diff --git a/translations/ar/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb b/translations/ar/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb
new file mode 100644
index 00000000..5d5446b5
--- /dev/null
+++ b/translations/ar/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb
@@ -0,0 +1,100 @@
+{
+ "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": [
+    "## 🔍 تحليل ارتباط بيرسون\n",
+    "\n",
+    "يعزز هذا الدفتر وحدة تصور البيانات من خلال توضيح كيفية تحليل العلاقة بين ميزات متعددة باستخدام ارتباط بيرسون.\n",
+    "\n",
+    "يقيس ارتباط بيرسون قوة واتجاه **العلاقة الخطية** بين متغيرين مستمرين. يُرجع قيمة بين -1 و 1:\n",
+    "- **+1** → علاقة إيجابية مثالية  \n",
+    "- **0** → لا توجد علاقة خطية  \n",
+    "- **-1** → علاقة سلبية مثالية\n",
+    "\n",
+    "هنا، قمنا بحساب مصفوفة الارتباط لدرجات الطلاب في الرياضيات، الفيزياء، الكيمياء، وعلم الأحياء. تساعدنا **خريطة الحرارة** الناتجة على فهم مدى قوة العلاقة بين درجات كل مادة والأخرى.\n",
+    "\n",
+    "يعد تحليل الارتباط هذا ضروريًا في **هندسة الميزات ومعالجة البيانات**، خاصة قبل بناء نماذج التعلم الآلي. يساعد في تحديد:\n",
+    "- الميزات الزائدة عن الحاجة\n",
+    "- المؤشرات القوية\n",
+    "- احتمالية التعدد الخطي\n",
+    "\n",
+    "هذا إضافة مفيدة إلى التصورات ذات المغزى عند تحليل مجموعات البيانات الواقعية.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**إخلاء المسؤولية**:  \nتمت ترجمة هذا المستند باستخدام خدمة الترجمة الآلية [Co-op Translator](https://github.com/Azure/co-op-translator). بينما نسعى لتحقيق الدقة، يرجى العلم أن الترجمات الآلية قد تحتوي على أخطاء أو معلومات غير دقيقة. يجب اعتبار المستند الأصلي بلغته الأصلية هو المصدر الموثوق. للحصول على معلومات حساسة أو هامة، يُوصى بالاستعانة بترجمة بشرية احترافية. نحن غير مسؤولين عن أي سوء فهم أو تفسيرات خاطئة تنشأ عن استخدام هذه الترجمة.\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": "2025-09-06T19:37:05+00:00",
+   "source_file": "3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb",
+   "language_code": "ar"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/translations/bg/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb b/translations/bg/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb
new file mode 100644
index 00000000..687b2350
--- /dev/null
+++ b/translations/bg/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb
@@ -0,0 +1,100 @@
+{
+ "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": [
+    "## 🔍 Анализ на Пиърсъновата корелация\n",
+    "\n",
+    "Този ноутбук разширява модула за визуализация на данни, като демонстрира как да се анализира връзката между множество характеристики, използвайки Пиърсъновата корелация.\n",
+    "\n",
+    "Пиърсъновата корелация измерва силата и посоката на **линейната връзка** между две непрекъснати променливи. Тя връща стойност между -1 и 1:\n",
+    "- **+1** → Перфектна положителна връзка  \n",
+    "- **0** → Липса на линейна връзка  \n",
+    "- **-1** → Перфектна отрицателна връзка\n",
+    "\n",
+    "Тук изчислихме корелационната матрица за оценките на учениците по Математика, Физика, Химия и Биология. Получената **топлинна карта** ни помага визуално да разберем колко силно са свързани оценките по различните предмети.\n",
+    "\n",
+    "Такъв анализ на корелацията е от съществено значение при **инженеринг на характеристики и предварителна обработка**, особено преди изграждането на модели за машинно обучение. Той помага да се идентифицират:\n",
+    "- Излишни характеристики\n",
+    "- Силни предиктори\n",
+    "- Потенциална мултиколинеарност\n",
+    "\n",
+    "Това е полезно допълнение към смислените визуализации при анализа на реални набори от данни.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Отказ от отговорност**:  \nТози документ е преведен с помощта на AI услуга за превод [Co-op Translator](https://github.com/Azure/co-op-translator). Въпреки че се стремим към точност, моля, имайте предвид, че автоматичните преводи може да съдържат грешки или неточности. Оригиналният документ на неговия изходен език трябва да се счита за авторитетен източник. За критична информация се препоръчва професионален превод от човек. Ние не носим отговорност за каквито и да е недоразумения или погрешни интерпретации, произтичащи от използването на този превод.\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": "2025-09-06T19:41:10+00:00",
+   "source_file": "3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb",
+   "language_code": "bg"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/translations/bn/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb b/translations/bn/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb
new file mode 100644
index 00000000..c5ab7b5d
--- /dev/null
+++ b/translations/bn/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb
@@ -0,0 +1,100 @@
+{
+ "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": [
+    "## 🔍 পিয়ারসন করেলেশন বিশ্লেষণ\n",
+    "\n",
+    "এই নোটবুকটি ডেটা ভিজ্যুয়ালাইজেশন মডিউলকে উন্নত করে দেখায় কীভাবে পিয়ারসন করেলেশন ব্যবহার করে একাধিক বৈশিষ্ট্যের মধ্যে সম্পর্ক বিশ্লেষণ করা যায়।\n",
+    "\n",
+    "পিয়ারসন করেলেশন দুটি ধারাবাহিক ভেরিয়েবলের মধ্যে **রৈখিক সম্পর্কের** শক্তি এবং দিক নির্ধারণ করে। এটি -1 থেকে 1 এর মধ্যে একটি মান প্রদান করে:\n",
+    "- **+1** → সম্পূর্ণ ইতিবাচক সম্পর্ক  \n",
+    "- **0** → কোনো রৈখিক সম্পর্ক নেই  \n",
+    "- **-1** → সম্পূর্ণ নেতিবাচক সম্পর্ক\n",
+    "\n",
+    "এখানে, আমরা গণনা করেছি ছাত্রদের গণিত, পদার্থবিজ্ঞান, রসায়ন এবং জীববিজ্ঞানের স্কোরের জন্য করেলেশন ম্যাট্রিক্স। প্রাপ্ত **হিটম্যাপ** আমাদের ভিজ্যুয়ালভাবে বুঝতে সাহায্য করে প্রতিটি বিষয়ের স্কোর একে অপরের সাথে কতটা শক্তিশালীভাবে সম্পর্কিত।\n",
+    "\n",
+    "এ ধরনের করেলেশন বিশ্লেষণ **ফিচার ইঞ্জিনিয়ারিং এবং প্রিপ্রসেসিং**-এ অত্যন্ত গুরুত্বপূর্ণ, বিশেষত মেশিন লার্নিং মডেল তৈরির আগে। এটি সাহায্য করে:\n",
+    "- অপ্রয়োজনীয় বৈশিষ্ট্য চিহ্নিত করতে  \n",
+    "- শক্তিশালী পূর্বাভাসকারী খুঁজে পেতে  \n",
+    "- সম্ভাব্য মাল্টিকলিনিয়ারিটি শনাক্ত করতে  \n",
+    "\n",
+    "বাস্তব-জগতের ডেটাসেট বিশ্লেষণের সময় অর্থবহ ভিজ্যুয়ালাইজেশনের জন্য এটি একটি কার্যকর সংযোজন।\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**অস্বীকৃতি**:  \nএই নথিটি AI অনুবাদ পরিষেবা [Co-op Translator](https://github.com/Azure/co-op-translator) ব্যবহার করে অনুবাদ করা হয়েছে। আমরা যথাসম্ভব সঠিক অনুবাদের চেষ্টা করি, তবে অনুগ্রহ করে মনে রাখবেন যে স্বয়ংক্রিয় অনুবাদে ত্রুটি বা অসঙ্গতি থাকতে পারে। নথিটির মূল ভাষায় লেখা সংস্করণটিকেই প্রামাণিক উৎস হিসেবে বিবেচনা করা উচিত। গুরুত্বপূর্ণ তথ্যের জন্য, পেশাদার মানব অনুবাদ ব্যবহার করার পরামর্শ দেওয়া হচ্ছে। এই অনুবাদ ব্যবহারের ফলে সৃষ্ট কোনো ভুল বোঝাবুঝি বা ভুল ব্যাখ্যার জন্য আমরা দায়ী নই।\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": "2025-09-06T19:38:09+00:00",
+   "source_file": "3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb",
+   "language_code": "bn"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/translations/br/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb b/translations/br/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb
new file mode 100644
index 00000000..d330d88e
--- /dev/null
+++ b/translations/br/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb
@@ -0,0 +1,100 @@
+{
+ "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": [
+    "## 🔍 Análise de Correlação de Pearson\n",
+    "\n",
+    "Este notebook aprimora o módulo de visualização de dados ao demonstrar como analisar a relação entre múltiplas variáveis usando a correlação de Pearson.\n",
+    "\n",
+    "A correlação de Pearson mede a força e a direção de uma **relação linear** entre duas variáveis contínuas. Ela retorna um valor entre -1 e 1:\n",
+    "- **+1** → Relação positiva perfeita  \n",
+    "- **0** → Nenhuma relação linear  \n",
+    "- **-1** → Relação negativa perfeita\n",
+    "\n",
+    "Aqui, calculamos a matriz de correlação para as notas dos alunos em Matemática, Física, Química e Biologia. O **heatmap** resultante nos ajuda a entender visualmente quão fortemente a nota de cada matéria está relacionada às outras.\n",
+    "\n",
+    "Esse tipo de análise de correlação é essencial na **engenharia de características e pré-processamento**, especialmente antes de construir modelos de aprendizado de máquina. Ele ajuda a identificar:\n",
+    "- Características redundantes\n",
+    "- Preditores fortes\n",
+    "- Potencial multicolinearidade\n",
+    "\n",
+    "É uma adição útil para visualizações significativas ao analisar conjuntos de dados do mundo real.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Aviso Legal**:  \nEste documento foi traduzido utilizando o serviço de tradução por IA [Co-op Translator](https://github.com/Azure/co-op-translator). Embora nos esforcemos para garantir a precisão, esteja ciente de que traduções automatizadas podem conter erros ou imprecisões. O documento original em seu idioma nativo deve ser considerado a fonte autoritativa. Para informações críticas, recomenda-se a tradução profissional realizada por humanos. Não nos responsabilizamos por quaisquer mal-entendidos ou interpretações equivocadas decorrentes do uso desta tradução.\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": "2025-09-06T19:38:46+00:00",
+   "source_file": "3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb",
+   "language_code": "br"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/translations/cs/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb b/translations/cs/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb
new file mode 100644
index 00000000..e29d1514
--- /dev/null
+++ b/translations/cs/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb
@@ -0,0 +1,100 @@
+{
+ "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": [
+    "## 🔍 Analýza Pearsonovy korelace\n",
+    "\n",
+    "Tento notebook rozšiřuje modul vizualizace dat tím, že ukazuje, jak analyzovat vztah mezi více vlastnostmi pomocí Pearsonovy korelace.\n",
+    "\n",
+    "Pearsonova korelace měří sílu a směr **lineárního vztahu** mezi dvěma spojitými proměnnými. Vrací hodnotu mezi -1 a 1:\n",
+    "- **+1** → Dokonalý pozitivní vztah  \n",
+    "- **0** → Žádný lineární vztah  \n",
+    "- **-1** → Dokonalý negativní vztah\n",
+    "\n",
+    "Zde jsme vypočítali korelační matici pro výsledky studentů v matematice, fyzice, chemii a biologii. Výsledná **heatmapa** nám pomáhá vizuálně pochopit, jak silně spolu jednotlivé výsledky z předmětů souvisí.\n",
+    "\n",
+    "Taková analýza korelace je klíčová při **tvorbě vlastností a předzpracování dat**, zejména před sestavením modelů strojového učení. Pomáhá identifikovat:\n",
+    "- Redundantní vlastnosti\n",
+    "- Silné prediktory\n",
+    "- Potenciální multikolinearitu\n",
+    "\n",
+    "Jedná se o užitečný doplněk k smysluplným vizualizacím při analýze reálných datových sad.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Prohlášení**:  \nTento dokument byl přeložen pomocí služby pro automatický překlad [Co-op Translator](https://github.com/Azure/co-op-translator). I když se snažíme o co největší přesnost, mějte prosím na paměti, že automatické překlady mohou obsahovat chyby nebo nepřesnosti. Za autoritativní zdroj by měl být považován původní dokument v jeho původním jazyce. Pro důležité informace doporučujeme profesionální lidský překlad. Neodpovídáme za žádná nedorozumění nebo nesprávné výklady vyplývající z použití tohoto překladu.\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": "2025-09-06T19:40:44+00:00",
+   "source_file": "3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb",
+   "language_code": "cs"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/translations/da/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb b/translations/da/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb
new file mode 100644
index 00000000..b5362d97
--- /dev/null
+++ b/translations/da/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb
@@ -0,0 +1,100 @@
+{
+ "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": [
+    "## 🔍 Pearson Korrelationsanalyse\n",
+    "\n",
+    "Denne notebook forbedrer datavisualiseringsmodulet ved at demonstrere, hvordan man analyserer forholdet mellem flere egenskaber ved hjælp af Pearson-korrelation.\n",
+    "\n",
+    "Pearson-korrelation måler styrken og retningen af et **lineært forhold** mellem to kontinuerlige variabler. Den returnerer en værdi mellem -1 og 1:\n",
+    "- **+1** → Perfekt positivt forhold  \n",
+    "- **0** → Intet lineært forhold  \n",
+    "- **-1** → Perfekt negativt forhold\n",
+    "\n",
+    "Her har vi beregnet korrelationsmatricen for elevresultater i Matematik, Fysik, Kemi og Biologi. Den resulterende **heatmap** hjælper os med visuelt at forstå, hvor stærkt hver fagkarakter er relateret til de andre.\n",
+    "\n",
+    "En sådan korrelationsanalyse er afgørende i **feature engineering og forbehandling**, især før opbygning af maskinlæringsmodeller. Den hjælper med at identificere:\n",
+    "- Redundante egenskaber\n",
+    "- Stærke prediktorer\n",
+    "- Potentiel multikollinearitet\n",
+    "\n",
+    "Dette er en nyttig tilføjelse til meningsfulde visualiseringer, når man analyserer virkelige datasæt.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Ansvarsfraskrivelse**:  \nDette dokument er blevet oversat ved hjælp af AI-oversættelsestjenesten [Co-op Translator](https://github.com/Azure/co-op-translator). Selvom vi bestræber os på nøjagtighed, skal du være opmærksom på, at automatiserede oversættelser kan indeholde fejl eller unøjagtigheder. Det originale dokument på dets oprindelige sprog bør betragtes som den autoritative kilde. For kritisk information anbefales professionel menneskelig oversættelse. Vi er ikke ansvarlige for eventuelle misforståelser eller fejltolkninger, der måtte opstå som følge af brugen af denne oversættelse.\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": "2025-09-06T19:39:32+00:00",
+   "source_file": "3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb",
+   "language_code": "da"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/translations/de/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb b/translations/de/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb
new file mode 100644
index 00000000..4ca9ab0e
--- /dev/null
+++ b/translations/de/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb
@@ -0,0 +1,100 @@
+{
+ "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": [
+    "## 🔍 Pearson-Korrelationsanalyse\n",
+    "\n",
+    "Dieses Notebook erweitert das Modul zur Datenvisualisierung, indem demonstriert wird, wie die Beziehung zwischen mehreren Merkmalen mithilfe der Pearson-Korrelation analysiert werden kann.\n",
+    "\n",
+    "Die Pearson-Korrelation misst die Stärke und Richtung einer **linearen Beziehung** zwischen zwei kontinuierlichen Variablen. Sie liefert einen Wert zwischen -1 und 1:\n",
+    "- **+1** → Perfekte positive Beziehung  \n",
+    "- **0** → Keine lineare Beziehung  \n",
+    "- **-1** → Perfekte negative Beziehung\n",
+    "\n",
+    "Hier haben wir die Korrelationsmatrix für Schülernoten in Mathematik, Physik, Chemie und Biologie berechnet. Die resultierende **Heatmap** hilft uns, visuell zu verstehen, wie stark die Noten in den einzelnen Fächern miteinander zusammenhängen.\n",
+    "\n",
+    "Eine solche Korrelationsanalyse ist entscheidend für die **Merkmalsauswahl und Vorverarbeitung**, insbesondere vor dem Aufbau von Machine-Learning-Modellen. Sie hilft dabei:\n",
+    "- Redundante Merkmale zu identifizieren\n",
+    "- Starke Prädiktoren zu erkennen\n",
+    "- Potenzielle Multikollinearität aufzudecken\n",
+    "\n",
+    "Dies ist eine nützliche Ergänzung zu aussagekräftigen Visualisierungen bei der Analyse von realen Datensätzen.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Haftungsausschluss**:  \nDieses Dokument wurde mit dem KI-Übersetzungsdienst [Co-op Translator](https://github.com/Azure/co-op-translator) übersetzt. Obwohl wir uns um Genauigkeit bemühen, weisen wir darauf hin, dass automatisierte Übersetzungen Fehler oder Ungenauigkeiten enthalten können. Das Originaldokument in seiner ursprünglichen Sprache sollte als maßgebliche Quelle betrachtet werden. Für kritische Informationen wird eine professionelle menschliche Übersetzung empfohlen. Wir übernehmen keine Haftung für Missverständnisse oder Fehlinterpretationen, die aus der Nutzung dieser Übersetzung entstehen.\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": "2025-09-06T19:36:52+00:00",
+   "source_file": "3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb",
+   "language_code": "de"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/translations/el/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb b/translations/el/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb
new file mode 100644
index 00000000..8dfeebe8
--- /dev/null
+++ b/translations/el/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb
@@ -0,0 +1,100 @@
+{
+ "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": [
+    "## 🔍 Ανάλυση Συσχέτισης Pearson\n",
+    "\n",
+    "Αυτό το σημειωματάριο ενισχύει τη μονάδα οπτικοποίησης δεδομένων, δείχνοντας πώς να αναλύσετε τη σχέση μεταξύ πολλαπλών χαρακτηριστικών χρησιμοποιώντας τη συσχέτιση Pearson.\n",
+    "\n",
+    "Η συσχέτιση Pearson μετρά τη δύναμη και την κατεύθυνση μιας **γραμμικής σχέσης** μεταξύ δύο συνεχών μεταβλητών. Επιστρέφει μια τιμή μεταξύ -1 και 1:\n",
+    "- **+1** → Τέλεια θετική σχέση  \n",
+    "- **0** → Καμία γραμμική σχέση  \n",
+    "- **-1** → Τέλεια αρνητική σχέση\n",
+    "\n",
+    "Εδώ, υπολογίσαμε τον πίνακα συσχέτισης για τις βαθμολογίες μαθητών στα Μαθηματικά, τη Φυσική, τη Χημεία και τη Βιολογία. Το προκύπτον **heatmap** μας βοηθά να κατανοήσουμε οπτικά πόσο ισχυρά σχετίζεται η βαθμολογία κάθε μαθήματος με τα υπόλοιπα.\n",
+    "\n",
+    "Μια τέτοια ανάλυση συσχέτισης είναι κρίσιμη στη **μηχανική χαρακτηριστικών και την προεπεξεργασία**, ειδικά πριν από την κατασκευή μοντέλων μηχανικής μάθησης. Βοηθά στον εντοπισμό:\n",
+    "- Πλεονασματικών χαρακτηριστικών\n",
+    "- Ισχυρών προβλεπτικών παραγόντων\n",
+    "- Πιθανής πολυσυγγραμμικότητας\n",
+    "\n",
+    "Αυτό αποτελεί μια χρήσιμη προσθήκη σε ουσιαστικές οπτικοποιήσεις κατά την ανάλυση συνόλων δεδομένων από τον πραγματικό κόσμο.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Αποποίηση ευθύνης**:  \nΑυτό το έγγραφο έχει μεταφραστεί χρησιμοποιώντας την υπηρεσία αυτόματης μετάφρασης [Co-op Translator](https://github.com/Azure/co-op-translator). Παρόλο που καταβάλλουμε προσπάθειες για ακρίβεια, παρακαλούμε να έχετε υπόψη ότι οι αυτόματες μεταφράσεις ενδέχεται να περιέχουν σφάλματα ή ανακρίβειες. Το πρωτότυπο έγγραφο στη μητρική του γλώσσα θα πρέπει να θεωρείται η αυθεντική πηγή. Για κρίσιμες πληροφορίες, συνιστάται επαγγελματική ανθρώπινη μετάφραση. Δεν φέρουμε ευθύνη για τυχόν παρεξηγήσεις ή εσφαλμένες ερμηνείες που προκύπτουν από τη χρήση αυτής της μετάφρασης.\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": "2025-09-06T19:39:13+00:00",
+   "source_file": "3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb",
+   "language_code": "el"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/translations/en/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb b/translations/en/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb
new file mode 100644
index 00000000..11c4850a
--- /dev/null
+++ b/translations/en/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb
@@ -0,0 +1,100 @@
+{
+ "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": [
+    "## 🔍 Pearson Correlation Analysis\n",
+    "\n",
+    "This notebook expands the data visualization module by showcasing how to examine the relationship between multiple features using Pearson correlation.\n",
+    "\n",
+    "Pearson correlation quantifies the strength and direction of a **linear relationship** between two continuous variables. It produces a value ranging from -1 to 1:\n",
+    "- **+1** → Perfect positive relationship  \n",
+    "- **0** → No linear relationship  \n",
+    "- **-1** → Perfect negative relationship\n",
+    "\n",
+    "In this example, we computed the correlation matrix for student scores in Math, Physics, Chemistry, and Biology. The resulting **heatmap** provides a visual representation of how strongly the scores in each subject are related to one another.\n",
+    "\n",
+    "Such correlation analysis is essential in **feature engineering and preprocessing**, particularly before developing machine learning models. It helps to identify:\n",
+    "- Redundant features\n",
+    "- Strong predictors\n",
+    "- Potential multicollinearity\n",
+    "\n",
+    "This serves as a valuable addition to insightful visualizations when working with real-world datasets.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Disclaimer**:  \nThis document has been translated using the AI translation service [Co-op Translator](https://github.com/Azure/co-op-translator). While we strive for accuracy, please note that automated translations may contain errors or inaccuracies. The original document in its native language should be regarded as the authoritative source. For critical information, professional human translation is recommended. We are not responsible for any misunderstandings or misinterpretations resulting from the use of this translation.\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": "2025-09-06T19:36:32+00:00",
+   "source_file": "3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb",
+   "language_code": "en"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/translations/es/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb b/translations/es/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb
new file mode 100644
index 00000000..fe8334fa
--- /dev/null
+++ b/translations/es/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb
@@ -0,0 +1,100 @@
+{
+ "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": [
+    "## 🔍 Análisis de Correlación de Pearson\n",
+    "\n",
+    "Este cuaderno mejora el módulo de visualización de datos al demostrar cómo analizar la relación entre múltiples características utilizando la correlación de Pearson.\n",
+    "\n",
+    "La correlación de Pearson mide la fuerza y dirección de una **relación lineal** entre dos variables continuas. Devuelve un valor entre -1 y 1:\n",
+    "- **+1** → Relación positiva perfecta  \n",
+    "- **0** → Sin relación lineal  \n",
+    "- **-1** → Relación negativa perfecta\n",
+    "\n",
+    "Aquí, calculamos la matriz de correlación para las calificaciones de los estudiantes en Matemáticas, Física, Química y Biología. El **mapa de calor** resultante nos ayuda a comprender visualmente qué tan fuertemente está relacionado el puntaje de cada materia con los demás.\n",
+    "\n",
+    "Este tipo de análisis de correlación es fundamental en **ingeniería de características y preprocesamiento**, especialmente antes de construir modelos de aprendizaje automático. Ayuda a identificar:\n",
+    "- Características redundantes\n",
+    "- Predictores fuertes\n",
+    "- Posible multicolinealidad\n",
+    "\n",
+    "Es una adición útil para visualizaciones significativas al analizar conjuntos de datos del mundo real.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Descargo de responsabilidad**:  \nEste documento ha sido traducido utilizando el servicio de traducción automática [Co-op Translator](https://github.com/Azure/co-op-translator). Si bien nos esforzamos por lograr precisión, tenga en cuenta que las traducciones automáticas pueden contener errores o imprecisiones. El documento original en su idioma nativo debe considerarse como la fuente autorizada. Para información crítica, se recomienda una traducción profesional realizada por humanos. No nos hacemos responsables de malentendidos o interpretaciones erróneas que puedan surgir del uso de esta traducción.\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": "2025-09-06T19:36:45+00:00",
+   "source_file": "3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb",
+   "language_code": "es"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/translations/fa/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb b/translations/fa/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb
new file mode 100644
index 00000000..aa8d5556
--- /dev/null
+++ b/translations/fa/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb
@@ -0,0 +1,100 @@
+{
+ "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": [
+    "## 🔍 تحلیل همبستگی پیرسون\n",
+    "\n",
+    "این نوت‌بوک ماژول بصری‌سازی داده را با نشان دادن چگونگی تحلیل رابطه بین چندین ویژگی با استفاده از همبستگی پیرسون بهبود می‌بخشد.\n",
+    "\n",
+    "همبستگی پیرسون قدرت و جهت یک **رابطه خطی** بین دو متغیر پیوسته را اندازه‌گیری می‌کند. این مقدار عددی بین -1 و 1 بازمی‌گرداند:\n",
+    "- **+1** → رابطه مثبت کامل  \n",
+    "- **0** → عدم وجود رابطه خطی  \n",
+    "- **-1** → رابطه منفی کامل\n",
+    "\n",
+    "در اینجا، ماتریس همبستگی برای نمرات دانش‌آموزان در ریاضی، فیزیک، شیمی و زیست‌شناسی محاسبه شده است. **نقشه حرارتی** حاصل به ما کمک می‌کند تا به صورت بصری درک کنیم که نمرات هر درس تا چه حد با یکدیگر مرتبط هستند.\n",
+    "\n",
+    "چنین تحلیل همبستگی در **مهندسی ویژگی و پیش‌پردازش داده‌ها** بسیار مهم است، به‌ویژه قبل از ساخت مدل‌های یادگیری ماشین. این تحلیل به شناسایی موارد زیر کمک می‌کند:\n",
+    "- ویژگی‌های زائد  \n",
+    "- پیش‌بینی‌کننده‌های قوی  \n",
+    "- احتمال وجود هم‌خطی چندگانه  \n",
+    "\n",
+    "این یک افزودنی مفید به بصری‌سازی‌های معنادار هنگام تحلیل مجموعه داده‌های واقعی است.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**سلب مسئولیت**:  \nاین 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 شامل خطاها یا نادقتی‌ها باشند. سند اصلی به زبان اصلی آن باید به عنوان منبع معتبر در نظر گرفته شود. برای اطلاعات حساس، ترجمه حرفه‌ای انسانی توصیه می‌شود. ما هیچ مسئولیتی در قبال سوءتفاهم‌ها یا تفسیرهای نادرست ناشی از استفاده از این ترجمه نداریم.\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": "2025-09-06T19:37:11+00:00",
+   "source_file": "3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb",
+   "language_code": "fa"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/translations/fi/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb b/translations/fi/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb
new file mode 100644
index 00000000..925a2ce7
--- /dev/null
+++ b/translations/fi/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb
@@ -0,0 +1,100 @@
+{
+ "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": [
+    "## 🔍 Pearson-korrelaatioanalyysi\n",
+    "\n",
+    "Tämä muistikirja parantaa datan visualisointimoduulia esittelemällä, kuinka analysoida useiden ominaisuuksien välistä suhdetta Pearson-korrelaation avulla.\n",
+    "\n",
+    "Pearson-korrelaatio mittaa kahden jatkuvan muuttujan välisen **lineaarisen suhteen** voimakkuutta ja suuntaa. Se palauttaa arvon välillä -1 ja 1:\n",
+    "- **+1** → Täydellinen positiivinen suhde  \n",
+    "- **0** → Ei lineaarista suhdetta  \n",
+    "- **-1** → Täydellinen negatiivinen suhde\n",
+    "\n",
+    "Tässä laskimme korrelaatiomatriisin opiskelijoiden pisteille matematiikassa, fysiikassa, kemiassa ja biologiassa. Tuloksena oleva **lämpökartta** auttaa meitä visuaalisesti ymmärtämään, kuinka vahvasti kunkin aineen pisteet liittyvät toisiinsa.\n",
+    "\n",
+    "Tällainen korrelaatioanalyysi on tärkeää **ominaisuuksien suunnittelussa ja esikäsittelyssä**, erityisesti ennen koneoppimismallien rakentamista. Se auttaa tunnistamaan:\n",
+    "- Päällekkäiset ominaisuudet\n",
+    "- Vahvat ennustajat\n",
+    "- Mahdollisen multikollineaarisuuden\n",
+    "\n",
+    "Tämä on hyödyllinen lisä merkityksellisiin visualisointeihin, kun analysoidaan todellisia tietoaineistoja.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Vastuuvapauslauseke**:  \nTämä asiakirja on käännetty käyttämällä tekoälypohjaista käännöspalvelua [Co-op Translator](https://github.com/Azure/co-op-translator). Vaikka pyrimme tarkkuuteen, huomioithan, että automaattiset käännökset voivat sisältää virheitä tai epätarkkuuksia. Alkuperäistä asiakirjaa sen alkuperäisellä kielellä tulee pitää ensisijaisena lähteenä. Kriittisen tiedon osalta suositellaan ammattimaista ihmiskääntämistä. Emme ole vastuussa väärinkäsityksistä tai virhetulkinnoista, jotka johtuvat tämän käännöksen käytöstä.\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": "2025-09-06T19:39:46+00:00",
+   "source_file": "3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb",
+   "language_code": "fi"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/translations/fr/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb b/translations/fr/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb
new file mode 100644
index 00000000..534db0a6
--- /dev/null
+++ b/translations/fr/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb
@@ -0,0 +1,100 @@
+{
+ "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": [
+    "## 🔍 Analyse de la Corrélation de Pearson\n",
+    "\n",
+    "Ce notebook enrichit le module de visualisation des données en montrant comment analyser la relation entre plusieurs caractéristiques à l'aide de la corrélation de Pearson.\n",
+    "\n",
+    "La corrélation de Pearson mesure la force et la direction d'une **relation linéaire** entre deux variables continues. Elle renvoie une valeur comprise entre -1 et 1 :\n",
+    "- **+1** → Relation positive parfaite  \n",
+    "- **0** → Aucune relation linéaire  \n",
+    "- **-1** → Relation négative parfaite\n",
+    "\n",
+    "Ici, nous avons calculé la matrice de corrélation pour les notes des étudiants en Mathématiques, Physique, Chimie et Biologie. La **carte thermique** obtenue nous aide à comprendre visuellement la force de la relation entre les notes de chaque matière.\n",
+    "\n",
+    "Une telle analyse de corrélation est essentielle dans le cadre de **l'ingénierie des caractéristiques et du prétraitement**, en particulier avant de construire des modèles d'apprentissage automatique. Elle permet d'identifier :\n",
+    "- Les caractéristiques redondantes  \n",
+    "- Les prédicteurs forts  \n",
+    "- Les éventuelles multicolinéarités  \n",
+    "\n",
+    "C'est un ajout précieux pour des visualisations significatives lors de l'analyse de jeux de données réels.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Avertissement** :  \nCe document a été traduit à l'aide du service de traduction automatique [Co-op Translator](https://github.com/Azure/co-op-translator). Bien que nous nous efforcions d'assurer l'exactitude, veuillez noter que les traductions automatisées peuvent contenir des erreurs ou des inexactitudes. Le document original dans sa langue d'origine doit être considéré comme la source faisant autorité. Pour des informations critiques, il est recommandé de faire appel à une traduction humaine professionnelle. Nous déclinons toute responsabilité en cas de malentendus ou d'interprétations erronées résultant de l'utilisation de cette traduction.\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": "2025-09-06T19:36:39+00:00",
+   "source_file": "3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb",
+   "language_code": "fr"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/translations/he/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb b/translations/he/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb
new file mode 100644
index 00000000..1f45c07c
--- /dev/null
+++ b/translations/he/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb
@@ -0,0 +1,100 @@
+{
+ "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": [
+    "## 🔍 ניתוח מתאם פירסון\n",
+    "\n",
+    "מחברת זו משפרת את מודול הוויזואליזציה של הנתונים על ידי הדגמת האופן שבו ניתן לנתח את הקשר בין תכונות שונות באמצעות מתאם פירסון.\n",
+    "\n",
+    "מתאם פירסון מודד את העוצמה והכיוון של **קשר לינארי** בין שני משתנים רציפים. הוא מחזיר ערך בטווח שבין -1 ל-1:\n",
+    "- **+1** → קשר חיובי מושלם  \n",
+    "- **0** → אין קשר לינארי  \n",
+    "- **-1** → קשר שלילי מושלם  \n",
+    "\n",
+    "כאן חישבנו את מטריצת המתאם עבור ציוני תלמידים במתמטיקה, פיזיקה, כימיה וביולוגיה. ה-**heatmap** שנוצר מסייע לנו להבין באופן חזותי עד כמה הציון בכל מקצוע קשור לציונים במקצועות האחרים.\n",
+    "\n",
+    "ניתוח מתאם מסוג זה הוא קריטי ב-**הנדסת תכונות ועיבוד מקדים**, במיוחד לפני בניית מודלים של למידת מכונה. הוא מסייע בזיהוי:\n",
+    "- תכונות מיותרות  \n",
+    "- מנבאים חזקים  \n",
+    "- פוטנציאל לקיום רב-קולינאריות  \n",
+    "\n",
+    "זהו תוספת מועילה ליצירת ויזואליזציות משמעותיות בעת ניתוח מערכי נתונים מהעולם האמיתי.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**כתב ויתור**:  \nמסמך זה תורגם באמצעות שירות תרגום מבוסס בינה מלאכותית [Co-op Translator](https://github.com/Azure/co-op-translator). למרות שאנו שואפים לדיוק, יש לקחת בחשבון שתרגומים אוטומטיים עשויים להכיל שגיאות או אי-דיוקים. המסמך המקורי בשפתו המקורית נחשב למקור הסמכותי. למידע קריטי, מומלץ להשתמש בתרגום מקצועי על ידי בני אדם. איננו נושאים באחריות לכל אי-הבנה או פרשנות שגויה הנובעת משימוש בתרגום זה.  \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": "2025-09-06T19:39:59+00:00",
+   "source_file": "3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb",
+   "language_code": "he"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/translations/hi/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb b/translations/hi/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb
new file mode 100644
index 00000000..f548b293
--- /dev/null
+++ b/translations/hi/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb
@@ -0,0 +1,100 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b1e38707",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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XcxYBAADgFMEiAAAAnCJYBAAAgFMEiwAAAPfh119/NdUMChQoYEpmOSpflpDWX61evbqp5KCVDfTymwnp1ZW0ekNAQIDUqVPHVNJIWNmgf//+plqElgHTKhxaw9eWXqlMS7RpwX2tnqDVKu5WvswRgkUAAID7EB4ebsqkaXCXFFrKTQM4LZ22fft2U++0V69edrVptS6sXk5W67Ru3brVnF8vy2l7udSBAweaklfffPONqZF6+vRpUzrNQuvT6uNonV29YISWbNOgVC9zmxyshgYAAEglXl5epqav1tF1Rq+Y9OOPP5oLIVjoJU21Fu+yZcvMvmYSa9WqZYrlK70ClV4KVC+Jqldb0vqywcHB5vrzTzzxhPWqVHqlMK1P+9BDD5nL3mrtWg0i8+XLZ9ro1cX08S9cuGDquyYFmUUAAIAE9CpcepWu6zab5cpc90uDuYSXQ9WsoR5XmgnUCwjYttErg+m+pY3eHh0dbddGLzahlwy1tNF/9apilkDR8jj6XHbv3u15V3DJXKRzWncBbtR+bt+07gLc6MT1O5ezQ8Zwdnria47jwXVwRc8HMnZ4tUcZc2UjWzokfL9XuFJ6+VHbAE7pvgZxejUtvZKYDiE7amO5pr2eQzODOXLkSNRGb7vb41hu87hgEQAAIL3QS8bqnEFbuhglIyJYBAAAHsnLy3Wz6TQwdFVwGBISkmjVsu4HBQVJ5syZzXXvdXPURu9rOYcOV+s8R9vsYsI2CVdQW85paZMUzFkEAABwo7p168qqVavsjq1cudIcVzq8XKNGDbs2usBF9y1t9HZfX1+7Nvv37zelcixt9N9du3bZraDWx9GgtHz58knuL5lFAADgkbzSSc7r5s2bcujQIbvSOFoSJ1euXGbBiQ5pnzp1SubOnWtu79u3r1nlPHToUOnRo4esXr1avv76a7NC2kKHwLt16yY1a9aU2rVry5QpU0yJnu7du5vbs2fPLj179jTt9HE0ANSV0hog6kpo1bx5cxMUPvvsszJhwgQzT3HkyJGmNmNysqYEiwAAAPfhr7/+MjUTLSxzHTXY07qGZ86cMRk/i2LFipnAUOskTp06VQoVKiSffvqpWals0bFjR1PeRmsiapBXtWpVU1bHdsHK+++/b1ZJazFuXamt9//www+tt+tQ9g8//CD9+vUzQWRgYKDp07hx4zyzziKroTMWVkNnLKyGzlhYDZ2xpOVq6Kyh3Vx27pvH5rjs3J6GzCIAAPBIrlzggn/wKgMAAMApMosAAMBjL60H1yOzCAAAAKfILAIAAA9FzssdeJUBAADgFJlFAADgkVgN7R68ygAAAHCKzCIAAPBIZBbdg1cZAAAATpFZBAAAHsmLnJdbECwCAACPxDC0e/AqAwAAwCkyiwAAwCORWXQPXmUAAAA4RWYRAAB4JDKL7sGrDAAAAKfILAIAAI/kJV5p3YUMgcwiAAAAnCKzCAAAPBJzFt2DYBEAAHgkgkX34FUGAACAU2QWAQCARyKz6B68ygAAAHCKzCIAAPBQ5LzcgVcZAAAATpFZBAAAHok5i+7BqwwAAACnyCwCAACPRGbRPQgWAQCAR/JigNQteJUBAADgFJlFAADgkRiGdg9eZQAAADhFZhEAAHgkLy+vtO5ChkBmEQAAAE6RWQQAAB6JOYvuwasMAAAAp8gsAgAAj0SdxXQcLMbGxsrnn38uq1atkvPnz0tcXJzd7atXr06t/gEAADjEMHQ6Dhb/85//mGCxdevWUrFiRVYjAQAAPKBSFCx+9dVX8vXXX0urVq1Sv0cAAABJQGbRPVL0Kvv5+UnJkiVTvzcAAADw/GBx8ODBMnXqVImPj0/9HgEAACRxgYurNqRgGPrxxx9PtIjl559/lgoVKoivr6/dbYsXL07qaQEAAJCOJTl0zp49u93Wvn17adSokeTJkyfRbQAAAC6ncxZdtSXTjBkzJDQ0VAICAqROnTqyadMmp22jo6Nl3LhxUqJECdO+SpUqsmzZMrs2N27ckJdfflmKFi0qmTNnlnr16snmzZvtn76Xl8Ptvffes7bRPiW8/Z133nFNZnH27NnJOjGcq1+7rAzs+6hUr1Rc8ufLKU/1miTfr/grrbuFZDq/Zo2cW7lCoq9dk8yFCkmRTp0lsFgxh23jY2PkzM/L5NKGPyT66lUJCAmRgu0fl+wVK1rbxEZEyOmlS+Xq9m0SfeOGZClcWAp37CSBoaFufFZwpn1ofulcsqDk8veTw9fDZcquw7L36k2HbX28vOTZUoWkZeG8kifAX07cvC0f7Tkqmy5ctbbJ7OMjvcoWkYb5c0tOf185cC1cPvj7iOxzck6kT7UqhUivJytJhVK5JV/uQOk35hf55Y/jad0tuNnChQtl0KBBMnPmTBMoTpkyRVq0aCH79++XvHnzJmo/cuRImT9/vnzyySdStmxZWb58uUnC/fHHH1KtWjXTplevXvL333/LvHnzpECBAqZ9s2bNZM+ePVKwYEHT5syZM3bn1RHfnj17SocOHeyOa2Dau3dv6362bNmS9fxSNCj/r3/9S65e/eeXnsX169fNbbi7wCz+smtPmLw8clZadwUpdHnzZjn57TeSv/WjUm7ESMlSqLAc/GCqRF+/7rD9qSVL5eJvv5qAssKYsRLcsKEcnvmR3AoLs7Y5PneuXN+7R0K795Dyo0ZLUPnycuD9yRJ15Yobnxkc+VeBPPJihWLy+f4w6bVumxy6Fi6THqooOfzsp+BY9C5bVNoWDZEpu47Is2u2yNLjZ+St2uWkVFCgtc2rVUtKreAc8sbWA9Jt7TbZfOGqvF+3ouQJ8HPjM8P9yhyQSfYduSxjp29I665k2NXQrtqSY/LkySYY6969u5QvX94EjVmyZJFZsxx/zmsA+Nprr5mqMsWLF5d+/fqZrydNmmRuv337tixatEgmTJggDRs2NIuKx4wZY/796KOPrOcJCQmx25YuXSpNmjQx57SlwaFtu8DAf34XuSxYXLt2rURFRSU6HhERIb/99ltKTpmhrFi7Q8ZO/Fq+W0420VOd+2Wl5GnQQPLUry+ZCxSQIl26iLefn1z643eH7S9v/FNCWj4i2StVEv/gYAlu1NhkFc+tXGluj4uKkivbtkqhDh0kW+nSEpA3rxRo09b8e2HdOjc/OyTUsURB+T7srPx04rwcu3lbJu48JBGxsdK6SD6H7VsUDpZ5B0/Kn+evyJlbkbLk2FnZcO6KdCp5Jxvg5+0tjfLnkY/2HJMdl6/LqfAImb0/zPz7WGiIm58d7sevm0/K+59vkZW/k01MC86GYVNjS6qoqCjZsmWLyfpZeHt7m/0NGxz/EREZGWmGn23pUPP69evN1zExMeYCKHdrk9C5c+fkxx9/NJnFhHTYOXfu3CZrqUPUen6X1VncuXOn9WtNg549e9a6r09Kx9stqVHgQRUXE2MygvkfecR6zMvbW7KVLSc3jxxxeh/vBAvBvH395ObhQ+breL0KUlyceGWyb+Pl62ttg7SRyctLSmfPKvMPnrAe0zoQf128KhVyOh7K8fX2lqgEV7bS/Uq5gqzD1Jm8vRK1iYyNlcq5mPcNpAca0Olmy9/f32y2Ll68aGKgfPns/3jU/X379okjOkSt2UjNGuq8Rb0ini4O1vNYMoF169aV8ePHS7ly5cy5vvzySxN8OitdOGfOHHO/hAuSX3rpJalevbrkypXLDHMPHz7cDF/r47skWKxatao14nY03KwR77Rp05JzSsDjxNy8aQK7TNnufPBb+AZlk4iz9vNHLILKVzDZyKylSpnM4o19+0wmUf6//JRPQIAEFi8uZ376UQLy5xffoCC5vGmThB85Iv4O5rvAfbL7+ZrA7nJktN3xK5HRUjRrFof32XT+inQsXkB2XLpmsoU1gnNIw5Dc4v3/2YrbsbGy6/J16Va6iBy7sV+uREZJs0LBUiFXkJwKv+2W5wU8CFxZ4ubtt9+WsWPH2h0bPXq0GQ6+X1p+UIetdb6ixlQaMOoQtu2wtQ5V9+jRwyThfHx8TMDXuXNnk8V0RO/bpUuXRNlInUtpUblyZVMru0+fPub5JQx8UyVYPHr0qKmtqGPhusonODjYeps+uE7i1CeUkmg9Pj5WvLzufV/AExXu2FGOz5sru0eP0nETEzDmqVdfLtoMWxfr0UOOzZkju14dqmMYkqVIEclVq7bcCmN4y9PoQpWhVUrJ/H/VMH8PnL51W346cc5u2FrnKg6vWkqWtKgtMXHxcuDaTVl16oLJYgJIe5qBsw20lKPgSqvCaOyjw8C2dF/nBzqi8dOSJUvM9L1Lly6ZBSzDhg2zm2uoAeS6deskPDzcrAnJnz+/dOzYMdF8RKVTAHUxjS60uRddgKPD0MeOHZMyZcpIqgeLunxbxSUYOkmNaN0nqIL4Zq90X+cF3CFT1qwmmIu5Yb+YJfr6DfF1UjrKN1s2KflCf4mLjjaZSd8cOeTU4sXinyePtY1/cF4p88oQiY2MlLiI2+KbPYcc+e9/xc+mDdzvWlS0CeZy+dtPEdAVzJciEs/dVlejYuS1zXvFz9tLgvx85WJElPQtFyqnwyOsbU7fipABf+ySAB9vCczkI5cio2VMjTJy5tY/bQCk3eX+HA05O6LJsho1apih5Mcee8waJ+n+iy++KHejWUDNHGopHV3Q8tRTTyVqo4tRdLty5YpZNa2LXhL67LPPTB+0BM+9bN++3cypdLRKO1WvDW07bzEsLCzRYpe2bdsmO1rPW6HX/XQFcBvvTJlM1u/63n2So2o165zDG/v2St4mTe5+X19f8cuZ05TSubptq+SsUTNRGx9/f7PF6F+Te3ZLwcftSyDAvWLi72T9auTJIb+dvWyO6WCy7i8+6njagUVUXLwJFHWOYqMCuWXNqYuJ2kTExpktq6+P1M6b05TYAeBZBg0aJN26dZOaNWtK7dq1TekczQjq0LLq2rWrCQo1WaY2btwop06dMtP79F8d2tYAc+jQodZzamCoo7ma/Tt06JAMGTLEDFtbzmmhWcdvvvnGupLals5x1MfSFdI6n1H3Bw4cKM8884zkzJnTtcHikSNHTD2gXbt2mbF2y2X/LKuHLBM0kxOtZ6QhaC2dU8JmxWNo4WCpXL6oXLl6U06cvpSmfUPS5Gv2bzn2+WwJDC0qWUKLyflVv5gVzbnr1Te3H509S/xy5DC1FFX40SMSdeWqqZ0YdfWqnPn+e/Nzk69FC+s5r+3ebeYwag3GyPPn5eSib83XeerXS7PniTsWHj4lr1UrLfuu3ZS9V27Ik8ULmDqJOrSsRlQrLRcjIuXjvXemDJTPkVXyZPaXg9duSnCAv/QoU8RcQOyLQyet56wdnMP8eyL8thQMzCwvlA+VsBu35Kew82n0LJESWQIySdEC/8xfLhSSVcoVzyVXb0TKmQvhadq3DCEZq5ZdqWPHjnLhwgUZNWqUWfyrQaAu+rUsetHEmmbzLHT4WWstajyVNWtWUzZH5yjmyHHn94K6du2aSa6dPHnSLE7R2olvvvlmoqvmffXVV+bzROczJqSxlt6uwahO/ytWrJgJFhMm7O7FKz4FF3hu06aNGZ//9NNPzQPr/EUdc9drRk+cOFEefvjh5J5SMhdJ/CQfVA8/VE5WfD0q0fF536yT5wfPlIyg/dy+4unOr1kt51asMLUV7xTl7iSBxe7MJdk/aaL4584toc/d+QvwxoH9EvbFFxJ54YJ4+/ubEjoaSGpAaXH5r7/k1P8Wm6LdPlmySM7q1aXgY4+JT2bHiyg8yYnrnv/H4OM2RbkPXQ+XqbsOy57/L6D9Qb1KcvZWhLy1/aDZr5o7SAZXLin5swTI7ZhYU0Jn5p5jcinyn1GYJgXySJ9yRU0weSM6RtaeuSif7D0u4TF3/2PbE5ydvl8yitqVQ2TBxNaJji9ecUBenZgxSskdXJG4VIu7lK79ocvOfWDTCy47t6dJUbCokzn12tC6qkYv76fBoqZJ9ZgGjNu2bUt2RzJSsIgHI1hExgoWkXQZKVhEGgeLD7kwWPyTYNEiRTNDdZjZcqkYDRxPnz5tXQCjq3EAAADcMgztqg33N2exYsWKsmPHDjMErUuwdWWOrgb673//63BJNwAAADJQsKiTMnWVj9ISODqHUecp6qVkdCIlAACAy5EBTL/Bol6mxqJUqVLmcjaXL182y7CTcz1FAAAAPEDBol52JilsL1cDAADgEq6ryY2UBouff/65WcRSrVo1a21FAAAAPLiSFSz269dPvvzyS3ONaK0grhXAtVAkAACAu8Uz9S39JXBnzJghZ86cMZej+f7776Vw4cLmOoaWS9IAAAAgg4/266Vj9JIyK1euNNeGrlChgrzwwgsSGhoqN2/euZoBAACAy3m5cMP9rYa20OscWq4Nfa/rQQMAAKQqb6K6dJlZ1AtR67zFf//731K6dGnZtWuXTJ8+3VwkWy+GDQAAgAyaWdThZi26rXMVtYyOBo16uT8AAAC3Y4FL+gsWZ86cKUWKFDGX9Fu3bp3ZHFm8eHFq9Q8AAACeEix27dqVK7QAAID0gZAkfRblBgAAQMZxX6uhAQAA0gyrod2CqyoCAADAKTKLAADAM7GOwi0IFgEAgGciVnQLhqEBAADgFJlFAADgmVjg4hZkFgEAAOAUmUUAAOCZSCy6BZlFAAAAOEVmEQAAeKR4Sue4BZlFAAAAOEVmEQAAeCZWQ7sFmUUAAAA4RWYRAAB4JhKLbkGwCAAAPBMLXNyCYWgAAAA4RWYRAAB4Jha4uAWZRQAAADhFZhEAAHgmEotuQWYRAAAATpFZBAAAnonV0G5BZhEAAABOkVkEAACeicyiWxAsAgAAz8T4qFvwMgMAAMApMosAAMAzMQztFmQWAQAA4BSZRQAA4JlILLoFmUUAAAA4RbAIAAA8Ury3l8u25JoxY4aEhoZKQECA1KlTRzZt2uS0bXR0tIwbN05KlChh2lepUkWWLVtm1+bGjRvy8ssvS9GiRSVz5sxSr1492bx5s12b5557Try8vOy2li1b2rW5fPmydOnSRYKCgiRHjhzSs2dPuXnzZrKeG8EiAADAfVi4cKEMGjRIRo8eLVu3bjXBX4sWLeT8+fMO248cOVI+/vhjmTZtmuzZs0f69u0r7du3l23btlnb9OrVS1auXCnz5s2TXbt2SfPmzaVZs2Zy6tQpu3NpcHjmzBnr9uWXX9rdroHi7t27zbl++OEH+fXXX+X5559P1vPzio+Pj5d0IHORzmndBbhR+7l907oLcKMT133Sugtwo7PT96d1F+BGB1f0TLPHLvG0fWCUmg5/kfS4pE6dOlKrVi2ZPn262Y+Li5PChQvLgAEDZNiwYYnaFyhQQEaMGCH9+/e3HuvQoYPJIM6fP19u374t2bJlk6VLl0rr1q2tbWrUqCGPPPKIvPHGG9bM4tWrV2XJkiUO+7V3714pX768yUjWrFnTHNMMZqtWreTkyZOmH0lBZhEAAHgmLxduSRQVFSVbtmwxWT8Lb29vs79hwwaH94mMjDTDz7Y0UFy/fr35OiYmRmJjY+/axmLt2rWSN29eKVOmjPTr108uXbpkvU0fX4eeLYGi0n5p/zZu3Jjk50iwCAAA4CCgu379ut2mxxK6ePGiCezy5ctnd1z3z5496/DcOkQ9efJkOXjwoMlC6hDx4sWLzTCy0qxi3bp1Zfz48XL69Glzfs04avBnaWMZgp47d66sWrVK3n33XVm3bp3JPGp7pY+vgaStTJkySa5cuZz2zRGCRQAA4Jl0IYqLtrfffluyZ89ut+mx1DB16lQpVaqUlC1bVvz8/OTFF1+U7t27m4yfhc5V1JmCBQsWFH9/f/nggw+kc+fOdm06deokbdu2lUqVKsljjz1m5iTqkLNmG1MTwSIAAEACw4cPl2vXrtlteiyhPHnyiI+Pj5w7d87uuO6HhIQ4PHdwcLCZZxgeHi7Hjx+Xffv2SdasWaV48eLWNrpSWjOFunL5xIkTZnW1rqK2bZOQ3qb9OXTokNnXx0+4yEaHuHWFtLO+OUKwCAAAPPdyfy7aNJun5WaCbDY9lpBmBnXhiQ4FW+jQsu7rUPLd6JxEzRxqALdo0SJp165dojaBgYGSP39+uXLliixfvtxhGwtdtKJzFrW90sfXBTA6p9Ji9erVpn+6KCepuIILAADAfRg0aJB069bNLCSpXbu2TJkyxWQNdWhZde3a1QSFlmFsXVyiJXCqVq1q/h0zZowJ4IYOHWo9pwaGOgytC1c0UzhkyBAzbG05p2Ycx44da1ZRa5bw8OHD5v4lS5Y0cyJVuXLlzLzG3r17y8yZM01mUoe8dfg6qSuh01WwSCmVjOV/XWemdRfgRrfDxqZ1F+BGJb4MS+suIKNIJ5f769ixo1y4cEFGjRplFo5oEKglaiyLXsLCwuzmGkZERJhai0eOHDHDz1rKRuco6splC8uwt2YLdUGKBoVvvvmm+Pr6mtt16Hvnzp0yZ84ckz3U4E9rMeqiGNsM6IIFC0yA2LRpU9MHPY/Of/TIOotPr12X1l2AGxEsZiwEixlLic5/pXUX4EaHv3w6zR67RLeFLjv34TkdXXZuT5NuMosAAADJkoLL8iH5CBYBAIBnIlh0C1ZDAwAAwCkyiwAAwCPFk1h0CzKLAAAAcIrMIgAA8EzMWXQLMosAAABwiswiAADwTHppPrgcmUUAAAA4RWYRAAB4JuYsugXBIgAA8EyMj7oFLzMAAACcIrMIAAA8Ewtc3ILMIgAAAJwiswgAADwTC1zcgswiAAAAnCKzCAAAPFI8cxbdgswiAAAAnCKzCAAAPBMpL7cgWAQAAJ6JBS5uQUwOAAAAp8gsAgAAz8QCF7cgswgAAACnyCwCAADPxJxFtyCzCAAAAKfILAIAAM9EYtEtyCwCAADAKTKLAADAI8UzZ9EtCBYBAIBnIlh0C4ahAQAA4BSZRQAA4Jkoyu0WZBYBAADgFJlFAADgmUh5pd+X+fbt23Lr1i3r/vHjx2XKlCmyYsWK1OwbAAAAPDFYbNeuncydO9d8ffXqValTp45MmjTJHP/oo49Su48AAACO5yy6asP9BYtbt26Vhx9+2Hz97bffSr58+Ux2UQPIDz74ICWnBAAAwIMyZ1GHoLNly2a+1qHnxx9/XLy9veWhhx4yQSMAAIDLUWcx/WYWS5YsKUuWLJETJ07I8uXLpXnz5ub4+fPnJSgoKLX7CAAA4DhYdNWG+wsWR40aJa+88oqEhoaa+Yp169a1ZhmrVauWklMCAADgQRmGfuKJJ6RBgwZy5swZqVKlivV406ZNpX379qnZPwAAAIfiWYiSfoPFa9euiZ+fX6Isog5PZ8pE6UYAAIAMPQzdqVMn+eqrrxId//rrr81tAAAAboliXLXBKkUvx8aNG6VJkyaJjjdu3NjcBgAAgAdDisaMIyMjJSYmJtHx6Ohoc3UXAAAAl2POYvrNLNauXVv++9//Jjo+c+ZMqVGjRmr0CwAAAJ4aLL7xxhvy6aefSsOGDWXs2LFm069nzZolb731Vur3EgAAIB3XWZwxY4YpKRgQEGDKCm7atMlpWx2JHTdunJQoUcK018oyy5Yts2tz48YNefnll6Vo0aKSOXNmqVevnmzevNnuHK+++qpUqlRJAgMDpUCBAtK1a1c5ffq03Xm0T15eXnbbO++84/pgsX79+rJhwwYpXLiwWdTy/fffm5XQO3futF4GEAAAICNYuHChDBo0SEaPHm0uiazBX4sWLczFShwZOXKkfPzxxzJt2jTZs2eP9O3b15Qe3LZtm7VNr169ZOXKlTJv3jzZtWuXuQBKs2bN5NSpU9ar6eljvf766+bfxYsXy/79+6Vt27aJHk8DUy13aNkGDBiQrOfnFR8fHy/pwNNr16V1F+BG/+s6M627ADe6HTY2rbsANyrR+a+07gLc6PCXT6fZYxd9b7XLzn18yL+S3LZOnTpSq1YtmT59utmPi4szCTUNyoYNG5aovWYBR4wYIf3797ce69Chg8kgzp8/36z/0MsqL126VFq3bm1to1P9HnnkETPC64hmHnWqoF56uUiRItbMomYodUupJGcWr1+/bvf13TYAAACX83Ldpot5E8Y3kZGRiboQFRUlW7ZsMVk/C29vb7Ovo7CO6Hl0+NmWBorr1683X+si4tjY2Lu2cVYHW4eZc+TIYXdch51z585t6mO/9957Dhcpp0qwmDNnTms6VTuh+wk3y3EAAABP9vbbb0v27NntNj2W0MWLF01gly9fPrvjun/27FmH59Yh6smTJ8vBgwdNFlKHm3UYWYeIlWYV9VLK48ePN3MQ9fyacdTg09ImoYiICDOHsXPnzhIUFGQ9/tJLL5na2GvWrJE+ffqYtSVDhw51Temc1atXS65cuczX+oBI7PyaNXJu5QqJvnZNMhcqJEU6dZbAYsUcto2PjZEzPy+TSxv+kOirVyUgJEQKtn9cslesaG0TGxEhp5culavbt0n0jRuSpXBhKdyxkwSGhrrxWeF+1a9dVgb2fVSqVyou+fPllKd6TZLvVzBM52kWLPhRPvtssVy4cEXKli0mr7/eRypXLu2wbXR0jHz88TeyZMlqOXfukhQrVlBeeeU5adjwn2oR+st/2rQv5bvv1sjFi1clb95c0r59U3nhhY4mMwDPUKtssPR+tLxULJ5T8uXMIn0n/Sor/zqZ1t3KMOJTsBAlqYYPH27mIdry9/eX1DB16lTp3bu3lC1b1vy860KX7t27m4XCFjpXsUePHlKwYEHx8fGR6tWrm0BQs5gJ6WKXp556SnRm4UcffWR3m+1zqFy5srkCnwaNGvgm9fkkOVhs1KiRw69xx+XNm+Xkt99Ikae7mADx/KpVcvCDqVJh7DjxtYnwLU4tWSqXN22Uos88awLF63t2y+GZH0nZoa9Klv+fZ3B87ly5ffqUhHbvIb45csjljX/KgfcnS4UxY8WPDK7HCMziL7v2hMnchWtl4SeD07o7SIGffvpN3n77Uxk7tr9UqVJa5sz5Tnr2HCXLls2U3Lnth3vUlCnzTRD4xhsDpHjxQvLbb1vlxRffkq++miDly5cwbT75ZJF8+eVP8u67A6VkySLy99+HZPjwqZItWxbp2jXxBHWkT1n8M8m+sCvy7drD8tHghmndHaQiDaSSEkzlyZPHBHPnzp2zO677ISEhDu8THBwsS5YsMdnAS5cumTmMOrexePHi1jYaQK5bt07Cw8PNEHj+/PmlY8eOdm1sA0Wdp6iJPdusorP5lToMfezYMSlTpoy4bDW0Lu+2HTPX5eJVq1aVp59+Wq5cuSIZ0blfVkqeBg0kT/36krlAASnSpYt4+/nJpT9+d9heA7+Qlo9I9kqVxD84WIIbNTZZxXMrV5rb46Ki5Mq2rVKoQwfJVrq0BOTNKwXatDX/XljHYiBPsmLtDhk78Wv5bjnZRE81e/YSeeqpFtKhQzMT2I0d+4IEBPjLokV3fl4TWrp0jfTt+5Q0alRTChcOkaefbiWNGtWQWbOWWNts27ZXmjZ9SBo3riWFCuWTli3rS4MGVWXnzoNufGa4X+t2nJHJX++UFWQT04Zm4V21JZGfn59ZeLJq1SrrMR1a1n0dSr4bnZOomUMN3hYtWiTt2rVL1EbL4migqPHV8uXL7dpYAkUdzv7ll1/MvMR72b59u5lTmTdv3iQ/xxQFi0OGDLEuZNHl3JribNWqlRw9ejRRyjYjiIuJkVthYRJUrpz1mJe3t2QrW05uHjni9D7evr52x7x9/eTm4UPm6/i4OP1uE69M9m28fH2tbQC4XlRUtOzefUjq1atiPaa/aOvVqyrbtu13eB/9Be7nZ/+zqxmKrVv3WPerVSsnf/65Q44evVMGY9++o7Jly167oWoAnmHQoEHyySefyJw5c2Tv3r3Sr18/kxHUoWWl9Q91WNtCL42scxSPHDkiv/32m7Rs2dIEmLZzCTUw1OScxlY6p1Evs6zD1pZz6u+ZJ554Qv766y9ZsGCBmdqicyR100U3Suc4TpkyRXbs2GEeS9sNHDhQnnnmmWStMUnR5f604+XLlzdfayTcpk0bM2FS6/xo0JjRxNy8aQK7TNnsU7++Qdkk4qzjiahB5SuYbGTWUqVMZvHGvn0mkyj/X8nIJyBAAosXlzM//SgB+fOboezLmzZJ+JEj4p+MvwYA3J8rV65LbGyc5M5t/4tVh5+PHHGcTWrQoJp8/vkSqVWrohQpEiIbNuyQlSv/MOexeP75J+TmzVvyyCP9xMfH29w2cOCz0rZtY5c/J+CB4cI5i8nRsWNHuXDhgowaNcoEazraqoGeZdFLWFiY+SPTQoeftdaiBnBZs2Y1sZPOUbRdxawrmzXAPHnypFkzoqV13nzzTfH9/0ST1lv87rvvzNf6eLZ0bUnjxo3NH6m6uGXMmDFmBXaxYsVMsJjcxF6KgkVNuWoxSKVpT42YlT6ZpJTO0Q4nXH4eGxUlPn5+klEU7thRjs+bK7tHjzLpbg0Y89SrLxdthq2L9eghx+bMkV2vDtVUhpnLmKtWbbkVdjxN+w7g7kaMeF5GjpxmAkEdzSpcOL88/ngzWbToF2ubn39eL99/v04mTXrFDG3v3XvEzIu0LHQB4FlefPFFszmydu1au31d+6HFuO9Gh5d1c0brJ96rVLYuivnzzz/lfqUoWGzQoIGJSvVKLno5G61crg4cOCCFChW65/11BY5eItBWxW7dpNJzd1KrniZT1qwmmIu5YR8oR1+/Ib7Zszu8j2+2bFLyhf4SFx1tMpO6gOXU4sXinyePtY1/cF4p88oQiY2MlLiI2+KbPYcc+e9/xc+mDQDXypkzyGT+Ll2yn4996dJVyZPH8TBOrlzZ5cMPR0pkZJRcvXrDBIATJ86RwoX/Ka0xYcJsk11s3frOoogyZULl9OkLZhU1wSKQROkjsfjAS9GcRa1QnilTJvn222/NEm2dnKl+/vlnM+5+L5pW1fSq7Vb+6S7iqbwzZTJZv+t791mP6ZzDG/v2StYEq5YS3dfX987K5rhYubptq+SoYp9KVj7+/iZQjNEVUXt2O2wDwDV07mGFCiVlw4ad1mM6t0iHlqtVu/tKQn9/P8mXL7fExMTKihV/mAUtFhERkYlK5GhQmk4uqgV4BB3ZddWG+8ws6iVkfvjhh0TH33///RQvR/f0Ieh8zf4txz6fLYGhRSVLqJbO+cWsaM5dr765/ejsWeKXI4eppajCjx6RqCtXTe3EqKtX5cz335sPiXwtWljPeW33bjOHUUvrRJ4/LycXfWu+zlO/Xpo9T6SsdE6J0H/KJ4QWDpbK5YvKlas35cTpS2naNyRN9+6Pyauvvi8VK5Y0tRXnzFkqt29HmKFlNXToZBMUDh7czezv2LHf1FcsV664+XfatC9MgNmr152ff9WkSS2ZOfNrKVAg2DoMrauuO3T4d5o9T6SsdE7RkKzW/ULBgVKuaA65ejNKzly6M10LyJDBoo619+zZU5588klz6RmI5KpVS2Ju3pDT330n0devm6LcpV56yVpjMeryZbssgg4/n/5uqUReuCDe/v6mhE5ojx6SKUsWa5vY27fl1P8Wm6LdPlmySM7q1aXgY4+Jl0+K3jakkeqVi8uKr0dZ9yeMvjPHd9436+T5wVwj2xO0avWwXL58TT74YIEpyq1B4KefjrUOQ585c0G8bSba6/Cz1lo8ceKsZMkSYEroTJgwSIKC/gkqRo7sI1OnLpCxYz+SS5eumaHqjh1bSv/+ndLkOSJlKhXPJV+M+ucybyO73lnNvmjdERk68/7niuHuqF/vHl7xKRjz0ItRf/HFF2aRik6+1MDxoYf+GV5JiafXUjswI/lfV4KkjOR2mP0cZTzYSnSmpmhGcvjLp9PssYvNcF3scLQ/FyCxSNGovNbs0WsVzp4921wvumHDhqaUzsSJExNVMAcAAHhAa3JnCCmewqkLXB5//HFZunSpqQGkV295/fXXpXDhwvLYY4+ZS84AAADAs933eh8tnTN69GiZNGmSuXSMrnTW6yQ++uij8sorr6ROLwEAABLQtQCu2vCPFK2U0KFnrTSuw9B6PUK9gsuXX34pLVq0sL7Azz33nCmjo0PTAAAAyEDBohbeLlGihPTo0cMEhcHBwYnaVK5cWWrVqpUafQQAAEiEBGA6DhZXrVolDz/88F3bBAUFmWsTAgAAuALBYjqes3ivQBEAAAAZOFjU8jjPPvusFChQwKyK9vHxsdsAAABczcvbdRvucxha5ymGhYWZUjn58+dn1RAAAMADKkXB4vr16+W3336TqlWrpn6PAAAAkoBclXukKNGqhbdTcJVAAAAAZJTL/Q0bNkyOHTuW+j0CAABIAm8v121IwTB0zpw57eYmhoeHm1qLWbJkEV9fX7u2ly9fTuppAQAA8CAEi5pNBAAASC+Ys5jOgsVu3bpJbGysuXzfd999J1FRUdK0aVNzXejMmTO7tpcAAAAJECymwzmLb731lrz22muSNWtWKViwoEydOlX69+/vut4BAADAc4LFuXPnyocffijLly+XJUuWyPfffy8LFiyQuLg41/UQAADAAV1L4aoNKQwWtRB3q1atrPvNmjUzL+jp06eTcxoAAAA8iEW5Y2JiJCAgwO6YroSOjo5O7X4BAADcFZflS4fBohbi1kv9+fv7W49FRERI3759JTAw0Hps8eLFqdtLAAAApP9gUVdEJ/TMM8+kZn8AAACShKmF6TBYnD17tut6AgAAAM8OFgEAANILMovuQbAIAAA8EsGie7COCAAAAE6RWQQAAB7Jm8yiW5BZBAAAgFNkFgEAgEdizqJ7kFkEAACAU2QWAQCARyKz6B5kFgEAAOAUmUUAAOCRvFgO7RYEiwAAwCMxDO0eDEMDAADAKTKLAADAI5FZdA8yiwAAAHCKzCIAAPBIZBbdg8wiAAAAnCKzCAAAPBKVc9yDzCIAAMB9mjFjhoSGhkpAQIDUqVNHNm3a5LRtdHS0jBs3TkqUKGHaV6lSRZYtW2bX5saNG/Lyyy9L0aJFJXPmzFKvXj3ZvHmzXZv4+HgZNWqU5M+f37Rp1qyZHDx40K7N5cuXpUuXLhIUFCQ5cuSQnj17ys2bN5P13AgWAQCAx85ZdNWWHAsXLpRBgwbJ6NGjZevWrSb4a9GihZw/f95h+5EjR8rHH38s06ZNkz179kjfvn2lffv2sm3bNmubXr16ycqVK2XevHmya9cuad68uQkGT506ZW0zYcIE+eCDD2TmzJmyceNGCQwMNI8bERFhbaOB4u7du825fvjhB/n111/l+eefT97rHK9haTrw9Np1ad0FuNH/us5M6y7AjW6HjU3rLsCNSnT+K627ADc6/OXTafbYDZaud9m517drkOS2derUkVq1asn06dPNflxcnBQuXFgGDBggw4YNS9S+QIECMmLECOnfv7/1WIcOHUx2cP78+XL79m3Jli2bLF26VFq3bm1tU6NGDXnkkUfkjTfeMFlFPc/gwYPllVdeMbdfu3ZN8uXLJ59//rl06tRJ9u7dK+XLlzcZyZo1a5o2msFs1aqVnDx50tw/KcgsAgAApFBUVJRs2bLFZP0svL29zf6GDRsc3icyMtIMP9vSQHH9+jvBb0xMjMTGxt61zdGjR+Xs2bN2j5s9e3YTuFoeV//VoWdLoKi0vfZPM5FJRbAIAAA8kiuHoTWgu379ut2mxxK6ePGiCew0o2dL9zWYc0SHiidPnmzmF2oWUoeIFy9eLGfOnDG3a1axbt26Mn78eDl9+rQ5v2YcNfiztLGc+26Pq//mzZvX7vZMmTJJrly5nPbNEYJFAACABN5++22Tqctus+mx1DB16lQpVaqUlC1bVvz8/OTFF1+U7t27m4yfhc5V1KHmggULir+/v5mb2LlzZ7s27kKwCAAAPJKXl5fLtuHDh5s5gNdsNj2WUJ48ecTHx0fOnTtnd1z3Q0JCHPY7ODhYlixZIuHh4XL8+HHZt2+fZM2aVYoXL25toyul161bZ1Yunzhxwqyu1lXUljaWc9/tcfXfhItsdIhbV0g765sjBIsAAAAJaDZPy80E2Wx6LCHNDOrCk1WrVlmP6dCy7utQ8t3onETNHGoAt2jRImnXrl2iNrrCWUvjXLlyRZYvX25tU6xYMRPw2T6uDpXrXETL4+q/V69eNXMqLVavXm36p3Mbk4qi3AAAwCOll8v9DRo0SLp162YWktSuXVumTJlisoY6tKy6du1qgkLLMLYGdFoCp2rVqubfMWPGmABu6NCh1nNqYKjD0GXKlJFDhw7JkCFDzLC15Zya/dQ6jLoyWoe0NXh8/fXXzQrnxx57zLQpV66ctGzZUnr37m3K62hmUoe8daV0UldCK4JFAACA+9CxY0e5cOGCKZCtC0c0CNQSNZbFJ2FhYXZzDbUOotZaPHLkiBl+1lI2OkdRVy5bWIa9tcSNLkjR0jpvvvmm+Pr6WttocKlBqdZN1AxigwYNzOParqJesGCBCRCbNm1q+qDn0fmPyUGdRaQJ6ixmLNRZzFios5ixpGWdxcY//u6yc69tXd9l5/Y0ZBYBAIBHSi/D0A86FrgAAAAg/WcWT1z3SesuwI0YlsxYMhcZndZdgBuFdOiU1l1ABuFNZtEtyCwCAAAg/WcWAQAAkoPMonuQWQQAAIBTZBYBAIBH8vZKF9X/HnhkFgEAAOAUmUUAAOCRmLPoHgSLAADAIzE86h68zgAAAHCKzCIAAPBILHBxDzKLAAAAcIrMIgAA8EgscHEPMosAAABwiswiAADwSGS83IPXGQAAAE6RWQQAAB6JOYvuQWYRAAAATpFZBAAAHsmLOotuQbAIAAA8EsPQ7sEwNAAAAJwiswgAADwSGS/34HUGAACAU2QWAQCAR/JmgYtbkFkEAACAU2QWAQCAR2I1dDrOLM6ePVtu3bqV+r0BAACA5weLw4YNk5CQEOnZs6f88ccfqd8rAACAJAQxrtrwjxS9HqdOnZI5c+bIxYsXpXHjxlK2bFl599135ezZsyk5HQAAQIqGoV214T6DxUyZMkn79u1l6dKlcuLECendu7csWLBAihQpIm3btjXH4+LiUnJqAAAApCP3nWnNly+fNGjQQOrWrSve3t6ya9cu6datm5QoUULWrl2bOr0EAABwUDrHVRtSIVg8d+6cTJw4USpUqGCGoq9fvy4//PCDHD161AxTP/XUUyZoBAAAQAYrndOmTRtZvny5lC5d2gxBd+3aVXLlymW9PTAwUAYPHizvvfdeavYVAADAirmF6ThYzJs3r6xbt84MPTsTHBxssowAAADIQMFidHS0HDt2TPLkyXPXdl5eXlK0aNH76RsAAIBTlLhJp6+zr6+v7Ny50zW9AQAAgOcH5c8884x89tlnqd8bAACAJGI1dDqesxgTEyOzZs2SX375RWrUqGEWtNiaPHlyavUPAADAIRa4pONg8e+//5bq1aubrw8cOJDafQIAAIAnB4tr1qxJ/Z4AAAAkA5nFdDxnsUePHnLjxo1Ex8PDw81tAAAAyMDB4pw5c+T27duJjuuxuXPnpka/AAAA7hnEuGpDCoeh9ZJ+8fHxZtPMYkBAgPW22NhY+emnn0zBbgAAAGTAYDFHjhym2LZueqm/hPT42LFjU7N/AAAADlHixj28k7uwZdWqVSaz+O2338rq1aut2/r16yUsLExGjBjhut4CAACkQzNmzJDQ0FAz6lqnTh3ZtGnTXa+GN27cOClRooRpX6VKFVm2bJldGx2xff3116VYsWKSOXNm03b8+PEmBrOwJPASbu+99561jfYp4e3vvPOO6zKLjRo1Mv/qNZ+LFCliHhAAACAjr4ZeuHChDBo0SGbOnGkCxSlTpkiLFi1k//79DqfnjRw5UubPny+ffPKJlC1bVpYvXy7t27eXP/74Q6pVq2bavPvuu/LRRx+ZdSIVKlSQv/76S7p37y7Zs2eXl156ybQ5c+aM3Xl//vln6dmzp3To0MHuuAamvXv3tu5ny5YtWc8vRXM49+7dK7///rtdNF21alV5+umn5cqVKyk5JQAAgEcucJk8ebIJxjSYK1++vAkas2TJYi5g4si8efPktddek1atWknx4sWlX79+5utJkyZZ22jg2K5dO2ndurXJDj7xxBPSvHlzu4xlSEiI3bZ06VJp0qSJOactDQ5t2yW8mEpSXudkGzJkiFnsonbt2mWiaX2SmnHUrwEAADxZZGSkiXWu22x6LKGoqCjZsmWLNGvWzHrM29vb7G/YsMHpuW0XCSsdatYpfRb16tUzU/8sFz/ZsWOHuf2RRx5xeM5z587Jjz/+aDKLCemwc+7cuU3WUoeo9Up8Li/KrUGhRs5q0aJF0qZNG3nrrbdk69atJmgEAADw5GHot99+O9Gi3dGjR8uYMWPsjl28eNHML8yXL5/dcd3ft2+fw3PrELVmIxs2bGjmImpQuHjxYnMei2HDhpkAVYepfXx8zG1vvvmmdOnSxeE5dbhaM4iPP/643XEdstar7uXKlctkK4cPH26Gr5NzaeYUBYt+fn5y69Yt87VeH7pr167ma+2IJeMIAADgqTSoSjha6u/vnyrnnjp1qhm21kBQ139owKhD2LbD1l9//bUsWLBAvvjiCzNncfv27fLyyy9LgQIFpFu3bonOqffVQDJhxtL2OVSuXNnEcH369DHBcFKfT4qCxQYNGpgHr1+/vhk714mdSlOlhQoVSskpAQAAksXLhaVzNJBKSjCVJ08ek/nTYWBbuq/zAx0JDg6WJUuWSEREhFy6dMkEgJpJtJ1rqFP+9FinTp3MfqVKleT48eMmyEsYLP72229mMY0lHrsbXYCjw9DHjh2TMmXKiMvmLE6fPl0yZcpkyufoSp2CBQtaV+G0bNkyJacEAADwOH5+flKjRg0zlGwRFxdn9uvWrXvX+2oWUGMoDd50Wp8uaLHQEVyd+2hLg1I9d0KfffaZ6YOW4LkXzVDqeZNzEZUUZRa1bM4PP/yQ6Pj777+fktMBAAB4bOmcQYMGmWxfzZo1pXbt2qZ0Tnh4uBlaVjpdT4NCzQqqjRs3yqlTp0wlGf1X50FqEDh06FDrOXU9iM5R1JhLh6G3bdtm5hn26NHD7rF1+t8333xjt5LaQhfY6GPpCmmdz6j7AwcOlGeeeUZy5syZ+sGidiYoKMj69d1Y2mU07UPzS+eSBSWXv58cvh4uU3Ydlr1Xbzps6+PlJc+WKiQtC+eVPAH+cuLmbfloz1HZdOGqtU1mHx/pVbaINMyfW3L6+8qBa+Hywd9HZJ+Tc8K9Fiz4UT77bLFcuHBFypYtJq+/3kcqV058ZSMVHR0jH3/8jSxZslrOnbskxYoVlFdeeU4aNqxhbaOTl6dN+1K++26NXLx4VfLmzSXt2zeVF17oSE1TD1K/dlkZ2PdRqV6puOTPl1Oe6jVJvl/xV1p3C8n0bP1i8vy/SkpwNn/Ze/q6jFm8U3aE/fP72VYmby/p16yUdKhVREKyB8iR8zflnR/2yK/7zlvbBPpnkkGPlJUWlfJL7qz+svvUNRn3v12y84Tjc8KzdOzYUS5cuCCjRo2Ss2fPmiBQi2xbFr3oRUtss4Q6/Ky1Fo8cOSJZs2Y1i4O1nI5eKc9i2rRppij3Cy+8IOfPnzdD1TrXUB/D1ldffWUKdXfu3DlRv3QYXW/XYFRXYGuBbw0Wk1u5xivethT4XWjqU1fPaNpSn7CjDy89lR63Xc2TVA9/989ycU/0rwJ5ZES10jJp5yHZc+WGPFm8oDQpkEeeXr1FrkZFJ2rft1yoNC8ULBN2HJLjN29Jnbw55cUKxaTfbzvl4PVw02ZMjTJSPFsWmbTzsFyMjJLmhfLKU8ULyLNrtsrFiCjxZL+19exriP/0028ydOhkGTu2v1SpUlrmzPlOli1bL8uWzZTcuf/5Ybd4773PTRD4xhsDpHjxQvLbb1vlnXc+k6++miDly5cwbWbO/Fpmz14i7747UEqWLCJ//31Ihg+fKgMHPiNdu7YVT5a5yGjJKJo3riJ1a5aRbbuOyMJPBmfIYDGkw505Vp6qddUCMqlLdRn5zU7ZfvyK9GhUXFpVKSBN314ll24m/t376qPl5bEahWT419vl8Pmb0rBMXhnZrqJ0+OA32XPqmmkzrWtNKZ0/m7z+zU45dz3CtO/RqIQ0f3e1nLsWIZ7s6Pv/DJ2624i//hn6TW1v1mzqsnN7miRnFvWSfrra2XLZP9jrWKKgfB92Vn46cecvyYk7D0ndfDmldZF8suDQyUTtWxQOlrkHTsqf5+8UMV9y7KzUyJNDOpUsKOO3HhA/b29plD+PvLZpj+y4fCeTO3t/mNTPl0seCw2RT/eFufkZwpYGdU891UI6dLhTV2vs2Bdk7drNsmjRSnn++ScTtV+6dI306/eUNGpU0+w//XQr2bBhu8yatUQmThxsjm3btleaNn1IGjeuZfYLFconP/64TnbuPOjW54b7s2LtDrPBc/VqXFIWbjgu326683t2xDc7pEm5fPJknaIyc1Xin8f2NQvLjJUHZO3eO7//F/xxTOqXDpbejUvIwAVbxd/XW1pWzi/Pz9okm45cMm2mLt8vTSuEyDP1QmXSz47Lq+DeuDZ0OgsWLZf6S/g1RDJ5eUnp7Fll/sET1mP67fvXxatSIafjS+r4entLVIJJqrpfKVeQdZhahzYStomMjZXKubK75HkgaaKiomX37kPSp88T1mOaba9Xr6ps27bf6XVA/fx8Ew0PbN26x7pfrVo5+frr5XL06CkzTL1v31HZsmWvDBuWuMAqANfw9fGSioWyy4e/3CmErHT87feDF6R6UcdzvPwyeUtkjP2IWmR0rNQsntt8ncnbWzL5eJtjtiJs2gDpWYoWuFjG23fu3GnG0ROuzGnb1rOHzJIru5+vCewuR9oPN1+JjJaiWbM4vM+m81ekY/ECsuPSNTkVHiE1gnNIw5Dc4v3/w/u3Y2Nl1+Xr0q10ETl2Y79ciYySZoWCpUKuIDkVftstzwuOXblyXWJj4yR3bvsPDh1+PnIkcRZZNWhQTT7/fInUqlVRihQJkQ0bdsjKlX+Y81g8//wTcvPmLXnkkX7i4+Ntbhs48Flp27axy58TgDtyBvqbwO7iDfsrdeh+ibyO//jXuYk9G5eQTYcvyfFL4VK/VLC0qJxfvP9/9UV4ZIxsOXpZBjQvI4fO3ZSLNyKkbfVCUj00lxy/eGfaETx7gcuDLkXBok7a1JU9WrU8oaTMWdRJlgkvmRMXHSXevn6SUehClaFVSsn8f9Uwf7WevnVbfjpxzgxbW7yx9YAMr1pKlrSoLTFx8XLg2k1ZdeqCyWLCs4wY8byMHDnNBIL690Dhwvnl8cebyaJFv1jb/Pzzevn++3UyadIrZs7i3r1H5O23P7UudAGQPulClbc7VpVfhjc1c/fDLt2SbzedkCdrF7G2GbRgi0zoVE02jm0hMbFxsvvkNfl+60mpWDjxHGfggQgWBwwYIE8++aRZkZPw8jYpvYRO4U7dpWhn++XgnuJaVLQJ5nL52w8z6grmS04WolyNipHXNu8VP28vCfLzNQtWdNHL6fB/JjqfvhUhA/7YJQE+3hKYyUcuRUabRS9nbnn2ZGhPlzNnkMn8Xbp0Z76pxaVLVyVPHsfDVLlyZZcPPxwpkZFRcvXqDRMATpw4RwoX/ufnZ8KE2Sa72Lp1Q7NfpkyonD59wayiJlgE3ONKeKQJ5vJksy/GrPsXrjv+3Xs5PEr6zNpkhqNzBvqZBSu66CXs8j9ZQw0gO834XTL7+UjWgExy4XqkWfQSdonM4v0gs+geKSrKrVXJddl1SgJFyyV0rl27ZrcVfuIZ8VQx8XeyfrpAxUK/f3V/95Ubd71vVFy8CRR1jmKjArll/dnLidpExMaZQDGrr4/UzptTfjt7Z4I00obOPaxQoaRs2LDTekynYujQcrVqd6+G7+/vJ/ny5ZaYmFhZseIPs6DFIiIiMlGVAQ1Kk1iwAEAqiI6Nl79PXjMLVCz0x7JeqWDZetz+D8SEomLiTKCo05J0QcvKXWcTtbkdFWsCxaDMvtKwbF755e/EbYAHIrP4xBNPyNq1a821DFPrEjqePgS98PApea1aadl37absNaVzCpg6iTq0rLSszsWISPl473GzXz5HVsmT2V8OXrspwQH+0qNMEfEWL/nCZuV07eA7weeJ8NtSMDCzvFA+VMJu3JKfwv6p3YW00b37Y/Lqq+9LxYolTW3FOXOWyu3bEWZoWWlZHQ0KBw++c0mmHTv2m/qK5coVN/9Om/aFCTB79frngu9NmtQy5XMKFAi2DkPrqusOHf6dZs8TyReYxV9KhP5zia/QwsFSuXxRuXL1ppw4zR96nuDTtYdk0tPVTQ3EHaZ0TgnJ4ucj3268szpabzt77ba89+Nes1+1SE7Jlz1A9py+Zuos/qdFWTNf8ePV/6ycblgm2ESdWoMxNE+gDG9bQQ6fuyHf/P85kTI+ad2BDCJTSi/3p8PQei1CvVahr6/98OtLL70kGc3q0xclh5+v9CxTxBTlPnQ9XF7582+zyEXly+xvlyHy8/GW3mWLSv4sAXI7JtaU0NGSOTdtVtQF+maSPuWKmmDyRnSMrD1zUT7Ze1xiyTSluVatHpbLl6/JBx8sMEW5NQj89NOx1mHoM2cuWCe3Kx1+njJlvpw4cVayZAkwJXQmTBgkQUH/zD8dObKPTJ26QMaO/UguXbpmhqo7dmwp/ft7ds26jKZ65eKy4ut/iuZOGN3V/Dvvm3Xy/OCZadgzJNWP20+bwtmDWpaVPEH+svfUdXnu4z/l4s07c+0L5MwscTa/h7U0zuBW5aRI7ixmMYuW0Bm0YKvciIixtsmW2VeGtC4vITkC5NqtaFm247RM/GmvmcIEpHdJLsqd8BqEffv2Ndc0zJ07t93QmX6tFckzWlFuZKyi3EiejFSUG55flBueU5T7re0rXXbu16oyqnNfmcURI0aYBSrDhg1LdJFrAAAAd2CBi3ukKNKLiooy10EkUAQAAHiwpSja69atmyxcuDD1ewMAAJCMzKKrNtznMLQW3Z4wYYIsX75cKleunGiBy+TJk1NyWgAAADwIweKuXbukWrVq5uu///7b7raEdeIAAABcwYeQI/0Gi2vWrEn9ngAAACDdua8VKocOHTJD0bdv3zb7XGkCAAC4C3MW03GweOnSJWnatKmULl1aWrVqJWfOnDHHe/bsKYMHD07tPgIAAMCTgsWBAweaRS1hYWGSJUsW63Etp7Ns2bLU7B8AAIBD3l7xLttwn3MWV6xYYYafCxUqZHe8VKlScvz4nWsfAwAAuBLDxek4sxgeHm6XUbS4fPmy+Pv7p0a/AAAA4KnB4sMPPyxz5861K5cTFxdnai82adIkNfsHAADgkI8LN9znMLQGhbrA5a+//jKX/hs6dKjs3r3bZBZ///33lJwSAAAAD0pmsWLFinLgwAFp0KCBtGvXzgxLP/7447Jt2zYpUaJE6vcSAAAgAUrnpOPMosqePbuMGDEidXsDAACAByNYvHr1qmzatEnOnz9v5iva6tq1a2r0DQAAwClK3KTjYPH777+XLl26yM2bNyUoKMjuetD6NcEiAABABp6zqFdp6dGjhwkWNcN45coV66aLXAAAAFzNx8t1G+4zs3jq1Cl56aWXHNZaBAAAcAcWoqTjzGKLFi1M2RwAAAA82JKcWfzuu++sX7du3VqGDBkie/bskUqVKpnrRNtq27Zt6vYSAAAgATKL6SxYfOyxxxIdGzduXKJjusAlNjb2/nsGAAAAzwkWE5bHAQAASEtkFtPhnMXVq1dL+fLl5fr164luu3btmlSoUEF+++231OwfAAAAPCVYnDJlivTu3dvUVnR0RZc+ffrI5MmTU7N/AAAADvl4xbtsQwqDxR07dkjLli2d3t68eXPZsmVLck4JAACAB6XO4rlz5xKtfLY7WaZMcuHChdToFwAAQOrX/4NrX+eCBQvK33//7fT2nTt3Sv78+ZPfCwAAAHh+sNiqVSt5/fXXJSIiItFtt2/fltGjR8ujjz6amv0DAABwuhraVRtSOAw9cuRIWbx4sZQuXVpefPFFKVOmjDm+b98+mTFjhqmvOGLEiOScEgAAIEUI6tJhsJgvXz75448/pF+/fjJ8+HCJj4+3FuLWSwBqwKhtAAAAkAGDRVW0aFH56aef5MqVK3Lo0CETMJYqVUpy5szpmh4CAAA4QImbdBosWmhwWKtWrdTtDQAAAB6MYBEAACAtMWfRPShRBAAAAKfILAIAAI9EZtE9yCwCAADcpxkzZkhoaKgEBARInTp1ZNOmTU7bRkdHy7hx46REiRKmfZUqVWTZsmV2bbQcoda2LlasmGTOnNm0HT9+vLUSjXruuedMRRrbLeFlmS9fvixdunSRoKAgyZEjh/Ts2VNu3ryZrOdGZhEAAHik9JJZXLhwoQwaNEhmzpxpAsUpU6aYkoL79++XvHnzOqxbPX/+fPnkk0+kbNmysnz5cmnfvr0pT1itWjXT5t1335WPPvpI5syZIxUqVJC//vpLunfvLtmzZ5eXXnrJei4NDmfPnm3d9/f3t3ssDRTPnDkjK1euNEGqnuP555+XL774IsnPzyveNkRNQw9/tz6tuwA3+q1t4h8ePLgyFxmd1l2AG4V06JTWXYAbHX2/XZo99rKTP7vs3C0LPZLktnXq1DEVYqZPn2724+LipHDhwjJgwAAZNmxYovYFChQwFzHp37+/9ViHDh1MBlGDSKVXxNPa1Z999pnTNppZvHr1qixZssRhv/bu3Svly5eXzZs3S82aNc0xzWDqFflOnjxp+pEUDEMDAAAkEBkZKdevX7fb9FhCUVFRsmXLFmnWrJn1mLe3t9nfsGGD03Pr8LMtDQLXr/8ncVavXj1ZtWqVHDhwwOzv2LHD3P7II/ZB7Nq1a032Uq+qpxdNuXTpkvU2fXwderYEikr7pf3buHFjkl8LgkUAAOCRvL3iXba9/fbbZsg3u82mxxK6ePGimV+Y8Ap2un/27FmH/dYh6smTJ8vBgwdNFlKHiPVyyjpcbKEZyU6dOplhal9fXzM8/fLLL5thZdsh6Llz55qgUoet161bZ4JJ7Y/Sx084DJ4pUybJlSuX0745wpxFAACABPSyxjoP0VbC+YApNXXqVOndu7cJBHVRii5e0bmEs2bNsrb5+uuvZcGCBWZuoc5Z3L59uwkWdei4W7dupo0GkxaVKlWSypUrm3NptrFp06aSWggWAQCAR3Ll8KgGhkkJDvPkySM+Pj5y7tw5u+O6HxIS4vA+wcHBZp5hRESEGTbWAFAzicWLF7e2GTJkiDW7aAkGjx8/brKblmAxIb2/9kcvx6zBoj7++fPn7drExMSYFdLO+uYIw9AAAAAp5OfnJzVq1DBDwRY6tKz7devWvet9dd5iwYIFTQC3aNEiadfun8VCt27dMnMLbWlQqud2RhetaPCZP39+s6+PrwtgdE6lxerVq805dFFOUpFZBAAAHim9lM4ZNGiQyfbpQpLatWub0jnh4eFmaFl17drVBIWWOY+6uOTUqVNStWpV8++YMWNMADd06FDrOdu0aSNvvvmmFClSxAxDb9u2zcxz7NGjh7ldayWOHTvWrJDWLOHhw4fN/UuWLGnmRKpy5cqZeY065K1lfbR0zosvvmiylUldCa0IFgEAAO5Dx44d5cKFCzJq1CizcESDQC1RY1n0EhYWZpcl1OFnrbV45MgRyZo1qyllM2/ePLNy2WLatGmmKPcLL7xghpI1uOvTp495DEuWcefOnaYOo2YP9fbmzZubwt22w+c671EDRB2W1j5ocPnBBx8k6/lRZxFpgjqLGQt1FjMW6ixmLGlZZ3HdmZ9cdu5G+Vu57NyehswiAADwSFriBq7HAhcAAAA4RWYRAAB4pPSywOVBR2YRAAAATpFZBAAAHonMonuQWQQAAED6L51Tqvlnad0FuFFc7sxp3QW4UVxIYFp3AW50dtFXad0FuNHtsC/T7LE3nv/RZeeuk7e1y87tacgsAgAAwCnmLAIAAI/kxZxFtyBYBAAAHolY0T0YhgYAAIBTZBYBAIBHYhjaPcgsAgAAwCkyiwAAwCOR8XIPXmcAAAA4RWYRAAB4JC+vdHFdkQcemUUAAAA4RWYRAAB4JBZDuwfBIgAA8EiUznEPhqEBAADgFJlFAADgkUgsugeZRQAAADhFZhEAAHgkb1KLbkFmEQAAAE6RWQQAAB6JxKJ7kFkEAACAU2QWAQCAR6LOonsQLAIAAI9ErOgeDEMDAADAKTKLAADAI5FZdA8yiwAAAHCKzCIAAPBIFOV2DzKLAAAAcIrMIgAA8EgkFt2DzCIAAACcIrMIAAA8kpdXfFp3IUMgWAQAAB6JYWj3YBgaAAAATpFZBAAAHolrQ7sHmUUAAAA4RWYRAAB4JDJe6fh1Dg0NlXHjxklYWFjq9wgAAACeHSy+/PLLsnjxYilevLj8+9//lq+++koiIyNTv3cAAAB3mbPoqg2pECxu375dNm3aJOXKlZMBAwZI/vz55cUXX5StW7em5JQAAAB40Ib7q1evLh988IGcPn1aRo8eLZ9++qnUqlVLqlatKrNmzZL4eIplAgAA1/By4ZZcM2bMMNP0AgICpE6dOiah5kx0dLSZzleiRAnTvkqVKrJs2TK7NrGxsfL6669LsWLFJHPmzKbt+PHjrbGVnuPVV1+VSpUqSWBgoBQoUEC6du1qYjJb2icvLy+77Z133nHfAhft6P/+9z+ZPXu2rFy5Uh566CHp2bOnnDx5Ul577TX55Zdf5IsvvrifhwAAAHAovQwXL1y4UAYNGiQzZ840geKUKVOkRYsWsn//fsmbN2+i9iNHjpT58+fLJ598ImXLlpXly5dL+/bt5Y8//pBq1aqZNu+++6589NFHMmfOHKlQoYL89ddf0r17d8mePbu89NJLcuvWLTOaqwGlBptXrlyR//znP9K2bVvT1pYGpr1797buZ8uWLVnPzys+Bek/7ZwGiF9++aV4e3ubSLZXr17mCVv8/fffJst4+/btJJ2zVPPPktsNeLC43JnTugtwo7iQwLTuAtzo7KKv0roLcKPbYV+m2WOfCP/eZecuHNgmyW3r1KljYp7p06eb/bi4OClcuLCZpjds2LBE7TULOGLECOnfv7/1WIcOHUwGUYNI9eijj0q+fPnks88+c9omoc2bN0vt2rXl+PHjUqRIEWtmUacP6ubWYWh9QQ4ePGgi3lOnTsnEiRPtAkWladNOnTqluGMAAADpfRg6KipKtmzZIs2aNbMe00Sa7m/YsMHhfXRRsA4/29IgcP369db9evXqyapVq+TAgQNmf8eOHeb2Rx55xGlfrl27ZoaZc+TIYXdch51z585tspbvvfeexMTEuH4Y+siRI1K0aNG7ttHxc80+AgAAeBoN6BJWevH39zebrYsXL5r5hZoFtKX7+/btc3huHaKePHmyNGzY0MxF1KBQq8zoeSw0I3n9+nWTjPPx8TG3vfnmm9KlSxeH54yIiDBzGDt37ixBQUHW4zpkrWtMcuXKZYa5hw8fLmfOnDGP79LM4r0CRQAAAFfz9nLd9vbbb5v5gdltNj2WGqZOnSqlSpUygaCfn5+pJqPzETUjafH111/LggULzNoPnf6ncxd1JFf/dbSG5KmnnjKLX3TU15bOpWzcuLFUrlxZ+vbtK5MmTZJp06Ylq+RhijKLOXPmNGnOhPSYplVLliwpzz33nHniAAAAnkYzcBpo2UqYVVR58uQxmb9z587ZHdf9kJAQcSQ4OFiWLFlisoGXLl0ycxg1k6j1qy2GDBlijlmm9OmqZ52LqAFrt27dEgWKetvq1avtsorO5lfqMPSxY8ekTJky4rLM4qhRo0z027p1axk7dqzZ9Gs9ppM1S5cuLf369TOrfAAAADxtzqIGhhp4BdlsjoJFzQzWqFHDDCVb6AIX3a9bt+5d+68JtoIFC5rgbdGiRdKuXTvrbbra2TbTqDQo1XMnDBR1HYlWoNF5ifeidbL1vI5WaadqZlEnWL7xxhsmnWnr448/lhUrVpgnrOlOrcFou1QbAADgQTNo0CCT7atZs6ZZjaylc8LDw60jrFo1RoNCyzD2xo0bzQJhrUut/44ZM8YEgUOHDrWes02bNmaOoq5q1tI527ZtM/MMe/ToYQ0Un3jiCTNE/cMPP5g5jWfPnjW36fxEDWJ1gY0+VpMmTUy5HN0fOHCgPPPMM2aU2KXBotYD0vo/CTVt2lQGDx5svm7VqpXD5eIAAACpwcsrfVz8o2PHjnLhwgUz8qoBmwaBWmTbsuglLCzMLkuow89aa1EXDGfNmtXETPPmzbNbxazzCrWG4gsvvCDnz583Q9V9+vQxj6E0yPzuu+/M1/p4ttasWWPmKWomVC/JrMGozlHUSjUaLCYcXndJnUWNcvXBdLP1/vvvm01flJ07d0rz5s2tUe69UGcxY6HOYsZCncWMhTqLGUta1lk8e/tOsOQKIZnbuuzcniZFmUWNdHVOokaumm61FIL86aefTPVypVd0adSoUer2FgAA4P+lkwu4PPBSFCzqPMTy5cubSuVaF0jpipp169aZIpLKMhwNAADwIF/u70GX4mtD169f32wAAAB4cKU4WNRVN1ojaO/evWZfV+roxat1WTfurlalEOn1ZCWpUCq35MsdKP3G/CK//HE8rbsFF6lVNlh6P1peKhbPKflyZpG+k36VlX+dTOtuIZmerV9Mnv9XSQnO5i97T1+XMYt3yo6wqw7bZvL2kn7NSkmHWkUkJHuAHDl/U975YY/8uu+8tU2gfyYZ9EhZaVEpv+TO6i+7T12Tcf/bJTtPOD4n0qf6tcvKwL6PSvVKxSV/vpzyVK9J8v2Kv9K6WxkGiUX3SFGdxUOHDkm5cuXMUnAdhtZNl2FrwHj48OHU7+UDJnNAJtl35LKMne74mpF4sGTxzyT7wq7ImFl8gHiq1lULyIjHKsjU5fvl0UnrZO/pazKnT13JndXPYfvBrcrJ03VDTUD573dXy4I/jsnH3WtL+YLZrW3e6VhVGpQJlkELtkrL99bIb/vPy7x+9SRfdvvrxSJ9C8ziL7v2hMnLI2eldVeA9JVZ1OsM6rUM//zzT1PLR2kFcg0Y9bYff/wxtfv5QPl180mzIWNYt+OM2eC5ejUuKQs3HJdvN4WZ/RHf7JAm5fLJk3WKysxVBxO1b1+zsMxYeUDW7r2TSdRgsX7pYOnduIQMXLBV/H29pWXl/PL8rE2y6cgl00YD0aYVQuSZeqEy6WfH15NF+rNi7Q6zwYMyXnBPsKgLWWwDRaVVw9955x3mMQJ4oPj6eEnFQtnlw18OWI9pwbHfD16Q6kUdF7X1y+QtkTGxdscio2OlZvE7V1fI5O0tmXy8zTFbETZtAMCjg3It8njjxo1Ex2/evGkqhgPAgyJnoL8J7C7eiLQ7rvvBQY6HjHVuYs/GJSQ0T6BZrdmgdLC0qJxfgoPuXCosPDJGthy9LAOal5G8QQHi7SXyWI1CUj00l9kHkDT68+WqDfcZLD766KPy/PPPm0vIaE1v3TTTqJf/00Uu96JVxK9fv263xcdFp6QrAJDu6EKVYxfC5ZfhTeXAe21kbIfK8u2mExL/zyVdZdCCLWZy/saxLWT/e23kuYeLy/dbT0pc8q+TAADpbxhar/ms10DUC2T7+vqaY3oRbA0Up06des/767URx44da3csZ/E2krvEPxfQBoD04Ep4pMTExkmebHeygha6f+F6hMP7XA6Pkj6zNpnh6JyBfnLuWoS8+mh5Cbscbm0TdumWdJrxu2T285GsAZnkwvVImda1poRd+qcNgHshBZhug0W9duHSpUvl4MGDsm/fnYnYujq6ZMmSSbr/8OHDE12XsPrjX6SkKwDgUtGx8fL3yWtmgcrKv+9cvlSHqOqVCpa564/e9b5RMXEmUNRSOrqg5cftpxO1uR0Va7agzL7SsGxeeef73S57LsCDxotgMX3XWVSlSpUyW0rmPOpmy8v7ToYyI8gSkEmKFgiy7hcKySrliueSqzci5cwFsgoPYumcoiFZrfuFggOlXNEccvVmlJy5dCtN+4ak+XTtIZn0dHVTA3HH8SvSo1EJyeLnI99uvLM6Wm87e+22vPfjnbqzVYvkNCVw9py+Zuos/qdFWfH29pKPV/+zcrphmWATdWoNRp3bOLxtBTl87oZ88//nhOeUzikRGmLdDy0cLJXLF5UrV2/KidN3VroDGSZYTJgJvJvJkyentD8ZQsXSeWTBxNbW/RF9HzL/Ll5xQF6d+Fsa9gyuUKl4LvliVDPr/siuNcy/i9YdkaEz/0zDniGpNCOohbMHtSwreYL8Ze+p6/Lcx3/KxZt3Fr0UyJnZbq6hlsbRWotFcmcxi1m0hI7WU7wREWNtky2zrwxpXV5CcgTItVvRsmzHaZn4016JiWPOoiepXrm4rPh6lHV/wuiu5t9536yT5wfPTMOeZQxeXhTPcQeveF2dkgRNmjRJ2gm9vGT16tXJ7kip5p8l+z7wXHG5M6d1F+BGcSGBad0FuNHZRV+ldRfgRrfDvkyzx74a9ZPLzp3Dr5XLzv3AZhbXrFnj2p4AAAAkC3MW3eG+87cnT540GwAAAB48KQoW4+LiZNy4cZI9e3YpWrSo2XSF9Pjx481tAAAA7lgN7ar/cJ+roUeMGCGfffaZ3eX91q9fL2PGjJGIiAh58803U3JaAAAAPAjB4pw5c+TTTz+1u1pL5cqVpWDBgvLCCy8QLAIAADcgA5hug8XLly9L2bJlEx3XY3obAACAq1E6xz1S9CpXqVJFpk+fnui4HtPbAAAAkIEzixMmTJDWrVvLL7/8Yq4PrTZs2CAnTpyQn35yXc0jAACAfzAMnW4zi40aNZIDBw5I+/bt5erVq2Z7/PHHZf/+/fLwww+nfi8BAADgWdeGLlCgAAtZAABAmqHETToLFnfu3CkVK1YUb29v8/Xd6MpoAAAAZKBgsWrVqnL27FnJmzev+VqvAe3ostJ6PDY2NrX7CQAAYIfMYjoLFo8ePSrBwcHWrwEAAPDgS3KwqJf0s8iaNavkzp3bfK0roD/55BO5ffu2KdLNAhcAAOAe1FlMd6/yrl27JDQ01AxFawHu7du3S61ateT999+X//73v9KkSRNZsmSJ63oLAABgM/XNVRtSGCwOHTpUKlWqJL/++qs0btxYHn30UVNv8dq1a3LlyhXp06ePuV40AAAAMmDpnM2bN8vq1avName9UotmE/Va0LpCWg0YMEAeeughV/UVAADABhnAdJdZ1Os+h4SEWOctBgYGSs6cOa2369c3btxI/V4CAADAM4pyJxzHZ1wfAACkBUrnpNNg8bnnnhN/f3/zdUREhPTt29dkGFVkZGTq9xAAAACeESx269bNbv+ZZ55J1KZr16733ysAAIB7onROugsWZ8+e7bqeAAAAwPOHoQEAANID5iy6B8EiAADwSCyydQ8G+wEAAOAUmUUAAOChyCy6A5lFAAAAOEVmEQAAeCQvcl5uwasMAAAAp8gsAgAAD8WcRXcgswgAAACnCBYBAIDH1ll01ZZcM2bMkNDQUAkICJA6derIpk2bnLaNjo6WcePGSYkSJUz7KlWqyLJly+zaxMbGyuuvvy7FihWTzJkzm7bjx4+X+Ph4axv9etSoUZI/f37TplmzZnLw4EG781y+fFm6dOkiQUFBkiNHDunZs6fcvHkzWc+NYBEAAHgoLxduSbdw4UIZNGiQjB49WrZu3WqCvxYtWsj58+cdth85cqR8/PHHMm3aNNmzZ4/07dtX2rdvL9u2bbO2effdd+Wjjz6S6dOny969e83+hAkTzH0sdP+DDz6QmTNnysaNGyUwMNA8bkREhLWNBoq7d++WlStXyg8//CC//vqrPP/888l7leNtQ9Q0VKr5Z2ndBbhRXO7Mad0FuFFcSGBadwFudHbRV2ndBbjR7bAv0+yxo+K2uOzcft41kty2Tp06UqtWLRPYqbi4OClcuLAMGDBAhg0blqh9gQIFZMSIEdK/f3/rsQ4dOpjs4Pz5883+o48+Kvny5ZPPPvvMYRsN3/Q8gwcPlldeecXcfu3aNXOfzz//XDp16mSCzPLly8vmzZulZs2apo1mMFu1aiUnT540908KMosAAMBjS+e4aouMjJTr16/bbXosoaioKNmyZYsZArbw9vY2+xs2bHDYbz2PDj/b0iBw/fr11v169erJqlWr5MCBA2Z/x44d5vZHHnnE7B89elTOnj1r97jZs2c3gavlcfVfHXq2BIpK22v/NBOZVASLAAAACbz99tsm+Mpus+mxhC5evGjmF2pGz5buazDniA4VT5482cwv1CykDhEvXrxYzpw5Y22jGUnNDpYtW1Z8fX2lWrVq8vLLL5thZWU5990eV//Nmzev3e2ZMmWSXLlyOe2bI5TOAQAAHsp1pXOGDx9u5iHa8vf3l9QwdepU6d27twkEdTGNLl7p3r27zJo1y9rm66+/lgULFsgXX3whFSpUkO3bt5tgUYeOu3XrJu5EsAgAAJCABoZJCQ7z5MkjPj4+cu7cObvjuh8SEuLwPsHBwbJkyRKzEOXSpUsmANRMYvHixa1thgwZYs0uqkqVKsnx48dNdlODRcu59XF0NbTt41atWtV8rW0SLrKJiYkxK6Sd9c0RhqEBAIBH8nLhf0nl5+cnNWrUMPMLLXRoWffr1q171/vqvMWCBQuaAG7RokXSrl076223bt0ycwttaVCq51ZaUkcDPtvH1XmVOhfR8rj679WrV82cSovVq1ebc+jcxqQiswgAAHAfBg0aZLJ9upCkdu3aMmXKFAkPDzdDy6pr164mKLTMedSA7tSpUyYDqP+OGTPGBHBDhw61nrNNmzby5ptvSpEiRcwwtJbV0XmOPXr0MLfr8LUOS7/xxhtSqlQpEzxqXUbNUj722GOmTbly5aRly5ZmyFvL62h9xxdffNFkK5O6EloRLAIAAI+UkuLZrtCxY0e5cOGCKZCtC0c0CNQSNZbFJ2FhYXZZQh1+1lqLR44ckaxZs5pSNvPmzTMrly20nqIGfy+88IIZStbgrk+fPuYxLDS41KBU6yZqBrFBgwbmcW1XWuu8Rw0QmzZtavqg5Xe0NmNyUGcRaYI6ixkLdRYzFuosZixpWWcxNv5vl53bx6uiy87taZizCAAAAKcYhgYAAB4pOQtRkHJkFgEAAOAUmUUAAOChyCy6A5lFAAAAOEVmEQAAeKT0UjrnQUdmEQAAAE6RWQQAAB6KnJc7ECwCAACPROkc9yAkBwAAQPq/3F9GFBkZaS4qPnz4cPH390/r7sDFeL8zFt7vjIX3Gw8ygsU0dP36dcmePbtcu3ZNgoKC0ro7cDHe74yF9ztj4f3Gg4xhaAAAADhFsAgAAACnCBYBAADgFMFiGtJJ0KNHj2YydAbB+52x8H5nLLzfeJCxwAUAAABOkVkEAACAUwSLAAAAcIpgEQAAAE4RLHqA5557Th577LG07kaG9/nnn0uOHDlcdv61a9eKl5eXXL161WWPgX/oa71kyRK3Py7vc/p07Ngx875s3749yfdp3LixvPzyyy7tF5AeECymQiCnv2D69u2b6Lb+/fub27SNq35ZwTXvp25+fn5SsmRJGTdunMTExLj8sevVqydnzpwxV4HA/Tt79qwMGDBAihcvblaoFi5cWNq0aSOrVq1K034l530msHTNz7ZuuXPnlpYtW8rOnTvN7fr9oe9LxYoV07qrQLpDsJgK9JfMV199Jbdv37Yei4iIkC+++EKKFCmSpn1D8ukHiH5oHDx4UAYPHixjxoyR9957z+WPq8FpSEiI+SDD/dE/vGrUqCGrV682792uXbtk2bJl0qRJE/NHXFpyxfscFRWVaufKCD/buukfDZkyZZJHH33U3Obj42PeFz0GwB7BYiqoXr26CRgXL15sPaZfa6BYrVo16zH9sGrQoIEZytS/avWX1OHDh623FytWzPyr99EPEh3isDVx4kTJnz+/ua9+4EVHR7vl+WU0moXSD42iRYtKv379pFmzZvLdd99Zb1++fLmUK1dOsmbNav3wUb/++qv4+vqajJYtHaZ6+OGHzdfHjx832a2cOXNKYGCgVKhQQX766SenWaTff//dfB9kyZLF3KdFixZy5coVc9u3334rlSpVksyZM5vvCe1neHi4W16j9O6FF14wr+WmTZukQ4cOUrp0afNaDxo0SP78809ru4sXL0r79u3N61uqVCm791n9/fff8sgjj5j3Ol++fPLss8+a+1joe6PZS32P9f3RNp988ol5H7p37y7ZsmUz2emff/7Zep+E77Oz7wkNeDW4VXqb7SiFPu6LL75oHjdPnjzm+6JHjx7WwMdCf0fkzZtXPvvsMxe90p75s61b1apVZdiwYXLixAm5cOGCw5GddevWSe3atc399Hevtr/bKIP+bHbt2tW8X/o9pd87+kenLf3+0M8LvV2/9yZPnmyd3qJ98Pb2lr/++svuPlOmTDG/j+Li4lL9NQGSgmAxlegv6tmzZ1v3Z82aZT4sbOkHiH5Y6S8C/atWfynoLwvLLwD9YFO//PKLCUBsg881a9aYwFL/nTNnjpk/pxtcT4MxS+bm1q1bJmifN2+eCQ7DwsLklVdeMbc1bNjQDHnqbbYf1gsWLDDfH0qD/MjISHNfzXa9++67JhBxRD+0mjZtKuXLl5cNGzbI+vXrTVARGxtrvj86d+5szrt3714TgDz++ONC2VSRy5cvmz/M9LXW4Csh23mnY8eOlaeeesoMRbZq1Uq6dOli7q80mPvXv/5l/njTn1k957lz50x7W/rzqAGb/vxq4Kh/YDz55JNmuHnr1q3SvHlzE2Tq944jzr4nNKBYtGiRabN//37znk+dOtXucTVLqX9QzJw5U3r16mX6aPnjRf3www/mcTt27JgKr+yD5ebNmzJ//nwTzOsfWwmdOnXKfE/UqlVLduzYIR999JEJut944w2n59RgXr9X9I8O/ZnVn0c9h+UPe32vdMrSf/7zH/Pz/e9//1vefPNN6/1DQ0PNH322nyVK9/Xc+pkBpAktyo2U69atW3y7du3iz58/H+/v7x9/7NgxswUEBMRfuHDB3KZtHNHb9S3YtWuX2T969KjZ37ZtW6LHKFq0aHxMTIz12JNPPhnfsWNHFz+7jPt+qri4uPiVK1ea9/WVV16Jnz17tnl/Dh06ZG0/Y8aM+Hz58ln333333fhy5cpZ9xctWhSfNWvW+Js3b5r9SpUqxY8ZM8bhY69Zs8ac/8qVK2a/c+fO8fXr13fYdsuWLaatfq/B3saNG81rs3jx4ru20zYjR4607ut7pMd+/vlnsz9+/Pj45s2b293nxIkTps3+/fvNfqNGjeIbNGhgvV1/RgMDA+OfffZZ67EzZ86Y+2zYsMHh+5yc7wkLfdxq1aolal++fHnzPWjRpk2b+Oeee+6ur0NG+tn28fEx749u+rrmz5/f/Cw5+v372muvxZcpU8b8HrD9edef59jYWOv78J///Md8feDAAXP/33//3dr+4sWL8ZkzZ47/+uuvzb7+zm7durVdv7p06RKfPXt26/7ChQvjc+bMGR8REWH2tX9eXl6mf0Ba4c+UVBIcHCytW7c22T79K1C/1myDLR2O0GyQZp+CgoLMX5FKs1P3okNTOqfGQodEzp8/74JnAs3GaGYnICDADCNpVkbnLSodOipRooTT90H/+j906JB1qFO/HzQTZclwvfTSSyYzUb9+fXNpMMvk+rtlFh2pUqWKuU2HoTWLpUNbluHpjC452dXKlStbv9b3SH8uLe+nZpM0k6/fC5atbNmy5jbb6SO259CfUc1S6ftioUPTytnPa3K+J2zpnMyENLtoyUppFlSHvy1ZbYgZ1tefK900E6zD9/ozrlMBEtKMfd26de3mlup7pBnJkydPOmyv8x3r1KljPabfC2XKlDG3WTLEOqxtK+G+Vr7Q76P//e9/1t8h2m/L5wWQFggWU5H+UtYfbB0ecvQLWocQdYhLP9g3btxotqROTte5cLb0FxjzV1z7gaLBvS5a0vfTEuw5eh9sgxOdH6bvs35gO/qw1g/zI0eOmGFJHXKsWbOmTJs2zenwtzP6YbJy5Upzfh2m1nPoh9LRo0clo9O5h/q+7Nu3775+rjQo0PfSElxYNv2+0CkHdzuH7TFLsOHs5zU53xO2HA2x63w5PZcOgeoQq86DtsyXxZ3XTIedddPh5U8//dRMD9LfyemFTi3Q91F/h+hngy6UJOBHWiNYTEW62EF/uHV+iv7FauvSpUvmr8qRI0eajJAukEiYCdJfEkrnpCHtP1B0gVJKVkbqh//ChQvlv//9r8lCajbCls5F03lLOidVV1s7+6DSjNXdyrxoEKLn1nl327ZtM98/lmxERpYrVy7z8zdjxgyHC36SWoZGF67t3r3bZHQsAYZlcxSo3Q9n3xPJ/Z2gmSzNTGmgoX+4Jpw3jcQ/QzoP0LaShYX+jrbMO7TQOYe6aKlQoUIO2+viF0sSwPb3vv5Bp/QPus2bN9vdL+G+5XeIzl3/8MMPzTl1PjKQlggWU5Fme3S4Yc+ePXZDxkpXx+kvcg0gdJhSS3roYhdbmpXSbJJlIv21a9fc/AyQGjRQ0eFMHVpM+GGtq1d1NbVmAHXxgw5z6oeMI8OHDzcfJLqyV4cmNVOmk+x1Na5+IL311ltmMr1OY9AgQ1d0OjtXRqOBogZYOsSni0Q0G6g/mx988IEZWkwKXXiiIwE6dUTfBx161vdO39PU/IPubt8TugJWAxqdGqHvr2Y770UDDc2G6/Pt1q1bqvXzQaALibRagW76+uiCJEsGOSH9udOV0tpGf/aWLl1qpgno721HC000o92uXTvp3bu3WYym0xieeeYZKViwoDmu9Fy60l1XQOv35Mcff2xGBxKWUdL3/6GHHpJXX33VfP/dbZQBcAeCxVSmQYJuCekvF63FuGXLFlP0deDAgYlq92kWSz/M9BdIgQIFrL9g4Fn0vda5ixpQ6HCSLT2mQYh+GGgmWku6aPbAEb1txYoV5kNHgx4NcvQDS79P9HtMV8/qSkttpxnrSZMmmflXEDMvWAMvnVKgmTr9mdOVp5qp1YA7KfRnUDNJ+p7pimadh6iBna6mTs1VqXf7ntBAQzPHWrJF5z5quZx70dW0OpdW/2jR54B/6B/i+tropnML9Y+Ab775JlGZMstrr4Gdzm3UOcKa+e3Zs6f5WXNGM7o6l1RLGOnPq2Yl9RyWaQk6EqAr1zVY1HNqf/SzQOdHJ6SPpSNVDEEjPfDSVS5p3QngQaO/6DUTlLBuH+BqminTQEcDF4Yv0z/NRGrm8rfffrM7Pn78eBPIJnXBE+BKlKoHUpFOHdBFCjopnUAR7qQLaHSKgmaYNfvZtm3btO4SHNA6rZrl1nmvOgStUwZsRxc02Nfi3NOnT79rTUfAnQgWgVSkUwd02EqHrPQDAXAXnbuqq5918YUubuGydemT/n6YMGGC3Lhxw0yX0KlHOs/UQqcafPnll2ahEkPQSC8YhgYAAIBTLHABAACAUwSLAAAAcIpgEQAAAE4RLAIAAMApgkUAAAA4RbAIAAAApwgWAQAA4BTBIgAAAJwiWAQAAIA483+Df+ge33lzowAAAABJRU5ErkJggg==",
+      "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": [
+    "## 🔍 पियरसन सहसंबंध विश्लेषण\n",
+    "\n",
+    "यह नोटबुक डेटा विज़ुअलाइज़ेशन मॉड्यूल को बेहतर बनाती है और दिखाती है कि कई फीचर्स के बीच संबंध का विश्लेषण पियरसन सहसंबंध का उपयोग करके कैसे किया जा सकता है।\n",
+    "\n",
+    "पियरसन सहसंबंध दो सतत चर के बीच **रेखीय संबंध** की ताकत और दिशा को मापता है। यह -1 से 1 के बीच एक मान लौटाता है:\n",
+    "- **+1** → पूर्ण सकारात्मक संबंध  \n",
+    "- **0** → कोई रेखीय संबंध नहीं  \n",
+    "- **-1** → पूर्ण नकारात्मक संबंध\n",
+    "\n",
+    "यहां, हमने गणित, भौतिकी, रसायन विज्ञान और जीवविज्ञान में छात्र अंकों के लिए सहसंबंध मैट्रिक्स की गणना की। उत्पन्न **हीटमैप** हमें यह दृश्य रूप से समझने में मदद करता है कि प्रत्येक विषय के अंक एक-दूसरे से कितने मजबूत तरीके से संबंधित हैं।\n",
+    "\n",
+    "ऐसा सहसंबंध विश्लेषण **फीचर इंजीनियरिंग और प्रीप्रोसेसिंग** में विशेष रूप से मशीन लर्निंग मॉडल बनाने से पहले बहुत महत्वपूर्ण है। यह पहचानने में मदद करता है:\n",
+    "- अनावश्यक फीचर्स  \n",
+    "- मजबूत भविष्यवक्ता  \n",
+    "- संभावित मल्टीकोलिनियरिटी  \n",
+    "\n",
+    "यह वास्तविक दुनिया के डेटा सेट का विश्लेषण करते समय सार्थक विज़ुअलाइज़ेशन में एक उपयोगी जोड़ है।\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**अस्वीकरण**:  \nयह दस्तावेज़ AI अनुवाद सेवा [Co-op Translator](https://github.com/Azure/co-op-translator) का उपयोग करके अनुवादित किया गया है। जबकि हम सटीकता के लिए प्रयास करते हैं, कृपया ध्यान दें कि स्वचालित अनुवाद में त्रुटियां या अशुद्धियां हो सकती हैं। मूल भाषा में उपलब्ध मूल दस्तावेज़ को आधिकारिक स्रोत माना जाना चाहिए। महत्वपूर्ण जानकारी के लिए, पेशेवर मानव अनुवाद की सिफारिश की जाती है। इस अनुवाद के उपयोग से उत्पन्न किसी भी गलतफहमी या गलत व्याख्या के लिए हम उत्तरदायी नहीं हैं।  \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": "2025-09-06T19:38:02+00:00",
+   "source_file": "3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb",
+   "language_code": "hi"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/translations/hk/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb b/translations/hk/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb
new file mode 100644
index 00000000..0686c14d
--- /dev/null
+++ b/translations/hk/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb
@@ -0,0 +1,100 @@
+{
+ "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": [
+    "## 🔍 皮爾遜相關性分析\n",
+    "\n",
+    "此筆記本增強了數據可視化模組，展示如何使用皮爾遜相關性分析多個特徵之間的關係。\n",
+    "\n",
+    "皮爾遜相關性衡量兩個連續變數之間**線性關係**的強度和方向。它返回一個介於 -1 和 1 之間的值：\n",
+    "- **+1** → 完美正相關  \n",
+    "- **0** → 無線性關係  \n",
+    "- **-1** → 完美負相關\n",
+    "\n",
+    "在這裡，我們計算了數學、物理、化學和生物學的學生成績的相關矩陣。生成的**熱圖**幫助我們直觀地了解每個科目成績之間的相關性強度。\n",
+    "\n",
+    "這種相關性分析在**特徵工程和預處理**中至關重要，尤其是在建立機器學習模型之前。它有助於識別：\n",
+    "- 冗餘特徵\n",
+    "- 強預測因子\n",
+    "- 潛在的多重共線性\n",
+    "\n",
+    "在分析真實世界數據集時，這是一個有助於生成有意義可視化的實用工具。\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**免責聲明**：  \n本文件已使用人工智能翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 進行翻譯。儘管我們致力於提供準確的翻譯，但請注意，自動翻譯可能包含錯誤或不準確之處。原始文件的母語版本應被視為權威來源。對於重要信息，建議使用專業人工翻譯。我們對因使用此翻譯而引起的任何誤解或錯誤解釋概不負責。\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": "2025-09-06T19:37:36+00:00",
+   "source_file": "3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb",
+   "language_code": "hk"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/translations/hr/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb b/translations/hr/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb
new file mode 100644
index 00000000..c0a631a1
--- /dev/null
+++ b/translations/hr/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb
@@ -0,0 +1,100 @@
+{
+ "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": [
+    "## 🔍 Analiza Pearsonove korelacije\n",
+    "\n",
+    "Ovaj bilježnik proširuje modul za vizualizaciju podataka demonstrirajući kako analizirati odnos između više značajki koristeći Pearsonovu korelaciju.\n",
+    "\n",
+    "Pearsonova korelacija mjeri jačinu i smjer **linearnog odnosa** između dvije kontinuirane varijable. Vraća vrijednost između -1 i 1:\n",
+    "- **+1** → Savršen pozitivan odnos  \n",
+    "- **0** → Nema linearnog odnosa  \n",
+    "- **-1** → Savršen negativan odnos\n",
+    "\n",
+    "Ovdje smo izračunali matricu korelacije za ocjene učenika iz Matematike, Fizike, Kemije i Biologije. Dobiveni **toplinski prikaz** pomaže nam vizualno razumjeti koliko su ocjene iz svakog predmeta međusobno povezane.\n",
+    "\n",
+    "Takva analiza korelacije ključna je u **inženjeringu značajki i predobradi podataka**, posebno prije izrade modela strojnog učenja. Pomaže u identificiranju:\n",
+    "- Redundantnih značajki\n",
+    "- Jakih prediktora\n",
+    "- Potencijalne multikolinearnosti\n",
+    "\n",
+    "Ovo je korisno proširenje za stvaranje smislenih vizualizacija prilikom analize stvarnih skupova podataka.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Odricanje od odgovornosti**:  \nOvaj dokument je preveden korištenjem AI usluge za prevođenje [Co-op Translator](https://github.com/Azure/co-op-translator). Iako nastojimo osigurati točnost, imajte na umu da automatski prijevodi mogu sadržavati pogreške ili netočnosti. Izvorni dokument na izvornom jeziku treba smatrati mjerodavnim izvorom. Za ključne informacije preporučuje se profesionalni prijevod od strane stručnjaka. Ne preuzimamo odgovornost za bilo kakve nesporazume ili pogrešne interpretacije proizašle iz korištenja ovog prijevoda.\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": "2025-09-06T19:41:24+00:00",
+   "source_file": "3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb",
+   "language_code": "hr"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/translations/hu/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb b/translations/hu/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb
new file mode 100644
index 00000000..451230cd
--- /dev/null
+++ b/translations/hu/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb
@@ -0,0 +1,100 @@
+{
+ "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": [
+    "## 🔍 Pearson-korreláció elemzése\n",
+    "\n",
+    "Ez a jegyzetfüzet továbbfejleszti az adatvizualizációs modult azáltal, hogy bemutatja, hogyan elemezhetjük több jellemző közötti kapcsolatot Pearson-korreláció segítségével.\n",
+    "\n",
+    "A Pearson-korreláció a két folytonos változó közötti **lineáris kapcsolat** erősségét és irányát méri. Az eredmény egy -1 és 1 közötti érték:\n",
+    "- **+1** → Tökéletes pozitív kapcsolat  \n",
+    "- **0** → Nincs lineáris kapcsolat  \n",
+    "- **-1** → Tökéletes negatív kapcsolat\n",
+    "\n",
+    "Ebben az esetben kiszámítottuk a korrelációs mátrixot a diákok Matematikából, Fizikából, Kémiából és Biológiából elért pontszámai alapján. Az eredményül kapott **hőtérkép** vizuálisan segít megérteni, hogy az egyes tantárgyi pontszámok mennyire erősen kapcsolódnak egymáshoz.\n",
+    "\n",
+    "Az ilyen korrelációs elemzés kulcsfontosságú a **jellemzők tervezésében és előfeldolgozásában**, különösen gépi tanulási modellek építése előtt. Segít az alábbiak azonosításában:\n",
+    "- Felesleges jellemzők\n",
+    "- Erős előrejelzők\n",
+    "- Potenciális multikollinearitás\n",
+    "\n",
+    "Ez hasznos kiegészítése a valós adathalmazok elemzésekor alkalmazott értelmes vizualizációknak.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Felelősségkizárás**:  \nEz a dokumentum az [Co-op Translator](https://github.com/Azure/co-op-translator) AI fordítási szolgáltatás segítségével lett lefordítva. Bár törekszünk a pontosságra, kérjük, vegye figyelembe, hogy az automatikus fordítások hibákat vagy pontatlanságokat tartalmazhatnak. Az eredeti dokumentum az eredeti nyelvén tekintendő hiteles forrásnak. Kritikus információk esetén javasolt professzionális, emberi fordítást igénybe venni. Nem vállalunk felelősséget a fordítás használatából eredő félreértésekért vagy téves értelmezésekért.\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": "2025-09-06T19:40:38+00:00",
+   "source_file": "3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb",
+   "language_code": "hu"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/translations/id/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb b/translations/id/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb
new file mode 100644
index 00000000..8388878a
--- /dev/null
+++ b/translations/id/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb
@@ -0,0 +1,100 @@
+{
+ "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": [
+    "## 🔍 Analisis Korelasi Pearson\n",
+    "\n",
+    "Notebook ini meningkatkan modul visualisasi data dengan menunjukkan cara menganalisis hubungan antara beberapa fitur menggunakan korelasi Pearson.\n",
+    "\n",
+    "Korelasi Pearson mengukur kekuatan dan arah **hubungan linear** antara dua variabel kontinu. Korelasi ini menghasilkan nilai antara -1 dan 1:\n",
+    "- **+1** → Hubungan positif sempurna  \n",
+    "- **0** → Tidak ada hubungan linear  \n",
+    "- **-1** → Hubungan negatif sempurna\n",
+    "\n",
+    "Di sini, kami menghitung matriks korelasi untuk nilai siswa dalam Matematika, Fisika, Kimia, dan Biologi. **Heatmap** yang dihasilkan membantu kita memahami secara visual seberapa kuat hubungan antara nilai setiap mata pelajaran.\n",
+    "\n",
+    "Analisis korelasi semacam ini sangat penting dalam **rekayasa fitur dan praproses data**, terutama sebelum membangun model pembelajaran mesin. Analisis ini membantu mengidentifikasi:\n",
+    "- Fitur yang redundan\n",
+    "- Prediktor yang kuat\n",
+    "- Potensi multikolinearitas\n",
+    "\n",
+    "Ini adalah tambahan yang bermanfaat untuk visualisasi yang bermakna saat menganalisis dataset dunia nyata.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Penafian**:  \nDokumen ini telah diterjemahkan menggunakan layanan terjemahan AI [Co-op Translator](https://github.com/Azure/co-op-translator). Meskipun kami berupaya untuk memberikan hasil yang akurat, harap diperhatikan bahwa terjemahan otomatis mungkin mengandung kesalahan atau ketidakakuratan. Dokumen asli dalam bahasa aslinya harus dianggap sebagai sumber yang berwenang. Untuk informasi yang bersifat kritis, disarankan menggunakan jasa terjemahan manusia profesional. Kami tidak bertanggung jawab atas kesalahpahaman atau penafsiran yang keliru yang timbul dari penggunaan terjemahan ini.\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": "2025-09-06T19:40:11+00:00",
+   "source_file": "3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb",
+   "language_code": "id"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/translations/it/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb b/translations/it/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb
new file mode 100644
index 00000000..095d12dc
--- /dev/null
+++ b/translations/it/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb
@@ -0,0 +1,100 @@
+{
+ "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": [
+    "## 🔍 Analisi della Correlazione di Pearson\n",
+    "\n",
+    "Questo notebook arricchisce il modulo di visualizzazione dei dati dimostrando come analizzare la relazione tra più caratteristiche utilizzando la correlazione di Pearson.\n",
+    "\n",
+    "La correlazione di Pearson misura la forza e la direzione di una **relazione lineare** tra due variabili continue. Restituisce un valore compreso tra -1 e 1:\n",
+    "- **+1** → Relazione positiva perfetta  \n",
+    "- **0** → Nessuna relazione lineare  \n",
+    "- **-1** → Relazione negativa perfetta\n",
+    "\n",
+    "Qui, abbiamo calcolato la matrice di correlazione per i punteggi degli studenti in Matematica, Fisica, Chimica e Biologia. La **heatmap** risultante ci aiuta a comprendere visivamente quanto ciascun punteggio di materia sia correlato agli altri.\n",
+    "\n",
+    "Un'analisi di correlazione di questo tipo è fondamentale nella **feature engineering e nel preprocessing**, soprattutto prima di costruire modelli di machine learning. Aiuta a identificare:\n",
+    "- Caratteristiche ridondanti\n",
+    "- Predittori forti\n",
+    "- Potenziale multicollinearità\n",
+    "\n",
+    "Questa è un'aggiunta utile per visualizzazioni significative quando si analizzano dataset reali.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Disclaimer**:  \nQuesto documento è stato tradotto utilizzando il servizio di traduzione automatica [Co-op Translator](https://github.com/Azure/co-op-translator). Sebbene ci impegniamo per garantire l'accuratezza, si prega di notare che le traduzioni automatiche possono contenere errori o imprecisioni. Il documento originale nella sua lingua nativa dovrebbe essere considerato la fonte autorevole. Per informazioni critiche, si raccomanda una traduzione professionale effettuata da un traduttore umano. Non siamo responsabili per eventuali incomprensioni o interpretazioni errate derivanti dall'uso di questa traduzione.\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": "2025-09-06T19:38:52+00:00",
+   "source_file": "3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb",
+   "language_code": "it"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/translations/ja/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb b/translations/ja/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb
new file mode 100644
index 00000000..db89b4a9
--- /dev/null
+++ b/translations/ja/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb
@@ -0,0 +1,100 @@
+{
+ "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": [
+    "## 🔍 ピアソン相関分析\n",
+    "\n",
+    "このノートブックでは、複数の特徴間の関係をピアソン相関を用いて分析する方法を示し、データ可視化モジュールを強化します。\n",
+    "\n",
+    "ピアソン相関は、2つの連続変数間の**線形関係**の強さと方向を測定します。結果は-1から1の間の値を返します：\n",
+    "- **+1** → 完全な正の関係  \n",
+    "- **0** → 線形関係なし  \n",
+    "- **-1** → 完全な負の関係\n",
+    "\n",
+    "ここでは、数学、物理、化学、生物の学生のスコアに基づいて相関行列を計算しました。得られた**ヒートマップ**は、各科目のスコアが他の科目とどれだけ強く関連しているかを視覚的に理解するのに役立ちます。\n",
+    "\n",
+    "このような相関分析は、特に機械学習モデルを構築する前の**特徴エンジニアリングや前処理**において重要です。この分析により以下を特定できます：\n",
+    "- 冗長な特徴  \n",
+    "- 強力な予測因子  \n",
+    "- 潜在的な多重共線性  \n",
+    "\n",
+    "実世界のデータセットを分析する際に、意味のある可視化を補完する有用な手法です。\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**免責事項**:  \nこの文書は、AI翻訳サービス [Co-op Translator](https://github.com/Azure/co-op-translator) を使用して翻訳されています。正確性を追求しておりますが、自動翻訳には誤りや不正確な表現が含まれる可能性があります。元の言語で記載された原文が正式な情報源と見なされるべきです。重要な情報については、専門の人間による翻訳を推奨します。この翻訳の使用に起因する誤解や誤認について、当社は一切の責任を負いません。\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": "2025-09-06T19:37:49+00:00",
+   "source_file": "3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb",
+   "language_code": "ja"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/translations/ko/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb b/translations/ko/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb
new file mode 100644
index 00000000..d22cfe7e
--- /dev/null
+++ b/translations/ko/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb
@@ -0,0 +1,100 @@
+{
+ "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": [
+    "## 🔍 피어슨 상관분석\n",
+    "\n",
+    "이 노트북은 데이터 시각화 모듈을 확장하여 여러 특성 간의 관계를 피어슨 상관분석을 통해 분석하는 방법을 보여줍니다.\n",
+    "\n",
+    "피어슨 상관계수는 두 연속형 변수 간의 **선형 관계**의 강도와 방향을 측정합니다. 이 값은 -1에서 1 사이의 값을 반환합니다:\n",
+    "- **+1** → 완벽한 양의 상관관계  \n",
+    "- **0** → 선형 관계 없음  \n",
+    "- **-1** → 완벽한 음의 상관관계\n",
+    "\n",
+    "여기서는 수학, 물리, 화학, 생물 과목에서 학생 점수의 상관행렬을 계산했습니다. 결과로 나온 **히트맵**은 각 과목 점수 간의 관계가 얼마나 강한지 시각적으로 이해하는 데 도움을 줍니다.\n",
+    "\n",
+    "이러한 상관분석은 특히 머신러닝 모델을 구축하기 전에 **특성 엔지니어링 및 전처리**에서 매우 중요합니다. 이를 통해 다음을 식별할 수 있습니다:\n",
+    "- 중복된 특성  \n",
+    "- 강력한 예측 변수  \n",
+    "- 잠재적 다중공선성  \n",
+    "\n",
+    "이는 실제 데이터셋을 분석할 때 의미 있는 시각화를 추가하는 데 유용한 도구입니다.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**면책 조항**:  \n이 문서는 AI 번역 서비스 [Co-op Translator](https://github.com/Azure/co-op-translator)를 사용하여 번역되었습니다. 정확성을 위해 최선을 다하고 있으나, 자동 번역에는 오류나 부정확성이 포함될 수 있습니다. 원본 문서를 해당 언어로 작성된 상태에서 권위 있는 자료로 간주해야 합니다. 중요한 정보의 경우, 전문적인 인간 번역을 권장합니다. 이 번역 사용으로 인해 발생할 수 있는 오해나 잘못된 해석에 대해 당사는 책임을 지지 않습니다.  \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": "2025-09-06T19:37:55+00:00",
+   "source_file": "3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb",
+   "language_code": "ko"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/translations/lt/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb b/translations/lt/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb
new file mode 100644
index 00000000..91b0db93
--- /dev/null
+++ b/translations/lt/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb
@@ -0,0 +1,100 @@
+{
+ "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": [
+    "## 🔍 Pearson koreliacijos analizė\n",
+    "\n",
+    "Ši užrašų knygelė praplečia duomenų vizualizacijos modulį, parodydama, kaip analizuoti ryšį tarp kelių savybių naudojant Pearson koreliaciją.\n",
+    "\n",
+    "Pearson koreliacija matuoja **linijinio ryšio** stiprumą ir kryptį tarp dviejų nenutrūkstamų kintamųjų. Ji grąžina reikšmę tarp -1 ir 1:\n",
+    "- **+1** → Tobulas teigiamas ryšys  \n",
+    "- **0** → Nėra linijinio ryšio  \n",
+    "- **-1** → Tobulas neigiamas ryšys\n",
+    "\n",
+    "Čia buvo apskaičiuota koreliacijos matrica studentų rezultatams Matematikos, Fizikos, Chemijos ir Biologijos dalykuose. Sukurtas **šilumos žemėlapis** padeda vizualiai suprasti, kaip stipriai kiekvieno dalyko rezultatai yra susiję tarpusavyje.\n",
+    "\n",
+    "Tokia koreliacijos analizė yra labai svarbi **savybių inžinerijoje ir išankstiniame apdorojime**, ypač prieš kuriant mašininio mokymosi modelius. Ji padeda nustatyti:\n",
+    "- Perteklines savybes\n",
+    "- Stiprius prognozavimo veiksnius\n",
+    "- Potencialią multikolinearumo problemą\n",
+    "\n",
+    "Tai naudinga priemonė prasmingoms vizualizacijoms, analizuojant realaus pasaulio duomenų rinkinius.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Atsakomybės apribojimas**:  \nŠis dokumentas buvo išverstas naudojant dirbtinio intelekto vertimo paslaugą [Co-op Translator](https://github.com/Azure/co-op-translator). Nors siekiame tikslumo, atkreipiame dėmesį, kad automatiniai vertimai gali turėti klaidų ar netikslumų. Originalus dokumentas jo gimtąja kalba turėtų būti laikomas autoritetingu šaltiniu. Dėl svarbios informacijos rekomenduojame kreiptis į profesionalius vertėjus. Mes neprisiimame atsakomybės už nesusipratimus ar klaidingus aiškinimus, kylančius dėl šio vertimo naudojimo.\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": "2025-09-06T19:42:01+00:00",
+   "source_file": "3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb",
+   "language_code": "lt"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/translations/mo/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb b/translations/mo/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb
new file mode 100644
index 00000000..9ee1b470
--- /dev/null
+++ b/translations/mo/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb
@@ -0,0 +1,100 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b1e38707",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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XcxYBAADgFMEiAAAAnCJYBAAAgFMEiwAAAPfh119/NdUMChQoYEpmOSpflpDWX61evbqp5KCVDfTymwnp1ZW0ekNAQIDUqVPHVNJIWNmgf//+plqElgHTKhxaw9eWXqlMS7RpwX2tnqDVKu5WvswRgkUAAID7EB4ebsqkaXCXFFrKTQM4LZ22fft2U++0V69edrVptS6sXk5W67Ru3brVnF8vy2l7udSBAweaklfffPONqZF6+vRpUzrNQuvT6uNonV29YISWbNOgVC9zmxyshgYAAEglXl5epqav1tF1Rq+Y9OOPP5oLIVjoJU21Fu+yZcvMvmYSa9WqZYrlK70ClV4KVC+Jqldb0vqywcHB5vrzTzzxhPWqVHqlMK1P+9BDD5nL3mrtWg0i8+XLZ9ro1cX08S9cuGDquyYFmUUAAIAE9CpcepWu6zab5cpc90uDuYSXQ9WsoR5XmgnUCwjYttErg+m+pY3eHh0dbddGLzahlwy1tNF/9apilkDR8jj6XHbv3u15V3DJXKRzWncBbtR+bt+07gLc6MT1O5ezQ8Zwdnria47jwXVwRc8HMnZ4tUcZc2UjWzokfL9XuFJ6+VHbAE7pvgZxejUtvZKYDiE7amO5pr2eQzODOXLkSNRGb7vb41hu87hgEQAAIL3QS8bqnEFbuhglIyJYBAAAHsnLy3Wz6TQwdFVwGBISkmjVsu4HBQVJ5syZzXXvdXPURu9rOYcOV+s8R9vsYsI2CVdQW85paZMUzFkEAABwo7p168qqVavsjq1cudIcVzq8XKNGDbs2usBF9y1t9HZfX1+7Nvv37zelcixt9N9du3bZraDWx9GgtHz58knuL5lFAADgkbzSSc7r5s2bcujQIbvSOFoSJ1euXGbBiQ5pnzp1SubOnWtu79u3r1nlPHToUOnRo4esXr1avv76a7NC2kKHwLt16yY1a9aU2rVry5QpU0yJnu7du5vbs2fPLj179jTt9HE0ANSV0hog6kpo1bx5cxMUPvvsszJhwgQzT3HkyJGmNmNysqYEiwAAAPfhr7/+MjUTLSxzHTXY07qGZ86cMRk/i2LFipnAUOskTp06VQoVKiSffvqpWals0bFjR1PeRmsiapBXtWpVU1bHdsHK+++/b1ZJazFuXamt9//www+tt+tQ9g8//CD9+vUzQWRgYKDp07hx4zyzziKroTMWVkNnLKyGzlhYDZ2xpOVq6Kyh3Vx27pvH5rjs3J6GzCIAAPBIrlzggn/wKgMAAMApMosAAMBjL60H1yOzCAAAAKfILAIAAA9FzssdeJUBAADgFJlFAADgkVgN7R68ygAAAHCKzCIAAPBIZBbdg1cZAAAATpFZBAAAHsmLnJdbECwCAACPxDC0e/AqAwAAwCkyiwAAwCORWXQPXmUAAAA4RWYRAAB4JDKL7sGrDAAAAKfILAIAAI/kJV5p3YUMgcwiAAAAnCKzCAAAPBJzFt2DYBEAAHgkgkX34FUGAACAU2QWAQCARyKz6B68ygAAAHCKzCIAAPBQ5LzcgVcZAAAATpFZBAAAHok5i+7BqwwAAACnyCwCAACPRGbRPQgWAQCAR/JigNQteJUBAADgFJlFAADgkRiGdg9eZQAAADhFZhEAAHgkLy+vtO5ChkBmEQAAAE6RWQQAAB6JOYvuwasMAAAAp8gsAgAAj0SdxXQcLMbGxsrnn38uq1atkvPnz0tcXJzd7atXr06t/gEAADjEMHQ6Dhb/85//mGCxdevWUrFiRVYjAQAAPKBSFCx+9dVX8vXXX0urVq1Sv0cAAABJQGbRPVL0Kvv5+UnJkiVTvzcAAADw/GBx8ODBMnXqVImPj0/9HgEAACRxgYurNqRgGPrxxx9PtIjl559/lgoVKoivr6/dbYsXL07qaQEAAJCOJTl0zp49u93Wvn17adSokeTJkyfRbQAAAC6ncxZdtSXTjBkzJDQ0VAICAqROnTqyadMmp22jo6Nl3LhxUqJECdO+SpUqsmzZMrs2N27ckJdfflmKFi0qmTNnlnr16snmzZvtn76Xl8Ptvffes7bRPiW8/Z133nFNZnH27NnJOjGcq1+7rAzs+6hUr1Rc8ufLKU/1miTfr/grrbuFZDq/Zo2cW7lCoq9dk8yFCkmRTp0lsFgxh23jY2PkzM/L5NKGPyT66lUJCAmRgu0fl+wVK1rbxEZEyOmlS+Xq9m0SfeOGZClcWAp37CSBoaFufFZwpn1ofulcsqDk8veTw9fDZcquw7L36k2HbX28vOTZUoWkZeG8kifAX07cvC0f7Tkqmy5ctbbJ7OMjvcoWkYb5c0tOf185cC1cPvj7iOxzck6kT7UqhUivJytJhVK5JV/uQOk35hf55Y/jad0tuNnChQtl0KBBMnPmTBMoTpkyRVq0aCH79++XvHnzJmo/cuRImT9/vnzyySdStmxZWb58uUnC/fHHH1KtWjXTplevXvL333/LvHnzpECBAqZ9s2bNZM+ePVKwYEHT5syZM3bn1RHfnj17SocOHeyOa2Dau3dv6362bNmS9fxSNCj/r3/9S65e/eeXnsX169fNbbi7wCz+smtPmLw8clZadwUpdHnzZjn57TeSv/WjUm7ESMlSqLAc/GCqRF+/7rD9qSVL5eJvv5qAssKYsRLcsKEcnvmR3AoLs7Y5PneuXN+7R0K795Dyo0ZLUPnycuD9yRJ15Yobnxkc+VeBPPJihWLy+f4w6bVumxy6Fi6THqooOfzsp+BY9C5bVNoWDZEpu47Is2u2yNLjZ+St2uWkVFCgtc2rVUtKreAc8sbWA9Jt7TbZfOGqvF+3ouQJ8HPjM8P9yhyQSfYduSxjp29I665k2NXQrtqSY/LkySYY6969u5QvX94EjVmyZJFZsxx/zmsA+Nprr5mqMsWLF5d+/fqZrydNmmRuv337tixatEgmTJggDRs2NIuKx4wZY/796KOPrOcJCQmx25YuXSpNmjQx57SlwaFtu8DAf34XuSxYXLt2rURFRSU6HhERIb/99ltKTpmhrFi7Q8ZO/Fq+W0420VOd+2Wl5GnQQPLUry+ZCxSQIl26iLefn1z643eH7S9v/FNCWj4i2StVEv/gYAlu1NhkFc+tXGluj4uKkivbtkqhDh0kW+nSEpA3rxRo09b8e2HdOjc/OyTUsURB+T7srPx04rwcu3lbJu48JBGxsdK6SD6H7VsUDpZ5B0/Kn+evyJlbkbLk2FnZcO6KdCp5Jxvg5+0tjfLnkY/2HJMdl6/LqfAImb0/zPz7WGiIm58d7sevm0/K+59vkZW/k01MC86GYVNjS6qoqCjZsmWLyfpZeHt7m/0NGxz/EREZGWmGn23pUPP69evN1zExMeYCKHdrk9C5c+fkxx9/NJnFhHTYOXfu3CZrqUPUen6X1VncuXOn9WtNg549e9a6r09Kx9stqVHgQRUXE2MygvkfecR6zMvbW7KVLSc3jxxxeh/vBAvBvH395ObhQ+breL0KUlyceGWyb+Pl62ttg7SRyctLSmfPKvMPnrAe0zoQf128KhVyOh7K8fX2lqgEV7bS/Uq5gqzD1Jm8vRK1iYyNlcq5mPcNpAca0Olmy9/f32y2Ll68aGKgfPns/3jU/X379okjOkSt2UjNGuq8Rb0ini4O1vNYMoF169aV8ePHS7ly5cy5vvzySxN8OitdOGfOHHO/hAuSX3rpJalevbrkypXLDHMPHz7cDF/r47skWKxatao14nY03KwR77Rp05JzSsDjxNy8aQK7TNnufPBb+AZlk4iz9vNHLILKVzDZyKylSpnM4o19+0wmUf6//JRPQIAEFi8uZ376UQLy5xffoCC5vGmThB85Iv4O5rvAfbL7+ZrA7nJktN3xK5HRUjRrFof32XT+inQsXkB2XLpmsoU1gnNIw5Dc4v3/2YrbsbGy6/J16Va6iBy7sV+uREZJs0LBUiFXkJwKv+2W5wU8CFxZ4ubtt9+WsWPH2h0bPXq0GQ6+X1p+UIetdb6ixlQaMOoQtu2wtQ5V9+jRwyThfHx8TMDXuXNnk8V0RO/bpUuXRNlInUtpUblyZVMru0+fPub5JQx8UyVYPHr0qKmtqGPhusonODjYeps+uE7i1CeUkmg9Pj5WvLzufV/AExXu2FGOz5sru0eP0nETEzDmqVdfLtoMWxfr0UOOzZkju14dqmMYkqVIEclVq7bcCmN4y9PoQpWhVUrJ/H/VMH8PnL51W346cc5u2FrnKg6vWkqWtKgtMXHxcuDaTVl16oLJYgJIe5qBsw20lKPgSqvCaOyjw8C2dF/nBzqi8dOSJUvM9L1Lly6ZBSzDhg2zm2uoAeS6deskPDzcrAnJnz+/dOzYMdF8RKVTAHUxjS60uRddgKPD0MeOHZMyZcpIqgeLunxbxSUYOkmNaN0nqIL4Zq90X+cF3CFT1qwmmIu5Yb+YJfr6DfF1UjrKN1s2KflCf4mLjjaZSd8cOeTU4sXinyePtY1/cF4p88oQiY2MlLiI2+KbPYcc+e9/xc+mDdzvWlS0CeZy+dtPEdAVzJciEs/dVlejYuS1zXvFz9tLgvx85WJElPQtFyqnwyOsbU7fipABf+ySAB9vCczkI5cio2VMjTJy5tY/bQCk3eX+HA05O6LJsho1apih5Mcee8waJ+n+iy++KHejWUDNHGopHV3Q8tRTTyVqo4tRdLty5YpZNa2LXhL67LPPTB+0BM+9bN++3cypdLRKO1WvDW07bzEsLCzRYpe2bdsmO1rPW6HX/XQFcBvvTJlM1u/63n2So2o165zDG/v2St4mTe5+X19f8cuZ05TSubptq+SsUTNRGx9/f7PF6F+Te3ZLwcftSyDAvWLi72T9auTJIb+dvWyO6WCy7i8+6njagUVUXLwJFHWOYqMCuWXNqYuJ2kTExpktq6+P1M6b05TYAeBZBg0aJN26dZOaNWtK7dq1TekczQjq0LLq2rWrCQo1WaY2btwop06dMtP79F8d2tYAc+jQodZzamCoo7ma/Tt06JAMGTLEDFtbzmmhWcdvvvnGupLals5x1MfSFdI6n1H3Bw4cKM8884zkzJnTtcHikSNHTD2gXbt2mbF2y2X/LKuHLBM0kxOtZ6QhaC2dU8JmxWNo4WCpXL6oXLl6U06cvpSmfUPS5Gv2bzn2+WwJDC0qWUKLyflVv5gVzbnr1Te3H509S/xy5DC1FFX40SMSdeWqqZ0YdfWqnPn+e/Nzk69FC+s5r+3ebeYwag3GyPPn5eSib83XeerXS7PniTsWHj4lr1UrLfuu3ZS9V27Ik8ULmDqJOrSsRlQrLRcjIuXjvXemDJTPkVXyZPaXg9duSnCAv/QoU8RcQOyLQyet56wdnMP8eyL8thQMzCwvlA+VsBu35Kew82n0LJESWQIySdEC/8xfLhSSVcoVzyVXb0TKmQvhadq3DCEZq5ZdqWPHjnLhwgUZNWqUWfyrQaAu+rUsetHEmmbzLHT4WWstajyVNWtWUzZH5yjmyHHn94K6du2aSa6dPHnSLE7R2olvvvlmoqvmffXVV+bzROczJqSxlt6uwahO/ytWrJgJFhMm7O7FKz4FF3hu06aNGZ//9NNPzQPr/EUdc9drRk+cOFEefvjh5J5SMhdJ/CQfVA8/VE5WfD0q0fF536yT5wfPlIyg/dy+4unOr1kt51asMLUV7xTl7iSBxe7MJdk/aaL4584toc/d+QvwxoH9EvbFFxJ54YJ4+/ubEjoaSGpAaXH5r7/k1P8Wm6LdPlmySM7q1aXgY4+JT2bHiyg8yYnrnv/H4OM2RbkPXQ+XqbsOy57/L6D9Qb1KcvZWhLy1/aDZr5o7SAZXLin5swTI7ZhYU0Jn5p5jcinyn1GYJgXySJ9yRU0weSM6RtaeuSif7D0u4TF3/2PbE5ydvl8yitqVQ2TBxNaJji9ecUBenZgxSskdXJG4VIu7lK79ocvOfWDTCy47t6dJUbCokzn12tC6qkYv76fBoqZJ9ZgGjNu2bUt2RzJSsIgHI1hExgoWkXQZKVhEGgeLD7kwWPyTYNEiRTNDdZjZcqkYDRxPnz5tXQCjq3EAAADcMgztqg33N2exYsWKsmPHDjMErUuwdWWOrgb673//63BJNwAAADJQsKiTMnWVj9ISODqHUecp6qVkdCIlAACAy5EBTL/Bol6mxqJUqVLmcjaXL182y7CTcz1FAAAAPEDBol52JilsL1cDAADgEq6ryY2UBouff/65WcRSrVo1a21FAAAAPLiSFSz269dPvvzyS3ONaK0grhXAtVAkAACAu8Uz9S39JXBnzJghZ86cMZej+f7776Vw4cLmOoaWS9IAAAAgg4/266Vj9JIyK1euNNeGrlChgrzwwgsSGhoqN2/euZoBAACAy3m5cMP9rYa20OscWq4Nfa/rQQMAAKQqb6K6dJlZ1AtR67zFf//731K6dGnZtWuXTJ8+3VwkWy+GDQAAgAyaWdThZi26rXMVtYyOBo16uT8AAAC3Y4FL+gsWZ86cKUWKFDGX9Fu3bp3ZHFm8eHFq9Q8AAACeEix27dqVK7QAAID0gZAkfRblBgAAQMZxX6uhAQAA0gyrod2CqyoCAADAKTKLAADAM7GOwi0IFgEAgGciVnQLhqEBAADgFJlFAADgmVjg4hZkFgEAAOAUmUUAAOCZSCy6BZlFAAAAOEVmEQAAeKR4Sue4BZlFAAAAOEVmEQAAeCZWQ7sFmUUAAAA4RWYRAAB4JhKLbkGwCAAAPBMLXNyCYWgAAAA4RWYRAAB4Jha4uAWZRQAAADhFZhEAAHgmEotuQWYRAAAATpFZBAAAnonV0G5BZhEAAABOkVkEAACeicyiWxAsAgAAz8T4qFvwMgMAAMApMosAAMAzMQztFmQWAQAA4BSZRQAA4JlILLoFmUUAAAA4RbAIAAA8Ury3l8u25JoxY4aEhoZKQECA1KlTRzZt2uS0bXR0tIwbN05KlChh2lepUkWWLVtm1+bGjRvy8ssvS9GiRSVz5sxSr1492bx5s12b5557Try8vOy2li1b2rW5fPmydOnSRYKCgiRHjhzSs2dPuXnzZrKeG8EiAADAfVi4cKEMGjRIRo8eLVu3bjXBX4sWLeT8+fMO248cOVI+/vhjmTZtmuzZs0f69u0r7du3l23btlnb9OrVS1auXCnz5s2TXbt2SfPmzaVZs2Zy6tQpu3NpcHjmzBnr9uWXX9rdroHi7t27zbl++OEH+fXXX+X5559P1vPzio+Pj5d0IHORzmndBbhR+7l907oLcKMT133Sugtwo7PT96d1F+BGB1f0TLPHLvG0fWCUmg5/kfS4pE6dOlKrVi2ZPn262Y+Li5PChQvLgAEDZNiwYYnaFyhQQEaMGCH9+/e3HuvQoYPJIM6fP19u374t2bJlk6VLl0rr1q2tbWrUqCGPPPKIvPHGG9bM4tWrV2XJkiUO+7V3714pX768yUjWrFnTHNMMZqtWreTkyZOmH0lBZhEAAHgmLxduSRQVFSVbtmwxWT8Lb29vs79hwwaH94mMjDTDz7Y0UFy/fr35OiYmRmJjY+/axmLt2rWSN29eKVOmjPTr108uXbpkvU0fX4eeLYGi0n5p/zZu3Jjk50iwCAAA4CCgu379ut2mxxK6ePGiCezy5ctnd1z3z5496/DcOkQ9efJkOXjwoMlC6hDx4sWLzTCy0qxi3bp1Zfz48XL69Glzfs04avBnaWMZgp47d66sWrVK3n33XVm3bp3JPGp7pY+vgaStTJkySa5cuZz2zRGCRQAA4Jl0IYqLtrfffluyZ89ut+mx1DB16lQpVaqUlC1bVvz8/OTFF1+U7t27m4yfhc5V1JmCBQsWFH9/f/nggw+kc+fOdm06deokbdu2lUqVKsljjz1m5iTqkLNmG1MTwSIAAEACw4cPl2vXrtlteiyhPHnyiI+Pj5w7d87uuO6HhIQ4PHdwcLCZZxgeHi7Hjx+Xffv2SdasWaV48eLWNrpSWjOFunL5xIkTZnW1rqK2bZOQ3qb9OXTokNnXx0+4yEaHuHWFtLO+OUKwCAAAPPdyfy7aNJun5WaCbDY9lpBmBnXhiQ4FW+jQsu7rUPLd6JxEzRxqALdo0SJp165dojaBgYGSP39+uXLliixfvtxhGwtdtKJzFrW90sfXBTA6p9Ji9erVpn+6KCepuIILAADAfRg0aJB069bNLCSpXbu2TJkyxWQNdWhZde3a1QSFlmFsXVyiJXCqVq1q/h0zZowJ4IYOHWo9pwaGOgytC1c0UzhkyBAzbG05p2Ycx44da1ZRa5bw8OHD5v4lS5Y0cyJVuXLlzLzG3r17y8yZM01mUoe8dfg6qSuh01WwSCmVjOV/XWemdRfgRrfDxqZ1F+BGJb4MS+suIKNIJ5f769ixo1y4cEFGjRplFo5oEKglaiyLXsLCwuzmGkZERJhai0eOHDHDz1rKRuco6splC8uwt2YLdUGKBoVvvvmm+Pr6mtt16Hvnzp0yZ84ckz3U4E9rMeqiGNsM6IIFC0yA2LRpU9MHPY/Of/TIOotPr12X1l2AGxEsZiwEixlLic5/pXUX4EaHv3w6zR67RLeFLjv34TkdXXZuT5NuMosAAADJkoLL8iH5CBYBAIBnIlh0C1ZDAwAAwCkyiwAAwCPFk1h0CzKLAAAAcIrMIgAA8EzMWXQLMosAAABwiswiAADwTHppPrgcmUUAAAA4RWYRAAB4JuYsugXBIgAA8EyMj7oFLzMAAACcIrMIAAA8Ewtc3ILMIgAAAJwiswgAADwTC1zcgswiAAAAnCKzCAAAPFI8cxbdgswiAAAAnCKzCAAAPBMpL7cgWAQAAJ6JBS5uQUwOAAAAp8gsAgAAz8QCF7cgswgAAACnyCwCAADPxJxFtyCzCAAAAKfILAIAAM9EYtEtyCwCAADAKTKLAADAI8UzZ9EtCBYBAIBnIlh0C4ahAQAA4BSZRQAA4Jkoyu0WZBYBAADgFJlFAADgmUh5pd+X+fbt23Lr1i3r/vHjx2XKlCmyYsWK1OwbAAAAPDFYbNeuncydO9d8ffXqValTp45MmjTJHP/oo49Su48AAACO5yy6asP9BYtbt26Vhx9+2Hz97bffSr58+Ux2UQPIDz74ICWnBAAAwIMyZ1GHoLNly2a+1qHnxx9/XLy9veWhhx4yQSMAAIDLUWcx/WYWS5YsKUuWLJETJ07I8uXLpXnz5ub4+fPnJSgoKLX7CAAA4DhYdNWG+wsWR40aJa+88oqEhoaa+Yp169a1ZhmrVauWklMCAADgQRmGfuKJJ6RBgwZy5swZqVKlivV406ZNpX379qnZPwAAAIfiWYiSfoPFa9euiZ+fX6Isog5PZ8pE6UYAAIAMPQzdqVMn+eqrrxId//rrr81tAAAAboliXLXBKkUvx8aNG6VJkyaJjjdu3NjcBgAAgAdDisaMIyMjJSYmJtHx6Ohoc3UXAAAAl2POYvrNLNauXVv++9//Jjo+c+ZMqVGjRmr0CwAAAJ4aLL7xxhvy6aefSsOGDWXs2LFm069nzZolb731Vur3EgAAIB3XWZwxY4YpKRgQEGDKCm7atMlpWx2JHTdunJQoUcK018oyy5Yts2tz48YNefnll6Vo0aKSOXNmqVevnmzevNnuHK+++qpUqlRJAgMDpUCBAtK1a1c5ffq03Xm0T15eXnbbO++84/pgsX79+rJhwwYpXLiwWdTy/fffm5XQO3futF4GEAAAICNYuHChDBo0SEaPHm0uiazBX4sWLczFShwZOXKkfPzxxzJt2jTZs2eP9O3b15Qe3LZtm7VNr169ZOXKlTJv3jzZtWuXuQBKs2bN5NSpU9ar6eljvf766+bfxYsXy/79+6Vt27aJHk8DUy13aNkGDBiQrOfnFR8fHy/pwNNr16V1F+BG/+s6M627ADe6HTY2rbsANyrR+a+07gLc6PCXT6fZYxd9b7XLzn18yL+S3LZOnTpSq1YtmT59utmPi4szCTUNyoYNG5aovWYBR4wYIf3797ce69Chg8kgzp8/36z/0MsqL126VFq3bm1to1P9HnnkETPC64hmHnWqoF56uUiRItbMomYodUupJGcWr1+/bvf13TYAAACX83Ldpot5E8Y3kZGRiboQFRUlW7ZsMVk/C29vb7Ovo7CO6Hl0+NmWBorr1683X+si4tjY2Lu2cVYHW4eZc+TIYXdch51z585t6mO/9957Dhcpp0qwmDNnTms6VTuh+wk3y3EAAABP9vbbb0v27NntNj2W0MWLF01gly9fPrvjun/27FmH59Yh6smTJ8vBgwdNFlKHm3UYWYeIlWYV9VLK48ePN3MQ9fyacdTg09ImoYiICDOHsXPnzhIUFGQ9/tJLL5na2GvWrJE+ffqYtSVDhw51Temc1atXS65cuczX+oBI7PyaNXJu5QqJvnZNMhcqJEU6dZbAYsUcto2PjZEzPy+TSxv+kOirVyUgJEQKtn9cslesaG0TGxEhp5culavbt0n0jRuSpXBhKdyxkwSGhrrxWeF+1a9dVgb2fVSqVyou+fPllKd6TZLvVzBM52kWLPhRPvtssVy4cEXKli0mr7/eRypXLu2wbXR0jHz88TeyZMlqOXfukhQrVlBeeeU5adjwn2oR+st/2rQv5bvv1sjFi1clb95c0r59U3nhhY4mMwDPUKtssPR+tLxULJ5T8uXMIn0n/Sor/zqZ1t3KMOJTsBAlqYYPH27mIdry9/eX1DB16lTp3bu3lC1b1vy860KX7t27m4XCFjpXsUePHlKwYEHx8fGR6tWrm0BQs5gJ6WKXp556SnRm4UcffWR3m+1zqFy5srkCnwaNGvgm9fkkOVhs1KiRw69xx+XNm+Xkt99Ikae7mADx/KpVcvCDqVJh7DjxtYnwLU4tWSqXN22Uos88awLF63t2y+GZH0nZoa9Klv+fZ3B87ly5ffqUhHbvIb45csjljX/KgfcnS4UxY8WPDK7HCMziL7v2hMnchWtl4SeD07o7SIGffvpN3n77Uxk7tr9UqVJa5sz5Tnr2HCXLls2U3Lnth3vUlCnzTRD4xhsDpHjxQvLbb1vlxRffkq++miDly5cwbT75ZJF8+eVP8u67A6VkySLy99+HZPjwqZItWxbp2jXxBHWkT1n8M8m+sCvy7drD8tHghmndHaQiDaSSEkzlyZPHBHPnzp2zO677ISEhDu8THBwsS5YsMdnAS5cumTmMOrexePHi1jYaQK5bt07Cw8PNEHj+/PmlY8eOdm1sA0Wdp6iJPdusorP5lToMfezYMSlTpoy4bDW0Lu+2HTPX5eJVq1aVp59+Wq5cuSIZ0blfVkqeBg0kT/36krlAASnSpYt4+/nJpT9+d9heA7+Qlo9I9kqVxD84WIIbNTZZxXMrV5rb46Ki5Mq2rVKoQwfJVrq0BOTNKwXatDX/XljHYiBPsmLtDhk78Wv5bjnZRE81e/YSeeqpFtKhQzMT2I0d+4IEBPjLokV3fl4TWrp0jfTt+5Q0alRTChcOkaefbiWNGtWQWbOWWNts27ZXmjZ9SBo3riWFCuWTli3rS4MGVWXnzoNufGa4X+t2nJHJX++UFWQT04Zm4V21JZGfn59ZeLJq1SrrMR1a1n0dSr4bnZOomUMN3hYtWiTt2rVL1EbL4migqPHV8uXL7dpYAkUdzv7ll1/MvMR72b59u5lTmTdv3iQ/xxQFi0OGDLEuZNHl3JribNWqlRw9ejRRyjYjiIuJkVthYRJUrpz1mJe3t2QrW05uHjni9D7evr52x7x9/eTm4UPm6/i4OP1uE69M9m28fH2tbQC4XlRUtOzefUjq1atiPaa/aOvVqyrbtu13eB/9Be7nZ/+zqxmKrVv3WPerVSsnf/65Q44evVMGY9++o7Jly167oWoAnmHQoEHyySefyJw5c2Tv3r3Sr18/kxHUoWWl9Q91WNtCL42scxSPHDkiv/32m7Rs2dIEmLZzCTUw1OScxlY6p1Evs6zD1pZz6u+ZJ554Qv766y9ZsGCBmdqicyR100U3Suc4TpkyRXbs2GEeS9sNHDhQnnnmmWStMUnR5f604+XLlzdfayTcpk0bM2FS6/xo0JjRxNy8aQK7TNnsU7++Qdkk4qzjiahB5SuYbGTWUqVMZvHGvn0mkyj/X8nIJyBAAosXlzM//SgB+fOboezLmzZJ+JEj4p+MvwYA3J8rV65LbGyc5M5t/4tVh5+PHHGcTWrQoJp8/vkSqVWrohQpEiIbNuyQlSv/MOexeP75J+TmzVvyyCP9xMfH29w2cOCz0rZtY5c/J+CB4cI5i8nRsWNHuXDhgowaNcoEazraqoGeZdFLWFiY+SPTQoeftdaiBnBZs2Y1sZPOUbRdxawrmzXAPHnypFkzoqV13nzzTfH9/0ST1lv87rvvzNf6eLZ0bUnjxo3NH6m6uGXMmDFmBXaxYsVMsJjcxF6KgkVNuWoxSKVpT42YlT6ZpJTO0Q4nXH4eGxUlPn5+klEU7thRjs+bK7tHjzLpbg0Y89SrLxdthq2L9eghx+bMkV2vDtVUhpnLmKtWbbkVdjxN+w7g7kaMeF5GjpxmAkEdzSpcOL88/ngzWbToF2ubn39eL99/v04mTXrFDG3v3XvEzIu0LHQB4FlefPFFszmydu1au31d+6HFuO9Gh5d1c0brJ96rVLYuivnzzz/lfqUoWGzQoIGJSvVKLno5G61crg4cOCCFChW65/11BY5eItBWxW7dpNJzd1KrniZT1qwmmIu5YR8oR1+/Ib7Zszu8j2+2bFLyhf4SFx1tMpO6gOXU4sXinyePtY1/cF4p88oQiY2MlLiI2+KbPYcc+e9/xc+mDQDXypkzyGT+Ll2yn4996dJVyZPH8TBOrlzZ5cMPR0pkZJRcvXrDBIATJ86RwoX/Ka0xYcJsk11s3frOoogyZULl9OkLZhU1wSKQROkjsfjAS9GcRa1QnilTJvn222/NEm2dnKl+/vlnM+5+L5pW1fSq7Vb+6S7iqbwzZTJZv+t791mP6ZzDG/v2StYEq5YS3dfX987K5rhYubptq+SoYp9KVj7+/iZQjNEVUXt2O2wDwDV07mGFCiVlw4ad1mM6t0iHlqtVu/tKQn9/P8mXL7fExMTKihV/mAUtFhERkYlK5GhQmk4uqgV4BB3ZddWG+8ws6iVkfvjhh0TH33///RQvR/f0Ieh8zf4txz6fLYGhRSVLqJbO+cWsaM5dr765/ejsWeKXI4eppajCjx6RqCtXTe3EqKtX5cz335sPiXwtWljPeW33bjOHUUvrRJ4/LycXfWu+zlO/Xpo9T6SsdE6J0H/KJ4QWDpbK5YvKlas35cTpS2naNyRN9+6Pyauvvi8VK5Y0tRXnzFkqt29HmKFlNXToZBMUDh7czezv2LHf1FcsV664+XfatC9MgNmr152ff9WkSS2ZOfNrKVAg2DoMrauuO3T4d5o9T6SsdE7RkKzW/ULBgVKuaA65ejNKzly6M10LyJDBoo619+zZU5588klz6RmI5KpVS2Ju3pDT330n0devm6LcpV56yVpjMeryZbssgg4/n/5uqUReuCDe/v6mhE5ojx6SKUsWa5vY27fl1P8Wm6LdPlmySM7q1aXgY4+Jl0+K3jakkeqVi8uKr0dZ9yeMvjPHd9436+T5wVwj2xO0avWwXL58TT74YIEpyq1B4KefjrUOQ585c0G8bSba6/Cz1lo8ceKsZMkSYEroTJgwSIKC/gkqRo7sI1OnLpCxYz+SS5eumaHqjh1bSv/+ndLkOSJlKhXPJV+M+ucybyO73lnNvmjdERk68/7niuHuqF/vHl7xKRjz0ItRf/HFF2aRik6+1MDxoYf+GV5JiafXUjswI/lfV4KkjOR2mP0cZTzYSnSmpmhGcvjLp9PssYvNcF3scLQ/FyCxSNGovNbs0WsVzp4921wvumHDhqaUzsSJExNVMAcAAHhAa3JnCCmewqkLXB5//HFZunSpqQGkV295/fXXpXDhwvLYY4+ZS84AAADAs933eh8tnTN69GiZNGmSuXSMrnTW6yQ++uij8sorr6ROLwEAABLQtQCu2vCPFK2U0KFnrTSuw9B6PUK9gsuXX34pLVq0sL7Azz33nCmjo0PTAAAAyEDBohbeLlGihPTo0cMEhcHBwYnaVK5cWWrVqpUafQQAAEiEBGA6DhZXrVolDz/88F3bBAUFmWsTAgAAuALBYjqes3ivQBEAAAAZOFjU8jjPPvusFChQwKyK9vHxsdsAAABczcvbdRvucxha5ymGhYWZUjn58+dn1RAAAMADKkXB4vr16+W3336TqlWrpn6PAAAAkoBclXukKNGqhbdTcJVAAAAAZJTL/Q0bNkyOHTuW+j0CAABIAm8v121IwTB0zpw57eYmhoeHm1qLWbJkEV9fX7u2ly9fTuppAQAA8CAEi5pNBAAASC+Ys5jOgsVu3bpJbGysuXzfd999J1FRUdK0aVNzXejMmTO7tpcAAAAJECymwzmLb731lrz22muSNWtWKViwoEydOlX69+/vut4BAADAc4LFuXPnyocffijLly+XJUuWyPfffy8LFiyQuLg41/UQAADAAV1L4aoNKQwWtRB3q1atrPvNmjUzL+jp06eTcxoAAAA8iEW5Y2JiJCAgwO6YroSOjo5O7X4BAADcFZflS4fBohbi1kv9+fv7W49FRERI3759JTAw0Hps8eLFqdtLAAAApP9gUVdEJ/TMM8+kZn8AAACShKmF6TBYnD17tut6AgAAAM8OFgEAANILMovuQbAIAAA8EsGie7COCAAAAE6RWQQAAB7Jm8yiW5BZBAAAgFNkFgEAgEdizqJ7kFkEAACAU2QWAQCARyKz6B5kFgEAAOAUmUUAAOCRvFgO7RYEiwAAwCMxDO0eDEMDAADAKTKLAADAI5FZdA8yiwAAAHCKzCIAAPBIZBbdg8wiAAAAnCKzCAAAPBKVc9yDzCIAAMB9mjFjhoSGhkpAQIDUqVNHNm3a5LRtdHS0jBs3TkqUKGHaV6lSRZYtW2bX5saNG/Lyyy9L0aJFJXPmzFKvXj3ZvHmzXZv4+HgZNWqU5M+f37Rp1qyZHDx40K7N5cuXpUuXLhIUFCQ5cuSQnj17ys2bN5P13AgWAQCAx85ZdNWWHAsXLpRBgwbJ6NGjZevWrSb4a9GihZw/f95h+5EjR8rHH38s06ZNkz179kjfvn2lffv2sm3bNmubXr16ycqVK2XevHmya9cuad68uQkGT506ZW0zYcIE+eCDD2TmzJmyceNGCQwMNI8bERFhbaOB4u7du825fvjhB/n111/l+eefT97rHK9haTrw9Np1ad0FuNH/us5M6y7AjW6HjU3rLsCNSnT+K627ADc6/OXTafbYDZaud9m517drkOS2derUkVq1asn06dPNflxcnBQuXFgGDBggw4YNS9S+QIECMmLECOnfv7/1WIcOHUx2cP78+XL79m3Jli2bLF26VFq3bm1tU6NGDXnkkUfkjTfeMFlFPc/gwYPllVdeMbdfu3ZN8uXLJ59//rl06tRJ9u7dK+XLlzcZyZo1a5o2msFs1aqVnDx50tw/KcgsAgAApFBUVJRs2bLFZP0svL29zf6GDRsc3icyMtIMP9vSQHH9+jvBb0xMjMTGxt61zdGjR+Xs2bN2j5s9e3YTuFoeV//VoWdLoKi0vfZPM5FJRbAIAAA8kiuHoTWgu379ut2mxxK6ePGiCew0o2dL9zWYc0SHiidPnmzmF2oWUoeIFy9eLGfOnDG3a1axbt26Mn78eDl9+rQ5v2YcNfiztLGc+26Pq//mzZvX7vZMmTJJrly5nPbNEYJFAACABN5++22Tqctus+mx1DB16lQpVaqUlC1bVvz8/OTFF1+U7t27m4yfhc5V1KHmggULir+/v5mb2LlzZ7s27kKwCAAAPJKXl5fLtuHDh5s5gNdsNj2WUJ48ecTHx0fOnTtnd1z3Q0JCHPY7ODhYlixZIuHh4XL8+HHZt2+fZM2aVYoXL25toyul161bZ1Yunzhxwqyu1lXUljaWc9/tcfXfhItsdIhbV0g765sjBIsAAAAJaDZPy80E2Wx6LCHNDOrCk1WrVlmP6dCy7utQ8t3onETNHGoAt2jRImnXrl2iNrrCWUvjXLlyRZYvX25tU6xYMRPw2T6uDpXrXETL4+q/V69eNXMqLVavXm36p3Mbk4qi3AAAwCOll8v9DRo0SLp162YWktSuXVumTJlisoY6tKy6du1qgkLLMLYGdFoCp2rVqubfMWPGmABu6NCh1nNqYKjD0GXKlJFDhw7JkCFDzLC15Zya/dQ6jLoyWoe0NXh8/fXXzQrnxx57zLQpV66ctGzZUnr37m3K62hmUoe8daV0UldCK4JFAACA+9CxY0e5cOGCKZCtC0c0CNQSNZbFJ2FhYXZzDbUOotZaPHLkiBl+1lI2OkdRVy5bWIa9tcSNLkjR0jpvvvmm+Pr6WttocKlBqdZN1AxigwYNzOParqJesGCBCRCbNm1q+qDn0fmPyUGdRaQJ6ixmLNRZzFios5ixpGWdxcY//u6yc69tXd9l5/Y0ZBYBAIBHSi/D0A86FrgAAAAg/WcWT1z3SesuwI0YlsxYMhcZndZdgBuFdOiU1l1ABuFNZtEtyCwCAAAg/WcWAQAAkoPMonuQWQQAAIBTZBYBAIBH8vZKF9X/HnhkFgEAAOAUmUUAAOCRmLPoHgSLAADAIzE86h68zgAAAHCKzCIAAPBILHBxDzKLAAAAcIrMIgAA8EgscHEPMosAAABwiswiAADwSGS83IPXGQAAAE6RWQQAAB6JOYvuQWYRAAAATpFZBAAAHsmLOotuQbAIAAA8EsPQ7sEwNAAAAJwiswgAADwSGS/34HUGAACAU2QWAQCAR/JmgYtbkFkEAACAU2QWAQCAR2I1dDrOLM6ePVtu3bqV+r0BAACA5weLw4YNk5CQEOnZs6f88ccfqd8rAACAJAQxrtrwjxS9HqdOnZI5c+bIxYsXpXHjxlK2bFl599135ezZsyk5HQAAQIqGoV214T6DxUyZMkn79u1l6dKlcuLECendu7csWLBAihQpIm3btjXH4+LiUnJqAAAApCP3nWnNly+fNGjQQOrWrSve3t6ya9cu6datm5QoUULWrl2bOr0EAABwUDrHVRtSIVg8d+6cTJw4USpUqGCGoq9fvy4//PCDHD161AxTP/XUUyZoBAAAQAYrndOmTRtZvny5lC5d2gxBd+3aVXLlymW9PTAwUAYPHizvvfdeavYVAADAirmF6ThYzJs3r6xbt84MPTsTHBxssowAAADIQMFidHS0HDt2TPLkyXPXdl5eXlK0aNH76RsAAIBTlLhJp6+zr6+v7Ny50zW9AQAAgOcH5c8884x89tlnqd8bAACAJGI1dDqesxgTEyOzZs2SX375RWrUqGEWtNiaPHlyavUPAADAIRa4pONg8e+//5bq1aubrw8cOJDafQIAAIAnB4tr1qxJ/Z4AAAAkA5nFdDxnsUePHnLjxo1Ex8PDw81tAAAAyMDB4pw5c+T27duJjuuxuXPnpka/AAAA7hnEuGpDCoeh9ZJ+8fHxZtPMYkBAgPW22NhY+emnn0zBbgAAAGTAYDFHjhym2LZueqm/hPT42LFjU7N/AAAADlHixj28k7uwZdWqVSaz+O2338rq1aut2/r16yUsLExGjBjhut4CAACkQzNmzJDQ0FAz6lqnTh3ZtGnTXa+GN27cOClRooRpX6VKFVm2bJldGx2xff3116VYsWKSOXNm03b8+PEmBrOwJPASbu+99561jfYp4e3vvPOO6zKLjRo1Mv/qNZ+LFCliHhAAACAjr4ZeuHChDBo0SGbOnGkCxSlTpkiLFi1k//79DqfnjRw5UubPny+ffPKJlC1bVpYvXy7t27eXP/74Q6pVq2bavPvuu/LRRx+ZdSIVKlSQv/76S7p37y7Zs2eXl156ybQ5c+aM3Xl//vln6dmzp3To0MHuuAamvXv3tu5ny5YtWc8vRXM49+7dK7///rtdNF21alV5+umn5cqVKyk5JQAAgEcucJk8ebIJxjSYK1++vAkas2TJYi5g4si8efPktddek1atWknx4sWlX79+5utJkyZZ22jg2K5dO2ndurXJDj7xxBPSvHlzu4xlSEiI3bZ06VJp0qSJOactDQ5t2yW8mEpSXudkGzJkiFnsonbt2mWiaX2SmnHUrwEAADxZZGSkiXWu22x6LKGoqCjZsmWLNGvWzHrM29vb7G/YsMHpuW0XCSsdatYpfRb16tUzU/8sFz/ZsWOHuf2RRx5xeM5z587Jjz/+aDKLCemwc+7cuU3WUoeo9Up8Li/KrUGhRs5q0aJF0qZNG3nrrbdk69atJmgEAADw5GHot99+O9Gi3dGjR8uYMWPsjl28eNHML8yXL5/dcd3ft2+fw3PrELVmIxs2bGjmImpQuHjxYnMei2HDhpkAVYepfXx8zG1vvvmmdOnSxeE5dbhaM4iPP/643XEdstar7uXKlctkK4cPH26Gr5NzaeYUBYt+fn5y69Yt87VeH7pr167ma+2IJeMIAADgqTSoSjha6u/vnyrnnjp1qhm21kBQ139owKhD2LbD1l9//bUsWLBAvvjiCzNncfv27fLyyy9LgQIFpFu3bonOqffVQDJhxtL2OVSuXNnEcH369DHBcFKfT4qCxQYNGpgHr1+/vhk714mdSlOlhQoVSskpAQAAksXLhaVzNJBKSjCVJ08ek/nTYWBbuq/zAx0JDg6WJUuWSEREhFy6dMkEgJpJtJ1rqFP+9FinTp3MfqVKleT48eMmyEsYLP72229mMY0lHrsbXYCjw9DHjh2TMmXKiMvmLE6fPl0yZcpkyufoSp2CBQtaV+G0bNkyJacEAADwOH5+flKjRg0zlGwRFxdn9uvWrXvX+2oWUGMoDd50Wp8uaLHQEVyd+2hLg1I9d0KfffaZ6YOW4LkXzVDqeZNzEZUUZRa1bM4PP/yQ6Pj777+fktMBAAB4bOmcQYMGmWxfzZo1pXbt2qZ0Tnh4uBlaVjpdT4NCzQqqjRs3yqlTp0wlGf1X50FqEDh06FDrOXU9iM5R1JhLh6G3bdtm5hn26NHD7rF1+t8333xjt5LaQhfY6GPpCmmdz6j7AwcOlGeeeUZy5syZ+sGidiYoKMj69d1Y2mU07UPzS+eSBSWXv58cvh4uU3Ydlr1Xbzps6+PlJc+WKiQtC+eVPAH+cuLmbfloz1HZdOGqtU1mHx/pVbaINMyfW3L6+8qBa+Hywd9HZJ+Tc8K9Fiz4UT77bLFcuHBFypYtJq+/3kcqV058ZSMVHR0jH3/8jSxZslrOnbskxYoVlFdeeU4aNqxhbaOTl6dN+1K++26NXLx4VfLmzSXt2zeVF17oSE1TD1K/dlkZ2PdRqV6puOTPl1Oe6jVJvl/xV1p3C8n0bP1i8vy/SkpwNn/Ze/q6jFm8U3aE/fP72VYmby/p16yUdKhVREKyB8iR8zflnR/2yK/7zlvbBPpnkkGPlJUWlfJL7qz+svvUNRn3v12y84Tjc8KzdOzYUS5cuCCjRo2Ss2fPmiBQi2xbFr3oRUtss4Q6/Ky1Fo8cOSJZs2Y1i4O1nI5eKc9i2rRppij3Cy+8IOfPnzdD1TrXUB/D1ldffWUKdXfu3DlRv3QYXW/XYFRXYGuBbw0Wk1u5xivethT4XWjqU1fPaNpSn7CjDy89lR63Xc2TVA9/989ycU/0rwJ5ZES10jJp5yHZc+WGPFm8oDQpkEeeXr1FrkZFJ2rft1yoNC8ULBN2HJLjN29Jnbw55cUKxaTfbzvl4PVw02ZMjTJSPFsWmbTzsFyMjJLmhfLKU8ULyLNrtsrFiCjxZL+19exriP/0028ydOhkGTu2v1SpUlrmzPlOli1bL8uWzZTcuf/5Ybd4773PTRD4xhsDpHjxQvLbb1vlnXc+k6++miDly5cwbWbO/Fpmz14i7747UEqWLCJ//31Ihg+fKgMHPiNdu7YVT5a5yGjJKJo3riJ1a5aRbbuOyMJPBmfIYDGkw505Vp6qddUCMqlLdRn5zU7ZfvyK9GhUXFpVKSBN314ll24m/t376qPl5bEahWT419vl8Pmb0rBMXhnZrqJ0+OA32XPqmmkzrWtNKZ0/m7z+zU45dz3CtO/RqIQ0f3e1nLsWIZ7s6Pv/DJ2624i//hn6TW1v1mzqsnN7miRnFvWSfrra2XLZP9jrWKKgfB92Vn46cecvyYk7D0ndfDmldZF8suDQyUTtWxQOlrkHTsqf5+8UMV9y7KzUyJNDOpUsKOO3HhA/b29plD+PvLZpj+y4fCeTO3t/mNTPl0seCw2RT/eFufkZwpYGdU891UI6dLhTV2vs2Bdk7drNsmjRSnn++ScTtV+6dI306/eUNGpU0+w//XQr2bBhu8yatUQmThxsjm3btleaNn1IGjeuZfYLFconP/64TnbuPOjW54b7s2LtDrPBc/VqXFIWbjgu326683t2xDc7pEm5fPJknaIyc1Xin8f2NQvLjJUHZO3eO7//F/xxTOqXDpbejUvIwAVbxd/XW1pWzi/Pz9okm45cMm2mLt8vTSuEyDP1QmXSz47Lq+DeuDZ0OgsWLZf6S/g1RDJ5eUnp7Fll/sET1mP67fvXxatSIafjS+r4entLVIJJqrpfKVeQdZhahzYStomMjZXKubK75HkgaaKiomX37kPSp88T1mOaba9Xr6ps27bf6XVA/fx8Ew0PbN26x7pfrVo5+frr5XL06CkzTL1v31HZsmWvDBuWuMAqANfw9fGSioWyy4e/3CmErHT87feDF6R6UcdzvPwyeUtkjP2IWmR0rNQsntt8ncnbWzL5eJtjtiJs2gDpWYoWuFjG23fu3GnG0ROuzGnb1rOHzJIru5+vCewuR9oPN1+JjJaiWbM4vM+m81ekY/ECsuPSNTkVHiE1gnNIw5Dc4v3/w/u3Y2Nl1+Xr0q10ETl2Y79ciYySZoWCpUKuIDkVftstzwuOXblyXWJj4yR3bvsPDh1+PnIkcRZZNWhQTT7/fInUqlVRihQJkQ0bdsjKlX+Y81g8//wTcvPmLXnkkX7i4+Ntbhs48Flp27axy58TgDtyBvqbwO7iDfsrdeh+ibyO//jXuYk9G5eQTYcvyfFL4VK/VLC0qJxfvP9/9UV4ZIxsOXpZBjQvI4fO3ZSLNyKkbfVCUj00lxy/eGfaETx7gcuDLkXBok7a1JU9WrU8oaTMWdRJlgkvmRMXHSXevn6SUehClaFVSsn8f9Uwf7WevnVbfjpxzgxbW7yx9YAMr1pKlrSoLTFx8XLg2k1ZdeqCyWLCs4wY8byMHDnNBIL690Dhwvnl8cebyaJFv1jb/Pzzevn++3UyadIrZs7i3r1H5O23P7UudAGQPulClbc7VpVfhjc1c/fDLt2SbzedkCdrF7G2GbRgi0zoVE02jm0hMbFxsvvkNfl+60mpWDjxHGfggQgWBwwYIE8++aRZkZPw8jYpvYRO4U7dpWhn++XgnuJaVLQJ5nL52w8z6grmS04WolyNipHXNu8VP28vCfLzNQtWdNHL6fB/JjqfvhUhA/7YJQE+3hKYyUcuRUabRS9nbnn2ZGhPlzNnkMn8Xbp0Z76pxaVLVyVPHsfDVLlyZZcPPxwpkZFRcvXqDRMATpw4RwoX/ufnZ8KE2Sa72Lp1Q7NfpkyonD59wayiJlgE3ONKeKQJ5vJksy/GrPsXrjv+3Xs5PEr6zNpkhqNzBvqZBSu66CXs8j9ZQw0gO834XTL7+UjWgExy4XqkWfQSdonM4v0gs+geKSrKrVXJddl1SgJFyyV0rl27ZrcVfuIZ8VQx8XeyfrpAxUK/f3V/95Ubd71vVFy8CRR1jmKjArll/dnLidpExMaZQDGrr4/UzptTfjt7Z4I00obOPaxQoaRs2LDTekynYujQcrVqd6+G7+/vJ/ny5ZaYmFhZseIPs6DFIiIiMlGVAQ1Kk1iwAEAqiI6Nl79PXjMLVCz0x7JeqWDZetz+D8SEomLiTKCo05J0QcvKXWcTtbkdFWsCxaDMvtKwbF755e/EbYAHIrP4xBNPyNq1a821DFPrEjqePgS98PApea1aadl37absNaVzCpg6iTq0rLSszsWISPl473GzXz5HVsmT2V8OXrspwQH+0qNMEfEWL/nCZuV07eA7weeJ8NtSMDCzvFA+VMJu3JKfwv6p3YW00b37Y/Lqq+9LxYolTW3FOXOWyu3bEWZoWWlZHQ0KBw++c0mmHTv2m/qK5coVN/9Om/aFCTB79frngu9NmtQy5XMKFAi2DkPrqusOHf6dZs8TyReYxV9KhP5zia/QwsFSuXxRuXL1ppw4zR96nuDTtYdk0tPVTQ3EHaZ0TgnJ4ucj3268szpabzt77ba89+Nes1+1SE7Jlz1A9py+Zuos/qdFWTNf8ePV/6ycblgm2ESdWoMxNE+gDG9bQQ6fuyHf/P85kTI+ad2BDCJTSi/3p8PQei1CvVahr6/98OtLL70kGc3q0xclh5+v9CxTxBTlPnQ9XF7582+zyEXly+xvlyHy8/GW3mWLSv4sAXI7JtaU0NGSOTdtVtQF+maSPuWKmmDyRnSMrD1zUT7Ze1xiyTSluVatHpbLl6/JBx8sMEW5NQj89NOx1mHoM2cuWCe3Kx1+njJlvpw4cVayZAkwJXQmTBgkQUH/zD8dObKPTJ26QMaO/UguXbpmhqo7dmwp/ft7ds26jKZ65eKy4ut/iuZOGN3V/Dvvm3Xy/OCZadgzJNWP20+bwtmDWpaVPEH+svfUdXnu4z/l4s07c+0L5MwscTa/h7U0zuBW5aRI7ixmMYuW0Bm0YKvciIixtsmW2VeGtC4vITkC5NqtaFm247RM/GmvmcIEpHdJLsqd8BqEffv2Ndc0zJ07t93QmX6tFckzWlFuZKyi3EiejFSUG55flBueU5T7re0rXXbu16oyqnNfmcURI0aYBSrDhg1LdJFrAAAAd2CBi3ukKNKLiooy10EkUAQAAHiwpSja69atmyxcuDD1ewMAAJCMzKKrNtznMLQW3Z4wYYIsX75cKleunGiBy+TJk1NyWgAAADwIweKuXbukWrVq5uu///7b7raEdeIAAABcwYeQI/0Gi2vWrEn9ngAAACDdua8VKocOHTJD0bdv3zb7XGkCAAC4C3MW03GweOnSJWnatKmULl1aWrVqJWfOnDHHe/bsKYMHD07tPgIAAMCTgsWBAweaRS1hYWGSJUsW63Etp7Ns2bLU7B8AAIBD3l7xLttwn3MWV6xYYYafCxUqZHe8VKlScvz4nWsfAwAAuBLDxek4sxgeHm6XUbS4fPmy+Pv7p0a/AAAA4KnB4sMPPyxz5861K5cTFxdnai82adIkNfsHAADgkI8LN9znMLQGhbrA5a+//jKX/hs6dKjs3r3bZBZ///33lJwSAAAAD0pmsWLFinLgwAFp0KCBtGvXzgxLP/7447Jt2zYpUaJE6vcSAAAgAUrnpOPMosqePbuMGDEidXsDAACAByNYvHr1qmzatEnOnz9v5iva6tq1a2r0DQAAwClK3KTjYPH777+XLl26yM2bNyUoKMjuetD6NcEiAABABp6zqFdp6dGjhwkWNcN45coV66aLXAAAAFzNx8t1G+4zs3jq1Cl56aWXHNZaBAAAcAcWoqTjzGKLFi1M2RwAAAA82JKcWfzuu++sX7du3VqGDBkie/bskUqVKpnrRNtq27Zt6vYSAAAgATKL6SxYfOyxxxIdGzduXKJjusAlNjb2/nsGAAAAzwkWE5bHAQAASEtkFtPhnMXVq1dL+fLl5fr164luu3btmlSoUEF+++231OwfAAAAPCVYnDJlivTu3dvUVnR0RZc+ffrI5MmTU7N/AAAADvl4xbtsQwqDxR07dkjLli2d3t68eXPZsmVLck4JAACAB6XO4rlz5xKtfLY7WaZMcuHChdToFwAAQOrX/4NrX+eCBQvK33//7fT2nTt3Sv78+ZPfCwAAAHh+sNiqVSt5/fXXJSIiItFtt2/fltGjR8ujjz6amv0DAABwuhraVRtSOAw9cuRIWbx4sZQuXVpefPFFKVOmjDm+b98+mTFjhqmvOGLEiOScEgAAIEUI6tJhsJgvXz75448/pF+/fjJ8+HCJj4+3FuLWSwBqwKhtAAAAkAGDRVW0aFH56aef5MqVK3Lo0CETMJYqVUpy5szpmh4CAAA4QImbdBosWmhwWKtWrdTtDQAAAB6MYBEAACAtMWfRPShRBAAAAKfILAIAAI9EZtE9yCwCAADcpxkzZkhoaKgEBARInTp1ZNOmTU7bRkdHy7hx46REiRKmfZUqVWTZsmV2bbQcoda2LlasmGTOnNm0HT9+vLUSjXruuedMRRrbLeFlmS9fvixdunSRoKAgyZEjh/Ts2VNu3ryZrOdGZhEAAHik9JJZXLhwoQwaNEhmzpxpAsUpU6aYkoL79++XvHnzOqxbPX/+fPnkk0+kbNmysnz5cmnfvr0pT1itWjXT5t1335WPPvpI5syZIxUqVJC//vpLunfvLtmzZ5eXXnrJei4NDmfPnm3d9/f3t3ssDRTPnDkjK1euNEGqnuP555+XL774IsnPzyveNkRNQw9/tz6tuwA3+q1t4h8ePLgyFxmd1l2AG4V06JTWXYAbHX2/XZo99rKTP7vs3C0LPZLktnXq1DEVYqZPn2724+LipHDhwjJgwAAZNmxYovYFChQwFzHp37+/9ViHDh1MBlGDSKVXxNPa1Z999pnTNppZvHr1qixZssRhv/bu3Svly5eXzZs3S82aNc0xzWDqFflOnjxp+pEUDEMDAAAkEBkZKdevX7fb9FhCUVFRsmXLFmnWrJn1mLe3t9nfsGGD03Pr8LMtDQLXr/8ncVavXj1ZtWqVHDhwwOzv2LHD3P7II/ZB7Nq1a032Uq+qpxdNuXTpkvU2fXwderYEikr7pf3buHFjkl8LgkUAAOCRvL3iXba9/fbbZsg3u82mxxK6ePGimV+Y8Ap2un/27FmH/dYh6smTJ8vBgwdNFlKHiPVyyjpcbKEZyU6dOplhal9fXzM8/fLLL5thZdsh6Llz55qgUoet161bZ4JJ7Y/Sx084DJ4pUybJlSuX0745wpxFAACABPSyxjoP0VbC+YApNXXqVOndu7cJBHVRii5e0bmEs2bNsrb5+uuvZcGCBWZuoc5Z3L59uwkWdei4W7dupo0GkxaVKlWSypUrm3NptrFp06aSWggWAQCAR3Ll8KgGhkkJDvPkySM+Pj5y7tw5u+O6HxIS4vA+wcHBZp5hRESEGTbWAFAzicWLF7e2GTJkiDW7aAkGjx8/brKblmAxIb2/9kcvx6zBoj7++fPn7drExMSYFdLO+uYIw9AAAAAp5OfnJzVq1DBDwRY6tKz7devWvet9dd5iwYIFTQC3aNEiadfun8VCt27dMnMLbWlQqud2RhetaPCZP39+s6+PrwtgdE6lxerVq805dFFOUpFZBAAAHim9lM4ZNGiQyfbpQpLatWub0jnh4eFmaFl17drVBIWWOY+6uOTUqVNStWpV8++YMWNMADd06FDrOdu0aSNvvvmmFClSxAxDb9u2zcxz7NGjh7ldayWOHTvWrJDWLOHhw4fN/UuWLGnmRKpy5cqZeY065K1lfbR0zosvvmiylUldCa0IFgEAAO5Dx44d5cKFCzJq1CizcESDQC1RY1n0EhYWZpcl1OFnrbV45MgRyZo1qyllM2/ePLNy2WLatGmmKPcLL7xghpI1uOvTp495DEuWcefOnaYOo2YP9fbmzZubwt22w+c671EDRB2W1j5ocPnBBx8k6/lRZxFpgjqLGQt1FjMW6ixmLGlZZ3HdmZ9cdu5G+Vu57NyehswiAADwSFriBq7HAhcAAAA4RWYRAAB4pPSywOVBR2YRAAAATpFZBAAAHonMonuQWQQAAED6L51Tqvlnad0FuFFc7sxp3QW4UVxIYFp3AW50dtFXad0FuNHtsC/T7LE3nv/RZeeuk7e1y87tacgsAgAAwCnmLAIAAI/kxZxFtyBYBAAAHolY0T0YhgYAAIBTZBYBAIBHYhjaPcgsAgAAwCkyiwAAwCOR8XIPXmcAAAA4RWYRAAB4JC+vdHFdkQcemUUAAAA4RWYRAAB4JBZDuwfBIgAA8EiUznEPhqEBAADgFJlFAADgkUgsugeZRQAAADhFZhEAAHgkb1KLbkFmEQAAAE6RWQQAAB6JxKJ7kFkEAACAU2QWAQCAR6LOonsQLAIAAI9ErOgeDEMDAADAKTKLAADAI5FZdA8yiwAAAHCKzCIAAPBIFOV2DzKLAAAAcIrMIgAA8EgkFt2DzCIAAACcIrMIAAA8kpdXfFp3IUMgWAQAAB6JYWj3YBgaAAAATpFZBAAAHolrQ7sHmUUAAAA4RWYRAAB4JDJe6fh1Dg0NlXHjxklYWFjq9wgAAACeHSy+/PLLsnjxYilevLj8+9//lq+++koiIyNTv3cAAAB3mbPoqg2pECxu375dNm3aJOXKlZMBAwZI/vz55cUXX5StW7em5JQAAAB40Ib7q1evLh988IGcPn1aRo8eLZ9++qnUqlVLqlatKrNmzZL4eIplAgAA1/By4ZZcM2bMMNP0AgICpE6dOiah5kx0dLSZzleiRAnTvkqVKrJs2TK7NrGxsfL6669LsWLFJHPmzKbt+PHjrbGVnuPVV1+VSpUqSWBgoBQoUEC6du1qYjJb2icvLy+77Z133nHfAhft6P/+9z+ZPXu2rFy5Uh566CHp2bOnnDx5Ul577TX55Zdf5IsvvrifhwAAAHAovQwXL1y4UAYNGiQzZ840geKUKVOkRYsWsn//fsmbN2+i9iNHjpT58+fLJ598ImXLlpXly5dL+/bt5Y8//pBq1aqZNu+++6589NFHMmfOHKlQoYL89ddf0r17d8mePbu89NJLcuvWLTOaqwGlBptXrlyR//znP9K2bVvT1pYGpr1797buZ8uWLVnPzys+Bek/7ZwGiF9++aV4e3ubSLZXr17mCVv8/fffJst4+/btJJ2zVPPPktsNeLC43JnTugtwo7iQwLTuAtzo7KKv0roLcKPbYV+m2WOfCP/eZecuHNgmyW3r1KljYp7p06eb/bi4OClcuLCZpjds2LBE7TULOGLECOnfv7/1WIcOHUwGUYNI9eijj0q+fPnks88+c9omoc2bN0vt2rXl+PHjUqRIEWtmUacP6ubWYWh9QQ4ePGgi3lOnTsnEiRPtAkWladNOnTqluGMAAADpfRg6KipKtmzZIs2aNbMe00Sa7m/YsMHhfXRRsA4/29IgcP369db9evXqyapVq+TAgQNmf8eOHeb2Rx55xGlfrl27ZoaZc+TIYXdch51z585tspbvvfeexMTEuH4Y+siRI1K0aNG7ttHxc80+AgAAeBoN6BJWevH39zebrYsXL5r5hZoFtKX7+/btc3huHaKePHmyNGzY0MxF1KBQq8zoeSw0I3n9+nWTjPPx8TG3vfnmm9KlSxeH54yIiDBzGDt37ixBQUHW4zpkrWtMcuXKZYa5hw8fLmfOnDGP79LM4r0CRQAAAFfz9nLd9vbbb5v5gdltNj2WGqZOnSqlSpUygaCfn5+pJqPzETUjafH111/LggULzNoPnf6ncxd1JFf/dbSG5KmnnjKLX3TU15bOpWzcuLFUrlxZ+vbtK5MmTZJp06Ylq+RhijKLOXPmNGnOhPSYplVLliwpzz33nHniAAAAnkYzcBpo2UqYVVR58uQxmb9z587ZHdf9kJAQcSQ4OFiWLFlisoGXLl0ycxg1k6j1qy2GDBlijlmm9OmqZ52LqAFrt27dEgWKetvq1avtsorO5lfqMPSxY8ekTJky4rLM4qhRo0z027p1axk7dqzZ9Gs9ppM1S5cuLf369TOrfAAAADxtzqIGhhp4BdlsjoJFzQzWqFHDDCVb6AIX3a9bt+5d+68JtoIFC5rgbdGiRdKuXTvrbbra2TbTqDQo1XMnDBR1HYlWoNF5ifeidbL1vI5WaadqZlEnWL7xxhsmnWnr448/lhUrVpgnrOlOrcFou1QbAADgQTNo0CCT7atZs6ZZjaylc8LDw60jrFo1RoNCyzD2xo0bzQJhrUut/44ZM8YEgUOHDrWes02bNmaOoq5q1tI527ZtM/MMe/ToYQ0Un3jiCTNE/cMPP5g5jWfPnjW36fxEDWJ1gY0+VpMmTUy5HN0fOHCgPPPMM2aU2KXBotYD0vo/CTVt2lQGDx5svm7VqpXD5eIAAACpwcsrfVz8o2PHjnLhwgUz8qoBmwaBWmTbsuglLCzMLkuow89aa1EXDGfNmtXETPPmzbNbxazzCrWG4gsvvCDnz583Q9V9+vQxj6E0yPzuu+/M1/p4ttasWWPmKWomVC/JrMGozlHUSjUaLCYcXndJnUWNcvXBdLP1/vvvm01flJ07d0rz5s2tUe69UGcxY6HOYsZCncWMhTqLGUta1lk8e/tOsOQKIZnbuuzcniZFmUWNdHVOokaumm61FIL86aefTPVypVd0adSoUer2FgAA4P+lkwu4PPBSFCzqPMTy5cubSuVaF0jpipp169aZIpLKMhwNAADwIF/u70GX4mtD169f32wAAAB4cKU4WNRVN1ojaO/evWZfV+roxat1WTfurlalEOn1ZCWpUCq35MsdKP3G/CK//HE8rbsFF6lVNlh6P1peKhbPKflyZpG+k36VlX+dTOtuIZmerV9Mnv9XSQnO5i97T1+XMYt3yo6wqw7bZvL2kn7NSkmHWkUkJHuAHDl/U975YY/8uu+8tU2gfyYZ9EhZaVEpv+TO6i+7T12Tcf/bJTtPOD4n0qf6tcvKwL6PSvVKxSV/vpzyVK9J8v2Kv9K6WxkGiUX3SFGdxUOHDkm5cuXMUnAdhtZNl2FrwHj48OHU7+UDJnNAJtl35LKMne74mpF4sGTxzyT7wq7ImFl8gHiq1lULyIjHKsjU5fvl0UnrZO/pazKnT13JndXPYfvBrcrJ03VDTUD573dXy4I/jsnH3WtL+YLZrW3e6VhVGpQJlkELtkrL99bIb/vPy7x+9SRfdvvrxSJ9C8ziL7v2hMnLI2eldVeA9JVZ1OsM6rUM//zzT1PLR2kFcg0Y9bYff/wxtfv5QPl180mzIWNYt+OM2eC5ejUuKQs3HJdvN4WZ/RHf7JAm5fLJk3WKysxVBxO1b1+zsMxYeUDW7r2TSdRgsX7pYOnduIQMXLBV/H29pWXl/PL8rE2y6cgl00YD0aYVQuSZeqEy6WfH15NF+rNi7Q6zwYMyXnBPsKgLWWwDRaVVw9955x3mMQJ4oPj6eEnFQtnlw18OWI9pwbHfD16Q6kUdF7X1y+QtkTGxdscio2OlZvE7V1fI5O0tmXy8zTFbETZtAMCjg3It8njjxo1Ex2/evGkqhgPAgyJnoL8J7C7eiLQ7rvvBQY6HjHVuYs/GJSQ0T6BZrdmgdLC0qJxfgoPuXCosPDJGthy9LAOal5G8QQHi7SXyWI1CUj00l9kHkDT68+WqDfcZLD766KPy/PPPm0vIaE1v3TTTqJf/00Uu96JVxK9fv263xcdFp6QrAJDu6EKVYxfC5ZfhTeXAe21kbIfK8u2mExL/zyVdZdCCLWZy/saxLWT/e23kuYeLy/dbT0pc8q+TAADpbxhar/ms10DUC2T7+vqaY3oRbA0Up06des/767URx44da3csZ/E2krvEPxfQBoD04Ep4pMTExkmebHeygha6f+F6hMP7XA6Pkj6zNpnh6JyBfnLuWoS8+mh5Cbscbm0TdumWdJrxu2T285GsAZnkwvVImda1poRd+qcNgHshBZhug0W9duHSpUvl4MGDsm/fnYnYujq6ZMmSSbr/8OHDE12XsPrjX6SkKwDgUtGx8fL3yWtmgcrKv+9cvlSHqOqVCpa564/e9b5RMXEmUNRSOrqg5cftpxO1uR0Va7agzL7SsGxeeef73S57LsCDxotgMX3XWVSlSpUyW0rmPOpmy8v7ToYyI8gSkEmKFgiy7hcKySrliueSqzci5cwFsgoPYumcoiFZrfuFggOlXNEccvVmlJy5dCtN+4ak+XTtIZn0dHVTA3HH8SvSo1EJyeLnI99uvLM6Wm87e+22vPfjnbqzVYvkNCVw9py+Zuos/qdFWfH29pKPV/+zcrphmWATdWoNRp3bOLxtBTl87oZ88//nhOeUzikRGmLdDy0cLJXLF5UrV2/KidN3VroDGSZYTJgJvJvJkyentD8ZQsXSeWTBxNbW/RF9HzL/Ll5xQF6d+Fsa9gyuUKl4LvliVDPr/siuNcy/i9YdkaEz/0zDniGpNCOohbMHtSwreYL8Ze+p6/Lcx3/KxZt3Fr0UyJnZbq6hlsbRWotFcmcxi1m0hI7WU7wREWNtky2zrwxpXV5CcgTItVvRsmzHaZn4016JiWPOoiepXrm4rPh6lHV/wuiu5t9536yT5wfPTMOeZQxeXhTPcQeveF2dkgRNmjRJ2gm9vGT16tXJ7kip5p8l+z7wXHG5M6d1F+BGcSGBad0FuNHZRV+ldRfgRrfDvkyzx74a9ZPLzp3Dr5XLzv3AZhbXrFnj2p4AAAAkC3MW3eG+87cnT540GwAAAB48KQoW4+LiZNy4cZI9e3YpWrSo2XSF9Pjx481tAAAA7lgN7ar/cJ+roUeMGCGfffaZ3eX91q9fL2PGjJGIiAh58803U3JaAAAAPAjB4pw5c+TTTz+1u1pL5cqVpWDBgvLCCy8QLAIAADcgA5hug8XLly9L2bJlEx3XY3obAACAq1E6xz1S9CpXqVJFpk+fnui4HtPbAAAAkIEzixMmTJDWrVvLL7/8Yq4PrTZs2CAnTpyQn35yXc0jAACAfzAMnW4zi40aNZIDBw5I+/bt5erVq2Z7/PHHZf/+/fLwww+nfi8BAADgWdeGLlCgAAtZAABAmqHETToLFnfu3CkVK1YUb29v8/Xd6MpoAAAAZKBgsWrVqnL27FnJmzev+VqvAe3ostJ6PDY2NrX7CQAAYIfMYjoLFo8ePSrBwcHWrwEAAPDgS3KwqJf0s8iaNavkzp3bfK0roD/55BO5ffu2KdLNAhcAAOAe1FlMd6/yrl27JDQ01AxFawHu7du3S61ateT999+X//73v9KkSRNZsmSJ63oLAABgM/XNVRtSGCwOHTpUKlWqJL/++qs0btxYHn30UVNv8dq1a3LlyhXp06ePuV40AAAAMmDpnM2bN8vq1avName9UotmE/Va0LpCWg0YMEAeeughV/UVAADABhnAdJdZ1Os+h4SEWOctBgYGSs6cOa2369c3btxI/V4CAADAM4pyJxzHZ1wfAACkBUrnpNNg8bnnnhN/f3/zdUREhPTt29dkGFVkZGTq9xAAAACeESx269bNbv+ZZ55J1KZr16733ysAAIB7onROugsWZ8+e7bqeAAAAwPOHoQEAANID5iy6B8EiAADwSCyydQ8G+wEAAOAUmUUAAOChyCy6A5lFAAAAOEVmEQAAeCQvcl5uwasMAAAAp8gsAgAAD8WcRXcgswgAAACnCBYBAIDH1ll01ZZcM2bMkNDQUAkICJA6derIpk2bnLaNjo6WcePGSYkSJUz7KlWqyLJly+zaxMbGyuuvvy7FihWTzJkzm7bjx4+X+Ph4axv9etSoUZI/f37TplmzZnLw4EG781y+fFm6dOkiQUFBkiNHDunZs6fcvHkzWc+NYBEAAHgoLxduSbdw4UIZNGiQjB49WrZu3WqCvxYtWsj58+cdth85cqR8/PHHMm3aNNmzZ4/07dtX2rdvL9u2bbO2effdd+Wjjz6S6dOny969e83+hAkTzH0sdP+DDz6QmTNnysaNGyUwMNA8bkREhLWNBoq7d++WlStXyg8//CC//vqrPP/888l7leNtQ9Q0VKr5Z2ndBbhRXO7Mad0FuFFcSGBadwFudHbRV2ndBbjR7bAv0+yxo+K2uOzcft41kty2Tp06UqtWLRPYqbi4OClcuLAMGDBAhg0blqh9gQIFZMSIEdK/f3/rsQ4dOpjs4Pz5883+o48+Kvny5ZPPPvvMYRsN3/Q8gwcPlldeecXcfu3aNXOfzz//XDp16mSCzPLly8vmzZulZs2apo1mMFu1aiUnT540908KMosAAMBjS+e4aouMjJTr16/bbXosoaioKNmyZYsZArbw9vY2+xs2bHDYbz2PDj/b0iBw/fr11v169erJqlWr5MCBA2Z/x44d5vZHHnnE7B89elTOnj1r97jZs2c3gavlcfVfHXq2BIpK22v/NBOZVASLAAAACbz99tsm+Mpus+mxhC5evGjmF2pGz5buazDniA4VT5482cwv1CykDhEvXrxYzpw5Y22jGUnNDpYtW1Z8fX2lWrVq8vLLL5thZWU5990eV//Nmzev3e2ZMmWSXLlyOe2bI5TOAQAAHsp1pXOGDx9u5iHa8vf3l9QwdepU6d27twkEdTGNLl7p3r27zJo1y9rm66+/lgULFsgXX3whFSpUkO3bt5tgUYeOu3XrJu5EsAgAAJCABoZJCQ7z5MkjPj4+cu7cObvjuh8SEuLwPsHBwbJkyRKzEOXSpUsmANRMYvHixa1thgwZYs0uqkqVKsnx48dNdlODRcu59XF0NbTt41atWtV8rW0SLrKJiYkxK6Sd9c0RhqEBAIBH8nLhf0nl5+cnNWrUMPMLLXRoWffr1q171/vqvMWCBQuaAG7RokXSrl076223bt0ycwttaVCq51ZaUkcDPtvH1XmVOhfR8rj679WrV82cSovVq1ebc+jcxqQiswgAAHAfBg0aZLJ9upCkdu3aMmXKFAkPDzdDy6pr164mKLTMedSA7tSpUyYDqP+OGTPGBHBDhw61nrNNmzby5ptvSpEiRcwwtJbV0XmOPXr0MLfr8LUOS7/xxhtSqlQpEzxqXUbNUj722GOmTbly5aRly5ZmyFvL62h9xxdffNFkK5O6EloRLAIAAI+UkuLZrtCxY0e5cOGCKZCtC0c0CNQSNZbFJ2FhYXZZQh1+1lqLR44ckaxZs5pSNvPmzTMrly20nqIGfy+88IIZStbgrk+fPuYxLDS41KBU6yZqBrFBgwbmcW1XWuu8Rw0QmzZtavqg5Xe0NmNyUGcRaYI6ixkLdRYzFuosZixpWWcxNv5vl53bx6uiy87taZizCAAAAKcYhgYAAB4pOQtRkHJkFgEAAOAUmUUAAOChyCy6A5lFAAAAOEVmEQAAeKT0UjrnQUdmEQAAAE6RWQQAAB6KnJc7ECwCAACPROkc9yAkBwAAQPq/3F9GFBkZaS4qPnz4cPH390/r7sDFeL8zFt7vjIX3Gw8ygsU0dP36dcmePbtcu3ZNgoKC0ro7cDHe74yF9ztj4f3Gg4xhaAAAADhFsAgAAACnCBYBAADgFMFiGtJJ0KNHj2YydAbB+52x8H5nLLzfeJCxwAUAAABOkVkEAACAUwSLAAAAcIpgEQAAAE4RLHqA5557Th577LG07kaG9/nnn0uOHDlcdv61a9eKl5eXXL161WWPgX/oa71kyRK3Py7vc/p07Ngx875s3749yfdp3LixvPzyyy7tF5AeECymQiCnv2D69u2b6Lb+/fub27SNq35ZwTXvp25+fn5SsmRJGTdunMTExLj8sevVqydnzpwxV4HA/Tt79qwMGDBAihcvblaoFi5cWNq0aSOrVq1K034l530msHTNz7ZuuXPnlpYtW8rOnTvN7fr9oe9LxYoV07qrQLpDsJgK9JfMV199Jbdv37Yei4iIkC+++EKKFCmSpn1D8ukHiH5oHDx4UAYPHixjxoyR9957z+WPq8FpSEiI+SDD/dE/vGrUqCGrV682792uXbtk2bJl0qRJE/NHXFpyxfscFRWVaufKCD/buukfDZkyZZJHH33U3Obj42PeFz0GwB7BYiqoXr26CRgXL15sPaZfa6BYrVo16zH9sGrQoIEZytS/avWX1OHDh623FytWzPyr99EPEh3isDVx4kTJnz+/ua9+4EVHR7vl+WU0moXSD42iRYtKv379pFmzZvLdd99Zb1++fLmUK1dOsmbNav3wUb/++qv4+vqajJYtHaZ6+OGHzdfHjx832a2cOXNKYGCgVKhQQX766SenWaTff//dfB9kyZLF3KdFixZy5coVc9u3334rlSpVksyZM5vvCe1neHi4W16j9O6FF14wr+WmTZukQ4cOUrp0afNaDxo0SP78809ru4sXL0r79u3N61uqVCm791n9/fff8sgjj5j3Ol++fPLss8+a+1joe6PZS32P9f3RNp988ol5H7p37y7ZsmUz2emff/7Zep+E77Oz7wkNeDW4VXqb7SiFPu6LL75oHjdPnjzm+6JHjx7WwMdCf0fkzZtXPvvsMxe90p75s61b1apVZdiwYXLixAm5cOGCw5GddevWSe3atc399Hevtr/bKIP+bHbt2tW8X/o9pd87+kenLf3+0M8LvV2/9yZPnmyd3qJ98Pb2lr/++svuPlOmTDG/j+Li4lL9NQGSgmAxlegv6tmzZ1v3Z82aZT4sbOkHiH5Y6S8C/atWfynoLwvLLwD9YFO//PKLCUBsg881a9aYwFL/nTNnjpk/pxtcT4MxS+bm1q1bJmifN2+eCQ7DwsLklVdeMbc1bNjQDHnqbbYf1gsWLDDfH0qD/MjISHNfzXa9++67JhBxRD+0mjZtKuXLl5cNGzbI+vXrTVARGxtrvj86d+5szrt3714TgDz++ONC2VSRy5cvmz/M9LXW4Csh23mnY8eOlaeeesoMRbZq1Uq6dOli7q80mPvXv/5l/njTn1k957lz50x7W/rzqAGb/vxq4Kh/YDz55JNmuHnr1q3SvHlzE2Tq944jzr4nNKBYtGiRabN//37znk+dOtXucTVLqX9QzJw5U3r16mX6aPnjRf3www/mcTt27JgKr+yD5ebNmzJ//nwTzOsfWwmdOnXKfE/UqlVLduzYIR999JEJut944w2n59RgXr9X9I8O/ZnVn0c9h+UPe32vdMrSf/7zH/Pz/e9//1vefPNN6/1DQ0PNH322nyVK9/Xc+pkBpAktyo2U69atW3y7du3iz58/H+/v7x9/7NgxswUEBMRfuHDB3KZtHNHb9S3YtWuX2T969KjZ37ZtW6LHKFq0aHxMTIz12JNPPhnfsWNHFz+7jPt+qri4uPiVK1ea9/WVV16Jnz17tnl/Dh06ZG0/Y8aM+Hz58ln333333fhy5cpZ9xctWhSfNWvW+Js3b5r9SpUqxY8ZM8bhY69Zs8ac/8qVK2a/c+fO8fXr13fYdsuWLaatfq/B3saNG81rs3jx4ru20zYjR4607ut7pMd+/vlnsz9+/Pj45s2b293nxIkTps3+/fvNfqNGjeIbNGhgvV1/RgMDA+OfffZZ67EzZ86Y+2zYsMHh+5yc7wkLfdxq1aolal++fHnzPWjRpk2b+Oeee+6ur0NG+tn28fEx749u+rrmz5/f/Cw5+v372muvxZcpU8b8HrD9edef59jYWOv78J///Md8feDAAXP/33//3dr+4sWL8ZkzZ47/+uuvzb7+zm7durVdv7p06RKfPXt26/7ChQvjc+bMGR8REWH2tX9eXl6mf0Ba4c+UVBIcHCytW7c22T79K1C/1myDLR2O0GyQZp+CgoLMX5FKs1P3okNTOqfGQodEzp8/74JnAs3GaGYnICDADCNpVkbnLSodOipRooTT90H/+j906JB1qFO/HzQTZclwvfTSSyYzUb9+fXNpMMvk+rtlFh2pUqWKuU2HoTWLpUNbluHpjC452dXKlStbv9b3SH8uLe+nZpM0k6/fC5atbNmy5jbb6SO259CfUc1S6ftioUPTytnPa3K+J2zpnMyENLtoyUppFlSHvy1ZbYgZ1tefK900E6zD9/ozrlMBEtKMfd26de3mlup7pBnJkydPOmyv8x3r1KljPabfC2XKlDG3WTLEOqxtK+G+Vr7Q76P//e9/1t8h2m/L5wWQFggWU5H+UtYfbB0ecvQLWocQdYhLP9g3btxotqROTte5cLb0FxjzV1z7gaLBvS5a0vfTEuw5eh9sgxOdH6bvs35gO/qw1g/zI0eOmGFJHXKsWbOmTJs2zenwtzP6YbJy5Upzfh2m1nPoh9LRo0clo9O5h/q+7Nu3775+rjQo0PfSElxYNv2+0CkHdzuH7TFLsOHs5zU53xO2HA2x63w5PZcOgeoQq86DtsyXxZ3XTIedddPh5U8//dRMD9LfyemFTi3Q91F/h+hngy6UJOBHWiNYTEW62EF/uHV+iv7FauvSpUvmr8qRI0eajJAukEiYCdJfEkrnpCHtP1B0gVJKVkbqh//ChQvlv//9r8lCajbCls5F03lLOidVV1s7+6DSjNXdyrxoEKLn1nl327ZtM98/lmxERpYrVy7z8zdjxgyHC36SWoZGF67t3r3bZHQsAYZlcxSo3Q9n3xPJ/Z2gmSzNTGmgoX+4Jpw3jcQ/QzoP0LaShYX+jrbMO7TQOYe6aKlQoUIO2+viF0sSwPb3vv5Bp/QPus2bN9vdL+G+5XeIzl3/8MMPzTl1PjKQlggWU5Fme3S4Yc+ePXZDxkpXx+kvcg0gdJhSS3roYhdbmpXSbJJlIv21a9fc/AyQGjRQ0eFMHVpM+GGtq1d1NbVmAHXxgw5z6oeMI8OHDzcfJLqyV4cmNVOmk+x1Na5+IL311ltmMr1OY9AgQ1d0OjtXRqOBogZYOsSni0Q0G6g/mx988IEZWkwKXXiiIwE6dUTfBx161vdO39PU/IPubt8TugJWAxqdGqHvr2Y770UDDc2G6/Pt1q1bqvXzQaALibRagW76+uiCJEsGOSH9udOV0tpGf/aWLl1qpgno721HC000o92uXTvp3bu3WYym0xieeeYZKViwoDmu9Fy60l1XQOv35Mcff2xGBxKWUdL3/6GHHpJXX33VfP/dbZQBcAeCxVSmQYJuCekvF63FuGXLFlP0deDAgYlq92kWSz/M9BdIgQIFrL9g4Fn0vda5ixpQ6HCSLT2mQYh+GGgmWku6aPbAEb1txYoV5kNHgx4NcvQDS79P9HtMV8/qSkttpxnrSZMmmflXEDMvWAMvnVKgmTr9mdOVp5qp1YA7KfRnUDNJ+p7pimadh6iBna6mTs1VqXf7ntBAQzPHWrJF5z5quZx70dW0OpdW/2jR54B/6B/i+tropnML9Y+Ab775JlGZMstrr4Gdzm3UOcKa+e3Zs6f5WXNGM7o6l1RLGOnPq2Yl9RyWaQk6EqAr1zVY1HNqf/SzQOdHJ6SPpSNVDEEjPfDSVS5p3QngQaO/6DUTlLBuH+BqminTQEcDF4Yv0z/NRGrm8rfffrM7Pn78eBPIJnXBE+BKlKoHUpFOHdBFCjopnUAR7qQLaHSKgmaYNfvZtm3btO4SHNA6rZrl1nmvOgStUwZsRxc02Nfi3NOnT79rTUfAnQgWgVSkUwd02EqHrPQDAXAXnbuqq5918YUubuGydemT/n6YMGGC3Lhxw0yX0KlHOs/UQqcafPnll2ahEkPQSC8YhgYAAIBTLHABAACAUwSLAAAAcIpgEQAAAE4RLAIAAMApgkUAAAA4RbAIAAAApwgWAQAA4BTBIgAAAJwiWAQAAIA483+Df+ge33lzowAAAABJRU5ErkJggg==",
+      "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": [
+    "## 🔍 皮爾森相關性分析\n",
+    "\n",
+    "此筆記本通過展示如何使用皮爾森相關性分析多個特徵之間的關係，來增強數據可視化模組。\n",
+    "\n",
+    "皮爾森相關性衡量兩個連續變數之間**線性關係**的強度和方向。它返回一個介於 -1 和 1 之間的值：\n",
+    "- **+1** → 完美的正相關  \n",
+    "- **0** → 無線性關係  \n",
+    "- **-1** → 完美的負相關\n",
+    "\n",
+    "在這裡，我們計算了數學、物理、化學和生物學的學生成績之間的相關矩陣。生成的**熱圖**幫助我們直觀地理解每門學科成績之間的相關性強度。\n",
+    "\n",
+    "這種相關性分析在**特徵工程和預處理**中至關重要，特別是在構建機器學習模型之前。它有助於識別：\n",
+    "- 冗餘特徵  \n",
+    "- 強效預測因子  \n",
+    "- 潛在的多重共線性  \n",
+    "\n",
+    "在分析真實世界數據集時，這是對有意義的可視化的一個有益補充。\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**免責聲明**：  \n本文件使用 AI 翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 進行翻譯。我們致力於提供準確的翻譯，但請注意，自動翻譯可能包含錯誤或不準確之處。應以原始語言的文件作為權威來源。對於關鍵資訊，建議尋求專業人工翻譯。我們對於因使用此翻譯而產生的任何誤解或錯誤解讀概不負責。\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": "2025-09-06T19:37:30+00:00",
+   "source_file": "3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb",
+   "language_code": "mo"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/translations/mr/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb b/translations/mr/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb
new file mode 100644
index 00000000..b13fb53f
--- /dev/null
+++ b/translations/mr/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb
@@ -0,0 +1,100 @@
+{
+ "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": [
+    "## 🔍 पिअर्सन सहसंबंध विश्लेषण\n",
+    "\n",
+    "या नोटबुकमध्ये डेटा व्हिज्युअलायझेशन मॉड्यूल सुधारित करून अनेक वैशिष्ट्यांमधील संबंध पिअर्सन सहसंबंध वापरून कसा विश्लेषित करायचा हे दाखवले आहे.\n",
+    "\n",
+    "पिअर्सन सहसंबंध दोन सातत्यपूर्ण चलांमधील **रेखीय संबंधाचा** ताकद आणि दिशा मोजतो. तो -1 ते 1 दरम्यान मूल्य परत करतो:\n",
+    "- **+1** → परिपूर्ण सकारात्मक संबंध  \n",
+    "- **0** → कोणताही रेखीय संबंध नाही  \n",
+    "- **-1** → परिपूर्ण नकारात्मक संबंध\n",
+    "\n",
+    "येथे, आम्ही गणित, भौतिकशास्त्र, रसायनशास्त्र आणि जीवशास्त्रातील विद्यार्थ्यांच्या गुणांसाठी सहसंबंध मॅट्रिक्सची गणना केली. परिणामी **हीटमॅप** आपल्याला प्रत्येक विषयाच्या गुणांचे इतरांशी किती मजबूत संबंध आहे हे दृश्यात्मकपणे समजून घेण्यास मदत करते.\n",
+    "\n",
+    "अशा प्रकारचा सहसंबंध विश्लेषण **वैशिष्ट्य अभियांत्रिकी आणि पूर्वप्रक्रिया** मध्ये अत्यंत महत्त्वाचा आहे, विशेषतः मशीन लर्निंग मॉडेल तयार करण्यापूर्वी. यामुळे खालील गोष्टी ओळखण्यास मदत होते:\n",
+    "- अनावश्यक वैशिष्ट्ये  \n",
+    "- मजबूत भविष्यवाणी करणारे घटक  \n",
+    "- संभाव्य बहुसंरेखीयता  \n",
+    "\n",
+    "हे वास्तविक-जगातील डेटासेटचे विश्लेषण करताना अर्थपूर्ण व्हिज्युअलायझेशनमध्ये उपयुक्त भर घालते.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**अस्वीकरण**:  \nहा दस्तऐवज AI भाषांतर सेवा [Co-op Translator](https://github.com/Azure/co-op-translator) चा वापर करून भाषांतरित करण्यात आला आहे. आम्ही अचूकतेसाठी प्रयत्नशील असलो तरी, कृपया लक्षात घ्या की स्वयंचलित भाषांतरांमध्ये त्रुटी किंवा अचूकतेचा अभाव असू शकतो. मूळ भाषेतील मूळ दस्तऐवज हा अधिकृत स्रोत मानला जावा. महत्त्वाच्या माहितीसाठी व्यावसायिक मानवी भाषांतराची शिफारस केली जाते. या भाषांतराचा वापर केल्यामुळे उद्भवणाऱ्या कोणत्याही गैरसमज किंवा चुकीच्या अर्थासाठी आम्ही जबाबदार राहणार नाही.\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": "2025-09-06T19:38:17+00:00",
+   "source_file": "3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb",
+   "language_code": "mr"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/translations/ms/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb b/translations/ms/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb
new file mode 100644
index 00000000..00034a17
--- /dev/null
+++ b/translations/ms/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb
@@ -0,0 +1,100 @@
+{
+ "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": [
+    "## 🔍 Analisis Korelasi Pearson\n",
+    "\n",
+    "Notebook ini mempertingkatkan modul visualisasi data dengan menunjukkan cara menganalisis hubungan antara pelbagai ciri menggunakan korelasi Pearson.\n",
+    "\n",
+    "Korelasi Pearson mengukur kekuatan dan arah **hubungan linear** antara dua pemboleh ubah berterusan. Ia memberikan nilai antara -1 dan 1:\n",
+    "- **+1** → Hubungan positif sempurna  \n",
+    "- **0** → Tiada hubungan linear  \n",
+    "- **-1** → Hubungan negatif sempurna\n",
+    "\n",
+    "Di sini, kami mengira matriks korelasi untuk markah pelajar dalam Matematik, Fizik, Kimia, dan Biologi. **Heatmap** yang dihasilkan membantu kita memahami secara visual sejauh mana kekuatan hubungan antara markah setiap subjek.\n",
+    "\n",
+    "Analisis korelasi seperti ini sangat penting dalam **kejuruteraan ciri dan prapemprosesan**, terutamanya sebelum membina model pembelajaran mesin. Ia membantu mengenal pasti:\n",
+    "- Ciri yang berlebihan  \n",
+    "- Peramal yang kuat  \n",
+    "- Potensi multikolineariti  \n",
+    "\n",
+    "Ini adalah tambahan yang berguna untuk visualisasi yang bermakna semasa menganalisis set data dunia sebenar.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Penafian**:  \nDokumen ini telah diterjemahkan menggunakan perkhidmatan terjemahan AI [Co-op Translator](https://github.com/Azure/co-op-translator). Walaupun kami berusaha untuk memastikan ketepatan, sila ambil maklum bahawa terjemahan automatik mungkin mengandungi kesilapan atau ketidaktepatan. Dokumen asal dalam bahasa asalnya harus dianggap sebagai sumber yang berwibawa. Untuk maklumat penting, terjemahan manusia profesional adalah disyorkan. Kami tidak bertanggungjawab atas sebarang salah faham atau salah tafsir yang timbul daripada penggunaan terjemahan ini.\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": "2025-09-06T19:40:18+00:00",
+   "source_file": "3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb",
+   "language_code": "ms"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/translations/my/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb b/translations/my/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb
new file mode 100644
index 00000000..5d62e022
--- /dev/null
+++ b/translations/my/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb
@@ -0,0 +1,100 @@
+{
+ "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": [
+    "## 🔍 Pearson Correlation Analysis\n",
+    "\n",
+    "ဒီ notebook က data visualization module ကို တိုးတက်အောင်လုပ်ပေးပြီး Pearson correlation ကို အသုံးပြုကာ အမျိုးမျိုးသော features တွေကြားရှိ ဆက်စပ်မှုကို ဘယ်လိုခွဲခြားလေ့လာရမယ်ဆိုတာ ပြသပေးပါတယ်။\n",
+    "\n",
+    "Pearson correlation က အဆက်မပြတ် variables နှစ်ခုကြားရှိ **linear relationship** ရဲ့ အားကောင်းမှုနဲ့ ဦးတည်မှုကို တိုင်းတာပေးပါတယ်။ အဲဒီ correlation က -1 နဲ့ 1 ကြားမှာ တန်ဖိုးတစ်ခုကို ပြန်ပေးပါတယ်။\n",
+    "- **+1** → အပြည့်အဝ အပေါင်းသင့်သော ဆက်စပ်မှု  \n",
+    "- **0** → Linear ဆက်စပ်မှု မရှိ  \n",
+    "- **-1** → အပြည့်အဝ အပေါင်းမသင့်သော ဆက်စပ်မှု  \n",
+    "\n",
+    "ဒီမှာတော့ ကျောင်းသားတွေ Math, Physics, Chemistry, နဲ့ Biology အတန်းများရဲ့ အမှတ်များအတွက် correlation matrix ကို တွက်ချက်ထားပါတယ်။ ထွက်လာတဲ့ **heatmap** က အတန်းတစ်ခုရဲ့ အမှတ်နဲ့ အခြားအတန်းရဲ့ အမှတ်ကြားမှာ ဘယ်လောက်အဆင်ပြေစပ်နေတယ်ဆိုတာကို ရှင်းလင်းစွာ မြင်နိုင်အောင် ကူညီပေးပါတယ်။\n",
+    "\n",
+    "ဒီလို correlation analysis က **feature engineering နဲ့ preprocessing** အတွက် အရေးကြီးပါတယ်၊ အထူးသဖြင့် machine learning models တည်ဆောက်မယ့်အခါမှာပါ။ ဒါက အောက်ပါအရာတွေကို ရှာဖွေဖို့ ကူညီပေးပါတယ်။\n",
+    "- မလိုအပ်တဲ့ features  \n",
+    "- အားကောင်းတဲ့ predictors  \n",
+    "- Multicollinearity ဖြစ်နိုင်မှု  \n",
+    "\n",
+    "ဒီလို correlation analysis က အမှန်တကယ့် data sets တွေကို လေ့လာတဲ့အခါမှာ အဓိက visualization တွေကို ပိုပြီး အဓိပ္ပါယ်ရှိအောင် ကူညီပေးနိုင်ပါတယ်။\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**ဝက်ဘ်ဆိုက်မှတ်ချက်**:  \nဤစာရွက်စာတမ်းကို AI ဘာသာပြန်ဝန်ဆောင်မှု [Co-op Translator](https://github.com/Azure/co-op-translator) ကို အသုံးပြု၍ ဘာသာပြန်ထားပါသည်။ ကျွန်ုပ်တို့သည် တိကျမှန်ကန်မှုအတွက် ကြိုးစားနေသော်လည်း၊ အလိုအလျောက်ဘာသာပြန်မှုများတွင် အမှားများ သို့မဟုတ် မမှန်ကန်မှုများ ပါဝင်နိုင်သည်ကို ကျေးဇူးပြု၍ သတိပြုပါ။ မူရင်းဘာသာစကားဖြင့် ရေးသားထားသော စာရွက်စာတမ်းကို အာဏာတည်သော ရင်းမြစ်အဖြစ် သတ်မှတ်သင့်ပါသည်။ အရေးကြီးသော အချက်အလက်များအတွက် လူက ဘာသာပြန်မှုကို အသုံးပြုရန် အကြံပြုပါသည်။ ဤဘာသာပြန်မှုကို အသုံးပြုခြင်းမှ ဖြစ်ပေါ်လာသော နားလည်မှုမှားမှုများ သို့မဟုတ် အဓိပ္ပါယ်မှားမှုများအတွက် ကျွန်ုပ်တို့သည် တာဝန်မယူပါ။\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": "2025-09-06T19:41:40+00:00",
+   "source_file": "3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb",
+   "language_code": "my"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/translations/ne/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb b/translations/ne/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb
new file mode 100644
index 00000000..ce741af1
--- /dev/null
+++ b/translations/ne/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb
@@ -0,0 +1,100 @@
+{
+ "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": [
+    "## 🔍 पियर्सन सम्बन्ध विश्लेषण\n",
+    "\n",
+    "यो नोटबुकले डेटा भिजुअलाइजेसन मोड्युललाई सुधार गर्दै धेरै विशेषताहरू बीचको सम्बन्धलाई पियर्सन सम्बन्ध प्रयोग गरेर विश्लेषण गर्ने तरिका देखाउँछ।\n",
+    "\n",
+    "पियर्सन सम्बन्धले दुई निरन्तर चरहरू बीचको **रेखीय सम्बन्धको बल र दिशा** मापन गर्दछ। यसले -1 देखि 1 सम्मको मान फर्काउँछ:\n",
+    "- **+1** → पूर्ण सकारात्मक सम्बन्ध  \n",
+    "- **0** → कुनै रेखीय सम्बन्ध छैन  \n",
+    "- **-1** → पूर्ण नकारात्मक सम्बन्ध\n",
+    "\n",
+    "यहाँ, हामीले गणित, भौतिकी, रसायनशास्त्र, र जीवविज्ञानमा विद्यार्थीको स्कोरको लागि सम्बन्ध म्याट्रिक्स गणना गरेका छौं। परिणामस्वरूप **हिटम्याप** ले प्रत्येक विषयको स्कोर एक-अर्कासँग कत्तिको बलियो सम्बन्धित छ भन्ने कुरा दृश्यात्मक रूपमा बुझ्न मद्दत गर्दछ।\n",
+    "\n",
+    "यस्तो सम्बन्ध विश्लेषण **फिचर इन्जिनियरिङ र प्रि-प्रोसेसिङ** मा विशेष गरी मेसिन लर्निङ मोडेल निर्माण गर्नु अघि महत्त्वपूर्ण हुन्छ। यसले निम्न कुराहरू पहिचान गर्न मद्दत गर्दछ:\n",
+    "- अनावश्यक विशेषताहरू  \n",
+    "- बलियो भविष्यवाणीकर्ताहरू  \n",
+    "- सम्भावित बहुविधता (मल्टिकोलिनियरिटी)\n",
+    "\n",
+    "यो वास्तविक-विश्वको डेटासेट विश्लेषण गर्दा अर्थपूर्ण भिजुअलाइजेसनमा उपयोगी थप हो।\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**अस्वीकरण**:  \nयो दस्तावेज़ AI अनुवाद सेवा [Co-op Translator](https://github.com/Azure/co-op-translator) प्रयोग गरी अनुवाद गरिएको हो। हामी यथासम्भव सटीकता सुनिश्चित गर्न प्रयास गर्छौं, तर कृपया ध्यान दिनुहोस् कि स्वचालित अनुवादहरूमा त्रुटिहरू वा अशुद्धताहरू हुन सक्छन्। यसको मूल भाषामा रहेको मूल दस्तावेज़लाई आधिकारिक स्रोत मानिनुपर्छ। महत्त्वपूर्ण जानकारीका लागि, व्यावसायिक मानव अनुवाद सिफारिस गरिन्छ। यस अनुवादको प्रयोगबाट उत्पन्न हुने कुनै पनि गलतफहमी वा गलत व्याख्याका लागि हामी जिम्मेवार हुने छैनौं।\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": "2025-09-06T19:38:25+00:00",
+   "source_file": "3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb",
+   "language_code": "ne"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/translations/nl/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb b/translations/nl/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb
new file mode 100644
index 00000000..8539a4f9
--- /dev/null
+++ b/translations/nl/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb
@@ -0,0 +1,100 @@
+{
+ "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": [
+    "## 🔍 Pearson-correlatieanalyse\n",
+    "\n",
+    "Deze notebook breidt de module voor datavisualisatie uit door te laten zien hoe je de relatie tussen meerdere kenmerken kunt analyseren met behulp van de Pearson-correlatie.\n",
+    "\n",
+    "De Pearson-correlatie meet de sterkte en richting van een **lineaire relatie** tussen twee continue variabelen. Het geeft een waarde terug tussen -1 en 1:\n",
+    "- **+1** → Perfecte positieve relatie  \n",
+    "- **0** → Geen lineaire relatie  \n",
+    "- **-1** → Perfecte negatieve relatie\n",
+    "\n",
+    "Hier hebben we de correlatiematrix berekend voor de scores van studenten in Wiskunde, Natuurkunde, Scheikunde en Biologie. De resulterende **heatmap** helpt ons visueel te begrijpen hoe sterk de scores van elk vak met elkaar samenhangen.\n",
+    "\n",
+    "Een dergelijke correlatieanalyse is van cruciaal belang bij **feature engineering en preprocessing**, vooral voordat machine learning-modellen worden gebouwd. Het helpt bij het identificeren van:\n",
+    "- Overbodige kenmerken\n",
+    "- Sterke voorspellers\n",
+    "- Potentiële multicollineariteit\n",
+    "\n",
+    "Dit is een waardevolle toevoeging aan betekenisvolle visualisaties bij het analyseren van datasets uit de praktijk.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Disclaimer**:  \nDit document is vertaald met behulp van de AI-vertalingsservice [Co-op Translator](https://github.com/Azure/co-op-translator). Hoewel we streven naar nauwkeurigheid, willen we u erop wijzen dat geautomatiseerde vertalingen fouten of onnauwkeurigheden kunnen bevatten. Het originele document in de oorspronkelijke taal moet worden beschouwd als de gezaghebbende bron. Voor kritieke informatie wordt professionele menselijke vertaling aanbevolen. Wij zijn niet aansprakelijk voor misverstanden of verkeerde interpretaties die voortvloeien uit het gebruik van deze vertaling.\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": "2025-09-06T19:39:52+00:00",
+   "source_file": "3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb",
+   "language_code": "nl"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/translations/no/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb b/translations/no/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb
new file mode 100644
index 00000000..36481d9f
--- /dev/null
+++ b/translations/no/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb
@@ -0,0 +1,100 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b1e38707",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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XcxYBAADgFMEiAAAAnCJYBAAAgFMEiwAAAPfh119/NdUMChQoYEpmOSpflpDWX61evbqp5KCVDfTymwnp1ZW0ekNAQIDUqVPHVNJIWNmgf//+plqElgHTKhxaw9eWXqlMS7RpwX2tnqDVKu5WvswRgkUAAID7EB4ebsqkaXCXFFrKTQM4LZ22fft2U++0V69edrVptS6sXk5W67Ru3brVnF8vy2l7udSBAweaklfffPONqZF6+vRpUzrNQuvT6uNonV29YISWbNOgVC9zmxyshgYAAEglXl5epqav1tF1Rq+Y9OOPP5oLIVjoJU21Fu+yZcvMvmYSa9WqZYrlK70ClV4KVC+Jqldb0vqywcHB5vrzTzzxhPWqVHqlMK1P+9BDD5nL3mrtWg0i8+XLZ9ro1cX08S9cuGDquyYFmUUAAIAE9CpcepWu6zab5cpc90uDuYSXQ9WsoR5XmgnUCwjYttErg+m+pY3eHh0dbddGLzahlwy1tNF/9apilkDR8jj6XHbv3u15V3DJXKRzWncBbtR+bt+07gLc6MT1O5ezQ8Zwdnria47jwXVwRc8HMnZ4tUcZc2UjWzokfL9XuFJ6+VHbAE7pvgZxejUtvZKYDiE7amO5pr2eQzODOXLkSNRGb7vb41hu87hgEQAAIL3QS8bqnEFbuhglIyJYBAAAHsnLy3Wz6TQwdFVwGBISkmjVsu4HBQVJ5syZzXXvdXPURu9rOYcOV+s8R9vsYsI2CVdQW85paZMUzFkEAABwo7p168qqVavsjq1cudIcVzq8XKNGDbs2usBF9y1t9HZfX1+7Nvv37zelcixt9N9du3bZraDWx9GgtHz58knuL5lFAADgkbzSSc7r5s2bcujQIbvSOFoSJ1euXGbBiQ5pnzp1SubOnWtu79u3r1nlPHToUOnRo4esXr1avv76a7NC2kKHwLt16yY1a9aU2rVry5QpU0yJnu7du5vbs2fPLj179jTt9HE0ANSV0hog6kpo1bx5cxMUPvvsszJhwgQzT3HkyJGmNmNysqYEiwAAAPfhr7/+MjUTLSxzHTXY07qGZ86cMRk/i2LFipnAUOskTp06VQoVKiSffvqpWals0bFjR1PeRmsiapBXtWpVU1bHdsHK+++/b1ZJazFuXamt9//www+tt+tQ9g8//CD9+vUzQWRgYKDp07hx4zyzziKroTMWVkNnLKyGzlhYDZ2xpOVq6Kyh3Vx27pvH5rjs3J6GzCIAAPBIrlzggn/wKgMAAMApMosAAMBjL60H1yOzCAAAAKfILAIAAA9FzssdeJUBAADgFJlFAADgkVgN7R68ygAAAHCKzCIAAPBIZBbdg1cZAAAATpFZBAAAHsmLnJdbECwCAACPxDC0e/AqAwAAwCkyiwAAwCORWXQPXmUAAAA4RWYRAAB4JDKL7sGrDAAAAKfILAIAAI/kJV5p3YUMgcwiAAAAnCKzCAAAPBJzFt2DYBEAAHgkgkX34FUGAACAU2QWAQCARyKz6B68ygAAAHCKzCIAAPBQ5LzcgVcZAAAATpFZBAAAHok5i+7BqwwAAACnyCwCAACPRGbRPQgWAQCAR/JigNQteJUBAADgFJlFAADgkRiGdg9eZQAAADhFZhEAAHgkLy+vtO5ChkBmEQAAAE6RWQQAAB6JOYvuwasMAAAAp8gsAgAAj0SdxXQcLMbGxsrnn38uq1atkvPnz0tcXJzd7atXr06t/gEAADjEMHQ6Dhb/85//mGCxdevWUrFiRVYjAQAAPKBSFCx+9dVX8vXXX0urVq1Sv0cAAABJQGbRPVL0Kvv5+UnJkiVTvzcAAADw/GBx8ODBMnXqVImPj0/9HgEAACRxgYurNqRgGPrxxx9PtIjl559/lgoVKoivr6/dbYsXL07qaQEAAJCOJTl0zp49u93Wvn17adSokeTJkyfRbQAAAC6ncxZdtSXTjBkzJDQ0VAICAqROnTqyadMmp22jo6Nl3LhxUqJECdO+SpUqsmzZMrs2N27ckJdfflmKFi0qmTNnlnr16snmzZvtn76Xl8Ptvffes7bRPiW8/Z133nFNZnH27NnJOjGcq1+7rAzs+6hUr1Rc8ufLKU/1miTfr/grrbuFZDq/Zo2cW7lCoq9dk8yFCkmRTp0lsFgxh23jY2PkzM/L5NKGPyT66lUJCAmRgu0fl+wVK1rbxEZEyOmlS+Xq9m0SfeOGZClcWAp37CSBoaFufFZwpn1ofulcsqDk8veTw9fDZcquw7L36k2HbX28vOTZUoWkZeG8kifAX07cvC0f7Tkqmy5ctbbJ7OMjvcoWkYb5c0tOf185cC1cPvj7iOxzck6kT7UqhUivJytJhVK5JV/uQOk35hf55Y/jad0tuNnChQtl0KBBMnPmTBMoTpkyRVq0aCH79++XvHnzJmo/cuRImT9/vnzyySdStmxZWb58uUnC/fHHH1KtWjXTplevXvL333/LvHnzpECBAqZ9s2bNZM+ePVKwYEHT5syZM3bn1RHfnj17SocOHeyOa2Dau3dv6362bNmS9fxSNCj/r3/9S65e/eeXnsX169fNbbi7wCz+smtPmLw8clZadwUpdHnzZjn57TeSv/WjUm7ESMlSqLAc/GCqRF+/7rD9qSVL5eJvv5qAssKYsRLcsKEcnvmR3AoLs7Y5PneuXN+7R0K795Dyo0ZLUPnycuD9yRJ15Yobnxkc+VeBPPJihWLy+f4w6bVumxy6Fi6THqooOfzsp+BY9C5bVNoWDZEpu47Is2u2yNLjZ+St2uWkVFCgtc2rVUtKreAc8sbWA9Jt7TbZfOGqvF+3ouQJ8HPjM8P9yhyQSfYduSxjp29I665k2NXQrtqSY/LkySYY6969u5QvX94EjVmyZJFZsxx/zmsA+Nprr5mqMsWLF5d+/fqZrydNmmRuv337tixatEgmTJggDRs2NIuKx4wZY/796KOPrOcJCQmx25YuXSpNmjQx57SlwaFtu8DAf34XuSxYXLt2rURFRSU6HhERIb/99ltKTpmhrFi7Q8ZO/Fq+W0420VOd+2Wl5GnQQPLUry+ZCxSQIl26iLefn1z643eH7S9v/FNCWj4i2StVEv/gYAlu1NhkFc+tXGluj4uKkivbtkqhDh0kW+nSEpA3rxRo09b8e2HdOjc/OyTUsURB+T7srPx04rwcu3lbJu48JBGxsdK6SD6H7VsUDpZ5B0/Kn+evyJlbkbLk2FnZcO6KdCp5Jxvg5+0tjfLnkY/2HJMdl6/LqfAImb0/zPz7WGiIm58d7sevm0/K+59vkZW/k01MC86GYVNjS6qoqCjZsmWLyfpZeHt7m/0NGxz/EREZGWmGn23pUPP69evN1zExMeYCKHdrk9C5c+fkxx9/NJnFhHTYOXfu3CZrqUPUen6X1VncuXOn9WtNg549e9a6r09Kx9stqVHgQRUXE2MygvkfecR6zMvbW7KVLSc3jxxxeh/vBAvBvH395ObhQ+breL0KUlyceGWyb+Pl62ttg7SRyctLSmfPKvMPnrAe0zoQf128KhVyOh7K8fX2lqgEV7bS/Uq5gqzD1Jm8vRK1iYyNlcq5mPcNpAca0Olmy9/f32y2Ll68aGKgfPns/3jU/X379okjOkSt2UjNGuq8Rb0ini4O1vNYMoF169aV8ePHS7ly5cy5vvzySxN8OitdOGfOHHO/hAuSX3rpJalevbrkypXLDHMPHz7cDF/r47skWKxatao14nY03KwR77Rp05JzSsDjxNy8aQK7TNnufPBb+AZlk4iz9vNHLILKVzDZyKylSpnM4o19+0wmUf6//JRPQIAEFi8uZ376UQLy5xffoCC5vGmThB85Iv4O5rvAfbL7+ZrA7nJktN3xK5HRUjRrFof32XT+inQsXkB2XLpmsoU1gnNIw5Dc4v3/2YrbsbGy6/J16Va6iBy7sV+uREZJs0LBUiFXkJwKv+2W5wU8CFxZ4ubtt9+WsWPH2h0bPXq0GQ6+X1p+UIetdb6ixlQaMOoQtu2wtQ5V9+jRwyThfHx8TMDXuXNnk8V0RO/bpUuXRNlInUtpUblyZVMru0+fPub5JQx8UyVYPHr0qKmtqGPhusonODjYeps+uE7i1CeUkmg9Pj5WvLzufV/AExXu2FGOz5sru0eP0nETEzDmqVdfLtoMWxfr0UOOzZkju14dqmMYkqVIEclVq7bcCmN4y9PoQpWhVUrJ/H/VMH8PnL51W346cc5u2FrnKg6vWkqWtKgtMXHxcuDaTVl16oLJYgJIe5qBsw20lKPgSqvCaOyjw8C2dF/nBzqi8dOSJUvM9L1Lly6ZBSzDhg2zm2uoAeS6deskPDzcrAnJnz+/dOzYMdF8RKVTAHUxjS60uRddgKPD0MeOHZMyZcpIqgeLunxbxSUYOkmNaN0nqIL4Zq90X+cF3CFT1qwmmIu5Yb+YJfr6DfF1UjrKN1s2KflCf4mLjjaZSd8cOeTU4sXinyePtY1/cF4p88oQiY2MlLiI2+KbPYcc+e9/xc+mDdzvWlS0CeZy+dtPEdAVzJciEs/dVlejYuS1zXvFz9tLgvx85WJElPQtFyqnwyOsbU7fipABf+ySAB9vCczkI5cio2VMjTJy5tY/bQCk3eX+HA05O6LJsho1apih5Mcee8waJ+n+iy++KHejWUDNHGopHV3Q8tRTTyVqo4tRdLty5YpZNa2LXhL67LPPTB+0BM+9bN++3cypdLRKO1WvDW07bzEsLCzRYpe2bdsmO1rPW6HX/XQFcBvvTJlM1u/63n2So2o165zDG/v2St4mTe5+X19f8cuZ05TSubptq+SsUTNRGx9/f7PF6F+Te3ZLwcftSyDAvWLi72T9auTJIb+dvWyO6WCy7i8+6njagUVUXLwJFHWOYqMCuWXNqYuJ2kTExpktq6+P1M6b05TYAeBZBg0aJN26dZOaNWtK7dq1TekczQjq0LLq2rWrCQo1WaY2btwop06dMtP79F8d2tYAc+jQodZzamCoo7ma/Tt06JAMGTLEDFtbzmmhWcdvvvnGupLals5x1MfSFdI6n1H3Bw4cKM8884zkzJnTtcHikSNHTD2gXbt2mbF2y2X/LKuHLBM0kxOtZ6QhaC2dU8JmxWNo4WCpXL6oXLl6U06cvpSmfUPS5Gv2bzn2+WwJDC0qWUKLyflVv5gVzbnr1Te3H509S/xy5DC1FFX40SMSdeWqqZ0YdfWqnPn+e/Nzk69FC+s5r+3ebeYwag3GyPPn5eSib83XeerXS7PniTsWHj4lr1UrLfuu3ZS9V27Ik8ULmDqJOrSsRlQrLRcjIuXjvXemDJTPkVXyZPaXg9duSnCAv/QoU8RcQOyLQyet56wdnMP8eyL8thQMzCwvlA+VsBu35Kew82n0LJESWQIySdEC/8xfLhSSVcoVzyVXb0TKmQvhadq3DCEZq5ZdqWPHjnLhwgUZNWqUWfyrQaAu+rUsetHEmmbzLHT4WWstajyVNWtWUzZH5yjmyHHn94K6du2aSa6dPHnSLE7R2olvvvlmoqvmffXVV+bzROczJqSxlt6uwahO/ytWrJgJFhMm7O7FKz4FF3hu06aNGZ//9NNPzQPr/EUdc9drRk+cOFEefvjh5J5SMhdJ/CQfVA8/VE5WfD0q0fF536yT5wfPlIyg/dy+4unOr1kt51asMLUV7xTl7iSBxe7MJdk/aaL4584toc/d+QvwxoH9EvbFFxJ54YJ4+/ubEjoaSGpAaXH5r7/k1P8Wm6LdPlmySM7q1aXgY4+JT2bHiyg8yYnrnv/H4OM2RbkPXQ+XqbsOy57/L6D9Qb1KcvZWhLy1/aDZr5o7SAZXLin5swTI7ZhYU0Jn5p5jcinyn1GYJgXySJ9yRU0weSM6RtaeuSif7D0u4TF3/2PbE5ydvl8yitqVQ2TBxNaJji9ecUBenZgxSskdXJG4VIu7lK79ocvOfWDTCy47t6dJUbCokzn12tC6qkYv76fBoqZJ9ZgGjNu2bUt2RzJSsIgHI1hExgoWkXQZKVhEGgeLD7kwWPyTYNEiRTNDdZjZcqkYDRxPnz5tXQCjq3EAAADcMgztqg33N2exYsWKsmPHDjMErUuwdWWOrgb673//63BJNwAAADJQsKiTMnWVj9ISODqHUecp6qVkdCIlAACAy5EBTL/Bol6mxqJUqVLmcjaXL182y7CTcz1FAAAAPEDBol52JilsL1cDAADgEq6ryY2UBouff/65WcRSrVo1a21FAAAAPLiSFSz269dPvvzyS3ONaK0grhXAtVAkAACAu8Uz9S39JXBnzJghZ86cMZej+f7776Vw4cLmOoaWS9IAAAAgg4/266Vj9JIyK1euNNeGrlChgrzwwgsSGhoqN2/euZoBAACAy3m5cMP9rYa20OscWq4Nfa/rQQMAAKQqb6K6dJlZ1AtR67zFf//731K6dGnZtWuXTJ8+3VwkWy+GDQAAgAyaWdThZi26rXMVtYyOBo16uT8AAAC3Y4FL+gsWZ86cKUWKFDGX9Fu3bp3ZHFm8eHFq9Q8AAACeEix27dqVK7QAAID0gZAkfRblBgAAQMZxX6uhAQAA0gyrod2CqyoCAADAKTKLAADAM7GOwi0IFgEAgGciVnQLhqEBAADgFJlFAADgmVjg4hZkFgEAAOAUmUUAAOCZSCy6BZlFAAAAOEVmEQAAeKR4Sue4BZlFAAAAOEVmEQAAeCZWQ7sFmUUAAAA4RWYRAAB4JhKLbkGwCAAAPBMLXNyCYWgAAAA4RWYRAAB4Jha4uAWZRQAAADhFZhEAAHgmEotuQWYRAAAATpFZBAAAnonV0G5BZhEAAABOkVkEAACeicyiWxAsAgAAz8T4qFvwMgMAAMApMosAAMAzMQztFmQWAQAA4BSZRQAA4JlILLoFmUUAAAA4RbAIAAA8Ury3l8u25JoxY4aEhoZKQECA1KlTRzZt2uS0bXR0tIwbN05KlChh2lepUkWWLVtm1+bGjRvy8ssvS9GiRSVz5sxSr1492bx5s12b5557Try8vOy2li1b2rW5fPmydOnSRYKCgiRHjhzSs2dPuXnzZrKeG8EiAADAfVi4cKEMGjRIRo8eLVu3bjXBX4sWLeT8+fMO248cOVI+/vhjmTZtmuzZs0f69u0r7du3l23btlnb9OrVS1auXCnz5s2TXbt2SfPmzaVZs2Zy6tQpu3NpcHjmzBnr9uWXX9rdroHi7t27zbl++OEH+fXXX+X5559P1vPzio+Pj5d0IHORzmndBbhR+7l907oLcKMT133Sugtwo7PT96d1F+BGB1f0TLPHLvG0fWCUmg5/kfS4pE6dOlKrVi2ZPn262Y+Li5PChQvLgAEDZNiwYYnaFyhQQEaMGCH9+/e3HuvQoYPJIM6fP19u374t2bJlk6VLl0rr1q2tbWrUqCGPPPKIvPHGG9bM4tWrV2XJkiUO+7V3714pX768yUjWrFnTHNMMZqtWreTkyZOmH0lBZhEAAHgmLxduSRQVFSVbtmwxWT8Lb29vs79hwwaH94mMjDTDz7Y0UFy/fr35OiYmRmJjY+/axmLt2rWSN29eKVOmjPTr108uXbpkvU0fX4eeLYGi0n5p/zZu3Jjk50iwCAAA4CCgu379ut2mxxK6ePGiCezy5ctnd1z3z5496/DcOkQ9efJkOXjwoMlC6hDx4sWLzTCy0qxi3bp1Zfz48XL69Glzfs04avBnaWMZgp47d66sWrVK3n33XVm3bp3JPGp7pY+vgaStTJkySa5cuZz2zRGCRQAA4Jl0IYqLtrfffluyZ89ut+mx1DB16lQpVaqUlC1bVvz8/OTFF1+U7t27m4yfhc5V1JmCBQsWFH9/f/nggw+kc+fOdm06deokbdu2lUqVKsljjz1m5iTqkLNmG1MTwSIAAEACw4cPl2vXrtlteiyhPHnyiI+Pj5w7d87uuO6HhIQ4PHdwcLCZZxgeHi7Hjx+Xffv2SdasWaV48eLWNrpSWjOFunL5xIkTZnW1rqK2bZOQ3qb9OXTokNnXx0+4yEaHuHWFtLO+OUKwCAAAPPdyfy7aNJun5WaCbDY9lpBmBnXhiQ4FW+jQsu7rUPLd6JxEzRxqALdo0SJp165dojaBgYGSP39+uXLliixfvtxhGwtdtKJzFrW90sfXBTA6p9Ji9erVpn+6KCepuIILAADAfRg0aJB069bNLCSpXbu2TJkyxWQNdWhZde3a1QSFlmFsXVyiJXCqVq1q/h0zZowJ4IYOHWo9pwaGOgytC1c0UzhkyBAzbG05p2Ycx44da1ZRa5bw8OHD5v4lS5Y0cyJVuXLlzLzG3r17y8yZM01mUoe8dfg6qSuh01WwSCmVjOV/XWemdRfgRrfDxqZ1F+BGJb4MS+suIKNIJ5f769ixo1y4cEFGjRplFo5oEKglaiyLXsLCwuzmGkZERJhai0eOHDHDz1rKRuco6splC8uwt2YLdUGKBoVvvvmm+Pr6mtt16Hvnzp0yZ84ckz3U4E9rMeqiGNsM6IIFC0yA2LRpU9MHPY/Of/TIOotPr12X1l2AGxEsZiwEixlLic5/pXUX4EaHv3w6zR67RLeFLjv34TkdXXZuT5NuMosAAADJkoLL8iH5CBYBAIBnIlh0C1ZDAwAAwCkyiwAAwCPFk1h0CzKLAAAAcIrMIgAA8EzMWXQLMosAAABwiswiAADwTHppPrgcmUUAAAA4RWYRAAB4JuYsugXBIgAA8EyMj7oFLzMAAACcIrMIAAA8Ewtc3ILMIgAAAJwiswgAADwTC1zcgswiAAAAnCKzCAAAPFI8cxbdgswiAAAAnCKzCAAAPBMpL7cgWAQAAJ6JBS5uQUwOAAAAp8gsAgAAz8QCF7cgswgAAACnyCwCAADPxJxFtyCzCAAAAKfILAIAAM9EYtEtyCwCAADAKTKLAADAI8UzZ9EtCBYBAIBnIlh0C4ahAQAA4BSZRQAA4Jkoyu0WZBYBAADgFJlFAADgmUh5pd+X+fbt23Lr1i3r/vHjx2XKlCmyYsWK1OwbAAAAPDFYbNeuncydO9d8ffXqValTp45MmjTJHP/oo49Su48AAACO5yy6asP9BYtbt26Vhx9+2Hz97bffSr58+Ux2UQPIDz74ICWnBAAAwIMyZ1GHoLNly2a+1qHnxx9/XLy9veWhhx4yQSMAAIDLUWcx/WYWS5YsKUuWLJETJ07I8uXLpXnz5ub4+fPnJSgoKLX7CAAA4DhYdNWG+wsWR40aJa+88oqEhoaa+Yp169a1ZhmrVauWklMCAADgQRmGfuKJJ6RBgwZy5swZqVKlivV406ZNpX379qnZPwAAAIfiWYiSfoPFa9euiZ+fX6Isog5PZ8pE6UYAAIAMPQzdqVMn+eqrrxId//rrr81tAAAAboliXLXBKkUvx8aNG6VJkyaJjjdu3NjcBgAAgAdDisaMIyMjJSYmJtHx6Ohoc3UXAAAAl2POYvrNLNauXVv++9//Jjo+c+ZMqVGjRmr0CwAAAJ4aLL7xxhvy6aefSsOGDWXs2LFm069nzZolb731Vur3EgAAIB3XWZwxY4YpKRgQEGDKCm7atMlpWx2JHTdunJQoUcK018oyy5Yts2tz48YNefnll6Vo0aKSOXNmqVevnmzevNnuHK+++qpUqlRJAgMDpUCBAtK1a1c5ffq03Xm0T15eXnbbO++84/pgsX79+rJhwwYpXLiwWdTy/fffm5XQO3futF4GEAAAICNYuHChDBo0SEaPHm0uiazBX4sWLczFShwZOXKkfPzxxzJt2jTZs2eP9O3b15Qe3LZtm7VNr169ZOXKlTJv3jzZtWuXuQBKs2bN5NSpU9ar6eljvf766+bfxYsXy/79+6Vt27aJHk8DUy13aNkGDBiQrOfnFR8fHy/pwNNr16V1F+BG/+s6M627ADe6HTY2rbsANyrR+a+07gLc6PCXT6fZYxd9b7XLzn18yL+S3LZOnTpSq1YtmT59utmPi4szCTUNyoYNG5aovWYBR4wYIf3797ce69Chg8kgzp8/36z/0MsqL126VFq3bm1to1P9HnnkETPC64hmHnWqoF56uUiRItbMomYodUupJGcWr1+/bvf13TYAAACX83Ldpot5E8Y3kZGRiboQFRUlW7ZsMVk/C29vb7Ovo7CO6Hl0+NmWBorr1683X+si4tjY2Lu2cVYHW4eZc+TIYXdch51z585t6mO/9957Dhcpp0qwmDNnTms6VTuh+wk3y3EAAABP9vbbb0v27NntNj2W0MWLF01gly9fPrvjun/27FmH59Yh6smTJ8vBgwdNFlKHm3UYWYeIlWYV9VLK48ePN3MQ9fyacdTg09ImoYiICDOHsXPnzhIUFGQ9/tJLL5na2GvWrJE+ffqYtSVDhw51Temc1atXS65cuczX+oBI7PyaNXJu5QqJvnZNMhcqJEU6dZbAYsUcto2PjZEzPy+TSxv+kOirVyUgJEQKtn9cslesaG0TGxEhp5culavbt0n0jRuSpXBhKdyxkwSGhrrxWeF+1a9dVgb2fVSqVyou+fPllKd6TZLvVzBM52kWLPhRPvtssVy4cEXKli0mr7/eRypXLu2wbXR0jHz88TeyZMlqOXfukhQrVlBeeeU5adjwn2oR+st/2rQv5bvv1sjFi1clb95c0r59U3nhhY4mMwDPUKtssPR+tLxULJ5T8uXMIn0n/Sor/zqZ1t3KMOJTsBAlqYYPH27mIdry9/eX1DB16lTp3bu3lC1b1vy860KX7t27m4XCFjpXsUePHlKwYEHx8fGR6tWrm0BQs5gJ6WKXp556SnRm4UcffWR3m+1zqFy5srkCnwaNGvgm9fkkOVhs1KiRw69xx+XNm+Xkt99Ikae7mADx/KpVcvCDqVJh7DjxtYnwLU4tWSqXN22Uos88awLF63t2y+GZH0nZoa9Klv+fZ3B87ly5ffqUhHbvIb45csjljX/KgfcnS4UxY8WPDK7HCMziL7v2hMnchWtl4SeD07o7SIGffvpN3n77Uxk7tr9UqVJa5sz5Tnr2HCXLls2U3Lnth3vUlCnzTRD4xhsDpHjxQvLbb1vlxRffkq++miDly5cwbT75ZJF8+eVP8u67A6VkySLy99+HZPjwqZItWxbp2jXxBHWkT1n8M8m+sCvy7drD8tHghmndHaQiDaSSEkzlyZPHBHPnzp2zO677ISEhDu8THBwsS5YsMdnAS5cumTmMOrexePHi1jYaQK5bt07Cw8PNEHj+/PmlY8eOdm1sA0Wdp6iJPdusorP5lToMfezYMSlTpoy4bDW0Lu+2HTPX5eJVq1aVp59+Wq5cuSIZ0blfVkqeBg0kT/36krlAASnSpYt4+/nJpT9+d9heA7+Qlo9I9kqVxD84WIIbNTZZxXMrV5rb46Ki5Mq2rVKoQwfJVrq0BOTNKwXatDX/XljHYiBPsmLtDhk78Wv5bjnZRE81e/YSeeqpFtKhQzMT2I0d+4IEBPjLokV3fl4TWrp0jfTt+5Q0alRTChcOkaefbiWNGtWQWbOWWNts27ZXmjZ9SBo3riWFCuWTli3rS4MGVWXnzoNufGa4X+t2nJHJX++UFWQT04Zm4V21JZGfn59ZeLJq1SrrMR1a1n0dSr4bnZOomUMN3hYtWiTt2rVL1EbL4migqPHV8uXL7dpYAkUdzv7ll1/MvMR72b59u5lTmTdv3iQ/xxQFi0OGDLEuZNHl3JribNWqlRw9ejRRyjYjiIuJkVthYRJUrpz1mJe3t2QrW05uHjni9D7evr52x7x9/eTm4UPm6/i4OP1uE69M9m28fH2tbQC4XlRUtOzefUjq1atiPaa/aOvVqyrbtu13eB/9Be7nZ/+zqxmKrVv3WPerVSsnf/65Q44evVMGY9++o7Jly167oWoAnmHQoEHyySefyJw5c2Tv3r3Sr18/kxHUoWWl9Q91WNtCL42scxSPHDkiv/32m7Rs2dIEmLZzCTUw1OScxlY6p1Evs6zD1pZz6u+ZJ554Qv766y9ZsGCBmdqicyR100U3Suc4TpkyRXbs2GEeS9sNHDhQnnnmmWStMUnR5f604+XLlzdfayTcpk0bM2FS6/xo0JjRxNy8aQK7TNnsU7++Qdkk4qzjiahB5SuYbGTWUqVMZvHGvn0mkyj/X8nIJyBAAosXlzM//SgB+fOboezLmzZJ+JEj4p+MvwYA3J8rV65LbGyc5M5t/4tVh5+PHHGcTWrQoJp8/vkSqVWrohQpEiIbNuyQlSv/MOexeP75J+TmzVvyyCP9xMfH29w2cOCz0rZtY5c/J+CB4cI5i8nRsWNHuXDhgowaNcoEazraqoGeZdFLWFiY+SPTQoeftdaiBnBZs2Y1sZPOUbRdxawrmzXAPHnypFkzoqV13nzzTfH9/0ST1lv87rvvzNf6eLZ0bUnjxo3NH6m6uGXMmDFmBXaxYsVMsJjcxF6KgkVNuWoxSKVpT42YlT6ZpJTO0Q4nXH4eGxUlPn5+klEU7thRjs+bK7tHjzLpbg0Y89SrLxdthq2L9eghx+bMkV2vDtVUhpnLmKtWbbkVdjxN+w7g7kaMeF5GjpxmAkEdzSpcOL88/ngzWbToF2ubn39eL99/v04mTXrFDG3v3XvEzIu0LHQB4FlefPFFszmydu1au31d+6HFuO9Gh5d1c0brJ96rVLYuivnzzz/lfqUoWGzQoIGJSvVKLno5G61crg4cOCCFChW65/11BY5eItBWxW7dpNJzd1KrniZT1qwmmIu5YR8oR1+/Ib7Zszu8j2+2bFLyhf4SFx1tMpO6gOXU4sXinyePtY1/cF4p88oQiY2MlLiI2+KbPYcc+e9/xc+mDQDXypkzyGT+Ll2yn4996dJVyZPH8TBOrlzZ5cMPR0pkZJRcvXrDBIATJ86RwoX/Ka0xYcJsk11s3frOoogyZULl9OkLZhU1wSKQROkjsfjAS9GcRa1QnilTJvn222/NEm2dnKl+/vlnM+5+L5pW1fSq7Vb+6S7iqbwzZTJZv+t791mP6ZzDG/v2StYEq5YS3dfX987K5rhYubptq+SoYp9KVj7+/iZQjNEVUXt2O2wDwDV07mGFCiVlw4ad1mM6t0iHlqtVu/tKQn9/P8mXL7fExMTKihV/mAUtFhERkYlK5GhQmk4uqgV4BB3ZddWG+8ws6iVkfvjhh0TH33///RQvR/f0Ieh8zf4txz6fLYGhRSVLqJbO+cWsaM5dr765/ejsWeKXI4eppajCjx6RqCtXTe3EqKtX5cz335sPiXwtWljPeW33bjOHUUvrRJ4/LycXfWu+zlO/Xpo9T6SsdE6J0H/KJ4QWDpbK5YvKlas35cTpS2naNyRN9+6Pyauvvi8VK5Y0tRXnzFkqt29HmKFlNXToZBMUDh7czezv2LHf1FcsV664+XfatC9MgNmr152ff9WkSS2ZOfNrKVAg2DoMrauuO3T4d5o9T6SsdE7RkKzW/ULBgVKuaA65ejNKzly6M10LyJDBoo619+zZU5588klz6RmI5KpVS2Ju3pDT330n0devm6LcpV56yVpjMeryZbssgg4/n/5uqUReuCDe/v6mhE5ojx6SKUsWa5vY27fl1P8Wm6LdPlmySM7q1aXgY4+Jl0+K3jakkeqVi8uKr0dZ9yeMvjPHd9436+T5wVwj2xO0avWwXL58TT74YIEpyq1B4KefjrUOQ585c0G8bSba6/Cz1lo8ceKsZMkSYEroTJgwSIKC/gkqRo7sI1OnLpCxYz+SS5eumaHqjh1bSv/+ndLkOSJlKhXPJV+M+ucybyO73lnNvmjdERk68/7niuHuqF/vHl7xKRjz0ItRf/HFF2aRik6+1MDxoYf+GV5JiafXUjswI/lfV4KkjOR2mP0cZTzYSnSmpmhGcvjLp9PssYvNcF3scLQ/FyCxSNGovNbs0WsVzp4921wvumHDhqaUzsSJExNVMAcAAHhAa3JnCCmewqkLXB5//HFZunSpqQGkV295/fXXpXDhwvLYY4+ZS84AAADAs933eh8tnTN69GiZNGmSuXSMrnTW6yQ++uij8sorr6ROLwEAABLQtQCu2vCPFK2U0KFnrTSuw9B6PUK9gsuXX34pLVq0sL7Azz33nCmjo0PTAAAAyEDBohbeLlGihPTo0cMEhcHBwYnaVK5cWWrVqpUafQQAAEiEBGA6DhZXrVolDz/88F3bBAUFmWsTAgAAuALBYjqes3ivQBEAAAAZOFjU8jjPPvusFChQwKyK9vHxsdsAAABczcvbdRvucxha5ymGhYWZUjn58+dn1RAAAMADKkXB4vr16+W3336TqlWrpn6PAAAAkoBclXukKNGqhbdTcJVAAAAAZJTL/Q0bNkyOHTuW+j0CAABIAm8v121IwTB0zpw57eYmhoeHm1qLWbJkEV9fX7u2ly9fTuppAQAA8CAEi5pNBAAASC+Ys5jOgsVu3bpJbGysuXzfd999J1FRUdK0aVNzXejMmTO7tpcAAAAJECymwzmLb731lrz22muSNWtWKViwoEydOlX69+/vut4BAADAc4LFuXPnyocffijLly+XJUuWyPfffy8LFiyQuLg41/UQAADAAV1L4aoNKQwWtRB3q1atrPvNmjUzL+jp06eTcxoAAAA8iEW5Y2JiJCAgwO6YroSOjo5O7X4BAADcFZflS4fBohbi1kv9+fv7W49FRERI3759JTAw0Hps8eLFqdtLAAAApP9gUVdEJ/TMM8+kZn8AAACShKmF6TBYnD17tut6AgAAAM8OFgEAANILMovuQbAIAAA8EsGie7COCAAAAE6RWQQAAB7Jm8yiW5BZBAAAgFNkFgEAgEdizqJ7kFkEAACAU2QWAQCARyKz6B5kFgEAAOAUmUUAAOCRvFgO7RYEiwAAwCMxDO0eDEMDAADAKTKLAADAI5FZdA8yiwAAAHCKzCIAAPBIZBbdg8wiAAAAnCKzCAAAPBKVc9yDzCIAAMB9mjFjhoSGhkpAQIDUqVNHNm3a5LRtdHS0jBs3TkqUKGHaV6lSRZYtW2bX5saNG/Lyyy9L0aJFJXPmzFKvXj3ZvHmzXZv4+HgZNWqU5M+f37Rp1qyZHDx40K7N5cuXpUuXLhIUFCQ5cuSQnj17ys2bN5P13AgWAQCAx85ZdNWWHAsXLpRBgwbJ6NGjZevWrSb4a9GihZw/f95h+5EjR8rHH38s06ZNkz179kjfvn2lffv2sm3bNmubXr16ycqVK2XevHmya9cuad68uQkGT506ZW0zYcIE+eCDD2TmzJmyceNGCQwMNI8bERFhbaOB4u7du825fvjhB/n111/l+eefT97rHK9haTrw9Np1ad0FuNH/us5M6y7AjW6HjU3rLsCNSnT+K627ADc6/OXTafbYDZaud9m517drkOS2derUkVq1asn06dPNflxcnBQuXFgGDBggw4YNS9S+QIECMmLECOnfv7/1WIcOHUx2cP78+XL79m3Jli2bLF26VFq3bm1tU6NGDXnkkUfkjTfeMFlFPc/gwYPllVdeMbdfu3ZN8uXLJ59//rl06tRJ9u7dK+XLlzcZyZo1a5o2msFs1aqVnDx50tw/KcgsAgAApFBUVJRs2bLFZP0svL29zf6GDRsc3icyMtIMP9vSQHH9+jvBb0xMjMTGxt61zdGjR+Xs2bN2j5s9e3YTuFoeV//VoWdLoKi0vfZPM5FJRbAIAAA8kiuHoTWgu379ut2mxxK6ePGiCew0o2dL9zWYc0SHiidPnmzmF2oWUoeIFy9eLGfOnDG3a1axbt26Mn78eDl9+rQ5v2YcNfiztLGc+26Pq//mzZvX7vZMmTJJrly5nPbNEYJFAACABN5++22Tqctus+mx1DB16lQpVaqUlC1bVvz8/OTFF1+U7t27m4yfhc5V1KHmggULir+/v5mb2LlzZ7s27kKwCAAAPJKXl5fLtuHDh5s5gNdsNj2WUJ48ecTHx0fOnTtnd1z3Q0JCHPY7ODhYlixZIuHh4XL8+HHZt2+fZM2aVYoXL25toyul161bZ1Yunzhxwqyu1lXUljaWc9/tcfXfhItsdIhbV0g765sjBIsAAAAJaDZPy80E2Wx6LCHNDOrCk1WrVlmP6dCy7utQ8t3onETNHGoAt2jRImnXrl2iNrrCWUvjXLlyRZYvX25tU6xYMRPw2T6uDpXrXETL4+q/V69eNXMqLVavXm36p3Mbk4qi3AAAwCOll8v9DRo0SLp162YWktSuXVumTJlisoY6tKy6du1qgkLLMLYGdFoCp2rVqubfMWPGmABu6NCh1nNqYKjD0GXKlJFDhw7JkCFDzLC15Zya/dQ6jLoyWoe0NXh8/fXXzQrnxx57zLQpV66ctGzZUnr37m3K62hmUoe8daV0UldCK4JFAACA+9CxY0e5cOGCKZCtC0c0CNQSNZbFJ2FhYXZzDbUOotZaPHLkiBl+1lI2OkdRVy5bWIa9tcSNLkjR0jpvvvmm+Pr6WttocKlBqdZN1AxigwYNzOParqJesGCBCRCbNm1q+qDn0fmPyUGdRaQJ6ixmLNRZzFios5ixpGWdxcY//u6yc69tXd9l5/Y0ZBYBAIBHSi/D0A86FrgAAAAg/WcWT1z3SesuwI0YlsxYMhcZndZdgBuFdOiU1l1ABuFNZtEtyCwCAAAg/WcWAQAAkoPMonuQWQQAAIBTZBYBAIBH8vZKF9X/HnhkFgEAAOAUmUUAAOCRmLPoHgSLAADAIzE86h68zgAAAHCKzCIAAPBILHBxDzKLAAAAcIrMIgAA8EgscHEPMosAAABwiswiAADwSGS83IPXGQAAAE6RWQQAAB6JOYvuQWYRAAAATpFZBAAAHsmLOotuQbAIAAA8EsPQ7sEwNAAAAJwiswgAADwSGS/34HUGAACAU2QWAQCAR/JmgYtbkFkEAACAU2QWAQCAR2I1dDrOLM6ePVtu3bqV+r0BAACA5weLw4YNk5CQEOnZs6f88ccfqd8rAACAJAQxrtrwjxS9HqdOnZI5c+bIxYsXpXHjxlK2bFl599135ezZsyk5HQAAQIqGoV214T6DxUyZMkn79u1l6dKlcuLECendu7csWLBAihQpIm3btjXH4+LiUnJqAAAApCP3nWnNly+fNGjQQOrWrSve3t6ya9cu6datm5QoUULWrl2bOr0EAABwUDrHVRtSIVg8d+6cTJw4USpUqGCGoq9fvy4//PCDHD161AxTP/XUUyZoBAAAQAYrndOmTRtZvny5lC5d2gxBd+3aVXLlymW9PTAwUAYPHizvvfdeavYVAADAirmF6ThYzJs3r6xbt84MPTsTHBxssowAAADIQMFidHS0HDt2TPLkyXPXdl5eXlK0aNH76RsAAIBTlLhJp6+zr6+v7Ny50zW9AQAAgOcH5c8884x89tlnqd8bAACAJGI1dDqesxgTEyOzZs2SX375RWrUqGEWtNiaPHlyavUPAADAIRa4pONg8e+//5bq1aubrw8cOJDafQIAAIAnB4tr1qxJ/Z4AAAAkA5nFdDxnsUePHnLjxo1Ex8PDw81tAAAAyMDB4pw5c+T27duJjuuxuXPnpka/AAAA7hnEuGpDCoeh9ZJ+8fHxZtPMYkBAgPW22NhY+emnn0zBbgAAAGTAYDFHjhym2LZueqm/hPT42LFjU7N/AAAADlHixj28k7uwZdWqVSaz+O2338rq1aut2/r16yUsLExGjBjhut4CAACkQzNmzJDQ0FAz6lqnTh3ZtGnTXa+GN27cOClRooRpX6VKFVm2bJldGx2xff3116VYsWKSOXNm03b8+PEmBrOwJPASbu+99561jfYp4e3vvPOO6zKLjRo1Mv/qNZ+LFCliHhAAACAjr4ZeuHChDBo0SGbOnGkCxSlTpkiLFi1k//79DqfnjRw5UubPny+ffPKJlC1bVpYvXy7t27eXP/74Q6pVq2bavPvuu/LRRx+ZdSIVKlSQv/76S7p37y7Zs2eXl156ybQ5c+aM3Xl//vln6dmzp3To0MHuuAamvXv3tu5ny5YtWc8vRXM49+7dK7///rtdNF21alV5+umn5cqVKyk5JQAAgEcucJk8ebIJxjSYK1++vAkas2TJYi5g4si8efPktddek1atWknx4sWlX79+5utJkyZZ22jg2K5dO2ndurXJDj7xxBPSvHlzu4xlSEiI3bZ06VJp0qSJOactDQ5t2yW8mEpSXudkGzJkiFnsonbt2mWiaX2SmnHUrwEAADxZZGSkiXWu22x6LKGoqCjZsmWLNGvWzHrM29vb7G/YsMHpuW0XCSsdatYpfRb16tUzU/8sFz/ZsWOHuf2RRx5xeM5z587Jjz/+aDKLCemwc+7cuU3WUoeo9Up8Li/KrUGhRs5q0aJF0qZNG3nrrbdk69atJmgEAADw5GHot99+O9Gi3dGjR8uYMWPsjl28eNHML8yXL5/dcd3ft2+fw3PrELVmIxs2bGjmImpQuHjxYnMei2HDhpkAVYepfXx8zG1vvvmmdOnSxeE5dbhaM4iPP/643XEdstar7uXKlctkK4cPH26Gr5NzaeYUBYt+fn5y69Yt87VeH7pr167ma+2IJeMIAADgqTSoSjha6u/vnyrnnjp1qhm21kBQ139owKhD2LbD1l9//bUsWLBAvvjiCzNncfv27fLyyy9LgQIFpFu3bonOqffVQDJhxtL2OVSuXNnEcH369DHBcFKfT4qCxQYNGpgHr1+/vhk714mdSlOlhQoVSskpAQAAksXLhaVzNJBKSjCVJ08ek/nTYWBbuq/zAx0JDg6WJUuWSEREhFy6dMkEgJpJtJ1rqFP+9FinTp3MfqVKleT48eMmyEsYLP72229mMY0lHrsbXYCjw9DHjh2TMmXKiMvmLE6fPl0yZcpkyufoSp2CBQtaV+G0bNkyJacEAADwOH5+flKjRg0zlGwRFxdn9uvWrXvX+2oWUGMoDd50Wp8uaLHQEVyd+2hLg1I9d0KfffaZ6YOW4LkXzVDqeZNzEZUUZRa1bM4PP/yQ6Pj777+fktMBAAB4bOmcQYMGmWxfzZo1pXbt2qZ0Tnh4uBlaVjpdT4NCzQqqjRs3yqlTp0wlGf1X50FqEDh06FDrOXU9iM5R1JhLh6G3bdtm5hn26NHD7rF1+t8333xjt5LaQhfY6GPpCmmdz6j7AwcOlGeeeUZy5syZ+sGidiYoKMj69d1Y2mU07UPzS+eSBSWXv58cvh4uU3Ydlr1Xbzps6+PlJc+WKiQtC+eVPAH+cuLmbfloz1HZdOGqtU1mHx/pVbaINMyfW3L6+8qBa+Hywd9HZJ+Tc8K9Fiz4UT77bLFcuHBFypYtJq+/3kcqV058ZSMVHR0jH3/8jSxZslrOnbskxYoVlFdeeU4aNqxhbaOTl6dN+1K++26NXLx4VfLmzSXt2zeVF17oSE1TD1K/dlkZ2PdRqV6puOTPl1Oe6jVJvl/xV1p3C8n0bP1i8vy/SkpwNn/Ze/q6jFm8U3aE/fP72VYmby/p16yUdKhVREKyB8iR8zflnR/2yK/7zlvbBPpnkkGPlJUWlfJL7qz+svvUNRn3v12y84Tjc8KzdOzYUS5cuCCjRo2Ss2fPmiBQi2xbFr3oRUtss4Q6/Ky1Fo8cOSJZs2Y1i4O1nI5eKc9i2rRppij3Cy+8IOfPnzdD1TrXUB/D1ldffWUKdXfu3DlRv3QYXW/XYFRXYGuBbw0Wk1u5xivethT4XWjqU1fPaNpSn7CjDy89lR63Xc2TVA9/989ycU/0rwJ5ZES10jJp5yHZc+WGPFm8oDQpkEeeXr1FrkZFJ2rft1yoNC8ULBN2HJLjN29Jnbw55cUKxaTfbzvl4PVw02ZMjTJSPFsWmbTzsFyMjJLmhfLKU8ULyLNrtsrFiCjxZL+19exriP/0028ydOhkGTu2v1SpUlrmzPlOli1bL8uWzZTcuf/5Ybd4773PTRD4xhsDpHjxQvLbb1vlnXc+k6++miDly5cwbWbO/Fpmz14i7747UEqWLCJ//31Ihg+fKgMHPiNdu7YVT5a5yGjJKJo3riJ1a5aRbbuOyMJPBmfIYDGkw505Vp6qddUCMqlLdRn5zU7ZfvyK9GhUXFpVKSBN314ll24m/t376qPl5bEahWT419vl8Pmb0rBMXhnZrqJ0+OA32XPqmmkzrWtNKZ0/m7z+zU45dz3CtO/RqIQ0f3e1nLsWIZ7s6Pv/DJ2624i//hn6TW1v1mzqsnN7miRnFvWSfrra2XLZP9jrWKKgfB92Vn46cecvyYk7D0ndfDmldZF8suDQyUTtWxQOlrkHTsqf5+8UMV9y7KzUyJNDOpUsKOO3HhA/b29plD+PvLZpj+y4fCeTO3t/mNTPl0seCw2RT/eFufkZwpYGdU891UI6dLhTV2vs2Bdk7drNsmjRSnn++ScTtV+6dI306/eUNGpU0+w//XQr2bBhu8yatUQmThxsjm3btleaNn1IGjeuZfYLFconP/64TnbuPOjW54b7s2LtDrPBc/VqXFIWbjgu326683t2xDc7pEm5fPJknaIyc1Xin8f2NQvLjJUHZO3eO7//F/xxTOqXDpbejUvIwAVbxd/XW1pWzi/Pz9okm45cMm2mLt8vTSuEyDP1QmXSz47Lq+DeuDZ0OgsWLZf6S/g1RDJ5eUnp7Fll/sET1mP67fvXxatSIafjS+r4entLVIJJqrpfKVeQdZhahzYStomMjZXKubK75HkgaaKiomX37kPSp88T1mOaba9Xr6ps27bf6XVA/fx8Ew0PbN26x7pfrVo5+frr5XL06CkzTL1v31HZsmWvDBuWuMAqANfw9fGSioWyy4e/3CmErHT87feDF6R6UcdzvPwyeUtkjP2IWmR0rNQsntt8ncnbWzL5eJtjtiJs2gDpWYoWuFjG23fu3GnG0ROuzGnb1rOHzJIru5+vCewuR9oPN1+JjJaiWbM4vM+m81ekY/ECsuPSNTkVHiE1gnNIw5Dc4v3/w/u3Y2Nl1+Xr0q10ETl2Y79ciYySZoWCpUKuIDkVftstzwuOXblyXWJj4yR3bvsPDh1+PnIkcRZZNWhQTT7/fInUqlVRihQJkQ0bdsjKlX+Y81g8//wTcvPmLXnkkX7i4+Ntbhs48Flp27axy58TgDtyBvqbwO7iDfsrdeh+ibyO//jXuYk9G5eQTYcvyfFL4VK/VLC0qJxfvP9/9UV4ZIxsOXpZBjQvI4fO3ZSLNyKkbfVCUj00lxy/eGfaETx7gcuDLkXBok7a1JU9WrU8oaTMWdRJlgkvmRMXHSXevn6SUehClaFVSsn8f9Uwf7WevnVbfjpxzgxbW7yx9YAMr1pKlrSoLTFx8XLg2k1ZdeqCyWLCs4wY8byMHDnNBIL690Dhwvnl8cebyaJFv1jb/Pzzevn++3UyadIrZs7i3r1H5O23P7UudAGQPulClbc7VpVfhjc1c/fDLt2SbzedkCdrF7G2GbRgi0zoVE02jm0hMbFxsvvkNfl+60mpWDjxHGfggQgWBwwYIE8++aRZkZPw8jYpvYRO4U7dpWhn++XgnuJaVLQJ5nL52w8z6grmS04WolyNipHXNu8VP28vCfLzNQtWdNHL6fB/JjqfvhUhA/7YJQE+3hKYyUcuRUabRS9nbnn2ZGhPlzNnkMn8Xbp0Z76pxaVLVyVPHsfDVLlyZZcPPxwpkZFRcvXqDRMATpw4RwoX/ufnZ8KE2Sa72Lp1Q7NfpkyonD59wayiJlgE3ONKeKQJ5vJksy/GrPsXrjv+3Xs5PEr6zNpkhqNzBvqZBSu66CXs8j9ZQw0gO834XTL7+UjWgExy4XqkWfQSdonM4v0gs+geKSrKrVXJddl1SgJFyyV0rl27ZrcVfuIZ8VQx8XeyfrpAxUK/f3V/95Ubd71vVFy8CRR1jmKjArll/dnLidpExMaZQDGrr4/UzptTfjt7Z4I00obOPaxQoaRs2LDTekynYujQcrVqd6+G7+/vJ/ny5ZaYmFhZseIPs6DFIiIiMlGVAQ1Kk1iwAEAqiI6Nl79PXjMLVCz0x7JeqWDZetz+D8SEomLiTKCo05J0QcvKXWcTtbkdFWsCxaDMvtKwbF755e/EbYAHIrP4xBNPyNq1a821DFPrEjqePgS98PApea1aadl37absNaVzCpg6iTq0rLSszsWISPl473GzXz5HVsmT2V8OXrspwQH+0qNMEfEWL/nCZuV07eA7weeJ8NtSMDCzvFA+VMJu3JKfwv6p3YW00b37Y/Lqq+9LxYolTW3FOXOWyu3bEWZoWWlZHQ0KBw++c0mmHTv2m/qK5coVN/9Om/aFCTB79frngu9NmtQy5XMKFAi2DkPrqusOHf6dZs8TyReYxV9KhP5zia/QwsFSuXxRuXL1ppw4zR96nuDTtYdk0tPVTQ3EHaZ0TgnJ4ucj3268szpabzt77ba89+Nes1+1SE7Jlz1A9py+Zuos/qdFWTNf8ePV/6ycblgm2ESdWoMxNE+gDG9bQQ6fuyHf/P85kTI+ad2BDCJTSi/3p8PQei1CvVahr6/98OtLL70kGc3q0xclh5+v9CxTxBTlPnQ9XF7582+zyEXly+xvlyHy8/GW3mWLSv4sAXI7JtaU0NGSOTdtVtQF+maSPuWKmmDyRnSMrD1zUT7Ze1xiyTSluVatHpbLl6/JBx8sMEW5NQj89NOx1mHoM2cuWCe3Kx1+njJlvpw4cVayZAkwJXQmTBgkQUH/zD8dObKPTJ26QMaO/UguXbpmhqo7dmwp/ft7ds26jKZ65eKy4ut/iuZOGN3V/Dvvm3Xy/OCZadgzJNWP20+bwtmDWpaVPEH+svfUdXnu4z/l4s07c+0L5MwscTa/h7U0zuBW5aRI7ixmMYuW0Bm0YKvciIixtsmW2VeGtC4vITkC5NqtaFm247RM/GmvmcIEpHdJLsqd8BqEffv2Ndc0zJ07t93QmX6tFckzWlFuZKyi3EiejFSUG55flBueU5T7re0rXXbu16oyqnNfmcURI0aYBSrDhg1LdJFrAAAAd2CBi3ukKNKLiooy10EkUAQAAHiwpSja69atmyxcuDD1ewMAAJCMzKKrNtznMLQW3Z4wYYIsX75cKleunGiBy+TJk1NyWgAAADwIweKuXbukWrVq5uu///7b7raEdeIAAABcwYeQI/0Gi2vWrEn9ngAAACDdua8VKocOHTJD0bdv3zb7XGkCAAC4C3MW03GweOnSJWnatKmULl1aWrVqJWfOnDHHe/bsKYMHD07tPgIAAMCTgsWBAweaRS1hYWGSJUsW63Etp7Ns2bLU7B8AAIBD3l7xLttwn3MWV6xYYYafCxUqZHe8VKlScvz4nWsfAwAAuBLDxek4sxgeHm6XUbS4fPmy+Pv7p0a/AAAA4KnB4sMPPyxz5861K5cTFxdnai82adIkNfsHAADgkI8LN9znMLQGhbrA5a+//jKX/hs6dKjs3r3bZBZ///33lJwSAAAAD0pmsWLFinLgwAFp0KCBtGvXzgxLP/7447Jt2zYpUaJE6vcSAAAgAUrnpOPMosqePbuMGDEidXsDAACAByNYvHr1qmzatEnOnz9v5iva6tq1a2r0DQAAwClK3KTjYPH777+XLl26yM2bNyUoKMjuetD6NcEiAABABp6zqFdp6dGjhwkWNcN45coV66aLXAAAAFzNx8t1G+4zs3jq1Cl56aWXHNZaBAAAcAcWoqTjzGKLFi1M2RwAAAA82JKcWfzuu++sX7du3VqGDBkie/bskUqVKpnrRNtq27Zt6vYSAAAgATKL6SxYfOyxxxIdGzduXKJjusAlNjb2/nsGAAAAzwkWE5bHAQAASEtkFtPhnMXVq1dL+fLl5fr164luu3btmlSoUEF+++231OwfAAAAPCVYnDJlivTu3dvUVnR0RZc+ffrI5MmTU7N/AAAADvl4xbtsQwqDxR07dkjLli2d3t68eXPZsmVLck4JAACAB6XO4rlz5xKtfLY7WaZMcuHChdToFwAAQOrX/4NrX+eCBQvK33//7fT2nTt3Sv78+ZPfCwAAAHh+sNiqVSt5/fXXJSIiItFtt2/fltGjR8ujjz6amv0DAABwuhraVRtSOAw9cuRIWbx4sZQuXVpefPFFKVOmjDm+b98+mTFjhqmvOGLEiOScEgAAIEUI6tJhsJgvXz75448/pF+/fjJ8+HCJj4+3FuLWSwBqwKhtAAAAkAGDRVW0aFH56aef5MqVK3Lo0CETMJYqVUpy5szpmh4CAAA4QImbdBosWmhwWKtWrdTtDQAAAB6MYBEAACAtMWfRPShRBAAAAKfILAIAAI9EZtE9yCwCAADcpxkzZkhoaKgEBARInTp1ZNOmTU7bRkdHy7hx46REiRKmfZUqVWTZsmV2bbQcoda2LlasmGTOnNm0HT9+vLUSjXruuedMRRrbLeFlmS9fvixdunSRoKAgyZEjh/Ts2VNu3ryZrOdGZhEAAHik9JJZXLhwoQwaNEhmzpxpAsUpU6aYkoL79++XvHnzOqxbPX/+fPnkk0+kbNmysnz5cmnfvr0pT1itWjXT5t1335WPPvpI5syZIxUqVJC//vpLunfvLtmzZ5eXXnrJei4NDmfPnm3d9/f3t3ssDRTPnDkjK1euNEGqnuP555+XL774IsnPzyveNkRNQw9/tz6tuwA3+q1t4h8ePLgyFxmd1l2AG4V06JTWXYAbHX2/XZo99rKTP7vs3C0LPZLktnXq1DEVYqZPn2724+LipHDhwjJgwAAZNmxYovYFChQwFzHp37+/9ViHDh1MBlGDSKVXxNPa1Z999pnTNppZvHr1qixZssRhv/bu3Svly5eXzZs3S82aNc0xzWDqFflOnjxp+pEUDEMDAAAkEBkZKdevX7fb9FhCUVFRsmXLFmnWrJn1mLe3t9nfsGGD03Pr8LMtDQLXr/8ncVavXj1ZtWqVHDhwwOzv2LHD3P7II/ZB7Nq1a032Uq+qpxdNuXTpkvU2fXwderYEikr7pf3buHFjkl8LgkUAAOCRvL3iXba9/fbbZsg3u82mxxK6ePGimV+Y8Ap2un/27FmH/dYh6smTJ8vBgwdNFlKHiPVyyjpcbKEZyU6dOplhal9fXzM8/fLLL5thZdsh6Llz55qgUoet161bZ4JJ7Y/Sx084DJ4pUybJlSuX0745wpxFAACABPSyxjoP0VbC+YApNXXqVOndu7cJBHVRii5e0bmEs2bNsrb5+uuvZcGCBWZuoc5Z3L59uwkWdei4W7dupo0GkxaVKlWSypUrm3NptrFp06aSWggWAQCAR3Ll8KgGhkkJDvPkySM+Pj5y7tw5u+O6HxIS4vA+wcHBZp5hRESEGTbWAFAzicWLF7e2GTJkiDW7aAkGjx8/brKblmAxIb2/9kcvx6zBoj7++fPn7drExMSYFdLO+uYIw9AAAAAp5OfnJzVq1DBDwRY6tKz7devWvet9dd5iwYIFTQC3aNEiadfun8VCt27dMnMLbWlQqud2RhetaPCZP39+s6+PrwtgdE6lxerVq805dFFOUpFZBAAAHim9lM4ZNGiQyfbpQpLatWub0jnh4eFmaFl17drVBIWWOY+6uOTUqVNStWpV8++YMWNMADd06FDrOdu0aSNvvvmmFClSxAxDb9u2zcxz7NGjh7ldayWOHTvWrJDWLOHhw4fN/UuWLGnmRKpy5cqZeY065K1lfbR0zosvvmiylUldCa0IFgEAAO5Dx44d5cKFCzJq1CizcESDQC1RY1n0EhYWZpcl1OFnrbV45MgRyZo1qyllM2/ePLNy2WLatGmmKPcLL7xghpI1uOvTp495DEuWcefOnaYOo2YP9fbmzZubwt22w+c671EDRB2W1j5ocPnBBx8k6/lRZxFpgjqLGQt1FjMW6ixmLGlZZ3HdmZ9cdu5G+Vu57NyehswiAADwSFriBq7HAhcAAAA4RWYRAAB4pPSywOVBR2YRAAAATpFZBAAAHonMonuQWQQAAED6L51Tqvlnad0FuFFc7sxp3QW4UVxIYFp3AW50dtFXad0FuNHtsC/T7LE3nv/RZeeuk7e1y87tacgsAgAAwCnmLAIAAI/kxZxFtyBYBAAAHolY0T0YhgYAAIBTZBYBAIBHYhjaPcgsAgAAwCkyiwAAwCOR8XIPXmcAAAA4RWYRAAB4JC+vdHFdkQcemUUAAAA4RWYRAAB4JBZDuwfBIgAA8EiUznEPhqEBAADgFJlFAADgkUgsugeZRQAAADhFZhEAAHgkb1KLbkFmEQAAAE6RWQQAAB6JxKJ7kFkEAACAU2QWAQCAR6LOonsQLAIAAI9ErOgeDEMDAADAKTKLAADAI5FZdA8yiwAAAHCKzCIAAPBIFOV2DzKLAAAAcIrMIgAA8EgkFt2DzCIAAACcIrMIAAA8kpdXfFp3IUMgWAQAAB6JYWj3YBgaAAAATpFZBAAAHolrQ7sHmUUAAAA4RWYRAAB4JDJe6fh1Dg0NlXHjxklYWFjq9wgAAACeHSy+/PLLsnjxYilevLj8+9//lq+++koiIyNTv3cAAAB3mbPoqg2pECxu375dNm3aJOXKlZMBAwZI/vz55cUXX5StW7em5JQAAAB40Ib7q1evLh988IGcPn1aRo8eLZ9++qnUqlVLqlatKrNmzZL4eIplAgAA1/By4ZZcM2bMMNP0AgICpE6dOiah5kx0dLSZzleiRAnTvkqVKrJs2TK7NrGxsfL6669LsWLFJHPmzKbt+PHjrbGVnuPVV1+VSpUqSWBgoBQoUEC6du1qYjJb2icvLy+77Z133nHfAhft6P/+9z+ZPXu2rFy5Uh566CHp2bOnnDx5Ul577TX55Zdf5IsvvrifhwAAAHAovQwXL1y4UAYNGiQzZ840geKUKVOkRYsWsn//fsmbN2+i9iNHjpT58+fLJ598ImXLlpXly5dL+/bt5Y8//pBq1aqZNu+++6589NFHMmfOHKlQoYL89ddf0r17d8mePbu89NJLcuvWLTOaqwGlBptXrlyR//znP9K2bVvT1pYGpr1797buZ8uWLVnPzys+Bek/7ZwGiF9++aV4e3ubSLZXr17mCVv8/fffJst4+/btJJ2zVPPPktsNeLC43JnTugtwo7iQwLTuAtzo7KKv0roLcKPbYV+m2WOfCP/eZecuHNgmyW3r1KljYp7p06eb/bi4OClcuLCZpjds2LBE7TULOGLECOnfv7/1WIcOHUwGUYNI9eijj0q+fPnks88+c9omoc2bN0vt2rXl+PHjUqRIEWtmUacP6ubWYWh9QQ4ePGgi3lOnTsnEiRPtAkWladNOnTqluGMAAADpfRg6KipKtmzZIs2aNbMe00Sa7m/YsMHhfXRRsA4/29IgcP369db9evXqyapVq+TAgQNmf8eOHeb2Rx55xGlfrl27ZoaZc+TIYXdch51z585tspbvvfeexMTEuH4Y+siRI1K0aNG7ttHxc80+AgAAeBoN6BJWevH39zebrYsXL5r5hZoFtKX7+/btc3huHaKePHmyNGzY0MxF1KBQq8zoeSw0I3n9+nWTjPPx8TG3vfnmm9KlSxeH54yIiDBzGDt37ixBQUHW4zpkrWtMcuXKZYa5hw8fLmfOnDGP79LM4r0CRQAAAFfz9nLd9vbbb5v5gdltNj2WGqZOnSqlSpUygaCfn5+pJqPzETUjafH111/LggULzNoPnf6ncxd1JFf/dbSG5KmnnjKLX3TU15bOpWzcuLFUrlxZ+vbtK5MmTZJp06Ylq+RhijKLOXPmNGnOhPSYplVLliwpzz33nHniAAAAnkYzcBpo2UqYVVR58uQxmb9z587ZHdf9kJAQcSQ4OFiWLFlisoGXLl0ycxg1k6j1qy2GDBlijlmm9OmqZ52LqAFrt27dEgWKetvq1avtsorO5lfqMPSxY8ekTJky4rLM4qhRo0z027p1axk7dqzZ9Gs9ppM1S5cuLf369TOrfAAAADxtzqIGhhp4BdlsjoJFzQzWqFHDDCVb6AIX3a9bt+5d+68JtoIFC5rgbdGiRdKuXTvrbbra2TbTqDQo1XMnDBR1HYlWoNF5ifeidbL1vI5WaadqZlEnWL7xxhsmnWnr448/lhUrVpgnrOlOrcFou1QbAADgQTNo0CCT7atZs6ZZjaylc8LDw60jrFo1RoNCyzD2xo0bzQJhrUut/44ZM8YEgUOHDrWes02bNmaOoq5q1tI527ZtM/MMe/ToYQ0Un3jiCTNE/cMPP5g5jWfPnjW36fxEDWJ1gY0+VpMmTUy5HN0fOHCgPPPMM2aU2KXBotYD0vo/CTVt2lQGDx5svm7VqpXD5eIAAACpwcsrfVz8o2PHjnLhwgUz8qoBmwaBWmTbsuglLCzMLkuow89aa1EXDGfNmtXETPPmzbNbxazzCrWG4gsvvCDnz583Q9V9+vQxj6E0yPzuu+/M1/p4ttasWWPmKWomVC/JrMGozlHUSjUaLCYcXndJnUWNcvXBdLP1/vvvm01flJ07d0rz5s2tUe69UGcxY6HOYsZCncWMhTqLGUta1lk8e/tOsOQKIZnbuuzcniZFmUWNdHVOokaumm61FIL86aefTPVypVd0adSoUer2FgAA4P+lkwu4PPBSFCzqPMTy5cubSuVaF0jpipp169aZIpLKMhwNAADwIF/u70GX4mtD169f32wAAAB4cKU4WNRVN1ojaO/evWZfV+roxat1WTfurlalEOn1ZCWpUCq35MsdKP3G/CK//HE8rbsFF6lVNlh6P1peKhbPKflyZpG+k36VlX+dTOtuIZmerV9Mnv9XSQnO5i97T1+XMYt3yo6wqw7bZvL2kn7NSkmHWkUkJHuAHDl/U975YY/8uu+8tU2gfyYZ9EhZaVEpv+TO6i+7T12Tcf/bJTtPOD4n0qf6tcvKwL6PSvVKxSV/vpzyVK9J8v2Kv9K6WxkGiUX3SFGdxUOHDkm5cuXMUnAdhtZNl2FrwHj48OHU7+UDJnNAJtl35LKMne74mpF4sGTxzyT7wq7ImFl8gHiq1lULyIjHKsjU5fvl0UnrZO/pazKnT13JndXPYfvBrcrJ03VDTUD573dXy4I/jsnH3WtL+YLZrW3e6VhVGpQJlkELtkrL99bIb/vPy7x+9SRfdvvrxSJ9C8ziL7v2hMnLI2eldVeA9JVZ1OsM6rUM//zzT1PLR2kFcg0Y9bYff/wxtfv5QPl180mzIWNYt+OM2eC5ejUuKQs3HJdvN4WZ/RHf7JAm5fLJk3WKysxVBxO1b1+zsMxYeUDW7r2TSdRgsX7pYOnduIQMXLBV/H29pWXl/PL8rE2y6cgl00YD0aYVQuSZeqEy6WfH15NF+rNi7Q6zwYMyXnBPsKgLWWwDRaVVw9955x3mMQJ4oPj6eEnFQtnlw18OWI9pwbHfD16Q6kUdF7X1y+QtkTGxdscio2OlZvE7V1fI5O0tmXy8zTFbETZtAMCjg3It8njjxo1Ex2/evGkqhgPAgyJnoL8J7C7eiLQ7rvvBQY6HjHVuYs/GJSQ0T6BZrdmgdLC0qJxfgoPuXCosPDJGthy9LAOal5G8QQHi7SXyWI1CUj00l9kHkDT68+WqDfcZLD766KPy/PPPm0vIaE1v3TTTqJf/00Uu96JVxK9fv263xcdFp6QrAJDu6EKVYxfC5ZfhTeXAe21kbIfK8u2mExL/zyVdZdCCLWZy/saxLWT/e23kuYeLy/dbT0pc8q+TAADpbxhar/ms10DUC2T7+vqaY3oRbA0Up06des/767URx44da3csZ/E2krvEPxfQBoD04Ep4pMTExkmebHeygha6f+F6hMP7XA6Pkj6zNpnh6JyBfnLuWoS8+mh5Cbscbm0TdumWdJrxu2T285GsAZnkwvVImda1poRd+qcNgHshBZhug0W9duHSpUvl4MGDsm/fnYnYujq6ZMmSSbr/8OHDE12XsPrjX6SkKwDgUtGx8fL3yWtmgcrKv+9cvlSHqOqVCpa564/e9b5RMXEmUNRSOrqg5cftpxO1uR0Va7agzL7SsGxeeef73S57LsCDxotgMX3XWVSlSpUyW0rmPOpmy8v7ToYyI8gSkEmKFgiy7hcKySrliueSqzci5cwFsgoPYumcoiFZrfuFggOlXNEccvVmlJy5dCtN+4ak+XTtIZn0dHVTA3HH8SvSo1EJyeLnI99uvLM6Wm87e+22vPfjnbqzVYvkNCVw9py+Zuos/qdFWfH29pKPV/+zcrphmWATdWoNRp3bOLxtBTl87oZ88//nhOeUzikRGmLdDy0cLJXLF5UrV2/KidN3VroDGSZYTJgJvJvJkyentD8ZQsXSeWTBxNbW/RF9HzL/Ll5xQF6d+Fsa9gyuUKl4LvliVDPr/siuNcy/i9YdkaEz/0zDniGpNCOohbMHtSwreYL8Ze+p6/Lcx3/KxZt3Fr0UyJnZbq6hlsbRWotFcmcxi1m0hI7WU7wREWNtky2zrwxpXV5CcgTItVvRsmzHaZn4016JiWPOoiepXrm4rPh6lHV/wuiu5t9536yT5wfPTMOeZQxeXhTPcQeveF2dkgRNmjRJ2gm9vGT16tXJ7kip5p8l+z7wXHG5M6d1F+BGcSGBad0FuNHZRV+ldRfgRrfDvkyzx74a9ZPLzp3Dr5XLzv3AZhbXrFnj2p4AAAAkC3MW3eG+87cnT540GwAAAB48KQoW4+LiZNy4cZI9e3YpWrSo2XSF9Pjx481tAAAA7lgN7ar/cJ+roUeMGCGfffaZ3eX91q9fL2PGjJGIiAh58803U3JaAAAAPAjB4pw5c+TTTz+1u1pL5cqVpWDBgvLCCy8QLAIAADcgA5hug8XLly9L2bJlEx3XY3obAACAq1E6xz1S9CpXqVJFpk+fnui4HtPbAAAAkIEzixMmTJDWrVvLL7/8Yq4PrTZs2CAnTpyQn35yXc0jAACAfzAMnW4zi40aNZIDBw5I+/bt5erVq2Z7/PHHZf/+/fLwww+nfi8BAADgWdeGLlCgAAtZAABAmqHETToLFnfu3CkVK1YUb29v8/Xd6MpoAAAAZKBgsWrVqnL27FnJmzev+VqvAe3ostJ6PDY2NrX7CQAAYIfMYjoLFo8ePSrBwcHWrwEAAPDgS3KwqJf0s8iaNavkzp3bfK0roD/55BO5ffu2KdLNAhcAAOAe1FlMd6/yrl27JDQ01AxFawHu7du3S61ateT999+X//73v9KkSRNZsmSJ63oLAABgM/XNVRtSGCwOHTpUKlWqJL/++qs0btxYHn30UVNv8dq1a3LlyhXp06ePuV40AAAAMmDpnM2bN8vq1avName9UotmE/Va0LpCWg0YMEAeeughV/UVAADABhnAdJdZ1Os+h4SEWOctBgYGSs6cOa2369c3btxI/V4CAADAM4pyJxzHZ1wfAACkBUrnpNNg8bnnnhN/f3/zdUREhPTt29dkGFVkZGTq9xAAAACeESx269bNbv+ZZ55J1KZr16733ysAAIB7onROugsWZ8+e7bqeAAAAwPOHoQEAANID5iy6B8EiAADwSCyydQ8G+wEAAOAUmUUAAOChyCy6A5lFAAAAOEVmEQAAeCQvcl5uwasMAAAAp8gsAgAAD8WcRXcgswgAAACnCBYBAIDH1ll01ZZcM2bMkNDQUAkICJA6derIpk2bnLaNjo6WcePGSYkSJUz7KlWqyLJly+zaxMbGyuuvvy7FihWTzJkzm7bjx4+X+Ph4axv9etSoUZI/f37TplmzZnLw4EG781y+fFm6dOkiQUFBkiNHDunZs6fcvHkzWc+NYBEAAHgoLxduSbdw4UIZNGiQjB49WrZu3WqCvxYtWsj58+cdth85cqR8/PHHMm3aNNmzZ4/07dtX2rdvL9u2bbO2effdd+Wjjz6S6dOny969e83+hAkTzH0sdP+DDz6QmTNnysaNGyUwMNA8bkREhLWNBoq7d++WlStXyg8//CC//vqrPP/888l7leNtQ9Q0VKr5Z2ndBbhRXO7Mad0FuFFcSGBadwFudHbRV2ndBbjR7bAv0+yxo+K2uOzcft41kty2Tp06UqtWLRPYqbi4OClcuLAMGDBAhg0blqh9gQIFZMSIEdK/f3/rsQ4dOpjs4Pz5883+o48+Kvny5ZPPPvvMYRsN3/Q8gwcPlldeecXcfu3aNXOfzz//XDp16mSCzPLly8vmzZulZs2apo1mMFu1aiUnT540908KMosAAMBjS+e4aouMjJTr16/bbXosoaioKNmyZYsZArbw9vY2+xs2bHDYbz2PDj/b0iBw/fr11v169erJqlWr5MCBA2Z/x44d5vZHHnnE7B89elTOnj1r97jZs2c3gavlcfVfHXq2BIpK22v/NBOZVASLAAAACbz99tsm+Mpus+mxhC5evGjmF2pGz5buazDniA4VT5482cwv1CykDhEvXrxYzpw5Y22jGUnNDpYtW1Z8fX2lWrVq8vLLL5thZWU5990eV//Nmzev3e2ZMmWSXLlyOe2bI5TOAQAAHsp1pXOGDx9u5iHa8vf3l9QwdepU6d27twkEdTGNLl7p3r27zJo1y9rm66+/lgULFsgXX3whFSpUkO3bt5tgUYeOu3XrJu5EsAgAAJCABoZJCQ7z5MkjPj4+cu7cObvjuh8SEuLwPsHBwbJkyRKzEOXSpUsmANRMYvHixa1thgwZYs0uqkqVKsnx48dNdlODRcu59XF0NbTt41atWtV8rW0SLrKJiYkxK6Sd9c0RhqEBAIBH8nLhf0nl5+cnNWrUMPMLLXRoWffr1q171/vqvMWCBQuaAG7RokXSrl076223bt0ycwttaVCq51ZaUkcDPtvH1XmVOhfR8rj679WrV82cSovVq1ebc+jcxqQiswgAAHAfBg0aZLJ9upCkdu3aMmXKFAkPDzdDy6pr164mKLTMedSA7tSpUyYDqP+OGTPGBHBDhw61nrNNmzby5ptvSpEiRcwwtJbV0XmOPXr0MLfr8LUOS7/xxhtSqlQpEzxqXUbNUj722GOmTbly5aRly5ZmyFvL62h9xxdffNFkK5O6EloRLAIAAI+UkuLZrtCxY0e5cOGCKZCtC0c0CNQSNZbFJ2FhYXZZQh1+1lqLR44ckaxZs5pSNvPmzTMrly20nqIGfy+88IIZStbgrk+fPuYxLDS41KBU6yZqBrFBgwbmcW1XWuu8Rw0QmzZtavqg5Xe0NmNyUGcRaYI6ixkLdRYzFuosZixpWWcxNv5vl53bx6uiy87taZizCAAAAKcYhgYAAB4pOQtRkHJkFgEAAOAUmUUAAOChyCy6A5lFAAAAOEVmEQAAeKT0UjrnQUdmEQAAAE6RWQQAAB6KnJc7ECwCAACPROkc9yAkBwAAQPq/3F9GFBkZaS4qPnz4cPH390/r7sDFeL8zFt7vjIX3Gw8ygsU0dP36dcmePbtcu3ZNgoKC0ro7cDHe74yF9ztj4f3Gg4xhaAAAADhFsAgAAACnCBYBAADgFMFiGtJJ0KNHj2YydAbB+52x8H5nLLzfeJCxwAUAAABOkVkEAACAUwSLAAAAcIpgEQAAAE4RLHqA5557Th577LG07kaG9/nnn0uOHDlcdv61a9eKl5eXXL161WWPgX/oa71kyRK3Py7vc/p07Ngx875s3749yfdp3LixvPzyyy7tF5AeECymQiCnv2D69u2b6Lb+/fub27SNq35ZwTXvp25+fn5SsmRJGTdunMTExLj8sevVqydnzpwxV4HA/Tt79qwMGDBAihcvblaoFi5cWNq0aSOrVq1K034l530msHTNz7ZuuXPnlpYtW8rOnTvN7fr9oe9LxYoV07qrQLpDsJgK9JfMV199Jbdv37Yei4iIkC+++EKKFCmSpn1D8ukHiH5oHDx4UAYPHixjxoyR9957z+WPq8FpSEiI+SDD/dE/vGrUqCGrV682792uXbtk2bJl0qRJE/NHXFpyxfscFRWVaufKCD/buukfDZkyZZJHH33U3Obj42PeFz0GwB7BYiqoXr26CRgXL15sPaZfa6BYrVo16zH9sGrQoIEZytS/avWX1OHDh623FytWzPyr99EPEh3isDVx4kTJnz+/ua9+4EVHR7vl+WU0moXSD42iRYtKv379pFmzZvLdd99Zb1++fLmUK1dOsmbNav3wUb/++qv4+vqajJYtHaZ6+OGHzdfHjx832a2cOXNKYGCgVKhQQX766SenWaTff//dfB9kyZLF3KdFixZy5coVc9u3334rlSpVksyZM5vvCe1neHi4W16j9O6FF14wr+WmTZukQ4cOUrp0afNaDxo0SP78809ru4sXL0r79u3N61uqVCm791n9/fff8sgjj5j3Ol++fPLss8+a+1joe6PZS32P9f3RNp988ol5H7p37y7ZsmUz2emff/7Zep+E77Oz7wkNeDW4VXqb7SiFPu6LL75oHjdPnjzm+6JHjx7WwMdCf0fkzZtXPvvsMxe90p75s61b1apVZdiwYXLixAm5cOGCw5GddevWSe3atc399Hevtr/bKIP+bHbt2tW8X/o9pd87+kenLf3+0M8LvV2/9yZPnmyd3qJ98Pb2lr/++svuPlOmTDG/j+Li4lL9NQGSgmAxlegv6tmzZ1v3Z82aZT4sbOkHiH5Y6S8C/atWfynoLwvLLwD9YFO//PKLCUBsg881a9aYwFL/nTNnjpk/pxtcT4MxS+bm1q1bJmifN2+eCQ7DwsLklVdeMbc1bNjQDHnqbbYf1gsWLDDfH0qD/MjISHNfzXa9++67JhBxRD+0mjZtKuXLl5cNGzbI+vXrTVARGxtrvj86d+5szrt3714TgDz++ONC2VSRy5cvmz/M9LXW4Csh23mnY8eOlaeeesoMRbZq1Uq6dOli7q80mPvXv/5l/njTn1k957lz50x7W/rzqAGb/vxq4Kh/YDz55JNmuHnr1q3SvHlzE2Tq944jzr4nNKBYtGiRabN//37znk+dOtXucTVLqX9QzJw5U3r16mX6aPnjRf3www/mcTt27JgKr+yD5ebNmzJ//nwTzOsfWwmdOnXKfE/UqlVLduzYIR999JEJut944w2n59RgXr9X9I8O/ZnVn0c9h+UPe32vdMrSf/7zH/Pz/e9//1vefPNN6/1DQ0PNH322nyVK9/Xc+pkBpAktyo2U69atW3y7du3iz58/H+/v7x9/7NgxswUEBMRfuHDB3KZtHNHb9S3YtWuX2T969KjZ37ZtW6LHKFq0aHxMTIz12JNPPhnfsWNHFz+7jPt+qri4uPiVK1ea9/WVV16Jnz17tnl/Dh06ZG0/Y8aM+Hz58ln333333fhy5cpZ9xctWhSfNWvW+Js3b5r9SpUqxY8ZM8bhY69Zs8ac/8qVK2a/c+fO8fXr13fYdsuWLaatfq/B3saNG81rs3jx4ru20zYjR4607ut7pMd+/vlnsz9+/Pj45s2b293nxIkTps3+/fvNfqNGjeIbNGhgvV1/RgMDA+OfffZZ67EzZ86Y+2zYsMHh+5yc7wkLfdxq1aolal++fHnzPWjRpk2b+Oeee+6ur0NG+tn28fEx749u+rrmz5/f/Cw5+v372muvxZcpU8b8HrD9edef59jYWOv78J///Md8feDAAXP/33//3dr+4sWL8ZkzZ47/+uuvzb7+zm7durVdv7p06RKfPXt26/7ChQvjc+bMGR8REWH2tX9eXl6mf0Ba4c+UVBIcHCytW7c22T79K1C/1myDLR2O0GyQZp+CgoLMX5FKs1P3okNTOqfGQodEzp8/74JnAs3GaGYnICDADCNpVkbnLSodOipRooTT90H/+j906JB1qFO/HzQTZclwvfTSSyYzUb9+fXNpMMvk+rtlFh2pUqWKuU2HoTWLpUNbluHpjC452dXKlStbv9b3SH8uLe+nZpM0k6/fC5atbNmy5jbb6SO259CfUc1S6ftioUPTytnPa3K+J2zpnMyENLtoyUppFlSHvy1ZbYgZ1tefK900E6zD9/ozrlMBEtKMfd26de3mlup7pBnJkydPOmyv8x3r1KljPabfC2XKlDG3WTLEOqxtK+G+Vr7Q76P//e9/1t8h2m/L5wWQFggWU5H+UtYfbB0ecvQLWocQdYhLP9g3btxotqROTte5cLb0FxjzV1z7gaLBvS5a0vfTEuw5eh9sgxOdH6bvs35gO/qw1g/zI0eOmGFJHXKsWbOmTJs2zenwtzP6YbJy5Upzfh2m1nPoh9LRo0clo9O5h/q+7Nu3775+rjQo0PfSElxYNv2+0CkHdzuH7TFLsOHs5zU53xO2HA2x63w5PZcOgeoQq86DtsyXxZ3XTIedddPh5U8//dRMD9LfyemFTi3Q91F/h+hngy6UJOBHWiNYTEW62EF/uHV+iv7FauvSpUvmr8qRI0eajJAukEiYCdJfEkrnpCHtP1B0gVJKVkbqh//ChQvlv//9r8lCajbCls5F03lLOidVV1s7+6DSjNXdyrxoEKLn1nl327ZtM98/lmxERpYrVy7z8zdjxgyHC36SWoZGF67t3r3bZHQsAYZlcxSo3Q9n3xPJ/Z2gmSzNTGmgoX+4Jpw3jcQ/QzoP0LaShYX+jrbMO7TQOYe6aKlQoUIO2+viF0sSwPb3vv5Bp/QPus2bN9vdL+G+5XeIzl3/8MMPzTl1PjKQlggWU5Fme3S4Yc+ePXZDxkpXx+kvcg0gdJhSS3roYhdbmpXSbJJlIv21a9fc/AyQGjRQ0eFMHVpM+GGtq1d1NbVmAHXxgw5z6oeMI8OHDzcfJLqyV4cmNVOmk+x1Na5+IL311ltmMr1OY9AgQ1d0OjtXRqOBogZYOsSni0Q0G6g/mx988IEZWkwKXXiiIwE6dUTfBx161vdO39PU/IPubt8TugJWAxqdGqHvr2Y770UDDc2G6/Pt1q1bqvXzQaALibRagW76+uiCJEsGOSH9udOV0tpGf/aWLl1qpgno721HC000o92uXTvp3bu3WYym0xieeeYZKViwoDmu9Fy60l1XQOv35Mcff2xGBxKWUdL3/6GHHpJXX33VfP/dbZQBcAeCxVSmQYJuCekvF63FuGXLFlP0deDAgYlq92kWSz/M9BdIgQIFrL9g4Fn0vda5ixpQ6HCSLT2mQYh+GGgmWku6aPbAEb1txYoV5kNHgx4NcvQDS79P9HtMV8/qSkttpxnrSZMmmflXEDMvWAMvnVKgmTr9mdOVp5qp1YA7KfRnUDNJ+p7pimadh6iBna6mTs1VqXf7ntBAQzPHWrJF5z5quZx70dW0OpdW/2jR54B/6B/i+tropnML9Y+Ab775JlGZMstrr4Gdzm3UOcKa+e3Zs6f5WXNGM7o6l1RLGOnPq2Yl9RyWaQk6EqAr1zVY1HNqf/SzQOdHJ6SPpSNVDEEjPfDSVS5p3QngQaO/6DUTlLBuH+BqminTQEcDF4Yv0z/NRGrm8rfffrM7Pn78eBPIJnXBE+BKlKoHUpFOHdBFCjopnUAR7qQLaHSKgmaYNfvZtm3btO4SHNA6rZrl1nmvOgStUwZsRxc02Nfi3NOnT79rTUfAnQgWgVSkUwd02EqHrPQDAXAXnbuqq5918YUubuGydemT/n6YMGGC3Lhxw0yX0KlHOs/UQqcafPnll2ahEkPQSC8YhgYAAIBTLHABAACAUwSLAAAAcIpgEQAAAE4RLAIAAMApgkUAAAA4RbAIAAAApwgWAQAA4BTBIgAAAJwiWAQAAIA483+Df+ge33lzowAAAABJRU5ErkJggg==",
+      "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": [
+    "## 🔍 Pearson Korrelasjonsanalyse\n",
+    "\n",
+    "Denne notatboken forbedrer datavisualiseringsmodulen ved å demonstrere hvordan man analyserer forholdet mellom flere variabler ved hjelp av Pearson-korrelasjon.\n",
+    "\n",
+    "Pearson-korrelasjon måler styrken og retningen til et **lineært forhold** mellom to kontinuerlige variabler. Den gir en verdi mellom -1 og 1:\n",
+    "- **+1** → Perfekt positivt forhold  \n",
+    "- **0** → Ingen lineær sammenheng  \n",
+    "- **-1** → Perfekt negativt forhold\n",
+    "\n",
+    "Her har vi beregnet korrelasjonsmatrisen for studentresultater i Matematikk, Fysikk, Kjemi og Biologi. Den resulterende **varmekartet** hjelper oss med å visuelt forstå hvor sterkt hver fagkarakter er relatert til de andre.\n",
+    "\n",
+    "En slik korrelasjonsanalyse er avgjørende i **funksjonsutvikling og forbehandling**, spesielt før man bygger maskinlæringsmodeller. Den hjelper med å identifisere:\n",
+    "- Overflødige funksjoner\n",
+    "- Sterke prediktorer\n",
+    "- Potensiell multikollinearitet\n",
+    "\n",
+    "Dette er et nyttig tillegg til meningsfulle visualiseringer når man analyserer datasett fra virkeligheten.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Ansvarsfraskrivelse**:  \nDette dokumentet er oversatt ved hjelp av AI-oversettelsestjenesten [Co-op Translator](https://github.com/Azure/co-op-translator). Selv om vi tilstreber nøyaktighet, vennligst vær oppmerksom på at automatiske oversettelser kan inneholde feil eller unøyaktigheter. Det originale dokumentet på sitt opprinnelige språk bør anses som den autoritative kilden. For kritisk informasjon anbefales profesjonell menneskelig oversettelse. Vi er ikke ansvarlige for eventuelle misforståelser eller feiltolkninger som oppstår ved bruk av denne oversettelsen.\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": "2025-09-06T19:39:39+00:00",
+   "source_file": "3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb",
+   "language_code": "no"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/translations/pa/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb b/translations/pa/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb
new file mode 100644
index 00000000..138e141f
--- /dev/null
+++ b/translations/pa/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb
@@ -0,0 +1,100 @@
+{
+ "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": [
+    "## 🔍 ਪੀਅਰਸਨ ਸਬੰਧ ਵਿਸ਼ਲੇਸ਼ਣ\n",
+    "\n",
+    "ਇਹ ਨੋਟਬੁੱਕ ਡਾਟਾ ਵਿਜੁਅਲਾਈਜ਼ੇਸ਼ਨ ਮੋਡੀਊਲ ਨੂੰ ਹੋਰ ਬਿਹਤਰ ਬਣਾਉਂਦੀ ਹੈ ਅਤੇ ਦਿਖਾਉਂਦੀ ਹੈ ਕਿ ਕਿਵੇਂ ਕਈ ਫੀਚਰਾਂ ਦੇ ਵਿਚਕਾਰ ਸਬੰਧ ਦਾ ਵਿਸ਼ਲੇਸ਼ਣ ਪੀਅਰਸਨ ਸਬੰਧ ਦੀ ਵਰਤੋਂ ਕਰਕੇ ਕੀਤਾ ਜਾ ਸਕਦਾ ਹੈ।\n",
+    "\n",
+    "ਪੀਅਰਸਨ ਸਬੰਧ ਦੋ ਲਗਾਤਾਰ ਵੈਰੀਏਬਲਾਂ ਦੇ ਵਿਚਕਾਰ **ਰੇਖੀ ਸਬੰਧ** ਦੀ ਮਜ਼ਬੂਤੀ ਅਤੇ ਦਿਸ਼ਾ ਨੂੰ ਮਾਪਦਾ ਹੈ। ਇਹ -1 ਤੋਂ 1 ਦੇ ਵਿਚਕਾਰ ਇੱਕ ਮੁੱਲ ਵਾਪਸ ਕਰਦਾ ਹੈ:\n",
+    "- **+1** → ਪੂਰਨ ਸਕਾਰਾਤਮਕ ਸਬੰਧ  \n",
+    "- **0** → ਕੋਈ ਰੇਖੀ ਸਬੰਧ ਨਹੀਂ  \n",
+    "- **-1** → ਪੂਰਨ ਨਕਾਰਾਤਮਕ ਸਬੰਧ\n",
+    "\n",
+    "ਇੱਥੇ, ਅਸੀਂ ਗਣਿਤ, ਭੌਤਿਕ ਵਿਗਿਆਨ, ਰਸਾਇਣ ਵਿਗਿਆਨ ਅਤੇ ਜੀਵ ਵਿਗਿਆਨ ਵਿੱਚ ਵਿਦਿਆਰਥੀਆਂ ਦੇ ਅੰਕਾਂ ਲਈ ਸਬੰਧ ਮੈਟ੍ਰਿਕਸ ਦੀ ਗਣਨਾ ਕੀਤੀ। resulting **ਹੀਟਮੈਪ** ਸਾਨੂੰ ਦ੍ਰਿਸ਼ਟੀਗੋਚਰ ਤੌਰ 'ਤੇ ਸਮਝਣ ਵਿੱਚ ਮਦਦ ਕਰਦਾ ਹੈ ਕਿ ਹਰ ਵਿਸ਼ੇ ਦੇ ਅੰਕ ਹੋਰਾਂ ਨਾਲ ਕਿੰਨੇ ਮਜ਼ਬੂਤ ਸਬੰਧਿਤ ਹਨ।\n",
+    "\n",
+    "ਇਸ ਤਰ੍ਹਾਂ ਦਾ ਸਬੰਧ ਵਿਸ਼ਲੇਸ਼ਣ **ਫੀਚਰ ਇੰਜੀਨੀਅਰਿੰਗ ਅਤੇ ਪ੍ਰੀ-ਪ੍ਰੋਸੈਸਿੰਗ** ਵਿੱਚ ਬਹੁਤ ਮਹੱਤਵਪੂਰਨ ਹੈ, ਖਾਸ ਕਰਕੇ ਮਸ਼ੀਨ ਲਰਨਿੰਗ ਮਾਡਲ ਬਣਾਉਣ ਤੋਂ ਪਹਿਲਾਂ। ਇਹ ਮਦਦ ਕਰਦਾ ਹੈ:\n",
+    "- ਫਾਲਤੂ ਫੀਚਰਾਂ ਦੀ ਪਛਾਣ  \n",
+    "- ਮਜ਼ਬੂਤ ਪੂਵਾਂਨੁਮਾਨਕ  \n",
+    "- ਸੰਭਾਵਿਤ ਮਲਟੀਕੋਲੀਨੀਅਰਿਟੀ\n",
+    "\n",
+    "ਇਹ ਅਸਲ-ਦੁਨੀਆ ਦੇ ਡਾਟਾਸੈਟਾਂ ਦਾ ਵਿਸ਼ਲੇਸ਼ਣ ਕਰਦੇ ਸਮੇਂ ਅਰਥਪੂਰਨ ਵਿਜੁਅਲਾਈਜ਼ੇਸ਼ਨ ਵਿੱਚ ਇੱਕ ਲਾਭਦਾਇਕ ਸ਼ਾਮਿਲ ਹੈ।\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**ਅਸਵੀਕਰਤੀ**:  \nਇਹ ਦਸਤਾਵੇਜ਼ AI ਅਨੁਵਾਦ ਸੇਵਾ [Co-op Translator](https://github.com/Azure/co-op-translator) ਦੀ ਵਰਤੋਂ ਕਰਕੇ ਅਨੁਵਾਦ ਕੀਤਾ ਗਿਆ ਹੈ। ਜਦੋਂ ਕਿ ਅਸੀਂ ਸਹੀ ਹੋਣ ਦਾ ਯਤਨ ਕਰਦੇ ਹਾਂ, ਕਿਰਪਾ ਕਰਕੇ ਧਿਆਨ ਦਿਓ ਕਿ ਸਵੈਚਾਲਿਤ ਅਨੁਵਾਦਾਂ ਵਿੱਚ ਗਲਤੀਆਂ ਜਾਂ ਅਸੁੱਤੀਆਂ ਹੋ ਸਕਦੀਆਂ ਹਨ। ਇਸ ਦੀ ਮੂਲ ਭਾਸ਼ਾ ਵਿੱਚ ਮੌਜੂਦ ਮੂਲ ਦਸਤਾਵੇਜ਼ ਨੂੰ ਪ੍ਰਮਾਣਿਕ ਸਰੋਤ ਮੰਨਿਆ ਜਾਣਾ ਚਾਹੀਦਾ ਹੈ। ਮਹੱਤਵਪੂਰਨ ਜਾਣਕਾਰੀ ਲਈ, ਪੇਸ਼ੇਵਰ ਮਨੁੱਖੀ ਅਨੁਵਾਦ ਦੀ ਸਿਫਾਰਸ਼ ਕੀਤੀ ਜਾਂਦੀ ਹੈ। ਇਸ ਅਨੁਵਾਦ ਦੇ ਪ੍ਰਯੋਗ ਤੋਂ ਪੈਦਾ ਹੋਣ ਵਾਲੇ ਕਿਸੇ ਵੀ ਗਲਤਫਹਿਮੀ ਜਾਂ ਗਲਤ ਵਿਆਖਿਆ ਲਈ ਅਸੀਂ ਜ਼ਿੰਮੇਵਾਰ ਨਹੀਂ ਹਾਂ।  \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": "2025-09-06T19:38:35+00:00",
+   "source_file": "3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb",
+   "language_code": "pa"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/translations/pl/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb b/translations/pl/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb
new file mode 100644
index 00000000..d2efb030
--- /dev/null
+++ b/translations/pl/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb
@@ -0,0 +1,100 @@
+{
+ "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": [
+    "## 🔍 Analiza korelacji Pearsona\n",
+    "\n",
+    "Ten notebook rozszerza moduł wizualizacji danych, pokazując, jak analizować zależność między wieloma cechami za pomocą korelacji Pearsona.\n",
+    "\n",
+    "Korelacja Pearsona mierzy siłę i kierunek **liniowej zależności** między dwiema zmiennymi ciągłymi. Zwraca wartość w przedziale od -1 do 1:\n",
+    "- **+1** → Idealna dodatnia zależność  \n",
+    "- **0** → Brak liniowej zależności  \n",
+    "- **-1** → Idealna ujemna zależność\n",
+    "\n",
+    "Tutaj obliczyliśmy macierz korelacji dla wyników uczniów z Matematyki, Fizyki, Chemii i Biologii. Powstała **mapa cieplna** pozwala wizualnie zrozumieć, jak silnie wyniki z każdego przedmiotu są ze sobą powiązane.\n",
+    "\n",
+    "Taka analiza korelacji jest kluczowa w **inżynierii cech i wstępnym przetwarzaniu danych**, szczególnie przed budowaniem modeli uczenia maszynowego. Pomaga zidentyfikować:\n",
+    "- Zbędne cechy\n",
+    "- Silne predyktory\n",
+    "- Potencjalną wielokolinearność\n",
+    "\n",
+    "To przydatne uzupełnienie znaczących wizualizacji podczas analizy rzeczywistych zbiorów danych.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Zastrzeżenie**:  \nTen dokument został przetłumaczony za pomocą usługi tłumaczeniowej AI [Co-op Translator](https://github.com/Azure/co-op-translator). Chociaż dokładamy wszelkich starań, aby tłumaczenie było precyzyjne, prosimy pamiętać, że automatyczne tłumaczenia mogą zawierać błędy lub nieścisłości. Oryginalny dokument w jego rodzimym języku powinien być uznawany za wiarygodne źródło. W przypadku informacji krytycznych zaleca się skorzystanie z profesjonalnego tłumaczenia wykonanego przez człowieka. Nie ponosimy odpowiedzialności za jakiekolwiek nieporozumienia lub błędne interpretacje wynikające z korzystania z tego tłumaczenia.\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": "2025-09-06T19:38:59+00:00",
+   "source_file": "3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb",
+   "language_code": "pl"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/translations/pt/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb b/translations/pt/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb
new file mode 100644
index 00000000..29df8c39
--- /dev/null
+++ b/translations/pt/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb
@@ -0,0 +1,100 @@
+{
+ "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": [
+    "## 🔍 Análise de Correlação de Pearson\n",
+    "\n",
+    "Este notebook melhora o módulo de visualização de dados ao demonstrar como analisar a relação entre múltiplas variáveis utilizando a correlação de Pearson.\n",
+    "\n",
+    "A correlação de Pearson mede a força e a direção de uma **relação linear** entre duas variáveis contínuas. Ela retorna um valor entre -1 e 1:\n",
+    "- **+1** → Relação positiva perfeita  \n",
+    "- **0** → Nenhuma relação linear  \n",
+    "- **-1** → Relação negativa perfeita\n",
+    "\n",
+    "Aqui, calculámos a matriz de correlação para as notas dos alunos em Matemática, Física, Química e Biologia. O **heatmap** resultante ajuda-nos a compreender visualmente quão fortemente as notas de cada disciplina estão relacionadas entre si.\n",
+    "\n",
+    "Este tipo de análise de correlação é essencial na **engenharia de características e pré-processamento**, especialmente antes de construir modelos de machine learning. Ele ajuda a identificar:\n",
+    "- Características redundantes\n",
+    "- Preditores fortes\n",
+    "- Potencial multicolinearidade\n",
+    "\n",
+    "É uma adição útil para visualizações significativas ao analisar conjuntos de dados do mundo real.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Aviso Legal**:  \nEste documento foi traduzido utilizando o serviço de tradução por IA [Co-op Translator](https://github.com/Azure/co-op-translator). Embora nos esforcemos para garantir a precisão, esteja ciente de que traduções automáticas podem conter erros ou imprecisões. O documento original na sua língua nativa deve ser considerado a fonte oficial. Para informações críticas, recomenda-se uma tradução profissional realizada por humanos. Não nos responsabilizamos por quaisquer mal-entendidos ou interpretações incorretas resultantes do uso desta tradução.\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": "2025-09-06T19:38:41+00:00",
+   "source_file": "3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb",
+   "language_code": "pt"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/translations/ro/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb b/translations/ro/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb
new file mode 100644
index 00000000..0f0a27f8
--- /dev/null
+++ b/translations/ro/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb
@@ -0,0 +1,100 @@
+{
+ "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": [
+    "## 🔍 Analiza Corelației Pearson\n",
+    "\n",
+    "Acest notebook îmbunătățește modulul de vizualizare a datelor, demonstrând cum să analizăm relația dintre mai multe caracteristici utilizând corelația Pearson.\n",
+    "\n",
+    "Corelația Pearson măsoară forța și direcția unei **relații liniare** între două variabile continue. Aceasta returnează o valoare între -1 și 1:\n",
+    "- **+1** → Relație pozitivă perfectă  \n",
+    "- **0** → Nicio relație liniară  \n",
+    "- **-1** → Relație negativă perfectă\n",
+    "\n",
+    "Aici, am calculat matricea de corelație pentru notele studenților la Matematică, Fizică, Chimie și Biologie. **Heatmap-ul** rezultat ne ajută să înțelegem vizual cât de puternic este legat scorul fiecărei materii de celelalte.\n",
+    "\n",
+    "O astfel de analiză a corelației este esențială în **ingineria și preprocesarea caracteristicilor**, mai ales înainte de a construi modele de învățare automată. Aceasta ajută la identificarea:\n",
+    "- Caracteristicilor redundante\n",
+    "- Predictorilor puternici\n",
+    "- Posibilei multicolinearități\n",
+    "\n",
+    "Aceasta reprezintă o completare utilă pentru vizualizări semnificative atunci când analizăm seturi de date din lumea reală.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Declinarea responsabilității**:  \nAcest document a fost tradus folosind serviciul de traducere AI [Co-op Translator](https://github.com/Azure/co-op-translator). Deși depunem eforturi pentru a asigura acuratețea, vă rugăm să aveți în vedere că traducerile automate pot conține erori sau inexactități. Documentul original în limba sa nativă ar trebui considerat sursa autoritară. Pentru informații critice, se recomandă traducerea profesională realizată de un specialist uman. Nu ne asumăm răspunderea pentru eventualele neînțelegeri sau interpretări greșite care pot apărea din utilizarea acestei traduceri.\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": "2025-09-06T19:41:04+00:00",
+   "source_file": "3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb",
+   "language_code": "ro"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/translations/ru/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb b/translations/ru/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb
new file mode 100644
index 00000000..229f8243
--- /dev/null
+++ b/translations/ru/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb
@@ -0,0 +1,100 @@
+{
+ "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": [
+    "## 🔍 Анализ корреляции Пирсона\n",
+    "\n",
+    "Этот ноутбук расширяет модуль визуализации данных, демонстрируя, как анализировать взаимосвязь между несколькими признаками с использованием корреляции Пирсона.\n",
+    "\n",
+    "Корреляция Пирсона измеряет силу и направление **линейной зависимости** между двумя непрерывными переменными. Она возвращает значение в диапазоне от -1 до 1:\n",
+    "- **+1** → Идеальная положительная зависимость  \n",
+    "- **0** → Отсутствие линейной зависимости  \n",
+    "- **-1** → Идеальная отрицательная зависимость\n",
+    "\n",
+    "Здесь мы рассчитали матрицу корреляции для оценок студентов по математике, физике, химии и биологии. Полученная **тепловая карта** помогает визуально понять, насколько сильно оценки по каждому предмету связаны друг с другом.\n",
+    "\n",
+    "Такой анализ корреляции имеет решающее значение в **инженерии признаков и предобработке данных**, особенно перед построением моделей машинного обучения. Он помогает выявить:\n",
+    "- Избыточные признаки\n",
+    "- Сильные предикторы\n",
+    "- Потенциальную мультиколлинеарность\n",
+    "\n",
+    "Это полезное дополнение к содержательным визуализациям при анализе реальных наборов данных.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Отказ от ответственности**:  \nЭтот документ был переведен с использованием сервиса автоматического перевода [Co-op Translator](https://github.com/Azure/co-op-translator). Хотя мы стремимся к точности, пожалуйста, имейте в виду, что автоматические переводы могут содержать ошибки или неточности. Оригинальный документ на его исходном языке следует считать авторитетным источником. Для получения критически важной информации рекомендуется профессиональный перевод человеком. Мы не несем ответственности за любые недоразумения или неправильные интерпретации, возникшие в результате использования данного перевода.\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": "2025-09-06T19:36:59+00:00",
+   "source_file": "3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb",
+   "language_code": "ru"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/translations/sk/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb b/translations/sk/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb
new file mode 100644
index 00000000..9025e718
--- /dev/null
+++ b/translations/sk/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb
@@ -0,0 +1,100 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b1e38707",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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XcxYBAADgFMEiAAAAnCJYBAAAgFMEiwAAAPfh119/NdUMChQoYEpmOSpflpDWX61evbqp5KCVDfTymwnp1ZW0ekNAQIDUqVPHVNJIWNmgf//+plqElgHTKhxaw9eWXqlMS7RpwX2tnqDVKu5WvswRgkUAAID7EB4ebsqkaXCXFFrKTQM4LZ22fft2U++0V69edrVptS6sXk5W67Ru3brVnF8vy2l7udSBAweaklfffPONqZF6+vRpUzrNQuvT6uNonV29YISWbNOgVC9zmxyshgYAAEglXl5epqav1tF1Rq+Y9OOPP5oLIVjoJU21Fu+yZcvMvmYSa9WqZYrlK70ClV4KVC+Jqldb0vqywcHB5vrzTzzxhPWqVHqlMK1P+9BDD5nL3mrtWg0i8+XLZ9ro1cX08S9cuGDquyYFmUUAAIAE9CpcepWu6zab5cpc90uDuYSXQ9WsoR5XmgnUCwjYttErg+m+pY3eHh0dbddGLzahlwy1tNF/9apilkDR8jj6XHbv3u15V3DJXKRzWncBbtR+bt+07gLc6MT1O5ezQ8Zwdnria47jwXVwRc8HMnZ4tUcZc2UjWzokfL9XuFJ6+VHbAE7pvgZxejUtvZKYDiE7amO5pr2eQzODOXLkSNRGb7vb41hu87hgEQAAIL3QS8bqnEFbuhglIyJYBAAAHsnLy3Wz6TQwdFVwGBISkmjVsu4HBQVJ5syZzXXvdXPURu9rOYcOV+s8R9vsYsI2CVdQW85paZMUzFkEAABwo7p168qqVavsjq1cudIcVzq8XKNGDbs2usBF9y1t9HZfX1+7Nvv37zelcixt9N9du3bZraDWx9GgtHz58knuL5lFAADgkbzSSc7r5s2bcujQIbvSOFoSJ1euXGbBiQ5pnzp1SubOnWtu79u3r1nlPHToUOnRo4esXr1avv76a7NC2kKHwLt16yY1a9aU2rVry5QpU0yJnu7du5vbs2fPLj179jTt9HE0ANSV0hog6kpo1bx5cxMUPvvsszJhwgQzT3HkyJGmNmNysqYEiwAAAPfhr7/+MjUTLSxzHTXY07qGZ86cMRk/i2LFipnAUOskTp06VQoVKiSffvqpWals0bFjR1PeRmsiapBXtWpVU1bHdsHK+++/b1ZJazFuXamt9//www+tt+tQ9g8//CD9+vUzQWRgYKDp07hx4zyzziKroTMWVkNnLKyGzlhYDZ2xpOVq6Kyh3Vx27pvH5rjs3J6GzCIAAPBIrlzggn/wKgMAAMApMosAAMBjL60H1yOzCAAAAKfILAIAAA9FzssdeJUBAADgFJlFAADgkVgN7R68ygAAAHCKzCIAAPBIZBbdg1cZAAAATpFZBAAAHsmLnJdbECwCAACPxDC0e/AqAwAAwCkyiwAAwCORWXQPXmUAAAA4RWYRAAB4JDKL7sGrDAAAAKfILAIAAI/kJV5p3YUMgcwiAAAAnCKzCAAAPBJzFt2DYBEAAHgkgkX34FUGAACAU2QWAQCARyKz6B68ygAAAHCKzCIAAPBQ5LzcgVcZAAAATpFZBAAAHok5i+7BqwwAAACnyCwCAACPRGbRPQgWAQCAR/JigNQteJUBAADgFJlFAADgkRiGdg9eZQAAADhFZhEAAHgkLy+vtO5ChkBmEQAAAE6RWQQAAB6JOYvuwasMAAAAp8gsAgAAj0SdxXQcLMbGxsrnn38uq1atkvPnz0tcXJzd7atXr06t/gEAADjEMHQ6Dhb/85//mGCxdevWUrFiRVYjAQAAPKBSFCx+9dVX8vXXX0urVq1Sv0cAAABJQGbRPVL0Kvv5+UnJkiVTvzcAAADw/GBx8ODBMnXqVImPj0/9HgEAACRxgYurNqRgGPrxxx9PtIjl559/lgoVKoivr6/dbYsXL07qaQEAAJCOJTl0zp49u93Wvn17adSokeTJkyfRbQAAAC6ncxZdtSXTjBkzJDQ0VAICAqROnTqyadMmp22jo6Nl3LhxUqJECdO+SpUqsmzZMrs2N27ckJdfflmKFi0qmTNnlnr16snmzZvtn76Xl8Ptvffes7bRPiW8/Z133nFNZnH27NnJOjGcq1+7rAzs+6hUr1Rc8ufLKU/1miTfr/grrbuFZDq/Zo2cW7lCoq9dk8yFCkmRTp0lsFgxh23jY2PkzM/L5NKGPyT66lUJCAmRgu0fl+wVK1rbxEZEyOmlS+Xq9m0SfeOGZClcWAp37CSBoaFufFZwpn1ofulcsqDk8veTw9fDZcquw7L36k2HbX28vOTZUoWkZeG8kifAX07cvC0f7Tkqmy5ctbbJ7OMjvcoWkYb5c0tOf185cC1cPvj7iOxzck6kT7UqhUivJytJhVK5JV/uQOk35hf55Y/jad0tuNnChQtl0KBBMnPmTBMoTpkyRVq0aCH79++XvHnzJmo/cuRImT9/vnzyySdStmxZWb58uUnC/fHHH1KtWjXTplevXvL333/LvHnzpECBAqZ9s2bNZM+ePVKwYEHT5syZM3bn1RHfnj17SocOHeyOa2Dau3dv6362bNmS9fxSNCj/r3/9S65e/eeXnsX169fNbbi7wCz+smtPmLw8clZadwUpdHnzZjn57TeSv/WjUm7ESMlSqLAc/GCqRF+/7rD9qSVL5eJvv5qAssKYsRLcsKEcnvmR3AoLs7Y5PneuXN+7R0K795Dyo0ZLUPnycuD9yRJ15Yobnxkc+VeBPPJihWLy+f4w6bVumxy6Fi6THqooOfzsp+BY9C5bVNoWDZEpu47Is2u2yNLjZ+St2uWkVFCgtc2rVUtKreAc8sbWA9Jt7TbZfOGqvF+3ouQJ8HPjM8P9yhyQSfYduSxjp29I665k2NXQrtqSY/LkySYY6969u5QvX94EjVmyZJFZsxx/zmsA+Nprr5mqMsWLF5d+/fqZrydNmmRuv337tixatEgmTJggDRs2NIuKx4wZY/796KOPrOcJCQmx25YuXSpNmjQx57SlwaFtu8DAf34XuSxYXLt2rURFRSU6HhERIb/99ltKTpmhrFi7Q8ZO/Fq+W0420VOd+2Wl5GnQQPLUry+ZCxSQIl26iLefn1z643eH7S9v/FNCWj4i2StVEv/gYAlu1NhkFc+tXGluj4uKkivbtkqhDh0kW+nSEpA3rxRo09b8e2HdOjc/OyTUsURB+T7srPx04rwcu3lbJu48JBGxsdK6SD6H7VsUDpZ5B0/Kn+evyJlbkbLk2FnZcO6KdCp5Jxvg5+0tjfLnkY/2HJMdl6/LqfAImb0/zPz7WGiIm58d7sevm0/K+59vkZW/k01MC86GYVNjS6qoqCjZsmWLyfpZeHt7m/0NGxz/EREZGWmGn23pUPP69evN1zExMeYCKHdrk9C5c+fkxx9/NJnFhHTYOXfu3CZrqUPUen6X1VncuXOn9WtNg549e9a6r09Kx9stqVHgQRUXE2MygvkfecR6zMvbW7KVLSc3jxxxeh/vBAvBvH395ObhQ+breL0KUlyceGWyb+Pl62ttg7SRyctLSmfPKvMPnrAe0zoQf128KhVyOh7K8fX2lqgEV7bS/Uq5gqzD1Jm8vRK1iYyNlcq5mPcNpAca0Olmy9/f32y2Ll68aGKgfPns/3jU/X379okjOkSt2UjNGuq8Rb0ini4O1vNYMoF169aV8ePHS7ly5cy5vvzySxN8OitdOGfOHHO/hAuSX3rpJalevbrkypXLDHMPHz7cDF/r47skWKxatao14nY03KwR77Rp05JzSsDjxNy8aQK7TNnufPBb+AZlk4iz9vNHLILKVzDZyKylSpnM4o19+0wmUf6//JRPQIAEFi8uZ376UQLy5xffoCC5vGmThB85Iv4O5rvAfbL7+ZrA7nJktN3xK5HRUjRrFof32XT+inQsXkB2XLpmsoU1gnNIw5Dc4v3/2YrbsbGy6/J16Va6iBy7sV+uREZJs0LBUiFXkJwKv+2W5wU8CFxZ4ubtt9+WsWPH2h0bPXq0GQ6+X1p+UIetdb6ixlQaMOoQtu2wtQ5V9+jRwyThfHx8TMDXuXNnk8V0RO/bpUuXRNlInUtpUblyZVMru0+fPub5JQx8UyVYPHr0qKmtqGPhusonODjYeps+uE7i1CeUkmg9Pj5WvLzufV/AExXu2FGOz5sru0eP0nETEzDmqVdfLtoMWxfr0UOOzZkju14dqmMYkqVIEclVq7bcCmN4y9PoQpWhVUrJ/H/VMH8PnL51W346cc5u2FrnKg6vWkqWtKgtMXHxcuDaTVl16oLJYgJIe5qBsw20lKPgSqvCaOyjw8C2dF/nBzqi8dOSJUvM9L1Lly6ZBSzDhg2zm2uoAeS6deskPDzcrAnJnz+/dOzYMdF8RKVTAHUxjS60uRddgKPD0MeOHZMyZcpIqgeLunxbxSUYOkmNaN0nqIL4Zq90X+cF3CFT1qwmmIu5Yb+YJfr6DfF1UjrKN1s2KflCf4mLjjaZSd8cOeTU4sXinyePtY1/cF4p88oQiY2MlLiI2+KbPYcc+e9/xc+mDdzvWlS0CeZy+dtPEdAVzJciEs/dVlejYuS1zXvFz9tLgvx85WJElPQtFyqnwyOsbU7fipABf+ySAB9vCczkI5cio2VMjTJy5tY/bQCk3eX+HA05O6LJsho1apih5Mcee8waJ+n+iy++KHejWUDNHGopHV3Q8tRTTyVqo4tRdLty5YpZNa2LXhL67LPPTB+0BM+9bN++3cypdLRKO1WvDW07bzEsLCzRYpe2bdsmO1rPW6HX/XQFcBvvTJlM1u/63n2So2o165zDG/v2St4mTe5+X19f8cuZ05TSubptq+SsUTNRGx9/f7PF6F+Te3ZLwcftSyDAvWLi72T9auTJIb+dvWyO6WCy7i8+6njagUVUXLwJFHWOYqMCuWXNqYuJ2kTExpktq6+P1M6b05TYAeBZBg0aJN26dZOaNWtK7dq1TekczQjq0LLq2rWrCQo1WaY2btwop06dMtP79F8d2tYAc+jQodZzamCoo7ma/Tt06JAMGTLEDFtbzmmhWcdvvvnGupLals5x1MfSFdI6n1H3Bw4cKM8884zkzJnTtcHikSNHTD2gXbt2mbF2y2X/LKuHLBM0kxOtZ6QhaC2dU8JmxWNo4WCpXL6oXLl6U06cvpSmfUPS5Gv2bzn2+WwJDC0qWUKLyflVv5gVzbnr1Te3H509S/xy5DC1FFX40SMSdeWqqZ0YdfWqnPn+e/Nzk69FC+s5r+3ebeYwag3GyPPn5eSib83XeerXS7PniTsWHj4lr1UrLfuu3ZS9V27Ik8ULmDqJOrSsRlQrLRcjIuXjvXemDJTPkVXyZPaXg9duSnCAv/QoU8RcQOyLQyet56wdnMP8eyL8thQMzCwvlA+VsBu35Kew82n0LJESWQIySdEC/8xfLhSSVcoVzyVXb0TKmQvhadq3DCEZq5ZdqWPHjnLhwgUZNWqUWfyrQaAu+rUsetHEmmbzLHT4WWstajyVNWtWUzZH5yjmyHHn94K6du2aSa6dPHnSLE7R2olvvvlmoqvmffXVV+bzROczJqSxlt6uwahO/ytWrJgJFhMm7O7FKz4FF3hu06aNGZ//9NNPzQPr/EUdc9drRk+cOFEefvjh5J5SMhdJ/CQfVA8/VE5WfD0q0fF536yT5wfPlIyg/dy+4unOr1kt51asMLUV7xTl7iSBxe7MJdk/aaL4584toc/d+QvwxoH9EvbFFxJ54YJ4+/ubEjoaSGpAaXH5r7/k1P8Wm6LdPlmySM7q1aXgY4+JT2bHiyg8yYnrnv/H4OM2RbkPXQ+XqbsOy57/L6D9Qb1KcvZWhLy1/aDZr5o7SAZXLin5swTI7ZhYU0Jn5p5jcinyn1GYJgXySJ9yRU0weSM6RtaeuSif7D0u4TF3/2PbE5ydvl8yitqVQ2TBxNaJji9ecUBenZgxSskdXJG4VIu7lK79ocvOfWDTCy47t6dJUbCokzn12tC6qkYv76fBoqZJ9ZgGjNu2bUt2RzJSsIgHI1hExgoWkXQZKVhEGgeLD7kwWPyTYNEiRTNDdZjZcqkYDRxPnz5tXQCjq3EAAADcMgztqg33N2exYsWKsmPHDjMErUuwdWWOrgb673//63BJNwAAADJQsKiTMnWVj9ISODqHUecp6qVkdCIlAACAy5EBTL/Bol6mxqJUqVLmcjaXL182y7CTcz1FAAAAPEDBol52JilsL1cDAADgEq6ryY2UBouff/65WcRSrVo1a21FAAAAPLiSFSz269dPvvzyS3ONaK0grhXAtVAkAACAu8Uz9S39JXBnzJghZ86cMZej+f7776Vw4cLmOoaWS9IAAAAgg4/266Vj9JIyK1euNNeGrlChgrzwwgsSGhoqN2/euZoBAACAy3m5cMP9rYa20OscWq4Nfa/rQQMAAKQqb6K6dJlZ1AtR67zFf//731K6dGnZtWuXTJ8+3VwkWy+GDQAAgAyaWdThZi26rXMVtYyOBo16uT8AAAC3Y4FL+gsWZ86cKUWKFDGX9Fu3bp3ZHFm8eHFq9Q8AAACeEix27dqVK7QAAID0gZAkfRblBgAAQMZxX6uhAQAA0gyrod2CqyoCAADAKTKLAADAM7GOwi0IFgEAgGciVnQLhqEBAADgFJlFAADgmVjg4hZkFgEAAOAUmUUAAOCZSCy6BZlFAAAAOEVmEQAAeKR4Sue4BZlFAAAAOEVmEQAAeCZWQ7sFmUUAAAA4RWYRAAB4JhKLbkGwCAAAPBMLXNyCYWgAAAA4RWYRAAB4Jha4uAWZRQAAADhFZhEAAHgmEotuQWYRAAAATpFZBAAAnonV0G5BZhEAAABOkVkEAACeicyiWxAsAgAAz8T4qFvwMgMAAMApMosAAMAzMQztFmQWAQAA4BSZRQAA4JlILLoFmUUAAAA4RbAIAAA8Ury3l8u25JoxY4aEhoZKQECA1KlTRzZt2uS0bXR0tIwbN05KlChh2lepUkWWLVtm1+bGjRvy8ssvS9GiRSVz5sxSr1492bx5s12b5557Try8vOy2li1b2rW5fPmydOnSRYKCgiRHjhzSs2dPuXnzZrKeG8EiAADAfVi4cKEMGjRIRo8eLVu3bjXBX4sWLeT8+fMO248cOVI+/vhjmTZtmuzZs0f69u0r7du3l23btlnb9OrVS1auXCnz5s2TXbt2SfPmzaVZs2Zy6tQpu3NpcHjmzBnr9uWXX9rdroHi7t27zbl++OEH+fXXX+X5559P1vPzio+Pj5d0IHORzmndBbhR+7l907oLcKMT133Sugtwo7PT96d1F+BGB1f0TLPHLvG0fWCUmg5/kfS4pE6dOlKrVi2ZPn262Y+Li5PChQvLgAEDZNiwYYnaFyhQQEaMGCH9+/e3HuvQoYPJIM6fP19u374t2bJlk6VLl0rr1q2tbWrUqCGPPPKIvPHGG9bM4tWrV2XJkiUO+7V3714pX768yUjWrFnTHNMMZqtWreTkyZOmH0lBZhEAAHgmLxduSRQVFSVbtmwxWT8Lb29vs79hwwaH94mMjDTDz7Y0UFy/fr35OiYmRmJjY+/axmLt2rWSN29eKVOmjPTr108uXbpkvU0fX4eeLYGi0n5p/zZu3Jjk50iwCAAA4CCgu379ut2mxxK6ePGiCezy5ctnd1z3z5496/DcOkQ9efJkOXjwoMlC6hDx4sWLzTCy0qxi3bp1Zfz48XL69Glzfs04avBnaWMZgp47d66sWrVK3n33XVm3bp3JPGp7pY+vgaStTJkySa5cuZz2zRGCRQAA4Jl0IYqLtrfffluyZ89ut+mx1DB16lQpVaqUlC1bVvz8/OTFF1+U7t27m4yfhc5V1JmCBQsWFH9/f/nggw+kc+fOdm06deokbdu2lUqVKsljjz1m5iTqkLNmG1MTwSIAAEACw4cPl2vXrtlteiyhPHnyiI+Pj5w7d87uuO6HhIQ4PHdwcLCZZxgeHi7Hjx+Xffv2SdasWaV48eLWNrpSWjOFunL5xIkTZnW1rqK2bZOQ3qb9OXTokNnXx0+4yEaHuHWFtLO+OUKwCAAAPPdyfy7aNJun5WaCbDY9lpBmBnXhiQ4FW+jQsu7rUPLd6JxEzRxqALdo0SJp165dojaBgYGSP39+uXLliixfvtxhGwtdtKJzFrW90sfXBTA6p9Ji9erVpn+6KCepuIILAADAfRg0aJB069bNLCSpXbu2TJkyxWQNdWhZde3a1QSFlmFsXVyiJXCqVq1q/h0zZowJ4IYOHWo9pwaGOgytC1c0UzhkyBAzbG05p2Ycx44da1ZRa5bw8OHD5v4lS5Y0cyJVuXLlzLzG3r17y8yZM01mUoe8dfg6qSuh01WwSCmVjOV/XWemdRfgRrfDxqZ1F+BGJb4MS+suIKNIJ5f769ixo1y4cEFGjRplFo5oEKglaiyLXsLCwuzmGkZERJhai0eOHDHDz1rKRuco6splC8uwt2YLdUGKBoVvvvmm+Pr6mtt16Hvnzp0yZ84ckz3U4E9rMeqiGNsM6IIFC0yA2LRpU9MHPY/Of/TIOotPr12X1l2AGxEsZiwEixlLic5/pXUX4EaHv3w6zR67RLeFLjv34TkdXXZuT5NuMosAAADJkoLL8iH5CBYBAIBnIlh0C1ZDAwAAwCkyiwAAwCPFk1h0CzKLAAAAcIrMIgAA8EzMWXQLMosAAABwiswiAADwTHppPrgcmUUAAAA4RWYRAAB4JuYsugXBIgAA8EyMj7oFLzMAAACcIrMIAAA8Ewtc3ILMIgAAAJwiswgAADwTC1zcgswiAAAAnCKzCAAAPFI8cxbdgswiAAAAnCKzCAAAPBMpL7cgWAQAAJ6JBS5uQUwOAAAAp8gsAgAAz8QCF7cgswgAAACnyCwCAADPxJxFtyCzCAAAAKfILAIAAM9EYtEtyCwCAADAKTKLAADAI8UzZ9EtCBYBAIBnIlh0C4ahAQAA4BSZRQAA4Jkoyu0WZBYBAADgFJlFAADgmUh5pd+X+fbt23Lr1i3r/vHjx2XKlCmyYsWK1OwbAAAAPDFYbNeuncydO9d8ffXqValTp45MmjTJHP/oo49Su48AAACO5yy6asP9BYtbt26Vhx9+2Hz97bffSr58+Ux2UQPIDz74ICWnBAAAwIMyZ1GHoLNly2a+1qHnxx9/XLy9veWhhx4yQSMAAIDLUWcx/WYWS5YsKUuWLJETJ07I8uXLpXnz5ub4+fPnJSgoKLX7CAAA4DhYdNWG+wsWR40aJa+88oqEhoaa+Yp169a1ZhmrVauWklMCAADgQRmGfuKJJ6RBgwZy5swZqVKlivV406ZNpX379qnZPwAAAIfiWYiSfoPFa9euiZ+fX6Isog5PZ8pE6UYAAIAMPQzdqVMn+eqrrxId//rrr81tAAAAboliXLXBKkUvx8aNG6VJkyaJjjdu3NjcBgAAgAdDisaMIyMjJSYmJtHx6Ohoc3UXAAAAl2POYvrNLNauXVv++9//Jjo+c+ZMqVGjRmr0CwAAAJ4aLL7xxhvy6aefSsOGDWXs2LFm069nzZolb731Vur3EgAAIB3XWZwxY4YpKRgQEGDKCm7atMlpWx2JHTdunJQoUcK018oyy5Yts2tz48YNefnll6Vo0aKSOXNmqVevnmzevNnuHK+++qpUqlRJAgMDpUCBAtK1a1c5ffq03Xm0T15eXnbbO++84/pgsX79+rJhwwYpXLiwWdTy/fffm5XQO3futF4GEAAAICNYuHChDBo0SEaPHm0uiazBX4sWLczFShwZOXKkfPzxxzJt2jTZs2eP9O3b15Qe3LZtm7VNr169ZOXKlTJv3jzZtWuXuQBKs2bN5NSpU9ar6eljvf766+bfxYsXy/79+6Vt27aJHk8DUy13aNkGDBiQrOfnFR8fHy/pwNNr16V1F+BG/+s6M627ADe6HTY2rbsANyrR+a+07gLc6PCXT6fZYxd9b7XLzn18yL+S3LZOnTpSq1YtmT59utmPi4szCTUNyoYNG5aovWYBR4wYIf3797ce69Chg8kgzp8/36z/0MsqL126VFq3bm1to1P9HnnkETPC64hmHnWqoF56uUiRItbMomYodUupJGcWr1+/bvf13TYAAACX83Ldpot5E8Y3kZGRiboQFRUlW7ZsMVk/C29vb7Ovo7CO6Hl0+NmWBorr1683X+si4tjY2Lu2cVYHW4eZc+TIYXdch51z585t6mO/9957Dhcpp0qwmDNnTms6VTuh+wk3y3EAAABP9vbbb0v27NntNj2W0MWLF01gly9fPrvjun/27FmH59Yh6smTJ8vBgwdNFlKHm3UYWYeIlWYV9VLK48ePN3MQ9fyacdTg09ImoYiICDOHsXPnzhIUFGQ9/tJLL5na2GvWrJE+ffqYtSVDhw51Temc1atXS65cuczX+oBI7PyaNXJu5QqJvnZNMhcqJEU6dZbAYsUcto2PjZEzPy+TSxv+kOirVyUgJEQKtn9cslesaG0TGxEhp5culavbt0n0jRuSpXBhKdyxkwSGhrrxWeF+1a9dVgb2fVSqVyou+fPllKd6TZLvVzBM52kWLPhRPvtssVy4cEXKli0mr7/eRypXLu2wbXR0jHz88TeyZMlqOXfukhQrVlBeeeU5adjwn2oR+st/2rQv5bvv1sjFi1clb95c0r59U3nhhY4mMwDPUKtssPR+tLxULJ5T8uXMIn0n/Sor/zqZ1t3KMOJTsBAlqYYPH27mIdry9/eX1DB16lTp3bu3lC1b1vy860KX7t27m4XCFjpXsUePHlKwYEHx8fGR6tWrm0BQs5gJ6WKXp556SnRm4UcffWR3m+1zqFy5srkCnwaNGvgm9fkkOVhs1KiRw69xx+XNm+Xkt99Ikae7mADx/KpVcvCDqVJh7DjxtYnwLU4tWSqXN22Uos88awLF63t2y+GZH0nZoa9Klv+fZ3B87ly5ffqUhHbvIb45csjljX/KgfcnS4UxY8WPDK7HCMziL7v2hMnchWtl4SeD07o7SIGffvpN3n77Uxk7tr9UqVJa5sz5Tnr2HCXLls2U3Lnth3vUlCnzTRD4xhsDpHjxQvLbb1vlxRffkq++miDly5cwbT75ZJF8+eVP8u67A6VkySLy99+HZPjwqZItWxbp2jXxBHWkT1n8M8m+sCvy7drD8tHghmndHaQiDaSSEkzlyZPHBHPnzp2zO677ISEhDu8THBwsS5YsMdnAS5cumTmMOrexePHi1jYaQK5bt07Cw8PNEHj+/PmlY8eOdm1sA0Wdp6iJPdusorP5lToMfezYMSlTpoy4bDW0Lu+2HTPX5eJVq1aVp59+Wq5cuSIZ0blfVkqeBg0kT/36krlAASnSpYt4+/nJpT9+d9heA7+Qlo9I9kqVxD84WIIbNTZZxXMrV5rb46Ki5Mq2rVKoQwfJVrq0BOTNKwXatDX/XljHYiBPsmLtDhk78Wv5bjnZRE81e/YSeeqpFtKhQzMT2I0d+4IEBPjLokV3fl4TWrp0jfTt+5Q0alRTChcOkaefbiWNGtWQWbOWWNts27ZXmjZ9SBo3riWFCuWTli3rS4MGVWXnzoNufGa4X+t2nJHJX++UFWQT04Zm4V21JZGfn59ZeLJq1SrrMR1a1n0dSr4bnZOomUMN3hYtWiTt2rVL1EbL4migqPHV8uXL7dpYAkUdzv7ll1/MvMR72b59u5lTmTdv3iQ/xxQFi0OGDLEuZNHl3JribNWqlRw9ejRRyjYjiIuJkVthYRJUrpz1mJe3t2QrW05uHjni9D7evr52x7x9/eTm4UPm6/i4OP1uE69M9m28fH2tbQC4XlRUtOzefUjq1atiPaa/aOvVqyrbtu13eB/9Be7nZ/+zqxmKrVv3WPerVSsnf/65Q44evVMGY9++o7Jly167oWoAnmHQoEHyySefyJw5c2Tv3r3Sr18/kxHUoWWl9Q91WNtCL42scxSPHDkiv/32m7Rs2dIEmLZzCTUw1OScxlY6p1Evs6zD1pZz6u+ZJ554Qv766y9ZsGCBmdqicyR100U3Suc4TpkyRXbs2GEeS9sNHDhQnnnmmWStMUnR5f604+XLlzdfayTcpk0bM2FS6/xo0JjRxNy8aQK7TNnsU7++Qdkk4qzjiahB5SuYbGTWUqVMZvHGvn0mkyj/X8nIJyBAAosXlzM//SgB+fOboezLmzZJ+JEj4p+MvwYA3J8rV65LbGyc5M5t/4tVh5+PHHGcTWrQoJp8/vkSqVWrohQpEiIbNuyQlSv/MOexeP75J+TmzVvyyCP9xMfH29w2cOCz0rZtY5c/J+CB4cI5i8nRsWNHuXDhgowaNcoEazraqoGeZdFLWFiY+SPTQoeftdaiBnBZs2Y1sZPOUbRdxawrmzXAPHnypFkzoqV13nzzTfH9/0ST1lv87rvvzNf6eLZ0bUnjxo3NH6m6uGXMmDFmBXaxYsVMsJjcxF6KgkVNuWoxSKVpT42YlT6ZpJTO0Q4nXH4eGxUlPn5+klEU7thRjs+bK7tHjzLpbg0Y89SrLxdthq2L9eghx+bMkV2vDtVUhpnLmKtWbbkVdjxN+w7g7kaMeF5GjpxmAkEdzSpcOL88/ngzWbToF2ubn39eL99/v04mTXrFDG3v3XvEzIu0LHQB4FlefPFFszmydu1au31d+6HFuO9Gh5d1c0brJ96rVLYuivnzzz/lfqUoWGzQoIGJSvVKLno5G61crg4cOCCFChW65/11BY5eItBWxW7dpNJzd1KrniZT1qwmmIu5YR8oR1+/Ib7Zszu8j2+2bFLyhf4SFx1tMpO6gOXU4sXinyePtY1/cF4p88oQiY2MlLiI2+KbPYcc+e9/xc+mDQDXypkzyGT+Ll2yn4996dJVyZPH8TBOrlzZ5cMPR0pkZJRcvXrDBIATJ86RwoX/Ka0xYcJsk11s3frOoogyZULl9OkLZhU1wSKQROkjsfjAS9GcRa1QnilTJvn222/NEm2dnKl+/vlnM+5+L5pW1fSq7Vb+6S7iqbwzZTJZv+t791mP6ZzDG/v2StYEq5YS3dfX987K5rhYubptq+SoYp9KVj7+/iZQjNEVUXt2O2wDwDV07mGFCiVlw4ad1mM6t0iHlqtVu/tKQn9/P8mXL7fExMTKihV/mAUtFhERkYlK5GhQmk4uqgV4BB3ZddWG+8ws6iVkfvjhh0TH33///RQvR/f0Ieh8zf4txz6fLYGhRSVLqJbO+cWsaM5dr765/ejsWeKXI4eppajCjx6RqCtXTe3EqKtX5cz335sPiXwtWljPeW33bjOHUUvrRJ4/LycXfWu+zlO/Xpo9T6SsdE6J0H/KJ4QWDpbK5YvKlas35cTpS2naNyRN9+6Pyauvvi8VK5Y0tRXnzFkqt29HmKFlNXToZBMUDh7czezv2LHf1FcsV664+XfatC9MgNmr152ff9WkSS2ZOfNrKVAg2DoMrauuO3T4d5o9T6SsdE7RkKzW/ULBgVKuaA65ejNKzly6M10LyJDBoo619+zZU5588klz6RmI5KpVS2Ju3pDT330n0devm6LcpV56yVpjMeryZbssgg4/n/5uqUReuCDe/v6mhE5ojx6SKUsWa5vY27fl1P8Wm6LdPlmySM7q1aXgY4+Jl0+K3jakkeqVi8uKr0dZ9yeMvjPHd9436+T5wVwj2xO0avWwXL58TT74YIEpyq1B4KefjrUOQ585c0G8bSba6/Cz1lo8ceKsZMkSYEroTJgwSIKC/gkqRo7sI1OnLpCxYz+SS5eumaHqjh1bSv/+ndLkOSJlKhXPJV+M+ucybyO73lnNvmjdERk68/7niuHuqF/vHl7xKRjz0ItRf/HFF2aRik6+1MDxoYf+GV5JiafXUjswI/lfV4KkjOR2mP0cZTzYSnSmpmhGcvjLp9PssYvNcF3scLQ/FyCxSNGovNbs0WsVzp4921wvumHDhqaUzsSJExNVMAcAAHhAa3JnCCmewqkLXB5//HFZunSpqQGkV295/fXXpXDhwvLYY4+ZS84AAADAs933eh8tnTN69GiZNGmSuXSMrnTW6yQ++uij8sorr6ROLwEAABLQtQCu2vCPFK2U0KFnrTSuw9B6PUK9gsuXX34pLVq0sL7Azz33nCmjo0PTAAAAyEDBohbeLlGihPTo0cMEhcHBwYnaVK5cWWrVqpUafQQAAEiEBGA6DhZXrVolDz/88F3bBAUFmWsTAgAAuALBYjqes3ivQBEAAAAZOFjU8jjPPvusFChQwKyK9vHxsdsAAABczcvbdRvucxha5ymGhYWZUjn58+dn1RAAAMADKkXB4vr16+W3336TqlWrpn6PAAAAkoBclXukKNGqhbdTcJVAAAAAZJTL/Q0bNkyOHTuW+j0CAABIAm8v121IwTB0zpw57eYmhoeHm1qLWbJkEV9fX7u2ly9fTuppAQAA8CAEi5pNBAAASC+Ys5jOgsVu3bpJbGysuXzfd999J1FRUdK0aVNzXejMmTO7tpcAAAAJECymwzmLb731lrz22muSNWtWKViwoEydOlX69+/vut4BAADAc4LFuXPnyocffijLly+XJUuWyPfffy8LFiyQuLg41/UQAADAAV1L4aoNKQwWtRB3q1atrPvNmjUzL+jp06eTcxoAAAA8iEW5Y2JiJCAgwO6YroSOjo5O7X4BAADcFZflS4fBohbi1kv9+fv7W49FRERI3759JTAw0Hps8eLFqdtLAAAApP9gUVdEJ/TMM8+kZn8AAACShKmF6TBYnD17tut6AgAAAM8OFgEAANILMovuQbAIAAA8EsGie7COCAAAAE6RWQQAAB7Jm8yiW5BZBAAAgFNkFgEAgEdizqJ7kFkEAACAU2QWAQCARyKz6B5kFgEAAOAUmUUAAOCRvFgO7RYEiwAAwCMxDO0eDEMDAADAKTKLAADAI5FZdA8yiwAAAHCKzCIAAPBIZBbdg8wiAAAAnCKzCAAAPBKVc9yDzCIAAMB9mjFjhoSGhkpAQIDUqVNHNm3a5LRtdHS0jBs3TkqUKGHaV6lSRZYtW2bX5saNG/Lyyy9L0aJFJXPmzFKvXj3ZvHmzXZv4+HgZNWqU5M+f37Rp1qyZHDx40K7N5cuXpUuXLhIUFCQ5cuSQnj17ys2bN5P13AgWAQCAx85ZdNWWHAsXLpRBgwbJ6NGjZevWrSb4a9GihZw/f95h+5EjR8rHH38s06ZNkz179kjfvn2lffv2sm3bNmubXr16ycqVK2XevHmya9cuad68uQkGT506ZW0zYcIE+eCDD2TmzJmyceNGCQwMNI8bERFhbaOB4u7du825fvjhB/n111/l+eefT97rHK9haTrw9Np1ad0FuNH/us5M6y7AjW6HjU3rLsCNSnT+K627ADc6/OXTafbYDZaud9m517drkOS2derUkVq1asn06dPNflxcnBQuXFgGDBggw4YNS9S+QIECMmLECOnfv7/1WIcOHUx2cP78+XL79m3Jli2bLF26VFq3bm1tU6NGDXnkkUfkjTfeMFlFPc/gwYPllVdeMbdfu3ZN8uXLJ59//rl06tRJ9u7dK+XLlzcZyZo1a5o2msFs1aqVnDx50tw/KcgsAgAApFBUVJRs2bLFZP0svL29zf6GDRsc3icyMtIMP9vSQHH9+jvBb0xMjMTGxt61zdGjR+Xs2bN2j5s9e3YTuFoeV//VoWdLoKi0vfZPM5FJRbAIAAA8kiuHoTWgu379ut2mxxK6ePGiCew0o2dL9zWYc0SHiidPnmzmF2oWUoeIFy9eLGfOnDG3a1axbt26Mn78eDl9+rQ5v2YcNfiztLGc+26Pq//mzZvX7vZMmTJJrly5nPbNEYJFAACABN5++22Tqctus+mx1DB16lQpVaqUlC1bVvz8/OTFF1+U7t27m4yfhc5V1KHmggULir+/v5mb2LlzZ7s27kKwCAAAPJKXl5fLtuHDh5s5gNdsNj2WUJ48ecTHx0fOnTtnd1z3Q0JCHPY7ODhYlixZIuHh4XL8+HHZt2+fZM2aVYoXL25toyul161bZ1Yunzhxwqyu1lXUljaWc9/tcfXfhItsdIhbV0g765sjBIsAAAAJaDZPy80E2Wx6LCHNDOrCk1WrVlmP6dCy7utQ8t3onETNHGoAt2jRImnXrl2iNrrCWUvjXLlyRZYvX25tU6xYMRPw2T6uDpXrXETL4+q/V69eNXMqLVavXm36p3Mbk4qi3AAAwCOll8v9DRo0SLp162YWktSuXVumTJlisoY6tKy6du1qgkLLMLYGdFoCp2rVqubfMWPGmABu6NCh1nNqYKjD0GXKlJFDhw7JkCFDzLC15Zya/dQ6jLoyWoe0NXh8/fXXzQrnxx57zLQpV66ctGzZUnr37m3K62hmUoe8daV0UldCK4JFAACA+9CxY0e5cOGCKZCtC0c0CNQSNZbFJ2FhYXZzDbUOotZaPHLkiBl+1lI2OkdRVy5bWIa9tcSNLkjR0jpvvvmm+Pr6WttocKlBqdZN1AxigwYNzOParqJesGCBCRCbNm1q+qDn0fmPyUGdRaQJ6ixmLNRZzFios5ixpGWdxcY//u6yc69tXd9l5/Y0ZBYBAIBHSi/D0A86FrgAAAAg/WcWT1z3SesuwI0YlsxYMhcZndZdgBuFdOiU1l1ABuFNZtEtyCwCAAAg/WcWAQAAkoPMonuQWQQAAIBTZBYBAIBH8vZKF9X/HnhkFgEAAOAUmUUAAOCRmLPoHgSLAADAIzE86h68zgAAAHCKzCIAAPBILHBxDzKLAAAAcIrMIgAA8EgscHEPMosAAABwiswiAADwSGS83IPXGQAAAE6RWQQAAB6JOYvuQWYRAAAATpFZBAAAHsmLOotuQbAIAAA8EsPQ7sEwNAAAAJwiswgAADwSGS/34HUGAACAU2QWAQCAR/JmgYtbkFkEAACAU2QWAQCAR2I1dDrOLM6ePVtu3bqV+r0BAACA5weLw4YNk5CQEOnZs6f88ccfqd8rAACAJAQxrtrwjxS9HqdOnZI5c+bIxYsXpXHjxlK2bFl599135ezZsyk5HQAAQIqGoV214T6DxUyZMkn79u1l6dKlcuLECendu7csWLBAihQpIm3btjXH4+LiUnJqAAAApCP3nWnNly+fNGjQQOrWrSve3t6ya9cu6datm5QoUULWrl2bOr0EAABwUDrHVRtSIVg8d+6cTJw4USpUqGCGoq9fvy4//PCDHD161AxTP/XUUyZoBAAAQAYrndOmTRtZvny5lC5d2gxBd+3aVXLlymW9PTAwUAYPHizvvfdeavYVAADAirmF6ThYzJs3r6xbt84MPTsTHBxssowAAADIQMFidHS0HDt2TPLkyXPXdl5eXlK0aNH76RsAAIBTlLhJp6+zr6+v7Ny50zW9AQAAgOcH5c8884x89tlnqd8bAACAJGI1dDqesxgTEyOzZs2SX375RWrUqGEWtNiaPHlyavUPAADAIRa4pONg8e+//5bq1aubrw8cOJDafQIAAIAnB4tr1qxJ/Z4AAAAkA5nFdDxnsUePHnLjxo1Ex8PDw81tAAAAyMDB4pw5c+T27duJjuuxuXPnpka/AAAA7hnEuGpDCoeh9ZJ+8fHxZtPMYkBAgPW22NhY+emnn0zBbgAAAGTAYDFHjhym2LZueqm/hPT42LFjU7N/AAAADlHixj28k7uwZdWqVSaz+O2338rq1aut2/r16yUsLExGjBjhut4CAACkQzNmzJDQ0FAz6lqnTh3ZtGnTXa+GN27cOClRooRpX6VKFVm2bJldGx2xff3116VYsWKSOXNm03b8+PEmBrOwJPASbu+99561jfYp4e3vvPOO6zKLjRo1Mv/qNZ+LFCliHhAAACAjr4ZeuHChDBo0SGbOnGkCxSlTpkiLFi1k//79DqfnjRw5UubPny+ffPKJlC1bVpYvXy7t27eXP/74Q6pVq2bavPvuu/LRRx+ZdSIVKlSQv/76S7p37y7Zs2eXl156ybQ5c+aM3Xl//vln6dmzp3To0MHuuAamvXv3tu5ny5YtWc8vRXM49+7dK7///rtdNF21alV5+umn5cqVKyk5JQAAgEcucJk8ebIJxjSYK1++vAkas2TJYi5g4si8efPktddek1atWknx4sWlX79+5utJkyZZ22jg2K5dO2ndurXJDj7xxBPSvHlzu4xlSEiI3bZ06VJp0qSJOactDQ5t2yW8mEpSXudkGzJkiFnsonbt2mWiaX2SmnHUrwEAADxZZGSkiXWu22x6LKGoqCjZsmWLNGvWzHrM29vb7G/YsMHpuW0XCSsdatYpfRb16tUzU/8sFz/ZsWOHuf2RRx5xeM5z587Jjz/+aDKLCemwc+7cuU3WUoeo9Up8Li/KrUGhRs5q0aJF0qZNG3nrrbdk69atJmgEAADw5GHot99+O9Gi3dGjR8uYMWPsjl28eNHML8yXL5/dcd3ft2+fw3PrELVmIxs2bGjmImpQuHjxYnMei2HDhpkAVYepfXx8zG1vvvmmdOnSxeE5dbhaM4iPP/643XEdstar7uXKlctkK4cPH26Gr5NzaeYUBYt+fn5y69Yt87VeH7pr167ma+2IJeMIAADgqTSoSjha6u/vnyrnnjp1qhm21kBQ139owKhD2LbD1l9//bUsWLBAvvjiCzNncfv27fLyyy9LgQIFpFu3bonOqffVQDJhxtL2OVSuXNnEcH369DHBcFKfT4qCxQYNGpgHr1+/vhk714mdSlOlhQoVSskpAQAAksXLhaVzNJBKSjCVJ08ek/nTYWBbuq/zAx0JDg6WJUuWSEREhFy6dMkEgJpJtJ1rqFP+9FinTp3MfqVKleT48eMmyEsYLP72229mMY0lHrsbXYCjw9DHjh2TMmXKiMvmLE6fPl0yZcpkyufoSp2CBQtaV+G0bNkyJacEAADwOH5+flKjRg0zlGwRFxdn9uvWrXvX+2oWUGMoDd50Wp8uaLHQEVyd+2hLg1I9d0KfffaZ6YOW4LkXzVDqeZNzEZUUZRa1bM4PP/yQ6Pj777+fktMBAAB4bOmcQYMGmWxfzZo1pXbt2qZ0Tnh4uBlaVjpdT4NCzQqqjRs3yqlTp0wlGf1X50FqEDh06FDrOXU9iM5R1JhLh6G3bdtm5hn26NHD7rF1+t8333xjt5LaQhfY6GPpCmmdz6j7AwcOlGeeeUZy5syZ+sGidiYoKMj69d1Y2mU07UPzS+eSBSWXv58cvh4uU3Ydlr1Xbzps6+PlJc+WKiQtC+eVPAH+cuLmbfloz1HZdOGqtU1mHx/pVbaINMyfW3L6+8qBa+Hywd9HZJ+Tc8K9Fiz4UT77bLFcuHBFypYtJq+/3kcqV058ZSMVHR0jH3/8jSxZslrOnbskxYoVlFdeeU4aNqxhbaOTl6dN+1K++26NXLx4VfLmzSXt2zeVF17oSE1TD1K/dlkZ2PdRqV6puOTPl1Oe6jVJvl/xV1p3C8n0bP1i8vy/SkpwNn/Ze/q6jFm8U3aE/fP72VYmby/p16yUdKhVREKyB8iR8zflnR/2yK/7zlvbBPpnkkGPlJUWlfJL7qz+svvUNRn3v12y84Tjc8KzdOzYUS5cuCCjRo2Ss2fPmiBQi2xbFr3oRUtss4Q6/Ky1Fo8cOSJZs2Y1i4O1nI5eKc9i2rRppij3Cy+8IOfPnzdD1TrXUB/D1ldffWUKdXfu3DlRv3QYXW/XYFRXYGuBbw0Wk1u5xivethT4XWjqU1fPaNpSn7CjDy89lR63Xc2TVA9/989ycU/0rwJ5ZES10jJp5yHZc+WGPFm8oDQpkEeeXr1FrkZFJ2rft1yoNC8ULBN2HJLjN29Jnbw55cUKxaTfbzvl4PVw02ZMjTJSPFsWmbTzsFyMjJLmhfLKU8ULyLNrtsrFiCjxZL+19exriP/0028ydOhkGTu2v1SpUlrmzPlOli1bL8uWzZTcuf/5Ybd4773PTRD4xhsDpHjxQvLbb1vlnXc+k6++miDly5cwbWbO/Fpmz14i7747UEqWLCJ//31Ihg+fKgMHPiNdu7YVT5a5yGjJKJo3riJ1a5aRbbuOyMJPBmfIYDGkw505Vp6qddUCMqlLdRn5zU7ZfvyK9GhUXFpVKSBN314ll24m/t376qPl5bEahWT419vl8Pmb0rBMXhnZrqJ0+OA32XPqmmkzrWtNKZ0/m7z+zU45dz3CtO/RqIQ0f3e1nLsWIZ7s6Pv/DJ2624i//hn6TW1v1mzqsnN7miRnFvWSfrra2XLZP9jrWKKgfB92Vn46cecvyYk7D0ndfDmldZF8suDQyUTtWxQOlrkHTsqf5+8UMV9y7KzUyJNDOpUsKOO3HhA/b29plD+PvLZpj+y4fCeTO3t/mNTPl0seCw2RT/eFufkZwpYGdU891UI6dLhTV2vs2Bdk7drNsmjRSnn++ScTtV+6dI306/eUNGpU0+w//XQr2bBhu8yatUQmThxsjm3btleaNn1IGjeuZfYLFconP/64TnbuPOjW54b7s2LtDrPBc/VqXFIWbjgu326683t2xDc7pEm5fPJknaIyc1Xin8f2NQvLjJUHZO3eO7//F/xxTOqXDpbejUvIwAVbxd/XW1pWzi/Pz9okm45cMm2mLt8vTSuEyDP1QmXSz47Lq+DeuDZ0OgsWLZf6S/g1RDJ5eUnp7Fll/sET1mP67fvXxatSIafjS+r4entLVIJJqrpfKVeQdZhahzYStomMjZXKubK75HkgaaKiomX37kPSp88T1mOaba9Xr6ps27bf6XVA/fx8Ew0PbN26x7pfrVo5+frr5XL06CkzTL1v31HZsmWvDBuWuMAqANfw9fGSioWyy4e/3CmErHT87feDF6R6UcdzvPwyeUtkjP2IWmR0rNQsntt8ncnbWzL5eJtjtiJs2gDpWYoWuFjG23fu3GnG0ROuzGnb1rOHzJIru5+vCewuR9oPN1+JjJaiWbM4vM+m81ekY/ECsuPSNTkVHiE1gnNIw5Dc4v3/w/u3Y2Nl1+Xr0q10ETl2Y79ciYySZoWCpUKuIDkVftstzwuOXblyXWJj4yR3bvsPDh1+PnIkcRZZNWhQTT7/fInUqlVRihQJkQ0bdsjKlX+Y81g8//wTcvPmLXnkkX7i4+Ntbhs48Flp27axy58TgDtyBvqbwO7iDfsrdeh+ibyO//jXuYk9G5eQTYcvyfFL4VK/VLC0qJxfvP9/9UV4ZIxsOXpZBjQvI4fO3ZSLNyKkbfVCUj00lxy/eGfaETx7gcuDLkXBok7a1JU9WrU8oaTMWdRJlgkvmRMXHSXevn6SUehClaFVSsn8f9Uwf7WevnVbfjpxzgxbW7yx9YAMr1pKlrSoLTFx8XLg2k1ZdeqCyWLCs4wY8byMHDnNBIL690Dhwvnl8cebyaJFv1jb/Pzzevn++3UyadIrZs7i3r1H5O23P7UudAGQPulClbc7VpVfhjc1c/fDLt2SbzedkCdrF7G2GbRgi0zoVE02jm0hMbFxsvvkNfl+60mpWDjxHGfggQgWBwwYIE8++aRZkZPw8jYpvYRO4U7dpWhn++XgnuJaVLQJ5nL52w8z6grmS04WolyNipHXNu8VP28vCfLzNQtWdNHL6fB/JjqfvhUhA/7YJQE+3hKYyUcuRUabRS9nbnn2ZGhPlzNnkMn8Xbp0Z76pxaVLVyVPHsfDVLlyZZcPPxwpkZFRcvXqDRMATpw4RwoX/ufnZ8KE2Sa72Lp1Q7NfpkyonD59wayiJlgE3ONKeKQJ5vJksy/GrPsXrjv+3Xs5PEr6zNpkhqNzBvqZBSu66CXs8j9ZQw0gO834XTL7+UjWgExy4XqkWfQSdonM4v0gs+geKSrKrVXJddl1SgJFyyV0rl27ZrcVfuIZ8VQx8XeyfrpAxUK/f3V/95Ubd71vVFy8CRR1jmKjArll/dnLidpExMaZQDGrr4/UzptTfjt7Z4I00obOPaxQoaRs2LDTekynYujQcrVqd6+G7+/vJ/ny5ZaYmFhZseIPs6DFIiIiMlGVAQ1Kk1iwAEAqiI6Nl79PXjMLVCz0x7JeqWDZetz+D8SEomLiTKCo05J0QcvKXWcTtbkdFWsCxaDMvtKwbF755e/EbYAHIrP4xBNPyNq1a821DFPrEjqePgS98PApea1aadl37absNaVzCpg6iTq0rLSszsWISPl473GzXz5HVsmT2V8OXrspwQH+0qNMEfEWL/nCZuV07eA7weeJ8NtSMDCzvFA+VMJu3JKfwv6p3YW00b37Y/Lqq+9LxYolTW3FOXOWyu3bEWZoWWlZHQ0KBw++c0mmHTv2m/qK5coVN/9Om/aFCTB79frngu9NmtQy5XMKFAi2DkPrqusOHf6dZs8TyReYxV9KhP5zia/QwsFSuXxRuXL1ppw4zR96nuDTtYdk0tPVTQ3EHaZ0TgnJ4ucj3268szpabzt77ba89+Nes1+1SE7Jlz1A9py+Zuos/qdFWTNf8ePV/6ycblgm2ESdWoMxNE+gDG9bQQ6fuyHf/P85kTI+ad2BDCJTSi/3p8PQei1CvVahr6/98OtLL70kGc3q0xclh5+v9CxTxBTlPnQ9XF7582+zyEXly+xvlyHy8/GW3mWLSv4sAXI7JtaU0NGSOTdtVtQF+maSPuWKmmDyRnSMrD1zUT7Ze1xiyTSluVatHpbLl6/JBx8sMEW5NQj89NOx1mHoM2cuWCe3Kx1+njJlvpw4cVayZAkwJXQmTBgkQUH/zD8dObKPTJ26QMaO/UguXbpmhqo7dmwp/ft7ds26jKZ65eKy4ut/iuZOGN3V/Dvvm3Xy/OCZadgzJNWP20+bwtmDWpaVPEH+svfUdXnu4z/l4s07c+0L5MwscTa/h7U0zuBW5aRI7ixmMYuW0Bm0YKvciIixtsmW2VeGtC4vITkC5NqtaFm247RM/GmvmcIEpHdJLsqd8BqEffv2Ndc0zJ07t93QmX6tFckzWlFuZKyi3EiejFSUG55flBueU5T7re0rXXbu16oyqnNfmcURI0aYBSrDhg1LdJFrAAAAd2CBi3ukKNKLiooy10EkUAQAAHiwpSja69atmyxcuDD1ewMAAJCMzKKrNtznMLQW3Z4wYYIsX75cKleunGiBy+TJk1NyWgAAADwIweKuXbukWrVq5uu///7b7raEdeIAAABcwYeQI/0Gi2vWrEn9ngAAACDdua8VKocOHTJD0bdv3zb7XGkCAAC4C3MW03GweOnSJWnatKmULl1aWrVqJWfOnDHHe/bsKYMHD07tPgIAAMCTgsWBAweaRS1hYWGSJUsW63Etp7Ns2bLU7B8AAIBD3l7xLttwn3MWV6xYYYafCxUqZHe8VKlScvz4nWsfAwAAuBLDxek4sxgeHm6XUbS4fPmy+Pv7p0a/AAAA4KnB4sMPPyxz5861K5cTFxdnai82adIkNfsHAADgkI8LN9znMLQGhbrA5a+//jKX/hs6dKjs3r3bZBZ///33lJwSAAAAD0pmsWLFinLgwAFp0KCBtGvXzgxLP/7447Jt2zYpUaJE6vcSAAAgAUrnpOPMosqePbuMGDEidXsDAACAByNYvHr1qmzatEnOnz9v5iva6tq1a2r0DQAAwClK3KTjYPH777+XLl26yM2bNyUoKMjuetD6NcEiAABABp6zqFdp6dGjhwkWNcN45coV66aLXAAAAFzNx8t1G+4zs3jq1Cl56aWXHNZaBAAAcAcWoqTjzGKLFi1M2RwAAAA82JKcWfzuu++sX7du3VqGDBkie/bskUqVKpnrRNtq27Zt6vYSAAAgATKL6SxYfOyxxxIdGzduXKJjusAlNjb2/nsGAAAAzwkWE5bHAQAASEtkFtPhnMXVq1dL+fLl5fr164luu3btmlSoUEF+++231OwfAAAAPCVYnDJlivTu3dvUVnR0RZc+ffrI5MmTU7N/AAAADvl4xbtsQwqDxR07dkjLli2d3t68eXPZsmVLck4JAACAB6XO4rlz5xKtfLY7WaZMcuHChdToFwAAQOrX/4NrX+eCBQvK33//7fT2nTt3Sv78+ZPfCwAAAHh+sNiqVSt5/fXXJSIiItFtt2/fltGjR8ujjz6amv0DAABwuhraVRtSOAw9cuRIWbx4sZQuXVpefPFFKVOmjDm+b98+mTFjhqmvOGLEiOScEgAAIEUI6tJhsJgvXz75448/pF+/fjJ8+HCJj4+3FuLWSwBqwKhtAAAAkAGDRVW0aFH56aef5MqVK3Lo0CETMJYqVUpy5szpmh4CAAA4QImbdBosWmhwWKtWrdTtDQAAAB6MYBEAACAtMWfRPShRBAAAAKfILAIAAI9EZtE9yCwCAADcpxkzZkhoaKgEBARInTp1ZNOmTU7bRkdHy7hx46REiRKmfZUqVWTZsmV2bbQcoda2LlasmGTOnNm0HT9+vLUSjXruuedMRRrbLeFlmS9fvixdunSRoKAgyZEjh/Ts2VNu3ryZrOdGZhEAAHik9JJZXLhwoQwaNEhmzpxpAsUpU6aYkoL79++XvHnzOqxbPX/+fPnkk0+kbNmysnz5cmnfvr0pT1itWjXT5t1335WPPvpI5syZIxUqVJC//vpLunfvLtmzZ5eXXnrJei4NDmfPnm3d9/f3t3ssDRTPnDkjK1euNEGqnuP555+XL774IsnPzyveNkRNQw9/tz6tuwA3+q1t4h8ePLgyFxmd1l2AG4V06JTWXYAbHX2/XZo99rKTP7vs3C0LPZLktnXq1DEVYqZPn2724+LipHDhwjJgwAAZNmxYovYFChQwFzHp37+/9ViHDh1MBlGDSKVXxNPa1Z999pnTNppZvHr1qixZssRhv/bu3Svly5eXzZs3S82aNc0xzWDqFflOnjxp+pEUDEMDAAAkEBkZKdevX7fb9FhCUVFRsmXLFmnWrJn1mLe3t9nfsGGD03Pr8LMtDQLXr/8ncVavXj1ZtWqVHDhwwOzv2LHD3P7II/ZB7Nq1a032Uq+qpxdNuXTpkvU2fXwderYEikr7pf3buHFjkl8LgkUAAOCRvL3iXba9/fbbZsg3u82mxxK6ePGimV+Y8Ap2un/27FmH/dYh6smTJ8vBgwdNFlKHiPVyyjpcbKEZyU6dOplhal9fXzM8/fLLL5thZdsh6Llz55qgUoet161bZ4JJ7Y/Sx084DJ4pUybJlSuX0745wpxFAACABPSyxjoP0VbC+YApNXXqVOndu7cJBHVRii5e0bmEs2bNsrb5+uuvZcGCBWZuoc5Z3L59uwkWdei4W7dupo0GkxaVKlWSypUrm3NptrFp06aSWggWAQCAR3Ll8KgGhkkJDvPkySM+Pj5y7tw5u+O6HxIS4vA+wcHBZp5hRESEGTbWAFAzicWLF7e2GTJkiDW7aAkGjx8/brKblmAxIb2/9kcvx6zBoj7++fPn7drExMSYFdLO+uYIw9AAAAAp5OfnJzVq1DBDwRY6tKz7devWvet9dd5iwYIFTQC3aNEiadfun8VCt27dMnMLbWlQqud2RhetaPCZP39+s6+PrwtgdE6lxerVq805dFFOUpFZBAAAHim9lM4ZNGiQyfbpQpLatWub0jnh4eFmaFl17drVBIWWOY+6uOTUqVNStWpV8++YMWNMADd06FDrOdu0aSNvvvmmFClSxAxDb9u2zcxz7NGjh7ldayWOHTvWrJDWLOHhw4fN/UuWLGnmRKpy5cqZeY065K1lfbR0zosvvmiylUldCa0IFgEAAO5Dx44d5cKFCzJq1CizcESDQC1RY1n0EhYWZpcl1OFnrbV45MgRyZo1qyllM2/ePLNy2WLatGmmKPcLL7xghpI1uOvTp495DEuWcefOnaYOo2YP9fbmzZubwt22w+c671EDRB2W1j5ocPnBBx8k6/lRZxFpgjqLGQt1FjMW6ixmLGlZZ3HdmZ9cdu5G+Vu57NyehswiAADwSFriBq7HAhcAAAA4RWYRAAB4pPSywOVBR2YRAAAATpFZBAAAHonMonuQWQQAAED6L51Tqvlnad0FuFFc7sxp3QW4UVxIYFp3AW50dtFXad0FuNHtsC/T7LE3nv/RZeeuk7e1y87tacgsAgAAwCnmLAIAAI/kxZxFtyBYBAAAHolY0T0YhgYAAIBTZBYBAIBHYhjaPcgsAgAAwCkyiwAAwCOR8XIPXmcAAAA4RWYRAAB4JC+vdHFdkQcemUUAAAA4RWYRAAB4JBZDuwfBIgAA8EiUznEPhqEBAADgFJlFAADgkUgsugeZRQAAADhFZhEAAHgkb1KLbkFmEQAAAE6RWQQAAB6JxKJ7kFkEAACAU2QWAQCAR6LOonsQLAIAAI9ErOgeDEMDAADAKTKLAADAI5FZdA8yiwAAAHCKzCIAAPBIFOV2DzKLAAAAcIrMIgAA8EgkFt2DzCIAAACcIrMIAAA8kpdXfFp3IUMgWAQAAB6JYWj3YBgaAAAATpFZBAAAHolrQ7sHmUUAAAA4RWYRAAB4JDJe6fh1Dg0NlXHjxklYWFjq9wgAAACeHSy+/PLLsnjxYilevLj8+9//lq+++koiIyNTv3cAAAB3mbPoqg2pECxu375dNm3aJOXKlZMBAwZI/vz55cUXX5StW7em5JQAAAB40Ib7q1evLh988IGcPn1aRo8eLZ9++qnUqlVLqlatKrNmzZL4eIplAgAA1/By4ZZcM2bMMNP0AgICpE6dOiah5kx0dLSZzleiRAnTvkqVKrJs2TK7NrGxsfL6669LsWLFJHPmzKbt+PHjrbGVnuPVV1+VSpUqSWBgoBQoUEC6du1qYjJb2icvLy+77Z133nHfAhft6P/+9z+ZPXu2rFy5Uh566CHp2bOnnDx5Ul577TX55Zdf5IsvvrifhwAAAHAovQwXL1y4UAYNGiQzZ840geKUKVOkRYsWsn//fsmbN2+i9iNHjpT58+fLJ598ImXLlpXly5dL+/bt5Y8//pBq1aqZNu+++6589NFHMmfOHKlQoYL89ddf0r17d8mePbu89NJLcuvWLTOaqwGlBptXrlyR//znP9K2bVvT1pYGpr1797buZ8uWLVnPzys+Bek/7ZwGiF9++aV4e3ubSLZXr17mCVv8/fffJst4+/btJJ2zVPPPktsNeLC43JnTugtwo7iQwLTuAtzo7KKv0roLcKPbYV+m2WOfCP/eZecuHNgmyW3r1KljYp7p06eb/bi4OClcuLCZpjds2LBE7TULOGLECOnfv7/1WIcOHUwGUYNI9eijj0q+fPnks88+c9omoc2bN0vt2rXl+PHjUqRIEWtmUacP6ubWYWh9QQ4ePGgi3lOnTsnEiRPtAkWladNOnTqluGMAAADpfRg6KipKtmzZIs2aNbMe00Sa7m/YsMHhfXRRsA4/29IgcP369db9evXqyapVq+TAgQNmf8eOHeb2Rx55xGlfrl27ZoaZc+TIYXdch51z585tspbvvfeexMTEuH4Y+siRI1K0aNG7ttHxc80+AgAAeBoN6BJWevH39zebrYsXL5r5hZoFtKX7+/btc3huHaKePHmyNGzY0MxF1KBQq8zoeSw0I3n9+nWTjPPx8TG3vfnmm9KlSxeH54yIiDBzGDt37ixBQUHW4zpkrWtMcuXKZYa5hw8fLmfOnDGP79LM4r0CRQAAAFfz9nLd9vbbb5v5gdltNj2WGqZOnSqlSpUygaCfn5+pJqPzETUjafH111/LggULzNoPnf6ncxd1JFf/dbSG5KmnnjKLX3TU15bOpWzcuLFUrlxZ+vbtK5MmTZJp06Ylq+RhijKLOXPmNGnOhPSYplVLliwpzz33nHniAAAAnkYzcBpo2UqYVVR58uQxmb9z587ZHdf9kJAQcSQ4OFiWLFlisoGXLl0ycxg1k6j1qy2GDBlijlmm9OmqZ52LqAFrt27dEgWKetvq1avtsorO5lfqMPSxY8ekTJky4rLM4qhRo0z027p1axk7dqzZ9Gs9ppM1S5cuLf369TOrfAAAADxtzqIGhhp4BdlsjoJFzQzWqFHDDCVb6AIX3a9bt+5d+68JtoIFC5rgbdGiRdKuXTvrbbra2TbTqDQo1XMnDBR1HYlWoNF5ifeidbL1vI5WaadqZlEnWL7xxhsmnWnr448/lhUrVpgnrOlOrcFou1QbAADgQTNo0CCT7atZs6ZZjaylc8LDw60jrFo1RoNCyzD2xo0bzQJhrUut/44ZM8YEgUOHDrWes02bNmaOoq5q1tI527ZtM/MMe/ToYQ0Un3jiCTNE/cMPP5g5jWfPnjW36fxEDWJ1gY0+VpMmTUy5HN0fOHCgPPPMM2aU2KXBotYD0vo/CTVt2lQGDx5svm7VqpXD5eIAAACpwcsrfVz8o2PHjnLhwgUz8qoBmwaBWmTbsuglLCzMLkuow89aa1EXDGfNmtXETPPmzbNbxazzCrWG4gsvvCDnz583Q9V9+vQxj6E0yPzuu+/M1/p4ttasWWPmKWomVC/JrMGozlHUSjUaLCYcXndJnUWNcvXBdLP1/vvvm01flJ07d0rz5s2tUe69UGcxY6HOYsZCncWMhTqLGUta1lk8e/tOsOQKIZnbuuzcniZFmUWNdHVOokaumm61FIL86aefTPVypVd0adSoUer2FgAA4P+lkwu4PPBSFCzqPMTy5cubSuVaF0jpipp169aZIpLKMhwNAADwIF/u70GX4mtD169f32wAAAB4cKU4WNRVN1ojaO/evWZfV+roxat1WTfurlalEOn1ZCWpUCq35MsdKP3G/CK//HE8rbsFF6lVNlh6P1peKhbPKflyZpG+k36VlX+dTOtuIZmerV9Mnv9XSQnO5i97T1+XMYt3yo6wqw7bZvL2kn7NSkmHWkUkJHuAHDl/U975YY/8uu+8tU2gfyYZ9EhZaVEpv+TO6i+7T12Tcf/bJTtPOD4n0qf6tcvKwL6PSvVKxSV/vpzyVK9J8v2Kv9K6WxkGiUX3SFGdxUOHDkm5cuXMUnAdhtZNl2FrwHj48OHU7+UDJnNAJtl35LKMne74mpF4sGTxzyT7wq7ImFl8gHiq1lULyIjHKsjU5fvl0UnrZO/pazKnT13JndXPYfvBrcrJ03VDTUD573dXy4I/jsnH3WtL+YLZrW3e6VhVGpQJlkELtkrL99bIb/vPy7x+9SRfdvvrxSJ9C8ziL7v2hMnLI2eldVeA9JVZ1OsM6rUM//zzT1PLR2kFcg0Y9bYff/wxtfv5QPl180mzIWNYt+OM2eC5ejUuKQs3HJdvN4WZ/RHf7JAm5fLJk3WKysxVBxO1b1+zsMxYeUDW7r2TSdRgsX7pYOnduIQMXLBV/H29pWXl/PL8rE2y6cgl00YD0aYVQuSZeqEy6WfH15NF+rNi7Q6zwYMyXnBPsKgLWWwDRaVVw9955x3mMQJ4oPj6eEnFQtnlw18OWI9pwbHfD16Q6kUdF7X1y+QtkTGxdscio2OlZvE7V1fI5O0tmXy8zTFbETZtAMCjg3It8njjxo1Ex2/evGkqhgPAgyJnoL8J7C7eiLQ7rvvBQY6HjHVuYs/GJSQ0T6BZrdmgdLC0qJxfgoPuXCosPDJGthy9LAOal5G8QQHi7SXyWI1CUj00l9kHkDT68+WqDfcZLD766KPy/PPPm0vIaE1v3TTTqJf/00Uu96JVxK9fv263xcdFp6QrAJDu6EKVYxfC5ZfhTeXAe21kbIfK8u2mExL/zyVdZdCCLWZy/saxLWT/e23kuYeLy/dbT0pc8q+TAADpbxhar/ms10DUC2T7+vqaY3oRbA0Up06des/767URx44da3csZ/E2krvEPxfQBoD04Ep4pMTExkmebHeygha6f+F6hMP7XA6Pkj6zNpnh6JyBfnLuWoS8+mh5Cbscbm0TdumWdJrxu2T285GsAZnkwvVImda1poRd+qcNgHshBZhug0W9duHSpUvl4MGDsm/fnYnYujq6ZMmSSbr/8OHDE12XsPrjX6SkKwDgUtGx8fL3yWtmgcrKv+9cvlSHqOqVCpa564/e9b5RMXEmUNRSOrqg5cftpxO1uR0Va7agzL7SsGxeeef73S57LsCDxotgMX3XWVSlSpUyW0rmPOpmy8v7ToYyI8gSkEmKFgiy7hcKySrliueSqzci5cwFsgoPYumcoiFZrfuFggOlXNEccvVmlJy5dCtN+4ak+XTtIZn0dHVTA3HH8SvSo1EJyeLnI99uvLM6Wm87e+22vPfjnbqzVYvkNCVw9py+Zuos/qdFWfH29pKPV/+zcrphmWATdWoNRp3bOLxtBTl87oZ88//nhOeUzikRGmLdDy0cLJXLF5UrV2/KidN3VroDGSZYTJgJvJvJkyentD8ZQsXSeWTBxNbW/RF9HzL/Ll5xQF6d+Fsa9gyuUKl4LvliVDPr/siuNcy/i9YdkaEz/0zDniGpNCOohbMHtSwreYL8Ze+p6/Lcx3/KxZt3Fr0UyJnZbq6hlsbRWotFcmcxi1m0hI7WU7wREWNtky2zrwxpXV5CcgTItVvRsmzHaZn4016JiWPOoiepXrm4rPh6lHV/wuiu5t9536yT5wfPTMOeZQxeXhTPcQeveF2dkgRNmjRJ2gm9vGT16tXJ7kip5p8l+z7wXHG5M6d1F+BGcSGBad0FuNHZRV+ldRfgRrfDvkyzx74a9ZPLzp3Dr5XLzv3AZhbXrFnj2p4AAAAkC3MW3eG+87cnT540GwAAAB48KQoW4+LiZNy4cZI9e3YpWrSo2XSF9Pjx481tAAAA7lgN7ar/cJ+roUeMGCGfffaZ3eX91q9fL2PGjJGIiAh58803U3JaAAAAPAjB4pw5c+TTTz+1u1pL5cqVpWDBgvLCCy8QLAIAADcgA5hug8XLly9L2bJlEx3XY3obAACAq1E6xz1S9CpXqVJFpk+fnui4HtPbAAAAkIEzixMmTJDWrVvLL7/8Yq4PrTZs2CAnTpyQn35yXc0jAACAfzAMnW4zi40aNZIDBw5I+/bt5erVq2Z7/PHHZf/+/fLwww+nfi8BAADgWdeGLlCgAAtZAABAmqHETToLFnfu3CkVK1YUb29v8/Xd6MpoAAAAZKBgsWrVqnL27FnJmzev+VqvAe3ostJ6PDY2NrX7CQAAYIfMYjoLFo8ePSrBwcHWrwEAAPDgS3KwqJf0s8iaNavkzp3bfK0roD/55BO5ffu2KdLNAhcAAOAe1FlMd6/yrl27JDQ01AxFawHu7du3S61ateT999+X//73v9KkSRNZsmSJ63oLAABgM/XNVRtSGCwOHTpUKlWqJL/++qs0btxYHn30UVNv8dq1a3LlyhXp06ePuV40AAAAMmDpnM2bN8vq1avName9UotmE/Va0LpCWg0YMEAeeughV/UVAADABhnAdJdZ1Os+h4SEWOctBgYGSs6cOa2369c3btxI/V4CAADAM4pyJxzHZ1wfAACkBUrnpNNg8bnnnhN/f3/zdUREhPTt29dkGFVkZGTq9xAAAACeESx269bNbv+ZZ55J1KZr16733ysAAIB7onROugsWZ8+e7bqeAAAAwPOHoQEAANID5iy6B8EiAADwSCyydQ8G+wEAAOAUmUUAAOChyCy6A5lFAAAAOEVmEQAAeCQvcl5uwasMAAAAp8gsAgAAD8WcRXcgswgAAACnCBYBAIDH1ll01ZZcM2bMkNDQUAkICJA6derIpk2bnLaNjo6WcePGSYkSJUz7KlWqyLJly+zaxMbGyuuvvy7FihWTzJkzm7bjx4+X+Ph4axv9etSoUZI/f37TplmzZnLw4EG781y+fFm6dOkiQUFBkiNHDunZs6fcvHkzWc+NYBEAAHgoLxduSbdw4UIZNGiQjB49WrZu3WqCvxYtWsj58+cdth85cqR8/PHHMm3aNNmzZ4/07dtX2rdvL9u2bbO2effdd+Wjjz6S6dOny969e83+hAkTzH0sdP+DDz6QmTNnysaNGyUwMNA8bkREhLWNBoq7d++WlStXyg8//CC//vqrPP/888l7leNtQ9Q0VKr5Z2ndBbhRXO7Mad0FuFFcSGBadwFudHbRV2ndBbjR7bAv0+yxo+K2uOzcft41kty2Tp06UqtWLRPYqbi4OClcuLAMGDBAhg0blqh9gQIFZMSIEdK/f3/rsQ4dOpjs4Pz5883+o48+Kvny5ZPPPvvMYRsN3/Q8gwcPlldeecXcfu3aNXOfzz//XDp16mSCzPLly8vmzZulZs2apo1mMFu1aiUnT540908KMosAAMBjS+e4aouMjJTr16/bbXosoaioKNmyZYsZArbw9vY2+xs2bHDYbz2PDj/b0iBw/fr11v169erJqlWr5MCBA2Z/x44d5vZHHnnE7B89elTOnj1r97jZs2c3gavlcfVfHXq2BIpK22v/NBOZVASLAAAACbz99tsm+Mpus+mxhC5evGjmF2pGz5buazDniA4VT5482cwv1CykDhEvXrxYzpw5Y22jGUnNDpYtW1Z8fX2lWrVq8vLLL5thZWU5990eV//Nmzev3e2ZMmWSXLlyOe2bI5TOAQAAHsp1pXOGDx9u5iHa8vf3l9QwdepU6d27twkEdTGNLl7p3r27zJo1y9rm66+/lgULFsgXX3whFSpUkO3bt5tgUYeOu3XrJu5EsAgAAJCABoZJCQ7z5MkjPj4+cu7cObvjuh8SEuLwPsHBwbJkyRKzEOXSpUsmANRMYvHixa1thgwZYs0uqkqVKsnx48dNdlODRcu59XF0NbTt41atWtV8rW0SLrKJiYkxK6Sd9c0RhqEBAIBH8nLhf0nl5+cnNWrUMPMLLXRoWffr1q171/vqvMWCBQuaAG7RokXSrl076223bt0ycwttaVCq51ZaUkcDPtvH1XmVOhfR8rj679WrV82cSovVq1ebc+jcxqQiswgAAHAfBg0aZLJ9upCkdu3aMmXKFAkPDzdDy6pr164mKLTMedSA7tSpUyYDqP+OGTPGBHBDhw61nrNNmzby5ptvSpEiRcwwtJbV0XmOPXr0MLfr8LUOS7/xxhtSqlQpEzxqXUbNUj722GOmTbly5aRly5ZmyFvL62h9xxdffNFkK5O6EloRLAIAAI+UkuLZrtCxY0e5cOGCKZCtC0c0CNQSNZbFJ2FhYXZZQh1+1lqLR44ckaxZs5pSNvPmzTMrly20nqIGfy+88IIZStbgrk+fPuYxLDS41KBU6yZqBrFBgwbmcW1XWuu8Rw0QmzZtavqg5Xe0NmNyUGcRaYI6ixkLdRYzFuosZixpWWcxNv5vl53bx6uiy87taZizCAAAAKcYhgYAAB4pOQtRkHJkFgEAAOAUmUUAAOChyCy6A5lFAAAAOEVmEQAAeKT0UjrnQUdmEQAAAE6RWQQAAB6KnJc7ECwCAACPROkc9yAkBwAAQPq/3F9GFBkZaS4qPnz4cPH390/r7sDFeL8zFt7vjIX3Gw8ygsU0dP36dcmePbtcu3ZNgoKC0ro7cDHe74yF9ztj4f3Gg4xhaAAAADhFsAgAAACnCBYBAADgFMFiGtJJ0KNHj2YydAbB+52x8H5nLLzfeJCxwAUAAABOkVkEAACAUwSLAAAAcIpgEQAAAE4RLHqA5557Th577LG07kaG9/nnn0uOHDlcdv61a9eKl5eXXL161WWPgX/oa71kyRK3Py7vc/p07Ngx875s3749yfdp3LixvPzyyy7tF5AeECymQiCnv2D69u2b6Lb+/fub27SNq35ZwTXvp25+fn5SsmRJGTdunMTExLj8sevVqydnzpwxV4HA/Tt79qwMGDBAihcvblaoFi5cWNq0aSOrVq1K034l530msHTNz7ZuuXPnlpYtW8rOnTvN7fr9oe9LxYoV07qrQLpDsJgK9JfMV199Jbdv37Yei4iIkC+++EKKFCmSpn1D8ukHiH5oHDx4UAYPHixjxoyR9957z+WPq8FpSEiI+SDD/dE/vGrUqCGrV682792uXbtk2bJl0qRJE/NHXFpyxfscFRWVaufKCD/buukfDZkyZZJHH33U3Obj42PeFz0GwB7BYiqoXr26CRgXL15sPaZfa6BYrVo16zH9sGrQoIEZytS/avWX1OHDh623FytWzPyr99EPEh3isDVx4kTJnz+/ua9+4EVHR7vl+WU0moXSD42iRYtKv379pFmzZvLdd99Zb1++fLmUK1dOsmbNav3wUb/++qv4+vqajJYtHaZ6+OGHzdfHjx832a2cOXNKYGCgVKhQQX766SenWaTff//dfB9kyZLF3KdFixZy5coVc9u3334rlSpVksyZM5vvCe1neHi4W16j9O6FF14wr+WmTZukQ4cOUrp0afNaDxo0SP78809ru4sXL0r79u3N61uqVCm791n9/fff8sgjj5j3Ol++fPLss8+a+1joe6PZS32P9f3RNp988ol5H7p37y7ZsmUz2emff/7Zep+E77Oz7wkNeDW4VXqb7SiFPu6LL75oHjdPnjzm+6JHjx7WwMdCf0fkzZtXPvvsMxe90p75s61b1apVZdiwYXLixAm5cOGCw5GddevWSe3atc399Hevtr/bKIP+bHbt2tW8X/o9pd87+kenLf3+0M8LvV2/9yZPnmyd3qJ98Pb2lr/++svuPlOmTDG/j+Li4lL9NQGSgmAxlegv6tmzZ1v3Z82aZT4sbOkHiH5Y6S8C/atWfynoLwvLLwD9YFO//PKLCUBsg881a9aYwFL/nTNnjpk/pxtcT4MxS+bm1q1bJmifN2+eCQ7DwsLklVdeMbc1bNjQDHnqbbYf1gsWLDDfH0qD/MjISHNfzXa9++67JhBxRD+0mjZtKuXLl5cNGzbI+vXrTVARGxtrvj86d+5szrt3714TgDz++ONC2VSRy5cvmz/M9LXW4Csh23mnY8eOlaeeesoMRbZq1Uq6dOli7q80mPvXv/5l/njTn1k957lz50x7W/rzqAGb/vxq4Kh/YDz55JNmuHnr1q3SvHlzE2Tq944jzr4nNKBYtGiRabN//37znk+dOtXucTVLqX9QzJw5U3r16mX6aPnjRf3www/mcTt27JgKr+yD5ebNmzJ//nwTzOsfWwmdOnXKfE/UqlVLduzYIR999JEJut944w2n59RgXr9X9I8O/ZnVn0c9h+UPe32vdMrSf/7zH/Pz/e9//1vefPNN6/1DQ0PNH322nyVK9/Xc+pkBpAktyo2U69atW3y7du3iz58/H+/v7x9/7NgxswUEBMRfuHDB3KZtHNHb9S3YtWuX2T969KjZ37ZtW6LHKFq0aHxMTIz12JNPPhnfsWNHFz+7jPt+qri4uPiVK1ea9/WVV16Jnz17tnl/Dh06ZG0/Y8aM+Hz58ln333333fhy5cpZ9xctWhSfNWvW+Js3b5r9SpUqxY8ZM8bhY69Zs8ac/8qVK2a/c+fO8fXr13fYdsuWLaatfq/B3saNG81rs3jx4ru20zYjR4607ut7pMd+/vlnsz9+/Pj45s2b293nxIkTps3+/fvNfqNGjeIbNGhgvV1/RgMDA+OfffZZ67EzZ86Y+2zYsMHh+5yc7wkLfdxq1aolal++fHnzPWjRpk2b+Oeee+6ur0NG+tn28fEx749u+rrmz5/f/Cw5+v372muvxZcpU8b8HrD9edef59jYWOv78J///Md8feDAAXP/33//3dr+4sWL8ZkzZ47/+uuvzb7+zm7durVdv7p06RKfPXt26/7ChQvjc+bMGR8REWH2tX9eXl6mf0Ba4c+UVBIcHCytW7c22T79K1C/1myDLR2O0GyQZp+CgoLMX5FKs1P3okNTOqfGQodEzp8/74JnAs3GaGYnICDADCNpVkbnLSodOipRooTT90H/+j906JB1qFO/HzQTZclwvfTSSyYzUb9+fXNpMMvk+rtlFh2pUqWKuU2HoTWLpUNbluHpjC452dXKlStbv9b3SH8uLe+nZpM0k6/fC5atbNmy5jbb6SO259CfUc1S6ftioUPTytnPa3K+J2zpnMyENLtoyUppFlSHvy1ZbYgZ1tefK900E6zD9/ozrlMBEtKMfd26de3mlup7pBnJkydPOmyv8x3r1KljPabfC2XKlDG3WTLEOqxtK+G+Vr7Q76P//e9/1t8h2m/L5wWQFggWU5H+UtYfbB0ecvQLWocQdYhLP9g3btxotqROTte5cLb0FxjzV1z7gaLBvS5a0vfTEuw5eh9sgxOdH6bvs35gO/qw1g/zI0eOmGFJHXKsWbOmTJs2zenwtzP6YbJy5Upzfh2m1nPoh9LRo0clo9O5h/q+7Nu3775+rjQo0PfSElxYNv2+0CkHdzuH7TFLsOHs5zU53xO2HA2x63w5PZcOgeoQq86DtsyXxZ3XTIedddPh5U8//dRMD9LfyemFTi3Q91F/h+hngy6UJOBHWiNYTEW62EF/uHV+iv7FauvSpUvmr8qRI0eajJAukEiYCdJfEkrnpCHtP1B0gVJKVkbqh//ChQvlv//9r8lCajbCls5F03lLOidVV1s7+6DSjNXdyrxoEKLn1nl327ZtM98/lmxERpYrVy7z8zdjxgyHC36SWoZGF67t3r3bZHQsAYZlcxSo3Q9n3xPJ/Z2gmSzNTGmgoX+4Jpw3jcQ/QzoP0LaShYX+jrbMO7TQOYe6aKlQoUIO2+viF0sSwPb3vv5Bp/QPus2bN9vdL+G+5XeIzl3/8MMPzTl1PjKQlggWU5Fme3S4Yc+ePXZDxkpXx+kvcg0gdJhSS3roYhdbmpXSbJJlIv21a9fc/AyQGjRQ0eFMHVpM+GGtq1d1NbVmAHXxgw5z6oeMI8OHDzcfJLqyV4cmNVOmk+x1Na5+IL311ltmMr1OY9AgQ1d0OjtXRqOBogZYOsSni0Q0G6g/mx988IEZWkwKXXiiIwE6dUTfBx161vdO39PU/IPubt8TugJWAxqdGqHvr2Y770UDDc2G6/Pt1q1bqvXzQaALibRagW76+uiCJEsGOSH9udOV0tpGf/aWLl1qpgno721HC000o92uXTvp3bu3WYym0xieeeYZKViwoDmu9Fy60l1XQOv35Mcff2xGBxKWUdL3/6GHHpJXX33VfP/dbZQBcAeCxVSmQYJuCekvF63FuGXLFlP0deDAgYlq92kWSz/M9BdIgQIFrL9g4Fn0vda5ixpQ6HCSLT2mQYh+GGgmWku6aPbAEb1txYoV5kNHgx4NcvQDS79P9HtMV8/qSkttpxnrSZMmmflXEDMvWAMvnVKgmTr9mdOVp5qp1YA7KfRnUDNJ+p7pimadh6iBna6mTs1VqXf7ntBAQzPHWrJF5z5quZx70dW0OpdW/2jR54B/6B/i+tropnML9Y+Ab775JlGZMstrr4Gdzm3UOcKa+e3Zs6f5WXNGM7o6l1RLGOnPq2Yl9RyWaQk6EqAr1zVY1HNqf/SzQOdHJ6SPpSNVDEEjPfDSVS5p3QngQaO/6DUTlLBuH+BqminTQEcDF4Yv0z/NRGrm8rfffrM7Pn78eBPIJnXBE+BKlKoHUpFOHdBFCjopnUAR7qQLaHSKgmaYNfvZtm3btO4SHNA6rZrl1nmvOgStUwZsRxc02Nfi3NOnT79rTUfAnQgWgVSkUwd02EqHrPQDAXAXnbuqq5918YUubuGydemT/n6YMGGC3Lhxw0yX0KlHOs/UQqcafPnll2ahEkPQSC8YhgYAAIBTLHABAACAUwSLAAAAcIpgEQAAAE4RLAIAAMApgkUAAAA4RbAIAAAApwgWAQAA4BTBIgAAAJwiWAQAAIA483+Df+ge33lzowAAAABJRU5ErkJggg==",
+      "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": [
+    "## 🔍 Pearsonova korelačná analýza\n",
+    "\n",
+    "Tento notebook rozširuje modul vizualizácie údajov tým, že ukazuje, ako analyzovať vzťah medzi viacerými vlastnosťami pomocou Pearsonovej korelácie.\n",
+    "\n",
+    "Pearsonova korelácia meria silu a smer **lineárneho vzťahu** medzi dvoma spojitými premennými. Vracia hodnotu v rozmedzí od -1 do 1:\n",
+    "- **+1** → Dokonalý pozitívny vzťah  \n",
+    "- **0** → Žiadny lineárny vzťah  \n",
+    "- **-1** → Dokonalý negatívny vzťah\n",
+    "\n",
+    "Tu sme vypočítali korelačnú maticu pre študentské výsledky z matematiky, fyziky, chémie a biológie. Výsledná **teplotná mapa** nám vizuálne pomáha pochopiť, ako silno sú výsledky z jednotlivých predmetov navzájom prepojené.\n",
+    "\n",
+    "Takáto korelačná analýza je kľúčová pri **inžinierstve vlastností a predspracovaní údajov**, najmä pred vytváraním modelov strojového učenia. Pomáha identifikovať:\n",
+    "- Redundantné vlastnosti\n",
+    "- Silné prediktory\n",
+    "- Potenciálnu multikolinearitu\n",
+    "\n",
+    "Je to užitočný doplnok k zmysluplným vizualizáciám pri analýze reálnych dátových súborov.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Upozornenie**:  \nTento dokument bol preložený pomocou služby AI prekladu [Co-op Translator](https://github.com/Azure/co-op-translator). Aj keď sa snažíme o presnosť, upozorňujeme, že automatizované preklady môžu obsahovať chyby alebo nepresnosti. Pôvodný dokument v jeho pôvodnom jazyku by mal byť považovaný za autoritatívny zdroj. Pre kritické informácie sa odporúča profesionálny ľudský preklad. Nezodpovedáme za akékoľvek nedorozumenia alebo nesprávne interpretácie vyplývajúce z použitia tohto prekladu.\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": "2025-09-06T19:40:57+00:00",
+   "source_file": "3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb",
+   "language_code": "sk"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/translations/sl/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb b/translations/sl/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb
new file mode 100644
index 00000000..366909a8
--- /dev/null
+++ b/translations/sl/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb
@@ -0,0 +1,100 @@
+{
+ "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": [
+    "## 🔍 Analiza Pearsonove korelacije\n",
+    "\n",
+    "Ta beležnica izboljšuje modul za vizualizacijo podatkov z demonstracijo, kako analizirati razmerje med več značilnostmi z uporabo Pearsonove korelacije.\n",
+    "\n",
+    "Pearsonova korelacija meri moč in smer **linearnega razmerja** med dvema zveznima spremenljivkama. Vrne vrednost med -1 in 1:\n",
+    "- **+1** → Popolno pozitivno razmerje  \n",
+    "- **0** → Brez linearnega razmerja  \n",
+    "- **-1** → Popolno negativno razmerje\n",
+    "\n",
+    "Tukaj smo izračunali korelacijsko matriko za ocene dijakov pri matematiki, fiziki, kemiji in biologiji. Nastali **toplotni zemljevid** nam vizualno pomaga razumeti, kako močno so ocene posameznih predmetov povezane med seboj.\n",
+    "\n",
+    "Takšna analiza korelacije je ključnega pomena pri **inženiringu značilnosti in predobdelavi podatkov**, še posebej pred gradnjo modelov strojnega učenja. Pomaga pri prepoznavanju:\n",
+    "- Odvečnih značilnosti\n",
+    "- Močnih napovednih spremenljivk\n",
+    "- Potencialne multikolinearnosti\n",
+    "\n",
+    "To je koristna dopolnitev smiselnih vizualizacij pri analizi podatkov iz resničnega sveta.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Omejitev odgovornosti**:  \nTa dokument je bil preveden z uporabo storitve za strojno prevajanje [Co-op Translator](https://github.com/Azure/co-op-translator). Čeprav si prizadevamo za natančnost, vas prosimo, da se zavedate, da lahko avtomatizirani prevodi vsebujejo napake ali netočnosti. Izvirni dokument v njegovem izvirnem jeziku je treba obravnavati kot avtoritativni vir. Za ključne informacije priporočamo strokovno človeško prevajanje. Ne prevzemamo odgovornosti za morebitna nesporazumevanja ali napačne razlage, ki izhajajo iz uporabe tega prevoda.\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": "2025-09-06T19:41:31+00:00",
+   "source_file": "3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb",
+   "language_code": "sl"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/translations/sr/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb b/translations/sr/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb
new file mode 100644
index 00000000..e35b5db2
--- /dev/null
+++ b/translations/sr/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb
@@ -0,0 +1,100 @@
+{
+ "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": [
+    "## 🔍 Анализа Пирсонове корелације\n",
+    "\n",
+    "Овај бележник унапређује модул за визуелизацију података демонстрирајући како анализирати однос између више карактеристика користећи Пирсонову корелацију.\n",
+    "\n",
+    "Пирсонова корелација мери јачину и смер **линеарне везе** између две континуиране променљиве. Она враћа вредност између -1 и 1:\n",
+    "- **+1** → Савршена позитивна веза  \n",
+    "- **0** → Нема линеарне везе  \n",
+    "- **-1** → Савршена негативна веза\n",
+    "\n",
+    "Овде смо израчунали матрицу корелације за резултате ученика из математике, физике, хемије и биологије. Добијени **топлотни графикон (heatmap)** нам визуелно помаже да разумемо колико су снажно резултати из сваког предмета међусобно повезани.\n",
+    "\n",
+    "Оваква анализа корелације је од суштинског значаја у **инжењерингу карактеристика и претпроцесирању**, посебно пре изградње модела машинског учења. Она помаже у идентификовању:\n",
+    "- Редундантних карактеристика\n",
+    "- Снажних предиктора\n",
+    "- Потенцијалне мултиколинеарности\n",
+    "\n",
+    "Ово је користан додатак значајним визуелизацијама приликом анализе скупова података из стварног света.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Одрицање од одговорности**:  \nОвај документ је преведен коришћењем услуге за превођење помоћу вештачке интелигенције [Co-op Translator](https://github.com/Azure/co-op-translator). Иако тежимо тачности, молимо вас да имате у виду да аутоматски преводи могу садржати грешке или нетачности. Оригинални документ на изворном језику треба сматрати ауторитативним извором. За критичне информације препоручује се професионални превод од стране људи. Не сносимо одговорност за било каква погрешна тумачења или неспоразуме који могу произаћи из коришћења овог превода.\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": "2025-09-06T19:41:18+00:00",
+   "source_file": "3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb",
+   "language_code": "sr"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/translations/sv/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb b/translations/sv/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb
new file mode 100644
index 00000000..1a4bb27b
--- /dev/null
+++ b/translations/sv/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb
@@ -0,0 +1,100 @@
+{
+ "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": [
+    "## 🔍 Pearson-korrelationsanalys\n",
+    "\n",
+    "Den här notebooken förbättrar modulen för datavisualisering genom att visa hur man analyserar sambandet mellan flera egenskaper med hjälp av Pearson-korrelation.\n",
+    "\n",
+    "Pearson-korrelation mäter styrkan och riktningen av ett **linjärt samband** mellan två kontinuerliga variabler. Den returnerar ett värde mellan -1 och 1:\n",
+    "- **+1** → Perfekt positivt samband  \n",
+    "- **0** → Inget linjärt samband  \n",
+    "- **-1** → Perfekt negativt samband\n",
+    "\n",
+    "Här har vi beräknat korrelationsmatrisen för studenters resultat i Matematik, Fysik, Kemi och Biologi. Den resulterande **värmekartan** hjälper oss att visuellt förstå hur starkt varje ämnesresultat är relaterat till de andra.\n",
+    "\n",
+    "En sådan korrelationsanalys är avgörande inom **feature engineering och förbearbetning**, särskilt innan man bygger maskininlärningsmodeller. Den hjälper till att identifiera:\n",
+    "- Redundanta egenskaper\n",
+    "- Starka prediktorer\n",
+    "- Potentiell multikollinearitet\n",
+    "\n",
+    "Detta är ett värdefullt tillskott till meningsfulla visualiseringar vid analys av verkliga dataset.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Ansvarsfriskrivning**:  \nDetta dokument har översatts med hjälp av AI-översättningstjänsten [Co-op Translator](https://github.com/Azure/co-op-translator). Även om vi strävar efter noggrannhet, vänligen notera att automatiska översättningar kan innehålla fel eller felaktigheter. Det ursprungliga dokumentet på dess originalspråk bör betraktas som den auktoritativa källan. För kritisk information rekommenderas professionell mänsklig översättning. Vi ansvarar inte för eventuella missförstånd eller feltolkningar som uppstår vid användning av denna översättning.\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": "2025-09-06T19:39:26+00:00",
+   "source_file": "3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb",
+   "language_code": "sv"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/translations/sw/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb b/translations/sw/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb
new file mode 100644
index 00000000..442ec4bc
--- /dev/null
+++ b/translations/sw/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb
@@ -0,0 +1,100 @@
+{
+ "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": [
+    "## 🔍 Uchambuzi wa Uhusiano wa Pearson\n",
+    "\n",
+    "Notebook hii inaboresha moduli ya taswira ya data kwa kuonyesha jinsi ya kuchambua uhusiano kati ya vipengele vingi kwa kutumia uhusiano wa Pearson.\n",
+    "\n",
+    "Uhusiano wa Pearson hupima nguvu na mwelekeo wa **uhusiano wa mstari** kati ya vigezo viwili vinavyoendelea. Unatoa thamani kati ya -1 na 1:\n",
+    "- **+1** → Uhusiano mzuri kabisa  \n",
+    "- **0** → Hakuna uhusiano wa mstari  \n",
+    "- **-1** → Uhusiano mbaya kabisa  \n",
+    "\n",
+    "Hapa, tumekokotoa matriki ya uhusiano kwa alama za wanafunzi katika Hisabati, Fizikia, Kemia, na Biolojia. **Heatmap** inayotokana inatusaidia kuelewa kwa taswira jinsi alama za kila somo zinavyohusiana kwa nguvu na nyingine.\n",
+    "\n",
+    "Uchambuzi wa uhusiano kama huu ni muhimu sana katika **utengenezaji wa vipengele na usindikaji wa awali**, hasa kabla ya kujenga mifano ya kujifunza kwa mashine. Husaidia kutambua:\n",
+    "- Vipengele vilivyo na taarifa zinazojirudia\n",
+    "- Vitabiri vyenye nguvu\n",
+    "- Uwezekano wa multicollinearity\n",
+    "\n",
+    "Hii ni nyongeza muhimu kwa taswira zenye maana wakati wa kuchambua seti za data za ulimwengu halisi.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Kanusho**:  \nHati hii imetafsiriwa kwa kutumia huduma ya tafsiri ya AI [Co-op Translator](https://github.com/Azure/co-op-translator). Ingawa tunajitahidi kwa usahihi, tafadhali fahamu kuwa tafsiri za kiotomatiki zinaweza kuwa na makosa au kutokuwa sahihi. Hati ya asili katika lugha yake ya awali inapaswa kuzingatiwa kama chanzo cha mamlaka. Kwa taarifa muhimu, inashauriwa kutumia huduma ya tafsiri ya kitaalamu ya binadamu. Hatutawajibika kwa maelewano mabaya au tafsiri zisizo sahihi zinazotokana na matumizi ya tafsiri hii.\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": "2025-09-06T19:40:31+00:00",
+   "source_file": "3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb",
+   "language_code": "sw"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/translations/th/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb b/translations/th/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb
new file mode 100644
index 00000000..3353d075
--- /dev/null
+++ b/translations/th/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb
@@ -0,0 +1,100 @@
+{
+ "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": [
+    "## 🔍 การวิเคราะห์ความสัมพันธ์แบบเพียร์สัน\n",
+    "\n",
+    "โน้ตบุ๊กนี้ช่วยเพิ่มประสิทธิภาพของโมดูลการแสดงผลข้อมูล โดยแสดงวิธีการวิเคราะห์ความสัมพันธ์ระหว่างคุณลักษณะหลายตัวด้วยการวิเคราะห์ความสัมพันธ์แบบเพียร์สัน\n",
+    "\n",
+    "การวิเคราะห์ความสัมพันธ์แบบเพียร์สันวัดความแข็งแกร่งและทิศทางของ **ความสัมพันธ์เชิงเส้น** ระหว่างตัวแปรต่อเนื่องสองตัว โดยให้ค่าระหว่าง -1 ถึง 1:\n",
+    "- **+1** → ความสัมพันธ์เชิงบวกที่สมบูรณ์แบบ  \n",
+    "- **0** → ไม่มีความสัมพันธ์เชิงเส้น  \n",
+    "- **-1** → ความสัมพันธ์เชิงลบที่สมบูรณ์แบบ\n",
+    "\n",
+    "ในที่นี้ เราได้คำนวณเมทริกซ์ความสัมพันธ์สำหรับคะแนนนักเรียนในวิชาคณิตศาสตร์ ฟิสิกส์ เคมี และชีววิทยา **ฮีทแมป** ที่ได้ช่วยให้เราเข้าใจภาพรวมว่าคะแนนในแต่ละวิชามีความสัมพันธ์กันมากน้อยเพียงใด\n",
+    "\n",
+    "การวิเคราะห์ความสัมพันธ์แบบนี้มีความสำคัญอย่างยิ่งใน **การสร้างคุณลักษณะและการเตรียมข้อมูล** โดยเฉพาะก่อนการสร้างโมเดลการเรียนรู้ของเครื่อง ช่วยให้เราสามารถระบุ:\n",
+    "- คุณลักษณะที่ซ้ำซ้อน  \n",
+    "- ตัวทำนายที่แข็งแกร่ง  \n",
+    "- ความเป็นไปได้ของปัญหาความสัมพันธ์เชิงพหุ\n",
+    "\n",
+    "นี่เป็นส่วนเสริมที่มีประโยชน์สำหรับการสร้างภาพข้อมูลที่มีความหมายเมื่อวิเคราะห์ชุดข้อมูลในโลกจริง\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**ข้อจำกัดความรับผิดชอบ**:  \nเอกสารนี้ได้รับการแปลโดยใช้บริการแปลภาษา AI [Co-op Translator](https://github.com/Azure/co-op-translator) แม้ว่าเราจะพยายามให้การแปลมีความถูกต้อง แต่โปรดทราบว่าการแปลอัตโนมัติอาจมีข้อผิดพลาดหรือความไม่แม่นยำ เอกสารต้นฉบับในภาษาดั้งเดิมควรถือเป็นแหล่งข้อมูลที่เชื่อถือได้ สำหรับข้อมูลที่สำคัญ แนะนำให้ใช้บริการแปลภาษาจากผู้เชี่ยวชาญ เราไม่รับผิดชอบต่อความเข้าใจผิดหรือการตีความที่ผิดพลาดซึ่งเกิดจากการใช้การแปลนี้\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": "2025-09-06T19:39:20+00:00",
+   "source_file": "3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb",
+   "language_code": "th"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/translations/tl/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb b/translations/tl/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb
new file mode 100644
index 00000000..d9b7d88f
--- /dev/null
+++ b/translations/tl/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb
@@ -0,0 +1,100 @@
+{
+ "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": [
+    "## 🔍 Pagsusuri ng Pearson Correlation\n",
+    "\n",
+    "Ang notebook na ito ay nagpapahusay sa data visualization module sa pamamagitan ng pagpapakita kung paano suriin ang relasyon sa pagitan ng maraming katangian gamit ang Pearson correlation.\n",
+    "\n",
+    "Ang Pearson correlation ay sumusukat sa lakas at direksyon ng isang **linear na relasyon** sa pagitan ng dalawang tuloy-tuloy na variable. Nagbibigay ito ng halaga sa pagitan ng -1 at 1:\n",
+    "- **+1** → Perpektong positibong relasyon  \n",
+    "- **0** → Walang linear na relasyon  \n",
+    "- **-1** → Perpektong negatibong relasyon\n",
+    "\n",
+    "Dito, kinalkula namin ang correlation matrix para sa mga marka ng estudyante sa Math, Physics, Chemistry, at Biology. Ang nabuong **heatmap** ay tumutulong sa atin na maunawaan nang biswal kung gaano kalakas ang kaugnayan ng bawat marka ng asignatura sa isa't isa.\n",
+    "\n",
+    "Ang ganitong pagsusuri ng correlation ay mahalaga sa **feature engineering at preprocessing**, lalo na bago bumuo ng mga machine learning model. Nakakatulong ito upang matukoy ang:\n",
+    "- Mga redundant na katangian  \n",
+    "- Malalakas na predictor  \n",
+    "- Posibleng multicollinearity  \n",
+    "\n",
+    "Ito ay isang kapaki-pakinabang na karagdagan sa makabuluhang mga visualization kapag sinusuri ang mga totoong datos.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Paunawa**:  \nAng dokumentong ito ay isinalin gamit ang AI translation service na [Co-op Translator](https://github.com/Azure/co-op-translator). Bagama't sinisikap naming maging tumpak, pakitandaan na ang mga awtomatikong pagsasalin ay maaaring maglaman ng mga pagkakamali o hindi pagkakatugma. Ang orihinal na dokumento sa kanyang orihinal na wika ang dapat ituring na opisyal na sanggunian. Para sa mahalagang impormasyon, inirerekomenda ang propesyonal na pagsasalin ng tao. Hindi kami mananagot sa anumang hindi pagkakaunawaan o maling interpretasyon na maaaring magmula sa paggamit ng pagsasaling ito.\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": "2025-09-06T19:40:24+00:00",
+   "source_file": "3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb",
+   "language_code": "tl"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/translations/tr/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb b/translations/tr/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb
new file mode 100644
index 00000000..7bd0b22e
--- /dev/null
+++ b/translations/tr/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb
@@ -0,0 +1,100 @@
+{
+ "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": [
+    "## 🔍 Pearson Korelasyon Analizi\n",
+    "\n",
+    "Bu notebook, birden fazla özelliğin ilişkisini Pearson korelasyonu kullanarak analiz etmenin nasıl yapılacağını göstererek veri görselleştirme modülünü geliştirir.\n",
+    "\n",
+    "Pearson korelasyonu, iki sürekli değişken arasındaki **doğrusal ilişkinin** gücünü ve yönünü ölçer. -1 ile 1 arasında bir değer döndürür:\n",
+    "- **+1** → Mükemmel pozitif ilişki  \n",
+    "- **0** → Doğrusal ilişki yok  \n",
+    "- **-1** → Mükemmel negatif ilişki\n",
+    "\n",
+    "Burada, Matematik, Fizik, Kimya ve Biyoloji derslerindeki öğrenci notları için korelasyon matrisini hesapladık. Ortaya çıkan **ısı haritası**, her bir ders notunun diğerleriyle ne kadar güçlü bir şekilde ilişkili olduğunu görsel olarak anlamamıza yardımcı olur.\n",
+    "\n",
+    "Bu tür korelasyon analizi, özellikle makine öğrenimi modelleri oluşturmadan önce, **özellik mühendisliği ve ön işleme** aşamalarında çok önemlidir. Şunları belirlemeye yardımcı olur:\n",
+    "- Gereksiz özellikler\n",
+    "- Güçlü tahmin ediciler\n",
+    "- Potansiyel çoklu bağlantılılık\n",
+    "\n",
+    "Gerçek dünya veri setlerini analiz ederken anlamlı görselleştirmelere değerli bir katkı sağlar.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Feragatname**:  \nBu belge, [Co-op Translator](https://github.com/Azure/co-op-translator) adlı yapay zeka çeviri hizmeti kullanılarak çevrilmiştir. Doğruluk için çaba göstersek de, otomatik çevirilerin hata veya yanlışlıklar içerebileceğini lütfen unutmayın. Belgenin orijinal dili, yetkili kaynak olarak kabul edilmelidir. Kritik bilgiler için profesyonel insan çevirisi önerilir. Bu çevirinin kullanımından kaynaklanan yanlış anlama veya yanlış yorumlamalardan sorumlu değiliz.\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": "2025-09-06T19:39:05+00:00",
+   "source_file": "3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb",
+   "language_code": "tr"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/translations/tw/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb b/translations/tw/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb
new file mode 100644
index 00000000..983bb69e
--- /dev/null
+++ b/translations/tw/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb
@@ -0,0 +1,100 @@
+{
+ "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": [
+    "## 🔍 皮爾森相關性分析\n",
+    "\n",
+    "此筆記本增強了數據可視化模組，展示如何使用皮爾森相關性分析多個特徵之間的關係。\n",
+    "\n",
+    "皮爾森相關性衡量兩個連續變數之間**線性關係**的強度和方向。它返回一個介於 -1 和 1 之間的值：\n",
+    "- **+1** → 完美的正相關  \n",
+    "- **0** → 無線性關係  \n",
+    "- **-1** → 完美的負相關\n",
+    "\n",
+    "在這裡，我們計算了數學、物理、化學和生物學的學生成績的相關矩陣。生成的**熱圖**幫助我們直觀地了解每個科目成績之間的相關性強度。\n",
+    "\n",
+    "這種相關性分析在**特徵工程和預處理**中至關重要，尤其是在建立機器學習模型之前。它有助於識別：\n",
+    "- 冗餘特徵\n",
+    "- 強預測因子\n",
+    "- 潛在的多重共線性\n",
+    "\n",
+    "在分析真實世界數據集時，這是一個有助於生成有意義可視化的實用工具。\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**免責聲明**：  \n本文件已使用 AI 翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 進行翻譯。我們致力於提供準確的翻譯，但請注意，自動翻譯可能包含錯誤或不準確之處。應以原始語言的文件作為權威來源。對於關鍵資訊，建議尋求專業人工翻譯。我們對因使用此翻譯而引起的任何誤解或錯誤解讀概不負責。\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": "2025-09-06T19:37:42+00:00",
+   "source_file": "3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb",
+   "language_code": "tw"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/translations/uk/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb b/translations/uk/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb
new file mode 100644
index 00000000..0145d768
--- /dev/null
+++ b/translations/uk/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb
@@ -0,0 +1,100 @@
+{
+ "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": [
+    "## 🔍 Аналіз кореляції Пірсона\n",
+    "\n",
+    "Цей ноутбук розширює модуль візуалізації даних, демонструючи, як аналізувати взаємозв’язок між кількома ознаками за допомогою кореляції Пірсона.\n",
+    "\n",
+    "Кореляція Пірсона вимірює силу та напрямок **лінійного зв’язку** між двома безперервними змінними. Вона повертає значення в діапазоні від -1 до 1:\n",
+    "- **+1** → Ідеальний позитивний зв’язок  \n",
+    "- **0** → Відсутність лінійного зв’язку  \n",
+    "- **-1** → Ідеальний негативний зв’язок\n",
+    "\n",
+    "Тут ми обчислили матрицю кореляції для оцінок студентів з математики, фізики, хімії та біології. Отримана **теплова карта** допомагає візуально зрозуміти, наскільки сильно оцінка з одного предмета пов’язана з іншими.\n",
+    "\n",
+    "Такий аналіз кореляції є важливим у **інженерії ознак та попередній обробці даних**, особливо перед створенням моделей машинного навчання. Він допомагає визначити:\n",
+    "- Надлишкові ознаки\n",
+    "- Сильні предиктори\n",
+    "- Потенційну мультиколінеарність\n",
+    "\n",
+    "Це корисне доповнення до змістовних візуалізацій під час аналізу реальних наборів даних.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Відмова від відповідальності**:  \nЦей документ було перекладено за допомогою сервісу автоматичного перекладу [Co-op Translator](https://github.com/Azure/co-op-translator). Хоча ми прагнемо до точності, звертаємо вашу увагу, що автоматичні переклади можуть містити помилки або неточності. Оригінальний документ мовою оригіналу слід вважати авторитетним джерелом. Для критично важливої інформації рекомендується звертатися до професійного людського перекладу. Ми не несемо відповідальності за будь-які непорозуміння або неправильні тлумачення, що виникли внаслідок використання цього перекладу.\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": "2025-09-06T19:41:55+00:00",
+   "source_file": "3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb",
+   "language_code": "uk"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/translations/ur/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb b/translations/ur/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb
new file mode 100644
index 00000000..4454a1da
--- /dev/null
+++ b/translations/ur/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb
@@ -0,0 +1,100 @@
+{
+ "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": [
+    "## 🔍 پیئرسن تعلق کا تجزیہ\n",
+    "\n",
+    "یہ نوٹ بک ڈیٹا ویژولائزیشن ماڈیول کو بہتر بناتی ہے اور یہ دکھاتی ہے کہ متعدد خصوصیات کے درمیان تعلق کو پیئرسن تعلق کے ذریعے کیسے تجزیہ کیا جا سکتا ہے۔\n",
+    "\n",
+    "پیئرسن تعلق دو مسلسل متغیرات کے درمیان **لکیری تعلق** کی طاقت اور سمت کو ماپتا ہے۔ یہ -1 سے 1 کے درمیان ایک قدر واپس کرتا ہے:\n",
+    "- **+1** → مکمل مثبت تعلق  \n",
+    "- **0** → کوئی لکیری تعلق نہیں  \n",
+    "- **-1** → مکمل منفی تعلق\n",
+    "\n",
+    "یہاں، ہم نے ریاضی، فزکس، کیمسٹری، اور بائیولوجی میں طلباء کے اسکورز کے لیے تعلق میٹرکس کا حساب لگایا۔ نتیجے میں بننے والا **ہیٹ میپ** ہمیں بصری طور پر سمجھنے میں مدد دیتا ہے کہ ہر مضمون کے اسکورز ایک دوسرے سے کتنے مضبوطی سے جڑے ہوئے ہیں۔\n",
+    "\n",
+    "ایسا تعلق تجزیہ **فیچر انجینئرنگ اور پری پروسیسنگ** میں بہت اہم ہے، خاص طور پر مشین لرننگ ماڈلز بنانے سے پہلے۔ یہ مدد کرتا ہے:\n",
+    "- غیر ضروری خصوصیات کی شناخت  \n",
+    "- مضبوط پیش گوئی کرنے والے عوامل  \n",
+    "- ممکنہ ملٹی کولینیئرٹی  \n",
+    "\n",
+    "یہ حقیقی دنیا کے ڈیٹا سیٹس کا تجزیہ کرتے وقت بامعنی ویژولائزیشنز میں ایک مفید اضافہ ہے۔\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**ڈسکلیمر**:  \nیہ دستاویز AI ترجمہ سروس [Co-op Translator](https://github.com/Azure/co-op-translator) کا استعمال کرتے ہوئے ترجمہ کی گئی ہے۔ ہم درستگی کے لیے کوشش کرتے ہیں، لیکن براہ کرم آگاہ رہیں کہ خودکار ترجمے میں غلطیاں یا عدم درستگی ہو سکتی ہیں۔ اصل دستاویز، جو اس کی مقامی زبان میں ہے، کو مستند ذریعہ سمجھا جانا چاہیے۔ اہم معلومات کے لیے، پیشہ ور انسانی ترجمہ کی سفارش کی جاتی ہے۔ اس ترجمے کے استعمال سے پیدا ہونے والی کسی بھی غلط فہمی یا غلط تشریح کے لیے ہم ذمہ دار نہیں ہیں۔\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": "2025-09-06T19:37:18+00:00",
+   "source_file": "3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb",
+   "language_code": "ur"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ No newline at end of file
diff --git a/translations/vi/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb b/translations/vi/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb
new file mode 100644
index 00000000..cfe46d7a
--- /dev/null
+++ b/translations/vi/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb
@@ -0,0 +1,100 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "b1e38707",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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XcxYBAADgFMEiAAAAnCJYBAAAgFMEiwAAAPfh119/NdUMChQoYEpmOSpflpDWX61evbqp5KCVDfTymwnp1ZW0ekNAQIDUqVPHVNJIWNmgf//+plqElgHTKhxaw9eWXqlMS7RpwX2tnqDVKu5WvswRgkUAAID7EB4ebsqkaXCXFFrKTQM4LZ22fft2U++0V69edrVptS6sXk5W67Ru3brVnF8vy2l7udSBAweaklfffPONqZF6+vRpUzrNQuvT6uNonV29YISWbNOgVC9zmxyshgYAAEglXl5epqav1tF1Rq+Y9OOPP5oLIVjoJU21Fu+yZcvMvmYSa9WqZYrlK70ClV4KVC+Jqldb0vqywcHB5vrzTzzxhPWqVHqlMK1P+9BDD5nL3mrtWg0i8+XLZ9ro1cX08S9cuGDquyYFmUUAAIAE9CpcepWu6zab5cpc90uDuYSXQ9WsoR5XmgnUCwjYttErg+m+pY3eHh0dbddGLzahlwy1tNF/9apilkDR8jj6XHbv3u15V3DJXKRzWncBbtR+bt+07gLc6MT1O5ezQ8Zwdnria47jwXVwRc8HMnZ4tUcZc2UjWzokfL9XuFJ6+VHbAE7pvgZxejUtvZKYDiE7amO5pr2eQzODOXLkSNRGb7vb41hu87hgEQAAIL3QS8bqnEFbuhglIyJYBAAAHsnLy3Wz6TQwdFVwGBISkmjVsu4HBQVJ5syZzXXvdXPURu9rOYcOV+s8R9vsYsI2CVdQW85paZMUzFkEAABwo7p168qqVavsjq1cudIcVzq8XKNGDbs2usBF9y1t9HZfX1+7Nvv37zelcixt9N9du3bZraDWx9GgtHz58knuL5lFAADgkbzSSc7r5s2bcujQIbvSOFoSJ1euXGbBiQ5pnzp1SubOnWtu79u3r1nlPHToUOnRo4esXr1avv76a7NC2kKHwLt16yY1a9aU2rVry5QpU0yJnu7du5vbs2fPLj179jTt9HE0ANSV0hog6kpo1bx5cxMUPvvsszJhwgQzT3HkyJGmNmNysqYEiwAAAPfhr7/+MjUTLSxzHTXY07qGZ86cMRk/i2LFipnAUOskTp06VQoVKiSffvqpWals0bFjR1PeRmsiapBXtWpVU1bHdsHK+++/b1ZJazFuXamt9//www+tt+tQ9g8//CD9+vUzQWRgYKDp07hx4zyzziKroTMWVkNnLKyGzlhYDZ2xpOVq6Kyh3Vx27pvH5rjs3J6GzCIAAPBIrlzggn/wKgMAAMApMosAAMBjL60H1yOzCAAAAKfILAIAAA9FzssdeJUBAADgFJlFAADgkVgN7R68ygAAAHCKzCIAAPBIZBbdg1cZAAAATpFZBAAAHsmLnJdbECwCAACPxDC0e/AqAwAAwCkyiwAAwCORWXQPXmUAAAA4RWYRAAB4JDKL7sGrDAAAAKfILAIAAI/kJV5p3YUMgcwiAAAAnCKzCAAAPBJzFt2DYBEAAHgkgkX34FUGAACAU2QWAQCARyKz6B68ygAAAHCKzCIAAPBQ5LzcgVcZAAAATpFZBAAAHok5i+7BqwwAAACnyCwCAACPRGbRPQgWAQCAR/JigNQteJUBAADgFJlFAADgkRiGdg9eZQAAADhFZhEAAHgkLy+vtO5ChkBmEQAAAE6RWQQAAB6JOYvuwasMAAAAp8gsAgAAj0SdxXQcLMbGxsrnn38uq1atkvPnz0tcXJzd7atXr06t/gEAADjEMHQ6Dhb/85//mGCxdevWUrFiRVYjAQAAPKBSFCx+9dVX8vXXX0urVq1Sv0cAAABJQGbRPVL0Kvv5+UnJkiVTvzcAAADw/GBx8ODBMnXqVImPj0/9HgEAACRxgYurNqRgGPrxxx9PtIjl559/lgoVKoivr6/dbYsXL07qaQEAAJCOJTl0zp49u93Wvn17adSokeTJkyfRbQAAAC6ncxZdtSXTjBkzJDQ0VAICAqROnTqyadMmp22jo6Nl3LhxUqJECdO+SpUqsmzZMrs2N27ckJdfflmKFi0qmTNnlnr16snmzZvtn76Xl8Ptvffes7bRPiW8/Z133nFNZnH27NnJOjGcq1+7rAzs+6hUr1Rc8ufLKU/1miTfr/grrbuFZDq/Zo2cW7lCoq9dk8yFCkmRTp0lsFgxh23jY2PkzM/L5NKGPyT66lUJCAmRgu0fl+wVK1rbxEZEyOmlS+Xq9m0SfeOGZClcWAp37CSBoaFufFZwpn1ofulcsqDk8veTw9fDZcquw7L36k2HbX28vOTZUoWkZeG8kifAX07cvC0f7Tkqmy5ctbbJ7OMjvcoWkYb5c0tOf185cC1cPvj7iOxzck6kT7UqhUivJytJhVK5JV/uQOk35hf55Y/jad0tuNnChQtl0KBBMnPmTBMoTpkyRVq0aCH79++XvHnzJmo/cuRImT9/vnzyySdStmxZWb58uUnC/fHHH1KtWjXTplevXvL333/LvHnzpECBAqZ9s2bNZM+ePVKwYEHT5syZM3bn1RHfnj17SocOHeyOa2Dau3dv6362bNmS9fxSNCj/r3/9S65e/eeXnsX169fNbbi7wCz+smtPmLw8clZadwUpdHnzZjn57TeSv/WjUm7ESMlSqLAc/GCqRF+/7rD9qSVL5eJvv5qAssKYsRLcsKEcnvmR3AoLs7Y5PneuXN+7R0K795Dyo0ZLUPnycuD9yRJ15Yobnxkc+VeBPPJihWLy+f4w6bVumxy6Fi6THqooOfzsp+BY9C5bVNoWDZEpu47Is2u2yNLjZ+St2uWkVFCgtc2rVUtKreAc8sbWA9Jt7TbZfOGqvF+3ouQJ8HPjM8P9yhyQSfYduSxjp29I665k2NXQrtqSY/LkySYY6969u5QvX94EjVmyZJFZsxx/zmsA+Nprr5mqMsWLF5d+/fqZrydNmmRuv337tixatEgmTJggDRs2NIuKx4wZY/796KOPrOcJCQmx25YuXSpNmjQx57SlwaFtu8DAf34XuSxYXLt2rURFRSU6HhERIb/99ltKTpmhrFi7Q8ZO/Fq+W0420VOd+2Wl5GnQQPLUry+ZCxSQIl26iLefn1z643eH7S9v/FNCWj4i2StVEv/gYAlu1NhkFc+tXGluj4uKkivbtkqhDh0kW+nSEpA3rxRo09b8e2HdOjc/OyTUsURB+T7srPx04rwcu3lbJu48JBGxsdK6SD6H7VsUDpZ5B0/Kn+evyJlbkbLk2FnZcO6KdCp5Jxvg5+0tjfLnkY/2HJMdl6/LqfAImb0/zPz7WGiIm58d7sevm0/K+59vkZW/k01MC86GYVNjS6qoqCjZsmWLyfpZeHt7m/0NGxz/EREZGWmGn23pUPP69evN1zExMeYCKHdrk9C5c+fkxx9/NJnFhHTYOXfu3CZrqUPUen6X1VncuXOn9WtNg549e9a6r09Kx9stqVHgQRUXE2MygvkfecR6zMvbW7KVLSc3jxxxeh/vBAvBvH395ObhQ+breL0KUlyceGWyb+Pl62ttg7SRyctLSmfPKvMPnrAe0zoQf128KhVyOh7K8fX2lqgEV7bS/Uq5gqzD1Jm8vRK1iYyNlcq5mPcNpAca0Olmy9/f32y2Ll68aGKgfPns/3jU/X379okjOkSt2UjNGuq8Rb0ini4O1vNYMoF169aV8ePHS7ly5cy5vvzySxN8OitdOGfOHHO/hAuSX3rpJalevbrkypXLDHMPHz7cDF/r47skWKxatao14nY03KwR77Rp05JzSsDjxNy8aQK7TNnufPBb+AZlk4iz9vNHLILKVzDZyKylSpnM4o19+0wmUf6//JRPQIAEFi8uZ376UQLy5xffoCC5vGmThB85Iv4O5rvAfbL7+ZrA7nJktN3xK5HRUjRrFof32XT+inQsXkB2XLpmsoU1gnNIw5Dc4v3/2YrbsbGy6/J16Va6iBy7sV+uREZJs0LBUiFXkJwKv+2W5wU8CFxZ4ubtt9+WsWPH2h0bPXq0GQ6+X1p+UIetdb6ixlQaMOoQtu2wtQ5V9+jRwyThfHx8TMDXuXNnk8V0RO/bpUuXRNlInUtpUblyZVMru0+fPub5JQx8UyVYPHr0qKmtqGPhusonODjYeps+uE7i1CeUkmg9Pj5WvLzufV/AExXu2FGOz5sru0eP0nETEzDmqVdfLtoMWxfr0UOOzZkju14dqmMYkqVIEclVq7bcCmN4y9PoQpWhVUrJ/H/VMH8PnL51W346cc5u2FrnKg6vWkqWtKgtMXHxcuDaTVl16oLJYgJIe5qBsw20lKPgSqvCaOyjw8C2dF/nBzqi8dOSJUvM9L1Lly6ZBSzDhg2zm2uoAeS6deskPDzcrAnJnz+/dOzYMdF8RKVTAHUxjS60uRddgKPD0MeOHZMyZcpIqgeLunxbxSUYOkmNaN0nqIL4Zq90X+cF3CFT1qwmmIu5Yb+YJfr6DfF1UjrKN1s2KflCf4mLjjaZSd8cOeTU4sXinyePtY1/cF4p88oQiY2MlLiI2+KbPYcc+e9/xc+mDdzvWlS0CeZy+dtPEdAVzJciEs/dVlejYuS1zXvFz9tLgvx85WJElPQtFyqnwyOsbU7fipABf+ySAB9vCczkI5cio2VMjTJy5tY/bQCk3eX+HA05O6LJsho1apih5Mcee8waJ+n+iy++KHejWUDNHGopHV3Q8tRTTyVqo4tRdLty5YpZNa2LXhL67LPPTB+0BM+9bN++3cypdLRKO1WvDW07bzEsLCzRYpe2bdsmO1rPW6HX/XQFcBvvTJlM1u/63n2So2o165zDG/v2St4mTe5+X19f8cuZ05TSubptq+SsUTNRGx9/f7PF6F+Te3ZLwcftSyDAvWLi72T9auTJIb+dvWyO6WCy7i8+6njagUVUXLwJFHWOYqMCuWXNqYuJ2kTExpktq6+P1M6b05TYAeBZBg0aJN26dZOaNWtK7dq1TekczQjq0LLq2rWrCQo1WaY2btwop06dMtP79F8d2tYAc+jQodZzamCoo7ma/Tt06JAMGTLEDFtbzmmhWcdvvvnGupLals5x1MfSFdI6n1H3Bw4cKM8884zkzJnTtcHikSNHTD2gXbt2mbF2y2X/LKuHLBM0kxOtZ6QhaC2dU8JmxWNo4WCpXL6oXLl6U06cvpSmfUPS5Gv2bzn2+WwJDC0qWUKLyflVv5gVzbnr1Te3H509S/xy5DC1FFX40SMSdeWqqZ0YdfWqnPn+e/Nzk69FC+s5r+3ebeYwag3GyPPn5eSib83XeerXS7PniTsWHj4lr1UrLfuu3ZS9V27Ik8ULmDqJOrSsRlQrLRcjIuXjvXemDJTPkVXyZPaXg9duSnCAv/QoU8RcQOyLQyet56wdnMP8eyL8thQMzCwvlA+VsBu35Kew82n0LJESWQIySdEC/8xfLhSSVcoVzyVXb0TKmQvhadq3DCEZq5ZdqWPHjnLhwgUZNWqUWfyrQaAu+rUsetHEmmbzLHT4WWstajyVNWtWUzZH5yjmyHHn94K6du2aSa6dPHnSLE7R2olvvvlmoqvmffXVV+bzROczJqSxlt6uwahO/ytWrJgJFhMm7O7FKz4FF3hu06aNGZ//9NNPzQPr/EUdc9drRk+cOFEefvjh5J5SMhdJ/CQfVA8/VE5WfD0q0fF536yT5wfPlIyg/dy+4unOr1kt51asMLUV7xTl7iSBxe7MJdk/aaL4584toc/d+QvwxoH9EvbFFxJ54YJ4+/ubEjoaSGpAaXH5r7/k1P8Wm6LdPlmySM7q1aXgY4+JT2bHiyg8yYnrnv/H4OM2RbkPXQ+XqbsOy57/L6D9Qb1KcvZWhLy1/aDZr5o7SAZXLin5swTI7ZhYU0Jn5p5jcinyn1GYJgXySJ9yRU0weSM6RtaeuSif7D0u4TF3/2PbE5ydvl8yitqVQ2TBxNaJji9ecUBenZgxSskdXJG4VIu7lK79ocvOfWDTCy47t6dJUbCokzn12tC6qkYv76fBoqZJ9ZgGjNu2bUt2RzJSsIgHI1hExgoWkXQZKVhEGgeLD7kwWPyTYNEiRTNDdZjZcqkYDRxPnz5tXQCjq3EAAADcMgztqg33N2exYsWKsmPHDjMErUuwdWWOrgb673//63BJNwAAADJQsKiTMnWVj9ISODqHUecp6qVkdCIlAACAy5EBTL/Bol6mxqJUqVLmcjaXL182y7CTcz1FAAAAPEDBol52JilsL1cDAADgEq6ryY2UBouff/65WcRSrVo1a21FAAAAPLiSFSz269dPvvzyS3ONaK0grhXAtVAkAACAu8Uz9S39JXBnzJghZ86cMZej+f7776Vw4cLmOoaWS9IAAAAgg4/266Vj9JIyK1euNNeGrlChgrzwwgsSGhoqN2/euZoBAACAy3m5cMP9rYa20OscWq4Nfa/rQQMAAKQqb6K6dJlZ1AtR67zFf//731K6dGnZtWuXTJ8+3VwkWy+GDQAAgAyaWdThZi26rXMVtYyOBo16uT8AAAC3Y4FL+gsWZ86cKUWKFDGX9Fu3bp3ZHFm8eHFq9Q8AAACeEix27dqVK7QAAID0gZAkfRblBgAAQMZxX6uhAQAA0gyrod2CqyoCAADAKTKLAADAM7GOwi0IFgEAgGciVnQLhqEBAADgFJlFAADgmVjg4hZkFgEAAOAUmUUAAOCZSCy6BZlFAAAAOEVmEQAAeKR4Sue4BZlFAAAAOEVmEQAAeCZWQ7sFmUUAAAA4RWYRAAB4JhKLbkGwCAAAPBMLXNyCYWgAAAA4RWYRAAB4Jha4uAWZRQAAADhFZhEAAHgmEotuQWYRAAAATpFZBAAAnonV0G5BZhEAAABOkVkEAACeicyiWxAsAgAAz8T4qFvwMgMAAMApMosAAMAzMQztFmQWAQAA4BSZRQAA4JlILLoFmUUAAAA4RbAIAAA8Ury3l8u25JoxY4aEhoZKQECA1KlTRzZt2uS0bXR0tIwbN05KlChh2lepUkWWLVtm1+bGjRvy8ssvS9GiRSVz5sxSr1492bx5s12b5557Try8vOy2li1b2rW5fPmydOnSRYKCgiRHjhzSs2dPuXnzZrKeG8EiAADAfVi4cKEMGjRIRo8eLVu3bjXBX4sWLeT8+fMO248cOVI+/vhjmTZtmuzZs0f69u0r7du3l23btlnb9OrVS1auXCnz5s2TXbt2SfPmzaVZs2Zy6tQpu3NpcHjmzBnr9uWXX9rdroHi7t27zbl++OEH+fXXX+X5559P1vPzio+Pj5d0IHORzmndBbhR+7l907oLcKMT133Sugtwo7PT96d1F+BGB1f0TLPHLvG0fWCUmg5/kfS4pE6dOlKrVi2ZPn262Y+Li5PChQvLgAEDZNiwYYnaFyhQQEaMGCH9+/e3HuvQoYPJIM6fP19u374t2bJlk6VLl0rr1q2tbWrUqCGPPPKIvPHGG9bM4tWrV2XJkiUO+7V3714pX768yUjWrFnTHNMMZqtWreTkyZOmH0lBZhEAAHgmLxduSRQVFSVbtmwxWT8Lb29vs79hwwaH94mMjDTDz7Y0UFy/fr35OiYmRmJjY+/axmLt2rWSN29eKVOmjPTr108uXbpkvU0fX4eeLYGi0n5p/zZu3Jjk50iwCAAA4CCgu379ut2mxxK6ePGiCezy5ctnd1z3z5496/DcOkQ9efJkOXjwoMlC6hDx4sWLzTCy0qxi3bp1Zfz48XL69Glzfs04avBnaWMZgp47d66sWrVK3n33XVm3bp3JPGp7pY+vgaStTJkySa5cuZz2zRGCRQAA4Jl0IYqLtrfffluyZ89ut+mx1DB16lQpVaqUlC1bVvz8/OTFF1+U7t27m4yfhc5V1JmCBQsWFH9/f/nggw+kc+fOdm06deokbdu2lUqVKsljjz1m5iTqkLNmG1MTwSIAAEACw4cPl2vXrtlteiyhPHnyiI+Pj5w7d87uuO6HhIQ4PHdwcLCZZxgeHi7Hjx+Xffv2SdasWaV48eLWNrpSWjOFunL5xIkTZnW1rqK2bZOQ3qb9OXTokNnXx0+4yEaHuHWFtLO+OUKwCAAAPPdyfy7aNJun5WaCbDY9lpBmBnXhiQ4FW+jQsu7rUPLd6JxEzRxqALdo0SJp165dojaBgYGSP39+uXLliixfvtxhGwtdtKJzFrW90sfXBTA6p9Ji9erVpn+6KCepuIILAADAfRg0aJB069bNLCSpXbu2TJkyxWQNdWhZde3a1QSFlmFsXVyiJXCqVq1q/h0zZowJ4IYOHWo9pwaGOgytC1c0UzhkyBAzbG05p2Ycx44da1ZRa5bw8OHD5v4lS5Y0cyJVuXLlzLzG3r17y8yZM01mUoe8dfg6qSuh01WwSCmVjOV/XWemdRfgRrfDxqZ1F+BGJb4MS+suIKNIJ5f769ixo1y4cEFGjRplFo5oEKglaiyLXsLCwuzmGkZERJhai0eOHDHDz1rKRuco6splC8uwt2YLdUGKBoVvvvmm+Pr6mtt16Hvnzp0yZ84ckz3U4E9rMeqiGNsM6IIFC0yA2LRpU9MHPY/Of/TIOotPr12X1l2AGxEsZiwEixlLic5/pXUX4EaHv3w6zR67RLeFLjv34TkdXXZuT5NuMosAAADJkoLL8iH5CBYBAIBnIlh0C1ZDAwAAwCkyiwAAwCPFk1h0CzKLAAAAcIrMIgAA8EzMWXQLMosAAABwiswiAADwTHppPrgcmUUAAAA4RWYRAAB4JuYsugXBIgAA8EyMj7oFLzMAAACcIrMIAAA8Ewtc3ILMIgAAAJwiswgAADwTC1zcgswiAAAAnCKzCAAAPFI8cxbdgswiAAAAnCKzCAAAPBMpL7cgWAQAAJ6JBS5uQUwOAAAAp8gsAgAAz8QCF7cgswgAAACnyCwCAADPxJxFtyCzCAAAAKfILAIAAM9EYtEtyCwCAADAKTKLAADAI8UzZ9EtCBYBAIBnIlh0C4ahAQAA4BSZRQAA4Jkoyu0WZBYBAADgFJlFAADgmUh5pd+X+fbt23Lr1i3r/vHjx2XKlCmyYsWK1OwbAAAAPDFYbNeuncydO9d8ffXqValTp45MmjTJHP/oo49Su48AAACO5yy6asP9BYtbt26Vhx9+2Hz97bffSr58+Ux2UQPIDz74ICWnBAAAwIMyZ1GHoLNly2a+1qHnxx9/XLy9veWhhx4yQSMAAIDLUWcx/WYWS5YsKUuWLJETJ07I8uXLpXnz5ub4+fPnJSgoKLX7CAAA4DhYdNWG+wsWR40aJa+88oqEhoaa+Yp169a1ZhmrVauWklMCAADgQRmGfuKJJ6RBgwZy5swZqVKlivV406ZNpX379qnZPwAAAIfiWYiSfoPFa9euiZ+fX6Isog5PZ8pE6UYAAIAMPQzdqVMn+eqrrxId//rrr81tAAAAboliXLXBKkUvx8aNG6VJkyaJjjdu3NjcBgAAgAdDisaMIyMjJSYmJtHx6Ohoc3UXAAAAl2POYvrNLNauXVv++9//Jjo+c+ZMqVGjRmr0CwAAAJ4aLL7xxhvy6aefSsOGDWXs2LFm069nzZolb731Vur3EgAAIB3XWZwxY4YpKRgQEGDKCm7atMlpWx2JHTdunJQoUcK018oyy5Yts2tz48YNefnll6Vo0aKSOXNmqVevnmzevNnuHK+++qpUqlRJAgMDpUCBAtK1a1c5ffq03Xm0T15eXnbbO++84/pgsX79+rJhwwYpXLiwWdTy/fffm5XQO3futF4GEAAAICNYuHChDBo0SEaPHm0uiazBX4sWLczFShwZOXKkfPzxxzJt2jTZs2eP9O3b15Qe3LZtm7VNr169ZOXKlTJv3jzZtWuXuQBKs2bN5NSpU9ar6eljvf766+bfxYsXy/79+6Vt27aJHk8DUy13aNkGDBiQrOfnFR8fHy/pwNNr16V1F+BG/+s6M627ADe6HTY2rbsANyrR+a+07gLc6PCXT6fZYxd9b7XLzn18yL+S3LZOnTpSq1YtmT59utmPi4szCTUNyoYNG5aovWYBR4wYIf3797ce69Chg8kgzp8/36z/0MsqL126VFq3bm1to1P9HnnkETPC64hmHnWqoF56uUiRItbMomYodUupJGcWr1+/bvf13TYAAACX83Ldpot5E8Y3kZGRiboQFRUlW7ZsMVk/C29vb7Ovo7CO6Hl0+NmWBorr1683X+si4tjY2Lu2cVYHW4eZc+TIYXdch51z585t6mO/9957Dhcpp0qwmDNnTms6VTuh+wk3y3EAAABP9vbbb0v27NntNj2W0MWLF01gly9fPrvjun/27FmH59Yh6smTJ8vBgwdNFlKHm3UYWYeIlWYV9VLK48ePN3MQ9fyacdTg09ImoYiICDOHsXPnzhIUFGQ9/tJLL5na2GvWrJE+ffqYtSVDhw51Temc1atXS65cuczX+oBI7PyaNXJu5QqJvnZNMhcqJEU6dZbAYsUcto2PjZEzPy+TSxv+kOirVyUgJEQKtn9cslesaG0TGxEhp5culavbt0n0jRuSpXBhKdyxkwSGhrrxWeF+1a9dVgb2fVSqVyou+fPllKd6TZLvVzBM52kWLPhRPvtssVy4cEXKli0mr7/eRypXLu2wbXR0jHz88TeyZMlqOXfukhQrVlBeeeU5adjwn2oR+st/2rQv5bvv1sjFi1clb95c0r59U3nhhY4mMwDPUKtssPR+tLxULJ5T8uXMIn0n/Sor/zqZ1t3KMOJTsBAlqYYPH27mIdry9/eX1DB16lTp3bu3lC1b1vy860KX7t27m4XCFjpXsUePHlKwYEHx8fGR6tWrm0BQs5gJ6WKXp556SnRm4UcffWR3m+1zqFy5srkCnwaNGvgm9fkkOVhs1KiRw69xx+XNm+Xkt99Ikae7mADx/KpVcvCDqVJh7DjxtYnwLU4tWSqXN22Uos88awLF63t2y+GZH0nZoa9Klv+fZ3B87ly5ffqUhHbvIb45csjljX/KgfcnS4UxY8WPDK7HCMziL7v2hMnchWtl4SeD07o7SIGffvpN3n77Uxk7tr9UqVJa5sz5Tnr2HCXLls2U3Lnth3vUlCnzTRD4xhsDpHjxQvLbb1vlxRffkq++miDly5cwbT75ZJF8+eVP8u67A6VkySLy99+HZPjwqZItWxbp2jXxBHWkT1n8M8m+sCvy7drD8tHghmndHaQiDaSSEkzlyZPHBHPnzp2zO677ISEhDu8THBwsS5YsMdnAS5cumTmMOrexePHi1jYaQK5bt07Cw8PNEHj+/PmlY8eOdm1sA0Wdp6iJPdusorP5lToMfezYMSlTpoy4bDW0Lu+2HTPX5eJVq1aVp59+Wq5cuSIZ0blfVkqeBg0kT/36krlAASnSpYt4+/nJpT9+d9heA7+Qlo9I9kqVxD84WIIbNTZZxXMrV5rb46Ki5Mq2rVKoQwfJVrq0BOTNKwXatDX/XljHYiBPsmLtDhk78Wv5bjnZRE81e/YSeeqpFtKhQzMT2I0d+4IEBPjLokV3fl4TWrp0jfTt+5Q0alRTChcOkaefbiWNGtWQWbOWWNts27ZXmjZ9SBo3riWFCuWTli3rS4MGVWXnzoNufGa4X+t2nJHJX++UFWQT04Zm4V21JZGfn59ZeLJq1SrrMR1a1n0dSr4bnZOomUMN3hYtWiTt2rVL1EbL4migqPHV8uXL7dpYAkUdzv7ll1/MvMR72b59u5lTmTdv3iQ/xxQFi0OGDLEuZNHl3JribNWqlRw9ejRRyjYjiIuJkVthYRJUrpz1mJe3t2QrW05uHjni9D7evr52x7x9/eTm4UPm6/i4OP1uE69M9m28fH2tbQC4XlRUtOzefUjq1atiPaa/aOvVqyrbtu13eB/9Be7nZ/+zqxmKrVv3WPerVSsnf/65Q44evVMGY9++o7Jly167oWoAnmHQoEHyySefyJw5c2Tv3r3Sr18/kxHUoWWl9Q91WNtCL42scxSPHDkiv/32m7Rs2dIEmLZzCTUw1OScxlY6p1Evs6zD1pZz6u+ZJ554Qv766y9ZsGCBmdqicyR100U3Suc4TpkyRXbs2GEeS9sNHDhQnnnmmWStMUnR5f604+XLlzdfayTcpk0bM2FS6/xo0JjRxNy8aQK7TNnsU7++Qdkk4qzjiahB5SuYbGTWUqVMZvHGvn0mkyj/X8nIJyBAAosXlzM//SgB+fOboezLmzZJ+JEj4p+MvwYA3J8rV65LbGyc5M5t/4tVh5+PHHGcTWrQoJp8/vkSqVWrohQpEiIbNuyQlSv/MOexeP75J+TmzVvyyCP9xMfH29w2cOCz0rZtY5c/J+CB4cI5i8nRsWNHuXDhgowaNcoEazraqoGeZdFLWFiY+SPTQoeftdaiBnBZs2Y1sZPOUbRdxawrmzXAPHnypFkzoqV13nzzTfH9/0ST1lv87rvvzNf6eLZ0bUnjxo3NH6m6uGXMmDFmBXaxYsVMsJjcxF6KgkVNuWoxSKVpT42YlT6ZpJTO0Q4nXH4eGxUlPn5+klEU7thRjs+bK7tHjzLpbg0Y89SrLxdthq2L9eghx+bMkV2vDtVUhpnLmKtWbbkVdjxN+w7g7kaMeF5GjpxmAkEdzSpcOL88/ngzWbToF2ubn39eL99/v04mTXrFDG3v3XvEzIu0LHQB4FlefPFFszmydu1au31d+6HFuO9Gh5d1c0brJ96rVLYuivnzzz/lfqUoWGzQoIGJSvVKLno5G61crg4cOCCFChW65/11BY5eItBWxW7dpNJzd1KrniZT1qwmmIu5YR8oR1+/Ib7Zszu8j2+2bFLyhf4SFx1tMpO6gOXU4sXinyePtY1/cF4p88oQiY2MlLiI2+KbPYcc+e9/xc+mDQDXypkzyGT+Ll2yn4996dJVyZPH8TBOrlzZ5cMPR0pkZJRcvXrDBIATJ86RwoX/Ka0xYcJsk11s3frOoogyZULl9OkLZhU1wSKQROkjsfjAS9GcRa1QnilTJvn222/NEm2dnKl+/vlnM+5+L5pW1fSq7Vb+6S7iqbwzZTJZv+t791mP6ZzDG/v2StYEq5YS3dfX987K5rhYubptq+SoYp9KVj7+/iZQjNEVUXt2O2wDwDV07mGFCiVlw4ad1mM6t0iHlqtVu/tKQn9/P8mXL7fExMTKihV/mAUtFhERkYlK5GhQmk4uqgV4BB3ZddWG+8ws6iVkfvjhh0TH33///RQvR/f0Ieh8zf4txz6fLYGhRSVLqJbO+cWsaM5dr765/ejsWeKXI4eppajCjx6RqCtXTe3EqKtX5cz335sPiXwtWljPeW33bjOHUUvrRJ4/LycXfWu+zlO/Xpo9T6SsdE6J0H/KJ4QWDpbK5YvKlas35cTpS2naNyRN9+6Pyauvvi8VK5Y0tRXnzFkqt29HmKFlNXToZBMUDh7czezv2LHf1FcsV664+XfatC9MgNmr152ff9WkSS2ZOfNrKVAg2DoMrauuO3T4d5o9T6SsdE7RkKzW/ULBgVKuaA65ejNKzly6M10LyJDBoo619+zZU5588klz6RmI5KpVS2Ju3pDT330n0devm6LcpV56yVpjMeryZbssgg4/n/5uqUReuCDe/v6mhE5ojx6SKUsWa5vY27fl1P8Wm6LdPlmySM7q1aXgY4+Jl0+K3jakkeqVi8uKr0dZ9yeMvjPHd9436+T5wVwj2xO0avWwXL58TT74YIEpyq1B4KefjrUOQ585c0G8bSba6/Cz1lo8ceKsZMkSYEroTJgwSIKC/gkqRo7sI1OnLpCxYz+SS5eumaHqjh1bSv/+ndLkOSJlKhXPJV+M+ucybyO73lnNvmjdERk68/7niuHuqF/vHl7xKRjz0ItRf/HFF2aRik6+1MDxoYf+GV5JiafXUjswI/lfV4KkjOR2mP0cZTzYSnSmpmhGcvjLp9PssYvNcF3scLQ/FyCxSNGovNbs0WsVzp4921wvumHDhqaUzsSJExNVMAcAAHhAa3JnCCmewqkLXB5//HFZunSpqQGkV295/fXXpXDhwvLYY4+ZS84AAADAs933eh8tnTN69GiZNGmSuXSMrnTW6yQ++uij8sorr6ROLwEAABLQtQCu2vCPFK2U0KFnrTSuw9B6PUK9gsuXX34pLVq0sL7Azz33nCmjo0PTAAAAyEDBohbeLlGihPTo0cMEhcHBwYnaVK5cWWrVqpUafQQAAEiEBGA6DhZXrVolDz/88F3bBAUFmWsTAgAAuALBYjqes3ivQBEAAAAZOFjU8jjPPvusFChQwKyK9vHxsdsAAABczcvbdRvucxha5ymGhYWZUjn58+dn1RAAAMADKkXB4vr16+W3336TqlWrpn6PAAAAkoBclXukKNGqhbdTcJVAAAAAZJTL/Q0bNkyOHTuW+j0CAABIAm8v121IwTB0zpw57eYmhoeHm1qLWbJkEV9fX7u2ly9fTuppAQAA8CAEi5pNBAAASC+Ys5jOgsVu3bpJbGysuXzfd999J1FRUdK0aVNzXejMmTO7tpcAAAAJECymwzmLb731lrz22muSNWtWKViwoEydOlX69+/vut4BAADAc4LFuXPnyocffijLly+XJUuWyPfffy8LFiyQuLg41/UQAADAAV1L4aoNKQwWtRB3q1atrPvNmjUzL+jp06eTcxoAAAA8iEW5Y2JiJCAgwO6YroSOjo5O7X4BAADcFZflS4fBohbi1kv9+fv7W49FRERI3759JTAw0Hps8eLFqdtLAAAApP9gUVdEJ/TMM8+kZn8AAACShKmF6TBYnD17tut6AgAAAM8OFgEAANILMovuQbAIAAA8EsGie7COCAAAAE6RWQQAAB7Jm8yiW5BZBAAAgFNkFgEAgEdizqJ7kFkEAACAU2QWAQCARyKz6B5kFgEAAOAUmUUAAOCRvFgO7RYEiwAAwCMxDO0eDEMDAADAKTKLAADAI5FZdA8yiwAAAHCKzCIAAPBIZBbdg8wiAAAAnCKzCAAAPBKVc9yDzCIAAMB9mjFjhoSGhkpAQIDUqVNHNm3a5LRtdHS0jBs3TkqUKGHaV6lSRZYtW2bX5saNG/Lyyy9L0aJFJXPmzFKvXj3ZvHmzXZv4+HgZNWqU5M+f37Rp1qyZHDx40K7N5cuXpUuXLhIUFCQ5cuSQnj17ys2bN5P13AgWAQCAx85ZdNWWHAsXLpRBgwbJ6NGjZevWrSb4a9GihZw/f95h+5EjR8rHH38s06ZNkz179kjfvn2lffv2sm3bNmubXr16ycqVK2XevHmya9cuad68uQkGT506ZW0zYcIE+eCDD2TmzJmyceNGCQwMNI8bERFhbaOB4u7du825fvjhB/n111/l+eefT97rHK9haTrw9Np1ad0FuNH/us5M6y7AjW6HjU3rLsCNSnT+K627ADc6/OXTafbYDZaud9m517drkOS2derUkVq1asn06dPNflxcnBQuXFgGDBggw4YNS9S+QIECMmLECOnfv7/1WIcOHUx2cP78+XL79m3Jli2bLF26VFq3bm1tU6NGDXnkkUfkjTfeMFlFPc/gwYPllVdeMbdfu3ZN8uXLJ59//rl06tRJ9u7dK+XLlzcZyZo1a5o2msFs1aqVnDx50tw/KcgsAgAApFBUVJRs2bLFZP0svL29zf6GDRsc3icyMtIMP9vSQHH9+jvBb0xMjMTGxt61zdGjR+Xs2bN2j5s9e3YTuFoeV//VoWdLoKi0vfZPM5FJRbAIAAA8kiuHoTWgu379ut2mxxK6ePGiCew0o2dL9zWYc0SHiidPnmzmF2oWUoeIFy9eLGfOnDG3a1axbt26Mn78eDl9+rQ5v2YcNfiztLGc+26Pq//mzZvX7vZMmTJJrly5nPbNEYJFAACABN5++22Tqctus+mx1DB16lQpVaqUlC1bVvz8/OTFF1+U7t27m4yfhc5V1KHmggULir+/v5mb2LlzZ7s27kKwCAAAPJKXl5fLtuHDh5s5gNdsNj2WUJ48ecTHx0fOnTtnd1z3Q0JCHPY7ODhYlixZIuHh4XL8+HHZt2+fZM2aVYoXL25toyul161bZ1Yunzhxwqyu1lXUljaWc9/tcfXfhItsdIhbV0g765sjBIsAAAAJaDZPy80E2Wx6LCHNDOrCk1WrVlmP6dCy7utQ8t3onETNHGoAt2jRImnXrl2iNrrCWUvjXLlyRZYvX25tU6xYMRPw2T6uDpXrXETL4+q/V69eNXMqLVavXm36p3Mbk4qi3AAAwCOll8v9DRo0SLp162YWktSuXVumTJlisoY6tKy6du1qgkLLMLYGdFoCp2rVqubfMWPGmABu6NCh1nNqYKjD0GXKlJFDhw7JkCFDzLC15Zya/dQ6jLoyWoe0NXh8/fXXzQrnxx57zLQpV66ctGzZUnr37m3K62hmUoe8daV0UldCK4JFAACA+9CxY0e5cOGCKZCtC0c0CNQSNZbFJ2FhYXZzDbUOotZaPHLkiBl+1lI2OkdRVy5bWIa9tcSNLkjR0jpvvvmm+Pr6WttocKlBqdZN1AxigwYNzOParqJesGCBCRCbNm1q+qDn0fmPyUGdRaQJ6ixmLNRZzFios5ixpGWdxcY//u6yc69tXd9l5/Y0ZBYBAIBHSi/D0A86FrgAAAAg/WcWT1z3SesuwI0YlsxYMhcZndZdgBuFdOiU1l1ABuFNZtEtyCwCAAAg/WcWAQAAkoPMonuQWQQAAIBTZBYBAIBH8vZKF9X/HnhkFgEAAOAUmUUAAOCRmLPoHgSLAADAIzE86h68zgAAAHCKzCIAAPBILHBxDzKLAAAAcIrMIgAA8EgscHEPMosAAABwiswiAADwSGS83IPXGQAAAE6RWQQAAB6JOYvuQWYRAAAATpFZBAAAHsmLOotuQbAIAAA8EsPQ7sEwNAAAAJwiswgAADwSGS/34HUGAACAU2QWAQCAR/JmgYtbkFkEAACAU2QWAQCAR2I1dDrOLM6ePVtu3bqV+r0BAACA5weLw4YNk5CQEOnZs6f88ccfqd8rAACAJAQxrtrwjxS9HqdOnZI5c+bIxYsXpXHjxlK2bFl599135ezZsyk5HQAAQIqGoV214T6DxUyZMkn79u1l6dKlcuLECendu7csWLBAihQpIm3btjXH4+LiUnJqAAAApCP3nWnNly+fNGjQQOrWrSve3t6ya9cu6datm5QoUULWrl2bOr0EAABwUDrHVRtSIVg8d+6cTJw4USpUqGCGoq9fvy4//PCDHD161AxTP/XUUyZoBAAAQAYrndOmTRtZvny5lC5d2gxBd+3aVXLlymW9PTAwUAYPHizvvfdeavYVAADAirmF6ThYzJs3r6xbt84MPTsTHBxssowAAADIQMFidHS0HDt2TPLkyXPXdl5eXlK0aNH76RsAAIBTlLhJp6+zr6+v7Ny50zW9AQAAgOcH5c8884x89tlnqd8bAACAJGI1dDqesxgTEyOzZs2SX375RWrUqGEWtNiaPHlyavUPAADAIRa4pONg8e+//5bq1aubrw8cOJDafQIAAIAnB4tr1qxJ/Z4AAAAkA5nFdDxnsUePHnLjxo1Ex8PDw81tAAAAyMDB4pw5c+T27duJjuuxuXPnpka/AAAA7hnEuGpDCoeh9ZJ+8fHxZtPMYkBAgPW22NhY+emnn0zBbgAAAGTAYDFHjhym2LZueqm/hPT42LFjU7N/AAAADlHixj28k7uwZdWqVSaz+O2338rq1aut2/r16yUsLExGjBjhut4CAACkQzNmzJDQ0FAz6lqnTh3ZtGnTXa+GN27cOClRooRpX6VKFVm2bJldGx2xff3116VYsWKSOXNm03b8+PEmBrOwJPASbu+99561jfYp4e3vvPOO6zKLjRo1Mv/qNZ+LFCliHhAAACAjr4ZeuHChDBo0SGbOnGkCxSlTpkiLFi1k//79DqfnjRw5UubPny+ffPKJlC1bVpYvXy7t27eXP/74Q6pVq2bavPvuu/LRRx+ZdSIVKlSQv/76S7p37y7Zs2eXl156ybQ5c+aM3Xl//vln6dmzp3To0MHuuAamvXv3tu5ny5YtWc8vRXM49+7dK7///rtdNF21alV5+umn5cqVKyk5JQAAgEcucJk8ebIJxjSYK1++vAkas2TJYi5g4si8efPktddek1atWknx4sWlX79+5utJkyZZ22jg2K5dO2ndurXJDj7xxBPSvHlzu4xlSEiI3bZ06VJp0qSJOactDQ5t2yW8mEpSXudkGzJkiFnsonbt2mWiaX2SmnHUrwEAADxZZGSkiXWu22x6LKGoqCjZsmWLNGvWzHrM29vb7G/YsMHpuW0XCSsdatYpfRb16tUzU/8sFz/ZsWOHuf2RRx5xeM5z587Jjz/+aDKLCemwc+7cuU3WUoeo9Up8Li/KrUGhRs5q0aJF0qZNG3nrrbdk69atJmgEAADw5GHot99+O9Gi3dGjR8uYMWPsjl28eNHML8yXL5/dcd3ft2+fw3PrELVmIxs2bGjmImpQuHjxYnMei2HDhpkAVYepfXx8zG1vvvmmdOnSxeE5dbhaM4iPP/643XEdstar7uXKlctkK4cPH26Gr5NzaeYUBYt+fn5y69Yt87VeH7pr167ma+2IJeMIAADgqTSoSjha6u/vnyrnnjp1qhm21kBQ139owKhD2LbD1l9//bUsWLBAvvjiCzNncfv27fLyyy9LgQIFpFu3bonOqffVQDJhxtL2OVSuXNnEcH369DHBcFKfT4qCxQYNGpgHr1+/vhk714mdSlOlhQoVSskpAQAAksXLhaVzNJBKSjCVJ08ek/nTYWBbuq/zAx0JDg6WJUuWSEREhFy6dMkEgJpJtJ1rqFP+9FinTp3MfqVKleT48eMmyEsYLP72229mMY0lHrsbXYCjw9DHjh2TMmXKiMvmLE6fPl0yZcpkyufoSp2CBQtaV+G0bNkyJacEAADwOH5+flKjRg0zlGwRFxdn9uvWrXvX+2oWUGMoDd50Wp8uaLHQEVyd+2hLg1I9d0KfffaZ6YOW4LkXzVDqeZNzEZUUZRa1bM4PP/yQ6Pj777+fktMBAAB4bOmcQYMGmWxfzZo1pXbt2qZ0Tnh4uBlaVjpdT4NCzQqqjRs3yqlTp0wlGf1X50FqEDh06FDrOXU9iM5R1JhLh6G3bdtm5hn26NHD7rF1+t8333xjt5LaQhfY6GPpCmmdz6j7AwcOlGeeeUZy5syZ+sGidiYoKMj69d1Y2mU07UPzS+eSBSWXv58cvh4uU3Ydlr1Xbzps6+PlJc+WKiQtC+eVPAH+cuLmbfloz1HZdOGqtU1mHx/pVbaINMyfW3L6+8qBa+Hywd9HZJ+Tc8K9Fiz4UT77bLFcuHBFypYtJq+/3kcqV058ZSMVHR0jH3/8jSxZslrOnbskxYoVlFdeeU4aNqxhbaOTl6dN+1K++26NXLx4VfLmzSXt2zeVF17oSE1TD1K/dlkZ2PdRqV6puOTPl1Oe6jVJvl/xV1p3C8n0bP1i8vy/SkpwNn/Ze/q6jFm8U3aE/fP72VYmby/p16yUdKhVREKyB8iR8zflnR/2yK/7zlvbBPpnkkGPlJUWlfJL7qz+svvUNRn3v12y84Tjc8KzdOzYUS5cuCCjRo2Ss2fPmiBQi2xbFr3oRUtss4Q6/Ky1Fo8cOSJZs2Y1i4O1nI5eKc9i2rRppij3Cy+8IOfPnzdD1TrXUB/D1ldffWUKdXfu3DlRv3QYXW/XYFRXYGuBbw0Wk1u5xivethT4XWjqU1fPaNpSn7CjDy89lR63Xc2TVA9/989ycU/0rwJ5ZES10jJp5yHZc+WGPFm8oDQpkEeeXr1FrkZFJ2rft1yoNC8ULBN2HJLjN29Jnbw55cUKxaTfbzvl4PVw02ZMjTJSPFsWmbTzsFyMjJLmhfLKU8ULyLNrtsrFiCjxZL+19exriP/0028ydOhkGTu2v1SpUlrmzPlOli1bL8uWzZTcuf/5Ybd4773PTRD4xhsDpHjxQvLbb1vlnXc+k6++miDly5cwbWbO/Fpmz14i7747UEqWLCJ//31Ihg+fKgMHPiNdu7YVT5a5yGjJKJo3riJ1a5aRbbuOyMJPBmfIYDGkw505Vp6qddUCMqlLdRn5zU7ZfvyK9GhUXFpVKSBN314ll24m/t376qPl5bEahWT419vl8Pmb0rBMXhnZrqJ0+OA32XPqmmkzrWtNKZ0/m7z+zU45dz3CtO/RqIQ0f3e1nLsWIZ7s6Pv/DJ2624i//hn6TW1v1mzqsnN7miRnFvWSfrra2XLZP9jrWKKgfB92Vn46cecvyYk7D0ndfDmldZF8suDQyUTtWxQOlrkHTsqf5+8UMV9y7KzUyJNDOpUsKOO3HhA/b29plD+PvLZpj+y4fCeTO3t/mNTPl0seCw2RT/eFufkZwpYGdU891UI6dLhTV2vs2Bdk7drNsmjRSnn++ScTtV+6dI306/eUNGpU0+w//XQr2bBhu8yatUQmThxsjm3btleaNn1IGjeuZfYLFconP/64TnbuPOjW54b7s2LtDrPBc/VqXFIWbjgu326683t2xDc7pEm5fPJknaIyc1Xin8f2NQvLjJUHZO3eO7//F/xxTOqXDpbejUvIwAVbxd/XW1pWzi/Pz9okm45cMm2mLt8vTSuEyDP1QmXSz47Lq+DeuDZ0OgsWLZf6S/g1RDJ5eUnp7Fll/sET1mP67fvXxatSIafjS+r4entLVIJJqrpfKVeQdZhahzYStomMjZXKubK75HkgaaKiomX37kPSp88T1mOaba9Xr6ps27bf6XVA/fx8Ew0PbN26x7pfrVo5+frr5XL06CkzTL1v31HZsmWvDBuWuMAqANfw9fGSioWyy4e/3CmErHT87feDF6R6UcdzvPwyeUtkjP2IWmR0rNQsntt8ncnbWzL5eJtjtiJs2gDpWYoWuFjG23fu3GnG0ROuzGnb1rOHzJIru5+vCewuR9oPN1+JjJaiWbM4vM+m81ekY/ECsuPSNTkVHiE1gnNIw5Dc4v3/w/u3Y2Nl1+Xr0q10ETl2Y79ciYySZoWCpUKuIDkVftstzwuOXblyXWJj4yR3bvsPDh1+PnIkcRZZNWhQTT7/fInUqlVRihQJkQ0bdsjKlX+Y81g8//wTcvPmLXnkkX7i4+Ntbhs48Flp27axy58TgDtyBvqbwO7iDfsrdeh+ibyO//jXuYk9G5eQTYcvyfFL4VK/VLC0qJxfvP9/9UV4ZIxsOXpZBjQvI4fO3ZSLNyKkbfVCUj00lxy/eGfaETx7gcuDLkXBok7a1JU9WrU8oaTMWdRJlgkvmRMXHSXevn6SUehClaFVSsn8f9Uwf7WevnVbfjpxzgxbW7yx9YAMr1pKlrSoLTFx8XLg2k1ZdeqCyWLCs4wY8byMHDnNBIL690Dhwvnl8cebyaJFv1jb/Pzzevn++3UyadIrZs7i3r1H5O23P7UudAGQPulClbc7VpVfhjc1c/fDLt2SbzedkCdrF7G2GbRgi0zoVE02jm0hMbFxsvvkNfl+60mpWDjxHGfggQgWBwwYIE8++aRZkZPw8jYpvYRO4U7dpWhn++XgnuJaVLQJ5nL52w8z6grmS04WolyNipHXNu8VP28vCfLzNQtWdNHL6fB/JjqfvhUhA/7YJQE+3hKYyUcuRUabRS9nbnn2ZGhPlzNnkMn8Xbp0Z76pxaVLVyVPHsfDVLlyZZcPPxwpkZFRcvXqDRMATpw4RwoX/ufnZ8KE2Sa72Lp1Q7NfpkyonD59wayiJlgE3ONKeKQJ5vJksy/GrPsXrjv+3Xs5PEr6zNpkhqNzBvqZBSu66CXs8j9ZQw0gO834XTL7+UjWgExy4XqkWfQSdonM4v0gs+geKSrKrVXJddl1SgJFyyV0rl27ZrcVfuIZ8VQx8XeyfrpAxUK/f3V/95Ubd71vVFy8CRR1jmKjArll/dnLidpExMaZQDGrr4/UzptTfjt7Z4I00obOPaxQoaRs2LDTekynYujQcrVqd6+G7+/vJ/ny5ZaYmFhZseIPs6DFIiIiMlGVAQ1Kk1iwAEAqiI6Nl79PXjMLVCz0x7JeqWDZetz+D8SEomLiTKCo05J0QcvKXWcTtbkdFWsCxaDMvtKwbF755e/EbYAHIrP4xBNPyNq1a821DFPrEjqePgS98PApea1aadl37absNaVzCpg6iTq0rLSszsWISPl473GzXz5HVsmT2V8OXrspwQH+0qNMEfEWL/nCZuV07eA7weeJ8NtSMDCzvFA+VMJu3JKfwv6p3YW00b37Y/Lqq+9LxYolTW3FOXOWyu3bEWZoWWlZHQ0KBw++c0mmHTv2m/qK5coVN/9Om/aFCTB79frngu9NmtQy5XMKFAi2DkPrqusOHf6dZs8TyReYxV9KhP5zia/QwsFSuXxRuXL1ppw4zR96nuDTtYdk0tPVTQ3EHaZ0TgnJ4ucj3268szpabzt77ba89+Nes1+1SE7Jlz1A9py+Zuos/qdFWTNf8ePV/6ycblgm2ESdWoMxNE+gDG9bQQ6fuyHf/P85kTI+ad2BDCJTSi/3p8PQei1CvVahr6/98OtLL70kGc3q0xclh5+v9CxTxBTlPnQ9XF7582+zyEXly+xvlyHy8/GW3mWLSv4sAXI7JtaU0NGSOTdtVtQF+maSPuWKmmDyRnSMrD1zUT7Ze1xiyTSluVatHpbLl6/JBx8sMEW5NQj89NOx1mHoM2cuWCe3Kx1+njJlvpw4cVayZAkwJXQmTBgkQUH/zD8dObKPTJ26QMaO/UguXbpmhqo7dmwp/ft7ds26jKZ65eKy4ut/iuZOGN3V/Dvvm3Xy/OCZadgzJNWP20+bwtmDWpaVPEH+svfUdXnu4z/l4s07c+0L5MwscTa/h7U0zuBW5aRI7ixmMYuW0Bm0YKvciIixtsmW2VeGtC4vITkC5NqtaFm247RM/GmvmcIEpHdJLsqd8BqEffv2Ndc0zJ07t93QmX6tFckzWlFuZKyi3EiejFSUG55flBueU5T7re0rXXbu16oyqnNfmcURI0aYBSrDhg1LdJFrAAAAd2CBi3ukKNKLiooy10EkUAQAAHiwpSja69atmyxcuDD1ewMAAJCMzKKrNtznMLQW3Z4wYYIsX75cKleunGiBy+TJk1NyWgAAADwIweKuXbukWrVq5uu///7b7raEdeIAAABcwYeQI/0Gi2vWrEn9ngAAACDdua8VKocOHTJD0bdv3zb7XGkCAAC4C3MW03GweOnSJWnatKmULl1aWrVqJWfOnDHHe/bsKYMHD07tPgIAAMCTgsWBAweaRS1hYWGSJUsW63Etp7Ns2bLU7B8AAIBD3l7xLttwn3MWV6xYYYafCxUqZHe8VKlScvz4nWsfAwAAuBLDxek4sxgeHm6XUbS4fPmy+Pv7p0a/AAAA4KnB4sMPPyxz5861K5cTFxdnai82adIkNfsHAADgkI8LN9znMLQGhbrA5a+//jKX/hs6dKjs3r3bZBZ///33lJwSAAAAD0pmsWLFinLgwAFp0KCBtGvXzgxLP/7447Jt2zYpUaJE6vcSAAAgAUrnpOPMosqePbuMGDEidXsDAACAByNYvHr1qmzatEnOnz9v5iva6tq1a2r0DQAAwClK3KTjYPH777+XLl26yM2bNyUoKMjuetD6NcEiAABABp6zqFdp6dGjhwkWNcN45coV66aLXAAAAFzNx8t1G+4zs3jq1Cl56aWXHNZaBAAAcAcWoqTjzGKLFi1M2RwAAAA82JKcWfzuu++sX7du3VqGDBkie/bskUqVKpnrRNtq27Zt6vYSAAAgATKL6SxYfOyxxxIdGzduXKJjusAlNjb2/nsGAAAAzwkWE5bHAQAASEtkFtPhnMXVq1dL+fLl5fr164luu3btmlSoUEF+++231OwfAAAAPCVYnDJlivTu3dvUVnR0RZc+ffrI5MmTU7N/AAAADvl4xbtsQwqDxR07dkjLli2d3t68eXPZsmVLck4JAACAB6XO4rlz5xKtfLY7WaZMcuHChdToFwAAQOrX/4NrX+eCBQvK33//7fT2nTt3Sv78+ZPfCwAAAHh+sNiqVSt5/fXXJSIiItFtt2/fltGjR8ujjz6amv0DAABwuhraVRtSOAw9cuRIWbx4sZQuXVpefPFFKVOmjDm+b98+mTFjhqmvOGLEiOScEgAAIEUI6tJhsJgvXz75448/pF+/fjJ8+HCJj4+3FuLWSwBqwKhtAAAAkAGDRVW0aFH56aef5MqVK3Lo0CETMJYqVUpy5szpmh4CAAA4QImbdBosWmhwWKtWrdTtDQAAAB6MYBEAACAtMWfRPShRBAAAAKfILAIAAI9EZtE9yCwCAADcpxkzZkhoaKgEBARInTp1ZNOmTU7bRkdHy7hx46REiRKmfZUqVWTZsmV2bbQcoda2LlasmGTOnNm0HT9+vLUSjXruuedMRRrbLeFlmS9fvixdunSRoKAgyZEjh/Ts2VNu3ryZrOdGZhEAAHik9JJZXLhwoQwaNEhmzpxpAsUpU6aYkoL79++XvHnzOqxbPX/+fPnkk0+kbNmysnz5cmnfvr0pT1itWjXT5t1335WPPvpI5syZIxUqVJC//vpLunfvLtmzZ5eXXnrJei4NDmfPnm3d9/f3t3ssDRTPnDkjK1euNEGqnuP555+XL774IsnPzyveNkRNQw9/tz6tuwA3+q1t4h8ePLgyFxmd1l2AG4V06JTWXYAbHX2/XZo99rKTP7vs3C0LPZLktnXq1DEVYqZPn2724+LipHDhwjJgwAAZNmxYovYFChQwFzHp37+/9ViHDh1MBlGDSKVXxNPa1Z999pnTNppZvHr1qixZssRhv/bu3Svly5eXzZs3S82aNc0xzWDqFflOnjxp+pEUDEMDAAAkEBkZKdevX7fb9FhCUVFRsmXLFmnWrJn1mLe3t9nfsGGD03Pr8LMtDQLXr/8ncVavXj1ZtWqVHDhwwOzv2LHD3P7II/ZB7Nq1a032Uq+qpxdNuXTpkvU2fXwderYEikr7pf3buHFjkl8LgkUAAOCRvL3iXba9/fbbZsg3u82mxxK6ePGimV+Y8Ap2un/27FmH/dYh6smTJ8vBgwdNFlKHiPVyyjpcbKEZyU6dOplhal9fXzM8/fLLL5thZdsh6Llz55qgUoet161bZ4JJ7Y/Sx084DJ4pUybJlSuX0745wpxFAACABPSyxjoP0VbC+YApNXXqVOndu7cJBHVRii5e0bmEs2bNsrb5+uuvZcGCBWZuoc5Z3L59uwkWdei4W7dupo0GkxaVKlWSypUrm3NptrFp06aSWggWAQCAR3Ll8KgGhkkJDvPkySM+Pj5y7tw5u+O6HxIS4vA+wcHBZp5hRESEGTbWAFAzicWLF7e2GTJkiDW7aAkGjx8/brKblmAxIb2/9kcvx6zBoj7++fPn7drExMSYFdLO+uYIw9AAAAAp5OfnJzVq1DBDwRY6tKz7devWvet9dd5iwYIFTQC3aNEiadfun8VCt27dMnMLbWlQqud2RhetaPCZP39+s6+PrwtgdE6lxerVq805dFFOUpFZBAAAHim9lM4ZNGiQyfbpQpLatWub0jnh4eFmaFl17drVBIWWOY+6uOTUqVNStWpV8++YMWNMADd06FDrOdu0aSNvvvmmFClSxAxDb9u2zcxz7NGjh7ldayWOHTvWrJDWLOHhw4fN/UuWLGnmRKpy5cqZeY065K1lfbR0zosvvmiylUldCa0IFgEAAO5Dx44d5cKFCzJq1CizcESDQC1RY1n0EhYWZpcl1OFnrbV45MgRyZo1qyllM2/ePLNy2WLatGmmKPcLL7xghpI1uOvTp495DEuWcefOnaYOo2YP9fbmzZubwt22w+c671EDRB2W1j5ocPnBBx8k6/lRZxFpgjqLGQt1FjMW6ixmLGlZZ3HdmZ9cdu5G+Vu57NyehswiAADwSFriBq7HAhcAAAA4RWYRAAB4pPSywOVBR2YRAAAATpFZBAAAHonMonuQWQQAAED6L51Tqvlnad0FuFFc7sxp3QW4UVxIYFp3AW50dtFXad0FuNHtsC/T7LE3nv/RZeeuk7e1y87tacgsAgAAwCnmLAIAAI/kxZxFtyBYBAAAHolY0T0YhgYAAIBTZBYBAIBHYhjaPcgsAgAAwCkyiwAAwCOR8XIPXmcAAAA4RWYRAAB4JC+vdHFdkQcemUUAAAA4RWYRAAB4JBZDuwfBIgAA8EiUznEPhqEBAADgFJlFAADgkUgsugeZRQAAADhFZhEAAHgkb1KLbkFmEQAAAE6RWQQAAB6JxKJ7kFkEAACAU2QWAQCAR6LOonsQLAIAAI9ErOgeDEMDAADAKTKLAADAI5FZdA8yiwAAAHCKzCIAAPBIFOV2DzKLAAAAcIrMIgAA8EgkFt2DzCIAAACcIrMIAAA8kpdXfFp3IUMgWAQAAB6JYWj3YBgaAAAATpFZBAAAHolrQ7sHmUUAAAA4RWYRAAB4JDJe6fh1Dg0NlXHjxklYWFjq9wgAAACeHSy+/PLLsnjxYilevLj8+9//lq+++koiIyNTv3cAAAB3mbPoqg2pECxu375dNm3aJOXKlZMBAwZI/vz55cUXX5StW7em5JQAAAB40Ib7q1evLh988IGcPn1aRo8eLZ9++qnUqlVLqlatKrNmzZL4eIplAgAA1/By4ZZcM2bMMNP0AgICpE6dOiah5kx0dLSZzleiRAnTvkqVKrJs2TK7NrGxsfL6669LsWLFJHPmzKbt+PHjrbGVnuPVV1+VSpUqSWBgoBQoUEC6du1qYjJb2icvLy+77Z133nHfAhft6P/+9z+ZPXu2rFy5Uh566CHp2bOnnDx5Ul577TX55Zdf5IsvvrifhwAAAHAovQwXL1y4UAYNGiQzZ840geKUKVOkRYsWsn//fsmbN2+i9iNHjpT58+fLJ598ImXLlpXly5dL+/bt5Y8//pBq1aqZNu+++6589NFHMmfOHKlQoYL89ddf0r17d8mePbu89NJLcuvWLTOaqwGlBptXrlyR//znP9K2bVvT1pYGpr1797buZ8uWLVnPzys+Bek/7ZwGiF9++aV4e3ubSLZXr17mCVv8/fffJst4+/btJJ2zVPPPktsNeLC43JnTugtwo7iQwLTuAtzo7KKv0roLcKPbYV+m2WOfCP/eZecuHNgmyW3r1KljYp7p06eb/bi4OClcuLCZpjds2LBE7TULOGLECOnfv7/1WIcOHUwGUYNI9eijj0q+fPnks88+c9omoc2bN0vt2rXl+PHjUqRIEWtmUacP6ubWYWh9QQ4ePGgi3lOnTsnEiRPtAkWladNOnTqluGMAAADpfRg6KipKtmzZIs2aNbMe00Sa7m/YsMHhfXRRsA4/29IgcP369db9evXqyapVq+TAgQNmf8eOHeb2Rx55xGlfrl27ZoaZc+TIYXdch51z585tspbvvfeexMTEuH4Y+siRI1K0aNG7ttHxc80+AgAAeBoN6BJWevH39zebrYsXL5r5hZoFtKX7+/btc3huHaKePHmyNGzY0MxF1KBQq8zoeSw0I3n9+nWTjPPx8TG3vfnmm9KlSxeH54yIiDBzGDt37ixBQUHW4zpkrWtMcuXKZYa5hw8fLmfOnDGP79LM4r0CRQAAAFfz9nLd9vbbb5v5gdltNj2WGqZOnSqlSpUygaCfn5+pJqPzETUjafH111/LggULzNoPnf6ncxd1JFf/dbSG5KmnnjKLX3TU15bOpWzcuLFUrlxZ+vbtK5MmTZJp06Ylq+RhijKLOXPmNGnOhPSYplVLliwpzz33nHniAAAAnkYzcBpo2UqYVVR58uQxmb9z587ZHdf9kJAQcSQ4OFiWLFlisoGXLl0ycxg1k6j1qy2GDBlijlmm9OmqZ52LqAFrt27dEgWKetvq1avtsorO5lfqMPSxY8ekTJky4rLM4qhRo0z027p1axk7dqzZ9Gs9ppM1S5cuLf369TOrfAAAADxtzqIGhhp4BdlsjoJFzQzWqFHDDCVb6AIX3a9bt+5d+68JtoIFC5rgbdGiRdKuXTvrbbra2TbTqDQo1XMnDBR1HYlWoNF5ifeidbL1vI5WaadqZlEnWL7xxhsmnWnr448/lhUrVpgnrOlOrcFou1QbAADgQTNo0CCT7atZs6ZZjaylc8LDw60jrFo1RoNCyzD2xo0bzQJhrUut/44ZM8YEgUOHDrWes02bNmaOoq5q1tI527ZtM/MMe/ToYQ0Un3jiCTNE/cMPP5g5jWfPnjW36fxEDWJ1gY0+VpMmTUy5HN0fOHCgPPPMM2aU2KXBotYD0vo/CTVt2lQGDx5svm7VqpXD5eIAAACpwcsrfVz8o2PHjnLhwgUz8qoBmwaBWmTbsuglLCzMLkuow89aa1EXDGfNmtXETPPmzbNbxazzCrWG4gsvvCDnz583Q9V9+vQxj6E0yPzuu+/M1/p4ttasWWPmKWomVC/JrMGozlHUSjUaLCYcXndJnUWNcvXBdLP1/vvvm01flJ07d0rz5s2tUe69UGcxY6HOYsZCncWMhTqLGUta1lk8e/tOsOQKIZnbuuzcniZFmUWNdHVOokaumm61FIL86aefTPVypVd0adSoUer2FgAA4P+lkwu4PPBSFCzqPMTy5cubSuVaF0jpipp169aZIpLKMhwNAADwIF/u70GX4mtD169f32wAAAB4cKU4WNRVN1ojaO/evWZfV+roxat1WTfurlalEOn1ZCWpUCq35MsdKP3G/CK//HE8rbsFF6lVNlh6P1peKhbPKflyZpG+k36VlX+dTOtuIZmerV9Mnv9XSQnO5i97T1+XMYt3yo6wqw7bZvL2kn7NSkmHWkUkJHuAHDl/U975YY/8uu+8tU2gfyYZ9EhZaVEpv+TO6i+7T12Tcf/bJTtPOD4n0qf6tcvKwL6PSvVKxSV/vpzyVK9J8v2Kv9K6WxkGiUX3SFGdxUOHDkm5cuXMUnAdhtZNl2FrwHj48OHU7+UDJnNAJtl35LKMne74mpF4sGTxzyT7wq7ImFl8gHiq1lULyIjHKsjU5fvl0UnrZO/pazKnT13JndXPYfvBrcrJ03VDTUD573dXy4I/jsnH3WtL+YLZrW3e6VhVGpQJlkELtkrL99bIb/vPy7x+9SRfdvvrxSJ9C8ziL7v2hMnLI2eldVeA9JVZ1OsM6rUM//zzT1PLR2kFcg0Y9bYff/wxtfv5QPl180mzIWNYt+OM2eC5ejUuKQs3HJdvN4WZ/RHf7JAm5fLJk3WKysxVBxO1b1+zsMxYeUDW7r2TSdRgsX7pYOnduIQMXLBV/H29pWXl/PL8rE2y6cgl00YD0aYVQuSZeqEy6WfH15NF+rNi7Q6zwYMyXnBPsKgLWWwDRaVVw9955x3mMQJ4oPj6eEnFQtnlw18OWI9pwbHfD16Q6kUdF7X1y+QtkTGxdscio2OlZvE7V1fI5O0tmXy8zTFbETZtAMCjg3It8njjxo1Ex2/evGkqhgPAgyJnoL8J7C7eiLQ7rvvBQY6HjHVuYs/GJSQ0T6BZrdmgdLC0qJxfgoPuXCosPDJGthy9LAOal5G8QQHi7SXyWI1CUj00l9kHkDT68+WqDfcZLD766KPy/PPPm0vIaE1v3TTTqJf/00Uu96JVxK9fv263xcdFp6QrAJDu6EKVYxfC5ZfhTeXAe21kbIfK8u2mExL/zyVdZdCCLWZy/saxLWT/e23kuYeLy/dbT0pc8q+TAADpbxhar/ms10DUC2T7+vqaY3oRbA0Up06des/767URx44da3csZ/E2krvEPxfQBoD04Ep4pMTExkmebHeygha6f+F6hMP7XA6Pkj6zNpnh6JyBfnLuWoS8+mh5Cbscbm0TdumWdJrxu2T285GsAZnkwvVImda1poRd+qcNgHshBZhug0W9duHSpUvl4MGDsm/fnYnYujq6ZMmSSbr/8OHDE12XsPrjX6SkKwDgUtGx8fL3yWtmgcrKv+9cvlSHqOqVCpa564/e9b5RMXEmUNRSOrqg5cftpxO1uR0Va7agzL7SsGxeeef73S57LsCDxotgMX3XWVSlSpUyW0rmPOpmy8v7ToYyI8gSkEmKFgiy7hcKySrliueSqzci5cwFsgoPYumcoiFZrfuFggOlXNEccvVmlJy5dCtN+4ak+XTtIZn0dHVTA3HH8SvSo1EJyeLnI99uvLM6Wm87e+22vPfjnbqzVYvkNCVw9py+Zuos/qdFWfH29pKPV/+zcrphmWATdWoNRp3bOLxtBTl87oZ88//nhOeUzikRGmLdDy0cLJXLF5UrV2/KidN3VroDGSZYTJgJvJvJkyentD8ZQsXSeWTBxNbW/RF9HzL/Ll5xQF6d+Fsa9gyuUKl4LvliVDPr/siuNcy/i9YdkaEz/0zDniGpNCOohbMHtSwreYL8Ze+p6/Lcx3/KxZt3Fr0UyJnZbq6hlsbRWotFcmcxi1m0hI7WU7wREWNtky2zrwxpXV5CcgTItVvRsmzHaZn4016JiWPOoiepXrm4rPh6lHV/wuiu5t9536yT5wfPTMOeZQxeXhTPcQeveF2dkgRNmjRJ2gm9vGT16tXJ7kip5p8l+z7wXHG5M6d1F+BGcSGBad0FuNHZRV+ldRfgRrfDvkyzx74a9ZPLzp3Dr5XLzv3AZhbXrFnj2p4AAAAkC3MW3eG+87cnT540GwAAAB48KQoW4+LiZNy4cZI9e3YpWrSo2XSF9Pjx481tAAAA7lgN7ar/cJ+roUeMGCGfffaZ3eX91q9fL2PGjJGIiAh58803U3JaAAAAPAjB4pw5c+TTTz+1u1pL5cqVpWDBgvLCCy8QLAIAADcgA5hug8XLly9L2bJlEx3XY3obAACAq1E6xz1S9CpXqVJFpk+fnui4HtPbAAAAkIEzixMmTJDWrVvLL7/8Yq4PrTZs2CAnTpyQn35yXc0jAACAfzAMnW4zi40aNZIDBw5I+/bt5erVq2Z7/PHHZf/+/fLwww+nfi8BAADgWdeGLlCgAAtZAABAmqHETToLFnfu3CkVK1YUb29v8/Xd6MpoAAAAZKBgsWrVqnL27FnJmzev+VqvAe3ostJ6PDY2NrX7CQAAYIfMYjoLFo8ePSrBwcHWrwEAAPDgS3KwqJf0s8iaNavkzp3bfK0roD/55BO5ffu2KdLNAhcAAOAe1FlMd6/yrl27JDQ01AxFawHu7du3S61ateT999+X//73v9KkSRNZsmSJ63oLAABgM/XNVRtSGCwOHTpUKlWqJL/++qs0btxYHn30UVNv8dq1a3LlyhXp06ePuV40AAAAMmDpnM2bN8vq1avName9UotmE/Va0LpCWg0YMEAeeughV/UVAADABhnAdJdZ1Os+h4SEWOctBgYGSs6cOa2369c3btxI/V4CAADAM4pyJxzHZ1wfAACkBUrnpNNg8bnnnhN/f3/zdUREhPTt29dkGFVkZGTq9xAAAACeESx269bNbv+ZZ55J1KZr16733ysAAIB7onROugsWZ8+e7bqeAAAAwPOHoQEAANID5iy6B8EiAADwSCyydQ8G+wEAAOAUmUUAAOChyCy6A5lFAAAAOEVmEQAAeCQvcl5uwasMAAAAp8gsAgAAD8WcRXcgswgAAACnCBYBAIDH1ll01ZZcM2bMkNDQUAkICJA6derIpk2bnLaNjo6WcePGSYkSJUz7KlWqyLJly+zaxMbGyuuvvy7FihWTzJkzm7bjx4+X+Ph4axv9etSoUZI/f37TplmzZnLw4EG781y+fFm6dOkiQUFBkiNHDunZs6fcvHkzWc+NYBEAAHgoLxduSbdw4UIZNGiQjB49WrZu3WqCvxYtWsj58+cdth85cqR8/PHHMm3aNNmzZ4/07dtX2rdvL9u2bbO2effdd+Wjjz6S6dOny969e83+hAkTzH0sdP+DDz6QmTNnysaNGyUwMNA8bkREhLWNBoq7d++WlStXyg8//CC//vqrPP/888l7leNtQ9Q0VKr5Z2ndBbhRXO7Mad0FuFFcSGBadwFudHbRV2ndBbjR7bAv0+yxo+K2uOzcft41kty2Tp06UqtWLRPYqbi4OClcuLAMGDBAhg0blqh9gQIFZMSIEdK/f3/rsQ4dOpjs4Pz5883+o48+Kvny5ZPPPvvMYRsN3/Q8gwcPlldeecXcfu3aNXOfzz//XDp16mSCzPLly8vmzZulZs2apo1mMFu1aiUnT540908KMosAAMBjS+e4aouMjJTr16/bbXosoaioKNmyZYsZArbw9vY2+xs2bHDYbz2PDj/b0iBw/fr11v169erJqlWr5MCBA2Z/x44d5vZHHnnE7B89elTOnj1r97jZs2c3gavlcfVfHXq2BIpK22v/NBOZVASLAAAACbz99tsm+Mpus+mxhC5evGjmF2pGz5buazDniA4VT5482cwv1CykDhEvXrxYzpw5Y22jGUnNDpYtW1Z8fX2lWrVq8vLLL5thZWU5990eV//Nmzev3e2ZMmWSXLlyOe2bI5TOAQAAHsp1pXOGDx9u5iHa8vf3l9QwdepU6d27twkEdTGNLl7p3r27zJo1y9rm66+/lgULFsgXX3whFSpUkO3bt5tgUYeOu3XrJu5EsAgAAJCABoZJCQ7z5MkjPj4+cu7cObvjuh8SEuLwPsHBwbJkyRKzEOXSpUsmANRMYvHixa1thgwZYs0uqkqVKsnx48dNdlODRcu59XF0NbTt41atWtV8rW0SLrKJiYkxK6Sd9c0RhqEBAIBH8nLhf0nl5+cnNWrUMPMLLXRoWffr1q171/vqvMWCBQuaAG7RokXSrl076223bt0ycwttaVCq51ZaUkcDPtvH1XmVOhfR8rj679WrV82cSovVq1ebc+jcxqQiswgAAHAfBg0aZLJ9upCkdu3aMmXKFAkPDzdDy6pr164mKLTMedSA7tSpUyYDqP+OGTPGBHBDhw61nrNNmzby5ptvSpEiRcwwtJbV0XmOPXr0MLfr8LUOS7/xxhtSqlQpEzxqXUbNUj722GOmTbly5aRly5ZmyFvL62h9xxdffNFkK5O6EloRLAIAAI+UkuLZrtCxY0e5cOGCKZCtC0c0CNQSNZbFJ2FhYXZZQh1+1lqLR44ckaxZs5pSNvPmzTMrly20nqIGfy+88IIZStbgrk+fPuYxLDS41KBU6yZqBrFBgwbmcW1XWuu8Rw0QmzZtavqg5Xe0NmNyUGcRaYI6ixkLdRYzFuosZixpWWcxNv5vl53bx6uiy87taZizCAAAAKcYhgYAAB4pOQtRkHJkFgEAAOAUmUUAAOChyCy6A5lFAAAAOEVmEQAAeKT0UjrnQUdmEQAAAE6RWQQAAB6KnJc7ECwCAACPROkc9yAkBwAAQPq/3F9GFBkZaS4qPnz4cPH390/r7sDFeL8zFt7vjIX3Gw8ygsU0dP36dcmePbtcu3ZNgoKC0ro7cDHe74yF9ztj4f3Gg4xhaAAAADhFsAgAAACnCBYBAADgFMFiGtJJ0KNHj2YydAbB+52x8H5nLLzfeJCxwAUAAABOkVkEAACAUwSLAAAAcIpgEQAAAE4RLHqA5557Th577LG07kaG9/nnn0uOHDlcdv61a9eKl5eXXL161WWPgX/oa71kyRK3Py7vc/p07Ngx875s3749yfdp3LixvPzyyy7tF5AeECymQiCnv2D69u2b6Lb+/fub27SNq35ZwTXvp25+fn5SsmRJGTdunMTExLj8sevVqydnzpwxV4HA/Tt79qwMGDBAihcvblaoFi5cWNq0aSOrVq1K034l530msHTNz7ZuuXPnlpYtW8rOnTvN7fr9oe9LxYoV07qrQLpDsJgK9JfMV199Jbdv37Yei4iIkC+++EKKFCmSpn1D8ukHiH5oHDx4UAYPHixjxoyR9957z+WPq8FpSEiI+SDD/dE/vGrUqCGrV682792uXbtk2bJl0qRJE/NHXFpyxfscFRWVaufKCD/buukfDZkyZZJHH33U3Obj42PeFz0GwB7BYiqoXr26CRgXL15sPaZfa6BYrVo16zH9sGrQoIEZytS/avWX1OHDh623FytWzPyr99EPEh3isDVx4kTJnz+/ua9+4EVHR7vl+WU0moXSD42iRYtKv379pFmzZvLdd99Zb1++fLmUK1dOsmbNav3wUb/++qv4+vqajJYtHaZ6+OGHzdfHjx832a2cOXNKYGCgVKhQQX766SenWaTff//dfB9kyZLF3KdFixZy5coVc9u3334rlSpVksyZM5vvCe1neHi4W16j9O6FF14wr+WmTZukQ4cOUrp0afNaDxo0SP78809ru4sXL0r79u3N61uqVCm791n9/fff8sgjj5j3Ol++fPLss8+a+1joe6PZS32P9f3RNp988ol5H7p37y7ZsmUz2emff/7Zep+E77Oz7wkNeDW4VXqb7SiFPu6LL75oHjdPnjzm+6JHjx7WwMdCf0fkzZtXPvvsMxe90p75s61b1apVZdiwYXLixAm5cOGCw5GddevWSe3atc399Hevtr/bKIP+bHbt2tW8X/o9pd87+kenLf3+0M8LvV2/9yZPnmyd3qJ98Pb2lr/++svuPlOmTDG/j+Li4lL9NQGSgmAxlegv6tmzZ1v3Z82aZT4sbOkHiH5Y6S8C/atWfynoLwvLLwD9YFO//PKLCUBsg881a9aYwFL/nTNnjpk/pxtcT4MxS+bm1q1bJmifN2+eCQ7DwsLklVdeMbc1bNjQDHnqbbYf1gsWLDDfH0qD/MjISHNfzXa9++67JhBxRD+0mjZtKuXLl5cNGzbI+vXrTVARGxtrvj86d+5szrt3714TgDz++ONC2VSRy5cvmz/M9LXW4Csh23mnY8eOlaeeesoMRbZq1Uq6dOli7q80mPvXv/5l/njTn1k957lz50x7W/rzqAGb/vxq4Kh/YDz55JNmuHnr1q3SvHlzE2Tq944jzr4nNKBYtGiRabN//37znk+dOtXucTVLqX9QzJw5U3r16mX6aPnjRf3www/mcTt27JgKr+yD5ebNmzJ//nwTzOsfWwmdOnXKfE/UqlVLduzYIR999JEJut944w2n59RgXr9X9I8O/ZnVn0c9h+UPe32vdMrSf/7zH/Pz/e9//1vefPNN6/1DQ0PNH322nyVK9/Xc+pkBpAktyo2U69atW3y7du3iz58/H+/v7x9/7NgxswUEBMRfuHDB3KZtHNHb9S3YtWuX2T969KjZ37ZtW6LHKFq0aHxMTIz12JNPPhnfsWNHFz+7jPt+qri4uPiVK1ea9/WVV16Jnz17tnl/Dh06ZG0/Y8aM+Hz58ln333333fhy5cpZ9xctWhSfNWvW+Js3b5r9SpUqxY8ZM8bhY69Zs8ac/8qVK2a/c+fO8fXr13fYdsuWLaatfq/B3saNG81rs3jx4ru20zYjR4607ut7pMd+/vlnsz9+/Pj45s2b293nxIkTps3+/fvNfqNGjeIbNGhgvV1/RgMDA+OfffZZ67EzZ86Y+2zYsMHh+5yc7wkLfdxq1aolal++fHnzPWjRpk2b+Oeee+6ur0NG+tn28fEx749u+rrmz5/f/Cw5+v372muvxZcpU8b8HrD9edef59jYWOv78J///Md8feDAAXP/33//3dr+4sWL8ZkzZ47/+uuvzb7+zm7durVdv7p06RKfPXt26/7ChQvjc+bMGR8REWH2tX9eXl6mf0Ba4c+UVBIcHCytW7c22T79K1C/1myDLR2O0GyQZp+CgoLMX5FKs1P3okNTOqfGQodEzp8/74JnAs3GaGYnICDADCNpVkbnLSodOipRooTT90H/+j906JB1qFO/HzQTZclwvfTSSyYzUb9+fXNpMMvk+rtlFh2pUqWKuU2HoTWLpUNbluHpjC452dXKlStbv9b3SH8uLe+nZpM0k6/fC5atbNmy5jbb6SO259CfUc1S6ftioUPTytnPa3K+J2zpnMyENLtoyUppFlSHvy1ZbYgZ1tefK900E6zD9/ozrlMBEtKMfd26de3mlup7pBnJkydPOmyv8x3r1KljPabfC2XKlDG3WTLEOqxtK+G+Vr7Q76P//e9/1t8h2m/L5wWQFggWU5H+UtYfbB0ecvQLWocQdYhLP9g3btxotqROTte5cLb0FxjzV1z7gaLBvS5a0vfTEuw5eh9sgxOdH6bvs35gO/qw1g/zI0eOmGFJHXKsWbOmTJs2zenwtzP6YbJy5Upzfh2m1nPoh9LRo0clo9O5h/q+7Nu3775+rjQo0PfSElxYNv2+0CkHdzuH7TFLsOHs5zU53xO2HA2x63w5PZcOgeoQq86DtsyXxZ3XTIedddPh5U8//dRMD9LfyemFTi3Q91F/h+hngy6UJOBHWiNYTEW62EF/uHV+iv7FauvSpUvmr8qRI0eajJAukEiYCdJfEkrnpCHtP1B0gVJKVkbqh//ChQvlv//9r8lCajbCls5F03lLOidVV1s7+6DSjNXdyrxoEKLn1nl327ZtM98/lmxERpYrVy7z8zdjxgyHC36SWoZGF67t3r3bZHQsAYZlcxSo3Q9n3xPJ/Z2gmSzNTGmgoX+4Jpw3jcQ/QzoP0LaShYX+jrbMO7TQOYe6aKlQoUIO2+viF0sSwPb3vv5Bp/QPus2bN9vdL+G+5XeIzl3/8MMPzTl1PjKQlggWU5Fme3S4Yc+ePXZDxkpXx+kvcg0gdJhSS3roYhdbmpXSbJJlIv21a9fc/AyQGjRQ0eFMHVpM+GGtq1d1NbVmAHXxgw5z6oeMI8OHDzcfJLqyV4cmNVOmk+x1Na5+IL311ltmMr1OY9AgQ1d0OjtXRqOBogZYOsSni0Q0G6g/mx988IEZWkwKXXiiIwE6dUTfBx161vdO39PU/IPubt8TugJWAxqdGqHvr2Y770UDDc2G6/Pt1q1bqvXzQaALibRagW76+uiCJEsGOSH9udOV0tpGf/aWLl1qpgno721HC000o92uXTvp3bu3WYym0xieeeYZKViwoDmu9Fy60l1XQOv35Mcff2xGBxKWUdL3/6GHHpJXX33VfP/dbZQBcAeCxVSmQYJuCekvF63FuGXLFlP0deDAgYlq92kWSz/M9BdIgQIFrL9g4Fn0vda5ixpQ6HCSLT2mQYh+GGgmWku6aPbAEb1txYoV5kNHgx4NcvQDS79P9HtMV8/qSkttpxnrSZMmmflXEDMvWAMvnVKgmTr9mdOVp5qp1YA7KfRnUDNJ+p7pimadh6iBna6mTs1VqXf7ntBAQzPHWrJF5z5quZx70dW0OpdW/2jR54B/6B/i+tropnML9Y+Ab775JlGZMstrr4Gdzm3UOcKa+e3Zs6f5WXNGM7o6l1RLGOnPq2Yl9RyWaQk6EqAr1zVY1HNqf/SzQOdHJ6SPpSNVDEEjPfDSVS5p3QngQaO/6DUTlLBuH+BqminTQEcDF4Yv0z/NRGrm8rfffrM7Pn78eBPIJnXBE+BKlKoHUpFOHdBFCjopnUAR7qQLaHSKgmaYNfvZtm3btO4SHNA6rZrl1nmvOgStUwZsRxc02Nfi3NOnT79rTUfAnQgWgVSkUwd02EqHrPQDAXAXnbuqq5918YUubuGydemT/n6YMGGC3Lhxw0yX0KlHOs/UQqcafPnll2ahEkPQSC8YhgYAAIBTLHABAACAUwSLAAAAcIpgEQAAAE4RLAIAAMApgkUAAAA4RbAIAAAApwgWAQAA4BTBIgAAAJwiWAQAAIA483+Df+ge33lzowAAAABJRU5ErkJggg==",
+      "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": [
+    "## 🔍 Phân Tích Tương Quan Pearson\n",
+    "\n",
+    "Notebook này nâng cao mô-đun trực quan hóa dữ liệu bằng cách minh họa cách phân tích mối quan hệ giữa nhiều đặc điểm sử dụng tương quan Pearson.\n",
+    "\n",
+    "Tương quan Pearson đo lường mức độ mạnh và hướng của mối quan hệ **tuyến tính** giữa hai biến liên tục. Nó trả về một giá trị nằm trong khoảng từ -1 đến 1:\n",
+    "- **+1** → Mối quan hệ dương hoàn hảo  \n",
+    "- **0** → Không có mối quan hệ tuyến tính  \n",
+    "- **-1** → Mối quan hệ âm hoàn hảo\n",
+    "\n",
+    "Ở đây, chúng tôi đã tính toán ma trận tương quan cho điểm số của học sinh trong các môn Toán, Vật lý, Hóa học và Sinh học. **Heatmap** thu được giúp chúng ta hiểu một cách trực quan mức độ liên quan mạnh mẽ giữa điểm số của từng môn học.\n",
+    "\n",
+    "Phân tích tương quan như vậy rất quan trọng trong **kỹ thuật đặc trưng và tiền xử lý**, đặc biệt trước khi xây dựng các mô hình học máy. Nó giúp xác định:\n",
+    "- Các đặc điểm dư thừa\n",
+    "- Các yếu tố dự đoán mạnh\n",
+    "- Khả năng đa cộng tuyến\n",
+    "\n",
+    "Đây là một bổ sung hữu ích cho các trực quan hóa có ý nghĩa khi phân tích các tập dữ liệu thực tế.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n---\n\n**Tuyên bố miễn trừ trách nhiệm**:  \nTài liệu này đã được dịch bằng dịch vụ dịch thuật AI [Co-op Translator](https://github.com/Azure/co-op-translator). Mặc dù chúng tôi cố gắng đảm bảo độ chính xác, xin lưu ý rằng các bản dịch tự động có thể chứa lỗi hoặc sự không chính xác. Tài liệu gốc bằng ngôn ngữ bản địa nên được coi là nguồn tham khảo chính thức. Đối với các thông tin quan trọng, nên sử dụng dịch vụ dịch thuật chuyên nghiệp từ con người. Chúng tôi không chịu trách nhiệm cho bất kỳ sự hiểu lầm hoặc diễn giải sai nào phát sinh từ việc sử dụng bản dịch này.\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": "2025-09-06T19:40:05+00:00",
+   "source_file": "3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb",
+   "language_code": "vi"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
\ 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
new file mode 100644
index 00000000..9ed1ef3b
--- /dev/null
+++ b/translations/zh/3-Data-Visualization/13-meaningful-visualizations/correlation-analysis.ipynb
@@ -0,0 +1,100 @@
+{
+ "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