfbD3RY1up023UX3LlU385mSDoQeLPtt/S5lHdTzTr7iQ7LzPp7rK2Q9Ezg88Cutp8r6QDgeNsf3lJqKmaPvv6KfbPtZ7ddC4Ckkze03PaXN7R8okm6gWpKip/Uj48GPmf7gJbqGZjtN9be6YhB2EuV9AuqrraFtg+u226y/dwWatkKOML2z/r93t2QdAXwHuCctn9XbdVUzB59fST7VkkzbP9+AOrpGOSStqPqDmjbupGQB7D9U0lr2ypmwLZfp73TEQOxlwpge3nzWyLQStdg/Y3rs8DBbbx/F7a3/ctRv6vW/tZrfa2pmKCvTQNulvRL1j+SfXx7JT26t/piqlEbs4CfUB1tb9MVks4BzqcKrxOBH4/szba01zoQ28/2cf18v020XNKRgCVNBd5BNRy0LT+Q9BrgWy0Pie3kLklPp/o7H7mY0h/aLam/NRXTdQMg6ZhO7bav6Hct8Gg9fwu8lGp65qOAp7V58GyEpA0ND7Ttvu+1DuD2e0Ondttf6Xcto0maDnyaarjuVlSzy77D9t0t1XM/8ESqbxV/phpuads7tVFPk6SnUc0QeSRwD3AH8NrRx4JKrqmooB8kklYAv6c64PJt2/dLumOSDEcLQFLzXIftgBcA19hufRhqbDxJTwS2GoQRZiP6VVNpUyAcLmmxpD9JeljSOkn3tVTORcBTqbpEXlFv0IH5VJW0q6RzJV1SP95P0ikt1zRI2w/bb2vc/oHqrOEd2qqnSdLTJH1X0mpJqyR9p95LbLOmV0v6pKqpI17ZZi1Nkp4i6TNUXaY/lvRpSU/ZkmoqKuiBz1L1g98GPAF4E3B2G4XYfiewN9WBvWOpLna+i6QTJQ1CWJxH9XX/qfXj31CdLdumgdl+Y3iAapsOgq9RnTW8G9U2/AbV8ZZWSPoc8I/AjcBNwD9KGpRtdwGwGngN1Uil1cDXW62ozzUV1XWj+iK7km4YGSYo6dqR4Ust1zaVxgFZj3G19j7Ws9j285q/H0nX2T6oxZoGavtJ+i6PfQubAuwHXGj7vW3U09T8HTXarm9rCgtJvwb2HTkQWw+5vNn2vm3U09Rp2KKkG23vv6XUVNqomwdVXdf2OkkfpTqK3cq3lvrg1OhP0ZGxVH+RdDXwAds/6G9lj3qg/qo48h/zcKDtCbsGZvvVPs5j23At8DvbrV7cXo9NInaJpNOo9gxHRk0taq0wWAbMAEYOJu5Ztw2CyyTNpfoGBNUe9KUt1gN9rqm0Pfq9gFXAVOBdwJOoTgIalD844NHhls8FvtrWSRv1MMqz6jpuoppV8wTbN7RRT13TQGy/xof06Im6DDxENcdMKx/SGtBJxOoTgJ5HNbqM+v5iqjmUWhniPGo7PhF4pF60FfCnNkYEtVVTUUE/2aiaGOucPr/n84Dltv+oavrdN1P1E94CnO6Wp5UddIPwIT2IRg2NFfDfgLnAW6C9IbJRKSroG3s762lrL2cQqZrr/YW210j671Rf/d8GHETVx9ra0MHJtP3a+JAe9f5TgJcBM2l0wbrP0xSPqulgqvNG/oZqXPi33NJ03KOpmktmJuv/rtqcvbKvNZXWRz/cuL8d1R/cBi+MsAWa0thrPxGYb/ubwDclXddeWcAk2n5thnztu8D/oxrl8sg4604YVZNznVTf7qIaOaJBOrtY0gLgAOBmHvtdtTpNcb9rKmqPvhNJS23/ddt1DApJNwEH2V5bj5SYZ/vKkWWD1h2R7ddZp1E3LdXxCNVY8FNGjqVIun2QvoVJusX2fm3X0dTvmorao9f6sw5uRbWHWNTP2APnU81zcxfVqeojs1c+g5ZH3WT7bZRLJM2yfVnLdbyaqi/+R5K+T9UV2OlAcZt+Lmk/27e0XUhDX2sqao9e68/fsha4E/i47VvbqWgw1UMpdwMucz3Xev0VfIc2p+DN9uuepFcB/071gfgXWp5bpj7zew5VF87zga8AFw/AB9HIgeKFVFcte4jHfletfSPqd01FBX3ElqI+cD0HuHHQZouUNI3q+MqJtl8wAPUso7pAynrHM1qe1KyvNRUV9JK2pRoqOJP1j2Sf2VZN0b1sv+5JuhI41nZrB2InC0k/t31E23U09bum0vo/v0PVz7yU6utQTC7Zft27nWoyrEto/K7aHF45wK6V9DWqkUrN31Wbwyv7WlNpQb+H7dltFxGbLNuve3fUt23qW4ztCVRhOqvR1urwSvpcU2ldN/OBs2zf2HYtsfGy/TaepO0H4UI2MdhKm6b4aGCpqmuP3iDpRlUXwY7JIduvS5KOkHQL8Ov68YH1VMExiqRnSvpBfQ4Jkg6Q9M9bUk2l7dHv1am9zaPr0b1sv+5J+gXVjIcLG9NMD9wJb4OgnnDtPcA5g/K76ndNRfXRjwSCpF2oTqGPSSTbb+PYXi6td27SurZqGXDb2/7lqN/V2raKqfW1pqK6biQdL+k2qoNUV1CdcHNJq0VF17L9NspySUcCljRV0v8EftV2UQPqLklP57FrL5xAda2DNvW1ptK6bq6nOivvP20fLOk44HW2W70WanQn2697kqYDnwZeSHVW5WXA2zPN9OOpupbufOBI4B6qHYnXtnzCVKeaXmf7zgl5v8KCfuRSdNcDB9t+RC1eXi02TrZf9yQdZftn47VFNaWz7XX1NA1b2b6/7ZpG9KumorpugP9SdeHtnwBflfRpqgs6x+SQ7de9TvO8D8Tc7wPojnro7uHAn9ouBkDSv0h6su0HbN8vaZqkD0/Y+xW2R7891RzdAl4H7ER1JaB8nZ0Esv3GJ+kIqq/77wQ+1Vi0E/CqfPt5vPrv6uVUs2weAvwHcIHtn7ZY07UeddF7SdfYPmSs52yOIkbdaMMX4j5dUmvX+IzxSfqp7aOB/8tj23Fk+31Y0hrgY7YzTrw6C3YHqv+7Ozba76Mabhmj1CeUXQhcWE+49mmqg/1TWixriqRtbT8EIOkJwLYT9WZF7dF3olzjc9KT9BTgKtvParuWQSFpL9u/k7QT1fS2A9PvPIjqaYFPBGYDS4Cv11dWa6ue9wKvAL5UN72R6pyIj07I+5Ue9CPU8jU+Y/NI2s1220PiBoakYaqQGNmrvxf4e9tL26tqMEm6E7iWaq9+4cg1GNomaTbVqCmAy21fOmHvtaUEfURJ6qkh3mp75AphRwOfG4TLCw4aSTvZvq/tOkarzwTfx/Z/1scRpkzUN7PSRt1EbCnWjYQ8QH1gse2zPQfVTpIulrSqvn1T0h5tFiTpH4CLgJFeht2Bb0/U+yXoIyanKySdI+lYScfUE5r9WNIhWv/au1F1cS0EnlrfvstjfeNteStwFNVBdGzfBuwyUW9WxKibiC3QyDDKM0a1H0w1cun5/S1noA3Zbgb7eZLe2VYxtYdsPzwy142krXn8yMGeSdBHTEK2j2u7hknkbkmvA86vH58E3N1iPVB9I3s/8ARJLwLeQvVNY0LkYGzEJCRpV+BfgKfafomk/YAjbJ/bcmkDpz7oeRZwBNVe81VU8wL9vsWatgJOobrClIBLgS9O1IXeE/QRk5Cqa8V+iepEwAPrr/7X2t6/5dKiS5KGAGyvnuj3StdNxOQ03faFkt4HYHutpMxH3yDp9A0stu0P9a2YmqpO+TOAU6kHw9Tb7SzbZ07U+2bUTcTk9EB9xvDIfOaHU500FY95oMMNqi6T97ZU07uoRts8z/bOtncGDgOOkvSuiXrTdN1ETEL1EMqzqKb3uAkYAk6wnWvsdiBpR+AdVCF/IfAJ26taqONa4EW27xrVPgRcNnqis17JHn3EJCLpeZL+yvY1wDHA+4GHqC48sqLV4gaQpJ3r6X9voOqqPsT2e9sI+drU0SEPj/bTT52oN03QR0wu5wAP1/ePBD4AnE11laL5bRU1iCR9DFgM3A/sb/uDtu9puayHN3HZZknXTcQk0rzilqSzgdW2P1g/vs72QS2WN1AkPUL1bWct65+MJKqDsTu1UNM6Ol9MR8B2tidkrz6jbiImlymStra9FngBMK+xLP+fG2wPXI+F7VbmwM8fRsTkcj7VWZV3AX+muuwikp5BRt3EGNJ1EzHJ1EMpd6MapfFA3fZMYIf6IG3EehL0ERGFG7g+rIiI6K0EfURE4RL0ERGFS9BHRBQuQR8RUbj/D2N5LVp4WgrKAAAAAElFTkSuQmCC",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     }
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n---\n\n**免责声明**：  \n本文档使用AI翻译服务[Co-op Translator](https://github.com/Azure/co-op-translator)进行翻译。尽管我们努力确保准确性，但请注意，自动翻译可能包含错误或不准确之处。应以原始语言的文档作为权威来源。对于关键信息，建议使用专业人工翻译。对于因使用本翻译而引起的任何误解或误读，我们概不负责。\n"
-   ]
-  }
- ],
- "metadata": {
-  "orig_nbformat": 4,
-  "language_info": {
-   "name": "python",
-   "version": "3.9.7",
-   "mimetype": "text/x-python",
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "pygments_lexer": "ipython3",
-   "nbconvert_exporter": "python",
-   "file_extension": ".py"
-  },
-  "kernelspec": {
-   "name": "python3",
-   "display_name": "Python 3.9.7 64-bit ('venv': venv)"
-  },
-  "interpreter": {
-   "hash": "6b9b57232c4b57163d057191678da2030059e733b8becc68f245de5a75abe84e"
-  },
-  "coopTranslator": {
-   "original_hash": "ea753231e2dbdcf8d2e7e976784cbe2a",
-   "translation_date": "2025-09-02T08:28:31+00:00",
-   "source_file": "2-Working-With-Data/08-data-preparation/assignment.ipynb",
-   "language_code": "zh"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
\ No newline at end of file
diff --git a/translations/zh/2-Working-With-Data/08-data-preparation/assignment.md b/translations/zh/2-Working-With-Data/08-data-preparation/assignment.md
deleted file mode 100644
index 106ccd9d..00000000
--- a/translations/zh/2-Working-With-Data/08-data-preparation/assignment.md
+++ /dev/null
@@ -1,26 +0,0 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "f9d5a7275e046223fa6474477674b810",
-  "translation_date": "2025-08-25T16:23:33+00:00",
-  "source_file": "2-Working-With-Data/08-data-preparation/assignment.md",
-  "language_code": "zh"
-}
--->
-# 评估表单数据
-
-一位客户正在测试一个[小型表单](../../../../2-Working-With-Data/08-data-preparation/index.html)，以收集有关其客户群的一些基本数据。他们将测试结果带给你，希望你验证他们收集的数据。你可以在浏览器中打开 `index.html` 页面查看表单。
-
-你已获得一个[包含表单记录的 CSV 数据集](../../../../data/form.csv)，其中包括表单的条目以及一些基本的可视化图表。客户指出其中一些可视化图表看起来不正确，但他们不确定如何解决这些问题。你可以在[任务笔记本](../../../../2-Working-With-Data/08-data-preparation/assignment.ipynb)中进行探索。
-
-## 指导
-
-使用本课程中的技术提出建议，以确保表单能够收集准确且一致的信息。
-
-## 评分标准
-
-优秀 | 合格 | 需要改进
---- | --- | --- |
-
-**免责声明**：  
-本文档使用AI翻译服务[Co-op Translator](https://github.com/Azure/co-op-translator)进行翻译。虽然我们尽力确保翻译的准确性，但请注意，自动翻译可能包含错误或不准确之处。原始语言的文档应被视为权威来源。对于关键信息，建议使用专业人工翻译。我们对因使用此翻译而产生的任何误解或误读不承担责任。
\ No newline at end of file
diff --git a/translations/zh/2-Working-With-Data/08-data-preparation/notebook.ipynb b/translations/zh/2-Working-With-Data/08-data-preparation/notebook.ipynb
deleted file mode 100644
index cb3b969e..00000000
--- a/translations/zh/2-Working-With-Data/08-data-preparation/notebook.ipynb
+++ /dev/null
@@ -1,4241 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "rQ8UhzFpgRra"
-   },
-   "source": [
-    "# 数据准备\n",
-    "\n",
-    "[原始笔记本来源于 *Data Science: Introduction to Machine Learning for Data Science Python and Machine Learning Studio by Lee Stott*](https://github.com/leestott/intro-Datascience/blob/master/Course%20Materials/4-Cleaning_and_Manipulating-Reference.ipynb)\n",
-    "\n",
-    "## 探索 `DataFrame` 信息\n",
-    "\n",
-    "> **学习目标：** 在本小节结束时，您应该能够熟练地查找存储在 pandas DataFrame 中的数据的一般信息。\n",
-    "\n",
-    "当您将数据加载到 pandas 中时，它很可能会以 `DataFrame` 的形式存在。然而，如果您的 `DataFrame` 数据集有 60,000 行和 400 列，您该如何开始了解您正在处理的数据呢？幸运的是，pandas 提供了一些方便的工具，可以快速查看 `DataFrame` 的整体信息，以及前几行和后几行数据。\n",
-    "\n",
-    "为了探索这些功能，我们将导入 Python 的 scikit-learn 库，并使用一个每位数据科学家都见过无数次的经典数据集：英国生物学家 Ronald Fisher 在其 1936 年论文《多重测量在分类问题中的应用》中使用的 *Iris* 数据集：\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": true,
-    "id": "hB1RofhdgRrp",
-    "trusted": false
-   },
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "from sklearn.datasets import load_iris\n",
-    "\n",
-    "iris = load_iris()\n",
-    "iris_df = pd.DataFrame(data=iris['data'], columns=iris['feature_names'])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "AGA0A_Y8hMdz"
-   },
-   "source": [
-    "### `DataFrame.shape`\n",
-    "我们已经将鸢尾花数据集加载到变量 `iris_df` 中。在深入分析数据之前，了解我们拥有的数据点数量以及数据集的整体规模是非常有价值的。查看我们正在处理的数据量是很有帮助的。\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "LOe5jQohhulf",
-    "outputId": "fb0577ac-3b4a-4623-cb41-20e1b264b3e9"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(150, 4)"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "iris_df.shape"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "smE7AGzOhxk2"
-   },
-   "source": [
-    "我们正在处理包含150行和4列的数据。每一行代表一个数据点，每一列代表与数据框相关的一个特征。基本上，这里有150个数据点，每个数据点包含4个特征。\n",
-    "\n",
-    "`shape` 是数据框的一个属性，而不是一个函数，这就是为什么它没有以一对括号结尾。\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "d3AZKs0PinGP"
-   },
-   "source": [
-    "### `DataFrame.columns`\n",
-    "现在让我们来看数据的4列。每一列具体代表什么？`columns`属性将为我们提供数据框中列的名称。\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "YPGh_ziji-CY",
-    "outputId": "74e7a43a-77cc-4c80-da56-7f50767c37a0"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Index(['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)',\n",
-       "       'petal width (cm)'],\n",
-       "      dtype='object')"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "iris_df.columns"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "TsobcU_VjCC_"
-   },
-   "source": [
-    "正如我们所看到的，有四（4）列。`columns`属性告诉我们列的名称，基本上没有其他信息。当我们想要识别数据集包含的特征时，这个属性显得尤为重要。\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "2UTlvkjmgRrs"
-   },
-   "source": [
-    "### `DataFrame.info`\n",
-    "通过 `shape` 属性提供的数据量以及通过 `columns` 属性提供的特征或列名，可以让我们对数据集有一些了解。现在，我们希望更深入地探索数据集。`DataFrame.info()` 函数在这方面非常有用。\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "dHHRyG0_gRrt",
-    "outputId": "d8fb0c40-4f18-4e19-da48-c8db77d1d3a5",
-    "trusted": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "<class 'pandas.core.frame.DataFrame'>\n",
-      "RangeIndex: 150 entries, 0 to 149\n",
-      "Data columns (total 4 columns):\n",
-      " #   Column             Non-Null Count  Dtype  \n",
-      "---  ------             --------------  -----  \n",
-      " 0   sepal length (cm)  150 non-null    float64\n",
-      " 1   sepal width (cm)   150 non-null    float64\n",
-      " 2   petal length (cm)  150 non-null    float64\n",
-      " 3   petal width (cm)   150 non-null    float64\n",
-      "dtypes: float64(4)\n",
-      "memory usage: 4.8 KB\n"
-     ]
-    }
-   ],
-   "source": [
-    "iris_df.info()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "1XgVMpvigRru"
-   },
-   "source": [
-    "从这里，我们可以做出以下几点观察：\n",
-    "1. 每列的数据类型：在这个数据集中，所有数据都存储为64位浮点数。\n",
-    "2. 非空值的数量：处理空值是数据准备中的重要步骤。这将在后续的笔记本中处理。\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "IYlyxbpWFEF4"
-   },
-   "source": [
-    "### DataFrame.describe()\n",
-    "假设我们的数据集中有大量的数值数据。可以对每一列单独进行单变量统计计算，例如均值、中位数、四分位数等。`DataFrame.describe()`函数为我们提供了数据集中数值列的统计摘要。\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 297
-    },
-    "id": "tWV-CMstFIRA",
-    "outputId": "4fc49941-bc13-4b0c-a412-cb39e7d3f289"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>sepal length (cm)</th>\n",
-       "      <th>sepal width (cm)</th>\n",
-       "      <th>petal length (cm)</th>\n",
-       "      <th>petal width (cm)</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>count</th>\n",
-       "      <td>150.000000</td>\n",
-       "      <td>150.000000</td>\n",
-       "      <td>150.000000</td>\n",
-       "      <td>150.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>mean</th>\n",
-       "      <td>5.843333</td>\n",
-       "      <td>3.057333</td>\n",
-       "      <td>3.758000</td>\n",
-       "      <td>1.199333</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>std</th>\n",
-       "      <td>0.828066</td>\n",
-       "      <td>0.435866</td>\n",
-       "      <td>1.765298</td>\n",
-       "      <td>0.762238</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>min</th>\n",
-       "      <td>4.300000</td>\n",
-       "      <td>2.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.100000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>25%</th>\n",
-       "      <td>5.100000</td>\n",
-       "      <td>2.800000</td>\n",
-       "      <td>1.600000</td>\n",
-       "      <td>0.300000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>50%</th>\n",
-       "      <td>5.800000</td>\n",
-       "      <td>3.000000</td>\n",
-       "      <td>4.350000</td>\n",
-       "      <td>1.300000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>75%</th>\n",
-       "      <td>6.400000</td>\n",
-       "      <td>3.300000</td>\n",
-       "      <td>5.100000</td>\n",
-       "      <td>1.800000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>max</th>\n",
-       "      <td>7.900000</td>\n",
-       "      <td>4.400000</td>\n",
-       "      <td>6.900000</td>\n",
-       "      <td>2.500000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "       sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)\n",
-       "count         150.000000        150.000000         150.000000        150.000000\n",
-       "mean            5.843333          3.057333           3.758000          1.199333\n",
-       "std             0.828066          0.435866           1.765298          0.762238\n",
-       "min             4.300000          2.000000           1.000000          0.100000\n",
-       "25%             5.100000          2.800000           1.600000          0.300000\n",
-       "50%             5.800000          3.000000           4.350000          1.300000\n",
-       "75%             6.400000          3.300000           5.100000          1.800000\n",
-       "max             7.900000          4.400000           6.900000          2.500000"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "iris_df.describe()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "zjjtW5hPGMuM"
-   },
-   "source": [
-    "上面的输出显示了每列的总数据点数、均值、标准差、最小值、下四分位数(25%)、中位数(50%)、上四分位数(75%)和最大值。\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "-lviAu99gRrv"
-   },
-   "source": [
-    "### `DataFrame.head`\n",
-    "通过以上所有函数和属性，我们已经对数据集有了一个总体的了解。我们知道数据点的数量、特征的数量、每个特征的数据类型以及每个特征的非空值数量。\n",
-    "\n",
-    "现在是时候查看数据本身了。让我们看看我们的 `DataFrame` 的前几行（前几个数据点）是什么样子的：\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 204
-    },
-    "id": "DZMJZh0OgRrw",
-    "outputId": "d9393ee5-c106-4797-f815-218f17160e00",
-    "trusted": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>sepal length (cm)</th>\n",
-       "      <th>sepal width (cm)</th>\n",
-       "      <th>petal length (cm)</th>\n",
-       "      <th>petal width (cm)</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>5.1</td>\n",
-       "      <td>3.5</td>\n",
-       "      <td>1.4</td>\n",
-       "      <td>0.2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>4.9</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>1.4</td>\n",
-       "      <td>0.2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>4.7</td>\n",
-       "      <td>3.2</td>\n",
-       "      <td>1.3</td>\n",
-       "      <td>0.2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>4.6</td>\n",
-       "      <td>3.1</td>\n",
-       "      <td>1.5</td>\n",
-       "      <td>0.2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>5.0</td>\n",
-       "      <td>3.6</td>\n",
-       "      <td>1.4</td>\n",
-       "      <td>0.2</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)\n",
-       "0                5.1               3.5                1.4               0.2\n",
-       "1                4.9               3.0                1.4               0.2\n",
-       "2                4.7               3.2                1.3               0.2\n",
-       "3                4.6               3.1                1.5               0.2\n",
-       "4                5.0               3.6                1.4               0.2"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "iris_df.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "EBHEimZuEFQK"
-   },
-   "source": [
-    "在这里的输出中，我们可以看到数据集的五(5)条记录。如果查看左侧的索引，我们会发现这些是前五行。\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "oj7GkrTdgRry"
-   },
-   "source": [
-    "### 练习：\n",
-    "\n",
-    "从上面的例子可以看出，默认情况下，`DataFrame.head` 会返回 `DataFrame` 的前五行。在下面的代码单元中，你能找到一种方法来显示超过五行吗？\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": true,
-    "id": "EKRmRFFegRrz",
-    "trusted": false
-   },
-   "outputs": [],
-   "source": [
-    "# Hint: Consult the documentation by using iris_df.head?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "BJ_cpZqNgRr1"
-   },
-   "source": [
-    "### `DataFrame.tail`\n",
-    "另一种查看数据的方法是从末尾开始（而不是开头）。`DataFrame.head` 的反面是 `DataFrame.tail`，它返回 `DataFrame` 的最后五行：\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 0
-    },
-    "id": "heanjfGWgRr2",
-    "outputId": "6ae09a21-fe09-4110-b0d7-1a1fbf34d7f3",
-    "trusted": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>sepal length (cm)</th>\n",
-       "      <th>sepal width (cm)</th>\n",
-       "      <th>petal length (cm)</th>\n",
-       "      <th>petal width (cm)</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>145</th>\n",
-       "      <td>6.7</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>5.2</td>\n",
-       "      <td>2.3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>146</th>\n",
-       "      <td>6.3</td>\n",
-       "      <td>2.5</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>1.9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>147</th>\n",
-       "      <td>6.5</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>5.2</td>\n",
-       "      <td>2.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>148</th>\n",
-       "      <td>6.2</td>\n",
-       "      <td>3.4</td>\n",
-       "      <td>5.4</td>\n",
-       "      <td>2.3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>149</th>\n",
-       "      <td>5.9</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>5.1</td>\n",
-       "      <td>1.8</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)\n",
-       "145                6.7               3.0                5.2               2.3\n",
-       "146                6.3               2.5                5.0               1.9\n",
-       "147                6.5               3.0                5.2               2.0\n",
-       "148                6.2               3.4                5.4               2.3\n",
-       "149                5.9               3.0                5.1               1.8"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "iris_df.tail()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "31kBWfyLgRr3"
-   },
-   "source": [
-    "在实际操作中，能够轻松查看 `DataFrame` 的前几行或后几行非常有用，尤其是在检查有序数据集中的异常值时。\n",
-    "\n",
-    "上面通过代码示例展示的所有函数和属性，都能帮助我们了解数据的整体情况。\n",
-    "\n",
-    "> **要点:** 仅仅通过查看 `DataFrame` 的元数据或其中的前几行和后几行数据，就可以快速了解所处理数据的大小、形状和内容。\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "TvurZyLSDxq_"
-   },
-   "source": [
-    "### 缺失数据\n",
-    "让我们深入了解缺失数据。缺失数据是指某些列中没有存储任何值。\n",
-    "\n",
-    "举个例子：假设某人对自己的体重很在意，因此在调查中没有填写体重字段。那么，这个人的体重值就会缺失。\n",
-    "\n",
-    "在现实世界的数据集中，缺失值是非常常见的。\n",
-    "\n",
-    "**Pandas如何处理缺失数据**\n",
-    "\n",
-    "Pandas处理缺失值有两种方式。第一种方式你在之前的章节中已经见过：`NaN`，即“非数字”（Not a Number）。这是一个特殊值，属于IEEE浮点数规范的一部分，仅用于表示缺失的浮点值。\n",
-    "\n",
-    "对于非浮点类型的缺失值，Pandas使用Python的`None`对象。虽然遇到两种不同类型的值来表示同样的含义可能会让人感到困惑，但这种设计选择有其合理的编程原因。在实际应用中，这种方式使Pandas能够在绝大多数情况下提供一个良好的折中方案。尽管如此，`None`和`NaN`都存在一些限制，需要注意它们的使用方式。\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "lOHqUlZFgRr5"
-   },
-   "source": [
-    "### `None`：非浮点型缺失数据\n",
-    "由于 `None` 来源于 Python，它不能用于数据类型不是 `'object'` 的 NumPy 和 pandas 数组。请记住，NumPy 数组（以及 pandas 中的数据结构）只能包含一种数据类型。这赋予了它们在大规模数据和计算工作中的强大能力，但也限制了它们的灵活性。这类数组必须提升为“最低公分母”，即能够包含数组中所有内容的数据类型。当数组中包含 `None` 时，这意味着你正在处理 Python 对象。\n",
-    "\n",
-    "为了更直观地理解这一点，请看以下示例数组（注意它的 `dtype`）：\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "QIoNdY4ngRr7",
-    "outputId": "92779f18-62f4-4a03-eca2-e9a101604336",
-    "trusted": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([2, None, 6, 8], dtype=object)"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "example1 = np.array([2, None, 6, 8])\n",
-    "example1"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "pdlgPNbhgRr7"
-   },
-   "source": [
-    "数据类型向上转换的现实带来了两个副作用。首先，操作将在解释型 Python 代码的层面上进行，而不是编译型 NumPy 代码的层面上。基本上，这意味着任何涉及包含 `None` 的 `Series` 或 `DataFrame` 的操作都会变得更慢。虽然你可能不会注意到这种性能下降，但对于大型数据集来说，这可能会成为一个问题。\n",
-    "\n",
-    "第二个副作用源于第一个副作用。由于 `None` 本质上将 `Series` 或 `DataFrame` 拉回到原生 Python 的世界，因此在包含 `None` 值的数组上使用 NumPy/pandas 的聚合函数（如 `sum()` 或 `min()`）通常会产生错误：\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 292
-    },
-    "id": "gWbx-KB9gRr8",
-    "outputId": "ecba710a-22ec-41d5-a39c-11f67e645b50",
-    "trusted": false
-   },
-   "outputs": [
-    {
-     "ename": "TypeError",
-     "evalue": "ignored",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-10-ce9901ad18bd>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mexample1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/numpy/core/_methods.py\u001b[0m in \u001b[0;36m_sum\u001b[0;34m(a, axis, dtype, out, keepdims, initial, where)\u001b[0m\n\u001b[1;32m     45\u001b[0m def _sum(a, axis=None, dtype=None, out=None, keepdims=False,\n\u001b[1;32m     46\u001b[0m          initial=_NoValue, where=True):\n\u001b[0;32m---> 47\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mumr_sum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeepdims\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minitial\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwhere\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     48\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     49\u001b[0m def _prod(a, axis=None, dtype=None, out=None, keepdims=False,\n",
-      "\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for +: 'int' and 'NoneType'"
-     ]
-    }
-   ],
-   "source": [
-    "example1.sum()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "LcEwO8UogRr9"
-   },
-   "source": [
-    "**关键点**：整数与`None`值之间的加法（以及其他操作）是未定义的，这可能会限制您对包含它们的数据集的操作。\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "pWvVHvETgRr9"
-   },
-   "source": [
-    "### `NaN`：缺失的浮点值\n",
-    "\n",
-    "与 `None` 不同，NumPy（因此也包括 pandas）支持 `NaN`，以便进行快速的矢量化操作和 ufuncs。坏消息是，任何对 `NaN` 进行的算术运算结果都会是 `NaN`。例如：\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "rcFYfMG9gRr9",
-    "outputId": "699e81b7-5c11-4b46-df1d-06071768690f",
-    "trusted": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "nan"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.nan + 1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "BW3zQD2-gRr-",
-    "outputId": "4525b6c4-495d-4f7b-a979-efce1dae9bd0",
-    "trusted": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "nan"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.nan * 0"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "fU5IPRcCgRr-"
-   },
-   "source": [
-    "好消息：在包含 `NaN` 的数组上运行聚合不会出现错误。坏消息：结果并不总是有用：\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "LCInVgSSgRr_",
-    "outputId": "fa06495a-0930-4867-87c5-6023031ea8b5",
-    "trusted": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(nan, nan, nan)"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "example2 = np.array([2, np.nan, 6, 8]) \n",
-    "example2.sum(), example2.min(), example2.max()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "nhlnNJT7gRr_"
-   },
-   "source": [
-    "### 练习:\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": true,
-    "id": "yan3QRaOgRr_",
-    "trusted": false
-   },
-   "outputs": [],
-   "source": [
-    "# What happens if you add np.nan and None together?\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "_iDvIRC8gRsA"
-   },
-   "source": [
-    "记住：`NaN`仅用于表示缺失的浮点值；整数、字符串或布尔值没有对应的`NaN`。\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "kj6EKdsAgRsA"
-   },
-   "source": [
-    "### `NaN` 和 `None`：pandas 中的空值\n",
-    "\n",
-    "尽管 `NaN` 和 `None` 的行为可能略有不同，但 pandas 仍然能够将它们互换处理。为了更好地理解这一点，来看一个整数的 `Series`：\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "Nji-KGdNgRsA",
-    "outputId": "36aa14d2-8efa-4bfd-c0ed-682991288822",
-    "trusted": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0    1\n",
-       "1    2\n",
-       "2    3\n",
-       "dtype: int64"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "int_series = pd.Series([1, 2, 3], dtype=int)\n",
-    "int_series"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "WklCzqb8gRsB"
-   },
-   "source": [
-    "### 练习:\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "collapsed": true,
-    "id": "Cy-gqX5-gRsB",
-    "trusted": false
-   },
-   "outputs": [],
-   "source": [
-    "# Now set an element of int_series equal to None.\n",
-    "# How does that element show up in the Series?\n",
-    "# What is the dtype of the Series?\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "WjMQwltNgRsB"
-   },
-   "source": [
-    "在将数据类型向上转换以实现 `Series` 和 `DataFrame` 数据同质化的过程中，pandas 会主动在 `None` 和 `NaN` 之间切换缺失值。由于这一设计特性，将 `None` 和 `NaN` 视为 pandas 中两种不同形式的“空值”是很有帮助的。事实上，pandas 中一些处理缺失值的核心方法名称也反映了这一点：\n",
-    "\n",
-    "- `isnull()`：生成一个布尔掩码，用于标识缺失值\n",
-    "- `notnull()`：与 `isnull()` 相反\n",
-    "- `dropna()`：返回过滤后的数据版本\n",
-    "- `fillna()`：返回一个填充或插补缺失值后的数据副本\n",
-    "\n",
-    "这些方法非常重要，掌握并熟悉它们是关键。接下来我们将逐一深入了解这些方法。\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "Yh5ifd9FgRsB"
-   },
-   "source": [
-    "### 检测空值\n",
-    "\n",
-    "既然我们已经了解了缺失值的重要性，那么在处理它们之前，我们需要在数据集中检测它们。  \n",
-    "`isnull()` 和 `notnull()` 是检测空数据的主要方法。两者都会返回数据的布尔掩码。\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "collapsed": true,
-    "id": "e-vFp5lvgRsC",
-    "trusted": false
-   },
-   "outputs": [],
-   "source": [
-    "example3 = pd.Series([0, np.nan, '', None])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "1XdaJJ7PgRsC",
-    "outputId": "92fc363a-1874-471f-846d-f4f9ce1f51d0",
-    "trusted": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0    False\n",
-       "1     True\n",
-       "2    False\n",
-       "3     True\n",
-       "dtype: bool"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "example3.isnull()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "PaSZ0SQygRsC"
-   },
-   "source": [
-    "仔细观察输出结果，有什么让你感到意外吗？虽然 `0` 是一个算术上的空值，但它仍然是一个完全合法的整数，pandas 也将其视为整数。而 `''` 则稍微复杂一些。虽然我们在第 1 节中使用它来表示空字符串值，但它本质上仍然是一个字符串对象，而不是 pandas 所认为的空值。\n",
-    "\n",
-    "现在，让我们换个角度，以更贴近实际使用的方法来应用这些方法。你可以直接将布尔掩码用作 ``Series`` 或 ``DataFrame`` 的索引，这在处理孤立的缺失值（或存在值）时非常有用。\n",
-    "\n",
-    "如果我们想要统计缺失值的总数，只需对 `isnull()` 方法生成的掩码进行求和即可。\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "JCcQVoPkHDUv",
-    "outputId": "001daa72-54f8-4bd5-842a-4df627a79d4d"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "example3.isnull().sum()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "PlBqEo3mgRsC"
-   },
-   "source": [
-    "### 练习:\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {
-    "collapsed": true,
-    "id": "ggDVf5uygRsD",
-    "trusted": false
-   },
-   "outputs": [],
-   "source": [
-    "# Try running example3[example3.notnull()].\n",
-    "# Before you do so, what do you expect to see?\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "D_jWN7mHgRsD"
-   },
-   "source": [
-    "**关键点**：当在数据框中使用 `isnull()` 和 `notnull()` 方法时，它们会产生类似的结果：显示结果及其索引，这将在处理数据时极大地帮助您。\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "BvnoojWsgRr4"
-   },
-   "source": [
-    "### 处理缺失数据\n",
-    "\n",
-    "> **学习目标：** 在本小节结束时，您应该了解如何以及何时替换或删除 DataFrame 中的空值。\n",
-    "\n",
-    "机器学习模型本身无法处理缺失数据。因此，在将数据传入模型之前，我们需要处理这些缺失值。\n",
-    "\n",
-    "处理缺失数据的方法会带来微妙的权衡，并可能影响您的最终分析结果以及实际应用效果。\n",
-    "\n",
-    "处理缺失数据主要有两种方法：\n",
-    "\n",
-    "1.   删除包含缺失值的行\n",
-    "2.   用其他值替换缺失值\n",
-    "\n",
-    "我们将详细讨论这两种方法及其优缺点。\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "3VaYC1TvgRsD"
-   },
-   "source": [
-    "### 删除空值\n",
-    "\n",
-    "我们传递给模型的数据量直接影响其性能。删除空值意味着减少数据点的数量，从而缩小数据集的规模。因此，当数据集较大时，建议删除包含空值的行。\n",
-    "\n",
-    "另一种情况可能是某一行或列有大量缺失值。在这种情况下，可以删除这些行或列，因为它们的大部分数据都缺失，对我们的分析贡献不大。\n",
-    "\n",
-    "除了识别缺失值之外，pandas 还提供了一种方便的方法来从 `Series` 和 `DataFrame` 中移除空值。为了更好地理解这一点，我们可以回到 `example3`。`DataFrame.dropna()` 函数可以帮助我们删除包含空值的行。\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "7uIvS097gRsD",
-    "outputId": "c13fc117-4ca1-4145-a0aa-42ac89e6e218",
-    "trusted": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0    0\n",
-       "2     \n",
-       "dtype: object"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "example3 = example3.dropna()\n",
-    "example3"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "hil2cr64gRsD"
-   },
-   "source": [
-    "请注意，这应该看起来像您从 `example3[example3.notnull()]` 的输出。这里的区别在于，`dropna` 不仅仅是基于掩码值进行索引，而是从 `Series` `example3` 中移除了那些缺失值。\n",
-    "\n",
-    "由于 DataFrame 是二维的，因此它提供了更多删除数据的选项。\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 142
-    },
-    "id": "an-l74sPgRsE",
-    "outputId": "340876a0-63ad-40f6-bd54-6240cdae50ab",
-    "trusted": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
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-       "\n",
-       "    .dataframe thead th {\n",
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-       "      <th>0</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>7</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2.0</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>9</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
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-       "     0    1  2\n",
-       "0  1.0  NaN  7\n",
-       "1  2.0  5.0  8\n",
-       "2  NaN  6.0  9"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "example4 = pd.DataFrame([[1,      np.nan, 7], \n",
-    "                         [2,      5,      8], \n",
-    "                         [np.nan, 6,      9]])\n",
-    "example4"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "66wwdHZrgRsE"
-   },
-   "source": [
-    "（你是否注意到，为了容纳 `NaN` 值，pandas 将两列数据类型提升为浮点型？）\n",
-    "\n",
-    "你无法从 `DataFrame` 中删除单个值，因此必须删除整行或整列。根据你的需求，你可能需要选择其中一种方式，因此 pandas 提供了两种选项。在数据科学中，列通常表示变量，行表示观测值，因此你更可能删除数据的行；`dropna()` 的默认设置是删除所有包含任何空值的行：\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 80
-    },
-    "id": "jAVU24RXgRsE",
-    "outputId": "0b5e5aee-7187-4d3f-b583-a44136ae5f80",
-    "trusted": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
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-       "\n",
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-       "<table border=\"1\" class=\"dataframe\">\n",
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-       "      <td>5.0</td>\n",
-       "      <td>8</td>\n",
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-      "text/plain": [
-       "     0    1  2\n",
-       "1  2.0  5.0  8"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "example4.dropna()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "TrQRBuTDgRsE"
-   },
-   "source": [
-    "如果需要，您可以从列中删除NA值。使用`axis=1`来实现：\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 142
-    },
-    "id": "GrBhxu9GgRsE",
-    "outputId": "ff4001f3-2e61-4509-d60e-0093d1068437",
-    "trusted": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>2</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>7</td>\n",
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-       "      <td>8</td>\n",
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-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>9</td>\n",
-       "    </tr>\n",
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-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   2\n",
-       "0  7\n",
-       "1  8\n",
-       "2  9"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "example4.dropna(axis='columns')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "KWXiKTfMgRsF"
-   },
-   "source": [
-    "请注意，这可能会丢弃许多您可能希望保留的数据，尤其是在较小的数据集中。如果您只想删除包含多个甚至全部空值的行或列，该怎么办？您可以在 `dropna` 中通过 `how` 和 `thresh` 参数来指定这些设置。\n",
-    "\n",
-    "默认情况下，`how='any'`（如果您想自己检查或查看该方法的其他参数，可以在代码单元中运行 `example4.dropna?`）。您也可以选择指定 `how='all'`，这样只会删除包含所有空值的行或列。让我们扩展示例 `DataFrame`，在接下来的练习中看看它的实际效果。\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 142
-    },
-    "id": "Bcf_JWTsgRsF",
-    "outputId": "72e0b1b8-52fa-4923-98ce-b6fbed6e44b1",
-    "trusted": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
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-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>0</th>\n",
-       "      <th>1</th>\n",
-       "      <th>2</th>\n",
-       "      <th>3</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>7</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2.0</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>8</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>9</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     0    1  2   3\n",
-       "0  1.0  NaN  7 NaN\n",
-       "1  2.0  5.0  8 NaN\n",
-       "2  NaN  6.0  9 NaN"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "example4[3] = np.nan\n",
-    "example4"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "pNZer7q9JPNC"
-   },
-   "source": [
-    "> 关键要点：\n",
-    "1. 只有在数据集足够大的情况下，删除空值才是一个好主意。\n",
-    "2. 如果某行或某列的大部分数据缺失，可以直接删除整行或整列。\n",
-    "3. `DataFrame.dropna(axis=)` 方法可以用来删除空值。`axis` 参数表示是删除行还是列。\n",
-    "4. 还可以使用 `how` 参数。默认情况下，它设置为 `any`，因此只会删除包含任意空值的行或列。可以将其设置为 `all`，以指定仅删除所有值均为空的行或列。\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "oXXSfQFHgRsF"
-   },
-   "source": [
-    "### 练习:\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {
-    "collapsed": true,
-    "id": "ExUwQRxpgRsF",
-    "trusted": false
-   },
-   "outputs": [],
-   "source": [
-    "# How might you go about dropping just column 3?\n",
-    "# Hint: remember that you will need to supply both the axis parameter and the how parameter.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "38kwAihWgRsG"
-   },
-   "source": [
-    "`thresh` 参数为您提供了更细粒度的控制：您可以设置一行或一列需要具有的 *非空* 值的数量，以便保留该行或列：\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 80
-    },
-    "id": "M9dCNMaagRsG",
-    "outputId": "8093713a-54d2-4e54-c73f-4eea315cb6f2",
-    "trusted": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>0</th>\n",
-       "      <th>1</th>\n",
-       "      <th>2</th>\n",
-       "      <th>3</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2.0</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>8</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     0    1  2   3\n",
-       "1  2.0  5.0  8 NaN"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "example4.dropna(axis='rows', thresh=3)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "fmSFnzZegRsG"
-   },
-   "source": [
-    "这里，第一行和最后一行已被删除，因为它们仅包含两个非空值。\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "mCcxLGyUgRsG"
-   },
-   "source": [
-    "### 填充空值\n",
-    "\n",
-    "有时，用可能有效的值填充缺失值是合理的。填充空值有几种方法。第一种是使用领域知识（即数据集所基于主题的知识）来近似缺失值。\n",
-    "\n",
-    "你可以使用 `isnull` 来直接处理，但这可能会很繁琐，尤其是当你有很多值需要填充时。由于这是数据科学中非常常见的任务，pandas 提供了 `fillna` 方法，它会返回一个 `Series` 或 `DataFrame` 的副本，其中的缺失值被替换为你选择的值。让我们创建另一个示例 `Series` 来看看它在实际中的工作方式。\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "CE8S7louLezV"
-   },
-   "source": [
-    "### 分类数据（非数值型）\n",
-    "首先让我们来看非数值型数据。在数据集中，我们会有一些包含分类数据的列。例如：性别、True 或 False 等。\n",
-    "\n",
-    "在大多数情况下，我们会用该列的`众数`来替换缺失值。比如说，我们有100个数据点，其中90个是True，8个是False，还有2个未填写。那么我们可以将这2个未填写的值填充为True，基于整个列的情况。\n",
-    "\n",
-    "当然，在这里我们也可以使用领域知识。让我们来看一个用众数填充的例子。\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 204
-    },
-    "id": "MY5faq4yLdpQ",
-    "outputId": "19ab472e-1eed-4de8-f8a7-db2a3af3cb1a"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
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-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "   0   1      2\n",
-       "0  1   2   True\n",
-       "1  3   4   None\n",
-       "2  5   6  False\n",
-       "3  7   8   True\n",
-       "4  9  10   True"
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "fill_with_mode = pd.DataFrame([[1,2,\"True\"],\n",
-    "                               [3,4,None],\n",
-    "                               [5,6,\"False\"],\n",
-    "                               [7,8,\"True\"],\n",
-    "                               [9,10,\"True\"]])\n",
-    "\n",
-    "fill_with_mode"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "MLAoMQOfNPlA"
-   },
-   "source": [
-    "现在，让我们先找到众数，然后用众数填充 `None` 值。\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "WKy-9Y2tN5jv",
-    "outputId": "8da9fa16-e08c-447e-dea1-d4b1db2feebf"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True     3\n",
-       "False    1\n",
-       "Name: 2, dtype: int64"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "fill_with_mode[2].value_counts()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "6iNz_zG_OKrx"
-   },
-   "source": [
-    "所以，我们将用True替换None\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {
-    "id": "TxPKteRvNPOs"
-   },
-   "outputs": [],
-   "source": [
-    "fill_with_mode[2].fillna('True',inplace=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 204
-    },
-    "id": "tvas7c9_OPWE",
-    "outputId": "ec3c8e44-d644-475e-9e22-c65101965850"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "   0   1      2\n",
-       "0  1   2   True\n",
-       "1  3   4   True\n",
-       "2  5   6  False\n",
-       "3  7   8   True\n",
-       "4  9  10   True"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "fill_with_mode"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "SktitLxxOR16"
-   },
-   "source": [
-    "正如我们所见，空值已被替换。不言而喻，我们本可以在 `'True'` 的位置写任何内容，它都会被替换。\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "heYe1I0dOmQ_"
-   },
-   "source": [
-    "### 数值数据\n",
-    "现在来说说数值数据。这里有两种常见的方法来替换缺失值：\n",
-    "\n",
-    "1. 用行的中位数替换  \n",
-    "2. 用行的平均值替换  \n",
-    "\n",
-    "当数据存在偏斜且有异常值时，我们使用中位数替换。这是因为中位数对异常值具有鲁棒性。\n",
-    "\n",
-    "当数据是标准化的，我们可以使用平均值，因为在这种情况下，平均值和中位数会非常接近。\n",
-    "\n",
-    "首先，让我们选取一个正态分布的列，并用该列的平均值填充缺失值。\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 204
-    },
-    "id": "09HM_2feOj5Y",
-    "outputId": "7e309013-9acb-411c-9b06-4de795bbeeff"
-   },
-   "outputs": [
-    {
-     "data": {
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-       "      <td>9</td>\n",
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-       "     0  1  2\n",
-       "0 -2.0  0  1\n",
-       "1 -1.0  2  3\n",
-       "2  NaN  4  5\n",
-       "3  1.0  6  7\n",
-       "4  2.0  8  9"
-      ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "fill_with_mean = pd.DataFrame([[-2,0,1],\n",
-    "                               [-1,2,3],\n",
-    "                               [np.nan,4,5],\n",
-    "                               [1,6,7],\n",
-    "                               [2,8,9]])\n",
-    "\n",
-    "fill_with_mean"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "ka7-wNfzSxbx"
-   },
-   "source": [
-    "列的平均值是\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "XYtYEf5BSxFL",
-    "outputId": "68a78d18-f0e5-4a9a-a959-2c3676a57c70"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.0"
-      ]
-     },
-     "execution_count": 33,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.mean(fill_with_mean[0])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "oBSRGxKRS39K"
-   },
-   "source": [
-    "用均值填充\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 204
-    },
-    "id": "FzncQLmuS5jh",
-    "outputId": "00f74fff-01f4-4024-c261-796f50f01d2e"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "      <th>4</th>\n",
-       "      <td>2.0</td>\n",
-       "      <td>8</td>\n",
-       "      <td>9</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     0  1  2\n",
-       "0 -2.0  0  1\n",
-       "1 -1.0  2  3\n",
-       "2  0.0  4  5\n",
-       "3  1.0  6  7\n",
-       "4  2.0  8  9"
-      ]
-     },
-     "execution_count": 34,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "fill_with_mean[0].fillna(np.mean(fill_with_mean[0]),inplace=True)\n",
-    "fill_with_mean"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "CwpVFCrPTC5z"
-   },
-   "source": [
-    "正如我们所见，缺失值已被其平均值替换。\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "jIvF13a1i00Z"
-   },
-   "source": [
-    "现在让我们尝试另一个数据框，这次我们将用列的中位数替换None值。\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 204
-    },
-    "id": "DA59Bqo3jBYZ",
-    "outputId": "85dae6ec-7394-4c36-fda0-e04769ec4a32"
-   },
-   "outputs": [
-    {
-     "data": {
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-       "      <th>2</th>\n",
-       "      <td>0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>7</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>2</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>9</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   0    1  2\n",
-       "0 -2  0.0  1\n",
-       "1 -1  2.0  3\n",
-       "2  0  NaN  5\n",
-       "3  1  6.0  7\n",
-       "4  2  8.0  9"
-      ]
-     },
-     "execution_count": 35,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "fill_with_median = pd.DataFrame([[-2,0,1],\n",
-    "                               [-1,2,3],\n",
-    "                               [0,np.nan,5],\n",
-    "                               [1,6,7],\n",
-    "                               [2,8,9]])\n",
-    "\n",
-    "fill_with_median"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "mM1GpXYmjHnc"
-   },
-   "source": [
-    "第二列的中位数是\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "uiDy5v3xjHHX",
-    "outputId": "564b6b74-2004-4486-90d4-b39330a64b88"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "4.0"
-      ]
-     },
-     "execution_count": 36,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "fill_with_median[1].median()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "z9PLF75Jj_1s"
-   },
-   "source": [
-    "用中位数填充\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 204
-    },
-    "id": "lFKbOxCMkBbg",
-    "outputId": "a8bd18fb-2765-47d4-e5fe-e965f57ed1f4"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>0</th>\n",
-       "      <th>1</th>\n",
-       "      <th>2</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>-2</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>-1</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>0</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>7</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>2</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>9</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   0    1  2\n",
-       "0 -2  0.0  1\n",
-       "1 -1  2.0  3\n",
-       "2  0  4.0  5\n",
-       "3  1  6.0  7\n",
-       "4  2  8.0  9"
-      ]
-     },
-     "execution_count": 37,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "fill_with_median[1].fillna(fill_with_median[1].median(),inplace=True)\n",
-    "fill_with_median"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "8JtQ53GSkKWC"
-   },
-   "source": [
-    "正如我们所见，NaN值已被该列的中位数替换\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "0ybtWLDdgRsG",
-    "outputId": "b8c238ef-6024-4ee2-be2b-aa1f0fcac61d",
-    "trusted": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "a    1.0\n",
-       "b    NaN\n",
-       "c    2.0\n",
-       "d    NaN\n",
-       "e    3.0\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 38,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "example5 = pd.Series([1, np.nan, 2, None, 3], index=list('abcde'))\n",
-    "example5"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "yrsigxRggRsH"
-   },
-   "source": [
-    "您可以使用单个值（例如 `0`）填充所有空条目：\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "KXMIPsQdgRsH",
-    "outputId": "aeedfa0a-a421-4c2f-cb0d-183ce8f0c91d",
-    "trusted": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "a    1.0\n",
-       "b    0.0\n",
-       "c    2.0\n",
-       "d    0.0\n",
-       "e    3.0\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 39,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "example5.fillna(0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "RRlI5f_hkfKe"
-   },
-   "source": [
-    "> 关键要点：\n",
-    "1. 填补缺失值应在数据较少或有明确填补策略时进行。\n",
-    "2. 可以利用领域知识通过估算来填补缺失值。\n",
-    "3. 对于分类数据，通常用列的众数替代缺失值。\n",
-    "4. 对于数值型数据，缺失值通常用列的平均值（针对归一化数据集）或中位数填补。\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "FI9MmqFJgRsH"
-   },
-   "source": [
-    "### 练习:\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {
-    "collapsed": true,
-    "id": "af-ezpXdgRsH",
-    "trusted": false
-   },
-   "outputs": [],
-   "source": [
-    "# What happens if you try to fill null values with a string, like ''?\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "kq3hw1kLgRsI"
-   },
-   "source": [
-    "您可以使用**前向填充**空值，即使用最后一个有效值填充空值：\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "vO3BuNrggRsI",
-    "outputId": "e2bc591b-0b48-4e88-ee65-754f2737c196",
-    "trusted": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "a    1.0\n",
-       "b    1.0\n",
-       "c    2.0\n",
-       "d    2.0\n",
-       "e    3.0\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 41,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "example5.fillna(method='ffill')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "nDXeYuHzgRsI"
-   },
-   "source": [
-    "您还可以**回填**，将下一个有效值向后传播以填充空值：\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "4M5onHcEgRsI",
-    "outputId": "8f32b185-40dd-4a9f-bd85-54d6b6a414fe",
-    "trusted": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "a    1.0\n",
-       "b    2.0\n",
-       "c    2.0\n",
-       "d    3.0\n",
-       "e    3.0\n",
-       "dtype: float64"
-      ]
-     },
-     "execution_count": 42,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "example5.fillna(method='bfill')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": true,
-    "id": "MbBzTom5gRsI"
-   },
-   "source": [
-    "正如您可能猜到的，这与DataFrame的操作方式相同，但您也可以指定一个`axis`来填充空值：\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 142
-    },
-    "id": "aRpIvo4ZgRsI",
-    "outputId": "905a980a-a808-4eca-d0ba-224bd7d85955",
-    "trusted": false
-   },
-   "outputs": [
-    {
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-       "     0    1  2   3\n",
-       "0  1.0  NaN  7 NaN\n",
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-     "execution_count": 43,
-     "metadata": {},
-     "output_type": "execute_result"
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-   ],
-   "source": [
-    "example4"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
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-    {
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-       "<style scoped>\n",
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-       "      <th>2</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>9.0</td>\n",
-       "      <td>9.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     0    1    2    3\n",
-       "0  1.0  1.0  7.0  7.0\n",
-       "1  2.0  5.0  8.0  8.0\n",
-       "2  NaN  6.0  9.0  9.0"
-      ]
-     },
-     "execution_count": 44,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "example4.fillna(method='ffill', axis=1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "ZeMc-I1EgRsI"
-   },
-   "source": [
-    "请注意，当没有可用于向前填充的先前值时，空值将保留。\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "eeAoOU0RgRsJ"
-   },
-   "source": [
-    "### 练习:\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {
-    "collapsed": true,
-    "id": "e8S-CjW8gRsJ",
-    "trusted": false
-   },
-   "outputs": [],
-   "source": [
-    "# What output does example4.fillna(method='bfill', axis=1) produce?\n",
-    "# What about example4.fillna(method='ffill') or example4.fillna(method='bfill')?\n",
-    "# Can you think of a longer code snippet to write that can fill all of the null values in example4?\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "YHgy0lIrgRsJ"
-   },
-   "source": [
-    "你可以创造性地使用 `fillna`。例如，让我们再次查看 `example4`，但这次我们用 `DataFrame` 中所有值的平均值来填充缺失值：\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 142
-    },
-    "id": "OtYVErEygRsJ",
-    "outputId": "708b1e67-45ca-44bf-a5ee-8b2de09ece73",
-    "trusted": false
-   },
-   "outputs": [
-    {
-     "data": {
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
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-       "      <td>1.0</td>\n",
-       "      <td>5.5</td>\n",
-       "      <td>7</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2.0</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>8</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1.5</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>9</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     0    1  2   3\n",
-       "0  1.0  5.5  7 NaN\n",
-       "1  2.0  5.0  8 NaN\n",
-       "2  1.5  6.0  9 NaN"
-      ]
-     },
-     "execution_count": 46,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "example4.fillna(example4.mean())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "zpMvCkLSgRsJ"
-   },
-   "source": [
-    "注意，第3列仍然没有值：默认方向是按行填充值。\n",
-    "\n",
-    "> **要点：** 处理数据集中的缺失值有多种方法。具体采用哪种策略（删除、替换，甚至如何替换）应根据数据的具体情况来决定。随着你处理和接触更多的数据集，你会对如何处理缺失值有更好的理解。\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "bauDnESIl9FH"
-   },
-   "source": [
-    "### 编码分类数据\n",
-    "\n",
-    "机器学习模型只能处理数字和任何形式的数值数据。它无法区分“是”和“否”，但可以区分0和1。因此，在填充缺失值之后，我们需要将分类数据编码为某种数值形式，以便模型能够理解。\n",
-    "\n",
-    "编码可以通过两种方式完成。接下来我们将讨论这两种方法。\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "uDq9SxB7mu5i"
-   },
-   "source": [
-    "**标签编码**\n",
-    "\n",
-    "标签编码基本上是将每个类别转换为一个数字。例如，假设我们有一个航空乘客的数据集，其中有一列包含他们的舱位类别，类别包括 ['商务舱', '经济舱', '头等舱']。如果对其进行标签编码，它将被转换为 [0,1,2]。让我们通过代码示例来看看。由于我们将在后续笔记本中学习 `scikit-learn`，这里不会使用它。\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 235
-    },
-    "id": "1vGz7uZyoWHL",
-    "outputId": "9e252855-d193-4103-a54d-028ea7787b34"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>ID</th>\n",
-       "      <th>class</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>10</td>\n",
-       "      <td>business class</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>20</td>\n",
-       "      <td>first class</td>\n",
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-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>30</td>\n",
-       "      <td>economy class</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>40</td>\n",
-       "      <td>economy class</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>50</td>\n",
-       "      <td>economy class</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>60</td>\n",
-       "      <td>business class</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   ID           class\n",
-       "0  10  business class\n",
-       "1  20     first class\n",
-       "2  30   economy class\n",
-       "3  40   economy class\n",
-       "4  50   economy class\n",
-       "5  60  business class"
-      ]
-     },
-     "execution_count": 47,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "label = pd.DataFrame([\n",
-    "                      [10,'business class'],\n",
-    "                      [20,'first class'],\n",
-    "                      [30, 'economy class'],\n",
-    "                      [40, 'economy class'],\n",
-    "                      [50, 'economy class'],\n",
-    "                      [60, 'business class']\n",
-    "],columns=['ID','class'])\n",
-    "label"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "IDHnkwTYov-h"
-   },
-   "source": [
-    "要对第一列进行标签编码，我们首先需要描述一个从每个类别到数字的映射，然后进行替换。\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 235
-    },
-    "id": "ZC5URJG3o1ES",
-    "outputId": "aab0f1e7-e0f3-4c14-8459-9f9168c85437"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>ID</th>\n",
-       "      <th>class</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>10</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>20</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>30</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>40</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>50</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>60</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   ID  class\n",
-       "0  10      0\n",
-       "1  20      2\n",
-       "2  30      1\n",
-       "3  40      1\n",
-       "4  50      1\n",
-       "5  60      0"
-      ]
-     },
-     "execution_count": 48,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "class_labels = {'business class':0,'economy class':1,'first class':2}\n",
-    "label['class'] = label['class'].replace(class_labels)\n",
-    "label"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "ftnF-TyapOPt"
-   },
-   "source": [
-    "正如我们所见，输出与我们预期的结果一致。那么，我们什么时候使用标签编码呢？标签编码适用于以下一种或两种情况：  \n",
-    "1. 当类别数量较多时  \n",
-    "2. 当类别是有序的时  \n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "eQPAPVwsqWT7"
-   },
-   "source": [
-    "**独热编码**\n",
-    "\n",
-    "另一种编码方式是独热编码。在这种编码方式中，每个列的类别都会作为单独的列添加到数据集中，并且每个数据点根据是否包含该类别会被赋值为0或1。因此，如果有n个不同的类别，就会向数据框中添加n列。\n",
-    "\n",
-    "例如，我们仍然使用飞机舱位的例子。类别是：['商务舱', '经济舱', '头等舱']。如果我们进行独热编码，数据集中将会添加以下三列：['class_business class', 'class_economy class', 'class_first class']。\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 235
-    },
-    "id": "ZM0eVh0ArKUL",
-    "outputId": "83238a76-b3a5-418d-c0b6-605b02b6891b"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>ID</th>\n",
-       "      <th>class</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>10</td>\n",
-       "      <td>business class</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>20</td>\n",
-       "      <td>first class</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>30</td>\n",
-       "      <td>economy class</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>40</td>\n",
-       "      <td>economy class</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>50</td>\n",
-       "      <td>economy class</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>60</td>\n",
-       "      <td>business class</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   ID           class\n",
-       "0  10  business class\n",
-       "1  20     first class\n",
-       "2  30   economy class\n",
-       "3  40   economy class\n",
-       "4  50   economy class\n",
-       "5  60  business class"
-      ]
-     },
-     "execution_count": 49,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "one_hot = pd.DataFrame([\n",
-    "                      [10,'business class'],\n",
-    "                      [20,'first class'],\n",
-    "                      [30, 'economy class'],\n",
-    "                      [40, 'economy class'],\n",
-    "                      [50, 'economy class'],\n",
-    "                      [60, 'business class']\n",
-    "],columns=['ID','class'])\n",
-    "one_hot"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "aVnZ7paDrWmb"
-   },
-   "source": [
-    "让我们对第一列进行独热编码\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "metadata": {
-    "id": "RUPxf7egrYKr"
-   },
-   "outputs": [],
-   "source": [
-    "one_hot_data = pd.get_dummies(one_hot,columns=['class'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 235
-    },
-    "id": "TM37pHsFr4ge",
-    "outputId": "7be15f53-79b2-447a-979c-822658339a9e"
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
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-       "\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>ID</th>\n",
-       "      <th>class_business class</th>\n",
-       "      <th>class_economy class</th>\n",
-       "      <th>class_first class</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>10</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>20</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>30</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>40</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>50</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>60</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   ID  class_business class  class_economy class  class_first class\n",
-       "0  10                     1                    0                  0\n",
-       "1  20                     0                    0                  1\n",
-       "2  30                     0                    1                  0\n",
-       "3  40                     0                    1                  0\n",
-       "4  50                     0                    1                  0\n",
-       "5  60                     1                    0                  0"
-      ]
-     },
-     "execution_count": 51,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "one_hot_data"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "_zXRLOjXujdA"
-   },
-   "source": [
-    "每个独热编码列包含0或1，指定该数据点是否属于该类别。\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "bDnC4NQOu0qr"
-   },
-   "source": [
-    "我们什么时候使用独热编码？独热编码在以下一种或两种情况下使用：\n",
-    "\n",
-    "1. 当类别数量和数据集规模较小时。\n",
-    "2. 当类别没有特定顺序时。\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "XnUmci_4uvyu"
-   },
-   "source": [
-    "> 关键要点：\n",
-    "1. 编码用于将非数值数据转换为数值数据。\n",
-    "2. 编码分为两种类型：标签编码和独热编码，可根据数据集的需求进行选择。\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "K8UXOJYRgRsJ"
-   },
-   "source": [
-    "## 删除重复数据\n",
-    "\n",
-    "> **学习目标：** 在本小节结束时，您应该能够熟练识别并删除 DataFrame 中的重复值。\n",
-    "\n",
-    "除了缺失数据之外，您在实际数据集中还会经常遇到重复数据。幸运的是，pandas 提供了一种简单的方法来检测和删除重复条目。\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "qrEG-Wa0gRsJ"
-   },
-   "source": [
-    "### 识别重复项：`duplicated`\n",
-    "\n",
-    "您可以使用 pandas 中的 `duplicated` 方法轻松识别重复值。该方法返回一个布尔掩码，指示 `DataFrame` 中的某个条目是否是之前条目的重复项。让我们创建另一个示例 `DataFrame` 来实际查看这一功能。\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 204
-    },
-    "id": "ZLu6FEnZgRsJ",
-    "outputId": "376512d1-d842-4db1-aea3-71052aeeecaf",
-    "trusted": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>letters</th>\n",
-       "      <th>numbers</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>A</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>B</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>A</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>B</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>B</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "  letters  numbers\n",
-       "0       A        1\n",
-       "1       B        2\n",
-       "2       A        1\n",
-       "3       B        3\n",
-       "4       B        3"
-      ]
-     },
-     "execution_count": 52,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "example6 = pd.DataFrame({'letters': ['A','B'] * 2 + ['B'],\n",
-    "                         'numbers': [1, 2, 1, 3, 3]})\n",
-    "example6"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
-    },
-    "id": "cIduB5oBgRsK",
-    "outputId": "3da27b3d-4d69-4e1d-bb52-0af21bae87f2",
-    "trusted": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0    False\n",
-       "1    False\n",
-       "2     True\n",
-       "3    False\n",
-       "4     True\n",
-       "dtype: bool"
-      ]
-     },
-     "execution_count": 53,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "example6.duplicated()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "0eDRJD4SgRsK"
-   },
-   "source": [
-    "### 删除重复项：`drop_duplicates`\n",
-    "`drop_duplicates` 仅返回数据的副本，其中所有 `duplicated` 值均为 `False`：\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 142
-    },
-    "id": "w_YPpqIqgRsK",
-    "outputId": "ac66bd2f-8671-4744-87f5-8b8d96553dea",
-    "trusted": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "\n",
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-       "\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>letters</th>\n",
-       "      <th>numbers</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>A</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>B</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>B</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "  letters  numbers\n",
-       "0       A        1\n",
-       "1       B        2\n",
-       "3       B        3"
-      ]
-     },
-     "execution_count": 54,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "example6.drop_duplicates()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "69AqoCZAgRsK"
-   },
-   "source": [
-    "`duplicated` 和 `drop_duplicates` 默认会考虑所有列，但您可以指定它们仅检查 `DataFrame` 中的部分列：\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 55,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 111
-    },
-    "id": "BILjDs67gRsK",
-    "outputId": "ef6dcc08-db8b-4352-c44e-5aa9e2bec0d3",
-    "trusted": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>letters</th>\n",
-       "      <th>numbers</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>A</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>B</td>\n",
-       "      <td>2</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "  letters  numbers\n",
-       "0       A        1\n",
-       "1       B        2"
-      ]
-     },
-     "execution_count": 55,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "example6.drop_duplicates(['letters'])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "GvX4og1EgRsL"
-   },
-   "source": [
-    "> **要点:** 删除重复数据是几乎每个数据科学项目的重要组成部分。重复数据可能会改变分析结果，并导致不准确的结论！\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 真实世界中的数据质量检查\n",
-    "\n",
-    "> **学习目标：** 在本节结束时，您应该能够熟练检测和纠正常见的真实世界数据质量问题，包括不一致的分类值、异常的数值（离群值）以及具有变体的重复实体。\n",
-    "\n",
-    "虽然缺失值和完全重复是常见问题，但真实世界的数据集通常还包含更为隐蔽的问题：\n",
-    "\n",
-    "1. **不一致的分类值**：同一类别的拼写不同（例如，“USA”、“U.S.A”、“United States”）\n",
-    "2. **异常的数值**：极端的离群值，可能是数据录入错误（例如，年龄 = 999）\n",
-    "3. **近似重复的行**：表示同一实体但存在细微差异的记录\n",
-    "\n",
-    "让我们来探讨检测和处理这些问题的技术。\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 创建一个示例“脏”数据集\n",
-    "\n",
-    "首先，让我们创建一个包含我们在实际数据中常见问题类型的示例数据集：\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "\n",
-    "# Create a sample dataset with quality issues\n",
-    "dirty_data = pd.DataFrame({\n",
-    "    'customer_id': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12],\n",
-    "    'name': ['John Smith', 'Jane Doe', 'John Smith', 'Bob Johnson', \n",
-    "             'Alice Williams', 'Charlie Brown', 'John  Smith', 'Eva Martinez',\n",
-    "             'Bob Johnson', 'Diana Prince', 'Frank Castle', 'Alice Williams'],\n",
-    "    'age': [25, 32, 25, 45, 28, 199, 25, 31, 45, 27, -5, 28],\n",
-    "    'country': ['USA', 'UK', 'U.S.A', 'Canada', 'USA', 'United Kingdom',\n",
-    "                'United States', 'Mexico', 'canada', 'USA', 'UK', 'usa'],\n",
-    "    'purchase_amount': [100.50, 250.00, 105.00, 320.00, 180.00, 90.00,\n",
-    "                       102.00, 275.00, 325.00, 195.00, 410.00, 185.00]\n",
-    "})\n",
-    "\n",
-    "print(\"Sample 'Dirty' Dataset:\")\n",
-    "print(dirty_data)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 1. 检测不一致的分类值\n",
-    "\n",
-    "注意，`country` 列中同一个国家有多种表示方式。让我们来识别这些不一致之处：\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Check unique values in the country column\n",
-    "print(\"Unique country values:\")\n",
-    "print(dirty_data['country'].unique())\n",
-    "print(f\"\\nTotal unique values: {dirty_data['country'].nunique()}\")\n",
-    "\n",
-    "# Count occurrences of each variation\n",
-    "print(\"\\nValue counts:\")\n",
-    "print(dirty_data['country'].value_counts())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### 标准化分类值\n",
-    "\n",
-    "我们可以创建一个映射来标准化这些值。一种简单的方法是将值转换为小写并创建一个映射字典：\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create a standardization mapping\n",
-    "country_mapping = {\n",
-    "    'usa': 'USA',\n",
-    "    'u.s.a': 'USA',\n",
-    "    'united states': 'USA',\n",
-    "    'uk': 'UK',\n",
-    "    'united kingdom': 'UK',\n",
-    "    'canada': 'Canada',\n",
-    "    'mexico': 'Mexico'\n",
-    "}\n",
-    "\n",
-    "# Standardize the country column\n",
-    "dirty_data['country_clean'] = dirty_data['country'].str.lower().map(country_mapping)\n",
-    "\n",
-    "print(\"Before standardization:\")\n",
-    "print(dirty_data['country'].value_counts())\n",
-    "print(\"\\nAfter standardization:\")\n",
-    "print(dirty_data[['country_clean']].value_counts())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**替代方法：使用模糊匹配**\n",
-    "\n",
-    "对于更复杂的情况，我们可以使用 `rapidfuzz` 库进行模糊字符串匹配，以自动检测相似的字符串：\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "try:\n",
-    "    from rapidfuzz import process, fuzz\n",
-    "except ImportError:\n",
-    "    print(\"rapidfuzz is not installed. Please install it with 'pip install rapidfuzz' to use fuzzy matching.\")\n",
-    "    process = None\n",
-    "    fuzz = None\n",
-    "\n",
-    "# Get unique countries\n",
-    "unique_countries = dirty_data['country'].unique()\n",
-    "\n",
-    "# For each country, find similar matches\n",
-    "if process is not None and fuzz is not None:\n",
-    "    print(\"Finding similar country names (similarity > 70%):\")\n",
-    "    for country in unique_countries:\n",
-    "        matches = process.extract(country, unique_countries, scorer=fuzz.ratio, limit=3)\n",
-    "        # Filter matches with similarity > 70 and not identical\n",
-    "        similar = [m for m in matches if m[1] > 70 and m[0] != country]\n",
-    "        if similar:\n",
-    "            print(f\"\\n'{country}' is similar to:\")\n",
-    "            for match, score, _ in similar:\n",
-    "                print(f\"  - '{match}' (similarity: {score}%)\")\n",
-    "else:\n",
-    "    print(\"Skipping fuzzy matching because rapidfuzz is not available.\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 2. 检测异常数值（离群值）\n",
-    "\n",
-    "查看 `age` 列，我们发现一些可疑的值，比如 199 和 -5。让我们使用统计方法来检测这些离群值。\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Display basic statistics\n",
-    "print(\"Age column statistics:\")\n",
-    "print(dirty_data['age'].describe())\n",
-    "\n",
-    "# Identify impossible values using domain knowledge\n",
-    "print(\"\\nRows with impossible age values (< 0 or > 120):\")\n",
-    "impossible_ages = dirty_data[(dirty_data['age'] < 0) | (dirty_data['age'] > 120)]\n",
-    "print(impossible_ages[['customer_id', 'name', 'age']])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### 使用 IQR（四分位距）方法\n",
-    "\n",
-    "IQR 方法是一种稳健的统计技术，用于检测异常值，对极端值的敏感性较低：\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Calculate IQR for age (excluding impossible values)\n",
-    "valid_ages = dirty_data[(dirty_data['age'] >= 0) & (dirty_data['age'] <= 120)]['age']\n",
-    "\n",
-    "Q1 = valid_ages.quantile(0.25)\n",
-    "Q3 = valid_ages.quantile(0.75)\n",
-    "IQR = Q3 - Q1\n",
-    "\n",
-    "# Define outlier bounds\n",
-    "lower_bound = Q1 - 1.5 * IQR\n",
-    "upper_bound = Q3 + 1.5 * IQR\n",
-    "\n",
-    "print(f\"IQR-based outlier bounds for age: [{lower_bound:.2f}, {upper_bound:.2f}]\")\n",
-    "\n",
-    "# Identify outliers\n",
-    "age_outliers = dirty_data[(dirty_data['age'] < lower_bound) | (dirty_data['age'] > upper_bound)]\n",
-    "print(f\"\\nRows with age outliers:\")\n",
-    "print(age_outliers[['customer_id', 'name', 'age']])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### 使用 Z-Score 方法\n",
-    "\n",
-    "Z-Score 方法通过与平均值的标准差来识别异常值：\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "try:\n",
-    "    from scipy import stats\n",
-    "except ImportError:\n",
-    "    print(\"scipy is required for Z-score calculation. Please install it with 'pip install scipy' and rerun this cell.\")\n",
-    "else:\n",
-    "    # Calculate Z-scores for age, handling NaN values\n",
-    "    age_nonan = dirty_data['age'].dropna()\n",
-    "    zscores = np.abs(stats.zscore(age_nonan))\n",
-    "    dirty_data['age_zscore'] = np.nan\n",
-    "    dirty_data.loc[age_nonan.index, 'age_zscore'] = zscores\n",
-    "\n",
-    "    # Typically, Z-score > 3 indicates an outlier\n",
-    "    print(\"Rows with age Z-score > 3:\")\n",
-    "    zscore_outliers = dirty_data[dirty_data['age_zscore'] > 3]\n",
-    "    print(zscore_outliers[['customer_id', 'name', 'age', 'age_zscore']])\n",
-    "\n",
-    "    # Clean up the temporary column\n",
-    "    dirty_data = dirty_data.drop('age_zscore', axis=1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### 处理异常值\n",
-    "\n",
-    "一旦检测到异常值，可以通过以下几种方式处理：\n",
-    "1. **删除**：删除包含异常值的行（如果它们是错误数据）\n",
-    "2. **限制**：用边界值替换\n",
-    "3. **替换为 NaN**：将其视为缺失数据并使用插补技术处理\n",
-    "4. **保留**：如果它们是合法的极端值\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create a cleaned version by replacing impossible ages with NaN\n",
-    "dirty_data['age_clean'] = dirty_data['age'].apply(\n",
-    "    lambda x: np.nan if (x < 0 or x > 120) else x\n",
-    ")\n",
-    "\n",
-    "print(\"Age column before and after cleaning:\")\n",
-    "print(dirty_data[['customer_id', 'name', 'age', 'age_clean']])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 3. 检测近似重复行\n",
-    "\n",
-    "注意，我们的数据集中有多个“John Smith”的条目，且值略有不同。让我们根据名字相似性来识别潜在的重复项。\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# First, let's look at exact name matches (ignoring extra whitespace)\n",
-    "dirty_data['name_normalized'] = dirty_data['name'].str.strip().str.lower()\n",
-    "\n",
-    "print(\"Checking for duplicate names:\")\n",
-    "duplicate_names = dirty_data[dirty_data.duplicated(['name_normalized'], keep=False)]\n",
-    "print(duplicate_names.sort_values('name_normalized')[['customer_id', 'name', 'age', 'country']])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### 使用模糊匹配查找近似重复项\n",
-    "\n",
-    "为了进行更复杂的重复检测，我们可以使用模糊匹配来查找相似的名称：\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "try:\n",
-    "    from rapidfuzz import process, fuzz\n",
-    "\n",
-    "    # Function to find potential duplicates\n",
-    "    def find_near_duplicates(df, column, threshold=90):\n",
-    "        \"\"\"\n",
-    "        Find near-duplicate entries in a column using fuzzy matching.\n",
-    "        \n",
-    "        Parameters:\n",
-    "        - df: DataFrame\n",
-    "        - column: Column name to check for duplicates\n",
-    "        - threshold: Similarity threshold (0-100)\n",
-    "        \n",
-    "        Returns: List of potential duplicate groups\n",
-    "        \"\"\"\n",
-    "        values = df[column].unique()\n",
-    "        duplicate_groups = []\n",
-    "        checked = set()\n",
-    "        \n",
-    "        for value in values:\n",
-    "            if value in checked:\n",
-    "                continue\n",
-    "                \n",
-    "            # Find similar values\n",
-    "            matches = process.extract(value, values, scorer=fuzz.ratio, limit=len(values))\n",
-    "            similar = [m[0] for m in matches if m[1] >= threshold]\n",
-    "            \n",
-    "            if len(similar) > 1:\n",
-    "                duplicate_groups.append(similar)\n",
-    "                checked.update(similar)\n",
-    "        \n",
-    "        return duplicate_groups\n",
-    "\n",
-    "    # Find near-duplicate names\n",
-    "    duplicate_groups = find_near_duplicates(dirty_data, 'name', threshold=90)\n",
-    "\n",
-    "    print(\"Potential duplicate groups:\")\n",
-    "    for i, group in enumerate(duplicate_groups, 1):\n",
-    "        print(f\"\\nGroup {i}:\")\n",
-    "        for name in group:\n",
-    "            matching_rows = dirty_data[dirty_data['name'] == name]\n",
-    "            print(f\"  '{name}': {len(matching_rows)} occurrence(s)\")\n",
-    "            for _, row in matching_rows.iterrows():\n",
-    "                print(f\"    - Customer {row['customer_id']}: age={row['age']}, country={row['country']}\")\n",
-    "except ImportError:\n",
-    "    print(\"rapidfuzz is not installed. Skipping fuzzy matching for near-duplicates.\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### 处理重复项\n",
-    "\n",
-    "一旦识别出重复项，你需要决定如何处理：\n",
-    "1. **保留第一次出现**：使用 `drop_duplicates(keep='first')`\n",
-    "2. **保留最后一次出现**：使用 `drop_duplicates(keep='last')`\n",
-    "3. **汇总信息**：合并重复行中的信息\n",
-    "4. **人工审核**：标记以供人工审核\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Example: Remove duplicates based on normalized name, keeping first occurrence\n",
-    "cleaned_data = dirty_data.drop_duplicates(subset=['name_normalized'], keep='first')\n",
-    "\n",
-    "print(f\"Original dataset: {len(dirty_data)} rows\")\n",
-    "print(f\"After removing name duplicates: {len(cleaned_data)} rows\")\n",
-    "print(f\"Removed: {len(dirty_data) - len(cleaned_data)} duplicate rows\")\n",
-    "\n",
-    "print(\"\\nCleaned dataset:\")\n",
-    "print(cleaned_data[['customer_id', 'name', 'age', 'country_clean']])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 总结：完整的数据清洗流程\n",
-    "\n",
-    "让我们将所有内容整合成一个全面的清洗流程：\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def clean_dataset(df):\n",
-    "    \"\"\"\n",
-    "    Comprehensive data cleaning function.\n",
-    "    \"\"\"\n",
-    "    # Create a copy to avoid modifying the original\n",
-    "    cleaned = df.copy()\n",
-    "    \n",
-    "    # 1. Standardize categorical values (country)\n",
-    "    country_mapping = {\n",
-    "        'usa': 'USA', 'u.s.a': 'USA', 'united states': 'USA',\n",
-    "        'uk': 'UK', 'united kingdom': 'UK',\n",
-    "        'canada': 'Canada', 'mexico': 'Mexico'\n",
-    "    }\n",
-    "    cleaned['country'] = cleaned['country'].str.lower().map(country_mapping)\n",
-    "    \n",
-    "    # 2. Clean abnormal age values\n",
-    "    cleaned['age'] = cleaned['age'].apply(\n",
-    "        lambda x: np.nan if (x < 0 or x > 120) else x\n",
-    "    )\n",
-    "    \n",
-    "    # 3. Remove near-duplicate names (normalize whitespace)\n",
-    "    cleaned['name'] = cleaned['name'].str.strip()\n",
-    "    cleaned = cleaned.drop_duplicates(subset=['name'], keep='first')\n",
-    "    \n",
-    "    return cleaned\n",
-    "\n",
-    "# Apply the cleaning pipeline\n",
-    "final_cleaned_data = clean_dataset(dirty_data)\n",
-    "\n",
-    "print(\"Before cleaning:\")\n",
-    "print(f\"  Rows: {len(dirty_data)}\")\n",
-    "print(f\"  Unique countries: {dirty_data['country'].nunique()}\")\n",
-    "print(f\"  Invalid ages: {((dirty_data['age'] < 0) | (dirty_data['age'] > 120)).sum()}\")\n",
-    "\n",
-    "print(\"\\nAfter cleaning:\")\n",
-    "print(f\"  Rows: {len(final_cleaned_data)}\")\n",
-    "print(f\"  Unique countries: {final_cleaned_data['country'].nunique()}\")\n",
-    "print(f\"  Invalid ages: {((final_cleaned_data['age'] < 0) | (final_cleaned_data['age'] > 120)).sum()}\")\n",
-    "\n",
-    "print(\"\\nCleaned dataset:\")\n",
-    "print(final_cleaned_data[['customer_id', 'name', 'age', 'country', 'purchase_amount']])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 🎯 挑战练习\n",
-    "\n",
-    "现在轮到你了！下面是一行包含多个质量问题的新数据。你能否：\n",
-    "\n",
-    "1. 找出这行数据中的所有问题\n",
-    "2. 编写代码清理每个问题\n",
-    "3. 将清理后的数据添加到数据集中\n",
-    "\n",
-    "以下是有问题的数据：\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# New problematic row\n",
-    "new_row = pd.DataFrame({\n",
-    "    'customer_id': [13],\n",
-    "    'name': ['  Diana  Prince  '],  # Extra whitespace\n",
-    "    'age': [250],  # Impossible age\n",
-    "    'country': ['U.S.A.'],  # Inconsistent format\n",
-    "    'purchase_amount': [150.00]\n",
-    "})\n",
-    "\n",
-    "print(\"New row to clean:\")\n",
-    "print(new_row)\n",
-    "\n",
-    "# TODO: Your code here to clean this row\n",
-    "# Hints:\n",
-    "# 1. Strip whitespace from the name\n",
-    "# 2. Check if the name is a duplicate (Diana Prince already exists)\n",
-    "# 3. Handle the impossible age value\n",
-    "# 4. Standardize the country name\n",
-    "\n",
-    "# Example solution (uncomment and modify as needed):\n",
-    "# new_row_cleaned = new_row.copy()\n",
-    "# new_row_cleaned['name'] = new_row_cleaned['name'].str.strip()\n",
-    "# new_row_cleaned['age'] = np.nan  # Invalid age\n",
-    "# new_row_cleaned['country'] = 'USA'  # Standardized\n",
-    "# print(\"\\nCleaned row:\")\n",
-    "# print(new_row_cleaned)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### 关键要点\n",
-    "\n",
-    "1. **类别不一致**在实际数据中很常见。务必检查唯一值，并通过映射或模糊匹配来标准化它们。\n",
-    "\n",
-    "2. **异常值**会显著影响你的分析。结合领域知识和统计方法（IQR、Z分数）来检测它们。\n",
-    "\n",
-    "3. **近似重复项**比完全重复项更难检测。可以考虑使用模糊匹配并规范化数据（如转换为小写、去除空格）来识别它们。\n",
-    "\n",
-    "4. **数据清理是一个迭代过程**。可能需要应用多种技术并审查结果，才能最终确定清理后的数据集。\n",
-    "\n",
-    "5. **记录你的决策**。记录你应用了哪些清理步骤以及原因，这对于可重复性和透明性非常重要。\n",
-    "\n",
-    "> **最佳实践：**始终保留一份原始“脏”数据的副本。切勿覆盖源数据文件——创建清理后的版本，并使用清晰的命名规则，例如`data_cleaned.csv`。\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n---\n\n**免责声明**：  \n本文档使用AI翻译服务 [Co-op Translator](https://github.com/Azure/co-op-translator) 进行翻译。尽管我们努力确保翻译的准确性，但请注意，自动翻译可能包含错误或不准确之处。原始语言的文档应被视为权威来源。对于关键信息，建议使用专业人工翻译。我们对因使用此翻译而产生的任何误解或误读不承担责任。\n"
-   ]
-  }
- ],
- "metadata": {
-  "anaconda-cloud": {},
-  "colab": {
-   "name": "notebook.ipynb",
-   "provenance": []
-  },
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.5.4"
-  },
-  "coopTranslator": {
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-   "language_code": "zh"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 0
-}
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diff --git a/translations/zh/2-Working-With-Data/README.md b/translations/zh/2-Working-With-Data/README.md
deleted file mode 100644
index b9c77df4..00000000
--- a/translations/zh/2-Working-With-Data/README.md
+++ /dev/null
@@ -1,29 +0,0 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
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-  "source_file": "2-Working-With-Data/README.md",
-  "language_code": "zh"
-}
--->
-# 数据处理
-
-![data love](../../../translated_images/zh-CN/data-love.a22ef29e6742c852505ada062920956d3d7604870b281a8ca7c7ac6f37381d5a.jpg)  
-> 图片由 <a href="https://unsplash.com/@swimstaralex?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Alexander Sinn</a> 提供，来自 <a href="https://unsplash.com/s/photos/data?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>  
-
-在这些课程中，您将学习一些管理、操作和在应用程序中使用数据的方法。您将了解关系型和非关系型数据库，以及数据如何存储在其中。您将学习使用 Python 管理数据的基础知识，并探索多种使用 Python 管理和挖掘数据的方法。
-
-### 主题
-
-1. [关系型数据库](05-relational-databases/README.md)  
-2. [非关系型数据库](06-non-relational/README.md)  
-3. [使用 Python](07-python/README.md)  
-4. [准备数据](08-data-preparation/README.md)  
-
-### 致谢
-
-这些课程由 [Christopher Harrison](https://twitter.com/geektrainer)、[Dmitry Soshnikov](https://twitter.com/shwars) 和 [Jasmine Greenaway](https://twitter.com/paladique) ❤️ 精心编写。
-
-**免责声明**：  
-本文档使用AI翻译服务 [Co-op Translator](https://github.com/Azure/co-op-translator) 进行翻译。尽管我们努力确保翻译的准确性，但请注意，自动翻译可能包含错误或不准确之处。应以原始语言的文档作为权威来源。对于关键信息，建议使用专业人工翻译。我们不对因使用此翻译而产生的任何误解或误读承担责任。
\ No newline at end of file
diff --git a/translations/zh/3-Data-Visualization/09-visualization-quantities/README.md b/translations/zh/3-Data-Visualization/09-visualization-quantities/README.md
deleted file mode 100644
index a8266bd0..00000000
--- a/translations/zh/3-Data-Visualization/09-visualization-quantities/README.md
+++ /dev/null
@@ -1,222 +0,0 @@
-<!--
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-  "source_file": "3-Data-Visualization/09-visualization-quantities/README.md",
-  "language_code": "zh"
-}
--->
-# 可视化数量
-
-|![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/09-Visualizing-Quantities.png)|
-|:---:|
-| 可视化数量 - _Sketchnote by [@nitya](https://twitter.com/nitya)_ |
-
-在本课中，您将学习如何使用众多可用的 Python 库之一，围绕数量的概念创建有趣的可视化。通过一个关于明尼苏达州鸟类的清理数据集，您可以了解许多关于当地野生动物的有趣事实。
-
-## [课前测验](https://ff-quizzes.netlify.app/en/ds/quiz/16)
-
-## 使用 Matplotlib 观察翼展
-
-一个非常优秀的库可以用来创建各种简单和复杂的图表和图形，它就是 [Matplotlib](https://matplotlib.org/stable/index.html)。一般来说，使用这些库绘制数据的过程包括：确定数据框中您想要处理的部分，对数据进行必要的转换，分配 x 和 y 轴的值，决定显示哪种图表，然后显示图表。Matplotlib 提供了多种可视化方式，但在本课中，我们将重点关注最适合可视化数量的图表类型：折线图、散点图和柱状图。
-
-> ✅ 根据数据结构和您想讲述的故事选择最佳图表：
-> - 分析时间趋势：折线图
-> - 比较数值：柱状图、条形图、饼图、散点图
-> - 展示部分与整体的关系：饼图
-> - 展示数据分布：散点图、柱状图
-> - 展示趋势：折线图、条形图
-> - 展示数值之间的关系：折线图、散点图、气泡图
-
-如果您有一个数据集并需要了解某个项目的数量，第一步通常是检查其数值。
-
-✅ Matplotlib 有非常好的“速查表”，可以在 [这里](https://matplotlib.org/cheatsheets/cheatsheets.pdf) 找到。
-
-## 绘制鸟类翼展值的折线图
-
-打开本课文件夹根目录中的 `notebook.ipynb` 文件并添加一个单元格。
-
-> 注意：数据存储在本仓库的 `/data` 文件夹中。
-
-```python
-import pandas as pd
-import matplotlib.pyplot as plt
-birds = pd.read_csv('../../data/birds.csv')
-birds.head()
-```  
-这些数据是文本和数字的混合：
-
-|      | 名称                         | 学名                   | 类别                  | 目           | 科       | 属          | 保护状态           | 最小长度 | 最大长度 | 最小体重     | 最大体重     | 最小翼展     | 最大翼展     |
-| ---: | :--------------------------- | :--------------------- | :-------------------- | :----------- | :------- | :---------- | :----------------- | --------: | --------: | ----------: | ----------: | ----------: | ----------: |
-|    0 | 黑腹哨鸭                     | Dendrocygna autumnalis | 鸭/鹅/水禽            | 雁形目       | 鸭科     | 哨鸭属      | LC                 |        47 |        56 |         652 |        1020 |          76 |          94 |
-|    1 | 棕哨鸭                       | Dendrocygna bicolor    | 鸭/鹅/水禽            | 雁形目       | 鸭科     | 哨鸭属      | LC                 |        45 |        53 |         712 |        1050 |          85 |          93 |
-|    2 | 雪鹅                         | Anser caerulescens     | 鸭/鹅/水禽            | 雁形目       | 鸭科     | 雁属        | LC                 |        64 |        79 |        2050 |        4050 |         135 |         165 |
-|    3 | 罗斯鹅                       | Anser rossii           | 鸭/鹅/水禽            | 雁形目       | 鸭科     | 雁属        | LC                 |      57.3 |        64 |        1066 |        1567 |         113 |         116 |
-|    4 | 大白额鹅                     | Anser albifrons        | 鸭/鹅/水禽            | 雁形目       | 鸭科     | 雁属        | LC                 |        64 |        81 |        1930 |        3310 |         130 |         165 |
-
-让我们从使用基本折线图绘制一些数值数据开始。假设您想查看这些有趣鸟类的最大翼展。
-
-```python
-wingspan = birds['MaxWingspan'] 
-wingspan.plot()
-```  
-![最大翼展](../../../../3-Data-Visualization/09-visualization-quantities/images/max-wingspan-02.png)
-
-您立刻注意到了什么？似乎至少有一个异常值——这翼展也太大了吧！2300 厘米的翼展相当于 23 米——明尼苏达州有翼龙在飞吗？让我们调查一下。
-
-虽然您可以在 Excel 中快速排序找到这些异常值（可能是输入错误），但继续从图表中进行可视化处理。
-
-为 x 轴添加标签以显示涉及的鸟类类型：
-
-```
-plt.title('Max Wingspan in Centimeters')
-plt.ylabel('Wingspan (CM)')
-plt.xlabel('Birds')
-plt.xticks(rotation=45)
-x = birds['Name'] 
-y = birds['MaxWingspan']
-
-plt.plot(x, y)
-
-plt.show()
-```  
-![带标签的翼展](../../../../3-Data-Visualization/09-visualization-quantities/images/max-wingspan-labels-02.png)
-
-即使标签旋转了 45 度，仍然太多了，无法阅读。让我们尝试另一种策略：仅标记异常值，并在图表内设置标签。您可以使用散点图为标签腾出更多空间：
-
-```python
-plt.title('Max Wingspan in Centimeters')
-plt.ylabel('Wingspan (CM)')
-plt.tick_params(axis='both',which='both',labelbottom=False,bottom=False)
-
-for i in range(len(birds)):
-    x = birds['Name'][i]
-    y = birds['MaxWingspan'][i]
-    plt.plot(x, y, 'bo')
-    if birds['MaxWingspan'][i] > 500:
-        plt.text(x, y * (1 - 0.05), birds['Name'][i], fontsize=12)
-    
-plt.show()
-```  
-这里发生了什么？您使用 `tick_params` 隐藏了底部标签，然后对鸟类数据集进行了循环。通过使用 `bo` 绘制带有小圆蓝点的图表，您检查了任何最大翼展超过 500 的鸟类，并在点旁边显示其标签。您在 y 轴上稍微偏移了标签 (`y * (1 - 0.05)`) 并使用鸟类名称作为标签。
-
-您发现了什么？
-
-![异常值](../../../../3-Data-Visualization/09-visualization-quantities/images/labeled-wingspan-02.png)
-
-## 筛选数据
-
-秃鹰和草原隼虽然可能是非常大的鸟类，但它们的最大翼展似乎被错误标记了，多加了一个 `0`。遇到翼展 25 米的秃鹰的可能性不大，但如果真的遇到了，请告诉我们！让我们创建一个新的数据框，去掉这两个异常值：
-
-```python
-plt.title('Max Wingspan in Centimeters')
-plt.ylabel('Wingspan (CM)')
-plt.xlabel('Birds')
-plt.tick_params(axis='both',which='both',labelbottom=False,bottom=False)
-for i in range(len(birds)):
-    x = birds['Name'][i]
-    y = birds['MaxWingspan'][i]
-    if birds['Name'][i] not in ['Bald eagle', 'Prairie falcon']:
-        plt.plot(x, y, 'bo')
-plt.show()
-```  
-
-通过筛选异常值，您的数据现在更加连贯和易于理解。
-
-![翼展散点图](../../../../3-Data-Visualization/09-visualization-quantities/images/scatterplot-wingspan-02.png)
-
-现在我们至少在翼展方面有了一个更干净的数据集，让我们进一步了解这些鸟类。
-
-虽然折线图和散点图可以显示数据值及其分布的信息，但我们想要思考这个数据集中固有的数值。您可以创建可视化来回答以下关于数量的问题：
-
-> 有多少种鸟类类别？它们的数量是多少？  
-> 有多少鸟类是灭绝的、濒危的、稀有的或常见的？  
-> 在林奈分类法中，各种属和目有多少？
-
-## 探索柱状图
-
-当您需要展示数据分组时，柱状图非常实用。让我们探索这个数据集中存在的鸟类类别，看看哪种类别的数量最多。
-
-在 notebook 文件中创建一个基本柱状图。
-
-✅ 注意，您可以筛选掉我们在上一节中识别的两个异常鸟类，编辑它们翼展中的错误，或者将它们保留在这些不依赖翼展值的练习中。
-
-如果您想创建柱状图，可以选择您想要关注的数据。柱状图可以从原始数据创建：
-
-```python
-birds.plot(x='Category',
-        kind='bar',
-        stacked=True,
-        title='Birds of Minnesota')
-
-```  
-![完整数据柱状图](../../../../3-Data-Visualization/09-visualization-quantities/images/full-data-bar-02.png)
-
-然而，这个柱状图难以阅读，因为数据没有分组。您需要选择您想要绘制的数据，因此让我们根据鸟类类别查看鸟类的长度。
-
-筛选数据以仅包含鸟类类别。
-
-✅ 注意，您使用 Pandas 管理数据，然后让 Matplotlib 进行图表绘制。
-
-由于类别较多，您可以垂直显示此图表并调整其高度以适应所有数据：
-
-```python
-category_count = birds.value_counts(birds['Category'].values, sort=True)
-plt.rcParams['figure.figsize'] = [6, 12]
-category_count.plot.barh()
-```  
-![类别和长度](../../../../3-Data-Visualization/09-visualization-quantities/images/category-counts-02.png)
-
-这个柱状图很好地展示了每个类别中鸟类的数量。一眼就能看出，这个地区数量最多的鸟类是鸭/鹅/水禽类别。明尼苏达州是“万湖之地”，这并不令人惊讶！
-
-✅ 尝试对这个数据集进行其他计数。有任何让您感到惊讶的发现吗？
-
-## 数据比较
-
-您可以通过创建新轴尝试不同的分组数据比较。尝试比较鸟类的最大长度，基于其类别：
-
-```python
-maxlength = birds['MaxLength']
-plt.barh(y=birds['Category'], width=maxlength)
-plt.rcParams['figure.figsize'] = [6, 12]
-plt.show()
-```  
-![数据比较](../../../../3-Data-Visualization/09-visualization-quantities/images/category-length-02.png)
-
-这里没有什么令人惊讶的：与鹈鹕或鹅相比，蜂鸟的最大长度最小。当数据符合逻辑时，这是件好事！
-
-您可以通过叠加数据创建更有趣的柱状图。让我们叠加最小长度和最大长度在给定鸟类类别上的数据：
-
-```python
-minLength = birds['MinLength']
-maxLength = birds['MaxLength']
-category = birds['Category']
-
-plt.barh(category, maxLength)
-plt.barh(category, minLength)
-
-plt.show()
-```  
-在这个图表中，您可以看到每个鸟类类别的最小长度和最大长度范围。可以肯定地说，根据这些数据，鸟越大，其长度范围越大。真是令人着迷！
-
-![叠加值](../../../../3-Data-Visualization/09-visualization-quantities/images/superimposed-02.png)
-
-## 🚀 挑战
-
-这个鸟类数据集提供了关于特定生态系统中不同类型鸟类的大量信息。在网上搜索，看看是否能找到其他与鸟类相关的数据集。练习围绕这些鸟类构建图表和图形，发现您之前未曾意识到的事实。
-
-## [课后测验](https://ff-quizzes.netlify.app/en/ds/quiz/17)
-
-## 复习与自学
-
-本课的第一部分为您提供了一些关于如何使用 Matplotlib 可视化数量的信息。研究其他可视化数据集的方法。[Plotly](https://github.com/plotly/plotly.py) 是我们不会在这些课程中涵盖的一个库，可以看看它能提供什么。
-
-## 作业
-
-[折线图、散点图和柱状图](assignment.md)
-
----
-
-**免责声明**：  
-本文档使用AI翻译服务[Co-op Translator](https://github.com/Azure/co-op-translator)进行翻译。尽管我们努力确保翻译的准确性，但请注意，自动翻译可能包含错误或不准确之处。原始语言的文档应被视为权威来源。对于关键信息，建议使用专业人工翻译。我们对因使用此翻译而产生的任何误解或误读不承担责任。
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diff --git a/translations/zh/3-Data-Visualization/09-visualization-quantities/assignment.md b/translations/zh/3-Data-Visualization/09-visualization-quantities/assignment.md
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-  "translation_date": "2025-08-25T18:39:15+00:00",
-  "source_file": "3-Data-Visualization/09-visualization-quantities/assignment.md",
-  "language_code": "zh"
-}
--->
-# 折线图、散点图和柱状图
-
-## 说明
-
-在本课中，你学习了如何使用折线图、散点图和柱状图来展示数据集中的有趣事实。在本次作业中，请深入挖掘数据集，发现关于某种鸟类的一个事实。例如，可以创建一个笔记本，展示你能找到的关于雪雁的所有有趣数据。使用上述三种图表，在你的笔记本中讲述一个故事。
-
-## 评分标准
-
-优秀 | 合格 | 需改进
---- | --- | --- |
-笔记本包含良好的注释、清晰的故事叙述以及吸引人的图表 | 笔记本缺少其中一个要素 | 笔记本缺少两个要素
-
-**免责声明**：  
-本文档使用AI翻译服务 [Co-op Translator](https://github.com/Azure/co-op-translator) 进行翻译。尽管我们努力确保翻译的准确性，但请注意，自动翻译可能包含错误或不准确之处。应以原文档的原始语言版本为权威来源。对于关键信息，建议使用专业人工翻译。我们对因使用此翻译而引起的任何误解或误读不承担责任。
\ No newline at end of file
diff --git a/translations/zh/3-Data-Visualization/09-visualization-quantities/notebook.ipynb b/translations/zh/3-Data-Visualization/09-visualization-quantities/notebook.ipynb
deleted file mode 100644
index 36a74a24..00000000
--- a/translations/zh/3-Data-Visualization/09-visualization-quantities/notebook.ipynb
+++ /dev/null
@@ -1,96 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "source": [
-    "# 一起来了解鸟类吧\n",
-    "\n",
-    "鸟类是地球上最迷人的生物之一。它们的多样性、适应性以及与环境的互动方式让人惊叹不已。\n",
-    "\n",
-    "## 鸟类的基本特征\n",
-    "\n",
-    "鸟类有一些独特的特征，使它们与其他动物区别开来：\n",
-    "\n",
-    "- **羽毛**：羽毛是鸟类的标志性特征，它们不仅提供保温，还能帮助飞行。\n",
-    "- **喙**：鸟类没有牙齿，它们用喙来进食、筑巢和防御。\n",
-    "- **轻骨骼**：鸟类的骨骼轻便但坚固，这使得它们能够飞行。\n",
-    "- **产卵**：所有鸟类都通过产卵繁殖后代。\n",
-    "\n",
-    "## 鸟类的多样性\n",
-    "\n",
-    "世界上有超过10,000种鸟类，它们分布在各种栖息地中，从热带雨林到极地冰原。以下是一些主要的鸟类分类：\n",
-    "\n",
-    "- **猛禽**：如鹰、隼和猫头鹰，它们以锐利的视力和强大的捕猎能力著称。\n",
-    "- **水鸟**：如鸭子、鹈鹕和企鹅，它们适应了水生环境。\n",
-    "- **鸣禽**：如麻雀、画眉和知更鸟，它们以美妙的歌声闻名。\n",
-    "\n",
-    "## 鸟类的行为\n",
-    "\n",
-    "鸟类的行为多种多样，以下是一些有趣的例子：\n",
-    "\n",
-    "- **迁徙**：许多鸟类会在季节变化时迁徙，飞行数千公里寻找适宜的栖息地。\n",
-    "- **求偶展示**：雄性鸟类通常会通过色彩鲜艳的羽毛或复杂的舞蹈来吸引配偶。\n",
-    "- **筑巢**：鸟类会用树枝、草叶甚至人类的废弃物来建造巢穴，为后代提供安全的环境。\n",
-    "\n",
-    "## 如何观察鸟类\n",
-    "\n",
-    "观察鸟类是一项有趣且放松的活动。以下是一些小贴士：\n",
-    "\n",
-    "1. **选择合适的地点**：公园、湿地和森林是观察鸟类的好地方。\n",
-    "2. **带上工具**：双筒望远镜和鸟类图鉴会帮助你更好地识别鸟类。\n",
-    "3. **保持安静**：鸟类对声音很敏感，尽量避免发出噪音。\n",
-    "4. **耐心等待**：有时需要一些时间才能看到鸟类的活动。\n",
-    "\n",
-    "## 保护鸟类\n",
-    "\n",
-    "随着栖息地的减少和气候变化的影响，许多鸟类面临生存威胁。我们可以通过以下方式帮助保护它们：\n",
-    "\n",
-    "- **种植本地植物**：为鸟类提供食物和栖息地。\n",
-    "- **减少使用化学品**：避免杀虫剂和除草剂对鸟类造成伤害。\n",
-    "- **支持保护组织**：参与或捐助致力于鸟类保护的机构。\n",
-    "\n",
-    "## 结语\n",
-    "\n",
-    "鸟类是自然界的奇迹，它们的存在让我们的世界更加丰富多彩。无论是通过观察它们的行为，还是参与保护它们的行动，我们都能从中获得乐趣并为生态环境做出贡献。\n"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n---\n\n**免责声明**：  \n本文档使用AI翻译服务 [Co-op Translator](https://github.com/Azure/co-op-translator) 进行翻译。尽管我们努力确保翻译的准确性，但请注意，自动翻译可能包含错误或不准确之处。原始语言的文档应被视为权威来源。对于重要信息，建议使用专业人工翻译。我们不对因使用此翻译而产生的任何误解或误读承担责任。\n"
-   ]
-  }
- ],
- "metadata": {
-  "orig_nbformat": 4,
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-   "nbconvert_exporter": "python",
-   "file_extension": ".py"
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-   "translation_date": "2025-09-02T08:47:49+00:00",
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-   "language_code": "zh"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
\ No newline at end of file
diff --git a/translations/zh/3-Data-Visualization/09-visualization-quantities/solution/notebook.ipynb b/translations/zh/3-Data-Visualization/09-visualization-quantities/solution/notebook.ipynb
deleted file mode 100644
index c76ece64..00000000
--- a/translations/zh/3-Data-Visualization/09-visualization-quantities/solution/notebook.ipynb
+++ /dev/null
@@ -1,569 +0,0 @@
-{
- "metadata": {
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-  },
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-   "original_hash": "b19267e699cacd851d2bf56fe99fc589",
-   "translation_date": "2025-09-02T08:55:30+00:00",
-   "source_file": "3-Data-Visualization/09-visualization-quantities/solution/notebook.ipynb",
-   "language_code": "zh"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2,
- "cells": [
-  {
-   "cell_type": "markdown",
-   "source": [],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "导入pandas和matplotlib以及数据\n"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "source": [
-    "import pandas as pd\n",
-    "import matplotlib.pyplot as plt\n",
-    "birds = pd.read_csv('../../../data/birds.csv')\n",
-    "birds.head()\n"
-   ],
-   "outputs": [
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "                           Name          ScientificName  \\\n",
-       "0  Black-bellied whistling-duck  Dendrocygna autumnalis   \n",
-       "1        Fulvous whistling-duck     Dendrocygna bicolor   \n",
-       "2                    Snow goose      Anser caerulescens   \n",
-       "3                  Ross's goose            Anser rossii   \n",
-       "4   Greater white-fronted goose         Anser albifrons   \n",
-       "\n",
-       "                Category         Order    Family        Genus  \\\n",
-       "0  Ducks/Geese/Waterfowl  Anseriformes  Anatidae  Dendrocygna   \n",
-       "1  Ducks/Geese/Waterfowl  Anseriformes  Anatidae  Dendrocygna   \n",
-       "2  Ducks/Geese/Waterfowl  Anseriformes  Anatidae        Anser   \n",
-       "3  Ducks/Geese/Waterfowl  Anseriformes  Anatidae        Anser   \n",
-       "4  Ducks/Geese/Waterfowl  Anseriformes  Anatidae        Anser   \n",
-       "\n",
-       "  ConservationStatus  MinLength  MaxLength  MinBodyMass  MaxBodyMass  \\\n",
-       "0                 LC       47.0       56.0        652.0       1020.0   \n",
-       "1                 LC       45.0       53.0        712.0       1050.0   \n",
-       "2                 LC       64.0       79.0       2050.0       4050.0   \n",
-       "3                 LC       57.3       64.0       1066.0       1567.0   \n",
-       "4                 LC       64.0       81.0       1930.0       3310.0   \n",
-       "\n",
-       "   MinWingspan  MaxWingspan  \n",
-       "0         76.0         94.0  \n",
-       "1         85.0         93.0  \n",
-       "2        135.0        165.0  \n",
-       "3        113.0        116.0  \n",
-       "4        130.0        165.0  "
-      ],
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Name</th>\n",
-       "      <th>ScientificName</th>\n",
-       "      <th>Category</th>\n",
-       "      <th>Order</th>\n",
-       "      <th>Family</th>\n",
-       "      <th>Genus</th>\n",
-       "      <th>ConservationStatus</th>\n",
-       "      <th>MinLength</th>\n",
-       "      <th>MaxLength</th>\n",
-       "      <th>MinBodyMass</th>\n",
-       "      <th>MaxBodyMass</th>\n",
-       "      <th>MinWingspan</th>\n",
-       "      <th>MaxWingspan</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Black-bellied whistling-duck</td>\n",
-       "      <td>Dendrocygna autumnalis</td>\n",
-       "      <td>Ducks/Geese/Waterfowl</td>\n",
-       "      <td>Anseriformes</td>\n",
-       "      <td>Anatidae</td>\n",
-       "      <td>Dendrocygna</td>\n",
-       "      <td>LC</td>\n",
-       "      <td>47.0</td>\n",
-       "      <td>56.0</td>\n",
-       "      <td>652.0</td>\n",
-       "      <td>1020.0</td>\n",
-       "      <td>76.0</td>\n",
-       "      <td>94.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Fulvous whistling-duck</td>\n",
-       "      <td>Dendrocygna bicolor</td>\n",
-       "      <td>Ducks/Geese/Waterfowl</td>\n",
-       "      <td>Anseriformes</td>\n",
-       "      <td>Anatidae</td>\n",
-       "      <td>Dendrocygna</td>\n",
-       "      <td>LC</td>\n",
-       "      <td>45.0</td>\n",
-       "      <td>53.0</td>\n",
-       "      <td>712.0</td>\n",
-       "      <td>1050.0</td>\n",
-       "      <td>85.0</td>\n",
-       "      <td>93.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Snow goose</td>\n",
-       "      <td>Anser caerulescens</td>\n",
-       "      <td>Ducks/Geese/Waterfowl</td>\n",
-       "      <td>Anseriformes</td>\n",
-       "      <td>Anatidae</td>\n",
-       "      <td>Anser</td>\n",
-       "      <td>LC</td>\n",
-       "      <td>64.0</td>\n",
-       "      <td>79.0</td>\n",
-       "      <td>2050.0</td>\n",
-       "      <td>4050.0</td>\n",
-       "      <td>135.0</td>\n",
-       "      <td>165.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Ross's goose</td>\n",
-       "      <td>Anser rossii</td>\n",
-       "      <td>Ducks/Geese/Waterfowl</td>\n",
-       "      <td>Anseriformes</td>\n",
-       "      <td>Anatidae</td>\n",
-       "      <td>Anser</td>\n",
-       "      <td>LC</td>\n",
-       "      <td>57.3</td>\n",
-       "      <td>64.0</td>\n",
-       "      <td>1066.0</td>\n",
-       "      <td>1567.0</td>\n",
-       "      <td>113.0</td>\n",
-       "      <td>116.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Greater white-fronted goose</td>\n",
-       "      <td>Anser albifrons</td>\n",
-       "      <td>Ducks/Geese/Waterfowl</td>\n",
-       "      <td>Anseriformes</td>\n",
-       "      <td>Anatidae</td>\n",
-       "      <td>Anser</td>\n",
-       "      <td>LC</td>\n",
-       "      <td>64.0</td>\n",
-       "      <td>81.0</td>\n",
-       "      <td>1930.0</td>\n",
-       "      <td>3310.0</td>\n",
-       "      <td>130.0</td>\n",
-       "      <td>165.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 2
-    }
-   ],
-   "metadata": {
-    "tags": []
-   }
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "让我们通过展示一个非常基础的折线图来直观地了解这些鸟类的翼展\n"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "source": [
-    "\n",
-    "plt.title('Max Wingspan in Centimeters')\n",
-    "plt.ylabel('Wingspan (CM)')\n",
-    "plt.xlabel('Birds')\n",
-    "wingspan = birds.MaxWingspan \n",
-    "wingspan.plot()\n"
-   ],
-   "outputs": [
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa009b17940>"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 19
-    },
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "<Figure size 432x864 with 1 Axes>"
-      ],
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-     },
-     "metadata": {
-      "needs_background": "light"
-     }
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "显示鸟类名称与其翼展的对应关系\n"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "source": [
-    "\n",
-    "plt.title('Max Wingspan in Centimeters')\n",
-    "plt.ylabel('Wingspan (CM)')\n",
-    "plt.xlabel('Birds')\n",
-    "plt.xticks(rotation=45)\n",
-    "x = birds['Name'] \n",
-    "y = birds['MaxWingspan']\n",
-    "\n",
-    "plt.plot(x, y)\n",
-    "\n",
-    "plt.show()"
-   ],
-   "outputs": [
-    {
-     "output_type": "stream",
-     "name": "stderr",
-     "text": [
-      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/matplotlib/cbook/__init__.py:1402: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
-      "  x[:, None]\n",
-      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/matplotlib/axes/_base.py:276: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
-      "  x = x[:, np.newaxis]\n",
-      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/matplotlib/axes/_base.py:278: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
-      "  y = y[:, np.newaxis]\n"
-     ]
-    },
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "<Figure size 432x864 with 1 Axes>"
-      ],
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xlink:href=\"#m9a96a52268\" y=\"674.638125\"/>\n      </g>\n     </g>\n     <g id=\"text_57\">\n      <!-- Horned grebe -->\n      <g transform=\"translate(84.620799 735.714116)rotate(-45)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-72\"/>\n       <use x=\"75.195312\" xlink:href=\"#DejaVuSans-111\"/>\n       <use x=\"136.376953\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"177.474609\" xlink:href=\"#DejaVuSans-110\"/>\n       <use x=\"240.853516\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"302.376953\" xlink:href=\"#DejaVuSans-100\"/>\n       <use x=\"365.853516\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"397.640625\" xlink:href=\"#DejaVuSans-103\"/>\n       <use x=\"461.117188\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"502.199219\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"563.722656\" xlink:href=\"#DejaVuSans-98\"/>\n       <use x=\"627.199219\" xlink:href=\"#DejaVuSans-101\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_58\">\n     <g id=\"line2d_58\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"107.708219\" xlink:href=\"#m9a96a52268\" y=\"674.638125\"/>\n      </g>\n     </g>\n     <g id=\"text_58\">\n      <!-- Red-necked grebe -->\n      <g transform=\"translate(77.267825 751.794166)rotate(-45)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-82\"/>\n       <use x=\"69.419922\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"130.943359\" xlink:href=\"#DejaVuSans-100\"/>\n       <use x=\"194.419922\" xlink:href=\"#DejaVuSans-45\"/>\n       <use x=\"230.503906\" xlink:href=\"#DejaVuSans-110\"/>\n       <use x=\"293.882812\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"355.40625\" xlink:href=\"#DejaVuSans-99\"/>\n       <use x=\"410.386719\" xlink:href=\"#DejaVuSans-107\"/>\n       <use x=\"468.25\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"529.773438\" xlink:href=\"#DejaVuSans-100\"/>\n       <use x=\"593.25\" 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xlink:href=\"#DejaVuSans-67\"/>\n       <use x=\"69.824219\" xlink:href=\"#DejaVuSans-108\"/>\n       <use x=\"97.607422\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"158.886719\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"200\" xlink:href=\"#DejaVuSans-107\"/>\n       <use x=\"257.910156\" xlink:href=\"#DejaVuSans-39\"/>\n       <use x=\"285.400391\" xlink:href=\"#DejaVuSans-115\"/>\n       <use x=\"337.5\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"369.287109\" xlink:href=\"#DejaVuSans-103\"/>\n       <use x=\"432.763672\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"473.845703\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"535.369141\" xlink:href=\"#DejaVuSans-98\"/>\n       <use x=\"598.845703\" xlink:href=\"#DejaVuSans-101\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_62\">\n     <g id=\"line2d_62\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"110.456424\" xlink:href=\"#m9a96a52268\" y=\"674.638125\"/>\n      </g>\n  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xlink:href=\"#m9a96a52268\" y=\"674.638125\"/>\n      </g>\n     </g>\n     <g id=\"text_63\">\n      <!-- Band-tailed pigeon -->\n      <g transform=\"translate(79.630267 753.939793)rotate(-45)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-66\"/>\n       <use x=\"68.603516\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"129.882812\" xlink:href=\"#DejaVuSans-110\"/>\n       <use x=\"193.261719\" xlink:href=\"#DejaVuSans-100\"/>\n       <use x=\"256.738281\" xlink:href=\"#DejaVuSans-45\"/>\n       <use x=\"292.822266\" xlink:href=\"#DejaVuSans-116\"/>\n       <use x=\"332.03125\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"393.310547\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"421.09375\" xlink:href=\"#DejaVuSans-108\"/>\n       <use x=\"448.876953\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"510.400391\" xlink:href=\"#DejaVuSans-100\"/>\n       <use x=\"573.876953\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"605.664062\" xlink:href=\"#DejaVuSans-112\"/>\n       <use x=\"669.140625\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"696.923828\" xlink:href=\"#DejaVuSans-103\"/>\n       <use x=\"760.400391\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"821.923828\" xlink:href=\"#DejaVuSans-111\"/>\n       <use x=\"883.105469\" xlink:href=\"#DejaVuSans-110\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_64\">\n     <g id=\"line2d_64\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"111.830526\" xlink:href=\"#m9a96a52268\" y=\"674.638125\"/>\n      </g>\n     </g>\n     <g id=\"text_64\">\n      <!-- Eurasian collared-dove -->\n      <g transform=\"translate(73.270004 768.03442)rotate(-45)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-69\"/>\n       <use x=\"63.183594\" xlink:href=\"#DejaVuSans-117\"/>\n       <use x=\"126.5625\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"167.675781\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"228.955078\" 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x=\"1025.115234\" xlink:href=\"#DejaVuSans-118\"/>\n       <use x=\"1084.294922\" xlink:href=\"#DejaVuSans-101\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_65\">\n     <g id=\"line2d_65\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"112.517577\" xlink:href=\"#m9a96a52268\" y=\"674.638125\"/>\n      </g>\n     </g>\n     <g id=\"text_65\">\n      <!-- Passenger pigeon -->\n      <g transform=\"translate(83.025148 749.898236)rotate(-45)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-80\"/>\n       <use x=\"60.240234\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"121.519531\" xlink:href=\"#DejaVuSans-115\"/>\n       <use x=\"173.619141\" xlink:href=\"#DejaVuSans-115\"/>\n       <use x=\"225.71875\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"287.242188\" xlink:href=\"#DejaVuSans-110\"/>\n       <use x=\"350.621094\" xlink:href=\"#DejaVuSans-103\"/>\n       <use x=\"414.097656\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"475.621094\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"516.734375\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"548.521484\" xlink:href=\"#DejaVuSans-112\"/>\n       <use x=\"611.998047\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"639.78125\" xlink:href=\"#DejaVuSans-103\"/>\n       <use x=\"703.257812\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"764.78125\" xlink:href=\"#DejaVuSans-111\"/>\n       <use x=\"825.962891\" xlink:href=\"#DejaVuSans-110\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_66\">\n     <g id=\"line2d_66\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"113.204628\" xlink:href=\"#m9a96a52268\" y=\"674.638125\"/>\n      </g>\n     </g>\n     <g id=\"text_66\">\n      <!-- Inca dove -->\n      <defs>\n       <path d=\"M 9.8125 72.90625 \nL 19.671875 72.90625 \nL 19.671875 0 \nL 9.8125 0 \nz\n\" id=\"DejaVuSans-73\"/>\n      </defs>\n      <g transform=\"translate(97.962058 721.398518)rotate(-45)scale(0.1 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x=\"69.824219\" xlink:href=\"#DejaVuSans-111\"/>\n       <use x=\"131.005859\" xlink:href=\"#DejaVuSans-109\"/>\n       <use x=\"228.417969\" xlink:href=\"#DejaVuSans-109\"/>\n       <use x=\"325.830078\" xlink:href=\"#DejaVuSans-111\"/>\n       <use x=\"387.011719\" xlink:href=\"#DejaVuSans-110\"/>\n       <use x=\"450.390625\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"482.177734\" xlink:href=\"#DejaVuSans-103\"/>\n       <use x=\"545.654297\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"586.736328\" xlink:href=\"#DejaVuSans-111\"/>\n       <use x=\"647.917969\" xlink:href=\"#DejaVuSans-117\"/>\n       <use x=\"711.296875\" xlink:href=\"#DejaVuSans-110\"/>\n       <use x=\"774.675781\" xlink:href=\"#DejaVuSans-100\"/>\n       <use x=\"838.152344\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"869.939453\" xlink:href=\"#DejaVuSans-100\"/>\n       <use x=\"933.416016\" xlink:href=\"#DejaVuSans-111\"/>\n       <use x=\"994.597656\" xlink:href=\"#DejaVuSans-118\"/>\n       <use x=\"1053.777344\" xlink:href=\"#DejaVuSans-101\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_68\">\n     <g id=\"line2d_68\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"114.57873\" xlink:href=\"#m9a96a52268\" y=\"674.638125\"/>\n      </g>\n     </g>\n     <g id=\"text_68\">\n      <!-- White-winged dove -->\n      <g transform=\"translate(82.395981 755.278877)rotate(-45)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-87\"/>\n       <use x=\"98.876953\" xlink:href=\"#DejaVuSans-104\"/>\n       <use x=\"162.255859\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"190.039062\" xlink:href=\"#DejaVuSans-116\"/>\n       <use x=\"229.248047\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"290.771484\" xlink:href=\"#DejaVuSans-45\"/>\n       <use x=\"326.855469\" xlink:href=\"#DejaVuSans-119\"/>\n       <use x=\"408.642578\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"436.425781\" xlink:href=\"#DejaVuSans-110\"/>\n       <use 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y=\"674.638125\"/>\n      </g>\n     </g>\n     <g id=\"text_86\">\n      <!-- King rail -->\n      <g transform=\"translate(114.402791 715.999095)rotate(-45)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-75\"/>\n       <use x=\"65.576172\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"93.359375\" xlink:href=\"#DejaVuSans-110\"/>\n       <use x=\"156.738281\" xlink:href=\"#DejaVuSans-103\"/>\n       <use x=\"220.214844\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"252.001953\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"293.115234\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"354.394531\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"382.177734\" xlink:href=\"#DejaVuSans-108\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_87\">\n     <g id=\"line2d_87\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"127.632701\" xlink:href=\"#m9a96a52268\" y=\"674.638125\"/>\n      </g>\n     </g>\n     <g id=\"text_87\">\n      <!-- Virginia rail -->\n      <defs>\n       <path d=\"M 28.609375 0 \nL 0.78125 72.90625 \nL 11.078125 72.90625 \nL 34.1875 11.53125 \nL 57.328125 72.90625 \nL 67.578125 72.90625 \nL 39.796875 0 \nz\n\" id=\"DejaVuSans-86\"/>\n      </defs>\n      <g transform=\"translate(109.407024 727.364732)rotate(-45)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-86\"/>\n       <use x=\"68.376953\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"96.160156\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"137.257812\" xlink:href=\"#DejaVuSans-103\"/>\n       <use x=\"200.734375\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"228.517578\" xlink:href=\"#DejaVuSans-110\"/>\n       <use x=\"291.896484\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"319.679688\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"380.958984\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"412.746094\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"453.859375\" xlink:href=\"#DejaVuSans-97\"/>\n       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xlink:href=\"#m9a96a52268\" y=\"674.638125\"/>\n      </g>\n     </g>\n     <g id=\"text_102\">\n      <!-- Piping plover -->\n      <g transform=\"translate(116.829673 733.130967)rotate(-45)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-80\"/>\n       <use x=\"60.271484\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"88.054688\" xlink:href=\"#DejaVuSans-112\"/>\n       <use x=\"151.53125\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"179.314453\" xlink:href=\"#DejaVuSans-110\"/>\n       <use x=\"242.693359\" xlink:href=\"#DejaVuSans-103\"/>\n       <use x=\"306.169922\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"337.957031\" xlink:href=\"#DejaVuSans-112\"/>\n       <use x=\"401.433594\" xlink:href=\"#DejaVuSans-108\"/>\n       <use x=\"429.216797\" xlink:href=\"#DejaVuSans-111\"/>\n       <use x=\"490.398438\" xlink:href=\"#DejaVuSans-118\"/>\n       <use x=\"549.578125\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"611.101562\" 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transform=\"translate(127.252407 752.134461)rotate(-45)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-83\"/>\n       <use x=\"63.476562\" xlink:href=\"#DejaVuSans-112\"/>\n       <use x=\"126.953125\" xlink:href=\"#DejaVuSans-111\"/>\n       <use x=\"188.134766\" xlink:href=\"#DejaVuSans-116\"/>\n       <use x=\"227.34375\" xlink:href=\"#DejaVuSans-116\"/>\n       <use x=\"266.552734\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"328.076172\" xlink:href=\"#DejaVuSans-100\"/>\n       <use x=\"391.552734\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"423.339844\" xlink:href=\"#DejaVuSans-115\"/>\n       <use x=\"475.439453\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"536.71875\" xlink:href=\"#DejaVuSans-110\"/>\n       <use x=\"600.097656\" xlink:href=\"#DejaVuSans-100\"/>\n       <use x=\"663.574219\" xlink:href=\"#DejaVuSans-112\"/>\n       <use x=\"727.050781\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"754.833984\" xlink:href=\"#DejaVuSans-112\"/>\n    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y=\"674.638125\"/>\n      </g>\n     </g>\n     <g id=\"text_135\">\n      <!-- Greater yellowlegs -->\n      <g transform=\"translate(129.358691 753.418302)rotate(-45)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-71\"/>\n       <use x=\"77.490234\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"118.572266\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"180.095703\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"241.375\" xlink:href=\"#DejaVuSans-116\"/>\n       <use x=\"280.583984\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"342.107422\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"383.220703\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"415.007812\" xlink:href=\"#DejaVuSans-121\"/>\n       <use x=\"474.1875\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"535.710938\" xlink:href=\"#DejaVuSans-108\"/>\n       <use x=\"563.494141\" xlink:href=\"#DejaVuSans-108\"/>\n       <use x=\"591.277344\" xlink:href=\"#DejaVuSans-111\"/>\n       <use x=\"652.458984\" xlink:href=\"#DejaVuSans-119\"/>\n       <use x=\"734.246094\" xlink:href=\"#DejaVuSans-108\"/>\n       <use x=\"762.029297\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"823.552734\" xlink:href=\"#DejaVuSans-103\"/>\n       <use x=\"887.029297\" xlink:href=\"#DejaVuSans-115\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_136\">\n     <g id=\"line2d_136\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"161.298205\" xlink:href=\"#m9a96a52268\" y=\"674.638125\"/>\n      </g>\n     </g>\n     <g id=\"text_136\">\n      <!-- Wilson's phalarope -->\n      <g transform=\"translate(129.771186 753.967415)rotate(-45)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-87\"/>\n       <use x=\"98.845703\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"126.628906\" xlink:href=\"#DejaVuSans-108\"/>\n       <use x=\"154.412109\" xlink:href=\"#DejaVuSans-115\"/>\n       <use x=\"206.511719\" xlink:href=\"#DejaVuSans-111\"/>\n       <use 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y=\"674.638125\"/>\n      </g>\n     </g>\n     <g id=\"text_137\">\n      <!-- Red-necked phalarope -->\n      <g transform=\"translate(124.000916 766.882057)rotate(-45)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-82\"/>\n       <use x=\"69.419922\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"130.943359\" xlink:href=\"#DejaVuSans-100\"/>\n       <use x=\"194.419922\" xlink:href=\"#DejaVuSans-45\"/>\n       <use x=\"230.503906\" xlink:href=\"#DejaVuSans-110\"/>\n       <use x=\"293.882812\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"355.40625\" xlink:href=\"#DejaVuSans-99\"/>\n       <use x=\"410.386719\" xlink:href=\"#DejaVuSans-107\"/>\n       <use x=\"468.25\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"529.773438\" xlink:href=\"#DejaVuSans-100\"/>\n       <use x=\"593.25\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"625.037109\" xlink:href=\"#DejaVuSans-112\"/>\n       <use x=\"688.513672\" xlink:href=\"#DejaVuSans-104\"/>\n       <use 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id=\"text_164\">\n      <!-- Great black-backed gull -->\n      <g transform=\"translate(140.389096 771.206457)rotate(-45)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-71\"/>\n       <use x=\"77.490234\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"118.572266\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"180.095703\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"241.375\" xlink:href=\"#DejaVuSans-116\"/>\n       <use x=\"280.583984\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"312.371094\" xlink:href=\"#DejaVuSans-98\"/>\n       <use x=\"375.847656\" xlink:href=\"#DejaVuSans-108\"/>\n       <use x=\"403.630859\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"464.910156\" xlink:href=\"#DejaVuSans-99\"/>\n       <use x=\"519.890625\" xlink:href=\"#DejaVuSans-107\"/>\n       <use x=\"577.800781\" xlink:href=\"#DejaVuSans-45\"/>\n       <use x=\"613.884766\" xlink:href=\"#DejaVuSans-98\"/>\n       <use x=\"677.361328\" xlink:href=\"#DejaVuSans-97\"/>\n       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id=\"line2d_167\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"182.596789\" xlink:href=\"#m9a96a52268\" y=\"674.638125\"/>\n      </g>\n     </g>\n     <g id=\"text_167\">\n      <!-- Caspian tern -->\n      <g transform=\"translate(162.058099 731.990757)rotate(-45)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-67\"/>\n       <use x=\"69.824219\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"131.103516\" xlink:href=\"#DejaVuSans-115\"/>\n       <use x=\"183.203125\" xlink:href=\"#DejaVuSans-112\"/>\n       <use x=\"246.679688\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"274.462891\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"335.742188\" xlink:href=\"#DejaVuSans-110\"/>\n       <use x=\"399.121094\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"430.908203\" xlink:href=\"#DejaVuSans-116\"/>\n       <use x=\"470.117188\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"531.640625\" xlink:href=\"#DejaVuSans-114\"/>\n       <use 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xlink:href=\"#DejaVuSans-110\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_169\">\n     <g id=\"line2d_169\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"183.970891\" xlink:href=\"#m9a96a52268\" y=\"674.638125\"/>\n      </g>\n     </g>\n     <g id=\"text_169\">\n      <!-- Common tern -->\n      <g transform=\"translate(161.619688 735.615784)rotate(-45)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-67\"/>\n       <use x=\"69.824219\" xlink:href=\"#DejaVuSans-111\"/>\n       <use x=\"131.005859\" xlink:href=\"#DejaVuSans-109\"/>\n       <use x=\"228.417969\" xlink:href=\"#DejaVuSans-109\"/>\n       <use x=\"325.830078\" xlink:href=\"#DejaVuSans-111\"/>\n       <use x=\"387.011719\" xlink:href=\"#DejaVuSans-110\"/>\n       <use x=\"450.390625\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"482.177734\" xlink:href=\"#DejaVuSans-116\"/>\n       <use x=\"521.386719\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"582.910156\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"624.007812\" xlink:href=\"#DejaVuSans-110\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_170\">\n     <g id=\"line2d_170\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"184.657942\" xlink:href=\"#m9a96a52268\" y=\"674.638125\"/>\n      </g>\n     </g>\n     <g id=\"text_170\">\n      <!-- Arctic tern -->\n      <g transform=\"translate(168.103357 724.022547)rotate(-45)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-65\"/>\n       <use x=\"68.408203\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"109.490234\" xlink:href=\"#DejaVuSans-99\"/>\n       <use x=\"164.470703\" xlink:href=\"#DejaVuSans-116\"/>\n       <use x=\"203.679688\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"231.462891\" xlink:href=\"#DejaVuSans-99\"/>\n       <use x=\"286.443359\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"318.230469\" xlink:href=\"#DejaVuSans-116\"/>\n       <use x=\"357.439453\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"418.962891\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"460.060547\" xlink:href=\"#DejaVuSans-110\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_171\">\n     <g id=\"line2d_171\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"185.344993\" xlink:href=\"#m9a96a52268\" y=\"674.638125\"/>\n      </g>\n     </g>\n     <g id=\"text_171\">\n      <!-- Forster's tern -->\n      <g transform=\"translate(163.598698 734.405969)rotate(-45)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-70\"/>\n       <use x=\"57.472656\" xlink:href=\"#DejaVuSans-111\"/>\n       <use x=\"118.654297\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"159.767578\" xlink:href=\"#DejaVuSans-115\"/>\n       <use x=\"211.867188\" xlink:href=\"#DejaVuSans-116\"/>\n       <use x=\"251.076172\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"312.599609\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"353.712891\" xlink:href=\"#DejaVuSans-39\"/>\n       <use x=\"381.203125\" xlink:href=\"#DejaVuSans-115\"/>\n       <use x=\"433.302734\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"465.089844\" xlink:href=\"#DejaVuSans-116\"/>\n       <use x=\"504.298828\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"565.822266\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"606.919922\" xlink:href=\"#DejaVuSans-110\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_172\">\n     <g id=\"line2d_172\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"186.032045\" xlink:href=\"#m9a96a52268\" y=\"674.638125\"/>\n      </g>\n     </g>\n     <g id=\"text_172\">\n      <!-- Sandwich tern -->\n      <g transform=\"translate(162.650012 737.677442)rotate(-45)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-83\"/>\n       <use x=\"63.476562\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"124.755859\" xlink:href=\"#DejaVuSans-110\"/>\n       <use x=\"188.134766\" xlink:href=\"#DejaVuSans-100\"/>\n       <use x=\"251.611328\" xlink:href=\"#DejaVuSans-119\"/>\n       <use x=\"333.398438\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"361.181641\" xlink:href=\"#DejaVuSans-99\"/>\n       <use x=\"416.162109\" xlink:href=\"#DejaVuSans-104\"/>\n       <use x=\"479.541016\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"511.328125\" xlink:href=\"#DejaVuSans-116\"/>\n       <use x=\"550.537109\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"612.060547\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"653.158203\" xlink:href=\"#DejaVuSans-110\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_173\">\n     <g id=\"line2d_173\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"186.719096\" xlink:href=\"#m9a96a52268\" y=\"674.638125\"/>\n      </g>\n     </g>\n     <g id=\"text_173\">\n      <!-- Elegant tern -->\n      <g transform=\"translate(166.862101 730.627367)rotate(-45)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-69\"/>\n       <use x=\"63.183594\" xlink:href=\"#DejaVuSans-108\"/>\n       <use x=\"90.966797\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"152.490234\" xlink:href=\"#DejaVuSans-103\"/>\n       <use x=\"215.966797\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"277.246094\" xlink:href=\"#DejaVuSans-110\"/>\n       <use x=\"340.625\" xlink:href=\"#DejaVuSans-116\"/>\n       <use x=\"379.833984\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"411.621094\" xlink:href=\"#DejaVuSans-116\"/>\n       <use x=\"450.830078\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"512.353516\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"553.451172\" xlink:href=\"#DejaVuSans-110\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_174\">\n     <g id=\"line2d_174\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"187.406147\" xlink:href=\"#m9a96a52268\" y=\"674.638125\"/>\n      </g>\n     </g>\n     <g id=\"text_174\">\n      <!-- Red-throated loon -->\n      <g transform=\"translate(157.319306 751.087059)rotate(-45)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-82\"/>\n       <use x=\"69.419922\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"130.943359\" xlink:href=\"#DejaVuSans-100\"/>\n       <use x=\"194.419922\" xlink:href=\"#DejaVuSans-45\"/>\n       <use x=\"230.503906\" xlink:href=\"#DejaVuSans-116\"/>\n       <use x=\"269.712891\" xlink:href=\"#DejaVuSans-104\"/>\n       <use x=\"333.091797\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"374.173828\" xlink:href=\"#DejaVuSans-111\"/>\n       <use x=\"435.355469\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"496.634766\" xlink:href=\"#DejaVuSans-116\"/>\n       <use x=\"535.84375\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"597.367188\" xlink:href=\"#DejaVuSans-100\"/>\n       <use x=\"660.84375\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"692.630859\" xlink:href=\"#DejaVuSans-108\"/>\n       <use x=\"720.414062\" 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id=\"text_204\">\n      <!-- Swallow-tailed kite -->\n      <g transform=\"translate(176.719918 753.5089)rotate(-45)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-83\"/>\n       <use x=\"63.476562\" xlink:href=\"#DejaVuSans-119\"/>\n       <use x=\"145.263672\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"206.542969\" xlink:href=\"#DejaVuSans-108\"/>\n       <use x=\"234.326172\" xlink:href=\"#DejaVuSans-108\"/>\n       <use x=\"262.109375\" xlink:href=\"#DejaVuSans-111\"/>\n       <use x=\"323.291016\" xlink:href=\"#DejaVuSans-119\"/>\n       <use x=\"405.078125\" xlink:href=\"#DejaVuSans-45\"/>\n       <use x=\"441.162109\" xlink:href=\"#DejaVuSans-116\"/>\n       <use x=\"480.371094\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"541.650391\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"569.433594\" xlink:href=\"#DejaVuSans-108\"/>\n       <use x=\"597.216797\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"658.740234\" xlink:href=\"#DejaVuSans-100\"/>\n     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y=\"674.638125\"/>\n      </g>\n     </g>\n     <g id=\"text_207\">\n      <!-- Sharp-shinned hawk -->\n      <g transform=\"translate(175.969217 759.132609)rotate(-45)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-83\"/>\n       <use x=\"63.476562\" xlink:href=\"#DejaVuSans-104\"/>\n       <use x=\"126.855469\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"188.134766\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"229.248047\" xlink:href=\"#DejaVuSans-112\"/>\n       <use x=\"292.724609\" xlink:href=\"#DejaVuSans-45\"/>\n       <use x=\"328.808594\" xlink:href=\"#DejaVuSans-115\"/>\n       <use x=\"380.908203\" xlink:href=\"#DejaVuSans-104\"/>\n       <use x=\"444.287109\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"472.070312\" xlink:href=\"#DejaVuSans-110\"/>\n       <use x=\"535.449219\" xlink:href=\"#DejaVuSans-110\"/>\n       <use x=\"598.828125\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"660.351562\" xlink:href=\"#DejaVuSans-100\"/>\n       <use 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style=\"stroke:#000000;stroke-width:0.8;\" x=\"212.139986\" xlink:href=\"#m9a96a52268\" y=\"674.638125\"/>\n      </g>\n     </g>\n     <g id=\"text_210\">\n      <!-- Bald eagle -->\n      <g transform=\"translate(195.404205 724.38494)rotate(-45)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-66\"/>\n       <use x=\"68.603516\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"129.882812\" xlink:href=\"#DejaVuSans-108\"/>\n       <use x=\"157.666016\" xlink:href=\"#DejaVuSans-100\"/>\n       <use x=\"221.142578\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"252.929688\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"314.453125\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"375.732422\" xlink:href=\"#DejaVuSans-103\"/>\n       <use x=\"439.208984\" xlink:href=\"#DejaVuSans-108\"/>\n       <use x=\"466.992188\" xlink:href=\"#DejaVuSans-101\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_211\">\n     <g id=\"line2d_211\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"212.827037\" xlink:href=\"#m9a96a52268\" y=\"674.638125\"/>\n      </g>\n     </g>\n     <g id=\"text_211\">\n      <!-- Mississippi kite -->\n      <g transform=\"translate(188.228008 740.111436)rotate(-45)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-77\"/>\n       <use x=\"86.279297\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"114.0625\" xlink:href=\"#DejaVuSans-115\"/>\n       <use x=\"166.162109\" xlink:href=\"#DejaVuSans-115\"/>\n       <use x=\"218.261719\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"246.044922\" xlink:href=\"#DejaVuSans-115\"/>\n       <use x=\"298.144531\" xlink:href=\"#DejaVuSans-115\"/>\n       <use x=\"350.244141\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"378.027344\" xlink:href=\"#DejaVuSans-112\"/>\n       <use x=\"441.503906\" xlink:href=\"#DejaVuSans-112\"/>\n       <use x=\"504.980469\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"532.763672\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"564.550781\" xlink:href=\"#DejaVuSans-107\"/>\n       <use x=\"622.460938\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"650.244141\" xlink:href=\"#DejaVuSans-116\"/>\n       <use x=\"689.453125\" xlink:href=\"#DejaVuSans-101\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_212\">\n     <g id=\"line2d_212\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"213.514088\" xlink:href=\"#m9a96a52268\" y=\"674.638125\"/>\n      </g>\n     </g>\n     <g id=\"text_212\">\n      <!-- Red-shouldered hawk -->\n      <g transform=\"translate(177.084831 763.771892)rotate(-45)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-82\"/>\n       <use x=\"69.419922\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"130.943359\" xlink:href=\"#DejaVuSans-100\"/>\n       <use x=\"194.419922\" xlink:href=\"#DejaVuSans-45\"/>\n       <use x=\"230.503906\" xlink:href=\"#DejaVuSans-115\"/>\n       <use x=\"282.603516\" xlink:href=\"#DejaVuSans-104\"/>\n       <use x=\"345.982422\" xlink:href=\"#DejaVuSans-111\"/>\n       <use x=\"407.164062\" xlink:href=\"#DejaVuSans-117\"/>\n       <use x=\"470.542969\" xlink:href=\"#DejaVuSans-108\"/>\n       <use x=\"498.326172\" xlink:href=\"#DejaVuSans-100\"/>\n       <use x=\"561.802734\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"623.326172\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"664.408203\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"725.931641\" xlink:href=\"#DejaVuSans-100\"/>\n       <use x=\"789.408203\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"821.195312\" xlink:href=\"#DejaVuSans-104\"/>\n       <use x=\"884.574219\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"945.853516\" xlink:href=\"#DejaVuSans-119\"/>\n       <use x=\"1027.640625\" xlink:href=\"#DejaVuSans-107\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_213\">\n     <g id=\"line2d_213\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"214.20114\" xlink:href=\"#m9a96a52268\" y=\"674.638125\"/>\n      </g>\n     </g>\n     <g id=\"text_213\">\n      <!-- Broad-winged hawk -->\n      <g transform=\"translate(181.175938 756.96378)rotate(-45)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-66\"/>\n       <use x=\"68.603516\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"109.685547\" xlink:href=\"#DejaVuSans-111\"/>\n       <use x=\"170.867188\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"232.146484\" xlink:href=\"#DejaVuSans-100\"/>\n       <use x=\"295.623047\" xlink:href=\"#DejaVuSans-45\"/>\n       <use x=\"331.707031\" xlink:href=\"#DejaVuSans-119\"/>\n       <use x=\"413.494141\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"441.277344\" xlink:href=\"#DejaVuSans-110\"/>\n       <use x=\"504.65625\" xlink:href=\"#DejaVuSans-103\"/>\n       <use x=\"568.132812\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"629.65625\" xlink:href=\"#DejaVuSans-100\"/>\n       <use x=\"693.132812\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"724.919922\" xlink:href=\"#DejaVuSans-104\"/>\n       <use x=\"788.298828\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"849.578125\" xlink:href=\"#DejaVuSans-119\"/>\n       <use x=\"931.365234\" xlink:href=\"#DejaVuSans-107\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_214\">\n     <g id=\"line2d_214\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"214.888191\" xlink:href=\"#m9a96a52268\" y=\"674.638125\"/>\n      </g>\n     </g>\n     <g id=\"text_214\">\n      <!-- Swainson's hawk -->\n      <g transform=\"translate(186.785116 747.119528)rotate(-45)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-83\"/>\n       <use x=\"63.476562\" xlink:href=\"#DejaVuSans-119\"/>\n       <use x=\"145.263672\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"206.542969\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"234.326172\" xlink:href=\"#DejaVuSans-110\"/>\n       <use x=\"297.705078\" xlink:href=\"#DejaVuSans-115\"/>\n       <use x=\"349.804688\" xlink:href=\"#DejaVuSans-111\"/>\n       <use x=\"410.986328\" xlink:href=\"#DejaVuSans-110\"/>\n       <use x=\"474.365234\" xlink:href=\"#DejaVuSans-39\"/>\n       <use x=\"501.855469\" xlink:href=\"#DejaVuSans-115\"/>\n       <use x=\"553.955078\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"585.742188\" xlink:href=\"#DejaVuSans-104\"/>\n       <use x=\"649.121094\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"710.400391\" xlink:href=\"#DejaVuSans-119\"/>\n       <use x=\"792.1875\" xlink:href=\"#DejaVuSans-107\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_215\">\n     <g id=\"line2d_215\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"215.575242\" xlink:href=\"#m9a96a52268\" y=\"674.638125\"/>\n      </g>\n     </g>\n     <g id=\"text_215\">\n      <!-- Red-tailed hawk -->\n      <g transform=\"translate(188.969797 744.124267)rotate(-45)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-82\"/>\n       <use x=\"69.419922\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"130.943359\" xlink:href=\"#DejaVuSans-100\"/>\n       <use x=\"194.419922\" xlink:href=\"#DejaVuSans-45\"/>\n       <use x=\"230.503906\" xlink:href=\"#DejaVuSans-116\"/>\n       <use x=\"269.712891\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"330.992188\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"358.775391\" xlink:href=\"#DejaVuSans-108\"/>\n       <use x=\"386.558594\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"448.082031\" xlink:href=\"#DejaVuSans-100\"/>\n       <use x=\"511.558594\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"543.345703\" xlink:href=\"#DejaVuSans-104\"/>\n       <use x=\"606.724609\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"668.003906\" xlink:href=\"#DejaVuSans-119\"/>\n       <use x=\"749.791016\" xlink:href=\"#DejaVuSans-107\"/>\n      </g>\n     </g>\n    </g>\n    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xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"634.703125\" xlink:href=\"#DejaVuSans-100\"/>\n       <use x=\"698.179688\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"729.966797\" xlink:href=\"#DejaVuSans-104\"/>\n       <use x=\"793.345703\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"854.625\" xlink:href=\"#DejaVuSans-119\"/>\n       <use x=\"936.412109\" xlink:href=\"#DejaVuSans-107\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_217\">\n     <g id=\"line2d_217\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"216.949344\" xlink:href=\"#m9a96a52268\" y=\"674.638125\"/>\n      </g>\n     </g>\n     <g id=\"text_217\">\n      <!-- Ferruginous hawk -->\n      <g transform=\"translate(187.364659 750.082747)rotate(-45)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-70\"/>\n       <use x=\"57.441406\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"118.964844\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"160.0625\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"201.175781\" xlink:href=\"#DejaVuSans-117\"/>\n       <use x=\"264.554688\" xlink:href=\"#DejaVuSans-103\"/>\n       <use x=\"328.03125\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"355.814453\" xlink:href=\"#DejaVuSans-110\"/>\n       <use x=\"419.193359\" xlink:href=\"#DejaVuSans-111\"/>\n       <use x=\"480.375\" xlink:href=\"#DejaVuSans-117\"/>\n       <use x=\"543.753906\" xlink:href=\"#DejaVuSans-115\"/>\n       <use x=\"595.853516\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"627.640625\" xlink:href=\"#DejaVuSans-104\"/>\n       <use x=\"691.019531\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"752.298828\" xlink:href=\"#DejaVuSans-119\"/>\n       <use x=\"834.085938\" xlink:href=\"#DejaVuSans-107\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_218\">\n     <g id=\"line2d_218\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"217.636395\" xlink:href=\"#m9a96a52268\" y=\"674.638125\"/>\n      </g>\n     </g>\n     <g id=\"text_218\">\n      <!-- Barn owl -->\n      <g transform=\"translate(204.141152 717.903864)rotate(-45)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-66\"/>\n       <use x=\"68.603516\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"129.882812\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"170.980469\" xlink:href=\"#DejaVuSans-110\"/>\n       <use x=\"234.359375\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"266.146484\" xlink:href=\"#DejaVuSans-111\"/>\n       <use x=\"327.328125\" xlink:href=\"#DejaVuSans-119\"/>\n       <use x=\"409.115234\" xlink:href=\"#DejaVuSans-108\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_219\">\n     <g id=\"line2d_219\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"218.323446\" xlink:href=\"#m9a96a52268\" y=\"674.638125\"/>\n      </g>\n     </g>\n     <g id=\"text_219\">\n      <!-- Eastern screech-owl -->\n      <g transform=\"translate(184.566831 758.426607)rotate(-45)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-69\"/>\n       <use x=\"63.183594\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"124.462891\" xlink:href=\"#DejaVuSans-115\"/>\n       <use x=\"176.5625\" xlink:href=\"#DejaVuSans-116\"/>\n       <use x=\"215.771484\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"277.294922\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"318.392578\" xlink:href=\"#DejaVuSans-110\"/>\n       <use x=\"381.771484\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"413.558594\" xlink:href=\"#DejaVuSans-115\"/>\n       <use x=\"465.658203\" xlink:href=\"#DejaVuSans-99\"/>\n       <use x=\"520.638672\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"561.720703\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"623.244141\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"684.767578\" xlink:href=\"#DejaVuSans-99\"/>\n       <use x=\"739.748047\" xlink:href=\"#DejaVuSans-104\"/>\n       <use x=\"803.126953\" xlink:href=\"#DejaVuSans-45\"/>\n       <use x=\"839.226562\" xlink:href=\"#DejaVuSans-111\"/>\n       <use x=\"900.408203\" xlink:href=\"#DejaVuSans-119\"/>\n       <use x=\"982.195312\" xlink:href=\"#DejaVuSans-108\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_220\">\n     <g id=\"line2d_220\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"219.010497\" xlink:href=\"#m9a96a52268\" y=\"674.638125\"/>\n      </g>\n     </g>\n     <g id=\"text_220\">\n      <!-- Great horned owl -->\n      <g transform=\"translate(190.24009 748.454192)rotate(-45)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-71\"/>\n       <use x=\"77.490234\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"118.572266\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"180.095703\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"241.375\" xlink:href=\"#DejaVuSans-116\"/>\n       <use x=\"280.583984\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"312.371094\" xlink:href=\"#DejaVuSans-104\"/>\n       <use x=\"375.75\" xlink:href=\"#DejaVuSans-111\"/>\n       <use x=\"436.931641\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"478.029297\" xlink:href=\"#DejaVuSans-110\"/>\n       <use x=\"541.408203\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"602.931641\" xlink:href=\"#DejaVuSans-100\"/>\n       <use x=\"666.408203\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"698.195312\" xlink:href=\"#DejaVuSans-111\"/>\n       <use x=\"759.376953\" xlink:href=\"#DejaVuSans-119\"/>\n       <use x=\"841.164062\" xlink:href=\"#DejaVuSans-108\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_221\">\n     <g id=\"line2d_221\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"219.697548\" xlink:href=\"#m9a96a52268\" y=\"674.638125\"/>\n      </g>\n     </g>\n     <g id=\"text_221\">\n      <!-- Snowy owl -->\n      <g transform=\"translate(202.855701 724.597072)rotate(-45)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-83\"/>\n       <use x=\"63.476562\" xlink:href=\"#DejaVuSans-110\"/>\n       <use x=\"126.855469\" xlink:href=\"#DejaVuSans-111\"/>\n       <use x=\"188.037109\" xlink:href=\"#DejaVuSans-119\"/>\n       <use x=\"269.824219\" xlink:href=\"#DejaVuSans-121\"/>\n       <use x=\"329.003906\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"360.791016\" xlink:href=\"#DejaVuSans-111\"/>\n       <use x=\"421.972656\" xlink:href=\"#DejaVuSans-119\"/>\n       <use x=\"503.759766\" xlink:href=\"#DejaVuSans-108\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_222\">\n     <g id=\"line2d_222\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"220.384599\" xlink:href=\"#m9a96a52268\" y=\"674.638125\"/>\n      </g>\n     </g>\n     <g id=\"text_222\">\n      <!-- Northern hawk owl -->\n      <g transform=\"translate(188.948179 753.786219)rotate(-45)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-78\"/>\n       <use x=\"74.804688\" xlink:href=\"#DejaVuSans-111\"/>\n       <use x=\"135.986328\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"177.099609\" xlink:href=\"#DejaVuSans-116\"/>\n       <use x=\"216.308594\" xlink:href=\"#DejaVuSans-104\"/>\n       <use x=\"279.6875\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"341.210938\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"382.308594\" xlink:href=\"#DejaVuSans-110\"/>\n       <use x=\"445.6875\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"477.474609\" xlink:href=\"#DejaVuSans-104\"/>\n       <use x=\"540.853516\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"602.132812\" xlink:href=\"#DejaVuSans-119\"/>\n       <use x=\"683.919922\" xlink:href=\"#DejaVuSans-107\"/>\n       <use x=\"741.830078\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"773.617188\" xlink:href=\"#DejaVuSans-111\"/>\n       <use x=\"834.798828\" xlink:href=\"#DejaVuSans-119\"/>\n       <use x=\"916.585938\" xlink:href=\"#DejaVuSans-108\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_223\">\n     <g id=\"line2d_223\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"221.071651\" xlink:href=\"#m9a96a52268\" y=\"674.638125\"/>\n      </g>\n     </g>\n     <g id=\"text_223\">\n      <!-- Burrowing owl -->\n      <g transform=\"translate(197.768615 737.519448)rotate(-45)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-66\"/>\n       <use x=\"68.603516\" xlink:href=\"#DejaVuSans-117\"/>\n       <use x=\"131.982422\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"173.080078\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"214.162109\" xlink:href=\"#DejaVuSans-111\"/>\n       <use x=\"275.34375\" xlink:href=\"#DejaVuSans-119\"/>\n       <use x=\"357.130859\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"384.914062\" xlink:href=\"#DejaVuSans-110\"/>\n       <use x=\"448.292969\" xlink:href=\"#DejaVuSans-103\"/>\n       <use x=\"511.769531\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"543.556641\" 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transform=\"translate(197.866321 746.942751)rotate(-45)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-66\"/>\n       <use x=\"68.603516\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"130.126953\" xlink:href=\"#DejaVuSans-108\"/>\n       <use x=\"157.910156\" xlink:href=\"#DejaVuSans-116\"/>\n       <use x=\"197.119141\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"258.642578\" xlink:href=\"#DejaVuSans-100\"/>\n       <use x=\"322.119141\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"353.90625\" xlink:href=\"#DejaVuSans-107\"/>\n       <use x=\"411.816406\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"439.599609\" xlink:href=\"#DejaVuSans-110\"/>\n       <use x=\"502.978516\" xlink:href=\"#DejaVuSans-103\"/>\n       <use x=\"566.455078\" xlink:href=\"#DejaVuSans-102\"/>\n       <use x=\"601.660156\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"629.443359\" xlink:href=\"#DejaVuSans-115\"/>\n       <use x=\"681.542969\" xlink:href=\"#DejaVuSans-104\"/>\n  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<use x=\"390.267578\" xlink:href=\"#DejaVuSans-119\"/>\n       <use x=\"472.054688\" xlink:href=\"#DejaVuSans-111\"/>\n       <use x=\"533.236328\" xlink:href=\"#DejaVuSans-111\"/>\n       <use x=\"594.417969\" xlink:href=\"#DejaVuSans-100\"/>\n       <use x=\"657.894531\" xlink:href=\"#DejaVuSans-112\"/>\n       <use x=\"721.371094\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"782.894531\" xlink:href=\"#DejaVuSans-99\"/>\n       <use x=\"837.875\" xlink:href=\"#DejaVuSans-107\"/>\n       <use x=\"895.738281\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"957.261719\" xlink:href=\"#DejaVuSans-114\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_232\">\n     <g id=\"line2d_232\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"227.25511\" xlink:href=\"#m9a96a52268\" y=\"674.638125\"/>\n      </g>\n     </g>\n     <g id=\"text_232\">\n      <!-- Red-headed woodpecker -->\n      <g transform=\"translate(185.184467 775.054665)rotate(-45)scale(0.1 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<use x=\"289.03125\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"320.818359\" xlink:href=\"#DejaVuSans-119\"/>\n       <use x=\"402.605469\" xlink:href=\"#DejaVuSans-111\"/>\n       <use x=\"463.787109\" xlink:href=\"#DejaVuSans-111\"/>\n       <use x=\"524.96875\" xlink:href=\"#DejaVuSans-100\"/>\n       <use x=\"588.445312\" xlink:href=\"#DejaVuSans-112\"/>\n       <use x=\"651.921875\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"713.445312\" xlink:href=\"#DejaVuSans-99\"/>\n       <use x=\"768.425781\" xlink:href=\"#DejaVuSans-107\"/>\n       <use x=\"826.289062\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"887.8125\" xlink:href=\"#DejaVuSans-114\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_234\">\n     <g id=\"line2d_234\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"228.629213\" xlink:href=\"#m9a96a52268\" y=\"674.638125\"/>\n      </g>\n     </g>\n     <g id=\"text_234\">\n      <!-- Red-bellied woodpecker -->\n      <g transform=\"translate(188.019186 772.13343)rotate(-45)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-82\"/>\n       <use x=\"69.419922\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"130.943359\" xlink:href=\"#DejaVuSans-100\"/>\n       <use x=\"194.419922\" xlink:href=\"#DejaVuSans-45\"/>\n       <use x=\"230.503906\" xlink:href=\"#DejaVuSans-98\"/>\n       <use x=\"293.980469\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"355.503906\" xlink:href=\"#DejaVuSans-108\"/>\n       <use x=\"383.287109\" xlink:href=\"#DejaVuSans-108\"/>\n       <use x=\"411.070312\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"438.853516\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"500.376953\" xlink:href=\"#DejaVuSans-100\"/>\n       <use x=\"563.853516\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"595.640625\" xlink:href=\"#DejaVuSans-119\"/>\n       <use x=\"677.427734\" xlink:href=\"#DejaVuSans-111\"/>\n       <use x=\"738.609375\" xlink:href=\"#DejaVuSans-111\"/>\n       <use x=\"799.791016\" xlink:href=\"#DejaVuSans-100\"/>\n       <use x=\"863.267578\" xlink:href=\"#DejaVuSans-112\"/>\n       <use x=\"926.744141\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"988.267578\" xlink:href=\"#DejaVuSans-99\"/>\n       <use x=\"1043.248047\" xlink:href=\"#DejaVuSans-107\"/>\n       <use x=\"1101.111328\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"1162.634766\" xlink:href=\"#DejaVuSans-114\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_235\">\n     <g id=\"line2d_235\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"229.316264\" xlink:href=\"#m9a96a52268\" y=\"674.638125\"/>\n      </g>\n     </g>\n     <g id=\"text_235\">\n      <!-- Williamson's sapsucker -->\n      <g transform=\"translate(190.096697 769.352511)rotate(-45)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-87\"/>\n       <use x=\"98.845703\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"126.628906\" xlink:href=\"#DejaVuSans-108\"/>\n  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xlink:href=\"#DejaVuSans-45\"/>\n       <use x=\"357.023438\" xlink:href=\"#DejaVuSans-98\"/>\n       <use x=\"420.5\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"482.023438\" xlink:href=\"#DejaVuSans-108\"/>\n       <use x=\"509.806641\" xlink:href=\"#DejaVuSans-108\"/>\n       <use x=\"537.589844\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"565.373047\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"626.896484\" xlink:href=\"#DejaVuSans-100\"/>\n       <use x=\"690.373047\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"722.160156\" xlink:href=\"#DejaVuSans-115\"/>\n       <use x=\"774.259766\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"835.539062\" xlink:href=\"#DejaVuSans-112\"/>\n       <use x=\"899.015625\" xlink:href=\"#DejaVuSans-115\"/>\n       <use x=\"951.115234\" xlink:href=\"#DejaVuSans-117\"/>\n       <use x=\"1014.494141\" xlink:href=\"#DejaVuSans-99\"/>\n       <use x=\"1069.474609\" xlink:href=\"#DejaVuSans-107\"/>\n       <use x=\"1127.337891\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"1188.861328\" xlink:href=\"#DejaVuSans-114\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_237\">\n     <g id=\"line2d_237\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"230.690366\" xlink:href=\"#m9a96a52268\" y=\"674.638125\"/>\n      </g>\n     </g>\n     <g id=\"text_237\">\n      <!-- American three-toed woodpecker -->\n      <g transform=\"translate(173.393724 805.50666)rotate(-45)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-65\"/>\n       <use x=\"68.408203\" xlink:href=\"#DejaVuSans-109\"/>\n       <use x=\"165.820312\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"227.34375\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"268.457031\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"296.240234\" xlink:href=\"#DejaVuSans-99\"/>\n       <use x=\"351.220703\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"412.5\" xlink:href=\"#DejaVuSans-110\"/>\n       <use x=\"475.878906\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"507.666016\" xlink:href=\"#DejaVuSans-116\"/>\n       <use x=\"546.875\" xlink:href=\"#DejaVuSans-104\"/>\n       <use x=\"610.253906\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"651.335938\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"712.859375\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"774.382812\" xlink:href=\"#DejaVuSans-45\"/>\n       <use x=\"810.466797\" xlink:href=\"#DejaVuSans-116\"/>\n       <use x=\"849.675781\" xlink:href=\"#DejaVuSans-111\"/>\n       <use x=\"910.857422\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"972.380859\" xlink:href=\"#DejaVuSans-100\"/>\n       <use x=\"1035.857422\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"1067.644531\" xlink:href=\"#DejaVuSans-119\"/>\n       <use x=\"1149.431641\" xlink:href=\"#DejaVuSans-111\"/>\n       <use x=\"1210.613281\" xlink:href=\"#DejaVuSans-111\"/>\n       <use x=\"1271.794922\" xlink:href=\"#DejaVuSans-100\"/>\n       <use x=\"1335.271484\" xlink:href=\"#DejaVuSans-112\"/>\n       <use x=\"1398.748047\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"1460.271484\" xlink:href=\"#DejaVuSans-99\"/>\n       <use x=\"1515.251953\" xlink:href=\"#DejaVuSans-107\"/>\n       <use x=\"1573.115234\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"1634.638672\" xlink:href=\"#DejaVuSans-114\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_238\">\n     <g id=\"line2d_238\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"231.377417\" xlink:href=\"#m9a96a52268\" y=\"674.638125\"/>\n      </g>\n     </g>\n     <g id=\"text_238\">\n      <!-- Black-backed woodpecker -->\n      <g transform=\"translate(187.041822 779.584568)rotate(-45)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-66\"/>\n       <use x=\"68.603516\" xlink:href=\"#DejaVuSans-108\"/>\n       <use x=\"96.386719\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"157.666016\" xlink:href=\"#DejaVuSans-99\"/>\n       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<use x=\"456.103516\" xlink:href=\"#DejaVuSans-111\"/>\n       <use x=\"517.285156\" xlink:href=\"#DejaVuSans-111\"/>\n       <use x=\"578.466797\" xlink:href=\"#DejaVuSans-100\"/>\n       <use x=\"641.943359\" xlink:href=\"#DejaVuSans-112\"/>\n       <use x=\"705.419922\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"766.943359\" xlink:href=\"#DejaVuSans-99\"/>\n       <use x=\"821.923828\" xlink:href=\"#DejaVuSans-107\"/>\n       <use x=\"879.787109\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"941.310547\" xlink:href=\"#DejaVuSans-114\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_240\">\n     <g id=\"line2d_240\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"232.751519\" xlink:href=\"#m9a96a52268\" y=\"674.638125\"/>\n      </g>\n     </g>\n     <g id=\"text_240\">\n      <!-- Hairy woodpecker -->\n      <g transform=\"translate(202.72434 750.967735)rotate(-45)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-72\"/>\n       <use x=\"75.195312\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"136.474609\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"164.257812\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"205.371094\" xlink:href=\"#DejaVuSans-121\"/>\n       <use x=\"264.550781\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"296.337891\" xlink:href=\"#DejaVuSans-119\"/>\n       <use x=\"378.125\" xlink:href=\"#DejaVuSans-111\"/>\n       <use x=\"439.306641\" xlink:href=\"#DejaVuSans-111\"/>\n       <use x=\"500.488281\" xlink:href=\"#DejaVuSans-100\"/>\n       <use x=\"563.964844\" xlink:href=\"#DejaVuSans-112\"/>\n       <use x=\"627.441406\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"688.964844\" xlink:href=\"#DejaVuSans-99\"/>\n       <use x=\"743.945312\" xlink:href=\"#DejaVuSans-107\"/>\n       <use x=\"801.808594\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"863.332031\" xlink:href=\"#DejaVuSans-114\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_241\">\n     <g id=\"line2d_241\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"233.43857\" xlink:href=\"#m9a96a52268\" y=\"674.638125\"/>\n      </g>\n     </g>\n     <g id=\"text_241\">\n      <!-- Northern flicker -->\n      <g transform=\"translate(207.681101 742.428316)rotate(-45)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-78\"/>\n       <use x=\"74.804688\" xlink:href=\"#DejaVuSans-111\"/>\n       <use x=\"135.986328\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"177.099609\" xlink:href=\"#DejaVuSans-116\"/>\n       <use x=\"216.308594\" xlink:href=\"#DejaVuSans-104\"/>\n       <use x=\"279.6875\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"341.210938\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"382.308594\" xlink:href=\"#DejaVuSans-110\"/>\n       <use x=\"445.6875\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"477.474609\" xlink:href=\"#DejaVuSans-102\"/>\n       <use x=\"512.679688\" xlink:href=\"#DejaVuSans-108\"/>\n       <use x=\"540.462891\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"568.246094\" xlink:href=\"#DejaVuSans-99\"/>\n       <use x=\"623.226562\" xlink:href=\"#DejaVuSans-107\"/>\n       <use x=\"681.089844\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"742.613281\" xlink:href=\"#DejaVuSans-114\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_242\">\n     <g id=\"line2d_242\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"234.125621\" xlink:href=\"#m9a96a52268\" y=\"674.638125\"/>\n      </g>\n     </g>\n     <g id=\"text_242\">\n      <!-- Pileated woodpecker -->\n      <g transform=\"translate(199.20891 760.746801)rotate(-45)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-80\"/>\n       <use x=\"60.271484\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"88.054688\" xlink:href=\"#DejaVuSans-108\"/>\n       <use x=\"115.837891\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"177.361328\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"238.640625\" xlink:href=\"#DejaVuSans-116\"/>\n       <use x=\"277.849609\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"339.373047\" xlink:href=\"#DejaVuSans-100\"/>\n       <use x=\"402.849609\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"434.636719\" xlink:href=\"#DejaVuSans-119\"/>\n       <use x=\"516.423828\" xlink:href=\"#DejaVuSans-111\"/>\n       <use x=\"577.605469\" xlink:href=\"#DejaVuSans-111\"/>\n       <use x=\"638.787109\" xlink:href=\"#DejaVuSans-100\"/>\n       <use x=\"702.263672\" xlink:href=\"#DejaVuSans-112\"/>\n       <use x=\"765.740234\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"827.263672\" xlink:href=\"#DejaVuSans-99\"/>\n       <use x=\"882.244141\" xlink:href=\"#DejaVuSans-107\"/>\n       <use x=\"940.107422\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"1001.630859\" xlink:href=\"#DejaVuSans-114\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_243\">\n     <g id=\"line2d_243\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"234.812673\" xlink:href=\"#m9a96a52268\" y=\"674.638125\"/>\n      </g>\n     </g>\n     <g id=\"text_243\">\n      <!-- Crested caracara -->\n      <g transform=\"translate(206.434489 747.669745)rotate(-45)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-67\"/>\n       <use x=\"69.824219\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"110.90625\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"172.429688\" xlink:href=\"#DejaVuSans-115\"/>\n       <use x=\"224.529297\" xlink:href=\"#DejaVuSans-116\"/>\n       <use x=\"263.738281\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"325.261719\" xlink:href=\"#DejaVuSans-100\"/>\n       <use x=\"388.738281\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"420.525391\" xlink:href=\"#DejaVuSans-99\"/>\n       <use x=\"475.505859\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"536.785156\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"577.898438\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"639.177734\" xlink:href=\"#DejaVuSans-99\"/>\n       <use x=\"694.158203\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"755.4375\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"796.550781\" xlink:href=\"#DejaVuSans-97\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_244\">\n     <g id=\"line2d_244\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"235.499724\" xlink:href=\"#m9a96a52268\" y=\"674.638125\"/>\n      </g>\n     </g>\n     <g id=\"text_244\">\n      <!-- American kestrel -->\n      <g transform=\"translate(207.443605 747.025615)rotate(-45)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-65\"/>\n       <use x=\"68.408203\" xlink:href=\"#DejaVuSans-109\"/>\n       <use x=\"165.820312\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"227.34375\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"268.457031\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"296.240234\" xlink:href=\"#DejaVuSans-99\"/>\n       <use x=\"351.220703\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"412.5\" xlink:href=\"#DejaVuSans-110\"/>\n       <use x=\"475.878906\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"507.666016\" xlink:href=\"#DejaVuSans-107\"/>\n       <use x=\"565.529297\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"627.052734\" xlink:href=\"#DejaVuSans-115\"/>\n       <use x=\"679.152344\" xlink:href=\"#DejaVuSans-116\"/>\n       <use x=\"718.361328\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"759.443359\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"820.966797\" xlink:href=\"#DejaVuSans-108\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_245\">\n     <g id=\"line2d_245\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"236.186775\" xlink:href=\"#m9a96a52268\" y=\"674.638125\"/>\n      </g>\n     </g>\n     <g id=\"text_245\">\n      <!-- Merlin -->\n      <g transform=\"translate(227.253475 708.779977)rotate(-45)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-77\"/>\n       <use x=\"86.279297\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"147.802734\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"188.916016\" xlink:href=\"#DejaVuSans-108\"/>\n       <use x=\"216.699219\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"244.482422\" xlink:href=\"#DejaVuSans-110\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_246\">\n     <g id=\"line2d_246\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"236.873826\" xlink:href=\"#m9a96a52268\" y=\"674.638125\"/>\n      </g>\n     </g>\n     <g id=\"text_246\">\n      <!-- Gyrfalcon -->\n      <g transform=\"translate(221.798088 721.064852)rotate(-45)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-71\"/>\n       <use x=\"77.490234\" xlink:href=\"#DejaVuSans-121\"/>\n       <use x=\"136.669922\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"177.783203\" xlink:href=\"#DejaVuSans-102\"/>\n       <use x=\"212.988281\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"274.267578\" xlink:href=\"#DejaVuSans-108\"/>\n       <use x=\"302.050781\" xlink:href=\"#DejaVuSans-99\"/>\n       <use x=\"357.03125\" xlink:href=\"#DejaVuSans-111\"/>\n       <use x=\"418.212891\" xlink:href=\"#DejaVuSans-110\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_247\">\n     <g id=\"line2d_247\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"237.560877\" xlink:href=\"#m9a96a52268\" y=\"674.638125\"/>\n      </g>\n     </g>\n     <g id=\"text_247\">\n      <!-- Peregrine falcon -->\n      <g transform=\"translate(210.617346 744.800438)rotate(-45)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-80\"/>\n       <use x=\"60.255859\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"121.779297\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"162.861328\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"224.384766\" xlink:href=\"#DejaVuSans-103\"/>\n       <use x=\"287.861328\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"328.974609\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"356.757812\" xlink:href=\"#DejaVuSans-110\"/>\n       <use x=\"420.136719\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"481.660156\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"513.447266\" xlink:href=\"#DejaVuSans-102\"/>\n       <use x=\"548.652344\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"609.931641\" xlink:href=\"#DejaVuSans-108\"/>\n       <use x=\"637.714844\" xlink:href=\"#DejaVuSans-99\"/>\n       <use x=\"692.695312\" xlink:href=\"#DejaVuSans-111\"/>\n       <use x=\"753.876953\" xlink:href=\"#DejaVuSans-110\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_248\">\n     <g id=\"line2d_248\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"238.247928\" xlink:href=\"#m9a96a52268\" y=\"674.638125\"/>\n      </g>\n     </g>\n     <g id=\"text_248\">\n      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y=\"674.638125\"/>\n      </g>\n     </g>\n     <g id=\"text_250\">\n      <!-- Great crested flycatcher -->\n      <g transform=\"translate(198.556804 773.04383)rotate(-45)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-71\"/>\n       <use x=\"77.490234\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"118.572266\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"180.095703\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"241.375\" xlink:href=\"#DejaVuSans-116\"/>\n       <use x=\"280.583984\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"312.371094\" xlink:href=\"#DejaVuSans-99\"/>\n       <use x=\"367.351562\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"408.433594\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"469.957031\" xlink:href=\"#DejaVuSans-115\"/>\n       <use x=\"522.056641\" xlink:href=\"#DejaVuSans-116\"/>\n       <use x=\"561.265625\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"622.789062\" xlink:href=\"#DejaVuSans-100\"/>\n       <use x=\"686.265625\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"718.052734\" xlink:href=\"#DejaVuSans-102\"/>\n       <use x=\"753.257812\" xlink:href=\"#DejaVuSans-108\"/>\n       <use x=\"781.041016\" xlink:href=\"#DejaVuSans-121\"/>\n       <use x=\"840.220703\" xlink:href=\"#DejaVuSans-99\"/>\n       <use x=\"895.201172\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"956.480469\" xlink:href=\"#DejaVuSans-116\"/>\n       <use x=\"995.689453\" xlink:href=\"#DejaVuSans-99\"/>\n       <use x=\"1050.669922\" xlink:href=\"#DejaVuSans-104\"/>\n       <use x=\"1114.048828\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"1175.572266\" xlink:href=\"#DejaVuSans-114\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_251\">\n     <g id=\"line2d_251\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"240.309081\" xlink:href=\"#m9a96a52268\" y=\"674.638125\"/>\n      </g>\n     </g>\n     <g id=\"text_251\">\n      <!-- Tropical kingbird -->\n      <g transform=\"translate(212.610935 746.309669)rotate(-45)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-84\"/>\n       <use x=\"60.865234\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"101.947266\" xlink:href=\"#DejaVuSans-111\"/>\n       <use x=\"163.128906\" xlink:href=\"#DejaVuSans-112\"/>\n       <use x=\"226.605469\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"254.388672\" xlink:href=\"#DejaVuSans-99\"/>\n       <use x=\"309.369141\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"370.648438\" xlink:href=\"#DejaVuSans-108\"/>\n       <use x=\"398.431641\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"430.21875\" xlink:href=\"#DejaVuSans-107\"/>\n       <use x=\"488.128906\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"515.912109\" xlink:href=\"#DejaVuSans-110\"/>\n       <use x=\"579.291016\" xlink:href=\"#DejaVuSans-103\"/>\n       <use x=\"642.767578\" xlink:href=\"#DejaVuSans-98\"/>\n       <use x=\"706.244141\" xlink:href=\"#DejaVuSans-105\"/>\n     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x=\"406.054688\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"437.841797\" xlink:href=\"#DejaVuSans-107\"/>\n       <use x=\"495.751953\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"523.535156\" xlink:href=\"#DejaVuSans-110\"/>\n       <use x=\"586.914062\" xlink:href=\"#DejaVuSans-103\"/>\n       <use x=\"650.390625\" xlink:href=\"#DejaVuSans-98\"/>\n       <use x=\"713.867188\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"741.650391\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"782.748047\" xlink:href=\"#DejaVuSans-100\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_253\">\n     <g id=\"line2d_253\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"241.683183\" xlink:href=\"#m9a96a52268\" y=\"674.638125\"/>\n      </g>\n     </g>\n     <g id=\"text_253\">\n      <!-- Western kingbird -->\n      <g transform=\"translate(213.306657 747.66643)rotate(-45)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-87\"/>\n       <use x=\"98.798828\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"160.322266\" xlink:href=\"#DejaVuSans-115\"/>\n       <use x=\"212.421875\" xlink:href=\"#DejaVuSans-116\"/>\n       <use x=\"251.630859\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"313.154297\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"354.251953\" xlink:href=\"#DejaVuSans-110\"/>\n       <use x=\"417.630859\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"449.417969\" xlink:href=\"#DejaVuSans-107\"/>\n       <use x=\"507.328125\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"535.111328\" xlink:href=\"#DejaVuSans-110\"/>\n       <use x=\"598.490234\" xlink:href=\"#DejaVuSans-103\"/>\n       <use x=\"661.966797\" xlink:href=\"#DejaVuSans-98\"/>\n       <use x=\"725.443359\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"753.226562\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"794.324219\" xlink:href=\"#DejaVuSans-100\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_254\">\n     <g id=\"line2d_254\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"242.370235\" xlink:href=\"#m9a96a52268\" y=\"674.638125\"/>\n      </g>\n     </g>\n     <g id=\"text_254\">\n      <!-- Eastern kingbird -->\n      <g transform=\"translate(215.261528 745.13079)rotate(-45)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-69\"/>\n       <use x=\"63.183594\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"124.462891\" xlink:href=\"#DejaVuSans-115\"/>\n       <use x=\"176.5625\" xlink:href=\"#DejaVuSans-116\"/>\n       <use x=\"215.771484\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"277.294922\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"318.392578\" xlink:href=\"#DejaVuSans-110\"/>\n       <use x=\"381.771484\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"413.558594\" xlink:href=\"#DejaVuSans-107\"/>\n       <use x=\"471.46875\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"499.251953\" xlink:href=\"#DejaVuSans-110\"/>\n       <use x=\"562.630859\" xlink:href=\"#DejaVuSans-103\"/>\n       <use x=\"626.107422\" xlink:href=\"#DejaVuSans-98\"/>\n       <use x=\"689.583984\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"717.367188\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"758.464844\" xlink:href=\"#DejaVuSans-100\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_255\">\n     <g id=\"line2d_255\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"243.057286\" xlink:href=\"#m9a96a52268\" y=\"674.638125\"/>\n      </g>\n     </g>\n     <g id=\"text_255\">\n      <!-- Scissor-tailed flycatcher -->\n      <g transform=\"translate(202.57487 771.878209)rotate(-45)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-83\"/>\n       <use x=\"63.476562\" xlink:href=\"#DejaVuSans-99\"/>\n       <use x=\"118.457031\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"146.240234\" xlink:href=\"#DejaVuSans-115\"/>\n       <use x=\"198.339844\" xlink:href=\"#DejaVuSans-115\"/>\n       <use x=\"250.439453\" xlink:href=\"#DejaVuSans-111\"/>\n       <use x=\"311.621094\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"352.640625\" xlink:href=\"#DejaVuSans-45\"/>\n       <use x=\"388.724609\" xlink:href=\"#DejaVuSans-116\"/>\n       <use x=\"427.933594\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"489.212891\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"516.996094\" xlink:href=\"#DejaVuSans-108\"/>\n       <use x=\"544.779297\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"606.302734\" xlink:href=\"#DejaVuSans-100\"/>\n       <use x=\"669.779297\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"701.566406\" xlink:href=\"#DejaVuSans-102\"/>\n       <use x=\"736.771484\" xlink:href=\"#DejaVuSans-108\"/>\n       <use x=\"764.554688\" xlink:href=\"#DejaVuSans-121\"/>\n       <use x=\"823.734375\" xlink:href=\"#DejaVuSans-99\"/>\n       <use x=\"878.714844\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"939.994141\" xlink:href=\"#DejaVuSans-116\"/>\n       <use x=\"979.203125\" xlink:href=\"#DejaVuSans-99\"/>\n       <use x=\"1034.183594\" xlink:href=\"#DejaVuSans-104\"/>\n       <use x=\"1097.5625\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"1159.085938\" xlink:href=\"#DejaVuSans-114\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_256\">\n     <g id=\"line2d_256\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"243.744337\" xlink:href=\"#m9a96a52268\" y=\"674.638125\"/>\n      </g>\n     </g>\n     <g id=\"text_256\">\n      <!-- Fork-tailed flycatcher -->\n      <g transform=\"translate(208.033787 762.334477)rotate(-45)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-70\"/>\n       <use x=\"57.472656\" xlink:href=\"#DejaVuSans-111\"/>\n       <use x=\"118.654297\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"159.767578\" xlink:href=\"#DejaVuSans-107\"/>\n       <use x=\"217.677734\" xlink:href=\"#DejaVuSans-45\"/>\n       <use x=\"253.761719\" xlink:href=\"#DejaVuSans-116\"/>\n       <use x=\"292.970703\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"354.25\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"382.033203\" xlink:href=\"#DejaVuSans-108\"/>\n       <use x=\"409.816406\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"471.339844\" xlink:href=\"#DejaVuSans-100\"/>\n       <use x=\"534.816406\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"566.603516\" xlink:href=\"#DejaVuSans-102\"/>\n       <use x=\"601.808594\" xlink:href=\"#DejaVuSans-108\"/>\n       <use x=\"629.591797\" xlink:href=\"#DejaVuSans-121\"/>\n       <use x=\"688.771484\" xlink:href=\"#DejaVuSans-99\"/>\n       <use x=\"743.751953\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"805.03125\" xlink:href=\"#DejaVuSans-116\"/>\n       <use x=\"844.240234\" xlink:href=\"#DejaVuSans-99\"/>\n       <use x=\"899.220703\" xlink:href=\"#DejaVuSans-104\"/>\n       <use x=\"962.599609\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"1024.123047\" xlink:href=\"#DejaVuSans-114\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_257\">\n     <g id=\"line2d_257\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"244.431388\" xlink:href=\"#m9a96a52268\" y=\"674.638125\"/>\n      </g>\n     </g>\n     <g id=\"text_257\">\n      <!-- Olive-sided flycatcher -->\n      <g transform=\"translate(207.84966 764.076832)rotate(-45)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-79\"/>\n       <use x=\"78.710938\" xlink:href=\"#DejaVuSans-108\"/>\n       <use x=\"106.494141\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"134.277344\" xlink:href=\"#DejaVuSans-118\"/>\n       <use x=\"193.457031\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"254.980469\" xlink:href=\"#DejaVuSans-45\"/>\n       <use x=\"291.064453\" xlink:href=\"#DejaVuSans-115\"/>\n       <use x=\"343.164062\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"370.947266\" xlink:href=\"#DejaVuSans-100\"/>\n       <use x=\"434.423828\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"495.947266\" xlink:href=\"#DejaVuSans-100\"/>\n       <use x=\"559.423828\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"591.210938\" xlink:href=\"#DejaVuSans-102\"/>\n       <use x=\"626.416016\" xlink:href=\"#DejaVuSans-108\"/>\n       <use x=\"654.199219\" xlink:href=\"#DejaVuSans-121\"/>\n       <use x=\"713.378906\" xlink:href=\"#DejaVuSans-99\"/>\n       <use x=\"768.359375\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"829.638672\" xlink:href=\"#DejaVuSans-116\"/>\n       <use x=\"868.847656\" xlink:href=\"#DejaVuSans-99\"/>\n       <use x=\"923.828125\" xlink:href=\"#DejaVuSans-104\"/>\n       <use x=\"987.207031\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"1048.730469\" xlink:href=\"#DejaVuSans-114\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_258\">\n     <g id=\"line2d_258\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"245.118439\" xlink:href=\"#m9a96a52268\" y=\"674.638125\"/>\n      </g>\n     </g>\n     <g id=\"text_258\">\n      <!-- Western wood-pewee -->\n      <g transform=\"translate(208.780332 763.589591)rotate(-45)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-87\"/>\n       <use x=\"98.798828\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"160.322266\" xlink:href=\"#DejaVuSans-115\"/>\n       <use x=\"212.421875\" xlink:href=\"#DejaVuSans-116\"/>\n       <use x=\"251.630859\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"313.154297\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"354.251953\" xlink:href=\"#DejaVuSans-110\"/>\n       <use x=\"417.630859\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"449.417969\" xlink:href=\"#DejaVuSans-119\"/>\n       <use x=\"531.205078\" xlink:href=\"#DejaVuSans-111\"/>\n       <use x=\"592.386719\" xlink:href=\"#DejaVuSans-111\"/>\n       <use x=\"653.568359\" xlink:href=\"#DejaVuSans-100\"/>\n       <use x=\"717.044922\" xlink:href=\"#DejaVuSans-45\"/>\n       <use x=\"753.128906\" xlink:href=\"#DejaVuSans-112\"/>\n   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id=\"text_260\">\n      <!-- Yellow-bellied flycatcher -->\n      <g transform=\"translate(205.281474 773.335511)rotate(-45)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-89\"/>\n       <use x=\"60.880859\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"122.404297\" xlink:href=\"#DejaVuSans-108\"/>\n       <use x=\"150.1875\" xlink:href=\"#DejaVuSans-108\"/>\n       <use x=\"177.970703\" xlink:href=\"#DejaVuSans-111\"/>\n       <use x=\"239.152344\" xlink:href=\"#DejaVuSans-119\"/>\n       <use x=\"320.939453\" xlink:href=\"#DejaVuSans-45\"/>\n       <use x=\"357.023438\" xlink:href=\"#DejaVuSans-98\"/>\n       <use x=\"420.5\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"482.023438\" xlink:href=\"#DejaVuSans-108\"/>\n       <use x=\"509.806641\" xlink:href=\"#DejaVuSans-108\"/>\n       <use x=\"537.589844\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"565.373047\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"626.896484\" xlink:href=\"#DejaVuSans-100\"/>\n   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transform=\"translate(216.214944 752.842673)rotate(-45)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-65\"/>\n       <use x=\"68.392578\" xlink:href=\"#DejaVuSans-99\"/>\n       <use x=\"123.373047\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"184.652344\" xlink:href=\"#DejaVuSans-100\"/>\n       <use x=\"248.128906\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"275.912109\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"337.191406\" xlink:href=\"#DejaVuSans-110\"/>\n       <use x=\"400.570312\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"432.357422\" xlink:href=\"#DejaVuSans-102\"/>\n       <use x=\"467.5625\" xlink:href=\"#DejaVuSans-108\"/>\n       <use x=\"495.345703\" xlink:href=\"#DejaVuSans-121\"/>\n       <use x=\"554.525391\" xlink:href=\"#DejaVuSans-99\"/>\n       <use x=\"609.505859\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"670.785156\" xlink:href=\"#DejaVuSans-116\"/>\n       <use x=\"709.994141\" xlink:href=\"#DejaVuSans-99\"/>\n       <use x=\"764.974609\" xlink:href=\"#DejaVuSans-104\"/>\n       <use x=\"828.353516\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"889.876953\" xlink:href=\"#DejaVuSans-114\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_262\">\n     <g id=\"line2d_262\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"247.866643\" xlink:href=\"#m9a96a52268\" y=\"674.638125\"/>\n      </g>\n     </g>\n     <g id=\"text_262\">\n      <!-- Alder flycatcher -->\n      <g transform=\"translate(221.790424 743.065817)rotate(-45)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-65\"/>\n       <use x=\"68.408203\" xlink:href=\"#DejaVuSans-108\"/>\n       <use x=\"96.191406\" xlink:href=\"#DejaVuSans-100\"/>\n       <use x=\"159.667969\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"221.191406\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"262.304688\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"294.091797\" xlink:href=\"#DejaVuSans-102\"/>\n       <use x=\"329.296875\" xlink:href=\"#DejaVuSans-108\"/>\n       <use x=\"357.080078\" xlink:href=\"#DejaVuSans-121\"/>\n       <use x=\"416.259766\" xlink:href=\"#DejaVuSans-99\"/>\n       <use x=\"471.240234\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"532.519531\" xlink:href=\"#DejaVuSans-116\"/>\n       <use x=\"571.728516\" xlink:href=\"#DejaVuSans-99\"/>\n       <use x=\"626.708984\" xlink:href=\"#DejaVuSans-104\"/>\n       <use x=\"690.087891\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"751.611328\" xlink:href=\"#DejaVuSans-114\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_263\">\n     <g id=\"line2d_263\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"248.553694\" xlink:href=\"#m9a96a52268\" y=\"674.638125\"/>\n      </g>\n     </g>\n     <g id=\"text_263\">\n      <!-- Willow flycatcher -->\n      <g transform=\"translate(220.255613 747.509541)rotate(-45)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-87\"/>\n       <use 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</g>\n     </g>\n    </g>\n    <g id=\"xtick_264\">\n     <g id=\"line2d_264\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"249.240746\" xlink:href=\"#m9a96a52268\" y=\"674.638125\"/>\n      </g>\n     </g>\n     <g id=\"text_264\">\n      <!-- Least flycatcher -->\n      <g transform=\"translate(222.899361 743.596147)rotate(-45)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-76\"/>\n       <use x=\"55.697266\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"117.220703\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"178.5\" xlink:href=\"#DejaVuSans-115\"/>\n       <use x=\"230.599609\" xlink:href=\"#DejaVuSans-116\"/>\n       <use x=\"269.808594\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"301.595703\" xlink:href=\"#DejaVuSans-102\"/>\n       <use x=\"336.800781\" xlink:href=\"#DejaVuSans-108\"/>\n       <use x=\"364.583984\" xlink:href=\"#DejaVuSans-121\"/>\n       <use x=\"423.763672\" xlink:href=\"#DejaVuSans-99\"/>\n       <use x=\"478.744141\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"540.023438\" xlink:href=\"#DejaVuSans-116\"/>\n       <use x=\"579.232422\" xlink:href=\"#DejaVuSans-99\"/>\n       <use x=\"634.212891\" xlink:href=\"#DejaVuSans-104\"/>\n       <use x=\"697.591797\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"759.115234\" xlink:href=\"#DejaVuSans-114\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_265\">\n     <g id=\"line2d_265\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"249.927797\" xlink:href=\"#m9a96a52268\" y=\"674.638125\"/>\n      </g>\n     </g>\n     <g id=\"text_265\">\n      <!-- Eastern phoebe -->\n      <g transform=\"translate(224.01399 742.74099)rotate(-45)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-69\"/>\n       <use x=\"63.183594\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"124.462891\" xlink:href=\"#DejaVuSans-115\"/>\n       <use x=\"176.5625\" xlink:href=\"#DejaVuSans-116\"/>\n       <use 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xlink:href=\"#DejaVuSans-110\"/>\n       <use x=\"564.208984\" xlink:href=\"#DejaVuSans-99\"/>\n       <use x=\"619.189453\" xlink:href=\"#DejaVuSans-104\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_340\">\n     <g id=\"line2d_340\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"301.456629\" xlink:href=\"#m9a96a52268\" y=\"674.638125\"/>\n      </g>\n     </g>\n     <g id=\"text_340\">\n      <!-- Common redpoll -->\n      <g transform=\"translate(274.115903 745.594829)rotate(-45)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-67\"/>\n       <use x=\"69.824219\" xlink:href=\"#DejaVuSans-111\"/>\n       <use x=\"131.005859\" xlink:href=\"#DejaVuSans-109\"/>\n       <use x=\"228.417969\" xlink:href=\"#DejaVuSans-109\"/>\n       <use x=\"325.830078\" xlink:href=\"#DejaVuSans-111\"/>\n       <use x=\"387.011719\" xlink:href=\"#DejaVuSans-110\"/>\n       <use x=\"450.390625\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"482.177734\" 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y=\"674.638125\"/>\n      </g>\n     </g>\n     <g id=\"text_343\">\n      <!-- White-winged crossbill -->\n      <g transform=\"translate(265.576532 766.795879)rotate(-45)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-87\"/>\n       <use x=\"98.876953\" xlink:href=\"#DejaVuSans-104\"/>\n       <use x=\"162.255859\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"190.039062\" xlink:href=\"#DejaVuSans-116\"/>\n       <use x=\"229.248047\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"290.771484\" xlink:href=\"#DejaVuSans-45\"/>\n       <use x=\"326.855469\" xlink:href=\"#DejaVuSans-119\"/>\n       <use x=\"408.642578\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"436.425781\" xlink:href=\"#DejaVuSans-110\"/>\n       <use x=\"499.804688\" xlink:href=\"#DejaVuSans-103\"/>\n       <use x=\"563.28125\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"624.804688\" xlink:href=\"#DejaVuSans-100\"/>\n       <use x=\"688.28125\" xlink:href=\"#DejaVuSans-32\"/>\n       <use 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x=\"849.951172\" xlink:href=\"#DejaVuSans-99\"/>\n       <use x=\"904.931641\" xlink:href=\"#DejaVuSans-104\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_346\">\n     <g id=\"line2d_346\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"305.578936\" xlink:href=\"#m9a96a52268\" y=\"674.638125\"/>\n      </g>\n     </g>\n     <g id=\"text_346\">\n      <!-- Lapland longspur -->\n      <g transform=\"translate(276.980333 748.110582)rotate(-45)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-76\"/>\n       <use x=\"55.712891\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"116.992188\" xlink:href=\"#DejaVuSans-112\"/>\n       <use x=\"180.46875\" xlink:href=\"#DejaVuSans-108\"/>\n       <use x=\"208.251953\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"269.53125\" xlink:href=\"#DejaVuSans-110\"/>\n       <use x=\"332.910156\" xlink:href=\"#DejaVuSans-100\"/>\n       <use x=\"396.386719\" xlink:href=\"#DejaVuSans-32\"/>\n       <use 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style=\"stroke:#000000;stroke-width:0.8;\" x=\"307.640089\" xlink:href=\"#m9a96a52268\" y=\"674.638125\"/>\n      </g>\n     </g>\n     <g id=\"text_349\">\n      <!-- Thick-billed longspur -->\n      <g transform=\"translate(272.796297 760.60096)rotate(-45)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-84\"/>\n       <use x=\"61.083984\" xlink:href=\"#DejaVuSans-104\"/>\n       <use x=\"124.462891\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"152.246094\" xlink:href=\"#DejaVuSans-99\"/>\n       <use x=\"207.226562\" xlink:href=\"#DejaVuSans-107\"/>\n       <use x=\"265.136719\" xlink:href=\"#DejaVuSans-45\"/>\n       <use x=\"301.220703\" xlink:href=\"#DejaVuSans-98\"/>\n       <use x=\"364.697266\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"392.480469\" xlink:href=\"#DejaVuSans-108\"/>\n       <use x=\"420.263672\" xlink:href=\"#DejaVuSans-108\"/>\n       <use x=\"448.046875\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"509.570312\" 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x=\"565.871094\" xlink:href=\"#DejaVuSans-111\"/>\n       <use x=\"627.052734\" xlink:href=\"#DejaVuSans-108\"/>\n       <use x=\"654.835938\" xlink:href=\"#DejaVuSans-101\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_384\">\n     <g id=\"line2d_384\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"331.686877\" xlink:href=\"#m9a96a52268\" y=\"674.638125\"/>\n      </g>\n     </g>\n     <g id=\"text_384\">\n      <!-- Bullock's oriole -->\n      <g transform=\"translate(306.995592 740.295947)rotate(-45)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-66\"/>\n       <use x=\"68.603516\" xlink:href=\"#DejaVuSans-117\"/>\n       <use x=\"131.982422\" xlink:href=\"#DejaVuSans-108\"/>\n       <use x=\"159.765625\" xlink:href=\"#DejaVuSans-108\"/>\n       <use x=\"187.548828\" xlink:href=\"#DejaVuSans-111\"/>\n       <use x=\"248.730469\" xlink:href=\"#DejaVuSans-99\"/>\n       <use x=\"303.710938\" xlink:href=\"#DejaVuSans-107\"/>\n       <use 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id=\"line2d_386\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"333.06098\" xlink:href=\"#m9a96a52268\" y=\"674.638125\"/>\n      </g>\n     </g>\n     <g id=\"text_386\">\n      <!-- Scott's oriole -->\n      <g transform=\"translate(312.031182 732.972972)rotate(-45)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-83\"/>\n       <use x=\"63.476562\" xlink:href=\"#DejaVuSans-99\"/>\n       <use x=\"118.457031\" xlink:href=\"#DejaVuSans-111\"/>\n       <use x=\"179.638672\" xlink:href=\"#DejaVuSans-116\"/>\n       <use x=\"218.847656\" xlink:href=\"#DejaVuSans-116\"/>\n       <use x=\"258.056641\" xlink:href=\"#DejaVuSans-39\"/>\n       <use x=\"285.546875\" xlink:href=\"#DejaVuSans-115\"/>\n       <use x=\"337.646484\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"369.433594\" xlink:href=\"#DejaVuSans-111\"/>\n       <use x=\"430.615234\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"471.728516\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"499.511719\" xlink:href=\"#DejaVuSans-111\"/>\n       <use x=\"560.693359\" xlink:href=\"#DejaVuSans-108\"/>\n       <use x=\"588.476562\" xlink:href=\"#DejaVuSans-101\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_387\">\n     <g id=\"line2d_387\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"333.748031\" xlink:href=\"#m9a96a52268\" y=\"674.638125\"/>\n      </g>\n     </g>\n     <g id=\"text_387\">\n      <!-- Red-winged blackbird -->\n      <g transform=\"translate(297.338108 763.733222)rotate(-45)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-82\"/>\n       <use x=\"69.419922\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"130.943359\" xlink:href=\"#DejaVuSans-100\"/>\n       <use x=\"194.419922\" xlink:href=\"#DejaVuSans-45\"/>\n       <use x=\"230.503906\" xlink:href=\"#DejaVuSans-119\"/>\n       <use x=\"312.291016\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"340.074219\" xlink:href=\"#DejaVuSans-110\"/>\n       <use 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y=\"674.638125\"/>\n      </g>\n     </g>\n     <g id=\"text_388\">\n      <!-- Brown-headed cowbird -->\n      <g transform=\"translate(295.643646 768.496249)rotate(-45)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-66\"/>\n       <use x=\"68.603516\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"109.685547\" xlink:href=\"#DejaVuSans-111\"/>\n       <use x=\"170.867188\" xlink:href=\"#DejaVuSans-119\"/>\n       <use x=\"252.654297\" xlink:href=\"#DejaVuSans-110\"/>\n       <use x=\"316.033203\" xlink:href=\"#DejaVuSans-45\"/>\n       <use x=\"352.117188\" xlink:href=\"#DejaVuSans-104\"/>\n       <use x=\"415.496094\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"477.019531\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"538.298828\" xlink:href=\"#DejaVuSans-100\"/>\n       <use x=\"601.775391\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"663.298828\" xlink:href=\"#DejaVuSans-100\"/>\n       <use x=\"726.775391\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"758.5625\" xlink:href=\"#DejaVuSans-99\"/>\n       <use x=\"813.542969\" xlink:href=\"#DejaVuSans-111\"/>\n       <use x=\"874.724609\" xlink:href=\"#DejaVuSans-119\"/>\n       <use x=\"956.511719\" xlink:href=\"#DejaVuSans-98\"/>\n       <use x=\"1019.988281\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"1047.771484\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"1088.869141\" xlink:href=\"#DejaVuSans-100\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_389\">\n     <g id=\"line2d_389\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"335.122133\" xlink:href=\"#m9a96a52268\" y=\"674.638125\"/>\n      </g>\n     </g>\n     <g id=\"text_389\">\n      <!-- Rusty blackbird -->\n      <g transform=\"translate(309.625409 741.906825)rotate(-45)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-82\"/>\n       <use x=\"69.419922\" xlink:href=\"#DejaVuSans-117\"/>\n       <use x=\"132.798828\" xlink:href=\"#DejaVuSans-115\"/>\n       <use x=\"184.898438\" xlink:href=\"#DejaVuSans-116\"/>\n       <use x=\"224.107422\" xlink:href=\"#DejaVuSans-121\"/>\n       <use x=\"283.287109\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"315.074219\" xlink:href=\"#DejaVuSans-98\"/>\n       <use x=\"378.550781\" xlink:href=\"#DejaVuSans-108\"/>\n       <use x=\"406.333984\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"467.613281\" xlink:href=\"#DejaVuSans-99\"/>\n       <use x=\"522.59375\" xlink:href=\"#DejaVuSans-107\"/>\n       <use x=\"580.503906\" xlink:href=\"#DejaVuSans-98\"/>\n       <use x=\"643.980469\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"671.763672\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"712.861328\" xlink:href=\"#DejaVuSans-100\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_390\">\n     <g id=\"line2d_390\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"335.809184\" xlink:href=\"#m9a96a52268\" y=\"674.638125\"/>\n      </g>\n     </g>\n     <g id=\"text_390\">\n      <!-- Brewer's blackbird -->\n      <g transform=\"translate(304.940658 752.650428)rotate(-45)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-66\"/>\n       <use x=\"68.603516\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"109.685547\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"171.208984\" xlink:href=\"#DejaVuSans-119\"/>\n       <use x=\"252.996094\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"314.519531\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"355.632812\" xlink:href=\"#DejaVuSans-39\"/>\n       <use x=\"383.123047\" xlink:href=\"#DejaVuSans-115\"/>\n       <use x=\"435.222656\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"467.009766\" xlink:href=\"#DejaVuSans-98\"/>\n       <use x=\"530.486328\" xlink:href=\"#DejaVuSans-108\"/>\n       <use x=\"558.269531\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"619.548828\" xlink:href=\"#DejaVuSans-99\"/>\n       <use x=\"674.529297\" xlink:href=\"#DejaVuSans-107\"/>\n      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x=\"450.390625\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"482.177734\" xlink:href=\"#DejaVuSans-103\"/>\n       <use x=\"545.654297\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"586.767578\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"648.046875\" xlink:href=\"#DejaVuSans-99\"/>\n       <use x=\"703.027344\" xlink:href=\"#DejaVuSans-107\"/>\n       <use x=\"760.9375\" xlink:href=\"#DejaVuSans-108\"/>\n       <use x=\"788.720703\" xlink:href=\"#DejaVuSans-101\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_392\">\n     <g id=\"line2d_392\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"337.183286\" xlink:href=\"#m9a96a52268\" y=\"674.638125\"/>\n      </g>\n     </g>\n     <g id=\"text_392\">\n      <!-- Great-tailed grackle -->\n      <g transform=\"translate(303.864746 757.550457)rotate(-45)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-71\"/>\n       <use x=\"77.490234\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"118.572266\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"180.095703\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"241.375\" xlink:href=\"#DejaVuSans-116\"/>\n       <use x=\"280.583984\" xlink:href=\"#DejaVuSans-45\"/>\n       <use x=\"316.667969\" xlink:href=\"#DejaVuSans-116\"/>\n       <use x=\"355.876953\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"417.15625\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"444.939453\" xlink:href=\"#DejaVuSans-108\"/>\n       <use x=\"472.722656\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"534.246094\" xlink:href=\"#DejaVuSans-100\"/>\n       <use x=\"597.722656\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"629.509766\" xlink:href=\"#DejaVuSans-103\"/>\n       <use x=\"692.986328\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"734.099609\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"795.378906\" xlink:href=\"#DejaVuSans-99\"/>\n       <use x=\"850.359375\" xlink:href=\"#DejaVuSans-107\"/>\n       <use x=\"908.269531\" xlink:href=\"#DejaVuSans-108\"/>\n       <use x=\"936.052734\" xlink:href=\"#DejaVuSans-101\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_393\">\n     <g id=\"line2d_393\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"337.870337\" xlink:href=\"#m9a96a52268\" y=\"674.638125\"/>\n      </g>\n     </g>\n     <g id=\"text_393\">\n      <!-- Ovenbird -->\n      <g transform=\"translate(323.605563 719.442926)rotate(-45)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-79\"/>\n       <use x=\"78.710938\" xlink:href=\"#DejaVuSans-118\"/>\n       <use x=\"137.890625\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"199.414062\" xlink:href=\"#DejaVuSans-110\"/>\n       <use x=\"262.792969\" xlink:href=\"#DejaVuSans-98\"/>\n       <use x=\"326.269531\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"354.052734\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"395.150391\" xlink:href=\"#DejaVuSans-100\"/>\n      </g>\n     </g>\n    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xlink:href=\"#DejaVuSans-103\"/>\n       <use x=\"651.224609\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"683.011719\" xlink:href=\"#DejaVuSans-119\"/>\n       <use x=\"764.798828\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"826.078125\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"867.191406\" xlink:href=\"#DejaVuSans-98\"/>\n       <use x=\"930.667969\" xlink:href=\"#DejaVuSans-108\"/>\n       <use x=\"958.451172\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"1019.974609\" xlink:href=\"#DejaVuSans-114\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_395\">\n     <g id=\"line2d_395\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"339.244439\" xlink:href=\"#m9a96a52268\" y=\"674.638125\"/>\n      </g>\n     </g>\n     <g id=\"text_395\">\n      <!-- Louisiana waterthrush -->\n      <g transform=\"translate(301.842358 765.717541)rotate(-45)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-76\"/>\n       <use x=\"55.697266\" 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x=\"893.148438\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"934.261719\" xlink:href=\"#DejaVuSans-117\"/>\n       <use x=\"997.640625\" xlink:href=\"#DejaVuSans-115\"/>\n       <use x=\"1049.740234\" xlink:href=\"#DejaVuSans-104\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_396\">\n     <g id=\"line2d_396\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"339.93149\" xlink:href=\"#m9a96a52268\" y=\"674.638125\"/>\n      </g>\n     </g>\n     <g id=\"text_396\">\n      <!-- Northern waterthrush -->\n      <g transform=\"translate(303.525435 763.725488)rotate(-45)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-78\"/>\n       <use x=\"74.804688\" xlink:href=\"#DejaVuSans-111\"/>\n       <use x=\"135.986328\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"177.099609\" xlink:href=\"#DejaVuSans-116\"/>\n       <use x=\"216.308594\" xlink:href=\"#DejaVuSans-104\"/>\n       <use x=\"279.6875\" xlink:href=\"#DejaVuSans-101\"/>\n       <use 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xlink:href=\"#m9a96a52268\" y=\"674.638125\"/>\n      </g>\n     </g>\n     <g id=\"text_403\">\n      <!-- Nashville warbler -->\n      <g transform=\"translate(316.092527 748.210019)rotate(-45)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-78\"/>\n       <use x=\"74.804688\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"136.083984\" xlink:href=\"#DejaVuSans-115\"/>\n       <use x=\"188.183594\" xlink:href=\"#DejaVuSans-104\"/>\n       <use x=\"251.5625\" xlink:href=\"#DejaVuSans-118\"/>\n       <use x=\"310.742188\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"338.525391\" xlink:href=\"#DejaVuSans-108\"/>\n       <use x=\"366.308594\" xlink:href=\"#DejaVuSans-108\"/>\n       <use x=\"394.091797\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"455.615234\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"487.402344\" xlink:href=\"#DejaVuSans-119\"/>\n       <use x=\"569.189453\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"630.46875\" 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x=\"346.11495\" xlink:href=\"#m9a96a52268\" y=\"674.638125\"/>\n      </g>\n     </g>\n     <g id=\"text_405\">\n      <!-- MacGillivray's warbler -->\n      <g transform=\"translate(309.127189 764.8889)rotate(-45)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-77\"/>\n       <use x=\"86.279297\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"147.558594\" xlink:href=\"#DejaVuSans-99\"/>\n       <use x=\"202.539062\" xlink:href=\"#DejaVuSans-71\"/>\n       <use x=\"280.029297\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"307.8125\" xlink:href=\"#DejaVuSans-108\"/>\n       <use x=\"335.595703\" xlink:href=\"#DejaVuSans-108\"/>\n       <use x=\"363.378906\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"391.162109\" xlink:href=\"#DejaVuSans-118\"/>\n       <use x=\"450.341797\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"491.455078\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"552.734375\" xlink:href=\"#DejaVuSans-121\"/>\n       <use x=\"611.914062\" 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xlink:href=\"#DejaVuSans-108\"/>\n       <use x=\"693.261719\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"754.785156\" xlink:href=\"#DejaVuSans-114\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_410\">\n     <g id=\"line2d_410\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"349.550206\" xlink:href=\"#m9a96a52268\" y=\"674.638125\"/>\n      </g>\n     </g>\n     <g id=\"text_410\">\n      <!-- American redstart -->\n      <g transform=\"translate(319.446792 751.120205)rotate(-45)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-65\"/>\n       <use x=\"68.408203\" xlink:href=\"#DejaVuSans-109\"/>\n       <use x=\"165.820312\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"227.34375\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"268.457031\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"296.240234\" xlink:href=\"#DejaVuSans-99\"/>\n       <use x=\"351.220703\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"412.5\" 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xlink:href=\"#DejaVuSans-75\"/>\n       <use x=\"65.576172\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"93.359375\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"134.472656\" xlink:href=\"#DejaVuSans-116\"/>\n       <use x=\"173.681641\" xlink:href=\"#DejaVuSans-108\"/>\n       <use x=\"201.464844\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"262.744141\" xlink:href=\"#DejaVuSans-110\"/>\n       <use x=\"326.123047\" xlink:href=\"#DejaVuSans-100\"/>\n       <use x=\"389.599609\" xlink:href=\"#DejaVuSans-39\"/>\n       <use x=\"417.089844\" xlink:href=\"#DejaVuSans-115\"/>\n       <use x=\"469.189453\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"500.976562\" xlink:href=\"#DejaVuSans-119\"/>\n       <use x=\"582.763672\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"644.042969\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"685.15625\" xlink:href=\"#DejaVuSans-98\"/>\n       <use x=\"748.632812\" xlink:href=\"#DejaVuSans-108\"/>\n       <use x=\"776.416016\" 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xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"526.416016\" xlink:href=\"#DejaVuSans-119\"/>\n       <use x=\"608.203125\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"669.482422\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"710.595703\" xlink:href=\"#DejaVuSans-98\"/>\n       <use x=\"774.072266\" xlink:href=\"#DejaVuSans-108\"/>\n       <use x=\"801.855469\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"863.378906\" xlink:href=\"#DejaVuSans-114\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_413\">\n     <g id=\"line2d_413\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"351.611359\" xlink:href=\"#m9a96a52268\" y=\"674.638125\"/>\n      </g>\n     </g>\n     <g id=\"text_413\">\n      <!-- Cerulean warbler -->\n      <g transform=\"translate(323.168541 747.799013)rotate(-45)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-67\"/>\n       <use x=\"69.824219\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"131.347656\" 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x=\"352.29841\" xlink:href=\"#m9a96a52268\" y=\"674.638125\"/>\n      </g>\n     </g>\n     <g id=\"text_414\">\n      <!-- Northern parula -->\n      <g transform=\"translate(326.114467 743.281264)rotate(-45)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-78\"/>\n       <use x=\"74.804688\" xlink:href=\"#DejaVuSans-111\"/>\n       <use x=\"135.986328\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"177.099609\" xlink:href=\"#DejaVuSans-116\"/>\n       <use x=\"216.308594\" xlink:href=\"#DejaVuSans-104\"/>\n       <use x=\"279.6875\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"341.210938\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"382.308594\" xlink:href=\"#DejaVuSans-110\"/>\n       <use x=\"445.6875\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"477.474609\" xlink:href=\"#DejaVuSans-112\"/>\n       <use x=\"540.951172\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"602.230469\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"643.34375\" xlink:href=\"#DejaVuSans-117\"/>\n       <use x=\"706.722656\" xlink:href=\"#DejaVuSans-108\"/>\n       <use x=\"734.505859\" xlink:href=\"#DejaVuSans-97\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_415\">\n     <g id=\"line2d_415\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"352.985461\" xlink:href=\"#m9a96a52268\" y=\"674.638125\"/>\n      </g>\n     </g>\n     <g id=\"text_415\">\n      <!-- Magnolia warbler -->\n      <g transform=\"translate(324.449283 747.985733)rotate(-45)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-77\"/>\n       <use x=\"86.279297\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"147.558594\" xlink:href=\"#DejaVuSans-103\"/>\n       <use x=\"211.035156\" xlink:href=\"#DejaVuSans-110\"/>\n       <use x=\"274.414062\" xlink:href=\"#DejaVuSans-111\"/>\n       <use x=\"335.595703\" xlink:href=\"#DejaVuSans-108\"/>\n       <use x=\"363.378906\" xlink:href=\"#DejaVuSans-105\"/>\n       <use x=\"391.162109\" 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xlink:href=\"#DejaVuSans-108\"/>\n       <use x=\"976.027344\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"1037.550781\" xlink:href=\"#DejaVuSans-114\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_417\">\n     <g id=\"line2d_417\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"354.359564\" xlink:href=\"#m9a96a52268\" y=\"674.638125\"/>\n      </g>\n     </g>\n     <g id=\"text_417\">\n      <!-- Blackburnian warbler -->\n      <g transform=\"translate(318.686026 762.260451)rotate(-45)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-66\"/>\n       <use x=\"68.603516\" xlink:href=\"#DejaVuSans-108\"/>\n       <use x=\"96.386719\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"157.666016\" xlink:href=\"#DejaVuSans-99\"/>\n       <use x=\"212.646484\" xlink:href=\"#DejaVuSans-107\"/>\n       <use x=\"270.556641\" xlink:href=\"#DejaVuSans-98\"/>\n       <use x=\"334.033203\" xlink:href=\"#DejaVuSans-117\"/>\n       <use x=\"397.412109\" 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<g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"367.413534\" xlink:href=\"#m9a96a52268\" y=\"674.638125\"/>\n      </g>\n     </g>\n     <g id=\"text_436\">\n      <!-- Western tanager -->\n      <g transform=\"translate(339.642468 746.45551)rotate(-45)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-87\"/>\n       <use x=\"98.798828\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"160.322266\" xlink:href=\"#DejaVuSans-115\"/>\n       <use x=\"212.421875\" xlink:href=\"#DejaVuSans-116\"/>\n       <use x=\"251.630859\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"313.154297\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"354.251953\" xlink:href=\"#DejaVuSans-110\"/>\n       <use x=\"417.630859\" xlink:href=\"#DejaVuSans-32\"/>\n       <use x=\"449.417969\" xlink:href=\"#DejaVuSans-116\"/>\n       <use x=\"488.626953\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"549.90625\" xlink:href=\"#DejaVuSans-110\"/>\n       <use x=\"613.285156\" xlink:href=\"#DejaVuSans-97\"/>\n       <use x=\"674.564453\" xlink:href=\"#DejaVuSans-103\"/>\n       <use x=\"738.041016\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"799.564453\" xlink:href=\"#DejaVuSans-114\"/>\n      </g>\n     </g>\n    </g>\n    <g id=\"xtick_437\">\n     <g id=\"line2d_437\">\n      <g>\n       <use style=\"stroke:#000000;stroke-width:0.8;\" x=\"368.100585\" xlink:href=\"#m9a96a52268\" y=\"674.638125\"/>\n      </g>\n     </g>\n     <g id=\"text_437\">\n      <!-- Northern cardinal -->\n      <g transform=\"translate(338.990988 749.132572)rotate(-45)scale(0.1 -0.1)\">\n       <use xlink:href=\"#DejaVuSans-78\"/>\n       <use x=\"74.804688\" xlink:href=\"#DejaVuSans-111\"/>\n       <use x=\"135.986328\" xlink:href=\"#DejaVuSans-114\"/>\n       <use x=\"177.099609\" xlink:href=\"#DejaVuSans-116\"/>\n       <use x=\"216.308594\" xlink:href=\"#DejaVuSans-104\"/>\n       <use x=\"279.6875\" xlink:href=\"#DejaVuSans-101\"/>\n       <use x=\"341.210938\" 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8IqgAAIlGUAEAEo2gAjxjZQrAL4IKAJBoBBUAINEIKsAzuv4AvwgqAECiEVQAgEQjqAAAiUZQAQASjaACACQaQQUASDSCCvCM9nTAL4IKAJBoBBUAINEIKsAzFqUF/CKoAACJRlABABKNoAI8o+sP8IugAgAkGkEFAEg0ggrwjMof4BdBBQBINIIKAJBoBBUAINEIKsAzR3864BVBBQBINIIKAJBoBBXgGYU/wC+CCgCQaAQVACDRCCrAM5r+AL8IKgBAohFUAIBEI6gA76j9AT4RVACARCOoAACJRlABABKNoAI8oz0d8IugAgAkGkEFAEg0ggrwjMof4BdBBQBINIIKAJBoBBXgGV1/gF8EFQAg0QgqAECiEVSAZ47aH+AVQQUASDSCCgCQaAQVACDRCCrAM/ZQAX4RVACARCOoAACJRlABntGdDvhFUAEAEo2gAgAkGkEFeObo+wO8IqgAAIlGUAEAEi22oDKzG81ss5ktTtv2FTNbZ2bPhh/npl33WTNbZmavmNlZadvPDrctM7PPxDVewBsqf4BXcc6ofiXp7Czbv+ecmxt+3CNJZnawpHdLOiS8z0/MrNzMyiX9WNI5kg6W9J7wtgCAElER1wM75x4ys5l53vx8Sbc659okrTCzZZKOCa9b5px7TZLM7Nbwtks8DxcAkFCDsY/qo2b2fFgaHB1umyJpTdpt1obb+tveh5ldaWYLzWxhQ0NDHOMGcuI8VEA8Ch1U10uaLWmupA2S/tvXAzvnbnDOzXPOzRs/fryvhwX2GHEF+BVb6S8b59ym6LKZ/VzSXeGX6yRNS7vp1HCbcmwHEsXMWD8JiEFBZ1RmNjntywslRR2Bd0p6t5lVm9ksSftLekrSAkn7m9ksM6tS0HBxZyHHDAAYXLHNqMzs95JOkzTOzNZK+rKk08xsroLqyEpJH5Yk59yLZnabgiaJTklXO+e6wsf5qKS/SyqXdKNz7sW4xgz4wKQK8CvOrr/3ZNn8yxy3v07SdVm23yPpHo9DAwAUEVamAAAkGkEFeMaitIBfBBUAINEIKgBAohFUgGd0/QF+EVQAgEQjqAAAiUZQAZ6wKC0QD4IK8Iy4AvwiqABPzGywhwAMSQQV4AmlPyAeBBXgGYEF+EVQAZ5Q+gPiQVABABKNoAI8o/AH+EVQAQASjaACACQaQQX4Ru0P8IqgAgAkGkEFAEg0ggrwhAN9gXgQVIBnjp1UgFcEFeAJK1MA8SCoAE8o/QHxIKgAz8grwC+CCvCE0h8QD4IK8ITSHxAPggrwjLwC/CKoAE8o/QHxIKgAAIlGUAGeUfkD/CKoAACJRlABABKNoAI8oT0diAdBBXhGYAF+EVSAJ7SnA/EgqABPmEkB8SCoAM+IK8AvggrwhNIfEA+CCvCE0h8QD4IK8Iy8AvwiqABPKP0B8SCoAE8o/QHxIKgA7wgswCeCCvCE0h8QD4IKAJBoBBXgCfuogHjkHVRmNszMyuMcDDAUkFeAX/0GlZmVmdl7zexuM9ss6WVJG8xsiZl928z2K9wwgeRjHxUQj1wzqvmSZkv6rKRJzrlpzrkJkk6S9ISk/zKz9xVgjEBRoPQHxKMix3Vvcs519N7onNsm6Y+S/mhmlbGNDChSxBXgV66gGpGrlOGc25YtyIBSZWbsoAJikCuotkhaK6kz/Do9tZykfeMaFFCMKP0B8cgVVD+QdLqkRyX9XtIjjv9EYED8lwB+9dtM4Zz7d0lzJf1B0iWSnjGzb5nZrEINDigmdP0B8ch5HJULzJf0aUk/lXS5pDcVYmBAsaHgAMSj39KfmQ2TdL6kiySNl/QnSUc751YXaGxAUXL0/QFe5dpHtVnSUkm3hp+dpHlmNk+SnHN/in94QPGg6w+IR66g+oOCcDow/EjnFMywAIQo/QHx6DeonHOXFXAcAABklWutv0+Y2QezbP+gmf17vMMCihcTK8CvXF1/F0u6Ocv230j6QDzDAYoX7elAPHIFVUU/a/21K3OVCgBiHxUQl1xBVWZmE3tvzLYNQA/iCvArV1B9W9LdZnaqmY0IP06TdJek7xRkdEARofQHxCNX19/NZtYg6RpJhyp4o/iipC855/5aoPEBRYPSHxCPXMdRKQwkQgnYAwQW4Feu9vQvmNmYHNe/0czOi2dYQPGh9AfEI9eM6gVJfzGzVkmLJDVIqpG0v4JV1e+T9PXYRwgUCWZSQDxy7aO6Q9IdZra/pBMlTZa0S9JvJV3pnGspzBABAKUs5z4qSXLOLVWwKC2AHFiUFohHzvNRAcgfpT8gHgQVACDRCCrAMyZWgF8D7qMys/GSPiRpZvrtnXMsTAsAiN2AQSXpDkkPK2hH74p3OAAAZMonqOqcc/8Z+0iAIcKxLC3gVT77qO4ys3NjHwlQ5FiZAohHPkH1cQVh1WJmu8ys0cx2xT0woNjQng7EI58DfkcUYiDAUEFeAX7ls49KZjZawRp/NdE259xDcQ0KKEasTAHEI5/29CsUlP+mSnpW0nGSHpf0xniHBhQXSn9APPLdR/UGSaucc6dLOlLSjlhHBRQx8grwK5+ganXOtUqSmVU7516WdGC8wwKKD11/QDzy2Ue11szqJf1Z0r1mtl3SqniHBRQfSn9APPLp+rswvPgVM5svaZSkv8U6KgAAQvl2/R0l6SRJTtKjzrn2WEcFFDHmVYBfA+6jMrMvSfq1pLGSxkm6ycy+EPfAAACQ8ptRXSzpiLSGim8qaFP/WpwDAwBAyq/rb73SDvSVVC1pXTzDAYofTRWAX/nMqHZKetHM7lVQfn+zpKfM7AeS5Jz7WIzjAwCUuHyC6vbwI/JAPEMBAKCvfNrTfx1dDtf8m+acez7WUQFFjMIf4Fc+XX8PmNlIMxsjaZGkn5vZd+MfGlBcWJkCiEc+zRSjnHO7JL1N0s3OuWMlvSneYQHFhyYKIB75BFWFmU2W9C5Jd8U8HqD4kVeAV/kE1TWS/i5pmXNugZntK2lpvMMCig+lPyAeAwaVc+4PzrnDnXMfCb9+zTn39viHBhSXQpX+nl+7Q+f98GG1tHcV5PmAwZbPiRN/kGXzTkkLnXN3+B8SgFyuvWuJFq/bpRfW7dQxs8YM9nCA2OVT+quRNFdBuW+ppMMVnO33g2b2/RjHBhQlx04qwKt8Dvg9XNKJzrkuSTKz6yU9rGA19RdiHBuALGguRKnJZ0Y1WtLwtK+HSRoTBldbLKMCACCUz4zqW5KeNbMHJJmkUyR93cyGSbovxrEBRSnuGQ/NhSg1+Syh9Eszu0fSMeGmzznn1oeX/yO2kQHIitIfSk0+pb/odg2Stkvaz8xOiW9IAAD0yKc9/b8kXSTpRUnd4WYn6aEYxwUUrbgnPJT+UGry2Ud1gaQDnXM0TgAJQOkPpSaf0t9rkirjHgiAPcPMCqUinxlVs4Kuv/uV1o7OmX2B7Ao142FmhVKRT1DdGX4AyMHMSA8gBnt0hl8A/Sv0+ago/aFU9BtUZnabc+5dZvaCsjQyOecOj3VkAAAo94zq4+Hn8woxEGCoYFFawK9cQXWRmT0maZFzrrNQAwIAIF2uoJoq6fuS5oTlv0clPSbpMefctkIMDgCAfoPKOfcpSTKzKknzJJ0g6XJJN5jZDufcwYUZIlBcaPwD/MqnPb1W0khJo8KP9eI8VACAAsnV9XeDpEMkNUp6UkHZ77vOue0FGhsAADmXUJouqVrSRknrJK2VtKMQgwKKGZU/wK9c+6jONjNTMKs6QdInJR1qZtskPe6c+3KBxggAKGE591G54FD7xWa2Q9LO8OM8BSdRJKiAQcCMDaWm39KfmX3MzG41s9WSHlQQUC9LepukMQM9sJndaGabzWxx2rYxZnavmS0NP48Ot5uZ/cDMlpnZ82Z2VNp9Lg1vv9TMLn0d3ytQGLT9AV7l2kc1U9IfJB3rnJvtnLvEOXe9c+4551x3jvtFfiXp7F7bPiPpfufc/pLuD7+WpHMk7R9+XCnpeikINgUzt2MVzuKicAMAlIZ+g8o59wnn3B+dcxv25oGdcw9J6n1g8PmSokVuf63gpIzR9ptd4AlJ9WY2WdJZku51zm0Luw3vVd/wA0oSa9KiVORz4kSfJqYF30ZJE8PLUyStSbvd2nBbf9v7MLMrzWyhmS1saGjwO2oggSgwolQUOqhSwkYNb/9rzrkbnHPznHPzxo8f7+thgT1GgAB+FTqoNoUlPYWfN4fb10malna7qeG2/rYDJY/SH0pFoYPqTklR596lku5I2/7+sPvvOEk7wxLh3yWdaWajwyaKM8NtQMlj5oZSkc9af3vFzH4v6TRJ48xsrYLuvW9Kus3MPihplaR3hTe/R9K5kpZJalaw+K2cc9vM7FpJC8LbXcPK7Ug6utMBv2ILKufce/q56owst3WSru7ncW6UdKPHoQFDAqU/lIpBa6YA8PowcUOpIKgAzxy1P8ArggooUpT+UCoIKqDIMGNDqSGoAM+IEcAvggooMsFp4oDSQVABRYbSH0oNQQUASDSCCvAs7gkPpT+UGoIKKDKU/lBqCCoAQKIRVIBncc93KP2h1BBUQJGh9IdSQ1ABRYq4QqkgqADPmPEAfhFUQJEiD1EqCCqgSDFzQ6kgqAAAiUZQAUWK+RRKBUEFFCkqfygVBBUAINEIKsCzQs10HMU/lAiCCihW5BRKBEEFAEg0ggrwrFAlOSZUKBUEFVBkCCiUGoIKKFK0p6NUEFSAZ7Gfij56HuZWKBEEFVBkiCeUGoIKKFKU/lAqCCrAs0LlBzmFUkFQAQASjaACihTno0KpIKiAIkVMoVQQVIBnTHQAvwgqoFgRiCgRBBUAINEIKsCzwi1Ky5QKpYGgAooU+8JQKggqAECiEVSAZwU7FT0zKpQIggooUuQUSgVBBRQZZlIoNQQVUKRYQgmlgqACioyFZ04kplAqCCqgyDCRQqkhqIAiRWChVBBUgGfsOwL8IqiAokUgojQQVECRYuKGUkFQAZ4RIIBfBBVQpMhDlAqCCihSzNxQKggqwDPyA/CLoAKKFCdORKkgqIAiRekPpYKgAjwjQAC/CCqgSJGHKBUEFVBkCCiUGoIK8KTQAcKagigVBBXgWdzdeBbrowPJQ1ABnhQqQJhHodQQVECRovKHUkFQAZ7FHSDRzI0DflEqCCqgyBBPKDUEFVCkKP2hVBBUgCeu1+fYn4+gQokgqAAAiUZQAUWKCRVKBUEFeJIqxVGTA7wiqIAixRJKKBUEFVCkiCmUCoIKAJBoBBXgWcFmOkypUCIIKqBIsYQSSgVBBXhAYwMQH4IK8KxQmUU2olQQVECRIqdQKggqoNgwlUKJIagAD9Kzo1BNDuQVSgVBBQBINIIKKFK0p6NUEFSAB+mRQdcf4BdBBQBINIIKKFJMqFAqCCrAg0FZmYLaH0oEQQV4RnwAfhFUQJEiEFEqCCrAg8EIDSp/KBUEFeAZAQL4RVABRYpTi6BUEFSAB4XMDOIJpYagAjwr2KK0BXkWYPARVECRsfAzlT+UCoIKKDLkE0oNQQV4kFHuK9SitIV5GmDQEVRAkekp/RFVKA0EFVBkiCeUGoIK8IDJDRAfggrwLO7MousPpYagAooM+YRSQ1ABRapQBxYDg42gAjyjGw/wi6ACihR5iFJBUAEeDMqZ6Av/lMCgIKgAz5jpAH4RVECRIhBRKggqwIPB6MCj6w+lgqACPCM+AL8IKqBIUfpDqSCoAACJRlABHjC7AeJDUAGeFSq0WAEDpYKgAooM+YRSQ1ABHgxGdhBYKBUEFeBZ3Mc3mUXPA5QGggooMsykUGoIKsCDwWhsILBQKggqwLOCdf1R/EOJIKgAAIk2KEFlZivN7AUze9bMFobbxpjZvWa2NPw8OtxuZvYDM1tmZs+b2VGDMWYgF7r+gPgM5ozqdOfcXOfcvPDrz0i63zm3v6T7w68l6RxJ+4cfV0q6vuAjBRKInEKpSFLp73xJvw4v/1rSBWnbb3aBJyTVm9nkwRggkATsm0KpGaygcpL+YWZPm9mV4baJzrkN4eWNkiaGl6dIWpN237XhtgxmdqWZLTSzhQ0NDXGNG8iqkGW41HNR+0OJqBik5z3JObfOzCZIutfMXk6/0jnnzGyP/gudczdIukGS5s2bx38wAAwRgzKjcs6tC/0RlaoAACAASURBVD9vlnS7pGMkbYpKeuHnzeHN10malnb3qeE2IJHiPqYqenjejaFUFDyozGyYmY2ILks6U9JiSXdKujS82aWS7ggv3ynp/WH333GSdqaVCIGSQ+UPpWYwSn8TJd1uwYJlFZJucc79zcwWSLrNzD4oaZWkd4W3v0fSuZKWSWqWdHnhhwwMoKD7qEgolJaCB5Vz7jVJR2TZvlXSGVm2O0lXF2BogBeFihG6/1AqktSeDmAPMLFCqSCoAA8KObshoFBqCCrAs8ItSguUBoIKKDLR7I2ZFUoFQQV4MCgrUwAlgqACPCvU/iq6/lAqCCqgyBBPKDUEFeBBIcPDsYYSSgxBBRQpcgqlgqACPIu72YGAQqkhqAAPCrr+XlT5o/0PJYKgAooMq6ej1BBUgGfkB+AXQQUUmajkRyCiVBBUgAcFbU+PPpNUKBEEFeAZAQL4RVABRabneF8SEaWBoAI8KGx3OgGF0kJQAd4VaFFa8golgqACigwBhVJDUAEecCp6ID4EFVCkWEIJpYKgAjwrVH4QUygVBBXgQ0FPRU9EobQQVECRYWUKlBqCCvCscKU/kgqlgaACPCjsqegL+GRAAhBUQJGJZlIEFkoFQQV4RkkO8IugAopMz6K0QGkgqAAPCrsobeGfExhMBBXgGQEC+EVQAUWmJwhJRJQGggrwoLANFHT9obQQVACARCOoAM/inuikuv6YUaFEEFSAB4PS9cc+KpQIggooMqyejlJDUAGeFWxRWvIKJYKgAjwofM8fUDoIKqDIsIQSSg1BBXhWqCYHSn8oFQQV4EEhGxxopkCpIaiAIkN7OkoNQQX4Vqj8IKdQIggqlLxfPPya1mxrHuxh5I+AQokhqFDSdjZ36Gt3v6T3/PyJ1/U4g7MyBVAaCCqUNgs+bWtqH9xx7AWaKlAqCCqUtO7u4MW+o6vb22PGvygtAYXSQlChpHW5KKiK58Wf0h9KDUGFktbVXXwv90yoUGoIKpS0OIKqUKU5AgulgqBCSfMVVIXt+gtPRV+4pwQGFUGFkkbpD0g+ggolrSuGV/2CLUxBYqFEEFQoad5KfwUsxNH1h1JDUKGkFWPpj4RCqSGoUNLi6frz/pD9PFGBngcYZARVnm58ZIWeeG3rYA8DnhV31x9JhdJQMdgDKAa7Wjt0zV1LJEkrv/mWQR4NfGnr7FJnEZb+6KFAqWFGlYcnlgczqWFV5YM8EvjS2Nqho6+9T/e9tCm1rdi66IpsuMBeI6jysGDlNknS7AnDB3kk8GVbU7t2t3Vqddp5qNo9LUwb+6K0MT8+kDQEVR627g5OAdHc3jXII4EvLR3B77Kto+d32tq+50F135JNWrGlqaDhEc38mFGhVBBUeWhq75Qk7WjuGOSRxG/zrlbdu2TTwDcsctGbjpb0oOrc8zciV9y8UKd/5wFfw8pLz3FUJBVKA0GVh+hFbVdLR9Htx9hTF/7kMX3o5oWp8zQNVa3h77S1o2cW1eJpxjzU/0aAQiOo8rC7LZhRtXd1Z7wDH4rW7WiRFM/SQkkS/R5b036fr+fkiYUMp+iphvivCEghqPLQ3NbzYrazZeiX/ySps4hOJLg3UvuoOnvCaU+bKTo9nhV4bwzt3xDQg6DKQ1N7p6orgh9VyQRV9+C+CMetuT3bjGrPXvr7CzYCBPCLoMpDU1unptTXSiqNhgqpSNfA2wOtHkp/7WmzscFYMZ3SH0oFQZWHpvYuTRhZHVwO91cNRekvgns6uyg2LVmaKTo69z6oCiUznIb27wiIEFQD6OjqVntnt0bVVoZfD90Xh8a0EB7qM6pspb893UfV1l9QDe0fHVBwBNUAokaK+toqSUN7382WxrbU5dfTAVcMooBKX+tv0eodmvmZu/Vaw+68HiM92ApVhkt/Gkp/KBUE1QCig31H1QUzqqHcDdeU1t041GdU2Q4zuP2ZtZKkvy7emNdjDE7pb2j/XoBsCKoBNEdBFZb+inG17XylzxaH8vcpZV8Oa1hVcDKBXXl2dg7GrNP1cxkYygiqAewOZxmpoBrCJbHutHfrQ7nEKWWfUdWGq+Pn29mZOaNK68YrUIQwu0Kp4HxUA2huy5xRdQzhmUZ6WXMolzilniWU0lWW5Xes3OZdrdrd1pmArj+gNDCjGkBUIhpZAjOq9P1SQ730l21GFW3b0dKe874fu/UZvfG/H9Tz63bGMrZcXMbMDSgNBNUAohW1h1cHk8+hPNNID6euIV76y7aPKgqqnS25j5V7ds0OSdIfn16b9fqCdQAO3T9FIANBNYDogNARNUFQdQzhF/CMGdUQDmQp8/ipSEvaKvn9aWnvSh0/lV4iJJyA+BBUA4he0KKgGsov4J1FXvr79P89p0O+9Dc9tnzLgLfNWfpr7r/0t7xhdyosGlsHd5WS4vsNAXuHZoos5r+yWWu3t2jd9haNDo+fqquKgmooz6iKuz39jmfXq62zW0s37dYJs8flvG3v0l+Z9cyo+l1xQkFQRfo75Ut/s57P3/6CTj9wgt508MScY8sXXX8oFQRVFpfftCB1+fCpoyRJNZVlqiy3od31l1H6K65AbuvsKckNdHzTk69tVWNrT9nOTKqqKEsFT66Q3tYUzLbGDKtKXZYGPn/X9qZ2/e7J1frdk6u18ptvyf3N5LBmW/Ne3xcoVpT+BtDY2hm8kJWXqaKsrOhewPdEMXb9RWNet70ltS3XeowPL23QRTc8kbEYbUWZqbI881+hv9lKdIzVhBHVGds7OnP/vKIOwRlj63LebiBv/t5Dr+v+QDEiqAbQ3N6p2spymZkqymxIL0qbvv+tGJZQWrW1SbM/d4/++sIGrU6baeSaUS1Ysa3PtjIzVfUKqv7KfztbOjSypkJ14cHBkYx1/8K9Rzua2/Xj+cv0WsNuPRd2Cs6ZNGKA7wpAb5T+BrCrpTO1YkFFuQ3pFRvSw6kYFqVdtHq7JOlff7dI82aMTm3PNet9bUtTn23ZZlQt7V2qqSzvc9vtze0aPaxK1RW9gipLsN22cI2+/fdXdOez63XIlJGS1Od+r0ccu6icc/rSHS/qHUdP1RHT6v0/AbAXmFENoKWjSzXh2X0rysti7frr6OpWQ9oK5oWWeRxV8mdU6eW+hauC0Cozqb3L6b4lm7R1d9+f5fKGnqCqDYOorMxUWWEZt4uOn+ttR3OH6msrVVOZ+a+TLdgXrQpmUc0dnXnvP9sTcSzVtKu1U795YpXe94snvT82sLcIqjxE76wryyzWfTef+9MLesN196mtnxfJuGV0/RVBiXPV1mZNHFmtw6YEDS/lZabaynJtb2rXFTcv1BU3L8y4vXNOK7b0dO2NrK1I3S/bjCqbHc3tqq+r6jPb6h1AzrnUjK+5rSs14+rocmrt6NKX7lisLVmCdLBF33d1JS8NSA7+GvNQXRmV/uJtprj7hQ2ScrdHx2kwmyl2t3Xqa3ct2aMzKK/a2qwZY4apOpzxVleUqbKiLHUg7qqtmR1ymxvbMpooRtQEhx5UlPXdR9Vf6/mOlg7V11WmnjOScVp6J93zwkZtbmxTfV2lmto704KqW39atE43P75KP5m/PO/vNZs4Sn+724Kfnc8SJfB6EVR5iMo8FTG3p5dbUH5q6xicoBrMJZT++PRa/eKRFfrx/GV532fl1ibNGFuXevdfVVGmyvKeoKooyyznrey1f2pkTbSLtu+MKtvKFVJ66a//ZgpJ+s0TKzV7/DBdctwMtXZ0p2Yqnd3d2rgzKFn2bsjIpavbqbvX3148QRWMs6qClwYkB80UvWTbh1CbKv3FO6MKc2oQS3/pzRSFnVGNHR6cQfmFPBd6bW7v1ObGNs0cN0xbw+OZqivKVG6mHf0EVe8ZVrTQsHNOleW99lFlebPQ1e20q7VD9XVVfVZY7/3z2tbUrv0njNDIcNYWLXTb0enUsLtnvPk64At/1UGT4+8Y3B2utrEnYwPixl9jL9lKPjWVaV1/Mb6Al4UvrNleJAthMJspotnByxsb87it09/Cs/DOGFuXKttVV5SrsqIstQRSRa9Z0tLNjRnhFYVIt3N57aPa1dIh5xSU/ir7L/39Y8kmvbppt+rrKlVXHfztbGsKgq29qzt10O5ApxNJ19XttHjdroxtcTRT7A5Lr8yokCT8NfaSreTTU/ori7X0VxaV/pIwoypw6S96oW9obOu37Bb5x5JN+sRtz0mSZo4dlgqN6rD0Fx2UW5E2S3LO6d4lm3TcvmNT26Jmiq5u1+eFOdsblmimFuyjKk9dloK29d7q66pSZw2OwrOzu1urtgUlyD0JqmziKf0xo0Ly8NfYS2t73xfomoq0rr8YS39lqdJfAmZUBS79pX/PD73akLp80c8e19W3LMq4bXoL//SxdakX1aqKMlWUWSpk0mdPyxt2a+XWZp1z2KTUz3nyqFpJQUt2NKOKSoBRWG7e1aq/LQ6aXKIwCrr+gtuPGRaULNdu77u0UX1dZWo/VPSz7eh0am6LTify+oIqDrvDpaWYUSFJ+GvsJdvxM+vDnd9xl/7MMl8kC62ru1vl4at4obv+2tN+7g8vDVY/d87pyRXbdPfzGzJum75q+ciantlNdUVZxgtsRVnP5XU7WiUFK0NE22ePH5a6Pgqo6Lxj0e/gfxes0VW/XaSXNuzSznCmVl9bmSo3jh8eLKXUe/9XdLth1Zm7gTu6ulML4uYbVL2bKE7ef5zmTBoRy+rpTVF7Ol1/SBCCqpdo38Tbjpyi/73yOEk9+00qy8v2qiT2ysZGrciyIkJvSZhRVZSZyssKvwJH1DU3dlhVqvyU7eDnNdua9eCrmyVJj33mjZKU1p5enrGvKb30F4XCqNpKTR0dzKT2HT88dX30wjw87ATsOYlicL9bnlydaoior6tK/Y7GjegJKsvsx1B9XVWfzr62zu7UYz+1cptWbR3476J3GfKtR+yTKjn6Fr0J6NWHAgwquv56id5Jv/3oqTpm1hi9a95UnXvYZElBKWlPZ1TOOZ31/WAh0YFWzU7toxqsGVWXS5XLCj+jCl74R9ZWpn4HL6U1VkQl15O/NV+SNHV0rfapDwInmkVVV5apO23HTXnaq+3OsGw3qrZKv73iWD22fKtmjeuZUUWdncOrKyW1pBpaotC8dcHq1FJao+sq1dwebI9mVOt3tmh0XeaK6vV1fWdUu9OOE3NOOuv7D+nla8/J+bNpas88tiw6ri+OKVV0HFt7ERzwjdJBUPUSvXutqSyTmelb7zgidV1FedkeL4ETnbY8Hz3NFP0/x86WDm3e1ar9J/pvVe7sdj2lvwK/UEVBNay6PBVUyzb3rCJxxncf1LbdPSGQPnOKZkMVZZZR+ktvDkmfUVVVlOkdR0/NeP6atGOxyixYZf3f3rifdrd1qqq8TO1d3brhodckBQcKN4X7mcaHMyrnpHHD+wZV7xnVrtbMcl8+HZ7RPq2e77dMJpOT/1lvFKQdgzSrB7Kh9NdL9MKRbUHSin6WUFqyfle/S+488VqwWvfMPE7vEJWOcu2jetdPH4/tVA9d3U4V5WUqL7OCt6e3dXWrqqJMNRXlqaBOPyX8qq3NakybjaQvP9TTKp554O7Gna1atyPYv7ijuUO1leX9NgnUVPWEXbeTFqzcrj88vVa72zo1Z/IIffjUfVO3LS8znbhfcGLGk/Ybl5qFjhueeeqPYVUVqRNuRqIJ35RwNpjPaT96n+SxqqKsT5nRl1RQFcGixCgdBFUvf3luvaR+girLEkqtHV069wcP67hv3K87nl2n1b12qreEZZtsjycF3WgzP3O3nnhta14zqlc2BeWwOM7uGs2oKsvLCr+PqrNb1eVlqqnsmVE1t/e/nFJ6Q0V1RgNFzyv45sY2nfjNf+rLdyzWLx5ZkXW/zg2XHK0fv/eongVqTfrsOXMkSZ/+v+f1wCsNGlZVoY+fsX/G/d588ES9+NWzdMS0+lR5Lz2oPnbG/po6urbf1Sc+ffaBOvPgiRmlyv70/jlE328c7enRGy6CCklCUKVZtbUptd7e8Oq+VdHei9Ku3d6sDTuDbrKdLR36+K3P6mO3PpNxnyh0ei+xE4nOU/SLh19L7cDOp+svjpUjurq7e5op+nn8zY2t2tncoc2NrV6fu72zW9WVZaquKEv9zHrPJCJT6mt17QWHpr5O71CrzDJj+vXjqyRl7rOKnHnIJL3l8MmpoOrocvrwqbN1yxXHpm4zvKbvzEhSKqCGhWGUHlSfePMBMrOMEE2/PLy6QhNH1qRWgsilqb136a9cZrHsokr97bGPCknCPqo008fU6faPnKDV25o1cWRNn+vT29MbGtt0+nceSDVaRJ5ds0Nnf/8h3fVvJ6mivCz1j9/f+n3R4y1vaEqtTNH7ttub2jW8piKjrNXa2eX9WJeu7uDFvMyylzidczrmuvtTX7/nmOn65JkH9Cl57Y22zm5V9ZlRZQ+qR8Nuv0j6z6EyR7tartXKa1PHOwU/+xP2G6dTDxivB19tSL1puevfTso6A0rNqEZU6cjp9Rk/DzNTmUndLgints721PMNr6nQ7rZOOedShyZk09zWd0Zliqf2Fx2ewYwKSUJQpTEzHTl9tI6cPjrr9RVpJbHF63aqo8vpnnAG9qaDJuq+lzZJCtrZ/7horcxM978ctFL3Xm3iyde2ar8Jw7WlKXjxXLOtOdU23ZaxErfTkdfeq7cesU/GC3drR1dqCSBfohlVtwtWUnjglc067cAJqet39Xr3//unVqu+rlL/efac1/3c7Z3BPqrqyrLUfsLm9k5NH1OXcfbebHpmKn2XQkqXq3EhKs2mzySjRokoqA4NTyfSW3rp7/aPnNj3+qoKNbZ1alh1RWpdwtrKcg2vrlBHl1NbZ3e/pWGp74wqavyIo/wbvUmK88B2YE9R+tsDlWmnon9xfbB4avT1t95xuE4Kd7BLwUGrn/6/57U2PLlfW0e37nlhg/76wgZ1dTtddMMTeuuPHtXW3dHSOi61Izu99Bd1q9353PpUEEaP51u0j6qizHTfS5t12U0LtHlXT4lvw86WPvepy/ECuydSQZXWTNHc3qWxw6t08weO0f4TgmOespXv0sMpW+kvH1HpL71EOyEMqmi9vv4MC68f38/MMlUiTCsn11VVaER4zFbjAOW/3vuoqspjLP2lZlSU/pAcBNUeSG+meHH9rlTnVWW5aXRdZcbO+rXbM1/UG9s69ZHfLdK//m5RqgS1bkdLxllot4ShlT6j2rK77xpywW38H2vV1e1UUZZ5LNKrm3paxDfs6Ltfqr8z4e6p9qjrr7IsdRxZc3uXhlVV6JQDxuuOjwYzlfQ3A5Ge7LJ+S39HTKvXPR87ud/nr0nto+r52Y8KV1cfaDmpaD2//kqgUZANTwu8uqry1Ext9wDn4Grq3Z4e40kNo1lnf/tUgcFAUO2BinJTe1e32jq7tGzzbp203zhVVZRpwogamZlG11Wlbts7qNJ9456XUpefXLGtz/XRjGrt9mZ95HdPZ32MH89fPuALXL6WN+zWrtaO1IzqsKn1qeuWbu456HZ9lhnV1n6CNB/ppav2cB9V+oyqqa0zte+orqpCd1x9on588VE5H7O8LPuf9BUnzdLB+4zs9361WUp/0XM3D9Dckr6PKpvhWWZUNZXlqRM3DtRQ0fvYq2j5pji6/qI3CeyjQpIQVHvgxNnj1NHl9LMHX9Pqbc2aM2mETj1gvGaHZanohai8zHLuuP/zs+tTlzfsbO1zTEz0Qn317xZlzGjS3f7MOv3XX19+Pd+OpCAUz/jvB/WBmxaEx1GZfvieI7XiG+eqvq5SSzdnn1FdevwM1ddV9jvjy8cVv16oS375pKSw66+iXDWVwcG1Xd1OLR1dqY46KZgVZevGTNffTDPa39Sf2qrgXyH9BToKr/6OkYtEM6axw3KX/tJb1dNnVI1tudf8W9frTc+o2sqczRevR2t0JmIO+EWCEFR74JQDxuv4fcfq5sdXqq2zW9PG1OkH7z5SP31f8C4/miGkL3ba202XvSF1+RNvPkBS35LR06u266O3LNJza3OfRHBbllNLRJZuatTMz9ydan/vzxOvbZUkLVy1XR1dPYvSmpkOnjxSD77SkFpWZ/3OFk0cWa2fXHyUvvwvh+jQfUZpa1P/gZzLyi1Nuv/lzXp46RY1tnb0HPAbhkNbZ5ea2rpUm6UtvDeXdql3qHzjbYfpR+89UsfMHJPzMWrS2tMjpxwwXmUmve+4GTnve/SM0TrtwPH9dmFGre0Zs7XK8tQ+qoFmVGu2N2eUlaPuUN8Tqu5ul1ohhH1USBKCag8dP3tsahYxbUydaqvKe16Iwpbu6WP6D6rjZ/ecD+mKk2fpl5fO03feeUTGbdbtaNFdvVYMl6QDJg7P3NDPa8kDrwSNEFKwRl0uD6adUmPFlqaMA2Y/8eYDtG5Hi256dIWkYEY1pb5W5x42WWVlprHDq7Rld5v+8tz6VJjl6+8vbkxdfmrFtrRmiuBPsq2jWy3tnRkzqv5MHxOs7nDsrLF9WtpH1lTqvMP3Sb2496en9Nczk5g4skavfeMtOnpG9i7QyIVHTtWvLj+m3+ujfVPps7WyMksFVUOO2bcUlJGj73HfcH1Ck7zX/qL9UpVhiTuOrkJgbxBUe+iYWT3vzKMXj8gh4T6Q4/bN/u69oswy2pDrqip0xkETdcTUnrbnOZN61vA77cDxqctfv/AwvfngiRmP19LRpbbOLnV0devUb8/XXc8HJcXLblqQWjoo2wrk6ZY39KzevXZ7S0ZX3byZY3T8vmP1h6fXyjmnDTtbUgvBSkGpa822Fv3b75/R9Q8s7/c5bnxkhe58br0eW74ltW3Jhl0aN7xKFWWmp1dtV1t4XFj08znl2/PV1N7V78oO6Q6aPFIP/cfpuuLkWam1Gr92waG68MgpOuOgCQPcOxDtj4pjJvG5cw/SW4/YR+cdvk/G9qmj6zRzbJ0+f/tifeHPL2S9b2tHlxoa23TaAeN1wdx99ItL50lSLEsoRftGo31nhV5Gqxjd9fx6nfbt+ezTixlBtYfeMHOM3jhngqbU16aOe4pceOQU/f3fT9Fb52a+IEWzlOi0E5Xlma8yI9KOh3rr3H30wZNm6YFPnaZfXX6MTj1gvKbU1+q9x07XoftkHsfzz5c3683ffUibG9u0amuzPnpL5qoYktQwwD6kddubdXhaUPZu/77gyH20amuzlm7erQ07WzOCKn2msbUp+/M0t3fqmruW6GO/f0bv/fmTqQBdsn6X5k4brRlj67S8YXdqCaVoRhW1bNcNsE8qMn1sncwsVfrbb8Jwfe+iuTmPT0oXnRwzjjMbTxhZox+850iNDLsIo1JveZnpU2cdKEn67ROrtTLLqWCiEzLOGj9M33/3kRmnJvEdI1HHXzTTo/w3sI/e8oxWbm3W5gHeEOL1Iaj2UHmZ6cbL3qBH/vP0PieXMzMdOGlEnwNxo3McRcf7PP7ZM/Twp0/PeMzIsKoKffG8gzUzLPH8+gPHpFZiOOewyfrZJUdnPPbqbc0Zq4r3PlBzw47s3Ycbd7ZqxZYmrdvRoiPSuvx6d80dNiW47r//8YraOrs1eVTPih3nHjZJFx87XVL2M9xK0rOrM/eRff3ul7S9qV3LG3br4H1Gavb44Vre0JQq/fUOlnxKf+mi1u2Bmi56i2ZUcVa7ol/zrHE9M/HzDt9HT33uDJWZ9MdFa/vc57k1wX7KOZMyOxZN/scazaiinx0t6vnb3s8bNfjByhR7KVfXVe8X26rUac6Dz9mOt4nWuMu1soIkHTurb1lxS1pDw9uufyzjus2NbWpp70q9EEfO/N6DqZUmZo0bphE1FWps7czYRyVJsycEgfn3F4ODjaPTt0vBz+C6Cw/T7rZOLVy5XVKwH+ZTf3hOZxw0UbPGDtN7f/FkxuPd/cIG3f/yJnU76ajp9ero6tb8VzarpqI8dRxVunyaKdJ9551H6PZn1qXKsPmKZnK9Z7s+RetCzhybuQ9zwsgazZ1Wr788t14vb2zUuYdN0oVHBqchWbhqu0ZUV+iAXqd18dn1t3Fnq256dIVuXbBGUvqMqrBBtWZbs6oqyrIuXzYYOru69fLGxn5XJEkPp1xdvnj9CKoCiMIn26oKkcOmjNLCVdv7BEVv2U4Rnn4s0/NZOgXXbG/u80KXvhzSlNG1Gj+iWo2tnX3G2Pv5epc7JWn/CcN1x7Prdckvn0ydRv6OZ9fLTBoZrlG4taldbz9qquZMGqHrwuPIjpk1Rlt3t6ujy6mjq1O1leV91rBLbz7Jx8SRNbrq1Nl7dB8peOH/0nkH64T99uz59sQb50zQhBHV+nCW8Z16wAR9775XtXJrs+5dskk7mjv08NItemb1dh05Y3TWv52dLR36zt9f0fSxdXrn0VP3Orxufnylfhaea0vqKUVHQfW3xRs0fcywnMeh7YmHlzboxkdWqLPb6b3HTNc5h01WZ1e33n3DE5pSX6vbrjrey/O8Xt+991X95IHl+t5FR2jr7nZdfuIslZeZ1mxr1sjaSt31Qk/D0zZmVLEiqAogepdelWO2dMoB47Vw1fbUPpz+VGdpgd4ULnO0+KtnaXtTu25/Zp2+e++rqeuXbtrdJ6jSzzk1e/xwjRterdcamrIG5cfO2F+vbNyli4+dkXWm8t5jZ+iJ17alQirinPTJMw9UmUlfvONFlZl0+Ykz9c2/vaz9JwxXXVWF3nRQT4PI2YdO0qrwNCmzxw/Tzy45OnXepkL4wEmzYn38fepr9dTn35T1uouPm66u7m4dNWO0vvOPV/TVvyxJXTcvS9ehKSj7/mj+MknSTY+u1NxpozRvxhi9vddJIQeyvtff3Iiw9NfR6dTW2aWrfrtI0sBnqO7P06u2a/nm3XrnvKlq7ejWlTc/rdbOLjkndTuncw6brL8u3qh1O1q0fmeLGhrbMo57W7+jRZNH1QwYxBt2tujr97ysb7ztsD0u/WYTHbrx2stHLgAAIABJREFU//73OUnBm8mjZ4zOOMv0lPpardvRQlDFjKAqgGhGVZGjrHTFybO0YWeL3nPM9JyPla3N+tFlW1RdUaZhVeUaXl2nS4+fmRFUV9+ySKOHHasTZvcsPzQ6PFj3f949V/tNGJ5apy7bO/foeK/+jBkWnN5dkv6wcI227G7Xf/0tOBh5zqQRYQPGi7r4uBmqKC/Toi++ObW/ZlRdZXCA8ZYmHTl9tOZMGql3zZuq/zx7jsZ6WJW9WIwbXq1PnBk0Vhw5LQir3zwRnJ4kW1BFx9Adt+8YHT1jtH48f7le2rBLv39qjQ6dMkoHTsp8Y/K3xRs1dXStPnrLIn3olH118bE9x4at3tas4/cdq8fDF+Zon+op356v9xwzLXW73zy+UpccPzPv76mzq1u3LlijL/x5sSTpD0+v0Yn7jVNLR5du/sAx+vuLG/W7J1fr3Tc8ride26ZRtZXa2dKhvzy3PvWmYf7Lm3X5rxbozIMn6rPnHqRZ4/o/9OPLd7yofyzZpHMOndTnrAZ7o/cuwFc2NWZULDbsbNXNHzhGl930VL/NRHF7ZOkWtXd16fQDJ8R2EHgS0ExRANEJEXOV9eqqKvSNtx2uSaP2vD7/2PKtauvsTv2hjqzt+/7jmbSmBuecdjR36COnzdb5c6dIUqpJYtOu13eeqXfOm6Z/Pa2ntDVn0khNG1Onld98i+ZOCxozRtVWZnQ6/ssR++hj4YkJa6vK9a13HFFSIdXbqLpKXXvBoanW/LnT6/vcZlK4H+ejp++vy07InAn++dl1GV8/t2aHrvrt0zrvh49o5dZm3fHM+ozrV29ryTjU4thZY1MHrf/+qWC/1T6javTlO19UY2v/q2is39Gip1f1LAn268dX6Qt/Xpw6WHnByu36/n1LVVluesPMMakmnugs2J888wAdM3OMfvbQ8lRjx73hQsz/WLJJ193dM8tcvG6nbn58pXa1duihVxu0aVerljUEq6jsaO7Q6q3NqXU0nXP62+INeS851tXt9NMHl2f8z0jSl+54Udfd85JGVFfozo+eqPmfPE0n7jdOY4ZVZazZGbeHXm3QFb9eqLdf/5je98sn9YFfLdQ1dy0Z+I5FjBlVAfQcSFmY9wVmpg+dPEvHzx6rBSu36/oHlmesF9fU3qXObpex2sGlJ8zULx5ZkVqI9fWaMKJamxvbNCrLWXWRnzs/epJe3dSY9aSN115wqD7/loM0dXSdunsd73T9A8s1d1q9Tj1gvCrLy1LlwUhj+IK9o7ldVRVl2rK7TdPH1qm+rlI7mjt04KThuv+Tp+nCnzyqZ1bv0PQxdbr2gkN16Y1P6Y9Pr9XKrc16asU2fe7cgzR3er3qKstVVma64MePanNjm5Zed44qy8u0LFwn8sFPna6Lf/mEFq/bpaNnjNYbZo5RbVW5zjhogs6YM0GfPXeOmtq6dNiUUdpvwnC99+dP6pYnV2vWuGG69anVOmPOBE0ZXatbn1qj8374sA6ePFJ/eW6DWjq69MjSLfrHkk0aXVeZ2u96+zNr9bnbX9CI6got+MKb9OqmRl3120UaUV2h7100V/cs3qCOLqdrzz9E9eH6nM+t2aFnVm/XhUdN1U2PrtD371ua9Xdy9emzdeGRU7XfhJ7DBMYMq9Yrm3br5sdX6q1H7JN6zD3V1e30xGtbdeysMSovM+1u68x4Qxf50fxlemrFNlVXlOnKU/bVrpYO3fToSl187HT98+XNamrr0r+/af8hNcMiqApg33HDtE99jT591us/b1O6GWPrUvt0bv5A5soIn3/LwZKkN86ZqLuf35CxTl/UrVRf2/MPNW1MneZ/6jRvQfW3fz8lr7PXon/7TRie8YKYLr1zNL0c/NHT99OP5i/Th3/zdOqULW2d3XrDzNFasHK7TtpvnJ5auU2X3fSUHnilQaPDNxIHTR6hMXVV2tHcoSj3Jo+q0TMK/s6i2fBX0vadXXrTU+rqdvrieQfrgyfNSh1L9PV7XtLKLU1q6ejSEdPqNaquUrd9+PjUySMjY4dX65dpS4pJ0gmzx+n4fcemZgij6yp10RumaZ/6Wv3uydXasKNVi9ftkhTsI/rHkk2qLDdNGFGj0XXdatjdpgVhB2pjW6f+unhDqiO1sa1TV9y8MPVcf3luva45/xD9/OHXtGZbsJ/u3pc26YfvOUozxw7Tpl2tmjSqRsOrK/TPlzfrD0+v1SfefGCf8viR0+t1y5Or9dyaHfrVYyv1/950gB56tUEXHjlF9XVV+vKdi/UfZ83R48u36s0HT9RX7nxRY4ZV6fjZY3XB3Cm6+pZFqqoo00sbdmnDzlZdMHcfrd3eosXrd+q3HzxWh04ZpV2tHXp02RZt3d0eLLF2+n765JnBWaQbGtt064I1etN3H5IkveWwyXIunoPCBwtBFZPLTpipXz22UpI0Z/II/YfHkPrnJ0/ViJpK1VSW6Yiv/kM/fM9ROuWA8f3efvKoGt353Hpd9IZpOnG/calzXNX3mu3kqv/vqTHDqjRm2N69s8Teu+T4GTp23zG65JdPadzwKo0dVq3yMtPP3z9PtVXlum3BGj2ybIseeCVYOmt7c4cuO2GmTj9wgmoqy/WFPy/WtNFBGXDSyKCRZfqYOo2qrdQRU0fpubU7NbKmQrtaO1PNOL96bIXem7Zv9aZHV6Yuv+XwYF9Rtllhf6694FBd8ONH9S9HTNY15x+aqkQsueYsbW/q0P/732d19en7yUz63O0v6KxDJukzZ89RZ7fTO3/2uJ5bs0NnzJmgFVuaUo0Qh+wzUp8680B1O6dZ44bpT4vW6Ufzl+lLd7yYet6rTp2tnz64XN+/71Vdc/7/Z++84+26qjv/PU+9y5YlV3DHxkxoNgRCQjch1FACSWCSyYTJDEyYDAmQAAmYNBhKgBQIKUwYakJoNjbFDtgGXLAN7t2WbcmSrK4nvV7O/PHbP/a6+51733uSbEn2WZ/P/dx7T9l17dX32v+po03PPX0N57zscY0+3Lf/4mnct22Q41ct5nNX3sebv6CN91+6Ju+Le80nLwfgIxdl3/G3btrEe85V/UcsXcDO5Hf82rUbWLpgLsNjk7z67y9vHKOzzzjypxrT6mUL+J1nnsTNG/p5wqNW8ObnnjptyrBDDVpG9SDBOS97HOe87HFcdudWntKw92lfIGYnuPt900dieQ/V6/7pSu7+yxexc9CMqmUkDzdYvXQBa5Yt4Iu/8zSecNzKKfvnnpA0o1858zje+aLHcsvGfn4unfH1cycfwXf/4Nk/fdYZ5S1wfPF3ns72wVGOWbGQXUNjfPUn93Pl3dv51k2beOy7v9XYnqbtDNPBKWuWctW7nj+l7QvmzuGoFXP4wu887afXLnlb3jg/v6/iBWccyQO7hnnrL57GikXz+L8/XMsxKxfx7NPWdAhib/3F01i5eB4X37aFt/7iaVRpbKqKjk3thqqquu6xW7k4BxO94HFHcfeWPbzk8cfwiYvvYsueEZ7/2DV86of3sGT+HDbtGub0o5fxd7/+ZD575X189vJ7+aMXnc5zTlvDwMg4AyPj3LhhFz9/ymo27RrmTZ+/hrOOP/ynJzS874JbOPuMI386j4Z3vuixsxvkQwyqh2PiybPOOqu++uqrp3/wEQJv+tw1XHBDTgJ74hFLWLt1gAvf8kxOLcLWWzg04ep7tnPtup284RdOmvbZ/uGxKdlTmuB9F9zCJy+9mz84+zG8OQW7lLBx1xDv/+at3LpxN8cetogP/8oTuHLtNm7btIePXHQ7b3r2ybz9hfvX5H2oQ13Xe+0/2jOiRM0Plv+pqqpr6ro+60EpfB/gkGFUVVW9EPgYMAf4p7qu39/t2ZZRdcLm3cNcfc8OLr5tM8Njk+wYHGXVkvl84NVP6Ho0RQstPNA/zB9++Xr+6jVPnLUZd2BknD8//2be/NxTO/JDtnBwQ8uo9gGqqpoD3A6cDawHrgJ+ra7rxpjMllG10EILLcweDlZGdaiI008F7qzr+u66rkeBLwIvP8BtaqGFFlpo4SGAQ4VRHQusC//Xp2s/haqqfqeqqqurqrp6y5YttNBCCy208PCAQ4VRTQt1Xf9DXddn1XV91urV3UO1W2ihhRZaOLTgUGFU9wOPCv+PS9daaKGFFlp4mMOhwqiuAk6tqurEqqrmA78KnHuA29RCCy200MJDAIfEht+6rserqvpd4NsoPP1TdV3fNM1rLbTQQgstPAzgkGBUAHVdXwBccKDb0UILLbTQwkMLh4rpr4UWWmihhUcotIyqhRZaaKGFgxpaRtVCCy200MJBDS2jaqGFFlpo4aCGllG10EILLbRwUEPLqFpooYUWWjiooWVULbTQQgstHNTQMqoWWmihhRYOamgZVQsttNBCCwc1tIyqhRZaaKGFgxpaRtVCCy200MJBDS2jaqGFFlpo4aCGllG10EILLbRwUEPLqFpooYUWWjiooWVULbTQQgstHNTQMqoWWmihhRYOamgZVQsttNBCCwc1tIyqhRZaaKGFgxpaRtVCCy200MJBDS2jaqGFFlpo4aCGllG10EILLbRwUENV1/WBbsN+h6qqtgD37kMRJwK7gWXFNw3Xun0/nJ89mNrycH72YGpL++zB15Zez25l7+D4uq5X7+W7Dxo8LDWquq5X13V91t5+gIVoosvvpmuPxGcPprY8nJ89mNrSPnvwtaXrvX2gfwcdk4KHKaNqoYUWWmjh4QMto2qhhRZaaOGghjnnnHPOgW7DQQfvfe97Hwt8FRgsvq9puNbt++H87MHUlofzswdTW9pnD762dH32nHPOuYaHETwsgylaaKGFFlp4+EBr+muhhRZaaOGghpZRHaRQVVXXuamqqtrHsvfp/VDO3JmWVVXVnPT83L2oZ8Z4WlXVwlmWPSd+F/f2apz21/jubR1VVS1O31P6tA/19U1X716U+aCP00xgf47T/oBuOFyOV1VV88Lvn86Pn4u/e9RVNf0+2KBlVA8RVFV1RESsLs8cmXDrKOAV6Z2+qqqOrqrqu+mZ+XVd1+m5k6uqekVVQHquz5/0P95fGf6unIborayq6pji2pqqqlYB3wb+saqqM8O94wvkX5D687vAbcA7q6r6+aqqXl9V1YqqqpaHNs8Nba/SeBwDvKqqqtVlP1L/Twxjtxp4e1VVz0z3V6Xrb6mq6sVVVT2pqqpTQjlHAX9XVdWLgK9WVfXEqqpOSt//E1hSVdXhRX9WNlyrQl+PSb8PT9+rq6r6uV5jHNrznKqqnh/G+OmVGPzx6fOsqqreV1XVGcDZqb7VVVW9qqqqeVVVHZXG42tVVf0m8MrUn67MO7Td8Lyqqt4axvuESozvrKqqzgfWhOf7wnsuZ3Esr/xd/LeQcFQxrzMmmGEeYx/mht9rqrwWYpuMa7+AcPiE9P+4qqo+UlXV0cX4TLd2F1Z5ra1o6K/72hc+xzWM1WHAX6Z5WBDuzwd+JpT5OOD9VVU9vqqqJwAXVFW1pJYv59g0D48CFlRaB68O5c2thC8VsCqVf2yiKwuqqjo9Pbcm3XteVVWfDG1cWs5Bt7kpf1dVtabXOHYd39ZHNXuoqqqqpxk4P1NV1UnAycA7gU8CC9Bm5EvS/RPRgt0JXAy8HxhChP07wDzgeuAvgOXA36T/JwC/lu7/JD37H8Cvp+e+BSwBXljX9XsTko4CZwN/BTwHeB7wSuAPgFV1XV+bFtRk6sZ84KPA3cC/ooVyFLAS2AP8EXBsau/zgdWprO8ip+5j0WL5a+CtqT2PT209O7XnYuC9wEuAZwLvS+OxGvhDYCNwSurz9tS3X03lvQPYkN5/JfBu4E2p7m8C/5DGe1sar+Wp3R8DngHcA/x3tP/ksFT+CHAZ8CvAAHBJemYj2qPy58Am4J+BJ9V1fVGa7znAC4C3AYcDP0zlPz/1+TLgPcAtYXznAWMJD44GvgCcCnw4jcHPpHF/ZRqvbwKvA65GOPT/gN9L4/OZVM9hwGLg54ErgOPTPP5MXdebqqp6OnBnXddbAo7+AvBahEcPAOeltr8BeBXwCmBumvc5wItTuy+qquppSOB9JfCLwLXAlcCTUz//GbgdOAL4b6ndu4HnAl9HjO/l6TMXWJTmwX1+E3ASsLSu65+Y4KV2vzC99zzg79D6egxaI/+OcO9Y4H+htbIyte/lCEfWpb48GvhBqncC+A3gVuDX6rq+v5LQchZak0Noje4APljX9XhVVSuATwHfB56e+v2O1McR4CbgjcB1wOY0tiuAt6A1exLwJeBMtG5fB5wGbEHr4cmpjW9I9b8L+O0012tT3/8MGEttOAfYle7/I/DmhANfS2O8Lc3BsQg/N6WxeT/wmwjv96TxOy/Nw4lpTL+SxuIjqd331HW9B6CqqicCd6Q2AjwO0Ytj6rr+dFVVL0ht+S1g23Q0NELLqGYJCSmX1HW9oZJke1td1xPhvqWIUxEh+jyarD2IgNyIiMsa4D8BL0UIdi3wBISs1wFPS+UsBS5ARP6ZQD9C9jkICf87IuqjCMlfhgjZVuAqoAZuRkg+gJBsA1pQi9GiMuG8HBhGi3Uk1XMcWjRzUj/mp7r6ENI+HSF8nd69ByEo6ffC1Pfx9DkZEaM6lTme6htK/b4deBIi0hPAhxByrwAqxAjuRwx0MVqki4EnAt9DxOZE4Azg2YggzU1zMT/V6zm6Dy3GvjQG46kdc9NnQbq2Fi3y5yLmW6exXYII/BGIKNwG/BsiLJ7vOakNS1K7LwT+CTGXd6H5PQ/N+2Sanz7gR6k/jwd+NpX3aOBfgP+MGN61qU19CK8WpXE6CeHJUamfu9Hc/wbwAUSMbkzE/lQkOKxCRGsSODe9O5qefSki6CvTeFhgeC9i6D9EeDiRxuqkVM6i1Pfh1MaV6ZkNCK8uTWP0QOrfL6cx35Xm6MepP2tSHW9M97ci4vnR9MzzUEaGEYTrj0njfWWak19OZYylee1DxHp5GrcbUv+WIBw8MtX7I8RMP4Bw54VIwHkPWVi4AAmZf5H6NJ7+/w1iVguBP0HMY0Vq4yLE6BaSBanfT/1Zi3C3L43J0jSXd6Y5OgHh4rcQMz85zdPNSNhblv7PBy5Ca2ABwtnr0xwMI/zrQ2v0PsTk/wbh8Ugap08jBj8fwbo0r99O4/RSxMwHqqp6KRIe/wdav7tSeaOp79cCHwTeW9f11TMR9jugruv2M8MPWjwvRFrDXyFEXtjw3C8hgnsBQtwRtEhMDK9I10YTYk6k+9vQAhhLz/Ujwjme3q3TJ/6+Pf0fQtLZCCJMd6AF81m0mN+BFnh8t24oc2uqb3eq/wpE5NzG+P4YIr6j6f9EUeYQIlST4ZlunwG0gHand/z+WjIzq8nSfp3KvDu9txUR3J+gBT7U0NdufZ/u/jiSbpuevwYtyl2I4F6f5qB8bihdH0yfoWK83EcLDnXqR3+45vH0O6Op73vCZzJ87gtlD6TvP0R4PB8RxN2p3l1IKr8svWtcGUWMq2ksB8i4PU7GgaZxnGy4PpE+w13uj6Xyh9L/nalPv53GYQLh9jid49g0p5MN99yGsVC/5+fS1PeJMEajaYyHU7t+nOZnByL0G0IbBpGgN4EY4QsQM9nT0Da/sw3hUJ3qnAxj9E9orQ+mMm4Oc+o1W45f7O9E+r8JrdnJcP1WpDkPF3M1Ep6zwGFh7jLgpYnevQExxecDn0v9vQ/h0xXAf0GM/FbgV/eK9h5o4n+ofZB0cmFCqpc33D8FSX9fTwgxgDQPI/1oWhg/QpKRNZhhRAw3kwmdidd4+L8lvR+R/fb03J5U32VIkhxCUvk7EuLcQiaOJZE0IdiZylyXyv1JuubFPIKYRySW3Qi8GdwQmRiVCzT2xcx7Z6rDz40D68NYfIJMwK9P1+8EvogW4gfoJPpxUfZiVPGZ+E68No7MgH6+H5l9Lk592Y2YVxNh9/v+vQ0x2viMiYPrfCDMT2RwZZsmivdiXTWdRO+EhKvPCOXfn+bITGc4zV+co9gn4/IkItB+t3ymZNrlZzj83oiIfmR2I+na9WG8f5L+j5MFoEhUewkk3YS+3eHeIDL51uHePeH/CFprG5BA6HKHinJ3FfU2MdI4XxauBtFatbC4I43tBNKkvkxeE19N4+a5GCvKL8eiP302pe+xVN/3wth7HqNwOZT+D5Bx8NNI+zw7Pf9e4E+RiXo3cBeyPLwJaZMfRhaQn4vC/0zobmv6mwFUVdVX1/Vk+P8S4EWI0HwTmcxAkuoy4PXIB/SSdG0PkmCHkZnIgz6J1G8j2RyyaaIXjKdnoxPTZRHKN5L5uV1I9S+dn+W7Rsx56dnh1C/fX4vMECtCGbGe8prL7EMI3tfwbPmONcll5AUdIwbHQ5ttjlqGGFYfkjKf06WO2YDH0u2dQGM/gExFkBf03HTPuFLOoxnRolD23cgstSw8Z0KwOJUR57Eiz385Jk1tr4pv0vvfR+bpTaldTwn1zEHECrLZB0TQFjN1ruMYleMFecxmAmaOS0IZQ4gxnZmubUl9WIdMzyb+c8M7cY2VdTfhqttp3BxP18aQia7sr4WmHyNT1yK0vmzO83PG++mgbJOF13npU9btYJTY5qqhnG59Ba2ZFXSOj+tsmi8LLR6bBcgPfA4S0L+H/FxjaK5elMoaRIy+D5lbLwA+U9f197u0awq0jGoWUFXVa9Giubuu6xurqvoAmohPIl/FOHLK/iNiSHchJLYP5A7k76nS/wppLUehCYTuSAWdSGdkiYTKBCEStoFU/+L0zBCZUHaD7QgJlyDEjVFjLncslftQhvbGhV8jCfCw4pqJ6f4ItY0EdoxMMDahBb4DODpdswN5MZ1QEukm4jUdoe8GlpinmwM/tz79PiFd3418IMOIIZlBzaOzv4ZxMoF8MMC4NYrw2n0rGYcFgsH0bZ9nnd6d1RaFov6dZJwyU/AcmhGW63ArEtzmkTWkWW/DSNCNqQ/R2a8htEb3x1xMJ0hEQdaWAAupNdKaPoKClI5CwtcNyAUyn6ztno9Mqiei8fuLuq53zqiFB9qUdqh8kOPyLiRBfAbZdOchCeKiNBmfRbZj28/HkUkoqv3R31LaoXuZSLo9Yx+BpZ2r0+9tdJqEepVdmgq6+cPKcqYz6zSV3e1eHJdxsvlhV3gvmqBs678GLZQm89RM657p87vDb5sy7aPzODf5Cprm7EvF3M50LLv5YKbr4wTyT12f2rsBmX6Mk6OhjGhC3NvPbN9vMpWOkoncKGII14dndtJpbitxfTZtKOctmtxj+4ynl5PxwAEUpUl7b3DR8xGfGUDr2f/ti+xFN0q8iOs6/t7JVJyqQ9nxWftHh5GQdhPSbu0OGEJMahK5Rz6NXBxjiJH1ISHgKOAxs6G/rUY1A6iq6g1IMzoHaRsvR4zrK0j1/zIKI34Zisw6Fk3WXIRUixAyzEMTfHj6diRbRfYxrWxoQs1USc4amZnTz5IX7TjS0Co6JaFuJggvrJ0ogtAmQtcXJa5tSHqM0E/WCGcLw0gybNIgbHPfQY5gI12zpmemtYI85hNM1Qb2BjxmUVMdotP8NVOzVo3G7oj0f5wsdcdF2GQ+La+Xc1mabrtpYzvRuNRozOcWdbue0qToKLKyXVXD79lAHIM4xpAJ5ABZeh8lazaTSEh5YrrWbTxie+nRzjif1s77wnULTYel5zcji8MS8pprwrmZjE25vq01us39qezSUtBrTfcCj21f8X4TLttCMY78mceQLRnfQD6q+SjSd0u63ocE9hMRzt2K/MaLkeXpf9R1PTibBrcbfmcGp6DQ2BNrhaJfgpjUb6DQ13G0X2UATWQ0VSxBkuB9aLxXIQQ8nE5E8T6VJsnB4e9G5jEyg5uLQtltt19GDgOeyQKBTIhWoAU4Gp6xVOvnD2MqeLHOFmqm+toi9KV2Rb+eF9NStHg3kv1689P3PPa+PTW5/5FJ3UNmgBeTNdmxspAEUUo1XB3KnkuOiIz+BbdjMvyOTm2366c+UzrXcfQJDiPG+rn02+Hli1M/7A+KeGicmghlzi/qM25NR/ynAzMpm6chawwOwvl6avv9ZD+v2/wUOpkl4V4JFZ3t9PNj4T5IU7JZ26bOCTT/Xwzv29TndyOTimPVTfAoYTx8LIi5rytQAIkDsazhzJZ+R/wpmTsN/y3QWAA8BtGyfjRHL05t+y4K3joCzdH5iE4sR0L6e9B6/SDwxdkyKWh9VFMgxvdXVbW8ruv+9Pt9aE/JWbX2UJ2JwnuXI03qGKRtPZVMfB9APowoJd2A9ktZeopS5WwgvhcR1zZ+L5wxppeGo0T4j2ifTpMDeX/4fbrBTMr3GDYRHGbw/t5AbJfrhyx5m5lF7bV8320zc3OgwAQSDI5i6nz00n5n2k/XfSvySy1E5prlSPMvNfTSb7Y3xHC2YKY8J/yPwUJlAEq3gJ29qdfm4vmhjMFUV4U0qAVkjd9afNQw3f4YIGRmMo/emm4cc69Z0wRvVzku1HEHYgAryQEb3creXxC1uhVoTD6MtgisSO34AgpcOoy8l/JMJGhckvqxGvi7uq6/uTeNaBlVF6iq6n+jTadzgXfW2qH+52jX+DnkjAbLUBaAEbIWtYKpCDyMQkufgIhGt4itaAJpQr5I+KI07midXhF13aDJjBOd6b0WQpP5YYypElspfU+E+/ZBLKBzYW9HmqfHxPuVjkvPmLnuD2LarY+ROUai2i16sSzH4xjNO3Hubd6MhIfwnJnhbIiRhZgoDO1CDNIOeQdQxLKb8GB/E8ERpvbVEBm+9345W0UTwS/bNtu2GncdqOC5jSbfLWg9Lwrv+DvOoevt1Z5oWrNwaXNipBXWZgcQ4zwylOe2RsHIWnfJvGLboinT96OZL87LXchC5PZ8B21afhba6rKWnOFjLhJ8zkMbrY8nm+o/C/xWXdd1FPr3BlpG1QApZcqrkaZ0LZJk3lXX9TVVVVnjuAARmMcghlbRGYUEnc5Mh3tbOotui7eXAAAgAElEQVSErrQNj9NJBMvFFzciLkLSzuGpjFJ76ga2U0c/iZG1F4Hal6im6UxFtn3HukbIxLVCJtRHT1PObMFEouyzw8QXkvtsQtXNL1WWMY5MHm8k+x8dvBAjBJtC2k24jVdRy+1GpGsUlfhd4DUI5zald/2JMBMf20x9YBF6PVfigbWoDQiPl6T67kRCyQJy0MLSGdbfC6KAF02sc+hkOuuRpWSEHLIdx6HUBGfTLgsTFlT6wv/oq3swhAVoFjAjLmxAZvUz0v+b0X6oo1Aw2TKUHusFKLvLdjKuzkX+q4XA9XVdf7Dc4jNbaBkVTMndlzSnT6LIvucjRvUslB5kM4pmOZWcZmaMbA5Yiuy3pN8wvdrfC+yPihIy5DRGZmj3oiCO0qHreiIx9vsXoRBS39+DEHUxzSbDCPtr8ZQS8gjZ3wQ5hNoLdgAx50gg9qWtTXvS4juO5IOsGZXMpNSsLPlaYHG4dWTwpWnNUVY2/zS1JRKv0mRcRpztQQQ+7s9yX2ejge5PIhmtBf3p/+FkjWAItd1CVyTms2WUTdD07mXI1+WxjHi2DWkIMHV/0Wz2hpUQGaXbBVkAXRye29d+RzyO896k2Za4fAeicdvT933kTBuvIDPV9yGf3WsRHbqxruvfqKrqZcBddV3ftBft7oBHfDBF4ZP6w6qq3o7U3CUol9VL67p+C1o8r0e5zeahaL8JsrlqJUKwCURYbaf2/poyjLRbVFIJNjvtpNOf4AVvKeU4svO1CaKznPT+i9J1O7OXk/PhRRt8E+wL8Yrtjk77iqkSf5Sgo7ljMlybTovs1dbS52UYTt9mmvZjWBM1mMmVvrLoaF/A1OCW0nQzh8wIY4BGSaDi/EUzzm4yrs1H+Pg1hKeRWH0/PEcow/hKw71u/3tBtCZQ/K6QOe1whHseL0eqOvy6yQReCgTd6m6C0iS7B/mU55IDhvrD/SPCb2uzUaOO9cxmbJx9xaHhVap3AM0jTA0p74XDTXWb6cW1E3HXTKrUKC10jyP/+lVImP1Eah+IGd1NDm5ZSg7m+n1gTaWM/d/YH0wKWkZFYFIvRyHen0vXdqDxeWFVVeegiXUm5qch56EXozccOpqmRiascbJj1hJilKQidENEvxvD1mM9Lm8ukp7LcpoYop83gV1CRtjS5zMdQ+qlzttZDZ3RgybS3YikI+Ic/eS8h2PkUP/9bQoxczCj8Jz6mn+7bfFaE8wjB09A3lALWUNz2Y4EdJ9iaHI3Ih3xqEJEflOqYw5KjvsOxAwga2tPQXtayjLN6OI4jPSovwlKZu2PcW4emfAa5qFN735nMvUlEufpTIgldHveOOb5W0qOcLRVYTvZBL0Ejcu29F6Z+SUyrqgdNTH8MXJezj4UcLAY+bnXI/pigcjCmxnNdOPeZK0pNWevubjuDLbaQGZSTl318whnTkHC8KkoZdJJyOc+B7lC3oGsUEOIwS3dF1NfCY9Y01+l85Qm67reUVXVMrSR98y6rh+T7i9FR2a8HjGmdQhhT0YMYV7676MPamQWXI0kpYVosstw7ibpqCmarXwnRj/Z7OCs3DOB0sTm7xJxu5md4nszZRKlHT+WV4ffVXHddaxHY920R2t/MyrQvFkgiJJsE8FsmsfS7+gIMmu6yxre8xiNhXevQRnYa7LgYIbm33MRQbkP4aQzpK9K94ZQlv4/QzgZA1xuR1ngK7LJupd5urQCTCfh98rG4fm1Wc9mTKcZI43ZHrqnz9pX8LYCm9hsEnXbHKXmtk/XV+idpqkmZ5CvyYEbFZq/9YjGVGhD8+FkP+zemPyacKxXGdE/6me/hfxTx5G3WtyM/PGjKAhkHOHbCFo7N6Z7n6/r+iuzbHNvONAZHw5QlokXoR3T/wb8Wbr2OBQg8dfhucUoX9+lyLF7OTnjuY+usA9jIvyvyYk9nbyzafd3NI8MhN9R5Z8kBxVcQE5K66MRynL82xrMKGKgg3RmH7CmUralTHhr82W3ts/kM0FzG8v7vrc1tKUps0aZeLPXuE401NPtf1P7er3vHfrluDeVMdalvm7j1TROHpsyE/1PkD9hOzoG5H4ybpRzPtNxm831pvu9MnQ4yjNmVBlMY1mTk6PuC87F9owwtX17iv/OkO6xv6uYU89L0ykA99Mbx8fJCaF3h2e8rp19w5aF/qKM6ca92xzE303jGdfRMAqecLt2k5Nqm759BQk+3u91PRLkT0bh6lcAp9tItV9p9oFmGgeASb0Q5eN7OdrV/llgfrp3Oopk+SsU7bOafPjZl9NEjpDT5wySif1ImswdyAQTic0uMiNqQn4jh3d/35ue97EBLmc3YjrjdJqfuiGjieZoKncgXJ9JeqVI6Ge6WHql+OlWTiSmNjvsDH0amUG53cZikpwZ2//HyJndBxBhcBuaCNF4l98ub90Mx6YkDk196NaviaIMZ1JfizQAvzfA/iHy+/qZrm/l0R7+3Sst0K6GcpvqaRLAJtHaiULXj8lHpdzKVDx0Rvi4TibIaYJKHG5qyyCZ8VhgcRvj2r8RCcPGT7fT41L2aSKV67q3k3F9V7ge6ykFiV2hz7cjYf3fU1u+hGjlJWG+/jbRyfelNl5U0NbjHjS6faAZx0PMpA5Pk/WK9P+piPl8HKWf70NJYy9CO60/hE7htBQ0mBAiIlBN82IzMkREKyX8urjna/YV2OEbtazI8KYjSJYA/W48NyguhJlqKNMR06b6hxueL5msz1maCO9sm2EdM/kM0pmnbxRlvbegcSUSCjYjhu7x79bfphxwk2TBwv9HinLMILtJvb207vIzhMxGPn4h5kp0VgeX79/Dxfv7a3ynwwcLcU33zHRnioczZe69tMf4/FvIJ/v63mw0mJl8fGSK6yjPg9rX8r1mZjtOk8jsaP/vi5Cv7G/RxvCNaPvN7WTz7A/Q2VIDSKD+3YeEdh9o5nEAmNWLkankCShx4nuRPfhKpF3ZLPh1pB0NBUQeIZulIlI7UsYLbxhpVU1aT7dPKcmXJkAv+Jkgrd/fjfY+xAP3IvPaG6LjZ2ZiNov1dXt+okt5+5NgNBGtQfJhiDuQGfhLacyiZN2rz5eREwvbtFYebNhkvpwoyvQ9S/Ez6dN4avdmxNR9mF9st9u0pbg+gaTpbnPSJL034Wc3/CsZjjXXXn0boTtuNr3XDQdL3IlMO86Ln9uFNqsaR/dXouWyfTvJhw/+MMx7/yzmfLrPbJmeD4nsRwxrNwqK2JDatgP57n+M/KZu8xDwdhRkcQ/wqJZRPTjM6oVpwP8oXFuKfFHfQyf4Xp0myGr4DqQSe1F5MWxBjC9qT/YPdZOQo2YT3zGibUJMcz0iRF7kURsqs11HAhaRdgtyjP51wzPd2jcbyX4mn26L3/3uJv3tD0ZV+g5cX6nt+VC4+F5Z/+XF/xEkXZbXmghP1Cri+DYR9V7Stk8Stj/jttCncaYe3re/57JpXGN9xnubboeQ38wHNJbMeyaaTFMfup0YPV6UafNYqdlfSqcZzifrbm2Yv70VFm3O9/Uv0bk+fZLwnT360+1TWnNiDsDp5vyq8M4Q0po+gGjNMGJUl5APct2C/OMD6Z07kPlvIbDsIaHZB5ppHEBmdTZa5D5I8LeQhPzt9DEzcXLIPWSHqydsAjGVaF5rWnS9NJiI0CaeE2SzzgTS6JqQb3OxMOICiAgdkbhkajMlDt18B1u7vF8uqiYzaU32tXQjzvvKrMp5aBqDXsRvoCir1Ay20xyAUtZfMr6m+Whqc2Q2Juz+P4CsAqNd3p3JmOyt6amXJuE23xB+30wW8KIZ3HntmsZoJnVG0+F4qKNG/rudqdxPkpnRerTNJM6BhUtHT/qeD/Ccbo7ieJZz5uCDS8j+TNMXa5ElbpZ9bZqnyBAnQrlN42Q/2WQYl3Fk3tuE9tctBs4la+d7yKbwHcg68ziUD/TLoJOiH4rPIzY8HaCqql9C6W0+DvwqkjRegpjXYeT9RvZZOCNCnb5vRlLFiS4yfU+iyT+6uNYt952J5CQyO56JbMXTHVVRhgG73F4h5k3g+/G5+1BqlJmExjpZpxdFmWYm5rKrmJoL8E4UOQQ5VP/B2uM3QQ4/jnMzgcZ7Czk0mXA/tmcI9dlbD7ah/juL/CSd+9ziuLqsOFaT5DD4pjkdRdrc49FG2T5yxo4RRFidE7HMYBIh4lzZp72BmA9ykM69YhU5JNvtXQ58HuXLdBtqmkPPZ9o+jx10HrVRHvgJnXPTNM77A+JcGpfNPEbJ+zOdwNWnJkDnqdHTrdkyB2WJZ9CZ89FMbB5iQCMok8SL0TacJShd3LForfYjH9SVwC+i7RKXI1fJnwNvrev6ipkPy77BI5pRAVRV9SqUvv/fUSDF3yKJIZ6T1EdO4mkpPGYb6LbPxte8QbXcW2KkNuEaQ8zvJPLeobhguy0uE6sFiGCsJp8jE+tz+yz1NSWdLZ9z+yKUC8JBA0tDG+vw7D0oFY2v28y5KLVjHflYAL8/W2Y73bM+/8uMwWPv/G4WPqCZSK4j7+lypusmIlsyJTNAwrWq+N+NYO4hn0G0Ezm1T0/1vxv4KJpzE6L70/eji7Y04d8gwpmSKc8GYhqnmqwdH0Ee413k89cWpfsWAu1D66OTqUyQ95R1m/fIJOP/IfJ+rHJu7Xfx3seY5iriax+d2cwpnoHOOabLOzUK2T4RJZYdIuP2fHI6sG6JgZugCTe7vROfdds3oHRI48iV4UwYzyRbiRz082OUQg4kzK9EZsIP1nV9bo827nd4xDKqqqqWI43nbcAzEAGwWW8r8DdI27L07zN5nENtLdI4akSgjSxNyNFN6rGEdTh50fWTz4BZxFTEbJKkQAv+JrQofB7VPLofIzJCTgtkwuC2Roj1l2V5oXuxuk1N2cCheYx8vay714LtpTFEAuL/falNd6b2Pjr15WK0EJsk9yaitAkRYY9BFAacnTxm1I4bXGeyabrEnUk6yxlABN2bvc9D0nCTFN0NyvnsdR7YTGAPOdkyqV3zyDn6KnIY9Bja9jGKNPbHhHJmI4hAd5wdRuaq49N/m9jGySnOKqZaK7rNS9mu8jkLfV6T1px8HIizyFhgsLC2N9ps1Ah7PVPig7XazQiHx5Gb44WI9nhP6FrghPQZRRF+zwTOrev6NVVVVcCquq63lvlRH2x4xKVQqgTzUM6+t6IzpuxrmoOk5uNQ9EtNNsEZuXw+zdEI8U2ojBwlAR4v7sfsC/PJCTjvRYtqGZ0Z2Ms5mpfaG8u0yeMMcjqaXkwKOk/V9UJqIhSx/pjaZwItRmdUMDOImloJ3oBsrQa6E6heRKsbk9oT/pfCgtvkbAfzkObarZ7IdA1HkonEHjq1gOV0jrW1HI+rU0J1G2cTlFh/hfbsWBIfRnji+n616CN0ajiGyYZrfnYPewcu00KaYQF5jAbIGV0WojHwMyeHMmAqI58OPB7OP+dAjoVIY4vr4FYkfNqU783Q9uu4fm8JiVDOVZOmPheZ7H1/IfLPzSenaLL2vYfM0GYDxqXpzJWRvkBmoGbYj0Na1NGpjQsQrVmYfn8n9WkAMaofASdXVfWkWrAV4KFkUvAIZFRpgF+GJOnXIORehZyI3t8wH1hDZlCelAfIZgUv8F4TFs1mkVjExWBEsga0m2bmUiLfLqbWvQDlK+wLz80UJmZ4v2Rulpy9kHaH+suoJ/swYg65SNBnC6XWs7S4PgctNOdqO40sGFQof9n2hnKjOcfmXtcRnewlY6nD+zG7O+n5m4pnyjl1FJb3d1Uot5r7tRr5EFxvLwEjXouaR5kyaxmduQcjdMOJ6NgHEbnt5MwGxudlSIM6GRG/sfQdtV4ngY1jHE+YLsH1WlCyufke8rpajsbN5TwJCZ8WqJz8FjrHaT7dhawmDRtEOyq0J9OwC1k2HKBh/L6bnI+zF+3dVyYQ580C0jxyMMm5aC28HAnaD6R3zkQnRKxD43or8PfIijDMgYQDHX13gCL+Po0mz5Et3mw6SGcU2wRiXneS1W4zGptlyvDnusunjNr5MRmJd6N0JE0bSXuV5/f93o3MLONE3aOeblFoMZptlM6UTDVCZG+Gvjdc/yFwHYq08nPXNZQ9k7bOtA/lWJfponr1uQzv93zHNDkxMitGWA6h0N1YtsPSm7ID9Gr/t8nRbHeSsyGU+33K6LPJUEavyLyZXo/lNWXlcMSmQ+PdrhE62+hnmqLSyhD9Xu3bgxhiDMO3aa8mp7SaQAT45uK5bUW93caxxIFy7rwBtsS1beR9l5NIQ9mW/q9nZnumyijecvx77R8ry9mCcOca5Ne8HWlKI0hIuAQxpvNRmidvL/gJ8KxEL9ccaJr9iNKoqqo6q6qqo9HehcuRFLaQnPhzEbKdO8t3H/I73IYmviKbYWIghKHuUb2fc3Tf48lS+AJysse4T6Mb2D7vjZx96doZdJ5RNditgHBvOtOGr8Xr5dlKIGl0OdkP4WNQno4WtHPUbSabs5rKnilMFr/jePUhgmVNYUGqM/ZnvPhvaIqinEuO4KzD9YpOZ/xCpPFEiXY+6r/npJf243m/DZloFiF8+B55G4UjIueRj8goyzF+dMOhXubObteazE4VnQdt+qiS3and68jzbC2zScsvr3Xz31VIA7gd+Rediy5qivaV9SGryEnk+SiP5phLtpA0jWOTKdbvzyFHecb1ejid50k9B62LgdQeB2H1gmi27gvfMaLSEC01JQO2BeBRaF/oNsSkzkRr0VtutqU2fgxl6Pkiwr3PVFU1h2arw0MKj4hgCp8uWVXVZ5DJby0KntiGCNYa8nEI0OlwLh2i08FuOg+rM9TkbOcmctB5MCL09iv5vRgCG6/PFGYa+luW2xQoYkmuHB+30YvMx8r7Hb+/t8JSjAyDvG/nCWQf2BXp/2JyeHTsW5Njuhyb8v8oObQ+EsBYljcPN2XON0Tzl4WKeUhQOpzOCM6SsEVBqakPnrc4R65zbwMnJlDk4SqmRssNotDm45GJrR/N9wk093lfwOVZq3D+v8PIZrt1yFy1CM37OnK2+Lg+y3XUBLGvkIOr7PdZR84JeBr5dAMfFdKHNLsFKOrUvkgLe+uRcFO2YRLRp8Vk37jXzQ0oT2kfOSgKOmnDBNlPtgBZOb4G/JdU3/Z0bQnC1zOBp9R1fVdVVXMRg19Z17V9bwcUHtaMypEpVVWdhBzhn0J219PQBD0aaVdPRRMaCdIuhNAxnLmUaJoYxPUIWU9N/32MAuG/j+uYaQRQlJYuR0j+NDoXXRm6HolTL3DfmphjGVkV+9vExKCTkdkE5LGNx8rPFHrV2TT+cUziGMw0rHcXIijQvB2gDNmPIcmxjkEkED2uuVtAJ6GMEX4VIn53oci48mDI6SL7SiiZegkzFXQirljAMv5uQzg9l3y0yQ3AWWTC2cRQXfd09e8mn/4L0gbWp///ivzNR5Ijae9G2rxNYNDsN3wAEeXYptiWMgw+mhrNwLxpNx7BY/NjKYC67H6Ep5vTtZXkgwj/GHh/KmczYnjnA79MpxnSa8r1+OiXbSiTxONRwNhmlL/UdWxA6/ABFES2BO0hfTcw/FAHSswEHu6M6vnAbyBGtBpJ1DuRE/Et6OTKxWR12SGsk8gObkknSikzIRDeg9XRnPQ9gojYSuTPOJnmSJ6SsI4D/4G0v0el9/vCMzHcvBuUGlH5bBNTiAvd5h+b2uK+o0hwtqLxroty4jszJY5Nz5XMfxJJiEektl4KPLtL/yiux31f0zHBkhmXUN4v5zCORWnu24rmFqRVDSAmNUZm7vblzGeq5j5TLbkblG3rNj9x788QIrgOKHDU3JJ03/vASuFlJoJZU91ep7chDcmnGZyIhALvc/NpvHentlmTmYtwJ2qbDpbxeDb1vUY+m8eSt2OYFsSyAL6B8oXOZCN+hCh8lJuVt5E3CDvEHjKTGkD4f26q4znINPpMFDj25HTvG4gx/VekAS9FtOQq4C+B79d13S2w5oDCw9JHFULQP452wf8QEa89iMC/AngWOZtChSZ6EWJkfciEEW3Vcdd/hKYwUyNZjF6K/pLDUltOoXu4aVzcO9Pvs5HK72jEiPgL6E6YY5kjdIfSTu++2bfgcYjSuRf+HLRg7gD+kKx57ErfNzaU3w3Kdk8U9+Y39OMHZEb4THKgRwllvd32UEVGEp9tiuKMZZuAN5UdhZ547RYUEWjn/CpkUppAfbUW7ghLEJFx+qBu/ZgNWFKPPqn70HzGZxaTgwLs3+1Pz/XRGcyzlByo5DKHmDnE/Jl+3/65CaRN/TNi8o9KbRsij/NaxLisBXo8Hc0Z/aRLyFGD0dfndhyFiDvhGQdUWIitkGuhjMgt57wJLBA6mjHi96rUZgvVPsV5YfqMozl4PKJtz05jNJja8hikRT0H+VrPQ9akBWjezgDuOViZFDz8NapXobOl7iPnqHob2bZsR+Nc5Jh9FDnXXjxdtI+c7qQ0w0SiVtq8vcfIKVt8b6aaWfRJ2b9VnupbmoGatIYmac4mjW6S3ghTtcKafPKqwdrjrlSeF1MkeE19mi00+ROiOWkP2f/XR3fbfzfo5rOCqVk+JhBxPJLZ96cmM/c+siY1jnBrDBHYU8hzPY4EqTVFXbuR+eaU9L+bBjdde3qZsu9Cvqa+cH8zCvtfhUzQ25HW4mwPxgG/U7arl8bmdpRMs5z33Sga8lREhE9ARHtXqn9eeDfWH3Emao+uz/PgZ70OBsNzK5F/ZxWiLceT90ytRXPiE3tnCnXqx9Fd2mrYhJimYQC5M05FwuCRSPtbnp49KfXpSPJ83I0sHici5rsJpUl6NDDSmv4eZEg7p9egyRtCeay+gpDrerI2cwSZiSwkS4CW1qDziO5oruhmGvH70SxlibApk8RMzV5W75cg5np0KrPJL1IyzW5BAXvDLCbRuJqBxzHx+N2BpLe4J2wxnbb5/Qn2F8T9buUib2LcZUYGm35i/sEytU4sI5Yd6y8JfpN/w8TQR8isIgsgJnaTZAbWDf/2J/Qytfn6NkQAn0nW5O5D68k+npKwlv+bzJOlQORnarKAYGGxxOc7EcP89XTNGp19aN0Esdn45BxMUTJdr21rN4Z9mZu4xtzOpujiXXQGBn2FfHzRU5HweAKas4+me8eE9m5CFqPzEC38BHB1XddRez6o4OFm+nseCl29FZ2jYufqXDQhj0KEfj4KTTex34HstFH1dcqkEvGiv6W8bgZis5PNZd7UaKmsm4kuluky5oe2HJvu7aGTAEcfkcFBIPF/bGtZd6zX5ZXvxwUU33NY8qlo/8+PUh1N0Y97A2UbvZ+kImfgKE11TYEvhtK34LYuIJuzornKjMwQA0yacv3VxXO+5rbMTfWsRoTY+9KiafWI8E63udtf0FRmjNoEjed2ZKb0RttjkBTu/kVTd5M20ERvSgtF1HCWkMOso8nV43JKqt+br2NYftSaSnzuxpQjODrPPrYYZbmDbEZ09vxS65sNRHxZRh5Hm9SjGR46I4cnkc99E8Jb+6q/i4LHXpSeOQfNmyMkv4no4ePRer1rL9v+kMDDjVHNR5O4Ai2kP0XqsFPrx4SRp6H+D6KJ+zmyORA6pdgyJLskHr7mZ2I9k2TpfFHDe343LhQvNPvLohljbupfXLBlRFeMCortboJycdmGv764vit92wdhf9ffAu8hH4dwOTKN3EU2e5qxlDBTdb5so3PdGa5CC3E3+dyh8v2SYdkUG+fUBDemvfG9aG4ttbNI1CNuRIZWzs/i1I+N5HyPltR3MT0xnSD7VOJRGTR8zwT8rI+DsIl6OyKCyxDRW5Xa64i7qMEuIgt7JaPoBnFebGr3+97kekfxfIXG6BYyQ9lIXrODoVw/Hwl7E5Q+zTnAV1M9HlubbEdTfZDzcs7WNGV/lNsVmXzpC6+KZyyY1UiLcuo1+9V3oMCPF6bnX4UCUK5GUYB/g/bmPQF4V13Xd9V13WuODjwc6B3H+5hh4hhkZ52DpPk7kCY1hHak/xP5COiLUGCFT7SsEVG7nUzY7QvygqmLz+QMrhkBfY7RLWTTWLcd5S7Hp66a4NbkBWgzU3ynzLgQ2122a7LL9Vh2WVY/spt7oQ6lfu0mH33izO3OBDCBDoizr28mYziTT5kloMwccU9q70zG2mWUByUOhfeajkW3SXk2/ZhEGvAA+SyryTTW3uNzL5lQer+Sz1FyOT9q6Jdx1memzXa8m3CkzGgxjgKRdpCPvP8+Yl4TyF9VnsfVDQd7tckMv8y+MNHQt53hOa+ZcaZmfeh2DlsvHJsMv2PWkcHimVhuPMZjpvjss6/imDSdWxfPtbIpchKty+8js95TUaDV2tQWZ9u5B9GPr6Fgo9OAN6fr/0DKPHEofA5ZjaqqqtPRoXFnAYuSffU7wAvQ4j8NeD2aqD1IY3oNQgYfh9CHGFxFzrFmU0SvPTd1cX2yeMa+r7XIfu9nopYVyyDdW4o0QOeE20Hn+TT9xbs2e+0M5ZSRO362jGgq+2EJdjhcX0b2k0ySM3cPIfv3halNCxHhugelRzobjbkJ//4Ap6HxGEdtaC4yYyylU4sx1A3f3o5gGCPPfU1OILqVPL+LyES5mwnYRDPCFrJD3tpvnepbndpuDcTmptgX0LHfFNeipG1N2MQ0PtMNSk0+lmk860OBAfcjptCH8kmuJJvVY51Ro+xWd5PpO5rsakSIYwJj+6R8grLnxLkDa6bm6St9o9NFtcV+uDwzFVsUrPVGBuN6pjs/LoK3kjgPpcctrj+PRzRZzkWCzSQSDN+J1tvr0ZyMIHy7CLlAjkCmwT+u6/o2tKZHgE/VdX3JLNp7YOFAc8q91KROQAT9t9P/vvR9IkLwm9HkjJAZUJT6v04metZeLC2WWkX5v/zsCs9tK+7FUzy3NLwbpTL/3hbKdWivn+9vKMMhqPFk01Lqa5IoXW4pxY03PFuTtY8RFBL8ZWTe+w5Zy9iJmNWGLmV0639sz4+LVxMAACAASURBVETDNf929vVufYoacVN/LbFPNpQf3/F5PB7D8rnpjiOPYdUmqv1ob8s6ZB69lan41jQGsb4mfIlt7jYu081BqUX5+m5yUtM6tfv6UH9855Nd5nMmn4Hi+di32HebHKPW24Tr5br1vaY8e93WhU2rO+nEg3E6TwCe7Xh7Pv1OWVavj/s7giw170Zr/3bg99B63JTm7JI0f6OI9lyDDjy8FnjZgabhs/0cklF/VVX9FtpP9C40KU9EUt8oUm0fjRD6+8AbEeEcIp8iW5NDfyfIB/tNl7W8lNJLH4clTEukdijHlDexnPJdnyK8J/1fTnamWkq1fTo6quOZM25X9FFNBzF0vkIL4SJkVogwjhbASpSCyqljvgH8duiXD69r2khr6HY9jqH9JE1Ha8whO7GjJBsd6tE3tBtFqW1Ae12mc3yPIaHmuIZ7g6mck+gejbcZSbPbUTjwWeRw9HtQyproO4m4YGgaI+NR1eOZXhDrK+saQNqcI+c2kwORdpKd8T7dOJYTT6edLRhXbUZzFGA8QuZ85HOZJJ/EfDjZn1hmnegWIBKj6sr6faii4RZES+5E9ME+5ugz3luI0cHd5tDt8n1noD8C4fNa1O/Hkf2Ic5EQ+QI0R0MobRJo3nbWdX35Q32e1L7CocqongV8BE3eYkRYHXb5DCQ5nI52ZJ+KzBb/DvxvNOlzEKIMIRNLeXy3iV001fUVz1jK8n8vlHJX+URxz6lOSphADs5nhbLtnK7pveepLKep7YT/TRFK65GjPKaFMaOIJpSdiIDfiyKGdpKPc+hGZJuOBe8FZRs3k7Mf+BMPEexP7fBRDi5jHDEVp0QyIa4RThxJTkhsM1Oc5/tTvU43c1S4537H859K5jFEDm9eQJaINyKhqdsYzWSeZ8ugIhgnnbeujE702DmBalNQ0WyYa7d7NmlNt/VjAh3093jyBt7dZKYSBbXZgvtc7j/rJ59WbLN4eSjmbGAznUfMuJ5eYzdG3j93Xnr/RBQk9psoCng56v9qRD+Wpc+xqb3vQT7Gn9R13Wuz/0ENh6qPyki6Kv2+HaUIeSKSus9Ek3sYmsQV6drFZDPGOJKw7YOIEUFNCVYnimf6kBmn9MUsDL8tpRLu2RkaTYaWHI9Iz8XzpryvpkSy0uwQ27op/Dez8TMxSmtz6M9x5JM+PQaO2quRqapGi+BUJBCAtIpjQ3tivbHfvaBG0qLB0q3LW0NnwIFNgG7rcrRQq6KM+WRiNkD2+wyG3/emMq0BfjE925f65XQ8PvfIQoATjkb/wW46zVCLUrmL0v8L0v8TizEapXMvka/3h2vRpBUFpOnAZqwIxgFHp3qcJxETddSchSSPt/sV6y7bMZ1PzG0prQDO8FCCzXU/S+dJ2nEfkbeBdIO64bf7BTm03cEKPlPtKKTJDaA1uiu0e6bg+lan73uK9sSz6WxZ8RjNQ/3vR9aNJyOa8yK0Jh6NXAU2Ly9Fmt/RyCT/RuBKlClm1SzafPDBgbY97oV/aj6yxZ6LJIwKHe61FfgzRKS3Ion3VuAzZOIxhiZ9ECHpGJlRNdmEvUjMqDaTDx+zzfj7KPmmj3ieqb15Y3r+dqYSA/suuvlQyoio0h5fvjtId/t86Z8or42Rtc870WLdST7kL0Yl7WqoY7qP7f6lL2gMeDudkXk7wrO7yUeJxLEpf7t9GxAx9LvjqV8xQMWRf96n1t+jXP/3nPkzHt61adTzMdjwXFO5/pQRhrP1hcykzNgGn5i7HeG523oPnb4hPztd+3vNefw/gPb9xLkepzMK0/UYT0q83VmU36tN0We1tbg2gUy1xkFvuN9MJz1oKq9XXXWos4wOjKHq5RqcILsFvFVgC9qvuDON0Sjw+0jrtI/+TSgs/SfAyw803d7XzyGnUdV1PYqO4H5a+o724rcgqeI7aIIfA/wCGTEsTX+HbPJZRUYQ0CQbuaKU24ek8yWIIG9O156BNh7ayRq1rp82m6nIuoUcVg950+wk2a8VEdd9jOX1kY/+dvv9XJSUfa6RNZBYnv0mdbgWwZFGtyEptkZ+KucaixFey5k9RFNbud/qHeSTRYeRNuf8bAvR2F1F54nBkAmM27UNScc2wS4lj7czbZhILiRvwo0Rl92ObDf+WcuZgzR3+3lGgC+Qs23HufF8+H8MxnEfXbaf21fYQaf2Mhm+d6K+H4aEQI/18XSeLAyzy95eQmlmW4RM3hbeQMLEfLTWvH/IPklrtRHHbd4l3G+CiGteO6YP/WhslpOFMOPMajI9iDhKj7ris/5uOkXYmmUZrVmRE/1+mUwfPC6byObvL6MgsSUoVdzzEG16Z13XX09Zew5ZOCR9VABVVZ2Fsol7l/gOREznI+eineALURj189BEOxeYkc72ZkswRv554Z6fs21/E4qeOQ0t4jmI8awO75vhRLBEeAdSzxeQszEbMTcjtX6ieN8LDJrTw8TfEco0Pq4r2tzrcN399QmttoFvROPmYzCi6WsmAk9ZR3SgD6U22rkczwCy0DCExitmlRgjbzxdQSZApUmp6diPCLENcbN2KWB081HUaHzWpGds3v0wkmq3Av8JEZF40GCcA8gBCTF/4974Q2K/PA7QadLzWH4FeDXZBDWEfHNnpP+lHytCN5xrumdhwITaZi7P+R5yxo7ok4zth5xyKfqTI96WUPqXzQz6yFsStiO8PhaNzUI0VmYC9OhnN2iat3itaexGyJltfMbXKQi/L0LbGJ6MXBbfR37h5cit4ejIDyG/FA6WONQCJ5rgkNOoAgwiIrUG7SD/a+SgPhZJzP+ATH/zUMJFI9wShJw+Sr2PvLu7j6nE26Gz3j+xJ9U5L9VnouP9Roao5fi7DzGpIbLvJL4ziBaozVsGMzjv5WhaPN0Wkttv8AKFHO5uTSDWdx95I6P7vg0tiJWhfSUh6QYmJn4+vj8vXB9GvhwfKe73ltCZ5cIHGJ6E5rsiZxiP0qmDJ2okpVtzMnjel5AzTtv36HmfLp/bBCImf4LMwI7c/FlEXH4htdH+S2sIkUlF4SPi4Lbw/HTgfpVz44wKi8iJg03gX5Wef4DsYzuePM5mUjZPdasrQsycENMBzSebuPrTf2tQHp9LkBB0N1Nxq2Kqv7OJicZ3ok/W//vQ/NhfvQsJjh4bB1mVUatNfS3ri9aO8n7Zl9guEAM28741fW5K7ZuLLETDyDf1ErLQuyO9s72u63dPadwhzqQADrjtcS/9VMtRhMt/RdKE91f8Ojld0jbkszJxvYxse54k5+iKO9yj+WWSTMi9uIbpPGLb1+N+mNJPFe9Z83NwhPdq2G9S+su8c93+Nfs4yr1EpR089svP3Emn7d5HUTftd3G03L3kPR/2t7i86fYSxbpjWzahPGOxzRNo5/wPyTb39yKp8WryKa72K8XyRsL/wVBuzCoQfRuxj93aOQl8viivDr/jtdE0Nt9I8+VMCfZF+CC/G8gm1nKuyjrK+ZxtxoPo47B/w2UPIqa0HhHBETLeOeODfZJlvU3+tfJ++T/ukZpEIdVbUhnGIc9VP1rX9gWN0n1Moj+0zCIywVT8dFDCWHimTmNwX/o9lObpVjr3Os3007THKq7fpnEqs24MIa3ogdSOH6ODFEdQ5N8PyEFPw2kc/hRt/L0NuPhA0+cHheYf6AbsJaM6AwUhvAKZfH47TbBNch9JyD8IvAH5DIyc5Ya+MvDAjCVmQoiE8GrE5EZoJvJGvsnwrpHUARMTZId+dMSXDMaRb9Gx2kRkm4hvyQi8+GPwhcdja/i/mc7oOuc/LBnkTD4ToZzY9s3kIAoTKUuG3mw7gYjpjUjKdjnDdDrOIyFzGzeFumJ0ZSlAROd9JEwmxveT8aQc06Hw32mEvLl8O50E0dJ7JFbD5A2ZcayaCP5sP2bsPo7D5TuLyXokhHwP4fntCK/9/jpEILu1pde1JiK9nbwmRtGJvOeRIwxr8uZij6tN+tPhVrdPtzaOh/oGyGZtpwe7hdmnyYo41RTcM91nK/Iv3YLw8CrgvyHc/xpaG5eg7CRfA/4nEuocEHYdYrinAU8/0PT5wfgccqa/qqrOAD6HTDWL67reVdf1PwOfRZP+ISTdfgWpy/8L+Ds6w0qNiJbqTDBIz2xHWluFJJtrEQIuBJ6ETBN21tcNzfRJok419HtoEdq0Y4fo5xFRtUnBeboMy8PzZei1pcYIpTMWMjFeQF4YfeHbASWWNn1IIuQUPzblOGVThOHwuy5+e4ydLmZXKt+ZwW9L9Y8gW/xKOkPzj0Um1WeQGb63G1hiNpjp9aV37COMh81VKGLU/fReIvc1BnPMQT6AuD8nzoH3GPmoDlAo8I10nsJqs2I0+fn9uKeul+9iJhDHYhHC21XIN+a2b031rkF7yJ6Z/l+Vvj3Gy1HCUr9XznlT+6J5zFqmifVhqT1XI+b/c0ir3pzq9RlW3m9Wk9dfKXwZepn73J4ara+YPszmPEc4ej+e/Wenk/3Xs9kCYBOy/0c/ZxOMkq0/hyPf0zLy6d/PRvufnozoTx+ibU9B/vZ/Rczt/yDB/dK6rm+r02beGbb7kIFDKpiiqqrlSKL4LFrgT0O+qRqFMv8KkhRtb38CWpAODLCz1sThOuRnMjGDfKS5Ha3ryM7lDyE1GzIy+r3yQEPCc65/C51+rdKZ7uftfyn3c/m+645BGH5uvHjP/12XgzS88OPCXVCU2STINB2oGK/FRWo/yCbyMetD5Eg7+5U2ImK2guxPAc2BiX5NPtzSEP1t3m/mcHEfhbAHzenxocwbkMBRISn2VKZu+u4FZkI3kw+7OwwF7byafL6UfZHlURYRYuDEvkLU1kx87ff0+OxC/hfQ3N+CNsh/IV3zXHpzdNzrFftRBvsY4iGT/j1M3vwcc0fuJkdZTpLnwEKU+1QGZTTNUbzuPph52CJhJugTky3wxCjbmBmjFzS1w2Uv6HK/nGvTjutRANj70//jUV7SjUi4OA1pmM5FekT6/XIkYH4IMdg/ruv6izNo+yEHh5pGNYQYx41kaelPkAb1b4iQnQT8DMo99gmEoIvQYthITqpaI+RwyHAfMoGMIVXb0vYxqbw+4I/IfqOSUXixkd6zpmEHcJlkdIi8MJ0p2897v5D383ihQQ4CiUxmPXnDrE08o4hhx5RO8Z27yFF316U2eP+Mz/eJkrQJSNMiLpmU22pHt1PPzEnt/EtEMG3+/CDaaPsuspYxiKLmdpAJyTxkt4fsEO9DPoZ/IW/yNaGsEFM8EvlG+lM5TyJre6cgfHJEnM2kTWDz3e70fTKScBenel+LNIE9aL5nsslyOibVTZKMGob7UqH1YcHrE2Qp3UeHWFBzCP/hqd3OxOAI0SV0ajBlSHXUGPaE3wvIQRmLyGHUaxGBPY8cOGLfXU0eB6+n/vC/hHIDs5+LOGoBZg6aG0cKWhBaQc4Ib9N3RWc0cIReEn2854CIoeK6+9gUkOK9iqej5LKr0PisQWN2CxrjLyFLUY0E8j9CWtXvAx+jeVweHnCgbY+z9E2tROaiu1D+qichwjqCHO9bELMZQTbd7YiIRx+GbeClv2crkmwskW5DRPFzCKHvovsm3NIfNIKY3YXhvjUq7wu5m+ZNuPGzLZRtRhLt35NF/TYl9DM1wGO8eNbMbgDtu7gDbewsN0t228TczQZfbl7clcrfmn7/PSKCy8mJWS9CQsR/0JmE9F8RI3B5Jq4PhLHbk+ZnhE7f0P1kYlEjX43HviYzSZt/o6mq7KODECbTHF5ONk+uA34HmZf9nvtwc1GG7zuV0nQ+x+k+fn+IfK7VLrJP7FuprT9J165OdW1CKcVcxm3k4Iky6KQMnuiGC9E3cxFahzavbUxjsofsi7kB4dvtTN1Y3eR/6pWMuGk9+kiViM8b6UzsPInw/tIZjnfTXDYFwnQLOInP7kCJYkdRkufPIj/TnyI8tkY+hKxG6xHNuBX55k9DgWSPRnn9bgJOO9A0+kGj/Qe6AbNkVKchInUdSpe0Eu1oX5c+t5MJiDMB7EDhzg4SsBM72tK3kgmRo9sc4bcLEb2dSIqJ4dFmeiWy7kr3fkTWjvzs7tS27eTggSaG17Qw9nS5Nxnq3RGub0/jYqJeMp3vIULmbB2TZOd6E5Eo2zcaxrtkgruRz8ZZLMw8dqFsISehRek5uI6cFeFCMrHaVZRtwmNpOJr7xshZ6h3tFoNntof3HUwRgyLKeWyKJvsu8hkMI0K7k6yxjBXPbizej+0v53Giob5ehNgM7/5Q170IRzaSAwXGUhvfkNrtKEO3Y1Mo14EN0zHOQXLkmQNlYvCDI9LWkoNUnGrs39A5cQ5eMEH2mpwJo3Dfy/v2U8bnbOYbAv4f0r7jnDsCciZMMD7TxExLIS1et/Bin/gkokn3IMb+35GwPYyEiLgeb0rj9x3gtYkW2m1zLFrDjz/Q9PnB/Bwypr8URPFZJI1tB/4CSRIOST8WOf6vIOewW4Qk9+cjhLTfyXbzeQgBlpBNDpNooXshX458EMtRiLdhHjKj3EteiAOIUDrwwrneoDNnXB/yaSyn0+QBORuAr9Xpezc5Lx1k00RcHA6+sAnAGQZ8VIF9V37355G0PSeV79N5T6UzB1m3bNEODinvOapxEXk/yj2IcS1Ge0CuRWbbq9BcbCJnXT+THATiPl1MZ/j4ULpmk51TzKwgp0qyOcz7oewD204mzJ6Xsg8VnZtuSWU8O72zIJUzh4xnpbN/DZ3gdsR6xsLzTT6fuuGaNZA+ZJoeRbi/HI2frQ5bUh+WIsl7IzKn3onG/H1oPUH2n9ShHSa0vm9TcNxAuz5dP5dM8HcjE9/q9P9WxLTmA89FQqZ9U4vIpnFCHU0Qx63JZDoHzUu0VowgTXIeOjj1uDQ2LutwRDua6ugFTfsHK0R7Shgmm9znk4WLJYgRzUUn7zrjzelk2lQjAX0O8La6rv+1qqo5deJSdV3fD5xd1/X1M2z3IQmHBKOqqmoRMq18FDkQ16KUK59C/oWfQcxqEQp6ODe9eg9CEmcvsJ16JJVxabq+HeXOmkBIvh4t/s+jBTSU3nsOWZqz9HkC0shsV59DzuZwElqEG8hHp5uJ2BQSpWxSG+NBiNvIDl47pUn1uN6Y3fwwD1v6XkxnwtYJsp9nDvDLqfyN6bmohTQRSegkyCvIIe4GE54TUULMD6eyjgJ+iZycdRJtil0IPJ3M3JbR6bjvQ0drfy30YSk6OsOnDTtCcSzd247mw/OyLZVjAtmfnosMufT5eAyG0fHdZp7HpDofS/aHkdoQx6wvlAPNjMjv2N/ZRPxGi/8ryIcYWlAxk+4n551cjdbA/Uij+hhiUgsRITwTEcHrEcOK/kyDx2Buav9NCBfNWE5KfXgF2f+0COGVnf+no4CNH5Ij3AyjTD08sBcYN7tBHwpGsHA1l5w02cE9jwt9nGkgS9yEH/3Jk+H+BMJxg8duEZ2WjlUo/P9HSAg2g12C5nAQrdE/Jmv996Expq7rDl9UXdfd0ns9fOBAq3SzMPsdlb7PQJL5ecDH0URejsxJt9K5MdUb4taRDxAz4ViPImtuI5ulnocI7ob032aAr9LpO7mZvCnQppR3IyJ8DZ1mI0v/rj/ar33ulA9ILPdV1Yiw2NnqPR9NprgNdNrxdzHVhGPTyBDKCu16/hAx117ml26+CV93do+dZGl2PYpkuiKN7U+QRnIy8iE6I7s3xjaZdGJ/h9P/G9JzO+j0LU2Gut22neG7vFcz1T9SbvQcQqbIu5FwE9/fUZQVTT3RRFWag3rtAWp6vuybn/tgmuctaCPo3Qh/P4ZMqZcjLWI8PfN9pMGeTw71t8AVx2a0qCf2zwTZ/d5ENjmOImHC+PwFZEb2erg31GN/655QnuuImdrdztimuP+tJlsa4vtx/6LN05uL52azX8qBNNFsWpM3ee+hc5Ox73sN3oL2d15DPuzwThTIshtZfCYQ3fnN9PtatK5/GwUbfRs49UDT4gNC/w90A2bJrOalyf0GMk99ICGb9x9NIgnyP8gbawfShF9P5ybTyYQs30nvDqdFdkNaRI7uM2FfGxDRm1L/OdwfQ5FnXlRj4dkJsukxLviI1E0bUkvitpl8aJ99XRNI2rqDqUTeC9FZEjbReSyJgw8uK96P0l/J6EqC4E3Mm1O5tyMbuwNZdgIvTPPnIzQ2ogV7Z+pL9BvtLsr1XjQzkfXkIJVRcgZpt303nQy3JPpmfL6/g855mAzXxlN9W8jEcScZ13qNTfQ5WQN3Pycani+ZUhzjeG8POUrtVnSUgxn9vcjfcREijJci5mRT7GQaQ5sFHZwS/VTOrHFfqNNCgZ/ZSfZPfjaMv/3Bl6E1dzlZUIvvlnNzH2KgXis+kXsw1B8ZT2yX8fxSOoUMW1JGUp0xg0kc++k2DkcfZxn8MlA8N8bUYA37p4aRFvVLSGAzg7sz9d3PnoOSbXtj9mvRNpsLeRgHSzysGFUidk9BRPEcxDyGwyTXSGJfgUxK1mQcUrwjIcYkebFvQ2bF+9L1LQlxvlQgXJ3esbnODvTvIXOInaP3k4nLFvKxIEbWbsQtSq5mJJFglc7raDq0xhUX3SBiPq7nTjKDHgQ+jcym1uhcdsxaUS7aqL2YmTgYxcdaXJvG4VnA2Wk8bgD+Ko3TxxGhfAkiaO9AAQqRUfwAEdrYhnWh72ZeQ6lOE9HB8O3+Oy1T1IivozNgJGoIkVHdmsbY4ei3IQJyD9KquzGXceQXie13BFxJ4LoxKRNdMzlrBYPIZDSZ2rcljWGNNKqL07yen+bl83RG+O1Jn/9AAl/EObdtSyrDUaRjqewRciDF1WFcXw78XzJz2Ez2AzrIwj6uoVBX/Dip9M7wXJyXm5iKk3cU12IGkAlyoIvnbzqhoNt9MyFrtQ7EGaYzwGmIHBTkdy0E7EZ4swYFk4whQfdGhIsWHDeThZFPImvED4BXH2ja2zKq2TOrsxBhvB9pStvJ4cH3hskeT/fuQlLmMDIbOqWLI6P+AkmmF4WF2U8+k2dHQDgTEZvxdqX/3yPn+doaFkaUHiNDjYjv+6PkQI4oOUfGYRNLf5d6SgYYmep/kE0w9yFiuz0suvGGssyU4v894bkhFEZ+Pjnq8HLEsH4DMayvpvG7CDGti5EJ94ZU1v0obDlKppvJ/rmtoW0WBDajhd6f3rGp0223OapGAo0JWGTsMUIsmtx2kk1Wu1E6m8vIueg8T8afyGQ9Rh5X3/PeP+PBTM7ucn9GyYEKO8PYrEu/NyKivRWZoFchH9IzEEOZJJvaanIARNRurG25XgckGWe3kDXJ9Uhz8ru3oKAm4/9utA3B+wP3pDm4nE6i39TnyJxKU23UsK0VR9PtMArljhpz/J4uBVj5jj9eh4OIYV9H1mLNEON68EGLO5EmdCHZD7wW4a0FwytTOe9DGtdAuv575ETCyxPdqw407T1Qn0MimCJCVVUrEXJ8DC1GZ/l+MZr8R6NNcgsR8njD5/eQw/KJCCFuRk7MK1BWi3cibawmZ3JwaqFF5DQ4kB3am5Dzdhtint7J72i+MXLwhqO9vMkWhMgOKuhDxP1IcuDDEvLmU0cZzUtle1OpQ2/t2PVZQ3V6bx1iUPcjwlWhyKdF5ChIRyfGiCdHX7lO930UEaYfpue3IHPG8xGTWoec2Uei8PM3IaLlQIbjUeqevyUHI6xCx4zfRQ4cWIVMJGeTD78klTuQnl2FTFv2wTjKbwjhgc+Vejc5A0Gdxq+fzrlw5obzkNDj0PI+tGfv3tT+pyJN5ohUfpleyYEwh6X7Nls9hhz1B8KRGCRhsGAQN4z3Iaf7IMK7lWg++8iM6AbgpQiPHcjw7NSvu5DWagJ8NDkKcizU48CCOQi3dqXyNyBivQjh+io0L9aSTkfBJpvS87tRiiRvzVhCTnBsU+3d5LE3gyK9S3rXGR4sjGxJ989PY3Qt2T9Eav+CUIb75e+mYBbX7+jRmhy9Z/P/crIg9yjy5nMfV7KMPF/z02cMaYF1Gpd3p7Gbg3xQ30rj8VgkEL8FBaZsQkLznWgeR+u67geoE7d6JMKcc84550C3YcZQVdXzkR/pZxDD+QY6lrkfRd/F3GA3IgTyRtlnIEloHkKIc8lS7dEoGslS0Y70v4/OdCg/Jud2uwIh7QCKrjLSekFMkMPBY2SR08ZAZmxeTMcW/xfQGc5sxmZGNYwI4k7y4ulDJpRVqS9LUzv/BWk37scJyAwxHy36MjNADEEeozP90po0Pr+FfIVL0mdH+uxG6ayWIeL5OuQIfnt671QUUHErIlhHpXYsQwTuhNT2NUjzOpV8wKUZsxnvo9H8/picheBmMjF+AB1l4TB0CwWLiz67b6Opjc9M/f4awpU3kI+IcfodQnmQTVjxgMQPIyL+GjpxArKQ4/yKZmSWzJ3Z4SYkXNgSsBCZj74LvBKZ9waQyfq5KIjh+SjTh3HsFIRPX09jWobkV6kMh6hHv+lyJCBchbQiM6t+ZM4aQHO4kJyJ/RcQjh6HmMApaD7vIR+l4dRiS8iZRpaRzZMLUv+XkcPs56GoywXpexHZVLgY+XOch9F4a+gWeh4FSDOzxYgRLiev1340B99M144gb8p3RhWXdVsak88A/xmle1ucxuKI9NmKMk08Od37EfA/0Hr+BPCluq7XdmnzIwsOtEo3Q1OfN7f9PDld/8cRQfw2WZOwZPR2coSNJaFhlBPrVch08XXk3L8IRb39PpLYvEH1OqSJWP3vR9rb35D9WT8mB3B4UUdnaumDig7pmOVigLyxNfqJHBFlydW+tkFyQIdNJReStYBb0BEo0e8Sj1awabN0dMfvXXRGDrqtlyNNyYTm3xEBOQX43XTtnWm+XoEEhglEVD+BNM/1ZH/SJkRYP4XMIhcg6fuzKEXMUBpn+1ps5htBJhSb10bSO+cj/+JWsjnVZr1Jcoooz8uuMGdOYOpgFWeP30l27sfITG8Kd7BCvOexvI2ccVMBUgAAIABJREFUiXxDqLcOZXiMY2aN25BJdZicieV2ZCr6Yqr319K47UQS+lXIX/S6NMb2Gdo0bdx5gGy29BiV5sY6jdOnycEIVyGi+0Uk9W9JfXsqMj2OIMY+QI6a7Sf7ijxO/alPxvtbyPhgn5bx3oE00XRu36g1z3emsXKGkrjmbqJzzHt9PPZub4009vUoVPwDqZ9eixtTm24npzbbiM7C+yeUDskm8fuRAPWpUM81aZ2cjWjV1Wn8rgdecqDp7sH0OeANmAWz+mVEjG9EBPzdadGspdNZfiGZMRmxLV0Nko84cModE5bNyHwSw2+vJ/sxJlNdMauFnbTRxzNYLJaJLr9jFoDNiDDZL3B1KuN+lB15HVrYbocZ1BgiFsPp2eG0aOzDsC/ChNxRg2WgRvTTuE3u1zidzOoqlJH+feSoOOdI+79oE+88pOneipjNWYiIXIgI7c3IQWwi9CVkyl2bPl9N91+PNLMNSIr9ODnE+CqkGfxnss/uSiQ0bExzZ8HAzMREeQOdGoPxxGm0RhHD/EB45h7gbeRjIOKYfCK1PwZJrA3lRgLbhAuegwvI2oQzqGxL43U8OqNrEzK1fi699wASSn6I9ge9AeGKJf2RMI/G3eivcZSdI/eiYGTfzC6yX+gKZB59C9I+noW2IEwgYSJmNvkxMmHZJOzyHKV7SxqnS8jbOswoyrG5mRzc4S0oFtgcVON58Vq0plWWFb/L34PkzBCO/v0oOjX3DxBu3o4EgC1kHLsktfu8NCcvRanDvokCh8zMBpGAZaHyB+QclbZ+nBwF9PZziDAqZAO/Ji3EMxFhG0qL8iVpIX0MES/vR/AC3IEkdjOBQeQYfxOdqVPMONaRJecaaVVnkaPshsiO/8g4vDCGQ1kOZY5EaSQ84wXiKCJnZ5gkO7rNyO6g8yBFv/vPiHjsQlrkcvI+sP+CmMokWtAXM9VB3UQwTZzXpnLvSHVMojDgS1AU3wPAhcVczUMmue8i6fEzyIz2ErKkehky0fwWOQpzI9oq8L8R0V2HCN9XkRZ3TSpvmKwVXYwk5j9J13+Yfk+Sw6zdr+HQdxPjUSSJf4ocRfgv5GCLmBtuDGnkA3TijTfLnpvqNYOwIBMjPUsBIe6fc52XpnG6FeH0diRYrUeE/lLk1zg1tfU8ZAL7PPLp9SPN8ur031GSJv5eF5FAT6R27yAHkRifLybvS3Joew38OM33KUhruBzh3Efp3LNoRvtvaazjfjkLhbYiWMO0kFWTk+X2I2HOOBu3MEQN7A5ysE23QJdSg6pTP/3/ujTu9yFTnJmtg6iuRXh5KVoXxjNHIf4/FGL+7+TEu29EZu4bEfP6E/Iaf3b6vOdA09qD9XPAGzADJtWHGNWF4dppCWn3oDD1SWTffTOS6n9AZ4TRzWmB2QwwgDZLXoOkvkhAvLjWks0fx5MP+7s0ILTfWRcWWtTgHP0TzXlefHYm+x2/7/1ekSBaar8gLQxL0pYch5DE/1WkBawlH59xVhqTzamvO0JdJqSx7+6XyxhN77wWMZ+J1MadiPCciwj4W9Ji/F3ErI5CfqIvI8Z2Q3p2D1rQpyFN7HVpLieQlvDiVP5FKK3M3amdX0CMzFki3p3aeyPZbDKOiMg2RMBLBhxNq9chQv+oVNYg8nnax+LsJGvpTAbs051rhFdfCe98AgVieP6HmHqqr4m+y4sbRZ1M+U6yJnQPEmCuQP6oDyFCuBz5fz6GmP4Sspa1O42fCXzsf3/4Lk1+FqocvLIzzMVEGmdH7A0gZroQ5akbRBrtNqRFX0oWBmwiHSYLb2YUN4UyN6Sxtu/4diQQ3UnWCG12rxFebktjE8c0bgS3lmiGHU3zxoUYNu+o2o2pLeeTN5jvRKeGfwIJW0NoHV5K3jf4ttTmb5HzCo6nvq1L7TAzfiMSLi9J4/zKA01vD9bPQX0eVVVVpyJJ/MsoBcsXge/Xdb27qqr/gyTJ1yEz1NvImz0fQKr0qchJuQ4RzT2I8D0JIf9yJA2fhRy29yMneYWQ6GnIhOVs6M9CRNiH7N2ANDzQ4pgf7pvZbEYIfiQiLKRrA+maowOXkhnWlWQzzxNR8MfbkWS9Lv13qpb/3955h1tVXP3/MxQbqKAi2GOLCipgTTAGY4m9409jXutrjAXra1fsLQnGCqgosUSNsSJCLIANxYKCgFQFLAjS5dIEZH5/fNdyNkdAyrnce+Os59nPOWefvafP6mvNWvZec+Rksp7dv8Ke6U8iem2svtI8Ze4pV4eUesa9sG5DqaM2RaqPv1q770MH4I2y9n5vfZiJUidNRQRyL1LcSGcbw6bA/8YY/xFC2A8xDedYmVOs3B6IQfkWEZ0drQ9HkRxXJllb/Lyp8YjwfIeQaFN7388Xq2Vj35DkjdbJ2jsdqWtWR0SxIZr/+mieW5BSItWx8boROUnsYL/7keL8GiEk7gdDugOGvz+LdBzHNcju9w2yJe2A1tvOiIgegex+Q+xQvOeBYTHGS0MIawANY4xjQwir2VjtZf0ZbeNWPKKjiKRXR+tpOtJWuLqvkbXjdeQmPR4h4zNsrusiRH02Kfh6MytjIMlLb027txrp2JlN7NPzQBZVgWuTjoJZ1+rthdZBY7TfR6G1WBsh/InI+aU+ySnG++n58uaSDqpcpfD/eORBOds+A9LK7Grf21k9e9o4TrJ6/mNj3JB02OckhFNm2P9t0d5bBUmdm6P1vSGyI05E2o5u1h9ijKNCCCFWZ6RcVVDVlPInpKnDEWdSG6URuRvFPB2NFvsMtJHaog3k9iFPKzORtHGmIEJ0FClp5QzEjc0iccWTkfT1lNW/ISkzwW0kMf8btBCLOnDXpRdVPW4/ca7Vsyd7Rna3DUxAEss39k53hGjeQ1z6G6Ts4h8hLnYB4kjfAE4ujFsLJF15OwYhQu42IY8zKdpOppDiVLwPQxEXfRmSniYib7KtCvPzCinzxMH2/F5I7dEfcfzOWb+IbE7HkSSqGxBiOBNJQ06UZ1o72iNCcyQK4r6KFAf2WGEMJ1v7XPfvWSs+JEkKzt1/gwhZf+AAhEjcyWQUOrZjpM2bn+7swdELrG8eRzPb5qeXlbGotEoLUEzZlJJ7xcwKkxAB+g4R6a2RGm0oIpzHIoIEWvN32vdmNg5r2u+TCvW7BOuqMJdC/P/h9tsJy1Trm0tbLyN72EUIyXqgeD+0RyqsfS4luo23Au2VU2wOXiOtqcmk4HA/jsbHw22980iq2jHWhz5ICnb14KckNaY7vpQ6Ujjj5+vDVaw+JguQ2neMPTOShbPvT0bq5D9ZP65DzhVfojX0Kdqfr1nbos3Vm2jvDkCq+b1trD2Oqj9ShQ63ea1V1bi2ul/VKo7Kj1A2LpEYY1e0eW6JOm7+ZbTQDkIb/2S0iS5CC+V9UqaEuUjamII4utlIMrsHcWBvIa5uU7Tw6qHNvI415zdIArkGJUydj4z7D6AFuaq9+xWJixuNOKzi5oN0zHckHU3eGHGc45FaYB0UazMNberHEVe5E+Jwv0cbow6SlPa19vwWqQ/ahBB+E0JYByGPa6xtLlmcb/2dw8LHvQfE9TUgHeW+kbX3I0QgnFi8EWPsiYgENt7jgKtCCBsizvOPiDPeBDEZ+yKJ9wskTR1tY7uj9WMOOkjxVuuvS0avIyl4QyTRvYgYlk3QGvg7ktD85FPPEr+utb02mvfvbPwnkGJfuiOm4HW0br5E66KOzemRCGm+haSbWSQ3aw83qLCyZiKbHPa7Acn+AkmtdZaNo8f8LCC5p9e18t5FiG8/hMCORESpPVrvl4cQbkIS6M4hhG2tf7OitAxnI4eTAdavQ0iHYK5nz7rU4Tafr0knLg9Aa6evtfs3SJrphJD0hWi+6yFp4l7kcu5EJpCkpfMR0XSJ0XMtLrD5cJf82nbPxzWSpP5xNq8VSJLZsjB+jW2MXTLzOLiZhTLc3R9r8xirp17h/+aIQXDHiHWt//NJiaydudmF5LSxvo1dLcRs7YuYmd8j4lQfMV3NSPYpP0zzPLR2RwMDYoyOJzIsDqqaUhoXuDlwin3fHk38n+33Nmijrmu/GwOr2veAiJVnjpiI7A7uwvw5WnRTSVzVOEQE6tqzC5C08DYiasMQUrsBcaQfIuT7GFroj1pdxUSqRceIClJWDFexLCC5CU8vtCciDrUvaRN/gBDHWWhzvGxXbyQldUAb5ElSTE59JKUMQkj/OUSk/4mQ0WssfOqoZ9Xw7AJ+/zoknfZCKp3paNOdbW15ysfexv9kJFFdY7/XRDFQQxFRdZflrxGx8kS+ryNm4ijEdc9DGQXeLIzfIGQbmYvcfc+2cR+D1IYgIna3tb2YyqYfSYKYj5DkKyRnlNmIcG5m5ayKpKjxhXeetblwm840krTmc/2NlTmShMCKnn5OsPzep4j4OUIdTJKqxiFV6UCrew/k0TgIIdPNkPPQrfbcGMS9H29jd47VdQ9aozNJ9jH3TnUJxJ09XHp5keRu/Raa/+l2vxNwUGHOt7OxG2X9vcza7NLQRBbOizmU5KL9kb1X9Hh9i8SsFW1pbkeM9swTVq/b0jzrRXHv+fvFbBWTSNLyFMQIjLayh5AcOvqh9f8CiSlZgDQfPUmOTm/aePexd101vQeSMkchhqOY29PDHV6x8u60ud+nqnFvTbmqi0S1NnBbCOFUkjrhf0II9yCJYhfgUOMgxwGPhhCeQ5LVZ4izq4sWVkPEbc9AiOwzZNsaZP81sstVOOOQbeIBREzqoQX7W7SgN0aIcnu0UY5BiP9V0nlMniAWRDRWZ+HzZxYgzsq9yCaRcoL9GklEH1i5zaxdm9q7uyHOdgYi2u8hbmw+Ik4BIY+rEAf+NVKlvWFtXt3G71uEkGfbOETE/c21MmYgjrWDldPF+rUNkhLao8362xDCmiGEPZA69p9AsxDCJTHGChvzjRBycCM0iPN8iiTlvWR93wkh1v0R9+6ZNbZGxO1TJFk1R0S9K9AlhFALSXCrIgRQzBzidsPpCAGNsnH9g83lO2jNHWjPHYeILmiOawGH2RzdYff9DDNXH81GEls9NOfzSWmbfF95QlvPeLABsnv1sTLWQ6rbWTbeOyGitzcwO8b4KLIJPoS483/EGC+z9ych1etZaJ7bIgZoNyS1TrO2VSDpYZq1a3W07kbY86ORqngmkiBaofX2Fum8rn1CCIeHEHZFe2muzUsg2Y+8z1MRIu+GNBh1bKyn2Bh6thUPdP4Nab9AUg/WQlL3h0ji3Qatqzmk4zDco3CsveuSWV1Sns+Gdr8OWsdNC/O4FUkC28n6sjXCBe6Svx7pnK9xSMKeg9b57VbutkhK2oLkyDEZMZOTED57DeGVo6xt58UYe5Fh6aCqKSVQ2z53QovfueXVULLNSxDCGYyQzSdowQxBSOAVhCDuIHFO/ZERuotdxyBE67r46UiVMhot8tZIdK+HVCcjEGfqsR/PIq7qFsTJ9kCcvAfOeiJN925yaWoGIpTO2brXWX/rx1jrwyxry6WkwMU3SGqjttb+udafeki10gEhvx1sbPaxct012V20PSeg5zmcS4rVmYPsXXOt30eizfdnkg3vUZuTMxECmogI4hloA++JdO6XIiIxhuSJNZJkm3nT2rwTIlKXIMLzsM3HacjA7Da6K2wc6iIEsjeSfPdHjMIkG8PppNiooh3oW6sn2Dg1QwixNim2605ru0u+j5GCyN0rbzjpdOCppDCFqUg693AGjyUqnnrsbZmC1vdTJBfwbkjFdzWSAO+1eesJrFPYI6cgZNcISTDfIOm1vX0fhdbZXSQP0DYkm6hz96+Sjqu/HRGC961tJyKpdYG1sxfyLmyBnArus3KeRfvN45im2RycYfM7EzEh3ax9p1h/JyPGxZm0u1k4R14f++6u+O4K3g8RK0f4XxXmxjUWbgP2sA/3unSpx13Y3W68wMq7GOEVD/p2KeodUpC12zr7WnuG2e8OhbkZQco3Osl+f4gI+PFo3z2CaYnytRx0oqobYJO9sX22RIjtPPtdCyGVUxESXs0W7gsIaY1GhOp9xJV3Qxu+O+KML7LF+DYiRk+QjpEYb4v0YKTLfwRxSc+S4o2mIAQ4xjZQSyRBfG3/P4s4JT/vytUZvonmIAcAV08V3dD7oo3/IkIuN9u9oYiwus3iM+vft4hAH4g46FrAtiXj+HdE3N8hZQZwTy6Pg1qAEIk7LEywPnifBtj77gTS3t55EXGj/ZC3285Wp6cK+n+IgF+IEPuXCHE1Q0TP23GijfsT1t//kOxBX9h/YxFi+8LmtzEiYkcjAj0aEYs7EJG5HCElDwgdVBjva2ysTrKy1i+0excb5wkkw/6XyBFgRKGMR5BBvYjMinbIbwvPdkPMhjMsxeDgF0hnW92AJNIP0Tr7u7XlCCRROyJ0z9wNETM1Dnjb7q1pY+SMzjiEoAcjteF7JW3zwNtpyMvwRETcXiFlRP8dWuMHoH1zNCLyZ6B9+KzVNwBJHv2tDfciRqafff8Ird0nkfpwCklV6irH+0lhGs5Y+P6Yj5guZ6w8FdpMpPosxkD5unZJ6fvC/WGF55zBcrXsWzY/lyKG6HvSCd1TkBr7MJKa9Ba0PwfZe1ciXDHKyvNg8CE2zy8hbcd6NjbtET7LzhM1iVAhBNLIJvRPdq8FIlZnYkiwBCHeZIupP0KOoxCyexepJV5EhKUV4nDa2cJ5CiHaO0jE4WikWx4CHGj1uIQyHOne7ycFqv4ZqSq+QYTr17YRfcPNJ53B5JvlW9tcA0kxM8PRhu2FOLJ7rO51kAqwF0I0fyCdVNsHcXiHI47/RERQbrdn2yDkMJGknnEEOcjaOJDEMb9j4+SZ5j9G7ueTSGljrkfEpCEJafxvyRyegDbwKLRpR6BN/y9EqB6z+Rxs/bjeyrrRxsYz1Lsn3zwrb5pdhyIiMxSpVva39n9F8rC6GSHOl5B0NgetiYgIwt42rs0K7d4GOa98Ys8PRMyLx7nMRdL116S4u2j3vyCdIVRqV3Gvt3k2vh1JHPznCLm7d915Vu8pNr+dEXO1OTLwb1NY++sgYtbW+vp/SPp0adkZAWcwZpMYALdPfmvfX7Fxb4vWzWSbu7cR49YPqYGfQkzS/fZ5H1KTzrZnt0eZUzwzzDy0rnqQEtQ6UfgOqafvIzGLTjArrIxZpADp0YgQePC6ayg8tsxjDD3AujRwfZ79N4mFU1R5sPhwRJCnof001ublURKj5nuuBylTTDu0Pz3TxF8RXvDsJzcj9d5EK2MgSXuzd1Uj/Jp6VXkDbCO2QdzmSQViNR5oa7+3QjYKj2/6DiHlYYgbGo4cHtx9dwf7XJd0zMQsxCE1RobnkYib/Rg4zp5fFUlXHUjxTwvs3cFIHeh6ZlfbnYaQy2tIkivmHPRNM5PkpvsUkrDGIO72VivnbsS5u7piLEklNg4hkB5oI5+PJInjkOqov23M7gjxfkwKjPSNXIGIqrvmViDbx0Arq5uN47n237GIE3Ynl4vRBm9dmLeTSdzkJKRKcueNfRCRG2ZtOdTKnI8Q1oUkd/vn0eZ/zsbHjfkTSO7QzRDBmY5scP+0cR9q7b0eEcVHbazfsPYtQMhpu0K7z0LI/l2bg8esjB6I8LvqqCOS1jwOzcd0HsnpwdW6Y1mYy/fsGt9Ymz1l0BeIQNyAiM121qbGSJrpgSShGSTm7Uyk8u1rdXYhIeMXSOrjT1jYScL7MY3kEt7e+v0iIr7XINXjUMSwzUSEc0+kxtwGhRt8hqQEV23OQ+tpK9IxKteRDlz8Fq2NIqH8EjEQT5MI6UC03waSCIqr6joipmQmWscTSGrrSQhHuM3Q58WJ5mhSpogxpAM6XesxFhHaF218PiNJwI2Q56VLob9Da3qajfFXJKZvFiL8T1rZbqaYg9bSBTbP+1Y1nq3J18omSE1I8R7bARcV/vOYqdMRgXkAid2tbeI/sAX+GTLSzkFIcDCWwBGzdy2m7hYlvw9AG/5+hAAesI3hR3XfQkIwdyOEMBVJOc1t8U9FiOVc25gnkg5RHGDlehDyXxDSHYbsMuORTeAt64+nKnJpwyWpde25B5AH3rOIGFxr/djM6hpsm+YDtJkfQ4jIN7Aj7OORHcljaw6wqzvazMOBQ63sdohDPBUhqRHIgcWl27MR8XgTbfzO1tfnkNPArghhdCC5Bbexvk5BCLuTjfEzCGl0Rdz7HghhbAA0tvbsiJDH8Qgp9iAhwDkImXyIVKceANoU2Kkw73vaeN1m49OTZCuZhtRWbmCfg1Rjj9n1DkK+znwchtaQS1QepxZtrG5FzM2NiBhXAO2sHYvN40Y6iPBgxFS9Z+M5EzElxRyOUxDj9BTpiPORNreuinIPvPH2zBtIG/C4/fc35G69GVqjT5HOcLva5vlIJJmfjPbMfKv7YsQ4PWntnG31ep3TEWMyjmQfGoaI7wibi/fQ3jmSRADcE9WJlhMal8Rcei2qtyPCEZMR8Rhj781D63A8aW92QfvFPRjdxjef5D3sadfGIO3NtYjQvmplvo/WnNu1/mXvv4zFNS5pnvNVDQkV8owZAfzWfh9hi+ECknrP7QBXIDXHXbYofonsU1fbBuiONnBnoPmSFgQl+uDic4ibnGjl3WAbqAIZrA8kueC63WQMQpyf2jv/tsU8ARGzumije1omz1ZwKekIjC4Imf7TFvep1pb/hwj1iyhW6I+kwwZHIOS8mj3bHnG/6yO13KukgFnPGTcVIbWxJDfaBfZeHaRWqUCEabTVcQjJJboVUv/caOUfhVQc7p22LyltUner9wtEyNohlUh7pO56F/ibtf1ckuPCKBt/Vw12sf4+jpCb2y43KqyhK61v11q9HqjZn+QGX5u0pra0tuxqv3dAxHFXRLjdycFTZI1CkpoTne5Iyp1jz3yOpLZo/XiS5HSxABGmCdauO5Ad6mGEOB9H6+TXS1qrJNvsYQjBd7U2uAQ4jJTEdS5SgbpE585EfUipuj61/gxFCPl768d8m/uHgK0L7Vjd6lyAVIJTEZN0EdozvyCdAzYOSQ5vk5IyjyGFaHyP1sWrNjdTkMTfE6neR9rv8UjyOMH65gTf1eyzSNnK70Vrr1Td589/aP3qTmLc5iPGsT3JPjXTnhuMGMFzSI4zz1g5vr/dvX6KldOHlEndUy8NQWrqD4Hjqxq5/zddK4tINUWcx2kl9w9CG/tC+/1LW9AtEdd/rS36I5EKYh3SMQK3IylnDZbDOImMw240HWCbqAfa8LehzX6E/f4KqTjutAV+ACmTxGCgs5XZACH5g5Fq6zuEWF4mRfBvirjZ1ogz7Y8kgBGImxxn/RqIbBgTrH31gTqF9t9uG6YRQiB9SA4YrvZxZDrDNvBExHnehBDQpUh12QchCE9NdJK16zdWlxPIRvau2yAG2vuDrIyLkIQ1AiHEg+3epYhYPGnvTixc3yK706FWzr5I1XQfIsKeeaE+ItYeRzbTrqtInqOHYMStME47IcnmZqvnRoSc/kKSnNyzcwDJc9Dn/VMbz54IAY2w9hSTprqadzJCeENtfEYDd1s7GiJCeznQaDFrcm9E+F3KHYiQt6vTZiKJezSSWicV5tm9T92bcwhifGba2LZEBOcytO48G4anQNoeOf5cgPZUV5uvwTbeoxCB/AdygW9sY+FxTS7dDbe+T7Oxc0ejCpuD3mgNXYeYtQftmeNsTL9CWpXnrcwhpGNiplh7K5BkdyYLe/k5sepLyvR+L5LYptoz11jd/uxRJIlvAtqLzawPHyP8dDtJhT4K7Y2PSRqMPjbOs5GDzlHW7iZkiapmECqE/D4FutnvOrbJdkec22G2YF+yyd278G4zuz/ennkCcT2P2uK+ZTnbtIstvEcQsrwdcb+vkSSPDxFBuQkh3X3t3Q723FukaP6ilObeey1Q/MUGJGLyhS38VxHS2ght/LFItVPXNtXXKOvEKUhq3BJx2S2QfcVVc7fahvkFUr8MRBu8NykhrttPdkTc5DiSumx35JE01sZiENDSyj4dIdz1rO41EaPQ1N6tsHYORAzFqUhymIEQ6eFWzn7WpwNs3L5DBMNtS28jlev6yM7U1vpwvL2/FeJuf2vj+SGSAHZEyPkE7LC6kjluULKObrB58eztc23sXGV0GyLQnmLoGaSm/NTG51VSpuwFCNn3IxEKD5idgZBvA7Rux9lcu4RXdzFr8gibs96IEHZEKuZz0Vq92cbVVW6T0N7oX6j/V0ia/cbmszcF5xdEYKYhBN/c+von5LzRyuakG2Is1kQI9wsbhwtY2HtwEyQB/4V0fIwTcHfQmWhj3t/Gezhaz/1srB+x/3dCRHomWlvnIg3Dy1af9/t9tEb/Ym242ca4I+lYlKKE45LblzaPD9hYPmjzMhMxjLdbeV/ZO/uT1IadEJP1AsnN/1tkRx1kdfckpXs6yMZ6/apG7v9N18qpJCXqPBsRm9sK/9VByPwEYA+75xzyZkjcvgWJ/6MQYjwQeYBNQohsqSUq24C9EIK8nhRPM4p07IPrpQeTMiTca5spIL38WHt/Ksno3QoR28dKnnfPwgdtA91oz6+BEMt31p4mdr1gG+BwUtxQFxu7QWiDP4gIwwfWjsGk84ymIsK+ACHzedbedxBn7Y4Mbi+7B6marrQxdRfuDe1zG2vTlYhYetb0STYfHyMbwxYk9997EWFshohuW+QxOZTkldnfyumKbJa32eU2sSMRI/Oltf9ghIwH2LztZnWdUjLH+yJu/0609n5p43q9zcNw+xxtYz8QISlX8V1vvx9AKug51oYOJGQ6lnREyXSbl+Ps/mikpm4A7LcUa9KdRC5G6/0qRChPRYiyNynfpMfijSE5N4xBCLqLlfc0Io7r2O8iI7UL2ovnF753QHZG73c/m7MWNl/nkOKZ+iCidLjNzUskD8tpaD12ICH0CTYWzyMC29Pm5SZr56GIYToQEaXTbfy+snJcsvo3WtfvIOL5EClqQud7AAAgAElEQVRf4bskVaM/Px3Z2qL992ukCnQVpbvKD0WM1GlWr58ycBZiUKeitTTR+uaHdo63+18g4ncrIvqfAOtVNWL/b7tWXkWJILxTuLdKyTOb+cZCHnhb22K7iqRq2xNx1wFYfSnrdm52O0Q0Hkf68ZsRJ3s1yVB7A0KAM21RHkvyFByKbEjvAUeUbHzf7AeXPP+YbaQjbDP8AyHGE+z9hkiaG07yelwPIe+WCKEMsE29JyLMRyPp5CiE7CeTjLd/I7kKt0UE5CKEbDvaM7Ws3dOAviVj9RjGMNjvprZBr7axWxNJR7fY5nwAEaWHrNzmaMP70ScTESF7BGUTeAsRs9cQNzsQIYa2SB3ZzOo91Ob7ZntnLEIMjyAC2x4R+paYG3ehzS1ISU//bvM6ABGnb0jHSbg3n7t0R5I0dS9Cmq4SfBetjcE23h539qqVNRQxFDuTuPMtiutvMWuzESKs7iRykpX1OSKEg+z3LSSb59VWx8NI6nAX+3lIJTYc7Z3F2W13R0h6O+SdWYGk90cQ0TkAIeAbEGJ3l/29keQ73ur9zuqrjYiYS5vu6PA12huP2bjcauW9jTK+1EWqzV5I1dvN+uXpqi5BjKznvnQc8irJyeJJtH5eQPuhwtq3D/LW+xgRxoAREKRSH2lz+oY9f7eVcw5SIQ8otKkrYgY+RuvnLlJy6ReAswp4a6OqRur/jdfKrSypGzz7RD3M7oKM3O/bpm2OEN9oW9SjEHLegqRK+sUy1n0EQrjuvnwjUjE8h5DBl4gwjUEqrhbW1uMLZZyDkOdCDhyIa6/AiFfh+cYIEW1si/g20gGGE5BKZQuEhK5E3GBre7duoRznjLdFiHIEQhw7Ikm0AiHah22cetlG6kryfmtldV6wiPkoSoQjSe79Ln3ejpBFOxu7C0mE+jq797dCua6WG4GI0SiEeDvaf88iTvwlRKgHIyTRxOb3JhTg2hshu5727jO2Ll5CXH2TJcx3U0RARiMpYCLJ+8tVWG8gqXCy/faA4ddJuR+HWtvOs/bMt/ZW2PydYe91Rlx4E4RQD1yKNenz+ZDVMQ0h52E2v/1JsVEzbQz+jIhoOxvj7kjK8ODwz1iKc42Qus2l5B3Rmj8VqeJOsbZ1QEh+rLXhciSpd0C2no/RnrkQEUu3mXmQrjMLoxARXouUvWETxPi8bO15EBEdt6W1s/I3s+dHIYZuf5JU+bmN2VuIGD5emOeuiNg+h/Z6aUzmHYjJepl0RMeTyIlpLokQnWn99zyPQ9C+eNDeu9zm5Q9Wdg7mrYRr5VeYJJCLEWJuY/e3QtLTArTxz0EeU8cgbqczQsqfYDaaZahzU8QproE4VlcdtELE5BBEmE5FiKiFLeZdSYcG/ihotKSO+vYZSu67J9dqCAn3Rc4CfUi2hjbWtrOR+q4hJa72CPG+gRDHCNtEZ1uZo+yaQfKiegghvysKbXBOetPCpvX56GjlH1yosw6S5N5EdrL9bcMOQwi/HUIEf8eIW+HdXRHCeMrqfYN0uN61iCl5HJOKrU2usvu93WuJEMo0hJCvxQg4JVk5FjEfDZFUM8/WUSeSl5t7Cp6DOOQOaD0OsznpgwjIcyQC1tf6MAUhsaHW1tk2f0fb3HVBaaVqla6FRczn60hdtIqN/zEI8c8ipVO6GiHkfkg91gYxDu5+vYAUMH4EYgzCUtT9gY1Lw8JaqIv22AP2WYxDnGNt62vPHk5yfR9OSgP1pY3DyzbXnqZrOtoDza1f69n39sgm1oOUbPoMtO/+iLQBc5HE9oL1+3Ik7V5nbRhk47gDstfujFR9rdC+7Ym0GbeR1NEPWpnD0D4ZYOM+mRQUPMHm6F6Eg9xG5QHxx6J9cRklUn2+ykw3qqTShDAvItki9kMidVf772B7dg1b0FfYAt3P7i9pIzrRcFvXRrZIL0Pc566Ia38eId86yH7yIXB0SVm7sIig0UXUGUo/EUJ4C1Nx2iaaiJKLghClI+/zEaLacBFl10VIaQxCmKeTjg7/mpQvrgIh0/0QUtgK2K2krLUWUf5CEiEpTqoBQtj3W53vI2LREKl83kUSYR0WtoP4GJyEJIGHScb/kxCC8XiqixBSr4MI3q3IFtcTIea1bd6nIkLzSMnc/mgdIGmgl83vLLvcPvg96Wwiz834LClX260ICY0jZXFw21F/G3+PM/oO6GF17odUSCcvxfp3B6MXC7/HW79H2phVIDXuUIS4XRX9GCKGXW0etrQ2bbqUe8+l5NIMIycW5v+PVs9JhfXQghRs3w3tiyNImR+mIIbCs8Z0Q7bBPa2tc4DDrLw7EGFfG+3/D0gMyJk2961J8WnHkM7D+tza7+nNOtv3F0nq9OIerIfWbGebx3ttjsfbGF+D1vFlpHOwLkDrcj5iar1tW5Ps1k8hyfMuSjxN81X+qw5VA/2QLnd6CGE4QtgDESJ31+anQwhPxxhPCCHMBe6LMU72AqKtnFKwDOsdQgijgTGWgX0CQoTHI0TyUQhhU7TQtkcbZXOkk+9q5YQo6BdC2B74PsY4fHEd8vbEGGMIoVbUGTNDQggjgd1DCH2QPv7fwJQQQnOkxmmLEOMe6LynrxdR9rwQQi8k8TWxvryANs1pSDIZYON4DLIjXA5cH2P8tKS4imL/rPwPQggbALX9vn1OCyE8gNRRRyKnl4gknUsxg3SMcX5h/IsnlI5Hkt6JSBL8FiG82xGCfQl40sZqQQihAbJNHW7zcpyNy/UI8e2GEDkxxu+L416ovykirA8hr1JQlvhTEbJ/Bali10J20L8hBHWklX8wsm/sbO3d2+7XRgRuA8T03IRspy1trTUE/i/G+BA/ATaffwC6hxDaIiJXGxHspxGyXgtJBX9E0scYJP175pEjbVzftXbN/ql6DWaj9fa03wghnIyQ+BohhI1jjPeEEOoAH/laDiE0IiUNvhPNzyREJHrZuAWE3BugvXwyyQu1NXBYCOFTRCx/GWN8JoTwL7Se7gohDEPz9McY4+AQwlFI3fs9Uk/OsXc3QgzsN4iQuMp1f2vvwzbOEZgZQuiMpMdDEO5xz8A+SDXeHTmGTEB751ob07lIRTkmhPCFjVsDRMQ2Rszl1THGr5Zy7DMsL6xsykjidtwWEZC76beIQ3oQIfQuiHu7HnGVuy1F2U3R4jsNLfi7SW7SR1jZ9yHEOxQt3MYIEWxUbN8K9tFTOdVCi/4W+70G2ggvo43y58I7ayymLLfhbUHKhj4WqTpuQkjjC6SiWBsZlIeyFDaSknpWRQxC0YZVB9nY7kbSyBOImI9EEsFi6yAxH73s3UOQ5HYrQhYDEUHoRPIuvAYxKi41n4mIy5E+nkuaI6RC+wwR8VUQAnIvQ8/FdretDQ+H6G/t+8Ke642ka8/W/RJiCNoi4j8eOXg0R0jsD/bOacu6fkjOAX2R7edLJH2+hqS7jgjZ/wlJFm8jG+uhNp6vIOS5VHNNkpIHkdyoPcNIQ4Ts+yJ7VZ2Sd09HxGk/Gw93/z7XxuJIpC6cQDopeSQiVO8jO6xLM5+i9boDItA7UpJSqlCvq9/nWPmNEaHqgIjYV2hvu1p/+8X0fXWb12sRg+hhJHfZmuhkZV1BkmjnIMZyNikn5QOFMhuUC2fk6yfWbpVUmmwR+9jC/4CU0+5rkgi/qy2on0zmSAFJ+eJBnE+7wjOb2sY/n+S0ULZFhgjThsgOcjZScdZHxLMYH7arbZIN/L3FlNcEIchWSPp7HMV0zEDpWt5H6pDZ9l8vSuxMy9j+XxuiOaNwb1VDDpciJP0e4uqPKB07EhPiDEMjpNbqi4jTJvb/9jZObRBS+wQh4G3suZeQ+qc/si3uvwx98FCIsxDyvZKUTmiBIaHz7dl6yIB+EUJe7yMCtTtiak5BaqBPkcThCVtvQ4jX11DdYv+Xoa2bIk5+qtVzHfIK/QZJhIMRc7IaIgjTrd4hSCIaBpy7HPP8Q4YR++1q1N2RCq1R6Zok5VXsY+vB1XZXkxiIk5G9ahJSlc5C6r+FmEBEmC5HDOrSMKB7ICaj6LBzHmLSLgMucRywDGvEky1vi2xyHtD8GcIPrmo9wOqfTYFI5WvlXiu3snTc9N9JThRtEYI83Bb5Z4izPQMh/mWJkfoBSdnvKxHReME2/u4UAkHL2C/f6I6wWhpi6Ya4tX+QEuzWQsj7ARTUuNj+ISnqDsSx32iby7M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-     },
-     "metadata": {
-      "needs_background": "light"
-     }
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "source": [
-    "plt.title('Max Wingspan in Centimeters')\n",
-    "plt.ylabel('Wingspan (CM)')\n",
-    "plt.tick_params(axis='both',which='both',labelbottom=False,bottom=False)\n",
-    "\n",
-    "for i in range(len(birds)):\n",
-    "    x = birds['Name'][i]\n",
-    "    y = birds['MaxWingspan'][i]\n",
-    "    plt.plot(x, y, 'bo')\n",
-    "    if birds['MaxWingspan'][i] > 500:\n",
-    "        plt.text(x, y * (1 - 0.05), birds['Name'][i], fontsize=12)\n",
-    "    \n",
-    "plt.show()\n",
-    "    \n",
-    "\n",
-    "    \n"
-   ],
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
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"
-     },
-     "metadata": {
-      "needs_background": "light"
-     }
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "移除那些异常值并重新绘制翼展\n"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "source": [
-    "plt.title('Max Wingspan in Centimeters')\n",
-    "plt.ylabel('Wingspan (CM)')\n",
-    "plt.xlabel('Birds')\n",
-    "plt.tick_params(axis='both',which='both',labelbottom=False,bottom=False)\n",
-    "for i in range(len(birds)):\n",
-    "    x = birds['Name'][i]\n",
-    "    y = birds['MaxWingspan'][i]\n",
-    "    if birds['Name'][i] not in ['Bald eagle', 'Prairie falcon']:\n",
-    "        plt.plot(x, y, 'bo')\n",
-    "plt.show()\n",
-    "    "
-   ],
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "<Figure size 432x864 with 1 Axes>"
-      ],
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"
-     },
-     "metadata": {
-      "needs_background": "light"
-     }
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "使用柱状图显示鸟类的类别\n"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "source": [
-    "birds.plot(x='Category',\n",
-    "        kind='bar',\n",
-    "        stacked=True,\n",
-    "        title='Birds of Minnesota')"
-   ],
-   "outputs": [
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa009bc4320>"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 23
-    },
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "<Figure size 432x864 with 1 Axes>"
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-     },
-     "metadata": {
-      "needs_background": "light"
-     }
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "显示各类别中的鸟类数量，并水平显示标签以便我们可以阅读类别名称\n"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "source": [
-    "category_count = birds.value_counts(birds['Category'].values, sort=True)\n",
-    "plt.rcParams['figure.figsize'] = [6, 12]\n",
-    "category_count.plot.barh()"
-   ],
-   "outputs": [
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa00a4b04a8>"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 42
-    },
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "<Figure size 432x864 with 1 Axes>"
-      ],
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-     },
-     "metadata": {
-      "needs_background": "light"
-     }
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "显示每个类别的最大长度\n"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "source": [
-    "maxlength = birds['MaxLength']\n",
-    "plt.barh(y=birds['Category'], width=maxlength)\n",
-    "plt.rcParams['figure.figsize'] = [6, 12]\n",
-    "plt.show()\n"
-   ],
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "<Figure size 432x864 with 1 Axes>"
-      ],
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"
-     },
-     "metadata": {
-      "needs_background": "light"
-     }
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "叠加每个类别中鸟类的最小和最大长度\n"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 77,
-   "source": [
-    "minLength = birds['MinLength']\n",
-    "maxLength = birds['MaxLength']\n",
-    "category = birds['Category']\n",
-    "\n",
-    "plt.barh(category, maxLength)\n",
-    "plt.barh(category, minLength)\n",
-    "\n",
-    "plt.show()"
-   ],
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "<Figure size 432x864 with 1 Axes>"
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"
-     },
-     "metadata": {
-      "needs_background": "light"
-     }
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [],
-   "outputs": [],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n---\n\n**免责声明**：  \n本文档使用AI翻译服务 [Co-op Translator](https://github.com/Azure/co-op-translator) 进行翻译。尽管我们努力确保翻译的准确性，但请注意，自动翻译可能包含错误或不准确之处。原始语言的文档应被视为权威来源。对于关键信息，建议使用专业人工翻译。我们不对因使用此翻译而产生的任何误解或误读承担责任。\n"
-   ]
-  }
- ]
-}
\ No newline at end of file
diff --git a/translations/zh/3-Data-Visualization/10-visualization-distributions/README.md b/translations/zh/3-Data-Visualization/10-visualization-distributions/README.md
deleted file mode 100644
index 36bcdb1b..00000000
--- a/translations/zh/3-Data-Visualization/10-visualization-distributions/README.md
+++ /dev/null
@@ -1,217 +0,0 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "80a20467e046d312809d008395051fc7",
-  "translation_date": "2025-09-05T11:36:56+00:00",
-  "source_file": "3-Data-Visualization/10-visualization-distributions/README.md",
-  "language_code": "zh"
-}
--->
-# 可视化分布
-
-|![由 [(@sketchthedocs)](https://sketchthedocs.dev) 绘制的速记图](../../sketchnotes/10-Visualizing-Distributions.png)|
-|:---:|
-| 可视化分布 - _速记图由 [@nitya](https://twitter.com/nitya) 绘制_ |
-
-在上一节课中，你学习了关于明尼苏达州鸟类数据集的一些有趣事实。通过可视化异常值，你发现了一些错误数据，并观察了不同鸟类类别的最大长度差异。
-
-## [课前测验](https://ff-quizzes.netlify.app/en/ds/quiz/18)
-## 探索鸟类数据集
-
-另一种深入了解数据的方法是查看其分布，即数据沿轴的组织方式。例如，你可能想了解明尼苏达州鸟类数据集中最大翼展或最大体重的一般分布情况。
-
-让我们来发现一些关于这个数据集分布的事实。在本课程文件夹根目录的 _notebook.ipynb_ 文件中，导入 Pandas、Matplotlib 和你的数据：
-
-```python
-import pandas as pd
-import matplotlib.pyplot as plt
-birds = pd.read_csv('../../data/birds.csv')
-birds.head()
-```
-
-|      | 名称                         | 学名                   | 类别                  | 目           | 科       | 属          | 保护状态           | 最小长度 | 最大长度 | 最小体重     | 最大体重     | 最小翼展     | 最大翼展     |
-| ---: | :--------------------------- | :--------------------- | :-------------------- | :----------- | :------- | :---------- | :----------------- | --------: | --------: | ----------: | ----------: | ----------: | ----------: |
-|    0 | 黑腹树鸭                     | Dendrocygna autumnalis | 鸭/鹅/水禽            | 雁形目       | 鸭科     | 树鸭属      | LC                 |        47 |        56 |         652 |        1020 |          76 |          94 |
-|    1 | 棕树鸭                       | Dendrocygna bicolor    | 鸭/鹅/水禽            | 雁形目       | 鸭科     | 树鸭属      | LC                 |        45 |        53 |         712 |        1050 |          85 |          93 |
-|    2 | 雪鹅                         | Anser caerulescens     | 鸭/鹅/水禽            | 雁形目       | 鸭科     | 雁属        | LC                 |        64 |        79 |        2050 |        4050 |         135 |         165 |
-|    3 | 罗氏鹅                       | Anser rossii           | 鸭/鹅/水禽            | 雁形目       | 鸭科     | 雁属        | LC                 |      57.3 |        64 |        1066 |        1567 |         113 |         116 |
-|    4 | 大白额雁                     | Anser albifrons        | 鸭/鹅/水禽            | 雁形目       | 鸭科     | 雁属        | LC                 |        64 |        81 |        1930 |        3310 |         130 |         165 |
-
-通常，你可以通过使用散点图快速查看数据的分布方式，就像我们在上一节课中所做的那样：
-
-```python
-birds.plot(kind='scatter',x='MaxLength',y='Order',figsize=(12,8))
-
-plt.title('Max Length per Order')
-plt.ylabel('Order')
-plt.xlabel('Max Length')
-
-plt.show()
-```
-![每个目最大长度](../../../../3-Data-Visualization/10-visualization-distributions/images/scatter-wb.png)
-
-这提供了每个鸟类目身体长度的一般分布概览，但这并不是显示真实分布的最佳方式。通常通过创建直方图来完成这一任务。
-
-## 使用直方图
-
-Matplotlib 提供了非常好的方法来使用直方图可视化数据分布。这种类型的图表类似于柱状图，通过柱子的升降可以看到分布情况。要构建直方图，你需要数值数据。构建直方图时，可以将图表类型定义为 'hist' 来表示直方图。此图表显示了整个数据集范围内最大体重的分布。通过将数据数组分成较小的区间，它可以显示数据值的分布：
-
-```python
-birds['MaxBodyMass'].plot(kind = 'hist', bins = 10, figsize = (12,12))
-plt.show()
-```
-![整个数据集的分布](../../../../3-Data-Visualization/10-visualization-distributions/images/dist1-wb.png)
-
-如你所见，这个数据集中的大多数 400 多种鸟类的最大体重都在 2000 以下。通过将 `bins` 参数设置为更高的值，比如 30，可以获得更多的洞察：
-
-```python
-birds['MaxBodyMass'].plot(kind = 'hist', bins = 30, figsize = (12,12))
-plt.show()
-```
-![使用更大区间参数的分布](../../../../3-Data-Visualization/10-visualization-distributions/images/dist2-wb.png)
-
-此图表以更细致的方式显示了分布。通过确保仅选择特定范围内的数据，可以创建一个不那么偏向左侧的图表：
-
-过滤数据以仅获取体重低于 60 的鸟类，并显示 40 个 `bins`：
-
-```python
-filteredBirds = birds[(birds['MaxBodyMass'] > 1) & (birds['MaxBodyMass'] < 60)]      
-filteredBirds['MaxBodyMass'].plot(kind = 'hist',bins = 40,figsize = (12,12))
-plt.show()     
-```
-![过滤后的直方图](../../../../3-Data-Visualization/10-visualization-distributions/images/dist3-wb.png)
-
-✅ 尝试其他过滤器和数据点。为了查看数据的完整分布，移除 `['MaxBodyMass']` 过滤器以显示带标签的分布。
-
-直方图还提供了一些不错的颜色和标签增强功能可以尝试：
-
-创建一个二维直方图以比较两个分布之间的关系。让我们比较 `MaxBodyMass` 和 `MaxLength`。Matplotlib 提供了一种内置方法，通过更亮的颜色显示收敛：
-
-```python
-x = filteredBirds['MaxBodyMass']
-y = filteredBirds['MaxLength']
-
-fig, ax = plt.subplots(tight_layout=True)
-hist = ax.hist2d(x, y)
-```
-可以看到，这两个元素沿预期轴存在预期的相关性，并且有一个特别强的收敛点：
-
-![二维图](../../../../3-Data-Visualization/10-visualization-distributions/images/2D-wb.png)
-
-直方图默认适用于数值数据。如果需要查看基于文本数据的分布怎么办？
-## 使用文本数据探索数据集分布
-
-此数据集还包括关于鸟类类别及其属、种、科以及保护状态的良好信息。让我们深入了解这些保护信息。根据保护状态，鸟类的分布是什么样的？
-
-> ✅ 在数据集中，使用了几个缩写来描述保护状态。这些缩写来自 [IUCN 红色名录分类](https://www.iucnredlist.org/)，这是一个记录物种状态的组织。
-> 
-> - CR: 极危
-> - EN: 濒危
-> - EX: 灭绝
-> - LC: 无危
-> - NT: 近危
-> - VU: 易危
-
-这些是基于文本的值，因此需要进行转换以创建直方图。使用过滤后的鸟类数据框，显示其保护状态及其最小翼展。你看到了什么？
-
-```python
-x1 = filteredBirds.loc[filteredBirds.ConservationStatus=='EX', 'MinWingspan']
-x2 = filteredBirds.loc[filteredBirds.ConservationStatus=='CR', 'MinWingspan']
-x3 = filteredBirds.loc[filteredBirds.ConservationStatus=='EN', 'MinWingspan']
-x4 = filteredBirds.loc[filteredBirds.ConservationStatus=='NT', 'MinWingspan']
-x5 = filteredBirds.loc[filteredBirds.ConservationStatus=='VU', 'MinWingspan']
-x6 = filteredBirds.loc[filteredBirds.ConservationStatus=='LC', 'MinWingspan']
-
-kwargs = dict(alpha=0.5, bins=20)
-
-plt.hist(x1, **kwargs, color='red', label='Extinct')
-plt.hist(x2, **kwargs, color='orange', label='Critically Endangered')
-plt.hist(x3, **kwargs, color='yellow', label='Endangered')
-plt.hist(x4, **kwargs, color='green', label='Near Threatened')
-plt.hist(x5, **kwargs, color='blue', label='Vulnerable')
-plt.hist(x6, **kwargs, color='gray', label='Least Concern')
-
-plt.gca().set(title='Conservation Status', ylabel='Min Wingspan')
-plt.legend();
-```
-
-![翼展与保护状态的关联](../../../../3-Data-Visualization/10-visualization-distributions/images/histogram-conservation-wb.png)
-
-似乎最小翼展与保护状态之间没有明显的相关性。使用此方法测试数据集中的其他元素。你可以尝试不同的过滤器。是否发现任何相关性？
-
-## 密度图
-
-你可能注意到我们目前看到的直方图是“阶梯状”的，并没有平滑地呈弧形。为了显示更平滑的密度图，可以尝试使用密度图。
-
-要使用密度图，请熟悉一个新的绘图库 [Seaborn](https://seaborn.pydata.org/generated/seaborn.kdeplot.html)。
-
-加载 Seaborn，尝试一个基本的密度图：
-
-```python
-import seaborn as sns
-import matplotlib.pyplot as plt
-sns.kdeplot(filteredBirds['MinWingspan'])
-plt.show()
-```
-![密度图](../../../../3-Data-Visualization/10-visualization-distributions/images/density1.png)
-
-你可以看到这个图与之前的最小翼展数据图相呼应，只是稍微平滑了一些。根据 Seaborn 的文档，“与直方图相比，KDE 可以生成一个更简洁、更易于解释的图，尤其是在绘制多个分布时。但如果底层分布是有界的或不平滑的，它可能会引入失真。与直方图一样，表示的质量也取决于选择良好的平滑参数。” [来源](https://seaborn.pydata.org/generated/seaborn.kdeplot.html)。换句话说，异常值总是会使你的图表表现不佳。
-
-如果你想重新审视第二个图表中那个不平滑的最大体重线，可以通过使用这种方法很好地将其平滑：
-
-```python
-sns.kdeplot(filteredBirds['MaxBodyMass'])
-plt.show()
-```
-![平滑的体重线](../../../../3-Data-Visualization/10-visualization-distributions/images/density2.png)
-
-如果你想要一个平滑但不过于平滑的线条，可以编辑 `bw_adjust` 参数：
-
-```python
-sns.kdeplot(filteredBirds['MaxBodyMass'], bw_adjust=.2)
-plt.show()
-```
-![较少平滑的体重线](../../../../3-Data-Visualization/10-visualization-distributions/images/density3.png)
-
-✅ 阅读此类型图表可用的参数并进行实验！
-
-这种类型的图表提供了非常直观的可视化。例如，通过几行代码，你可以显示每个鸟类目最大体重的密度：
-
-```python
-sns.kdeplot(
-   data=filteredBirds, x="MaxBodyMass", hue="Order",
-   fill=True, common_norm=False, palette="crest",
-   alpha=.5, linewidth=0,
-)
-```
-
-![每个目体重密度](../../../../3-Data-Visualization/10-visualization-distributions/images/density4.png)
-
-你还可以在一个图表中映射多个变量的密度。测试鸟类的最大长度和最小长度与其保护状态的关系：
-
-```python
-sns.kdeplot(data=filteredBirds, x="MinLength", y="MaxLength", hue="ConservationStatus")
-```
-
-![多个密度图，叠加](../../../../3-Data-Visualization/10-visualization-distributions/images/multi.png)
-
-也许值得研究一下根据长度分布的“易危”鸟类群是否有意义。
-
-## 🚀 挑战
-
-直方图是一种比基本散点图、柱状图或折线图更复杂的图表类型。上网搜索直方图的优秀使用案例。它们是如何使用的，展示了什么内容，以及它们通常在哪些领域或研究领域中使用？
-
-## [课后测验](https://ff-quizzes.netlify.app/en/ds/quiz/19)
-
-## 复习与自学
-
-在本课中，你使用了 Matplotlib 并开始使用 Seaborn 来展示更复杂的图表。研究 Seaborn 中的 `kdeplot`，一种“一维或多维的连续概率密度曲线”。阅读 [文档](https://seaborn.pydata.org/generated/seaborn.kdeplot.html) 以了解其工作原理。
-
-## 作业
-
-[应用你的技能](assignment.md)
-
----
-
-**免责声明**：  
-本文档使用AI翻译服务[Co-op Translator](https://github.com/Azure/co-op-translator)进行翻译。尽管我们努力确保准确性，但请注意，自动翻译可能包含错误或不准确之处。应以原始语言的文档作为权威来源。对于关键信息，建议使用专业人工翻译。因使用本翻译而引起的任何误解或误读，我们概不负责。
\ No newline at end of file
diff --git a/translations/zh/3-Data-Visualization/10-visualization-distributions/assignment.md b/translations/zh/3-Data-Visualization/10-visualization-distributions/assignment.md
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index b08c8652..00000000
--- a/translations/zh/3-Data-Visualization/10-visualization-distributions/assignment.md
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-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "40eeb9b9f94009c537c7811f9f27f037",
-  "translation_date": "2025-08-25T18:07:50+00:00",
-  "source_file": "3-Data-Visualization/10-visualization-distributions/assignment.md",
-  "language_code": "zh"
-}
--->
-# 运用你的技能
-
-## 说明
-
-到目前为止，你已经使用了明尼苏达州鸟类数据集来探索鸟类数量和种群密度的信息。尝试使用不同的数据集来练习这些技术，可以从 [Kaggle](https://www.kaggle.com/) 获取数据集。创建一个笔记本来讲述关于这个数据集的故事，并确保在讨论时使用直方图。
-
-## 评分标准
-
-优秀 | 合格 | 需改进
---- | --- | -- |
-笔记本包含关于数据集的注释，包括其来源，并使用至少5个直方图来发现数据的相关信息。 | 笔记本包含不完整的注释或存在错误。 | 笔记本没有注释且存在错误。
-
-**免责声明**：  
-本文档使用AI翻译服务[Co-op Translator](https://github.com/Azure/co-op-translator)进行翻译。尽管我们努力确保准确性，但请注意，自动翻译可能包含错误或不准确之处。应以原始语言的文档作为权威来源。对于关键信息，建议使用专业人工翻译。因使用本翻译而引起的任何误解或误读，我们概不负责。
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--- a/translations/zh/3-Data-Visualization/10-visualization-distributions/notebook.ipynb
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-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "source": [
-    "鸟类分布\n"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n---\n\n**免责声明**：  \n本文档使用AI翻译服务[Co-op Translator](https://github.com/Azure/co-op-translator)进行翻译。尽管我们努力确保准确性，但请注意，自动翻译可能包含错误或不准确之处。应以原始语言的文档作为权威来源。对于关键信息，建议使用专业人工翻译。因使用本翻译而引起的任何误解或误读，我们概不负责。\n"
-   ]
-  }
- ],
- "metadata": {
-  "orig_nbformat": 4,
-  "language_info": {
-   "name": "python"
-  },
-  "coopTranslator": {
-   "original_hash": "e5272cbcbffd1ddcc09e44d3d8e7e8cd",
-   "translation_date": "2025-09-02T09:06:25+00:00",
-   "source_file": "3-Data-Visualization/10-visualization-distributions/notebook.ipynb",
-   "language_code": "zh"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
\ No newline at end of file
diff --git a/translations/zh/3-Data-Visualization/10-visualization-distributions/solution/notebook.ipynb b/translations/zh/3-Data-Visualization/10-visualization-distributions/solution/notebook.ipynb
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index ed33d302..00000000
--- a/translations/zh/3-Data-Visualization/10-visualization-distributions/solution/notebook.ipynb
+++ /dev/null
@@ -1,571 +0,0 @@
-{
- "metadata": {
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.0"
-  },
-  "orig_nbformat": 4,
-  "kernelspec": {
-   "name": "python3",
-   "display_name": "Python 3.7.0 64-bit"
-  },
-  "interpreter": {
-   "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d"
-  },
-  "coopTranslator": {
-   "original_hash": "7fbf767d839628bf761fd53b8824bae0",
-   "translation_date": "2025-09-02T09:15:22+00:00",
-   "source_file": "3-Data-Visualization/10-visualization-distributions/solution/notebook.ipynb",
-   "language_code": "zh"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2,
- "cells": [
-  {
-   "cell_type": "markdown",
-   "source": [
-    "# 鸟类分布\n"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "可视化数据集\n"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "source": [
-    "import pandas as pd\n",
-    "import matplotlib.pyplot as plt\n",
-    "birds = pd.read_csv('../../../data/birds.csv')\n",
-    "birds.head()"
-   ],
-   "outputs": [
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "                           Name          ScientificName  \\\n",
-       "0  Black-bellied whistling-duck  Dendrocygna autumnalis   \n",
-       "1        Fulvous whistling-duck     Dendrocygna bicolor   \n",
-       "2                    Snow goose      Anser caerulescens   \n",
-       "3                  Ross's goose            Anser rossii   \n",
-       "4   Greater white-fronted goose         Anser albifrons   \n",
-       "\n",
-       "                Category         Order    Family        Genus  \\\n",
-       "0  Ducks/Geese/Waterfowl  Anseriformes  Anatidae  Dendrocygna   \n",
-       "1  Ducks/Geese/Waterfowl  Anseriformes  Anatidae  Dendrocygna   \n",
-       "2  Ducks/Geese/Waterfowl  Anseriformes  Anatidae        Anser   \n",
-       "3  Ducks/Geese/Waterfowl  Anseriformes  Anatidae        Anser   \n",
-       "4  Ducks/Geese/Waterfowl  Anseriformes  Anatidae        Anser   \n",
-       "\n",
-       "  ConservationStatus  MinLength  MaxLength  MinBodyMass  MaxBodyMass  \\\n",
-       "0                 LC       47.0       56.0        652.0       1020.0   \n",
-       "1                 LC       45.0       53.0        712.0       1050.0   \n",
-       "2                 LC       64.0       79.0       2050.0       4050.0   \n",
-       "3                 LC       57.3       64.0       1066.0       1567.0   \n",
-       "4                 LC       64.0       81.0       1930.0       3310.0   \n",
-       "\n",
-       "   MinWingspan  MaxWingspan  \n",
-       "0         76.0         94.0  \n",
-       "1         85.0         93.0  \n",
-       "2        135.0        165.0  \n",
-       "3        113.0        116.0  \n",
-       "4        130.0        165.0  "
-      ],
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Name</th>\n",
-       "      <th>ScientificName</th>\n",
-       "      <th>Category</th>\n",
-       "      <th>Order</th>\n",
-       "      <th>Family</th>\n",
-       "      <th>Genus</th>\n",
-       "      <th>ConservationStatus</th>\n",
-       "      <th>MinLength</th>\n",
-       "      <th>MaxLength</th>\n",
-       "      <th>MinBodyMass</th>\n",
-       "      <th>MaxBodyMass</th>\n",
-       "      <th>MinWingspan</th>\n",
-       "      <th>MaxWingspan</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Black-bellied whistling-duck</td>\n",
-       "      <td>Dendrocygna autumnalis</td>\n",
-       "      <td>Ducks/Geese/Waterfowl</td>\n",
-       "      <td>Anseriformes</td>\n",
-       "      <td>Anatidae</td>\n",
-       "      <td>Dendrocygna</td>\n",
-       "      <td>LC</td>\n",
-       "      <td>47.0</td>\n",
-       "      <td>56.0</td>\n",
-       "      <td>652.0</td>\n",
-       "      <td>1020.0</td>\n",
-       "      <td>76.0</td>\n",
-       "      <td>94.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Fulvous whistling-duck</td>\n",
-       "      <td>Dendrocygna bicolor</td>\n",
-       "      <td>Ducks/Geese/Waterfowl</td>\n",
-       "      <td>Anseriformes</td>\n",
-       "      <td>Anatidae</td>\n",
-       "      <td>Dendrocygna</td>\n",
-       "      <td>LC</td>\n",
-       "      <td>45.0</td>\n",
-       "      <td>53.0</td>\n",
-       "      <td>712.0</td>\n",
-       "      <td>1050.0</td>\n",
-       "      <td>85.0</td>\n",
-       "      <td>93.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Snow goose</td>\n",
-       "      <td>Anser caerulescens</td>\n",
-       "      <td>Ducks/Geese/Waterfowl</td>\n",
-       "      <td>Anseriformes</td>\n",
-       "      <td>Anatidae</td>\n",
-       "      <td>Anser</td>\n",
-       "      <td>LC</td>\n",
-       "      <td>64.0</td>\n",
-       "      <td>79.0</td>\n",
-       "      <td>2050.0</td>\n",
-       "      <td>4050.0</td>\n",
-       "      <td>135.0</td>\n",
-       "      <td>165.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Ross's goose</td>\n",
-       "      <td>Anser rossii</td>\n",
-       "      <td>Ducks/Geese/Waterfowl</td>\n",
-       "      <td>Anseriformes</td>\n",
-       "      <td>Anatidae</td>\n",
-       "      <td>Anser</td>\n",
-       "      <td>LC</td>\n",
-       "      <td>57.3</td>\n",
-       "      <td>64.0</td>\n",
-       "      <td>1066.0</td>\n",
-       "      <td>1567.0</td>\n",
-       "      <td>113.0</td>\n",
-       "      <td>116.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Greater white-fronted goose</td>\n",
-       "      <td>Anser albifrons</td>\n",
-       "      <td>Ducks/Geese/Waterfowl</td>\n",
-       "      <td>Anseriformes</td>\n",
-       "      <td>Anatidae</td>\n",
-       "      <td>Anser</td>\n",
-       "      <td>LC</td>\n",
-       "      <td>64.0</td>\n",
-       "      <td>81.0</td>\n",
-       "      <td>1930.0</td>\n",
-       "      <td>3310.0</td>\n",
-       "      <td>130.0</td>\n",
-       "      <td>165.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 3
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "显示 MaxBodyMass 数据的直方图\n"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "source": [
-    "birds['MaxBodyMass'].plot(kind = 'hist',bins = 10,figsize = (12,12))\n",
-    "plt.show()"
-   ],
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "<Figure size 864x864 with 1 Axes>"
-      ],
-      "image/png": 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"
-     },
-     "metadata": {
-      "needs_background": "light"
-     }
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "尝试使用箱子\n"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "source": [
-    "birds['MaxBodyMass'].plot(kind = 'hist',bins = 30,figsize = (12,12))\n",
-    "plt.show()"
-   ],
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "<Figure size 864x864 with 1 Axes>"
-      ],
-      "image/png": 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"
-     },
-     "metadata": {
-      "needs_background": "light"
-     }
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "筛选数据并创建一个新的直方图\n"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "source": [
-    "filteredBirds = birds[(birds['MaxBodyMass'] > 1) & (birds['MaxBodyMass'] < 60)]      \n",
-    "filteredBirds['MaxBodyMass'].plot(kind = 'hist',bins = 40,figsize = (12,12))\n",
-    "plt.show()        \n"
-   ],
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "<Figure size 864x864 with 1 Axes>"
-      ],
-      "image/png": "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"
-     },
-     "metadata": {
-      "needs_background": "light"
-     }
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "创建一个二维直方图，显示最大体重与最大长度之间的关系\n"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "source": [
-    "from matplotlib import colors\n",
-    "from matplotlib.ticker import PercentFormatter\n",
-    "\n",
-    "x = filteredBirds['MaxBodyMass']\n",
-    "y = filteredBirds['MaxLength']\n",
-    "\n",
-    "fig, ax = plt.subplots(tight_layout=True)\n",
-    "hist = ax.hist2d(x, y)"
-   ],
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ],
-      "image/png": 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n4948p4D/SnJvki3d2mB/phb8e1Aar6oqyaDfp5/kRcDXgPdV1a9GvwyPDHl/qupZYH2Sc4FvAJf3PFITkrwBOFBV9ya5pu95GvSaqtqb5AJge5Ifzr9yaD9TnkGdvP1J1gJ03w/0PE9vkswyitOXqurr3bL7M09VPQXsAF4NnJvkuV8K1wF7exusP1cDb0zyKPAVRk/tfQ73BoCq2tt9P8DoF5uNDPhnykCdvG8Cm7vLm4E7e5ylN93rBrcAe6rqM/OuGvz+JFnTnTmRZBVwLaPX6HYAb+luNsi9qaqbqmpdVV0CvA24p6regXtDknOSvPi5y8BfALsZ8M+UnyRxAkm+DFzD6OPu9wMfAf4VuB34fUZ/OuStVXXsGyle8JK8Bvhv4AF+91rChxm9DjXo/Unyx4xezF7O6JfA26vq75L8AaOzhtXAfcBfVtWh/ibtV/cU399U1RvcG+j24Bvd4QzwL1X18STnM9CfKQMlSWqST/FJkppkoCRJTTJQkqQmGShJUpMMlCSpSQZKktQkAyVJatL/AQEZvoB0TfH/AAAAAElFTkSuQmCC"
-     },
-     "metadata": {
-      "needs_background": "light"
-     }
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "使用过滤后的数据集，创建一个标记和堆叠的直方图，将ConservationStatus与MaxBodyMass叠加在一起\n"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "source": [
-    "x1 = filteredBirds.loc[filteredBirds.ConservationStatus=='EX', 'MinWingspan']\n",
-    "x2 = filteredBirds.loc[filteredBirds.ConservationStatus=='CR', 'MinWingspan']\n",
-    "x3 = filteredBirds.loc[filteredBirds.ConservationStatus=='EN', 'MinWingspan']\n",
-    "x4 = filteredBirds.loc[filteredBirds.ConservationStatus=='NT', 'MinWingspan']\n",
-    "x5 = filteredBirds.loc[filteredBirds.ConservationStatus=='VU', 'MinWingspan']\n",
-    "x6 = filteredBirds.loc[filteredBirds.ConservationStatus=='LC', 'MinWingspan']\n",
-    "\n",
-    "kwargs = dict(alpha=0.5, bins=20)\n",
-    "\n",
-    "plt.hist(x1, **kwargs, color='red', label='Extinct')\n",
-    "plt.hist(x2, **kwargs, color='orange', label='Critically Endangered')\n",
-    "plt.hist(x3, **kwargs, color='yellow', label='Endangered')\n",
-    "plt.hist(x4, **kwargs, color='green', label='Near Threatened')\n",
-    "plt.hist(x5, **kwargs, color='blue', label='Vulnerable')\n",
-    "plt.hist(x6, **kwargs, color='gray', label='Least Concern')\n",
-    "\n",
-    "plt.gca().set(title='Conservation Status', ylabel='Max Body Mass')\n",
-    "plt.legend();"
-   ],
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ],
-      "image/png": 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KK9m7d2+h61xzzTXMmjWLTZs2cfXVVxd5n7kloqMpD52ens6iRYt49913qVGjBqmpqZH4EhISMLOot+Xu3H777fzqV786bHpOTs5h76e707dvX+bMmXPYcrG6ZqGmHhEBoE+fPuzbt++wM8qVK1eyePHiQtcrrKRy3759efTRRyPj3377LTt37qRmzZrUqVOHzZs389prrx0ztkGDBvHPf/6TjIwM+vfvH+URFa5Xr17Mnj0bgNdeey3ynIEdO3ZwyimnUKNGDT788EPee++9Y26rS5cukesXeX/h9O/fn5kzZ0ba6zds2MCWLVuOuv7bb7/NJ598AgRNSh9//DEtW7YkJyeHdevWARzxxVBcOuMXKa+i6H5ZksyM+fPnM2bMGO6//36qVatGYmIi06ZNY8OGDQWu165dOypVqkT79u258sorI80aAHfeeSc33HADbdq0oVKlSkyYMIHBgwfToUMHWrZsyZlnnhlp3ihMlSpV6N27N3Xr1qVSpUpHXeZoJaUnT55c4DYnTJjA0KFDad26Nd26daNp06aR9R5//HHOPfdcWrRoEWmOKcy0adMYPnw49957LwMGDKBOnToA9OvXj7Vr19K1a1cguDj8zDPPHHEMDRs2ZNasWQwdOpR9+/YBMGnSJM455xymT5/OwIEDqVGjBj179iyR5xaoLLOUeyrLLIcOHaJjx448//zzkfbu8uT777+nevXqmBlz585lzpw5/O1vfyvVGIpSllln/CJSrq1Zs4YLLriAQYMGlcukD7B06VJGjRqFu1O3bl1mzpxZ1iEVSolfRMq1Vq1a8emnn5Z1GIXq2bNn1A+hLw9idnHXzM40szfMbI2ZrTaz0eH0iWa2wcyywtf5sYpBRESOFMsz/gPAre6+zMxqA0vNbGE4b6q7T4nhvkVEpAAxS/zuvhHYGA7vMrO1wBmx2p+IiESnVNr4zSwR6AC8D3QHRpnZFUAmwa+Cb4+yzkhgJBDpZiUnvuL00BGRkhXzxG9mtYB5wBh332lmjwG/Azz8+0fgiFvx3H06MB2C7pyxjlOk/JlY6ttTaeP4ENPEb2YJBEn/WXd/EcDdN+eZPwOI33dfpJxRaeP4EMtePQY8Cax19z/lmd44z2KDgFWxikFESoZKG1cssfx67A5cDvTJ13XzD2b2gZmtBHoDNxe6FREpNSptHB9i2avnLcCOMuvVWO1TRI6PShvHB925KyJRUWnjikNXQkSk2OK5tPGJTGf8IuXWxFLfo0obxweVZZZSVVo3cKks84mnPJQ2PpGpLLOInHBOtNLGJzIlfhEpF0600sYnMl3cFRGJM0r8IiJxRolfRCTOqI1fKqSi9h46EXsBiRSXEr9IOTUxfWLJbi/12NszM2655Rb++Mc/AjBlyhR2797NxIklF8u2bds477zzANi0aROVKlWiYcOGADz33HMMHjyYVatKvnZjVlYWX331FeefH9unvZ4IZZ/V1CMiEVWrVuXFF1/k66+/LtHt5i3LUL9+fbKyssjKyuK6667j5ptvjoxXqVKlyNuLVlZWFq++qlJhoMQvInlUrlyZkSNHMnXq1CPmbd26lYsvvpjOnTvTuXNn3n77bQCWLFlC165d6dChA926deOjjz4CYNasWVx44YX06dMncoYfjYMHD3LttdfSunVr+vXrx549e4CgOW7MmDEkJyfz4IMPFimeH374gbvuuou0tLRI1dHvvvuOq6++mpSUFDp06BC5WWzWrFkMHjyYAQMG0Lx5c8aOHRuJraKUfVZTj4gc5oYbbqBdu3aHJTwIyibffPPN9OjRgy+++IL+/fuzdu1aWrZsyeLFi6lcuTKLFi1i/PjxkZo7y5YtY+XKldSrVy/q/WdnZzNnzhxmzJjBkCFDmDdvHsOHDweCipu5d/EPGzasSPHcc889ZGZm8sgjjwAwfvx4+vTpw8yZM9m+fTspKSn89Kc/BYJfB8uXL6dq1aq0aNGCG2+8kerVq0fKPtesWZP777+fP/3pT4wdO5Zrr72Wf/3rX/zoRz/i0ksvPe5/g1hT4heRw5x88slcccUVPPTQQ1SvXj0yfdGiRaxZsyYyvnPnTnbv3s2OHTsYMWIE2dnZmBn79++PLNO3b98iJX2AZs2aReoFderUiZycnMi8vEm1OPHktWDBAl5++eXIswX27t0beZ7AeeedF6kV1KpVKz7//HO2b99eYco+K/GLyBHGjBlDx44dueqqqyLTDh06xHvvvUe1atUOW3bUqFH07t2b+fPnk5OTc1gPqbxlk6OVW/4ZgrLNuU09+bdXnHjycnfmzZtHixYtDpv+/vvvHxHDgQMHKlTZZ7Xxi8gR6tWrx5AhQ3jyyScj0/r168fDDz8cGc9NeDt27OCMM84Agvbx0lLUeGrXrn1YVc7+/fvz8MMPRx4XuXz58kL3V5HKPuuMX6Sciqb7ZSzdeuutkfZwCJ6oldv+f+DAAXr16sXjjz/O2LFjGTFiBJMmTWLgwIGlFl9R4+nduzeTJ08mKSmJ22+/nd/+9reMGTOGdu3acejQIZo1a1ZoF8yKVPZZZZmlVJVWWeaiKg83cMV7WWY5PkUpy6ymHhGROKPELyISZ5T4RUTijBK/iEicUeIXEYkzSvwiInFG/fhFyqkSrIQc1fZ69+7NuHHj6N+/f2TatGnT+Oijj3jssceOuk5qaipTpkwhOfmIHoMlprAyx4mJiWRmZtKgQYOY7b8i0hm/iAAwdOhQ5s6de9i0uXPnMnTo0JjvuzhllqX4Ypb4zexMM3vDzNaY2WozGx1Or2dmC80sO/x7SqxiEJHoXXLJJbzyyiv88MMPAOTk5PDVV19x8OBBLrjggshyo0aNOmpphlq1anHHHXfQvn17unTpwubNm4GCyzlPnDiRyy+/nO7du3P55ZeTk5NDz5496dixIx07duSdd96JbHvnzp0MHDiQFi1acN1113Ho0KEj9v/MM8+QkpJCUlISv/rVrzh48GBJvj0VyjETv5n9wsxqh8N3mtmLZtYxim0fAG5191ZAF+AGM2sFjANed/fmwOvhuIiUsXr16pGSksJrr70GBGf7Q4YMwcyiWv+7776jS5curFixgl69ejFjxgzgP+WcMzIymDdvHtdcc01knTVr1rBo0SLmzJnDqaeeysKFC1m2bBlpaWncdNNNkeWWLFnCww8/zJo1a1i3bt0RNe/Xrl1LWloab7/9NllZWVSqVIlnn332eN+SCiuaNv7fuvvzZtYD+CnwAPAY8OPCVnL3jcDGcHiXma0FzgAuAlLDxf4KpAP/rzjBi0jJym3uueiii5g7dy5PPvlk1HVnqlSpEvll0KlTJxYuXAgUXD4Z4MILL4yUft6/fz+jRo2KJO6PP/44sk5KSgpnn312JMa33nqLSy65JDL/9ddfZ+nSpXTu3BmAPXv2cOqppxb3bajwokn8ub+XBgLT3f0VM5tUlJ2YWSLQAXgfaBR+KQBsAhoVsM5IYCRA06ZNi7I7ESmmiy66iJtvvplly5bx/fff06lTJ956663Dmlb27t171HUTEhIivw5ySxlDweWT4fAyy1OnTqVRo0asWLGCQ4cOHbZ8/l8d+cfdnREjRnDfffcV8YjjUzRt/BvM7AngUuBVM6sa5XoAmFktYB4wxt135p3nQYW4o1aJc/fp7p7s7sm5D2IWkdiqVasWvXv35uqrr45c1D3rrLNYs2YN+/btY/v27bz++utF2mZB5ZPz27FjB40bN+akk07i6aefPqyNfsmSJXz22WccOnSItLQ0evTocdi65513Hi+88AJbtmwB4JtvvuHzzz8vUpzxJJoz/iHAAGCKu283s8bAbdFs3MwSCJL+s+6e2yi32cwau/vGcFtbihO4SEVX0t05ozV06FAGDRoU6eFz5plnMmTIENq0aUOzZs3o0KFDkbZXUPnk/K6//nouvvhinnrqKQYMGHDYr4HOnTszatQoPvnkE3r37s2gQYMOW7dVq1ZMmjSJfv36cejQIRISEnj00Uc566yzivEOVHzHLMtsZv8FrHf3fWaWCrQDnnL37cdYzwja8L9x9zF5pj8AbHP3yWY2Dqjn7mML2g6oLHNForLMBVNZZjkeJV2WeR5w0Mx+BEwHzgRmR7Fed+ByoI+ZZYWv84HJQF8zyya4WDw5im2JiEgJiaap55C7HzCzwcDD7v6wmRX+jDLA3d8CCuoHdl5RghQRkZITzRn/fjMbClwB5N4znRC7kEREJJaiSfxXAV2Be939MzNrBjwd27BERCRWjtnU4+5rgJvyjH8G3B/LoEREJHaOmfjNrDlwH9AKiNxR4e5nxzAuERGJkWgu7v4vMAGYCvQmaPpRVU+RGCvprq/RdFmtVatWpJxCSdq+fTuzZ8/m+uuvP+r8TZs2MWbMGDIyMqhbty6NGjVi2rRpnHPOOSUei0SXwKu7++sEff4/d/eJBOUbRESisn37dv785z8fdZ67M2jQIFJTU1m3bh1Lly7lvvvui1T3LC3xVBo6msS/z8xOArLNbJSZDQJqxTguESknCiqrvGTJErp27UqHDh3o1q0bH330EQCrV6+OlEdu164d2dnZjBs3jnXr1pGUlMRttx1+4/8bb7xBQkIC1113XWRa+/bt6dmzJ+7ObbfdRps2bWjbti1paWlA8GsoNTWVSy65hJYtW3LZZZeRezNqRkYG3bp1o3379qSkpLBr1y4OHjzIbbfdRufOnWnXrh1PPPFEZDs9e/bkwgsvpFWrVuTk5HDuuedy7bXX0rp1a/r168eePXti/h6XtmiaekYDNQgu8P4O6AOMiGVQIlJ+5JZV7tGjB1988QX9+/dn7dq1tGzZksWLF1O5cmUWLVrE+PHjmTdvHo8//jijR4/msssu44cffuDgwYNMnjyZVatWHbVOz6pVq+jUqdNR9/3iiy+SlZXFihUr+Prrr+ncuTO9evUCYPny5axevZrTTz+d7t278/bbb5OSksKll15KWloanTt3ZufOnVSvXp0nn3ySOnXqkJGRwb59++jevTv9+vUDYNmyZaxatYpmzZqRk5NDdnY2c+bMYcaMGQwZMoR58+YxfPjw2L3BZSCaXj0Z4eBugvZ9EYkjBZVV3rFjByNGjCA7OxszY//+/QB07dqVe++9l/Xr1zN48GCaN29e7H2/9dZbDB06lEqVKtGoUSN+8pOfkJGRwcknn0xKSgpNmjQBICkpiZycHOrUqUPjxo0j5ZlPPvlkABYsWMDKlSt54YUXgKAgXHZ2NlWqVCElJYVmzZpF9tmsWTOSkpKAoLx0Tk5OseMvrwpM/Gb2cmEruvuFJR+OiJQ3BZVVHjVqFL1792b+/Pnk5ORELh4PGzaMH//4x7zyyiucf/75PPHEE5Fa+kfTunXrSEIuiqpVq0aG85aBPhp35+GHHz7secIQNPXkLQZ3tO1WxKaewtr4uwJNgMXAFOCP+V4iEgcKKqu8Y8cOzjjjDIDDHsX46aefcvbZZ3PTTTdx0UUXsXLlSmrXrl3gA1369OnDvn37mD59emTaypUrWbx4MT179iQtLY2DBw+ydetW3nzzTVJSUgqMtUWLFmzcuJGMjKChYteuXRw4cID+/fvz2GOPRX6VfPzxx3z33XfFe0MqgMKaek4D+gJDgWHAK8Acd19dGoGJxLuyqBj6/fffR5pPAG655ZYCyyqPHTuWESNGMGnSJAYO/E9Hv+eee46nn36ahIQETjvtNMaPH0+9evXo3r07bdq04Wc/+xkPPPBAZHkzY/78+YwZM4b777+fatWqkZiYyLRp0+jRowfvvvsu7du3x8z4wx/+wGmnncaHH3541PirVKlCWloaN954I3v27KF69eosWrSIa665hpycHDp27Ii707BhQ1566aXYvZHl3DHLMgOED18ZSvDYxbvd/ZFYB5aXyjJXHCrLXDCVZZbjUZSyzIVe3A0T/kCCpJ8IPATML7FIRUSk1BV2cfcpoA3wKsFZ/qpSi0pERGKmsDP+4cB3BP34b8rzcGMjeFzuyTGOTaRcK06z1bGalNz9iAeJixxLNE32eRWY+N1d9XhESlG1atXYtm0b9evXV/KXqLk727ZtO6K7bWGiuXNXREpBkyZNWL9+PVu3bi3rUOQEU61atcN6YwSPzU4AAAuTSURBVB2LEr9IOZGQkHDYHaQisaLmHBGROHPMxG9mN5rZKaURjIiIxF40Z/yNgAwze87MBpiuOomInNCOmfjd/U6gOfAkcCVBXf7fm9l/xTg2ERGJgaja+D3oJLopfB0ATgFeMLM/xDA2ERGJgWgetj4auAL4GvgLcJu77899KhcwNrYhiohISYqmO2c9YLC7f553orsfMrMLYhOWiIjESmG1euqFgw/mGwfA3b9x97UxjE1ERGKgsDb+pUBm+Hcr8DFB087WcFqhzGymmW0xs1V5pk00sw1mlhW+zj++8EVEpKgKTPzu3szdzwYWAf/t7g3cvT5wAbAgim3PAgYcZfpUd08KX68WJ2gRESm+aHr1dMmboN39NaDbsVZy9zeBb44jNhERiYFoEv9XZnanmSWGrzuAr45jn6PMbGXYFFTgHcFmNtLMMs0sU0WrRERKTjSJfyjQkODJW/OBU8NpxfEY8F9AErCRQh7a7u7T3T3Z3ZMbNmxYzN2JiEh+x+zO6e7fAKPNrHYw6ruLuzN335w7bGYzgH8Ud1siIlI80RRpa2tmy4FVwGozW2pmbYqzMzNrnGd0ULhNEREpRdHcwPUEcIu7vwFgZqnAdI5xgdfM5gCpQAMzWw9MAFLNLAlwIAf4VXEDFxGR4okm8dfMTfoA7p5uZjWPtZK7H+06wJNFCU5EREpeNIn/UzP7LfB0OD4c+DR2IYmISCxFk/ivBu4GXgzHF4fTRCqM9PT0sg5BpNRE06vnW+CmUohFRERKQYG9esysgZlNMLObzKyWmT1mZqvM7G9m9qPSDFJEREpOYd05ZwNVCZ6+tQT4DLiEoO/9X2IfmoiIxEJhTT2N3H18+Izdz90992lbH5rZDaUQm4iIxEBhZ/wHIfLYxa/zzTsUs4hERCSmCjvjP9vMXgYszzDheLOYRyYiIjFRWOK/KM/wlHzz8o+LSBSK0200NTW1xOOQ+FZg4nf3f5dmICIiUjqiKcssIiIViBK/iEiciaYsc7WjTGsQm3BERCTWojnjzzCzLrkjZnYx8E7sQhIRkViKpkjbMGCmmaUDpwP1gT6xDEpERGInmiJtH5jZvQRlmXcBvdx9fcwjk3JPFS1FTkzHTPxm9iTBA9LbAecA/zCzh9390VgHJyIiJS+aNv4PgN7u/pm7/x/wY6BjbMMSEZFYiaapZ1q+8R3AL2MWkYiIxFQ0TT3NgfuAVkCka6e7nx3DuEREJEaiaer5X+Ax4ADQG3gKeCaWQYmISOxEk/iru/vrgLn75+4+ERgY27BERCRWounHv8/MTgKyzWwUsAGoFduwREQkVqI54x8N1CB44Hon4HJgRCyDEhGR2ImmV09GOLgbuCq24YiISKwVmPjzPHHrqNz9wpIPR0REYq2wM/6uwJfAHOB9gkcuRs3MZgIXAFvcvU04rR6QBiQCOcAQd/+2yFGLiEixFdbGfxowHmgDPAj0Bb52939H+XSuWcCAfNPGAa+7e3Pg9XBcRERKUYGJ390Puvs/3X0E0AX4BEgPe/Yck7u/CXyTb/JFwF/D4b8CPy96yCIicjwKvbhrZlUJ+uwPJWieeQiYfxz7a+TuG8PhTUCjQvY9EhgJ0LRp0+PYpYiI5FXYxd2nCJp5XgXudvdVJbljd3cz80LmTwemAyQnJxe4nIiIFE1hbfzDgeYE/fjfMbOd4WuXme0s5v42m1ljgPDvlmJuR0REiqmwNv6T3L12+Do5z6u2u59czP29zH9u/hoB/K2Y2xERkWKK5s7dYjGzOcC7QAszW29mvwQmA33NLBv4aTguIiKlKJpaPcXi7kMLmHVerPYpIiLHFrMzfhERKZ+U+EVE4kzMmnpEpGSkp6fHfB+pqakx34eUHzrjFxGJM0r8IiJxRolfRCTOKPGLiMQZJX4RkTijxC8iEmeU+EVE4owSv4hInFHiFxGJM0r8IiJxRolfRCTOKPGLiMQZJX4RkTij6pwVVFErOqo6o0j80Bm/iEicUeIXEYkzSvwiInFGiV9EJM4o8YuIxBklfhGROKPELyISZ5T4RUTijBK/iEicUeIXEYkzZVKywcxygF3AQeCAuyeXRRwiIvGoLGv19Hb3r8tw/yIicUlNPSIicaaszvgdWGBmDjzh7tPzL2BmI4GRAE2bNi3l8EQkFopaNRZUOTYWyuqMv4e7dwR+BtxgZr3yL+Du09092d2TGzZsWPoRiohUUGWS+N19Q/h3CzAfSCmLOERE4lGpJ34zq2lmtXOHgX7AqtKOQ0QkXpVFG38jYL6Z5e5/trv/swziEBGJS6We+N39U6B9ae9XREQC6s4pIhJn9LB1EalQ1GX02HTGLyISZ5T4RUTijBK/iEicUeIXEYkzSvwiInFGiV9EJM6oO6eckNJz0gudn5qYWipxxLPidJuU8kFn/CIicUaJX0Qkzijxi4jEGSV+EZE4o8QvIhJnlPhFROKMEr+ISJxRP/4TQGn0l1af7PiW/9+/sPskdI/EiU9n/CIicUaJX0Qkzijxi4jEGSV+EZE4o8QvIhJnlPhFROKMunPGEXXRC5Tk+5CeDqlRrJK7z5ysRBKTcoq0j+L+21SU0tXltTtzajT/8GW0n2PRGb+ISJxR4hcRiTNK/CIicaZMEr+ZDTCzj8zsEzMbVxYxiIjEq1JP/GZWCXgU+BnQChhqZq1KOw4RkXhVFmf8KcAn7v6pu/8AzAUuKoM4RETikrl76e7Q7BJggLtfE45fDvzY3UflW24kMDIcbQF8VMxdNgC+Lua6Jyodc3zQMceH4znms9y9Yf6J5bYfv7tPB6Yf73bMLNPdk0sgpBOGjjk+6JjjQyyOuSyaejYAZ+YZbxJOExGRUlAWiT8DaG5mzcysCvA/wMtlEIeISFwq9aYedz9gZqOA/wMqATPdfXUMd3nczUUnIB1zfNAxx4cSP+ZSv7grIiJlS3fuiojEGSV+EZE4U6ETfzyUhjCzmWa2xcxW5ZlWz8wWmll2+PeUsoyxJJnZmWb2hpmtMbPVZjY6nF6Rj7mamS0xsxXhMd8dTm9mZu+Hn++0sLNEhWJmlcxsuZn9Ixyv0MdsZjlm9oGZZZlZZjitxD/bFTbxx1FpiFnAgHzTxgGvu3tz4PVwvKI4ANzq7q2ALsAN4b9rRT7mfUAfd28PJAEDzKwLcD8w1d1/BHwL/LIMY4yV0cDaPOPxcMy93T0pT9/9Ev9sV9jET5yUhnD3N4Fv8k2+CPhrOPxX4OelGlQMuftGd18WDu8iSApnULGP2d19dziaEL4c6AO8EE6vUMcMYGZNgIHAX8Jxo4IfcwFK/LNdkRP/GcCXecbXh9PiQSN33xgObwIalWUwsWJmiUAH4H0q+DGHTR5ZwBZgIbAO2O7uB8JFKuLnexowFjgUjten4h+zAwvMbGlYtgZi8NkutyUbpGS4u5tZheuza2a1gHnAGHffGZwMBiriMbv7QSDJzOoC84GWZRxSTJnZBcAWd19qZqllHU8p6uHuG8zsVGChmX2Yd2ZJfbYr8hl/PJeG2GxmjQHCv1vKOJ4SZWYJBEn/WXd/MZxcoY85l7tvB94AugJ1zSz35K2ifb67AxeaWQ5BM20f4EEq9jHj7hvCv1sIvuBTiMFnuyIn/nguDfEyMCIcHgH8rQxjKVFhO++TwFp3/1OeWRX5mBuGZ/qYWXWgL8G1jTeAS8LFKtQxu/vt7t7E3RMJ/u/+y90vowIfs5nVNLPaucNAP2AVMfhsV+g7d83sfIJ2wtzSEPeWcUglzszmAKkEpVs3AxOAl4DngKbA58AQd89/AfiEZGY9gMXAB/yn7Xc8QTt/RT3mdgQX9SoRnKw95+73mNnZBGfD9YDlwHB331d2kcZG2NTzG3e/oCIfc3hs88PRysBsd7/XzOpTwp/tCp34RUTkSBW5qUdERI5CiV9EJM4o8YuIxBklfhGROKPELyISZ5T4RUTijBK/iEic+f+0XA0mY7YBpgAAAABJRU5ErkJggg=="
-     },
-     "metadata": {
-      "needs_background": "light"
-     }
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "使用Seaborn，创建一个关于MinWingspan的平滑图\n"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "source": [
-    "import seaborn as sns\n",
-    "import matplotlib.pyplot as plt\n",
-    "sns.kdeplot(filteredBirds['MinWingspan'])\n",
-    "plt.show()"
-   ],
-   "outputs": [
-    {
-     "output_type": "stream",
-     "name": "stderr",
-     "text": [
-      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/matplotlib/cbook/__init__.py:1402: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
-      "  x[:, None]\n",
-      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/matplotlib/axes/_base.py:276: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
-      "  x = x[:, np.newaxis]\n",
-      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/matplotlib/axes/_base.py:278: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
-      "  y = y[:, np.newaxis]\n"
-     ]
-    },
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ],
-      "image/png": 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"
-     },
-     "metadata": {
-      "needs_background": "light"
-     }
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "尝试绘制关于MaxBodyMass的kdeplot\n"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "source": [
-    "sns.kdeplot(filteredBirds['MaxBodyMass'])\n",
-    "plt.show()"
-   ],
-   "outputs": [
-    {
-     "output_type": "stream",
-     "name": "stderr",
-     "text": [
-      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/matplotlib/cbook/__init__.py:1402: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
-      "  x[:, None]\n",
-      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/matplotlib/axes/_base.py:276: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
-      "  x = x[:, np.newaxis]\n",
-      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/matplotlib/axes/_base.py:278: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
-      "  y = y[:, np.newaxis]\n"
-     ]
-    },
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ],
-      "image/png": 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"
-     },
-     "metadata": {
-      "needs_background": "light"
-     }
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "尝试调整绘图平滑参数\n"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "source": [
-    "sns.kdeplot(filteredBirds['MaxBodyMass'], bw_adjust=.2)\n",
-    "plt.show()"
-   ],
-   "outputs": [
-    {
-     "output_type": "stream",
-     "name": "stderr",
-     "text": [
-      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/matplotlib/cbook/__init__.py:1402: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
-      "  x[:, None]\n",
-      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/matplotlib/axes/_base.py:276: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
-      "  x = x[:, np.newaxis]\n",
-      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/matplotlib/axes/_base.py:278: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
-      "  y = y[:, np.newaxis]\n"
-     ]
-    },
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ],
-      "image/png": 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"
-     },
-     "metadata": {
-      "needs_background": "light"
-     }
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "创建一个二维核密度估计图（kdeplot），比较 MinLength 和 MaxLength，并使用 hue 显示 ConservationStatus\n"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "source": [
-    "sns.kdeplot(data=filteredBirds, x=\"MinLength\", y=\"MaxLength\", hue=\"ConservationStatus\")\n"
-   ],
-   "outputs": [
-    {
-     "output_type": "stream",
-     "name": "stderr",
-     "text": [
-      "/Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages/seaborn/distributions.py:1078: UserWarning: Dataset has 0 variance; skipping density estimate.\n",
-      "  warnings.warn(msg, UserWarning)\n"
-     ]
-    },
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7fe4e053cf98>"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 13
-    },
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ],
-      "image/png": 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"
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-     "metadata": {
-      "needs_background": "light"
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-   "metadata": {}
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-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n---\n\n**免责声明**：  \n本文档使用AI翻译服务[Co-op Translator](https://github.com/Azure/co-op-translator)进行翻译。尽管我们努力确保翻译的准确性，但请注意，自动翻译可能包含错误或不准确之处。原始语言的文档应被视为权威来源。对于关键信息，建议使用专业人工翻译。我们不对因使用此翻译而产生的任何误解或误读承担责任。\n"
-   ]
-  }
- ]
-}
\ No newline at end of file
diff --git a/translations/zh/3-Data-Visualization/11-visualization-proportions/README.md b/translations/zh/3-Data-Visualization/11-visualization-proportions/README.md
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index 5380f539..00000000
--- a/translations/zh/3-Data-Visualization/11-visualization-proportions/README.md
+++ /dev/null
@@ -1,205 +0,0 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
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-  "source_file": "3-Data-Visualization/11-visualization-proportions/README.md",
-  "language_code": "zh"
-}
--->
-# 可视化比例
-
-|![由 [(@sketchthedocs)](https://sketchthedocs.dev) 绘制的草图笔记](../../sketchnotes/11-Visualizing-Proportions.png)|
-|:---:|
-|可视化比例 - _草图笔记由 [@nitya](https://twitter.com/nitya) 绘制_ |
-
-在本课中，你将使用一个以自然为主题的数据集来可视化比例，例如在一个关于蘑菇的数据集中，不同类型的真菌数量占比。我们将通过一个来自 Audubon 的数据集来探索这些迷人的真菌，该数据集列出了 Agaricus 和 Lepiota 家族中 23 种有鳃蘑菇的详细信息。你将尝试使用以下美味的可视化方法：
-
-- 饼图 🥧
-- 环形图 🍩
-- 华夫图 🧇
-
-> 💡 一个非常有趣的项目 [Charticulator](https://charticulator.com) 由微软研究院开发，提供了一个免费的拖放式数据可视化界面。在他们的一个教程中也使用了这个蘑菇数据集！因此，你可以一边探索数据一边学习这个工具库：[Charticulator 教程](https://charticulator.com/tutorials/tutorial4.html)。
-
-## [课前测验](https://ff-quizzes.netlify.app/en/ds/quiz/20)
-
-## 了解你的蘑菇 🍄
-
-蘑菇非常有趣。让我们导入一个数据集来研究它们：
-
-```python
-import pandas as pd
-import matplotlib.pyplot as plt
-mushrooms = pd.read_csv('../../data/mushrooms.csv')
-mushrooms.head()
-```
-一个包含丰富分析数据的表格被打印出来：
-
-
-| 类别       | 菌盖形状 | 菌盖表面 | 菌盖颜色 | 是否有瘀伤 | 气味    | 鳃附着方式   | 鳃间距     | 鳃大小   | 鳃颜色   | 茎形状     | 茎根部   | 茎环上表面            | 茎环下表面            | 茎环上颜色            | 茎环下颜色            | 幔类型     | 幔颜色     | 环数量     | 环类型     | 孢子打印颜色       | 种群       | 栖息地   |
-| --------- | --------- | --------- | --------- | --------- | ------- | ------------- | ----------- | --------- | --------- | --------- | --------- | -------------------- | -------------------- | -------------------- | -------------------- | --------- | --------- | --------- | --------- | ----------------- | --------- | ------- |
-| 有毒      | 凸形      | 光滑      | 棕色      | 有瘀伤    | 刺鼻    | 游离          | 紧密        | 狭窄      | 黑色      | 膨大      | 等粗      | 光滑                 | 光滑                 | 白色                 | 白色                 | 部分      | 白色      | 一个      | 垂饰      | 黑色             | 分散      | 城市     |
-| 可食用    | 凸形      | 光滑      | 黄色      | 有瘀伤    | 杏仁味  | 游离          | 紧密        | 宽        | 黑色      | 膨大      | 棍棒状   | 光滑                 | 光滑                 | 白色                 | 白色                 | 部分      | 白色      | 一个      | 垂饰      | 棕色             | 众多      | 草地     |
-| 可食用    | 钟形      | 光滑      | 白色      | 有瘀伤    | 茴香味  | 游离          | 紧密        | 宽        | 棕色      | 膨大      | 棍棒状   | 光滑                 | 光滑                 | 白色                 | 白色                 | 部分      | 白色      | 一个      | 垂饰      | 棕色             | 众多      | 草原     |
-| 有毒      | 凸形      | 鳞状      | 白色      | 有瘀伤    | 刺鼻    | 游离          | 紧密        | 狭窄      | 棕色      | 膨大      | 等粗      | 光滑                 | 光滑                 | 白色                 | 白色                 | 部分      | 白色      | 一个      | 垂饰      | 黑色             | 分散      | 城市     |
-
-你会立刻注意到，所有数据都是文本格式。为了在图表中使用这些数据，你需要将其转换。事实上，大部分数据是以对象形式表示的：
-
-```python
-print(mushrooms.select_dtypes(["object"]).columns)
-```
-
-输出为：
-
-```output
-Index(['class', 'cap-shape', 'cap-surface', 'cap-color', 'bruises', 'odor',
-       'gill-attachment', 'gill-spacing', 'gill-size', 'gill-color',
-       'stalk-shape', 'stalk-root', 'stalk-surface-above-ring',
-       'stalk-surface-below-ring', 'stalk-color-above-ring',
-       'stalk-color-below-ring', 'veil-type', 'veil-color', 'ring-number',
-       'ring-type', 'spore-print-color', 'population', 'habitat'],
-      dtype='object')
-```
-将这些数据转换，将 'class' 列转换为类别：
-
-```python
-cols = mushrooms.select_dtypes(["object"]).columns
-mushrooms[cols] = mushrooms[cols].astype('category')
-```
-
-```python
-edibleclass=mushrooms.groupby(['class']).count()
-edibleclass
-```
-
-现在，如果打印出蘑菇数据，你会看到它已经根据有毒/可食用类别分组：
-
-|           | 菌盖形状 | 菌盖表面 | 菌盖颜色 | 是否有瘀伤 | 气味 | 鳃附着方式   | 鳃间距     | 鳃大小   | 鳃颜色   | 茎形状     | ... | 茎环下表面            | 茎环上颜色            | 茎环下颜色            | 幔类型     | 幔颜色     | 环数量     | 环类型     | 孢子打印颜色       | 种群       | 栖息地   |
-| --------- | --------- | --------- | --------- | --------- | ---- | ------------- | ----------- | --------- | --------- | --------- | --- | -------------------- | -------------------- | -------------------- | --------- | --------- | --------- | --------- | ----------------- | --------- | ------- |
-| 类别      |           |           |           |           |      |               |             |           |           |           |     |                      |                      |                      |           |           |           |           |                   |           |         |
-| 可食用    | 4208      | 4208      | 4208      | 4208      | 4208 | 4208          | 4208        | 4208      | 4208      | 4208      | ... | 4208                 | 4208                 | 4208                 | 4208      | 4208      | 4208      | 4208      | 4208              | 4208      | 4208    |
-| 有毒      | 3916      | 3916      | 3916      | 3916      | 3916 | 3916          | 3916        | 3916      | 3916      | 3916      | ... | 3916                 | 3916                 | 3916                 | 3916      | 3916      | 3916      | 3916      | 3916              | 3916      | 3916    |
-
-如果按照此表中呈现的顺序创建类别标签，你可以绘制一个饼图：
-
-## 饼图！
-
-```python
-labels=['Edible','Poisonous']
-plt.pie(edibleclass['population'],labels=labels,autopct='%.1f %%')
-plt.title('Edible?')
-plt.show()
-```
-瞧，一个饼图展示了蘑菇数据中这两类的比例。这里正确设置标签顺序非常重要，因此务必验证标签数组的顺序！
-
-![饼图](../../../../3-Data-Visualization/11-visualization-proportions/images/pie1-wb.png)
-
-## 环形图！
-
-环形图是饼图的一种变体，中间有一个空洞，看起来更有趣。让我们用这种方法查看数据。
-
-看看蘑菇生长的各种栖息地：
-
-```python
-habitat=mushrooms.groupby(['habitat']).count()
-habitat
-```
-这里，你将数据按栖息地分组。共有 7 种栖息地，因此将它们用作环形图的标签：
-
-```python
-labels=['Grasses','Leaves','Meadows','Paths','Urban','Waste','Wood']
-
-plt.pie(habitat['class'], labels=labels,
-        autopct='%1.1f%%', pctdistance=0.85)
-  
-center_circle = plt.Circle((0, 0), 0.40, fc='white')
-fig = plt.gcf()
-
-fig.gca().add_artist(center_circle)
-  
-plt.title('Mushroom Habitats')
-  
-plt.show()
-```
-
-![环形图](../../../../3-Data-Visualization/11-visualization-proportions/images/donut-wb.png)
-
-这段代码绘制了一个图表和一个中心圆，然后将中心圆添加到图表中。通过更改 `0.40` 的值可以调整中心圆的宽度。
-
-环形图可以通过多种方式调整标签的显示，特别是可以突出显示标签以提高可读性。更多信息请参考[文档](https://matplotlib.org/stable/gallery/pie_and_polar_charts/pie_and_donut_labels.html?highlight=donut)。
-
-现在你已经知道如何分组数据并将其显示为饼图或环形图，可以尝试其他类型的图表。试试华夫图，这是一种不同的数量可视化方式。
-
-## 华夫图！
-
-华夫图是一种二维方块数组的数量可视化方式。试着用这个数据集可视化蘑菇菌盖颜色的不同数量。为此，你需要安装一个名为 [PyWaffle](https://pypi.org/project/pywaffle/) 的辅助库并使用 Matplotlib：
-
-```python
-pip install pywaffle
-```
-
-选择一部分数据进行分组：
-
-```python
-capcolor=mushrooms.groupby(['cap-color']).count()
-capcolor
-```
-
-通过创建标签并分组数据来生成华夫图：
-
-```python
-import pandas as pd
-import matplotlib.pyplot as plt
-from pywaffle import Waffle
-  
-data ={'color': ['brown', 'buff', 'cinnamon', 'green', 'pink', 'purple', 'red', 'white', 'yellow'],
-    'amount': capcolor['class']
-     }
-  
-df = pd.DataFrame(data)
-  
-fig = plt.figure(
-    FigureClass = Waffle,
-    rows = 100,
-    values = df.amount,
-    labels = list(df.color),
-    figsize = (30,30),
-    colors=["brown", "tan", "maroon", "green", "pink", "purple", "red", "whitesmoke", "yellow"],
-)
-```
-
-使用华夫图，你可以清楚地看到这个蘑菇数据集中菌盖颜色的比例。有趣的是，有许多绿色菌盖的蘑菇！
-
-![华夫图](../../../../3-Data-Visualization/11-visualization-proportions/images/waffle.png)
-
-✅ PyWaffle 支持在图表中使用任何 [Font Awesome](https://fontawesome.com/) 提供的图标。尝试用图标代替方块，创建更有趣的华夫图。
-
-在本课中，你学习了三种可视化比例的方法。首先，你需要将数据分组为类别，然后决定哪种方式最适合展示数据——饼图、环形图或华夫图。它们都很有趣，并能快速呈现数据集的概况。
-
-## 🚀 挑战
-
-尝试在 [Charticulator](https://charticulator.com) 中重新创建这些有趣的图表。
-## [课后测验](https://ff-quizzes.netlify.app/en/ds/quiz/21)
-
-## 复习与自学
-
-有时很难决定何时使用饼图、环形图或华夫图。以下是一些相关文章：
-
-https://www.beautiful.ai/blog/battle-of-the-charts-pie-chart-vs-donut-chart
-
-https://medium.com/@hypsypops/pie-chart-vs-donut-chart-showdown-in-the-ring-5d24fd86a9ce
-
-https://www.mit.edu/~mbarker/formula1/f1help/11-ch-c6.htm
-
-https://medium.datadriveninvestor.com/data-visualization-done-the-right-way-with-tableau-waffle-chart-fdf2a19be402
-
-做一些研究，了解更多关于这个选择的相关信息。
-
-## 作业
-
-[在 Excel 中尝试](assignment.md)
-
----
-
-**免责声明**：  
-本文档使用AI翻译服务[Co-op Translator](https://github.com/Azure/co-op-translator)进行翻译。尽管我们努力确保准确性，但请注意，自动翻译可能包含错误或不准确之处。应以原始语言的文档作为权威来源。对于关键信息，建议使用专业人工翻译。因使用本翻译而导致的任何误解或误读，我们概不负责。
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-<!--
-CO_OP_TRANSLATOR_METADATA:
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-  "source_file": "3-Data-Visualization/11-visualization-proportions/assignment.md",
-  "language_code": "zh"
-}
--->
-# 在 Excel 中试一试
-
-## 操作指南
-
-你知道吗？你可以在 Excel 中创建甜甜圈图、饼图和华夫图！使用你选择的数据集，在 Excel 表格中创建这三种图表。
-
-## 评分标准
-
-| 优秀                                                   | 合格                                            | 需要改进                                              |
-| ------------------------------------------------------ | ----------------------------------------------- | ----------------------------------------------------- |
-| 提交的 Excel 表格包含所有三种图表                      | 提交的 Excel 表格包含两种图表                   | 提交的 Excel 表格仅包含一种图表                      |
-
-**免责声明**：  
-本文档使用AI翻译服务 [Co-op Translator](https://github.com/Azure/co-op-translator) 进行翻译。尽管我们努力确保翻译的准确性，但请注意，自动翻译可能包含错误或不准确之处。应以原始语言的文档作为权威来源。对于关键信息，建议使用专业人工翻译。我们对因使用此翻译而产生的任何误解或误读不承担责任。
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-  },
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-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n---\n\n**免责声明**：  \n本文档使用AI翻译服务[Co-op Translator](https://github.com/Azure/co-op-translator)进行翻译。尽管我们努力确保准确性，但请注意，自动翻译可能包含错误或不准确之处。应以原始语言的文档作为权威来源。对于关键信息，建议使用专业人工翻译。对于因使用本翻译而引起的任何误解或误读，我们概不负责。\n"
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diff --git a/translations/zh/3-Data-Visualization/11-visualization-proportions/solution/notebook.ipynb b/translations/zh/3-Data-Visualization/11-visualization-proportions/solution/notebook.ipynb
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--- a/translations/zh/3-Data-Visualization/11-visualization-proportions/solution/notebook.ipynb
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-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "source": [],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "导入蘑菇数据集\n"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "source": [
-    "import pandas as pd\n",
-    "import matplotlib.pyplot as plt\n",
-    "mushrooms = pd.read_csv('../../../data/mushrooms.csv')\n",
-    "mushrooms.head()"
-   ],
-   "outputs": [
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "       class cap-shape cap-surface cap-color     bruises     odor  \\\n",
-       "0  Poisonous    Convex      Smooth     Brown     Bruises  Pungent   \n",
-       "1     Edible    Convex      Smooth    Yellow     Bruises   Almond   \n",
-       "2     Edible      Bell      Smooth     White     Bruises    Anise   \n",
-       "3  Poisonous    Convex       Scaly     White     Bruises  Pungent   \n",
-       "4     Edible    Convex      Smooth     Green  No Bruises     None   \n",
-       "\n",
-       "  gill-attachment gill-spacing gill-size gill-color  ...  \\\n",
-       "0            Free        Close    Narrow      Black  ...   \n",
-       "1            Free        Close     Broad      Black  ...   \n",
-       "2            Free        Close     Broad      Brown  ...   \n",
-       "3            Free        Close    Narrow      Brown  ...   \n",
-       "4            Free      Crowded     Broad      Black  ...   \n",
-       "\n",
-       "  stalk-surface-below-ring stalk-color-above-ring stalk-color-below-ring  \\\n",
-       "0                   Smooth                  White                  White   \n",
-       "1                   Smooth                  White                  White   \n",
-       "2                   Smooth                  White                  White   \n",
-       "3                   Smooth                  White                  White   \n",
-       "4                   Smooth                  White                  White   \n",
-       "\n",
-       "  veil-type veil-color ring-number   ring-type spore-print-color population  \\\n",
-       "0   Partial      White         One     Pendant             Black  Scattered   \n",
-       "1   Partial      White         One     Pendant             Brown   Numerous   \n",
-       "2   Partial      White         One     Pendant             Brown   Numerous   \n",
-       "3   Partial      White         One     Pendant             Black  Scattered   \n",
-       "4   Partial      White         One  Evanescent             Brown   Abundant   \n",
-       "\n",
-       "   habitat  \n",
-       "0    Urban  \n",
-       "1  Grasses  \n",
-       "2  Meadows  \n",
-       "3    Urban  \n",
-       "4  Grasses  \n",
-       "\n",
-       "[5 rows x 23 columns]"
-      ],
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>class</th>\n",
-       "      <th>cap-shape</th>\n",
-       "      <th>cap-surface</th>\n",
-       "      <th>cap-color</th>\n",
-       "      <th>bruises</th>\n",
-       "      <th>odor</th>\n",
-       "      <th>gill-attachment</th>\n",
-       "      <th>gill-spacing</th>\n",
-       "      <th>gill-size</th>\n",
-       "      <th>gill-color</th>\n",
-       "      <th>...</th>\n",
-       "      <th>stalk-surface-below-ring</th>\n",
-       "      <th>stalk-color-above-ring</th>\n",
-       "      <th>stalk-color-below-ring</th>\n",
-       "      <th>veil-type</th>\n",
-       "      <th>veil-color</th>\n",
-       "      <th>ring-number</th>\n",
-       "      <th>ring-type</th>\n",
-       "      <th>spore-print-color</th>\n",
-       "      <th>population</th>\n",
-       "      <th>habitat</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>Poisonous</td>\n",
-       "      <td>Convex</td>\n",
-       "      <td>Smooth</td>\n",
-       "      <td>Brown</td>\n",
-       "      <td>Bruises</td>\n",
-       "      <td>Pungent</td>\n",
-       "      <td>Free</td>\n",
-       "      <td>Close</td>\n",
-       "      <td>Narrow</td>\n",
-       "      <td>Black</td>\n",
-       "      <td>...</td>\n",
-       "      <td>Smooth</td>\n",
-       "      <td>White</td>\n",
-       "      <td>White</td>\n",
-       "      <td>Partial</td>\n",
-       "      <td>White</td>\n",
-       "      <td>One</td>\n",
-       "      <td>Pendant</td>\n",
-       "      <td>Black</td>\n",
-       "      <td>Scattered</td>\n",
-       "      <td>Urban</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>Edible</td>\n",
-       "      <td>Convex</td>\n",
-       "      <td>Smooth</td>\n",
-       "      <td>Yellow</td>\n",
-       "      <td>Bruises</td>\n",
-       "      <td>Almond</td>\n",
-       "      <td>Free</td>\n",
-       "      <td>Close</td>\n",
-       "      <td>Broad</td>\n",
-       "      <td>Black</td>\n",
-       "      <td>...</td>\n",
-       "      <td>Smooth</td>\n",
-       "      <td>White</td>\n",
-       "      <td>White</td>\n",
-       "      <td>Partial</td>\n",
-       "      <td>White</td>\n",
-       "      <td>One</td>\n",
-       "      <td>Pendant</td>\n",
-       "      <td>Brown</td>\n",
-       "      <td>Numerous</td>\n",
-       "      <td>Grasses</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>Edible</td>\n",
-       "      <td>Bell</td>\n",
-       "      <td>Smooth</td>\n",
-       "      <td>White</td>\n",
-       "      <td>Bruises</td>\n",
-       "      <td>Anise</td>\n",
-       "      <td>Free</td>\n",
-       "      <td>Close</td>\n",
-       "      <td>Broad</td>\n",
-       "      <td>Brown</td>\n",
-       "      <td>...</td>\n",
-       "      <td>Smooth</td>\n",
-       "      <td>White</td>\n",
-       "      <td>White</td>\n",
-       "      <td>Partial</td>\n",
-       "      <td>White</td>\n",
-       "      <td>One</td>\n",
-       "      <td>Pendant</td>\n",
-       "      <td>Brown</td>\n",
-       "      <td>Numerous</td>\n",
-       "      <td>Meadows</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>Poisonous</td>\n",
-       "      <td>Convex</td>\n",
-       "      <td>Scaly</td>\n",
-       "      <td>White</td>\n",
-       "      <td>Bruises</td>\n",
-       "      <td>Pungent</td>\n",
-       "      <td>Free</td>\n",
-       "      <td>Close</td>\n",
-       "      <td>Narrow</td>\n",
-       "      <td>Brown</td>\n",
-       "      <td>...</td>\n",
-       "      <td>Smooth</td>\n",
-       "      <td>White</td>\n",
-       "      <td>White</td>\n",
-       "      <td>Partial</td>\n",
-       "      <td>White</td>\n",
-       "      <td>One</td>\n",
-       "      <td>Pendant</td>\n",
-       "      <td>Black</td>\n",
-       "      <td>Scattered</td>\n",
-       "      <td>Urban</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>Edible</td>\n",
-       "      <td>Convex</td>\n",
-       "      <td>Smooth</td>\n",
-       "      <td>Green</td>\n",
-       "      <td>No Bruises</td>\n",
-       "      <td>None</td>\n",
-       "      <td>Free</td>\n",
-       "      <td>Crowded</td>\n",
-       "      <td>Broad</td>\n",
-       "      <td>Black</td>\n",
-       "      <td>...</td>\n",
-       "      <td>Smooth</td>\n",
-       "      <td>White</td>\n",
-       "      <td>White</td>\n",
-       "      <td>Partial</td>\n",
-       "      <td>White</td>\n",
-       "      <td>One</td>\n",
-       "      <td>Evanescent</td>\n",
-       "      <td>Brown</td>\n",
-       "      <td>Abundant</td>\n",
-       "      <td>Grasses</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>5 rows × 23 columns</p>\n",
-       "</div>"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 4
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "# 饼图\n",
-    "\n",
-    "创建一个饼图，显示有毒蘑菇与可食用蘑菇的比例\n"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "source": [
-    "print(mushrooms.select_dtypes([\"object\"]).columns)"
-   ],
-   "outputs": [
-    {
-     "output_type": "stream",
-     "name": "stdout",
-     "text": [
-      "Index(['class', 'cap-shape', 'cap-surface', 'cap-color', 'bruises', 'odor',\n",
-      "       'gill-attachment', 'gill-spacing', 'gill-size', 'gill-color',\n",
-      "       'stalk-shape', 'stalk-root', 'stalk-surface-above-ring',\n",
-      "       'stalk-surface-below-ring', 'stalk-color-above-ring',\n",
-      "       'stalk-color-below-ring', 'veil-type', 'veil-color', 'ring-number',\n",
-      "       'ring-type', 'spore-print-color', 'population', 'habitat'],\n",
-      "      dtype='object')\n"
-     ]
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "source": [
-    "cols = mushrooms.select_dtypes([\"object\"]).columns\n",
-    "mushrooms[cols] = mushrooms[cols].astype('category')"
-   ],
-   "outputs": [],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "source": [
-    "edibleclass=mushrooms.groupby(['class']).count()\n",
-    "edibleclass"
-   ],
-   "outputs": [
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "           cap-shape  cap-surface  cap-color  bruises  odor  gill-attachment  \\\n",
-       "class                                                                          \n",
-       "Edible          4208         4208       4208     4208  4208             4208   \n",
-       "Poisonous       3916         3916       3916     3916  3916             3916   \n",
-       "\n",
-       "           gill-spacing  gill-size  gill-color  stalk-shape  ...  \\\n",
-       "class                                                        ...   \n",
-       "Edible             4208       4208        4208         4208  ...   \n",
-       "Poisonous          3916       3916        3916         3916  ...   \n",
-       "\n",
-       "           stalk-surface-below-ring  stalk-color-above-ring  \\\n",
-       "class                                                         \n",
-       "Edible                         4208                    4208   \n",
-       "Poisonous                      3916                    3916   \n",
-       "\n",
-       "           stalk-color-below-ring  veil-type  veil-color  ring-number  \\\n",
-       "class                                                                   \n",
-       "Edible                       4208       4208        4208         4208   \n",
-       "Poisonous                    3916       3916        3916         3916   \n",
-       "\n",
-       "           ring-type  spore-print-color  population  habitat  \n",
-       "class                                                         \n",
-       "Edible          4208               4208        4208     4208  \n",
-       "Poisonous       3916               3916        3916     3916  \n",
-       "\n",
-       "[2 rows x 22 columns]"
-      ],
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>cap-shape</th>\n",
-       "      <th>cap-surface</th>\n",
-       "      <th>cap-color</th>\n",
-       "      <th>bruises</th>\n",
-       "      <th>odor</th>\n",
-       "      <th>gill-attachment</th>\n",
-       "      <th>gill-spacing</th>\n",
-       "      <th>gill-size</th>\n",
-       "      <th>gill-color</th>\n",
-       "      <th>stalk-shape</th>\n",
-       "      <th>...</th>\n",
-       "      <th>stalk-surface-below-ring</th>\n",
-       "      <th>stalk-color-above-ring</th>\n",
-       "      <th>stalk-color-below-ring</th>\n",
-       "      <th>veil-type</th>\n",
-       "      <th>veil-color</th>\n",
-       "      <th>ring-number</th>\n",
-       "      <th>ring-type</th>\n",
-       "      <th>spore-print-color</th>\n",
-       "      <th>population</th>\n",
-       "      <th>habitat</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>class</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Edible</th>\n",
-       "      <td>4208</td>\n",
-       "      <td>4208</td>\n",
-       "      <td>4208</td>\n",
-       "      <td>4208</td>\n",
-       "      <td>4208</td>\n",
-       "      <td>4208</td>\n",
-       "      <td>4208</td>\n",
-       "      <td>4208</td>\n",
-       "      <td>4208</td>\n",
-       "      <td>4208</td>\n",
-       "      <td>...</td>\n",
-       "      <td>4208</td>\n",
-       "      <td>4208</td>\n",
-       "      <td>4208</td>\n",
-       "      <td>4208</td>\n",
-       "      <td>4208</td>\n",
-       "      <td>4208</td>\n",
-       "      <td>4208</td>\n",
-       "      <td>4208</td>\n",
-       "      <td>4208</td>\n",
-       "      <td>4208</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Poisonous</th>\n",
-       "      <td>3916</td>\n",
-       "      <td>3916</td>\n",
-       "      <td>3916</td>\n",
-       "      <td>3916</td>\n",
-       "      <td>3916</td>\n",
-       "      <td>3916</td>\n",
-       "      <td>3916</td>\n",
-       "      <td>3916</td>\n",
-       "      <td>3916</td>\n",
-       "      <td>3916</td>\n",
-       "      <td>...</td>\n",
-       "      <td>3916</td>\n",
-       "      <td>3916</td>\n",
-       "      <td>3916</td>\n",
-       "      <td>3916</td>\n",
-       "      <td>3916</td>\n",
-       "      <td>3916</td>\n",
-       "      <td>3916</td>\n",
-       "      <td>3916</td>\n",
-       "      <td>3916</td>\n",
-       "      <td>3916</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>2 rows × 22 columns</p>\n",
-       "</div>"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 7
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "source": [
-    "labels=['Edible','Poisonous']\n",
-    "plt.pie(edibleclass['population'],labels=labels,autopct='%.1f %%')\n",
-    "plt.title('Edible?')\n",
-    "plt.show()"
-   ],
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ],
-      "image/png": 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"
-     },
-     "metadata": {}
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "source": [
-    "capcolor=mushrooms.groupby(['cap-color']).count()\n",
-    "capcolor"
-   ],
-   "outputs": [
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "           class  cap-shape  cap-surface  bruises  odor  gill-attachment  \\\n",
-       "cap-color                                                                  \n",
-       "Brown       2284       2284         2284     2284  2284             2284   \n",
-       "Buff         168        168          168      168   168              168   \n",
-       "Cinnamon      44         44           44       44    44               44   \n",
-       "Green       1856       1856         1856     1856  1856             1856   \n",
-       "Pink         144        144          144      144   144              144   \n",
-       "Purple        16         16           16       16    16               16   \n",
-       "Red         1500       1500         1500     1500  1500             1500   \n",
-       "White       1040       1040         1040     1040  1040             1040   \n",
-       "Yellow      1072       1072         1072     1072  1072             1072   \n",
-       "\n",
-       "           gill-spacing  gill-size  gill-color  stalk-shape  ...  \\\n",
-       "cap-color                                                    ...   \n",
-       "Brown              2284       2284        2284         2284  ...   \n",
-       "Buff                168        168         168          168  ...   \n",
-       "Cinnamon             44         44          44           44  ...   \n",
-       "Green              1856       1856        1856         1856  ...   \n",
-       "Pink                144        144         144          144  ...   \n",
-       "Purple               16         16          16           16  ...   \n",
-       "Red                1500       1500        1500         1500  ...   \n",
-       "White              1040       1040        1040         1040  ...   \n",
-       "Yellow             1072       1072        1072         1072  ...   \n",
-       "\n",
-       "           stalk-surface-below-ring  stalk-color-above-ring  \\\n",
