{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Выгрузка в датафрейм первого набора (Объекты вокруг земли)\n",
    "\n",
    "https://www.kaggle.com/datasets/sameepvani/nasa-nearest-earth-objects"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "В космическом пространстве существует бесконечное количество объектов. Некоторые из них находятся ближе, чем мы думаем. Хотя нам может казаться, что расстояние в 70 000 км не может причинить нам вред, в астрономическом масштабе это очень маленькое расстояние, которое может нарушить многие природные явления. Таким образом, эти объекты/астероиды могут причинить вред. Поэтому разумно знать, что нас окружает и что может причинить нам вред. Таким образом, этот набор данных содержит список сертифицированных НАСА астероидов, которые классифицируются как ближайшие к Земле объекты.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Проблемная область: угроза космических объектов\n",
    "\n",
    "Объект наблюдения: астероиды и другие малые тела Солнечной системы\n",
    "\n",
    "\n",
    "Бизнес-цели: \n",
    "1. Мониторинг и предупреждение о космических угрозах\n",
    "Разработка платформы для регулярного мониторинга потенциально опасных космических объектов с использованием данных из вашего датасета. Это может включать:\n",
    "\n",
    "Система раннего предупреждения: Создание алгоритмов и моделей, которые бы заранее сообщали о приближении объектов (например, астероидов) к орбите Земли.\n",
    "Информационные каналы: Предоставление информации о пространственном расположении и траекториях объектов для правительственных организаций и агентств.\n",
    "\n",
    "2. Консультационные услуги для правительств и бизнеса\n",
    "Предоставление специализированных услуг по анализу угроз космических объектов, которые могут включать:\n",
    "\n",
    "Оценка рисков: Анализ угроз и оценка вероятности столкновения для государственных структур, научных организаций и приватного сектора.\n",
    "Разработка стратегий защиты: Консультирование по вопросам разработки мер защиты и методов нейтрализации потенциальных угроз.\n",
    "\n",
    "3. Привлечение инвестиций в космическую отрасль\n",
    "Использование данных для привлечение инвестиций в проекты, связанные с изучением космических объектов и их безопасностью:\n",
    "\n",
    "Создание стартапов: Поддержка идеи создания стартапов, занимающихся космическими технологиями и защитой Земли от космических угроз.\n",
    "Привлечение финансирования: Разработка бизнес-стратегий для получения грантов и финансирования от организаций, заинтересованных в космической безопасности.\n",
    "\n",
    "\n",
    "Актуальность: \n",
    "Изучение космических объектов и их взаимодействие с Землей представляет значительный научный интерес. Это также является основой для международного сотрудничества, где государства, организации и научные институты объединяют усилия для наблюдений, исследований и разработки совместных планов по предотвращению угроз."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['id', 'name', 'est_diameter_min', 'est_diameter_max',\n",
      "       'relative_velocity', 'miss_distance', 'orbiting_body', 'sentry_object',\n",
      "       'absolute_magnitude', 'hazardous'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# вывод всех столбцов\n",
    "df = pd.read_csv(\".//static//csv//neo.csv\")\n",
    "print(df.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Атрибуты: имя объекта, минимальный и максимальный оценочные диаметры, относительная скорость, расстояние промаха, орбитальное тело, объекты программы \"Сентри\", абсолютная звездная величина, опасность"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Проверяем на выбросы"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Количество выбросов в столбце 'relative_velocity': 1574\n",
      "Количество выбросов в столбце 'miss_distance': 0\n",
      "Количество выбросов в столбце 'absolute_magnitude': 101\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1000 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Выбираем столбцы для анализа\n",
    "columns_to_check = ['relative_velocity', 'miss_distance', 'absolute_magnitude']\n",
    "\n",
    "# Функция для подсчета выбросов\n",
    "def count_outliers(df, columns):\n",
    "    outliers_count = {}\n",
    "    for col in columns:\n",
    "        Q1 = df[col].quantile(0.25)\n",
    "        Q3 = df[col].quantile(0.75)\n",
    "        IQR = Q3 - Q1\n",
    "        lower_bound = Q1 - 1.5 * IQR\n",
    "        upper_bound = Q3 + 1.5 * IQR\n",
    "        \n",
    "        # Считаем количество выбросов\n",
    "        outliers = df[(df[col] < lower_bound) | (df[col] > upper_bound)]\n",
    "        outliers_count[col] = len(outliers)\n",
    "    \n",
    "    return outliers_count\n",
    "\n",
    "# Подсчитываем выбросы\n",
    "outliers_count = count_outliers(df, columns_to_check)\n",
    "\n",
    "# Выводим количество выбросов для каждого столбца\n",
    "for col, count in outliers_count.items():\n",
    "    print(f\"Количество выбросов в столбце '{col}': {count}\")\n",
    "\n",
    "# Создаем диаграммы размахов\n",
    "plt.figure(figsize=(15, 10))\n",
    "for i, col in enumerate(columns_to_check, 1):\n",
    "    plt.subplot(2, 2, i)\n",
    "    sns.boxplot(x=df[col])\n",
    "    plt.title(f'Box Plot of {col}')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "В столбцах 'relative_velocity'и 'absolute_magnitude' присутствуют выбросы. Очистим их."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Количество выбросов в столбце 'relative_velocity': 1574\n",
      "Количество выбросов в столбце 'absolute_magnitude': 101\n",
      "Количество удаленных строк: 1678\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Выбираем столбцы для анализа\n",
    "columns_to_check = ['relative_velocity', 'absolute_magnitude']\n",
    "\n",
    "# Функция для подсчета выбросов\n",
    "def count_outliers(df, columns):\n",
    "    outliers_count = {}\n",
    "    for col in columns:\n",
    "        Q1 = df[col].quantile(0.25)\n",
    "        Q3 = df[col].quantile(0.75)\n",
    "        IQR = Q3 - Q1\n",
    "        lower_bound = Q1 - 1.5 * IQR\n",
    "        upper_bound = Q3 + 1.5 * IQR\n",
    "        \n",
    "        # Считаем количество выбросов\n",
    "        outliers = df[(df[col] < lower_bound) | (df[col] > upper_bound)]\n",
    "        outliers_count[col] = len(outliers)\n",
    "    \n",
    "    return outliers_count\n",
    "\n",
    "# Подсчитываем выбросы\n",
    "outliers_count = count_outliers(df, columns_to_check)\n",
    "\n",
    "# Выводим количество выбросов для каждого столбца\n",
    "for col, count in outliers_count.items():\n",
    "    print(f\"Количество выбросов в столбце '{col}': {count}\")\n",
    "\n",
    "# Выбираем столбцы для очистки\n",
    "columns_to_clean = ['relative_velocity', 'absolute_magnitude']\n",
    "\n",
    "# Функция для удаления выбросов\n",
    "def remove_outliers(df, columns):\n",
    "    for col in columns:\n",
    "        Q1 = df[col].quantile(0.25)\n",
    "        Q3 = df[col].quantile(0.75)\n",
    "        IQR = Q3 - Q1\n",
    "        lower_bound = Q1 - 1.5 * IQR\n",
    "        upper_bound = Q3 + 1.5 * IQR\n",
    "        \n",
    "        # Удаляем строки, содержащие выбросы\n",
    "        df = df[(df[col] >= lower_bound) & (df[col] <= upper_bound)]\n",
    "    \n",
    "    return df\n",
    "\n",
    "# Удаляем выбросы\n",
    "df_cleaned = remove_outliers(df, columns_to_clean)\n",
    "\n",
    "# Выводим количество удаленных строк\n",
    "print(f\"Количество удаленных строк: {len(df) - len(df_cleaned)}\")\n",
    "\n",
    "# Создаем диаграммы размаха для очищенных данных\n",
    "plt.figure(figsize=(15, 6))\n",
    "\n",
    "# Диаграмма размаха для relative_velocity\n",
    "plt.subplot(1, 2, 1)\n",
    "sns.boxplot(x=df_cleaned['relative_velocity'])\n",
    "plt.title('Box Plot of Relative Velocity (Cleaned)')\n",
    "plt.xlabel('Relative Velocity')\n",
    "\n",
    "# Диаграмма размаха для absolute_magnitude\n",
    "plt.subplot(1, 2, 2)\n",
    "sns.boxplot(x=df_cleaned['absolute_magnitude'])\n",
    "plt.title('Box Plot of Absolute Magnitude (Cleaned)')\n",
    "plt.xlabel('Absolute Magnitude')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# Сохраняем очищенный датасет\n",
    "df_cleaned.to_csv(\".//static//csv//neo.csv\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Выбросов стало меньше."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Количество пропущенных значений в каждом столбце:\n",
      "id                    0\n",
      "name                  0\n",
      "est_diameter_min      0\n",
      "est_diameter_max      0\n",
      "relative_velocity     0\n",
      "miss_distance         0\n",
      "orbiting_body         0\n",
      "sentry_object         0\n",
      "absolute_magnitude    0\n",
      "hazardous             0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Проверка на пропущенные значения\n",
    "missing_values = df.isnull().sum()\n",
    "\n",
    "# Вывод результатов\n",
    "print(\"Количество пропущенных значений в каждом столбце:\")\n",
    "print(missing_values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Пропущенных значений в датасете нет. Можно перейти к созданию выборок"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        id                 name  est_diameter_min  est_diameter_max  \\\n",
      "0  2162635  162635 (2000 SS164)          1.198271          2.679415   \n",
      "1  2277475    277475 (2005 WK4)          0.265800          0.594347   \n",
      "2  2512244   512244 (2015 YE18)          0.722030          1.614507   \n",
      "3  3596030          (2012 BV13)          0.096506          0.215794   \n",
      "4  3667127          (2014 GE35)          0.255009          0.570217   \n",
      "\n",
      "   relative_velocity  miss_distance orbiting_body  sentry_object  \\\n",
      "0       13569.249224   5.483974e+07         Earth          False   \n",
      "1       73588.726663   6.143813e+07         Earth          False   \n",
      "2      114258.692129   4.979872e+07         Earth          False   \n",
      "3       24764.303138   2.543497e+07         Earth          False   \n",
      "4       42737.733765   4.627557e+07         Earth          False   \n",
      "\n",
      "   absolute_magnitude  hazardous  \n",
      "0               16.73      False  \n",
      "1               20.00       True  \n",
      "2               17.83      False  \n",
      "3               22.20      False  \n",
      "4               20.09       True  \n",
      "Размер обучающей выборки: (53494, 9)\n",
      "Размер контрольной выборки: (17832, 9)\n",
      "Размер тестовой выборки: (17832, 9)\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "print(df.head())  # Вывод первых строк DataFrame\n",
    "\n",
    "# Разделение на признаки (X) и целевую переменную (y)\n",
    "# Предположим, что 'hazardous' - это целевая переменная\n",
    "X = df.drop('hazardous', axis=1)\n",
    "y = df['hazardous']\n",
    "\n",
    "# Разбиение на обучающую и остальную выборку (контрольную + тестовую)\n",
    "X_train, X_rem, y_train, y_rem = train_test_split(X, y, train_size=0.6, random_state=42)\n",
    "\n",
    "# Разбиение остатка на контрольную и тестовую выборки\n",
    "X_val, X_test, y_val, y_test = train_test_split(X_rem, y_rem, test_size=0.5, random_state=42)\n",
    "\n",
    "# Вывод размеров выборок\n",
    "print(\"Размер обучающей выборки:\", X_train.shape)\n",
    "print(\"Размер контрольной выборки:\", X_val.shape)\n",
    "print(\"Размер тестовой выборки:\", X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Распределение классов в обучающей выборке:\n",
      "hazardous\n",
      "False    0.905989\n",
      "True     0.094011\n",
      "Name: proportion, dtype: float64\n",
      "\n",
      "Распределение классов в контрольной выборке:\n",
      "hazardous\n",
      "False    0.905956\n",
      "True     0.094044\n",
      "Name: proportion, dtype: float64\n",
      "\n",
      "Распределение классов в тестовой выборке:\n",
      "hazardous\n",
      "False    0.906012\n",
      "True     0.093988\n",
      "Name: proportion, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "\n",
    "# Разделение на признаки (X) и целевую переменную (y)\n",
    "X = df.drop('hazardous', axis=1)\n",
    "y = df['hazardous']\n",
    "\n",
    "# Разбиение на обучающую и остальную выборку (контрольную + тестовую)\n",
    "X_train, X_rem, y_train, y_rem = train_test_split(X, y, train_size=0.6, random_state=42, stratify=y)\n",
    "\n",
    "# Разбиение остатка на контрольную и тестовую выборки\n",
    "X_val, X_test, y_val, y_test = train_test_split(X_rem, y_rem, test_size=0.5, random_state=42, stratify=y_rem)\n",
    "\n",
    "# Функция для анализа сбалансированности\n",
    "def analyze_balance(y_train, y_val, y_test):\n",
    "    print(\"Распределение классов в обучающей выборке:\")\n",
    "    print(y_train.value_counts(normalize=True))\n",
    "    \n",
    "    print(\"\\nРаспределение классов в контрольной выборке:\")\n",
    "    print(y_val.value_counts(normalize=True))\n",
    "    \n",
    "    print(\"\\nРаспределение классов в тестовой выборке:\")\n",
    "    print(y_test.value_counts(normalize=True))\n",
    "\n",
    "# Анализ сбалансированности\n",
    "analyze_balance(y_train, y_val, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Выборки сбалансированы."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Выгрузка в датафрейм второго набор (цены на бриллианты)\n",
    "\n",
    "https://www.kaggle.com/datasets/nancyalaswad90/diamonds-prices"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "В этом документе рассматривается набор данных, содержащий цены и атрибуты примерно для 54 000 бриллиантов круглой огранки.\n",
    "В наборе данных 53 940 бриллиантов с 10 характеристиками (карат, огранка, цвет, чистота, глубина, таблица, цена, x, y и z)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Проблемная область: Данные о ценах на бриллианты, включая их характеристики\n",
    "\n",
    "\n",
    "Объект наблюдения: бриллианты\n",
    "\n",
    "\n",
    "Пример бизнес-цели:\n",
    "\n",
    "Анализ рынка: Исследование ценовых трендов на бриллианты в зависимости от различных факторов, таких как качество, размер, цвет и форма. Это позволит оценить динамику цен на рынке и выявить актуальные тренды.\n",
    "\n",
    "Оптимизация ценовой политики: На основе анализа данных компании смогут создавать более точные и обоснованные ценовые стратегии. Это поможет установить конкурентоспособные цены и увеличить маржинальность продаж.\n",
    "\n",
    "Прогнозирование цен: Применение методов машинного обучения для прогнозирования будущих цен на бриллианты. Это поможет бизнесу заранее оценить, когда лучше закупать изделия или проводить распродажи.\n",
    "\n",
    "Сегментация клиентов: Использование данных для сегментации клиентов по их предпочтениям и покупательскому поведению. Это позволит создать целевые маркетинговые кампании и персонализированные предложения.\n",
    "\n",
    "\n",
    "Актуальность датасета с ценами на бриллианты обусловлена несколькими факторами, влияющими на рынок и потребительские предпочтения. Рассмотрим основные аспекты, подчеркивающие важность такого датасета:\n",
    "\n",
    "Рынковая динамика: Цены на бриллианты подвержены влиянию различных факторов, таких как экономические условия, изменения в спросе и предложении, а также сезонные колебания. Датасет позволяет отслеживать эти изменения и предсказывать тенденции.\n",
    "\n",
    "Инвестиционная привлекательность: Бриллианты все чаще рассматриваются как инвестиционный актив. Актуальный датасет помогает инвесторам оценить риски и потенциал доходности, анализируя исторические данные о ценах и их изменениях.\n",
    "\n",
    "Ценовая прозрачность: Для потребителей доступ к объективным данным о ценах помогает сделать осознанный выбор при покупке. Это актуально в условиях разнообразия предложений и поставщиков на рынке.\n",
    "\n",
    "Анализ качества: Бриллианты различаются по множеству характеристик (чистота, цвет, огранка и т.д.). Датасет может быть использован для анализа зависимости цен от этих параметров, что поможет покупателям выбрать качественные изделия по выгодным ценам.\n",
    "\n",
    "Маркетинговые стратегии: Компании могут использовать данные для разработки более целенаправленных и эффективных маркетинговых кампаний, ориентируясь на актуальные тренды и предпочтения аудитории.\n",
    "Образование и осведомленность: Датасет может служить полезным ресурсом для обучения как профессионалов, так и потребителей. Правильное понимание рынка помогает избежать мошенничества и недобросовестной практики.\n",
    "\n",
    "Контекст глобальных изменений: В условиях глобализации и влияния различных политических, экономических и социальных факторов на рынок бриллиантов, актуальный датасет становится важным инструментом для анализа, прогноза и адаптации стратегий бизнеса."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Unnamed: 0', 'carat', 'cut', 'color', 'clarity', 'depth', 'table',\n",
      "       'price', 'x', 'y', 'z'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv(\".//static//csv//Diamonds Prices2022.csv\")\n",
    "print(df.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Атрибуты: Unnamed: 0 - Неопределено: 0 (или Неизвестный: 0)\n",
    "carat - Караты\n",
    "cut - Огранка\n",
    "color - Цвет\n",
    "clarity - Чистота\n",
    "depth - Глубина\n",
    "table - Площадь огранки\n",
    "price - Цена\n",
    "x - Ширина (или X-координата)\n",
    "y - Длина (или Y-координата)\n",
    "z - Высота (или Z-координата)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Загрузка данных\n",
    "df = pd.read_csv(\".//static//csv//Diamonds Prices2022.csv\")\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(df['price'], df['carat'])\n",
    "plt.xlabel('price')\n",
    "plt.ylabel('carat')\n",
    "plt.title('Scatter Plot of price vs carat')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Сильнейший выброс был замечен при значении ≈ 17500."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Используем метод метод межквартильного размаха для удаления выбросов."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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LQHz+85/f5boKxWziL68xQKH4M+e1r30tDz/8MG984xt56qmnuPHGG1mxYgXz5s3jv//7vxvr3XHHHURRxNVXXz2pR0jTtMa/U6lU49+lUonBwUHOPPNMqtUqL7zwwh4f58qVK1mzZg3vete7GBoaYnBwkMHBQSqVCmeffTYPPPDApOzE3/7t3+7WPt7znvfQ0dHB3LlzOf/886lUKvzgBz/gpJNOmnL9np4eVq5cyaWXXkpra2vj82OPPZbXvva1/OY3v9n9EwUee+wx+vv7+bu/+7tx/Ujnn38+hx9+OL/+9a93e5tRFHHHHXdwwQUXTHk+9XuYSCQa9zcMQ4aGhshmsxx22GE88cQTk753ySWXjLvne7KNmfKOd7yD/v5+7r///sZnP/vZz4iiqKEcWO+Buv/++xkZGdmj/VxxxRWNf9czL57n8fvf/37cem95y1t2muUEGBgY4IEHHuA973kPCxcuHLesfs2FEPz85z/nggsuQAjReLYHBwdZsWIFo6OjO71uw8PDCCHGZY7Gcuutt3LSSSexfPlyAHK5HOeff/5eyeMf6Pfxda97HaOjo9xyyy2NzNOeXLeLL74Yy7IaP59yyikIISb1EJ1yyils2bKFIAgAGu/yRz/60XHr1bOO9Xcyn89z3nnn8dOf/nRcFvC2227j1FNPnfQMTMXll18+7nfqmWeeSRiGbNq0CYB77rmHIAj4u7/7u3Hfu/LKK3e5bZC/l0E+B9NRX1YsFme0zX3F3XffTaFQ4KKLLhp3Pw3D4JRTTuG+++6b9J0PfOAD436uZ8DuuusuqtXqTvdXf2cGBwf30RkoFAcGVbKoUPwZcvLJJ/OLX/wCz/N46qmn+OUvf8lXvvIV3vrWt7Jy5UqOPPJI1q1bh67rHHnkkTvd1rPPPsunPvUp7r333kl/zEdHR/f4GNesWQMwSYhg4vbHDkqXLFmyW/u4+uqrOfPMMzEMg/b2do444ghMc/pfe/UB0mGHHTZp2RFHHMFdd921W03mM9nu4YcfzoMPPrhb2wMZGBSLxV2WpUZRxNe+9jW++c1vsmHDBsIwbCybqnRzqmu8u9uYKa973etoamritttu4+yzzwbkQPf444/n0EMPBWQw+PnPf56rrrqKrq4uTj31VN7whjdw8cUXN8rzdoau6yxdunTcZ/VtT/RKmsnzVbdN2Nl1HxgYoFAo8N3vfpfvfve7U64zE4EdMUUZYKFQ4De/+Q1XXHEFa9eubXx+xhln8POf/5zVq1c3zm93OJDvY7lc5pe//CU/+clPxk0G7cl1mxgQ1QfvCxYsmPR5FEWMjo7S1tbGpk2b0HW9EdTW6e7uprm5ufHOgpw4uOOOO3j44Yc5/fTTWbduHY8//viUNh9TMfEY69ewPsFQ39fEY2ltbZ02KB9LPdgqlUrTigHNJGjbH9Sfq9e85jVTLs/n8+N+Nk2T+fPnj/tsyZIlfPSjH+XLX/4yt956K2eeeSZvfOMb+eu//utx5Yqw450ZGwArFC8FVECmUPwZY9s2J598MieffDKHHnool112GbfffjvXXHPNjL5fKBQ466yzyOfzXHfddSxbtoxkMskTTzzBJz7xiSn7a2ZK/btf+MIXpjWszWaz436emLnZFcccc8xu9Uv8OfK5z32OT3/607znPe/h+uuvp7W1FV3X+fCHPzzl/ZvqGu/uNmZKIpHgwgsv5Je//CXf/OY36evr46GHHuJzn/vcuPU+/OEPc8EFF3DHHXdw11138elPf5obbriBe++9l5e97GV7vP+J7O7zNR31a/LXf/3X0wY4xx577LTfb21tRdO0KTOCt99+O67r8qUvfYkvfelLk5bfeuutfOYzn9njYz5Q7+OFF15ItVrlfe97H694xStYsGDBHl236RT9pvt8YpA7k4H7BRdcQDqd5qc//Smnn346P/3pT9F1nbe97W27/O7uHMuecsQRR3DHHXfw9NNP88pXvnLKdZ5++mmAxgTcdOc9drJlX1C/p7fccsuUEygTJ8jGZuPH8qUvfYlLL72U//qv/+J3v/sdH/rQh7jhhhv44x//OC6Aq78z9b5eheKlggrIFIq/EOplbT09PQAsW7aMKIp47rnnph2A3X///QwNDfGLX/xi3B/6DRs2TFp3uj/w031eF6vI5/OzJmhatGgRAC+++OKkZS+88ALt7e2N7NjuzMCO3e7EmeIXX3yxsXx36OjoIJ/Ps2rVqp2u97Of/YxXv/rV/Md//Me4zwuFwowHLTPdxp7MSr/jHe/gBz/4Affccw/PP/88QogpjY6XLVvGVVddxVVXXcWaNWs4/vjj+dKXvsR//ud/7nT7URSxfv36cVmj1atXA+xSEGQq6tm2nV33jo4OcrkcYRju0bNtmibLli2b8j279dZbOfroo6ecVPnOd77Dj370o50GZLPpffzXf/1XfvnLX/LZz36Wb3/723t93XaHRYsWEUURa9as4Ygjjmh83tfXR6FQGPdOZjIZ3vCGN3D77bfz5S9/mdtuu40zzzxzkiDO3hwLwNq1a8dlHYeGhmZUpvuGN7yBG264gR/+8IdTBmRhGPKjH/2IlpYWzjjjDGBHlm6isf3YzGCdvck21Z+rzs7Ovb6nxxxzDMcccwyf+tSn+MMf/sAZZ5zBt7/9bf7lX/6lsU79nRl7TxWKlwKqh0yh+DPjvvvum3Lmtd4zUS+bu/DCC9F1neuuu25SlqP+/frM7tjteZ7HN7/5zUnbz2QyU5Yw1gOYiX/4TzzxRJYtW8YXv/hFyuXypO8NDAxMe477izlz5nD88cfzgx/8YNzxrlq1it/97ne8/vWvb3w23XlNxUknnURnZyff/va3cV238flvf/tbnn/+ec4///zdPlZd17nwwgv5n//5Hx577LFJy8few4nPw+233862bdtmvK+ZbmN3rkmdc845h9bWVm677TZuu+02Xv7yl48blFar1UlKc8uWLSOXy427ljvj3/7t3xr/FkLwb//2b1iW1SiT3B06Ojp45Stfyfe//302b948btnYa/6Wt7yFn//851MGbjN5tk877bRJ93XLli088MADvP3tb+etb33rpP8uu+wy1q5dyyOPPDLtdmfT+7hs2TLe8pa3cPPNN9Pb27tPrttMqb/LE8sOv/zlLwNMeiff8Y53sH37dr73ve/x1FNPTTlpsKecffbZmKbJt771rXGfj31ud8bpp5/OOeecw0033cSvfvWrScs/+clPsnr1aj7+8Y83spqLFi3CMAweeOCBcetO97sddu+9rrNixQry+Tyf+9zn8H1/0vKZ3NNisdjo/atzzDHHoOv6pN8Bjz/+OJqmcdppp+32sSoUBxOVIVMo/sy48sorqVar/NVf/RWHH344nufxhz/8gdtuu43Fixdz2WWXAbJf4ZOf/CTXX389Z555Jm9+85tJJBI8+uijzJ07lxtuuIHTTz+dlpYWLrnkEj70oQ+haRq33HLLlAHfiSeeyG233cZHP/pRTj75ZLLZLBdccAHLli2jubmZb3/72+RyOTKZDKeccgpLlizhe9/7Hueddx5HHXUUl112GfPmzWPbtm3cd9995PP5hpzzgeQLX/gC5513Hqeddhrvfe97G7L3TU1NXHvttePOF+Rg553vfCeWZXHBBRdM2V9mWRaf//znueyyyzjrrLO46KKLGrL3ixcv5iMf+cgeHevnPvc5fve733HWWWdx+eWXc8QRR9DT08Ptt9/Ogw8+SHNzM294wxu47rrruOyyyzj99NN55plnuPXWWyf1Ve2MmW5jZ/d6OizL4s1vfjM/+clPqFQqfPGLXxy3fPXq1Zx99tm8/e1v58gjj8Q0TX75y1/S19fHO9/5zl0eezKZ5M477+SSSy7hlFNO4be//S2//vWv+ed//uddCnhMx9e//nVe8YpXcMIJJ3D55ZezZMkSNm7cyK9//WtWrlwJyOzPfffdxymnnML73vc+jjzySIaHh3niiSf4/e9/z/Dw8E738aY3vYlbbrllXE/Yj370I4QQ44x/x/L6178e0zS59dZbOeWUU6ZcZ7a9jx/72Mf46U9/yle/+lX+9V//da+v20w57rjjuOSSS/jud7/bKM3+05/+xA9+8AMuvPBCXv3qV49b//Wvfz25XI5/+Id/aASO+4quri7+/u//ni996Uu88Y1v5HWvex1PPfUUv/3tb2lvb59RhuqHP/whZ599Nm9605t417vexZlnnonruvziF7/g/vvv5x3veAcf+9jHGus3NTXxtre9jW984xtomsayZcv41a9+NWVvY/133Yc+9CFWrFiBYRgzevdAZly/9a1v8f/9f/8fJ5xwAu985zvp6Ohg8+bN/PrXv+aMM87YZeB57733csUVV/C2t72NQw89lCAIuOWWW6a8D3fffTdnnHHGXvW2KhQHhQMp6ahQKPY/v/3tb8V73vMecfjhh4tsNits2xbLly8XV155pejr65u0/ve//33xspe9TCQSCdHS0iLOOusscffddzeWP/TQQ+LUU08VqVRKzJ07tyGjzwTJ5HK5LN71rneJ5uZmQWwMXee//uu/xJFHHilM05wkqfzkk0+KN7/5zaKtrU0kEgmxaNEi8fa3v13cc889jXXqMtt1mfJdMZ0R7USmM/39/e9/L8444wyRSqVEPp8XF1xwwThj6DrXX3+9mDdvntB1fUYS+LfddlvjWre2tk4yhhZi92TvhRBi06ZN4uKLLxYdHR0ikUiIpUuXig9+8IMNY2jHccRVV13VMJw944wzxMMPPyzOOusscdZZZzW2s7NrNtNtCLHzez0dd999twCEpmliy5Yt45YNDg6KD37wg+Lwww8XmUxGNDU1iVNOOUX89Kc/3eV2pzKG7urqEtdcc804Gf+xxtATme4ZWbVqlfirv/or0dzcLJLJpDjssMPEpz/96XHr9PX1iQ9+8INiwYIFwrIs0d3dLc4++2zx3e9+d5fH7rquaG9vF9dff33js2OOOUYsXLhwp9971ateJTo7O4Xv+1PK3gsx+97HV73qVSKfzzdk4Gdy3abb5nTvz1TH7Pu++MxnPiOWLFkiLMsSCxYsmGQMPZZ3v/vdDRn7qZhO9n7isUwlOR8Egfj0pz8turu7RSqVEq95zWvE888/L9ra2sTf/u3fTrm/iZRKJXHttdeKo446SqRSKZHL5cQZZ5whbr755nGy+3UGBgbEW97yFpFOp0VLS4t4//vf3zDrHvtMBEEgrrzyStHR0SE0TdsjY+j77rtPrFixQjQ1NYlkMimWLVsmLr30UvHYY4811pnueV2/fr14z3veI5YtWyaSyaRobW0Vr371q8Xvf//7cesVCgVh27b43ve+N6PrpVDMJjQh9lFXqUKhUCgUs4hLL72Un/3sZ1OW4L0UuP7667nppptYs2bNtMIQij9fCoUCLS0t/Mu//Auf/OQnD/bhzHq++tWvcuONN7Ju3bp9JtCjUBwoVA+ZQqFQKBSzkI985COUy2V+8pOfHOxDUexnarXapM/q/W2vetWrDuzBvATxfZ8vf/nLfOpTn1LBmOIlieohUygUCoViFpLNZmfkV6Z46XPbbbdx88038/rXv55sNsuDDz7Ij3/8Y84999yGMqJieizLmiSyo1C8lFABmUKhUCgUCsVB5Nhjj8U0TW688UaKxWJD6GOspLtCofjzRfWQKRQKhUKhUCgUCsVBQvWQKRQKhUKhUCgUCsVBQgVkCoVCoVAoFAqFQnGQUD1k+4goiti+fTu5XG5GJo4KhUKhUCgUCoXizxMhBKVSiblz56LrO8+BqYBsH7F9+3YWLFhwsA9DoVAoFAqFQqFQzBK2bNnC/Pnzd7qOCsj2EblcDpAXPZ/PH+SjUSgUCoVCoVAoFAeLYrHIggULGjHCzlAB2T6iXqaYz+dVQKZQKBQKhUKhUChm1MqkRD0UCoVCoVAoFAqF4iChAjKFQqFQKBQKhUKhOEiogEyhUCgUCoVCoVAoDhIqIFMoFAqFQqFQKBSKg4QKyBQKhUKhUCgUCoXiIKECMoVCoVAoFAqFQqE4SKiATKFQKBQKhUKhUCgOEiogUygUCoVCoVAoFIqDhArIFAqFQqFQKBQKheIgoQIyhUKhUCgUCoVCoThIqIBMoVAoFAqFQqFQKA4SKiBTKBQKhUKhUCgUioOECsgUCoVCoVAoFAqF4iBhHuwDUCgUCoViT4kiwbZCjYoXkLFN5jWn0HVt2vWDIOKJLSMMVTzaMjYnLGjBNPU93t5fKuo6zg4m3ofj5zXTV3b32XXfnft4oO+5esYUf06ogEyhUCgUL0nW9pe4a1Uf6wbKOEFI0jRY0p5hXkuKlG1MChTueb6Pmx/ayMahCn4YYRk6i9syXHrGYs4+omvK7S3ryLLi6C6Wd+YO8tnuPftqAHvP833c9NAG1g2U8YII29RZ1pHlsjOW7LPrOPFY5+ST9BSdGR/7vhys7+229lfgcM/zffzH/23guZ5RvCDC0DWaUxa5tEXSNMgmTE5Y0MLC9vSU78NUx9aVTbByW4GhikfNC9k2UmPDYKVxH5d2ZDhuQTMducS4c1nbX+LOVb08s22UqheQtk2OmdfE647u3i/vzv54xmZTsKnYM17K90kTQoiDfRB/DhSLRZqamhgdHSWfzx/sw1EoFIo/W6JI8NC6QX78p81U3IDOXAJD19kyUuWFniKOH5GydFK22Qi4AG747QuUHJ+2jE3KNqh5IUMVj1zS4pLTFvFCb4nhisecpiRp26TqBfSMOrRmbC47Y/FeDyx3NVjYn4OJvR3A1o/t3hf6+M4D6xmt+ZiahqYJhNAIhaA1k+B9Zy7Z6+s48Vi9IML1IxKWjm3quzz2fRlY7+229leQf8/zffzD7U9RqPpMN4jTAV0D09DJJgxStsmitjRvOG4uxy9oZqDksnJzgVXbZRBVdgOGSx5VP8QLI/wwImUZHD4nz4KWNIWqx7qBCkEYsaA1TXs2wdKODO25BL95poctQ1V0DdAANAxN49DuHB8+5xCWd+b22fO9tr/ETQ9t3OtnbGwAmbLktTl5SStHdOfHHduf+0TN3jJbgqDZeJ92JzZQAdk+QgVkCoVCsf9Z21/izmd6+c2qXvqLDgKBECAQlJ0QgSBhGjSlTJpSFsNVn4xtkLQMhioeC1tS6PrYDEHE+sEKtqlzSGeO4+Y3jVsuhGBNf5lj5jXxt2ct2+OBxq4GCzMZTOzpwKc+gB0qe+STJrquEUWCohPQlt31ALZ+bGv6ity/epCS42PqkE1aJEyDUAgcL6DmhWSSFod0ZXn5olYMY/rrCEyZAXu+t8ivn+rBDSLmNidx/IjHNw1TqPpkEgaHd+fJJsxpj32qwXrFDVg/WCaTMLno5Qs5bUnbjLJtezvw3xeBw1QEQcSrv3QfW0acGX+nNW3RnDbpK3kIIX92g4hICNK2gR9GDJY9wkgGcoYBYQSRAEPXaElb+GEUx1oa+aRJZ85muOLTW3Lx4wxdU8qiPZvANDRKNR8vFLz2yC7e+fIF3P1s/7jne7psW52pnneAb92/jlXbRzmkM4um7Vh/pu/q2v4SX/39Gl7sLVHxAtw4AEXIZ/qouXletqCFFUd3AeyXe/jnwmwJghq/40ouuqEhIoGma0ShoC2XOGj3aXdiA1WyqFAoFIqXBPU/ultHqpQcHz+McPwQPxRE8TqGBkEYUfVCOnNJFraYrB2oUPVqLGvPjgu2al7AcMXH8yNGqj4aGkEoWNaZoTWTAEDTNOY0JVnbX2ZbocaC1vQeH/eOQV2Kqhewavso20drvObwTu59oX/a5ZfFGb49GfhEkeCuVX1sHq7iByFr+kuNcs2OrE3FC/jds30sbc8CTBoArx8sN44dIah6Abomr0vNDzF0jSAS1PwILwKv6vPU5hF6Cg4vW9jMkni7Y6/jH9YN8tSW0UkZMNvU2DJSo+wELGpN05612DBYpeaHmDr0l1xGa0N0ZBO0pMcfez3IvGtVH8MVrzFYH664rOuvMFRxGa35vNhbYk4+SdI2dpptm2pbALmkRTZhsqa/PG7f0133Pf3+zvjjuqHdCsYAhqs+XhBR80IioOqF8r4g34NQyABMABEgwh3fDSNBoeZjaOAGAgGMVH02Ddca69QTY4V4P/NbUrRlE/QVHf539QCDZRcvEOSTJvmkRaHq8V8rt/Pzx7eOy7bVA7SBkstTWwqsH6iMe96PW9DEuoEyc5qS44IxmNm7GkWCHz2ymT+uH6Ls+ASRIIx2nEMQCbYXapi6xrZClaRl7NE9nC1Zo/1B/dwmTp5M9XvrQARB9Xfthd4i/aMOo05AGAk5QZA06WxK7vG7diBRAZlCoVAoZj1jB7hzm5Ks3FKg5ASNQKxOKCAMBZEXUvVDmk2LjG0wWvMJxY61a15Ab9HBDwRoMssWRhF9RYeS63P8guZGUJayDfrijMpMjnNi5mdnA/PVfWVufmgjmYTBIZ1Zym7ISNXDNnSWd2RYO1DhR49sxvFDRqr+tAHbdAOfbYUaT24ZYctwlWKtXt4mAI2Rikc+ZZEw9UlBkuzHS7N5uEbPaI3lHVk2DVYII4Ftahi6ThAKSk4QZ1p27NMPobfocP+LAwCNoCxlG6ztL/OjP21GCJjTlMTxDZkBq8lMZiSgOW0xUHYZrnpU3ICaHxIJSFoGQgh0XWOg7GLoGk9sHmkMvrcVauMG68MVl5VbCtS8kGzSRAC9ozVGa7Js9cRFrSQtfcrrOHFbY5nJwH9vv78z/uvpbbu1fp2yF076TABVX0z6rP6JroEQ4IcCfyfbFoCuawghKLsBfSWXRa1pmlIWGwYrJC2DfMJk41CFqhdQrPloQMo28YIIISL+Ow7Q2rI2Q2UP09BZ1pGhOW0TRYJnto3ybM8oZSdgbpwtm8iu3tWtI1Xufq6PQtUDAUIDTZPBWCTA8SO2jtQ4aWELL/aXGCi5nLioZcptZRMmj28a5rFNLZy0qPUvosSxfm5r+0s821NsTJ505GwMXdsnEw67y7ZCjQfXDrC2r0wQCVK2gWVo+KFguOpTdAJsQ+eC4+bu0YTagUIFZAqFQqGY9Ywd4I7WfMpTBGNj8UNBT6FG1ZVZHARU3JDmtCxtGq74VD2ZnfFCOfwcLEshg4xvsm6gQkvalpkgLyRhGmTsnf/JnGog1p5NsH6wzMLW9JQD81zS5Nntoxw5J8fjmwoMVz2CKMLUdVrTNl1NCf64foiOXIJj5zVNGbDtbOBTcnzW9pcZigMY09DR0BDITOJQ2cUPI370yCYEWiPg216o8vMnttFbdEhZBpuHq5iN7WtoyFK2qhfG4d2OQbyhy7K3qhfw6MZhFrVm0DToLzpsHanSkUvw8sWtaJrG8z0jhJFgYUuK3qJUB2zP2mQTJttHHYYqHglTJx1f+3pWrjVjM1R22TIss6UAFS/ACULSdoooEjy7vUih6tOasbENnQHHJYygK5fADSI2DlU4aVELyzsyPL1tlP/84yYuO2MJC1rS47Y1Fbsa+O/t93fG5qHqbn+nztj7NBOi3VhZA3RdJwwjRqs+bj7CCwReGFGourh+SDZhUnLkBIimgRuEbB2pMlL1CKOIMBL0jTqEQlCoegyUHPIpi7Rt0pwyqXohZTegK59gTlNq0js19l2dKku1tq9EX9EhikDXGZcd0zR5vmUn4E8bhyl7IYMlF8QI3U1OI3Nez7oOVlyKNZ/v/O96Hl04Mk2J48HJGu0Pxmb6swkDjR2TJ2UvaExi7Yuqgt2hWPVZ3VfGDyOaUhZOEDVEbvJJk9Gaz5q+MsWqD6379VD2ChWQKRQKhWLWM3aA2190CGcwUNQ1+b0wDDENGTxEUUTZCxmputT8HZkdXZMz/FUvxA0iDK3CYV05ckmTnlGHY+Y1NXpYpmK6ssTnekbZPFSlM5cgl7Qmfc/QZcC3dqCMhkY2aWIZJn4Y0V9yGCg7jNZkZuyxTSP0l9xGyWFnLrHLgU/J8RmueIRCQASOHxAJga5pWIYU4xgsuQyWPU5Z0krZDdk4VOHJzSMMlBy8EFw/ohwHPUKAF0Tolk4kdgzux94OTdMwDY0oEAyWPJ7cMkIQRqwfrFKLA5DHNo2QS5r0jtZIWQaaJs99tOZTdUNyKYukqeOHEUnLAIgzKYIgFGBBwjIoOVKMAiBjmyRNg+2FKhuHqqztL6NpsjzPMjSqboBt6liGjmXqDFc8tgxX6Su69JUc1vSX6Sk4HDu/meMWNJE0DapeMOV921WQXj+WPf3+zqh5O8tV7Zz9KRpQ8yMMXQY1gRBsHa4iNJk1jsKI1mYbL4hw/IikbWBoGiXHp+zKErMFrWlKbsDmoSqWIYN+mbkWIIQcdAey5PJ/Vw+yrD3D8q5sI5MthGi8qzU/4Fv3r5uUpRqteQShLLsMxszoiHhfIEs2+0sunfkEhapMn/WXZOZ8SXuGDYMVql6IrslnMGnqPLNtdK9KHGc7E0twhyoeYSTIpyyyCRiueOMmsfZmwmF32TBcpuYFCOR9G/u3wdDANnWqXsCG4TJHzW/a78ezp6iATKFQKBSznrED3N7izPpnwgjStsawJ2hKWSRtg+d6S4RhhBPs+Ktt6JC1TfxIEGkRfigYLLsMl116i7J5/9yjunYqiT1dWeLyjizrByq82FeiPZuYNKMfRhFeGOF4IfNa0vGANcTQZBZoy0iVihuytr9EyQknlRz2l1zmNad2kqkJ5T6CiCiSWQD5bYEXyJ9BDlwe31RguOKytVCj4sp96QAaGLoMjiJAF+AFgqk0wSxDbtOJg12B4NENQ1imQVPKIpG2sQyNF3tL1PyQMBIkTJ2mlEVTysIyNEpuQDZpYpsym+cFEX4Q4oWyXLG/5FCsGei6Rto2yCblUGZec4rmtMXdz/UhhDw+25TBnCx9jGiz5XYFchC5avsoQkAmIddL2QartsvBdXPaomfUIWMblF0p/GDHioW7CtLnNadY1pFl1fZRsglz3H2Pooh1A2WWtGeIhCCKxG4N0G1zdg7mRfw/GjKoKcQlskJAyQ1lb5YhnyPQqMbPuh/JrNT2gkPCkvdb13RySYtQCCpugOOHCCFksK/J+75+qMJw1ePERS0kLSP+vk4mYfDv967D9UPyKYt80mqUPA6UqjvNrNcJoggRCZKWQRhBW04KBK3cUgAgDCOKToBl6qwdKNOathmquFTdgJcvadvnZar7kj3pb5tYgmsbenwv5fubTZoMVzxKTkA+Ze3VhMPuommyj7Ve6VDvZxTIEvaaH5EwtEn3ZLahAjKFQqFQzHrGDnAdf2azrl4QUnQgaZnMbU5R80JcPxw3Mw6gCUhYOgnA8SAUsoyxp+jwqsM6Ofeonfd+7KxfKJ+ymNOUpKfgUKhKSfGaF5KyDbpzSfpLLklL9k5tG6nhxMp3uqaRNDVZ8hWE9I6GpGyThCUzC6EQeH5IXxycpuMs0kTqvTF1wYZJKZJ4AL21UCNhyt4Lx5e9RjpjBtmaaFgFCCEzit7E6xiv68cZiB27kAM4y9AJwojtBYeqFzZmsv0wpBb3yGUS8hiGKh66JoNlL74mpqGRtWXEV6j5mIbG/JYUuYQccG8ZqTJc8XBi1TzHl/dbi0tWQTT6CL1ACr9oQFc+iRdnHVvSNrmkzGbMbTLQNbjr2T6ZYYxD2bqc+86CdF3XWHF0F9tHa6zpl89GyjboKdRYta0or5GAr/1+zW73F2UTkzNus4H6ILiedY7iZwvkoLg26qBrsWhIfDl1XUNH3tuKFzBaq99rHU2Tz6DrR1imTjZh4oVycuHQzixFx2f9YIWH1g0yrzmNoYEf6tz6yGYKVZ+EoWGauizV1XVaUhYlZ2a/O/wgoqfoMrc5ianrDFd9gjCiv+hgGhpCaCQtgzlNSUxD9jQGYUQ1nmSYirFZo4Ml+rGn/W0TS3BzSZOWtM1AycHO2FiGTsUN8MJoXKZyZ1UF+4qWhCWz5ux43up3oP5M+qGgZZa+N3VUQKZQKBSKWc/YAe4LPTOZ4wY/gpSucdz8JtYOlBmpeJgamCaMHZcFAkpOQEvaJpOQAUQQRbzhuDn89SmLdzlQ2lm/kBYP3jcPV/nvp3oIoqiREUmYOnOb0+SSBoMlr9GQnjR0an5If1lmBYJIBkBWKE2YNV1mCXTLwKn5lByfaIpsVRQJRh05kBw7QKkzttzQ8UPmNKUYLMs+q3oJZ32gE0YyKLMMGSTmUyZCwHDFb2QckpZOGInYm6wevsD8liRtmQRDFY/hqkfNixrZt/oxBBEEXogfRqRtkzAIcYJIBrhaRMYy0HW9MchvTplU/Ag3CFk7UOK/Vm7jma2jPLllhKoXxMehSdl2ZNbO0DSqngzURmvyAWhOy0Fa2QnozCfJJc1GNmPTcFVe10bjlTb5IjJ9xmF5Z47LzljcGACv7S+zZbiKZeq8bGETc5vTe9RfdMy8PPevGd7legcaMy5XrN9TS4ekbVJ1g0YQFo65nHKyQMSBt45laIx4gcziCnldnSAiiATJWDAkiGTwZpkaGjIoKtV81nslskmLuc0pyk6A4wdUXPlMzmlKNYKmijuzgCyIBFokn+OufILVfSWGKh5OINADQTZh0JaxyCTkMNrO6PSO1qh5IUXHoyVjT9pmPWs0WHL5/XN9B8xEu86u1F539vxNLMHVNI3lnVnpXVfx5O8lTWY31/SXd1lVsC/pr06umKg/X7tabzahAjKFQqFQvCRY3pnj8O4cdz41M5W5lqROU8Ziy3CFwZKLpkFbNkHNi3CDHX04AjmDnwkiwjgTk0lYHN6dn9GAYlf9QoOleCCoganrmKYssSk5AWv6imiahkBg6zpeEFKLwA8jGokdwNQ13FAQOD65hImu63hBRMqWmbGNQ1UWx2qGsGMm/P9W9+OPiV81ZLAFcvBcXxRGssTPDXYEb8GYTJeUBxeNQc6x85r5/05bzHW/epaNQ1V0ZBmXHzV8gdEEmAaNvhJd06jFaTVjzDGMJQgFpg5VN8LQpABHKORgP2mb8TYCRqoeUQQbB6t8+perSNsm81uSBKHA0OSAXcTiJQBJU6fmBbhhyNZCjXzSImXpCDSGKx4p22RZx45y02QsZNKZS7DiyK5JJYt1MZVIiEn+WmMzDss7cyx+ZYbHNg/z40c24wYJTl7UgmHI+7Yn/UXLug++16mpMynTPPY505G9O7YBFYhFYOR36sF4PfDSdZmRCiIN2zQQQUgQRYxUA2lpIaDq+ji+TsKUhu9r+6XiZ3PKouxI2f6hskvvqIMTyAxvyjLwQynZP7cpSWvGplDzZnR+QQQEIZuGqg0Z/Jxt4PoRaUuWyxZqPklLGm4Tn69AvosLWtKT/Ax7Rh3mNCX58Z82s6avPC7rumGgwgu9pYaJ9u4wk2zb3towTFWC25qxOX5BM2v7S2waqpJLmgRhxDHzmnZZVbAvGSz56PHsztjfJ2OVQnVNrjebUQGZQqFQKF4S3PN8Hz94eBNFd7J891SU/YhUEFFw5MAun9QpOSFeIGfJJ+aUHD+kKWXiBBGHduU4YcHUctcTGTtYmdhvlLZ0nthcQNM1Du/MEApZmjVc8TAQ1AKBiGTmq14qKOKBakLXCGNNxFQsouEEESU3JGNDxjawDJ2yFzBQchuZt7Ez4ZapywAsDr7GjqHHnn/RCVi1vdj4tF5OuENXccdnOtCetTENjaUdWUarvizpEmO2KyBhylKxQs3H9QUjVbexz6lEWRq9R9WAlGVQ9gLKnkd7xiaM5P0JI0Gx5qNrGt15u2FmHEYRG4equHGQasdZRtvUsXSNmi8b6HRNZ35zmlzKZONghYob0JVPsqwjS+uYrMZAyaXmhcxpSqLrOvmUPu5Y5zQleWLzCC/2lfCCaJf+cU9vLfDM9lFSlsETm0f3yutuUUtmp8sPBBODsYnUg6+SK8tCTUNHILAQeFGcTRUiLn/VCIQgCiJ0wI/LdKVqowzeBRqREPihIBSCIIxoyyYYrnixT52GocuJjUiAHcueCyJGq4JcwiSXNLHiQESHGfWSyYkR+Q6VvAjT0DAMnZSl4wQRw1WfFmC47FKoBYRRRE+hxp3P9nHMvDxz4lLpnlGH1rRNoerx9NZRbEMjl7Kw4p66Us3nqS0FfvTIZj51/pE7DcrHBmDT+bVNLEHcWxuG6UpwLUOagZ+8pJXzj5nDEXPyB9xzrbspganrjUqAcQJDyHtt6jrdTYkDdkx7ggrIFAqFQjHrCYKImx/aSMnxSVo6/sQGpikIQ+JBkojL8iI0DUzDQNOihsltnaRt4EeC1kyCS05fjGnq0226QX1wdEh3lsc3D3NnnDWpz3x7fkTVC5jblMQ0TQwhGCxLwYtM0kbzAoq1IB48ysgpisA0NLw422TEctzZhEHCMnbIO3sh/VUXXYO7n+2lv+jy2qM6ufvZ/sZM+MahCmZc7qXHJWP14Gzc9Y2kkMHEM26UNYodnlRJS2ek6vGjP22mZ7SGF+743thQOQxl+WJPwd2pEMXY0kW5L4FlarRZNn1FB8vUiSKBocuMiwZ05hJU3JAgEnRlLHIJk+2FWjxYF1i6NIZ1/ZCWpiStGY3hSoBtavzLm4+iOWnz/Qc3smGozLHzmqbIZtRI2wadueSUxzw2g3bCwpYpMw5j/eNStkHKMsgkjIZi35563T25qbDLdfYHU0nmG/GCemVnFK8js2U7VExTtvStc4VcQ9c0WtIWtmlQcn2iSJb++pEY92zahi7f90BONoRC4PoRzSmLbSM1RqoebhDhAelYuRHqz3lEEMkexe2FmuxBiw9I20VEtuMcYnVPZE+bbRoYuoYTy6qXah6jVU+W1yIz2bous66PbBhmWUeW9myCY+Y1cfS8PP/62xcw4kz9eAGfBNsLVe5/sZ9zj+ri5YvbpgxqxvaADca2D5ahc/S8PEvbs9OWIO4LG4aJJbh9RYeEaXDs/OYDmhGbyDmHdpEwDdz4HowtiQX5LGQtg3MO7TooxzdTVECmUCgUilnPE1tG2DhUoS1jU5thH0iIDGYsS8MJZNCRtOISFkOOyLxgxwBQAw7vznPJ6Ys5+4hd//GeODha11/GCSLStoEdB3OBkCIFiTHS7fXMjQwOZVmdpkHC1KXJcjxoTJo6YRTho8VqYVLRzI0EQ2XZc6ZpGkvbM8xrSbFq+yir+0tU3KDhe9aatkknTKpuiK5FGJqGG0yRnoqJ2BEgjRfmIM50aLRkbLrySR7dNMK6gUqsRqfjRxEi3DHODQQEfhT3Fk2YlR+z/bofVP3nsisDmKaUiWXqOH5Edz7B9lGHirvDFLjkBpi6RsaWsvm5lMVIVfbTFWp+7GsFflQlYRo0JS2WdmRpSSVY0Jrmr09byE0PbWTtQKUx41/PZrRlEyQtg5ofkjMmB+ZjM2hTZRy68zv8446b30zJCbAMHV3Tac0Yk2TCd0eV7unthV2ucyAwkM9DGAmZ1UWWqdbv49gSxrIbYutyfV2TAYAQUKh6lN2QSAj5rho6hi6wDVm+64URuq5hGFJMJWebDFVcegr1fjIdI5TBkR/ueGrrwhp16XyQqo+6BrYJM9QFktuIz83WNbS491PXpJ1CJRa5qWfoskmL5pSF44c4fkRrxubD5xzC/JY0D6wZYLTqk01KRUhpeC5LhaWyf4Rf8fn679dwxvLCpCzX2Mx3dz7B9kItFuyRfVuZhElrJjFlCeK+smFY3plj6auyB0WQZDoGqh7dTQkZ2Iu4HDZeFiF/33flEwxUPRYkZ2/YM3uPTKFQKBSKmKGKhx/KcrSUCUV3Zt8bG+BEIu5RMuo9LRqGLhCRnF3/9BuO5PVHz5lRZmyqwZFp6DSZGpZpcEhXlo5sktGKy6+f7aWn4GC1SdELOfiMA7Y4ODJ12ZtkmzqjNR/bkFkhv66GIMCJe2vcIMLUNVKWQVs2wbELmsmnbHJJiyc2jzBQcjmsSw7k8imLRa1p1vaXCYXeuBYTGatOFiEHrWMny+uD7fas9JL65ZPbKXs78mGBH2EbGrbJpMyjLB+rb2X8PgWTy9+SlkHFk8qLKUtmJNYNVnCDiJobEkVVTFOnKWnRlLYIIlkil7ENTF2T1ynuPRJIlb4wFOjAwtZ0Q/ltuhn/Y+Y1cc4RXdz9XN+UZahS9n76DJqIA8LBksv8Fln+NVaVrjVjj5MJn6nXXZ1ybf97O03FxCdHEKttxtfZ1KdR8kSq3AWhvE9d+QRVL6TqhYB8H+SzIMsSTV0nlzQRwqTsyKD7pMWtlJyAvqIjLReIaM/YJC2drQUZzRs68jkTMojStVhEJE6bNKdMan4kJw52w5Etm7BIWrKkOBKCtG0g4l7TYs0nbgckENJeQQrTGGgarOkrATQCFj+KGChJSwnb1IkiGawGoUDXZBY4kzAnZbkm9oCVnIBCzaclY2Mb2qQAf2IJ4s5sGHZXFVHXtYMq2z+RkuMjBOSTJqW4n7COocl3r77ebEYFZAqFQqGY9bTF0so1L5QpFWbWR+aO+ess4p/rSm0iVs7LJQ0O7cpyaHduxmWKOxsc9RUdtgzXMHWdntEamqYx6vhsHCyTsk2EkH0wugA3jLBNaaTqBDJLoGsaNV92jxmGhm0YUpCgGvfLaBodWZt5LWmWd+YavU8NdcChKgMllznNKTRN49j5zVS9kIGSM0llztDGG+LWmapyKQJ6R92GKIOhje8F8+KgR17VsU31Op05EzcUjQHrVD1I9X4P25SBVd3nKRVvzNDk4DqKEyGGDtmkRdnxsTP2GMNfMa4HTkMG4hUvpFAdL+qwsxl/XYfne4uTZO91TaM9l6AlbTFQcukekyUbrris66+wZbhC0QlY21+i7AQs68yMU6VLJ0z8MGSk6s3I624sYXhwArKJCKA2JtsaRDsC+4k0An4hKLsBCUMDU4/LAWXmzDZkn2QkBGFctptKyKxlOmFyxJw8a/pKDJRcsgkpOS99sHy8IGwI09SDw0js2G5HJkGEQNflhMdEefRpjxdZ8piydQxDJwwilnZkKDkBL/aW5ARGJM/L0GVmruYJal6IHYv3rB+ssLAtw+K2NELUe1WlUmHVlxk229RwfFn3mUuYzGtJNYRjlrZnJ/WAeWFEEEZYsSroRB+wiSWI0/WANfrbDqAq4r6m7AYU42ArY+uyHlXEkXhsczFa8xsG8rMVFZApFAqFYtZzwoIWFrdleK6niDODPpupqLeNhAK0SJA0DVoyFod158gn7RmbmE43OAp0GCwFstm+7LJ+oIxl6LSmLQbKHm4giERAEEozaA2pKpdNGOSSMhtQqPlyRl+AYUhZbx3pM6ZnbIpOgG3onHVoJy0Ze1K5XEcuQdo22D5aawQKrRmbI+bkqLk+xTF6/3XFxUBMPzCdSH29+ncninPUS4TqgV4goCllMq8lTckN5EDV0IlENElhsa68GEQyaLZNnYoTYMRlmSNVDyE80rbOgpYUhThTlLQMKUnuBbi+rJmsG1qbujSE1eIOvae2jrJ1pMrCth3CGLuc8R8je1/3L6u4PrZpsHm4xqLWNMu7pMLlyi0Fqm6AALIJk4Spj+sZk6p0ZfpLDo4vy1eP280enPIMRW32N1M9M2M/M8c8HwlTa2R8R2sBaUunNWPTnU+yYbAs1Qotg1BI9VEnCElr0pQ5FEJeUyEouQHtuQQZ22S4KjPJCUPDDyBCk+qemjQE15HPUiIWEAkjQc2VGZRdiXrIoE5DxL2LNT8iirPrKdtA1zQO6c6xcvMIYawMWvdOE3EZYc2TJbN1A3VN08gnLWp+JHvQNA0/DOP+VhFL/Uc8uaVAb9GluynRyHJN7AEbb8ysjfMBg6lLEOsZ4TtX9caS+yFp2+DYec275YM320jbUk1TCjdZ44LKKBIUnQBTl5nN2YwKyBQKhUIx6zFNnXOP6uLJLQWq/szLjcaia2Dr4ISyXPGM5W0s78iwfqjG8s7sjE1MpxochXHZTyTAMjRqnkzk1aXkW9IWhq5RiUtqROzldfyCJipuSM9oDdvU6cwlcYOQhKnjeAEFJ8SKy/C68kkO6TR5rqckG9i1ybPZjh+xoDVNxjYbM+GOH/JibwkvErRlbQZKMks0Vrhgd4mQmYGpECLOYmkyi6VpUlTE1KRwCEB72qQYl2rJgbgUWdE0mUGwdNlbV/VkmVix5hPG19YJItYPVcklTAoVj4VtaXpGYwGHSKDrkDJ1aaKta3HwqFFxAwaKDusGyuMCsinPL86ChpFoyN4PlB3W9JWxdAiFRlPSwglC1g9VGKp6JC2dYs3HNHSa07a8325AS1r2tq0bqHDSohZOWtTM09tGWdqe5bIzFjO/Jb1bmYnant60A4yhaxia9H+zTR0hBAkBFTcklzSpuAGFqkcYCSnQEoqGQiYCRmIzZk2DF3uLbBqqMr81zfLOLCBY3VtmoOTKjGsoM1QtaSmD35610QHLNJnTlKBn1CEII4w4A76rK6ghAztNh6Z4m5EGCUP2NB43v5nOnM3KTSNxhk9v2Elo8WyFLNOMSFpynzU/ZF5LCk2D4YqHG4QNg3A0ME2NpCnLlvtLDqOOR1sm0cjcju0Bm2jM7IdRLHqj77oEMU72Cvk/jYDxpUrVC7EMjUhI5UtgR4YMWRpqGlpcIjt7UQGZQqFQKGY9USTYMFghaWr4gTauFHGmhEIqL4Lsc3p80wjrB6sc1p3brXKdiYOjbMIgDAWOH5FPmrF3kiBpm1hx6V1b1uaCY7rpK3kMVTyKNZ+XLWhiuBrghy66pmHoOh25BFuGKwDU4ll06TUmlQVaM/akDFid+kDshIUtnHNEF797rpent47yYl+RmheyrD3LkvY0v1i5TZZH7SX1bMBE6n1oGtCctDANnXWDFfwgitXyYLAaNMRD3NhzLR3LaPv1stJ6300QSS8yQ2duU7IR/JZqPpomA7TlHVleNr+J/3p6OynLiHt4xt9Py9SpeiGD5V17UY3Nguq6Ti6p8WKvTxgJ2nNJvDDC8SOOnJOnNw7yHC+kI5dsyOiDzJiNVH1sU2ew7NIz6lB2A+a3pHn3qQt3GRhORSRm98CyThhJMZu68mEmYTUyjNVYXTASoOkaUST7Pf0owoqzmnWxkIxtYhk6aJCydGxT57GNI1g6zGtJNbzphiseZSfAjkshF7VlWNaRZf2A9CxrzdjU/JmZAwvk74qEpZOyDPncRXD0vKZGEH3/6n5MXZNZ91hNop5MDWMLCrNuuYA8j/ZsgvaszfaCw7ZCNS5NlsqlUvhH9obapk5f0QEhs+NT9YDVS2CHKh5BGDGnOQWIaY2Zx/a9zmtJkbZNql7Asz1FeorOjI3JZxvZpEk+ZTFcdinGFid1LEMjnzRpSllkZ7GgB6iATKFQKBQvAbaOVPnj+iGyCZPFbWme7ylR2c1MwcTYoegECLTdbvaeODgqu2GjvLDmR3hBCEIgIoETCZJxpqbqC+a1pOlqSrJxsMKbT1pALmFR8QKe2lLgf1ZuZ91AmaGKRyQElqHT3ZSkPZvADyMGyi7DVY/WjE1bJrHTXpD6CTuxeELKMhAIKn6Ipes4M+zB29m1tGIRhalCO8uQmTHb0jHr5Ytx9qo+Q6/pxINygdCkv1MoBJmEiaHBSM1vBHf1AKvkBkSRIGlo+JqOZei8bEEzAugrO40B8qTjFQI/Eli6RlvWnmKNHUSRYN1AmYGyQzYhe/5KTsBw1SMb9+xYhk7ZDUgnTE5e3Eo2YfLUlgLHzW9mfmuqEQzWSxSHKi7Fms9wxeXERa17JRNeF4SZ7UQCvDDC0CFlyOvoxPKGjh9B3CtWD97llyDQZLlqNmHQlLY5el4THdkE2YTBmv4Km4eqAGi6NIq2DB0rrneteSFHzc2TTlh4QYgbRAxWXGxTZ7jqkzD1KeX7pzx+pN/cso5sHIgnxgXRuqaRTpqYgcANwriHTtpXaJpG2jJImLLnEMb/3jh5cQvzRlPc83wfYSjIJAzcUJAZo9AKO8zNp+oBy6dMDunMsGp7kSiSvXKjtWBKY+a9NYaezeQSFinLoOgEsTH8jmVBKEsW57WkySUmq0vOJlRAplAoFIpZz/rBCqNVn7acjR9J4QcvimL1vj2jPWORtAzWD1SmNWQda8I6VvChPjha3Vcmio1qU5bOYDxbHQkZ8KUsg7a8jaYxqb8jl7BY0JpmbX+Jp7eOkk2anL6sjQfXDlKoBdi6VG3LJUxStomV1tg8UqMrn+QDZy3jnhf6J6kD1oOxmx7ayFDZI2kbJGORjNV9ZVZuHcXdRyVvdal6Q4+FDZC9ORnbIIgikpZJzQsxkKVkIHvpiASZhE7Vl+IqxEIOfhDiBxEd2QRCCLotQ64vIJvQiYRUtbMNnbRtIvwQ09DpyifJpyxWbhkhYRp4oaDqRXH/mECIuM8vEnRkbZa2T5+VqlsZPL21wLqBCtsLDl25JK1ZmyCKsAw5bPLDCDMuEdM0je6mJM/1SPPjsZm51ozNyYtb6BmtMVzxef9ZSzlpUeteDXqTif3TC1M/IlODPUmgWrGQRj1BEQFRKPBDcHw/LgOMe6zilfT6f3EvI8jvCwTZhMmx85tY0p5t7COXNHl2u8uRc3IUHSmKUnaD2Pg3RXc+AWj81QnzeHrLKE9sHqZY80laBlEkA6eZnFr9WmwdqeEHEYvbs7zm8M5xQc6S9gwd2SSFqkfW1ql40uJC1zQyto5AozltsyR+3sb+3lg7UCFh6nHpZkjJDUlaBs1pCy+MKDsB2aQpbT7iX3LTqYK+6bi5HLegudFbN5UM/d4aQ89murIJClWfIPawm9ibGkSC0ZpPV1YZQysUCoVCsdcITUqYF6o+RcefJCixO2hI5cZUwqKv6PDI+qFJYg9jfcacICRpGizryDYa4F9zeCc3P7SRtf2lRlbL1DW68gn8UCqtaZpgsOySSZi4QUgUReP6OybOXPeMOpi6TtqS2vyuHzFU8WjXNMquDAq9IKTo+rz/lUvpiZXU6gMxgG/dv47Nw1X8IGT7qMNA2cUPooYM/L6ifv11scO/rC0rJd2DUCreDZY98kmL7qYUndkED64bxA8jNE3HNgRBJL2aXD9kuBrFvkqCOc0pWjM2T28pUPNDnEDIQXsYyf66IMI2ZTmZH4/AOnNJ0naF0JU9fmFcOtro0dE1TF3nV0/18LpjuidlqMaWdM1tTjJa8+kZdegt1ugt1nCDCNsIpAy+E9CZTzYktU1doyllM1z1WBB7wI2l7IacuKhlr4MxgO7c/htYWroMHPydeNVNRyQmD4bHIthhIA07jKTr/6+P+bk7n8DQdDYMVmhKWQ0Dbek3FtGSSXBYd4KSEzTsCHJJk1AINg5W6Mgl+MCrlvHYpha+/LvV9JccDEPD1GcWzGpIxb5QwLLOLEnL4N4X+lnUlm48Nwta0py6pJW7n+/D0nVSlonQBJqQWV8/Epy2tJUFLTsCnLFB1dPbCtIgPmGSSUhFSCcIMSOdznyyEVxOFObYEx+wfWEMPVtZua3AcMWb9tmLBAyVPVZuK/DyJW0H9uB2AxWQKRQKhWLWs6Q9Q8oy2F6oAWKvg7GUHHmiaRpNaYuhsteQp4bxg/M5TUnSdoqqFzT8gV5zeCf3vtBPJmGMz2oZshwrG/fLOH6IFwiqXsCf1g+RsMxxPWtbhqusGyiTsnQe31Rg+2iNQs2PFd4ATc7u6poUp9B0jY1DVb7zv+s5YWELK47u4vDufOPctgxXeXLLCFuGq1III4rwgsmKhvsSX0ixlCPm5Dh6fkvDq+uF3hIrtxY4eVEr81tTDFU80rZByZE+XVEkle/C2IagNWNT8wIO6cxyWHeOnlEHQ5c9IG4gA/FQyGxcPmXEPSEaVTdgw0CFwYpL2ZVlakFsKFyvzJR9OgZLOtKs2l5kTX+Z84+bwxHd+UYgO7Gk65CuHEMVj6Gy2/CgGql6JE3p/7asI9sQLOktupy2tJWaH7G6r0wuaWLosg+qFPcQ7itZ8a78/slgmJp8vvYkGIPJipvTIdihqFl/Lk0NLEsnCAVuKLOabbnEJH+tMJKlvGEkRW3yqfFlaDU3aKgL6rrGCQtapAKhF7KwJcVwZdf9gyADQyeIMA2ddMJkeUd2Ulmfrmu869SFrBsss2pbUWbfhMyQJkyDo+flueiUhZPueT2o2jpS5fsPbmTDUJlj5uapeNE4r7u1A5UphTn2xAdsXxlDz0b6RsfI+zNesKX+c8UL6BudWf/gweKld+UVCoVC8RfHvKYUzSmL7YXatF5HE0lbMpMyNhgxNDkbnDB3iA1MdE/aVb/F6r4SN/9hIxnb5NCuHCVH9hK5gZSzrlR9Rms+UbRjcBBEMFjxSFlRo2et3qu0abhCKVYRTNkGSUsGikEYoWt6wxPL1DQSlkHS1GnNWJPMY0Gan67tLzNUdmVGyND3iwhEvcysPsPfljaI0NA1aUYdRZH0REqYGHFLjG3oGHrcM0bs6tWQpZcCCC2ZBIWaz2MbRxiueow6Po4foYsIT8h9CgRRJKh6Ia0ZmzX9JRxfDmRNXfbulKMIr266HYtBZBIGg2WPpKGzeaTGC71FjpyTZ3lnjuMWNE1R0iWPUpZlakSx6bAbRPhhRBjJe1nv3bvolIVsGqpy80MbeXb7KH4YYRk6i9syvO2k+ftMMKHszSyo2F302L5pXz0tRvx8COR9Q+x4H6L4Xupxuath6I23UEcqbQKTDLRLTsDiNukDVg9+6kylLthTdEhYOs0pqXQZTCcNOgV+BIKIlGnstKwvl7RozVi4gdE4L1mOOH3Pkq5rLGzL8NenLeSmhzaybrDKnKYkzWmLmheydqCyT73B9qUx9Gxj3WCp8Tt+4t0d+7ytGywdyMPabVRAplAoFIpZT0/RoSVj05VP0jPDmU43jOjO2rRkE2wYrCCEIJfQcSMtNoYWRFHEaNWnKbWj12NX/Rayj6XIaUvbGj5kYSSDhEZp4Jgg0NQgYRl05RMgBOsHKnzzvnUsbc/w9NYCq3tLhELQmrZJx6pulViIo+qFOL70C2rN2vSXPJrTFhnbpCuX5Jnto/znHzdx2RlLWNCSpuT4jSxAKlZR2x9EQDIWH2jL2hiaRs+oQ7E2QFcuEQ9+ZU/HQ+uGWNSaZmlnhjAUBKGgOWlQ9aXkfXc+gWVobCk4tI0zuDXJWAalWkAQCSyDhorlaKyml7YNglDQkrboL3kEQiDCqHH9deSg3wsFZSeg7EgFuua01cjMrdo+yrM9o5SdgLnxgFQIwbr+Cm4gewNLYYRAkE5YJA2Nihfw+KYRjpyTH9e7V8+anrq0FUPX46AtmFTutjcUKvtH9l7am+15KtWMvz02oKv3jFkGCCEVNOvBOMj7EyInJ4QmRSwsU/aIDlU8MmMMtHsKNUIER8/Ps7avzOreEnNbUjs1OK548jk5cVErGwYrbBoq79Y5RRGkbfmcTyzrG2uN8Lqjuim74aQM166EMqbrC5tKmGNv+HM2hk5bMwtlZrrewWJ2H51CoVAoFOwYWJ2+rJ3/W93HloK7y+80JS0CpDfV/NYUW0ZqjDjSkDmKBJt8KbduWwbnjOn12FW/Rb2PRde1WDkuZKTiUfVCxBR9WmGsMJhLmCQsg60jVe59oZ/BeU1kEwa6rhNFEeXY2LU5ZeGFAieQQhSBdJdl3WAlFs8Q/O+aAYJQ9mA911Nk/UCFly9pleIZQqDFKYmxRs67M9Q22CFfPx1VPyJjG+STJi/2lnBDwSCwabiGqcMhXTkWt2V4sbfE+sEKfSWHMIqwDI2SW+/7kaauZS+kOWXheiEp28T1Q3pHnTjDKbANDU3XpKmvrmFpGqYOgyWXzlySkaqPZcqsouuHaJqGrkkzaEPXiYSQapi6zGikbIORqodtGhzSlOKprQWGyi4VNyCfsig5AT3FGqM1H9ePZPCOwAsiwhDSCekDddEpCzlhQQvbRmvc9NAGto5UOXZeE7q+QwlRCLFPVexK7q6f/YPBxNA/2mEFRRSNfx7ry+uZDS+s6wlKqXldk3YHI4Hszdw8XGWoLL27ntk6KtczdfrKLtmEOa3Bcb1UL2npnLy4hfasRf+qXoIZxLT1u9RbdGnNJieV9U20RsinxqtfzlQoY0/7wnaXAxX8HWi68zPL6s10vYOFCsgUCoVCMeupD6wcX4oq7AoNeNmCJqp+xKahKpoGWjw1b8ZfD6MIX8gB+unL2xsDoJ31W0SRoLfo4IcR6wfKbByssG2kStmdXr1NIMUoBLLczfUj2dNQqrF5ROAFYUPMwoulC7NJk5Gq/E4oZLlj0jTobkpg6jpbRqrS6FQITENn/WCFnlGHdFzuqCH9j8YW3+0O9fK1XfWe1byQ57YXpSH2GKW8IIIXekr0jtbIJixsQ2Ok6uOHEfmURSUWY+gZdTF0jeaUxcLODM9uLxIKKXvemU8yWJYebVHcM5RJmBw1J0/RCdhWqDFQcsmnQrqbUmRsg81DVTSkQEJd0EPTwNR0XEKiSF7niSqJS9szDJQ81g+WOW5+M24QMlzxqHlxnSJaQ149jKRRda/hMFL1+M4D63l6a4Fnto9KkZFAsKwz0xCi2JWK3XRKntORe4n0+dRFPICG8MpEQY/6v+tZM12DjmyCloxNsepR86Wf2PbRGo4f0p5NkLINClWP3lGHwbLHorY06Zb0lAbHY0v1lndksE1jRiXPGmCZ0ossiMSUZX37UihjT/rC9oTlnTkWvzLDE1tGGKp4tGVsTljQgmnqu/7yLKU1Z2FoO+9hNDS53mzmpfFWKxQKheIvmnnNKZrTFnc/14cQEQY773VJmBpdTSnaswnmNad4YvMI81vSVByPUSeUfjW6RkvKoj1n82JviVcf1omua+MGcRnbaJQi9RYdXuwp0l9yCCN4dOMIuiYH/7sKeEIB2wuOHMzHCpHbRhw68wnSttEwzI0EOH5dylsjYWi4PgRRRMqysAyd/pJLzQulsXI8urQNGCw5jW2YhhYb7O5+dgxk/8yuqA+qIyHV+YJo/H4EUKhK3zBd07BNHSE0DA3aMhYJ25TBlhB4fsj6gQoVNyRp6nQ2Jan5IbqmSQEWDGpeIAfluQSHz8mzZbjKQ+sGWd6R5fA5eTYOSUNtrS6AEm9b9i7FwakGQRjih9E4lcR0wqQ9a5OJPZk0BFU32BGQarK5qipCTF0jFFI98/bHtmAZBinbIGUZZBIG/SWHkutz/ILmRlA23eB8V0qeU2GZ+0f2/kAgkNlXIw52xg6iDU0a+Tp+iOtLJU7DgM3DVcJIsKg1ja7r1LyAkhNg6RpuCKVawJzFiSkNjuules/3Frnr2T7Krj+jZ1sAUSiPKWnpU5otvxSFMqZ63h7dMLLT5222U3JkSXfVCwljFc+xQb4Rm86XnNltqD57nhKFQqFQKHZGPHgzdB3D0BpeRhPRgKRtNrIfaduUJr62QcI26U5Ycc5D+l+VnYAnNo80shdjB3F3PtuHF4SUXV8GF0L6btX/4IcCwhmq0lW9gKSly1IupO2rFPjQ8AJZPlkPGjRNel0FkRTmsHSoeCG1Qo2qGzayQEEkz6FQlUIhlqERRFLsIIhk0JkwwQ32reQ9jN/edINcOSiK+3ncAD8S6LrGsvbMuLK+yJY9N0EYkbGl6IChaXHAJq+5Yeh4gVSi0zSN5rRFezaJG8qSwpoXxpk9ee4IIXuUIlkqV8+SVrxonEoiyMFzezbR8K96cO1Ao6RO13YIkgiBVK0EEILhiscrlnfIAMHQ0TWd1owxSR1wqsH5rpQ8xwYWY5nlrTC7JASiQPZW5tMWjheSTZrU3AA3FAzF5b+tGZvufILntheZ15JC13WEEAxXfPxQ9vOZYcSo41N2Qw7pnKyEOI74nZopgZDCQEEkOH7++HLIKJKWCvmUybqB8pRlqrNNKGNPn7fZTlvGpjltk7FDRqo+XrijNDZhaLSkLSzToC2zc0P4g81L/LVWKBQKxV8C2wpSDv7kxS1sGKywfSfCHsaE6pvRmiw9MzQZzNX8HQauKUvHDyO2DFcb6od16gIZNS+g5kc7ApA99PMydQ03Dt4sQyObtKh4IX4oZ3brJYJCgPBlr1V93ZRlEERSPt8JBAlTI4zAiAOvIIJswsSKpKlsc8qi5ofU/JAwgpRl4MSlkQeDlG3i1TzCUJCxTUaqPtmkiWXI619yZClqzQ8YjU1xhZCiGzU/JG3phKHAMuqZth1S871Fh7ue7cPxA+k9Fke8ugaWqSPioCqMBJoOXfkEi9syREKWHmYTRmPwfMayds5Y1k5nzubJzSOEcXZPtuTFZXeahoikcEhbJtEQemlJ2wyUpEDCRHXAiYPzXSl57iywiMSEQOMlSOwHTsWVGcMgijBNnYQthT/aszYa0Ft0CCJZlgsyGK75Ibapo2lg6Rq1SFCN+wYnloaOFd5YcWQX/7VyG+BPe1xjkSbnJppgXDnk2CzTYNlly3CVnoLD0fPyzGlOzUqhjL153mY7JyxoYXFbhtX9JY6ak2XUifCjCEvXaUrqbCt6LGnPcMKCloN9qDtFBWQKhUKhmPXU+zWWtmdJmDpr+8oIIYONsTFGvVRNCNmPJYSgr+gigKoXouuyP8nQdEIhqHjSO8gPBWV3h3rajx7ZzIu9JcIwREwQI5ipTdPEUsGaHxJR7ymTJW9AQ2Bg7Lq6PBFMXWaIkpbOaDVoeER5gVQdtAw5g18foGpIf6TD5+SouiGbhysMVTxa0ibzmrMMVXzWDVZneNVnfm47I4p7cNBkaefS9gxeKD29ym4gvcjiWe0ogpGqx0jVw9K1uO9OStynTJ2mtI0fiEYJ2enL2/nlk9tAk9eing3dkYWUgbCmQSJpkklI6fSnt41S7w0zNI1Dx3jDASxoS5MwDfwwwtC1uI9IZtksQ0PTZCCZjTNemqaxvDNL2Q0Yrnikx6gD9hYnD853peS5s56z9vTsnumfCYHYkc0VAvxQkE9acaZYkDANckmTTUNSHdWPX5JQyMyUockAzY+zwGlLlnFOLA2dKLyxO1Mp85qTpBMmTWmrUQ5Z9x+sZ5nmNqdoz9qs2l7kyc0FBsse7dnErBPK2JvnbbZjmjqXnrGYG377AtuKsi+u2Zb2AduK0pj+ktMXz/o+ORWQKRQKhWLWM7Zfw4/l1KeaxxWAiOXnCzWPkapPS8bG0HWCMCJv641Bsalp6KZO0QkwdUE6FgvZOlLlf1cPUHZln4qp18W5d4+6D9NYL5x6ZiBCBh/1wKYePBD/fz5lYRuyX8YLI4YrPqYOuaTJaC2Q2xFSiVFHZgrkzyFJy2BhS5p8ymJ5Z4YH1wySScoywJHqvvGwEkw2YZ0uSKt5IWEsPmIaGglT57gFeUpOwEDZYU1fmYCIjGlQcUPcICQII8JII2kZJC1pLeCF8koFYcQx85o454gu7n5uR/aj7IZsGCzz+MYRar4UWQkigWFKdcasbTKnKUnRkUFg44pP8SDlUxbNGZuRiid9pSwDDYGI9y/PXx8XqrdmbI5f0Mza/jL9JQfHl9mc4+Y3Txqc740gRG+5ttN781LBH1NyLIVSPNy4t7Ov6FCsGbJc1JQiHi1pq1HGGgqBLuSz1Za1mdOUBCb3bU28zi3pBJtHdq1S2Z426G5OjVPiHO8/uCPLtKA1w7zmFE9vG2Vpe5bLzljM/Jb0rMo07UsBktnI2UdI24mbH9rIxqEKwxUPy9A5rCvHJacvbiyfzaiATKFQKBSznrFCG00ps1GmN1UAEMTNXTUv4uTFrXQ3JXly8wiuBk4QxRky2f/lxT+bhiZVC4F1A2X6iw46e+flZZpSWKPiyv6wlKWTtAxGnSAOLnYcv5Rn3yEVbmgy02UaBpEXInTQdb0hfBCJOKAQgCYzfGGsKji/JUU+ZaFpGrZpMKc5xbqBCqur3rhB8N6i67GPVLSjgX669SIhCOP+MCeQ1zmXNHmx1yeMBK0Zm+GqH/fYCWxDl2VsGrRkEkSRNN1+2YJmPvLaQ5nfkp6U/cglNbxAkE1JR6yaHxGEAlMXtKYtIqDoBJx7RCcVL9qpZ1QuYXFIZ5a1/WWKNT8O4OQd0zWN9th7baTms3CMQXFrxuakRc27HJzvjSDEaG3/+JAdTPwgwkNmObO2AZpGoeZjmzpzmhJ4oWDzSI22tE3C1Cg5MguasAyOX9DSsI5YN1BmSbssRw2CiGLNx/VD+osO3U1Jjp6T5altxV0eT2fWnqTEOdF/cCy6rrOsI0uh6kvLhb0MxnZHeXMm674UBUh2l7OP6OKsQzpesgqSL90rr1AoFIq/GMYZm/aV8MNop8VHpg5vPmEe5x8zlxf6iuRTFjU3JBIRVS8iFAJD00jbUoghnTDIxop7g2WPINqRMZvpn/OJGSLZu6TJnjZNIxWLVWiaLKOL4kwfyBJFXZPCFUII3LhUri5g0ZlLEIQRhZpPyjZkmV/c3+SFUkY/ZRl05ZMcM6+5Uba5veAwWvMpVD2CfRiMQZzh06SipReKKfvTdCCbsIiEwA0imtMWTWmb1X1lckmTwYqLbeqMVH1MXZdqhbbBUMmTXmy+DF5bMzZHzcvH108OeCfO+te9w7wgwjB0miyj0Y8UhBHFqo+ha1Q8Kb0/loklW/OaU7xsQQuuH+HnQ/pLXqMvpTNnYxkGh3ZlqfnRJKPd7QWH5rTNacsnD9zrjJ1gyCbMcevtShBiTu6lX7I4EU3TpGWDvkP0piUtn5uEafDeMxfxm6d72ThUwQtEXMZrcHh3jrRt8EKv9OILQjkpcf2vnsP1I2xTY8tIjed7SixqTdOWS5C2dare9EGtqYNumpSdYJwS51j/wanYV1mm3VHenOm6O3veJgayUSy881LENHVevqTtYB/GHqECMoVCoVC8JFjemeM1h3fy5KbhKQf/YwOiMJJSx/Vsx8LWNOv6yxRqYdyzJQgEVP2Q5pTBgtY0uYQcpLdl7ViAQ5oMOzNxkZ3iWLK2iRfI/Rm6YKTmNwIx09DwAT2Ky/400HSdjGU0lOCKTiCDNB2SpoGvwbzmNId0ZbF0ne0FKW5SqPokTIPmtMUx8/LkUyYlx6dn1ME2NLaN1Agj0ZCm3xf5lboapMzoaWQsDS+K8AO5LIzXkUqQUUPxseQGDJc9dE2jUPUo1nyaUhad+SStGZuntxaoeUEjo+gHglzKxNQ1ekYdvEBQ8aSU/sTshxuEjFZ9hBCkbZNQyLxdPmkRRILBskfFDXCDEBgfkE0cTI+dABgquyxoTWPo0oOs5AS0ZRNcdMpCgHFGu3WfuSDSuePJbdxp9k45QB43wTAhoNuZIEQUCYar0wva7Ev2xC5hT7edS5osbU8TRDQmS0wdeosejh+ypD3Df1x8Eiu3FRiqeGwcrPDg6gE2D1d5sbdEzZelui9b2ERnLskTm0cYqfo0pywO6cyxtr/E+iFpTt6SsvB8d9peUNvUKTkB+ZQ9TokzjASWoUvRmAkIIegvujh+SLHm73ZQU89yPd9b5NdP9eD6IfmURT5pEUWCZ7ZNVkLcHdXE6Z63nkKNVduK+HEg+7Xfr9ml7YJi/6ACMoVCoVC8JFjbX+KXT25joDR1D8jYPqxICIbLsl9qXnOKha1pVm0bBbTYGFprfKfmhyyMsyIAyzqyNKdtto/W2A2VbATyj6plaRi6TjZp4HhSZl2Ps2JeGBHGmaW0qaPrOhUvIJ+0SFk6VV9KqrckLNAEFVeGNn4Y0plLkktJ9cGKFzSyRUlb9oylLIOiE+AOVkiYBkfPbWKk5lJ2AzTAtgwIQrx9YMcj2GH6G0QRARq6pmOZAlPTCUSEqWukbAPXj0iaOh35BGEEHbkEjh8SRjqL2zIsaE3RlUuydqDMSMXDDyOiWOhBAF7RJW0bpDwDBPQXHe55rp+1/aVG9mNhawrLNHCDiESswugFskTSNmUkahm67EWbIsCeqmRreWeOy85Y3Ai4ql5AwjQ4dkJP2NJXZeVguqfIr5/pwdRD5janSMflrtPJik/cfl/RIWEa0wpC1LMhT28t7f0NnAH7MyAbW6orkEbohZpU10yaBqWaT3/ZJYwEjq/x/x7YwAkLC6w4uovWjM2DawbJpSxOaZa9XUNlj4Sp0Tvqygx3KFjYkmKk6jNS9Th5cSvrYiP3Qs3HMHRaLIEndNwgikt+5THVvIgwiujM2w0lzkwcsLfnbPpKDh1Zm76y9AP0Q9n/tnm4Ri5p8uNHNu+Wt1f9vq7tL/FsT1FOsBgapimtFkxdpyVlUfECfvdsH4tbM2wbrfGff9zE1pHqOMn9nakmTnze1vaX2TJcxTJ1XrawibnN6T8LGfyXKiogUygUCsWsp658+NSWQtw/NRmBLDeqL43i720ZqTJc8dA0aQSdsI2GIbHrywHV2LnseU0pbEObcTBmAgFgadKoORIalqY1AgoAL5SlQbalS8U4DTIJg1ogyCYt2uOSRIC21hSHduYoOT5Jy0QD1g6W2DJcoehKU2NpbI2cxW/PcsScHD2jDgnL4Pxj5pC2DVZuLvDQmsGGWaoXRAQzDMZ2Zbw9lpRp4IURkZCBlGEKOtI2h83NM1jyKFQ9OnMJ/Ejg+BEtaZtc0mR1XwnL1Fk3UOGP64YYrnhUpjA0CwWU3JCKJ42if/b4VvxQMKcpyUmLWnl43SCrtheJIlkW6QYhpi7LUFsz0gfMMmSZY106fyw7KxFc3plrBFzT9ejUzcT/e+V2vCDi0K7cjGXFZ7J9GJ8NMfUD0xMTIUVodB38/eCpWz9DQ4NFrTI7NlL1GK7IzKmuabRnbExDpzVjsWr7KNsKVZKW0ZBv7xl1KDkBTSmLbNKkryjN0eU11Bv2A4d15Th5cSstaYtHNgxjGzpOECIiQRDtCMbqx9Q76jJSHaI19q6qehFJS2dOU5IXekr8acMIINVDg1Cg6RpduQQnLmolaek8s22U1X0lzj9uDkd055mTT9ITZ2DH3uOx9zWbkAbxXhBScUW8vxSmoTFQdjF0jf9bM0Ch6rNpqMIz20dJWfI73U1J0rH3Yi5pTquaWH/eto5U+f6DG9E0dhnQATPuZ1PsOSogUygUCsWsZ+tIlT+uH6LsBoxWp+7R2DHrrmEYMFB0+Pf71vLoxmGe7y1i6Rp+FOE7IvYw0unKJ5nTlGSk6jcGL1tHqgxX/RlnCOpHEwppdluX3C+7AndMgBEKcLxIytOHglEnxNA1lnVkWNqe4dmeIkJAc8omEnDcghbOPaqLh9cNce+L/bhBSNoyCDV5tn5I3FOmk0/Z5JIWa/rL/HH9EI4vTVJb03bjPLzd6CHTNNDFzMobU7ZOq2VR8yP8SGDrGpahs2GgghtENMX9WmN7cjRNY25zisc3jbBuoIIfhJjGzgd5kYDBkstgyeWkxa1omoYfupiG3J8TSS8qDRo9dpGIcAMa3mxeELF91CFlmzMqEQQZcO1KCnxvZMV3tf2JHlIvbhva6bHsKTpyUiFECuOYGqRMELqBH84sIpuovDkVph4L2ET1SRQ5OXLSohZKTsCTWwpoQEfWplCT5aFzmmSg/NTWAoMll0O7czy2aYQtI7KkNGHqZB3pa+f4O/z2LEOn7AaNXsx8ykJDk89aoOEE4biJl0bJswDHjxiJfw9EyLLKtG1S8QK8IJRG7tJrAoSg6ASUXR9DtxitemwarvJCb5H5LSm8QJCwdGxTb/R5vfaoTu5+tr9xXwfLLsWa9EhrSpnUfNkzOrdJlvP2jNboHXXQNejIJUlZBqYOL/aWeL5H9smmbZPWtM3i9jRuEE7Zz6brMrNedHyWdWTHGVrD+Of1D+sGeWrL6Iz62RR7hwrIFAqFQjHrWT9YYdtIjaIT7DRI8iPQNYGJxg8f3iizQ5pGxQ0a5sCGrpFLWpi2LG0LIumJtW6gzJx8krue76Xk+LtVrmUAhiGDBh2pGheGUgjAMqX6H8SD1XjDRiwcYhoaVT/kjcfO5fiFzbTnEo2Z6CgSfOa/n8MyNHIJm6on5d8NXScZ+zitG6hw9NxmdF2jO5/gj+uH6MglOG5+M6NVjz9uGGZ39Txm6rUG0t/N1A0cP8INQxw0Rh3Z5xUKQcUJ0A2NtkxiXE9O0jTYNlIDBAlTpzKDWspAyAE8SMPvp7eO4gURyzsylN1A9s2ZOqYGBSdgy3CNtqxNVy6Jaegc2pWlJWOzfqCyyxLB3WF/yopPDPY0w2Sm5sa7QxRv1dQ1jFBgmhqRpkvFT3YdaGkzWAdi+wd0DANsQ8c2NNK2ydqBCtmESc0PySRkCWPKNsc9My1pm+d7ipQ3BZScIFZbjYg8mX21Tekv6AUhKdsYp5QI0v/PCyMSQmdeU5Lne0uN97yuvArxxArghRHtGZu5TUmGKx5PbSkgBHRkLcquNCBuTtpYBhSdkEc3DtOWsXF8KWBT9UI2DFaoeCEtaYsTFraQtAxWbR9ldX+JihuwsDWNpmkyg93wFJQG6DUvbCjBur5UBu3OSxXVSAgGy7JnUgj5uy1p6vSXHIYqsu9xOtXEmTyva/vL/OhPmxGCXfaozRZ2R51ytnFQtSAfeOABLrjgAubOnYumadxxxx3jlgshuPrqq5kzZw6pVIpzzjmHNWvWjFtneHiYd7/73eTzeZqbm3nve99LuVwet87TTz/NmWeeSTKZZMGCBdx4442TjuX222/n8MMPJ5lMcswxx/Cb3/xmn5+vQqFQKPaMIIgouTIYM3f191WAqct+qtGaz2jVwwukuqIbCNxADmBsQ2N1X5n7X+jnxd4i/++B9Vx286P8/LGtDbNmDTlQm476H1Fp+Kw1Zq1DIQd0hi5HdkY8kT72j66lx8HFGM347qYkh3fnGxmTO5/rYU1/idaMzdzmFJ35BCnLIJ8yycVGx4WqT09RelOFEYzWfFrSslQvY5tTCqDsS0quzDpV3IAgrJcNRg3T31DEF2OCOP7G4XKjv03XtZ1e57FsGCzz6MZh/m/1IOsGyhSdgJ5RWdKVT1kkLYNaEGGg4QUhrhcyWHZpSltcdMpC/u5Vy/nIaw/lyrMP4SOvPZS/PWvZXg8sx8qKT8XeyIrvGDzL724f2X8+ZELITKqua3RkErRmbLIJEyO+P2PHtmN7NmGHn95M9mEbOm1pi6akRVs2wVtOms/Rc5sYrrgNm4HOfJLjFzQ3ygaFkKXFJSegr+gQCVnWlzQN6QAhImpeQBTFJuxRRNkJaM3IElkhBP0lGYTXvJDne0rjJirG/ts0pRqqH0iVR8Mw0OJ+T9vQMQwD25KKkLomZe9TlsFQWXoftmZsDF1jpOJR8yIWtqQIQsHGoSrZhMkhnVmGyi5bhqukYlPruv1GEImGmbofSi87xw+p+vIZSljSqiEMZRCatAyStpwQAalOWaj5eEHEnHxyynuwq+e16gYMll0qbsAhnVlySasxkXVIZ5bhisfvnu2bUuDkYLG2v8S37l/HV+5ezdfvWcNX7l7Nt+5fx9r+A9Nzubcc1ICsUqlw3HHH8e///u9TLr/xxhv5+te/zre//W0eeeQRMpkMK1aswHF2KAy9+93v5tlnn+Xuu+/mV7/6FQ888ACXX355Y3mxWOTcc89l0aJFPP7443zhC1/g2muv5bvf/W5jnT/84Q9cdNFFvPe97+XJJ5/kwgsv5MILL2TVqlX77+QVCoVCMWO2FCoIsUPdb2cDvwgouQFOHHiNDQM05CzqSNWjv+QSRVGcVdMYKNVY3V9iuLJDNETAtAGNDqQsjYQpB6tJy8DQpXFt/ctBJCW6dV0jZeskLakeB5BOGCQtWWY1tznJsz1FbnpoI2v7S43BxS1/2MxI1WOo7NIz6qIhy7D0WNDDMqTyXy3OLhXjzF4+9hp6rre434QZxiKQ2atIyHI0M66T1HXIJqSCnhCwbqASz+hLSf4oEuhIVUTTmNmQZEvBZeWWAk4QYhk6liEl8HtG4+1pWly2qqHrOrqho03o+VrQmm4EvvtiBr0uK94z6iAm9DjWe9SWd2anlLHfFRMHz1O02e0T6ldf1yCbMHjVYZ286bg5HDe/iZa0RcY2ydo6uYSBASQtbdIgclovOna8swJI2joJ0yCdNFnQmuaoOU184FXLeP9ZyzhqbhPHzm/ipEUtjWBsuOLy2MYRHts4hOPLLHG95zJlGxi6DppGJOT11nWNzSM1DENnUVuashuwpr9MLmmRsjQKVX+nPZJ+OLWoiYifb5CTKRrxhAOyzDeM5C+c7QWHTYNVyl4QZ25lae1wxaPkBI2ywKoXNkSKEqYhy3uFoFDzKVR9ql5AT8Fh43CVSAia0xYJ06DshhiGNE53YkPGMJLB20jVpzltY5s6PcWpFTnHPq9RJP3a6iWTURSxfrACaCxtz+yyBHc2UO/FW7V9lOa0xdL2LM1p2XdY/5062zmoJYvnnXce55133pTLhBB89atf5VOf+hRvetObAPjhD39IV1cXd9xxB+985zt5/vnnufPOO3n00Uc56aSTAPjGN77B61//er74xS8yd+5cbr31VjzP4/vf/z62bXPUUUexcuVKvvzlLzcCt6997Wu87nWv42Mf+xgA119/PXfffTf/9m//xre//e0DcCUUCoVCsTOCcLzU+u4EGVE9UNB2/OyFoiGVnTY0nCAiEegsbEmxcajS+O7O+sjqPSSWrhFFEa1pizCWVwc5QJOIWEQEhJBb1IHmdJJ0wqBQ8wGNQzqzrOkv86NHNjd6wNqyFrahI5BBhxsEmIZUhkua8v/lOQmiKGKk6tGcsjF0+Xd0oDy1IuW+pn6dIgGmrqPrGp4fIgQkbYNCLcA2dQbLLj2jDmU3IJswpbm0YciePkObcSWeF0QMlGTvkG2aJE2NYlzC1paxWdaRoeyF1LyQkxa1yAHkBPPnfcmeytjPhIkeUobOjMVZdocISMSD/EhAf8ll3UCFoYpLIGRpYL1cVNO1uHSWGYnfTFxlqOzh2SHz2tKcsLClUVp20qJWHl04wqrto411hysyAK+6AW4Y+4TpGm4oCByfXMIkZelUPHk8AmhKWcxvTpOw9NgeIeKYeU0c0ZXjvhcHZPZaY9pS3jASDeuGsb8BZNC1Yz1D1+JMsGjYVZS9IC7flDL5Sav+7soySi9eryOXIG0bbB+t0d0keyubUhb9RbeRIZPvknyXo9jKI5c0Gap4GLoMjApVj4oXyvLNUNDVlGRRW5pizZ+2RLb+vD7fW+SuZ/saFhGgYWga3U1J2rM2mcRkE2nYd55r+4KJPZYzFdSZbcxa++oNGzbQ29vLOeec0/isqamJU045hYcffhiAhx9+mObm5kYwBnDOOeeg6zqPPPJIY51XvvKV2PYOI8UVK1bw4osvMjIy0lhn7H7q69T3MxWu61IsFsf9p1AoFIr9w5zmJLYpZ6RnWiUzNkiAHYFcQ4VRCDK2QVvGIghl6Y+uS4EMnZ0HYyDLlAJBPBATWKZs2m9JywAhaZkYmvT+8gJZyhdGESIC29JJWTqWoRNEUUN0oN4Dtq1Q45DObEOC3wsiEoaG40c4ftjoeytUfcIo4sXeEnc920c2aXLqklbWDlR4dMMwA8UDE5CNvU71AWpdln9xW5qOXJIg9g4brrgcM6+Jd5+6kExCCm1EUUQiLt3aFUlzR2bQ9UMcN6Tmx0bawQ7jXj+ImNucYm6suLe/Z/XrsuJHz22iUPWlxHrV55h5TXvVb1MfPLdmbNb0l0nvp6l0nXjgLyLcMOLxzSP0FmsYukZr2iZhGQShDDySpt5QrtwTkpZGLQjZGkvF17cz8VyLNY81fWUpdqFppG0Dy9DJ2iZJUyeMpAInQFvaZl5Liua0xbtPWcRNl57MNRccNa40teT7+EEYT1js+jjrQVsURYhI2ih4oXxe/VCQT8kJk6oXUfNCBOAHMpQzdR1L1xBCa6gh1rywofLp+BELWtO0ZRKs6S9TcqR/mabJQC+TsJjfkmJOc4q2TIKEpVNxQ4QQ2IaOqcsSxzlNSfJJ2Wt35iHtnLSohZRlzLxEVpvwD036B9qmvl9KcPc1uyOoM5s5+FdyGnp7ewHo6uoa93lXV1djWW9vL52dneOWm6ZJa2vruHWWLFkyaRv1ZS0tLfT29u50P1Nxww038JnPfGYPzkyhUCgUu8u5h3eTSz7HQJx9mgljvbLqjA3m3EAwWvMbJWaJuJawOWViWzphLAMXVwQBO/pmrHjAIoioebIRPwzlDHUQgWXsKDHTY+lwDZnZEEDGMrBNnYoTEIYysBBCNHrA6tLpmqbxsoUt3PtCP/1lb1IwamjQHPeLCaDmhqTbDLYMV6l5IcldNtzte7xQ4Mfy8qaukU/ZHDMvTc9ojeGKz/vPWspJi1rZVqhxaFeWVduKDFb8Ro/dzhIuqbh/xvGlzH4QCdwoQpNOArh+RN+ow3DFa4iIABRrflzO5VFy9r0gRp2ZytjvyXbrHlKPbRwCd9+fg2nI0t2aJ+9AaAkKVZ9I+IDADyK8SProeVqIbWjxAHj3i2It3aAlJcvt7n6uj4tOXogZv39jz/XpbQW2jEip+658ks5sggfXDeKHcjLFNHSCMKI5bdOSMhko+7Rnk7x8SSumqU9Sr+wvyt8f0S4M0usZtFRchrx5pEZz2ua4BRme2lpguOJLZce0Rc0PKcRm0AhwAkEQBdiGRiDAcaSvGPE7CjvKWE9Y2MI5R3Rx93PyXIcqHi1pO74fWtwbB93NKZZ2ZljXX+HpbaMsbc/QnLLoGa1hGnIS6dj5zTSl7Z3aONSpZ5XCSLDiyC7KbogXRtiGfL/W9JepeCHbCw6HdpnjAp2ZbP9Asj8FdQ4kszYgm+380z/9Ex/96EcbPxeLRRYsWHAQj0ihUCj+fNF1jaS5d0UdUw0by7G/lVT5C0haBqGAtowt5bJ9OfCsGzpHyGCsM5eg7IVUHOlPtKgtQ8nxcYOIXNIibQu8MMQPBBEQhnEfTdz/FgLbC1I10jKlb9H2gkPKNsb1gNWPXMTNcGOHvxpSCODw7hwtaZueQo0Xeks8tXUUDUHKMqjtjrP1PqQus5+0DFrSsey9G3LiohZOWtTa8O46Yk6eNX0l/EhmvHaVltxR+in3IuLshaZpGLq2o0w0Xj5a81jXX2a46lHzpeDDHU9uxzb1/aYQNxOZ/D2hHuz96slNDJT3fUBm6Dq6Jg25IwGWIXupIiEtBNz4HahPdAjEuBmOcZd+J+hAezZBW1a+YxsGKzyxZYSXL2lrrFM/1wfWDPC9B9eztE32BAHMG0qxbqBMoSYQCMJQMFB0GKno2JbBOUtbWdAy9fXvyidkCfMujlEDEpZOIg4ELV2XP5vSzHzjYBU3CNkyUiOM5OTD8s4s/WWXkuNLb7I4oHP8iJovyz2zCYNC1aev6DbKWJd35ljeOf5cm1LmuCAplzRj9UjBkvYMo3EJsB4/94d0ZsinTEqOP6MS2bFZJVkVMP5369zmFJuHqyRMfZ+X4O5rxvZY5pKTSyxnUzZvZ8zao+vu7gagr6+POXPmND7v6+vj+OOPb6zT398/7ntBEDA8PNz4fnd3N319fePWqf+8q3Xqy6cikUiQSCT24MwUCoVCsbs8vnmYocrMs2MzpT64rPkRGwer9I86ZJMWSzqy2IbGk5tGKHkhGnLGWg7UDGp+XVDAxDI0Ng9X8UNBwpCeSmUvJIwbVPR4P2Msixit+riWTtqWJq6modFXrFHzo1ikQB5fFAlWbi4A0JmzqHpCZl4SJmlLo+RGvNhXakhtZxIGJcenK58c19dyIBmb5aqXaK7pL089iBPyGuYSJoEQbBuZWoSgvs2aL42xZaZSQ0NaAFixsmVzRopPdGZt+ssej6wfJps0ySYM/ECnOW+xebjCTQ9tnJWy3btC1zUCsX8GwVEkiGQiR6ot+hHphEnVC6kFYUN5tI4b7AjCNGQmuCGouRMEkLSktHvKlibPQxVvkmT5nHyStG2QiL3FwELTNBa1pdk4WMGNZFpUi4VK3VBOhpy+vH3aQOGwruyMSp4zFjSlbRa0pPnoaw/lhAUt9BQdnu8p8utneujK2Ri6ztoBWU6ZtqTcfiTANgwSJjhBJHtTLSnt70eCqiv7Go9b0DzOakHXNZZ1ZOnMyt8FUwVJNTegPZvgsjOWoGuyL22w5LJyc4H1gxU2DlZmbOMwk6xSwtQ5/7g5rOkts26gvE9tIvYlE3ssZ3M2b2fM2oBsyZIldHd3c8899zQCsGKxyCOPPMIHPvABAE477TQKhQKPP/44J554IgD33nsvURRxyimnNNb55Cc/ie/7WJaMnO+++24OO+wwWlpaGuvcc889fPjDH27s/+677+a00047QGerUCgUip3xYk+pEQTtLzSg7Ee4oce8lpChspyF7k6YdDUlsQydgThoSidMqm5AJmUyryXN2oEKxZpHNZanrsdBGrEqmwYiGu/v5QURTSmZafD8uKFe12hOW/SMuuSSFj3FWmz+bMhBphZimToZW0fTdExdMFBy8eOZ86ofEAkZNGZsg0J13wexu0LXdgSfQSTYMFTh1KVtkwZx2wry3A7rzrK6t8xg2WsEcromg7C66IJezyyOkdGvL5NqezqGrtGRTWDqOiO1AMeTxritGZuKG5JOmBw5RyoGvlQa/adipLp/Si7dUIxTLy06IU4gsE1tSuGOscnMeq/mTIoXBeAGEVmIe6p0HC/kW/evaxgQe0EUGzzLIO2ZbUWWtqfpbkqxaahKJmGQsXUKToBlGLRlbDpzCSzD4MXeEq8+rHPK+/rU1tEZHWNbNokTylLkExa0YJo685pT/PfK7XhBxOFzmig5ARuGqsxpTmMbGr2jNdxY+TMdZ9f8MGJuU4qMrdMz6pKwDC57xWJevrht0vHNNLBY0DJGGbQbTl/WvlslslHcy+n6If1Fh+4JvVdCCPqLLo4f0pyyeP8rl9ITl/zNRm+v/SmocyA5qAFZuVxm7dq1jZ83bNjAypUraW1tZeHChXz4wx/mX/7lXzjkkENYsmQJn/70p5k7dy4XXnghAEcccQSve93reN/73se3v/1tfN/niiuu4J3vfCdz584F4F3vehef+cxneO9738snPvEJVq1axde+9jW+8pWvNPb793//95x11ll86Utf4vzzz+cnP/kJjz322DhpfIVCoVAcPHqLzj6Tb59Y9lf/t6FrpE2dWhDy3PYimYRJ0jIxTY2hikexJvucUraBHwhs00BoGpuGqxSqHl4YYWiaVEhjR2ZsbBw5LnskoK8ofYsSpkFzyuKw7iygNUqFXD+MBUd0an6EHqs6VrwQx/fxQ7mvkaqH1xfRlJKeUVEsqmHNUEp+X5MydFqzNhUv5NyjunjXyxdNGhBVPOl1NFRxpfF10qTme/IaTSgrC8SObEw0JtiV/Xlaw/PNDSKOmJ9n03CV/qKDEOD4IXPiwW5dRn1so//+KC/cHXbXzDZh7L+JibHvWEQ9yzP1uvVDrAfGu/t+RlHEUMVjfkuKP20YolALmNOUxPENHl43yEDZRdeksmDFDXl8cwFTLwAyC6Xr0JlLcvS8JjqyCXJJk7Ib7PS+bh+dmbBDyQ3pyCcb0vELWtOTxCO8MCIII6ykGWf7TCh7GJomA1lDJ0SWfBZqAc0Zm7aMTXPanvL+7mlgsTslsmv7S9y1qo+1/SW2jNR4vqfEotY0y7uytGYSDFdc1vaV2TRcJZc0+fEjm3l0wwgrju7i8O78jPaxN+ypsfPYvsPZnM3bGQc1IHvsscd49atf3fi53pN1ySWXcPPNN/Pxj3+cSqXC5ZdfTqFQ4BWveAV33nknyeQOo7tbb72VK664grPPPhtd13nLW97C17/+9cbypqYmfve73/HBD36QE088kfb2dq6++upxXmWnn346P/rRj/jUpz7FP//zP3PIIYdwxx13cPTRRx+Aq6BQKBSKXdGes3e90gTqAhw6MHZMOTYYG0vdMyubNBgs+1g62KY0Qx0ou9IHTYOqF+KHAV35BF4QMVhxcfy4PNEATQfCyX0qhjZeVMQEhAampjOvOYkXRPSMOrRlErz9ZFkq9MiGIQRynzqyR8oNpA/T2HMwdI2qG1DzQprSlpScT5gzVi7cGzTAQJ67qeukbIMwEhQd2ZN3aFduykFV2jIYLLtU3YDOvBzkDpRklqweKMtS0R1iKPX9ZWydprRNyQ2kF5mu4fghxTibeHh3jr5iDUvXOXFRC3ObU+OyAHvT6L+ng8apqA+Q65mhpGmwrCPLiqOnH0TOa8mxrTQ65bIDia7tuSdazQsouQH5pMWiljSFmjQg5v9n70+DJMvS80zsOefczdfwWDIj96zKylp6qapeATRANBY22BBaYxiOBDNQkAwG42g4MybJODQbM9IMQxuSRoMAjmAYkhIJ/MAQ1KAhUSYBIgmqQYAgADa6u3qprqVrz8yqXGOP8N3vds7Rj3PdwyMyIjNyiczo5n3NujMrw8P9+r3XI857vu97XuDla2tsDFI84cLWrRV4ShSkUve6oedaVn0lmK36NCuuA+pu1zVU7vNwNxRJI1R84lxrBzp+d5tfoCSekq5V2XObKEoKZqo+WW4YpJpsCkV/ohkC4o6zTIdpLMZZXZuDlJMzEZ86P8e3rm5yZWPA5jDl4vE6l1b7tEcux+yT52aJfMl3bnW41Rkdeovv/XwWpnVYQJ1HpcdqyH70R3/0tgDFaQkh+Lt/9+/yd//u3933MXNzc3zxi1+84+u88MIL/If/8B/u+Jif+Zmf4Wd+5mfufMClSpUqVeqxaKERouCOYa67JQVFbpKd5DbtXohNL/IrvmKUu13vXFsSbTlTD4v2KUMUKJQQBbxDszlIixwySYwuKIp2Kn9sp4x1xzSuKASBxFrIjLMg8/WQlW4MFp5bbPC55xb5wvMn+K9/+2Uur/eJlEPpT6Oox8cvBUglijkfSyXw2BykE8T2YcrijKUQkshXeFISKMtmEVD7sdOtfb8PBONGudmqPzk/YzMG4EuJh2FUXMPFhs/p2SpSSm61RwxSjS8d8S/OXBbbsXpIpmEm8qiHty917nfQ/0EXjbufa3qBXA0qDNP8rgtgYQ+XFndQbuKDdBDnFp5dbPBTL5zk61c2OTkTYYzltVsd3l3pY61ltuqjjQs7D5RkoR7QHeUk2jBfC5mr+WwNMy6vDZgtSKN3u64XF+t4Ymfr8F4KPUmS7Xyu3fCIRuQxWw1Y68UEtQAhXKuwMXCiGbLUiak1Qp4/PcPJZsjl9eGBZpkOw1jsldXViOD7npzn0mqPqxsDXrqySeBJLizUuHi8MakmP4osr/v9LOzWYQF1HoWO7AxZqVKlSpUqNdaFY3VCXzI8wCpwOvDVWouegvftZ8Yiz81vxZmhn7g8oVGac6s9KnbBDUpKEC5fSBsHl5ipBDgMhZ60cO0HDbDsDKK1xrUVGmsZpZp6qBgbFItbXJybr/HciQZXN4cOoZ0ZcrNz0Tw2MZEnCT2HLZ+t+mRacXVjeFeU/MOQtqCLPLExgj/yFCdnIlb6yZ6LpFGmWai7hezmICVQksATjLLtk2RxOW/T77kbG2QnplHxqQSKfpKzNUzJixP/8tU2eZETtdqL+eqVDYfAP15jrhbe96D/w1o0woOF2W4kD6t593b5RYfr/ZgtVUA99vpWfypAerbq8Ytf+BD/i4+c5NJ6nz97d43Law5O0Y3zyX2ddhMqgSI3lmogEMJRDlNt6CU5czWfeuQ2HnpxTiPy7npdLx6ruzy19M5bO6PM8I0PtviJDy9OnmuvGa+Lx+v0k5yNQUquDadbFTqjjLdX+khcTtsr19u8JpxRuLjojNbdDNbDNhb7ZXXN1QI+/cQcs1Wfb11r8/GzLZ6auh/h9iyvh214vleCnR9UpSErVapUqVJHXnXfQ+5Xetola12bohSC1IwzxiDL914sesqR/gapLgARTsbCWi8p5pksSeZapKzYxqu7IF07CZIWYn9DtlujfNsirg8SuknGXC1goR4wytyC8WZ7BELwmafmeGe5z3o/mcyn+dLtyNuCNphpU5AV4d2VPs3I48RMxOYwneD3D0PT5lAbBzHxlGC26uEruW/7WC3wWKiHLNQDljoJy50RUkiqfhGiXTxpZsCTbvHYjx3ab7WfstJzM0ZjFLuriEoGSY4VMBP5VAOPODPcbA/pxCnPLjYYZeaeB/0fZNG4Fz3w5etbvHxtk/na7bTmuy2AxSH5sXFz6/1WvvQdjstYqIeK0Fcca0S8cKaFlILuKOPyap9Lq/3boCDauqiEsdHrxTlp7oA5/STn8vqAhXqINq4qutw9GMAh8CSjVN/x89CJM6qhv6Otea8Zr2bF4+njNV6/2UVbB5gZV+mqgcsaTHPDMNV044xf/9OEhXrE86dn+MmPnnhks013oioKIWhW3HttVPzbwpXhcLO87iXY+bu1+nUQlYasVKlSpUrdUQ9zZuZ+tdQdTXDnd1uPjhdawlgmHXtWoKTFmD1asiyMsnySvzT+epLbXZADS6otSgo8BZ6SrjJTgDWm/NU9adJaaV3LY+DJSZvUeCH13IkZPnSiyWs3Onz9/c0dpMV+kjPmf5vxG8ANvGXaZXU1IgdGuFur1v1IUFRWCkN0cqbCbNVjrZ+y0U+o7jPHNl1x+NT5FkudiG9e3aISKCoKbnQSktwQKsHZ2Qq5hat6xDBx12q3PAm1SFHzFccbIe1RTj1SVPyArUHKRj/lHdvjp54/dc8thve7aLy02uNL31nm9Zsdhmk+ub9ybflgY8BMxefG1nb1bqw7LYBb4eF89jSOBHrQlsW9tN/3hp5ASpip+izUA95Z7vGvX13inaU2l1b72zRNbt800RaGaT5B6oe+ZLEZ0hlmLLVHDnyRaV4807rrnNUHG0N8KYjuUm23RcX56uZwxzXda8YryQ2LjXBChLRYnlqoEPo+wyzn5uYIrTVxBu+vDdkaZLy/NuDt5R5//XNPPxJTdresLpelJt3Pjz10mFle3yvBzg+q0pCVKlWqVKl99TBnZh7sOAZYXChzeqet+ClpoKog8D1GmSZSirlA0I7dvNd8LXBD+5khy7YhGRMPN/Vc0/+ujauIZbkmLQzPOLvpfuQrwfm5Kr4SXG/HpLnhZNPBq3YvpF44M8OV9QGbRSZbqg1JrlFSILGkBnxPUA0U81WfjWGKNq4l8qCVu3uVEOB7krygSzpEv3No4/bLvTRdcbi0NuBEM+REM2KpMyJVktlqSKo1MxUfpSTt4j3vNmNjE5BpiFPD2VYVpRT1yLV4fuxMCyEEW8OUUar5wgsnUFLy9nL3wBsM97NovLTa49f+6D3eXe4Vob6azijHWMtMxSfyFUoKVnsxvSTjY2dbE1N2pwVwag6PnGnYzhO7V+13BgUQeAopBHGq6YxS/l/fvI6SrlI1fTn3s0hJgd8HQTPyOVYPma/6fLA54uxslb/1k89xZrbKUje+63XNzZ3z+SRuBsxYuL45pBfvjBmYnvEa55J50lFCNwYp1lpev9UrKtbbs6O1QE3mzNJM8+r1Nl986Rq/+IUPH/oG192Q+r0454mFGt0450RBaJ3++mFmeT3MYOejsHl4vyoNWalSpUqV2lMPc2bmQRUVVZaDmjFwMy2ZEeRJTm4gDCE1gnroFeAI9/fcZpO5pTs9ey1QpMYBPzLjFnaehGrokeSG0e7k3IMcI24RMShmWlrVYAdqe/dCSkrJx8/N8qfvrLI+yNBmXNVzR+4rwdnZKvXIY22QMkoyjL1zO9mDSojt+aBcu/yoONPUI4/52nb75V7aXXEIPIkUbp7vzGzEBxsDjHUzZkoK4jRHCkeVzLVrwxQC/OK/R5l2OWWAr1z1MDOWhXpANVS8fqPDP/vzq3Tj7J42GO510WiM5YsvXePV620CJahHHuuZRgi38OqOMmqRiyiYq+6EUwB3XACP8sOtFBzUuAvAF5BOz0Xu8RgloBF5aG1Y6ydsDlJWuwnn56tsFDl50+2+e1XZnOG21ALJsUZIqg39OGe+HjJbC7jRHvH7ry/fdePo3GyFOHcmMJSQ7PGRNUB3lFOvuFDsfpLvudCfziV7ZrHhojHijM1+OrkHx+/HFO2XVVw1vFEAfF66ssGNrSHn5msHO+n3qbsh9efrIT/z3HH++O3VR57l9bCCnY/K5uH9qjRkpUqVKlXqNh21QeuPn525I5V3L01ChAuNMk0FwXwzZJDm9JPctepIyWiK37jXq1hASMFCJURIuLUV4ylXifKkoJcY5NRjLeBNEQP3PUZcrtYw0zwxX+P8fHUHanuvhVQ9VNRCj2Gmd1TzlHCkyGqgMMaSZJrNYfrQ8tv2U25cppSQbm4vN5ZTrcqBMN9wO1VuvecAD6/f6hBnBsg5Vg/ZGMQkuTPBvhIIYUlz8BUFZdFVUjrDjIqvGMQ5WrvzYK1lqT3i+uYQIeCpY/V72mC410Xjja0hX7uygRIwP0XqrPiuUjRIcpLM0IwEW8OMwJOs9xOWOjH9JL/jAni+EgAHy9M6DPnKBXC72cQ7VJoEBMq1zW4NM6yF0FPk2hAowbsrfQapqz7Z4v/G82KCndUyXzoTDo66WA08jjcjzs9Xubk14otfv4a13HXjaLWfbMdH3GH/pJ/mDHPNfC1kc5DuCK0eL/RfPDuzo43VE4LO0G2SRIEizQ05dscGQpxpN28qHB5/o59yZX1w6IYMDobUPz9ffeRZXg8j2PkobR7er0pDVqpUqVKlbtNRG7ReH6T4SqKNuW84ReS7MNn1vgN1RL7El5LNYpce9g+NBpiv+SzUQ5Y7LqTaQSsUnVE2aUtS0gE2kiI3K1CQ7FMgmsb4XzxW58UzMwxSTZKZHSZmeiF1abXHG0tdAD5xdgZj4Du3uviepFqER99sjybzNp6ScB+Vu4NqfGcYQBiYqSo+dX6WE82Q1291eXKhhrGWPDcsFS19d2slOj9f4weenOdWZ8RvfvkDvnx5jTduthmkZhK0naUGVSz4x8+ihDv3vSRHbw3pxjm+J3n9ZocbW0OWOgmhL3nh9EzRUnnwDYY7LRpvtWNCX+4g6F1ZH9AZZsw3HI5dj+EvBRAm9F273pnZCqPUsDFI6I4yNgcJnzw/t+8C2Bg7MSaPQy4DTFILPUJPstKNJ7NYUkDgSzCuhTPNNUnuKriZNhyrh0S+4mZ7RLdIm1a7qmtjsIdg5+fjzGzEfC1krZ/SqgZ8/GyLZsWnF2es9xOEgBeL1lTY/7puDTNXibvrR8IZqF6c8X//ygc0Ip9Ts5UdC/03ljr045xThQnvp86MCSlcoHvxGtqCKeIwjIVEG6LiNR617obUPwhy/zDaAh8kf2168/DisRr9RLM1dNTWi8dqXFobfFdQGktDVqpUqVKlbtNRG7TeGmZUA4VSkkGc35cpM4DVhlHmFvbWKoY230FWnDZguytLo8xwqz1ifeCqToNEM1NRNCs+o0zjKbdLrq3r4ZMFVGM/jRebFnhvpesIchY+cqqJsRZj7I6F0hOfrfGlN5e41R7x9LE6F47V2BikXF4fkBtDP8lJtSWLLUo6KEFyiL2KAtd+Zabas3JjyYzh3765OgGK/L1//SZJZgh9SeDJ21qJ7tRq1Kh4XN0YkheAlul2Otd25mAmuTFutk9AmmuGaU41cJRJLFxaG5Bkhu+/MDsxY5P3sWuD4XSrsueCcz+gQ5IZciP5vW/f5EveMk8dqzNb8102W7HoVkIghcAUVaDxYrwR+Xz0VJWlzojNQcZf+5ELfOr83J4Lx/F5emu5cxiX844SOFKpxM3lXdscUPEVjUCQ5u5anJ+vUA88bnYSwLrIieKCBVJgrSXJNbm2pMLQjHx3zfQ28dBO/amL152peEghkVIyWwuIMzMxXlfWB4DgwkLtQBtHrcgnzR0caDoTcLeSzM1cxpnhG1c3Od6IyKzlRDOaLPRfu9lho58wSBxyf3OQYbBoc3vb57gC6Kig7tx0hhkzlYAnFw6/OjatuyH17/T1w2wLvN/8tfHmYcWXfOtqm81hSm4MnpTMVQNOzITfFZTG0pCVKlWqVKnb9DAHrR+G5msBlcBjRgk2sWzF9xIR7eRa15jskMfFouuANH3Weombb1KCqHjbg6mZpnFVKtfjqGN74HbBzWHK+sAF4Fpr+R//6L09TcvL17a4ujmkPcrYGmYsNkMqgWKtlzuoxvgYDJOK0mFJCYe3D5VilG8vYF+6skkj8nl2sY6QgneWe/STnMgTnFuoQQSv32hzqzPix4u5lb1aja5v9vmz99YBNzuUa4veRb6MM5e51k8t2loCJakGnguoVoI0dwuz060KN7aGLmNuF7QAtjcY3lrq8i9fubXvgnNvoIPmVKtCNfAmx+4rQcVXtIcZi01nRCu+ZJBqZ5QzTegrZqvus9VPNJ88P3tHMzZuyfI8H0hve8xhSuIqk45SWpgOkzNAICTUPY9BYlDSZdGNK2CeEvhKEviS9X462aAYZZZBmtz9dQU8s1hnlFk2BynV0CPTeoK5r4ce1kItvP1nFNy+cWSLzAA3dwim+DGy+3OSW1d9NcbdUyu9hOXuGvXQbcCcLlpy13opb9zqoITgZntErvdvUZ5UAC1sDFKMtXzmwhxnZ4+uSZjWo2gLvJ/8tUGas95P2BgkJJmhHnn4yiPThtVeTCdOma+FR57SWBqyUqVKlSp1mx7WoPXD0ifOzvLEfI3Xb3ZI8ns3Y1C0QUlBVtAAM1Ngtu/RtQRKMl8P2OingEAbixKO/phZM3m+g+Y5CRwUQxRVhUxbZireZKEzbVrmar6jDhZ0vs4odZldxu6YJ5v+8zA0xpNn2hB5ioVaSBQobrVH1AKP+arP+xsD1voJoyLfzVhY6iZEnqRV8TnWjLi2MaQWKp5ZbNzWbvZn766y1kuohz6VQDpaZOZgDuNTa3Dzd0rCTCXgxEzEs4sNjtdDrmwM6Cc59dDjWC2kn+Ss9RJ6cU6zsnMBP0o1SW74/deXHOXyDgtOKcVtQIfdx/7uSo9W1We1m7AxSGlEHq1qwCiL6cY5QsATrQgBvLfav+OczO55zndvtVk5tCu7tzTsGOqyxX9KXNTBhWM12oOU65sj0ty1FXsS5qquetxPHPJfSlFUiO7+mgpn6Ja7CT988RhX1ges9mLizEww98+fmeF3X7554I2jziinEqgiYH0nA3T6kCJPEPpqMus3zipvj/LJfXSsHtKo+CwXldJ6cHu8g6fA6p3V8DQ3RIHixbMt/sr3nzvSbXRjHbWZ4mlVfcV6P2GY5BxvRsW8pkYJwVwtYKUbg2Xf+I2jotKQlSpVqlSp2/QwBq0fpjxP8rFzLb72/gb36ccQRQvhtE+6l9bHii8LM1T8KQSbg5TcgrHOTNmiJW13yO1+cgHWECoXzjpM3QzYx8+2uHisxqs32vzjf/8ezcjnoyebLPdiADqjjJPNiNV+Qj/Oirkil7M2NoR7ZTo9TCnh7pNjjZBW1WdrmGItpFq7mT9PkhUtfRZXlRC4ubbNYUZ7lBH6ih979tie7Wa+pyZB00IIPCVoKIknBf04IysCoT0p+NjZFp/78CIvvb/JKNX8q9eWaI/cTI+SgpnIoxp6dEZZYei3F+/WWm61Y5LM4Em9p8HaveCcnrEER01MtSFQknro0Yh82sOUM7MV1vspvaJiVC0W7ZEvmakEdEb5Xedkds9zNiuPd2E5vq+1ASktSkgurfZphpJmpOjEtiALWrpJTq4tuXYwFnvAz4UvtyesNvopidZ86nyL1252uLBQ5xd+6AnOFJWl1653DrxxNF8LaEQ+jdDSGWX0E9cuKXAQnnFOX9VXxef69g0b14rsPqe1UcYzi3V8Jbm+ObptBhULnicwxRMrAQuNkL/43HE+/wiDoR9UR22meFpFzZNUW25uDRlmZvK5r/qyiN7YP37jqKg0ZKVKlSpVak89yKD1w1aeG1651p4QBHv7kTLuoDEyf7/MsL1w2xIHYDDWcrJVZbbqk2pHzHv+9AxLnZiVXsxyJ6YWKhqhIjOWjX6KtkzIi/sZI4N7UW0F/cTgK9dKeW1zSJwZbrSHRZXI45VrbVJtMNa1jG0OUuqhV+DAFalxfEdRtEx6EtJDcmQGEEWZRCk3H7QxSB3WXcBcLaA9TBmmbsHrSwr8vsWTgkrFZTYluUHuAzeYrfggXBUu8JSrrOACbOdqAf1Ek2rNX/3hJ/g//OgzLHVjvvSdZd681SU3lkqg8AvK3+bQtXjWQo+b7RGRr3ZsMIS+JDeSU63KgRac4xnLOJO8vdSbzK1oY9HaIqVrp3xivsZiI8QCUkLV93j+dJOPnZtloREeaE5m9zzn2iDb97GPWg6w4j6LudE0Qp/QU3gSBklOXLTNWu7tXhTjmTtjSIxmo5fSjzWtasBnLs5PrtG9bhyNK+1vLnWpBpJEG7J8vKR3clwSF/4+NmPT1WcpXKTCMDMMkpzzc1VOz1aph102BgmeEG5Wzowr31APJNXQI9WGz3/kBH/1h57E8w4vT+5h66jNFE9rlGmqgeTGVs6ooFiO0TD9xJFnT7WiO8ZvHAWVhqxUqVKlSu2r+x20fth6+foWH2wMON2qkOea3trwvp5HsLfxYo9/84QzGw5c4So0QriZmEGSUw09vu/JOS6t9hmmmk+ea/HUsTqXVt3sk5QCa4yj0N1he1YIt1jPtHEzNkJwZb2PJ+XEDG4O0sm8m1dUD5LckmTpZME7Xw3wPcFaz+UgWWPxhJ3s+j9saeuqEHGm2ew7qpnxLAbBrfaI9jCbQBN0gTK3xQyNEILIU/STnJV+zIk9Wl9Pzjgqn9vxTovXc+dACUi05XSryn/92aeRUpBqzQcbA0aZZr7mT+AdoSfwJWwOXSXxE2dnubo53LHBcHGxzu99+ybVfWYidy84a4FHmhtevrZFri31yCPXgqXOiDgz+ErQqgacnavQi3NCX/GF50/yoZPNe/787J7nvDsh8PEoUookM4xyPcneGt+b9ypfSZSAkXVzXOuDhFYluA2eMp7tO+jGkedJ/tJHFvnWtS02U73j2CaVZSGoh64KuUm2Zyuwti5qIdOW9ijjzJxrY60VGyRzoSQzzpS3qj5GG7qJRgr41geb5NreEYRx1AKOj9pM8bSqvmJrmJFrR161CERREVNYcm0cFKpsWSxVqlSpUt/Nup9B64etjUFKpg2VQHFjcP9AAzv5v9uNmQAi5RZbmYFaqKj4ik6co6RgEGfUA1cBU1ISKLfg78YZrYrPkwv1CQku9KSrDCnFsCgN+NK9YDb1ohImRkNJSHIQuErL8YabU0sLkIVkvFMv8D2ByA1pYXSqgeLkTMggNfgqA20Z7WrPPAwZYGuQMlPxmYl8Lq25KpIqwp0nj5ssdpkQJEUBQ2kPsttAG9ZaVvsZL55p8s2rbQaZIZACJV3VITYWXwr+kxdPca095A++s8JLVzbYLO6N9iinGihCT6ItpLmlGngYa/n+p+b4zz55Zsdi92Z7xJe85QMvOE82I5LMLfTOzbqq2novxlhoRopurDEFle/kjOC91T6XVvt87kP33ua7e56z4ns8aqjHQTTKNRKxI2Xhfi1E4ElC5eY9I8/BT6qBug2eMj3bd5CNI2MsL1/bQhuz49imfw6k2rqvCzmhMU5XygSOGik8ST7KGRYVwjHs4/31AYl29/+4Uphpd39fWKhxerZyRxDGUQw4PmozxdMy1tIduapxq+JPNgHG16oT5/TibEKDPaoqDVmpUqVKlTrymq8F+MplNyX32Xpyp+oYuIVXjltI+cKQGUsyyie4fDfQPyDyZRHkanl3pU8lcAvGtV7CYjFUXvEVvSSnk20jvbXZSXSUUweT5sZV4Iqv+Upyc2vExiDZsTPvACBuPsL3JDozWAuZtlxZH2KspZ/khxk9tkMCZxR9KTgxE3Jta0Sq3bkbt4hOy1hnYL1iFqwaKOqRx7srfU61dmZ7BZ5ksVnh/FzCWjdhkGny3J3DZuhxerbCIMn5zS+/z9YwK6qZksATDv6R5ORa4SlJLfBoVj02+ylbw4wfuLBzg+FeF5xLXdfm2Kr4LthZSYapM+5xbol8hZKCfqJpVvwHmrHZ3ZbnHdGN/lxD6O9kyd/PEnhsgnqJJvAkzeJ6HGS2727n9trGgC+/t+42Xzx3rcYfFTl1vBuDjECJCRkRXFW2FnpUfOmOL87xlZjMjwoheOFMi2GqWS9w+OPoioqvmK+HvHC2RbPi5tj2AmEc1YDjhzVTfBiVvw82hu56BopEWwJP4hW3YZK7Tbzx455YqD/Qax2mSkNWqlSpUqWOvKZnP0b3MKcwDdhQcrsiNV4zToMvIl+hhMvS8pQiyV2rYTVUbg4k9NgaZWTakGvL9a0RSWYIPMHmMOPK+hqhclj8fpIzTPLJ6wh2zq5JsRNwoC1YbQk9Qa4dYt9YUFIyZrSNW6UAksxMKkxau7yn3Lj8sXulRj6oPCWxuAWPKfrp7mR8cwN58bhWxSdUgkGSc21zSOjJSbZXL7a8v+Hyrp470SDwFFZCI/B46liNQar52pUNjjVCXjzT4lZ7hJICJSWzFeUQ84HiRCMk9F17pK8k87XgtmO61wXnIM0JPMknz8/x/vqA5c6IJDdEvjN/rapPkmvSIuPuQWdsptvyvn1t676e434kcfftQeLsxtTNB9V47m+uFtCseFjrKkuwE57SiLx7NrrfvLpFN85chMKu9zQ+coEzXqNUT34+KAH1yKMWKLR1mYTawsXFOhcW6pN7plnxOD9fYaOfuDBwYxHSVbA/dLLBXHHv7TWXeJRJhvDgM8WHWfnzPMls6DFIckaZISsMci3wqIaOlnnUVRqyUqVKlSp15DWe/fj29TbpAQtk44pYLVAIgZvlEq66RO7AC8aMyX9wohlirTNjQkBvlE2+L9PGLT5OVdAGTs9WMNbh7mcqPlnuqiidoZ5UR8Queogatywa9+fYtAgg8NwiNMncjr1ONfXAgSeGu2ZdxjIWdLGqzLTL4xokLmtN4ebS8juPrz2wxpCDuVrIUjcmKQKxRXHyk7sMsFUDxbn5WgHWUHziXIuXr7VRwr1nJQSBEqwPM6qB4WNnW8zVQgC00XRGGc8susVcLVBUA0VnlNOqKIIi7ys1lqCAjjy72OATZ2f3PJbxgvNL31nm9ZsdhqmmGiheON26bdE4nqmJfMmnn5jlVjvim1e3qASKRgFvyM12W+vDmLEZt+V9/f01lrt3z/B6UDl4hUBYiz7gZ+5BK7MzkcfzZ5qT0GhPSXpxTpIb3vlg67bQ3ycWqiS5PrDRHab5pAV4P1ng+VMN6pHPe2t9Nvspw6LaNaZlGgvHGxF/4yee5fx8dWI0Lq32ubzWB6AZ+QziDE+6mdO3lnrMVPzJ/bvbpB9lkuFY9ztTfJiVvycXarQqAYMk50TTtW2P75FaIFnrP54A7ntVachKlSpVqtSRlzGW99cHE6DFQU2GFA4xjoBhqgvSm/tuW7ihQAlmIp9ekvPUQp0TMxFvLfc4PhPhCVjqJNQaIc+fmuFUK6Kf5Lz0/ia10MMTgqsbA1b7CVobAk8S5xpt3PMK4WZSLJDqbZz3mDyohGvbU0KQ5Tlx5spmoSfd9+6zwt0NS8i0YbmbbEM0cBRENYXyPiyt9lKGmXHVA+Nmtay1rsK4j8btYZuDlO4o5+KxOq/f6vD/feUmUjiQymovZWuY0osl9VCRacPltQGz1QAhBN04K1rKLN8sFutCuOrbSk9PqqPZxgBrBTNVn5964eTdqwvFybXu/7C7Zk+MsRhraVY8Lq/1ef7UDPXQY6bi0x66OcN+7DKRGpH3UGdspBScm6kCmw/0PAfRTOSRa8vwAeh0B/msSuAHLsyS5ZqrWzHXt0Y8u+jz4plZXjg7w2995YMd8JTp0N+NQcLZueqBjK4xllQfLCw99BQL9YheovnMk/P8ydurLPeTSQviiUbIf/7DT/IXP7QIwIUfrXNja8iv/uG7vLdiaVU8wuJzIIRAa8NKN+b1mx0++7SLedht0o8yyXBa9zpTfNiVv7OzVX7gyTn+zRvLRds2jLe6pIDAV3zuuyCAuzRkpUqVKlXqyOvG1pA/fXfNYdLF3dvylChmwgy0RxlKiNu+xxZVqshTLDRCuqOM9X5CNfTItIMyrPcTTIF2f2Opy1InZqERuCDSNC9mltzwf+hLhokmNxZjISkCqNVUy9d0WLQFar4iVJJhptFWgHAGQBuLi53e+43u/te9QqgP24hNa5RqNzNjIdUGhXsPe8kr1lzaQpwb/vidFWqBzzDL6Y4yPCWo+h7HGgEzFZ9+4sJ4lXQUw17coBF5bA1TKr7ineUuuXEmthZ4dGSO1prMjmmUpsiO0vzRmyts9NI926Smd/FPz27DI95Y6rLUjfmFH3oCYFINWe8nXF7r852bDqEOLnh4Y5CwUA95Yr5KP8kfem7fVvJogB5x7kAqgRKM7vNmutN3jSvYQoCnFNYKR9VMNXGqMdZVOjYGCev9lCfnq1PkTIVfFVzbGrGYG042ozsex7hd7vdfvXmg4357ucuVjSG9OOPd5S5pbpiJPKQURdVW8tL7m3zmqflJWDjAuys9Qk+y0HDH0/OL1tnifd3cGtGNM5qRf5tJP8okwwfRYVf+pBT80NML/NHbq/Ri97N+DA/KjKUR+fzgxYUjH8D93XVVS5UqVarUf5S6vNZnuROT5gfb4Z6eEzMG8l3fFRY4e21crpevJJ9+Yo43lrq8s9JjmOSTtpcTMyGNyJ/syt9sDxkkmsiTzDUCRpkm0w6vjNiuCmi2F52+dPNeaQERkMBCzVV6toYZ2hiUklRDt3ALPUlm3HOK4gn3et/3Ui08TOVmuw0szrbnyKY1PtbdOPT2MGdruL3rnxtLlrtQ58VmSKatm9vLDZ1hyuYgYbkbc3qmQj/O+WBjSOQJ2kPDINXkRQ7bWErATKQIfWfiXr/Zvq1N6iC7+F986RpxptkaZpyciYh8xbXNIe1RRqY1MxVnIIepJtOGG1sjFurhbTM2Dwo26A4fTYVklBlSwW2L6P00hnEcZN5M4lp4PSVIcsu7Kz3qoZu98xOJ7wn+5atL/PZL1xilGm0tl9cHHK+HNCrus9iPc1rVgMCTLHXjfRfz00b7oNlf3TinJSVSCHpxVsx7ShYq7vXiVPPyB1v8439/if/mc89wdrbKlfUBnWHGfCOYnLO5WkiqY+IC2jNKc261Y1ZkssOk7666vnB6ZmI+4fGTDB9Eh135M8by9lKP83NVTs2ErPZSMmPwpeR4I8BXineWe/zYs8ePtCkrDVmpUqVKldqho5aBA7DeS4gzfaDF3kGUaIu2loqvsLhd3I+fbfH5Dy/y6vU2760NkLngifkKSjlK13hX/tLagFwbaoE3CbDNtCE3zgjsLiZYW7QoCrcINcb9tzEGIaUDXNiicmQt41PdijwHB0n3N6FHwYzt15i4O1Jg/N+7H7/Xe9DWLYo9JTk1E7E1TOnGllGBmv+BC/M8f2aG9//9AG0M7ZGjqxljKE7nxCA0Ip9EW5ICjf3Rk01W++mONqm77eKfaIY7ACIAby1t4UnBhxbrrPVTWtWAj59tUQ8Vr9/qcmGhzi/80BOcma3uoOg9KNhgtX/482NjGeuynA5q/A8KlPE98KQk1xZrIdeG2aqbA4ozzdvLblMkztznKvSEqzC1R8xmmmrgcbwZcX6+SneU7buY3220jTa8drN71+MLfUVURF6IouIySDVJe+QiLYwlNZZ/9+YKwzjnxbOzzNZ8XJF7+/6pBIoTzYjNQUI/cWHR/STnL1xcmJj06XtivZ9wfXPIUjvmo6ebnGxV7plkeNR02JW/8Wf36cU6tUCx1IkZZpqqrzg5EzFI9WOfvTuISkNWqlSpUqUmOooZOFBkVz1k9yFwuHhjHTXxZmfEhypNFmci3l3t06z4bA1zhMwnsyDWOHjEKMvpxDn1yCuOz+HosTtpipNKUIGml0KgpEUbaMc5ka8QCKTEGS/hjFmKJY9zfCkOPUvsYWh6wT4dLzCN7J8mWh5kgW+BrWHKmdkKJ2cipEiQAn7hLzzBjzx9nLeXu2z0UxqhhwV6SU5ejDt50rVxWmCUaTdLlhtGmebbN9pcLAK8x4u0u+3ia8MEICKEoDvK2Bqm1CMfpVz2XJwZhBAo5T4z7WHmFvMPGWku7KO5I0IPfCmJcxetMM7Bs7jr6BVtwdO55wc1brkBrc1k88KZsC7agC4+j75yIezaWnxP4RlDbqDiK37gyTmaRTtrkpl9F/O7jfbxxu2Ezb0kcATQ61tDjHH2XglBbiw61UVAuUXjhkK/c6uDrxzevj3MWGzKibGvBIqTXsSttjNV/6e/eJHve2IeKcVt98SpVoWFesB3bnX59rU26/10zyrrd5MOO8Ns/NmNM8nbS70d4Jdb7fiewS+PS6UhK1WqVKlSwNHNwBnrYVeDsmLx54ye4dvX2vTjnNmq28Wdq3m8tzJgkOYTg+VJQT30MMUCPc41zcjDGlMs0hxMY1yhmT5uIQSeEpjconGh0doUyPrxY3AZZBVf0U9z+un24nv38x0lTR+TxBEeEUX2GkzODfb2x99JxsJmPyH0FZXQ4+xshYvH3MxOP8kZZZpGxacWKGQ3YRDnyAKWMp7ly3KL70k8T6KNZaOXoI1lvhZOFml328UfA0SaxddSbci1wS8Mua8cSW8/zP3DAhsYY+k9ooVlxXPIcD8z9EYZUorJNUy0G9CbNmOwf7V0t6bp+IGiqDC7tl7L9p9prrHWVaAboccw1S78uzhFd1vM7zba3USjGAdJ7K9+nHN9a0hWgGmEcBsuxdvGV8JV9ozLnHtivsa7Kz1aVZ/VbsLGIKURefhKkmlDL86RUvBjzx6bmLH97omzczVOtyq8drOzZ5X1u00PK8NsP9UCjzQ3DwX88jh1tI+uVKlSpUo9Eh31DJzD0nTVbXOQ8sr1NrXQI8k0by31sNadA2stwzQn05ZunFHxHZI+1Zb1Xoq2O9sRYQ/whrZkBXFRFI8QU9gOKcaoeoOxioqv0NoFU3u4ReRRNGPTGkcIjIfppIJnFuts9lPW+ilaWySugmU4GCa9n2rOzFXxpOQT52Yni+965FEJFL04pzPM6CUZxrproNmeaVOqOMvWLfRb1YB+nLuMOd+1o95tF39rmNKqBBQUewIl8ZQkK7LjMm1Qcn/M/cMAG4yr14PRozFkuXXthF4x/6ikq+QmmWthNHZXZXRnysOBJNgOTK8WlM44NwTKQXgc4NEickPbZASenMxhLYuE0JdcXHQY9r1aq3cb7WGqD1TGM1i2Bin5OMzd2EmouxC4VkvhPpd+EXFxqlUhN5aZyOdGezRB5IM7fy+ebfFXvv/c5Bin7wm4PWNtryrrd6seNMPsTjrZjEiKVuZzs5X7Br88bpWGrFSpUqVKHf0MHCEOHWAhcSCDUeYodgKo+hIpIM6dear47u8GWKiHjDJNL3G78ONsscATGGsn7XNj3WbQcofJV8LtIlvjvscCOs6oBS5LK88MeXF8B4UmPC65pZCrBDpkv+D0bJXPXlzgy5fWeX99hO8JfAGbxUzX3TRTUTRDj4VmtGMnvRH6zNcC3up2ybSbIfOUm9OZNtrWWnJcdSfwFJ50TZV22gzfZRf/TKvKUwt1lrox9dDhzENPsjVIOd4I7oq5f1CwwW4C5Nby8EDn7kE0ynQBaLHO5FpAW5QS+DAB2Gjjzh/Wkk6d9zFB8U6txsVT4lHMVBY01LToZRRFW2QlcEHtoyKEcLUX0wh9ciP5vW/f5Eve8p6t1buNdsWXB2p9Nsa1F08I6my/j7ERxboNh6AAhVQCRehJfvrjp3l3qeey7LKcqu/xwpkZPv/REzuO7U6tdveTsXbUdb8ZZnfTUjcm9CWtis/WMCsqZPKewC9HQaUhK1WqVKlSRz4DZ7biH3p16DYYBzDIDIOCGiiB3LhKgbVusddLNPnWkM1xS6MnCZVklOd3NJBSgJDCzefgZuSmX19b6CWaQIkd33NksIr7SANW20n2msDy5o0OV1YHPH2izrFmxFcvbxIDFU+RHiBx+FY7xlrJf/7MsR0L2pPNCE9KlBTUAkWcG6SQSOHIlFnRLqktBFIUC3tJP9XUI4/5miNkjnW3XXyAX/uj9/iDN1bQ1pLm+sCY+wcBG+yuXq9s9e/pmtyrJnNihkmdURTH4aqa7t+kcC28SrnKWLqr2mm5c8VsnBNncZs+o9yRBsFVTwXj+ApB6EkqnmRrlBF6ivmaj68Up1rb8QR7tVbvNtrW3jkUeqzcgBznFe7x9W1ojCArzsf4Gn7oRJMff+Y4L1/fYmOQMl8L+MTZ2R2ER2Ms3VHG5iDl7aUuQoiixfG7r9XuXnSvGWYH0SDNCTzJJ8/P8f76gK1hSj9xkSQHAb8cFX3vXOVSpUqVKnXfOuoZODc7h18RuJtEsS1uDWQYuknOp87NcqIZstFPkEIQ55ruXfrwxqHIEmcC9TT9YkqWYlanUL7P446apt+9rwS2qDQMizmaStGatjtweS8p4Ewrop/m/NZXr3KqVZmE8Y53xhfqIbk2NKsBWW5Y76eM0hxPWJdpFngkuUYIN5t3vBHSjHxyY+iOsknYL9x5F//Sas8dVOFYAk8xUxF3xdzDg4ENdlevB4ccMGd3/Slw7bjTc1+egHqoiDNNmt8brGW8ryClwBbG3didUQXj1zcGpHIVuqyIp/CkINOWj5xqHKi1etpo/7u3Vw50DjwpJsZTwCSQXhctyVP7JPhK7riGoyzn1//syg4w0jfe35pU78atp++tdHlvpc8gyZmvFXOQnviua7V73FTe8e+uyJd8+olZenG+o/XzbuCXo6KjfXSlSpUqVeqR6LBJWA+qXG8bmUfpSaYXmOOgaQ2gLa9e3+LW1ohTrQhPSQZpfqCZKIN70vsN2z2IAiXwJQyzg1UEDkMCiHzFcyca9EYZbyx1GaWaVsUZ/txY+nF+xwBr3xM0KyHzdbdA/a2vfMCPPH0Mz5N77owjYLbm04jcbNko0zQijyfqNU61KvhSsNQe8frNDo3I43deurZjsQx77+KPq1TaWD7/4UX6iZ4s+mqB3BdzP9aDgA12V6/zR9yzKtie9Qukq3ppACHwlCTWO2/6OwW3R56YzFHmevve1NaZ73GIui+3zY827l6RAuZrIaMsZ64W3lNr9dhoG2t47UbbVVDvUJwNPAEadG6pBBJfCnxPobVxIe7Gooo26s4opT3MmKsFPHuiwW995eq+YKQff+44f/z2KpuDlHqoaEQexlrao5w4N5yccT9Lvlta7Y4ClXf6d9fTx+s0K9sbikfhd9dBVRqyUqVKlSq1Y8H47kqfRuShpEAbSy/Oma8/3gyck60IVaDMH6Wm15W7X9uTgvV+wlo/QWuHsldiO3fsUWhsGMfzOp4CaxwgASAwekeV7VEryTQfbAywFuoFJa8eOSqaNAYhQej9TXamLcNM0/LcvNj76wNevr7F9z05f8ed8XqoeHelx3dudjnRjJir+7SHKdc2BiS5pVUL+OS5WSJf3pUiaozlm1c3efnaJvOFEZhe9AEHAjDcL9hgd/V6rqK4fE9X4cE0vpcD5WAm2ljy3NJP9I6exEnUwR1utyTfe4NAsN2mKHFGPtUGYyxKCpqRx+nZCtVA8dZyb0K73K07tVZLKfj0k3PUAtcW6AnD/nwUFxxvpKYeeA4mUsQaRL6jquZFZTXODJ86P8fnPrTIH765Pxjp3ZUe/+wrH1ALPJ5ZrLMxSPGk4HSrQnuY0h5lLHVijtXD74pWu6NC5T1siuOjUmnISpUqVaoU4BaMP/7ccf7Zn3/AG7c6ZNrgK8kT8zV+5lNnHivy/scvHi8WOEenZy/JDbLAq+emmAvj8MzYNPY+UA4CYgrzF3puAZkZsMIW2UlMZnIepSQFcQ9ItauCnZ2rEmea1V6CBebrIVc3Bmjtjl0KB3LYXS3TFm5tjRgmmmbFLaQ3BqmbZ7KWZsXj8lqfF07P3LYzLqXkQ6eavLfc47WbHdJiXq8RKE6danCsEQLc1uoGTFqw1noJr15v8+1rbd641WWm4nNjK+Sp4zXmauHk9aaNwJ1auO4HbLC7er05uvvc3WFICuHgOhImn8MptOJB7rT9HuMXPYDaOHqhtpb5qk/oe4wyzbMnGjy7WOf1W90dtMvdultr9afOzXHxeJ03l7p4SuEZvYOMCm5TxVeSauARZy5rUACNio8UDtiTpDkIyfFGyH/5I0/xqfNzdwUjNSKPN251+cyFeYQQE1KnpySnWhUaFZ9Rqvno6RlOtSpHutXuqFF5D5Pi+Kh09K5yqVKlSpV6LLq02uOP316lFip+4MIcSkq0cRk6f/z2Kufnq4/tF9vryx3EEdrgFLh1aGbsDvraYVbGxusabZ3RUoUpEwZ8TxXBvS5zSxuX7RR6iix9tAt4w3aIsMEtrq11VElPSnpxTiP0iHw1oezl2u5Lj5TSte31k4zIV8Sp5p/8yWUur/VZ7ydc3xxyqx1z4ViN2WowqerGmebK+oA408zVfNrDHIm7Zq9c79CIfJ5cqO9odfvK5XVeud7m9Zsd1vsxK92Uiq+4sFBlpuKjpGC1F9NLMj52tjUxZWMjsN5L+Hdvrt6xhetewQa7KwC9+GB0yoetOLf4RjtTJqAWKpJMH6hN907ypMt386RlkDrOfDNyoI5B6siD7y73uLox5OkT9QntshH599xa7XmS//rHLvJ3/tWbbPQTlBBYYSfZgUrAsXrIEwtVPKWKqp/LCtwaZiRa40nJ4kwFT0o+89Q8nzo/h5TirmAkJV08woQSGnnMVgPWeq6KUw89cm0IiyiGo9xqdxSpvIdFcXxUKg1ZqVKlSpXaseP5zGLjtoXO484he3eljz4Ir/qQNX7nqsBxy2IW5VEc2biFFApceIGoM0Cc5SjpZl20cSZotuq7trLHoOlLtd5LaQ9SlJIEnqSf5FxeH1Ar2ir1VDbbXudRSUnkCTaHOdVA8dKVdTqx5uRMxKlWhcATfPtah1vt0SQf7vxclc1hyiDJiTzhZr5yg5Ku+hFnmleutzk/V5uARi6t9vmNP7vCcicmNy7XKM0NAyWw1gXOjoPDt4YZl9cGzFYDwC2eT85E/JvXl9gaZg+9hWu6AvD1Kxv39RwPQy6Xy6Kka19MDthONyYmTldAx/9mLSS5JpfSbSwIWxTdBJkxRJ7CU6LA7At+6OkF/vjt1fturR5DYX7zy1d4a6lHL8mQFlpVn2dPNjnZjOiOcqJA8YlzLV6+2ibONGdmK7teK7wnkqY2Dkoyrl4LIbh4vE4/ydkcpASeRAgXF/Heav9It9odVSrvYVAcH5VKQ1aqVKlSpY7kjue0fCkeeCf+QTVuxUMU9DcEgSfRWh/6zJjAGRcp3aIjUpKkeF1PQKAUFutIbdKRBa0tKg6PWQaHRBfGUAsV1cCjF+ds9NPJ18fAlr00iDP6QhB5CiUES92EF8+0EEKwOUhY6SbMRIrIk8zXA55ZbHBtc8D76wM8JTBWIoWYVBgdVMKy0U9Z6o443aoyTHJutkcMk5xKoCataYHnCHqrvQRPCSJfsTV0AcXr/YSlTkw/yZmrBmBha5gdWgvXuAJwba3L//PbS/f1HA9DoiDrdEbpHcEYt32fAL+II6gFEgmkxoWlu7ZfjQISbVAqp+I5IuZHT89wrB5SDxWX1ga8s9zjx549xm995ep9t1afn6/yA0/Oo4S7jv0kx2LZ7KeMUu1C2a3lpfc3SXNDkhl0kT3nWuFavHB2htxYrm8OOd2q3BWM1Itznlio0Y1zFo2hn2iMtVw8VmOpM+La5ohG5KpkR73V7qhTeb8bVZ6pUqVKlSp1ZHc8xxokj6dNa1pjOuK4vSk3FpNqHlUNyuCMYOi5ylw18ABBLVR85GSTbuyqNv04px75LNQjlID2AQOYD1sWWO9neDKDcf5U8e9jlPhebYsGZ8gbkSLRhtlqgBCuanV5dcAo1Sw0IlJtiDNDI/KZr4WkuSHXEPmufTM3tmhLswgBqTYME421livrA3pxhpKuQtFLcoaJnlTULLDRT/mhiwusdBM2BkmRI5XwyfNzPH9mht99+eZ9bWjcCzZcSsHz51qPxZBFHvhSkluKwOgdI2R3lGUbiqOEq3pWfYnKLda6yqc2liQ3k/kqIQS+EsxW/cl84MmZiJevbfHOSu++W6vHMIqNfsqJmQiEZa0f0ymgGlJCPfD45Pk5LizUGaY5t9ojQl/xhedPUg0Ur1xr87sv37ytLfVOcIn5esjPPHec3/32zUmW3fhTIIHnTjb42U+f40Mnm0e+1e6oUnkfN4L/QVQaslKlSpUqdeR3PN/feDw5ZBJuozvaqb88KjM2TVLUBqQvqYWuknNmrspzJ5sAzNUCvnWtzdPH6jx1vM6X3zsaZmxa2ky9HxziPDe3mzEJKAVnWlXmaz43OzGjVNMI3T3Yi3M2h6mDLgiBr1w7ZDqFYc8NpLnBUxIpXGC1ti5LTmAZpjnvrfYdnEVbcm2wuOfylHueTLvMtFxbBknOJ8+1uLI+YKOf8Jc/cZqf/PBJLq3372tD436w4e3B45ohgxzDuYUqK90EbQ15bu+5OuwpF67eHuXk2qAtDJKciq+Yq/poC8cbIaEnWe0lvHajw8fPtWhGPpGvuLY55Hgj5ONnW5P4gYrvc6IZcWltcMdK5Lg1+9rmkCzXvHJ9i7VesqPFVhvo6JyX3t+kEiieXKjzzKKrcn7tygZxpm9rS339Zod3V3p84cWT/ORHT/Dq9TZX1gYTuMRHT83wwtkZRplmmORT1WB3jKKoal84VnvgDoRHYUqOItnwKCD4H0SlIStVqlSpUkd2x3Os5FHz7nE7+ZEvyXIzMQ9we/CxMY4OqIpyj+Hhz5SNn9tTbsHVqCg84WZu5mqBA2VEHk8s1Li8NqBTgB98Tz3kIzmYxudrr6s2DsUenydjth+/47wV1RclBUIIlBAufLvok0u1ITcGX7mlTKYdNCRQkpmKN3ktUcz6IQQBzmBl1v27EPD86RlmKh7f/GATKaAWeoAllYIkNxhri3ZZy2s32ry11GVY5Jv9y1du8cH6kI+dbU02NOqhd1s47V4bGgfBhj8xV+Pl61tsDFLmawGfODvLN97femjX6V6VA1fWhwRKMF/zybVl7R4NYp5bMmUcURF3fcfzU76nCIrq5ebAId83Bin9OGexGdGouHNZCxTfutpmc+igH56UzFUDTsyEd2ytvtke8e3rW1zfHNIZpnTjfM+qrC1M4jc+2OT8XA0hXBD2n7yzyrFGyPc9MYeUDvWYaUN7mHBlfcgrN7Z4drHB86dn+M8+cZqFRsh6L+GVa23+P9+6wRtLXfpxzrnZCidbFaqhN4lpuJuZPIgepSk5SmTDo4LgfxCVhqxUqVKlSh3JHc9pzVUf/a8rKcWkzQ3czr42Fr8IvlUK95exEbPbpu1e5SzAzoqbgsnM2HzVpxfnDDKDsbDaTQk9ST3yeHu5y6WpBenZuSq1wO3oq8eAphzDGqYXuncLLJhU/wpja+z2/5Y6IzaHKXPVAE+5oNyTrYrDhkvpZoikYK2XUA89unEK1rW7WW0nIAoptoPFfSWoBYr/1SfP8FMfPcXXP9iYzI2BRRShxy4EePs4O6OMTLv8qUAJLq8N+GB9yFtLXRabEe+t9slzw9YomxiF2YqP50k+c2F+sqFxEGz4/+3fX2a1G3N1cziZkzo/X+Xdle5DvV4HVcHVILcuzmCmEhAoydYwu2O4925pHHGzXswTamsnFbHNQcog02z0U6RwZk0KgQWWuyOurGuUgOtbQ9LcgVZ85eIQVnsxnThlvhbu21rdizMurfbZ6CdYuz/Z0+Bak9f7Ke+sdOnHmuXOiFudmFRbvnW1zVPHawB87comG/0EbVzF9W3b44P1Ie+s9PnLHz+9Iwha4OAh68X7/NjZ1o52zAeZ030cpuQokA2PGoL/flUaslKlSpUqBRytHc/dOvkYQCJaW2yRdyQEBJ5E4nKY4kw7s2ZBFou68WL/XuTJbSCHsRZpLJlxoA6l3GLUk4JEW5SS1IRglGosljTXxJlgthrgKcFKd8T1rSE/8eFFfvbT5/jDN1f40hu3HuYpOZAmkzFT80W7z8v0/NgYljJuG9u9SI4LEzr0NOfnq8zXQt5b7XOiGdKq+FxZ79OLc3Jj2RykLHViIs8t4kMlkdJVxfKiKlb1nRGYqfpcOOYWac2KT6sWsDVIGWWuspUWlVE5dWyZtnjStdwZ60xZkhteu9Hh2ROGpfaIXpIzXwuYKXKlrmwMaEY+z55oTBaEd4Po5Nrwb99bI/Id2KISKNrDlFeutx8bOdMWF2183a5tDpG7yIkHVcVXPHeyyYlmxFvLPaSUSClpRD4bw6z4bDncvLGW9V7R6qgESW7xEs1ic/vchZ4iqElWujFYd433Ui/O2Bw4mIyrcO1febe46/36zY6rbAp3DPVQsdqL6cbOdK90Y5SA0Jdk2lLxFbk2vHJti6X2iGONaBIErY2lWfGph7A5SCekTiHEA83pPk5T8rjJhkcdSHVQlYasVKlSpUpNdBR2PPfScyeaRJ4gvp/V3wNICeFIfQZONKMJSv5me4TNcjf79CAvYN18UaZda1xi9PifCZTAIDBFhlct9BhlBqVc5cVXklxbtoYpC/WwQEBaBHDxeJ2Lx+ssdYa8frP34CfiHmWsM5v7JRVM/7OUBWXR7m1opQCtDev9hJmqz1/7kQt852aXK2sDegUy3FrXalgNJKNUszHIMRZ8zxnmWuhRDxWBp7DG4PseZ2crNEJXnWiEPk8fr3NptU93lDHK9AQw4UnwpCTJNJ4n8IQgM+68p0Wlx1jLq9fbnJ+vcqwRsjXM6IwyPCm5sFDDk7KgAx4H4PJan7V+TD10NMzphaQxlreXumTacH6uSj3yGaU5vTjHe4wfwzHUZqzhPbQR766QPn28zqefmANguZuw1osJagHGQpprZHGOjbVFILVzg4F0GxL7R2AILGJ/Ymeqwbqqd3xA45NkhjMtn9VeSuQrQk/RjCQ3toZsDjJ8JaiEPtq6KmzFVwQVn5tbQy6t9rlwrLYjCDrTdlLd3hyk9OKcZmHe73dO93vFlNyPpoFUY5rldLvw4wZSHVSlIStVqlSpUju0e8fTFGjnx2nQPnFmlkbkEfcfHdDA4AKLLa46NoZFDFP3C38Mp3gQi2iL5ws8STbVHmct9BODko4m6ElBZjIHm5CSxaajCA5STWeUEXmKxZmIE01nBsYLrzOtR7/4qgXSVTZgf0c2JU9KrDU7KlFjjatnuQFrLB+sD/ji167x/JkZPvPUHG8udagEHo1AkmgYpYY4N3gFKEQA9cinl+SkuWGuJjjTquApySfOzU5aCE+3Knz87CxJZsiamhtbI7LcEPqSWqBItatIWmPJi3k2wTi0WmOMe925WsBzJ5q3LQr7ST4Jnn71eofXbrS5vDbgVjtmsRHx1PHaJGR6qTtia5QResoRHq1lc+BaJauhTzt+PBWyB9HunLlqqCbGYTqLy1hLlht8VcAuinMNgsiXjmxqLKGvXBtg5OEr17bq6KIe87WA0T48flm0ovaTnIP6ydCTbA4zIl8S+QFrvQSBpTPKiXODsQI7ylylNfIn83DVyGNjkE2IlNNB0EEtwFeSQQGhedA53aNOyT1MjYFUt9pDljvJnnOF3w0I/qN9dKVKlSpV6rHqqJCrlnsxqhiif1QSuOqX1hZjNBukbAxSKr4izYybMxGuAnO/yBFtYZSZCbRECAjGAdAWsHY7+8xaMm0IPMlcLZgALtqjjI+emeH8XBVtnWkZpDnGWDz1aCuKAB872+Lqep+VXrrj3xV7VxOFsFgkCDNZsY9zycZmbNw66EJ5M/7Vq0t0RylbhRkVQrJQ92gPMyxQCxSj3C3SB0mOEoJMGzqjjNCTPHeyuWMmcnqGcqOfMFcLeeNWZ2KIfA/awxQQk1lCd9SCwJd0Rq6FdYxrH88FjTUOnv7i169hLZxqRRPM+nJ3xMYg4enFBsfqIYM4J9OG+VrgNgJywyjTxUL/7vN4j0p3bvi7XeNjluByv4rK4Fwt4GNnW1xa7fHeSh9jIdHu/ldSFO25hs0i2NtYOD1TmVQp+0mOJyXHm25DAsS+i+8n5qsuskLbA5/HVBu83JACoyynM3Kmxit+HFlrXXaaFVQDOTGaSrjrleTjz/bhBUEfdUruYep0q0Kr6vOHb64QKEGj4k/mCqfbuB8XkOqg+t67MqVKlSpV6qHoKJGrLq/1HRrbczMkj4q5OMnJshAVkIfOMJ28/sOqE5riuSJPMlsL6AwzZ0AKE1b1PSzu78bAIHGUv9xYVJHXBNsLr/Vewr97c5U/eOPR51V9/f1NfE/SjDzao3wSD7BfXSfNLb6CQElyYwgkIIoZH+uMqK+EOxe55drmkMhX5MYRGgNPMMw0ce5a2SqBq74IXDXFWOFmyYR7jtxYhknOlfUBoacmFd/pGcpLqz1CX9GPc87PV6mHHkvtEcZCnG4Hgfdi17JmbGFQitmn3RWyYZKz3k8Qgkmo9dOLDTYGKRv9hDjXrPViWtWAUaoRBXof3D1grEUJ999HwYwB+Moh4u+li1gA1UDRqAQ74EG+EsxUfD5yusk33t8izjRekQE3nkXMtSbOwCvuk0+dn51g76dJhQepMknpjuUgVbJenDFI8qISN76nmIRiZwYC6a7PMDO0isHJYZJTD32SXO9pPq9uDB9aEPRRp+Qeusb34G6I0VQb91FXachKlSpVqtRtOmrkqvV+Sm5sgYnWdJLDt2QSCD1HWsyMW8QtNgK2BhmdAqxwgI68A7/WmEMwTDVnZiv0k9y1VmlLnGv8gjiX5JoPNgaoAqgQepKXr25xozHE9xRPH6/zb15fYmuYUQ8f/a95bUBqQyd3GVP73R1e0aI4U/EcuIMCoAFUPEVQGGBfSgQuO8ziqmTzxcxce5SSa1eBcufK0QiHWc4g0ZPndDM7imGqaYSKt5d7/F/+4B0+fLLJxeONScX34vEGT3zWoea/c6vLVy+tI4BhsfrOpi64wEEfUm1RUlDxPZa6MVvD7DbKoi6IGBcWalOL5e2GV09K4sw48IM2aGNY7sTk2tCo+BMzKY+AGwsUpLqYc/QEeXawgxK4qlI1VPzCD57nOzd7O+BBL5xpsdgM+eYHW46uKIvtkOLphXBZckpANfS4tDbg5ExEq+rmry6tDe5aZfpgY4gvBZHvCLIHOea8yKyzmMmc4+53nBpAa3qjlH7okWiDBT779Dyh7+1pPj/95BxfeP7kQwmCPuqU3MPUzfaI9ijj00/MstRJdlRNF5u3t3EfVZWGrFSpUqVK3aajNiQ+Xw8cbTA3pI8ok0wDw6nt/04x11ML1cSQPSzVAkE9cnligyQnmK1wqlIhzjS3OiOS1JAX1R9tLKk2jMatfFg2+imrvYS5WsCxesAoMzx9vM5X3n10gdoTaqJw94gwe7eFjZH441MbZ4a5qk8l9FjpJuTGgRwybchygyqqf6ZA4rdqjkrXqnjcVNK10vqSwHNAj26cuUoV4EtHr0u0YdTTRbVN0qr6aGPxlNhR8QV2tOiu9hI+WB8yynLSXfjHMUZfWFeFCD3B5jAjTmOaFa+ArhhXifMlxxshtQIiYq3l8uoAa+GpYzU6o4yb7Ziqrzg2V2G1m9IeZaz3U/qpJlCCYaIf2gbAQbT72oXKbRwUHXik2hnSg6oIFCD0FE8s1Pnhp4/fBg/6k3dXUQICXxav5UimQrioAllkx714ZgYhxI7w5QNXmYSD9eyOZtj3mIv7ThekzmlJHHkx15ZMg7Hu/quGHi+ebfFf/dhFgNvItS+caT10cu1RpuQepsbzcxcW6pyZrd5WnZ5u4z7KKg1ZqVKlSpW6TUdtSPypY3Va1YBbndGOXKhHqcj3GKQaa8xksfqw5nmUUkgpCH1nMPqJJvKVw7VrqASSwFP0E120TRVh0YCxrnKmpGCY5Lx6o8Nnnz6GEIJ2/Giuz3i6b7yA9ZSrZmTj2Ti2z9PufDIhLINUo5RkpuLTGblA4PHCP8+dsfOVoBJ4k6qftjBfC+gnOd04J/IdTESb7dDneuQReArPWNYHKZ4UHKuHpNrQT3LS3HLxWI1LawO++NI14kyzNcw4OROx2tV8sD5glOV4UpDr22emvCKPLDOWYaZZnAkx2rDSdblUSgpmIp9m5Ko4g8QR9XpxzubQQSmEEHTjHCHgeCMkCjxOtCRKuVy09jAjy53JjXzlSIGPQNMzX5aivW8XCfOg9/5k+rNA2Q+SfE9cuhSCMFAYA7qoMIJrM80KkA5Y/v07q/zYs8cn4csHhQ09MV/FWhfmHPmS9ACV9jFAf/q+lVOzfJVAobVhlLkK2slWxI8+c5zPf/TExAQ9KnLto6DkGmOPFIV39/zc7vnNUZJ/V8zPHe2jK1WqVKlSj0VHbUj89EyFQInHZsYAKp5AKUV3pCfhx+Mw6Ac5LIFrwzPFAl4KQS/OSLKc9si14REoPGkJPEGcFS14oUdeLC5PzETUAsWVjQE32yNutofUw+ZtVZ3D0ngGbvz3NHfkONi/ZXGsTIM2DjMf+YpRpidmbKxxq2LkOdKewLA1yFiohzy32ODd1R4bg3RSofOUQOHmyJLMkBqHsJcCbrRHjFJNZgzfvr7Fai/mxEzE165scKwR8uKZFtbCazc65MYyXwvoxhqL3lFV0RYULng6KCp1nWFO6ElOz1YmQdNppklzjbZwZb3PC6dn2Bymjq6pAmLjjFo98giLvlVfSSqB4tPnZ+mMMjb6GT/xkUVGSc5v/Ml79B7hZv/4UtgD3EreVC6ZV7SfOoPuzpGxMEhyevHetNQnF2ocq0es9WIy41p1jbWTz73AXdu1XsK/enWJd1b6/PXPPX3gKr0QjoQ4ygyZ2f9TqyhmRwElwfckcWrQjKvS7i/WOlNe9T0QjsL5M588y//6k2d3mJRHmdV1mK91VCBP0/pemZ8rDVmpUqVKlbpNR+2X3I2tIZvD7LES5uLcUlOuf0pJqAaeWzAah1ncDfpQ0rWz5fvka41lcSS2TGdI6cxWnGm6sSHwFKGQKCnoFNUuYyH0FVJKApxRGaa527VOcrSFP3t3jbeXegj76BDp0xXDaSO433sfL2pF0frnaIJmez5/1/drC91RjpQxSREWbbEkueFUM3LVozjn7HyVGxsD1gYZnVGOkg6QoYR7jc1+gi2uYXeU0Y9zbrVHjDLNM4sNhBDc6gxpjzIqgTvPnjITuMv0PWiLN6CNRWuIc82pVmXn5yX02OgnaOsW8H/wxgpJptkYJHSGGbZ4/wu1cPJ9mXbVocj3mKuHCDHgLzy9wDPHG/zuK9fprccPeLW2z/G9fJ7u9Phm6JFkuQvgxpkwY50pjXyPqi8Z5gZr7L5VvrOzVZ47UefqxgBrLbXAY5S50G9w90oj9JirBfTjnFevt/niS9f4xS98+EBVmlGmOT1bQQhY7caM7vBGxwZ0vNmglEBrO7lf7bgSi/vZEHgKT8LxZvQ9Oat1lCBP0/pemZ97tAzhUqVKlSr1XaHxL7m5mqOh9WIHKejF2QMjmu9H37y6xSDJHythLs41w9QtlJUSNCKf+aqP70l8TxB5gkYwDiOVhJ5rQzzIMYeea7dLMjcYFvmK4/WQejEzluQGJSDTrmVxELug4CTX5Nqw1IkZpRolx9ldgvV+wtV2etfXfhjyhKsY3sv1GRucvCDXaba/vxZIqoGk4ksiT+AXq5VRbtgcpCS5Yb7mc6wRIgV8sDUi05ZjTYeTjwKPM7MVmpFH6Lm2tzgzZIVx9qSgEfqEvsJay2ovceTKoh1yHD7sK+FmiKaOWwr3fgUQKonCMsodlGSU5txqx4ymWnmFEIS+Is60q3YWFbzQU2TGIqwt4BHO0ltr6cc5c7WARuTtqEZLKaj500fzYLrXz5Nl/4VjkusJSdMCce6MeW6gF+esDzKssUSBcmHP+6hVce879BXaWtKiZVUJh8H3PPfZmq+HGGP4k3dW+foHG25j5C6qBR4L9ZAXzszwoZONyX21W+P9BE8wCaneXfG1uOtocTELkSdZqEc8uVC763F8t2k35KkR+SjpfgY+fbzO5iDl376xcqBrcBgaz8999NQM7WHGB+sD2sOM50/PPDajeK8qK2SlSpUqVWpPHaUh8WGc3xNA4FBk3aJT4PDra30HYZirBligWfGp+ZK1fkrkK3qjjLVecrCntlAPFQhBPVB86FST7jBjtZ/Qi3PiVDtaoO+Q3xoweU6Sj4EXltCT5AZ8D6q+QlsYPgIAiqAAbsi7QxKmNa6oAZPqEWwvgidPbl0VcPr1ZiseBgda8aTkwnyNzGhutmOy3PDkQg0pJfXQZ3OQ0I+zifmo+JJK4E2w8nLSJmkZZTnWBpgJuty1zI2JfNNVMoBUa6zdBkRIIeknGanWnGhGVAIPawszqC1SwOc/vEg/0az1E95b6ZPlmo1hykrHfb4GSU4l8HjqWB3gtmp0Z3Cwe+qwtLsSPD6vyT4X380Jjiu5mlY12Ne0jIl5P/jUPLfaMR+sD+jHOb5y5jfwFbm2dGNX2ezFOWv9lH/wpXf4seeO85NTc1t7aVz5/9r7GwhcXtzGYP+w+WPNkGFqGCY5IFBi25TVQ4/FZojvSZLMbZR85sIcZ2ePLsnvfnXUIE976VHMzx2mSkNWqlSpUqX21VH5JafF7XCIRy1rLUnmVuPjkOJ+qpFC4HsCa2Br5GbucmOKrx3sgKPA4eqrgeL1mx0+WB9gLDQj38EntAMGpNq4+RYLILDWoq3Dj7uwYsFsxed4I2Rr6Eh9hykH2yjyqO7i/aZPxfjuGcM3JNsRAtN3lquQuPc+DiKuh6564inJ04t1fClZ7sSsdVM2Bym+klxeH3C8HtKo+MzXA9LcINHbBEixfe7S3FANPZJU8/Zyn5tbMZtDF7Mwbq2bPiY79T6M3a52JZlGSffcSYGwXxCCQeKqZb4SnGpVkFLSrEiaFZ/ZasCl1T6ZsWz0E5Y6bkH7zGIDX4k9q9GjR9eFelcd5LMocPenMa5apo3lZCPa87G7iXmNyOPLl9apBV6RRQfDNOfG1shVVq0Dfdxsj/iXr9zi7eUef/1zT+9ryqQUPHeywe++cpPeKEMJQdV34dvTBMVAusemuSOANkIXmeDy7wy5ttRCb9Jq6ynJh081+Cvff+6h/Gw8auCMowZ52k+PclbvYas0ZKVKlSpV6o46Cr/kFhrRJHfrcUgw1cYkBYvNCItlrcidQrj5pvl6yBPzVV690XEtbYFHUsx+7fZm424pA5xpVfjU+Vm2hhlff38TJSVnZyukuVtwNiKPNDfEmcYKR/YbAxPyvECDS0HdVyw23QxF5EtuteNDDdG2gJISX8Ewda/kFdWiafM1bWLG3+cr8IohHG1c9chYFzdgjAv/HhvRsRkDmKv6tGohm4N0sjCPM03gSzwpma8H9EY5y92YYaapBh7z9ZB+kiMFVHyPNDNo60K1a6GiGnqs9xI2Bilr/YT5akDoCQovddt7AXdMVd+jEXnUI8XGICNQEilgkGrXOukpjjdCukmGtpLIU5OQYIC5WsCnzrc42Yx4e6XLubkqkSfpjjKSbO/AYHtEoqEPehRFRBdKSeq+xFjLKzfbfN+T87c9djdM6HSrQi30SDIXCp0bQ5rbSXVVSUHgCVpVnyw3d50pM8by9lKPk82IRqi4vDbA9ySh5+Y0c+OuzUyk8JQi04YPnWyijSEKPL7w/EmqgeLbV11O3TDLqfoeL5yZ2UFVfBAdRXDGUYM8fS+qPHOlSpUqVerI63g9dHk/j8mRTWabQoW1Lrw5n0I+WuuqOb1RyltLPYZp7iiIub7tOcaabv1a6Y745tUtGpHnEPbFWlJbi7GWiq8Iin9UUvDpJ+bojjKubQ5Z7iZ4UjBbdTNRoywvkO76roTDh6FcGwIlHOyggJiMSXRjkzX93idtigZEwVQvRqsA9/jBrlbL8X8FHkSBg8zUQ48bWyNqgTOh/dgBPKqBx/F6yGovoVUN+PjZFtZalruxM7R2GxxicYv0fpzjKcHpVoiv3G5/nJkdRnD8eIELOLbAqVZEI/LYGmacma2Qa8sozalHPv0kY7HpiIGj3JDkmq9c2WCxEfHU8RpztZDNQcLl1QErvZhRpjljLMebFT71xOy+gcEnqgHrg31xFEdSY0LmsXrAINVsDPaebdwNE2pWfBbqAZfXBpOe1jFkw+I+H83Ao1mYhJVuzEtXNrixNeTc/O1tkePWu6cX64zSnNVuQhRIAqWoh26mrzPKmK26OIUkcdftk+fndhjjH3xq4VAqWEcVnHHUIE/fiyoNWalSpUqVOvI61giLBc/jqw5YC3GmEUIwTPUUYt2ZiKCANGwMErS2pMoQH3CGa5hq3l7qoqSkFnrMVAI2BymBkghcpUgXQISqrzjejPjQySZnZiv88dtrZNrQGaakxs2TmWm3d8inLDMuwNdXYkJXnLQGOg4hHttGrRkp4tygtTObojjMaughYEKT3EvH6hHDVDsSZa4ZJjnNyHftpNowU/FJM40IPWZrAXHmcPeNyKPiK/pJziA1VAKFL6VbgMcOFjNfC3j+dItG5PPWUpdb7RGeKmh6ciriwI6jCgpjbiyVwOP50y0ALq32We3FpLnh2taQaoGvX+rELHViVrojeknGkws13l8fMExc5tq52SqnZytc3xoySHMuHKvtucgPwocH9XiYGueV7XW7+Z7LydsYuPnK+Vqw93PsIuadaIaESuFLQaa3q4NjuqGUAq+YBRRCMFP12einXFkf7GnIxq13cSZ5Z7lPL8npJ67tdGvooiSshWcX6+TGsjnI+Gs/coFPnJ1lqRvz9nJ3YsCmuwaMsVzfHD6QQdsNzhibnkbkUw893lvt82/fWOHCQv2Rty9+r5AMj7JKQ1aqVKlSpY68rm2O8KVEYB6bJTOT/7M7crdy7RaIM5H7lbrRT0itI8t5B1igWCjmwyza5rSUz5nZiM4oZ7UXo40lzg2zFQ+lZLFQTJit+Awzw3MnG3zz6ha5sQTFqniyMH6EJ8tiJ9lsxoIntvOjDM6kRb6iFvrUQ0sv1gyzHKUEcxUfJRVbo3RfDylxUJVKoLi8PiDNNHFmWO/FtEcp87WQj5+b5f31AZuDlGrokWnN1jBlqWMmbZ+6CCceg0JCz7UZjqtrQggqgZoYryBwBijXlmrg2thGhSHvJznPzja4eLzBXGEyPnW+xas3OvRjn3rk8eKZGQcYiXwGqWaY5nRHGd++ujUxFM2Kx9OLDZqVgEbk33Hxfa5V5eUb/Yd+/XZXA8fa73oEyn0tKYrAoiBt7rUHkeYWo3MQglY14GOFed1LY5jQl76zzNff32SpM6JV9ZHC4eU3+y7gO1RiAvlIc1NkuN3581YL3D3w8rUtstxQDRXDRBOnmq7J2Sg2Qd5e7lGPfD5zYZ5W1efX/+zKjhbCC8dqvHi2xbFGyFov4dXrba6sDR6oxfBO4Axws5PfurrJN6/O8qnzc4/c/BwlyNP3okpDVqpUqVKljrystW6e6DEfx+72tQlYzkIvzqiHPoszITc2Y4QQeErsS5+bVjdxwAmJM3Qvvb9JLVAI6cxBOspY7iSuKifhz95N+fr7W3zkVNNBKQKFNZZRMRvlYBvikQRD+xIiBYlx2WzPnajzznKPQaonxjVQbu4O4SqJ1hhC32OprVmoh0VosCDJNL50FYvcWHLtFvmeAm0FnVFO6KmiRdQ9btsxWGYqHh8725pUqeLMMMo05+er5MbwkVNNbrVjVnsJmTH4UrLYDGlGHm8u9VjrJZxsVfCVKJ7W9WFaXEumryShJyZVyA+dbNKIHHAiN2ZSMZitBQSe5NxcFSldBWeuFkyO7WZ7yPog5VgjZLEZ8dSxOnO1AGstvdiFS796o71n693okNp2x2j53bfMfq/mKYnEkmo7qRbvR7O3QGZB4migK/3k7nOp1plgbS113+NYPcBXklezLsYaaqGHlC6zT1uHpe8MM2Yq+1McTzYjksywNcw4N1tBjTLawwxt3GZCri1WWW62Y6pBxotnZvi//vFll3XXci2Et9pD/uUrt/h/f+sG8/WAjb4DyXz0dJMLC/X7bjHcD5wxbmtdHyR0Rxm//qdX+Ma5rccyU3ZUIE/fiyoNWalSpUqVOvIKlCS5G8bvMWuM9TaJRUgXlNuJD045LPKlC3PnkN7VQDFb9cm1Q90LKAAErmXv7eUuxroZj1qg2BikrHQTIl8SKMFS9/BzyAJP4nkKk2ustTyxUOP7npjlm9favLfcoxIoTjRDNII4M3zoRIPlbsKNrSEWF9q8OFMhzw3XNgcEnkBJifMxZpLlJrHEuTvHs1UfT4YgIMk0x+oB7VHO5bUBnzo/y6fOt3jtZocLC3V+4YeeoJ/m/OM/vsSpVpUzs1V6sSNXBsrlxuXG8MHGkFudESdmIkJfEQWKUWrIjGPyjzH5cW5RUnKsEfC/+8HzXF4Z3FYxuLhY5/e+fZPqLsjBXC3g00/MMr8e8NXL67xwpsWFhRpCiMnCe3OYkmlX/fvNL3/A//Yz53YsvEf54ZDspHSm0xOWdBfMZLc84T6TFgg9Pckbu+PzFxsOK92Yd1e6+y7kp+eoTs9GbAwScmN4b3WANgVtNLe0TU7kS6Rwgc0baYqxd0bPL3VjQl/SqvhsDjMGSY4vBUoIUm0QssidE9CPM/4fX79O4EnOz1U51gjojFLeW+2jjWshXunESCnQxvDeap9a6DFXC++rxXAvcMbmIOGV621GqSbwJDMVn7ma/1hnyo4C5Ol7UaUhK1WqVKlSR1432sMJFv1hSQCRB6OHtL7tpwaBmVQKwmJuJr+PA1cSBG5WbVig1+uBQ2XUIo8TjRBfCd5Z6TPKNIvNECEEzcinM8pdhUcIFHDYlHQpBLVAsVD3WekkvLXU40bo0RlmIMVkJkwpyZnZKmfnqpyZrWCswVeST5xrcapV4a2lbtFx5hav1hZthH7RJpi58+hJwYmZyiSn65XrbdqjnMCTrPcTljox/STnzGyVn/uBc5ybr3F9c7hjsdus7CTFxZnh7FyVWuDx7kofKS21QJHndlJltFhGmcaTjszYjDxybfjf/4UnWeknOyoGN9sjvuQt70mlE0JQ8RXVwKPiy4kZGy+865FH4Ekg5/2NPv/Tn3+wY+Ft7eFUI0TRYjqez7rTGOJsLeB0q4In4Z0iS82TLvpg92MVrqqbG3cvdkc5v/4nV3h7qX9blWf3HBXAe6t9rqzFyAKpUg0lSkmGSU4vyakHHkmuUUry4tnWHdHzg9TdJ588P8dbSx2WOiMo7rNxyPvWIENKQd33WO2l1EPl8gATB34ZpZr5ekgvybm5NeJMq0I98tgcpFxeGzBbDe4rm2s3OAPg8uqAUbEBsTXMON6MODlTmZyXxzVTVurha7/A9VKlSpUq9R+pxgPqby93ub45xDxsJ3QfWm7Hh/K8qb7b1Mm9adzGmBvYGKZUvYP/mp0+jtBTzNUCl99koRZ6NCsBtcgj15YkNyx3XeUgN3BlbcCt9ghtDBVfMkw13VF2qMj7sWSR6RWnBm1dkPHl9QHaWmYrPhLBej8t6HUuV+3S2oCLxxr86DPHGKSustaIFJ6UBerfkBUDaEJANZCueiPhh56a51PnZ5mrBZM2wGONiNxYuqOMzUHCR0/N8JMfPUFe3Msni7bApU6MtTvv5zEl7hPnZvlPXjzJIMl5/UaXbpyTGYPAIiUY42bY+nHGej/hyvqA//O/eZu/+s+/ybsrPZ470eTsXBUpxWRxvd/r9eKcJ+Zr9OIcY8xk4T1XCwgKs7HYjHjh9Aybg5R/+8bK5HP49LG92/EeVKkuZv+KllElbl8kSpzBSnJD4Mli1kkgpWCxGXKssct84q7fmE45VjWQfOdWh//pzz/g0mpv8u97zlGN8+mkxPckubYs1HxqoYN91EIXov3TL566YwYZbFeh1noxt9ojksyQFHESg1S764FFa8PmICPJNJvD1P05SLm5NZpQBqVw2X9COpM9NmW9YgOiEiiSXDNI8wP9TB2DM+ZqAe+t9lnqxKwPEgJPsjXMJmHh4xy9acNX6rtfZYWsVKlSpUpNdBQzcMDRDR+2lHBVgHuxm3ciyd3+/BJtD/7s049Uwi3QfM9h7FVBLVTASGtudRzFLy+qN3FuWOvFDFNNo+I7KuMjmrkz1rLcTdAFBGMckNwdZZNqS+S51rJLq30CJScgAIBf+6P3+IM3VtDG4ElIchgW1TC0m4uTwlXGFmrBpHVwrHEb4FJnxOYg43/5wklubo343Zdv7riHnzvZuCMl7tkTDf747VVqoeIHLsyx3I155VqbuAj4VgKS1AFdfOUCuKNA8e5Kj1/6/70NwF/8kHtPd6PSzdcDfuZTZ/jjt1d57WaHlV5MLVSk2tCP88niW0p5W6VlHDR9GLK48y+EM9O+EnTjnEQbF8VQOKtBknNza+jmyCS0qj5z9chVnAqNow9yC9Juz5cJoBJ4XDxW47WbHf7nr13lF37oSc7OVm+bo+rFOXExv9WPc0aZJskNxrr5vWbkkRv4zz974UCgi9OtChbLV65soLVBSTfnOY6yMNZd2zh3X3P/kwyKmItMW+LctQ8aa1HSBcKDmy8cFCHusJ3Ntd5L+Hdvrh7oZ+o0OOPla5t0RxkzFZ/jU3OGYx2VMOZSD0elIStVqlSpUsDRzcABWGxFD/05dVENEPbgbX33UnEy1jJI788SiQIEIYu6whi0oI0tKkgWJUEVFAZRtEZ244w4y5EINAIp7ENv9dytXrJ99sbL4URbBJbIE3ieQmCLebiAv/L95yaL50l1pCilVHyPQXr73JuxkBWAhz+/vMH5uSoXF+vM1cLJY/qJg3d844NNtobZnvfwjz93nLeXerfNfH3uQ4v84ZuuVe6ZRXeP39yKUUqirSbLdtrwXFvWBynVTBF5kq1Bym995QN+5OljeEVV9CBUuvPzVf7nr13lvVVHTfSV3LH4ttaSa8taP+HyWp/TrQpXD6laPNaY+imlazOthh75KMNXglFmJpEKS92EaqAIlItqkLjZsukgcDtl4qQYB4JLhLB862qblV7sqkHtmBfOtHjx7MyO1tJUG3JjmK0GNCOfXpIzSjWfPD/LqVYFbS0frA9oVvwDte0ZY7m24SpUUdEKm2uLUs58ae3Od8WXjDJD5Cm0MZiCdKoN3Nwa0YtyZ0QrPok21K0l0wYlpZutK6quJ2ci/s3rS/vej3v9TB2DM755dZZf/9MrzNV8Ts5UbiMvlmHM31sqr2KpUqVKlTrSGTgATx87HCO418zLw1Kc2/tqh1QCjDEYI9HGFBlMBmMMw1STaVNUbCSpsS4IuRmw2k3op5phZqn6ghMzIdZYbnSSh/7e9tJ47sja7UqittD0XRtid5SxrGLqoYeUYnLPaWP5Sx86zlI75t+9s7pjZum2v1uoRx5X1l2Y8rMnmtRDj16cu2wrC1vDbN97+J3lHn/tsxdYKioL0zNf061y3VHG5bU+3eHebZ8WyLRBCI9h5qqRby51+dKbS/zUR09NPiN3o9I50/YkS+2YSmFYG5G3A/IxDo3+nZeu8cbNLnH2aCoio0zjFxlgAMPUFHAV9/5rviLNNaOieiQFVEOfiu+qWuP4A3D3tAWUdBCVy2vD7ey63EFbXr/Z4WZ7SKvqs9Rx90mgJJ6UZAWAJcsNp1oVTrWcQRkl+T2Zkpevb7HaSzg1E5Hkhn6iyXROpi1KuLvN4Mx9pZjxW+klaGORwm3c5MbSHmWEvuTFMzOs9zM2Bim5NpxsVQDLe6t95qp3vx/3+5kqpeBT5+f4xrktvnOrc9v7KMOYv/dUzpCVKlWqVKk7ZuAchXkFrQ9vGuqw7OX9Pq+vQApJtwgsPj0boaRgtZcyTPVkRm2UG6QQnJiJmK9HPHuiwcmZEE/Cx862+E8/dpqzcw+/srjnMcudCwpXcXCLV21ci5qxlvVewqW1PsbYyT1X8SUvX+vw9aubdEbuPfvSQVGkgEBCLZB4ErqF8QqUYKkT8x/eW+NrVzYYJDkvnJ2hPcrueg8vdWPOzlV3zHxtt8q5hX2caTYGiWtPlHsvlnID2hhy7Yzy1iDln3/lKv/kTy7vmIsaU+mmX29aZ2ervHCmRZKbHWbslettVrojcm05N1vlVCviO7c6bPUPt0I21iAxdEfpJJQcto2VJ6EaKiqBhwUGqSZOczYHqYs62LXLkRlQQtCI1KQClmSa5U7C1jDl0mqfzjDl+tYIAcxWg6JqaGlVfLYGKRuDdMcc1diUXDxeP7Ap2RikZNrQqgWcnIk4ORNxYiZipuITFFEH4Kp4i40QbRxNMvQkFndPKykIPUnkuSraxWM1lHQzZYGSdEY5z5+e4SefP3Gg+3G/n6m7Z8p6cUZuDL04c4avDGP+nlJZIStVqlSpUvtm4Iz1uOcV/vTS+kN/TovLxzLWIbv3C8B91Mo0VAKIfImnJLXAo6dytFcEGhf9i0K42azIL9obpaQR+myqrKhCSQLPv9NLPRR5OPT9KN02zdZaRJHhlWoHTUhzi7GG3/naVa5vjHh6sc56P2FjkBBnbj5nXIExFox27ZZBMX+mlCDJLVfXB1QCxelWhVwbPnyqibHwp++u0Y9zTu2zOL/TPbwbOb7UGaGNMyAIgd3nzujHOUpK1+5mXVvmvbb4Ts+bvbvSpxEp3l7usdlPiHzvttDoV69uHOCqPLiUhFFmGGau2uVLJnOCnpKkuWv7izNNkls2hxn1yCfyXEV0+pQJXJtnklmU0IVRd2d1puLTiBRr/WRidn7+B5/gtesdLq/1t6+/FDx9vEaz4tGLs8ns39iUjE3+nfKx5osZxPYwJckM3TifVL9CXxEWc2KRL4sYC0dlzLXBCzxqoeIjJ5t045y1fsL1zSEzFZ+ffvEUL55tsdAIJ6/97mrvgX+mlmHM//GoNGSlSpUqVWrPDJxpPe55hSQ9HHi71nbSknYYZuygzzmNp9dFyHTFVxyvh/STHKUEH12oYxG8udTHYmiGHqmBzWHGKV8Brs2sEfnEucYYQz08/EYYI2AwbcagMLgFcCTLsQWJzxeCeuRylN5Z6XKzPWKUaSTuPYNre5S49sSp3G0sriqSGcuZeogFtoYps7WQ2YrPly+tsz5IqYUeF4/d3gZ2p3t4N3J8XBGyFGHB+713C4FwodhKChqhxxMLNS6tDe6pxffi8QY//txx/tmff8Cr17fYGmaOdCglTy5UJzAHIQTxIQVD71Yj8hhlhiRz19biZsQ8X+EV2XDjPC5woeDWumgAY3dWiN1YnSDVmmSYE3kKpdy81XwtJPIVoafYKEzOfC3gv/rRpyYGa72X8Mq1NlfWB3ywPrjNlBwURvSJs7Mcb4S8frPjgDhuzwAhYJBohHDV3HNzVVa6CUluiHzpNjiE4MxcledONgF3732wMeCvfN85Pvv0sduu8/TP1HFb7XT23UF/ppZhzP9xqDRkpUqVKlXqtgXpdIvNUZhXmAkP59fVvdg8VSzeDto8eS/L5unjELhqRJJrbrRHNCu+m7tZHxUEN0umoRO7+ZneKGXTl1jjDMIPX5wnzg1/8MYK/eTgwdT3q/2gIRMzZV21zwKR7ypbzYrPK9e32CxayLyiMjKeU5p+ziQ3k7klKWC+5iOEIM01npSsdEf82btrrPdjktyy2o157XqbJxZqtKq+C6ZuRHe8h3dTEVWx2D0IEGVUJCJ7CN5Y6rE+SGlWfL51dZNvXp09EP3v0mpvQnj86OkZ3lruUQ8VWW54f31QBAI7gEn/ECmL0+rHOZEn8QJJP3UwDymh4ktCT6GtZZjkrh1RwvGGy8LrJ32CAp2vrQPRnJiJMMaBUDINgQfNQFEPPSyWJHPkwtBX9OKcfpLvDCA+AT/41MKepuReYESOXCq2q7FFCPQ4lF1Yh+SfqQTM1UKM7eAXkI5q6E/aJcG1NR6rRwUN8/brO/6Z+rX3N8hzw9bItRx6UjJb8fE8yWcuzB/oZ2oZxnwwHaRKelRVGrJSpUqVKnVXTPfjnldY6h2NrB0pthfpFd8t4JJ8iir3AM/t2uNc3pWSilakaA9z1vsJFU8S54bc2AncIs4hznMkkOkRlcDjo6eb/NQLp/jdb9+cgCcet+qhj8W1H56ZdWZMCMFsNSDJNLLIVfKVxFc5u4uhZqpSVvEljcjRB/txjqckL13ZJM4NwrpzkxvLzU7MzU5MoCDw1I6Kyu57eLyIy43lJz96glevt3lvuXegUG9j3T0RepLZWoCv4J3lnoOxKMWv/+kVvnFu646xEbuBOkudGF8KJIL5enhb4PA9JCk8kDIDeWoIVUFILF53mGqsdYYkUNtZYb6CtGgz9T05aT31lGCm4lMNPDwluL4Z4yuJMZa1foqxFimEi0aQgmqgqEe3L0/3MiX3CiO6tjHg8uoAT7ng9VzbSRtmoFxraq4tZ+cqbPRTQl/Rj3POz1e5eLwxqVQeZJNKSsFzJxv87is36cUZ87WAmYrPKNVc2RjQjHyePdE4cobhu9XUXFrt8aXvLPP6zQ7DNKcaeG6W76MnvitaO0tDVqpUqVKlgKM9r7DcfTSkwDvJFPju8ayZMS4Y9l6zzPaTnWrzUhKEkPhK0k1y0txlYcE2An8sIRx+u1qgx//Vq7dY6cbMVT22Brcj5B+1klwT+YpjjZDnT7cmi2YlXNWsVXUtsqNMu7mzPc6mADzPLZrHWU+Rr1juuJZHJQRSSTCWbOoEaePaH+Ncc2V9wNWN4Y77eK9WtwsLNX70ueO8eqPN1c3RjlDj3UfmK4g8he+5UOv1wmCMfdxs1b/rTNk03ORbV9tsDBI6Rfh0M/JpVLYDhxuR5yo82aNxZRaIC4MscCYtNa6NUU3NlFngyvqIoDBaVgpy3L0ZFOcGwJMKJSlmCg2VQBEpSVaQCz0lODNboRHe3ja9l1GYhhEBdEfZjrbA3Rlu37y6RT/JqQWKLHdbKGNT7UuB70lGmeaZxQaf+cEF3lrq8vuvL5FkGl8JcmMOvElljOXtpR4nmxHH6gFbw4zOKMOTkgsLNTwpeWe5x489e/zIGJ6jmkN5N11a7fFrf/Qe7y73iuxH96l9f23A28u9uwaGHwWVhqxUqVKlSk10VOcVTjYeDS3wTrK4iljgObhEOoZr8HAMmRm/CM6cjLLt4OcxQn5ckCgI3Q4lrgQfOd2kN8p5Y6nLIHHmzVWcHv1184QLAx4r8CTPnmjsqDAAaOtaFaUUHK8HbI1y0twQ+II8366Utao+kScwVtCJM1Z7CU8fr9OIPN5d6SGFC/fNiwyp8ayTLrDrxxohs1Wf6+2Y3/rKB/zwUwus9BPeWu7y+68ukRTBw5NWt5sdvvzeGoNU04gUSWYm1xp2Xu9cQ4xrs1xJHSAiVK7ih4B66HGqVbkj4nyQ5hO4SZIZ6pHHqZkKS52Y9ihjlGlqoWJrmLLcjZmt+gw7j95oS7Zbaw2ukisBT+EqXL5kVIQrZ9pQ8SXWioJGqFnrxbSHmdtQKMxYrg3GuPPVqnjEucFXkpPNnZ/3/YzC04t14lwTZ5K3l3psDtNJW+BcNeCJhSpJrifgjDjXaGOJM7dg95VCFNU/bSx5ZrAWUm04O1fl7FyVC8dq97VJNTaLTy/W95wh6yf5DrP4uHWUcyjvJGMsX3zpGq9ebxMoQaPi4ysXldAbZbx6vc0XX7rGL37hw4/999iddKSx91pr/rv/7r/jySefpFKp8NRTT/H3/t7fw07V6621/O2//bc5efIklUqFz33uc7z33ns7nmdzc5Of+7mfo9ls0mq1+Kt/9a/S7/d3POa1117jh3/4h4miiLNnz/Irv/Irj+Q9lipVqtRR090w3Y9DHzpdf9yHABRB0tgd4bf7NQU+yFnLdNH+tevf9bh9r9jVlzgwyZu3eqwNUuqB58JujWGY5qz3Hv3CXUlBLZCTBcZ8PeST51o7zJi1riJyvBEhheDKxpDl7ohhqkkKCEg1UJyYifjkuVnOztaIihBiJXGEu62Rq4ZZ6yiNZvt8GbbPvzYWpRTztYB3V3r87X/1Hf7ev36Dv//7b/LVKxu0h0kR6itY7ye8cavDy9e3WOunDBONJ2GxGbDYDGmEinogqAcusluKgh5YQFSkcKHYmXYZW5mxd0WcV33Fej9hEOfM1QIHegjdRshs1QFaOsOMUap5/vQMzx1vHuLV21+W7Q0BcPde5Dv6obWCUElOzEREnmurHGWGVBv6Scb7631WeykaB7uoRd6kHXSu5nO86cAeC42IwJMsdbfR/mOj8J1bHVpVnwsLdVpF5fH3X19ia5Dy8rUtVnsxkS+ZrQZEvmS1F/Otq1skuZmAM546VnOVPu2C1WVR8ZbCVaVz7T7bTx2rTV7/4vEG/9WPPsV/8xPP8H/8i0/z3/zEM/yXP/LUXc3JdJSCEIJmxWehHk5adiuB2mEWH6d2t342It9BaiKfp4/X2Ryk/Ns3VjCHnTJ/H7qxNeRrVzZQwv2sCT2FFILQU8zXQ6SAl65scGNr+LgP9Y460obsl3/5l/kn/+Sf8I//8T/mrbfe4pd/+Zf5lV/5Ff7RP/pHk8f8yq/8Cv/wH/5D/uk//ae89NJL1Go1Pv/5zxPH2x/mn/u5n+ONN97gD//wD/nX//pf82d/9mf8F//FfzH5erfb5S/9pb/E+fPn+da3vsU/+Af/gP/+v//v+Y3f+I1H+n5LlSpVqtTeOtZ8/DvI4BZu2ooDma0HWbrc7XsNRQWo+HOtH5NkGkuBxi8WTo9jgsxYSDIzMUX9OOMP3lzl+uZgR47SmVaVF8/O0I8zhknuvse6qlOmLbmxNCKfW50R64OUiq9YbEZcPF5na5hxaa2PBVKzXQ2bPobxe5/eUNgapvzJO2u8cr3Nciemn2S8u9Lna1c2ee1Gm//w3jqbw8wt6nBUwyS3tIe5g48oST0KQLh7QBV5XNo4Ew0uisCTDhwxDlW+0+J7XGfdHSNeCRQnZyJmKwELjYhf+AtP8F/+yFPk9vHMBY7vtbEsMMwcyj7Xho1ByvXNIWkxl2UMeLJo6y2+zxOCeqQ4M+vIkbmxbA0zrLEszlT4xLkWoScn52naKFw8VsNadw2thYvHasSp5lY7ZmuQMVv1dyzGZ6s+7VFGmptJxe3ETIVq6IFw84HGWqy1RfSFwy3WQo8TMzvnwnZvUgFc3xzy9nKX65vDPY3KNGVxLz1ucu20jnoO5Z10ZX1AZ5jRrPp7HvtMcR9cWR88piM8mB7/XXAHfeUrX+Gnf/qn+cIXvgDAE088we/8zu/w9a9/HXA7bL/2a7/GL/7iL/LTP/3TAPzzf/7PWVxc5Pd+7/f42Z/9Wd566y2+9KUv8Y1vfINPfepTAPyjf/SP+Kmf+in+h//hf+DUqVP89m//Nmma8pu/+ZsEQcBHPvIRXnnlFX71V391h3ErVapUqVKPR+bxbyIDboZGcnvlai89ylwzX0kGqUZrU7Rfge/vn591mMqmFqeeAoRgc5Dy55c3ODNbYaEe8sLpFj/x4UX+6Z9eYpS5gOtG6DKtcmOx1sE51vuxI95VfbaGGfXIY6WboLU+EALR4maDxhCGNLds9JMdaP4kM4wyzVJnhLEuiDg3ljjTSCmKioqhPXTBze1RRl5UL3MDOtVTc4UWIwwISa4tvSSjWYAc9lt8D5KcWugM20o3oVX1CTzXctWPc1q1gPlaQKsauOM5IlvpdupPi6voTl8RZ+CczawGHo1QTR7nScGpVoVGcW4+enqGkzMRy92EONN0R9lt4eHfutq+rSWxUfFIctfSuTnMCJVESLAGEm1oVYNJxe3sXJUkNzx1rMa7K31GqSazdtKyaC3UAsWFYzWSfP9P+EHnrB4XufZ+oBxHPYfybrIC9t8me/wdHgfRkTZkP/iDP8hv/MZv8O677/LMM8/w6quv8uUvf5lf/dVfBeD9999neXmZz33uc5PvmZmZ4fu///v56le/ys/+7M/y1a9+lVarNTFjAJ/73OeQUvLSSy/xl//yX+arX/0qn/3sZwmC7XaKz3/+8/zyL/8yW1tbzM7O3nZsSZKQJNtD5t1u9zBOQalSpUoB373kq4elt5bbj/sQgJ2Vl7vJk87AHUQPat4ybSatWODaBuNHBH7YT5Hnqg21QLHZTxgkFiUE81VHSVzujnh7ucdMxWU8jTJD4IHJDW4Ey9IdZrQqDogwDiFe7SXFzJIiO0A+3XLXzS61R1mRnQW+V5jVorI2ytz5a1V8QKC1QUqBEi5wWxtNZpx5klIUs3l2UpkTbAdaDzOLEJrcGF6+2ubWVrwv4vzSao/f+/Ytljsx2rhZtUGaUw0U1cDjeDPiRDMExMTMnWg+/nnKsSTsyPFTYruKJoEst3hKsFB3pjLVhpvtmK1hxmIzpB565NoQ54ZvfrDF1c0hjcjjd166xjfe37otPDz0HHLfWstKL2al5+YGz7QqfLAxZK2XuBZVKWhVfJ474QzS2EjUAo9zcy4O4e2lnptpMxalBLM1Rz1sRsG+Vat7Rew/anLt/UI5jnoO5Z305EKNViWgPcxYbMrbjG9nmDFTCXhyoXaHZ3n8Onpndkp/82/+TbrdLs899xxKKbTW/P2///f5uZ/7OQCWl5cBWFxc3PF9i4uLk68tLy9z/PjxHV/3PI+5ubkdj3nyySdve47x1/YyZL/0S7/E3/k7f+chvMtSpUqVurO+W8lXD1PvrR3tdpO9dFAz9rCUaDuZsT4Ksx5uYS7oxVmBtYc0NzQrPm8sdfnWtS3W+0kxd6RIc4O2liy39OKUbpyTZJpRpjk/X+N4I+TLl9aLb9tqLQABAABJREFUNjMY5QdLkdPWsjnMJtj2aqigeA5t3SxRbsbgFEN3ZEi0M4XagtGGyHPmTwio+AolJTrJwFiEFNvh0VPmzLU0in0R5+PF/UY/4XgjpD3MWKhLOqOcwJN86ESDM7MVLq0NdlRSTs88nizA3dpdqNvNj5munF3bGLLeT1lshtQChe9JNgfppBL45q0Og1TTqgZ88twskS/3DA9vD7MJJr/iSzJtiYu8Pl8JTs9WkEJgrCXNNJdW+5ydq06MxHTV6j/92CmWuwnDTFP1FSeaIZfXh1w8Xt+zarW7fbKfaLaGKYGSXDy2dxj4/ZJr72cD7kGgHEc9h/JOOjtb5QeenOMP31phY5A6EukY6hHnGGv5zIU5zs4ejbb3/XSkDdm/+Bf/gt/+7d/mi1/84qSN8K//9b/OqVOn+Pmf//nHemx/62/9Lf7G3/gbk//udrucPXv2MR5RqVKlvhf13Uq+etiahjl9N+mgla87odVhZ+VhWuPAZG1BFQhvQ9GCdX+H/FAkhION9JOMqIBUONCIJtOWp4/X+crldUapRtidC83IlzSiCuEgJdUjLszX+Pj5Wa5uDhmmeWE2DzbHB64iVg0UnTifolQKPFkEfU/dW71Y40kHqqgErsUuLkwhgBSSeuhRCz03GyQLI2LdNQiUo0amuZuHM9buiTifXtw/s9jgWCPiletthmlOqxrQjzOubQ0ZZYb5+s5KylZ8NNrGdt9f1rrWsb2+boFhknN1QzNbdea0O8q4tjlyZE1PcGGhtoPEWQ+928PDfYUSEm0t/SQnK+YlO6OMCws1pNy2iSZQXNsasTg1QzZdtbq8PuTkTMTiTMQo1VxeH06qVuBmxMaG6GQz4uXrW7x8bRNfSb55dYutYbajffLETLgnNfFeybX3swF3r3lsu3XUcyjvJCkF/5sfOMdqP+HdlR69qc+HkoIXz7b4K99/7kge+7SOtCH7b//b/5a/+Tf/Jj/7sz8LwPPPP8/Vq1f5pV/6JX7+53+eEydOALCyssLJkycn37eyssLHPvYxAE6cOMHq6uqO583znM3Nzcn3nzhxgpWVlR2PGf/3+DG7FYYhYRg++JssVapUqX30oL9kv5d0dubotGndi+7FFE2bst0aB/OOi26ecC2BXlFZGgMKpJgiMT5GjQ1hklusdSj0CYK/+ONYPeRtetxsjwg9QZxbpkOCLZaZSrA9M2XHWHs38LNfBdIvTFKq3Qzbj1xcYJBlfPnSljNJRUubkgKDRWdTx42jMiIso9QZS4Ul//+z9+dRkmVneTf62/tMMWZGzjVlDd1VPUo9a2haCAwC6QO8JC4GhFhY2BjZQoIls3xh2YBs9AEy9jJI4GWJ4bNs8yG4d9kXFsOHUCOZSUNL6m713OquecrKOeY44973j30icp4zq7Kqz2+p1V2VkREnTkSc2M9+3/d5ANcywdZKa6LEOCoqBZE2bY0kCs+xAPAc4wj5+iMVjg0WVlicLzdRGCy6PDBe4fRUk/l2SKJhqh7w8LEBfuCR8SULcb0Pqp+w8j2mVvvLFBNobaIiZpohpycbWFJybKiI1iaI+WB/fkllZrXw8MWPLIQwYeDamL90Zwy71ZGmH6+YIYONq1YAn/jrMz1BFMYmdy1KFKenmkRJgiUlo30eAwWXKFFMNXxqfshQ0Vt1zmq1UOvV2O4G3FZMOdY6jv2cQ7kRJ0fLfOhtp/jMc2kwdBRTcGzuO9LP27Ng6J3TbreX7HYAWJaFUuYqfOLECQ4cOMDnPve5ngCr1+s88cQTvP/97wfg0UcfpVqt8uSTT/Lwww8D8PnPfx6lFG9605t6t/m5n/s5oijCcUzv7OOPP86dd965artiRkZGxvVgN75kbxX2R01g71m+nu2ZRWiw01KMUuA5kpJnESRgWxJbGpMJP1po41s837MXbLb6JwSEcYIfQ9GzEVrz1fNzXJprEUQJ7dAEOxc9i7xj9UKCAR69fZDRssnxMiG+miR9UpbRQCuIFChhdscLns2BSp6Gb2PLeWJlzEKEMNWrKF5p0KIAErBsU3EME42UCzb8F2fbzLVC/Mi0NVpCsHg/pOQ5lHIWIBgsuD2L88WmCKuZKAwWXd5wfICGH9OJYibrPu968PCKxaRr7xNXjy0gtO65bmrAjxWlnJWKdMFYX37FNQ5WCw9XqRFHOlPnamZbISdHSrRC00LYDGJsKRnty3FsqEC9E60QSSdHyxx/a5GnLs0z2woZKro8ND7A+blW2kYa0pez0RrOTrdS4xWZbnyA1Nq0XFqSvGvjFiWTdR+0iTHYDjvZgNstU479mkO5GU6OlvmJf3BzHjvsc0H2D//hP+SXf/mXOXr0KPfeey9PP/00v/Zrv8Y//af/FDALkg996EP80i/9EqdOneLEiRP8wi/8AocOHeJd73oXAHfffTfveMc7+PEf/3E++clPEkURH/zgB3n3u9/NoUOHAHjPe97DL/7iL/JjP/Zj/OzP/izPP/88H//4x/n1X//1G/XUMzIyMm5656tdZX8UBa4LtjAiJkrnmkz4rmlzcyxJIhRRopltx+RtiRDgOCZk1wzmJ7h2aje+h4rMthas3tdDpwtorYzRiZkdC4li1QuQVlrT8OM0H0rg2RLXlhwbLPKDbzjK4y9O8rULcxirD42V5kYptfpbI9EgJWbOSGkOVfJUCg6zzQiBmRmLEzNHZqXVRzBVMIWxrO+6ARZdi7wjCRI4PdWk4EjGBwvMtgI6QQIoQqVxLUFf3ma05FLtxIz2eZRzZpm13BRhLROFbl6VEDBQ8Ch7Sw0WVOr+eNMhoGBLpBQEseLoYJFHjlV47mqdmUZAK4jpy680k1gcHj5W9ogSbeb+hDFWmWwEOFJQ8izuOdS3avhyEKkVZhTL2wI9S/L4C9eYboZcqXawBJybaTLdDIhiRV/OphMmhEmCY0ljMBMr5toRh3oCzEQXrPZ+3MxM2E424HbTlGOz1bz9yM187PtakP3mb/4mv/ALv8BP/MRPMDU1xaFDh/jn//yf8+EPf7h3m5/5mZ+h1Wrxvve9j2q1ylve8hY+85nPkMsttLf8/u//Ph/84Af59m//dqSUfN/3fR+/8Ru/0ft5f38/n/3sZ/nABz7Aww8/zPDwMB/+8Iczy/uMjIwbyo1yvtqPjo71TrTxjW4RYg0lx0IkRnjlXJm60WmKrkUrTGini/IobVUUQlJ0LRJtHBcTtTzVag+Oc5O6oOtqWPQsOqGimhpsCCGQaKQ2FdBEQztMyDuSPs/lQCXH81drPDhV58ffcoJjw3mevVw17WNKpWJsdaUuMULQj8yCvuHHDJc9/FiTdA07EnrGHVJ0HTQFTioINTBWzjFYdGgFCRfm2ri2QAgTCl3OObSCmE60kLN1Zd5nuhEy2pfj9pFSr1VvuSnCdkwUuiLif7+8dAxjt9jLmAbPtgBNO0xQCs7ONBECDvR5TDdCzs40uf9IZcl5SBLFhbk2BddCa5hrRZTz5poYJYr5dkTesXDKOeY7EUdhiahb7zwuroKh4dWpJl8441NP5ww926KUs0mUxrYk7Uih0rwySwj82ASJt4OYZioCSzmboaLbmzdc/HibmQnbyQbczWzKkWHY14KsXC7zsY99jI997GNr3kYIwUc+8hE+8pGPrHmbwcFBPv3pT6/7WPfddx9/93d/t91DzcjIyNh1bsSX7OmpBp95Pu3DD2MKrs3rD/fzjhvchz9Rb9+wx94Ku7Wo7UQJjiUYLbs4luTwQJ43HBugFSqmmwHPX6lRbQW04wSVQCIT2pGZuXIdqzd342jFXrjfb+V52pYgb0v6Cw4XZs3rKNOWweUxAhqIE8VE3Wei7mNJwfnZNv+fr1zm2+4eYajkmRY0P6KzSr9it5oIprWxHcR87fwc8+0I2zLGEfOtkIYfm6wpDZ5tZrjm0oDiWGksTCtowTPVD9NCqTnUn2O2FTLdDAjihDA2ktAW9Gbjwlgx2wiYawU4lljVFGGrJgqLZ4vkzdexSCdKlpjSzDZDGn6VkZLHaJ9HMW3J656HV641eOpiFT9K0FqT6IicJQlih5xrYUvJSNnDlpI7xkp0IrWp89htC7w41yaKE164WmMuDZrOWYI4USSYOcJGYDYObClwLGkCpJURfXFisuuC2Pz7YCW/Ip4AtjYTtpMNuJvZlCPDsK8FWUZGRsZrmev9JXt6qsHH/upVXrnWINFd02rBuekWL19r8KG3nbphosxie3MZ1xOBEQPRanaIW8SxBHnHIudYZsHXn8eyLPryFrFSOJZASknOBl+b1j8nbZeypUBbEoRpzduLskd3Pm0zd11wLEqeTT3NAeu+Wx1L9ATNYsJFOssWUHItXplscHamuRCArY0bYzNcKsosaUKI/ThBa1NhnKj5nBgpcudYHzlHcmW+bUw2plugNeMDeWIFrVARxokJpU4UWmtaQcyVakQnSogixStTTTxLMlxymKwpYqGRgLTAkRbDZZcwSphtxzxxdo63nBpe0xRhsyYKy2eLrs61gIDdZqtvk25VsYsjQFqCWOkVs32LPxKONNe2KFFcrXXQwE99+ymeu1znzHSTZy/XePlaHY2poHmuxUTVpxVEqDacGClyoC9Hw48ZKnn80JuOAmzKjOJKtcPTl+a5NNem3olohwmJMmY4jVjTzYMWAkRqTCOE+UwrrdDaHPeh/hzNMKETJjx8bIADfR7PXa1zYrhoNhrSE7OVmbCdbsDdzKYcGZkgy8jIyNjXXK8vWaU0n37iIs9cquJagnLeWchy6UQ8c6nKp5+4yM9/9z03ZJf1cCXHVy/VrvvjbgVB6tC3CwwWXA5V8lxMxYNgwfr/zFQLreH2kSINP2amFeDZFp4l6cQJiVK4tsSPzLxLvAeDZEb4bZy1JoFKwUVgnAkBLIwpiXFiXP98xUnXERGm6r4xVBCQcyzTqsmCwFOkuWFKMZh3aIQJnTDm7gMlTo6VTdaYhjvGyjxzuYpWmrxrI6XElVD2LOppW1qgjJidrPskyhylAoJQoWxN3DAzZq4tsaQgSjPU+nMOXjlHwQtpBDFvu2eU73rdoTU/M5sxUVg+W1Tyrs/SzTQZrm0Ms1yQxRosZVTMeoYykcLkt6V/nmsFHKnkecvJES7Otvjp/+8z5ByL44NmEwLAHpDMNn1mWhEvXW0wVPS470hlyTVwM2YUDT/i9FST2WZgPq/aBMfFeqmITJTGSec2lTKzhIkS6VyixVw7Ik4UByt5Eq357ItTROlM4sf/6lVuHylx/3j/lmbCdmMD7mY25XitkwmyjIyMjH3O9fiSvTzf5stnZ7EEDJW83gLCsy3cknEQe+LsLJfn2xwdKu7a426Wsrf/K2Tr2X5vlWrHLOjDdB7syfNzTNYDxvo95tohdtoK1/BjIqWMeE4dLqJEU3IlRddmtrX7lRQwz3UzOk9hzDXQxqRDBAlCmgyx9bTrYnfJqzWfOPXyT7SpXnSiBM+WPWOP7qEoDc0goR0kJMIYdrxwtcFL15p4jqTg2oyWPQpuak+fBhSXcjaVgksnUnTSVt0wTkz7ohDYlkTpGDChw+1wwcpfp4v63rELQX/BoRnEWFJu+DndyIhg+WzRRiJ21xBGlKFXF1fLq2AaI2ItzGu00WF2fxzGiqcuVrlttMxUM2A6DcruijEw81OHBwrk3bWF7vLzqJRekiV2uJKn4Ucm10wp4mShIrYclX6OBIAUhGkVzRKCQ5Uc1+oBSplA8K9frGJbkjvHSlSKLkppnrtS44WJGk0/5tAaFa3VZsJ2YwPuZja2eC2TCbKMjIyMm4C9/pI9O9Oi1o4YKrur7ub2FxxmmyFnZ1o3RJCdm98bYbFfMa18CZYU5GyLWMNkvcNU06cVxPiRoh3EKEybXidMEEApZzNQcCm4Fp0gItiohHUduFbzGSp63D5coN6poTAZYuuFfXdbxhTQDhIcW5g8ssSELycaglitWsFRLIjjREMzXfDKQNByYuZTAYaA8cE87VAx3w6JlaIvb1N0jWlEK4yxpMCWRoRpBQq9JHS7Eykzq2cJXEv2jqUTmurkUBpyvB4bmegsny06N3t95imT9PyvNbK21quXYF47S4JQG0dWxAraaZjvbBoCnXdXbsBsReiuZaRRKdiEiSKI1pe13dw/LSBvSQRmtlBKI87fef8hXn+4wudfnuKFiRoWcH6uTTzTxJaSgbxDrDXtIF7TQXKtmbCsyvXaJBNkGRkZGRmAWXyINb35buxiQO2lf/suY6WtXDupYygNRc8ybonCuP6BoNoKqbYjI8SEwLZMC5uUgjBKmG9H5GxJ3Y+IEoVtGTfBvUSy8FxXe859ORvbEkw1QzzbIukFPK/N4uqZgl47GOl/e2mrYCdc/33RrdqYSod5XCk0s80A17aIleaRYxWaQdKzSy+6kr95dZpqO0xn9QSWEARSoZcdt5m01OmskXlNlFLMtkLuHCvz0PjAuoJrMw58y2eL4l2YUdwMggUXys3gCBDSvN+kBM8S+HrlB2FpvHMaxi3M6zhUNCY2nTCh5AnCtBXUEgLXlpsSuusZabSDmCgV8hs9dynN8x8te2itQEhGyx7/4ltu55Fjg1ypdrj85TYNPyJOdBpKbRMlxninG9K+moPkRjNhWZXrtUcmyDIyMjIyODFcpJJ3qbYjxvrkisVDrR3Rn3c5MXz9q2NgbOBvFlZZg26ZnGPmV6JEE6uEeidMRcMiQaDNDFN3xinnSDrtiPkooWBLhosuc62QvQ5xs4SZCQti1ZspEyzMGAlhFtazrYBEmRkcRyjCNKR5M3RvJtP7NgJt8817XWGWpMK+O3/Wl3c4Pd3iYH+OSsGhEyacmWkzVMpxZqpljEuUJkqSJSKy25HnSFITEE1iKYJYMVEP6cs5vPebjnN+rrWm4AJWCIdmJ+Lxl67xuZcn+a7XH+Afv+k4rmstmS3qy9lMt/Y+BkKuIca6jpKCpTOEkQZbgWsbY49Ym/dFvEw0d41dxKL/Hix6ADw0PsDxoSIvTtSp2SF+bGb6pBDkbIEfa+491MdD4wOrHvNG4cp//fJk7zmt11UphBFFUpjq6IH+PLaUPHr7EI8cG0RKQcOPuDjbJlFqZZt3UTLbDEi0MZnJnA8zNiITZBkZGRkZjA8UePOJQR5/aZLZVkg5Zy+YevgxSmsevW2Q8YHCDckpEzfRmmU3anmJ0un8lVk21vwYpYwg6Ra8FNAKE1qh+ZtuRUNp0sBl49TYifc2ODzSEIdqSWubxggVgckWA7ClxLMlg0WHhh/R50hqrZhGuLkSngBsW2BLiVLGVKOLTG+wkb7TmFZHY40Pj942xHwrWjGvM9bn8bXzcwSRQgqdLs51r/LZFRLDJZf5TkycKIJYU+2Yyth7v+k4x4YKa1ZqrlTb5BxriXB49nKVpy7M00xbUZ+7XOV/fPECP/7W2/iRR48vzBZNXh9zm7UqY7FecCBcjCXMz+LYOE8OFB2SRNNapYq5WNg6Fljp9cO2Jd957xjPXK7SDBIKro1nC4JYM9OKyNkW33HPGLa9eiPlRuHKhdQQJf1YrSnIbEswWHDRGo4NF/Asi+Gyt0RANYOYTpRQztmrPpbnWDT8mO+4Z4yJqp85H2asSybIMjIyMjKQUvCeNx9lqhnwymSDhr+wiLek4P7xCj/0pqOcnWluKuR0t2kEeysq9hOC1BxBmlbNbhXCkesbaXRFEBiziXaU0Imuz3nTLAjFniEHRijFicKxLXKuJO9Y3HOon5mGz6tTTUKl1nXkW4wlTUugYwk6y9vOxPrOfssRgB8n2FLwz996GxOpuUJ3g+HFiRqWlGhMv6NOZ8e6Vb/uY1tScv/hPkp5h/lWyA+98Rjf+8BhpBR84q/PrFmpeeZylZlGwBtPDPXE2BdOz6TufhIhjOHEtXqH//iX3wDgRx49zm3fWuL5K1Veme5s8pnuHnZaMesK0u7fddsaHQscIfDT+axmJ8Rf4+0nMC6dUgrKnsPxtPKulKbeiTk1WmKq4VPrxDR8E8I8Ws4xWvbMBlE6z7WcjcKVC56NIG0rZnVhCeBZxknRkoIzUy2ODxX5trtGl1zjSjmbvGsRRIqSp1d0FQSRouBa3HWwzLseOJzNhGWsSybIMjIyMjIAM0z+obed4jPPpcHQUUzBsbnvSD9vf90BYGWL1Vohp7tNrbP3LVo3GgE49oLoMu59Rt5IjKvdZgWHTKtj7eD6n7fFC1wF+LExvqjkPTzHwpECgcCRgig2lcBui+N680qxMgI1SRRKm0V19/bLA6bXI9GgYjNL9qdfv8p0I+TtrxvjrgN9vdu0w4S8I9HaMk6X3XbFZQt4lc742VIyUHA5NmTsy9er1IBpa5tqBNQ6IQXH4qkL8yRKk3dMu3DXUKLkWTSChP/r78/xgw+P47oWp0ZK/AWTm3y2O0ekBhe2BZ6UtEMjhiWmPVEjUInJ6OruCGigE4NjS/OaqYXzJoG8a6qcrmNzaqxEf87MhHXP2/3jFYquxUTNpx0lFByLg/05WmGyxCp+ORuFKw/kHVzL9Fu6aRZalKwUZX6cMFwy1vpFz1S6Pv/yFMeGCr1rXNlzODpY4NJcu+fU2e0qaPoxti0ZH8hT9pxsJixjQzJBlpGRkZHR4+RomZ/4BysdvoB1d/yXh5zuNrVOuOv3uZ/o7trbQhJo1bPb7p5JKdI2uU3Op3XihLwr8RyLYK9dPdZg8YyQHyVMNQPGBwq8OtWgE5mstMUufpsxj1CY4GhLgmdJgkix1WfXPae2JRgoOqtuKJRyds8ZT/uKJDXu6FWGJNiWsfL/xrUGQawZ7fP4gycu8tVz85waK61aqZlrBZyZajFR79DwY756bo68a9MM4rQyZo6uZxIiJQVXMFX3+ezL1/ie+w4z1u9t8RnvDK1NJUxpQbLIJ960fxph60hBopbayEsBRVeSJMLk4yULDpgCwXDZY7Ts8c2nRnrXmMUVLikFhweWipi8ywqr+MVsFK7cChW3j5a4MNtGa41tSZJk6XvIEmbOMe9aDJVcBoseWutVg5wfHB8giBSxUsy3I5pBjC0lI2UPW0oeOjqwZpBzRsZiMkGWkZGRkbGE1XZzL821txRyutuEawUG3SI4lhEYYaJJNOQdaRa5GqLEZF4hNm/PoTTMtCL0DoKqt9ICuB6mwiIII8VMw6fg2SilU8ORrT+GBLTCCNdtHKQQYEtT1XJti+OV/JLFNkDTj8k7pkKTaCh5Du0oots5qzRINNVOZPKqBLiWxJaC567UeGWqQRirJZWauVbA1y9V6YQJOVtS8mw0MNM0j+Gmr68JmTaCUQqBZ0M71FyrmeiHz700tcUztn26n3Qh6ZnHLP6ZhrT6ZX7iWNAbCdRmXi9nWzgahE562WX3Hu6jkncYLueWzGVtVOFayyq+y0bhykMll+9/5Ai/9vgrfONafUUOmWPB+ECRoaLDfDvizHSLgYK7YZDzbDPgyEAeKxWmDT9mqORlph0ZmyYTZBkZGRkZG7LRbMZqIaebZTMmITeTqcd2cKXAkgKhTHWs4NocH8wRKc2rUy06kdq8GoNei92OrPd38LvdWSsw/847klgpqp2IMFZIKVZt5dvK/Qsh0tkxvaVj9SyBbVsmO8yWSxbbXzwzwzOXarw6WefyfIcgMuIpUQqlBd14rDjRqeOlwpGmbe9qrUOsNGNlD8syz+9q1efUqEUjiHn2co16J2K07DHfjjgymCdOjJMmxOY1XkSsNFNxgGtLLCE4kFbGnr96fUw9gF5rYpSKrCXOhAIKjjHcEFJQyTsEkQkzl6noTRJNKBR5W9LSqRtIyv3jAyuMLRZXuIqutSSOoORZ61rFd9koXPnCbJuGH5uqH0vNcHRafZVSUsrZzLVCGr7JEVsvyPkzz6dt3mFCwbW473Blz+dqM24tMkGWkZGRkbGE1QTSTneu12IzOUwAw0WHydaNab27HjQjDVHSW/B2ophzcx1GSh55xzIhyFuw078+SVVr0xVjtjTtX1E6hxVraAYJnmOqSetZj69535jFvmtJWmGy5d9PlMYT0J938GyjsPKuxempJp/+ykW0NrNb5ZyN0pogSoyZiBQUXJsoSdCOIEhNQaQ0wi5KNI4lmG4GWFIwWHQJk4S/fGGSIEqYaQXYUtIIYoaKHq8/XAHglWs1phrBiuoTmDbOTqQYLDh8511mjrPRvr5zgcut/gGKqTKN0w0EtCaKNUFixHLOsbAFBIkmiBWOlAwVXCxp/u5HHj3Gd73u0IqNl27V6aVrdf7yhUkSveBpaQnBHQfKm6o6rRWurJTmF//kRVpBTDlnM9+J08wxgSvAj00+2EDB5KG1AiMwYYNrXHqYOh0yXC/0PCNjNTJBlpGRkZHRYy2B9B33jq47m7GZnevVHmuzJiG3dsPiAr0ZJUuiFVytdki0Ju9YxIkiUnpDa/f9hKk+GCs+nQ7ACbn6In8rmByyZNX7sVJvie57xk5vkOiFyloYK1zLMllmWtMOYmaaAULA/UcqzLZC7HROqNoOmW2FRInGThR9OYecLbkw1wHLVDMTrZHp7JFrmwyqiZrP0YG8seNPj1L0DnRp/XL5OVjvz9f75dfL/g30xKjSafC2gDBRjBQ9OpGp9hVcixzgR4rRPo+yZ3Gp6nPnWJl33HNw41a+3rkSC3/eAqu1Xn/twhyvTjVIlCLSZkbPkmamLU6MuOyECXU/IudYWFLiWnLNa9zia9jhgTwF16YdxrwwUWei7u+p0VHGrUUmyDIyMjIygI0F0rfdNbrmbMZWQ043CnBdPkBfzrmAv4fPfn/R8uO0pc5UGg71FwiiiEjB5erG5+FGa7buuyBWJoS5krOp+ZFxEkznp7r5aVJsLUy7a3CyrMPPzJaxqL01vUPLAse20jZOI8DCBK5U2/ztqwmjZS8VuYLbhosIYapvtmXaLLXWWAIirQnihERJ6oFOqzdmQyKMFUXX7rVAerbF5WqHsT6P77x7lDPTLZ69XCPvWAyk80nPXanRCmKuVte2sLcEuLYkiJKeqUclL7jW2vtXOGeDFIJ2tPSx3HS2Le8uuHgWXIsHjw5wfCjPhbkOX79UpREkpt1SmvfAparfC8xeK0ese11IlObt94ytaFk8Pd3akXnQdCMwDojSzKuhNaHS2EIbR8goIdEw34rIOQkHK3nAGHosv8Zt9RqWkbEemSDLyMjIyNjU4uIb1xq899HjPP7i6rMZW9kJ3ijAdfkAfV/B3dXnu99xbUGcQDuIkVLQiWIODxTxo3hTggy2X33aKSL9p6uXlNb4scaSFqWcMb8IEo0UojejtJUKqKQbfL1At94kWbBp72JbFjnHAkdTa0emUoamEyZM1n0mGwF9OYfDlRxFz+lVzbTWXJrrkCjVU5ix0kzUFxw/G0Fi5oY8m7wrCWOFYwmaYQxaU3QtnrpYY7YV0IlN5aUTJZTzNhdm20SxIk669bOF5wLgWsamXUjBfDvqmXq87lCFa6/Ob+GMbQ8pTAXMkQvi105nw5TuZokJgkTTChKeuzzPixN1Cq5FybNpdiLaoXGP9CPVC8z+9rvHVn08pTRfuzDHUxfnGCp6CCF6TpdddmoepNEorRHSQkpBzrWIg4RYayzMezJRGiHN83ctSa0Tr3qN2+o1LCNjPTJBlpGRkZGxZHEBUO9EvZ3pcs7uLS7+4f2HeP+33r7jkNOtmoR0OsHOnuDNhiZ1egsRAoZLHg8frfDlc3OAmc1Si3LJBAvCZrUWs+vNYoHVDhWhVIyWPUbKOeZbIUprmkHcM1NYLkbWY7kYW/G46RyTwBg0PHb7IKWcw1MX5qn7EcQL5ymIFbYUdKKE+XbElWqbyVrAXCvgSrVDJ0pSV0Yj9Ja78glMK6QJK1Y4loVM5+ZsS3JpvmNm0nI2h/rzTNR8qh1jj94KYlxbYlsmmkAAlmXKf4nWxpFQSIJYLTH1OD5ShusgyNqRqQzmbEmkjFFHN1fOEtAKTfusLY3TYjtUuDagNf0FhzCWlPIO73zgEN965ygPjQ+sWRnrtko/dXGeF67W6c87XJ73uH20yGBxweZ/J+ZBAMeHi+RdGz9KyNkmDy9nm3OcKEWsNLYl+I67x3j7vQcYLntrXuMWX8O0Ns6Ki6+ZOz3WjNcWmSDLyMjIyOgtLvxI8vJEg7l2SKwUtpQMFlyODxcI4oRWGO9KyOlWTUKuNG5dQ4/V6MSapB3h2pI40cZ0YrpFwbWMCFBGdNip8OiKmv3gJbD8EKy0VNcMEu484KA0tIMIz3bphHE62yVQWq8QPNvBsSFK18BF16beiTk93eJK1TfzTpiQYyGMSYdWiihOmGkEfPnMLAXXwrUtEqWN8NULLoMAzqIZtZ47HxBECseSoAVB+gthlDDWn0/bGI2L4FwrYLoZEGsoSEE5ZxPGCbEys3GLK5vVdoDCzLJ1TT2u1zylwLzHwnRo0bUWssZiDTaanGORKIWvNEXPRghjQkIn5tRIGduSlDyHR44Nrrlps7hVerDo0J93sKRgquHTCCIeGK/0RNl2zYO69Odc7hgr8fyVOjOtCKEXBX4LE9h954ES/+ybb+PYULH3e0ppLs21VzU6ulptc60WrLhmHuj3dnSsGVtnM469+5XsXZKRkZFxC7DTL6KiaxPGiqcuzhMnmlLOxrFsokQx1fCZbQWMDxZ2bXGxUYDr8gF6T+4DpXGdCVNLdM+W/IO7RumECc9cquJYgjDp5j4Zp8Juftl+wUkLIbaUOLak4AgageLMdItvPjnC2ZkmZ6ab2JaFm7bBubZksrHzAPBEgSWNwAsTxVTTVFcTrRfcCxXkHEHekYBFvRPSSttDC65NKzD2/LYUSGFaFRNtRIljCXTqfiiFwJVpK5yiJyY6UUIQKeNgsoi8a3HQztGJFHESMlz2GC55aGAibUlc/DKGialAfdPtQ0w2g54QuB4sWMF3Z+WMUYYjzPWmlHNwpKDaUViWYLTskXMsOlFClGjuPFBGStFr2ztcya8aON9tlT45UqThx+Qci2o7YrTsLskCA7ZlHrSYw5U8dx/s46WJOio1yDEejqbC7NqCB48OML4okHo9o6NKweHxFydxLUE57/SumZP1Dpfm23zHPWNZMPR1YrOOvfuVTJBlZGRk3OTsxhfRwb4cQaSYb0ccHcgTJRo/tfseKDhcnO8wFisO9uV25Zg3CnBdPkD/yLF+vjGztvnBrYoGosTMGcWxotaJGCg4TDdCEm3ysHrCWy+dG7tRM2TpoeDaFpW8TTtSBIkxp5hvhcx3AvrzDvce6qPeiTk8UGCq7jNV94GtC7LlM2iJgrInKbgW9SAhTjR+nPSqiAunSxArM0sWxIpYQ5IkzDQD/NiYO6g09Ll7IuOuxWVaWXEtkx8XxgqFxo8Ut40UKbgWz12u4dmSuVaYbnBIokTR9GMGiw5+GONHxjRECknOMa1zi100HctYyD95YZ5f++w3ODla5txsY8vnaDtYQMKCYImUmYmzLZkaYxjRm6R/X87ZyFSAV9shkTJmLpN1n5cm6vzJ16+uuEbdP97PmekmeUfy5IUqc+2QdhhT78Q0AvNen0kdK5tBvGXzoNWotkOC2OT6WYuen9amhbXaXngPrmd0dKXaxu8FtC07nvRNc3PUZm5+uq/TbDOkL2fTl3NQSvPclZWOvfuVTJBlZGRk3MRsxTp+PSbqPp4jKTgWZ2ZaKAXdpYqU0J9zcG3JRN3ftQH1jQJcFx93pbA7QvBmJNHwG3/1CgrRyzeyLFCJmWmy0oWfLY1dfjdg+EYWzGIFcZgQpDNYlhA4thEks82Qb7ljlNcf6eePnrpCpeBwfKjAK5N1Lsy22Wpz6vIWvuGSg2dLqu0YCcy0Avxw4Zwk2gidRCkavkYpTbdRMFEm7NmVgkSa9rzFbaBK06tOghHLUbJwG5mKt6Jn49iS8cE87VAx3w5pBjG2lIz25RgreygF7ShhqhHQDGLyjkQrRZiY5ySBnCUYKZvqkG0Jnr9a40unZ7d4hjZm1Rk+AYV05ivR5jVMlAaUMWqJEmKlsaRkqOwhpeydk65dfFfs/vlzE4SxWnGNemGixlTd71UUSzmbci5HwY2YqgfMNEMcKZlrBTx8bHDL5kHLuTzf5pnLNZQ26rynr+lWBDXPXK5xeb7NkYHCukZHz1yuMtMIeOTYANfqwZLXeKwvx4E+L51LzEw99pKuIdXFuTZxrDg/2+q1jg7kHVphfFO4XWaCLCMjI+MmZTdtl1thTBgrrNQ73NzVQuXFkhDGilYY72qf/loBrsvv78J8e1v3f6vQioyPoMCICYlAoLEkjJQ8osQoh3Le4UrVXyIabiTdqpSZPUoo52x+5NFjvOOeg1ypdejL25yZbnLf4X6kkAgBYgsW+MuRwjgTVv24FxotMHNsiytPUQICjS1NhVHorjukoL/g0A4TxCYG2pLUWEUAOUfSn7eZagbMtgJyjkWsNI8cq6xq3/6td45wrR7w3OUqnSgxbo6AYxtbeUsILEtQ70TkXRvXtjjVn+d/v3xtm2dnJQJTJYpZyG+DNHzbMvEEnViRxIoD/Tmm6z6NwJzXVphQdi0G+z0sIVBKESaauVbISNmjmM49BpHClgl3jJVXXKO+fqnKhdk2BddirG/BrbA/71L2bC7OtXEsyTsfPMx33XtwTVOQzXJmusm1mt+bD+yKSAClFInSXKv5nJluIoRY10VxoOByerJJOecwPlhYYeqRaM35mVZm6rHHXKl2ePrSPNMNf0W7fTek/amL8/teGGeCLCMjI+MmZTdtlwuOxUwzIE40t48UiRKTs2QJMzNztdrh8nyb56/U+KsXJzk73VqzPXKrgm0zJiGvTrY2eVZubboVHluSzjbBRD3AtYwToxALVbQbTbfTrze3pY0wOz5c4JN/e4bnrtSYafpM1kOuVn3yrkWyAzEGRlDMdyL8aGlo9Gr6VKc/FULgWKbqKC2zQDcZZBu7P6pFP4+VYroZkrMlfqw5MVzgUH+eV6dalHM2lhQ045iJms9QyeWH3nSUC7NtrtXaXKl2eo6FZm5NUPBsLCFoBTGaBEeaYy05En8X3E9ytpnxCxMFyVLBKjCVrslG2HvuZ6dbCAGDRYejQ0WmG745zxpCpXh1qokfq/R9qfjLFycZHyzgOZJDlfyq16jRssczlxTeMqHVCWNmmyHNIEGKhD/7+lUuzXZ2PA800wgIYuNo6dhWmn+nEQgs20JFCUFq8HKwkl/XCbacs0FAw48YKLorLPo7QZyZelwHGn7Exdk2iVIMpddAMAYtbtGEtF+aa9Pwoxt8pOuTvUsyMjIyblK2ah2/Ht3FqU4XqF7qytAJY65WA6qdGDoRv/7Zb1D0HF53uI/bhksr2iOBPRmsbvg7N3u4VUiUETaLl7dSCOZaEYkKVgQm3yi6C/nFC/1OmPChP3gGMAt+0/qmmG0GJErtuM2yW7mxFpvEYCq8iVoprBxL4NmSvGucDlVq1pEk5lic9Pe697NWiLUtTUuf0lDtxNiWwLEkDxyt8GfPTPDC1RpRYlwYjw8V+f5HjgDw+ZenKLgWRc9UVHK2yTILYkXO0VgWsGwvo+TZzHS2/3noxgFY0sK2BLYliTpx7zl2q7Dh8t5RkbouJpojA3nuPdTP6akGp6ebtPy4N18npDDVoljTn7cpuOaf1bAtaV4Dx2K2FeJZkiAx74co0XiOpOjaFDx7y23Yq7Hw0TBOmBrzegrR3UAw/6/Y2AnWloL+vMtcO2R8sLChMVHG3tAMYpPtl7NXFf2eY9HwY5rB/q5UZoIsIyMj4yZlq9bx69GJEoZLLkLQMyGIE8VEzcePFJ4jSRKN0oIk3Q0vejaDRa/XHvnpJy7ip3lOO5lnW41ueG7GAkvc+GKNYy8VP/uRWMPpaVPtzNnStMIm2phn7NJjKKXJuV1LivRx13j7uLZpy9MI/NgYbMSJJkpPZKxSsSUFUaIpuBZJkhDGps0PTPSAma8yC/GBgoPAOAL+zTemKHoWb75tEEvKdG4t5nMvTZJzLOZaIeODRS7MmsyzWBlXzU6U0AxjcraFJY1ome9E9OUd5ts72+nPOQIpJANpa2asVK+aaQnzXJNV3kgW4KVzgF+/VOWd9x/mkWMDTDcCGp2IwYJN0XNAiLRNVXFlvkPBs7l9pLSigqS1pu6H2JY5lsm6z3TdpxMboV50LPpzDp5jMVBwKee21oa9GiNlD8cSBLE2rpHdrmxtqqhCaDxbMFxyUVovbald1N6oteZaPeDR2wbpRGpTxkQZe0MpzXwLIkXJ0yuEcRApE1ae29+SZ38fXUZGRkbGmmzVOn49iq7NcMljuOQyUQuYbwVMNwKCRDFQMCYJ082QStGh7NnMtcKeHbUQggN9Hl8+O8tI2eP+I5UdzbOtRnvFdn3GYhQQrJeYvA/xY7WlQOjNkncs+vM2c6lwWdw2udjivKvRnNSyPEoUc+2QJHVWlCyEQXerP83AtLt1nRddaXbhtdDkbIuD/XnKOZumH3O52mG2FfKmE0MrPptdQ4g3nhgCoOCZKlDTj+hEiXFujBQSsKQkjDXPX64xVfPp7PCjEESaogf3H60w2wh4daqJZ2ukBEca18i5tpGb3eeqtQmtNuaEmtlmyES9Q8mzmW+HdKvr1U6M0hopupECmkYn4uxMc8l1Ya4VcHqyyYW5Nn6keOFKHc8WVAoOwo9NLp1SzLZCbhsp9qofW2nDXo3bhovkHJsgNu+Nxa2sAsxrall86fQsf/7sNWbSdreJqs/rDvdxsJJfIrh+6E1HATZlTJSxN5Q9h6ODBS7NtVd1NLVtyfhAnrK3ctNyP5EJsoyMjIyblK1ax6/HYnH3yLEKEzWfr12YZ9S1KLkW1+oBthQUXcvMseSMKGv4MX15h0RBrRMtGdzvshsLKdsSpryScdOwGdv9vXhF22Hca+nUmMqOlAutho6ESC1UfISUkDpYmna+tHlNLgRCd++re7xap4JNSmwBoTLv85JnAdAMY9CaA33rG0IkSlMpOAwUXKYbPgf7c0SJZrYVMlHrkCiNFJq+nI1nS87OtlA7KIPK1NwkjBV526JScLnvSD/1jrGxf2miTr0dLzxPTAUwSZWsLQVRbPLd2oHJG2uFRoR1IkXetbCEJNGaVpggUgkshOhdo/wo4ckL81Q7EZW82eC5UvOJlKaWCrqcaxHCikrdVtqwV8O8RjaNTkRC18hlUVSEMNX4V6YanBwtc6iSZ7jk8vzVOk9frDLTDBkueSsE12aMiTL2hsOVPA+ODxBEiliZ6Jau2+VI2cOWkoeODuz71tFMkGVkZGTcxGzFOn49Fou709MtvLSdzLEkc+0Ix5LkHYt2kFDKmfmYVmBcxQDqfoQG+lZpnYSdL6TKnkV1n88AvJbp5nstrjjcSPncrYQJYf5baXotagnCuImiuXOsTKRgqu7TjhJyjgVap7NkSW+2SGD2A7r3uZBnpgmVaVkUQCt1U5RC4LkW/Xl31eNbbghxcrREM4iZb0cUPZsoTrAtgWeZDRDbkihMhafZDoi3+1HongOtmWz4PHrbMK8/0s9/+d+nefFqnVhp8q6kFS70eEYqrSgq0MK0hGml0Gi0UkSx7m3WdEWILQSWY9HyIxSa77h7lGo75vRUgxcm6jT9mNuGixzoy/HStQZHBvI0/Yi5dkicaGypKHo2pZyFH6nexs9W2rBXoxWY6pvnWMRKpa6SXdFobO81ZpHfbQMfHyxyuJLn2Ss1bhsu8U8eO86RgcISwbUZY6KMvWHxd9dsM+DIQL7XdtvwY4ZK3k3ROpoJsoyMjIybnM1ax2/mfrri7tkrVfxIEcahWXxKQSdOqM9H9OUcynm7lzWktWa+HVLJu+lCdyU7XUgNFmwu1TNBth+xF9ml7we6tTEhFsRTb4ZMQN6WOKl5xMFKnrG+HBfm2oQXFHlH0vQj6n6MSvPKtFoQmoJ0ZsySROlf5m2JkII40XSihAP9OaJEUQjtVT8PWuv08yCZqPkcGcgzWHR5YLzC6akmk/UO1U5MwbW5Y6zEwf4CBc/q2alfnWvRqgbbOjcinRHzbIu333uA97zxGHGs+Pmqjx8nDBYcYqVph0sNVhSmbbNbsXJS05JrdR8hBDJdAAshFmUkG3MMKQS3j5Z44/EhvnZhjt/6m7MMFh0O9ueZbYXEiTIuhTmbcs5mouZTcC0O9nsgBNV2SJioXTHKaAamAnegz8OPElqhSlssTfWvHSYgWBEbYRwhc1yca3Gt7nNkIBNf+4nlG5Pt0Dhc3nekctO0jmaCLCMjI+MWYLd2aLvi7vJ8m197/BWeODtL3pH0Fcws2ETNp9qJqPkRt40UAc2rU02OVArcPlxiou5TzjlL2rSUUpyZbnJiuIjSJoh3q2Kxvr8di/cFezGPtRmUNtlZloB2dOOVWdG1UnMTlQacLxhzWFIiJeQ9Gyd1eRRCMFhwsS3BVD3AjxVJWinptixa6UxZzjEuiEmiEAJsaSEl9OVtBIK7D/ahtGao6BnBtezzsHh2yrWNoPnLFyZ780l3HSgRJQntMOGNJ4Y4PlRY0fKod7DRb0kT9C4FnBo185xfv1IliBMKro0fm77O1dpN1SJR6tnGFTGKlWkBTTTznQjXkhRciZTmPAkhepbwUgr68g6eIxnrMzb4riWNy2NizExKOYf+MMGxLebbEa4tEUIQxsY4Y6dGGV0DiCTRHKrkey2XjU5MK4wIYoUl4NXJBkprCq5NO4y5VvOZbYXUOxG/9Tdn+erR+R07x2bsLru1MXmjyARZRkZGRsYSpBQcGSiki9TujI1pOxwpu0zWA4Iood6JqbajXnskwKe+cH7JPNtEtcPzV+pEiXE1+/hfvbotG/z8DgNhXyvcCDmkMGYQpjRy481Xco5EAM3AHJvEVGlc28KSprUwThSHB/I0/BitNSXPSp0NEyRppYcFkRunGWFhavihEdhSMNbnMd+OmGmEjPbl8GzJqbHyqp+HJbNTBZeHjw7gR/GK+aSHjg1QKbip6+nKxaRjbW+B6UhTnYtiKDiC0b4cALMtY6E/WHCYboT4ccx6nqZSwFDJY7DgYAkYLueo+zF+FBPGCVGiyNlGaA2WXE6OlHqGCsudYcs5uzc/5xZdokSRd23uPlDmWt3nwmybcur4uhtGGYsNIObbEbZMK3CxaVu0pMSWcHGuzcW5dm8mVwpBOWfjORY5W/LclZ07x2bsPjdz62gmyDIyMjIylqCU5msX5rgw1+Leg33U/GjJoPRdB8r05WxiBT/0pqM8cmywtwu5uG3k9FSTS3NtHFvy4NF+DlUK27bB79vnlsU3mhu5ByxIs7tufHEMgHYQIaVpF1SJEWiWMBW8gYIRBlGiOT5YwHOMA2jJMwtvAWghkGLhnHYdF5UCYZn2NbTZYPBsi4E8NCzBieEiP/jGcR45OoidbiB0Pw/LZ6dOjpYZLLqAt2I+6VB/nt/627Nruqe6lgP4Wz4viTazblJCzjHPF2CoaObcZlshUoBnWYTJSmHtpIYgQsBsM6DWyXP/kQpRAlfm25RcSStMCCKFZUlOjZZItMksU1oTp3b2y63ku/Nz3fbFg5U8lYJDJ0p4w4lBvvv1B7n7YB8H+3JM1H1evlbfdvVjsQFElCScnm7RCRU5R5KzLWqdmEipNFxd0PITlNZESUzDjyl4FqenmwwWXFphvCPn2IyMxWTfcBkZGRkZPU5PNfjM89f44pkZvnGtQX/eYbTscfeBMgXP7s2xJFpzfqbVa0Xqsrjl8b/+/XmEYEmGz2o2+MCGbSY5x7p+J+EmwwKENILhRphRatg3YgxM26RjxSRphljeNe8dP1LU/ZhToyUO9ufQCP6P1x/g2Us1vnBmhiBSaXVNoNEkKiFdl2MJ076Yl4KEBcfGuXaIa5nF/DcmG3zq78/z5JFqrwLc/Twsn51aLLKklNw+UqLajoyBhy3XdU+tlGyY3Pp5UdqY0w/kHSwpekG5DxyumBbEZghar/n+sSxBzjJZaq0wJufIJYKqHUT0FVxipWh2IibrAUIIzk63+D//7EWCNM8wjNUKK/lTo0Wev1pHKdPGWOvES+Z/Tk81+K2/PbvjwPnFBhCX5loUXIuBooMtJUGU4EUKYvOaoKERRKlDp5mN82xjbjTdDLCk4KmL89t2jt0tlNI3bZtexgKZIMvIyMjIAIwY+9hfvcor1xr4cUIQK2abIdV2xGwr4s23DfbCXTtBvKZJh5TG5rruR9w+UloSqApLbfC/eGaGZy7VNlxoRSpbYKyGSXqCkmvjR0kv0Pi1jHFINDNgOrV4L7oW5bKLFJI7x8oUczbnZ1qMlD3e/623M9rn8epUg+GiR8mzaAYJV2sdE3CcKILEmN5rAQ4CZZmA9KZv5qZG+jwSZcTf8grwarNTy1nuQrqee2q1E/KlM/PbOjdaQ61jPrvdoNzJZkDOMVb1a719LKDkObi2oBUm+FHcM/hYbEgy3w7xQ0XNjxkpWTx0tELOsXnywhzVTsRAweGhowOrWsm/8/5D3D9eYbjsLREWp6cafOoL55lrhbsSON89t//3ly9werqFk2arFVybIFJUCg5BrKh1ImINloKCK3FtSTeffrDoMptmlDX8Gzfgenqq0XuP7ESo3irczOI0E2QZGRkZN4D99sWhlObTT1zkmUtVHMtYWPuRTSeMQWkm6z7PXanx1lMjABu6nbXCGD9OKLir/zzvWpyeavLpr1xEazZcaBXdbIZsMXlHUHYt5jsxsQLXEgzkPYJEMVEPb/ThXRcszIzYcg2xuFpnArNNXlY+0ZRzNpHSS1w/pRS88cQgI6UcrSCm5Fm4tql6+VFC0bNQQYJSupdpJoXAEgvvydlmSF/e5ImVcyuD0JfPTi1nNRfStUwKfu/L55YEW2/6fAkzfxYp4wZZsE3lsN6OmGmGmC5LYeY9l/2ugp57ojHlMeegy2DR5Q3HB6h3Ir5ybo4gTvjWO0boL7h89fw8idIcHcgz3444P9vmkWMDG1rJdx/rL5+fZK4Vcmq0tGuB80aUnWCi6pvsqlbEXCug2omwQ4FSmihVX5JuZIIwTpOppaiUgloaen3Xgb7rfv3ebaF6s3Ozi9NMkGVkZGRcZ/bjF8fl+TZfPjtrdsljzXw7IowVkTKW0FJoLs+1mah1aAbJhm5n6y1AtdZM1X0uz7cZKXu84dggrTBhPm3/OjlS5PR0a8lCK+dkgmwxSsNcJyFRRpDMtIxDXN61NhXIfLPjSHBtizBOSDSrVna656Hbgtht0bPlyg2F8YECbz4xyP/zwjXOTLdQGhKlCGJFOzJCxrMlsQZPSCwpiNGmbQ+o+zFFT/eCoUueqQp97cIAjxwbXBK8vtpc2FobHKuZFNw/XjHiaIsvsrHxl0hh7OuvNXyOj5Q4N9ekE8YUPQfPErSChEa4dIZMYyqNsRJobQRRmCiUUjTT7DXXkmg01U7E+GCB/oJLw4+Zb4eUcg5SyhWB8otbNVe7llypdjgzbdo2dztwfnygwOGBPI+/OIlrCQqejd2J8KOEJNG9VlUhIEoUSWCcIKNYMdcMqaWVsf/fk1e4MNO5rtfvvRKqNytdcTrbDOnL2fTlHJTSN5X5SibIMjIyMq4j+3VX8+xMi5lmgB8lqeMYgBmUiVJnuTjRXKn6vOXk8IZuZ2stQLu232dmWnRC48p2tWoMCoQwC8bRsrdiofWNa829PgU3FUFs0rYkxtdQCmiHCZ0oueXFGJiw4ihc39Gxex7CtLJlWhljzs20TRjzmKk+dc0ijg0XkGCsz6XAkmaeKUhMgLBjyV5mVTlnU22HoMFPFDnHODhernaYrAXMtIIVFunrzYV1NzgALs21162cB5FaEcK9GTTQCBIKrrGSf3WyyRuPD/XEUJwootjkjUmxUuRGSpGTptXxsduH8GPFX74wmVaMjPSN0+ranWN9xq4+UcSJwknbI5cHym8UGL+40q61Cfrtir9yamG/k8D53ptEmFw1pTRJorEsIzy10FjSvO5RbKIOphoBQaxwbUl/3mGo5F736/deCtWbja44vTjXJo4V52dbxEphS8lA3rlpzFcyQZaRkZFxndjPu5paa1pBQhgnvbwmIcyiRGAqMZYUfO8Dh/i+h8c3PL7Fw/Or2X4XXAspTGWhEykEqRuelMy3QqYaAYcr+d5Cq2vNnbGAwBgtoDSObQK6g+vt6HETIFkQL0mimW2FJFrze186nxpdGLOGS/MdokQxkLchnYN0pGSk5NAME1p+zOuPVJiq+1yab+NHilhpKnmHSsGl7kc8d6UGmt5ifbDoLFmsrzUX1hVj/+WvT/PclRrt0ARDv/5wP+943YEli/xGJ1oIut4G7VARScXjL0xS78QcquTI2ZK6H6MxAcmeJQhizaI8bUqeTX/e4c4DfXzXfYf4o6evLAosM9eDriOlH8WAtyxnTBAlqhcoDxsHxncr7Verba7VAubaYW+xPVhwOdDvbTpwfnmbuNammveG4wNM1AKm6j6J1r0Z2Lwj8SPjvClSd0qzWZWkxiiSQ5U8B/tNVfN6Xr830xK+I6F6E3Gl2uHpS/NMN3ziRFPK2TiWTZSofWW+shGZIMvIyMi4TuznXc2cI4kThdLgSmP7bY4LbEsSK2NZfWRw87Nui40Jltt+j5Vz/M2r072MH4yTOK5lQmAn66ZqVkjdFVfxQXjNo4BEaWxLYAtoZ2JsVRJtWhwRkCiYbfg0/YggTmgEpso2kHewLclI2SOIEmxLcmqsxEgpRzlnM1Hr8OWzc8y3Q65WO8bwIVEmOFprHEvSTm3zR8seU42QSsGh6Noc6Mv1WnD/xbfczvtXmQs7O9PsGeosrjidm27x8rUGH3rbqZ4oe3WqteMqqNJwbNDj+as1Ls01cW3TbuilxhUagWODSjSRBkvC+GCBbzk1wjted5DHX5wkUZq33zO2pGWx6Eo++9IUz1+pc7iSX5Iz5hQcmn7MaJ85p+u1anY5nFrgd9sKy3mnt9ierHe4NN/mO+4ZW/P3u6zWJt6Xc5hpBtx3pMKRgQIX5tqEFxR5x6IVRPhpsLWUAk8Kip7NbDNgqOiaAGzPtFx2r+XX8/q9nZnEW5WGH3Fxtk2iFEMlr/d6eLaFW5T7wnxlM9z6r1RGRkbGPmE/72p2IoUtBYqFlqXuolClu8a2FHSirW3Nr2X7XeuEBHFiAnYtE8EbK/N4OcfC70Q0/AiVDtAfH/CYbt36u71bQWBErNKadqR71Z7Xgiyz2HwEtWcLtNJESdq255vFth8pEqWRQjPb0ri2ZLhUouTZTNZ9Ls11GC55AIyUPRKlefLCPAIoeDZ5V9P0EzqR4uJcm4Jr0Z+zOTPTQqXOfV8+N9er5CxerC9esC821FkQHZIoUTQ6Ec9cqvLpJy7y8999D1KK3mbFTtAaZloxp8bKPHO5ii0EBdcmitP2SwF+lNDsinwNF2fbPGHPYtuSpy7OMVQ0i9+u82qX1x3q4+mLVZ69UuP2kRLHhwvMtQIuzneo5B2ODxVoBvGSVs11N3kWtRUuIR2k22ivZq028TPTJidxuOQyPlhksOBS9GxyjkWl4NAIYjphwh2jJep+zJVqxwhVSzLWb1qyB9MMN7i+1+/tziTeijSDmE6UUM7Zq250eo5Fw497M6T7lUyQZWRkZFwnFu9qljx7xTzEjdzVlEJQyNmIMOnZpxt5pHtBsqbNcOulqtVsv6udGDTYliDRxjREKZ1W4hayo87Ptjk+XGK4nAdau/uk9xlbFVOOJdDaCA2BMSDYT3lge4UlzHtKLXIDXG3maTFCGGOP7n/blkTECjudFYpi03441woJY00rNEHFpqLjUc7ZNIOYRJuqrSUFlhAUPWgG5vfbYcJ0M8CWkgP9HuWcQ5Qopho+NT9kqOjRCuMVrXNJovjy2Vkswcod/pJksu7zxNlZLs+3OTpUpNHe/k6/FKZaGCtohDFCCAYKLqc1PHR0gNNTTWqdiHZsZkkl4DmiV8X++uUaT1+s4dqCsb4cl+c9bh8tMlj0eo9xsJJnphlyYrhItW0qkeODBUZjhWdLap0IP1K9Vs31Zq6uVDtL2grn22EvoH6sL8eBPo/5drRmVWq9NvH7DvczUfNXreYNFByiWHGokueug31ordHnTSX0oaMVDlVWxhdcz+v3ai3hq80k7ueZqd2ilM4SBpGi5OkV4jSIFAV3IeZhv7K/jy4jIyPjFqK7q/nlc7PEsWK+Ey0ZPrZtyaO3Dd2QXc0Tw0X6cg6NTpzu7hs0JnA4ShSe7ZJzZGp7vbUv+tVabKQU5NNKQJCYeRWloOjZFDyL1qIdzenmzqsC+52taCmBcQtUWvScMF8LYgyM8EqWPdn1xJhSSxdpOcfML+p0Q8CYUSTEieZq1SfnmMwpMGYOL0/UaYcJsdLkbPP+DyLTaiuEYLjoopVmshng2hYnhgq97L1u29Rk3QcNU3Wfz704taR1TgrBTDPgwBqtzP0Fh9lmyNmZFkeHikw1tvdZsDDVQuOnobEw9u4SQagUjiV55/0HmagG/PWrU9RVhFbgRxqNph2GvfcdSKSAqYZPI4h4YLzSE2WdMGG45PFPHjuBFKInPLvmKVuJ+uh2Fdw2XOLIQGHFJlY3oH6tqtTiNnGAeida8vtbqeadHClz70HJxCoVyhtRlVovq24joXsrUfYcjg4WuDTXZq4VpjNk5nul6cfYtmR8IE/ZW9nauZ/IBFlGRkbGdUJKwV0Hy/zR16/Q8COGii79eYdOmHB2tkVfzuHOA+Ubsqt5uD9vLJ2VcXCzLJGaIWjCxAyzV9sRf/jERZ48X92yxfPyFpuBgkPOsYhis3tJZGY3DvR5uLZkqhHSn3c5MVwE4KXMZXFJBc3kSWFUBa8dMQYbC9fllUbXNtb0YWIqPuY2AiHMDF6iTLunBuP+J8wCO1Ga2aYyAiZtjzMBysbgZrDoUnRtXNs4B042A3Pfq1aRBX6s+J9PXiZK9JLWuWcvV2n6MUFBkV91zbj0/qZbwYbnaDUUC4HZtoRzMw3OzbRQWuGHCV+/VKXeCXFsy1QD042ZtDhmXAeBUEGiFXOtiKODJlvszHSLgYJp3+sKk/FVcsW2Olu1fCNneXvkegH1sCDo/Ejy8kRjhSnI8WHTPrrZah7Ap75wft9UpdbKqruex3CjMzUPV/I8OD5AECmTKdeOelXUkbKHLSUPHR3Y9+2bmSDLyMi4rtzoi/eNRCnNyxMNDvblGCm5zLcjap0IW0puGy5iS8k3rjX4B3eOXvdzcrXWIYgSPEvSXecnWhMr45omhVmE2pbclsXz8habA30eh/pznJ1p0QgSco7FSNlDSMFc28yOPXrbIOMDZgHX2d/z2NcNgWmZO1jxmKoHNIOFSSopwLEgStavGN3qCAFCm/OBADe1WgcjStphTD5dwAexUR06VXGuZdoP/YjUXQ8cKRBCk2jjCppzrNRCXzFQMG6knSjBtgRlz151l76UtjzOtUIeOjqwpHXu3oN9fONag8l6QF/O7lXXwAjDWjtasjnh2du7Nix+S5RyNnU/IYwjpBQcruQJYsXzV+tESUK3ON09B2gTiK1099pgZvHOzCgGCsYcY6Lm0wziXRUmO52VKro2Yax46uL8Cge+qYbPbCtgfLCwpWrefqtKrZZVd73YD5mai79bZpsBRwbyWFKQKBOTMFTybor2zUyQZWRkXDf2w8X7RtJtnzk1Vlp1hqwZxDfMZfHsTAs/UhwZzNPyY1phQjtMkIK0hcsiTBRRojk1WtqWxfPyFptKwaU/H+KnPf6dKIHICL/7xyv80JuO9u67YENzf89k7zkaUxk7WMlRytnGkEJGZh4P8CyQ0iJOEiSwA2f0mxopjHiIFZCKKGvRW7QdaTqRUfg9v4iud0VqjGKnYk5CavxhZteUhiBOsKSk6UeERRfHEtT9mP68y/1H+plsBEzVAyKlcKRktM+jL+fw4kR9VYfV/oLL+GCB87NtphoBA0V3wdTDj1dsTrzx+CDPXNl+xVgAjU5sHFSlQKbVQluanLAgWhYKrcwvqWVvqJJnESuYaYY4UjLXCnj42OCuCpOdzkod7MsRRKZqcnQgv6SV1CkILs53GIsVh/vz2PbS8Pm1rsH7oSq1H9hPmZrLv1vaoamc3nekctO0b2aCLCMj47rQvXjPNkP6cjZ9OQelNM9dubGByNeTxS6Lq7mT3ejsGC0g79hU8i4NP+ZqrYNjSVzLzJ1027l2YtG/fDEz0wh4+sI8z1+t045iCo7NfUf6efuy7KXXHyrwpYvtvXjaNwWONEK1UnBTp8AY27I4MuAQxk1ibdwxUQmJXt7k9tpitYwurY34iNPSYU+Ipf+t0/9OFhnKNIMEISHnmtZaKTRSmLmyJFF0lKLuR7TChIGCy+sP9THdClFKESlFGCuwQSmTh1RwLUbLuRXHJoTZgJhvm6y9hr/w+V9tc+IH3nSM3/3CxS27adpp6yHSBDg7UoAw5iTd6tzJkSJX5tpcbZhjkZjrwmoOnqWczVDR42q1Q86xeN9bb+MNx4d2XZjsZFZqou7jOZJK3mG+Ha2oXFYKLq5t5sK2ch27kVWp/cB+zNS82YVyJsgyMjL2nO7F++JcmzhWnJ9tLTGzaIXxDQtEvp7s5+yYE8NFKnmXajtirM/Dtoyzmglw1aai6VgM5B201sSJZroZcGa6ueUvvSWLmQPwTbcPb/glWsrlgNeuIBsuuhwfKiAty7SOGW2MHyVGUGhFvMyMJcPQNfBIlMZK/7ur2WyZhhkLgULjSGPaEaUOjonCVIyEMM5+ZY+5Vkg7SlDKWG7fdaCP937TcQB+8U9fZK4VYAnT5tgJBXOtkL68w7HBgrHntuSKY8w7Fvce6ufEUIGLc511NyduHy7z5hMDfOnc/JbOQ5zmsTmWMfPIp86prSCmFSSMlj2klPQVXCYaIUKk76MF53tgwdHTs+3UsdLCc2wO9O/d4ne7i+1WGOPakoePDXJuprXEpXG0L8exoQL1TvSaCFDeTfZrpqZSmolah9lWyFDR5WBf7qZZU2SCLCMjY8+5Uu3w9KV5phv+ij7+6WaAJQVPXZy/Ia1615P9nB0zPlDgzScGefylSWZbIZ4lEZiqWKI0WsORgTyxUnzt/DyTDZ9OlPAHT1zkhSv1JW2nW50T3Mxu81cv1Hbz6d50TLVCgkRxaqyP77r3AE9drBJECSdHi5yfbTPbCldUfV7LOAKi9CS4lmnJ8yONEJB3JH6kiFNzjqGiixCSqYbfE2KChUparMCSpjpWzjsMl1yuVo3T3r/8zjv43geOIKXgl/78RcI4wbEWWYcIsBAkiamYXa363DG2+mf/oaMDvO+bb9vQiVBKwXfdd4ivXZhni7GARAqi0PxSJ0p6lS+lNSKtq7q2IO9KosRkfMWLHGPSTk7yroVry9683FDRNS3He8h2qlLdTbCcI3nD8YFV28SDSL0mApR3k/2Yqfm5lyb5b184z/nZFlFiXEOPDxX50ceO8+13j12349gu2TswIyNjz2n4ERdn2yRKrczZKUpmmwGX5to0/FvbuWE/Z8dIKXjPm48y1Qz4xrUGzTAmVpooSsg7FmN9OY4OFnjmco12EBMrODpQ4FAlt2RmANiTOcG9XuztdxIF8+2Ypy/O0/Bjjg4WuGOsnJosLpVfr3UxZrF0fm6xu2KiTKWsa1ShlKbWiRBC9ASILc3vdw0tBGbGTClFnGg6YUK54DI+kOfNtw1j25KLsy2+fHaWXGqxHSWaRGssIXAswVQjoNYOOTyQX/ezb9tyQ9ERx4o/fvoKUgospTcdkL0aQRzTlWJaK5RS1DoJo+Uc8+2QMEpwUgHr2IIo1liWoJRzCGLFaJoFBmJfiprFm2CnRktL2sSVUpyZbnJiuIjSeltxHq9V9lu3x+demuSjf/Fyz724+9l6ZarBR//iZYB9L8r236cnIyPjlqMZxKZVJ2ev2t7gORYNP6YZ3PptI/s5O+bkaJnvffAw//Xvz/LKVBOtNSq1VR8pe5ybaTHXDMg5Nn15m1NjZfryLuWcw6tTTT79xEX8KGG+He36kLctIXitulSQzvJgxMUrk3XuOVhGCMHVWptOpLAkPZvy1zoJLDkRSi8VaH6ssS2BY5laWCdSPcHWDZiWUlDMWySJyeAKY0WSGnqM9eVWWGmfnWlRa0cMlV2klHjLuhK7WWJvvm2IuVbIc1dqtMOEgmtx3+HKljYsnrw4x6tTTSwpcFxBJ1TbEmVRotN2TdPKWe1ETDdDlIa+vE3Js2kqjZ8oLMtkrh2seNx1sI+xvhyuJSm6kueu1vetqFm8CfbKZJNyzsaSgvl2yNnpFnFiqv8f/6tXX1MGUztlP3V7xLHiv33hPA1/qXFLOScpuhYX5zv89y+e51tOjawwbtlPZIIsIyNjzynlbPKuRRApSp5ecfEOUpe9Uu61cUnar8PHp6ca/NHTV5iqB+QdC8+W+FFC04958sIcWptMJ0tKTgwXGCya3CEhBAf6PL58dpaRssf9Ryq7PuR9+6DDs1O3dgV1PazUJjBKTBTBXCvkUCXPTDMgiJPeXE+8SIhYsKPqya2KmQ3TeLbEkWZ2rJ228UkpkFJgC4FnSxIJnTCm4NrkXMntIyXT5riKlbYW9Nr+VrLo71MXEW3+D603L6OV0nzl3BydMEHrdM5ty2fA0D10lbZuNoIEIeBA2aNSdOmECWGcUMo7fPOpYWabIVLA4YECeddiotrhC6frps1zH4uak6Nlvu2uUf7bF87zwtUanTChE5mZ2AeP9nPXgf4b5g54s7Kfuj2eujTP+dkWQ0V3SWSEOU7JUNHl3EyLpy7N88YTQ3t+PNvltbH6ycjIuKGUPYejgwUuzbVXzeix0zafsrdqKuotyU5dunY7z00pzaefuMgzl6q4lgm9jRPNRNjpLSUdS3KkkidKFOdmWvTnHQaLHmCqM7VOxB1j5T0Z8j42NsCzU1Pbfn77lU3Pe2mzkdG9/cW5Np0o4ex0s+cqKKBnd9+zec9KZquitMkby+UdpNYUPUkcK0bKLonStCNFO0wYyLsUXQuNmbOSUqxqpb3UFEeu2HSqtSNyjsWXz84SJZrDA3kKrp0KgTqvTjX57vsPcudYGQG0o2TF57obG/L5l6eIthk0150BU5i2MyHSrEEhGB/M05ezqXbiXj7iydEytpQcHSzyTx+7jc++eI3nrtSYaQZM1gLyrhE1hyqFfStqTk81+PzLUxQ9izedGOSVyQazzRDXFlya65BzbEZKHidHipyebr0mDKZ2g/3S7THbCokS1XNHXU7etZhrhcy2wutyPNslE2QZGRl7zuFKngfHBwgiRaxMJkzX6Wqk7K1o/8lYn73Ic7s83+bLZ2exBAyVjMiaafgoDQNFl0YnJkgUQpjqwGTd59nLNR48WqEv51D3IzTQt8o8Aex8yPv0ZH1bv7ff2eyyWgEirYgAXK2aENQgXqiPdO3bu2TLyfWJNTT9iLxjMVh0qLZj4gQOVXI0A0W1E3LXwTJ3jBZ5fqLBbcMl/sljxzkyUFixWF9uilNetOnUzRKr5F3CWC3ZtIgSRa0dcmGuzTOXq+QdCyFguOQxXPJ6n2ugl/k0WnK39XwlYElj7CGFqdCVPQeNZq4VMVb2eN3hfppBsmo+4n1H+k1FT2lmGgFhknCkkKPomTbA6215vplNqcX27HeMlVNTD005b9P0Y6abHWaaASMlj8Gix4F+74ZlQd6M7Iduj6E0t68TJpRzK1sSO6Ex2hkqbu9zc73IBFlGRsaes7i9YbYZcGQgjyUFidI0/HjV9p/XGputeO1VGOfiGRghBEFk2npc2+z251xJ0FHMtALqnYh2lDDbCmn6MaNljyRdcK7i6A3sfMj7wnywrd+7VVgeytuJFP5qgVspSVYZ2xQCCGLFRM1HSkHsa+YnQixp7PFfudbg/Gyb8cECj54cWlH97bLYFOeVycaKLLFTYyWkEByq5Hv3MdcK+PqlKp0wIe9KZpohkWvjWIJZAcMll+ev1rhSbZNzrF7m0/np7YVCKxa9j7T5TPphgkivxWenmwSx5vbRIsPppgyYzZTTU00+/ZWLaG3m4XKORdGzmWmFtC5VeWC8wmDR21I1fCdV/s1uSi23Zw8TRTuM6YTGmCjnWOi08jnV8Kn5IUNFL7PB3wI3OpPtofEBjg8VeWWqQdG1VhjqzLZC7hwr89D4wA07xs2QCbKMjIzrwvL2hnYY49nWqu0/rzU2u7jY6zDOxTMwSWroYQmjsKSQSGkWcW2dkHMkthRoNGdnW5RzNvcd7udaPaCcc3Z9yPu1K9UNyytfJnvsBh3MLYQZHdNIAXlLYktoh4owgZJro9AmdyyI+b+/dGFJ1Wr5NevkaJkPve0Un3nOtPUtzhI7daDMHz99hYJro7Wm7kc8e7lGvRMxWva4WjWRIANFl0LaYnWtHvDw0QrPXqkx0wh44wkjCDux6rWmbhfHgigx9yETjWMLHNtiquHTCKKewNJaM1X3uTzfZqTs8cbjg8y1IxKl6cs7lDwzz3hmusVAwWzmbKYavpMq/1Y2pRbbs2ut8aOEeicmUarnDtiJEqQQFD2bqYZPGCtya+0s7UN2u339ZsO2JT/62HF+8U9f5KVrjV4GoNaCRGuGih7v/abj+9rQAzJBlpGRcR3ZD+0N+42tLC72Moxz8QzMaFnQCRPiRNEBcrbEj82ipexZWFLSChNiZQyzbxsumpDvgksuUiuGvK9WfTxHcnLMvPbbec3lNmdmbjUcC+LMqWPXURr8MKHgWRRcm0RpBooOrmWMPxItCGNFf95etxp9crTMT/yDlde4K9UOn7GvcbXa5lotYLLuM9nwcSxJECvagQkwtqVxMyzlbOZaIc0gYaDgcnqySZJ+BnLO9heWFl23TiPsHWly1rSGMHWQnG9HnJluoYEzk03OzLTopOLqyQvVNDje5JR5tuwda8OP6cs7G1bDd1Ll3+qmVNeevXver9U6tMPYbGh0IjxHorVmphkQxopOlNAOEn7/iYv84BvH9/1G4V60r9+MHBsqcGK4QCuICeIErUAIjWdbHB8ucGxo/7efZoIsIyPjunKj2xv2E1tdXOxlGGd3BuZPnr3K81d94kSZhRoJlgDHluQci7G+fC9XqVJweXC8Ql/eoRnEzLcjvvehwzx7qdYb8g5ilc4OSv746St8xr62rQVDpscMKsl8OvaKBGPc0Z93KHo2s62QogtjfTnCRFHtRIDg1Ghp3Wr0ate4w5U8lYLD4y9O4loC25I4lsSxBK0gphMphtKwZTAGOq3AhBiXczYIk+c4UHRxLbmt94AtwRICrTWhMsYvSVpmCxPNdDOkGSYMFJxUwHRopdb8XeE11fCp+yE5R9L0I9x0fqd7rIur4Qf7clyaay8RpsCOqvxb3ZRaft6717EwUQSxwo8TU+lXGqSpkrmW5KVrdT71hfP7ypxkOXvVvn6z0f0eLbg2P/SGI1yrB7SjhIJjcaDP48xM+6YwaskEWUZGRsYNYquLi70M45RScGKkmFpdqzSfyAihRAOxouRaKGC+HdGXd7nvSIX+ghmU7orBkbLH+7/1dq5UO7w0UefPn5vAlgmHKotd5ba+YAgyFQJkNvZ7TaK0ESxp1aovjeJwLEkzFR3brkZ338NCmLYqzOer+9lfPPcXJQpLSlzLJND1513m2iHjgwUipbclyCxhDiFKRVi87E6CSBElCj8077KiZ3P7SAnHEpydaRnXyYLLfDukZMneXFt3zjSMTXV8sOhy54Eyv/W3Z1dUbu4f799RlX9bm1LLzrstBVJa+CSEkSbGuGzmHZtSzgIEJ0dKTDaCfbuQ3+v29ZuJxd+jlmVxeGDp+2anDr/Xi/3dUJmRkZFxC7OwuFhdQOVdiyBOeouLbhjnRM1fkV3U3Zk+OVra1pxWHCs++8IkUggkpo0pMTFJvfmtuh/T8mNG+3LpjMmCa9ViMSil4HAlz6uTzZ6rXDnn9JzYTo2WmGuFfPaFSdQmS1/LF4+vRXbQqZaxWTTMtiIuz7fxY8VcK+Rq1aeR2sC76WzR8s/mRlypdqh2It5wfIDRcq4XuO5HCWXPolKwCaIEP/1nthlScC0KjuTsTIs7xkr05x1emWwy29yewU2QYIKQ1/i5bQm0Bj9SKK05OlDgynybr1+qMlUPOD/T5uVrdZTW+JHi1GiJkbJHtR0hgDhRvP5wP9921yiff3mK56/WqBQcbhsuUSk4PH+1xqe/cpGZZrAwS9eJmGkaoyCt9YbndfGm1Gos35Ra67zHiaI/Z5tz7NqMlnMc7PeIE81g0aUv7yxZyO83trKZd6uz1e/R/UpWIcvIyMi4QWy14rWXYZxPXZrnpWt1OlFMopfu1inMjIkUUM7ZPHy0siSAczXTjr2cd3utkmnSvSfWGqk1aQcblhA0g4i6H3HbSNG0D7L1anR30XjbcIkjAwUafsx0M+DVySZxklDMOVyZ73BuptVziIsSxR9+7TI5xwRSu7YkiBSX57e/yF7PCERg5kXbsSKINd+YrKGR5F0Lz5E0/YR2mHB5vm0MPzAxF/ce6uPRk8O87lAfDxyu8Dt/f27Nys0zl6vMNgOuVNtM1gLm2iGxUthSMlhwOdDvrXteu5tSz1+tUfLsDc2D1jvvrSDCkgohjIHRfDsi75qq4GbNSW4Ue9m+frOxl50j15P9fXQZGRkZtzBbXVzA3oVxTtcCqu2QOJ0tkUL07PyE1iQaokRT8mxOT7c2FIPZgmF3kSzM+2TsHVqBtgSOFCitCNPWwcVyeDuuocsXjX15889AweX0VJPL820SZQLCcraZJWuHZnOknLM5MlAg50iuzHfQyzMQdokg0QSJNrNlQBBrRsoL1yWZN2Y/rTBhrhUy1fCxhMRzJF87P8fzl2v8VWmKszNNjg4WVt2IuW24yOX5Dk+cnSPvSMp5B8eyiRLFZL3Dpfk233HP2JrndaubUuud9xeu1qh2GmhlKodj/TluHyn1Kv/7eSF/q4iQ3WA736P7kVv/lcrIyMjYp2y34rUXbpXTLZ9YGQ0mpViwmU/FmUpM1eD+8X4KrrOhGMwWDLtLpsWuE8KIX9sSJMqYZ0gBriW4Mt/mK+dm6c+7DJe3lp241qJxsOjy8NF+rtU6jPXl+JZTwwgh+PrlGtV2yEjJpdqJOT/b4rbhAvVOxPQ2WxY3S3eWTS4TVI4lsTxjfKI0CA3FnLVkPvTFiRoXZ9uMlr1VP/d51yZWpiLF8kw3YXaANjqj3U2pzzyfxgukxiP3Ha6sMAta77x/022DzLdDlNK85eQQ/altP+z/hfytIkKWsx0L/73sHLmeZN+EGRkZGTeQ7Va8dtutcvGsmDZOA2iMrf3iebXhssd7Hz2x4ZfmrbpgyLg56Wa3bXSbcs7MNgkh6M879OUczkw10rwymG3OU847/ONHj22pGr3eovHMdBMQPHysQqXocbXWod6JKHo2Uhp3w4lah9lmQKI0riXobHOosmt7vxmB7zkWnUjh2tJUzTSEsSbv2rSDmHaU8PDxwSVtiSdHSpydbvGNyQbDJW9FlWy6EaCU5v4j/dT9mKl6QKQUjpSM9nkc7De2+5tqZdbmH23+b8VcLWy8WL/3UD8AU80Qy5I3zUL+VhEhi9mJhf9edY5cTzJBlpGRkXGD2Q/5bCXPwbZMW1y8JHXY/FsKsKS53UZisLvLeepAiVcmG7wy2eRQZfsLhs0af7xWsNPsqIzNs6EYE6biYwmB61jGwCZMmKr7JApcCQhwLYtOGPHfv3ie0bLHjzx6fMV9Ld/lP9iXY6LuEyvNO153gGcuVTk73eotGk8MF9Eaco7F187PM1HrMNUIyDmSumPTX7CptyMKns3B/hzVTghBtK3zsJFL52Lh6lqCXCrKolSkFl0bS0I7iFedD+2aYUxUfeqdqOfCCt2NmA4F12Kw6FLvxF1NZR4zPQe1TrRhqHTX7v3wwEJ17oWJOhN1f4V760aLdeCmXMjfCiKky25Y+O+H79GdkAmyjIyMjH3Ajc5ne+T4ACXPodpefaGnNZQ9h0eOD6x7P8t3OcM0h+ziXBvPllteMHTvL2OBTIztAakKCRKFwgiDajtEA3lHkCBwpQls1tpirh3xf/39OX7w4XFsW/YWgdONoCe4Fr//PUfi2pKcbXHbcJH/10OHGS57FF0bpTX/55+9yFMX54kT4zSYc4yVfCuMaQYRUaIZ7U8t+PdwfdmtlGsgiBXHhwqp46pOLePh3GybgmtzfKi44veFENxxoMx0M+D0dJM7xspLNmKGSh6J0jx9qUqcaPryNo4liRLFdDPoWfuv1cq8Xbv3jRbrN+tC/mYXIbC7Fv43+nt0J2SCLCMjIyOD8UqBg/056n4ECixrYWGWJICEg5Uc45W1v+zW2uW8Wu3gORbf/fqD3H2wb9MLhu79zTbDXXuetwqbacHLWJvu+ROr/KUAXMu06DkSEkx+Vc61EEIghKDg2kzVfX7viQv4keLMdJOZZsCluTaOJXnd4T4qeZcnL8xR7UQMFBweOjpAzrGWVHLGBwvEqWibb0ccHcgjhKDu2LTCmJwtqHZMXUsCV+Y7zK2xabJbWMJUDIuezaWqz1DRXSSqQvKuxVDBZaYZcmCVKlnesbhjrMxtw0VmmuGSys233TnKL/8/L/Wea9et1bMtnILg4nyHsVhxsC+36rHtxL11vcX6zbyQv5mPHTJH3i6ZIMvIyMi4AWxneHkvmaj7HKrkqbYjY0W9yNLPdSQDRZeD/Xkm6v6qX4rr7XLeMWZ2OU9PNXnb3ZtvU/zL5ye5ONcmzkpCK8jE2PZxJQgpCGONawskmigByxI8cnwAP1K8OtUAzILQlUaMOdZC1INnC1qB5s+eu8qBvjwH+jyuVjsmSF0pXplsYFuSRGmODuSZb0ecn23zyLEBTo2Wluz6T9R9PEdSyTvMtyNKOZtKwaETJdT9BNeSBHHCpbk2iQbbsgiS3Y8I92yB1BpfQb/n8G+/+x4+/dVLnJ9tMdcymyI5x+Jgv8dcO+LvT89wbLDAybGSscFPc8VOTze551Af/+ptdzLZDJZc465UOyuea7dC1vRjKgUX15ZrXmcWu7dqrWn4JqzbtSTlnL1n7q377Xp9K5E58hoyQZaRkZFxndnJ8PJe0QpjXFvy4NEKX784x2w7IlEaSwoGCw4PjFd6t1ttRuapS/M8dXGOoaKH1tDwo95CqeTZlDybJy/M8bULAzw0PsBE+gW71uLmSrXD05fmmW74xEkmPzJ2j0iBjcZO3ey1ENiWwJKC4bLHwb4cNT+i1o7wbGOqsXznPoiMU6BWmlOjJRp+bCphRRfXEukCMuFIxVS8XFsyUe1wtZLjUH9+ya5/97P38LFBzs20mE+zufryJrhYCsHVWgc/VoyU3LSVcfcFWaI0sQZHSo4NFXjdeIVP3XuApy7N8/yVOl86M4MUcHiggB8pnrwwx9nZFnPtkJOjJa7VfCZqPnY6e/Y7f3+Ot79ujLsO9PUeY7Xn2gxibCkZ7ctxbMg4SW4UDH212ubaNnLMFrNZkbXfrte3mjjMHHkN+/7ZXblyhZ/92Z/lL/7iL2i325w8eZJPfepTPPLII4AZEv23//bf8ju/8ztUq1Uee+wxPvGJT3Dq1KnefczNzfGTP/mT/Omf/ilSSr7v+76Pj3/845RKpd5tnn32WT7wgQ/w1a9+lZGREX7yJ3+Sn/mZn7nuzzcjI+PWZjeGl/eComsTxorTU03aocK1LLBMU1c7THjhap2ToyVmGgGfe3FqxYxYlCjOz7bIORKlTLXBkoJEaZJEIyX4keLXPvsKjiWXzNSstrhp+BEXZ00201DJQ063Muv3jF1BY9oRPVuijEEfVmpaU2tHNP2EB45UqLUjZltBz3Wxi1KKVhjj2Rb3H+lHCEGYKOJE4aTiLedYzLZCOlHMTFPRjmKCSPHk+Xmu9vscHy4QxElvUZ2zzdzYG44PLKn6lDyLM9MtmkFEwbVwLbmkUrcVbEwrYrzoOS/e6+hmEB4bzNOXN9EWhyt5Hjk2yFfPzWNbslf9LufgjSeGOD3V4PR0k79/dYaiZ3GwkufOsT5yjlz1mrbecy3nbJqBOU/rBUNXCg6PvziJa4kt55h12azI2m/X6/0mDneDzJHXsL1P9XVifn6exx57DMdx+Iu/+AtefPFF/tN/+k8MDCwMlf+H//Af+I3f+A0++clP8sQTT1AsFnn729+O7/u92/zwD/8wL7zwAo8//jh/9md/xt/+7d/yvve9r/fzer3Od37nd3Ls2DGefPJJ/uN//I/8u3/37/jt3/7t6/p8MzIybm2Wt/WVcw6WFJRzDqdGS8y1Qj77wuQNcRU82JdjvmXmPbQ2xgIlzyHvWmitmaz7XK36/PmzV3n+ao1KwaGSd7k01+aVqYYJiZWCajtithVS70QIBPWO+XO1HWFJwVTD55WpBpfn21TyDpWCw/NXa3zqC+c5nbaJATSDmE6U4DmWOXfX/Yxk3Mq4lqCcsxkuuYAmTBR+pHj+So1WEPO2e8Z4/7fejmcbA492mJAoRTtMmGtHuLbk+HCBct5L709iW5IoVThG7Gmu1UzLnhSCnGNs1acaPk9emCeIVa/CcftIiYmaWbf05R2GSx59ecfMk/kRwyWPt5wcYbQvTyvYXuuWFiClyViDhfk5CzMzl3eMec/52RYvXKnxB09c5BN/fYYvnplZdcZnsOjyyLEBxsoeri159LZhHrt9mJE0g2y1a9p6zxVgouZzcrS0/uK7e3lcNceMDXPMuiKrex27bbi06nVov12vN3vcNxtdC//BosurU00afkSsFA0/4tWp5k1p4b8d9rUg+9Vf/VXGx8f51Kc+xRvf+EZOnDjBd37nd3L77bcDRjl/7GMf4+d//ud55zvfyX333cf/+B//g6tXr/LHf/zHALz00kt85jOf4Xd/93d505vexFve8hZ+8zd/kz/8wz/k6tWrAPz+7/8+YRjyX//rf+Xee+/l3e9+Nz/1Uz/Fr/3ar92op56RkXELspXh5evN1VqHajvEkgIhJUmiiZKEJNEIKbGkYKLW4UrV59RoiZJnc26m1ZuRAWj6MVGi6ctZKA2TdR+lzJ+jRNP0zULy6ECeONGcn21T8uxVFzeldB6k4cdcmb/+5yPj1kUCSkO9E1PvROQdi0re5dhQkQfHKxRci8+/PMWjtw/x/377nRzoyxNECfPtiCBKONif531vvY07x/pop6115ZzNQMGl6UemopY+VpgocrapFOdd07o7UHCodiLC1LxiowXpUMljfLBA3rV4w3FjDLJthESgkXSrhGY+ruQ5CCGxpfn7JNEc6HN5/mqNT3/lIjPNgMIqVatmkBAmmqJn90xPeg+1yjVtJ4tvpTRfuzDHhbkW9x7sY7Ts4UeK+XaIHynG+nK84fhAL8dsNbYisvbT9Xq/icPdpmvh/7pD/VTbEednWlTbEa8/3L+lKqRSmktzbV6+VufSXPumOh/7umXxT/7kT3j729/O93//9/M3f/M3HD58mJ/4iZ/gx3/8xwE4d+4c165d421ve1vvd/r7+3nTm97El770Jd797nfzpS99iUql0mtxBHjb296GlJInnniC7/3e7+VLX/oSb33rW3HdhbyMt7/97fzqr/4q8/PzSypyXYIgIAiC3p/r9fpenIKMjIxbiP08vHx2poUfKYZLLnPNkEaUoE0+NAXHopKzmW1HvcXg1WqHa7UOeccswqQUvdYjP+2J6oQxpZyDH5tZtDBRSGlc6ko5m7lWSMOPe9lFi520yp7DUNHlpXq9V3XIyNgNCq7Ziw4ThWM5aK2JlSaIE87OthnIO7SjhM++MMm/+Jbb+cGHx/nsy9e4Vgs40O/xnXcdwLYln/jrM0varE6OlmgGMbOtED+McSyJRFD3E3KORSXvEK5hXrFeptTb7h7j8Rcnef5qLW0Z3N7zFsK0W0opkVL1HCNztiSIVdpaLHGBWGtaoeLUaIlnLleZbQa0grhXyeoSJoogbZ1zV2mlXO2atp38rG6r3lMX53nhap3+vMNgweXuA2UKnt1reUy05vxMa81r6FZE1n66Xr8WnAh3auF/s7dzbluQ/d7v/R6f/OQnOXfuHF/60pc4duwYH/vYxzhx4gTvfOc7d+Xgzp49yyc+8Ql++qd/mn/zb/4NX/3qV/mpn/opXNflve99L9euXQNgbGxsye+NjY31fnbt2jVGR0eX/Ny2bQYHB5fc5sSJEyvuo/uz1QTZRz/6UX7xF39xV55nRkbGa4P9PrwcKUWtmdAJk4XZEg2tMCFSmkRpwjjha+fnuVrrMN0IsCVoBEprwlhhCYHWoNAoDVGikAgSzM+n6z5+qKgUHBKlCFM3x+WLm4N9OWwp0QgskQmy1woCU8HajmXFZqIAJMbOvR0mxAn4kRFLY32mza6bh2VJwVMX53sL3O+57/CK+3r768a4Wuvw6pRZKPflbU6NFnn+ah2EwJHGEEQpjWUJ/DjBVmubV6y3IJWS3mNVCiuvHZvBsSR5x2Kg5DDbCBkue3SCmHaoCBKFFALXMgYknUjRjhKEENw2XGS6EXJ2psn9RypLBIEjBVGsGS7ZlHMrr1trXdO2svhePMc1WHToz5vq0HQzoBnGPDBe6QnFThD3Hm8184utiKz9dL1u+Mb91rWlyYRcZjRzqzgRbtfCf7/N+m2Hbb2LPvGJT/DhD3+YD33oQ/zyL/8ySWq/WqlU+NjHPrZrgkwpxSOPPMKv/MqvAPDggw/y/PPP88lPfpL3vve9u/IY2+Vf/+t/zU//9E/3/lyv1xkfH7+BR5SRkbHb7Lab1X4eXj4+VCCIFM1g5VI40dCJFFLA6ckGnmvaCW1LEEQJiTK771IIHFuitUYrM6siMHMrlpBE0uzOt8KYTmR227u76ssXNxN1n1gpBJroJmo7ydgZmu2JMQBbGgfF9VAYW/ruZ8+zjeCwrIU8LLcomU0zxRr+2plfa1V63nn/IQ5W8vz5sxMMFh0O9OXS1r6NzSvWWpAufqwnL2x90W0J+JaTw5wcK/HVC/PM1ENcKRkcKFDrRCQ1Tc6R2FIQJqaiXUir4QXPzNoV05Deg/25Xi7ZtXrASNmj4KxcTm50TdvM4nt5qx7A5Xmf6YbPQMFY55+ZbjFQMB1O3cfrRDGf+OszK6ol94/3b1pk7Zfr9empBn/89FXOTDU5P9Mk59gMFlxuHy0yWPRWHPdrjd0Mlr6RbOuV+83f/E1+53d+h3e96138+3//73t//8gjj/Cv/tW/2rWDO3jwIPfcc8+Sv7v77rv5X//rfwFw4MABACYnJzl48GDvNpOTkzzwwAO920xNTS25jziOmZub6/3+gQMHmJycXHKb7p+7t1mO53l4nrfNZ5aRkbHf2Yv2h+78xOJd9YXAVf+GDi8rpenE6y+FlYbJZshICSxhEyeaWIFnQZiAFGahknckdT8Bbay0S55FK0hwLEmiNcX050VPU/LsVRc3DT9ithlS9my0B+2qv+6xZWR0xZjF+qJuthX3Kml1P+GVqSYH+nO9Rb0QAs8x84vNDQw01qr0AExUfZ6/WkMIsaTVb7uL+e5j/cfPRnz+GzOb/j0LsKWgmLOxLIu7D5Q5O91mqhlQ9CzyjslYE2l1uxMmDJVcDvabcOZOmDBc8vjehw7z7KXaEvF535F+vv+RI3z+5ak9uaat1qrXbQ2dT81VZpoBEzWfZhAzWHS580CZ//7FC6tWS65U21QKDhM1f0ORtR+u193Kz2wzYLTsUW1HeLYxR2oEEQ+MVxgouK8ZJ8LVuFXaObclyM6dO8eDDz644u89z6PVau34oLo89thjfOMb31jyd6+88grHjh0D4MSJExw4cIDPfe5zPQFWr9d54okneP/73w/Ao48+SrVa5cknn+Thhx8G4POf/zxKKd70pjf1bvNzP/dzRFGE45iL5uOPP86dd965artiRkbGrc1etj9sZ37ievDkxXnieONKlASmmyGT9YBEmzaxjjbVia4dd91PsKUgUUaEzrQiBGZR6IeaTpiQtyVSwLX6wkJq8eKm67JYzptdzsuZIMvYJAnrizIT5mCQAtpBzIXZFkGUMFBwcSxBECkKrkVplTa85axV6dmLxbyUAmuLQ2SOLRECOmnAejHncGwoz1Qj4OJ8h6GCi2cLGn6M0uA5Fg+MDyClXCJSHrvduCiu1jVwbKiwJ9e01VoMB4suD4xXOD3VZLYVUO9EzLUCHj422Ju3W69acqjfYqDgbup1uZHX68WVnzvGyoyUc3z9UpVWEFP0bJp+xIsTdUZKOYZKrw0nwtXYT7N+O2FbguzEiRN8/etf7wmjLp/5zGe4++67d+XAAP7lv/yXfNM3fRO/8iu/wg/8wA/wla98hd/+7d/u2dELIfjQhz7EL/3SL3Hq1ClOnDjBL/zCL3Do0CHe9a53Aaai9o53vIMf//Ef55Of/CRRFPHBD36Qd7/73Rw6dAiA97znPfziL/4iP/ZjP8bP/uzP8vzzz/Pxj3+cX//1X9+155KRkXFzcD3aH1bbVT/Yl2Oi7vPytfoNCfu8Wutsylo+WGawoTFW2YmCREK/ZyOFIFGKTpSY+RlpZlMsKbGVsRiPtaaauqE9dvvwispj12UxiBSlrBkhY4usVyGzJdhSEiemdVFpTRBrLs11mG+F2LakP+cwPlik7G1vXgv2bjF/cqS08Y1STNaYwpVWrwWxEyYcHSzyjx4Z58+fmeD8bIswNjOfOcfiwaP9jA/mafgRV6s+niM5OWauV4cr+TVbKndiyLAWa81xDRZd3nB8gIlah7lWxD//ltt45NjgkmoJQL0TLWkVPdifY74drVrtW+t12avnthHLKz+Lheh8OyTRMFUPePjYAD/wyPi+n5HaK/bTrN9O2NbR/fRP/zQf+MAH8H2TV/OVr3yFP/iDP+CjH/0ov/u7v7trB/eGN7yBP/qjP+Jf/+t/zUc+8hFOnDjBxz72MX74h3+4d5uf+ZmfodVq8b73vY9qtcpb3vIWPvOZz5DL5Xq3+f3f/30++MEP8u3f/u29YOjf+I3f6P28v7+fz372s3zgAx/g4YcfZnh4mA9/+MNLssoyMjJeG1yv9ofFu+qnpxr81t+evaHuUJ61Aytt0oBZDa8/3M/JkSJnplv87aszlDyL8YE8sYJEa6JYUe+EzKW23xamVWg5Zc/h6GCBS3NtZlvhjo4tI2MxriUpuBYN37grdvcYFNCKEpxEIYGjgwUOV/I7miXdzGJ+q/c/VN68SEw0KAUFV3Cgz1tS8fqBh8f5Rw8e4alL86kzZMLluTbnZtucn2kRpKHvsZL88dNX+Ix9bd3r0nYNGdZjvTkuMLb7Dx8b4JFjg0gpetUSP5K8PNFgrh0SK4UtJYMFtxfIPVL2eP+33r7p874Xz20j1qoOdgO1O1HMZN3nXQ8efs2KMdjfs9lbYVuC7J/9s39GPp/n53/+52m327znPe/h0KFDfPzjH+fd7373rh7g93zP9/A93/M9a/5cCMFHPvIRPvKRj6x5m8HBQT796U+v+zj33Xcff/d3f7ft48zIyLg1uN7tD/vFHer20eKO76MTK75xrU6tbbKFCq5NzpFIKfEsQSeMmWuHhLHCkRLPthjr93hhos5E3V/yXA9X8jw4PsB8O2SqlrUrZuweSms0RoDptO1WpH/OpS52nUhRbYecnm7w+AtTO9osWW8xv51Z1fMzHWwBm+gwhvQ5WgImG+GK9mApBW88MbRwblJx+NJEnT9/bgJbJhyq5Cm49g25Lm11jqvo2oSx4qmL88SJppSzcSybKFFMNXxmWwHjgwWKrn1DRNZWWKvy051JFAIGCt6Oqri3Avth1m832HYw9A//8A/z6quv0mw2uXbtGpcvX+bHfuzHdvPYMjIyMq47i78EV2M32x/2U9inJ3dWIevix4qzsy0mGwGjfS6lnMNcK8SPEmabIUFk2sRyrkXBs/Cc1YOhpRTcdbBMrRMTxJtppszI2Bxxoqn7sWmnFeBYAsz/sKRksOjSn7d55nKN//z50zx/tUal4HDbcIlKweH5qzU+9YXznJ5q7Og4upsxW73/nGPh2JKCY7HRElOmz6sZJMw2gw2DdqUUHK7keXWySRgr7hgr3/AQ4q2EBh/syxFEivl2xEDBwbMtpBB4trUikHu/0638TNT8FV0E3crPydHShpWfmzksebPsVrD0jWTbph5xHHPq1CkKhQKFgtlhePXVV3Ech+PHj+/mMWZkZGRcN65n+8N+cof60rnpXbmfKNacGisRJYpOmHDHWIlr9YCpuk/Nj3AsSdG18GyJ0hBEZtpn+XNVSvPyRIP+vE2wz4exM24udPp/SoPnStBmfsqzLY4P5ck5Nn6UcG6mRaXg8Njtw7s+S7qTWdVHjg9Q8hwafkTOgiBh1flPCeQsIzIjpfmWO4f5kTef2PB499N1qctm57gm6mbmrZI3lvimQiaJ1gjk3s/sRuXnZg9L3gonR8scf2ux14I7VHR5aHwA29527em6sq2j/NEf/VG++MUvrvj7J554gh/90R/d6TFlZGRk3DC6X4KDRePC1fBN+13Dj3h1qrmr7Q8L7ZGr743lXYsgTnalPXKjXdJqe3uPITGOdt2zcedYiTccH+T1h/vRCObaIY8cq/C6I/0MFF1GSh5KaaYaATU/4rkrNb52fh4/SpY81yvVDk9fmqcVxLg3yRdqxs2BBlzL/DtONJEyomW47CKFea+FsZkvGyy4G4qS7bAV0bOcY4NF7h/vI1IKfw0x1sWPwY81UgiGy6YqtFG15Hpel9Zj+TULYHywwF0H+hgfLKx6DW6F5nrx8LFBRso5/Egx3w7xI8VoX46HjlbwbLnvHfe67KTys90K7M1Kdxb7D79yiT995ip/+JVL/Nbfnr1pnue2KmRPP/00jz322Iq/f/Ob38wHP/jBHR9URkZGxo3kelkdXy93qNV2SW8bKXL/eIWRskfRtXuhq1tFY0Kh0ab1a6zPWCIuDpM9Pd2i5NlYAqYaPlGiyTkWB/tz2JZYMdsBJofs4mybRCmGSh5nZto7OgcZGV2KjkQhgIQ40TiWwLEkdT+mGSTkbEmoNLYUHFijtW2ns6Q7nVXtyznYUvRC01drQhPCiLVYaSwJV+Y7/Je/Ps3Z6da61ZL94Fq33cpO99hzjuyZX6wVyL0Ts5bryXZcHm+VsOTNsl9msXfCtj5NQggajZWKs1arkSTrB4tmZGRk3AxcD6vj69EeudoX1dVqmz/5+lX+15OXGR8sMFzy6HO397w09PLIPFvywkSDa/WQA/1eL0z2mUtVnrlcpdqOaIcJIyWXoZJpvwFwCoKL8x3GFs129HLIciud1TIyYOMA6LW470gZ23L4+uV52mGCFJBzJE4qcKqdCA0MFV1sa/X33k5FyU5Ez+X5Ni9fazBUdEFr5jsRnWilJFucUBHGmk/93RnKeY/XHe7jtuHSmgvWG+1at5PF9eJjPzVaWjOQuxPFfOKvz9w0rXxbNSDZj22ne8WtIj631Qfy1re+lY9+9KNLxFeSJHz0ox/lLW95y64dXEZGRsaNpPsluF6LzE7vfy/bI1czDal1Ql6dapIohdIQxor+vM3XLtW3/TwEUPRsjg8V0539Dl89P0+l4JiKmYYgVAhhLMfbUUIrjGgGEY1OxFw7WjLbAUtzyFazxc/I2O7270TNfL5AYgmBFoIwSvBjRaI0ni3py9mM9nlM1IIdGSqsxU4MG87OtKi1o3SzQvbaLDci0SYf8NWpJrVOuKZJx/Vs217OTo2ONnPsdx4o89+/eOGWbuXbL22nW2U7BiQ7af/dT2xra+dXf/VXeetb38qdd97JN3/zNwPwd3/3d9TrdT7/+c/v6gFmZGRkbJd2O+K3v3iGy3M+RwZzvO+bbqdQ2F8WwXvZHrn8i0przZmpFp0wYajkESaKaicCBCV3cy6LaXdiD9eCgmNxoGIqXlGiej2M1XbIf/vCeebbEYMll8GiR6IU1+oBF2fb2JbEscwA/l0HzPPsLhAW55DNZTlkGWuQdyRFz6bWMkG5m/HjnGuHDBY9XFtiCeMCGqdCrODajPV5HOzPUfdjPFvuiZX2YsOGVyablHM2lhQkStPwY4ZK699/pBTTjQSNsbRf83HSD6wCXFsyVPKYa4WcmW4xkM7HrVYtuV5t28vZjcrOesf+trvHePzFm7+ashH7oe10q2y3TfV6R9XsFdt6Je655x6effZZ/vN//s8888wz5PN5/vE//sd88IMfZHBwcLePMSMjI2PL/NwfPcf//NplgmRhifaJ/32Of/TIEX75e19/A49sJXvVHrn8i6rhmxywUtoG6FiSZmBmLEq5zX0ddMWYI4399t0H+5ACqp2Y+XaILSVjfTnGyh4vX2swUva4/0iFhh+jtFls5ixBIizynsVQ0SOME05PNZfMkHVzyEwwreL0dGtH5yLj1sMSMNbn8a13jFJ0JX/+3DXOz228Cz5UcLn7UB8vT9QZKLoMFl2mGgGVgsuD4xX68g6J1gQzLb77/oO8eq25J6Lk5GiZb7trlE994RzPXJ4nihWOLTk5UuL7Hzmy5v0fHyqgNbSjBFuwZiyELUwGWfen1VYICEo5i7lWSMOP6cs7ay5Yr0fb9nJ2a3G91rG/Vlr5bnTb6VbZSZvqzSg+V2PbR3fo0CF+5Vd+ZTePJSMjI2NX+Lk/eo4/+MpFlDZ92SJdmASJ4g++chFg34myvQgpXf5FFSZG3DiWufRHicKWEteSHCh7W7rvRIEfJUzVfQ7257n7QJmCZ/eG56vtiFon4o6xMkIISp5Fkmj8SNGXs43ZQGKqEiXPWjFDtriCMNsMdvW8ZNz8OJJ0Q8E0Ltq2zUDR3ZQgGy4bt8/TliRKNK4lKHo29U5EMzQipbuIu/tAH2+7a2xPRMnpqQZ/9PQVJms+jiWxpEAKwbWazx89fYVjQ4VVF6FCCHKOxXw7IlinpUvppaY7liVohTFBnJB3LcJ0s2q9Bev1Dk/ezcX1asd+q1RTNuJmCkve6QzYzSY+12LTguzZZ5/lda97HVJKnn322XVve9999+34wDIyMjK2Q7sd8T+/dhmlzQ7x4gu4UppYw/988jI/9/a79l374m6z/IvKtSS2NJk8riVp+jGjfTnKOZtvVDfvYthtW0wUoDVTzYBmGPNAWl0AqPvGGKEvXVQ1gwTLMgtJPzaPnyhFJ0poBnrVfKDFrUefeWFyd09Oxk2LBKQQKK1phzHz7Yj+gotnb25xaaXtu54tmar7CDSdWBFEiifPz3Olr4NtSx69bagnvnZblCil+fQTF3nmUhXXEgwW3V5eVqMT8cylKp9+4iI//933rFiEtoKYRGnQekUL8ZLHwFQRLUuYWAohyNmSdmhErGOJfbdg3evF9a1STdkMN6rtdKvstGp5M4nP9dj0O+6BBx7g2rVrjI6O8sADD/TmEZYjhMicFjMyMm4Yv/3FMwSJMou2ZRdgKQUy0QSx4re/eIYPve2uG3OQq7AXFszLv6gO9HlU8g4TtQ7/f/b+PMiy7CzvhX9r7enMOU81T60e1OqWuluiW8IgkECE5WsbZG5czGUyhI2MsRERNtdfgMPgAa4dBtsfWAasKzkuln3xZ4O5CCEJCSHTklqtnqfqrjlryDnPfM4e1/r+WPucyqmqMqsyqzKr9i+iparMU5n77HPO3utd7/M+j22ZeZnjY2ZH8nJ18wPPesX/z7VChgsOUaL6cykA1U7IYN7FSv0GwkRhSXNzrXVC2mFCrDRxopkYyHF4pECjG11TNvUvP//mLZ2LjLsHKcCxJUGUkChNO4zQWmNt0o1ztuGz3I2pd0OWWiEaKDgSzxbYluDsUptKzuH+yfKOLeIuVTt8/ewSlsDMc8YKP0qwhGCk5DHX8Hnm7BKXqh0OjRRX/dumH9EKYqQFSXztgkxiZuyklORdC6U0fqxQWqO0ph3EzDWCXbVg3enF9d3STdksd0J2ulW2o2u5V4rP67HpguzcuXOMjY31/5yRkZGxG7m0bFz6rrU268l3eo/bDdzsMPNmWHujcm2JFAJLCu4bL1LJ2zT9iNnGzckCLSmodWMsAXGiGCzYKCU4MFjg+GiJmYZPOef0u3O2ZYqytTM7K/OB1iKluG4nIOMeQ4AfJn1b9zdnmrT9hHoQYwtj936990onTKjkXZpC4KZdqXaYUPRsLCE4NlrElpI3Z5t8x/3jO7J47TkllnIWV2o+3ShBaRPgnHcsCp6k1o04u9heVZAppbmw1CGIEsIb7H3nXInS4FmSsZJHrBTzjSD9PAmW2yGPHx7edQvWnVxc3y3dlK1wu2WnW2W7upZ7ofi8HpsuyA4fPgxAFEX84i/+Ir/wC7/A0aNHd+zAMjIyMm6GA8NmBulaTum9r/ced6fpDTMvtUIqOZtKzkEpzSuXty/Qcu2NarEZ8OJ0jbOLbc4vtvFsi4GcxeJN+GbkbEkYK1phQitM+Ma5Kg9Olvn+Jw5weKTAJ58+v2F3rpJ3eeTAIAMFd1M701kxltGj59MjhJHkObbk7FKbnGMxWvaodSLCRK26BojefxJc26IVxLT8CMcymwRxopms5Hjy2Eh/g2CnzR1WOiUax0dJojXtMKYTsi4Drbdx8z9PzRMkN/5EFByLgmtjWQI/TrCl5P7JMpWcTazgb337MZ44PLwrF6w7ubi+G7opdxPb2bXc7cXn9diySNZxHP7bf/tv/MIv/MJOHE9GRkbGLfE333ucj//pOYJEoZReN0OmMAHGf/O9x+/cQa44ns+9Osf0coc4VpxfahMrY7QxlHdoh/G2WTCvulFNwnuPj65a7Pza597gzNLWc1oW2xGWuDpXJgV9U4Kf+eB9m+rO3a070xk7h5RGkufYEoHg6EiRThDTFCZbz1OCRKWmFoAQmkjBWPHqBoAfJVhSkbMtxioeQoj+fztt7tBzSvSjhIG803/f20IgbdMdK1s2R0bMZ3alC51rbS53bKzs8YEHxmmHijCdGy15FqcX2jx+cGDXFmM9dnJxvde7KXcT92LXciNuamrxr/7Vv8rv//7v87GPfWy7jycjIyPjligUHP7aEwf4z9+YJtYgVuwk9wqGv/b4gV1h6HG51uWFi1UWmj5xoinlbBzLJkoUC60ASwqen67uyC792sXO2BZdFleSaLPodS3BYN4hUXqVKcFHb9Cd28zOdCZZvLu4ldfTljBadAgVlD2bgbzNxeUO1W5IFCuEFEiglHPIOVaaxaXpRArPkWgNkwM5FlsBMp2Hb/kxias35Ty4HQghqOQcupEyJje2xErllmGssKSk7DkIIda50L00vby582RJTi902DeYY7BgnCNPL7TvmQXujdjL3ZS7jaxreZMF2X333ccv/dIv8fTTT/P4449TLK4eOP27f/fvbsvBZWRkZNwM/+x738GlaoevvLW4atEngL9w3+iusbxv+hHTSx0SpRgpeX2phmdbuEXJUivg4nKHph/t+LGMV25Nwqkxrm55x8JzrHWmBNfrzm1mZ7oItG7pCDPuFnK2pNqNSbRx87yw3O0HJAsBjhBEiaIVxIyXcxwYzDFUdHj69BLdSPWlT0GU0A4T8o51250Hu1HC/qE8QsByOyRckSUmgMmKx77BPN0oWedC1w43E38NjU7EYD5mermDZ8u7boG7E0ZIGXeOe71reVMF2Sc+8QkGBwd57rnneO6551Z9TwiRFWQZGRl3lC++Mcflms9QXiKl1R+WVyrhcs3ni2/M8YEHJ7b8c7d7AdAKYrpRQjlnb2j36zkWTT+mFex8Jk79Foq+XrdDa53mHgkGCg5LrZCzi20ODBXWnbet7Ewrpclioe8ubqXb2Q6NY2fekbSjpP+zkjTqwnMsbAlBovGjhEPDBS4stal3I5R5o1JOA5G7kaIb3X7nwaJrM1ryGC25XKn5zDcDIqVwpGSi4jE1kAMERdde50JXcDcnWZyouBRcc/17/MgwD++r8NjBIWx7c/9+N3MzRkhZAbf7uZe7ljdVkK10WexZ369dTGRkZGTcCeJY8amnz9P0I46PV5Dy6uJDKcV0tct//Op5vv2+sS0tTG60ALiZm30pZ5N3LYJIUfL0umHmIFIUXItSbuczcbr+5nbdN0JA394+iBV5t/dVuFLr8vEvn7lpB8neec/kihnQmwcDxzJdpmTN2zZW0A4iPNtCCM3F5Q4vX66zbzDPYMGl4UfMt0IWWgGebQGCSCns2+w8uNLI4N1HhmgFybo5r16H7nKtu8qFrrBJGaVrWTS6EReWO0wvd3hlqsKz56rb4t56J1k5Tzc1kKPg5umEMa9eubYR0lYLuKx4y7jd3PRd/hOf+AS/9mu/xqlTpwAjY/yZn/kZfuInfmLbDi4jIyNjqzx/scr5pTYjRXdVMQYgpWSk6HJusc3zF6u85+jIpn7mjRYA3/nAOCdnmlsuOsqew6HhAheXOyy3w3SGzNhwt/wY25YcHMpT9nZ+3u3c0s0LAhVgcXVjTmtNvRORcyy+fnaJKNGbXjitZOV5z8hwLVAKY3sfa9Pt2oBYQRIm/e8XHIktTZC0UhopzM+JEk3eMXEMBc/m4FCBv/Xtx2+L2cVKI4PTC22mBq4957XWhW4wv7nrwZV6ByktBgtmrtO2xKY/e7uVtfN0vWtOOedQ8mxOzbfWGSFttYDbyRiSjIxrcVMF2T/6R/+IX/3VX+Wnf/qneeqppwD42te+xsc+9jGmp6f5pV/6pW09yIyMjIzNstQOiRJF3rU2/H7etVhuhyxtcpF/owXACxdr/NsvnmKykmMg72zJtn7/YJ53HRwiiBSxUlQ7JvDVlpKxsoctJY8dGrotIaWNW5xTixQ40khDl9ohSmsG8y5hrHjbRHlTC6eVrD3vn72lo8vYy+QdwYBn0Yo07SC5od6xL6FN/z5d7TJT9821IVbkbIHj2WiMuUfRlVys+bi25LGDQ7etE7JZI4O1LnSdTTo/tkLFkZE8GhPU7toW9w3kb/jZ282snadbiRAm43BlXMFWC7ib6b5lZGwHN1WQffzjH+e3f/u3+YEf+IH+1/7yX/7LPPLII/z0T/90VpBlZGTcMUaKLo4l6YYJ5dx6SWI3THAs0ynbDNdbAAB0gpgr9S4SuLDc2ZJt/cqF1lIr4MBQHksKEqVp+jEjJe+2uaHZ8tbnShIN7SDGcyzumyghhWDfYH5TC6e13Oi8Z9w7CAGBMoYxrcCYb0iuX5eJFd9sdEPGynk8W4KGUGmSyFwHlNbUujGDBRfXlsw0/Ns6w7JZI4OVxdsfvnx5Uz/byDsFYZpB5lpyU5+963GnpXxr5+nWsjauYOV1BKDRjfrS0HLOXnUu9g/mt9x9y8jYLm6qIIuiiCeeeGLd1x9//HHieOeHzzMyMjKuxWMHhzgyUuSt+SZF11o3Q7bUDrl/osxjB4fW/ds4Vjx/scpSO2Sk6PLYwaHrLgCafsxSO6AbJiy2Q0ZK7pZt69fuknfCGM+2eOTA4Kpd8p1eCLny1qe0VFqQHRzOc3i4wEuX6lRyNkXXXnesN8p5utHCK+PeIYg0WisKztX3qEzbYBtNPiq9uljzI8VyO0RrKOds/CihGyXEStENjdvhkdEijW60I7ljN/rsbtbIoFe8VTsBb8zeWGJsS9m39B+v5Cins6g3m7G23VK+m7mmFV171TzdWtbGFfSuI34kOTnTZLkT9jfNhgsuR0YLBHFCO4y33H3LyNhObqog+6Ef+iE+/vGP86u/+qurvv5bv/Vb/OAP/uC2HFhGRkbGzWDbkh993xF++bMnma52GSm6/ZDJpXZIJefwI+89ss7Q44tvzPGpp89zfqlNlCgcS3JkpMhfenTqmguAIE6otiPQMFxwUpOArdvW32iXfDsWQjda/LSj7bHNKBds3php8Px0jURp3pprMlRwedtkmclKrr8zfaOcp5ULr5K386YmGbuXnCNxpKDZ644JiK/zdl37LUsK/CghiJN0fkz3H6m1qd6CaGdyx7a7iJFS8O1vG+MTf37+hkY3tjSW+nnX5vjY1Y7PzWSsbbeU72bPy9p5urVGSGvjCoquTRgrnp+urst6nG/6LLUDDg4XNnSzXMtOh4Vn3NvckqnH5z//eZ588kkAnnnmGaanp/nhH/5hfvZnf7b/uLVFW0ZGRsZO07O07xVYy+0Qx5LcP1HmR957pP/9XpHypZNzfPLp83TDhJHS1QLurfkmv/WVLo8cGGCm7q9bAASxcUYr52w8Z/XM2lZt66+1S74dC6HNLH70NtkYXljsYklwLYlnG0nYTL3LbN1noGAzkHcZK7k4tsVTx0auOR/XW3h9/dwScXzzDpAZex/HkuQdi3aaFYa+cbB0zoIwAWFUijiWIAk19W6MLQW2ZSR8Bc9mvulzqdblux6a2NZ5zZ2aR7IsgXWDohSgFSa8bajAifEyw6lE+2Yy1m7GSON63Mp5WTtPNzWQ61+vZ+r+uriCqUqOIDLzuYeG8n3FhGdbOAXBdLXLRKyYquSYafhb6r5lZGwnN/WuevXVV3nssccAOHPmDACjo6OMjo7y6quv9h+X6f4zMjLuFB94cIJvv29snQSx1xnrFSmn5hr8z9NLtIOYkaJjFmtSUs5Jiq7FdLXLxeUO942X1y0AZhsBri3xrPUGItthW78dC6HNLn72D3h88+JNHeYqTECvQKV6slhpEgUaTbUdEUaKuYbPSNHjR9975JrHLaXggakyv/fi5dsSjJ1xe7lRQbWSZjcmiBU5xyIKkhv+YwsjW5RSGHMfbWYyzfsQlNZ4QiDQJEr3j2Y7VywrP7snxoq0gsQYa1iSE2NFTi+0b3oeqdqOsC1BfJ2KzBKwbzBPJefgWKIvz1xbtGxGNridUr7tuKZt1gwFYKbh4zmSwbxDtROtc7JdOTu41e7bbuFOz/VlbA83tUr40z/90+0+joyMjAxge28uti03tLbvFSlLrZAgMuHMOVvSiRRRw2eykiPv2n2b/LlGwF//lsPM1PxVC4C3T1XohDHVdsRcI2Cw4ODa22dbf6sLoestfoquxcuX6/zO1y/wY+87Sq27fUWPBlSiidK2mxSmA6fSmR/XkgRxwtOnF/mO+8ev6bJ4cqbJVCXHWMm8Bhl3D1tqyIr0PaWuFk29PDKt1/+sBNDKZOPFicK1BEXPph1EJEk66xgqLAmq4TNUcLl/skS1E23bfFDvs5t3JM9dqK2bXZoc8G56HimNX8cW+ppdMp3aTHbChOnlDp4t1xUtm5UNbqeUb7uKu82aobTDGNeWPH54mHOLbaqdsO9kO17JcXik0J8d3Gr3bTew1y36t7uY3MvFadZ3zcjI2DXs9ND4WMHl82/O8t+fv2IkdDmbi9UuQZSQSIFrSxIlWO5E7HMshBB9m/y8a/HR9x/v/7zFZsALF6oEkablR4CgHcYUXIuCa2+Lbf2tLISU0nzzwjLPTy8zUvRWfW+5HXBmvs1c0+fUfIuZms8Ll+o3dYxrEUCitCnAAEeCm0oXo1gzWnIZK3nMNwOeObvEpWqHA0OFdTfR3sLtvokSJc/ma2eXt+X4MvYeni2o5GyqHbNp4Fi9vDuTJbYWW4CUphOWKJCOZWbH0uJNYr5fztlMVPKEsVlwh7GmHcbbsqhrhzGLrYCldmCC39fMLtX9kJGid1PzSIeGCwhhPl+9Sdi1ol4p4chInkiB51h8+B1TPDhV6T+XlZtSlZx93biOrRpp3Oi8bFdxtxkzlN6x5xzJu48M0fTjVS6LrSAmiFT/2LfSfbvT7HWL/u2+3+/14jQryDIyMnYFOz00frna5fxih24UEaVRRrY03aLeWitKNEKYAissuniOtcomv7cAOD3f5LOvzrLcDnlgsoQQ0OxGJErhWILDw8bq/Xq29ZtZ9K01tli7mLjWQqj33J+frvLalQYDeYdLVY/j40UAXrxYoxsmFD0jtcy7Ft1oe+a0ep2Lnm+CFAIpRCohg7xjOo8DBYelVsjTpxdZbkfrbqL3TZQyl8WMPrVuTKxMb0gKsIVEoc1nFiPRE8KEQnuOpOBYVNOur2dL/CghUeb9WfAsXFsCAs82n6W5hg8a5hs+X3x9/pYXdQXHYrEV0AmMw2GvG9Qz/On9voKzcV7i9fAjhWdLgljRE1yuxZICz7E5Omhyx07Pt/jgg1dlip97dY7p5Q5xrDi/1L5uXMd2Svm2s7jbDCuP/b7xEpUVodrXOvbNdt/uJNs913e72Yn7/V4uTiEryDIyMnYBOz00fmYh4PUrdWJlFm49YgXVToQEYjSW0EgpCGJFrDTOBjb5Gx1r0XM4Pd+i2g6odSMuVbv8xXfsu+YibrM7eSuNLaI4YaEV9h0gr2WMsfK5DxcdBvIOlhTMN30avpk96YYJw0WXMP1ZQwWXnDThzreKwmRArZSTKWUWzp4jcaRZCIEgShSfe32WvGOvu4m+Nd8kjBVXah1m65lc8V4mSDQS+kWIQCAloARSgGMLLCDRAiE1rm0RJObNl2iNI8FyLTphgm0JSp6FEJJulJD03WwEfqz4/z13iSjRt7yo0+nPvPZkmvnezXjpFFwLS8q+2+TanyEwGyGOJTaUAV6udXnhYpWFpm8+l6mcUWvNfDNAo/nKKcUTR4Z44vDwtkr5bvec1s0e+2ajCO4Ue9mif7vv93u9OO2RFWQZGRl3nJ0aGj8+WuRyvcs3zi33i7GNZi40pnuTKEgSM+jfDCIWWmqdTf5GxzpcdPtymGonpBsm/KVHpzg8Ulz3u7ayk9cztvh/vnmR5XaAJQRCaLQWGxpjrL0xAVyq+iw0fYYKDvPNgE6Y9Bc7K/OJBgo2zcb22DmvVJGFiSZMdD836nI9IGdH/UWkUnqd6cHx0SIvX66x0Ap5LZWLZty7WBjZotKmgIkS3TfnUBoEov+e8yxjxlPrRqQ1GYvtiIG8QyVvIxD4scaSyshrE81yGFJK5WvL7ZDHDg1dd97y4FDhhgu7bpQwWnIRwljPrzWTKOVsRoou3SjZ8vnohKaQVNfYQOmdm56cc60MsOlHTC916EYxWkOtE6G0iQBQShMpU5j9+y+f4fHD1f5G0XZI+e7EnNZekiFulr1s0b/dxeReLk5XkhVkGRkZW2InhmZ3Ymg8ThR/8NIV5psBfmqbfs0BeMxCLhameFAa/FBx/+Rqm/zrHasQgkreoeBZnF9sb7jQ2orJxv6BPJfrXf7fl67QDWIcqzctIhACLMQ6Y4yNbkwnxku0gphqJ8KSgm4Y40eKVhCvyidybRvYuZu3FJB3JAiodU1BNlJ0GSm6fPNClflmQJSoq/b7WrPUDlFAkNne39N4tkRK0+0pSYtE69S90xRmQaywU+MPjWChGaC1pveRmRrwiBONH2mGig5RrKh1o34sw3glR9mzeX2msW5Rt9G85SMHBm8oYSy6NqMlj9GSy0w9WGcmMVnxAHFT0ry8bRFEyYah2D0SpXHkxrljrSCm4Uf4UZJ+9iVKQStIiFPZthRm7mrtRtF2SPnuRIG0F2SIW+F2Sz+3k+0uJvdycbqS3fdKZWRk7Fp2amh2u4fGp5fbnJ5vEfU6M5sgiBW2hIIjcWzJ3/6OY/zge1YHSCulaXQjgihhvuEzucGOXO9YC47FxeXOhkYVUwM5ABrdiDBRdMKY2brPfDPg1HyLU3MtpBAkSvHK5TqJ1gzlHco5B8eWWKkcaaUxxqGR4oY3puGiyzsPDnJ6vsmlapcw0dS7IUdGi6vyiW5mp/5GCMycXo9WkJBzLLMQBvwo4fnpKk0/IVGaWCnCdFffS7Wlo0WXTrj9x5axe+l9onod7URrbGnhWMa+fLEVkIRxv0OkNIQaXMsU/r1CDYy7ohCSoaJNM4hZbpkIjP2DBe6bKDFW8ih5Fi9crFFwLcbLuf5xLLeDDectNyNhXCnNe+LwIK0g6c9/ljyL0wvtDaV5m9nwulBtEyfX36RIFNT8iLFKbp0MsOBaRInpoFVyxryoEyVoDa4tCGPTYRvIO4yVc+skX9vRZbgTBdJulyFuhb1q0Q/bX0zu5eJ0Jbv76DIyMnYNOzk0u503l5wlubDUIYgVQwXHhDJv1tFdCBAwVHB599GRVcVYrxg9Pd/kYrXLGzNNDg8XODFRYjh1Mewd69RAjj948QpnF9v9wvXYWJHhostCyydRijdmGlQ7EZ0wptGNkEIwWnaxhOBytUM3VjhSkChN3rWMJb8Kmazk8Bwz75F3LRaaAd84t8yBocJ1bky9QRONLQVhotEq9cVOafjbv3uoMfJFW4CVtivyjsSSgiBKWGqHLLbMItqS5rE6Df3tJhopYajoMDWQY/lCbduPL2N3UnAtJNrMgSVm3rDoWpQ8myQt0BACS5K+j817RmkTtyCFwJICSwimBjyGCi7VTkTBtY08D3j7vjJTg3m6YcLphTYjJY+cY9GNEsqWRGvNmfn2hvOW5dyN51JWSvNOL7SZGsgxWHD6v28jad5mN7x0clUWvPI36zV/7oYJp+Zb635XJ0xwLIHSEj9WWEIQJaZblmhjCCKFSE2Odk7ydTcVSLebvWjR32O7i8m9XJyuJCvIMjIybshOD81u581ltukTxgo7fay1yYB6tWI1YwsordhNW1uMPnF4mOcuLHN2qc1yJ+Txw0PkHIuZup8aaAT9wqzg5rlS6/AHL16hE8Z0wqTvaDZe9voW8RrNYjNEA5WczaGhPOeXOkSJIo9FPl0sLncihjDhsM0gIogU//35y8w1Ar7r7ePrbky9Xf5OECOF5L7xMn6ccG65Q7Ub8fjhodQs4WbsBW6M1XNcTFTarYB2EBNEcf+cJ9rkRhkzAlBpgK/WZs6tmmTB0PcCtgQ0VDyLbpxgaY1IINaKWir582NFojSuBGkLdKyZKOfQWrHciYiVJu+Y4q2ctwHB/RNlhBB0o5izC20enCrT9BPOL7b7UrkPPjjBF16f6392mn7Mcifsh7qvnLfcbJGyFWneVja8lrsBaPpzmb3LotYmg61HtRPynqMj635XKWdTyTt0gwSNphWY0Gzbwsgc0w68m25I3SnJ117OlLod7NXZuO0uJvdycbqSrCDLyMi4IbdjaHa7bi7VTmRyioSkG6ktuZjF6bZzkGiu1LscGDLPZW0xWs7Be46OcHq+yYWlDs9dqPLQVIWH9w2w1AqYafj9xy63jQwxUcrIqRIzNC9QzDZ8EqXxbAvHEtS6EUJAzjFzJ36UECuod2NytsJzJC0/ohvGJMoM4Jc8m32DXn/h9p0PjPdvTJMVj1NzLRrdCNuSVPI2jxwcBOgf+1fPLFFwrGvO120HOcfCjxIaQdyXRq5VSPaKM4urDo1Kw5Wav3MHlrErcC2wpSDRxnmzHsTkbItKzsKWiiBO6EYKK5XpSSEIlfEptC3BSMkl51jkvZDZus9YyWO45KKBWickUrpvsHFgqMDf+NZjSCHWLfSlhCv1Lm/NtVBa0w4i0A5NFVNYMW8Jmy9SNiPN2+qG13DJw7aMfb2QVzcvhARbX/3z9z62nx9+8ui6hWjZczg0XODicoc4UeRsD6UDLCnS2TtzrfBsI9O8E5KvvZ4pdbvYq7Nx211M7tXidCVZQZaRkXFDtnNo9nq7nttxcxkpuuRdG9cy9vWtYHO7unY6rxLGmoWmz2/92VmeO1/j0YMDGxajw0WXJw4PMVRwWWoFfOChcR7eN8C//eLp/mNXyp5GSh4tP2apbYqjIFGE6WB+ECVYlsSWgkgpY0qAIOeYxyllZtwipdDa2HrbUpoF5nCefYOmcDw13+LN2SY/8tQRvvD6HC9frnGx2iHnWExUchwfK/Vnxt59ZJhKzubVyw3KFW+jU7ItRAriMElne4yExJHXfvxaKVawhTnAjL2JEMJkhEmNY0HJsyl5NgXXZrEVECuFawv8SKHSOaecJQgThRQizb4TDBUcap2QIC3cokRhSYmbShB70qVruSSeGC/znQ+M86mnz3N6vkm1E1G1IoYLLkf3FfqfHdhakXIjad5WN7wmyjkqOZtGkKCUMhJDTOREojSWJal4Fg9NDWz4PPcP5nnXwSGCSBErRbUd4khBkCgGcjaWlEwN5Cnn7Dsi+bobMqVuJ3tV+rndxeReLU57ZAVZRkbGDdmuodnN7Hre6s3lsYNDHBkp8tZ8k4ODOdqhQztsEifr83pWkqTSn94simcbh7HXZuq0/Jh9axYjPfe1xXZAoxvxe89f4Zvnaiy2gv5jV8qehBAIaYblSTRRfLV7FylItELYZm7DF4rBvEuiNXnHnNMgToji3oLUHOtYOcc79g/2F3G9hdv/8ug+Pvr+43zl1AL/4c/PcmykxGDBWbfYq3djEq05PlbimXPVmz7nN6I3Swa9jDKJFHpFBtTG/wauvh4Zdye2NGHJ3TBBxRrXlpQ9m4mBPBNlj3YYEyWaRCkKrqQTxLhSIiWUbDNTVuvGTDgWsdJU8g6uJVlqh8SJYmowD+gNZ6nWcnq+yZdOzlP0LN53YpS35postUJsCecW2wzkHYaL3rYXKVvd8Hrs4BAPTg3w8uUaWpn8tDjN/is4FkIKHto30M9OXMtKiddSK+DAUJ52EHNytkkriCnlbQ6PFGgF8W2XfN0tmVIZm2O7i8m9WpwCXGefMiMjI8PQG5qdqftpqO9VeouTE+Ol6y5Oeruer16pM1hwODZqioRXr9T5ZLojvR3YtuRH33eEcs7hYs0niJO+/fO1EBhTCc82ZgAAjiW5b7xEO4hZagW0V3TaenNZ800fW4p0oeZwbqnFxeUOV2odAMLE7ED3LOuVglhpglhhSbMYFVwtOjqRJlEQRIpG1+SZldN5spGCg22ZnfDBvMP9k2WePDayatc+71oEcUI7jJFScHysxHgph50GxK6k6ccstAKK3lVp0m1BpEXpJsWkWS12dyKg3ynVWlPyLAqeTcmzeOTAII8dHERpI/EdKZrFuC0FQgpipSi6FiNFj7xjOsVLrZBqO2SikuOhqUrfmMK1JPVuzDv2D1yzs6KU5sJSm9/5+gUuVTvcN15i/1CBRw8OMVzyEFLS6Eacmm/R6IabKu62wsoNr41Yu+HVu8aNlXN4tmSs7DJe8Rgru3i2ZLycW5WduBE9idc79g+a7qQQHBsrcf9EhYNDeRrdiFonuu552wm20i3MyLibyDpkGRkZN+RWh2Zv965nLzesJz0KYo0Q4KTVz1oXdZ0+R6VNi0wK3Z91OjZaZKEZcnaxxaMHBgH6MsShgkO1EzFeyTE1kGcytZh+9XKD/YN5XEtiSxMG61rSLLjSMNtkRcdubdGhNLRDhSMV4xUPyxLkHIupgTyNbshDUxUemKpc03K/t3C7nvtUECe0g5gjI0XKObsvedppktQJbytZz6l3QcYepff6paOdjBRcJJp2pIkS1S+2Yq2pdSK+fm7Z/D1R1P0IR0qKnsnFAgtpSTpRYnKzlGIgZ6StOcfIE4UQ/JVH9/HowUFGy951pUu9rv3Ll2q8cqVO3rGIYs3x8SLDRS+NjGgx3/S5uNxhIO/w6IHBbZ1LuRmXuA88OMGVWpff/srZ/iyqJU3B8sNPHV6VnXgtNpJ4TVVyzKTduDsh+bpbMqUyMrZKVpBlZGRsilsZmr0dpiBr+cCDE/yF46P8p2cv8O+/fIZEwaEhj26kObvUJlHGCj5JHeGV0ghMsSSl4PR8i3aQcGS0wGjJpZgWjiXPZrEd4NqSaidaFa4shODhfRVemK7x8uU6x0aLDOYdZupd7HShCDcufHrrn0TDQjPAFqIv4XpzrtWfkVnJRgu36xXSl2td8o7NvsF03m1bzvrm0KwviteysgiT4qrkMWPv0QsFj7UmTjRDBZeSZ7HQCsmlEQ6J0gx6Fp0godoOiZVKXTcFQRRTT2MZrBWXD0sK8o6FbUkKnuTQSIH/7d2HeHCqsqlCYuWsUt41TqZFz2K+6dMMIt55cJDhose7jwxR7YScX2rzA+85xLfdN7atRcrNbHidnm/yBy9dYa7hEyUmd00lmtm6zx+8dIWnjo9sqmDcSOJ1JyVfd0umVEbGVsne0RkZGZvmZodm78SuZ2/n+/npKokyzobnq8aBbSDnsNyJSLReNccFxvZ5sOBQydvMN32W2gEHhwv8wHsO8dLFGl89s8RyK6CSd5io5FaFKwNMDeZZbIUcHS1S78a4tuznIo2XPC4sGTnj9bo+niMpuxadUCGBt+8fYN9AjtMLbZ46Nkw3UjdcuPXMU2Kl+Z6HJ3npYo2zC+1+If2eIyMcHzWOkHrFebhd3Oj39YuxnT6QjB1HAJ3QfMAUcKnawXV6kkOLVhAzUvII0rnKWOm+lFejVxXjKs2pU5gCpOCCbUlGiy5Rojk93+KDD95YSri2a9/0YxxLIoVkuGix3A45s9BmqOAihMCxJGMlY4yzEx2jrWx4KaX5lT86yQvTNZTSeLY0OX4KwljxwnSNX/nsSX7rh57Yc3NWeyVTKrPkz9husoIsI+MeZ6s3lpsZmr3du569ne+lVkjOlhRzDmhNw4+Za/g4lkSg+7NjcVqMSYykajDvkHNsXEsyXe0yESvGyi5osEVvjuVq2PLa5zJa8vix9x3tW2svNgNenK7xZ6fmAbOgtFP51kadIokgSEBKE+I6vdSm7ceMlD1+4FsOAVx34baRecqx0SLf99j+VRKus4stPvn0eU7Nt7blvO8EPUvvjL3LykgFKczmh6ONC2LdVwzkzTWh1jGdMc8SSCkJEzNvuZLe9GGvYIsSjWcLFtohthQ8P13lcq3L/sH8da9ra7v25ZzNUMFloWk2Nko5m+V2SNOPKefs21IIbHbD6/xCi6+eXUIpTcG92nmXFthS0gkVXzuzxPmFFodGijx/scpSO2Sk6PLYwaHrzpbdafZCplRmyZ+xE2QFWUbGPczturHczl3P3s739HKHOFYsd0KafkQUK8qehUaigdGSB1pR9RO0NhlhUgocKWmHCXnXzFgNFlyiRPHxL58ljBXHx4uEiWKm7jPf9GmFcV/adE1r7Ul47/FRKgWbVy7V0yH6a1cZQZLQjZJ+Z+ClS3WmBnJ8ZP8BYqUpujZ/69uObTjrcS3L6NdmGsw0fH7sfUf6BfXKXfnPvjp7y+c+I+NGCG0yx9CadhCTKEXZc1j2Q3Nd0FDMmZmyTpgQxIqVS++Vc5cas2FxueZTydmEAi4ud3h9ps7/ePEyr1yu0wlNhtg79g/wPQ9P9q9ra7v2QghOjJdoBTHL7ZCCZxMlCdVOyGzj9hUCm9nw+sKbc/hRkuYtClQaLC8QaTdP0I0SPv6Vs8zWfc4vtYkSYy50ZKTIj77vyKZmzO4UuzlTKrPkz9gpsoIsI+Me5XbeWG7nruflWpcXLlZZaPrEiaaUs9k3kGem7lP3EyxhJFH7h/JG7uM6+FFCJefQDiI6kaLejcjZFhMDOQ4NF3h+uoolBY8dGkIIwX0TZdphQieIWW4Zx8UHJss0/YSR0sbPRUrBA5MVBgsuTT8miBM2GAUDjOFH71uOBSNFh4VWwG/+2Vn++LUZDg4V+wvMByYr/X93M+YpvV35f/n5N2/53O8EGtO5zJpkexux4g/DeYdWlNANEyKlqHdD9g8VyDmSxVbQLyx6nVEBWJYgSa5Ka3uS3163rB0maK3pRopPpd3xRF99xLmFNidnm/zMB+/jxHh5Vde+5Nk0/RilNSfGisw2fOabAX6k6EbJtpt43CrNbgypzX0YK5TWJgxamJk70lnYz706Q961GSm5/evtW/NNfvmzJwF2fVG22zKlMkv+jJ0kK8gyMu5B7sSN5Xbtejb9iOmlDolSjJQ8hBB4tunSLbcDltohYawIY8X+oQITZY83ZpvkHIvBginOat2Ihw8McHi4wEzdpxsmqwxJhosuR0cLvDhdo56GPS82A06Ml/n+Jw5c01p7rOwxVHBZbodIjCQx3KAoUxjzAo2RIHXDhCRRxArOLXToBsm6BSZc3zwFTODucxeW+eaFIZ44PNx/bXfz4iFnS+JEZVlke5zey5doWO5GDORtPEvgx4LRksfjhwZp+jEvX6qbzpUwuWJgCo2N5hx7RZkUgpwtafgx3SjizIIx38k5Vj+g3Y8SXrpY49PPTPPzH36o37X/+rkl4lhR7UbESmFLyWDOZrjo8vA+Y/l+4BpB0jfLrc4fvW2yhBAY91iMuYmQ5hyFKwbu2oGJzbClwJaSck5SdC2mq13+41fP8+33je16+eJuypS6E+ZUGfcOWUGWkXEPcqduLLdj17MVxHQjsxBZ+dzyrsU+J48lBXMNn6NjRR7ZPwjAbCNgoekzVHCIlcZKZT9Ggtil4FqMl3P9n7XcDji32MaxBAeG8rSChAcny1hS8KWT8xweKawqynrS0NPzTerdqF9c5G2JitSqGRtIjSyEMRiJlSZOFJYU2BYkSiOlJFFq1QIT4MxCi4WWT8mz0Vr3n//aEOvf/LOzPHuouutnHiQwWfGIEsXlenCnDydjmwjjhOW2xrEEni0ZLrqcXmgzWfHYP5DnzGKLhh9DOueZaCN1XPkxWflnITR+bD4jfqRRqblFvWu6XlII8o5Eac0zZ5e4VO1waKTIA1Nlfu/FyzT9iJGiy0DeoRsmnFvuUMk5fOt9oxwaKW7rc98Omfh33z9J3nmNVhinjqzmOa91Ii26kk6kiBo+k5UceddGSslI0eXcYpvnL1Z5z9GRbX1+dzOZJX/GTpIVZBkZ9yB38say07uepZxtApIjRcnT6wpOSwhKnkOU9HbfzezIYivgzEI7XSRJXrhQ5WUpOTBc4OBwwRR5lkRr3c8hGy56VLshWmtsS3B8tMSZxfaq7uJKaWjJsyjnbCwBC+2QbqxQmIDc3lGGygRGe7bEsS1q3QhLGCe5JFFEWpMkivFKjrmGzzNnl/jz0wu8cqnBy5dqnFloc6XmM1HOcXzcLCZfvFijGya4tuyHWO+VmYdqN6KUWVzfdSit6IRgScn3Pbafswsdziy0GCg4uJak4Ucode2svh5mHE1QSIsPraEbRiTaxrUllpAkStP0YzTGJOTMQosDQwVOzjSZquQYK7lUOxH1boQtJcdGi9hS8uZsk++4f3zbNoy2Sya+0Ak5Pl7k9St1YmVMb1Y22nudQ8+1cCyLbpSw3InYl3YM865xkVxqh9vyvO4VMkv+jJ0ke9dkZNyD3M03lrLncGi4wMXljimCcjaOZcKZm92YWGuGiw4CeGu2yb6hPIlSRIkiSBSWlBQ9x+ik0vykiTTwuTdrstwJUVpzcrZBNzKmA39+apHXLje4f7Lc7y7uH8z3paEnxopML3dNoHTRZbzscnaxQzdKGCm6TFY8qt2YC0sdco5FKWfTCRO0BmkJwlgRpzM0S+2IWEHBkyy0Aj759HlyjsW+wRz1bsRM3Weu0aXhR9iW2DDEGlglTd2VpCvLdpDtON9NRMq8tFJCnCiGiy4fevsUl2tdvnRyjtPzLbphjJZGlnc9tWrOlgwVHaqdED8yGxx+rJFS4VgmbLobKmJlZMpCJPzhizM4luTMQov7Jkr9z3WYBriXczatIN5WlcB2ysTbYcz+oTwDeYcXp6u0gqtWra6EwYLLUickSjSuDa5tZM9hrPAcM0vmWKZTltm3b569YsmfsTfZe6utjIyMW+ZuvrHsH8zzroNDBJEiShIWmiFRuhjrpHJGKQULrYCK51DzI5bTubJ37KswNVCg4Fm4lqTkWZxeaCOAoYLLqfkWni2pdQKW2yGJAseSlHIWIFhqhzx7fpkT42XaYdyXhuYdyXMXasw2fJY7IUvtgDjRRIlGYSSTDT9mtOTi2RaR0mit+zM0Uapp1KmMMecYO/12aKRZTT/i4X0VWkHCaNmj2gmJE021HRDEivFybl2INbBKmrobURrqfrwqDDjj7kBjcrMSrWmn2Q+xUvz+i5dpBUaKd61iTGI6QhIj4V1oBjiWpOBYBLFCCrOBEa3Q8PVcVAVwsdbm09+YpuXH7BvMI4Sgkl+9MbXdKoHtlIn3NtQmx3M8sr/CixfrvD7bYDDvMFxwCBNFK4wJYk3eMRLsSCsSrVFKsdQOuX+iTCVv8/Evn7kl+eS9VNDtBUv+jL1LVpBlZNyDrLyxvDXXMjI6KfrSnms5Be4Fes/tjdkGJ2e6+FFMK4xpdJOrDm1KEycxTT+mGcYcGMrz6IERpgby6xZLUwOmmPnex/bz8sU6L12sspx2qIquRd61cCwzGO9agmonYnq5Tc6SJoOsFbDUDggiRTlnUe8audDKhabS0AoSOmGXyYpHKzASI8+2+iG4QvTkljaubWErzXInRCvI2xbfvFCl2jHGBInSJIkJ1G0FCZV8wuSAKcJXhljvlZmHtbMxGbuDoiNoR9oY1AAJm3fD7DlnxoniSq3Dx798hmfOLvHa5QZRos37XYJI1v9MSwocmf5Orcm5FmXPRkpBECtCZdwVw8RoHnOuRCkjX847kuNjJa7UfZZaAe0gXleMwfarBLZTJr5yQ+2+8RL3TZRZbIfkHImUkk435tBwgbl0o8e1zdn2o4T5ZkAl5/BdD03wf39t+pbkk/diHtdutuTP2NtkBVlGxj3KifEy3/nAOJ96+jyvXamvyqm5llPgXqLpR1Q7Ed0wphupNSYA5v9jBdV2RBRrvvX42IbOhL2F0ljZ46PvP84fvnKFr55ZwrE0Jc9CSlOMaU0/xLYdxP2d7sVWQCeIGa8YU5BOGK9bYK50oKt2I942UabaDmkHMamLNRLIOxaWND/fjxJiZYb5n59exnNsxivGxTFKFI1uRKKNXGn/YIF3HhzoH2uPvSBN7c3DZOwubMznx5GAEAg0Um/sGnotpDCbEZ99eZZKwcUSui/LldCfIet9Knvvg7GSixDQ8GOCMMGRgsmBPBNlj0RpltsRfhj3zXPC2EiR847FcNGEvh8bLbLQDDm72OLRA4M7rhLYTpn42k7NZMVjMO8wU+9iS4ltSU6Ml9g/mOet2SaL7TA1PFHcP1Hmh586wpuzzVuST97LeVy70ZI/Y++ze+/CGRkZO8rp+SZfOjlP0bN48tgwVurc1/TjDZ0C9wpKaT79zDRvzjZJEhP6vK4A0mZ+xYK0ixRzbrHN2ybLax6nmW8E+FFCoxsBRqKYcyQIgR9rICGMEzO/suIX/dM/fIMfft9hQKRecaT5Y+tXrCuLDj9UOELwkXftx7Ikn3l5hsVWQK0b4kcxndAsYoUw8zMRCo1xhFxuh7hpty6MVV9W+eZckzhRnJgoMVz0+s9tL0hTBeBYYpWdd8adJwGSJO2OSc1Gddi1iumVkkOJ6R49fmSYly7Wrn5dCuJrvOaOLRkveyTLXWKleezQEMfGzBzkbCOg2okIVvzbWEGiFHnXYmogTzlnk2jNaMmlmBYgOyk/U0qjtKaStzmz0OKR/as3R27ms7i2U+PaklhpWkFEwbV4Y6YOGBfLdx4c5LsemuTh/RUeOzjETMPnj16ZuWn5ZJbHtfss+TP2PllBlpGxB7lV3f7KG+rbJsrrdof38g31UrXDn721QCuIsaXYML9KYxZpvaemgddnGxwfK9IOE8JE0QljZmpdppe7lHM2//mZaZ49V2XfoLGPjpOEZjdetfADcISZ9Zpt+Px/v3iavCPxbMlSOyRYsWu/9nhW/tmyJG/MmUXWQMGh1o0QQqQLu7RroM3MTe/vni1pBglnF9tmXgaTPeTZZhbu7FKb5U7I44eHyDnWnpl5cGyBLSBMbvzYjNtHr3MlpNmkiBNFoo1DqFJXH7MRQpjNkB49kxm0vhr4rDSWNJ/TtcSJYrkTUfBsY7zjmp/W9GOaftTfPFl7vI1uRJwohBB0g5jRkteXIu+U/GylrG+xFXBxucNMzefh/RWmBvO3VACeGC9z5NuKPH+xyqtXGkSJYqEZrDrv0pKMlDy+7W2j/edzq/LJLI8rI2P7yQqyjIw9xnbo9u+WG+pGhemZhRbzDZ9EKcL4+gv5lcXRQsPnPz87TSFd3NU7EQkwXHA5NlbCsSSvXK5zqdrGtSRXal30BivOSINnSTwbFps+GhgtudiWpB3dWM9lwqAFJ8aKvHixyun5Fg0/wrMElmvTCWOU0kTq6nPrRoputNrCWgBFz6KSt3lo3wBLrYALSx2eu1DloakKD+8b4JGDA8RKc3G5c8PjulMIIMmEi7uS3saGbZlYBlvAeMmjEyUstULQ6181W/ZmwEzXU6Rf++b5Khernas/V4PUV3/PSqqdiBEhGR9w2T+YZ6buc2m5a/L2Ftumg7zi31nC/Ben3eJ3Hx7qd6Ted3yU9x0f3RH52VpZ377BPKMll1evNHhhusZiK2S05N10AXh6vskfvzrLy5dqvDnXpBsqjo7k2TdYoODZuJak6EpeudLgd75+gR9731EODhVuWT6Z5XFlZGw/WUGWkbGH2C7d/m6+oa4ssgqOhQa6UbJuoXStwlSkWUNblbgFkSJWmm6U4EjZdzqsdUJeu1Kn4NoM5R1aQUTDj0Cksy0b/JowVtS6Gq3NwrLaicjZEnuTdoENP+SbF6pcrhrrfgRIIRBCkyi9YddgLRpj6FF0FWMlj2OjRQ4M5VluR3zb28a4XO3ye89f7p+73UqiNDlH4N/pA8m4Jn6ksISRrHWihESZ0Gel9aoNERv6rq5BlJB3zPvulSsNbCkZKtjUOxatIEHBhjLIgi2ZGsjRDIxRz3uPj/HZV2dp+hF2WnT1DEMkYFkCIcAWpjPX8mOePrvE2/cNrOpIbffG07VkfQeHi+wbyPHshSpDBYf/9d0HeOLQMLYtb/ATV3N6vsm//pNTvDXbxI8TltshlhCcmm+z3Il58tgwsVI8P91krulzar7FTM3nkQODfNfbx2/JZfdujk3JyLhTZJ+WjIw9wnbq9lfeUDfK4LmVG+pW5ZQrH7/QDHhxusarV+ostgIanQjHkoyWPUZLXr8TCFyzMG370U3NG9kWeLYgVhArnc5iaZQ2w/AlF+ZbAVGc0PRjxksetW5Ed4OulwbCWONYYCnTPYgUdKPNFbivXG6ScySVnDn/eVsSa1OYbqYYk/Q6S1DrhhQciRAiDZMO+MIbc2jNqnO3W4kVG57jjN2DJHUK9Y2M0LUlUxUPaVkstkzEQzdMiDHzmgXHYqToMVZxOT3XotqJuG+shGVJJgfyTFc7RGmmmCmqIElMcTVQdHFsi+OVHJaUvDhdY7LiMVZyOb3QAkzhZafXHNuSaXdMgzCbJEMFd8dNJ66lQlhuB5yZb7PYCrlY7eJHiucO1LZsNf/pZ6Z56WINxxLYUiCEwLaMU+5cw+fZ81VsKfCjhKJnCt+8a/U3777zgfGbtm+/m2NTMjLuFFlBlpGxR9hOmWHvhvr1c0vEsaLaNXbptpQM5R1sW/LUsZEt31C3KqdcO19xZqGFH5lA13YQk2iNa1mEiUqlPnUu10xw8rUK0y+/MbulY+4RJ+DagkQrwkjh2QLHkoSJwu+YUNWSZxYtUaIYKrhUcjan59skpPM0rN7Vj5SRSo2XPcJYsdDc3CCUxnQdwihEC/Aci7IlqXaCTf17lR6LFBAnmrNLbd42UaETGBt+IVjlLLfRLvduQcOGc3cZuwfHMnNk7VDR8GPGyi5jFXPtCKKEdphQciWtIMGSghPjJebqXV670iCKNVLCazMNJgdyjJY8ppQJYo9iMyPpWZKJisv9kxUmKrn+xtFM3efrZ5d48tgwUwN5XEvy561FLGkiKDSmS5d3bYQgDXFP+N7H9u24YdFGKoTldsCLF2t0w42LpM0WiZeqHb5+dolEa3SsaQUJfpQQxeZ10MD0cpvRksdkJUeYOugOFVzKObN59+Zskx956ghfeH3r9u1ZHldGxvaTFWQZGXuE7ZQZSil4YKrM7714maYfMVJ0Gcg7dMOEs0ttKjmH+yfLW7qh9uSUS62QSs6mknNQSvPK5Y0XGyvll5MVjzMLLTN0r0w54ViSsmuCXuebAWcX2/yFE6O8fLnOYjPgPUdHNixM14/zb44EaAZXCyY/1gRxgmsJLCGM3XyYECUJWkMnihnOO6uGVcSKP4Nxc0QIiq7FSNHY0c81V896XQudHpNI7fSNpHLzz6dn/KGAxVbIiTHF2cU2IDg2WtzQ4n834tmQsy3qfubqcTtxUgXdppqTQqxyD212Q67UjMX8cNEjTHzCRJFzLVp+zMm5JonSuLaFa2mkFHSChEvVLn6UMJB3eeeBATRwcrbJ44eHePtUZV1sgyUFUWIs7YUQvGN/hRcv1mgFMVoawxGd7k6YoOmEqYE83/Pg1Ladp2uxVtantebMfJtumDBcdDcskjarcDi72E47j8Zh1bUFiTKbR2H6OsRKY6fnq+Wb2I1yzl61efe/PLqPj77/+E3Nz2V5XBkZ20tWkGVk7BG2U7evlObkTJOpSo7RosNCM6QVxDhScmykiG1J3pxt8h33j6+7Oa+VJE5Vclyud/mdr1/grbkmloDzS+1VHbd2GK9abKyVXza6EZdrXaQQlHMO1U6ElBpLSgqupB3EXKp2afoxQwUjc0qu0TaxtrHQ0ECQaFwLbCnNoi5KsCTM1n0uLHX79ZdmdYBxr0ultObCchcAf5OSxbXH4EfK1H1b7BT1lsjTi23qnSjtQLgUvd3bEVuLa1mUPDsryG4zAwWHONHUujd+zwaxRoqrexNCmFD0MEmYrOSYrORYbge0goQw0dhaM172KOccFlohjiUoOJJaN0Zr+JYjwwwUTK7WlZrPaNFbV4wB6ayaiesAsCyLxw4P8dUzS/ixSmWLmiBS/Y79j3/rUVx352cm18r6mn7McieklMqQr1UkbUbhoLWRgEoBRc90/3AhCcz1ued4qbSJvsi7tpmvTa+NKzfvbsW+PcvjysjYPrKCLCNjj7Cduv2e/HGs7DJTM06Avf8AxsruhouDtZLEMFb4UUI3Sji30CZIFJ4lVwUUL7QCLCl4frra/3lr5ZfVToQfJRRcC506pMWx2e11bYnnSDphQrUTUckbq+umHzFUdNc9t+Hi9hcbJg9J48caz7GIlUozyK6NwMyloYWZn1H6pn0CpTBdzSS6uZ9gSYFG0/IjIqU5u9ji+IqdeL3VSm8LrA313QqWMMHWnWyG7LYzWnSRQlLvNjf12vWcDXvkbIkfG3v6fQM50wVf7CCA8bLLvsE8QghaQUw7TMg7kqJn0wkT2lHMAA5NP+bIaJGGHzOp9bprXtOPOTJSpOnH6PT7jxwYBOD5C8s0A+PPGSWKqYE8P/6tR/mhp45s30m6DmtlfZ4tiZIE15Y3LJJuRN61kMLIMXsBBI4lKXmkkmrzWoSxYt9ogeNjJYZXXCu303Qjy+PKyNgesoIsI2OPsJ26/XZoZomW2gFBpKjkbRxL9guoRhAxUvRWLQ7WShK1hjdnmyx3QrRKXQ01JJZivgGuJcm7Nm5RspTm7zT9qP/7V8kv00OOE+OQGCszfN8MItzYwrGEeUg6rD+Qd1nuhBwcLqxbpHnW9u9+K4xccSBnA5JaJ+of9vUWq0kC62Opt07Rs4nj5KacBi0BYaJo1rpcSEzf7vR8i1eKNd51eIiBvMOZ+fYtH+O1uNVnL4UJt63etBg142a4sNzhwEAe1xa4tiQIEyJ1/ddTYzYPbCnwY4UlBZ0gZqkdUG1HtFPLxU6YMFMPVkkau+nsaJwollsh7SBhpOTx/Q+M86WT87w116Kcs7GkMa5o+jEjJZfvf+IAXzo5v+qaeHS0iCOhEyrevn+Ah/dXeGhfhSgxEQ+32sXZrHHRSlnfy5dr+JECYiYquVsqkip5h8GiS7Ud0o3MppUljGRbCvN6gWCk5PLYwQGsFdfEzHQjI2N3khVkGRl7iO3S7Rcc437WCYxsplfUeLaFW5TMNXzQ5nFw1eFxerlDHCvOLbaYrft0wgQpNCINYHZTW/dWEDPXDDg8bCGEwHMsmn5MKzAF3lr55VDewbYkzSBGYORPCrMYD+MEPzZFyWDOZrYR8NSxYbqR2rAw3aluSpgkdCKBFKbTZYnVEsW13GohsrLYa3QihEjd7Lb6cwTUuxFKm4WybUssIZhvBXzp5DzDBZfdOk6mdE+mmbl63G6kECy2I+JE40pNybMJEt0vqq6FLWGinGO5E6YbLAl+PUEIwUjBod6N4BqSxoYfp9lmmifWXNM++fQ5XrpUJYoVji05MVbi+584wAcenODwSGHdNfGdh4b57rcbR9bPvTrHn55cuOncxpVs1bioJ+u7VO3wf/35ec4ttXhk/8AqCeZWi6Sy53DfeInT8y3q3Yh2YDqEWveKYlOUVTsxn399fltCqDMyMnaWrCDLyNhjbIdu3yxvjQvZxpjv9ZbBl2tdXrhYZaHpEye67z4oMDIhlXa0tAbbEuhE0+xGBLHCsyVBpCi4Vn9+Yq38spxzyNuSlm86gUJrU3ykc2JaaxwJc42AkbLHD3zLIYANC9OTs42bOa03JE7MLrZIS6IbOetLUpfDnuEHWysrVj42Sb9gi607DvZymQquec3iRDFayRElFnPNgCt1n8H87rwVaIzcLFbZwvF2kyhFnG46NEOFIxW2df2srN77c2owx2jZ40qtw2LL5I3dN17i+FiJr5xaZLkdUslZ+LFeJWlshwn7Bwt89P3Hefdhk811er7J771wmbm6j2NJLGm6QLN1n9974TKHRwrXvCaeXWxtS25jj5vNgZRScGikyP/+1CE++fR5Ti+0b0nhsH8wz7sODnGl3mWpaTahetcFCQwWHN42UWZqIMdrM9sTQp2RkbGz7M67cEZGxnW5Vd1+N0oYLZnOyHLbDJr3JIstP6aUsxkpunQjsxve9COmlzokSjFS8qh3I+J0oF5K08UCQAgSbdzNokTR9CPawnRlDg7lKadmEivll2/NtZBSI4Sxtw4ThUh3eBOtUWkmmCUlh0YL/K9PHOwvJjZahP0f//2lWzu518CRZuc5XlGJ9dZOvZ3plZQ8i4JrMdsMTXEm2ZJL4kbcYGztmkgBYaKJE9BoltsRBUfiSEEUa6xdvFPeCuJUgpVxO/Fj4+7Z20gw+XzmDdzr1ApMpxgBjgBpSSPLE4J3HxnkG+c1tmXx2KFBpgZytIKEY2NFGn5EwzfzVO0gYq4BS+0QgaDo2fzXb17i+Qs1vuvt4/yXb1zkpYs1XEswXHT716lmN+KlizU+/cw0P//hh9ZdE+NY8bvPXuTCUpsTY6X+3O3N5DbC9uRAbpfCQUpBJW8zvdSlEyqkuDoDrIBmEJN3JYdGTBj8y5frHBst8WPvO8KBoULWGcvI2IVkBVlGxj1I0bUZLXmMllxm6gHVjnFZtKVkvGJ2q0H05xlaQUw3SvqOYH1EL+9KIITGEsZAIkyMu2CYaA4O5bCl5LFDQ6vkOCfGy3znA+N86unznJpvUu2E2FKSdyR518J1LBwpGMg7TA7k8KOEv/qu/asWLRsVpo8eHOR3v3l5289ZpExwtEQRpoWV0qbY6dndr6yXJsoOQlrMt4zNfXIHfSkSDSoxRW/PbKQbKYLYyC9zzs67zt0stiVQKjP1uJNYwrwOYbojoDDy5LxjYUlBNzJREL3NCkvA6QWTg+XZFq4tee5CzcgYlaLk2bR8Y+QTJYpOaL72rkMDPDA50O86vTnX4LUrDSwBIyVvtbS6ZKTVz5xd4lK1w6GRYv94T883+d1nL/HZV2expIl9GC64HB8vMlz0tuxqCLeWA7l25uxvfdsxZlIDj5tROMSx4vdfuEyUJDiWUTIopa9mISrNydkmD+8bRErJ8bEStU5kZsyyYiwjY1eSFWQZGXc5Gw2gr5QMPn5ogNlGQCdKKDgWE2WPV2caHB0totIOVSlnk3ctgkhR8jQ5W2Knw/VCgkbgWCbAWGtja51zbB7ZX0Fps5haK8c5Pd/kSyfnKXpmB/31Kw3s1MJaCMGBoQLjZY+pgRztMKHWifodtuvx+IGhHTmPds/pUK2e5LqWhLAZaIaKZtj+TpsE9swWRFowSyHwVdIvIGfqN2MXcrvQd7SYvZfpvT8SDcQaC+jZ/ERJLykvfazWxAryjmQg73L/ZJl3HBjgP371PM9PV4kTnXbibWwZ0fYjQGMJQd6RnBgtMDWQx5JXu1hPn1lkvuFzdIPcPCEEAwWHpVbI2cV2vyDryQovLLWxpDG2SJRmvunTDCLeeXCQ4aK3JVdDuPkcyOvNnD0wWdnU717Lc9PLnJpv4VqSkmfR9BMSlWAJcGxJlGiWWyEz9S77hwpbfq4ZGRm3n6wgy8i4i7neYuBDD0/wxmyDz78+T5LaJ4ex2a3OORKt4d/8ySmOj5V49OAAh4YLXFzusNwOKXo2Zc+m1o0IE2PCkbMlwwWH5U6E1jBUcPFsi/smyuvkOCvlP2+bMF+vdmIu1zokiaLux9S7EfMlj8vVLrYteerYyKYG3r92fnFHzmWowFWaKK0Ocrbod5g2ohFEWJZIF5J33pSiVwtLIWgHcbqgNqitDqbdRsIYXFsQ36xeM2NbSDCdL6GvyuPC5OoUas/o5q+8az9/+/0nmKrkuFjrsNQOWGyFHB0pIKWkG8Yst03XWGsTuD5VybHUiXjxYq1fMAkhGC64xMp02jf+5K/PSOxdV06MlVhshSTqqlnRcjvkzEKboYK7Zev3m8mBvNmZsxvx1lyLMFbYEuabYX+eNdEQhebriTYy0P1DhW21uc/IyNgZ9pQw/1d+5VcQQvAzP/Mz/a/5vs9P/dRPMTIyQqlU4iMf+Qhzc3Or/t309DQf/vCHKRQKjI+P8/f//t8njlfvFH35y1/msccew/M8Tpw4wac+9anb8IwyMnaO3mLg1St1BgsOx0ZLDBYcXr1ST3eQO+aB6ZomjDW1ToQfm9mOA0P5/uP/6JUZDg0XGC/nGCt7BLEi51p4ttXvuFipa9hoyeXJ4yP8f/7ig/zsd9/P3/wLx/Bsi5OzDS4ud/odu5XyHyEEY2WXlh9T68Y4lkRrTaI1Z5fazNZ97p8sb0puM1MLduqUmmDbtFvjp8WYc42raDdULLUCkyG2Y0e0OXpdukSbkGk/1qs6e7u51LEEOJnM6o7Tew+vfa/0ijNLQjlnk3csulHMb37lLL/yRye5VO0Sxoozi21qnZClVkgQKYQ0+YJgujrDRVMknVlo93PxJioejhQ0utG6rDytNfVOxEDe5eio6Y6tvK5U8g5DBZeWH/Uzyko5m+V2SKMbMVP3OTFe2nCTRyljj7/ymtVTFczU/Q2PZe3PWztzVs45/e7ffeMlltshn39t7qY2Q3KORaIUrVCtMxfSmM+6Tl1VNzq2jIyM3cee2S559tln+c3f/E0eeeSRVV//2Mc+xmc+8xn+63/9rwwMDPB3/s7f4fu+7/t4+umnAUiShA9/+MNMTk7y1a9+lZmZGX74h38Yx3H45//8nwNw7tw5PvzhD/OTP/mT/Kf/9J/44he/yE/8xE8wNTXFhz70odv+XDMybpUbDaC/NdfiU0+fp+hZfOihCZp+zPMXa0gBYyWXWjfm/FKHJw4Pcd94iVPzLfYNWBwcLrDcDjgwZKRF1XZoum+RYqKSY7Tk8ciBAT708CQnxsucnm/ym185u65Dd99EaZX8R2vNYjOk5NkUXE03SvAjTZxojo0WsaXkzdkm33H/+A2LsqmB3I6f35VcS44oMEWbwBh8oDWdUHF90/CtY2GkiBs1kKyeTFGIvvOITo+t9yW4+v+7kUTfuhnK3cKthGzfKgrTHevhpEYSSoPnSAZzFkJK/uytBRZbAVGiybsWlZyDVYCFZsjlapdYaVxbUnSN6c2Vuk+YKPKu3S+Ymn5MJe/gWJKxco5OlLDUDimvMB9q+jFKa546NszBITOztVJWKITgxHiJVhD3jYssKfCjmNMLLQ6PFDd0NbyRqmCzOZC3MnN2Ix7dX1kl45VXP96rXq+SZ3FqvpXZ3Gdk7AH2REHWarX4wR/8QX77t3+bf/pP/2n/6/V6nU984hN8+tOf5ju/8zsB+OQnP8mDDz7I17/+dZ588kk+//nP8/rrr/Mnf/InTExM8M53vpN/8k/+CT/3cz/HP/7H/xjXdfn3//7fc/ToUf7Vv/pXADz44IP8+Z//Ob/2a7+WFWQZu5IbBZPeaDFQztm8dqXOk8eGkVIihCCMFUNFD8uSlHKsWhhNDeSodiK+97H9vHyxzpmFFp0wpug5fO+7DvDowUFGy96qY7meXOet+WYqjzTyn6Yfs9wJGS65uGkeWTdMePzwEPsG87SCeNOLlyOjN+8+uZ301ksaKHsW+wbznF1ss9zZ3jmOBPAs8IQgTMy8lecIBnM2sYamH5N3bI6O5Kj5CXONwAT3RjFBsvpYdyO97su9jiONMcyWc+jYvgJOr/mz60iUgoJr4SdAklDvxgwWHN53fJSmb7rdoCk4kqUwJlaaHEYSLTDdnm6oqOSMa2s7iAkThdaa2UbA++8fY7YRcGq+SdO/+tmxpODRg4P8wLcc6l/71soKh4su7zw4yOn5FtVOiB8lJAoe2ldZ5dbaYzMSw826JN7szNlmODnfXCXWvNaGyqVal++4fyKzuc/I2APsiYLsp37qp/jwhz/MBz/4wVUF2XPPPUcURXzwgx/sf+2BBx7g0KFDfO1rX+PJJ5/ka1/7Gu94xzuYmJjoP+ZDH/oQH/3oR3nttdd417vexde+9rVVP6P3mJXSyLUEQUAQXJVGNRo7k32UkbGWzQST3mgxYElBlKi+zLCXT+WkOWErF0ZwdfEwVvb46PuP3zAD7cYduiZBrLhS83nbhG1+v1I4lvn9UazYN5hnaiBH0zcOj9VOSNOPbnh+Xrq4+z6LC62ob+W/E4QxJMK4KEoJtrQQQhpHRSkZKjo0AkXOtrAldMP4jhuNbIV4F8+47TS9gsq2BFJCkugtxR/s1JmzLYnAuKva0uSDNfyQIFZp2LjZ+Mk5krMLbaSAvGvTDmKkFLTDmLofMVrycG3JUjvEs69uDvU6O73MwT9+ZZZXLtfpRDEFx17Vie+xNt9QCGOV/+4jQzS6EacXWrx93wB//7vvx17zWVw9f1akFZhrjmtJTowVOb3Q5vOvzfGT336cj24iB/JmZs42y3wjRFpGRmqiLNa8NulT+5ajI/zktx/POmMZGXuAXV+Q/Zf/8l94/vnnefbZZ9d9b3Z2Ftd1GRwcXPX1iYkJZmdn+49ZWYz1vt/73vUe02g06Ha75PPrF7W//Mu/zC/+4i/e9PPKyLgZNjskfqPFQJJmiCWpnbhrSWzLuHN59tVizU1bEysXD5vJQLtRh27fYJ7p5Q6eLTk136Lk2X2ziTA28qXRktu3yu5GMUrB779wBdeW193tvVLvbPW07jjG5S1Eip1ZHmtSJzxt7MhtCXU/xpIC25LcP1mh1gmZa/h0I2Pbv/JV2R22I9cmvlEK912KtSYIfDidiWqG219NO9J0WjZb7PmxwrEEuXSOVGvTnTWdL311zqoXjC4EjiUQgtQm37gyLrYCBvMOAmj4kTHySNS6rtPf/o4bF0Er8w3XygrnmgGHR4p8/xMH1hVjcPWalXdWW/TbUjJccJkc8FZ16W90DdyoOOzRm+t6x/6Bm5rrmhzwcKS5ZiuZECrdj+BwpUBaFnGieGBqc3O3GRkZd55dXZBdvHiRv/f3/h5f+MIXyOVu71zIjfiH//Af8rM/+7P9vzcaDQ4ePHgHjyjjbmcrwaQ3Wgw0/ZgjI0WafozWmnLOZqjgstD0cQoOLT9mvJKjnLNvuHjYSD65GbmOZ0s+/OgUp2ZbnJ5vAlDrRBweKTBW9ji32KYbJhQ9iyiWDFYcppfbfPLp89d1Jyu4u0/fZlmApi8R3Cl6znc6TFZFEyhlBkyiRPVNBHZ7EbaSe7hB1n+N7LSbrYW4mje1rb/JOB4KpYk2eb6jRJN3NEGc0A4SYqXRGl6/0qAVKCYGPPxYsW8wR8uPaYcJApFKEs38ox8l1DDXpcG8y1//lkN85wMT6wquzWwEwc2HL7fDmMVWwFI7MPEeqUV/lCjmmz51P2Sk6G1aYni94nCjmbOt8N0PTPIvy28x2+gylLeJlUBphRQSW2qq3ZipgTzf/cDkln92RkbGnWFXF2TPPfcc8/PzPPbYY/2vJUnCV77yFX7913+dz33uc4RhSK1WW9Ulm5ubY3LSXIgmJyf5xje+sern9lwYVz5mrTPj3NwclUplw+4YgOd5eJ53y88xI2OzbGVIfP9gnkcODvDaTJ2XLtU4Nlqk4Nn9xcBIyeX7nzjAl07O9xcLR0aNYcd0tctg3uHISIFWEF938XAt+eSjBweu2aHTWjPfCPCjhMG80w9JfWOmwWdemcEPEy5Xu3SCmFLOoR3EFDybh6YGGCo4qwrPjRYzE6XdtXkDoBWIHeqO9TDyRPOHnGMhtMaPEqQUvHCxSskzhghCCjyR5kitbmLsShxpntsONIV2PYk2XTKZZuA1/Bgn7YBut+Q0Uia+oveJMoHvrHPxY8X3NdAOEixpCqy8I40MWgjmGl3mWz5hbAx/yp7NXDNASmj7aQdNCFTarXVti0RrLix1thyUvJYT42WObUJWuJKCY7HYCugEZjNqVQh10YRQo83jtnIcN1Mc3gjXtfiJv3CU//OP32ShFWH1KnRtXq+8a/Pj33oU17VuOG+ckZGxO9jVBdkHPvABXnnllVVf+7Ef+zEeeOABfu7nfo6DBw/iOA5f/OIX+chHPgLAm2++yfT0NE899RQATz31FP/sn/0z5ufnGR8fB+ALX/gClUqFhx56qP+YP/qjP1r1e77whS/0f0ZGxm5gs0Pib8w0+B8vXuaVy3UWWz6NbsxCM2C05DFa8lYtBg6PFPqLhSBOODhcYDxWeLak3o3wo/XSod4N/o3ZBp95aYYg3QHvySdfuVznzdkGUgrOLLR4ZP8AMt3dX24HnJ5rcWG5Qzln85+fmebZc1U+9PAE3/32SY6NFfndZy/y+kwDSwqCWDFeyXF8rMRw0QW4oTvZYPq43UTc8wbfQRSm8NOAVnH/1wmM5C9ONAXXxAlIKXGk6aYBO7LA3y5yjpHEhd17M9RWpMVY0e1JA6ETJmz3G0pwtRPZcwaN4oTkGl1dzzYmI5EyoemjRQfbshguucSJcUrtBjFBrGj7Zh4171g0/BjPNg6LUZrrt28gT8mzmG8GPHN2iUvVTj/o+WbZbEetR69vrLlWsWK+t9WzfjPF4WZ46vgID0yWeO1Kw8z6KkCAa0semCzx1PGRTc0bZ2Rk7A52dUFWLpd5+OGHV32tWCwyMjLS//qP//iP87M/+7MMDw9TqVT46Z/+aZ566imefPJJAL77u7+bhx56iB/6oR/iX/yLf8Hs7Cw///M/z0/91E/1O1w/+ZM/ya//+q/zD/7BP+Bv/I2/wZe+9CV+93d/l8985jO39wlnZFyHzQyJB7Hivzw7zaXlbj/sWWuI01XV9z22n/ceH+0vBjZaLExVcsyk7l9rFw+9G/zp+SavzTRo+TGHhwuMld2+UUi9E3JhudM3sJip+Ty8v0LOsXjuQpVaN2Kw4PL4oSFyjlw3//ZXH9vPW/NNJit58o5FObdacnk9dzKlNLXujY0/7lZ6i8XeHJAFaAHDRQc/UtS7MSCIE41rX11cOpYkCndYT3mTDBccEg21e7QgixWUPZt9g3kkmks1H7UDOQUrf6IUxvinZ1u1Vt4qAKUgZ1vEYcJQ3iHv2hQ8h3fsHwTg9HyL+UaXhp+w2A44PlYi71pcqXXJu6bLFCWKnGPhpgHqAwWHpVbI2cX2LRdkW6UbJYyWXISgb5Pfs9hv+TGlnM1I0aUbbf1zstXi8Eb05Otj5Rw/9tQgr820aAQRFc/hockSr8+1+PU/PY1KTKj2yg2zWw2lzsjI2Bl2dUG2GX7t134NKSUf+chHCIKAD33oQ/y7f/fv+t+3LIs//MM/5KMf/ShPPfUUxWKRH/mRH+GXfumX+o85evQon/nMZ/jYxz7Gv/k3/4YDBw7wH/7Df8gs7zN2FTeaC7tS86l2QubqPp4tKac5PlGiaHYjzi22+dLJed57fHTVz91osbDy772Q1JUdsXLOQgCDBYeFVkArjDk6WuTcYptOOvcVJprDQwVmGj7PT9cACGPFsZEikwO5fg7WSgezY6Mlyp7DcMGj4FpbcifrFYtfeG32Fs/03UOCyY4quhb7BvJcqXVMtylR6RySSSXzbEE7vMMHuwESyLk2A3mHSzX/Th/ObWVl/8SPEy7XfJppyHGyg91MiflcJsq4J4aJWleMCWFk0p20OBFCMF7JcWK83O9kv/vIENVOkdevNCjnzWaS0qC0JowVQaxQ2vyuSzWfvGNR8DYvB9xuiq6dqghcZuoB1U5IK4ixpWS8kmOy4gHiplwRt5uVBiQvXmr2DUhqHXOd19psTOVdi2OjRfKuJIjVOsfIa8m+MzIybj93/sqyRb785S+v+nsul+M3fuM3+I3f+I1r/pvDhw+vkySu5f3vfz8vvPDCdhxiRsaOcKMhcc+W1NohthSMlLzVMxAlMwOxVTnQ6fkmf/zqLC9fqvHmXJNuqDg2WsC2PBKlqeQdSh4stUNevFgDTAHXSbt1Apgse3SihHYQ8/D+Cs1uzBuzzWs6mN2MO9lK98lSbs9d1nYUjckjmmuGgJEuCiGIlLFPF+hdG7ws0pkmvQMdod2O5mrgbzdSiE6IZ1tEqTNqz7pmu146gZlX640WtsOEjcqjfrA4mkT3Xh+VvkarXyc/UgwWHD78yD6u1Lq8fKkOCFqBcQAteTZeOjvWCiJq3ZCJSo6jo7e3OwarN7yeODxIK0gIE1PElDyL0wvtm3ZF3G42MiCJE8FMvYsfKUATJZqca/HWXIuTs00qeYeCa2/oGJmRkXHnyVYuGRl7iOsNiQ8WHL55fpmRsruh6cdW5UCn55v86z85xVuzTfw4YbkdYgnBW3MtZhsBIjWG8GyJZ0tm635qay2wpCDnSPKuxWI7JE4UDT/m/GIbpbmug9lW3cnWuk+eW2ztyLnfywSxJopjbEvg2hbDRZdaJ6TpKyxLMFpyafi7TxKYaLhS62Lfo8nQK90lO5FCCih4NkGUGBMXSxDGW8sluxY6/X2WIC3Ujdx1o6EplTqGSkzRWPcT3pxtstSOePLYMMCqWdH/+dYCx8dK/PVvOcil5Q5nFlsmEy8NjTYIEqUYKjjsH8jfdjOKlded0wttpgZyDBYcumHC6YX2LbkibjdrDUiCWLHQDOhGCVGs+vOgC80QS4DnWCRKk7PlTTlGZmRk7DxZQZaRsce41pD4V04toAWI6wylbxalNJ9+ZpqXLtZwLUHesXAsiWMJolix1Aoo5mykiHCLrnHBS4ztcsmz8GNF0bUpezZ4MFPv0gpiPEdycKhAGCv8KMFKg1vXOphtxZ1srfukLe78gmm3oYyhHVGiUTrBloLhgottSUquxYHBAmcXd19+m6S3+N+lLbzbiMAUZe0w7HcOk9g4I6LXz3ndDIqrRaBtmTmx6x1POWebjpkwBjEz9S7PnFsGoN6JKHk2x8ZKOJbk1SsNTi20GCg4TA3kaXQjwhWtWSlgYiDHYMHl6+eWeOli/babUeyUK+J20zMgCRNtHGnDhIYf9R0xV74XlIYgSmh0YazkbXi9zcjIuPNkBVlGxh5ko7mvo6NFBvMutU7EREWuk/rVOxEDeXdTcqBL1Q5fP7uEJWCk5BHGCikEUgjyrk07iAkiRSUnWG6H/XwrKUQaGCsZLrorZJPG3S+KFZerXfxYobR5fM6WxhlwjYPZZt3J1rpPztTvrVmjzSC5al+uEiP7HCm6vPPgAO0guSmjgtuBBoaKDvsH83z9XPVOH84dRZMWqOlfRCovXGlLb6eP245XM4xXyhPBksZVsVdCDeYtHMciTjRjJZdWELPcCTm/2Kacsyk4NgrNqfkmtpQM5R2CRNEOYp46NsK5xTbzzYBIKRwpmah4HBktcrna5dPfmEZr46h6u80odsoVcTvpRgkFVzJTTwhjhSPFqvdB748yjUxIlPk3sdKYCb+bc4zMyMjYObKCLCPjLuHgUIEnjw7zhTfmWGqHlFe4hDX9GKU1Tx0b5uDQjWcGzi62qXeivvzRtSV5R9IOE/KOxHMk3TDhwFDeSHrmW1hpCHHJM2YcGk0QJTiWwI+0cW1LFN1IkXctcpYkSl0RXVuyz5XrCoPNuJOtdZ+crrZv6TzejfQW1mAW1hMV4zB7aq5FO0g4NHLn52J6rA09Pjpa5NEDg/d8QQZXiy/N6kJMAMXUuMGxBN3NJjvfgN57RgFuGlKmEo0EbEsSpp3wSt6hkndwbcnFagdHClxbrDIWWmgFKG3mSxOlec/RYZqpHb5rSco5m6YfsdgycuhHDwz2N3TKOYeSZ98wg3C72G5XxO2m4Fh00mtx0bVWOctKcbXL2ZtBFBi3zqYf4UfJLTlGZmRk7AxZQZaRcZcgpeCvP3mI+VbAW3NNmitmgiwpePTgID/wLYc2vZBZKX8UQjBc9AgTn25kumVaGxe2gbzknQcHmWv6LLdC2mFMOzSD5SCQEgqujSUleVvi2hbdyLisCSEYKjgoremGyU1JaNaagFxPZnWvoiG1FjcLNIAgVsw3fBRQbu8e6ZJe8+c3Z5smxDrjmkgBQwUPBDS6EX5kPvsC01W7mUkhS5guXE9VGCuNtaLr7seanGOt6oTbUvSLgXXGQkXJYtMnTsxs2ORAjkr+qouq1pqzi21AcGy0uOEc7I0yCO8VepJFx7IYKzlorWkFZq5Q6KtFWe9S35sJjBLF/qHCrnKMzMjIMGSfxoyMu4gT42V+5oP38cevzPLK5TqdKKbg2DxyYIAPPTy5aanPRvLHvGsxWcmx3A5Y7kRYwhgtvGP/IA8fqPB7z19mproImIV/f39dgx/GeLYk59hMVDyiRJNos8BzLMF8M7xpCc1aE5DdIyzaPVjChPf26prFZtCXidpS4O3CWZJ+d0aboizj2kgJ7zw0yJGRAi9fqvP06UUsy7y2Qaw2bcW40rlRSoFAY0tTAFRyDt0oRqRNlbwjmajk+pliWmsafozEmPZsVFDlXJtOpCi41oaGPSXPRmsoeuvjLuD6GYT3Er3MtDBOOLvU6c/i9eSqkp77pSnKbEtgCbMpd2KstKscIzMyMgxZQZaRcZdxYrzM3/6OW5uBuJb8UUpwbclAzubJYyN872P7eflind9//jIvXKzRjhI8SzBUdMm7Nkob2WInTJBCUM45VDsRpZyRGUaJYrkT4dmSvCs5t9hm/0D+msHU13vOvWH8Z88u3uopvOvQGvy0GlMaEmWMPUBjS8Fuc5VfeTi1TsguGt/ZlSgFsVJIKZkcyOGmc5lxz8N+E/TMQfph4VKQIJDprGclZ3N4pMBCM2CxHeLZ5nqgtO7LosEUU1qbDvraOdYgUgzkHT7yxAHOzLXXGWe848AAv/f85b78eC3XyiC81yi6Nq4tSbR5wWwpsMXVQPheISZMYBxaKyp5h6Jrc2q+zUhp9zhGZmRkGO7tq1pGRsaGXE/+aFuSx48M8Jce3cfnXjN28yXPImdLpioe1U7EcjukojQF12ZyIE/Zs3h9psnBoTx1P+6HriZKkySaEGiFMb/9lbN84n+ew3Mkri235K7WG8b/xP88s8NnZ++hMFImwdVB/1iZHCkdK+YawZ0+xA252iXbDg/Buxe1IgJMaSi4Fp0oNc6RYvXA2TWwU9OO3mJeAyMFB88xgc6PHx6i5Fq8cqWOa0ssIdbJoh/aV6EbmoiMXiZgb4as5cfYtuTgUJ63Tw3w3Q9Orts0Anj5Yn1LGYT3IlOVHEGk6IQJx0YLxAqqnZCZhk8SazTg2YJKzmGpHaKBMFY8c26ZIyNFvv+JA7vGMTIjI8OQFWQZGXcZp+ebfdtmP07wLMlY2eOJo8M8OFlhqpJb1YFa+/deR+p68sfvemiSL7x+NftrqR2SKM1waqs83wwYLLi86+Ag5ZxNrRNxcq7FYjvgyaPDtEPFQsvn1FyLSCck2tjfL7Z8at2YwbzD44eHyTlyS+5qUgqi+N6WM10LCViWuNptSnRfyebs0pgvY6ku8GxJFGQGBGtZVaYKU7Q0uxFFz0HpiMGcRTNQJCq+bk0mMYWYJQWuFExUPFzLIlaKRGkGCg7nFlvM1gNsS7B/MI8ljMRYStZdF75+dolYKaqdiFYQY0tzDbKl5LFDQ/1rzEZzYFvJILxXmWn4eI5kMO9Q68aUcjbjlRxSCmbqPlGsCBNNrRtTztkcGy0xWvawhCBWmi+dnOfwSCEryjIydhFZQZaRcRdxer7JJ58+z3I7ZGoghx9J3pxt8sy5Zf74tVn2D+YRCJpBRJxobEtQ9hyENLviBdfmHfsH+J503uxa8se12V+uJbEt2Q+KHiq6+JGi7ke8NddirunTCWNmG5rPvz7P2/dXWGyG+FGCbUnKjmVylRQcGspT7UScX2rzxOEh7hsvbcldzbEk/WTUDMAUXJYlkRgzD0uYvwsNSinquzAUuofWetdJKncbxrBBc2q+heda7BvMMVuHWjfsu7j0LfPX4Fr0Iy0QMF7J8dSxEYIo4dUrDYI4ZqEZIBBMDea4f6JCzpFcrnaNc+uJUR7eV+Gxg0PYqYzxSr3LUivgwFC+777a9GNGSt4NC6q9kgV2J2mHMa4tefzwMOcW233FAcBowem71zpSkLclS52Qmh/14wc6UXJb3CozMjI2T1aQZWTsQeJY8fzFKkvtkJGiy2MHh5BS8LlXTdfqxFiRS7Uur1yuE8WKsZLLcifi5Ut1OmGySvwlMDlhY2UX17Y4t9Dm5GyTn/ngfZwYL2+4k702+6ucsxkquCw0fdyii2NJltshr1yuo5UmVnB8tMTUQI7XZho8e75KGCeUcw4TlRwTZY83ZpuUcjZSSko5m+V2SNOPqeSdLbmr5e1sgbEWKcFJZWg9FzyURqONA9suczHsSSsTbTpk0T0aDG2lGQAbPX2bqwYOlhR0woRvPTHEiYkSv/O1C1gydUkVepW7pi3MpkUlb+OHMQqBJQVHRouUXAstBJeqXQquxV96xxQXlzvMNHxOjJWo5B2EECy3AxrdiAvLHaaXO7wyVeHZc9W+tHhlQdUJYzzb4pEDg5suqPZCFtidpBf1kXMkTxweZKbuM98MuFTt4DoWeSloBQkIqPkJuUQzNZDHtgQLrQBLCp6frt7zbpUZGbuJrCDLyNhjfPGNOT719HnOL7WJEhPCfGSkyF96dIozCy3yjuSbF6qcmm/RDRNyjiRRmmonTO3or87m9EZP/FjR8GOOjLi0/JiXLtb49DPT/PyHH1q3CFJK0+hGBFHCfMNnMu2SnRgvmXDYdohrSdpBTJJIcq5NJW9z30SZ4aLLgaE8Xzu7xGIz4L3HRhgquiy1Q2KlcCxzSXIsSSswGUWwNXc1x7HYnmjcu4cghlgoM/sjjZ25SjtPAnAsCHfJKesVY7pvUKAR9/BCvDfbtQ4JJVvi2BaDBZe/+4ETvOfICJeqHRZbAXGiOT5WpBUknF1sIdE4tjRGHwImK8b8Y3q5g2NJ/uI7pvDDmNeuNGlHJhW66cdUuxFvmyj3TTaW2wEvXqzRDRMGCw6JMp32tdLiWy2odnsW2J2kF/Xx9XNLxLGi2gmZbwWEsWIgZ5No8/m2paCcs+lGilo3Yt9AjuGiy1Ir4OJyh6Yf3fiXZWRk3BaygiwjYw/xxTfm+OXPnqTpR4wU3f58xVvzTX79T9tUcuYj3fLjNKTZNtlEftQvxmxhMsZ6i7yelKnZjdFaM1LymGv4PHN2iUvVDodGiv3f35tPOz3f5GK1yxszTQ4N55kazFNwbU6MFZmpdzk938aPEoquzXjZ48S4KcYApJQcHS0y1wj6WWSuJbGlGf73UvdFW0pcyww3bcVdLYrvzW7KjUg0dCOFwFiWI6ATmr/Hu6QYg6sFWaxNh0gKkZo77K4u3lbpFb5Rsrln4li9HECBFOpqZxNzXiYHcjhSECv4jvvHeM+REaTsRUcINKIf6i6FQPXmBNOayI8SFluh6aQQ8x+fPkfRc3h4f4X7Jsp0wpg3ZhtML3UYL3sm7F1rzsy36YYJw0UXjTGTcG2L+wby66TFWUG1M0gpeGCqzO+9eJmmH5nrvtY4lqTWjfuFr2Nb/fdAN0wIY4XnWHiORdOP+zLHjIyMO88uHeXOyMhYSRwrvnpmgX/5uZMstwIODuZwLUkYK1xLcnAwT8uPObvQppra1IORMhlb8xU70yvWtivjwhRQ7cQIIRgoONS6URrUaujNp716pc5Q0eWJw8N4tuTVKw2++PocX3lrnm9eWOatuRYa0+WyLZGGlkYstozMSSlNzraQUnB2sYVSinLOZrhgunNKGUe24aJLOWf33dVOjJdu6K6mlGa5nS0yrofGWKT74dXsot1Uwop0s8CxBPsG8+wfypO39/6tyuwtCAqu7Heor9cvsqXJ83Mk5B0L24x4YafRE1IIHNtaF/jey6jqyX79yEQcCCBMjIW9AGYbAbVuhOeYzRClBYlSnJpvUe+GlHMOx0eL+HHCC9M16t2Qhh+x3An7OWMrN07WBjdn7BxKaU7ONJmq5Dg2WiRONEGs04Bwh4JzNfZAp3mPWpvsx178QMG1KOWyPfmMjN1C9mnMyNjl9CSKJ2cbLLVChIBXLjdwbWOkobVGaYiShG6k0anjYRQb22vHkii1ogWyQTRRr0aLlUJrTRhpwkSx0AxQ6dZ8bz7tvvFSuhgzjmuOJfGjhGon6svMCo5E2hI/Vrw11+LkbJNK3sGSgjjRpiCIFGGs+OPX5jg2VmSg4DDb6DK93GWw6HJ4pEAriDftrtbr3nV3U3WxS9kt8sSNEAJKrkXeszE+E4L7J8p8/Xz1Th/aNbEwc3qxWv3Z6gX0FlyLoZJL0bURKM4sdogSIxm1WF0Uu+lnquCaTnGv0z1UdNBK040UlZzN2ybKPHpwcF3ge9G1GS15jJZcZuoB8w0fjSnizGdfEMQJCMVwwcWzJQutkMGiQ9kzRdyZhTYaODPXIko055baBFFCOe+kGWE5s9Hix4xXcv0NoOtJi5XS2UzYNtEzVbpvokTJs7lS6/LNC1XyrkXZs2kGMa0gQWC64lZ6npNEsxyG/fiB8jUCuDMyMm4/WUGWkbGLWSlR9GyZzv6Yne5YJRQ9s0vd2wkFs7ibb4WrnOlWLnt0/3/Sv68o0GwhuFztsNyJEAL+4MXLzDV83nlwcJWrYk+6FMSKgiOJE0U3SpDChNR2VxiHCMB1JFYQE8bmcSAouhKl4FI15FK1Q9G1sS1BzrEYLjg0uhF+mHBwOM8TR4bxbAul9IaLuF73bqkVbtepv+vZrSJAKaDoSqRl5HdBknCp2rnTh7UhEtPJA5Pr1utgaW0kl7k0T09rSJTGtSW1ToItJZCAFmlQt+5LNV1bMpB3KHk2UaIIooSRssfbxss0/Yica/Phd0zx4FRlw6KmN1/06pU6TxwepOnHvHCxRq0TMlp0mG2EtEOYGshR8mxmGwG2FBRdI28r5WxmUpfERGnGyi6z9QA/UjT8Lu0wIk4Uecem4NkcHyv188JWSotXFmALzYCXLtY4u9DGj5Mt5QtmrGelqZIQppM8VfNZaPrgQdE1hh+OJbEE1P0Yz5YordfFD2RkZOwOsoIsI2OXEseKTz19nqYfcWjISBKvYBbSVjro3w5is/spzO48QBCvX2av/IpKg1+lXi1VswTUuxHdSKExN/XLtS5/8OIVvnFuGYB96Q286cfMNLq0gxilzSKyEyZ9t/nUHK6/8+9HpiPW+54tSWfGNKSmDcMll0f2D/QXnY8dGuRy1Weh4fP7L1zmj+3ZDRdxSmk+9+oc08sd4mx+bNPsxmIMzMzYUjcm75h5xqVWkBbxdx4L8/5V6X+ODUN5h1hBM4iQQmAJU2AlscZ1LEquRTtMqHcjcrbF1IBHrWs2PO4fL1LrJsw1fRxL4kpohaaj8cThIc4udphv+iy1QpoDMY8eHLqhU6GUop/ldXqhzdRAjrfvq/DchSqX6gF5x6IsbRzbotqJKKQbIbEy1xVbSurdyGQUDuQIE0XeiWgGEZ0wMbOI9YCKF/P4yFB/NnRlcHM3ivn4l89wZqHFYmog4ViSh/dXODZaohPGW8oXzFhNz2XRdCud9aZKtqSUc7CEoBVEjJc9HpgsU/TsTccPZGRk3F6ygiwjY5fy/MUq55fajBRdpJQ4aahvgtmBF6ktuF4R8LtZhDALN53o/sLckoJ2mCCFoOjZHBwyNsnNbsSpuWZ/N7ySdwjihHonQmtN3rXxo6QfPOukxaLiejMymlaY9DPL2kHCcjuk5NnsG8zzwsUa//35y0wN5NiXGoZ0wphXLtd5a67Jhx+d4sHJSj8T7YWLVRaavnGQy9jTlFxjOlD3Y67UulhCMFp0uVwP7vShrfPujBJoBQmuLRGpfbzWJubBElD2LBwpcQsW7TBKC5wEzxZ4tkvNT7AtM+fpWpIwUeQcywQ02xbvPjJEtRNyfqnND7znEN9239h1F9G9rlSsNN/z8GS/KxXECQeHC4xHCoXm/GKbdhAzkc4gnVlo9yMrOmFMnOi+DHGu7lP3Y4TWVPLGVbETxnSjhG+er5IkmomBfLrQd7l/ssx//OoFltshkxWPK7Vu2gU082lFz2a46FHy7C3lC2ZcZWUXtOSZeb7hoss7Dw5yer7JhaUO5ZzNgaE8YZoNKYQgUWwpfiAjI+P2kRVkGRm7lKV2SJQo8q4FgBYCz7YI0tmwXiV1o2KsZ4fQK5BGiw7NICbRmp5XgnHhMj8p5wgmyi5Fz1we3JIkqndpdiPOLrZ4ZP8A1U5IECs828ywBWlnyvgWCHpl3kblkcB084RSfZmU55gOW7UTUc45VNshc2n2UW/BESWKeifkwnKHk7MNHpqqcHy8xGDB4dRcE601kwN5WGhv8Fsz9gojJY+Bgku+FTLT8JkYyDGySwqytQggiBOidPYySsyMpOkaG5OMRjc2smKleTGo4VhGwlhwBVGsCEnljomi6NoMFsyGR5gYB1LHkoyVchwfu37R0puhPLPQ6ssCj40W+b7H9jNa9kzHq5LjSr3L//Xn5zm3ZD7LUprFeiuIWWqHdMK438FeapksQAEMFd2+XFmlGXbtIOHr55aZKHscGS3x1x7fz8mZZn/WtOnH1LoRQ0UX1xL9+bShgrvOBCRzZNw8K7ugp+aNlDzvWjiWYCDv8O6jw31Z61Qlx0w615fN7mVk7F6ygiwjY5cykgYsd8OEck5iCTNfZVvCWBhvthskjGMbmJ37H/yWg5yYrPDKpQavXa6TdwR+rPnmhSpeWqHVuhE5xyKf2swXPJtWEFDrRPzxa3N0w5ggTghiMw9jpS5uGowM8RpIwLLSRSv0A2sTpVFaM9/0mV7ucG6xRaLgmfPLHKznGSt7nFtsr8o+6oQx/+PFK1Q7IY1ulM5I3MoZz9gN9KIQPNe8F3OOKdpzFvi7Q7kImPdyzrEIY5W+f032U09SXM7ZtAJjNa4B15IUXQtLQDtSWBIsYbpqhcE8fpQwXvaIlCZWxrlQKcWZhRZHR4uotBC60QxlJWdTyTkopXn1SoOZhs+Pve9Iv+A5NFLkf3/qEJ98+nxf0ljJ29w3XuTVKw0sKbEtTTuMybuWkS+nmyJgXp9IGXfXUs4mThTHx0vkHIv/9+UZ2kHMoeECQgjCRBEnCid1ZVwb+L6VfMGM1awN4J5r+NcM4M6K3YyM3U9WkGVk7FIeOzjEkZEib803KboWri3JOxa1brIlaZ7Z7TZyFTOvJfmLD+/jwmKXct7hvvESF5Y7WNJ04Cwp6EYJy52IIaDaNvMj3TDh0nIHhMC1jMQqUppEaaTQm55J0kr3B8y6YUIQJencm+ali1USbeyapYBGN+LNIOatuSZFz2aykkMDcw2fN2abNLvmuExXwix+M/Y2rTBhAtDKyGhV6k6zm5whLWE2Ex6YLDHXCFJ7+asxAlrBUitCc1W2G4sES5riBg3VTsTx0SLVTkTJs3AsyXLHGGZMDeapdUOePt3ouzH+mz85takZyvNLbWJl7OiH8g7tMF4nC7zWYv6vPLqPd+wf5Esn5zm31KLgSM4ttvvGJUppOmFiiqu0g97wNQXP5sRYieenqyw0A+6fMMfnWsYJNko0nm26fe0Vge9byRfMWM+J8TJHvq3I8xerLLVDRooujx0cwr4LYiIyMu41sqtgRsYuxbYlP/q+I/zyZ08yXe2aWTKh0yDXzaO0MdVQmF39K9UuXz2zuMo1cSjvkHMs/FhRdI0zXMuP6IYmYDpONEKYWZdCunis5B2WOyF+EPdDp2+ExrjP9eikQcWWNJ2CMDadhp5Nd84xlvq9RSCY0Otqau3v2pK8Z5P4EULKvkV/xt4ljBOUUgSJYiDvEEYJyrXuWFaa5GpQtWMZYw+FKRTLOYcnj47w8uU63zxfJdYKlYC0BCpZLdvtBXMHcYRtCVQC882QomfRCROOjhQ5t9RGKdNBfnG6hmNL3nVogH2DhWsaYaydoSzlbBzLODQutAIsKXh+urpOFnhivMyx95dWWdH35G3vTUaodkJm6l00mm4QY1sWoTIB1SXP5AgGsTEgKaRdzKmBHBeWOiw0A6YG85RzNkMFtz+fFiUKK80tW2kCkrn93Ryn55v88auzvHK5TieMKbg239i/zPesiULIyMjY/WQFWUbGLuYDD04A8Kmnz3N+qc1M3QfMrntqnH1DYnXV9bCcs6l2Ij79jWlaftx3TazkHfYP5o0sMFJGKhkZg4+eSYFA48cKx5Yk2gSLxokm0eDZgu4G7o5r2egRJv9M94+zp8hSaaCpZ5sFa9OPuLDUphUkdKOkX8jZUvfd7aQjibZYsGbsLlxL9rPo7p8sc2a+xXT1zgYNC5FmjQGRMjOSAnhzttk3zfDT7mzRlVhS0kzide93897WhLHZTBgsOHSjhEY3IlZqXYeqN+MFUM45GxphNP2I6aUOiVKMlLz+xoVnW7hFyVLqctj0o+s+x/NLbf7Hi5f71vTVdshsvUuUaLqhwrESHMvCTjvpSmm6YcJIyWVqIAfAWNmj4FpcqXeZTDd7eu5/S+2w3/0Dzan51qbyBTM25vR8k3/9J6d4a7ZJoo3CAATnFtqcnG3yMx+8LyvKMjL2EFlBlpGxy/nAgxN8+31j/M4z5/k/P/smjqWpeDbdSNHYZPGhMTKrUs7YHsda0wli2oGZ5RBC8MiBQTphwmIroOlHhLGRIkohyDkSmZpvtMMEIYz1fd4xczGLrZDtMFLvLcusNMupHSVYCNDGzr/WjXBtCylMMdYJE1TqOKkAS2cdsr2OHyWMlByGCw4CM/8yHiuWmwHRHXh5FVflk2Y+UiPEiqiHJEoXxMZhNFYQqaTf0VuZ96YBx5KEsSKKFcdHi/ixYqkV8L2P7ed7HppipuHTeDFKTTzWS89Kns1zF5b55oUhnjg8TCswjofl3NU5rx7GMMei6ce0gtVzWitNQNZa0w/mXc7Mt/BjxXDBoRUkxEqjtck8bAfmeXqOxTsPDvWP048UB4cLFF27bzaxcj5NKSN1rndj3rF/IHP7u0mU0nz6mWleuljDtQTlvINjSaJE0exGvHSxxqefmebnP/wQSulM0piRsQfICrKMjGJDr3IAAH/gSURBVD2AbZtddw2UPRvbtnA0iCDZVBlUcAT7BguUcjYLrcDY52s4u9ji0QODfdvkA0M55mod/DSLLNEghaboWQSRQinIOxatIMKPFOPlIpWcTcuP8NP13kpXx7UIjPucIyV+nCAB2xIEkSYBbMssWBOlSbQmSTR+EvdljjK1Eu+m1tzJypWu1v0ctIy9y/6hPIeHi2jg8SPDPLyvwjv3D/JT//fX+MKp+m09ll4xZTq3Gp1+boQmDWQ3kr3+46XAkhCmb9i14dta6X6YmVKa12caNPyYcs7mi6/Pc2Gxy30TpX7o70qW2wFn5tsstgMa3Yjf/LOzPHuoyvHxInnXfD5Lnl5VlGmtCSLTzS7lroY1vzHb4DMvzRDEiqmB1db0b801sdPP4OHhAtVOlEZTxNQ6EUGc0A4Tpip53nV4iKOjxf7vmqn7PHZoiA8+OMEXXl8/n/bowcFVjo8zDZ+Ts43M/W+LXKp2+PrZJSzB+q5oSTLX8Hnm7BL/zzen+aOXZzm/1CZKjPLhyEiRH33fkb76IiMjY3eQFWQZGXsE4zYHWpubryVEv7C6HmYGRiBTqVFPxpRoKKzYyZ5r+Dx/oYYfK3KOKZJkGjg93whxLIEmwbMkUhhJYTswWVHNFZ26lTXR2gVpzjaFn+dYXKp2+7Njtp2QxFefmzH2AEsoCo5NksSkniS0gwSlWNWBkAKziEzUDc9Hxu6m4JqOzoXlDtPLHV6ZqvDseJX2bVKi9uS9ecditOyw3AppR4owMZ8lW5pNg1gZ2SJCpDWWRmthCra0rlj7VtQYq3swmwmzjYCJgRyPHxoi50hevVLnrfkmYaz6ob9girEXL9bohibzbCDvMFx0+o8fKbost0OT5Zez+92Slh9j25KDQ3mWWyEff+MMp+ebvDbToOXHHB4ukHfkKmt643qYcGAwj5TGSdGPFN95/zjtMOHicocLS22OjOYZLbnEStENE2bqfl+CeGK8zInx1fNpKwuu0/NNfvMrZ1dZ9G9kWJKxMWcX29Q7ESNld8Ou6EDB4dJyl3/7xdMorRkpuuRdi26Y8NZ8k1/+7EmArCjLyNhFZAVZRsYe4YkjQ5Q8h1YQ41gC25Jm4XeDAsS1BUGsmV7qADCYd5BS0PZjHj88iB8pTs+3eObsMt0oIedYJNrkDPU6UwKzA25bkiBWaDRKwWzdRwOeLUmUYq3J4boZmrR91isQtTY/1xISgQmt7YRXHRv9WBPEJgfp/9/en8fJdVYH/v/nuWvt1fsiqbXL+4ZXjCGY4NgkDoGE34SQhC8wgTAM5DeETELynQTCZDIQwoRJJkxCgGAmmUCSAcIEiMEYbGIwGCwbLGPLkqzNknrvrr3u+nz/uFWlbnW31C231FrO+/USuLtvVd2qW9V9zz3nOSeXssi7JrP1YEEwloxl06gkUjyNV/fisoy3zZowDag0Airq+HgDy1TsOlrie/vPfHbMIAmmbMtgfXea7qzDQD6Z3XV01qOYsdjam6HqxxwrNUnZyTiKuhfgG6CUxjIMAn38FTZoldOSZIOBVjkwbOnLcNX6LnqyDgBZx+QHz81S8ZLZZdesL6KUYt94MvKhO2MzUw8YKKQYLiYZtHZGqz+fIopjZuoBVS/EMgz68y6WYbCxJ8OXnzjW6eiogK6MzUTVY7Lm4YVxp+QxZZtM1fxOd8t2Z8RQw/ruDIPFFK5tsrU/y2w96GTATixBNAy1aLv1dov+6ZrPcDFFxkkv2bBELE0rUCyRUdTJXLlIay4ZyM1Zh5iUmB+aafCpbx/gpTv6pXxRiHOEBGRCnAfiOFnLdc2GAg8/O025GeKYBtEySvQMBZZt4AUxR2cbVJsBM/WAWGu+/MNj3H7pAFevL/KveyZwLQPDUDiGSTPwO9mmpPEGpG0DTUwQ6k7GKusYuJZJ1deEJwmGkiG6msmqT8qJsA3VauahWw08IIoXyyok/2rNELQm41g0Ax/TSDJp7e3jmNYV+HMx1Di3mGr+a1dwzWWvRzxTknJUC42iN+uggZm6j2OZ7Cim+Zddq/+YpmoNKm+9EKYBadtiqJiiuxUkGYaBa1ko5bXKhS3SOsI2jc6FBdu2sOOwk6V1TTPJ6rY7hpqKwUKKrGMyUfEIYs1g3uVFW3uxrGTwe7skcazSpNwMcC2fY7NNtvRnmax5OJbBTD0g7Vhs6891MiPrutIcmq6TdSy8MGJDdxrTUESxptIM6c26nTb7W3sz/ODILJNVn660RU/WYbziJWvhwhjXNnEtAwX4UUwa5nVGhKRVfV/O5U23bcFQakUDh9st+tuDo9vPYamGJWJxW/qydKUdZusBgwVjQZnqeKWJ1pq+rLNgHaJhGPRmHfZP1th5eIabt/Se7d0XQixCAjIhznFzF98HkSbnmFS8kLq/sIvbYoIYrFZYU/Uiaq0TbwU8PVbh4HSDwbyblEOZioxj4QXHt4HjQVGoY2wFUXstjUpaeTeDmFM1WUxKwZJ24XUvCShNpQg0rYzcyUMpDdT9iGZr3zKuRRTFNMMYw1BkHJOsY3Kk5C3jVbm4hToJRtoBw8kC6bNBkWRuixmbfKvJjN8aOu6YyQmnbbDqawRd2yAIY1KWgWsmYxY2dKWItKLuh5hKYZuKhh+2hqsnXUeTmYBJg5uUZRBFmoxjcuX6IvsnqkxWfRzL6Lyfi2mbIIo5OO3hh0nr+Jmazz//8BjXbeyimLY7JYlZNwnQNvdmODzT4IkjJfwgpjfnMFBIsa0/18moAaSdJIi6+9ph9oxW2TdRpe6HnSHBV28o8vmdR5it+/zt3slW5gSmaj7PzTToyyX3NdM5uU+adTT8mLyblD0OFFLkXItS3WfvRJUr1hVYX0yvOLtyZLYxb9zGXO22+XvHqwta9Iv5RrozvHBLD/c9NcZUzSc/p0y10gwJIo1tGhQz9qK3Tzsm0zWfqZp/lvdcCLEUCciEOIedWN6zritZt/HD52aZqvr4jRADcC1AGdSDmFZVVCe7FccaL9KdMj9Nsg4mbSfDpht+yJ5xnygG0zCpNAO8MFmLtTDfpIl00rkt9CKUXl7rfUjKtjJ2MiQ2iGIaYYzdupJvGnCqUWaaJAMWRknwFrYWqRft5Eq/2RokLZZn7lo7b40GaltGe71WslYpipMTyTiOmakFdGVstNZorRnIwJHq6j5+EMas60rzc9evZ/9knW/tnWT/VIOolbU1VGuNo6kYzKc6J7K5lEVXxqERNCk3Q2zTIJ+ykm0sg1jDrdv66M7YHJ6q8Y1nJnj6WNKevJCyiHXyXp6q+Ty4e4KerEOsNYOFFH7rfb2xJ8sVwwUeOTDNZMXj6vVF1nWlFwQy7eHKlw8VuOOywQXrtp4Zr7B7rMwzoxVinQyJN3Syhs2LNMdKHsWMjW0Zndb067vSNIOIQzMNutI23Rmbb++b5FipiWUmZY0f/eazK17zVfPDRRuWtKUds7WGLVz05yJhGIpffOFGxqseu0crTNd8dOsCmWuZXDKUY/9EjWYQY5vmgts3/CTD2zsnsBdCrC0JyIQ4Ry1V3jPSk2VdMcX9T41RaYaYpkIpCFpZjnYg1bZYkwutk7Uh7aYa9VYcU/EWZsbm3y4pJ4t1km2LOL5GZjkc08APgs4yL6+1c+Yy76A9pDfWSVbHtRT9eQfLUBwrNWlKm8XT0i5hPFssoJi1W+uTIkwDvCAmimOma15SUhsn6wK/s3+a7oyNaaeA5qruR0/WYX0xxRNHyszWfSpeiB/GKHW8uYdSit6cw01bupmo+IStdVphHFNIW2Qcg2YQ41omYRRz7Uj3vLVUvh/xr3unSLsmG7pSuJbJsVISyCk0FS+i4oUUUhZhpDFNxYbuTGdN1xXDBb7bnGGi6nXmBrbFccy+iSpb+rKdNV8nZpYcpdg/WSeKIeMYaBSRjtFR0ogk0sm6vRs3dXFwukEcK4ppm/68y0CYdJJ84kiJMEqaQ2zoyZB3LZ44svI1X1nHImWZ8xqWzNUOLrOOnJqcyvaBPD/7gvV88qH97Juo4kcxjmmwsTvD/3PbJv7m24d4ZrxC1jHnlS3GccxUzefSwTzXj3Sv4TMQQswlv/WEOEctVd7TXmtS9iIiDVGo8QHHPJ7RmntuvdiqqliTDFcOkv92TshQLXZubgB9OYcoSk5IT7btUmbqAaY6/njtYG75FXOaQtomjGI29WVo+jF1P2K2HhBEMWnbpObLYOiVOtsFi1olAfVw0aUrEzNe9qj7IWGsqfkRadtkqOiST9lUmgFPt9rDr7ZYa/ZN1sk4ZjLPjlZjD8CxzOSCRatpDCTBznTNm7dOq9wISDkWd189zOXDhQVrqR4/MstouclQIUXaSYKQjGMyWfOT7LBKSjF1nMzZc22Dvtzx7nkZ16Iv55B1j3dETTsmx2Yb7DpSJog0WsOffm3Pop0Kd42WCMIIw6DVkOf482n/f6RhthEsaE0/mHP50H3PJLeLNfUgYs94Bcsw6E7b1PxwRWu+1nel2dafY9fREjnXWrD26VipydXri6zvWjyDJo7bO17h60+Pk0tZvHhHH6aRNFaqNEMe3D3JnVcOcqzc5NBMY16XxamaTyFl84YXbZaGHkKcQyQgE+IcNbe8J441x8oNxisez03XUaikwYDRWgekk5M6U7FgLdeJJ9tWa+hyUs6icCxjWQN3Y5ImC16oOydyi93/yWhIZjC154qRXLWvnqpescW1THoyNn6k+emr1/HQnkl2j1VoBhGWqcg6JtN1KVtcqbRtEXhnp0ysfQpe9yImqh6bWhmX0XKT2XqIaxsUXAsDlaxzqXoEkV7WiIeViqII2zEYLTfxgiQzlrINFIpsymIo7+JYBuOVpDTs3T95GU88V563TuvEjNiJpmp+crHAMZO5YGFMqRFiKYVtKfxWG/xQa7ozDoZSTFZ9NvcmM8XaTTR+9vr1/PBwiX0TVfaOV5NBzpbBCzYWWdeVWbRTYRxrdo9WO2sF4XgnSXTymW5nnW/Z0svbbt8+L7A6PF3n2cnkuYaRntdSf6LqYRqKnYdmlr3myzAUd101yNFSY15weWLbfGnocXJzqycuGcwvCGz3jFepNEN++xWX8alvH+DAVI3pmo9tGlw6mOcNL5I5ZEKcayQgE+Ic1S7v2T1aZvdohZm6TzOM0VqTsU0KaQuj1XRAqSTAWk6mKYxbreJJyrG01vPa1bdLtdprzcI5nQ/rXoxtJUFczY9a7eZXZu5jRYAfaSxgOeGAUprZRsjmvgwHp2pkXYvLhgo8M14h55jSX/E0NcIzn1V0LUUw52pBGGumqz4516In42AYiiCK2dybYbbhMz7t0QyjVidPk5RtEHirW5JaDzT1ICCMYmKSgc/NIG4NK49QSmEYSXOEqapPrOGtP7aVnYdnmKr59GYdrh/pPmmmoTfrYJsGs3UfL4gpN8N5DUMcM2l0k3MthospglgzXfOptAZGt7NGt23r47ZtfTw3U+evHzqAUnDN+uKclubzOxXGWnPfk+M8sm+qE8gaqlVe3MoG2qYijjVKwaVDSUB5eLreWYNWrgccmqoTxfHCAcSteYaHp+tUmsu/CLJ9IM+bbtvcaVS0VNt8sbTlNkd55bXr+OQbb1rR+1UIsTYkIBPiHLW+K41G8+1np9CxxrVNzFZrw7ofUfMjbCtpu93woxVlD9qBWxTH+OH8oEozf/2MZWii1iDmGLAUmK0r2M83AFKQlFwtc/tyMybtJPOYnpttEoYxYxWPmhfiBTE5d+ECdnFqq3l6dmKJrKWSctogPt5Ypr1BEGv2T9Y4rGpolTRseWasnARCrfNMUyWldtEZWOTWDDWmSrJv7SAFkvckRMk6xc6zgqOzjQUDjb+3f+akzS2uH+lmIO+y62gJtEajkvVxShNESTDUnbXpz6eYqQdkXIsgipip+4yWF2aNlFKUm0Gn9X25EXTWD+VTSVC389AMu8eSAdMjPSkefy7JoLc/98lnG6JIoxWkLZOsa/IXD+yb99xcy6DcDOjJLj6A2LWTId7VRbKrcayXHAy9fSDP1tuXHhwtTm4lzVEsy5DW9kKcByQgE+IcFcfJMOc41riW2RqAnJzAaZLyrShIThxP11LN9WJodWvUnZM3Wq3SG6GmHiyv5f6pnE6+I4gjdh0pU0wng4PzKRMvTAZmL3ZiKE5tNUMdoxWwa+isb/Ii5pW5zhVr8PXxvfBazSZsU2GopMNhFOtlZVBPV+dzoJL3utYQRpooTjo8luoBKdvkO89OEUR6RQONDUPRnbWTGXs6yToD87LL+ZTNdSNd7JuoMV5JmtM0gohrN3QtyBq1T8abgcHTxypM15NGI5Zh0JNx2NSX4dB0nYG8y/Ubu5mq+WRTFqX68c9sex1Zu2SxN+vwfx49gtbMe267R8s0/IiKGS665ssLYjKOSS41/1Ri7qiOdnB34vq2pQZHi1OT5ihCXHjk0yrEOWrn4RnGKx7riim8MKbmRURaE0er34TBWmTtWaQhiuZnPBZvhb96TBYPL02VnOgXUiZVP2a84mEbiuFWG/DerOq00xcrF63iHDKtk2OVS1lMVoPkeK7w7tuZ0/b5f3AGW0DOXV+FPh6saKAZRvi1mFhrutIOfhjPW7OznIHGz83UOTrbpDtj4QXJ3LzWQ2GbCstQVJshlqm4cVMXPzxSYmtfjjfdtpkN3ZkF95d1LPwwZuehGYJI41rJYHatNWOVJqPlBnU/4toNxdb8NoVlGNgmzO13077oknFNGmFEtRly3UjXvOe2YyDPM2NVGkG0YN5VtZlkX0a60+Td40HBiaM6lhu4iuWT5ihCXHgkIBPiHNVuBjBQSGMqRTMI2TMe0YhXP+g42fnugtb3q/7oc/bjhK9tIwkWDTOZ7WSZJq4JXhi1BlMnJyJpx2SokOq0TBcrs5pzoV1bkbLMzjy8lTI43ilU62SN4Zk0997nrq9SCpp+RD5ts2Mwh6HUonPATjXQ+NnJGqV6wHBXGtc0kuHsfki5EaJ1jGUmswCPzjYxjaTl/S+9cCMbe7OL7u9wIYUXxExUfFKWYrZ+fGZaylLU/BjLUPTn3M5twjgplbTQKBPiOGleYqCJ4ySjMph3Fzy3QtpmY0+ScSukLBpBTNULsQyD/ryLZRhcv7G7c+K/1KiO5QSuYvmkOYoQFx5Z2SnEOardDKDhJ80FlDIIz1AG6FxthhHpJHPnR3EnS9YeqeO3Gpy0pR2T4WJqjfb0/NaVXr1rc305l239Wbw5KdeVnBauZY5z7vw921RsH8jzqmvX8Qs3b8SxDDJLlIClHRMvjJYcaKwVKJIGIYW0zXAxzcaeDPlUUsoYxlD1Qq5eXzxlBulYuUkYJzPbZhth0hnSMlAKZhshkU7C2cmqDySfHR0npceWZbSyZYq0bWFZyfOJY73oybtSikuG8qQdk4xjcvX6Ajdu6ubq9QWKaZuNvZl5J/7LbTZxZLax9EEQy9JujnLVuiKz9YADkzVm68Gy3kNCiHOPZMiEOEddP9LN5t5sZ7hnw4+42OYex5rOfCiIUV7SaMFSySyn6Zo/rxV39QzMqroYxEst8FohBVimwWwjJGWb0AixjSSwXslDGOr4GqezyZzzuIWUzfteeQWb+5PmE/dao4uu2dFaM15O1n2VG8GC4GZLX5autMNsPWCwYMzL6g5bKY7OJhmN///Lt3Pz5t5TZjUqzYCpqk/etdBAI4jxwhilFN0ZG7SmGSYNNYaKqSQgI5lnFkVJyaSpFBpN1rHIuzBZ9ah4IYNaU2mG85qEpG2TSwbzbO3LMln1O+3+rznJ+rblNJsQz580RxHiwiEBmRDngMU6klmWwRtv28z7/+VpDk4nzT0udEutT2s3Wmj4EVpBT9ZhpCdDrDUz9WBeGdXeidrZ3u3zXsVbnbb3hkqO4UAhxToDJioetmngqiRwWFEn0Nb/myTvibMRnIWt4D9lG5iGYrLus9VQS67Zma757B2vcHCqTj5l8envHlrQdXGkO8MLt/Rw31NjC9ZhVZohhqF42aX9ywrGIMmkNYKklDLnJuvJIq0xVTKOouqFhDWfjGOyZ7yKYylMA+JIYxqKQsqmJ+uQdUxsUzFV9ck4FsdKDWZqPjP1ZAyAZRp0Z2wsw+DWbb386ku2cqwVTC114i/NJs4+aY4ixIVBfisKscZO1pHs5ZcPcnS2wce/+SwHZy78Mp/FztcNkmCs3VDEMQ229ufozTl4QcSG7jSmkXTiq0iG7LSs1vX0jGNy3YYu1nenOVZqkLaT2XCWqcgAlWUMALfU/OBLA8pgVSOykzWmUa0B1M1WIwtYfM1OI4jYeXCG2UZAd8bm+o3dpGxzQfMKw1D84gs3Ml71eGasMu89ahqKa0e6eN0tG5ed1cilrKREMojJueDax0c9tDsfFtM2r7lxA/vGavzwyGxS+hxFWCiKaQvHMvCimEozwrZN1qdtZmoBjaBJb9ahmLFp+BHPTtbIp2wuHconDTxOceIvzSaEEOL0SEAmxBo6VUeyH79sgO/un8a2jGUPTz7fzT1ZNtX8LniWATnX4vBMHctI2qJHOuh0mrtmQxf3Pjm2Rnt+/urK2FSawbwufKej5kV878A0R0tpLNPg0uE8R2YaeGEMankBh20ZaK07g6KjCNKWQRTHNE7jA9BeKB1zfBiy1nrR8l/XAksZNMOYhtYU56ytmzvQeO94hR8dK1Nphmzty7J9IEdPNmmisVjziu0Ded55xw7ufWKUJ46UqAchGdvimg1F7rpqaEXrffJu0mjj8HR90ZLddufDK4eL3Hn5UGeQ9PcPTjNb9xgve0S6idkqcSymbbIpm0LKJoxiZhoB5UaAaRhs7ctimQa7Ryu87NKBUwaN0mxCCCFOjwRkQqyRU3Uke2aswv/8xl4OTtWpesFFEYwZHM9QAGRdE6UUfhgnjT1Mg1jHmMqkP+/SDGJcy+Dua4e5fKjA+q40H/zK7jV9Ducb20i67gWrULUYA2OVJtP1gGLG5udesJ4fHStzaKpGzQupedFJE10GkLYNdgzkWNeVZabu89ihWYJoZYPP2/oyNvm0Rd2Pmap5ZGwzyaZqCBbJpioUqh0sKFB6fuDQXrPz/YPTfPTBffRmXYZOaGCxVNfF7QN5/v3Lnv96n/VdaV4w0o0XxIRxvKBkd27nQ8NQbOzN8pJL+vj67nFmWk1ArNYsw+lGQKRhk21y+XCBnGstWENW9cIlO0guZm7gum+iyli5iWuZXL2+OG/N2ckGRwshxMVGAjIh1sipOpLlXItvPjPRWrC/Rjt5lmnmt2CPYk0Ux53W56aCZhgT6xDTUOwYyLJ3osbesSp3XCZX3leinTlyrCS7slorFP0IojjCr8R8budzbO7LMlRIU/MCJqs+cXy8tT0kWSuztTNpx+IFG7uxTYPurM267hSWCY8dKjHbKh9ciYoXknZNujI2zSCiN2fjh1BqLH5fcavkzzIgY1vMNheOUDAMRSFt49omA4WFn93keSzevGI11vvMzUJNVb0FJbu9OXdeFiqONd/aO4kfRjiGOj7lnWRhphcm2avrN3ajVPLclvNcTuZUzSYWK9Pe2p/l2pEu+vOuBGhCiIuOBGRCrJFTdSRrBBGNIDlRNi78fh7AwnU9fqt7nGq1vG/4yesRGDGPHZzhWDHNUNHtXMGXtSnL45jgmgb1IKa2jHVdK6FIguoo0kxUPExDkbIt0k67bbxuHdP564u0TppOXL+xG62T+V1j5SaFlMObXrSJp46V+NKu8RXtSy5l4Yca19Ks704zVfPZ2J0mZRs0p+qgIIx0530Xx8mg5K60hUbRm3UWvd+1bl5xYhbqZJ0Pn5up851np0hZBht7MguagByZqTNT8xmvNFnXtTBYPN3nslTwuViZ9tHZOv/38aN89tHnGOnJtEYn5OY1RxFCiAuZBGRCrJFTndSNl5vHTxTP7q6dMzRgGYo41EnmTLWH9ipsy2Cs0mSi2qSQtvnXPRNMn0YW5WKgSEoTTdMgipMe9DU/PiPvK83xtX+Rbh2/OOborE8YazZ0p6l5IeVmSNSaj2UYSTa0EYTc//QYL7t0kJ+9fv28bMnffvcAX941vqJMnmsabOrNEsRJo4uaH3FoukFP1iFlKepBMkRZGYq+rE0x45C1TQ7PNrh0MMf1I92L3u+50LxiuS3P24Ope/MOSql5TUAAenIOlWad/ZM1hovpM/pcFivTnq557BmvEsUxsU4uwhTT1oLmKEIIcSGTgEyIVXA66yFOdVJ3Mc7Uci2FHx7PWBhAHMedDouWqTot8IMomb80VfNRMw2em36GQnrxjMbFyCDJKoYa0o7CMU1Mw6DuhzQDfUaHgRtGEpHFOgmee3MuDT+kEsVMVD2CMKYRRLRiwyQLrJLgbaLi8c8/OMrusQrvvGNHJ8tyyWCerGNS90++Dq3NUnDFujw1L1lndbTUZDDvMq1hpu5jmWYykVnBYM5hoJii4Uccnm1QSNm84UWbsSxj0fs+V5pXLLcEsj2YejFKGaQck1zKPuPP5cQyba01+8ZrNPyI3pxLtRkyVm6yoTvN9v6kHHlucxQhhLhQSUAmxPN0srb1J7uye6qTukzq4vt4zg3GIDlfbq8vMozkJDtsbTBe9pIufLEmBkqNAD+8WHOJC5mt9vHJeawi6yQt6EuNOMlKzWmesqqP255TQLJcyTKS8sTevMtkPWC2HnSGRM99+Fgna8h6sg7VZsgPDs/yd989xO/efQWGobhxYw8bezM8PVpZumd9iwLSrsWB6QbopDkMwKbeLAP5kFjDrdv68MKIh56Z4NBMgyMzDWzT4NLBPG940WZefvngSR9juc0r1tpSg6khufBTqgf05VK86bbN7HqufEafy4ll2pVmyHTdxzIVR2eb1P0QL4z5/sEZ1p1QjiyztoQQF7KL74xPiFV0qrb1S5XbhGHMzsMzTNV8btjUzZGZBvtba2baJ0KmKvLowdmz/6TW0Inn2e1sWLvZRzPSna/9KOpkVdCaVGvmlUhEOnn9Bgsuaduk6oUYShHHYFuqVbqYZNJMIwl+n8/r1wmWdRIIapIW8+lWiZzCIG4dv+TrxdYMJuuV3FzSSOK7z07x3Eydjb1ZDENx2WCe/ZO11jqok+yLSZKF8yPWd6XxozhpFJJx2NiTYc94lWYQ8e9euo23vHhr57PYm3W4fqR7yczYiZZbNriWTjWYOtaaW7f28JLt/bxke/8ZfS4nlmn7UUzdD2n4EWGsW+sNDdKOyXilSanp05t1V9RQRAghzkcSkAlxmk7Vtv7EWURt9z81xj3fOsCBqRpB60RxU0+GV163jmtHujonQv/r4f1r9dTW1Imzx9on3u3uiyd26NOtM3xDQXAmUj7nqfZaLtcy2dKXxY80E5Um42UPANcy0HOGcZ1sWPJSLPP44O54znEyVDL0uCvt4NomWmvGK81kTZmZPNBic8C8MAkSCmmbYsZmqurz7GSNjb1Zjsw2QClu297LriMlRstLrxcMIwiJqTQCjmgwTcWG7gz5lLVoW/qbt/Su8JkftxqdE8+klQ6mPpPP5cQybdtU1P2IIIzJOCbNMCbrWORdC1wYKzdBQ+aEdW9CCHGhWd5lQCHEAqdqWz/3pK/t/qfGeP+/PM0z4xXyKYv13WnyKYs9E1U+9q/7OTKTnCAahiKOL87gIjohGDvxVWifxyuOd/TTGmpeKAFZi9H6Z5sGhbTFW1+6jU++4Sb+/cu2k3FMLEORdy3cViYo1qfXOCaMoDvr0EqMdYK6WEPKSuZi+VGyzs9rlZPqGKIlHizWSVe/xPzPVLvc7bKhIrfv6CdtK0zAVuCaSdOSts7+KMVsI6DUCLBNRaUZorUm7Zh4YUTND4ljzeHpOk+Pljk8Xb8gP3ftwdQ/c806tvfnWNeVYnt/jp+5Zh2vvWmEsPUanOnn3i7T7sk67BmvUvOS4xFrTTNMLk71ZJ05v08VGiWZbyHEBU8yZOKsupCGgZ6qbf2J83vCMOaebx2g0gzY2J0miDR+mAxg3did5tBMg099+wAv3dGPZRlMVKVj4MlokhI5oxUFRFrhyoV0ADKOQc61KDdDjs40ydoWhqG4en2Rkd40+yfqNENN2rGIdED4PALZuhdiGaoViGmiqHVsYk2pEeBYBqah2NidoVQvEbUzaCy+hi3SMXEcU6oHFNMOW/qywPFyt6OzdZ4ZqybBd6tMUs8Zaj13uLgfxdiGIghjdh0pMVZq0pN1GSq6uJbJZMXjaz8a44kjJep+SMaxuHp9kVdcNXRGBhg/3/t6PrffPpDnzbel+fudhzgy08SxFGEU8/mdR1a09vX5mrv2buehaRQKw0hajvRkbVzbwAsjqs2QXMqiN+vQWI2p5UIIcQ6TgEycNafb/KJt7rqrla71OBNWOoto5+EZDkzVyLkmx0peq8ucxlDJOpuca7J/ssbOwzPcvKUXR/LXy9Jek9Qejiug5sf4UYBjKep+yHf2T/HAMxPsm6iSdSwMlXzfsQwcy0DH0bwy0ZXwgpi0baBVUkwam8lasGSot2ZbX3vgr8PvfK6UdF6EZITBIo85XmlS8yIc2+SOrT2MdCcldOu70nRlbO770RhaxxhKEeqFexxD506bQUxkQLbVIMcwFGPlBodn6ty0uYdPP3KIPWNVIn08x7d/osbTo0mHR+B5/c6a6/n+/nu+t/+bhw/w8X99lrGKR9gaBO6YBteNdHHDpp5lrX1dLe21d98/2M1HH3wWy4BSI2S2ESQdMA2DgUKKoYJL0pBGTlWEEBc2+S0nzorTbX7Rtti6q829Wd5426m7oZ0pK51FNFXzafgRDV8TaZLMgTKItKbmh9S8pMPg7tEKN27qwZfyu5PqlCy2vk6aSpzOSqgLj2qtpwsijWMqvr57jLzrUEhZbB/Ik7JMfnSsTCOIcK2kTMwyFc/Neit+rDAGwzBwbBNTKSKtsXzwY41hKH7lJVu4YWMPn3vsOUwT4vB484/FeCEEUUCPoXjR9r75GaD2OkJltErdlrF/GqrNJPhsBlGr/bvm6dEylWaIbSpSttlpw94MIn5weJb/+Y19pGyDmXpwWr+z5lru77+lMmBL3f6JIyWeGatw97XDXD5UWDJj9jcPH+CP7t1Nww+TURIkFzIacdLRMGUnQ6VPtvZ1tRmG4sZNPXxv4wy7jpa4aXM3VS/Cj5KqgZxrsneidsbnuQkhxLlAAjJxxp1u84u29rqrSjOgN+t0WsM/M17h/f/yNMCaBGVz29Y/M1rBMCGOk/bscQS9eXfe/J6eTNJVTGtNPmXTjt90rAmjmLofoRR8ddco5UbYafculjb3fLwRxjjm+Vn+utqS4dlJ2V4QacqNgCjS/OhYKSmTtQy292dohprenMtg3uWZ0dJpBWSmStrVt9/PllKYrk3cDCjXffaOV/n+gVm+tXcSvcxOjrFOWqJ/6YfHeNmlAxiG4shsg9lGwKVDOZ48Ul60Kchi+xZpCDSEfsx42aMn6zDSk2bXkTJp2yTWipl6QNTq8pdxkiYk/7pngsuHC1w30rXi31nznssyf//FWnPfk+MLMmA/ceUA9z05vuD2QRRTqvscbK1/u2K4wPaB/IKMme9H/MUDe6n7IZZKhoPHUZwEZq33x/cOTHPlcAHTNBY0PDmT5v4O3TtRY7iYoitj0/Aj9k7Uzto8NyGEWGsSkIkzbiXNL048AZi77mqkK00tiCg3AizTYKQrzeHZ+euuzrbtA3kuG8rz8T2TTFSaRFpjKkV/PsWbd/TNOzEaKKRwraT9uNYapRRBFFP1QsJWlwPTSK4MP3GkxGy9edafz/nkxBN7Ux3vuHixaw/SbucLj0w3aIQxYaw7772Jikc+ZTNV84m1xrZO78+Bu8jNlALbMqh6If/8w2N0ZxyGiy6GYSRXLU7CaXVhDKOYB3aPc2iqxub+HDU/ZLLqMVXzlt2Nau67wVAwUHBRwHMzDRp+RBTHcxqMJCWLDT/CMiCINSnbWPHvrBMt5/ffzkMz7B6r4IfxggzaM+MVal7Ixp5M5/bTNY/HD8/S8CO6MjZRrLFMtWj27itPjTJe8TAVOLaZNO5ojUQwW8Po6l7EvskqlwwWFqx9PdPOl3luQghxJklAJs64lTa/mKu97so1Fc+0Zgfp1slEyjbpSlvz1l2dbfc/NcanHj5Iww+Tk71W2VPdC/nUwwdZ15XuZO+8MGZTb5p9EzXKzZC0bdIMQvwwnlN6pXlqrMpwIcXRmcaSjyvmsxSkLDNZC7RUC7+LjIZOedpsI8Qwkq6LpmEQxZogjJmoeLiWwY/tSHF0un5aj1MPgUZAxjWxW2ldrZNySa0hCCN2DOQo1X30MvJjhlJYloEXRJSbAd87OM3m/hwZ22Sy6jFZ9Sg1gmXtW6STQL0dB9mmQTFts3+qlgSnQRLImIbR+uxCGMc0fN1qTBITx5qqF3ZK6fIpa0VBy6l+/6Vsk0PTdQbyLtdv7F6QQdt5aIaJiselg/nOa7tvvEbDj+hpdbicqfs4lsmOYnpB9u7p0QpxDK6lOpnT9uvRHhAeapis+lwyuHDt69lwPsxzE0KIM0kCMnHGrbT5xVxTNZ9KM6Duh2itWidPSfOGhh/RDEKyTnKV/2xrZ+9maj4pS1FpHm/SkbIUMzV/XvYu61hs7MnSlbHZPVplpuZTD2K0TsocM46FYyrSjslE1aPiSWex5Qp10lnQMg3qpZWX3V2o2qGpJjnxb5/eWoZC2QZ1P8aPYvxQs9J3mwmd23hhTKhj8q6FYRjJ4OZY45gGW/qySfv5eoCOdWcflgrN/EgT67g1Vw6OlZqd7SvNgOlaQLiC5i2GSkrjolgTtEqG281ENEmWqH3eH6OJ4+OvxQ8Ol9g7VsNs/d6xDIOejNPp0ricoOVUv/8mKh4NPzppBu3gVJ2JisdwV5pKM2S67pNrzVTzwwjLMHBMY9HsXSFlg5o/u89QyethzAmRXctYdO3r2XKuz3MTQogzSVapiDOu3fziWKm5oKSsfQKwfSC36AlAV8pulRaBaxvJiSTJCaVrG0Qx1IOQrkVOdM60nYdn2DNeIYpj6kGMZSbdEi1TUQ9iojjmmbEKOw/PAMdfB9s0+Zlrhrh2pIhrGqRtRV/GxjIUaTsZitqTdQhCCchWYqoeMFGRYAxOnOCVZIlY5LPX/nap4Xdmki2HASgj+X/HStqWR5Gm0ozwghgFpG2DvrzLYCGN1pqpuk+swTbhVA8VRho/SjI5g4UUANVGSLkZAZrlLhU0VZIw9UNNFMNkxWPfZBJgJdmiJDCJtSaMY7wgTpqUqOQ1rHlJmWS5EZCyTFK2wVi5wfcOzNCVsZcVtJz691+DjGMykE8tevv+vEvGMTlaaqC1xo9iwjhpbKS1ptoM6ck65FudJOfOWAO444oBUraZBLpxUq5qGQpDKWKdZBEtBYMFlz3jVVm3JYQQa0AyZOKMm7twe894spai3ZjjWKl50hMArY6fwLTXvsz9OvmP+dudLRMVj2ozxDIWaWpgm9S9gKoXdoKE9uvw1GiZ+56aoOoF+FGMqaDsRaQdk56sQzOImK4F1AJZD7USoVQqdrRL09qJJMNQhDEodby9exwn2+jWSXl+scVgS9y3Jgl0ihmLvqzLVNUjiJIOixnHIOtayYBzpThWajBa8jg4VU1a68etodUGizfm0Mf33bEUN23uBmD/dJUgjMg4Fl4QEi3jesXc+Mc2k8ARDVEcY5lJmWsQJ0FY2CqxNBSYpiKOkmy3Y5mEkWam7tOXc1v1fnpB0LuUU/3+6825pGyTRhCRX6STTzOIGenJkHWSBiA5NxlbUPOScue0Y7Gt/3izjxMrDrb05njR1h4eeGaSZphcOLJUMnOgvZyvJ+cQx8i6LSGEWCMSkImz4nQXbpcaIWnHpBnE+JHGULqzLia52m6Qsg1KjbOzAH0uTXJlXRkmJ1QaJes0DIM4iJLtWu2sn52sUffCZH2Pal2lRqN0MrOp1PCpemFSTqQWH5wrxKnEtLJXJvgRnRbxoW7V6qkky2QAGIruTNL1c7lDA5RKApehQoobN/Wwd7zKWKVJuRGwuTfLbdv7+IkrhvjM9w5x34/GcExFIW0zWfMJomTsw8n6r2iS7FZfNoVpGK3HTAIqQylcy8BbxlpBTRKIpW2TgUKKrGNiGbB/qt5aD2aiMKk0kwYfpqFwzFbmyIDBYoogjKn5EaVWlmywmMzHmqkHy+5EeLLff3dcPsh9Pxo76fiM6zd2d7bbO14BYLYesKk3w/aBPD1ZZ972c0sODUPx2z91ObONH/Lk0XKSYdNJ1xfXUuwYzPFrP34Jlw8v3TZfCCHEmSUBmThrTmfhdm/WIZ+ySdkRM7UAPzx+FueYiq6MhW2a9LZOSM6mzX1Z0o5FM4hIWca85xHHGj+MybSG8P7FA/vYO17hyWNlqs2Qke4Ucdqm7ofJ8FqtqXkh1WaIIulQJ7HYyl3sU8gUSUmgUkmGpx2zRBoMdJIlIgnYokgTA8WURd2LCON42a9d1jboK6QAhW0a3Lylh2OlBtO1gLe+dCs3bupJNuwsXEoyTcWUzUzdTwIyWHQ9maHAMhUD+RSb+7I0giQV1p9zybkWXhgT6eUFDY6lyNhJEJVP2QRRzEwjpJh2sIyAMIacY+LmTSYqGtcyCeOYIEo6LPZmHQylaAYRs42AqzYU2dSTIdKaA5O1FXUiPNnvP8PglBUE2wfybB9Ibv/UsTJfeuIYXhBhm6rViGTpioPtA3k+8Jpr+PIPjvLQ3ikqXkDetXnJjl5+8pp1khETQog1JgGZOKtWunD7+pFuBvIuPzpWxjUh49q02yxGYUipEXLFugzXj3Sfwb1eXDHlcMlgjh8dKyddEx0T21AErYYjlmmwvjvNV54cww9jcq7ZWv8GPzxSxg+TE+BYH+92FumklCte5tBbMd/F/JK1i920TgIaSC4M2EaSjY3j5Gexan+EFK6puHFTF1dtKHLvrtFlPY6pIOdaZB0LL0wG+QJUvYgbNnVz46YeDENxeLrObCPgps3dHCt5zNR9Uo5JKrDwo2Tmnh9oHEt12rZnHIuMY7G+K8W6rjSgOqV31490s2Mgz+OHZ2kGJ69XbL8WN2zsJtYw2wiSQDDWRJFGtZp8GAaUmiGOpdCAH8VYhqI/76BUUgbrttbIZRyLnoyDUoqGF55WJ8Klfv8tt4KgffuRngxb+7MrqjjYPpDnHS+/hJ+9QToZCiHEuUYCMnFOMwzFpp4MT49WiFA4hsI2DIJY42NgKNjck1mTk4r1XWlevL0fP4wZrzQpNUIareGyPVmH/rybnLQGEZcM5pmq+ZSbAZMVr7PeqR1AaI6XJ7aqiYRYtvaasfbaLhVpjFb5nakU124ocnC6zmx7ALKp6M7aXDqUJ59yeOW16/CDkF1Hy6d8LMcyKHsRjbBOV8bGD+NFm0G0271v7cuxoTtDpZm0jq97EUdnaxyZbTJR9XAtk8sGc6zrzpJxTRzTIGMbPHpolsFiiiOzdYYLKSzL4LqNXTz87NRJS3nbjUZMpRjIu2zszVJphkxUm+wZqxIS49omKctgc2+GfRM1qs2gNSRas70/x3Axzf6pGjM1n4G8Q7UZMlBIkU9ZZ6wT4UorCE6n4kA6GQohxLlJAjJxTjsy2wCluHVbD7tHq5QaQSfo6c0lGSqNWvZajtU0d7F+X87BNJK1J4aCKNakHIuaF7KuK510NlOKmVpAENPpErfoiWX77PqizveIlUjZBrFOmlJEcVKa6JgGuZRNww8Z6c1y/aZujpWa1IOIjG0yXEwRAwcmazSCCLXMJosp2yBtm0zWAqxmRBDFi2ZmTmz3XkgnnVCnlYehjFbWycS1TKbrAet7NF0Zm2dGK+w8NEMjiLBHDR7ZP8W2/hyvv3UT3947iZqzjnQxhgLLMOjOOsw0AjYC+ZTF7tEkGO3JOoxXfLoyNmnHpDdrU24G2KZBFCfB5dFSMgOw1AiZqnn05Vw292aoeuEpGxE9HysNmCTAEkKIC4MEZOKc1r7KftlQkcuHCkueUK5kLcdqOrHUyAuTDmfbB3JsH8zxT48dIdMqa2p3VTxVvBXHYJoSjInl604nzSAqXhIg9WZd1nenKaQsfnSsQhxrDMNgfff8k/daM+iU3mWd5Y2OqLWa0vTnHDZ0Z/jFWzZ2yhTnard7n9usYrrm8fjhWepeiEJx6WCB4WKKJ4+VeezQLLvHquyfrBG2hjArNHUv4vHDs+werVDzwuTCx0lSZJYJpmkwWHBZ1xqUnHMtJmseGs2+yRpxDH4UcXCqhqGSbGEUQxhB1Q8IGzHFtEMxbVP3k9f0uZkGfTlXOhEKIYRYdRKQiXPaiVfZT3ZCeSrtToervX5iqdKhI7MN7rVGqfshGdvk6dFKJ4OmT9JlzjQUvrRwF8tkAlop1hVTWFWfrozDC0a6yKeSNumb+7KUmyFDi4yNmFt6t647jWMmw5hPdjnANA0UipRt4FgGhbS96OfoxHbvQwWXPWNVyo0AyzQopC12DCYdAjd0p/nB4RJPjZYIo4i0ZZByLUyVtNL3/JCZup+07j/F6+GHYJsa2zT4qWuG+eHhEjsPTTNd9QniGFMphgouFS9MPodoSvWAINYUUjaXD+aYmPM65lyTJ46W2dqX4023bWZD99qUSAshhLhwndODod///vdz0003kc/nGRgY4NWvfjW7d++et02z2eTtb387vb295HI5XvOa1zA2NjZvm0OHDnH33XeTyWQYGBjgN3/zNwnD+RmVBx54gOuvvx7Xddm+fTv33HPPmX56Yhmez1DpufaOV/iLB/bx4fue4c/u38OH73um0/lwNbRLhy4bKiTzlwzV2ffv7J/ifz18gKeOJY/VHsa6VMyVso0VDekVFy8FZFyTlG1yeLZJyrG4Yl0Bw1DsnajRm3N544s205tz2DNepdIMCOOYSjPgmbEqjmWwfTC5mHDHJYMMFzOYRtJYZrGQwzZgW1+Wbf1ZgkgzVfXI2OaS+9fOIF+1rsjRUpPDM3Us02CwkOK6ka5Ou3bDMMilTGbqSelgNuUkQ+BVMgQ+49q0Y6BT5Y4NI5kzdqzUpDfr8Lbbt/GrP7aVQtoi55hs68+SspNRGinHJO0knRsbfkTOtTBNk+6sgxfGKKUwTZNt/TlKjaDTel8IIYRYTef0Wd+DDz7I29/+dr7zne9w3333EQQBd955J7VarbPNr//6r/PP//zP/OM//iMPPvggR48e5ed+7uc6P4+iiLvvvhvf9/n2t7/Npz71Ke655x7e8573dLbZv38/d999Ny972ct4/PHHeec738mb3/xmvvKVr5zV5ysWal9l78kuPKFcrJHAYvaOV/jktw6w62iJrozN1r4cXRmbXUdLfPJbB1YtKFts35thxJ6xKhVvGVNsWxzTwDxxsJkQi0i6diq29mW5ZCDPSHeaciNgth5w1boir7hqiPXdaV5x1RBXriswWw84MFnj0HSdmhdS80L+6bEjfPi+Z/jEtw/wM9etS0psddINdC5DQX/e7cwFA4VGnTJA2j6Q5223b+N1N29kS3+W27b1ceOm7k4w1lbzIuI4ef8vNtfPnvO9kz1mxjIwDcVMzafSDDAMxVAxTcq2sEwT1cq6xVq3Pme6c3/t59xeT9buIJl2TLwwWrPSaCGEEBe2c7pk8d5775339T333MPAwACPPvooP/ZjP0apVOITn/gEf/d3f8eP//iPA/DJT36Syy+/nO985zu88IUv5Ktf/So/+tGP+NrXvsbg4CDXXXcdf/AHf8C73/1ufv/3fx/HcfjLv/xLtmzZwn/7b/8NgMsvv5yHHnqID3/4w9x1111n/XlfLJZbQni6Q6Xbj/GVXWNM13y292epehEzdR/HNNjen2XvRI2vPjnG1r4cwGmXNC72XMIw5v8+fpQoTk74lrMqzFRQ9UOCUNaQiaWZrSHOjm0w0pvhrS/dxvUj3RwrN6n5IZMVj8cPzfL5nUdohhEpy2RrX5afu349dT/iS08cwzIi1nWlyTgWdT9k19ESPdmkUc6jB2cXlNQqoO5HHJiqYxjQk3XoyzmdOWEnYxiKbf05BnKpTkv+ciNZU5kMaLaIO+1FFVonLeiTtW9JgxJlnKyVx/zHipVCx5qan+xbI4joyyWt7Kdrfmt9WvIYUaxxLIM41gStWRNBFGMaBo6ZRGgNPzqtNvdCCCHEcpxXf11KpRIAPT3J0NFHH32UIAi44447OttcdtllbNy4kYcffpgXvvCFPPzww1x99dUMDg52trnrrrt429vexpNPPskLXvACHn744Xn30d7mne9855L74nkenud1vi6XT90uWhy3d7zSCbDaJ4zb+nPcddXSM3RW2uIZkgBr30SVtG3w6MFZpus+YRxjGQY9GYehosve8Srf3jfJDw6Xlr0/y3kuKdvg6GxjRfPEIg2RBGPiFFxL0Z1JhhZ7QUzOtbAsg5GeDHvHK/zLrlGmaz7DxRQZJ03dD3nyWJmjpQYp28QPYy4ZzHfWlOVTNjnX4ktPHGP/ZA1NsjZtbqgV6SSwaWfQwigJZJYbpMwt4Q3DmJlG0PksdqdtgjgmZZvUvICqFyQdSFvTo03Fsj9HQQyubWBZyfw1SNai9uVc+nJOMhOtlvzubgYRXWmbrGsyVQto+DF5Nz4rbe6FEEKItvMmIIvjmHe+853cdtttXHXVVQCMjo7iOA5dXV3zth0cHGR0dLSzzdxgrP3z9s9Otk25XKbRaJBOL/wj/P73v5/3ve99q/LcLjbtEsITTxh3HS1xtNTgTbdtXjQIOp0WzzU/ZLLqMVXzaAYxrmXgWiZaa8YqTUpNn7Rt8XePHEJrVrQ/c5/LVNWnkLIopGziWPPEkRKjpXrnivtymGqJNvhCzGEAfbkUgwWXmh9RaYZUvaSUbm5GeMdAbkHA9YPnZpmseNy8pXdegw+AKNIcnqoTa0i3gplGOH9AeRBqRgbTOKbi8GwTP4wZLqSWt9+G4rLhPJ9//AiVZkBvNuli2PAjnp2qkU9ZjHSleWa8im49T6MViAUr+FxkHQPLNOjKOGzpywLzOz7euKmLqhcxUfXYM1YliCKaQcz6rjTNIOLQTIOutH1W2twLIYQQcB4FZG9/+9vZtWsXDz300FrvCgC/8zu/w7ve9a7O1+VymZGRkTXco/PDqU4Y94xXOyWEq3Hyk7FNJqses40AA5itB8RaYyhF2jZo+CHjkUfGMblupGtF+9N+Loem64RhzIGpWueKf1faSmaorYAEY6LtZFPolAGVZjJTqz3LK5dKfpW3M8LDxdSCgEupJKu2d6xKtMiFgiePlfBj3RrLkHT1UCQzv9rJqhiYqnqkHYuujINjGRwrN5d1oSSONU8fqzBcSNGfc5ipB5QaAZZhsLUvi6Fg/2Qdy0xGP+hW85uVfiwsI1lDduvWHkZaXVnndnzcO1FjuJhiU28Gx1TsOlpG65hi2qY/7zIQJhduSo2AZrD4jDUhhBBiNZ0XAdk73vEOvvjFL/LNb36TDRs2dL4/NDSE7/vMzs7Oy5KNjY0xNDTU2eaRRx6Zd3/tLoxztzmxM+PY2BiFQmHR7BiA67q4rvu8n9vFZu4JIyxcRzJcTLF3vLpqg5414IUxlUaAaShc28RUBpHWVL2QINIoBQN5B63haKlOw49IOybDhfSS+xPHmu8fnOZf904wXm6ilCKfsrBNq9PBbrYePO/9FxcXW0FfwWW6FuCHMQbzywZNBXnXwjQNZhsBlqnY0J0m7yYzxNpz+zJOGq01lWbY+XzlXAsDhR/HjJabdGXseUFbuZlk2QyVNNForyEzVBKItb+uBzGb+pKAptwIlt3oov3Z3zGYI2Ob7JusUvVCcq7Ftr4cz05Wma37rCumCcKIihfRCCK0hpSVNBCpB3HS5p/FV5O1Z/xdO9LF627ZOO8iylJrUV917TquHemiL++SdSyGC6nOWrzVHI8hhBBCLOWcDsi01vzar/0an//853nggQfYsmXLvJ/fcMMN2LbN/fffz2te8xoAdu/ezaFDh7j11lsBuPXWW/nDP/xDxsfHGRgYAOC+++6jUChwxRVXdLb58pe/PO++77vvvs59iNXTPmFsBgZPH6ssWNO1uS+zqt3Mal5IGCXzl46ffCZnlqrVYS2ONWNlj0f2zzDbSDIPpqHoSttcvaHY2e+29pqxRw9Os+tICa013RmHrGPixRHjZY+6Hy6j/YAQCUUS+FitNn/daZtGEBLEmkZrKJ1jKmzLxFBJ04uutEUzjLFNg+FCijjWlBsBXhCxd7xCqREwU0/WaUWxJoo0sY5p+smg5UojYPtgjp5scmEp785vXz83M2W0UnYxcMlgjhs3dVP1QrwgXvYasvZnf7wc8cPnSvM+a7uOlCmkbYJY45gGA3mXqhdxtNTANg1sQ1FrlWUqQ2GiOxm0dqsPw0j284Vbe3nby7Y/r7Woq3ExSAghhFiuczoge/vb387f/d3f8YUvfIF8Pt9Z81UsFkmn0xSLRX7lV36Fd73rXfT09FAoFPi1X/s1br31Vl74whcCcOedd3LFFVfw+te/ng9+8IOMjo7yu7/7u7z97W/vZLj+3b/7d/z5n/85v/Vbv8W//bf/lq9//ev8wz/8A1/60pfW7LlfKE7sPphpNRTYeWiGMNLkWlmlIIoZrzSZqnmM9GRWpZtZHGuenazihRHdGZso1jSCmKA1IDfrWBRScKzUZOehGUBhmQrHVElpVs3noT2TXLGu0NmfuevfUnbSqc22TGp+hBc2iVuZN0MdP1EU4lT6sjaWASUvou5H9GYdNvWk0Uqxe6xCxjZIWQYp18ZQilhr/CAil07KBr+zf4ofHC6xd7zCvoka42UP11IMFlOkLJNjpQYNP+rMxwN4dqrGdN3nhk3dpGyTlGXimAZ+FOMF8fxywdZ/uJbBjRuTpkorbXSRdSxmaj4/OlomjDWulQyWDsIkY3d0pkEMjJabNIKYtJMEnwqoehGNVsfE8IRyy5hkPlrasRjIp3jHy3ewpdU1dTGnsxZVCCGEOJPO6YDsL/7iLwC4/fbb533/k5/8JG984xsB+PCHP4xhGLzmNa/B8zzuuusu/uf//J+dbU3T5Itf/CJve9vbuPXWW8lms7zhDW/gP//n/9zZZsuWLXzpS1/i13/91/nTP/1TNmzYwMc//nFpef88LdZ9cEtvhpmaz0w9YGN3GqM108i1TOyM4tBMg8EVNAo41WM/enCaqhdS9aDgWvTnHGwrmfNlm4qpWoDWmoavMRQ0gqQ0S7UCKk8pDk7V8KOIMIzntdA/OFXHUIoo1mSdJCir+yGmUpiGAnUaC2DERanmRziWQcZJuvqlbJPJmk/Nj+jOONy6tZcDU3Vm6j5eFGEZBgOFFP35FEdm63ziX/fj2ibDRZdiymay6uNHmvGyh2WqVkdEk1hr0o7JVeuK7JuocnCqzqMHZ7hiuMC1G7uxbZMv/fAYSzX63D6QpRFGHJtaeaOLwZzL0dkm9SDCMRVVLyTWraYhreybqZKAq9L0qXqKIIqpxzoZCq+SzNiJay0dA8I4acm/uS/Dpp7s8zoWQgghxNmmtD5x2ow4HeVymWKxSKlUolAorPXurLmFnRSTWUf7JqrsHavitIa3JhkygyCKqTRDtE7Wc73rzku5cVPPitZutLNxT42W+dIPjuGFMfmUyfcOzFBqJGtyUrbBcDGFZRpUW+trjs40Tto8wDbghVt62dCTZv9Una60TakRMFb2GC01CGMwjaRkqubFOCZEcTJ8VoilzC2gTVkGaSfJUHlhxGAhRRhr6n5E1jZ4ySUD5FNWZ01Y3QsZLTUZrzQZr3ikHZNtfTmGiimeGk0GnVebQVISHGmyjkXWtcilTEBx69Ze8imLY6UG07WAt750K9ePdPObn/0B//yDo4RLpHbXd6W4eXMPOwbzK2508cj+Kf793z7aKlVc+HkzANMExzST+WCR7mSYDZUMawaN34oWWx3xcSxFuw3K1euLfOYtt3ZKP4UQQoi1spLY4JzOkInz08k6Ka7vSvP0aIW+tE3GMZmpB1S9sLPGRRmKA1N1Pvrgs3xv48yy5oDB8YzY3vEKTx4rU22GbOrJ0JfLMlxME8eayI4pNZM21v05l/68y0zN7wRjS3W2C2M4PFPn6bEK5WaAbSgcy6QrY5NL2VSbAZHWBK3e3GEESkkwJpY2fzVj8l9RFFNrDUNu+BHru9MMFVLsHqvy7GSVazd0UUjbTNc89k5UqXshXhjjWia9WYeJqsdkzcMLYwYLKQopC8cyGC01k6/TFhqYqfv4UYxSioFCirofUUjbPDdT56E9k9imQU9a0Qwh0hpTKVxTU/Y1DT/k528c4ZatvStudDFR8ah60ZLBXtz6H9tRNEM9r9w3qVKMiSKwTIii46+jbRgU0xYp22Ss7LHz8Aw3b+ld0b4JIYQQa0kCMrHqTtZ627VMcq5F1Qt5QavN/ES1yZ6xKiExrm2Ssgx6svYp54AtlRFTQFfGZqLqUfVDtvRlqXohdS8g7STr1bb254i1JojiU1YVaqDcDOjK2MzUfaJYE0SaSmutWKyTK/jt81MNWAboSNaQicTcYH/u2sL2+8exDJp+RKwh5ZhYpgINacekL+eQbY1gGCq47BmrUm4EWKZB2rEwVEQuZaOAsXKTuh8RhMlnKedaSfksybpJP0zKHR0zySA1/AjXMsk6Ft99doqqF5JxTGzbxLbnP4ecSta3HZltnFbXwSjSNJeKxlpinQxrztkGsYa6H5OyFUGkCcLkdXNQmEYrS9YqL4akg2rN95mq+SveNyGEEGItSUAmVt3c1tsnyqcs+nIOB6fq+FFMX85l92jSba0nm8wmGiikGC4mt11qDthSGTHLdIhiTSFtk3NhuuYzWfW5dkORfRM1pmoenhfjhRE3bOqh6gV8/+AscPLlXkEYMVWlU2oVztlazfn/drc8wzAwdUx8mhFZ+0RdnL/mZsHa74u49YVqBfG69f9eEKFRGAZkHJNCKrmgMF33GenJ8LqbN/LDwyV+eGSWwzN1UrbJYCHFYN7lqdEKQaRxzGQmWakRMlH1WFdMYagkUGkEEfk4ptoMGSikyKeStWpzG3M0w6TFvLlEsGUaSXfHZhgt+vNTqXjLGwMRx5ps1k2ai0QBacemYCpmGz6NIFlPZijV+X3g2ib1IKI2G5Kyk2yhEEIIcT6RgEysuqxjkbJM6n5IPjX/MrtSSZe38bLHkdkGQaSZrHk4lsFMPclgbes/Xua42BywuevTcu78jNh03U/KB6Oki1suZTFd87l0MM9Nm7sXrJn5+EPPLus5eaHGNKJFgzZFa04TYCnoz7s0g4hau1vBaZCKx3NPO8tlGUnzCW8ZcYmhwLUNgkijY03aNvDDGKf1/4aZdBEMY03GSboORq0gvjtjd5rc3Lqll9u29fHNPRN8/KFn2dqboyuTfLZGyx5HZutEkaYRRnhhlHQU9WukbIP1XSkaQcyhmQZdGYdNvRmqXlK6O7cxx/aBHK5l0PAjrJSal91OGt9EuJbB9oGlOxiezP7J2rK2M1trxcIoJmWZRFrjKEXOsWgEAaGGjJm0vLfNJNunjZjpekhXxuG69V2ntX9CCCHEWpGVz2LVre9Ks60/x7FSkxN7xmidtJ7/8csGuGlzD9M1j3JrHtFAIcV1I130zLnCnXbMeXPJTlyf5lhm0uXQtejJOoStZgCVZtI90TYNojjGb53lVr2IGzZ105Wx+eg3n+Vfd48v6zn5MTSWaD0XA46RnFjHGrYP5Lh0qEDhhGB0JU4vByHOpO6Mhd3KEsVaYZAEXI65cFtF8ss145jcvLmHl17Sz1AxBUolXQKVYrgrxU2buunPu+RTNqahsIzk/doIImbqAV2ZpK39sXITw1Bs688xkEthmaozW68/71Bthsw2ArSGtJ2U/HqtRjm2abKxJ8MlA3lGutOUGwGz9YCr1xfnlQPfuLGH7QO5VjORpPW9H0Z4QUzdjwljzY7BXKft/UoVUsu8/tf6HeFYJkPFFLZpJOMq4uR1NRU0Q41G41oKP4opNyNSdjJYfqzqndb+CSGEEGtFMmRi1RmG4q6rBjlaarBnPFlLlnZMGn7UuSr/uls2srUvx/cPTvPRB5+lJ2szXEwvWHM2d40LLFyf5pgGlml0MmL5VgdEy1BM13wcy2itnYnZM16lJ+tw6VCeT337INM1n7S7Oh8BP9adDMpM3edllw4QRRGTsp7lgmGZBgM5k3IzpB5EWCb0pG1CFA0/Qus4WevUynD15R1cy8SPNNdsKHD5UJ4njpT44ZES16wvcvX6ItP1gPGqR3fGYbaetLkPY00YaQaLKTb1Zig3gs4FifbFjl1HS+Ra793Jik/OtUi3mta4lkHaNrl6XYEohqtagde6Yppj5eaSA5Ety+Dfv2w7v/tPu5iqemitO+WWKEVXxuaV167jWLm56DDlU7lhc/eyZvPFKLKOSU/WJe2YpGyT6ZrHVM3HUNCbdWgGSYDYCGJMQ9Gbc7hqfREFqzZUXgghhDhbJCATZ8T2gTxvum1zZw7ZWLmJa5lcvb44r132jZt6+N7GGXYdLS24jxPXuMDC9Wn5lEV3xmGi0sTJOtimgWUodgzmmap6HJyqk09ZhFHM1euL3HH5IPf96HiG7alj5SW7K65U+z7SjsUzY1X2LbNES5w/Uo6JaRnUZxpkHItGqGmGITnXwrWsZIZWI8ALNYWURTZlM13zqTRD8imLlG1x5XCRlG0ev6BgGFimYriYYrzi0ZVxeMFI0lGx6oV4Qdy5IHHixY6cazFZ88ilLLwgIpdy2DGYoz/nkk8lzXNm6wFKKSzLOOVA5E29GS4ZzPFEECVrymLQrXe20nD/U+P84HCJbf25ZXdAbVvXlSGXsig3lw6YTAVZN2n/bxgQa41hJE1PcikL1zR54dYehgouo2WPehCRsU2GiylqfsRsPViVofJCCCHE2SR/ucQZs30gz9bbcxyZbSx5VX452bS5w2dPXJ+mVLL2peqF8zJiadukmLa5aUsPd189zOXDBdZ3pRdk2FzLWPXZzcN5mwf3TFM6yYmnOP94QcTm3iwz9STraSiFaSrqfoTfaj9vm4qMYxHGITU/Ju1CEEXM1H1Gy016cw7/5sYNfP3p8U7XxK60zbFSA8s0KKQdrtnQRTHjLHpBAuZf7Nh5aJpyI6CYthksJtmzE0t+x1pZsVNplwNnHItfvHmE0bLHeKXJczMNdBwTk6x7K6atU3ZAXfT1C2MuGczx1LEKdX/hekxLJftrGYpSMySINU5rnphlGtyyuZeBvNsq3zRY3308uFzqtRJCCCHOBxKQiTPKMNQpr8ovN5sGC0u2lFL0ZB2uG+li73hlXkbsmg1dC25/YoYtjFa/Mf2XnhjHNJeeaybOT+1g31IKy1AEUYxlmJiGwo80UawxDQi1JpuyGCy4lJshzSBZE3btnPfjpt5M5/3uWEYS3BmKHQNZCmmLSjNY9IJEW/tix/cPdq+o5Pdk5l6sME2TdV1pjs42MZSip5DGj2JmGwGg2DGQW7ID6lKyjsXGnixdGZunjpaZqQcEcUzcmi02kE+RcS0292Z4drJGw4/oStv05Vyu2VDkrquGAPjktw4s6+KNEEIIcb6QgEycE5aTTYOlM2q2qRbNiJ14+xMzbKeai3Q6YmAg61Cu+9QkSXbWOCYE0ZkLgvOujdYaP9Z0Z50kMxYlQZgXxgRRjB8lrey39GW4bVsvTxwts7Uvx5tu28yG7kzn/Xji+32y4vH4oVmenaxxYLK25AWJuQxDrbjk92ROvFhRaYZM131yqeTCh20aVL2wM1R6sQ6oJzP3YsrPvmA9x0oeTxwtUWsGDBVcSs2I3pzLpUMFLhnM88MjpUVfu+VevBFCCCHOFxKQiXPGcrJpsHRG7cSMWBxrDk/XOwFef8bhscPTTNd9np2s8qItvYSxJmmyvXon8rYBShnEajktDMRqMEjK2kxDY6CoBav7upsKXMtguuaTcSyuXFfkqWNlpqoeqjVxrBFEOJbBQN5ha1+WfZN1NnRn+KUXbmRjb3bhPs99vw/Bi7b1nfKCxGL3sZKS35M58WKFH8WEcYxtJn8mkozg8aHSKymHPHFf903WO01JihmHUjOaN/JCqaSjZHv929z9X+7FGyGEEOJ8IQGZOC9tH8iz8cUZvvr0KKMlj6Giy52XDeG0epDvHa9w765RnjhSou6HTFZ8jszUqXjH167seq5ExjFXNRiDpEX+ZNUjjKRgcaUWC45PVvppAqYJpmFwyUCOXMqm7kfsPDS7KvsSkwRjvVmHUjNkU2+G7QN5erIOxbTNnrEyz07WKKRtMraJYyn68ilArThrs9wLEidaScnvyZxYDtxuOBJEMY5pzBsqDSsrh1xsX+eufxsopFa0/u10XyshhBDiXCQBmThnhWHMzsMzTNV8erMO1490Y7UW+d//1Bj3fOsAB6ZqBFGMbRr8/SPP8cbbNrOpN8N//9oenhmtEGlNqe4zWQsW3r+G8nKm+54GX4KxFXNNyLoWcRzjRUlGBpLAKIzn5xpTFqRti2YQYZkGW/qz/L93X8H1I908N1Pnlz/xXZ6bbT6v/bFMGMyn+Dc3jXDZUIEvPXEML4iwTdXKHCm6Mg63bktz99XDXDqURwH1IDrrWZvVyBqdmG07seFIZk4G6/k00TgT69+EEEKI85n8pRPnjDjWnRPKxw/P8sUfHOXgVL0TcG3uzfLG2zYD8P5/eZpKM6A363RKtJ4Zq/Cf//lJhospDk01SDkGeddi38TCYEyce4IY/FDjWCbbe1M4lslU1SOMYiYqPn6UzHozTYVrmRiGQU/WpBnGdKWPB+wbe7Ns6EmvOCAzSLJxtqXIuxabe7O8ZEcfP3X1cBJE9GdPWSa7EnPf76sRwK1G1ujEbNvpNBxZ7r6u5vo3IYQQ4nwmAZlYM3NPSCcqHj84PMuzEzUOTdfYPVpFoxksuAwU0knANV7hD7/0I1K2SaUZMNKVphZElBsBUQymAcdKTQ7PNHAtRY/hUvOeX5ZEnD0KGCy4xFozVQm4fH2KF4x08cxolbo/ixXG2IaiJ+eSdixirfGCCNe28MOInYdnuHFTD4emauwbr2GboOMkE7qcx+7KWMng4XyaGzZ2k7INnhqrMvatA5327qu1dmnveKUT9DTDiJRlntZsr7l8P1qyhPdUTgwO3/pjWztDpE+n4chyrOb6NyGEEOJ8prTWUlu1CsrlMsVikVKpRKFQWOvdOeftHa/wL0+M8r0D00xUPWbrATnHZEtfhh8cKVNuBDimwjINerIu2VYnxb0TNep+yEDOpdQMqfshsU4625kGWIaiGWqyjiKK1RnpoijOnA1dKQCm6z6mUtyypRs/gsMzddYVU4SRptQMCeOYKNZEkUYZCi+IuHJdkes3dgOaP//GXjKOiWMqys1kJtiJTI6vTYuBQsriyuECQ8Wk/bpjGuRck70TNa5eX+TfvXTbqgQHe8crfPJbB5iu+QwXU2Qci7ofdoKQlcz2avubhw/w8X/dz0SlSaQ1plL051O8+SVbeP2tm0+5P6cKDlc7m7fU43thUqa4fSAnXROFEEKc11YSG0iGTJx1e8cr/MEXn2LXkRLNIMQPYyIN48DB6RphDIaCKFZEfkTVC0nbyVqSIIrxQs3hRcrRwhjCODnF9kONYZzlJyaet8mqR9q2cE2Dmh/x/YOzNIKIINKMlpr0ZR0uHc6Tsk32jFUJiXFtk5Rl0JO12XW0xES12ZoJplAqKblbTAxYBrRH0W3oTkrjnhqtEMZJR8GejMNQ0V1Re/eTaQ9fnq757BjIddZN5VM2Odda8WwvSIKxP/7KbrwgIm2bmKYiijSjpQZ//JXdAEsGZQuDwzR1P1ww+PlMNtGQrolCCCEudhKQibMqjjUf+cZevn9wGt06adZAK44iavXYSL7WndsEYchsY/lDvaK5dyrOG81Q40cB6CRgChohrplkPuNYM1nzqB6MGCy4RLGmJ+swUw8YKKQYLiYB1VS1iQJqXoQiGcy8mCToP94spNb08cOYXMrCNpPgf7zSpNT06c26y27vfjJzhy+f2MTidGZ7+X7Ex/91f9Jy30wywnGgMZTCMhWNIOITD+3ntTeMLChfPBPB4ek6kwHfmczuCSGEEKtBAjJxVh2aqvHQnkmCMEZrTSM4dcv502lL3y5jFOefuXG0YwKteW5aJ8OJ617I/omQkZ4M0/VgXvc/gCuGCzw9WqXqhad8D2gFSifvLy/SDBadzv24lomTNRgrN0FDxl7eeqyTmTt8WWtNpZkMWnZMg3zKWvFsr68+PdraP00zOP5kIzRBlASdo6UmX316lJ++Zv282652cHguOhNr9YQQQojVJgGZOKu+u3+ambrP2VjaJfHYhcEyFUGoiNFEsSaMNZGGo7MN+nIuW9Zl5s2vyrg2rmVQaZ78PaA1GAY4lkEQxkDSzr3ihYRRjGUa5JKIEI1alfdTe/jy0dk6oyWP6bq/oDxyJa3ej8428aM4eS6KpDyzNbgt1powhkjHHF2kxHducLiYlQaH55rllmMKIYQQa00CMnFSJ5sFtlL3PzXGR7+596wEY+LC4EXgRRGGSgIox4Cca1JpRhRSFrap2D9Zo5i26cm6AExUmjSCCNPgpO+1tGNSTJlEWlFpJqMRdh0tE7QCHKXANg368y59OYdG8Pxn1q3vStOVsbnvR2M4piKftjvlkWPlBodn6vzEFYPLbvXumIpYJzGYYSg6ea5WcBZHOnndzIUleu3gsO6H5FP2gp+fz3PAzqVyTCGEEOJUzr+/tOKsWWz4cnsW2MsvH1zxfb3/X55mtCRt6MXKtcsYYzSgsE2FaRqdNWT7Jmp0Z5Is2f7JGl6QBFW2kQRyi7W+78na2IbR6cQ5VfOIojlZNQ2xjpmseKzvSq9eYNJ+gBObjagktbWS8GBddxpTtUp046TjZOdhWi+aoZLtTrS+K822/hy7jpbIuda8ssXzfQ7YxVCOKYQQ4sIhAZlYVDuAWjB8ebzC+//laYBlB2VhGHPPtw5QaQYUHEU9kGJCcXrCSBNEMV0Zi+5MEow5lsFk1eNYqUnVC7EMg7i1eCzSS/d2qTYj4jigEUb4c5JfqvUPkmCuEcYcnKoxmHMX3MdKG0YcmW0w2wi4aXM3x0oeM3W/s8+DhRRDBZeZerDsQMExTbKuRdULidpBGXQa5SgFOdfCMReuf7uQ54Bd6OWYQgghLiwSkIkF5gZQG7vTGK3+8fmUQdYxOTTT4FPfPsBLd/Qvq3xx5+EZDkzViCLNZE3qFcXpi1vrvvpyLteNdLFvosZUzaPcCJiuedywqYcwjnnkwPQpm2zW/KRM0TFN/Oh4RNa+maFa/61hth7w2HMz3LK1r7Pd6TSMaAcKW/tybOjOLGjqEWnNgcnasgOFLX1ZNnRnOFZqUPdC/Oj4k3ZMRca1GC6m2dKXXfT22wfyvOm2zZ3nMVZurtrg57V0IZdjCiGEuPDIXyOxQDuA6s06nWCszTAMerMO+ydr7Dw8w81bek95f1M1n+maT81//mtwxMXNMhXrutIkZYtGK9PUYLoW8NaXbuXGTT18/amxZU08iDUM5l00UD3hvalJsmtGq0eGH8bsHqt0ArLTbRhxYqBQSM8PFhpeuKJAYaQ7wwu39HDfU2MUUyYxyZoyQ4GBJtKKW7f2MNK9dLbtQpwDdiGXYwohhLjwyOhcscBUzSeIYtLO4m2+045JEMVM1fxl3V/etiQYE6dFMf+q0bqCS1/OJYpj/NZE56oXccOmbm7c1INhKKaqy1un2J22WN+dwVwi8DAA9PHyv4afPN6JDSPyKRvTUORTNjsGckzXfL765BjxIlFhO1A4VmqiT+jJ3w4Utg/klh0oGIbiF1+4kWtHunBsC9s0cC0D2zRwbItrR7p43S0bTxlcteeAXTZUYKQnc14HY3C8HLMn67BnvEqlGRDGMZVmwJ7x6nldjimEEOLCIxkysUBv1sE2DRp+RD61MGZv+BG2mWTKlmPnwYnV3kVxkbCN1lDoUGOZCsMwqXkhSin8MF705Pqzjz63rPtu349rLn5dKmmEnwRkhoKt/UmW6fk0jDgT67a2D+R55x07uPeJUZ44UqIehGRsi2s2FLnrqqHztuzw+bpQyzGFEEJceCQgEwtcP9LN5t4sz4xXyDomQaSJtMZUSXe7qZrPpYN5rh/pXtb93fOdw2d4j8WFKtIQRRrbMrh2QxHTUBycqpNPWYRRvOjJ9b7J+rLuu9FapmUtEZDB8fVkjplkwOJYs2+iykS1Sc610FovCMpO1TDiTAQK2wfy/PuXXVhlh6vhQizHFEIIceGRgEwsYFkGb7xtM+/75x/x1GgFUymU0mitiLSmN+vyhhdtXvY8slJdOpmJ0xNpyLsmL9jYzfaBHEdnG9y0pYe7rx7m8uHCoifXzVazjlPRQBzHSeZNLd6Nsd1xMWVbzNR9/uKBffzwuVn2TdQ4OttkMJ9i20C2MwMNltcw4kwECu2yQzGfvC5CCCHOdRKQiXnabbwjrRkuuNS8EC+M0DEopXEtk819GTb1Lv8ER/oqiuUyWjWCMWAqeNHWXnqyNmGr0+E1G7pOmUVyTagt4xqAARyabmAZzBk+ljBJujkqlQRjlgGfe/QIlmmwritFqRFwrNRkrNyg4gVcN9JFT9ZdUcMICRSEEEIIARKQiTnabbz3jld48liZajNke1+GYsbBsgwytslQwWXfZJ2vPjnG1r7csq7oy9QxsVymSrJijmlw+XCe//JzV2MotaIs0kAhxfTEqRt79GZMUrbJaLnZuWhgAI6lcCwTQymyjoFSBo0gou6H3LCpB6UUOwbz1Pzke+VG0iji8iHFaNmThhFCCCGEWBEJyAQwv413zjVRQFfGZqoeUA/jTgYAmNe0YH1XWtZniA6DxTOi7UBrLktBeML3Yg35lMX67jR3XTnESPfKO/5tKGZ4ehkBmUHMZUM5tvRlePJomYYfEUYRKdukL+dgWyZ+EKFRhHHMuq50Z71YT9bhupEu9o5XGa80OTxdp5i2uXYZGTwhhBBCiLkkIBML2nhP1XyiWFNI2+RcmK757Juo0Z1xUEp1mhY8dazM/3386IoG44oLl6nANEC32hOGMWRsg2YYo1QSgLXXaTmmIpey8AKN0hGGaVJI22zpzeJaiv5C+rSzTI1weUWyjVBz6VABAC/UHJmpE8UxpWbITD2gP2cwUEjhhRG2qRjIp+bdvifrcNPmbmbqPgemarzu5o382I5+uSAhhBBCiBWRgEwsaOPtmAaWaRBEGtcyyKUspms+lWZIIW3T8CO8MOZLTxzDC2IKKYtCqwPdE0fmD8b1Zf7YRcVQBqYFXqSxDE1P1mYg7zJa9rBNA6UUWscEkaY37zKUT7F/qkYYaUZ6MvTmXLYP5J5XlulwaXldFqOYTsZr+0COqhdS9wLSjkUQxWztzxFrjWunqHkhjSAif0JHRqWSAdX9uRTb+pdXwiuEEEIIMZcEZIKaH9IMIzJO0oQgn7LozjhMVJrYGZs4hrofMl1PyhmPzjbxgphm4BFGMXvGKwRRjG0aFFMm39o7wRceP0LBNUDJ7PGLRaTBjGMikqAk5Vj05FJYlkl/QdEMYi4fyjNa9hivNJmq+gwX07z6uvVcO9JFX95dlbJX11x8oPmJ5ra7n1uCOFXz8LwYL4y4YVMPd1w+yH0/GmPX0RI515rX5n4lTTyEEEIIIRYjAZkg61ikLJO6H5JP2Sil2D6QY7LqsW+yRhRpwjjm8YMz/OhICdc2qDSThgZ+GFNuhos27qh6kh270NhG8i/UEEVgmjA3CerHYBma7oxN2rZwWqMRbNOg5oVkXIubNmfOaJnfC7d1s2eyccrtBvLzB5u3SxCPlRpM1wLe+tKt3LipB8NQGAarOsxZCCGEEKJN0heC9V1ptvXnOFZqonU7tNKARmvwoxjLMCh7Ic9O1th1tMLB6ToTVZ/SEsGYuLBYrVhDGWCaJo5logywDIMTwxBTgW2amKYiaHXyCKIY0zBwWmWLZ7LM7913Xrms7Tb2ZOa834+rehE3bOruBGNwfJjzVeuKzNYDDkzWmK0HXL2+2CnPFUIIIYQ4HZIhExiG4q6rBjlaavDMWJV8yuTp0QrlRkgURzSCmEYg08QudCYwN6epANtMAqwgBqWBGLShiXTSoMOPYmwj6ayoNbiWwjYUlWZAjIWhAuyMTbUZMlBIkU9ZZ7zML5d1+OlrhvniD48tuc1LL+ljqDuzoozXmRjmLIQQQgghAZkAkpPNH79sgHu+dYAfHJ5hqubjn9inXFwwbAUpx0yCqjBCKdBaEcUaQ0F/zqHuhdSDGK0UlpnM5GqEMUGsCaMke6oUKENhoMg7JoahCGNNHMU0/YiMbXJopkFX2mZzb4aqF56VMr8//8XrgZ2LBmU/fc0wf/6L13fm7u2bqDJWbuJaJlevL560oYgMcxZCCCHEalN6sZodsWLlcplisUipVKJQKKz17ixbHGuOzDZ4arTMl35wjIYfMl33+P7B0lrvmjgDTOBnXzDEbTsGSTkmvVmHqwYLfH3vOKMlj8mKxw8Pz3B4tkm1GVL2AgylGMq75DM2z003qHkhkGTIbFNhGIqMbbKhOwlUpmseVS+kGcRcNpwnn7JxLaP1z3zeXRRXolrz+W9f381z00029KT4jR+/lFz2+Nqx9vtfMl5CCCGEWE0riQ0kIFsl52NAtne8wj/tfI7P7nyO6VpATNKwoR7IW+JCYBmQsVtZqyjJbF0ymOOzb70Nx1m6E2EYxuxsZUkPTNZ46JkJDs00CKKkbNW1DExD8dxMA9cyKaQsenMu6dZ9aq0pN0Omax6/eddl/Ox16zlWbkrQI4QQQoiLxkpiAylZvEjtHa/wur/6DhNVf973A2mMeM4wVdJK/lQcE1zTQBkGKdug3AgIY03KSoKxWINWiqFCinfecelJgzEAyzK4eUtv5+u3vHhrJ0DrzTpct76Lnc/N8P9+bhdeELGuK4VhzO8P1PAj+nIpbt7Sg2UZUuYnhBBCCLEECcguQnGs+X8+8d0FwZg4d3SlbS4ZzPLIgdlTbjvSk2VbX4a0a3P31cPM1H3+72NHeHayhh/FpE2D7QM53njbFl5++eCK9+XEAA3g5s293H5JP/c9NcZ0PSCfsrBNgyCKqTRDYq25dWsPI90SiAkhhBBCnIwEZBehp47McrTkrfVuiCX8/25cx3/56at57xd3LSsgC6OY6zb2zFuX9ZoXbJiX1bp+pBvLWr0pF4ah+MUXbmS86vHMWIVKM+z8zDQU14508bpbNkppohBCCCHEKUhAdhH6r19+eq13QSwh75j82ssuIZWy6Mm6y7rNi3f08e9eum1e8LNYVmu1bR/I8847dnDvE6M8caREPQjJ2BbXbChy11VDMptLCCGEEGIZJCC7CD07VVvrXRCLSFmKn79ppFPmd/dVQ/zFg8+e8navu3FkzTJR2wfy/PuXyWwuIYQQQojTJQHZRSjnrF7pmjh9jgmmYWAZit6MyVUjvfPK/L53aHpZ9/O9Q9NcNdJ9Jnf1pGQ2lxBCCCHE6ZOA7CK0pdtmz2RjrXfjotSTtXjrS7bT9AN+NFo9aZnfkZnmsu5zudsJIYQQQohzjwRkF5k41ty/p7zWu3FR6s85fOA11/DyyweXNZA4l0ra0ytgse737e+3txNCCCGEEOcfCcguInvHK3xl1xgyauzscU1F1rW4fCjHv33Jtk7b+eWU+d1+2QB//vV9hDGYgGEkAZgC4hgikkHet182cKafhhBCCCGEOEMkILtI7B2v8MlvHWC6JrPHzjRLwXDR5c6rhrlxc89pt53vzaTY1p9jz3iVSIOOwWgNi45J/ntrf47eTOrMPBEhhBBCCHHGSUB2gZutNPmdf3qCnQdLaGJ+5irJppwJ7WJD21S87fatvP2lO3Cc51dKuL4rzc9cu57P7TzMczMNvEgTt2oXXVOxoTvNq65bz/qu9PPbeSGEEEIIsWYkILvAjM/W+NW/fZTRsk+54VMP5q8++vh3jqzRnl2YDAWOaaC1Jow1WcfkzsuHn3cwBklZ411XDXK01GC8VKfsRXhBhGubFFyTgWKGO68clBbzQgghhBDnMQnILiA/9kf3c0g67p01BmC1giFlKDKWgWub1IJw1R5j+0CeN922ma/sGmPfRBUvjHAtk+0DOe68clCGLwshhBBCnOckILtASDB2ZqnWv7j1dcYCy0o+PqZSZBwDwzDIOCa51Op+rLYP5Nl6uwxfFkIIIYS4EElAdgEYn61JMHaGKCCfMunPpaj7AVP1kLRl0JdzcS0DZSTNNrwoxjINRrrT5F171fdDhi8LIYQQQlyYJCC7APzq3zy61rtw3urL2qQdi+maT91PBgI4lkJrTRRDyjYpph1QkEs5FNIOOdfCsQxm6gFhGGMZBgN5F8swuH5jtzTZEEIIIYQQyyYB2QXgyKxkx5bLNuCWrT3UvIhywweVtKLvyTq4VohtKjZ0Z0jZJmgNSmEYkLEtrtlQ5LLhAl9/epypqseG7jSmoYhiTaUZ0ptzpcmGEEIIIYRYEQnILgA9WYeJWrDWu3HOMgHThKFCirf82DZef+tm9o5XuPeJUZ44UqIehGRsi6vXF7huYzd9eZesYzFcSHGs3FywbmtTb6bTZKPuh7iWyTUbuqTJhhBCCCGEWDGltdan3kycSrlcplgsUiqVKBQKZ/WxHz08wWs+8shZfcxzSXvccgw4JiilcAyDywfTXL9lgL68y1DR5c7Lhua1o49jfdqNMp7PbYUQQgghxIVtJbGBZMguAC9Y30chZVJuRmu9K2eUCXRlbTZ0ZdgxlOW377yU7xycYbTkMVR0+fHtA+waKzNV8+nNOlw/0o1lGUve3/NplCFNNoQQQgghxGqQDNkqWcsMGcDe8Qo/8z8eoh7Ei/4865p4XkQEpGxFIzh7h70/a+PHMaXGygNGU8Gm3gx9OZcXb+/jp64ZlrJAIYQQQghxTpMM2UVo+0Ce//trL+Zz332Wv3v0GDU/QmsYzCpu2THMcFea3aNlHjtUohlEFFKKcnN5A4z7czY518I2DbJGxOFKhB/G5FyTn71mgJ5iHg0MFV1etLmH//HgXp6bblJwLTZ0uRwu+XhhxCP7p5mo+id9rKytMA2DnGvymuuGuGXHUGdNl5QFCiGEEEKIC41kyFbJWmfI2uaubZqseDx+aJZnJ2t4YYRrmWzty7C+O0PKMTkwWeOrjx9k11iTUIOl4MoBhys2DZJzLdZ3p/g3140w1Qw6a6WWanSxnP3JOhZ52+C/3PsUz003WV902D6UY6YWLfpYEoAJIYQQQojz0UpiAwnIVsm5EpCd6FTNJ8IwZufhmWWvuxJCCCGEEEKcnJQsio5TNZ+wLIObt/SexT0SQgghhBBCtEkqRAghhBBCCCHWiARkQgghhBBCCLFGJCA7wUc+8hE2b95MKpXilltu4ZFHLt6By0IIIYQQQogzSwKyOf7+7/+ed73rXbz3ve9l586dXHvttdx1112Mj4+v9a4JIYQQQgghLkASkM3xJ3/yJ7zlLW/hTW96E1dccQV/+Zd/SSaT4a//+q/XeteEEEIIIYQQFyAJyFp83+fRRx/ljjvu6HzPMAzuuOMOHn744QXbe55HuVye908IIYQQQgghVkICspbJyUmiKGJwcHDe9wcHBxkdHV2w/fvf/36KxWLn38jIyNnaVSGEEEIIIcQFQgKy0/Q7v/M7lEqlzr/Dhw+v9S4JIYQQQgghzjMyGLqlr68P0zQZGxub9/2xsTGGhoYWbO+6Lq7rnq3dE0IIIYQQQlyAJEPW4jgON9xwA/fff3/ne3Ecc//993Prrbeu4Z4JIYQQQgghLlSSIZvjXe96F294wxu48cYbufnmm/nv//2/U6vVeNOb3rTWuyaEEEIIIYS4AElANsdrX/taJiYmeM973sPo6CjXXXcd995774JGH0IIIYQQQgixGpTWWq/1TlwIyuUyxWKRUqlEoVBY690RQgghhBBCrJGVxAaSIVsl7bhW5pEJIYQQQghxcWvHBMvJfUlAtkoqlQqAzCMTQgghhBBCAEmMUCwWT7qNlCyukjiOOXr0KPl8HqXUWu/OqiqXy4yMjHD48GEpx1xDchzODXIczg1yHNaeHINzgxyHc4Mch3PDuXQctNZUKhXWrVuHYZy8sb1kyFaJYRhs2LBhrXfjjCoUCmv+5hZyHM4VchzODXIc1p4cg3ODHIdzgxyHc8O5chxOlRlrkzlkQgghhBBCCLFGJCATQgghhBBCiDUiAZk4Jdd1ee9734vrumu9Kxc1OQ7nBjkO5wY5DmtPjsG5QY7DuUGOw7nhfD0O0tRDCCGEEEIIIdaIZMiEEEIIIYQQYo1IQCaEEEIIIYQQa0QCMiGEEEIIIYRYIxKQCSGEEEIIIcQakYDsIvWRj3yEzZs3k0qluOWWW3jkkUeW3PZjH/sYL3nJS+ju7qa7u5s77rhjwfZvfOMbUUrN+/eKV7ziTD+N89pKjsE999yz4PVNpVLzttFa8573vIfh4WHS6TR33HEHe/bsOdNP47y3kuNw++23LzgOSinuvvvuzjbyWVi5b37zm7zyla9k3bp1KKX4p3/6p1Pe5oEHHuD666/HdV22b9/OPffcs2CblRxbsfLj8LnPfY6f+ImfoL+/n0KhwK233spXvvKVedv8/u///oLPw2WXXXYGn8X5baXH4IEHHlj0d9Lo6Oi87eSzsDIrPQ6L/d5XSnHllVd2tpHPwsq9//3v56abbiKfzzMwMMCrX/1qdu/efcrb/eM//iOXXXYZqVSKq6++mi9/+cvzfn4uni9JQHYR+vu//3ve9a538d73vpedO3dy7bXXctdddzE+Pr7o9g888ACve93r+MY3vsHDDz/MyMgId955J0eOHJm33Ste8QqOHTvW+ffpT3/6bDyd89JKjwEkU+fnvr4HDx6c9/MPfvCD/Nmf/Rl/+Zd/yXe/+12y2Sx33XUXzWbzTD+d89ZKj8PnPve5ecdg165dmKbJv/k3/2bedvJZWJlarca1117LRz7ykWVtv3//fu6++25e9rKX8fjjj/POd76TN7/5zfOCgdP5jF3sVnocvvnNb/ITP/ETfPnLX+bRRx/lZS97Ga985St57LHH5m135ZVXzvs8PPTQQ2di9y8IKz0Gbbt37573Gg8MDHR+Jp+FlVvpcfjTP/3Tea//4cOH6enpWfC3QT4LK/Pggw/y9re/ne985zvcd999BEHAnXfeSa1WW/I23/72t3nd617Hr/zKr/DYY4/x6le/mle/+tXs2rWrs805eb6kxUXn5ptv1m9/+9s7X0dRpNetW6ff//73L+v2YRjqfD6vP/WpT3W+94Y3vEG/6lWvWu1dvWCt9Bh88pOf1MViccn7i+NYDw0N6T/+4z/ufG92dla7rqs//elPr9p+X2ie72fhwx/+sM7n87parXa+J5+F5wfQn//850+6zW/91m/pK6+8ct73Xvva1+q77rqr8/XzPbYXu+Uch8VcccUV+n3ve1/n6/e+97362muvXb0du4gs5xh84xvf0ICemZlZchv5LDw/p/NZ+PznP6+VUvrAgQOd78ln4fkbHx/XgH7wwQeX3Obnf/7n9d133z3ve7fccot+61vfqrU+d8+XJEN2kfF9n0cffZQ77rij8z3DMLjjjjt4+OGHl3Uf9XqdIAjo6emZ9/0HHniAgYEBLr30Ut72trcxNTW1qvt+oTjdY1CtVtm0aRMjIyO86lWv4sknn+z8bP/+/YyOjs67z2KxyC233LLs43qxWY3Pwic+8Ql+4Rd+gWw2O+/78lk4sx5++OF5xw3grrvu6hy31Ti2YuXiOKZSqSz427Bnzx7WrVvH1q1b+aVf+iUOHTq0Rnt44bruuusYHh7mJ37iJ/jWt77V+b58FtbGJz7xCe644w42bdo07/vyWXh+SqUSwILfMXOd6u/DuXq+JAHZRWZycpIoihgcHJz3/cHBwQU150t597vfzbp16+a9mV/xilfwv/7X/+L+++/nj/7oj3jwwQf5yZ/8SaIoWtX9vxCczjG49NJL+eu//mu+8IUv8Ld/+7fEccyLXvQinnvuOYDO7Z7Pcb3YPN/PwiOPPMKuXbt485vfPO/78lk480ZHRxc9buVymUajsSq/58TKfehDH6JarfLzP//zne/dcsst3HPPPdx77738xV/8Bfv37+clL3kJlUplDff0wjE8PMxf/uVf8tnPfpbPfvazjIyMcPvtt7Nz505gdf7mi5U5evQo//Iv/7Lgb4N8Fp6fOI555zvfyW233cZVV1215HZL/X1ov9/P1fMla80eWZyXPvCBD/CZz3yGBx54YF5TiV/4hV/o/PfVV1/NNddcw7Zt23jggQd4+ctfvha7ekG59dZbufXWWztfv+hFL+Lyyy/nox/9KH/wB3+whnt28frEJz7B1Vdfzc033zzv+/JZEBejv/u7v+N973sfX/jCF+atX/rJn/zJzn9fc8013HLLLWzatIl/+Id/4Fd+5VfWYlcvKJdeeimXXnpp5+sXvehF7Nu3jw9/+MP8zd/8zRru2cXrU5/6FF1dXbz61a+e9335LDw/b3/729m1a9cFu+5OMmQXmb6+PkzTZGxsbN73x8bGGBoaOultP/ShD/GBD3yAr371q1xzzTUn3Xbr1q309fWxd+/e573PF5rncwzabNvmBS94Qef1bd/u+dznxeb5HIdarcZnPvOZZf0Rlc/C6hsaGlr0uBUKBdLp9Kp8xsTyfeYzn+HNb34z//AP/7CgVOhEXV1dXHLJJfJ5OINuvvnmzusrn4WzS2vNX//1X/P6178ex3FOuq18FpbvHe94B1/84hf5xje+wYYNG0667VJ/H9rv93P1fEkCsouM4zjccMMN3H///Z3vxXHM/fffPy8Dc6IPfvCD/MEf/AH33nsvN9544ykf57nnnmNqaorh4eFV2e8Lyekeg7miKOKJJ57ovL5btmxhaGho3n2Wy2W++93vLvs+LzbP5zj84z/+I57n8cu//MunfBz5LKy+W2+9dd5xA7jvvvs6x201PmNieT796U/zpje9iU9/+tPzxj8spVqtsm/fPvk8nEGPP/545/WVz8LZ9eCDD7J3795lXayTz8Kpaa15xzvewec//3m+/vWvs2XLllPe5lR/H87Z86U1ayci1sxnPvMZ7bquvueee/SPfvQj/au/+qu6q6tLj46Oaq21fv3rX69/+7d/u7P9Bz7wAe04jv4//+f/6GPHjnX+VSoVrbXWlUpF/8f/+B/1ww8/rPfv36+/9rWv6euvv17v2LFDN5vNNXmO57qVHoP3ve99+itf+Yret2+ffvTRR/Uv/MIv6FQqpZ988snONh/4wAd0V1eX/sIXvqB/+MMf6le96lV6y5YtutFonPXnd75Y6XFoe/GLX6xf+9rXLvi+fBZOT6VS0Y899ph+7LHHNKD/5E/+RD/22GP64MGDWmutf/u3f1u//vWv72z/7LPP6kwmo3/zN39TP/XUU/ojH/mINk1T33vvvZ1tTnVsxUIrPQ7/+3//b21Zlv7IRz4y72/D7OxsZ5vf+I3f0A888IDev3+//ta3vqXvuOMO3dfXp8fHx8/68zsfrPQYfPjDH9b/9E//pPfs2aOfeOIJ/R/+w3/QhmHor33ta51t5LOwcis9Dm2//Mu/rG+55ZZF71M+Cyv3tre9TReLRf3AAw/M+x1Tr9c725z4d/pb3/qWtixLf+hDH9JPPfWUfu9736tt29ZPPPFEZ5tz8XxJArKL1P/4H/9Db9y4UTuOo2+++Wb9ne98p/Ozl770pfoNb3hD5+tNmzZpYMG/9773vVprrev1ur7zzjt1f3+/tm1bb9q0Sb/lLW+RX/ansJJj8M53vrOz7eDgoP6pn/opvXPnznn3F8ex/r3f+z09ODioXdfVL3/5y/Xu3bvP1tM5b63kOGit9dNPP60B/dWvfnXBfcln4fS0W3ef+K/92r/hDW/QL33pSxfc5rrrrtOO4+itW7fqT37ykwvu92THViy00uPw0pe+9KTba52MIxgeHtaO4+j169fr1772tXrv3r1n94mdR1Z6DP7oj/5Ib9u2TadSKd3T06Nvv/12/fWvf33B/cpnYWVO53fS7OysTqfT+q/+6q8WvU/5LKzcYscAmPf7frG/0//wD/+gL7nkEu04jr7yyiv1l770pXk/PxfPl5TWWp+x9JsQQgghhBBCiCXJGjIhhBBCCCGEWCMSkAkhhBBCCCHEGpGATAghhBBCCCHWiARkQgghhBBCCLFGJCATQgghhBBCiDUiAZkQQgghhBBCrBEJyIQQQgghhBBijUhAJoQQQgghhBBrRAIyIYQQQgghhFgjEpAJIYQQa+zAgQMopXj88cfXeleEEEKcZRKQCSGEEGeI7/trvQtCCCHOcRKQCSGEEHPEccwHP/hBtm/fjuu6bNy4kT/8wz8E4N3vfjeXXHIJmUyGrVu38nu/93sEQdC57e///u9z3XXX8fGPf5wtW7aQSqUAuPfee3nxi19MV1cXvb29/PRP/zT79u3r3G7Lli0AvOAFL0Apxe233372nrAQQog1Za31DgghhBDnkt/5nd/hYx/7GB/+8Id58YtfzLFjx3j66acByOfz3HPPPaxbt44nnniCt7zlLeTzeX7rt36rc/u9e/fy2c9+ls997nOYpglArVbjXe96F9dccw3VapX3vOc9/OzP/iyPP/44hmHwyCOPcPPNN/O1r32NK6+8Esdx1uS5CyGEOPuU1lqv9U4IIYQQ54JKpUJ/fz9//ud/zpvf/OZTbv+hD32Iz3zmM3z/+98HkgzZf/2v/5UjR47Q39+/5O0mJyfp7+/niSee4KqrruLAgQNs2bKFxx57jOuuu261no4QQojzgJQsCiGEEC1PPfUUnufx8pe/fNGf//3f/z233XYbQ0ND5HI5fvd3f5dDhw7N22bTpk0LgrE9e/bwute9jq1bt1IoFNi8eTPAgtsKIYS4+EhAJoQQQrSk0+klf/bwww/zS7/0S/zUT/0UX/ziF3nsscf4T//pPy1o3JHNZhfc9pWvfCXT09N87GMf47vf/S7f/e53AWn6IYQQQtaQCSGEEB07duwgnU5z//33LyhZ/Pa3v82mTZv4T//pP3W+d/DgwVPe59TUFLt37+ZjH/sYL3nJSwB46KGH5m3TXjMWRdHzfQpCCCHOMxKQCSGEEC2pVIp3v/vd/NZv/RaO43DbbbcxMTHBk08+yY4dOzh06BCf+cxnuOmmm/jSl77E5z//+VPeZ3d3N729vfzVX/0Vw8PDHDp0iN/+7d+et83AwADpdJp7772XDRs2kEqlKBaLZ+ppCiGEOIdIyaIQQggxx+/93u/xG7/xG7znPe/h8ssv57WvfS3j4+P8zM/8DL/+67/OO97xDq677jq+/e1v83u/93unvD/DMPjMZz7Do48+ylVXXcWv//qv88d//MfztrEsiz/7sz/jox/9KOvWreNVr3rVmXp6QgghzjHSZVEIIYQQQggh1ohkyIQQQgghhBBijUhAJoQQQgghhBBrRAIyIYQQQgghhFgjEpAJIYQQQgghxBqRgEwIIYQQQggh1ogEZEIIIYQQQgixRiQgE0IIIYQQQog1IgGZEEIIIYQQQqwRCciEEEIIIYQQYo1IQCaEEEIIIYQQa0QCMiGEEEIIIYRYI/8fpT/BkNQYy1YAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Количество строк до удаления выбросов: 53943\n",
      "Количество строк после удаления выбросов: 49517\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Загрузка данных\n",
    "df = pd.read_csv(\".//static//csv//Diamonds Prices2022.csv\")\n",
    "\n",
    "# Выбор столбцов для анализа\n",
    "column1 = 'carat'\n",
    "column2 = 'price'\n",
    "\n",
    "\n",
    "# Функция для удаления выбросов\n",
    "def remove_outliers(df, column):\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",
    "    return df[(df[column] >= lower_bound) & (df[column] <= upper_bound)]\n",
    "\n",
    "# Удаление выбросов для каждого столбца\n",
    "df_cleaned = df.copy()\n",
    "for column in [column1, column2]:\n",
    "    df_cleaned = remove_outliers(df_cleaned, column)\n",
    "\n",
    "# Построение точечной диаграммы после удаления выбросов\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(df_cleaned[column1], df_cleaned[column2], alpha=0.5)\n",
    "plt.xlabel(column1)\n",
    "plt.ylabel(column2)\n",
    "plt.title(f'Scatter Plot of {column1} vs {column2} (After Removing Outliers)')\n",
    "plt.show()\n",
    "\n",
    "# Вывод количества строк до и после удаления выбросов\n",
    "print(f\"Количество строк до удаления выбросов: {len(df)}\")\n",
    "print(f\"Количество строк после удаления выбросов: {len(df_cleaned)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Количество пропущенных значений в каждом столбце:\n",
      "Unnamed: 0    0\n",
      "carat         0\n",
      "cut           0\n",
      "color         0\n",
      "clarity       0\n",
      "depth         0\n",
      "table         0\n",
      "price         0\n",
      "x             0\n",
      "y             0\n",
      "z             0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Проверка на пропущенные значения\n",
    "missing_values = df.isnull().sum()\n",
    "\n",
    "# Вывод результатов\n",
    "print(\"Количество пропущенных значений в каждом столбце:\")\n",
    "print(missing_values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Пустые строки не обнаружены."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Создадим выборки."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Размер обучающей выборки: 32365\n",
      "Размер контрольной выборки: 10789\n",
      "Размер тестовой выборки: 10789\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Загрузка данных\n",
    "df = pd.read_csv(\".//static//csv//Diamonds Prices2022.csv\")\n",
    "\n",
    "# Выбор признаков и целевой переменной\n",
    "X = df.drop('price', axis=1)  # Признаки (все столбцы, кроме 'Price')\n",
    "y = df['price']  # Целевая переменная ('Price')\n",
    "\n",
    "# Разбиение данных на обучающую и оставшуюся часть (контрольную + тестовую)\n",
    "X_train, X_temp, y_train, y_temp = train_test_split(X, y, test_size=0.4, random_state=42)\n",
    "\n",
    "# Разбиение оставшейся части на контрольную и тестовую выборки\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",
    "# Вывод размеров выборок\n",
    "print(f\"Размер обучающей выборки: {X_train.shape[0]}\")\n",
    "print(f\"Размер контрольной выборки: {X_val.shape[0]}\")\n",
    "print(f\"Размер тестовой выборки: {X_test.shape[0]}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Проанализируем сбалансированность выборок"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Распределение Price в обучающей выборке:\n",
      "price\n",
      "327      1\n",
      "334      1\n",
      "336      1\n",
      "337      1\n",
      "338      1\n",
      "        ..\n",
      "18791    1\n",
      "18795    2\n",
      "18797    1\n",
      "18804    1\n",
      "18806    1\n",
      "Name: count, Length: 9476, dtype: int64\n",
      "Процент положительных значений: 100.00%\n",
      "Процент отрицательных значений: 0.00%\n",
      "\n",
      "Необходима аугментация данных для балансировки классов.\n",
      "\n",
      "Распределение Price в контрольной выборке:\n",
      "price\n",
      "326      2\n",
      "340      1\n",
      "344      1\n",
      "354      1\n",
      "357      1\n",
      "        ..\n",
      "18781    1\n",
      "18784    1\n",
      "18791    1\n",
      "18803    1\n",
      "18823    1\n",
      "Name: count, Length: 5389, dtype: int64\n",
      "Процент положительных значений: 100.00%\n",
      "Процент отрицательных значений: 0.00%\n",
      "\n",
      "Необходима аугментация данных для балансировки классов.\n",
      "\n",
      "Распределение Price в тестовой выборке:\n",
      "price\n",
      "335      1\n",
      "336      1\n",
      "337      1\n",
      "351      1\n",
      "353      1\n",
      "        ..\n",
      "18766    1\n",
      "18768    1\n",
      "18780    1\n",
      "18788    1\n",
      "18818    1\n",
      "Name: count, Length: 5308, dtype: int64\n",
      "Процент положительных значений: 100.00%\n",
      "Процент отрицательных значений: 0.00%\n",
      "\n",
      "Необходима аугментация данных для балансировки классов.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Загрузка данных\n",
    "df = pd.read_csv(\".//static//csv//Diamonds Prices2022.csv\")\n",
    "\n",
    "# Выбор признаков и целевой переменной\n",
    "X = df.drop('price', axis=1)  # Признаки (все столбцы, кроме 'Price')\n",
    "y = df['price']  # Целевая переменная ('Price')\n",
    "\n",
    "# Разбиение данных на обучающую и оставшуюся часть (контрольную + тестовую)\n",
    "X_train, X_temp, y_train, y_temp = train_test_split(X, y, test_size=0.4, random_state=42)\n",
    "\n",
    "# Разбиение оставшейся части на контрольную и тестовую выборки\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",
    "# Функция для анализа распределения и вывода результатов\n",
    "def analyze_distribution(data, title):\n",
    "    print(f\"Распределение Price в {title}:\")\n",
    "    distribution = data.value_counts().sort_index()\n",
    "    print(distribution)\n",
    "    total = len(data)\n",
    "    positive_count = (data > 0).sum()\n",
    "    negative_count = (data < 0).sum()\n",
    "    positive_percent = (positive_count / total) * 100\n",
    "    negative_percent = (negative_count / total) * 100\n",
    "    print(f\"Процент положительных значений: {positive_percent:.2f}%\")\n",
    "    print(f\"Процент отрицательных значений: {negative_percent:.2f}%\")\n",
    "    print(\"\\nНеобходима аугментация данных для балансировки классов.\\n\")\n",
    "\n",
    "# