diff --git a/README.md b/README.md index 2c0873d..1edd463 100644 --- a/README.md +++ b/README.md @@ -1,3 +1,38 @@ +<<<<<<< HEAD +# Exercício de Casa 🏠 + +## Projeto da Semana 12 + +Nesta semana iremos trabalhar novamente com os dados do navio "Titanic", que contém informações sobre os passageiros que estavam a bordo da viagem. Você deve gerar um notebook em arquivo .ipynb com análises básicas para esse dataset (Titanic) ou para outro dataset (caso desejar) utilizando Pandas, Matplotlib e Numpy. + +Formato do notebook: + + ● Introdução + ○ Breve descrição da base de dados + + ● Processamento + ○ Função de processamento + ○ Leitura e tratamento do DF + + ● Visualização dos Dados + ○ gerar gráficos + ○ extrair ideias e conclusões + +Atividades obrigatórias no desenvolvimento: + ● 3 diferentes gráficos + +--- + +Terminou o exercício? Dá uma olhada nessa checklist e confere se tá tudo certinho, combinado?! + +- [ ] Fiz o fork do repositório. +- [ ] Clonei o fork na minha máquina (`git clone url-do-meu-fork`). +- [ ] Resolvi o exercício. +- [ ] Adicionei as mudanças. (`git add .` para adicionar todos os arquivos, ou `git add nome_do_arquivo` para adicionar um arquivo específico) +- [ ] Commitei a cada mudança significativa ou na finalização do exercício (`git commit -m "Mensagem do commit"`) +- [ ] Pushei os commits na minha branch (`git push origin nome-da-branch`) +- [ ] Criei um Pull Request seguindo as orientaçoes que estao nesse [documento](https://github.com/mflilian/repo-example/blob/main/exercicios/para-casa/instrucoes-pull-request.md). +=======

logo reprograma

@@ -72,3 +107,4 @@ O que veremos na aula de hoje? Desenvolvido com :purple_heart:

+>>>>>>> a6dbd2ba07e88899736f47b2e58ee8a118fcd839 diff --git a/assets/exercicio.casa/12.semana copy.ipynb b/assets/exercicio.casa/12.semana copy.ipynb new file mode 100644 index 0000000..2c699ad --- /dev/null +++ b/assets/exercicio.casa/12.semana copy.ipynb @@ -0,0 +1,369 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Introdução: \n", + "O código realiza análise de dados do Titanic, utilizei as bibliotecas aplicadas nas últimas semanas Panda, Numpy e Matplotlib.\n", + "\n", + "Utilizei a leitura pela url externa e armazena um DataFrame.\n", + "\n", + "Exibe uma breve descrição dos primeiros registros do DataFrame para fornecer uma visão inicial dos dados.\n", + "\n", + "Gráficos: \n", + "\n", + "Inseri mais de 3 gráficos como insights para análises mais profundas sobre os dados iniciais do csv.\n", + "\n", + "Os insights que apliquei nos gráficos obtive da dinâmica da primeira aula, foram perguntas que me fiz sobre o estudo feito no banco de dados como:\n", + "\n", + "- Contagem de passageiros por porto de embarque\n", + "\n", + "- Taxa de sobrevivência por classe (trago uma adendo, porque me questionei sobre a posição das cabines e taxa de sobrevivência pelo valor do ticket, não consegui cruzar esses dados).\n", + "\n", + "- Distribuição de tarifas pagas.\n", + "\n", + "- Distribuição de idades dos passageiros por classe." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "url = 'https://raw.githubusercontent.com/datasciencedojo/datasets/master/titanic.csv'\n", + "df = pd.read_csv(url)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " PassengerId Survived Pclass \\\n", + "0 1 0 3 \n", + "1 2 1 1 \n", + "2 3 1 3 \n", + "3 4 1 1 \n", + "4 5 0 3 \n", + "\n", + " Name Sex Age SibSp \\\n", + "0 Braund, Mr. Owen Harris male 22.0 1 \n", + "1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n", + "2 Heikkinen, Miss. Laina female 26.0 0 \n", + "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n", + "4 Allen, Mr. William Henry male 35.0 0 \n", + "\n", + " Parch Ticket Fare Cabin Embarked \n", + "0 0 A/5 21171 7.2500 NaN S \n", + "1 0 PC 17599 71.2833 C85 C \n", + "2 0 STON/O2. 3101282 7.9250 NaN S \n", + "3 0 113803 53.1000 C123 S \n", + "4 0 373450 8.0500 NaN S \n" + ] + } + ], + "source": [ + "print(df.head())\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "survival_by_class = df.groupby('Pclass')['Survived'].mean()\n", + "survival_by_class.plot(kind='bar', color=['lightgreen', 'lightblue', 'lightcoral'])\n", + "plt.title('Taxa de Sobrevivência por Classe')\n", + "plt.xlabel('Classe')\n", + "plt.ylabel('Taxa de Sobrevivência')\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(df['Fare'].dropna(), bins=20, color='gold')\n", + "plt.title('Distribuição de Tarifas Pagas pelos Passageiros')\n", + "plt.xlabel('Tarifa')\n", + "plt.ylabel('Frequência')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "port_counts = df['Embarked'].value_counts()\n", + "port_counts.plot(kind='bar', color='lightblue')\n", + "plt.title('Contagem de Passageiros por Porto de Embarque')\n", + "plt.xlabel('Porto de Embarque')\n", + "plt.ylabel('Contagem')\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "gender_counts = df['Sex'].value_counts()\n", + "gender_counts.plot(kind='bar', color=['lightblue', 'lightcoral'])\n", + "plt.title('Contagem de Passageiros por Gênero')\n", + "plt.xlabel('Gênero')\n", + "plt.ylabel('Contagem')\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "survival_by_class = df.groupby('Pclass')['Survived'].mean()\n", + "survival_by_class.plot(kind='bar', color='lightgreen')\n", + "plt.title('Taxa de Sobrevivência por Classe')\n", + "plt.xlabel('Classe')\n", + "plt.ylabel('Taxa de Sobrevivência')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(8, 6))\n", + "for pclass in df['Pclass'].unique():\n", + " age_data = df[df['Pclass'] == pclass]['Age'].dropna()\n", + " plt.hist(age_data, bins=20, alpha=0.6, label=f'Classe {pclass}')\n", + "plt.title('Distribuição de Idades dos Passageiros por Classe')\n", + "plt.xlabel('Idade')\n", + "plt.ylabel('Frequência')\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "url = 'https://raw.githubusercontent.com/datasciencedojo/datasets/master/titanic.csv'\n", + "df = pd.read_csv(url)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(8, 6))" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "class_names = ['Primeira Classe', 'Segunda Classe', 'Terceira Classe']\n", + "\n", + "for pclass in df['Pclass'].unique():\n", + " age_data = df[df['Pclass'] == pclass]['Age'].dropna()\n", + " plt.hist(age_data, bins=20, alpha=0.6, label=class_names[pclass - 1])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: matplotlib in c:\\users\\webfoco\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (3.8.0)\n", + "Requirement already satisfied: contourpy>=1.0.1 in c:\\users\\webfoco\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from matplotlib) (1.1.1)\n", + "Requirement already satisfied: cycler>=0.10 in c:\\users\\webfoco\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from matplotlib) (0.12.1)\n", + "Requirement already satisfied: fonttools>=4.22.0 in c:\\users\\webfoco\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from matplotlib) (4.43.1)\n", + "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\users\\webfoco\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from matplotlib) (1.4.5)\n", + "Requirement already satisfied: numpy<2,>=1.21 in c:\\users\\webfoco\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from matplotlib) (1.26.0)\n", + "Requirement already satisfied: packaging>=20.0 in c:\\users\\webfoco\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from matplotlib) (23.2)\n", + "Requirement already satisfied: pillow>=6.2.0 in c:\\users\\webfoco\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from matplotlib) (10.1.0)\n", + "Requirement already satisfied: pyparsing>=2.3.1 in c:\\users\\webfoco\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from matplotlib) (3.1.1)\n", + "Requirement already satisfied: python-dateutil>=2.7 in c:\\users\\webfoco\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from matplotlib) (2.8.2)\n", + "Requirement already satisfied: six>=1.5 in c:\\users\\webfoco\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "[notice] A new release of pip is available: 23.2.1 -> 23.3.1\n", + "[notice] To update, run: C:\\Users\\Webfoco\\AppData\\Local\\Microsoft\\WindowsApps\\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\\python.exe -m pip install --upgrade pip\n" + ] + } + ], + "source": [ + "!pip install matplotlib" + ] + } + ], + "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": "3.11.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/assets/exercicio.casa/12.semana.ipynb b/assets/exercicio.casa/12.semana.ipynb new file mode 100644 index 0000000..2c699ad --- /dev/null +++ b/assets/exercicio.casa/12.semana.ipynb @@ -0,0 +1,369 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "Introdução: \n", + "O código realiza análise de dados do Titanic, utilizei as bibliotecas aplicadas nas últimas semanas Panda, Numpy e Matplotlib.\n", + "\n", + "Utilizei a leitura pela url externa e armazena um DataFrame.\n", + "\n", + "Exibe uma breve descrição dos primeiros registros do DataFrame para fornecer uma visão inicial dos dados.\n", + "\n", + "Gráficos: \n", + "\n", + "Inseri mais de 3 gráficos como insights para análises mais profundas sobre os dados iniciais do csv.\n", + "\n", + "Os insights que apliquei nos gráficos obtive da dinâmica da primeira aula, foram perguntas que me fiz sobre o estudo feito no banco de dados como:\n", + "\n", + "- Contagem de passageiros por porto de embarque\n", + "\n", + "- Taxa de sobrevivência por classe (trago uma adendo, porque me questionei sobre a posição das cabines e taxa de sobrevivência pelo valor do ticket, não consegui cruzar esses dados).\n", + "\n", + "- Distribuição de tarifas pagas.\n", + "\n", + "- Distribuição de idades dos passageiros por classe." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "url = 'https://raw.githubusercontent.com/datasciencedojo/datasets/master/titanic.csv'\n", + "df = pd.read_csv(url)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " PassengerId Survived Pclass \\\n", + "0 1 0 3 \n", + "1 2 1 1 \n", + "2 3 1 3 \n", + "3 4 1 1 \n", + "4 5 0 3 \n", + "\n", + " Name Sex Age SibSp \\\n", + "0 Braund, Mr. Owen Harris male 22.0 1 \n", + "1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n", + "2 Heikkinen, Miss. Laina female 26.0 0 \n", + "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n", + "4 Allen, Mr. William Henry male 35.0 0 \n", + "\n", + " Parch Ticket Fare Cabin Embarked \n", + "0 0 A/5 21171 7.2500 NaN S \n", + "1 0 PC 17599 71.2833 C85 C \n", + "2 0 STON/O2. 3101282 7.9250 NaN S \n", + "3 0 113803 53.1000 C123 S \n", + "4 0 373450 8.0500 NaN S \n" + ] + } + ], + "source": [ + "print(df.head())\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "survival_by_class = df.groupby('Pclass')['Survived'].mean()\n", + "survival_by_class.plot(kind='bar', color=['lightgreen', 'lightblue', 'lightcoral'])\n", + "plt.title('Taxa de Sobrevivência por Classe')\n", + "plt.xlabel('Classe')\n", + "plt.ylabel('Taxa de Sobrevivência')\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(df['Fare'].dropna(), bins=20, color='gold')\n", + "plt.title('Distribuição de Tarifas Pagas pelos Passageiros')\n", + "plt.xlabel('Tarifa')\n", + "plt.ylabel('Frequência')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "port_counts = df['Embarked'].value_counts()\n", + "port_counts.plot(kind='bar', color='lightblue')\n", + "plt.title('Contagem de Passageiros por Porto de Embarque')\n", + "plt.xlabel('Porto de Embarque')\n", + "plt.ylabel('Contagem')\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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GBgaqcuXKzjlJBe/rP9fi4+OjGjVqFPpdu/766+Xj4/OX6y0I6SdOnCjU98knn+js2bP69ttvNXToUGd7wXPdu3fvCz5uVlaWyz8Xf/7dKej7/fffFRQUdNnv8aK275dfflGtWrVc3tPS+deqoB9lD2EHpS4oKEhVqlTR7t27L+t+l3qBtj9PcCzwxw+6Czl27Jhat26toKAgjR49WtHR0fL19dWOHTv03HPPXXAvw8Xk5+frxhtv1KRJk4rs/3OIuVD9V7JdlyI/P182m02fffZZkesq6r/yojRr1sx5NtafbdiwQffee69atWql6dOnq3LlyvL29ta8efNcJhb7+fnpyy+/1Lp167Rs2TKtWLFC77//vtq0aaNVq1bJ09NT9erVU3JyspYuXaoVK1boww8/1PTp0/Xyyy9r1KhRkqQBAwZo3rx5GjRokGJjY+VwOGSz2dS9e/div5aStHDhQkVERBTq/6uzrwq2rbhatWql/fv365NPPtGqVav0z3/+U2+88YZmzpzplrOULnVbatasKS8vryJ/51u3bi2p8HNX8Fz/4x//cNnj9kd/fk/+1e/I5b7Hr+S1QtlC2IFb3H333Zo9e7aSkpKK/I/6jyIjI5Wfn6+ffvrJ+d+TJKWlpenYsWOKjIy87PVfKDh98cUXysjI0EcffaRWrVo52wvOJPpjTZK0b98+5/U/pPNnjf38888u/2lHR0fr22+/Vdu2bUv9irphYWHy8/Mr8rBHcnKyy+3o6GgZYxQVFaXatWtflXo+/PBD+fr6auXKlbLb7c72efPmFRrr4eGhtm3bqm3btpo0aZLGjRunF198UevWrVO7du0kSQEBAXrwwQf14IMPKjc3V126dNGrr76q4cOHy9fXV0uWLFHv3r31+uuvOx/3zJkzhc4sioyM1L59+wrV8Oe26OhoSVKlSpWcNVypsLAw+fv7F3o9JGnv3r3y8PBwCcQhISFKSEhQQkKCTpw4oVatWumVV165pLDz008/ubxfT5w4oSNHjuiuu+6S9P/f18nJyS6H1HJzc3Xw4MFib3NAQIBuv/12rV+/Xr/99pvzkObFFDzXQUFBJfZcl8R7PDIyUt99953y8/Nd9u4UHOIuzt8jXH3M2YFbDBs2TAEBAXrssceKvLru/v379dZbb0mS8w/xm2++6TKmYE9Jp06dLnv9AQEBklToQ6/gv70/7i3Jzc3V9OnTXcY1bdpUoaGhmjNnjs6dO+dsX7RoUaHDSt26ddNvv/2mOXPmFKrj9OnTOnny5GXXf6k8PT0VHx+vjz/+WCkpKc72H374QStXrnQZ26VLF3l6emrUqFGF9hYZY5SRkVEi9dhsNpfT+H/++Wd9/PHHLuMyMzML3bfgv/uCQ19/rsfHx0cxMTEyxujs2bPO9f15W6ZMmVLoMgLx8fFKSkrSN99841LDn7+LKT4+XkFBQRo3bpxzHX909OjRIrb64jw9PdW+fXt98sknLqe4p6Wl6d1331WLFi2ch8f+vM2BgYGqWbPmJV9RePbs2S51z5gxQ+fOnVPHjh0lnZ9v5ePjo8mTJ7s8b3PnzlVWVlaxftcKvPzyy8rLy9MjjzxS5OGsP79OTZo0UXR0tF577bUixxfnuS6J9/hdd92l1NRUvf/++862c+fOacqUKQoMDHTuqULZwp4duEV0dLTeffddPfjgg6pXr57LFZQ3b96sxYsX69FHH5UkNWrUSL1799bs2bOdh5m++uorvf322+rcubPLf6qXqnHjxvL09NTf//53ZWVlyW63q02bNrrttttUsWJF9e7dWwMHDpTNZtPChQsL/WH08fFxfldPmzZt1K1bN/3888+aP3++oqOjXfbg9OzZUx988IGefPJJrVu3TnFxccrLy9PevXv1wQcfaOXKlRc87FMSRo0apRUrVqhly5Z66qmnnH+Y69evr++++845Ljo6WmPHjtXw4cP1888/q3PnzqpQoYIOHjyo//znP3r88cdd5lQUR6dOnTRp0iR16NBBDz/8sNLT0zVt2jTVrFnTpZbRo0fryy+/VKdOnRQZGan09HRNnz5dN9xwg1q0aCFJat++vSIiIhQXF6fw8HD98MMPmjp1qjp16uScI3L33Xdr4cKFcjgciomJUVJSkj7//HOFhoa61DVs2DC98847uvPOOzVgwAAFBATon//8p6pVq6bMzEzn6xkUFKQZM2aoZ8+euvnmm9W9e3eFhYUpJSVFy5YtU1xcnKZOnXrZz8vYsWOd1xV66qmn5OXlpVmzZiknJ0cTJ050jouJidHtt9+uJk2aKCQkRF9//bWWLFmi/v37X9J6cnNz1bZtW3Xr1k3JycmaPn26WrRooXvvvVfS+b1Mw4cP16hRo9ShQwfde++9znG33HKLHnnkkcvetgItW7bU1KlTNWDAANWqVct5BeXc3Fz9+OOPWrRokXx8fJyHBz08PPTPf/5THTt2VP369ZWQkKDrr79ev/32m9atW6egoCB9+umnl1VDSbzHH3/8cc2aNUuPPvqotm/frurVq2vJkiXatGmT3nzzzUs+8QKlrLRP/wL+6McffzR9+/Y11atXNz4+PqZChQomLi7OTJkyxXmatzHGnD171owaNcpERUUZb29vU7VqVTN8+HCXMcacP/W8qFNzW7du7XI6uDHGzJkzx9SoUcN5CnbBaeibNm0yt956q/Hz8zNVqlQxw4YNMytXrizyVPXJkyebyMhIY7fbTbNmzcymTZtMkyZNTIcOHVzG5ebmmr///e+mfv36xm63m4oVK5omTZqYUaNGmaysLOc4SSYxMdHlvgcPHjSSzD/+8Q+X9nXr1hlJZvHixRd9jo0xZv369aZJkybGx8fH1KhRw8ycObPIU6ONMebDDz80LVq0MAEBASYgIMDUrVvXJCYmmuTk5Iuu40LX2fmzuXPnmlq1ahm73W7q1q1r5s2bV6iWNWvWmPvuu89UqVLF+Pj4mCpVqpiHHnrI/Pjjj84xs2bNMq1atTKhoaHGbreb6Oho8+yzz7o8n7///rtJSEgw1113nQkMDDTx8fFm7969JjIy0vTu3dulrp07d5qWLVsau91ubrjhBjN+/HgzefJkI8l5DaYC69atM/Hx8cbhcBhfX18THR1tHn30UfP11187x1zo1PM/v74FduzYYeLj401gYKDx9/c3d9xxh9m8ebPLmLFjx5pmzZqZ4OBg4+fnZ+rWrWteffVVl9PJi1Lw2qxfv948/vjjpmLFiiYwMND06NHDZGRkFBo/depUU7duXePt7W3Cw8NNv379Cl1nqXXr1n956n9Rdu7caXr16mWqVatmfHx8TEBAgGnYsKF55plnzL59+4oc36VLF+frHBkZabp162bWrFnjHFPwXP/5UgUF2/3nyzNcynv8YtuXlpbmfF/5+PiYG2+80cybN++ynwuUHpsxJTS7EYDy8/MVFhamLl26FHnYCuXLoEGDNGvWLJ04ceKCk1/Lg/nz5yshIUHbtm27qnsRgbKKOTtAMZ05c6bQ4a0FCxYoMzOz0NdFoOw7ffq0y+2MjAwtXLhQLVq0KNdBBwBzdoBi27JliwYPHqwHHnhAoaGh2rFjh+bOnasGDRo4v3sL5UdsbKxuv/121atXT2lpaZo7d66ys7P10ksvubs0AFeIsAMUU/Xq1VW1alVNnjxZmZmZCgkJUa9evTRhwoRLutAaypa77rpLS5Ys0ezZs2Wz2XTzzTdr7ty5LpcgAFA+MWcHAABYGnN2AACApRF2AACApTFnR+dPFz58+LAqVKhQ6pfzBwAAxWOM0fHjx1WlSpVCX87654Fu9euvv5oePXqYkJAQ4+vraxo0aOByUbL8/Hzz0ksvmYiICOPr62vatm3rcmExY4zJyMgwDz/8sKlQoYJxOBzm//7v/8zx48cvuYZDhw4ZSSwsLCwsLCzlcDl06NBFP+fdumfn999/V1xcnO644w599tlnCgsL008//aSKFSs6x0ycOFGTJ0/W22+/raioKL300kuKj4/X999/L19fX0lSjx49dOTIEa1evVpnz55VQkKCHn/8cZdvUb6Ygst7Hzp0yPkdNAAAoGzLzs5W1apV//JrOtx6Ntbzzz+vTZs2acOGDUX2G2NUpUoVPfPMM87vK8nKylJ4eLjmz5+v7t2764cfflBMTIzLlUFXrFihu+66S7/++quqVKnyl3VkZ2fL4XAoKyuLsAMAQDlxqZ/fbp2g/N///ldNmzbVAw88oEqVKummm25yucT+wYMHlZqaqnbt2jnbHA6HmjdvrqSkJElSUlKSgoODXS6B3q5dO3l4eGjr1q1FrjcnJ0fZ2dkuCwAAsCa3hp0DBw5oxowZqlWrllauXKl+/fpp4MCBevvttyVJqampkqTw8HCX+4WHhzv7UlNTValSJZd+Ly8vhYSEOMf82fjx4+VwOJxL1apVS3rTAABAGeHWsJOfn6+bb75Z48aN00033aTHH39cffv21cyZM6/qeocPH66srCzncujQoau6PgAA4D5uDTuVK1dWTEyMS1u9evWUkpIiSYqIiJAkpaWluYxJS0tz9kVERCg9Pd2l/9y5c8rMzHSO+TO73a6goCCXBQAAWJNbw05cXJySk5Nd2n788UdFRkZKkqKiohQREaE1a9Y4+7Ozs7V161bFxsZKOv/lfceOHdP27dudY9auXav8/Hw1b968FLYCAACUZW499Xzw4MG67bbbNG7cOHXr1k1fffWVZs+erdmzZ0uSbDabBg0apLFjx6pWrVrOU8+rVKmizp07Szq/J6hDhw7Ow19nz55V//791b1790s6EwsAAFib278IdOnSpRo+fLh++uknRUVFaciQIerbt6+z3xijkSNHavbs2Tp27JhatGih6dOnq3bt2s4xmZmZ6t+/vz799FN5eHioa9eumjx5sgIDAy+pBk49BwCg/LnUz2+3h52ygLADAED5Uy6uswMAAHC1EXYAAIClEXYAAIClEXYAAIClEXYAAIClEXYAAIClEXYAAIClufUKynC/j5KPuLsElKIudSq7uwQAKHXs2QEAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJZG2AEAAJbm1rDzyiuvyGazuSx169Z19p85c0aJiYkKDQ1VYGCgunbtqrS0NJfHSElJUadOneTv769KlSrp2Wef1blz50p7UwAAQBnl5e4C6tevr88//9x528vr/5c0ePBgLVu2TIsXL5bD4VD//v3VpUsXbdq0SZKUl5enTp06KSIiQps3b9aRI0fUq1cveXt7a9y4caW+LQAAoOxxe9jx8vJSREREofasrCzNnTtX7777rtq0aSNJmjdvnurVq6ctW7bo1ltv1apVq/T999/r888/V3h4uBo3bqwxY8boueee0yuvvCIfH5/S3hwAAFDGuH3Ozk8//aQqVaqoRo0a6tGjh1JSUiRJ27dv19mzZ9WuXTvn2Lp166patWpKSkqSJCUlJenGG29UeHi4c0x8fLyys7O1Z8+eC64zJydH2dnZLgsAALAmt4ad5s2ba/78+VqxYoVmzJihgwcPqmXLljp+/LhSU1Pl4+Oj4OBgl/uEh4crNTVVkpSamuoSdAr6C/ouZPz48XI4HM6latWqJbthAACgzHDrYayOHTs6f27YsKGaN2+uyMhIffDBB/Lz87tq6x0+fLiGDBnivJ2dnU3gAQDAotx+GOuPgoODVbt2be3bt08RERHKzc3VsWPHXMakpaU55/hEREQUOjur4HZR84AK2O12BQUFuSwAAMCaylTYOXHihPbv36/KlSurSZMm8vb21po1a5z9ycnJSklJUWxsrCQpNjZWu3btUnp6unPM6tWrFRQUpJiYmFKvHwAAlD1uPYw1dOhQ3XPPPYqMjNThw4c1cuRIeXp66qGHHpLD4VCfPn00ZMgQhYSEKCgoSAMGDFBsbKxuvfVWSVL79u0VExOjnj17auLEiUpNTdWIESOUmJgou93uzk0DAABlhFvDzq+//qqHHnpIGRkZCgsLU4sWLbRlyxaFhYVJkt544w15eHioa9euysnJUXx8vKZPn+68v6enp5YuXap+/fopNjZWAQEB6t27t0aPHu2uTQIAAGWMzRhj3F2Eu2VnZ8vhcCgrK+uam7/zUfIRd5eAUtSlTmV3lwAAJeZSP7/L1JwdAACAkkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAlkbYAQAAllZmws6ECRNks9k0aNAgZ9uZM2eUmJio0NBQBQYGqmvXrkpLS3O5X0pKijp16iR/f39VqlRJzz77rM6dO1fK1QMAgLKqTISdbdu2adasWWrYsKFL++DBg/Xpp59q8eLFWr9+vQ4fPqwuXbo4+/Py8tSpUyfl5uZq8+bNevvttzV//ny9/PLLpb0JAACgjHJ72Dlx4oR69OihOXPmqGLFis72rKwszZ07V5MmTVKbNm3UpEkTzZs3T5s3b9aWLVskSatWrdL333+vd955R40bN1bHjh01ZswYTZs2Tbm5ue7aJAAAUIa4PewkJiaqU6dOateunUv79u3bdfbsWZf2unXrqlq1akpKSpIkJSUl6cYbb1R4eLhzTHx8vLKzs7Vnz54LrjMnJ0fZ2dkuCwAAsCYvd678vffe044dO7Rt27ZCfampqfLx8VFwcLBLe3h4uFJTU51j/hh0CvoL+i5k/PjxGjVq1BVWDwAAygO37dk5dOiQnn76aS1atEi+vr6luu7hw4crKyvLuRw6dKhU1w8AAEqP28LO9u3blZ6erptvvlleXl7y8vLS+vXrNXnyZHl5eSk8PFy5ubk6duyYy/3S0tIUEREhSYqIiCh0dlbB7YIxRbHb7QoKCnJZAACANbkt7LRt21a7du3SN99841yaNm2qHj16OH/29vbWmjVrnPdJTk5WSkqKYmNjJUmxsbHatWuX0tPTnWNWr16toKAgxcTElPo2AQCAssdtc3YqVKigBg0auLQFBAQoNDTU2d6nTx8NGTJEISEhCgoK0oABAxQbG6tbb71VktS+fXvFxMSoZ8+emjhxolJTUzVixAglJibKbreX+jYBAICyx60TlP/KG2+8IQ8PD3Xt2lU5OTmKj4/X9OnTnf2enp5aunSp+vXrp9jYWAUEBKh3794aPXq0G6sGAABlic0YY9xdhLtlZ2fL4XAoKyvrmpu/81HyEXeXgFLUpU5ld5cAACXmUj+/3X6dHQAAgKuJsAMAACyNsAMAACyNsAMAACyNsAMAACyNsAMAACyNsAMAACyNsAMAACyNsAMAACyNsAMAACyNsAMAACyNsAMAACyNsAMAACyNsAMAACyNsAMAACyNsAMAACyNsAMAACyNsAMAACyNsAMAACyNsAMAACzNqzh3OnPmjKZMmaJ169YpPT1d+fn5Lv07duwokeIAAACuVLHCTp8+fbRq1Srdf//9atasmWw2W0nXBQAAUCKKFXaWLl2q5cuXKy4urqTrAQAAKFHFmrNz/fXXq0KFCiVdCwAAQIkrVth5/fXX9dxzz+mXX34p6XoAAABKVLEOYzVt2lRnzpxRjRo15O/vL29vb5f+zMzMEikOAADgShUr7Dz00EP67bffNG7cOIWHhzNBGQAAlFnFCjubN29WUlKSGjVqVNL1AAAAlKhizdmpW7euTp8+XdK1AAAAlLhihZ0JEybomWee0RdffKGMjAxlZ2e7LAAAAGVFsQ5jdejQQZLUtm1bl3ZjjGw2m/Ly8q68MgAAgBJQrLCzbt26kq4DAADgqihW2GndunVJ1wEAAHBVFPtbzzds2KBHHnlEt912m3777TdJ0sKFC7Vx48YSKw4AAOBKFSvsfPjhh4qPj5efn5927NihnJwcSVJWVpbGjRtXogUCAABciWKFnbFjx2rmzJmaM2eOy9WT4+LitGPHjhIrDgAA4EoVK+wkJyerVatWhdodDoeOHTt2pTUBAACUmGKFnYiICO3bt69Q+8aNG1WjRo0rLgoAAKCkFCvs9O3bV08//bS2bt0qm82mw4cPa9GiRRo6dKj69etX0jUCAAAUW7FOPX/++eeVn5+vtm3b6tSpU2rVqpXsdruGDh2qAQMGlHSNAAAAxWYzxpji3jk3N1f79u3TiRMnFBMTo8DAwJKsrdRkZ2fL4XAoKytLQUFB7i6nVH2UfMTdJaAUdalT2d0lAECJudTP72Lt2Sng4+OjmJiYK3kIAACAq6pYYedvf/ubbDZboXabzSZfX1/VrFlTDz/8sOrUqXPFBQIAAFyJYk1QdjgcWrt2rXbs2CGbzSabzaadO3dq7dq1OnfunN5//301atRImzZtKul6AQAALkux9uxERETo4Ycf1tSpU+XhcT4v5efn6+mnn1aFChX03nvv6cknn9Rzzz3H10cAAAC3KtYE5bCwMG3atEm1a9d2af/xxx9122236X//+5927dqlli1blouLDDJBGdcKJihfW7JGjXJ3CShFjpEj3V1CqbvUz+9iHcY6d+6c9u7dW6h97969ysvLkyT5+voWOa8HAACgNBXrMFbPnj3Vp08fvfDCC7rlllskSdu2bdO4cePUq1cvSdL69etVv379kqsUAACgGIoVdt544w2Fh4dr4sSJSktLkySFh4dr8ODBeu655yRJ7du3V4cOHUquUgAAgGIoVtjx9PTUiy++qBdffFHZ2dmSVOhYWbVq1a68OgAAgCtUrDk7fxQUFFTsSb0zZsxQw4YNnY8RGxurzz77zNl/5swZJSYmKjQ0VIGBgeratatzT1KBlJQUderUSf7+/qpUqZKeffZZnTt37oq2CQAAWEexr6C8ZMkSffDBB0pJSVFubq5L344dOy7pMW644QZNmDBBtWrVkjFGb7/9tu677z7t3LlT9evX1+DBg7Vs2TItXrxYDodD/fv3V5cuXZzX78nLy1OnTp0UERGhzZs368iRI+rVq5e8vb01bty44m4aAACwkGLt2Zk8ebISEhIUHh6unTt3qlmzZgoNDdWBAwfUsWPHS36ce+65R3fddZdq1aql2rVr69VXX1VgYKC2bNmirKwszZ07V5MmTVKbNm3UpEkTzZs3T5s3b9aWLVskSatWrdL333+vd955R40bN1bHjh01ZswYTZs2rVAAAwAA16ZihZ3p06dr9uzZmjJlinx8fDRs2DCtXr1aAwcOVFZWVrEKycvL03vvvaeTJ08qNjZW27dv19mzZ9WuXTvnmLp166patWpKSkqSJCUlJenGG29UeHi4c0x8fLyys7O1Z8+eC64rJydH2dnZLgsAALCmYoWdlJQU3XbbbZIkPz8/HT9+XNL5U9L//e9/X9Zj7dq1S4GBgbLb7XryySf1n//8RzExMUpNTZWPj4+Cg4NdxoeHhys1NVWSlJqa6hJ0CvoL+i5k/PjxcjgczqVq1aqXVTMAACg/ihV2IiIilJmZKen8WVcFh5UOHjyoy70gc506dfTNN99o69at6tevn3r37q3vv/++OGVdsuHDhysrK8u5HDp06KquDwAAuE+xJii3adNG//3vf3XTTTcpISFBgwcP1pIlS/T111+rS5cul/VYPj4+qlmzpiSpSZMm2rZtm9566y09+OCDys3N1bFjx1z27qSlpSkiIkLS+dD11VdfuTxewdlaBWOKYrfbZbfbL6tOAABQPhUr7MyePVv5+fmS5Dw1fPPmzbr33nv1xBNPXFFB+fn5ysnJUZMmTeTt7a01a9aoa9eukqTk5GSlpKQoNjZWkhQbG6tXX31V6enpqlSpkiRp9erVCgoKUkxMzBXVAQAArKFYYefXX391mefSvXt3de/eXcYYHTp06JIvKDh8+HB17NhR1apV0/Hjx/Xuu+/qiy++0MqVK+VwONSnTx8NGTJEISEhCgoK0oABAxQbG6tbb71V0vmrNMfExKhnz56aOHGiUlNTNWLECCUmJrLnBgAASCpm2ImKitKRI0ece1MKZGZmKioqyvlloH8lPT1dvXr10pEjR+RwONSwYUOtXLlSd955p6TzX0vh4eGhrl27KicnR/Hx8Zo+fbrz/p6enlq6dKn69eun2NhYBQQEqHfv3ho9enRxNgsAAFhQscKOMabIbzQ/ceKEfH19L/lx5s6de9F+X19fTZs2TdOmTbvgmMjISC1fvvyS1wkAAK4tlxV2hgwZIkmy2Wx66aWX5O/v7+zLy8vT1q1b1bhx4xItEAAA4EpcVtjZuXOnpPN7dnbt2iUfHx9nn4+Pjxo1aqShQ4eWbIUAAABX4LLCzrp16yRJCQkJeuutt4r9BaAAAAClpVhzdubNm1fSdQAAAFwVxQo7J0+e1IQJE7RmzRqlp6c7r7lT4MCBAyVSHAAAwJUqVth57LHHtH79evXs2VOVK1cu8swsAACAsqBYYeezzz7TsmXLFBcXV9L1AAAAlKhifRFoxYoVFRISUtK1AAAAlLhihZ0xY8bo5Zdf1qlTp0q6HgAAgBJVrMNYr7/+uvbv36/w8HBVr15d3t7eLv07duwokeIAAACuVLHCTufOnUu4DAAAgKujWGFn5MiRJV0HAADAVVGssFNg+/bt+uGHHyRJ9evX10033VQiRQEAAJSUYoWd9PR0de/eXV988YWCg4MlSceOHdMdd9yh9957T2FhYSVZIwAAQLEV62ysAQMG6Pjx49qzZ48yMzOVmZmp3bt3Kzs7WwMHDizpGgEAAIqtWHt2VqxYoc8//1z16tVztsXExGjatGlq3759iRUHAABwpYq1Zyc/P7/Q6eaS5O3tXeh7sgAAANypWGGnTZs2evrpp3X48GFn22+//abBgwerbdu2JVYcAADAlSpW2Jk6daqys7NVvXp1RUdHKzo6WlFRUcrOztaUKVNKukYAAIBiK9acnapVq2rHjh36/PPPtXfvXklSvXr11K5duxItDgAA4Epd1p6dtWvXKiYmRtnZ2bLZbLrzzjs1YMAADRgwQLfccovq16+vDRs2XK1aAQAALttlhZ0333xTffv2VVBQUKE+h8OhJ554QpMmTSqx4gAAAK7UZYWdb7/9Vh06dLhgf/v27bV9+/YrLgoAAKCkXFbYSUtLK/KU8wJeXl46evToFRcFAABQUi4r7Fx//fXavXv3Bfu/++47Va5c+YqLAgAAKCmXFXbuuusuvfTSSzpz5kyhvtOnT2vkyJG6++67S6w4AACAK3VZp56PGDFCH330kWrXrq3+/furTp06kqS9e/dq2rRpysvL04svvnhVCgUAACiOywo74eHh2rx5s/r166fhw4fLGCNJstlsio+P17Rp0xQeHn5VCgUAACiOy76oYGRkpJYvX67ff/9d+/btkzFGtWrVUsWKFa9GfQAAAFekWFdQlqSKFSvqlltuKclaAAAASlyxvhsLAACgvCDsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAAS3Nr2Bk/frxuueUWVahQQZUqVVLnzp2VnJzsMubMmTNKTExUaGioAgMD1bVrV6WlpbmMSUlJUadOneTv769KlSrp2Wef1blz50pzUwAAQBnl1rCzfv16JSYmasuWLVq9erXOnj2r9u3b6+TJk84xgwcP1qeffqrFixdr/fr1Onz4sLp06eLsz8vLU6dOnZSbm6vNmzfr7bff1vz58/Xyyy+7Y5MAAEAZYzPGGHcXUeDo0aOqVKmS1q9fr1atWikrK0thYWF69913df/990uS9u7dq3r16ikpKUm33nqrPvvsM9199906fPiwwsPDJUkzZ87Uc889p6NHj8rHx+cv15udnS2Hw6GsrCwFBQVd1W0saz5KPuLuElCKutSp7O4SUIqyRo1ydwkoRY6RI91dQqm71M/vMjVnJysrS5IUEhIiSdq+fbvOnj2rdu3aOcfUrVtX1apVU1JSkiQpKSlJN954ozPoSFJ8fLyys7O1Z8+eIteTk5Oj7OxslwUAAFhTmQk7+fn5GjRokOLi4tSgQQNJUmpqqnx8fBQcHOwyNjw8XKmpqc4xfww6Bf0FfUUZP368HA6Hc6latWoJbw0AACgrykzYSUxM1O7du/Xee+9d9XUNHz5cWVlZzuXQoUNXfZ0AAMA9vNxdgCT1799fS5cu1ZdffqkbbrjB2R4REaHc3FwdO3bMZe9OWlqaIiIinGO++uorl8crOFurYMyf2e122e32Et4KAABQFrl1z44xRv3799d//vMfrV27VlFRUS79TZo0kbe3t9asWeNsS05OVkpKimJjYyVJsbGx2rVrl9LT051jVq9eraCgIMXExJTOhgAAgDLLrXt2EhMT9e677+qTTz5RhQoVnHNsHA6H/Pz85HA41KdPHw0ZMkQhISEKCgrSgAEDFBsbq1tvvVWS1L59e8XExKhnz56aOHGiUlNTNWLECCUmJrL3BgAAuDfszJgxQ5J0++23u7TPmzdPjz76qCTpjTfekIeHh7p27aqcnBzFx8dr+vTpzrGenp5aunSp+vXrp9jYWAUEBKh3794aPXp0aW0GAAAow9wadi7lEj++vr6aNm2apk2bdsExkZGRWr58eUmWBgAALKLMnI0FAABwNRB2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApRF2AACApbk17Hz55Ze65557VKVKFdlsNn388ccu/cYYvfzyy6pcubL8/PzUrl07/fTTTy5jMjMz1aNHDwUFBSk4OFh9+vTRiRMnSnErAABAWebWsHPy5Ek1atRI06ZNK7J/4sSJmjx5smbOnKmtW7cqICBA8fHxOnPmjHNMjx49tGfPHq1evVpLly7Vl19+qccff7y0NgEAAJRxXu5ceceOHdWxY8ci+4wxevPNNzVixAjdd999kqQFCxYoPDxcH3/8sbp3764ffvhBK1as0LZt29S0aVNJ0pQpU3TXXXfptddeU5UqVUptWwAAQNlUZufsHDx4UKmpqWrXrp2zzeFwqHnz5kpKSpIkJSUlKTg42Bl0JKldu3by8PDQ1q1bL/jYOTk5ys7OdlkAAIA1ldmwk5qaKkkKDw93aQ8PD3f2paamqlKlSi79Xl5eCgkJcY4pyvjx4+VwOJxL1apVS7h6AABQVpTZsHM1DR8+XFlZWc7l0KFD7i4JAABcJWU27EREREiS0tLSXNrT0tKcfREREUpPT3fpP3funDIzM51jimK32xUUFOSyAAAAayqzYScqKkoRERFas2aNsy07O1tbt25VbGysJCk2NlbHjh3T9u3bnWPWrl2r/Px8NW/evNRrBgAAZY9bz8Y6ceKE9u3b57x98OBBffPNNwoJCVG1atU0aNAgjR07VrVq1VJUVJReeuklValSRZ07d5Yk1atXTx06dFDfvn01c+ZMnT17Vv3791f37t05EwsAAEhyc9j5+uuvdccddzhvDxkyRJLUu3dvzZ8/X8OGDdPJkyf1+OOP69ixY2rRooVWrFghX19f530WLVqk/v37q23btvLw8FDXrl01efLkUt8WAABQNtmMMcbdRbhbdna2HA6HsrKyrrn5Ox8lH3F3CShFXepUdncJKEVZo0a5uwSUIsfIke4uodRd6ud3mZ2zAwAAUBIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIIOwAAwNIsE3amTZum6tWry9fXV82bN9dXX33l7pIAAEAZYImw8/7772vIkCEaOXKkduzYoUaNGik+Pl7p6enuLg0AALiZJcLOpEmT1LdvXyUkJCgmJkYzZ86Uv7+//vWvf7m7NAAA4GZe7i7gSuXm5mr79u0aPny4s83Dw0Pt2rVTUlJSkffJyclRTk6O83ZWVpYkKTs7++oWWwadOnHc3SWgFGVnB7i7BJSi7DNn3F0CSpHtGvwMK/jcNsZcdFy5Dzv/+9//lJeXp/DwcJf28PBw7d27t8j7jB8/XqNGjSrUXrVq1atSIwAAV92ECe6uwG2OHz8uh8Nxwf5yH3aKY/jw4RoyZIjzdn5+vjIzMxUaGiqbzebGylAasrOzVbVqVR06dEhBQUHuLgdACeL3+9pijNHx48dVpUqVi44r92Hnuuuuk6enp9LS0lza09LSFBERUeR97Ha77Ha7S1twcPDVKhFlVFBQEH8MAYvi9/vacbE9OgXK/QRlHx8fNWnSRGvWrHG25efna82aNYqNjXVjZQAAoCwo93t2JGnIkCHq3bu3mjZtqmbNmunNN9/UyZMnlZCQ4O7SAACAm1ki7Dz44IM6evSoXn75ZaWmpqpx48ZasWJFoUnLgHT+MObIkSMLHcoEUP7x+42i2Mxfna8FAABQjpX7OTsAAAAXQ9gBAACWRtgBAACWRtgBAACWRtgBAACWRtgBAJR7+/bt08qVK3X69GlJf/3FkLi2EHYAAOVWRkaG2rVrp9q1a+uuu+7SkSNHJEl9+vTRM8884+bqUFYQdnDN2LBhgx555BHFxsbqt99+kyQtXLhQGzdudHNlAIpr8ODB8vLyUkpKivz9/Z3tDz74oFasWOHGylCWEHZwTfjwww8VHx8vPz8/7dy5Uzk5OZKkrKwsjRs3zs3VASiuVatW6e9//7tuuOEGl/ZatWrpl19+cVNVKGsIO7gmjB07VjNnztScOXPk7e3tbI+Li9OOHTvcWBmAK3Hy5EmXPToFMjMz+coIOBF2cE1ITk5Wq1atCrU7HA4dO3as9AsCUCJatmypBQsWOG/bbDbl5+dr4sSJuuOOO9xYGcoSS3wRKPBXIiIitG/fPlWvXt2lfePGjapRo4Z7igJwxSZOnKi2bdvq66+/Vm5uroYNG6Y9e/YoMzNTmzZtcnd5KCPYs4NrQt++ffX0009r69atstlsOnz4sBYtWqShQ4eqX79+7i4PQDE1aNBAP/74o1q0aKH77rtPJ0+eVJcuXbRz505FR0e7uzyUEXzrOa4JxhiNGzdO48eP16lTpyRJdrtdQ4cO1ZgxY9xcHQDgaiLs4JqSm5urffv26cSJE4qJiVFgYKC7SwJwmb777rtLHtuwYcOrWAnKC8IOAKBc8fDwkM1m+8urJNtsNuXl5ZVSVSjLmKAMy+rSpcslj/3oo4+uYiUAStLBgwfdXQLKGcIOLMvhcLi7BABXQWRkpLtLQDnDYSwAQLn3/fffKyUlRbm5uS7t9957r5sqQlnCnh0AQLl14MAB/e1vf9OuXbtc5vHYbDZJYs4OJBF2cA1ZsmSJPvjggyL/++MrI4Dy6emnn1ZUVJTWrFmjqKgoffXVV8rIyNAzzzyj1157zd3loYzgooK4JkyePFkJCQkKDw/Xzp071axZM4WGhurAgQPq2LGju8sDUExJSUkaPXq0rrvuOnl4eMjDw0MtWrTQ+PHjNXDgQHeXhzKCsINrwvTp0zV79mxNmTJFPj4+GjZsmFavXq2BAwcqKyvL3eUBKKa8vDxVqFBBknTdddfp8OHDks5PYk5OTnZnaShDCDu4JqSkpOi2226TJPn5+en48eOSpJ49e+rf//63O0sDcAUaNGigb7/9VpLUvHlzTZw4UZs2bdLo0aP53js4EXZwTYiIiFBmZqYkqVq1atqyZYuk89fr4IREoPwaMWKE8vPzJUmjR4/WwYMH1bJlSy1fvlyTJ092c3UoK5igjGtCmzZt9N///lc33XSTEhISNHjwYC1ZskRff/31ZV18EEDZEh8f7/y5Zs2a2rt3rzIzM1WxYkXnGVkA19nBNSE/P1/5+fny8jqf799//31t2rRJtWrV0pNPPilvb283VwgAuFoIO7hmnDlzRt99953S09Odu72l89fjuOeee9xYGYDiOnPmjKZMmaJ169YV+t2WuKwEzuMwFq4JK1asUM+ePZWRkVGojy8LBMqvPn36aNWqVbr//vvVrFkzDl2hSOzZwTWhVq1aat++vV5++WWFh4e7uxwAJcThcGj58uWKi4tzdykowzgbC9eEtLQ0DRkyhKADWMz111/vvM4OcCGEHVwT7r//fn3xxRfuLgNACXv99df13HPP6ZdffnF3KSjDOIyFa8KpU6f0wAMPKCwsTDfeeGOhs6+4rDxQPh09elTdunXTl19+KX9//0K/2wXX18K1jbCDa8LcuXP15JNPytfXV6GhoS6TGG02mw4cOODG6gAUV7t27ZSSkqI+ffooPDy80ATl3r17u6kylCWEHVwTIiIiNHDgQD3//PPy8ODoLWAV/v7+SkpKUqNGjdxdCsow/urjmpCbm6sHH3yQoANYTN26dXX69Gl3l4Eyjr/8uCb07t1b77//vrvLAFDCJkyYoGeeeUZffPGFMjIylJ2d7bIAEoexcI0YOHCgFixYoEaNGqlhw4aFJjFOmjTJTZUBuBIFe2v/PFfHGMMFQ+HEFZRxTdi1a5duuukmSdLu3btd+rjiKlB+rVu3zt0loBxgzw4AALA05uwAAMq1DRs26JFHHtFtt92m3377TZK0cOFCbdy40c2Voawg7AAAyq0PP/xQ8fHx8vPz044dO5STkyNJysrK0rhx49xcHcoKwg4AoNwaO3asZs6cqTlz5riceBAXF6cdO3a4sTKUJYQdAEC5lZycrFatWhVqdzgcOnbsWOkXhDKJsAMAKLciIiK0b9++Qu0bN25UjRo13FARyiLCDgCg3Orbt6+efvppbd26VTabTYcPH9aiRYs0dOhQ9evXz93loYzgOjsAgHLlu+++U4MGDeTh4aHhw4crPz9fbdu21alTp9SqVSvZ7XYNHTpUAwYMcHepKCO4zg4AoFzx9PTUkSNHVKlSJdWoUUPbtm1ThQoVtG/fPp04cUIxMTEKDAx0d5koQ9izAwAoV4KDg3Xw4EFVqlRJP//8s/Lz8+Xj46OYmBh3l4YyirADAChXunbtqtatW6ty5cqy2Wxq2rSpPD09ixx74MCBUq4OZRFhBwBQrsyePVtdunTRvn37NHDgQPXt21cVKlRwd1kow5izAwAotxISEjR58mTCDi6KsAMAACyN6+wAAABLI+wAAABLI+wAAABLI+wAKHdWrlypOXPmuLsMAOUEp54DKFd+/fVXPfXUUwoLC9MNN9ygjh07urskAGUce3YAlAmpqal6+umnVbNmTfn6+io8PFxxcXGaMWOGTp065Rz3xBNPaOrUqVqyZIleeOEFZWVlubFqAOUBp54DcLsDBw4oLi5OwcHBGjVqlG688UbZ7Xbt2rVLs2fP1hNPPKF7773XLbUZY5SXlycvL3aEA+UVe3YAuN1TTz0lLy8vff311+rWrZvq1aunGjVq6L777tOyZct0zz33SJKOHTumxx57TGFhYQoKClKbNm307bffOh/nlVdeUePGjbVw4UJVr15dDodD3bt31/Hjx51j8vPzNX78eEVFRcnPz0+NGjXSkiVLnP1ffPGFbDabPvvsMzVp0kR2u10bN25UTk6OBg4cqEqVKsnX11ctWrTQtm3bSu9JAlBshB0AbpWRkaFVq1YpMTFRAQEBRY6x2WySpAceeEDp6en67LPPtH37dt18881q27atMjMznWP379+vjz/+WEuXLtXSpUu1fv16TZgwwdk/fvx4LViwQDNnztSePXs0ePBgPfLII1q/fr3LOp9//nlNmDBBP/zwgxo2bKhhw4bpww8/1Ntvv60dO3aoZs2aio+Pd1k3gDLKAIAbbdmyxUgyH330kUt7aGioCQgIMAEBAWbYsGFmw4YNJigoyJw5c8ZlXHR0tJk1a5YxxpiRI0caf39/k52d7ex/9tlnTfPmzY0xxpw5c8b4+/ubzZs3uzxGnz59zEMPPWSMMWbdunVGkvn444+d/SdOnDDe3t5m0aJFzrbc3FxTpUoVM3HixBJ4FgBcTRyEBlAmffXVV8rPz1ePHj2Uk5Ojb7/9VidOnFBoaKjLuNOnT2v//v3O29WrV3f5nqTKlSsrPT1dkrRv3z6dOnVKd955p8tj5Obm6qabbnJpa9q0qfPn/fv36+zZs4qLi3O2eXt7q1mzZvrhhx+ufGMBXFWEHQBuVbNmTdlsNiUnJ7u016hRQ5Lk5+cnSTpx4oQqV66sL774otBjBAcHO3/29vZ26bPZbMrPz3c+hiQtW7ZM119/vcs4u93ucvtCh9QAlD+EHQBuFRoaqjvvvFNTp07VgAEDLhgybr75ZqWmpsrLy0vVq1cv1rpiYmJkt9uVkpKi1q1bX/L9oqOj5ePjo02bNikyMlKSdPbsWW3btk2DBg0qVi0ASg9hB4DbTZ8+XXFxcWratKleeeUVNWzYUB4eHtq2bZv27t2rJk2aqF27doqNjVXnzp01ceJE1a5dW4cPH9ayZcv0t7/9zeWw04VUqFBBQ4cO1eDBg5Wfn68WLVooKytLmzZtUlBQkHr37l3k/QICAtSvXz89++yzCgkJUbVq1TRx4kSdOnVKffr0KemnA0AJI+wAcLvo6Gjt3LlT48aN0/Dhw/Xrr7/KbrcrJiZGQ4cO1VNPPSWbzably5frxRdfVEJCgo4ePaqIiAi1atVK4eHhl7yuMWPGKCwsTOPHj9eBAwcUHBysm2++WS+88MJF7zdhwgTl5+erZ8+eOn78uJo2baqVK1eqYsWKV7r5AK4yLioIAAAsjevsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAASyPsAAAAS/t/RKzPYvLtU1QAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "gender_counts = df['Sex'].value_counts()\n", + "gender_counts.plot(kind='bar', color=['lightblue', 'lightcoral'])\n", + "plt.title('Contagem de Passageiros por Gênero')\n", + "plt.xlabel('Gênero')\n", + "plt.ylabel('Contagem')\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "survival_by_class = df.groupby('Pclass')['Survived'].mean()\n", + "survival_by_class.plot(kind='bar', color='lightgreen')\n", + "plt.title('Taxa de Sobrevivência por Classe')\n", + "plt.xlabel('Classe')\n", + "plt.ylabel('Taxa de Sobrevivência')\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(8, 6))\n", + "for pclass in df['Pclass'].unique():\n", + " age_data = df[df['Pclass'] == pclass]['Age'].dropna()\n", + " plt.hist(age_data, bins=20, alpha=0.6, label=f'Classe {pclass}')\n", + "plt.title('Distribuição de Idades dos Passageiros por Classe')\n", + "plt.xlabel('Idade')\n", + "plt.ylabel('Frequência')\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "url = 'https://raw.githubusercontent.com/datasciencedojo/datasets/master/titanic.csv'\n", + "df = pd.read_csv(url)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(8, 6))" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "class_names = ['Primeira Classe', 'Segunda Classe', 'Terceira Classe']\n", + "\n", + "for pclass in df['Pclass'].unique():\n", + " age_data = df[df['Pclass'] == pclass]['Age'].dropna()\n", + " plt.hist(age_data, bins=20, alpha=0.6, label=class_names[pclass - 1])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: matplotlib in c:\\users\\webfoco\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (3.8.0)\n", + "Requirement already satisfied: contourpy>=1.0.1 in c:\\users\\webfoco\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from matplotlib) (1.1.1)\n", + "Requirement already satisfied: cycler>=0.10 in c:\\users\\webfoco\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from matplotlib) (0.12.1)\n", + "Requirement already satisfied: fonttools>=4.22.0 in c:\\users\\webfoco\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from matplotlib) (4.43.1)\n", + "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\users\\webfoco\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from matplotlib) (1.4.5)\n", + "Requirement already satisfied: numpy<2,>=1.21 in c:\\users\\webfoco\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from matplotlib) (1.26.0)\n", + "Requirement already satisfied: packaging>=20.0 in c:\\users\\webfoco\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from matplotlib) (23.2)\n", + "Requirement already satisfied: pillow>=6.2.0 in c:\\users\\webfoco\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from matplotlib) (10.1.0)\n", + "Requirement already satisfied: pyparsing>=2.3.1 in c:\\users\\webfoco\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from matplotlib) (3.1.1)\n", + "Requirement already satisfied: python-dateutil>=2.7 in c:\\users\\webfoco\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from matplotlib) (2.8.2)\n", + "Requirement already satisfied: six>=1.5 in c:\\users\\webfoco\\appdata\\local\\packages\\pythonsoftwarefoundation.python.3.11_qbz5n2kfra8p0\\localcache\\local-packages\\python311\\site-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "[notice] A new release of pip is available: 23.2.1 -> 23.3.1\n", + "[notice] To update, run: C:\\Users\\Webfoco\\AppData\\Local\\Microsoft\\WindowsApps\\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\\python.exe -m pip install --upgrade pip\n" + ] + } + ], + "source": [ + "!pip install matplotlib" + ] + } + ], + "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": "3.11.6" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/assets/exercicio.casa/semana12.ipynb b/assets/exercicio.casa/semana12.ipynb new file mode 100644 index 0000000..e69de29 diff --git a/exercicios/para-casa/README.md b/exercicios/para-casa/README.md deleted file mode 100644 index ac13de2..0000000 --- a/exercicios/para-casa/README.md +++ /dev/null @@ -1,33 +0,0 @@ -# Exercício de Casa 🏠 - -## Projeto da Semana 12 - -Nesta semana iremos trabalhar novamente com os dados do navio "Titanic", que contém informações sobre os passageiros que estavam a bordo da viagem. Você deve gerar um notebook em arquivo .ipynb com análises básicas para esse dataset (Titanic) ou para outro dataset (caso desejar) utilizando Pandas, Matplotlib e Numpy. - -Formato do notebook: - - ● Introdução - ○ Breve descrição da base de dados - - ● Processamento - ○ Função de processamento - ○ Leitura e tratamento do DF - - ● Visualização dos Dados - ○ gerar gráficos - ○ extrair ideias e conclusões - -Atividades obrigatórias no desenvolvimento: - ● 3 diferentes gráficos - ---- - -Terminou o exercício? Dá uma olhada nessa checklist e confere se tá tudo certinho, combinado?! - -- [ ] Fiz o fork do repositório. -- [ ] Clonei o fork na minha máquina (`git clone url-do-meu-fork`). -- [ ] Resolvi o exercício. -- [ ] Adicionei as mudanças. (`git add .` para adicionar todos os arquivos, ou `git add nome_do_arquivo` para adicionar um arquivo específico) -- [ ] Commitei a cada mudança significativa ou na finalização do exercício (`git commit -m "Mensagem do commit"`) -- [ ] Pushei os commits na minha branch (`git push origin nome-da-branch`) -- [ ] Criei um Pull Request seguindo as orientaçoes que estao nesse [documento](https://github.com/mflilian/repo-example/blob/main/exercicios/para-casa/instrucoes-pull-request.md). diff --git a/exercicios/para-sala/README.md b/exercicios/para-sala/README.md deleted file mode 100644 index 1d08782..0000000 --- a/exercicios/para-sala/README.md +++ /dev/null @@ -1,15 +0,0 @@ -# Exercício de Sala 🏫 - -## Semana 12 - -Essa semana teremos 5 exercícios que iremos resolver no horário de aula do sábado, por isso, os exercícios estarão no arquivo .ipynb --> [Material da aula](https://github.com/reprograma/on26-python-s12-pandas-numpy-II/tree/main/material) ---- - -Terminou o exercício? Dá uma olhada nessa checklist e confere se tá tudo certinho, combinado?! - -- [ ] Fiz o fork do repositório. -- [ ] Clonei o fork na minha máquina (`git clone url-do-meu-fork`). -- [ ] Resolvi o exercício. -- [ ] Adicionei as mudanças. (`git add .` para adicionar todos os arquivos, ou `git add nome_do_arquivo` para adicionar um arquivo específico) -- [ ] Commitei a cada mudança significativa ou na finalização do exercício (`git commit -m "Mensagem do commit"`) -- [ ] Pushei os commits na minha branch (`git push origin nome-da-branch`) diff --git a/exercicios/para-casa/instrucoes-pull-request.md b/instrucoes-pull-request.md similarity index 100% rename from exercicios/para-casa/instrucoes-pull-request.md rename to instrucoes-pull-request.md diff --git a/material/aula_revisao.ipynb b/material/aula_revisao.ipynb deleted file mode 100644 index 916e3c7..0000000 --- a/material/aula_revisao.ipynb +++ /dev/null @@ -1,2858 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Revisão Conteúdo Semana 12" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Bibliotecas" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Carregando os dados" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
0103Braund, Mr. Owen Harrismale22.010A/5 211717.2500NaNS
1211Cumings, Mrs. John Bradley (Florence Briggs Th...female38.010PC 1759971.2833C85C
2313Heikkinen, Miss. Lainafemale26.000STON/O2. 31012827.9250NaNS
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000C123S
4503Allen, Mr. William Henrymale35.0003734508.0500NaNS
.......................................
88688702Montvila, Rev. Juozasmale27.00021153613.0000NaNS
88788811Graham, Miss. Margaret Edithfemale19.00011205330.0000B42S
88888903Johnston, Miss. Catherine Helen \"Carrie\"femaleNaN12W./C. 660723.4500NaNS
88989011Behr, Mr. Karl Howellmale26.00011136930.0000C148C
89089103Dooley, Mr. Patrickmale32.0003703767.7500NaNQ
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" - ], - "text/plain": [ - " PassengerId Survived Pclass \\\n", - "0 1 0 3 \n", - "1 2 1 1 \n", - "2 3 1 3 \n", - "3 4 1 1 \n", - "4 5 0 3 \n", - ".. ... ... ... \n", - "886 887 0 2 \n", - "887 888 1 1 \n", - "888 889 0 3 \n", - "889 890 1 1 \n", - "890 891 0 3 \n", - "\n", - " Name Sex Age SibSp \\\n", - "0 Braund, Mr. Owen Harris male 22.0 1 \n", - "1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n", - "2 Heikkinen, Miss. Laina female 26.0 0 \n", - "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n", - "4 Allen, Mr. William Henry male 35.0 0 \n", - ".. ... ... ... ... \n", - "886 Montvila, Rev. Juozas male 27.0 0 \n", - "887 Graham, Miss. Margaret Edith female 19.0 0 \n", - "888 Johnston, Miss. Catherine Helen \"Carrie\" female NaN 1 \n", - "889 Behr, Mr. Karl Howell male 26.0 0 \n", - "890 Dooley, Mr. Patrick male 32.0 0 \n", - "\n", - " Parch Ticket Fare Cabin Embarked \n", - "0 0 A/5 21171 7.2500 NaN S \n", - "1 0 PC 17599 71.2833 C85 C \n", - "2 0 STON/O2. 3101282 7.9250 NaN S \n", - "3 0 113803 53.1000 C123 S \n", - "4 0 373450 8.0500 NaN S \n", - ".. ... ... ... ... ... \n", - "886 0 211536 13.0000 NaN S \n", - "887 0 112053 30.0000 B42 S \n", - "888 2 W./C. 6607 23.4500 NaN S \n", - "889 0 111369 30.0000 C148 C \n", - "890 0 370376 7.7500 NaN Q \n", - "\n", - "[891 rows x 12 columns]" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# carregando os dados\n", - "df = pd.read_csv('titanic.csv')\n", - "df" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Mudando o display da tela" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# Option\n", - "display_settings = {\n", - " 'max_columns': 999,\n", - " 'width': 4096, \n", - " 'max_rows': 999,\n", - " 'precision': 2,\n", - "}\n", - "\n", - "for op, value in display_settings.items():\n", - " pd.set_option(\"display.{}\".format(op), value)" - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
0103Braund, Mr. Owen Harrismale22.0010A/5 211717.25NaNS
1211Cumings, Mrs. John Bradley (Florence Briggs Th...female38.0010PC 1759971.28C85C
2313Heikkinen, Miss. Lainafemale26.0000STON/O2. 31012827.92NaNS
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.001011380353.10C123S
4503Allen, Mr. William Henrymale35.00003734508.05NaNS
5603Moran, Mr. JamesmaleNaN003308778.46NaNQ
6701McCarthy, Mr. Timothy Jmale54.00001746351.86E46S
7803Palsson, Master. Gosta Leonardmale2.003134990921.07NaNS
8913Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)female27.000234774211.13NaNS
91012Nasser, Mrs. Nicholas (Adele Achem)female14.001023773630.07NaNC
101113Sandstrom, Miss. Marguerite Rutfemale4.0011PP 954916.70G6S
111211Bonnell, Miss. Elizabethfemale58.000011378326.55C103S
121303Saundercock, Mr. William Henrymale20.0000A/5. 21518.05NaNS
131403Andersson, Mr. Anders Johanmale39.001534708231.27NaNS
141503Vestrom, Miss. Hulda Amanda Adolfinafemale14.00003504067.85NaNS
151612Hewlett, Mrs. (Mary D Kingcome)female55.000024870616.00NaNS
161703Rice, Master. Eugenemale2.004138265229.12NaNQ
171812Williams, Mr. Charles EugenemaleNaN0024437313.00NaNS
181903Vander Planke, Mrs. Julius (Emelia Maria Vande...female31.001034576318.00NaNS
192013Masselmani, Mrs. FatimafemaleNaN0026497.22NaNC
202102Fynney, Mr. Joseph Jmale35.000023986526.00NaNS
212212Beesley, Mr. Lawrencemale34.000024869813.00D56S
222313McGowan, Miss. Anna \"Annie\"female15.00003309238.03NaNQ
232411Sloper, Mr. William Thompsonmale28.000011378835.50A6S
242503Palsson, Miss. Torborg Danirafemale8.003134990921.07NaNS
252613Asplund, Mrs. Carl Oscar (Selma Augusta Emilia...female38.001534707731.39NaNS
262703Emir, Mr. Farred ChehabmaleNaN0026317.22NaNC
272801Fortune, Mr. Charles Alexandermale19.003219950263.00C23 C25 C27S
282913O'Dwyer, Miss. Ellen \"Nellie\"femaleNaN003309597.88NaNQ
293003Todoroff, Mr. LaliomaleNaN003492167.90NaNS
303101Uruchurtu, Don. Manuel Emale40.0000PC 1760127.72NaNC
313211Spencer, Mrs. William Augustus (Marie Eugenie)femaleNaN10PC 17569146.52B78C
323313Glynn, Miss. Mary AgathafemaleNaN003356777.75NaNQ
333402Wheadon, Mr. Edward Hmale66.0000C.A. 2457910.50NaNS
343501Meyer, Mr. Edgar Josephmale28.0010PC 1760482.17NaNC
353601Holverson, Mr. Alexander Oskarmale42.001011378952.00NaNS
363713Mamee, Mr. HannamaleNaN0026777.23NaNC
373803Cann, Mr. Ernest Charlesmale21.0000A./5. 21528.05NaNS
383903Vander Planke, Miss. Augusta Mariafemale18.002034576418.00NaNS
394013Nicola-Yarred, Miss. Jamilafemale14.0010265111.24NaNC
404103Ahlin, Mrs. Johan (Johanna Persdotter Larsson)female40.001075469.47NaNS
414202Turpin, Mrs. William John Robert (Dorothy Ann ...female27.00101166821.00NaNS
424303Kraeff, Mr. TheodormaleNaN003492537.90NaNC
434412Laroche, Miss. Simonne Marie Anne Andreefemale3.0012SC/Paris 212341.58NaNC
444513Devaney, Miss. Margaret Deliafemale19.00003309587.88NaNQ
454603Rogers, Mr. William JohnmaleNaN00S.C./A.4. 235678.05NaNS
464703Lennon, Mr. DenismaleNaN1037037115.50NaNQ
474813O'Driscoll, Miss. BridgetfemaleNaN00143117.75NaNQ
484903Samaan, Mr. YoussefmaleNaN20266221.68NaNC
495003Arnold-Franchi, Mrs. Josef (Josefine Franchi)female18.001034923717.80NaNS
505103Panula, Master. Juha Niilomale7.0041310129539.69NaNS
515203Nosworthy, Mr. Richard Catermale21.0000A/4. 398867.80NaNS
525311Harper, Mrs. Henry Sleeper (Myna Haxtun)female49.0010PC 1757276.73D33C
535412Faunthorpe, Mrs. Lizzie (Elizabeth Anne Wilkin...female29.0010292626.00NaNS
545501Ostby, Mr. Engelhart Corneliusmale65.000111350961.98B30C
555611Woolner, Mr. HughmaleNaN001994735.50C52S
565712Rugg, Miss. Emilyfemale21.0000C.A. 3102610.50NaNS
575803Novel, Mr. Mansouermale28.500026977.23NaNC
585912West, Miss. Constance Miriumfemale5.0012C.A. 3465127.75NaNS
596003Goodwin, Master. William Frederickmale11.0052CA 214446.90NaNS
606103Sirayanian, Mr. Orsenmale22.000026697.23NaNC
616211Icard, Miss. Ameliefemale38.000011357280.00B28NaN
626301Harris, Mr. Henry Birkhardtmale45.00103697383.47C83S
636403Skoog, Master. Haraldmale4.003234708827.90NaNS
646501Stewart, Mr. Albert AmaleNaN00PC 1760527.72NaNC
656613Moubarek, Master. GeriosmaleNaN11266115.25NaNC
666712Nye, Mrs. (Elizabeth Ramell)female29.0000C.A. 2939510.50F33S
676803Crease, Mr. Ernest Jamesmale19.0000S.P. 34648.16NaNS
686913Andersson, Miss. Erna Alexandrafemale17.004231012817.92NaNS
697003Kink, Mr. Vincenzmale26.00203151518.66NaNS
707102Jenkin, Mr. Stephen Curnowmale32.0000C.A. 3311110.50NaNS
717203Goodwin, Miss. Lillian Amyfemale16.0052CA 214446.90NaNS
727302Hood, Mr. Ambrose Jrmale21.0000S.O.C. 1487973.50NaNS
737403Chronopoulos, Mr. Apostolosmale26.0010268014.45NaNC
747513Bing, Mr. Leemale32.0000160156.50NaNS
757603Moen, Mr. Sigurd Hansenmale25.00003481237.65F G73S
767703Staneff, Mr. IvanmaleNaN003492087.90NaNS
777803Moutal, Mr. Rahamin HaimmaleNaN003747468.05NaNS
787912Caldwell, Master. Alden Gatesmale0.830224873829.00NaNS
798013Dowdell, Miss. Elizabethfemale30.000036451612.47NaNS
808103Waelens, Mr. Achillemale22.00003457679.00NaNS
818213Sheerlinck, Mr. Jan Baptistmale29.00003457799.50NaNS
828313McDermott, Miss. Brigdet DeliafemaleNaN003309327.79NaNQ
838401Carrau, Mr. Francisco Mmale28.000011305947.10NaNS
848512Ilett, Miss. Berthafemale17.0000SO/C 1488510.50NaNS
858613Backstrom, Mrs. Karl Alfred (Maria Mathilda Gu...female33.0030310127815.85NaNS
868703Ford, Mr. William Nealmale16.0013W./C. 660834.38NaNS
878803Slocovski, Mr. Selman FrancismaleNaN00SOTON/OQ 3920868.05NaNS
888911Fortune, Miss. Mabel Helenfemale23.003219950263.00C23 C25 C27S
899003Celotti, Mr. Francescomale24.00003432758.05NaNS
909103Christmann, Mr. Emilmale29.00003432768.05NaNS
919203Andreasson, Mr. Paul Edvinmale20.00003474667.85NaNS
929301Chaffee, Mr. Herbert Fullermale46.0010W.E.P. 573461.17E31S
939403Dean, Mr. Bertram Frankmale26.0012C.A. 231520.57NaNS
949503Coxon, Mr. Danielmale59.00003645007.25NaNS
959603Shorney, Mr. Charles JosephmaleNaN003749108.05NaNS
969701Goldschmidt, Mr. George Bmale71.0000PC 1775434.65A5C
979811Greenfield, Mr. William Bertrammale23.0001PC 1775963.36D10 D12C
989912Doling, Mrs. John T (Ada Julia Bone)female34.000123191923.00NaNS
9910002Kantor, Mr. Sinaimale34.001024436726.00NaNS
\n", - "
" - ], - "text/plain": [ - " PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked\n", - "0 1 0 3 Braund, Mr. Owen Harris male 22.00 1 0 A/5 21171 7.25 NaN S\n", - "1 2 1 1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.00 1 0 PC 17599 71.28 C85 C\n", - "2 3 1 3 Heikkinen, Miss. Laina female 26.00 0 0 STON/O2. 3101282 7.92 NaN S\n", - "3 4 1 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.00 1 0 113803 53.10 C123 S\n", - "4 5 0 3 Allen, Mr. William Henry male 35.00 0 0 373450 8.05 NaN S\n", - "5 6 0 3 Moran, Mr. James male NaN 0 0 330877 8.46 NaN Q\n", - "6 7 0 1 McCarthy, Mr. Timothy J male 54.00 0 0 17463 51.86 E46 S\n", - "7 8 0 3 Palsson, Master. Gosta Leonard male 2.00 3 1 349909 21.07 NaN S\n", - "8 9 1 3 Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg) female 27.00 0 2 347742 11.13 NaN S\n", - "9 10 1 2 Nasser, Mrs. Nicholas (Adele Achem) female 14.00 1 0 237736 30.07 NaN C\n", - "10 11 1 3 Sandstrom, Miss. Marguerite Rut female 4.00 1 1 PP 9549 16.70 G6 S\n", - "11 12 1 1 Bonnell, Miss. Elizabeth female 58.00 0 0 113783 26.55 C103 S\n", - "12 13 0 3 Saundercock, Mr. William Henry male 20.00 0 0 A/5. 2151 8.05 NaN S\n", - "13 14 0 3 Andersson, Mr. Anders Johan male 39.00 1 5 347082 31.27 NaN S\n", - "14 15 0 3 Vestrom, Miss. Hulda Amanda Adolfina female 14.00 0 0 350406 7.85 NaN S\n", - "15 16 1 2 Hewlett, Mrs. (Mary D Kingcome) female 55.00 0 0 248706 16.00 NaN S\n", - "16 17 0 3 Rice, Master. Eugene male 2.00 4 1 382652 29.12 NaN Q\n", - "17 18 1 2 Williams, Mr. Charles Eugene male NaN 0 0 244373 13.00 NaN S\n", - "18 19 0 3 Vander Planke, Mrs. Julius (Emelia Maria Vande... female 31.00 1 0 345763 18.00 NaN S\n", - "19 20 1 3 Masselmani, Mrs. Fatima female NaN 0 0 2649 7.22 NaN C\n", - "20 21 0 2 Fynney, Mr. Joseph J male 35.00 0 0 239865 26.00 NaN S\n", - "21 22 1 2 Beesley, Mr. Lawrence male 34.00 0 0 248698 13.00 D56 S\n", - "22 23 1 3 McGowan, Miss. Anna \"Annie\" female 15.00 0 0 330923 8.03 NaN Q\n", - "23 24 1 1 Sloper, Mr. William Thompson male 28.00 0 0 113788 35.50 A6 S\n", - "24 25 0 3 Palsson, Miss. Torborg Danira female 8.00 3 1 349909 21.07 NaN S\n", - "25 26 1 3 Asplund, Mrs. Carl Oscar (Selma Augusta Emilia... female 38.00 1 5 347077 31.39 NaN S\n", - "26 27 0 3 Emir, Mr. Farred Chehab male NaN 0 0 2631 7.22 NaN C\n", - "27 28 0 1 Fortune, Mr. Charles Alexander male 19.00 3 2 19950 263.00 C23 C25 C27 S\n", - "28 29 1 3 O'Dwyer, Miss. Ellen \"Nellie\" female NaN 0 0 330959 7.88 NaN Q\n", - "29 30 0 3 Todoroff, Mr. Lalio male NaN 0 0 349216 7.90 NaN S\n", - "30 31 0 1 Uruchurtu, Don. Manuel E male 40.00 0 0 PC 17601 27.72 NaN C\n", - "31 32 1 1 Spencer, Mrs. William Augustus (Marie Eugenie) female NaN 1 0 PC 17569 146.52 B78 C\n", - "32 33 1 3 Glynn, Miss. Mary Agatha female NaN 0 0 335677 7.75 NaN Q\n", - "33 34 0 2 Wheadon, Mr. Edward H male 66.00 0 0 C.A. 24579 10.50 NaN S\n", - "34 35 0 1 Meyer, Mr. Edgar Joseph male 28.00 1 0 PC 17604 82.17 NaN C\n", - "35 36 0 1 Holverson, Mr. Alexander Oskar male 42.00 1 0 113789 52.00 NaN S\n", - "36 37 1 3 Mamee, Mr. Hanna male NaN 0 0 2677 7.23 NaN C\n", - "37 38 0 3 Cann, Mr. Ernest Charles male 21.00 0 0 A./5. 2152 8.05 NaN S\n", - "38 39 0 3 Vander Planke, Miss. Augusta Maria female 18.00 2 0 345764 18.00 NaN S\n", - "39 40 1 3 Nicola-Yarred, Miss. Jamila female 14.00 1 0 2651 11.24 NaN C\n", - "40 41 0 3 Ahlin, Mrs. Johan (Johanna Persdotter Larsson) female 40.00 1 0 7546 9.47 NaN S\n", - "41 42 0 2 Turpin, Mrs. William John Robert (Dorothy Ann ... female 27.00 1 0 11668 21.00 NaN S\n", - "42 43 0 3 Kraeff, Mr. Theodor male NaN 0 0 349253 7.90 NaN C\n", - "43 44 1 2 Laroche, Miss. Simonne Marie Anne Andree female 3.00 1 2 SC/Paris 2123 41.58 NaN C\n", - "44 45 1 3 Devaney, Miss. Margaret Delia female 19.00 0 0 330958 7.88 NaN Q\n", - "45 46 0 3 Rogers, Mr. William John male NaN 0 0 S.C./A.4. 23567 8.05 NaN S\n", - "46 47 0 3 Lennon, Mr. Denis male NaN 1 0 370371 15.50 NaN Q\n", - "47 48 1 3 O'Driscoll, Miss. Bridget female NaN 0 0 14311 7.75 NaN Q\n", - "48 49 0 3 Samaan, Mr. Youssef male NaN 2 0 2662 21.68 NaN C\n", - "49 50 0 3 Arnold-Franchi, Mrs. Josef (Josefine Franchi) female 18.00 1 0 349237 17.80 NaN S\n", - "50 51 0 3 Panula, Master. Juha Niilo male 7.00 4 1 3101295 39.69 NaN S\n", - "51 52 0 3 Nosworthy, Mr. Richard Cater male 21.00 0 0 A/4. 39886 7.80 NaN S\n", - "52 53 1 1 Harper, Mrs. Henry Sleeper (Myna Haxtun) female 49.00 1 0 PC 17572 76.73 D33 C\n", - "53 54 1 2 Faunthorpe, Mrs. Lizzie (Elizabeth Anne Wilkin... female 29.00 1 0 2926 26.00 NaN S\n", - "54 55 0 1 Ostby, Mr. Engelhart Cornelius male 65.00 0 1 113509 61.98 B30 C\n", - "55 56 1 1 Woolner, Mr. Hugh male NaN 0 0 19947 35.50 C52 S\n", - "56 57 1 2 Rugg, Miss. Emily female 21.00 0 0 C.A. 31026 10.50 NaN S\n", - "57 58 0 3 Novel, Mr. Mansouer male 28.50 0 0 2697 7.23 NaN C\n", - "58 59 1 2 West, Miss. Constance Mirium female 5.00 1 2 C.A. 34651 27.75 NaN S\n", - "59 60 0 3 Goodwin, Master. William Frederick male 11.00 5 2 CA 2144 46.90 NaN S\n", - "60 61 0 3 Sirayanian, Mr. Orsen male 22.00 0 0 2669 7.23 NaN C\n", - "61 62 1 1 Icard, Miss. Amelie female 38.00 0 0 113572 80.00 B28 NaN\n", - "62 63 0 1 Harris, Mr. Henry Birkhardt male 45.00 1 0 36973 83.47 C83 S\n", - "63 64 0 3 Skoog, Master. Harald male 4.00 3 2 347088 27.90 NaN S\n", - "64 65 0 1 Stewart, Mr. Albert A male NaN 0 0 PC 17605 27.72 NaN C\n", - "65 66 1 3 Moubarek, Master. Gerios male NaN 1 1 2661 15.25 NaN C\n", - "66 67 1 2 Nye, Mrs. (Elizabeth Ramell) female 29.00 0 0 C.A. 29395 10.50 F33 S\n", - "67 68 0 3 Crease, Mr. Ernest James male 19.00 0 0 S.P. 3464 8.16 NaN S\n", - "68 69 1 3 Andersson, Miss. Erna Alexandra female 17.00 4 2 3101281 7.92 NaN S\n", - "69 70 0 3 Kink, Mr. Vincenz male 26.00 2 0 315151 8.66 NaN S\n", - "70 71 0 2 Jenkin, Mr. Stephen Curnow male 32.00 0 0 C.A. 33111 10.50 NaN S\n", - "71 72 0 3 Goodwin, Miss. Lillian Amy female 16.00 5 2 CA 2144 46.90 NaN S\n", - "72 73 0 2 Hood, Mr. Ambrose Jr male 21.00 0 0 S.O.C. 14879 73.50 NaN S\n", - "73 74 0 3 Chronopoulos, Mr. Apostolos male 26.00 1 0 2680 14.45 NaN C\n", - "74 75 1 3 Bing, Mr. Lee male 32.00 0 0 1601 56.50 NaN S\n", - "75 76 0 3 Moen, Mr. Sigurd Hansen male 25.00 0 0 348123 7.65 F G73 S\n", - "76 77 0 3 Staneff, Mr. Ivan male NaN 0 0 349208 7.90 NaN S\n", - "77 78 0 3 Moutal, Mr. Rahamin Haim male NaN 0 0 374746 8.05 NaN S\n", - "78 79 1 2 Caldwell, Master. Alden Gates male 0.83 0 2 248738 29.00 NaN S\n", - "79 80 1 3 Dowdell, Miss. Elizabeth female 30.00 0 0 364516 12.47 NaN S\n", - "80 81 0 3 Waelens, Mr. Achille male 22.00 0 0 345767 9.00 NaN S\n", - "81 82 1 3 Sheerlinck, Mr. Jan Baptist male 29.00 0 0 345779 9.50 NaN S\n", - "82 83 1 3 McDermott, Miss. Brigdet Delia female NaN 0 0 330932 7.79 NaN Q\n", - "83 84 0 1 Carrau, Mr. Francisco M male 28.00 0 0 113059 47.10 NaN S\n", - "84 85 1 2 Ilett, Miss. Bertha female 17.00 0 0 SO/C 14885 10.50 NaN S\n", - "85 86 1 3 Backstrom, Mrs. Karl Alfred (Maria Mathilda Gu... female 33.00 3 0 3101278 15.85 NaN S\n", - "86 87 0 3 Ford, Mr. William Neal male 16.00 1 3 W./C. 6608 34.38 NaN S\n", - "87 88 0 3 Slocovski, Mr. Selman Francis male NaN 0 0 SOTON/OQ 392086 8.05 NaN S\n", - "88 89 1 1 Fortune, Miss. Mabel Helen female 23.00 3 2 19950 263.00 C23 C25 C27 S\n", - "89 90 0 3 Celotti, Mr. Francesco male 24.00 0 0 343275 8.05 NaN S\n", - "90 91 0 3 Christmann, Mr. Emil male 29.00 0 0 343276 8.05 NaN S\n", - "91 92 0 3 Andreasson, Mr. Paul Edvin male 20.00 0 0 347466 7.85 NaN S\n", - "92 93 0 1 Chaffee, Mr. Herbert Fuller male 46.00 1 0 W.E.P. 5734 61.17 E31 S\n", - "93 94 0 3 Dean, Mr. Bertram Frank male 26.00 1 2 C.A. 2315 20.57 NaN S\n", - "94 95 0 3 Coxon, Mr. Daniel male 59.00 0 0 364500 7.25 NaN S\n", - "95 96 0 3 Shorney, Mr. Charles Joseph male NaN 0 0 374910 8.05 NaN S\n", - "96 97 0 1 Goldschmidt, Mr. George B male 71.00 0 0 PC 17754 34.65 A5 C\n", - "97 98 1 1 Greenfield, Mr. William Bertram male 23.00 0 1 PC 17759 63.36 D10 D12 C\n", - "98 99 1 2 Doling, Mrs. John T (Ada Julia Bone) female 34.00 0 1 231919 23.00 NaN S\n", - "99 100 0 2 Kantor, Mr. Sinai male 34.00 1 0 244367 26.00 NaN S" - ] - }, - "execution_count": 64, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.head(100)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Gráficos" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Gráfico númerico (1 variável)\n", - "df['Age'].plot.hist(bins=20, edgecolor='black', color=\"orange\") \n", - "\n", - "plt.xlabel('Classe', fontweight='bold') \n", - "plt.ylabel('Quantidade') \n", - "plt.title('Distribuição de Classes', color='darkred')\n", - "\n", - "plt.tick_params(axis='x', colors='red')\n", - "plt.tick_params(axis='y', colors='purple')" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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countmeanstdmin0.5%10%50%80%99.5%max
Age714.029.714.530.420.814.028.041.070.7280.0
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" - ], - "text/plain": [ - " count mean std min 0.5% 10% 50% 80% 99.5% max\n", - "Age 714.0 29.7 14.53 0.42 0.8 14.0 28.0 41.0 70.72 80.0" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# análise descritiva da var 'Age'\n", - "df[['Age']].describe(percentiles=[0.005, 0.10, 0.80, 0.995]).T" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
80380413Thomas, Master. Assad Alexandermale0.420126258.52NaNC
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" - ], - "text/plain": [ - " PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked\n", - "803 804 1 3 Thomas, Master. Assad Alexander male 0.42 0 1 2625 8.52 NaN C" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.loc[(df['Age'] == 0.42), :]" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
63063111Barkworth, Mr. Algernon Henry Wilsonmale80.0002704230.0A23S
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" - ], - "text/plain": [ - " PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked\n", - "630 631 1 1 Barkworth, Mr. Algernon Henry Wilson male 80.0 0 0 27042 30.0 A23 S" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.loc[(df['Age'] == 80), :]" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'Distribuição de Classes')" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Gráfico númerico (1 variável)\n", - "\n", - "# filtro com condicional\n", - "filtro_hist = (df['Age'] > 0.8) & (df['Age'] < 70.72)\n", - "\n", - "# gerando o histograma\n", - "df.loc[filtro_hist, 'Age'].plot.hist(bins=20, edgecolor='black')\n", - "\n", - "plt.xlabel('Classe') \n", - "plt.ylabel('Quantidade') \n", - "plt.title('Distribuição de Classes')" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
0103Braund, Mr. Owen Harrismale22.010A/5 211717.25NaNS
1211Cumings, Mrs. John Bradley (Florence Briggs Th...female38.010PC 1759971.28C85C
\n", - "
" - ], - "text/plain": [ - " PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked\n", - "0 1 0 3 Braund, Mr. Owen Harris male 22.0 1 0 A/5 21171 7.25 NaN S\n", - "1 2 1 1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 0 PC 17599 71.28 C85 C" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.head(2)" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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12HWmDgIwM6sye06UagAQQgVr5/Pq9fyWSi8tF9WimfOoVM2WhLqEpz1qB97tUZZEqLLe511tgCQQ0KqiIOx/z1KouI8sZSkUKneJx5xH03yYJgR86v1sjzrW46+4yFCCGatXgtJTVu/Rb6kFOh9Ur7nMOgrAwoISyYZsADgfTyiXlVhEJ5QQ7+lWYnNVoRKXqVhUnd01SCG4LJjFY8fwelcxOWnj8/pom/CDgMOjyv3kr9kGQFd9BjFzluTe57nhgZfz7if/GGvqIA4O4IjCbzyybHH8kUf42h+8jRO7dpFcyDO1+TYA1j81gSE1diXOMB3txEhP8Ucdw1i2ZNWSWuye1V8KQCSidvwDyXEAPHE3uu5m4ewpDLfFwryFVd8GQ2OsMOsYy04xW9OOKznGpnCG6aU8DTaMFmwKRjul4hgeDywkYtiaRFMGALmEykDKWGpRjlYyl6Z1tdi260pEfGW1xOd0ZWH4/VAul7E1CaZ6zrZ/PvtIYdjqX1lbLewFoaIObjuGYdQAEHCr9zcN9Rq6S1kGvkrQe7xG/b4noz7PnqxyJ01XEqJa8k8h0Dl7VqDrOtF5VzWe4PJ4ScQiGG4N34EHEW43d0WH0YXO5WdVcP6LC0p05+rU975YPgVAOq3SfVc39KCbAtmoMzV9Gl+4hthMMwhoPvADAPZf2YKJDekZ9K9cRel7b2LfaPzfvE4cfnNwROE3FNM0md87xswn92OctsgsxcnGnkMiOTvnwWxfgZye5a2La7Gkxfe71GLzW9qjAIwPLSGk5IlMmKJWT6FwgoYGQSKVIN5cRhYs1q++AqtcJtq4gJSQ2XYzAC9bUFbD7lYVF7i1fhSA7oJanCd91yKx6OxUnzNdayFzFtGaFtKLcwjdJlnx6/vnlHtpptKXqAm1qBkVUchqyqfvV2s4lvu8KEjr57OP1KOq+6iSvVQQkqCuIZBoQu3s/S5VA1GUKjYgKp1TPUnlFjoTUCrWszSJFDpPLNYS8hpM6ZIOj4bXnMTj6aZQkLTUNakFvBJPqG1fgbQ0mupsRDFPacMKklqBLeEeIrklcvUbOJQM0VbrY8CQRA2NnvR3EcLFiePqi65IKEtqLH8CgIZlO5CWRluzwD1wAFEb5Z9blFVy+6xKAT6Qa+LWf9nNH/3kKPuT2X/3GnL4v4kjCr9hSGkxMPAcn/vc53joyJPY6TLhWJhQqJ6lmXHqWxewLcncJW8F4Krn1eJwV+4sBZeP6MyzXBRNMpsosLGok7clJ0O3A5LVa5TrZiykFuauqBISqzwEwEK0D4D1Q2oXvdurts0XacrK8CSUe2dE3wRAQ6N670RYuaY6WvuwLYtgJE8ha2LVNiGmlTtquqyOrbWUe0kU1YKfEcr15fMpITDdEvnvWQoVUchULIWcgNC55qUVUfBVWlzkTbX4W6Zy/7hnVdD9qHeBgOGnOTFFOdJF2jRob1DHrPEoV5lldwHQUnGFnYsneAIq/lCfU2J5crVa6K+01OfZ57sEgLYuZZ1cHYihywIe91YyGUlLfRPhBQMRMDh44iE0XSeVUAHt9vHHABi6egUZUeKimlX0TZ9AesL8ycTF6jkf3HxokDccG+F0xhGH3zQcUfgN4vjiEHv2voS5+T8jm00xMnOWeEsZWbK5ZMOtABRTewAYWarBijai9Q/ykmwvyVKK+1fuRCB5f/1uALzTKoPlCWsrAB73YQAG4mNIIfHGPei6QezsCYRuM7NoYLs9BI4O4ZY6e3OTSAQdyQMAxObU6522VCaR368W/EVdLUwNERVc9vqUdVDuWo/MZum0apgpKiEKVgrYzMI5UVALp8ejHpsuCaZa9M+lZ54Tg/PuI/V82jRw6YKSgJBesS5kpRlepXVFpphQn6WkAsqu8ZOIaA3zeoYev3JdLQaWq89Wq6yLXqFcbKmkEqzGohKac/GEQlb9Xbj/cQDuaVLWx1VT6vmvxJTYTkaV62h7+V4AZmZ7AFjjURXSyfASllWieflmChk30TqDwHN3IXw+Ptc9CMDtS+rz7298DTMFN8tbQhysCOhjiymeOfUZ+vv/hHx+CoffDBxR+A3g5FyKWw4NcvOJLHOmm1Jpim3blNvjoEftRiOLEULBOmITQ0Sb45SLNotX/Q4Atx5RC96dlXYRG2L3EzAs+kfi1NqCQ1nBkncbxdI43d2QyWaItZaReYu+VVdQyueobVSvWdh2IzKd4YZMD/FigtMtazDSk2yvSXF2IUNYCo7nDKQwkFLtuheKyi8frrTCFpp6XGxSi21foY50OUPKF8GTU9lOxUqri3Sl9cQ5USi7AAkul7dqKfx8l1RXJQCdMnV85wbsVGYp2JVYhq9iRaSyKiMpn/XiDRhomQRmu8r+6dbVsSNal3rvkFrEO00lnlNTSljq5j3VeIIvFCYZDxCqceEaOgxdHRx3z7My2ElbfJxC7RqeW4qwrCHAiKFcUW3ZezCMWk72a7hcLjomgyDg4NCD6jMLJSJdReVKmrtqHTN6mg3hXraP7Ue6g/zppApcu1ZGqtfN1oBNT/obzM7dzZ6913DgwH0Ui8V/4wpz+L+EIwr/l8ks8Jc/PcxNn32G/HyOgi25z/d+APyBJ/F44OzsBLG2ErJos3OjshasomrGNlLsRHp8BJ47Tl+pkYHMOPuXXYCWX+TPOk9TtiR9SbWr3u99AwDd3WqnPupXrpDuWlWPIMQYAPFKdfPlU2qx3tOgdrWvrhvFlrC8CDlbsuC9nFJpmlBYMJdYQAqJp1gpJjuXgRRWGTq9lcydmUgbWi6GT7fI5CqtLqR6zuVSrimz0rLa6wlUYwrnu6SqH+fcR2nTwOtSC3lQU693rhme11SisJScxeX1Us67CAXUDjvdpL5bb8VNdbSkPueCRyCA5txTGEaEmRmbhtp63IXz8YRo6yoEgiafcsVNb1RW01W6WqwPBi4DoHGZimFc7VXnVcrt2DasaurBVRTYzRrz86NEWzpJzEXxBgwij30ddJ1/Wa12/bdXztGhplcznveysi3MYe85ewluk9+s/tvt7uO++w7w+c9/nsOHDyPl+eMc/m/hiML/QSzbInvg6/D5C7hs8cfYEoonl9CRPJAKseC7knJ5ke3blRvmgD4MQM1ilGAwyvzISWoaU+QyJolrbwfL4u0Dqi/R9+rULvgW8yEAZoYTADyc78TGQLIPTYPB2Bi2LvFVXEjxiWOAZCav8vE7+tUOe7eudusXCuUaCaTU4zGP8m93dtiUSiWyURuZsNB1N9lKBlJWV2mg7Sm1cE+FahFI1gRzJLNKBJK2WsQNo7KoV6avud0+bOtcTEFxbrqC8QJLwes+N2BH7ZBLJSVM3pKyIBbjEwQiKiYQMNQx83XquZ6cijc8k2rEY2ic1W16vFoli2klIGgNqED4uXiCXnH9RGePAPBUh3qNq+aURfe1uNr1n61R73FBQWUSDQ2p/5cVlSynoZSyRkIN29VnCcyiFbJkd27guHue5cEOrhh8DukK8N4pJTRiRZhzXB0u0Jq9r/JI4/TpDQBkMhkO7znIwhePUprK4PB/D0cU/o9xeu4Er73/tXx8+jEoJrl6/g5WB3OMzWfZkRLYwA+N3wNANx4nGBRMzk8z115AFi12VmIL2MrPP+Jaj9R0Wp88SZ3t54nEaWZr2gnOH+TGhhiTiznWlQSzJZux0GsxzSXWrrXJ5/PMtxWReYu1Ky8jl0pQ05gknShTWr0NBsfoMaMcSo9RNLy0JdT7JedVXGFQKtdQXZ0SrqVwESxJe+tq0rE5hGaTLqkFv77Sz2LGq2oZVvvTpApl3BISlapkvbJglytuII/Hj2X92+6jc6KQNHU8FfeRvzJLoViqNOPLq0ph0yzhqRTO+U21uz8bVoLUszSJdAXYkwjT0RDAFILVbnVMoaAsgCZTWQDn4gnZRBOaLggceBARDvNAcIQWXwNrZk5SjK7gicVaepqCjOuSdX6oLezB61nJ5ISkPlpH3YwLETI40v8InkCQxdk2NF3Q8MwdAHxnixKZt5bdCCTHml/JSM7L6vYIRz1KHnXgluLnq9eU23UFE+NKMjVN48JsL6XxNPP/dJj5B09SzDnV0f+XcETh/wh2Nsvsxz6O/bsfYGhxgHuXjnNg2QWIUoYvNatA5NixBYI2PJ8xGA29AcvKsHWb2rEfQFkL0aU6AoEos0NHCdVmSS6VyVzxW8hMlndOrFLpqd3KJfTummcACM+oBfNZ7XoAmltU4dnIuVGUdZsAcLlVgDXZdy1IyctinRStIoc6NqBnZrikNsnobBqvhBPFSoqnV8UI4m61K22pX45tmQRqCqSSFlIIgvPquWlDCUCvN4WUEJWCRKVHRbV9tq4WfLfLh21WLIVzalDBsC2k7saSGi5DiUIAFYMpVJrhebMC/Oq1XZ4a9VmzqjHeyUACj+6hPT5OrmYFUgpqalVcpqfiRostqO9XH/dV4wmh+iZyaQ8NDQItHSe9uZeSsLjSqwTkSPByAGo71d9e4VJdVjNZZT2sCXQBsBRcQEqbxu4dWGWdZU0l9JlRzE2redw3Rpu/iRvPPI00fLx3+goArBdYCTdHktQWVMKBrvk5eLCj+tymtrWEY5WWgQKeffJO7njP7zG473kc/m/giML/Acb7F/nRp48Re/BxxOlh/nxcLdofD3sxNYNlk/dwW8ss8WyJzQtqIfyO9UpsdKR8gro6jZnYLDPLCsiCxSUbbwUpcbuUC2KsQQUh1z81gY7gJ7lxioaX5XMP0Ogp0z8UJyIFT6UDFPQWyuX9+PyCofkxLJfEt+RF03SSsyrQOW+oLKKNw2r3ubtGuVBeGR2hbElWlDXGizY5VzemWUlnLSUAqPUr/7zXl8Iq21gdqzBmlOtlpjKDuUNXx0YsiFf8+qLSPvvcnGa361+7j85nH5lgVNxOrnMDdpTw5HIabrcbvSyw3Oq1zg3X8cTGQQiOeRZY5mtCkzZzXpURZJ8LMpcPABpjZzU1VCepvWB+gqogbyyruoHDy5VYXbmkxOaOhHLhjNSoiRAbst9UtQknAmiaRtd0BDTB/tP3I4RGLqNahrSeuAuABy5Srq+3iCi6tOhveQUDWR9rOyMcd1diLZrgJblPVq8ty7qWREI9F/D7WT/RUH0u25xn9OwRMktx7v303/Lkt58nlyrh8L8bRxT+F1PM5XjkXz7Pru89R2wqz/j1Koi8/u5+VpfrGcpM8J111yCQfFi/AyEkR47P02YJTuclxyPvxraLbNqkGtkdMAeRQhJdqsPvjzA9cAB/pMDCnEV++42qmC3WR6KU5IGVlyBKWT7UcYyiabM+ZVOUkuOh27HtIn19BUqlErNtBWTOYu2qS0ktzBKqzTK/YGHVtRA8MowhNfZYykW0Xaidb01aLdaTvmswzTj19YK5pFr4A7YK4mrnMpA61mLH4kRsL1OmSl1t0SrpqSYUbElJ1EBlp1+uzGl2GR4s88XuI/tc8ZplIXWVPnpuPrNPKrdLJnO+xcW5MZxSKreVa3oA0dhAWivS61JCcabS/jrh1zAENOZ24fEsI5+T1aE65+IJCNUKI3xmF+g6d9WNEXaH2Hr2MOVINw8u1LOiNcS0JtkRNAmUh/G4LyCdkixv6caTE5jNknh8iqbe9WSTPpqadTzHnoaeTr4bOUm9p5ZbTj+N1D28f1a1OC/2nrcSXh2aJlg6DYDb1czeveefu6hmPa5Kzw8RMnjs4Nerz7X3XcbJ5wp87yN7Gdw/9/OXqsP/IhxR+F9KYSjBoa/8hONPPEw5+zBCsxmddpG99DXIXI4PPqt2dF/MjzEXacUfO8Ynu49StiQdZ9UC+a3CTkwRolzeRUuLYG5xnumqtXAb0rbx+48BMNGr2llcvVvtuO+s+Oivy6lgZGxE+cofK28CIBpVPY+GNeX+6anbDIAvMIm0Ib3tZchUiuuzPZxOj7MUqKNlScUVcjH1+UY0tTNubzfJ5/PkIjZ6Rl2yZlG5pgr1qtBrfamBmUrq6rkCNm/FSii42pC22umXK03wXC4v0rY5P1rnhUN2TGxdWQrnRUF9v0wG/JWZ0EW7Mp6zHEDTBcbUEKU2lTbbXSmWPpBvRRMwakiWewWGTCOl+sxN1AAqniCERjJehz9o4O5/DnttL1N6issCyzBsk+NhFQyOdChRvFTbC8DsnLJEVpaUi2kgrs6h7lbWYuf8swDsvrwBKeCN7hY8ZoEzrbfQnw7Q11VDf8VKiOgaV6Y/zjmWEldQqmz8WxuaWTYaqD43yBEKBXVOg7UNLM2r9ytkyww+eITJP3435tISDv/7cEThfxmmaXJ61xFiXztOZ6KXmpoWlqbP0tB2BoD+2muwgzV49hzn9sV15Mwcn+xVN+yrEl+nxVviyOBiNTj8XOQvkFj0rVNumv3FM0ghqU3W4/eHmRnYgzdYYnIWSqu3oZ0Y4PpcD6fTZznUuQVP/AxvbJ1ibD7L6rLGiRws+nZSKBylrk4wMj9O2SPxJ/wIoZFZVNbAYq2qbr5iKoxEsrd9PXp2nitqlxidTqMjOWmqIq6aaAKApZoiMmMSCjeQSyqxyfnVbntFNsxSKUne7SdSVoKhl9RilzdasWy10z83ktNVWfQ1XVZjCufGcbosE7tiKQijMjvBTqBpXopFScClROHcGM5CzqtaZtsWyUa1cPZWFswnlxpoq/WTF7DarRbJVEplKzUm/dV4QrR1GeW8i6ZwDiElQ2tqALgyo17nO6nNaAIGwho+TbAmfQeGEeVkv044FKZp0o2ocXH89BNEGluIzdQRqnERfOp7iIZ6vtjUT8gV5LYzzyJ1Nx+Yu1p9h55Q9dp6XfA0Xktldnm9azh0ULm8hBBcVFpZbTlutQgOHru/+nfh5hsxy5XYS9ig88FPkH7oIUZffgvxg3v/zevY4dcXRxT+F5FOT/LVr36VH+66h3iTicxbXLNWzTSY7H+UcF2WTNJk+uY/A+D6uyeps/08stTP89070HIxvtKhehfJUwk0KfleZiV5o5Ni8Xm6uwWxpUUmunLIvMXOjbdhmSahyEmQML3ldQC89rByn3y3Xi3Ib/c/BUD9rArm7vH8FqrtRQrTNJluyyGzJmtXXkp8agxfqMBM3I3t9tB5UjWQ2x1Qi+kra0fIlSyWlzX68zqW8OJyKR973K125p0ta0nHZhFCkkG5bjozapGfrmnDm1fuC1lpdZHVm7Csynzlqigo/7qmn7cUeEH2kVURDSqi4LPi6JVaAZ+mBCOdjyM0jULGQ8ir3mu2Vh3fk5g7P1invtLBVapg/vSUF6/HSyimV+MJwdpKtlVMieaDrfO4NTeXnD2EGWrnrrlGVrZHmNcklwczuOw4yO1YFqyt6UFIQcwzDVISad6BQNDNIMK2OHV1DwVh8lp/N4FimqHWl3EkFWR9d5TTLiWczW6Ni5KfqF5royNbqydkffsqonMq60q4NXYNfL96XNuai4jP1FUfr4s/gr6kalWsYoHfOfIB/n7/31OujEJ1+PXHEYX/JUxP38XBQy+hrW0C27Z5Sh7DdEv0GcklW1+LbZmY+YcRQjI4GyS/9XrkfIyPnFDBy78JCMq6m76pH3B1XZzh2QzbM4K0ZfNI8IMALF9xHIADuTPYmqQu2YDXF2Zm4DncvjJjc17MtuUEnj/B2nIDTyTOMFvTRvvMo/T68/QPxQlIeDTXii3c+HzK9TQk1e6zt0G5kEI105QKFoWtL0GcGWGZWcPugtr5b5VqUWzI2BRsyYLvCsqlIYSAmJkAoLGmC8s0CdTkSefUDrVpSS1u06F69OwcQkjK+YooaPVIWcbjOT+S06iIgtDsF9QpVNxHVhmr0ttI6uoWcdsxdK3S9wj1t8nMPP5ILVKKaiB6NFzAEAbLYqOkQqrfkB5Rx3eW96HrIaambFprmxBSVOMJptmKEBA4eD+iuZHnfJPsCPfgL2Y4WXMFAIF2JYAX2U8AMDzShBCCnvkoGIL9J+/D5fWyNL8Mt1en9vGvIQIB/qlzAJ/u5Y2De5CawZ/NX6s+f895d9DrffswKnETr2cnQ8pwxOv1smmqpXrcQnSWhcp4UX+4hnRiW/W55S15As/+pPr44dt6GDBifPPkN3nbQ29Bxsdw+PXHEYVfc/KWzR+dGuezsx4sK0codA/NzRrx5BKHlqkZyO3pbpoae4lPjdHQfhop4dSyVyE9PhofPMg1uW7GstN8Y901CNvk70PfA2DyeAy/hJ+mGljybKVQOMzq1ZJ4comzXVlk3uKSTa/BLBWpqRvAtiTzl74FTJO3D3RgSpMfdm1CWCU+1HaQfMliUwYWyjbDoTdSLI6wbJng7NwEJb+NPxFACI1CWrm6lpZdCFLy8sVOZvILjDX00hw/oBb0eGU+g/tCLDtHa6tgLqUW0IhLuV+8/jSZlIntCxKuxCFmfCGEXWalP08hrwQgI1TBXDCoUfo5UdA1CT/fEM8qY1YsBbsyn9lrzoNQi/K5FhfxpWl8YfXa51pm9/sTtPubcFklJt0qdpDya3iEoC7/LC7XckDQYqjd9XjsJLrLTSIWpa7BQF+cYWGTys66quL+ujOzGV0TnAoK6lwaPZk78XqWM35W0tXUgT+pUW60SKbmae7dTrmo012zhJaOM311H/N6hleGVhLNLjLWehMHkiE29NYyUBkx1+PV2Jz8FABCuDlypLd6/V1Yvx53rhJcrnPx1P5vV5+r7XwppYJyMYVqXLTf+7Hqc4lrL+CrdSeqj1+SKyH+5TI4de+/usYdfr1wROHXmLFYlvf87Dg/mI3z7WQbo6E3Ytk51m/Yg67DsYnTKjBcsrm85zYQoupGSiyWmXv5B8C2efuDJobU+Ep2kKnaTqKzz/GBZQPE0kW2xizKUnK3990AdHSqQOWBzGlsXVKfasLrDTI79Awut8lIog47Uk/bkyeJ2j5+UpigpHu4JHkvurBJjqpMomfEVQB098xj2zYTLRlk1mT1ip0snD2D22syU1AL6sYRtTjtblqOlo9xbd0SZ6fTCCkZkGqBamktkslmKARtPEXlvtG0JZBQ7t6AZ1b566cMtWCvCWZIV9o4pFEZND6/rIqCLiruEN3mXMeGc+UKumViCiUalq5aU3jseWSlZYbXdIEuSKbm8fhqAPAkp8EwOOGep8et3Ez9pso8GndLVvkkuixQLKoMo4ZsoBpPqGtfjm1qNKIsque6i2hC44qzx7CCLfxgtpnVnRHimuRq/zyaLJHNqjjRalSl+cl51aSwUFiD0KBp97fBMPjnleMYmsFvjx5BCp0/X7weISDe5a9eZ693PYyouNU0cQ0LC+qENNY20DNSiTkIOLT0WLXgr3XlBcSmlPtQCFg3fTdaJqEed7Tx3i1nqq9/bbSP1514GIpJ+MEbGNr1fUzLxuHXE0cUfk0pDu7idV98mgf3TXLJkvJ7f7rwSjLuVRQKZ7j0MrVrfjJ1iELQRsxZXLXjzRU30iMIITm92EBp7UUwMMoHxzdSsIr87TI11/f2/NcIGSbHjs/TbAkeS3mZDtxMoXCajRtNkqkko11pZM5i5+ZbKeVzRJtGKBUsFq95OzKd4V2Tq4kXEzy48hKM1ATv6hhjaCbNClPj6bSPvNGJpqmd/7CprJrljRcgbZtI/SyppTKllVsJHxlBR7DbpVblW2qGSeTKLLM0ThTU7jwcVmmmidoSJG00Ta9mIJVaVyJn5jGkxoymFrTlnhRLlVYXqUrcweeV1TnNhlA7XE2T50Whcu4N28SsuI8sDQK6QEgT21IBZm/JUGM4pawO13HPjSBamygJi15bubT2ZJtoDHtYErDKpSyJxVi4OlTnXDzBG1IZRJHh5xE+Lz+LDLM+1E19Zp4z0SuQUuBuU4K0rXxPtTbB7/PTMuFD1Lo4Pfgsjd1rSMcDdDTbGGdPkrpsA2dcMV4aWU1zYpLxtpeyeynCxuV1DFWshA1+WJ36kvreRpR9++qr1+DFrEarTKnLt5YYGFYFbZ5AkGzu4upxK5tS+PY9UDl5Bv/ySj/JSrFgm7+Jj5w8X9iWadjCzY/4ecPX9rKYcZrr/TriiMKvIbnnP4fnuy/nu60/BODIgRnWlQRx0+bL7o9XhtE/RF+fJJ/P81z9MFJIGhab6WjrIz41SkP7GWxLcnrdbyM1nXU/VXGApxIn2bV8J0Zqgn/pfpZC2aZnQt3AdwoVtG5ofB4hJAeSpzENSUOqBY83wMLoM+iGxXBpGdLtZcNTk+gIvutSu+83GKrVc9N8kbKUHAneTrkcY80amJifphCyCSQDIARmQbVuTq67FplIcm22hwPZcSyhs8VWboe2vGSqZJNxr0LXVDV03JODsk1LyyryKRVQztd0gGmyplzPtKXcSMtcCfIlC7+ElF1ZzH12dSSnXhEFFVP4uS6pVplSxVIoaRCquJDMiih4ChrSW/nPEsoKcY2fotCiLJ/uQg4pdJ6M19JcmaHQLQcAwdmzOs11jejm+XhCsdCMx6fjPfIEhY1qzsFVlTf4QXYzLl1wMiBY5tFozd6Px7OFVEqypr4XzRLM6qqC3B3Yos5bpTvqNzcm0ITG2ybOIIXGXy5djyZgbtl5K+F14nvnr7vc1eRySizWtK+gvjImTgQMHj98R/W4pp4bKWaVpVVT56Ll7vNuo8FXbeExv+rT5NJc/H2iQKigsrRsfz2vS7yDvKWzZyTOb39zP8fTTouMXzccUfg1omAWeO+u9/IH87swNZ2eibv47PJDmLYkcWCBeltwMCt4JPJpQNLY9ACRiGB0dpyh3hSYkp2tr0DXDaZOPkK4LkNs3mTx5X+CzOf5s2fVLvBvPCUKLh8XzXybLZEMh87EWFsWHM4KzoR/l2LxLNu2lUhn0ox2pZA5k0s23Uo+naSu5Sy5tEni2rchp2Z4y2Ifp9JjHOnYTP3MLrZE0pwZjOOV8FhJjYxsbRtHSsl4UxqZMVm9/GIWzvajuyzmXaoB3JVTEdLlDMfb19MYP4AmbGRc7SQnvVdRLI1gGBCz1ALT1rCCVGwGhCTrVovxmkItM5XK59ZKcVudLUjYanHzuE1KlcR77ZyloJ93Y0hxPtBcFspSKAlRnaVQLlea4WW16hhOywrg9eto6ThLDZV5Cal5ypEukmUDT0S9TntpNx53hxqCUxmqMx47iScQJLkQojlaQlgmx1eq46+aGcD2N/CdmVbWLKshKSRXe9ViOzenXGrL47UIl8a+E/cQrK0nNt1IfaOBb98DlLf28Yx3nKtr1tC9MMxU6w3sWoyycWU9o5XvfEmoTGfmx+rceLrZt1d9frfbzZa59up5GXWdJJtV57Opdz3zk+o5TROsHf4+WkEVDVobV/OXPYerf/fH/hWsmzpeObc6H/e9l+PpigvOpZFcGeJlh4a4Zz7x87eCw68QRxR+TTAXFhh/759ycPgZ9iUH+OTGGwB4+czneG3LDLF0kY7TKn//XHzBNONs33ECkDw3e4RUvYmMlbl2x+1YpomZfxQhJCez3ZS7+qq1C1O5Ob7SdxXCzPOFepUtop9OIqTkW+UbsISXYOgpXC44ED+F5ZI0ZFpxu/0sTjyL0GxGPBuRQpwvZmtoQUibP2/aS7pgsikLp/OSed81lMv78XoFgwWVWrqiaStmqUS0cZH5BQu7tpllp5R7aHddG1phiRvr40zPqIyeYW09Upbp7BTMZZQbps7filUuE4wUyJiVlM+0j4VCnLLupl6q48IWJCtjNV1uE9u2MQ2JLtXvhHix+0gXOgJJicp8Zu38LIVi0YXL5cIoCYoo66pU8BEMKktiuhaVChobrQ7WyQd1Arogmt8DKDdRUzFcjSfUtqluqbVJ5YP/WeMk3YE2uhaGGaq7AktqiFa1kG7J34lhROk/odPW2EJo0SDfUCKXS1LbfiFIwbLUQfU6O9RnetvsBBLBhxM3omuC6Y5K/QXwqvKXq9ffzPTOar3GtqZ1+NJqabCbdfYe/qk6f14v5fJl1b9ZXb+A94jKhBKhEB+5NoFVccJdUbOWNx1/qHrsU+2/x9emzvdQWr2jlQFDkrdtfrd/jGcnnsLh1wNHFH4NWJzOMPjHH8R68HE+82Q7htT4XuIYP+67FmGV+Hjpk6wO5jg1keTiOevn4guH2Lkzi2maPOU+iWVIInM1LO/eVnUjmSWboYveBajahXo7wB3pM5yt76Fl6mHe1jbB4HSa7TmNsYLNwcj7KJfn2b49QzaXZbAricyaXLLlVrJLMRpap0jGVaM8/fgA12V7eDR5mvlIC1ti9+LTLfJnVXrjHs+rse08fX0lZmKzZKIWgVQQhEBaQ6q6efvL0E6P0G5G2FNpR/GyyBCzyQLNluCkqdpCNzblSKaSlLySAJWJav4UybRaiFqSAolkJtpGxFTxBr8JcbNSlFZpn225QXUPAk17caDZ0JQFUayknRaEJKgpq6CQ16stLgqW+n75jJeAoQRiOJijxdeAr5RjVFMW0JQHVnstNCzSGVVlXjfvqcYTXN4uAEJHHoYVXZxxxbjKpSyfH+e34HVpnPBLNvghWjwAbMOyYE2lf1T/9C50l4tErBt/yCD0xDdhZTc/DJ/m4sqozdm263h8sZaNK+s4W7F6bghnacyrBd3rvYD+fnWOamuirBypUSfEEDw7+qPqddqy8iXk0pXBQA0GjXefr2t4/LXLOe1S7rAWXwMfO72n+txc6zW8ZXBn9fG2Tc3sCZyfx/CGyDTFwbdx8uR7sZ16hl85jij8ipkaWOSuTx3icPcbscN1uPf389ljqr3DxwvDHOrcgp6d40e1X8KnW+w/MsfW/IvjC7pxHz09gvn4Asd65sGWbI1ej9vlY+rkI4Rqs8zM2CRe+k7kfIy/Or6Ckl3iEx3KDfE++XU8ms3ssRheCd/Jbaao1eP2PIHfLzgYO0nZI2nMtuF2+UjMPgtCcrbxUgBee9SPaZv8sHsLWm6B93YOcGYyRbep8Ui2CUv4qK1TbS/G65PItMnK3guJjZ9ACEmstg9sm5fHOzmWGiPrCbG5ElfoKsDJnIYl/AQDaqFP1JUwsmpR1/UEpbyF1dBBNKbcTdOhBvwFFW9wl22Spo2NXm2fbbpltToX8YKYAmAI9brFyrjNgpAEhXI55fM6fo8ShUwxgccfwCzp+CsurWP+GD0etaAfLbUR8bmY0SSrDPW5Z6Z9NETVUJ1z8YRsupFonYExNcT4OiV+Vy5MYvtq+eZ0B6u7omQFXOFSNR+jI814PB7aJwNQbzA0eoCW5Vsp5V30eCbQSkV2Xapaed++lEAi+OvUS9E1wUS7shJcQnBz4TPq66Nz6uS66vV4sasP3VLnI9EQZ2ZWxX4alq1kbqILAE0XrDnxDbSSOp/Jq7fwpXrlJjKEwSfTFpF8AoBypIeXT72h+vrruqI813h+2bk6XOD6xB+q8zN7F8eO/TWFQgGHXx2OKPwKObP7WR743PvRjSxLsTIDL/0otuGm8YED/PnEZkzb5N1+i5loB6H5A9zdo/oMnd03S4d1Pr4gZZnunsfx+QUHx08w315ELpW5bvvvYJkmVkFlI/Xb6zFbumh86BDXZXt4PnGGR1Zdhid+hs/2HGAuVWBb3GaxbLMr/CFMM8m2bXHy+TwDnUvIjMnOLbeSWpihvm2W+TmL/NYbCD53gtXlen5cmKSsu3m1/QgA7bESS6bNYOi3KRSOUFMjGMyo5nsrm7ZRzGaINi8xs+TBNtxsHhWY0mR/xwbqFw/g0iRGokRJSub8V8G56W3+HDJl4vdHsEqVDKTu9fjmK9PKfCFclTbWWtHGAkpGS7V9tumy0Srd7zRhV6eI2ciqpVBA/cyK87MUMhkIuNTCmsrF8EdUrYE3M4fweRkwFumpuJ2eTTXSVml50WWfQtf8TEzYtAaVtTAeO0mwtp5swk9TJTPp8Y4kDd5aNkweY6zucoq2htXswxCwPvNNPJ5exsYkqxp7MEqCqcq40rK5DsOlUffk1xDNjXyloZ+N4V62je1nvvUqHlyoZ9PqeiYqVsIrwgtEisr373JdweSkijEsb+2meUKJoYi6eHzfHQDoLjdSuxpREc++mincp5QlINpb+cDWoeo1/a7gKjZNHAFAuvz8gf1uZisT89pr/QysDGBX2oms88MbU7+PqLicPJ4uHn3U4I477iCdTv//3zgO/604ovArIr1nmhN3PUhqYRZZuge312R6RjJx698AsOl7h3htcjXx4hJ/2NFF3u1n1cQP+LueY6QLJsFjCbySanyhVJpi584xAJ4sHKXkswnM+OhbdXnFjTRAMW8xduWfgG1z+4NlDKnxST1DzhPkhoVv0OMvcOLYPI224AfpDjLuVSCeoKZG4/D8KUpeSVOuHZfLSzb2HAATK28C0+T3BjtZLC7x0MpLCM/t5eq6OIODcdwSnuJywGZtX4aFpRjJepNgOgRCoItRSnmLwrYbiBwZRUjYE4oiiilublhgflYFMcdcF1IsjuP3CxYrlbedbevJVTKQCo09iCnVXmHa7UWUs7R5i9iVVhc5o+1FonDeUpAvdh9VLIWCdOPSBWUBAVTcJJOR+Co9kRKpWTwBtSP3LE4g25qRAnqLRaTLz55EiECNWmDbis/jcqu5Ck1mpBpPiDSr1taR8X2IaA2PBEa43NeGQPLT0lb8bp3jPslFwTJ+c4xcTtUmrEg1IDw6B07cS11HL8lYiK6GLPriDMevWkZJWNyeVW6YT2RvxqULxlrVZwnogmsz6hrT9SAHDqigsWEYbIt3Va/PY9mnMU1lIbWtuY5MQmVeNTTp1N/9d1T+iK+/MsSips7PJTWreeux83GE79S/m4cWlHAGPQbm5lpSlWB+i1vjD0vvx1VpNOhyNXDk8GVks5LZ2Vm+/a1vUVp0MpN+FTii8Ctg9rlhkncPc2HkJhobu0nMTuDxPIKm2wxPe5l/1V+AZfGqb4+xo9jG6fRZ/mKdGrBy69xneGXTPKPzGS6YKL0ovlAsPs3WrSpraE/rOEhY770Evz/M1MmHCdVmGZ/WSF/1Bhgc5S/ObmQuH+NLay9HFJN8ue1+8iWLFZNFCrbkft8HsO0cWy6YplAscKZjsWItvIalmXHqWxeYnBGUVm6l7YlTRG0fd1ZmDPxp7bMkcmU25QXPp11kXcsJVDqunq1dUi6knh3Ep08gkSwtuwgZX+LqfDe7TRV0vik8xMRilqiEM7IbkHR22sxXgs1N0S5SCyoDKR9oQeZyLDNrmKlc1X3BDKW8igfk9GaoLO6mYUOli6kQVjUXVcUUlCjkpQvfualrZBHCRT4P/opbaXFpCpe3BlAts3PNKsbRk14kF1mBJTVKIRcRQ6OmeJBSSS2+9XEfsk7FEzS9E8Ot4T/4MInNPVhIrkrGkZ4IX59axqruKAUBl4jdCGHQfyJIY10D0TkXubochUIGf81WENB88AeIUIjPd5xmebCDy4eeI9ZyBT+ba2Tj6nqmKlbCbcEx/OYYAOXyNSQrM7YvaOkjkFAnrthmcfLM0wDUtnWxMKnadRhujdUHvoSoFLANv2ILDwZUP6dGbz2fGDhY3fWf6ngtHxpVTQ81AZ0XNjNWqY3wa4L3aZ8nUFYWhq4HGBx8CbGYel7TNLaLlcS+dIxyZVPg8D+HIwr/wzz22GN84+kfkmg0kRmTq9peTyTSxMLYaWrqnkUiORFvIXXd25DpDH/6gxLtZoRHlvr5l403Iqwin7Q/Ra8/z8FTC1yc4kXxhWDwXlpaNAamhhnrySBTJtdtUW4ku6iykfoDl2JFG+m7W9UufDt1iuHGlfRO/pSXN81z6PQCK03BfakwMf/lmOZTNDZqHJ47Rclv05zvxDDcFFK7QcLM1t9CptO8c3I1J1KjHGvfyOr5+6lzl7HGM1jA4cBbKRSHaO8QDCRUzcGK5m1klxapaUgzU1Q7yquno4xkJpmLtLLRPI6U0FsQHC8oX359fZZ4cgnTLalxN2CVSwTCBbKVZnV9xTqmbGURrPClyVVaXeT0RqRUC0xZtxGVoiwhzvc+ksiqpZCXLrwu5ULyywyGoV7fK10Ir06xkEVoYdUye3KQxXolFt2xUeZ8KlYz7xWs9arddjxeUx2qk2ABhCAVr6epzkYU8xzolQQMPxeOHWS84XKylkapyUtAF6xK3YHHs4VkUrLW1wXA0fEn8IVriM200NoscJ/Zz/g1a1jS8ryt7EEg+dv8y3HpgqGWSnDYpXFpSgWH3e5W9u5R1cqRUJi1Y+r8C5/OE8e+BYCm6xj+66pZSX2+IVzDRwCw1q/iQ73q37rQ+bucIJpVYp1p2MIrR15aveYvuKCFQ97Kgg+8P3BfNcgthIu52VcwMX4+8HxN03aax73Y6TILXz5GdmLx/3VLOfwX44jC/yADA39DLPYsxWKRB8r7SdeZyESZG1bejt8fZvrMARrbjoCEw/YFarDN1AyfergerzT4Qqqfx1dcipGe4q6Gr+DRbE7sn2GleT6+YNk51q3bjWHA07HDZKIWnimDzetfwuLkCA3tA+TSJpMveX+1dsG0TT7W2oGQNh/3fBuQ+M+k1Dxn/R1IWWbDxlFKpRIn22LItIotxCaGqG2OMzrnxWrpZuO5YrbGNkQxzQc7TnByPEGbJXikuAaA5b0xEqkE8eYyocy5kZvjqrp5xRa6TyUA2N22ltrFA3g0G1+yzFzJJuXuw+ubRUpJsq6Mt6SEwhdMkyoov3VvNshMSbkkul1Jktlz/Y/qsM+1zxY2mC9og3cupiDAVRGFnDTwutXt4ZVJNK3SKsN0gU/9XspgtWX2RNSi3lNLJJ9gQHbgdWmM6xYrddXob/ysQWtYBZLHF04Sbe6gmHNTnxsBw+Cn0VF2hrpxWSXuKW0j5DU47pVcEUjjkknm53oxDIPOyRA0GpydOEbDsh1IS6Nj8gmE280Xlo/S5m/iJWd2EW++hB/PNrFxbQOzFSvht/xHcdsqwB1fvJxzM4Yu9q9DL6uFf8I/TCql3HDtfVeTiqlq8JYWjdp7Pg2ACAX52HVpTKGsjHeE1rD1rEqFfWGBGsDmlXU8U3d+mXlH5BTLU+eH8+Syr+LUqfPjUK9o30bnC+Y2FGtK3PHhdzI9cOpf3U8O/z04ovA/xFMjdzMx+VWamn/CihWCXD7HA+IQuYgFMZObNrwLl8vLxIknaewYwjIlh+tfRnn5ZvRjZ/j8/rVIJH/OAoNNq4jM7eHHvQ9SMm1KB2PU2OfjC4XiAJdeukCpVOLpoOp4ukpuIRJurLqRhqe95C56GZ49x/md2DoOJAe5d/WVBOcP8rHufk5PptiWEzyXcXE2+DqKxWfo6BQcmTlFIWjTUliGYbgxC/uwLcncZaqY7c2La3kkeZpYqImXFB5UO/1Fk+GCzXTgJjT9AELAWCiOTJks795Ock51Rk2uvw7t1DDNVpDdXjeilOWWpnmW5pXbZ9J7Fbat3BXxQB6RtEEING2JdNJE6gbtSZ35QhxL6LTqSySyJYSUZERNtX12WTOroiDEC1JSX2Ap5Cw3nnPuIzuBqDTD85Z0LI9aDMtFP8FKy+zBYJYerypKO1BopaMhgIVgmd2P291GMmnTTBRcGoOj+wnWqTqG0InHMNevYF7PcFWugHQH+fL0MlZ2RykJuNB+FMOI0N9vsKK5G3dBMF4+jabrpBIrqKlzEXjmxyxdvp4RY4m3VkZt/n3xFXgMjcFmJZYdHo1tSRUL8Hr7OHxYWUFdzR20nVXxAhoNnjuoqugjTa3EplVLFLdXZ/lzn0VUTtRTt66k362E48KaVfxOJY7w8wVq3U1BDnedK/2GV0aWuDjxF+dvCvlyDhxwVR9e2LGJ5UPnJ71ZrfCzPZ+hkM3wk0/8JVNnHGH4n8ARhf8B/mJwkjeNdzMYvh3bztHW/hN6egTpTJoHPEfIh1Tvopdv/yM0TWf8+L00tE9TyFkcXf8OrPo2Io8f4mMjW8iZOf6wPkLCX8v6ie/y0e5+ZhMFVgznQMpqfMG0HqZvnWR6YYaTvXFk1uSa9W+pupEQkpPNN2H7glxXqV34B+KkvRFem/waDe4yseOLeCR8234NNhqrV5/CNE36W+aRaZOLN7+a+dGTRBpSjCTrscN1XLs7r9JTey7At3iC1zTPMjwYx5CS5123UC7Ps2qVZDA+hhSSla3bSc5NEazJM+9eBpbFK+I97MlNIRHcGBxkbC6DT8KQ1ke5PE9NjWCRNLJk09zUi1WKYVuSctda6uImpjSZr2mlkUVMW1IjBWnC2HYew1CWgqxaCtYLAs0CQ6hbIicNXIYSBY8dh8rITU9Bp6wrl1Ah58WPckkd8y7QU5mz8GS8nkitWmhbC08jhHInNaT82HUCyyphyzbCURfu4aOcWRPC0AwuPXuIqcbLSJsGuSYPDS6N7sz3EWIHpgkr800In86B4/fTvHwzhYyb7lI/CMFX18fUqM1Tu0g0X8SdMy1sWNvAXKUX1Os9T6PLPCAYHroAUL77HWlVUIcu2D31M5ASITR8NS/BttS56NOP4xpXxXXpKzbzhUYVG6r31PI3g0fRpBLJFxao1fhdJDbUUKgYARcFLV6R/P3qPeF2XcMzz5wXgM0dfawbPD+XwW7R+Omez1Qb8BkeH0/deZbZyoQ/h/8+HFH4b+YTD5zixJlFSlLyicyNDIXfimVl6Vx2N11dgkQqwcPBY5R8Nvo03HzxH4GUTJ/6MbUtcVJLZU5e/SFsb4CVP9zP2+LrmMzN8icrt2BqBm9Y+DQ3NcQ4PrLEJXH5gviCTmPj/dTUCPZOHiXeUsaYhou2vIrFyREa2wdJLZWZffmfIRdU7cJicYl/WnspenaOLy97gplEnu1LklN5yYnIH1Eo7GfFCjg2fZp82KK11I2uu9HkwWqjPP34ANfkuvlRcZqy5uKdwV0sZkpsKmg8nqnDFCHa2qdIZzMstpYJZ2sA8IcnWJiXWLVNbBmFeHGJgebVbCgfw7QlK0uC/rJK5+zotJjPqWB0e+Mq8mmVgVRqX0NwXlkDU6FGoqZylURtQVIqV1MgINScZvNce4sXtLmAqihkLAPDpf7tsWLYlf5JnqxGUebQdINC1o2vGEeEw0wYSXpKJWx/A8M5HzLkosGlESqdJpNpxOPxEI7pJFlA0w2SC7U0eRMAPNA8y9ZwD+F8kgfMbdT4XRx321ztn0VIk9GRZqKRGuqn3aSjacrlArZYj9evE3nsaxR3rGOfZ4o3ultwW0U+U3oFXpfGqSa1C1/tE/QlP6c+v+cSRipdaTe3rSW0qCyGdFOa8UlVG9LedxmJeRVDaW+F6P1fAEC0NvP+HSoNVhMaf1N0U59RFsMLC9QMTVC/o6nanLDXq/E7uT9Ak0pMvd4dPPFEc/W8r2lfwZahpvP/D806P9v/WSxLHR+oqcUXvY3EnJt7P3+U+bOp/+c95/CfwxGF/0b2PvcEX356hGPPTXJRGkpS8vHMTRVhSNHdcw8dnRqxpUUerVMFYt4pFzfufCeWaRIb/SHhugwLcxYjr/xbJHDDt05zVa6LfckB/m7j9Qgzz2fF39PpK3Dw4Aybilo1vmCaS2zbfhyQPGEdo+yRLMuvor6+k6lTDxOszXFmvobixiuqtQs/SPRzqmUtm6a/x2W1CU4em6NOCr5TuBRTBOnqPoxlWZxomkOmTC7e/Cpmho4Qqs0yYnYh3V5+60iQWDHOIysvYdnsw3T6CojJLCnL5nT4rVjWPtxuGPHHkMkyPd0XkI2fwrYlmW0vI3p0TKWmNi6jZvEwAd0mnLI4kxeYIkRtNEkssYhlSOqC7aQWpgFJoXYZ+owSgplAhEBRiUXIgpStdvF+f2Ukp1RzmsHCruiCjcSo3BJZy1Wdz+y1FjBNH4Zh4CoKcuUUgZpaQOBNTmO3qVhBb2aJZFilmS76NNZ4VcB7btZPW20zQgrGF05S196DWdaJzhxBdLSx3zPNVWUd6fLzxalelvdEMYVgW+luPJ4eRkcla8M9CASHRx4m2tJJYi5Kd2gBLZ/hx9vM6qjNVNN2vjndzvq1DcTOWQn63WhYaJqHI4fVjIdgIMC6cfW5RcTFY/u/oX5f20C8Mm/Z49fpeVLNWUDX+earaohpyjL6nXAfF47uA/51gdrG7a2ccKv3rjU0/sT+azyW+r/wetfy9K5V1eB1T8syLhrtOJ8i3Gjws8P/SKmsKtsD0Tq8NbeRraTElvImQ3fcR2Fg4Je7GR1+YRxR+O9i9z+z49FX8JXle7AlHNk9xcWpFwuDaSZYvvxeWlsFM7E5nmg6g+WShKaDXHXhb1PK58gs/Ah/qMj4tMbMbR9D5gu843sJVpTr+H7iOD/uuxYjNc7Pmr6GLiSz+2ZptsX5+ELhMDt3ZkikEhzsmEIWLK5c8QYs00QWH0FKyakVr8PWDG5/sIxuw8cam8Aq85nID8gWLdZMlZgq2eyOfJBC4QTr1tscnzpDtsamzexB13TcriNkUybJa95K8LnjrCrXc6dHIswCH2o7TP/oEs224Cl7J5aVpW+dyWBsDFuXrG69kMXJEXyBIot167Bjca4odLFbtxHlHK9smiEby1GWkln/tbg909i2TarOJChqMEsl/OECOW8jcjFO1PYx7fbizqmFyFeWJCqDcfz+8yM5vZ4AYFd7ZkshX2Ap6FVRcFvzlE1PtcVFprCEN6Qql13zI2SaVBZPz+I4k64udE0wZtis0KbRNC/j41IN1anEE3yRXjRd4D/wALMbWwG4cvIEcw07WSobJOs99Hg1mnMPk89tRAhB90wEu0lnemaAUMN2NF3Q8Mw3kGuW87PgIK+rjNr8J+sV+Nw6/Y3q+24L2PSkv1W5KK+ppn1eFF6Pq6gW4lPlvZRKKm4Tbr4RqzJveb25D2NmDICxWy7gvqBKId0aWcE7jlbiCC4/77TeUy1Q29rXyHPKyMAjBB9w31EtlHO7O9i7ZyvFovoM7Y2tXD6pOr0C0GBw7/HPU6w02AtE6/BEbiObrMQ8BGyKDBH93kcZf9vbKI2P/zJ3pMMviCMK/w3cM3wPX519BoBrJz/H91Y8iZRwaPcUlyTleWEIvQXTjLN6zYM0N2tMzE+xq30YS5c0zDdx8ZZXk0suYRV/istX5vRsDfGX/TFyPsbH7g0QkV4+XhjmYOcFRGef44e9j5DIlWnoT+KS5+sXNP1eenoEJybPMNWVR5u1uHzbG6pupHjMJHbL+2BwlA+ObeRYapifrr2aupldvLtzhEMn5+k1BXdmVlMw2mlu3oOUNsfrp5FJk4s2v5rpgf34IwVGfJuQlsXvDXZwLDXCibb1XJG+D4lkRdxib0Yn7e6jvn6IfD7PfFuRcC4KUhKqna1WN18zXcvB9Cgl3cMNgSFGZtK4JIy6tmFW8tuXggVcWeX+8AUzZCpDcNaVGpjWBVphiYjLxChLlir+ca/XpmwrUXC7fSCtavbRC2MKactAGAKfJtBlgVLRIOBVopBMz+P2qcI19/gpFupdhN0h6tNz9FudtNX6KQhYZh3D7V6BZamhOufiCaViK40NAi2T4OllOdaGumhOTPGQvJD6kId+t8VVniEEOv39YXpbuvBmNMZyx/EEgizOttHZVMKYHuHxS4L4dC9vGNxDpvECvjy5jHV9DcQrVsJtUlkALlcde/coIWtrbKVzTH2XcqvkaL+a29225uLqvOVlrRbhR1SWkN23gj9fcQSAWk8Nfzd6El2qAPu369/DIzH1uqvaw+xrOx84fk/oadqyPwPAMGo5cfxKUqnzA3yuml9dzXoSdS4eOPklcjnlGgpE6/FEbiOXrDTw02CL7wS1P1PtOayFGMOf+DCxfOyXuTUdfgEcUfgvZqx/Nx9+7sP8Y+oEn9p8ExLBRRNf4d6VagjJgT3TXLJUEYbszQyF3kK5vEBf38M0NAhGZs7yfNdZbCHpSC5n07rrSc5N4zEeQjMsjmZWkLnsVsSZEf7x2V4sy+Q9/jIz0Q42T3yTv+g6o5rbzZSr8QUb6O55HL9f8FTiMIWQTXOinbbWVcqNFM1xKtFKacUW1v7sOH2lRj5rzZH0R/n94tfwaTaRoQwpy+bR4AcpFofZsqVM/9QAmVqLdnM5mtDw+4+RWCyTvfy1tD9xmojt5c6mTlzJUX6vfZzxoSWQkoP+36ZYPEwkojHiXkAmynR3baaYOU0xb1G84Dp6TicpWEUOd25gXekohbLNclNwxl6GaaVobhYsamlkysTrDaLrS6Sy6nJemQszXfFfrwtmEEWLpUpg2e0xq9PXPB4/5yczV7KPKq0c0qYOhqjOUigUDPwutWONJ6fRjTAen46WjHG2pkyPV7li9mSbqK9Xx7Xkd1Eut6PrOjXzLpIs4PL6SC6EaSiNIwIB7gkNcaUIInUP/zy9nO7uGmwh2JK/E493M4mEzSqzFRE0OHTiQRq7d2CVddr670a0t/K1+n5eFVajNr8oX0XAo3OiQe30rw4XaMmq6y6TvopCAYQQXFRYgZAC4dF56tR3gXPzlreqfwcNuh5WVc8iEOATN+QwhY1A8ImSn8akmhB3quO1/OWoylBqDHuYXBumVNn0vyUyzrrkZwHQNB9jozcxN6fOdE24hutS63BXotCi1sWDA18mXSlKDNbW4428pioImi64QD9AzQNfrN5n5uY1vGPHSd7x2DvIlDK/1D3q8P/GEYX/QvLHT1B447v43KF16Ai+lTjGX2y5EVMzWD/+HR5b8RN0YXNg3zSXLtovEoZSeZYNGx+jrk5wZmqYfb1TSGmzqrSFVcsvJjY+SCT6DNKWHPZdSXH9pfifPcrfn95CvJiotsJ42+Lfc119nAPH59mRPV+/UCpNcfHOUfKFPM/UDSFNi0s6Xo20bGT5USzLZvCCt2MXCnzg2TqWSkk+u/piXMlRvtDzPCfPJrigIPhJqomkZzM10WfQdcnR2ilkssyFm17FzMAevMESZ5suR6bTvGtqDQ8lTxMLNvJm1xPMJQtsKGo8UliOLS3W9mUYmh/DcklWt15UHdMZ774Y/eQwjVaQ3TWNhBePEDAs6jM2xwuVrJ62EvN5NY6zo60PqxwjlzaxQ7V0ptzMlFVNwip/GrNgkbUkpgjicVtVUXC7fEhpIasxBaoxhZRpIHWtOkshl9Pw6x7QYGlpBkSIUEgtamcCaXp0P1JoPBmvRQu7aXNr+M0xluJRmuuaMMxz8YSVSCkIn9lFdvNyisLiqtlhYk07mS+6WKxzsSkgiRQPszC/gmAgSNOkh0Qojm3bZDOraGrS8Rx9ikNXtiE0nTePHCFXv5EvTHSxtq+BJQE6cEvx8wB4PL3s368K2Da0r6FmXllXs5EJ4vEpAGo7b6rOW16X2YW+qBb+Z29bzTG3csO9JbKOnSOq59ELC9S8Lg3vtgYWKy0srg/nuDrxHkA13FuMvZLRUfVcMBDkJaXNeDOVuc81Lh4Z+TrJSm1EsLYeT/jWqstId2lsM58m/Og3qvdZ7pJN3H7dGItajtPx07x315+C5XRX/a/CEYX/IlKLefY8MI5dLFH/0AG+/vRagtLNPUvHefemqykaXpZP/IRdvd/Do9nsPzDDpQsvFIbfplSaZtPmJ6mpEZyYOMOh5XNQttnsuoJlHRuYGThEQ+tBykWbw11vwGxfQcfd+/ijuY3VVhiinOUL+j/Q5i0ysG+GblOrxheKxWfYuq3I2dkJBpYnYd7k6h1vZXFimMb2QeZmLZZu/mM8e4/z9tg67kr0c6x9I5fPfZO+UJbk8TjYkru976FUmmLrtjynp4ZI1Vt02CuQtiQUOcncnEXhguvYtGsK2zb5ce9WmmaeYH0oi3c6z9mizVTwFQSD/ZRKJWbbCkTytdiWSbh+ntliPZgmtyx1scfOIMwCtzVNU4zliZVtkp7NRCIJFhIxbE3SHO2pTmErd6+ncclmJh9DIuh2JylWWl0UXO24XCVKlZ4+btfPu48krkqztqRpYOuCUGUgTS6n4cOD8BtIaWOaAQKVrqtHvfP0mHZ1sE7Kr7HGq3z04+MuWjzn4wlufxf+kIGn/3mOLjdo9zezcu4Mj7KDpoiXUy6bK4yjGHqYEycM1tT2oCE4OPAgTb3rySW9dMaeR0Rr+KfWk9wUWUNzYpKvaq8m5DU4Wq8W9psiCWoLagGfmrwIKQU+r4+Nk5UsnwaDXfuUldC6aiuxKWXp9LQWCT51JwCZyzfzj01HAdgcWc4fHHsY+NcFaudmIwBsCkh+K/WOaruLQvFVnDhRcd15vNyoXVBtpyHCBo9PfIf4khrVGqxtqAiCshAMt8a27MMEdn2/ep8t3rCVt13aT05TIhB0BXjT9DDc84e//E3r8G/iiMJ/AWahxAP/+BFmcgUGbvtH7GAE33NH+cr9HTRbQZ5aOsXvrttJ2huhffJ+nun6BgHDYv+hGS6dtyrC8LKKMEywddvTRCIahyf6Ob4ihixYXFRzM02NvUz0P01jxxlyaZPjF74XO1zHzm8f42WZFTyy1M+XNt6IKznKPS13UCqXMY4s4n9BfCEQuI/WVo3np4+QbDCpXaije9mmqhupv7gSs30F1949Sa3l42O1EWQ5z5ea7mYynmNHUvJIys9M4EZ8vifweOBIeByZKHPh5lcyM/Acbl+ZiVUvQ05M86bFtfyoNIMpND7YvI/+kTh1UvCsoeZBt7ZqDGuzyESZrs6N2KUhkktlyr2b2Dqmcyp9lqQ/yvX+IcZm0ggpGfdegWFMYJommTqLGk8TqYVpJJJiywrCCzlKdolYuIkOI0Emd67VRSuGUaJUVo9dhkdZCi8YsnPOfZQydSxdEBJKUNJpiVe6kJWYZynvw28mEfW1LGo5erIJ4pXBOmfd0CvGcbmaWFqyaSqGq/GEfLaZ5lAWhOCuhrNc6W5Aai6+ML2Szu4aDE1jXeabCE3VJiyP1WI1CRZiY+jujYRqXASf/C6jV60kq5d568Rp8nXr+PR4L6v7GkgJiVcT3Jj7JABe79ZqxfCFdetw5zXQYP/8Q0hpq3nL2YsACEYMOu/7OACiuZEPXKjSTyPuMJ8cG8CwzX9VoPbC2Qhtbo13Fv4UQyp3jhA3sW+vslBcLhcv8W8nvFAZgxoyeGr2+yzExtR71zbgDr2mKghur862+N34n7+7ep+NvWo779h8pDrIp8lXzzeTkotG98HRO2HXp/7jN7FDFUcU/pPYts3YfSe4qO4mUpM/paRPcfwln8Rq6UY/fobP/yjM6nI9B5ODvHXlBmLBRhqnH+e59i9S5y6z//Asl86aqo6hIgzF4hjbtj9LMCjYO3GUMysSyIzJlW2vo6ammfFj99PYPnm+3bYUvPE7M6wvNfLPlVYYdTO7uHP5U0ws5lg/VqjGFyxZoq/veYSweNI4gSlsdtTfhJACWX6Ucsli5LI/Ri7E+MixFZxKj/GDvmvpmLyf17dMc+boPFEJ3xNvxTTjbNueZGB6hESjSae9Cts0qakbUI3yVmzhur0F5guLPLZiJ9vi96JJi7VLNo+nI5S1KCtWLDIyP07ZI1ndcTELYyfQDJvkhuuJHj2LLW32dGxgTfEIqbxJj6UxJNZQKg2jaRAPFfBZfsxSkWCkSC7cjntWjY6cjjTRRJxkVlkGOb0JTS9QrMwBcLm8SGlVs49soURB6h6kFFgaBLQSAp1s1sZnurEqYzjzWQ/e3Bxmm6qd6I1PMap10RzxkhSSZeZRNE2JRN2CmyQLym8fD1C7cAK5upezRoKr4rPEmy5iquBhrtbg4mARnznO2GgLnU3tBJZ0hlKHCTe0EJupo1sMobldfK5nuDpq8xvGa4j4XByuVbfza0JTBEtnKk30lM+/qa6RnhGVJZVtyTMyptpSNPa+VM1bFrBu4UG01CJoGt95TR3zlfTTj1sRmhNqat5T7b9bLVB74WyEkK7xPu3T1WZ7bveVPL1LBeM1TeOG6HbqZlQQWgQMnon9hNk5VZ0erGvAHbyVXEoJgjdgsHX6u/gOKMsEITj6hm28b+Wh6n23MtjJd6dmWTl3uvq7scHjPHB85pe/iR1ehCMK/0nODHyLu0YeYMqX4ebV76QwvQfde5yD2z9Aac0O5Og4H/2WySWFDk6nz/KmZV1M1nZSM/s8u5r+kTZvkf1H57hkukzJtvlE9mUMh95MsTjMRRftIRAQPDNxkJHeNDJR5voVt+P3R5g8+RPqWmPVdttyKcFf3mVQZ/urrTC2T3yNP102xJGBRS5JyGp8oVAc5NLL5oktLXK0ew4ZL3PdjrdX3EjDTE5D6rrbaXj4ENfnevh8aYLFYAN/od1Brliib6bMwaxgIHw7uv44waDgcOAsMlFmx6ZbmBt+FsNlMrP19ehHz3B1vos7vQI9O8u7O4aYHl4ib9mcDL0Nw3UQ0ywz3ZqjJl9HuVgg2hhj3tOFnI9xaaGT3YEgocWjRFwmzTlJf6ke2y7Q0SFY1DNolSJXbyBN1hVFzi3gljoz/ii1VoxUoYxbQlarRxMFyuWychXpHpAWtn3efWQAGJX5zBoERRHdCAMCb0mnpBXxBsNYZR3P4gSppiA+w0fL0gRHS600NfgRQHP+SXLZZuqjdbjzmup31LoKISB48AFG+2qJuiNsHj/Ck+Ii2mp9DLgkl/AcHk8XIyOS1Vo7ImxwtP9Ralp24Pbo1D7+NRauWMekkeT22XEKtav51PhyVvbVk9EgomtckVZN7wzjSqan1Xe72F6tgsshg8cO3gGoecsLE20ArGjOVnflEy/fys+CarjOm2o2cPmQapM+23otbxm8BHjxbAQdeL/vLurzKuPO672AJ584P+P52qYdNI1Xsoh8Os8nfsbUtFrMQ3WNuAO3Vie6+UMGW4e/iveYei0Mg6fetpmPd5yf/XxRzSq+OXiMpqRyO0kET3X8PlcM3caf/PAoJ6acquf/DI4o/Cf48WyMJ7KL7Nz5HAeLezlaO8NVza/Hk1nE43uaAz1vJnfxLcj5GH/01QVekV7JRG6WNzXVMtC0muDCIR6r+wd6/XkOHJ9n55RJ2bb5ePblDIfeTKE4wMU7D+D1wpPT+5nozkGszE0b3omuuZgf/iGR+nSl3fYHkaPjfPaJdkrlAn9QHybpj/LOxKe4vG6Jowdm6CtrL5jv/DDr1ksOT/Qz11EkNBNg9YqdTJ16iFA0xwnXNqxoE297oESxlOfTK7fjWzzB3/cc4Uj/Al2m4Nvll1K2S2zbtsDwzBjxljLLWEMpnyfaNMLYgg+zpYvfOhLiSGqYk619vFY8wlQ8z/qSzhPWDkqlGVauFAwzg1wq09m+HiFHmZ+XWNFGrpupZ09xHmGVeG3TNHa8yEBeUtZqaWousFBcQhYs6uuXoesJMmUvmCZryw1MeXyESvNICVEpyGi1IFRRlOkGl+7Gtl9gKaBEwTbUAlYSAj959EoHVm9RJ29lq8N13FMDzNXqdPmaEEieSTXhrvGwzKvhseaYm/PTFmysxhM01zLqG3S0+CyPtMW5LNCBJgT/NLOK1q4IQV2wMv1N8vnNeL1eWif8xP3zGB43S/PL6I4m0TJLfKVvjotrVrF2up/vum8l4nNzsGIlvC54Gq81g66H2L+/BYC17Suru/QhjlLIp3B5vZTKanJeOOqi7Z6PASDX9PJnq1QcYX24hz8+qnbrpZoebpl6PfCvZyP8QeQo3elvq3PkWckzT/dVCwKvaN9Gx6hKfxVenX25h6uV06G6RozAq6uCEIwYXND/Bdyn96vjfT7uevtq/rnhWPWee3l0PV84+hTBgkpdla4AX2r6K367Ilb5ssX77jpGrGT+x25qB0cU/qMMT8/zzbvP8C+Ja7jX9/ts2Pggcc8xdjUNscF3JW3uRlz6fRyJXknixncg0xl+68tD/G5sPQuFOL8d0TjcsRnf4gkeDP8N60NZDvTPc9FEGVMqYRgJvYlC4SSXXnYEt1vy6NxeZjoL1T5JVqlMau5HBCJ5TsRbSV33Ntz7+/nHYxuZys3xJyu3YJXzfMX1GaJGidT+eaK2qMYXGhruJxrVeDJ3mJLPZlPgSly6G1l+jEK2zPj174fBMT44tpF7lo5zsPMCXh7/Bs3uAvUjWUYKNoci78OWT1BXJzjkGUMuKWthYfQZwGThstsJPXucFWYd321eRmR2N5fVJgjO5DmQ1Uh6NtPRMcXY3AQlv83azp3Exo9jS5vstptZfirFVG6O8fpurvUPMDmTxgKm/dcQCsaYT8SQQtLevBq7HCOVUqvR6nwNM4aGJ68C0BEL0tRApX225ZYYursSUzhnKYAhlfsIoKCdm6Wg3C7urEa2lMATiKLpAn1qkNFwkV4jWB2sk/PrrHGnEcLF2bPQZEaq8YRMsoEGOYtoqOcJ/xhXpdMkG7czlvcyFTW4MpDEJXOc7A+zpqEHQ2ocOH0/zb3bMUs6TXu/Q+6i9Rx2z3B7fIlidCWfOLuS5X0N5AQ0uzUuSioroVy6hnRK4na72TKrrAGrRXDgmJre17LyJeTTXoSAdeM/RsumED4ff3tjiZKwCLmCfGpiBJddRrr8vMtUBWo/PxvhtsgC2xJ/rc6Pu4X9+y+iUFDPvbDBnXBrHCo9UXVbheqbMAKvIZ9WAhyOuthy6NO4RpQAiJoIX317B9+PnHcPvSO8jo8duh9XZY6zFWzlD32f4O/Orqges6I1xNTaMG/vH8O0z7fjdvjFcUThP0CymKT+vlfzp6578exZYF+sky/6P0d952NE2k7zQPQYTWIVm1svwy7+iDPuXmZf81fIUomrv3aUD01sIV3O8LueLE/3Xow7McRPfR9lR02Kg6cW2DFWwrJtPpa9pSIMx7ns8uMYhs1Di3tYaC9W+iT9MflUgnL2p3h85Wq77YYH9vPnE5tVK4xNN+BODHFv27eJZQp0nUmTNC2+7P44ZSvN1m1Hyeay7G4eQybLXLf1d4hNDNHYMczotIvspa+p1i58LOLFLib5asdDnBhdYlNB8O3cBRQJsGnzBGOz48RaS3SJtRQyaepaxxlO1WP7wrxjoJOHkgMsBep4b91znBiOE5Wwz/cmLPsAmmYz0ZKhplBPIZMi2pgkVr8BvX+IOtvP7uYVrM4fYSFVpM0SjBlbEdpZSqUS2ahNQ6iTXHqOctHGbO2lK+1jWppouQU8mk3QhDQB7HMzFVwVUbB/PtAMdmW6WkFIfDINMoCu67gLgnRuEcMdIRjSEZbJ6UCSHkuSrwzWmfJArxjD41mOaUL9opckC0QaW8mnPUSGnyW+eRle3cNFYwfYpV/EsoYAI4bkQusRPN5NLC3ZLE80YDZJ4kvTFApraG+RuEZP8IMLCmrU5tkDfN97K5GAlwNRFUh+vXcvhkzjdrexd69q4LejaT3ejIZwaewaVFk8L5y3vKpxCe9BNT51z219HHYrn/xfU0dbXFUMv7BA7YWzES4Nlbk5+S4ADCPCqZPXkUgoUX5RgzuXxjGeY2BYZUOF65sw/K8mX7EQonUGm/Z8AmNCNd0TTY186q01POxXgW5DM/iobyW/f/SB6j2Yq9/ITYW/5t75hurvLljTwMC6MHOa5PlEho+PTP/HbvDfcBxR+CWRUvLsj/6R26JuGqynubfhSxR3j1GedvNR1xcoREfo23CSe117kSLE1aveQCH+Q2Y0F2O/9Rlsw8X67+zjMye3UDAL/JGc5b7VV2GkxrlT/yuurotz6EyMraNFbFu+QBiOcPkVJxHC4sHUPuItZbxTBjfufCephVkM7QEQZrXd9rlxnt9fOsaP+q6lafoxvr38aU6OJ7l43j4fXygc4ZJLUgxOjzLam8E35WZj37VMnXqIYE2e/tprsHQPH3i2jqHMBN9ddy2rJn/MDQ2L5PqXSJQsdoU/RLn0NK2tGgddI8ilMts3vozF8WcoF8vEr/kdOp48g8/U+PHy7fQt3I9PlOhLSh7Nd1G2sqxbZzFsTiOXynS09WEYY8wkPEgJtyR62OPWCSweo8FdpiMvOW13UCqO4fEIliJFQlqUdExlIJWW9dGcFEybaYS0WRPM4TUlKenHslT9gumy0YULWalulkLDlhIDiXXOUhASv0xiSz8Bv8q2SaTmEFqYgM8CTeOoe46eXIZZbw81fhezmqSjfAizvIxQMIQ/pTO+cJJw4wo8fh3vkSfZ3V3monAvXrPIF2bX0rgsTKNLozPzQ2ILK2mubyKyYHAmvo/G7jWk4wHahx7EWr+KBwPD3J4tUarp4a/HVtPTV09BqKZzm1N/D0Bs4TIlSNE6Vowo19dC3RwLC2Nq3rKu5i1H6wxa7lbZRrlLNvEPLUcAeF3NBq4ZUD79FxaovXA2wiqf4K2ZdyGkiaZ5mBh/GdPTShDWvrDBnSE4ZeyrTnILNzSjv0AQ6hoMNjz1V9V2GqK7k794o8Y+j6qfCBh+vmA3cMvJx6r34FTbS7ho7k84lVFuKUMTXHBhG891uql07sAjBL7Uw8zPP/yfu+F/A3FE4Zek/5EBOr53io881cS7vILnayRPRj5C5MwJWoYKfNj8cwb8jVx48QEet3cz68lz0+rfozD/EGl9gTOv+ix2pJ62n+3jK3s3oNuSPy8O8931N6BnZ/mK/WFe3jTPkcFFtgznkS8ShoNcedUAllXigfw+kg0moekgV1/4FhYnhgmFn6KQN1W77WhzdZznJyqtMHZOfoU/6BzlwJFZLiic748ktPvp7YWn5w+RqbVYo23H7wmB9RiZZJnpm/+sWrvwxfwoC+FG/s7/XcZjWXakBN9PLyPj6qWvb4CJuSnm24t06+vIJhZpaJti2OrBKhR559QaflCaxS5l+LOOU8wPJ5kq2UwEb6WhcZiJ+WkKIZu1yy5haeY4xZxF4YLr2TaqszdzFltKbmuawlgqcTzvxcams1OyaGRw592UCwUCoRKFhh6ii0WmC6pCdnUgjV6ySdhuLCuDEEoUDGGomAIgNR2pgSHB0tSClRXgk0ks00vAo3JRF5cmse0gAXKI5kZyWpmexDSDdNLWEEAHmvK7SCSitEWaqvEEW7bTXFNCM3R+WjPClYUy6YYLGMj6OFtjcI1/BpceULUJnk5EjYsTp5/CHdhCXYOBb9/9PLzTy4pgJ5cPPc+P/bcRDfo4EKlYCa6HENLE613P0aMq7fNifQ2aJRB1Lp7ap/ofta25jsySD00XrBn8DqKYRzQ18IGLxwBYE+riT4+pthcvLFB74WyEBpfGu80PVQb2aCSWXsWQ6jxCb2sXF45UGtzpggHPEY6dfBxQgqB5zwtCY5PO+of/vFooZ/et4A9fk+aMS7WuaPTW8820xsWje6v3356O3+GSkTeQLKvvGA246bq8rdpvCaDdo/E33i9yQeLjnDr9fvJ5p0fSL4MjCr8EU6dPcvrAg+xd+XbQN/GF74e4L5HhYz2r+Wf5Ya7PPkvbkSRfSN/CI97XsG37Y5xgH0drZriu8y0Q78fST3D02k9Q7lxF+MlDfP2x5dTYXv42c5LPb3opWn6Rzxb+kte3THN0OM6GwRyiIgyqwd1errpqmFKpwAPWfjK1FvXzjVy85dXMDh2lvnk/qaUyp67+EFZZ8qc/KNFcCvAef5nZmjbek/wkF9YkGd87S6ul4gtpVy9d3Y/hcpns8p/GypW5duNbiY0P0tgxzOBskPzW67nupxP4SoJP9m4iMreHD3WfZvjYPC4L7g+8n0JxN93dggNiCBlX1kJi5lmyqTKpq9/K5l3TLBYWeXzFTm4uP8h4LMvaksYz+ksoFg8RCMB4U5posYHM4gLh+gxLPRdTd3SCdDlDf9s6rvadYXYuy5Jpk/Bup6Exx0I5gUyWcbm8eINpcv4mfHNJ8maehL+WXk8SWbRIWAYgVadUXaILA1kRBXQdKSWGlJiaB5cuKAvw2EuUyx78bh/CrZHLJSmXfPiKi5Ra63BpLjpjYxwstOCPeun1CVz2EhMTLppFFLtOYEuTdLyOusRpihtXkNHLXHH2CM+5d9LTFGRct9lWvAtd2w646JwMseCZIlBTR2y6ka7MIURXO9+s6edtZRdmZBkfGetjWV8dBQEb/bAq9S+AYGhwMwAr23ponPSAgEOJx7Ask9q27uq85TW1syrDR9P4/msamdUzBF0B/mFqHLdVfFGB2gtnI3g1wftdXyFUUoORTPOVHD2qitjaG1u5bKIHzRagwUign8MnVOO8cEMLmvfVFDKqcV5Li8ba+96nUmCB0rZ1/O7NM8zoypJbEezkuzNzrJo9CYA0vHyj5UO8dvDKaofV3uYg8uLGakdWUC6tj5TfRVPu0crnS3P8xB9i207F8y+KIwq/IKV8Ee2RHOGyjUu/m5PePsZ3vIv33pGleUDwpuVreEn5Dj4Z+CHe3bM8G1vFV70fo3v1o+RCR9lVN8iO6E1EzBKasYtDm/+Y4sYrcB3o50t3N9Bl1vDl5HE+uuWlyFKGj2X+knd0nOXE6BJ9Z7IIW/LR7Csqk9We56qrx9T0Nv0QubBNR3I5m9fdwOTJZ2nsOMn8nMXoK/8Ge3qWTz1cT66Q4Q86uijIMnf4PovbzhM5niBXVvULxfICO3cOMxObpb93Ede0xvZNL2fq1EMEInlOLXsVVjrHR46t4OGlfp7v3sGbM19DlPNsnCtzX7KGRd9Olq84xvTCLLOdRXr09aRiszS0zTLq34Q9Oc0b42u406cTWDjCy5vmic4WeDITpKhFWbcuz2BhEhkv09a2Bq9vnJlSA3JunosLHeyua2NV7giTiznqpGDccyl+/xzziRjYVNxOCbJaGDGt2iZM1bTQaSQp5y2WTLWYBAIapm6hCQO7MsQFTcfWVEzBFG58lalrXitGseTCr3nBr35XyHrxpqZJNPpZ5m9GlxaPxxsoBQ1Wu5K4XA3EYpKGpJ8kC9S2dVMqGIQOP8jJVX42hXuoycb557k+6paFWe4TNOYfY2yshZVNPbgsjX0n76Ou40L8QRehx+9g3xXNtAWaueHM09wduJWaoJ8DYfV9Xiu+B4DHfSmjoxLDMNgaW6Y+a2uJgaE9at6y71qkFNQ3GjT89G/V+bn5An4SUr78D4smOhbHXlSgZmiChh3NzGgSAfxp4PFqPyVdfwm7n1cWVGNdA1fPr1IN7gSMR4bYf/ReACKNLeieV1YFob0VVt/1HrSsyiBKX7mZt109SFKoVuMX1qziW4PHaE4oF5IVaOR9wU/wkdE11ftxy5p6hjdEmNXOz35+e2SY3029Dq819aJ7N5cb4ejR+/5jN/5vII4o/II89cxdPOY9Qa++lR2dN5Bf/C5JLc3Rqz/BzidN3vVMlDfX1pByD/FQ3Wcw9g+SmvDzCfc/YbQeo6H7GPdHjtLlvYDloV40eS+Hl72S7OWvRZwZ4VN3urig1MIPl47zvs3XY9om74v/Fe9bNkj/2QSrT2XQK8IwEnoDxeIzXHX1JKl0koe8Ryj4LFaWNrNm5SWMH3uIxo5xzk7rzNz6seo4zzPps3xw3eW44wPc2/E9RuYyXDBZekH9wnNs315k3+QxFltL9JT6iATrwXqMxGKJuZd/gIaHD3FDtpe/CQhkboF/6drFkRPztFkaP3L9AYXCEdaskRywB7GXSmzbcBOZ+PMsLZpkL7uV6/eUOJQc4nTLWv4w/Awnh+IYpuRY8HZC4X5mYrNkohZ9nZeSXjhJMl6m1LuB62cb2C2K+BZP0Owp0Z2HQbEaKVU/p1zYoqWuF9uMkc67kLkcXWYNM8FamkWcQr7MUmWwjt8vKWs2utSR1jlLwVAN8aSkrHnwupR7wmMtUCy68AkPtsdGd7kp5ty45keZiUK3K1wdrDPnhR4xgqYtrw7VGV84SaCml9p6A31mlHuap7nScpFt2MTxdIChiMZV7kE8nmUMD8PKbCOlJotcPkEi1k23ZxIjHOALTf28hQgy2MJfnl1PW18dRQGXhMp0Zn6Mpnk5fLgLgG0t6/EnNUTA4PEjdwDQvvYaUotBdJfG6mNfRTNLyFU9fGCNyvZ5dXQ9N5x5CoAnXzBBbeP2Vo671Xm7PTLMmpQauOPxXMpTT6pAcjQS5brEOlwFDQRM1Z5l96GfAEoQNPcryWeVy2hZq8XyH74bUVSpwXMv3cbtO45TFOr/4WXR9fzzC1JOC3VreVX5Y/xoVg3l0TXB1h2tPN/pqU51q3NpfCzwIy5PvK/aXuMcXu8aTp18FT/72VGGh4f/M0vAbwyOKPwCPDc3iPB8lsauk/zUu5ec7uJla99FeWEPmmsv+1f9Lh5zPZ//cYQvFkt8q7mWe30fpnv0MLUDRT5ifYTpiM6GzYd4wLMft9HEjvYbsAo/pr92G/GXvRs5NcMHvp7m+lwPDy/1864Nl5PXDd6x8FE+2t3P6YkkK/rTuCzJx7KvZDT0BkqlJ7n66hniySUeiRynpFts1C+jq3MjEyfuor51gdNzqt32uXGejy71/3/svVWYXNf17fvbu2oXc1Uzc6tJzGyWzMxsJ3ac2DHFdhwzJaYY4phix8wgg9hialGr1S21Ws3MXdXFXPs+tCxZcc4995z/fQnMx1r7QV/rm3OsNeccY/D6xCWk967i7aLt7G0YZo73mL+zVvcdaWmwPlZHJBHjxAlXj7eRsttoHE0iUjqT61aG6fMN8k7FSUzv/5DJRjdp7X62eCU6DReRmbWHgZEB+rJDFEgTcfV3YU8fpjNtEYr94yZBH6XlkT+wEosiSJUPNsSnEAo1kJoq0uVwY4sm4+rvQm8O4q06jaJGL/u97QQlDZem9KB1RzkYsRGJ9GIwCLisEayaVILeQbzuKLJKQ3nIQZ9ajyMxgjcQJSZDWJGCRpMgIsQQUZA4MmhGEMd5CrJMRFCjOfJSUMcGCQZEtLJEVBnFYD3CUehsoM0UoiAh4jYVo5UUdIkJsiK7CQZSSbelIigVNLfvJhZNJ1kxAnlZHJAGOaHvENWaeRSnmxgQZSYHPyQUnIzdYsPer6JhYCuphdOIx9Q4Nr7N4RMK0euMnNO4me9Nl2A26NltBAG4IPo6ALJ8EqOjMhaTmZL2cSZxh3QIn8+FOSWDkf7xW3a5sRNV424ErYZnT48RFuIUG7K5t2687z+QfjLXHXFQ+6k3whkmL4vGfgeARjORjRtyAQGD3sBp4UlojijUDib1sXXP+JaTOTkdQXX+UUDITw+T/8ntiEe0p5ovmsFvqvbxo7/OTeZKnvjJyulQ+onMH76HWs/4JpVFJ1G4IIOtFuFobk7WyzzJveT4jukjHfkPRSGezvp1046qsy5btoxAIPB/XgD+w+K/oPC/id6xIH+uDfGw6k2cJjez5+5ma2w7+yx9zLdfRJpkRZS/pNFQQc+kX/Dge3GCnXF+XVDK/fLTXBZeQ1LtGC/5LmOLbgHTZ25is7iDUU2cUwquIuZZRqfWQd8lT5AYc3PDG91cMVbG9rHD3DhhOh6NkSsGnuKFwhoO93rIPeBBnYDHA0eAIbqOxScMMeQc4YekBqKJGLPMZ5CSlMdA82dYkj1H5bZ/tPP8UQpjcc+r/DKzi4O7+imMiTwfOg+PMpuy8u0Egm52Z/Ug9ieYN+1S+g6tRGcK0FhxDXJrN/d3TORNfwv9xmRetn0xPv8IC3woX0wwdJhJkxLsiTaRcEaYWrmUsLeawYEEoSkncfl+Iys8TbglDQ9m1uJud1PrF3BqZlFc7KLZ1408GiUjvQS9qZchTR7KAy0YYxJ7siZygqaR0UE/LUGZsOggJyeBU+VHFzeMayAlxoXxCvx6+iUlpugwrh+lLpSZaDQJYsQRZOFY+0ihOKKSKhMVJNSSiFoQUMp+/H4BTVwiLAfRGGxHJbMP6EbJD/rpkXLJStKjFEWSgpsYGjKQprSRsAsICoGxEQuWrl30VqVSaMgia7ST14YqMGUZmKJPYAof4NAhM2WGPAS7xOGWHcTiFeQ6AigDLl7ObeJKKQ2F1s79HZNIL7cTFWCJyUdScAOSlMTO6nEgmKOtQBkTSKQqqN731RG/5dNIxEVSUhXYl43rA+2+qILd6j60Si3PDvSjjoWOI6iVZBzzRpiqT3CR52YA1Op8tm2dSCwGGo2GpcIxgbuR1GE27hwnsZlT0hFU5xHyj7eMitN85Hx8J0IiDgoFu6+Zxv0F47IVSkHJo9pibqldfjTvarOuYnb7tQxHxv8NeSkGxLkp7FcfewlcYh7idv8VGCKHj8tZSUpiZPgKNm608eNDEMDr9bJm1X+3kf538V9Q+H8LWUax/lFiextIbQrxSOBXrNZcxMTJy4lY97PCXkeSspAF+RcQHv2MMYWb/QsfZclqmQuq9VyekUGxsJrX9O+gr+5n3fAk3tfcRfnkdTSrd1JvHOTUnGuR3ZsZlfy0XfoicVnkrDcbuGNgEnWeNq4umMCQKZVzep7lraIdtPR7ydw/hjYuHwGGy4nFVrNo8Sh9wwOsT2siGoqyKP1SDDobY72foTcHx+W2K+Zx2nuNLA7k8HuGaUku5B7/01Tq3cT2jhCPyryheoJQpIP58wdo6GmiOy9ApjcXhzULIb6OkaEoo2ffSdk3deQHTfwxr4yUvh+4OauT6KExDvllDppvw+HYyujYCD25QQrVkxnpasaW4qSn9GyMW+vJDhn4omgWJ/qX0zrgozgqsEtzOZKqhmHXCB5HjLKcBQTdjQwOySR0Zs5257PDZKPQX0vHkA9dAvp0p2CzexiOuRG9EA0F0RrDhDNKyHQr6JVjaEODBCNxdDIElWmoVDEixBBlkUTsyEqqQgkCKBMJIqiQJAUm5fiN1OcHTURJMOZF0lgwGASQJBqkEfLdgzTEszDZNRRrZZRE6eyEJL8BN8PYM4tQKJVo965mQ5aPExQWAo5KajxGmkwiCxX70Ggm4XJBbp+ZfrEDe1YB7mEjabWfMbC4nIBW4KLDW1lpuRizSc8ug4xKEDgj9DwAXu9iwuFxa8v0Tg0oBbZ1fAFAZvlCxobMKFUixTv/gpCIE5xTxdPptQA8oMwgb7j1OIJasklN7xFvhBy1yK9Ct6OQg0hSMvtq5hMIyEiSxFLtDEwj4222sbQx1u0YN+Uxp2QgSMcAYUKKi8yP70GQZQSVitU3VPLMkfVXvVLHKyRz7pGVU1mh4tP0ezmn+TTi8nh5mlzioH2imb4j8wODQuAB4w+cOXYzCjl0XMpqNNPZs3sJhw79PJ3zUrMpb7QS2D/0Py4N/87xX1D4f4kvDn/Ot0IHnynu5cLweiw7R9gxXMzTmtcQUhoon7iP74Xt9KtDnF54E6L7EIJyG3vKbsLiKeXpZWYeVkjsNntZbXoSy77DDHeZeVr1HPbCvcQcNWyyNDMv5UK0gU6C4mEazn6OuCWFWX/fw5MtU2j19XBVRjodSQWc2P0ynxWto23QR0rtGLoYPB44n3bj5cTjK1iw0EXXYA9bstuIeyOcVng9YkIg7P4SURmmNu8KovYsbv54jIyglt84THgEeN/wEgG/l5IWPzU+WGN+nmhsLZVVCTY59+HXxliYdwkjXS0kZ7XR4M8jklLAvVvtbHA1sLlgDrdH32JgxM0sn8B7oYUEoiNMmxZmd7CRhDPClKqlxCO76e4XieZUcHNLDp9FB1GMtXFdRjfJQxFWBzIJRwcoKIAOmwtbLIWRrhYkdRjfjDOZ2SFRHXOhcTaQrg5TGBFoV05Fo+5nyDOMHIhjtaShM3gJWbOxOWP0x/0o/IMIgow9IRBQJCOpokSJISQE4vGfto8SSLJMGBVKScSgABDxeRNoQiK+kBNRacIghRHSU5BFgdyRdnb6U0kYJUokJ2p1AfG4AuuQRNdwA2pDPin2BKJGxQpDKycMdbJbO4+STDNjCij3vcvISDGFaXloYgp2H/gWnWUaaWkCqua9vFbay6XaXDRKHfd1TCa5zEZMEDjXNIQ5vB+NupA9u9UoFApmuMf9l8eSXPT1N2GwJ+EcqgKgQnMYqf0AosPO7+eNi9udY63kzEPrgWMENbVSRDtt3N/ZrBC5mz+iifWgUBhpajqN0dHEuMCdZSa2I9IZ3nQfq7ePt7HMKRnwE0CocAyQ9ukfxv/EBj0f/6KQt2zjMhfJGvv4yukRj4aE1sbD5ie4p2383ywKMG16Ojtyj80PirUCf1Q+RannmOEOgCBIxGLns3ZNyVF3tx9DkiQWZ87gxI4itB4FY9+0EvdF/sf14d81/gsK/4sIDwxRec/7dPWFubKogqnxr/jO/heUew6jb43zh9iD7NJNZdbsH2hV7WKztZmp5tMoMOQjRz+j0VzBQOk1PP6ewIHBOI9lZ/CO6lEm91SjOxTlkcTj+FLcpBfvYYWhlhLzAtIUGuLCFvYv/AORgioKP9/FX/dNYjA4zNV2PQfTK5jR/RbLi76nc9iHY58TY0w+Cgyy/D3z53to7etge14XCWeEM6puIezxopC/JxwMc2DWXcSDCR7/To/P7+bO4ilIrsN8l/M5dW0u5rmOzRccju/RGyJstTQhD0U5YebV9DWuRK3z0zL710e5C09pYsi+Hl7M201H3TBj4QTV5vsxGDfi9bvoyvVTpJ7MYNtBTA4P/TOuIGt9IwG/l/VFc7hBs4HGZifeSII2w+Xk5A7QNNYFI1FSUwow2wcYTZqIo66HFl83w6YULkvpwuSJ0RjPJBZvxef3EzLEyUovRyG5CagdGIa89IWdCPEIhboQpjj4BAdKZWTckjPO8dtHAijlBGFUKJQiRjGBUmlElgXUARG3fxhZNqKNewimW8nQJqOKR1jntDGqEciTW4jHskm1JaMUxucJoUAKDn8r3skF2HU2yvsO8uZoFdpMA3MNIfS4OXhAojiSSjApjKwQGOlPI6t3I765FbRpPVzeXM0P1osxm03sNsjoFQIn+ca3h7q7ZyPLMCWjDINTgWCVWLdr3JDGlDLut5yeJmD79gUQBD67OI1ehYcCQya/P7ABOJ6gNmFWOoclGaUA92g+xRraiSBI9PedQ0+3jCAI4wJ33eNzgkBGiBXbxofPlpRMUJ47rroKTLR2kvzFYwAIdhuv3JDKV8YmAAoNWXzYP3x05TRiLeJS+Une7RuX5DBplRQvyGKr7dj84HSTj98Hr8Ua2n1crqrVOXR0XMqO7TpAOO4sMzmd85VzKWgxHv0tEYgx9s1/h87/q/gvKPwvYsO37TRP/Q2XfObjlq1WbtJJLEvS8K10H0vdG0na4+Sb0Vm8qnmSpIItJOXVsky3G0lycFLB5URd3zKmdFE3/yEuXq5g5l4VV+dkc4XwJrfElmGpGeMl/43sM06gauo21mh2YtLlU2GdSDz2HfurbiQ47dSjhj2RUIDr9TF25U6nvPsj1hV+Se+oD/NeF5afvBgQvmHuXD+NPS3sLuxDGIxz1vTbGOvvQm9ch8sZoemMx5Fbe3hxawG7x8alMLJ7vuO1wp3U7O2nKjzOXxgTU5kytZbuoV4OF47hGE0hPbkYMbGO/v44Y6ffwilfdxPyeflbxYmcPPx3HLKbqcNxPvSV4U1IzJjhY4+vkYQryuSK0xDlvbSP6IlqrfyqbwIf6VSk9a8jU/IwyQdbxJOR5V14/WM4U2OU5y0kEmiif0xDYnCEGeEMqtPLWKRqxDMcpD6oIhpz4XAIjFmjJJmySURH8MZ1KPpH8ES8+DQmJui96GLgFSwoFGEiifGXQuJI01lWKsZlLuQEISQUkohBjCGKJkRRRAoJuMYGiMV0aP1DOJO05KstRE05+OMSHUqZrGg1Y247aRoHCbuApFXjHjVgrFvD3kKBxZp0QrZSdoxZaDQIzGMLCnEGGrWJlB419b2bSMqbhcmsRr/lcz6Y5ON8YxFmWeR3ndOwT7ARR+BiQwe6WAca9QwaG8GoN1DeMS73UO/fQiwWIaNs3G9ZpVVQuGXc17j/jGl8ZmpEq9Dw7NAo2kgAX/LUowS16RNTjnoj3Gbac2R4K+DzXsDhI237RRnHBO5CGVG+2/oiAJbUTGTlOYQDKgQBJhsOY/963NNByEjjiWt0bNR2AjDTXMx7zQeOynE70+az0HU/O8fGdZJyk/So5qVSqxnfelILArebarjMfTWSfLwCqkq1mC2bF9DVefzrQKFQMC9rKqd2l2JwKX6W3/4RJ+179/xfVIZ///gvKPyTGKw5TGv1CwSV3eycdj+qxGReeVfFQE+MK4vKmC9/xsemV1HvaiXSruJ+4XnazGZmzNrKVsV2GvQjnJJ1HfpQP7KwiT0VN5E+XMwD3xn4rdGCrKnjfd3LmHZ1s3JkLp9rrqFq6nqqNTtwayXmZ5xFPLSMg9mn4Dn5WrTb9vO35ZkYwyI3K1ysK5pPfvdXbM7/kFG3F92eUaxHgKHDcBmi4itmzQ5S193IvuJBFP0yZ835LYOtB7AlVx+V2/7RzvNHKYxT+17hytRuhnYPIUXG5wvB8EHmzfewvbeWMXuU2Wln4+zpIDmrnYOJSiKSkUfrinjbe5huo4PX07+jtn4IfVTmB+P9qNTricQ8dOT6KNZOpb+lFp3Ry/Ci65mysY8adwtNyYXcn7YHf6eXTV4dPoyUlSfoMI5ij6Uw3HmIWCxCaOopLBlIYYdWTb5/H20DXsKxBE7tHDIzY4xqApgUNkLeQTw+AdnpwprQ0mdOp0jrRRVN4MOEKIbGLTljMonYkUmkoBjfPkokCMkSgkLAIEYQRRN6nR4BAaerj3BQh8rZRa8lQZ6sxGkoItOhQ1QI2IPb6O1RkRI2HeEnlGC2Skhdh/jK3s5i1xA1+vmUZpuJSQKF3vfp7MygzJKP4JDo6KrF4yoiL9pAfFIpWwy9XN1eywb7xZjNZnbrEtglkfmeJxEEiQMHxjeKZpsqUEYEIhlxDh7ehM5sxesa91uupBZlTzMU5XJPeT0A96qyKBw8PE5Qc91EMK6gItfK9tTx4nmZeYApY+MezYnEOdTUjM8NZmdNPnrjjmTE+WbbONhYUrOQFeOAIIoCU1S1WL9/afzvWpTLPZfFqFUNAHCmtZJX6zZhDI0X94asS5nZeRP9ofF206QiO12TLfSIx4x7ntS8zjT3E8flqEJhxOe9jHU/ZBIOH5+/KfZkztfOo7TZMs6s/kkIOgUdliY+3/4Ua958ifB/t5F+Fv8FhX+IYDDIYM0g55Tehsbfj1L4miZdIY0LH+Ts76LcusHELTo1KxwiyzW/Z+HwFpJqxnjLfTYfa35NSdUPRBx7WWWpp1A/mwrbJOKhzzhsq2Ak9zKe+kDJ916Rj5IlvtE9QlZ9PT2dSTynepzsimq6TdU0GEY5KfMyhMBqWu2FDJ93H2J9Ey9/ZqQwbObOeA9flZ1ERu9KNue8jdfrRbVrBEcMHgteQIfhMiTpS2bMDLO36wAHi5yoe5Usnftreg9Vk5xZf1Ru+0c7zydDrdRkTuTB0NNkKZykNHio843zFwS+Jz8/wQahnrg7yskzr6evcQVKlY+OxXfiWF3Did4snswqIrf7G85y9JPdGeRLTyqjilymT3ey232I+FiUSeWnIEm1tHmSibv8XOUs4+P0fGaPfUdbv5uMmMB+w/WkprTT7OxAHo2SZMvGmjyMK38uxYd97Az2oXY1kakKUBQV6FLNx2IZYzTuRh3W4BnpI+SPEbelUBVOpt9oJ1s5hhhO4Jb1CEKQaCwCMRlZToAgg3jspRCQVaAUMAhhQI9eo0PQKYknogR9KlR9LbQY/RSEQrSLudjtOkq1CSSFkcFBGfuwiq7hBpTqXFI0buJlBfg0MtM79/GWswpVup7FBhd6yUprq0D+kJWeRAuphZORE1os69/m+9lKzjCXkhwO8bvO6ZgnWEkIApfp9qNKjKBQLKa/P0FWcgbZ7XoErYL19eObP7bM04mElGSly5hXvoag0fDnMyAkxDjdWsF5DeuOI6j96I0QR2CxMcySsXFrS0k6hW1bx9dBp2RVUN48LooXS4dvtv8ZZBlrWvYxQFAITGU75tVvAhCfWMKvzx2jTTluevQLcyVPHlk5lUUl32beydLmM4kmBEQBpk9LozpfQ+BIHZ9vjPJo7NekBo7fGNJoyjl44Dz27Tv+BSCKIjOzJnH6QMXRAfhPI5IRZ3n3m+zc9zUAPpeTrZ+89z8vGv9m8V9Q+Idoa/uIdd6N7LC1U65ewJycMwkNf0FUeYBdFbciaxfywtsio10Jri4oZon8AX/TvYm+uoP+LhsPKl7Cl+KitKqaFZodeDUqTs27GjzrcEmj1M++n+u+05ByQMmvs7P4k+pZThrYiKIhxmM8gZDbiyJ9J5uNzcxPvwh1cB8DOpHuy54h0d3PI+9FmR1M56FgE29VLSGpbwNbMl8lEfQi7BwmOfojMFyKWv0506ZF2NG9j6ZCN8Y+PSfOuo6u+rUkZ7Ufldue+34dSz153KGLMqjW8onpFXoHXcwaiPG+O4M242Xk5K4lFHVRkzuAedBMXuYkRHkdXX0C3kWXce2KMLtdzfxQPJ9HpHepax6hKCzwreZOENajUPppz/VSoptGf9NuFCo/zpN/wSk7Iyz3NOGPB7gtq43MoQg/RKsIR/aQwM9oepSKvIXEoy0MRByo6luIBAO0JBdxaXInVl+CJoqRVL0MekeQx2LEIzE0hjCR3EqKAkZ6NXoyRCeJcByPrAHZTzgWRY6N30YVigSyKJIQZJSJOKGEElkhoiNIIqFHJ2mQNaAzWRBkBYqeJuq1o+R7htgfSUcwSZQoh1Eqi8dNdWJKmtt34/ckYe2roaXcwnxDLrI5l81jdg4aBGbGVhEOTyE3JQt9WGLPge+RhSryjSOIqVY+tTRxXXcjWx0XYbRY2KNNkKUWme7+E0qFid27UhEEgdnBcXe3Hl0bbvfguN9yXzIavZL8H8bbNzUXVrJD00OuPoMHD44L0/1IUNOrFUe9Ecp0cLXvFkTiaNSz2bB+XNSuLLOYyS3jHs7xNIFl1c+RSMSxpmUTF84mHJBQSiLTI+sxrv8AgNDsKm5c0s2QwodSUPKwrpjfHFk5ldVm/mh7nFtbpgJg1CgpWZB1VGxPBG78p+xkEUE4g3U/TGF4OHFc3totNs41zqOy2Y4Y/4fXgUnJIe1evt76LF7P8HFn+9esYLCj8/+2XPxbxn9B4SdR5w3wXVhi0pRv0WfW8oVmBwPqCEuzf0mKoIboJ3Rq7Bxc9Cin/aDkt2uN3KrXsdYeY6Xu98zo3Ya1zsfzgV+wWncGk6etoVVfTbWxkwWpF2OXQ8QS66ip+AXF3UXctErNLxxJzFR/yx9iH2LYPcoLwd/Q6kgie8I2VulrqUw6BXvCiVvsoPniF4kHokcNe17wHuS5yWdgHqhmQ8oLqCMe4tVDpEUEHgteQKfhErS6z5k8Jcbmnj20F/hwDCUxd+pFdNd/gyN9aFxue9KJXPlBP5leNb/JykXwNPNd7lfsrhtkemB8vuDEzJw5LezvbmAgM8RUyym4+3vG20j6+cRHA/yhYyJ/UvoQ3M08lVeP0DjGGreOPs0CpkztY4+rgfhYlKoJJ6LT1dEaL0A82MZsXxpfFs7iMnEtTc1OWv0wrJ1NeXmINt0I9kQaI50HcLsjRDKKOcudz47kPBZIjQRHghyMWIlGW/D4PERUcTLTS9EZfURSCsn2qOiXVDhkJ5FgDHdCQpb9RKIROMJwFhXjL4Xx9lGcgKwioRDQESAR16BTaIirEmhNNvQmBQqVklbJRf5IB1u9Kbh1CvLkw+OkNUMyCbuAzmIh5Neh27uSVWnDLPb5qTUsYEKOBb1aINv/NQ0NZkrIxG/3o7PacY9YSdr6DtsWOjjBUkq2d5S7umZhLLUiCwJXqDehkIOEwyfj88lMzJww7nmcrGTr3k/H/ZZ9swCoDG9HMdRFeFYlT2XuQ61Q8+yIG13Yd5SgJgiQOzONDqVMikrktujvkRIuNJpyNm0qRJahMD2PWW2Z45yOVJFvdr9APB47CgiRoISkFpnu/h79lvE12LGTp3LdwkZ8QgS9UsfLQgrnHxxfOY2a87hG+SSv92QDkO3QoZ2Xyr4j84Mf2cmL/oGdLEnJDA1ezuZN1qMGPj/GlKwKzhqdhHVQOv5AAH96kGWHX6TuJyqrP4beYiOz4hK2fDp81FPjv/FfUDgasizz2somVo1M4jHN3xixhpg7bzNt6l2sth/EoSrm5MKriY9uIK6oZk/xtYSsS3n2bQFfB1ybV8gFwju8IL2NcUc39X0FPKV6Dl3eIez5O/lWv5sk/QSmJc8jFvyCpuRKPKkX8sRHSl6P6ThgGeQTzbMk7+ngm5FTWK4/k/JJ61ivqybJUkG+NplgYjsHT/8jMUPSUcOed8bqeGDKUrQjdfxgfwab7CZUPUhGBB4NXkin4WKMxs+ZODHO+r5d9OQGyBzLZ1LFqfQf/gyT3cW+pHOI2HJ54CsFzrER7q9YSF7PMl4s2EvLzn5MoSPzhcguZs4MscFXQygS5tQZN9LXuBJB9NKz5B4mfFNHkkfBa+WLuHDsLXzOUWb4BT4RrycW24hOH6Ql102pYQb9zTtJxP14TryGy+tMfBobxjy4g1L1KJMCAjtUl2C2HKJ5pIOEM4rFmIIt2Yl74hJmdarZoUyQ56uho89LZyiOHwvp6QJj9ihpjkKUSjcBUwZJrgR9QhxLbJhAMIorpiCe8BGJREAe92kWFYlxldQjPIVAQnkEFHxEo2q0gpqoGEats2LQJJAzUkjW2tEl4lSPmeiUZDLC2xkeNpESN4/7J6QUk+wQUJi07NYNMr+zhnfHJiKm6zlR24tWXUEwoCWtW8P+jnUYk2aQnRJFirl5LfkAN/Z3Up10EUarld1amVKtQLn7RdSqLHbu1KHT6qjqTgWFwI7eb0CWSc4/nXBARW56DOMP7yLYbdy/YFyB9HeaPEoGGo4jqE2bmsZerYxOFLhX8QqGyGHUqmx2bJ9KJAJZKRnM68pDTAjIKQq+2fsi0WgIa3oOcc4hEpRQaxVMH/oC7a7xV0DP2dP5xbT9xIQESRob7/hE5rXuAMCdMosTPQ+waXScaDexwEbvFCtdivGC/L9iJ2s0s9i187Sjw+4fw2wyc459PlOaU8Y1l34Sgk2iRtjI99teIhTyHXcmKhRkV56EoLqS4Z5UBts9NO4Y+J8XkX+T+C8o/Bj1X3C792UUO5uxHo7wJ/91vKO+B3v+Fooq97BK3MoBwyALky+mxFhA1P8BgxqR2gWPsmiHiTtW6rhTZ2CTLcAq3f1MbqtGUx/m8eh97LVUMGX6OrZrt9OlC3JK1tVIgWpc0jAHpvyOW74zEGxT8kS6kfc0j1HZWENLRyZ/Ud9L4cSt1Ji2EzKamGqbRTi8nLp5dxAumHzUsOcbVz23Tz4Zwd3GcuOT5Cic+LYPkh0eB4YO/QWYLZ9TXpFgzeBOBjKDFIcnUZw3E2f3ZyjVXvZX/YqYN86fN2SywXmI1ycu4az+lzjH3oNqv5NDR+YLGu13WKxBtqe0o+vTUJI/G1FeT2ufGv+kU7h3s5X3PYdoN1h5M+cHeutGOOATaTZeSdXEdvaOHCI2FqW8aAEGyyHaDVMxbalH74mzqWAW9ybvINLlZY0/FX+4GYMhzFBGmMr8RQhCB8OafJL397DX24Hg6SJT8pAXU9CrO4m09DBObRCbLp1EbAS/woJpJEB/PIguNIjbH8UVg3jcSzweJ66U0aj1CKKMIIjIyEiJOP6ERFQcl82ORFRoZRWhhB+lyoJe9ONPM1OgthM0F5Jk0oFSwBquobNTwDGqoWu4AUHMwhHpYmhSFtNN+ag0DtaNJVOvk5kW/oLR0RImJOWjsEsMOtsZHcggveEbGk7IY5q1iFJnD3d2z0FdbAHgcsUyBGSGhuYTj8NsW+W46U+Kl66eA6QWVjHck4HOqCRn1ZMgCHx9cQZdijFOtZZz0YE1yJL+KEFt0hFvBBG4W7+S5MAPSJKd/fsX4fPJpNiTOWGwGGVMgGQl3+7/C5FIYBwQ5LOJhJRo9Uqmdb2HZt86EAQOXjaDO8rGvZQLDVl8ODBKaf/4ymlr1vnM6rmFruC429v0qWnsKtDgP1LL/xk7WRTVRCMXsHZNET7f8Tf5yqxSznFPxdGrOj6PlQJjaS4+r32a5tad/GMk507AnnsDQz1VxKLHZhI7lrUSCf3XwhP+CwoARCJBLm7/mH0mN6tUd3Fj5HvUW/pwdRn5nfwc2/RzmDJtOXLKHr4y7CamNnNG4S9R+Q8jy2vZl3su7rQLePJdkWiryHW5uVypfIvHeRdzdR8bBqfzsuoPZFXuIJRazQ/GBqYknU6WpCUaX0Nt5Q1MbSnmnHUSv0xN4WbNG1w0upLIAZknhEcwlbQwlLSNJr2HBalnkwiu4EDFRQRmnXXUsGejs4GbKuYSCQ3zhfpRyjQjuLYPkBsWeDR4ER3687HZPqO0NM4q506Gk0JMFOeTZi8gNPYF0YiPQyc+gLK+gxfrJvJXz0HWF8ziscjT6EMjTOwM8747g1b9hUwo20rXcAetBV4qVXPxDw+QnNVOQ+oZKOs7uH5wAk+kZ1HR8ymVqgGmjSR4P3oGoUg19qQgTTkuJhhnMdC8Hb8/gG/WudzUksNHejUTh7+nu8+JPizTYriS0tIx2lTDOBLpOLvrGBxOEPdGKAtaqc2s5OKkTlL8cdoUkzAZRxmR3egTRkK+QbwRNap+J71hJ1JggDF/BF8sTgwlGo1AXCWjUmlRKGTko+2jGP64YhwUEm5CYSWauBJ/xA2CEW3YyYhDRb6gZlCbT0qSjgnaKGpVLpLSgD4g0dK5B7fTgfnQBrblBDkhIlNvWkhprpVMnUBqZDcHD6goGHXQGTlESsEs7DYV2padvJx9mOudo+xOvhC91U6NVma6PkG+9z00monU1SlIc6SQ26ZHMCv5YfffkTRawpFxj+IK9zoUzkEGl07jI/MhsnSpPNywDYB3jxDU8lIM7D/ijfBLcyPFnjdRiDraWk9neFjGarZy8lg5UkhEcEh8f+CvhIIebBm5xDiLSEiJ3qRkatPrqA9uB6WSrddO4ZGccdmKGeZi3m05QJqrG1kQWZt5Kyc2n08wrkCvVlA+P5MtDhFZEDAoBP5gWvczdrJanU9r68VUV2uPy1WDXs/pyXOZ2ZyBFD7+dUCykm2Bb1m9/Q3i8ePJaTqThazKS/C4l+B16n9eBBIJer/d+D8pI/828V9QAJwffMlD78fZPRzj4sIyLNIOdlgeYGbfdhy7XGwcmsTDqtdwJoWYPmsTtaodbDY1U2E+ganJC4i5P8OpcrN31kPMrEvjru803Ks1scvqYoX2ASqb9hA9JPJQ4kl6k3UUV25ilW4HCkMqc1JOJe5fRlNqJWHrufzhYwVPqSyYDNX8Kf4Gut1OXgjdzkimhDZvK1t1rcxLPx8pvIWm7Om4l9x01LCnfqyN64on4pbDfCA+zCzDEMPb+ig4CgznkZT8BQUFMVb6duK0hJhlOgODygLx73C5wrSf9xSOlXu4r3sSv2eYNoOFz22v0tgyyJwxmedD5zEqS8xfMMCWgRo8UphTJt9AX+NKYgkvA2ffx8nLuuh09rOieB7PGT/iQP0QvgDUmO+ktPQQNcOHiHoilOTPxuJooitjMdkbGjk81kWrycE9WY3kjsbYLCxGramlZaiD+FgUvcqCyT6Gb+ZZLB1MY4c1hXnKQ8RGwxyKZ6BQ9jDkG0XhE/GO9OFxx0kMjeIPeQnLCZKkMGZZIKTMQK+HqArUah2CIIPi2KDZl5CICAKahItQUIEmKuENOonH9ajdfXRb4uSFwzTJ2ajMKooVg8iJXDLMKSTsAsakVCRJi7pjP9+a21ncXc8HnonEU7UsVh1GoZiBw5SKOahiz4EV+Lwl5IzuoPfEcjItqUzrP8xdPXNRFo+r0V0s/x0QaW6aCMCcWAmCLNAY3U0kEjjqt1yQFsKw+TMoyOGeigNIosQzrgCGkIeGrEt5uH0CFp2Eu8pCUIBzzG7mjd2PICgZHj6Pjg4Zo97AktBENH4RwS6xvPE1/H4XtoxcovKZRIMSRovE1P0vomquQdBq+PbGMl5K2Q/A6dYKXqvfjCnoRlYZeCnpUW5sGZ9zZNp1GOelsUc7fusv0gr8UfknJrj/elw+StJJbN40l57u418HpRmFnBeYQVqX5rjfBZXIQFIvn+56kp7ehuPPRJGsisUotFcx3JP+s9xXSCITUlzM3HQf4SfuItrb+7Nv/tPiPx4U/G4Pqw5n0FP5C676NMR9a8y8EElwZ24BF8t/40vjCzjqGrAcDPKC72reUd9BaslWMkp28a16G726EKfl3IiDEHJ8GQcyFzGccyWPfKBAblZyQ3Ymv1S9wf3RDzBVD/GZ8yw+1F7LhCkbaLFuo1Y/yOLMy9FHD+KUBjhUdTu//cbEgUEdXzmCfKh6gsx9rXw5ejabLLPJLV/HD7paJqUuxZxopsthZ+DCh8cNe9YWMOAe4urcAgbUal6PP8ipln76tvZRFBoHhnbdWaSmfUl2doQV4d24tSEWpV6CEIqh1a2la1Cg/6LHmPRxDWeNZvMbh4mYr51l+d9St6ef1OD4fCES20hZWZhNukNIgwomlp6EgnUcHrIQzKjk4bpCnsWFNFbPr9Mbye8O8X5gBt5IK1lZIRqznJSb5jDYspVRZ4Rg3jRu7p3AxxmFnBNfTWuzk2qvGldCJCMjykBGkMrCRUiqLkaTqig57Kda9pPrqaGrz8vBoEQo3InXP0YkGkWl1CGpgsTTCymLJtNvSafc4MOaEAgqM9DpBeJSApWkRRDjIBxZSU3E8caUhETQJEbHxfBCImPeQaIRHeqhdpoNPgq8TvaG0vHpFeQmGnC77aQKVtwMY7AXkmrwE5xURL45A6usZPVYOge0MpMC79HVmUmplI3X4sGRU4yIEePWz3iluJMb/BFqUi5Aa0umViNzojFEmn8FKtUCOjpkKjJLsA5IxNKh9uAaknJKGOzOwWiRyPruMQS1mhfPEgmIUe7UFVHedwBf8lTOb1t61BuhT5SZaYhznvtXAAQD53PwoIBWo2WJMA2de5wZvarlb3i9I9gy8oglziIalDDbJCbvfhplZwOCycTfb8zhA8t4Ib7RXMkfa1YgxSPEjJncrH6KP3flA1CZb2VwqpUO5XihX2rycX/oeqyhYy0epcKEe+wy1q9LI/KTi75Wo+XUtNnMa81BFTy+ZMXTRH4Y/ZBNuz6AfxgWJ+UUk5R/I8O9k4lGfr6impMeZ27Ty6R9+gdErxM5HGboxRf/xzXlXz3+40GhY8MOAs638CgHqZ58D/7kM3nwrQhnbVdzg0HHO6k6XhUf4GE+xLC9k7E2A/clnmWrcSZTpq/A49jF98Z92PUTWJh5LnhX4VF0sGf6fUxsK+J3X0s8oLZQaxnkW82DlDfUMNxk5GH+SDzfibVgCyu0eylyzKdIl0o4tJL9Fdex6GAxU7cruSPVzLPaZ5jXUs3BjkLe1NxEQdVGtpqqyUieTqYyyoh6mI7L/oyivpXXliUhuANclWKnzZTEc6EHuNDRTffWXkqDAo8GL6FddwaZWV+Tlh5ihbwHrxjilILr8A8NYHNs59CgBefS33D++x1kupTcWTyFgt6veSK3Ft+eYdq9ImvMz2G3f09UHqI+f4Si+CQirjGSMto5VHQp9nX1THda+cuEBfwy+BYtrQPYA7DZdD+5efuoHWok4o1QmD0da0obPWXnMnVTH8s9LcjeFmZo+6kMCtTqryc3b5BWxRBJcgbugQP0u3WoDnbQ6ekjEHGSIbqwRWSGNbPIygK3PUp2Whk6o59wdhmlQQt9RgdFOg/GOPgVqWg1CaJKGZWkRRSPbx954wpCgow6NozPB+qAiNPVS9CnRtnTSK12iIKRdja4kuhRQXp4K729apLcOrqGG4jHM7EO11NfpGIxOg5ZFlKSZ6XCkMCOl64uNVm9empaV6NQTyJfbMU9ZwKCRc/8rlru7l2AXGRCAZwTeRlR1FKzNwe1Ws3k/nQEtciGQx8c8Vs+AUEQqOj/FtE3xv4Lqtim6eZEaxmX1686jqD2ozdCvkbkF4HbUMghBM5i924VkiSxRDsd04gCwSyxtuMdxsYGjgDCmURCSmwOJZO2PIqytwUh2cGfr7ezQt+KUlDykK6EW4+snPqSprAk8Airhu3j84Mpqewt1OIVjrCTzTVc7r4aKeE6mocaTRV1dedQV3c89yA/LZfzY7OOsqh/DEGnpMN8mC+2P8XIyPF2m1qjmezKi/B4Tscz+vNWkSNZyWzftxR8dCvKzuNfFp7vvifU2Pg/KSn/8vEfDQpulxtFq4azSm/BGB6F2EcMqmR2LXgCfXwmL/1NJP2AknMy0jlsHuAH7e+4wLUa445hqgeqeFj6C+60AFVTN7JDu4UafT8LUi8mX5tMNPQpTSlV9BTeyH2fqJAOS/wyM4Xfal7ldv+naHaN8qbnetaYTqJ88lp2mLfhMqpYkHYWBFfRklmFqDmL2z8XuV/v4BTjl9zk+hJPnYI/8Xsc5fU02LYhW9MoM+bhie+l6fznkAc8PPORRJZHxdUWJfuTC3jE8wDXp7bRtrWXCYFxYGjTLiU7Zxl2R4CV0j4C0RBnVP6K4dZ6kjPq2O8vwjt5KXd9GqFvdJA/TjqVCwdeYL62i7wmHx+502nWnc2UqfvY21vHkCXAyRXX0H94JeGol+Gz7+baFWG+cjfSojfwau42VIfdfOrLYzQepKg4SEPmCOWWOQy3b6FvBCJKOxeM5PN10Sxut2xB7vazNlIG7KZjuJOoJ4qUkJDUboJl8znDnceurCouTuogMyDTqZ5LcnIAly5IsiUXpTRGyJFHrldDn9ZEnuRGG5UJiEmoNQmiygSSUo0g/ISnEI/ijUkEBRl1fBi/X0AVVRCK+hEFDcpEiLBWgQmRUczEVAK2eBdOp4TZI9HWU4vHacWwdwXLUro5ob+Jj3yTCSdrWKjYSyQylZLkPESrAn/ch2c0GduGt3l3oovroxL1KeehsqdQp5Y50zyGLVRNInESLleCmUmVqP0iA6ZenM7eo37LxSketDu/JzKjgsez95GhS+HRQzuOI6j96I1gVYrclXgUTbwflXQCmzebUSgURwXuBKOSdb0fMOrswZ6ZfxQQkpKVVK5/EMVwD0JOJg9dJbFd041OqeMlIYULDo7bX3ZlnsnM/ttp9mvRqRRUzM9kS5KChCAcYyePHWMnCygQOJsf1lYxOnrspq9SqTgxYyYntBeg8R1fpiIZcZZ3vc7O2mXH/S6IIlnlC5EMVzPUk4nwDzpIepOSqer9VH5+E9o9/0RCW6lk5NSpPN/29v+orvyrx380KLR2fMW38U1UmzvI187g1MJrkHyHIPElHfpUahY9SUlHLn99T0t7r8DFeflkqTfwg+Fhypt3o6/18ar3Kt5W/4b08p2Y87bzja6asMHEqVlXoQ7X4KeWvVN+S9HAZO75XMGjKhuHrV18rXqY0vp6mtuyeFL5CNayw4Qyt7JZ28KctPOw0c2o1EdT8S3csczAaq+ZTmsLL8f/jHbvCC+E7yRSFGQscxMdhjDT7fMIhtdzcMlDRBQm7n3by+yxJH6hDrA1ZxJ3OR/mjoxGWrb2UOGHx4KX0KY9jfyCbzGavKzS1RH2Rjlr+m30NmwkKaOVfdrFhGyFPLPKwTfOQ3xRuoCn488QGelnznCC50PnMZKIMHfeGBui+4k7ZaaXn4HIBg6NpRMRbNzXVsHjySnMHvgAnbePCg+s0t9Hamo1dYONhH1R8tInYkvppH/mlZy2M8LH8VEKh1Yx3D+E1w89qukUF0foSwtQVXgCWn0PYwXzmNOpYYfByByxAcEVoUkuRG8YZgQvZslBIjZKQJtM6hj0SyoyFGMoozI+0YZaHSOmSCBJGgQxgSAqSMjjMwVPTCQiCmiEGBq1DlGnRG+2YzAIxDOSydMk4TEVk56ko0wbRqksIt2aSsIqYknNxmqREO06omYNOQE/K1zZNOqgzPcuhxrMFHqSaffXYUmbQZ7NQ6wohU5blFPadnN33yJihUY0osCSwNNIUhK7dlpJsjoobDOBQ8mm3R9gy8hjqKcIs00ifdnjCFYLf1g4gFJU8rQ7ginoPkpQ+9EbQSUI3Kt6D3N4HxrNdDZsyBgXuEueQUq3GsGgZNPQZwwPd2DPzCcaP4NISElKqkj5intQOAeRJxRw+0VBGqThoyun81t3ICOwOetmFrRcij+mIN2qxbwgnd1H5gf/jJ2sUqXS13cZmzebjvouA2SnZHCBMIe8VsNx+SqYlRzS7hknoXlHjjtzZBWSVHA9w31TiYSObxUpVSLlSQNMX3sn5tVvIPwjJ0EQ8C6azJO3pfKrybV8PLySfUP7/n+oMP+a8R8LCm3+ENUJJzNmfos5Zy9fi1vYZ+xjovEkFuacD2NbkeVVNCRNp2HWfZy21cIjX+t4P6Tk7qw0fqn8K68p/0rqniY8TVoeiD/Jdss0Jk1fSa9tBz/oGyh3nEiVpYq4/3M6Hem0ld7CnV8b0DaouTndxj26v3Cz+0uEXV6eD95FXVIxeRWrWWuoxuGopMJURDC0irrya1iyfwLJtWqeTZJ5Q/kERfsb+Wz0EupSSzEUraNa28HslCXI4Y00zPkFoawqbnijm/NH87mVQVYWzeWXQ4/xSE4dh7f1UuE7Agyakykq/g61bow11gPEXQnOmvNbuuu/w5LUS23eFciuBC/vLuOpUBu1tjS+dLxB/f5+cn3j8wWENaSmediV2UV2oBjZF8Ce1kbz1F9Q+k09iREv3xTN5pWkrxioH2GV20afkEJlZYCG9GEqbPMY7d5Cp1OH0OunaFTD5uwq7s+so3A0TrXqfFLTumiln2Qy8Y0eZCCWREpdHzvCw2R79tI/4ONA2IwgdDAUGEUd1hD2D+LDgHUkTK8ok8woQjiOFzMqKUZUEUdS/PhSOMJTiEUJyiq0SgGFwoReo0NWy6gNVvRSBG+KkQJRQ4+Uh9aqoUjsJxzKIE2y42YYjSmf5EQfnZVJLJZsNFkXUphvZZYhiFlKQUw4sPu11LdsYmw4l5SdH7Jshsw1mGlMOQulPY0DKpkLjb0YIofxuE8gHJaZI05ARGD3yCoEUUSpPQVBEChv/xQx5OfbS7LpUI5xm76Yqp79DGSME9R+6o1wu3Ermf6v0WhK2byplEQCFmdMJ7Ndh6BTsM25jP6BZuxZBURi4y+EjDSBCd/cjegbIzq1jJvOGqJH6abAkMkHA04m9DcgSzreTH2Iq5rnA1CRa2V0up12ReIIO7ntZ+xktXoO1TtOoaXlWE4qlUoWZE7j5K4SdO6ftJHEIyS0xhepa1h3XB5rDCayqy7E6zsTz4jxuDNBGHd7m1v/NCmfP4YYPJ6vABCeWcnLv83j+tn1R/WZAF6pfeV/UF3+teM/FhQ+3tnF+66TeVj9Fi1WO7PnfI86cw9fqrZxSDfCbPu5zEg9gZjre6JspybvXHqKb+BXX6q4fJ2KO3UWPkqSeUf1ILcGPse8vY89vRU8qHieQE6AosoNrDdso0Mf4uTMq7DLQwSim9hXeSNZnrn87hOBP0oOuqytfKJ8hKLaBnb2TOEl1W/JnriT1pSttBoCLEg5Gym8kdbscgyczlXLBO61WPit/nXO6NjA3vYqPjZcREb5Wjbpapmcegq6+D4OV5yKb8bZnPVmA7f2VXBvuI2PK07i0r4/8nzeHhq29VLlhUeDl9KmOZHSCSsQpBHWJTeiGFawdM6v6Dv0OWrdEAdm3YVxVwcPtVRyhy6KL9LPF/nf07NrgAGPyGrzM2RlraVnrJXODA8nFF1Bf9NKfEEfzpNv4p7NNv4cH8Tg2skSfQtTR+N8pfoNFusWGoYbCfujZCeVYknuZmjR9VxRb+Yjg5ZTAsvpbHHyg89BKNbAoKeHiC+KEIwSiviIKCzYxmQGlJCaGCIUlBlLCATCLuK+GD7nAN6AAs2gm/5ECGtsmHg4jhcjSmWEmBBHUqgQxASyICAL4zOFuKDCqABRMKJTaYkqo0gaK7q4m2GHkvxonIZEJmGDktxEPcPDRpL8BrqGG4iEUzG3bGNdppsTRnr51D8Ff5KauWzCOVrCBF0OY8ZRkvOmkGITUUo+NiUPc3bLTu4ZWEy40IhZKbLI+yQaTQl79kiUZBSS1KsikBairX0vmWUn4hnVMyFpFM2+dQwvmcb7lgYWWCZwdd3qcYJaz+XHeSNcbe6hyv08KlUGu3bOJByGOVmTyW8xImgVVHuX0917cBwQomcQDSvITk9Q/OUdiEEf/gWTuP7kNlxikOnmIt5rOUi6q4u4PpXbdU/yZEcxANMnp1JTrMUtyEfYyV+waOzuo+xkUdQSDl3ID2sL8PuP3djTHCmcr55LcYv5OBE7wSZRI2/4GQlNEEQyy+Yjma5mqDvrZ62ilFSR2aOfkPvRHSj623+W//GqEt77TSlXnnCILZqun53v7N9J00DN/6zI/IvGfyYojLZycscbSNtasDQEed+1lEdUr9FpMzJ7znJI280Xmh106PycmHollZaJxMc+wy8eZlfVLYSs5/D4uwrK9kpcmpJKt7mNVdr7OK1nA+o9Xt52X8FbmpvIrtqJImcr32p3Y7OVMyvpRITAcvrMag6X3savv7dhqldzW4qJB3QvccPw1wT3Rnkq+gdGcjWYCtexVltLVdppZCi9DEs9tOf+gt9+beT9mJUM60budb/HcK2K58XbSa7cw05LNTkps0lWDNCeXYBz6W+OGPZM5infIV6dtJQze57jbwVbqd/WwyTPODC0qhdRXr6aiDzAxvRmtEM6Fk+7ipGOz4jJYzSf8ThFX+3n3P5Mbs3KIW/wW36Xth/LgTE+G8vgsOZkZs9uYtPoPoLBCHOrLkTBRg6GixH7w1zcm82LpXO4l7/TdGCQg24VrarZTJrspT51kEr7AjwDW2nzpWDc28nIyAi9yjgn69vJ88Mh/aVMKAvSk+6jsvAEjJY+PJOWcMZgOjvSSrjI3kFuEHq0J5GVFcdtjmIzZhCLBZBdfvojbgyRYULBGB5Zi1IZISLEUSpU4wVLEIkjoxRENGolRkUCBCM6hZoIIUSFEU1gmE5zlDyfk2pfKgNqSAtuobtHic2lpmuwgZDfirZ7PzVWFxXOPr5359Klh2L/VzQ26sjpM7G3aSWh0AQyWlezab6Fq6Q02pOXINgzOSgluEx/CE28n86OGUiSimnDWQgGJetq/n7Ub9nmUJL89RMIedncW3WIFK2DJw7vBknHLUcIaj96I5xiCnLy2G9RKq00HDwJtzvB1KwKypptCGoFe4Jr6ejajyOrkEjkdKJhBXnpUQo+vR0hEmJkyTRumHuQkBBjqbWC1+u3YAq6CTgqOSvyGMsGk9FKCqrmZ7IleXx+MM5Ovo8c38dH006tLqS56UJ27Tq2UiqKIrOzJrOkrxzj6E/aPkoBV+oRElrbruPS156ZT3LRdYz0TycaPF7awmSVmC5WU/7JzWjqtvw894ty+ebmSi49vZXvDS0/PwcWWCbwUdRC8ZpH/i+Ky79+/EeCwhstXzCia2G9+nYekD9BW92C8UCI98bO5FHpFXqT1Myas4JQ8k6+0FczpIdTs26gSJ9J3PshoyonO2f8Hmt4Hs+/rcDXruayzDSq9Kv4THqCsvr9eBrUPBJ9jJ22KVROXcVh+xZ26bqZk3EBOWoVwfBy9pdfjCNyEnd+DM8rHQzbGnmHJ8jd28zKwSV8ZLiY3KoN7LRvQ3ZkMMUyiWBwNQcmXMZ5NWUEm42scgzySvxP6PcM8VL4dhQThmlN24KUnE+hTkOfOUrfJU+Q/8VuXt03iTfcB3hiyhks6nmVz4p+YP/2Hqa4ZR4NXkqLeh5VE3/AG+1la3YH1hEH08vPxD/yBU5fiK4Ln+S09xrJGBT4Q8UCLhv+M2WJdqb2RsfnC7KLSZPH2GxuItmTgRSRsSa30rbgt5y0rJPNo+20aJU8kbmT4t4IH8qXotVuoMXZRDAYIdWUh8naw+ii67ipJYePM4u5Sb8RqSfARnkeVuthWqK9JItZhLyHGdLmM6E5xA61ktniQSR3hDaxCrvDi8sQJCO5GK3RTyS9GKNPRgiN4g9EGUtoEMUQUWIoFSoQEqBQkkBGgQK1pMAgxpETOrSCmmDciywb0Ti7aNS7yXf1Ue1NJqoBmxjDpElGMIuYU3NJMUcYm5jNfH0G7baF5ObZWGhwolZUkmXJRWEWEQw6JIUZffduPk3v5MKWndw7eCK+fAOpKpFZ7qfQqGfR1ATTUyvQekRaxTqCQR9ay2mAggmH3kWBzF/Olggq4jztA0vAybuOO1g7YjvqjVClg8s9N6MQ1XR1nkl/f4LyzGImtSQjSCL7Yhtpad+NI7uIcGQp0YiSwrQguR/fjhCP0XbBDH41qZY4MjdYKvljzUqkeISBjFOYO3g3B7160ixa7AvS2KUbv/kfYycf2+KRlKewccNs+vqOvQ6SrA7OM86nvNmGmPjJTT9ZydbAN6zZcTwJTa03kF11Hn7/2biHTcfltEqroMraxdTlt2Jc//7Pcl7ISmfj9VO4+PwePrT8E69OYJFlAp+ETbyybzWVPXXQthF69v5fVJh/7fiPA4Xo4CDT3trPWleCswtK6DR2s1K6g0f4AOPOZnT1Id4ZO5cnVS8wlCIyc/ZKxhzVfKXfiV+vY2n2L8lSaYn6P6TPqGHPjAeZ1F7GEx8qWe7R8/s0I3dr/sLjkTdJ2tlBbdcEHhSfIlwQJKP0B9botxE0m1mUfC666A4GdT4aJtzGDesysNVquNOh4WH9C1zV+z2D+zU8mfgDYskowZz17NH2MiflLLTx3bRmTSA5cjqnrhB4xKzkSdVzTDxQx8fOK+jITSGY9wN9JpFJxhJGhcO0X/o81o2HeHvTBL53NnLP5NOY2PM+3xd+x74dPUxxyTwavIwW1WwmT17PcKCd6rweMsZyKcmehRz+hm6nkqGld3LzR2N0DA3wetki/iw/w2hbFyWuBG+onkCtWQnqfg5ljzIv83wGWtbi8ocYm3IeD+8v5DG7ldNG38PV0UPII1Knu4wpU53UJw1QlbQIn3M77RSRvbmNje5uzGM78Q/20+pVMZIIEYgPEA5GiXu8uDwxVO2jHPD2k+bZx8ign4ZYGjrtIKOiD7suHUkaI5xWzISglSGNhkQoyFhciSAEiBJHIUgIQhwQkJFRiErUkohBjBCPa9GiwhdyEYvqkPpbadH7sKHCaDMzQRNClnNJ1zhwM4ykzcXuamR3foIT3C6+DE7BnaRmVmwF3V2ZFIdSaXbXoDJMIddXS90J2Vygy6bPfgJxRxaNUoIrNDuRCFFfX4LVbKGk3Uo8TWT3/u+O+i2XWfpQH9zOgQsmsknbyS3GUqZ01RwlqP3ojZChEvl15HcoZT9O5/m0tsoUpucxsy0TQamgXtzB4ZbtOLKLCIXHAaEk1UP2x3chCAL7rprOvUU1KAQFD+hKuG3fcgRkdmddx+y2q3FFlZTnWBibYaNFKY+zk43rj2MnK5VWXK7LWb8+hdiPzqeCwLSsSs4YrsIyeOx1IKhFBpJ6+HTXk/T2/qRwCwKZZXNRm69lqDuXn7qriaJAUZqf2bsewfH1nxAix3s2C0kO9l41ncsvH+WvyXX8g70CAgInWsr4PGTg5X2rKe87cPwHW5//H1acf734jwOFmm1juNLO5/rPYjz1tYEDgwKn5xdwwDLE19JdPCW8g333YVR1If7uvoCnpGdxpseYPnM1A/btLDPsQTCkcHrODaQIPmKhT2lLKaKh6i4u2J7BDd+KPK6ysczh40PNA1w78g1StZMPXZfzN+0NZE3eRTBrM6s09ZSmnki5MYdwYBkNxaeg50zu/ETgZcGB31HP69EnSd3Tzieuq1jvmE9G2Uo2GKrJS51LgU5kWNlFb/q13LxMx6sKC2eaPuWK7pXs6JjJctspGEpWstfQzVTbXAKxXTSd9ySqplH+tjyTfSMd/GbiYvIGv2Fd4efU7ephqjNxBBhmMm36Zno8zezJ76coNJF0Sz5q1Soa3Km4J5/F49/p+dDdwqbUPL5OeYvmPX143ApWmZ6itHQLNcN1uMQgiyZeikLYwAHlNCx7+8kZUPBFwVRey1iBocnDe5ETEBTb6fQ0E4hESNKlodL14Z52Htd057Isbyq/T9tDkTNOje5aSkrddCV7qShYhNHaj6/qZBaMJtNktJAe7aPLryCa6GE46ESPGTk+SsiSRb5PR78xmSylC29MACFIVI6iFJRwdNAsIAhKJEmBQYgQi6nRxiU8gVEiIS1SaBi7wYJTX4DJqqFQ7MbrSSYlbKJruIGgLwVj3RpWJfUxY7Cdbz2FuAwy+eFdDA+aSfbqae3fR8CThmXPl7yZ3cblrXv4/fDJuHP1FGhEJnmeRRRPZGAgwRx1OQpBweaWTzDak3EOVZGUoiBp2R+JTi3j0ex9zLGUcP3+lUcJaj96I2gVIr9TvIA+2ko0cj71dSLZPwrcCSKN0h4ONm48CgixiIKy5GEyPrkPJIl110/kqYx9aJVaXhLSuOjgWmSFmvfT7ufC5pOQZYHpE1OoLdXhEn7CTvYcG9BqNJOo3XcWB+qPlRmr2cI51nlMak5GETtWoeNpAj+MfMimXR8eR0KzpueQWnwdI/0zifxDqygjHeb0/p2sj3+HYrT/uDPBZKLxkhlcd12QP2XsIyLEjz9H4GRrOZ8Hdbywb9VRjaZ/jMjgYXpHnP/nheZfOP6jQCEcCNBY/SpOlZftE++kr+QGzlmt4sWPdYx0Kliak80O6xgfSffyjPAWKXsOoawL8Xf3pfxRegpfVojJ01fTYdvG97p9GCxFnJx5BeZ4O8HIcg7mn0xfzg389hsrM7coudGewpi1ga+V93NC83bc+xU8GXmY3cmTKJm4ghrH+DB5YeqFOIR2RqQO6ifcwpU7Ckneq+Z+u4JHdc9zSdcaWg6l8SfhLixVh2nP2MyIRcM0yyzCoQ0cKr6EC3eW0NRvxeXYz2Puv9Jfq+MV6UZsZVvZYdhPVdIihNhuGk+5nVhAw8ufGXENj3LDhJnYRjexKe99Gvb0MG0kziPBy2iRpjFj5nZaXYfYnztIlTAPg6DHbN3CPnkaIVMxL24t4H55iGF5lA9yVhKvGeGbsUwaVfOYO7ebDdRjHLOilzWY7K10nXw31y4P8Xq4H9vYFgpCraSOyWwz3srUqYPU2XqZmLSYsLeaduNUpm0e5LOEi7me5fS1ulgTKkKrraMl3EOKmE0s1Mxo8iTmdmnZ4cjkfFs7mSHok0qJyk7EgEjIP4hfZSfDo6BPZ6ZI60WMyOPy2YkYCkGJQBxEEVEUSSjUKKVxL4VIVIUmqsTtG0IUdcRSLOQpdHSIucSMEtnxOvr6NNjHNPSPtSIpTcjJKkosqfRZZpGRa+MEbQ+R6GQmmPJx6gaxZc4gT9NP1/xcTjJnM2aeTcSRQ5Mkc7m0CklhYPeuZArSc0nr0jBqH2JoqB1j8lIEJEr3vo5o1PPgCSM4tDaebK5F1tm51HUTolJFbLINvyDzO+03OAKbEMXTqa7WkGJPZvFACcqESIuunv0H15KUU0woNA4IlbYeUj97GEGn4/NfFPO6ox6H2sbf/RILWreT0Dm4z/QkD7SXo5FEJs3LZEuqkjjCz9jJgqBETpzDD2srcbmO6VxPzCrjbNcU7H3HROwEnZJ282G+2P7H40hoKq2O7KpzCYbOY2zIfFweW+1KZsbWU/LRLagOH+/XLGi1dJ87g1tuFngwrwaveLwtmyiILLFW8FVAw/M1KykZ+OetJF/yVF5JeYSSgYf5y+ae/7uC8y8a/1mgsHeUxRkXYoqOEg/8Ha+yg51F19Ay+bcs2OPg1XfUCM1KzkzPYK09wFuqB/mz8AZZNQcQ94f5m/tKnpYeIZzrpWLKGhotW1mjO0iGfRoLU85GH92NL76DfRVXETecxyMfaJAatFybamOeeRmvxZ6hcG8jdW0lPCw8QqwkgKVoDat01ZhTKphmmYYcWEFz/gwUqgu49RMlb8pJyPY9vBR4GuPuft7w3EJjTh764uVs1TYwOeVUzEIj7Tml5HtPp3KDkrcsPp5N/AlTzSAvh3+DuqydWscO8lJnYxJaaJp9MQFHGY+8F8U0FOGagjKUvv1syX6Tptpupg3Fxl8M0iRmz67m4Gg9DdkjzDQsRfZ4sCXvZV/SOUj9Ao8emMCtDiO5zuVcb9lHaWuA50Pn4hR6yM4boSarj5n20xlpW8eQP44/fQa3Nefz56KpPKV9D2f9KJ/5KwiKBxkKteKLRrEqraAcxpc5kwV9VnY60jnX0IjJA23KcpS6QfzRCJGxUQZ9KlIbhqgWoswQDqJ3x+hQzSEtI4xHFSbu9+OLabGNRumTVBSoPWhjEEIiEo8iCkogAYKIKIjEj4JCgHBIQhNSEIz5Mahl3Mk6CmIyddEMRrUCGeHdhANWlDoFhqQ8ksVhDk8wsjgYYVlkKqMOiWnhz2k6bCd/yEpN82p8YwUkbf+A14u6uKqjjvtHT2UkR8dEHZR4XicUPJFQSGSGKxfBJrFh17tkls3FOWCjTN+G1LKPFRfn0i6N8aeghM3v5HHt3Rzw6Y96I/zKXE+B913UqoVs2mjFZrZyylg5UlSgw9jI3rrlJOUUEwwuIRZTMMncQtJXTyFYLbx+Ywafmw6Tb8jkgyEn5X0HCNlKuSD+BJ/0p5Fi1pA8P51qvYxKELjdvO84drJKlUFP92Vs3Wo8euE3GoycmTSP6c1pKCPHXgfhIyS0Xf9AQsuYMAut/XqGuvP4ab9Ho1cyydjMpGW3oN/65fHJrVQyvGQad/9az52lNQyJ/uOOFYKC060VfO1T8nTNCgoH/0GHG5ARGEw/kd9bn6Oi606e6SxClgW+qunF6Y/87Pt/1/iPAQVZTvBh/XfUGHrJ0U3lrKJfkynpibjfISTUsj9rCQdm3U9lWyGvvimSdEDigqQUvnZEeFH1OC8JfyW/tg5qI/zNfTXPSfeTKHRRMnE1tdYtbNI1U5J8AjPtC1GH1uFUtLJ38q2kBk/kkXcFdg9beChF5EHDC9zrfg9t9QBfjFzCW7qryZy8i+HsDWxXdzAt/Sxy1GFGEzUcmHAD5++rJG2XhkdtMo9rn+G8tvXUNFfyiuZGkiq3sydpG+a0ciYYLAwqOxh2XMUV32h5UdJwq/ZV5h7aw4djVzFSrKY7ewO61BKyNH5aJ0zFU3UGt/1tmMp+LVdlZuKLdrMl/RU6D3QzbWAcGJqlKubO28Pe4Rqa05wsTL0Yf28TJvsh9lf9ivStvVzQnsodRZO4yvUiaZ5myofivKF6HLN1FX3BJnqNXhZPvHK8jWQ9kaIfuukYHKJZk+CXlj2UDsdZq/8dEyd1sd/SzaTUE4mHd9GVdSJLqqN8aNJztfQDur4g26WzKcgfpsvupjx3HlrjECFrCZFRHxZfA+7hAE1yAVarG5cxRLIll1AkhH7YR59CIFPpwhAHn2AmGo+ikBXAePtIFATiohpRKaLDRzCoQB1XotDq0AkBBm0i+QEP270pxPVgEvWk6ZMYYxhRysbSsZNVaUPM72/hW28JskkmQ3ZjELNQ6EGTlEmaMYa73EqlPY2IfhIBRz6tSplLhY9Rq3PYuVPH1Ixy9GMKatzrUBuMeFxTSU1VYP/mWUZPncrfbQe5yVTO9I7dbMi8mbd7s456I1xoHmXW2MNoNJPZsCEbo8HEaaFJqAMi3ZY2dtYuIymnhGBwCfG4ginaA9i++TNCWgp/utbED7p2ppqLeK+lgQxnF8Ppi1kweh81biMTssx4ZzhokmQyVCJPad5g2tjjR3NMrZ7P9u0n0tZ2rP0zIbOIc33TSelWH/3tRxLasn8goVnTskkrvY7RgTmE/cdaRQqlQGnqGLO2/B7bdy8gxH8icS2KeBZP4bHbkrllUi0dyrHj8l4pKDnLWsE3HoE/1qwgf+jnG0eyQk1L1vlco/8LM9uu56P+tOPOw7EEH1b/57iz/ceAQv/IFiqqPkGXvYvvxc2sNzWi1mVwTv5vmGAsIub6kmhiI4eTJ1Ez73GyRyfz8psiJXuUXGNL4v3kOE+qn+avvEhpXR2x2ihveq7jBdXvUBQPkV++mmrrFnZqu5mccSaTLRMRAt8xqPdTU3Unczun86tP4vxVTmKVY4h3VA9xefdKRvfA08H72Zs2keyK5eywb8FntzDHehKq6FY6svKI6i/jls9UvBNLQuuo5mnvnxF2OXkpeAdjxUoCBWs4bPAy3TqXeGQbhwvO45IdJWx22Si0r+ZXPV+yqX0BW9NnEClcidOmo8xoozPNwNDJv+GyN1o4syuZqxxGekUvG5L/zEBjB1P7ouMvBmUp8xfUUj2yhw77GKcUXMdo2zbUhg4OnfgAcz5vJa0X/lQ2j5fF5xg62EnEqWSV8SEmT9nLZl8NSq8Wu2RHb26j7+Tb+d1mK4+bdVzsf4+hQz2sc6czKjjxyu14YxGMso5AzE1MTkXqD+GN9SANd7HXYyWmOExLoItUZS5yvAVX4XxOG0xhvz2VjHA3zUEDanU/owovKdZcJI0XOaKiPxEmFSfqqIxf4SBBFBEFEB+fJ4gKYqIaQSGgS3gIBkVUghK11oo24qTdHCHXM0KPMp1CtZ9wOIOUmIWu4YMEfSlohw9Akg6PYSJJOXYWqxpxuSZQksjg8OguYvEK0vZ/wd8rRrmm+zAPuU5jMEfHfGOULN8XDAzMw6A3Ud7uIJQepallB7as05FlFcU7XkLMzuDeyYeZaS7ml/tXHiGozTnqjTDXEONs969QqwvZurUSlUrLEnkqOrdIv72b7Xs/Jym3hEDwNBIJJVMVe7CseBXyc/j95bBH3ccSawVv1G/BHByjLusKZrVfz1BYYnpVCvUT9DhF+Sfs5FUAiKKOYPBiflibSzAwDgg6rY4lqXOY25KNKnTkti+CPz3A140vHEdCkzRasqvOJhQ+H9eg5bi8zU5PMKf1r6R/cj+iZ/S4s9DsKl68LYcbZtVRrxo67kwpKjnXWsm37gRP1KwgZ6TtZzVBVpvZm3UtS4RXOKn5/KPmP/8YNr2KfYEgscR/hjvbfwwo3NeTwZ9UL9BkTWPytGXklO1gr3o7y0x7cekkTs64lmmO+Sh9W4hFl9FhTWXXnMcwRxfyzN9E5mxR8ltDEq+miNyrfYE3eI6J9bWEa6K84fkFL6lvR1vWS2bZGrZYNlOvG2FW+gWUG7KJ+b+kK8nOoaJbuWJnKSetEbjbaCfu2Md7iYeYdWAvB5qKeIz7iU8IIhWu4gdNDYXpi5hgsDEW2szBCVdwRuNMsnaoedYS4XHVs5x2eCsbuk7iU8tZWMvWsN20l+LUhSQre2jPKqTSuRTrLh077W086XmB3v0m3tVdgqp0LQ2mYSaZyxjWDtF1/iMsfucAtzTncZ1RpkGnZI31adwtHUztifBI8HJalEXMX1DH5tFd9Bg8nFH+K4abVxATh2g781Gu/HCAQ4NDfJ1ZxFcpf2dszwCrx7I4JE1iyrQ+tjnaqNIuxNW5md6AkricwUntFj7NKef51B8o7I3wreYOysub2GfsYlLaiYjyHnrLzuWKejOfZBRzt2MHOa4E9dqzcaSM4E1EiDgHGUwkUdYcZofJztmWVvQ+ARcJhsNjWFRJKKUxItYcgqEwtsQIykgCv5iEUoocIUol4MhLISaqQSmgld3EYxpkVQKFZELj6afV4MMuazDbdBQInYyOWkjy6hgJ9mPRKhgsc7A4ruDb6HSG7Uomhz6lo81GmlfPgL8bndJMwuTHlGpGUhXjcRTSoUhwfvR1NJrJHKgXmWMoRylJrK99d9xvuTeZCukgyr5mXj1Hg0Kj5Y+t9cTMuZzTc/lRb4QircD1/ltQKx3U7J1DPCaxRD0D06iC4ZRBNu/+iKTcUgKB0wCJ6bHNmNb+nURFMbde4KZZGuU6cyV/qlmJJMt8kX4PZzUvRaFQMnluJlvSlCQEgRv+gZ2sURfTdPhC9uw+NicoTM/jvMhMMjqOeSEI9h9JaC8TDh1r7WSUzkDvuJ6h7oLjpC4cyUpmB5ZT+NFvkNqP3wiKTyzlnVtLuGpRA9s03cedKUUlF1gr+d4V49Ga5WSNdvysFsSMGazOvJUZwZc4v/lkGn26n30DUJRuZNK8TEbmJrHCkGDFiPv/a7n5l47/CFAYGRmhdlkjqoYoXzpP4k7pHdboTyW1dAuTpm1k2L6Dz7TbadF7mOY4i4Xp52KOdRENfki/UaR65oOgPYOHPlSz5AcFj6ocPJOi5Nf613iLp5nesJdATYzXPbfwV/UtGMq7SCpZwwbLFloNQRZkXEKR3kjQ/xXN+ZMZSLme279PxVGj4Td2HUttn/G07yUcu7r4duhC3jZcTurEnXTkbKDJ4GdW0hIsQgNdyQY85qu48Ss9n0Tt2JM385TrJbx7Yjwf/S1y+TAdORuIpWRQZUxnSNmJ33Q5Z3yv4W+aCA/wHNbafl6N/RKprI59lgOU26YTEA7TdvFTlH9zmMf3l/Ar5Rjb7UksNzxBvKuNKV1RHg1eQYsynwULD7J+bCeDaj9nTL4FZ+dXuCIBek++kwe+UvA3fz/tKh9/TVtJSoOHF4Pn4ZVakIydtCQ7OaHyShTCRhqyz2PxikG+8A+T6VuPpr+FJqeRToWBhKYTtxxFF1PgCofQdcvUjw1T5NuIu83JhsRs0tO76TA7Kc2eBcphlE6RfREv0+WDmH0xulQzEFQjqKNa5LiTUHI+yW4FuugIcjiOX3SglCKIsghyHFkQUQgiUUGFrBDRJFzIcT1RMQKCCdVoFx6LyLAmD8EskR2rZXTIhEapRGfNxeFrYUtOgEUD7XwXKMNiiWFN2MjT5TOi7ENnnUZW30a+mBbl+oFuHvWcTm+WhiUmH0nBTTQ2VpKdkklmu45OdSNRIvh9s8hIB+v3f6Hx/Mls1HfxVFiDPeTjltgdhBR63FUW9CqRO+IPoxMiNDaeitstcpppBrYBJc5UJ+ur3yE5dwKBwKkIgsR0/2r0mz4hPLOSG0/vZVgZ5AF9KbfXLkfWWHjM+jh3tU0kyaQmfUEGOwwyNqXIY/ovWXyUnSygVCxhw4aZ9PePD5PVajUnp89kUVs+Gv+R0qIUcKU6+Xzf8SQ0c0oG6ROuYXRwHiH/MUDRm5RM1daNi9btWnF8IhfnsezmSi5d2sIKfetxR5IocbG1khXOKA/VLCfD+XOWcshexvtp91M++id+2TKL4Yj0s28khcCUUge5J2RRX2miWi/z4xjk3d6Rn33/7xj/EaAgN7xFjeF2fp/4BPveQ9h2uWjozueu2DO8qb6dQKaXWXO+R5G9g2+1W6jWd5Jlnc5p2deQqZSJ+t5lVD3Crsm347NfzB3LLFz6vcgrsoOHU1RcYXyXd+UnmXtoJ569cV73/obXtTdimdiGuXgNa01bGDApWJR6MTmaKJ7ACg6WnYmovpB7PtbR3uPgaUeAR/TP8tvBT3DvjPBC4G5qMytwTPieLdZtGNPLmWwqIRjcwKGSc1jcvpjszWpeNvt5TPk0Cxp2saz/QrZmzEAq+Z4aQycTbXORY3tpyT2bC7YW8n3AyFmmDzj58Hbed16FpzTIodRt5CRPQ0ETzefeT8quYf6yvZTfxwb4PiOfz1SPoOlvZlJHmMeCV9CiyGLBwkOs9e5kTAizZPIv8Q19QVdQy2jFeTyzLo17NHHS/T9wtmIvpb1R3lA9RnrWOvb79hMJi6TpMlHrWxiY9wt+tzeD5/IqeMH0CeYWH58qbqSgsI592g4mp52EpKylf+plXNOYxvLMYi4x1OJzSoyKQTqDXaQq81AI7bhLT6BkUIUi2kFwJEirWIkj2Y9PiBAODBE0pJHv1+MWE8RCcXyCDUkVhTjIxEEQEUSBqKAioRDQyH5UgpZQIkAspkcZGSZDpadZzsatFcmKNWGWUhhjmIScibFlC01pUVAVYM6yMV/cTXd3FoUuO3Wdm4gGs9CO7mM0U4FVyGDEUUS/Es4IPY9KtZDeHoFZ/gLkVAU7ar4kKW8poKVww7PEJk/godwarjeVM6d9J+867mCD007SzFScSrhHegtzpJG+3rPp65M5JWkmyT1q3Oke1u54k+S8CfgDp6BQqJjuXIZu+zI8J07h+sVNxFTi+MrpgTVELAVcKTzJ271ZlGSaCMxKolFKMFkv85RwH7m+jwBQKm04Ry9nwwbHUe5BTmoWF8hzyGn7iYjdURLam0dJaJJaQ3bVmURiF+IcsB39dFy0bojp6+7GvPL140TrhOwM1t84mYvP6+ajfyCeqRVqLrVUsmI0xB9qlpPmOv7lADCWOoc/JT1Jae8feKC9nHDi52UvyaRmxrQ0pBMy2J6jplFK/OybbWM+WgOhn/3+7xb/9qAgR6P8rnM7lxSU0GLq4e/iPXyue44Tejeh29SP/7CaVzzX8rD0EvW2AiqnLietZDPb9ZtZpatHYU5nadYvKdankfB/hltsZF/lNQxkXst1GzL4xRcyX4Ts3J2i5kzLZ3yYeIQTDm/HvUfmNc9t/E13DfaJTWiLVrPKsA2fzcwCx3mkqwYZje6iruJKcv1LueVD+DycytakTl4XHuKc5o3UH8rnCfku4uVBwoWr2K5toSr1FLK1LnpMAUZtV3Ltt1a+DtpIT1rHoyOv0b3PyAvCL1GXH6AuZRtp6dPI0njoyMxlav9SggfNxOzbuKvvfdZ1nMD+3CJGCtZiTC3FLg3RdPL1aAYk3liTwwuBHt7Pq+Jd4SGSRhqpag/xeOhKWhTpzJvfyKrgLgIxmRMqriAe/JpD8TyimjIe2p3DbZk5XOX7K4aew4jDClbq72HajANsVB+kRDkNX99OuiImjL0qxJ4QrRo/J8k1REeUHJSq0Fg6GBPCaMIxhiIKMuu9fBvzcb68BstAiF2ai8nMGcYthom6+xnWFjCvS89uWyrZoU4OR1OwmF24dCE0qPCJZrI8EsMqJbFgGC8m1JoEMYUM8pHtI0QiR0BBTxidUkMg6oaEjoQuTn4c9obSwSCjTthIFSx0jx5CjjqI2uLMVepYEZ/BkE3JxNBKgmMZSGoZXVIxuZEm1s3Rcb1zlMe9Z9CdoeFc0xC2aDN792QxKaMM05jEts6vjvotV8b3oAyM8tBJTiZbCrll/6qjBLWJM9I5oEpwp34D6f7leD3n09QEi9Onk9GhxZceYNW2V0nOK8PvPwVJpWZa34do96ym/6wZ3Di9DqPGxN8DKha0bseVOpfFY39gm8vMtIpkGsqMjAoyF5uHj2MnazRTqdl7BgcPjl+fJUliUeZ0TuosQusZLyf/KxJaesk0DCnXM9RdhPxjYRYgPz3CnAPPkPL5I4h+z9HvhWQHu6+axuWXjfCao/444plGoeYKSxUrh7z8ft9yUseOd0yTBQU9GUv5jelFJnX8mle7c/9pfSjNNFE1P5P+WXY220Wc4j+fG5gUIheYR3EOfvN/VH/+FePfHhRGdh3g7hcHuf8bHf1dSpZkpPHnND3z1F+xx3AHdwQ/IXn3YVR7fWwamMXtwmt8oz+HpPI6JkxaQ3fSNr7UVTNiUHJS5lVMskxECq3FG9vGgeLTaC24hXNrJnDHBzG2uuzckqphvvU7Pkw8wKnNW3DugVe9v+Ud/RUkTTqIsmAtK/U7EJKzmGc7FbvYwKDUTV3pjSxuX8DpX8d5Um3DmLSdV/yPU1xziOV95/CO8WLsFTs4lLmBMbuJaebpENlOU/GJzOxbQt4mDW8bx3hYeJop9fv5YPRamgoz8RWvYMgmMdGUz7Cqh4T6Imb+YGSlYYCHfX+mp97O5+bTCZWsxpdkIV8n0jr9JKLqYl792sFXrl5eLJ7OX2MPkT92kPLWIE+ErqRVmcLceU2sjO4mEVYxvWApkriC/aZFmLrUnN9g48GSKbymeg5/bSebnFkckorIL+6iNqWfRSWXIwrrOVx+NVetDPOMTsUvYu8TbRjmo9hZpGXuY6/UxuT0k1Cp6xiacjFntNqo14s4nC1s8+dhtbXQrh+lMHUKQdlLcluAap2OpYZmwl4FSMOMKv0kGTMJJRIkuxL06UwkM4Yb/bh8tpAYbx+JIqIoEEE17s8sh9AKarxBJ0o0uJK0FAR9HJQzydF48fqSSPLrcUWdOBR+aosULBzp47twJdlWP2I4j1Ixi4bB7YSDxVgal1OTGyI7amXAUYpLBSf7niSeOJloREtldxJjSS6GnJ2EI/PJSY9jWv03Vl+cz4guxtPtjQSTJnF+29Kj3gjXm9sp87xMPH4u+/YpmZs1hfxWI8GMMMu3vUxyfhl+38moNWqmtf4NTd0WGi+ZwW3lNeQaMvhwyEV5bz2Hsy5mVtfNDEU1TJmTwdYMCZVS4H7jBs4au2ncjEeQiMfPZe2aCbjd44UzPSmN86W5FLaYjorYxdME1g5/cBwJzZycRnrZ1TiHFhD0HttCSklVMNf5Obkf3Y6y79ggWDCbOHTJDK69LsAzGbXHEc+0Cg1XWypZOeDmnn3fk+Q5pmwKIEt6GrIu5ULVX5nXegXfDSX9rCZoJJFpZUlknZhFbbmRXTqZmCD87DuAqfoEvzNt56X4NZw79gtCvc+TSMT+6bf/LvFvDwr7m3xsnfdHBguuZtGeJN56TcGibSpejGu4IC+PNks370v38a7yORZ2bkG3ZZCulhQeCT3IS6rf4coWmDrtG+LZW/hat4UGnYspGWcxx34iNhrxBb+nOaeS+gl3ML9tGr9/O8bhfgs3JuuYaFvDx/Hfc1bLZoZ3C/zVewfvGy8iZdJ+4gVrWaXZjSmjklmmOegT1fRaZZqyfsGVuyaSu03NA0aRc20f8djIa0R3ennFexv7c8vRl37HNtMesjPmUmpQ0a/qYdBxOVesSmWVz0J+0ir+MPA2Bw7k85b2EpRlm6mx1lPqmIlKOExXxmmcuTmfbyICv1S+QmpdL3/jSuITttNudzHBlEJnYQ6e/FN45iOJuqERHpkwmz+GHmaSbz8TmgI8GbqSVqWNWXOaWCnsRROyUZoyDa12PbUFV1Cx2YOjI8HbOaV8kvQOmlonrwXPI2JoZFg4iEuMkmcqBlUroxUXcsWBJD7OKuA+y3ocAzK7NGdiS+tiVIygCgTojVuYstvHx0YDt5k2Yx+VaZYKGIp3kCrlopQ6CaZMYXQswDT5AGZPgh5FFs7YGA5DJrLgxeAM06/Wko4TT0KLSooRExPI8nj7SCEIhJGIi6BOxNCiwh/1olFAn1UmOxjCp0siX2hndNiEUVahNmZjGahhR6YXm5yEOsPOHHkT7e1pZPhNeMUIaUoftbPMXB8I86fAWbRnqLnY0I5ZiLCz2sRsSyUqjZr1u98lrXgpyHpy1/yJsZOn8pb9II/HjCRFo1zsuonsVCu7MiSWmnwsHrsLpfI0tm/TMzWrkgnNVsIZMb7d9iIp+eX4vSej1auZevAVVM01VF87jQfzaphiLuT91kOku3pZnnk7pzafjVGvJWthBtuNUKgV+KPyaco8fwFApcqiu+tStm8zAAIKhYK5WVM4rXcCBue4xLWgV9JubuSL7X9kdHS8haNUqcmuOp1I4mKc/fajOWm0SExX7qL8k5tQ79949HdBq6XzvBnc8kuRh/Jq8AnHuAE6pY5rLZWs6ndy177lOHzHbxsldA62Zv2ShbGXWdp8Jnvcx8toA6SaNUyfkQ6L0tiapaJZ+fMWEYBdErnMPMDL6me4w3chE93PIcnjQ+ZIZIRR56b/80L0LxT/1qAQ9QYZrvsGIu/hUXSwP+tU9iz8IyrFidz5sYLHPlERapE4LyWVP6dqWKj9iu3aO/mN9zPSq1sI1cb5cvR87la8yC7HRCZM+wFb0Tq2WLawWdNKavI0Tkg+n0zJR8D7GZ0pDmoq76ZiZBEPvQ3ONjM3OvRkOzbyUfxeLmxdz9Bukb967+Rj83mkTNpLIH8NazX7SMucxTRzFYrIBtqzsnBarubmlZm4m228anXyoPZZbur8hkN1mTwj30q03IuraCWNRg+TbfPRc4CW/ClUjp5N/kYtnxmHeSj+Jwr2N/Om50aGSwW6835AnVZEoR460zKZ2XUK3W0mSq3fcl7Tet4fuwJv6QCNyQ2UWksYSBIYmHMd977txd/r566y2dwXeIIF0b0UH/bzx+DVtElmps9sZpV6H/ZoFunaDLT6ag7MuINzvnaxb8hFvT7CE9aVZLeFeF16iMLibWxL1JIulBAeqqVdSCO/Nsp2l5ui8A8IrR0sC8zAnFTHPkXL+GtBcxBX8RnkdYpo4geJd4+wTbGE7LwhnMowcV8foymTmNNrxC04iY2GaJemotQPYxAsKJRuEgoHfQqBbOUYoZgCSRUlJiSQE+ODZlEQCaNGIQHo0MYlwokwuoSHDnMYW1yNZFGTE6tHDqbiZoR4LB21p5Eig541iZk47QrKIrXYyWMo0YmgqCT5wNesLnIzIaCiy15GRC0w3/MErrFFOMyp5LTrqQ9uxZKRw2B3NhWBLSi1cPeUw1xjqWB+204e197FkGCjt8xEhUHmEs/NqNVz2LghiYrMEia1JBFNT/DNtj+TkleOz3sSeqOGKTXPo+o9zIobK3g+tZbTrOW8Wb8NUyLBs47HuKVlOsXpJsKzHDRIMktMfv4Quh5bqBoAlWoh27Yupr19/NafbEviPN08JjRbj4rYhdNjLO98nV21x1oraUVTMKXewFB3CXJ8vMyoNOPM6Wkrb8P4w7vHElWSGDp9Onf/Ws/dJTUMKY5JZOuVOm4wV7K6d4g79i3H5j9+2Bs157Ms8y4mep7niuaFdAU1/GOU5VioWJBJ9ywbW6wCY/+k6gnAHEOMPxjX8efoZZw+dsvRv8E/Rn//V/+70vMvHf/WoDDQ2MtM81ksLbyRPI2NyOjnxKPLGNTJ7Jh4O62Vt1LZVc5f/ipz+joFHweNnJedQbu1nQ80v+c1+c8saNmGcusotR1l3BP7Mx8bLsFU0U5+5XLa0jbzrWovsj2Dk1Ivp1hnJOr7nD5TlL1Vt5EeWsID76kQDxr4pc2IOWkH78fv5dL2tQzsEnnFexdfWM8ieeJuxgrWskF1iIKsxUwy5hAKrKO5eBZq4RJu/ELPBlcy+5MbeS76KHPr97Cy60zeN5+PrnwbdWmbUGUUM9GUzqjQRF/yRVywPo8NY2ZKHN9zT8/77GiayTeOk4lNWEWLzUWVtQSXehCVeD75W8y0W+r43cBbrO08icYCBx2ZW8lxlBHQOelc+juu//sg6e3wq9Lp/NL3HGclqslv9PJ06CraVAYmTW1mtb6ebCowxQSU2gM0Lfo9v/0izl9jQRzRzSwI7ELTJ7Jc9xsmTW5kp72DhfkXIcobaZnyC27boOfZzDyes3xJWneUddqrScvpYEQZQgq46VJlc8pemc9SsrlSs5MulwmloZt27Qh59nLciQTlrTF2Wx0URNo4nMjDahsjJMjIiVFimlRG4zGylC7ksAKlIvKTl4KAiEAQCUmRIBHXoYkpiRBH4x/GZYFBRTZBvUhGrI8UhZ3esWY0gpmuAjWL3E6+j02i1DyK11VAkS+Z5uF9GAQjXWUKLokLPBc6m5Z0FZfpajGqktlXIzE7Ukw0XeZw23YSwgkUpEcxbv6EN8/Vk2vJ4Nb9q9mQeTMfDuagm5aMVivwq9Ad6NUF/D/cvWd4pVd19v97Tu+96khHvWskjTS9F/fesOkEDBiSN/SEFtLgJS8QSgDTMTbFBWxs4+6xZ8bj6X3Ue5dO773/P4wzZmI72IH8E3x/PNr7eZ7r0rXWvfde+173/n2NNDnrWTfjouwUePToN7E1dJBIXIJOL6f36JeRRpf5xQcauNs4zHsNq/jq6acR1A5ul/wLdy7WsabDyliXlphY4GP6s7wz9m6k5QhisZpU6q08/5ybbLaCIAisq+nmat8q9IHzTewEvZQRxQkeOfT1CyI0ncWOq/PdRII7SL90VCSIoLkqzcZTXzqvnM5lzgeoSER8dx///FEb/6f7zEXCM61Uwx36VTyz5OWjZ5/AkL64/1DK2ssP7P9Im/+f+dhUH4nixW5rSpmYNatsOHfXcLpNzUllhRKvPCJyykS8V7/A92T/xF8lbqM9/j3Elcyr5hOxWI1ctoOxUQeZzKuPeTNA8oeH/PliJPIMJ6Vh6jW1NJYdXOF6PxVzhbnEMP7YXWT0HUzb2khv/hdqdVHe9fzvkGeXGdwg5901DtpNIm6OPMQni0EORq7ha2P9+F0uvuf8GFpDkSvq97HK8Sjp9Gp+l7XQqG5mm+utFJUZToeewa+ys9jzQRyiPJ/79QN4W6V8tEvPDtVJ7vE8ylNz1/DrmU3c2fBJmiwhrtTejS82x9BCM72uSxGV0wz6DjLadjVtMTA/9Cg/2aSlwf4cb4/s4198t/Ljhg/T2zjFjvRjnJzqYZV4C/HkLLM1tTTG16Hd9xiPb/Xwd9mv8POzt/GDxvdxffvjjC76qZb0kwjPsmLbza4D59jbO88nM9/iruH34Glzcq3mWbTibRRDy0zf8BmufuBb2K6r4X0tXXx/9odotXnuGd7Cv3a8m08pf05nzzjPDom5IraJM/FnKejULGz5GP/0+I/49C0S7vL+hLOTFk6oOmk2VmG1TbDgs9JiXsV8bo60bTttE0eYs4dpCZ7jYGQNm40PcG7Bze6qSzgRO0vGuIGg/0VuL+/nPu9lnG7fiUM6h0O+icXCMEKlmlOiRS6Xj/PdRCdabYiYNEslnyRrrEObGMEpRCgXBESK7Es1hZcKzSKBXEWKRFKmmJcjr0iQyHTIgx5KWhEjgWqk2hKVvBlrVsNYMU51aZGnGtL8dU5N2WFhQ/EBAstuWkUlpLoOXJNPcedlUb4cr3CXuQupUmBN7KvMrbyFVdWtmBYVPLXyU6raLicV0+J+4vNM3tTHUeMkv1lIEHTu5PapTfRscjKigC/xNQxiEYcPrabKfL7BHXYxDx/9Opa6dhLx3RiNMrr2/iNSIce/vd/GYeUMn1e18dYzTxC3reWG4IdZzCnp31jFQR24ZCI+IfkRjuhTACgU7QycW4vPd353YNIb2SFahWnypSucIkg50uw5ddcFzYFYKsPVvpvgSiuhlZfXmlVOgYaz9yDbe+yi2Mxs7OaHGxIcVgxc9LtOpuWdylreMX4IXebiJnUVBILOHXwndzU/X6x61Zh3mZQ4m42c0wscFABeeUQkEWCbJsv2yh4ak79EyL12jUAQJMjlqwkGmxgZlpLLVYASTU2j9PX1vea8P2e8aUnBm4kT0Z5jy9bTCEI/04tO9kXlNKvraRCaub5uFRlTljHvUeKpJ4iY+lhuvgm5VEtNcpyv3OUh0ybiqS4DX3KJuEE8xS/jD5Mp9PCTsR0cqrTxfP1ufmG6hEssM2zqvwt1aYa9lWr0NLDKfRUakYSh3AGCpQKB7tvQixV8/ImHSThSfKbbwGrjOX6y8jgvzF3JL2e28N2GT9JsC3Cl/h4WwtPEVlrpdVxKrhRkPDxIoOFWLp3xIjv3It/emOEW1d3olhv5t5XrONv0Ia7tfApP5ClyS/30phsYCR0h7riO6w4NcrR2kq66R7h8ro4faG+itsHPZaanyc9uoDWpYowq1k83Mabdz5X19zAyfDm/aLqJ2zp+i2SyH3sox9S1f0X/3nuxpEz8RU8dP1j5FTpVln8b2s3Xu97Np5T30NIxzvNjEi4VLudw4GGixnWoWm7mMwee5m8u1fDdpW/zlnN/z0/X3cTnrF9kxKdih7CJ2eiTTCt3svXQQb52q4RvSn/F8HQbT6y5g+sbn8Q/3YQ0E2bRuIWbz51mzw4Jrd5hDhX6+Kj75wRSWcrpFWLV67B4FuiSjSKPQ8wuRSRNopfqSUusmKICZlUIcgIVZYGCqIRIEFMRREgQyFSkSMRl8nkZsoqAINYjKi9Qh4ihUg0OZZyo30htSYZEWYNm6jTCRin7vd2krQJ1+QBl0TZG/ccQSdaTMD7P5VIp381fyUSjjNvl+1FVVrO4KOfWvJNlzSxijYrAUhPr889QqTPzD41n+Xq5Bkslw7ald7Cm284RLfyT4jc4cpOcPXclWqWNnd4WRBYJD5/4Bpa6VhLx3VjMUjqe+hxivZx/ulXBlNLPt3CxY+hZZqtv4JrZW5ArFbi32TkkK7NVW+AvUp9EkVsGBMSiq9j7vIlS6TwhrK7poHfOgbhwfpUtmKWcDD3D1KGXG9E5mnooFDfjX3z56MZgltLifw7Nfb++KC6Lve38YluZp9QXJ3yDTM+7FDW8ffwgmuzwRX+riGXMOa/ii5FL2Tv7StWxIJy3AC261ZyVl5l+jaJxrVzEFYox+lM/QR2fftUx/w6Fop1EopORYRXx+L/fSHr5ZtLQ0NCblhSESuU/uli/OXD3yArf8ARQqwR2yOdYnXsQZ3GaUnk1c7NWwkENLaY66iMmDAUlEWWAgZm95KVl1MZe4uFq7HpwLB5APXuYmY3V/LJuBYfVzM3RCP3RMPtVV/JvwbVoa52s2GU06xPsKj5EY2kJz0obsUA1XUIdzrSG+ewQc/EppKp+5BUr7vlnKcoXub8vRY1NxVs9y5wQX8LdpW3kGgw0W3xckf8VRb+bbKCN7lQ1kdwC8+klRKzGHTpHXDXCs2tyvCeV4Gx+B7/T70Zdl+WW8n1k5mtwxXtQptJMJRfRVOox+h/nqU0p3pLPc0/xbSw2u7lF/hDCrJPWSCez4UFE1KOJPc3Q+iT1iUZ+6biWa0xPoZ124A45mUmEqDtzkGRVmi9uWOZ74STL8l18MXUV/m4Dn1TejSspsDzVxc5IK/tX7kek301TdByvcZjBNQVu9xX5QOmjCH0K/rbwJSYGruDS7Cpe9D+GXrkbQ+geBnaWcAQ6+EnNtbzfcCfRwW52ZFZxKjFK/cIKd28b5hNZNZ+yfoJ32X9EcWgL1aECKzk7uuR96LpCfK30eXZ0HcM8oKMxLMeTMZMW7mWNK81njH/HBxw/Rjl4FcGVAZxxLb/bdIhVoV6eXr2Nt3iepGvuEk7Gl6hffArfrhS/SnyMvr5JqgcKdEarmEiIcAZ/TXpDnLtj/4eadRnWTJ+lb2UnJ+MnqA2UuLfvKJ/NF/hrzd8TbJDyhczbGBt9Kx2yfpriJh489RXMdbdjkwnUPv7PfOEONWtsVfzt4D4+KP8KHk0DZ1pVfER/hvWJbzEzczOpmJGr4r3INVIeOfctjNWNxKO7sNsktP7u04hcZv72+gRxtcCdiTKdK8Psq/5L3je5iSanlmCXHr+ownv1sxfEaFKpFc/KFYy+dINUp9WxQ96DbeklYZlEIGwJsff4zyi91HtIY7aid1xBaOXlGz4KtYQ2yRiGJ+5EVHy5UFxpbeDh3Sru179svANglOl5t7yat4+/iCp3sYdyRa7jjO0mvuDdzHBC/YoY18gltLeambfJWBC/etFYIRLYqUmyrfQ7apIPXbAFfTXIZC4K+T4mJ00XBHmvBUEQ+NSnPoVa/crv+nPHm5YUfvPA3Xx1xIjZakJTrWFCK0Kvhu2yaXqzv8ZR9pHP9zI5aaCYNdOicVPr16OSy/CU5xiYeh6VrRpB0k4u5aBG4cd86rdgLHKgV8Ke6hBXyczcsDhKVtXND1LbGZS3IXJryJoqXC49Tl/2UQqpTmanHDTLmmmMmoiIVhj2HEVmWIWo4MYdPI4kfZaH1xZRVsl5h3eZEfF2flLcSbbeSKvVy2X5+8h6a6mEOulKOfGkRvGW0kgrHbiXnmei1st0R5F3hDP8NH8zYzWddDhn2JHaS3hmNZ2pJhYiZ0nipCqyzLTtLKLGAuZYMz8zXscq9wRrQ0MoF7cgiYbw5qRUBYc5uHqe7RUl31e9m676cVoWItT6VjETm6dqZglRYZTP7vbz1WyJkrCav43fTLhXz8eU9+CIiwnNdbMl2sTexV8i0l5N5+IeXuz14qwpYI238D3tLTQ0rXBFbA+5mZ04EmUmU3FqVzw83nWK94oDfDr3WWQb9dyeuB/FxFX4lk6AdD2K3C9Rt4f4UfIjVK3Ln0/G3m2cjk/TNHucQ9fE8PmuJrHWytbp06zyrmEgHsMceRR1b5qvyT7L22ofRDe0idTKGNaYmsc3HaQxuJaj/f1cNX+QzuW1DMUSaBOPYu5K8P9kn+fm+t+gHlyLzLNEqdTEpP5ebtRk+bj573h71V2UhzZSHS6xkDZTv3Ivw5cXWPZewr1t6/i49mGa0kFGzrVzjb+b49mnqBicZFO99O35G/a/rYEXG5L8YugIv7J9mh/H+giuMXOlwct1sY8S8N/G0qKOawtrUMlkPDb0HXRVdcSju6iyi2h+6FOUOur4yGUraLQGvr+yjCsZ4ruGz/D1hUb6262cqpGhkor4hOJB6hO/AkChWMexo20XVsRd1a30L7qQ5l5acdslHFx4iOWV8wldLJXiattFyNtGqXj+9pFILNBsjeJ45luIIy/fDBJqq9lzmYUfm4cu0hmY5EbeK6vi1rEDqPIXdzQtaarYZ7yZzy2swZ97perYbVFhaTJwVieQfvVNAa1KgctkA/QmfnyhJcerQSIxILCG+Xkn0//55uEVuPbaa+nv739jk/4M8KYkhVBihbkH30WvZ4yAZSN7Kuv4nqcZncWC1qVhUifCrKmwTTpBd+Z+7GRIpzsZH9ehEDlolrqo8WkR6wRmE0NMLJ3AULWKXLYJpVSHKz6AYeAJPKttPNAcQmZXc0siSX80xvPyy/lBdA2aegcrVjHrtR625X6BqaBjatKNmUba4g4q4jQDyy8g6Goo5VpwZ6ZQe/fzzFpI1ol4t9/PnLCBHxd2k2ow0W5Z5tL8g6RWapFGumhLW5mNnSEqUqIq1VA19xgv9sbRukpsiKr5VuUtBBttXKnZQ3UghsizluqUkrH4CPJKE9bAM+zZEOPaQpl7ym8l3GLlFuFhStMdNMacTMSnMacVzJlfpM5e4MnSbZRbJOyKHMe2sBFPeBpDqIxh9hk+e22CzyDFXKjlw7G3k+gz8hHl3VijMpKLvayP1/L84n2IldfRPfZLfn55jLfKk+xP3cS+hs3cZHkY7YxAu38bM54DZCQb6Jy4m5/eVuGOsIEvWD7EDc4HKA862JLq4XRimsaZYe672sOl4Sa+23gzn5J8D9nIzSx5TuDM2Hmydx870w38pO0G3pd6APfk1QxGZ6lbepHgjhg/zX+G69qexTrQQdm7gCGq4JlNB3AGNzHe38z2qREaVmqZT8qISO5nq63Epxyf5qO676Icfgtz3lPURqQ8su00a0Kd3NdxBX+Z/zmm0euZ8B3EUajj+brH+aCQ4WPafyTXCJ/M3MHxY9dxqXwzcgocnnqEQvktrIs9Tl7r5xNbZngglCKh2sRbFm5At6WKWmOW9yfeRTZ1C8PDOq4TrUMrKHli9E40Djfx6E5q7BXqf/0pcuvb+fCWCdqMtXx74gxqsYqP8GmeDVnpXefkoF6gV13hw/m/Q5MfQxBkFArXcuTw+d4/apWa7dpequbPHwMJchEr2nkOnLzvgubA3riKYnkLqejLfY3cVWVqj/4Y6czL9QHBbuXYlbV8yzFwvm7zEixyE++VOnjL+AGU+fRFcZsztfKQ4ia+ON9BpiS+6G8iAVY1mMjWqDgnf/WUpRYLXKqOsKX4EM7Uk686BkAkkiOV9uPz1TMyLKJUes2hrzJXhNtWTb3YTqO6muq397z+yX8meFOSwtkXH+I7oz9mSpdmp6qaXbEIGxYHSZj7eF60ge972pAZrWirNUzpRNg0FbZKR+lO34tFJCYeb2V8TItFVUNjxYEroCGnzzLmPUIw40Nj7iEVr8WuLmKfeg55eoqTa3Q85g6wU2XihuUJMopu7kxsZ1bbSsalwmDJcmnlKdryo3g8reTC9XQUajCWpQz7D5GWSSkVO6kqhjDNPsWL/QLzTSXeHQoRrPTxg+KlJOssdFgXuST/CPGlOjSxVTRmDIyHTpCR2tDntRi9T/LE+iQblAUk6TbuUl6LoqHIjZWHyM1VUx/rIR1fwF+QYE+kWdYfpdCcRxvv4H7rlWx0nqBhKYzTt5ZAZJJy0YksswfP6gTZ5CaONfRwE0+imVxPPuxDlFTgOP4LvnRLmXcplbSndLwv9l4S/Wb+Wnk35pCCgqeP3mQVB5YfQiy/hu7B7/GNm7P8YyrC32c+xvzqFj4l/yrewTXsTq/jSPBpjLKN5IoPoGyP8lTorcz2t/KJ4o8Rj15PcOkUEmkfEcl9rHOE+bzsH7i8ZQ+6oWpqghDIWolI72WzvcinrZ/m/xh+gGb4Jmb9g9QFw4xsn+ZI/HZ6Vo1QfdaF3O9DHZGyZ+MLmMI78a22sGbUh3NFSTRtZtxxPxskNr7b+lbemXqQhqkrORedp37xAHO74zwV+wD1fUEaR/z0hTsZiEdomn2Kw9fmyHi3cVfLRj6n/gmWiJrY0iq2+Bt4ZPzbGGpvxSGq4Dp4Jx96R5LPKWxsiqRYt/Jx6je7SRnhM9n3Iy1t5dgxC9cqN2AqqHhq8kcobU5i0Z3U2wvU3v83RC5dzV/1neMSYwdfPreXgqmdG8N/jb+iw7rewaCszG36AFfHPoa4kkUur2ViYjsL8+fDv8XVyAaPG1n6fJG45BTYN/6rC5oDtdGC0XU5wWX7hTgzWyW0LDyG8tjjF34TDHqGr2zhX2uHL9IZ2BQW3ie2csvYC8iLF7eKiNk38NPKNXx7oeEVsaxXSmlpNTNtlbDyGmrjHlWFSyUn6Ur8CGn5tVzSBBSKbqLRVoaHFKTTrz/tSSQS6m011FXsOD2ql7u+igWqvrABkeLNVZp9U5LC0Dfug9/+FIkKlle7eM4d44DBxyZtLbtTGbYsnKWo7+AF8Ua+7+ugordfIIgqbZktkkFWpe/DJNIRCTcxOamhWltHY86KNaEmpgwyOLefilqFWN5JMeugWljGdPYRkvVyfteeIuqScEsyw5pYkj2Sy7gnvQZZrZWITcQu5RAbso9SStSxNOemRWikJq1lLj2MN+sDcQ+Wchnr9O8Y6Cxxri3P2+NxssVOvl+6nHidlQ7LPLtzjxNZaMCc6KQmq2EkeIyCtAFbOguZ/Ty3PscN+SzHcrvZb99Ii2uOHYkDpObX0JJ0MhkdQKg0YA3s58DaILvLIn7O2yi2yLkm9wzi2TWYIhWWsnls4XFOrZ6nPV/Fz+03crVxD6bJRlSBEomMlJp9P+bOW2RsMyvZHqnwrviHSPZZ+UvVPRgDSsS+NbSmjRzx7EEuu5TWke/y9bcU+XowwQdKn6W81sgnit8iMHY1bUktI8kA9fNT/GrLGJ/KFfmY9gtsb3oRw3CFDfFeziYWaZo+yp7rooi92xnu6+RG7x7a5ndyJraE27OP6JYYdxU/xTWtz2Mb6CTvncUSFrF/2zEKkRtQrErSdE6JLhhHERKxd8N+lNEryPUIdAxUMPqyiOIqDnY/hTOxlrOrO9g1PUijp5HZhIQ0v6bfleWzps9wu/2nSIcvJ7M0grzSzpjp11yvTvA3mr9H2ljgw7m/4+CLu7i5tJmF8gAhChSya1n91Ce4+/0udC4Vnxs/zrWFL6NqbWLcIeGLfBZTpZr9+6u52rgJe1LNs3N3IbPYiEV20GxL47r/MyzesJZPtZ3mLwyr+MSZJ1muvoor527DajYQ6TYQl8LHVHt/T4y2k4MvVpPLgUKuYJupF/fs+bNxQS1hRjzI8XO/A0AkllDdsZOQr4NS4fzqXaWV0FY6h/7pHyKUzy+zBZWK2Su7+GrTOMHfM7hxKK3cLjJz0+gLyEovu6BVBDGeqkv51+QV/NZne0UM19s1GBv1nNZA9lWOiAwSEZervWzK3Yclc+A1c4Fc3kAm08PYqJZQ6PWnOplMRoPVTX3Jhn1FcZE50O/D9PY2VN2vVE3/OeNNRwq5dIpHvvEEUb8Jk1GKvbiIfuQ55L4JIv0NHGos8phpnk59DbtzJXYsDCBV1XJYtokfBDpJa6rQVWuY0Yuo1hbZLDrHqswD6AUbfn8dC/N66rV11MdNGCtKPOVZhucPorI1UCg0oxLrqAqeRLtwkJF+A4/UB1ijM3CjZ46MtIvvJbexbGol7pTTbAqxo/Ag9lyZuZl6bKVmmpMWYqUVJsODiORdaMs6nDNPMNOQ5FBXjpvTKaT5Ju4sXUmszkaHdY6d2aeJzDdiT3Ziy0gYjZyhLG6jKjpPUHmakd4i1yYE7inexHy9m0t0L+DyxVF716GJZ5jLRDBnJIQUL5JtzSNNdvOocxeXmvZinldRG2xhLjqDLiNl1n6IJoOY++XvoL12goZZsPtsrCQy1O2/l99cK8VRLeM2X4K3Jz9Cos/Bh1R3o/OqUAfXU5OSczZyErVoLY6Fu7jvmjIfDYj5hOLjNHb42BU8gH7mEsKesySFHpoWf8kL15XQ+9bySNsOPiH6AaLR64kuD6Cgg1HzA1yjgU9b/4YPmX+McugavN4hqiOwf+tZiuGrKK2WsGo4jdlboRRXc7zzcYzpjSz12ukbTmDwlpAE4YWNe6lEr0O1Kk7zWSOSgA9DqMjhLccJRN6CY5WPunM6NP4IkpSBg+1P0pNu5Tedl/G26KM0zlzOudgETbMDPHdNCLl3Kz9s3sQ/Kb5CZbkJY3wNTSkjz479DKRvZ533IZab09zdF+KXo6f5iu7vOa3q5Vyjgi8pfkRdOcre51u5zLqR6oiW55Z+gdRoIhrZQZslhvM3f8/g29fwf91n+YyqlbcOPcuRmg/w9skd9LVZOO2WU60S+Gj5q5iyRxGLtcSi13LmzPnkXu+sZXOwAUXi/O4g5yry/Nm7SSTP+xbYGjqosI1E5PzxkkQqosXow/bkNxElzyt8BZkM76U9fK1zlgVx9EIcVilt3C4YuXHsBaSl3ys4S5SMO6/jHwI7ORbVXRS7EpFAV5OJpEvJkOyVaUkA1mlK7BIdpC1+F5JK8hVjAKRSK+XyGmamrSwsvP70plKqaDS7qc2ZsXrkF3lIv+ac1TZMt7W+7nf8OeBNRwqBgWmO3Pcr5pYHMNe0IlM1kYjakYi1OJURjPNHUA/tI9NVy9lWKQ9ZF7CaLOwuSdm1PIJBbOKYYjM/CXYRVNagq9EwpxPh1hXYLDpFZ+YhNNTg8VTj95hpUtdSFzYgl4uYSwwxFxpBbV5FNl2HXZbDOv40ZVWYPd0lZmvK3JjNsy6W5WnRLn5dWA+1RrCWuER8gO7cKQKeZsrRZtqzTsSlLMOBo1SUDcjLTlyLewlYPTzfneOqUg5DtoY7S1cRrnXQaZlhR/Y5QnPNVKc60KULTCTGEAltuPynGKmZpdiQpztm5YeymxAaJVzLY5TnamiMtuGJjZMtO7GFTnC0d4XNSPmF+K2omrLsjB9HM7+BfNhDPq+nXNpPvi3JUP5acs1SNvnHcSx3sRgJ4D7yDAe3lYk3C/zlsp93pD9BvK+KD6juQbOiwRzagCGZZzy1iClfRSn3O6Y3FGgMNvJ9+81c7XgM1biUtaHNnA7txSJaxYzmYbaYQ3yJz9LRs0DTuIfVoT4G4h7qFg8wtTvKgfhfUNvrp2m4SJVPRjytZ9zxEN1iF/d2XcV13oO4Z9tZSYiYtf8Wl7iBvb3r2D05j3FJDqEyB9Y/RybxFlztSzSdcxP3TlMVibG0YYwH85/gLY2PYx/YiNc7ijsUYXjbFCdif0FL9zw1Q3KcfgjnbESk97HBluBz6i9gaYrzzuz3OHV8MzfE1rDX90vk9t04iiVsY/fywev93BvNMq28ii/GLmOhV88ndHvozR/gwAtr2GJZR4PfwH7f/Qg6HdHwdjpNfmyP/AsvvKebu5wTfEWwsWP2FHdZ/4Yvz7ezeo2Tg0aBK3Upbkt8FGk5gkLRydkzawgEykilUrbYemmcPp+UBb2Uodwhhkb3AaA2mDBVX05g+SUHMgHqnQVqDtyJZGny/G9iMbGdvXyrx8Pw7xncuFR2PlDRc93YC0jLhQu/l5Vmjllu4vPLG5lJX6w6NqllNLaamDRL8L7KEZFVKuJK1SLrM/dgyJ161bgXi9VIxGtZWqpmfFzg9WY1rUZLk7EGd9qM2SO9oNJ+vRCppTg/vx5B9Mbm/W/Gm44UxvacQTgVR5OXkjMW8KSmGZ5+EbFKgc7aRqFQQypuxGksYgkPoT31BBWnhrEuPY86V8iZ5ewStOz2TlNTFHNKtZWfRVYxL6tFX61lwSCmTpdlk3CcjsxjKMp1LC5WkY7ZaZbV4I7oKClyjHmPEaskkaq6IF+FqzCHYewpFjuUPNocpdmo4Qb/EmlRJ99PbSdgayLulLBGO8OW/KMIMTvBxSaaS7VYcjJGw8fJSDVISo24gqdJScd4ti/HNkmRqpSdH5Suxl/rpMsyxdbMC0TmW6lNtiFNJ5hJLaGoNGDz7edQl59mY4FiqpsHDJfS6F5ia/wI5YU1VCfUTCUX0Gd1JCT7SbVnKSXX8FzNRq5WP4t2pgZHyMhCMoYpPsV4zwKqXDeH6vu5unAA68wa/BEPzoFzTLaEOdWT53NLHt6b+STh1bXcrr4H5ZKGquBGJIkQ87k8rmiOUesh3NVxTkSv4cW2tfy16jtEhzfSn3IzkPDQODvAA1escGPMxVfc7+Sv1D9EMnoNiZVhtIVaDjU8xq6yk+8238p7Mg/hnrqMkZgPVfwZ7B3wVetH+YDsFzjGrmYqFiIhfpJms4l7uq7jqqUhTPNGioECh9fvIZR8B52tYzSea8fjm8YZGaXUn+J72o9yh+Zn2MauZyLsRRPfg62zxJcNn+CDprvRD1/DvH+IWn+cZ3dOYA5u4nvNW/mS5LP4x9bTm9uEKBdhMjFNpbiJnj2f4Z/fr+KdGg39CQlXeD6IaLOda40TXJK+kxPHd9KtX037ipUXww9R1qiIhrfRrVvE8vR3+O17m3jG7kKaZAABAABJREFU5uW7yQodER9/K/kMz8Wrcay3M6qAv9SdYW30i4AIQbiKgy8aKZeh2lbF1ngr6qgIRJB0pHnuJRGaSCzG1b6DiL+L4ktHRTa7mKbxX6M4u/dCjGU2d/O99TGOyV++1eNWOXl/RcO1Yy8g+b2GcQVdLU9rb+bvFnqJFS4+e29yatE06Dijhtx/yKliYLM2z06epynxc0SVV3okv7qw7A/DqDfSpK+mJm7E6JNcaOb3uiEAFglRSZAZ31nWvu82nE0tb+wZ/4vx5qqQAHPlZxiS5JDKDdSpqqgS13BZ1XuR6KTn/4neM0TiYyR1baQtDcTX/i0GnRxbdp5PPbYHWdHPYo+MH7qdjDsy7JQt8X84RlsyxWBkK7+Y72FU3MDB6s3cZ9hKoy7LxtYjdOR+B8VGDszaEOWqaXL3syqhJSkNM7L0DMtaI4s9b0Mj0nL7sSPIUoM8213NYIOfqzT3syZW4OnYDh4XrWOw+uOYrQl2GZ8kmz3LkdlGqlQdtGf0LCZHmLPIEAu3ce3ZWcif4Ld9YS7T3k3zkpkfLF7ND2tup6ttElP6MaLz7TQoOymk/Mw7G+nwbUYzvpdDfaN8ND3EwYHL+Hn1rWzrOkTFk8Tp7Scf80FhN3XHxznTeYYPrIxxn/w2qpr99OuGqV7pwyfO0TRazazrCDcurPAb041c1nEA22QHwd4eameWMScn+OxGKz9d+RofPvdJftbzbt5b/XOWy8eoq2zCVphlSWegZ7aJJ7XD3Kz4DTOjNu7q/zB/3fITliccGBMJZmsv4ZpDDxDYOEf17DzPrrmGDfpBOjNrGIqH2D6oIL9mnvJyhUS9FJ88C2Ufxnwtk6pJRNE8yVo1KWmJQiWFOQZie4ZiXopYmkUsSCgIJSRUyIpkyMt5ZIgQidQk5EkceSU6XZZc3EYll0JV0jDZAJV8AyZbgrTHSVW2gETUgMf4MNfHc/yTcSOX65YwplzEy7U4Mxoen/olEvU7WTNzFwdurqfVVuHSmVF2Rv8B2wYHtcYou5JfYWj4Wlr0XbQvWTgcf5SyRkUsvI1e5SSmfffw0w+4GbPF+KUnigUtNxa+SELsQLLZQFAp8C8vqZOlUhvLS5czPg5isZgtNT20ThkQKgKCWcqJ0NNMHzoJgLWuDcTbCSyfry1o9FLa0kfQPfCzC7FVXN3OPdtKPKN6WXhWp3bxwZKSq0ZeQFx5+RpP2tLNvZIb+cpCCwX/y0lXKhZY1WwmXKVk6FU8C6rlIq5QTLE2/VM08bFX/B3+sLDs1WA1WWhUV+OO6tH5JOD7T4e/AoJaQk6fYyU1xcj0QRIzL/dgsp1rflORwptqp7CSjvD00FepTj2FWu4in2vC4zEwPy/BbnDiltlxRjUYUgpyhjye9DSjM4eQGfQo9S1k0y6EigGHLIRx7jCquROEeqo52JDjRVuYDWobuyNBeiMBJnWbuTexmtM0oa/RsWIU0aRLs75ykI7cASq5JmZnrGiopTFvw5ZT4S3NM+U/g1TfQDFXj01IY5l8hogzwxPtKSw2BdcH/GRo50fZHQQd9RQcFbYoTtGfO0LEW48k2kZz2kYq52EuOQfiNmyZOPLYC+xdnaXOUGJVXM9d5atZqqmh2zLGhuRxEgsdNCYbiScX8RVKmDMKisX9DK/OsiWj4F7xTSTrNVwtPIt4vhZ31M18fAZlzkCW/cTbs2TTGzhWt4qrxM+jme5EHsqRzIopiA4gbiiyV3wLLTVTNEzpkXorCP44suiL/PjSLP/i9fM3+Y+x3N3EX6h/gXROR1NoM8HgIOFKHa2z+/jlrmU+ms/wadFncPfG2Lp8nPqFnYwFj2Ap1HLE/SQ3SsX8neXjvMf6c1TDu0l7JzCkDLzYsw9Hah0jPY1smZzHvqwlE5NwtvNJyrnLkHZm6DjnIO6dxxyfptKT4pfW27kt/xym8VZSwQyn1z3FcPpDXF27F/fQJpbDQZLS57AaXTzds5Ed435UKyXkoTxn1h1kOvluOtrHqBmooehdxBSXsX/NIeyxddzZtIN/EX+S8TM7uDy9hdHIPnLqNpyZPIrw0/zLpVHunR7nI6J/JNzURcFV5qOpO1heuBZ1vpUNS26OZ54hI4N4ZDu94rMYzj7Kv75DSd6m5NsTZ8ka+rl66Z246xycqVOwRlfgvelPoSguoVBs4MjhFpLJCg6LnW3ZdnRB8UsitCB7j99NqVREpTNgrr2CwNL5thFSuYg2zSKWx795oUdRpa2Rh3Yr+bXu5STdoKnmgwUZV44fQFR5ObmHnNu5M38Vdy3XXBSbVq2culYTIyYxIeHilCMTBHZo02wrPUlt8gFEvPKO6BsRlv07qqwOGhQuaoJa1BHxH57w+xAJVKwiIoKfqZVTzC6c5bXOpKo7urjtH/7fG3v+/2K8qUjhwUkf/zbnY0laplddols8Q0t+H87sYeSyNlJJN/PzamJRNbVGF9UlE3a/Epnq/C5i2nMab2wWg7ODCrVk4lZsuiKW0CCawWfJNJo41SLiaYePNr2V3ckE6wMrLGrW8etUH0doRVetw2MU0aJLsL7yAm35U+RSjSzM2bCK6qhPW9BVJMwmhvCkFpGo2xEVnVQlp9CsvMhAp5jjzVl2VUqsjZd5orSd5+TryblUNJo97Cg8gSyqJr7SSlOuBkU+z0RkkJKkFmNOjDa0l8OdSXSOImvjGn5evoqF6jq6LWOsTZwks7iK+qSbQHyaWEmNNZnErzpKrK1AfdLFzzXXU+32syV5Asn8WrSxIiuZNJaEl5HmUVqlOu5X3Uxb9RStSxnsvnqW4kG0yRmWuxYJlHaRrFew3uNHt2wjGUlinnyCb12T40vhMF/K/xXTnW28R/tLxNN62kObWAocJyVeRcv0w/zymiR3JFR83vDXXFbzNJoxDX2xDgbiS9QvnOHA5QGE0A4mupu50nOQ5qUNjMYiyDNP42gp8TXLR7hd8Suc41cyHQ2REj9NlcXOo107uWTKg3y5gjwaINQzxm/Vf8k7JY9iHe0jFkgzuOYJjmY/wjuqHsU1sgNPKMaS82mK0i3kO0p0nnUS8c1jjY3D6gTf1/41t6t+hXP8asYjHnTxfRi6knxF/rd0NUyyOXycwvwWGtMaTgUOIxF20HXon/nUu0t8J5nkBfFbeUK5jeVmBV8o/DXpwA7yoUa2rjRyNr+PpKRILLKNvtIRdDMv8IVbCtTY7PzLwD6Gq97KrVOXsbrfyWGTiPfqZ9ke/TQSkYRs9lqOHVUiEolY5+qmY8aEqCRcJEITRCKqO7YTDayikJcgiKDRkcH13LcR+89bWQp11Tx7mZUfmwcvxFiTpoY7chIum3zxAhlURFLmq67ky9HLeDZouigmW6t1yOt1nFFWKPyHU5pGhYgr5MOsTv4IZfGV9plvVFgmCAI1NhcNUifVPs2FAvrrhaCVkNFlWI6PMzJ1kHT69XkyiyUS/upnDyCVyf/w4D8DvKlI4ZknHuSHAyVmCkbqXDrEZgUrahERBfQr06wSjdGUfRZbaRaR0EE44mBmWo5SYsWtdFCV0mGKKSgYinhS04zPH0VusiBTNZJOVKFRKLHlZtEN70GkzjLaqeWJKj96i47dmQJb/AuEVb38NtvH/lIHmmo9fqOIFn2M9aV9tBaGScYbWFmwUyWupz5hRCwuMBE8RUIoIYha0aDGvnKIgniWvauKUCXmunCUTKmVnxV2EHDWoHTk2C7eT1NqCd9iK1XZJuxZGdOxAVIiI9qCCZN/H2ebwpRrC2yKq3igfCXTrnp6LKOsiZ0ls9RDQ8rJcmySTNmCLTrDRPUYBmeRVKqfZ+yb2WQ5Qe1KGptvFaHYIuQ0lCoHibWliWY3M1bfwGX5QxhmV5OM+pEm8yy6T6CUNXOkZh2XpU9jmWslFIrgPPc7vntdgb9NR7kz+35G27t4p+5XiKaMrApvZMp3kJJsLXWL9/HsVQVWhVr5qfs6PqD9CeXR7ej9ccIFBxQepKYpwb/KP80Ndb/DONRP3ruAMS5wZt0xllK3Udc+T9NALXHfIubYNKXexPldQfZ5TJMtZEJxpvv28TQf5wO6+7ANbSUUTDHW9xh7i5/iDssvsQ/uxh9OMdb5GOOl97DdfZj6oQ0sh0PEZXuwG1080bWFnTPzmJaUFGJiTrQ/jSPTx50tO/kyf8vI6cu5LrWB55buQaS+mb7Rn3HfFVnWWwRakjY+ln4Hnh49/yj5MspYPb75JnZ7WxkqHSImZEjEttOX3osydJZPXhNmt7mWTw4+z/22j/P/PKupWudgQSNcUCfL5Q2MjW5laamMxWhme7kTo0+KIBezop27IEKz1rYgSHcSD50/KnI6BRrP/QLZyBEABLuNo1e6+bffE561amu5IwuXTBy80CqiItMwYL+RL/i2MhB/2YZTLhHR1WLG71S8wrNAKRK4RBNjS/ERqlOvdDETieTIpGvwemsZGRH/QWGZWCymzlZDnWDH5VVd0Fm8LkgEyhYR4coKE4snWFwe/sNzXgO3/N2XqF3V+1+e/78JbypSOPCbt9E7cwiVVMeSro+j5Q4eCtUyXbJQ69IhMstZUAkUlQJrFFE6KwM0Zh/HJFQoFJvxec0sLiqo0rmoFiw4QmpUUtmFXUQw60VraaVYcFMpmnFIQxhnDqGIjTPfZWZPTZS4U8quoogdvgVyslYeK6zhuWIXcpeBoElEmy7C2uLzNBVmiIXrCHlcuEVu3Ck9WSKM+09TVlmg1IC1EMewuI/ZuixH2nKsE5dYkxB4qriNfap1lKql9OrG2Jh9kZSvGnmkg7qMAV9yikCxgLJSgzVwjEnXItHmAlviCh6pXM54VRO9lmFWx4YoLq7GnTQzn5iGkg197AyDbUu0K8UcKl7BUp2DS0X7Uc/VYYsaWEwE0SfjzNSfo1ph5GHtDayxncM9p0PtVxJL5snJDyOtlvOM7np2qE7inGwlGo7gPPkMd12Z4gPlBA9k3s7p1j7eprsX0aSJ3vAGRgMHkEjXoYvdj29DmunwlQx2dfKB3L3oJq5myX8OS0bPgY4X6Ms38Ou2K7k1+jQ1sxuZjifJih6nxmHkl803cI3/NMZZO4VwhonVe9lf+mveZX0Y59A2/KEE0x2Pc6zyIW61/Y6qgS0Eg0km+55kf+VjvF/7EKaRdcRDUeZ69/Gs6CO8V/0g1uGNBCNpxjqfZC7/Vnqah6kfXIXHN48tOkRldYTvyj/OutrTNC/P41jeRj4yh6esxJXIEpcc5OD6Ap9d8nJF7NOk1jv4iOZealNZJofbuDzcxUT5BKFygmR8G/3hJxBX5vjI7iU+qHXztqlTfEHxGQ4Wm0n3GDHrynwo/wW0+VGk0ks4+KKTfB76a7pYNWtDUhQoVQnsG/0lofASSq0eS93l+JdcCAjoTVLaQntRv3A/AILRwNCVLXytZpC06PzNoXZtHR/KVNg5+TIZlNR2Dphu5nOL6/BkZRfiz65X4G4xMWQUiPyHXUGnCi6TnqE78SNkpYsNct6osEwqldJgc1NXtuFYUb7cjuN1QDBISamSLMZGGZ06eKHL6x+LDTe/lc23vvNP8qz/abxpSCHiW+CrD36EfZolXBo7fRI9/akkazxjGJCyrO/jWLmdh8L1zJatVLu0VEwK5lSgVkGfPEB7+QT16afRSqykU3UsLmlJxQ24NVVU5wxYoyqKmgIr6Wmml08hM9kRSxvIpR2YFWXMgXNoZg8RatZxoCHLVFWRLSIFOwNLyEU1PF5YyzOlVUicZiJmEe26IGtKz9OYWyYYrCUZqKWeKpwZLaH8ArOxSQRFHdKyA3tkFEn6DEc6SsTdFa5MpMgWWvhVeQcBZxUOe4ht5efQRSDj6aIxV0Uu5Wc+40Mu1GEJj7JiGGOpvcCWtJyny5cw4myl1zJET2SMynIfzqSG+cQcsoIFaeYwE11h2ktGHlRch8GdZHPiDKqFfoRojGRGhKh8gmhrAm9xG75aC9sSw5gXOghGAshyc0RavQxJr6bOuUjTpI1MII3lzGF+uzXI1Yok+1M3cqhpLbfp70c0aaE/so6B4EF0oi4i4oeoqUvwy+z70fWW2LgwTKtnHcOxRaq9p1jYFmRf5nb62gapHXRT8HkxRBPMrRtiT/mvuMXxONXDW/GEovhtzxJT7kDbHKNjoJFwIEzI8TyTqhvocQ1Qf66biC/BdN+zHBL/H94l3YN6vIVMdJFk+yRP297O9dFzyGb0CJFlEt2T/Fp7B+8SPYZtfC3eaJ7JmscxV7r5UetO/rHyj8wMXseuXCsv+p9EKbuUlsFv8A+3FrlneZF35v6BSF8L15kPsz5zgjOnerg60cscA/gKIdLJ7fStPEhRF+YTW2b4R5GFLUE/78p8nLy1noF6BdcazquT5SIZodA1DAyI0ev07JB0Y12RXSRCEwQR1R1biYV7yGclKFRiWqWTGJ/4DqJiHkGtZuaKTr7aNEZIdL71RJeung+limyfOnQhxnLGFh5R3sQ/z3eRKr28Iu9w6xHXaTmtLF/kWaATi7hME2Rz7gFsmedeEbNvRFimUChotLipzVuwrSiQvA4NAQBSESULBItLTCwcY8U78frmvQ7orE501gYEiQuNqZGrPrz5T/bs/0m8aUjBs+8kvn/7DvK5AUrNtXgadJx15nhWt4BMq6VfbqE/k6XPN4mzUGJF38fxcjsPR+qZxYHTqaFokjOvAru6Qq90idbiIepyR5CJ6olGnczPqVGLHdRIbDjjGnQoiIqDTHvPEC1FUOlbyGerUUm0WDMz6Cb3k7MUOdEscKImyWq5mh1hP9aSiWfK63iy2EPFYSZuEdGu9bO2+DzufAi/100xVk9D0YE5L2MxOYYvH0YQN6ErKbF4DxMyLHOos0i7okhfQsYzpc0c1K5BVl1mneIEnclJYsttOFP1aNIFplMzIFRjiXuJSk8xvarAhpyUF0o7GXS00WsZZlVkEtHSGixJMfMpL5qcijyH8XWksaSbeMRyCf3WczQsF7D4G/DFfShTOZZdJzCrbTxhvIot+lM4p2soB7OUkwl8tWdISNcRq9OybjGDsCxFNXKOFzsX6LamGE1exp66LdxqeADxhJW+aD+DkeNYsjYGqvawW5Pny4pPsLPxAOYxC1V+BcGMirDyEdrMKr7jfi/vKD6Mc3Iri9EUAf2TaDXtjHbWsGFIRsIbQJ49jbzeyIHOtWwdlJDy+SkrjhFwbEZVG6PtbCNxb4yZ1Xs5Kv8Aby8dRZiyk48NIW7Isa91GztGSiQ9ASTFE6jddvZ2rGXLWJbiShR9dIn46gXuln6EHTXPoxvP0e/fyrnlJynKd9Mz+DO+eX2cTxejPJB/N6frNtDsWuGG7N2cOLaeq7Nr8FTGWMn5SKe20Tf7C2J1Bf5hzSzfTgm4Sgau8byX6q5aTlvFfEx9Xp2sUHRz+lQvoVCF7po2Vi9UIc0JF4nQzDWNSBS7iQU1LzWti+F49luIwz4EmQzPZT18tX2GJcn58/NuXSMfSmbZOn3kQmzFbWv5GdfzrcV6Ki9d31TKxHS2mFl2yJn9Dx1K+9UVdouP0BH/yQUby3/HGxGWadRqGk1uajNmLCuy160hEIxSksoY8+ERxqYOUShk//CkP/RMkQijsxa1sQ6oIh41kUu93KxPIhPxgW9uQyT+8/cte9OQwuGHjjB2OEqprMJqKGMoetEsD6AcOoDIrCbUYmPUVWGf0YtPV6Zf6aA/X6Q/ME9dKoHHsJoTlQ4eidQzK1Rhr9KQM8hYVEG9pki3eJqW/H7cxWnK5SYCfjPeFT0OdRWuogl7TA3KIiupaWb9g8j0DhDVUSnYsAkRDHOHEZcXGG5T8qI7gVuvYmc0RG1OxQuVdTxWXE3ebiVtEdGu9bCmuJeqXBKfpwZJopH6nAVlscxsbJiEICAWGrGkwqgiRxhsTONpLLErnSObbeYB8U4iVWaazPNsyh+i7LOiDHfgzMhZTE6RKRswpXLkOMp4V5a+soSTxW2cdnTSYxqiKzyHZKUPfaLEUjqKPlMhpDlKpa5MOLeRc9Wt7JYcQj9fhyYkJZTKICkOEmmNsFzeTbZOwlqfF92yi2g0Tkp7nIrNxnHHRi6JT6GcsyCenmbCPoyuMUM0upHfuXdzs/E3SCfs9MZWMRAdpDpa4vmuk1xV0fEV+/t5l+F+NCPbSfhnMMUFTvedIJG+Allnnu4RNQVvBF10iUTvCg8Z38/bCvvRTDaSjXiIdI2yz3Ib1wVnyM6CKHeUQn0LvmY9PWfspLwxZnte4JTqPdySGiE7JYfsYUr1jSy2WVl91kbMH8Xvep4Jxc301pyjfqgPbyhG0PAsGlU7d7Xu4jOlbxMZuwZ3QsJEKkJ1MMGI4zj6hiymeCvfV92IrLnIh3P/xOmTO7k0v45waYbFzBLZzHZWj/6IhV4JP+zx8n2Pl5hqM+9avBbn+iriRvhI+V+xZI8B1/Dii1rUKi071D04FhQvidAOMjS6H4VGi63hcnyLNQgI1FRVqDv2E6TTZ0EsJrqrl2/1rDAiDQCwWt/Eh2IpNs2eN8OpCCJ8zt18PX0lv/E6LsRZlVFJVYuRAb2I+O/dIjJLRVyuWmFj9hevsLF8I8IyvU5Pk74Gd9KEyfv6NASCXETRVMZXWGBs9giB4NzrzhuvBYlMhqmqEYWulmLRTjxkoJj/z28wveWza7DV6v7TMX8OeNOQwovfvouhM88jyCXobfVI5FXkc1YSES16vQyTNI4+Molq/AiypIdkm4uZWjmHzGEG9DFWqZ30lwT6Q0u0xMP4dT2cFDp4NNLAnLgai0NN2iDFpxZoVZ8vWjfnnsdRSZHN1LG8bCCXtFMjt+NM6TAWFcTFQWYDA8RLKWTaJorZKgwSAbP/LIrQWRaa5LxYm0Jqk7ErkaQtI+I4/TxaWEPS5iBvgTbtMmuKL2BNZwl4a1GnGqnNGSnn48wmxsmLTSjKDiyRUbLCMCc6izj0RVYnlDxX2sJRQy9GV4JNoiM4IgkKnm5qMmaiqSWC+QqGrJxK8QgT7UnaJSJGC5s4ZutmtXmQ9uAKCk8vingWbyaNPhNlqWoAs0HBYeFKqK2wKT6CbqGTVCyCKJMlYDmBSu9gn3kn22RncUw3kgjHEEqzJOojnNBfxSbpOJbJGgpLy8SEY4R6c2jC3dxbfSW3mB5CMuGkO9rKUHwOt2+BvZtnaU2180jzbt6RfRTH1C6mIksYEicRdyf5se6veJfyUeyjG/FFkixVP41fdhW1dfM0D7YT9UdZan6Ww/I7eLtqL7KxNuKJc4hrYLijjfVntGQ8Eea7DzOgeyvXB5aIzaQQqU4y4byMJucktee6iYT8+FcdY6/2L3hL9iiKKQeVsJ9A7zkekPwVV1U9RHnAxI74Rg77H0OjuBTX8k/59TUVPuEr8P7yJ8j3KvlM8fOMn9vKltx60sUVZpOz5PPbWH3uu5zdquDZtgTfnhlhn+7dfDO6mfxqE+3GJG9NfBS1RM783KVMTUFbdRPrlmuQ5UQkHSn2nLiLQiGLq2MzsUgvhYwUk0VCy/ITqI6c72WU3tLD99ZFOf6S8GyNvpkPRWOsnzuvV6hIFEw6r+WfQ7s4GNYD501sOuuMlNwazspLVF4ysRGAjZoiu4QXaEn87CIbyzciLLMYzTRqqqmJGjAEXod0SgBMEhLyKLOBASamj1zwePivQq7WYKxqQqZ0k89ZiQW1VMpvbNW/7a0trNpR/Ud9x/8GvClIoZAvMP69Q+iCIsRaGTlllnDBy3JgjIWVETRWB2qDGwQ7qYQJSlrMugKG7DKahTMoZ05QqLezVK/hpD3FIUOAWp2V/rKU/qiPjoifiLaTU0Inv4vWMyetxWhXk9BLiGuhUxmjszJAU34/hoqSeMzFyrIBNS5cFTPOpAaxtMxKaprF6ARijYNy2Y0CPdbULJrlo0QcBQ435Im5BLZms/QkygzRyyPFNYSt1ZStFVo1C6wpHESfKhPy1GPK1lKT0xPPrLCYXgZxNbqCDF34GPN2H3MtJdYXyhSyTfxatoNslYZV+hG648MUvK3YEzWQirGUiaApGJDmTjDZHMKtErOUX8shWy+9piHaA36Unm5E8QSRbBFVbp7FhhmsEgdPai+j3TZNy1IRg89BIB5BUp4l0uhnXrwLqStL71IJ8YqSXCpI2D3MgnonDkuIhgkHGU8ISfgAZ7fkaQ0385Oq67nR/DCyyWo6Im7GEiGqvWc4t81HKH45iVV6NszNUbXsIpgss2B7AoOsi9GOejZO5CmupJClJqClyJ76S7h0OkF6OQGSoyxXb8Xs8tN8ro1gbIFk/TzDzRvYfk5MfiXM/KoTjBmv5/KFOLmlKCnnYfYa3setysOIRuvIJE8h1Mo43baKjUNKYr4QeelBxNYWHmjfzh2pXyGbuIr48iBROuka+gVfvyXN1yNh3pv/PJF+J58WfxHf6Gr6Musp50NMxiYoFbfRfepb7LtMyWxTmS9PnOHrio9zXN7DZJOK9+jPsjb6z8jlmzhyuIlyWcF2Qy/Vs6qXRWizJzG56pGqdxML6FBpJLRWBjA89QOEcolCfwf3bC7yrHoGgPX6Fu6IRFg7f75tRFlh5IT1Zv5uZSOTqfNtsV82sZGyIH45TThkIq5UzrEu8zN0uYvtNF9dWPZKOCx2GpUuqkM6tOE/rCEQlGLyxiLe7CxjM4cIR1becJ74fWhMFvS2RsQyF5mUlXhYhfAqHs5vBG0bHOz+i44/6hn/G/CmIAWvd4qHH/4VoZAIq8GCTWnCUtZhjivQxqQIejFpWZJgeokF7xDhhAe9vR6ZykWxYCMV06PTyjCJIuiCY6jGDiNSl/A3mRmsKvCiKYDSqKUfJX3xEN1hDyl1G6eETh6PNzArrUVvVxPTSSjoKqxSBGgrn6QpfwJ50UowaCccsGKXOKnK6THnVSSFEHOhYRKVPBJFPULRgbkURb9ygrzMw6mmCvO1JdaUS/QnCsxXOnmktA6PqQbBVqFNM0t/4SjKuIi4rxF71o0tp8CXmsWfTyAV1WJOhqkUTjDclEFuLdCT1LC/spmTpi5qHB42Fk8g92vRhNrRpYospL3IiwYUmRHm3IsYjWKiudUcsPTRax6mNRBC4+kkn4iRyZQRF4fwtwSRlDo45Ohnm+wU1lk3QrhAPpUhZj5BxeDihG0924tjmGbriEVCpAxniJpbiVeZWDsnJrecRTX/Ai/szrIh4uJOx23cYHkU5YSb+rCF2WQeU3Q/ibVRni7+Bb3Nw7hHXJR9ETSROAv9QxwQbudq8/O4RtYQDsZYbn6O44r3cqPmAJrRDmKJEYq1WcZbOtlwRkU6EMXXdpbJqp3sGC1TXAmz0HmWGetl7JwsUlkJsdB2jEHjW7nWv0JqPkNRc5Qh+7X0WodwDa0iFgzh6TzEo7IPc5P9F+QG29mQbuZsYopaX4ynWo9zizzE91PvZ3hVJx9S/YDijJOm2AbkuSRj0REqpW10H/9XfnOtDGWNjI/OjfHB/CfJ1LYz7RLzCclPcGX2kUlfy/Hjchqr6tjor0eRFV8QoUkUCuyNl+NfdCOWimk1BbA98Q1EySiV9iZ+s0vOg7pxADYaWvlQKETfwmkAiroantXdwhcWegnlz5+R15hV2JqNnNFxwcTmvK9xju2VZ8/7GldeXpn/u7BsYsKI1/vq6UQQBKqtVdTLnFQHtKhif2AV/lIriZgkxIzvLJMzx6lUXp9o7VVejsHuQmtuAFEVyZiJTELxh+e9QZiq1Lzt79f/yZ/7/zfeFKQwuPI881NfQlOJIpXWUyxUEYsZ8HikhEISrHoLNoUJS1GLKS5Hm5JRNkBSiOJLzDG7PEBFJqAx1SFIHGRTZiolPRZ1Fn1qAc3cKWSRaRJNVsarRRyyhIlZRKwWa1mTjNMT8VKU1XFa1MkTiWZmZbWobWpiejESfZlV0iXaikdpKE5Rylbh95koJFy4RDYcaS0KocJKapqV5DyC0gHlajRlMebQIKLcKKN1FUbrC7TIKqxL5AkVm3msso45Qz0Se5lW9TR9+RPIYnIy/iYc2Sp0WVhJzxEriVCXregTEwSVY8y0legUoJBu5GHVDsQuEX2Kc9SH/eDtxppU4Ut7KRaUaDJLeG1jiB0C+Uw3L1jX0GMeodWXQONtIZmIUMmUyUpPk6nPs1LZSsSlZVNiEt1CM/FoGKG0QKTex6x8F2Z7hLYZDXlvgQKTRGsrjFnWsS3iRzQnRzV1mGd3RNmdsvBN67u5wfoYqolaqgJKvFkpldJTWOrz/Fj7l9xoeYKq4c0shzyIS8dR1GrY07SLS+YCFJaKkDtLodbBQksVa84aSfvDLLefZMx2NbtnMpSXwiy0n2bGcg07p3OwHGKhc4gF+y52jZbJ+yJ4WoeYdW1j+wgUPBFW2o5yyvA2rovMkJuDUvEERZebJ9o3clPwKWqmL2PEsw+1bBPS9H2E1mTIhdbxkPtSrrE8gX05izmwEWO6yFBkAFFlC50nv8GPrq2w1qrkmkCMW8MfRtZbh9xa4H2ZT6IXKxgZ3kQwKGWrZTX1MxoqdjGHFh5i2TOBq30jyVg/ubSUuqoiNQe/h3RhHKHezdOXmfipaQiALYY2PhT00bN4DoCMpYv7pTfy5fk2CmXhNU1sLvI1LrysIHs9wjKRSEStrZp6sQOXV4089Z8TgaCWkDPkWElNn28lEQ/8l/KBSCzB5KpHqa+lXHYSjxgoZF7p4PangFonwagpoisE0IQm6f/m3yCS/3mL2N4UpPBPA/P8LBhBLxfRIs/RKPJSXR6nKncUY2ECmayeYtFFIm7A65MTDEix6q3YZEYsRS3GmBxNQUZRWyJWCrISmWTJN4bCaEGhqaZUtpFNmdEqpRgrQbTeYVTzpyg4FMzVKThuTTBrK9Au19GXTtMb8SERuzgj6uDZVDNT8gYUViUxnQStIccq8SytxaNUF/ykEi6CPhuqoouqghFrXkW6HGExPkGiUkQkqUVRNmFKzKGMnWXRkWawoYRJX2ZDIk8hX8uTrGNU34zCXqRFPU1f7jRCRE0x0EJV3oo0lWYxu0ShYkCXEyPLnmHWFaRQVaA9reOgaDMj5iYarXP0JwcQe5swx+2kkyFiuTLabIKQ/iwFNwjZTvZb1tBtHKPVm0PtqyUWDyPOFkjoTlJ2ajgt20WVzU/Hchm5V08yESWlP0PGUsOYvYut8UWU81bSqWXi1bOMWXbSV/aimdQjHz/Oc5u9XJbX8Q3Te7nG/hT6iQYM/grxjBKf5gkaTAZ+XnMrb8nuxTrdRSicZKnhOeakN7LKNUrdYAexYAhf6xGOm97G9eEFMjMF8ubDvOB4JzcWp0iOlclUneJI1a1c5QlTWoyx2DaO17mZ7UOQiARIuT3462pZc8ZEKD5DvjbCTEML68/pSPnDeFte5AnFHbzNdg+l4S04QnkWMgZapx7k3pvyvCek5x/0H6SnYYy1wXOIF7fgSkkYCJ9GwmbaTn+Lb15f4K1KgdqMnTsibyfba2WnaYYd0b9FLrmEAwdsVJmr2RJtQp2Vsayd48WT92F0upFrLyHq12O1i2mefAjF6T0ITjuHr6jmO/ZBikKZ7YZ2PuRfoWv5vCo57NjCD4rX8KMlNwA6pYTWVgszFgnLLx0RyQWBXdokW0uP4U4+eEGf8HqEZecNadzUY8Ox8nuGNK8GkQAWMWGRn2nPaWbmT79mK4n/DFKFEpOrEbm6lkLeRjyku2AT+ieDADqDFKMyiy7nReUbRzF+DHFg6aJhdb/5NcpVq/607/7/GW8KUvjC3U/y68kyFr0Kq1mFVC8jrRazIquQkopoVhRpkoRwV2aoyp/CmjuFUlZFqVhFImnC71MQDMqxaK3YpEYsBTXGqAK1SE5WmSGc87IUHCOc9qIyViORVZHLmhEqBkyyDPr4DOr504iEEN56HWedeYZsKVwaDf25PD3RELqKiTOiTp7LNDMpb0RiUZHWizDp03SJxmkrnMSczxGOOEiGqrGUHThzWlRFAV9mDm/WQ0ViQ1Kxo8+l0UbOElWtMNRYoWwvsS5dRJF1sEdYx1ltOyp7gVb1JN3ZAYSwHoKtOPN6iukoS9kA0ooFXSpCWnSG+eYcLoWYUrqRxzXb0LnSrK2cwxyQogo2I01m8GYSaHMlEsqTJOtLSPLtHLCso1s/RrOniMrvIhqPIM4niNoHQFPDcfMG1snHcMxWUQhlKRSWiblXmNFsp1YfwD1lJRWKkjQOsGTtx67LUzWuRzx+jsPds+wQFHzL9H6utD+LabwJhS9NMaNgqnoPenEXI20tbJkLIlkUEMdXSHYu85z9Fq4NzFKYE1MQHWOhZiNuqw/rUBOR9BFWGtqoNYUQD9RQkO7hmfq3cV3cQ2E2xXzLDKHqtWw/KyeYGCLu1mOwZlAP1pEuPM9A/W569TMoh+tJpY6TrjXzQmsPO+ZP0rmyhbOxQWo9MR5ce46PFjJ8XPQZLD1Zbks+TGJqJy1pHadDJ1AIG2ga+C5fuz7HxwtpvJWt3Fm+Al+bhg8qH6Q58ySBwFWMjcrY5OileVpH2Sli3+gvSWTCOFoux79Yh0YnpS1zHN2zP0UwGhi4qpmv1QySE0rsNLZzh3eRjpVhKiIJi1VX8v9il/JkwAJAvU2DseliE5tX9zX+w8IyuVxOg9VNXcGKY0WB+D/2s/g9/HsriZXEJMOTB153K4nfh1JnwOBoRKqoIZexEQup4Y12Ov1PIBIJGExiDLI02vQKqpVh5KNHESVey9XtZTj/75cw3Hzzn+xb/ifwZ08KlUqFwHf7sMZWyOob8SsbmRbcnMk6eSFmZTZvoMqiQmdUUNJKCctFrMjLVCkEmqUxaoUFqouDOLJH0YpVlMvVJFNm/D4F4ZASk+o8UZhzakxxBQqZlJQkSTC9yFJgnJy4gEJbQwU7hZwJjVSOoeBFuzKIPDZJrFrBaFWFs/YkMpOC/nyZ3ngEc1HDoKiTvblWxhWNiMxKcnoBhz5OZ2WQ1sIgiqycSNBOKVaDs2zBmleRL8RYSc0SL5cRi6vRFOXoomMUmWCkrkCspkxvvoQpbWG/sIaTmg7U9gJt6km60iNUwhakoWZsOQWJTAh/Nom6aESRnyJgmCZeW6Qha+KweDOLNiftugnaostIPB3oExJ86SDynIis9AzRhgzyYisvmvpZpZ+iyQMqn41IPIxQ9hOtmSMt62HO6WJzYhHNoot4NEheO0TEVsOytZ5NgRgsycgwjrdGT9bqZNW0jMrENIO1w6xRiblT9wEud+7FOtYC/hjSeIHJjuMEyldgbQ7SNlRDLBAmKz9KzNqLuKFE+7ka4iEvoZZxpl2b2TYskAr5iTV4iFbX0XvGQiy7h5PNO9haCJMez7PYMk/S1cOm0zqimec43bSFzUKA1LBApO4MZxw3cuVyjNxikkjdEZ7U387bjb9CPnwZiZVhpJV2gvLf0OEM8/3Mh0mstvHhwg8IjV9Kd9rGqfAxlMIa6sZ/zNevzfKP0SCPlN/GYfMmEnUiPlT4AlaRnJMnelBKbGxNtaHLyZkWD3Di3GO42jeQTKylXJTTqlnG8sQ3EUtg+opOvtI4SlSU5RJjO3esLNDqHaEiUzNkv4F/8G/ndExz3sSm0USy+mUTG7VY4FJNhC2Fi32N/5CwTKVUnReTZc1YV+SIS6+RlCUCFYuIUMXD5NIJFpaG3nCM66yOCyKxdMJykT/0HwuJTITRKGAQJ9DEF1AtDiIbO3ahGeAbhek978b+2c/+yb7vfwJ/9qTgjy5z68M3kZeLaFI5aBApaSqUaEyGaQotYM5nSemaWJE3MF6p4UTawf6IlbJSf9GuwiMDsVKgRZ6mTvDgLo/gyB3HTJZKpYZUykwwoCQS0mBQWLBKDFiyakwpBRKZiIQQwRufxRudQ1CpkSpcFAtWRBUDBlEafWQSpX+IgjrNjFvKWXuGpE1ET1mgJxGnKq9kVNTOC/kWRhVNlE0qMJap1oRoLw/QXJiklNCRCLuQp6txFvRoCyKCmUV8uSBlkRlFxYwhsYKQH2LOnsBTX6ZZKOFKGzhKP4fVq9A68rQoJ+lIjSGEXcjDDRizAuFMgGQBtFkxRc7gdUdR6aQImSb26DZicwTpz42g9rjRRfVEkxHIQUk0TLgugowmjhrX0q6bpmlFgsJvJJoIgXieSHWcJfkmFNY83ctl8CrIZpeI1Swxq99KiyKIddJCOjlHpCbEvHUNG3xpmIwxqz1Ok03gZ6r3savqRRxjreT9YZTRMJ7uMY7K3sEO03FcI11EQ0GCTUc4Znwr16UnKUwpyKkPc6zmBi4vLZAZBX/9CLOu3ewcLZNIjrFU76RDnCI1DAutXiq2Zlad1pEUHue5lpu5zBOj4I8RrlsmXt3A6rNGIvFhUnVwsrGVvqlFWn31jCcLVHueYHxXCk/gEo509PF/JN8lNLKd/lQNpyLH0Aq9OBZ+zg+uyPHFgJ9/zn+IhaYu6p0Bro19EqF0CUeO6Flf1UPbtImCs8hzZ+9CotGgNFxK1G+gwZnF9fy3kUa9rFzew1fbpvBIklxqaOeO5RmafeOUVVYOmm/hs4vrWM7KMaplNLWYmLS8bGLTo4JLJScu8jX+Q8IynVZHo6EGd8qE2fvahjSCXkpanWQxNsbo1EGy2Vd3SHvVuYIIY5UbtbGeCk6SURPZlOwPT3wdkCnFmPQV9ETRRGdRzp1BOnkG4Y+8zvr7UG/ahPuun/7Jnvc/gT97UogdPsbSHXcgNmrJ19iIONQsm2Fcl+K00k9YVaJJab+YLILzGCtiotomFiV1DJeqOZKwczJtQ2/Unt9VaKSElSLiigpuZZEGcYDayiTO/CkcJQ8iqkinLQSDamIRHXqpBavYgCmrwphVIEjLxEoBPJFpwvkgEpUFQeSkUjSjEsnRZ5dR+4cRFxdYcckYdObxOso0ScT0JJO4c3KmaOZgsY1hRTNFkxKZqYhb6aG9NEhtbol01EIuUosxb8daUFHJJfFklomVyihEDjTpPIrsCH7tEosNFWzKErUpPYN0c0DV8xJBTNOamEQI1qKO16BJF/BkgwgFOcpCnIT8HOH6PPaijRPSjURtWrrkE9QFSsj8tZRSGTLpFDBPtMaDWNLAcUMfbdoFmpalSAM6EvEQJdUY0SoFE9qNdMqXccxYyEZiZJWjBJzVJCxO+hdLZHwZEsYx5p19dGazKMcrBEqHMNQXeVDxHjY7j1Ez1kbSH0SZnCfV6ecp89u4PnMG2ZSVfGaCRIMET62b/gEjseg8sfoEuSobTWetpErPcKD5KnYEEqRDMRLuErWqAolzEhbbQqjMddSckuEzHWWyfifbR0TEUyeZamyjSxqiMKwmbjvGE7b3cJvmQWwjVzIdGqXGH+bpzRNsTtRxZ80t/IXuLtKjq1iTaOF05Cj6SifawAP89pICf+uP8ZHMXxFYVcMNun30Zn/L7MxuElEz24udGHNKhnIHGZ89hrPlMgJLDTgcYhoGf4V89AiRXb18o2eZSVmYyw0d3LE0RaN/gryhkcfUN/H3892kiuLzJjaNOs6ozpvY6CUirlD72JS794KvsVisRixey/JrCMtMeiON+hrcMQNG32sUa6UiyhYIFJeZWDzKiuf1t5IQS2WYXA0otbWUSg7iIT2F/B9v86LSni8A60tB1KFpFFMnkc6P/OGJfyQkTifN+/b+4YH/i/FnTwrn9s5x+KFZtHoJWkUBTTmKMulB4Z9BNjuAJBen5HYQd2pZsYiZMmQ5pwqyrM5Sp7LRKFLSWCjSlAzTGFpGJ1IRVDcxJ3JzLu/iYMLOIjZMZvWFXUVIBjpNhUZphDrmqCqcw1WcQYaBTMZGOKQhHtGjEVuxinSYM2oMRTklcZ5w1oMvPkeinEGqclIu25BU9OjLaXThCaSpSWKGHCOuMktVJSxKgZ50lrqMlMVKA4cr7QzIWygZ5ajMOerlC7QVx7Clg6SjLiqxWmwFE7qcQDTrx5cLg2BEXVCgysyRkIyxXFdBYszTmNYzW+lgn6oPjT1Hi3KG5vgs4kADmoQNaSqHJxNBWZBRqcySsC6Qt8oRci0cMqzGbVmhO76IfLkBeVJENBFGEMIk7dOUVLWc1ffTqlmkfkmBJKgimQqQ148QtdYwZ2phY9KHcsFMMrNIrGqZOfMGVhUjqGdUJEQTLLhd2GQy7KNiktGjVNqTPCN9J+uqTuMeaSUeDCMUhyjUyzlVu4Gdc0myS3lSxkMM2K9jm7BIZVxF1HKUc+5r2D2bIRldJlivplGSIzku4G1K41JrKJ0qsNgex6Zzoj1RZKppHrG9g47TCoKqvZxsvJrd01nSoWWi9UlmGmpYNVpC68lTyBgYtT7KLqWIfzZ+hGuqfodszEZ/rIPT4ROYyk2QeYRTm4u8JSDmb0rvJ9mp5XbhG1RVxBw+1ECnpZNVczbSlhR7Tt6Ftb6LVHIdMpmatugLaPbfS3pzN3eui3Ba4eMqQzsfWBynPjBN0tbPPcJ1/OtCExKR6CUTGwVj0sqr+hr/vrBseEhC/j+YmtnMVhpVLmoienTBV0/Q51tJxFmIjDA2eYh84fUdt8jVGozORmSql0RiIR2V0h/XGkJrkGBU5dHlfah8EygmjiHxzf9Rz3wjEExGii4rCYcWr0XMFZ//AUrJn+6I6/9v/NmTwt6f/ZCh/XvQmOwotTYkMhOgJ5/XkE6oEVCg04FWnEGVD6GMLCBfGUe2Mg42I8kqAz6blFlDnmFNjGlNiiq1iaZ/J4tEmPqoD7XEgE/ZwGTFzZmck2NpJ2mlFa1RTkkjJaoUUVGXcSvS1AtLVJeGcRUn0JXFZLM2whEtyagJFRas6DFnlejKMvKkCaSXCKS95MUCIokToWxGWZGhTy2hSExSEHmYdsGCs4xgLNOTzVOflRMo1nBE6OCc4jxJ6I0pGmUztOSn0CYzZGO1yGLVWEsqxOksgUKAWL6MqmJAnY1SqAzhdSbJOArU5fWESs3sVfWjtOVoUczRFFtC5G9Gl9RRzGQJZ+IoC5CXDBB2J9GI7JyTb6BgE9FbnsK8bEEZ1RJLhKlU4mSNo2SMNYxoV9Ok9lC3qEAIykjnfKQskwRMvZSMSrqXyhQ8RTKKCTw1VQhaIy0zEtKxRXzuAgVTLS3jUFg+Saw7zBHRbfS4hqgbaSEWipCVHydqa6dYJ6VryE4q6CXcOM189Vo2DUtJxCbxNuipVZQoDMBSaxCDoQbjiQLjzR4c+lpUJ5LMtaaoVxkons3hac5Tp1RROFthpSWIzOKm9oyKqOIFnqi7jRskT+Ea385UNIoy9Tyajjg/rfwlPW3DuCazrAr1MhA5jSnnIip9kmRnDne0njvVt6BpyvC29OfIx7cwPWljh6gbS17F8dBThNJeVKbLSMcstMqnMT35bUrdTdy1JccLqiWuNrTxwYVRaoJz+Kt28W+Zq7jX48SilVPfamLUKCYoqryqr/F/JixzWZ00yKuoDurOW3X+BwgyEUVzGX9hkbH5I/j9s68rPtVGCwZ7A2JZDZmU5Y8SiQkiMBglGOQZtJkV1CsjyMeOIooF//DkPxKCSkW52k7aoSdglbFgKDKuTnBOEcAvvvh47MFrH6TV1Prf/k3/XfizJ4XAXUPkl+JUNAJ5SY50JUEsEyAcX8EXmCFbTKEx2ZFrrEhkJioVPfmchkxSjUKhQKssoCnHUaZ8KALTyBdHkYgyZKrNBG0KFkwlRnUJJnUpjGodTSIFjfnzO4u6ZBSZ2MyyrJ6xcjUn0k5GKi4ErQGpXkZGJSarrmBUF2gQ+aipjFNdmMRSSpDLWYlF9KTjZhQlKxZ0mPJKNCUJ6XKMQGqJcCFMWaxBEGzIKno0hRTq2DQUZ/FYssw7K6RsJdqKJeqyctIFB8dFnZxStCAYpRiNcRrF0zTnppEm5BTD9WjTVgw5Mel8FG8mggQt6lwZKpOEdStEa0rYK1ry+VqeU69Fac3TrFigKepD8DehTSpJZ1LkMmmESpKCZpioS0Gl2Mo5YzNNOg/tgRQSr4tsIkGhlKCkHSJiczKj6qFBFaRmUQGBCmmWiTpCrOj6qVMksU3rSSWWidl9eOy9rIpnYLFIxDyLv6qb7sUixelB/F0+BiQ30O6comGkiWggQsxyiHnTbtqMXszDDpKFM0zVtdGiTCMeluNxjRCqXsvaczJ88heYbt3KljMVJiynEKp7cRyPMN2aokMsJToOsUYZ7qyUlCfBSr2BlkKJ1EyOUGOMoNtK15CRtM+LOeRjYsM0k/EboEvMusUxWjzrGY6cxZI2MWd4Hrs7yUJsO0+7trHOeY6t2QcYGtyATdLG6iUnMX2IF8/dj71pJyFvE42WFFXPfBMcGn6zS86j+mmu17fz/rlBXDEf01XX8MXwLl4IGWl16VDU6zitqlAWXulr/FrCMpFIRI3NRb3EgcunQfkqhjSCWUpcHmEuOMj41FFKpVf6JF88QcBgc6Ex1yNIqkhFLaQT/7U7+2KpCKNRdL4AnFxCtXS+ACzKvP76xBuGRIJQZSdbZSJsVbBsrDClSzOoCDEpDb3ux3xzxze5pPaS/77v/G/Gnz0p3PndOynkCxhUWnRSNVpUaIpyNGkp6rgYeVlKRStQlBXJkCSeCxFOrOAPzZHKx1EbrMjVFsRSE+XyecIoZNWoVVK0kgyqQghldAm5ZwJZfImiVX1R3WJGl0GiU9AoUtCUL9CYDONO5xBJLCyI6xgqujiTq2JGUo1cr6KkkZJRCyg0RWpkEdyVGapLUziLfso5PbGYgWzcgqxgxVLRYSwoURUF4sUgoayfaCFBRWJCiglFUY42tYS4MEtc6mWhWiDsKFEtKlKfVVLKWTgl6eSkvBWJSYzZEKJZmKM2NY8oYYJwHYa8BnmmQCgfI1EooC4okFS8pKQTRN15lHItklw1+1VrkFoLNEkXaYpGEPwNaJJSopko5IqUJPOkbD5KaieTstWILSVWp5dRLDsQRyGVD1NRjxFwmFlSd1GvjFI1r6QSLJCVzOKvEuE3dNCbjCFdlJMSTbBYY0ev0OCckhITT7JUW0d7TIRoZJbltjlmpFdR71ykYaSOWDBMvPo4A9Zr2ZVZpjQtI2o7yVjtbrZPlkinBhltbqc3UKYYm+J0awcb5iXESgdY7thM+6kw480JOgoyUktZRlpc9C8qyOYPcrJ9KzsmKiSKB3i65RquLB7BONlCLJplrH4PxlI3h9v6uDb8Aq6FLUxGhrHElQxWvUC/McVjqVsYamnnFtVd1GQrnD1TzxZpD7a8ikMLD1HRaslk1mPWyqk78VNkpSBPXWLkV9ZJrte38v7ZQZzpGKdtN/F5z2Zmsxq6WswEHHImpBVcMhFXKl/yNc6PvaawTCwWU293U4eNKo8KWeZiIhAUYvKmIr7sHKOzhwiHl/nPIBJLMFbVojbUUyo7SIRN5LNvvB4gU4gxGioYhBia6BzKubNIJk8jKv4BEvovQrBaKLgsxOwavGaBWV2OYVWEQZmfvPAHXH1eBz615lO8p/M9f4Iv/Z/BnzUpVCoVDry4FrHYiICZUslANqchnVISi0kIhSCXk6HX6NDLtegkKrRlJZq8DHVSiiotRqKSUpQXyYrSJPIRoikvgcgCiVwEhdaEXGVBJDFSLunJ5dQIZS0aRQUNCVRpH4rADLLwHII0Q8KhuVC3mNdnyesl1IsVNOXzNKSiVGehJLYwI9QymK9iuFKDR+5AopeTU4mR6kqYlKnz12RL01QXl5HkZCTiRnIJO5KsBXNZh74kR1EoE835CRciJIp5RGIrSjSoMhnk+XlylVlWrHnCVRXUqiK1ORXyjIEz0vMkITeBRR+gqbyAK7WCKFqNKOrCkBNTzOfwZ6PIilKkpQw50QgpZ5K8QYUmV8VhZT+CuUyTZImmSIqKvwZlWkIsGaRSKYJ8mIBLQkncwpTBTad0haplOZKwlkTSi6Caw+dU49V04pYlcC6oKYTjFJRzLFdZKWiddC0XyIWiBKx+IrZOOn0FshEvi7VybJgwDPpZqhlmSbmb6io/dSNukiEP8doxRlw72TWTJxVYwdNUwqLRohiAhXo/Nq0TYTBFqFmgJm6gEJtkuLWf/oEQE80xmpN68uElzrT2sHUwzXD1JDZjNfLTOZYbIxSrDawaqCEQCJOXvIjFruaemlu5tfw49sntzIXHscQETjYcZZciyw+K7yPZYeTt5S+TWO6lEmtlbcBNQDLPwMILqC2XQsFOi+dp1LNHOHiFix87x7hW38L7Z85hKVV4Xn8zf7fQj0ipxd1iZMgoIiW62NdYIpK8qrBMJpNRb3VTV7biXFYiyf/e0c3vt5Lwn2Vq5gTl8msnRalCgamqEbnGTbFgJxbSUyq8MZGYUi3BpCuhK4dQh6ZRzpxGMjuI8CdOQ4JWQ7naTtKuw2+VMq8vMKaJc07uJyL6r103fb14W9vb+Nz6z/23vuO/E3/WpBDIF9h1fBSntIxdksEixDETwFReQV+cRZcfR1sJIJHYEQQzpaKBXE5LOn2eNMJhgVJRjV6lRSfToBNUaEty1FkZ6rQEZUVGWVkmJ86SKsaIpn0Eo0skchGkai0ypRlBbKRU0lEsaJEKKtSSPOpCBFV8CVloDmnBR8YguVC3WDIUSBgFqsUymgp5atNpnAUxBSxMVGoYKlUzJnITV5koa6WINGXUmhwuiZea8izVxRW0uSzJhIlCyo4oZcVc1qIryRBn88SKIcL5GJmKGLlgQFGQosr5KVVmCakCRFwCRX2emqIGdUbPsLSDE/IWVKYSNq2fxsICzmScSrgeRcqIKlMhmk+RzaWQlcQURLMUDCtEbQp0xSrOynsomgUaxR4awzkqfgeytJhE0kdJlqConSVmdrGiaEOpK9AdjiP12knHfZRky3irREQ1bbikGewLanLRIEnDMj5HE1axCMuMnFRlmkW3BQdqdHM5fBYfJUM97uEky8YTeHXbsNnj1A47SEdn8DelydrdtA6qiQknGW5ay4bFCunMOQZb+tgyWmS4aoJahRXm4pxpbmHzeIzxxiB1QSfJ9ADxltUYB1bwNyuoD+vIxsc40L6BXbFZmFUijs6Q61rhN5r3caP2aexjm1kJTWOO5jnafpLdyPiG4nZqmrxcnr2fkYF+eunFUVByYOrXKOwdpGLttJRHMR29l3NX1PPd2gmu1jXyvplT6CRGfiO7kf+70E6Dy4ioVssZVZk6hfj3fI0XX1VYplQof8+QRo749wxpBNX5VhKe9Ayj0weJxf+jC9rLUGr1GJxNSBXV5LI24kHNBT+F1wONXopRnUeX96MJTCCfPI5kZea/HvD/AYJMRsVlJ+M0ErbKWTJWmNSmOKcIMC+J/sne80axo2YH39n1nf+x9/+x+LMmhYHlKB/+1Wl0GhlKlRSxUkJZLiYrF4iLISSGtLiCQy7CISlgEyWxCBHMFR+G0gL64jT6whxKiQpBsFAqGcnlNKTTSuIxCbGolEpZg06hQy9Ro60o0RTkqDNSNEUpEomYnDRLupQglvETTnhIFGIgkyFVmBFEBkpFHZQ1yEQKNCRRZQIoIvNIcl7Kohghq5wFU4kVU4mYsYJZJqYxn8edLWDNS8lUrIyWa5gQ1TAmdpPVaEAjoNbmMSkjuCvzVBc9mLIh8mkjxaQdIWHDUNGiLYgRcllipSihXIIySpQVNYpsDkFYIiXMEreXSVgLWFBjSOuZlLVyStGM2pjHrg7QkF3EloByqBZlWoYoXyGciSAtVChKoiCfIOiUosTJhKKDjF5Ks8RHXaCEELJBMk8qH0aQLRBwlMkrGljR2VldDKBZMlKJJslKV/BbC8T0zTglRexzKlLpFSKWMDFLG03JIuLlND5zgIytmYb5AmFhkZirmobpAn7xaXyWPkzWPDUjFtKpUWYbzbjUMpRDEjyuWWTmeoyDWRabczTEDaTjc3ibVuEYnmG+2UnvjMB4o4/axWrmdeO4lTYKy0FGmtvpG04y5V5CZlfTOlhDPBDC23yIc5JbWes6jWukm3BwCUMkzqnuQbrzTn5kuYVdtidwR+ME51axPtbIcn6YpcwyxeImqmUpHAd+wMyWKr7dNMMlhhreN3UKibqBH5au4W5fI50tZlbsMjwyLvI1fjVhmVatodHkxp02YfH8niHNS60kIiI/U97TzMy9disJrcWO3tqASFpNOmEh+TpFYoIAepMUgzyDLutB5R1DPnYEcdj3xwe5ICA4bOSrzERtKjwmgRldlkFViBFZgBL/+9JXu6mdX1/76//pz/gv48+aFPLjeyg9+/ekZVaiYjNBwcxKWc9iQc90Tst4SsN8ToVBrUSvkaFUS5EoxZTlYnIyEQmpQERcoSwDu6yCXZzFKopjIYSxvIKhNIehOIe+kkYkMlEqGcnnNKTTKuJxCemkgkpJj06mQStSoasoUedkaPIylBUJJWmRTDlBPBcikvKRKibIC2UkCjOCYKBc0iFBjawiRlOKokwsI814oOwnqS2xbAavsUzMVEGpgIZCAVdewJSXkShZGaOGCUkt01IXJY0Mta6ARp3AJfZQXfRgz3qpZNSUkw6I29GWVWgKApVclmgpQSSXRiaoUOYkiIiQY5qMPkbCXkImU2HMGFmQNnFW0YDGmMWhDNGYXsIQ01EK21BnpaRzGfLpKGVRCbF0nLCjRFnmwCdrIaZX0SLyU+OvIIStFOMRUuI4gnIGr81BRNWIRV6gzlNEHJCQEJYJWpIkjM04BTDPyUkV5glUlcjr6mldLBLLLuGpNVKTVSP4AizVaWnySoilzrLiakVvEuEa1hOvDDJV30FvskjW62Wm2U6XX064MEqirgPr2CIzTW7apxN46wpUz2mZawhSP2tnpH6JFl81i7JJbLoqKgsBTra3stmfJDtfIKY/TEC3GXljirbRKhL+ILpYgNHuMdSZXp6t28jN8p8Qn66jNtWHPSfn2PzjiPXr0VSM1J74Kf52Gd/pXOb/Y+8/Yy7b0vtO7LfCjienN9Vb+Ya+3Ww21aRECpQGskRaljXSwBrbMCxLwsDCGPpgG8bAMIzBQB8cgPlgwBH2F3nssQxBUIA1lkYOEzUaMYns5u10Y9WtenM6cee9gj+cU3VvUyRneNXk1R3rqVq19tl77XDq7PX/P2E9a/0Lw33+/Me/RtX7ffybmz/Or7YPXi9ic9zx/PHdusYd6XHup/j44xknu8SyYX/I08F9HmYjRp9ZkEb0NGWv4jz7gO9/9A/J88U/0XeEkAwP79MdPwZxRLYYUeb/2UFhqQSjsWIYZPTyU9KT7xK+/0vIfP1P1ZfFcIC9t8fmsM/VWPFi0PD9zpJfj6/JxO9ObOFHKZGKmEZD9oIeT6Mxf/lPfHkT2L7UpPD/+uBv83//3r/NVEVM0UytY9Y2TOuCablimt0xLpaIeESV7JMFUxZqwg0jzuyIF22Pj8seH5VdCj14TRxBovHxljiyADINKoa9wLAnC6ZiwZhrRvaEoT1hYm+JRIizI5qmR14kZJuQuuxB26enO/TFLgDehiQuIARKCjbNnFV1S24yStegwiGIIcL3CIiJnSctbwiLC6S/oeGOywncjD2bMfiO5aG1HDQhwyZiYWZ8qB/ynn7EaTQj6Vt63ZxxfMd9e8VBfUFSgssOYbVPxyYkjceZhkWbk9clqUvQztCIU2x4QrZvqXoJo2bErXrCd+JHdIYFh8GcN4tr4vkUln2iRrPJrmmxyOCUcrhgOdinDJ5w2+vzDnfsXYOejyiyS1w0526SUQ4eskn3+PH1HeH5kI15yWK4YTN7zIGXDF4ocl5ydb/LQEyYvCi5GN7C6AF7LzIuZzlTM8Wdf4fTR8f0BilH30uYx+/yyZPfx09+5LkNv8fq/o9x9INrXr6R8PazhI+fXPH4xT7PHl7x+MWM0wdz7r8Ycvog58GLDs8ebXh8MuHj8Sd097ocvDujKL7D6mHMJ48e8wc+klSXBfHqjLMf+5iz5ue4fnPIn2j/HS4/+El+avM2n6x+laUKoH2Hp8/+LuXwmv/jN2/5qdGYP/f8Xa56f4i/fP1HKEcPcA87fNSB/3L3jp+t/zqHzS/8E4lls9GUJ917PFgOGbxakEZtp5KYc8mHZ/+YFyfv/hN9RQUB46MnJIOHWHPAej6irX/7eEAQScZDwUCu6K5ekLx8l+CDX0E29efqryKJ8fcOKA4G3O5FnAwsH/Qyvh1fc6E2n+uav9uS6IRZOGQadNiTEVOvmFnLrK2ZVRmzfMFsc02//MwcTkLCv3EL8kc8Kd/vkXypSeHkr/6fWfz9v0fVi8i7mlVXMI8tN0nLZVhwGmScBBuiKGEa9JnplInQzLxgagzTpmJaZsyKBZNqQ6Q6VNGUdTBlLidc+RFndsjzus+zuse5HyPilE4nfE0cTSjJA4EJPWHsmQQ1M7lhwh1jd87InjF2d/RdBn5A0/Qo8oSySDFVH9/06coOfZ/QNRGp08RW4WjI2gXrZkFuC3JTgu4hxABNSuAikrYmLq+Q/hbrLlmkOXcTST721N2WQwSzNqZXJdy5PT4KH/Fd/ZAi7TDoF/Q7S46446C6ZFzlkB3h13vEbURSe4w1LOoNrm2JnMaoDU58hBnl3E0SOnZMLh/wXuc+aa/mnrzjjc0GfzslzLu4siSv73BhBskJ57MZPjjmujPlq8wZXWqiRcKiOUcmN5we9qniB9ynZnSSUJaXLIcr1nvH7LUh/ZOWVXTJ/N4B91cas7jl5kGXo0XA0twgB/fof/w+Lx736QxGHHw/5G74fVYHX+PRD0o+eZTxdDnhRL3kIDriprrinpjxcfea+1fH3N7bcHgCH97f4/jFJ5w+fcz9j17w3XeO+f0nnvL8huzJR/za0c/xc2c3mDNQ2XOWXznll+Sf4ejhRzy5uqJz9dPsZZLvzn8FL38/Dxfvocpf4d/6AxlfmXT5sycf8IP0j/I/v/5Zeg/v8XIvYNJ3/Lz6Bb66+bfohk9/KLHscHrAk+SI+3d9ursFacRAU3QLTpY/+E2nkojSDqOjbZJYW++zuuvhfpsksbijGfUtAzenu3hG8uxb6GfvIn6bwPNvKkohDveojiYsZwnnY/ioX/BucssH+u5HOW/dP5V0dMosGjLTHaYyYs9LZnaLCXtVzjSfM1tf060/J1n9ax9Ab/9H+9C/R/KlJoVf/Zvvcvr9WyIqAlsSthlBuUTlc/T6Bj2/QM4vUMLBqI8Zdqn7MUUvYN2RLFPPTdJyFZachQXXUYVMIqa6w1TFTL1k5jzTtmFa50yLFaOmpu8VTThlpafciRGXfsxJO+CTps+FH3ItxwRJTJhqfKxpI0kVgIo9QWwZ64qZWDLhmqE9Z+LmjNyS0ELT9CjLlKbs4eo+vhrQpUPXxXR8QGwluvWUbkNm1hS2YGNKLCFa9glJCIwmrjcod4flmpJL1iPIJ4Kq19BXmrFJScuEuTzk/fAhHwTHRF3LoLtiEm04qK85rBbobIpdzwjriKSR1KZlXS7QxuE0OHmGjM642k+QaorlHh93Dkk6LQ9Zcn9ZYu4mxEVEkV9RhxVh8JKX+31UdMh1ssdX/Ir+eYRaGjJ1ybKXUc0OccGMN64KzCJj0VmyPpyx16Z0TnOuhgvM4D7HFwVX3RVhfEB4O2e93+Xg4xtePvCE3UP237OcH5wzih8gT8+4fPqIex9fc/bkkNmLE4p79xDrZ4zKA5b7hvR0w+r+Mfr2JeNoyinXTCZ90ndj5pNf4ltHf5I/mp8inqfY6gPKJ3P+/f6f4Q+P/z/4j495Z/kOL+bfIheHTCtH7+Lv8zd/uuR4FvLfuDjlHwU/z/9p/TN0nsx4MZb8C91z/mD1Vzli+TqxbLOB470jngSHHF/3SNby9VQSt/acD05+kbPz93+oL3SGY4YHb6DCe1TFHuu7FH6LJLHtGgAtg/aGzu1HxB/+Cvr0P//UFABiMsYcz1jvd7kcK54PKn7QWfHr4RWV+NHNJfQ7lV7Q3YK9SpnJcKcAWvaaimm1YbYD+7TJf3cf5C/9Aux/OVdh+1KTwn/4f/2rXD17htQJQiZAjPcxzoWYNsQ0AU0V4n1AnEiiEGJtCUVD6EoCkxNUK3SxQK9v0csrVHaLlC1+0KEZpJS9kE1HsezAXWK4imquopJN4mlSyVgnzETA1LG1PtqKSZUxah0DKxGyy0JPuWXEhRtyYoacuy1xzPUQnYSIWNHGEhcKotgQxYaBzpkyZ+SvGLslQ7tk0K6wbUpVdXD1AFsN8OWAjk/p+IjESSIjcG1F4TJyV5G1BbWThLJD6ELCRqDdGiduqf0FTZJRjgXloEVEIcO2Q1J3udNHvBc+5Coa0e/lDJKMPbvguLqlm3dplxN0FRG3AXVbUeS3eClxYU4onjGfKLLuhEDs8yI5IE4db5gl0zuLX87QecHc3xKGc84nArozFtEhb5kVvYuEdrOijuZcHShM9JAHZUV0aVmEd6zvDZiUKdFNxs20JpIz4tsFq8OI2S3cDDwHVxUXkwzdf8Ds/YyPHzveWnQ5iy44aA8561xxfDPk/CAlnC+YKMWqH2JvS0ZpxHm/5Ogk4AdvdvnGRzHr5td47+GP8zW5IP6gR+m+R/5A8Yt7P8sf8/8R+vnvZ7qGD7Mzeu4B09O/x7/3EysGB/Cnb9f8P+3P8ffVH6C63yMd1/xR9Q/4Wvn/xjdf5YMPRtzcbBekeST2uXfZJSrEbzuVxGDviN70CULdI19PKNa/yUpiAgbDgGFS0q+v6Fz+gOj9X0bd/vb5B69P73Rwx/vkBwNupgEvhy3vdzd8O77hVv4ug+pvkEHYZxb2mekOMxEydbBnLdOmZFasmRVbsI//c0638bsuf+HvwuM//EU/xeeSLzUpnP71X6W+XmN8Q+NqaltStRlVk1FUa4pySZYtyMoVYRwTpl3CuIMOO6ggRagYIbZk4lyENVsysW2IEBFhIIkCRyRbQl8Rmpyg2WxJJLtDb26RJkP5HNONKPsxeVex6gjmieUmblgkhjIVVAn0guC19TFpa6Z1Td8JelYR25C1HHHNmHM75JIh12rKpRixDvuISGFjSRA54qQljBoGesOEW8Zuyciu6ZsFYWNo6z6+GeLKARQDEpeQoIkN6MbS+JrcNWRtQWVaQhERGY2ygFpTiysQV7QDSzFwlGlM13RJbJ8rfY9n0SFVGjBIckZqw/1qySz3NKsxqkiI2oC6XFM2K9rIkagLsjTncjqho6ach/skieSt6o74JiZcp2yaC9Ab7vo1zWREro953G7onEVU9ppVp6LaGxLbIfvnJUs1Z33QYVR0kMsVd3uKvTxiTUakhzRuRbcMWcbXiOEbTD664cVbfR593HD2OGRyktFOhpTqls5tTNJ1bOIOrpwzzDpc743w+Rmzbh8+XPLJGyF7ccjovZSN+A43h/vc3hvztZtLDi++wfnyA6w9Yv/ql/nVp6eo+4Y/uvD89ebn+NbwJ1gfKr7Ze8nP1H+DUZvy4sUhL18GPJ7d55Hf4/AiIXQaM/Fctye8/+IXubreZp5JpRgdPSIdPML7Q9aLEc1vWElMqt0aAEFOLz/7dA2AbPnbdyKtEfcOKA9HLGYxp+PtsM53k1ue6X8yQP2jFIFgGPaZhQNmOmUqNHtOMDUte03JtNwwy+bM1leE9vPFMb4w+W/+3+Crf/qLforPJV9qUvj//s1/l3yVEQpN6ANCpwicIjCK0EiCRqBrudWOhYJIQiBw2uGUxQhD62saV9PYgqrJKZuMslqTl0uqJqcVhiBKCaIOOkx/U6vEthFChEgCtAwIpSGkJjQFQbshKJfofIFs10ifg8uwkSfvh6w7gkXimCeWogNVAnXqCQOxtT6sYWwsPavpGE1iFY3rcs2YS8bcqDGXasyFnFAECSqBOLbEUUMcl/T0mrFfM7QbBu2KfrNCtAnUI3w5whd9QhcSO4FuLMoKSt+wMRV1kxN6RWAlTkErFwhxgQpX5EPBvJcS+R6xH3ITHHARjRAx9KOC+2bDYZZjV31kMSCqJWVxQ6ZKQp1jg1tO9oYk4YC74IBuqHmc3aBvh4i8pJS3ZEnFer+Dlfe5XxfEV47ML1jsGUT0gKN5QV5nbGaKQdXFlivKwYD+KmfdD+nmNVaFNPYSP3xKcn5JfrRHdHNF0JmSJRXBsqZrEsq+wqoQ425Qm5CR0Dy7l/D0PcHl7GP85BH3fqBZBu/y8uArDPduOXh+SP824CIvmaxveTH9dapHNT+56fHXzM/z0d4bdA5Kflb+R3yl+pCbq3t8/Czl0eghD9spB+cxuh+wjla8uP0O7330C1jbEEQxo3tPiLsPadt91r8hSez1GgByTXfzkvTlu4Tv/TKiqX7zjiIEYm9KezRltd/hYix43q/4bmfJd4IrjPicax//FiKFZBQOtpq9SpkJvdXsTcu0LpmVK/ayOZP1FYFrf6T3/mdG/vT/Hr75577op/hc8qUmhV/8pX+dulphbYAxAa1RtI2ibRV1LahqQVVCVUFVCYIgIg4joiAiVAGxCgllQCgCQjShV4ROE1hFYCShUQStJPCSwClEIPHKYZXFiJbWN7SuojYFVZtT1huqJqNxDS0trbfoINlZJQlCxuBjnIvBh0gRoAhQXhFgCd02LqKrFbrdIHwBPsP7DUbVbLqSVQqbFOpU0CTQpBYfWibOM7KCrgtITUhsAjAhc0bc6AnXesK5HHOhRqhYksQtaVwRRwVdXTKyGf12zaBZ0zcW6hEUQ2zZQZuAsHWEVmOcpzAVdbNCGgdS0miDVrdEXJD3BNf9FIIhEQM24YirYEASGw5FzXG2QK0T3GZEWrQs3DVCV1i94WocIHtdcnFAV2vuL9fI25jGXbOJLZs9QBxzWNXou5p5nFNPOvSLGLkpyMaObpVQ+xypO9i2RIsQLx2uvYPeQ8TqgjCekQUZ0caTiAAbw0omaO9AtzhbomrJWPRYVp8wf/CEN94XLKLv8sm9r/NGeMbs2dvMl9dEhWMV/RLrNwuebPb5G/LnuTma8OboGT/T/Ce4uy7Pn404Sh/wsByzN0+xQ8dl/QnvPftPuZufkvT6DA/eIEjvU5cz1re910liUaIYDz19t9iuAfD8WwQffes3DQCLXg97f5/soM/1RO+yeFd8O7pmJX8LwvgdiBKKcTRgFvSZqWSn2cO0bZjVBXvlmml2y2Rzg3ZfXFzhixSPgDDF/LH/GcFP/3e/6Mf5XPKlJoW//O6/R20KYioiSiJfEPqc2GdotyFyGwK3JHQrtF0S+RKtQqTsIEUKImar8Ud4F2FdgLUhxmjaVmFaTdNI6lpQ1xJjFM5GeBsSqJBIR4QyIJKakFfEorcWi9lq2IFXBEgCJ1Fe4JTD0NJS0+wIpW5yalfTYmhdS+MajPefWiUiRvjoUxJBIa0kcJ7A5OgmQ4oCKPA+w7CmZUOeQN2VtKmgTaCNDTayJEKRupjUhoRtSGBCSjXgWk+4VGOu1Ih1kJJGDWlckoY1qarpm4xxmzExJWGV4Mo+pkiRtSZyAcpIWlNRVQta32ACgVY1kbiiDCpuRx2KzpCeSNnoAQs1YC8w3KuWpGsByx6qqdiIK5zyrHo19STF+yNSLzi8K3BFy1KX5GOD0EfMigqTt2SDCiVHpOuSrGMJTYL1JVqGOOVxjUW4DbJzQN0u6NchTRxRS4fwHu0dlYgQAgKgSGHvxZKXTye88bFjnn7E2YM3+dqiIDobkG9yWvltFk83DIv7/J3OHyM6avhJ/S0OVxecvzxk7B/woBgxqVPW4ZznV9/mw2e/TGc0ob/3BBUcU2ZTNosU2K4BMO4Z+uaWzt1HJB/+Y/TLH/zQOy+iaJfFO+RuFnMytHzYy/j1+JYT/Ttf2hJAC80kHjILesxUwgzF1Hr2TMOsKpgWS/ayO8bZDdL/aK2KL1q8UBB2cEEHqzsY3aFVCbXqUMuEUiSUIib3CZmPyXzEysWsbcTChCxsyKINuWlCbhvNbRvgveBf+/m3+O//sTe/6K/3ueRLTQp//m9/m9I5vBI4KXBKYCW0AoyARkIjBI3wVAJq4UEJYiVIJKTSk0pHLByJMCSiJRYNMQ0xNRElMSWhL4h8Tug2RH5D4DJCXxDTEvuaEIsQEfgIz9al5Gy4s2A0rdGvLRhrQ7ARzoZgQnARgdhZK7/RDYZCe4FGoJ1AORDe0fqGxpbUtsIIQ+MtxhkaZ2hcS+s8iBglQ7QI0F6hnERZUNYjaUCUeJljyGjZIFjhIodJBTZxtJGjSDWoiNDEhDYm9DGelNtgwp0esNQJJtAkYU0SGBJZMbEVk6ZkWBlsmdJmKcokBK0iaBrydslGZKjAE6mSWmy4GyTkw5SBSFjLCUKl7NkN41WDX0R4tyFXBWXqKEaKwO0TO8FosSF3lqJf4fWUYVlSGI9NFVEjMDQIEWKlRTuPtxJPhgyn1KJCtxbpJTgLYUOuxwxMjXHQryUvJo7HVwE3nXOK/QOevOxSXW+w4hmL+zd4/xb/YPBTPJie8fX8u5SXQ9LiEQ/KMakTXOQf897zf4RIQrqTx3hxRLacUOUh/YFmlNb06is61+8Tv//LqOuX2xdbytdZvIu9hIux4KNeyffSOT/4HWTxBjJgGg13mn3EFMWe9btcnpy9fMU0u2Gc3SL+GcwM/s3EywAfdl+DeKtTWpVSy/Q1iBck5GwBfONj1jZmZUOWNuSujZi3AXdtyHUTsGr/6Rf0+c3kL/2Rp/xP/itf+V259u+2fKlJ4R/+X/51SiOoCCmdpvQBpdMUXlPYgNwpMhuQGUVmNRuryYyiIqAlJAgUUSAJtUJrQaAlalekEkgtEVKAkqB4TTxOgpECIwRGgpEeIUEpkNojpUcpTyy3ZBOLlkRsiWZLNtWOVIod4VTEviWkIfQtkWsIfU3gHM5vycXZkNZorAnwLgK7Ld5EYEK8CRE2JvCaV/ZEgEB5gfYgLCjnkcbicLRYDJbWWRpnqJ2htS3e260t4hXaCoT3eCVx0mFljhUZigwt1khd0saSIgnYxCFVGBH4lIAOmoRGxayDlJXq4LUk0S1DaZm0NcNygy4VphigqoCwaSnJqP0CoQWVduRJSzkOSEWfihmx9xysl5ALWtewTgym65B+SseW6AzyqMJGHZLGYjA4GaBbg9MK4zzCO4Qt0VEfKyzOOQI0vikxaQ9dlTRpjG83TKouV90lvXjI6FlM3r5guX/FJn6HZ5Nj3g6esX9bIG6fcNj2oMz55OZdFmZB0nuAdYdsliM6acwwzOkVn1kDYH2HGA0xx3ts9ntcTV5l8a74dnhJIX9rX/ur7NmZ7jJTMTN2CVVNw6zOmeYL9jY3DIrFFw72Xsf4oIsNOlid0uoOjUppZEIlE0oSCmIytpr42kWsbcTKhSxMxLyNuGsC7tqAmyYk/6dckOf3Sv6Vn33EX/5TX/uiH+NzyZeaFP7q3/5voZ0lcI7QO4Ld9rY2BNYSOkNgWwJnCG1LYFoC2xDYFu09GoWWCi9DnIqwKsLJECMjrAhpZUQrQloR0oiAhpCGgJqQGk3lQ0ofUO1IqfgMKdUENDLctpXb8xqx3XYy+JR4lEAosc1MlXJn+YCXAqk8WnuUcrvi0dKglEVLi1aGiHrnQquIaIn8rjhDQENoW0LXoG1DaBuUB1wMNkLYbY2JwMRgApwJwe6sC7ZkIqxHeYnyEuEkBk/rDNY2WFth2hKcwUuBUxKjPEI5AlERijVelLSiJY9DNp2ELE0IVEQiNNoHrFWXgj4dLehQM2hK+oVBbAJsI9C+YENBEYW4uKDpamJ61GbE2CxQBRTKYAOLVwmB84jW0mgIraaRLaEXGC/wwiK8AxEiAO9bNAohBWWgSMsGRMq6n3G47tEs7piP7lgOn5D3O/xYcU5wdcSwiCk2N1yWFzg1xJk9lB/SEyW97ITk5DtEL95FTLvkh0NupyEnQ8P73Yxfj6+5/A2LsyQq3mn2XWYyYuYlM+uYtTXTKmMvXzLdXDMol78r/emVP9wFHZzuYnRKq1MalVKLhEqmbFWZrSa+cTFrF7JyMUsbsWwD5ibirg24bbaldl8OEP9Ry5/96Qf8L/5rX/+iH+Nzye+O7fR7IM453HsBpQrYCLfV2IWjlf513QpPKxyttNttbWkDTysstbC0wtIIhxF+B8LgtNyBsSMQLYHwBKIhEIpQSAIhCZCEQqIRBECIIPDstqHnPGM8ofdo73ek5bdE9Yq0vEcjkUi0lUgjUMht8QLpJdJLhJdILzAipCX4DCEFVD6kItiSlQxpRLSrA3KRcidC6h0JlSKkEgElIY0MUAq0cgTKoQOHThxaGrTaFiUNWji0dChpCXCE3hC6lsC1xM4SO0tkHYmzpNairMKZAG8Uzmhsq3HtBGefoJwi8YKugeM7i7wy1L6ikjUVBUOu0OoCJzRGCVpluNSGtgtNqhEqpOe6NK5D6e8xdA3dds2kvKIxCaVs8MojhANvaVRIx3loLU3YELYKGxiE0XincDi8L5FonBS0zhC2ktALGqkhbdi7VFx1L1g/PWAYzPjKlSN+IcmqPot6Tu6HxE2fo/WKKP8A0fzHLCcBZ2PPLzwo+c7bt3yolyRBw17kmOoOMxmx77v8WRsza7ZTJUzzOXubG7rV72z+oN+JPzz3MRsfsXYxq9/CHz5vNbb6/08Q/1GLsV9aXfvLSwreWN66/uZW43M7zc87hHMIZ/HOIbzdjtLY7cNbhLW7dnbrS7YW4Xe1Ndtj1oDfju4QAoTweOERwgIGIcALhxAADqTAK4lXCq+2207JndtF4pXGSXA6wUmwUmCVoJFsPyuBFWAVWOkxUmCFxyowwmOkx8kWlMGLEq8ESLb3lYAS4MErwIKWW49XKjy8ch8IDziE9LBtjvAa1UpUK5F+R0leIHbbeIXwErza+t5RCKERSAQKLwIaNA2ShYgwQmOkxEiNTRRtKnHSY0SD8ZpWKgwaZIgWmlDERIItYQKB2JJOaFoC5+m0jrg1qJXANpLGC3A1oc8xHkoJuQIfZAipQVq8AKcdyrSswoCecCS1phYVrYyIvMNit0F/pzHeIJB44ailZzuwWFJXBdeHU/bylOPnKeXmmtZpWlbY9hO8v+TFyHI1MNz2DX21nT5lZmpmbcXb5YZ/JV8x28xJ622s4FN/eIrV3U/94cET7qZf4/Q38YdvbMTSRSxNyLyNmJuA2ybkptEs2wD+GcnV+ufyw9K6L29A/ktLCg54MTngU4Tj9TA+wSsoFK8xcbvzMx5WL16fI3YX+NSR9pnpAfxv9MqK3zDz8Kt7iB2BbO8uxHYlWiF3x/Db8eJ82vbVnYTg9TOAR4vtDyM8CLctuztv220vtf2WfncN7xHi1ectGQi/bSPx2+mSvd8uZvK6ndt9VYEX2/1bwgOP39bi1YNsiXD7VR1eeLxwW41bbNsK0SJxILb3FNLt/g8c0tvtc+32+d1P4IXAAU5sv52V232NlFRCsFQSL0BqixceKd3WxbY7X6MRUuG8QwiF8wLZGoxq0L4mCzQiaNE2RLWCWmkU4J0HY7DGk7UFcRATaonyFqctk7WgXrzE+iV30Yq7mSdQMDAwbB33moB3XExhE7L5DsR3Wvi7n/WH+4A78Rl/ePGf/W7/c/kvgHx5DYUvLyngPRN/DXwGaOUrghA7UH59ZFuLz3wW4tMjn2n7abvfeEzs/n7mWp5/ct+urdiRkNiB7ut/P/sMr67pef08n239m11je+anbT97/g8viC7w/hWYexyA8Pgdo/hXf3Zf41MS8J8CtvevJzDzwm95Zedd8K/24bdU9aodrywRtyUFuSMHAQi72/fqs9u6e+SWnIxQGKmwKIzaWhVWSKxQtF7irMChMH4bAI9p8a5BOoOXLa2wKGOQtSNoQoTzW0tHSHzQUgqBokS2kkaB9RaLwbSGn/uX/iX+xl/5q/T3Q3wYcxulEAzQHOGEBK+pnedaaO6CFgKBFcHWPBNby9CK7Y8xkI6+kNwXCs9ugAIei8AJhxXgPDjxGXJ8TeWfbrvd++K8wHmJR+L89rhD4rzY7du1d3J3rthe33+67Xfbr388tr/vNqbC6/qVovFZUPPef/p5pxG9Voz8D1/H7w7+xuOvLg3+M+12197d44f2757D77a9//Q5/Kt7feacz97D/zOAyL34ywutX9onV1IxHf4YiF3n2oEZOy3XvdZu/Q+DHh73CrheacS8AsLdMT7Tjk+v9+rF8z/0ZwuAr15eh/v0mP9sDc7vepvYgqXc1UI4pNxq6VvAfPX5lbvnFZBuAVa+cmWJT9sI3E4LtyA9Avu6vRAexzYu4aTGCo0RAVYonAgwQmPRWKFebxuhMCis2O63XuKcwL+qndwCjRNYJ3EOvBM4J4h9S+xbIgyxa4i93YbnXYsRFiugxdEKi8EhjSNqHFFrCRuLshpFiPQhykoCb5AeIhEQynAHwiDjhiIIyFRC4zp0bUEtLUIEeO/RKqBKJK2vCUpPpDzWSoyWKOO27hwv6SSK/8ff+mukI42SAREgVI12DUYpJBbVCkKrsImkDDXeSGK/pgnAO4e2BuHc1lr1Egx4K8E6AmMIvN/mqyARPgQ0eI1CI1+doyRG7NyOYms9ITxeWJAGIe3u97Y7svUItbMQpXv9nmy3LV4YkG57vjB4YbFi+y5bIbCAQ2w/w9bVKeTWetuavTtCU59acjsCc0LuzhW7GpzfPTeSnV2IReGE2l4DtTu2JX6PxCJxqF0td8d3tRefbiO27+D2zd7WXuz27/b5rdVpHbv+tmXdH963I0jvt8TJpwQjdgqY8FuXsfRbNUvyyhrfAozcWeFSiNfLiIqdN0D6nXK41/3dB8HfJfnSkoKX8OuDf4iUHvlqOKjcDgcV0qMkO+DdAankh0H4FQDzGRDebb/SYLfuFQe71xCx7UbwqjaAw3uHIdgBbYAh3AGvxvgAK8Ot1kuwA9mQlggrAiwh5tU5BBgCWrbA3KIxaNpdJ3J2y4DeSXaosX3BLVuQdtttZwTO7kB6G0LBW0h8TVc0JNR0aEhpSF1FQrM95mtinxH7ZqtBC4eRjkZYnPS00mKkwwiLcI64cUStJzKesHGErUNaiRcpji5OJFgSnA9wu04ocGivSGSPSERoIowW2xJaVFzjo4osCsl0Qu4DrBcM3Ya1zKhtTlo0qLaDqCVppRnpkjaFleyg/XZBFiU1ZSzwtiStQ6oYZN3itUba7buhxfZF8sLTT0PwEik1Xm0HADhh0ezcVEKQ9SDalHSqBJs4GhmjXIP3BmEUzknCJiR2Ci0shBbigDwZswgiKgStUNshyeRIsm3muzDU3kNjieuWuK5JqoawMWivUV4hvcYSYYkxLsQ5uQUmJ5Ae5CuEc36nJGmkShFSI4RCSoUUGiUloRAotkWK3aAGBOCwbP9vvNgOuPBiW5A7Nx+8ti7dTnFyOJwHKxyWHy4ei5d2O+JLNttavPps8ViQdtunpN31sa1FqV735W3/FdJu+/hOKfpUcdpRhXSv+6kQ2/4pdv0XZT7TZ+12LXEM3ltgOxTbeY/1Evu6x2v8TnFyIsCLcLcvwAmNI8ALjUPjdsqUEwGgedx5B3j0ewGFP3L50pKClJL0rY8xxFgZURNhxHYYqRXRDmRDjNiVHSi3BLvtV6C7dVO0XtEiMX7rqmiRtF5gvKTx0PqtGf6KG8SrbbftmNp7tAflQPldTsCrzmpBbO15cB7vPN56nPU46wh8S0fUpL4loSalJhVrUl8T+2a3ryGR2zp9nfPQgDA0CloJjYJGehoBjXI0wlHvShN4rBOERqFaSdhKglaiGoFuQTcgG48XCbXoIGSKokcgElIS8GobL/AenMUJixOggohAxwQqRssIIoFVWzKx0qJ0iY5LVNzQhIJ12OVWJ2xcQGEk3lrGbkXXZeSqwbSG7sqQzBUdEzNwDansYFUXEUSYXsplMiCIN1i5xq3XBG1Imoek3ZpGBDjfIKTHek9UCupEEtUNjVYI55EKrBU459CBRBoPPsApB/pTv4eUConHS0EtNGlhqdIE2oagCWijnZboAOvpmIQoqMiSmFUwIGJNpXNkdUe4UcRNQtTURNahRR8lBgihMYFAJA0qzqhTyEYpWRBzJTWFFRRW0TpJ6itSV5OYhsQ24GocNY4GS43xNV40hMYQG0PUtES1RRpwtYAGvI+wPsYTI4nRPkb6EOEjhAvBB1uiBLwUWLWzxKXHviIAHM5bnLN4DPgGvEVJiRYSJSSBEMRiS64Ksa2FRBGipEbLECUDtNBbshIaiUL57Tneb12S23jTjnz8rhbb57C7UYNWOJz0W1ISbmcJeSx2u8/v2mMx3mH99vmtdxhnsW67ba3FOIuxBmu33+8VSSnl0Vqg9W6YuAKlQe+UUaUgUCClQypIXABfzoTmL3eewh/4D95FOE/od6DsBGoHzlsr+lNgFvaVJuXAgbcO53bAbBzWeozd1cbRWgfWkIotCKfU9FRLX7X0VEtPt3RlS1c0pLIlFQ3JTguPXmVE+5rQVQS+Bt9SSUGjBJWESgpqCbWAUikKpSlVQCEFhZAUAkoBxkHYCMJWErWCsBYELYTNtsSNJ20EcQPKhwjVQYjOdvZXmQAJTuwyrWWAVR6nHE46nNj61J03eCGIVESkAkIdEMmAQGic2FkIajcqSjlkVKOiChWWuLBgE8SsdMJCJRQ+pLYRdaPQzjA1Gf02o6WmljWqKRitS7qlI2CA8wmthUB06Yo+NtC4XkXdNVzFXUob0PW31H5JZynQZcTIhfhIsuh1aLRFt3OCIiKSkiwK0a5A24haWkIvqPEoqZDGYXaOaiUVwluc27qy8A1ahDtfvUMJUFptXYJOECBoA0dkFbVwhAZsoLDKI5uauEnQUcs8GoOeQ7VGlSm90pGIFCJB3Zfcxh3WIiLxSyQrCirCTctwVRLbCGVDjFMYI+mQ0hU9CBLqQGKVxakaFZfouEZENVmg2AQRSx2SqYCKkMZoaqOoTUjVBMTWkNqari0Y2YyB3dCzGzwVVrQY1eKUoVUtjWiohMG5FrkbmaaMRNYC2YKuPar2BLUjqUG7ELUjGCFiJBH4EAjxLsDpBKtjjIqwMtxaxwKccDt36yubwuG92UZepEHQstXqDfht7Mi5Fm8brG3wtgUPWqjtwG4hUSi00ARBTKAjtAoJ1LbWKtiRULAjII16de5ulJ30ArGzwLdc4HduwJ0VITxObUe3WcV2W/ktIe2Kkx4nHdM3Dnnjj/34FwmPn1u+1KTwp/7Nv0NoSwZBy0AbesrQlQ1d1dKVDR3ZvAb15PXUFVuwjnxN4CoCV6FthXIVytZgG0opKKWkDEJKHVEEMUUQUeiAUoUUUlJIRSElpRCUwlPgKbyl9JbWWuJWEDWCuBHErSBtJb1W020VnUa8BvKotkTV1u2CTEB18DLByQQvY6xOsWEHo1OMjjEiwMmtSe6E2XYo3+Bo8H6rPYZKE+uI8DMT/gVCowlxWmACsc3jkGxdQ8IgogIZ54goRwQ5tXasdIdMd8lUl5yE2gRUTUDdxERNy8RsmNoVPbeilgWlKFG2YrRp6GUtghGNn2JchHCelJiuHtFGIVVkCDpL6q7lMh5x5yLidk3Hz6lNxfDOEFcJiVcoPUF0Gi5HXaxrsf6aeKEY1BGiK5knXYS9JclCXCfAuJbQCOpAEBixzd8WEuXVdigxYL1EeosVAmk9Vhm0UxghkFIgvX8d8NVsg8XCC6Sz2FAhWodQCiMNuvQkSrJMejiuCDLNqAqQseJ22KeSHievUOuMTpaQVC2RGBKILiYR+OGaRdrhNugwbyUDu2bgVuS+pbU5w/WG4aYksSGtn1C7LtKCNB7tNWHUJwg6aKUxymCEwSjw2qDiBhkV+DDDBjmZDljrhEKn5DolFxGVCahNSNPG1G1A1WjqRhObmonfMLLb0ncZfbemZzd0ZYmTLUYbGmlopKWShkoYcglrqdhIgbMCVQtUy27WYtC1I26g2yrSGpIa4mrXDwwIYiDEixhPgJcRLuxgww4uTLA6wegIK6Od6zXA+q01ZRxbJee1i8gilUFKu3M/GYR4RTjtlnC8wbsG71qcbXC2wZpdaWtMsy3OWMIgIgxTwjAhDLYl0BFBEBGoV1ZzyPTrT3njT/zsF4iOn1++1KRQ/G++QWEKyjClCJIdeIeUOqRQAYXSFFJRyq3mXQgo8JQ4CmcovaFwLaWrqduGpBF0jGJkY4Y2ZGBC+q2ma/RngNwT156wsoSVQZctom63QK7TXelg0wE26WHCLiZIsTqhldE2ZuA1XrQ4YfC0eBoQDUI0QI33Fd5WCG+IVEykdgAvwp023yGSMUJqWgWt8rTSUWNphKUSDU6XqKhCRjkizJFBDrqg1F1y3SfTfVYyZeNTahPRNAlVFVLXAWlbcugWHNk7ZuYOKwsKVVMGNbKpGWaGNPfQdmnYw/sxgVNgDVpqkmSEClKawNAGJWFvTd11XMRDrn1EVUuOzS3GZ/gqZ3ZXkJgB1ihSMUVEKXaScd7rUlqPFqckt5ZOHtIRfZqx5DaKEeaczjKhEwTc9GJUuaBnU1YxdCpHE4NqJYIGg9rGCpzbBqR2oC/81t3glUPXHh8o8NtAppICbyy7ABTSO5wAqTS0LQQxxm/olQGbXhcrbkmWAV0VcD0Y4oIlIpvTWcUMXIIPYoo9wVnYJzRzWjEnXBSMN4LQRLQmYOSnuCSliVuC3pyip7mIety4gKwN6JucWbNBuJyGFc5mjPOSflYh6pSGCc6N0S5BtRYnHK2EIEgIdUqqAgIht7EhBUZv5wbzqoE4R8QZIsjxwQarasqgQ6675CohkykbkbL2HRqT0LQhTRPQNoq6Vbitn5WkLRn5jJHbMHBbMhn6DWOZMSSnR4ampJGGWjoq5SiEpVCCjQ5ZK8VaSjYCNjgyZ5ANBI0nqCFsPN1W0280faPo1Nu+mVSeqHaEpSGoWnTRQGPwPtzGBXSKS/u4pIeNu7gwxYbbvmlfkcwu/mfQW4vNClojMK2nbR3WGrR2qMBuZxjQr0jn1UAAgxCGpz/1Dt/84z/zRUPk55IvNSn86b/1L7JZ3zK2MSMXMzQRAxPQNwHdVtFt5RbIa4hrR1g7wqpFly2qbJBFjShLrA+2L0l3jOuOdoDex0Q9bJhiVIJREa2ItnEIq2iM2AL6DsylapGqQcoGRLX199oSZytsU6KlJtbxdkEbnZCEPZKgQ6hTQhGjfYATgkZZGumohaURhkoZKtFQ+xoRVqgoRwYlMsgJwpIgrHDKkwVD1qrPRvZYiS4r+tSmQ12HNHVIU2uaAqZuzbG748jdsW9uiNhQhi15WJOpFlU3pJkiziSq0DimKDFD+QHSWow0OC1JoiGpSvCBp9QtPm4IeyuqtOU07nMtOmRVSr/OOWjnlGK7ONHhokTZGbVNiV1CN5xSdB2M5rzsTslaR99dwDJnsgqIfYQORmwO4E5GCPOC7nXAiC7LaYdKzYnvoBNEzLuCdG0waYSoSlwUEtQOo7ZDXoVQ4Nx2aKYQqNbSBm7rw7YSJySRtzjhMRK0Fwgn8WobN4KWVkqkUqi6pYljZFkQBilFVBLNIU1CbroJoj6nN4/oqC6bvZC51nh/Su/a0C0kMRNE0MHM1lz0R1wbxay9IvcZ/cWSycaCG9O0IV2bkEYT6ljR6JKov8F2Ky6SlEvdITMJmyqk19TsN0sm5oZWbChUhrAlw6wmygQi11g/RjNB+yEYtVsnw9KIFikUaRCRqHA70strbCCxgaJRnhpH7Q02LBBRti1BhtAZQVhhFRS6R6ZSMpWyocNGDFjTZ+NSmjbczTysaJutO0q1DlqHrS2qrRn6DcMdiYzImKqCscwZiS2ZdN2GxG6wvqaUjkJ5cinYBDHrMGatAzY7K2UjYO0tG2/YuJqqbQhqR9TAyEQMTcTQBPRaRa/VdBpIa0FcuR25tOjSoIoKWdSQF/iiwgUJvjfEdYafkkzS21kzKTZIOP7DX+Pxf/X3f9EQ+bnkS00Kz//Mv0z50TPcYIrtj7eg3hlikwEm7v4Gt0tES0DrFK2VNC00jQdalG7QYYvSBq0ahKhB1OArnKtxpsS2BW1d4K0hlDGxTukkAzrxgDjsEusOoUwIRIh2AdJKGrmdRqOWljowVKrdmtg0VL6hcRUqKtBhiQ5LwrgmimrCoEKqkkKlrNWAjeyzEgNWYsySIblJsE1IWyuaSuFqkFXLvltxz91y6O7Yc7dMWdCELZvAsNYtS9niG0maa+K1JF45NCNCsYcUY7xPsNLRaoMRljTs0dMJgVRUgaUOQCQ5qregSkpOoxF3asimjqnLgONmzqy9JpcLonLD/rzCu0NqOyEwMAxntGmE6ayw45Jn6YxlBYftBXW14vCmRbddUrGH7WvmU8edDAnsC4aXkn6T4IddzgcRqjphdJdCr8Ndp6V3W6I6AwqxoVsnZHFD4iK8qWi12hKCAaccyjoUglr77brT0uBVTNS2NMKAiqBtUULitdoNO5RIa7HSYUNNWhqyVNLPHMtBQrxcEYddbrueeLFmXHdZ7qXkQYFeLRgtNbGY0OxbPukMiewltlixf+OQbUjPz7BJFzG65XaU8Ins4ZuWveaGwq0YrOZMVy3GH2FMh6DxJPGIKOzTBpZa1+h+huouuYkCzqM+S9Ejq2M2RUq3Kbnf3PKgOceLFVVQkOsS0ZYkmSLcSPTao92IUEyQboRzXVygaLWnFS2Nr8HVpEFIoiNiERITEakUpzWt8jTSUwtLIRpKUeGDAhFuXZIyyIiiijCs0LqgUppMdtjIHhvRJZMjNoxY02flUzY2pG00phG4VhAYCFqHaj20DlNb2trgmoaB2zDwGROZsx+UTHXBZEcm/R2ZpHZNbNbodk2JZaMEWdhlE6Wsw4R1ELJRARulWAvYCM/GW9a+ZW1rNrYkawtEa5jYlLFLGJmIkY12Voum20g6jeDNn/uX+cq/8M9XXvs9l3/nf/1rnLy3RIeWIGoJQoPWDVK3W42dGti6YqwpsabE1Ftwb8oCLQK6nRG9dEyaDEmjHnHQJVIpoYzQPkQ5jTQCb7Yve60NdWSpA7sFedFS0VDamtLWOFmig4owKkkSQ5K2RGGNDkqU3K7ktpYj1nKwq/dYMmbp+2xcgqkVplbYWqJrh6gtomo58HPu+Tl77pYj5hyKBVIVbALLMjDMleVaS0St6aw1aQbdpaVXJ2i1jxBTHAOMjmhDSysajK/ohgldnZKIGBtCHUhK1aI6a0T3liouuYzGXAdTNk3KOu/SL0veaM/oukvWak1aFoznDdbew7oDAuMIVYe4M6YMW/RgTjbyfBSO2JQB95tLSrvg6GpB1ExwbZdBtE8zybmYRtwZQdSeMr1s6dQpUTzl+lCSy5Lk7o5J1qWZ9riJK7rXGf1wyPXAMLwx5KMO8WZF0+2S5CV5qLer7lmPBOwu78+6hthoqlQTZSVlt0MnLynTkKCsECrCCo+Xu2xtp1DeI21DFSkSG9HaDTLs0zZLhr7D1QB61zmdsM/FOERtLpgtQoJun0+mXYQ/o3uV08lDuvKQZlZxMuyTm4bIXdG9vmVUJLR1h7GY0Xa7+N4d9djyUdLnukoZNxnj+prK39LPVkwWDbU/xpkpYePxSpEmY6IgpNGGSjt0L0N0bsnjhqtowG04YtV2yKou6zyhX2c8bS94YM7p2juqqCILKlaqxFcQ5wHRCtKVJW26BHKKZIRjQB30aQOBkYaWGusKvN0QKUmiY2IRkciENOwTkWCV3BGHoZKGUrXkVJS+Ap0jwxwV5ERxQxw3BGGJVjle5JQyZi27ZKJHJvfYyMmORHosfcLCRhRGYhuBbwWp9UQGAuORrcU3W4ukri1laairmq7P2A9KDsOKg7BgpgqmqmAocgZkdP2Gjt0Qt0vCdo2ql9i2IIs6bJI+m7jHKkzYhDEbvSWVtZT83Nf/Al97+se/WID8nPKlJoW/97/7X/HBL/wDpFD0+zN6nQmddPhbau/KKkQroHa0zlDHnjp2NKGl0i31zlVT+h3ItxVVu9Xao7ih1/MkqSGOG8KgQgclUmZ4n5HLhLUYspZ7bNQBSzFjxZCF77K0EaZR2EoiW4gaj6otrrJQ1xy6OQf+jntizn214EDM6bNkoy3LwHGrPFdhyJUUyFLR20g6a89kLRhvBIGc4tUUL8ZU4Zg2lBjd0vgC63K6YURPd+jKBKUj6lBQKEelSlRvgUhvaKKSm2Sfq2DKsumRFV3qTPO0veDt5gVGzllHOZ2soH8HtjlAcIy22wSnbjpDhpIyqokmC256kmd6xLqMeFjc4LhmsLhltEkp7YyhH+A7Q9z0ipfjIcvak7ZnTK9WROWArjykPqj5uNcjbl4yOm/pmRnZvZC7oGJwsWIsRpwdxAS3F4zlhKteyXSuuR3BaKVYRzWhilG1Q9Jg1DbJLkCAlNSBQrQZSdtlngb0yiVaJOQRKN+gjNjFHjxGaWQoCGoNpqTqhPQzWHRbJivJzSxgeLXBdycs0g2ji5pONOLlXgLVC6ZXgq7fI7/nOY1SVPOS2WVBUHXoiyPMuOZmT/KJ7zCrrqjaO+5fzZHNAa6JGeo9TCfGpGvUeMXzXpdzBuR5wHG5YGTOKcQtvXzDYG5ozSHS3kMbRassKkjpR11CKakCT6k9It1A9wabzJlHA27DCddyQFb12BRdsjxkWi95Yi942F5w6C5pw5IsqFmokmspoNQkmSRdeUYrz6AI0ExAjrBySJ1MqVSKVZZW1FhfAjnerhGuIVExsYxIdZdONCRRPSIf0cI2eC0MZdBSqJaCmsJVlKZEBDk6zImTliRpieKGMChROkeIDdaXrEVKLgdk8oBM7bEREzYMWfkuS5+wciFzoyhbjzbQdZAaiOzWIhGtwzdbi6SuDWVp2JQtm6plpkvuxzVHUcVBULIXlExkzlhuyeTwj/xFJk+/+UVD5OeSLzUp3P6d96l/9RbXWtrYUyeeJrZbV4c2VLKloqXyNaWpKdqKoi5pTUmaWvp96HTcZ16qCqVyhMzwfk3jatZyzEYfk6kj1nKfFWMW9Fm4Dnc2pK4FooGkhaT16Mbha0tTGKgqjrjjSMx5FCy5rxccMGdk72hkySLw3GrJZZRyEWiucIS5pL+B0VpysFFMVo6g7SKCKUZPaJIZZTigVQYjaqzP8W5FqgO6QZeuSklUHxNKSu3IZEsdbFC9O1Ryg40abpN9LvWMGzsiL3pkWcKkWvJj7ScctyfkUcYiyOlkhs6tgnpKwEOkC2mlIUx69IMudWho05pgdM1pL+KlnrHKUu4Xd3TtKb655eimobZPUW3AID6k6lqY3fJBf0JewrA9Y3B9TacYk9oZfhxxfeiZG0O8OWXvOiRVhyzvC25Vweh8zriZcfmggy3P2btNWB0PcKtLBnLCQmf0my6FXKPCPnFWUsQOqzTKSYTYzl3lhUOaGmVCAiVZxiFhsySsQ6RWeCkxbOeRUgikNdTS0sYBaa3x7Ya636N7l7PY79G/WmAHM3Jxy95NxOqwTy6umZ47uuGU50cx3l4yPt/QrSboQYfTQ83atOjqjPsXBaYZMVb3qAYt9X7B96IxuqjpN5cE+QX7ty21fYyuJd10H5mElFFBNLnjsit4Hk2YV31kBm+XJ3TsKetwSSfP6d0JXDVG+4dosx3KWwtLN+ow0ClSSepIUkiHi1eI3g0iviGPNLfRjGs95saNyYs+WR5R55JZveCJ3VoYD/wVsV6xDmuWuuVCC66EJMwk6UbQX8HBWjDYuSy9GGGCMU1nRhkOqUWAocFQImSBYIMzG0y9IZYBsYrphAN6yZhUb62OwIUY5ymFoQoMVfjD5JG3JaXNUaogSRu6XUeStsRRgw6KbV8XG7xfUdmaTI4o9D1ydUgmZ2zEmDWD1ySysAFzI5kbjzKeoRd03SuLxKNbvyMSy1/++bf5+sHgi4bIzyVfalL4R//xP+Q//aV/RFGWRBEMBtDt+h3QN4RRQ7DT5oXI8H6FNQty0ScLHpKp+6zVAWsxY8mQhe8xdzF3RpPvcgH6RtAx/rUrp60MZdEiqoJDMedpuOJJuORYL9nnjrG9JTAL7gLPTRhykfS4DGMuJVzZhiQXjNaCgzzkcK2YLC1JBj6YYYMpdfeAKp1SyngL+hR4scHbJZGUdHWXnu7RC0cIHVFoRy5b1rLEx0tU5w6d3kLUchMfcqmmXPo98rJPtkkgM3zNnvCV9hlDrlkkJXe6IF5JuneKoBgSicfgezTa4BSMkiGhCsgji+9tYHTJi7TPqdpjse5xXMw5bp+TccPBbQbVAzATIhGR9KZUaYbdW/G9dEqdw6w+obM4p7+eIs2IuLtHc3jH99Ip/fqM/tUN3WxEEh9w+8Cy8CXDi2sm1YzsXsqN3rB3WhD1Z5z1a/bOaqq9MTY7Jw4OqO0NKpqi1ys2PU3sUsK6wguL8ezmyNrOi6SEwgctK72dJsNgEEYi/TZz1wkPWuFUSNJIVJWx