-       "cap-color                                                     \n",
-       "Brown                          2284                    2284   \n",
-       "Buff                            168                     168   \n",
-       "Cinnamon                         44                      44   \n",
-       "Green                          1856                    1856   \n",
-       "Pink                            144                     144   \n",
-       "Purple                           16                      16   \n",
-       "Red                            1500                    1500   \n",
-       "White                          1040                    1040   \n",
-       "Yellow                         1072                    1072   \n",
-       "\n",
-       "           stalk-color-below-ring  veil-type  veil-color  ring-number  \\\n",
-       "cap-color                                                               \n",
-       "Brown                        2284       2284        2284         2284   \n",
-       "Buff                          168        168         168          168   \n",
-       "Cinnamon                       44         44          44           44   \n",
-       "Green                        1856       1856        1856         1856   \n",
-       "Pink                          144        144         144          144   \n",
-       "Purple                         16         16          16           16   \n",
-       "Red                          1500       1500        1500         1500   \n",
-       "White                        1040       1040        1040         1040   \n",
-       "Yellow                       1072       1072        1072         1072   \n",
-       "\n",
-       "           ring-type  spore-print-color  population  habitat  \n",
-       "cap-color                                                     \n",
-       "Brown           2284               2284        2284     2284  \n",
-       "Buff             168                168         168      168  \n",
-       "Cinnamon          44                 44          44       44  \n",
-       "Green           1856               1856        1856     1856  \n",
-       "Pink             144                144         144      144  \n",
-       "Purple            16                 16          16       16  \n",
-       "Red             1500               1500        1500     1500  \n",
-       "White           1040               1040        1040     1040  \n",
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-     "metadata": {},
-     "execution_count": 9
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-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "# 圆环图\n"
-   ],
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-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "source": [
-    "habitat=mushrooms.groupby(['habitat']).count()\n",
-    "habitat"
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-   "outputs": [
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-       "         class  cap-shape  cap-surface  cap-color  bruises  odor  \\\n",
-       "habitat                                                            \n",
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-       "Paths     1144       1144         1144       1144     1144  1144   \n",
-       "Urban      368        368          368        368      368   368   \n",
-       "Waste      192        192          192        192      192   192   \n",
-       "Wood      3148       3148         3148       3148     3148  3148   \n",
-       "\n",
-       "         gill-attachment  gill-spacing  gill-size  gill-color  ...  \\\n",
-       "habitat                                                        ...   \n",
-       "Grasses             2148          2148       2148        2148  ...   \n",
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-       "Waste                192           192        192         192  ...   \n",
-       "Wood                3148          3148       3148        3148  ...   \n",
-       "\n",
-       "         stalk-surface-above-ring  stalk-surface-below-ring  \\\n",
-       "habitat                                                       \n",
-       "Grasses                      2148                      2148   \n",
-       "Leaves                        832                       832   \n",
-       "Meadows                       292                       292   \n",
-       "Paths                        1144                      1144   \n",
-       "Urban                         368                       368   \n",
-       "Waste                         192                       192   \n",
-       "Wood                         3148                      3148   \n",
-       "\n",
-       "         stalk-color-above-ring  stalk-color-below-ring  veil-type  \\\n",
-       "habitat                                                              \n",
-       "Grasses                    2148                    2148       2148   \n",
-       "Leaves                      832                     832        832   \n",
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-       "Urban                       368                     368        368   \n",
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-       "Wood                       3148                    3148       3148   \n",
-       "\n",
-       "         veil-color  ring-number  ring-type  spore-print-color  population  \n",
-       "habitat                                                                     \n",
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-       "Wood           3148         3148       3148               3148        3148  \n",
-       "\n",
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-       "      <td>...</td>\n",
-       "      <td>368</td>\n",
-       "      <td>368</td>\n",
-       "      <td>368</td>\n",
-       "      <td>368</td>\n",
-       "      <td>368</td>\n",
-       "      <td>368</td>\n",
-       "      <td>368</td>\n",
-       "      <td>368</td>\n",
-       "      <td>368</td>\n",
-       "      <td>368</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Waste</th>\n",
-       "      <td>192</td>\n",
-       "      <td>192</td>\n",
-       "      <td>192</td>\n",
-       "      <td>192</td>\n",
-       "      <td>192</td>\n",
-       "      <td>192</td>\n",
-       "      <td>192</td>\n",
-       "      <td>192</td>\n",
-       "      <td>192</td>\n",
-       "      <td>192</td>\n",
-       "      <td>...</td>\n",
-       "      <td>192</td>\n",
-       "      <td>192</td>\n",
-       "      <td>192</td>\n",
-       "      <td>192</td>\n",
-       "      <td>192</td>\n",
-       "      <td>192</td>\n",
-       "      <td>192</td>\n",
-       "      <td>192</td>\n",
-       "      <td>192</td>\n",
-       "      <td>192</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Wood</th>\n",
-       "      <td>3148</td>\n",
-       "      <td>3148</td>\n",
-       "      <td>3148</td>\n",
-       "      <td>3148</td>\n",
-       "      <td>3148</td>\n",
-       "      <td>3148</td>\n",
-       "      <td>3148</td>\n",
-       "      <td>3148</td>\n",
-       "      <td>3148</td>\n",
-       "      <td>3148</td>\n",
-       "      <td>...</td>\n",
-       "      <td>3148</td>\n",
-       "      <td>3148</td>\n",
-       "      <td>3148</td>\n",
-       "      <td>3148</td>\n",
-       "      <td>3148</td>\n",
-       "      <td>3148</td>\n",
-       "      <td>3148</td>\n",
-       "      <td>3148</td>\n",
-       "      <td>3148</td>\n",
-       "      <td>3148</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>7 rows × 22 columns</p>\n",
-       "</div>"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 10
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "source": [
-    "  \n",
-    "labels=['Grasses','Leaves','Meadows','Paths','Urban','Waste','Wood']\n",
-    "\n",
-    "plt.pie(habitat['class'], labels=labels,\n",
-    "        autopct='%1.1f%%', pctdistance=0.85)\n",
-    "  \n",
-    "center_circle = plt.Circle((0, 0), 0.40, fc='white')\n",
-    "fig = plt.gcf()\n",
-    "\n",
-    "fig.gca().add_artist(center_circle)\n",
-    "  \n",
-    "# Adding Title of chart\n",
-    "plt.title('Mushroom Habitats')\n",
-    "  \n",
-    "plt.show()"
-   ],
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ],
-      "image/png": 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"
-     },
-     "metadata": {}
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "华夫图表\n"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "source": [
-    "pip install pywaffle"
-   ],
-   "outputs": [
-    {
-     "output_type": "stream",
-     "name": "stdout",
-     "text": [
-      "Requirement already satisfied: pywaffle in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (0.6.3)\n",
-      "Requirement already satisfied: matplotlib in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from pywaffle) (3.1.0)\n",
-      "Requirement already satisfied: python-dateutil>=2.1 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from matplotlib->pywaffle) (2.8.0)\n",
-      "Requirement already satisfied: numpy>=1.11 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from matplotlib->pywaffle) (1.19.2)\n",
-      "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from matplotlib->pywaffle) (2.4.0)\n",
-      "Requirement already satisfied: cycler>=0.10 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from matplotlib->pywaffle) (0.10.0)\n",
-      "Requirement already satisfied: kiwisolver>=1.0.1 in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from matplotlib->pywaffle) (1.1.0)\n",
-      "Requirement already satisfied: six>=1.5 in /Users/jenlooper/Library/Python/3.7/lib/python/site-packages (from python-dateutil>=2.1->matplotlib->pywaffle) (1.12.0)\n",
-      "Requirement already satisfied: setuptools in /Library/Frameworks/Python.framework/Versions/3.7/lib/python3.7/site-packages (from kiwisolver>=1.0.1->matplotlib->pywaffle) (45.1.0)\n",
-      "\u001b[33mWARNING: You are using pip version 20.2.3; however, version 21.2.3 is available.\n",
-      "You should consider upgrading via the '/Library/Frameworks/Python.framework/Versions/3.7/bin/python3.7 -m pip install --upgrade pip' command.\u001b[0m\n",
-      "Note: you may need to restart the kernel to use updated packages.\n"
-     ]
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "source": [
-    "import pandas as pd\n",
-    "import matplotlib.pyplot as plt\n",
-    "from pywaffle import Waffle\n",
-    "  \n",
-    "# creation of a dataframe\n",
-    "\n",
-    "\n",
-    "data ={'color': ['brown', 'buff', 'cinnamon', 'green', 'pink', 'purple', 'red', 'white', 'yellow'],\n",
-    "    'amount': capcolor['class']\n",
-    "     }\n",
-    "  \n",
-    "df = pd.DataFrame(data)\n",
-    "  \n",
-    "# To plot the waffle Chart\n",
-    "fig = plt.figure(\n",
-    "    FigureClass = Waffle,\n",
-    "    rows = 100,\n",
-    "    values = df.amount,\n",
-    "    labels = list(df.color),\n",
-    "    figsize = (30,30),\n",
-    "    colors=[\"brown\", \"tan\", \"maroon\", \"green\", \"pink\", \"purple\", \"red\", \"whitesmoke\", \"yellow\"],\n",
-    ")\n",
-    "\n",
-    "\n"
-   ],
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "<Waffle size 2160x2160 with 1 Axes>"
-      ],
-      "image/png": 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\ No newline at end of file
diff --git a/translations/zh/3-Data-Visualization/12-visualization-relationships/README.md b/translations/zh/3-Data-Visualization/12-visualization-relationships/README.md
deleted file mode 100644
index a12f1924..00000000
--- a/translations/zh/3-Data-Visualization/12-visualization-relationships/README.md
+++ /dev/null
@@ -1,188 +0,0 @@
-<!--
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-}
--->
-# 可视化关系：关于蜂蜜的一切 🍯
-
-|![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/12-Visualizing-Relationships.png)|
-|:---:|
-|可视化关系 - _Sketchnote by [@nitya](https://twitter.com/nitya)_ |
-
-延续我们研究的自然主题，让我们探索一些有趣的可视化方法，展示不同类型蜂蜜之间的关系。这些数据集来源于[美国农业部](https://www.nass.usda.gov/About_NASS/index.php)。
-
-这个包含约600项的数据集展示了美国多个州的蜂蜜生产情况。例如，你可以查看每个州从1998年至2012年间的蜂群数量、每群产量、总产量、库存、每磅价格以及蜂蜜生产的价值，每年每州一行数据。
-
-我们可以通过可视化来研究某个州每年的生产情况与该州蜂蜜价格之间的关系。或者，你也可以可视化各州每群蜂蜜产量之间的关系。这段时间涵盖了2006年首次出现的严重“蜂群崩溃失调”（CCD，Colony Collapse Disorder）（http://npic.orst.edu/envir/ccd.html），因此这是一个值得研究的深刻数据集。🐝
-
-## [课前测验](https://ff-quizzes.netlify.app/en/ds/quiz/22)
-
-在本课中，你可以使用之前用过的Seaborn库，这是一个很好的工具，用于可视化变量之间的关系。特别有趣的是使用Seaborn的`relplot`函数，它可以快速生成散点图和折线图来可视化“[统计关系](https://seaborn.pydata.org/tutorial/relational.html?highlight=relationships)”，帮助数据科学家更好地理解变量之间的关联。
-
-## 散点图
-
-使用散点图展示蜂蜜价格如何逐年在各州演变。Seaborn的`relplot`可以方便地对州数据进行分组，并显示分类数据和数值数据的点。
-
-首先导入数据和Seaborn：
-
-```python
-import pandas as pd
-import matplotlib.pyplot as plt
-import seaborn as sns
-honey = pd.read_csv('../../data/honey.csv')
-honey.head()
-```
-你会注意到蜂蜜数据中有几个有趣的列，包括年份和每磅价格。让我们按美国州分组来探索这些数据：
-
-| 州   | 蜂群数量 | 每群产量 | 总产量   | 库存     | 每磅价格   | 生产价值   | 年份 |
-| ----- | ------ | ----------- | --------- | -------- | ---------- | --------- | ---- |
-| AL    | 16000  | 71          | 1136000   | 159000   | 0.72       | 818000    | 1998 |
-| AZ    | 55000  | 60          | 3300000   | 1485000  | 0.64       | 2112000   | 1998 |
-| AR    | 53000  | 65          | 3445000   | 1688000  | 0.59       | 2033000   | 1998 |
-| CA    | 450000 | 83          | 37350000  | 12326000 | 0.62       | 23157000  | 1998 |
-| CO    | 27000  | 72          | 1944000   | 1594000  | 0.7        | 1361000   | 1998 |
-
-创建一个基础散点图，展示每磅蜂蜜价格与其来源州之间的关系。将`y`轴设置得足够高以显示所有州：
-
-```python
-sns.relplot(x="priceperlb", y="state", data=honey, height=15, aspect=.5);
-```
-![scatterplot 1](../../../../translated_images/zh-CN/scatter1.5e1aa5fd6706c5d12b5e503ccb77f8a930f8620f539f524ddf56a16c039a5d2f.png)
-
-现在，用蜂蜜色调展示同样的数据，显示价格如何逐年变化。你可以通过添加一个“hue”参数来展示逐年的变化：
-
-> ✅ 了解更多关于[Seaborn中可用的颜色调色板](https://seaborn.pydata.org/tutorial/color_palettes.html) - 尝试一个美丽的彩虹色调！
-
-```python
-sns.relplot(x="priceperlb", y="state", hue="year", palette="YlOrBr", data=honey, height=15, aspect=.5);
-```
-![scatterplot 2](../../../../translated_images/zh-CN/scatter2.c0041a58621ca702990b001aa0b20cd68c1e1814417139af8a7211a2bed51c5f.png)
-
-通过这个颜色方案的变化，你可以明显看到蜂蜜每磅价格在逐年强劲增长。如果你查看数据中的一个样本集（例如选择亚利桑那州），你会发现价格逐年上涨的模式，虽然有少数例外：
-
-| 州   | 蜂群数量 | 每群产量 | 总产量   | 库存     | 每磅价格   | 生产价值   | 年份 |
-| ----- | ------ | ----------- | --------- | ------- | ---------- | --------- | ---- |
-| AZ    | 55000  | 60          | 3300000   | 1485000 | 0.64       | 2112000   | 1998 |
-| AZ    | 52000  | 62          | 3224000   | 1548000 | 0.62       | 1999000   | 1999 |
-| AZ    | 40000  | 59          | 2360000   | 1322000 | 0.73       | 1723000   | 2000 |
-| AZ    | 43000  | 59          | 2537000   | 1142000 | 0.72       | 1827000   | 2001 |
-| AZ    | 38000  | 63          | 2394000   | 1197000 | 1.08       | 2586000   | 2002 |
-| AZ    | 35000  | 72          | 2520000   | 983000  | 1.34       | 3377000   | 2003 |
-| AZ    | 32000  | 55          | 1760000   | 774000  | 1.11       | 1954000   | 2004 |
-| AZ    | 36000  | 50          | 1800000   | 720000  | 1.04       | 1872000   | 2005 |
-| AZ    | 30000  | 65          | 1950000   | 839000  | 0.91       | 1775000   | 2006 |
-| AZ    | 30000  | 64          | 1920000   | 902000  | 1.26       | 2419000   | 2007 |
-| AZ    | 25000  | 64          | 1600000   | 336000  | 1.26       | 2016000   | 2008 |
-| AZ    | 20000  | 52          | 1040000   | 562000  | 1.45       | 1508000   | 2009 |
-| AZ    | 24000  | 77          | 1848000   | 665000  | 1.52       | 2809000   | 2010 |
-| AZ    | 23000  | 53          | 1219000   | 427000  | 1.55       | 1889000   | 2011 |
-| AZ    | 22000  | 46          | 1012000   | 253000  | 1.79       | 1811000   | 2012 |
-
-另一种可视化这种变化的方法是使用大小而不是颜色。对于色盲用户，这可能是一个更好的选择。编辑你的可视化，显示价格的增长通过点的大小来表示：
-
-```python
-sns.relplot(x="priceperlb", y="state", size="year", data=honey, height=15, aspect=.5);
-```
-你可以看到点的大小逐渐增加。
-
-![scatterplot 3](../../../../translated_images/zh-CN/scatter3.3c160a3d1dcb36b37900ebb4cf97f34036f28ae2b7b8e6062766c7c1dfc00853.png)
-
-这是否是一个简单的供需问题？由于气候变化和蜂群崩溃等因素，蜂蜜的供应逐年减少，因此价格上涨？
-
-为了发现数据集中某些变量之间的相关性，让我们探索一些折线图。
-
-## 折线图
-
-问题：蜂蜜每磅价格是否逐年明显上涨？你可以通过创建一个单一折线图来最容易地发现这一点：
-
-```python
-sns.relplot(x="year", y="priceperlb", kind="line", data=honey);
-```
-答案：是的，除了2003年左右的一些例外：
-
-![line chart 1](../../../../translated_images/zh-CN/line1.f36eb465229a3b1fe385cdc93861aab3939de987d504b05de0b6cd567ef79f43.png)
-
-✅ 由于Seaborn对数据进行聚合，它通过绘制均值和均值周围的95%置信区间来显示“每个x值的多个测量值”。[来源](https://seaborn.pydata.org/tutorial/relational.html)。这种耗时的行为可以通过添加`ci=None`来禁用。
-
-问题：那么，在2003年我们是否也能看到蜂蜜供应的激增？如果你查看逐年的总产量呢？
-
-```python
-sns.relplot(x="year", y="totalprod", kind="line", data=honey);
-```
-
-![line chart 2](../../../../translated_images/zh-CN/line2.a5b3493dc01058af6402e657aaa9ae1125fafb5e7d6630c777aa60f900a544e4.png)
-
-答案：并不完全。如果你查看总产量，实际上在那一年似乎有所增加，尽管总体而言蜂蜜的产量在这些年间呈下降趋势。
-
-问题：在这种情况下，是什么导致了2003年蜂蜜价格的激增？
-
-为了发现这一点，你可以探索一个Facet Grid。
-
-## Facet Grids
-
-Facet Grids可以选择数据集中的一个方面（在我们的例子中，你可以选择“年份”，以避免生成过多的Facet）。然后，Seaborn可以为你选择的x和y坐标生成每个Facet的图表，以便更容易进行视觉比较。2003年在这种比较中是否显得特别突出？
-
-通过继续使用`relplot`创建一个Facet Grid，正如[Seaborn文档](https://seaborn.pydata.org/generated/seaborn.FacetGrid.html?highlight=facetgrid#seaborn.FacetGrid)所推荐的那样。
-
-```python
-sns.relplot(
-    data=honey, 
-    x="yieldpercol", y="numcol",
-    col="year", 
-    col_wrap=3,
-    kind="line"
-    )
-```
-在这个可视化中，你可以比较逐年的每群产量和蜂群数量，并将列的wrap设置为3：
-
-![facet grid](../../../../translated_images/zh-CN/facet.6a34851dcd540050dcc0ead741be35075d776741668dd0e42f482c89b114c217.png)
-
-对于这个数据集，逐年和各州之间的蜂群数量及其产量并没有特别显著的变化。是否有其他方法可以找到这两个变量之间的相关性？
-
-## 双折线图
-
-尝试通过将两个折线图叠加在一起来创建一个多折线图，使用Seaborn的'despine'移除顶部和右侧的轴线，并使用`ax.twinx` [源自Matplotlib](https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.twinx.html)。Twins允许图表共享x轴并显示两个y轴。因此，叠加显示每群产量和蜂群数量：
-
-```python
-fig, ax = plt.subplots(figsize=(12,6))
-lineplot = sns.lineplot(x=honey['year'], y=honey['numcol'], data=honey, 
-                        label = 'Number of bee colonies', legend=False)
-sns.despine()
-plt.ylabel('# colonies')
-plt.title('Honey Production Year over Year');
-
-ax2 = ax.twinx()
-lineplot2 = sns.lineplot(x=honey['year'], y=honey['yieldpercol'], ax=ax2, color="r", 
-                         label ='Yield per colony', legend=False) 
-sns.despine(right=False)
-plt.ylabel('colony yield')
-ax.figure.legend();
-```
-![superimposed plots](../../../../translated_images/zh-CN/dual-line.a4c28ce659603fab2c003f4df816733df2bf41d1facb7de27989ec9afbf01b33.png)
-
-虽然2003年没有明显的异常，但这确实让我们以一个稍微乐观的结论结束这节课：尽管蜂群数量总体上在下降，但蜂群数量正在趋于稳定，尽管每群产量在减少。
-
-加油，蜜蜂们，加油！
-
-🐝❤️
-## 🚀 挑战
-
-在本课中，你学习了更多关于散点图和折线图网格的其他用途，包括Facet Grids。挑战自己使用不同的数据集创建一个Facet Grid，也许是你之前使用过的数据集。注意它们的创建时间以及如何谨慎选择需要绘制的网格数量。
-
-## [课后测验](https://ff-quizzes.netlify.app/en/ds/quiz/23)
-
-## 复习与自学
-
-折线图可以很简单也可以很复杂。阅读[Seaborn文档](https://seaborn.pydata.org/generated/seaborn.lineplot.html)中关于构建折线图的各种方法。尝试使用文档中列出的其他方法来增强你在本课中构建的折线图。
-## 作业
-
-[深入蜂巢](assignment.md)
-
----
-
-**免责声明**：  
-本文档使用AI翻译服务[Co-op Translator](https://github.com/Azure/co-op-translator)进行翻译。尽管我们努力确保翻译的准确性，但请注意，自动翻译可能包含错误或不准确之处。原始语言的文档应被视为权威来源。对于关键信息，建议使用专业人工翻译。我们不对因使用此翻译而产生的任何误解或误读承担责任。
\ No newline at end of file
diff --git a/translations/zh/3-Data-Visualization/12-visualization-relationships/assignment.md b/translations/zh/3-Data-Visualization/12-visualization-relationships/assignment.md
deleted file mode 100644
index ef0305a8..00000000
--- a/translations/zh/3-Data-Visualization/12-visualization-relationships/assignment.md
+++ /dev/null
@@ -1,23 +0,0 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "680419753c086eef51be86607c623945",
-  "translation_date": "2025-08-25T18:12:09+00:00",
-  "source_file": "3-Data-Visualization/12-visualization-relationships/assignment.md",
-  "language_code": "zh"
-}
--->
-# 探索蜂巢
-
-## 指导
-
-在本课中，你开始研究一个关于蜜蜂及其蜂蜜产量的数据集，这段时间内蜜蜂群体数量整体上有所减少。深入挖掘这个数据集，构建一个笔记本，讲述蜜蜂群体健康状况的故事，按州和年份进行分析。你是否在这个数据集中发现了什么有趣的内容？
-
-## 评分标准
-
-| 卓越表现                                                                                                                                               | 合格表现                                 | 需要改进                                 |
-| ----------------------------------------------------------------------------------------------------------------------------------------------------- | ---------------------------------------- | ---------------------------------------- |
-| 提供了一个笔记本，包含一个故事，并配有至少三个不同的图表，展示数据集的各个方面，按州和年份进行分析                                                   | 笔记本缺少其中一个要素                   | 笔记本缺少其中两个要素                   |
-
-**免责声明**：  
-本文档使用AI翻译服务[Co-op Translator](https://github.com/Azure/co-op-translator)进行翻译。尽管我们努力确保翻译的准确性，但请注意，自动翻译可能包含错误或不准确之处。原始语言的文档应被视为权威来源。对于关键信息，建议使用专业人工翻译。我们对因使用此翻译而产生的任何误解或误读不承担责任。
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diff --git a/translations/zh/3-Data-Visualization/12-visualization-relationships/notebook.ipynb b/translations/zh/3-Data-Visualization/12-visualization-relationships/notebook.ipynb
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-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "source": [
-    "可视化蜂蜜生产 🍯 🐝\n"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n---\n\n**免责声明**：  \n本文档使用AI翻译服务[Co-op Translator](https://github.com/Azure/co-op-translator)进行翻译。尽管我们努力确保准确性，但请注意，自动翻译可能包含错误或不准确之处。应以原始语言的文档作为权威来源。对于关键信息，建议使用专业人工翻译。因使用本翻译而导致的任何误解或误读，我们概不负责。\n"
-   ]
-  }
- ],
- "metadata": {
-  "orig_nbformat": 4,
-  "language_info": {
-   "name": "python"
-  },
-  "coopTranslator": {
-   "original_hash": "0f988634b7192626d91cc33b4b6388c5",
-   "translation_date": "2025-09-02T08:58:00+00:00",
-   "source_file": "3-Data-Visualization/12-visualization-relationships/notebook.ipynb",
-   "language_code": "zh"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
\ No newline at end of file
diff --git a/translations/zh/3-Data-Visualization/12-visualization-relationships/solution/notebook.ipynb b/translations/zh/3-Data-Visualization/12-visualization-relationships/solution/notebook.ipynb
deleted file mode 100644
index 010e4fc6..00000000
--- a/translations/zh/3-Data-Visualization/12-visualization-relationships/solution/notebook.ipynb
+++ /dev/null
@@ -1,391 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "source": [
-    "# 可视化蜂蜜生产 🍯 🐝\n"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 177,
-   "source": [
-    "import pandas as pd\n",
-    "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns\n",
-    "honey = pd.read_csv('../../../data/honey.csv')\n",
-    "honey.head()"
-   ],
-   "outputs": [
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "  state    numcol  yieldpercol   totalprod      stocks  priceperlb  \\\n",
-       "0    AL   16000.0           71   1136000.0    159000.0        0.72   \n",
-       "1    AZ   55000.0           60   3300000.0   1485000.0        0.64   \n",
-       "2    AR   53000.0           65   3445000.0   1688000.0        0.59   \n",
-       "3    CA  450000.0           83  37350000.0  12326000.0        0.62   \n",
-       "4    CO   27000.0           72   1944000.0   1594000.0        0.70   \n",
-       "\n",
-       "    prodvalue  year  \n",
-       "0    818000.0  1998  \n",
-       "1   2112000.0  1998  \n",
-       "2   2033000.0  1998  \n",
-       "3  23157000.0  1998  \n",
-       "4   1361000.0  1998  "
-      ],
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>state</th>\n",
-       "      <th>numcol</th>\n",
-       "      <th>yieldpercol</th>\n",
-       "      <th>totalprod</th>\n",
-       "      <th>stocks</th>\n",
-       "      <th>priceperlb</th>\n",
-       "      <th>prodvalue</th>\n",
-       "      <th>year</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>AL</td>\n",
-       "      <td>16000.0</td>\n",
-       "      <td>71</td>\n",
-       "      <td>1136000.0</td>\n",
-       "      <td>159000.0</td>\n",
-       "      <td>0.72</td>\n",
-       "      <td>818000.0</td>\n",
-       "      <td>1998</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>AZ</td>\n",
-       "      <td>55000.0</td>\n",
-       "      <td>60</td>\n",
-       "      <td>3300000.0</td>\n",
-       "      <td>1485000.0</td>\n",
-       "      <td>0.64</td>\n",
-       "      <td>2112000.0</td>\n",
-       "      <td>1998</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>AR</td>\n",
-       "      <td>53000.0</td>\n",
-       "      <td>65</td>\n",
-       "      <td>3445000.0</td>\n",
-       "      <td>1688000.0</td>\n",
-       "      <td>0.59</td>\n",
-       "      <td>2033000.0</td>\n",
-       "      <td>1998</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>CA</td>\n",
-       "      <td>450000.0</td>\n",
-       "      <td>83</td>\n",
-       "      <td>37350000.0</td>\n",
-       "      <td>12326000.0</td>\n",
-       "      <td>0.62</td>\n",
-       "      <td>23157000.0</td>\n",
-       "      <td>1998</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>CO</td>\n",
-       "      <td>27000.0</td>\n",
-       "      <td>72</td>\n",
-       "      <td>1944000.0</td>\n",
-       "      <td>1594000.0</td>\n",
-       "      <td>0.70</td>\n",
-       "      <td>1361000.0</td>\n",
-       "      <td>1998</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 177
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "使用散点图显示一个州与当地蜂蜜每磅价格之间的关系\n"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 178,
-   "source": [
-    "sns.relplot(x=\"priceperlb\", y=\"state\", data=honey, height=15, aspect=.5);\n"
-   ],
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
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3vF7mzrPvv8fR3dGSds508aSLWyS3sxo9nW0pMfV0tsHtWnjVK9t6Kp13u6p12wdRe6qHdLFuufriooyViCqDKR6PG/6I2caNG7Flyxa0t7cnj/n9frS3t8PhcODuu+/GypUrk+d2796NH/3oR9i7dy+czuyXk4y+4uHxOOE7PgUpJONUMIL6uurkB3fiWF2NFWF5Bo4a6+y5ueGYsLCvEeHOmafBaUMsFk8/pylz3AWppqtmn/XwS9Nwu2pmi475D5YmZFtPpfMKx3PKV9Se6mFerMsaF8PvDxQ6KqFK4TdEPTHf8lYK+Wa64iGk8DCakMKjyDdZT8y3vFVavkDl5cx8y1sp5FvwWy1EREREAAsPIiIiEoiFBxEREQnDwoOIiIiEYeFBREREwrDwICIiImFYeBAREZEwLDyIiIhIGBYeREREJAwLDyIiIhKGhQcREREJw8KDiIiIhGHhQURERMIIKTwCgQBuv/12XHHFFbjqqquwefNm/PWvf02eHxsbQ2trK/bs2SMinPyZAOmMjHFfENL0DFCV5bUpS//55/WISc2YavqcbfPmuwG8c2oagUhUn3i1UBOnGXhvKoy//f00fIEwYDZ4vnzaG6mYYiEiSsNi9ASxWAzXX389Vq5ciUceeQQWiwUvvPACrr/+ejz22GNYvHgxBgcHsW7dOvT392PdunVGh5QfEzA6fhp9A8MIy1Gc57aja20LdgyOICxHYbOa0dPZhoGnx/COPwSb1YzeLi9WNC0C4gv7LzivQ0yqxszUJ0Ob7o4WNHocaF7izD1evXMzA8NvnMD//r/v78HX/qEN3g81AFED5sunvZGKKRYiIgWGX/E4ePAg3nvvPfT29sJima1zVq1ahe3btyMWi2FmZga7d+/Gli1bcPjwYYyPjxsdUl6kkJz8YAeANd7GZNEBAGE5ih2DI1jjbUy+7hsYhhSS0/aff16PmNSMqaZPujb9e8dwdFLKK14t1MT53qlwsuhItPnf/3cE750KGzJfPu2NVEyxEBEpMfyKx+HDh9HW1oaqqtQa59JLLwUAPP3001iyZAmWLVuGtWvXor+/H9/5znc0zeF2O3SLV4nH4wQAHDviS36wAwBMSH2Ns69Nqa9DchTNTQ0L+887n4tcxszUB5jNV6lNLB7PK14t1OT2t79LaduclML4yAfPUTWP4v6mmU9rfKJoiSWRbyWptJyZb3kr5XwNLzyqqqoQjytf5x0cHMQVV1wBANiwYQO2bt2Km2++GdXV1arn8PsDiMWMu5bs8Tjh800BAOw2C2xWc8oHfLrXcy9t26xm2K1m+HxTiv0T53ORy5iZ+gDIGGuVyZRXvFqoyc29yJa2zWKXTVWMavZXKV8j9jNXamOZm2+lqLScmW95K4V8MxVGht9qufDCC3H48OEFxcfdd9+Nxx9/HAcOHMBvfvMbtLe349Zbb4UkSXjqqaeMDitnrloLeru8s8UFgKFXJtDT2ZZ8nXjGY2h4Ivm6t8sLl92atv/883rEpGZMNX3StenuaEHzElde8WqhJk7PIhu+9g+pe/C1f2jDufU2Q+bLp72RiikWIiIlpnimyxE6iMfjuPrqq3HJJZfgpptugtlsxtDQEG655RZce+21+Mtf/oL7778/2f7ee+/FX/7yFzz44IOq5xB5xQPA7F8OhGScCkZQX1cNV50VUjDDa7s19eG++f3nn89FLmMq9EnJ92yb41IYNTYLXLUWOGosYh9WVJObefZZjxPSNBpcNbNFh8oHS7Pub7a1NGI/c6UillL4bUlvlZYz8y1vpZBvpisehhceAHDixAls374dhw4dgsViweLFi7Ft2zZ897vfxZYtW9De3p5s6/f70d7ejsHBQTQ3N6saX3jhUeaYb3mrtHyBysuZ+Za3Usg3U+Fh+DMeANDQ0IB/+Zd/WXD80UcfXXDM7Xbj1VdfFREWERERCcb/z6VEREQkDAsPIiIiEoaFBxEREQnDwoOIiIiEYeFBREREwrDwICIiImFYeBAREZEwLDyIiIhIGBYeREREJAwLDyIiIhKGhQcREREJw8KDiIiIhGHhQURERMIY/r9OGwgEcNddd+HFF1+E2WyGy+XCtm3b8JGPfATBYBA//elP8eyzz6K2thYOhwPf/OY38elPf9rosIxlAqSQjFOBCOqdNrhqLUA8/Tmz2YTTQRnT4Rmcs6gmtW0h49TSJpdx84lN6dz843YLpGD2/CZ9AfhOTRdmD0Q6uz7Hjvhgt1n0228iIg0MLTxisRiuv/56rFy5Eo888ggsFgteeOEFXH/99XjsscfQ29uLFStW4LHHHkN1dTUOHz6MG264AXfddRdWrlxpZGjGMQGj46fRNzCMsByFzWpGb5cXK5oWAUg9d57bjv912Yfw612HFrY1+sM+S5xZ2yjFl0sfjbGlPbd0EUbfTl3brrUt2DE4ohyHCTg6OYUJXwD9e8fE74FIavZFj70jIsrC0FstBw8exHvvvYfe3l5YLLM1zqpVq7B9+3Y8//zzmJycxC233ILq6moAwIc//GHceOON+Nd//VcjwzKUFJKTH9wAEJaj6BsYhhSSF5xb421MFh3z2xYyTi1tchk3n9iUzvmlyIK1TRQdmfI7Oikli45c4y0FRu03EZFWhl7xOHz4MNra2lBVlVrfXHrppbj//vtx4YUXwmQypZz75Cc/ibvuukvTPG63I+9Ys/F4nKraHTviS35wJ4TlKEJyFIgj9ZwJim2bmxryjjnnODGbb6Y2SvHl0kdTbPPX8Oy5k4Gw5rU9dsSHWDxesD0QSc2+6LF3xUztz3C5YL7lrZTzNbTwqKqqQjye/hqtyWRCNBpdcFyWtf925fcHEIsZdy3Y43HC55tS1dZus8BmNad8gNusZtitZsBkSnsuXVu18+UqY5wAfL6pjG2U4sulj6bYFNZwscOmeW3tNguqFMYTsQciqdkXPfauWGn5GS4HzLe8lUK+mQojQ2+1XHjhhTh8+PCC4uPuu+/G9PQ0Dh06tKDQGB4eRltbm5FhGcpVa0Fvlxe2s1/gifvkLrt1wbmhVyZw/VUXpm1byDi1tMll3HxiUzrndlUvWNuezras+TUvcaG7o6UgeyCSUftNRKSVKa50SUIH8XgcV199NS655BLcdNNNMJvNGBoawi233IJHHnkE27Ztw/nnn4/vfe97sFqtOHToEHp6ejQ/XFpMVzwAvP+XAcEI6uuqZz+45/9FxtlzZkvV+3/V4rKltjWaQpwp+WbKReO4esSW8dz843XW2b9qyRSHCZDjJvhOnSnMHoh0dn1CchR2q1lxPfLeuyJUCr8h6on5lrdSyDfTFQ9DCw8AOHHiBLZv345Dhw7BYrFg8eLF2LZtGz784Q9jenoaP/vZz7B//36YzWYsWrQIvb29mv+ctugKjxLHfMtbpeULVF7OzLe8lUK+BS08RGDhoS/mW94qLV+g8nJmvuWtFPIt2DMeRERERHOx8CAiIiJhWHgQERGRMCw8iIiISBgWHkRERCQMCw8iIiIShoUHERERCcPCg4iIiIRh4UFERETCsPAgIiIiYVh4EBERkTAsPIiIiEgYFh5EREQkjEXkZBMTE7j88svR3Nyccry+vh5XXXUVOjs7RYajjQkITM9AOjOD6fAMzllUA1etBcj0P4pbBfilCPzSNNyLauB2VgMxY2OUQjJOBSKod9qyx5em73EpjHelMKotVTgzLcNhr85pnJxiUKJ1HfWIQYe11GUNjFhPIqICElp4AMC5556LXbt2pRzbtm2b6DA0icXiODo5hQlfAP17xxCWo7BZzejt8mJF06L0XwRVwKtHT2DH4EiyfU9nGy5qbjCm+DABo+On0TcwrC6+LH27O1pgqzbj0aE38aXLV+Q8juoYlGhdRz1i0Hktc14DI9aTiKjAeKtFhXeOB3F0UkoWHQAQlqPoGxiGFJLT9vFLkeSXZaL9jsER+KWIITFKITn5BaUmvmx9+/eOQQrKWONtzGsctX2VaF1HPWLQey1zXQMj1pOIqNCEX/F47733cNVVVyVfb9y4Me8x3W5H3mNkMnLEh1g8nvwCSAjLUYTkKJqbGhb0eePYZNr2JwNhLF/m1j3GY0d8muJT0zcWjwOm/MdR01eJ1nXMNQaPx5n3GPn2NXKs+ebmWykqLWfmW95KOd+yuNXi9wcQixl37bnBVYsqkwk2qznli8BmNcNuNcPnm1rYx2lL236xw5a2fb7sNoum+NT0rTKZEIvF8x5HTV8lWtcxlxg8HmfKOSPWMpc1MGI9gYX5VoJKy5n5lrdSyDdTYcRbLSqcd04dmpe4Zp97sJoBIHm/3WW3pu3jdlajp7MtpX1PZxvcrmpDYnTVWtDb5VUdX7a+3R0tcNVZMTQ8kdc4avsq0bqOesSg91rmugZGrCcRUaGZ4vG4sMfUJiYmcO2112Lfvn0px7dt24ZPfepTOf9Vi9FXPDweJ3zHp1L/qsVlm/0CUPtXLa6a2S9LEX/VEoygvq46e3xp+h6XwrDXWmGzVOFMWIajxprTODnFoETrOmqMIe1vDzqspS5rYMB6lsJvS3qrtJyZb3krhXwzXfEQfqtFyQ9/+EPceeedyde//vWv8YlPfKKAEc0TBxw2Cxw2S8qxjGKA21ENt6M6+dpQccBVa4Wr1pp8nUvfxJt6sT2/cTT3VaJ1HfWIQae1zHl+I8YiIioCQq94GEXIFY8iry71xHzLW6XlC1Rezsy3vJVCvnzGg4iIiIoCCw8iIiIShoUHERERCcPCg4iIiIRh4UFERETCsPAgIiIiYVh4EBERkTAsPIiIiEgYFh5EREQkDAsPIiIiEoaFBxEREQnDwoOIiIiEYeFBREREwggpPGZmZvDLX/4S69evx4YNG7Bu3Trs2LEDc/+HcX/yk59g1apViEQiIkIyngmQzsh4890A3jk1jUAkCphSz02eCMEfiGD8eBDS9Mz753Wcf9xnwNi5zGkCpGkZR49NYeJECCfPyMrvPqNiVxg3FovnN9+8cQPhGbHrniYGYfMSEWlkETHJ7bffjuPHj2Pnzp1wuVwIBAL4+te/DqfTiX/6p3/CzMwMnnjiCXzsYx/Dk08+iSuvvFJEWMYxAaPjp9E3MIywHIXNakZ3RwsaPQ40f8CJ0bdP4/dPjqJj5VLs3DuWbNPb5cWKpkVAPPsUWufXbexc5kT69Ti3oRYrmuqBmIDYlcZdugjPj7yDnz38cm7zKez1Y39+C1NB2fh1z5Sb0fMSEeXA8Csex44dw+7du/HjH/8YLpcLAOBwOHDbbbfhnHPOAQD813/9F84//3x88YtfxM6dO40OyXBSSE5+CQBAWI6if+8Yjk5K8EsR9A0MY423MVl0JNr0DQxDCsmGzK/X2LnMqbQe/9+7QfiliOpxjIjPL0WSRUcu8ynl1v7xJiHrrhSDiHmJiHJh+BWP1157Dc3NzVi0aFHK8ebmZjQ3NwMABgcHcfnll+PSSy/FLbfcgiNHjuCCCy5QPYfb7dA15nQ8HqfqtseO+JJfAglhOYpYPI6TgfDsORPStgnJUTQ3NeQVq9L8WsbWkm+2ORFPn2tiPZYvc+sau5b4kvuR43xK4yZudei1p7nEkGlerftbDiotZ+Zb3ko5XyG3Wkym9284P/nkk/jlL3+JWCyG6upq/PrXv8azzz6LO++8EzU1NbjsssvQ39+PW2+9VfX4fn8AsZhx15Q9Hid8vinV7e02C2xWc8qXgc1qRpXJhMUOG2xWc/LY/DZ2q1nTXFrmVzu21nyzzQmTKeN6zJ0r39i1xpfYj1znUxo3cYtDrz3NJQaleXPZ31JXaTkz3/JWCvlmKowMv9XykY98BEePHkUgEAAAXH755di1axd++ctf4uTJk9i9ezfi8Tj+8R//Ee3t7Xj++eexa9cuTE9PGx2aYVy1FvR2eVMKjO6OFjQvccHtqkZvlxdDr0xgU0dLSpveLi9cdqsh8+s1di5zKq3H+f9PHdyuaiGxK43rdlVjy9UX5zyfUm77XhoXsu5KMYiYl4goF6b43D8tMchtt90Gv9+P7du3w+VyIRqN4uGHH8bvfvc71NbW4sYbb8SGDRsAALFYDJ///Odx0003obOzU9X4xXbFA8DsXxmEZByXwqixWVQjT/oAACAASURBVOCqtcBRY5n9TfjsucC0DJvVguC0jPq66tkvCr3SODvHqWBE89g5V9OZ5jz7Vxe+02HYrFWoq7FiscOa+mCpDrHnEp/b7cBbEydzn2/euFVVJpyYCuu/pxpiyDRvKfy2pLdKy5n5lrdSyDfTFQ8hhUcsFsNvf/tbPProo4jH44hEIvB6veju7sbXv/51PPPMM7Ba3//t7MEHH8Sjjz6KgYEBVeMXZeFRwphveau0fIHKy5n5lrdSyLfghYfRWHjoi/mWt0rLF6i8nJlveSuFfAv6jAcRERFRAgsPIiIiEoaFBxEREQnDwoOIiIiEYeFBREREwrDwICIiImFYeBAREZEwLDyIiIhIGBYeREREJAwLDyIiIhKGhQcREREJw8KDiIiIhClI4TExMYH29vYFx1tbWxXPERERUenjFY9iZgKkMzLGfUFI0zNA1ezryRMh+AMRjB8/e9yk0N6UcXRjYsw2p8gYE3MdD8IfiGDyRCj3OasAfyCCsUkJ/mBE3U9OplwLsVckDve3tFTafhU4X4vY6Ug1EzA6fhp9A8MIy1Gc57aja20LBp4eQ8fKpdi5dwxhOQqb1YzeLi9WLF2E0bffb5883rQIiIuJMeucWtvrHNumjhbsPfg2vnT5Cm1zVgGvHj2BHYMjybF6OttwUXMDEFM/fzJXCFwHEk/k+5zyV2n7VQT5FuyKx3vvvYerrroq5f/ofVJITr4xAGCNtxE7BkewxtuYLDoAICxH0TcwDL8USWmfOC6FZGExZptTa3u9Y9u5dwxrvI2a5/RLkWTRkRhrx+AI/FJE0/yJeUWuA4nH/S0tlbZfxZBvwa54nHvuudi1a1fKsdbW1pzGcrsdeoSUkcfjNHyOuY4d8SXfGAAA0+wbJPnfOcJyFCcC4bTHQ3IUzU0NmudXk++CGLPMqbV9PpTmSqzf/Dkz5fvGscm0Y50MhLF8mVvT/CE5CsTT76ER66BE9Pu5GIjKWeT7PJNK2+Nc8y2W/dKqlPMti1stfn8AsZhx14g8Hid8vinDxk/HbrPAZjWnvEFsVnPyv/OPNzhsaY/brWbNsavNVylGpTm1ts+H4vrFF86ZLd8GZ/q1XeywKfbLlCtMJmHrkE4h3s+FJjJnke9zJZW2x/nkWwz7pVUp5JupMOLDpUXKVWtBb5c3WWwMvTKBns42DL0ygU0dLSlFSG+XF25XdUr7xHGX3Sosxmxzam2vd2ybOlowNDyheU63sxo9nW0pY/V0tsHtqtY0f2JeketA4nF/S0ul7Vcx5GuKx+PCH5+ZmJjAtddei3379qUcb21txZ/+9Ke05zIpxyseAGafPA7JOBWMoL6uGq46K6SgjMC0DJvVguC0PHvcbp19KGh++8RxjTTlq3VOnWLUGltdjRVheQaOGuuCOVXlWzX7rIdfmobbVTNbdCg9WJpm/gW5ilyHeSrtt2GgADkXcH+BytvjvPMt8H5pVQr5ZrriUZDCQ29lW3gUCPMtb5WWL1B5OTPf8lYK+fJWCxERERUFFh5EREQkDAsPIiIiEoaFBxEREQnDwoOIiIiEYeFBREREwrDwICIiImFYeBAREZEwLDyIiIhIGBYeREREJAwLDyIiIhKGhQcREREJw8KDiIiIhLEUYtLW1la8/vrrmJiYwOWXX47m5mYAwPT0NFpbW3HbbbfhnHPOKURoREREZKCCX/E499xzsWvXLuzatQtPPvkkli5dit7e3kKHlSIWi0M6I2PcF4Q0PQOY5jUwQdX5yRMh+AMRjB9XaJdNtnm0Uhgva76i41Qat0rDPBli0pTvnHECkag+eRq1XkRERaggVzyUmEwmfPOb38Qll1yCv/3tb1i+fHmhQwJMwPMj7+BnD7+MsByFzWpGb5cXK5oWAfHZ86Pjp9E3MJzx/O+fHEXHyqXYuXcsfTsVcWScJ4e80o63dFHmfEXHmWHcns42DDw9hnf8oczzZIoJWfZXYRxnnRVfWL0M/bnup9HrRURUpAp+xWO+6upqLF26FG+++WahQwEASCE5+aUEAGE5ir6BYUghOXk+8aWR6fwab2Oy6EjXTk0cmebJJa904/mlSMZ8RceZadwdgyNY423MOk+mmLLtr9I47R9vShYd+eRp1HoRERWrorrikWAymVBTU6O6vdvtMCyWY0d8yS+FhLAcRUiOormpQf15EzK2yzcOvfI6GQgXVZzZxp17W0JpnkwxIa5+X1LGyXM/1cSWz3pl4/E4DRu7WFVazsy3vJVyvkVXeEQiEbz11lu44IILVPfx+wOIxYy5Lm23WWCzmlO+HGxWM+xWM3y+KdXnE8eV2uUbh155LXbYiirObOPOvR2hNE+mmGAyqY53/jh65GnUemXi8TgNG7tYVVrOzLe8lUK+mQqjorrVEovFcO+99+Kiiy5CU1NTocMBALhqLdhy9cUpxUNvlxcuuzV5vrfLm/X80CsT2NTRothOTRyZ5sklr3TjuV3VGfMVHWemcXs62zA0PJF1nkwxZdtfpXH2/fc4uvPYTzWxERGVI1M8Hhf+CJvSn9PGYjGsWLEC3/ve91BfX696PCOveACzt3LemjiJU8EI6uuqZ78U5k5nmr1Xn+18YFqGzWpBcFpO3y6bbPNopTBe1nxFx6k0bp0VUlDlPBli0pTvnHEanDbEYvH88zRqvRSUwm9Lequ0nJlveSuFfDNd8ShI4aE3owuPUthkPTHf8lZp+QKVlzPzLW+lkG/J3GohIiKi8sbCg4iIiIRh4UFERETCsPAgIiIiYVh4EBERkTAsPIiIiEgYFh5EREQkDAsPIiIiEoaFBxEREQnDwoOIiIiEYeFBREREwrDwICIiImFYeBAREZEwBS08WltbAQAHDx7E5s2bCxkKERERCWApdABUhkyAFJIROCPDVm1BcFpGvcOGKhNwQgqj3mmDq9YCxAsT16lARFsMufbLlwkITM9AOjODcGQGLns1wpEZOOzVhVm/szEVZC20mhenOxbPeN5lt0AKGpRXqaxZOSmVNS+VOHXGwoP0ZQJGx0/j90+OomPlUuzcO4awHIXNakZ3Rwse+/NbmArK6O3yYkXTInE/ZGfj6hsYTsaTjCHXfkbGbgKOTk5hwhdA/5w13NTRgr0H38aXLl8hdv3OxlSQtdAqTZxbrr4YLR9wzsaZ5nxPZxsGnh7DO/6QvnmVypqVk1JZ81KJ0wB8xoN0JYVk9A0MY423MVl0AEBYjqJ/7xjaP96EsBxF38AwpJAsPK658aiJIdd+esR7dFJKFh2JuXfuHcMab6Pw9UvEVIi10CpdnD97+OVknOnO7xgcwRpvY/K1XnmVypqVk1JZ81KJ0whlccXD7XYYPofH4zR8jmKSa77Hjvhmf5BMSP5AJSSOJ/4dkqNobmrIN1Rtcc2LJ3T2mFK+mfoZGfuxIz7E4nHFNcw3hlz2t1BroVW2OJXOJ96b89sbGYuRKvUzq1zep9mU8v6WReHh9wcQm38PV0cejxM+35Rh4xebfPK12yywWc0AAJvVnPKDZbOak5cQbVYz7FazsHVNxDU/HvvZWJXiyNTPyNjtNguqTCbFNcwnhlz3t1BroVW2OJXOz728rVdehVqzSv7MKpf3aSalsL+ZCiPeaiFduWot6O3yYuiVCWzqaEkpQro7WrDvpfHkvUyX3So8rrnxqIkh1356xNu8xIXueWu4qaMFQ8MTwtcvEVMh1kKrdHFuufriZJzpzvd0tmFoeCL5Wq+8SmXNykmprHmpxGkEUzweL9hjLK2trXj99ddx8OBBXHfddaipqUme27hxI+644w5V4/CKh77yzjfxVy3TMmzWs3/VUleNqioTTkyFUV9XPfvDVai/aglGUmLImq9CPxHxLvirFnkGjhprXjHktb+FWgut5sW5rHEx/P6A4nlXnXX2r1qMyKsAa1bxn1kl+j5VG2cp7G+mKx4FLTz0wsJDX8y3vFVavkDl5cx8y1sp5MtbLURERFQUWHgQERGRMCw8iIiISBgWHkRERCQMCw8iIiIShoUHERERCcPCg4iIiIRh4UFERETCsPAgIiIiYVh4EBERkTAsPIiIiEgYFh5EREQkDAsPIiIiEqYghUdraysAYGJiAq2trXjuuedSzre3t2NiYqIQoREREZGBCn7Fw2q14gc/+AECgUChQyldJkA6I2PcF4Q0PQOYUo9PngjBH4hg/Pi887mMmUd8b74bwDunphGIRPMbL884kjlVzb4eOeLLnKOatZjTJhCJ6rd2Rsl1f7X00/M9pJdijImowlgKHcC5556L1atX4yc/+QnuvPPOQodTekzA6Php9A0MIyxHYbOa0dvlxYqlizD69mn8/slRdKxcip17x1LPNy0C4umHjMXi6cfM0EdLfN0dLWj0ONC8xKl9vFzNi+M8tx1da1uwY3Akc45K6zu33Zw2zjorvrB6Gfo1rLdoOe+vmrXIpa0oxRgTUQUq+BUPANi2bRueffbZBbdcKDspJCc/SAEgLEfRNzAMvxRB38Aw1ngbk0XH3PNSSFYc853jwbRjZuqjJb7+vWM4OinlNF6u5sexxtuYLDoScaXLUWl957ab26b9403JoiPTuIWU6/6qWYtc2opSjDERVaKCX/EAAIfDgTvvvBM/+MEPsHv3bs393W6HAVGl8nichs+Ri2NHfMkP0oSwHMWJQHj2uAlpz4fkKJqbGtKOOaIwZqY+WuOLxeM5jZerBXGoXBel+Oe2S2mTw3qLluv+qlmLXNqKEopEiy4mIxXrZ5ZRmG/pKIrCAwA+85nPJG+5aOX3BxCLGXet1ONxwuebMmz8fNhtFtis5pQPVJvVjAaHDTarOfl6/nm71ayYU4OrVnMfrfFVmUw5jZcrpTiy5ajUb267+W30Wjuj5Lq/atYil7YieDzOoovJSMX8mWUE5lt8MhVGRXGrJSFxy+W9994rdCglw1VrQW+XN6XI6O3ywu2qRm+XF0OvTGBTR8uC8y67VXHM886pSztmpj5a4uvuaEHzEldO4+VqfhxDr0ygp7Mta45K6zu33dw2+/57HN0a11u0XPdXzVrk0laUYoyJqBKZ4vG48MeqWltb8frrr2NiYgLXXnst9u3blzz37LPP4itf+Qr+9Kc/obGxUdV4lXzFA8Dsk/ohGaeCEdTXVc9+kMbfPx6YlmGzWhCcllPPK/B4nPAdn0o/Zh7xHZfCqLFZ4Kq1wFFjEf9A3/x1qrNCCsoIyVHYrWblHJXWV6FNg9OGWCyuz9oZIK/9VbMWubQ1WPJnuIhiMlLRf2bpjPkWn0xXPApSeOit4gsPnTHf8lZp+QKVlzPzLW+lkG/J3GohIiKi8sbCg4iIiIRh4UFERETCsPAgIiIiYVh4EBERkTAsPIiIiEgYFh5EREQkDAsPIiIiEoaFBxEREQnDwoOIiIiEYeFBREREwrDwICIiImFYeBAREZEwBSs8WltbU16PjY2htbUVe/bsKVBEBjIB0hkZ474gApFo8t/S9AwC4Znkv2Fa2D7leCHpFZPWcTK1VzqXOH48CH8ggskTIV1injwRgj8QwfjxItqXhPlrUSXoPVSM71UiKmqWQgeQMDg4iHXr1qG/vx/r1q0rdDj6MQGj46fRNzAMZ50VX1i9DP17xxCWo7BZzejuaMFjf34LU0EZvV1erFi6CKNvz7ZPtOnt8mJF0yIgXvgc8opJ6ziZ2kPhXJr129TRgr0H38aXLl+Rc8y/f3IUHSuXYuecvSv4vsyLMZHzeW47uta2YMfgiLGx6vW+IKKKUhS3WmZmZrB7925s2bIFhw8fxvj4eKFD0o0UkpMfzO0fb0oWHQAQlqPo3zuG9o83ISxH0TcwDL8USbZPtOkbGIYUkosih3xi0jpOpvZK59Kt3869Y1jjbcwr5jXexmTRkc8aGGH+WqzxNiaLDsC4WPV6XxBRZSmKKx779+/HkiVLsGzZMqxduxb9/f34zne+o7q/2+0wMLpZHo8zp37HjviSH8ww4f1/nxWWo8nL02E5ihOBcNo2ITmK5qaGnGLIxdx8U3LIIyat42Rqj3j6tTypsH6JtVeaS2l/kzEo7J3ofUlnwTqpiDXX93PGedPMU0z0yLmUMN/yVsr5FkXhMTg4iCuuuAIAsGHDBmzduhU333wzqqurVfX3+wOIxYy7tuvxOOHzTeXU126zwGY1Jz+g5/478TpxWdpmNaPBYUvbxm415xyDVvPznZ9DrjFpHSdTe5hMac8tVlg/xJXnyrS/iRgS4xRyX5QorZNSrPm8n9XMWwxrMp9eOZcK5lveSiHfTIVRwW+1+P1+HDhwAL/5zW/Q3t6OW2+9FZIk4amnnip0aLpw1VrQ2+WFzWrGvv8eR3dHS8oXWXdHC/a9NJ68P+52VSfbJ9r0dnnhsluLIod8YtI6Tqb2SufSrd+mjhYMDU/kFfPQKxPYNG/vCr0vCfPXYuiVCfR0thkeq17vCyKqLKZ4PF6Qx8BaW1vx+uuv47e//S2ee+453H///clz9957L/7yl7/gwQcfVDVWMV/xADD75H9IxqlgBA1OG2KxOE4FI6ivq0ZVlQknpsKor6ue/cCOp7ZPOS5I2nz1iknrOJnaK52bc7yuxoqwPANHjVVxrqz7e3a8wLQMm9WC4LRckH3JaP5a1FkhBdOvm66/LRX4vapWKfyGqCfmW95KId9MVzwKfqtlcHAQW7ZsSTl2zTXX4P7778fRo0fR3NxcoMh0FAdctVa4at//TXDuvx02S7Jd2vbF8EGuV0xax8nUXuncgvWu1jVmtyPP8YwwP+eYoPdQMb5XiaioFazweP311wEAjz766IJzbrcbr776quiQiIiIyGAFf8aDiIiIKgcLDyIiIhKGhQcREREJw8KDiIiIhGHhQURERMKw8CAiIiJhWHgQERGRMCw8iIiISBgWHkRERCQMCw8iIiIShoUHERERCcPCg4iIiIRh4UFERETCCCk8Dh48iM2bNydfBwIBbNq0CT/+8Y/xt7/9Dddeey2uvPJKfOELX8D3v/99hEIhEWGVHxMgnZEx7gtCmp4BTArHlNpqGVfDucmTIbw3FcYb70jwByPGvutyyGvkiC/3tRHBiHjyGTPfeIptfYlIKIvoCYPBIL761a/ik5/8JLZu3Yr169fjRz/6ET72sY8hFovh9ttvx89//nPccsstokMrbSZgdPw0+gaGEZajsFnN2HrNxYjMxFKO9XZ5sWLpIoy+fXrh8aZFQDz7uMm2yH7u90+OomPlUuzcO5Zs09PZhouaG4CY8WugOS8tayOClpxUisXiuY+ZbzwG5ENEpUXorZZQKIQbbrgBq1atwtatWwEAx48fx/T09GwwVVX4xje+gfXr14sMqyxIITn5YQ4AYTmKo5PSgmN9A8PwS5G0x6WQrGrcRFs159Z4G5NFR6LNjsER+KWIkDXQmpeWtRFBS05qvXM8mPOY+cZjRD5EVFqEXfE4c+YMvva1r+GNN97AL37xi+TxW265BTfeeCPOPfdcrFy5Ep/73Ofw2c9+VtPYbrdD52gX8nichs+Rj2NHfMkP84RYPL7gWFiO4kQgnPZ4SI6iuakBwPv5phs30RZxZD9nSt/mZCCM5cvcuSecRqZYE3lla6tmbUTSkpNaI3mMmW88RuSjVrH/DOuN+Za3Us5XWOExMjKCb33rW/jgBz+IW2+9Fffddx8AoLOzE5///Ofx/PPP489//jO2bduGjRs34vvf/77qsf3+AGIx467TejxO+HxTho2vB7vNApvVnPKhXmUyLThms5rR4LClPW63muHzTaXkm27cRFsojD/3XOLY/DaLHTbd1zRTrPPnUmqbbW1E05KTWg2u2pzHzDceI/JRoxR+hvXEfMtbKeSbqTASdqvF6/XipptuwrZt2/DGG2/g4Ycfxv/8z//gF7/4BRwOBzo6OvDDH/4Q/f39+MMf/iAqrLLhqrWgt8ub8mXfvMS14FhvlxduV3Xa4y67VdW4ibZqzg29MoFNHS0pbXo62+B2VQtZA615aVkbEbTkpNZ559TlPGa+8RiRDxGVFlM8Hjf8ka6DBw/ivvvuw4MPPggAePXVV/HlL38Z999/P2688Ubcc889+PSnPw0AePrpp/Fv//ZvePjhh1WPzyseZ5lm76GfCkZQX1ed/DBfcCyu0PbsEi7IN0NbNecC0zIsFjNOByJocNpmiw69HyxVE6tC25Achd1qVrU2BaFzPB6PE77jU7mPmW88BVjfkvkZ1gnzLW+lkG+mKx7C/6oFAC666CJcd911+OEPf4h7770XfX19uPXWW2G1WrFs2TLcfffdhQir9MUBV60Vrlpr8jWQ/phSWy3jaj13rtM2e9yooiNbPAptm5saZn+Ic1kbEYyIJ58x842n2NaXiIQScsXDaLzioS/mW94qLV+g8nJmvuWtFPItimc8iIiIiFh4EBERkTAsPIiIiEgYFh5EREQkDAsPIiIiEoaFBxEREQnDwoOIiIiEYeFBREREwrDwICIiImFYeBAREZEwLDyIiIhIGBYeREREJAwLDyIiIhJGSOFx8OBBbN68Ofk6EAhg06ZNWLt2Ldrb2xEKhZLnjh8/jtWrV+Ovf/2riNCo0EyAdEbGuC8IaXoGMOXRxwQEwjOYPDWNN98NqB9Pz9hzyUdUbPn0zTZeofIuhvUW/b4TIbGux4PwByKYPBEqj7yoKFhETxgMBvHVr34Vn/zkJ7F161b09vbi5z//OW655RYAwB133IFrrrkGH/nIR0SHRqKZgNHx0+gbGEZYjsJmNaO3y4sVTYuAuMY+Sxfh6N+nMOELoH/vmPrxdIx96zUXIzIT05aPEXJZ1wx9s+aVz3yFylPHGI5OCnzfiZBmXTd1tGDvwbfxpctXlG5eVDSE3moJhUK44YYbsGrVKmzduhUAcNttt+HRRx/F4cOHsW/fPvz9739HT0+PyLCoQKSQnPxwA4CwHEXfwDCkkKy5j1+K4OiklPzwVzuenrEfnZQ052OEXNY1U99seeUzXz4KNe/8GES+70RIt647945hjbexpPOi4iHsiseZM2fwta99DW+88QZ+8YtfJI+fc8452LZtG26//XZMTU3h3nvvhcWiLSy326F3uAt4PE7D5ygmIvI9dsSX/HBLCMtRhOQompsaNPU5GQgjFo9rHi9Ba77p4shnfj2pWVelfHPJK5d91IPWeY14Tx874iuafZ8v13yV1hWm4shLCT+jS4ewwmNkZATf+ta38MEPfhC33nor7rvvvuS5K6+8En/4wx+wfv16NDc3ax7b7w8gFjPu2p/H44TPN2XY+MVGVL52mwU2qznlQ85mNcNuNSvOr9RnscOGKpNJ83hAbvmmiyPX+fWWbV0z5ZtLXrnsox60zGvUe9pusxTNvs+VT75K64p44fNSws/o4pOpMBJ2q8Xr9eKmm27Ctm3b8MYbb+Dhhx9OOf+BD3wAH/jAB0SFQ0XAVWtBb5d39kMNSN4bd9mtmvu4XdVoXuJCd0eLpvH0jL15iUtzPkbIZV0z9c2WVz7z5aNQ886PQeT7ToR067qpowVDwxMlnRcVD1M8Hjf8MaGDBw/ivvvuw4MPPggAePXVV/HlL38ZAwMDuOCCCwAA27Ztw6c+9Sl0dnZqHp9XPPQlNF/T7D3lU8EI6uuqZz/UVDwAmbaPCQhMz0A6M4Pp8AzOcdlUjZdzvuniQA75GCHDumbNN5e8ctlHPaic19D3dI7vOyPlne+cda2rsSIsz8BRYy14Xkr4GV18Ml3xEP5XLQBw0UUX4brrrsOWLVvwH//xH7DZbIUIg4pBHHDVWuGqtSZf59wnDjhsFjhslpS2hlGIQ3M+RshlXbP0zThePvPlo1DzzotB6PtOhPnriurkcaJ8CbniYTRe8dAX8y1vlZYvUHk5M9/yVgr5FsUzHkREREQsPIiIiEgYFh5EREQkDAsPIiIiEoaFBxEREQnDwoOIiIiEYeFBREREwrDwICIiImFYeBAREZEwLDyIiIhIGBYeREREJAwLDyIiIhJGWOFx8OBBbN68Oe25sbExtLa2Ys+ePaLCISIiogIoiiseg4ODWLduHfr7+wsdyiwTIJ2RMe4LQpqewcxMLOU1TOnbT54IwR+IYPy4QjuV86Xtp6aNHn30GlPvdvNVAf5ABGOTEvzBSG7v5LNzjxzxiV8f0WMRERUJS6EDmJmZwe7du/HQQw+hu7sb4+PjaGpqKlxAJmB0/DT6BoYRlqM4z21H19pW7Bh8DWE5CpvVjN4uL1Y0LQLi77f//ZOj6Fi5FDv3jqVvp3K+tP3UtMll3DzXRnFMvdvNVwW8evQEdgyOJPv1dLbhouYGIKZzLlroOaYR8RERFYGCX/HYv38/lixZgmXLlmHt2rUFv+ohheTkhz0ArPE2JosOAAjLUfQNDEMKySnt13gbk0VHunZq50vXT02bXMbVSu2Yerebzy9FkkVHot+OwRH4pYjuuWih55hGxEdEVAwKfsVjcHAQV1xxBQBgw4YN2Lp1K26++WZUV1erHsPtdugWz7EjvuSHPQDAhNTXmH0dkqNobmp4v32WdqrnS9NPTZtcxs3E43HmPKbe7eZ749hk2n4nA2EsX+bOmls+c4sa04j45kq3v+Wu0nJmvuWtlPMtaOHh9/tx4MABHDp0CA888ADi8TgkScJTTz2VLEbUjRNALKbP9We7zQKb1ZzyoZ/utd1qhs83lWyfrZ3W+eb2U9Mml3GVeDzOtG3Ujql3u/kanLa0/RY7bFlzy3duUWMaEV+C0v6Ws0rLmfmWt1LIN1NhVNBbLbt378aqVatw4MAB7Nu3D8888wx6enqwc+fOgsXkqrWgt8ubLCaGXplAT+dHU4qL3i4vXHZrSvuhVyawqaNFsZ3a+dL1U9Mml3G1Ujum3u3mczur0dPZltKvp7MNbpf6q2SFXJ9CxUdEVAxM8XhcyKNqBw8exHXXXYeamprkMY/Hg23btqG9vT15zO/3KtiJSAAAIABJREFUo729HYODg2hublY1tp5XPADM/jVBSMapYAT1ddVYuqQeb0+eSr522a0LHpKUQjIC0zJsVguC03L6dirnS9tPTZtcxk0jYzWtdky9281XNfush1+ahttVM1t0qH2wdN7cITkKu9Wsfm4VY2rOx+ix5iiF35b0Vmk5M9/yVgr5ZrriIazwMJLuhcc8pbDJemK+5a3S8gUqL2fmW95KId+ivdVCRERElYWFBxEREQnDwoOIiIiEYeFBREREwrDwICIiImFYeBAREZEwLDyIiIhIGBYeREREJAwLDyIiIhKGhQcREREJw8KDiIiIhGHhQURERMKw8CAiIiJhLEYOPjExgc997nPYtGkT7rjjjuTx0dFRfPGLX8T27dtx3333oaamBlarNXn+wx/+MLZv325kaERERFQAhhYeAFBfX4+hoSFEo1GYzWYAwOOPP46GhoZkm1/96ldobGw0OpTiYQKkkIxTgQjqnTa4ai1APMt5zDtmt0AKZhijWGXLPV0bLbmqGT+PeKtMwAkpvHBsvefNR5b1c8fimdsX43upFGIkIlUMLzzq6uqwfPlyvPjii1i1ahUA4LnnnsPq1auNnro4mYDR8dPoGxhGWI7CZjWjt8uLFU2LZj9I05zfes3FiMzEUo71dLZh4OkxvOMPLRyjWGXLPU2b89x2dK1twY7BEeU+WsbPM97ujhY89ue3MBWU3x8bOs+bDxXrt+Xqi9HyAafi+63o3kulECMRqSbkGY/169djz549AIDXXnsNra2tKbdWbrjhBlx11VXJ//vjH/8oIqyCkEJy8gMUAMJyFH0Dw5BCsuL5o5PSgmM7BkewxtuYdoxilS33dG3WeBuTX5pKfbSMn2+8/XvH0P7xppSx9Z43H2rW72cPv5zx/VZs76VSiJGI1DP8igcAXHbZZbjnnnsQi8XwxBNPYP369Xj88ceT5/O91eJ2O/QIMyOPx6nLOMeO+JIfoAlhOYqQHEVzU0Pa87F4PG0fmNKPoQe98p0rW+5p25iQtY+W8ZWky1dpvMS6J8ZGXH2MRtO6fvmsmSh6xWjEe7qYMd/yVsr5Cik8HA4Hli9fjpdeegkvvPACvv3tb6cUHvny+wOIzb9vrSOPxwmfb0qXsew2C2xWc8oHqc1qht1qhs83lfZ8lcmUts/cy8xzx8iXnvnOlS33TG0y9dEyfjpK+SqNl1j3xNhQ2B+99kMLreuX65qJpEeMRr2nixXzLW+lkG+mwkjYn9OuX78ed911Fy688EJYLELqnaLkqrWgt8s7+wUGJO9Xu+xWxfPNS1wLjvV0tmFoeCLtGMUqW+7p2gy9MoGezraMfbSMn2+83R0t2PfSeMrYes+bDzXrt+XqizO+34rtvVQKMRKReqZ4PG7YpYKJiQlce+212LdvH4LBIFavXo0dO3bg05/+NLZt24ZPfepTaf+ctra2Fv39/arnKaUrHgDef0I/GEF9XfXsB2i6v2qZex7zjtVZZ/9SQWmMPBhaTWfLPV0bLbmqGX+ejPnOG6+qyoQTU+GFY+cwr2GyrN+yxsXw+wPK7QsZu5I8YyyF3xD1xHzLWynkm+mKh6GFhyglV3gUOeZb3iotX6Dycma+5a0U8i2KWy1ERERELDyIiIhIGBYeREREJAwLDyIiIhKGhQcREREJw8KDiIiIhGHhQURERMKw8CAiIiJhWHgQERGRMCw8iIiISBgWHkRERCQMCw8iIiIShoUHERERCWNo4TExMYHW1lbcdtttKcdHR0fR2tqKwcFBtLe3Y8OGDbjqqquwYcMGdHd347XXXjMyLCIiIioQi9ET1NfXY2hoCNFoFGazGQDw+OOPo6GhIdnmV7/6FRobGwEA+/fvx/XXX48nnngipQ0VIRMghWScCkRQ77TBVTv7dlpwLF7gOPVS7PnOi6/KBJyQwoWPi4hoDsMLj7q6OixfvhwvvvgiVq1aBQB47rnnsHr16rTtP/vZz+KjH/0o/vM//xPXXnut0eFRrkzA6Php9A0MIyxHYbOasfWaixGZiaUc6+3yYkXTotL/0kuTb2+XF9WWKvz0/7xc+HzTxNfd0YLH/vwWpoJy+ewDEZU8Ic94rF+/Hnv27AEAvPbaa2htbYXValVs/6EPfQhvvvmmiNAoR1JITn7JAUBYjuLopLTgWN/AMKSQXMhQdZEu376BYRydlIoi33Tx9e8dQ/vHm8pqH4io9Bl+xQMALrvsMtxzzz2IxWJ44oknsH79ejz++OOK7U0mE2pqalSP73Y79AgzI4/HafgcxSRbvseO+JJfcgmxeHzBsbAcRUiOormpuG+b5ZJvWI4iFo8vOFaIfJXigyl9XJX2fgYqL2fmW95KOV8hhYfD4cDy5cvx0ksv4YUXXsC3v/3tjIXH66+/jnXr1qke3+8PIBYz7hqyx+OEzzdl2PjFRk2+dpsFNqs55cuuymRacMxmNcNuNRf1+uWar81qRpXJlNKuUPkqxZe4tTI3rkp7PwP8GS53zLf4ZCqMhP057fr163HXXXfhwgsvhMWiXO/s27cPo6OjWL9+vajQKAeuWgt6u7yzX26Y/WJrXuJacKy3ywuXXfm2WqlIl29vlxfNS1xFkW+6+Lo7WrDvpfGy2gciKn1CrngAs7dbvv/97+Nb3/rWgnM33HBD8pmPxYsX4/7774fDYfztE8pDHFjRtAg/uWk1TgUjqK+rTn6xLThWDg80Fnu+aeKrqjJh2RJXee0DEZU8UzweL/mPI95q0RfzLW+Vli9QeTkz3/JWCvkWxa0WIiIiIhYeREREJAwLDyIiIhKGhQcREREJw8KDiIiIhGHhQURERMKw8CAiIiJhWHgQERGRMCw8iIiISBgWHkRERCQMCw8iIiIShoUHERERCcPCg4iIiIQRXnhMTEygtbUVt912W8rx0dFRtLa2YnBwEJs3b8bBgwdFh0ZEREQGK8gVj/r6egwNDSEajSaPPf7442hoaChEOPoxAdIZGeO+IKTpGcA051wV4A9EMDYpwR+M5L7ymeYQ0V/DHG++G8A7p6YRiESBKjHzBsIzmDw1jTffDczOk8u8+axRKexPIeYiIjrLUohJ6+rqsHz5crz44otYtWoVAOC5557D6tWrCxGOPkzA6Php9A0MIyxHYbOa0dvlxYqmRYAJePXoCewYHEme6+lsw0XNDUBMpzniAvrnGON1X1iB2horfvnH1wyd9+jkFCZ8AfTvHUNYjuI8tx1da1tS1j3rvPmsUSnsTyHmIiKao2DPeKxfvx579uwBALz22mtobW2F1WotVDh5k0Jy8kMcAMJyFH0Dw5BCMvxSJPnllzi3Y3AEfimi2xwi+uc6x+mgnCw6jJz36KSULDoAYI23ccG6Z5s3nzUqhf0pxFxERHMV5IoHAFx22WW45557EIvF8MQTT2D9+vV4/PHHcxrL7XboHN1CHo8z4/ljR3zJD/GEsBxFSI5iKiSnPXcyEMbyZW7VMWSao7kp+20qLf3/f/buPbqN8s4f/3ssjeWLLBs7yhZqzIJDHLNJ8VJIgkm2EOpt3Sa4DT0myy5uz6aUkAVzutAepzSwqVloSgOpuWVp2W+htKWmNSSFXEhx2U3chtIUl1BMQhJ+eIVJozgYRZItjSX9/nCkWLYuM9LMo4vfr3N6Go2ey+fzPIr00cwoJMtXyxzBUCituNXOO20eCZrzTWeNRe5Purz+gLC5skWqr+lcxXzzWy7nm7HCw2q1Yt68edi/fz/27duH22+/PeXCY3jYjWDQuPPDdnsZnM5TCduUWMywyKaoN3OLbEKJbIJcVhDzubOslqTjqp1DzThq+6vJV8scBZKUVtxq5403j5Z801ljUfuTLru9TNhc2SKd13QuYr75LRfyTVQYZfTntM3Nzdi0aRPmz58PszljNZAubMVmtLc2wCKbACByzdxWIqOqrBBrVi6Iem7NygWoshXqNoeI/qnOUV4q4+ZrP2H4vLXn2LCqaW5knj2vO6ate7J501mjXNifTMxFRDSZFAqFhN5K5nA40NbWht7eXng8HjQ2NmLLli24/PLL0dHRgYULF+K5557DLbfcgkWLFqkaMxvOeACY+JWAV8GIx4+K0sKJN/FwWAXAsMuPYdcYqmxFE0WHlhtL1cyhU/+0q+nTc5xw+VBkMcNWbIa12AyXJ424Vc7rHhuHa3QcY75xzLJZYCuVk847Ld901ljA/qQrkq+AubJFLnxD1BPzzW+5kG+iMx7CCw8jZE3hkSeYb36bafkCMy9n5pvfciHfrL3UQkRERDMLCw8iIiIShoUHERERCcPCg4iIiIRh4UFERETCsPAgIiIiYVh4EBERkTAsPIiIiEgYFh5EREQkDAsPIiIiEoaFBxEREQnDwoOIiIiEYeFBREREwhhaeDgcDtTV1eGuu+6KOj4wMIC6ujr09PRg2bJlePDBB6Oe7+joQE9Pj5Gh6UMCXKMKBp0euMbGASnOsQJg2O3HoSEXhj1+ceWe2vi0jFGgsb8o8fKasvYeJZA8drVrpHUtjZRNsRARJWA2eoKKigrs2bMHgUAAJpMJALB9+3ZUVlZG2jz55JNoamrC/PnzjQ5HPxIwMPgRurr74VMCsMgmtLc2oNBcgO//7E+RY3dcfwk8vnE89qs3IsfWrFyAi2srgaDY+O64/hL4x4PTYq6vKQdC6sZYs3IBun9zCB8Me5P3FyXOXtT/bTn+fPgktvQciBy/sWU+dr36/2HwmPtM7GrGmpqj2nYiZFMsRERJGP7du7S0FPX19Xjttdcix/r6+tDY2Bh5fNNNN2HdunXw+/1Gh6Mbl1eJvNEDgE8JoKu7H0eGXFHHjgy5IkVH+NiWngMYdhmba6z4jgy5Ysbs8iqqx9jScwBLG6pV9Rcl3l4cH/FFio7w8R9ufRNf+NScuLHHGyvVdiJkUyxERMkYfsYDAJqbm7Fr1y4sXrwYb7zxBurq6hAKnfkqtmLFChw4cACPPPIIvv71r2sev6rKqme4MdntZVGPjx12Rt7ow3xKAMFQ9FfMYCgUs92Hbh/mnV9lTLBx4osXi1cJoLamMuq43V4WN8fJp/Hj9RcpXpwnXb6Yx8f8Zz6gvaefD+9vvLGm5qi2nQipxDL19TwTzLScmW9+y+V8hRQeV111FTZv3oxgMIgdO3agubkZ27dvj2qzYcMGtLS0oKmpSfP4w8NuBIPGnVO228vgdJ6KOlZiMcMim6Le8C2yCQVS9MX1AkmK2e4sq2XamHqKFV+8WEpkU1Qs4Xzj5Tj59H2s/qLFi7PKZol5vKjQFPlziTzx53D88caamqPadiJojSXW6znfzbScmW9+y4V8ExVGQm5ztFqtmDdvHvbv3499+/ZFXWYJs9vt6OjowLp166Ao2X+K2FZsRntrw8QHMRC5rl57ji3qWO05Ntx87Seijq1ZuQBVtkLh8dWeY4sZs61EVj3GmpULsKffoaq/KPH2wl5hwZqVC6KO39gyH8//z+G4sccbK9V2ImRTLEREyUihUMiwUwUOhwNtbW3o7e3FCy+8gKeffhoXXnghOjs70dHRgYULF+Lhhx/GU089herqifsG1q5di//93//Fd77zHaxcuVLVPJk44wFg4pcEXgUjHj8qSgsjb/TTjknAsMuPYdcYqmxFE0WHkTeWao1vytJF5Tt1jFIZLk/i/hkRK9cQJn7VMmntiywmDH80FtVm2v7GG0vtnJmgIZZc+Lakt5mWM/PNb7mQb6IzHkIutQATl1vuvPNO3HbbbQnbbdiwAcuXLxcUVZpCgK1Yhq1YjjwGYhwLAVXWQlRZT5/lEFF0aIlPyxhBjf1FiZMrglPWHkDprNLoNmrHSrWdCNkUCxFRAoae8RAlY2c88hTzzW8zLV9g5uXMfPNbLuSb8Xs8iIiIiAAWHkRERCQQCw8iIiIShoUHERERCcPCg4iIiIRh4UFERETCsPAgIiIiYVh4EBERkTAsPIiIiEgYFh5EREQkDAsPIiIiEoaFBxEREQnDwoOIiIiEMbTwcDgcqKurw1133RV1fGBgAHV1dejp6cHbb7+NtrY2XHPNNfj85z+PO++8E16v18iwUicBrlEFg04PXGPjgBTnWLL+JzwYdvsxdNKbvE+iuVW0HzrpxbDbn9qcRkuUkwS4feMYGhnD/w17Ijlojj2V/VG7xnrIxJxERBlkNnqCiooK7NmzB4FAACaTCQCwfft2VFZWAgC+/vWv495778Xf//3fIxgMYsOGDfjBD36AdevWGR2aNhIwMPgRurr74VMCsMgmtLc2oNBcgO//7E9Rx+pryoFQ8v7XNc3F7lffw798tj52nyRzx+1zuv3TOwfQtOg8/GL3Ie1zGi1RTgCODJ2Cw+nGS6++Ny2HhLmrnUPF/qieJ1WZmJOIKMMMv9RSWlqK+vp6vPbaa5FjfX19aGxsBACcOHECY2NjE8EUFOCWW25Bc3Oz0WFp5vIqkQ8IAPApAXR19+PIkGvaMZdXUdX/F7sPYWlDddw+yeaO1yfcfmlDdeQDW+ucRkuUk8ur4MiQC8+cjnVqDmpj17JuWtdYD5mYk4go0ww/4wEAzc3N2LVrFxYvXow33ngDdXV1CIUmvtKtW7cON998M2bPno1Fixbh6quvxpVXXqlp/KoqqwFRR/P6A5EPiDCfEkAwFJp2zKsEUFtTGXX82GFnzP6Q4vdJ1jden0j702OnMqfdXhbzuF4S5YQQEAyFEuaQKHY1c0ztG29/1cyTKq37qiej9zcbzbScmW9+y+V8hRQeV111FTZv3oxgMIgdO3agubkZ27dvBwCsXLkS//iP/4jf//73+N3vfoeOjg6sWLECd955p+rxh4fdCAaNOzdtt5ehxGKGRTZFfVBYZBMKpOiL8hbZhBLZBKfzVNTxeP0Rit8nWd94fcLtw+20zmm3l8WNRS+JcoIkoUCSEuaQaL3UzDG5b6L9VTNPqjIxJyBmf7PNTMuZ+ea3XMg3UWEk5FctVqsV8+bNw/79+7Fv377IZZbx8XE88sgjsFqtaGpqwt13341nnnkGzz77rIiwNLEVm9He2hD1Ydje2oDac2zTjtlKZFX9r2uaiz39jrh9ks0dr0+4/Z7XHbiuaW5KcxotUU62YjNqz7FhVdPcmDmojV3LumldYz1kYk4iokyTQqGQYacKHA4H2tra0NvbixdeeAFPP/00LrzwQnR2dqKjowPz5s3DY489hs2bN+Pyyy8HAPzmN7/BE088gZ///Oeq5xFxxsPpPDXxCwSvghGPHxWlhZEPiGnHEtwkGm5bWiTDp4zDWiQn7hOjb9J5JrV3jymwyGZ4xhTVcwqrphPlJAHusXG4RscRCARQYpHhGVPU5a52jtMS7q/RN3lmYM5c+Lakt5mWM/PNb7mQb6IzHkIutQATl1vuvPNO3HbbbZFjNpsNjz/+OO6//358+9vfhizLOP/88/HAAw+ICkubEGArlmErliOPgdjHVPVHYfI+SeZW277KWqh9TqMlyikEWC1mWC1nXqKRHLTErmXdtK6xHjIxJxFRBhl6xkMUYWc8Zgjmm99mWr7AzMuZ+ea3XMg34/d4EBEREQEsPIiIiEggFh5EREQkDAsPIiIiEoaFBxEREQnDwoOIiIiEYeFBREREwrDwICIiImFYeBAREZEwLDyIiIhIGBYeREREJAwLDyIiIhKGhQcREREJY1jh4XA4UFdXh7vuuivq+MDAAOrq6tDT0wMA+PGPf4zm5mYsX74cLS0t+OlPf2pUSMaTANeogkGnB66xcUBS0aYgTh81Y4mKWe8xjJzz9PGjf3Xjg5ExuP0B49ZOa2xG9dM49vvH3amPLep1ma+4fkQwGzl4RUUF9uzZg0AgAJPJBADYvn07KisrAQAPPfQQXnvtNfzkJz/BrFmzcPLkSaxduxYjIyP4t3/7NyND058EDAx+hK7ufviUACyyCe2tDaivKQdCsducXVWC1k/PxZaeA9F9zivHwHtJxjIw5qpKq755p9Ney5wx1m1V01xU262oPadM37XTGluy/PRYF6PHNjLGmYDrRwTA4EstpaWlqK+vx2uvvRY51tfXh8bGRoyOjuKJJ57Avffei1mzZgEAKisrcc899+BHP/oRRkdHjQxNdy6vEnlDAQCfEkBXdz9cXiVum6UN1ZGiY3KfYZc/6VhGxvzBCU/aY8SLVWt7LWPEWrdndh/CkSGX7munNbZk8+uxLkaPbWSMMwHXj2iCoWc8AKC5uRm7du3C4sWL8cYbb6Curg6hUAijo6MoLi5GdXV1VPs5c+agsLAQR48exd/93d+pmqOqSsM39BTZ7WUJnz922Bl5QwnzKQF4lQBqaypjt5EQs8+Hbl/SsfQQL+aTp0axoNae1hjxYtXaXsuc8dYtGAolHT/Z/qqVan56rIvRYxsZowh67XGqRK9fpvMVjfnmDsMLj6uuugqbN29GMBjEjh070NzcjO3bt0OSJAQCgZh9xsfHNc0xPOxGMGjcuUq7vQxO56mEbUosZlhkU9Qbi0U2oUQ2RfrGazP18VlWS9Kx9BAvnsqyYtXzqMk7nfZa5oy3bgWSlHB8NfurVqr56bEuRo9tZIxG03OPUyVy/bIhX5GYb/ZJVBgZ/qsWq9WKefPmYf/+/di3bx8aGxsBAEVFRVAUBUePHo1q/8477yAYDOKCCy4wOjRd2YrNaG9tgEWeuJclfP3WViLHbbPndQfWrFwwrU+VrTDpWEbGfPas0rTHiBer1vZaxoi1bqua5qL2HJvua6c1tmTz67EuRo9tZIwzAdePaIIUCoUMOVXgcDjQ1taG3t5evPDCC3j66adx4YUXorOzEx0dHVi4cCFOnDiB3/72t3j44YdRVVWF4eFh/Nu//RuWLl2q6ebSbDjjAWDijnWvghGPHxWlhRNvKFPDmtqmVIbLE6OPmrH0EGMe+yyN1bTWWPXILd4Yp4+fcPlQZDHDVmyGtciccHzdvz2kmp+Rez5p7I9VWVFYEEptbFGvS51lzTdEQeuXNfkKwnyzT6IzHoZfagEmLrfceeeduO2226KOf+1rX0NZWRm+8pWvIBQKQZIkrFq1Cv/8z/8sIiz9hQBbsQxbsRx5nLRNME4fNWOJilnvMYycc+rxVMdPR6r5Gbnnk8a2262pv2mJel3mK64fkXFnPETKmjMeeYL55reZli8w83JmvvktF/LN6D0eRERERGEsPIiIiEgYFh5EREQkDAsPIiIiEoaFBxEREQnDwoOIiIiEYeFBREREwrDwICIiImFYeBAREZEwLDyIiIhIGBYeREREJAwLDyIiIhKGhQcREREJY3jh4XA4UFdXh7vuuivq+MDAAOrq6vCrX/0KLS0taGlpwfz58/G5z30OLS0t2LBhg9Gh6UcCXKMKBk94MOz2Y+ikF66xcaDg9HGnJ/ZjyYAY1Iwdq+2kY+8fd8fvr2Ueo8Vbd2l6m6GTXgy7/Rg8kULcqeacqbXKpj0iIprCLGKSiooK7NmzB4FAACaTCQCwfft2VFZWQpIkbN26FQCwbNkyPP7446iurhYRlj4kYGDwI3R198OnBGCRTbiuaS52v/oeWj89F92/OYQPhr04u6oErZ+eiy09ByLt2lsbUF9TDoT0jyHu2DHa3nH9JfCPB5P31zKP0RKs+798tn4iJky0eXrnAJoWnYdf7D6kPe5Uc87UWmXTHhERxSDkUktpaSnq6+vx2muvRY719fWhsbFRxPSGcnmVyJs8APiUAH6x+xCWNlRjS88BLG2YKKLCjye36+ruh8urGBJDvLFjtT0y5FLVX8s8Rku07uGYwm2WNlRHig6tcaeac6bWKpv2iIgoFiFnPACgubkZu3btwuLFi/HGG2+grq4OoZA+X8Gqqqy6jJOI3V4W8/ixw87Im3yYTwkA0pn/B3Dm8ZR2XiWA2prKtGKLF0OssWO1DYZCqvprmcdoydbdqwSA0JS9mNJ2ctxa9zdZzplaK7Xzxss3n820nJlvfsvlfFUXHjt37sTAwADWrFmDl19+GcuXL9c00VVXXYXNmzcjGAxix44daG5uxvbt2zUHHMvwsBvBoHHnke32Mjidp2I+V2IxwyKbot7sLbIJCJ35/8nHp7YrkU1xx1YrXgyxxo7VtkCSVPXXMo/Rkq17iWwCTucVfi5e3Knsb7KcM7VWauZNlG++mmk5M9/8lgv5JiqMVF1qefzxx/Hzn/8cO3fuxNjYGB5++GE88sgjmoKwWq2YN28e9u/fj3379uXFZRYAsBWb0d7aEPUBd13TXOzpd2DNygXY0+8AAOx5feLx5HbtrQ2wlciGxBBv7Fhta8+xqeqvZR6jJVr3cEzhNnted+C6prkpxZ1qzplaq2zaIyKiWKSQiusdLS0tePbZZ9Ha2ornn38eLpcL1113HXbs2JF0AofDgba2NvT29uKFF17A008/jQsvvBCdnZ3o6OjAwoULsXLlSgATN5c+9dRTmm8uzeQZDwATvyLwKhjx+FFaJMOnjMNaJMNWKsPlmTheUVo4/XGJrN8Nf5NiSDp2rLY4c+xjVVYUFoRi99cyj9HirfvkmE63cY8psMhmeMaUaXFr2V9NOWdqrZLMmwvflvQ203JmvvktF/JNdMZD1aUWs9mMwsLCyGObzQazWfvtIVdddRXuvPNO3HbbbZr7ZrUQYCuWYSsOf6s8vVbBKcenPtbzQ2hqDInGjtM2fMxut8Z/UWuZx2jx1j0Uv02VNUYbrfOo7ZuptcqmPSIimkJV9XD22WfjlVdegSRJ8Pv9eOKJJ/Dxj39c1QTV1dXo7e0FMPHrlj//+c+R57773e9GtQ23IyIiovykqvBYv349vvnNb+LgwYNoaGjAxRdfjE2bNhkdGxEREeUZ1ddLnnzySYyOjiIQCMBqteLw4cNGxkVERER5KOGvWkZGRjAyMoIbb7wRH330EXw+HwKBAE6cOIG1a9eKipGIiIjyRMIzHrfffjv6+voAAIsWLTrTyWwxdpCjAAAgAElEQVTGpz/9aWMjIyIioryTsPB44oknAADr1q3DfffdJyQgIiIiyl+q7vG47777MDIygtHRUYRCIQQCAQwODuKKK64wOj4iIiLKI6oKj66uLvzXf/0XAMBkMkFRFMyZMwe//vWvDQ2OiIiI8ouqfzL9+eefx29/+1t85jOfwUsvvYTvfve7mDNnjtGxERERUZ5RVXhUVlZi9uzZuOCCC/D222+jpaUF7733ntGxERERUZ5RVXiYzWYMDg7iggsuwB//+EeMj4/D5XIZHRsRERHlGVWFx0033YT169fjyiuvxO7du3HllVdi8eLFRsdGREREeUbVzaUXXXQRnnzySQAT93u89957KChQVbMQERERRWj6l0tHRkbg8/kwa9Ys3HLLLaoncTgcqKuri/xjZGHLli2Dw+HA/Pnz0dLSEvW/n/70p6llRERERFlL879cKkkSTCaT5n+5VJZlrF+/Htu2bYPVao16bvbs2di6daum8bKKBLi8CkbcflSUWVAgASddPlSUWWArNif/z5JP6a+qT4wx3GPjcI2OY8w3jlnlRdHjqJ1DAt4/7saJD72wFJrhGVNQYU0xJlGS5RZrbUrMcHkUuEcVfDg6jlMen7g89dhvkeMSEelI9b9cunbtWpx77rnYtWsX3nnnHXz5y1/WNNHs2bPR2NiIjRs3orOzM/WIs40EDAx+hK7ufviUACyyCaua5uLF372LUx4F7a0NqK8pj/8BEKN/0j4xxjgydAoOpxvP7D40fRyonON0LE/vHEDTovPwi1hjZdsHWbL1i7M2N1/7Cez6/bu47O/OFpunHvstclwiIp2p/lXLD3/4Qxw5cgT33HMP3n//fdx5552aJ+vo6MDevXunXXI5fvz4tEstBw8e1Dx+Jri8SuTNHgB8SgDP7D6EZZ+sgU8JoKu7Hy6voql/sj6xxjgy5Ip8sE4dR+0c4XZLG6ojH8apxiRKstzirc1jv3oDy5fWCs9Tj/0WOS4Rkd5U3Vz6l7/8Bb/85S/x+OOP4wtf+AJuv/12XHvttZons1qt6OzsjFxyCUv3UktVlTV5ozTZ7WUxjx877Iy82Yf5lAAgnfmzVwmgtqZSU/9EfWKNEQyF4o6DEFTNEYlFUtc+GyRbv0RrM+YLCM9Tj/3WY9x4r+d8NtNyZr75LZfzVVV4hEIhFBQUoK+vD2vWrAEAjI6OpjThkiVLIpdc9DI87EYwaNz5ZLu9DE7nqZjPlVjMsMimqDd9i2yKnN62yCaUyCbN/RP1iTVGgSTFHQcJnps8RziW8PPpxCRKsvVLtDbFFpPwPPXY73THTfR6zlczLWfmm99yId9EhZGqSy01NTW48cYb4XA4sHDhQtx+++2YN29eygGFL7kcP3485TGyha3YjPbWhqgP7FVNc9G7fzBynd1WImvqn6xPrDFqz7FhVdPcmOOonSPcbs/rDlwXZ6xskyy3eGtz87WfwK/3HBGepx77LXJcIiK9SaFQKOmpAq/Xi927d+OTn/wkqqur8fOf/xxf+MIXUFxcrGoSh8OBtrY29Pb2Ro7t3bsXq1evxssvv4zPfvazqK2tjepz2WWX4dvf/raq8TN5xgPAmV8TePyoKC1EQYGEk6d8qCgtnHjjV/urltP9VfWJMUbULzdsluhx1M4hAf6ghBMjHljk079qSTUmUZLlFmttSuWJX7WMKSguKpz4VYuoPPXY7zTGzYVvS3qbaTkz3/yWC/kmOuOhqvDIdhkvPPIM881vMy1fYOblzHzzWy7km/alFiIiIiI9sPAgIiIiYVh4EBERkTAsPIiIiEgYFh5EREQkDAsPIiIiEoaFBxEREQnDwoOIiIiEYeFBREREwrDwICIiImFYeBAREZEwLDyIiIhIGBYeREREJIzhhYfD4UBdXR36+vqiji9btgyf+tSn8MILL0Qd93q9WLRoEU6ePGl0aERERCSYkDMesixj/fr1cLvdUcdvvfXWaYXHSy+9hEWLFqGyslJEaJSPJMA1qmDQ6YFrbBwomPJY0tA3Udt0+ujZPx1a585krNkgm/OfFNv7x93qY8vmnIwyE3POImYRk8yePRuNjY3YuHEjOjs7I8c/8YlP4Hvf+x5GRkZQUVEBANi2bRu+/OUviwiL8pEEDAx+hK7ufviUACyyCWtWLkD3bw7hg2EvLLIJ7a0NqK8pB0LJ+8Ztm04fPfunQ+vcmYw1G2Rz/qnGls05GWUm5pxlhN3j0dHRgb1790ZdcikpKcHVV1+NnTt3AgD++te/4t1338XSpUtFhUV5xuVVIm8oAOBTAtjScwBLG6ojj7u6++HyKqr6xmubTh89+6dD69yZjDUbZHP+qcaWzTkZZSbmnG2EnPEAAKvVis7OTqxfvx7btm2LHL/22muxefNmrFq1Cr/+9a9xzTXXoKBAWz1UVWXVO9xp7PYyw+fIJrma77HDzsgbSphPCUSdSvUpAXiVAGprzlzOs9vL4vad2lbNfIn66Nk/Vankm6lY9ZLuazqb8081tmzOSSu1+5svOefqezQgsPAAgCVLlkQuuYRdeumlcDqd+OCDD7Bt2zY8/PDDmscdHnYjGDTuHJndXgan85Rh42ebXM63xGKGRTZFvbFYZFPUKVSLbEKJbIrkGM43Xt/JbdXOl6iPnv1TkWq+mYhVL3q8prM5/1Rjy+actNCyv/mQcy68RycqjIT/nDZ8yeX48eORY1/84hfx2GOPoby8HDU1NaJDojxiKzajvbVhotgAIvd47Ol3RB63tzbAViKr6huvbTp99OyfDq1zZzLWbJDN+acaWzbnZJSZmHO2kUKhkKG30zgcDrS1taG3tzdybO/evVi9ejVefvllVFdX49ixY7j66qvxn//5n/jCF76geQ6e8dBXzucrTVzHHfH4UVFaCFupDJdn0uMSOeoMSFS+U/tOaatqPjV99OyvUVr5Co5VL7q9prM5/0mxfazKisKCkOobnLM2J5U072+O55wL79GJzngYXniIwMJDX8w3v820fIGZlzPzzW+5kG9WXWohIiKimYuFBxEREQnDwoOIiIiEYeFBREREwrDwICIiImFYeBAREZEwLDyIiIhIGBYeREREJAwLDyIiIhKGhQcREREJw8KDiIiIhGHhQURERMKw8CAiIiJhhBUeDocDdXV16Ovrizq+bNkyOByOyOOenh50dHSICouIiIgEEnrGQ5ZlrF+/Hm63W+S0xpEA16iCQacHrrFxQIpzTK+x9WirYs4Dh52pj5MoFr3iTGVuncd3+wPGzqUxnnhrPXTSi2G3H4MnMhhnqozeT6Jk+Bo0hFnkZLNnz0ZjYyM2btyIzs5OkVPrTwIGBj9CV3c/fEoAFtmEO66/BP7xYNSx9tYG1NeUA6H0xo47jpa2es2ZyhjQKU4j41c5flmpjM83no9ndh8yZi6N8cRb66d3DqBp0Xn4xZQ4qyqtgoJMg9H7SZQMX4OGEX6PR0dHB/bu3TvtkkuucXmVyAsSAHxKAEeGXNOOdXX3w+VV0h473jha2uo1Zypj6BWnkfGrHX/ZJ2siRYcRc2mNZ2oM4eeWNlRHio7JbT444REWZ6qM3k+iZPgaNI7QMx4AYLVa0dnZifXr12Pbtm26jFlVZfw3OLu9LOrxscPOyAsyLBgKTTvmUwLwKgHU1lSqnivW2PHG0dJWrzlTGQMh6BJnKnOrGX/q/iYcXzI2FzVUrXWcOE+eGsWCWruQOFOl1+t6smR7nG+Yb3qMeA3qKZf3V3jhAQBLliyJXHIBgGPHjkGWZfzN3/wNQqEQTCaTpvGGh90IBo0792W3l8HpPBV1rMRihkU2Rb0wCyRp2jGLbEKJbJrWP5FYY8cbR0tbveZMZQzotDZGxB9rf5ONb2QuaqhZ63hxVpYVC4szVXq9rsPU7HE+Yb7p0/s1qKdc2N9EhVHGfk4bvuRy/Phx/OAHP8BvfvMbAMDBgwdx7rnnZios1WzFZrS3NkS9wdeeY5t2rL21AbYSOe2x442jpa1ec6Yyhl5xGhm/2vF7/ziIVU1zDZtLazxTYwg/t+d1B66LEefZs0qFxZkqo/eTKBm+Bo0jhUIhIbfJOBwOtLW1obe3N3Js7969WL16NXbu3IlvfvObcLvd+NjHPobNmzejvLxc9diZOOMBYOKOZ6+CEY8fFaWFkRfktGOphBZr7HjjaGmrYk6vEkCJbEptnESx6BVnKnMnoPrbw6TxK8ssCAZDxuWihoq1do8psMhmeMaUSBv7rOz/tgRA19dLLnxD1BPz1YnR71kpyoX9TXTGQ1jhYaSMFR55ivnmt5mWLzDzcma++S0X8s3KSy1EREQ087DwICIiImFYeBAREZEwLDyIiIhIGBYeREREJAwLDyIiIhKGhQcREREJw8KDiIiIhGHhQURERMKw8CAiIiJhWHgQERGRMCw8iIiISBgWHkRERCSMoYWHw+FAXV0d+vr6oo4vW7Ys8txdd90V9dzAwADq6urQ09NjZGhERESUAYaf8ZBlGevXr4fb7Z72XEVFBfbs2YNAIBA5tn37dlRWVhodlngS4BpVMOj0wDU2DkhJnpMAt28cQyNjOPpX9/Q+mYxXS5uwAmDY7cfB9134q8sHz3jAuHySxTXl+WAwpGq8o39144ORMbj9KmLXsjZExL8zM4jZ6Almz56NxsZGbNy4EZ2dnVHPlZaWYt68eXjttdewePFiAEBfXx8aGxuNDkssCRgY/Ahd3f3wKQFYZBPaWxtQX1MOIPZzpUVmvPuBC8/sPjS9T5LPyXQFg6H48YbnTpTT1PgKgD8fOYktPQcibW9smY9Z5UWomV2qbz7J4orx/Nf/6RLM/XhZ7DhitF/VNBfVditqz1HfR9TeEeUk/p2ZUYTc49HR0YG9e/dOu+QCAM3Nzdi1axcA4I033kBdXR1kWRYRljAurxL5CwUAPiWAru5+uLxK3Oe8vkCk6Jjax2gfnPDEjVdNTlMNu/yRoiPc9odb34TXF9A9n2RxxXr+wZ//KW4csdo/s/sQjgy5NPURtXdEuYh/Z2YWw894AIDVakVnZyfWr1+Pbdu2RT131VVXYfPmzQgGg9ixYweam5uxfft2TeNXVVn1DDcmu70s5b7HDjsjf6HCfEoAXiUAhBDzuVH/eNw+tTXGXoo6kCDe8NyJcpoa3zvHhuLmqHc+yeLSEnei8YKhkOY+IvZOrXRez7lqpuWcS/nq8Xcml/LVQy7nK6TwAIAlS5ZELrlMZrVaMW/ePOzfvx/79u3D7bffrrnwGB52J79Onwa7vQxO56mU+5dYzLDIpqi/WBbZhBLZBEhSzOeKC+P3SScWNSptxUnnTpTT1Pgqyyxxc9Q7n2RxaYk70XgFkqS5j4i9UyPd13Mummk551q+6f6dybV805UL+SYqjIT+nDZ8yeX48eNRx5ubm7Fp0ybMnz8fZrOwWkgYW7EZ7a0NsMgmAIhcv7SVyHGfK7FM3EsQq4/Rzp5VGjdeNTlNVVVWiDUrF0S1vbFlPkosJt3zSRZXrOe//k+XxI0jVvtVTXNRe45NUx9Re0eUi/h3ZmaRQqGQYacKHA4H2tra0NvbGzm2d+9erF69Gi+//HLkOY/Hg8bGRmzZsgWXX345Ojo6sHDhQqxcuVLVPNl+xgPAxB3bXgUjHj8qSgsn/kKFEjwHwD02DtfoOMZ845hls0T3MZDdXgbniVPx41WT01QFE/d6nHCNobzUgrISM0oLTcbkkyyuKc+fX30Whoen/+pqavsTLh+KLGbYis2wFpkTx65lbQTLhW9LeptpOedkvmn8ncnJfNOQC/kmOuNhaOEhSk4UHjmE+ea3mZYvMPNyZr75LRfyzZpLLURERDSzsfAgIiIiYVh4EBERkTAsPIiIiEgYFh5EREQkDAsPIiIiEoaFBxEREQnDwoOIiIiEYeFBREREwrDwICIiImFYeBAREZEwLDyIiIhIGBYeREREJIzhhYfD4UBdXR36+vqiji9btgw33XQT2tvbo47v3bsXV199NdzuBP+Z8kyTANeogkGnB66xcUBKsY+WcVKZM1fEyy3Rmp3wwHnKh3ePu/HhqALXWAbXJlv2ZlIcnvEAht1+HBpyYdjjz8xXjGxZFyLKKmYRk8iyjPXr12Pbtm2wWq2R47fffjvWrFmD3t5eLFu2DF6vF//xH/+Be++9N6pdVpGAgcGP0NXdD58SgEU2ob21AfU15UBIW59CcwG+/7M/JR8nlTlzRbzczivHwHvJ1+zLn6+H47gHP9v1dmbWJlv2ZlIcNR+z4jOL/hY/3PpmJKY1Kxfg4tpKICg+nrx7zRJRWoR8D5o9ezYaGxuxcePGqOMlJSW455578J3vfAderxddXV1YtmwZFi1aJCKslLi8SuTNFAB8SgBd3f1weRXNfY4MuVSNk8qcuSJebsMuv6o1c3mUSNExuZ2otcmWvZkcxxc+NSdSdIRj2tJzAMMuf0biCceQL69ZIkqPkDMeANDR0YEVK1agr68PV1xxReR4Y2MjlixZgnXr1uHo0aN49tlnNY9dVWX82RG7vQwAcOywM/JmGuZTAvAqAdTWVMbsG69PMBSadizWOKnMma5wvkaLl9tJt0/VmgVDIV3WJtV8M7E3yeIY8wVixvSh24d551cBMH5/s2VdJhP1ms4WzDe/5XK+wgoPq9WKzs7OyCWXyTo6OnDllVfikUceQVFRkeaxh4fdCAaNO39rt5fB6TwFACixmGGRTVFvqhbZhBLZFGkzVbw+BVL0Re9446QyZzom52u0eLlVWi2q1qxAktJem3TyFb03auIoLjLFjOksqwVO5ykh+5st6xIm8jWdDZhvfsuFfBMVRkJvOVuyZEnMSy5WqxU2mw0f//jHRYaTEluxGe2tDbDIJgCIXLu2lcia+9SeY1M1Tipz5op4uVXZClWtma1UxvWfmZextcmWvZkcx3OvHMaNLfOjYlqzcgGqbIUZiSccQ768ZokoPVIoFDL0Vi+Hw4G2tjb09vYCANxuN1asWAGn04mdO3eiuroawMSvXJ566qnIYy1EnvEAMHG3vlfBiMePitLCiTfTZNPH6gMN46QyZ4qEV9PxckuyZkUWMzyjCiqsFpgKgBF3amuTdr4C90ZtHFXlRRjzBTDsGkOVrWii6Dh9Y6mw/c2WdUFufEPUE/PNb7mQb6IzHsIutYSFL7msXr1a9NT6CQG2Yhm2YjnyONU+qsdJZc5cES83NWtWZokMYyvK0Npky95MiaNUNqHKevosh6hfsySIJ69es0SUMsMLj+rq6sjZjrAlS5bg4MGDUcemtiEiIqL8w3+5lIiIiIRh4UFERETCsPAgIiIiYVh4EBERkTAsPIiIiEgYFh5EREQkDAsPIiIiEoaFBxEREQnDwoOIiIiEYeFBREREwrDwICIiImFYeBAREZEwLDyIiIhIGMMLD4fDgbq6OvT19UUdX7ZsGRwOB9xuNzZs2IDly5ejpaUFN9xwA/7yl78YHRblEwlwjSoYdHrg9gcif3aNjcPtG4/8GdL09q6xcQSDCf577VPaxhsjclyEWHNLgNs3jqGRMRz9q1tsTJlci3jU7rGW2PXOMxvXjdTj/qXMLGISWZaxfv16bNu2DVarNXI8FArhxhtvxKJFi/D888/DbDZj3759uPHGG/Hiiy/irLPOEhEe5TIJGBj8CF3d/SgrlfH5xvPxzO5D8CkBWGQTVjXNxYu/exenPAraWxtQf145Bt6baB9u8/V/ugRzP14GTP1smjR2uG28MdpbG1BfUz59DAPznTx3aZEZ737gispdSEzx1kjEWmiIKeYea4ld7zyzcd1IPe5fWoRcapk9ezYaGxuxcePGqOP79u3D8ePH0d7eDrN5ogZavHgx7rvvPgSDQRGhUY5zeZXIX/5ln6yJfPACgE8J4Jndh7DskzXwKQF0dfdj2OWPtA+3efDnf4LLqyQcO9w23hhd3f0xxzAy38lze32BabmLiClePCLWQktMsfZYS+x655mN60bqcf/SI+SMBwB0dHRgxYoV6OvrwxVXXAEAcLlcWLBgAQoKouufT33qU5rGrqqyJm+UJru9zPA5skmu5HvssDPylx8Szvz5NJ8SiJwC9SkBnHT7YrbxKgHU1lTGH3tS2w81jKG3eDGN+sc1xaTX/saLR8RapBuTltj1zlPEuuXK32G9iMw3G173uby/wgoPq9WKzs7OyCUXACgoKEAolP55qeFhd+Lr9Gmy28vgdJ4ybPxsk0v5lljMsMimyJvA5D+HH4dPfVpkEyqtlphtSmTTtJynjh1ue5aGMfQWL6biwtjHY8Wk5/7Gi0fEWqQbk5bY9c7T6HXLpb/DehCdb6Zf97mwv4kKI6G/almyZEnUJZf58+fjrbfemlZ8PPDAA9i3b5/I0ChH2YrNaG9tgEU2ofePg1jVNHei2AAi93j07h+MXIOtshVG2ofbfP2fLoGtRE44drhtvDHaWxtijmFkvpPnLrGYpuUuIqZ48YhYCy0xxdpjLbHrnWc2rhupx/1LjxTS45RDAg6HA21tbejt7QUAuN1urFixAk6nEzt27MA3vvENXHHFFVi7di1MJhP27NmDdevW4fnnn8esWbNUzcEzHvrKuXyliWuuIx4/KsssCAZDGPH4UVFaiIICCSdP+VBRWjjxphCKbl9RWojzq8/C8LA76diJxogcF5xvZG4A7rFxuEbHMeYbxyybJW5Muu9vJtdCZUxx91hL7HrnaeC65dzf4TRlJN8Mvu5zYX8TnfEQdqklLHzJZfXq1ZAkCY8++ijuu+8+LF++HGazGWeddRYef/xx1UUHEUKArViGrfjMt43Jf7ZazJF2sdoXFCT4HdzUseOMIfSDNs7cVov5TK4iY8rkWsSjdo+1xK53ntm4bqQe9y9lhhce1dXVkbMdYUuWLMHBgwcjj++//36jwyAiIqIswH+5lIiIiIRh4UFERETCsPAgIiIiYVh4EBERkTAsPIiIiEgYFh5EREQkDAsPIiIiEoaFBxEREQnDwoOIiIiEYeFBREREwrDwICIiImFYeBAREZEwLDyIiIhIGMMLD4fDgbq6OvT19UUdX7ZsGRwOB+bPn4+Wlha0tLTgM5/5DNrb23HixAmjw9KHBLhGFQye8GDY7cfQSS9cY+OANOk5p+fMsan9Yj2nde5kY8RrpyUGPeLVWwEw7Pbj0JALH44qcI0liS9WDqePHTjs1C+vTK1VNu4REVEMZhGTyLKM9evXY9u2bbBarVHPzZ49G1u3bgUAhEIhPPDAA2hvb8fPfvYzEaGlTgIGBj9CV3c/fEoAFtmE65rmYver7+FfPluPQnMBvv+zP0Wea29tQH1NOYDp/SLPhVKfO+YY8dqdV46B91TGoHYukQqAPx85iS09B1BWKuPzjefjmd2H4scXI4c7rr8E/vGgvnllaq2ycY+IiOIQcqll9uzZaGxsxMaNGxO2kyQJt956K9555x28/fbbIkJLmcurRN7oAcCnBPCL3YewtKEaXd39ODLkinquq7sfLq8Ss1/4uXTmjjVGvHbDLr/qGPSIV2/DLj+29ByATwlg2SdrIkVHvPhi5XBkyKV7Xplaq2zcIyKieISc8QCAjo4OrFixAn19fbjiiivitissLMR5552Ho0ePYt68earGrqqyJm+UJru9LOrxscPOyBt9mE8JANLE/wdDoWnPeZUAEELMfl4lgNqaSlWxxJt76hjx2p10+5L2D+erdi6R3jk2dCYmKfl6xsohGArpnlem1iqVeae+nmeCmZYz881vuZyvsMLDarWis7MzcsklEUmSUFRUpHrs4WE3gkHjzinb7WVwOk9FHSuxmGGRTVFv+BbZBIQm/r9Air7IbpFNKJFNgCTF7Fcim6bNEU+8uaeOEa9dpdWSsP/kfNXOJVJlWXT8yeKLlUOBDvswVabWSuu8sV7P+W6m5cx881su5JuoMBL6q5YlS5YkveTi9/vx7rvvYs6cOQIj085WbEZ7a8NEsQFE7vHY0+9Ae2sDas+xRT3X3toAW4kcs1/4uXTmjjVGvHZVtkLVMegRr96qygqxZuUCWGQTev84iFVNcxPGFyuH2nNsuueVqbXKxj0iIopHCoVCht5+5nA40NbWht7eXgCA2+3GihUr4HQ6sXPnzqjngsEgHnzwQbz11lt44oknVM+RiTMeACZ+SeBVMOLxo7RIhk8Zh7VIjrzhh5+rKC2cOBaa3m/ac2qpHSNeuwT9p+WrR7x6K5i412PYNQZ7RTFMBcCIO0F8sXLAxDGvEkCJbNInr0ytlYZ5c+Hbkt5mWs7MN7/lQr6JzngIu9QSFr7ksnr1agDA8ePH0dLSAmCi8Kivr8emTZtEh5WaEGArlmErDn+zLIwcB6Y8F0rQL5UPJrVjxGunJQY94tVbEKiyFqLKWhg5ZCtKEF+cHGzFMmprKif+EuuRV6bWKhv3iIgoBsMLj+rq6sgZjbAlS5bg4MGDAIA333zT6BCIiIgoS/BfLiUiIiJhWHgQERGRMCw8iIiISBgWHkRERCQMCw8iIiIShoUHERERCcPCg4iIiIRh4UFERETCsPAgIiIiYVh4EBERkTAsPIiIiEgYFh5EREQkDAsPIiIiEsbwwsPj8WDDhg1oamrCNddcg+uvvx6///3vAQAdHR3o6emJav/QQw/hoYceMjqs7CIBrlEFg04PXGPjgKSiTYGKPinG8v5xt/7jimACjp/y4e33P4LT7QNMULe2YVraJpKJOXNJopzTWY9cWctciZPIIGYjBw+FQlizZg3q6+vx4osvorCwEG+99Ra+9rWvYdOmTUZOnTskYGDwI3R198OnBGCRTWhvbUB9TTkQit9mzcoF6P7NIXww7I3dx6hYspUJ6H/nJP7ruQOR2G/64gLMrihC5/97LXk+euWuZZxcXu9UJcoZaaxHrqxlrsRJZCBDz3j84Q9/wNDQENatW4fCwkIAwEUXXYSbb74Zjz76qJFT5+QqVeIAACAASURBVAyXV4m8CQGATwmgq7sfLq+SsM2WngNY2lAdt49RsWSr4yO+SNEBTMT+X88dQCAEVfnolbuWcXJ5vVOVKOd01iNX1jJX4iQykqFnPA4cOID58+dDkqLPJV522WXYtGkTzj77bHR1deHJJ5+MPHfixAmsWrVK0zxVVVZd4k3Ebi8zZNxjh52RN6EwnxKAVwmgtqYyYZvJp2in9jEqlmz19vuumLG7vP5px2Ll4/UHdMldyxpmcr2Nej0nkyhnTCoSpz6XbD3UrGWmcp5M5J5nQ74iMd/cYWjhIUkSAoHAtOOKcqa6b29vx8qVKyOPU7m/Y3jYjWDQuPOUdnsZnM5ThoxdYjHDIpui3owssgklsikyZ7w2k0/NTu1jVCzZqqrcEjN2W0lhVLtY+djtZbrlrmWcTK23ka/nZBLlDElKeT2SrWUmc55M1J5nS76iMN/sk6gwMvRSy8UXX4w333wzqtAAgP7+fixYsMDIqXOGrdiM9taGiUICiFzztZXICdusWbkAe/odcfsYFUu2spdbcNMXF0TFftMXF8AkQVU+euWuZZxcXu9UJco5nfXIlbXMlTiJjCSFQiFDb2n66le/inPPPRff+ta3IMsy3nzzTaxZswabNm3Cc889h4ULF8Y843HrrbeqniOXz3gAmLjL3atgxONHRWnhxJtQjBsRo9qUynB5kvRJMRZ/UMKxYbe+44pgmrjX46RrDJW2IsyusADB5Gsb2V81+6CGlnH0mlODjH9bSpRzOuuRoG/Gc55MwJ5nVb4CMN/sk+iMh6GXWgDg4YcfxoMPPojly5fDZDKhvLwc999/PxYtWoTnnnvO6OlzQwiwFcuwFcuRx0nbBFX0STGWj8+2olAK6TuuCAFgdpkFs8sskceAhnVSsw9qaBlHrzlzSaKc01mPXFnLXImTyCCGn/EQIefPeGQZ5pvfZlq+wMzLmfnmt1zIN2P3eBARERFNxsKDiIiIhGHhQURERMKw8CAiIiJhWHgQERGRMCw8iIiISBgWHkRERCQMCw8iIiIShoUHERERCcPCg4iIiIRh4UFERETCsPAgIiIiYYQUHh6PBxs2bEBTUxOuueYaXH/99fj9738PALjhhhvw6quvRtq+8MILuPLKK3H06FERoREREZFAZqMnCIVCWLNmDerr6/Hiiy+isLAQb731Fr72ta9h06ZNUW137NiBBx54AD/+8Y/xt3/7t0aHRtlAAlxeBe5RBZZCMzxjCiqsFtiKzbH/c+Gn24+4/agoS9BOa9s0YjdsfFFzEBEJZHjh8Yc//AFDQ0N46qmnIEkSAOCiiy7CzTffjEcffTTS7qWXXsIDDzyAJ598Eueee67RYVE2kICBwY/w9M4BNC06D7/YfQg+JQCLbEJ7awPqa8qjP2RPt+/q7k/cTmvbNGI3bHxRcxARCWb4pZYDBw5g/vz5kaIj7LLLLsOBAwcAAC+//DL+/d//HStWrGDRMYO4vAq6uvuxtKE6UnQAgE8JoKu7Hy6vErN9snZa26YTu1Hji5qDiEg0w894SJKEQCAw7biinHnz7O3txY9+9CO0t7fjqquuwoIFCzTNUVVlTTvOZOz2MsPnyCYi8j122DnxoSoh8uEa5lMC8CoB1NZUTm+fpJ3WtoD2fLWOnwoj55hpr2dg5uXMfPNbLudreOFx8cUX4yc/+QkURYEsy5Hj/f39WLBgAYLBIO6++24sXrwYd9xxB+644w709PSgtLRU9RzDw24Eg8ade7bby+B0njJs/GwjKt8SixkW2QQAsMimqA9Zi2xCiWyKiiPcPlk7rW1TyVfL+Kkyao6Z9noGZl7OzDe/5UK+iQojwy+1XHrppZgzZw7uvffeyFmON998E4899hjWrl0LACgsLAQAtLa24vzzz8d3vvMdo8OiLGArNqO9tQF7Xnfguqa5UUVIe2sDbCVyzPbJ2mltm07sRo0vag4iItGkUChk+G1qY2NjePDBB/HKK6/AZDKhvLwc7e3tuPzyy3HDDTfglltuwaJFiwAAw8PDWL58Ob71rW9hxYoVqsbnGQ99Cc03/KuWMQUW+fSvWkoLJz5cE/2qxeNP3E5D25Tz1RJLqgyYY6a9noGZlzPzzW+5kG+iMx5CCg+jsfDQF/PNbzMtX2Dm5cx881su5JvRSy1EREREYSw8iIiISBgWHkRERCQMCw8iIiIShoUHERERCcPCg4iIiIRh4UFERETCsPAgIiIiYVh4EBERkTAsPIiIiEgYFh5EREQkDAsPIiIiEoaFBxEREQljNnoCj8eD73//+9i7dy+Ki4thtVpx66234vLLL0dHRwf27duH8vJyAMDo6CgqKipw3333oba21ujQiIiISDBDz3iEQiGsWbMGsizjxRdfxLZt2/Dtb38b3/jGN/Dqq68CANrb27F161Zs3boVL730Ei6++GI89NBDRoaVGRLgGlUw6PTANTYOSCm0UztGqu1VjuP2jac+ptb81B7TK2ct/eO11Wvds91MyZOIdGXoGY8//OEPGBoawlNPPQVJmnhXuuiii3DzzTfj0Ucfxdlnnx3V3u/3w+l0Rs6A5A0JGBj8CF3d/fApAVhkE9pbG1BfUw6EVLaDyjG0zplC7Kua5uLF372LUx5F25ga87vj+kvgHw9Oa19oLsD3f/an9NconTWL1/a8cgy8p8O6Zzu9Xl9ENOMYesbjwIEDmD9/fqToCLvssstw4MABAEBXVxeuueYa/MM//AM+//nP4+yzz8Y3vvENI8MSzuVVIm/QAOBTAujq7ofLq6hup3YMrXOmEvszuw9h2SdrNI+pNb8jQ66Y7Y8MuXRZIy3xqW077PLrsu7ZTq/XFxHNPIae8ZAkCYFAYNpxRTnz5tTe3o6VK1fi6NGj+Nd//VcsWrQIVqtV0zxVVdrap8JuL0u577HDzsgbdJhPCcCrBFBbU6mqHUJQNYbWOeMJ5xtvnPBpdS1jas0vGArFbB8MhaYdS2WNJrPbyzStWby2J92+tNZdlHRez0D6r69MSDfnXMN881su52to4XHxxRfjJz/5CRRFgSzLkeP9/f1YsGBBVNsLLrgAd9xxB771rW9h165dKCtTv6jDw24Eg8ad37Xby+B0nkq5f4nFDItsinqjtsgmlMimqHETtYMkqRpD65yxTM433jjh0+lqx0wlv4I4ORdMOYOW6hpNzVfLmsVrW2m1pLzuoqT7egbSe31lgh455xLmm99yId9EhZGhl1ouvfRSzJkzB/fee2/kLMebb76Jxx57DGvXrp3Wfvny5Tj33HPx6KOPGhmWcLZiM9pbGyY+sIHI9XBbiay6ndoxtM6ZSuyrmuaid/+g5jG15ld7ji1m+9pzbLqskZb41LatshXqsu7ZTq/XFxHNPFIoFDL0VrCxsTE8+OCDeOWVV2AymVBeXo729vbIz2kXLlyIlStXRtrv378fX/nKV7B9+3ace+65qubI9jMeACZ+AeBVMOLxo6K0cOINOs5NoXHbqR1D65xTTMt3yjgFBRJOnvJpGjPl/KDyWKprNDVfLf3jtU1x3UXR7dtSluc5WS58Q9QT881vuZBvojMehhceIuRE4ZFDmG9+m2n5AjMvZ+ab33Ih34xdaiEiIiKajIUHERERCcPCg4iIiIRh4UFERETCsPAgIiIiYVh4EBERkTAsPIiIiEgYFh5EREQkDAsPIiIiEoaFBxEREQnDwoOIiIiEYeFBREREwrDwICIiImHMIiZxOBz47Gc/i9raWkiSBEVRMHv2bNx333342Mc+BgC49tprYbfbsWXLFhEhERERUQYIO+Mxe/ZsbN26Fc8//zxefPFFzJ8/H52dnQCAgwcPQpZlvP322/jggw9EhZTdCoBhtx+HhlwY9vgndkoCXKMKBp0euMbGAUnlWKn2U8vo8bXONaWN2zduTGyn5xk66cWw24/BE1PmELkuqdAaX7bno0WyvSMiwwg54xHLpZdeit7eXgBAT08PrrjiCoyMjKC7uxu33XZbpsLKDgXAn4+cxJaeA/ApAVhkE9asXICzrBbc99QfI8faWxtQX1MOhBKMJQEDgx+hq7tfWz+1jB5f61wx2qxqmosXf/cuTnkU/WI7Pc/TOwfQtOg8/GL3oeiYzivHwHuC1iUVWvdN5D4bLdne5WJORDkkI/d4KIqCHTt24JJLLoGiKNi2bRuam5vR3NyMX/7ylxgfH89EWFlj2OWPFB0A4FMC2NJzAF5fIOpYV3c/XF4l4VgurxL5sNDSTy2jx9c6V6w2z+w+hGWfrNE1tvA8SxuqIx9ck2MadvmFrUsqtO6byH02WrK9y8WciHKJsDMex48fR0tLCwDA7/fjE5/4BG6//Xb8z//8D+x2O+bMmYNQKISCggL89re/RVNTk+qxq6qsRoUdYbeXGT5H2DvHhiJvhmE+JYBR//i0Y14lgNqayrhjHTvsjDlWsn5q8011/FSomStem/Ap9Hixad3fyDwSYs73odsnbF1S4fUHNMUncp+NEt7jZHuXSzklIvI9Kxsw39whrPAI3+Mx1a9+9St88MEHWLZsGQDA7XbjmWee0VR4DA+7EQwad27Ubi+D03nKsPGnqiyzwCKbot4ULbIJxYXR22WRTSiRTQljK7GYY46VqJ+WfFMZP1Vq5orXJnzqPFZsqexveJ7wmFPnO8saew+NWBet7PYyzfsmcp+NMHmPk+1druSUiOj3rExjvtknUWGU0Z/TnjhxAn19fXjhhRfQ29uL3t5ePP/889i3bx/+7//+L5OhZVRVWSHWrFwQ9ea4ZuUClFhMUcfaWxtgK5ETjmUrNqO9tUFzP7WMHl/rXLHarGqai979g7rGFp5nz+sOXNc0d1pMVbZCYeuSCq37JnKfjZZs73IxJ6JcIoVCIcNvo3I4HGhra4vcTBr23//933j99dfx0EMPRR2/9dZbcd555+GOO+5QNX6+nfEAMPGrFpcfw64xVNmKUGUrBEIT16dHPH5UlBZOvEGqSVvS1k9zvhrHT4uauaa0KSiQcPKUL277lPf39DzuMQUW2QzPmBI9h8h10SCSr9b4sjQfNabtcbK9y3G58I1YT8w3+yQ64yGk8DBaXhYeGcR889tMyxeYeTkz3/yWC/lm7aUWIiIimllYeBAREZEwLDyIiIhIGBYeREREJAwLDyIiIhKGhQcREREJw8KDiIiIhGHhQURERMKw8CAiIiJhWHgQERGRMCw8iIiISBgWHkRERCQMCw8iIiISxixikp07d+Lxxx/H+Pg4QqEQWlpa8NWvfhU33HADjh07hpKSEgQCARQWFuK2227Dpz71KRFhERERkWCGFx5//etfsXHjRvT09OCss86Cx+PBDTfcgPPPPx8AcM8992DRokUAgAMHDuCrX/0qfvrTn2LOnDlGh6YPCXB5FYy4/agos6BAAk66fKgos8BWYobLc+Y5W7EZCCXuH7ONinl16ycBQ043nCNjGPONY1Z5kfqxRVCTdwEw7PLj5KkxVFgtUMYDsBYXxl3/94+7ceyER9s66hWrETI1LxGRCoYXHh9++CEURcHY2BgAoLS0FN/97ndhsVimtV2wYAGam5vx7LPPYt26dUaHlj4JGBj8CF3d/fApAVhkE1Y1zcWLv3sXpzwK1qxcgO7fHMIHw15YZBPaWxtQX1N+5kMgRv9pbVTOq0s/CTgydAoOpxvP7D6kbWwR1ORdAPz5yEls6TkQaXNd01zsfvU9/Mtn6/VZf71iNUKm5iUiUsnwezzmzZuHq6++Gp/+9KfxpS99Cffffz+CwSDOO++8mO0vvPBCHD161OiwdOHyKpE3eADwKQE8s/sQln2yBj4lgC09B7C0oTryXFd3P1xeJWH/qW3UzqtHP5dXwZEhV6To0DK2CGryHnb5I0VHuM0vdh/C0oZq3dZfr1iNkKl5iYjUEnKPx4YNG7B27Vrs3bsXe/fuRWtrK77//e/HbCtJEoqKijSNX1Vl1SPMhOz2smnHjh12Rt7gw3xKAJCm/zn82KsEUFtTmbD/5DaxGNXv2GEngqFQSmOLoCbvd44Nxd0TvdZfr1iNoHbeWK/nfDfTcma++S2X8zW88HjllVfg9Xrxuc99Dtdeey2uvfZadHd345e//GXM9gcPHkRtba2mOYaH3QgGjTuPbLeXwek8Ne14icUMi2yKeqO3yKbIKe3Jfw4/LpFNkbHi9Z/cJhaj+pVYzCiQpJTGFkFN3pVllrh7otf66xWrEdTMG+/1nM9mWs7MN7/lQr6JCiPDL7UUFRVh06ZNcDgcAIBQKITDhw+jvr5+Wts33ngDu3btwpe+9CWjw9KFrdiM9taGiQ82IHKPR+/+QVhkE9asXIA9/Y7Ic+2tDbCVyAn7T22jdl49+tmKzag9x4ZVTXM1jy2CmryrygqxZuWCqDbXNc3Fnn6HbuuvV6xGyNS8RERqSaFQyPBbzp577jk88cQTUJSJ68xLly7FN7/5TaxevTryc1pJklBcXIz29nZcfvnlmsbP1BkPAGd+QeDxo6K0EAUFEk6e8qGitBC2UnniVy2nn7OVyPF/1ZKojYp5desnAUpIgnNkdOJXLTaL+rFFUJN35FctPpRbCzE+HoC1SI67/v6ghGPDbm3rqFesRkgyby58W9LbTMuZ+ea3XMg30RkPIYWH0TJaeOQh5pvfZlq+wMzLmfnmt1zIN6OXWoiIiIjCWHgQERGRMCw8iIiISBgWHkRERCQMCw8iIiIShoUHERERCcPCg4iIiIRh4UFERETCsPAgIiIiYVh4EBERkTAsPIiIiEgYFh5EREQkDAsPIiIiEsZs9AQbNmzAn/70JyiKgsHBQdTW1gIA2tra8L3vfQ8vvvgiZs2aBQBwOBxoa2tDb2+v0WFRLpj0n3cvLZLh84/DWlIIW7E55n/e3j02DtfoOMZ845hVXgRbiRkuj4IRtx8VZZbY/QAEgyG4RpO30xRzrLESPZfKeCnG5Q9JKCxA4nH0mjdd6cZxuv+xw06UWMyZy0MPBcCwy49h1xiqyotQVVYIBDMdFJF2hhced999N4AzRcXWrVsBAD09PfB4PLj77rvxyCOPGB0G5RoJGBj8CF3d/fApAVhkE65rmovdr76Hf/ls/f/f3r3HR1Xe+QP/zD2ZZCYx4+BKIRSjUdZForAlIVArGAHBC6FGCgWLbrd4C9pqVXRrq4UC8lo1YqF1V9taV8xqlkt0+RnBdg1YbCCp0SK3UgISMATC5EJmJjPn90fMMTOZ+5zzzIXP+x+cc57L9/s8c/nmzIyDMfk5Pi/oh4534lhbFzbU7Zfb3z33KrxRtw+t7T0wGXSorCjy7fdl3w+bW/Hs63vkfgHbxRizPBZCnAs2T6jxonzxjWocpeaNV7xxJEseStACfzl0GutrmuVclpSPxbiCPBYflHIS+lbL9OnTceTIEWzZsiWRYVAScvS45RcMAHC6PXijbj+mFI1AVXUTHD1un7aHjjvkomOg/bq3PsaUohHybf9+A30Hio5Q7WKNeWCsUOdiGU+puNScN17xxpEseSih3eGSiw6gP5f1Nc1od7gSHBlR9FS/4hGKwWDAL37xC/zgBz9ASUlJzOPYbNkKRhWY3W5RfY5kkuh8Txxsk59kBzjdHkDT/2+P24OC/Dy5rVeSgrYffHtwv1Dz+LeLJ+YetweQEPU8SsUW7ThKrkk84o0jWfJQwoETxwPmcqbLiStG2wL2SfRjWDTmmzoSWngAwNixY3HbbbfhySefxGOPPRbTGO3tXfB61bt2ardb0NbWqdr4ySYZ8jWb9DAZdD5PtiaDDpD6/zUbdHKMZpMeWo0maPvBtwf3CzWPf7t4YjYbdECQ+ELNo1Rs0Y6j5JrEI944kiUPJeRZTAFzuSDbFDCXZHgMi8R8k0+owigpvtVy77334siRI6itrU10KJQkrJl6VFYU9RcPgPwZjw+ajqGyoghWs8GnbcFwK+aVFfq0v3vuVfig6Zh827/fQN8Hv3ONT79A7WKNeWCsUOdiGU+puNScN17xxpEseSjBZjFiSflYn1yWlI+FzWpMcGRE0dNIkiTkY1b+31ipqanBRx99hJUrVwIAmpubsXDhQuTl5UX9rRZe8VBW0uTr/60Wdx+yMwz9LxzhvtViNcGaZej/Vku3C7lZxsD90P9W3eFjZ8K2izbmIWOFOhfLeDHG9Q+2bBi1UmTfalFiTeIRbxxf9u9xe2A26BKXhxIGf6vFmtFfdAT5YGnSPIYFYb7JJ9QVj4S/1TJg7NixuOOOO/hBU/qKBFgzDbBmDvyFapSPB2qbbdIj2zToLu316x/kBUer1UTULqaYpQjPxTJejHHZ7dnhn7SUmjde8cbxZf+C/Lz+nFO16AAAL2DLNsKWbZRvE6UiYVc81MQrHspivuntfMsXOP9yZr7pLRXyTfrPeBAREdH5gYUHERERCcPCg4iIiIRh4UFERETCsPAgIiIiYVh4EBERkTAsPIiIiEgYFh5EREQkDAsPIiIiEoaFBxEREQnDwoOIiIiEYeFBREREwrDwICIiImH04ZvE72c/+xn27NkDt9uNlpYWFBQUAADKysrw0Ucf4be//S00Gg0kScLdd9+N0tJSLFy4UERo8dEAjh43OrpcyLWYYM3U9//s9pfHu865YTLq4XT3wWTQo7vXjdzsQe3UjMWsh6M7QGx+7U85nMg06WExG5Bt0sUfV7A1UUqYNY86Hw3w+RddOHGqW514I6H2mvnNc+JgG8wmfWJyJaLznpDC48knnwQAHDt2DIsWLcKmTZsAAB6PBx988AF+85vfYPHixXj55Zeh1WpTpujY23IWVdVNcLo9MBl0qKwowphROdh75Cx+v3UvyiaOQt2uIyibOApv1O33bZefo9yTvl8sF9vMqLi+EOtrmgPPGSD2eWWFGGHPRsHw4D9lHPOaKJVrmDUPmk+wudWON56clI4hGXIlIkKC32rR6XR45pln8Ktf/QqbN2/G66+/jhUrViQypIg5etzykzgAON0eVFU3od3hQlV1E6YUjcAbdfvlf/3bOXrcqsUypWiEXHQEmjNQ7Bvq9uPQcUdccQVbE6VyDbfm0eajdryREBVDMuRKRAQIuuIRSn5+PpYuXYqHH34Yv/nNb5Cbmxv1GDZbtgqR+bLbfa8EnDjYJj+JD3C6PTjT5ew/roHvv37tetweFOTnKRLbkFjCzBksdq8koefL4/75xhQHlM012PinB9bc7/hAPsHmVjveSIiKIRlyTbRY7tOpjPmmt1TON+GFBwA0NDTgwgsvxP/93/+hpKQk6v7t7V3wetW7Xmy3W9DW1ulzzGzSw2TQ+TyZmww6XJBtgsmgk28P/OvfzmzQDRkzVsFiCTZnsPZajQbmL2OOJbZg4yqVa7Dx875c82D5BJtb7XgjISqGZMg1kQI9htMZ801vqZBvqMIo4d9qeeutt3D48GG8+eab2LRpEz788MNEhxQRa6YelRVFPsVFZUURbFYjKiuK8EHjMdxeVij/69/OajaoFssHjcewpHxs0DkDxT6vrBAFw61xxRVsTZTKNdyaR5uP2vFGQlQMyZArEREAaCRJEvbRsoEPl27fvh0A8Le//Q3f/e538fvf/x6XXHIJ6urq8POf/xybN29GTk5OxOMm4ooHgK++jdDtQm6Wsf9JfPC3WnrdMBn8vtUyuJ2S/GPJMvR/q8U/Nr/2pxxOZHz5DYfsjP5vOcRVTQdbE6WEWfNA+YQbz+XV4ER7l3p7E47aa+Y3T4/bA7NBl5hcEyQV/kJUEvNNb6mQb6grHgl7q8XlcuGHP/whli5diksuuQRA/9dr33//fTz55JN47rnnEhVa5CTAmmmANdMg3w54HEYAgC3b6NtOzVi8QWIL1l6puIKtiVIiXvMI55aArw3LhlEjRd5HaWqvmd88Bfl5/U9a50nRQUTJRWjhMWLECPlqh9FoxMaNG4e0SZVvtRAREVH0Ev4ZDyIiIjp/sPAgIiIiYVh4EBERkTAsPIiIiEgYFh5EREQkDAsPIiIiEoaFBxEREQnDwoOIiIiEYeFBREREwrDwICIiImFYeBAREZEwLDyIiIhIGBYeREREJIyQX6c9duwYFi1aJP8y7YDLL78c119/PY4dO4aenh6cOnUK+fn5AICHHnoIU6ZMERGecjSAo8eNji4Xci0mWDP1/T89rgXaHS60O3phy8mASa/FaYfTt02C4szLyYDX4/WNOYZxci0mWM16OLoD5B9rfN0uZGUY4HT1IdtsjG+tgu1NuDaIoJ8asajRl4goCQgpPEJ58cUXAQC7du3C2rVr8eqrryY4ohhpgL0tZ1FV3QSn2wOTQYfKiiKM+XoO/nLwNNbXNMvH55UV4u2dh9HZ7e5vk58j7sVjUJyWLANmTRqNDXX7fWK25WVHNY7T7cHFNjMqri/0yTOm3AKs4+1lhajbdQTfnTEmtrUKtjeDxwrSxqjXYs1/7Ykvp2hjUaMvEVGS4FstCnH0uOUXBABwuj2oqm7CFx1O+cV44PiGuv2YOj5fbuPocSckzqnj8+WiY3DMrae6oxoHAKYUjRiSZyy5BVrHN+r2Y0rRiJjXKtjeDB4rWJtDxx1x5xRtLGr0JSJKFgm/4qEEmy2Cv9DjZLdbQp4/cbBNfkEY4HR7cNrhDHgcmq/+u8ftQUF+nqLxRhSnBoFj7jyHsQX2yMcJMVa0uQVbx4HxY1mrYGMOHqvH5QnYxitJIftFK5JY1OjrL9z9OR2dbzkz3/SWyvkKKTy02qEXViRJgkajUWT89vYueL3qXWu22y1oa+sM2cZs0sNk0Pm8MJgMOtispoDHBy6Nmww6mA26sOMrxT/OQLHlWTJjztf/drS5BRsXUuxrFWzMgbHsdkvQNlq/+2i8+xUuFrX6DhbJ/TndnG85M9/0lgr5hiqMhLzVYrVa0dnpu0jt7e3IyckRMb0Q1kw9KiuK+l8kAfn9d3uuCUvKx/ocn1dWiO27W+Q2dc5QvwAAIABJREFUVrMhIXFub2jBvLLCITFffGFWVOMAwAeNx4bkGUtugdbx9rJCfNB0LOa1CrY3g8cK1qZguDXunKKNRY2+RETJQiNJkpCPpX3729/G97//fUyfPh1A/4dKDxw4gOeeew5AfB8uTYYrHgB8vo2Rm2Xsf0EI9q2WTqdvG5EGxZlnMcHrlXxitl8YY75Zhv5vtfjnH0d8WRkGON19yM4wxLdWwfYGg/Y3UBsE7xezELGo2vdLqfDXktLOt5yZb3pLhXxDXfEQ9hmPZ555Bj/96U/x4osvwu124/LLL8dPfvITUdOLIQHWTAOsmQb5NgDAC9iyjbBlG+Wm2Sa9bxuR/OMEhsYcyzjeIPnHHZ8xvvECjRlorCBtFMkp2ljU6EtElASEFR6jR4/Gb3/726DnJ06ciIkTJ4oKh4iIiBKAX6clIiIiYVh4EBERkTAsPIiIiEgYFh5EREQkDAsPIiIiEoaFBxEREQnDwoOIiIiEYeFBREREwrDwICIiImFYeBAREZEwLDyIiIhIGBYeREREJIyQH4mbP38+5s+fj9mzZ8vHenp6cO211+Kiiy6CTqdDS0sLLrzwQpjNZowYMQIvvviiiNCIiIhIICGFR3l5OWpra30Kj3fffRclJSWoqqoCACxcuBD33Xdf+vxCrQZw9LjR0eVCrsUEa6a+/yfMgx1XY65Iz8cr0vG1QLvDhXZHL2w5GbBZjIBXwThioQE+/6ILJ051K782aq97jHN7vRIc51SIK9Z8I+mXyLVMZ1xXSgAhhcfMmTOxevVqdHR0IDc3FwCwefNm3HHHHSKmF08D7G05i6rqJjjdHpgMOlRWFGHMqBzsPRLgeH5O7A/2YHMNjBnuvFq5+o+vBf5y6DTW1zTL7ZaUj8W4grzEFR9qro3a6x7r3AA+bG7Fs6/vUTauWPONpF8i1zKdcV0pQYR8xiMrKwvTpk3D1q1bAQAnT57E4cOHMWXKFBHTC+foccsPZgBwuj2oqm5Cu8MV8Lijx634XANjhjsfr0jHb3e45KJjoN36mma0O1yKxBELNddG7XWPdW5Hj1suOpSMK9Z8I+mXyLVMZ1xXShQhVzwAYO7cuXjuuecwb948bNmyBTfffDO0WmXqHpstW5FxQrHbLRG3PXGwTX4wD3C6PTjd5Qx4vMftQUF+XkxxBZtrYMxw54OJNN9Ixz9w4njAdme6nLhitC2iuZQW69okeux45oYEVeKKNd9I+im1ltE8htNBuHwTeR9VA/c3dQgrPCZMmIC2tja0trZi8+bNWLt2rWJjt7d3wetV79qg3W5BW1tnxO3NJj1MBp3Pg9pk0CEv2xTwuNmgi2r8SOYaGDPc+UCiyTfS8fMsgXO/INsUc+7ximVtkmHseOaGRqNKXLHmG0k/JdYy2sdwqosk30TeR5XG/U0+oQojoV+nnTNnDtatW4ecnBzk5+eLnFooa6YelRVFMBl0ACC/d2qzGgMet5oNis81MGa48/GKdHybxYgl5WN92i0pHwub1ahIHLFQc23UXvdY57Zm6vHgd65RPK5Y842kXyLXMp1xXSlRNJIkCfsY0YkTJzBt2jQsX74ct956q8+5eL7VkmxXPAB89Wnxbhdys4z9D2YpxPF4hBszyjmjzjfS8Qd/q8Wa0V90JMG3WlxeDU60dym3H4PGVnyvFZjbZsvG4WNnlI8r1nwj6RfnWqbCX4hKijjfRN5HFcT9TT6hrngILTzUkpSFRwpjvuntfMsXOP9yZr7pLRXyTZq3WoiIiOj8xsKDiIiIhGHhQURERMKw8CAiIiJhWHgQERGRMCw8iIiISBgWHkRERCQMCw8iIiIShoUHERERCcPCg4iIiIRh4UFERETCsPAgIiIiYVh4EBERkTCqFx7z589HbW2tz7Genh5MnDgRp0+fBgDMnTsXS5YsUTsUIiIiSjDVC4/y8vIhhce7776LiRMnIi8vD/v27YPBYMBnn32G1tZWtcMhkTSA45wbLW3dcPT2AZo420YzXhTxNR9sU2Y8v3EVizNR80Q7vhZo73Jh/3EH2rtd6j67fBnb3052obWjF10uj3rrTPGL974q6jEVi2SLLdniCUCv9gQzZ87E6tWr0dHRgdzcXADA5s2bcccddwAAampqUFpaio6ODlRXV2Pp0qVqh0QiaIC9LWdRVd0Ep9sDk0GHyooijMnPAaQY2kYzntLxJcO4oueJdnwt8JdDp7G+plluv6R8LMYV5AFeBeIJE9u8skKMsGejYLhF2XWm+MV7XxX1mIpFssWWbPEEofoVj6ysLEybNg1bt24FAJw8eRKHDx/GlClT4Ha7sXnzZsycORMzZ87Em2++ib6+PrVDIgEcPW75zg8ATrcHVdVNcPS4Y2obzXhKx5cM44qeJ9rx2x0uuegYaL++phntDpci8YSLbUPdfhw67lB8nSl+8d5XRT2mYpFssSVbPMGofsUD6P8Mx3PPPYd58+Zhy5YtuPnmm6HVarF9+3bY7XZceumlkCQJWq0W77//PsrKyqIa32bLVinyr9jtFtXnSCbx5nviYJt85x/gdHvQ4/agID8v6rbRjKd0fMkwrtLzhNvfaMc/cOJ4wPZnupy4YrQtbDzRCBabV5JC5s/HcGLEe1+NtH8i8hX1eA8kUL6JjCcaQgqPCRMmoK2tDa2trdi8eTPWrl0LAHjrrbfQ2tqKqVOnAgC6urqwYcOGqAuP9vYueL3qXUey2y1oa+tUbfxko0S+ZpMeJoPO50FgMuhgNuiGjB1J22jGUzq+ZBhXyXki2d9ox8+zmAK2vyDbpPhjJ1hsWo0maHx8DCdOvI+JSPonKl9Rj3d/wfJNVDyBhCoEhX2dds6cOVi3bh1ycnKQn5+PU6dOYceOHaitrcX27duxfft2bNy4EX/6059w9OhRUWGRSqyZelRWFMFk0AGA/F6j1WyIqW004ykdXzKMK3qeaMe3WYxYUj7Wp/2S8rGwWY2KxBMutnllhSgYblV8nSl+8d5XRT2mYpFssSVbPMFoJEkS8pGTEydOYNq0aVi+fDluvfVWvPzyy2hsbMQLL7zg0+7+++/HqFGj8NBDD0U8Nq94KEuxfDX97zl2dLuQm2Xsv/MH26ZI2kYzXhTx9bg9MBt08Y+nVpwKzxPx/kY7vrb/sx7tjl7YrBn9RYfSHyz1i+2Uw4kMkx7WTD2yM/RB4+NjOMHifUyE6Z/QfEU93gcJmW8C4gkk1BUPYYWHmlh4KIv5prfzLV/g/MuZ+aa3VMg3Kd5qISIiImLhQURERMKw8CAiIiJhWHgQERGRMCw8iIiISBgWHkRERCQMCw8iIiIShoUHERERCcPCg4iIiIRh4UFERETCsPAgIiIiYVh4EBERkTAsPIiIiEgYVQqPxYsXo66uTr69atUqXH311XC5XPKxyZMn4+jRowCAuXPnYsmSJWqEQkRERElElcKjpKQEe/bskW/v3LkT48aNw+7duwEAR44cgdlsxsiRI7Fv3z4YDAZ89tlnaG1tVSMcdWkAxzk3Wtq64ejtQ5ezT/5vaH3PQRPZGEHbCYgfWuDzL7rExaIBupx9ON7Ri7+d7EJ7twvHT/coP3ewNdaEyffLfn872YXWjl50uTxi9ycaibwfqSld8yI6T+nVGLS4uBgrVqwAAJw8eRJGoxEzZsxAfX09SkpK0NDQgEmTJgEAampqUFpaio6ODlRXV2Pp0qVqhKQODbC35SyqqpvgdHtgMugwr6wQb+88jM5uN5aUj0X1e/vR2t4Dk0GHyooijMnPAaTQYwRsJyD+i21mVFxfiPU1zWJi0QCHjnfiWFsXNtTtl+ecP/1ybP3w7/jujDHKzB1sjUflYO+REGsfZH9H2LNRMNyi/v5EI5H3IzWla15E5zFVrnhceeWVaGlpgdPpRH19PUpLS1FaWor6+noAQENDAyZPngy3243Nmzdj5syZmDlzJt5880309fWpEZIqHD1u+QkRAJxuDzbU7cfU8flwuj1YX9OMKUUj5HNV1U1w9LjDjhGonYj4pxSNkIsOEbE4etw4dNwhFx0Dc/7X/9uHKUUjFJs72Bq3O1wh1z7Y/h467hCyP9FI5P1ITemaF9H5TJUrHjqdDuPGjUNzczPq6+uxYMECjBw5Er29vTh79iwaGxuxbNky/PGPf4Tdbsell14KSZKg1Wrx/vvvo6ysLKr5bLZsNdLwYbdbhhw7cbBNfkIc4HR/dSl+8H8P3O5xe1CQnxd2DP92ahgytwZCYzlxsA1eSQq6hkrNHWyNT3c5Q+YbrJ9XkoTsTzSivR8Fuj8nIyUfH6mSs1KYb3pL5XxVKTyArz7n8fHHH+OZZ56Rj23btg25ubmwWCx466230NraiqlTpwIAurq6sGHDhqgLj/b2Lni96l13tdstaGvrHHLcbNLDZND5PDGaDDr5EvDg/x64bTbofMYKNoZ/OzUEm1tULGaTHlqNJugaKjV3sDzzsk0h8w3WT6vRCNmfaERzPwp2f05GSj0+UilnJTDf9JYK+YYqjFT7Om1xcTE2bdqEwsJC6PX99U1paSleeeUVlJaW4tSpU9ixYwdqa2uxfft2bN++HRs3bsSf/vQn+dsuyc6aqUdlRVH/CyUgfwZg++4WmAw6LCkfiw+ajsnnKiuKYDUbwo4RqJ2I+D9oPIYl5WOFxWLN1KNguBXzygp95pw//XJ80HRMsbmDrbHNagy59sH2t2C4Vcj+RCOR9yM1pWteROczjSRJql0qKC0txT333IMFCxYAADo7O1FcXIxXX30VTU1NaGxsxAsvvODT5/7778eoUaPw0EMPRTxPoq54AOj/xH2PGx3dLuRmGaHVanC604ncLCOsWQY4ur86ZzUbAn8gzm+MoO3U4D93lgEujwYn2rvExKIBunr74DjXh15nH3KyjXC6+pCdYVB27mBrrAFc3hD5ftnvlMOJDJMe1kw9sjP0yfnBxgjvR6nw15IPBR4fKZdznJhvekuFfENd8VC18BAloYVHGmK+6e18yxc4/3JmvuktFfJNyFstRERERP5YeBAREZEwLDyIiIhIGBYeREREJAwLDyIiIhKGhQcREREJw8KDiIiIhGHhQURERMKw8CAiIiJhWHgQERGRMCw8iIiISBgWHkRERCQMCw8iIiISRpXCY/Hixairq5Nvr1q1CldffTVcLpd8bPLkybjrrruG9H3sscfwu9/9To2wiIiIKMFUKTxKSkqwZ88e+fbOnTsxbtw47N69GwBw5MgRGI1GNDU1ob29XW537tw5vP/++7jpppvUCIuUpAEc59xoaeuGo7cP0IQ5HknfEG26nH2h2/v1+9vJLrR29KLL5QndPlVFsobx9FdqH5Wi1lz+42oF5hQpketMJIBejUGLi4uxYsUKAMDJkydhNBoxY8YM1NfXo6SkBA0NDfjmN7+J3t5evPPOO1i4cCEA4L333kNxcTEuuOACNcIipWiAvS1nUVXdBKfbA5NBh8qKIowZlYO9RwIcz88BpDB9w7SZV1aIt3ceRme3e2j7EHHNKyvECHs2CoZbhrZPVZGsYQherxS6f4x7FE0MIvONZtwl5WNR/d5+tLb3qJtTHDEmPCaiOKlyxePKK69ES0sLnE4n6uvrUVpaitLSUtTX1wMAGhoaMHnyZJSXl6O2tlbut3HjRsydO1eNkEhBjh63/EQIAE63B1XVTWh3uAIed/S4w/YN12ZD3X5MHZ8fsH24foeOOwK2T1WRrGEorae6Q/aPdY+iiSEaas0VaNz1Nc2YUjRC0XmUjjHRMRHFS5UrHjqdDuPGjUNzczPq6+uxYMECjBw5Er29vTh79iwaGxuxbNkyZGdn48yZMzh69CgyMjLw97//HaWlpVHPZ7Nlq5CFL7vdovocySRUvicOtslPhAOcbg/OdDkDHu9xe1CQnxeybyRtBi4x+7cPF5dXkgK2jzTfZBPJGobSHKZ/PHsUaQzRUGou/z0Odz+LdR4lxZN7Kt2nlcB8U4cqhQfw1ec8Pv74YzzzzDPysW3btiE3NxcWS/+i3XrrraitrUVGRgZuvvlmaLXRX4Rpb++C16vedUe73YK2tk7Vxk824fI1m/QwGXQ+T4gmgw4XZJsCHjcbdPJ4wfpG0mbg0rJ/+3BxaTWagO0jzTfZRLKGoeRZM0P2j2ePIo0hGkrMFWiPw93PYplHabHmnmr36Xgx3+QTqjBS7eu0xcXF2LRpEwoLC6HX99c3paWleOWVV3yuasyZMwd1dXXYunUrysvL1QqHFGTN1KOyoqj/SRqQ33e2WY0Bj1vNhrB9w7WZV1aI7btbArYP169guDVg+1QVyRqGcvGFWSH7x7pH0cQQDbXmCjTukvKx+KDpmKLzKB1jomMiipdGkiTVLhWUlpbinnvuwYIFCwAAnZ2dKC4uxquvvoprrrlGbnfXXXfB6/XilVdeiWkeXvFQVkT5avrff+7odiE3y9j/RCiFOB5J3xBttFoNTnc6g7f363fK4USGSQ9rph7ZGfqQH8RLyf2NZA2DsNstaDvVGbq/UvuolDjnCrrH/uNmGeDoFpRTpGLIPSXv03Fgvskn1BUPVQsPUVh4KIv5prfzLV/g/MuZ+aa3VMg3IW+1EBEREflj4UFERETCsPAgIiIiYVh4EBERkTAsPIiIiEgYFh5EREQkDAsPIiIiEoaFBxEREQnDwoOIiIiEYeFBREREwrDwICIiImFYeBAREZEwLDyIiIhIGGGFx+LFi1FXVyffXrVqFa6++mq4XC752OTJk/HII4+gpqZGVFjiaYH2Lhf2H3egvdvVvwMawHHOjZa2bjh6+wBNmDGiba82v3i6nH1DYxvUpsvliT3+eHL376vtv918sC30WMm4P0rOkWz3JyJKa3pRE5WUlGDPnj0oKysDAOzcuRPjxo3D7t27UVJSgiNHjsBsNkOjSeNnPS3wl0Onsb6mGU63ByaDDkvKx+KCbBN+8bsG+VhlRRHG5OcAUoAxNMDelrOoqm6KrL3aAsQzr6wQb+88jM5ud39so3Kw90h/G0uWAbMmjcaGuv3Rxx9P7n59L7aZUXF9oc9eBBwr2jlF7I+ScyTb/YmI0p6wKx7FxcVobGwEAJw8eRJGoxEzZsxAfX09AKChoQGTJk0SFU5CtDtc8gsdADjdHqyvaUaP0+NzrKq6CY4ed8AxHD1u+UUikvZqCxTPhrr9mDo+X46t3eGS20wdny8XHdHGH0/u/n2nFI0YsheBxop2ThH7o+QcyXZ/IqL0J+yKx5VXXomWlhY4nU7U19ejtLQUpaWluO+++/Dwww+joaEB06ZNw3vvvRf12DZbtgoR+7LbLXGPceDEcfkJfoDT7cE5V9+QYz1uDwry84aMceJgW8AxgrWPVaT5Botn4HK90+3BmS7nV200iDn+eHIf0jfCOKKdU8T+KDHHwP6Kuj8lAyUew6mE+aa3VM5XWOGh0+kwbtw4NDc3o76+HgsWLMDIkSPR29uLs2fPorGxEcuWLYup8Ghv74LXq951Ybvdgra2zrjHybOYYDLofJ7oTQYdMo2+22Ay6GA26ALOaTbpA44RrH0sosk3WDwDl+lNBh0uyPbNO9b448k9WN9wY0U7p4j9iXeOwfsrIt5koNRjOFUw3/SWCvmGKoyEfqtl4HMeH3/8MYqKiuRj27ZtQ25uLiyW1K3gImGzGLGkfGz/CzMgf8bDbNL5HKusKILVbAg4hjVTj8qKoojbqy1QPPPKCrF9d4scm81qlNtsb2jBvLLCmOKPJ3f/vh80HhuyF4HGinZOEfuj5BzJdn8iovSnkSRJ2EfI9u/fjwcffBD5+flYt24dAKCurg5VVVW44YYbcP/99+PRRx/FN77xDZSXl0c8bqpc8QDQ/60Whwvtjl7YrBmwWY2A1P9ee0e3C7lZxv4n/VDpaKJsH6Wo8/WLR6vV4HSn0ze2QW3yLCZ4vVJs8ceTu3/fLAMc3W70uD0wG3TBx4p2TpX3J945huyviHgTLBX+QlQS801vqZBvqCsewt5qAYDCwkJ0dHRg/vz58rHi4mI88MADKC0tFRlK4ngBW7YRtmyjfBsArJkGWDO//Csz3JO+FGV7tfnHAyDbpJfPBWsTU/zx5O7f19t/uyA/r/9BHGysaOcUsT9KzpFs9yciSmtCCw8A2LFjh89ti8WCTz/9VL69cuVK0SERERGRIPw/lxIREZEwLDyIiIhIGBYeREREJAwLDyIiIhKGhQcREREJw8KDiIiIhGHhQURERMKw8CAiIiJhWHgQERGRMCw8iIiISBgWHkRERCQMCw8iIiIShoUHERERCaNK4bF48WLU1dXJt1etWoWrr74aLpdLPnbVVVfhn//5n3Hy5En5WG9vL8rKyvD++++rEZZ6NIDjnBstbd1w9Pahy9kn/zc0EZwXEFPQeQa163J54DjnRvPBtshii3QOpfjPpw0wf4B8FIkv3lyVWivRa05EpDC9GoOWlJRgz549KCsrAwDs3LkT48aNw+7du1FSUoIjR47gH/7hHzBt2jQ8/fTTWLt2LQDg+eefx4QJE3DdddepEZY6NMDelrOoqm6C0+2ByaDDvLJCvL3zMDq73Xho/jVw9XmDnq+sKMKY/BxAUjemgPMMamfJMmDWpNHYULc/dJ9o51App4ttZlRcX4j1Nc0+8xv1Wqz5rz3R5xPF3FGPpdRaiV5zIiIVqHLFo7i4GI2NjQCAkydPwmg0YsaMGaivrwcANDQ0YNKkSVi6dCkOHDiAbdu2Ye/evairq8Pjjz+uRkiqcfS45RcCAHC6PdhQtx9Tx+fD6fbg0HFHyPNV1U1w9LhVjynQPIPbTR2fL79Ih+oT7Rxq5TSlaIRcdAye/9BxR0z5RDN3tGMptVai15yISA2qXPG48sor0dLSAqfTifr6epSWlqK0tBT33XcfHn74YTQ0NGDatGnIyMjA8uXL8dhjj8Fms+Hpp59GdnZ21PPZbNH3iZbdbgl4/MTBNvmFYIDT7ZEvgXslKeR5p9uDHrcHBfl5isUaLCb/eXzaaRBRn2jnUMqQ+YLE65WkkOeDxRdsfwPOHWYspfsrPQ4QOt90db7lzHzTWyrnq0rhodPpMG7cODQ3N6O+vh4LFizAyJEj0dvbi7Nnz6KxsRHLli0DAEyYMAETJkyAJEkoKSmJab729i54vepda7bbLWhr6wx4zmzSw2TQ+bwgmAw6+dK3VqMJed5k0MFs0AUdPxbBYvKfx79dJH2inUPtnPxvazWakOcDxRdqf0PNHWmuSq2VUuOEyzcdnW85M9/0lgr5hiqMVPtWy8DnPD7++GMUFRXJx7Zt24bc3FxYLF8FNXz4cHzta19TKxRVWTP1qKwo6i8mAPkzHNt3t8Bk0KFguDXk+cqKIljNBtVjCjTP4HbbG1owr6wwbJ9o51Arpw8aj2FJ+dgh8xcMt8aUTzRzRzuWUmsles2JiNSgkSRJlUsF+/fvx4MPPoj8/HysW7cOAFBXV4eqqirccMMNuP/+++W2L7zwAgD4HItGIq94AOj/pkGPGx3dLuRmGaHVanC604ncLKP8ohDyvEofxhw8Z9B5BrXLs5jg9UrocXtgNujCxxbpHErxny/LAEe33/wYmk+4+CL66yHeXJVaKwXGSYW/lpR2vuXMfNNbKuQb6oqHKm+1AEBhYSE6Ojowf/58+VhxcTEeeOABlJaWqjVtYkiANdMAa+ZXf3lmm/TyOSD8edVjCjZPgNgL8vP679ThYot0DqX4z+cNPL9/PorEF2+uSq2V6DUnIlKYaoUHAOzYscPntsViwaeffjqkXaxXOoiIiCi18P9cSkRERMKw8CAiIiJhWHgQERGRMCw8iIiISBgWHkRERCQMCw8iIiIShoUHERERCcPCg4iIiIRh4UFERETCsPAgIiIiYVh4EBERkTAsPIiIiEgYFh5EREQkjKq/Tjtg69at+PWvf42+vj5IkoRbbrkF//Iv/wIA2LhxI1599VX09fXB6/Xitttuw6JFi0SElVo0gKPHjY4uF3ItJlgz9enzk+jpnJtauGZElKJULzxOnjyJVatWoaamBhdccAG6u7uxcOFCjB49GqdOncKGDRvwq1/9CsOGDYPD4cCdd96JzMxM3HbbbWqHljo0wN6Ws6iqboLT7YHJoENlRRHG5Oek/otNOuemFq4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-     },
-     "metadata": {}
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "显示此关系以及一个新的黄色散点图，其色调由年份决定。\n"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 179,
-   "source": [
-    "sns.relplot(x=\"priceperlb\", y=\"state\", hue=\"year\", palette=\"YlOrBr\", data=honey, height=15, aspect=.5);\n"
-   ],
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "<Figure size 604.1x1080 with 1 Axes>"
-      ],
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-     },
-     "metadata": {}
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "再试一次这个图，这次通过散点图中点的大小来显示价格变化\n"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 180,
-   "source": [
-    "sns.relplot(x=\"priceperlb\", y=\"state\", size=\"year\", data=honey, height=15, aspect=.5);\n"
-   ],
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "<Figure size 604.1x1080 with 1 Axes>"
-      ],
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"
-     },
-     "metadata": {}
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "探索一些折线图选项\n"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 181,
-   "source": [
-    "sns.relplot(x=\"year\", y=\"priceperlb\", kind=\"line\", data=honey);"
-   ],
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "<Figure size 360x360 with 1 Axes>"
-      ],
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"
-     },
-     "metadata": {}
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 201,
-   "source": [
-    "sns.relplot(x=\"year\", y=\"totalprod\", kind=\"line\", data=honey);"
-   ],
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "<Figure size 360x360 with 1 Axes>"
-      ],
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"
-     },
-     "metadata": {}
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "构建一个分面网格\n"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 189,
-   "source": [
-    "\n",
-    "sns.relplot(\n",
-    "    data=honey, \n",
-    "    x=\"yieldpercol\", y=\"numcol\",\n",
-    "    col=\"year\", \n",
-    "    col_wrap=3,\n",
-    "    kind=\"line\"\n",
-    ")\n"
-   ],
-   "outputs": [
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": [
-       "<seaborn.axisgrid.FacetGrid at 0x7fec8d8419b0>"
-      ]
-     },
-     "metadata": {},
-     "execution_count": 189
-    },
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "<Figure size 1080x1800 with 15 Axes>"
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-     },
-     "metadata": {}
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "创建一个双线图（此解决方案由 Kedar Ghule 提出：https://kedar.hashnode.dev/how-to-combine-two-line-charts-in-seaborn-and-python）\n"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 200,
-   "source": [
-    "fig, ax = plt.subplots(figsize=(12,6))\n",
-    "lineplot = sns.lineplot(x=honey['year'], y=honey['numcol'], data=honey, \n",
-    "                        label = 'Number of bee colonies', legend=False)\n",
-    "sns.despine()\n",
-    "plt.ylabel('# colonies')\n",
-    "plt.title('Honey Production Year over Year');\n",
-    "\n",
-    "ax2 = ax.twinx()\n",
-    "lineplot2 = sns.lineplot(x=honey['year'], y=honey['yieldpercol'], ax=ax2, color=\"r\", \n",
-    "                         label ='Yield per colony', legend=False) \n",
-    "sns.despine(right=False)\n",
-    "plt.ylabel('colony yield')\n",
-    "ax.figure.legend();"
-   ],
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": [
-       "<Figure size 864x432 with 2 Axes>"
-      ],
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"
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deleted file mode 100644
index cb20de8f..00000000
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--->
-# 制作有意义的数据可视化
-
-|![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/13-MeaningfulViz.png)|
-|:---:|
-| 有意义的数据可视化 - _Sketchnote by [@nitya](https://twitter.com/nitya)_ |
-
-> “如果你对数据施加足够的压力，它会承认任何事情” -- [Ronald Coase](https://en.wikiquote.org/wiki/Ronald_Coase)
-
-数据科学家的基本技能之一是能够创建有意义的数据可视化，以帮助回答你可能提出的问题。在可视化数据之前，你需要确保数据已经像之前课程中那样被清理和准备好。之后，你可以开始决定如何最好地呈现数据。
-
-在本课中，你将学习：
-
-1. 如何选择正确的图表类型
-2. 如何避免误导性图表
-3. 如何使用颜色
-4. 如何为图表设计样式以提高可读性
-5. 如何构建动画或3D图表解决方案
-6. 如何构建创意可视化
-
-## [课前测验](https://ff-quizzes.netlify.app/en/ds/quiz/24)
-
-## 选择正确的图表类型
-
-在之前的课程中，你已经尝试使用 Matplotlib 和 Seaborn 构建各种有趣的数据可视化。通常，你可以使用以下表格选择适合问题的[正确图表类型](https://chartio.com/learn/charts/how-to-select-a-data-vizualization/)：
-
-| 你的需求是：             | 你应该使用：                 |
-| ----------------------- | --------------------------- |
-| 展示随时间变化的数据趋势 | 折线图                       |
-| 比较类别               | 柱状图、饼图                 |
-| 比较总量               | 饼图、堆叠柱状图             |
-| 展示关系               | 散点图、折线图、分面图、双折线图 |
-| 展示分布               | 散点图、直方图、箱线图       |
-| 展示比例               | 饼图、环形图、华夫图         |
-
-> ✅ 根据数据的组成，你可能需要将其从文本转换为数值，以支持某些图表类型。
-
-## 避免误导
-
-即使数据科学家谨慎选择了适合数据的正确图表类型，数据仍然可能以某种方式被展示以证明某种观点，通常会以牺牲数据本身为代价。有许多误导性图表和信息图的例子！
-
-[![Alberto Cairo 的《How Charts Lie》](../../../../3-Data-Visualization/13-meaningful-visualizations/images/tornado.png)](https://www.youtube.com/watch?v=oX74Nge8Wkw "How charts lie")
-
-> 🎥 点击上方图片观看关于误导性图表的会议演讲
-
-这个图表颠倒了 X 轴，以日期为基础展示了与事实相反的内容：
-
-![bad chart 1](../../../../3-Data-Visualization/13-meaningful-visualizations/images/bad-chart-1.png)
-
-[这个图表](https://media.firstcoastnews.com/assets/WTLV/images/170ae16f-4643-438f-b689-50d66ca6a8d8/170ae16f-4643-438f-b689-50d66ca6a8d8_1140x641.jpg)更加误导，因为视觉上吸引人们得出结论认为随着时间推移，各县的 COVID 病例数量有所下降。实际上，如果仔细查看日期，你会发现日期被重新排列以制造这种误导性的下降趋势。
-
-![bad chart 2](../../../../3-Data-Visualization/13-meaningful-visualizations/images/bad-chart-2.jpg)
-
-这个臭名昭著的例子使用颜色和颠倒的 Y 轴来误导：本应得出枪支死亡人数在通过支持枪支的立法后激增的结论，实际上视觉上却让人误以为情况正好相反：
-
-![bad chart 3](../../../../3-Data-Visualization/13-meaningful-visualizations/images/bad-chart-3.jpg)
-
-这个奇怪的图表展示了比例如何被操纵，效果令人捧腹：
-
-![bad chart 4](../../../../3-Data-Visualization/13-meaningful-visualizations/images/bad-chart-4.jpg)
-
-比较不可比的事物是另一种不正当的手段。有一个[精彩的网站](https://tylervigen.com/spurious-correlations)专门展示“虚假的相关性”，例如缅因州的离婚率与人造黄油的消费量之间的“事实”相关性。Reddit 上也有一个小组收集了[数据的丑陋用法](https://www.reddit.com/r/dataisugly/top/?t=all)。
-
-理解眼睛如何容易被误导性图表欺骗是很重要的。即使数据科学家的意图是好的，选择错误的图表类型（例如显示过多类别的饼图）也可能具有误导性。
-
-## 颜色
-
-你在上面“佛罗里达枪支暴力”图表中看到，颜色可以为图表提供额外的意义层次，尤其是那些没有使用 Matplotlib 和 Seaborn 等库设计的图表，这些库自带各种经过验证的颜色库和调色板。如果你手动制作图表，可以稍微研究一下[颜色理论](https://colormatters.com/color-and-design/basic-color-theory)。
-
-> ✅ 在设计图表时，请注意可访问性是可视化的重要方面。你的部分用户可能是色盲——你的图表是否对视觉障碍用户友好？
-
-选择图表颜色时要小心，因为颜色可能传递你未曾预料的含义。上面“身高”图表中的“粉色女士”传递了一种明显的“女性化”意义，这增加了图表本身的怪异感。
-
-虽然[颜色含义](https://colormatters.com/color-symbolism/the-meanings-of-colors)可能因地区而异，并且根据其色调会有所变化，但一般来说，颜色含义包括：
-
-| 颜色   | 含义                 |
-| ------ | ------------------- |
-| 红色   | 力量                 |
-| 蓝色   | 信任、忠诚           |
-| 黄色   | 快乐、警告           |
-| 绿色   | 生态、幸运、嫉妒     |
-| 紫色   | 快乐                 |
-| 橙色   | 活力                 |
-
-如果你需要使用自定义颜色构建图表，请确保你的图表既可访问，又与试图传递的含义一致。
-
-## 为图表设计样式以提高可读性
-
-如果图表不可读，它就没有意义！花点时间考虑调整图表的宽度和高度，以便与数据良好匹配。如果需要显示一个变量（例如所有50个州），尽量将它们垂直显示在 Y 轴上，以避免水平滚动的图表。
-
-标注你的轴线，必要时提供图例，并提供工具提示以更好地理解数据。
-
-如果你的数据在 X 轴上是文本且较长，可以将文本倾斜以提高可读性。[Matplotlib](https://matplotlib.org/stable/tutorials/toolkits/mplot3d.html) 提供了 3D 绘图功能，如果你的数据支持它。可以使用 `mpl_toolkits.mplot3d` 生成复杂的数据可视化。
-
-![3d plots](../../../../3-Data-Visualization/13-meaningful-visualizations/images/3d.png)
-
-## 动画和3D图表展示
-
-如今一些最佳的数据可视化是动画化的。Shirley Wu 使用 D3 制作了令人惊叹的作品，例如“[电影之花](http://bl.ocks.org/sxywu/raw/d612c6c653fb8b4d7ff3d422be164a5d/)”，其中每朵花都是一部电影的可视化。另一个为《卫报》制作的例子是“Bussed Out”，一个结合 Greensock 和 D3 的交互式体验，外加滚动叙事文章格式，展示纽约市如何通过将人们送出城市来处理无家可归问题。
-
-![busing](../../../../3-Data-Visualization/13-meaningful-visualizations/images/busing.png)
-
-> “Bussed Out: How America Moves its Homeless” 来自 [卫报](https://www.theguardian.com/us-news/ng-interactive/2017/dec/20/bussed-out-america-moves-homeless-people-country-study)。可视化由 Nadieh Bremer 和 Shirley Wu 制作
-
-虽然本课不足以深入教授这些强大的可视化库，但你可以尝试在 Vue.js 应用中使用 D3，利用一个库来展示《危险关系》这本书的动画社交网络可视化。
-
-> 《危险关系》是一部书信体小说，即以一系列信件形式呈现的小说。由 Choderlos de Laclos 于1782年创作，讲述了18世纪末法国贵族中两位主角——瓦尔蒙子爵和梅尔特伊侯爵——之间恶毒、道德败坏的社交操纵故事。两人最终都遭遇了悲剧，但在此之前造成了巨大的社会破坏。小说通过他们圈子中写给各种人的信件展开，策划复仇或单纯制造麻烦。创建这些信件的可视化，发现叙事中的主要关键人物。
-
-你将完成一个网络应用程序，展示这个社交网络的动画视图。它使用一个库来创建[网络可视化](https://github.com/emiliorizzo/vue-d3-network)，基于 Vue.js 和 D3。当应用程序运行时，你可以在屏幕上拖动节点以重新排列数据。
-
-![liaisons](../../../../3-Data-Visualization/13-meaningful-visualizations/images/liaisons.png)
-
-## 项目：使用 D3.js 构建一个网络图表
-
-> 本课文件夹包含一个 `solution` 文件夹，你可以在其中找到完整的项目供参考。
-
-1. 按照起始文件夹根目录中的 README.md 文件中的说明操作。在安装项目依赖项之前，请确保你的机器上运行了 NPM 和 Node.js。
-
-2. 打开 `starter/src` 文件夹。你会发现一个 `assets` 文件夹，其中包含一个 .json 文件，记录了小说中的所有信件，编号，并带有“to”和“from”注释。
-
-3. 完成 `components/Nodes.vue` 中的代码以启用可视化。找到名为 `createLinks()` 的方法，并添加以下嵌套循环。
-
-循环遍历 .json 对象以捕获信件的“to”和“from”数据，并构建 `links` 对象，以便可视化库可以使用它：
-
-```javascript
-//loop through letters
-      let f = 0;
-      let t = 0;
-      for (var i = 0; i < letters.length; i++) {
-          for (var j = 0; j < characters.length; j++) {
-              
-            if (characters[j] == letters[i].from) {
-              f = j;
-            }
-            if (characters[j] == letters[i].to) {
-              t = j;
-            }
-        }
-        this.links.push({ sid: f, tid: t });
-      }
-  ```
-
-从终端运行你的应用程序（npm run serve），享受可视化效果！
-
-## 🚀 挑战
-
-浏览互联网，发现误导性可视化。作者是如何误导用户的，这是否是故意的？尝试纠正这些可视化，展示它们应该如何呈现。
-
-## [课后测验](https://ff-quizzes.netlify.app/en/ds/quiz/25)
-
-## 复习与自学
-
-以下是一些关于误导性数据可视化的文章：
-
-https://gizmodo.com/how-to-lie-with-data-visualization-1563576606
-
-http://ixd.prattsi.org/2017/12/visual-lies-usability-in-deceptive-data-visualizations/
-
-看看这些关于历史资产和文物的有趣可视化：
-
-https://handbook.pubpub.org/
-
-阅读这篇关于动画如何增强可视化的文章：
-
-https://medium.com/@EvanSinar/use-animation-to-supercharge-data-visualization-cd905a882ad4
-
-## 作业
-
-[创建你自己的自定义可视化](assignment.md)
-
----
-
-**免责声明**：  
-本文档使用AI翻译服务[Co-op Translator](https://github.com/Azure/co-op-translator)进行翻译。尽管我们努力确保翻译的准确性，但请注意，自动翻译可能包含错误或不准确之处。原始语言的文档应被视为权威来源。对于关键信息，建议使用专业人工翻译。我们不对因使用此翻译而产生的任何误解或误读承担责任。
\ No newline at end of file
diff --git a/translations/zh/3-Data-Visualization/13-meaningful-visualizations/assignment.md b/translations/zh/3-Data-Visualization/13-meaningful-visualizations/assignment.md
deleted file mode 100644
index 650e3243..00000000
--- a/translations/zh/3-Data-Visualization/13-meaningful-visualizations/assignment.md
+++ /dev/null
@@ -1,23 +0,0 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "e56df4c0f49357e30ac8fc77aa439dd4",
-  "translation_date": "2025-08-25T18:02:36+00:00",
-  "source_file": "3-Data-Visualization/13-meaningful-visualizations/assignment.md",
-  "language_code": "zh"
-}
--->
-# 构建你自己的自定义可视化
-
-## 指南
-
-使用本项目中的代码示例创建一个社交网络，模拟你自己的社交互动数据。你可以绘制你的社交媒体使用情况，或者制作一个家庭成员的关系图。创建一个有趣的网页应用，展示一个独特的社交网络可视化。
-
-## 评分标准
-
-优秀 | 合格 | 需要改进
---- | --- | --- |
-提供一个包含正常运行代码的 GitHub 仓库（尝试将其部署为静态网页应用），并附有注释清晰的 README 文件，解释项目内容 | 仓库无法正常运行或文档不够完善 | 仓库无法正常运行且文档不够完善
-
-**免责声明**：  
-本文档使用AI翻译服务[Co-op Translator](https://github.com/Azure/co-op-translator)进行翻译。尽管我们努力确保准确性，但请注意，自动翻译可能包含错误或不准确之处。应以原始语言的文档作为权威来源。对于关键信息，建议使用专业人工翻译。因使用本翻译而导致的任何误解或误读，我们概不负责。
\ No newline at end of file
diff --git a/translations/zh/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb b/translations/zh/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb
deleted file mode 100644
index 9ed1ef3b..00000000
--- a/translations/zh/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb
+++ /dev/null
@@ -1,100 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "b1e38707",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x600 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import pandas as pd\n",
-    "import seaborn as sns\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "data = {\n",
-    "    'Math': [80, 85, 90, 95, 100],\n",
-    "    'Physics': [82, 88, 91, 97, 99],\n",
-    "    'Chemistry': [78, 83, 88, 90, 94],\n",
-    "    'Biology': [76, 81, 85, 89, 92]\n",
-    "}\n",
-    "\n",
-    "df = pd.DataFrame(data)\n",
-    "corr = df.corr()\n",
-    "\n",
-    "plt.figure(figsize=(8, 6))\n",
-    "sns.heatmap(corr, annot=True, cmap='YlGnBu')\n",
-    "plt.title(\"Pearson Correlation Matrix\")\n",
-    "plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "da3c9cb7",
-   "metadata": {},
-   "source": [
-    "## 🔍 皮尔逊相关性分析\n",
-    "\n",
-    "本笔记本通过展示如何使用皮尔逊相关性分析多个特征之间的关系，增强了数据可视化模块。\n",
-    "\n",
-    "皮尔逊相关性衡量两个连续变量之间**线性关系**的强度和方向。它返回一个介于 -1 和 1 之间的值：\n",
-    "- **+1** → 完美的正相关关系  \n",
-    "- **0** → 无线性关系  \n",
-    "- **-1** → 完美的负相关关系\n",
-    "\n",
-    "在这里，我们计算了学生在数学、物理、化学和生物学科成绩的相关性矩阵。生成的**热力图**帮助我们直观地理解各学科成绩之间的相关性强弱。\n",
-    "\n",
-    "这种相关性分析在**特征工程和预处理**中至关重要，尤其是在构建机器学习模型之前。它有助于识别：\n",
-    "- 冗余特征\n",
-    "- 强预测因子\n",
-    "- 潜在的多重共线性\n",
-    "\n",
-    "在分析真实世界数据集时，这是一种对有意义的可视化的有益补充。\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n---\n\n**免责声明**：  \n本文档使用AI翻译服务[Co-op Translator](https://github.com/Azure/co-op-translator)进行翻译。尽管我们努力确保准确性，但请注意，自动翻译可能包含错误或不准确之处。应以原始语言的文档作为权威来源。对于关键信息，建议使用专业人工翻译。因使用本翻译而导致的任何误解或误读，我们概不负责。\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.13.1"
-  },
-  "coopTranslator": {
-   "original_hash": "8ad076c4d4df20aa5939d32b7a50baff",
-   "translation_date": "2025-09-06T19:37:23+00:00",
-   "source_file": "3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb",
-   "language_code": "zh"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
\ No newline at end of file
diff --git a/translations/zh/3-Data-Visualization/13-meaningful-visualizations/solution/README.md b/translations/zh/3-Data-Visualization/13-meaningful-visualizations/solution/README.md
deleted file mode 100644
index 5dea4ba3..00000000
--- a/translations/zh/3-Data-Visualization/13-meaningful-visualizations/solution/README.md
+++ /dev/null
@@ -1,38 +0,0 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "5c51a54dd89075a7a362890117b7ed9e",
-  "translation_date": "2025-08-25T18:03:58+00:00",
-  "source_file": "3-Data-Visualization/13-meaningful-visualizations/solution/README.md",
-  "language_code": "zh"
-}
--->
-# 危险关系数据可视化项目
-
-在开始之前，请确保您的计算机上已安装并运行了 NPM 和 Node。安装依赖项（npm install），然后在本地运行项目（npm run serve）：
-
-## 项目设置
-```
-npm install
-```
-
-### 编译并热加载用于开发
-```
-npm run serve
-```
-
-### 编译并压缩用于生产
-```
-npm run build
-```
-
-### 检查并修复文件
-```
-npm run lint
-```
-
-### 自定义配置
-请参阅[配置参考](https://cli.vuejs.org/config/)。
-
-**免责声明**：  
-本文档使用AI翻译服务 [Co-op Translator](https://github.com/Azure/co-op-translator) 进行翻译。尽管我们努力确保翻译的准确性，但请注意，自动翻译可能包含错误或不准确之处。应以原始语言的文档作为权威来源。对于重要信息，建议使用专业人工翻译。我们对因使用此翻译而产生的任何误解或误读不承担责任。
\ No newline at end of file
diff --git a/translations/zh/3-Data-Visualization/13-meaningful-visualizations/starter/README.md b/translations/zh/3-Data-Visualization/13-meaningful-visualizations/starter/README.md
deleted file mode 100644
index ce028630..00000000
--- a/translations/zh/3-Data-Visualization/13-meaningful-visualizations/starter/README.md
+++ /dev/null
@@ -1,38 +0,0 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "5c51a54dd89075a7a362890117b7ed9e",
-  "translation_date": "2025-08-25T18:03:21+00:00",
-  "source_file": "3-Data-Visualization/13-meaningful-visualizations/starter/README.md",
-  "language_code": "zh"
-}
--->
-# 危险关系数据可视化项目
-
-开始之前，请确保您的电脑上已安装 NPM 和 Node。安装依赖项（npm install），然后在本地运行项目（npm run serve）：
-
-## 项目设置
-```
-npm install
-```
-
-### 编译并热加载开发环境
-```
-npm run serve
-```
-
-### 编译并压缩生产环境
-```
-npm run build
-```
-
-### 检查并修复文件
-```
-npm run lint
-```
-
-### 自定义配置
-请参阅 [配置参考](https://cli.vuejs.org/config/)。
-
-**免责声明**：  
-本文档使用AI翻译服务 [Co-op Translator](https://github.com/Azure/co-op-translator) 进行翻译。尽管我们努力确保翻译的准确性，但请注意，自动翻译可能包含错误或不准确之处。原始语言的文档应被视为权威来源。对于重要信息，建议使用专业人工翻译。我们不对因使用此翻译而产生的任何误解或误读承担责任。
\ No newline at end of file
diff --git a/translations/zh/3-Data-Visualization/R/09-visualization-quantities/README.md b/translations/zh/3-Data-Visualization/R/09-visualization-quantities/README.md
deleted file mode 100644
index c7c59130..00000000
--- a/translations/zh/3-Data-Visualization/R/09-visualization-quantities/README.md
+++ /dev/null
@@ -1,231 +0,0 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "22acf28f518a4769ea14fa42f4734b9f",
-  "translation_date": "2025-08-25T18:20:34+00:00",
-  "source_file": "3-Data-Visualization/R/09-visualization-quantities/README.md",
-  "language_code": "zh"
-}
--->
-# 可视化数量
-|![由 [(@sketchthedocs)](https://sketchthedocs.dev) 绘制的草图笔记](https://github.com/microsoft/Data-Science-For-Beginners/blob/main/sketchnotes/09-Visualizing-Quantities.png)|
-|:---:|
-| 可视化数量 - _草图笔记由 [@nitya](https://twitter.com/nitya) 绘制_ |
-
-在本课中，你将学习如何使用一些 R 中可用的包库，围绕数量这一概念创建有趣的可视化。通过一个关于明尼苏达州鸟类的清理数据集，你可以了解许多关于当地野生动物的有趣事实。
-
-## [课前测验](https://purple-hill-04aebfb03.1.azurestaticapps.net/quiz/16)
-
-## 使用 ggplot2 观察翼展
-[ggplot2](https://cran.r-project.org/web/packages/ggplot2/index.html) 是一个非常优秀的库，可以用来创建各种简单或复杂的图表。一般来说，使用这些库绘制数据图的过程包括：确定数据框中要处理的部分，对数据进行必要的转换，指定 x 和 y 轴的值，选择图表类型，然后展示图表。
-
-`ggplot2` 是一个基于《图形语法》（The Grammar of Graphics）声明式创建图形的系统。《图形语法》是一种数据可视化的通用方案，将图形分解为语义组件，如比例和层次。换句话说，`ggplot2` 通过少量代码即可轻松创建单变量或多变量数据的图表，这使其成为 R 中最受欢迎的可视化包。用户只需告诉 `ggplot2` 如何将变量映射到美学属性、使用哪些图形元素，剩下的由 `ggplot2` 处理。
-
-> ✅ 图表 = 数据 + 美学 + 几何
-> - 数据：指数据集
-> - 美学：表示要研究的变量（x 和 y 变量）
-> - 几何：指图表类型（折线图、柱状图等）
-
-根据你的数据和想通过图表讲述的故事，选择最合适的几何类型（图表类型）。
-
-> - 分析趋势：折线图、柱状图
-> - 比较数值：条形图、柱状图、饼图、散点图
-> - 展示部分与整体的关系：饼图
-> - 展示数据分布：散点图、条形图
-> - 展示数值之间的关系：折线图、散点图、气泡图
-
-✅ 你还可以查看这个描述性的 [ggplot2 速查表](https://nyu-cdsc.github.io/learningr/assets/data-visualization-2.1.pdf)。
-
-## 绘制鸟类翼展值的折线图
-
-打开 R 控制台并导入数据集。
-> 注意：数据集存储在本仓库的 `/data` 文件夹中。
-
-让我们导入数据集并观察数据的头部（前 5 行）。
-
-```r
-birds <- read.csv("../../data/birds.csv",fileEncoding="UTF-8-BOM")
-head(birds)
-```
-数据头部包含文本和数字的混合：
-
-|      | 名称                          | 学名                   | 类别                  | 目           | 科       | 属          | 保护状态         | 最小长度 | 最大长度 | 最小体重   | 最大体重   | 最小翼展   | 最大翼展   |
-| ---: | :--------------------------- | :--------------------- | :-------------------- | :----------- | :------- | :---------- | :---------------- | --------: | --------: | ----------: | ----------: | ----------: | ----------: |
-|    0 | 黑腹树鸭                     | Dendrocygna autumnalis | 鸭/鹅/水禽            | 雁形目       | 鸭科     | 树鸭属       | LC                 |        47 |        56 |         652 |        1020 |          76 |          94 |
-|    1 | 棕树鸭                       | Dendrocygna bicolor    | 鸭/鹅/水禽            | 雁形目       | 鸭科     | 树鸭属       | LC                 |        45 |        53 |         712 |        1050 |          85 |          93 |
-|    2 | 雪鹅                         | Anser caerulescens     | 鸭/鹅/水禽            | 雁形目       | 鸭科     | 鹅属         | LC                 |        64 |        79 |        2050 |        4050 |         135 |         165 |
-|    3 | 罗斯氏鹅                     | Anser rossii           | 鸭/鹅/水禽            | 雁形目       | 鸭科     | 鹅属         | LC                 |      57.3 |        64 |        1066 |        1567 |         113 |         116 |
-|    4 | 大白额雁                     | Anser albifrons        | 鸭/鹅/水禽            | 雁形目       | 鸭科     | 鹅属         | LC                 |        64 |        81 |        1930 |        3310 |         130 |         165 |
-
-让我们从绘制一些数值数据的基本折线图开始。假设你想查看这些有趣鸟类的最大翼展。
-
-```r
-install.packages("ggplot2")
-library("ggplot2")
-ggplot(data=birds, aes(x=Name, y=MaxWingspan,group=1)) +
-  geom_line() 
-```
-在这里，你安装了 `ggplot2` 包并通过 `library("ggplot2")` 命令将其导入工作区。要在 ggplot 中绘制任何图表，使用 `ggplot()` 函数，并将数据集、x 和 y 变量作为属性指定。在这种情况下，我们使用 `geom_line()` 函数，因为我们要绘制折线图。
-
-![最大翼展折线图](../../../../../translated_images/zh-CN/MaxWingspan-lineplot.b12169f99d26fdd263f291008dfd73c18a4ba8f3d32b1fda3d74af51a0a28616.png)
-
-你立即注意到了什么？似乎至少有一个异常值——那是一个相当惊人的翼展！2000+ 厘米的翼展超过了 20 米——难道明尼苏达州有翼龙在飞翔？让我们调查一下。
-
-虽然你可以在 Excel 中快速排序找到这些可能是输入错误的异常值，但我们继续通过图表进行可视化分析。
-
-为 x 轴添加标签以显示涉及哪些鸟类：
-
-```r
-ggplot(data=birds, aes(x=Name, y=MaxWingspan,group=1)) +
-  geom_line() +
-  theme(axis.text.x = element_text(angle = 45, hjust=1))+
-  xlab("Birds") +
-  ylab("Wingspan (CM)") +
-  ggtitle("Max Wingspan in Centimeters")
-```
-我们在 `theme` 中指定了角度，并在 `xlab()` 和 `ylab()` 中分别指定了 x 和 y 轴的标签。`ggtitle()` 为图表命名。
-
-![改进后的最大翼展折线图](../../../../../translated_images/zh-CN/MaxWingspan-lineplot-improved.04b73b4d5a59552a6bc7590678899718e1f065abe9eada9ebb4148939b622fd4.png)
-
-即使将标签旋转到 45 度，仍然太多了，难以阅读。让我们尝试另一种策略：仅标记那些异常值，并在图表内设置标签。你可以使用散点图来腾出更多空间进行标记：
-
-```r
-ggplot(data=birds, aes(x=Name, y=MaxWingspan,group=1)) +
-  geom_point() +
-  geom_text(aes(label=ifelse(MaxWingspan>500,as.character(Name),'')),hjust=0,vjust=0) + 
-  theme(axis.title.x=element_blank(), axis.text.x=element_blank(), axis.ticks.x=element_blank())
-  ylab("Wingspan (CM)") +
-  ggtitle("Max Wingspan in Centimeters") + 
-```
-这里发生了什么？你使用了 `geom_point()` 函数绘制散点图。通过这种方式，你为 `MaxWingspan > 500` 的鸟类添加了标签，同时隐藏了 x 轴上的标签以减少图表的杂乱。
-
-你发现了什么？
-
-![最大翼展散点图](../../../../../translated_images/zh-CN/MaxWingspan-scatterplot.60dc9e0e19d32700283558f253841fdab5104abb62bc96f7d97f9c0ee857fa8b.png)
-
-## 筛选数据
-
-无论是秃鹰还是草原隼，虽然可能是非常大的鸟类，但它们的最大翼展似乎被错误标记，多加了一个 0。遇到翼展 25 米的秃鹰的可能性不大，但如果真的遇到，请告诉我们！让我们创建一个新的数据框，去掉这两个异常值：
-
-```r
-birds_filtered <- subset(birds, MaxWingspan < 500)
-
-ggplot(data=birds_filtered, aes(x=Name, y=MaxWingspan,group=1)) +
-  geom_point() +
-  ylab("Wingspan (CM)") +
-  xlab("Birds") +
-  ggtitle("Max Wingspan in Centimeters") + 
-  geom_text(aes(label=ifelse(MaxWingspan>500,as.character(Name),'')),hjust=0,vjust=0) +
-  theme(axis.text.x=element_blank(), axis.ticks.x=element_blank())
-```
-我们创建了一个新的数据框 `birds_filtered`，然后绘制了一个散点图。通过筛选掉异常值，你的数据现在更加连贯且易于理解。
-
-![改进后的最大翼展散点图](../../../../../translated_images/zh-CN/MaxWingspan-scatterplot-improved.7d0af81658c65f3e75b8fedeb2335399e31108257e48db15d875ece608272051.png)
-
-现在我们至少在翼展方面有了一个更干净的数据集，让我们进一步探索这些鸟类。
-
-虽然折线图和散点图可以显示数据值及其分布的信息，但我们还想思考数据集中固有的值。你可以创建可视化来回答以下关于数量的问题：
-
-> 有多少种鸟类类别？它们的数量是多少？
-> 有多少鸟类是灭绝的、濒危的、稀有的或常见的？
-> 根据林奈分类法，有多少种不同的属和目？
-
-## 探索条形图
-
-当你需要展示数据分组时，条形图非常实用。让我们探索数据集中存在的鸟类类别，看看哪种类别最常见。
-让我们在筛选后的数据上创建一个条形图。
-
-```r
-install.packages("dplyr")
-install.packages("tidyverse")
-
-library(lubridate)
-library(scales)
-library(dplyr)
-library(ggplot2)
-library(tidyverse)
-
-birds_filtered %>% group_by(Category) %>%
-  summarise(n=n(),
-  MinLength = mean(MinLength),
-  MaxLength = mean(MaxLength),
-  MinBodyMass = mean(MinBodyMass),
-  MaxBodyMass = mean(MaxBodyMass),
-  MinWingspan=mean(MinWingspan),
-  MaxWingspan=mean(MaxWingspan)) %>% 
-  gather("key", "value", - c(Category, n)) %>%
-  ggplot(aes(x = Category, y = value, group = key, fill = key)) +
-  geom_bar(stat = "identity") +
-  scale_fill_manual(values = c("#D62728", "#FF7F0E", "#8C564B","#2CA02C", "#1F77B4", "#9467BD")) +                   
-  xlab("Category")+ggtitle("Birds of Minnesota")
-
-```
-在以下代码片段中，我们安装了 [dplyr](https://www.rdocumentation.org/packages/dplyr/versions/0.7.8) 和 [lubridate](https://www.rdocumentation.org/packages/lubridate/versions/1.8.0) 包，以帮助操作和分组数据，从而绘制堆叠条形图。首先，你按鸟类的 `Category` 分组数据，然后汇总 `MinLength`、`MaxLength`、`MinBodyMass`、`MaxBodyMass`、`MinWingspan`、`MaxWingspan` 列。接着，使用 `ggplot2` 包绘制条形图，并为不同类别指定颜色和标签。
-
-![堆叠条形图](../../../../../translated_images/zh-CN/stacked-bar-chart.0c92264e89da7b391a7490224d1e7059a020e8b74dcd354414aeac78871c02f1.png)
-
-然而，这个条形图由于数据未分组过多而难以阅读。你需要选择要绘制的数据，因此让我们根据鸟类类别查看其长度。
-
-筛选数据以仅包含鸟类的类别。
-
-由于类别较多，你可以垂直显示此图表并调整其高度以适应所有数据：
-
-```r
-birds_count<-dplyr::count(birds_filtered, Category, sort = TRUE)
-birds_count$Category <- factor(birds_count$Category, levels = birds_count$Category)
-ggplot(birds_count,aes(Category,n))+geom_bar(stat="identity")+coord_flip()
-```
-你首先统计 `Category` 列中的唯一值，然后将它们排序到一个新的数据框 `birds_count` 中。接着，将这些排序后的数据按相同顺序分级，以便按排序方式绘制。使用 `ggplot2` 绘制条形图。`coord_flip()` 将条形图水平显示。
-
-![类别长度](../../../../../translated_images/zh-CN/category-length.7e34c296690e85d64f7e4d25a56077442683eca96c4f5b4eae120a64c0755636.png)
-
-这个条形图很好地展示了每个类别中鸟类的数量。一眼就能看出，这个地区数量最多的鸟类是鸭/鹅/水禽类别。明尼苏达州是“万湖之地”，这并不令人意外！
-
-✅ 尝试对该数据集进行其他计数。有什么让你感到惊讶的吗？
-
-## 比较数据
-
-你可以通过创建新的轴来尝试不同的分组数据比较。尝试比较基于类别的鸟类最大长度：
-
-```r
-birds_grouped <- birds_filtered %>%
-  group_by(Category) %>%
-  summarise(
-  MaxLength = max(MaxLength, na.rm = T),
-  MinLength = max(MinLength, na.rm = T)
-           ) %>%
-  arrange(Category)
-  
-ggplot(birds_grouped,aes(Category,MaxLength))+geom_bar(stat="identity")+coord_flip()
-```
-我们按 `Category` 对 `birds_filtered` 数据进行分组，然后绘制条形图。
-
-![比较数据](../../../../../translated_images/zh-CN/comparingdata.f486a450d61c7ca5416f27f3f55a6a4465d00df3be5e6d33936e9b07b95e2fdd.png)
-
-这里没有什么令人意外的：蜂鸟的最大长度最小，而鹈鹕或鹅的最大长度较大。当数据符合逻辑时，这是好事！
-
-你可以通过叠加数据创建更有趣的条形图可视化。让我们在给定的鸟类类别上叠加最小和最大长度：
-
-```r
-ggplot(data=birds_grouped, aes(x=Category)) +
-  geom_bar(aes(y=MaxLength), stat="identity", position ="identity",  fill='blue') +
-  geom_bar(aes(y=MinLength), stat="identity", position="identity", fill='orange')+
-  coord_flip()
-```
-![叠加值](../../../../../translated_images/zh-CN/superimposed-values.5363f0705a1da4167625a373a1064331ea3cb7a06a297297d0734fcc9b3819a0.png)
-
-## 🚀 挑战
-
-这个鸟类数据集提供了关于特定生态系统中不同类型鸟类的大量信息。在互联网上搜索，看看是否能找到其他与鸟类相关的数据集。练习围绕这些鸟类构建图表和图形，发现你之前未曾意识到的事实。
-
-## [课后测验](https://purple-hill-04aebfb03.1.azurestaticapps.net/quiz/17)
-
-## 复习与自学
-
-本课为你提供了一些关于如何使用 `ggplot2` 可视化数量的信息。研究其他可视化数据集的方法。查找并研究可以使用其他包（如 [Lattice](https://stat.ethz.ch/R-manual/R-devel/library/lattice/html/Lattice.html) 和 [Plotly](https://github.com/plotly/plotly.R#readme)）进行可视化的数据集。
-
-## 作业
-[折线图、散点图和条形图](assignment.md)
-
-**免责声明**：  
-本文档使用AI翻译服务 [Co-op Translator](https://github.com/Azure/co-op-translator) 进行翻译。尽管我们努力确保翻译的准确性，但请注意，自动翻译可能包含错误或不准确之处。应以原始语言的文档作为权威来源。对于重要信息，建议使用专业人工翻译。我们对因使用此翻译而产生的任何误解或误读不承担责任。
\ No newline at end of file
diff --git a/translations/zh/3-Data-Visualization/R/09-visualization-quantities/assignment.md b/translations/zh/3-Data-Visualization/R/09-visualization-quantities/assignment.md
deleted file mode 100644
index 2216bb3b..00000000
--- a/translations/zh/3-Data-Visualization/R/09-visualization-quantities/assignment.md
+++ /dev/null
@@ -1,23 +0,0 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "0ea21b6513df5ade7419c6b7d65f10b1",
-  "translation_date": "2025-08-25T18:25:50+00:00",
-  "source_file": "3-Data-Visualization/R/09-visualization-quantities/assignment.md",
-  "language_code": "zh"
-}
--->
-# 折线图、散点图和柱状图
-
-## 说明
-
-在本课中，你学习了如何使用折线图、散点图和柱状图来展示数据集中的有趣事实。在本次作业中，请深入挖掘数据集，发现关于某种鸟类的一个事实。例如，创建一个脚本，展示你能找到的关于雪雁的所有有趣数据。使用上述三种图表，在你的笔记本中讲述一个故事。
-
-## 评分标准
-
-优秀 | 合格 | 需改进
---- | --- | --- |
-脚本包含良好的注释、清晰的故事叙述以及吸引人的图表 | 脚本缺少其中一个要素 | 脚本缺少其中两个要素
-
-**免责声明**：  
-本文档使用AI翻译服务 [Co-op Translator](https://github.com/Azure/co-op-translator) 进行翻译。尽管我们努力确保翻译的准确性，但请注意，自动翻译可能包含错误或不准确之处。应以原始语言的文档作为权威来源。对于重要信息，建议使用专业人工翻译。我们对因使用此翻译而产生的任何误解或误读不承担责任。
\ No newline at end of file
diff --git a/translations/zh/3-Data-Visualization/R/10-visualization-distributions/README.md b/translations/zh/3-Data-Visualization/R/10-visualization-distributions/README.md
deleted file mode 100644
index 9211d75e..00000000
--- a/translations/zh/3-Data-Visualization/R/10-visualization-distributions/README.md
+++ /dev/null
@@ -1,183 +0,0 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "ea67c0c40808fd723594de6896c37ccf",
-  "translation_date": "2025-08-25T18:12:47+00:00",
-  "source_file": "3-Data-Visualization/R/10-visualization-distributions/README.md",
-  "language_code": "zh"
-}
--->
-# 可视化分布
-
-|![由 [(@sketchthedocs)](https://sketchthedocs.dev) 绘制的速记图](https://github.com/microsoft/Data-Science-For-Beginners/blob/main/sketchnotes/10-Visualizing-Distributions.png)|
-|:---:|
-| 可视化分布 - _速记图作者 [@nitya](https://twitter.com/nitya)_ |
-
-在上一节课中，你学习了关于明尼苏达州鸟类数据集的一些有趣事实。通过可视化异常值，你发现了一些错误数据，并通过最大长度观察了鸟类类别之间的差异。
-
-## [课前测验](https://purple-hill-04aebfb03.1.azurestaticapps.net/quiz/18)
-## 探索鸟类数据集
-
-另一种深入了解数据的方法是查看其分布，即数据如何沿轴排列。例如，你可能想了解这个数据集中鸟类的最大翼展或最大体重的一般分布情况。
-
-让我们来发现一些关于这个数据集分布的事实。在你的 R 控制台中，导入 `ggplot2` 和数据库。像上一节一样，从数据库中移除异常值。
-
-```r
-library(ggplot2)
-
-birds <- read.csv("../../data/birds.csv",fileEncoding="UTF-8-BOM")
-
-birds_filtered <- subset(birds, MaxWingspan < 500)
-head(birds_filtered)
-```
-|      | 名称                         | 学名                   | 类别                  | 目           | 科       | 属          | 保护状态           | 最小长度 | 最大长度 | 最小体重     | 最大体重     | 最小翼展     | 最大翼展     |
-| ---: | :--------------------------- | :--------------------- | :-------------------- | :----------- | :------- | :---------- | :----------------- | --------: | --------: | ----------: | ----------: | ----------: | ----------: |
-|    0 | 黑腹叫鸭                     | Dendrocygna autumnalis | 鸭/鹅/水禽            | 雁形目       | 鸭科     | 栖鸭属      | LC                 |        47 |        56 |         652 |        1020 |          76 |          94 |
-|    1 | 棕腹叫鸭                     | Dendrocygna bicolor    | 鸭/鹅/水禽            | 雁形目       | 鸭科     | 栖鸭属      | LC                 |        45 |        53 |         712 |        1050 |          85 |          93 |
-|    2 | 雪鹅                         | Anser caerulescens     | 鸭/鹅/水禽            | 雁形目       | 鸭科     | 雁属        | LC                 |        64 |        79 |        2050 |        4050 |         135 |         165 |
-|    3 | 罗氏鹅                       | Anser rossii           | 鸭/鹅/水禽            | 雁形目       | 鸭科     | 雁属        | LC                 |      57.3 |        64 |        1066 |        1567 |         113 |         116 |
-|    4 | 大白额雁                     | Anser albifrons        | 鸭/鹅/水禽            | 雁形目       | 鸭科     | 雁属        | LC                 |        64 |        81 |        1930 |        3310 |         130 |         165 |
-
-通常，你可以通过使用散点图快速查看数据的分布方式，就像我们在上一节课中所做的那样：
-
-```r
-ggplot(data=birds_filtered, aes(x=Order, y=MaxLength,group=1)) +
-  geom_point() +
-  ggtitle("Max Length per order") + coord_flip()
-```
-![每目最大长度](../../../../../translated_images/zh-CN/max-length-per-order.e5b283d952c78c12b091307c5d3cf67132dad6fefe80a073353b9dc5c2bd3eb8.png)
-
-这提供了每个鸟类目身体长度的一般分布概览，但这并不是显示真实分布的最佳方式。通常通过创建直方图来完成这一任务。
-
-## 使用直方图
-
-`ggplot2` 提供了非常好的方法来使用直方图可视化数据分布。这种图表类似于柱状图，通过柱子的升降可以看到分布情况。要构建直方图，你需要数值数据。构建直方图时，可以将图表类型定义为“hist”以表示直方图。此图表显示整个数据集范围内最大体重的分布。通过将数据数组划分为较小的区间，它可以显示数据值的分布：
-
-```r
-ggplot(data = birds_filtered, aes(x = MaxBodyMass)) + 
-  geom_histogram(bins=10)+ylab('Frequency')
-```
-![整个数据集的分布](../../../../../translated_images/zh-CN/distribution-over-the-entire-dataset.d22afd3fa96be854e4c82213fedec9e3703cba753d07fad4606aadf58cf7e78e.png)
-
-如你所见，这个数据集中的 400 多种鸟类大多数最大体重都在 2000 以下。通过将 `bins` 参数更改为更高的数字，例如 30，可以获得更多数据洞察：
-
-```r
-ggplot(data = birds_filtered, aes(x = MaxBodyMass)) + geom_histogram(bins=30)+ylab('Frequency')
-```
-
-![30个区间的分布](../../../../../translated_images/zh-CN/distribution-30bins.6a3921ea7a421bf71f06bf5231009e43d1146f1b8da8dc254e99b5779a4983e5.png)
-
-此图表以更细致的方式显示分布。通过确保仅选择特定范围内的数据，可以创建一个偏向左侧较少的图表：
-
-过滤数据，仅获取体重低于 60 的鸟类，并显示 30 个 `bins`：
-
-```r
-birds_filtered_1 <- subset(birds_filtered, MaxBodyMass > 1 & MaxBodyMass < 60)
-ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + 
-  geom_histogram(bins=30)+ylab('Frequency')
-```
-
-![过滤后的直方图](../../../../../translated_images/zh-CN/filtered-histogram.6bf5d2bfd82533220e1bd4bc4f7d14308f43746ed66721d9ec8f460732be6674.png)
-
-✅ 尝试其他过滤器和数据点。要查看数据的完整分布，请移除 `['MaxBodyMass']` 过滤器以显示带标签的分布。
-
-直方图还提供了一些不错的颜色和标签增强功能可以尝试：
-
-创建一个二维直方图来比较两个分布之间的关系。让我们比较 `MaxBodyMass` 和 `MaxLength`。`ggplot2` 提供了一种内置方法，通过更亮的颜色显示收敛：
-
-```r
-ggplot(data=birds_filtered_1, aes(x=MaxBodyMass, y=MaxLength) ) +
-  geom_bin2d() +scale_fill_continuous(type = "viridis")
-```
-可以看到这两个元素沿预期轴存在预期的相关性，其中一个点的收敛特别强：
-
-![二维图](../../../../../translated_images/zh-CN/2d-plot.c504786f439bd7ebceebf2465c70ca3b124103e06c7ff7214bf24e26f7aec21e.png)
-
-直方图默认适用于数值数据。如果需要根据文本数据查看分布该怎么办？
-
-## 使用文本数据探索数据集分布
-
-此数据集还包括关于鸟类类别及其属、种、科以及保护状态的良好信息。让我们深入了解这些保护信息。根据保护状态，鸟类的分布情况如何？
-
-> ✅ 在数据集中，使用了一些缩写来描述保护状态。这些缩写来自 [IUCN 红色名录类别](https://www.iucnredlist.org/)，这是一个记录物种状态的组织。
-> 
-> - CR: 极危
-> - EN: 濒危
-> - EX: 灭绝
-> - LC: 无危
-> - NT: 近危
-> - VU: 易危
-
-这些是基于文本的值，因此需要进行转换以创建直方图。使用过滤后的鸟类数据框，显示其保护状态及其最小翼展。你看到了什么？
-
-```r
-birds_filtered_1$ConservationStatus[birds_filtered_1$ConservationStatus == 'EX'] <- 'x1' 
-birds_filtered_1$ConservationStatus[birds_filtered_1$ConservationStatus == 'CR'] <- 'x2'
-birds_filtered_1$ConservationStatus[birds_filtered_1$ConservationStatus == 'EN'] <- 'x3'
-birds_filtered_1$ConservationStatus[birds_filtered_1$ConservationStatus == 'NT'] <- 'x4'
-birds_filtered_1$ConservationStatus[birds_filtered_1$ConservationStatus == 'VU'] <- 'x5'
-birds_filtered_1$ConservationStatus[birds_filtered_1$ConservationStatus == 'LC'] <- 'x6'
-
-ggplot(data=birds_filtered_1, aes(x = MinWingspan, fill = ConservationStatus)) +
-  geom_histogram(position = "identity", alpha = 0.4, bins = 20) +
-  scale_fill_manual(name="Conservation Status",values=c("red","green","blue","pink"),labels=c("Endangered","Near Threathened","Vulnerable","Least Concern"))
-```
-
-![翼展与保护状态的关联](../../../../../translated_images/zh-CN/wingspan-conservation-collation.4024e9aa6910866aa82f0c6cb6a6b4b925bd10079e6b0ef8f92eefa5a6792f76.png)
-
-最小翼展与保护状态之间似乎没有明显的相关性。使用此方法测试数据集中的其他元素。你可以尝试不同的过滤器。是否发现任何相关性？
-
-## 密度图
-
-你可能注意到我们迄今为止看到的直方图是“阶梯状”的，并没有平滑地呈弧形。要显示更平滑的密度图，可以尝试密度图。
-
-现在让我们来使用密度图！
-
-```r
-ggplot(data = birds_filtered_1, aes(x = MinWingspan)) + 
-  geom_density()
-```
-![密度图](../../../../../translated_images/zh-CN/density-plot.675ccf865b76c690487fb7f69420a8444a3515f03bad5482886232d4330f5c85.png)
-
-你可以看到此图与之前的最小翼展数据图相呼应；它只是稍微平滑了一些。如果你想重新创建第二个图表中那个不平滑的最大体重线，可以通过这种方法很好地将其平滑化：
-
-```r
-ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + 
-  geom_density()
-```
-![体重密度](../../../../../translated_images/zh-CN/bodymass-smooth.d31ce526d82b0a1f19a073815dea28ecfbe58145ec5337e4ef7e8cdac81120b3.png)
-
-如果你想要一个平滑但不过于平滑的线条，可以编辑 `adjust` 参数：
-
-```r
-ggplot(data = birds_filtered_1, aes(x = MaxBodyMass)) + 
-  geom_density(adjust = 1/5)
-```
-![较少平滑的体重](../../../../../translated_images/zh-CN/less-smooth-bodymass.10f4db8b683cc17d17b2d33f22405413142004467a1493d416608dafecfdee23.png)
-
-✅ 阅读有关此类图表可用参数的内容并进行实验！
-
-这种图表提供了非常直观的可视化。例如，通过几行代码，你可以显示每个鸟类目最大体重的密度：
-
-```r
-ggplot(data=birds_filtered_1,aes(x = MaxBodyMass, fill = Order)) +
-  geom_density(alpha=0.5)
-```
-![每目体重](../../../../../translated_images/zh-CN/bodymass-per-order.9d2b065dd931b928c839d8cdbee63067ab1ae52218a1b90717f4bc744354f485.png)
-
-## 🚀 挑战
-
-直方图是一种比基本散点图、柱状图或折线图更复杂的图表类型。上网搜索直方图的优秀使用案例。它们是如何使用的，展示了什么内容，以及它们通常在哪些领域或研究领域中使用？
-
-## [课后测验](https://purple-hill-04aebfb03.1.azurestaticapps.net/quiz/19)
-
-## 复习与自学
-
-在本课中，你使用了 `ggplot2` 并开始展示更复杂的图表。研究一下 `geom_density_2d()`，它是一种“一维或多维的连续概率密度曲线”。阅读 [文档](https://ggplot2.tidyverse.org/reference/geom_density_2d.html) 以了解其工作原理。
-
-## 作业
-
-[应用你的技能](assignment.md)
-
-**免责声明**：  
-本文档使用AI翻译服务 [Co-op Translator](https://github.com/Azure/co-op-translator) 进行翻译。尽管我们努力确保翻译的准确性，但请注意，自动翻译可能包含错误或不准确之处。原始语言的文档应被视为权威来源。对于关键信息，建议使用专业人工翻译。我们不对因使用此翻译而产生的任何误解或误读承担责任。
\ No newline at end of file
diff --git a/translations/zh/3-Data-Visualization/R/10-visualization-distributions/assignment.md b/translations/zh/3-Data-Visualization/R/10-visualization-distributions/assignment.md
deleted file mode 100644
index 573d3456..00000000
--- a/translations/zh/3-Data-Visualization/R/10-visualization-distributions/assignment.md
+++ /dev/null
@@ -1,23 +0,0 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "a233d542512136c4dd29aad38ca0175f",
-  "translation_date": "2025-08-25T18:15:57+00:00",
-  "source_file": "3-Data-Visualization/R/10-visualization-distributions/assignment.md",
-  "language_code": "zh"
-}
--->
-# 应用你的技能
-
-## 说明
-
-到目前为止，你已经使用了明尼苏达州鸟类数据集，探索了有关鸟类数量和种群密度的信息。现在，尝试使用一个不同的数据集来练习这些技术，可以从 [Kaggle](https://www.kaggle.com/) 获取数据集。编写一个 R 脚本，讲述关于这个数据集的故事，并确保在分析中使用直方图。
-
-## 评分标准
-
-优秀 | 合格 | 需要改进
---- | --- | -- |
-脚本包含关于数据集及其来源的注释，并使用至少 5 个直方图来探索数据中的信息。 | 脚本包含不完整的注释或存在错误。 | 脚本没有注释且包含错误。
-
-**免责声明**：  
-本文档使用AI翻译服务 [Co-op Translator](https://github.com/Azure/co-op-translator) 进行翻译。尽管我们努力确保翻译的准确性，但请注意，自动翻译可能包含错误或不准确之处。原始语言的文档应被视为权威来源。对于重要信息，建议使用专业人工翻译。我们不对因使用此翻译而产生的任何误解或误读承担责任。
\ No newline at end of file
diff --git a/translations/zh/3-Data-Visualization/R/11-visualization-proportions/README.md b/translations/zh/3-Data-Visualization/R/11-visualization-proportions/README.md
deleted file mode 100644
index 7d635a11..00000000
--- a/translations/zh/3-Data-Visualization/R/11-visualization-proportions/README.md
+++ /dev/null
@@ -1,198 +0,0 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "47028abaaafa2bcb1079702d20569066",
-  "translation_date": "2025-08-25T18:30:29+00:00",
-  "source_file": "3-Data-Visualization/R/11-visualization-proportions/README.md",
-  "language_code": "zh"
-}
--->
-# 可视化比例
-
-|![由 [(@sketchthedocs)](https://sketchthedocs.dev) 绘制的速记图](../../../sketchnotes/11-Visualizing-Proportions.png)|
-|:---:|
-|可视化比例 - _速记图由 [@nitya](https://twitter.com/nitya) 绘制_ |
-
-在本课中，你将使用一个以自然为主题的数据集来可视化比例，例如在关于蘑菇的数据集中有多少种不同类型的真菌。让我们通过一个来自 Audubon 的数据集来探索这些迷人的真菌，该数据集列出了 Agaricus 和 Lepiota 家族中 23 种有鳃蘑菇的详细信息。你将尝试一些有趣的可视化方法，例如：
-
-- 饼图 🥧
-- 环形图 🍩
-- 华夫图 🧇
-
-> 💡 一个非常有趣的项目 [Charticulator](https://charticulator.com) 由微软研究院开发，提供了一个免费的拖放界面用于数据可视化。在他们的一个教程中也使用了这个蘑菇数据集！因此，你可以同时探索数据并学习这个库：[Charticulator 教程](https://charticulator.com/tutorials/tutorial4.html)。
-
-## [课前测验](https://purple-hill-04aebfb03.1.azurestaticapps.net/quiz/20)
-
-## 了解你的蘑菇 🍄
-
-蘑菇非常有趣。让我们导入一个数据集来研究它们：
-
-```r
-mushrooms = read.csv('../../data/mushrooms.csv')
-head(mushrooms)
-```
-打印出一个表格，其中包含一些适合分析的优秀数据：
-
-| 类别       | 帽型       | 帽表面       | 帽颜色       | 是否有瘀伤 | 气味       | 鳃附着方式       | 鳃间距       | 鳃大小       | 鳃颜色       | 茎形状       | 茎根部       | 环上方茎表面       | 环下方茎表面       | 环上方茎颜色       | 环下方茎颜色       | 面纱类型       | 面纱颜色       | 环数量       | 环类型       | 孢子印颜色       | 种群数量       | 栖息地       |
-| --------- | --------- | ----------- | --------- | ------- | ------- | --------------- | ------------ | --------- | ---------- | ----------- | ---------- | ------------------------ | ------------------------ | ---------------------- | ---------------------- | --------- | ---------- | ----------- | --------- | ----------------- | ---------- | ------- |
-| 有毒      | 凸形       | 光滑        | 棕色        | 有瘀伤   | 刺鼻    | 自由            | 紧密        | 狭窄       | 黑色        | 扩大        | 等长        | 光滑                   | 光滑                   | 白色                  | 白色                  | 部分       | 白色       | 一个         | 垂饰       | 黑色             | 分散       | 城市       |
-| 可食用    | 凸形       | 光滑        | 黄色        | 有瘀伤   | 杏仁    | 自由            | 紧密        | 宽广       | 黑色        | 扩大        | 棍状       | 光滑                   | 光滑                   | 白色                  | 白色                  | 部分       | 白色       | 一个         | 垂饰       | 棕色             | 众多       | 草地       |
-| 可食用    | 钟形       | 光滑        | 白色        | 有瘀伤   | 茴香    | 自由            | 紧密        | 宽广       | 棕色        | 扩大        | 棍状       | 光滑                   | 光滑                   | 白色                  | 白色                  | 部分       | 白色       | 一个         | 垂饰       | 棕色             | 众多       | 草地       |
-| 有毒      | 凸形       | 鳞状        | 白色        | 有瘀伤   | 刺鼻    | 自由            | 紧密        | 狭窄       | 棕色        | 扩大        | 等长        | 光滑                   | 光滑                   | 白色                  | 白色                  | 部分       | 白色       | 一个         | 垂饰       | 黑色             | 分散       | 城市       |
-| 可食用    | 凸形       | 光滑        | 绿色        | 无瘀伤   | 无气味  | 自由            | 拥挤        | 宽广       | 黑色        | 锥形        | 等长        | 光滑                   | 光滑                   | 白色                  | 白色                  | 部分       | 白色       | 一个         | 消失       | 棕色             | 丰富       | 草地       |
-| 可食用    | 凸形       | 鳞状        | 黄色        | 有瘀伤   | 杏仁    | 自由            | 紧密        | 宽广       | 棕色        | 扩大        | 棍状        | 光滑                   | 光滑                   | 白色                  | 白色                  | 部分       | 白色       | 一个         | 垂饰       | 黑色             | 众多       | 草地       |
-
-你会立即注意到所有数据都是文本格式。为了在图表中使用这些数据，你需要将其转换。事实上，大部分数据是以对象形式表示的：
-
-```r
-names(mushrooms)
-```
-
-输出为：
-
-```output
-[1] "class"                    "cap.shape"               
- [3] "cap.surface"              "cap.color"               
- [5] "bruises"                  "odor"                    
- [7] "gill.attachment"          "gill.spacing"            
- [9] "gill.size"                "gill.color"              
-[11] "stalk.shape"              "stalk.root"              
-[13] "stalk.surface.above.ring" "stalk.surface.below.ring"
-[15] "stalk.color.above.ring"   "stalk.color.below.ring"  
-[17] "veil.type"                "veil.color"              
-[19] "ring.number"              "ring.type"               
-[21] "spore.print.color"        "population"              
-[23] "habitat"            
-```
-将数据中的“类别”列转换为分类：
-
-```r
-library(dplyr)
-grouped=mushrooms %>%
-  group_by(class) %>%
-  summarise(count=n())
-```
-
-现在，如果打印出蘑菇数据，你会看到它已经根据有毒/可食用类别分组：
-
-```r
-View(grouped)
-```
-
-| 类别       | 数量       |
-| --------- | --------- |
-| 可食用    | 4208      |
-| 有毒      | 3916      |
-
-如果按照此表中呈现的顺序创建类别标签，你可以绘制一个饼图。
-
-## 饼图！
-
-```r
-pie(grouped$count,grouped$class, main="Edible?")
-```
-瞧，一个饼图展示了根据蘑菇的两种类别的数据比例。在这里，确保标签数组的顺序正确非常重要，因此务必验证标签的构建顺序！
-
-![饼图](../../../../../translated_images/zh-CN/pie1-wb.685df063673751f4b0b82127f7a52c7f9a920192f22ae61ad28412ba9ace97bf.png)
-
-## 环形图！
-
-环形图是一种更具视觉吸引力的饼图，它在中间有一个空洞。让我们用这种方法查看数据。
-
-看看蘑菇生长的各种栖息地：
-
-```r
-library(dplyr)
-habitat=mushrooms %>%
-  group_by(habitat) %>%
-  summarise(count=n())
-View(habitat)
-```
-输出为：
-
-| 栖息地       | 数量       |
-| --------- | --------- |
-| 草地       | 2148      |
-| 树叶       | 832       |
-| 草坪       | 292       |
-| 小径       | 1144      |
-| 城市       | 368       |
-| 废地       | 192       |
-| 木材       | 3148      |
-
-这里，你将数据按栖息地分组。共有 7 个栖息地，因此使用这些作为环形图的标签：
-
-```r
-library(ggplot2)
-library(webr)
-PieDonut(habitat, aes(habitat, count=count))
-```
-
-![环形图](../../../../../translated_images/zh-CN/donut-wb.34e6fb275da9d834c2205145e39a3de9b6878191dcdba6f7a9e85f4b520449bc.png)
-
-此代码使用了两个库——ggplot2 和 webr。通过 webr 库的 PieDonut 函数，我们可以轻松创建环形图！
-
-在 R 中，仅使用 ggplot2 库也可以制作环形图。你可以在 [这里](https://www.r-graph-gallery.com/128-ring-or-donut-plot.html) 学习更多并尝试自己制作。
-
-现在你已经知道如何分组数据并将其显示为饼图或环形图，你可以探索其他类型的图表。试试华夫图，这是一种不同的方式来探索数量。
-
-## 华夫图！
-
-“华夫”类型图表是一种以二维方块阵列可视化数量的方式。尝试可视化此数据集中蘑菇帽颜色的不同数量。为此，你需要安装一个辅助库 [waffle](https://cran.r-project.org/web/packages/waffle/waffle.pdf) 并使用它生成可视化：
-
-```r
-install.packages("waffle", repos = "https://cinc.rud.is")
-```
-
-选择数据的一部分进行分组：
-
-```r
-library(dplyr)
-cap_color=mushrooms %>%
-  group_by(cap.color) %>%
-  summarise(count=n())
-View(cap_color)
-```
-
-通过创建标签并分组数据来创建华夫图：
-
-```r
-library(waffle)
-names(cap_color$count) = paste0(cap_color$cap.color)
-waffle((cap_color$count/10), rows = 7, title = "Waffle Chart")+scale_fill_manual(values=c("brown", "#F0DC82", "#D2691E", "green", 
-                                                                                     "pink", "purple", "red", "grey", 
-                                                                                     "yellow","white"))
-```
-
-使用华夫图，你可以清楚地看到蘑菇数据集中帽颜色的比例。有趣的是，有许多绿色帽子的蘑菇！
-
-![华夫图](../../../../../translated_images/zh-CN/waffle.aaa75c5337735a6ef32ace0ffb6506ef49e5aefe870ffd72b1bb080f4843c217.png)
-
-在本课中，你学习了三种可视化比例的方法。首先，你需要将数据分组为类别，然后决定哪种方式最适合显示数据——饼图、环形图或华夫图。所有这些都很有趣，并能让用户快速了解数据集。
-
-## 🚀 挑战
-
-尝试在 [Charticulator](https://charticulator.com) 中重新创建这些有趣的图表。
-
-## [课后测验](https://purple-hill-04aebfb03.1.azurestaticapps.net/quiz/21)
-
-## 复习与自学
-
-有时不容易判断何时使用饼图、环形图或华夫图。以下是一些相关文章：
-
-https://www.beautiful.ai/blog/battle-of-the-charts-pie-chart-vs-donut-chart
-
-https://medium.com/@hypsypops/pie-chart-vs-donut-chart-showdown-in-the-ring-5d24fd86a9ce
-
-https://www.mit.edu/~mbarker/formula1/f1help/11-ch-c6.htm
-
-https://medium.datadriveninvestor.com/data-visualization-done-the-right-way-with-tableau-waffle-chart-fdf2a19be402
-
-进行一些研究以获取更多关于这个选择的信息。
-
-## 作业
-
-[在 Excel 中尝试](assignment.md)
-
-**免责声明**：  
-本文档使用AI翻译服务[Co-op Translator](https://github.com/Azure/co-op-translator)进行翻译。尽管我们努力确保翻译的准确性，但请注意，自动翻译可能包含错误或不准确之处。原始语言的文档应被视为权威来源。对于关键信息，建议使用专业人工翻译。我们对因使用此翻译而产生的任何误解或误读不承担责任。
\ No newline at end of file
diff --git a/translations/zh/3-Data-Visualization/R/12-visualization-relationships/README.md b/translations/zh/3-Data-Visualization/R/12-visualization-relationships/README.md
deleted file mode 100644
index 194170e1..00000000
--- a/translations/zh/3-Data-Visualization/R/12-visualization-relationships/README.md
+++ /dev/null
@@ -1,177 +0,0 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "a33c5d4b4156a2b41788d8720b6f724c",
-  "translation_date": "2025-08-25T18:16:40+00:00",
-  "source_file": "3-Data-Visualization/R/12-visualization-relationships/README.md",
-  "language_code": "zh"
-}
--->
-# 可视化关系：关于蜂蜜的一切 🍯
-
-|![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../../sketchnotes/12-Visualizing-Relationships.png)|
-|:---:|
-|可视化关系 - _Sketchnote by [@nitya](https://twitter.com/nitya)_ |
-
-延续我们研究的自然主题，让我们探索一些有趣的可视化方法，展示不同类型蜂蜜之间的关系。这些数据来源于[美国农业部](https://www.nass.usda.gov/About_NASS/index.php)。
-
-这个包含约600项的数据集展示了美国多个州的蜂蜜生产情况。例如，你可以查看每个州从1998到2012年的蜂群数量、每群产量、总产量、库存、每磅价格以及蜂蜜生产的价值，每个州每年一行数据。
-
-我们可以通过可视化来研究某个州每年的生产情况与该州蜂蜜价格之间的关系。或者，你也可以可视化各州每群蜂蜜产量之间的关系。这段时间涵盖了2006年首次出现的毁灭性“蜂群崩溃综合症（CCD）”（http://npic.orst.edu/envir/ccd.html），因此这是一个值得研究的引人深思的数据集。🐝
-
-## [课前测验](https://purple-hill-04aebfb03.1.azurestaticapps.net/quiz/22)
-
-在本课中，你可以使用之前用过的ggplot2库，这是一个很好的工具，用于可视化变量之间的关系。特别有趣的是ggplot2的`geom_point`和`qplot`函数，它们可以快速生成散点图和折线图，用于可视化“[统计关系](https://ggplot2.tidyverse.org/)”，帮助数据科学家更好地理解变量之间的关联。
-
-## 散点图
-
-使用散点图展示蜂蜜价格在每个州逐年变化的情况。ggplot2通过`ggplot`和`geom_point`可以方便地对州数据进行分组，并显示分类和数值数据的点。
-
-让我们从导入数据和Seaborn开始：
-
-```r
-honey=read.csv('../../data/honey.csv')
-head(honey)
-```
-你会注意到蜂蜜数据中有几个有趣的列，包括年份和每磅价格。让我们按美国州分组来探索这些数据：
-
-| 州   | 蜂群数量 | 每群产量 | 总产量   | 库存     | 每磅价格   | 生产价值   | 年份 |
-| ----- | ------ | ----------- | --------- | -------- | ---------- | --------- | ---- |
-| AL    | 16000  | 71          | 1136000   | 159000   | 0.72       | 818000    | 1998 |
-| AZ    | 55000  | 60          | 3300000   | 1485000  | 0.64       | 2112000   | 1998 |
-| AR    | 53000  | 65          | 3445000   | 1688000  | 0.59       | 2033000   | 1998 |
-| CA    | 450000 | 83          | 37350000  | 12326000 | 0.62       | 23157000  | 1998 |
-| CO    | 27000  | 72          | 1944000   | 1594000  | 0.7        | 1361000   | 1998 |
-| FL    | 230000 | 98          |22540000   | 4508000  | 0.64       | 14426000  | 1998 |
-
-创建一个基础散点图，展示蜂蜜每磅价格与其来源州之间的关系。让`y`轴足够高以显示所有州：
-
-```r
-library(ggplot2)
-ggplot(honey, aes(x = priceperlb, y = state)) +
-  geom_point(colour = "blue")
-```
-![scatterplot 1](../../../../../translated_images/zh-CN/scatter1.86b8900674d88b26dd3353a83fe604e9ab3722c4680cc40ee9beb452ff02cdea.png)
-
-现在，用蜂蜜色调展示同样的数据，显示价格随年份的变化。你可以通过添加`scale_color_gradientn`参数来实现逐年变化的可视化：
-
-> ✅ 了解更多关于[scale_color_gradientn](https://www.rdocumentation.org/packages/ggplot2/versions/0.9.1/topics/scale_colour_gradientn)的信息 - 尝试一个美丽的彩虹色方案！
-
-```r
-ggplot(honey, aes(x = priceperlb, y = state, color=year)) +
-  geom_point()+scale_color_gradientn(colours = colorspace::heat_hcl(7))
-```
-![scatterplot 2](../../../../../translated_images/zh-CN/scatter2.4d1cbc693bad20e2b563888747eb6bdf65b73ce449d903f7cd4068a78502dcff.png)
-
-通过这个颜色方案的变化，你可以明显看到蜂蜜每磅价格在这些年间逐年上涨。如果你查看数据中的一个样本集（例如亚利桑那州），你会发现价格逐年上涨的模式，虽然有少数例外：
-
-| 州   | 蜂群数量 | 每群产量 | 总产量   | 库存     | 每磅价格   | 生产价值   | 年份 |
-| ----- | ------ | ----------- | --------- | ------- | ---------- | --------- | ---- |
-| AZ    | 55000  | 60          | 3300000   | 1485000 | 0.64       | 2112000   | 1998 |
-| AZ    | 52000  | 62          | 3224000   | 1548000 | 0.62       | 1999000   | 1999 |
-| AZ    | 40000  | 59          | 2360000   | 1322000 | 0.73       | 1723000   | 2000 |
-| AZ    | 43000  | 59          | 2537000   | 1142000 | 0.72       | 1827000   | 2001 |
-| AZ    | 38000  | 63          | 2394000   | 1197000 | 1.08       | 2586000   | 2002 |
-| AZ    | 35000  | 72          | 2520000   | 983000  | 1.34       | 3377000   | 2003 |
-| AZ    | 32000  | 55          | 1760000   | 774000  | 1.11       | 1954000   | 2004 |
-| AZ    | 36000  | 50          | 1800000   | 720000  | 1.04       | 1872000   | 2005 |
-| AZ    | 30000  | 65          | 1950000   | 839000  | 0.91       | 1775000   | 2006 |
-| AZ    | 30000  | 64          | 1920000   | 902000  | 1.26       | 2419000   | 2007 |
-| AZ    | 25000  | 64          | 1600000   | 336000  | 1.26       | 2016000   | 2008 |
-| AZ    | 20000  | 52          | 1040000   | 562000  | 1.45       | 1508000   | 2009 |
-| AZ    | 24000  | 77          | 1848000   | 665000  | 1.52       | 2809000   | 2010 |
-| AZ    | 23000  | 53          | 1219000   | 427000  | 1.55       | 1889000   | 2011 |
-| AZ    | 22000  | 46          | 1012000   | 253000  | 1.79       | 1811000   | 2012 |
-
-另一种可视化这种变化的方法是使用大小而不是颜色。对于色盲用户，这可能是一个更好的选择。编辑你的可视化，用点的大小来展示价格的增长：
-
-```r
-ggplot(honey, aes(x = priceperlb, y = state)) +
-  geom_point(aes(size = year),colour = "blue") +
-  scale_size_continuous(range = c(0.25, 3))
-```
-你可以看到点的大小逐渐增大。
-
-![scatterplot 3](../../../../../translated_images/zh-CN/scatter3.722d21e6f20b3ea2e18339bb9b10d75906126715eb7d5fdc88fe74dcb6d7066a.png)
-
-这是否是一个简单的供需关系？由于气候变化和蜂群崩溃等因素，是否导致蜂蜜的供应逐年减少，从而价格上涨？
-
-为了发现数据集中一些变量之间的相关性，让我们探索一些折线图。
-
-## 折线图
-
-问题：蜂蜜每磅价格是否逐年明显上涨？你可以通过创建一个单一折线图来最容易地发现这一点：
-
-```r
-qplot(honey$year,honey$priceperlb, geom='smooth', span =0.5, xlab = "year",ylab = "priceperlb")
-```
-答案：是的，除了2003年左右的一些例外：
-
-![line chart 1](../../../../../translated_images/zh-CN/line1.299b576fbb2a59e60a59e7130030f59836891f90302be084e4e8d14da0562e2a.png)
-
-问题：那么在2003年，我们是否也能看到蜂蜜供应的激增？如果你查看逐年的总产量呢？
-
-```python
-qplot(honey$year,honey$totalprod, geom='smooth', span =0.5, xlab = "year",ylab = "totalprod")
-```
-
-![line chart 2](../../../../../translated_images/zh-CN/line2.3b18fcda7176ceba5b6689eaaabb817d49c965e986f11cac1ae3f424030c34d8.png)
-
-答案：并不明显。如果你查看总产量，实际上在那一年似乎有所增加，尽管总体而言蜂蜜的产量在这些年间是下降的。
-
-问题：在这种情况下，是什么导致了2003年蜂蜜价格的激增？
-
-为了发现这一点，你可以探索一个分面网格。
-
-## 分面网格
-
-分面网格可以选择数据集的一个维度（在我们的例子中，你可以选择“年份”，以避免生成过多的分面）。Seaborn可以为你选择的x和y坐标生成每个分面的图表，以便更容易进行视觉比较。2003年在这种比较中是否显得特别突出？
-
-使用`facet_wrap`创建一个分面网格，推荐参考[ggplot2的文档](https://ggplot2.tidyverse.org/reference/facet_wrap.html)。
-
-```r
-ggplot(honey, aes(x=yieldpercol, y = numcol,group = 1)) + 
-  geom_line() + facet_wrap(vars(year))
-```
-在这个可视化中，你可以比较逐年蜂群产量和蜂群数量，并将列数设置为3：
-
-![facet grid](../../../../../translated_images/zh-CN/facet.491ad90d61c2a7cc69b50c929f80786c749e38217ccedbf1e22ed8909b65987c.png)
-
-对于这个数据集，逐年和各州之间，蜂群数量和产量并没有特别突出的变化。是否有其他方法可以发现这两个变量之间的相关性？
-
-## 双折线图
-
-尝试使用R的`par`和`plot`函数，通过叠加两个折线图来创建多线图。我们将在x轴上绘制年份，并显示两个y轴。展示每群产量和蜂群数量的叠加：
-
-```r
-par(mar = c(5, 4, 4, 4) + 0.3)              
-plot(honey$year, honey$numcol, pch = 16, col = 2,type="l")              
-par(new = TRUE)                             
-plot(honey$year, honey$yieldpercol, pch = 17, col = 3,              
-     axes = FALSE, xlab = "", ylab = "",type="l")
-axis(side = 4, at = pretty(range(y2)))      
-mtext("colony yield", side = 4, line = 3)   
-```
-![superimposed plots](../../../../../translated_images/zh-CN/dual-line.fc4665f360a54018d7df9bc6abcc26460112e17dcbda18d3b9ae6109b32b36c3.png)
-
-虽然2003年没有明显的异常，但这让我们可以以一个稍微乐观的结论结束这节课：尽管蜂群数量总体上在下降，但蜂群数量正在趋于稳定，尽管每群产量在减少。
-
-加油，蜜蜂们！
-
-🐝❤️
-## 🚀 挑战
-
-在本课中，你学习了更多关于散点图和线网格的其他用途，包括分面网格。挑战自己使用不同的数据集创建一个分面网格，也许是你之前课程中使用过的数据集。注意创建这些网格所需的时间，以及如何谨慎选择需要绘制的网格数量。
-
-## [课后测验](https://purple-hill-04aebfb03.1.azurestaticapps.net/quiz/23)
-
-## 复习与自学
-
-折线图可以简单也可以非常复杂。阅读[ggplot2文档](https://ggplot2.tidyverse.org/reference/geom_path.html#:~:text=geom_line()%20connects%20them%20in,which%20cases%20are%20connected%20together)，了解构建折线图的各种方法。尝试使用文档中列出的其他方法来增强你在本课中构建的折线图。
-
-## 作业
-
-[深入蜂巢](assignment.md)
-
-**免责声明**：  
-本文档使用AI翻译服务[Co-op Translator](https://github.com/Azure/co-op-translator)进行翻译。尽管我们努力确保准确性，但请注意，自动翻译可能包含错误或不准确之处。应以原始语言的文档作为权威来源。对于关键信息，建议使用专业人工翻译。对于因使用本翻译而引起的任何误解或误读，我们概不负责。
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diff --git a/translations/zh/3-Data-Visualization/R/13-meaningful-vizualizations/README.md b/translations/zh/3-Data-Visualization/R/13-meaningful-vizualizations/README.md
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index ebae3356..00000000
--- a/translations/zh/3-Data-Visualization/R/13-meaningful-vizualizations/README.md
+++ /dev/null
@@ -1,180 +0,0 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "b4039f1c76548d144a0aee0bf28304ec",
-  "translation_date": "2025-08-25T18:26:32+00:00",
-  "source_file": "3-Data-Visualization/R/13-meaningful-vizualizations/README.md",
-  "language_code": "zh"
-}
--->
-# 制作有意义的数据可视化
-
-|![ 由 [(@sketchthedocs)](https://sketchthedocs.dev) 绘制的速写笔记 ](../../../sketchnotes/13-MeaningfulViz.png)|
-|:---:|
-| 有意义的数据可视化 - _速写笔记由 [@nitya](https://twitter.com/nitya) 提供_ |
-
-> “如果你对数据施加足够的压力，它会承认任何事情。” -- [罗纳德·科斯](https://en.wikiquote.org/wiki/Ronald_Coase)
-
-数据科学家的基本技能之一是能够创建有意义的数据可视化，以帮助回答你可能提出的问题。在可视化数据之前，你需要确保数据已经像之前课程中那样被清理和准备好。之后，你就可以开始决定如何最好地呈现数据。
-
-在本课中，你将学习：
-
-1. 如何选择合适的图表类型  
-2. 如何避免误导性图表  
-3. 如何使用颜色  
-4. 如何为图表设计提高可读性  
-5. 如何构建动画或3D图表解决方案  
-6. 如何构建创意可视化  
-
-## [课前测验](https://purple-hill-04aebfb03.1.azurestaticapps.net/quiz/24)
-
-## 选择合适的图表类型
-
-在之前的课程中，你已经尝试使用 Matplotlib 和 Seaborn 构建各种有趣的数据可视化。通常，你可以使用以下表格选择适合你问题的[正确图表类型](https://chartio.com/learn/charts/how-to-select-a-data-vizualization/)：
-
-| 你的需求：               | 你应该使用：                 |
-| -------------------------- | ------------------------------- |
-| 展示随时间变化的数据趋势 | 折线图                          |
-| 比较类别                 | 条形图、饼图                    |
-| 比较总量                 | 饼图、堆叠条形图                |
-| 展示关系                 | 散点图、折线图、分面图、双折线图 |
-| 展示分布                 | 散点图、直方图、箱线图          |
-| 展示比例                 | 饼图、圆环图、华夫图            |
-
-> ✅ 根据数据的组成，你可能需要将其从文本转换为数值，以支持某些图表类型。
-
-## 避免误导性图表
-
-即使数据科学家小心选择了适合数据的正确图表，也有很多方法可以通过展示数据来证明某种观点，往往以牺牲数据本身为代价。有许多误导性图表和信息图的例子！
-
-[![Alberto Cairo 的《图表如何撒谎》](../../../../../translated_images/zh-CN/tornado.2880ffc7f135f82b5e5328624799010abefd1080ae4b7ecacbdc7d792f1d8849.png)](https://www.youtube.com/watch?v=oX74Nge8Wkw "图表如何撒谎")
-
-> 🎥 点击上方图片观看关于误导性图表的会议演讲
-
-这个图表颠倒了 X 轴的顺序，根据日期显示了与事实相反的内容：
-
-![错误图表 1](../../../../../translated_images/zh-CN/bad-chart-1.596bc93425a8ac301a28b8361f59a970276e7b961658ce849886aa1fed427341.png)
-
-[这个图表](https://media.firstcoastnews.com/assets/WTLV/images/170ae16f-4643-438f-b689-50d66ca6a8d8/170ae16f-4643-438f-b689-50d66ca6a8d8_1140x641.jpg) 更具误导性，因为视觉上会让人得出结论，随着时间推移，各县的 COVID 病例数在下降。实际上，如果仔细查看日期，你会发现它们被重新排列以制造这种误导性的下降趋势。
-
-![错误图表 2](../../../../../translated_images/zh-CN/bad-chart-2.62edf4d2f30f4e519f5ef50c07ce686e27b0196a364febf9a4d98eecd21f9f60.jpg)
-
-这个臭名昭著的例子同时使用了颜色和颠倒的 Y 轴来误导：本应得出枪支死亡人数在通过支持枪支的立法后激增的结论，但实际上视觉上被误导认为相反的情况是真实的：
-
-![错误图表 3](../../../../../translated_images/zh-CN/bad-chart-3.e201e2e915a230bc2cde289110604ec9abeb89be510bd82665bebc1228258972.jpg)
-
-这个奇怪的图表展示了比例如何被操纵，效果令人啼笑皆非：
-
-![错误图表 4](../../../../../translated_images/zh-CN/bad-chart-4.8872b2b881ffa96c3e0db10eb6aed7793efae2cac382c53932794260f7bfff07.jpg)
-
-比较不可比的事物是另一种阴险的技巧。有一个[精彩的网站](https://tylervigen.com/spurious-correlations) 专门展示“虚假的相关性”，比如缅因州的离婚率与人造黄油消费之间的“事实”相关性。Reddit 上还有一个小组收集了[数据的丑陋用法](https://www.reddit.com/r/dataisugly/top/?t=all)。
-
-理解眼睛如何容易被误导性图表欺骗是很重要的。即使数据科学家的意图是好的，选择错误的图表类型（例如显示过多类别的饼图）也可能具有误导性。
-
-## 颜色
-
-你在上面“佛罗里达枪支暴力”图表中看到，颜色可以为图表提供额外的意义层次，尤其是那些没有使用如 ggplot2 和 RColorBrewer 等库设计的图表，这些库自带各种经过验证的颜色库和调色板。如果你是手动制作图表，可以稍微研究一下[颜色理论](https://colormatters.com/color-and-design/basic-color-theory)。
-
-> ✅ 在设计图表时，请注意可访问性是可视化的重要方面。一些用户可能是色盲——你的图表是否对视觉障碍用户友好？
-
-选择图表颜色时要小心，因为颜色可能传递你未曾预料的含义。上面“身高”图表中的“粉红女士”传递了一种明显的“女性化”含义，这增加了图表本身的怪异感。
-
-虽然[颜色的含义](https://colormatters.com/color-symbolism/the-meanings-of-colors)可能因地区而异，并且根据其色调的不同而变化，但一般来说，颜色的含义包括：
-
-| 颜色  | 含义                 |
-| ------ | ------------------- |
-| 红色   | 力量                 |
-| 蓝色   | 信任、忠诚           |
-| 黄色   | 快乐、警告           |
-| 绿色   | 生态、幸运、嫉妒     |
-| 紫色   | 快乐                 |
-| 橙色   | 活力                 |
-
-如果你需要为图表选择自定义颜色，请确保你的图表既具有可访问性，又能与试图传递的含义相符。
-
-## 为图表设计提高可读性
-
-如果图表不可读，它们就没有意义！花点时间考虑调整图表的宽度和高度，使其与数据比例协调。如果需要显示一个变量（例如所有 50 个州），尽量将它们垂直显示在 Y 轴上，以避免水平滚动的图表。
-
-为你的轴添加标签，必要时提供图例，并提供工具提示以便更好地理解数据。
-
-如果你的数据在 X 轴上是文本且较长，可以将文本倾斜以提高可读性。[plot3D](https://cran.r-project.org/web/packages/plot3D/index.html) 提供了 3D 绘图功能，如果你的数据支持的话，可以用它制作复杂的数据可视化。
-
-![3D 图表](../../../../../translated_images/zh-CN/3d.db1734c151eee87d924989306a00e23f8cddac6a0aab122852ece220e9448def.png)
-
-## 动画和 3D 图表展示
-
-如今一些最佳的数据可视化是动画的。Shirley Wu 使用 D3 制作了许多惊艳的作品，例如“[电影之花](http://bl.ocks.org/sxywu/raw/d612c6c653fb8b4d7ff3d422be164a5d/)”，每朵花都是一部电影的可视化。另一个为《卫报》制作的例子是“Bussed Out”，一个结合了 Greensock 和 D3 的交互式体验，通过滚动叙事文章格式展示纽约市如何通过将无家可归者送出城市来处理其无家可归问题。
-
-![Bussed Out](../../../../../translated_images/zh-CN/busing.8157cf1bc89a3f65052d362a78c72f964982ceb9dcacbe44480e35909c3dce62.png)
-
-> “Bussed Out: 美国如何转移无家可归者” 来自 [卫报](https://www.theguardian.com/us-news/ng-interactive/2017/dec/20/bussed-out-america-moves-homeless-people-country-study)。可视化由 Nadieh Bremer 和 Shirley Wu 制作
-
-虽然本课不足以深入教授这些强大的可视化库，但你可以尝试在 Vue.js 应用中使用 D3，利用一个库展示《危险关系》这本书的动画社交网络可视化。
-
-> 《危险关系》是一部书信体小说，或以一系列信件形式呈现的小说。由 Choderlos de Laclos 于 1782 年创作，讲述了 18 世纪末法国贵族中两位主角——瓦尔蒙子爵和梅尔特伊侯爵夫人——之间恶毒且道德败坏的社交操纵。两人最终都走向毁灭，但在此之前造成了巨大的社会破坏。小说以他们写给圈内各种人的信件展开，策划复仇或单纯制造麻烦。创建一个这些信件的可视化，发现叙事中的主要关键人物。
-
-你将完成一个网络应用，展示这个社交网络的动画视图。它使用了一个库来创建[网络可视化](https://github.com/emiliorizzo/vue-d3-network)，基于 Vue.js 和 D3。当应用运行时，你可以在屏幕上拖动节点以重新排列数据。
-
-![危险关系](../../../../../translated_images/zh-CN/liaisons.90ce7360bcf8476558f700bbbaf198ad697d5b5cb2829ba141a89c0add7c6ecd.png)
-
-## 项目：使用 D3.js 构建一个展示网络的图表
-
-> 本课文件夹中包含一个 `solution` 文件夹，你可以在其中找到完整的项目供参考。
-
-1. 按照起始文件夹根目录中 README.md 文件的说明操作。在安装项目依赖项之前，确保你的机器上已运行 NPM 和 Node.js。
-
-2. 打开 `starter/src` 文件夹。你会发现一个 `assets` 文件夹，其中包含一个 .json 文件，记录了所有信件的编号，以及“发件人”和“收件人”信息。
-
-3. 完成 `components/Nodes.vue` 中的代码以启用可视化。找到名为 `createLinks()` 的方法，并添加以下嵌套循环。
-
-循环遍历 .json 对象，捕获信件的“发件人”和“收件人”数据，并构建 `links` 对象，以便可视化库可以使用它：
-
-```javascript
-//loop through letters
-      let f = 0;
-      let t = 0;
-      for (var i = 0; i < letters.length; i++) {
-          for (var j = 0; j < characters.length; j++) {
-              
-            if (characters[j] == letters[i].from) {
-              f = j;
-            }
-            if (characters[j] == letters[i].to) {
-              t = j;
-            }
-        }
-        this.links.push({ sid: f, tid: t });
-      }
-  ```
-
-从终端运行你的应用（npm run serve），享受可视化的乐趣！
-
-## 🚀 挑战
-
-浏览互联网，发现误导性可视化。作者是如何误导用户的？这是有意为之吗？尝试修正这些可视化，展示它们应该如何呈现。
-
-## [课后测验](https://purple-hill-04aebfb03.1.azurestaticapps.net/quiz/25)
-
-## 复习与自学
-
-以下是一些关于误导性数据可视化的文章：
-
-https://gizmodo.com/how-to-lie-with-data-visualization-1563576606
-
-http://ixd.prattsi.org/2017/12/visual-lies-usability-in-deceptive-data-visualizations/
-
-看看这些关于历史资产和文物的有趣可视化：
-
-https://handbook.pubpub.org/
-
-阅读这篇关于动画如何增强可视化的文章：
-
-https://medium.com/@EvanSinar/use-animation-to-supercharge-data-visualization-cd905a882ad4
-
-## 作业
-
-[创建你自己的自定义可视化](assignment.md)
-
-**免责声明**：  
-本文档使用AI翻译服务 [Co-op Translator](https://github.com/Azure/co-op-translator) 进行翻译。尽管我们努力确保翻译的准确性，但请注意，自动翻译可能包含错误或不准确之处。应以原文档的原始语言版本为权威来源。对于关键信息，建议使用专业人工翻译。我们对于因使用本翻译而引起的任何误解或误读不承担责任。
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-<!--
-CO_OP_TRANSLATOR_METADATA:
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-  "source_file": "3-Data-Visualization/README.md",
-  "language_code": "zh"
-}
--->
-# 可视化
-
-![一只蜜蜂停在薰衣草花上](../../../translated_images/zh-CN/bee.0aa1d91132b12e3a8994b9ca12816d05ce1642010d9b8be37f8d37365ba845cf.jpg)
-> 图片由 <a href="https://unsplash.com/@jenna2980?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Jenna Lee</a> 提供，来自 <a href="https://unsplash.com/s/photos/bees-in-a-meadow?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
-
-数据可视化是数据科学家最重要的任务之一。图片胜过千言万语，可视化可以帮助你识别数据中的各种有趣部分，例如峰值、异常值、分组、趋势等，从而帮助你理解数据背后的故事。
-
-在这五节课中，你将探索来自自然的数据，并使用各种技术创建有趣且美观的可视化。
-
-| 主题编号 | 主题 | 相关课程 | 作者 |
-| :-----------: | :--: | :-----------: | :----: |
-| 1. | 可视化数量 | <ul> <li> [Python](09-visualization-quantities/README.md)</li>  <li>[R](../../../3-Data-Visualization/R/09-visualization-quantities) </li> </ul>|<ul> <li> [Jen Looper](https://twitter.com/jenlooper)</li><li> [Vidushi Gupta](https://github.com/Vidushi-Gupta)</li> <li>[Jasleen Sondhi](https://github.com/jasleen101010)</li></ul> |
-| 2. | 可视化分布 | <ul> <li> [Python](10-visualization-distributions/README.md)</li>  <li>[R](../../../3-Data-Visualization/R/10-visualization-distributions) </li> </ul>|<ul> <li> [Jen Looper](https://twitter.com/jenlooper)</li><li> [Vidushi Gupta](https://github.com/Vidushi-Gupta)</li> <li>[Jasleen Sondhi](https://github.com/jasleen101010)</li></ul> |
-| 3. | 可视化比例 | <ul> <li> [Python](11-visualization-proportions/README.md)</li>  <li>[R](../../../3-Data-Visualization) </li> </ul>|<ul> <li> [Jen Looper](https://twitter.com/jenlooper)</li><li> [Vidushi Gupta](https://github.com/Vidushi-Gupta)</li> <li>[Jasleen Sondhi](https://github.com/jasleen101010)</li></ul> |
-| 4. | 可视化关系 | <ul> <li> [Python](12-visualization-relationships/README.md)</li>  <li>[R](../../../3-Data-Visualization) </li> </ul>|<ul> <li> [Jen Looper](https://twitter.com/jenlooper)</li><li> [Vidushi Gupta](https://github.com/Vidushi-Gupta)</li> <li>[Jasleen Sondhi](https://github.com/jasleen101010)</li></ul> |
-| 5. | 创建有意义的可视化 | <ul> <li> [Python](13-meaningful-visualizations/README.md)</li>  <li>[R](../../../3-Data-Visualization) </li> </ul>|<ul> <li> [Jen Looper](https://twitter.com/jenlooper)</li><li> [Vidushi Gupta](https://github.com/Vidushi-Gupta)</li> <li>[Jasleen Sondhi](https://github.com/jasleen101010)</li></ul> |
-
-### 致谢
-
-这些可视化课程由 [Jen Looper](https://twitter.com/jenlooper)、[Jasleen Sondhi](https://github.com/jasleen101010) 和 [Vidushi Gupta](https://github.com/Vidushi-Gupta) 🌸 编写。
-
-🍯 美国蜂蜜生产数据来源于 Jessica Li 在 [Kaggle](https://www.kaggle.com/jessicali9530/honey-production) 上的项目。该 [数据](https://usda.library.cornell.edu/concern/publications/rn301137d) 来自 [美国农业部](https://www.nass.usda.gov/About_NASS/index.php)。
-
-🍄 蘑菇数据同样来源于 [Kaggle](https://www.kaggle.com/hatterasdunton/mushroom-classification-updated-dataset)，由 Hatteras Dunton 修订。该数据集包括对应于伞菌科和鳞伞科中23种有鳃蘑菇的假设样本描述。蘑菇信息摘自《奥杜邦协会北美蘑菇野外指南》（1981年）。该数据集于1987年捐赠给 UCI ML 27。
-
-🦆 明尼苏达州鸟类数据来自 [Kaggle](https://www.kaggle.com/hannahcollins/minnesota-birds)，由 Hannah Collins 从 [维基百科](https://en.wikipedia.org/wiki/List_of_birds_of_Minnesota) 抓取。
-
-所有这些数据集均以 [CC0: Creative Commons](https://creativecommons.org/publicdomain/zero/1.0/) 授权。
-
-**免责声明**：  
-本文档使用AI翻译服务 [Co-op Translator](https://github.com/Azure/co-op-translator) 进行翻译。尽管我们努力确保翻译的准确性，但请注意，自动翻译可能包含错误或不准确之处。应以原始语言的文档作为权威来源。对于关键信息，建议使用专业人工翻译。我们不对因使用此翻译而产生的任何误解或误读承担责任。
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+++ /dev/null
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-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "07e12a25d20b8f191e3cb651c27fdb2b",
-  "translation_date": "2025-09-06T20:26:53+00:00",
-  "source_file": "4-Data-Science-Lifecycle/14-Introduction/README.md",
-  "language_code": "zh"
-}
--->
-# 数据科学生命周期简介
-
-|![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/14-DataScience-Lifecycle.png)|
-|:---:|
-| 数据科学生命周期简介 - _Sketchnote by [@nitya](https://twitter.com/nitya)_ |
-
-## [课前测验](https://ff-quizzes.netlify.app/en/ds/quiz/26)
-
-到目前为止，你可能已经意识到数据科学是一个过程。这个过程可以分为五个阶段：
-
-- 数据捕获
-- 数据处理
-- 数据分析
-- 数据沟通
-- 数据维护
-
-本课程重点讲解生命周期中的三个部分：数据捕获、数据处理和数据维护。
-
-![数据科学生命周期图示](../../../../translated_images/zh-CN/data-science-lifecycle.a1e362637503c4fb0cd5e859d7552edcdb4aa629a279727008baa121f2d33f32.jpg)
-> 图片来源：[伯克利信息学院](https://ischoolonline.berkeley.edu/data-science/what-is-data-science/)
-
-## 数据捕获
-
-生命周期的第一阶段非常重要，因为后续阶段都依赖于它。实际上，这个阶段可以看作是两个阶段的结合：获取数据以及定义需要解决的目标和问题。  
-定义项目目标需要深入了解问题或问题背景。首先，我们需要识别并获取那些需要解决问题的人。这些可能是企业的利益相关者或项目的赞助者，他们可以帮助确定谁或什么会从项目中受益，以及他们需要什么和为什么需要它。一个明确的目标应该是可衡量和量化的，以定义可接受的结果。
-
-数据科学家可能会问的问题：
-- 这个问题以前是否被研究过？发现了什么？
-- 所有相关人员是否都理解目标和目的？
-- 是否存在模糊性？如何减少模糊性？
-- 有哪些约束条件？
-- 最终结果可能是什么样子？
-- 有多少资源（时间、人力、计算能力）可用？
-
-接下来是识别、收集并最终探索实现这些目标所需的数据。在数据获取的这一步，数据科学家还需要评估数据的数量和质量。这需要一些数据探索，以确认所获取的数据是否能够支持实现预期结果。
-
-数据科学家可能会问关于数据的问题：
-- 我已经拥有了哪些数据？
-- 谁拥有这些数据？
-- 有哪些隐私问题？
-- 我是否有足够的数据来解决这个问题？
-- 数据的质量是否适合解决这个问题？
-- 如果通过这些数据发现了额外的信息，我们是否应该考虑改变或重新定义目标？
-
-## 数据处理
-
-生命周期的处理阶段专注于发现数据中的模式以及建模。一些处理阶段使用的技术需要统计方法来揭示模式。通常，对于一个大型数据集来说，这将是一个繁琐的任务，因此需要依赖计算机来加速处理过程。在这个阶段，数据科学和机器学习会交叉。正如你在第一课中学到的，机器学习是构建模型以理解数据的过程。模型是数据中变量之间关系的表示，帮助预测结果。
-
-此阶段常用的技术在《机器学习初学者》课程中有详细介绍。点击以下链接了解更多：
-
-- [分类](https://github.com/microsoft/ML-For-Beginners/tree/main/4-Classification)：将数据组织到类别中以提高使用效率。
-- [聚类](https://github.com/microsoft/ML-For-Beginners/tree/main/5-Clustering)：将数据分组到相似的群组中。
-- [回归](https://github.com/microsoft/ML-For-Beginners/tree/main/2-Regression)：确定变量之间的关系以预测或预估值。
-
-## 数据维护
-
-在生命周期图示中，你可能注意到维护位于数据捕获和数据处理之间。维护是一个持续的过程，贯穿项目的整个过程，涉及数据的管理、存储和安全性。
-
-### 数据存储
-数据存储的方式和位置会影响存储成本以及数据访问的性能。这些决策通常不会由数据科学家单独做出，但他们可能需要根据数据存储方式选择如何处理数据。
-
-以下是现代数据存储系统的一些方面，这些方面可能会影响决策：
-
-**本地存储 vs 外部存储 vs 公有云或私有云**
-
-本地存储指的是使用自己的设备管理数据，例如拥有一个存储数据的服务器，而外部存储依赖于你不拥有的设备，例如数据中心。公有云是存储数据的流行选择，它不需要了解数据具体存储的位置或方式，公有指的是所有使用云服务的人共享统一的基础设施。一些组织有严格的安全政策，要求完全访问存储数据的设备，因此会选择提供自身云服务的私有云。在[后续课程](https://github.com/microsoft/Data-Science-For-Beginners/tree/main/5-Data-Science-In-Cloud)中，你将学习更多关于云中的数据。
-
-**冷数据 vs 热数据**
-
-在训练模型时，你可能需要更多的训练数据。如果你对模型满意，更多的数据会到来以支持模型的用途。无论如何，随着数据的积累，存储和访问数据的成本都会增加。将很少使用的冷数据与频繁访问的热数据分离，可以通过硬件或软件服务实现更便宜的数据存储选项。如果需要访问冷数据，可能会比热数据的检索时间稍长。
-
-### 数据管理
-在处理数据时，你可能会发现一些数据需要使用[数据准备](https://github.com/microsoft/Data-Science-For-Beginners/tree/main/2-Working-With-Data/08-data-preparation)课程中介绍的技术进行清理，以构建准确的模型。当新数据到来时，也需要应用相同的技术以保持质量的一致性。一些项目会使用自动化工具进行清理、聚合和压缩，然后将数据移动到最终位置。Azure Data Factory 就是这些工具的一个例子。
-
-### 数据安全
-数据安全的主要目标之一是确保数据的收集和使用在控制范围内。保持数据安全包括限制只有需要的人才能访问数据，遵守当地法律法规，以及维护伦理标准，这些内容在[伦理课程](https://github.com/microsoft/Data-Science-For-Beginners/tree/main/1-Introduction/02-ethics)中有介绍。
-
-团队可能会采取以下措施以确保数据安全：
-- 确保所有数据都已加密
-- 向客户提供关于数据使用方式的信息
-- 移除已离开项目人员的数据访问权限
-- 仅允许特定项目成员修改数据
-
-## 🚀 挑战
-
-数据科学生命周期有许多版本，每个版本的步骤可能有不同的名称和阶段数量，但包含的过程与本课程中提到的相同。
-
-探索[团队数据科学过程生命周期](https://docs.microsoft.com/en-us/azure/architecture/data-science-process/lifecycle)和[跨行业数据挖掘标准过程](https://www.datascience-pm.com/crisp-dm-2/)。列举两者的三个相似点和不同点。
-
-|团队数据科学过程 (TDSP)|跨行业数据挖掘标准过程 (CRISP-DM)|
-|--|--|
-|![团队数据科学生命周期](../../../../translated_images/zh-CN/tdsp-lifecycle2.e19029d598e2e73d5ef8a4b98837d688ec6044fe332c905d4dbb69eb6d5c1d96.png) | ![数据科学过程联盟图片](../../../../translated_images/zh-CN/CRISP-DM.8bad2b4c66e62aa75278009e38e3e99902c73b0a6f63fd605a67c687a536698c.png) |
-| 图片来源：[Microsoft](https://docs.microsoft.comazure/architecture/data-science-process/lifecycle) | 图片来源：[数据科学过程联盟](https://www.datascience-pm.com/crisp-dm-2/) |
-
-## [课后测验](https://ff-quizzes.netlify.app/en/ds/quiz/27)
-
-## 复习与自学
-
-应用数据科学生命周期涉及多个角色和任务，其中一些可能专注于每个阶段的特定部分。团队数据科学过程提供了一些资源，解释了项目中可能涉及的角色和任务类型。
-
-* [团队数据科学过程中的角色和任务](https://docs.microsoft.com/en-us/azure/architecture/data-science-process/roles-tasks)
-* [执行数据科学任务：探索、建模和部署](https://docs.microsoft.com/en-us/azure/architecture/data-science-process/execute-data-science-tasks)
-
-## 作业
-
-[评估数据集](assignment.md)
-
----
-
-**免责声明**：  
-本文档使用AI翻译服务 [Co-op Translator](https://github.com/Azure/co-op-translator) 进行翻译。尽管我们努力确保翻译的准确性，但请注意，自动翻译可能包含错误或不准确之处。原始语言的文档应被视为权威来源。对于关键信息，建议使用专业人工翻译。我们不对因使用此翻译而产生的任何误解或误读承担责任。
\ No newline at end of file
diff --git a/translations/zh/4-Data-Science-Lifecycle/14-Introduction/assignment.md b/translations/zh/4-Data-Science-Lifecycle/14-Introduction/assignment.md
deleted file mode 100644
index cd61093f..00000000
--- a/translations/zh/4-Data-Science-Lifecycle/14-Introduction/assignment.md
+++ /dev/null
@@ -1,35 +0,0 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "564445c39ad29a491abcb9356fc4d47d",
-  "translation_date": "2025-08-25T17:44:36+00:00",
-  "source_file": "4-Data-Science-Lifecycle/14-Introduction/assignment.md",
-  "language_code": "zh"
-}
--->
-# 评估数据集
-
-一位客户向您的团队寻求帮助，调查纽约市出租车乘客的季节性消费习惯。
-
-他们想知道：**纽约市的黄色出租车乘客在冬季还是夏季给司机的小费更多？**
-
-您的团队正处于数据科学生命周期的[捕获](Readme.md#Capturing)阶段，而您负责处理数据集。您已获得一个笔记本和[数据](../../../../data/taxi.csv)供探索。
-
-在此目录中有一个[笔记本](../../../../4-Data-Science-Lifecycle/14-Introduction/notebook.ipynb)，使用 Python 从[纽约市出租车与豪华车委员会](https://docs.microsoft.com/en-us/azure/open-datasets/dataset-taxi-yellow?tabs=azureml-opendatasets)加载黄色出租车行程数据。
-您也可以在文本编辑器或电子表格软件（如 Excel）中打开出租车数据文件。
-
-## 指导
-
-- 评估此数据集中的数据是否能够帮助回答问题。
-- 探索[纽约市开放数据目录](https://data.cityofnewyork.us/browse?sortBy=most_accessed&utf8=%E2%9C%93)。确定一个可能有助于回答客户问题的额外数据集。
-- 提出三个问题，以便向客户寻求更多澄清并更好地理解问题。
-
-参考[数据集字典](https://www1.nyc.gov/assets/tlc/downloads/pdf/data_dictionary_trip_records_yellow.pdf)和[用户指南](https://www1.nyc.gov/assets/tlc/downloads/pdf/trip_record_user_guide.pdf)，以获取有关数据的更多信息。
-
-## 评分标准
-
-优秀 | 合格 | 需要改进
---- | --- | ---
-
-**免责声明**：  
-本文档使用AI翻译服务 [Co-op Translator](https://github.com/Azure/co-op-translator) 进行翻译。尽管我们努力确保翻译的准确性，但请注意，自动翻译可能包含错误或不准确之处。原始语言的文档应被视为权威来源。对于重要信息，建议使用专业人工翻译。我们不对因使用此翻译而产生的任何误解或误读承担责任。
\ No newline at end of file
diff --git a/translations/zh/4-Data-Science-Lifecycle/14-Introduction/notebook.ipynb b/translations/zh/4-Data-Science-Lifecycle/14-Introduction/notebook.ipynb
deleted file mode 100644
index 2cd9f3f6..00000000
--- a/translations/zh/4-Data-Science-Lifecycle/14-Introduction/notebook.ipynb
+++ /dev/null
@@ -1,140 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "source": [
-    "# 冬季和夏季的纽约出租车数据\n",
-    "\n",
-    "请参考[数据字典](https://www1.nyc.gov/assets/tlc/downloads/pdf/data_dictionary_trip_records_yellow.pdf)了解提供的列的详细信息。\n"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "#Install the pandas library\r\n",
-    "!pip install pandas"
-   ],
-   "outputs": [],
-   "metadata": {
-    "scrolled": true
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "source": [
-    "import pandas as pd\r\n",
-    "\r\n",
-    "path = '../../data/taxi.csv'\r\n",
-    "\r\n",
-    "#Load the csv file into a dataframe\r\n",
-    "df = pd.read_csv(path)\r\n",
-    "\r\n",
-    "#Print the dataframe\r\n",
-    "print(df)\r\n"
-   ],
-   "outputs": [
-    {
-     "output_type": "stream",
-     "name": "stdout",
-     "text": [
-      "     VendorID tpep_pickup_datetime tpep_dropoff_datetime  passenger_count  \\\n",
-      "0         2.0  2019-07-15 16:27:53   2019-07-15 16:44:21              3.0   \n",
-      "1         2.0  2019-07-17 20:26:35   2019-07-17 20:40:09              6.0   \n",
-      "2         2.0  2019-07-06 16:01:08   2019-07-06 16:10:25              1.0   \n",
-      "3         1.0  2019-07-18 22:32:23   2019-07-18 22:35:08              1.0   \n",
-      "4         2.0  2019-07-19 14:54:29   2019-07-19 15:19:08              1.0   \n",
-      "..        ...                  ...                   ...              ...   \n",
-      "195       2.0  2019-01-18 08:42:15   2019-01-18 08:56:57              1.0   \n",
-      "196       1.0  2019-01-19 04:34:45   2019-01-19 04:43:44              1.0   \n",
-      "197       2.0  2019-01-05 10:37:39   2019-01-05 10:42:03              1.0   \n",
-      "198       2.0  2019-01-23 10:36:29   2019-01-23 10:44:34              2.0   \n",
-      "199       2.0  2019-01-30 06:55:58   2019-01-30 07:07:02              5.0   \n",
-      "\n",
-      "     trip_distance  RatecodeID store_and_fwd_flag  PULocationID  DOLocationID  \\\n",
-      "0             2.02         1.0                  N           186           233   \n",
-      "1             1.59         1.0                  N           141           161   \n",
-      "2             1.69         1.0                  N           246           249   \n",
-      "3             0.90         1.0                  N           229           141   \n",
-      "4             4.79         1.0                  N           237           107   \n",
-      "..             ...         ...                ...           ...           ...   \n",
-      "195           1.18         1.0                  N            43           237   \n",
-      "196           2.30         1.0                  N           148           234   \n",
-      "197           0.83         1.0                  N           237           263   \n",
-      "198           1.12         1.0                  N           144           113   \n",
-      "199           2.41         1.0                  N           209           107   \n",
-      "\n",
-      "     payment_type  fare_amount  extra  mta_tax  tip_amount  tolls_amount  \\\n",
-      "0             1.0         12.0    1.0      0.5        4.08           0.0   \n",
-      "1             2.0         10.0    0.5      0.5        0.00           0.0   \n",
-      "2             2.0          8.5    0.0      0.5        0.00           0.0   \n",
-      "3             1.0          4.5    3.0      0.5        1.65           0.0   \n",
-      "4             1.0         19.5    0.0      0.5        5.70           0.0   \n",
-      "..            ...          ...    ...      ...         ...           ...   \n",
-      "195           1.0         10.0    0.0      0.5        2.16           0.0   \n",
-      "196           1.0          9.5    0.5      0.5        2.15           0.0   \n",
-      "197           1.0          5.0    0.0      0.5        1.16           0.0   \n",
-      "198           2.0          7.0    0.0      0.5        0.00           0.0   \n",
-      "199           1.0         10.5    0.0      0.5        1.00           0.0   \n",
-      "\n",
-      "     improvement_surcharge  total_amount  congestion_surcharge  \n",
-      "0                      0.3         20.38                   2.5  \n",
-      "1                      0.3         13.80                   2.5  \n",
-      "2                      0.3         11.80                   2.5  \n",
-      "3                      0.3          9.95                   2.5  \n",
-      "4                      0.3         28.50                   2.5  \n",
-      "..                     ...           ...                   ...  \n",
-      "195                    0.3         12.96                   0.0  \n",
-      "196                    0.3         12.95                   0.0  \n",
-      "197                    0.3          6.96                   0.0  \n",
-      "198                    0.3          7.80                   0.0  \n",
-      "199                    0.3         12.30                   0.0  \n",
-      "\n",
-      "[200 rows x 18 columns]\n"
-     ]
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n---\n\n**免责声明**：  \n本文档使用AI翻译服务[Co-op Translator](https://github.com/Azure/co-op-translator)进行翻译。尽管我们努力确保准确性，但请注意，自动翻译可能包含错误或不准确之处。应以原始语言的文档作为权威来源。对于关键信息，建议使用专业人工翻译。因使用本翻译而引起的任何误解或误读，我们概不负责。\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "name": "python3",
-   "display_name": "Python 3.9.7 64-bit ('venv': venv)"
-  },
-  "language_info": {
-   "mimetype": "text/x-python",
-   "name": "python",
-   "pygments_lexer": "ipython3",
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "version": "3.9.7",
-   "nbconvert_exporter": "python",
-   "file_extension": ".py"
-  },
-  "name": "04-nyc-taxi-join-weather-in-pandas",
-  "notebookId": 1709144033725344,
-  "interpreter": {
-   "hash": "6b9b57232c4b57163d057191678da2030059e733b8becc68f245de5a75abe84e"
-  },
-  "coopTranslator": {
-   "original_hash": "3bd4c20c4e8f3158f483f0f1cc543bb1",
-   "translation_date": "2025-09-02T08:36:28+00:00",
-   "source_file": "4-Data-Science-Lifecycle/14-Introduction/notebook.ipynb",
-   "language_code": "zh"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
\ No newline at end of file
diff --git a/translations/zh/4-Data-Science-Lifecycle/15-analyzing/README.md b/translations/zh/4-Data-Science-Lifecycle/15-analyzing/README.md
deleted file mode 100644
index 22dd4c4e..00000000
--- a/translations/zh/4-Data-Science-Lifecycle/15-analyzing/README.md
+++ /dev/null
@@ -1,64 +0,0 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "661dad02c3ac239644d34c1eb51e76f8",
-  "translation_date": "2025-09-06T20:26:41+00:00",
-  "source_file": "4-Data-Science-Lifecycle/15-analyzing/README.md",
-  "language_code": "zh"
-}
--->
-# 数据科学生命周期：分析
-
-|![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/15-Analyzing.png)|
-|:---:|
-| 数据科学生命周期：分析 - _Sketchnote by [@nitya](https://twitter.com/nitya)_ |
-
-## [课前测验](https://ff-quizzes.netlify.app/en/ds/quiz/28)
-
-数据生命周期中的分析阶段旨在确认数据是否能够回答提出的问题或解决特定问题。这一步还可以用于验证模型是否正确地解决了这些问题。本课程重点介绍探索性数据分析（EDA），这是一种定义数据特征和关系的技术，并可用于为建模准备数据。
-
-我们将使用 [Kaggle](https://www.kaggle.com/balaka18/email-spam-classification-dataset-csv/version/1) 提供的示例数据集，展示如何通过 Python 和 Pandas 库应用这些技术。该数据集包含一些常见单词在电子邮件中的出现次数，这些电子邮件的来源是匿名的。请使用本目录中的 [notebook](notebook.ipynb) 跟随学习。
-
-## 探索性数据分析
-
-生命周期的捕获阶段是获取数据以及明确问题和问题所在，但我们如何知道数据能够支持最终结果呢？  
-回想一下，数据科学家在获取数据时可能会问以下问题：
--   我是否有足够的数据来解决这个问题？
--   数据的质量是否适合解决这个问题？
--   如果通过数据发现了额外的信息，我们是否应该考虑改变或重新定义目标？
-
-探索性数据分析是了解数据的过程，可以用来回答这些问题，同时识别处理数据集时可能面临的挑战。让我们来看看实现这些目标的一些技术。
-
-## 数据分析、描述性统计和 Pandas
-
-我们如何评估是否有足够的数据来解决问题？数据分析可以通过描述性统计技术总结并收集数据集的一些总体信息。数据分析帮助我们了解可用的数据，而描述性统计帮助我们了解数据的数量。
-
-在之前的一些课程中，我们使用 Pandas 的 [`describe()` 函数](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.describe.html) 提供了一些描述性统计信息。它可以对数值数据提供计数、最大值和最小值、平均值、标准差以及分位数等信息。使用像 `describe()` 这样的描述性统计方法可以帮助你评估数据量是否足够，是否需要更多数据。
-
-## 抽样和查询
-
-在大型数据集中探索所有内容可能非常耗时，通常由计算机完成。然而，抽样是一种理解数据的有用工具，可以帮助我们更好地了解数据集的内容及其代表的意义。通过抽样，你可以应用概率和统计方法对数据得出一些总体结论。虽然没有明确规定应该抽取多少数据，但需要注意的是，抽取的数据越多，对数据的总体概括就越精确。
-
-Pandas 提供了 [`sample()` 函数](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.sample.html)，你可以传递参数来指定需要的随机样本数量。
-
-对数据进行一般查询可以帮助你回答一些普遍的问题和假设。与抽样不同，查询允许你控制并专注于数据中你感兴趣的特定部分。  
-Pandas 库中的 [`query()` 函数](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.query.html) 允许你选择列并通过检索的行获得关于数据的简单答案。
-
-## 使用可视化进行探索
-
-你不必等到数据完全清理和分析后再开始创建可视化。事实上，在探索阶段创建可视化可以帮助识别数据中的模式、关系和问题。此外，可视化为那些未直接参与数据管理的人提供了一种沟通方式，同时也可以是一个机会，用于分享和澄清捕获阶段未解决的额外问题。参考 [可视化部分](../../../../../../../../../3-Data-Visualization) 了解一些流行的可视化方法。
-
-## 探索以识别不一致性
-
-本课程中的所有主题都可以帮助识别缺失或不一致的值，而 Pandas 提供了一些函数来检查这些问题。[isna() 或 isnull()](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.isna.html) 可以检查缺失值。探索这些值为何会出现缺失是一个重要的步骤，这可以帮助你决定采取哪些 [措施来解决这些问题](/2-Working-With-Data/08-data-preparation/notebook.ipynb)。
-
-## [课后测验](https://ff-quizzes.netlify.app/en/ds/quiz/29)
-
-## 作业
-
-[探索答案](assignment.md)
-
----
-
-**免责声明**：  
-本文档使用AI翻译服务[Co-op Translator](https://github.com/Azure/co-op-translator)进行翻译。尽管我们努力确保准确性，但请注意，自动翻译可能包含错误或不准确之处。应以原始语言的文档作为权威来源。对于关键信息，建议使用专业人工翻译。因使用本翻译而导致的任何误解或误读，我们概不负责。
\ No newline at end of file
diff --git a/translations/zh/4-Data-Science-Lifecycle/15-analyzing/assignment.ipynb b/translations/zh/4-Data-Science-Lifecycle/15-analyzing/assignment.ipynb
deleted file mode 100644
index c63d26c4..00000000
--- a/translations/zh/4-Data-Science-Lifecycle/15-analyzing/assignment.ipynb
+++ /dev/null
@@ -1,154 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "source": [
-    "# 冬季和夏季的纽约出租车数据\n",
-    "\n",
-    "请参考 [数据字典](https://www1.nyc.gov/assets/tlc/downloads/pdf/data_dictionary_trip_records_yellow.pdf) 了解提供的列的详细信息。\n"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "#Install the pandas library\r\n",
-    "!pip install pandas"
-   ],
-   "outputs": [],
-   "metadata": {
-    "scrolled": true
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "source": [
-    "import pandas as pd\r\n",
-    "\r\n",
-    "path = '../../data/taxi.csv'\r\n",
-    "\r\n",
-    "#Load the csv file into a dataframe\r\n",
-    "df = pd.read_csv(path)\r\n",
-    "\r\n",
-    "#Print the dataframe\r\n",
-    "print(df)\r\n"
-   ],
-   "outputs": [
-    {
-     "output_type": "stream",
-     "name": "stdout",
-     "text": [
-      "     VendorID tpep_pickup_datetime tpep_dropoff_datetime  passenger_count  \\\n",
-      "0         2.0  2019-07-15 16:27:53   2019-07-15 16:44:21              3.0   \n",
-      "1         2.0  2019-07-17 20:26:35   2019-07-17 20:40:09              6.0   \n",
-      "2         2.0  2019-07-06 16:01:08   2019-07-06 16:10:25              1.0   \n",
-      "3         1.0  2019-07-18 22:32:23   2019-07-18 22:35:08              1.0   \n",
-      "4         2.0  2019-07-19 14:54:29   2019-07-19 15:19:08              1.0   \n",
-      "..        ...                  ...                   ...              ...   \n",
-      "195       2.0  2019-01-18 08:42:15   2019-01-18 08:56:57              1.0   \n",
-      "196       1.0  2019-01-19 04:34:45   2019-01-19 04:43:44              1.0   \n",
-      "197       2.0  2019-01-05 10:37:39   2019-01-05 10:42:03              1.0   \n",
-      "198       2.0  2019-01-23 10:36:29   2019-01-23 10:44:34              2.0   \n",
-      "199       2.0  2019-01-30 06:55:58   2019-01-30 07:07:02              5.0   \n",
-      "\n",
-      "     trip_distance  RatecodeID store_and_fwd_flag  PULocationID  DOLocationID  \\\n",
-      "0             2.02         1.0                  N           186           233   \n",
-      "1             1.59         1.0                  N           141           161   \n",
-      "2             1.69         1.0                  N           246           249   \n",
-      "3             0.90         1.0                  N           229           141   \n",
-      "4             4.79         1.0                  N           237           107   \n",
-      "..             ...         ...                ...           ...           ...   \n",
-      "195           1.18         1.0                  N            43           237   \n",
-      "196           2.30         1.0                  N           148           234   \n",
-      "197           0.83         1.0                  N           237           263   \n",
-      "198           1.12         1.0                  N           144           113   \n",
-      "199           2.41         1.0                  N           209           107   \n",
-      "\n",
-      "     payment_type  fare_amount  extra  mta_tax  tip_amount  tolls_amount  \\\n",
-      "0             1.0         12.0    1.0      0.5        4.08           0.0   \n",
-      "1             2.0         10.0    0.5      0.5        0.00           0.0   \n",
-      "2             2.0          8.5    0.0      0.5        0.00           0.0   \n",
-      "3             1.0          4.5    3.0      0.5        1.65           0.0   \n",
-      "4             1.0         19.5    0.0      0.5        5.70           0.0   \n",
-      "..            ...          ...    ...      ...         ...           ...   \n",
-      "195           1.0         10.0    0.0      0.5        2.16           0.0   \n",
-      "196           1.0          9.5    0.5      0.5        2.15           0.0   \n",
-      "197           1.0          5.0    0.0      0.5        1.16           0.0   \n",
-      "198           2.0          7.0    0.0      0.5        0.00           0.0   \n",
-      "199           1.0         10.5    0.0      0.5        1.00           0.0   \n",
-      "\n",
-      "     improvement_surcharge  total_amount  congestion_surcharge  \n",
-      "0                      0.3         20.38                   2.5  \n",
-      "1                      0.3         13.80                   2.5  \n",
-      "2                      0.3         11.80                   2.5  \n",
-      "3                      0.3          9.95                   2.5  \n",
-      "4                      0.3         28.50                   2.5  \n",
-      "..                     ...           ...                   ...  \n",
-      "195                    0.3         12.96                   0.0  \n",
-      "196                    0.3         12.95                   0.0  \n",
-      "197                    0.3          6.96                   0.0  \n",
-      "198                    0.3          7.80                   0.0  \n",
-      "199                    0.3         12.30                   0.0  \n",
-      "\n",
-      "[200 rows x 18 columns]\n"
-     ]
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "# 使用以下单元格进行自己的探索性数据分析\n"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [],
-   "outputs": [],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n---\n\n**免责声明**：  \n本文档使用AI翻译服务[Co-op Translator](https://github.com/Azure/co-op-translator)进行翻译。尽管我们努力确保准确性，但请注意，自动翻译可能包含错误或不准确之处。应以原始语言的文档作为权威来源。对于关键信息，建议使用专业人工翻译。因使用本翻译而引起的任何误解或误读，我们概不负责。\n"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "name": "python3",
-   "display_name": "Python 3.9.7 64-bit ('venv': venv)"
-  },
-  "language_info": {
-   "mimetype": "text/x-python",
-   "name": "python",
-   "pygments_lexer": "ipython3",
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "version": "3.9.7",
-   "nbconvert_exporter": "python",
-   "file_extension": ".py"
-  },
-  "name": "04-nyc-taxi-join-weather-in-pandas",
-  "notebookId": 1709144033725344,
-  "interpreter": {
-   "hash": "6b9b57232c4b57163d057191678da2030059e733b8becc68f245de5a75abe84e"
-  },
-  "coopTranslator": {
-   "original_hash": "7bca1c1abc1e55842817b62e44e1a963",
-   "translation_date": "2025-09-02T08:33:58+00:00",
-   "source_file": "4-Data-Science-Lifecycle/15-analyzing/assignment.ipynb",
-   "language_code": "zh"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
\ No newline at end of file
diff --git a/translations/zh/4-Data-Science-Lifecycle/15-analyzing/assignment.md b/translations/zh/4-Data-Science-Lifecycle/15-analyzing/assignment.md
deleted file mode 100644
index d3a4953d..00000000
--- a/translations/zh/4-Data-Science-Lifecycle/15-analyzing/assignment.md
+++ /dev/null
@@ -1,34 +0,0 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "fcc7547171f4530f159676dd73ed772e",
-  "translation_date": "2025-08-25T17:47:42+00:00",
-  "source_file": "4-Data-Science-Lifecycle/15-analyzing/assignment.md",
-  "language_code": "zh"
-}
--->
-# 探索答案
-
-这是上一节课[作业](../14-Introduction/assignment.md)的延续，我们之前简单浏览了数据集。现在我们将更深入地研究这些数据。
-
-客户想知道的问题是：**纽约市的黄色出租车乘客在冬季还是夏季给司机的小费更多？**
-
-您的团队目前处于数据科学生命周期的[分析](README.md)阶段，负责对数据集进行探索性数据分析。您已获得一个包含2019年1月和7月200笔出租车交易的笔记本和数据集。
-
-## 指导说明
-
-在此目录中，有一个[笔记本](../../../../4-Data-Science-Lifecycle/15-analyzing/assignment.ipynb)和来自[出租车与豪华轿车委员会](https://docs.microsoft.com/en-us/azure/open-datasets/dataset-taxi-yellow?tabs=azureml-opendatasets)的数据。有关数据的更多信息，请参考[数据字典](https://www1.nyc.gov/assets/tlc/downloads/pdf/data_dictionary_trip_records_yellow.pdf)和[用户指南](https://www1.nyc.gov/assets/tlc/downloads/pdf/trip_record_user_guide.pdf)。
-
-使用本课中的一些技术，在笔记本中进行自己的EDA（可以添加单元格），并回答以下问题：
-
-- 数据中还有哪些因素可能会影响小费金额？
-- 哪些列最有可能不需要用来回答客户的问题？
-- 根据目前提供的信息，数据是否显示出任何季节性小费行为的证据？
-
-## 评分标准
-
-优秀 | 合格 | 需要改进
---- | --- | ---
-
-**免责声明**：  
-本文档使用AI翻译服务 [Co-op Translator](https://github.com/Azure/co-op-translator) 进行翻译。尽管我们努力确保翻译的准确性，但请注意，自动翻译可能包含错误或不准确之处。应以原始语言的文档作为权威来源。对于重要信息，建议使用专业人工翻译。我们不对因使用此翻译而产生的任何误解或误读承担责任。
\ No newline at end of file
diff --git a/translations/zh/4-Data-Science-Lifecycle/15-analyzing/notebook.ipynb b/translations/zh/4-Data-Science-Lifecycle/15-analyzing/notebook.ipynb
deleted file mode 100644
index 8911b3ec..00000000
--- a/translations/zh/4-Data-Science-Lifecycle/15-analyzing/notebook.ipynb
+++ /dev/null
@@ -1,193 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "source": [
-    "# 数据分析\n",
-    "[课程](README.md)中提到的 Pandas 函数示例。\n"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "source": [
-    "import pandas as pd\r\n",
-    "import glob\r\n",
-    "\r\n",
-    "#Loading the dataset\r\n",
-    "path = '../../data/emails.csv'\r\n",
-    "email_df = pd.read_csv(path)"
-   ],
-   "outputs": [],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "source": [
-    "# Using Describe on the email dataset\r\n",
-    "print(email_df.describe())"
-   ],
-   "outputs": [
-    {
-     "output_type": "stream",
-     "name": "stdout",
-     "text": [
-      "              the          to         ect         and         for          of  \\\n",
-      "count  406.000000  406.000000  406.000000  406.000000  406.000000  406.000000   \n",
-      "mean     7.022167    6.519704    4.948276    3.059113    3.502463    2.662562   \n",
-      "std     10.945522    9.801907    9.293820    6.267806    4.901372    5.443939   \n",
-      "min      0.000000    0.000000    1.000000    0.000000    0.000000    0.000000   \n",
-      "25%      1.000000    1.000000    1.000000    0.000000    1.000000    0.000000   \n",
-      "50%      3.000000    3.000000    2.000000    1.000000    2.000000    1.000000   \n",
-      "75%      9.000000    7.750000    4.000000    3.000000    4.750000    3.000000   \n",
-      "max     99.000000   88.000000   79.000000   69.000000   39.000000   57.000000   \n",
-      "\n",
-      "                a         you          in          on          is        this  \\\n",
-      "count  406.000000  406.000000  406.000000  406.000000  406.000000  406.000000   \n",
-      "mean    57.017241    2.394089   10.817734   11.591133    5.901478    1.485222   \n",
-      "std     78.868243    4.067015   19.050972   16.407175    8.793103    2.912473   \n",
-      "min      0.000000    0.000000    0.000000    0.000000    0.000000    0.000000   \n",
-      "25%     15.000000    0.000000    1.250000    3.000000    1.000000    0.000000   \n",
-      "50%     29.000000    1.000000    5.000000    6.000000    3.000000    0.000000   \n",
-      "75%     61.000000    3.000000   12.000000   13.000000    7.000000    2.000000   \n",
-      "max    843.000000   31.000000  223.000000  125.000000   61.000000   24.000000   \n",
-      "\n",
-      "                i          be        that        will  \n",
-      "count  406.000000  406.000000  406.000000  406.000000  \n",
-      "mean    47.155172    2.950739    1.034483    0.955665  \n",
-      "std     71.043009    4.297865    1.904846    2.042271  \n",
-      "min      0.000000    0.000000    0.000000    0.000000  \n",
-      "25%     11.000000    1.000000    0.000000    0.000000  \n",
-      "50%     24.000000    1.000000    0.000000    0.000000  \n",
-      "75%     50.750000    3.000000    1.000000    1.000000  \n",
-      "max    754.000000   40.000000   14.000000   24.000000  \n"
-     ]
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "source": [
-    "# Sampling 10 emails\r\n",
-    "print(email_df.sample(10))"
-   ],
-   "outputs": [
-    {
-     "output_type": "stream",
-     "name": "stdout",
-     "text": [
-      "      Email No.  the  to  ect  and  for  of    a  you  in  on  is  this    i  \\\n",
-      "150   Email 151    0   1    2    0    3   0   15    0   0   5   0     0    7   \n",
-      "380  Email 5147    0   3    2    0    0   0    7    0   1   1   0     0    3   \n",
-      "19     Email 20    3   4   11    0    4   2   32    1   1   3   9     5   25   \n",
-      "300   Email 301    2   1    1    0    1   1   15    2   2   3   2     0    8   \n",
-      "307   Email 308    0   0    1    0    0   0    1    0   1   0   0     0    2   \n",
-      "167   Email 168    2   2    2    1    5   1   24    2   5   6   4     0   30   \n",
-      "320   Email 321   10  12    4    6    8   6  187    5  26  28  23     2  171   \n",
-      "61     Email 62    0   1    1    0    4   1   15    4   4   3   3     0   19   \n",
-      "26     Email 27    5   4    1    1    4   4   51    0   8   6   6     2   44   \n",
-      "73     Email 74    0   0    1    0    0   0    7    0   4   3   0     0    6   \n",
-      "\n",
-      "     be  that  will  \n",
-      "150   1     0     0  \n",
-      "380   0     0     0  \n",
-      "19    3     0     1  \n",
-      "300   0     0     0  \n",
-      "307   0     0     0  \n",
-      "167   2     0     0  \n",
-      "320   5     1     1  \n",
-      "61    2     0     0  \n",
-      "26    6     0     0  \n",
-      "73    0     0     0  \n"
-     ]
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "source": [
-    "# Returns rows where there are more occurrences of \"to\" than \"the\"\r\n",
-    "print(email_df.query('the < to'))"
-   ],
-   "outputs": [
-    {
-     "output_type": "stream",
-     "name": "stdout",
-     "text": [
-      "      Email No.  the  to  ect  and  for  of    a  you  in  on  is  this   i  \\\n",
-      "1       Email 2    8  13   24    6    6   2  102    1  18  21  13     0  61   \n",
-      "3       Email 4    0   5   22    0    5   1   51    2   1   5   9     2  16   \n",
-      "5       Email 6    4   5    1    4    2   3   45    1  16  12   8     1  52   \n",
-      "7       Email 8    0   2    2    3    1   2   21    6   2   6   2     0  28   \n",
-      "13     Email 14    4   5    7    1    5   1   37    1   8   8   6     1  43   \n",
-      "..          ...  ...  ..  ...  ...  ...  ..  ...  ...  ..  ..  ..   ...  ..   \n",
-      "390  Email 5157    4  13    1    0    3   1   48    2   8  26   9     1  45   \n",
-      "393  Email 5160    2  13    1    0    2   1   38    2   7  24   6     1  34   \n",
-      "396  Email 5163    2   3    1    2    1   2   32    0   7   3   2     0  26   \n",
-      "404  Email 5171    2   7    1    0    2   1   28    2   8  11   7     1  39   \n",
-      "405  Email 5172   22  24    5    1    6   5  148    8  23  13   5     4  99   \n",
-      "\n",
-      "     be  that  will  \n",
-      "1     4     2     0  \n",
-      "3     2     0     0  \n",
-      "5     2     0     0  \n",
-      "7     1     0     1  \n",
-      "13    1     0     1  \n",
-      "..   ..   ...   ...  \n",
-      "390   1     0     0  \n",
-      "393   1     0     0  \n",
-      "396   3     0     0  \n",
-      "404   1     0     0  \n",
-      "405   6     4     1  \n",
-      "\n",
-      "[169 rows x 17 columns]\n"
-     ]
-    }
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n---\n\n**免责声明**：  \n本文档使用AI翻译服务 [Co-op Translator](https://github.com/Azure/co-op-translator) 进行翻译。尽管我们努力确保翻译的准确性，但请注意，自动翻译可能包含错误或不准确之处。原始语言的文档应被视为权威来源。对于关键信息，建议使用专业人工翻译。我们不对因使用此翻译而产生的任何误解或误读承担责任。\n"
-   ]
-  }
- ],
- "metadata": {
-  "orig_nbformat": 4,
-  "language_info": {
-   "name": "python",
-   "version": "3.9.7",
-   "mimetype": "text/x-python",
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "pygments_lexer": "ipython3",
-   "nbconvert_exporter": "python",
-   "file_extension": ".py"
-  },
-  "kernelspec": {
-   "name": "python3",
-   "display_name": "Python 3.9.7 64-bit ('venv': venv)"
-  },
-  "interpreter": {
-   "hash": "6b9b57232c4b57163d057191678da2030059e733b8becc68f245de5a75abe84e"
-  },
-  "coopTranslator": {
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-   "language_code": "zh"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
\ No newline at end of file
diff --git a/translations/zh/4-Data-Science-Lifecycle/16-communication/README.md b/translations/zh/4-Data-Science-Lifecycle/16-communication/README.md
deleted file mode 100644
index e78e380a..00000000
--- a/translations/zh/4-Data-Science-Lifecycle/16-communication/README.md
+++ /dev/null
@@ -1,222 +0,0 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "215a3254ba5a222a57c5bb0192cea8e3",
-  "translation_date": "2025-09-06T20:27:10+00:00",
-  "source_file": "4-Data-Science-Lifecycle/16-communication/README.md",
-  "language_code": "zh"
-}
--->
-# 数据科学生命周期：沟通
-
-|![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev)](../../sketchnotes/16-Communicating.png)|
-|:---:|
-| 数据科学生命周期：沟通 - _Sketchnote by [@nitya](https://twitter.com/nitya)_ |
-
-## [课前测验](https://ff-quizzes.netlify.app/en/ds/quiz/30)
-
-通过上方的课前测验测试你对即将学习内容的了解！
-
-# 简介
-
-### 什么是沟通？
-让我们从定义沟通开始这节课。**沟通是传递或交换信息的过程。** 信息可以是想法、思考、感受、消息、隐秘信号、数据——任何一个**_发送者_**（发送信息的人）希望**_接收者_**（接收信息的人）理解的内容。在这节课中，我们将把发送者称为沟通者，而接收者称为受众。
-
-### 数据沟通与讲故事
-我们知道，沟通的目的是传递或交换信息。但在沟通数据时，你的目标不应该仅仅是向受众传递数字，而是要讲述一个由数据支撑的故事——有效的数据沟通与讲故事是密不可分的。你的受众更可能记住你讲述的故事，而不是你提供的数字。在这节课的后面，我们将讨论一些方法，帮助你通过讲故事更有效地沟通数据。
-
-### 沟通的类型
-在这节课中，我们将讨论两种不同类型的沟通：单向沟通和双向沟通。
-
-**单向沟通**是指发送者向接收者传递信息，而没有任何反馈或回应。我们每天都能看到单向沟通的例子——例如群发邮件、新闻报道最新事件，或者电视广告告诉你他们的产品有多好。在这些情况下，发送者并不寻求信息的交换，他们只是想传递或提供信息。
-
-**双向沟通**是指所有参与方既是发送者又是接收者。发送者首先向接收者沟通，接收者则提供反馈或回应。双向沟通是我们通常所认为的沟通方式。我们通常想到的是人与人之间的对话——无论是面对面交流，还是通过电话、社交媒体或短信。
-
-在沟通数据时，有些情况下你会使用单向沟通（比如在会议或大型群体中进行演讲，之后不会直接提问），而有些情况下你会使用双向沟通（比如用数据说服一些利益相关者支持，或者说服团队成员投入时间和精力开发新项目）。
-
-# 有效沟通
-
-### 作为沟通者的责任
-在沟通时，你的责任是确保接收者能够获取你希望他们获取的信息。在沟通数据时，你不仅希望接收者记住数字，还希望他们记住一个由数据支撑的故事。一个优秀的数据沟通者也是一个优秀的讲故事者。
-
-如何用数据讲故事？方法有无数种——但以下是我们将在这节课中讨论的6种方法：
-1. 了解你的受众、沟通渠道和沟通方式
-2. 从结果出发
-3. 像讲传统故事一样构建
-4. 使用有意义的词语和短语
-5. 运用情感
-
-以下将详细解释每种策略。
-
-### 1. 了解你的受众、沟通渠道和沟通方式
-你与家人沟通的方式可能与和朋友沟通的方式不同。你可能会使用不同的词语和短语，以便让对方更容易理解。沟通数据时也应该采取同样的方式。思考你正在与谁沟通，思考他们的目标以及他们对你所解释的情况的背景了解。
-
-你可以将大多数受众归类到某个类别中。在《哈佛商业评论》的一篇文章“[如何用数据讲故事](http://blogs.hbr.org/2013/04/how-to-tell-a-story-with-data/)”中，戴尔的战略顾问Jim Stikeleather将受众分为五类：
-
-- **初学者**：第一次接触主题，但不希望过于简化
-- **普通人**：对主题有所了解，但希望获得概述和主要内容
-- **管理者**：深入、可操作的理解，包括复杂性和相互关系，并能访问详细信息
-- **专家**：更倾向于探索和发现，而不是讲故事，关注细节
-- **高管**：只关注权重概率的意义和结论
-
-这些类别可以帮助你决定如何向受众展示数据。
-
-除了考虑受众的类别，你还应该考虑与受众沟通的渠道。如果你是写备忘录或邮件，与开会或在会议上演讲的方式会有所不同。
-
-在了解受众的基础上，了解你将如何与他们沟通（使用单向沟通还是双向沟通）也至关重要。
-
-如果你的受众主要是初学者，并且你使用单向沟通，你必须首先教育受众并提供适当的背景信息。然后你需要向他们展示你的数据，告诉他们数据的意义以及数据的重要性。在这种情况下，你可能需要专注于传递清晰的信息，因为你的受众无法直接向你提问。
-
-如果你的受众主要是管理者，并且你使用双向沟通，你可能不需要教育受众或提供太多背景信息。你可以直接讨论你收集的数据及其重要性。然而，在这种情况下，你需要关注时间和控制你的演示。当使用双向沟通时（尤其是面对寻求“可操作的复杂性和相互关系理解”的管理者受众），可能会出现问题，将讨论引向与你试图讲述的故事无关的方向。当这种情况发生时，你可以采取行动，将讨论拉回到你的故事上。
-
-### 2. 从结果出发
-从结果出发意味着在开始沟通之前，明确你希望受众获得的关键信息。提前思考你希望受众获取的信息，可以帮助你构建一个他们能够跟随的故事。从结果出发适用于单向沟通和双向沟通。
-
-如何从结果出发？在沟通数据之前，写下你的关键信息点。然后，在准备用数据讲述的故事的每一步，都问自己：“这如何融入我正在讲述的故事？”
-
-注意——虽然从结果出发是理想的，但你不应该只沟通支持你预期结果的数据。这种做法被称为“选择性呈现”，即沟通者只传递支持自己观点的数据，而忽略其他数据。
-
-如果你收集的所有数据都清楚地支持你的预期结果，那很好。但如果有数据不支持你的结果，甚至支持与你关键信息相反的观点，你也应该传递这些数据。如果出现这种情况，要坦诚地告诉受众为什么尽管所有数据并不完全支持你的故事，你仍然选择坚持自己的故事。
-
-### 3. 像讲传统故事一样构建
-一个传统故事通常分为五个阶段。你可能听过这些阶段被描述为：背景介绍、情节发展、高潮、情节收尾和结局。或者更容易记住的：背景、冲突、高潮、收尾、结论。在沟通你的数据和故事时，你可以采取类似的方式。
-
-你可以从背景开始，设定场景，确保你的受众都在同一页上。然后引入冲突。为什么需要收集这些数据？你试图解决什么问题？接下来是高潮。数据是什么？数据的意义是什么？数据告诉我们需要什么解决方案？然后是收尾，你可以重申问题和提出的解决方案。最后是结论，你可以总结关键信息点以及你建议团队采取的下一步行动。
-
-### 4. 使用有意义的词语和短语
-如果我们一起开发一个产品，我对你说“我们的用户在平台上注册的时间很长”，你会估计“很长”是多久？一个小时？一周？很难知道。如果我对整个受众说这句话，每个人可能会对用户注册时间有不同的理解。
-
-但如果我说“我们的用户平均花费3分钟注册并完成平台的使用”，那就更清晰了。
-
-在沟通数据时，很容易认为受众的思维方式和你一样。但事实并非总是如此。让数据及其意义清晰是你作为沟通者的责任。如果数据或故事不清晰，受众会难以跟随，也更难理解你的关键信息。
-
-通过使用有意义的词语和短语，而不是模糊的表达，可以更清晰地沟通数据。以下是一些例子：
-
-- 我们今年表现*令人印象深刻*！
-	- 一个人可能认为“令人印象深刻”意味着收入增长2%-3%，另一个人可能认为是50%-60%的增长。
-- 我们用户的成功率*显著*提高。
-	- 多大的增长才算显著？
-- 这个项目需要*大量*的努力。
-	- 多少努力才算大量？
-
-使用模糊的词语可能在引入更多数据或总结故事时有用。但请确保你的演示的每一部分对受众来说都是清晰的。
-
-### 5. 运用情感
-情感是讲故事的关键。在用数据讲故事时，情感更为重要。当你沟通数据时，一切都围绕你希望受众获取的关键信息展开。唤起受众的情感可以帮助他们产生共鸣，并更有可能采取行动。情感也能增加受众记住你的信息的可能性。
-
-你可能在电视广告中遇到过这种情况。有些广告非常沉重，用悲伤的情感与受众连接，让他们更关注广告中呈现的数据。或者，有些广告非常欢快，让你将数据与快乐的情感联系起来。
-
-如何在沟通数据时运用情感？以下是几种方法：
-
-- 使用推荐和个人故事
-	- 在收集数据时，尝试收集定量和定性数据，并在沟通时整合这两种数据。如果你的数据主要是定量的，尝试从个人那里收集故事，了解他们对数据所反映情况的体验。
-- 使用图像
-	- 图像可以帮助受众将自己代入某种情境。当你使用图像时，可以引导受众产生你希望他们对数据产生的情感。
-- 使用颜色
-	- 不同的颜色会唤起不同的情感。以下是一些常见颜色及其通常唤起的情感。注意，颜色在不同文化中可能有不同的含义。
-		- 蓝色通常唤起和平与信任的情感
-		- 绿色通常与自然和环境相关
-		- 红色通常代表激情和兴奋
-		- 黄色通常代表乐观和快乐
-
-# 沟通案例研究
-Emerson是一款移动应用的产品经理。他发现客户在周末提交的投诉和问题报告增加了42%。他还发现，如果客户提交的投诉在48小时内未得到回复，他们给应用评分1或2星的可能性增加了32%。
-
-经过研究，Emerson提出了几个解决问题的方案。他安排了一次30分钟的会议，与公司三位负责人沟通数据和提出的解决方案。
-
-在这次会议中，Emerson的目标是让公司负责人理解以下两个解决方案可以提高应用评分，而评分的提高可能会转化为更高的收入。
-
-**解决方案1.** 招聘客服人员在周末工作
-
-**解决方案2.** 购买新的客服工单系统，让客服人员可以轻松识别队列中等待时间最长的投诉——以便优先处理。
-
-在会议中，Emerson花了5分钟解释为什么应用评分低是个问题，10分钟解释研究过程和如何识别趋势，10分钟讨论一些最近的客户投诉，最后5分钟简单提及两个潜在解决方案。
-埃默森在这次会议中是否有效地传达了信息？
-
-在会议中，一位公司负责人专注于埃默森花了10分钟讲述的客户投诉。会议结束后，这些投诉成为这位负责人唯一记住的内容。另一位公司负责人主要关注埃默森描述的研究过程。第三位公司负责人记住了埃默森提出的解决方案，但不确定这些解决方案如何实施。
-
-在上述情境中，可以看到埃默森希望团队负责人从会议中获取的信息，与他们实际获取的信息之间存在显著差距。以下是埃默森可以考虑的另一种方法。
-
-埃默森如何改进这一方法？  
-背景、冲突、高潮、收尾、结论  
-**背景** - 埃默森可以用前5分钟介绍整个情况，并确保团队负责人理解这些问题如何影响公司关键指标，例如收入。
-
-可以这样展开：“目前，我们的应用在应用商店的评分是2.5。应用商店的评分对于应用商店优化至关重要，这会影响有多少用户在搜索中看到我们的应用，以及潜在用户如何看待我们的应用。当然，我们的用户数量直接与收入挂钩。”
-
-**冲突** 接下来，埃默森可以用大约5分钟时间讨论冲突。
-
-可以这样说：“用户在周末提交投诉和错误报告的比例高达42%。提交投诉后48小时内未得到回复的客户，其给我们应用评分超过2的可能性降低了32%。将我们应用在应用商店的评分提高到4，可以提升20-30%的可见度，我预计这将使收入增加10%。”当然，埃默森需要准备好为这些数据提供依据。
-
-**高潮** 在铺垫好基础后，埃默森可以用大约5分钟时间进入高潮部分。
-
-埃默森可以介绍提出的解决方案，阐述这些解决方案如何解决所列问题，如何将这些解决方案整合到现有工作流程中，解决方案的成本是多少，投资回报率（ROI）如何，甚至可以展示一些截图或线框图，说明解决方案实施后的效果。埃默森还可以分享一些用户的反馈，例如那些投诉超过48小时才得到处理的用户的评价，甚至可以引用公司现有客服代表对当前工单系统的评论。
-
-**收尾** 接下来，埃默森可以用5分钟时间重申公司面临的问题，回顾提出的解决方案，并再次说明为什么这些解决方案是正确的选择。
-
-**结论** 由于这是一次与少数利益相关者的会议，且会进行双向沟通，埃默森可以计划留出10分钟时间用于提问，以确保团队负责人在会议结束前能够澄清任何困惑之处。
-
-如果埃默森采用方法#2，团队负责人更有可能从会议中获取埃默森希望他们获取的信息——即投诉和错误处理方式可以改进，并且有两个解决方案可以实施以实现这一改进。这种方法将更有效地传达埃默森希望传递的数据和故事。
-
-# 结论  
-### 主要观点总结  
-- 沟通是传递或交换信息。  
-- 在传递数据时，目标不应该只是向观众传递数字，而是要传递一个由数据支持的故事。  
-- 沟通有两种类型：单向沟通（信息传递无意图获得回应）和双向沟通（信息在双方之间传递）。  
-- 有许多策略可以用来通过数据讲述故事，我们讨论了以下5种策略：  
-  - 了解你的观众、媒介和沟通方式  
-  - 从结果出发进行规划  
-  - 像讲述一个真实的故事一样展开  
-  - 使用有意义的词汇和短语  
-  - 运用情感  
-
-### 推荐的自学资源  
-[The Five C's of Storytelling - Articulate Persuasion](http://articulatepersuasion.com/the-five-cs-of-storytelling/)  
-
-[1.4 Your Responsibilities as a Communicator – Business Communication for Success (umn.edu)](https://open.lib.umn.edu/businesscommunication/chapter/1-4-your-responsibilities-as-a-communicator/)  
-
-[How to Tell a Story with Data (hbr.org)](https://hbr.org/2013/04/how-to-tell-a-story-with-data)  
-
-[Two-Way Communication: 4 Tips for a More Engaged Workplace (yourthoughtpartner.com)](https://www.yourthoughtpartner.com/blog/bid/59576/4-steps-to-increase-employee-engagement-through-two-way-communication)  
-
-[6 succinct steps to great data storytelling - BarnRaisers, LLC (barnraisersllc.com)](https://barnraisersllc.com/2021/05/02/6-succinct-steps-to-great-data-storytelling/)  
-
-[How to Tell a Story With Data | Lucidchart Blog](https://www.lucidchart.com/blog/how-to-tell-a-story-with-data)  
-
-[6 Cs of Effective Storytelling on Social Media | Cooler Insights](https://coolerinsights.com/2018/06/effective-storytelling-social-media/)  
-
-[The Importance of Emotions In Presentations | Ethos3 - A Presentation Training and Design Agency](https://ethos3.com/2015/02/the-importance-of-emotions-in-presentations/)  
-
-[Data storytelling: linking emotions and rational decisions (toucantoco.com)](https://www.toucantoco.com/en/blog/data-storytelling-dataviz)  
-
-[Emotional Advertising: How Brands Use Feelings to Get People to Buy (hubspot.com)](https://blog.hubspot.com/marketing/emotions-in-advertising-examples)  
-
-[Choosing Colors for Your Presentation Slides | Think Outside The Slide](https://www.thinkoutsidetheslide.com/choosing-colors-for-your-presentation-slides/)  
-
-[How To Present Data [10 Expert Tips] | ObservePoint](https://resources.observepoint.com/blog/10-tips-for-presenting-data)  
-
-[Microsoft Word - Persuasive Instructions.doc (tpsnva.org)](https://www.tpsnva.org/teach/lq/016/persinstr.pdf)  
-
-[The Power of Story for Your Data (thinkhdi.com)](https://www.thinkhdi.com/library/supportworld/2019/power-story-your-data.aspx)  
-
-[Common Mistakes in Data Presentation (perceptualedge.com)](https://www.perceptualedge.com/articles/ie/data_presentation.pdf)  
-
-[Infographic: Here are 15 Common Data Fallacies to Avoid (visualcapitalist.com)](https://www.visualcapitalist.com/here-are-15-common-data-fallacies-to-avoid/)  
-
-[Cherry Picking: When People Ignore Evidence that They Dislike – Effectiviology](https://effectiviology.com/cherry-picking/#How_to_avoid_cherry_picking)  
-
-[Tell Stories with Data: Communication in Data Science | by Sonali Verghese | Towards Data Science](https://towardsdatascience.com/tell-stories-with-data-communication-in-data-science-5266f7671d7)  
-
-[1. Communicating Data - Communicating Data with Tableau [Book] (oreilly.com)](https://www.oreilly.com/library/view/communicating-data-with/9781449372019/ch01.html)  
-
-## [课后测验](https://ff-quizzes.netlify.app/en/ds/quiz/31)  
-
-通过上面的课后测验复习你刚刚学到的内容！  
-
-## 作业  
-
-[市场研究](assignment.md)  
-
----
-
-**免责声明**：  
-本文档使用AI翻译服务[Co-op Translator](https://github.com/Azure/co-op-translator)进行翻译。尽管我们努力确保准确性，但请注意，自动翻译可能包含错误或不准确之处。应以原始语言的文档作为权威来源。对于关键信息，建议使用专业人工翻译。因使用本翻译而导致的任何误解或误读，我们概不负责。
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--->
-# 讲述一个故事
-
-## 指南
-
-数据科学的核心是讲故事。选择任意一个数据集，撰写一篇简短的文章，讲述你可以从中挖掘出的故事。你希望你的数据集能够揭示什么？如果它的揭示结果存在问题，你会怎么做？如果你的数据并不容易揭示其秘密，你又会如何应对？思考你的数据集可能呈现的各种情景，并将它们记录下来。
-
-## 评分标准
-
-优秀 | 合格 | 需要改进
---- | --- | --- |
-
-提供了一篇以 .doc 格式呈现的完整文章，清晰解释、记录并注明数据集来源，同时围绕数据集讲述了一个连贯的故事，并结合数据提供了详细的示例。| 提供了一篇较短的文章，细节较少 | 文章在上述某些方面存在不足。
-
-**免责声明**：  
-本文档使用AI翻译服务 [Co-op Translator](https://github.com/Azure/co-op-translator) 进行翻译。尽管我们努力确保翻译的准确性，但请注意，自动翻译可能包含错误或不准确之处。应以原始语言的文档作为权威来源。对于重要信息，建议使用专业人工翻译。我们不对因使用此翻译而产生的任何误解或误读承担责任。
\ No newline at end of file
diff --git a/translations/zh/4-Data-Science-Lifecycle/README.md b/translations/zh/4-Data-Science-Lifecycle/README.md
deleted file mode 100644
index d48a62bb..00000000
--- a/translations/zh/4-Data-Science-Lifecycle/README.md
+++ /dev/null
@@ -1,28 +0,0 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "dd173fd30fc039a7a299898920680723",
-  "translation_date": "2025-08-25T17:40:43+00:00",
-  "source_file": "4-Data-Science-Lifecycle/README.md",
-  "language_code": "zh"
-}
--->
-# 数据科学生命周期
-
-![communication](../../../translated_images/zh-CN/communication.06d8e2a88d30d168d661ad9f9f0a4f947ebff3719719cfdaf9ed00a406a01ead.jpg)
-> 图片由 <a href="https://unsplash.com/@headwayio?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Headway</a> 提供，来自 <a href="https://unsplash.com/s/photos/communication?utm_source=unsplash&utm_medium=referral&utm_content=creditCopyText">Unsplash</a>
-  
-在这些课程中，您将探索数据科学生命周期的一些方面，包括数据的分析和沟通。
-
-### 主题
-
-1. [简介](14-Introduction/README.md)
-2. [分析](15-analyzing/README.md)
-3. [沟通](16-communication/README.md)
-
-### 致谢
-
-这些课程由 [Jalen McGee](https://twitter.com/JalenMCG) 和 [Jasmine Greenaway](https://twitter.com/paladique) 用 ❤️ 编写
-
-**免责声明**：  
-本文档使用AI翻译服务[Co-op Translator](https://github.com/Azure/co-op-translator)进行翻译。虽然我们努力确保翻译的准确性，但请注意，自动翻译可能包含错误或不准确之处。原始语言的文档应被视为权威来源。对于关键信息，建议使用专业人工翻译。我们对因使用此翻译而产生的任何误解或误读不承担责任。
\ No newline at end of file
diff --git a/translations/zh/5-Data-Science-In-Cloud/17-Introduction/README.md b/translations/zh/5-Data-Science-In-Cloud/17-Introduction/README.md
deleted file mode 100644
index 9a6973aa..00000000
--- a/translations/zh/5-Data-Science-In-Cloud/17-Introduction/README.md
+++ /dev/null
@@ -1,116 +0,0 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "5f8e7cdefa096664ae86f795be571580",
-  "translation_date": "2025-09-05T11:28:32+00:00",
-  "source_file": "5-Data-Science-In-Cloud/17-Introduction/README.md",
-  "language_code": "zh"
-}
--->
-# 云端数据科学简介
-
-|![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/17-DataScience-Cloud.png)|
-|:---:|
-| 云端数据科学：简介 - _Sketchnote by [@nitya](https://twitter.com/nitya)_ |
-
-在本课程中，您将学习云的基本原理，了解为什么使用云服务来运行数据科学项目可能对您有吸引力，并且我们将查看一些在云端运行的数据科学项目示例。
-
-## [课前测验](https://ff-quizzes.netlify.app/en/ds/quiz/32)
-
-## 什么是云？
-
-云，或云计算，是通过互联网提供的按需付费的各种计算服务，这些服务托管在基础设施上。服务包括存储、数据库、网络、软件、分析和智能服务等解决方案。
-
-我们通常将公共云、私有云和混合云区分如下：
-
-* 公共云：公共云由第三方云服务提供商拥有和运营，通过互联网向公众提供其计算资源。
-* 私有云：指专门供单一企业或组织使用的云计算资源，服务和基础设施维护在私有网络上。
-* 混合云：混合云是结合公共云和私有云的系统。用户选择本地数据中心，同时允许数据和应用程序在一个或多个公共云上运行。
-
-大多数云计算服务分为三类：基础设施即服务（IaaS）、平台即服务（PaaS）和软件即服务（SaaS）。
-
-* 基础设施即服务（IaaS）：用户租用IT基础设施，例如服务器和虚拟机（VM）、存储、网络、操作系统。
-* 平台即服务（PaaS）：用户租用一个用于开发、测试、交付和管理软件应用程序的环境。用户无需担心设置或管理开发所需的服务器、存储、网络和数据库的底层基础设施。
-* 软件即服务（SaaS）：用户通过互联网按需访问软件应用程序，通常是基于订阅的。用户无需担心托管和管理软件应用程序、底层基础设施或维护，例如软件升级和安全补丁。
-
-一些最大的云服务提供商包括亚马逊AWS、谷歌云平台和微软Azure。
-
-## 为什么选择云进行数据科学？
-
-开发人员和IT专业人员选择使用云的原因有很多，包括以下几点：
-
-* 创新：您可以通过将云提供商创建的创新服务直接集成到您的应用程序中来增强应用程序功能。
-* 灵活性：您只需支付所需的服务费用，并可以从广泛的服务中进行选择。通常按需付费，并根据不断变化的需求调整服务。
-* 预算：您无需进行初期投资来购买硬件和软件、设置和运行本地数据中心，只需支付实际使用的费用。
-* 可扩展性：您的资源可以根据项目需求进行扩展，这意味着您的应用程序可以根据外部因素随时调整计算能力、存储和带宽。
-* 生产力：您可以专注于业务，而不是花时间处理可以由其他人管理的任务，例如管理数据中心。
-* 可靠性：云计算提供多种方式来持续备份您的数据，并可以设置灾难恢复计划，即使在危机时期也能保持业务和服务的运行。
-* 安全性：您可以受益于增强项目安全性的政策、技术和控制措施。
-
-以上是人们选择使用云服务的一些常见原因。现在我们对云是什么以及其主要优势有了更好的理解，让我们更具体地了解数据科学家和处理数据的开发人员的工作，以及云如何帮助他们解决可能面临的各种挑战：
-
-* 存储大量数据：与其购买、管理和保护大型服务器，不如直接将数据存储在云中，例如使用Azure Cosmos DB、Azure SQL Database和Azure Data Lake Storage。
-* 执行数据集成：数据集成是数据科学的重要组成部分，它使您能够从数据收集过渡到采取行动。通过云提供的数据集成服务，您可以从各种来源收集、转换和集成数据到单一数据仓库，例如使用Data Factory。
-* 处理数据：处理大量数据需要强大的计算能力，并不是每个人都能获得足够强大的机器，这也是许多人选择直接利用云的巨大计算能力来运行和部署解决方案的原因。
-* 使用数据分析服务：云服务如Azure Synapse Analytics、Azure Stream Analytics和Azure Databricks可以帮助您将数据转化为可操作的洞察。
-* 使用机器学习和数据智能服务：与其从零开始，您可以使用云提供商提供的机器学习算法，例如AzureML服务。您还可以使用认知服务，例如语音转文字、文字转语音、计算机视觉等。
-
-## 云端数据科学示例
-
-让我们通过几个场景使这一切更加具体。
-
-### 实时社交媒体情感分析
-
-我们从一个常见的机器学习入门场景开始：实时社交媒体情感分析。
-
-假设您运营一个新闻媒体网站，希望利用实时数据了解读者可能感兴趣的内容。为此，您可以构建一个程序，对Twitter上的相关主题进行实时情感分析。
-
-您关注的关键指标是特定主题（标签）的推文数量和情感，这些情感通过分析工具对指定主题进行情感分析来确定。
-
-创建此项目所需的步骤如下：
-
-* 创建一个事件中心以流式输入数据，收集来自Twitter的数据
-* 配置并启动Twitter客户端应用程序，调用Twitter流API
-* 创建一个流分析作业
-* 指定作业输入和查询
-* 创建输出接收器并指定作业输出
-* 启动作业
-
-查看完整流程，请参阅[文档](https://docs.microsoft.com/azure/stream-analytics/stream-analytics-twitter-sentiment-analysis-trends?WT.mc_id=academic-77958-bethanycheum&ocid=AID30411099)。
-
-### 科学论文分析
-
-让我们看另一个由本课程作者之一[Dmitry Soshnikov](http://soshnikov.com)创建的项目示例。
-
-Dmitry创建了一个分析COVID论文的工具。通过审查此项目，您将了解如何创建一个工具，从科学论文中提取知识，获得洞察，并帮助研究人员高效地浏览大量论文。
-
-以下是使用的不同步骤：
-
-* 使用[Text Analytics for Health](https://docs.microsoft.com/azure/cognitive-services/text-analytics/how-tos/text-analytics-for-health?WT.mc_id=academic-77958-bethanycheum&ocid=AID3041109)提取和预处理信息
-* 使用[Azure ML](https://azure.microsoft.com/services/machine-learning?WT.mc_id=academic-77958-bethanycheum&ocid=AID3041109)并行化处理
-* 使用[Cosmos DB](https://azure.microsoft.com/services/cosmos-db?WT.mc_id=academic-77958-bethanycheum&ocid=AID3041109)存储和查询信息
-* 使用Power BI创建交互式仪表板进行数据探索和可视化
-
-查看完整流程，请访问[Dmitry的博客](https://soshnikov.com/science/analyzing-medical-papers-with-azure-and-text-analytics-for-health/)。
-
-如您所见，我们可以通过多种方式利用云服务进行数据科学。
-
-## 脚注
-
-来源：
-* https://azure.microsoft.com/overview/what-is-cloud-computing?ocid=AID3041109  
-* https://docs.microsoft.com/azure/stream-analytics/stream-analytics-twitter-sentiment-analysis-trends?ocid=AID3041109  
-* https://soshnikov.com/science/analyzing-medical-papers-with-azure-and-text-analytics-for-health/  
-
-## 课后测验
-
-## [课后测验](https://ff-quizzes.netlify.app/en/ds/quiz/33)
-
-## 作业
-
-[市场研究](assignment.md)
-
----
-
-**免责声明**：  
-本文档使用AI翻译服务[Co-op Translator](https://github.com/Azure/co-op-translator)进行翻译。尽管我们努力确保准确性，但请注意，自动翻译可能包含错误或不准确之处。应以原始语言的文档作为权威来源。对于关键信息，建议使用专业人工翻译。因使用本翻译而导致的任何误解或误读，我们概不负责。
\ No newline at end of file
diff --git a/translations/zh/5-Data-Science-In-Cloud/17-Introduction/assignment.md b/translations/zh/5-Data-Science-In-Cloud/17-Introduction/assignment.md
deleted file mode 100644
index 8033f143..00000000
--- a/translations/zh/5-Data-Science-In-Cloud/17-Introduction/assignment.md
+++ /dev/null
@@ -1,23 +0,0 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "96f3696153d9ed54b19a1bb65438c104",
-  "translation_date": "2025-08-25T17:31:28+00:00",
-  "source_file": "5-Data-Science-In-Cloud/17-Introduction/assignment.md",
-  "language_code": "zh"
-}
--->
-# 市场调研
-
-## 说明
-
-在本课中，你了解到有几个重要的云服务提供商。进行市场调研，了解每个提供商能为数据科学家提供什么服务。这些服务是否具有可比性？撰写一篇文章，描述三个或更多云服务提供商的服务内容。
-
-## 评分标准
-
-优秀 | 合格 | 需改进
---- | --- | -- |
-一篇完整的文章描述了三个云服务提供商的数据科学服务，并区分了它们之间的差异。 | 提交了一篇较短的文章 | 提交了一篇文章，但未完成分析
-
-**免责声明**：  
-本文档使用AI翻译服务 [Co-op Translator](https://github.com/Azure/co-op-translator) 进行翻译。尽管我们努力确保翻译的准确性，但请注意，自动翻译可能包含错误或不准确之处。原始语言的文档应被视为权威来源。对于关键信息，建议使用专业人工翻译。我们不对因使用此翻译而产生的任何误解或误读承担责任。
\ No newline at end of file
diff --git a/translations/zh/5-Data-Science-In-Cloud/18-Low-Code/README.md b/translations/zh/5-Data-Science-In-Cloud/18-Low-Code/README.md
deleted file mode 100644
index ec15555a..00000000
--- a/translations/zh/5-Data-Science-In-Cloud/18-Low-Code/README.md
+++ /dev/null
@@ -1,349 +0,0 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "bd4da10766c64fce4294a98f6479dfb0",
-  "translation_date": "2025-09-05T11:27:03+00:00",
-  "source_file": "5-Data-Science-In-Cloud/18-Low-Code/README.md",
-  "language_code": "zh"
-}
--->
-# 云端数据科学：低代码/无代码方式
-
-|![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/18-DataScience-Cloud.png)|
-|:---:|
-| 云端数据科学：低代码 - _Sketchnote by [@nitya](https://twitter.com/nitya)_ |
-
-目录：
-
-- [云端数据科学：低代码/无代码方式](../../../../5-Data-Science-In-Cloud/18-Low-Code)
-  - [课前测验](../../../../5-Data-Science-In-Cloud/18-Low-Code)
-  - [1. 简介](../../../../5-Data-Science-In-Cloud/18-Low-Code)
-    - [1.1 什么是 Azure Machine Learning？](../../../../5-Data-Science-In-Cloud/18-Low-Code)
-    - [1.2 心力衰竭预测项目：](../../../../5-Data-Science-In-Cloud/18-Low-Code)
-    - [1.3 心力衰竭数据集：](../../../../5-Data-Science-In-Cloud/18-Low-Code)
-  - [2. 在 Azure ML Studio 中进行低代码/无代码模型训练](../../../../5-Data-Science-In-Cloud/18-Low-Code)
-    - [2.1 创建 Azure ML 工作区](../../../../5-Data-Science-In-Cloud/18-Low-Code)
-    - [2.2 计算资源](../../../../5-Data-Science-In-Cloud/18-Low-Code)
-      - [2.2.1 为计算资源选择合适的选项](../../../../5-Data-Science-In-Cloud/18-Low-Code)
-      - [2.2.2 创建计算集群](../../../../5-Data-Science-In-Cloud/18-Low-Code)
-    - [2.3 加载数据集](../../../../5-Data-Science-In-Cloud/18-Low-Code)
-    - [2.4 使用 AutoML 进行低代码/无代码训练](../../../../5-Data-Science-In-Cloud/18-Low-Code)
-  - [3. 低代码/无代码模型部署与端点使用](../../../../5-Data-Science-In-Cloud/18-Low-Code)
-    - [3.1 模型部署](../../../../5-Data-Science-In-Cloud/18-Low-Code)
-    - [3.2 端点使用](../../../../5-Data-Science-In-Cloud/18-Low-Code)
-  - [🚀 挑战](../../../../5-Data-Science-In-Cloud/18-Low-Code)
-  - [课后测验](../../../../5-Data-Science-In-Cloud/18-Low-Code)
-  - [复习与自学](../../../../5-Data-Science-In-Cloud/18-Low-Code)
-  - [作业](../../../../5-Data-Science-In-Cloud/18-Low-Code)
-  
-## [课前测验](https://ff-quizzes.netlify.app/en/ds/quiz/34)
-
-## 1. 简介
-### 1.1 什么是 Azure Machine Learning？
-
-Azure 云平台包含超过 200 种产品和云服务，旨在帮助您实现创新解决方案。  
-数据科学家通常需要花费大量时间探索和预处理数据，并尝试各种模型训练算法以生成准确的模型。这些任务耗时且常常无法高效利用昂贵的计算硬件。
-
-[Azure ML](https://docs.microsoft.com/azure/machine-learning/overview-what-is-azure-machine-learning?WT.mc_id=academic-77958-bethanycheum&ocid=AID3041109) 是一个基于云的平台，用于在 Azure 中构建和运行机器学习解决方案。它提供了广泛的功能和能力，帮助数据科学家准备数据、训练模型、发布预测服务并监控其使用情况。最重要的是，它通过自动化许多与模型训练相关的耗时任务来提高效率；同时，它支持使用可扩展的云计算资源来处理大量数据，仅在实际使用时产生费用。
-
-Azure ML 提供了开发者和数据科学家所需的所有工具，用于完成机器学习工作流。这些工具包括：
-
-- **Azure Machine Learning Studio**：一个基于 Web 的门户，提供低代码和无代码选项，用于模型训练、部署、自动化、跟踪和资产管理。Studio 与 Azure Machine Learning SDK 集成，提供无缝体验。
-- **Jupyter Notebooks**：快速原型设计和测试机器学习模型。
-- **Azure Machine Learning Designer**：允许通过拖放模块来构建实验，并在低代码环境中部署管道。
-- **自动化机器学习界面 (AutoML)**：自动化机器学习模型开发的迭代任务，能够以高效和高生产力的方式构建模型，同时保持模型质量。
-- **数据标注**：一个辅助机器学习工具，用于自动标注数据。
-- **Visual Studio Code 的机器学习扩展**：提供一个完整的开发环境，用于构建和管理机器学习项目。
-- **机器学习 CLI**：提供命令行工具，用于管理 Azure ML 资源。
-- **与开源框架集成**：支持 PyTorch、TensorFlow、Scikit-learn 等框架，用于训练、部署和管理机器学习的端到端流程。
-- **MLflow**：一个开源库，用于管理机器学习实验的生命周期。**MLFlow Tracking** 是 MLflow 的一个组件，用于记录和跟踪训练运行的指标和模型工件，无论实验环境如何。
-
-### 1.2 心力衰竭预测项目：
-
-毫无疑问，制作和构建项目是检验技能和知识的最佳方式。在本课程中，我们将探索两种不同的方式来构建一个数据科学项目，用于预测心力衰竭攻击。这两种方式分别是通过 Azure ML Studio 的低代码/无代码方式，以及通过 Azure ML SDK 的方式，如下图所示：
-
-![project-schema](../../../../5-Data-Science-In-Cloud/18-Low-Code/images/project-schema.PNG)
-
-每种方式都有其优缺点。低代码/无代码方式更容易入门，因为它涉及与图形用户界面 (GUI) 交互，不需要任何代码知识。这种方法可以快速测试项目的可行性并创建概念验证 (POC)。然而，随着项目的扩展并需要进入生产阶段，仅通过 GUI 创建资源变得不可行。此时，程序化地自动化所有内容（从资源创建到模型部署）变得至关重要，这也是学习如何使用 Azure ML SDK 的关键所在。
-
-|                   | 低代码/无代码 | Azure ML SDK              |
-|-------------------|------------------|---------------------------|
-| 代码知识要求      | 不需要          | 需要                      |
-| 开发时间          | 快速且简单      | 取决于代码熟练程度        |
-| 是否适合生产环境  | 否              | 是                        |
-
-### 1.3 心力衰竭数据集： 
-
-心血管疾病 (CVDs) 是全球范围内的头号死亡原因，占全球死亡人数的 31%。环境和行为风险因素（如吸烟、不健康饮食和肥胖、缺乏运动以及过量饮酒）可以作为估算模型的特征。能够估算 CVD 的发展概率对于预防高风险人群的心力衰竭发作具有重要意义。
-
-Kaggle 提供了一个公开的 [心力衰竭数据集](https://www.kaggle.com/andrewmvd/heart-failure-clinical-data)，我们将在本项目中使用该数据集。您可以现在下载数据集。该数据集是一个表格数据集，包含 13 列（12 个特征和 1 个目标变量）和 299 行。
-
-|    | 变量名称                  | 类型            | 描述                                                     | 示例               |
-|----|---------------------------|-----------------|----------------------------------------------------------|-------------------|
-| 1  | age                       | 数值型          | 患者年龄                                                | 25                |
-| 2  | anaemia                   | 布尔型          | 红细胞或血红蛋白减少                                    | 0 或 1            |
-| 3  | creatinine_phosphokinase  | 数值型          | 血液中 CPK 酶的水平                                     | 542               |
-| 4  | diabetes                  | 布尔型          | 患者是否患有糖尿病                                      | 0 或 1            |
-| 5  | ejection_fraction         | 数值型          | 每次心脏收缩时排出的血液百分比                          | 45                |
-| 6  | high_blood_pressure       | 布尔型          | 患者是否患有高血压                                      | 0 或 1            |
-| 7  | platelets                 | 数值型          | 血液中的血小板数量                                      | 149000            |
-| 8  | serum_creatinine          | 数值型          | 血液中的血清肌酐水平                                    | 0.5               |
-| 9  | serum_sodium              | 数值型          | 血液中的血清钠水平                                      | jun               |
-| 10 | sex                       | 布尔型          | 女性或男性                                              | 0 或 1            |
-| 11 | smoking                   | 布尔型          | 患者是否吸烟                                            | 0 或 1            |
-| 12 | time                      | 数值型          | 随访时间（天）                                          | 4                 |
-|----|---------------------------|-----------------|----------------------------------------------------------|-------------------|
-| 21 | DEATH_EVENT [目标]        | 布尔型          | 患者是否在随访期间死亡                                  | 0 或 1            |
-
-下载数据集后，我们就可以在 Azure 中开始项目了。
-
-## 2. 在 Azure ML Studio 中进行低代码/无代码模型训练
-### 2.1 创建 Azure ML 工作区
-要在 Azure ML 中训练模型，首先需要创建一个 Azure ML 工作区。工作区是 Azure Machine Learning 的顶级资源，提供一个集中式位置，用于管理您在使用 Azure Machine Learning 时创建的所有工件。工作区会记录所有训练运行的历史，包括日志、指标、输出以及脚本的快照。您可以使用这些信息来确定哪个训练运行生成了最佳模型。[了解更多](https://docs.microsoft.com/azure/machine-learning/concept-workspace?WT.mc_id=academic-77958-bethanycheum&ocid=AID3041109)
-
-建议使用与您的操作系统兼容的最新浏览器。以下浏览器受支持：
-
-- Microsoft Edge（最新版本的新 Microsoft Edge，不是旧版 Microsoft Edge）
-- Safari（最新版本，仅限 Mac）
-- Chrome（最新版本）
-- Firefox（最新版本）
-
-要使用 Azure Machine Learning，请在您的 Azure 订阅中创建一个工作区。然后，您可以使用此工作区来管理与机器学习工作负载相关的数据、计算资源、代码、模型和其他工件。
-
-> **_注意：_** 您的 Azure 订阅将在工作区存在期间因数据存储产生少量费用，因此建议在不再使用 Azure Machine Learning 工作区时将其删除。
-
-1. 使用与您的 Azure 订阅关联的 Microsoft 凭据登录 [Azure 门户](https://ms.portal.azure.com/)。
-2. 选择 **＋创建资源**
-   
-   ![workspace-1](../../../../5-Data-Science-In-Cloud/18-Low-Code/images/workspace-1.PNG)
-
-   搜索 Machine Learning 并选择 Machine Learning 磁贴
-
-   ![workspace-2](../../../../5-Data-Science-In-Cloud/18-Low-Code/images/workspace-2.PNG)
-
-   点击创建按钮
-
-   ![workspace-3](../../../../5-Data-Science-In-Cloud/18-Low-Code/images/workspace-3.PNG)
-
-   按以下设置填写：
-   - 订阅：您的 Azure 订阅
-   - 资源组：创建或选择一个资源组
-   - 工作区名称：输入一个唯一的工作区名称
-   - 区域：选择离您最近的地理区域
-   - 存储账户：注意将为您的工作区创建的默认新存储账户
-   - 密钥保管库：注意将为您的工作区创建的默认新密钥保管库
-   - 应用程序洞察：注意将为您的工作区创建的默认新应用程序洞察资源
-   - 容器注册表：无（第一次将模型部署到容器时会自动创建一个）
-
-    ![workspace-4](../../../../5-Data-Science-In-Cloud/18-Low-Code/images/workspace-4.PNG)
-
-   - 点击创建 + 审核，然后点击创建按钮
-3. 等待您的工作区创建完成（可能需要几分钟）。然后在门户中访问它。您可以通过 Machine Learning Azure 服务找到它。
-4. 在工作区的概览页面，启动 Azure Machine Learning Studio（或打开一个新的浏览器标签并导航到 https://ml.azure.com），使用您的 Microsoft 账户登录 Azure Machine Learning Studio。如果提示，请选择您的 Azure 目录和订阅，以及您的 Azure Machine Learning 工作区。
-   
-![workspace-5](../../../../5-Data-Science-In-Cloud/18-Low-Code/images/workspace-5.PNG)
-
-5. 在 Azure Machine Learning Studio 中，切换左上角的 ☰ 图标以查看界面中的各个页面。您可以使用这些页面来管理工作区中的资源。
-
-![workspace-6](../../../../5-Data-Science-In-Cloud/18-Low-Code/images/workspace-6.PNG)
-
-您可以使用 Azure 门户管理工作区，但对于数据科学家和机器学习运维工程师来说，Azure Machine Learning Studio 提供了一个更专注的用户界面，用于管理工作区资源。
-
-### 2.2 计算资源
-
-计算资源是基于云的资源，用于运行模型训练和数据探索过程。您可以创建四种类型的计算资源：
-
-- **计算实例**：数据科学家可以用来处理数据和模型的开发工作站。这涉及创建一个虚拟机 (VM) 并启动一个 notebook 实例。然后，您可以通过 notebook 调用计算集群来训练模型。
-- **计算集群**：可扩展的虚拟机集群，用于按需处理实验代码。训练模型时需要使用计算集群。计算集群还可以使用专门的 GPU 或 CPU 资源。
-- **推理集群**：用于部署使用您训练模型的预测服务的目标。
-- **附加计算**：链接到现有的 Azure 计算资源，例如虚拟机或 Azure Databricks 集群。
-
-#### 2.2.1 为计算资源选择合适的选项
-
-创建计算资源时需要考虑一些关键因素，这些选择可能是至关重要的决策。
-
-**需要 CPU 还是 GPU？**
-
-CPU（中央处理器）是执行计算机程序指令的电子电路。GPU（图形处理器）是一种专门的电子电路，可以以非常高的速度执行与图形相关的代码。
-
-CPU 和 GPU 架构的主要区别在于，CPU 设计用于快速处理广泛的任务（以 CPU 时钟速度衡量），但在任务并发性方面有限。而 GPU 设计用于并行计算，因此在深度学习任务中表现更佳。
-
-| CPU                                     | GPU                         |
-|-----------------------------------------|-----------------------------|
-| 成本较低                                | 成本较高                   |
-| 并发性较低                              | 并发性较高                 |
-| 深度学习模型训练速度较慢                | 深度学习的最佳选择         |
-
-**集群规模**
-
-较大的集群成本更高，但响应速度更快。因此，如果时间充裕但预算有限，可以选择较小的集群；反之，如果预算充足但时间有限，可以选择较大的集群。
-
-**虚拟机规格**
-
-根据时间和预算限制，可以调整 RAM、磁盘、核心数量和时钟速度的大小。增加这些参数会提高成本，但性能也会更好。
-
-**专用实例还是低优先级实例？**
-
-低优先级实例意味着它是可中断的：本质上，Microsoft Azure 可以将这些资源分配给其他任务，从而中断当前作业。而专用实例（不可中断）意味着作业不会在未经您许可的情况下被终止。这是另一个时间与成本的权衡，因为可中断实例比专用实例便宜。
-
-#### 2.2.2 创建计算集群
-
-在我们之前创建的 [Azure ML 工作区](https://ml.azure.com/) 中，进入计算选项，您将看到我们刚刚讨论的不同计算资源（例如计算实例、计算集群、推理集群和附加计算）。在这个项目中，我们需要一个计算集群来进行模型训练。在 Studio 中，点击“计算”菜单，然后点击“计算集群”标签页，点击“+ 新建”按钮以创建计算集群。
-
-![22](../../../../5-Data-Science-In-Cloud/18-Low-Code/images/cluster-1.PNG)
-
-1. 选择您的选项：专用 vs 低优先级，CPU 或 GPU，虚拟机规格和核心数量（对于本项目，可以保留默认设置）。
-2. 点击“下一步”按钮。
-
-![23](../../../../5-Data-Science-In-Cloud/18-Low-Code/images/cluster-2.PNG)
-
-3. 为集群命名。
-4. 选择您的选项：节点的最小/最大数量、闲置秒数后缩减规模、SSH 访问。注意，如果节点的最小数量为 0，当集群闲置时可以节省成本。注意，最大节点数量越高，训练时间越短。推荐的最大节点数量为 3。
-5. 点击“创建”按钮。这一步可能需要几分钟。
-
-![29](../../../../5-Data-Science-In-Cloud/18-Low-Code/images/cluster-3.PNG)
-
-太棒了！现在我们已经有了一个计算集群，接下来需要将数据加载到 Azure ML Studio。
-
-### 2.3 加载数据集
-
-1. 在我们之前创建的 [Azure ML 工作区](https://ml.azure.com/) 中，点击左侧菜单中的“数据集”，然后点击“+ 创建数据集”按钮以创建数据集。选择“从本地文件”选项并选择我们之前下载的 Kaggle 数据集。
-
-   ![24](../../../../5-Data-Science-In-Cloud/18-Low-Code/images/dataset-1.PNG)
-
-2. 为您的数据集命名、选择类型并添加描述。点击“下一步”。从文件中上传数据。点击“下一步”。
-
-   ![25](../../../../5-Data-Science-In-Cloud/18-Low-Code/images/dataset-2.PNG)
-
-3. 在 Schema 中，将以下特征的数据类型更改为布尔值：贫血、糖尿病、高血压、性别、吸烟和死亡事件（DEATH_EVENT）。点击“下一步”，然后点击“创建”。
-
-   ![26](../../../../5-Data-Science-In-Cloud/18-Low-Code/images/dataset-3.PNG)
-
-很好！现在数据集已经就位，计算集群也已创建，我们可以开始训练模型了！
-
-### 2.4 使用 AutoML 进行低代码/无代码训练
-
-传统的机器学习模型开发资源密集，需要显著的领域知识和时间来生成并比较多个模型。自动化机器学习（AutoML）是自动化机器学习模型开发中耗时、迭代任务的过程。它允许数据科学家、分析师和开发人员以高效和高生产力的方式构建机器学习模型，同时保持模型质量。它显著减少了生产就绪机器学习模型的时间，并且操作简单高效。[了解更多](https://docs.microsoft.com/azure/machine-learning/concept-automated-ml?WT.mc_id=academic-77958-bethanycheum&ocid=AID3041109)
-
-1. 在我们之前创建的 [Azure ML 工作区](https://ml.azure.com/) 中，点击左侧菜单中的“自动化机器学习”，选择您刚刚上传的数据集。点击“下一步”。
-
-   ![27](../../../../5-Data-Science-In-Cloud/18-Low-Code/images/aml-1.PNG)
-
-2. 输入一个新的实验名称，目标列（DEATH_EVENT）以及我们创建的计算集群。点击“下一步”。
-
-   ![28](../../../../5-Data-Science-In-Cloud/18-Low-Code/images/aml-2.PNG)
-
-3. 选择“分类”，然后点击“完成”。这一步可能需要 30 分钟到 1 小时，具体取决于您的计算集群规模。
-
-   ![30](../../../../5-Data-Science-In-Cloud/18-Low-Code/images/aml-3.PNG)
-
-4. 运行完成后，点击“自动化机器学习”标签页，点击您的运行记录，然后在“最佳模型摘要”卡片中点击算法。
-
-   ![31](../../../../5-Data-Science-In-Cloud/18-Low-Code/images/aml-4.PNG)
-
-在这里，您可以看到 AutoML 生成的最佳模型的详细描述。您还可以在“模型”标签中探索其他生成的模型。花几分钟时间探索“解释（预览）”按钮中的模型。一旦您选择了要使用的模型（这里我们选择 AutoML 选出的最佳模型），接下来我们将学习如何部署它。
-
-## 3. 低代码/无代码模型部署与端点使用
-### 3.1 模型部署
-
-自动化机器学习界面允许您将最佳模型部署为 Web 服务，仅需几个步骤。部署是模型的集成过程，使其能够基于新数据进行预测并识别潜在的机会领域。对于本项目，将模型部署为 Web 服务意味着医疗应用可以使用该模型实时预测患者的心脏病风险。
-
-在最佳模型描述中，点击“部署”按钮。
-
-![deploy-1](../../../../5-Data-Science-In-Cloud/18-Low-Code/images/deploy-1.PNG)
-
-15. 为其命名、添加描述、选择计算类型（Azure 容器实例），启用身份验证并点击“部署”。这一步可能需要约 20 分钟完成。部署过程包括注册模型、生成资源并为 Web 服务配置资源。部署状态下会显示状态消息。定期点击“刷新”以检查部署状态。当状态显示为“健康”时，表示已部署并运行。
-
-![deploy-2](../../../../5-Data-Science-In-Cloud/18-Low-Code/images/deploy-2.PNG)
-
-16. 部署完成后，点击“端点”标签页，点击您刚刚部署的端点。在这里，您可以找到有关端点的所有详细信息。
-
-![deploy-3](../../../../5-Data-Science-In-Cloud/18-Low-Code/images/deploy-3.PNG)
-
-太棒了！现在我们已经部署了一个模型，可以开始使用端点了。
-
-### 3.2 端点使用
-
-点击“使用”标签。在这里，您可以找到 REST 端点以及消费选项中的 Python 脚本。花点时间阅读 Python 代码。
-
-该脚本可以直接从您的本地机器运行，并将使用您的端点。
-
-![35](../../../../5-Data-Science-In-Cloud/18-Low-Code/images/consumption-1.PNG)
-
-花点时间查看以下两行代码：
-
-```python
-url = 'http://98e3715f-xxxx-xxxx-xxxx-9ec22d57b796.centralus.azurecontainer.io/score'
-api_key = '' # Replace this with the API key for the web service
-```  
-`url` 变量是消费标签中找到的 REST 端点，而 `api_key` 变量是消费标签中找到的主密钥（仅在启用身份验证的情况下）。这就是脚本如何使用端点。
-
-18. 运行脚本后，您应该看到以下输出：
-```python
-    b'"{\\"result\\": [true]}"'
-    ```  
-这意味着对于给定数据，心脏衰竭的预测结果为真。这是合理的，因为如果仔细查看脚本中自动生成的数据，所有值默认都是 0 和 false。您可以使用以下输入样本更改数据：
-
-```python
-data = {
-    "data":
-    [
-        {
-            'age': "0",
-            'anaemia': "false",
-            'creatinine_phosphokinase': "0",
-            'diabetes': "false",
-            'ejection_fraction': "0",
-            'high_blood_pressure': "false",
-            'platelets': "0",
-            'serum_creatinine': "0",
-            'serum_sodium': "0",
-            'sex': "false",
-            'smoking': "false",
-            'time': "0",
-        },
-        {
-            'age': "60",
-            'anaemia': "false",
-            'creatinine_phosphokinase': "500",
-            'diabetes': "false",
-            'ejection_fraction': "38",
-            'high_blood_pressure': "false",
-            'platelets': "260000",
-            'serum_creatinine': "1.40",
-            'serum_sodium': "137",
-            'sex': "false",
-            'smoking': "false",
-            'time': "130",
-        },
-    ],
-}
-```  
-脚本应返回：
-```python
-    b'"{\\"result\\": [true, false]}"'
-    ```  
-
-恭喜！您刚刚使用 Azure ML 部署并训练了模型！
-
-> **_注意：_** 完成项目后，请记得删除所有资源。
-## 🚀 挑战
-
-仔细查看 AutoML 为顶级模型生成的模型解释和详细信息。尝试理解为什么最佳模型优于其他模型。比较了哪些算法？它们之间有什么区别？为什么在这种情况下最佳模型表现更好？
-
-## [课后测验](https://ff-quizzes.netlify.app/en/ds/quiz/35)
-
-## 复习与自学
-
-在本课程中，您学习了如何在云端以低代码/无代码方式训练、部署和使用模型来预测心脏衰竭风险。如果尚未完成，请深入研究 AutoML 为顶级模型生成的模型解释，尝试理解为什么最佳模型优于其他模型。
-
-您可以通过阅读此 [文档](https://docs.microsoft.com/azure/machine-learning/tutorial-first-experiment-automated-ml?WT.mc_id=academic-77958-bethanycheum&ocid=AID3041109)进一步了解低代码/无代码 AutoML。
-
-## 作业
-
-[基于 Azure ML 的低代码/无代码数据科学项目](assignment.md)
-
----
-
-**免责声明**：  
-本文档使用AI翻译服务[Co-op Translator](https://github.com/Azure/co-op-translator)进行翻译。尽管我们努力确保准确性，但请注意，自动翻译可能包含错误或不准确之处。应以原始语言的文档作为权威来源。对于关键信息，建议使用专业人工翻译。因使用本翻译而引起的任何误解或误读，我们概不负责。  
\ No newline at end of file
diff --git a/translations/zh/5-Data-Science-In-Cloud/18-Low-Code/assignment.md b/translations/zh/5-Data-Science-In-Cloud/18-Low-Code/assignment.md
deleted file mode 100644
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-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
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-  "language_code": "zh"
-}
--->
-# 在 Azure ML 上进行低代码/无代码数据科学项目
-
-## 指南
-
-我们已经学习了如何使用 Azure ML 平台以低代码/无代码的方式训练、部署和使用模型。现在，请寻找一些可以用来训练另一个模型的数据，部署并使用它。你可以在 [Kaggle](https://kaggle.com) 和 [Azure Open Datasets](https://azure.microsoft.com/services/open-datasets/catalog?WT.mc_id=academic-77958-bethanycheum&ocid=AID3041109) 上查找数据集。
-
-## 评分标准
-
-| 卓越 | 合格 | 需要改进 |
-|------|------|----------|
-|上传数据时，如果有必要，你注意更改了特征的类型。同时，你对数据进行了清理（如果需要）。你通过 AutoML 对数据集进行了训练，并检查了模型的解释。你部署了最佳模型，并成功使用了它。|上传数据时，如果有必要，你注意更改了特征的类型。你通过 AutoML 对数据集进行了训练，部署了最佳模型，并成功使用了它。|你部署了 AutoML 训练的最佳模型，并成功使用了它。|
-
-**免责声明**：  
-本文档使用AI翻译服务[Co-op Translator](https://github.com/Azure/co-op-translator)进行翻译。尽管我们努力确保翻译的准确性，但请注意，自动翻译可能包含错误或不准确之处。原始语言的文档应被视为权威来源。对于重要信息，建议使用专业人工翻译。我们不对因使用此翻译而产生的任何误解或误读承担责任。
\ No newline at end of file
diff --git a/translations/zh/5-Data-Science-In-Cloud/19-Azure/README.md b/translations/zh/5-Data-Science-In-Cloud/19-Azure/README.md
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+++ /dev/null
@@ -1,325 +0,0 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "472d3fab1c5be50f387336e7a686dbe1",
-  "translation_date": "2025-09-05T11:29:32+00:00",
-  "source_file": "5-Data-Science-In-Cloud/19-Azure/README.md",
-  "language_code": "zh"
-}
--->
-# 云中的数据科学：使用 "Azure ML SDK"
-
-|![由 [(@sketchthedocs)](https://sketchthedocs.dev) 绘制的草图笔记](../../sketchnotes/19-DataScience-Cloud.png)|
-|:---:|
-| 云中的数据科学：Azure ML SDK - _草图笔记由 [@nitya](https://twitter.com/nitya) 绘制_ |
-
-目录：
-
-- [云中的数据科学：使用 "Azure ML SDK"](../../../../5-Data-Science-In-Cloud/19-Azure)
-  - [课前测验](../../../../5-Data-Science-In-Cloud/19-Azure)
-  - [1. 介绍](../../../../5-Data-Science-In-Cloud/19-Azure)
-    - [1.1 什么是 Azure ML SDK？](../../../../5-Data-Science-In-Cloud/19-Azure)
-    - [1.2 心力衰竭预测项目和数据集介绍](../../../../5-Data-Science-In-Cloud/19-Azure)
-  - [2. 使用 Azure ML SDK 训练模型](../../../../5-Data-Science-In-Cloud/19-Azure)
-    - [2.1 创建 Azure ML 工作区](../../../../5-Data-Science-In-Cloud/19-Azure)
-    - [2.2 创建计算实例](../../../../5-Data-Science-In-Cloud/19-Azure)
-    - [2.3 加载数据集](../../../../5-Data-Science-In-Cloud/19-Azure)
-    - [2.4 创建笔记本](../../../../5-Data-Science-In-Cloud/19-Azure)
-    - [2.5 训练模型](../../../../5-Data-Science-In-Cloud/19-Azure)
-      - [2.5.1 设置工作区、实验、计算集群和数据集](../../../../5-Data-Science-In-Cloud/19-Azure)
-      - [2.5.2 自动化机器学习配置与训练](../../../../5-Data-Science-In-Cloud/19-Azure)
-  - [3. 使用 Azure ML SDK 部署模型和消费端点](../../../../5-Data-Science-In-Cloud/19-Azure)
-    - [3.1 保存最佳模型](../../../../5-Data-Science-In-Cloud/19-Azure)
-    - [3.2 模型部署](../../../../5-Data-Science-In-Cloud/19-Azure)
-    - [3.3 消费端点](../../../../5-Data-Science-In-Cloud/19-Azure)
-  - [🚀 挑战](../../../../5-Data-Science-In-Cloud/19-Azure)
-  - [课后测验](../../../../5-Data-Science-In-Cloud/19-Azure)
-  - [复习与自学](../../../../5-Data-Science-In-Cloud/19-Azure)
-  - [作业](../../../../5-Data-Science-In-Cloud/19-Azure)
-
-## [课前测验](https://ff-quizzes.netlify.app/en/ds/quiz/36)
-
-## 1. 介绍
-
-### 1.1 什么是 Azure ML SDK？
-
-数据科学家和 AI 开发者使用 Azure Machine Learning SDK 来构建和运行基于 Azure Machine Learning 服务的机器学习工作流。你可以在任何 Python 环境中与该服务交互，包括 Jupyter Notebooks、Visual Studio Code 或你喜欢的 Python IDE。
-
-SDK 的主要功能包括：
-
-- 探索、准备和管理机器学习实验中使用的数据集的生命周期。
-- 管理云资源以监控、记录和组织机器学习实验。
-- 在本地或使用云资源（包括 GPU 加速的模型训练）训练模型。
-- 使用自动化机器学习（AutoML），通过配置参数和训练数据，自动迭代算法和超参数设置，找到最佳模型以进行预测。
-- 部署 Web 服务，将训练好的模型转换为可在任何应用程序中消费的 RESTful 服务。
-
-[了解更多关于 Azure Machine Learning SDK 的信息](https://docs.microsoft.com/python/api/overview/azure/ml?WT.mc_id=academic-77958-bethanycheum&ocid=AID3041109)
-
-在[上一课](../18-Low-Code/README.md)中，我们学习了如何以低代码/无代码的方式训练、部署和消费模型。我们使用了心力衰竭数据集来生成心力衰竭预测模型。在本课中，我们将使用 Azure Machine Learning SDK 完成相同的任务。
-
-![项目架构](../../../../5-Data-Science-In-Cloud/19-Azure/images/project-schema.PNG)
-
-### 1.2 心力衰竭预测项目和数据集介绍
-
-查看[这里](../18-Low-Code/README.md)了解心力衰竭预测项目和数据集的介绍。
-
-## 2. 使用 Azure ML SDK 训练模型
-
-### 2.1 创建 Azure ML 工作区
-
-为了简化操作，我们将在 Jupyter Notebook 中工作。这意味着你已经有一个工作区和一个计算实例。如果你已经有一个工作区，可以直接跳到 2.3 节的笔记本创建部分。
-
-如果没有，请按照[上一课](../18-Low-Code/README.md)中 **2.1 创建 Azure ML 工作区** 的说明创建一个工作区。
-
-### 2.2 创建计算实例
-
-在我们之前创建的 [Azure ML 工作区](https://ml.azure.com/)中，进入计算菜单，你会看到可用的不同计算资源。
-
-![计算实例](../../../../5-Data-Science-In-Cloud/19-Azure/images/compute-instance-1.PNG)
-
-让我们创建一个计算实例来提供 Jupyter Notebook。
-1. 点击 + New 按钮。
-2. 给你的计算实例命名。
-3. 选择你的选项：CPU 或 GPU、虚拟机大小和核心数量。
-4. 点击 Create 按钮。
-
-恭喜，你刚刚创建了一个计算实例！我们将在[创建笔记本部分](../../../../5-Data-Science-In-Cloud/19-Azure)中使用这个计算实例。
-
-### 2.3 加载数据集
-
-如果你还没有上传数据集，请参考[上一课](../18-Low-Code/README.md)中的 **2.3 加载数据集** 部分。
-
-### 2.4 创建笔记本
-
-> **_注意：_** 接下来的步骤中，你可以从头创建一个新的笔记本，或者上传我们之前创建的 [notebook](../../../../5-Data-Science-In-Cloud/19-Azure/notebook.ipynb) 到你的 Azure ML Studio。要上传，只需点击 "Notebook" 菜单并上传笔记本。
-
-笔记本是数据科学过程中非常重要的一部分。它们可以用于进行探索性数据分析（EDA）、调用计算集群训练模型、调用推理集群部署端点。
-
-要创建一个笔记本，我们需要一个提供 Jupyter Notebook 实例的计算节点。返回 [Azure ML 工作区](https://ml.azure.com/)，点击计算实例。在计算实例列表中，你应该能看到[我们之前创建的计算实例](../../../../5-Data-Science-In-Cloud/19-Azure)。
-
-1. 在 Applications 部分，点击 Jupyter 选项。
-2. 勾选 "Yes, I understand" 框并点击 Continue 按钮。
-![笔记本](../../../../5-Data-Science-In-Cloud/19-Azure/images/notebook-1.PNG)
-3. 这将打开一个新的浏览器标签页，显示你的 Jupyter Notebook 实例。点击 "New" 按钮创建一个笔记本。
-
-![笔记本](../../../../5-Data-Science-In-Cloud/19-Azure/images/notebook-2.PNG)
-
-现在我们有了一个笔记本，可以开始使用 Azure ML SDK 训练模型了。
-
-### 2.5 训练模型
-
-首先，如果你有任何疑问，请参考 [Azure ML SDK 文档](https://docs.microsoft.com/python/api/overview/azure/ml?WT.mc_id=academic-77958-bethanycheum&ocid=AID3041109)。它包含了理解本课中涉及模块的所有必要信息。
-
-#### 2.5.1 设置工作区、实验、计算集群和数据集
-
-你需要使用以下代码从配置文件加载 `workspace`：
-
-```python
-from azureml.core import Workspace
-ws = Workspace.from_config()
-```
-
-这将返回一个表示工作区的 `Workspace` 对象。然后你需要使用以下代码创建一个 `experiment`：
-
-```python
-from azureml.core import Experiment
-experiment_name = 'aml-experiment'
-experiment = Experiment(ws, experiment_name)
-```
-
-要从工作区获取或创建实验，你需要通过实验名称请求实验。实验名称必须为 3-36 个字符，以字母或数字开头，并且只能包含字母、数字、下划线和短划线。如果在工作区中找不到实验，将创建一个新实验。
-
-现在你需要使用以下代码为训练创建一个计算集群。注意，这一步可能需要几分钟。
-
-```python
-from azureml.core.compute import AmlCompute
-
-aml_name = "heart-f-cluster"
-try:
-    aml_compute = AmlCompute(ws, aml_name)
-    print('Found existing AML compute context.')
-except:
-    print('Creating new AML compute context.')
-    aml_config = AmlCompute.provisioning_configuration(vm_size = "Standard_D2_v2", min_nodes=1, max_nodes=3)
-    aml_compute = AmlCompute.create(ws, name = aml_name, provisioning_configuration = aml_config)
-    aml_compute.wait_for_completion(show_output = True)
-
-cts = ws.compute_targets
-compute_target = cts[aml_name]
-```
-
-你可以通过以下方式从工作区获取数据集：
-
-```python
-dataset = ws.datasets['heart-failure-records']
-df = dataset.to_pandas_dataframe()
-df.describe()
-```
-
-#### 2.5.2 自动化机器学习配置与训练
-
-要设置 AutoML 配置，请使用 [AutoMLConfig 类](https://docs.microsoft.com/python/api/azureml-train-automl-client/azureml.train.automl.automlconfig(class)?WT.mc_id=academic-77958-bethanycheum&ocid=AID3041109)。
-
-根据文档，有许多参数可以调整。对于本项目，我们将使用以下参数：
-
-- `experiment_timeout_minutes`：实验允许运行的最大时间（以分钟为单位），超时后实验将自动停止并生成结果。
-- `max_concurrent_iterations`：实验允许的最大并发训练迭代次数。
-- `primary_metric`：用于确定实验状态的主要指标。
-- `compute_target`：运行自动化机器学习实验的 Azure Machine Learning 计算目标。
-- `task`：要运行的任务类型。值可以是 'classification'、'regression' 或 'forecasting'，具体取决于要解决的自动化机器学习问题类型。
-- `training_data`：实验中使用的训练数据。应包含训练特征和标签列（可选的样本权重列）。
-- `label_column_name`：标签列的名称。
-- `path`：Azure Machine Learning 项目文件夹的完整路径。
-- `enable_early_stopping`：是否启用早停，如果短期内分数没有改善则终止。
-- `featurization`：指示是否自动进行特征化步骤，或使用自定义特征化。
-- `debug_log`：写入调试信息的日志文件。
-
-```python
-from azureml.train.automl import AutoMLConfig
-
-project_folder = './aml-project'
-
-automl_settings = {
-    "experiment_timeout_minutes": 20,
-    "max_concurrent_iterations": 3,
-    "primary_metric" : 'AUC_weighted'
-}
-
-automl_config = AutoMLConfig(compute_target=compute_target,
-                             task = "classification",
-                             training_data=dataset,
-                             label_column_name="DEATH_EVENT",
-                             path = project_folder,  
-                             enable_early_stopping= True,
-                             featurization= 'auto',
-                             debug_log = "automl_errors.log",
-                             **automl_settings
-                            )
-```
-
-现在你已经设置了配置，可以使用以下代码训练模型。这一步可能需要长达一小时，具体取决于集群大小。
-
-```python
-remote_run = experiment.submit(automl_config)
-```
-
-你可以运行 RunDetails 小部件来显示不同的实验。
-
-```python
-from azureml.widgets import RunDetails
-RunDetails(remote_run).show()
-```
-
-## 3. 使用 Azure ML SDK 部署模型和消费端点
-
-### 3.1 保存最佳模型
-
-`remote_run` 是一个 [AutoMLRun](https://docs.microsoft.com/python/api/azureml-train-automl-client/azureml.train.automl.run.automlrun?WT.mc_id=academic-77958-bethanycheum&ocid=AID3041109) 类型的对象。该对象包含 `get_output()` 方法，可返回最佳运行和相应的拟合模型。
-
-```python
-best_run, fitted_model = remote_run.get_output()
-```
-
-你可以通过打印 `fitted_model` 查看最佳模型使用的参数，并通过 [get_properties()](https://docs.microsoft.com/python/api/azureml-core/azureml.core.run(class)?view=azure-ml-py#azureml_core_Run_get_properties?WT.mc_id=academic-77958-bethanycheum&ocid=AID3041109) 方法查看最佳模型的属性。
-
-```python
-best_run.get_properties()
-```
-
-现在使用 [register_model](https://docs.microsoft.com/python/api/azureml-train-automl-client/azureml.train.automl.run.automlrun?view=azure-ml-py#register-model-model-name-none--description-none--tags-none--iteration-none--metric-none-?WT.mc_id=academic-77958-bethanycheum&ocid=AID3041109) 方法注册模型。
-
-```python
-model_name = best_run.properties['model_name']
-script_file_name = 'inference/score.py'
-best_run.download_file('outputs/scoring_file_v_1_0_0.py', 'inference/score.py')
-description = "aml heart failure project sdk"
-model = best_run.register_model(model_name = model_name,
-                                model_path = './outputs/',
-                                description = description,
-                                tags = None)
-```
-
-### 3.2 模型部署
-
-保存最佳模型后，我们可以使用 [InferenceConfig](https://docs.microsoft.com/python/api/azureml-core/azureml.core.model.inferenceconfig?view=azure-ml-py?ocid=AID3041109) 类部署它。InferenceConfig 表示用于部署的自定义环境的配置设置。[AciWebservice](https://docs.microsoft.com/python/api/azureml-core/azureml.core.webservice.aciwebservice?view=azure-ml-py) 类表示部署为 Azure 容器实例上的 Web 服务端点的机器学习模型。部署的服务由模型、脚本和相关文件创建。生成的 Web 服务是一个负载均衡的 HTTP 端点，带有 REST API。你可以向该 API 发送数据并接收模型返回的预测。
-
-模型通过 [deploy](https://docs.microsoft.com/python/api/azureml-core/azureml.core.model(class)?view=azure-ml-py#deploy-workspace--name--models--inference-config-none--deployment-config-none--deployment-target-none--overwrite-false--show-output-false-?WT.mc_id=academic-77958-bethanycheum&ocid=AID3041109) 方法部署。
-
-```python
-from azureml.core.model import InferenceConfig, Model
-from azureml.core.webservice import AciWebservice
-
-inference_config = InferenceConfig(entry_script=script_file_name, environment=best_run.get_environment())
-
-aciconfig = AciWebservice.deploy_configuration(cpu_cores = 1,
-                                               memory_gb = 1,
-                                               tags = {'type': "automl-heart-failure-prediction"},
-                                               description = 'Sample service for AutoML Heart Failure Prediction')
-
-aci_service_name = 'automl-hf-sdk'
-aci_service = Model.deploy(ws, aci_service_name, [model], inference_config, aciconfig)
-aci_service.wait_for_deployment(True)
-print(aci_service.state)
-```
-
-这一步可能需要几分钟。
-
-### 3.3 消费端点
-
-你可以通过创建一个示例输入来消费端点：
-
-```python
-data = {
-    "data":
-    [
-        {
-            'age': "60",
-            'anaemia': "false",
-            'creatinine_phosphokinase': "500",
-            'diabetes': "false",
-            'ejection_fraction': "38",
-            'high_blood_pressure': "false",
-            'platelets': "260000",
-            'serum_creatinine': "1.40",
-            'serum_sodium': "137",
-            'sex': "false",
-            'smoking': "false",
-            'time': "130",
-        },
-    ],
-}
-
-test_sample = str.encode(json.dumps(data))
-```
-
-然后将此输入发送到模型以获取预测：
-```python
-response = aci_service.run(input_data=test_sample)
-response
-```
-这应该输出 `'{"result": [false]}'`。这意味着我们发送到端点的患者输入生成了预测值 `false`，表示此人不太可能发生心脏病。
-
-恭喜你！你刚刚使用 Azure ML SDK 消耗了在 Azure ML 上部署和训练的模型！
-
-> **_注意:_** 完成项目后，别忘了删除所有资源。
-
-## 🚀 挑战
-
-通过 SDK 你可以做很多其他事情，但遗憾的是我们无法在这节课中全部讲解。不过好消息是，学会快速浏览 SDK 文档可以让你在学习中走得更远。查看 Azure ML SDK 文档，找到允许你创建管道的 `Pipeline` 类。管道是可以作为工作流执行的一系列步骤的集合。
-
-**提示:** 访问 [SDK 文档](https://docs.microsoft.com/python/api/overview/azure/ml/?view=azure-ml-py?WT.mc_id=academic-77958-bethanycheum&ocid=AID3041109)，在搜索栏中输入关键词，例如 "Pipeline"。你应该能在搜索结果中找到 `azureml.pipeline.core.Pipeline` 类。
-
-## [课后测验](https://ff-quizzes.netlify.app/en/ds/quiz/37)
-
-## 复习与自学
-
-在本课中，你学习了如何使用 Azure ML SDK 在云端训练、部署和消耗一个模型来预测心脏病风险。查看这个 [文档](https://docs.microsoft.com/python/api/overview/azure/ml/?view=azure-ml-py?WT.mc_id=academic-77958-bethanycheum&ocid=AID3041109)，获取有关 Azure ML SDK 的更多信息。尝试使用 Azure ML SDK 创建你自己的模型。
-
-## 作业
-
-[使用 Azure ML SDK 的数据科学项目](assignment.md)
-
----
-
-**免责声明**：  
-本文档使用AI翻译服务 [Co-op Translator](https://github.com/Azure/co-op-translator) 进行翻译。尽管我们努力确保翻译的准确性，但请注意，自动翻译可能包含错误或不准确之处。应以原始语言的文档作为权威来源。对于关键信息，建议使用专业人工翻译。我们不对因使用此翻译而产生的任何误解或误读承担责任。
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diff --git a/translations/zh/5-Data-Science-In-Cloud/19-Azure/assignment.md b/translations/zh/5-Data-Science-In-Cloud/19-Azure/assignment.md
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index f1788aac..00000000
--- a/translations/zh/5-Data-Science-In-Cloud/19-Azure/assignment.md
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@@ -1,23 +0,0 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "386efdbc19786951341f6956247ee990",
-  "translation_date": "2025-08-25T17:38:17+00:00",
-  "source_file": "5-Data-Science-In-Cloud/19-Azure/assignment.md",
-  "language_code": "zh"
-}
--->
-# 使用 Azure ML SDK 的数据科学项目
-
-## 说明
-
-我们已经学习了如何使用 Azure ML 平台通过 Azure ML SDK 进行模型的训练、部署和使用。现在，请寻找一些数据，用于训练另一个模型，部署并使用它。你可以在 [Kaggle](https://kaggle.com) 和 [Azure Open Datasets](https://azure.microsoft.com/services/open-datasets/catalog?WT.mc_id=academic-77958-bethanycheum&ocid=AID3041109) 上寻找数据集。
-
-## 评分标准
-
-| 卓越 | 合格 | 需要改进 |
-|------|------|----------|
-|在进行 AutoML 配置时，你查阅了 SDK 文档，了解可以使用哪些参数。你通过 Azure ML SDK 使用 AutoML 对数据集进行了训练，并检查了模型解释。你部署了最佳模型，并能够通过 Azure ML SDK 使用它。|你通过 Azure ML SDK 使用 AutoML 对数据集进行了训练，并检查了模型解释。你部署了最佳模型，并能够通过 Azure ML SDK 使用它。|你通过 Azure ML SDK 使用 AutoML 对数据集进行了训练。你部署了最佳模型，并能够通过 Azure ML SDK 使用它。|
-
-**免责声明**：  
-本文档使用AI翻译服务[Co-op Translator](https://github.com/Azure/co-op-translator)进行翻译。尽管我们努力确保翻译的准确性，但请注意，自动翻译可能包含错误或不准确之处。原始语言的文档应被视为权威来源。对于关键信息，建议使用专业人工翻译。我们不对因使用此翻译而产生的任何误解或误读承担责任。
\ No newline at end of file
diff --git a/translations/zh/5-Data-Science-In-Cloud/19-Azure/notebook.ipynb b/translations/zh/5-Data-Science-In-Cloud/19-Azure/notebook.ipynb
deleted file mode 100644
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-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "source": [
-    "# 云端数据科学：\"Azure ML SDK\" 方法\n",
-    "\n",
-    "## 介绍\n",
-    "\n",
-    "在本笔记本中，我们将学习如何使用 Azure ML SDK，通过 Azure ML 训练、部署和使用一个模型。\n",
-    "\n",
-    "前置条件：\n",
-    "1. 您已创建一个 Azure ML 工作区。\n",
-    "2. 您已将 [心力衰竭数据集](https://www.kaggle.com/andrewmvd/heart-failure-clinical-data) 加载到 Azure ML 中。\n",
-    "3. 您已将此笔记本上传到 Azure ML Studio。\n",
-    "\n",
-    "接下来的步骤是：\n",
-    "\n",
-    "1. 在现有工作区中创建一个实验。\n",
-    "2. 创建一个计算集群。\n",
-    "3. 加载数据集。\n",
-    "4. 使用 AutoMLConfig 配置 AutoML。\n",
-    "5. 运行 AutoML 实验。\n",
-    "6. 探索结果并获取最佳模型。\n",
-    "7. 注册最佳模型。\n",
-    "8. 部署最佳模型。\n",
-    "9. 使用端点。\n",
-    "\n",
-    "## Azure Machine Learning SDK 的特定导入\n"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "from azureml.core import Workspace, Experiment\n",
-    "from azureml.core.compute import AmlCompute\n",
-    "from azureml.train.automl import AutoMLConfig\n",
-    "from azureml.widgets import RunDetails\n",
-    "from azureml.core.model import InferenceConfig, Model\n",
-    "from azureml.core.webservice import AciWebservice"
-   ],
-   "outputs": [],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "## 初始化工作区\n",
-    "从已保存的配置中初始化一个工作区对象。请确保配置文件存在于 .\\config.json\n"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "ws = Workspace.from_config()\n",
-    "print(ws.name, ws.resource_group, ws.location, ws.subscription_id, sep = '\\n')"
-   ],
-   "outputs": [],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "## 创建一个 Azure ML 实验\n",
-    "\n",
-    "让我们在刚刚初始化的工作区中创建一个名为“aml-experiment”的实验。\n"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "experiment_name = 'aml-experiment'\n",
-    "experiment = Experiment(ws, experiment_name)\n",
-    "experiment"
-   ],
-   "outputs": [],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "## 创建计算群集\n",
-    "您需要为您的 AutoML 运行创建一个[计算目标](https://docs.microsoft.com/azure/machine-learning/concept-azure-machine-learning-architecture#compute-target)。\n"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "aml_name = \"heart-f-cluster\"\n",
-    "try:\n",
-    "    aml_compute = AmlCompute(ws, aml_name)\n",
-    "    print('Found existing AML compute context.')\n",
-    "except:\n",
-    "    print('Creating new AML compute context.')\n",
-    "    aml_config = AmlCompute.provisioning_configuration(vm_size = \"Standard_D2_v2\", min_nodes=1, max_nodes=3)\n",
-    "    aml_compute = AmlCompute.create(ws, name = aml_name, provisioning_configuration = aml_config)\n",
-    "    aml_compute.wait_for_completion(show_output = True)\n",
-    "\n",
-    "cts = ws.compute_targets\n",
-    "compute_target = cts[aml_name]"
-   ],
-   "outputs": [],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "## 数据\n",
-    "请确保您已将数据集上传到 Azure ML，并且密钥名称与数据集名称相同。\n"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "key = 'heart-failure-records'\n",
-    "dataset = ws.datasets[key]\n",
-    "df = dataset.to_pandas_dataframe()\n",
-    "df.describe()"
-   ],
-   "outputs": [],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "## 自动化机器学习配置\n"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "automl_settings = {\n",
-    "    \"experiment_timeout_minutes\": 20,\n",
-    "    \"max_concurrent_iterations\": 3,\n",
-    "    \"primary_metric\" : 'AUC_weighted'\n",
-    "}\n",
-    "\n",
-    "automl_config = AutoMLConfig(compute_target=compute_target,\n",
-    "                             task = \"classification\",\n",
-    "                             training_data=dataset,\n",
-    "                             label_column_name=\"DEATH_EVENT\",\n",
-    "                             enable_early_stopping= True,\n",
-    "                             featurization= 'auto',\n",
-    "                             debug_log = \"automl_errors.log\",\n",
-    "                             **automl_settings\n",
-    "                            )"
-   ],
-   "outputs": [],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "## 自动化机器学习运行\n"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "remote_run = experiment.submit(automl_config)"
-   ],
-   "outputs": [],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "RunDetails(remote_run).show()"
-   ],
-   "outputs": [],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "best_run, fitted_model = remote_run.get_output()"
-   ],
-   "outputs": [],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "best_run.get_properties()"
-   ],
-   "outputs": [],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "model_name = best_run.properties['model_name']\n",
-    "script_file_name = 'inference/score.py'\n",
-    "best_run.download_file('outputs/scoring_file_v_1_0_0.py', 'inference/score.py')\n",
-    "description = \"aml heart failure project sdk\"\n",
-    "model = best_run.register_model(model_name = model_name,\n",
-    "                                description = description,\n",
-    "                                tags = None)"
-   ],
-   "outputs": [],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "## 部署最佳模型\n",
-    "\n",
-    "运行以下代码以部署最佳模型。您可以在 Azure ML 门户中查看部署状态。此步骤可能需要几分钟时间。\n"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "inference_config = InferenceConfig(entry_script=script_file_name, environment=best_run.get_environment())\n",
-    "\n",
-    "aciconfig = AciWebservice.deploy_configuration(cpu_cores = 1,\n",
-    "                                               memory_gb = 1,\n",
-    "                                               tags = {'type': \"automl-heart-failure-prediction\"},\n",
-    "                                               description = 'Sample service for AutoML Heart Failure Prediction')\n",
-    "\n",
-    "aci_service_name = 'automl-hf-sdk'\n",
-    "aci_service = Model.deploy(ws, aci_service_name, [model], inference_config, aciconfig)\n",
-    "aci_service.wait_for_deployment(True)\n",
-    "print(aci_service.state)"
-   ],
-   "outputs": [],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "source": [
-    "## 使用端点\n",
-    "您可以向以下输入示例添加输入内容。\n"
-   ],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "data = {\n",
-    "    \"data\":\n",
-    "    [\n",
-    "        {\n",
-    "            'age': \"60\",\n",
-    "            'anaemia': \"false\",\n",
-    "            'creatinine_phosphokinase': \"500\",\n",
-    "            'diabetes': \"false\",\n",
-    "            'ejection_fraction': \"38\",\n",
-    "            'high_blood_pressure': \"false\",\n",
-    "            'platelets': \"260000\",\n",
-    "            'serum_creatinine': \"1.40\",\n",
-    "            'serum_sodium': \"137\",\n",
-    "            'sex': \"false\",\n",
-    "            'smoking': \"false\",\n",
-    "            'time': \"130\",\n",
-    "        },\n",
-    "    ],\n",
-    "}\n",
-    "\n",
-    "test_sample = str.encode(json.dumps(data))"
-   ],
-   "outputs": [],
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "source": [
-    "response = aci_service.run(input_data=test_sample)\n",
-    "response"
-   ],
-   "outputs": [],
-   "metadata": {}
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n---\n\n**免责声明**：  \n本文档使用AI翻译服务[Co-op Translator](https://github.com/Azure/co-op-translator)进行翻译。尽管我们努力确保准确性，但请注意，自动翻译可能包含错误或不准确之处。应以原始语言的文档作为权威来源。对于关键信息，建议使用专业人工翻译。对于因使用本翻译而引起的任何误解或误读，我们概不负责。\n"
-   ]
-  }
- ],
- "metadata": {
-  "orig_nbformat": 4,
-  "language_info": {
-   "name": "python"
-  },
-  "coopTranslator": {
-   "original_hash": "af42669556d5dc19fc4cc3866f7d2597",
-   "translation_date": "2025-09-02T05:44:34+00:00",
-   "source_file": "5-Data-Science-In-Cloud/19-Azure/notebook.ipynb",
-   "language_code": "zh"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
\ No newline at end of file
diff --git a/translations/zh/5-Data-Science-In-Cloud/19-Azure/solution/notebook.ipynb b/translations/zh/5-Data-Science-In-Cloud/19-Azure/solution/notebook.ipynb
deleted file mode 100644
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diff --git a/translations/zh/5-Data-Science-In-Cloud/README.md b/translations/zh/5-Data-Science-In-Cloud/README.md
deleted file mode 100644
index b1804266..00000000
--- a/translations/zh/5-Data-Science-In-Cloud/README.md
+++ /dev/null
@@ -1,32 +0,0 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "8dfe141a0f46f7d253e07f74913c7f44",
-  "translation_date": "2025-08-25T17:19:14+00:00",
-  "source_file": "5-Data-Science-In-Cloud/README.md",
-  "language_code": "zh"
-}
--->
-# 云中的数据科学
-
-![cloud-picture](../../../translated_images/zh-CN/cloud-picture.f5526de3c6c6387b2d656ba94f019b3352e5e3854a78440e4fb00c93e2dea675.jpg)
-
-> 图片由 [Jelleke Vanooteghem](https://unsplash.com/@ilumire) 提供，来自 [Unsplash](https://unsplash.com/s/photos/cloud?orientation=landscape)
-
-在处理大数据的数据科学时，云计算可以带来革命性的变化。在接下来的三节课中，我们将了解什么是云，以及为什么它非常有用。我们还将探索一个心力衰竭数据集，并构建一个模型来帮助评估某人发生心力衰竭的可能性。我们将利用云的强大功能，通过两种不同的方式来训练、部署和使用模型。一种方式是仅使用用户界面，以低代码/无代码的方式进行；另一种方式是使用 Azure Machine Learning 软件开发工具包 (Azure ML SDK)。
-
-![project-schema](../../../translated_images/zh-CN/project-schema.420e56d495624541eaecf2b737f138c86fb7d8162bb1c0bf8783c350872ffc4d.png)
-
-### 主题
-
-1. [为什么在数据科学中使用云？](17-Introduction/README.md)
-2. [云中的数据科学：低代码/无代码方式](18-Low-Code/README.md)
-3. [云中的数据科学：Azure ML SDK 方式](19-Azure/README.md)
-
-### 致谢
-这些课程由 [Maud Levy](https://twitter.com/maudstweets) 和 [Tiffany Souterre](https://twitter.com/TiffanySouterre) 带着 ☁️ 和 💕 编写。
-
-心力衰竭预测项目的数据来源于 [Kaggle](https://www.kaggle.com/andrewmvd/heart-failure-clinical-data) 上的 [Larxel](https://www.kaggle.com/andrewmvd)。数据遵循 [Attribution 4.0 International (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/) 许可协议。
-
-**免责声明**：  
-本文档使用AI翻译服务 [Co-op Translator](https://github.com/Azure/co-op-translator) 进行翻译。尽管我们努力确保翻译的准确性，但请注意，自动翻译可能包含错误或不准确之处。原始语言的文档应被视为权威来源。对于重要信息，建议使用专业人工翻译。我们对因使用此翻译而产生的任何误解或误读不承担责任。
\ No newline at end of file
diff --git a/translations/zh/6-Data-Science-In-Wild/20-Real-World-Examples/README.md b/translations/zh/6-Data-Science-In-Wild/20-Real-World-Examples/README.md
deleted file mode 100644
index 16f4bc33..00000000
--- a/translations/zh/6-Data-Science-In-Wild/20-Real-World-Examples/README.md
+++ /dev/null
@@ -1,156 +0,0 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "0f67a4139454816631526779a456b734",
-  "translation_date": "2025-09-06T18:16:26+00:00",
-  "source_file": "6-Data-Science-In-Wild/20-Real-World-Examples/README.md",
-  "language_code": "zh"
-}
--->
-# 数据科学在现实世界中的应用
-
-| ![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/20-DataScience-RealWorld.png) |
-| :--------------------------------------------------------------------------------------------------------------: |
-|               数据科学在现实世界中的应用 - _Sketchnote by [@nitya](https://twitter.com/nitya)_               |
-
-我们即将完成这段学习旅程！
-
-我们从数据科学和伦理的定义开始，探索了各种数据分析和可视化工具与技术，回顾了数据科学生命周期，并研究了如何通过云计算服务扩展和自动化数据科学工作流程。那么，你可能会问：_"如何将这些学习内容映射到现实世界的场景中？"_
-
-在本课中，我们将探讨数据科学在各行业中的实际应用，并深入研究在科研、数字人文和可持续发展领域的具体案例。我们还将讨论学生项目的机会，并提供一些有用的资源，帮助你继续学习之旅！
-
-## 课前测验
-
-## [课前测验](https://ff-quizzes.netlify.app/en/ds/quiz/38)
-
-## 数据科学 + 行业
-
-随着人工智能的普及，开发者现在可以更轻松地设计和集成基于人工智能的决策和数据驱动的洞察到用户体验和开发工作流程中。以下是数据科学在各行业中“应用”的一些实际案例：
-
- * [Google Flu Trends](https://www.wired.com/2015/10/can-learn-epic-failure-google-flu-trends/) 使用数据科学将搜索词与流感趋势相关联。尽管方法存在缺陷，但它提高了人们对数据驱动的医疗预测可能性（以及挑战）的认识。
-
- * [UPS 路线预测](https://www.technologyreview.com/2018/11/21/139000/how-ups-uses-ai-to-outsmart-bad-weather/) - 解释了 UPS 如何利用数据科学和机器学习预测最佳配送路线，考虑天气状况、交通模式、配送截止时间等因素。
-
- * [纽约出租车路线可视化](http://chriswhong.github.io/nyctaxi/) - 使用[信息自由法](https://chriswhong.com/open-data/foil_nyc_taxi/)收集的数据帮助可视化纽约出租车一天的运行情况，帮助我们了解它们如何在繁忙的城市中导航、赚取的收入以及每24小时内的行程时长。
-
- * [Uber 数据科学工作台](https://eng.uber.com/dsw/) - 每天从数百万次 Uber 行程中收集数据（如上下车地点、行程时长、偏好路线等），构建数据分析工具，用于定价、安全、欺诈检测和导航决策。
-
- * [体育分析](https://towardsdatascience.com/scope-of-analytics-in-sports-world-37ed09c39860) - 关注_预测分析_（团队和球员分析 - 想想[点球成金](https://datasciencedegree.wisconsin.edu/blog/moneyball-proves-importance-big-data-big-ideas/) - 以及粉丝管理）和_数据可视化_（团队和粉丝仪表盘、比赛等），应用包括人才选拔、体育博彩和库存/场馆管理。
-
- * [银行业中的数据科学](https://data-flair.training/blogs/data-science-in-banking/) - 强调数据科学在金融行业的价值，应用包括风险建模和欺诈检测、客户细分、实时预测和推荐系统。预测分析还推动了关键指标，如[信用评分](https://dzone.com/articles/using-big-data-and-predictive-analytics-for-credit)。
-
- * [医疗领域的数据科学](https://data-flair.training/blogs/data-science-in-healthcare/) - 强调应用包括医学影像（如 MRI、X光、CT扫描）、基因组学（DNA测序）、药物开发（风险评估、成功预测）、预测分析（患者护理和供应物流）、疾病追踪与预防等。
-
-![数据科学在现实世界中的应用](../../../../translated_images/zh-CN/data-science-applications.4e5019cd8790ebac2277ff5f08af386f8727cac5d30f77727c7090677e6adb9c.png) 图片来源：[Data Flair: 6 Amazing Data Science Applications ](https://data-flair.training/blogs/data-science-applications/)
-
-图中展示了其他领域和数据科学技术的应用案例。想探索更多应用？查看下面的[复习与自学](../../../../6-Data-Science-In-Wild/20-Real-World-Examples)部分。
-
-## 数据科学 + 科研
-
-| ![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/20-DataScience-Research.png) |
-| :---------------------------------------------------------------------------------------------------------------: |
-|              数据科学与科研 - _Sketchnote by [@nitya](https://twitter.com/nitya)_              |
-
-虽然现实世界的应用通常关注行业规模的用例，_科研_应用和项目可以从两个角度提供价值：
-
-* _创新机会_ - 探索先进概念的快速原型设计以及下一代应用的用户体验测试。
-* _部署挑战_ - 调查数据科学技术在现实世界中的潜在危害或意外后果。
-
-对于学生来说，这些科研项目不仅提供学习和协作的机会，还能加深对主题的理解，并拓宽与相关领域的人员或团队的接触和参与。科研项目是什么样的？它们如何产生影响？
-
-让我们看一个例子 - [MIT Gender Shades Study](http://gendershades.org/overview.html)，由 Joy Buolamwini（MIT Media Labs）发起，与 Timnit Gebru（当时在微软研究院）共同撰写了一篇[标志性研究论文](http://proceedings.mlr.press/v81/buolamwini18a/buolamwini18a.pdf)，重点研究：
-
- * **什么：** 研究项目的目标是_评估基于性别和肤色的自动化面部分析算法和数据集中的偏差_。
- * **为什么：** 面部分析被用于执法、机场安检、招聘系统等领域——这些场景中由于偏差导致的不准确分类可能对受影响的个人或群体造成经济和社会损害。理解（并消除或减轻）偏差是使用公平的关键。
- * **如何：** 研究人员发现现有基准主要使用肤色较浅的主体，并策划了一个新的数据集（1000+张图片），在性别和肤色上更加平衡。该数据集用于评估三个性别分类产品（来自微软、IBM和Face++）的准确性。
-
-结果显示，尽管总体分类准确性较高，但不同子群体之间的错误率存在显著差异——**性别错误分类**在女性或肤色较深的人群中更高，表明存在偏差。
-
-**关键成果：** 提高了对数据科学需要更多_代表性数据集_（平衡子群体）和更多_包容性团队_（多样化背景）的认识，以便在人工智能解决方案中更早地识别并消除或减轻这些偏差。像这样的研究努力对于许多组织定义_负责任的人工智能_原则和实践以提高其人工智能产品和流程的公平性也至关重要。
-
-**想了解微软相关的研究工作？**
-
-* 查看[微软研究项目](https://www.microsoft.com/research/research-area/artificial-intelligence/?facet%5Btax%5D%5Bmsr-research-area%5D%5B%5D=13556&facet%5Btax%5D%5Bmsr-content-type%5D%5B%5D=msr-project)中的人工智能领域。
-* 探索[微软研究数据科学暑期学校](https://www.microsoft.com/en-us/research/academic-program/data-science-summer-school/)的学生项目。
-* 查看[Fairlearn](https://fairlearn.org/)项目和[负责任的人工智能](https://www.microsoft.com/en-us/ai/responsible-ai?activetab=pivot1%3aprimaryr6)倡议。
-
-## 数据科学 + 人文学科
-
-| ![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/20-DataScience-Humanities.png) |
-| :---------------------------------------------------------------------------------------------------------------: |
-|              数据科学与数字人文学科 - _Sketchnote by [@nitya](https://twitter.com/nitya)_              |
-
-数字人文学科[被定义为](https://digitalhumanities.stanford.edu/about-dh-stanford)“结合计算方法与人文研究的一系列实践和方法”。[斯坦福项目](https://digitalhumanities.stanford.edu/projects)如_“重启历史”_和_“诗意思维”_展示了[数字人文学科与数据科学](https://digitalhumanities.stanford.edu/digital-humanities-and-data-science)之间的联系——强调网络分析、信息可视化、空间和文本分析等技术，这些技术可以帮助我们重新审视历史和文学数据集，从而获得新的洞察和视角。
-
-*想探索并扩展这一领域的项目？*
-
-查看["Emily Dickinson and the Meter of Mood"](https://gist.github.com/jlooper/ce4d102efd057137bc000db796bfd671)——这是一个来自[Jen Looper](https://twitter.com/jenlooper)的优秀案例，探讨如何利用数据科学重新审视熟悉的诗歌，并在新的背景下重新评估其意义及作者的贡献。例如，_我们能否通过分析诗歌的语气或情感来预测其创作的季节_——这又能告诉我们作者在相关时期的心境如何？
-
-为回答这个问题，我们遵循数据科学生命周期的步骤：
- * [`数据获取`](https://gist.github.com/jlooper/ce4d102efd057137bc000db796bfd671#acquiring-the-dataset) - 收集相关数据集进行分析。选项包括使用 API（如[Poetry DB API](https://poetrydb.org/index.html)）或使用工具（如[Scrapy](https://scrapy.org/)）抓取网页（如[Project Gutenberg](https://www.gutenberg.org/files/12242/12242-h/12242-h.htm)）。
- * [`数据清理`](https://gist.github.com/jlooper/ce4d102efd057137bc000db796bfd671#clean-the-data) - 解释如何使用基本工具（如 Visual Studio Code 和 Microsoft Excel）格式化、清理和简化文本。
- * [`数据分析`](https://gist.github.com/jlooper/ce4d102efd057137bc000db796bfd671#working-with-the-data-in-a-notebook) - 解释如何将数据集导入“笔记本”进行分析，使用 Python 包（如 pandas、numpy 和 matplotlib）组织和可视化数据。
- * [`情感分析`](https://gist.github.com/jlooper/ce4d102efd057137bc000db796bfd671#sentiment-analysis-using-cognitive-services) - 解释如何集成云服务（如文本分析），使用低代码工具（如[Power Automate](https://flow.microsoft.com/en-us/)）实现自动化数据处理工作流程。
-
-通过这一工作流程，我们可以探索季节对诗歌情感的影响，并帮助我们形成对作者的独特视角。自己试试吧——然后扩展笔记本，提出其他问题或以新的方式可视化数据！
-
-> 你可以使用[数字人文学科工具包](https://github.com/Digital-Humanities-Toolkit)中的一些工具来探索这些研究方向。
-
-## 数据科学 + 可持续发展
-
-| ![ Sketchnote by [(@sketchthedocs)](https://sketchthedocs.dev) ](../../sketchnotes/20-DataScience-Sustainability.png) |
-| :---------------------------------------------------------------------------------------------------------------: |
-|              数据科学与可持续发展 - _Sketchnote by [@nitya](https://twitter.com/nitya)_              |
-
-[2030年可持续发展议程](https://sdgs.un.org/2030agenda)——由所有联合国成员国于2015年通过——确定了17个目标，其中包括**保护地球**免受退化和气候变化影响。[微软可持续发展](https://www.microsoft.com/en-us/sustainability)倡议支持这些目标，探索技术解决方案如何支持并构建更可持续的未来，重点关注[四个目标](https://dev.to/azure/a-visual-guide-to-sustainable-software-engineering-53hh)——到2030年实现碳负、水正、零废物和生物多样性。
-
-以可扩展和及时的方式解决这些挑战需要云规模的思维——以及大规模数据。[Planetary Computer](https://planetarycomputer.microsoft.com/)倡议为数据科学家和开发者提供了四个组件来支持这一努力：
-
- * [数据目录](https://planetarycomputer.microsoft.com/catalog) - 提供数PB的地球系统数据（免费且托管于 Azure）。
- * [Planetary API](https://planetarycomputer.microsoft.com/docs/reference/stac/) - 帮助用户在空间和时间范围内搜索相关数据。
- * [Hub](https://planetarycomputer.microsoft.com/docs/overview/environment/) - 为科学家提供处理海量地理空间数据集的托管环境。
- * [应用](https://planetarycomputer.microsoft.com/applications) - 展示可持续发展洞察的用例和工具。
-**Planetary Computer 项目目前处于预览阶段（截至 2021 年 9 月）** - 以下是如何通过数据科学开始为可持续发展解决方案做出贡献的指南。
-
-* [申请访问权限](https://planetarycomputer.microsoft.com/account/request)，开始探索并与同行建立联系。
-* [查看文档](https://planetarycomputer.microsoft.com/docs/overview/about)，了解支持的数据集和 API。
-* 探索像 [生态系统监测](https://analytics-lab.org/ecosystemmonitoring/) 这样的应用，寻找应用创意的灵感。
-
-思考如何利用数据可视化揭示或放大与气候变化和森林砍伐等领域相关的洞察。或者思考如何利用这些洞察来创造新的用户体验，激励行为改变以实现更可持续的生活。
-
-## 数据科学 + 学生
-
-我们已经讨论了行业和研究中的实际应用，并探索了数字人文和可持续发展领域的数据科学应用示例。那么，作为数据科学初学者，你如何提升技能并分享你的专业知识呢？
-
-以下是一些数据科学学生项目的示例，供你参考。
-
- * [MSR 数据科学夏季学校](https://www.microsoft.com/en-us/research/academic-program/data-science-summer-school/#!projects)，以及 GitHub 上的 [项目](https://github.com/msr-ds3)，探索以下主题：
-    - [警察使用武力中的种族偏见](https://www.microsoft.com/en-us/research/video/data-science-summer-school-2019-replicating-an-empirical-analysis-of-racial-differences-in-police-use-of-force/) | [Github](https://github.com/msr-ds3/stop-question-frisk)
-    - [纽约地铁系统的可靠性](https://www.microsoft.com/en-us/research/video/data-science-summer-school-2018-exploring-the-reliability-of-the-nyc-subway-system/) | [Github](https://github.com/msr-ds3/nyctransit)
- * [数字化物质文化：探索 Sirkap 的社会经济分布](https://claremont.maps.arcgis.com/apps/Cascade/index.html?appid=bdf2aef0f45a4674ba41cd373fa23afc) - 来自 [Ornella Altunyan](https://twitter.com/ornelladotcom) 和 Claremont 团队，使用 [ArcGIS StoryMaps](https://storymaps.arcgis.com/)。
-
-## 🚀 挑战
-
-寻找推荐适合初学者的数据科学项目的文章，例如 [这 50 个主题领域](https://www.upgrad.com/blog/data-science-project-ideas-topics-beginners/)、[这 21 个项目创意](https://www.intellspot.com/data-science-project-ideas) 或 [这 16 个带源码的项目](https://data-flair.training/blogs/data-science-project-ideas/)，你可以拆解并重新组合这些项目。别忘了记录你的学习历程，并与我们分享你的洞察。
-
-## 课后测验
-
-## [课后测验](https://ff-quizzes.netlify.app/en/ds/quiz/39)
-
-## 复习与自学
-
-想要探索更多用例？以下是一些相关的文章：
- * [17 个数据科学应用及示例](https://builtin.com/data-science/data-science-applications-examples) - 2021 年 7 月
- * [11 个令人惊叹的现实世界数据科学应用](https://myblindbird.com/data-science-applications-real-world/) - 2021 年 5 月
- * [现实世界中的数据科学](https://towardsdatascience.com/data-science-in-the-real-world/home) - 文章合集
- * [12 个现实世界数据科学应用及示例](https://www.scaler.com/blog/data-science-applications/) - 2024 年 5 月
- * 数据科学在以下领域的应用：[教育](https://data-flair.training/blogs/data-science-in-education/)、[农业](https://data-flair.training/blogs/data-science-in-agriculture/)、[金融](https://data-flair.training/blogs/data-science-in-finance/)、[电影](https://data-flair.training/blogs/data-science-at-movies/)、[医疗保健](https://onlinedegrees.sandiego.edu/data-science-health-care/) 等。
-
-## 作业
-
-[探索一个 Planetary Computer 数据集](assignment.md)
-
----
-
-**免责声明**：  
-本文档使用AI翻译服务[Co-op Translator](https://github.com/Azure/co-op-translator)进行翻译。尽管我们努力确保准确性，但请注意，自动翻译可能包含错误或不准确之处。应以原始语言的文档作为权威来源。对于关键信息，建议使用专业人工翻译。因使用本翻译而导致的任何误解或误读，我们概不负责。
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diff --git a/translations/zh/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md b/translations/zh/6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md
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-<!--
-CO_OP_TRANSLATOR_METADATA:
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-  "source_file": "6-Data-Science-In-Wild/20-Real-World-Examples/assignment.md",
-  "language_code": "zh"
-}
--->
-# 探索行星计算机数据集
-
-## 说明
-
-在本课中，我们讨论了各种数据科学应用领域，并深入研究了与科研、可持续发展和数字人文相关的示例。在这个任务中，你将更详细地探索其中一个示例，并应用你在数据可视化和分析方面的学习，来从可持续发展数据中获取洞察。
-
-[行星计算机](https://planetarycomputer.microsoft.com/)项目提供了可以通过账户访问的数据集和API——如果你想尝试任务的额外步骤，可以申请一个账户以获得访问权限。该网站还提供了一个[Explorer](https://planetarycomputer.microsoft.com/explore)功能，你可以在无需创建账户的情况下使用它。
-
-`步骤：`
-Explorer界面（如下图所示）允许你选择一个数据集（从提供的选项中），一个预设查询（用于过滤数据）以及一个渲染选项（用于创建相关的可视化）。在这个任务中，你需要：
-
- 1. 阅读[Explorer文档](https://planetarycomputer.microsoft.com/docs/overview/explorer/)——了解选项。
- 2. 探索数据集[目录](https://planetarycomputer.microsoft.com/catalog)——了解每个数据集的用途。
- 3. 使用Explorer——选择一个感兴趣的数据集，选择一个相关的查询和渲染选项。
-
-![行星计算机Explorer](../../../../translated_images/zh-CN/planetary-computer-explorer.c1e95a9b053167d64e2e8e4347cfb689e47e2037c33103fc1bbea1a149d4f85b.png)
-
-`你的任务：`
-现在研究浏览器中渲染的可视化，并回答以下问题：
- * 数据集有哪些_特征_？
- * 可视化提供了哪些_洞察_或结果？
- * 这些洞察对项目的可持续发展目标有什么_意义_？
- * 可视化的_局限性_是什么（即，你没有获得哪些洞察？）
- * 如果你能获得原始数据，你会创建哪些_替代可视化_，为什么？
-
-`额外加分：`
-申请一个账户——并在获得批准后登录。
- * 使用 _Launch Hub_ 选项在Notebook中打开原始数据。
- * 交互式地探索数据，并实现你想到的替代可视化。
- * 现在分析你的自定义可视化——你是否能够获得之前遗漏的洞察？
-
-## 评分标准
-
-优秀 | 合格 | 需要改进
---- | --- | -- |
-回答了所有五个核心问题。学生清楚地指出当前和替代可视化如何提供关于可持续发展目标或结果的洞察。| 学生详细回答了至少前三个问题，表明他们对Explorer有实际经验。| 学生未能回答多个问题，或提供的细节不足——表明未对项目进行有意义的尝试。 |
-
-**免责声明**：  
-本文档使用AI翻译服务 [Co-op Translator](https://github.com/Azure/co-op-translator) 进行翻译。尽管我们努力确保翻译的准确性，但请注意，自动翻译可能包含错误或不准确之处。原始语言的文档应被视为权威来源。对于关键信息，建议使用专业人工翻译。我们不对因使用此翻译而产生的任何误解或误读承担责任。
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-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "07faf02ff163e609edf0b0308dc5d4e6",
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-  "source_file": "6-Data-Science-In-Wild/README.md",
-  "language_code": "zh"
-}
--->
-# 数据科学的实际应用
-
-数据科学在各行业中的真实应用。
-
-### 主题
-
-1. [现实世界中的数据科学](20-Real-World-Examples/README.md)
-
-### 致谢
-
-由 [Nitya Narasimhan](https://twitter.com/nitya) ❤️ 撰写
-
-**免责声明**：  
-本文档使用AI翻译服务 [Co-op Translator](https://github.com/Azure/co-op-translator) 进行翻译。尽管我们努力确保翻译的准确性，但请注意，自动翻译可能包含错误或不准确之处。应以原始语言的文档作为权威来源。对于重要信息，建议使用专业人工翻译。我们不对因使用此翻译而产生的任何误解或误读承担责任。
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-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "cc2e18ab65df63e75d3619c4752e9b22",
-  "translation_date": "2025-10-03T11:05:02+00:00",
-  "source_file": "AGENTS.md",
-  "language_code": "zh"
-}
--->
-# AGENTS.md
-
-## 项目概述
-
-《数据科学入门》是由 Microsoft Azure Cloud Advocates 创建的一个全面的10周、20课时的课程。该仓库是一个学习资源，通过基于项目的课程教授数据科学的基础概念，包括 Jupyter 笔记本、互动测验和实践作业。
-
-**关键技术：**
-- **Jupyter 笔记本**：使用 Python 3 的主要学习媒介
-- **Python 库**：pandas、numpy、matplotlib 用于数据分析和可视化
-- **Vue.js 2**：测验应用程序（quiz-app 文件夹）
-- **Docsify**：离线访问的文档站点生成器
-- **Node.js/npm**：JavaScript 组件的包管理
-- **Markdown**：所有课程内容和文档
-
-**架构：**
-- 多语言教育仓库，提供广泛的翻译
-- 结构化为课程模块（从1-Introduction到6-Data-Science-In-Wild）
-- 每节课包括 README、笔记本、作业和测验
-- 独立的 Vue.js 测验应用程序，用于课前/课后评估
-- 支持 GitHub Codespaces 和 VS Code 开发容器
-
-## 设置命令
-
-### 仓库设置
-```bash
-# Clone the repository (if not already cloned)
-git clone https://github.com/microsoft/Data-Science-For-Beginners.git
-cd Data-Science-For-Beginners
-```
-
-### Python 环境设置
-```bash
-# Create a virtual environment (recommended)
-python -m venv venv
-source venv/bin/activate  # On Windows: venv\Scripts\activate
-
-# Install common data science libraries (no requirements.txt exists)
-pip install jupyter pandas numpy matplotlib seaborn scikit-learn
-```
-
-### 测验应用程序设置
-```bash
-# Navigate to quiz app
-cd quiz-app
-
-# Install dependencies
-npm install
-
-# Start development server
-npm run serve
-
-# Build for production
-npm run build
-
-# Lint and fix files
-npm run lint
-```
-
-### Docsify 文档服务器
-```bash
-# Install Docsify globally
-npm install -g docsify-cli
-
-# Serve documentation locally
-docsify serve
-
-# Documentation will be available at localhost:3000
-```
-
-### 可视化项目设置
-对于像 meaningful-visualizations（第13课）这样的可视化项目：
-```bash
-# Navigate to starter or solution folder
-cd 3-Data-Visualization/13-meaningful-visualizations/starter
-
-# Install dependencies
-npm install
-
-# Start development server
-npm run serve
-
-# Build for production
-npm run build
-
-# Lint files
-npm run lint
-```
-
-
-## 开发工作流程
-
-### 使用 Jupyter 笔记本
-1. 在仓库根目录启动 Jupyter：`jupyter notebook`
-2. 导航到所需的课程文件夹
-3. 打开 `.ipynb` 文件以完成练习
-4. 笔记本是自包含的，包含解释和代码单元
-5. 大多数笔记本使用 pandas、numpy 和 matplotlib - 确保这些库已安装
-
-### 课程结构
-每节课通常包括：
-- `README.md` - 主要课程内容，包含理论和示例
-- `notebook.ipynb` - 实践 Jupyter 笔记本练习
-- `assignment.ipynb` 或 `assignment.md` - 实践作业
-- `solution/` 文件夹 - 解决方案笔记本和代码
-- `images/` 文件夹 - 支持的视觉材料
-
-### 测验应用程序开发
-- 使用 Vue.js 2 的应用程序，开发期间支持热重载
-- 测验存储在 `quiz-app/src/assets/translations/` 中
-- 每种语言都有自己的翻译文件夹（en, fr, es 等）
-- 测验编号从0开始，总计40个测验
-
-### 添加翻译
-- 翻译存储在仓库根目录的 `translations/` 文件夹中
-- 每种语言的课程结构与英文完全镜像
-- 通过 GitHub Actions 自动翻译（co-op-translator.yml）
-
-## 测试说明
-
-### 测验应用程序测试
-```bash
-cd quiz-app
-
-# Run lint checks
-npm run lint
-
-# Test build process
-npm run build
-
-# Manual testing: Start dev server and verify quiz functionality
-npm run serve
-```
-
-### 笔记本测试
-- 笔记本没有自动化测试框架
-- 手动验证：按顺序运行所有单元，确保没有错误
-- 验证数据文件是否可访问，输出是否正确生成
-- 检查可视化是否正确渲染
-
-### 文档测试
-```bash
-# Verify Docsify renders correctly
-docsify serve
-
-# Check for broken links manually by navigating through content
-# Verify all lesson links work in the rendered documentation
-```
-
-### 代码质量检查
-```bash
-# Vue.js projects (quiz-app and visualization projects)
-cd quiz-app  # or visualization project folder
-npm run lint
-
-# Python notebooks - manual verification recommended
-# Ensure imports work and cells execute without errors
-```
-
-
-## 代码风格指南
-
-### Python（Jupyter 笔记本）
-- 遵循 PEP 8 风格指南编写 Python 代码
-- 使用清晰的变量名，解释所分析的数据
-- 在代码单元之前包含解释性的 Markdown 单元
-- 代码单元专注于单一概念或操作
-- 使用 pandas 进行数据操作，matplotlib 进行可视化
-- 常见导入模式：
-  ```python
-  import pandas as pd
-  import numpy as np
-  import matplotlib.pyplot as plt
-  ```
-
-
-### JavaScript/Vue.js
-- 遵循 Vue.js 2 风格指南和最佳实践
-- ESLint 配置在 `quiz-app/package.json` 中
-- 使用 Vue 单文件组件（.vue 文件）
-- 维护基于组件的架构
-- 提交更改前运行 `npm run lint`
-
-### Markdown 文档
-- 使用清晰的标题层次结构（# ## ### 等）
-- 包含带语言说明符的代码块
-- 为图片添加替代文本
-- 链接相关课程和资源
-- 保持合理的行长度以提高可读性
-
-### 文件组织
-- 课程内容存储在编号文件夹中（01-defining-data-science 等）
-- 解决方案存储在专用的 `solution/` 子文件夹中
-- 翻译在 `translations/` 文件夹中镜像英文结构
-- 数据文件存储在 `data/` 或课程特定文件夹中
-
-## 构建和部署
-
-### 测验应用程序部署
-```bash
-cd quiz-app
-
-# Build production version
-npm run build
-
-# Output is in dist/ folder
-# Deploy dist/ folder to static hosting (Azure Static Web Apps, Netlify, etc.)
-```
-
-### Azure 静态 Web 应用部署
-测验应用程序可以部署到 Azure 静态 Web 应用：
-1. 创建 Azure 静态 Web 应用资源
-2. 连接到 GitHub 仓库
-3. 配置构建设置：
-   - 应用位置：`quiz-app`
-   - 输出位置：`dist`
-4. GitHub Actions 工作流将在推送时自动部署
-
-### 文档站点
-```bash
-# Build PDF from Docsify (optional)
-npm run convert
-
-# Docsify documentation is served directly from markdown files
-# No build step required for deployment
-# Deploy repository to static hosting with Docsify
-```
-
-### GitHub Codespaces
-- 仓库包含开发容器配置
-- Codespaces 自动设置 Python 和 Node.js 环境
-- 通过 GitHub UI 打开仓库中的 Codespace
-- 所有依赖项自动安装
-
-## 拉取请求指南
-
-### 提交前
-```bash
-# For Vue.js changes in quiz-app
-cd quiz-app
-npm run lint
-npm run build
-
-# Test changes locally
-npm run serve
-```
-
-### PR 标题格式
-- 使用清晰、描述性的标题
-- 格式：`[组件] 简短描述`
-- 示例：
-  - `[第7课] 修复 Python 笔记本导入错误`
-  - `[测验应用] 添加德语翻译`
-  - `[文档] 更新 README，添加新前置条件`
-
-### 必要检查
-- 确保所有代码运行无错误
-- 验证笔记本完全执行
-- 确认 Vue.js 应用程序成功构建
-- 检查文档链接是否有效
-- 如果修改了测验应用程序，请进行测试
-- 验证翻译保持一致的结构
-
-### 贡献指南
-- 遵循现有代码风格和模式
-- 为复杂逻辑添加解释性注释
-- 更新相关文档
-- 如果适用，跨不同课程模块测试更改
-- 查看 CONTRIBUTING.md 文件
-
-## 附加说明
-
-### 常用库
-- **pandas**：数据操作和分析
-- **numpy**：数值计算
-- **matplotlib**：数据可视化和绘图
-- **seaborn**：统计数据可视化（部分课程）
-- **scikit-learn**：机器学习（高级课程）
-
-### 使用数据文件
-- 数据文件位于 `data/` 文件夹或课程特定目录中
-- 大多数笔记本期望数据文件在相对路径中
-- CSV 文件是主要的数据格式
-- 部分课程使用 JSON 作为非关系数据示例
-
-### 多语言支持
-- 通过 GitHub Actions 提供40多种语言翻译
-- 翻译工作流在 `.github/workflows/co-op-translator.yml` 中
-- 翻译存储在 `translations/` 文件夹中，使用语言代码命名
-- 测验翻译存储在 `quiz-app/src/assets/translations/` 中
-
-### 开发环境选项
-1. **本地开发**：本地安装 Python、Jupyter、Node.js
-2. **GitHub Codespaces**：基于云的即时开发环境
-3. **VS Code 开发容器**：基于本地容器的开发
-4. **Binder**：在云中启动笔记本（如果已配置）
-
-### 课程内容指南
-- 每节课是独立的，但建立在之前的概念之上
-- 课前测验测试先前知识
-- 课后测验巩固学习
-- 作业提供实践练习
-- Sketchnotes 提供视觉总结
-
-### 常见问题排查
-
-**Jupyter 内核问题：**
-```bash
-# Ensure correct kernel is installed
-python -m ipykernel install --user --name=datascience
-```
-
-**npm 安装失败：**
-```bash
-# Clear npm cache and retry
-npm cache clean --force
-rm -rf node_modules package-lock.json
-npm install
-```
-
-**笔记本导入错误：**
-- 验证所有必需库是否已安装
-- 检查 Python 版本兼容性（推荐 Python 3.7+）
-- 确保虚拟环境已激活
-
-**Docsify 无法加载：**
-- 验证是否从仓库根目录提供服务
-- 检查 `index.html` 是否存在
-- 确保网络访问正常（端口 3000）
-
-### 性能注意事项
-- 大型数据集可能需要时间加载到笔记本中
-- 复杂图表的可视化渲染可能较慢
-- Vue.js 开发服务器支持热重载以快速迭代
-- 生产构建经过优化和压缩
-
-### 安全注意事项
-- 不应提交敏感数据或凭据
-- 在云课程中使用环境变量存储任何 API 密钥
-- 与 Azure 相关的课程可能需要 Azure 账户凭据
-- 保持依赖项更新以获取安全补丁
-
-## 贡献翻译
-- 通过 GitHub Actions 管理自动翻译
-- 欢迎手动修正以提高翻译准确性
-- 遵循现有翻译文件夹结构
-- 更新测验链接以包含语言参数：`?loc=fr`
-- 测试翻译课程以确保正确渲染
-
-## 相关资源
-- 主课程：https://aka.ms/datascience-beginners
-- Microsoft Learn：https://docs.microsoft.com/learn/
-- 学生中心：https://docs.microsoft.com/learn/student-hub
-- 讨论论坛：https://github.com/microsoft/Data-Science-For-Beginners/discussions
-- 其他 Microsoft 课程：ML for Beginners, AI for Beginners, Web Dev for Beginners
-
-## 项目维护
-- 定期更新以保持内容最新
-- 欢迎社区贡献
-- 在 GitHub 上跟踪问题
-- 由课程维护者审查 PR
-- 每月内容审查和更新
-
----
-
-**免责声明**：  
-本文档使用AI翻译服务 [Co-op Translator](https://github.com/Azure/co-op-translator) 进行翻译。尽管我们努力确保翻译的准确性，但请注意，自动翻译可能包含错误或不准确之处。应以原始语言的文档作为权威来源。对于关键信息，建议使用专业人工翻译。我们不对因使用此翻译而产生的任何误解或误读承担责任。
\ No newline at end of file
diff --git a/translations/zh/CODE_OF_CONDUCT.md b/translations/zh/CODE_OF_CONDUCT.md
deleted file mode 100644
index c4706928..00000000
--- a/translations/zh/CODE_OF_CONDUCT.md
+++ /dev/null
@@ -1,21 +0,0 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "c06b12caf3c901eb3156e3dd5b0aea56",
-  "translation_date": "2025-08-25T16:10:06+00:00",
-  "source_file": "CODE_OF_CONDUCT.md",
-  "language_code": "zh"
-}
--->
-# Microsoft 开源行为准则
-
-本项目已采用 [Microsoft 开源行为准则](https://opensource.microsoft.com/codeofconduct/)。
-
-资源：
-
-- [Microsoft 开源行为准则](https://opensource.microsoft.com/codeofconduct/)
-- [Microsoft 行为准则常见问题](https://opensource.microsoft.com/codeofconduct/faq/)
-- 如有疑问或需帮助，请联系 [opencode@microsoft.com](mailto:opencode@microsoft.com)
-
-**免责声明**：  
-本文档使用AI翻译服务 [Co-op Translator](https://github.com/Azure/co-op-translator) 进行翻译。尽管我们努力确保翻译的准确性，但请注意，自动翻译可能包含错误或不准确之处。原始语言的文档应被视为权威来源。对于关键信息，建议使用专业人工翻译。我们对因使用此翻译而产生的任何误解或误读不承担责任。
\ No newline at end of file
diff --git a/translations/zh/CONTRIBUTING.md b/translations/zh/CONTRIBUTING.md
deleted file mode 100644
index 5b8b19e5..00000000
--- a/translations/zh/CONTRIBUTING.md
+++ /dev/null
@@ -1,362 +0,0 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "10f86fb29b5407088445ac803b3d0ed1",
-  "translation_date": "2025-10-03T13:29:57+00:00",
-  "source_file": "CONTRIBUTING.md",
-  "language_code": "zh"
-}
--->
-# 贡献《数据科学入门》
-
-感谢您对《数据科学入门》课程的贡献兴趣！我们欢迎社区的贡献。
-
-## 目录
-
-- [行为准则](../..)
-- [我如何贡献？](../..)
-- [入门指南](../..)
-- [贡献指南](../..)
-- [拉取请求流程](../..)
-- [样式指南](../..)
-- [贡献者许可协议](../..)
-
-## 行为准则
-
-本项目采用了 [Microsoft 开源行为准则](https://opensource.microsoft.com/codeofconduct/)。
-有关更多信息，请参阅 [行为准则常见问题](https://opensource.microsoft.com/codeofconduct/faq/) 或通过 [opencode@microsoft.com](mailto:opencode@microsoft.com) 联系我们提出其他问题或意见。
-
-## 我如何贡献？
-
-### 报告错误
-
-在创建错误报告之前，请检查现有问题以避免重复。当您创建错误报告时，请尽可能提供详细信息：
-
-- **使用清晰且描述性的标题**
-- **描述重现问题的具体步骤**
-- **提供具体示例**（代码片段、截图）
-- **描述您观察到的行为以及您的期望**
-- **包括您的环境详细信息**（操作系统、Python版本、浏览器）
-
-### 提出改进建议
-
-我们欢迎改进建议！提出建议时：
-
-- **使用清晰且描述性的标题**
-- **提供建议改进的详细描述**
-- **解释为什么此改进会有用**
-- **列出其他项目中的类似功能（如果适用）**
-
-### 贡献文档
-
-文档改进总是受到欢迎：
-
-- **修复拼写和语法错误**
-- **提高解释的清晰度**
-- **补充缺失的文档**
-- **更新过时的信息**
-- **添加示例或使用案例**
-
-### 贡献代码
-
-我们欢迎以下代码贡献：
-
-- **新的课程或练习**
-- **错误修复**
-- **改进现有的笔记本**
-- **新的数据集或示例**
-- **测验应用程序的改进**
-
-## 入门指南
-
-### 前提条件
-
-在贡献之前，请确保您已具备以下条件：
-
-1. 一个 GitHub 账户
-2. 您的系统上已安装 Git
-3. 安装了 Python 3.7+ 和 Jupyter
-4. 安装了 Node.js 和 npm（用于测验应用程序贡献）
-5. 熟悉课程结构
-
-请参阅 [INSTALLATION.md](INSTALLATION.md) 获取详细的设置说明。
-
-### Fork 和克隆
-
-1. **在 GitHub 上 Fork 仓库**
-2. **本地克隆您的 Fork**：
-   ```bash
-   git clone https://github.com/YOUR-USERNAME/Data-Science-For-Beginners.git
-   cd Data-Science-For-Beginners
-   ```
-3. **添加上游远程仓库**：
-   ```bash
-   git remote add upstream https://github.com/microsoft/Data-Science-For-Beginners.git
-   ```
-
-### 创建分支
-
-为您的工作创建一个新分支：
-
-```bash
-git checkout -b feature/your-feature-name
-# or
-git checkout -b fix/your-bug-fix
-```
-
-分支命名约定：
-- `feature/` - 新功能或课程
-- `fix/` - 错误修复
-- `docs/` - 文档更改
-- `refactor/` - 代码重构
-
-## 贡献指南
-
-### 对课程内容的贡献
-
-贡献课程或修改现有课程时：
-
-1. **遵循现有结构**：
-   - README.md 包含课程内容
-   - Jupyter 笔记本包含练习
-   - 作业（如果适用）
-   - 链接到前测和后测
-
-2. **包括以下元素**：
-   - 清晰的学习目标
-   - 分步解释
-   - 带注释的代码示例
-   - 练习以供实践
-   - 额外资源的链接
-
-3. **确保可访问性**：
-   - 使用清晰、简单的语言
-   - 为图片提供替代文本
-   - 包含代码注释
-   - 考虑不同的学习风格
-
-### 对 Jupyter 笔记本的贡献
-
-1. **在提交之前清除所有输出**：
-   ```bash
-   jupyter nbconvert --clear-output --inplace notebook.ipynb
-   ```
-
-2. **包含带解释的 Markdown 单元格**
-
-3. **使用一致的格式**：
-   ```python
-   # Import libraries at the top
-   import pandas as pd
-   import numpy as np
-   import matplotlib.pyplot as plt
-   
-   # Use meaningful variable names
-   # Add comments for complex operations
-   # Follow PEP 8 style guidelines
-   ```
-
-4. **在提交之前完全测试您的笔记本**
-
-### 对 Python 代码的贡献
-
-遵循 [PEP 8](https://www.python.org/dev/peps/pep-0008/) 样式指南：
-
-```python
-# Good practices
-import pandas as pd
-
-def calculate_mean(data):
-    """Calculate the mean of a dataset.
-    
-    Args:
-        data (list): List of numerical values
-        
-    Returns:
-        float: Mean of the dataset
-    """
-    return sum(data) / len(data)
-```
-
-### 对测验应用程序的贡献
-
-修改测验应用程序时：
-
-1. **本地测试**：
-   ```bash
-   cd quiz-app
-   npm install
-   npm run serve
-   ```
-
-2. **运行代码检查工具**：
-   ```bash
-   npm run lint
-   ```
-
-3. **成功构建**：
-   ```bash
-   npm run build
-   ```
-
-4. **遵循 Vue.js 样式指南**和现有模式
-
-### 对翻译的贡献
-
-添加或更新翻译时：
-
-1. 遵循 `translations/` 文件夹中的结构
-2. 使用语言代码作为文件夹名称（例如，法语为 `fr`）
-3. 保持与英文版本相同的文件结构
-4. 更新测验链接以包含语言参数：`?loc=fr`
-5. 测试所有链接和格式
-
-## 拉取请求流程
-
-### 提交之前
-
-1. **使用最新更改更新您的分支**：
-   ```bash
-   git fetch upstream
-   git rebase upstream/main
-   ```
-
-2. **测试您的更改**：
-   - 运行所有修改过的笔记本
-   - 测试测验应用程序（如果已修改）
-   - 验证所有链接是否有效
-   - 检查拼写和语法错误
-
-3. **提交您的更改**：
-   ```bash
-   git add .
-   git commit -m "Brief description of changes"
-   ```
-   
-   编写清晰的提交消息：
-   - 使用现在时态（“添加功能”而不是“已添加功能”）
-   - 使用命令式语气（“移动光标到...”而不是“光标移动到...”）
-   - 限制第一行为 72 个字符
-   - 在相关时引用问题和拉取请求
-
-4. **推送到您的 Fork**：
-   ```bash
-   git push origin feature/your-feature-name
-   ```
-
-### 创建拉取请求
-
-1. 转到 [仓库](https://github.com/microsoft/Data-Science-For-Beginners)
-2. 点击“Pull requests” → “New pull request”
-3. 点击“compare across forks”
-4. 选择您的 Fork 和分支
-5. 点击“Create pull request”
-
-### PR 标题格式
-
-使用清晰、描述性的标题，遵循以下格式：
-
-```
-[Component] Brief description
-```
-
-示例：
-- `[Lesson 7] 修复 Python 笔记本导入错误`
-- `[Quiz App] 添加德语翻译`
-- `[Docs] 更新 README，添加新前提条件`
-- `[Fix] 修正可视化课程中的数据路径`
-
-### PR 描述
-
-在您的 PR 描述中包括：
-
-- **内容**：您做了哪些更改？
-- **原因**：为什么需要这些更改？
-- **方法**：您如何实现这些更改？
-- **测试**：您如何测试这些更改？
-- **截图**：对于视觉更改，请包含截图
-- **相关问题**：链接到相关问题（例如，“Fixes #123”）
-
-### 审核流程
-
-1. **自动检查**将运行在您的 PR 上
-2. **维护者将审核**您的贡献
-3. **根据反馈**进行额外提交
-4. 一旦获得批准，**维护者将合并**您的 PR
-
-### PR 合并后
-
-1. 删除您的分支：
-   ```bash
-   git branch -d feature/your-feature-name
-   git push origin --delete feature/your-feature-name
-   ```
-
-2. 更新您的 Fork：
-   ```bash
-   git checkout main
-   git pull upstream main
-   git push origin main
-   ```
-
-## 样式指南
-
-### Markdown
-
-- 使用一致的标题级别
-- 在章节之间添加空行
-- 使用带语言标识符的代码块：
-  ````markdown
-  ```python
-  import pandas as pd
-  ```
-  ````
-- 为图片添加替代文本：`![Alt text](../../translated_images/zh-CN/image.4ee84a82b5e4c9e6651b13fd27dcf615e427ec584929f2cef7167aa99151a77a.png)`
-- 保持合理的行长度（约 80-100 个字符）
-
-### Python
-
-- 遵循 PEP 8 样式指南
-- 使用有意义的变量名
-- 为函数添加文档字符串
-- 在适当的地方添加类型提示：
-  ```python
-  def process_data(df: pd.DataFrame) -> pd.DataFrame:
-      """Process the input dataframe."""
-      return df
-  ```
-
-### JavaScript/Vue.js
-
-- 遵循 Vue.js 2 样式指南
-- 使用提供的 ESLint 配置
-- 编写模块化、可重用的组件
-- 为复杂逻辑添加注释
-
-### 文件组织
-
-- 将相关文件放在一起
-- 使用描述性的文件名
-- 遵循现有的目录结构
-- 不要提交不必要的文件（如 .DS_Store、.pyc、node_modules 等）
-
-## 贡献者许可协议
-
-本项目欢迎贡献和建议。大多数贡献需要您同意贡献者许可协议 (CLA)，声明您有权并实际授予我们使用您的贡献的权利。详情请访问 https://cla.microsoft.com。
-
-当您提交拉取请求时，CLA-bot 会自动确定您是否需要提供 CLA，并适当装饰 PR（例如，标签、评论）。只需按照机器人提供的说明操作即可。您只需在所有使用我们 CLA 的仓库中执行一次此操作。
-
-## 有问题？
-
-- 查看我们的 [Discord 频道 #data-science-for-beginners](https://aka.ms/ds4beginners/discord)
-- 加入我们的 [Discord 社区](https://aka.ms/ds4beginners/discord)
-- 查看现有的 [问题](https://github.com/microsoft/Data-Science-For-Beginners/issues) 和 [拉取请求](https://github.com/microsoft/Data-Science-For-Beginners/pulls)
-
-## 感谢！
-
-您的贡献使这门课程对所有人都更好。感谢您抽出时间贡献！
-
----
-
-**免责声明**：  
-本文档使用AI翻译服务 [Co-op Translator](https://github.com/Azure/co-op-translator) 进行翻译。尽管我们努力确保翻译的准确性，但请注意，自动翻译可能包含错误或不准确之处。原始语言的文档应被视为权威来源。对于关键信息，建议使用专业人工翻译。我们不对因使用此翻译而产生的任何误解或误读承担责任。
\ No newline at end of file
diff --git a/translations/zh/INSTALLATION.md b/translations/zh/INSTALLATION.md
deleted file mode 100644
index d93c1887..00000000
--- a/translations/zh/INSTALLATION.md
+++ /dev/null
@@ -1,261 +0,0 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "a64d8afa22ffcc2016bb239188d6acb1",
-  "translation_date": "2025-10-03T15:15:57+00:00",
-  "source_file": "INSTALLATION.md",
-  "language_code": "zh"
-}
--->
-# 安装指南
-
-本指南将帮助您设置环境，以使用《数据科学入门》课程。
-
-## 目录
-
-- [先决条件](../..)
-- [快速开始选项](../..)
-- [本地安装](../..)
-- [验证安装](../..)
-
-## 先决条件
-
-在开始之前，您需要具备以下条件：
-
-- 基本的命令行/终端操作知识
-- 一个 GitHub 账户（免费）
-- 稳定的互联网连接以完成初始设置
-
-## 快速开始选项
-
-### 选项 1：GitHub Codespaces（推荐初学者使用）
-
-最简单的开始方式是使用 GitHub Codespaces，它提供了一个完整的开发环境，直接在浏览器中运行。
-
-1. 访问 [仓库](https://github.com/microsoft/Data-Science-For-Beginners)
-2. 点击 **Code** 下拉菜单
-3. 选择 **Codespaces** 标签
-4. 点击 **Create codespace on main**
-5. 等待环境初始化（2-3分钟）
-
-您的环境现在已经准备好，所有依赖项都已预安装！
-
-### 选项 2：本地开发
-
-如果您希望在自己的电脑上工作，请按照以下详细说明操作。
-
-## 本地安装
-
-### 步骤 1：安装 Git
-
-Git 是用于克隆仓库和跟踪更改的必要工具。
-
-**Windows:**
-- 从 [git-scm.com](https://git-scm.com/download/win) 下载
-- 使用默认设置运行安装程序
-
-**macOS:**
-- 使用 Homebrew 安装：`brew install git`
-- 或从 [git-scm.com](https://git-scm.com/download/mac) 下载
-
-**Linux:**
-```bash
-# Debian/Ubuntu
-sudo apt-get update
-sudo apt-get install git
-
-# Fedora
-sudo dnf install git
-
-# Arch
-sudo pacman -S git
-```
-
-### 步骤 2：克隆仓库
-
-```bash
-# Clone the repository
-git clone https://github.com/microsoft/Data-Science-For-Beginners.git
-
-# Navigate to the directory
-cd Data-Science-For-Beginners
-```
-
-### 步骤 3：安装 Python 和 Jupyter
-
-数据科学课程需要 Python 3.7 或更高版本。
-
-**Windows:**
-1. 从 [python.org](https://www.python.org/downloads/) 下载 Python
-2. 安装时勾选“Add Python to PATH”
-3. 验证安装：
-```bash
-python --version
-```
-
-**macOS:**
-```bash
-# Using Homebrew
-brew install python3
-
-# Verify installation
-python3 --version
-```
-
-**Linux:**
-```bash
-# Most Linux distributions come with Python pre-installed
-python3 --version
-
-# If not installed:
-# Debian/Ubuntu
-sudo apt-get install python3 python3-pip
-
-# Fedora
-sudo dnf install python3 python3-pip
-```
-
-### 步骤 4：设置 Python 环境
-
-建议使用虚拟环境来隔离依赖项。
-
-```bash
-# Create a virtual environment
-python -m venv venv
-
-# Activate the virtual environment
-# On Windows:
-venv\Scripts\activate
-
-# On macOS/Linux:
-source venv/bin/activate
-```
-
-### 步骤 5：安装 Python 包
-
-安装所需的数据科学库：
-
-```bash
-pip install jupyter pandas numpy matplotlib seaborn scikit-learn
-```
-
-### 步骤 6：安装 Node.js 和 npm（用于测验应用）
-
-测验应用需要 Node.js 和 npm。
-
-**Windows/macOS:**
-- 从 [nodejs.org](https://nodejs.org/) 下载（推荐 LTS 版本）
-- 运行安装程序
-
-**Linux:**
-```bash
-# Debian/Ubuntu
-# WARNING: Piping scripts from the internet directly into bash can be a security risk.
-# It is recommended to review the script before running it:
-#   curl -fsSL https://deb.nodesource.com/setup_lts.x -o setup_lts.x
-#   less setup_lts.x
-# Then run:
-#   sudo -E bash setup_lts.x
-#
-# Alternatively, you can use the one-liner below at your own risk:
-curl -fsSL https://deb.nodesource.com/setup_lts.x | sudo -E bash -
-sudo apt-get install -y nodejs
-
-# Fedora
-sudo dnf install nodejs
-
-# Verify installation
-node --version
-npm --version
-```
-
-### 步骤 7：安装测验应用依赖项
-
-```bash
-# Navigate to quiz app directory
-cd quiz-app
-
-# Install dependencies
-npm install
-
-# Return to root directory
-cd ..
-```
-
-### 步骤 8：安装 Docsify（可选）
-
-用于离线访问文档：
-
-```bash
-npm install -g docsify-cli
-```
-
-## 验证安装
-
-### 测试 Python 和 Jupyter
-
-```bash
-# Activate your virtual environment if not already activated
-# On Windows:
-venv\Scripts\activate
-# On macOS/Linux:
-source venv/bin/activate
-
-# Start Jupyter Notebook
-jupyter notebook
-```
-
-您的浏览器应该会打开 Jupyter 界面。您现在可以导航到任何课程的 `.ipynb` 文件。
-
-### 测试测验应用
-
-```bash
-# Navigate to quiz app
-cd quiz-app
-
-# Start development server
-npm run serve
-```
-
-测验应用应该可以通过 `http://localhost:8080`（如果 8080 端口被占用，则使用其他端口）访问。
-
-### 测试文档服务器
-
-```bash
-# From the root directory of the repository
-docsify serve
-```
-
-文档应该可以通过 `http://localhost:3000` 访问。
-
-## 使用 VS Code Dev Containers
-
-如果您安装了 Docker，可以使用 VS Code Dev Containers：
-
-1. 安装 [Docker Desktop](https://www.docker.com/products/docker-desktop)
-2. 安装 [Visual Studio Code](https://code.visualstudio.com/)
-3. 安装 [Remote - Containers 扩展](https://marketplace.visualstudio.com/items?itemName=ms-vscode-remote.remote-containers)
-4. 在 VS Code 中打开仓库
-5. 按 `F1` 并选择“Remote-Containers: Reopen in Container”
-6. 等待容器构建（仅首次需要）
-
-## 下一步
-
-- 查看 [README.md](README.md) 了解课程概览
-- 阅读 [USAGE.md](USAGE.md) 了解常见工作流程和示例
-- 如果遇到问题，请查看 [TROUBLESHOOTING.md](TROUBLESHOOTING.md)
-- 如果您想贡献，请阅读 [CONTRIBUTING.md](CONTRIBUTING.md)
-
-## 获取帮助
-
-如果您遇到问题：
-
-1. 查看 [TROUBLESHOOTING.md](TROUBLESHOOTING.md) 指南
-2. 搜索现有的 [GitHub Issues](https://github.com/microsoft/Data-Science-For-Beginners/issues)
-3. 加入我们的 [Discord 社区](https://aka.ms/ds4beginners/discord)
-4. 创建一个新问题，并详细描述您的问题
-
----
-
-**免责声明**：  
-本文档使用AI翻译服务 [Co-op Translator](https://github.com/Azure/co-op-translator) 进行翻译。尽管我们努力确保翻译的准确性，但请注意，自动翻译可能包含错误或不准确之处。原始语言的文档应被视为权威来源。对于关键信息，建议使用专业人工翻译。我们对因使用此翻译而产生的任何误解或误读不承担责任。
\ No newline at end of file
diff --git a/translations/zh/README.md b/translations/zh/README.md
deleted file mode 100644
index 90b7b2e3..00000000
--- a/translations/zh/README.md
+++ /dev/null
@@ -1,263 +0,0 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "33d252f7491b696d85df7f680e7e7b90",
-  "translation_date": "2026-01-16T09:22:53+00:00",
-  "source_file": "README.md",
-  "language_code": "zh"
-}
--->
-# 面向初学者的数据科学课程
-
-[![在 GitHub Codespaces 中打开](https://github.com/codespaces/badge.svg)](https://github.com/codespaces/new?hide_repo_select=true&ref=main&repo=344191198)
-
-[![GitHub 许可证](https://img.shields.io/github/license/microsoft/Data-Science-For-Beginners.svg)](https://github.com/microsoft/Data-Science-For-Beginners/blob/master/LICENSE)
-[![GitHub 贡献者](https://img.shields.io/github/contributors/microsoft/Data-Science-For-Beginners.svg)](https://GitHub.com/microsoft/Data-Science-For-Beginners/graphs/contributors/)
-[![GitHub 问题](https://img.shields.io/github/issues/microsoft/Data-Science-For-Beginners.svg)](https://GitHub.com/microsoft/Data-Science-For-Beginners/issues/)
-[![GitHub 拉取请求](https://img.shields.io/github/issues-pr/microsoft/Data-Science-For-Beginners.svg)](https://GitHub.com/microsoft/Data-Science-For-Beginners/pulls/)
-[![欢迎 PR](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)](http://makeapullrequest.com)
-
-[![GitHub 关注者](https://img.shields.io/github/watchers/microsoft/Data-Science-For-Beginners.svg?style=social&label=Watch)](https://GitHub.com/microsoft/Data-Science-For-Beginners/watchers/)
-[![GitHub 分叉](https://img.shields.io/github/forks/microsoft/Data-Science-For-Beginners.svg?style=social&label=Fork)](https://GitHub.com/microsoft/Data-Science-For-Beginners/network/)
-[![GitHub 星标](https://img.shields.io/github/stars/microsoft/Data-Science-For-Beginners.svg?style=social&label=Star)](https://GitHub.com/microsoft/Data-Science-For-Beginners/stargazers/)
-
-
-[![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG)
-
-[![Microsoft Foundry 开发者论坛](https://img.shields.io/badge/GitHub-Microsoft_Foundry_Developer_Forum-blue?style=for-the-badge&logo=github&color=000000&logoColor=fff)](https://aka.ms/foundry/forum)
-
-微软 Azure 云推广者很高兴提供一个为期 10 周、共 20 课的数据科学课程。每节课包括课前和课后测验、完成课程的书面指导、解决方案和作业。我们的项目驱动教学法使你在构建的过程中学习，这是一种经证实的新技能“扎根”的方法。
-
-**衷心感谢我们的作者：** [Jasmine Greenaway](https://www.twitter.com/paladique), [Dmitry Soshnikov](http://soshnikov.com), [Nitya Narasimhan](https://twitter.com/nitya), [Jalen McGee](https://twitter.com/JalenMcG), [Jen Looper](https://twitter.com/jenlooper), [Maud Levy](https://twitter.com/maudstweets), [Tiffany Souterre](https://twitter.com/TiffanySouterre), [Christopher Harrison](https://www.twitter.com/geektrainer)。
-
-**🙏 特别感谢 🙏 我们的 [Microsoft 学生大使](https://studentambassadors.microsoft.com/) 作者、审稿人和内容贡献者，** 尤其是 Aaryan Arora, [Aditya Garg](https://github.com/AdityaGarg00), [Alondra Sanchez](https://www.linkedin.com/in/alondra-sanchez-molina/), [Ankita Singh](https://www.linkedin.com/in/ankitasingh007), [Anupam Mishra](https://www.linkedin.com/in/anupam--mishra/), [Arpita Das](https://www.linkedin.com/in/arpitadas01/), ChhailBihari Dubey, [Dibri Nsofor](https://www.linkedin.com/in/dibrinsofor), [Dishita Bhasin](https://www.linkedin.com/in/dishita-bhasin-7065281bb), [Majd Safi](https://www.linkedin.com/in/majd-s/), [Max Blum](https://www.linkedin.com/in/max-blum-6036a1186/), [Miguel Correa](https://www.linkedin.com/in/miguelmque/), [Mohamma Iftekher (Iftu) Ebne Jalal](https://twitter.com/iftu119), [Nawrin Tabassum](https://www.linkedin.com/in/nawrin-tabassum), [Raymond Wangsa Putra](https://www.linkedin.com/in/raymond-wp/), [Rohit Yadav](https://www.linkedin.com/in/rty2423), Samridhi Sharma, [Sanya Sinha](https://www.linkedin.com/mwlite/in/sanya-sinha-13aab1200),
-[Sheena Narula](https://www.linkedin.com/in/sheena-narua-n/), [Tauqeer Ahmad](https://www.linkedin.com/in/tauqeerahmad5201/), Yogendrasingh Pawar , [Vidushi Gupta](https://www.linkedin.com/in/vidushi-gupta07/), [Jasleen Sondhi](https://www.linkedin.com/in/jasleen-sondhi/)
-
-|![由 @sketchthedocs 绘制的手绘笔记 https://sketchthedocs.dev](../../../../translated_images/zh-CN/00-Title.8af36cd35da1ac55.webp)|
-|:---:|
-| 面向初学者的数据科学 - _由 [@nitya](https://twitter.com/nitya) 绘制的手绘笔记_ |
-
-### 🌐 多语言支持
-
-#### 通过 GitHub Action 支持（自动且始终最新）
-
-<!-- CO-OP TRANSLATOR LANGUAGES TABLE START -->
-[阿拉伯语](../ar/README.md) | [孟加拉语](../bn/README.md) | [保加利亚语](../bg/README.md) | [缅甸语（缅甸）](../my/README.md) | [中文（简体）](./README.md) | [中文（繁体，香港）](../hk/README.md) | [中文（繁体，澳门）](../mo/README.md) | [中文（繁体，台湾）](../tw/README.md) | [克罗地亚语](../hr/README.md) | [捷克语](../cs/README.md) | [丹麦语](../da/README.md) | [荷兰语](../nl/README.md) | [爱沙尼亚语](../et/README.md) | [芬兰语](../fi/README.md) | [法语](../fr/README.md) | [德语](../de/README.md) | [希腊语](../el/README.md) | [希伯来语](../he/README.md) | [印地语](../hi/README.md) | [匈牙利语](../hu/README.md) | [印度尼西亚语](../id/README.md) | [意大利语](../it/README.md) | [日语](../ja/README.md) | [卡纳达语](../kn/README.md) | [韩语](../ko/README.md) | [立陶宛语](../lt/README.md) | [马来语](../ms/README.md) | [马拉雅拉姆语](../ml/README.md) | [马拉地语](../mr/README.md) | [尼泊尔语](../ne/README.md) | [尼日利亚皮钦语](../pcm/README.md) | [挪威语](../no/README.md) | [波斯语（法尔西语）](../fa/README.md) | [波兰语](../pl/README.md) | [葡萄牙语（巴西）](../br/README.md) | [葡萄牙语（葡萄牙）](../pt/README.md) | [旁遮普语（古鲁姆奇）](../pa/README.md) | [罗马尼亚语](../ro/README.md) | [俄语](../ru/README.md) | [塞尔维亚语（西里尔字母）](../sr/README.md) | [斯洛伐克语](../sk/README.md) | [斯洛文尼亚语](../sl/README.md) | [西班牙语](../es/README.md) | [斯瓦希里语](../sw/README.md) | [瑞典语](../sv/README.md) | [塔加洛语（菲律宾语）](../tl/README.md) | [泰米尔语](../ta/README.md) | [泰卢固语](../te/README.md) | [泰语](../th/README.md) | [土耳其语](../tr/README.md) | [乌克兰语](../uk/README.md) | [乌尔都语](../ur/README.md) | [越南语](../vi/README.md)
-
-> **更喜欢本地克隆？**
-
-> 此仓库包括 50 多种语言翻译，显著增加下载大小。要克隆时不包含翻译，请使用稀疏检出：
-> ```bash
-> git clone --filter=blob:none --sparse https://github.com/microsoft/Data-Science-For-Beginners.git
-> cd Data-Science-For-Beginners
-> git sparse-checkout set --no-cone '/*' '!translations' '!translated_images'
-> ```
-> 这样你就能获得完成课程所需的一切，下载速度更快。
-<!-- CO-OP TRANSLATOR LANGUAGES TABLE END -->
-
-**如果您希望支持额外的翻译语言，详情见 [这里](https://github.com/Azure/co-op-translator/blob/main/getting_started/supported-languages.md)**
-
-#### 加入我们的社区 
-[![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG)
-
-我们正在进行 Discord AI 学习系列，了解详情并加入我们，[AI 学习系列](https://aka.ms/learnwithai/discord)，时间为 2025 年 9 月 18 日至 30 日。你将获得使用 GitHub Copilot 进行数据科学的技巧和窍门。
-
-![AI 学习系列](../../../../translated_images/zh-CN/1.2b28cdc6205e26fe.webp)
-
-# 你是学生吗？
-
-使用以下资源开始：
-
-- [学生中心页面](https://docs.microsoft.com/en-gb/learn/student-hub?WT.mc_id=academic-77958-bethanycheum) 本页面包含初学者资源、学生包，甚至还有获取免费证书凭证的方法。建议将此页面加入书签并定期查看，因为我们至少每月更新内容。
-- [微软学习学生大使](https://studentambassadors.microsoft.com?WT.mc_id=academic-77958-bethanycheum) 加入全球学生大使社区，这或许是你进入微软的途径。
-
-# 开始
-
-## 📚 文档
-
-- **[安装指南](INSTALLATION.md)** - 面向初学者的逐步设置说明
-- **[使用指南](USAGE.md)** - 示例和常用工作流程
-- **[故障排除](TROUBLESHOOTING.md)** - 常见问题解决方案
-- **[贡献指南](CONTRIBUTING.md)** - 如何为此项目贡献代码
-- **[教师专用](for-teachers.md)** - 教学指导和课堂资源
-
-## 👨‍🎓 针对学生
-> **完全初学者**：刚接触数据科学？请从我们[适合初学者的示例](examples/README.md)开始！这些简单且注释详细的示例将帮助你在深入课程前理解基础概念。
-> **[学生](https://aka.ms/student-page)**：如要自行使用此课程，请fork整个仓库并自行完成练习，从课前测验开始。然后阅读课程内容并完成其他活动。尝试通过理解课程来创建项目，而非仅仅复制解决方案代码；不过，每个项目导向课程的 /solutions 文件夹中均提供了解决方案代码。另一种方式是与朋友组成学习小组，共同学习内容。欲进一步学习，推荐使用 [Microsoft Learn](https://docs.microsoft.com/en-us/users/jenlooper-2911/collections/qprpajyoy3x0g7?WT.mc_id=academic-77958-bethanycheum)。
-
-**快速开始：**
-1. 查阅 [安装指南](INSTALLATION.md) 设置开发环境
-2. 查看 [使用指南](USAGE.md) 学习如何使用课程
-3. 从第 1 课开始，按顺序完成
-4. 加入我们的 [Discord 社区](https://aka.ms/ds4beginners/discord) 寻求帮助
-
-## 👩‍🏫 针对教师
-
-> **教师朋友们**：我们提供了 [一些建议](for-teachers.md) 来帮助你使用本课程。欢迎在我们的[讨论论坛](https://github.com/microsoft/Data-Science-For-Beginners/discussions)反馈意见！
-
-## 团队介绍
-[![Promo video](../../ds-for-beginners.gif)](https://youtu.be/8mzavjQSMM4 "Promo video")
-
-**动图来自** [Mohit Jaisal](https://www.linkedin.com/in/mohitjaisal)
-
-> 🎥 点击上方图片观看关于该项目及其创建者的视频！
-
-## 教学理念
-
-我们在构建此课程时选择了两个教学原则：确保课程以项目为基础，并且包含频繁的测验。在本系列课程结束时，学生将学会数据科学的基本原理，包括伦理概念、数据准备、不同的数据处理方式、数据可视化、数据分析、数据科学的真实案例等。
-
-另外，课前的低风险测验可以帮助学生设定学习主题的意向，而课后的第二次测验则有助于巩固知识。该课程设计灵活且有趣，可以全部学习或部分学习。项目从小型开始，到10周课程末变得越来越复杂。
-
-> 查阅我们的[行为准则](CODE_OF_CONDUCT.md)、[贡献指南](CONTRIBUTING.md)和[翻译指南](TRANSLATIONS.md)。我们欢迎您的建设性反馈！
-
-## 每节课程包含：
-
-- 可选的手绘笔记
-- 可选的补充视频
-- 课前热身测验
-- 文字课程
-- 对于项目课程，提供逐步构建项目的指南
-- 知识点检测
-- 挑战任务
-- 补充阅读材料
-- 作业
-- [课后测验](https://ff-quizzes.netlify.app/en/)
-
-> **关于测验的说明**：所有测验都存放在 Quiz-App 文件夹中，共40个测验，每个测验三道题。测验链接嵌入课程，但测验应用也可以本地运行或部署到 Azure；请参照 `quiz-app` 文件夹中的说明。这些测验正在逐步完成本地化。
-
-## 🎓 适合初学者的示例
-
-**刚接触数据科学？** 我们专门创建了一个[示例目录](examples/README.md)，其中包含简单且注释详尽的代码，帮助你快速入门：
-
-- 🌟 **Hello World** - 你的第一个数据科学程序
-- 📂 **加载数据** - 学习如何读取和探索数据集
-- 📊 **简单分析** - 计算统计数据并发现模式
-- 📈 **基础可视化** - 创建图表和图形
-- 🔬 **真实项目** - 从头到尾的完整工作流程
-
-每个示例均附有详细注释，讲解每一步，非常适合完全初学者！
-
-👉 **[从示例开始](examples/README.md)** 👈
-
-## 课程列表
-
-
-|![ Sketchnote by @sketchthedocs https://sketchthedocs.dev](../../../../translated_images/zh-CN/00-Roadmap.4905d6567dff4753.webp)|
-|:---:|
-| 初学者数据科学路线图 - _手绘笔记来自 [@nitya](https://twitter.com/nitya)_ |
-
-
-| 课程编号 | 主题 | 课程分组 | 学习目标 | 课程链接 | 作者 |
-| :-----------: | :----------------------------------------: | :--------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------: | :----: |
-| 01 | 定义数据科学 | [介绍](1-Introduction/README.md) | 学习数据科学的基本概念及其与人工智能、机器学习和大数据的关联。 | [课程](1-Introduction/01-defining-data-science/README.md) [视频](https://youtu.be/beZ7Mb_oz9I) | [Dmitry](http://soshnikov.com) |
-| 02 | 数据科学伦理 | [介绍](1-Introduction/README.md) | 数据伦理的概念、挑战与框架。 | [课程](1-Introduction/02-ethics/README.md) | [Nitya](https://twitter.com/nitya) |
-| 03 | 定义数据 | [介绍](1-Introduction/README.md) | 数据的分类及其常见来源。 | [课程](1-Introduction/03-defining-data/README.md) | [Jasmine](https://www.twitter.com/paladique) |
-| 04 | 统计与概率入门 | [介绍](1-Introduction/README.md) | 使用概率与统计的数学技术理解数据。 | [课程](1-Introduction/04-stats-and-probability/README.md) [视频](https://youtu.be/Z5Zy85g4Yjw) | [Dmitry](http://soshnikov.com) |
-| 05 | 关系数据处理 | [数据操作](2-Working-With-Data/README.md) | 关系数据入门，使用结构化查询语言（SQL）探索和分析关系数据基础。 | [课程](2-Working-With-Data/05-relational-databases/README.md) | [Christopher](https://www.twitter.com/geektrainer) | | |
-| 06 | 非SQL数据处理 | [数据操作](2-Working-With-Data/README.md) | 非关系数据介绍、各种类型及文档数据库的探索和分析基础。 | [课程](2-Working-With-Data/06-non-relational/README.md) | [Jasmine](https://twitter.com/paladique)|
-| 07 | Python数据操作 | [数据操作](2-Working-With-Data/README.md) | 使用Python和Pandas等库进行数据探索的基础，推荐具备基础Python编程知识。 | [课程](2-Working-With-Data/07-python/README.md) [视频](https://youtu.be/dZjWOGbsN4Y) | [Dmitry](http://soshnikov.com) |
-| 08 | 数据准备 | [数据操作](2-Working-With-Data/README.md) | 数据清洗和转换技术，处理缺失、不准确或不完整数据的挑战。 | [课程](2-Working-With-Data/08-data-preparation/README.md) | [Jasmine](https://www.twitter.com/paladique) |
-| 09 | 数量可视化 | [数据可视化](3-Data-Visualization/README.md) | 学习使用 Matplotlib 可视化鸟类数据 🦆 | [课程](3-Data-Visualization/09-visualization-quantities/README.md) | [Jen](https://twitter.com/jenlooper) |
-| 10 | 数据分布可视化 | [数据可视化](3-Data-Visualization/README.md) | 可视化区间内的观测与趋势。 | [课程](3-Data-Visualization/10-visualization-distributions/README.md) | [Jen](https://twitter.com/jenlooper) |
-| 11 | 比例可视化 | [数据可视化](3-Data-Visualization/README.md) | 离散和分组百分比的可视化。 | [课程](3-Data-Visualization/11-visualization-proportions/README.md) | [Jen](https://twitter.com/jenlooper) |
-| 12 | 关系可视化 | [数据可视化](3-Data-Visualization/README.md) | 可视化数据集及变量间的连接和相关性。 | [课程](3-Data-Visualization/12-visualization-relationships/README.md) | [Jen](https://twitter.com/jenlooper) |
-| 13 | 有意义的可视化 | [数据可视化](3-Data-Visualization/README.md) | 制作有价值的可视化技术与指导，有效支持问题解决与洞察。 | [课程](3-Data-Visualization/13-meaningful-visualizations/README.md) | [Jen](https://twitter.com/jenlooper) |
-| 14 | 数据科学生命周期介绍 | [生命周期](4-Data-Science-Lifecycle/README.md) | 数据科学生命周期介绍及其首个步骤：数据获取与提取。 | [课程](4-Data-Science-Lifecycle/14-Introduction/README.md) | [Jasmine](https://twitter.com/paladique) |
-| 15 | 数据分析 | [生命周期](4-Data-Science-Lifecycle/README.md) | 数据科学生命周期中分析数据的技术。 | [课程](4-Data-Science-Lifecycle/15-analyzing/README.md) | [Jasmine](https://twitter.com/paladique) | | |
-| 16 | 结果沟通 | [生命周期](4-Data-Science-Lifecycle/README.md) | 数据科学生命周期中将数据洞察以便决策者易于理解的方式呈现。 | [课程](4-Data-Science-Lifecycle/16-communication/README.md) | [Jalen](https://twitter.com/JalenMcG) | | |
-| 17 | 云端数据科学 | [云端数据](5-Data-Science-In-Cloud/README.md) | 介绍云端数据科学及其优势。 | [课程](5-Data-Science-In-Cloud/17-Introduction/README.md) | [Tiffany](https://twitter.com/TiffanySouterre) 和 [Maud](https://twitter.com/maudstweets) |
-| 18 | 云端低代码模型训练 | [云端数据](5-Data-Science-In-Cloud/README.md) | 使用低代码工具训练模型。 |[课程](5-Data-Science-In-Cloud/18-Low-Code/README.md) | [Tiffany](https://twitter.com/TiffanySouterre) 和 [Maud](https://twitter.com/maudstweets) |
-| 19 | Azure机器学习模型部署 | [云端数据](5-Data-Science-In-Cloud/README.md) | 使用 Azure 机器学习工作室部署模型。 | [课程](5-Data-Science-In-Cloud/19-Azure/README.md)| [Tiffany](https://twitter.com/TiffanySouterre) 和 [Maud](https://twitter.com/maudstweets) |
-| 20 | 真实世界中的数据科学 | [实践应用](6-Data-Science-In-Wild/README.md) | 真实世界的数据科学项目实例。 | [课程](6-Data-Science-In-Wild/20-Real-World-Examples/README.md) | [Nitya](https://twitter.com/nitya) |
-
-## GitHub Codespaces
-
-按以下步骤在 Codespace 中打开此示例：
-1. 点击 Code 下拉菜单，选择“Open with Codespaces”选项。
-2. 在面板底部选择“+ New codespace”。
-更多信息请查阅 [GitHub 文档](https://docs.github.com/en/codespaces/developing-in-codespaces/creating-a-codespace-for-a-repository#creating-a-codespace)。
-
-## VSCode 远程容器
-
-使用本地机器和 VSCode 通过 VS Code Remote - Containers 扩展打开此仓库的容器，步骤如下：
-
-1. 如果是首次使用开发容器，请确保系统满足先决条件（例如安装了 Docker），详情参见 [入门文档](https://code.visualstudio.com/docs/devcontainers/containers#_getting-started)。
-
-使用此仓库时，您可以选择在隔离的 Docker 卷中打开仓库：
-
-**注意**：底层将使用 Remote-Containers 的 **Clone Repository in Container Volume...** 命令在 Docker 卷中克隆源代码，而非本地文件系统。[卷](https://docs.docker.com/storage/volumes/) 是持久化容器数据的首选方式。
-
-或者打开本地克隆或下载的仓库版本：
-
-- 将仓库克隆到本地文件系统。
-- 按 F1，选择 **Remote-Containers: Open Folder in Container...** 命令。
-- 选择克隆的文件夹，等待容器启动，然后开始使用。
-
-## 离线访问
-
-您可以使用 [Docsify](https://docsify.js.org/#/) 离线运行本文档。Fork 此仓库，在本地安装 Docsify（[安装指南](https://docsify.js.org/#/quickstart)），然后在仓库根目录输入 `docsify serve`。网站将通过本地的 3000 端口提供服务：`localhost:3000`。
-
-> 注意，笔记本文件不会通过 Docsify 渲染，当你需要运行笔记本时，请在 VS Code 中使用 Python 内核单独操作。
-
-## 其他课程
-
-我们的团队还出品其他课程！请查看：
-
-<!-- CO-OP TRANSLATOR OTHER COURSES START -->
-### LangChain
-[![LangChain4j for Beginners](https://img.shields.io/badge/LangChain4j%20for%20Beginners-22C55E?style=for-the-badge&&labelColor=E5E7EB&color=0553D6)](https://aka.ms/langchain4j-for-beginners)
-[![LangChain.js 入门](https://img.shields.io/badge/LangChain.js%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=0553D6)](https://aka.ms/langchainjs-for-beginners?WT.mc_id=m365-94501-dwahlin)
-
----
-
-### Azure / Edge / MCP / Agents
-[![AZD 入门](https://img.shields.io/badge/AZD%20for%20Beginners-0078D4?style=for-the-badge&labelColor=E5E7EB&color=0078D4)](https://github.com/microsoft/AZD-for-beginners?WT.mc_id=academic-105485-koreyst)
-[![Edge AI 入门](https://img.shields.io/badge/Edge%20AI%20for%20Beginners-00B8E4?style=for-the-badge&labelColor=E5E7EB&color=00B8E4)](https://github.com/microsoft/edgeai-for-beginners?WT.mc_id=academic-105485-koreyst)
-[![MCP 入门](https://img.shields.io/badge/MCP%20for%20Beginners-009688?style=for-the-badge&labelColor=E5E7EB&color=009688)](https://github.com/microsoft/mcp-for-beginners?WT.mc_id=academic-105485-koreyst)
-[![AI Agents 入门](https://img.shields.io/badge/AI%20Agents%20for%20Beginners-00C49A?style=for-the-badge&labelColor=E5E7EB&color=00C49A)](https://github.com/microsoft/ai-agents-for-beginners?WT.mc_id=academic-105485-koreyst)
-
----
- 
-### 生成式 AI 系列
-[![生成式 AI 入门](https://img.shields.io/badge/Generative%20AI%20for%20Beginners-8B5CF6?style=for-the-badge&labelColor=E5E7EB&color=8B5CF6)](https://github.com/microsoft/generative-ai-for-beginners?WT.mc_id=academic-105485-koreyst)
-[![生成式 AI (.NET)](https://img.shields.io/badge/Generative%20AI%20(.NET)-9333EA?style=for-the-badge&labelColor=E5E7EB&color=9333EA)](https://github.com/microsoft/Generative-AI-for-beginners-dotnet?WT.mc_id=academic-105485-koreyst)
-[![生成式 AI (Java)](https://img.shields.io/badge/Generative%20AI%20(Java)-C084FC?style=for-the-badge&labelColor=E5E7EB&color=C084FC)](https://github.com/microsoft/generative-ai-for-beginners-java?WT.mc_id=academic-105485-koreyst)
-[![生成式 AI (JavaScript)](https://img.shields.io/badge/Generative%20AI%20(JavaScript)-E879F9?style=for-the-badge&labelColor=E5E7EB&color=E879F9)](https://github.com/microsoft/generative-ai-with-javascript?WT.mc_id=academic-105485-koreyst)
-
----
- 
-### 核心学习
-[![机器学习入门](https://img.shields.io/badge/ML%20for%20Beginners-22C55E?style=for-the-badge&labelColor=E5E7EB&color=22C55E)](https://aka.ms/ml-beginners?WT.mc_id=academic-105485-koreyst)
-[![数据科学入门](https://img.shields.io/badge/Data%20Science%20for%20Beginners-84CC16?style=for-the-badge&labelColor=E5E7EB&color=84CC16)](https://aka.ms/datascience-beginners?WT.mc_id=academic-105485-koreyst)
-[![人工智能入门](https://img.shields.io/badge/AI%20for%20Beginners-A3E635?style=for-the-badge&labelColor=E5E7EB&color=A3E635)](https://aka.ms/ai-beginners?WT.mc_id=academic-105485-koreyst)
-[![网络安全入门](https://img.shields.io/badge/Cybersecurity%20for%20Beginners-F97316?style=for-the-badge&labelColor=E5E7EB&color=F97316)](https://github.com/microsoft/Security-101?WT.mc_id=academic-96948-sayoung)
-[![Web 开发入门](https://img.shields.io/badge/Web%20Dev%20for%20Beginners-EC4899?style=for-the-badge&labelColor=E5E7EB&color=EC4899)](https://aka.ms/webdev-beginners?WT.mc_id=academic-105485-koreyst)
-[![物联网入门](https://img.shields.io/badge/IoT%20for%20Beginners-14B8A6?style=for-the-badge&labelColor=E5E7EB&color=14B8A6)](https://aka.ms/iot-beginners?WT.mc_id=academic-105485-koreyst)
-[![XR 开发入门](https://img.shields.io/badge/XR%20Development%20for%20Beginners-38BDF8?style=for-the-badge&labelColor=E5E7EB&color=38BDF8)](https://github.com/microsoft/xr-development-for-beginners?WT.mc_id=academic-105485-koreyst)
-
----
- 
-### Copilot 系列
-[![用于 AI 配对编程的 Copilot](https://img.shields.io/badge/Copilot%20for%20AI%20Paired%20Programming-FACC15?style=for-the-badge&labelColor=E5E7EB&color=FACC15)](https://aka.ms/GitHubCopilotAI?WT.mc_id=academic-105485-koreyst)
-[![用于 C#/.NET 的 Copilot](https://img.shields.io/badge/Copilot%20for%20C%23/.NET-FBBF24?style=for-the-badge&labelColor=E5E7EB&color=FBBF24)](https://github.com/microsoft/mastering-github-copilot-for-dotnet-csharp-developers?WT.mc_id=academic-105485-koreyst)
-[![Copilot 冒险](https://img.shields.io/badge/Copilot%20Adventure-FDE68A?style=for-the-badge&labelColor=E5E7EB&color=FDE68A)](https://github.com/microsoft/CopilotAdventures?WT.mc_id=academic-105485-koreyst)
-<!-- CO-OP TRANSLATOR OTHER COURSES END -->
-
-## 获取帮助
-
-**遇到问题？** 查看我们的[故障排除指南](TROUBLESHOOTING.md)以获取常见问题的解决方案。
-
-如果遇到困难或对构建 AI 应用有任何疑问，欢迎加入学习者及有经验开发者的讨论社区，交流 MCP 相关内容。这里是一个支持性强的社区，欢迎提问并自由分享知识。
-
-[![Microsoft Foundry Discord](https://dcbadge.limes.pink/api/server/nTYy5BXMWG)](https://discord.gg/nTYy5BXMWG)
-
-如果有产品反馈或在构建过程中遇到错误，请访问：
-
-[![Microsoft Foundry Developer Forum](https://img.shields.io/badge/GitHub-Microsoft_Foundry_Developer_Forum-blue?style=for-the-badge&logo=github&color=000000&logoColor=fff)](https://aka.ms/foundry/forum)
-
----
-
-<!-- CO-OP TRANSLATOR DISCLAIMER START -->
-**免责声明**：  
-本文件使用 AI 翻译服务 [Co-op Translator](https://github.com/Azure/co-op-translator) 进行翻译。尽管我们力求准确，但请注意，自动翻译可能包含错误或不准确之处。请以原文（母语版本）为权威来源。对于重要信息，建议使用专业人工翻译。对于因使用本翻译而产生的任何误解或误读，我们不承担任何责任。
-<!-- CO-OP TRANSLATOR DISCLAIMER END -->
\ No newline at end of file
diff --git a/translations/zh/SECURITY.md b/translations/zh/SECURITY.md
deleted file mode 100644
index ec2fc662..00000000
--- a/translations/zh/SECURITY.md
+++ /dev/null
@@ -1,49 +0,0 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "0d575483100c332b2dbaefef915bb3c4",
-  "translation_date": "2025-08-25T16:10:40+00:00",
-  "source_file": "SECURITY.md",
-  "language_code": "zh"
-}
--->
-## 安全性
-
-Microsoft 非常重视我们软件产品和服务的安全性，这包括通过我们的 GitHub 组织管理的所有源代码库，这些组织包括 [Microsoft](https://github.com/Microsoft)、[Azure](https://github.com/Azure)、[DotNet](https://github.com/dotnet)、[AspNet](https://github.com/aspnet)、[Xamarin](https://github.com/xamarin) 和 [我们的 GitHub 组织](https://opensource.microsoft.com/)。
-
-如果您认为在任何 Microsoft 拥有的代码库中发现了符合 [Microsoft 对安全漏洞的定义](https://docs.microsoft.com/en-us/previous-versions/tn-archive/cc751383(v=technet.10)) 的安全漏洞，请按照以下描述向我们报告。
-
-## 报告安全问题
-
-**请不要通过公共 GitHub 问题报告安全漏洞。**
-
-相反，请通过 Microsoft 安全响应中心 (MSRC) 报告，网址为 [https://msrc.microsoft.com/create-report](https://msrc.microsoft.com/create-report)。
-
-如果您更愿意在不登录的情况下提交，请发送电子邮件至 [secure@microsoft.com](mailto:secure@microsoft.com)。如果可能，请使用我们的 PGP 密钥加密您的消息；您可以从 [Microsoft 安全响应中心 PGP 密钥页面](https://www.microsoft.com/en-us/msrc/pgp-key-msrc) 下载。
-
-您应该会在 24 小时内收到回复。如果由于某种原因未收到，请通过电子邮件跟进以确保我们收到了您的原始消息。更多信息请访问 [microsoft.com/msrc](https://www.microsoft.com/msrc)。
-
-请尽可能提供以下所需信息，以帮助我们更好地理解问题的性质和范围：
-
-  * 问题类型（例如，缓冲区溢出、SQL 注入、跨站脚本攻击等）
-  * 与问题表现相关的源文件的完整路径
-  * 受影响源代码的位置（标签/分支/提交或直接 URL）
-  * 复现问题所需的任何特殊配置
-  * 复现问题的分步说明
-  * 概念验证或漏洞利用代码（如果可能）
-  * 问题的影响，包括攻击者可能如何利用该问题
-
-这些信息将帮助我们更快地对您的报告进行分类和处理。
-
-如果您是为漏洞赏金计划报告问题，更完整的报告可能会获得更高的赏金奖励。有关我们当前计划的更多详细信息，请访问 [Microsoft 漏洞赏金计划](https://microsoft.com/msrc/bounty) 页面。
-
-## 首选语言
-
-我们更倾向于使用英语进行所有交流。
-
-## 政策
-
-Microsoft 遵循 [协调漏洞披露](https://www.microsoft.com/en-us/msrc/cvd) 原则。
-
-**免责声明**：  
-本文档使用AI翻译服务[Co-op Translator](https://github.com/Azure/co-op-translator)进行翻译。尽管我们努力确保准确性，但请注意，自动翻译可能包含错误或不准确之处。应以原始语言的文档作为权威来源。对于关键信息，建议使用专业人工翻译。对于因使用本翻译而引起的任何误解或误读，我们概不负责。
\ No newline at end of file
diff --git a/translations/zh/SUPPORT.md b/translations/zh/SUPPORT.md
deleted file mode 100644
index 303d9773..00000000
--- a/translations/zh/SUPPORT.md
+++ /dev/null
@@ -1,22 +0,0 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "872be8bc1b93ef1dd9ac3d6e8f99f6ab",
-  "translation_date": "2025-08-25T16:08:07+00:00",
-  "source_file": "SUPPORT.md",
-  "language_code": "zh"
-}
--->
-# 支持
-## 如何提交问题并获取帮助  
-
-此项目使用 GitHub Issues 来跟踪错误和功能请求。在提交新问题之前，请先搜索现有问题以避免重复。对于新问题，请将您的错误或功能请求提交为一个新问题。
-
-如果您在使用此项目时需要帮助或有疑问，请提交一个问题。
-
-## Microsoft 支持政策  
-
-对此存储库的支持仅限于上述列出的资源。
-
-**免责声明**：  
-本文档使用AI翻译服务 [Co-op Translator](https://github.com/Azure/co-op-translator) 进行翻译。尽管我们努力确保翻译的准确性，但请注意，自动翻译可能包含错误或不准确之处。原始语言的文档应被视为权威来源。对于重要信息，建议使用专业人工翻译。我们对因使用此翻译而产生的任何误解或误读不承担责任。
\ No newline at end of file
diff --git a/translations/zh/TROUBLESHOOTING.md b/translations/zh/TROUBLESHOOTING.md
deleted file mode 100644
index 44196537..00000000
--- a/translations/zh/TROUBLESHOOTING.md
+++ /dev/null
@@ -1,627 +0,0 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "93a6a8a8a209128cbfedcbc076ee21b0",
-  "translation_date": "2025-10-03T15:31:56+00:00",
-  "source_file": "TROUBLESHOOTING.md",
-  "language_code": "zh"
-}
--->
-# 故障排查指南
-
-本指南提供了解决使用《数据科学入门》课程时可能遇到的常见问题的方法。
-
-## 目录
-
-- [Python 和 Jupyter 问题](../..)
-- [包和依赖问题](../..)
-- [Jupyter Notebook 问题](../..)
-- [测验应用问题](../..)
-- [Git 和 GitHub 问题](../..)
-- [Docsify 文档问题](../..)
-- [数据和文件问题](../..)
-- [性能问题](../..)
-- [获取额外帮助](../..)
-
-## Python 和 Jupyter 问题
-
-### 找不到 Python 或版本错误
-
-**问题：** `python: command not found` 或 Python 版本错误
-
-**解决方法：**
-
-```bash
-# Check Python version
-python --version
-python3 --version
-
-# If Python 3 is installed as 'python3', create an alias
-# On macOS/Linux, add to ~/.bashrc or ~/.zshrc:
-alias python=python3
-alias pip=pip3
-
-# Or use python3 explicitly
-python3 -m pip install jupyter
-```
-
-**Windows 解决方法：**
-1. 从 [python.org](https://www.python.org/) 重新安装 Python
-2. 安装时勾选“Add Python to PATH”
-3. 重启终端/命令提示符
-
-### 虚拟环境激活问题
-
-**问题：** 虚拟环境无法激活
-
-**解决方法：**
-
-**Windows：**
-```bash
-# If you get execution policy error
-Set-ExecutionPolicy -ExecutionPolicy RemoteSigned -Scope CurrentUser
-
-# Then activate
-venv\Scripts\activate
-```
-
-**macOS/Linux：**
-```bash
-# Ensure the activate script is executable
-chmod +x venv/bin/activate
-
-# Then activate
-source venv/bin/activate
-```
-
-**验证激活：**
-```bash
-# Your prompt should show (venv)
-# Check Python location
-which python  # Should point to venv
-```
-
-### Jupyter 内核问题
-
-**问题：** “找不到内核”或“内核不断崩溃”
-
-**解决方法：**
-
-```bash
-# Reinstall kernel
-python -m ipykernel install --user --name=datascience --display-name="Python (Data Science)"
-
-# Or use the default kernel
-python -m ipykernel install --user
-
-# Restart Jupyter
-jupyter notebook
-```
-
-**问题：** Jupyter 中的 Python 版本错误
-
-**解决方法：**
-```bash
-# Install Jupyter in your virtual environment
-source venv/bin/activate  # Activate first
-pip install jupyter ipykernel
-
-# Register the kernel
-python -m ipykernel install --user --name=venv --display-name="Python (venv)"
-
-# In Jupyter, select Kernel -> Change kernel -> Python (venv)
-```
-
-## 包和依赖问题
-
-### 导入错误
-
-**问题：** `ModuleNotFoundError: No module named 'pandas'`（或其他包）
-
-**解决方法：**
-
-```bash
-# Ensure virtual environment is activated
-source venv/bin/activate  # macOS/Linux
-venv\Scripts\activate     # Windows
-
-# Install missing package
-pip install pandas
-
-# Install all common packages
-pip install jupyter pandas numpy matplotlib seaborn scikit-learn
-
-# Verify installation
-python -c "import pandas; print(pandas.__version__)"
-```
-
-### Pip 安装失败
-
-**问题：** `pip install` 因权限错误失败
-
-**解决方法：**
-
-```bash
-# Use --user flag
-pip install --user package-name
-
-# Or use virtual environment (recommended)
-python -m venv venv
-source venv/bin/activate
-pip install package-name
-```
-
-**问题：** `pip install` 因 SSL 证书错误失败
-
-**解决方法：**
-
-```bash
-# Update pip first
-python -m pip install --upgrade pip
-
-# Try installing with trusted host (temporary workaround)
-pip install --trusted-host pypi.org --trusted-host files.pythonhosted.org package-name
-```
-
-### 包版本冲突
-
-**问题：** 包版本不兼容
-
-**解决方法：**
-
-```bash
-# Create fresh virtual environment
-python -m venv venv-new
-source venv-new/bin/activate  # or venv-new\Scripts\activate on Windows
-
-# Install packages with specific versions if needed
-pip install pandas==1.3.0
-pip install numpy==1.21.0
-
-# Or let pip resolve dependencies
-pip install jupyter pandas numpy matplotlib seaborn scikit-learn
-```
-
-## Jupyter Notebook 问题
-
-### Jupyter 无法启动
-
-**问题：** `jupyter notebook` 命令未找到
-
-**解决方法：**
-
-```bash
-# Install Jupyter
-pip install jupyter
-
-# Or use python -m
-python -m jupyter notebook
-
-# Add to PATH if needed (macOS/Linux)
-export PATH="$HOME/.local/bin:$PATH"
-```
-
-### Notebook 无法加载或保存
-
-**问题：** “Notebook 加载失败”或保存错误
-
-**解决方法：**
-
-1. 检查文件权限
-```bash
-# Make sure you have write permissions
-ls -l notebook.ipynb
-chmod 644 notebook.ipynb  # If needed
-```
-
-2. 检查文件是否损坏
-```bash
-# Try opening in text editor to check JSON structure
-# Copy content to new notebook if corrupted
-```
-
-3. 清除 Jupyter 缓存
-```bash
-jupyter notebook --clear-cache
-```
-
-### 单元格无法执行
-
-**问题：** 单元格卡在“In [*]”或执行时间过长
-
-**解决方法：**
-
-1. **中断内核：** 点击“Interrupt”按钮或按 `I, I`
-2. **重启内核：** 内核菜单 → 重启
-3. **检查代码中的无限循环**
-4. **清除输出：** 单元格 → 所有输出 → 清除
-
-### 图表不显示
-
-**问题：** `matplotlib` 图表未在 Notebook 中显示
-
-**解决方法：**
-
-```python
-# Add magic command at the top of notebook
-%matplotlib inline
-
-import matplotlib.pyplot as plt
-
-# Create plot
-plt.plot([1, 2, 3, 4])
-plt.show()  # Make sure to call show()
-```
-
-**交互式图表的替代方法：**
-```python
-%matplotlib notebook
-# Or
-%matplotlib widget
-```
-
-## 测验应用问题
-
-### npm install 失败
-
-**问题：** `npm install` 期间出现错误
-
-**解决方法：**
-
-```bash
-# Clear npm cache
-npm cache clean --force
-
-# Remove node_modules and package-lock.json
-rm -rf node_modules package-lock.json
-
-# Reinstall
-npm install
-
-# If still failing, try with legacy peer deps
-npm install --legacy-peer-deps
-```
-
-### 测验应用无法启动
-
-**问题：** `npm run serve` 失败
-
-**解决方法：**
-
-```bash
-# Check Node.js version
-node --version  # Should be 12.x or higher
-
-# Reinstall dependencies
-cd quiz-app
-rm -rf node_modules package-lock.json
-npm install
-
-# Try different port
-npm run serve -- --port 8081
-```
-
-### 端口已被占用
-
-**问题：** “端口 8080 已被占用”
-
-**解决方法：**
-
-```bash
-# Find and kill process on port 8080
-# macOS/Linux:
-lsof -ti:8080 | xargs kill -9
-
-# Windows:
-netstat -ano | findstr :8080
-taskkill /PID <PID> /F
-
-# Or use a different port
-npm run serve -- --port 8081
-```
-
-### 测验无法加载或显示空白页面
-
-**问题：** 测验应用加载但显示空白页面
-
-**解决方法：**
-
-1. 检查浏览器控制台中的错误（按 F12）
-2. 清除浏览器缓存和 Cookie
-3. 尝试使用其他浏览器
-4. 确保启用了 JavaScript
-5. 检查是否有广告拦截器干扰
-
-```bash
-# Rebuild the app
-npm run build
-npm run serve
-```
-
-## Git 和 GitHub 问题
-
-### Git 未识别
-
-**问题：** `git: command not found`
-
-**解决方法：**
-
-**Windows：**
-- 从 [git-scm.com](https://git-scm.com/) 安装 Git
-- 安装后重启终端
-
-**macOS：**
-
-> **注意：** 如果尚未安装 Homebrew，请先按照 [https://brew.sh/](https://brew.sh/) 的说明进行安装。
-```bash
-# Install via Homebrew
-brew install git
-
-# Or install Xcode Command Line Tools
-xcode-select --install
-```
-
-**Linux：**
-```bash
-sudo apt-get install git  # Debian/Ubuntu
-sudo dnf install git      # Fedora
-```
-
-### 克隆失败
-
-**问题：** `git clone` 因身份验证错误失败
-
-**解决方法：**
-
-```bash
-# Use HTTPS URL
-git clone https://github.com/microsoft/Data-Science-For-Beginners.git
-
-# If you have 2FA enabled on GitHub, use Personal Access Token
-# Create token at: https://github.com/settings/tokens
-# Use token as password when prompted
-```
-
-### 权限被拒绝（publickey）
-
-**问题：** SSH 密钥认证失败
-
-**解决方法：**
-
-```bash
-# Generate SSH key
-ssh-keygen -t ed25519 -C "your_email@example.com"
-
-# Add key to ssh-agent
-eval "$(ssh-agent -s)"
-ssh-add ~/.ssh/id_ed25519
-
-# Add public key to GitHub
-# Copy key: cat ~/.ssh/id_ed25519.pub
-# Add at: https://github.com/settings/keys
-```
-
-## Docsify 文档问题
-
-### Docsify 命令未找到
-
-**问题：** `docsify: command not found`
-
-**解决方法：**
-
-```bash
-# Install globally
-npm install -g docsify-cli
-
-# If permission error on macOS/Linux
-sudo npm install -g docsify-cli
-
-# Verify installation
-docsify --version
-
-# If still not found, add npm global path
-# Find npm global path
-npm config get prefix
-
-# Add to PATH (add to ~/.bashrc or ~/.zshrc)
-export PATH="$PATH:/usr/local/bin"
-```
-
-### 文档无法加载
-
-**问题：** Docsify 服务启动但内容未加载
-
-**解决方法：**
-
-```bash
-# Ensure you're in the repository root
-cd Data-Science-For-Beginners
-
-# Check for index.html
-ls index.html
-
-# Serve with specific port
-docsify serve --port 3000
-
-# Check browser console for errors (F12)
-```
-
-### 图片无法显示
-
-**问题：** 图片显示为断链图标
-
-**解决方法：**
-
-1. 检查图片路径是否为相对路径
-2. 确保图片文件存在于仓库中
-3. 清除浏览器缓存
-4. 验证文件扩展名是否匹配（某些系统对大小写敏感）
-
-## 数据和文件问题
-
-### 文件未找到错误
-
-**问题：** 加载数据时出现 `FileNotFoundError`
-
-**解决方法：**
-
-```python
-import os
-
-# Check current working directory
-print(os.getcwd())
-
-# Use absolute path
-data_path = os.path.join(os.getcwd(), 'data', 'filename.csv')
-df = pd.read_csv(data_path)
-
-# Or use relative path from notebook location
-df = pd.read_csv('../data/filename.csv')
-
-# Verify file exists
-print(os.path.exists('data/filename.csv'))
-```
-
-### CSV 读取错误
-
-**问题：** 读取 CSV 文件时出现错误
-
-**解决方法：**
-
-```python
-import pandas as pd
-
-# Try different encodings
-df = pd.read_csv('file.csv', encoding='utf-8')
-# or
-df = pd.read_csv('file.csv', encoding='latin-1')
-# or
-df = pd.read_csv('file.csv', encoding='ISO-8859-1')
-
-# Handle missing values
-df = pd.read_csv('file.csv', na_values=['NA', 'N/A', ''])
-
-# Specify delimiter if not comma
-df = pd.read_csv('file.csv', delimiter=';')
-```
-
-### 大数据集导致内存错误
-
-**问题：** 加载大文件时出现 `MemoryError`
-
-**解决方法：**
-
-```python
-# Read in chunks
-chunk_size = 10000
-chunks = []
-for chunk in pd.read_csv('large_file.csv', chunksize=chunk_size):
-    # Process chunk
-    chunks.append(chunk)
-df = pd.concat(chunks)
-
-# Or read specific columns only
-df = pd.read_csv('file.csv', usecols=['col1', 'col2'])
-
-# Use more efficient data types
-df = pd.read_csv('file.csv', dtype={'column_name': 'int32'})
-```
-
-## 性能问题
-
-### Notebook 性能缓慢
-
-**问题：** Notebook 运行速度非常慢
-
-**解决方法：**
-
-1. **重启内核并清除输出**
-   - 内核 → 重启并清除输出
-
-2. **关闭未使用的 Notebook**
-
-3. **优化代码：**
-```python
-# Use vectorized operations instead of loops
-# Bad:
-result = []
-for x in data:
-    result.append(x * 2)
-
-# Good:
-result = data * 2  # NumPy/Pandas vectorization
-```
-
-4. **对大数据集进行采样：**
-```python
-# Work with sample during development
-df_sample = df.sample(n=1000)  # or df.head(1000)
-```
-
-### 浏览器崩溃
-
-**问题：** 浏览器崩溃或无响应
-
-**解决方法：**
-
-1. 关闭未使用的标签页
-2. 清除浏览器缓存
-3. 增加浏览器内存（Chrome：`chrome://settings/system`）
-4. 使用 JupyterLab 替代：
-```bash
-pip install jupyterlab
-jupyter lab
-```
-
-## 获取额外帮助
-
-### 在寻求帮助之前
-
-1. 查看本故障排查指南
-2. 搜索 [GitHub Issues](https://github.com/microsoft/Data-Science-For-Beginners/issues)
-3. 查看 [INSTALLATION.md](INSTALLATION.md) 和 [USAGE.md](USAGE.md)
-4. 尝试在线搜索错误信息
-
-### 如何寻求帮助
-
-在创建问题或寻求帮助时，请提供以下信息：
-
-1. **操作系统：** Windows、macOS 或 Linux（具体发行版）
-2. **Python 版本：** 运行 `python --version`
-3. **错误信息：** 复制完整的错误信息
-4. **复现步骤：** 错误发生前的操作
-5. **已尝试的解决方法：** 你已经尝试过的解决方案
-
-**示例：**
-```
-**Operating System:** macOS 12.0
-**Python Version:** 3.9.7
-**Error Message:** ModuleNotFoundError: No module named 'pandas'
-**Steps to Reproduce:**
-1. Activated virtual environment
-2. Started Jupyter notebook
-3. Tried to import pandas
-
-**What I've Tried:**
-- Ran pip install pandas
-- Restarted Jupyter
-```
-
-### 社区资源
-
-- **GitHub Issues：** [创建问题](https://github.com/microsoft/Data-Science-For-Beginners/issues/new)
-- **Discord：** [加入我们的社区](https://aka.ms/ds4beginners/discord)
-- **讨论区：** [GitHub Discussions](https://github.com/microsoft/Data-Science-For-Beginners/discussions)
-- **Microsoft Learn：** [问答论坛](https://docs.microsoft.com/answers/)
-
-### 相关文档
-
-- [INSTALLATION.md](INSTALLATION.md) - 安装说明
-- [USAGE.md](USAGE.md) - 如何使用课程
-- [CONTRIBUTING.md](CONTRIBUTING.md) - 如何贡献
-- [README.md](README.md) - 项目概述
-
----
-
-**免责声明**：  
-本文档使用AI翻译服务 [Co-op Translator](https://github.com/Azure/co-op-translator) 进行翻译。尽管我们努力确保翻译的准确性，但请注意，自动翻译可能包含错误或不准确之处。原始语言的文档应被视为权威来源。对于关键信息，建议使用专业人工翻译。我们对因使用此翻译而产生的任何误解或误读不承担责任。
\ No newline at end of file
diff --git a/translations/zh/USAGE.md b/translations/zh/USAGE.md
deleted file mode 100644
index fb42d31d..00000000
--- a/translations/zh/USAGE.md
+++ /dev/null
@@ -1,374 +0,0 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "f546349678757508d69ce9e1d2688446",
-  "translation_date": "2025-10-03T14:54:44+00:00",
-  "source_file": "USAGE.md",
-  "language_code": "zh"
-}
--->
-# 使用指南
-
-本指南提供了使用《数据科学入门》课程的示例和常见工作流程。
-
-## 目录
-
-- [如何使用本课程](../..)
-- [学习课程内容](../..)
-- [使用 Jupyter Notebooks](../..)
-- [使用测验应用程序](../..)
-- [常见工作流程](../..)
-- [自学者提示](../..)
-- [教师提示](../..)
-
-## 如何使用本课程
-
-本课程设计灵活，可通过多种方式使用：
-
-- **自学**：根据自己的节奏独立完成课程
-- **课堂教学**：作为结构化课程进行指导教学
-- **学习小组**：与同伴协作学习
-- **工作坊形式**：短期强化学习
-
-## 学习课程内容
-
-每节课遵循一致的结构，以最大化学习效果：
-
-### 课程结构
-
-1. **课前测验**：测试现有知识
-2. **手绘笔记**（可选）：关键概念的视觉总结
-3. **视频**（可选）：补充视频内容
-4. **书面课程**：核心概念和解释
-5. **Jupyter Notebook**：动手编码练习
-6. **作业**：练习所学内容
-7. **课后测验**：巩固理解
-
-### 课程学习示例流程
-
-```bash
-# 1. Navigate to the lesson directory
-cd 1-Introduction/01-defining-data-science
-
-# 2. Read the README.md
-# Open README.md in your browser or editor
-
-# 3. Take the pre-lesson quiz
-# Click the quiz link in the README
-
-# 4. Open the Jupyter notebook (if available)
-jupyter notebook
-
-# 5. Complete the exercises in the notebook
-
-# 6. Work on the assignment
-
-# 7. Take the post-lesson quiz
-```
-
-## 使用 Jupyter Notebooks
-
-### 启动 Jupyter
-
-```bash
-# Activate your virtual environment
-source venv/bin/activate  # On macOS/Linux
-# OR
-venv\Scripts\activate  # On Windows
-
-# Start Jupyter from the repository root
-jupyter notebook
-```
-
-### 运行 Notebook 单元格
-
-1. **执行单元格**：按 `Shift + Enter` 或点击“运行”按钮
-2. **运行所有单元格**：从菜单中选择“Cell” → “Run All”
-3. **重启内核**：如果遇到问题，选择“Kernel” → “Restart”
-
-### 示例：在 Notebook 中处理数据
-
-```python
-# Import required libraries
-import pandas as pd
-import numpy as np
-import matplotlib.pyplot as plt
-
-# Load a dataset
-df = pd.read_csv('data/sample.csv')
-
-# Explore the data
-df.head()
-df.info()
-df.describe()
-
-# Create a visualization
-plt.figure(figsize=(10, 6))
-plt.plot(df['column_name'])
-plt.title('Sample Visualization')
-plt.xlabel('X-axis Label')
-plt.ylabel('Y-axis Label')
-plt.show()
-```
-
-### 保存您的工作
-
-- Jupyter 会定期自动保存
-- 手动保存：按 `Ctrl + S`（macOS 上为 `Cmd + S`）
-- 您的进度保存在 `.ipynb` 文件中
-
-## 使用测验应用程序
-
-### 本地运行测验应用程序
-
-```bash
-# Navigate to quiz app directory
-cd quiz-app
-
-# Start the development server
-npm run serve
-
-# Access at http://localhost:8080
-```
-
-### 参加测验
-
-1. 课前测验链接位于每节课顶部
-2. 课后测验链接位于每节课底部
-3. 每个测验包含 3 个问题
-4. 测验旨在巩固学习，而非全面测试
-
-### 测验编号
-
-- 测验编号为 0-39（共 40 个测验）
-- 每节课通常有课前和课后测验
-- 测验 URL 包含测验编号：`https://ff-quizzes.netlify.app/en/ds/quiz/0`
-
-## 常见工作流程
-
-### 工作流程 1：完全初学者路径
-
-```bash
-# 1. Set up your environment (see INSTALLATION.md)
-
-# 2. Start with Lesson 1
-cd 1-Introduction/01-defining-data-science
-
-# 3. For each lesson:
-#    - Take pre-lesson quiz
-#    - Read the lesson content
-#    - Work through the notebook
-#    - Complete the assignment
-#    - Take post-lesson quiz
-
-# 4. Progress through all 20 lessons sequentially
-```
-
-### 工作流程 2：特定主题学习
-
-如果您对某个特定主题感兴趣：
-
-```bash
-# Example: Focus on Data Visualization
-cd 3-Data-Visualization
-
-# Explore lessons 9-13:
-# - Lesson 9: Visualizing Quantities
-# - Lesson 10: Visualizing Distributions
-# - Lesson 11: Visualizing Proportions
-# - Lesson 12: Visualizing Relationships
-# - Lesson 13: Meaningful Visualizations
-```
-
-### 工作流程 3：基于项目的学习
-
-```bash
-# 1. Review the Data Science Lifecycle lessons (14-16)
-cd 4-Data-Science-Lifecycle
-
-# 2. Work through a real-world example (Lesson 20)
-cd ../6-Data-Science-In-Wild/20-Real-World-Examples
-
-# 3. Apply concepts to your own project
-```
-
-### 工作流程 4：基于云的数据科学
-
-```bash
-# Learn about cloud data science (Lessons 17-19)
-cd 5-Data-Science-In-Cloud
-
-# 17: Introduction to Cloud Data Science
-# 18: Low-Code ML Tools
-# 19: Azure Machine Learning Studio
-```
-
-## 自学者提示
-
-### 保持条理
-
-```bash
-# Create a learning journal
-mkdir my-learning-journal
-
-# For each lesson, create notes
-echo "# Lesson 1 Notes" > my-learning-journal/lesson-01-notes.md
-```
-
-### 定期练习
-
-- 每天或每周安排固定时间学习
-- 每周至少完成一节课
-- 定期复习之前的课程
-
-### 参与社区
-
-- 加入 [Discord 社区](https://aka.ms/ds4beginners/discord)
-- 参与 Discord 中的 #Data-Science-for-Beginners 频道 [Discord Discussions](https://aka.ms/ds4beginners/discord)
-- 分享您的学习进度并提出问题
-
-### 创建自己的项目
-
-完成课程后，将概念应用到个人项目中：
-
-```python
-# Example: Analyze your own dataset
-import pandas as pd
-
-# Load your own data
-my_data = pd.read_csv('my-project/data.csv')
-
-# Apply techniques learned
-# - Data cleaning (Lesson 8)
-# - Exploratory data analysis (Lesson 7)
-# - Visualization (Lessons 9-13)
-# - Analysis (Lesson 15)
-```
-
-## 教师提示
-
-### 课堂设置
-
-1. 查看 [for-teachers.md](for-teachers.md) 获取详细指导
-2. 设置共享环境（GitHub Classroom 或 Codespaces）
-3. 建立沟通渠道（Discord、Slack 或 Teams）
-
-### 课程计划
-
-**建议的 10 周课程安排：**
-
-- **第 1-2 周**：介绍（第 1-4 课）
-- **第 3-4 周**：数据处理（第 5-8 课）
-- **第 5-6 周**：数据可视化（第 9-13 课）
-- **第 7-8 周**：数据科学生命周期（第 14-16 课）
-- **第 9 周**：云数据科学（第 17-19 课）
-- **第 10 周**：实际应用与最终项目（第 20 课）
-
-### 运行 Docsify 以离线访问
-
-```bash
-# Serve documentation locally for classroom use
-docsify serve
-
-# Students can access at localhost:3000
-# No internet required after initial setup
-```
-
-### 作业评分
-
-- 检查学生的 Notebook 是否完成练习
-- 通过测验分数检查理解情况
-- 使用数据科学生命周期原则评估最终项目
-
-### 创建作业
-
-```python
-# Example custom assignment template
-"""
-Assignment: [Topic]
-
-Objective: [Learning goal]
-
-Dataset: [Provide or have students find one]
-
-Tasks:
-1. Load and explore the dataset
-2. Clean and prepare the data
-3. Create at least 3 visualizations
-4. Perform analysis
-5. Communicate findings
-
-Deliverables:
-- Jupyter notebook with code and explanations
-- Written summary of findings
-"""
-```
-
-## 离线使用
-
-### 下载资源
-
-```bash
-# Clone the entire repository
-git clone https://github.com/microsoft/Data-Science-For-Beginners.git
-
-# Download datasets in advance
-# Most datasets are included in the repository
-```
-
-### 本地运行文档
-
-```bash
-# Serve with Docsify
-docsify serve
-
-# Access at localhost:3000
-```
-
-### 本地运行测验应用程序
-
-```bash
-cd quiz-app
-npm run serve
-```
-
-## 访问翻译内容
-
-翻译版本支持 40 多种语言：
-
-```bash
-# Access translated lessons
-cd translations/fr  # French
-cd translations/es  # Spanish
-cd translations/de  # German
-# ... and many more
-```
-
-每种翻译版本的结构与英文版保持一致。
-
-## 其他资源
-
-### 继续学习
-
-- [Microsoft Learn](https://docs.microsoft.com/learn/) - 更多学习路径
-- [Student Hub](https://docs.microsoft.com/learn/student-hub) - 学生资源
-- [Azure AI Foundry](https://aka.ms/foundry/forum) - 社区论坛
-
-### 相关课程
-
-- [AI 入门](https://aka.ms/ai-beginners)
-- [ML 入门](https://aka.ms/ml-beginners)
-- [Web 开发入门](https://aka.ms/webdev-beginners)
-- [生成式 AI 入门](https://aka.ms/genai-beginners)
-
-## 获取帮助
-
-- 查看 [TROUBLESHOOTING.md](TROUBLESHOOTING.md) 了解常见问题
-- 搜索 [GitHub Issues](https://github.com/microsoft/Data-Science-For-Beginners/issues)
-- 加入我们的 [Discord](https://aka.ms/ds4beginners/discord)
-- 查看 [CONTRIBUTING.md](CONTRIBUTING.md) 以报告问题或贡献内容
-
----
-
-**免责声明**：  
-本文档使用AI翻译服务 [Co-op Translator](https://github.com/Azure/co-op-translator) 进行翻译。尽管我们努力确保翻译的准确性，但请注意，自动翻译可能包含错误或不准确之处。原始语言的文档应被视为权威来源。对于关键信息，建议使用专业人工翻译。我们不对因使用此翻译而产生的任何误解或误读承担责任。
\ No newline at end of file
diff --git a/translations/zh/docs/_sidebar.md b/translations/zh/docs/_sidebar.md
deleted file mode 100644
index 2f68a5be..00000000
--- a/translations/zh/docs/_sidebar.md
+++ /dev/null
@@ -1,38 +0,0 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "3767555b3cc28a2865c79202f4374204",
-  "translation_date": "2025-08-25T16:36:56+00:00",
-  "source_file": "docs/_sidebar.md",
-  "language_code": "zh"
-}
--->
-- 介绍
-  - [定义数据科学](../1-Introduction/01-defining-data-science/README.md)
-  - [数据科学的伦理](../1-Introduction/02-ethics/README.md)
-  - [定义数据](../1-Introduction/03-defining-data/README.md)
-  - [概率与统计](../1-Introduction/04-stats-and-probability/README.md)
-- 数据处理
-  - [关系型数据库](../2-Working-With-Data/05-relational-databases/README.md)
-  - [非关系型数据库](../2-Working-With-Data/06-non-relational/README.md)
-  - [Python](../2-Working-With-Data/07-python/README.md)
-  - [数据准备](../2-Working-With-Data/08-data-preparation/README.md)
-- 数据可视化
-  - [数量可视化](../3-Data-Visualization/09-visualization-quantities/README.md)
-  - [分布可视化](../3-Data-Visualization/10-visualization-distributions/README.md)
-  - [比例可视化](../3-Data-Visualization/11-visualization-proportions/README.md)
-  - [关系可视化](../3-Data-Visualization/12-visualization-relationships/README.md)
-  - [有意义的可视化](../3-Data-Visualization/13-meaningful-visualizations/README.md)
-- 数据科学生命周期
-  - [介绍](../4-Data-Science-Lifecycle/14-Introduction/README.md)
-  - [分析](../4-Data-Science-Lifecycle/15-analyzing/README.md)
-  - [沟通](../4-Data-Science-Lifecycle/16-communication/README.md)
-- 云端数据科学
-  - [介绍](../5-Data-Science-In-Cloud/17-Introduction/README.md)
-  - [低代码](../5-Data-Science-In-Cloud/18-Low-Code/README.md)
-  - [Azure](../5-Data-Science-In-Cloud/19-Azure/README.md)
-- 数据科学的实际应用
-  - [实际中的数据科学](../6-Data-Science-In-Wild/README.md)
-
-**免责声明**：  
-本文档使用AI翻译服务[Co-op Translator](https://github.com/Azure/co-op-translator)进行翻译。虽然我们努力确保翻译的准确性，但请注意，自动翻译可能包含错误或不准确之处。原始语言的文档应被视为权威来源。对于重要信息，建议使用专业人工翻译。我们不对因使用此翻译而产生的任何误解或误读承担责任。
\ No newline at end of file
diff --git a/translations/zh/examples/README.md b/translations/zh/examples/README.md
deleted file mode 100644
index 7225c1c9..00000000
--- a/translations/zh/examples/README.md
+++ /dev/null
@@ -1,145 +0,0 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "9bef7fd96c8f262339933117d9b3e342",
-  "translation_date": "2025-10-03T12:57:17+00:00",
-  "source_file": "examples/README.md",
-  "language_code": "zh"
-}
--->
-# 初学者友好的数据科学示例
-
-欢迎来到示例目录！这套简单且注释清晰的示例旨在帮助您开始学习数据科学，即使您是完全的初学者。
-
-## 📚 您将在这里找到什么
-
-每个示例都是独立的，并包括：
-- **清晰的注释**，解释每一步操作
-- **简单易读的代码**，每次只展示一个概念
-- **真实世界的背景**，帮助您理解这些技术的使用场景和原因
-- **预期输出**，让您知道应该关注什么
-
-## 🚀 开始使用
-
-### 前置条件
-在运行这些示例之前，请确保您已经：
-- 安装了 Python 3.7 或更高版本
-- 对如何运行 Python 脚本有基本了解
-
-### 安装所需库
-```bash
-pip install pandas numpy matplotlib
-```
-
-## 📖 示例概览
-
-### 1. Hello World - 数据科学风格
-**文件名:** `01_hello_world_data_science.py`
-
-您的第一个数据科学程序！学习如何：
-- 加载一个简单的数据集
-- 显示数据的基本信息
-- 打印您的第一个数据科学输出
-
-非常适合希望看到第一个数据科学程序运行效果的绝对初学者。
-
----
-
-### 2. 加载和探索数据
-**文件名:** `02_loading_data.py`
-
-学习处理数据的基础知识：
-- 从 CSV 文件读取数据
-- 查看数据集的前几行
-- 获取数据的基本统计信息
-- 理解数据类型
-
-这是任何数据科学项目的第一步！
-
----
-
-### 3. 简单数据分析
-**文件名:** `03_simple_analysis.py`
-
-进行您的第一次数据分析：
-- 计算基本统计数据（均值、中位数、众数）
-- 找到最大值和最小值
-- 统计值的出现次数
-- 根据条件过滤数据
-
-了解如何回答关于数据的简单问题。
-
----
-
-### 4. 数据可视化基础
-**文件名:** `04_basic_visualization.py`
-
-创建您的第一个可视化：
-- 制作简单的柱状图
-- 创建折线图
-- 生成饼图
-- 将可视化保存为图片
-
-学习如何通过可视化传达您的发现！
-
----
-
-### 5. 使用真实数据
-**文件名:** `05_real_world_example.py`
-
-通过完整示例将所有内容结合起来：
-- 从存储库加载真实数据
-- 清理和准备数据
-- 进行分析
-- 创建有意义的可视化
-- 得出结论
-
-此示例展示了从头到尾的完整工作流程。
-
----
-
-## 🎯 如何使用这些示例
-
-1. **从头开始**：示例按难度排序编号。从 `01_hello_world_data_science.py` 开始，逐步学习。
-2. **阅读注释**：每个文件都有详细的注释，解释代码的功能和原因。仔细阅读！
-3. **尝试实验**：尝试修改代码。如果更改一个值会发生什么？打破代码并修复它——这是学习的方式！
-4. **运行代码**：执行每个示例并观察输出。与您的预期结果进行比较。
-5. **扩展它**：一旦理解了一个示例，尝试用自己的想法扩展它。
-
-## 💡 初学者提示
-
-- **不要着急**：在进入下一个示例之前，花时间理解每个示例
-- **自己输入代码**：不要只是复制粘贴。输入代码有助于学习和记忆
-- **查找不熟悉的概念**：如果看到不理解的内容，请在线搜索或参考主课程
-- **提出问题**：如果需要帮助，请加入 [讨论论坛](https://github.com/microsoft/Data-Science-For-Beginners/discussions)
-- **定期练习**：每天尝试编写一点代码，而不是每周进行一次长时间的学习
-
-## 🔗 下一步
-
-完成这些示例后，您可以：
-- 学习主课程的内容
-- 尝试每个课程文件夹中的作业
-- 探索 Jupyter 笔记本以进行更深入的学习
-- 创建自己的数据科学项目
-
-## 📚 其他资源
-
-- [主课程](../README.md) - 完整的20课课程
-- [教师指南](../for-teachers.md) - 在课堂中使用此课程
-- [Microsoft Learn](https://docs.microsoft.com/learn/) - 免费在线学习资源
-- [Python 文档](https://docs.python.org/3/) - 官方 Python 参考
-
-## 🤝 贡献
-
-发现了错误或有新示例的想法？我们欢迎您的贡献！请参阅我们的 [贡献指南](../CONTRIBUTING.md)。
-
----
-
-**祝学习愉快！🎉**
-
-记住：每个专家都曾是初学者。一步一步来，不要害怕犯错——它们是学习过程的一部分！
-
----
-
-**免责声明**：  
-本文档使用AI翻译服务 [Co-op Translator](https://github.com/Azure/co-op-translator) 进行翻译。尽管我们努力确保翻译的准确性，但请注意，自动翻译可能包含错误或不准确之处。原始语言的文档应被视为权威来源。对于关键信息，建议使用专业人工翻译。我们对因使用此翻译而产生的任何误解或误读不承担责任。
\ No newline at end of file
diff --git a/translations/zh/for-teachers.md b/translations/zh/for-teachers.md
deleted file mode 100644
index fcb9d495..00000000
--- a/translations/zh/for-teachers.md
+++ /dev/null
@@ -1,76 +0,0 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "f7440be10c17a8a9262713af3d2818a9",
-  "translation_date": "2025-09-06T19:52:53+00:00",
-  "source_file": "for-teachers.md",
-  "language_code": "zh"
-}
--->
-## 给教育工作者
-
-想在课堂上使用这套课程吗？请随意使用！
-
-事实上，您可以直接在 GitHub 上通过 GitHub Classroom 使用这套课程。
-
-要做到这一点，请先 fork 此仓库。您需要为每节课创建一个单独的仓库，因此需要将每个文件夹提取到一个独立的仓库中。这样，[GitHub Classroom](https://classroom.github.com/classrooms) 就可以分别识别每节课。
-
-这些[完整的说明](https://github.blog/2020-03-18-set-up-your-digital-classroom-with-github-classroom/)将帮助您了解如何设置您的课堂。
-
-## 按原样使用此仓库
-
-如果您希望按当前形式使用此仓库，而不使用 GitHub Classroom，也完全可以。您需要与学生沟通，一起完成哪一节课程。
-
-在在线教学环境中（如 Zoom、Teams 或其他平台），您可以为测验创建分组讨论室，并指导学生为学习做好准备。然后邀请学生参加测验，并在特定时间以“issue”的形式提交答案。如果您希望学生公开协作完成作业，也可以采用相同的方式。
-
-如果您更喜欢更私密的形式，可以让学生逐节 fork 课程到他们自己的 GitHub 私有仓库，并授予您访问权限。这样，他们可以私下完成测验和作业，并通过您课堂仓库中的 issue 提交给您。
-
-在在线课堂中，有很多方法可以让这套课程发挥作用。请告诉我们哪种方式最适合您！
-
-## 本课程包含内容：
-
-20 节课程，40 个测验，以及 20 个作业。课程配有手绘笔记，适合视觉型学习者。许多课程同时提供 Python 和 R 的版本，可以通过 VS Code 中的 Jupyter notebooks 完成。了解更多关于如何设置课堂以使用这套技术栈的信息：https://code.visualstudio.com/docs/datascience/jupyter-notebooks。
-
-所有手绘笔记，包括一张大幅海报，都在[这个文件夹](../../sketchnotes)中。
-
-您还可以使用 [Docsify](https://docsify.js.org/#/) 将这套课程作为一个独立的、离线友好的网站运行。[安装 Docsify](https://docsify.js.org/#/quickstart) 到您的本地机器，然后在本地副本的根文件夹中输入 `docsify serve`。网站将在本地的 3000 端口运行：`localhost:3000`。
-
-课程的离线友好版本将作为一个独立的网页打开：https://localhost:3000
-
-课程分为 6 个部分：
-
-- 1: 介绍
-    - 1: 数据科学的定义
-    - 2: 伦理
-    - 3: 数据的定义
-    - 4: 概率与统计概述
-- 2: 数据处理
-    - 5: 关系型数据库
-    - 6: 非关系型数据库
-    - 7: Python
-    - 8: 数据准备
-- 3: 数据可视化
-    - 9: 数量的可视化
-    - 10: 分布的可视化
-    - 11: 比例的可视化
-    - 12: 关系的可视化
-    - 13: 有意义的可视化
-- 4: 数据科学生命周期
-    - 14: 介绍
-    - 15: 分析
-    - 16: 沟通
-- 5: 云端数据科学
-    - 17: 介绍
-    - 18: 低代码选项
-    - 19: Azure
-- 6: 数据科学的实际应用
-    - 20: 概述
-
-## 请告诉我们您的想法！
-
-我们希望这套课程能为您和您的学生带来帮助。请在讨论区中给我们反馈！也可以在讨论区为您的学生创建一个课堂专区。
-
----
-
-**免责声明**：  
-本文档使用AI翻译服务[Co-op Translator](https://github.com/Azure/co-op-translator)进行翻译。尽管我们努力确保准确性，但请注意，自动翻译可能包含错误或不准确之处。应以原始语言的文档作为权威来源。对于关键信息，建议使用专业人工翻译。因使用本翻译而导致的任何误解或误读，我们概不负责。
\ No newline at end of file
diff --git a/translations/zh/quiz-app/README.md b/translations/zh/quiz-app/README.md
deleted file mode 100644
index b246bdd7..00000000
--- a/translations/zh/quiz-app/README.md
+++ /dev/null
@@ -1,137 +0,0 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "e92c33ea498915a13c9aec162616db18",
-  "translation_date": "2025-08-25T17:39:05+00:00",
-  "source_file": "quiz-app/README.md",
-  "language_code": "zh"
-}
--->
-# 测验
-
-这些测验是数据科学课程的课前和课后测验，课程网址为：https://aka.ms/datascience-beginners
-
-## 添加翻译后的测验集
-
-通过在 `assets/translations` 文件夹中创建匹配的测验结构来添加测验翻译。原始测验位于 `assets/translations/en` 文件夹中。测验分为几个组别。请确保编号与正确的测验部分对齐。在整个课程中共有 40 个测验，编号从 0 开始。
-
-编辑翻译后，请编辑翻译文件夹中的 `index.js` 文件，按照 `en` 文件夹中的约定导入所有文件。
-
-编辑 `assets/translations` 文件夹中的 `index.js` 文件以导入新的翻译文件。
-
-然后，编辑此应用中的 `App.vue` 文件中的下拉菜单以添加您的语言。将本地化缩写与您的语言文件夹名称匹配。
-
-最后，编辑翻译课程中的所有测验链接（如果存在），以包含本地化查询参数，例如：`?loc=fr`。
-
-## 项目设置
-
-```
-npm install
-```
-
-### 编译并热加载以进行开发
-
-```
-npm run serve
-```
-
-### 编译并压缩以进行生产环境
-
-```
-npm run build
-```
-
-### 检查并修复文件
-
-```
-npm run lint
-```
-
-### 自定义配置
-
-请参阅 [配置参考](https://cli.vuejs.org/config/)。
-
-致谢：感谢此测验应用的原始版本：https://github.com/arpan45/simple-quiz-vue
-
-## 部署到 Azure
-
-以下是帮助您入门的分步指南：
-
-1. Fork GitHub 仓库  
-确保您的静态 Web 应用代码位于您的 GitHub 仓库中。Fork 此仓库。
-
-2. 创建 Azure 静态 Web 应用  
-- 创建一个 [Azure 账户](http://azure.microsoft.com)  
-- 访问 [Azure 门户](https://portal.azure.com)  
-- 点击“创建资源”，搜索“静态 Web 应用”。  
-- 点击“创建”。
-
-3. 配置静态 Web 应用  
-- 基本信息：  
-  - 订阅：选择您的 Azure 订阅。  
-  - 资源组：创建一个新的资源组或使用现有的资源组。  
-  - 名称：为您的静态 Web 应用提供一个名称。  
-  - 区域：选择离您的用户最近的区域。  
-
-- #### 部署详情：  
-  - 来源：选择“GitHub”。  
-  - GitHub 账户：授权 Azure 访问您的 GitHub 账户。  
-  - 组织：选择您的 GitHub 组织。  
-  - 仓库：选择包含静态 Web 应用的仓库。  
-  - 分支：选择您要部署的分支。  
-
-- #### 构建详情：  
-  - 构建预设：选择您的应用所使用的框架（例如 React、Angular、Vue 等）。  
-  - 应用位置：指定包含应用代码的文件夹（例如，如果在根目录则为 `/`）。  
-  - API 位置：如果有 API，请指定其位置（可选）。  
-  - 输出位置：指定生成构建输出的文件夹（例如 build 或 dist）。  
-
-4. 审核并创建  
-审核您的设置并点击“创建”。Azure 将设置必要的资源并在您的仓库中创建一个 GitHub Actions 工作流。
-
-5. GitHub Actions 工作流  
-Azure 会自动在您的仓库中创建一个 GitHub Actions 工作流文件（.github/workflows/azure-static-web-apps-<name>.yml）。此工作流将处理构建和部署过程。
-
-6. 监控部署  
-进入 GitHub 仓库中的“Actions”标签。  
-您应该会看到一个工作流正在运行。此工作流将构建并部署您的静态 Web 应用到 Azure。  
-工作流完成后，您的应用将上线，您可以通过提供的 Azure URL 访问。
-
-### 示例工作流文件
-
-以下是 GitHub Actions 工作流文件的示例：  
-name: Azure Static Web Apps CI/CD  
-```
-on:
-  push:
-    branches:
-      - main
-  pull_request:
-    types: [opened, synchronize, reopened, closed]
-    branches:
-      - main
-
-jobs:
-  build_and_deploy_job:
-    runs-on: ubuntu-latest
-    name: Build and Deploy Job
-    steps:
-      - uses: actions/checkout@v2
-      - name: Build And Deploy
-        id: builddeploy
-        uses: Azure/static-web-apps-deploy@v1
-        with:
-          azure_static_web_apps_api_token: ${{ secrets.AZURE_STATIC_WEB_APPS_API_TOKEN }}
-          repo_token: ${{ secrets.GITHUB_TOKEN }}
-          action: "upload"
-          app_location: "quiz-app" # App source code path
-          api_location: ""API source code path optional
-          output_location: "dist" #Built app content directory - optional
-```
-
-### 其他资源
-- [Azure 静态 Web 应用文档](https://learn.microsoft.com/azure/static-web-apps/getting-started)  
-- [GitHub Actions 文档](https://docs.github.com/actions/use-cases-and-examples/deploying/deploying-to-azure-static-web-app)  
-
-**免责声明**：  
-本文档使用AI翻译服务 [Co-op Translator](https://github.com/Azure/co-op-translator) 进行翻译。尽管我们努力确保翻译的准确性，但请注意，自动翻译可能包含错误或不准确之处。应以原始语言的文档作为权威来源。对于关键信息，建议使用专业人工翻译。我们对因使用此翻译而产生的任何误解或误读不承担责任。
\ No newline at end of file
diff --git a/translations/zh/sketchnotes/README.md b/translations/zh/sketchnotes/README.md
deleted file mode 100644
index aa0f6a22..00000000
--- a/translations/zh/sketchnotes/README.md
+++ /dev/null
@@ -1,19 +0,0 @@
-<!--
-CO_OP_TRANSLATOR_METADATA:
-{
-  "original_hash": "3a848466cb63aff1a93411affb152c2a",
-  "translation_date": "2025-08-25T17:11:21+00:00",
-  "source_file": "sketchnotes/README.md",
-  "language_code": "zh"
-}
--->
-在这里查看所有手绘笔记！
-
-## 致谢
-
-Nitya Narasimhan，艺术家
-
-![路线图手绘笔记](../../../translated_images/zh-CN/00-Roadmap.4905d6567dff47532b9bfb8e0b8980fc6b0b1292eebb24181c1a9753b33bc0f5.png)
-
-**免责声明**：  
-本文档使用AI翻译服务 [Co-op Translator](https://github.com/Azure/co-op-translator) 进行翻译。尽管我们努力确保翻译的准确性，但请注意，自动翻译可能包含错误或不准确之处。应以原始语言的文档作为权威来源。对于重要信息，建议使用专业人工翻译。我们不对因使用此翻译而产生的任何误解或误读承担责任。
\ No newline at end of file