{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Лабораторная работа №3\n",
    "\n",
    "## Набор данных Students Performance in Exams (Успеваемость студентов на экзаменах)\n",
    "\n",
    "Выгрузка данных из CSV файла в датафрейм"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 363,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['gender', 'race/ethnicity', 'parental level of education', 'lunch',\n",
      "       'test preparation course', 'math score', 'reading score',\n",
      "       'writing score'],\n",
      "      dtype='object')\n",
      "\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1000 entries, 0 to 999\n",
      "Data columns (total 8 columns):\n",
      " #   Column                       Non-Null Count  Dtype \n",
      "---  ------                       --------------  ----- \n",
      " 0   gender                       1000 non-null   object\n",
      " 1   race/ethnicity               1000 non-null   object\n",
      " 2   parental level of education  1000 non-null   object\n",
      " 3   lunch                        1000 non-null   object\n",
      " 4   test preparation course      1000 non-null   object\n",
      " 5   math score                   1000 non-null   int64 \n",
      " 6   reading score                1000 non-null   int64 \n",
      " 7   writing score                1000 non-null   int64 \n",
      "dtypes: int64(3), object(5)\n",
      "memory usage: 62.6+ KB\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "\n",
    "# Загрузка данных\n",
    "df = pd.read_csv(\"..//..//static//csv//StudentsPerformance.csv\")\n",
    "\n",
    "# Вывод колонок\n",
    "print(df.columns)\n",
    "\n",
    "print()\n",
    "\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Описание набора  \n",
    "\n",
    "Контекст\n",
    "Оценки, полученные студентами\n",
    "\n",
    "Содержание\n",
    "Этот набор данных состоит из оценок, полученных учащимися по различным предметам.\n",
    "\n",
    "Вдохновение\n",
    "Понять влияние происхождения родителей, подготовки к тестированию и т. д. на успеваемость учащихся."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 364,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gender</th>\n",
       "      <th>race/ethnicity</th>\n",
       "      <th>parental level of education</th>\n",
       "      <th>lunch</th>\n",
       "      <th>test preparation course</th>\n",
       "      <th>math score</th>\n",
       "      <th>reading score</th>\n",
       "      <th>writing score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>female</td>\n",
       "      <td>group B</td>\n",
       "      <td>bachelor's degree</td>\n",
       "      <td>standard</td>\n",
       "      <td>none</td>\n",
       "      <td>72</td>\n",
       "      <td>72</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>female</td>\n",
       "      <td>group C</td>\n",
       "      <td>some college</td>\n",
       "      <td>standard</td>\n",
       "      <td>completed</td>\n",
       "      <td>69</td>\n",
       "      <td>90</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>female</td>\n",
       "      <td>group B</td>\n",
       "      <td>master's degree</td>\n",
       "      <td>standard</td>\n",
       "      <td>none</td>\n",
       "      <td>90</td>\n",
       "      <td>95</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>male</td>\n",
       "      <td>group A</td>\n",
       "      <td>associate's degree</td>\n",
       "      <td>free/reduced</td>\n",
       "      <td>none</td>\n",
       "      <td>47</td>\n",
       "      <td>57</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>male</td>\n",
       "      <td>group C</td>\n",
       "      <td>some college</td>\n",
       "      <td>standard</td>\n",
       "      <td>none</td>\n",
       "      <td>76</td>\n",
       "      <td>78</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   gender race/ethnicity parental level of education         lunch  \\\n",
       "0  female        group B           bachelor's degree      standard   \n",
       "1  female        group C                some college      standard   \n",
       "2  female        group B             master's degree      standard   \n",
       "3    male        group A          associate's degree  free/reduced   \n",
       "4    male        group C                some college      standard   \n",
       "\n",
       "  test preparation course  math score  reading score  writing score  \n",
       "0                    none          72             72             74  \n",
       "1               completed          69             90             88  \n",
       "2                    none          90             95             93  \n",
       "3                    none          47             57             44  \n",
       "4                    none          76             78             75  "
      ]
     },
     "execution_count": 364,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Вывод столбцов\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 365,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>math score</th>\n",
       "      <th>reading score</th>\n",
       "      <th>writing score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1000.00000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>66.08900</td>\n",
       "      <td>69.169000</td>\n",
       "      <td>68.054000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>15.16308</td>\n",
       "      <td>14.600192</td>\n",
       "      <td>15.195657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>57.00000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>57.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>66.00000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>69.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>77.00000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>79.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>100.00000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       math score  reading score  writing score\n",
       "count  1000.00000    1000.000000    1000.000000\n",
       "mean     66.08900      69.169000      68.054000\n",
       "std      15.16308      14.600192      15.195657\n",
       "min       0.00000      17.000000      10.000000\n",
       "25%      57.00000      59.000000      57.750000\n",
       "50%      66.00000      70.000000      69.000000\n",
       "75%      77.00000      79.000000      79.000000\n",
       "max     100.00000     100.000000     100.000000"
      ]
     },
     "execution_count": 365,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Краткая статистическая сводка для данных:\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Анализ содержимого\n",
    "\n",
    "*Объекты наблюдения:* студенты, участвующие в экзаменах.\n",
    "\n",
    "*Атрибуты объектов:* \n",
    "\n",
    "1. gender — пол: определяет гендерную принадлежность студента (мужской, женский).  \n",
    "2. race/ethnicity — этническая принадлежность: группа, к которой относится студент (например, различные расовые/этнические категории).  \n",
    "3. parental level of education — уровень образования родителей(например, среднее образование, высшее образование и т.д.).  \n",
    "4. lunch — тип обеда: информация о том, получает ли студент бесплатный или платный обед.  \n",
    "5. test preparation course — курс подготовки к тесту\n",
    "6. math score  — результаты экзаменов по математике.\n",
    "7. reading score — результаты экзаменов по чтению.\n",
    "8. writing score — результаты экзаменов по письму.\n",
    "\n",
    "\n",
    "### Бизнес-цель\n",
    "1.  Анализ факторов, влияющих на успеваемость студентов:\n",
    "\n",
    "    **Цель:** Исследовать, как различные факторы, такие как пол, этническая принадлежность, уровень образования родителей, тип обеда и наличие курса подготовки к тесту, влияют на оценки студентов по математике, чтению и письму.\n",
    "\n",
    "    **Эффект:** Это поможет образовательным учреждениям и политикам лучше понять, какие аспекты могут быть улучшены для повышения успеваемости студентов, а также выявить возможные неравенства в образовательных возможностях.\n",
    "\n",
    "2.  Прогнозирование успеваемости студентов\n",
    "\n",
    "    **Цель:** Разработать модель прогнозирования успеваемости студентов на основе имеющихся данных, таких как пол, раса/этническая принадлежность, уровень образования родителей, тип обеда и участие в подготовительных курсах.\n",
    "\n",
    "    **Эффект:** Это позволит предсказать, какие студенты могут столкнуться с трудностями в обучении, и принять меры для их поддержки. Например, образовательные учреждения могут инициировать дополнительные занятия или индивидуальные консультации для студентов, у которых ожидаются низкие результаты на экзаменах, тем самым повышая их шансы на успешную сдачу экзаменов.\n",
    "\n",
    "### Техническая цель\n",
    "1.  Разработка системы анализа факторов успеваемости студентов:\n",
    "\n",
    "    **Цель:** Создать аналитическую платформу, которая будет собирать, обрабатывать и визуализировать данные о студентах, включая их оценки и соответствующие факторы (пол, этническая принадлежность, уровень образования родителей, тип обеда, наличие подготовительных курсов).\n",
    "\n",
    "2.  Создание модели прогнозирования успеваемости студентов:\n",
    "\n",
    "    **Цель:** Разработать и внедрить предсказательную модель, которая будет оценивать вероятную успеваемость студентов на основании их характеристик и данных.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Анализ данных "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 366,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gender                         0\n",
       "race/ethnicity                 0\n",
       "parental level of education    0\n",
       "lunch                          0\n",
       "test preparation course        0\n",
       "math score                     0\n",
       "reading score                  0\n",
       "writing score                  0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 366,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Проверка на пропущенные данные\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Нет пропущенных данных"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "gender — пол: определяет гендерную принадлежность студента (мужской, женский).  \n",
    "race/ethnicity — этническая принадлежность: группа, к которой относится студент (например, различные расовые/этнические категории).  \n",
    "parental level of education — уровень образования родителей(например, среднее образование, высшее образование и т.д.).  \n",
    "lunch — тип обеда: информация о том, получает ли студент бесплатный или платный обед.  \n",
    "test preparation course — курс подготовки к тесту\n",
    "math score  — результаты экзаменов по математике.\n",
    "reading score — результаты экзаменов по чтению.\n",
    "writing score — результаты экзаменов по письму."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 367,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Построим графики boxplot для обнаружения выбросов по каждой характеристике\n",
    "plt.figure(figsize=(10, 10))\n",
    "\n",
    "# Создание boxplot\n",
    "for i, column in enumerate(['gender', 'race/ethnicity','parental level of education','lunch','test preparation course','math score','reading score','writing score'], 1):\n",
    "    plt.subplot(8, 3, i)\n",
    "    sns.boxplot(x=df[column])\n",
    "    plt.title(f\"Boxplot для {column}\")\n",
    "    \n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Для автоматического анализа выбросов мы можем использовать несколько методов, которые помогут оценить, являются ли данные выбросами и какие из них стоит оставить или удалить. Давайте используем следующие подходы:\n",
    "\n",
    "Метод межквартильного размаха (IQR) — это один из самых популярных способов для обнаружения выбросов. Мы будем искать значения, которые выходят за пределы \n",
    "1.5×IQR, где IQR — это разница между 75-м и 25-м процентилем данных.\n",
    "\n",
    "Z-оценка — этот метод позволяет определить, насколько отклоняется значение от среднего в терминах стандартного отклонения. Обычно выбросами считаются значения, для которых Z-оценка больше 3 или меньше -3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 368,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Выбросы по методу IQR:\n",
      "math score: 8 выбросов\n",
      "reading score: 6 выбросов\n",
      "writing score: 5 выбросов\n",
      "\n",
      "Выбросы по методу Z-оценки:\n",
      "math score: 4 выбросов\n",
      "reading score: 4 выбросов\n",
      "writing score: 4 выбросов\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "# Функция для поиска выбросов по методу IQR\n",
    "def find_outliers_iqr(df, columns):\n",
    "    outliers = {}\n",
    "    for column in columns:\n",
    "        Q1 = df[column].quantile(0.25)\n",
    "        Q3 = df[column].quantile(0.75)\n",
    "        IQR = Q3 - Q1\n",
    "        lower_bound = Q1 - 1.5 * IQR\n",
    "        upper_bound = Q3 + 1.5 * IQR\n",
    "        outliers[column] = df[(df[column] < lower_bound) | (df[column] > upper_bound)]\n",
    "    return outliers\n",
    "\n",
    "# Функция для поиска выбросов по методу Z-оценки\n",
    "def find_outliers_z(df, columns, threshold=3):\n",
    "    outliers = {}\n",
    "    for column in columns:\n",
    "        z_scores = (df[column] - df[column].mean()) / df[column].std()\n",
    "        outliers[column] = df[np.abs(z_scores) > threshold]\n",
    "    return outliers\n",
    "\n",
    "# Вычисление выбросов для математических, чтения и письма\n",
    "columns_to_check = ['math score', 'reading score', 'writing score']\n",
    "outliers_iqr = find_outliers_iqr(df, columns_to_check)\n",
    "outliers_z = find_outliers_z(df, columns_to_check)\n",
    "\n",
    "# Выводим результаты\n",
    "print(\"Выбросы по методу IQR:\")\n",
    "for col, data in outliers_iqr.items():\n",
    "    print(f\"{col}: {data.shape[0]} выбросов\")\n",
    "\n",
    "print(\"\\nВыбросы по методу Z-оценки:\")\n",
    "for col, data in outliers_z.items():\n",
    "    print(f\"{col}: {data.shape[0]} выбросов\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Так как выбросы по Z-оценке и IQR частично совпадают, это подтверждает их значимость в данных.\n",
    "\n",
    "Попробуем решить устранить проблему выбросов для writing score и reading score и math score, заменив их на медиану. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 369,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Функция для замены выбросов на медиану\n",
    "def replace_outliers_with_median(df, columns):\n",
    "    for column in columns:\n",
    "        # Находим границы по методу IQR\n",
    "        Q1 = df[column].quantile(0.25)\n",
    "        Q3 = df[column].quantile(0.75)\n",
    "        IQR = Q3 - Q1\n",
    "        lower_bound = Q1 - 1.5 * IQR\n",
    "        upper_bound = Q3 + 1.5 * IQR\n",
    "        \n",
    "        # Заменяем выбросы на медиану\n",
    "        median = df[column].median()\n",
    "        df[column] = df[column].apply(lambda x: median if x < lower_bound or x > upper_bound else x)\n",
    "    \n",
    "    return df\n",
    "\n",
    "# Заменим выбросы на медиану для баллов\n",
    "df = replace_outliers_with_median(df.copy(), columns_to_check)\n",
    "\n",
    "\n",
    "# Построим графики для проверки\n",
    "plt.figure(figsize=(10, 5))\n",
    "\n",
    "# Создание boxplot\n",
    "for i, column in enumerate(['math score','reading score','writing score'], 1):\n",
    "    plt.subplot(3, 3, i)\n",
    "    sns.boxplot(x=df[column])\n",
    "    plt.title(f\"Boxplot для {column}\")\n",
    "    \n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Связи между объектами:\n",
    "\n",
    " Анализ данных покажет, как каждый из атрибутов (пол, этническая принадлежность, уровень образования родителей, тип обеда, курс подготовки) влияет на оценки студентов, что поможет выявить ключевые факторы, способствующие или препятствующие успеваемости."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 370,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Расчет корреляции между оценками по математике, чтению и письму\n",
    "correlation_matrix = df[['math score', 'reading score', 'writing score']].corr()\n",
    "\n",
    "# Визуализация матрицы корреляций\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', fmt='.2f', linewidths=0.5)\n",
    "plt.title('Корреляция между оценками по математике, чтению и письму')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Корреляция между всеми тремя оценками высока, особенно между оценками по чтению и письму (0.95), а также между чтением и математикой (0.78). Это говорит о том, что студенты, которые показывают высокие результаты в одном из этих предметов, обычно показывают хорошие результаты и в других предметах.\n",
    "\n",
    "Математика имеет наиболее слабую корреляцию с другими предметами (0.78 и 0.76), но все равно достаточно значимую. Это может означать, что успехи в математике несколько менее связаны с успехами в чтении и письме, чем между чтением и письмом, но все же есть определенная связь.\n",
    "\n",
    "Чтение и письмо имеют почти идеальную корреляцию (0.95), что указывает на очень высокую взаимосвязь между этими двумя видами деятельности. Это может свидетельствовать о том, что навыки, необходимые для хорошего выполнения задания по чтению, схожи с теми, что необходимы для успешного написания текста.\n",
    "\n",
    "Если целью является улучшение успеваемости студентов, стоит сосредоточиться на факторах, которые могут улучшить все три навыка одновременно. Например, можно обратить внимание на методы, развивающие как читаемость, так и письмо, так как они сильно взаимосвязаны.\n",
    "Математика может потребовать отдельного внимания или подхода, так как ее корреляция с чтением и письмом ниже, чем между чтением и письмом."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 371,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x800 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Влияние пола на оценки\n",
    "plt.figure(figsize=(12, 8))\n",
    "\n",
    "# Boxplot для math score по полу\n",
    "plt.subplot(2, 2, 1)\n",
    "sns.boxplot(x='gender', y='math score', data=df)\n",
    "plt.title('Влияние пола на math score')\n",
    "\n",
    "# Boxplot для reading score по полу\n",
    "plt.subplot(2, 2, 2)\n",
    "sns.boxplot(x='gender', y='reading score', data=df)\n",
    "plt.title('Влияние пола на reading score')\n",
    "\n",
    "# Boxplot для writing score по полу\n",
    "plt.subplot(2, 2, 3)\n",
    "sns.boxplot(x='gender', y='writing score', data=df)\n",
    "plt.title('Влияние пола на writing score')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Женщины, как правило, демонстрируют лучшие результаты в чтении и письме, чем мужчины, в то время как в математике различие менее выражено.\n",
    "\n",
    "Для мужчин характерны выбросы с низкими оценками в чтении и письме, что может свидетельствовать о необходимости дополнительной поддержки или изменений в методиках обучения.\n",
    "\n",
    "Женщины могут иметь более высокие и стабильные результаты, что также заслуживает внимания для дальнейших исследований."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 372,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x1000 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Влияние этнической принадлежности на оценки\n",
    "plt.figure(figsize=(15, 10))\n",
    "\n",
    "# Boxplot для math score по этнической принадлежности\n",
    "plt.subplot(3, 2, 1)\n",
    "sns.boxplot(x='race/ethnicity', y='math score', data=df)\n",
    "plt.title('Влияние этнической принадлежности на math score')\n",
    "\n",
    "# Boxplot для reading score по этнической принадлежности\n",
    "plt.subplot(3, 2, 2)\n",
    "sns.boxplot(x='race/ethnicity', y='reading score', data=df)\n",
    "plt.title('Влияние этнической принадлежности на reading score')\n",
    "\n",
    "# Boxplot для writing score по этнической принадлежности\n",
    "plt.subplot(3, 2, 3)\n",
    "sns.boxplot(x='race/ethnicity', y='writing score', data=df)\n",
    "plt.title('Влияние этнической принадлежности на writing score')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Этническая принадлежность имеет значительное влияние на успеваемость студентов, особенно между группами с более высокими и более низкими результатами. Это подтверждается тем, что группы E и D стабильно показывают более высокие результаты, чем другие.\n",
    "\n",
    "Этот фактор следует учитывать при разработке методов поддержания успеха студентов, особенно в группах с более низкими результатами."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 373,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1000 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Влияние уровня образования родителей на оценки\n",
    "plt.figure(figsize=(15, 10))\n",
    "\n",
    "# Boxplot для math score по уровню образования родителей\n",
    "plt.subplot(3, 2, 1)\n",
    "sns.boxplot(x='parental level of education', y='math score', data=df)\n",
    "plt.title('Влияние уровня образования родителей на math score')\n",
    "\n",
    "# Boxplot для reading score по уровню образования родителей\n",
    "plt.subplot(3, 2, 2)\n",
    "sns.boxplot(x='parental level of education', y='reading score', data=df)\n",
    "plt.title('Влияние уровня образования родителей на reading score')\n",
    "\n",
    "# Boxplot для writing score по уровню образования родителей\n",
    "plt.subplot(3, 2, 3)\n",
    "sns.boxplot(x='parental level of education', y='writing score', data=df)\n",
    "plt.title('Влияние уровня образования родителей на writing score')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Уровень образования родителей оказывает значительное влияние на успеваемость студентов по всем предметам: математике, чтению и письму. Студенты с родителями, имеющими высшее образование (особенно степень бакалавра или выше), показывают наивысшие результаты, а также более стабильные оценки.  \n",
    "\n",
    "Студенты с родителями, имеющими среднее образование или диплом старшей школы, имеют более низкие результаты и большую вариативность, что может свидетельствовать о разнице в образовательных возможностях, доступных этим студентам.  \n",
    "\n",
    "Выбросы в некоторых группах (особенно у студентов с родителями с средним образованием и дипломом старшей школы) могут указывать на случаи, когда студенты из этих категорий столкнулись с дополнительными трудностями в учебе, возможно, из-за менее квалифицированных условий обучения или недостаточной поддержки."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 374,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x800 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Влияние курса подготовки на оценки\n",
    "plt.figure(figsize=(12, 8))\n",
    "\n",
    "# Boxplot для math score по курсу подготовки\n",
    "plt.subplot(2, 2, 1)\n",
    "sns.boxplot(x='test preparation course', y='math score', data=df)\n",
    "plt.title('Влияние курса подготовки на math score')\n",
    "\n",
    "# Boxplot для reading score по курсу подготовки\n",
    "plt.subplot(2, 2, 2)\n",
    "sns.boxplot(x='test preparation course', y='reading score', data=df)\n",
    "plt.title('Влияние курса подготовки на reading score')\n",
    "\n",
    "# Boxplot для writing score по курсу подготовки\n",
    "plt.subplot(2, 2, 3)\n",
    "sns.boxplot(x='test preparation course', y='writing score', data=df)\n",
    "plt.title('Влияние курса подготовки на writing score')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Курс подготовки оказывает положительное влияние на успеваемость студентов во всех трех предметах, особенно по математике и письму.  \n",
    "\n",
    "Студенты, прошедшие курс подготовки, имеют значительно более высокие оценки и меньше выбросов с низкими баллами.\n",
    "\n",
    "Это подтверждает важность подготовки студентов перед экзаменами, что помогает улучшить их успеваемость и снизить уровень неудач в учебе."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 375,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x800 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Влияние курса подготовки на оценки\n",
    "plt.figure(figsize=(12, 8))\n",
    "\n",
    "# Boxplot для math score по курсу подготовки\n",
    "plt.subplot(2, 2, 1)\n",
    "sns.boxplot(x='lunch', y='math score', data=df)\n",
    "plt.title('Влияние типа обеда на math score')\n",
    "\n",
    "# Boxplot для reading score по курсу подготовки\n",
    "plt.subplot(2, 2, 2)\n",
    "sns.boxplot(x='lunch', y='reading score', data=df)\n",
    "plt.title('Влияние типа обеда на reading score')\n",
    "\n",
    "# Boxplot для writing score по курсу подготовки\n",
    "plt.subplot(2, 2, 3)\n",
    "sns.boxplot(x='lunch', y='writing score', data=df)\n",
    "plt.title('Влияние типа обеда на writing score')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Тип обеда действительно может оказывать влияние на успеваемость студентов. Разница в оценках между группами standard и free/reduced указывает на то, что этот признак важен для анализа.\n",
    "\n",
    "Студенты с бесплатными обедами в среднем показывают меньше стабильности в результатах, что можно связать с другими социальными и экономическими факторами, влияющими на их учебный процесс."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 376,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p-значение: 0.48669808284196514\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.stats import chi2_contingency\n",
    "\n",
    "# Создаем кросс-таблицу\n",
    "crosstab = pd.crosstab(df['lunch'], df['race/ethnicity'])\n",
    "\n",
    "# Выполняем хи-квадрат тест\n",
    "chi2, p_value, dof, expected = chi2_contingency(crosstab)\n",
    "\n",
    "# Выводим результаты\n",
    "print(f\"p-значение: {p_value}\")\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Строим столбчатую диаграмму с накоплением\n",
    "crosstab = pd.crosstab(df['race/ethnicity'], df['lunch'])\n",
    "\n",
    "# Строим график\n",
    "crosstab.plot(kind='bar', stacked=True)\n",
    "plt.title('Тип обеда по этнической принадлежности')\n",
    "plt.xlabel('Этническая принадлежность')\n",
    "plt.ylabel('Частота')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "p-значение больше 0.05, это означает, что тип обеда, скорее всего, не зависит от этнической принадлежности студентов в данном наборе данных, и можно сделать вывод, что этническая принадлежность не оказывает влияния на распределение типов обеда среди студентов.\n",
    "\n",
    "Несмотря на различия в частотах типов обеда среди различных этнических групп (например, группа C имеет значительно больше студентов с стандартным обедом, чем с бесплатным), не существует статистически значимой связи между этими двумя переменными.\n",
    "\n",
    "Это может означать, что тип обеда для студентов в разных этнических группах распределяется случайным образом и не имеет четкой зависимости от этнической принадлежности."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Исходя из анализа и результатов, мы можем выбрать только те признаки, которые действительно оказывают влияние на успеваемость студентов:\n",
    "\n",
    "1. gender (пол) — оказывает влияние на успеваемость, особенно в письме и чтении.  \n",
    "2. parental level of education (уровень образования родителей) — важный фактор, сильно влияющий на успеваемость.\n",
    "3. test preparation course (курс подготовки к тесту) — влияет на результаты студентов, особенно по математике и письму.\n",
    "4. lunch (тип обеда) — имеет влияние, хоть и менее выраженное, но все же важно для предсказания успеваемости.\n",
    "5. race/ethnicity (этническая принадлежность) — оказывает влияние на успеваемость, особенно в различиях между группами с высокими и низкими результатами.\n",
    "\n",
    "Эти признаки имеют заметное влияние на успеваемость студентов, и они будут полезны для построения более точной модели."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Выявление проблем датасета\n",
    "\n",
    "Оценим распределение признаков и сбалансированность данных, смещение по каждому признаку. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 377,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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uAMa6devq3eeHH34wAOOBBx6ota36tbJ582YDMO66664a2//85z8bgPHDDz84rmvbtq0BGIsXL66x79NPP214enoae/bsqXH9448/bthsNiMlJeWkj0VOTu1c5JzFx8cTHBxMVFQU48ePx8vLi88//9zxjZ2rq6tjMabKykoyMzPx8vKic+fObNy40XGcTz/9lKCgIO6///5a9/HbU6TOxK233oq3t7fj8vXXX094eDjffvstAJs3b2bv3r38/ve/JzMzk4yMDDIyMigsLOTSSy9lxYoVtU57KSkpwc3N7aT3+9lnn2G327nxxhsdx8zIyCAsLIyOHTuyfPnyGvuXlZUBJ++7+fHHH+Pr68tll11W45h9+vTBy8ur1jHLy8tr7JeRkXHKmU+HDx9m7ty5TJ06FS8vr1r337VrV7p06VLjmNWnGf72/utSff+n+v1VKysrO2Uv0vz8fKxWa52zD07XnXfeWeN5NmDAAAzD4M4773RcZ7PZ6Nu3LwcOHKhx2xP7jmVnZ5Obm8uQIUNqPL9Pxx/+8Ical4cMGUJmZiZ5eXn13qZ6pvl3331Xb+uXhQsXYrfbefLJJ2stjFb9mJcuXUpZWRkPPvhgjX3uvvtufHx8+Oabb2rcztXVldtvv73GdQ3x/BCRc6MxuW4ak+vWVMfku+++G5vN5ri8ZMkScnJyuOmmm2o8VpvNxoABA2o81hPH5JKSEjIyMrjooosAHM/xyspKvvvuO8aOHUt0dLRj/65duzJq1KjTzjlp0qQar4chQ4ZQWVlJcnIyAMuWLaOiooL77ruvxu3qel3Vxc/Pj8LCQpYsWVLvPp9++im9evWqcyZ4dbZvv/2WsLCwGv2UnZ2deeCBBygoKOCnn36qcbtx48YRHBzsuJyVlcUPP/zAjTfeSH5+vuP3n5mZyahRo9i7dy+HDx8+rcckIiencbxuGsfr1tTG8SFDhpCWlsbu3buBqhnnQ4cOZciQIaxcuRKomp1uGEatmeh1fb5sKL/73e/w9/evkRNwfK4/cuQI27Zt49Zbb63xdxo2bBhxcXGnPH717+rrr7+mvLy8zn0+/fRTLBYLTz31VK1tJ47XAA8//HCN7f/3f/8HUOszeUxMTK33LR9//DFDhgzB39+/xvMqPj6eyspKVqxYccrHI/VTOxc5Z6+++iqdOnXCycmJ0NBQOnfuXKMIV9336bXXXiMxMZHKykrHturT0qDqVLXOnTvj5NSwT8uOHTvWuGyxWOjQoQNJSUlAVT9pgIkTJ9Z7jNzc3Br/6WZkZNQ67m/t3bsXwzDq3e+3p4ZV953+7eD622Pm5uYSEhJS5/b09PQal7///vsaH4JOx1NPPUVERAT33HNPrb7ee/fuJSEhod5j/vb+61Ldn7O6+HsqOTk5J/2dAAwcOJBXXnmFP/3pTzz66KP4+vrW6mN3Kid+iD4xX1RUVK3rf3vsr7/+mpkzZ7J58+YavcrO9A3qbzNUP+eys7Px8fGp8zYxMTE8/PDDvPTSSyxYsIAhQ4Zw9dVXc/PNNzsew/79+7FarXTr1q3e+67+wN+5c+ca17u4uNC+fXvH9mpt2rSptchLQzw/ROTcaEyum8bkujXVMTkmJqbG5ernRXVh4bdOHCOzsrKYPn06H3zwQa3fQfVaIceOHaO4uLjO50Pnzp0dH2JP5WTjNvxvbO3QoUON/QICAmo8h+tz33338dFHHzF69GjatGnDyJEjufHGG7n88ssd++zfv59x48ad9DjJycl07Nix1hfpXbt2rZGz2m9///v27cMwDKZOncrUqVPrvI/09PTTOuVdRE5O43jdNI7XramN49XF6ZUrVxIZGcmmTZuYOXMmwcHBjn7qK1euxMfHh169etW4bV2fLxvK2Y7X1dedanLcsGHDGDduHNOnT+cf//gHw4cPZ+zYsfz+9793fGmxf/9+IiIiCAgIqPc4ycnJWK3WWjnCwsLw8/M75XgNVc+rrVu36jN5I1ERXc5Z//79Hf2b6vLss88ydepU7rjjDp5++mkCAgKwWq08+OCDTWJhg+oML7zwAr17965znxMHmrKyMo4ePcpll112yuNaLBYWLVpUYzZVXccESE1NBaixwEZdxwwJCWHBggV1bv/tf5QDBgxg5syZNa575ZVX+OKLL+q8fUJCAu+88w7vvfdenf3f7HY7cXFxvPTSS3Xe/rcF57pUv8E6ncU5oOr30rZt25PuM378eDZu3MjcuXN58803T+u4v1XX36i+640TFkVZuXIlV199NUOHDuW1114jPDwcZ2dn3n77bd5///0GyXDi/dXl73//O7fddhtffPEF33//PQ888ACzZs3i119/rbEgW0Oqa9Xvhnh+iMi50Zhc/3E1JtfWVMfk344x1c+L//znP3X+TU4sEt1444388ssvPPLII/Tu3RsvLy/sdjuXX355gz/Hz3bcPl0hISFs3ryZ7777jkWLFrFo0SLefvttbr311lqLgjak+n7/f/7zn+udqV9X4UFEzpzG8fqPq3G8tqY2jkdERBATE8OKFSto164dhmEwcOBAgoOD+dOf/kRycjIrV65k0KBBtb7YrevzZUNp7PHaYrHwySef8Ouvv/LVV1/x3Xffcccdd/D3v/+dX3/99ZRfXNR1vNNR32fyyy67jEcffbTO23Tq1OmMskhNKqJLo/vkk08YMWIEb731Vo3rc3JyCAoKclyOjY1lzZo1lJeXN8gCHtWqvw2vZhgG+/bto2fPno77hapZTPHx8ac83pYtWygvLz/pm5vq4xqGQUxMzGn9R7Vz504sFkutmcC/PebSpUsZPHjwaQ0yQUFBtR7TyRYomTJlCr179+Z3v/tdvfe/ZcsWLr300rM+DbB65elT/f6g6pS5ffv21ZhxVRer1cqLL77Itm3bSExM5LXXXiMtLY2bb775rDKeiU8//RQ3Nze+++67GqfGvf3227X2PZdTJ08lLi6OuLg4nnjiCX755RcGDx7MvHnzmDlzJrGxsdjtdnbu3Fnvm9nqN1O7d++mffv2juvLyspITEw8rddGQzw/RKRxaUzWmHyi5jImVz8vQkJCTvq8yM7OZtmyZUyfPp0nn3zScf1vn3fBwcG4u7vXuh5wnILeEKrH1n379tWYLZaZmXnas/pcXFy46qqruOqqq7Db7dx333288cYbTJ06lQ4dOhAbG8v27dtPmWPr1q3Y7fYaRYtdu3bVyFmf6vcFzs7Op/W6FJHGo3Fc4/iJmuI4PmTIEFasWEFMTAy9e/fG29ubXr164evry+LFi9m4cSPTp08/rWPVpTE+Z544Xv9WXdfV56KLLuKiiy7imWee4f3332fChAl88MEH3HXXXcTGxvLdd9+RlZVV72z0tm3bYrfb2bt3r+NsMYC0tDRycnJOOV5D1fOqoKBA43UjUU90aXQ2m63WN3wff/xxrd6J48aNIyMjg1deeaXWMc7lG8J3332X/Px8x+VPPvmEo0ePMnr0aAD69OlDbGwsL774IgUFBbVuf+zYsVrZbTYbV1555Unv97rrrsNmszF9+vRa+Q3DIDMz03G5oqKCTz/9lP79+5/0W8obb7yRyspKnn766VrbKioqHKetnY3Vq1fzxRdf8Le//a3egenGG2/k8OHD/POf/6y1rbi4mMLCwlPezyeffELnzp3p0qXLKff94osvKC4urvf07RPNnTuXH374gQULFhAfH8/gwYNPeZuGYLPZsFgsNU6lTEpKqvMNlaen5zn9jeqSl5dHRUVFjevi4uKwWq2O1jJjx47FarUyY8aMWjNUqp+b8fHxuLi4MGfOnBrP17feeovc3FzGjBlzyiwN8fwQkcalMVlj8omay5g8atQofHx8ePbZZ+vsNVr9vKieafbbv/Hs2bNrXLbZbIwaNYqFCxeSkpLiuD4hIYHvvvvunLKe6NJLL8XJyYnXX3+9xvV1va7qcuLzEqoKG9WFquoxfty4cWzZsoXPP/+81u2rfw9XXHEFqampfPjhh45tFRUVzJ07Fy8vL4YNG3bSHCEhIQwfPpw33niDo0eP1tr+29eliDQejeMax0/UFMfxIUOGkJSUxIcffuho72K1Whk0aBAvvfQS5eXltfqhnwlPT0+ABv1cHRERQY8ePXj33XdrPG9/+ukntm3bdsrbZ2dn13peVk9eO3G8Ngyjzi8QThyvofb7luqzFU73M/nq1avrfD+Tk5NTq3YgZ0Yz0aXRXXnllcyYMYPbb7+dQYMGsW3bNhYsWFBjtitULVLy7rvv8vDDD7N27VqGDBlCYWEhS5cu5b777uOaa645q/sPCAjg4osv5vbbbyctLY3Zs2fToUMH7r77bqDqP/R//etfjB49mu7du3P77bfTpk0bDh8+zPLly/Hx8eGrr76isLCQV199lTlz5tCpUyd+/PFHx31U/0e7detWVq9ezcCBA4mNjWXmzJlMmTKFpKQkxo4di7e3N4mJiXz++edMmjSJP//5zyxdupSpU6eydetWvvrqq5M+lmHDhnHPPfcwa9YsNm/ezMiRI3F2dmbv3r18/PHHvPzyy1x//fVn9Xv6/vvvueyyy076jeUtt9zCRx99xB/+8AeWL1/O4MGDqaysZNeuXXz00Ud899139X4LfuDAAZ5//nnWrl3Lddddx3vvvefYtm7dOqBq4bDo6GjCwsJ46qmneO211xg0aBAjR448afYdO3bw6KOPMm3aNPr163cWj/7sjRkzhpdeeonLL7+c3//+96Snp/Pqq6/SoUMHtm7dWmPfPn36sHTpUl566SXHqW4DBgw4p/v/4YcfmDx5MjfccAOdOnWioqKC//znP9hsNkeP1A4dOvDXv/6Vp59+miFDhnDdddfh6urKunXriIiIYNasWQQHBzNlyhSmT5/O5ZdfztVXX83u3bt57bXX6Nev32nNPDiX54eInB8akzUmQ/Mbk318fHj99de55ZZbuPDCCxk/fjzBwcGkpKTwzTffMHjwYF555RV8fHwYOnQozz//POXl5bRp04bvv/+exMTEWsecPn06ixcvZsiQIdx3332OonL37t1rjd9nKzQ0lD/96U/8/e9/5+qrr+byyy9ny5YtLFq0iKCgoFPOprvrrrvIysrikksuITIykuTkZObOnUvv3r0dM9QeeeQRPvnkE2644QbuuOMO+vTpQ1ZWFl9++SXz5s2jV69eTJo0iTfeeIPbbruNDRs20K5dOz755BN+/vlnZs+eXWORwPq8+uqrXHzxxcTFxXH33XfTvn170tLSWL16NYcOHWLLli0N8jsTkZPTOK5xHJr2OF5dIN+9ezfPPvus4/qhQ4eyaNEiXF1dz+n9QWxsLH5+fsybNw9vb288PT0ZMGBAnf3Bz8Szzz7LNddcw+DBg7n99tvJzs7mlVdeoUePHnV+IXSi+fPn89prr3HttdcSGxtLfn4+//znP/Hx8XEUxkeMGMEtt9zCnDlz2Lt3r6PN3MqVKxkxYgSTJ0+mV69eTJw4kTfffJOcnByGDRvG2rVrmT9/PmPHjmXEiBGnfByPPPIIX375JVdeeSW33XYbffr0obCwkG3btvHJJ5+QlJRU46wVOUOGyFl6++23DcBYt27dSfcrKSkx/u///s8IDw833N3djcGDBxurV682hg0bZgwbNqzGvkVFRcZf//pXIyYmxnB2djbCwsKM66+/3ti/f79hGIaRmJhoAMYLL7xQ6366d+9e43jLly83AOO///2vMWXKFCMkJMRwd3c3xowZYyQnJ9e6/aZNm4zrrrvOCAwMNFxdXY22bdsaN954o7Fs2bIa932qn4kTJ9Y47qeffmpcfPHFhqenp+Hp6Wl06dLF+OMf/2js3r3bMAzDuP/++42hQ4caixcvrpXpqaeeMup6mb755ptGnz59DHd3d8Pb29uIi4szHn30UePIkSOOfdq2bWuMGTOm1m3/+Mc/1jomYFgsFmPDhg01rq/rb1RWVmY899xzRvfu3Q1XV1fD39/f6NOnjzF9+nQjNze31v1Vq36+nOrn7bffNg4dOmRERUUZDz74YJ3HBIynnnrKMIyq51fPnj2Niy++2KioqHDsc7LnSl25fvs8rv7dHzt2rMb1EydONDw9PWtc99ZbbxkdO3Y0XF1djS5duhhvv/12nX+7Xbt2GUOHDjXc3d1rPFfqu6/qbImJifXmP3DggHHHHXcYsbGxhpubmxEQEGCMGDHCWLp0aa19//3vfxsXXHCB4+82bNgwY8mSJTX2eeWVV4wuXboYzs7ORmhoqHHvvfca2dnZNfYZNmyY0b179zrznO3zQ0TOjcZkjckteUyutnz5cmPUqFGGr6+v4ebmZsTGxhq33XabsX79esc+hw4dMq699lrDz8/P8PX1NW644QbjyJEjNXJW++mnn4w+ffoYLi4uRvv27Y158+bV+Xdu27ZtjedSfTmrn+fLly93XFdRUWFMnTrVCAsLM9zd3Y1LLrnESEhIMAIDA40//OEPJ/19fPLJJ8bIkSONkJAQw8XFxYiOjjbuuece4+jRozX2y8zMNCZPnmy0adPGcHFxMSIjI42JEycaGRkZjn3S0tKM22+/3QgKCjJcXFyMuLg44+23365xnFP9nfbv32/ceuutRlhYmOHs7Gy0adPGuPLKK41PPvnkpI9DRE5N47jG8ZYwjlcLCQkxACMtLc1x3apVqwzAGDJkSK39T/b5sq7f2xdffGF069bNcHJycjxOw6j6rN62bdvTyl3X+4IPPvjA6NKli+Hq6mr06NHD+PLLL41x48YZXbp0Oenj3bhxo3HTTTcZ0dHRhqurqxESEmJceeWVNd6fGEbVe4IXXnjB6NKli+Hi4mIEBwcbo0ePrvFcKS8vN6ZPn+543UZFRRlTpkwxSkpKahyrvuekYRhGfn6+MWXKFKNDhw6Gi4uLERQUZAwaNMh48cUXjbKyspM+Fjk5i2E0UCd9kSbmxx9/ZMSIEXz88cdn/Q3yiZKSkoiJiSExMbHehTumTZtGUlIS77zzzjnfX0vzzjvvOH4/9Rk+fDi33XYbt91223nLJSIijU9jctOiMdlcOTk5+Pv7M3PmTP7617+aHUdE5JQ0jjctGsfPn969exMcHMySJUvMjiJNgHqii4iIiIiINILi4uJa11X3Oh0+fPj5DSMiIiJ1Ki8vr9Uv/Mcff2TLli0ar8VBPdFFTpOXlxcTJkw46eIkPXv2JCIi4jymaj5iY2O59tprT7rPZZdd5ljRXUREpD4ak8+NxuTz58MPP+Sdd97hiiuuwMvLi1WrVvHf//6XkSNHnrdF0EVEmhqN4+dG43jDO3z4MPHx8dx8881ERESwa9cu5s2bR1hYGH/4wx/MjidNhNq5SIvV0KeciYiIyNnRmCyt1caNG3n00UfZvHkzeXl5hIaGMm7cOGbOnHnS4pGISFOicVxautzcXCZNmsTPP//MsWPH8PT05NJLL+Vvf/ubvowQBxXRRURERERERERERETqoZ7oIiIiIiIiIiIiIiL1UBFdRERERERERERERKQeWlgUsNvtHDlyBG9vbywWi9lxREREADAMg/z8fCIiIrBa9b33iTR2i4hIU6Sxu34au0VEpCk63bFbRXTgyJEjREVFmR1DRESkTgcPHiQyMtLsGE2Kxm4REWnKNHbXprFbRESaslON3SqiA97e3kDVL8vHx8fkNCIiIlXy8vKIiopyjFPyPxq7RUSkKdLYXT+N3SIi0hSd7titIjo4TiXz8fHRYC4iIk2OTnmuTWO3iIg0ZRq7a9PYLSIiTdmpxm41aRMRERERERERERERqYeK6CIiIiIiIiIiIiIi9VARXURERERERERERESkHiqii4iIiIiIiIiIiIjUQ0V0EREREREREREREZF6qIguIiIiIiIiIiIiIlIPFdFFREREREREREREROqhIrqIiIiIiIiIiIiISD1URBcRERERERERERERqYeK6CIiIiIiIiIiIiIi9VARXURERERERERERESkHiqii4iIiIiIiIiIiIjUQ0V0EREREREREREREZF6qIguIiIiIiIiIiIiIlIPFdFFREREREREREREROqhIrqIiIiIiIiIiIiISD1URBcRERERERERERERqYeT2QFEpOVJSUkhIyOjQY4VFBREdHR0gxxLREREmpeGfE8Bel8hIiLS2DR2S0ulIrqINKiUlBS6dO1KcVFRgxzP3cODXQkJGjRFRERamYZ+TwF6XyEiItKYNHZLS6Yiuog0qIyMDIqLipjw2AuERsee07HSUvaz4LlHyMjI0IApIiLSyjTkewrQ+woREZHGprFbWjIV0UWkUYRGxxLZsbvZMURERKSZ03sKERGR5kVjt7REWlhURERERERERERERKQeKqKLiIiIiIiIiIiIiNRDRXQRERERERERERERkXqoiC4iIiKnbcWKFVx11VVERERgsVhYuHChY1t5eTmPPfYYcXFxeHp6EhERwa233sqRI0dqHCMrK4sJEybg4+ODn58fd955JwUFBef5kYiIiIiIiIicHhXRRURE5LQVFhbSq1cvXn311VrbioqK2LhxI1OnTmXjxo189tln7N69m6uvvrrGfhMmTGDHjh0sWbKEr7/+mhUrVjBp0qTz9RBEREREREREzoiT2QFERESk+Rg9ejSjR4+uc5uvry9Lliypcd0rr7xC//79SUlJITo6moSEBBYvXsy6devo27cvAHPnzuWKK67gxRdfJCIiotEfg4iIiIiIiMiZ0Ex0ERERaTS5ublYLBb8/PwAWL16NX5+fo4COkB8fDxWq5U1a9aYlFJERERERESkfpqJLiIiIo2ipKSExx57jJtuugkfHx8AUlNTCQkJqbGfk5MTAQEBpKam1nus0tJSSktLHZfz8vIaJ7SIiIiIiIjIb2gmuoiIiDS48vJybrzxRgzD4PXXXz/n482aNQtfX1/HT1RUVAOkFBERERERETk1FdFFRESkQVUX0JOTk1myZIljFjpAWFgY6enpNfavqKggKyuLsLCweo85ZcoUcnNzHT8HDx5stPwiIiIiIiIiJ1I7FxEREWkw1QX0vXv3snz5cgIDA2tsHzhwIDk5OWzYsIE+ffoA8MMPP2C32xkwYEC9x3V1dcXV1bVRs4uIiIiIiIjURUV0EREROW0FBQXs27fPcTkxMZHNmzcTEBBAeHg4119/PRs3buTrr7+msrLS0ec8ICAAFxcXunbtyuWXX87dd9/NvHnzKC8vZ/LkyYwfP56IiAizHpaIiIiIiIhIvVREFxERkdO2fv16RowY4bj88MMPAzBx4kSmTZvGl19+CUDv3r1r3G758uUMHz4cgAULFjB58mQuvfRSrFYr48aNY86cOeclv4iIiIiIiMiZUhFdRERETtvw4cMxDKPe7SfbVi0gIID333+/IWOJiIiIiIiINBotLCoiIiIiIiIiIiIiUg8V0UVERERERERERERE6qEiuoiIiIiIiIiIiIhIPVREFxEREREREWmhVqxYwVVXXUVERAQWi4WFCxfW2G4YBk8++STh4eG4u7sTHx/P3r17a+yTlZXFhAkT8PHxwc/PjzvvvJOCgoLz+ChERETMpSK6iIiIiIiISAtVWFhIr169ePXVV+vc/vzzzzNnzhzmzZvHmjVr8PT0ZNSoUZSUlDj2mTBhAjt27GDJkiV8/fXXrFixgkmTJp2vhyAiImI6J7MDiIiIiIiIiEjjGD16NKNHj65zm2EYzJ49myeeeIJrrrkGgHfffZfQ0FAWLlzI+PHjSUhIYPHixaxbt46+ffsCMHfuXK644gpefPFFIiIizttjERERMYtmoouIiIiIiIi0QomJiaSmphIfH++4ztfXlwEDBrB69WoAVq9ejZ+fn6OADhAfH4/VamXNmjX1Hru0tJS8vLwaPyIiIs2ViugiIiIiIiIirVBqaioAoaGhNa4PDQ11bEtNTSUkJKTGdicnJwICAhz71GXWrFn4+vo6fqKioho4vYiIyPmjIrqIiIiIiIiINKgpU6aQm5vr+Dl48KDZkURERM6aiugiIiIiIiIirVBYWBgAaWlpNa5PS0tzbAsLCyM9Pb3G9oqKCrKyshz71MXV1RUfH58aPyIiIs2ViugiIiIiIiIirVBMTAxhYWEsW7bMcV1eXh5r1qxh4MCBAAwcOJCcnBw2bNjg2OeHH37AbrczYMCA855ZRETEDE5mBxARERERERGRxlFQUMC+ffsclxMTE9m8eTMBAQFER0fz4IMPMnPmTDp27EhMTAxTp04lIiKCsWPHAtC1a1cuv/xy7r77bubNm0d5eTmTJ09m/PjxREREmPSoREREzi8V0UVERERERERaqPXr1zNixAjH5YcffhiAiRMn8s477/Doo49SWFjIpEmTyMnJ4eKLL2bx4sW4ubk5brNgwQImT57MpZdeitVqZdy4ccyZM+e8PxYRERGzqIguIiIiIiIi0kINHz4cwzDq3W6xWJgxYwYzZsyod5+AgADef//9xognIiLSLKgnuoiIiIiIiIiIiIhIPVREFxERERERERERERGph4roIiIiIiIiIiIiIiL1UBFdRERERERERERERKQeKqKLiIiIiIiIiIiIiNTDyewAItI0pKSkkJGRcc7HSUhIaIA0IiIiIiIiIiIiTYOK6CJCSkoKXbp2pbioqMGOWVBQ0GDHEhERERERERERMYuK6CJCRkYGxUVFTHjsBUKjY8/pWAlrf2LR/JcpKSlpoHQiIiIiIiIiIiLmURFdRBxCo2OJ7Nj9nI6RlrK/gdKIiIiIiIiISHNgGAb7s8rxumAMGzJtbC05SoXdwN3ZhrebExF+7oT7uuFsO/PlGRuqbWxQUBDR0dENcixpfVREFxERERERERERkTOWU1TG/F+S+XTjIVKyiggceS9JhUBh7RavNquFjiFe9Iz0JdzX/ZTHzss6BsDNN9/cIFndPTzYlZCgQrqcFRXRRURERERERERE5LSVV9p5a1Uiry3fR15JBQBuThayd6/jggsuICwsDJvVQlFZJdlFZRzKLqagtIJdqfnsSs2nbaAHg2ODCPZ2rfc+igvyABhzz1/p3LPPOeVNS9nPguceISMjQ0V0OSsqoouIiIiIiIiIiMhpOXCsgAc/3MzWQ7kAdAnz5g/DYgktT2XQgGl0H/EZkVF+NW5jGAZpeaVsPZzD7tR8kjOLSMlMoW87fwbEBGKzWuq9v8CItufcelbkXKmILiIiIiIiIiIiIqe0ZGcaD/x3E8Xllfi6O/PEmK5cd2EkNquFjRvT6r2dxWIhzNeNMN8w+rcL4Of9mexLL2BdUjZJmUWMiQvH1935PD4SkTNz5t38RUREREREREREpFX5z+ok7vnPeorLK7mofQCLHxzCDX2jTjqLvC5+Hi6MiQvnih5huDlZOZZfyofrDnI4u7iRkoucOxXRRUREREREREREpF7/XpXI1C92YDdgfL8o/nPngNNaHPRkOoZ68/sB0YR4u1JcXslnmw6xL732gqQiTYGK6CIiIiIiIiIiIlKn/65NYcbXOwGYPKIDs66Lw9nWMCVFbzdnru8TSYdgL+wGfLv9KLtT8xvk2CINSUV0ERERERERERERqWX5rnT+8vk2ACYNbc//jeyExXJm7VtOxdlmZXSPMLqEeWMY8N2OVPYf04x0aVpMLaKvWLGCq666ioiICCwWCwsXLqyx3TAMnnzyScLDw3F3dyc+Pp69e/fW2CcrK4sJEybg4+ODn58fd955JwUFeqGJiIiIiIiIiIicrb1p+dz/300YBvyubxRTRndp8AJ6NavVwshuoXQN98YAFm1PJZdzaxcj0pBMLaIXFhbSq1cvXn311Tq3P//888yZM4d58+axZs0aPD09GTVqFCUlJY59JkyYwI4dO1iyZAlff/01K1asYNKkSefrIYiIiIiIiIiIiLQoeSXl3PXuegpKK+gfE8DTY3s0WgG9msViIb5LKO2DPKm0G+wkCif/iEa9T5HT5WTmnY8ePZrRo0fXuc0wDGbPns0TTzzBNddcA8C7775LaGgoCxcuZPz48SQkJLB48WLWrVtH3759AZg7dy5XXHEFL774IhEReqGJiIiIiIiIiIicLsMw+Ovn20nOLKKNnzvzbu6Di9P5mYdrtVoY3SOMzzYd5mhuCSHjplJhNG7xXuR0mFpEP5nExERSU1OJj493XOfr68uAAQNYvXo148ePZ/Xq1fj5+TkK6ADx8fFYrVbWrFnDtddeW+exS0tLKS0tdVzOy8trvAciIiIiIiIiIiLSgFJSUsjIyGiQYwUFBREdHe24/PH6Q3y15Qg2q4W5v7+AAE+XBrmf0+VkszImLpx3V+2BwCgSSkvoYxiNPhNe5GSabBE9NTUVgNDQ0BrXh4aGOralpqYSEhJSY7uTkxMBAQGOfeoya9Yspk+f3sCJRUREREREREREGldKSgpdunaluKioQY7n7uHBroQEoqOjOZhVxLSvdgDwfyM7cWG0f4Pcx5nydHWiG4fYWB5BlrMbm1JyuLCtOVlEoAkX0RvTlClTePjhhx2X8/LyiIqKMjGRSMtmNwwOZxeTmFFIbnE5RWWVuDlb8XFzJjrQg7YBHjjZTF2iQURERERERKRZyMjIoLioiAmPvUBodOw5HSstZT8LnnuEjIwMoqKiePyzrRSVVdI/JoA/DD23Y58rL0rIXvYmgZffz8/7M4jwdyfMx83UTNJ6NdkielhYGABpaWmEh4c7rk9LS6N3796OfdLT02vcrqKigqysLMft6+Lq6oqrq2vDhxaRGuyGwc4jeaxJzKKgtKLOfbYezsXFZqVXlC992vrj6mQ7zylFREREREREmp/Q6FgiO3ZvsON9uO4gP+/LxM3ZyvPjemK1mt8+pWDLd3QZcxfHKt1ZvD2VCQOicdYkPDFBky2ix8TEEBYWxrJlyxxF87y8PNasWcO9994LwMCBA8nJyWHDhg306dMHgB9++AG73c6AAQPMii4iQJHdxgfrDnIsv2r9AVcnKx1CvAj2dsXDxUZpuZ3MgjL2HSugoLSCdUnZbDucyyWdQ+gY6m1yehEREREREZHWY922Xby8zw+A8d08yUrZTVbKmR0jISGh4YMBnVxyKLZ7k1tczs/7MhjeOeTUNxJpYKYW0QsKCti3b5/jcmJiIps3byYgIIDo6GgefPBBZs6cSceOHYmJiWHq1KlEREQwduxYALp27crll1/O3Xffzbx58ygvL2fy5MmMHz+eiIgIkx6ViLi178vGkmAqS0pxcbJyUUwAcZG+OFlrf1s8tFMQ+48V8sv+DLKLyvl2eyo9sooY1jnYhOQiIiIiIiIirUde1jEA/vrRWrziLqM0dR/Tn3+Y6Yb9rI9ZUFDQUPEAcLIYXNYtlM83HWbLoVw6hHgR6e/RoPchciqmFtHXr1/PiBEjHJer+5RPnDiRd955h0cffZTCwkImTZpETk4OF198MYsXL8bN7X/9jxYsWMDkyZO59NJLsVqtjBs3jjlz5pz3xyIiVdLxIeT6J6nESoSvG1fEhePpWv9/NRaLhQ4hXsQEefLrgUzWJ2ez/UgeOcXlXOh5HoOLiIiIiIiItDLFBXm4tumCV9xlAFzeqy0Br3xyVsdKWPsTi+a/TElJSUNGBCA6wIMebXzYfjiPpQnp3DwgWmuryXllahF9+PDhGIZR73aLxcKMGTOYMWNGvfsEBATw/vvvN0Y8ETlD+9IL2EMEFouFMFsR113YAdtp9lCzWS0M7hBEpL8732w7yqHsYgoKnLC6eTVyahEREREREZHWyQD84/8AQPcIH3p2DT3rY6Wl7G+gVHUb0iGYpIwicovLWZuUxaDYoEa9P5ET6SsbEWkQR3KKWbT9KGChYNtSOrnknHYB/URtAz0Zd2Ek7s42csqthNwwjeLysz+NTERERERERETqlo4vrmEdsGFnUGyg2XFOysXJyvDjrV83JGeTVVhmciJpTVREF5FzVlhawbfbjmI3IJA8MhfNwXIOi3iH+rgx7sI2uFgNXCO68Pwv2ZRVqJAuIiIiIiIi0lDKK+0kU1WUjnYuwMPF1IYVp6V9kCcxQZ7YDVi+K/2kHS5EGpKK6CJyTux2g2+3HaWwrJIATxc6cQTOYQGSaoFergwOrsBeVsyWtDJmfL2jAdKKiIiIiIiICMCmgzmU4UxFbjqRTg27GGhjsVgsDO8UjM1q4VBOMfuPFZodSVoJFdFF5JysT8nmSG4JLjYrV/YMx0bDfQsc4GqQ8eXzWID3fk1hwZrkBju2iIiIiIiISGtVUl7JhqRsAHJWvMtZdGM1jY+7M32i/QFYufcYFZU6c10an4roInLWMgpKWXMgE4DhnYPx93Bp8Pso3r+O38d5A/DUFzvYfDCnwe9DREREREREpDXZlJJDWaUdD0oo3PmT2XHOWJ+2/ni62sgrqWCT6gRyHqiILiJnxW43+H5nGnajqidZlzDvRruv67p4MrpHGBV2g/v/u5G8kvJGuy8RERERERGRlqykvNIxQS2aDGjAM8rPFxcnK4NjgwBYn5RNcXmlyYmkpVMRXUTOyrbDuRzLL8XVycolXUKwnMtKoqdgsVj427ieRPq7czCrmCmfbdPiISIiIiIiIiJnYWNKNmWVdoK8XAgk3+w4Z61LmDdBXi6UVdpZn5Rldhxp4Zr+srsi0uQUlVWw+ngbl0GxgXi6Nv5/Jb7uzsy96QJumLeab7YeZWS3UK7p3eaMjpGSkkJGRkaDZQoKCiI6OrrBjiciIiIiIiLSmIrL/jcL/aL2geRsNTfPubBYLAyODeKLLUfYciiXXlF++Lg5mx1LWigV0UXkjK3en0lpRdW31j3a+J63+70g2p/7L+nIP5buYdqXOxgUG0Swt+tp3TYlJYUuXbtSXFTUYHncPTzYlZCgQrqIiIiIiIg0CxtSsimvNAj2dqV9kCcbzQ50jtoGetDGz53DOcWsTcwivmuo2ZGkhVIRXUTOSGZBKduP5AEwvFMI1kZs41KX+0bEsnhHKglH83jyi+28fnOf07pdRkYGxUVFTHjsBUKjY885R1rKfhY89wgZGRkqoouIiIiIiEiTV1RWwZbqWegxAY3alvV8sVgsDIoN5OMNh0g4mkf/dgH4uGs2ujQ8FdFF5IxUt3GJDfakjb/7eb9/Z5uVF2/oyTWv/Myi7al8s/UoY3qGn/btQ6NjiezYvRETioiIiIiIiDQ9G5NzqLAbhHi7EhPkaXacBhPh506kvzuHsovZkJzNiC4hZkeSFkgLi4rIaUvNLWH/sUIswMD2gabl6B7hy33Dq2aTT/1iO5kFpaZlEREREREREWnqSsor2Xo4B4AB7VvGLPQT9W8XAMCOo3kUlFaYnEZaIhXRReS0/XKgalHOLuHeBHqdXi/yxjL5ko50CfMmq7CMp77cYWoWERERERERkaZs6+FcyisNAr1ciAlsObPQq0X6uxPu60al3WBjSrbZcaQFUhFdRE7L0dxiDmYVY7XARTHmzUKv5uJk5YXre2GzWvh661F+2nPM7EgiIiIiIiIiTU5FpZ3NKTkA9I32b3Gz0KGqN3r/mKrZ6NsO5VJUptno0rBURBeR07Iuqeqb3C5hPk1mkY64SF8mDmwHwFNfbKekvNLcQCIiIiIiIiJNTMLRfIrLK/F2c6JjqLfZcRpN2wAPQrxdqbAbbDr+pYFIQ1ERXURO6Vh+KYkZVb3Q+7bzNztODQ9d1pEQb1eSMot4c8UBs+OIiIiIiIiINBl2w2DD8fYmF0T5YbO2vFno1U6cjb71UK4m2kmDUhFdRE5pfXIWAB1DvPD3cDE5TU3ebs48cWU3AF5dvo+UzCKTE4m0bCtWrOCqq64iIiICi8XCwoULa2w3DIMnn3yS8PBw3N3diY+PZ+/evTX2ycrKYsKECfj4+ODn58edd95JQUHBeXwUIiIiIiKtw/70AnKLy3FzstKjja/ZcRpd+yBPAj1dKKu0s/VQrtlxpAVREV1ETiqvuJy9aVXFrb7HV7tuaq7qGc6g2EBKK+xM+2oHhmGYHUmkxSosLKRXr168+uqrdW5//vnnmTNnDvPmzWPNmjV4enoyatQoSkpKHPtMmDCBHTt2sGTJEr7++mtWrFjBpEmTztdDEBERERFpFQzDYH1y1Sz0nlF+ONtafhnQYrE4zqDfciiHSrvqA9IwWv6rR0TOyZZDORhAVIA7wd6uZsepk8ViYcY1PXC2WfhhVzpLdqaZHUmkxRo9ejQzZ87k2muvrbXNMAxmz57NE088wTXXXEPPnj159913OXLkiGPGekJCAosXL+Zf//oXAwYM4OKLL2bu3Ll88MEHHDly5Dw/GhERERGRlutgdjHp+aU4WS30jvQzO8550zHEG08XG0VllexNzzc7jrQQKqKLSL3KKuxsP5wHwAVRTasX+m91CPHiriHtAZj+1U6Ky9T7TOR8S0xMJDU1lfj4eMd1vr6+DBgwgNWrVwOwevVq/Pz86Nu3r2Of+Ph4rFYra9asqffYpaWl5OXl1fgREREREZH6bTreC717hA/uLjaT05w/NquFuMiq1jWbD+bobHVpECqii0i9dh7No6zSjr+HM+0CPcyOc0r3X9KBCF83DucUM++n/WbHEWl1UlNTAQgNDa1xfWhoqGNbamoqISEhNbY7OTkREBDg2Kcus2bNwtfX1/ETFRXVwOlFRERERFqO7MIyko6vGdY7ys/cMCaIa+OLzWIhLa+U1LySU99A5BRURBeROhmGweaDOUDVgGuxNP0VvD1cnPjLmK4AzPtpPweztMioSEsxZcoUcnNzHT8HDx40O5KIiIiISJNV/Xm+fZAnfh4u5oYxgYeLE53CvID//S5EzoWK6CJSp5SsInKLy3FxstI13MfsOKdtTFw4A9tXLTL6zDcJZscRaVXCwsIASEuruS5BWlqaY1tYWBjp6ek1tldUVJCVleXYpy6urq74+PjU+BERERERkdpKyivZebSq/WFrnIVerfqx70svoLjC3CzS/DmZHUBEmqath3IB6Bbm06xW8LZYLEy7ujtXzFnJ4h2prNqbwcUdg8yOJdIqxMTEEBYWxrJly+jduzcAeXl5rFmzhnvvvReAgQMHkpOTw4YNG+jTpw8AP/zwA3a7nQEDBpgVXaTVS0lJISMjo0GOFRQURHR0dIMcS0RERM7cjiN5VNgNAr1ciPR3NzuOaUK83Yjwc+NITgn7C1pPT3hpHCqii0gt+SXlJGYUAjgW42hOOod5c8tFbXnnlySmfbWDRX8aYnYkkRajoKCAffv2OS4nJiayefNmAgICiI6O5sEHH2TmzJl07NiRmJgYpk6dSkREBGPHjgWga9euXH755dx9993MmzeP8vJyJk+ezPjx44mIiDDpUYm0bikpKXTp2pXiooZpg+bu4cGuhAQV0kVERExgtxtsOZQDNJ/WrI2pd5QfR3JSSSywYnFqfW1tpOGoiC4itWw/nIcBRPq7E+DZPAeZh+I78eWWI+xLL2D+L0lc6Gl2IpGWYf369YwYMcJx+eGHHwZg4sSJvPPOOzz66KMUFhYyadIkcnJyuPjii1m8eDFubm6O2yxYsIDJkydz6aWXYrVaGTduHHPmzDnvj0VEqmRkZFBcVMSEx14gNDr2nI6VlrKfBc89QkZGhoroIiIiJtifUUB+SQVuzla6hHqbHcd0sUFeeLs5kV9SgUfXoWbHkWZMRXQRqcFuGOw4WtXKpWeb5jcLvZqvhzOPjurM459tY/bSvbw8MsDsSCItwvDhwzEMo97tFouFGTNmMGPGjHr3CQgI4P3332+MeCKtSkO1YElIqFpDJDQ6lsiO3c/5eCIiImKe6kU049r44tSMWrM2FqvVQlwbX37Zn4lXz5Fmx5FmTEV0EakhJbOIwtJK3J1ttA/2MjvOObmxbxTvr01h66Fc3tuWb3YcERGRBtPQLVigql2TiIiINF/peSUcySnBaoGekX5mx2kyuoX7sHp/Bm6R3TiYW86FZgeSZklFdBGpYcfxFbw7h3ljszbv3mlWq4XpV3fn2td+YXlSMS4Rnc2OJCIi0iAasgVLwtqfWDT/ZUpKShoonYiIiJihehZ6xxBvvFxV8qvm6epEmLvB0WILSxOLuWbEqW8j8lt6RYmIQ2klJB6rWlC0W7iPyWkaxgXR/lzfJ5JPNhwiIP4eTtKFQkREpNlpiBYsaSn7GyiNiIiImKWwtILdaVVnYPeO8jM3TBMU41XJ0WIrPyUXU1pRiauTzexI0syoOZKIOBwsslJpGIR4uxLs7Wp2nAbz2OVd8HC24BreiaRC/bcnIiIiIiIiLcu2w7nYDQj3dSPM183sOE1OqJtBRX4meaV2lu5MNzuONEOqJomIQ1JB1X8JLWUWerVgb1du7FbV3317jo2S8kqTE4mIiIiIiIg0jAq7na2HcgHNQq+P1QKF25YA8OH6gyankeZI7VxEBADnkPbklluxWSx0DvM2O06Du6KjJ28u2wFB0aw5kMWwzsFmRxIREREREZFWJCUlhYyMjHM+TkJCQo3Le9IKKC6vxMvVidhgr3M+fktVsHUJvoPGs3LvMQ5lFxHp72F2JGlGVEQXEQC84uIBaB/siZtzy+sN5mS1kL30DULHP8OWwzl0b+NDkFfLaVkjIiIiIiIiTVdKSgpdunaluKiowY5ZUFCAYRhsTskBoGekLzarpcGO39JU5KYRF+LCtvQyPl5/iIcu62R2JGlGVEQXEcorDTy7DwegW0TLauVyopLkLUS42zlSbOWn3ce47sI2WCx6gyEiIiIiIiKNKyMjg+KiIiY89gKh0bHndKyEtT+xaP7LlJSUcCSnhGMFpThZLfRo49tAaVuu+PYex4voB3ng0o760kFOm4roIsK6IyXY3H1wtxlEB7Ts05l6+leQVurKoZxi9qYX0Cm05bWuERERERERkaYpNDqWyI7dz+kYaSn7Hf/edDAbgC5h3ri3wLPKG9qANm74ujtzJLeEVfsyGNZJrV7l9GhhURHhx+RiAKI97Vhb+MxsTyfo29YfgJV7MyivtJucSEREREREROTMFdttHDhWCGhB0dPlYrNwTe8IABZuOmxyGmlOVEQXaeVyi8rZnFoKQLRnpclpzo++bf3xdnOioLSCdUlZZscRERGR01ReaSe7sIycojIKSyswDMPsSCIiIqY5UuGJAUQHeBCoNb9O2zW92wDw3Y5UisoqTE4jzYXauYi0ct/tSKXCDmXpifhEtzE7znnhZLMytGMw32w7ysbkHLqF++Dn4WJ2LBEREalDRqmFfbvTScosIre4vMY2T1cbbfzc6RHhS6S/u9Y6ERGRVsPi4s7Riqp2rJqFfmYujPYjOsCDlKwiluxMcxTVRU5GM9FFWrmvth4BoDBhhclJzq/YYE+iAtypNAxW7s0wO46IiIicwDAM1hwuIezWf/BTmjNbDuU6CuguNisuNisWoLC0kj1pBXy26TAfrDvI0dxic4OLiIicJ149LqUSK34ezrQLbNlrmzU0i+V/LV2+2HzE5DTSXGgmukgrllFQys/7qgrIRbtWAjeZG+g8slgsDO8UwoI1yRzIKCQps5B2gZ5mxxIREWn19qXn88TC7fx6IBvX8I7YLAadQn3oEOJFmK8bHi5VH2HKK+2k5ZWwN62AnUfzSM8v5eP1h7gg2o9BsUHYrJqVLiIiLZMBePe5CoDekX46E+ssXNO7DXN/2MeKPcfIKiwjwFNnp8vJaSa6SCu2aNtR7AZ0CHCmIifV7DjnXYCnC72On/b2055jVNrVV1VERMQsdrvBvJ/2M/rllfx6IAsXG+Su/ojREeWM7B5G+2AvRwEdwNlmJdLfgxFdQrhjcAxdwrwxgI0pOXy15QhlFVo8XOR0VFZWMnXqVGJiYnB3dyc2Npann366xpoDhmHw5JNPEh4ejru7O/Hx8ezdu9fE1CKtWzZeOAe0wYadruE+ZsdpljqEeNGjjQ8VdoNvtmo2upyaiugirdhXW44CcHGUm8lJzDMgJgAPFxs5ReVsOphtdhwREZFW6Vh+Kbf8ew1/W7SL8kqDS7qE8PKoYHJWvIur7dS3d3exMap7GGPiwnGyWkjOKuKTjYcoKW8di6aLnIvnnnuO119/nVdeeYWEhASee+45nn/+eebOnevY5/nnn2fOnDnMmzePNWvW4OnpyahRoygpKTExuUjrdZgAAMKdinBxUmnvbI093gt9oVq6yGnQK02klTqaW8zapCwABkW5m5zGPK5ONgbHBgGwNjGLglKtzC0iInI+bUzJ5sq5K/l5Xybuzjb+dl0cb03sS6jXmXee7BDixbg+kbg72ziWX8qXW45QXqkZ6SIn88svv3DNNdcwZswY2rVrx/XXX8/IkSNZu3YtUDULffbs2TzxxBNcc8019OzZk3fffZcjR46wcOFCc8OLtELH8kvJxRPDXkkbp0Kz4zRrV/WKwGKBDcnZHMwqMjuONHEqoou0Ut9srZqF3q+dP0EepzHFqwXrGu5NmI8b5ZWGo0e8iIiINL4vtxxh/Bu/kpZXSocQL766/2LG948+p96uYT5uXHdhG1ydrBzNLeHbbUexG2rZJlKfQYMGsWzZMvbs2QPAli1bWLVqFaNHjwYgMTGR1NRU4uPjHbfx9fVlwIABrF69ut7jlpaWkpeXV+NHRM7d5oM5ABTt+QU3q864OhehPm4Mig0E4IvNh01OI02diugirdRXW6pOV7qqV4TJScxnsVgY3jkYgF2p+RzJKTY5kYiISMtmGAZv/LSfB/67ibJKOyO7hbLwj4PpEOLVIMcP8nLl6l4ROFktJGUW8euBzAY5rkhL9PjjjzN+/Hi6dOmCs7MzF1xwAQ8++CATJkwAIDW1au2k0NDQGrcLDQ11bKvLrFmz8PX1dfxERUU13oMQaSUKSyvYnZoPQN66L0xO0zJcc0JLF0NfustJqIgu0golZxay5VAuVguM7hFudpwmIdTHje4RVQuy/Lj7mGasiYiINJJKu8G0L3cwa9EuAO4YHMO8m/vg5Xrm7VtOJsLPnfiuVUW/dUnZHC0++9ntIi3ZRx99xIIFC3j//ffZuHEj8+fP58UXX2T+/PnndNwpU6aQm5vr+Dl48GADJRZpvbYdzqXSMPCmmLIju8yO0yJc3iMMFycr+9IL2HlUZ8xI/VREF2mFvj7eymVQbBDB3q4mp2k6BsUG4upk5VhBKdsP55odR0REpMUpKa/k3vc2MH91MhYLPDGmK09e1Q2rtXEK3J3DvOkZ6QvAukwnbF4BjXI/Is3ZI4884piNHhcXxy233MJDDz3ErFmzAAgLCwMgLS2txu3S0tIc2+ri6uqKj49PjR8ROXsVlXa2Hqr6nBqBzrBqKD5uzow4fmb6om31n10joiK6SCv0v1YumoV+Ig8XJy5qX9UPbfX+TIrL1V9ORESkoWQVlnHTP3/l+51puDhZeeWmC7lrSPtGv98hHYMI8Xal3G4hYNRknaot8htFRUVYrTVLAzabDbu9alHemJgYwsLCWLZsmWN7Xl4ea9asYeDAgec1q0hrtjstn+LySrxcnQgi3+w4LcoVcVW1kW+3HdX7BKmXiugirczetHx2pebjbLMwqnv9M0daq55tfAn0dKGkws6v+/XtvoiISENIzixk3Ou/sCklB193Z967cwBjep6fL/OdrFZGdgvFioFHh/78mKy1T0ROdNVVV/HMM8/wzTffkJSUxOeff85LL73EtddeC1StH/Tggw8yc+ZMvvzyS7Zt28att95KREQEY8eONTe8SCthGAabji8o2jvKDzUoa1iXdAnBxWblQEYhe9IKzI4jTZSK6CKtzFfHW7kM7RiMn4eLyWmaHqv1f4uMbjucy7H8UpMTiYiING9bDuZw3Wu/kJhRSBs/dz69dyD9Y85vW5VAL1e6+VWdYfbWpjyN7yInmDt3Ltdffz333XcfXbt25c9//jP33HMPTz/9tGOfRx99lPvvv59JkybRr18/CgoKWLx4MW5ubiYmF2k9DmYXk1lQhpPV4ljLSxqOt5szQzsFAVWz0UXqoiK6SCtiGAZfO1q5RJicpumK9PegY4gXBvDjnnSdziUiInKWvt+Ryvg3fyWzsIwebXz4/L5BdAjxNiVLR287pan7KCo3eG6xFmMTqebt7c3s2bNJTk6muLiY/fv3M3PmTFxc/jfhxmKxMGPGDFJTUykpKWHp0qV06tTJxNQircumlGwAukX44OZsMzlNyzS6R9UZcou3qy+61E1FdJFWZMeRPA5kFOLqZCW+W6jZcZq0IR2DcLJaOJJTwu409ZsTERE5U2//nMg9722guLySYZ2C+WDSQEJ8zJu1arVA1vevAfDJhkOsT8oyLYuIiMjpyi4sIymzCKhq5SKNI75rKM42C7vT8tmXrpYuUpuK6CKtyFdbq2ahX9o1BC9XJ5PTNG3ebs70O36q+cq9GZRWaJFRERGR02Kx8tamXKZ/tRPDgJv6R/PWxL5N4r1H2dE9xMe4AzD1ix1U2nW2mYiING3VvdBjgjzxV0vWRuPr4czgDlUtXRZvV0sXqU1FdJFWoqqVS9VAcFVPtXI5HRdG++Hn7kxRWSVrDmi2moiIyKlU2CF47BS+2Vs1Y+7x0V149toeONmazseOm3v64OPmRMLRPD7beMjsOCIiIvUqLq8k4WgeABdoFnqju+J4S5dvt6mli9TWdN7Nikij2piSw+GcYjxdbIzoEmJ2nGbByWpl2PFFRjcfyiGjQIuQiYiI1KewtIIV6U54dBqIsxXm3nQBfxgWi8ViMTtaDT6uVv44ogMAf/9+D8VlOttMRESapq0Hc6iwGwR7uxLp7252nBbvsm6h2KwWdh7NIymj0Ow40sSoiC7SSnx1fEHRkd3DtBDJGWgX6ElssCeGAT/uPqZFRkVEROqQllfCB+sOkl1mpbI4j2nDApv0IuYTB7WjjZ87qXklvLXqgNlxREREaimvtLPlUC4AfaL9m9yX0i2Rv6cLg2IDAVikBUblN1REF2kFKu0G32w73sqlV7jJaZqfoR2DcbJaOJxTrEVGRUREfiPhaB4fbzhEQWkFXk4Gqf/5M12Dm3bPVjdnG4+M6gzAGz8dILeo3OREIiIiNe08kkdxeSU+bk50DPEyO06rMfp4S5dF6osuv6EiukgrsCYxk2P5pfi6O3Nxh2Cz4zQ7Pu7O9GtXtcjoKi0yKiIiAlR9Sf/TnmN8vzONSrtBu0APRoSVU5F9xOxop+XqXhF0DvUmv7RCs9FFRKRJsdsNNqZkA3BhW3+sVs1CP19Gdg/FaoGth3I5nFNsdhxpQlREF2kFvjq+oOjoHmG4OOllfzYubOuHr7szhWWVrE3UIqMiItK6FZVVsHDTYTYfzAGgf7sAru4VgUszepthtVr4U3xHAN7+OYmcojKTE4mIiFTZm15AXkkF7s42uoX7mB2nVQnycqVv26pJdEt3ppmcRpqSZvQ2V0TORnml3XEa0pU9m25v0qbOyWpleKeqWfybDuaQV6aZACIi0jql5ZXw37UHOZRTjLPNwpi4cAbGBjbLXq2Xdw+jS1j1bPREs+OIiIhgGAYbkqtmofeK8sXZptLd+RbfLQSApQkqosv/6JUo0sKt2pdBTlE5QV4uXNQ+wOw4zVq7oP8tMro5W4uziohI63Ni/3M/d2d+1zeKDs24T6vVauFPl2o2uoiINB0pWUUcKyjF2WahV6Sf2XFapcu6hQHw64FM8kq0bopUURFdpIX7aktVX9Ir4sJx0jfY52xox2BsVgvHSq14dB1qdhwREZHzwl5H//Px/aII9HI1O9o5G3V8NnpBaQX/WqnZ6CIiYq71x2ehd4/wxc1Zk7fMEHN8Al15pcFPu4+ZHUeaiCZdUausrGTq1KnExMTg7u5ObGwsTz/9NIZhOPYxDIMnn3yS8PBw3N3diY+PZ+/evSamFmk6Ssor+X5H1elHV/VSK5eG4OPuTP/ji4z6j7iT4nK7yYlEREQaV3FZJZ9vrt3/3LWFfLC3Wi08eLw3+ju/JJFdqNnoIiJijqO5xRzKLsZqgQui/cyO06rFdwsF1NJF/qdJF9Gfe+45Xn/9dV555RUSEhJ47rnneP7555k7d65jn+eff545c+Ywb9481qxZg6enJ6NGjaKkpMTE5CJNw097jlFQWkG4rxt9ov3NjtNiXBjth6eTgZN3IB/tLDA7joiISKNJzy/hv+tSOJTd/Pufn8zIbmF0Dfepmo2+6oDZcUREpJVak5gFQNdwH3zcnE1O07qNPF5EX74rnfJKTZ6TJl5E/+WXX7jmmmsYM2YM7dq14/rrr2fkyJGsXbsWqJqFPnv2bJ544gmuueYaevbsybvvvsuRI0dYuHChueFFmoDqVi5X9gzHam1ZH3bN5GSz0su/AoCv9xSy/5gK6SIi0vLsSs3j4/WHyC+pwLcF9D8/mare6B0AeHd1MvnqfyoiIudZal4JyZlFWCzQr53WMzNb7yh/Aj1dyCupYN3xLzekdWvSRfRBgwaxbNky9uzZA8CWLVtYtWoVo0ePBiAxMZHU1FTi4+Mdt/H19WXAgAGsXr3alMwiTUVRWQXLEtIBtXJpDOHuBkX71lJpwDPfJJgdR0REpMHY7QYr9x7jux1pVNgN2rag/ucnM7JbGO2DPckvqeC/a1PMjiMiIq3M2uOF2i5h3vi6axa62WxWC5d2DQFgiVq6COBkdoCTefzxx8nLy6NLly7YbDYqKyt55plnmDBhAgCpqakAhIaG1rhdaGioY1tdSktLKS0tdVzOy8trhPQi5lqakE5xeSVtAz2Ia+NrdpwWKXv5W3h16McPu9J569vVXBB29sWFoKAgoqOjGzCdiIjImSuvtLNoeyqJGYUA9Gvnz0XtA7G2sPYtdbFaLdwztD2PfbqNt1YlctugGFycmvScIxERaSHS80tIzCjEgmahNyXxXUP5aP0hluxM48kru7W4dnZyZpp0Ef2jjz5iwYIFvP/++3Tv3p3Nmzfz4IMPEhERwcSJE8/6uLNmzWL69OkNmFSk6alu5XJVzwj9R98I8rKOUZF1mJx1X+DTbyxTP9vM0X9PBuPseqW5e3iwKyFBhXQRETFNmWHlkw2HSM8vxWa1MKpbKB1Dvc2OdV6NvaANf/9+D2l5pXyx+TA39I0yO5KIiLQC1bPQO4V54+/hYnIaqXZxxyBcnawcyi5md1o+XcJ8zI4kJmrSRfRHHnmExx9/nPHjxwMQFxdHcnIys2bNYuLEiYSFhQGQlpZGeHi443ZpaWn07t273uNOmTKFhx9+2HE5Ly+PqCi9QZaWI7e4nJ92HwPUyqWxFBdUncEyoFME+60GBEVz9azP6OB95kX0tJT9LHjuETIyMlREFxERUzgFRrKxJIhSoxR3ZxtX9Qon3Nfd7FjnnauTjTsujuFvi3bx5ooDjLswUuvKiIhIozqWX8r+Y1VngPXXLPQmxcPFiSEdg1iakM6SHWkqordyTbqIXlRUhNVa8xRKm82G3V5VpIqJiSEsLIxly5Y5iuZ5eXmsWbOGe++9t97jurq64urasns6Suv2/Y5UyirtdAr1onNY65pBdr6FhEcSFhXC8t3H2J3vwkVx7XBztpkdS0RE5LTl4UbYzS9Sajjh6+7M2N4R+LXiWXC/HxDNKz/sY296AT/sSie+W+ipbyQiInKW1iYdn4Ue4kWAZ+sdf5uq+K6hVUX0hDTuv7Sj2XHERE26yd9VV13FM888wzfffENSUhKff/45L730Etdeey0AFouFBx98kJkzZ/Lll1+ybds2br31ViIiIhg7dqy54UVM9NXWo0BVKxdpfD0ifAn0dKGkws6vBzLNjiMiInLaDmcXs4NobG5e+FjL+F3fqFZdQAfwcXNmwoCqM8PeWLHf5DQiItKSpeWVsC+9AIB+MZqF3hRdcnxx0a2HcknPLzE5jZipSRfR586dy/XXX899991H165d+fOf/8w999zD008/7djn0Ucf5f7772fSpEn069ePgoICFi9ejJubm4nJRcyTWVDKz/syALhSrVzOC6vVwtBOwQBsO5xLdlGZyYlEREROLSWriIWbD1OJjeKkLfR0zcTdRWdTAdxxcQzONgvrkrLZkJxldhwREWmhftlfNQmrS5g3QV7qmNAUhXi70TPSF8DRNldapyZdRPf29mb27NkkJydTXFzM/v37mTlzJi4u/5sdY7FYmDFjBqmpqZSUlLB06VI6depkYmoRcy3ankql3SCujS8xQZ5mx2k1ogM8aBvogd2A1fs1G11ERJq2xIxCvtxyhAq7gT8FHPt0OjaLYXasJiPUx41rL2gDwBs/HTA5jYiItEQHs4pIySrCaoGL2geaHUdOYnjnqtnoP6qI3qo16SK6iJy5r7YcAeCqXuGn2FMa2sUdggDYm15Aaq5O8xIRkaYpObOQr7ceodJuEBvsSVcOYVToLKrfmjS0PQBLEtIcp9qLiIg0BMMw+Hl/1RnkcW188XV3NjmRnMyIzlVnnq/Ye4zySrvJacQsKqKLtCCpuSWORUnGqB/6eRfk5Uq38KrVulfuO4ZhaEafiIg0LUdzi/l661HsBnQI8WJ0j3CsaLyqS4cQb+K7hmIY8M8Vmo0uIiIN50BGIWl5pTjbLPRrp17oTV3PSD8CPF3IL6lgQ3K22XHEJCqii7QgX289gmFAn7b+tPFzNztOq3RR+wBsVgtHckpIzCg0O46IiIhDRkEpX2yuauESHeDB5d3DsFktZsdq0u4dXjUb/fNNh0nL01lmIiJy7gwDftlX1QK0d5Qfnq5OJieSU7FZLQw7vg7a8t3pJqcRs6iILtKCfLG5qpXL2N6ahW4WbzdnLojyA+DnfZnY7ZrdJyIi5sstLmfhpsOUVtgJ93Xjyp7hKqCfhj5tA+jb1p+ySjv//jnR7DgiItICJBdaySoqw83JSp+2/mbHkdM0osvxvui71Be9tVIRXaSF2H+sgG2Hc3GyWtTKxWR92/nj5lz1xmjn0Tyz44iISCtXXF7J55sOU1hWSaCnC1f3isDZpo8Bp+ueYbEAvP9rCvkl5SanERGR5szi7MaOXBsAfdsF4OpkMzmRnK6hHYOwWmB3Wj6Hc4rNjiMm0LtnkRaiehb6kI5BBHi6mJymdXN1stH/eF+7NYlZVGjhERERMUml3eDbrUfJLS7H282JsRe0wc1ZH9jPxKVdQogN9iS/tIIP1h40O46IiDRjvhfdQEmlBV93Z3pF+podR86An4cLF0ZXnTnwo1q6tEoqoou0AIZh8MXmwwCMvaCNyWkEIC7SFy9XJwpKK9h2ONfsOCIi0goZhsGPe9I5lFOMs83C1b0i8FLf1TNmtVqYNLSqN/pbqxIpq9CX4yIicuZSCyrw6X8tUDX5zUlnhTU71S1dlu9SEb010rtokRZgy6FckjOLcHe2Ed811Ow4DS4hIaFJHONMOFmtDIgJYNmudNYlZdOjja9OnRcRkfNqy6Fcth+uait2eY8wgrxcTU7UfI29oA0vfr+H1LwSvtpyhHF9Is2OJCIizcw7W/KwOLkQ4manfZCn2XHkLAzvHMwL3+3m532ZlJRX6uy+VkZFdJEWoHoW+sjuoS1qZe+8rKoFO26++eYGO2ZBQUGDHetUuob7sD45m9zicrYczKHv8RYvIiIijS05s5AVe6rG0Ys7BNE+yMvkRM2bq5ON2we34/nFu3lzxQGuu7ANFosWZhURkdOzam8Gaw+XYtgr6eVfqTGkmeoW7kOojytpeaWsTcxiaKdgsyPJedRyqm0irVRFpZ2vthwF4JreLWtB0eKCqtlzY+75K5179jmnYyWs/YlF81+mpKSkIaKdFpvVwoCYAL7fmcaG5GziIn21cIyIiDS6vOJyFm1PxQC6hntzYbSf2ZFahAkD2vLqD/vYnZbPj3uOMaJziNmRRESkGSivtDP9qx0A5G/8Bp92l5ucSM6WxWJhROcQPlh3kOW701VEb2VURBdp5lYfyCSjoBR/D2eGdGyZ/4EHRrQlsmP3czpGWsr+BkpzZjqHebMuKYvsonI2p+QwoH2gKTlERKR1qLQbfLv9KKUVdkJ9XLmkS4hmuzUQX3dnbuofzb9WJfLGT/tVRBcRaaJSUlLIyMhokGMFBQURHR19Tsd4d3Uye9ML8HaxcHDVArhORfTmbPjxIvqPu4/x1FVmp5HzSUV0kWaq+o3Bv9fmANA/3JltWzaf1bHOd7/w1sRqsXBR+0AWbU9lY0oOvaL81DdNWrTKykqmTZvGe++9R2pqKhEREdx222088cQTjkKeYRg89dRT/POf/yQnJ4fBgwfz+uuv07FjR5PTizR/q/ZlkJZXiquTlSt6hONk1XocDemOi2N455ckfj2QxZaDVeO6iIg0HSkpKXTp2pXioqIGOZ67hwe7EhLOupB+MKuIF7/bDcCEOB+mlBY2SC4xz+AOgThZLSRmFJKSWUR0oIfZkeQ8URFdpBmqfmNQUlZB5OT3sLp6MH/6ZN48vPOcjns++4W3Jh1DvFjn5UJGQRkbU7IZFBtkdiSRRvPcc8/x+uuvM3/+fLp378769eu5/fbb8fX15YEHHgDg+eefZ86cOcyfP5+YmBimTp3KqFGj2LlzJ25ubiY/ApHma196AZsP5gAwslsoPu7O5gZqgSL83Lm6VwSfbTrMmysO8OqEC82OJCIiJ8jIyKC4qIgJj71AaHTsOR0rLWU/C557hIyMjLMqohuGwV8+30ZxeSX9YwKIb+/ClHNKJE2Bt5szF0b7szYpi5/2HuOWwLZmR5LzREV0kWao+o3ByMfeYDceeNgM7psyk7M9W9uMfuGtieX4bPSvtx5ly8FcLoz212x0abF++eUXrrnmGsaMGQNAu3bt+O9//8vatWuBqg8Ts2fP5oknnuCaa64B4N133yU0NJSFCxcyfvx407KLNGe5xeUsSUgD4MJoP9oHayHRxnL30PZ8tukwi7YfJTmzkLaBnmZHEhGR3wiNjj3nlqDn6pMNh1i5NwNXJyt/uy6OnIN7TM0jDWdopyDWJmWxYs8xbrlIRfTWQkV0kWYs2y0cSqBHdCBR59Br26x+4a1J+yBPAr1cyCwoY8uhHAbEqDe6tEyDBg3izTffZM+ePXTq1IktW7awatUqXnrpJQASExNJTU0lPj7ecRtfX18GDBjA6tWr6y2il5aWUlpa6ricl5fXuA9EpBmx2w0WbT9KWYWdcF83nfHUyLqG+zCsUzA/7TnGv1Ym8vTYHmZHEhGRJiY9v4SZ31S1TX3osk60D/Zi40GTQ0mDGdYphBe/38Pq/ZmUV9pxtql9Xmugv7JIM2XzDiS9pGrqedcwb5PTyKlYLBb6tQ0AYHNKDmUVdpMTiTSOxx9/nPHjx9OlSxecnZ254IILePDBB5kwYQIAqampAISGhta4XWhoqGNbXWbNmoWvr6/jJyoqqvEehEgzsy4pi7S8UlycrFzeIwybVQuJNrZ7hrYH4OMNB8ksKD3F3iIi0tpM+3IHucXl9Gjjw10Xx5gdRxpY9wgfAj1dKCitYGNyttlx5DxREV2kmfLsNgKw0MbPHT8PF7PjyGnoGOqFn7szJRV2th3ONTuOSKP46KOPWLBgAe+//z4bN25k/vz5vPjii8yfP/+cjjtlyhRyc3MdPwcPaiqPCEB2qYU1SVkAjOgcjI+b+qCfDwNjA4lr40tJuZ13VyebHUdERJqQb7cd5dttqdisFp4b1xMnzVJucaxWCxd3rDrzb8XeYyankfNFr2SRZsgwDLziLgWga7hmoTcXVouFvu38AdiYkk1FpWajS8vzyCOPOGajx8XFccstt/DQQw8xa9YsAMLCwgBIS0urcbu0tDTHtrq4urri4+NT40ektbM4ubAu0wnDqFrEunOo3hOcLxaLhUnHZ6O/uzqJ4rJKkxOJiEhTcCi7iMc/3QrAvcNi6R7ha3IiaSxDOwYD8NMeFdFbCxXRRZqhPVnlOAdGYbMYdAzRB+bmpEuYD95uThSVVbLjiHo6S8tTVFSE1Vrz7YXNZsNur/rSKCYmhrCwMJYtW+bYnpeXx5o1axg4cOB5zSrS3PkNu438CgueLjZGdAnBcrYrjMtZGd0jjKgAd7KLyvl4g86OERFp7Soq7Tz4wWbySiroFeXHn+I7mh1JGtGQTlUz0bcfziNDrd1aBRXRRZqh5YnFALRxt+PipJdxc2KzWujbtmo2+vrkbCrthsmJRBrWVVddxTPPPMM333xDUlISn3/+OS+99BLXXnstUDV788EHH2TmzJl8+eWXbNu2jVtvvZWIiAjGjh1rbniRZmRrWik+fa8GIL5rKO7ONpMTtT5ONit3XVw1G/2fKw/oDDMRkVZuzg/7WJ+cjberE3PHX6DFJlu4EG83uoVXnR27am+GyWnkfNArWqSZKSmvZNXBqiJ6Wy99WGuOuoX74Olio6C0goRUzUaXlmXu3Llcf/313HfffXTt2pU///nP3HPPPTz99NOOfR599FHuv/9+Jk2aRL9+/SgoKGDx4sW4ubmZmFyk+Sgqq+C19VVra8R4VdIuyNPkRK3XDX0j8fdw5mBWMd9sO2p2HBERMcmvBzJ55Ye9AMy8tgfRgR4mJ5LzYWinqpYuK9TSpVVwMjuAiJyZJTvTKCo3qMhLJzjKz+w4chacbFYujPZn5b4MNiXnMDzQ7EQiDcfb25vZs2cze/bsevexWCzMmDGDGTNmnL9gIi3I37/fQ3phJRW56fSM9DM7TquVkpJCRkYGo2Jc+WBHOX9ftJ02lalYz6KtTlBQENHR0Y2QUkREGltWYRkPfrAZuwE39Inkmt5tzI4k58nQTkHM+2k/K/ZmYLcbWK1qrdeSqYgu0sx8suEQAAXbf8DS4zqT08jZ6t7GhzWJWWQVlZHqqYFWREROz+aDObz9cyIAmd+/ilPcX01O1DqlpKTQpWtXiouKsLp60ubet0nJ9WDoTZMp3vvrGR/P3cODXQkJKqSLiDQz5ZV2/rhgI6l5JbQP8mTa1d3NjiTnSUpKCrasY7g5WcgoKOWz5Wtp7+981sfTF+pNn4roIs3I4ZxiVu6tOk2ocPsPgIrozZWrk424Nr5sSMlmT5762IqIyKmVV9p5/NOt2A0YGu3Gfw5sMDtSq5WRkUFxURETHnuB0OhYtufY2J0Hncb/hUtCKziTyehpKftZ8NwjZGRk6MOziEgz88w3Caw+kImni415t/TB01VlttbgxC/Tg8c9iUeH/tz51xfJW/PJWR9TX6g3fXp1izQjH647iN2AHsEuJGcfMTuOnKNeUb5sOphNRqkVlzCt3C4iIif35ooD7ErNx9/Dmdt7+/AfswMJodGxRHbsTkBZBft/TiKnzEplYDvaBapPvYhIS/fhuhTe+SUJgH/8rjedQr3NDSTnzYlfpuf7d2RLNnQadStDb/39WR1PX6g3DyqiizQTFZV2PlyXAsDIWA++MTmPnDtvN2c6h3qTkJqPT/9rzY4jIiJN2IFjBby8rGrBsiev6oavkW5yIjmRh4sTcZG+bErJYW1iFm0DPLCcRW90ERFpHjYkZ/HEwu0APHxZJ0Z2DzM5kZghNDqW2DaxbFmdTGaZlZCYrrg4Wc2OJY1ERXSRZmL57mOk5ZUS4OnCgDZuZseRBnJBtD8Jqfl4dB5MWkGF2XFERKQJMgyDJ7/YQVmFnaGdghnbuw2bNqmI3tRcGO3P1kO5HM0t4XBOMZH+HmZHEhGRRnAou4g/vLeR8kqD0T3CmDyig9mR5AwkJCQ06DH8PFzwdXcmt7icQ9lFtA/2OufjS9OkIrpIM/H+mmQAru8TibOt2OQ00lCCvV0JcbOTXmLj672FjB5qdiIREWlqvt2Wyqp9Gbg4WZl5TQ/NcG6ivFyd6B7uw9bDuaxNzFIRXUSkBcouLGPiv9dyLL+ULmHevHhDL6xWjcvNQV5W1fpyN998c4Mds6CgAIDoAA+2Hc4lOVNF9JZMRXSRZuBwTjE/7qn6D398vyhyDu4xOZE0pE4+laSXWFl6oJicojL8PFzMjiQiIk1EYWkFM7/ZCcC9w2KJDlRh9lw19Ay0E/Vp68/2I7kczC7mSE4xEX7u53xfIiLSNJSUV3LXu+vZf6yQcF833r69nxYSbUaKC/IAGHPPX+ncs885HSth7U8smv8yJSUlALQNPF5Ezyo655zSdOnVLtIMfLjuIIYBA9sH0j7Yi40HzU4kDSnE1aAs7QCEtuf9tSncN1ynA4qISJW5P+zjaG4JUQHu3Ds81uw4zVpjzkCr5uPuTNdwH3YcyePXA5lcd2Fkg92XiIiYp9Ju8MB/N7EhORsfNyfm39GfcF99UdocBUa0JbJj93M6RlrK/hqXI/3dsVogt7hcE+NaMBXRRZq4ExcU/f0ArdLcElkskLd+IUFjHua91clMGtIeJ5sWIxERae32pRfw1qoDADx5ZXfcnG0mJ2reGnMG2on6twsg4WgeB7OLOZRdpLYuIiLNXNXaJNv5fmcaLk5W/jWxH51Cvc2OJU2Iq5ONcF93DucUk5xVpCJ6C6UiukgTd+KCoiO7h5odRxpJYcJK2l/3Z47klrBkZxqj48LNjiQiIiYyDINpX+6gvNLgki4hxHcNMTtSi9EYM9BO5OPuTPcIX7YdzuXXA1mMu9BdfexFRJqxTxMKeH97ARYLvPy73vSPCTA7kjRBbQM9OJxTTEpmEb0i/cyOI41AUx1FmrjqBUVv6BOJq5NmoLVYleWMbF81U+3tX5LMzSIiIqb7bsf/FhN96qpuKsI2M/3a+WOzWDicU8yhbC0ILyLSXHnGxfP+9qrWXdOu6q7JTlKvtgFVn+cPZhdRaTdMTiONQTPRRc6jlJQUMjIyTnv/o/kV/Li7qn9nnGc+GzduBBpmQSxpekbFevD57kLWJmax80ge3SJ8zI4kIiImKK2o5NlvdwEwaUh72gZ6mpxIzpS3mzM92viw5VAuqw9kEumv2egiIs3N0WILgZffD8C9w2OZOKiduYGkSQv2dsXd2UZxeSVHc4vVzq0FUhFd5DxJSUmhS9euFBed/mrN/pfchU+/sRTtX8dVw6fX2v7bxaykeQv0sDG6Rxhfbz3K/F+SeO76nmZHEhERE7z7SzIpWUWEeLtqMdFmrG+7ALYfyeNobgkpWUX6MkREpBlJzSthTYYTFquFYW3deXRUZ7MjSRNnsViIDvRgd2o+yZlaE6UlUhFd5DzJyMiguKiICY+9QGj0qT8Ql9vh28POVBhw2UW9CRvxmWPbyRazkubt9sHt+HrrURZuPsxjo7sQ4KkFSUREWpOswjLm/LAXgD+P6oynq96uN1derk70bOPLpoM5rD6QSXSAh2aji4g0A7nF5Xy5+QiVhoXixI388for9P+3nJa2AVVF9JSsIgabHUYanN6Vi5xnodGxp7WY1ZaDOVQYx/D3cKZPXIcag/bJFrOS5u3CaH/i2lQtRvbBuhTuG97B7EgiInIezV66h/ySCrqF+zDuwkiz48g56tPWn22Hc0nLK+VARiGxwV5mRxIRkZMoKa/ki82HKS6vxM/ZTsrCWTg9NsbsWNJMRB/vi56eX0pRWQUeLiq7tiRaWFSkCTIMgy2HcgDoFemnb71bEYvFwm3He+39Z3UyFZV2cwOJiMh5sy89nwVrUgB44squ2Kwa/5s7T1cnLoj2A+CX/ZnYDS00JiLSVFXaDb7ZdpTsonK8XJ0YFFyBUabFoeX0ebo6EeRVdTZ5Stbpt/KV5kFFdJEmKCWriOyiclxsVrqGa3HJ1ubKXuEEeblwNLeEJTvTzI4jIiLnybPf7qLSbnBZt1AGxQaZHUcaSJ9of1ydrGQVlrHraL7ZcUREpA6GYfDDrnQOZRfjbLNwda8I3DWJWM5C9RooyZkqorc0KqKLNEGbD+YA0C3CBxcnvUxbG1cnGzf2jQLg/bUpJqcREZHz4ed9GfywKx0nq4Upo7uYHUcakKuzjX7tAgBYfSBTZ5mJiDRB65Oz2Xk0Dwswukc4wd6uZkeSZqrt8ZYuKVlFGDoDrUVRdU6kickuKiPp+DeWvSJ9TU4jZrmpfzQWC6zcm0FyZqHZcUREpBEZhsFzi3cBcPNFbWmvvtktTq9IX7xcnSgorWDr4Vyz44iIyAlSsor4ZX8mAMM6BRMT5GlyImnOwv3ccLJaKCqrJKOgzOw40oBURBdpYrYerPpg1S7QAz8PF5PTiFmiAjwY1ikY0Gx0EZGWbtH2VLYeysXTxcbkS7SgdEvkZLMyIKZqNvq6pCxKKypNTiQiIgAFJRUs3p4KQPcIH3pF+ZkbSJo9J6uVSH93AJKzNCGuJVERXaQJKS6vZMfRqiJ6bw3erd7v+0cD8PH6Q/qwLSLSQlVU2nnxu90A3DWkPUFeOn28peoW7oO/hzMl5XY2JueYHUdEpNWrtBss2n6U4vJKgrxcGH58EpPIuVJf9JZJRXSRJmTrwRzKKw2CvV2JPt5HS1qvS7qEEObjRlZhmWN2hIiItCwfbzjEgYxCAjxduHtoe7PjSCOyWi0MjA0EYNPBbApLK0xOJCLSuq3en8mR3BJcbFbGxIXjZFOJTBpG28Cqes7RnBLKtRZKi6H/IUSaiPJKO5sP5QDQt60/FovF3EBiOieblfH9jy8wukYtXUREWpriskpmL90DwOQRHfBydTI5kTS2DsFehPq4Ul5psC4py+w4IiKt1oFjBWxIyQbgsm6haqUqDcrP3RkfNycqDYND2cVmx5EGoiK6SBOx40geJeV2fN2d6aAFxeS43/WLwmqBNYlZ7EvPNzuOiIg0oHd+SSItr5Q2fu5MuCja7DhyHlgsFgbHBgGw7XAuucXlJicSEWl9CksrWJqQDlS1Ue0Qos/f0rAsFouju0BypvqitxQqoos0AZV2g43HvwW/MNoPq1Wz0KVKuK87l3YNBWCBZqOLiLQYucXlvP7jPgAevqwTrk42kxPJ+RIV4EF0gAd2A1YfyDQ7johIq2IYBst3pzv6oF/cIcjsSNJCOfqiZ6kvekuhIrpIE7A3LZ/8kgrcnW10C/cxO440MRMGVM1O/HTDIUrKtcCoiEhL8NaqRPJKKugU6sXYC9qYHUfOs0HHe6PvTs0np0yTJ8R8hw8f5uabbyYwMBB3d3fi4uJYv369Y7thGDz55JOEh4fj7u5OfHw8e/fuNTGxyNnZnZrP/mOFWC0wslsYNk1gk0YSFeCOxQI5ReXk6cyzFkFFdBGTGYbB+uSqWegXRPtpMROpZWjHYCL93ckrqeDrrUfNjiMiIucop6iMf69KBOCh+E76AN8Khfq40fF4+4AdOToLQcyVnZ3N4MGDcXZ2ZtGiRezcuZO///3v+Pv7O/Z5/vnnmTNnDvPmzWPNmjV4enoyatQoSkpKTEwucmaKyyr5ae8xAAbEBBLs7WpyImnJXJ1shPm4AZqN3lKoWidisqTMIjILy3CxWenZxtfsONIEWa0WbupfNRt9wZpkk9OIiMi5+ufKAxSUVtA13IdR3cPMjiMmGRgbiMUCqSVWXCO7mx1HWrHnnnuOqKgo3n77bfr3709MTAwjR44kNjYWqJr0M3v2bJ544gmuueYaevbsybvvvsuRI0dYuHChueFFzsDKfccoKbcT6OVCn7b+p76ByDlqG6i+6C3JWRXR27dvT2Zm7f59OTk5tG/f/pxDibQm65OzAIhr44urs2YiSd1u7BuFk9XCppQcdhzJNTuONEMau0WahqzCMt7+OQmAh+I7ah2UVszfw4XuEVVt/PyGTcQwDJMTSVNzvsbuL7/8kr59+3LDDTcQEhLCBRdcwD//+U/H9sTERFJTU4mPj3dc5+vry4ABA1i9enW9xy0tLSUvL6/Gj4hZDmUXkXA0H4BLu4ToLDA5L9oGVPVFP5hdjN2ucb65O6sielJSEpWVtfvylpaWcvjw4XMOJdJaHMkp5khOCTaLhd7RfmbHkSYs2NuVUT2qZiu+rwVG5Sxo7BZpGt5YsZ+iskp6tPHhsm6hZscRkw2ICcRqMXCL7MaGo6Vmx5Em5nyN3QcOHOD111+nY8eOfPfdd9x777088MADzJ8/H4DU1FQAQkNr/p8VGhrq2FaXWbNm4evr6/iJiopqsMwiZ8JuN/hxT1Ubl7g2voT7upucSFqLEB9X3JyslFXYSc1T+6vmzulMdv7yyy8d//7uu+/w9f1f64nKykqWLVtGu3btGiycSEu34Xgv9C7h3ni5ntHLUVqhCf2j+WbrURZuOsxfruiKp54zcho0dos0HcfyS3n3l6q2XA9f1gmLRbPgWjsvVyc6eNnZk2/j/e353HWFobMT5LyP3Xa7nb59+/Lss88CcMEFF7B9+3bmzZvHxIkTz/q4U6ZM4eGHH3ZczsvLUyFdTLH9SC6ZBWW4OVkdCzuLnA9Wi4XoAA/2pBeQnFlEhJ++wGnOzqgCM3bsWAAsFkutwdTZ2Zl27drx97//vcHCibRkmQWlHMio6oulfmxyOgbGBhIT5EliRiHfbD3Kjf30IUROTWO3SNPxxk/7KS6vpFeUHyM6h5gdR5qITj6V7MooISnHk2+2HeWqXhFmRxKTne+xOzw8nG7dutW4rmvXrnz66acAhIVVnQ2ZlpZGeHi4Y5+0tDR69+5d73FdXV1xddXCjWKu0vJKfj1Q1UL1ovaBuKmFqpxn0YHHi+hZhQzUlzjN2hm1c7Hb7djtdqKjo0lPT3dcttvtlJaWsnv3bq688srGyirSomxIqZqF3iHYC38PF5PTSHNgsVi4oW8kAB+uP2hyGmkuNHaLNA3H8kv5z6+ahS61udogb+3nALy0ZA/llXaTE4nZzvfYPXjwYHbv3l3juj179tC2bVsAYmJiCAsLY9myZY7teXl5rFmzhoEDBzZYDpHGsC4pm+LySgI8XOjRxvfUNxBpYNEBVYuLpuWVUlxeu0WXNB9n1RM9MTGRoKCghs4i0mrkl5SzO7VqUZM+7TQLXU7f9RdGYrNa2JCczb70fLPjSDOisVvEXG+tSqS0wk7vKD+GdtRrUWrKW/8FPq5WEjMK+XTDIbPjSBNxvsbuhx56iF9//ZVnn32Wffv28f777/Pmm2/yxz/+EaiayPHggw8yc+ZMvvzyS7Zt28att95KRESEY9a8SFNUUFLB5kM5AFzcMUiLiYopvN2cCfSsmjh5MKvI5DRyLs66oe6yZctYtmyZ45vxE/373/8+52AiLdnGlBzsBkT6uxPm42Z2HGlGQnzcGNE5mKUJ6Xy0/hB/uaKr2ZGkGdHYLWKO3KJy3js+C33yiA6ahS61GGXFjOviydtb8nl52V7GXtBGLQcEOD9jd79+/fj888+ZMmUKM2bMICYmhtmzZzNhwgTHPo8++iiFhYVMmjSJnJwcLr74YhYvXoybmz7LSNO1JjGTSrtBhK8b7QI9zI4jrVh0oAeZhWUkZxbRKdTb7Dhyls5qJvr06dMZOXIky5YtIyMjg+zs7Bo/IlK/0krYcSQXgL7qhS5n4ca+Vb3QP9t4SKd8y2nT2C1invmrkygoraBLmDeXdFEvdKnbqA6ehPu6cTS3hAVrUsyOI03A+Ry7r7zySrZt20ZJSQkJCQncfffdNbZbLBZmzJhBamoqJSUlLF26lE6dOjVoBpGGlF8OO47mATCoQ5C+wBZTtT3e0iUlqwjDMExOI2frrGaiz5s3j3feeYdbbrmlofOItHgHCqyUVxoEe7s6emOJnIkRXUII8nIlo6CUZQnpXN4jzOxI0gxo7BYxR2FpBf/+ORGA+0Z0wKpTyaUeLjYLf7q0I49/to3Xlu/jd/2i8HI96xOHpQXQ2C1y9nbl2jAMaBfoQRs/d7PjSCvXxs8dm9VCQWkFWYVlBHpp0eXm6KzelZWVlTFo0KCGziLS4lmcXdmXX3Vqbt+2/vo2XM6Ks83K9X0imffTfj5cl6IiupwWjd0iZyclJYWMjIyzvv0XuwvIKSon3MtGnG95AyZr2hISEprEMZqbcX0ieWPFARIzCnl7VSL3X9rR7EhiIo3dImfHyS+Mg0VVjRcuah9ochoRcLJZifRzJzmriOSsIhXRm6mzKqLfddddvP/++0ydOrWh84i0aF5xl1Fmt+Dr7kyHYC+z40gzdmPfqiL6T3uOkZpbQpiv+lHKyWnsFjlzKSkpdOnaleKis1wEyuZMmz+8hZNXANs+eonus35mV0IC0dHRDRu0CcnLOgbAzTff3GDHLCgoaLBjNXXONisPXdaJB/67iTdXHODmi9rif3wxMml9NHaLnB2fi27AwELbQA9CtQaZNBHRgR5VRfTMIi6MVmvf5uisiuglJSW8+eabLF26lJ49e+Ls7Fxj+0svvdQg4URakgq7gU//awG4MNpPp3PLOWkf7EX/dgGsTcrikw0HmXyJZqrJyWnsFjlzGRkZFBcVMeGxFwiNjj3j2x/It7Ip2wl3m8HVV4zkv88tISMjo0UX0YsLqvrPjrnnr3Tu2eecjpWw9icWzX+ZkpKShojWbFwZF87rP+4n4Wge81bsZ8poLSLeWmnsFjlz6YUVePW4FIABMQEmpxH5n7YBHqwEDucUU1Fpx8l2VstUionOqoi+detWevfuDcD27dtrbFN7CpG6/XywGCffUFytBt3CfcyOIy3Ajf2iWJuUxUfrD3HfcPXZlZPT2C1y9kKjY4ns2P2MblNpN1iyOgmooH9sCEElVa1cGqpFSVNvdRIY0faMf2e/lZayv4HSNC9Wq4X/u6wTd727nv+sTuaeobEEaDZ6q6SxW+TMfbWnEIvNiWBXO+G+6oUuTUeApwterk4UlFZwOKeYtoGeZkeSM3RWRfTly5c3dA6RJutce6ECGIbBB5uzABsdvCv1jaPU6UwLIhGVdtydLKRkFfGf738lLqSqr1pQUFCLnuUoZ0djt8j5tSctn7ySCtydbfSI8GHP+s1Aw7Y5gdbV6qQ1ubRrCN0jfNhxJI+3Vh3gkVFdzI4kJtDYLXJmcorKWHqgGIDOPpUmpxGpyWKxEB3gwc6jeSRnFqmI3gw1+eXeDx8+zGOPPcaiRYsoKiqiQ4cO/D979x0dV3Wuf/x7Zka99y5ZrnLBvWCbjqmGhNiBkNgJ7SbcxPTchJ9DuNxwkxjSgNwQCIQACd1gOoaAAdNcJXfLvag3q4zaqMyc3x+yFRzcNdKZ8nzWmrWiKUfPibC2znv2fveTTz7J5MmTgZ7i5D333MPjjz9OY2MjM2fO5JFHHmHYMLU2kL7rcy/UgyIGTyb1yv/B09FGmtHhpXQSKPrSPzbxogXEjL+EHz/8Cgfe+j0AEZGRAd9zV0TEl5mmyZp99UBPCzeH3ebVNicQvK1OgoVhGNxy/jBu/EchT3+xn++fOZj4SM1GFxE5lmdW7qfDbdJZvZvUnByr44h8RV7SwSJ6fd9qTGKNUyqin3vuucdcPvbhhx+ecqAva2hoYObMmZx77rksXbqUlJQUdu7cSULCvxrw/+Y3v+GPf/wjTz/9NPn5+dx9991cdNFFbN26lfBwbSAhfdPXXqiHLK92UNcBzeuX4j53uhcTSiDoS2GlvsPgo2qIHXMO375oJg1lu3n2/p8EfM9dOXkDNXaLCOyqbaGhrYswh43TsuMOe80bbU4geFudBJMLRqZRkB7Dtqpm/vb5Pu64YLjVkWSAaewWOXGuLjdPfbEfAOfqVzGm3GJxIpGvyk2MxADqWztpdnUREx5y3M+I7zilIvqhvmyHdHV1sX79ejZv3sw111zjjVwA3H///eTk5PDkk0/2Ppefn9/7v03T5MEHH+TnP/85X//61wH4+9//TlpaGq+99hpXX32117JIcDuVXqiHVDS2U1dShoGH5rWvg4rochSnUljJMk02tpZwoKWT5qgs0lQ3l6MYqLFbJNiZpsnafQ0AjMuOJ8xhtziR+CubrWc2+o+eLeLJz/dywxn5xEXoYjuYaOyWYNLXNqrL9rZR19JBXIiH/ds+BVREF98THmInLTacKqeL/QfaGJMVd/wPic84pSL6Aw88cMTn/+d//serfRnfeOMNLrroIq688kqWL19OVlYWP/rRj/j+978PwN69e6mqqmLWrFm9n4mLi2PatGmsWLHiqEX0jo4OOjr+1VLD6XR6LbPIvyvc33MhnUoT+1rqLU4jgcYwDEZnxPLJzjq2Vjg5M+H4n5HgNFBjt0iwK2top6a5A4fNYHxOvNVxxM9dPDqdYanR7Kxp4ekv9nHL+WpZGUw0dkuw8EYb1fRrHiQsfSj73n8aPG79GxGfNSgpkiqni30HWlVE9zNe7Yk+f/58pk6dyu9+9zuvHG/Pnj088sgj3HHHHfzsZz9jzZo13HLLLYSGhnLNNddQVVUFQFpa2mGfS0tL633tSBYtWsQvfvELr2QUOZYDLR3sqWsFIJt6VlucRwJTQUYsn+86QE1zB41RR1/yK3Ik3h67RYJdUUnPzfNRGbFEhGoWuvSNzWZw8/nDuOX5dTzx2V6umzlIS79FY7cEnL62Ue1pcRmCDZNpI/N5fxXaM0R81qDkKFburaekvo1ujweHzWZ1JDlBXi2ir1ixwqt9yD0eD5MnT+bXv/41ABMmTGDz5s08+uijfVq+tnDhQu64447er51OJznadEL6QeHBC+khKVFE1HZanEYCVUSIncEpUeysaWFviwZgOTneHrtFgtmBlg72HeiZRTchN97aMBIwZp+WwUMf7GB3bSv/WLmfH50z1OpIYjGN3RKoTrWN6pYtVUAzI9JjSSLtuO8XsVJqTBiRoXbaOt2UN7STlxRldSQ5QadURJ8zZ85hX5umSWVlJWvXruXuu+/2SjCAjIwMRo0addhzI0eO5JVXXgEgPT0dgOrqajIyMnrfU11d/ZX+cV8WFhZGWFiY13KKHEmzq4vtVc0ATM5LpLzW4kAS0EZnxrKzpoXSVhuGI9TqOOKDBmrsFglm60obgZ6b5/GR+l0s3mG3GfzonKH8ePEGnvx8H9fPzCc8RKscgoHGbpHja+vsZmd1T+uWsdnxlB+9KYGITzAMg0FJUWytdLLvQJuK6H7klKYsxsXFHfZITEzknHPO4Z133uGee+7xWriZM2eyffv2w57bsWMHeXl5QM8mo+np6Sxbtqz3dafTyapVq5g+XZs3irXWlzbiMSE7IYL0OM0Ukf6VmxhJTLiDLtMgYph+/8lXDdTYLRKsWju62VbZc/N8Up42qBDv+tr4TDLiwqlt7uC1deVWx5EBorFb5Pi2VjhxmyapMWG67ha/kZ/cUzjfd7D9r/iHU5qJ/uSTT3o7xxHdfvvtzJgxg1//+tdcddVVrF69mscee4zHHnsM6Ll7c9ttt/HLX/6SYcOGkZ+fz913301mZiZXXHHFgGQUOZKObjeby3s2rJ2Uqwtp6X+GYTAqI5ZVe+uJGXeh1XHEBw3U2C0SrDaUNeI2TTLiwsmIi7A6jgSYELuNG87I55dvF/PYp3u4anIONpv2QQl0GrtFjs1jmmwsbwJgbLY2aBT/kZMYgc2AxvYuGtrU+tdf9KknemFhIcXFxQCMHj2aCRMmeCXUIVOmTOHVV19l4cKF3HvvveTn5/Pggw8yb9683vf89Kc/pbW1lR/84Ac0NjZyxhln8O6776pHnFhqc7mTTreHxKhQ8pIirY4jQaKniH6A8LxxVLV0Wx1HfFR/j90iwajL7WFjWc9F/ETdPJd+cvXUXB5atpM9ta18UFzNhaPTrY4kA0Rjt8iR7atrpdnVTbjDxoi0GKvjiJywMIedzPgIyhra2VfXSorVgeSEnFIRvaamhquvvpqPP/6Y+Ph4ABobGzn33HN54YUXSEnx3o//sssu47LLLjvq64ZhcO+993Lvvfd67XuK9IXbY7L+YE/UibnxGIZmCcnAiI0IIS3cpNplsGxvG5eeZXUi8SUDOXaLBJutFU46uj3ERYQwOEV9LaV/RIc5+O7pefz549385ZM9KqIHAY3dIsd26Ab2qMxYHPZT6lYsYpn85CjKGtrZe6CVlGir08iJOKXfMjfffDPNzc1s2bKF+vp66uvr2bx5M06nk1tuucXbGUX8yo7qZlo6uokKtTMiXXfDZWANinYD8OHedrrdHovTiC/R2C3SPzym2buh6ITceGy6eS796NoZgwi12yjc38DaffVWx5F+prFb5Oga2zrZX98GwGlZauUi/if/4Iai5Q3tdOnS3S+cUhH93Xff5c9//jMjR47sfW7UqFE8/PDDLF261GvhRPyNaZoUlTQAMC4nHodNd8NlYGVGmLhbG2lwefh4e63VccSHaOwW6R+7a1toau8iPMTGqIxYq+NIgEuNDWfOxCwA/vLJHovTSH/T2C1ydFsre/Ygy0uMJD4y1OI0IicvPjKEuIgQPCbUuDQJwx+cUoXP4/EQEhLyledDQkLweHT7RIJXSX0bdS2dhNgN3Q0XS9gMaNm8DIAX1pRanEZ8icZuEe8zTZOi/Y0AjM2KJ0RLyWUAfP+swRgGvL+1ml01LVbHkX6ksVvkyDymSXFlMwCjM3UDW/yTYRi9s9Gr2vU3pD84pZ/Seeedx6233kpFRUXvc+Xl5dx+++2cf/75Xgsn4m+KShoBGJ0ZR3iI3dowErRaNr4PwEfba6h2uixOI75CY7eI91U2uahyurAbBmOzdfNcBsaQlGhmjUwD4Kkv9lqcRvqTxm6RIyupb6Olo2dD0XztRSJ+bFByJABVLhXR/cEp/ZT+9Kc/4XQ6GTRoEEOGDGHIkCHk5+fjdDr5v//7P29nFPELtc0dlNS3YQATcuKtjiNBrLu+jJHJIbg9Ji8XllkdR3yExm4R7zvUwq0gI4aoMIfFaSSYXD8zH4BXCstpau+yOI30F43dIke2taKnlcuI9Bi1UBW/lpUQQYjdwOU2CE0bYnUcOY5T+ms/JyeHoqIiPvjgA7Zt2wbAyJEjmTVrllfDifiTQxfSw1KjiY346rJLkYE0a3AkxXVNvLS2lB+ePQSbTT3Wgt1Ajt3l5eXceeedLF26lLa2NoYOHcqTTz7J5MmTgZ4WGPfccw+PP/44jY2NzJw5k0ceeYRhw4Z5PYtIf3G2d7GnthXQzXMZeKcPTmREWgzbq5tZvLaU/zhzsNWRpB/oulvkq9q73L3j7+hMrQIT/+aw2chNjGR3bSsRw6ZZHUeO46Ru2X344YeMGjUKp9OJYRhccMEF3Hzzzdx8881MmTKF0aNH8+mnn/ZXVhGf1ezqYkd1T0+2iXkJFqcRgenZ4cSEOdh/oI2Vew5YHUcsNNBjd0NDAzNnziQkJISlS5eydetWfv/735OQ8K/fjb/5zW/44x//yKOPPsqqVauIiorioosuwuVS+yHxHxvLmzCBnIQIkqLDrI4jQcYwDK6dOQiAv6/Yj9tjWhtIvErX3SJHt72qGbdpkhITRkqMxl/xf4NTogGIHDbd4iRyPCdVRH/wwQf5/ve/T2zsVzduiIuL48Ybb+QPf/iD18KJ+Iv1pY14TMiOjyAtNtzqOCKEO2x8bXwmoA1Gg91Aj933338/OTk5PPnkk0ydOpX8/HwuvPBChgzpWZ5omiYPPvggP//5z/n617/O2LFj+fvf/05FRQWvvfaa13KI9Kcut4ct5U0AjNcsdLHIFeOziIsIoaS+jY+21VgdR7xI190iR3eolcvoDG0oKoGhZ3NRk9DUfKpbuq2OI8dwUkX0DRs2cPHFFx/19QsvvJDCwsI+hxLxJx3dbjaX9wzkmoUuvuTqKbkAvLu5iobWTovTiFUGeux+4403mDx5MldeeSWpqalMmDCBxx9/vPf1vXv3UlVVddhS9Li4OKZNm8aKFSuOetyOjg6cTudhDxGrbK9qxtXtITbcwaBkbWgm1ogItXP1lBwAnvpin7VhxKt03S1yZDXNLmpbOrAbBiPSY6yOI+IVEaF2ksN6VpStqeiwOI0cy0kV0aurqwkJOXqvZ4fDQW1tbZ9DifiT4spmOt0eEiJDGJQUaXUckV5jsmIZlRFLp9vDa+vLrY4jFhnosXvPnj29/c3fe+89fvjDH3LLLbfw9NNPA1BVVQVAWlraYZ9LS0vrfe1IFi1aRFxcXO8jJyfHa5lFToZpmqwvawRgXE48NkN7Toh15p+eh82Az3bVsfNga0Hxf7ruFjmyQ7PQh6REER5itziNiPdkRngAWF2h9pa+7KSK6FlZWWzevPmor2/cuJGMjIw+hxLxF6Zpsr60Eei5kDZ0IS0+xDAMrp7aU2h8YXUppql+qcFooMduj8fDxIkT+fWvf82ECRP4wQ9+wPe//30effTRPh134cKFNDU19T5KS9WmSKxR3tjOgZZOHDZDS8nFcjmJkcwa2XNT8ukV+6wNI16j626Rr+r2eNhW1XOzcFSmxl8JLBmRPUX0rbWdNLZpFbmvOqki+qWXXsrdd999xI2/2tvbueeee7jsssu8Fk7E1+070EZTexehDhsj0zWQi+/5+rgswhw2tlc3s6Gsyeo4YoGBHrszMjIYNWrUYc+NHDmSkpISANLT04GeWXZfVl1d3fvakYSFhREbG3vYQ8QKh26ej8yIJUyz4MQHHNpg9JXCcprau6wNI16h626Rr9pb10pHt4foMAc5iVoBLoEl2gGdNXvxmPCh9jnxWSdVRP/5z39OfX09w4cP5ze/+Q2vv/46r7/+Ovfffz8jRoygvr6eu+66q7+yivicQxfSozNjCXWc1D8nkQERFxnCpaf1zFR6cU2JxWnECgM9ds+cOZPt27cf9tyOHTvIy8sDID8/n/T0dJYtW9b7utPpZNWqVUyfrh3pxbc527vYU9sKwLjsOIvTiPSYPjiJEWkxtHe5WVJUZnUc8QJdd4t81faDs9BHpMeolZoEpLZdqwB4f2v1cd4pVnGczJvT0tL44osv+OEPf8jChQt7WwMYhsFFF13Eww8//JUepyKBqr61k5L6NgDGZcdbG0bkGL41JYdX15XzxvoKfj57FFFhJ/WrX/zcQI/dt99+OzNmzODXv/41V111FatXr+axxx7jscce6/2+t912G7/85S8ZNmwY+fn53H333WRmZnLFFVd4LYdIf9hY1oQJ5CRGkBQdZnUcEaDn9+p3puVyzxtbeH51CdfOGKQWg35O190ih2vvcrO3rucmdoE2FJUA1b5zJfEzrmb5jlpcXW71/fdBJ11JycvL45133qGhoYFdu3ZhmibDhg0jISGhP/KJ+KxDs9AHJ0cRF3H0jX9ErDYtP5H85Cj21rXy9sZKrpqiDRmDzUCO3VOmTOHVV19l4cKF3HvvveTn5/Pggw8yb9683vf89Kc/pbW1lR/84Ac0NjZyxhln8O677xIeHu71PCLe0uX2sLmipy3WeN08Fx9zxYQsFi0tZkd1C4X7G5g8KNHqSNJHuu4W+Zed1c14TEiODiVZN7ElQHVW7SIxwkZ9u5svdtdxXoFulvqaU56OmJCQwJQpU7yZRcRvdHS5Ka7s2Rl8fE68tWFEjsMwDL41JYf7lm7jhTUlKqIHsYEauy+77LJj9mo1DIN7772Xe++9t9+ziHjLtqpmOro9xEWEMCg5yuo4EoCKi4v79PkZWWF8uK+d/1u6nv+dPZTc3FwvJRMr6bpbhN4NRa3ah6yvv5+9fRwJXFMzw3l3dxvvb61WEd0HaU2/yCnYUumk22OSFBVKdkKE1XFEjmvOxCx+9952ikoa2VHdzPA0LYMUETlRpgkbDq5AG5sdp16s4lXO+loA5s+f36fjhGaOIOO7v+fj3U2MHD+Z4vVrVUgXEb/X1N5FZZMLAxg+wK1cvPX7+d+1tLR49XgSOKZmHSqi1/CrK0xsNv3N6UtURBc5SR7T7L2QHp8Tr56T4hdSY8I5f2Qq722p5vnVJdxz+WirI4mI+I3aDoMDrZ2E2A1GZ1gzC04CV3tLz+rG2TfexYixk075OKYJy6o8NBGKffB06urqVEQXEb+37eAK8JzESKIHeG8nb/1+PqR49XKWPv0QLperz8eSwDQ6JZSYcAd1LR0UljQwRe3ZfIqK6CInaf+BNpyubsIcNkZoUxPxI9+emst7W6p5ubCMn1w0gshQDQEiIidiT3PPxk4F6bGEaZMn6SdJmXlkD+vbTe4JkY18vL2W6PEX9W5GKSLir0zTpPhgKxcrNxT1xu9ngOqS3V5II4EsxG5wwcg0lqwr5+2NlSqi+xib1QFE/M3GskYARmXEEmLXPyHxH2cNSyE3MZJmVzdvbqiwOo6IiF+wRydS0d6z6mxsdpzFaUSOrSA9BrthEpqcx7a6LqvjiIj0SbWzg6b2Lhw2gyEp0VbHERkQl56WAcDSzZV4PLoh7ktUARQ5Cc72LvYdaAPgNF1Ii5+x2QzmTetZ1v33Ffs1Q01E5AREj70QE4PMuHCSo8OsjiNyTGEOOzmRHgD+uafN4jQiIn1TXNXTTmVIajShDpWvJDicOTyZmDAH1c6eli7iO/RbSOQkbCpvAiAnIYKEyFCL04icvCsn5xDqsLGlwsn6g739RUTkyNwek+jxFwO6eS7+Iz+6p4j+RWk7jW2dFqcRETk1bo/JjuqeVi4j1UZVgkiYw86sUWkAvL2x0uI08mUqooucILfHZEtFz51wXUiLv0qMCuWysT3Lw/6xcr/FaUREfNvayg4cMcmE2kyGpmoZufiHhFCTzpq9dHngDbVvExE/tf9AK64uD5GhdnISIq2OIzKg1NLFN6mILnKCdtW00N7lJirMzuBkXUiL//ru6XkAvLWxkoZWzVATETma93a1AjAoyoPDpj+bxT8YBrRsfB+Al9aWWpxGROTUbDu4oeiItBhsNsPiNCID68xhyUQfbOlSpJYuPkNXAyIn6FArlzGZcdg1iIsfG58Tz5isWDq7PSwu1MW1iMiR7KtrZX11J6bpIT/GbXUckZPSuvVjHDbYXO5k68GVlCIi/qKj282eup4b2QVq5SJBKDzEzqyRqQC8vUktXXyFw+oAIv7A2WlQ3tiOYfQU0UV8XXFx8TFfPyvDYHM5PLF8JxOjGrEZR78xlJycTG5urrcjioj4tOdWlwDg2ltEdN5Yi9OInBxPu5PJmeGsLHOxuLCUezJHWx1JROSE7appwe0xSYwKJSVGm3pLcLr0tAxeW1/B0k1V3D17lFZk+AAV0UVOwJ6WnkUbg5OjiA7XPxvxXc76WgDmz59/zPcZIWFk/+hpqonmzCt/gGtv0VHfGxEZybbiYhXSRSRouLrcLD7YBqN53Ttwjoro4n/OGxTByjIXr60rZ+ElIwl1aBGyiPiHbZU9rVwK0mMwjjHZRySQnTU8hegwB1VOF+tKG5iUl2h1pKCnaqDIcRgh4exv7bnoGJsdb20YkeNob+lZsj37xrsYMXbSMd+7ocHOrmYYf83/MCOl+4jvqS7ZzbP3/4S6ujoV0UUkaLyzqZKGti6SI23s373W6jgip2RCehipMWHUNHewrLiaSw5uUiYi4sucri7KGtsBGKFWLhLEDrV0eW19BW9vrFIR3QeoiC5yHFGjzqbbNIiPCCEnIcLqOCInJCkzj+xhx166HdXaya6V+6lstxGbPZzYiJABSici4tueWbkfgAsGR1JoeixOI3Jq7DaDOROzeXT5bhYXlqmILiJ+YfvBDUWz4yOIDdf1iQS3Qy1d3tlUyc9nj1RLF4tpTZ/IMZimScyESwE4LTtOS8kkoCREhfbeGDq0ca6ISLDbWuGkqKQRh83g/PxIq+OI9MmVk7MB+Hh7DdVOl8VpRESOzTShuLJnZW1Bhmahi5w1PIWY8J6WLqv21lsdJ+ipiC5yDDvquwhNG4LNMBmVEWt1HBGvO9SiaEuFk26PZluKiDy7qmcW+oWj00iMsFucRqRvhqREMykvAY8JS4rKrY4jInJMjZ0GDW1d2G0GQ1OjrY4jYrnwEDuXjulZSfbaOo3jVlMRXeQY3tvVBkBOpIfwEF1IS+AZnBxFVJid9i43u2parI4jImKplo7u3guU+dPyLE4j4h1XHZyNvriwFNM0LU4jInJ0h/YiG5ISRZhD198iAFdMyAJ69uxxdbktThPc1BNd5Cia2rr4oqxnQ5PB0ZqhK4HJZjM4LSuOlXvqWVfSyIi0GLUtEpGg9cb6Clo73QxOiWL6kCTWrSuxOpLIKSsuLgYg2+MhzG6wp7aVFz9YxfCk0JM6TnJysjYXF5H+Z7NT1tZTRB+ZrlXgIodMy08kIy6cyiYXH26r4VLtcWIZFdFFjuK19eV0uqGzZi8JOVlWxxHpN6dlxbFmXwM1zR1UNLnIitcGuiISnF5c01M0v3pKjm4oit9y1tcCMH/+/N7nki77MdGjz2XBb5+m/v1HT+p4EZGRbCsuViFdRPpVRP5EOjwGkaF2chO1J4nIITabwdfHZ/Ho8t28uq5cRXQLqYgucgSmafL86p4L6ZYN72FMvt7iRCL9JzLUQUF6DFsqnKwvaVQRXUSCUnGlkw1lTThsBnMmZlsdR+SUtbf0bMo3+8a7GDF2EgBV7Qaf10LS5Nl87+sXYjvBe0TVJbt59v6fUFdXpyK6iPSrqNHnAjA8LQbbif6SEgkS35jQU0T/eHsNDa2dJESd3Koy8Q4V0UWOYGNZE9uqmgm1Q+uWjwAV0SWwjc+JZ0uFk921LTS1dxEXEWJ1JBGRAfXimlIALhiVRnJ0mMVpRPouKTOP7GGjAcj0mKz7fC9tnW66EvIYkqIN+0TEd7R2eogcdjoAI9NjLE4j4ntGpMcwMiOW4konb2+qZP7p2rvHCtpYVOQIXji4nHt6dgSejlaL04j0v+ToMHISIzCBDWWNVscRERlQri43rx7cUPSqKTkWpxHxPpvNoOBgYaq40mlxGhGRw31R5sJwhBIb4iElRjeyRY7kGxMyAXjt4N+sMvBURBf5N60d3byxvgKAWYPV1kKCx4ScBAC2lDvp7NZmuiISPP65tZqm9i4y4sI5a1iK1XFE+kXBwY369tW14epyW5xGRORflu9vByA3yqM9SUSO4mvjsjAMWLu/gZIDbVbHCUoqoov8mzc3VNDa6WZwchSjktVnSoLHoKRIEiJD6HR72FLRZHUcEZEBc2hD0SsnZWNXH1YJUCkxYSRHh+I2TXZWt1gdR0QEgNL6NrbWdmKaHnIiNZFH5GjS48KZMSQJgNfXaza6FVREF/k3zx/sifqtKTm6Cy5BxTAMxufEA7ChrAmPaVobSERkAJTWt/H5rgMYBlw5Wa1cJLAdmo1eXKWWLiLiGw4VA137NxKpXftEjumK8VkALFlXjqnr9QGnIrrIlxRXOtlQ2kiI3WDupGyr44gMuJEZsYQ5bDS1d7G3TvsBiEjge2ltz83zM4Ymk5MYaXEakf41Ij0GA6hsctHY1ml1HBEJcqZpsqSop4jeuuVDi9OI+L5LTssgMtTO3rpW1uxrsDpO0FERXeRLXjw4C/2CUWkkR2tDEwk+IXYbp2XFAbCupNHaMCIi/cztMVm8tgyAqzQLXYJAdJij92bRtqpmi9OISLDbUNbEnrpWQu3QtmOF1XFEfF50mIPLx/ZsMPrCwXaEMnBURBc5yNXlZklRz4X01VNyLU4jYp2x2XHYDChvbKehUy2NRCRwfbKjliqni/jIEC4cnWZ1HJEBMTI9BugpomspuIhY6dWD19/TssIxO9stTiPiH66a0jPx451NlThdXRanCS7qOCVy0NLNlThd3WTFR3DG0GSr44hYJiY8hKGp0eyobmGXU/daRSRwHVqB9o0JWYQ57BanERkYQ1KjCdleQ1N7F5VNLjLjI6yOJCJByNXl5rX1FQCcMyiSZyzOI+ILiouLj/8m0yQn1kGps5uH31jJRUOjvvKW5ORkcnM1OdTbVEQXOej51f/aUNRm0+xbCW4TchLYUd1CaZsNe3Si1XFERLyutrmDD4qrgZ6xXyRYhNhtDE2JpriqmeIqp4roImKJf26tpqm9i8y4cMamhlodR8RSzvpaAObPn39C74+ZcgWJ5/0Hf3y7kJ/9/favvB4RGcm24mIV0r1MRXQRYHdtC6v31mMz4MrJ2lBUJD0unIy4cCqbXMRMutzqOCIiXrekqIxuj8m4nHgK0mOtjiMyoAoyYimuamZndQtnD0/BYdPKMxEZWIsPbuz9zUnZ2G2tFqcRsVZ7ixOA2TfexYixk477/g43vF1uEpYxjOsfeJX40H+1Z6su2c2z9/+Euro6FdG9TEV0Ef61nPvcEalkxGk2jgjA5LwE3txYScyES2nr8lgdR0TEa0zT5MWDF+9Xaxa6BKHshAiiwxy0dHSzt66VYakxVkcSkSBS1tDGZ7vqALhycg61+7ZZnEjENyRl5pE9bPQJvXdoVyU7a1qocyQzZlhqPycT0MaiInR2e3il8OCGolN1l07kkPzkKGIcJrawKP65u83qOCIiXrN2fwN7aluJDLVz+bhMq+OIDDibYTDi0Aajlc0WpxGRYLN4bRmmCTOHJpGTGGl1HBG/NDqzZyXltqpmut2a9DYQNBNdgt4HxdUcaO0kLTaMc0ekWB1HxGcYhsHwWDeF9Q5e3drE7DWFhNj7tl+ANjgREV9waAXa7NMyiA7Tn8MSnArSYyjc38C+A620d7mJCNHmuiLS/9wek5cPTmK7arJWg4mcqpzESGLCHTS7utlV26L2hANAVw0S9J5fXQLAlZNycNi1OEPky+I7quluDqc5Jonzr7+T1s3L+nQ8bXAiIlZrdnXx9sZKAK6eqot3CV7J0WGkRIdR29LBzupmxmbHWx1JRILA57vqKG9sJzbcwUWj062OI+K3bIbBqIxYVu2tZ3O5U0X0AaAiugS10vp/9WLTXXCRr+poddK89j0Szr2e/K/fxgX/uQDjFCeja4MTEfEFb26opL3LzZCUKCbmJlgdR8RSBekx1O7qYFuViugiMjBeOrgnyRUTsgjXChiRPhmdGcvqffWUN7ZT19JBcnSY1ZECmoroEtReWluKacIZQ5PJTVIvNpEjaV7/LsnnXktzt42uhDwGp0RbHUlE5JS9uKZnBdrVU3IxTvWuoEiAGJ4ew6e76qhsctHU3kVcRIjVkUQkgDW0dvLPLdWAJrGJeENMeAiDk6PYXdvKxrImzivQBqP9Sb0rJGh1uz29d8G1nFvk6MzONjIdrQAU7m+wOI2IyKkrrnSyoayJELvBNyZmWR1HxHLRYQ5yEiMA2FbltDiN+Ir77rsPwzC47bbbep9zuVwsWLCApKQkoqOjmTt3LtXV1daFFL/02vpyOt0eRmfGMiYrzuo4IgFh3MGVZNuqnHR0u60NE+BURJeg9fH2WqqdHSRGhXLBqDSr44j4tCxHKzYDKppcVDS2Wx1HROSUHNpQdNbINC13FTlo5MEeqtuqmjFN0+I0YrU1a9bwl7/8hbFjxx72/O23386bb77J4sWLWb58ORUVFcyZM8eilOKPTNPk2VU9q8G+NUWT2ES8JTshgsSoULrcJsWVzVbHCWgqokvQeuHgcu65E7MIc6gXm8ixhNk8vRuVFJVoNrqI+B9Xl5vX1pcDungX+bIhKdE4bAaNbV1UN3dYHUcs1NLSwrx583j88cdJSPjXnhFNTU088cQT/OEPf+C8885j0qRJPPnkk3zxxResXLnSwsTiT1bsOcCumhYiQ+18Y4JWg4l4i2EYjD24smNjWSO6H95/VESXoFTV5OLDbTUAfGuKNjgUORGT8noupnbXtlLf2mlxGhGRk/PPrdU0tnWRGRfOmcNSrI4j4jNCHTYGp0QBsK1SLV2C2YIFC5g9ezazZs067PnCwkK6uroOe76goIDc3FxWrFhx1ON1dHTgdDoPe0jwemblfgC+MSGLmHDtvyDiTSMzYgm122ho66KmQ3v+9BcV0SUoLV5biseEqYMSGZqqTRJFTkRiVCiDk3susjUbXUT8zaENRb85OQe7TRcXIl92aLXZjuoW3B5NYQtGL7zwAkVFRSxatOgrr1VVVREaGkp8fPxhz6elpVFVVXXUYy5atIi4uLjeR06OVgEFq2qni/cObij63el5FqcRCTyhDhsFGTEA7G5Wp4X+oiK6BB2Px+RFbSgqckoOzUbfVtlMS0e3xWlERE5MaX0bn+86gGHAlZOyrY4j4nPyEiOJCLHT3uWmpL7N6jgywEpLS7n11lt59tlnCQ8P99pxFy5cSFNTU++jtLTUa8cW//LcqhLcHpOpgxJ7b9qJiHcd2mC0st3AHqtVl/1BRXQJOp/tqqOsoZ3YcAeXnpZhdRwRv5IZH0FGXDhu02R9aaPVcURETshLB2+enzE0mZzESIvTiPgem81gRFrPDLZtVWq5EWwKCwupqalh4sSJOBwOHA4Hy5cv549//CMOh4O0tDQ6OztpbGw87HPV1dWkp6cf9bhhYWHExsYe9pDg0+X28PzqntVg8zULXaTfJEaFkp0QARjETLjU6jgBSUV0CTqHNhT9xoQswkO0zEXkZE0e1DMbfVNZEx1dbovTiIgcm9tjsnhtGaANRUWOZcTBZeB7alvp7PZYnEYG0vnnn8+mTZtYv35972Py5MnMmzev93+HhISwbNmy3s9s376dkpISpk+fbmFy8Qfvb62mprmD5OgwLh599JsuItJ343PiAYgefwntXRrLvc1hdQCRgVTX0sH7W3t6sV09VRuKipyK/KQokqJCOdDaycbyJqYMSrQ6kojIYUpKSqirqwOgsNJFldNFTKhBSkclRUVH79/774qLi/sroojPSYsJIz4yhMa2LnbVtjAqQ7OGg0VMTAxjxow57LmoqCiSkpJ6n7/hhhu44447SExMJDY2lptvvpnp06dz+umnWxFZ/MjfV+wD4NtTcwh1aB6nSH/KT44iymHSGh7Nx/vamTnN6kSBRUV0CSqvFJbR5TYZlxPPSF0YiJwSwzCYlJfAP7dWs760kQk58Tjs+oNYRHxDSUkJBSNH0t7W09c5+YqFRI2YSfnnr3L6//71lI7Z0tLizYgiPskwDArSY1i5p55tVU4V0eUwDzzwADabjblz59LR0cFFF13En//8Z6tjiY/bWd3Myj312G0G35mmSWwi/c1mGAyLcbO+wcFbO1v5fx4Tu82wOlbAUBFdgoZpmry4pqcn6re1nFukT4anxfDF7gO0dHRTXNXMaVlxVkcSEQGgrq6O9rY25t35W+KyhvBOeQgmMOfy2cTNPbn+kMWrl7P06YdwuVz9E1bExxSkx7JyTz2l9e3aQDzIffzxx4d9HR4ezsMPP8zDDz9sTSDxS099sQ+A8wtSyYiLsDaMSJDIi/JQWNFMJTF8UFzNRWqj5DV+NXXwvvvuwzAMbrvttt7nXC4XCxYsICkpiejoaObOnUt1dbV1IcVnrdpbz566VqJC7Vw+LtPqOCJ+zW4zmJgbD0Dh/gY8pmltIBGRf5OWO4SmiExMDNJjwxk9ehTZw0af1CMxPdvq0xAZUHERIWTEhQOwo6rZ4jQi4s8OtHTwcmHPniQ3nJFvcRqR4OGwQcv6dwF44tO9FqcJLH5TRF+zZg1/+ctfGDt27GHP33777bz55pssXryY5cuXU1FRwZw5cyxKKb7shYM7gn9tfCZRYVqEIdJXY7LiCHfYaGrvYneNWh2IiG8xTdhS0QTA6Ey1pRA5UQXpPRuMblMRXUT64JmVJXR0exibHcfUfO2hJDKQmovexGGD1fvq2VDaaHWcgOEXRfSWlhbmzZvH448/TkJCQu/zTU1NPPHEE/zhD3/gvPPOY9KkSTz55JN88cUXrFy50sLE4msa2zp5Z3PPRmJXT1EvNhFvCLHbGHtw9++1+xswNRtdRHzIgQ6DhrYuQuwGw9NirI4j4jeGpcVgM6C2pYOmTvVRFZGT5+py924o+v0zB2MY+l0iMpDcLfXMzOlpofTXzzQb3Vv8ooi+YMECZs+ezaxZsw57vrCwkK6ursOeLygoIDc3lxUrVhz1eB0dHTidzsMeEtheXVdOZ7eHkRmxjM1W72YRbxmfHY/DZlDT3EFpQ7vVcUREeu1r7fkzd1hqDKEOv/iTV8QnRITYyU+OAqCkTf92ROTkLSkq50BrJ1nxEVwyRv2YRazwteE9Y/k7myopb9S1ujf4/F9FL7zwAkVFRSxatOgrr1VVVREaGkp8fPxhz6elpVFVVXXUYy5atIi4uLjeR06ONpkMZKZp8sLqng1FvzM1R3fBRbwoItTe2yZh7f56i9OIiPQwQiMoO1j8UysXkZM34mBLl9JWG6C/nUXkxHk8Jn/9bA8A15+Rj8Pu82UnkYCUnxDCjCFJuD0mf9NsdK/w6d9mpaWl3HrrrTz77LOEh4d77bgLFy6kqamp91FaWuq1Y4vvWVfayPbqZsJDbHx9QpbVcUQCzsTcBAwDSuvbqXa6rI4jIkLUyLNwmwaJkaG9mySKyInLT4oi1GGj3W0QljPG6jgi4kc+3FbDntpWYsIdfGuKJiyKWOkHZw0G4PnVJTS0dlqcxv/5dBG9sLCQmpoaJk6ciMPhwOFwsHz5cv74xz/icDhIS0ujs7OTxsbGwz5XXV1NevrRlwyFhYURGxt72EMC16ENRWeflklseIjFaUQCT2xESG+/4cL9DRanERGB6LEXAT2z0LUCTeTkOew2hqVGAxA1+hxrw4iIX3n8055Z6N+Zlkt0mMPiNCLB7ezhKYzOjKWt081TX+yzOo7f8+ki+vnnn8+mTZtYv35972Py5MnMmzev93+HhISwbNmy3s9s376dkpISpk+fbmFy8RXNri7e3FAJwLen6i64SH+ZlNuz6fOumhYa23SHW0Sss6+xi7DM4RiYFGRoQ1GRU1VwsKVLVMEZdLq1ebiIHN+G0kZW7a3HYTO4bka+1XFEgp5hGPzwnCEAPL1iH60d3RYn8m8+fVswJiaGMWMOXz4YFRVFUlJS7/M33HADd9xxB4mJicTGxnLzzTczffp0Tj/9dCsii495bX0F7V1uhqZGMykvweo4IgErJSaMQUmR7DvQRmFJA+cXpFkdSUSC1LK9bQBkRphEhvr0n7oiPi0rPoJIu0lbWBRrK1zo6kpEjudPH+0C4GvjM0lXOzURn3DJmAwGJW1n34E2nl9dwn+cOdjqSH7Lp2ein4gHHniAyy67jLlz53LWWWeRnp7OkiVLrI4lPsA0TZ5ZsR+AedNytZxbpJ9NzksEoLiyWXe4RcQSri43y/e3AzAo2m1xGhH/ZhgGOVEegN5/VyIiR7O1wsn7W6sxDFhw7lCr44jIQXabwY1n98xG/+une+no1t/Ip8rviugff/wxDz74YO/X4eHhPPzww9TX19Pa2sqSJUuO2Q9dgseafQ1sr24mIsTOnInZVscRCXiZ8eGkx4bj9pisL220Oo6IBKGlmytp6TTpbqomLVztJ0T6Kjeq50K7qLKDem1IJiLH8KePdgJw2dhMhqREW5xGRL5szsQs0mLDqHK6eG1dudVx/JbfFdFFTtQ/VvbMQr9iQiZxEdpQVKS/GYbB5EE9bZM2ljfpDrcAcN9992EYBrfddlvvcy6XiwULFpCUlER0dDRz586lurraupASMJ5fVQpAy4Z/ogVoIn0XGwIdVbtwm/D2pkqr44iIj9pR3czSzVUA3HyeZqGL+Jowh53/OKOnjcujy/fg9miyyalQEV0CUk2zi3c39/yhP//0PIvTiASPwclRJESG0NntYXO50+o4YrE1a9bwl7/8hbFjxx72/O23386bb77J4sWLWb58ORUVFcyZM8eilBIodtW0sHpfPTYDWjZ9YHUckYDRuvVjAM1cE5Gj+tOHuzBNuGRMOsPTtKm3iC/69rRc4iJC2FvXyrsHb3rJyVERXQLSS2tK6XKbTMyNZ3RmnNVxRIKGYRi9m/iuK2mg2+OxOJFYpaWlhXnz5vH444+TkPCvjZ2bmpp44okn+MMf/sB5553HpEmTePLJJ/niiy9YuXKlhYnF372wugSAyRlhuFsOWJxGJHC0bV2OzYDC/Q3sP9BqdRwR8TG7a1t4a2MFADdpFrqIz4oOc3DNjEEA/PnjXZimZqOfLBXRJeC4PSbPreq5kP7udM1CFxloBemxRIc5aO10s62q2eo4YpEFCxYwe/ZsZs2addjzhYWFdHV1HfZ8QUEBubm5rFix4qjH6+jowOl0HvYQOcTV5eaVojIALhgSaXEakcDibm1gbGooAK+tq7A4jYj4moc/2oXHhFkj0zSBTcTHXTtjEBEhdrZUOPlkZ53VcfyOiugScD7cVkNFk4vEqFAuGZNhdRyRoGO3GUzIiQegaH8DHt3hDjovvPACRUVFLFq06CuvVVVVERoaSnx8/GHPp6WlUVV19GWFixYtIi4urveRk5Pj7djix97bUkVDWxeZceGMTwuzOo5IwDkrLwKA19aXa+aaiPTaV9fK6+t7bq7dcr5moYv4usSoUK6e2nMd9eePdlmcxv84rA4g4m2HNhS9anIO4SF2i9OIBKcxWXGs3ldPQ1sXe2pbGZoabXUkGSClpaXceuutvP/++4SHh3vtuAsXLuSOO+7o/drpdKqQLr2eP9jK5aopOdhtLRanEQk807LCiQhpYW9dKxvKmhh/8Ga5iPiXkpIS6uq8M/s0OTmZB784gNtjcu6IFMZmx3vluCLSv75/5mCeWbmfVXvrKdxfz6S8RKsj+Q0V0SWg7Ktr5ZMdtRgGzJuWa3UckaAV6rAxNjuONfsaWLu/niEpUVZHkgFSWFhITU0NEydO7H3O7XbzySef8Kc//Yn33nuPzs5OGhsbD5uNXl1dTXp6+lGPGxYWRliYZhjLV+2pbWHlnp4NRa+anEPVnmKrI4kEnIgQGxeNTuO19RW8UlimIrqIHyopKaFg5Eja29q8crzonBEkf+f3APz4whFeOaaI9L/M+Ai+MSGLl9aW8X8f7uKp66ZaHclvqIguAeXZVT2z0M8ZnkJOonqiilhpfE48RSWNVDs7KG9stzqODJDzzz+fTZs2HfbcddddR0FBAXfeeSc5OTmEhISwbNky5s6dC8D27dspKSlh+vTpVkQWP/fCmlIAzh2RSmZ8BEdvCiQifTF3Ujavra/g9fXl3DV7pFZ8iviZuro62tvamHfnb0nLHdKnY1WX7OafO5swgdljMxiTpV7oIv5kwblDeaWonI+317KhtJFxujl+QlREl4Dh6nLz0tqeTcW0oaiI9SJDHYzKiGVTeRNr9zcwWZPRg0JMTAxjxow57LmoqCiSkpJ6n7/hhhu44447SExMJDY2lptvvpnp06dz+umnWxFZ/FhHt5uXC3vG/m9P1Qo0kf40Y0gymXHhVDS5eH9rNZePy7Q6koicgrTcIWQPG92nYxzoMIgkBJsBd1ww3EvJRGSg5CVF8fXxmSwpKuf/PtzJX6+ZYnUkv6CNRSVgvLmhgqb2LrITIjh7eKrVcUQEmJSXgAHsP9BGY6dhdRzxEQ888ACXXXYZc+fO5ayzziI9PZ0lS5ZYHUv80D+3VFPf2kl6bDjnjEixOo5IQLPbDOZOygZg8cGbVyISfEzTZEtjz0qUcwdFMCRFex+J+KObzh2KzYAPimvYXN5kdRy/oCK6BIxnDm4oOm9aHnabinUiviAuIoRhaT1/WO9wasgJVh9//DEPPvhg79fh4eE8/PDD1NfX09raypIlS47ZD13kaL68oajDrt8xIv3tmweL6J/urKWySa3aRIJRaUM7tR02zO4urhoVY3UcETlFg1Oi+drBVWV/XLbT4jT+QVcbEhDWlzayoayJULuNqyZnWx1HRL5kUl4CAKVtNhxxaRanEZFAsbeulS92H8Aw4FtTcqyOIxIU8pKimJqfiGnCkqJyq+OIyAAzTZMvdtcB0Lz+HVKitDeCiD+76byhGAb8c2s1WyucVsfxeSqiS0D422d7Abh8XCZJ0WEWpxGRL0uNCSc3MRIwiJnyDavjiEiAeOHgLPRzhqeQFR9hcRqR4HHloZYua0sxTdPiNCIykHbVtFDt7MBumDStWGx1HBHpo6GpMcw+LQOAP32k2ejHoyK6+L3Kpnbe2VQJwHUzB1kbRkSOaPLB2ejRY2fh7PBYnEZE/J2ry82La0sB+M40bSYuMpAuPS2DyFA7+w60sXZ/g9VxRGSAdHs8fL77AADDYzx42hqtDSQiXnHzecMAeGdTFdurmi1O49tURBe/948V++n2mEzLT2RMVpzVcUTkCLITIogP9WALCWfprlar44iIn3tjfQWNbT2biZ9XoM3ERQZSVJijd9ba4oM3s0Qk8G0qa6KpvYvIUDvDY91WxxERLxmRHsMlY3r2p/rTR7ssTuPbVEQXv9be6ea5g8u5rz8j3+I0InI0hmEwPKZnBvrSXW20d+oPbxE5NaZp8tQX+wD47unaTFzECldO7tmH4O2NlbR1dlucRkT6m6vLzeq99QBMH5yEQ5UkkYBy03lDAXhrYwW7alosTuO79KtP/Nqr68ppbOsiJzGCWSO1YaGIL8uK9NDdWIWzw8PLhZq5JiKnpnB/A1srnYQ5bFw1WRuKilhhyqAE8pIiae10886mKqvjiEg/W7OvHle3h6SoUEZlxFodR0S8bHRmHBeMSsM04WHNRj8qFdHFb5mmyd8+79lQ9NoZ+ZqJJuLjbAY417wGwOOf7sXt0WZkInLynl6xH4ArxmeREBVqcRqR4GQYBt+c+K8NRkUkcDnbu9hQ2gTAzKHJ2HTdLRKQbjnYG/319eXsrVML1iNxWB1A5FR9urOOXTUtRIc5uGpydu/zJSUl1NXVeeV7FBcXe+U4ItKjZdP75M7+ISX1bby7uYrZYzOsjiQifqTa6WLpwc3EvztdG4qKWGnupGz+8MEOVu2tp+RAG7lJkVZHEpF+8PnuOtymSU5CBIP071wkYJ2WHcd5Bal8uK2Ghz/axe+uHGd1JJ+jIrr4rUOz0K+cnE1MeAjQU0AvGDmS9rY2r36vlhb1hBLxBrOrg0uHRvHS1hYe+2Q3l56WjmFoNouInJjnVpXQ7TGZnJegzcRFLJYZH8EZQ5P5dGcdLxeWcseFI6yOJCJeVuV0saO651r4jGHJ+rtdJMDdfN5QPtxWw6vryrnlvGG6Qf5vVEQXv7S9qpmPt9diGHDtjEG9z9fV1dHe1sa8O39LWu6QPn+f4tXLWfr0Q7hcrj4fS0R6XDI0ktd3tLGhrImVe+qZPiTJ6kgi4gc6uz29m4l/70tjv4hY55uTsvl0Zx2vFJVz26zhavMgEkBM0+SznT0rvEemx5AaE25xIhHpbxNyEzhreAqf7Kjlzx/v4r65Y62O5FNURBe/9NgnewC4eHQ6eUlRX3k9LXcI2cNG9/n7VJfs7vMxRORwceF2rpyczTMrS3jsk90qoovICVm6uZLa5g5SY8K4eHS61XFEBLhodDox4Q7KG9v5fHcdZw5LsTqSiHjJ3rpWyhvbsdsM/b0u4odOtT3xJdkePtnRs+fJ2SkuRg9KJzc318vp/JOK6OJ3KhrbeX19OQA3nt332eYiMvD+44zBPLuqhI+217K9qpkR6TFWRxIRH2aaJo9/2nMDfd60PEIdNosTiQhAeIidb0zI4u8r9vP86hIV0UUChNtj8tmunlnoE3Lie9uniojvc9bXAjB//vxTPkbqVfcSkT+R+Yueof2TJ9hWXKxCOiqiix/622d76faYnD44kfE58VbHEZFTMCg5iotHp7N0cxWPfbKH31+lTUtE5OhW7DnA5nInYQ6bNhQV8THfnprL31fs559bqqlpdqnlg0gA2FLRRENbFxEhdiYPSrA6joichPYWJwCzb7yLEWMnndIxDnQYfFwNMeMuxLlyMXV1dSqioyK6+Jmmti6eP9gPVbPQRfzbD84azNLNVbyxoZz/umg4GXERVkcSER91qI3blZOzSYwKtTiNiHzZyIxYJubGU1TSyOK1ZSw4d6jVkUSkDzq63azcUw/AtPxEwhx2ixOJyKlIysw75TbH2cDe7nL2H2gjbsbV3g3mx7QWVvzKM6v209rpZkRaDOcM13JREX82ITeBqfmJdLlNnvx8n9VxRMRHfXkz8f84Y7DVcUTkCL4zrWeFyAtrSvB4TIvTiEhfFO5voL3LTXxkCGOy4qyOIyIWOX1wz14IUaPPpaK52+I0vkFFdPEbri43T36+F4Abzx6MYRgWJxKRvvrPs3sKYs+tKsHp6rI4jYj4okO90C8alc6g5K9uJi4i1rtsbAax4Q5K69v59GAfZRHxP82uLopKGgE4Y2gydpuuuUWCVXpsOBkRHgybnZe2NFsdxyeonYv4jSVF5dS1dJIZF87l4zKtjiMiXnDO8FSGpUazs6aF51aV8J9q0yQiX1LtdPVuJv6DszULXcQqxcXFx33PmTmhvL2zmz+/t4GYlsQjvic5OVk9VUV82Io9B3B7TDLjwhmsG9ciQW9UnJvKdhuflrjYVdPM0NQYqyNZSkV08Qvdbg+PfbIbgBvOHEyIXYsoRAKBzWbwg7MG85OXN/Lk53u5buYg9V0UkV5Pfr6PLrfJ5LwEJuZqYzORgeasrwVg/vz5x31vSFIOmf/xCCtL25h69rdwt9R/5T0RkZFsKy5WIV3EB9U2d1Bc2TPb9MxhKVr5LSLEh5q0bf+CyBEzePCDnfzpOxOtjmQpFdHFL7y5sYJ9B9pIiAzh6ik5VscRES/6+vgsfvfP7VQ7O3h9fQVXTda/cRGBlo5unl21H+jZiFhEBl57ixOA2TfexYixk477/o+rPRzosHPhz/7GyDjPYa9Vl+zm2ft/Ql1dnYroIj7os4OtmIanRpMeF35CnzmRVSoDeRwR8b7Gz58jcsQM3t5UyU1VTgrSY62OZBkV0cXnuT0mf/pwFwD/ceZgosL0n61IIAl12LhuZj73Ld3G45/s4ZsTs7Gp/6JI0Ht25X6aXd0MTo5i1sg0q+OIBLWkzDyyh40+7vsmRzt5b2s1pR3hnD90EDbNZBXxCyX1bZTUt2EzYMbQ5OO+/2RWqZyMlpYWrx5PRPquq3YfM7LD+aLMxUMf7OSR+ce/qR6oVI0Un/fOpkp217YSFxHC96bnWR1HRPrBd6bl8qcPd7GzpoWPd9RwXoEKZiLBrK2zm7980rOh6H+eM0Q31kT8xNDUaJbvqKXZ1c2+ulYGp0RbHUlEjsM0TT4/OAt9bFY8cREhx/3Mya5SOZ7i1ctZ+vRDuFyuPh9LRLzvqtHRrCh3sXRzFVsqmhidGWd1JEuoiC4+zfOlWejXz8wnJvz4A7qI+J/Y8BC+My2Xxz7Zw6PL96iILhLk/rFiP/WtneQlRTJnQpbVcUTkBDnsNkZnxVG4v4H1ZY0qoov4gZ01LdQ0dxBqtzEl/+T2HznRVSrHU12yu8/HEJH+kxsXwuVjM3ljQwUPfrCTx7832epIllARXXxCSUkJdXV1X3l+ZVk726ubiQwxmBjdRFFR0TGPo15qIv7rupmDePLzvazeW8+6kgYmaBNBkaD05VnoN507FIc2ExfxK2Oz4ija30BpfTsHWjpIig6zOpKIHIXbY/LF7gMATMyNJzJUJSIRObJbzh/GWxsreH9rNZvKmjgtO/hmo+s3pFiupKSEgpEjaW9r+8prGdc+RGjaECo+fo6zfvnsCR9TvdRE/E9GXARfG5fFK0VlPPbJnqDutSYSbL58M/21bS3Ut3aSHm1nEDUUFdWe1LF0Q13EWrERIQxOiWJ3bSsbypo4ryDV6kgichRbKppoau8iIsSuCSwickxDU6O5YnwWS9aV88AHO/jbtVOsjjTgVEQXy9XV1dHe1sa8O39LWu6Q3ucr2gxW1IXgMEy+e9VcQr8997jHUi81Ef/2g7MG80pRGe9uqWJfXSuDkqOsjiQi/ezLN9ONkDCy/vNv2CPj2Pzi75h697JTPq5uqItYZ3xOPLtrWymudDJjSBLhIXarI4nIv+ns9rBqbz0A0/ITCXVo5ZeIHNvN5w/j9Q0VfLitJihXj6uILj4jLXdIbz810zT5dE0p0MH43EQGn8AO4aBeaiL+bkR6DOeOSOGj7bU8/ukefvWN06yOJCL97Ms30xvjh7G50UGUw+Qb/7kAm7HgpI+nG+oi1suKjyA5OpS6lk62VjiZmBdcF9ki/mB9aSNtnW7iIkIYkxV8bRlE5OTlJ0cxZ0IWiwvL+MP7O/jHDdOsjjSgVEQXn7SrtmdzkxC7wcQgu7MlEux+cNYQPtpey8uFZdx+wXCS1UtVJCgkZQ+hsDoMcDNjWDq5mbGndBzdUBexnmEYjMuJZ1lxDRvKGhmfG291JBH5krbObgr3NwAwfXASdpthcSIR8WVfbpd4Xlo3r9rg0511PP3uCk5LPfHr9eTkZHJzc/sj4oBQEV18jsc0Wbm7Z1nZhNwEIkK1/FMkmJw+OJFx2XFsKGvib5/t5acXF1gdSUQGwI5mG+1dPTPiCtJjrI4jIn1UkBbD5zvrcLq62VvXim6Ji/iONfsa6HR7SIkJY3hatNVxRMRHOet79iaaP3/+Yc8nzPpPYiddxv977guq/vFfJ3y8iMhIthUX+20hXUV08Tnbq5qpb+sk3GFjomatiAQdwzBYcO5QfvCPQv6+Yj83njWEuMgQq2OJSD+yRyeyw9lz03zmkCRsmhEn4vccdhujs+Io3N/A+tJGpunemIhPaO2GTZVNQM+Yaxgac0XkyNpbnADMvvEuRoyd1Pu8yw3vVpiEZRZw9W9fJTPSPO6xqkt28+z9P6Gurk5FdBFvcHtMVu45AMCkQQmEOTQLXSQYzRqZxoi0GLZXN/P0in3ccv4wqyOJSD+KP3M+btMgIy6coamaEScSKMZmx1FU0kBZQzsjwlSoE/EFWxvtuE2TnIQI8pKirI4jIn4gKTOvdw/DQyaG1LFmXwPb26OYPDYXWxDckNP2y+JTNlc04XR1ExlqZ1x2vNVxRMQiNpvBj84dAsDfPt9La0e3xYlEpL/sbewi6rRZAJw5LFkz4kQCSGx4CMMO3hjb7tSlp4jVQlIGUdLW829x5tBki9OIiD+blJtAmMNGfWsn26qarY4zIPSXjPiMbg+s2dvTC33qoERC7PrPUySYXTY2k0FJkTS2dfHcqhKr44hIPzBNk6c3ODEMG9mRbjLiIqyOJCJeNjkvEYCyNhuO+HSL04gEt/izrwEMhqVGkxYbbnUcEfFjYSF2Jg9KAGDlngN0ezwWJ+p/qlKKz9jdYqO1001suIMxWXFWxxERi9ltBj88p2c2+mOf7sHV5bY4kYh428fba9lY3YnZ3cWYeP0bFwlEKTFh5CVFAgaxU75hdRyRoLWlpoPIIVMwMJk+JMnqOCISAMZlxxMVaqfZ1c3mcqfVcfqdiujiE2zhMWxv6ul/Pm1wEnZtKCYiwDcmZJMZF05tcweLC8usjiMiXtTl9vDrd4oBcBa+QZR26hEJWJPzemaqRZ02i0aXbpiJDDTTNPn7xp52C/nRHhIiQy1OJCKBIMRuY1p+z0251Xvr6ewO7NnoKqKLT4ibeTVdpkFydCgF6TFWxxERHxHqsHHj2T2z0R/9eDdd7sAelEWCyROf7WVnTQsxoQZNK16yOo6I9KOs+AgSQz3YQsJ4e2er1XFEgs57W6rZWd+Fp9PFyDjdyBIR7xmVGUtcRAjtXW7WlTZYHadfqYgulqts7iZmwmwAzhyWEhQ7+orIifvWlBySo8Mob2znFc1GFwkIpfVtPPjBDgCuHR+L2aGimkggMwyD4bE9hbt3d7XR7OqyOJFI8Oh2e/jNe9sAcK59jXC7xYFEJKDYbQbTB/fMRi/a30h7Z+DeqFMRXSz3zKZmDLuDtHAPuYmRVscRER8THmLv7Y3+fx/uCvglYiKBzjRN7nptM64uDzOGJHFOnjYTFQkGmREmXQdKae0yeX61NgwXGSgvF5axp7aVmFAD56pXrI4jIgFoeFo0KdFhdLo9rN5Xb3WcfqMiuliqcH89K8pcmB43p2lDMRE5innTckmN6ZmN/tLaUqvjiEgfvLGhgk921BLqsPGrb5yGoRVoIkHBMKBp1RIA/vrpXm0YLjIA2jvdPHBw5dc3R8VgdrZbnEhEApFhGMwc2jMbfWNZI41tnRYn6h8qootlTNPkV2/3bCjWsukD4kJNixOJiK8KD7Gz4NyhAPzpw1268BbxU41tndz75lYAbj53KPnJURYnEpGB1LrlI5Ij7dQ0d/DMyv1WxxEJeE99sY9qZwdZ8RFcPESrvkWk/+QlRZGXGInHhM921Vkdp1+oiC6WeWdTFUUljYTZDZo+fcbqOCLi466emkNGXDhVThcvaBm4iF9a9M42DrR2Miw1unfTYBEJIp5urhoVDcAjH++mtaPb4kAigauxrZM/f7wLgB9fOJwQu1Z+iUj/OmNYMgawu7aV8obAW/miIrpYor3Tza/f6ZmFfkVBFO7WwN7BV0T6Lsxh56bzemajP/zxbs1GF/Ezy4qrefFgO6ZfzzmNUIf+DBUJRucMimBQUiQHWjt56ot9VscRCViPfLybZlc3BekxfH18ltVxRCQIJEeHMTozFoBPdtZimoHVcUJXL2KJR5fvpryxnaz4CK4YEW11HBHxE1dOyiErPoJaLQMX8Su1zR389OWNANxwRj5TBiVanEhErOKwGdw6axgAj32yB6ery+JEIoGnorGdJw/epLrz4gLsNs1CF5GBcfrgJELsBjXNHWyvbrY6jlc5rA4gwaesoY1Hl+8G4GeXjiSsu9LiRCLiL0IdNm45fyh3vrKJRz7ezdVTc4kO01Am4stM0+SnL2/gQGsnBekx/OSiEVZHEhGLfW1cFg9/tJtdNS088elebr9guNWRRALKgx/soLPbw9T8RM4ZkWJ1HBEJIlFhDiYPSmTF7gN8vusAQ1Oicdj/NYe7uLjYa98rOTmZ3Nxcrx3veFR5kAH363eK6ej2cPrgRC49LZ1161REF5ETN2diNo8u38PeulYe/2SPLrxFfNw/Vu7no+21hDpsPHT1BMJD7FZHEhGL2W0Gd1wwnB89W8QTn+3l2hmDSIgKtTqWSEDYWd3My4VlAPy/SwowDM1CF5GBNTEnnk1lTbR0dFNU2sjUQYk462sBmD9/vte+T0RkJNuKiweskK4iupyykpIS6upObsfdTdUdvLOpHpsB3xpqY926dV69CyUivs8b/+a/OTyE39bB45/uYd60XFJjw72QTES8bWd1M796u+ff/MJLChiRHmNxIhHxFRePTmdkRizFlU4e/WQ3Cy8ZeVKfP5VrkWMZ6NlsIv3lN+9tx2PCRaPTmJibYHUcEQlCDruNmUOTeG9LNWv31TM6I5b2FicAs2+8ixFjJ/X5e1SX7ObZ+39CXV2diuji20pKSigYOZL2trYT/5BhI+O6/yM0JY/GtW8x575HD3u5paXFyylFxJd4+85z5vf+QFvGcB5ctpNff+M0rxxTRLynvdPNzc+vo6Pbw9nDU7h2xiCrI4mID7HZDH58wXD+4+9refLzfcyflkdOYuQJffaUrkWOY6Bns4n0h7X76nl/azU2A35yUYHVcUQkiI1Ii2FdSSM1zR2s3HOA+IPPJ2XmkT1stJXRTpmK6HJK6urqaG9rY96dvyUtd8gJfWan08bGRgehNpN5X7uQ0G9cCEDx6uUsffohXC5Xf0YWEYt5885zdcluXn72CdLn3c+La0q5fmY+Q1O1SbGIrzBNk4VLNrKtqpnk6FB++82xWk4uIl9x/shUpg9OYsWeA9y3dBsPz5t4Qp87lWuRY7FiNpuIt5mmyf3vbgPgqsk5+ttYRCxlGAZnDUvh5aIytlQ4GU+Y1ZH6TEV06ZO03CEndAepxdVN8cp9gMkZw9IYnB3X+1p1ye7+CygiPsdbd547yrYwNTOM1RUd/ObdbTz2vcleSCci3vD0F/t4bX0FdpvBn74zUS2XROSIDMPgvy8fxew/fsrbmyr57p4DnD446YQ/f6LXInJsixYtYsmSJWzbto2IiAhmzJjB/fffz4gR/9oI2uVy8eMf/5gXXniBjo4OLrroIv785z+TlpZmYXL5smXFNazZ10CYw8Zts7RnkIhYLyshgqEp0eyqbWE3/j9e2I7/FpG++2RnLV1uk/TYcMZkxVodR0QCxPyxsdhtBv/cWs2affVWxxERYM2+en75pT7oJ1MQE5HgMzIjlu9M65n9/Ys3t+L2mBYnCj7Lly9nwYIFrFy5kvfff5+uri4uvPBCWltbe99z++238+abb7J48WKWL19ORUUFc+bMsTC1fJnbY/Kb93pmoV83M5/0ON28FhHfcOawZOw2AydRRI48y+o4faIiuvS7fQda2VnTggGcV5Cq5dwi4jXZsQ6umpwDwK/eLsajC28RS9U4Xfzo2SK6PSaXjc3ghjPyrY4kIn7gjgtGEBvuoLjSyUtrS62OE3Teffddrr32WkaPHs24ceN46qmnKCkpobCwEICmpiaeeOIJ/vCHP3DeeecxadIknnzySb744gtWrlxpcXoBWFJUxo7qFuIiQvjh2X1vcSQi4i2xESFMGdSzyXHCuTfQbfpvTVBFdOlX3W4PH2/v2UxwfE48KTH+3wNJRHzL7bOGERVqZ31pI68UlVkdRyRoubrc3PhMIbXNHQxPi+b+ueqDLiInJjEqtLf9xO/e247T1WVxouDW1NQEQGJiIgCFhYV0dXUxa9as3vcUFBSQm5vLihUrjnqcjo4OnE7nYQ/xPleXmwfe3wHAj84ZQlxkiMWJREQONyk3gXA6ccQksb8rxuo4p0w90aVfrdnXQFN7F9FhDi3nFpF+kRobzq2zhvHrd7Zx/7vbuHB0OnERungQOZKSkhLq6uq8cqzk5OTeDfg8HpPbX1zPupJG4iJCeHT+JKLC9GemiJy4707P49lV+9ld28oD7+/gnsvV69wKHo+H2267jZkzZzJmzBgAqqqqCA0NJT4+/rD3pqWlUVVVddRjLVq0iF/84hf9GVfo2YekoslFRlw418wYZHUcEZGvcNhtDKaareRQ3h1FfWsniVGhVsc6aT59daMNTvxbfWsnhfsbADhreDKhDi18EJH+ce2MfF5cU8ru2lYe/EAX3iJHUlJSQsHIkbS3tXnleBGRkWwrLiY3N5f7393G0s1VhNptPPbdSQxOifbK9xCR4BFit3HP5aP53t9W89QX+7h8XCYTcxOsjhV0FixYwObNm/nss8/6fKyFCxdyxx139H7tdDrJycnp83HlXxpaO/nTR7sAuOOC4YSH2C1OJCJyZIm00LZzFZHDpvHx9hq+MSHL71at+nQR/dAGJ1OmTKG7u5uf/exnXHjhhWzdupWoqCigZ4OTt99+m8WLFxMXF8dNN93EnDlz+Pzzzy1OH9xM02RZcTVu02RQUiRDdTEtIv0o1GHjf742mu8+sZq/r9jPt6bkUJCuTYxFvqyuro72tjbm3flb0nL71i+1umQ3z97/E+rq6lheYfKXT/YA8NsrxzJNK89E5BSdNTyFOROzWFJUzk9f3sjbt5xBmENFwYFy00038dZbb/HJJ5+QnZ3d+3x6ejqdnZ00NjYeNhu9urqa9PT0ox4vLCyMsDC18+xPf/xwJ82ubkZmxDJnYvbxPyAiYqGGZY8RNWwqpQ3t7KppYViaf7V28eki+rvvvnvY10899RSpqakUFhZy1lln9W5w8txzz3HeeecB8OSTTzJy5EhWrlzJ6aefbkVsATaVN1HR5CLEbnDuCG0mKiL978xhKVwyJp2lm6v479e38OIPTtfvHpEjSMsdQvYw76zWKKx0seizzQD814XD+fr4LK8cV0SC139fNopPdtSxq6aFP324ix9fOOL4H5I+MU2Tm2++mVdffZWPP/6Y/PzDN4WeNGkSISEhLFu2jLlz5wKwfft2SkpKmD59uhWRg8rRWrFVtXTzjxU9+49dOczBhvXrjnmc4uLifsknInKiupuqyXW0sL87hk921pGXFOVXXSt8uoj+7052g5OjFdE7Ojro6Ojo/VobnHhXs6uLz3cdAGDGkGRi1ZtYRAbIXbNH8tH2GlbvreeNDRUq6In0o5DUwfx+RSMeE66anM2Cc4daHUlEAkB8ZCj/+/XR/PDZIv788W4uHpPO6Mw4q2MFtAULFvDcc8/x+uuvExMT09vnPC4ujoiICOLi4rjhhhu44447SExMJDY2lptvvpnp06dr4lo/O1YrtuSv30lUwZm07ynkhvvvOeFjtrS0eDOiiMhJyQlppsGRgNPVzZp99cwcmmx1pBPmN0V0bXDiH0zT5KPttXS6PWTEhTM2W3/wisjAyU6IZME5Q/n9+zv437eKOXt4CvGR/rdhiYiva+uG1G/eg6vb5IyhyfzqG6dp5YeIeM0lp2X0ri776csbeW3BTELs/jNTzd888sgjAJxzzjmHPf/kk09y7bXXAvDAAw9gs9mYO3fuYXuRSf86Wiu2Ax0GH1eHACaXzxhL3DlLjnus4tXLWfr0Q7hcrn5MLCJybHYDzh6WwpsbKykqaWBkRqzfbDLqN0V0bXDiH3ZUt7C3rhWbAecXpGLTBbWIDLAfnD2Y1zdUsKumhXvf2sofrhpvdSSRgNLR7ebzWgeOmCRyYh38ef5EFbdE5ISdaEuJb+Z7+HSHwZYKJ3c/9ylXjf5X31S1pfAu0zSP+57w8HAefvhhHn744QFIJP/uy63YTNPki8IywMWojDhGj0o7oWNUl+zux4QiIicuPzmKQUmR7DvQxofbapg70T82GfWLIro2OPEP7Z1ulu/o6ck2ZVAiSdH6/1hEBl6Yw879c8fyzUe/YElROV8bl8k5I1KtjhU0Fi1axJIlS9i2bRsRERHMmDGD+++/nxEj/tXT1uVy8eMf/5gXXnjhsNlsaWkndhEo1nF7TN7ZVIWzy0Z3Sz0/n11AbLjatonI8Tnre64T5s+ff8KfiRp1DsmX/xfPb2riwZ8toKNsy2Gvqy2FBKPdta1UNrlw2AymazNvEfFDhtGzf+I/Vu6nvLGdLZVOxvhB6zafLqJrgxP/8tH2Gtq73CRFhTJ5UILVcUQkiE3KS+C6Gfn87fO93PXqZt67/Syiw44+5B1tw6ZTlZycTG5urteO50+WL1/OggULmDJlCt3d3fzsZz/jwgsvZOvWrURFRQFw++238/bbb7N48WLi4uK46aabmDNnDp9//rnF6eVYelq21VBS34bdMKl8+RekXP+S1bFExE+0t/TsQzX7xrsYMXbSCX9uzQE3Ja12Bn3vPmaldxFmV1sKCV5uj8lnu3r+Zp2Ym0B0uE+XdEREjio2IoTpg5P4dFcdn+2sY3ByFJGhvv07zafTaYMT/7GjupmdNS0YBlwwKg2HTcu6RcRa/3XRcN4vrqK0vp37l27jf68Yc8T3HWvDplMVERnJtuLioCykv/vuu4d9/dRTT5GamkphYSFnnXUWTU1NPPHEEzz33HOcd955QE/P1ZEjR7Jy5UqN3z5szf4GtlQ4MYBpyd3sqdaycBE5eUmZeb1tKU5Ear6HF9aU0NDWxWZXPF8bl6m2FBK0NpU30dTeRUSInUl5mrgmIv5tfE4826qbqW3u4JMddVw85uhdRXyBTxfRtcGJf2jt6Oaj7TUATMlLJC023OJEIiIQGergvjljmffXVfxj5X4uG5vBtCMseT3ahk2nqrpkN8/e/xPq6uqCsoj+75qamgBITEwEoLCwkK6uLmbNmtX7noKCAnJzc1mxYsVRi+gdHR10dHT0fu10Ovsxtfy7bVVOVuw+AMDZI1JIai+3OJGIBItQh41LxmTw4tpS9h1oY11po9WRRCzR0eVm9d56AE4fnEioQxPXRMS/2WwG5xek8uKaUrZXNzMyI4a8pCirYx2VTxfRtcGJ7zPNnjYuri4PydGhTM1PtDqSiEivmUOTuXpKDi+sKeXHizew9NYziTlK/+Yvb9gk3uHxeLjtttuYOXMmY8b0rASoqqoiNDT0sL1MANLS0npXnB3JokWL+MUvftGfceUoyhva+WBrz83yibnxjMuOp2yniugiMnBSYsI4a1gyH22v5fNddYwhwupIIgNu5Z562rvcJEaF+kXvYBGRE5EWG864nHjWlzby4bYa5p+eR4jdN28S+mYq8RulbTZ217ZiM+DCUenYbb6/m66IBJe7Zo8kJzGCsoZ27nl9y/E/IF6zYMECNm/ezAsvvNDnYy1cuJCmpqbeR2lpqRcSyvE0tHXy1sYK3KbJ0JRozhiabHUkEQlSp2XFMSw1Go8JxWRjj9GGihI8mjoNNpQ3AnD28BRsuu4WkQAyfXAS0WEOnK5uVh1cceOLVESXU2aPSWJ9gx2AqYMSSYkJsziRiMhXxYSH8MBV47EZsGRdOa+v1wzagXDTTTfx1ltv8dFHH5Gdnd37fHp6Op2dnTQ2Nh72/urqatLTj94DLywsjNjY2MMe0r/aO928vr4CV7eH9NhwLhqdhmHool1ErGEYBrNGppEUHUoXDlLm3I3b1O8kCQ4bGuyYJgxJiSI3MdLqOCIiXhXqsHHuiBQAikoaqG3uOM4nrKEiupwSj2mSdOntdHkMUmPCmDxIbVxExHdNHpTITecNA+Dnr22mrMF7m4jK4UzT5KabbuLVV1/lww8/JD8//7DXJ02aREhICMuWLet9bvv27ZSUlDB9+vSBjitH0e3x8NbGCprau4gNd3D5uAwcPrqsUkSCR6jDxtfGZuKgm7D0oWzvjD+hFqAi/ixyxExqO2zYbQZnDUuxOo6ISL8YnBLN0JRoTBM+3FaDxwfHd10NySl5a0crEYPGYzdMLh6tNi4i4vtuOW8oE3LjaXZ1c8dLG3B7fG9QDgQLFizgmWee4bnnniMmJoaqqiqqqqpob28HIC4ujhtuuIE77riDjz76iMLCQq677jqmT59+1E1FZWCZpskHW2uoaHL1FKzGZRIZ6tPb6IhIEImNCGEkZZjuLmrdEb0bLYoEoo5uk4RzbwBgcl4CsRFH3ttHRCQQnD0ihVC7jSqni01lTVbH+QoV0eWkba1w8symZgDGxrtJiAq1OJGIyPE57DYe/NZ4okLtrN5bz58+3GV1pID0yCOP0NTUxDnnnENGRkbv48UXX+x9zwMPPMBll13G3LlzOeuss0hPT2fJkiUWppYvW7W3nu3VzdgMmH1aBknRatcmIr4ljnbq//lnAFburWdbldPiRCL949VtLTjiUom0m0zKS7A6johIv4oOczBjaM+eJ1/sPkCLq9viRIdTEV1OiqvLzW0vrqPbA207V5Ef7bE6kojICctLiuLer48B4MFlO/hsZ53FiQKPaZpHfFx77bW97wkPD+fhhx+mvr6e1tZWlixZcsx+6DJwiiudvZv5nDsiVX1XRcRntWx8nyxHCwDvb61m/4FWixOJeNee2hZe3dbz3/hpCd2EqK2aiASBsVlxpMeG0+n28PGOGqvjHEa/heWk3P/uNnZUtxAXZuPAu39E+4uJiL+ZOymbb03OwTTh1hfWcaDNbXUkEZ9Q3tDOB8XVAEzKS2BMVpzFiUREjm1IiJPhqdF4THh7UyXVTpfVkUS8wjRN7np1M10eaN9bRFaE2hCKSHAwDIPzR6ZiM2B3bSu7a1usjtRLDS7lhL2/tZonP98HwE1T4/hBm+/1JxKR4FJcXHxKn7si12TVLgf7Gjv55YcVYLN7OZmIf2ls6+StjRV4TBiaEs3MIUlWRxIROS7DgAtGp9He5aa0oZ3X11dw5eRsEiLVblL82ytF5azYc4BQO5S/9zDG2Y9YHUlEZMAkR4cxKS+BNfsa+Gh7DdkJEYQ5rL9mVxFdTkhZQxv/tXgDADeckc+kDM3yEBHrOOtrAZg/f/4pH8MRn0HGtQ+ynyjiz/oeLS2+c4dbZCB1dnt4a2Mlrm4PabFhXDg6DeMEl5qd6o0sbx9DRIKXw2Zj9tgMXikqp7a5g9fWlXPV5ByiwnSpK/6pvrWTX729FYCrRsXwq6ZqixOJiAy8qYMS2VndQmN7F5/vOsB5BalWR1IRXY6vy+3h5ufX0dTexbiceO68uIDNG9dbHUtEglh7S88GYrNvvIsRYyed8nHK2wxW1kHctLmUttRT4K2AIn7CNE3e21LFgdZOokLtXDY284R6rnrjRta/040sETlVYQ47Xx+XyeLCMprau3h9fQVzJ2X5xKw1kZP1y7e30tDWRUF6DF8bEcWvrA4kImIBh93G+SNTeaWonE3lTRSkx5AZH2FtJku/u/iF3763nXUljcSEO/jTtycQ6lArfRHxDUmZeWQPG33Kn88GSpetpJwktnXGM6Glg+ToMO8FFPFxK/fUs6euFbvN4LKxmUSf4MxNb93IAihevZylTz+Ey6VVbiJy6qLCHFwxPpOX1pZR29LBWxsr+fr4TBw2XbuI//h8Vx1LisoxDFg05zTMur1WRxIRsUx2QiSjM2PZUuFkWXEN356WY+m4riK6HNOH26p57JM9APz2m+PISYy0OJGIiHcNooZd+0qIGDSBNzdUcPXUXCJCNHNNAt/O6mZW76sH4PyCVNLjwk/6GH29kQVQXbK7T58XETkkPjKUK8Zn8nJRGWUN7fxzSzUXj0nHdoItqkSs1NrRzcIlmwD47ul5TMhNoEhFdBEJcmcMTWZvXSv1bZ2s3dfA6YOt27tJt+XlqPYfaOW2F9YDcO2MQVw8Jt3aQCIi/cAA6l7/DeFGN05XN0s3VeLxmFbHEulXtc0d/HNrT4/VCbnxjMyItTiRiIh3pMaGc9nYTGwG7KxpYfn2WkxT47r4vkVLiympbyMzLpz/umiE1XFERHxCeIids4enALB2XwP1rZ2WZVERXY6orbObG/9RiNPVzYTceBZeqk7BIhK4PK5mRofVE2I3KG1o57NddVZHEuk3Lje8ubGCbo9JbmIkZwxJtjqSiIhX5SZGctHonglAG8ubWL233uJEIsf2yY5anllZAsBvrxxHbHiIxYlERHzHsNRo8pOjcJsmHxRXW3ZzXEV0+QrTNPnpyxvZVtVMcnQYj8ybpE15RCTgRdu6uWBUGgDrShsprnRanEikH9gcrKpz0OzqJj4ihEvGpGOzqc2BiASe4WkxnHNw5trKvfVsLGu0NpDIUTS1dfHTlzcCcM30PGYO1c1tEZEvMwyDc0akEGI3qGxysam8yZIcKqLLV/z10728tbESh83gkfkTT6lHqoiIPxqWGsPUQYkALNtWQ1WTNjqUwJI46wfUddgItdu4fFwm4er/LyIBbFxOfO+4/tH2WnbWNFucSOSrfvHmFqqcLvKTo/h/l4y0Oo6IiE+KDQ9hxsEVtJ/vOkB798BnUBFdDvP5rjoWLS0G4L8vH8WUg390iogEi9MHJ/YsFfOYvL2pktYOC0ZnkX7w3q5WYiZcCphcPCadxKhQqyOJiPS70wcnMiazZ9+H9zZXU1rfZnEikX95d3MVS9aVYzPgd1eOIyJUN7dFRI5mbHYc6bHhdLo9rG9wDPj3VxFdeu2qaeaHzxTiMeGbk7L57ul5VkcSERlwhmFw0eg0EiNDaeno5q2NlXR7PFbHEumTlXsO8Nd1PS2KxsS5yU+OsjiRiMjAMAyDcwtSGZLS00v1rY2V1DRrpZlYr7yxnTtf6WnjcuPZQ5iUl2BxIhER32YzDM4fmYrNgIp2GxHDpg/s9x/Q7yY+q66lg+ueWoPT1c2kvAR+ecUYDEM9UkUkOIU57Fw2LoMwh40qp4sPt9VYtnmJSF+V1rfxo2eLcJvQuvVjhsfqppCIBBebYXDx6HSy4iPodHt4fX0FjW2dVseSINbl9nDTc0U0tXcxLjuO22cNtzqSiIhfSI4O673pmHjBjXS5B+46XUV0wdXl5vt/X0tpfTu5iZE89t1J6pEqIkEvITKUS8akYwDFlc2sL220OpLISWvt6OYH/yikvrWTwQkODiz9P3SPXESCkcNu4/JxGSRHh9LW6ea19RW0d7mtjiVB6nfvbWddSSMx4Q7+9J2JhDpUmhEROVFTByWSGeGh7vX7CLEP3MWNflMHOY/H5MeLN7CupJG4iBCevG4KSdFhVscSEfEJeUlRnDmsZ/OST3fWsf9Aq8WJRE6cx2Nyx0vrKa50khwdxv+bmYjZ3WF1LBERy4Q57FwxPouYcAdN7V28rZZtYoEPt1Xzl0/2APDbb44lJzHS4kQiIv7FYbcxPaWbjvJtA/p9VUQPYqZp8ut3inl7YyUhdoNH509iSEq01bFERHzK+Jx4RmbEYAJLN1fRoOXf4ice/GAH722pJtRu4y/fnURypFaZiYhEhTn4+rhMQu02yhvbD7ZsszqVBIvKpnZ+/NIGAK6dMYiLx2RYnEhERE6UiuhB7E8f7uKvn+0F4L45Y5k+JMniRCIivscwDM4rSCUjLpyObg9vbqigo1vLv8W3vbmhgj9+uAuARXNO02ZlIiJfkhQdxqWnpWMYPS3btjt1WSz9z9Xl5j//UUhDWxenZcWx8NICqyOJiMhJ0F8LQerpL/bx+/d3AHD3ZaOYOynb4kQiIr7LYbMx+7QMosMcNLR18e7mKjyatiY+alNZE/+1uGeW2w/OGqwxXkTkCPKSojh7eAoAW5ocRI6YaXEiCWSmafL/XtnIhrIm4iNDePg7EwlzaIWYiIg/URE9CC0pKuOeN7YAcMv5w7jhjHyLE4mI+L6oMAeXjc3AbjPYd6CNL3YfsDqSyFfUOF18/+9r6ej2cO6IFO68WLPcRESOZlx2POOz4wFIOP8HdHTrBrn0j798sofX1ldgtxn8ed5EcpPUB11ExN+oiB5k3t5YyU9e3gj09GC7fdYwixOJiPiPtNhwLhiZBkDh/ga2VTktTiTyL64uN9//RyFVThdDU6P547cnYLcN3G71IiL+6MzhyQyOdlP9wl2EOfQ7U7zvw23V3P9uz+Z3/3P5KGYMSbY4kYiInAqH1QFk4Ly6rowfv7QBjwnnDorgsiwX69atO6VjFRcXezmdiIh/GJEeQ11LB2v3N/BBcQ0JkaGkxYZbHUuCnGma3PnKRjaUNhIXEcJfvzeZmPAQq2OJiPg8m2EwIdHNR/VlVkeRALSjuplbnl+PacJ3puUy//Q8qyOJiMgpUhE9SLy0ppQ7X9mICbRsfJ+nfvN/PGV6+nzclpaWvocTEfEz04ckcaC1k711rby1sZKrp+QQFaYhVazzu39u5/X1FThsBo/Mm8ig5CirI4mIiAS1isZ2rvnbalo6upman8j/XD4aw9BqBxERf6Ur/iDwj5X7ufu1zQA0r3uHiyfkk37Zy306ZvHq5Sx9+iFcLpc3IoqI+BWbYXDR6DReWlNGfVsnb22sZO6kLBw2dUmTgffcqhIe/mg3AIvmnMaMoVomLiIiYqXGtk6+97fVVDb1tFj7y/xJhDr0d6KIiD9TET2AmabJH5ft4oEPdgAwe1gkf77/z6R/fQnZw0b36djVJbu9EVFExG+FOexcPi6DF9aUUuV08eG2mt5+6SLeVFJSQl1d3RFfK6x0seizBgC+NTqaIbZaiopqj/hetWITERHpf+2dbq5/ag27alrIiAvn79dPJSEq1OpYIiLSRyqiB6gut4efLdnE4sKe3n4/OmcI56e08meLc4mIBJL4yFAuPS2D19aXU1zZTEp0GClWh5KAUlJSQsHIkbS3tX3ltdC0IaR95z5soRG0bHqf39z/EL85gWOqFZuIiEj/6HJ7WPBcEUUlPXuUPH39VDLjI6yOJSIiXqAiegBqdnXxo2eL+HRnHTYDfvH1MXz39DyKioqsjiYiEnByEyM5c2gyn+ys49OddcxMUa9L8Z66ujra29qYd+dvScsd0vu8swuWV4fQ6TFIDffwjUvPxjb77GMeS63YRERE+k+X28OtL6zjw201hIfY+Nu1kxmeFmN1LBER8RIV0QNMaX0b3//7WrZVNRMRYudP35nA+WovICLSr8bnxFPX0snWSier6hw4EjKtjiQBJi13SG8rtqb2Lt4tLKXT4yY1Jow5E7MIc9iPewy1YhMREekfhwro72yqItRu45F5k5iUl2h1LBER8SIV0QPIx9truPWF9TS1d5EcHcbfrp3M2Ox4q2OJiAQ8wzA4tyCFhrZOKptcpM69m063aXUsCUAtrm6WFJXR2uEmKSqUKyacWAFdRERE+se/F9Af/e5Ezi1ItTqWiIh4mbaHDgAej8kfl+3kuqfW0NTexbiceN64aaYK6CIiA8hhszH7tAyiHCbONa8RaldbF/Guts5uXl1XjtPVTVxECN+YkEVEiAroIiIiVuns/moB/bwCrQQXEQlEKqL7uQMtHXz/72v5w/s7ME34zrRcXrrxdG1eIiJigagwBxdkdNGy4T2ro0iAae+GV4rKqW/rJDrMwZwJWUSFaUGhiIiIVVo6urnh6TUqoIuIBAldffmx5Ttq+a/FG6ht7iDUYeOXV4zhqsk5VscSEQlqmoAu3maPTWF5TQit3f8qoMdGhFgdS0REJGjVNndw/VNr2FTeRGSonUfmT+Ls4SlWxxIRkX6kIno/KCkpoa6uzivHSk5OJjc397DnXF1u7n93G09+vg+AoanRPHT1eEZnxnnle4qIiIhvqGjuJn3e/bR2G8SGO5gzMZs4FdBFREQss/9AK9/722r2H2gjKSqUv107hXE58VbHEhGRfqYiupeVlJRQMHIk7W1tXjleRGQk24qLewvpRSUN3PnyRnbWtABwzfQ8Fl46knD1RBUREQkom8ub+PlHB3DEphLjMLlyUg7R4frTTURExCqr99bzw2cKOdDaSU5iBH+/fhr5yVFWxxIRkQGgKzEvq6uro72tjXl3/pa03CF9OlZ1yW6evf8n1NXVkZyeye/e28GTX+zFNCE5OpTffnOcdv0WEREJQO9uruL2F9fT3uWhs3oPZ03MVgFdRESkn5zIavL3drXy13VO3Cbkxzv4+cwYGkq201Dy1fd2dHQQFhbW51zFxcV9PoaIiHiHrsb6SVruELKHjfbKsdZXdXDrB59QWt8OwJyJWdw9exQJUaFeOb6IiIj4BtM0eXT5Hu5/dxsA49PDeOOB/0f4lGcsTiYiIhKYjrua3OYg8YIbiRl/CQCtxZ+w/J2H+Li74xhHNQDTaxlbWlq8diwRETk1KqL7sLZuSL5iIfd+Ug9AVnwEv/rGGM4ZodnnIiIigcbV5eauVzfzSlEZANfOGMTsTBevd3qnRZyIiIh81bFWk7d2w+o6B/WdNsBkTLyb4ReejnHR6Uc9XvHq5Sx9+iFm33gXI8ZO6lO2Q8dyuVx9Oo6IiPSdiug+qNvjYV1JI6sqQ4gaMRObAdfMGMSPLxxBdJh+ZCIiIoFmR3UzNz+3ju3VzdhtBvdcPorvTR9EUVGR1dFERESCwr+vJt9Z3cyH22ro7PYQ6rBx8ej0E+p/Xl2yG4CkzLw+r04/dCwREbGeKrI+Zv+BVj7eXktjexdg4Crdwp9vOJs553mnNYyIiIj4DtM0eX51Kfe+tQVXl4fk6DAe/NZ4zhiWbHU0ERGRoNTl9rB8Ry1bKpwApMeGc/GYdOIiQixOJiIiVlIR3Uc0tHXy2c469tS1AhAZamd0TAcv338n7Zc+Q1FR3wdsbUoiIiLiO2qaXdzz+haWbq4C4KzhKfz+ynGkxPR9IzIRERE5eWUNbSwrrjk4qQ2mDEpgWn4SdpthcTIREbGaiugWc3W5WbW3no1ljXhMMAwYlx3P6YMT2V34KQDz58/36vfUpiQiIiLW8XhMXlhTyn1Li3G6unHYDH568Qj+44zB2HSRLiIiMuCM0EiK6u3sLSkHICrMzkWj0slJjLQ4mYiI+AoV0S3i9phsKm9i1Z4DuLo9AAxKiuTMYSkkRoUC0N7Ss3zMGxuSgDYlERERsdr2qmbuenUTa/c3AHBaVhyL5pzGmKw4i5OJiIgEH9M0WVnWTuZ//Jm9LXYAxmTFcsbQZMIcdovTiYiIL1ERfYCZpsneulY+3VVHY1vPErGkqFDOHJZMXtKRNynxxoYkoE1JRERErFLZ1M6D7+9kcWEpHhOiQu38+MIRXDNjkJaIi4iInIKSkhLq6upO+fP7G7v423onm2o6ccQkE+UwuXhsNtkJmn0uIiJfpSL6AKpt7uDTnbWUNrQDEBFiZ/rgJEZnxmr5toiISABqau/i0eW7+dtne+k4uPLs4tHp/Pflo8iMj7A4nYiIiH8qKSmhYORI2tvaTvqztohY4mZ+h5gJl2DY7JjdnThXL2HW7AvJThjeD2lFRCQQqIg+AFo7ulmx50Dv7t52w2B8bjxTBiVoiZiIiEgAqml28bfP9vHsyv00d3QDMDkvgYWXFjApL9HidCIiIv6trq6O9rY25t35W9Jyh5zQZzo9sNNpZ1ezjW6zZxJbVoSHiJJVLPv0GbpmndWfkUVExM+piN6Put0eikobWbuvni63CcCw1GhmDk0mLiLE4nQiIiJySF+XhB9S2dzN+yVu3tneSOfBmecj0mL4r4tGMGtkKoahlWciIiLekpY75LitTzu63Wwoa6Jof0PvqrDUmDDOHJZMdkIkhe3bByKqiIj4ORXR+0lpq41/rtxPs6tn9llabBhnDUvR0m0REREf05cl4T0MwgdPInbiZUQMmdz77ITceH50zlDOL0hV2zYREZEB1tLRzfrSRjaVNdHp7imeJ0WFcvrgJIakROnGtoiInBQV0b1s+4FO0uf/jtUHHEA30WEOZg5NYkRajAZpERERH3QqS8IB2t1Q0mJjb6ud1u5DY7xJ2+61/P6Gi5h/0eka+0VERAZYfWsnhfsb2FblxNOzIJyEyBCmDkpkeHoMNo3NIiJyClRE96IvdtWxcNkBwrIKsBsmU/OTmZAbT4jdZnU0EREROY4TWRLu9pjsO9DKlgon+w60Yh68OA9z2BiVGUtqVw2P3f8LbFcMY926sD5nKi4u7vMxREREAp1pmpTUt7GhrIm9da29z2fGhTMpL4H8ZM08FxGRvlER3YumDU4iP97Bpk+WctXFZzMsXxuHiYiIBILa5g62VTkprmymvcvd+3xGXDijM2MZnhZDiN3G1lVbAJg/f75Xv39LS4tXjyciIhIIOt1QVNLAprImGtu7ep8fkhLFxNwEtVMVERGvURHdi+w2g0XnJzN94UNEXHa21XFERESkD5yuLrZXNbO9qpkDrZ29z0eG2hmZEcuojFgSo0IP+0x7ixOA2TfexYixk/qcoXj1cpY+/RAul6vPxxIREQkUO+s7SbrkVt6uCMFj9mwMHmq3MTIjhrHZ8V8Zn0VERPpKRXQvC7VriZiIiIi/cnW52VnTwvaqZsob23uftxsGg5IjGZURS15SFPbjbBSalJl33NYwJ6K6ZHefjyEiIhJIVu45wJ0fHCB67AV4TEiODmVsdjwj0mIIdaiVqoiI9A8V0UVERCSodbpNIkfMZEWtg+rSvbgPNToHsuMjGJEew9DUaMJD7BamFBEREYApgxLJirGzfcX7XHb+mYwdPVT9zkVEpN+piC4iIiJBa0NpI9e/UU3KFQupaAcwSY4OZUR6DCPSYogJD7E6ooiIiHyJ3Wbw4EUpTP35H0i69AwV0EVEZECoiC4iIiJBa3haDB4Tup01jM5OYvLIfJKjw6yOJSIiIsdwvLZqIiIi3qaGYSIiIhK0IkLt/O6CZMofuYEx8W4V0EVEREREROQrVEQXERGRoJYZ4wDM475PREREREREgpOK6CIiIiIiIiIiIiIiR6EiuoiIiIiIiIiIiIjIUQRMEf3hhx9m0KBBhIeHM23aNFavXm11JBERETkGjd0iIiL+RWO3iIgEq4Aoor/44ovccccd3HPPPRQVFTFu3DguuugiampqrI4mIiIiR6CxW0RExL9o7BYRkWAWEEX0P/zhD3z/+9/nuuuuY9SoUTz66KNERkbyt7/9zepoIiIicgQau0VERPyLxm4REQlmDqsD9FVnZyeFhYUsXLiw9zmbzcasWbNYsWLFET/T0dFBR0dH79dNTU0AOJ3OPudpaWkBoGznFjra2/p0rOqS3QBU7dvB7qjIPmfz5vGC4Vi+nE3nae2xfDmbztPaYwHUlu0FesaDvo4rhz5vmmafc/kSjd3WHC8YjuXL2XSe1h7Ll7PpPK09FmjsPhEau605XjAcy5ez6TytPZYvZ9N5WnsssGjsNv1ceXm5CZhffPHFYc//5Cc/MadOnXrEz9xzzz0moIceeuihhx5+8SgtLR2IIXXAaOzWQw899NAj0B8auzV266GHHnro4V+P443dfj8T/VQsXLiQO+64o/drj8dDfX09SUlJGIZhYbKeux85OTmUlpYSGxtraRZvCbRzCrTzAZ2Tv9A5+QdvnpNpmjQ3N5OZmemldP5roMfuQPxv85BAPTedl/8J1HPTefkfjd39Q9fdAyvQzinQzgd0Tv5C5+QfrBi7/b6InpycjN1up7q6+rDnq6urSU9PP+JnwsLCCAsLO+y5+Pj4/op4SmJjYwPmP+xDAu2cAu18QOfkL3RO/sFb5xQXF+eFNL7Fn8buQPxv85BAPTedl/8J1HPTefkfjd1H509j98kIxP+eA+2cAu18QOfkL3RO/mEgx26/31g0NDSUSZMmsWzZst7nPB4Py5YtY/r06RYmExERkSPR2C0iIuJfNHaLiEiw8/uZ6AB33HEH11xzDZMnT2bq1Kk8+OCDtLa2ct1111kdTURERI5AY7eIiIh/0dgtIiLBLCCK6N/61reora3lv//7v6mqqmL8+PG8++67pKWlWR3tpIWFhXHPPfd8ZdmbPwu0cwq08wGdk7/QOfmHQDyn/uDrY3cg/xwD9dx0Xv4nUM9N5+V/AvncvMnXx+6TEYg/80A7p0A7H9A5+Qudk3+w4pwM0zTNAftuIiIiIiIiIiIiIiJ+xO97oouIiIiIiIiIiIiI9BcV0UVEREREREREREREjkJFdBERERERERERERGRo1ARXURERERERERERETkKFREt8AjjzzC2LFjiY2NJTY2lunTp7N06dLe110uFwsWLCApKYno6Gjmzp1LdXW1hYlP3n333YdhGNx22229z/nbef3P//wPhmEc9igoKOh93d/O55Dy8nLmz59PUlISERERnHbaaaxdu7b3ddM0+e///m8yMjKIiIhg1qxZ7Ny508LExzZo0KCv/JwMw2DBggWA//2c3G43d999N/n5+URERDBkyBD+93//ly/vAe1vPyOA5uZmbrvtNvLy8oiIiGDGjBmsWbOm93VfP6dPPvmEyy+/nMzMTAzD4LXXXjvs9RPJX19fz7x584iNjSU+Pp4bbriBlpaWATwLOZJgGJMhMMZlCNyx+ZBAG6Mh8MbpQwJ1vAb/H7MP0dgtwTDGB8L4Hqhje6CN6YE2ngfqOO7vY7jPj92mDLg33njDfPvtt80dO3aY27dvN3/2s5+ZISEh5ubNm03TNM3//M//NHNycsxly5aZa9euNU8//XRzxowZFqc+catXrzYHDRpkjh071rz11lt7n/e387rnnnvM0aNHm5WVlb2P2tra3tf97XxM0zTr6+vNvLw889prrzVXrVpl7tmzx3zvvffMXbt29b7nvvvuM+Pi4szXXnvN3LBhg/m1r33NzM/PN9vb2y1MfnQ1NTWH/Yzef/99EzA/+ugj0zT97+f0q1/9ykxKSjLfeustc+/evebixYvN6Oho86GHHup9j7/9jEzTNK+66ipz1KhR5vLly82dO3ea99xzjxkbG2uWlZWZpun75/TOO++Yd911l7lkyRITMF999dXDXj+R/BdffLE5btw4c+XKleann35qDh061Pz2t789wGci/y7Qx2TTDJxx2TQDc2w+JBDHaNMMvHH6kEAdr03T/8fsQzR2S6CP8YEyvgfi2B6IY3qgjeeBOo77+xju62O3iug+IiEhwfzrX/9qNjY2miEhIebixYt7XysuLjYBc8WKFRYmPDHNzc3msGHDzPfff988++yzewdzfzyve+65xxw3btwRX/PH8zFN07zzzjvNM84446ivezweMz093fztb3/b+1xjY6MZFhZmPv/88wMRsc9uvfVWc8iQIabH4/HLn9Ps2bPN66+//rDn5syZY86bN880Tf/8GbW1tZl2u9186623Dnt+4sSJ5l133eV35/Tvg/mJ5N+6dasJmGvWrOl9z9KlS03DMMzy8vIByy4nJlDGZNMMrHHZNANzbD4kGMZo0/T/cfqQQByvTTPwxuxDNHbLIYEyxgfS+B6IY3swjOn+Pp4H4jgeaGO4L47daudiMbfbzQsvvEBrayvTp0+nsLCQrq4uZs2a1fuegoICcnNzWbFihYVJT8yCBQuYPXv2YfkBvz2vnTt3kpmZyeDBg5k3bx4lJSWA/57PG2+8weTJk7nyyitJTU1lwoQJPP74472v7927l6qqqsPOKy4ujmnTpvn0eR3S2dnJM888w/XXX49hGH75c5oxYwbLli1jx44dAGzYsIHPPvuMSy65BPDPn1F3dzdut5vw8PDDno+IiOCzzz7zy3P6shPJv2LFCuLj45k8eXLve2bNmoXNZmPVqlUDnlmOLNDGZAi8cRkCb2w+JNDHaAiMcfqQQByvIfDH7EM0dgefQBvjA218D7SxPdDH9EAYzwNxHA/0MdwXxm5Hn48gp2TTpk1Mnz4dl8tFdHQ0r776KqNGjWL9+vWEhoYSHx9/2PvT0tKoqqqyJuwJeuGFFygqKjqs39IhVVVVfnde06ZN46mnnmLEiBFUVlbyi1/8gjPPPJPNmzf75fkA7Nmzh0ceeYQ77riDn/3sZ6xZs4ZbbrmF0NBQrrnmmt7saWlph33O18/rkNdee43GxkauvfZawD//u/t//+//4XQ6KSgowG6343a7+dWvfsW8efMA/PJnFBMTw/Tp0/nf//1fRo4cSVpaGs8//zwrVqxg6NChfnlOX3Yi+auqqkhNTT3sdYfDQWJiol+cY6ALxDEZAm9chsAcmw8J9DEaAmOcPiQQx2sI/DH7EI3dwSMQx/hAG98DcWwP9DE9EMbzQBzHA30M94WxW0V0p3gXGQAAE/NJREFUi4wYMYL169fT1NTEyy+/zDXXXMPy5cutjnXKSktLufXWW3n//fe/ctfLXx26AwkwduxYpk2bRl5eHi+99BIREREWJjt1Ho+HyZMn8+tf/xqACRMmsHnzZh599FGuueYai9P13RNPPMEll1xCZmam1VFO2UsvvcSzzz7Lc889x+jRo1m/fj233XYbmZmZfv0z+sc//sH1119PVlYWdrudiRMn8u1vf5vCwkKro4kE3JgMgTkuQ2COzYcE+hgNgTFOHxKo4zVozJbAEmhjfCCO74E4tgf6mB4I43mgjuMaw/uX2rlYJDQ0lKFDhzJp0iQWLVrEuHHjeOihh0hPT6ezs5PGxsbD3l9dXU16ero1YU9AYWEhNTU1TJw4EYfDgcPhYPny5fzxj3/E4fj/7d17UFRlHwfw70Jy0VUh0AVUBLyxJCqpOSvZUjZipZmWFyrDG5NBiCkl5SXf1IxRK5zUFA0do7Qm85aBGbIqYyIKQkYLKl4qJtMgRA1l9/f+8b6cXGER04Jdvp+ZneE8z7k8z54ZvrvPnvOce6DRaGyyXzdyc3ND9+7dceLECZs9T97e3ggKCrIo02q1yu1yNW2/+SnaTb1fAHDmzBns2bMHU6ZMUcps8Ty9+uqrSEhIwLhx4xAcHIzx48fjlVdeweLFiwHY7jnq0qULDAYDKisrce7cOWRnZ+P69esICAiw2T7VaEj7vby8cP78eYv66upq/P777zbRR3tnb5kMNI9cBuwjm2vYc0YD9pPTNew1rwH7zuwazO7mw94yvjnkuz1kuz1nur3kub3muD1neFPIbg6iNxFmsxlVVVXo27cvWrRogW+//VapMxqNOHv2LHQ6XSO2sH6DBw9GQUEB8vLylFe/fv3w3HPPKX/bYr9uVFlZiZMnT8Lb29tmz1NoaCiMRqNFWVFRETp37gwA8Pf3h5eXl0W/KioqcOjQoSbdLwBISUlB+/bt8cQTTyhltnierly5AgcHy3/Njo6OMJvNAGz7HAFAq1at4O3tjbKyMqSnp2PEiBE236eGtF+n06G8vNziCoCMjAyYzWYMGDDgX28z1c/WMxloHrkM2Ec217DnjAbsJ6dr2HteA/aZ2TWY3c2XrWd8c8h3e8h2e850e8lze89xe8zwJpHdd/xoUrptCQkJYjAYpKSkRPLz8yUhIUFUKpXs3r1bRESmTp0qvr6+kpGRITk5OaLT6USn0zVyq2/fjU8JF7G9fs2cOVMyMzOlpKREsrKy5NFHHxVPT085f/68iNhef0REsrOz5Z577pFFixZJcXGxpKamSsuWLeXjjz9W1nnnnXfEzc1Ntm3bJvn5+TJixAjx9/eXq1evNmLL62cymcTX11dmzZpVq87WzlNkZKR06NBBdu7cKSUlJbJlyxbx9PSU1157TVnHFs9RWlqafP3113Lq1CnZvXu39O7dWwYMGCDXrl0Tkabfp0uXLklubq7k5uYKAHn33XclNzdXzpw5IyINa//QoUMlJCREDh06JAcOHJBu3bpJREREY3WJ/q+5ZLKI7eeyiH1mcw17zWgR+8rpGvaa1yK2n9k1mN3UXDLe1vPdHrPdXjPdnvLcXnPc1jO8qWc3B9EbwaRJk6Rz587i5OQk7dq1k8GDBytBLiJy9epViY6OFnd3d2nZsqWMHDlSSktLG7HFf8/NYW5r/Ro7dqx4e3uLk5OTdOjQQcaOHSsnTpxQ6m2tPzV27NghPXv2FGdnZwkMDJQ1a9ZY1JvNZpk7d65oNBpxdnaWwYMHi9FobKTWNkx6eroAqLOdtnaeKioqJC4uTnx9fcXFxUUCAgJk9uzZUlVVpaxji+do8+bNEhAQIE5OTuLl5SUxMTFSXl6u1Df1Pu3du1cA1HpFRkaKSMPaf/HiRYmIiBC1Wi1t2rSRiRMnyqVLlxqhN3Sj5pLJIrafyyL2m8017DGjRewrp2vYa16L2H5m12B2U3PJeFvPd3vNdnvMdHvKc3vNcVvP8Kae3SoRkTu/np2IiIiIiIiIiIiIyP5wTnQiIiIiIiIiIiIiIis4iE5EREREREREREREZAUH0YmIiIiIiIiIiIiIrOAgOhERERERERERERGRFRxEJyIiIiIiIiIiIiKygoPoRERERERERERERERWcBCdiIiIiIiIiIiIiMgKDqITUYOcPn0aKpUKeXl5jd0UIiIiQu1szszMhEqlQnl5eaO2i4iIqLmaMGECnnrqqXrXYV4T2aZ7GrsBRNT0TJgwAeXl5di6dWtjN4WIiIgaaODAgSgtLUXbtm0buylERETNUlJSEkREWQ4LC0OfPn3w/vvvK2XMayLbxCvRichuXLt2rbGbQEREZOHfzCYnJyd4eXlBpVL9a8f8N1y/fr2xm0BERFQvk8kEs9mMtm3bws3Nrd51mddEtomD6EQ2LCwsDLGxsZg+fTrc3d2h0WiQnJyMy5cvY+LEiWjdujW6du2Kr7/+WtnGZDJh8uTJ8Pf3h6urK3r06IGkpCSlfv78+diwYQO2bdsGlUoFlUqFzMxMpf7UqVN4+OGH0bJlS/Tu3RsHDx602j4Rwfz58+Hr6wtnZ2f4+Phg2rRpSn1VVRVmzZqFTp06wdnZGV27dsW6deuUeoPBgAceeADOzs7w9vZGQkICqqurLfr/8ssvY/r06fD09ER4eDgA4Pvvv8djjz0GtVoNjUaD8ePH48KFC3f0XhMRETXE382mtLQ0PPjgg3Bzc4OHhweGDRuGkydPWuw7OzsbISEhcHFxQb9+/ZCbm2tRf/Pt4evXr4ebmxvS09Oh1WqhVqsxdOhQlJaWKttUV1dj2rRpynFnzZqFyMjIem9FP3PmDIYPHw53d3e0atUK9913H3bt2qXUHz9+HMOGDUObNm3QunVrDBo0SOmL2WzGW2+9hY4dO8LZ2Rl9+vRBWlqasm3NFDWbN2+GXq+Hi4sLUlNTAQBr166FVquFi4sLAgMDsXLlyts4M0RERNbt3LkTbm5uMJlMAIC8vDyoVCokJCQo60yZMgXPP/88gL8ydvv27QgKCoKzszPOnj1rMZ3LhAkTYDAYkJSUpHy3Pn36NPOayEZxEJ3Ixm3YsAGenp7Izs5GbGwsXnrpJYwePRoDBw7E0aNHMWTIEIwfPx5XrlwB8L8w7NixIz7//HP88MMPmDdvHt544w189tlnAID4+HiMGTNGCe3S0lIMHDhQOd7s2bMRHx+PvLw8dO/eHRERERYD2zf64osv8N5772H16tUoLi7G1q1bERwcrNS/8MIL+PTTT7F8+XIUFhZi9erVUKvVAICff/4Zjz/+OPr3749jx45h1apVWLduHRYuXFir/05OTsjKysKHH36I8vJyPPLIIwgJCUFOTg7S0tLw66+/YsyYMXf1fSciIrLm72TT5cuXMWPGDOTk5ODbb7+Fg4MDRo4cCbPZDACorKzEsGHDEBQUhCNHjmD+/PmIj4+/ZVuuXLmCpUuXYuPGjdi3bx/Onj1rsV1iYiJSU1ORkpKCrKwsVFRU3HI6t5iYGFRVVWHfvn0oKChAYmKiRX4/9NBDcHZ2RkZGBo4cOYJJkyYpnxWSkpKwbNkyLF26FPn5+QgPD8eTTz6J4uJii2MkJCQgLi4OhYWFCA8PR2pqKubNm4dFixahsLAQb7/9NubOnYsNGzY06JwQERHVZ9CgQbh06ZLyA7XBYICnp6fFBWUGgwFhYWHK8pUrV5CYmIi1a9fi+PHjaN++vcU+k5KSoNPpEBUVpXy37tSpU53HZ14T2QAhIpul1+vlwQcfVJarq6ulVatWMn78eKWstLRUAMjBgwet7icmJkaefvppZTkyMlJGjBhhsU5JSYkAkLVr1yplx48fFwBSWFhY536XLVsm3bt3l2vXrtWqMxqNAkC++eabOrd94403pEePHmI2m5WyFStWiFqtFpPJpPQ/JCTEYrsFCxbIkCFDLMrOnTsnAMRoNNZ5LCIiorvlbmXTb7/9JgCkoKBARERWr14tHh4ecvXqVWWdVatWCQDJzc0VEZG9e/cKACkrKxMRkZSUFAEgJ06cULZZsWKFaDQaZVmj0ciSJUuU5erqavH19a31OeBGwcHBMn/+/DrrXn/9dfH3968z+0VEfHx8ZNGiRRZl/fv3l+joaBH56/PG+++/b7FOly5d5JNPPrEoW7Bggeh0OqvtJCIiuh3333+/kolPPfWULFq0SJycnOTSpUvy008/CQApKioSkb8yNi8vz2IfN3+X1uv1EhcXZ7EO85rINvFKdCIb16tXL+VvR0dHeHh4WFztrdFoAADnz59XylasWIG+ffuiXbt2UKvVWLNmDc6ePXvbx/P29q617xuNHj0aV69eRUBAAKKiovDll18qv2zn5eXB0dERer2+zm0LCwuh0+ks5okLDQ1FZWUlfvrpJ6Wsb9++FtsdO3YMe/fuhVqtVl6BgYEAUOu2eCIion/C38mm4uJiREREICAgAG3atIGfnx8AKPlcWFiIXr16wcXFRdmvTqe7ZVtatmyJLl26KMve3t5Kbv/xxx/49ddf8cADDyj1jo6Otdp/s2nTpmHhwoUIDQ3Fm2++ifz8fKUuLy8PgwYNQosWLWptV1FRgV9++QWhoaEW5aGhoSgsLLQo69evn/L35cuXcfLkSUyePNniPVy4cCGznYiI7hq9Xo/MzEyICPbv349Ro0ZBq9XiwIEDMBgM8PHxQbdu3ZT1nZycLL4f3wnmNVHTd09jN4CI7szNoadSqSzKagaha24H37RpE+Lj47Fs2TLodDq0bt0aS5YswaFDh277eDfv+2adOnWC0WjEnj178M033yA6OhpLliyBwWCAq6trwztZj1atWlksV1ZWYvjw4UhMTKy1bs2gPxER0T/p72TT8OHD0blzZyQnJ8PHxwdmsxk9e/a84weT1vU5QUTuaJ9TpkxBeHg4vvrqK+zevRuLFy/GsmXLEBsb+4/ke2VlJQAgOTkZAwYMsFjP0dHxrhyPiIgoLCwMH330EY4dO4YWLVogMDAQYWFhyMzMRFlZWa0LwFxdXe/aw0GZ10RNH69EJ2pmsrKyMHDgQERHRyMkJARdu3at9auwk5OT8kCVO+Xq6orhw4dj+fLlyMzMxMGDB1FQUIDg4GCYzWYYDIY6t9NqtTh48KDFB4esrCy0bt0aHTt2tHq8+++/H8ePH4efnx+6du1q8bp5UIOIiOjfcKtsunjxIoxGI+bMmYPBgwdDq9WirKzMYh9arRb5+fn4888/lbLvvvvujtrVtm1baDQaHD58WCkzmUw4evToLbft1KkTpk6dii1btmDmzJlITk4G8L871vbv34/r16/X2qZNmzbw8fFBVlaWRXlWVhaCgoKsHkuj0cDHxwenTp2q9f75+/s3tLtERET1qpkX/b333lMGzGsG0TMzMy3mQ2+ou/HdmnlN1DRwEJ2omenWrRtycnKQnp6OoqIizJ071yKMAcDPzw/5+fkwGo24cOFCncHaEOvXr8e6devw/fff49SpU/j444/h6uqKzp07w8/PD5GRkZg0aRK2bt2KkpISZGZmKg84jY6Oxrlz5xAbG4sff/wR27Ztw5tvvokZM2bAwcH6v66YmBj8/vvviIiIwOHDh3Hy5Emkp6dj4sSJd+2HASIiottxq2xyd3eHh4cH1qxZgxMnTiAjIwMzZsyw2Mezzz4LlUqFqKgo/PDDD9i1axeWLl16x22LjY3F4sWLsW3bNhiNRsTFxaGsrKzeK+umT5+O9PR0lJSU4OjRo9i7dy+0Wi0A4OWXX0ZFRQXGjRuHnJwcFBcXY+PGjTAajQCAV199FYmJidi8eTOMRiMSEhKQl5eHuLi4etv5n//8B4sXL8by5ctRVFSEgoICpKSk4N13373j94CIiAgA3N3d0atXL6SmpioD5g899BCOHj2KoqIiq1OR1sfPzw+HDh3C6dOnceHCBat3cd8K85qo8XEQnaiZefHFFzFq1CiMHTsWAwYMwMWLFxEdHW2xTlRUFHr06IF+/fqhXbt2tX6Bbig3NzckJycjNDQUvXr1wp49e7Bjxw54eHgAAFatWoVnnnkG0dHRCAwMRFRUFC5fvgwA6NChA3bt2oXs7Gz07t0bU6dOxeTJkzFnzpx6j1nzi7nJZMKQIUMQHByM6dOnw83Nrd7BdyIion/KrbLJwcEBmzZtwpEjR9CzZ0+88sorWLJkicU+1Go1duzYgYKCAoSEhGD27Nl1Tg9zu2bNmoWIiAi88MIL0Ol0UKvVCA8Pt5h7/WYmkwkxMTHQarUYOnQounfvjpUrVwIAPDw8kJGRgcrKSuj1evTt2xfJycnKberTpk3DjBkzMHPmTAQHByMtLQ3bt2+3mGO2LlOmTMHatWuRkpKC4OBg6PV6rF+/nle2ERHRXaXX62EymZRB9HvvvRdBQUHw8vJCjx49bnt/8fHxcHR0RFBQENq1a9fgZ5HdjHlN1PhUcqeTLBERERERkV0wm83QarUYM2YMFixY0NjNISIiojowr4n+fXywKBERERFRM3XmzBns3r0ber0eVVVV+OCDD1BSUoJnn322sZtGRERE/8e8Jmp8nNuAiIiIiKiZcnBwwPr169G/f3+EhoaioKAAe/bsUeZMJSIiosbHvCZqfJzOhYiIiIiIiIiIiIjICl6JTkRERERERERERERkBQfRiYiIiIiIiIiIiIis4CA6EREREREREREREZEVHEQnIiIiIiIiIiIiIrKCg+hERERERERERERERFZwEJ2IiIiIiIiIiIiIyAoOohMRERERERERERERWcFBdCIiIiIiIiIiIiIiKziITkRERERERERERERkxX8BtPuyKyf3HAEAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1500x1000 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x1000 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Коэффициенты асимметрии (skewness):\n",
      "math score: -0.06868258599956258\n",
      "reading score: -0.13966907569164858\n",
      "writing score: -0.16409966338743373\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.stats import skew\n",
    "\n",
    "# Строим гистограммы для количественных признаков\n",
    "def plot_histograms(df, numerical_columns):\n",
    "    plt.figure(figsize=(15, 10))\n",
    "    for i, column in enumerate(numerical_columns, 1):\n",
    "        plt.subplot(2, 3, i)\n",
    "        sns.histplot(df[column], kde=True, bins=20)\n",
    "        plt.title(f'Распределение для {column}')\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "# Строим барплоты для категориальных признаков\n",
    "def plot_barplots(df, categorical_columns):\n",
    "    plt.figure(figsize=(15, 10))\n",
    "    for i, column in enumerate(categorical_columns, 1):\n",
    "        plt.subplot(2, 3, i)\n",
    "        sns.countplot(data=df, x=column)\n",
    "        plt.title(f'Распределение для {column}')\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "# Вычисляем коэффициент асимметрии (skewness) для количественных признаков\n",
    "def check_skewness(df, numerical_columns):\n",
    "    skewness = {}\n",
    "    for column in numerical_columns:\n",
    "        skewness[column] = skew(df[column])\n",
    "    print(\"Коэффициенты асимметрии (skewness):\")\n",
    "    for column, value in skewness.items():\n",
    "        print(f\"{column}: {value}\")\n",
    "\n",
    "\n",
    "\n",
    "# Указываем признаки для анализа\n",
    "numerical_columns = ['math score', 'reading score', 'writing score']\n",
    "categorical_columns = ['gender', 'race/ethnicity', 'parental level of education', 'lunch', 'test preparation course']\n",
    "\n",
    "# Визуализируем распределения признаков\n",
    "plot_histograms(df, numerical_columns)\n",
    "plot_barplots(df, categorical_columns)\n",
    "\n",
    "# Проверяем на смещение (skewness)\n",
    "check_skewness(df, numerical_columns)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Распределение оценок:**\n",
    "\n",
    "Все три оценки (математика, чтение, письмо) имеют нормальное или близкое к нормальному распределение, с небольшим левым смещением. Это означает, что большинство студентов набирают средние баллы, а на крайних значениях (как низкие, так и высокие) распределение становится реже.\n",
    "\n",
    "Таким образом, модель, вероятно, будет работать хорошо, так как данные достаточно близки к нормальному распределению, что способствует лучшему обучению модели.\n",
    "\n",
    "**Смещение (Skewness):**\n",
    "\n",
    "Смещение не очень сильное. Для большинства признаков смещение не критичное, так как коэффициенты асимметрии близки к нулю, но все же указывают на небольшое смещение влево. Это может означать, что студенты с низкими оценками по этим предметам встречаются чаще, чем с высокими.\n",
    "\n",
    "**Группировка категориальных признаков:**\n",
    "\n",
    "1. Пол: Данные сбалансированы между мужчинами и женщинами.\n",
    "2. Этническая принадлежность: Распределение студентов неравномерное, с наибольшим количеством студентов в группе C, что важно учитывать при обучении модели.\n",
    "3. Уровень образования родителей: Студенты с родителями, имеющими диплом бакалавра, составляют наибольшую часть данных.\n",
    "4. Тип обеда: Преобладание студентов с стандартным обедом над бесплатным/сниженным.\n",
    "5. Курс подготовки: Большинство студентов не прошли курс подготовки, что нужно учитывать в модели.\n",
    "\n",
    "\n",
    "Для корректной работы модели стоит обратить внимание на несбалансированность данных по таким признакам, как тип обеда и курс подготовки, что может потребовать дополнительной обработки данных (например, с использованием методов балансировки данных)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Разбиение датасета на три части: обучающую, контрольную и тестовую выборки\n",
    "\n",
    "Обучающая выборка (training set): используется для обучения модели. Это основная часть данных, на которой модель обучается находить зависимости.\n",
    "\n",
    "Валидационная выборка (validation set): используется для настройки гиперпараметров модели. Во время обучения модель проверяется на этой выборке, чтобы выбрать оптимальные параметры и избежать переобучения.\n",
    "\n",
    "Тестовая выборка (test set): используется для окончательной проверки модели после завершения процесса обучения. Тестовые данные не используются в процессе обучения и позволяют объективно оценить качество модели."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 378,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Размер обучающей выборки: (700, 5)\n",
      "Размер контрольной выборки: (150, 5)\n",
      "Размер тестовой выборки: (150, 5)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Предполагаем, что df — это DataFrame, содержащий все ваши данные\n",
    "X = df[['gender', 'race/ethnicity', 'parental level of education', 'lunch', 'test preparation course']]  # Признаки\n",
    "y = df[['math score', 'reading score', 'writing score']]  # Несколько целевых признаков\n",
    "\n",
    "# Разбиение на обучающую и тестовую выборки\n",
    "X_train, X_temp, y_train, y_temp = train_test_split(X, y, test_size=0.3, random_state=42)\n",
    "\n",
    "# Разбиение временной выборки на контрольную и тестовую\n",
    "X_val, X_test, y_val, y_test = train_test_split(X_temp, y_temp, test_size=0.5, random_state=42)\n",
    "\n",
    "# Печатаем размеры выборок\n",
    "print(f\"Размер обучающей выборки: {X_train.shape}\")\n",
    "print(f\"Размер контрольной выборки: {X_val.shape}\")\n",
    "print(f\"Размер тестовой выборки: {X_test.shape}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### Оценка сбалансированности выборок\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Коэффициенты асимметрии (skewness) для целевых переменных:\n",
      "math score: -0.009831373587054385\n",
      "reading score: -0.09142001190462311\n",
      "writing score: -0.14592365848290204\n"
     ]
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.stats import skew\n",
    "\n",
    "# Строим гистограммы и рассчитываем skewness для количественных признаков\n",
    "def plot_numerical_balance(y_train):\n",
    "    plt.figure(figsize=(15, 5))\n",
    "    skewness = {}\n",
    "    for i, column in enumerate(y_train.columns, 1):\n",
    "        # Гистограмма с плотностью\n",
    "        plt.subplot(1, 3, i)\n",
    "        sns.histplot(y_train[column], kde=True, bins=20)\n",
    "        plt.title(f'Распределение для {column}')\n",
    "        \n",
    "        # Вычисление skewness\n",
    "        skewness[column] = skew(y_train[column])\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "    # Вывод коэффициентов асимметрии (skewness)\n",
    "    print(\"Коэффициенты асимметрии (skewness) для целевых переменных:\")\n",
    "    for column, value in skewness.items():\n",
    "        print(f\"{column}: {value}\")\n",
    "\n",
    "# Оценка сбалансированности количественных признаков\n",
    "plot_numerical_balance(y_train)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Все целевые переменные (оценки) имеют незначительное смещение влево, что говорит о том, что большинство студентов набирают средние и ниже средние баллы. Эти данные не требуют аугментации с точки зрения смещения."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "aimvenv",
   "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": "3.12.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}