7Gl6bUpTz6E/Qa9ucL0DSnPJQdbn5DCic33OuN3j7DjBVafMLjyd8JCz+5LSbehdXdJdd+np+zRHK747mNApbpDZGcc3nraeMtIHVH0P+3N+0B1xW8c8yK6w/pTZ4o541cfYB6jWk3ZmBFFAETbo0ZJssOJ5MuPKDZmvh8yKJT9WfYSQ16yiJVFW07lT6E2XSDxEmCltCJWsEdIzCjv0ZAphSKkFuWxpoiWye4tMriGuuYum2yVcxQGLZkReJJR5AIVlXC95Ys95YC54Iq+YiBsy3TAPGi5DzYnWzK0n3Qi6K8/BJmB/LRguGnSb4NQIE8+oewdU8ZhSdGicx1CBKFC6ADa4dk1TrRDGkKiUXjKhn07phANi1SF0EbrVtGY7aKOMLWXYUuodefiavC3J6py6zUmSdqssdi1J3BDFDUFQoGQOYo1zK6ydU/mIPHhAoe9RqH02YsZGvIqHdPhffu33cRB3vmiI/FzypSaFFy/+Bi9P/rcYs6D1kAePKYKHrNU9NmKPpZiwpM/cdZjbiFsjWbWevvWMjKRjPFHjELXDVoaqaNnkLbLOeBqteCtZ8zhccawW7HHH2NyQNtdshOE6irnoDLmIOlxoxaWw3JiK7saztwl4UCQcrhXjpaWzqPBigIlmNIN7lN19Sj2kQuBEgRc5QqxwZon2lq7u0Q+H9KMpsexQaU+uWlaqYiMyZLpEp3fE6RIRt9yE+1zoPc79EatqSJnF1BvB/eaGr5nnvGleIKIVt1HFlayJVhGDa0E37xGox3g/pQ4dtajoxSkj3aWNJFnokIMbzOCKF8ke53KPu9WAg3zFO/X7rINr+uuM5K6D82+hWki7M1QS0PTWFHsbvhvuYzaSe/ULwvyUyV0P08wYxoe0kw1nh5qrJqBffMK9iwzZHhH3x5zdM2xMyeTqgtFmht9PedZXDK5eMqv2uXqQwOaMg+WQkwcxw7MbzGQfsgtE5xC1uiMbdxmvPKUoaDoBwseErUM5iwS8dICg1ZrGKxyeEIcT2/0SgbJbrdCbljoyuLjHYFWzSGo6YkRbXaPSQ9rqnKGbcjKqODppafYn3AUL9k9LOp19PjwICFafMLvWdIJj7h40XHlI18/ZuwlIzAGME5ZHFT+gz0FxTrh+yXSeYuspg3CfpiOQe9c8Gya8cGPSdc29+jmNP+fgKsdVD9BmH4sjTSekQcgmssj+Ejs85yQdcKb3uS1G5KuYr9YveLv6ABMtuQ1WqNyT3inSeUgiHyH8AVUQUemG1mV0A81A9+jKDi7aKiQb0VBFS2R6i0pviZI166jPtZ5yKQ+58PtkZYeyCPEFiNzQr1Y8dhc88hc8Vdcc+0tqmTEPLJdhwGkUcyI9WWPormCyUTzIImZLz2BeE+Rg1Yi2d0AzOKJKJlSqR2EjGtviydFhhVIZ+AzbrmnKJU25IZYJg+4ew86Mbjwi0X0ikRCYAFEJ2qalii1VYikjQ6kaCtGQ+4rCVOR1wabIgIb+APo9T6djSdKWMKwIghIlM37mZ/4KSsVfLEB+TvlSk8J/eH3Kv/HxHbdGsDGWsROMrKBnINq5cmxpqEtDnrcsspquy/lqd8ObyYYn4ZJ7cs6ev2NobujU15h2xVXa47Iz4SLpcRFGXEjPpWu4a3IGG3hUdniQvwJ9Q/e2gFphkn2ayQOq/iFlPKbwHRrX4kSOCjIEK2yzAFPTC/oM4ymDZEYq+xghyJUh0zVLWZCLFTpdEnUWdLprRFhzFe5zIQ85Fw+4a0aUWYjZSOK84B37knfsJzwWp2RRyWVYcyYFnUXA8BrG6w6BeoQVR1SxpJIFgXJMowGRiikTSR5UqNEpVXfBSec+p36P+XLMNFvwzfr7VME11q4Zn0u8eQNtJjjtGaR7NImB0YLbScX7+gizUjwtP8I1Zxxca9r2ET01xA4izNEt30736K/vSLLn7N10CMwhahbx4p6kqnLGVycMNnuEowHvHUR0lh+zf50ix2Oejy17L67opkc8H5XcO2nY3B+ibs4R/fuQXWAHU7q3GYuRp+N6hJuCIjDYWOBVhDQavzMHJG43wkgg2a7nLA0I66m9o4kMPu7QKQWizFmMNbMiZu3mxPEBdXVGGhyy0Dcc3XU5fZCQXrxkau/zyX1FePcJ+/Mhbq/Dx8OIdP4h05uUrj5m/TDnI92hlz/n3vkGXx/STY8weze8P+mxKjV7xSf0lyckqxmintBJpvhUIcZzricN7wf7rFcdvpo9R4oX6GbO4ELizZsEZkCtLL10REeHFDE06QY5POGuCyfxMRfNhMV6RLLJ+MnmIx6aZ+TJhstgg8sVvRvF8FaSch84oorG1EFL5TcocgZBh77q7RITFZk0rGRJrlfozi1Bekens6YO4VrPuJIHXIj7XJkxRRFiCkVYWlxm6NQrHppz3lTXvKGvOfYXRO6OO224jCJO4g6ngebUNzRFy3AND7OEe1nIbOXp35aEywojB7TDezTje9SdPapgQOET8kpibEkQlYRhiZA5+A2mWdEUS8psgXaa0eCAYW+fXjohDfrEcmt1qEZB4TDGUHU8VWIoI0upt+RR+Ioaw3/nL/75LxoeP7d8qUnhV06X/E//1rsss4Zl3nAvKvlqN+PtZM2jYAv4sx3gp9UVqrzmNu5y0d/jMh1yESVcas2FN1zanHm14TAPeaMa8CCPOdgoxgtD5y5HLQraeI9m/wn16JgqnVLIPnkbYFyJDDKCIMP7JaZe4Oqcnu4z6h4wTPfpqD7ahuSqZRM0rIOKpc+o5ZqwM6fby+h01rig5CrY51IecyYecdmOqfMQm0lkZjiqr3nHfsJXxUtm6pKbqOVFaDkXmv5dwOzKs7dKUPohRt2jSlIqVWDdhnHUYaSHuDhiE1iqZIEanlB2Kl4kjzhz+9wtxow3K/5g/R1EcMlSrRhegV7NCP0bWAkqThjEXbLYoKeXnIwEH4t7tIuAH6u+T23POLi0mPYdIh8TDybY8S2f7Gte1gOOs+eMbk8Isgf05D5+ZvjgMMIWGyY3z+gtD0i7+7x4aCmrJfdO5qTyMaePBX5zztFlSn5/xNJdcHTR5+pRQnpxgRjew+anqM4xYn1BMZsxuSpZdmt8PGCQG0xlqKMaG0qkjhAmohaKDiWRMdBKWqewosaFhjoMiH2XdFOS65KyP2R/0XAXZyRyH1OeIzv3MPUJI7fPi0HJgxeW5aMJZXXC0WWH5rjHy6jm6PScbv2I8qHnk0AyvP2A0e2AXvSI4njJu+mQcX7C9OoEnd2nzx5mpKiOFnw72iNdV/Sb95neXiGyh+hmQJJOUHFAO9hQzK75QXrERT5lulrxlfr7rMJr+ouS6KZL4N/Gu4QqMAziPt2dMlCGOWJ8St1ZcNY55hMOmGdT1suIN6szvlG/z4E4YZHmnOgKk4f0ryUH15C6Q7w4okwOqUKoyXFuTiw9fdVjGI1J1ZAq8Gxkw1KVbOQKnd5+qvAEG+70mCt1yKV8zJnfZ950qDNFWHnC0tJsWuJyxUN/wVfCW97aEcawPWMlGi7jmNN0wEkYcSodp7ZArhuO8pgnRYejtWa6cvRuC4KbFdYltHuPaCb3ty6qaEQpOuSNpsgdUhfESY0OC4TI8W6NadbU+YJyPcfWDePxMePB4dZdFQ+3kzwSE6YJR3/xp75oePzc8qUmhWx+gf3rf4G0ukbnlxRKczE85KI74SLuchmEXEjPhau5bFesyw1vVkPeqHegv5KMFi3pXYG+WWJUj+bwDZrpA6reAWUwIncJReVBZoRxjlIbvFvSVnOaYk0vGDAdHDPs7NNVQyITU3vLJmxYRxUrWbI0G3ywJOmtGAwKkmSFUwWXep9L9Yhz8ZgzO6EoItxGEuaGIMv5invJV3nJV+VLhF5wHlk+igIunWJ8Izm8FhwuQpR6QBM+oEhG1Lqg9QtSBZNwSDcYUUaChaqwvSv04IS8q3kePeK0PWKxGDDYrPlD9bv01SkX8YpwAb3LiMh/BRhRBTWjZIwOA/JOjZy94Hm/zzN/hJsH/L7iXQp1xt55g6u+inYhvf4hTbekPrzjW/EByaplUH+Pw4sCVz2lnxzQ7q/4/l4HuckZ371Pf35AJz5m8Tjn1Etm1x8xXt5DHCV80Bfsn79gWD3i5LEkuXvBNDvkxUPF6OQKpvdpipf01BErf0kSHOLX1+T7Y/ZuKpaqoul36dWSKDdsVAWRp41jRNsllhVpUVK3CbUu8LHDBAM6rSPcNKw7FT4eM7nLue6VdNmjzc/xo2Pk/BQ5PKYpXzCtD/joCO49uyEaH/DxoOXokwsG+iHvP1IE6xfsnUo60UOuntbcNI7Zzft053v0o/tURwu+NRozXN3SW73P8G6CaiYk/T3s5I5P9gUftXs8WF6h3fvsXS8hfxtlukTpkCiJqDoVbu8FH/anvGgPKOYxP5N/l0i+YCMW9K8EyXJK4N+iVQGlqhjHXfq6SxUr1rqA0Tmyd8Z1OuF5eMxFdchy1UOtan6i/Ygfbz+gH1xzFRd8rC02DxldC44vPd12ipf3KDsPKKKYWhQ4vwAzp6sTBsGQUXxAIFMKaVnrioXKWbNGJXck3RW93oYonLOSIZfqkGv9Bhfc46UZkpUBshR0a4/MDeWmQZcrnnDJ15I73tI33PcXTJozjFlwEcec9CacRh1OtOTU11xUazqZ5c1yyKMi5WijGM8N3buS4GqOrTzt0Ru0s0fUg0PqZEwpexQmIi+grhqiTkOclChdbGOWdkNbrxjsTfkX/4f/oy8aHj+3fKlJwZuG/8G/++c4azdc1gt80/BOM+Vp1ed+FnOwfgX6Oepqjq0d7fFXaA6eUA+OKOMJheiSV5qqagmSnDjOEXKNMwuack6dzemGQ2ajh4w7B3SDEZFNoYGNrll3GtZ6q/Wv6jVBvGAwquj3c+J4gZFrLtURl/pNLsRjXroZ6yJC5IKksNhNy151xdd4wTeCMx75F6yDihcRvJ8k3DUwvZE8ulYc3kqkekCTPGCTHlDripYFyq+ZhAPG4RQRdlgHhrlaI0dnRL1zlp0+z8JHnDTHrOY9epsNf6j5Dvf4mMtORlZaxmeabnmM9E+pI4/Xjr14RJsois4GP3vOx71DnjdH+DvJH6x+jU1wzuiiRBdvI+2QpDNAJgFu75oPpiGflHu8vX6OrJ8xvRogmkM6g33a/Rt+fTIkWOaMF99jsDikq4+pjjO+0+0zW7zH7DyiEz/ik6ctJrvm8KQlHRzx3oFg+vI5E/GAD+8LDp5dwv598uaEWTXjtL/hwarLRXdD301o8ivK2R4HtyVzUWKGHdImIF63rMIC3/UoN0ZhSFaWUpXY2NHGQ7qNRa9bVr0C4n1mNxuuewWxnqHn12wO9xie3LG4P6V/dkm7f0RTvmRvvc/zB5LR6UsmPODDBwH96w+Yzu/RPAj4KJbsX73PaHGPcDzgg2OPzXOmN++RLh7QjY+xhwvenXZxS8uo+i77Fy2+ekI3nmJ6Hnt0xbv9KTerAT+2eR/Lc6bnNVTvIG1KkHbpxh2ytEXMnnM6CPlI3efibsab2Slfb77DIrmlLSv6p4pu/RDhH21diz5nGncY6D5NErJQJbZ3gRqcUHUkn0QPeO4fstiMyReag+KG39d8wI/zEQR3nEcN70UKv9ZMruHRlaCfd3H6HmX/EZtwTCMrHCu8u0GYkqHuM04PGYRThAvIVMMyqljKgqVZopI53X5Gv58TRnfUouRa7XGt3+JCPOLMzzhvEppC0K8hqRw+N+TrGpeveVtf8410zlvB9WvCCMsLrqOA0/4+J2mf0yDkBMOpzbkq77hfpLxdjXiUJ1sFctnSuclRl3e4vKI9egOz/5h6dI+6M6PSAwqXcPDWmJ/9b3/9i4bHzy1falIA+JX/8b9K+uwSdXWHXWXYe2/SHr1JPb5P1dmjUH1yE5Pl4FxJ2isIwm1g17YL6vyOYnVLLxqzP33EqHtELxiT+BRZSArRkvUa1lHNShQs2g1Zs6TTz5iMG7o7jaZhxZU64ir4Guc78L8tI2Qh6ZYWkRncesNX3AnfDM/4qnrJ2J1yHjo+SlPei0I2pePgSvDmbcjhtUfIQ+ruE9adY2ptaVni7RU9HTEJpwyjPYpQMFcF6+iOcHBG3LvmurPPx/oRp/V91ouU7irjZ5vv8jbvc93JOfUNo8uQyXVKJL5KHY7IVc4wjhkGfbJUUvevMJMTPuw+5pPiCHkHf7j8x2yiM7p3JdH8Ado/wEWKYTqiGOQUh5e8Gz2knWverL5NsrwgXL1F4AYkwynt4SW/OpyQznPGm3cZXh/QkcfYw5Zf30vprC85Orsibt7EP4Dv92IOLr7DdPGI6mHE87Dh4bMruoOHvLdnefjRNfbePVbNKUerGR8eOx4/t5w81jx4abg4ajladbjzd5jBIfu3G+7iAtuZMC5K2kKQd3J0NEA6QbpwrJMC01OodsBwkbNIMkxvn8N5zlWcodJDupcLru8lHJ82fPJA8ei54/Rpwv7H1xTHR7TFc2aLe5y8oehcf8xk8ZjLNwVlecvRi5pu9xEfPHK47Jqj0wVp+xSOHN/e79BbXjO9/oBw/ZRefITbW/PegeJ20+N++R3GNxeIzTuEdOj0xjTDNTeHS76rHuLvJD9e/hqVOGd4ItDNj+F9jEoihnGXTQx+8pLVKOOD5DGn60PE3PJfqn6NVL3gLFwT3SmGZwE9/yatukcRWRq/Zhb1GOkRTRIwlxVV94qgf4LsZZzG9/hYPuWi3GezjBGrlrfql/yE/ZCvyWfk4ZrnkeMHYQBrwf6V4Om1YrjQuOAe1fAp6/iASliMWCHFnLa8IhEB43jGpHuPrhhinGCja5ZxyUJkLJoVMpzTHxUMdkqYFzfciD43wTtcqzc484ec2D7XlSatPMNm646ymWG1qmiyDV9P7viJzh1vBTc88BdM2zM62UtWwnIyOuKkM+Y0SjhRcOoqTusFZZnxlWbCm+XW67C/EgznNeltxuhP/ikO/+J/74uGxs8tX3pS+IX/w7/PxY0iqxTZuiXu1MRpQRBmwApTz6myO/LFNd1kzMH0CeP+PfrhhMR30LWmLVuyvmGd1lut32UsqjWNWzKZNIzHDZ3OGh3c0rg5V+ohV+HXORdPeOlmnDcRMhMMa9BZS7GqmVZXfDM845vRKU/dJwh3y/Mk4P3ukPe0oNoYHt5o3r4NObxswe9Tjd5g039ILiRGLIEbtM2ZRFOmyT207rAMGm71hia+JhqcE/cXnMdHfKje4qw6IlskpMuMP9h+j5/wP2CdrvkgaAjvIg5PBH37lCZ4TBF7LBmHyYgw6jKPWvz4JdXwlvc6b/EyO0bfNvzR6pfI0gvkoqR3NSN0X6EOPZN0TJ2C2z/hg3GHj4r7vD1/ScwPmFxUyOrrBLpL0O9SH5/zK91D+ncbxsW7jC6mxOIYOQ14/rBhvZEc3n6b7t0jusMDnj2qqPI1917e0RVvcvqWwWTXHL1UyKMp73cb3vhojj0+4tpd8uBqxPOnAUfP7lg83Kd3ek5274j+yTXXD4c8OG24HKwJgyMGNytupzlOPuLgds4iLvH9kKiJSOaGZW+NTY7Yy1Ys25Z6EjHdKNbNhnI24/A842yv5sG6z3l6x0E55bR7x4PbMR88gCcfLlk+ntFuPuHg5pizNxVi/YKD8wn2YcoHCdw/+4B+9hbmgeW73Q77t7/O+HJIN3xIfX/JL/emHCyfM7t6gd68RS/Zx0xyPjlu+bA44J319wnLZ3Sv7yGbPbrdCaYD9uCE98d9Piru82B+zQP3LqW5oX+aEDdfp1UhMlJMogF5ImmG57SjMz7qPeJZfZ+72x4/lX/AT7S/zqKz4Nw3DM40B1cdQvkV8viAQpd4v2AS9ZnoKW0cMdcVm+gGPTgl7V9zm4x4pp/wsX3Cat3DrSBer/lG+zHflB/xhOfchiUfpCHf0wqx8hxfa968VkxuLFYeUk7eJOses/EhljVKz3HmBlMu6as+e8OHDKN9UtehsoZVvCMLn7Ool6AXjCcVg0FBnCyR8pK1M1uy0O9wIe5z6ia8aGOq0jNtoN94VGGpNw2LZUWdb/jJ3oJvpHPeDq+5zxWz5oxu/oKmXnE+usdpf4+TpMupDjih5dRs+Fe/8Zf4k298Oae4gP8CkMJ/8tf+Li+/94tU61uyxTXddMzB7CmT/jGDeEpCl6DW+MySJ5ZNr2UTVSxFzqLZsMhXhFHO3p5lOKpIkiVK3VDbO26Cp1wF33gN/i+aBF/ApIY4tzTrmnq94cfkGT+TXvBV9ZKj5hlLkfNhf8R7aZ/3hcGvWt68i3nnOmTvokT4GeX4bTbDR2QktHKFlLeY+oqh7jLrHDMK9im15y4suRUL6F3SG16hOhteRA/5SL7DWXlAMQ+JlwU/3X6fn+b7mPiO92LDugi491JwsByDfoesM6aQS7oB7AdT2jRmEW1g8jH5sOb70Vc4XR+T3BX8fPmLFJ1zyrxi/LJLzE9QaUkn6RAnCflgRbZ/zrvJm6yvu/xM/ivUnDA66aPsW0RJD3oB5f1TfiV9yOR6xbD+FuPzEdrfJxoNuXt8y/fbGQ9X32J6HpOIxzQPSn69N+D45tuMLmak0z2+fayYXX6H6fVD6schL2XB0+c57v4hp9zw5KzLyZsx/ZMT4tF9Fs0pe+6I6+CSiT9gWV3hR/fpn1+zuB9wMNfMbYWbdJmuKuatpd2TDIoUuSiZT3OEesT+1Zyr0RIZPWJ8cc3VPc/RvMuNvCPsHyGvzqiOjknOXuD2H9Kun7Pvj/nBpOTpx4bFG0PqzSccXR2weDNkUd9w/7mhO73Hrx0FzG6+y/h8RDI45PmTmmLTcHT9HvHiDZLBjNXjW74t9nm4/D7jqytU9hW66RQ7brg6XvJt84i3756j/Q8YnQbo+qv4MGTYGZAPcoqDE97tPOH8Zo+fWX2XOPiAutgwPO2S2K9TBgEy8OzFI6pEk3VvEZMPOOvv8QOecjnfY7Jc8EeqX2UcfsLHSU6+Cdh/CfubGV69zaYzpZQrcHOmYZ9peICNYu50yZ2+Ixic0R1cUSXwPHzEh+KrXBZjmlWAWrVMsiu+6T/k9wfPmLmXnEYt3+/0+J7yyKXjyU3IW9ea6XmFEzOq2dtk/Yes6NG4HBUuEdzSFJfI2jDrHzPrP6CvxoR1RC4aVp2GZVgwdxvm5RL0kum0ZTgsSZIlUl1Rmxtugze4Cb7OlXzMmT/ghenyshb0G5i1gk5lITcU64bbZUlTFvyBwYqf6NzxdnjDAy7Za07pFCeo//pfQTz8g180NH5u+dKTwsl/8GuY72UEdYDYeBrRshlaNmnDShcsbc6iXLHKlkwmltnMvvb3C3lF1d5wG36N6+AbnMknnLg9nrcpZeHZb6BXOuymYTmvGDdX/OHe1Wvtf1B+wstY895wn/ejhPddQbwwfG3e4+0bzd5ZAWZIuf9VstET1vSp2aCDW2x7SWBa9jpHzDoPCUlZBDU3YcZC3BINLhmObrFJybPgDT6QP8ZFMaNeaMJlwU817/GzfJ84uOD9juWZC9g/Uzw91YTyLfL0Kf8/dv6rSdI0TazEjsvvc621Cq1FZkaqysqqLK1ad0/39PRwBDCDATgLLHYXoNkaueTSyOUFubt3pNlibQEQYqanZ1p3aZFZVal1ZmREZES41lprzYtuDsDr6bUylPH8AfeLz895nvd197Y4YjwpYhP0mAU7DRVUxSIKa4i6QeCJfJVMzYWu0uS17k2G6gylQR9bTIludIKeUgeKMXa1mbp6wtR5TNBs5qg9R6CUxze9z7RZR1ecZzq1o9OaGBkmtHxp7ijmsOermEYPMKf1SMcBNAYrHV+Om2o3M5UgllwGRWcVlcVIdLZNqz7Cmw+i66wx8U95ohHxZ55gbC5TW5xS6deYiU+Y+O3EZHXmo1NKS0ZklRj2QYAjW4fFhJzIvJxApEMiIBJIjEh7O3jKeorSMgPTDO5UgbSni2VoY1qsU/ZNcbT19MsN6j4JzraGdr1Nz6XCWZpSlDcYGX1YUnlyAQP+WJf4jAx/bEpkVs5MqEt6yYApFkdj8hNTlJlNCJQXNQxaSVxpC905kbhsSCCaRidfJrnUo9Vq40vk0YyXGPl73NWb8dT2sSb7CON55BYVmdkGxx0b880HGHM1ZO01NBoLE+OYqi/Hfdk89kINy/Q+2nwbsbHBSKLGpDEy1MgZ2RKkLFP2WaSXU/JK+wZtTYpJZYApbUScbtISZMgUQxxKCwO1QE1VQWoNUjfI2Vcsk2p46RYlXOjuc3ryiJq6xoFigiovZzYOhmGAnmqJhkpLX1JFOilhURixq7xM5AJlRZeirAraNHpTEbmmRkLhISzbIDTy0WyIiI0Jk2qH+X6c84oIJ6RhlJMMEZWMfa2RfckQWWXMSlnFUkGOLdliOjbQc67++rMlNdMeDJELNWSyCuNBgW49j05uwGmZw6LxoJ7qkXYkNOUDGro+VUWH6qRJuV1jIqljt48wmXqo1TVk8gKjUYqa1E5JeYKCfIkMHpJjE7GBgnp/gmcswTQAsTtm3B7SrPfJVbp88s8uYdf9p/kbBfgSRCG7n+DmrRtUB00qzRqjcQenEyyW4d+e90/J0huWKAsnKCi2yUjnSfxG/tXeFPdAgrE3QdIa0qr2adXqnNXkuaDJsSZL4B1EoJfl2GjjUG/lUCEnOGhgrE45VTOyWJBhS7WYDjR0XRu0LAvUJGb64w5yoQLTPKN2EZNgxWWcxyiz0WVCWexQkNXpSIvoLAVMxjItxYSwYpVj6Qa5lpFxTYai2mVrcMxz0qc4ZDGO1SN2lQLanIyVhARTx0lPvUZDY6QvKaOUtHCJTlSihaKyR1ubRrCGyWst7MnWyJYdmCtVXu/fBHWa+KSPParA3FthoPDSUbRwq8wMNSIdY5a2vcBDYY1izszLzTt0xRC61BR1e5uhTMSsM9M1DKl7UtyTr+DJFjFMH2BOqpAMF9AZrfSdJR7YdYiFPvbmPfSFBTSim66vxh2tjZnyI6wpEY0wT2G5RbYL/lQMw2SV1MqQQb2AN64Ev5ljscNSqE1v1kplmMWbcxJZkOMKpejP+iAXQzT56NQzCCY340KehnsWZyJF0a/GlZNSEsr0DAsE0nnC3g6evpV2pcbYrcFcmlCWtOiYZwgkM6R94CtqSGgKOCQuap0sWr2bdjOFWekhK8vgrXg4nJ0wf1SkN+OkNkjjSdsoLsvptzJ4Ejqkfh2P1TJm0/sYOqt05wYcyNTM5O+iKy2iMluIzzUoNFT4G7fQZWdRKV1g7/PUN6FcMxLo3MSY6aPorKHSGJkYpbS8Me6L80zyUrYHNxl2C+iyPmTTGRClmNRG6toOI1eQp3o/x41ZnKUyFwY3yWoKyPMSbFkrMukmLQGksi4uwcpEraaobCCxBhkbWzwVlwgN5qkWNAQaaV4YPsChiHKs7hMbyXGmpCymFMiZo2VYpamUMpwWkY3LWJQmHOoAyETKyg45aY2RmEVvLqDVFSkodEQUGxxPl8i29cgaEpSNIZJqlW0iXBDjrI6P6U+LHOv07Gl07E/7KMoDtqp6lgpy7MkmtBX03Bu0bYvUFTYaXSkSeR1BrDEdF+m1sgybDZyWBRymGYxKG+JQzbg1pqkZ/joWsg6VUYNSq8KEFk7nBLO5h1rTQC4vMB6n6IwnlMUdioo1cpIAqamN+FBNeSTl4OJ/upfM8CWIQqmUYH///4FcUWIySdMbVqmIOxT/v/KfOogO1GS6U7xjCZbeBEV7RK8+IF/u4JWUuWQo/O30b22HKNPn2OrnqcbAkWxKqFfBV1WwUzOxUJBiSTSYdhV0/Sdo25aoSa2/mVIqyKQF+u0M4kSG27yATeVDMVZRVfQoqdrkxxUQ81hsVXS6AmWFQEi+zRFr5Fo6FHUJ0kqP1X6IS/KnzEuCRMUhdzVqhjVYTMiZzQmMxVVqhgU68i7jSQazQoVT9DJUieTkdUbGOCpTkrjOy5PpBsWSBXu1xBv9G8jUKY6lQ6wxBY66n6Fymaaig0UU0KlMlNUdpq4DwkYPB51FzIU654fXKMrKWCM6lONtBgLYtBaaxhZVb447rBPI5jFK7mFMKpAN1tFqjQytPSK+HqmanaXOVYxJEcV0HqVVS3SuQbUq4CvfRVteQ2U1czA/QChlcMUHqFSLBJeHCKUwzqQFyYyeQ+WI+XAewenjSN1iKTKisGxEXozimMxybK6wkNATnp8yE+4Tn1MyG5uQ8HfwZ/QkzVlsUjedUp26y818ukbUUcE5stOuNui7LDgzfdK2PLapm06pRt3jxpvIkgqY8UVbJOaUBCJDIgsaAuEK2QUbhkQYvXaGsLrKfFRJbkWNpBrCXpiluCKh18zhjYnIPWbuW0VmUrfRVVYQ3AJ3HCKO0j72pAyVfIGRr8kNsxV/MYSpkETZWEdnsNF3F3hgNTItSPAMb6BPSlD01xHVOqZ6BT1vmId6H4WChUutazSlKYxRFarhCdpKcKgNDDUiXXOcuq3OI8UaqYKTc/U9liWPCAt1NEk5rqKDiWKDpjhFJmngEu1IRT15ZZOxKYrSnCGiDrA/XaNYNqMod3m2/5hn2KUqVnigUdCvSFlMSPEVVIyFZWrGRdrSERNJDsmwhE2w4dTMIpGIlIUuOVmNjiyPzlzAaCrSUfSJylcJSbcIDR306nL0rTGDSg99K8dzYoyzigizgyNK0g4HRht7KjX7oxaq6ohTVSPLeRm2ZAtJZUjft0nbuUpDcFDtioynLVSaOlJpmWE3R7OcRiPX43EsYdX60EqNKLtKht0BDeOIurpPVdamMmxQalaYTNu4XFPMlj5aTfM3/kkhCA7OnvnJF63FvxP/yUdhPO7x3+6+T3zya/knelMcYwmOAag6Y4aNAeVyl3arxQvG4n80/YdRNcIkdGYOzV4OVWoOGRBrl1ht6NipGZnLgTlRY9qS0J3doWVfoS630ehNUSiryJUlhr0sw2YFt2kOl3EePRZ64wlldYeCokFxWEJjKGKz1RFVGbIyCyHlaQ6nSxSbasQ6UO6x0IvwkvKQDQ5JCR3u6/Qk+1Pmk3LW4xKUYy9NyzYNQctAkkc2KeEUnVjVXqrKETl5GZklimjKE1Qv8mS8QbVowFXJ89bwJgpVgj1hiCGhJJCzMhJO0FRPEKR9XCoHdbWUrjlMx1bjvnKbTN7Bq7W7qIUD2s0+9qSHqXyZqTjCrLXSMJcpuSvcHW8yn01jkt5HmwDl4ARKlRaJWUYpkOHuaJkTlfuoKhmE2hY6vZW2r8gNnZuF4i7mdA/VZAWJf8gNs4n54j1MCSsaq5cHcyPsuaeY0y6kfi37GlgIxdAb53lkmzIfTKB0+YjJS8wmDYSXNbhDUSZ+L/1SEqPGTXGYwa5wUu4W0RicDAplKp4ZfPEodZ8BU1pO1prCLvHQLjcpuTysJHLEZ8b4snrS+hQOPFRbBQSri3E+g8zppl9IYtR6yZPCVw9w6B0wF6rTmDf/+n6hOEtkTYox8xRzbYX8yphus4g3KkFt83LbJ8Wbf4QxaUdlchNebNOtDnEXDlDV11GbjaQWyoRaDhbbn2OIa5Ayh8ZgpOmPc0s7gyNTwzi9jSGhQjZcQ1SrkWpF+p4wT8w2guUZXqjcYSweoUxOMDU26SrMKIUpJpWFqrbDxLFP3GTnyWCVTkbBW50b6IUgx7Ielpgcd8XDQLlBQzVCOq3hVdlRiGZyijZdfQyNNUpeY+ax/ASphothUcpCM8LL00d4ZCH2NWMey5WY0xK24lKMDSNd4wYVjZ+upI1EmoVeGbvKgVM7jwyRsrJLTlGnNs2jNRWxWCpIlAUSMj8RxWmeTmbItUQMLVA2hrRKLRbGCV7UJjghCWHvHZFQStk3udgXlOwP6+iqE87UzH8bClm2Qc+/Sce9TlPjoTbU0O70UWsbKJRVxsMC3UaGTrWC276IyzKPSXSgmuiQNSX0JwMapt/EQtKiPKhTalRYWVnkG9/49hetxb8T/8lHAeDPPtinnG9Tr/ZIlzusiDVeMBb/o+k/yKhbIGib48jo4KlSwdG4TaZVZKdp5VTNyGxugjFeZVIf0Vs4S9u5Sl3poNqRIZPXEVQVxsMsnVoKg9KMz7aKRXSj7AnUlD2Kmg75aZXGuIDVXsdiriJTpEnI/YQV5ziYzFFuiugbUyblHr5OnFfEI3am+1TkNR4YjOwiwZGVciohx1pS0TFtU9XP0ZG2mJJEO53i0c0iKkzkhA4lZQ6VNYrCWGdf2GB/tE6joMZdyfKV8U00YpwH4gQhK2cpoUWi2KGu0TCWlPCKdtDoKGpKSB0HhIyz7LdXEXMd3updJqvJISZk2OobdEUroiBBozHRsKbJu3rcHWyynI1hkd1DlQJVZweJSkA06mj6ItxULeLL5tAN7mNOLSBXOpFblURn6+QrRmYbn2PILKLWuaktlNkf2ZgvfY6+uIHGoeWGV8lc5i6G7ByygMhjjZz56DFGxQqP5mT4w0+wyNZ47Bkxd5yGGRelbhJ/bY5jf5e5UJ/IkgV/KE1i3o8vGicVmCUQS1CYMeNJDIgGmsykzWT1SSwKL61yk7rHhTsapRhw40v2SPo7+BJGot4CcykHEV+R2YSFSKDLTHhKaNmENxhG4vFSHqXw5Lwcr8ixJw+w9NbYXZLhTj3E3NggtzagXa/ijw5RGWd5sjhCU4zhjE0QhUUaizV2J3aWqp+hT/pRqT0MvXVu28zYs0UM7UcYsuvIRTMyk0AlkOSGZI31XBgFjzDGDMjGawgaAYVWS9cZImjTsNtY41TxELv8Ps1mH2fCxUS+RkfRxiOaGWhVNAwJxvYkjzUbBKtzmAtVvtL/nJ4mRWgC3pAUR2OGjmadpnKAbFrCLTpQq2zklB3qqhQaW4SeDp4otznqz9MpimgrVZ4bPOY5+S5VZYW7WjXp7pTFhJyNKMjHLpqOE1SUNoaSKpCGXgWHxotLN4d8rKYsdMgp6xRHBVT6InZHHVFMkZdqiSrOEpSsctQzIG1KMbd/vU0MqhWeVSV5ThVjdXyMuh0kqLewZ3Swp5By0C9jrcHZuu1vQyFPFOl71+j4t2jp/NQmemq1MSptC0FVB0r0W1kaxRSiXI3XtYpdH0AvtyAOVEzrY7Sv+TA+5/+Cjfh340sRhQ/f/hHe/BW8gzDa+jE1mZxD+zyHOjOHcgmHgyr1Vo0LbSfbVT2B7BhjvMKk0qW3fJ62a42G6KLSEZlMG6h1VZjm6TZSTDs9/M51nPo5dBMjw/6Ykr5LQWiS71eYyPO43B0MhiIjaY6YYp2Q/BwHIx+1pgJzc8q40sPaTPKGKsg56T7DaZb7RjN3lHLkxTFnUmpm4yPG4iI1xwlqMoGpNMdkkMYuWPDqFunJZKSFOg0hhdGeYKQfsKs8ydP+Mp28gKea4WvTmxiVEW5rYFpUsh5WopRsUtN76ZLHLqgwqd3kxQ5D2xFdS5c78h1SOScX67usSO8TlLZxBVVox2doCDJsahUSrY6WLUTKIeV+Z5uNXBiL4g7y9Bhd8zQjUcCkN9F0x3los9LNqlkdXkGTkCMdraM3mGj5MlzVBVjOH6ArZhA722gsBsILFVplGe7iE3StEyi8U65ZDKxkr6PPr6Dwy3mgUzEXf4xpvMXBygR76gmWxibHqzJsiT1s43WeeAbMBcs0lu1IMxGsigBhbY6FnJ9oIM9MxEpstsJM3ER0tshM3EbYk2apYicjTWPSeGlWGgzcDnTxNNnAPL7wMYm5BWajMWJzPgKRBPH5AN5QhOaSB3kyjEkVIKYtMxszcrSmxhndxyJb46FnylzwGINmmbteOTPx+5i7m2TW+rRrdQKxLqJ2nuhKi165hyeTRD1YQxIYctVqYrawhynTRDFYQ2PRkZ3Pcr87z6nGp4jZNsrONhqtnqFlSDZQ4k5nk2eK9xlJjrBELEhYQ6GSodYaaNqjpJ1T7ve3cGZKnJ1+TkJSx3msRj3eoa6So1eAWW0nr2qD44CyWcldyUlyeSvb1UNemt4grqmS7MpZPJZi6SzQMKzTkreRTAu4RQcG0UVB6FJUZlHbIigMNQ6EVXYn21QqOmTFAcvtIG/IH+ORHLOvkXBTFJEVJpxOifjjI0biIjXnCaoSDVNZCSYppp0qLn0Al3Ye+VhFReiSExrkBkUU6gIOVxONJktb0iCmOENYdpL9kYtyS4aj8+ttolrsYOzmeMOY+vWxU/8pk26aA6uPfb2VPdmE/W4BT0PBuZqN5aIcW6KJIpKh71ykO3uStmmWOiYqdQkSaQeNoYlMVmbUL9CupmmWi3z3v/6/4N36/98pfOGE7/0L3kl9xpFkxGGvwKTT47m2h62KBn9mhC5WYlpp0Vt5hrZng4bKQ6Ur0h900Rp+8xXUdppWKY3bsoDXtopJbkfRVtJQ9ijqO+QldfLtAnpjHZerjVqTpUuZmPIcQdlp9oYuOi0ZluaEUamPtpHhLW2QC9IDVMMID01mbqk1FBs9dtJqNmMTlC0DLe9ZyiofPUkdCQnkwxZ+wwImpYeKok9KUWGkjWO1p6mr5TxSnOOoM8ewKMNTSfM1bmFXhLipk9KqyNk+lqMdL1E3rNKSVdBKu3g0MzTVMsqaJKLjmKf6RfbaG5Ad8p3eFXrqGMWWhLmwmYlyh5bQwSOa6OpE+o59YnY9D5qbnMwdYVPeYZwZY67t0BMF7DoLDVuZmLfPXmWZ5xtXGDXzGIs7yNU6pDY5sdkKsYqXtc7HGKMO5EIAqXvELbcGZz6KNVlFwxqj2R63NDZWs5+iK24h+KXc1utYTN3G3DlBcn2ItBTHkfbQWJXRaqbxpt3ENkX0iQPs0w2eOpvMhftEVpz4QkdMfW6apRR2lZvUNEdg7CGqzDAzdJIii19uI9MuYjK5aJZLyJwuJOkifZ8LdSJDx+dClUgy8TmZ5NLojU5SowyzzXlC7ir+kITwmhlXZB+TsMwT+5C5owIKv52gosfccQPB4+a+UcZcbBfzaIvUeodOrYk/0kClWia3XCfbEvAW7qOrbaOy63k6X6dbkOOq38SQ20KttTDw1Lnn0iGmRjgm1zBFXIylAcx6I21HnmOPhP3SIq/WPqNBAnvEwVS6ikwFep2ZmilF1V3l1nQHUhO+2vuYpCaPKiLDVV+jqfYxlFbxq+z0tGrK2gRKR5CnukWedDboZqS80rnLtuwhj7VdajU5G0dydMMlqqY12rImknEWt+jArPZRUvZIy/MI5ih6S46o4OWR7ByJhg15aYK6XOIVyWMuSR9RlVe4ZTCyNx4xk5ZzOi7HmJPQtZ2galmlNpYiUxSYjJJMOzW8pkWcmjmUAxVlsUtB1SQzKDGW53G52xgMBSYkSCqWiSoucDiZ5bCrwtiRYGqPGVX7FPINdsQMr+iSbElCeNr71MZt9h1z7GkM7EuGHLQzzLU1/7+hCCcZmrx0F87Qti7QkFupNOX0e33+4L97Fo1B/UUr8e/ElyIKj44/J/qX/xJfZog2mmdcbjJYOU/bt0VD46XaV9Nq9tGaGiiVZUb9DI1SHJVUw6xnG7smgGaoY9QZUTb1KKra5IYVqt08bncPm62OICapT7pExRcJSrd5MrAxbEqxtyYMyz1k1Txf1Yd4Tv4Ua+8pT/QGbunNPB20Wc0InE0KWBMDOvaTVOyb1CdSpIoso14Mk1xPwLSGXKIlJ7ZISfIoTUls9jRZQc9D+UWCLS/SIriqab4huY1LfsxNvYxsU8bOsQxr00/NvENT2UM6yeJX+5CrzSSFIjL7IR3LmFuy86RzTjYqx7zMVZ5oGqgSSvyFWdq6FQayKj6Nk6pmyti1S9Dq4GFtkzP5fRzCXXqFMfbiFi1Rj1NroGnqU/WnuDo+zenMHlLJLvaIn5HCj9lgouaPclW7wFImjKq5j750Gq3eQnMmx1VhjhPlTzFGDQjqOVrzdR5L7CzmrmCo7KD0Sbhu0rOcuoalfpLaap9ip00gPELpt7KvnbBwXEThtRNR1pmNCGTX9aiSR9glSxw7qsyGlByumJkLRSgtzGCMRJn6HYzSGTR2B41SDqPFSbWSx2R2UKvkMZodVCs5zCY7xXoeh9pJcpIj0JslbMmwlPBwONvDH26RWXVijh5gFOc5NLeZPR7QWdXRaKXwJD2UN6FXy+CIW5DOCRwoFMzFjjCwQXKjRavSIRAroVKs0Fiq82RkZ6n4CdrcBlqTk+JCirvDOU42PkIbNSCVzqI2GyjOR/l8uMmz5RuMmwksuZOM1Rr0OjMtzzEPrS4yWQevdT6kOC7iDrsZK1aQiWNMWislU46uO81N+Q6VlJ5vtS7T1YSpVaQsxGwMhVPUFS2sSjlmjZuM2GRse8rQ0uW24jTRqg99vs43BlexKY+4oQFJTs6JYzlKySpl0xodWRVGKVyiE4d6hrJiQFJeRGKIYbOnyYsqduUX2O/NMC3LkBa7bPQO+YrwBN/4gF2dkusaLeVal3NpLevhEfKmhtbMOcqaOZrDPkqxwKgfZ9KqE7Ct4dTOouz9epMoaFpkhmXa4xweTxeTuYxcFiWPloR4iWPJGvsDC602eLv/YZvo1yq8ZclyURVjZXyMpfaEtErNvnWWPbWWvWmXw1aKlY6Rcw0by4XfhCKUYOpbZPVnP/yidfh35ksRhWG+wJ3/4Wc0dT6qAy212giNoYVKXWEyytGuJujVqsz4TuAxL2GUWpE35TSVfUrGLgVZg2y7yFhSIeAfYDKVkcliFBGIia9wJFnnSd+MtCXB3hozLPUYVop8XR/mkvIQX3eXI42a2yYHdyZd7AW4mNEzE2oxlnmpz5ynJHcyooJMlmTcyuMzLuJWz9OWTMmo6+TIYLJnMJjSRAUPD2XPEa3bUZYmOKtJviW7g1f2lJt6JaGelFNBBf68mYblPFVRyXgawyUYsGlmyQht6roIWmeMPc0KjzvbTNITvtn9HJu4zwOZhLkDOabBKao6GzJZDY/WR1HdZeJ+xIF5jt3yGueLT/AId6iWx/iyqzRVDqwqkb5BSSewzzXVNtpEjxXJJ8jjMsThaVRqkZENkrMlDiqLnO28jyouIJ2sorMYSC2nOKr5WWl8iDG5glrvprRc4KhtZb74OfrqGQQffGYxspa5jKF0isnCiEOJgsVoBK1mnTszUpbC9zFMT/JwYcri0T5a3RJPzV1mjjtUNqwoE0dYZStE7EUWQkb2FpXMhFIkVhYJhIJU531oI0nwWRllM+isNiqVDE6dl/gwh38c4MiQZjkTIObPMRcxsb+gIBDMUlrxYogdoFctEjbWmQnJyW5qUaQP8DS3ubcqYzZ6D3P/JA/XpriT9zCXNmmtDkj3psxE02jFNRJrddrlAd5EBg1r9OZbXBN9nCh9gCHmQ6nxMAjU+NzsZSW7j7IcQ1/bQaMzMnBXeegWaWX0rI8+QBGTIQx3UKkVYNTQ8u9xU7PJMCnnxeEHZAZ1/BE/A3EFmbKHVeskry8w9oS4pdohlnHxeu0WbuERB5MJi08FVNMzVDU6pJIiAZWfjlokp0micR6Q0Lu5PTlLNatlvhLl29Or9MQUn6uUmBNSToaUTJWblI3L9KRlGCVwik5c6jmq8hEJZZmBKobdlaGnarGnfIaH400aFRF1ZYi6lOUrwhOekzyiJilyy2znlmSMJTfl2bSWQLDJUOmnHjhHSeaiN2kiiHkGnSiTVps51xYOzQxCR6Qm9Mjr2mTHZcqdHG53F6uthiAkaY4qpFQvEJKd4unYw3FHjrv3H7aJVK7FjLTEW+YUZ+QRZvuHiNVDomYvexYf+6LA3rhFsJniu9ZX+a/f+r9/0Tr8O/OliALAz//HnzLoxOg2U9RzCeyWWQKuDawqL6q+hnFzSNUyoKhpkx9XydbymMw9PJ4OOl2eKSFyUj9R8UWOpivs9nSoOmBvThiUe7SLJb5ujPGSeMh85yFxJdyx+rglm9CrtHkxZ2EtMkLMT2guXKRsXKY+nKIUswx7EdRjGXP2bQzYKCm7JIUKNWkSp7uASpfhULnKQ+mzpKpG1KURtkqS7yjvMMseN40qdkdStkNKlhMibdM5ylonQ0kcHQMCuhVaopSkkEXjPKRplHJTdpF0wcFMIcF3ppdJ6Epkmgq2nmqYis9QU4NO1sOuDZDWlpF5nvDIsMJ+cZWLxQf4hLtkGxPm4vO01LPoxAkKg4WWa5eHVh/ptJs3uu9Q7lZxp7fpq3RYDDZq/mOu6paYSWTQjO5hi60j01qROOFJYIQkBbbWbYz5HXQWK4nlDJmyEX/lJvr6WQQPfGY3spG9gja3gdIn57ZBx3L8FubeKUJbA1S5IxzZBSqb0KvE8WZmCW4JWKK7WCen2Pe0mQmW6aw5mCaPsEpXidpzLIZt7C1ICQTTxFeXmQ0Gacy7EKIxtC4rhWoOp2KWI32atdQcsZksgZCJ3UWB+eMUpVU/pvA+Wv08YW2JuYie/Q0Ts+EH6JQb7DrHLBwnEZ1u9jUT5kNZtJYZrjuUf/v+sxttmtUWgXAblW6Z2FqVdnGEJx1DM9pgOtPnssXJdv46uqgMhXwZhVPG/lybZkpHYHgFa3iWsdqJxmikNHfEFU5zNvuAyfQAVyhAVxnAptXRNA1oBsJ8LjmLKdngvOQjUu0+c9EAHfUyUkULh8ZLTl8Gzz73tFscFhc4VTzggvRz7ql6OIIKPJUFavo12pIsTkGFSeMnKdQYmo9Q2gvcF3fYa60yzkw433rEW9KbRDQNbsoE5iIyNiJKBtqTFA0LDKQFpoM4TtGBV7NIQzYhIVZoyOPY3VkUmjxPFZs8kp4n1tSjr0wZ5hucGR/yVdUevv4uuwYt17UGDjsNTud0nI7JMUcatF0nqLtPUhybmEiqKIUsvVYUOj3m3Sexq2YQfxOJgq5NdlIh18hid3Rxulqo1WkGowhZ5Umiymc5ms6z29Mi6U3x/0fbRKHc4CVTkZd1SbYI4mrtQytN4+X/Busz/+SLVuHfmS9NFO792x8jVgX0UzOyuoSOYkDJ0qcgb5Drlqg0i/j9ExyOJip1msEwRE65Q0R4joPJArtdFaYu2FsT+qUutUKVr5pivCoesdx7TGXS4LZznluCnHCjzHNFC6cTCqzHVbqOTWr+MxQnZqbSMjJ5gn49gUc/j9+4imSgJKtrk5AUGIspPJ4iU1WOfeUz3J2eplzVoCkNsVQSfE+8x8L0MbeMam4jYz2iYDskY6A9RdG0zECaRTbOMqtbQhAsRIUqA+MRekeah6ptHna3GaXhtfYtTsvvcFU7RROXs5p00DCeoyEv4xQEdFo3SV0WhXufu9oTHBYWuFR6wIxwm2hnwnLQR0e7gVzZxqx3U7KFSHnhdmWH14tXaSuPcB3aGCk3MGpEurYB6dkyD8qbvNx6h1Gmi6p3FqNBT8OX4TPjHJuZxwjFDKrOGfQWHccrBdpZJc7SQ/TtMwgeCVecvwlCcgmN28inHpHV5KeYSqforPbJDob4wx1UbgePdbAYSiM6XAQ1PbyhLpM1PcVamqXiEneXJCwH40xnzZQrSbzddZKeDN6QjkfLJtaCx+RX57Ech1C5jGRaRbzDRYLOFBthD/vzAzzHZdKbs3iPDhBsdoLyChtJP7eXFaweHyE4nESFJr6wSGNDpFuN404FKG5PGVRSOOIOWJRxLJExH4mgEzY53Ggj5pPYwwJq4zzRtTKdwgRnJoh2eAKFe8p1rxp/Oow2n0bo7aA3G8gtRLje2+T5+jtIU2Pkg1OYdDpazjKHPinRTIBX+m9TKzdxFM/SUclx6h2ULTlKviqf955hLR1mSfE5mdqExdgsTd0SUnkdl9ZPVltH4nnEgWGRh9UN3Nkc3558xFNdmV5WwWbYRFd/gZowQJiWmNEu0lbJSYpJ9M5DqgYl12WXSBdtqPNN3hzc4qzsLnf1Eh6NZJwMKViMKulaz1LQzjCS5hj3IzgFB379Km2mJNVVCiSxuXLoDQliCg+P5S+w23WjqkqQlXroqim+rd3nwvQhzUmeGzYv1+VThqUOL2XNrIb7KHJDWkvPU7WsUuwIKIQycmWWTi2CtDdiwbuDXR1AaIo0lD0Khg5ZqqRrWYymLh7vb4bFyTFliYWE6mWCkg32BhZiHQlzIwmm1n/YJoRhg//p90+yuTj7Ravw78yXJgq5GxEODg7IT6tk6gWmtJidG2OxVFEo4nQHSTKqFwkrnuFgMsOTthJnT4KtPaZb7FLM1/iKKcHr6iBr/UcMehnuuJa4rdHwoFtiraTh+ayewFGN8UBHY/VFSupZGt0BKm2OYS+CtNNhwX0Gu9xLazwkbWiQGGTRmrO4XAXaiip7wsvcHW/RrAhoSwPMlTjfVz9gafyQO0Y1n8nlLMRknDuWgnyVgmOHjqTOdBTGrXbiVM+TFTpkhThmd5CKXsk12ctkCxbsuTy/P73MUBXjukxgc0+GrbNKybTKQJLAr7Ij0ZrJ6GIoPUFuqs8Sys3wQukuC8JtnvZh48hBR7PDWFHDo/ORM+do+9JcHj/PfDLJguIKw6QMU/sZhiowGh3U/E+4qlvDEatgk17DHvQzEPwYTWbyC8d8PjjB87V3EeMiUskKKofI7kIHZaqLpfQUXfcMglvCZY+Bzexn6KMB1HYPtxeH+DIP0cVnUPs13DJqWYvexshp7q6CL3YLR/MMjzZleCN3sA2e4fb8mM2jQ5Q2DxFNg9mglr0tK0tHTxDtdvYVNU4mZzmYb+M4qpDbmMcTPETlNhDrVJhpLpP2pHCFtDxYsbMVOmQya6JQyzDX2OSpv447WGO6aqRcibFQXOPmmoKN4D008pPc9sPa8WO0wkluLkhZjHyOpXmG7HaHXqWCOyxFbZnl9tIIf/YxpogdldlPaK1IJyvBWdhD2zmN6BR4NN9FmexhbDzAVDqPXm+gHkjxqXGR9eQhkt4ezuRJxhodGqOR6uw+lxVnWI5H0StvYjjWIZOcRSoMMBicVBz7xFxqbpV3eDF/B4P6PtWChMXkPFX9ElJZGZ92hoymicT9kIjJxa3OWcRkm98bfEBLk2C/L2dnT4kg2aGkdzOaRHGLFkxqHzGhRtdwiNGZYE+1zu3haXo5AVshxe/KruOW7nHZqCXSmvBcUMQXV9B0X6QgehjLs4y7IRyinYBhnf5ESlJbIz1KY3ZksVpTFORS9pSvcG+0RLeqwFgb0s5WeUlxxFviE7ydB+waTFw3WLjTr7FSEHk+rcN3WGE4MdJcuURZM0ulOUWlKyOTZmiWQyhGUhZ9Z7CLfoSWQFPep2jqkpFUSNfzKIUm/sAAk7GERBqmOWySVb9KWH6ap2Mvu10F+iFcfnYNo6j4olX4d+ZLE4VkMsLjx/8LBkMRiTRMa9ggrX6DkPwseyMvBx0Z/gFYm2O6xR6ZXI03TBne0h6zMdxF0Qxy37nELb2J26M6+sqQVwtOVsJ9xFiV1solqs5tCj0tcmUJmTxFuxLGofEya9tGO9BTFDskVVWy/TQuTxmLNUVZOmJXeIu7wyWGVTma4gBzNc4PNPdZHd3njlHDJ0ol7qSUS0cyFH03Jd9z1CQTkATRTaXMG7doKyREhBxycwitPct98Sz3O1tMM1LO1R/wVflV7un7pBpynn0ignCekt4Mkzhz2nk6GoG8PojgSfCZ8BzxrIeXyrdZFm/xaCRhe99CX32enqJCQOclr+swCjzhc9V5ejGBb45+QXjYYjG0Ql3jwq3VU7U2yc6WuVnZ4Wu1d6k0ijhLF5CpleBQEpkvE8/62Rq8g+k4gETjQeqWcMevxJlMYcgnUQ9OIXhkXPFoWcvdxBSxoTLNcbRWQV4o4QpLEM2L3FwcM5u6iTm/RX99QK7bwR2SIsxpCckHeEM9FAsa4pM6gbCO1CkDusgjrKOzHPubuI9L9DYcjGNPsU5PkXNlMAdlHK97WAkeonBrCQ1rrJaWiQcq6I9bFLdncT59iNowT8RYZvZYz/62hYWje4iqTYK2Np5gE2Hu16/rCWsYbSgodio4wxqUCyL7EjlL4Rga7Qa3V8b4M3fRJxbQePR87NWwmbmMLr6A1uLleC1PNyPFWnyErnUGnU3P0XKWUtqCr/8JtvgWCo2RsXPCwcyQStLM6uQ9DMcmhvI17HoDVXuBqH/Eo/wG32y8TWpSYOF4lppmDpsoY2DQ0PM84JF5gf3sAt+qXGakOaCbljOfXaZiWEAqLeDXzZFRdZg6H5G3qrg6fJ5eQsq3O1dwC4+5IihYOpDhLfkp2c7SkeVRTxrMGdapC5BQptG5jhgZ2txUvsRefQZlbsxK7Sm/p7xOX57gQ5OJemHAS0E1trRALXCJgsIOigzDThC7YGfGtMF4JCelqxMfZdCaszhdObqyIgfCS9znFNG6GmdjQj/XxdaO833DU86O7tEa5bnhnOOaQkKqVuKVopNTcSnG/Qxd1yaNufOUZG7qzQFaYxHJNEWjGEQlUTPvO41d8CE0BVrSPiVLl6y0RrqZZziu/2bgrKBQxOn1I3SN3+SrJ/9vX7QGfyt8aaIwHvf51w//Bw6kO+wNnRx3YG4gxdIc0Sl1SWQbvGrM8VV9kO3RE3TVXZ7Y57htdnGbLsVqmbcqXk7GZZj30/Sty9QXL1KUeWn1umj0OYbdCINaiSXfWVyqOaYdKVlzi4SkSH2UJjBbQ6+PkpIa2VW+yZ3+DMqaBLHQx1SN8ge6h6wP7nLPqOUDtYgxPeGlYxFdWUt17iWKUh0SeRRpN8+i9QSizExMXaWkjOD2RcmqNVyVvkquaEaXq/L9yaf45Lu8q9dgjsnYDulpWC/9Zr3PMW9Yp6gaUzUcoPAUuKJ4kVTGwcvlW6wJN7mDjJO7Wkaq52koa8xqHFR1Ukaeuzw0L3OYWeR71ffIqqN49g2guIBc7KE2Oqj7H3BVt40u1mVB/iHaIwMT2Qlseh01d5o7LgemaAPj6CbO+EkUWhMDf4vP7C42Uo9RZ6oIk21Et5zP/CpWsvcxhdWIukWy6yVKJQX+ZBS94gTJzTqjUgVnREDlcnDVLrIdvo6BZ7i5CgvhGzi6z3B9Xcr60Q200me47x+xFowjuA0cCy1WQmYebppZOXyIYPET0tVYDVm5vmbk5PERCo/I03GTk9kF7s0PmQ9m6a3Y6eWCeLunOPA38B2XkS9piXeLrMUD3DupYu74Jqbps1yfn7IdvINGeoZrixJWo59j7VwgvN1Gno9jiTrQ+LR8YjOwkXwfffksipkJH5scnMq9hza1ic7i4HgtQzslYKvdQ1c5h9FiIrkS4VF1iVOdX2A69jFSe9GYDeQWQ3zaPstblV9Rq9awVy6AWoJotFKffcgV8SzmaJ0txQc0U1OctV/fJc1o7BT0PcaBx1xTnyeTsPCD1rvktDFkMTkz+XWKpllk5JjVz5NV9enbH9O2j7jCy5STWl6o3eV5+VU+0U9RJuScPtbRtDxPRZAxHYcJaPzoRTdRsUpDHcThiRBWubgmeYlyUYuQbfPi4D5fld8gomrxnlaHOj3hpSMlurKWytwL5CVmZIo0g/YRVsHGnHkbyVBJWt8gNskhUafxectIlBFCyh0eyZ7nfseCpQFCqU8jW+YbumNeF3bxNu7wxOTgmsnO9VEdS2nCq3kbi6EuikiJzvol6u6T5Acm+oM2WmOJyTBBLXeMTjCz4N3BJvgQGkq6kgEFa5e8vE6qmafVrXLx4jrPPfc7X7QGfyt8aaIA8I9uBimnm3SKXaLZBpcMBb5pCHNysoelcp+Q0c0tu59bsgkHzTSXqk4uZnR4D8uMq2PaJ1+nbFoj31Si0pSRyZO0SscYlWYWPWcwje00x33S5ibxfg6ELDMzNQQxSFS+wCPFa9ztujHUQF7oYarG+CPDQ7b6t7lv1PC+Vos0N+D1sA5bdEJ94RXySi9TRYpxJ4hfP49TtUBe7BCRpTA4ooiWDPeES9zpbCLLwmL5kD9UXiahrvC5ROTsgRJ3yUPB+QwdWQ49XQKGDRJik55lj6mzxsey1yikzLxSvcmmcJOrUimndjUgPEdN2cGn0jPSG6jbH5FxCXxee5bzmcf4VNepp6V4K+eoaeT49Q4KliLFQIVPqxf4Vul98tMk86FN2joTNqOD4twuH8jP80L6Gr1mHEv5Inq9lkYgw0e6FS7mP0WZGKKQbCJ45FzzK5lPH2CMThDFJVorVZ70nSxmP8PcPkd/ucn+yMBy/DFG2WmebrVR5ULYYz6kK1Ji4xHuyACtX0tQOcQV7iHOq4jRwhtUkz9lQBnfxdM9x+3ZIRvBBOKMyN6kydnYPO+vCDxzfIzSr+CxpMe56BwfLSs5FzxG4VOxJ+1wMuzl+paGE4d3kGk3iVla2ENtVPMq4pMWnpCK0aaCQquEJ2JCsiIhPpzgiXTQWj1c8ahYT36MqXKGwWqH/YGR+fQtDP0dBotNrsv9bJTeQZ8+jd5m5mg1RzOlwla/gan0DCazmdziMVcGJ3m5/jPkEZGJdBO70Uwp8JRPtNusRY+RKR7g35+lqfHh0Zkp2HJkA22uls/z7cJHFFRh/HsGxsKzjJU1PPoAKUOSkT/Gx9KXGEYl/OHgl+zpihiP5XirmxQMM0glKeZ1S+RVI1rWJ0wdFS7LXyWbtbGcP+IH8g95qGsTrst5YVeJXH6CnHGRoSSKbtJn3niCqnJEWJFC7wgjmHPcE57nVm8DeUGKkC3wu4rbnJ3c5I5BxUdKOYG4lOcPpShaZioLL5Ef61CISfrNI6xKK3O2E8j7KrL6JlEK9CRpAjMV1Jpj4lIve8rXuTXwM2nIsNVGVDJNFsdxvm98yqn+XbqDLDfcK1wTFTxuZni+bOdCWo3noMS4Ba3tV6mYVym01CBtotHlGfUTVNLHmHVO5r072JRelA0lvekA3TfmsG/5vmgF/lb4UkXhbz69hxh6l9PTJzgq98moNNxyLXNbkHOnk2G5rvnb6UA4TNFZe4564Bz5sY1uv43OmGfYi9DKJ1j0n8FnWEVoipR0XVLqKrFWCout9uuLYtkxIeECD2SXeNi24qhPmeY6WOpx/tj4iO3+TR7qtbyv19Motvlq1IT/sEMzcImsfo2htMh0dIRRqmbRvEMHCKmLdMUwXn+MhGDmc9kb5EtGxHSDb4xv8oz0Oh+b1aSr8OojJVLFSfKmZYYEcSjUOLQLhFQlsO/Rt3X5WPIW5aSW12o32Rauc1kh59QjEYXsOcqaKTbFFK3eS8p0xMhf4P3xaxijdb7FL3gwHbC176Fu2Ean7CIzWmn77/GZbgdZdMIF6S/px+Vohs+hVE2ZWEXyi1GuVs7xteZP6KcmKIfnsRr1lOeCvC8/xSuld1BG5Ujk6wguBbdmJfiTUQyJOoJsg/F8i8/EGU4WfoEuewa1T+Aju5Gt9HsYC2dgqc/BVMNi5Bit5gRXlySsRq5ga1/kyfYYY/IBvtIOD09IcYdu4+w/y2dLUs48vY/MsELGXEcf7DPastDOBVmon+Ta3JitYBx1QMFDSY/zIR+XNzXsHD1EYZwhqW9gCkLrhJVRZo+F6g5XVqXsHN1EKZzntm/CicPHCNptbvoknDq+iVZ2gRvrEwLJ65izW4zXewR7KuZjQXTKE8Q3ywzyfdzREirVGvXVInttLzPlDzAWzmOwmThczdBMaLG1P8dSeBaD0UBlNsL7wklezX1Ev1BB07mIUSvQdFXZ9yvJx+xcnP6MUUyOavgcCnGAwmylGbjPZ5odxlEpX5/8jP3egM3jBcrGBfTyLlqDh6xlj5avw0e9VzDEK/z++Jfc0rZwH8hxNk+QN/qRTmMs6FcpqsdUDAeoPGk+U75EqOjHls7wp5L3qahTXJGInNtX4Ck4KXgu0ZLWYBBmwbiKWm4joq5QUYbw+uPkRTnXFV/hqGZHlx9iK4b5e5pbuEcP+djm4Op4wJmYmrP7Q6ZTH6W5SxSGIoIqRa/5FLPCwoLtFGJXQ9bQJiYrUBkkmZmtYzBGKTNiX/wad8cbxJsKAs0po0KXVr7A71tDvCx/jLt2m32Ti2sWN9enLbrVOl8pe9mOgnE3Rs8y/7f3EfmyFJWmgajJM+jEqKSD2MwBvvl//D8hmnRftAJ/K3ypojCIXePyzf+eWxoNt/oF5I0+X634ORED45MEA62b5ubLlDQLFCqgNVaRy1M0S0cIEwUrgWexyT2MGxNy9g4JWYlsM4k/0MZuz9CbxjhSvck9yTPstbR4G1NG2Q7WRoy/b3rMie41Hht0vG8wk6rW+HrMxtKTJh37DnnnOZrjNnJ5CEm7wqrrPKqJibiuRmIaxelPIdNnuCO8zq3uOmJuii0X48/EK0zkYX5uMOKKSDl3oKTpfJmiqGU6PmRG60cjujhWZRBdezQsEj6cvkUzIfJa4zqnlNf5QFRy6qECNc9Q1OnQSaq4jMtEdAkUgad8IrxAKWbkjzs/J6jN4nmiRpC+QFtoEDAESJuTVGcqfFx7gW/mPqYkBFl44qOhXcat01NyZnjqFynFbOzwc3RPrfRVa9hNVnILe3w0OMdrtR8jhgxMhCVEl8CduTHOeA5DLoVqdBK5f8KHdienc++hiyyhtXu4t9bCnj7EHHaidji5PKdgNfEJ5vwOg80uufYIV3SMymXhoUHBSiiEaFjggR02ghGUVi9RfQ9buINuQcHhpMNi0EjotBHn4U0Mkks89rVYDObRBaQ8kgw4GXJw75Se5f1rKMSLRFxV3ME6mhklQboEjtU0djTUS2EWU/OET8uRZvcIJJcp7YxolTM4IzaEJRm7EjXL4UN06lM83mhhyh5jCTvR2F1cWx4xm36IOWRDbZolsZ4ikzfjqV7FUH0Gvc3A09UsjbgOe+9TbOkL6PQ6GoE0H5tXOBXfZdg7xJO+gEQrR2LVUFwI8WHjeb5b+hWJUY6l4CZVvQG/zkHWkqEyU+Hjxou8kr6JXHMfRVCBpfssFdWEgMpEQ6+k77xP1G7mav0C88ko35a+zWXNmMXHciy9U+SNHmSTGAuGNSqqKTntU4y+MDdUF3lcXUGbrPOD8WWc8kf83KDDFZZy5qlAw/kSBaWeKU8xSZTMG09SUvQJylJo7RGMthhPlKf4bPIs3ZKAPNPhTH+XHwg3GExjvGf38KjV5NWYia0nbQbqZYqBixT7ckRNkl7jKUa5iUXHDpqunpyuTVxZItNO4A80sdpSdCYJjtVf4QHnuNfW4WmBpjIglahzQZXgd/RP2e7eptfLcsO7znW1ipvtFBt1PS/nrCwEW8ifJuitXaQxe46S1EWpPMY5O+Cb/+yNL1p/vzW+XFEYD/jv/8UfcT6uxH1QYJKr09l5k6rrBPmugcGog96UY9iLUksHmfWeYNa6ha5rpCX59bFQbJijOciyuNjFYIxSH5d5qv42d6enCDZEAs0J/UwbeyvGn5gfc7J7jT2dmvdNdp7WK3wtaWdrt8VQXKAw+wKloRRBFaNfC7JgO4FLMUdB7BCUZ5AbYrg9McJKF59J3iRf1iOmO7w4uMu35J9yVw+fTuW8cKAikNSTD7xOU9ZCOgyxbN5irNRyJCYweffJGTV8NH6DXkLBG43rnFZ8zttake2HckyDHfJGN0qSzBo3iWkqKHz3uGfY4klulTcL17Bq75DNK1hJbVA0OnEopfSNOvozd/lce4ZuRMk3pz8mWh0TyF+grQGnyUcpcJ+PdaeZDydRK64T2F+ko3diMttILz/lWu00L7b+Gm3QwUg9h+gQeTA/wBBroC8doO2eQ+0SuTYnZSn9BENIRKVfJL+Rp1BSEUiG0MlPkt6qMCi0scckaMwBPplTsh39EEv9AolTXYbFBJ6ok+m2hEyngSuoQrKtINapshA0kTurY5TeZ7V4kisbMk4d3ECmepaQp4bnuIZxVsJjRqwGDQTPGLEf3cI2eo7Lc3Dh+CES/TJRSw9nqI7eqyGiHGMNjdAGBI7lUjyhBmqHgxsWkVOhm+hkz/Bws4s5u485MoMqoOYjq5nt9DsYimdQzkz5wOTiVPGXGGIn0dnsHKznGMenmEq7aDvn0Vt1PF3NUY0bcQ4+wZU4i6jT0fHWueWxYg7V0UqvM/t0lbbehNXopjR7nw9UF1iJRNCJV7E/MTIULqARu0iNFjr+21wznKYYNfKHvR9zX2iw/dBIR3uJgTzDnG6BpLaB3P/r5+NxfpWd7C6vKD7kfVHGiUcK9MPT5AwuJJMQy4YNaqKUtPYYq++QB+oT3GifRp7o81b3Bs9LP+Vdi5ZyccrrD+Wg2CJrOcFQlmDajrHqOI2AibC2TEESxjebpC1WuS18k5vteUylCaNUmW8rH/AVPics9HjHbCdZqfP1mIXFR3U6zh0K7nOUu6DSJunWD9BL9Sy7zqHtGilp2sTVFWL1BC5PE5crx0RyQFS4wK78JW70XMhbU9yNMfVMm2mjwN+zBbkkfYSzcptDs4erVh/X6RJrZHmrFuB8UsR5kGdSbKH6B/85c3/2u1+0/n5rfKmiABD8b/9HsiM7RYWPYnmC3lxFofj1NjDp9FlfeB6XOIesIqVs7pHS1Ig20wjqGrNzDdTqY0oT2Ff/DrfH66SacmbrE1rpNu5unD8xP+ZU53MONGret7q43yzyZtrO2b0+dI2U1t4gOzIhqJP0mvvYRSeLlh16IwjrS+QnEWbns4xUGW4L3+BaZxV9YYqYzvBn6qsExnf5mcNBrjTka49ElONFMq7zDKQp5P0Ma9Zz1AUIC2EcgUNiOjsfD19lFJfwRvM655Sf8zOdyNauAntznax5Cck0zLJxnYy6z9B1n7RNz+XmC8zFo3xL9iuuyOH8AxMN0wtMpRl8piVipiCtmRofNF7hjfQ1hppdXE/0jMTn0Ag9xlYtjfk93u6/yjcyH5Ifx5mPnWWoVSLYjCSXozzKbvLs4G8wHvgY6PyIDjWPF9sIkRH65j0M1Wcx2IzsrhVQxbtYMwnUnGC00OBz+Qxb+V9iLJ5HOT/mU5Wdk8kP0HeeobFdp1YdYY9OUNtcfOYSOXt8Bb30Ra5tjFkMfoazc4lbWxP84Rt4Os/x2TpsHXyGVv4il2elPHt0D4l+laSlhTXcwjQn4clkzPyRSOGcmVF6l5XiKT7bkrF58Bk6+ctcnodnn36OXLzI5zNw/vAqauVzXFmCreBHmHsvcnSiibIQwR52I84LfK6ycDL2MbrhM9Q3yyRrOmbje6ilp2ivlXjQ9bNYegdj6QJap447S10csSzabBLlaAedXc/T5SzVuAnn+GO8oR2kOi1D15SDuSHVqJl16S+xPnHSUK/iM1jIu0I88ZhJx5x8a/Qj4uUx/uIF6uoJMwY/SVOS9kyOdzuvsZ14yrb4McGKnO3wPHnzCippAY9xhZAuhiqwzyfiS8RSbl4vX2VLuMa7CoGL9xUI0nPkdDakkyBL+g3aKgURMYgzcMCBZp7Lg0tIU3Ci8ojfV3zIPf2YawMJr++rcWYM5ObeoE6H6egAh2BlVrtJQehxJE2itsRxOMMcK2a4JnuDWE2PJT9AlkvyZ4a7nO59zi2Tibc1asaFDt8Im/HulmjNPUfefppKZ4Ral6RTO0An1bLsPo+ha6YidknoqkSaSYzmOj5/GYVin6zUw57wVW6PFkm1pCy0p1DoEUtW+aopybe0B6y3bzHoZrnp3+aaRsuNbgZDC/678/9n1tZf+ILN99vjSxeF3SthDm9cZdSLUk4+xetYZdG9g3FkY9gckXd1SMjKxCpJ3O4eXm8JuWKfjMTDnvgNbgwXqLckzNQn1FJNvP0kf2p+zE77M440Au9bfVzrFnglY+XiASizI6onvk5a6keiyDMdPUXW6bLhfx51X0/cWCc4SmBxZrDZQxwpFvhM9ib5sgZNpsd2e5e/L1whKVT5ic7AbETKxV0JbfslMpoZprJDtKM+K5azZMQOcUUQz+wRh5o5Pum/gCQx4c3WdZ5RfMqPDRpW9hQECrNkHKcZccSSdpGmRk7BvMvEW+PtydeRRgb8w+FPeKAv43oiYhg9T0U9ZEZtoaKXMp65zafaZ6mFNfzh6Ec8GHU5cbhK0eggoLOSteXJzHW4mz7F77X/knR5iL3xIoJqzMAlIzJfIxGf4cT0p1j3Z+nr3KicOvaW6kzCcoyd61gLz2G2Woiuh0ilHfhqn6KvXUDjEfgooGU7cxl9eAGNzc2t1R7u9CPMkRlEl4mPvRpOxN/DUnuGxokG9WoPa0RA7dXyQCeyFgqh1C1x3aPg/NEd5JozXPNKOH90H4luiz3nkOVQDtFqIqPpo4kMsC5MeTqe4jqSMDptIN1IsBHycXxOhSL5iKXiSR6ckmCK3WW2eobHJ8eI2ccEkks0To2oVyvYIgbUARU31TpOhG+il1/gcKuKmMtgCRvQWvzcXOsSSO9iDDnRWmYIbaTppqQ4cgeohjsoPFKu+A2cStxBmeghkW6gtet4upynHLPimX6A73CLsV6P1K4mtpRlN7XB66MfMgoJSCUXMaklNG1jKvNx3qu+zA8KbxNRJth86KJiPIFFGDAymuj7bnLLfIJQzMuftH7GoTaD+4kS3egSZc0It1JArrOTMu4h86d5R/J16nEV32t+hF31gA8RePmegFS4QE5jRjo5YsW4RUcQCAohHIGnJDVGPpy+RT+jxJ0N8Z8JH1IQsvxUo2XrWMHO4ym1mdfIKB3IFEFo5thwP4tspCFsKJMeh5mZyzJVx3kgfoVPByeQV6RI0x3m2kf8qe42zs4dPnHO8o5ijCsNb4W0WPdKNNZfI2faptbpo9EnaVf30UxVrPqfxdS3UZN1SRrrhNsp5EKZufkmavURlXGPI/V3uTfd5kFbZKYLhsqQdLKBZVTij+3HXJw+xF66xbE1gO+PPkQtaL9o9f3W+NJFoZxO8eT//Su8+hVUdZGWfEDG2iQ+ypOrZVhYHOJwZJlMdkkpT/NY+RbX+j6mLfDWR5SSTWZH/yEEYVHO+44ZPukXuZAz8vKxgO6wQv3E18hoV+mP6yiUITrFCOv+Z3FKZigKHYJinpYkxvxClo4iz23xe3zeWcJcnDBNFvhj9S0uDD/lQ7uNT4dD3jrUMXcopbj8NYpSJZLpExwKM37dBlFNlZzyEO9MiMfqDS53n0UZH/Nm+xoXFZ/yI5OWuQMZS0k3Gc/zDDhmRu1CqrES0T7FMBvkPeWbpOM2frf+ESrNI0IVJeeOZ8naTiBKM7hMK8TMB7QDdd5rvcHF+D0suuvIjkX0o5fpiXVcxjkK3nvctK4yCCm4KP8xBLVIpM9h0UipuuoczCjphDTMSd/Fs79I3+BEsOt4ulyiHdZhH13BmbyIwWIitxTkVnODrdZPMSeeQW+18mQjhzrexJIooxG3aazniFTNLCQfoZefJ7tVol/oYolJ0JgCXF6QsxX7EEvlGUqn2rQqJewhA5oVKdExWCJjdD4VEVGCLdJC59QQV08whnuY/EqS0iGa6BTr4oTjMdiDEoRNBZF+C/+hwOi0mkyrwMyhkfGOjEyzivtYh2oTkv0J5ogUtUvPTaOGneMb6IWL3N3sYM4+wRaaQZzR8KHFwqn0L9EWz6FYGPKh2sfJ4s8wpM+jcem5ujhhNfkYbUiKTLXAeLbNu+ZlXs79CiEoMBKWUTt0HC4XKEZtzPAuvoMNujoDaquF3PIBH1Wf5/drPyRZ7uKqvchE1UNldtGcu83bypeZDSVYUH/I9FhAP3yZtlBl1rhA1BhjOJvgl4Ov4g1n+Jb8J3wikfL8XRNV60v0pHEWtfMUNVPa9gc0HWPeHn0VaWTAn/TfYaQ55vOhgjfvioy0l8ipDUhGB6yYthgq1RwKEWz+Iyr6Ce9Jv00tp0WbzPK/VV7GwiP+ymZDmhzx1iM5E8U6Ked5htI8w/YuAf0CPnGVvKrDoTSFUp/A64uSlgncVH6Huy0nrtKYRrzGV4UnfFdxjeEgzLvuJT4a1jid1fHSkQLtUYX6ya+S067T6HbQ6FO0KnuopyJr/ouYh3aa0x5JS4NIN0NvXGRpqY3eEKU9iBDVfJOH0me53jWj6U7xNie0sm0yuRr/xWafv/+973zR2vut8qWLAsDTf3WdCHlizQzdYZmVlT4mU5ze8IC4+g0eyV/mateOtgPO6pBcvMESKf7UvMup1mfElRLed83z0bDERkHDm2Ed1odpWuuvkbOfptoZotbFaJaeEDAtM2c8waA/JWquEu5ECcwVMBgPOVJuc0XyBvmKGkO2h6t6zD/RfYZ0csSPHF4qhS6/s6tBbDjIzL1OW1Jl0nvCvGkVsyLAsbZAXXWEOxDkrvgMn7XPoEr0ebNznefkl/krswHvkYytkJms/zU60ihOhQqLdo4DTQTzzGNu687wsLDOVmqfryt/xa8EJZfuaOgYXqclz7ComyGr7SGducNl3XPkwyb+Ye+H3BCanL3npGQ5g0XoMTTp6czf45ey1zkVPkClvkHggYO6fguf3kDeHea224cu2MEiv8zs3hpdoxnRbuB4pUAhYsc3fh9v+Cxqk4naQoJ3pTu8WPsrDEdrqMweSmtxHtfmWSq/g776LCqfjHcdNk7lf4kpdgoxoOJ9m4Wd9C8wFs/BZpNIW40vlkerW+Xysoyt8IdYG88RO9WlV4zgDzqRn5ySbPewh0V0q2OiAzBFpZgXp4RHYI6CaXlKcAjWsATj6pTQYIo1LMO0PCUykmCKSDDMKAjKZNgjTdQWB9fsap45+hSt8CI3NnvY0ndwxhZRrky5i5WNyE10igukt3IMciNs0Sp67SbpzTSlrAZvZhdxsoN0tss7hgWeK/0Ew9ESgslFYz7NO/JTvF75KzSHDgbaAIJdx9FyiXzEzrz0bby7i7QNNkwWF6XF+/x8+Crfy/yK2CjDWmiHmk6JzzhD1v2Ah84ZElEXf2/w77k9HHDmYJGcZRaXUkLPqKfnu8kD8xq7qWV+UHmfgW6fWkrBRnyJjG0dxSTCovkkQVURwXuPkNnDJ83n0Eeq/GN+QVKb5UFbxlfuqumZXiSv0sBwn1XzNmO5jgNVDJPnkIGhxofK3yVWsKBK1Pi+5BoXJlf4ld3G40aP7+zrsSQF8itfozSWIJcfIOs02fQ8DwOBsLFMrB9mbqGAoHnKnvACn/E8xaoSa2FAO5XjH1oe8crgCjHFgLftfq63C7yRsXNhf4Qi2aW28w2y4iLtXhuNPkGrvIc4UbI28zyWkYPuoE/K3iI6zFFqZlheHmCxJhkOd0mrLvJE8To3Bj4KHfjfe+z80bLri1beb5UvZRSuXXuHTvczNJog7UGCsPZ3eCB5lqttw6//2qIyJB2vsyZN86fmx5xsfUZaMeED9zLvj6v4izK+EbXguZekEzhHYeZ5ck0FGmOGfvMJqrGCDd/zqNt6kpYGR+MUCm2amZkUNVmF2+Lv81l7HltlSi9W5nviQ74x/ZD7eiV/oxJZiyl58f6QrvkcSfNJJooE4+YRG84LKCVGDrRZxrojrN4w18VXuN7cRBPv8ZXedS7JPuEvrUZsQSln9nVkZ79KW57FMO0wYzjBU00GlechCbOFD9uvYI6U+Mf8DZ8aO7j2Fbgap8kZrZhkDUzGeeLmXbqBBr9qf4WTsX12VB8QKwgs5s5R0slY0HuJm3I0FzK8W32DP8j9gqAyyYndZUomBwG9h6z/IZ+YT7F0HEOmuMXiwRY9gx7BYSSynCYam2F5+ks8T7eRGc10Z2q8Z17l1eyvEII6pJpfT8dv65a5WP4bjKFT6GwOdjcLGBJljNEhev06oa0CikwdY1SGzjjPtY0R7vRN7OElVKsSHkr0LIcP0WpPcnlJwnboI6ztSxyc6kP+gNlwgN5pCflGGdexFu32lHB3jD0kx7g6JTIEY0SKcU5GcjpFG5tgcKs50CiYieRRav187hF57uhjNMpXuLY5wJm6jiu+juREl0RLgSNSw6Bf4drGEE/mFrbwMqJHz/s+A9u5X2GK7aByGflsUcJ66haGoA2VfobKWpzrrXU22j/BFrmA0WIhv/yUjztnudT+S0x7swyMThR2LcfLZTJhF6uyX+B+PENd78VtdlOYu8MvFS/yXOQeTdVjNh4EKBrnmdGZSFsL1GbzvFd9jd/NvkdWd8z8fR0D9SsMZSnmjescG4NIZyP8bPItNME6/5Af8YF2yIl7AnLZJcrqMRbpAJt+iQNNCMvsI25qz3O/vIEzmuSfyn/GA0ObUFnKW3cFms7XKChFJMMnLJtPIJXr2ROTaF2HyC1prii/y+OqD3Wiy6XePX5P9h63DUp+IZXw0rGWrYddagtvklQEkIsxutUnLDl2cEvnyapbHEnTSNVJArMJqtIad8Xf43JvAXMNJKk2hnqcf2y+x1bzE+5anLxtMHFQzfK9jI+Tj1tQlVDd+SYZ2Qy9QRONPkGztIcwlrM+ewnrxMWgPSTtbBOd5klXkszPD3G68sBjSlIvF07+C5xqyxetvN8qX8oolGoP+PfhG9ydnuZWW8VsD0zlIfFYjW1llj8x/ToEBemA971rvD9poKkM+V7CxcLdLF39DOW1N0l1zIiaPNPJId1SmhPzL+OY+CkrOgS1BbKdKEvLVUTNE54KF7nMq+SrItZ8H2Uuxj8zXcfXu8kv3LN81m3xnWMLi4/aVNa/SUpqRyEeQzPLtvtF+hIZe2ISlS2E3hHhM+Eb3K4voo13+ergOi9JP+Yv7RY0EXjuoUh+7ps05DXEYZo16zOEVDX6lkeMXHV+xvcYhSX8ve47TLRP2W0KvPzARsbzCiOOWDVuEdaUEWbu8LH2RdIRG3/e+RH3dUU27+jo6l9jIk3hMa+Sct7j0OPiMDbPn/b+DY96Q9biz9DQgcs0S2H2Jm+LL/Js5D4N2S6rh6foGdQIdjOJlTgH8RVO8mOcT1YYGWxMvXAtYGI7socsm0UxOY3apeH6goT1+C6qyBRRtUZvNc/ng2VOFH+GrnwBcRbe0Xk4l/sJ+vw5lKt9ruNkJ3IFvfwFDrdrKPJJ7GE7YkDNDZ2eneNbaNQX+HRlwlr4MrbGcxyeHjAqHDB/5GV8Rka2UcMRVmNYnRLvS9HGJBh8IoeiAl+kiKDz8blb5PnjT1AJr/Dp+pC52Kc48qdpnWrRrHQwRtWobBY+9mk5HfsVuuozsFFnr21lKX4fnew81c0UibKZmeQD1JxmvFTjA8kqO7W/xhx/BqPNysFGjFZUh7l5FVP5Baw2M/G1Pe7ltjk5+muc++sM9SYkLhXHi1XSYQ9bsp/ifOSlpp/HZ3SR89/nY9MZPMEsLtXHOB+aaGmfwyL26Jg0DObv8DPpV/AE01wSfk64pGQ1fZqcQYtfVNPSqxl4b/LEOs+d3Elezt1gXfUpn/WVvHbXRN79Jh2CLGi8DDUGoto9LLNHfKD8GpGch9nkEX+u/DmfmKCYg7fuCFR9b1GQy2G4y4p5C4Xcwr4qicJ6jN4R4qrwda42VzGlhwTKB/xT1QdkFEX+wmjEFZfw5v0pI+UKad8LdCZVpuNdNGMZ667nmPRlhE1lwp0wc/Nl9IY9goo1rsm+xqOGjtnqhFKkxiVlkD/S3MRevcFl3xpvizKq5QrfT3pZfVBmNNJTOfl10lMvo0kDtTZOs7iHfChlc+EFbFMPw8aQrKtDTFokVkywtqblm9/8p1+07n7rfCmjMJ5O+e6VfaSZLpFYjdNi7jch+JQSfT7wrfM+bYbVJj9IB1h7UGbUE6me/japqY+ppIpSCFFN77LsO8esboNhe0LMXuewFcPuKuPxRClKOtwW/5Ar7QCu6pROtM5riif8ofwTMrIKP7J5aOZbfP+JHm1WILfxbUqjKTL5LtrhlA3785TkffblMSyeCApzjE+F73O/FkAX6/CN4TVelHzIDx12pPEJr95TUpr7BhX5ENngiE3rs+TUAzKaJ5hmIryj/DaZlI3z+Xu8KbzDD9UaXr0pMNa8RlnVwa2QoTR4SJof0vc3+UX3GyyEQnxd/BlXewIX9+ZI21ZwikP6RiOD2eu8o34F9XGb1xR/QyEpYum9wkSoo7X4qS9e56fTr/C1+GUykiBrofP0dHIEu5XUcoj7yZM8N/kLLHuzdPUeBKeeJ0tNhOAYdecW1vKLWK1mjjaOKUctOOtXMTSeReVWc3lO5ET6CvrgLFqLnyfbRQyJAvqoHKNxmftbdSzpEMawG9Fu5aMZNafiv8JYPs/wZINKdYIhpkC0Wrjs1PLS07fRKL/K59tdnImb+NPblM6MaFRyuI4N6DcmJHpS1AkJOpuOe2YVJ48fI2pOc3lRyqng+5i7LxE61WRSiOEKOlHNC1wTTZwJfYZe+SJPTlTQZuKYIk5UVitXlhQsZi9jC28iuoxcXlCynrmMMbSMzuzmeDNBNyFgzz9EGJxD5Ra5Nq9gI7qHLFVExmkMVhPHa1GSUR8B3mbm6UlGei1Tl5LjxSbJsJfTsr/B+shJTbfOjNFG2r3HXdcsvWMlLyj+hk5IQDV+DZRFrOZFit7r3LGvEw+7+PPuv+ey2Ob5m2YK9peQTiPMmbc50B+jmj/iJ3yHcWjCPx3+iLvGMpojJYvZJZL2TSSjfdbNZ0ipuzRNj5B5cvxC+nvUkhpO5B/yR8qf82OrlkkCXrmroDT7dcpymPQesmI9gUpqZ0+bYao/xOY55q7wIh/2zmDIThCTMf654TL64UN+6JwhVWry/X0jxhgUtr5NdqBC1ATpFA7Z8D+PfeIlo21xKEkxVqZYWMjRlYZ4rPpdPh7t0K9LsBUH5GMF/sT6lK/xGeNOhPcCG7xNG31pxO/EHczeSdHTzVDZeJPUwAGSOiptnEbhCbIBbC68hF3qZVIdIXnJjv+FlS9ad791vpRRAPi3711lOfNzTjY/pTZt86F/m/ekPSrVMr+fm+PE4yZkmtSf+R4Z1TLNTgedMU4j/wizysG69yJiXUPW2uJYmqE5SrG8UkKufMy++CaXeZFcVYmrMKCTyvDPrLc52/6QTxx+/loxZiem4pV7QwaadVLeF+lRYtx/gEvtZUFzgpS6ydNpGO9cnLEuzcfCH/Ck4kIXa/Ot8TVe5gP+0umglxrxtdsKqv6vkhdEJINHbNrO01bKORQP8S7sc039HPfK67gjSf4rxY941zzBfChnOb1I0rGNbHLIquUsB5o0+rl7fKR+iVjMxT9o/IyYMY5tV0A7fYWqUGFFv0DIlGM6f8RPet/iYvQ+Wv0N7A9/fdSgVdYYWU20lh7w087X+cPMTzmaZFmPPktfO0Vpt5FdPeZ66hxvDv8tmn0Hbe0SRpuN6NoxwfgC86Nf4ok8i95kpLR8zAe9c5zv/AW24AX0ViuRjTCVlBlP7g7i6BzyhQ7vKhc4X/hrdLnziPNS3tW6eSb1Y7S1Z5Fv1jjo2JiL7GPSnOfGdhtD5hHeYADNqoRH6JiPRtFoN7iyKONE6H0s7RcJn2ozLEbxHtnQbU6I9RXoYhNURguX3TpeO3wblfIrXDvZwZS6hS+8hPJkj0RLxBTrYDAEuLKsYCH1IdbEFqr1IVcnHk5FPkYrvUR9M0mqZMaf2EUjP0NnPcOd1jxrhbfR1J9F5RH5eEbH+dRlVIcmZLoZhvNVfq7b4quFv0Z8aqavm0Hp1LK71EF6LEMtucJC8ALoFAxcMoILbeJhPxdkf4XxkZW6boeATk/KESc8I/A0ssgfj/41T6qwlLtIVTNgwbhEyHxAfbbN+5XX+GbmMiP9A8QDEUv/RUrqLvNqG1WdgoHnFsc2F5+VL7Ad3+c7yp/yQ5XI6zdERrpXKYljNOMCC+bT7KsziO5HlM3w8/HvQFzCS/XrvCV7l39rM2GKSHn+nozC4rcpS0eM+w9ZsZxAK3FyoM/SVR3hDRyyK5zk7dHLqAsSRpEc/5n2Oju9j/mlO8CH/QbfCdpYu1OluvYVEsplZGKGXvM+NsHBiu08w+6UkKVMsBlhdr6KxXJAWmLkpvB9LrdcBJpTJqk203Ka/9L+iIudj0kpprztXuDdfo7NkpavhY24bkfo+E5QXn6FVMeMRFZHrY1Tzz9B0p/wO//N/xWDz/lFq+63zpc2CoPoZ/z0/v+T9+VjYrUMv1ec59yTAcrDNM3z3yZnPUWhMsZgy9Jv7jGq1zm59Dq2oZuarEvYWCZUCzM338RmPyY/hVuqP+KTtg9fbUI33uTk+Ig/13yKrHvAX/tWuN4q8YOQi5W7FaprXyUhLCEXE3SqD1l1ncEhmSVqqHI8PGZhOUlLVeBD4e8TLFtQR5t8hxu8PHmPv3K6KOcG/M4tBU3Xa2REG4zvs2RcQ6GwsasO45x7QlDn4p3ua2hDbf7x5Bc0tRFutpV8/aaOnP+btCUh5jUuBho9WcsD+v4WP+t8B28oyd+X/5CfKRS8dstMzvEKciL4LJtEbA/Jz8j4NPM8/6D8Y3Y1Kc7eslOwnsOjgpJlRGspyC/Lb/KPyn/Bbr/OSuoSQ80Qpc1BfnWfK7nn+U7/XyM5MNBRbeO2OEit3udy6SKXBv8W65MVJiYno0CH9+wLvJp+G8WxAZl2kfF8lV+KWzxb+0ssoXNo7TbubZXxRBNoogoMhhVC21mUyTb6+BijfpW7J5qYU4dYwwEULj0fuA08G/8ZusbztE9UaZcHaOJqRLOZjzw6Xj38KVrpN7h/qo4iv0/gqfvX9wsDFeZoG7Xew8czGi4d/xzd+A0OT9aRFeI4gg5UMyo+1ll4/vg9tPLXODpRRJXNoY9aUVnsfLoiZy77CY7gJoLXwPsBLVv5t7EGd1A5zVxf67MSf4zmWIPKuEhzJc6HvR1Ot/8Se/hZjBYLuZU9Pq6f5/zgL3DubSIxmul6B9zw21g4jjMYP2A2+RIKzZS2a0p4sUcsGOAF+V+i2jXS1lzEo1aQtVUoLVS5kn2eP6/9G65LmjzzeJms2cO8zkTK0Eay+IC/nn4H+1GW31X8iI+GCl6+7yHlfgZhGiVgOsG+/imG+QN+LP8erZDAP+j+gpb2mIOqgtfumkgFvkZPcoxXMKJT+9lVHeOef8KBZpZ3Oy+hjnT53vAKJyRX+Jc2G/NHUs4+kJFb/R2qkgHj7n2WrNsYpW6e6vNUFUfMzQc5Vnh4W/JNhkUlk0iN31Hc5dvjX3HDZOQvBMmvB6+7fQaqZdIzr1DvdxCEp3QLMU4svIa5ZydjaHEoTdMjyfJykansEQeqr/MpLxBsKFmoTihEa2xKEvwj421WKx/z0OrlHYuTy804r1bcvHKoxHgnSGf1OUrzL5Jq6DE5+3z7f/fqF625/1X40hEXpN0AAQAASURBVEZhPBnz//qf/4wLDzpo7odon3yDgv8iqYoSvbkI4yMqyadsLr5IQL3GqD4m7m5w2E2gUJVYXMwzljxmX/1tPpleIluX4y8OKUbz/Oe2h7zZe49HejU/NJoZ5lv8/r4JQ7hPYef7ZIZ6RM0x3eIRJ2deQTM0c2wuER8ds7KSoCR0eF/xxyTKBsRIg+9Jb/Dq+F3+yuUhVezzezeVdM3PkdTOAQ/wqVxYxQV2tUnUrj06liY/4Q8Yx2W8XLvOJcX7/GudgddvKRFkF8nqjIjjGEuWs+xqY5jn7vGR5lXCcS9/UH2PkX6XWlJkJXuKjMmCT5DSMxrpBz7nivFZ8kET/2T4r/iEERcfL5OxeFkw2IiZs1QWylzJvsCfN/4Vj1t9FvIvM1Z1kdvslNee8H7hZf43rX9JO6xiLL+A32Qmu3iPn45e5ner/wbJUz09zQoah5FHq00sxw3E6h6a9rPoHAZurXVYCocQYi0E1TaTpSLvTjY5W/0rDOnziH4N77rMnCz+DEv4FMoZLe/YzJzP/BRd7iyqjTZ3B07Ww3cxqp/n7okG2swB7qAPYUHkqsrE+ePL6IXXuHqyjTZ3j9kDP5oTU44GWpyxMjrdPJcX5WzGPsBcOkfvZJVGeYwmIaAxOriyILCWfA9z5iTCRoe7AxcbkevoFJfIbicZ5CSYYiVMmpMktmJ0MiK2RBC1YofRcp73xlvs1H+IJfEcepuZvc00sjDoKg9QtZ/FbLNwtBGhEHZgH7+PL/oiWqOKeiDFe9YtXgpfp9iL4C+8jko9ouEaElkYEg7O8Jrs3yHd1zMQX8QmDqlYp7SXjvh5/ev8YfbnPFXHOH/DRs52AZOijtTsouP/nKvmc8RCTv6L7r/nU32dnZsiQ+1bNORJlrVzFLTQd98iYTPyQf1lfOE4/1T+l/yVWcHqYwWe8jIJ2xaT0QM2LaepCzKC4h7+hX2uql7iRnUDbbjKP5J9iENyl//ZZObMU4GtxxKya9+jRpdR7x5Llm0sEh+HxgJ5jlhYipBSCrwn/wHZkhZ1os2p3i5/LrxPSl7j31kdDLNtfrBnQBsdUtj5HsmeEY0+RqNwl4BljUXDKfq9MSFbmcNqhJnZOg7nMaVJm/vqP+aD3hKqJliLfWLhEr9vC/E9xTWs5Zt85t/mbY2ah/UY3y3O8ezBFPWDINr/8v+A7w+//UVr7n8VvrRRAIj/T/+O47yeVNOEXKwiCGHKiQd4bSusup5BrKjIWdsElTnS9Thra20Mxj2yaLkh/hGftN0EGlOG8Sbudox/br6Kt36VXwY2+PGkwcWUgdfuDBlJPKRXvkalP0RQPmFSL7Ez8wb0lTw15ylOgiyvREkr5Lyr+EPyRTXKSJ3fk/86Bj9yeQmWOvzBbRVj1Q5R0zZS+RNMUxkLhh0OtAW6uj30vjC/Ev6QVMaCP37MPxd+xE9sApqgjJ1DGzH/a4ynj1gzbVJRSymb79P3tflx77tYjvL8V/J/x490Ul66pqJl/hptaYQ1wwaH+iyqhQf8kN/FeZjlm8KPeFwVWM88S1E3Ztm4SMh6QGpOwYPoFv+w9y/Zq04JVF9nIjZQ2JxUVh/yy/Kb/Gn9fyGfkiKMX8ZtUJEP7PNL/bN8J/429VwN5eh5XBYz8fXH3M2cYGP0N7iCz2O0mEmv73OrfIK11o+xpZ9H4zBxe73FQuwIVUSBQbdGbjNKNm/Gn3iETnmR1macVNmKL7qPSX2R4xNZ1Jky6qgJrdnJlRU5s+mPsEfW0G4NeTywMBsNYdSc5POtEebMVfyH82hPTNgfGPFFkxi0G3y2PsWdvoIztIT2xIRHAwvz4aeYNWe4ud3GlNnFdTyP3KPnA5+eE9mfY0qcQlxQ8Cutn3OFv0KfPYcwI/Arq5ud8t9gC51H67Bwb7OEJ5xDFS8iU2wjDYx41xngtfSvUBzqGGvnkbplXJ3TsxU8pFM/xtJ4DZdRR37+AT9RvsQPUj8lWa1ia7yBUTOm4moTnh8TCs7yNem/oX+oBcVrGIQGfYue3vJtftT/Di9Fb4H+Nv57Wnrat5gSwW/ZImx7QCUw5oPya7yauo5X+znRvMBOeIWkfRktaXzGbZ7oDrAsPOFnyu9SjBn5ZvVjFsVr/I1UxXevqmjZ36SknKAYhNiyXSKsrlHV7mL2B3lX+D2OCy60/x/2/vNLrupeuIXnrpxzdXXOOalbOUsoiyiSyJhsgzFOgMHYxoBNcjj2AdtgwOSMkEAChHJWK7U6qXPO3ZVzrrof8Hme+95x3/vtHJ5xfOYfUGPUGFVzjrV+e+3VP8Ujyi+IS3p5VaNl/QU1ZR0Ck7XX4SVIInSacsscrEIhvQY7o4keyitHcMoC7JPfTrvbRMZEFIO9n0cM+1GEW/ggr5LT3llu7c+m5OQMnvpLGVXVkxbPQrKdhMfF3LJN6PxGJk3fzB78iXGqqz1IpGcYlC/hmPgyjvp1VAQhNRbEMz3Dj3MusCF+kFRgkK+L5rFLmmTCPc17G9/Gll36bSvuP4X/1lGYGXJz5L2P8M22IEkINJZtxBy14RciDJrddLkHsWX6KSgcJ5Jsp1NzM3uTy5j2iSlyJhjvt3OnuZNr07txpD28n1vKac803xnKper4NP7KDYwYF5AQ7KTjzWhTCuqzLiKYTNChnSQsGaS0rI8hiYWd4uvx2+WIBjzcLDvJhvguPsrOo80d5LYzWkTpcoZsyxFk3cjDbuqtqxhW+xiVdlBYeoEjyk2ccNeg63Pwc+l2JtUTHIhIuOGYCkfuFjwyH/qUh3x9Ay3aXmwl59mj2sTAWB6X2A9SoD7Gab+SNa15jGYvQSkMkmdqoMd4nmiJl8+8W1g7cgKL/jiiTiUaLiYkGafYXMdI5mnacgoY7cviO6nX6JuRkhncjCB3IbFm4608w3bPZXzX+QaD9gjm8MVYNGDPneJITikNPV0E4+3kTW/EbFBiL+1ku2gVV7rfQnEhm6S+kFRBlC8zSlg3uQNVdw5yYzGBqiG+Ds9jnvd9TBPLUWbr2F8qo3piP5beGlQZVo7XR8kbbcbcX4rCauFgtUD+1CFyeqqRFqnZabKxfvAD5NG1xOqn8TrEKMeSGHUFHKwSkz95AFt/GbqGNOdjJkoGezCqF3C0IYJh4gwF3cXIyhTsVmWxtu9jFKLNOOvHic2mUI8ksGjLaWoIkDnahWEoH40pgxP1EYpHWtEO5KI35dJVP4F8JIZuZBS5rBFKPWyXN7DM+xYZPYswmKxMVXWwz7eY+ZF3yelZitKgw1MyzA71Uq6f/JDQUJqEeCG5pgymKk7wceRi7nK8zuRUEnV8IxkqmMl0M1ACfX1FXM0beIeUyNMXI5POIrHkECk9woeSKyjqGWGh6jM8g0qskQ145A6q9dVc0I2gKG3n3fSNmLpnuU/8Jh/IpVx2WMNMzhWE0xeo0dczro4SzzzNTIaCz0KXYOix83PJexw1RvGPiljdrGW09Cqiol7MaYF8XT0t6nEUmR0kzTN8Krkd35QG7eAQv9Z8xqhilrekMq7s0JHfLWa89nr8+EiETlFqqidLVEyf0UV/tJvyqnHC8jEOKu7imC+H/JkE4ZExfm4+To3/az7Pq+LjpIdNoxZWnvARVZcyWXYx9mASja4f99g5KvMXU6SqIxSJM2B10uUeIC8/QHbOIP7EIO3a29kfn8d0QES5J8n0oJfs5DQ/sp5jvncPTlspmTd9+m3r7T+N/9ZRADj9l/fIiheS9CQYywnSEx/DH52mttaPUnWOSVEuJ+Q3szdgozgAqdEAEvc4P884TaP7S45klfC+Wo5kNswtnWaMbXacS25mOJmLQjNK0HWaAmMl5Zp52KVB2qRjyHSj5Bd00iMpZQdXknRKSfW5+Y6iiY2xz/g4p5DTXj93nDMgD2QzVLCZtGyQtHeA+bkbsUujXBB1UVrVTZcyn+2RzWgGo1wdPchc0T7+ZjSz+awCQ6CGEUsNQuIsDZbljKnD+MzniGYH+TB6PboeFz8Xvc0OY5K6MxLUorXMqmIUKjSEdFpCucdpzajg/HAN93s/olk/wdzjGjymSxELg2SYq7EXHOWgeSHRLjHXSt9ifFSBMX4pYpkdsSUbf2UTH/uv4nszb9IdDJDn3IxSGcGXneRCqQR6xBileynvXIJgUBDMjXIkL4dlfWeIzk4iiy/DnGHmQu0o9EnQe06j8i9Hm6XnWGWSOYPnkQ9KUWmrCVQPccxfy/yZ7cj8S1EWp/hMXcxS+/toJhagKpPyuSqf1ePvI/cuQ1Xj5mQ0nzn9R9Ar1tPTMIVq0o56JAOl0cjBMjmVE19jGK5C15DgbMxGxUALFuVymuZ6MIx3k9FfgDjTwN58LQvGtqOemYemJsrBZAGLBr5EIVpLuG6YQbeNosFzqOUrCNYO0uYupmp8L7L4EmSlUbarKlnkeRdb31J0VjOdc0ZJDUjQOc6iDC9FazPRXOPC1BNA5GvB6F1DltnERNUZvnBfxKXR19C15ODTFWKyZDFTfZZd9g3cFnwFz4AMxGvJ0UgYz5hhqERCT18xN6ZfY2JUjiFxKSnxJCZLKZ6iw+zSr0V0IcrNkrc44ZexcKCRCbOBEqUeu06EUHSUPbo1jPVn8MPgB7QYp8hol5EZWMykQYch7SLLUEub5gK2slY+l1/N+FgGi2fOsEXyKS8ZDWw4I8cQrGbYXE8qcZpKcx1isZFmRS95JV1MqqV8xA3Ix8E23sNjqk84p03xSSrJjW1GbEMyxmq2Ekh7iIWaKDPVkS0qZ9DooivSS1nlJGllJ8eVt7E7UEGeM4W7d5bvm85wcWgHx6yZvK2SkjGZZmurCsVgEMeSmxgOZ6DWTxDxNSOLCzQWbUDlUTNpDdItjOOOjFNT40OpamZSlEuT/Eb2hLKxhtKYZ2L0DLh4fqONzUsavm21/afx3z4KU8f7OdZ1hn77IKVlMbKzBgjEe7mgvZ09ycVM+0WUu5IM9Tq5xtDHbdL9SPwdfFo8l09js6ybtLL+TIx0UM303OuYDMjRGPrxTp6jvugispPFjOv9tCYHMGdOkZnZygX5QrYnL0bqEBHrc3GHqolN0c/4JKeQIwE/d543oZvVMVR+NXHxJHFvC/Pz1hNBynlFHzlFfXi1IT4Q3Q7jEkom2/mx7APesxkQDcNFZ1QMl28lJu4lU6zArC6lWd1Fbvl5vlZdSt9EPsunmlgt/4KPBQ1XH9Uxnr+FeLqNOvNCOtQT6EtP8bHsGmLdaR5K/YNPFCk2H7Mylr0Ko9iO1JJLqPggO9SXoLngZoPyQ3wDalTpSxHLZpBYcghWHeeD4NXcM/EBbXEH1RPrSSvDpLL0jFVO0DFYwyLRu+ScL8ZryEGVYaGvZgJHbwY2vqRgaC0GoxZnRTvboxdxUfh1Mi4sRGPJwFHdyX7vAhoDH2IZW4naZuBEbZjqgTZkQwYM+nLG6vsJTGqxDnejVy3HWzvAuDOD/MFWjOrVjNYNI5pKIR+NYdVWcaIxSMZoO9aBYkQ2AwcKVTROfI52vAFtfZSTsWzq+09gVK6lo2EK7cQ06uFM1EYTh2vE5EwfIq+7Gkm+ni8yzSyc+Qjd6DyUpUq263JZ4ngP/ehi5AVavswx0mDfjq1nIRqbmeZ6B1n9MyhGZhHLGhAVRfncXM6G2Q/RXChGbMgmXOTgM0MjV01+SLpHSURViTbDQn/tCMO9BRRJt1PW0kDQoEJqMzNRNcDJoYVcmX4NUYeOoGolBVoloxljDBTL6ekr4TupV+ibkpMVvpiIeIIcSy3TucdostUx3m3lJ4lX2CFOsbEpm9GsxRjEs+iMJUzampjOVbLHuY6lI2dYpvqS3VEFl580M1x4CclEM/XmxQyrAsRsZ/FkJPkoei3qHh8PCtuZVQ9zMCzhxsNyXPlX4pAmSQfbmJ+zjll5jG5xB2WVnbQoqtkRXotxNEGVs5UfST9kj0XPV5Ewt583YZhQMlq9lRAuooETlJrqyBNXMmzw0B7ppaRsCrmmmdPK6/k8Mh+rK42vz8MVinZuE3YyLgnxli2PQccsd/Xnkn9sFO/cyxnRzSOW9CCXd+McbaehfB154nKC8RgDVhedrn6ysoPkF4wSTbTQq97KYWEN5wNyji+sxKqSf9tq+0/jv30UfD4fZ889jkRyiklJNcflN7AnYKEkCJKxIN7pGR7LbuYi/y66tBrez8il2T3O3SNF1B6ZIFC4lPG8NXgjYRTKDoLT/SwovxR9yMyQ1UNbqI/CYjsG4zlaFBv5NHEROodAqNfJPZqTbIx+xqc5RewJ+bir1Yp5SMJo/Y0EcRH3N1GfsxJFykizdgxVRg/yjD52yu9hZMaMtm+cJ9Wf0q928mVUxG1HlQRtm5lWKBFFWmnMXEefykXMcp5wVoD34zej6vbxC+Fdjhv9yHulVE5UMWqrQJ0aJt80lw5DK4rSYd6J3kxh9xA3Kd5jd0zJmrYKxjLLKFCKcBtkUHqE98TXUdQ9xHzN59CpAellyCVTiK05hCqP8X74Gm4f3067MM7cnuX4dQk01nwc1Wf5cmYjt4T/TqJLS0A5j3xzFhNVJ/jctYHLoq9gaC0hYsiGHCmHi42s6DtFYswNknkoszQcLlOwdOAY4gEpMm0NiYpJtifns9T/Nuah5SiyjByugqqxoxj6KtGZMzjV6CdvsBvlcBYGYw4nG8NkD7VjHC5GaTZxuEZE9vRh8i9UIRTo+CrLzPKxj1DaF6CuDXMslse8vn1olBsZrx9DNhVGPCpg1ZZxojFE9kg7xsFvPutIHRSOH8fWW4M8w8jR2iRlY6cw9JViMGXS0jiLecCFcsSOUjmXZMUUn6YWsjj4Bll9qzBaTAzUdTEwVkRO+AsyJtdhtRgYrTnP4anFzOVdCtrng15DKDfKsUIbVV1D+DlP+cg6ZJoUvsw0g+VRhnvyWC57F/NZM07DIkq0BoZsAwwUq+juLeWu1Mu026UUeTbil81SbKxjIPMMQ/kmTo008qDvDfZoPaw9pGU68zLSyQ4qzAtp1XegLu3j7eSt6LrsPCp6nTeMEtYdlZLSbMahCGEhikVfSYuqg7zyFr5UbqF3Ko+S4Q5+InuX121GbD0CC1o1DJVdQ1zShyrqpca0gm6tHaf8ArnFrRxVbGGftwbjUJg1kdPcwsd8kpXLCa+PO5tNqB06hiuvJpJ2EA2cpNhUQ6GkmlGdj5ZoLwXF0+gM52hVbGRHcg1Sl4BoyE95tI8HtXuRBNv5oLCOr/yT3DCex6JjDiLaEqaqLmPKI0ZnHsE7cxqTMoP63IuQO2VMZoboYhy7f5y6uhBabQsSfQOLap/9trX2n8p/+ygAfNyzjb97SpgOQJU7xUifm2WKEX6gPUyW4xC7i+bxgSyBxh7jO51WzGdGcC+9kWFZDYJklnTyPPiDLCi5BKlfTl+mi05fL5VVThSaM5xTXsv26BIszjT+Hgf36k6xKbKd7Tkl7Ix4ubPDRnZnjPF5t+JORklGj1NmridDKKLDOI1f0UVuURuHFVdxyFONvt/PXaJ9lKUP82ezhU0tKnImMunPW0eas5Roi5DJM2hRdVBQ0crXysvpmixgzlgLN8s+5CWtnq2HlHhsW/BJxylW2ghp1XhsTYxlGdjnWM2msWPk6o4wPaai3LGMaR1UanPp17mRl5/l7cRNzOttI9+wD32zhpjqUhTiSSQZuYQrjvBubCs3jHxJt3yAJS0N2A1ysixlOCoP8X7kSr4//TrDM1G08U1k6xRMF7azXbeKm0a24RkLgbCMbIuVwdoWWkbqKE99TN7AOkxmPeM1rRyzL6A2/CGZo2vQWQ00z5khs8+JcnQSqWIBkgo7nyTnstzzFvrxxchz9Xydr6fG8RnZ3fMQZRs5UCal2P41+Z1zEHKM7CtUUzX7Gba+BhRlaj7R5rFx4i0krqVoagMcjBWzpPdzpIrNBOsGcTl0mIZHyVAtpmvOGPoxH+JRyNTW0NIwhXXEjmQEMrS1DM4ZIDauxDTShVK+hHTFOJ/F57PI9zbm0ZWoMo2crAtR09uNaMSHIKtDkidid14WG0e+gh4JMXUl8iwtpyugsGuCaKSNLMdmMg0qpovP84lyHbeOfsSow4sxfDGZGhGT2ROcK8hB6ExQpN5JaVMG05YGKnQZ9Fm7GCjS0dVXwn3JlzjlkVI9fRFOlZdKQz1dplZ8pRG+nN3EbdM7GTd0U3xaTkp5GT7xIPWGBjrVM+hKTrBTfRmT/Ra+F9iGQ9fPzISMJV25DOashPgZ5masok/lIW49RygzwLvxW5D3x7g9vptsyUleVmq58aQSWaqeIWM96XQTRZpizNJCmrWjyC3dqDO6+FJxN+3OTBS9Xm6RHmdd7DPezivmgsPL3Wf1SANWhkq3EBPsRH0nKDJVUyytY1zr43y8H1vuNDZbM12SuewUtuD2SDCNhRHPjPB4xhFKXHvYVdTAO/iZO63mimYJskEfjhW3MBzNRaayQ7IN3+Qg86svIStZSCAVpd/qpNM5wJVXXUxpac23rbT/VP4lorB/ys0LX/YwNmrnZzntXBr7Ck/Sw4cFdXwWGuPS2Ww2nEkgzMSxL/sOQwETWuMEQdcpzHIr9VkXEQ+l6Lba6ff1UlfnRJA3c0Z9G9vDjeQ4U7i77dxvPMWm0KfsyC3l06iX27oyKTrnZ2rhbcwmBAROk6mwUaqYS6/RwXCyk8qabtrllXwc3Yx5NE6dq5n7xB/wZlYWkbE4lx+VMFFzCz6xA2loiMaMNbRrphEyWghlBHg7cSvKnjAPpT9hTDtGn13KhnNWBgsvJp08Q6N1Ja2qcUwlJ/lCs5nR/kx+HHyPs4YpKk4rEJSX4RUPUWdspE3Xj7S8h3eDN7Jp6BgSQxMFp7T49JeiEk8gycgjUn6Id5JbuWLwAOPqThafLmXaYqPYWMJUyWE+UlzMtQO7GUiOUDW+AZkmgS8nxrliA1ldTpJCEyUjGzDq5ThKetimWM319jcRXTAS1ZUhzpGyr9DK2qF9CH1SBG01QrGfbZp6Vnjewta1GJ3FyoWGURRDKTSjvSjlSxBVTfJ5pJFVs+8jcS9GXSZim7KUVbNvoZpcgLpUySfaQtZMvYlsegHaKimfiEvYNPE2Iv9y1DU+9sZKWTG4A7GwHnFdP83eUqr7D6FSridc182AM5fiwdOoFBcRqe2hxVVKzcg+xKxAUenik9QclnnewjCxDHmWkYMVYhYMH0bVn41GV4inupudweUsjLxJdt8aTGYDY7UtHJpZRH3yPQp7V6M16nCWdPGRfA23Tr9BtEdGSNlAriWXiepj7HBczG2hl4h2qgkpl1OiNzGWf47d1kWUdQ0h1hxh7olcJixlVBpz6bO201dooKu3lAcSf+NwSMK8kWXMaBPU6Kto1/dC+Qgf+reyevAURfoDOIaVlDiXMqWX/nP+pCGQdZKhTDN7natpGGxhq/xjXlVouHm/gtn8rfjFo2RL5OjVRTSrOiisaGaf8hKaZ8uw9g3zW8Xb7DGL6ZtNct0hCfbKrdhFaZKBJublriUqyDgj7aagdJCAepZPZffimlGR6rHzoO4gNZG9vJpXyvS0nztOqSGey2DxpSREM0R8Jyg0VlIia2BaHaA5NYDBOkVuXhvDomy+lNxCr1dJ4XQM+9AUv8g6zSrPdk5k5PO2VkXI7uGu3hyyjg3gm38FY+ZF+IJhtIZ+HMNN5GVUU21bhiyqIPunCxCJRN+20v5T+e/97f7JapuBXym+5LTyB+TKD/KzvCy+o9OS2ybhpX/IWHvKymjFdzlV/wOcMjuJ4JtY0h7W5d1AqXklZ1RjfKU6iqbkCDXz93LctICH+TNnxuYgPzrNzTO7+EL2MHFjKzdlWFC2K3nyLRlq4TLON9yDk3Z06VaWmzejsJbypaoJSck+dPVD/EF6P3snNpF1eoA/BX7PAvOXPKLWsPKrNGuGVtJReQuu6DGqNQYKcpZxSHMGXeVOTuQs4F3nbZSe7+GPyafYZppGf07NsrG19BcvQyN0UW1dySltC6rqI7wk+w6zLUqeDv2eXfppFu1TEVFfS0DoocY0j1ZjG7GKMd7x3MT1w3uIGk9TclyLV3cZavEEEmsesfIDvJveyvqBE0zpLrDkZBaTplzK9SWMZ5/iK+0qFvS30a8cprFvDhG5h4RRj7vIgX3QhEZ1grrOUhJyP25DnNaMfJaNNzPqjRFRziFDm4U/fwxhHCbSTtKSWgwaEzO2CLWOQWSTKQKyNF5thO50MRLpAIpELXqFhgmdnEWhC8icWjQKPWndNNawF7UbRDIlgtqFIhpDFgaRRI4gDaFIxSEpIAgi0qRJCGISEkikYogSclxiDWlFjHA8gCKiYEiaSUIbxhd1IfOZGVbZ8GYmSEejJB256M1+xEot0kQCXVBCXaKHIWEuHl0XMyEv0skqLFluMuM2EulzTAVmUI+VoM4LkxXLxSc9wWTAh2FiLhtUB/lUfDXTdT4sfjcO1xCqgXqWZp/ms9T1OOt9WF1tDPhGyZlYyCX+AzSXVmPy13Fi8Tg5jlG6PUOUzc6hbNhJZcUgf5Lexxplgqbi42S543R4+6n1lCDqquB21RscrlzAMe/V1GR5OVpyhMLxQYZDEiLTfeSOrMHSk+B2/T9oravnmcSP+UFIzN7L4oT4iPwZH+NRC11Th1gRaCDQfTnzxlu53fgSrgW5/FDzCLHZxdwj2PnH9Wkckh1Ude5Bqb6Ydv8YfRMHWR+pRzm4DntrPXeH3uB681uw1MRfrNdyV/hXXGy38FBihg+vEHhvSR/l/S9SNtyCWn0V00kT+6ffx+/s4ZLoHErty+lq2Uyqz8p9sT/zM+WzmKqCuJYV8LL+KpaH/8hwaA0vjE/zeDLEgRUJvv89GMjsoPbkb5g7/CnaZDFi1R2ElAUcG/+c8eLh//ZBgH+RKIhFArLqQq4sreS1qJhrD8n597+myLfX0bb0V7TmbyYktBH3vkG+TMulRd/DpK/ikKqXQ6oj2CqOUNZ4kH26NTyc/hPtIxUoj01x8+xOdssfImlo4YZMK+luJU+/LsYc38C5xgdwSEeRRQ+y2LSSvMwlfC1vxVtwiIKGo3you5JXffcRb/LxI8cHPK75N56wJAj06vjBF1mMZd3FuEmEPt3OEsvF9GiCTBXuQ1U7xO+EH9HbZuOBqU/YYnyPf5doueFTGUH1TUyqvFRo5Ggyq+jJPkyy2s5fw/eS2zrEo6J/4zVJmqt2mxjN3oIo3UGpZRGd1iY85TF22K/k3olPGDZ20HhIg910KVrpBOJ/BuEtrmNJ7zki+rMsOGpmMqOBcl0WfZYLNGeXoewJoNMeY94pG7M6DdnGMiLFp9jjXseNiffQdSqx6/PJ1BcSKeqkf7wQlfoMpWM1KGRR7OYxjrCYDfGvye2zkJRHcRvsnE3WUyicw+CpxKbSk7L1EJhVEYn6SIjk+BXQL85FIR5Hmi5DK5EzodRSHR2EsB6tTENUEcAWdyEOCMgkStKSb6IgJEAQBBAgjpiUOE0yGSMdV+IRq4mpUsTiIVJBIyGFAq8RJHGBhNeCXu8nrdAgTYnQhiUUiCeRRHOIijpwRjyYnWq6TIVEc0RoYgJ6n4QFoXa+VKxjqsyDPCLH5NGwLNTMNt1mwqUxjAE3Ae8kupFMtMVhFME6Oko6EMWMGJxpMkcEpGUJPIEVnJs/gdUTYNjVhX5oBdelt7OzcDVzQrkcnt9PttNDt7eHcvs8yoZnqSwf5Pey77NZFudw9Vly7S4u+MYp95hJdC/hu6LXGasr5OXod7laFmbbsgFKhvcQSJfTN3Ocea6FhC/U8wAvoWsU8TPdg1TZqzCWevi87hyVfduRyldwxnsasztI0egafO1VPJh4kTkVPWwrWcuDsZ9zr1OHqcTDC5dMkTf1CnmzfqLSFZz0HCY2O8DGwBICvZtIdpr4RfJZLso+gHdxHs/obuOh4M+4Z1bB3WI7L18rYvu8Hqo6/42SsU6U6muYTVvZO/EeHlcXm8J11LpX0Ne2Dm93FbeE3+A30p+TXzxDYEUmn2euZ23wKT6Mf4cfTQX5yOHAXZPgu7dF+GqFh6y+11l27rdkhlMIki3Urrn821bZfwn/ElEAyK7awlNfGvnxGxFQbeDURc8zYCwhkd5P3L2NKm05lxV+H0GTwZeaVs6qjlNUe5j8uuPsVF/Oz1J/pGeoAPWxKW5x7GKf4kHS+vNcn20jNKDk2X8I5PlXcnbuQ8yqvBD8nEbDHOqzN3NMMUhf1hHK5uzidEYDv40+wXSLkksHjvCW+BecsozyRsLKA++mMScup7NyE+HgVzToKjBnN3BIdxJr1S5OZC7iLfedZJ4b4sXYbzhpHWJyyMjWU1V0l24lGj3GfONC+rVhYqVfcaGwlB3T13Bp32Eu1b3HrpCOq05WMFi4CqNolAxbA0O5hxkqNrN3dC0Pz77OSf0IK/ZpmbJdgkEyidiSR7xsP2+LtjKn5wIaw1FqT2iZzlhBoVLGgHGC6SIY7bWxSfoRGefVzFrmU6LLYSrzHF/INnD19NecjyaQidaQo1AwmdHBHtFFXO/7HEm/FJfahkafiSfXjWosxBAekDSSqbYRyhpFOpXAHveRkGSSVirpN2RxUeA0+lE5KRmkDFPMeK24JS5IqQnLYUJuQ56eQZTOQSlIcMg05CbspJIaZGIpSUkERSqKkIBv/gZpEohJSdIkUjHSMQUpiYiQCiRpMYmgFq0qQFyuQJKSooqJyZB5MIb1xIVREvEERf4ZDivn4c0JkIymkDrzKTaMYgjlEBE3Mx12IJsqRp4bIz9hJpU6y0RgGul4DcU5owihOgZy24hGNWQ7bazwneWL7JXUKyQkEweYiMQotFewxnuKU0VzqI9kca6sBW3UjNc1iKR/GVuVn/CK7WrWxrUcqW0j05Ok291Oxex8KkcmqSwf5FnZD9giSrOnoYP86WF6gj5yPGISPWu5PfoukgYFTwg/4c6YhE83+8id+giFZC7nXU00BgqI9GzmCvdulhY38Wr+ZXzhuZ57BC9vbAlhdr5JVtDIcFhB7+QBVgfn4u29lNrRHu7T/BvJRWZ+ob+Xo44reDbgZf9mgf2lTTR0/B0zNczEszk88wk5foGlrhWMXLiUzGEvTwk/p66kh9GFZfxMeR+/93+fX80kuVrp5nc3Ceyr66Ku9TlKpvpQqK/Fjo29E+/icLezLlTNQt9yhjpWM9mxiC3BT/m98ENq8vrxLzVzpngJ10d+yoPJX7LRqeDrkREqMpP8ekuQP94qRQjtYTVfoDEqv12J/RfxLxMFk8KEsPb7nFjwONNKEaS2kfYcZr51JRuz78CjkrJde5Ze7QmqGw9hqTrHR8qbeCTxPMODmaiPTnKreycHFA+B7jxbc7JwjCh57h9QPruQs/MeZUonkAh8RJUmj2U5W+lQOThpPEpR7S7sBUp+ydN0DJRT1tzJK4lnKDHu5wd6I8v3ibiypZ72qvuwSwbIlE6xOPMKziomcBceQF0zxLP8lJ4LOdw+tosfaP7GE1o5F3+eJiOxhaG8EoxCH7WZazmubsFQvYc3tVtp6yrlUftbiC1NzA7oWTK6kuHcIgoUccS2AtzF+zidU8+Zvnp+7f0ru7RONu3VM5a9GaN4CpElj3jpXt6RbaW0q59i3R7yTquxmzaRIQswaYoRLRvlyMQy7kv8g9EpGQnlRjLkMUaM03Rm29D2uXGoe2joriAqdeLVS5jMkWAY8tCvnMYYWoJNIcZp7eRQfAkbUl9T0GMkKg/j0Po5LW9kXeQgtkE9SVmcgH6W3nARgnwYVbKODIWRiGEWs8+D1pEmLIkTkUdxRXT4FX7ESRNxSQq3zIAh4SSNAYVIQkgkQZsKfxMFQSDN/14pgEAqLkcmjxNQSZAmxEiTEpSKCLqoipTIjzwmIicywbC0hLh8HEfYheAsQDCDWKNAHo+hCiWoDfTwtXIFM8V+ZDEFZp+BRclzfC1bx1iFB2VUjdkrZ66niy+ty8kxgTpwjhHfGLLR+awxHOIjrsFeFyTDNcigpx/NyFKulH3Gq6ZrWCYXmFXsJZ7IRuXyI/TVcZnlC/6gu43NgojTJacxBzV0u1oonVlE9eg41RWD/EZ6P1vT8MXCfgrH2xkOgdbpQ9K3iSvdX1FYa+cx9Y+4wptB04YoosiH2GI5tAVGMDoDGAfXk93v5jb9q/TMqeTh1CN836OkZ3WC87b9VPQeIC1fxSnXIXK8aYrGV+Nob+An8RdZWNzMsZr53Jl6ghWOHK5Q2vndTQliye1U9exGpb6C3qCLMxPbWRwpoGx6Jd3nL2ahvYVfSx4jp8pNZ0Mt94ge5CP/zbww6WeZ0cfjt8KJsgs0ND9N8ewwcuV1uESZ7Bl7h0nPeS4KVbAiuILx7pX0ta5hrf8gf0x9l2VZ5/EuMDA+p44fJ+7isvgfyfYXsm2ghzvlKd5bHcH5o+u/ZYP91/EvEwWArJWVpGNvoQgOsDr/GpaarmFUHeZjdRPTxibmLjyIoqiTN+X38lj0Sab6TWiOTXCrZxeHlQ8iaJq5Ji+b0XE5z70h0DBSR/P8XzBqMhILvk+RQs26nFuY0KT4WnMMS/k+VFXjPC99hN2zm9GcnORR35s8qnmBn2WIcQ5peeQTLQ7rbQzllUF4N4sMS5FaizmsP0Zu9Rccy1jEP7z3YDgzzguRZ7BbOtjjN/PdXSYGc+/Anu6kWmNGk1lJe8ZR1DU9/DH5AIlzIf4QeZ4dphkqDssxJq9iUhOmWm1j1iglVbqbnab1DF2w8Zvwn3lPHeLyvSZG8jZgEk8jsuQSL93Du4qtZHWMsUj7Gco2FUHt5ajFE4RNekRlZ9nuvYL7Pe+yP5km372KtGiMiEFHvGSAtvFqLpFsI69FzYwpj3x9KYnCMxxxLWWT8DklbTq8yiRJrYGZ7DQZYy568YFkPjnKDBI5ncxMmZlUziBLN5KlNBGzDJOaTeOLBUmItcSUYgY0eSwKt6K0q5DLFAiGKVw+PfFkAASBmEyEW6QlKgoiTRuQICYgkaNJh0gnQEBEmhQJxCTEacRpEcm4HJk0hhwZaSTIE2nMaS/KhIZ4eoxgIowllKBVUYIvK44sIUMeNJGrnkIbyiAiamUm7EJiL0SUCTmCBnGsF3fAgXo6g0iOnNKklnT8NKPBGSTjjczLaKMtehEXamZQR3WovGHMY3KEEgG1v4Yz1f1ookbirilEA1Usz2riVeFWDMURNP6vmYkpKfDoUPZZWJ5/mj/Iv8saRYxO2yEMsSz6nM0UTy+henSE6spBnpTfz41x2LF8lMKxE0xGdSRmh9ENrWPx5HmWlJzlWettZNnriM0J0W3cTeHkDKMRFbNTzdTPrCB4oYKfCH9GN0/Mo7oHkNvnssJo55WN05QNvoZVqKYvGGVg8ghrg/Nxdl9C6cgYP1U+h3Khkj+ab+TPge/yR0cEd0OEv26YoGzgL+Q5/KQVF3PGdZLx6VNcEpmLanQV/c0rudL/GT9VPoemQeB01QKuTT7Oef8lvDXpoCw7xM++k6I1v53G07+mwDGJVHEdblE2e0bfYdR7huWBEtZHVjDTt4z2c5ew0NPC8/E7udx8EP8cNYEl5Twv2crC0J+Yja/gdyENDRkN37a+/sv4l4qCNc/CZSsfoFq9ig71LB8pjhO0nGLx0n2k8sb4u/whHo/+HE+/Gs2xCb7j28kR1YOINee4piCP7mk5z78tZklXKa3zHmPAWkws9CFZoiAbs28jrNWzQ3kSSeFhChqO8476Jv4a+D6x02GuGd/D+7Jfss/i4K9Y+PG7aYq8F9Facxu+yEEKFTAv6zKOyPsIFR5AXTPM06JH6OnO48qhA/xW+Xt+bRKoPipl+chyussuIRU9yCLTano0AULF+5gp0fKa927KWrt5Uvx7/qAWuOYzCU7LjXjSHTQaGrigs6Oq2Mubyhvxtop4MvFH/iFPcdU+C4N5azGLZxBZ84iX7OU95bXo22e5RPUhvn41guwKoA+1uZh0yRHeTVzHlrEDHFc7Wd5WiksVIttUTaLoONvDW7jH+zHtfglpxTqyZGKGTX0cUi9l89RRWmIp0vLl5KstBDLPczS4hLXsoahbT1AWxKVJ0Kqt5BLPUdTDYvyyFF6liE5tGetCTZhH5IQlUYJKH6OxLNLyGRTpMkwSNXGNE3PIg9otEJHEiYoSBGNyYrIo4pSOlChJTCxHnQyTTksRCd/8PuKISInTCIghIUUqjaGMyxBEEWRxMaaYi0lJDkmJA3fET8qXjUKXQKRUIk0kUYdTlIUGOCZfgDs3iCghRevLoFrey1hyDtO2UcIxMUZ3HoskZ9kvWstQlRt1VIPRDxX2KS7kljEHBbHUYSbDEXIdVWyIHORT23oWS0SEhAN44joKvWayRyOoKqK0BjbRvdBP9vQp+v0OKj3lZA+HKC4b5y/czVxTmEn1btSpEvodZyiaWkrd2ABVFUM8oXiAW6MiPr1ohuLR/biSebhnOsgdX03ekIOLM3fyTsHFdLs20Gh1sbOhnbKBg4REc2ib2cfyYCPOrvXc6NvGouJzfFC4gZf9d/OrYJAPrgKv8BklIxeISpZy3PEVJQE5RROrmGxdwP3Rl1mZd4TehipuE/0KubOeX0ccvH6tiLO5p2hsfREjNXhFNRyY+Qi5x8WlwSX4+y/C0V7NfdEXuFX/JumFWvYWrGRT5Gl8viV8NDGFvijCT+6EflsLC5oep8BtRyK/Dp80j70j7zDkO8XiQCEXx1fgHlzM2dOXU+Ue58n4Pdyh/5R4lZT4inze1VzM0fqnv1Vv/VfzLxUFANGiDD6RHEWUdYaly3fjz/TygvxJfhP8IaFeGaqj43zHv5Pj6geRqs9yXWEh5+wynnlfxtqzWVyY+zA92XOJxT5Dnxhife7NqPQlfK4+hyfrGHXzv+KIcTG/jj+N84KKOe3n+VD0BEb9ce4yW1h0RMx3DufTUfVDprVxZPHDrLBeQtCg55jhGEW1uzhiWcxrvntRnJrh96E/YzEf4y+CiR98KMVnuJlxkxSrZJz6zA0cVrVgqfyKXaYNHB5byVVDB7hO+w6voOO2r8z0Fd5APHaCRvNKTml6MFYe4a+iuxG3eHmcP/GqVMo1hzIZzF9NhsSOYM0lXryHDzRXIe/wcrP8TXqn1JjjlxCllxzLHHwFh/lIuYWq3h4i+vOsOKpnwlJCha6M0YxzfK1ZTeNQO83qGRoGqolKpogYtEQLZnEM61FommnozCEkdePWiBiy6SkYG6dTCCISLyZfZSGV1UqPoxSRuherrwqjQkbcMsCAr4ikagxNrIpMhR7BMoDDqcch8pJOm0jLpUwqM6iPDaJwCqQEEKl9BCMawpIwpKQkRRARK1ClQqSRICAAaVKCmIQ4hSgtIE0LSCRJFAkxKTykUknMMTfd0jzC5hTitAgCRowaF5agljgDeGIB1F4dDoMRiU6CIhJCEgpT6JnmqL4eMlNow35SAR+WWYGZrAwq0yqINTEanEUx1chKzXE+Fy5lrD6INeBnxtOPMNLIsowmPuBaPDVhbI6z9PlGKbA3stF/iPbSKgq9JRxcNE22y023p4NS+3wWzFxAXgUfRG8mKy9IKPUZMqoYtJ8hf3I5c8Z7qKwY4nHFA9wWlPDJehclo7sIUsnozGmqppeh6lNyi/5NjlYs4O3A7XxH8PDWhlnK+t9DJl/OaedhaoJmhIENFI9M8B39aww3lnEfj3Gzw4ioKsCHC3qo7XoNvXwlXQE7I9PHWR9aiKNnE7nDTh6VPYVxHryTfSk/CD3KgzMiajK9PHlzHJn/Y6p7vkKp3MJILMmhyfcoD+pY6VvO4IX1yAa1/Cr+KBtsB4kstfBxxiY2hJ/H5K1g28Qk0coYD9yZYsJ4jgXHf0me14NIdh1+WQH7ht+hz3ecBYE8tiRWEhxdyMkTl5HrDPNw7Ic8qPkHWdUpNlb/97sz4f+Lf7koZBbnsH59PzMW+IP8D/wucBfJXhGKo2PcEdzJSfVPkSvPcl1hEUdcUp76SM5lhw30zfkR7fnriCX2IwueYlX2lRQYFrJP3UmP4Rhz5n3FSJaRR4U/cmGkDEvTEL+NvcL96pd4IEvLxJiaJ96VEdVdS2fpWqKh7VSqs6jL3MheRTuJwkNoaob5jeiX9PQWcFHfSV6SPcUfrVHo1HDjiRLaK+/AFz1CrbYAlbWMs+bjWGvP8EfxA4y3avm5+w1UlpOcdRq4+kwFvSWbkSWbqbWuoUl/Dn1VM/+e+D6Wlgl+Jn2Rf4hUXH00j4H85dikLrDkEC/eyyfaK6AtzH2SVzjuU1Pmugi3ZJRy8wJGM09wxLwI6YUAy1Sfk3leyYxtHXkKEX26CabyZfj6VdQr91B/WsuUMYcSfSXR3Ca+iG7m9vA2QqMSXIa5FGnySeScpcm7gDXivZR0avHJA3jVUgbMNhqc3Tj9aUKKPLQqEwGzD7U9gCsWJi6xIZarsGsV1IeGUNslhGVxgrI045IMMhLjSOIWdHI1gtpJIKQgnoyQFlIkJSLCYjnKVJhUWoIg/POojpAmLgFRSoQ4LaAkiiIhJZFyEUpFkEQ12KU6AoYk8rgUbVxEbnqGGXEBYdU03kiQtLOATOMMVr+JsKidybATpivJsc1i82fhk51nKuJHaa9iruY8J1Ir6a32oInp0Pii2KYTePINVAWsjJrPEUtmkO1XUTDpIVSiRe+v5FjjBKaQkglXJ6KhpVwr28Yb1iu4OC7hTEkLhmgmQ47zWMaXc3ngS5zVmRwLXEGqJoQ88CliUS2j9rPkTq5g7mQnlRVD/FJ5P7f7pXy80U/R6KckxI10248y17mQaFcB9yn/ysicEp6I/5iHQlHe2RIja/ptzFTT4R9F7JiiZHIl4c58HhI/j3qehCf1dzHmXMV3cfCnrXGMzrfJs/sICnM5OrODyqCOoqmVjLQu5e7wa2zO/IqZhYU8IPsxzd7NvGK3c2ZFgjfWTFLd+QdyXX4E1dW0etton/maVaFyyl3LaW2+mOKZWZ5J/Yj6/G78y3N4WXcVl4SfpcFt4+OZaSbq4/zo7hROVROLjv+C3EAAQXodIUURe4feoct/hMZADlfHV5KYWMjxY5sxzGp4yDiJSBC+XWn9F/MvFwUAX/5z/Lv/BiQ9KSRHR7kj9DlN6p+iUJ7lxuJSvvCJ+NUOJdd9KWe45l7OF11FJNUEvq9YZFtDg2Uzp9WjHNUco7TuIKLiSZ6VPMUu+3oUJ5zc4tjFB/JfsMMS5A8iIz/9UKBxrJFzc36IR+hGl7zAmszrmNSmaDIeo7z+K46YlvKS//tITzt4IvgKS/Sf83OtiTs+AVPiYnoLFyGOHWKpdTPdai+egoPEy0P8KfoT5GftvJB4js/NdvTNShonVzKYV4NZNEqRbTHnzSeRVw7zUuReytp7uF/5Mu8m1FzVVMhg/iKypB7SpmxixfvZobuUUHuaB8V/Y1dUyYKh+cyoAtQZ53LB2Mxwnpmx3gzuE71O/7QCsewSZAzjN2iQlnZwYHYV34+9xaBdTkS3nkxZin7dCOfNFRQMjtKicVA0OweJeIZJbYBOcyGVE/2cF0URixeTqzASybhAc3AOc4UT5A/qiIk8OJRBWuU1bIgcxzIkxS/245HF6ZEWURdtRzMjJy1KklC5cIWNBBUB5OSgEStIyZ1II1EUIUiIISGCsCAjnYohQoaAQJpvBsxxsYCQFiFJgSYdJi5oSKWdBGIR0mETGk0MlUiJOBVDGk2RE5zglKwab1YCZUqNISajIjnIWdlc7HkhpCkNprCexkQbu+UrmSyNoIuq0QbjFDntdNtKqEOKED3BSMiBfraeddIDvKW4AklBHIvzHAP+EUzT87iMXbxv3sRa0jgVh0ikCzB4oyT7S1iedYJ/k9zOQm0Ej3g3AuV4HL0Ig6u5JfUuHRXVTHtWMbwogsm+jZSojsnZ82SOr2T+VAeVFSP8Unk/t/lkfLI5RMHY+wiShbTaD7LYNxdX5zweEP070UYTPxYe4j63jMMbUwTTO8h1RpiMGxiePsYy72LG2y/i7vAbNBR38EXJKn4d+hHPucKcWJOkqeA0tZ0foFReTLt/gOnZs2wMLma2Zz3m4TCPS36FpTbC3qKVXBd7io0OI98TnDxzo0C/+RTzW/6EPlVFRL6UQ1Pb8Hv6uDw0D83UMtrPbWSF9yRPix4iu9SFZ2khT8tu5pbYb7jEqeAdp5O2+Ql+erdAUDjGouO/IDsURZBcT1hZwr7Bt2kP7Kc+YGNrbBlK3xKysi75tnX1X86/ZBRWWzKo6HZxV2Qnp9U/RS0/w02lFXwcgEd3qrjjU4Gp0js4U3kHIaGDhO9j5pgaWWW7gR6Vhy8Ux8goO05OzUneVt7Nn0L3Q0uEeb2n+Ur5S0S6Zm6x2Zh7Usr9Ow30V97PsDWHVHAbDcYGCq1L+ELZjLToMOqqYX4jfpzugULmdzbztuQJPrb6aLMb+NEOA30l92KXT5EpcTLHton9yhaslbs5lTGXbc5rqWnr4A/S53jKKOXSL0VIZVczaUhTqEihsVbQl3mMaFmQN323M7/rPDeo32Z7RMdlzeUM5s4lS+YjZcoiVnSAXbqNuNul/IoXeQ8Fa9urmDBpqNVV0KLtIVrq4fjEYh6JvsoOpFRPL8YnGSbbVEcy/ygfpq5ly9QBvlbGqB2tIyoaBb0VcXEPrRNVXCL9jOJWJbP6DPJ0ZYjyznPOMYdV4v2Ud6jwyCMktBo81hSaSR8XZEFE4nnkKi2kMzoZceUSUU2ij1ZgUahIm4eZ9mURUrtQUYZFoUPQj+P26XBJ/aRTRsSCGK9MRX7SgSQkISFKERfShJGREhKI/zlTSAOC8E00REkQpUCVCuMUG0kqk0QTEVIBA0pVAENUQVKYxJ8MIQkZiGiUyJRi5PEgQiRKhs/HBU0RYmMKVSRAIuxD55LgyTBQhgJxrJnR4DTCVD31pk5aE0vpqvahixmQ+nwoJnQoixIo/RWcrp5AE8sg5ZslOVhOY84F3k1dR7gqgm3mICORIFW+EvIn3OgqouyMXEOsJorJsRN/OgetJ0Cwdwnfkb3J/qIlmN21nLwoQvbENqKiauz2dqzjK1k400JFxQi/VH6f2zxKdmyOkTvxHnLpEs45DrI0WIW9cxX3xV9G35DkEdUDLHXkEKsNcSq/ifKBk4TFCzgz/QUrw5V4ejdSP9XDrYbXmZhXzF38nJWOLOZbPPx5i5uy/r+QEdLjFSo4MvURdSErxTMr6D+/klvCb3GN5RP8i7N4SnU7L4fu5uVJJ9LyKE/cGEU/+x61g18jl1/BbFrLnrHXMQcSXBZchn9wOUNtK7g2/B6PKZ5FXpXCPr+CnyTv4qH4z7lzOsrLfg8Hl8Z45C4RydhBFh//BVmRJGnxDcQ1FewbeIeW4D7mFFUgkUi+bV39l/MvGQVBEHhvlReN7BS3llbxRjjFg1+pue/dOI6CGzlV+wP8khFi3rco0+SzKedOZlQptimPI847Sd283RzQruYXiWdw9Wqwne7lL6K/cqv8H3wvN4O+CTnPvSFCKt3E+epbCcf3YxWcrMm5mR6Vj3PGY1Q37OaQYTkvBn6AcMrDT33vcrPmTe63GNmwV8SiwQbaqm8kEv6SBl0NCksRJw3HKag7zN/k99A5UMrVo3u5Tfs6zygN3LtNynj2bbjSF6jVFuI36nDkHmKqQMd251WsHTjOOu2nHAjo2dhew1BODdmyIGljFtHCA+w2rWGmU8OTqRd5VSbj8qZ8RrJKKVdZ6NROo6roZLtnC3c5trFNE2HT6VymDEpq9A30m89xzLQQY48DhfYsy5u0TJmyKTNU47adZqdwCVc793MYEQpWohcHGdJOc1ZXS8N0J2ckcSTipWTKlTgN/ZxgEVti+7H2SvAp0iTVahxGBdnOWaYScWLSPPQyI3HdDDFnmimCJAUbSrGKsCqMIeRDCKaJS0XE5GLcchM5STvygEA8HSNBklhSTEqcRISEb3aP0iD88yBbGsTJNKpkELvYQFQtIBXkSBIK9FI/spiKaGrsm2GzJxu9wUNmWEMi1cNs1E3SWYzF6iHfryeSbmUq7EY0W0GpfoDpaA2D+XakKTO2kIpaXw9NlnrqxCCJHGc04sHmqmJdch/v6zfSoIwjDR5kJgal/kLmOHpwlFoxeSs4sNCBzRel29VC5vQiLgt+QX9FCSJPA0dWBCmYOMpYNEV5wEiwp4Zr9B/zbtbFLHVlsmt9mMLRzwgIZXjsXZjGVrFk9hwVlaP8QnkvN7rVfLEpQcb0O2ikCznjPMr8YCHOnnXcFPiI0opx/mS+kYBrMYtNHt5dPkxN9/tIles45TxIUUCGdWwViW4bj0p+i2KejN8bbuKwdwu/9bt46VoBl3QvVf37kKiu4Ly7Gbezg82hxcx0r0U3LOLXwqNkVXhorZnLltiTWDzlvOCZ5eONSbYtn6Tu3DPkeIOI1TfQFRji2PRH1AczWB1azkD7CkL9lXw/9lu+p3+b0BwV43PquDn8AC+mfsijEw5+H42wbVWEx++SIPbvYcmJX5ARhZT4ekQZdRjm53/bqvpW+JeMAoBQcwVfKvL4wT4NP/1HGH/mFk7OexSP1Ek8+CY5EgWXF99HTKFju+4sTlMTi5bsps9k4xHxi7RNlKE5McW9wW28Lf8l71nT/Eai5cc7pKxqL+Dc/MewKwIIwV0sydiAUV/FTtUpNMVHUFUM85T4KbqGiqls6+Aj6RM0myZ5J27k0fcleKw3MWLLQRY9wkrblXSq3cxmH0FVNcqzqccINSf5ue91TIYTbA8buecrM50VtxOOHWSBaRk9ah9C0X5asqo5MLWaq8b2UaPfw3m7jtU99QxnlZAji5AyZhIuPMh+yyrGOs38NvEiL6tFbD1oZjB3MTnSOBO6JPryk7wZvZXVw6cYMg6w8ZCGkewlFCk0XNCMEi4M0DtYwF3id/GNyvGYNmERR+jXzDCZpSE1kEamaWHpOT0uRRSzvghZXjvtM5Uslxygqk2BR5FArrWSzprCO6pkQjuFzV+NWhLDoZrmvFDLuthxMgZEBCRBfAoY0hSxLNqBZlZKWJ4krBAxqcylPjaAyiUhKo4RFadxifVkxWeQ+JOkSSPIosSTMhAlEKWl/2vQjABSAESIk2kUiQCzgoGoOok4JUWZEmFNeRiX5BEyJAAp8qiOLOksnmQOHq2LaFKEKmShVDJEp6iOybwQ8pQRa0RBXbCDrzSLUFiT6AIjTAVHSUzUUpoxxGBkAa01XvQxAymfk/RoHvl5M3RFVnB2rg+rP8yw+wLi8cVskezgTcNlXJJMMmo4iZRyQu4RIoNLuF72AR9nbuASn4qdS6bIdUxywddPY6ACUZ+FtRn7+ZP+Zm7wyPl4vZ+Soa/wkE/Y0Y9ubBXLZk9TXjHO46rvstWlYd/6JHr7e+iFOTR7zlATMhHqX89q5wmW5jXxaf46vvBfy71pFy9u8VPe93f0koV0BSYIOftZ4FzKcNty7o+8SE1RH4fLF3N/5FGenIniq4/w9spx5rT9GX26CgfZHJn4gLnhbIrty+k5v4Zrgx9yu+4toovNvGK4ih9GHuXnUyE26kI8emsKu/o485v/gC5VDtqNHJ/6imFvE+v81VQFl3Lu9DoMU3J+FXuAyyyHcc3X01U6n02+R/kidTO/Gx7hMSHNK2uCPHenEpVjF0ubHqexrgSp7L/v67H/v/jXjYIg8DPTjcT0Gzm59Cnssjjp2DsYEj4uK/8+WnkBXxna6VKeZN7Cw0Sz7Twj/QOfOtejP+tl2ehxDqgexa/p5vqsLGrPSvjZhzImi++kK2cZieg28mQKVmZfT7N6kg7DMermfs1B/Qr+HPoRotN+7nJ/yqPKF7gv00Bes4wbj2TTVvsAvnQL2eIwc2yb2KNqwVS+n8HsLF4Ofg/D2Qn+znN8bXHiHVJz+fkyOsuvJB05yGLrZpqUg5jK9/KlYSMtQ9XcOfMZOkMTI+MaFozOZ8SWS548QdJoI5R/iGOWRQx2Z/Kb2N/4ux6u36NloGATJmGChNGKvPgwb4luoaB7mErtXnLPKZjNvAI1I/h0KrRl59jhv5zv+raxXSaibLqRqDCAypCLsvAc+zyr+G7iYyZmpfiNa8hRqBjSDXBMvZglMy2clCSRipZhlqaZ1IxyQraYywNHCNil+DVF2JTZSDO7mZzNIKCewRIuRSOFgGaWgWQhZcluzNNSYukgfnGYUZGN4vgQahfE0xHCoijOlBY5XiRpLQqJFKRh4gkZSVEKAcl/JAFBAHFaQCCBKC2gSgaZFumIKONIEyJk8TTmmINecS5BcxJFSoEmIaYgOs5pWTXunCTqhBpdFIoCgxxT1iGY42hCbvwRBzhy0WdHKPCbmVVdIJRUkRPMYFH4DHsMi6iTJJEFjzERCZDnLWN1+BCHbQtZGxVhVzYRT+eT4U+TGsqiomCItxLXkS6NYZn9AkdSS1FQT3wgn+WZJ/md7Ha2pqKcLWzDFLXS5jxJrXsumWNh6oq6eEL+Xe72J/hgjYOSoUM4UjaSznFUY6tZ6TxBafkEv1bfwxaXgWNrEyh8H2FOldLm6yXbD/KR1ZROTLDFsoPTlXN5JvwDnvT6eOnqFHrHO2QF5MwmLVyY2cv64DzGujaw1HmOW/TvYF+Qzx3pn5HjKuV2sZvf3BhH4/qQ0olOROorOGU/SMgzwMXBxUz2rEY+rOaJ1EMUFE8zOq+Sm+IPMRBax7uTkww1Jnj6hji2oTeoG96DRHIxflkBXwz9HYJ2tvjnoXEs4vSpzdS4R3g++T3mZvXgWWLmePZylnqepDO5iVcHurlHIeL36328d18BeavrvmVDfXv8y0YBQL9uDcE8G6L0R0j9fWwqu5N8+VyO6vs5KD5GZf0pzKUneUP+I54P3ou8M05GywXeU/6Jy8TvcHdhIe0zYv74hhhTZAlnG35MQLiAOHiYtXnXI9Jk8rmyCXPpMeSlwzwlfobOkRLyzvXwoexpvPounlSY+NmHAvrkWjpKNpII72CeaRFSYz5HdCcoq9/Hh+rrODS1grqONv4qe5pfWRUsPiymxLmcvry5KOJnmW/bzCFVO7k1+3lDdSsj3VZ+4nkfp6mLYL+K2pkljFnM5MvSxA1WgnmHOWVtpLuvkMfDr/KWMcbW3XKGCq9GkbqAwVxJMOcou7SboTPC7bI36ZlVoRQ2Eku1k22qJ5Z7jI8kW6kZ7GbYMMzG4zomjRlUGWoYM7WyR7WWmpFuTmk9zO0rICgeR6SxoMjvo2+qgMWyg9S0ynGpRBi1eciyuxmezsGs6KRiSE9YZMepTDGsy6HKM8RIIklUVoZVZkVq6sPuNOJU+1ClSzHLVaT107iDZuJyP6pUFlqpAkHjIhzVEJPFkGFCIZaCNEQ8KSMpSiBCyjc7RmkQ0kjSAiIhQYIkoqSMqFhOWgbieApJLIk+6mFAaiOqjiOLpBCHo+hDPkZVmagVaeRRB4GoF8GbidoaoyykJZJuYybsR+UpYo60na9FKxmvSGAMpvAHpohPlpCbO4MjPIezNT70MQMh/yTR0WqqbT1sS17GdE2MrJmz9AbHKPHUssJ7grHSXCzeEvYs9ZPjGKXL20WNp47S6Sm0FRH+Hr2VSmuISPIrZKI5DDhOkTuzmIX2NjIqvTzOfdwTDfPJimmKx84yndAicc8iG1nFGtdRSsomeFJ9BxsdZppXJkhGd2CNmOgNOVC4XdgmV6AdFLhT8yojDSXcH3+Yx+1R9q1LMak6SMlYO1HpUo5Of8qScCHikRWIB7Q8In4K6VwZL5uu4a3gbfzN7uTLtQmOVfbQ2PIXVJKVTKdUHJv8iMXBAopcy7nQvJ7Lgp9xv/olIvO1fG5by9WR33CFXcav4z7+cHmMAwtnaTjxBDmBABLVzQxHnOwZ/wd5IQVXRpbhG1nIhfMXsza4j+f5CVkFLsIrMtmuX88i//OEogt4v6+T++ZdgUgm+7b19K3xLx0FgLqVuVxUtJU56jW0aCf5THQEW8lZqup38bXqYh5OPI93SIPq+BiPJj/gZcnjvGpT8phMxQNfKrj0sI72xb9gTGskHnqfCm0xCzOu4Jh6gD7dcebM/5oD2tX8IfQjJM1BrrZ/zQvy3/KLLB3uYTk/3aaiv/qHzKjjKKJNrM66jgsqB+PW42TUtPGs6JfMdmnZMrGXe1Wv8JDBxPc+FYjpr2XSIMPMOBUZqzmiOUNB3WFekDyAt03Kk+FXOGueQNMup8i7knGjmny5iJjBgj/3CM0ZNbQPlfNw4B22mwJc9rWE0fwbIH6GYvMiRqxNtGaUMdlr4nFe4n2RmgUDVTgUbupMi+g2NNNmLSPaA1tkn6DqlmPPvBSTyMeAykMi38XoiI0rpJ+R1S5l2jqfEnUeE4YL7JNfxCr7aQ5LQCFailYcZEQ5y3ntHOY5L3BelCIpX0C2Qk9QP0BropY1iRNkD4oJyCPElTLcWhVmr4txYsTFuejFOsSacbxeFS55EBFZaKVKBKWLYFhJUBpFjA656J8rhbiEpJCA9DcrhW/4j3VDjEQ6STqhRCZPoU3JEIkCRIiRjupRqtNY0wrE6Vn8qSCpQDZ6Y5jciIp4qofZqBdcBRRoRhlJlDOaH0KZtmIJiyn0jTNgzqchIYLYKaajYWy+QlbEjrFDu4paRQxF4AjTsRhF/kIW+8/Rn1fMYp+Oc+UjaJK5uD39REYWskXxOa/rruDqaIyunA40qWKGnc1op5ZwVWQbE5W5DAZWM7QgRubENmLiWnyuXmTjK7gy8DnpGiXPxL/HLWkfX84donC6j/GYGI3XjzCyivWeQxSXTfK05jZWOm30LYrjE31Nlk/EaCxF2DVIuX050e5sfij9A/55Nr6bfojr7Spi1VH21vVQ2/0xUsUlnLDvw+KPU+VcxmjbIn4Se47ywmGaq+ZxS/RXfH8mRWlWhGevjVDc9SI53iBp1SUcmfqcpH+SS0JLmOhegWjYxq8TP6U6fwjXokJ+lLqHD+K38droKHk5CX50e4owR1l4/veoU2WIDVdzZvoQ5z27WeDPY11iOX0dC5jpX8X1kb/xlPw5xCUJkivyeEl6KbdpXiancsu35qP/E/iXj0JuTQ2eQikfy08izjrH4iVf0KEr4BHJq7RMl6JvsrPReZDD6oeZ0I5yTVYW5a1innw9hTd7K+eLt5JMH0QR6ubiwrsIKBXsVJ4kp+IEsuIhfiP9HR1jpVjPDPK65E9kqo9yr9nGrV9JmDtQyfn67xGO7ydPKqU+YxNfq1rRlxzFnZ/m+fgjiM76+GXkDbJ1h3lRMPLQJ3IGS+/FLeqnQC7DYqrjjP4k2bVn+T0Pkzwf4d+S/852S4CyM1Is0bVM6UQUyKXE9GZ82Ue5YCujeaSW73s+5rh5llWHxNizbyQeb6LOsopWw1m8+SnOTDTycPhtXtWJufqwmVFrGRXqLFo1o4iLJzk1NZeHEm+zQ1CQ555PlC5MhnLkBU3siF3JTd6v+VwmwxaaT5oBfGoZkrwJxsazmC8/wpxWOQ61kmxtKYrsVjrcVVwkHKWkU4JHAUqVFal1DMe0DqfaToYvH4kQwC7z0SktZ0W0Fd20jJBCIKVU4FfL0Ab9ONNxkoIZFUoEuZNgUIZfFIG0FpkgBUmIWEJMQkgipMX/P4NmSUqAdIxEOkE6oUAqT6JOSiDtJZSKkAobUakjmKJy4ukxXFEfKW8mRpWdSNSGw+BDhBZLQkVVpIcDivkkrXF0QTuO8CSR6QpyM6ZxRKq4UBZAk7QiDQZg2oQxL0QsVMWpWh+GqAG3b4jgaD1Ljad5RbgSmy2OwfE1zpSKsqAV5biS7CIHb8avQ1UYRW/fRVgoRh0I4ulfyA3y9/g6ZwWLXVa2rwlSNLwHZ9qC3hfGO7CCO5Kv4qzJ5OXwbVys8HOsvJM8h4OhSBizP058aBUbvfsoLJvmefXNzHNmMdsQY1JzmFyXl9mkgWlnC3M9S5i9UM9DwjOIGxX8XPZdMtzlLDMGeGWTi9qOv6CWrKQ3NI3d1cbawGJ62tazzneIGw0f4lyQw/fSPyTmn8dvQ06evSbJrPokDV1vI5Vewng8zsnpT1geLKbQs5S2cxvYENzDQ8o/Em1QcbZ4CeuCT1Poy+cd+wx7l8V54TrIv/ASc6b2IwibSOjmsLv/HzijvWx011EbW8yZpmUwVcr9kUf4ofY9fJVybrl2AYjE37aWvlX+5aMAkLe0hOUrjuAxeXlW/jc+dK/B1uojq7ONr/XPs5RPuL24nFP2FP/+nor8qXLOLH0Cl9RNwvcBcy1LqTKsZr+uizH9SRrn72a/Zg3PhX+CvCXERRNHeF/+K17KlLHPLefJt0V4sm6gz1ZDMvgpi21rEbSZHFCfoGrOXvbq1vCB6xqsZ0Z5R/Ish81uWmdV3PW1ifbaewnH9lOnryauM9NtPo6uqo/fJX6GotnN30R/4GVrmqVHxShEm5lRRylUKInpTXizj9GTmc/J8bl8x7mTIeMQFaelhA3XEEw1M9e6mlOadhRlI2z3XMHVM3s4bnJw5QE5g4WXoGcap1aCubSJ92I3sW7yGPsNAS47rmfSaKFaX0ufrp1zhjlo+r3I1K2sPq1kWpdBub4Gt7mFryWbWe86zn6pBGV6EUrBzojCw4AhG/OEg7OaIJZwFTLBzYTMwwVNFfN8nQykRETl1WTKrQimQcaDOeSIB8mcEhNNuvFJEkzIs6mPD6L0yIjKBJDJicnFyKIRfCRJoUGGBEEcIh4XEfuPKACp9P/ePhLSMeLpOKmEEpkkjiouIZVyEYiFSAaNKFV+JBEVfpmLZFqGNqGhKD3BWWk1jpwUyqgCaSiK0p8mbpZTE1EQS7XiiMWxhLJZkDzD58rlGE0J9N5WRkLj6NxVrBIO8JFyPZWqGCrfYexJgbJgLlXuAbzFRsy+QvYu8JERiNPtOkeGs5HN4S8ZKC7G5i3hy+VB8qfPMxiyUx8oIDmUxVLbaX6vuJG7/VF2LJ2hYHaI/rCT6qCVme6F3C15if7qcr4MXU2DJciF7HNk+ZP0BWfJDUgIDa3iYt9uCspm+KP2RipdeUSrIvRknKZwdgQP+fTbj7Lcv4ihtuX8MP4HjNVhXjFeRXtgEz9IuXj+2hiF/S9hiVlxi3I4O72TDaE6fAPL0Y2J+bnkSdINct4zX8IzkR/w98kZhhvivLHJT/25Z7FE9SRV69g3+j7SoJtLQksY715KcriIn8d+woKsTnxLsnhOcj2PJh/lV+MzfEee4GfXhjhfM0Pj4cfIDYcQZDcyk07x+cCLqKNRrvItQO+Zz/Hj68n2wPOyv7Ehw/At2+jb53+iAFgsNvozf8tvQ99F1ZdAdGKQ38vf4Xfpx3khx8yDUinf36/hhs+gd+GD9JjqIfkpxqSfzQX3MKmK8YXsBIXVJxEXDPFb6b/ROlGGvmmUF8Qvs1K+jVtyclhyUsrWQwZa5j+CVzKKInKetTk306FyMGQ8QVH9cf4sf5Se0UJqu87zmuK3/CpbR16LhDWtxbRXXEcivIulto2MqqPYbcdJlbh5IfIApuZJXhY/zzNWOZfuE5NUX4Fd4aFYqSOiM+DOPMFgpo2jU4u5Zvprovo2VJ1ylMIGPJIB5pqXcFo9gK26mdejd9DQ34pR20TBeTn2zGsgcZZMYy3hzCY+U27B2D1LnXIfxS1SJnMvR5e2M6oOoy7u4+xMHT9If0ibT0ZMuxYlE4ypwiSyvcyO6JkjP0ZDiwS71kChphIhs42m6CKuS+5FNCHFrS8nX1kA1m46Q1WsTp0iZ0CEX5lCqtQj0tvx2uVMKP2o0iVoZWICMhcj6Rwq4wNoHBAVRYlIwSU3UZC0I4pIiEvEiEVSkESIJwRi/2v76J9/A0FADKRTMRKpJOm4HIksjjIuJp50EE7EkcY1WAQXY+JcvFYRctRoE2JywqO0yYvQKeKoIuM44g5izmJsxlnc0UIG8iMo0xlowkk0rhTpLCkVfh2jlhFE5GENS9A4RIjz06gCRZyo9aKP6pj29hIen8d65T5eU1zOOiFCJH2UlKgSRTCAZ3AOm7V7eEl1BTeFwpwpHcQQy6DDeZwy91xqHYPoyyI8nbiTLeIArXkdWCIGzrtOsTBYibunmluVb3KmZB59/oswFEaYUR3FGjHQ6e+nNKDBN7CKS/1fkFc6y7/rrifXU4KqJEJzQTulk22EJPW0zexmTaCR/o7VfCfyD8qKRvk6fxUvh+7m9y4H/35VElFgJ4WzwyQUqzg4+QF1ISvZjqVMdczlofivKSmcoLe2nisjT3C5U8FWZYjHbk4g93xG7dh+JIqrGYzMcNa+k5X+Mgp8i2k+vZGVgaM8LnuaWJWEsdo5bPQ9hj22nE+GhghWpPjpnUBoP4tb/4gqWYLcfBPtznMccnxAWdDI1Yll+MYayLQ9/m1q6P8Y/icK/+TSnFosZxzcENrDIeWD9GjtbMnJoaBDzO9fTRHXbeB07f3ERB3EnDtZmX8lOYo57Da049CfoXHBV+xXr+fpyE9RtoeZO9zELuUv2GWJ86eUkqffl6JJLaelZCuJ+OcUyk3UmtexW92GuvAE6cIZnkr/hmRHii2ze3lA/jfuz7Bwy1dirKFl9OQtQhTey6qsazirGkfIO8Zstpx/+G8jq2WIv8me41c2HTd9JSZg2YpLNk2J0kxYo8dlO/HN3cwzq9g4fhiz9iSuEQV5nkXMKoPUaMvpUM2SXXGcV/ge1gsTXKd4n067AlN0OUGhmwbLKjp05xmzGZgctPCo+HX2xJRYwouJpjvIMdSRyjrDNtF1NI63ccQQZE2zCZc8SLGxgUTGeb7iUi7xHmGPTII2uQBJepwJZQSfOU1iDNzqCWqGDISYJKSSkTIHCExJmFK7yHZnkUrZsUsiDKkKqQ0PM5oWEZMUYJYaEBsm8QRNKKQujG4J8aSfoBDHJTaSl5xBHhKTlEBSIiIhlkE8RYwk6X+uFP7vg2aIEUvGScUUSKVRSMoJKhOIRSpUaQm2hIteSR5+YxJ5VIQQiSAKKpEboCCiJJ7sxRkNow9nUJPuYo90IdHMGPrAJFOhMWKzVVRpe9iTXsZoaQKzf5ZRfy/M1LJcdpy3ZRsp1cbQuA/hTskpC9nIdAQQFQv0h5dyen6M3Olm+oMT1ARKybIHMJSEeS16PRm5MZTuL0iLGvB4+klPLGFr/D3sldnsC1+BrjBKOvQlStFczs/uZ66vkfSQlcuMX7A7ZzVKbw2z9XESsa8wJIto9bRQHTLh6l/JFcHPyS2x81ftNd9ceJQf5mhVHxUjR4nJlnJq5nPWBusZvrCaDf49rMpsor1qDj+MPswfp1zsWZWgJ7ud2v6diBVbaJrZjzzoZVlgGR3Na7k0+DnXGT/DszCTn6TupiuykVfts7y7NsbRxmnmnX4WdbKchGYpu4deRxOOcHl0CWPdiwgPV/Fw7KdclHEW32IL72guZmv8abbOxngxFOKVi4K8ebWE4jN/Ys7sQVLJtUitq9nX/w590VOsyphDdnb2t2yh/zP4nyj8E7Ncyu41M1Slv+DWsjoOOWP85WMDtRdMtKx5mgm5klT0PXKlejbk3Eaf2sNu6XEq60+Rzhnkadm/0zxVjubkJL8R3uZm0avcXliArlvMTz+S0Nf4Y8bUOlKBHazM2QIqC3uVTVQ3HuK8oZIXgveiOuviyeQbFCgP8LjKyGMfSfBk3sCETos8epalWVdxUN2FtewY7eZStrkuo6i9m3+T/Z6HrUbu+VyEM+dmPJJhSlU2QhotDttxprKVfOFYx4rhE9Sqv6bNrqRhuIpxg54CuYppTQp9yQneV9yEcCHME9K/8YZYy6q2fKa1Cqr1lZxTD6Ev6+VLzzpu8+7kHaOEy48bmNAbqdbV0q7pYsyqJzAo40rpZ+h6pExnb8QsitOrnMGXkSQwIqdGfoJ55yXM6DIp1VURNHVwiDVcFjpCR1xKRL0Yq0zGlHKKs/K5LA200ZMSE1PUYZMbCGnH6aKSJbEWDFNiggoJSpkBiXoan1eOUx5CLuSglAiExEFmBSNZiVnkAUgICZJigahEgSYVISIIwDdRIJ0mLYA4lYZUhHgqTjqmQCkO4RL0hLQiJIIcZUKMOWGnT5yFTB5DEXPiTXhJ+LIx6H2IIkYmrUHkIgummIycwCSzRgsNETHJRBu+lJjsqIWGcAvnDdUsjSVJJM4SxEBh2Eie2044T02mP5ujtV4MMR1jng7SU41cKtrB58bVXO2P0JU/iDpVzJi7Ben0Aq5KfERfcTmZvkK+XBGhcOwAjrScgqCKmd653CR/m5MFC8nyFXN0eYLc4fdISxbT7zhJmWsRmVMRFma38Lrhcpa6TDQtS6C2f4yaes46jjMnaGOmZwVXBreRU+rgFf0W5L46KjLD7J47StXAV6Tkazk6/SkrgmU4+1ZQ5xzgOuOnTDaUcFP05/xwOkK8Is6ny+00tP4FmXgtg2E7w+4mLg7NZ6RrGdmzER6TPkVsjoIvMtbyw9gj/G58GktBiidvTJDX9XdK3X1IFNfR5euhxfU1F3krKAwu4sypTSz2n+M3kl9CWRr//AquD9zPF9zKawM9LLWIufcGPwMlM8zd9zNyoiHSouuIaC1oVv5PEP6D/4nC/w3VvOt4wVrKd49ouefdIKMN99KceTGC+BAp5zE2ld2JVlbATkMLfuM5Gud9yT7VRn4TfRB1Z5Ti3mb2qR/ngt7OjzU6Ht6lorY3l3PzfkpUaEYZ7mZd3q20a2bo0pygqnE/bynuZf/sYiznBvlY/hynjG4+Dyh45CM5vXU/xCsZw5CYps6ygT2q85TWHmKfdi2HZhZT3d3Kk/I/85DZzI92SJksuhOfqIcydS4hjQaH9QSObAmfuS5hfv9Z1qm2syegZk1rHkMZlZjxkNJYiWQ2cVS3BGefjmd4lT8atdy8V81A3nJMgo9ZVZqM0lO8mb6T0r4eRNo2FjdJGCvcgjY1wYwmibW0jS/967kt+CXva5RUjBcREgbI1FUgzznPruSlXOE/yJdyObp4IzCIU5lGkmXHMamnVHaeOR0S3CoBm7oIla2Lfm8xK4Uz5A+I8KmkGBWZKE2DOFxGdLJJ8ibFRPHgl6XwKE2oAyHGpXHAhk6qRlB78CYMZKQdKPwp4sSJi9JEREq06TCplIjUf5xo/uZGTqRpgXQqSiwRR5ySok6HcIkMRDQCkpQMeSKNKhIkrlKQnZBCahJXzIMQsJIrGeO8uAp7ThpVVEI47CLizsVo8ZOM5NBTEEGZykAWihB3WDFkBfBHyjldHcUQU2EPDBGcrGWR5hyvizdTaIyjce7Hi47ikBnFtApLvo9PYpcTKI1jG/8Sn2ChIKTCNVDOatMJ/iK/ijt8AQ7WT2INSWhznWFeoAph1MIS2zleVFzN7c44b60LUza0k7C4nIB3EN3sYhZ42sgttvOU5FZumYVP18XJGnkLpXgJp2YPMj+Ux0TPSq4OfUR2iYvX9ZcT989jgSXCjmXT1PZ+jEi+kSNT25kfyiExthjrZJL7VH/DO9/G7fEfsdBjZpUxyh+uClN1/g/oEkX4pYUcm/qEVf4y5NOLcXbX80jsZ+QWzDLRUMPFwV9S77Xw63iQX10VYtJ8gXltLyBNLyKureeLwb9jisDlscWMdM/HPzyPH0Z/xhbzERzzjRzJWM6qwDNU+K1sm5ymdU6cx+6RoXJ8xeKOP1E/bzEqveHbVM//UfxPFP7viMT8ofDHyJhH06LH8YtniXrfotJQy3LztbRpp9jLMernniGZOcQz8r9weqYC9ckZHkxt4xf8jh8VFtA3Bc++KeDO3coF2zJS0U8oUxdSZbiI3bp2pDln0JZc4LeS53ENaKnsOcc78qf4bY6RxICYW/ZoaW38EdH0cQrkerINjexXnaZ6zn7eU95O53gxCwZP8UP5SzyiN/Pgp1KGy+4mKLRRoSkiqFIyYz6JOyfJx54tVHe3cr3iXd5JarjquJW+vBWoEr1YDNWMGU8xbtPRMlHJ94Lb+dSS5Pp9UobKbkQcO0eGrgq/9TT7NesQ9cS4X/Y25zwyVMIKYulWio3z8Zmb+Up5GZb+aWSqNlafkjGWuYACpZULqhHGTXqSwwJlsibmt0iYNhRRqilnVtPLUdlFNDrbOaAWo49VIU5OMi0P49HLSM0kGFF7ybebiSWncMuT+LQqcMYYVIZQpYqQCmFcohCj0mzqEoNEIlKicisakRqR0kk4rCItCaPzQzId+ea9R2IFunQIUgIpseR/vxBPAFEa0ukoKUCGGG06hF1kIKxKIomLSMQjxKN6VJo4lqiMaGqCWFqBMaWiMDbGWWkFOkUMVWiQmZgdkbeACkk3e8ULCGTHMfjGGAsPIXaWMV98lm3SleToYmgdx5mJRygK51AX6MKVa6bMb+ZonQ9DTMuA+zwyxxwuTn5KS3Y9az0Svp7vwRqCdudpKvx11HoGEJemeTV2PYW2OIT2IhXm0+s4Sa5rPvM9HWjKozwWv4vvB71sX+KgaLaHmZQUczBJYGgJV4Y+QlmV5Kep7/LgbIBXL45T2PsaMukqTk7vZnGwkLGuFVwbfp/MEjfvGC/BF1jERbow761xUdf5FhL5Zo5M7qQ8ZERvX0JqKJOHxU8TadTxuOhW/OFFPJT08+sbEtiG3iQ34CWtXsfesXcpDKlpCC+lrXkd14bf5lrjbvyLbPwidTO7kzfy9tg4LXPj/GNLiqoTvyUnHEKiuZ7z9pN0BY6y1l1FfmgBTSfX0xDo5hnhYeRFUZLLCvlJ+CaelTzCL4cHeEQs4YkNLk7dWkLxqopv2zz/R/E/Ufh/oGpsJLFkJVLx50j8Q1xR+QPSYj2f6c6RtJyncd4X7Fdu5onog2h6YljaO9in/y0RRSc32zK49piSK76W0Lb8cZyyAAnv56wpvIGkwsBX8pNUzTnOiEnDbxO/QNUW4kr3Hn4qeZF7crPZcFzG/K58WitvJRHfyRzTfESqDE6omqiYc5i/yh9hdsDARRNHuE7+No9rTDy8TcpA9b2EhfNU6coIqBRMm08RyA3znu86Sjq7uE/xGi/IdNy2V0d38RWII6coNy2jVXuGdKGdzzybWDF+nJCmk6IWCT7b1USTp2jMWMN5zXm82Uk6J4p5JPUBfzNouaTJyJRGS7W+hvPqfqLZPobGsviZ+AMORWXIREtJpdqRq2wYC1rYHVnP1tA+dilUmMJ1pFK9RJRy9Ll9DMzmcoX4IJZBCU5jNXnqPNyaQU6JF7MqdI7OtISoag5mmQK7zE6ntJIF0S5Gk1Ki8mIsMgMJzTQj6Xyq4gNonQJRGSilOsRyN+GwlJAsjlwwkE6GiZEkJMhRp0KIEgIp4T/OKXwTBXEK0qkoCDKkiFGlQjgEPRFlAnksRCgVJBkxolBFEMfUzJiSyAQ92rgEY9hBWKeiLCojnuojnJKREddRGe2lR1vEvGiaVKKNUFpDbsxIaWAEr83IHL+S7jwnCgpIhl24xiuoN/bwWmozWeYE+tm9hAQLhSEt0bEsirMneCF+DTX6OBL/PhDVEw9O4x2Zx/Xpt+gqqibHV8BXy6IUDe0gJC5GEfLjHVrIzfHXcNdk8I/YzazWhOnJ7sISUdLp7aIuZGO0axl3xF8kWqfhgdgD/Mrh5i+XRinvfAWJbA3Hp3ayLFjK0IXlXBd+i8xiNx8aNzMVWsEVyhCvbfJR1/EKUulmTs/sJyMoUOxdgqOnnkfSj5OqkfCu7mI+j93AC85ZfndZhJBwnJrxw0gUV3F6ej+x0BQbw4u50LKcUredxyS/IVCn5GjWcm6K/ZqfTDjZaICf3BJHEtjPvIEPIL2esDKfLwZfIjMq4/L4Uoa7GvEML+V7kce4xbCHiTk6Bormscz9a0LJBXwy0MMlm+5AEP9rP4L6/+R/ovD/wvxLyllQvIEG5TpOG0c4nDrOvIVniFmHeFbxN07MVqI9aeeO2Jf8VfRrns3L4cNAkj+9r0SZXMDZyjtJC4dQhydYn3sb7ZoZWmQnaJy/j13Ka3jbczna07M8J36LMulefmAw8cjnSpTppXRmLSMR+mbuMKtMckHdREHtSf4gfYJ4j4ir3XtYLtvBMzItD38ipbf+B0Q5RY2+Cp9SzqSpiUiulzcDN5Hb0ctj8r/wjMbIfbuUdFbcTDp2kHm2TRxXn8da0c5r0Tsp6upmreIzOuwycl31uKV2GswLOa0aIquijQ9C17Jo4jSduhkuPSZlqHgLyuQwHqUYW/E5PkrcwMLps+w3xrnihJoprYkqQx3dqh66dUVIhuPky06zsFXCpKmCYk0Rw8ohzqrmY5mcoUUbpmZIR1g0jkxpRm/rZ8yVxTLROQoGRHjVOmyKPKTGQQbDRSxNtWOZFBFQKjFIrUi1k3gDejIlU1jsaaJChKhUQFDEiQZF+MVRxIIeqVggQpyQIEOdDiMkRaQF4X89fZQWQJJOkxISCIIUaUqEKhViWjCgEZKIk15CqTCJoAGdzMMQ2bgtIE/IScVCRANWNIYImrCOUVsMWdqMKpoi5VGjssbRhC10FkRQpqwkwh7cM8XkmafYm1jKeGkSq7OL4eAwucFiFoePMZWTw1yfmiP1fgwxNT2uc5h8dWyI7mKyKJ+cQA47l0TIs7cyEnFQH8wlPJLHfOsF/iS5iu96fOxa5CLX76TXP8ycUA4T3Q3cIn+LjuJ6fKEFuKsTyDy7UAiNnJ7ew7JgOcMXFnGf8G945th4OHI/v/Q6+culYao6/oFYto5jkztYEaygv2MF14XfwFLs4VPTBgbCa7hJGuLFS4PUtb2IRLyONvd5ZCEvDYHFDLYv4cexX6MrCdGUv4zHYz/hpfEJ9iyLcbZ2isaOvyMSbWI06qbLc5DN/noCo/MJD1fzi/hPsBW48cwt41Lfw+hDFbzqcvHXNQEOL4/QeOSXmBI6ZIbraBr/iqHoGda7qsmPzOfk8XXUBEZ5Lv0A5lwfyeXZ/DF1Oa/Vf0JWxr/u6yz+//E/Ufh/QaHWYFtZx3bNaSQZrcxp3MU+5aX8Mvowmr4EiuZuvjL9kWzhENcWFFLTJuGH78YZWPATBtR5JCMfUG2sp1yzlN2GDrA1k115nD/Jn6FrsgDDuQE+0zxHq97F35JSnn5fymzxLYyqTaT9e1iXfxMd6llmjacxlrXzrPAU0o4Y34t8RoH8AH8RVDy0TU5nwwPEUseoNdTjVUqZMDSRyHPwSuh2bG3DPCf7M780mXngMxnd1XeTiB1gSeZlHFF3UlzTxF/5Mfq2GX4u+zuviDVsPmVi2FyETZRmShUns+w0b0nuwdg1yxXSbUxMSRCUa4mlmqk0LWVCd56T2oVI+qNcJd1BcliEK/syJIke/AopGUUd7A+u4KboXrYrVZj9FSTTPYgURiy5nZzz13Nreh9DfgkB83K0xBiWORnWZCGfDtKnDlI8pSPKFBGFBLnRScApJym3UzIuEMVDQi5BpvES8kpxy0KYA2oSKS9hcRKPzIg0EsElTgI6FGIZcSFGSFChToX/uVIAQRCRTn9z0Y4kBXEJCIIECSLUyQB+iQpjMo2Q9hKMh5HEtGSmZxiQ5OE3JlBGvLhjTuK+bGyqWVooYzonhTaWwhmZIuoqpFA9wn7m4cpNYvINMxYaRh8oYnHyGCe1c1kbihD8v9j7zyhLy2pRG76esHKsWpVz7srd1TnRNN3kDJIEQUQRFUxsBREMiIqKYBYQRcQAgiQlp6ahc6zu6so5V62qVSunJ34/2rPP+47xfec7+333Oex9dl9j1Fj/a605r3HPed9zSj0g1ODPQGy6ipr8GR5XzyO3QMc//wZZqYTStJOlsVo6Ar38jEu4Qk0wkzuA3VxG/9I+imMr2JzYhVJv47HsR2nL1UgLe7ELrRyZe5uOxHIsUz42FHTyJ+/5XBQSeeaMLFV9TyBaTufQ/OtsSK1goa+Vmy2PMttazY+zN/PlbJTfnJ+kue/PiLaz2D3zIluTTQx0beba9O/Iq4nx97yz6Mqew2eEJA9dlqXl6E+xsJHB5ASp5DinpdbRffR0bsr+lGWl44y1tPKx7D18c2oerd7gyQuyLN//fZxGExlnPe9O/ZE1sXIqk+voPnom12V+yeWBncTW5fNDPsJj3MrvRocQauCbHxcp7H+SttAH6OYlxCxeXhl7lLK0k4uNTYz0LmdxbBsfz9zHbd4XMDu83LB9xYeaZ/6jckoK/z/IbS7hzLM1soExfuh4lPcXmsk9EOKSxLs8I3+dPxb5uVcwuf8ffhr6Axza8E3S0ihq6DXOqbsJ1eriZes+GtsOEs2P8i1+hDQIdYOHedb2bR4szWdo1uRfnpHoXftVovIClsRRtpZewy7vCGLxYYyyGe7X78bZmeBb5l8wHZ38SbHxpResnFjxRTTjfdpzVxJxyEz69kPFLI+kP03g6AS/tD7EXYX5fOnvVoabb0HV32dD8TkccI9R27yX31tvw3Iiw89tD/O9vFxuft3OQM2lWDLHyPU0sJRzkAPulWRGbHxf/j2/cns585CfWaeDJl8jR51jeKpG2Bdazi3qKzye42NDVy5RS5S2wHpGnd0ccq/EPpKk0HqYDccszOStoNxRxKBjgkFPKfJkFs0xyuYumSWbSJWnET2nnwPmBs7KHuA4FlTXCtyCyqwcZcRehT8Sps+hYTcqwQixJGWZsxVhi6eYsqpIQgkyWeJClpCUQ4WxyLwoYuI+OeLCmiGJC4eRRtRO3jgSBRGTkycFyQRFBhEJ2RBw6AlMi4jXkMFYIqOpuA07+foifWIZBYKGVZkhrESxZwJUG8MckJvwOVSciX6C2RD+TCGt6jEOOZpZoyiQPY4iBCjNevBFNcQSE1WpZU+bQiCTYTB6HF+8kTPU1xgrruO0qMjOtgR+xUHv4n5KEi2cltpJvMbPdHYtR5erlI28REouIz8tMjPYwbXSn+itbKU0Uc4b6xUqB55Gt64ilOjHv7SKjfEDOGqy3KHcxNcWlvjNuWmWDT6LZlvLyNI+2pKrMSeKuMb7PEMNbfwtew0fF1P8eVuM5qEXEOxnsHf2RbYlW+nr2sR16UcIVMd4LW8bB5SL+LIW54cfUWg6+lNsZgczeoaZRBdnptdw7MjpXJh+jrPyDhBaWctlya9x8aLJthyT715jUHP4ZxQqIPrO442RJ8jPiGzVNnBo/2m0x4e4x/pDllocdFWs44L0fVwTTHGHKPCVSyJMlk+zdt992PUaHPmXs3PkWWb0bs5ebKZCWc3uXVupTYZ5ojqE2/pfb1fC/wynpPA/oG7ZLTxg3IVnWMc8MMRzuY+wSX+W6+uayYzr/OBxg0jlR+gsPBdJeg2vGuPMko/T5ZnjgLiXtet2sNu1iR8nbybQGeeKxFt8TfgJN1fV0HxM5pK3rHSuuQNNPIxXDbMycD5vek5QWHuI+TydhzKfx3t4iZ9bf8eYZ5rXYjKffclGV8cX0M13WZG3lrBDYsKzH7lynF9mP4evc4bfOR7i68X5fO51G9MNn0ARj7Iifz39riXyag7yqvMy0sN2viX+hd8WOrjhHRtjjZ9A196no+AsDtmPYpQF2RlaxeWxt/hHnsB179kZrr0MW3aArN1FblUnf5VupGBoAsnexaYjFiarLsChLTDlSFNU3cuu6Co+ob/B8w43+eFaVPrwOMvwlvSwU9vKhZldvGWzYzdaMbVRIjYTT8Esiws+1spHqR2WCLsLKXNWorrH6RLa2KQcZ0axkHHWk2v1EJbDjMvlNGtjTIgymlyEV3aRlBIsiHmUakGErIRi8WMTrGBJkTIduIw0gnZSBCfLR8bJnoJposgnpyDJxj8X7ljBo4no2hKIHlymhTxtgaTDTZkqgjqJgpUcw0mFMsmCO4/GDKj6MIaYR4HqJDeVhHyJ4rSX7sosDiOPeHqW6Gwd7a4enmIrNS4N1+L7ZKV8KtIe1Pl88ovD/FE5D1eJQe7M66iWSvIzFmZHmzjDs4e/2M/i40sx3u+IkqdYOLa4n47UMqxzHpYVT/CAeTmfXwzz7JYEVUv9BA2T8pSDqcHV3KD9mmhrLt9TbuZmLcbLGyLUBY+TsJSSTU5THF1D8aLCGQUHOFS+jm7lbM7yqryyMUTjxDsY9tPZN/d3tiXb6T1+Gh9L/4qcqgRv5W/lPfVyvp6N8d0rFeo6f4ZTryMsuRiI7Obc5Ep6OjewJnGMG33PE11dzCeSnyE3WcfXjAxfuzqDe+4fNC4dR3JcycG5HUSyI1yUXs1kfwfCbC3fVD5PbnmM7JoaLot8kbC+kb9OT7FjZYbfftRB/Qc/oC49hiFeyaKp8sbk41SlvFzKJmTlMgKBLR92evkPyykp/A9w2vx8y5/HpvBe3rR/jR258Em3lc+/7+e8VxRObP8us7KEEv8LHYWbqbWu4Y3cE2RzjlLX/BaP2+/hzYWVOPfP8TPHkzSLr3JjYSGf2+GmbrSUw003AW9QaS+iyrWaV52HWdZ+gB5vBb+JX4v30Dx/cP2K3f4khxdMbnzFyvEVt6Hrb9GRv5Elu8C4ez/2qmF+rnwR79EgT9h/wndKC7j+PQfRko+QkMapcZaz6DDRCg5xwldL70wll0TfZsgzS/1xmWzOuWTEHtYUbWOfe4Sq5sM8rt9M6cAQyyw7EEYEMoFz0czDtOVtpd9xnGFvHslxB/dY/8JLpp2CcANZaYDGnHUE3V2879iGeyRCrqWTjcdkZovWUWT10G+fJeIHZUpmjbSXZf0ywdxWypxFTFinOWFvxhMM0+3K0jDpICPMIVq9ePMmCUYCrBG7KZwWiDt85NkKkbwzjOsVtGgjOCMyKUcBbtmF6FwiaOZTogexpUQUixObaEO0pFF1Gy4hjaSBIQgnTwomIJhIhoAimwjISLqAqtmwWE08qkTCqSMJTmyahDWrITohP2sh4kwgCbl4VQkxJWHxG+SlXQyX6FiNAIaSIBYqozAnxAd6BzOVOrmRYSZTkxRly1mV3c98fhHrYhJH65M49QIm470QauJM/R/05LdwTlhjZ3sSv2Kne3Ef1akmViR6UKqtvKheiLXCxDvzD5BXsZgcwrLYwXnpF0kuc/Mb9RrWeXVC7hO49CoOz+9gfaKO6b52Pm57ktH6Ro4o51JfaDBZMEhBVmJSCZOfFtFnVrEmfozashle9m9DTLdRV2yyv22OZfMHMZ0bODz/KtsTy+k+tpnrMz/HXxVnZ+EW3tCu5jvJKPdeoVDZ9Wt8eiEpRwVHQq9xbqKd8b7VVIRT3G7/FbGVfr5vXkGXcQGPLi7yw3NjBPMGWTn4Z0zzPGa0GEfDr7NtqRF3ZCWD3WdxY+Z+LgnsI7Iml4fFC/iOeBc/Hu5nXb6N267PgHqAtT2/AnUFjuLzeaPvceLuIJu2bP6QM8t/bE5J4f8P57cWc33VKLc1LGffQpqH/+LGqrexv/ULmJZOsvM7uKDxFjKyjRct+6lvOY5ZNMh3rb8kMunGfniQ130/os+9yLckCz983o1pP40TBadhZp9jZeEWHNYS3rTup2P1bt53buWZ8Jl4Dk3zrPtB/pZvZWra5Oo3bBxv/yyG8TqrC7cQtsOo8wCuqn4e0v4F99El/uD8BT8tCXDOPhum4wwWrWnyMBDtAabcB4kUZPjH0uk0jhyhyfI2XQsiteMVzNktlFl8TDnSFNQc4Tn7J3D0prjP+lt+a3GzsTOHeZuFBk8txz0zlNUf46XUeWxc3M9buQJX77Ixlr+SAtFKv3uB3MpBDi818mle5xmnm+KFchRhkEJXA3J+L++IF9Aa6uaNHDuNYx4y0hS5jgrchUMc0lZyvraPTiworhVYtCXmrUliLgt6CCK2CA1TIhkhitXiw+ENEku4qRcnyAmaZGUTryWA6FgiobgpExaxx0ETTeySE0FOoeoybjKIqokpmP9csgMgoCOhSiCaIqJhoqpWZKuORZdZcplI2LFqBum0B4dLRVJdTOeZWE0XmpokFSvA641zwqhluszAm40zm5xAjJXTbh7lA+sKVmtZxMwxdLGYwqwTJRTAW5hmh7qemRqDgtm9hHSFhnQhzogVe2mWZ9WzEStMAlOvolvqyFFMpkZauNj6Mnvz1nLhosJfN6WpDPUwpyVYlspjfKCdq2zP0l22gvJEKe+uUSjr/xOCZTM9oQ9ozazEM+9gQ0E3r+WcQWssj6MrVVzBv2MzWjgS3EV7oojJ/tVcmv4zjtosv5IvpS2ai1ZnMlIxRk14CM21imOhN9me6qC78zRuyPwEb2WCD4o284J2HT+KLnHv5RmKe39PQHWguZfzweyznBFtIDbRgWO+gK9L3yHc4uBFz3Ye5lYeHx/n9dUZdp6msHLf/ch6B7qvmTcmHqc5kc8acwP79p7O2tQhvuH4GXNNTsZqOtgW/w6bIxYeTir8aOsSO8+y0PbmXZQYGST7dRRt70C0nyob/Y84JYX/CSxnfptzhwv5zB8ijG78Mr3OVmTxRXINge1FH6PLO8duYw8bNu2m21XOt9W7CPRnqBk7zAv2b/HrsiLeDit8/08CM62fZMxegBp7nm01HyVmFflA2kfHqh08b/8EO4NteA8P84L7fh4uCaCPmFz4ro3O1psx9FdZU7SdkN1kyHEAX80Jfmzeiaczwm+dj/D7Qg+txyz4E61MObw40pMUuprptB/CVTXA79I3kN89whesj/Ow5ODyHQ4GijdiTffhdVQx7zlMv7eUmakAt/MSvynK4ZPv2hiqvRQ504NgC2Ap7ORl+0dxDca4THqBhRmDbO6ZGOYRil1NZP1dvG27FO9oCKflBJuOW5kq3UKOaTLkXMSSP8fivI9rLW8Tn4dI8VZs6hJT9jhCToTUvJ0O6Rh1IxJL3gpKXeWELLP0WNqpiE9yzGXBbpRhaHNEZAXBmUaJSiSsScoXQRFSCLINmyNGJm0lR1jCnTDRRA2r6ECQMmiqhIv0SSlwsnxkYmAKJpogocsnbyTpaJiaDdmqETO9JDwnH7Cl9QRaxo/NkWbQLGUpDxyqylJmHi1WSLUwygGxCa9TxxntJaylKVZzqM2Mkc5z0ZCycaJKxWHksZAeJbNQzzpxN+841nJuIsGSbwLJrCeRmSU208iZ4uscylnJJYtJPlieJkez0bWwj6ZMA5XxMHK5xoPZK9ni0Elau7CYzXTOv0tLppWisEZx2RLf067gS8EFntyWpm5mF2lbOXImBsGVbE29g1yjcrf6Ub44s8ij29PUdP0GUdzKB9MvsilRT+/x9dyU/THZRpk7lI9x5YJKd7tK1NVFRTKE4mqmJ7yTbekVnOjcwo2ZB/FWJtlfvJGn9Bt5aGmRey9JEhh9mpKsguA7jXcn/8z6aBWWpQ6SY23crd+JUifQXbGGz+p388DYEFotPHq1heb37yNXd2MNXMTrA4/jULNcqG3gRGcHrlAR31ZvxVOWgg1VXLN0M8ekS/nLUC+ReoFvfMpGYPBvnNM4S1lj/YedTv7Dc0oK/xN4rV62n/4pjm77PnFxiVToz6yvOI9KaTlv5XcTdx+jfflrvGC/hccj55J/JMK12Xf4uvEAt9Yswzlo8JmnFXq23ENEiqGG3uTc2k8y7IxwXNpHy4q3edxxN4OzJfiP9fOM8/t8r7yc/D6BrbsddDbdiKm/wrrScwjZdAbtB8it7eQB7sZzLM6vnI/zcp6IZwBqx0oZ9i1DSh6myX8aH9iOUNm0n1+Y/4Kna5Ff2n7JPfn5fOEVK33NN2Jm36Mj/0z224/irBjihdjZNE4cRbP1UNkjES25BJ2DdBScyVFbF8m8BIOzJXxJ+AeP5Ae4YK+dGbuPOm89Xa4p7CUT9CyUc6v4Ok+73JTNFqJII1T7lpPy9PKe9QJypmfpdyXY2iWzZJGoz2ljwTrCIetGfMF5Ot06TaNWMkIIv60YV2CcPqWO083jBNMCafcyvLLMopRgylKGJZ5m1qrgzngxzAhpScdiS5BNSqRkhUBCQEfBEAVkaxZNl7CSRdbMk+s5hX+eFATQTBnNAoIhoRlZDM2BJGssmF5SbrDoJkk1gpbyUyAtMiyUkfbpuOKThNUIPj2Xan2EMUcZbaksKuOIUgk5qoVkJIAnkOKQ1s5otU5epJ+Z1CwVShHL0sNkS+xYlCrea85SmJhiND5MfaacikQYowxe17aTqYT8sZcxLE3YVYXZ0RYu4y9MVCyjPpHHi2syVA2+iGavx6akiUy2cXn2cWKNPh5TP8o2l8FEySQ5upfe8AnaEoUMdK/iRvVnxFr8fCVzC/eEF3nkvCxN/U8h2LdxOPgqW9LLGTqxjlv17xNv8/Kp1Kf58nSQf2zIIGTep1AxyTgrGY7tY1t6JV1HN3Nj5kd4KlMcLl3P742b+cVCkPsuSuKaf4XK9CKS7xzeHH2CtlgRxanVTPZt4kvZr+IrT7DU2sxFyW/yyZkQZ+Ra+frHDIqP/4661Diy4xqOzO5kVu/h/HA76dk2pobO5FPpr3NB4Aihtbn8xbKd28TvcMfYEJ+xu/jFDV5Kb7z6w00k/0k4JYX/STyrVtC4SSU7v4+Lm24jJcq8aN1P1bIenCV7edD+K07MlyLvneYR7x9p11/iurJyPnLQzek74OjGb6DL3QjhLraX3sAh/yTzzsNUNu7k57YfEZ1yU9B7nCds3+POyhpaT8isPuyms+5aTO1lNpSdz6JVY8B2kPzaI/xY+Ba+riQP2P7MAV+K8ITB2iM+eou2YCTeZW3BhexwdtHcvpdHLV/D3Zfll44n+GlpAZ/a6WS65SYMYR8byy5gr3uY+pZD/Fb6It6eIJ+X/sAzuoW27hyCFoEaVyW97gVqGzv5k3EjRSODYO1m5QkrM/VXQvYwdrkAW9EJXrVei280iGjpYctxG1MV23EqMcYdMQKlI/SEK7hJepf3sSDK7Rj6AKpsI790lM50I5eynyNY0FztCNoki9Ysdv8CySU7LWI/5VMSUUc+edYCMrYgo1ItDco4gzYbiMWgRUmKKnGLBzFjEJM1PHET3cyiSWCRFRRVwhB1bJp4UgrmyU8TAQ0JTQLBFFH0LLpqxyFnWDB9JJwGdiVCUk8jZL0UGtP0i2VUaRqSMo2ChVzNSUF2EcMvU5pxMFhmYjNyCGdnSYUqaLb08qa4ipW6gpQ8hilXElBkQvMV1OTM8QfldFqcJvbwHpAbcKgaM+P1bLW9zzuu9Vy5GOaD5RlydQsngvtpy9Tgi9jIK4nx3exl3JSKcqw5Rq7uomvhACsTVcwNt3Cp6026S1ZSkSxm94oshYNPIYgb2T/7ChvTLcwPtnGj9Y/MNdXxnH4NV1pV3t6QpGHhMJpzOePxw6zJrmJxaDmfl39GbHkhn0h+ke9OTPLo1gTe6RfJ0bwkbHlMpo5yhrKarqOb+UTmftwVKY6XreVh87M8PDvL/efGkJM7qUsMI3sv4c3hJ6hKeWnV19HTuZ1PZu6jrXCCzJpqzo/czoq4m7tNgds/EkPM7qNj5h9o+gUEjTR7Iy+xMVRNnb6a/fu3c1r6be6xP8rkMgfhZW2ctvQNKtRyfrP27v/S29T+LZySwr+BjnNP5+yOT9Hln+M9ZQ8bNh1iyiVwj/AA9gmQjgzwduDHjDnnuc1t47tvBCiYLeFQ06eRLTvwqlnW5lzIe3n9CHnH8VYc5IfWB5HHRGpGD/NT8Yd8vqqO7UftNJ3wc6zqCkzt72yquIgFq0q/9SBF9Qd4QPo2/p4098jPMe6a5VhQ47wdFrprLkNPv8aW8qt43ztIU/thnnPcjDlm4TbzZXb7M9R3S2DdQFwKUuuuYswRJ6+qi9cdV2IdNnnA8Rd+VFzILW9ZGay9GDHVidNSQtp3jN3OrdjGDL5t/wt/tDioH/QTkyK0F26i0zWCEQgxMRfgS5ZXedrlpmIih6w0RVNgHbO2Pg7aNuGYSOGRetjQa2M+0E6BxceofZGwS0CYF2kVO2kYkQn5Gyi0FTAtLzJirUZazDJpT9I4KZAVovhtRVi9c0xoZbQYo8TTkHWV4bY4iIppFuVcxHSWsKwjCz4ghSKYSHIGXRXQRR2bCsb/7aRgnpSCbCKaIqqewVCtuEmwKPrR7TrWdIiUmsCDkwJjgajdR4Wio5lzIObhUWVSCT92n8KwUcN4qY4vvcBcaoqAVkSrcpRYIMDyhMmJGh27kctkYgApVssG9S1m8qvZFFHZ26zh1Wz0hg5SpdbSluolU2Fhj76VcI1J/vDfwdaKoMZZmmziYvUPRGpLWNJXM9CoUzDwDIK0gfF4J6WZdpqTEzjK03w7+xH+ZW6O32zP0DD8d3CsIpjqpzrTQX5EYn1+P/vyN2DPNmGtEljMHaJINYjKDjQlRE1mNcJMKZ9wPk1keSWfz36RB2dmeODcJEW9f8CllxGWbATTfZyhrOHE0dO4KfM9nBVpespX8xPzNh6dnuLBbVFUjtAYOYrs+gjvjjxFriKx0VzPwYNbuTjzBBcHDpBeV8RN0ZuIm+v4bSjM/duWGG2IsrrzQUx1NWJuOy8PPUxl2svZ0mb271tJQdzFd7RbsZVksWyqoHvlNyDvVNnof5ZTUvg3IIoSzitqSHgHWLPmDXbYL+H+1Mep6k1RO32El2zf4PGSIn6fTPGzZ1ykA9s4FtiKZL5EtbuGWusaXvN3UVh9Ai1/mPvE75MzqLIquJe7zIf4dHUtV+93UzFawLHyizDUF9lcdRlBq0KvfIDS+j08IH2HnL4st/EyWWsPr8Z0rn1FoKvlkxjqa2ytuoIjvilKao+zx7GRkdlCNizswSvtp2tBp/WEnzFXObbUJA5LCTP2o8z4BDqD1WyP7uKYJ8amYzYWa6/EFA6xpvh89tt7cJQOsyO8gm3RPbwZsPCxvU5Gay9CTA2QlmUKKnp5yfIxvGMzaNZBtnTZmao5D0t6hllbmvKqQXalOrhA3cPzOT6qx11kxWnKPc2o7iEOWLaRtzDJQY9E64hIWoxQ5KpF8I7TJa2iJTPAEZcDm1aEac6Rkk08vkXicQet4jg5iyIJewEeyUNKjLMoFlCkhZi3WEDwYRpJVHR0EdBAFXRsOpgYCKbwr1NSVSQ0GURTQzU1dMWOhxgLgo+ACaIWIWMauHUbedoChkumMCszmy9gMb0oepxUNJ9S+wJ7zQY8bhNXuJuMaKMo68aesiLkGQwazfRXqeQv9RJUlqjK5hNIaehFJruN9QSrDAom3kKzFVOUcTE9Xscm93FetGzm2oUQuzsUcnWZ7sXDLE9XYy4Usyx/hl8bZ3NLcJ7nTlOoTIwQwqQy5WG0v4WPag8Taczjt/q1XGQ36VsWoVBTmdMTFGftRCbbOTP9d6jS+J56GdfOR3lpdRLfxPPY9HoGY30UZm1YwysoiylcnLuLheZGvqd9jgcWg9x3cZrKI7/Eqi8jaGaIqOOcpq6h6+gWPpX5Do6KDIOVK/mB+UV+MznOw6fHiLsHaAu+h2S9nD3j/8BiJDlLX8/B/RvYkN7FZz0vElrl58fqRbxmuZEnR4d5syPFaxc6aHvnG7iNAlyFl/NG7++AKJekVjM13MjS5HY+m/4i/1KzwKXtpyag/ls4JYV/Ix6vh+3bN/O48wFeDbUSOLjEx3mHu5Xv8+WGduanMnzrDwqTqz/HsFyMlvwrayvOwUMZL9sP0dTexYI3w33aXRT1pDgv+T43qo/wyfIKbtnlI3exguNFZ2JkX2BL9UcIWrP0SAepWLabh6z3kzOo8TH1LXKFPTyuwudegBMrv4AgvMP68vMYckYQ8o8z7nHw+tJqCvp7uIo/8QvJynWvi/TWXIKe2EF77jb2WI+SV3OMJ7LX4e8b5Xzhb7wX06kZCbAgm1Q4ihlyh6lt7OYpy2fx9C9ysfA8vYsastmOLg2wsmg7x639hL1ZQrNe7rC/wp+dbqpH3KTFeVoLNjEsD9LvqMQ2Y7BN3IlrAkKVZyJkJgha0xQUj9EbK+MK+QCHkNHtLRjqKEnJIJA/w2Q8wDbpBOEEpHxNyHqakCVF2gbEQZRClC+KZEUNvz0PyREmSDGl+gJzkgB4QM+SFXXSkgNRN1FFA6vy33oKJ8tH/Gv5yEQyVDRTQTBsuIwIQcFPvqYi6GFEyYNLtyCqMoLTRNQ9TAR0bJrEYnoWM1lIrdFDj7WWjmSarDQDZjFWXSe0UEStc5pXjFW0SzrW5DGQqnGpBrNTlaxydPOysJ4LY1FmSmJYtApGIkcJZBtYl9lBstJFD5uYqTHJH3oJwbocRVskE2zm3Oyfidf6ecu8gOJCGTG5F1Fv4eDM66xJN5GaquUc/0GOF66mMlXIkTaFnPEXEPUV7J18hfWxKnqOr+RT6gNEWzx8JvkZ7puZ5qFzUtQe/iWmcRp7Jl6mPVbM/Egbq9NHWV44znjNCp42P859iRjfvUyhft9DCFoHk5k5Mvocm/W1nOg8nU+lv4WtLMtoVQf3cjsPj43wh40x5ktmWD71DxAupnNuNylzlvOUNRw92EF9aoG7bL9musXNm+7N3Ct9jZ8PdqFXy/z0Bic1O39IhRFBdn2UztkPGDGOcOZ8E/5MO5MTH+eSqlPXT/+tnJLC/wNyczdxmrOK7O4pngj8kZXK81xX3cCabhvX/jVKz5nfYYk0qYVnObvxE2QEK6+J+1iz7gh9jmJ+lP40xV0xbjLf5iz1T3yypIg73gvgSC/jRO4m9MwLbK29iqBVoVs8SFXjB/zU+gM8QxoXpHbSZL7GD2WZO/4m0Lf+XxAs+2kOdBC2mMzaj6DlT/K79FW4jk/yoPXn3FVYyB0vWxno+BymuYMtlZezxztCS/tRnrR9BU9vkp87fst38wv51Jsy/ZUXYsQPkmupZt52nD5nKclpJ3fZX+aXRcVcu9PCaE47LiXOrD1NVV0fz4vX4R2dICmPcvoJO9N1FyMmhwjLOmVVw7yln0VDqJt3c5xs7RIJiTJ1uW2MytOM2oqxz+vUcoxlYzKLgTZyZCdTtghRu4i4JFAmDFEzJRFxlpBny2dRCDMhVyJEs8xYVUoWdFSSOAQvsiPMgu6nzAySVUC1+rFLVrKiRlpwIKgGqqBj0UxM04CTEy4wBRPVlFBlE8lQUA0NKxZcpIhLLnJVFVMPY+LBoYukU25sLpVhs4SlgIk7PcdSNkSe6aNSG0X3O6jOyPRVgN3wMJMYQUqU06btY95fxrpomu5qA6eRw1i0mxylmjXKTlIlXrxqKW83pSgOHSOMQm0ywNJMNSv8Y/zRPJ3rQwvsX64RMCR6FztpT5QSna5ns7+H1xyb+UgwzBObUtQO/x1cbSj6IrZYK+tTu9AqDO5KXclXZqb55fYM9T1/QrSfTldoB+vUFYTG27jJ9hTR1gp+ZnyKO9JxfnOBTkvvk4jWc9g58QynRevo7lzORZkn8ZUmOZC3nk4u5HZT58HLdZr2PoCpbWQoNohpRNhgrKX72FZuznwDa5nCRFUH95hf4dcjgzy3OsHIsigrRv6Cpp/HULSfeb2P85Md9He1khvz803xm8zVWRmv6OAa9V6+PDbE9oCbf/mYRs7ISyyP7kJRLyQsqOxc/CsrkqVcce4NSJL9Q80V/xk5JYX/h3yisZi3Vu5j1jrDjX4nX9lTwIpdGkdP+za6PExq9m0ubPws0/Yk72l7Wb9hD/vsp/Fw/GJyj4S5x/53mrSX+XQgh2+/lYcoLqfHsxIt9Txn1H2UeWuWLuEANU07+YXtflyjBluie9iq/5W7XS6++byVkfVfRpd7KLHkIok5HBMPkVd9kJ8Zt+M+FuEJ96N8v6KKm3d5CTZeh27ppyWwkhFnlJzybnY5ziAzaedW8XVeyndz2WE3880fQ5AOsqH0QvY6Byiu6eSv6QsoHOvDlLqp6ZdYqrkEQTxMc85mBqRuJpxu1Bkbdzlf5o8uF3UDdpJiiNaijfTKYyy6TPQ5C5+yvc2RtI7u6AD6cEn5ePJH2SudQUFolL0+B8sHISkkqfA2ErdM02tZhTUUZcCh0zRhkhHiFDgrwDXPqLiMquwkgw4HFj0XhAiKDE5XkkzGSoUUwpEUUBx+rKKVDApp0Y6hGSiCgawY/zrzCE66QUVClUxEXUE1FKzIuI0EolXCp0ukXSCZTkxDI53y4LMl6NFLkV0mjtg4qiiRqzqxZ0VMr8mMWcVQqUZOappgZp4SLUB5doFsQGLBXEZnjULeUjdLeorKdC5GNIA3P83vM6ez0i4ipTsxjWaWlHGE6DJOV14gWeFnhjUMNejkDzyPaO0grs0iRZvYknmZaJWVB5VruMhiMlkVwWv66A0dpS1azEBPOzcYvybeWMDjxnVcbTE5vEqhMjtHxlmAYMQJpJZTlsiyNm+Y/pIOZswtbM6xsmedwrLQfmTfFg4vvs62dDudR9bx8cz3SFYJ/NWyDVXv4CqXhccuE2k99BC6fjrdi4ewGmnWsZbertP5dObrWMqyzNQu5yvmHfx8pJ+3VqToXqWysvc3KOo2ZtUwQ+oBzg23Mj/aiLDQxj3ql1AqDLT2Rs6K3M2ZoSz3iDa+cFGQjHOMtQOPoWTWYyteRXaziC3g+tDyw39mTknh/yGCIOA7506ecubzi1cKcUVKObDsFqz2fZihQc6pvIluf5Bj2gHWrH2X1+w38kx4A84Di/zK9xRu9vEFr4MfvBZA9Wykz9mKmnye7Q0fY96a4Zh5gPrmHfza9gOsEyLtCwe5Qn2cL+fmcu/LbmY6Pk1KmsWViVEoN7BTPMyyll382noveQMZfuR6ln/ku1jWK2HPNjIvgjMTRRZyGZc7SfiXeC28ipKxE5TzHgPBLIUzBSyKKiXWAJPOJKW1fbxs/yT+0Qz32v/Eww4X6w8JzEpOSqz5DHlC1C3r5TnjCrzjI4StE5zR7WRm2WUYyRMooo2C8mHetnwEx9QMk7Ylzu1xMRNYjseUGHOF8eRNMrWUw7W2/RwwBUxLIzCKINjJKZimS2tkg97NQZcTq1oIQhBBtOH2LzKhFtJmjjEigG4tRVdCJEUVmz1CNi1TzCKOuElW9mATrGRMhZRg+6cU/nlSONlqBkxMQUA1/1k+0jOouorVlHEaSUyriFcXWPIIiNhJ6THUlJ9i5hkWy6hVNUR1EkEswKPJRCO55HiT7Fbr8LoFnKETYM0nP+sgEi6iwB/lJaWDZgvYYp0IUh2ikWZhqoJNwgeM5C5j21KMd9s0CvQEw5EBGlNFaKESqvJDPJw5g4+HFji4wiBgigyGu2mNFzM92swFrl0MF7bTmnDzyvIEpcefBGkTPeFdtBlt2JYK2RQY4EhgNZXpAvqadBzBt5G1Oo7P7aEpls/EQAvnpP+CVm7wK+N8TosITC0zSbqGKNaz6N5qZpRuTtNXMdi9ic9r32Khwc59mUuoS+WxJt/B8+fLLD/2MLp+Foend+DWDVaxjt6urdySuQtLqcJCXTtfNO7kJ8O97GvJcuA0kZVHf4qa3URUljmSfIszFhpQFptYmjydL6VvI1CUwLK+knODn8Ov1fB4JMX9m+fpXi/T8d43aFu5kpqVaz7sFPGfllNS+H+BZHPz07XfJ1V7AUd927DbX8dniGzwXcze/FHm5E6aW1/jL457+GCxFmHfLH/Ne5yoPMydFvjx33NIFp3FgLUGJfY3zmq4gTlrmk7jAI0t7/Ko/QcIUxJVk0f4rP4rbi0u5ptv5bBUczkhIQPhHppcm3nL1knHqv085bgT27jAR9T3SFoGORHMsHqfRL9/NVpkFy3e0/hAPkZl/QEeNm7FdWKB71oe5fs+Pze8AX2l56HH9lFsbWRAPMaSO0XPQjEXZj7gzTwn1x72MNF6PUZ2L6W2ZqalbnrstVhmRO52vcyTThf1PRIxIUpL4Tq67ZOY/mmmgz4+Y3+PF11uiiccqNIMDbkrmRVGOWFdjj2oUGoep3nMQqhwJTY1y4wjjsMzTyxsZ6vcTSKik8ptwcwGCVsyeNwLJGN2lsvjOMImKW8FdkEkJmdJyxJCGmSi5CYlFEHCLjtRyZIS3MiaQkYUwZT4bzOPABBMlH9KQVQyKIaC1ZBwGXE0q4BbFVhw6cimTDyzBFkv+cYEQWseVekMca+GYPpJmyebzbXCCEekZaxMJcnaFzCNYtJmmHiojBUcZtDdwGnhCL214DB9DIU6KTUracr2kCiycFjYQLhCIHf4dSRbM4KZZmm6hrOUp8hUFZAy2zhRr1LQ+7eT4y2UMXzZJlanD5AsF/hG4nJui4R5Z5NAZXaapNVBriITnmrkzMwzRCtlvhq/kjsmx/nZtiR1Rx8DeRu7p19gc6KB3q7V3Kx9n1Cjk09GP8FXx0f4zYYIgYFnsOmVzOtxBOK0sIql8VV8SfgRoVYPnw5fx7mLadwVVnaeIbF88EkM4Xz2jv+DXE1ihbiW/u4zuCV9J1KpwlJ9G5/Rv8YDw930NGrsOMfBiv33o2dXonmK2R16no3BarzpNob7z+MTqTtZkzeJubGYTy9dQ6/tIv48NsxbrQmO3XU2jee2fphp4T89p6Tw/xJreTml11+KJLxEfU4ry4Q1vF3Yh+nvo6TqdR51/pSRYB7qvkleyf8V/c4o95lZHnrBR7j2UoalErLRv3FW443M2lIc1Q/Q3PYOv3XejzJjJX+ki28aP+HWimq+8kGArH8zk6KH7ML7bMi7mHd9vbR3nOAd+xVMz/mpnjrKZu1v/ByBm/6u093yCUztDU6vvIo9/hHaVnTxrONLBMZUfuh+jkfLyvnUPj/TK25CshxgQ+XF7PeM0tB4hCeEm7H3zXG+/jx7o1ly5/JJCCFaCtbQ5Zuhpv4Ezyvn4Z8cYs46x7YeF3NNH0FPdWIlB0dgiJ3WK7FNJijTD1E7IrFQdw5GaoCEYFJcPslecwPVkQE+yPGxcsAkaipU5bYwLcwzZKvFvqTiYpzmGSthZxk5lhyCcpyQxY0lYZBrTlO8KJOwBvDKHqJCkgUxgJTWico6+QkRXdBwWjyI1hQpwYvLyJAWJQRkBAzg5MnPFEA1JRTJRNQy6BhYDQmLYWLKoApuIi4Du5ElocVxCy4K9HlEt0yxIjKeZ2IxHCwmJrGq+dRo3Wh+D8uSBt3l4DTtTEUGyTMKaVSOo+RZ0ahif3Wa/FAXKUmkIuUjvFDBMt88TykbuDQaYaTewGEGODG7mya9Ck/Kg7cwxUPJM/l4ZImjHQL5wEhsgOZIIUODzXxEfpF4TQ0ZlrNQK+CefQtDbWX/xMusidcw1NfBx8XHSTUV8wTX8XER3jtNoD7WheRfwZw6wHJzJanZNj5pf4Z0eyl3a5/jB8E57j8/Te3en6Irazk68wHFWSf+dDu2cCW32f9IoiOPqyOf4ZapSWbroXulzvLJl0C+mJ1Dz1CUtdMmrGGw9ww+k74DqVQlsayVT2p3c//QCWbrTF652EP7+98FpQkpr423p56kI1RKrbCSzqPncFH6F1zlP0RoVQ4/z57F711f4ldRk4+c/y8fbkL4P4BTUvh3oKwhl/Ov+wK5Wjmv5BynsGIIV8FOHnL8mtScnfSBEd7I+wkf5Fj4ZTbBg886CbZeyyi5pMN/45ymm5i1pTiiHaC1/V1+7/geiVkHrv4eHhR+xBdrGrhpfw4WpYEBazXK0qtsr7yOPXmjVNT30+2o4e1QM5auIe4Rfskd+YXc/ZqTkfVfQLIdYmXpGQy7l/AW93HM3sjYfB6rwwdIyIOkJxU84VJCQpZ80c2CJYuvpI9djgtwT5rc5/k7vyyr4uadNoarLkSLHcZh5pNy9nLYvgHvtME3XH/nDw4HTV0GYTI05K2gxzOLv3CAw9FKLjT28XxhEZuP6SyYFqr9jQw7gpieILFFBx+172e/YWKKtYjSBB4hD6d/khPiWryROU64bSwbM0iTpthbRVRYYEyuxYgqzFoVahYlsoJKjrOQNHEWpSK0pEJY1vHHdDQzi01yIsgZknhwmynSkoRgWkD4b43mk1dSFVNCFgVEXUEUrVhMCd2wIVphSfCTcJvYshGSWgKv4SCghcg6JDD8TORqePUooewieYaHYnWejFcmJpTTX6KQGx8loicpTvsgFcAeUPh7toM6pwVH+AgmtWTMCPFgFRvUV8iW5lOo5PPGsjglkzvBXUZAtTI+XMm5+vMoVaXYjFr2L8tQ2P0MgrSa6WwvpUYzDZkZxGKV7yXP49bpcR7folA/8Rqybw1hY5wKs43ipEhL/hz7fGupzgSYrjcRs4dxGvlMZ2bJ16yIkWZq0xHW5Y0zV9XGbvlyPiMK/OEiidbu34FwNjsGn6ItXExsupHytMq1/p2oHWVcHf8y94z08n5rmrnqKK2hnUj2S3ln4I+UZdw0iqsZ7juDz6a/AsUa6cYmPq7czb1DJ0hVSzx7pZeWd76NpFXiKD6NV4cepSEWYI1tLfv2nsam7Kt80fMPRls9dOVvJnH509gk24edDv7Tc0oK/04UrmpgfpNIU9sgiq+H71t+hmsG1GMDvOG/nxcL83gqFuEHT8nMrr6ZSdNFOvQc5zZ9ihlbikPKAdqXv8sfnfcSmncjdfXxmHw/d9a2cN5xN/mTfrq961GTf+fMhhvoypnDGugj5Iryu/gFWI7O8qT753yrehmf2Z9LpOoiUmIQny6A6WaYYxi5g/wpcQ6W7jE+bfyen1osXPG6Sm/xmWiRD6h2dNBpHkMIDPFadAUFkz3o9OIfMUgHTgNbP2sqzuGIZ4KSyuP8Pb2ZvOl+Jmwhtve5mGu5El05So5QSto+yAH7mfhnFC7kbSZCGdK565DkAQqt1aRtIxy2bscynyFX76Fl3MpS6VrMVJCgLUVe/iyDqQK2Sd3sd9ixZXMRpCAuIReHd55RoZGSzAxDTieBBRWdOB5LLpJjiQWhjIAWJmi14UoYGGSxCnYEOU0KJ24zTUqUAAnQ/y/fooBiilgQEIwsgmjFYkhomg3RqrNgeEm4DGzJBVQMXKoVNBeCy2BUKCMUMHAujaIKAoGsEy2Ti9Wnslerx+OVcS50ItnK8RgSwdl82uVBDlpbOWNpkf56CSdeBoKHqaGEYiVDNt/kyewWNtgs6NIIhlJLX2gfNUIdFUoMrUDnh/GzuSEW41iHTL5gMpGcoD4aoK93GVfqv0epL2S3dD4tAQfx3HncZi5D4V7qo7kM9ddzcebXBKusfDFyFV8dH+YnW6JUH3gYTdvIB8PPsXqpnO6uRs5LP4ZSZvK0cDql2SLKS5wcXwPN4b1Yveeyc/JpNi3V0n9iGasyBzktbwizvZZb1K/y45E+nlwdJuMepyl1HIvnMt7ofZyqhJ9aYRWjg9v5XObLUKyhNDdxbfZu7hnqQq6w8sS1Ppre+hZOswB36fn8o+fXlGRcbLOvZ/euVbQoI3zH+SR3nN9IvueUEP49OCWFf0c2bzkNW2kL3+Y+iic0LP29vOS4j99W1PLOUphv/1lnctMXmDEkksHnOLf5ZmbsSQ5mD9Cx8l2ecn6DqXkfeucQf3H8gO/UttHeb6HhiMCxkotBf5mt9Vcz4YixYOnCUtDJT/TPk3Miyu9z/8ifSkpY1i/hCxYwJuajLBxgmWMdO6VOGpr28Ij0NfL6EzzqfZIfVNbxxd05TK69BYvtEBuqL+Gwf4KW9h6est6ObyDJ9xxP8qDXy3k7FUacyxCik2RNCXveALvtFxOY0rnH9RJPOO20HFVZNE2qvbUM+BaoqBzg7fRqKpd62ZPj5NJuNzP+dizpGDOOOOUVU+xV2mhNdLMzL8CafoOwIVKZs4xJeZGI1cASFtksnkBZUkkWtKFlpolJKjm5ESaUAO2M0W+1IAj5CGIE0bRic8WZN/MoNYLMWqyAF1FMIxoSsiVLAgdOI0VaAJA52VM42VQQBBMVCSsgGAqCYEE2BTTNhmQ1mNG92CwicnoRyeLDacik0x7sTpUTShEBq4Q1MY5oKcRlWAgv+aiyLrBHX8aqVIq0J4GpFbKgTCFmymlRP8AscOExCnm/KkHB4jE0u4eStIfpyXI22HvodLVxTjDIa20qJdlJYqJOVcLPyFAN58lvkSqvolAtYueyJIVdfwVxNaOpY9SLTRRmZIoL4jypn86Vs7P8dtUSFUceB3EThxfeYoPaxNz4Sm6wPYfaXM6fpOu5xYSXz7LSOL8Dq38rxyPvsVVppb97PZ9Wv8v8MhefCX+Um6bG2dWSQbUOUiYkseav5XhsB9sTLRw5uJzzMr+jpDBKsqGZ+8Qv8cvxEX60MYicPkqdPoUj9zJe7/stDbEAFeYKJobO5HPpL2EW6xgtjVyV/QZ3DvdQUOri1x/zUv/mvfgED96Sy3m162F8psz5lg0c3N/GGZU30uhyfHiB/38Yp6Tw78y6upu5IGUlf+oEfxS/zY/r2uiej/C1JzOMn3EHQV0nMfcc57d8mll7goPZA6xc9R7POr7OUDAP5fAoz7sf4CfVzfhHdda/k6Rz2U1YrO+wqmI7McHguHaYspr3+Ll8H2XDGe50vs6IPUx3MM6md9N0lZ6PlnyNrZXXsCd3lLYV/bzmuAXnjMANwg46PSoFIyayWkfYTJJjWIiJBoavn25bHbE5Bx8V3uf1PB8f6fIx134dsuUIa0rP5aB1CG9hF6/Hl1M838uwI8b2PjfBtqtAO0KJpYFZ+um11eGdN7nV+Q4vCyY5M3Y0eY6movUMmlMsWlXERYlrHAfYa+iglSFaJim0VpK1TtJr3YCwmCHLLCtmHITd1bgFGyFbEottgWxUZqVlnGhaI+uuRFdDpCQNjztFWHVSKS4yL4IpeoEUqmhitakkDRtOM03qX6Wgn3y4dvICEioSFhMEIwuCFYshomRl7JLKnO4l3xSQ1AgIbuy6RCLhpEiKMEAp9YqKKYQw9FzSxEhFC6gxTpDw5tMST9FdDh5BZDI8QJmRS56qks6FN7SVVHjtOEKHMYwaIvosYqqaNdlXyRTb6bFsIFkukzPyJrrRwqwyRL5RT2u2i0iRxI8S53J9Ks2JDgsFosGcEqQ6lsNAbzWXZB8mWe/hEeMarhNEjq21Um6G0FwBrIKGPdXEsuwMJQVRdjrXUp3JJVYtojjGCAhWUg4HhpSgTlyOEWriU47nUZcXcUv6Nr4/NsCPNi2Qf+Ip7EYRaaeHBWGUM1jF0SMbuS79LbQSnZHCFTxlu5lfzQe5Z/s83sk3qBDiuAou4vX+x2gK51NktDM9eha3pr+IXqxDawOXp+/mC6M91Bd5+fENLmre+T4FkoSr4BrePPE4sqxw81kfo+DUnuV/V05J4d8ZWRS45/R6Hix4hW8uW83iVIIv/iHK8Fn3sKRniM+8wPmtn2HGnmB/5gArV+3kRcdX6FosIH1wnJdyfsrjFTVEp1Jc8GKErtVfxu7cTbW3AUnz8r52iLb293nC8V180yYbkweozr7Fz40Mn31OpW/tF7FaP2B9zUUMekM48gYYsfnYHarCNdzH5uxzPKFmOPfNFH15W8iG32OZcx37jC4CJQf5U/pCzL5JzlKe59VUkpoukznDilcXmbdkKKoYYYf9egqmFO52vsjjdhvtBzPMGTKF9gJGvGHqGsZ4nfMQJ6aJCVNsHnax0HgBaqobUXfiyZ/goPUihJk4Lr2P9nEb4cpNaIkxoqJKYfECnXozFYlhjvhzWDaukTQUin3VzJthZuU8HEmDcmOM/AWBlK8CUc0QlxWstijZtEyVvEREU9GsfgwtgSIYWK0qWV3EJ2ZJA5j/PCn8E1EwUU0Z6z/LR2BBNgSyioxPSLMo+MjTdDAiGIYHVcyQTXkoZpYZuYDqZIKFQhsWvCwkJvAKAarVPjSfBVMopqs4Q064H81qpzDlJrRYTKNznrf0NrZFFhlqsOLCzUDwMLVqIZZ0Ab7cNL9NbODy8BLdbVZyJZnhpW4aonlMTS7jbMdBFosaaMi4eKMxRuHxpzFYTX98Py1SE45UMStyZzno6WBVXOLdpgSB3ufQMq3sH3mZjqVSuo43cmXmx8zW2Lk1fDVfGRvgoQ2LlO1/BDXTwcGJt6iPB4jO1rMsO8mavCnS9Y38xXoT340nuf8ihbr3H0BNdzAaG0AWs6yyrGZ44Aw+m7mT2UqZt23r6HRcxoPxDHeeH6Lg+J8oFAVcxefzxtDjtIeKydVamR0/k9vSX8Qo0pFb67kk+XU+Mz7Amvwc7vuYlfKdP6FcTuPwX4PSLOFuK/iQIv3/XE5J4X8BDquEftXvKI3l86knFxg8916iRpzo9H8Xwt7UAVatfp+XHV/gwGIJ6f1T/CP/V7xYVMjx+QjX/zVKz5avY3Ecw29aKTBqeFM8ypq1R3jVeRuheReu4R4+lX2E2/Nz+fabOUxtug2sgxRa8tB1G/1aJ46CwzyiXIvl+CK/cjzCtypquGNnDuPrb8VqP8zG2ss4HpilpX2YVxyfp2hS5QH/3/lVRR237fEztuLjmOouWnybOWL0IPkH2BmtpXKxm15nmrMGPASXX4kkdFLnWsGQNsCcBZQFK7d6dvFMXiGrjqQJahZKXRWMeJcoLBrlYKqCteoJ3skrYl2fwaLppNhdwZQrgtU1zVLUwXmOPg5YJeSkF1FeIGApRrPMM2ZpxYgqhKQU9WEbcSkHj8VHTM6giWnMtEApC7gSoLrz0NXEyXcJchZdk/FKGdIYmEiYpv7PdwonDwsqElbTRDQUTNOCIRpoqhU3SYKCjzw1Q8YtIpgOkloMPeMjTx9F9tgpVUyG8lRcZAml5snTPXh1yHrhoLEMt8+CY74TQSrHlLKEF4po03YhFOZSnnXxTlWUgsWjCO5C8rN2xkaLOU14H7WkhCrFw+v1UYr6XsbqXo4ixSFRw9rMawSLZR5IXcB1ik7fCiuFosaSEac86WF0oJJzM48yX2nnrsRV/Etokae3S9TP78CWu5FJrYcOqQ0l1MY1rvcQmit42nI9X9AFnjnfQfPIc1gcZ/H++LNsCtfSebiJi9I/J10Cb9vWoYkruMTj4W+XuGnregRF28aeoRcoybgpMdtIBDdxu3Ef43VOfpk5g4y8hnsMG1+/LE3p7l/iE924S7fz1tgTrFwoxaM0E5w8k9vSX0ArMrC113Nh/C5unBzm7ECAb1wHxft+w9bNPtq3n/nhBvr/oZySwv8i3DYPt5z3TWauvp+kGSY2+SIXtn6WGXuCPckDrFm7izcdn2HnUhXZ/bP8vfC3vJ/j5NVwiC89lWLwzHvAOoIQnaNRXs/bnm5Wrh7gkG0z74cqyXaO84j1Ie6qaebWQwWk8jaxoGsoC93UiMvZIXTS1r6fJ2zfomxM4fv+V3mpsJA1/XY0WxtRM45HNcjoEiG5h1mbxuFQCaXBEyhmP8pkBkmtJsUiNTmNjPoi1DeO84b90xRMZPm6/Xl+Z7OwfF+CGd2N17Qy5YxT2zDF25ZrYSxKY+Z9tJkMydLNWGyDVLlbmFRHGZGK8C3CVfYD7NaziJk8JOsU5d5G5rVphuU6vBGdDuMYwoJCsrgDXZlCNUT8gTDD1OFLzjHidFEeNMiYCrmuIiJmgpAUwJ41yNXnccVBseUgmQJZSUOSMmiqgFfIkDZ1TEECUz+5ihMTODnmwgKgZzFNGV3QMFQ7bqLERQ+BbIaYR0bETiy9iFtyU6DPojklDCGXsVwFd3KarGiSm3WQSOSR706xI1vH6oxCKlfFVAuYjg1SIBVTpw0SyZHYJaymIMeJM3gIXa1mXhsnlypalUMs5ss8pZ7OFquddG4MUSvh+OR7NKdKiS42cbpniPHcFlbGDV5qilB49C9oxiq6lj5gubAMLdLE2d5eYmUNCEIzlNtAGMJh5jORGKU07SE8W8Pq7G5shQqvW9ZRm/GhV1iJFUcpFVNY8puYNQfZJK9gqH8jn9G+y1Stky8tXco5CxGUSivDKy20RnZhcV3MW31P0LZYhLpUhztZw+223zHX4uWLi5dQphbwSYef710tUP3OD7CbBbhKNvLu1J9ZPVeGPdPI4vRZfD59G1qhgXNFLedH7+Tq6Qku9+Wz57uXUXXRpg8zvP+P5pQU/hciBwKs+8RajMwBLmj9LNP2BLuT+1m3bg87HDfy+lI9+r45nit+ki6Xyu+SIb75VxjbdgcZIUhq9hDr/BfyfsEQNQ2jjFttPB47HeHwAn8NPMZvKpfRNCSRP2ylz9ZKOvQqp5dfxZ78Udo6RnjffgWJoJ2G0DHylA94LR5lwzsxen0byITeoc2zhd1SN7V1B3hcuAV7b5j7LL/nh7l+Pva2xkDJWWTDuygRGuhRukk5pjgULqYx2sUxt8HZgx4Wl1+FxdpFa2AzJ4xhFPskQ2E/F4uH+EdJJZeecDLlW4EWmSIsKZRXzbHXch7adARBH2LFhJ1w9ekoySE0TSJQGKRb3ogajDEvJVgz6ybiqUZU0oQcKXz+JSazPlZIE/Q7HLhCGqYYw2fLI21EWZCKEdI6GSFLYcpCRnZjE21kZA1BTKOrAm4hg6kZmKKVk7ePTq7kFPlno9kwEMx/SsFU0VQbbjOMxSbj1yDkNrEhEM8s4cFBjh4lZRcYF8pI+AUcwUFkWyFO3UJo0UuDMMass4L2yBLd5eAXFWbi45SkfGRTZRT6U/wt1ca2aJjRZQ7ckouhYCc1yTwSkVravUF2CB1cMDfFSy1JyiKdyDmVeBBZmC5nU+bPLJRaeVi9iKt1kcEOB0WSSlzUKFZczIxVsCX9Z2aKZb4WvoDbJgb4xYYwVYd/h8Fm9k6+xPp4Ld1dK/iY8iNm65x8JvxR/mWkm5+umaPw0BPomQZGo/34dQsFZgvWeB03u/6BvqKA62Of4a6hLp5qX0LU+6mUoriLz+L92WfZNF/D6EA1lUqGm71vk1kV4Or5j7EpkuXM3Fx+/VE39W/ei6BVYi9ezq7F51g3U4GYrCc8dxZfyHwetcDEs6KW88Jf5bzcjdy09tYPO7T/j+aUFP4X4/Y7uOrO77CQp7A7uZ/1G/byvuMang83Ixxc4C/FTzMjB/mhFuUHL7qY3XALIS1LbPINzqy8nsNFkzjzRki6BnhQ+STF3XF+lf8ih7wC3fMRtr2yRFf9DUjiW5zeeDWDvhAW/xDzthTPRVaQPT7FPfyKuwsLuONtN2ObPo/DcYgNdZfQE1igrnGcDxxX4Z4TuMXxAW8U5HNun5dww0XI9kE6Ks+kNxCkuWWMl+23kjuW5ivWv/E7m8jK3TGm9BysqSRBOU1p5RQf2D+GfzrDFbzBznQcx4IDXZqjpWwTQ/Z5HN4hemIBzhSP805+KRv6BYLkkCP7mPXECQRm6EoXs8oc4FBOPg2jCjHNpNhbzYIcR5dCGDGRNdYJBg0F3VKMJEVxmB5kZ4RFsYpsQmFJ1ilMWsiadmyijaygYQoK6AIuIYOkC5gWG6ZpnHymIJycgXSy0WxiiGCaFlRTwVCsuIwQok3Cr8GcU8VupkioCTyqHdPwI7kMTijFVIpWLOlJdD1AWoyhJPOpVA8j59hwCXkcLkrgX+xGdhcSUO3MTOfSQSdaXjGtSZ03qiIULBzB4q3AZ1qZnihkvfoSQrmHUdt69FIb7qldZFMNnJjfRYtYhZisZYU/yCHHCraE4zzbFKLo8J/QtJUcnX+HlUY94fkVfMS5C7m+nGP28zjN72Oy3UKZGEfMLUezxKmSW3CmKrnEexy5sZLnHDfwFUXgiQttNJ14HM3cxnv9f2H5QjHjg1W0Kj2sCswiN9XxoOVWHpye4ptb5yg48gdc5GLJb6BH3cv2eAuHD9TTkd3P+Tn92NaUcOnCLVwzPUFFsY+nr8mj+Z170TMNSHk17Iu+zMaZKvRYLdHgmXwhfStKoclFF6/Gv+1LH2Y4/5fglBT+NyD7HdRe3MFpWybZY7+cp8MrcBwK8VjBC2SNAe6SM/z4tXxCTZczoTiJz73Auc0305+zSETqxZ33Hj+V7qV2NMP11t3Y1UP8XI3xpecUBrfeid3VSbW3gawu0Z3pJKfoA35hfoGC/gSP5f2V31Y2cUlPDuncdcTNKLZ0BlO1M6aeIOMc4sXYCtS+cbamnuWv2RgrP4gxLFaRCXbhVHKZ1HpZsCboD/lZmTrGEY/E2UM+QsuvwObsYUXJNrqkMZy+E7wfr6Ys3EenR+bioVwWmi9Cy3Th1fNJiGN0W9eSv6hzqWUfu7QUcsyFaJ2lOm854/o8YTGDNSpwrr2XA5KOlHAiWRcodFeyqC0yKVfhThjU6iM4wgbpvFp0dREVA48nRZAC3EqUBZud3LiJYmq4bF4yZEmLdiy6idNMI6lgyDZMU/vX7+m/9xQMdKuEYEqoehZZtOEy45hWERkbCy4FWzaCJpq4FQuZtBe/LcsJrYhaJUsqVwLdz0Jikjw5lwp9gphb5JjQiDvHjnPuKIZRSkwMIWfLaFLeI55r46hlNZ48J67ZAyjZaiZTfZRbqijXYqQCJr+KrOfy8BJHVjopluMkJI2ShIfhwWK2ZR4nXuHkSfMSrhBtjKx0UWRRyNhkAoaVWLCK1Zm3iBbAj2Onc83kCL9vnafw0JNkky0cGHmFpsUCBnsq2ZJ+inQRPG+spzbrwVnmJLgMapnGVXQ6xxM72Zpu5vChVi5J/YB4EXR6Ohh1nsOdpp0HLhOofecHqKlmIqSIWGc5S1zF/r3LOSv9GxrzlvCuquLK+O18aaiTTIWVNy8vpPWD+8kk2jF8eRxJvc1pM7VklqpJhM7kAf973L6i4sML4v9CnJLC/yZ8Ph/NbfezM7MJ39ElHsh7HXd2H7e5BB7YUUYqfz0DehnZ2HOc3XYTM7YEPZmjVNW+xW+dD5I/a1Cz1MUFid/z5VwP33ungPl1nyLLAsbSJGXGMt7lGB0rj/A35zcpnjW4XNhPRJjg2EKYtg8i9DlXk1p8i1U5Z7HLMUBr+zGett1J2Vian+W9yK+rW/j84UKm138Su7OTDXWXcCwwTXPLIC9YPotnJMmX5Gf4rU1gza4I42YReniWhKHjyZ/kkP1iioMqX/Ls4Gm3g+bDMeZVGwHJz6Q3RlXNLLs5nczUIoo+zspJJ5H6bejKEHbVg+Scps9+BvawQp3ShTOokSxfBcYMNtWJzRNkXF6BHs2iGovUhG0kPWVo6QhxScHtTjFr+CgxF5i1uXBHFHSyOG1e0qZCCgeybuIw00iqiSFZwND/2+Tsk+Uj42T5SJdFBGQUPYNVtJycf2QRiMk5xFxgSwSRbT6cupVY3E6JuMSYUEJtIsxMgYxTkgjGJwlobtDysfh03knVsFrRSRRKCHoeYwsnKBMCuDUfYq7OU/F2ticSjDe68FrsjC71UBXNYW6umi22XlKFVSzLyLxRE6bg+AsgtDGp9lFvq8ev5lKWm+A1s4Oz5+f4U2OQokN/JJvt4NDU66zM1DA10cFH5ReQG/P5o+VabtEtvHOml4boPhyBrfRnD7KBZkaH13GT/jMWG9x8eulabh/u4pcrZgl0P4vFKCEu62jWBKvsHUyPb+Vz2n2MVNu5Z+lsmtNWVubn8tplebQee5hUcjNd07uwywYbXWs4fmwLVyfuxpKvYmuq4zPG17iv7yAnahSOnJPHiiM/IRlbRcZhpUvbxekzdZTnnkdLw5c/zPD9L8UpKfxvxG0P8ER7DfdWnKA68RqfzvPwvX2VmEYlXXIHgvEymxo/QlI3+CB5kI6OXbzo+iaJBSvZ3iG+pz/IV2pb+PyxElRbE+Oqj+jMG2wu+gh7CsZobR/nqHUzPUs5aAODXJP8Ld/0WLn9NZmRTV/E4TrE+rqLGMpdorB8nBPW5SwsOmiIdpHWB5idjeGdkJlTRMRYGEUViYkDjFtczIccbNGOss/n5OxhP0ttl2N39bOq6mxOuKYpLj7Bq8pasuMzoPRSMgGx2u3YHEM05K9hUJtCtU4xG3FyqeM4bxZUsLEf5oUiLFmVoCtJSekSnWYrRnCBIQesn/UQ8dahJOaJWDLk58UZNiuxxhcZcTmpD1lJCH7ckou4LYvNFiWelqmVl5iTZawJFchgl91ktCwpwYGomdiNJJIGpmzDNHX41/HZJgoSFtNAs4oIpoyiprAJFtxGnKwsEhb8pF0mlugsouRFEk2SMQdF5hQ2r4PyjE5fbhqvukBW1MlNO4hGcqm3LNIj1rAiPEd3OeQKccJahKKYh4WFIlZaRlnwVrEmHOH1yjAFwcPY/XXYZIjMF7M68xxLhVZeYBvbrG4ilQJOyc/Q4nFqQrn095dyrvpnjBovL1ou4RKrh/EVbkqsWTSXB48oYsaraVL7seVr/MNczbpoliO1WRxLB7GaBcxn58nBikNpoEh1cEbOONaGal5yXs/Xsia/vlCifu8vSKc2sGfwRSoTPuR0HZ5sJZ92vYG+IsA1C5/g0yNdDFbrzLRbaYvtBPkC3jnxJKVpN7W2dqYnzubT6duZLxaJlTbxffsd/HSgk38sCzO+1sPKwd8Rj24gKqRJrldZu3XDhxm2/+U4JYX/zeS5bZxx0fX8omUrdx6pwDnr5GjOudjtb9NSsgE55eYN9RAbNp5gt/0q9oUKiRya4mnfL/lp3QqaRi2UHk3R5duKobzCmS03MJC7CK5hYrZxHktuxzwa4vf+x/lx/QpuPlFMvGIbCSOClIhhyXjpSh3HmbOP32cvQj2xyDelx7k/P5/P7LAy0v5xRHM3a8su4JBrnIZlJ3hW/ASWoTif5Rl+ZzFZv3OJEaGCTHAAI2tBsY7Ra2nDt2jy5Zw9PFtay4XHLEx62kkt9qMo4A5M02m7kMCiyoXCXnZpSWwLMqYcpL5oFRNyCNk2ylTMydn2fg74C6gbzRJWRPKdpSw4Uzhd80yk3ayUx+lz+ymcTZPRMwQ8JUSlFKYQRk+L1FhCzAo6puhFkjLYRAc6aVKiF1E3sekpJNXAEC0newrmyUazwMmBeBZdR5UFQCKjprBiwYaKIpssGD78khU5G8I0PWSFDGrGR74+hOi2oEt5jORkcIaGsTqLcJoyiwtuao1uHHlu8nUP+wtj+Oe7sHuqsFkEFuYCtCjvQL6dYftKxEIH7sm9ZNNVjMa6qLWW4dQKKMjJ8rd0BxfPDPNMc5SSqZ04czpI2kIUSvWU63FseRp/TK/h4ukRnmiap2j/H8hmVrB/7GVWxCsY6G/hKv0xkvUevpm5ljuCczyyVaG28wk0fRMfDP+NVQtldB6t5JzUT5krEvmjsoG6rJuCYi9ja9w0KcdxBc7j/YlnWTdXyYljFSxX9rMqsICnrYo7hNu5f7iLny2fwprpocYawlN0CW/2/57WYBGWTB1KZAu369+kv8LCMVc7f/V+jl+PDvLL9mmiNbBq7jmaT7+RpnPP+LBD9r8cp6TwYeDwc+9pP6JGauBI5XW4vbspcpSSnyrnTfsx1qwbo1+u4cnoasQjIf5a/BTv5uXSNx/ivL/N0r3q87jc+2kuXk9KMzmW6KSk/F0etXyDuvEs3w68y1GPRGgmRtW+CH3W5SQXX2d9wYXs8w/TsXKIV513UD6r8VX/B7xZUMyGESeZnJUoQpACaz4hKY3VN0qfvIzMosz5wmF25fo4e8RPqPUSHK5BVteeR0/OPLV1w7zKhaSHw6xKvkVXeAkp5sEQ52kuXc9IzhIlJePs1DtIT80RNedYNekk0ngWgjlKrlnEkjrJsKWRvKjBNms3B40McsyCbAtRmlPPvB4mKaSwJgTW2qfpk0zkmIAgxchxFhFREyyJHpyKSZmwyKKWQZf9CGIai2lDsGRICzmYqoFpKtg04aQUDO1f9ykIwsmTgtU00CwioiCTVVNYTQlMB4LVZFb3km9KiHoMTXWS0qO4ZA/5+hQpu8iEVIHktWFfGMAw8khKcUT1ZLM55ZXptzTjzHXgnD2KqhQTVCYpspVQYQQJ+QWeSS5ne1phqsWH32phKjJE5ZLv5JsFduKqyGXOsQZLkRNH5BhKqoKjo2/TGC5gZKSOC3kZR10uO5wXc57Dz9RKLyW2DKK/AKvVxK3XUq5lqclNMJHThEOsJb/YS6zKoNSWxV7QQMwepMPZzlJwMzeLT5Jo9PGppeu4fbCTR9tm8I6/i0fyYsmrZNY6zDahnSOHV3Bp4l5ihTCZ28L7nmv5cSzLXdvmKdz3OB7Ji7d8A3sWX2T9dCVzk2X4sjXcKT9KX62Dv2bX0um7ikfnQ9y7boK8Wy5g+faqDztS/0tySgofElabg+rv3MWytSlsGZUGYxU78vupb5pmQY7w48xVVPTFuS/vHeLGKL/ILnLH8yZjZ9+FbB9FTsQpzFTyDp2sWXuC11xfxbIgUrzQy/Loc/xISPO5f2gMb/oSTvdh1tZdwLg3gs0/zqQssTtcRGZolI3xv/E7NcK21xbozzmN5NIOml0b2M8AZeWHeMq4HK0/wk36X/mtRWfDeyFGpQYSc8expd3Mq4PMSCKxJQuX2o7xRnEVHx0sINhyEYZ2giKhirHUKItiBjUs8zFvF28WVrJ5UGROrkSJzBGWMpRWROiRt6AupClIdxMIGiQq1yIIs3jNAEkjyKSlFU9So8EYZjGVRPFUIskRXKKPjBkjJJVjyxrk6UHsSRPVWwhmCkmXkOQMKcGHoZ7cuuY0ZAxJxjT+OeaCkxeQVFPCamgoMoiCgaorWA0Zw3AgWEymFDd5hoHqtoDpJJZexCM68ZkKKTv0asXUYgUpjqb4WUxOkW/NIWBqJNzwXrqWVSrEyhxIZg6jc52UZf0kk1W0upY4LtazOTj1zxLSERw5TRgOBSFTRkv2PaYDEr+MbuSK8BJ7V7soNyex5VUj20zEdCXN6lGieSY/D6/jI5N9/LZxhqL9T5BOtbN/+CWWL5Vy4kQtl2R+wlSFg9sXL+HW0S5+0jFDya5HSMWaOTzyBtVhP5G5cuq0eToCi9jqanjZcwPfSJs8dJ5G9XsPkowtp2/uEG5RptG5nFBwG5/Tvs9AlY2HYlvwCpV8wlXAz6+0U/Pm91BTdRi+XIalY2yLNNN5tJxKPcUX3a8x3eLlW6EziLq38vLqb1F++rkfcoT+1+WUFD5ERElk01XnsfWsG9lXNo4nfwrDsY8H+BrNEwrb6WR55G982WfjBztLCbVfQUhRCI/vYH3gIvYUjdHcOsOAXMVb4QqWjk7xoPxL7q1t51+OlRNuuoi0GcKMLOJO5bI/cZzi0vf5nfA58geT/CrveR6uaefW7jIW1l6PzTVEa+kmJrxRistn6LRtw7EkcrX9MO/n53POSA6Rlguxu4dZWXMW/XmLNDXN8I7147in0lzLa/xNzFJ9aJHZjA27YhC0p6ioDnHIfiXeYJazjQ94X4nimNbQxBD1xauYckfw+yc5mi3BH5vkuM/PpnkfEX896egEGV0lUBBlVFqOGlFwKpOUhyykC+vRtUVkRcbmjLIoVWOmNBzqAv6EiOLOQ9cSaIaJzaaREjxoqn5SCpqEIVowDO2fPQXzvw/E0zUUCWTBOLmSU5fQdTsOi8G05iVPyZLwW5GxE47P4TUcGHouFqfBkXQBddkMkRI3VtHJ7NIQeVk32UwxZc40u9VqVoVn6S6HPCGEZreSn3QxOeFnubEPV4mbRedykkVW3OO7SScrGVw4TB3FqOlGVnuCzHlraUuqvFERIvfoc2SSjXRN76BFKWV+ro1LLHvw1Rdy2H0+2z35zC73UWpPYwmUg0Ol2LKMHL2ATf5ZpOpqhtzncKE7n2NnFNKQPYYrfxvHYu+xIVXP0aONXJz8HsNFMo8mN1CXcVBVlEPv1nxaou8h2s7hnZ4/0DSXT3yhnICWz6dd75Jtz+Ga2Wu4eGacQLGPXZeU0NL5KxLRNUzFRki5ljiHDvbsbqBd2cM1Od341hcinPltHLWnSkYfJqek8B+AnO3VNK9fRqDgTR5xPERZ0MA1289t8Yf4l9pmPt9djpAuoFevJhN+ibNbP8lA3iKabQTFfpSfKx+loC/OY8V/5+WSCtxTCoXHEvTRSGLhNTaVXcrBggk6Vk7ygfMTOBcF1qrHSWsj9CwsUnYozLBaQmx6HyVqDQdTPfgD+3hKOZNET4iPKU/zmKSyecciI9YWYnMH8WcLGEoOkpTHORbxUxIdZMimsn7cTaT1fJzuEVpKT2fQNkdOTh87M7WoczMExTBrZzxEm85GlsYotlczkZ4lIqQQYiKXegfY782lZjjFkmLDJ/lYdKfJzY0yZJSiR8OMOmy0RNwknSVkEwskBAWfP0tQKCGdUIhIGmUpO4rNj5aNowg6drtOCheqopMVTOyGhCFImPo/bx8JAgInN69ZdQ1FNpHQUYwsFk1E1ax4RZUgXvKzScIeEYesEldjuLNWMhkvJZYUg2YJNfFFxvNNfGKcjKiSk7QTCnmoF8ex5uZSnhXYVbiEb6YTq7OarD2FrBZTn/2AOZ/I3zMdbFcFZppzyLVLBDOzlEY8DA8GWK88j1rq5HX5TM61+Qm2+sh3CCRlnXzFxfx0ER3pF5nOE7h/cRPXjJ/g0aZpivb+nlSilX2DL9K6UMTxznLOTj3EcJGFu4Lb+Oh4N081zOEfegNJLyZiJrDZTGqczZjJ1XzK/gp6q59Pha/nSwOH+X3jLI7IMYrsAr7yjXRlP2BLpIHDBytYqbzFysAS+R0V3JL9Inf37eed2ijhRhvLYztQ9bPZ2/8iTovIVs8aDuxfzbnGmzy5tpy2Mt+HHY7/5Tklhf8gtLauoqD9BaxRGwvHx3jM8iAPNK2nadxC9c55OsuuwOnYyarac0noGkeinVTXvMOTju9TN6dzltCJO72fx9IL3PBijMENn8ftPcKq2nOZtSdIScPELOM8E29n6fgcdxqP8/2iIr68J4fJzZ/F5T3O+mWX0l+4yLKmeQ44riY/ZHKT5xDvFpRwzlgO0WXnYHWO0165hZG8MI3NQXY5bqZ8XuV23w6ezitg+xGVCUcr8eBxrCkHUW2cMakEbwQ+lXOMNwuqOG1AYt5RT3JxmGxWxxdYoN92NjkRlQ0c54gaQ46IiJYlyguaCMoxrNZpplI2VskT9HvzqAiaxAw3NtNCwq7+8zpqDmImRtBmpyhtJSu70dU0WVHHZlNJ4ETJ6mREsKsCBjKGcXKfgvDPzoKGjMXQyMomkqmhGSpWTUJVLHiENDHRQ14mwYJLx24mMSRwZy3EEw5KhEUsbjcVaYVuXwJ3fAq7uxiHLLO44KFaOwR+K+P2Fuw5LpyznWSShUzHBiiXC7Do5RT5FN7ILGPr7PDJh2zBwzh9LaQdcXIslVQZQVJ+k8ciHVwy2cdfGhYp6n0Z0bKc0UwXbbZaMokOznP24q8tZsi9nU3+AuaX+ylzpLHn16I4k9S6l2FTmrnY00egqYSXXNfxRdXKX891Udf7ZzR9Mzv7nqZ1ppD+3mLalT1U5KaQq2p503c99yYNfnBWgoq3f0w6Xk9IC6M4E5zmWknX8fVcnribSB4kipfxF99neWh6gntXjmKPHafOsYgzcBmvH3uMkpSb02u309b6CPkO74cag6c4ySkp/Aei1V/EQx01PFP/Nn8vraV/foFLn56i/4y7cPt68Bh2chOFvG0eY936ft533kxwyUa0b5TPp37J10uruOdwJUsrryZrhlBDM+SmitmVOc6yZQf4q+3rNEyrfKfgfd4sLKF+QkZM5hJMm2SCIzgSHk7EerC4DvDX9DqWeha4IvM0j0kZtuwIMexaRXxhH0VaFV2JASR7J28mq4iPz+BNHiYUjGCoeRjSIvVFHUwEotQ1hNhvuRTLXJrTlffZmQ3jHk2Q1qJU5jUxk5OguGSRTtqJzMaxZIYoC0okatYjy/PkysXMpxcJIeFJwgbnNP1WCznTMbJahjxvGVFbBps1wqxmp9xcZM7hIzdukjZtWLGiWDUsFoWEacNmpknLVuy6iCFICIDBf7+SigCyYZCVTCRURNlycny2YsFLErvDQo6qM+vIYstGsNpzcBgWomErheY4Vq8V3VrAaE4Wx3Q3iAUkxTguKZ9ytZsFl8AepZ7VhkS0yofD4mEs2E1R1E0wWMhqsQd/aQ66vZ65IgH38AekEuX0zeylNltIeKmBM2x9+CoKiTk78BR6kIVZBK2AgfmjVC55GejLY0PqcUbyJL61cDofG+3i4eZpivY8Tireyr6+F2iaK+D4sUJOS/6a8TyBX0fXsjEcZrLMxPDGKHLoeErambOPscnaSm/vaq5Of4e+EgsPRTZRl7XRUlDAofPLaJ16HkXZyvvdT1GSclNoaSQV2cZtxg/prrDxx9Qa4s4N3Gu4ufucMEXvP4rf6iJQfR7T5ZN0XLgRu93+YYbeKf4vnJLCfzAqAk6KLv8BJ3Lr+PqLElNnfxWFBSLjB1jp3s6e4nGWNc8xKdr5c6yd7NEQfy74E0/UdtA8acU7qNCn1pJYeJUt1VdytHiGltZ5uqybmQg7UcdHWBN5ll+ZMa54Ncpg4zXI0h42NVxBd1GQ9uVz7HLdQmXI4Lbcg7xdVMk54wFitduQ7VM0FK1iMidGde0iRxxXUxky+Wr+AZ6raOa6gULmmy9CNLupdDTTr0whW0+wN1GAvDDBpCXDujkv0ZZzsDkmqfC3MK4HEeVh+hMu2sxRDueVsXneRyR3GZn4GGJawuJcZMK6Fk9Sp5FRBrQEhuJAssYIuEuImkkEcYFsWmCZPcKczYYvopFRReyyg6ysn5SCYcX9TynYVNART/YTTg5JRcREFAQshkFGMpB0FUm2YTEkMhkRNzGsNhm3KTHtSGNLBJEsXgyrQTrlJk8bQHPKTEvlBJxubLFxlIyfxdQUBbIfKwXY3Aavx6tZvXTyvUJAn8OeU4xXsDE346Mx/QrhHJm3jNVsN+3MtwQIOERiZpLCpIvRkRxWpf/CdJ7Mb5KbuSIS5r1VVipSXXgLV7NkD9LgqceiNbHVO4OrsowFzyZW5xSy2OanzJnEVdJMyhWhxduMmlrHtY49eFsC/NC4gTvmZnhwfYiy935BItLMsbEdFGVcuIxa3Fo9H3ftx96ey83hG/hC/wGerp9HEqapcsXJKb+AD6b+xsqpEiZHiyg0nNzifp9kWw7XT13MqliKs3JL+d01OdS98wNWnnk66666CkEUPtygO8X/jVNS+I+Izc23tt2P5xO3M5F2EJl5kXNaPslAQYi0NIro2Mkv+DyNYxm+mrubIEFejE5z2XPzDKz9HJ6co6yo3k5ITDOT6QP7Ph7Lno+9N8LDeX/jkbo1fGqwjGjDOajCIg5VQEsKjCb7UCzHeSHRxHzPPBcnnuYxIcXWdxcZ8W8gFd5DtaWVY8oIubmHeUlZw+JgiI3xl3k1vUjx4Tlmk1b0SIiEruL0zdJn20Jp1OCzecd5o6CCLQMyQU8TiVAvlqSVtDnNqNxCcdzkYm8/+11uqoeThLJOxIzGkjNDUVGaMbEZJZIlLz2ILaySKVqGLEfwWHKJKHGiyDizArXWJeYkEUckhW5kcTtyyYgqopQhbki4yJCSLdh0MJAQETH/OfdIwEQQwWKaZGQD0VAQJRsWUySdFnGbYUSbRNqSS8RlYFmaAcFLVkjjkL3ka+NEbdCnl1KHTLbAiWB6mV7oJy/rJpksoMkaQvUWUJ9MsLNgAd/EIUS5koi0SLGrlHwBNK/JXyKNbJsZ5M3qOAXzh3F4W4jaFqlwVeE3A5T6FTqlBlZGo7xdtoT/2N9JRKs4Mvga9cFcuk8UcHr6EcYKrXxjYRsfH+/i160zFO3+HcloM/v6XmTZXIDeE3msTj1DIhf2iS34xRKWFeQzsbGIBgbwFJzNgeCrrFko58ihEtal/4QrV4OKWt7NuZ7vxDTu3bpI8ds/w1BKEPwB5pxjnKG2sG9vFaszz7M8J0LJ6jKuj32Ozwzsx1lTiOupX1C3vuxDDrRT/H/jlBT+gyIIAmVXnM2ZN5SzpeNaYobCoXAny5a9x/OeH1AQMnEHhzh36fd8PeDl3r2lLGy8EcVcJDM/SkGqgg+MblZ09PCa+26qFk3OlLpIqaPsXpqm+e0peu1ryETfZU3xeRzNm2RFxxQ7XF+mbsHgq3n7eaOkjnMmA8QrT0OwzFLmq2PekSC3YJ5e63rcEZEr3Sd4v7CCiycLiay8DJdvjOU1ZzKSE6Kycobd4jbU6SQb0u+xMxPCMxAmms6Q5yhizpekqipOj2UrcijLCuUonZkwYkgHKUxlUSuLziQe7yIDeiHJpSizFpO2qI90Xi2GFsKuOsgYEUJSBW7VoExYZNbUEOMqiGlcdj8ZQUUUU2Q1AZ+YIS1ZsSomhvnPn79w8k/45//dYpqkRR1JzyJKVgRJQFUsOI0lTItIXMoBh4wlOoOuOUlqUXwWFy5BIm3n5A2kTIZgsR2fVSVlpsmJ2wjOO6k1e3EE7ARdLVhz3Njnu0gnChgNHqNMySWRqGKNbQZXUQEeuZChIgX3wHskY2WcGNtJTSzA9HQFpwvv46zwsNNxDhdYc5lcU0y5I46rsA7TrVHkqCHXzKPdF4XiKjKuDpoDBSy15VDuSuItXU7YFaTD10wkvInrpBfINHi5PfwRbhs6yk8bxwgcfhrUIjIWE8OjsMrfxsz0Vm5QfkhvqZXvBTdSl7WwLq+YDy6toPnE4yQiazk2tgOnVWZNTgf9/adzZeJOlgIgVtTwt/of8+0tP6CipOnDDK9T/A84JYX/4FS0ttB47Xb6ixfZsGGUo7aLObTkZ+rwNI84H+Gnjes5eywH27RAf6aC5OKrbK2/lu7SBSpqgkwIXt6NFDF9fIbbld/y/ZIK7jxcxvzpt+DyjlCV20TEzBDVR4gK47wWq2C2d5Zz48/wmJhg2ztLjOZvIRvfS6N3LcelccrKe3iVixDH43xMeJ2nHTIb98eYsDcTCx7GlwowHB0hzQjdMReB+DjDdoH1c17iLWfh9ExRV7iKSWsIz/+nvfuOrus+DDz/vf31/h56BwEWsIhFFIt6d5HlothyHGfc0jyJx5tsTiYnTjYzmV2fSTtOJs12JuNJNhsndiZW3G1ZkmXJokhKIkWRIEiCBAE8AA/v4fV33+37BygkZ3bPTmY2E0Xy7/M3D88hcS+/vL+aWOCUM0httULbLTG9HqK97Ri6sU5PZIRVbwNYZKWtccBY5mKql+kNg1aoQLe1huN4xOIdKuo2NNOj4JdY8zq4SgJNs4jocSxsoI3nSGRUC1NW0NwAL5CQAnnr7jWJAFneHD4yZQ/ZtZBkHV8JcB2dqF/C02WqcoqcHEZx69h2hIa5TpwIvp8jHfJ5qdvLRH2NKxmXuF0inOxD1xVqG3GGrWdoxBVOetMcCHTqkzniIZ319go91QjXrsXZ5TyOkwvxPeUwd8oJyjN5ctEAN6yS9sObO6A7X2EhJfHb6/t52/Vz/OeJIvlTn8fs7uCl64+zs9bLxbkR7nP+hJWBEP/bxr18aOECv7+rSO/Tn6VZ286JC48xsZbm6pUcO+3nSKYdOoUJisk7eTTax3fe1M/U1b/Gdo7y5Mt/zuRqmtp6Hz1BjHclLpK+Kc9PNH6Un5l9jsfGyniZLtPhZaLZt/Ctc/+RbWsZDHcU37yNj/Ep7jg6xLsffhgimdfuhRL+m0QUXgeUpMHDj7yDzMAR/sC8k+yFBn849C1eiod5vlzk7i8uMHvgx0lkXmRm+Faars1s/RzJ1OP8sfxTjC90+Y3+J3m8d4j0ikt0rs18J8vG4hNM6wc5GbrCrpnLfCPyc0yXPD7R+xxfHZzm/utZ2gM340nrZPQsdSx8fZllKcJKXaevNc96sEFs1cbWBvGkCoPpbZtfAON1Xo48wljN5ycyL/L1/BB3XDYoZfZg1meJmymWG0tUgxZWQ+KB2DwnesY5tpqgltuObV4nYseomiVWlR5yZsCx6DIXw1F6Vy0aToTAtGgaDum0w7o0iNtxSdqryC0bN9mLrJgYShTTs/D9Nr4bkFJsTFlGtX08f/Noi1fnFCQCJAl036ejuCiOiSTp+LJH4IeIeOtYKpT9JHlJwUtGkPwIlUaRuGNgdRMMKE2kaJLhTpsziSrR9csoWg8dpUU2nKeHBpUIfKU+yoF6aXNewVkmlh2DWIDi9jDmvEQxLvGfKju5e+0aj090KKydRo/uYF1aZltsBMnfydFYmWh/H25kF9l8hiDjkjCimIpDXDaQ7QEG/QZ9SZtqchwtNMlYPk9tJsVwrE16+CDr4WUORbdTLB7m3d5nuD4c4mMr9/HoldN8dbSM7lyjEJVJjxzkmnKeY/Y0p06NcUfrt+imweqd4Kns+/i1usUvHyuS/+5nMeQ0qeH9vGw/w9HVMZYWJrlr28f5l2O9SJKYP/jnTkThdUJRFHaOfZj/NDHCg5GrbKt8iU+EHH7lqTylO38Clwqt4iUGu1M8bcxy6OYFnon9FEpVQlub56bKF/lNuckHv+lz/fhPEk+/woGpN1FMNFEiRUp0+G4tR/HiCnfW/pLPUOee71S52n83vnOSPYU7OBdbZnpqgceNDzCwYvHz6af4i4EJ3nMhzdrOt6DIF5jKHuKStkIuf4mn/IPUrjfZ33qCJ80SqXNrbLR8NAc2QiYDQ03mQm+mr+lzf+gCJ0I6I5eblK0E3Y01mkGXQk+bRf0WYm2PndI1ZrGJLG3QdUzyqSGaEYtYrM1qkKXdtPGcGj0dHSfViySZ6BiYroWJhuFLpJQuHVlGc4KtKAQ3bl3bjIKEGviYsovsdEHS8HDRVY1Y0KCjSKx6cfI+mKkQuqZSb5eJmhrtdpR+aY1Y0sAN9bOa9AgtnMWx0pQ7S+SVFJ47xGTEYlEdYGajxHdyaySunsAPhijZiwwbPeBPsSfaxEr1M+SqvFhoErvwOO3aAC9ffYLRSpLLlwoccf4Ljd4Qn7Nu412NOt/eLzOw+gx6ZD9XOmeY8YcpLu/izcF/oT0S45OtN/GRpcv83u5Vep/+DM2NaU7Ofpnh9SSllTwjfondqRbZySG+k34v/4ul8kd3ugw/+bvUKzu4sPwcSdlgPL6DdvMuPswfcXEoxCdWjzNhKdyeHeTr7xxk+tlPUavMsNq6DrtkPvj+D5DN7n6tXyHhH0hE4XXm5v4UH3/vQ3z12Ef40flBpGaUi+1+2uWvcPeO93FxsEIyu8YGq/xZYw/tlzf4vczn+fT0YR69OkA3PkW57VK//iI9nSFO2LNMbjvD34Y+zvSaw7/tf4avDO/i/qU8ncJeLL+GbkHX9FjvXqXOEt+vp6ksFOmtPc1ztWXSZ1YpNnTaa1cJGhKV7nXKQZe1usaUe5XZWIxjpTTNXfcQTRbZMXyU5VidQk+JM+yls9ZhqnOal9vrSGUbnzrDPTupJE3y+RZXpSmsqkW/OcdKq4qjFzBCDXLJYRqKia7XKHox7HaLkq4zZEWxY3kCv40W6Jh2l44UIeQHJGQLU5JQbBc/kJACCWRuHIcHkgQK0mYUbBPYjIKhGIQDi7YSsOTEyTo2zaRGRLVBV4h2NapVlYJ/DaIqK9oww6EUimZid5MslS6SNSM0Glmm5OtkCxGasWnkTJRQeRazlePS0vP0NeKsrebZxymM3igvhI9wu5GjuruPfNxHSaSIaDq+1cOoewUrKfHVznYOl1d4vK9CYu4Juu0+5oqn6KvFWFnOMuU8T5DyuaBNkpIL9BVyNHemGY63yIzcQlGf55A6xZXLe3lb97c436PyKytHOL6+gtlnUNudYdq4TrxwH88sfJE9Sz3MzeYY9de4P7VC3009fLT1QX76wjM8PlSlMxFhr36eIz/0c4y/8zhyWH1N3xnhv4+IwutROMWjR/81D+3/Ya4e+jDJ7EtsHzhM1/R4oXKWwaEn+Xzol5gpe7w7+jIt5zpfqy5w9MsLzI28DcN4nuMz7+Fqb42e/jKLUoYXazFWLxU5tvEFPk2N+56oc23kQWROc3DkAS7k19i9u8SzkZ9kesPjX/ee4m9H9/K+6yNUb36EeHqRfdvuYaGnzvT2Oi+E38dw1eXDqdN8Pde/OXRUuAm7M0eOfq52ivhc4EwrCrVVlhSPA5UknelbCUXW6U9tY01pEAoVueBkqVaaNN06U/Uo3aEZVK1OyshR81tIcpmSpTCsVFiLZehr69hGEs9pITsygdTFlDPoPsTlLqYUoDgenhcgy6/OKWx+KciSRCBpWEqAbLW3bl8zFA1ZjhBosGBFydsmlRiEgzZGJI2hatRrBjnnPJ2wzCW/n0kUGkNp4mGFplMlXQ9RLBqMuqexkxpn5R0ckCLUpnrIRCVcFdLdMMtLMaY6j7EQl/nT2m7uLi/zxHiXwtop1PAOiu5VpoxB2u3d3K3P0jee4mTifh7S81w+Osiotky6/xDlcJE9sQnq9YM8rD6FOp7gPzhv5SMrC/zBnhI9T32aRmWKFy59g4FGgm6zlwIR7k2uMTzTwx/pP8rPF5f4P/YukHvys3hWH340QjvV4FhkhnMv7+WB5q+ykpaoZ8Z5Jv8j/Fq9w5femuWmj72d6cPiUpzXIxGF17HU/Q9w30cPksrojLkzPJO4zKFDa5wL3c9cPczCuWV+svtpPjk4zi+8MsHGLe/Bl8u41Q3C1Sgn66/Q1/8cX9R+nMkVm08OfI/HRvdw33IeMzVNy27hVavQkLlSu4Qnn+Zv22MsXqxwZ/MxPh802PfsGtf17bQqp+nxR5g1rxONnOVxe4qVa3Vm6k/wZGeV9JllSg0Fa2OdTtdGD1dY1G+irwPvyVzhRM8oR1Zi1PI78ewlEkGG1fY6ll+k0pbYpxe5mB1gbyNJJzOK75YJBzGqZoOO10LrBuyM1FkJx8i3ZCwlimM1CGxQtS5dJYvqBZtLUglQLJcg8JAlZesqTunG5jVfVrFVH7nbJghUXN/CkDQgQkiDRTtGvlNnLWJjdKuoRhJXd9GVBFn3GmUNXjR7mDA7FAsqSalOND2AFlbodlIMWs+xHJb4enOMA83Ne5sz3QUiqQnMWJv+xAAJJU9P3GVOHmZno8b3ChVi575Nu9bHy/NPMLyWYHY2wf7O57iUVviN0kHevnCWPxlbJPvsn9LYmODk7FcYKyaYPZ9id/svWUvC4/Z2htwQsUKKzo40I4km2ZFjLEgXOORPcu7cJPe1Psn5rMxftHZTkNLckhvk5TdPsH31a1jWUb579v9isJYgKo2geEf4CfWvmB8L8yul25nc91H+9wc/hdbf/9q+HML/MBGF17lIMsR9H/1p9EeGCSWr2PJJPmO9md5LbX5v6Ds82TuKt9ai55nrzAYz2K0nOT71LmYHK0xNV7mi7ma+plOeX+Jg5a/5bFDlgSebXJ98CEN7icNTDzHXt8Hu3XVejP4LRhvwaGqWU5kCu1cjdFNTeFRJaVnqkokRW+e6PIrcgOP6Fc6m8hwrZ2ltv4Noco0dY8co5luMjDY4r91FrOpwq3KOE6rE0OU6ZSdFvXQNr+WDusGKvps+C26NF7kYSzFSgqaWw2qXkDsyjl9nQx0l7Ups06usagbJhkfXD2F3mtg4RCIBppxBcQOimJj4yLaDH/jIsrq1n3lzn4JEIKnYKkjdNoGvbd6+hkoQREgqHlUSZDt1lkNd9FYJSU7QlUySoThhJYYfhhOtHNtqa8xmTKL166hGD221RSGaJapkiUXhhDnIvtJVvp1dIX75+zhOP8uNOQb9LM3GCIfUy+QGYlxMHud4tJf67n56Eh6R3ABKTCJtDJKVY0wmbdzMIGp4gv5sDmssSW8yIN43iZ2ymU5P4ruHeXv4ZdJTKf5Efjs/Vl7jD/eV6X3q09TLk5y58h0KZhQjGCIuTfJIbI70riy/2H6Uj86d5PfHLhNZOU0urpEbP8qs8zw3V0Y5dTLHtPN9PjTY5fMfu43ogXdvjsEJr1siCm8QhR2DPPLIj1MZ+m2GagGZxlVm1r/Iv1c7/MxTUVbv+xli6QUyRh6vDmcr50mlv8vnpR9mZKnLrw98ly+N38S9xTxWeIRax6S1dg2jGubcxkXCke/zmHOY7nyD98tf4y/SGR5+OcTq9jeh6ReZGbyVy6kyE+MlnjfeQU/F4f2xU3wj18ddl0OUB24mcK/QHxpnwSsRiVzhpDPM6nKT/vppLjZXodTFdlsUkkNspLoMDNpc1w4RbXnska8xq0B2uUnDMjBrG3SwSWccNrRpIpbPsFJhRQ6IVk1MW0ZyfSzNIxz2MaUkkhsQwcQMPCTbgcBHVm6Md0tsTTT7soqjBEjdJp6nYrkmOhq+FyIpO0QiOmnXY8Foom0s4/sx2m6NhBwh8PMM6i4dI8tIq8oLsTKhpZfx3CwVc5mClMK2BtipV8j3JPCjY/iZCEb1Ct12hrmFU/SuR5ifj7LT+jKllMYXWnu5q7rO05M2+dVTKPo0i51ZJumjXN7G7XwPaTDKX/p38a5Gk6/u8+i79HVcd4YXrn2D6VKOs2fSHGr9AZeTMn9W3cmOtolciGFOpRhNNsmN3MYV9wwHzFFeeGGQ483fYDklcUUfp5Y6xgdCg3zhwQSj3/s9auXtzJfOYkRkjvYdoJD/9/yr6VsYiBiv7Usg/KMQUXgD0bQE7x2/hV+ZGeY3hp/ls9uP88BSAWo687UEG4vfZn/uHs72rbDvphrnjQepNhTqi0vsLf8Nn/ErvOmJJos730k4eo4jOx7m6kCdbVNNLup3IDck+s0FTOs6V8urRM6vslzTqC6ew6iGubQxTyC9wHc6vSzOV5isPskT7SUyLyyw0ghRL16CGmx0ilS9NhstmW0sM5dIc6yap7PjViLxMiOFXayH26SSG1wJxrGqXQa7l5izqyjFKrbdIZccoBV3SKVtyvIIsunS45dY8220ShPLcokYCWzDJxT26EgJcH1CfoeO74LlEAQ+iqTibw0fbf4n15dUFFlBdrv4rrJ50Y6v4LoGcdkkHFJBT1KOumjrCziv7lXwQ9h2kkGlTioZwokM0E1ohFYu0mklWC7NkumE2agmmQwuomYMZrVdHFDi1Lb3kU94RHI9GGEN2c/T7y/RiQZ8oznCwdIC38yvEjv7DVrVHl6Zf5r+tSiXLkbZ0fkrrsYlPlfezpG1eZ4uVIisnkUhRVdxMMIqvbExwtIMb45dZ2hHls8b7+Ij9Tqf2V+l98k/olae4JWFZ0h7YfKRMbTgMO8LPYs9neCjq2/m0UvP8XxfA2dQZ1tsjUThflYLRY49cjdTU/tf0+de+MclovAGtH8kTeGHfoee6bfy5qc7XL35I6Tys+wcOUbDs5ivzqHpT/Ln3lsoXGvzW/1P8NcTB7l3JY+t9lJuOFQXXiZRz/BSY45c/gW+Jj1EpmjyC9nv8oWRPXzo2ijVo+8lmV1m98QdrOTbjI63uRS+j94WvDUxz4u5AW4t5+lsO0Y4XmZy+BBr+S7j410uhx9itO3zSPoSz+WGOLwSpdEzQ+Auk1Z7WXM3ULVFZu0Eq+stpM4ikZqL1TNFKNakkBmjEbKIRTus0YPTcUnZq6w4TYK2jax2iUezWJqHoTu0ieE7PobfwfQdAtcHyUNW/u4VePVLwZNUdFlFdi1cV8Gy2+iesnl8Nh30kEpHy5DQo6hmFdsMsVErErMNWu0IvaxgxDVWjVEmtBRef5qQHqbSXiFd01la1Biyvkc5ovCEOcnBVoNXhiDdmseIjlFTygyHe3DdHdwSXmdwOM1y5giH44O0ZgboTXkk+ifxUh4j6REMeSe3xcoMT+R4JX0/D+s9nD82wLB1gXDyAJeaL7Cr08/58/0ca/8u51Iyn1rbw4FKiXbBwJpMMpZskBu+nYudk9xUG+T06Tz7W3+MlwCjf4iT+Xfzs12F3zxSYXj1Se78F/dz+NF3i5VFb0AiCm9UssIP7XiU8T/5M7I7CqzPf58JaS+nE9c4cHCdM9H3Q0PCXl1i+/rf8lm/zFuebLO09z3Ekhc4PPM2lgpNCr01ilKOy02d0sIqY+Vv8TfmEtPfu851dYpO9UWGje3MKUV6e6/yffleYmsW74mc5BuZHu6+EqYyegxFuspIaheLWoVstsgZ6Sb89S4H/TOckB0G52qU3TTN8jxaQ6XUXKPp1miZMKOvcSkzwIFWlu7gTlS1RjrSQ13qoBs1il6CRsMibK7gdDq48R403SIWyWCpLpq+eWy25/joXgfTt/FRkeUARVYJ/t7mNW58KeiSguI7+J5Kt9tC8xRsSyVGA1mXaaoZ8moEUlEkKUylvkyspVKr6uS9eeyIwnwwwGSgsDGQIGl0iWcHUGIqKml6vAXKYfjyxgA3rc7xrewK8dmn6XZ6ubp2hv52ktWVHHu8p+nkDL5i7eeuZp1npgLyqyeRlCmu1s8yYRVYvD7IEedvWMlr/GblFt6xcJbPjsyTeubPaWwMMLd0kqwZQXJ7yCpZbk9Wmdie56vxR/ixlsUfH2rS88QfUiuPcqn4AnElxEhyEte+nfcpX2JxOMy/WryVo22Lj971s4x9+g8pDItjrt+oRBTe4LRUguM/NMP7f+13MKd0qtYCHs/wf9q3EZtv8pt9j/OFbYe5eyWH46VZrQaUr56i0BngjDvPyMhFHtc/yHTZ5ZcGTvPN0X08UOqj078XN6ij2gFWx6XUXKTtXuHZZoLr18oMV57iidYiuVPXWGrEqSyfI1QLca22iOu/wslOjOJylUT1LFfrq7DapG2aGIRoGDaFgkUxdJyBLtyZWGE2WWB6I0w7OYzvbRCVktScJpJUYtnRaTY71HEZtmK4uUEUrUvUSNKVHVSlSzsI4dgemtvG9GwCSUWSfWRJIZBubFwjgBtfCpqkEugaQaDS6TbRbBnLUokFNTAUanKSnKRhZWJEw4CuELE0ymWZrH2OmiFzptvDpNlmIRcQ766ih/voaG16ollkeYztEYt4JkPIKGCldYz2Mq6dZKl8kWw1xOJiiLHO17kagT9dH+fo0nkey18n9tLXadUKzC08R74SYWU5yqjzAn5MoqgNEDH6Gcv00d4zwHCyRW74Ztb06+xWx1i4to17up/hlazKv13ez/HVK6znJZyJJGPJJtmh2zhX+x571vs5fTrJ9u7XmUq7/OTD+/A+8E2OTjwodiW/wYko/ICI9WWZfOd+Hn30YeYLv0yqCdrGEuOlr/Af3RIPfddi+eD7SObm2L/jAdaSbWR9napX4olmmsuzFe5uPsZf6AH3vRCwOvUAofAc+ybv41pvnZ272pyP/TDb2gHvy1/hZM8Et1bydMYOYkQ2GMhuYyNukS90WDIOUTDhgdQSZ3rGubPWR2fmDuLJDcYH9lFJmhR6OiyoNxFpeexVF5jVNfrXbJpyGqu1hm7pbHTqWM46TStgTK+xGs8xYsVxkr0oSpewFqOLg6J0aQU6lu2hum1wXALNQFZeXX0U3FgxE9yYU1DQJJlA15AlDdNqojsy3a5CNKjiqhLlIEU+gHbKIKw6hGIZpLCMRIyMe5U1LeDZRo6J2grnUk0ipTmCIMeGWSQXJDA7PWyXl4jnw8yHd3NAS1Pd0U8h3iXVO4mX9hlIDhDWJtgb7dLTn6ORvol9yUE6M4P0pRwyQ7uw0iZTmTEc5wD3GhdJjyX5W+Ue3tVs8Te7TXpe+CuaG5OcPP9lBleirK4k6Q822J20GN1W4KnMu/iIJfG5Qx16nvhDqqVRFtbPEw5pzPTsIhb9OJ/bPcIH9w0R1pXX+jEW/gmIKPyAyef38mM738bPj/XyGxNn+MLUUe5ay+OZYZYrMqX5Zxjyt3FWvc6OHYt8P/bT7GoE/EjuIpeiEfxKC+1KmaUNjfLCaVLNDLOtayQSr/C0fxh5pcM79O/zjXSOe67G2Ji4HV2/zrbegyzHawwOVrmg3U227vHm6CzPpQrcXIzQ6N0NFClERijJdWKxEpf9YcxqlxH7Ehe9FrHrFeodldbGCm7bIaBJTR0m68BMtM5qNEN/x8CO5YA2uhTBDCwkuUPTV+naHrLbQXUDAj2MJAXI8qv/0AVbE82upKJLMoGhoajgeBaqJdHpQMQrYWmbR13kHJdaXCbst9BCKTzdJRmOY6g5kiGJpSDLSL3Eqcg6xqVTdFoJlkqzZNoR1tdjjLov0ImrPOts40CnvTmvUL+EYoywbi8yKOVo1Ic5IJ1F7QnzBDdzd8fmuWmJ/OpJfLYxVzrFWCPLpbks+zt/wvmExKeKOzm+fIETuRq6UyIZD5Hon8DMmOxJbaNSOcCD3l9ytUfnFxdv5q7r57hWcPFGE4ynG2SG7qTUu8oDb3sT+/c/gq7nXrsHVvgnJ6LwA+reqQKTP/TvGNr/Id5+NUfxlh8lVZhnZvx2aqpJ1Vyk673IY50JNi7XeL/yTf6id5QPXBlg4/j7SOVWmBq+hUrURAtVqXgu55saK0vr9JSf4cnWAj3Pz7NsZqmtniPRTXGts4IRusQpZ5T6Sptd3Rc4EXTon9ug7GdpV68RsaKstNcJgnkuWBGKpRaRxiWWm+v4tS6Oa5KM5GnFXPJ5n6q2g5QLU0aNVSNCoa1gG0l8t42GTtfbPCG14Sl0LBfLd4h5Gr4RRpZ9ZEn+r+YUJDxUdGQCTUVVJSRFRvcU2m2IeGt0ZIklJ0bO6VKJ+RhWDUVLbu5V0GIE5BnWuuRTIdzYMPF4Ak2x8N0Ya9VrpGoaS0sKg+aTXAvBF8vDHFiZ41vZIvFz36Fd6+Hiwgl6y1EuXw4z3flr5mISn16d4rbrL/HF3DXip79Cq5bn2spZ0p0QTjdNTpbZEXcYGM4zn7uHtxt9vHS0wGD5BIG0m9PzX2N8Mcn58xHG7GfJxQP6Rvp4Ifd2PuyG+PNbHKbUy9z7oXdx4NF3oGbEbWg/iEQUfpBJEreP3ceO3/p9pt5yAIJlJkJ7OZcssnffBmfjH6avFTDsLqPUz/FEfZ6R717murINq3mGydRNXImXmZqq8FL0/exsBfx43yVODOzg1o0C5sBuUOpElCgd18byylTdDgttic5GmaB9lZV6CVYaNBoWdqOB3bGxvSrVIITfDZjSyizGs+ztZLHH9hGNN+nNTdCKOaRSDmV1DKPrM6JUWNU00g2frhzDdZqonoppm/h+k6YHvmNjKhqxQAc9BJK/OdHMq0tSfZDAQ0GXZHxdRVUCVD2EIm/OKYTcFZpKwEI3Rt5sUgxb6K0SATf2KkgRbDvFgFwlmjQohUaY0JJ0h/OkEz7xXB9yXCERyRFTI6Qj0DayZDBoJBU0p4qiROn4LaLo4KXJyA6FaICSykF8kunUAObMMP1pi9zwXprxOlOxYZqNXdwuP4PZF+H36kd558JZPjt4mfiJv8bq5DAlCyWtsD0/idU9ykPKtygPhvnE6q3cHxnlX/7wpxj+pZ8j1RN5jR9M4bUkoiAgSRLj+3t45Jf+DdmHtuNHfBTlGR5zjhBa7PAL2af5m8nDvHdtgvbELTheA6u6gVSBufV5VPUU37F3Yi61eIv0LF9Pprn3apyN6buJRBfZOXKcYr7F5LYuV6IPs82E9/YscLp/F/fXh+jsvoN4usbE8H4qOYvhYZdi+HYGLbg7tcpsZoB99RRm/3ZUrU42PkBD6xKNtSnRB6ZLb1BiRfKI1Sy6vo7TbSI7Mh27i+836bgBCbmLaUSIeiq+FkKWfGRZIeDVe3b+3kQzEp6moMg+qh4CTUJVdCJ+lbocMG9GyLc3WDTa6JUlPDdKrV0i5hh02lF6KUJU5ao0xKQvUeoLkwiqhGL9mHqbnnCGwB9ml16jpxBlKb6Xg0ae6s4BCpEWyZ5pOrE2I/F+XGcHh7XrpPpjPKfdwt12wMntCoWVk7jeBOeXn2VkPcUrr0TZ0fwz5qMBJ7rDpOQkU+kBardsYzS0Qix3iLna82yvFHjppQS7zS/x8ESKz33sQfS3/jbDCXFWkSCiIPxXwtvSPPreH2HXTV/gSCxFdXWd6dJX+LxX4daTbVYm7ycau8yBHQ9S7O8wMtqlqE5Ra0OjtE66coInW9foef4qRbeH9sYFclI/C/46qdQiZ9mPsWFzt36eE/EkB5dDNPr2oCgr9CUnWQ+1yWTrXJNniLRc9mvXmQ1HGSvLtOOD+F6FmJqmFrTQ9RqrQRqr5ZBxVln1uhgbLUxbxmrXwfTpWiZdX8LwIKc7mFqYWKAT6GGQghtnH23+2V+9W8FFQQM8TUGVAhRNJ9DA0Aw0JYqmySzZUbKtMvNaHbV0DasTZqNeJGZq1Go6efcSrZDMOauXyU6LK1mXaG0BScpT6a6Q9eI0m3m2BXMEaZ1T/hQHzC6vDEGqMgvSIMuNOfq7KVZWssw432Q5qfDH5Z3cuXCGz+fniZ36Mq16lrXqVWKSQTLcS1Sb4u7YBsOTKb4Vvo93tTr81c46uWf+M7XSIHPXnycRj/Hg7W/j8M3f5GfHh+mNiWEi4e+IKAj/r3LxUT45M8K3Pn4bK/d8gpvMXlhqsFRWWb/2PAVnkIvBMoODi5w0HmGy7vPT/Rd5dmgPt1V76WbHsZ0WdqOJX/NY3ljCcc5xwkyyuNRgvHGSE26dvotlKuQwGwsk/DRrTgXDKDLn9dPZsBhzLnMRm1yxTSNIYLVLhJwwVevV5ahhag2LiLnCil1HWq9idgKsdhvXc9E0B0vJEw0gpzmYmkHEVfA1A0nybswp/N3wkXQjCjoSri6jSB6KahAoAYZmIMkxUipEQgYhOUQ97KFWluh2DNYrC0QbCuvrEhn7DOsaPNfMMlEr8mJ8g/DCWbpmmmLpItlmmJUVgyH7exTDEl+ujXBg7RLfyhaJnv0WjY0cs/PPki8ZLCxo9JvP0AxLLFAgpmcYSvVi7RxmMGORHdpHNbTONm2AtdI4R50vMZ9R+eTSHm5beImzmSZKUmZmV4g3/fSv0vvjNzFz814MQwwTCf9PYjui8P8pFEsxeOjH+Hf7P8T6jjkmzxfpzO2g41qs1ZcYdk/zbfNjZBaq3Bd+ml9PJLn/RY3a7geIh4oM5G6nFO8ymnYpho4QawX0s07Da9JobMCKRHXExa4vIoV9VrprjDqXOe/phEobpEIXmE0NoM871HplurUiUiRgw67iOEUWgVDTBGMd09TxXB2wiIVTWOGAeFymq+YJdyGrdemoOhFfJVANJMlHkhV8/u6YiwBpc/VRIOGqEgousmLgyz6GoiFJUZKKhxPRsOUcWT2JlLYIhXU6XQkDjW5XJ+kuUtXgTDvNkLvC2WEZ7do6naH3U6vPcSh8P2uWwtjEGSo6vGQWGKBDpTdAk7pEwiFsKYWSVOmL9KFru7glVKM2nOSMeZy7vCVO79TYc/V5qoUZXr76BIfTb+PZss7R7NNo0Q+SLORZiL2dX53YQfqzb2FnduS1fJSE1wkRBeEfRFIUCod2cPehHdi1oyzOXuVg2+Ba5M2E6yA114mYL/FUfIgfPaXz4q0DuM0v09//Vl4wlunrL/Nt/R4Gmz7vyc7zfGIXD66GMXdPEE/XiUZ20Ug4ZOMOFXUb4W7AoFKloiokWwGuGicITAw5RFdy0HWLppwjYgeMGk3WY1lGnSReoYAetolpBeyQf+OE1DyGBxnVxlRDRFyFQDUAH1l69RXYvH0NGdxAQSfA1RSUwEVWdDzJw1B0CCKkZIdWWMUMsuTVEG7WIBIKIJGBqEzKSKJrfQwpIKUieOoQ/ZEs0mBAMqlgqj1ISZV8KI+hjzOjubh9MUrcxP6QRnWnQt6oU49so6aUGVIL1OpD7DNO8FjmPv78wgw/3/4mHyjEOfKXc7SP30pgV9FjOkPxYWTpFn4qU6T3oUMM9/4Ok6oYEBD+4UQUhP9ueirMxC07mWAnFxtl6rNNdo1e5hnlJm6tq9gRjWbbxK4soBoy89YSBfUCp90PoZZMjshn+Uwkyh1LKo3BfWh6ieHkTsoJk/4+iyuhYwzW4b70KrPxcW7eiGNNThCJt0hFp2glPQpZqOkzFEyZPbEGq/E8Y50kbnYAXe8SN7JYmks47LMhZzB8SCkWpqoS8hV8VUeSbixJvTHBLN34YnBRUQNwVFACF0nR8SQHQ9bw/TBxuYsdMmg5aXKyTzcNEdXGjaRxdIekEQPyDGkdWskQFX+MSU2iMSiT1DpIyQFMvUMhlMGxB5gOLXMlM8jZ2g4OWhd4ZUhhz/VzVPKTXF07w2TqGN9sh5mKfIE54z6K9X5+M+6RSmZwd4QYzFq05X3UMhvcuftWktvfya2x2Gv7kAivWyIKwv8v04kc0zfn4OD/ysWVO3nkqRM0j/aQkktk0seoxi3Cepu6a3HBkogs1+mLPs9zqXHefVHjlf0F7M6LpOO7uMwivYkKV6U7CDVdDhpLvBJLsfOKgdkzhabXyRlDNMIWqZTFZXWSuB0wHaqxEkkwWAnjpPpQ1C5RLbV5GF7IxZTzqN7mVZwdWSXkSPiKDmxesrOZgmBrSaqDgh4EOKpEyLeRVB3Xd9DR8LwQcTrU9Ag1L0U+qNFMKIT8NqaRokuHhBLFcdIMhCvMx/tYaA4x6S1R7FHJmyu0YgU2uqvkjCylaorx6Eucjo3yzcVRftL+Gr+YTXDkS8vUDxxivX6G/fpd+EqMuJZir+7CcJLLwXHulppcvHOII2mJ+PEPkyxEX9NnQXhjEFEQ/nHICtMDh+G9h/H9gJVLVwiutbi0scpEusOl6DuYKkvsSK9SVKLQNPGXG5RHfJzWZbSwyoq3Tr+8wEU3j1ztMCFf5ouKwSMll2pPL4H/Ckl9L5eVFtFom5I0hNb1GFUrXNLD9HRU7GgWSTaJ6GmqiothuLSl5I0odKkoKiFPxlc0NqMg/b19CoAkbU40BwGWClHXBknDCSx01M3js2mC3sN6J0nOK1ONqeStKmhx2k6NuB+j3YlRiC9xMTLE+Y0+JjrnOZmOMvzSLPbQYYqNM2wP7uQFS2U8822u6A+xXOnh18NFVifCKCGVZFLHjY7hZDxGogM49jRHIucpTd9CSf8JfmxbHik19Fr+1IU3IBEF4R+dLEsMTE/CNAwCrdZ2aHQ5UIM3pa9wIjrFg4s63b3DRFMNZLkPy7dpd2uY9hKzrkxirU02OsvFzDCJRZmFXBSrUyKciFLzVknqVdaCLH7HpY91vqvKTDQCrHgKggVCyiCW1MVQLTrEkNyAmNRlSZaJOxAoGmAjodxYfbR5IF4AOMFmFGwV5K4FaNiehe7H6Dibh+K5usSalyDn2CzFVAbLJfzwCPXOOkkjQ7FukM9eZEM/wvOtPPe5y/xBNM1bL67Tit1DuXqOg/q9tDyduGxT0GWiuQQNYzc3hVNUd2rk1Q2q4XHKygp7B3fA8HZuHRlHVeOv4U9XeKMTURD+p4vFRnkgBg/0A+4nuFQ6y7aT57C1ApHWKn3xm6jEbUZSAeuhW8i0ISU1sOw6q+11pAWP2niA2y6iJGWq1BllnaIXhVabrLLGGhHi9YDVZATPbaHLIbo0iKoWbaLg+Jv3M0ubexZ8WQN8JEm+sXntxpyCvDl8pAU+luoje12CII7tmuh+iqqlEQ02sBWJZSfO8W6LE2GJwxtL2MntVLpL9OszlAOJsYGXWFYDKrUUA8YyC2MqiqtghA0UskhJhZ5oHkWeYEavUy9EmDV3cztlSndMcziaJrT/blI9IgLCPx0RBeGflmqwrf8QvO0QAPtsm87SBmvrFSakPp43tjGwAccza8yFJznQjWH3GkiSjWc5eG2XqlfDcZdZDBRidYtYeIVVt49QVcYc1HGtJqqvYXoWsmLSDkIEr97PLEnodoAf0SDY3KewtfKIgAAJO1DQfZ+mHKDYXYIgje3WiLky3a5M1CvTVmHBivJ2qcaCDtraNbqFEI3qNSIhmaoFM/4aqioTDofxooOMRXpxx1RywSyrAAADYUlEQVRSMZdueJCubtJrZPH9PTyc6uAe2c5g4hMcziZfy5+Q8ANOREF4Tam6TmK8l8R4L7CLw8Aj412sbRKtVoj751cJDkSJGS2y+jSdhEdM9+kGBnXbp9u00LxlVqwQSlnBtGQcs4HiKpiehSR1aAYGruMRCkxMKUBzAyxJAXwkZALcG8dc+ATS5vBR1Pfpqj6yYxL4Gl2nTcZVME2ZmL9GU4Z5M0LOKXGlEKCsWrjTmxPSiq4Si8bRtH6GVR8tE6aqjDGjx6nf3MtUr0ww/DYyAxkeGOxHFkdSC/+MiCgI/+z0xkMQ3wPsYdd+4F2w07LplI7g1n1u7TrY8Qy31yPYOY+updCymwSVALPj47brSBaYahdo0/Q1JNskJHcw0VFtn46kEtz4UghePTb7xs1rLjJ64NJVPCSrjeepmHYLzZLodCSyzjI1OaDVDpPQ69haHjkaIRxTCIX6kPp0duanyWaifDwRIdY3jBH/DL+cysBbxAU1wj9vIgrC64Jm6CSH+mAIsgwA93IU4OYpAvd+vtxZI71zlTsZprn+bqJKL5PeNhKJIhOEaccDVD+Bqkposg+KCoHH3+1O2IxCIIEdKBi+j6XL6LKPEYmiGiFi2SS52Cij2Qbv0XL03B3FS/0uv5MfZuBQjnf3jBFK3ookSfTf+D13vCZ/W4LwP04KgiD4b/8yQXjjCYKAwA8IggAfFyQJP1DRZAlZFv+jF34wiSgIgiAIW8ShKIIgCMIWEQVBEARhi4iCIAiCsEVEQRAEQdgioiAIgiBsEVEQBEEQtogoCIIgCFtEFARBEIQtIgqCIAjCFhEFQRAEYYuIgiAIgrBFREEQBEHYIqIgCIIgbBFREARBELaIKAiCIAhbRBQEQRCELSIKgiAIwhYRBUEQBGGLiIIgCIKwRURBEARB2CKiIAiCIGwRURAEQRC2iCgIgiAIW0QUBEEQhC0iCoIgCMIWEQVBEARhi4iCIAiCsEVEQRAEQdgioiAIgiBsEVEQBEEQtogoCIIgCFtEFARBEIQtIgqCIAjCFhEFQRAEYYuIgiAIgrBFREEQBEHYIqIgCIIgbBFREARBELaIKAiCIAhbRBQEQRCELSIKgiAIwhYRBUEQBGGLiIIgCIKwRURBEARB2CKiIAiCIGz5vwH1jxwbG3ASkgAAAABJRU5ErkJggg==", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# pie plot (variável categórica)\n", - "plt.pie(df['Ticket'].value_counts())\n", - "plt.show() " - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Pclass\n", - "3 491\n", - "1 216\n", - "2 184\n", - "Name: count, dtype: int64" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df['Pclass'].value_counts()" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# pie plot (variável categórica)\n", - "plt.pie(df['Pclass'].value_counts())\n", - "plt.show() " - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([3, 1, 2], dtype=int64)" - ] - }, - "execution_count": 35, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df['Pclass'].unique()" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Pclass\n", - "3 491\n", - "1 216\n", - "2 184\n", - "Name: count, dtype: int64" - ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df['Pclass'].value_counts()" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# pie plot (variável categórica)\n", - "plt.pie(df['Pclass'].value_counts(), labels=df['Pclass'].unique())\n", - "plt.show() " - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# pie plot\n", - "plt.pie(df['Pclass'].value_counts(), labels=df['Pclass'].unique(), explode = [0.1, 0.1, 0])\n", - "plt.show() " - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
0103Braund, Mr. Owen Harrismale22.010A/5 211717.25NaNS
1211Cumings, Mrs. John Bradley (Florence Briggs Th...female38.010PC 1759971.28C85C
2313Heikkinen, Miss. Lainafemale26.000STON/O2. 31012827.92NaNS
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.10C123S
4503Allen, Mr. William Henrymale35.0003734508.05NaNS
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" - ], - "text/plain": [ - " PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked\n", - "0 1 0 3 Braund, Mr. Owen Harris male 22.0 1 0 A/5 21171 7.25 NaN S\n", - "1 2 1 1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 0 PC 17599 71.28 C85 C\n", - "2 3 1 3 Heikkinen, Miss. Laina female 26.0 0 0 STON/O2. 3101282 7.92 NaN S\n", - "3 4 1 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 0 113803 53.10 C123 S\n", - "4 5 0 3 Allen, Mr. William Henry male 35.0 0 0 373450 8.05 NaN S" - ] - }, - "execution_count": 51, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# duas variáveis\n", - "plt.scatter(x=df['Age'], y=df['Fare']);" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Tratando dados nulos" - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "RangeIndex: 891 entries, 0 to 890\n", - "Data columns (total 12 columns):\n", - " # Column Non-Null Count Dtype \n", - "--- ------ -------------- ----- \n", - " 0 PassengerId 891 non-null int64 \n", - " 1 Survived 891 non-null int64 \n", - " 2 Pclass 891 non-null int64 \n", - " 3 Name 891 non-null object \n", - " 4 Sex 891 non-null object \n", - " 5 Age 714 non-null float64\n", - " 6 SibSp 891 non-null int64 \n", - " 7 Parch 891 non-null int64 \n", - " 8 Ticket 891 non-null object \n", - " 9 Fare 891 non-null float64\n", - " 10 Cabin 204 non-null object \n", - " 11 Embarked 889 non-null object \n", - "dtypes: float64(2), int64(5), object(5)\n", - "memory usage: 83.7+ KB\n" - ] - } - ], - "source": [ - "df.info()" - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(204, 12)" - ] - }, - "execution_count": 59, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df[df['Cabin'].notnull()].shape" - ] - }, - { - "cell_type": "code", - "execution_count": 72, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Sex\n", - "female 55\n", - "Name: PassengerId, dtype: int64" - ] - }, - "execution_count": 72, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_18_anos = df.loc[(df['Age'] < 18) & (df['Sex'] == 'female'), :]\n", - "df_18_anos.groupby('Sex')['PassengerId'].count()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Referência: Data Viz \n", - "\n", - "https://datavizproject.com/\n", - "\n", - "https://www.data-to-viz.com/\n", - "\n", - "\n", - "# Biblioteca Interativa: Plotly\n", - "\n", - "https://plotly.com/python/\n", - "\n", - "# Matplotlib\n", - "\n", - "https://www.geeksforgeeks.org/how-to-set-plot-background-color-in-matplotlib/" - ] - } - ], - "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": "3.11.4" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/material/aula_s12.ipynb b/material/aula_s12.ipynb deleted file mode 100644 index 6a3a013..0000000 --- a/material/aula_s12.ipynb +++ /dev/null @@ -1,4198 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "aMKnRGLzS_me" - }, - "source": [ - "# S12 -Tratamento de dados utilizando pandas e numpy - parte 2\n", - "\n", - "**Objetivo da aula:** Introduzir o conceito de visualização de dados e como utilizar consultas, filtros e agrupamentos em Pandas (com um pouco de matploblib) para a geração de gráficos com insights importantes ao contexto da análise. No fim, introduzir o conceito da biblioteca Numpy utilizando as duas bibliotecas (Numpy e Pandas) em um contexto de análise de dados.\n", - "\n", - "O dataset utilizado é o mesmo da aula anterior: 'Titanic'.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "PQilYsBgS_ml" - }, - "source": [ - "## Pensamento analítico\n", - "\n", - "A capacidade analítica já é considerada um requisito básico entre determinados cargos e essa é uma habilidade fundamental para desenvolvedores e analistas de dados.\n", - "\n", - "A capacidade analítica está relacionada a outra qualificação de destaque no mercado atualmente: a tomada de decisão. Profissionais que entendem o que os dados mostram conseguem tomar decisões certeiras diante da estratégia do negócio pois embasam as suas decisões sempre que precisam dar passos importantes, projetando cenários e interpretando dados.\n", - "\n", - "### Como desenvolver a Capacidade Analítica?\n", - "\n", - " - Desenvolver senso crítico na análise de situações\n", - " - Se expor a diferentes assuntos e ideias\n", - " - Fazer cursos relacionados à análise de dados\n", - " - Entender tipos de análises de dados\n", - " - Criar uma visão mais abrangente da sua área\n", - "\n", - "\n", - "\n", - "[Fonte](https://blog.somostera.com/lideranca-baseada-em-dados/capacidade-analitica)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "vZpmn9A8S_ml" - }, - "source": [ - "**Dinâmica**: 20 minutos\n", - "\n", - "***Quais são os insights que gostaríamos de obter a partir dos dados que obtemos do dataset?***\n", - "\n", - "**Exemplo:** % de sobrevivência dada a sua classe, sexo, preço da passagem, idade, etc\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "e_UiUp01S_mm" - }, - "source": [ - "#### Visualização de dados\n", - "\n", - "O conceito de visualização de dados é bem simples. Ela nada mais é que a representação gráfica, estática ou em movimento, da análise de dados e indicadores de natureza variada.\n", - "\n", - "[Fonte](https://www.oncase.com.br/blog/data-analytics/visualizacao-de-dados/)\n", - "\n", - "[Fonte](https://www.cursospm3.com.br/blog/data-visualization-o-que-e-dataviz-e-importancia-para-produto/)\n", - "\n", - "[Fonte](https://www.linkedin.com/pulse/por-que-visualiza%C3%A7%C3%A3o-de-dados-%C3%A9-importante-para-voc%C3%AA-e-veiga-r-/?originalSubdomain=pt)\n", - "\n", - "[Importância da visualização de dados na era do Big Data](https://lume.ufrgs.br/handle/10183/169109)\n", - "\n", - " \n", - "\n", - "### Quarteto de Anscombe\n", - "[Quarteto de Anscombe](https://acervolima.com/quarteto-de-anscombe/)\n", - "\n", - "O quarteto de Anscombe (Anscombe, 1973) é uma maneira de ilustrar a importância de visualizações na análise de dados. Deve-se sempre olhar um conjunto de dados graficamente antes de começar a qualquer análise. Em especial, são ilustrados diversos contextos nos quais as propriedades estatísticas básicas para descrever conjuntos de dados são inadequadas.\n", - "\n", - "[Fonte](https://storopoli.io/Estatistica/aux-Anscombe.html)\n", - "\n", - "[Fonte](https://spacedata.com.br/quarteto-de-anscombe-porque-voce-deve-sempre-visualizar-seus-dados/)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "_6BQoLOYS_m7" - }, - "source": [ - "## Importância de gráficos na visualização de dados\n", - "\n", - "Gráficos se propõem a diferentes usos: exploração, descoberta, interpretação, diagnóstico, comunicação, apresentação, destaque, clareza, contar uma história.\n", - "\n", - "Os gráficos são recursos visuais muito utilizados para facilitar a leitura e compreensão das informações e divulgação de pesquisas em jornais, revistas, panfletos, livros e televisão.\n", - "\n", - "[Fonte: Principais tipos de gráficos para educação básica](https://educa.ibge.gov.br/professores/educa-recursos/20773-tipos-de-graficos-no-ensino.html#:~:text=Os%20gr%C3%A1ficos%20s%C3%A3o%20recursos%20visuais,%2C%20panfletos%2C%20livros%20e%20televis%C3%A3o.)\n", - "\n", - "\n", - "---\n", - "\n", - "Gráficos de colunas\n", - "![](/S12/conteudo_e_codigo/images/colunas.png)\n", - "[Fonte](https://educa.ibge.gov.br/professores/educa-recursos/20773-tipos-de-graficos-no-ensino.html#:~:text=Os%20gr%C3%A1ficos%20s%C3%A3o%20recursos%20visuais,%2C%20panfletos%2C%20livros%20e%20televis%C3%A3o.)\n", - "\n", - "Gráficos de barras\n", - "![](/S12/conteudo_e_codigo/images/barras.png)\n", - "[Fonte](https://educa.ibge.gov.br/professores/educa-recursos/20773-tipos-de-graficos-no-ensino.html#:~:text=Os%20gr%C3%A1ficos%20s%C3%A3o%20recursos%20visuais,%2C%20panfletos%2C%20livros%20e%20televis%C3%A3o.)\n", - "\n", - "Gráficos de linhas\n", - "![](/S12/conteudo_e_codigo/images/linhas.png)\n", - "[Fonte](https://educa.ibge.gov.br/professores/educa-recursos/20773-tipos-de-graficos-no-ensino.html#:~:text=Os%20gr%C3%A1ficos%20s%C3%A3o%20recursos%20visuais,%2C%20panfletos%2C%20livros%20e%20televis%C3%A3o.)\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "# carregando a biblioteca \n", - "import pandas as pd" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "id": "_eorJ3GNS_mm", - "outputId": "14f2b233-cc14-4f8a-a041-98b4cb90d2cc" - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
0103Braund, Mr. Owen Harrismale22.010A/5 211717.2500NaNS
1211Cumings, Mrs. John Bradley (Florence Briggs Th...female38.010PC 1759971.2833C85C
2313Heikkinen, Miss. Lainafemale26.000STON/O2. 31012827.9250NaNS
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000C123S
4503Allen, Mr. William Henrymale35.0003734508.0500NaNS
.......................................
88688702Montvila, Rev. Juozasmale27.00021153613.0000NaNS
88788811Graham, Miss. Margaret Edithfemale19.00011205330.0000B42S
88888903Johnston, Miss. Catherine Helen \"Carrie\"femaleNaN12W./C. 660723.4500NaNS
88989011Behr, Mr. Karl Howellmale26.00011136930.0000C148C
89089103Dooley, Mr. Patrickmale32.0003703767.7500NaNQ
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891 rows × 12 columns

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" - ], - "text/plain": [ - " PassengerId Survived Pclass \\\n", - "0 1 0 3 \n", - "1 2 1 1 \n", - "2 3 1 3 \n", - "3 4 1 1 \n", - "4 5 0 3 \n", - ".. ... ... ... \n", - "886 887 0 2 \n", - "887 888 1 1 \n", - "888 889 0 3 \n", - "889 890 1 1 \n", - "890 891 0 3 \n", - "\n", - " Name Sex Age SibSp \\\n", - "0 Braund, Mr. Owen Harris male 22.0 1 \n", - "1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n", - "2 Heikkinen, Miss. Laina female 26.0 0 \n", - "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n", - "4 Allen, Mr. William Henry male 35.0 0 \n", - ".. ... ... ... ... \n", - "886 Montvila, Rev. Juozas male 27.0 0 \n", - "887 Graham, Miss. Margaret Edith female 19.0 0 \n", - "888 Johnston, Miss. Catherine Helen \"Carrie\" female NaN 1 \n", - "889 Behr, Mr. Karl Howell male 26.0 0 \n", - "890 Dooley, Mr. Patrick male 32.0 0 \n", - "\n", - " Parch Ticket Fare Cabin Embarked \n", - "0 0 A/5 21171 7.2500 NaN S \n", - "1 0 PC 17599 71.2833 C85 C \n", - "2 0 STON/O2. 3101282 7.9250 NaN S \n", - "3 0 113803 53.1000 C123 S \n", - "4 0 373450 8.0500 NaN S \n", - ".. ... ... ... ... ... \n", - "886 0 211536 13.0000 NaN S \n", - "887 0 112053 30.0000 B42 S \n", - "888 2 W./C. 6607 23.4500 NaN S \n", - "889 0 111369 30.0000 C148 C \n", - "890 0 370376 7.7500 NaN Q \n", - "\n", - "[891 rows x 12 columns]" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# carregando o dataset \n", - "df = pd.read_csv(\"titanic.csv\")\n", - "df" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "boKqOy_KS_ms" - }, - "source": [ - "Dever da aula passada: substituir as colunas em inglês pelas suas traduções em PT-BR. Um dos objetivos dessa aula é realizar consultas e filtragens, então as colunas em PT podem ajudar no entendimento." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "id": "ZuehjTU7S_mt", - "outputId": "6ce66e1f-01d4-4889-f97f-50d5edae71e6" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "['IdPassageiro',\n", - " 'Sobreviveu',\n", - " 'Classe',\n", - " 'Nome',\n", - " 'Sexo',\n", - " 'Idade',\n", - " 'NumeroIrmaos',\n", - " 'NumeroPais',\n", - " 'NumeroTicket',\n", - " 'PrecoTicket',\n", - " 'NumeroCabine',\n", - " 'PortoEmbarcacao']" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Resolução\n", - "traducoes = {\n", - " 'PassengerId': 'IdPassageiro',\n", - " 'Survived': 'Sobreviveu', # 0 = Não, 1 = Sim\n", - " 'Pclass': 'Classe', # 1, 2, 3\n", - " 'Name': 'Nome',\n", - " 'Sex': 'Sexo',\n", - " 'Age': 'Idade',\n", - " 'SibSp': 'NumeroIrmaos',\n", - " 'Parch': 'NumeroPais',\n", - " 'Ticket': 'NumeroTicket',\n", - " 'Fare': 'PrecoTicket',\n", - " 'Cabin' : 'NumeroCabine',\n", - " 'Embarked': 'PortoEmbarcacao' # C = Cherbourg, Q = Queenstown, S = Southampton\n", - "}\n", - "\n", - "novas_colunas = []\n", - "\n", - "for chave, valor in traducoes.items():\n", - " novas_colunas.append(valor)\n", - "\n", - "novas_colunas" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "id": "h6tvl5HPS_mw", - "outputId": "4639f186-8484-4780-f4dc-0a29f3cc759e" - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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IdPassageiroSobreviveuClasseNomeSexoIdadeNumeroIrmaosNumeroPaisNumeroTicketPrecoTicketNumeroCabinePortoEmbarcacao
0103Braund, Mr. Owen Harrismale22.010A/5 211717.2500NaNS
1211Cumings, Mrs. John Bradley (Florence Briggs Th...female38.010PC 1759971.2833C85C
2313Heikkinen, Miss. Lainafemale26.000STON/O2. 31012827.9250NaNS
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000C123S
4503Allen, Mr. William Henrymale35.0003734508.0500NaNS
.......................................
88688702Montvila, Rev. Juozasmale27.00021153613.0000NaNS
88788811Graham, Miss. Margaret Edithfemale19.00011205330.0000B42S
88888903Johnston, Miss. Catherine Helen \"Carrie\"femaleNaN12W./C. 660723.4500NaNS
88989011Behr, Mr. Karl Howellmale26.00011136930.0000C148C
89089103Dooley, Mr. Patrickmale32.0003703767.7500NaNQ
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891 rows × 12 columns

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" - ], - "text/plain": [ - " IdPassageiro Sobreviveu Classe \\\n", - "0 1 0 3 \n", - "1 2 1 1 \n", - "2 3 1 3 \n", - "3 4 1 1 \n", - "4 5 0 3 \n", - ".. ... ... ... \n", - "886 887 0 2 \n", - "887 888 1 1 \n", - "888 889 0 3 \n", - "889 890 1 1 \n", - "890 891 0 3 \n", - "\n", - " Nome Sexo Idade \\\n", - "0 Braund, Mr. Owen Harris male 22.0 \n", - "1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 \n", - "2 Heikkinen, Miss. Laina female 26.0 \n", - "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 \n", - "4 Allen, Mr. William Henry male 35.0 \n", - ".. ... ... ... \n", - "886 Montvila, Rev. Juozas male 27.0 \n", - "887 Graham, Miss. Margaret Edith female 19.0 \n", - "888 Johnston, Miss. Catherine Helen \"Carrie\" female NaN \n", - "889 Behr, Mr. Karl Howell male 26.0 \n", - "890 Dooley, Mr. Patrick male 32.0 \n", - "\n", - " NumeroIrmaos NumeroPais NumeroTicket PrecoTicket NumeroCabine \\\n", - "0 1 0 A/5 21171 7.2500 NaN \n", - "1 1 0 PC 17599 71.2833 C85 \n", - "2 0 0 STON/O2. 3101282 7.9250 NaN \n", - "3 1 0 113803 53.1000 C123 \n", - "4 0 0 373450 8.0500 NaN \n", - ".. ... ... ... ... ... \n", - "886 0 0 211536 13.0000 NaN \n", - "887 0 0 112053 30.0000 B42 \n", - "888 1 2 W./C. 6607 23.4500 NaN \n", - "889 0 0 111369 30.0000 C148 \n", - "890 0 0 370376 7.7500 NaN \n", - "\n", - " PortoEmbarcacao \n", - "0 S \n", - "1 C \n", - "2 S \n", - "3 S \n", - "4 S \n", - ".. ... \n", - "886 S \n", - "887 S \n", - "888 S \n", - "889 C \n", - "890 Q \n", - "\n", - "[891 rows x 12 columns]" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.columns = novas_colunas\n", - "df" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "JehELGwYS_mx" - }, - "source": [ - "## Pandas\n", - "\n", - "### Consultas e filtros\n", - "### Filtros\n", - "\n", - "A biblioteca pandas fornece diferentes maneiras de filtrar por valores de colunas, além de permitir a combinação de diferentes técnicas oferecidas, de forma a nos permitir manipular os dados com mais facilidade fornecendo subsets com as condições requeridas.\n", - "\n", - "\n", - "[Fonte](https://sparkbyexamples.com/pandas/pandas-filter-by-column-value/)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "id": "gZKfzWYAS_mx", - "outputId": "cdc7a73b-0f3b-42ce-cdf2-f8322b15e686" - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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IdPassageiroSobreviveuClasseNomeSexoIdadeNumeroIrmaosNumeroPaisNumeroTicketPrecoTicketNumeroCabinePortoEmbarcacao
0103Braund, Mr. Owen Harrismale22.010A/5 211717.2500NaNS
1211Cumings, Mrs. John Bradley (Florence Briggs Th...female38.010PC 1759971.2833C85C
2313Heikkinen, Miss. Lainafemale26.000STON/O2. 31012827.9250NaNS
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000C123S
4503Allen, Mr. William Henrymale35.0003734508.0500NaNS
.......................................
88688702Montvila, Rev. Juozasmale27.00021153613.0000NaNS
88788811Graham, Miss. Margaret Edithfemale19.00011205330.0000B42S
88888903Johnston, Miss. Catherine Helen \"Carrie\"femaleNaN12W./C. 660723.4500NaNS
88989011Behr, Mr. Karl Howellmale26.00011136930.0000C148C
89089103Dooley, Mr. Patrickmale32.0003703767.7500NaNQ
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891 rows × 12 columns

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" - ], - "text/plain": [ - " IdPassageiro Sobreviveu Classe \\\n", - "0 1 0 3 \n", - "1 2 1 1 \n", - "2 3 1 3 \n", - "3 4 1 1 \n", - "4 5 0 3 \n", - ".. ... ... ... \n", - "886 887 0 2 \n", - "887 888 1 1 \n", - "888 889 0 3 \n", - "889 890 1 1 \n", - "890 891 0 3 \n", - "\n", - " Nome Sexo Idade \\\n", - "0 Braund, Mr. Owen Harris male 22.0 \n", - "1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 \n", - "2 Heikkinen, Miss. Laina female 26.0 \n", - "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 \n", - "4 Allen, Mr. William Henry male 35.0 \n", - ".. ... ... ... \n", - "886 Montvila, Rev. Juozas male 27.0 \n", - "887 Graham, Miss. Margaret Edith female 19.0 \n", - "888 Johnston, Miss. Catherine Helen \"Carrie\" female NaN \n", - "889 Behr, Mr. Karl Howell male 26.0 \n", - "890 Dooley, Mr. Patrick male 32.0 \n", - "\n", - " NumeroIrmaos NumeroPais NumeroTicket PrecoTicket NumeroCabine \\\n", - "0 1 0 A/5 21171 7.2500 NaN \n", - "1 1 0 PC 17599 71.2833 C85 \n", - "2 0 0 STON/O2. 3101282 7.9250 NaN \n", - "3 1 0 113803 53.1000 C123 \n", - "4 0 0 373450 8.0500 NaN \n", - ".. ... ... ... ... ... \n", - "886 0 0 211536 13.0000 NaN \n", - "887 0 0 112053 30.0000 B42 \n", - "888 1 2 W./C. 6607 23.4500 NaN \n", - "889 0 0 111369 30.0000 C148 \n", - "890 0 0 370376 7.7500 NaN \n", - "\n", - " PortoEmbarcacao \n", - "0 S \n", - "1 C \n", - "2 S \n", - "3 S \n", - "4 S \n", - ".. ... \n", - "886 S \n", - "887 S \n", - "888 S \n", - "889 C \n", - "890 Q \n", - "\n", - "[891 rows x 12 columns]" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# dataframe originial\n", - "df" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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IdPassageiroSobreviveuClasseNomeSexoIdadeNumeroIrmaosNumeroPaisNumeroTicketPrecoTicketNumeroCabinePortoEmbarcacao
1211Cumings, Mrs. John Bradley (Florence Briggs Th...female38.010PC 1759971.2833C85C
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000C123S
4503Allen, Mr. William Henrymale35.0003734508.0500NaNS
6701McCarthy, Mr. Timothy Jmale54.0001746351.8625E46S
111211Bonnell, Miss. Elizabethfemale58.00011378326.5500C103S
.......................................
87387403Vander Cruyssen, Mr. Victormale47.0003457659.0000NaNS
87988011Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)female56.0011176783.1583C50C
88188203Markun, Mr. Johannmale33.0003492577.8958NaNS
88588603Rice, Mrs. William (Margaret Norton)female39.00538265229.1250NaNQ
89089103Dooley, Mr. Patrickmale32.0003703767.7500NaNQ
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305 rows × 12 columns

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" - ], - "text/plain": [ - " IdPassageiro Sobreviveu Classe \\\n", - "1 2 1 1 \n", - "3 4 1 1 \n", - "4 5 0 3 \n", - "6 7 0 1 \n", - "11 12 1 1 \n", - ".. ... ... ... \n", - "873 874 0 3 \n", - "879 880 1 1 \n", - "881 882 0 3 \n", - "885 886 0 3 \n", - "890 891 0 3 \n", - "\n", - " Nome Sexo Idade \\\n", - "1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 \n", - "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 \n", - "4 Allen, Mr. William Henry male 35.0 \n", - "6 McCarthy, Mr. Timothy J male 54.0 \n", - "11 Bonnell, Miss. Elizabeth female 58.0 \n", - ".. ... ... ... \n", - "873 Vander Cruyssen, Mr. Victor male 47.0 \n", - "879 Potter, Mrs. Thomas Jr (Lily Alexenia Wilson) female 56.0 \n", - "881 Markun, Mr. Johann male 33.0 \n", - "885 Rice, Mrs. William (Margaret Norton) female 39.0 \n", - "890 Dooley, Mr. Patrick male 32.0 \n", - "\n", - " NumeroIrmaos NumeroPais NumeroTicket PrecoTicket NumeroCabine \\\n", - "1 1 0 PC 17599 71.2833 C85 \n", - "3 1 0 113803 53.1000 C123 \n", - "4 0 0 373450 8.0500 NaN \n", - "6 0 0 17463 51.8625 E46 \n", - "11 0 0 113783 26.5500 C103 \n", - ".. ... ... ... ... ... \n", - "873 0 0 345765 9.0000 NaN \n", - "879 0 1 11767 83.1583 C50 \n", - "881 0 0 349257 7.8958 NaN \n", - "885 0 5 382652 29.1250 NaN \n", - "890 0 0 370376 7.7500 NaN \n", - "\n", - " PortoEmbarcacao \n", - "1 C \n", - "3 S \n", - "4 S \n", - "6 S \n", - "11 S \n", - ".. ... \n", - "873 S \n", - "879 C \n", - "881 S \n", - "885 Q \n", - "890 Q \n", - "\n", - "[305 rows x 12 columns]" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df[df['Idade'] > 30]" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "id": "xzHvkQRrS_mx", - "outputId": "ca4d371d-386e-433d-e49b-6e5a6749f767" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(305, 12)" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Vamos começar com um filtro baseado em uma única condição: idade > 30\n", - "df_filtrado = df[df['Idade'] > 30]\n", - "df_filtrado.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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IdPassageiroSobreviveuClasseNomeSexoIdadeNumeroIrmaosNumeroPaisNumeroTicketPrecoTicketNumeroCabinePortoEmbarcacao
1211Cumings, Mrs. John Bradley (Florence Briggs Th...female38.010PC 1759971.2833C85C
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000C123S
111211Bonnell, Miss. Elizabethfemale58.00011378326.5500C103S
151612Hewlett, Mrs. (Mary D Kingcome)female55.00024870616.0000NaNS
181903Vander Planke, Mrs. Julius (Emelia Maria Vande...female31.01034576318.0000NaNS
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86286311Swift, Mrs. Frederick Joel (Margaret Welles Ba...female48.0001746625.9292D17S
86586612Bystrom, Mrs. (Karolina)female42.00023685213.0000NaNS
87187211Beckwith, Mrs. Richard Leonard (Sallie Monypeny)female47.0111175152.5542D35S
87988011Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)female56.0011176783.1583C50C
88588603Rice, Mrs. William (Margaret Norton)female39.00538265229.1250NaNQ
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103 rows × 12 columns

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" - ], - "text/plain": [ - " IdPassageiro Sobreviveu Classe \\\n", - "1 2 1 1 \n", - "3 4 1 1 \n", - "11 12 1 1 \n", - "15 16 1 2 \n", - "18 19 0 3 \n", - ".. ... ... ... \n", - "862 863 1 1 \n", - "865 866 1 2 \n", - "871 872 1 1 \n", - "879 880 1 1 \n", - "885 886 0 3 \n", - "\n", - " Nome Sexo Idade \\\n", - "1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 \n", - "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 \n", - "11 Bonnell, Miss. Elizabeth female 58.0 \n", - "15 Hewlett, Mrs. (Mary D Kingcome) female 55.0 \n", - "18 Vander Planke, Mrs. Julius (Emelia Maria Vande... female 31.0 \n", - ".. ... ... ... \n", - "862 Swift, Mrs. Frederick Joel (Margaret Welles Ba... female 48.0 \n", - "865 Bystrom, Mrs. (Karolina) female 42.0 \n", - "871 Beckwith, Mrs. Richard Leonard (Sallie Monypeny) female 47.0 \n", - "879 Potter, Mrs. Thomas Jr (Lily Alexenia Wilson) female 56.0 \n", - "885 Rice, Mrs. William (Margaret Norton) female 39.0 \n", - "\n", - " NumeroIrmaos NumeroPais NumeroTicket PrecoTicket NumeroCabine \\\n", - "1 1 0 PC 17599 71.2833 C85 \n", - "3 1 0 113803 53.1000 C123 \n", - "11 0 0 113783 26.5500 C103 \n", - "15 0 0 248706 16.0000 NaN \n", - "18 1 0 345763 18.0000 NaN \n", - ".. ... ... ... ... ... \n", - "862 0 0 17466 25.9292 D17 \n", - "865 0 0 236852 13.0000 NaN \n", - "871 1 1 11751 52.5542 D35 \n", - "879 0 1 11767 83.1583 C50 \n", - "885 0 5 382652 29.1250 NaN \n", - "\n", - " PortoEmbarcacao \n", - "1 C \n", - "3 S \n", - "11 S \n", - "15 S \n", - "18 S \n", - ".. ... \n", - "862 S \n", - "865 S \n", - "871 S \n", - "879 C \n", - "885 Q \n", - "\n", - "[103 rows x 12 columns]" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df[(df['Idade'] > 30) & (df['Sexo'] == 'female')]" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "id": "ZFZ7LOT6S_my", - "outputId": "e41628cd-501b-4009-d643-c91f7fe9d7e4" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(103, 12)" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Agora um filtro com múltiplas condições\n", - "df_filtrado = df[(df['Idade'] > 30) & (df['Sexo'] == 'female')]\n", - "df_filtrado.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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IdPassageiroSobreviveuClasseNomeSexoIdadeNumeroIrmaosNumeroPaisNumeroTicketPrecoTicketNumeroCabinePortoEmbarcacao
85585613Aks, Mrs. Sam (Leah Rosen)female18.0013920919.35NaNS
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" - ], - "text/plain": [ - " IdPassageiro Sobreviveu Classe Nome Sexo \\\n", - "855 856 1 3 Aks, Mrs. Sam (Leah Rosen) female \n", - "\n", - " Idade NumeroIrmaos NumeroPais NumeroTicket PrecoTicket NumeroCabine \\\n", - "855 18.0 0 1 392091 9.35 NaN \n", - "\n", - " PortoEmbarcacao \n", - "855 S " - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df[df['Nome'].str.contains('Rose')]" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "id": "BpueQ0G0S_my", - "outputId": "ba8ca09f-6788-4332-9a11-786aa02700e4" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(1, 12)" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Selecionando entradas que possuem uma determinada 'string'\n", - "# Vamos procurar por nomes que possuam 'Good'\n", - "\n", - "df_filtrado = df[df['Nome'].str.contains('Jack')]\n", - "df_filtrado.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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IdPassageiroSobreviveuClasseNomeSexoIdadeNumeroIrmaosNumeroPaisNumeroTicketPrecoTicketNumeroCabinePortoEmbarcacao
76676701Brewe, Dr. Arthur JacksonmaleNaN0011237939.6NaNC
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" - ], - "text/plain": [ - " IdPassageiro Sobreviveu Classe Nome Sexo Idade \\\n", - "766 767 0 1 Brewe, Dr. Arthur Jackson male NaN \n", - "\n", - " NumeroIrmaos NumeroPais NumeroTicket PrecoTicket NumeroCabine \\\n", - "766 0 0 112379 39.6 NaN \n", - "\n", - " PortoEmbarcacao \n", - "766 C " - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df_filtrado" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "id": "ODR1BQRyS_my", - "outputId": "a2289b09-9eb0-460f-da3b-265bfaf400fb" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(77, 12)" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Por último, utilizando a função isin()\n", - "df_filtrado = df[df['PortoEmbarcacao'].isin(['Q'])]\n", - "df_filtrado.shape" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "8gULcYPzS_mz" - }, - "source": [ - "### Consultas e filtros\n", - "### Consultas\n", - "\n", - "Pandas também possui a função query(), que realiza consultas no DataFrame através de uma expressão booleana (T/F).\n", - "\n", - "**Docs**: [https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.query.html]" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "id": "W-Ktv45VS_mz", - "outputId": "00f9464e-df1b-4cd2-c0b3-30eac23016f1" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(491, 12)" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Para ilustrar essa função, vamos procurar por pessoas que estavam na classe 3:\n", - "df_consulta = df.query(\"Classe == 3\")\n", - "df_consulta.shape\n" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "id": "uVSmQ1zeS_mz", - "outputId": "555cf64e-c5a6-4b3d-d69a-89fb77dd1012" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(707, 12)" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# A função query também pode ser utilizada para filtrar baseada em uma lista de valores\n", - "df_consulta = df.query(\"Classe in (1, 3)\")\n", - "df_consulta.shape" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "zu1HgPXrS_m0" - }, - "source": [ - "### Relembrar Slicing e introduzir conceitos de loc e iloc:\n", - "\n", - "### Slicing: obter apenas uma parte do dataset\n" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "id": "H3dlaegrS_m0", - "outputId": "1c7ad974-196c-40db-8eb7-fa78f65ff480" - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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IdPassageiroSobreviveuClasseNomeSexoIdadeNumeroIrmaosNumeroPaisNumeroTicketPrecoTicketNumeroCabinePortoEmbarcacao
0103Braund, Mr. Owen Harrismale22.010A/5 211717.2500NaNS
1211Cumings, Mrs. John Bradley (Florence Briggs Th...female38.010PC 1759971.2833C85C
2313Heikkinen, Miss. Lainafemale26.000STON/O2. 31012827.9250NaNS
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000C123S
4503Allen, Mr. William Henrymale35.0003734508.0500NaNS
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IdPassageiroSobreviveuClasseNomeSexoIdadeNumeroIrmaosNumeroPaisNumeroTicketPrecoTicketNumeroCabinePortoEmbarcacao
101113Sandstrom, Miss. Marguerite Rutfemale4.011PP 954916.7000G6S
111211Bonnell, Miss. Elizabethfemale58.00011378326.5500C103S
121303Saundercock, Mr. William Henrymale20.000A/5. 21518.0500NaNS
131403Andersson, Mr. Anders Johanmale39.01534708231.2750NaNS
141503Vestrom, Miss. Hulda Amanda Adolfinafemale14.0003504067.8542NaNS
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" - ], - "text/plain": [ - " IdPassageiro Sobreviveu Classe Nome \\\n", - "10 11 1 3 Sandstrom, Miss. Marguerite Rut \n", - "11 12 1 1 Bonnell, Miss. Elizabeth \n", - "12 13 0 3 Saundercock, Mr. William Henry \n", - "13 14 0 3 Andersson, Mr. Anders Johan \n", - "14 15 0 3 Vestrom, Miss. Hulda Amanda Adolfina \n", - "\n", - " Sexo Idade NumeroIrmaos NumeroPais NumeroTicket PrecoTicket \\\n", - "10 female 4.0 1 1 PP 9549 16.7000 \n", - "11 female 58.0 0 0 113783 26.5500 \n", - "12 male 20.0 0 0 A/5. 2151 8.0500 \n", - "13 male 39.0 1 5 347082 31.2750 \n", - "14 female 14.0 0 0 350406 7.8542 \n", - "\n", - " NumeroCabine PortoEmbarcacao \n", - "10 G6 S \n", - "11 C103 S \n", - "12 NaN S \n", - "13 NaN S \n", - "14 NaN S " - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.iloc[10:15, :]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Exercício 04 parte 01\n", - "\n", - "- Realizar filtro com apenas uma única condição;\n", - "- Realizar filtro com múltiplas condições;\n", - "- Selecionar entradas com string específica;\n", - "- Realizar uma consulta passageiros da segunda e terceira classe;\n", - "- Obter subamostra do dataset com 30 amostras utilizando o método de slicing;\n", - "- Utilizar a função loc para obter a idade e sobrevivência do passageiro;\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Alomoço" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " " - ], - "text/plain": [ - "" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from IPython.display import IFrame\n", - "\n", - "# GIF link used as IFrame\n", - "iframe_url = \"https://giphy.com/embed/7AKbdZiyTx98fPHG0Z\"\n", - "\n", - "# resized output IFrame\n", - "IFrame(src=iframe_url, width=300, height=250)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "nES063ppS_m4" - }, - "source": [ - "### Agrupamento / Agregação\n", - "\n", - "Podemos criar um agrupamento de categorias e aplicar uma função às categorias. É um conceito simples, mas é uma técnica extremamente valiosa, amplamente utilizada na ciência de dados. Em projetos reais de ciência de dados, você lidará com grandes quantidades de dados e tentará várias vezes, portanto, para eficiência, usamos o conceito Groupby. O conceito de Groupby é realmente importante por causa de sua capacidade de resumir, agregar e agrupar dados com eficiência.\n", - "\n", - "\n", - "[Fonte](https://acervolima.com/pandas-groupby-resumindo-agregando-e-agrupando-dados-em-python/)" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": { - "id": "noSCxvLQS_m6", - "outputId": "fd434c19-19d3-4395-a595-83703e140d1b" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Agrupar passageiros por classe\n", - "dado_agrupado = df.groupby('Classe')\n", - "dado_agrupado" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Classe\n", - "1 216\n", - "2 184\n", - "3 491\n", - "Name: IdPassageiro, dtype: int64" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Contagem de passageiros em cada classe, usando os dados agrupados no passo anterior\n", - "contagem_passageiros = dado_agrupado['IdPassageiro'].count()\n", - "contagem_passageiros" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Classe\n", - "3 491\n", - "1 216\n", - "2 184\n", - "Name: count, dtype: int64" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# função value counts\n", - "df['Classe'].value_counts()" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": { - "id": "UxbPOZJfS_m7", - "outputId": "f2031638-fbad-45b1-94a6-1415795d6800" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Classe\n", - "1 38.233441\n", - "2 29.877630\n", - "3 25.140620\n", - "Name: Idade, dtype: float64" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Média de Idade de passageiros em cada classe\n", - "media_idade = dado_agrupado['Idade'].mean()\n", - "media_idade" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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IdPassageiroSobreviveuClasseNomeSexoIdadeNumeroIrmaosNumeroPaisNumeroTicketPrecoTicketNumeroCabinePortoEmbarcacao
969701Goldschmidt, Mr. George Bmale71.000PC 1775434.6542A5C
11611703Connors, Mr. Patrickmale70.5003703697.7500NaNQ
49349401Artagaveytia, Mr. Ramonmale71.000PC 1760949.5042NaNC
63063111Barkworth, Mr. Algernon Henry Wilsonmale80.0002704230.0000A23S
85185203Svensson, Mr. Johanmale74.0003470607.7750NaNS
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" - ], - "text/plain": [ - " IdPassageiro Sobreviveu Classe Nome \\\n", - "96 97 0 1 Goldschmidt, Mr. George B \n", - "116 117 0 3 Connors, Mr. Patrick \n", - "493 494 0 1 Artagaveytia, Mr. Ramon \n", - "630 631 1 1 Barkworth, Mr. Algernon Henry Wilson \n", - "851 852 0 3 Svensson, Mr. Johan \n", - "\n", - " Sexo Idade NumeroIrmaos NumeroPais NumeroTicket PrecoTicket \\\n", - "96 male 71.0 0 0 PC 17754 34.6542 \n", - "116 male 70.5 0 0 370369 7.7500 \n", - "493 male 71.0 0 0 PC 17609 49.5042 \n", - "630 male 80.0 0 0 27042 30.0000 \n", - "851 male 74.0 0 0 347060 7.7750 \n", - "\n", - " NumeroCabine PortoEmbarcacao \n", - "96 A5 C \n", - "116 NaN Q \n", - "493 NaN C \n", - "630 A23 S \n", - "851 NaN S " - ] - }, - "execution_count": 42, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df[df['Idade'] > 70]" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": { - "id": "jxk2rE1hS_m7", - "outputId": "55e2c10a-49b5-4a8a-f979-162b4bc89e1b" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Valor máximo do preço da passagem por classe\n", - "Classe\n", - "1 512.3292\n", - "2 73.5000\n", - "3 69.5500\n", - "Name: PrecoTicket, dtype: float64\n", - "Valor mínimo do preço da passagem por classe\n", - "Classe\n", - "1 0.0\n", - "2 0.0\n", - "3 0.0\n", - "Name: PrecoTicket, dtype: float64\n" - ] - } - ], - "source": [ - "# Obter o valor máximo e mínimo pago por passageiros em cada classe:\n", - "valor_max = dado_agrupado['PrecoTicket'].max()\n", - "valor_min = dado_agrupado['PrecoTicket'].min()\n", - "\n", - "print(\"Valor máximo do preço da passagem por classe\")\n", - "print(valor_max)\n", - "\n", - "print(\"Valor mínimo do preço da passagem por classe\")\n", - "print(valor_min)" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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Sexo
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" - ], - "text/plain": [ - " PrecoTicket Idade \\\n", - " count mean median min max count mean median \n", - "Sexo \n", - "female 314 44.479818 23.0 6.75 512.3292 261 27.915709 27.0 \n", - "male 577 25.523893 10.5 0.00 512.3292 453 30.726645 29.0 \n", - "\n", - " \n", - " min max \n", - "Sexo \n", - "female 0.75 63.0 \n", - "male 0.42 80.0 " - ] - }, - "execution_count": 49, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.groupby('Sexo').agg({'PrecoTicket': ['count', 'mean', 'median', 'min', 'max'], \n", - " 'Idade': ['count', 'mean', 'median', 'min', 'max']})" - ] - }, - { - "cell_type": "code", - "execution_count": 52, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " PrecoTicket \n", - " count mean median min max\n", - "Sexo PortoEmbarcacao \n", - "female C 73 75.169805 56.92920 7.2250 512.3292\n", - " Q 36 12.634958 7.76875 6.7500 90.0000\n", - " S 203 38.740929 24.15000 7.2500 263.0000\n", - "male C 95 48.262109 24.00000 4.0125 512.3292\n", - " Q 41 13.838922 7.75000 6.7500 90.0000\n", - " S 441 21.711996 10.50000 0.0000 263.0000" - ] - }, - "execution_count": 52, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.groupby(['Sexo', 'PortoEmbarcacao']).agg({'PrecoTicket': ['count', 'mean', 'median', 'min', 'max']})" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Exercício 04 parte 02\n", - "\n", - "- Agrupar passageiros por genero\n", - "- Obter a média de idade de passageiros por genero\n", - "- Obter o valor máximo e mínimo pago por passageiros em cada genero" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "6sNCu_PsS_m8" - }, - "source": [ - "## Pandas: Gerando Gráficos\n", - "\n", - "Pandas, em si, não é uma biblioteca de geração de gráficos - o seu segredo é a sua integração com bibliotecas de visualização, permitindo que as suas capacidades de manipulação atuem como ferramentas poderosas para gerar gráficos. Exemplos dessas libs são o Numpy, matplotlib e Seaborn.\n", - "\n", - "Pandas permite:\n", - "- manipulação e preparação de dados\n", - "- integração com bibliotecas de visualização\n", - "- seleção de dados\n", - "- permite fácil combinação de dados provenientes de diferentes datasets" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "RM_Xr6IzS_m8" - }, - "source": [ - "### Pandas + Matplotlib" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Iab5GB_jURLN" - }, - "source": [ - "Matplotlib, por sua vez, é uma biblioteca Python dedicada à visualização de dados. As figuras geradas, muitas vezes após tratamento de dados utilizando Pandas, são passíveis de fácil edição e customização via código e podem ser facilmente exportadas para outros formatos (inclusive de alta qualidade).\n", - "\n", - "Basicamente, a lib monta o gráfico a partir de seus dados em uma 'Figura' que pode conter um ou mais 'Eixos', que definem uma área onde pontos (relativos aos dados) podem ser delimitados.\n", - "\n", - "\n", - "[Fonte](https://matplotlib.org/)\n", - "\n", - "[Fonte](https://matplotlib.org/stable/tutorials/introductory/quick_start.html)" - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 448 - }, - "id": "KS8389rdXMEu", - "outputId": "386b4ea1-0ef0-4e2a-891a-e1ea0dee0ed5" - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# exemplo mais simples de gráfico:\n", - "import matplotlib.pyplot as plt\n", - "\n", - "fig, ax = plt.subplots()\n", - "ax.plot([1, 2, 3, 4], [2, 4, 6, 8]);" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "j-fE0SiHS_m8" - }, - "source": [ - "*** OBS: Deixar claro para alunas que matplotlib será visto em semanas seguintes - ele só está sendo introduzido nesse contexto para que possamos gerar gráficos simples com o uso de Pandas ***\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "metadata": { - "id": "bmZg5LjvS_nJ", - "outputId": "f3874a08-9ec1-4235-8db1-2250deeb54ea" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Classe\n", - "3 491\n", - "1 216\n", - "2 184\n", - "Name: count, dtype: int64" - ] - }, - "execution_count": 59, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Criação de um gráfico de barras: matplotlib + Pandas\n", - "# plot() + bar\n", - "contagem_passageiros = df['Classe'].value_counts()\n", - "contagem_passageiros" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Doc:** https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.plot.html" - ] - }, - { - "cell_type": "code", - "execution_count": 62, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# pandas: gráfico\n", - "contagem_passageiros.plot(kind='barh');" - ] - }, - { - "cell_type": "code", - "execution_count": 63, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "df['Idade'].plot(kind='hist')" - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "metadata": { - "id": "zpZmI2e9S_nJ", - "outputId": "3f16a4f4-3039-4426-ac46-d0f8178d87a6" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'Número de Passageiros por Classe')" - ] - }, - "execution_count": 68, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# título, legendas (Y e X)\n", - "contagem_passageiros = df['Classe'].value_counts()\n", - "contagem_passageiros.plot(kind='bar');\n", - "\n", - "plt.xlabel('Classe do Passageiro')\n", - "plt.ylabel('Quantidade')\n", - "plt.title('Número de Passageiros por Classe')\n" - ] - }, - { - "cell_type": "code", - "execution_count": 69, - "metadata": { - "id": "j7lUqI3BS_nK", - "outputId": "758058f2-185d-412d-835e-38d8ca773f3e" - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Criando um gráfico de barras para calcular a taxa de sobrevivência por sexo\n", - "# plot.bar()\n", - "taxa_sob_sexo = df.groupby('Sexo')['Sobreviveu'].mean()\n", - "taxa_sob_sexo.plot.bar()\n", - "\n", - "plt.xlabel('Sexo')\n", - "plt.ylabel('Taxa de Sobrevivência')\n", - "plt.title('Taxa de Sobrevivência por Sexo')\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 74, - "metadata": { - "id": "RzV9cvoOS_nK", - "outputId": "eea06c73-5805-427d-f2b1-b82d3481b8a8" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'Distribuição de Idade')" - ] - }, - "execution_count": 74, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Criando um histograma com a distribuição de idade em passageiros do Titanic\n", - "\n", - "df['Idade'].plot.hist(bins=10, edgecolor='black')\n", - "\n", - "plt.xlabel('Idade')\n", - "plt.ylabel('Quantidade')\n", - "plt.title('Distribuição de Idade')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Exercício 05\n", - "\n", - "- Gerar 3 gráficos com ideias de interesses\n" - ] - }, - { - "cell_type": "code", - "execution_count": 86, - "metadata": {}, - "outputs": [], - "source": [ - "# exemplo dúvida: como pegar uma única classe (a importância do reset_index())\n", - "agg_classe = df.groupby(['Classe', 'Sexo']).agg({'Sobreviveu': ['count']})\n", - "agg_classe.reset_index(inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 90, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
ClasseSexoSobreviveu
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01female94
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" - ], - "text/plain": [ - " Classe Sexo Sobreviveu\n", - " count\n", - "0 1 female 94" - ] - }, - "execution_count": 90, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "agg_classe[(agg_classe[( 'Classe', '')] == 1) & (agg_classe[( 'Sexo', '')] == 'female')]" - ] - }, - { - "cell_type": "code", - "execution_count": 88, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "MultiIndex([( 'Classe', ''),\n", - " ( 'Sexo', ''),\n", - " ('Sobreviveu', 'count')],\n", - " )" - ] - }, - "execution_count": 88, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "agg_classe.columns" - ] - }, - { - "cell_type": "code", - "execution_count": 91, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['IdPassageiro', 'Sobreviveu', 'Classe', 'Nome', 'Sexo', 'Idade',\n", - " 'NumeroIrmaos', 'NumeroPais', 'NumeroTicket', 'PrecoTicket',\n", - " 'NumeroCabine', 'PortoEmbarcacao'],\n", - " dtype='object')" - ] - }, - "execution_count": 91, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df[df['PortoEmbarcacao']]" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "kdYYrD87S_nK" - }, - "source": [ - "## Pandas: Gerando Gráficos\n", - "\n", - "### Pandas: Pandas + Numpy (funções básicas do numpy)\n", - "\n", - "[Numpy for absolute beginners](https://numpy.org/doc/stable/user/absolute_beginners.html)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "96dXic7LS_nL" - }, - "source": [ - "#### Biblioteca Numpy\n", - "\n", - "A biblioteca Numpy é uma biblioteca de computação científica cuja principal estrutura de dados são os arrays (similares a listas, porém de mais rápido processamento) e que oferece funções para cálculos com matrizes e álgebra linear, sendo uma das bibliotecas mais importantes para cientistas de dados.\n", - "\n", - "O foco dessa aula não é o Numpy, mas existem exemplos na parte de Pandas que contam com a biblioteca - apenas apresentar o conceito, mas dizer que assunto será retomado na aula seguinte." - ] - }, - { - "cell_type": "code", - "execution_count": 92, - "metadata": { - "id": "6qrRb5EIS_nL" - }, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import numpy as np" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "FCrGZFGoS_nL" - }, - "source": [ - "Numpy arrays permitem a utilização de diferentes operações,tornando-os uma estrutura de dados útil em manipulação de dados:\n", - " - Seleção de elementos de array\n", - " - Slicing de arrays\n", - " - Divisão de arrays\n", - " - Reshaping de arrays\n", - " - Combinação de arrays\n", - " - Operações Numéricas (min, max, media, etc)\n", - "\n", - "Numpy e Pandas são comumente utilizados para análise e manipulação de dados." - ] - }, - { - "cell_type": "code", - "execution_count": 93, - "metadata": { - "id": "suI74yqZS_nL", - "outputId": "eb87a166-4d92-4068-e5ad-e7ce283ed244" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[1, 2, 3, 4, 5]\n", - "[1 2 3 4 5]\n" - ] - } - ], - "source": [ - "# Conversão de lista (estrutura de dados Python) para um array unidimensional com uma linha e quatro colunas:\n", - "lista_1 = [1, 2, 3, 4, 5]\n", - "array_1 = np.array(lista_1)\n", - "\n", - "print(lista_1)\n", - "print(array_1)" - ] - }, - { - "cell_type": "code", - "execution_count": 98, - "metadata": { - "id": "DxCmoOkhS_nM", - "outputId": "2c7699a1-519f-4b81-d4e1-836895767375" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Uma lista de listas\n", - "[[1, 2, 3], [4, 5, 6]]\n", - "Array bidimensional\n", - "[[1 2 3]\n", - " [4 5 6]]\n" - ] - } - ], - "source": [ - "# Array bidimensional de uma lista Python (uma lista contendo listas)\n", - "lista_2 = [[1,2,3],[4,5,6]]\n", - "array_2 = np.array(lista_2)\n", - "\n", - "print('Uma lista de listas')\n", - "print(lista_2)\n", - "print('Array bidimensional')\n", - "print(array_2)" - ] - }, - { - "cell_type": "code", - "execution_count": 121, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[1, 2, 3]" - ] - }, - "execution_count": 121, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "lista_2[0]" - ] - }, - { - "cell_type": "code", - "execution_count": 122, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([1, 2, 3])" - ] - }, - "execution_count": 122, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "array_2[0]" - ] - }, - { - "cell_type": "code", - "execution_count": 99, - "metadata": { - "id": "KZ-JzM6US_nM", - "outputId": "9c7580bf-627b-4926-ed86-8441bb436861" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([10, 20, 30, 40, 50])" - ] - }, - "execution_count": 99, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# operações matemáticas podem ser realizadas em todos os valores de um array Numpy de uma vez (impossível em listas python sem loops)\n", - "\n", - "# loja com preços\n", - "array_precos = np.array([12, 22, 32, 42, 52])\n", - "\n", - "# queremos entrar em liquidação diminuindo 2 reais no preço de cada item\n", - "array_promocional = array_precos - 2\n", - "array_promocional\n" - ] - }, - { - "cell_type": "code", - "execution_count": 100, - "metadata": { - "id": "SWy82LKAS_nM", - "outputId": "ee7e4c2e-5553-405f-cbdf-878534650298" - }, - "outputs": [ - { - "ename": "TypeError", - "evalue": "unsupported operand type(s) for -: 'list' and 'int'", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32mc:\\Users\\Debora\\Documents\\GitHub\\on26-python-s12-pandas-numpy-II\\material\\aula_s12.ipynb Cell 70\u001b[0m line \u001b[0;36m3\n\u001b[0;32m 1\u001b[0m \u001b[39m# se fosse uma lista Python, seria impossível pois retornaria um TypeError\u001b[39;00m\n\u001b[0;32m 2\u001b[0m lista_precos \u001b[39m=\u001b[39m [\u001b[39m12\u001b[39m,\u001b[39m22\u001b[39m,\u001b[39m32\u001b[39m,\u001b[39m42\u001b[39m,\u001b[39m52\u001b[39m]\n\u001b[1;32m----> 3\u001b[0m lista_promocial \u001b[39m=\u001b[39m lista_precos \u001b[39m-\u001b[39;49m \u001b[39m2\u001b[39;49m\n", - "\u001b[1;31mTypeError\u001b[0m: unsupported operand type(s) for -: 'list' and 'int'" - ] - } - ], - "source": [ - "# se fosse uma lista Python, seria impossível pois retornaria um TypeError\n", - "lista_precos = [12,22,32,42,52]\n", - "lista_promocial = lista_precos - 2" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "v6P70zdwS_nM" - }, - "source": [ - "Séries são similares aos arrays NumPy, mas possuem mais funcionalidades que permitem que seus valores possam ser indexados e rotulados. Esses rótulos são úteis quando estamos utilizando porções de dados que possuem outros dados associados a eles.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 101, - "metadata": { - "id": "LljyqgRgS_nM", - "outputId": "17c514e4-0fa0-4d36-c6f8-46293589ce4b" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([100, 200, 300])" - ] - }, - "execution_count": 101, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Criando Séries em Pandas usando um array numpy:\n", - "precos = np.array([100,200,300])\n", - "precos" - ] - }, - { - "cell_type": "code", - "execution_count": 102, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 100\n", - "1 200\n", - "2 300\n", - "dtype: int32" - ] - }, - "execution_count": 102, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "serie_precos = pd.Series(precos)\n", - "serie_precos" - ] - }, - { - "cell_type": "code", - "execution_count": 103, - "metadata": { - "id": "4m4a1TDIS_nN", - "outputId": "d60ac614-bd45-430f-d375-bafdf4252478" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Bala 100\n", - "Chocolate 200\n", - "Bolo 300\n", - "dtype: int32" - ] - }, - "execution_count": 103, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# criando série e customizando índices da série\n", - "serie_precos_produtos = pd.Series(precos, index=['Bala', 'Chocolate', 'Bolo'])\n", - "serie_precos_produtos" - ] - }, - { - "cell_type": "code", - "execution_count": 104, - "metadata": { - "id": "ZxBABq3pS_nN", - "outputId": "d11786b2-ac75-43da-9ce9-5e3c8d3869d4" - }, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " A B C D\n", - "0 1 2 3 1\n", - "1 4 5 6 5" - ] - }, - "execution_count": 106, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df['D'] = [1,5]\n", - "df" - ] - }, - { - "cell_type": "code", - "execution_count": 107, - "metadata": { - "id": "LHfl6fALS_nN", - "outputId": "5805c47a-4db3-4a7f-8790-dad45dc7ff76" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "0 1\n", - "1 4\n", - "2 9\n", - "3 16\n", - "4 25\n", - "dtype: int32" - ] - }, - "execution_count": 107, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Operações matemáticas podem ser realizadas em séries e Dataframes Pandas usando funções do numpy\n", - "\n", - "# criando um array (numpay)\n", - "lista_1 = [1, 2, 3, 4, 5]\n", - "array_1 = np.array(lista_1)\n", - "\n", - "# criando uma series (pandas)\n", - "serie_precos = pd.Series(array_1)\n", - "\n", - "# calculo matematico\n", - "serie_precos_ao_quadrado = np.square(serie_precos)\n", - "serie_precos_ao_quadrado" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Exercício 06\n", - "\n", - "- Gerar arrays de números aleatórios de com tamanho de 5, 50 e 500 elementos\n", - "- Criar um DF Pandas a partir de um array numpy criado de uma lista Python\n", - "- Utilize métodos do Numpy e séries de Pandas para dobrar os números da lista" - ] - }, - { - "cell_type": "code", - "execution_count": 119, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([10, 23, 13, 32, 25, 11, 20, 49, 26, 6, 40, 14, 42, 17, 11, 6, 43,\n", - " 15, 19, 12, 29, 47, 5, 12, 42, 47, 40, 15, 7, 22, 8, 41, 35, 25,\n", - " 14, 5, 31, 13, 42, 49, 49, 47, 41, 37, 5, 22, 34, 21, 14, 10, 13,\n", - " 10, 37, 27, 24, 31, 34, 31, 23, 32, 23, 47, 22, 30, 14, 40, 31, 42,\n", - " 35, 21, 40, 42, 36, 37, 11, 8, 14, 46, 11, 28, 39, 35, 29, 5, 46,\n", - " 18, 24, 17, 29, 48, 35, 14, 38, 30, 11, 10, 47, 48, 38, 5, 37, 5,\n", - " 26, 10, 10, 29, 6, 49, 15, 48, 13, 30, 24, 19, 15, 40, 43, 5, 35,\n", - " 46, 45, 44, 28, 7, 13, 44, 5, 47, 14, 13, 17, 43, 33, 46, 32, 16,\n", - " 39, 46, 23, 38, 21, 46, 18, 31, 7, 43, 8, 31, 11, 18, 37, 40, 48,\n", - " 41, 13, 6, 33, 13, 11, 6, 15, 23, 30, 16, 31, 34, 16, 6, 14, 5,\n", - " 34, 27, 34, 45, 45, 6, 28, 15, 8, 27, 25, 22, 46, 32, 23, 49, 45,\n", - " 35, 30, 34, 45, 27, 27, 7, 16, 18, 7, 34, 47, 45, 30, 30, 46, 47,\n", - " 29, 29, 45, 16, 47, 22, 35, 9, 31, 49, 36, 9, 8, 21, 37, 47, 49,\n", - " 48, 38, 11, 11, 12, 11, 25, 31, 47, 13, 41, 33, 14, 6, 41, 24, 15,\n", - " 44, 33, 37, 28, 43, 33, 45, 9, 33, 29, 33, 40, 21, 33, 15, 47, 41,\n", - " 47, 22, 46, 27, 12, 10, 43, 40, 39, 19, 30, 17, 49, 44, 26, 8, 44,\n", - " 27, 42, 37, 48, 13, 12, 36, 15, 41, 6, 40, 19, 17, 14, 27, 39, 24,\n", - " 17, 29, 14, 24, 27, 21, 12, 44, 12, 39, 18, 7, 34, 20, 17, 19, 27,\n", - " 31, 19, 18, 13, 47, 38, 47, 14, 39, 26, 30, 6, 18, 37, 44, 42, 21,\n", - " 20, 6, 48, 6, 29, 7, 35, 47, 17, 22, 42, 32, 24, 48, 22, 28, 27,\n", - " 34, 21, 48, 7, 39, 26, 8, 43, 7, 19, 22, 49, 29, 6, 22, 30, 32,\n", - " 49, 35, 9, 40, 21, 18, 21, 33, 6, 14, 47, 6, 10, 46, 10, 24, 24,\n", - " 27, 18, 40, 34, 28, 45, 16, 38, 38, 45, 23, 37, 25, 46, 25, 37, 22,\n", - " 38, 30, 18, 44, 11, 39, 32, 26, 12, 7, 8, 7, 18, 23, 41, 37, 39,\n", - " 40, 45, 38, 43, 48, 19, 13, 25, 11, 37, 9, 11, 42, 18, 49, 24, 40,\n", - " 40, 17, 7, 31, 21, 37, 18, 15, 35, 49, 19, 41, 13, 42, 8, 30, 10,\n", - " 35, 12, 25, 40, 16, 42, 40, 42, 15, 25, 33, 30, 40, 30, 20, 22, 7,\n", - " 16, 8, 34, 8, 38, 8, 31, 9, 28, 23, 36, 8, 13, 38, 31, 29, 18,\n", - " 42, 45, 34, 41, 37, 28, 24, 36, 45, 49, 45, 23, 14, 25, 30, 27, 44,\n", - " 45, 19, 18, 24, 35, 34, 42])" - ] - }, - "execution_count": 119, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.random.randint(5, 50, 500)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Links Bibliotecas:\n", - "\n", - "https://pandas.pydata.org/\n", - "\n", - "https://numpy.org/pt/\n", - "\n", - "https://matplotlib.org/" - ] - } - ], - "metadata": { - "colab": { - "provenance": [] - }, - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "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": "3.11.4" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/material/titanic.csv b/material/titanic.csv deleted file mode 100644 index 5cc466e..0000000 --- a/material/titanic.csv +++ /dev/null @@ -1,892 +0,0 @@ -PassengerId,Survived,Pclass,Name,Sex,Age,SibSp,Parch,Ticket,Fare,Cabin,Embarked -1,0,3,"Braund, Mr. Owen Harris",male,22,1,0,A/5 21171,7.25,,S -2,1,1,"Cumings, Mrs. John Bradley (Florence Briggs Thayer)",female,38,1,0,PC 17599,71.2833,C85,C -3,1,3,"Heikkinen, Miss. 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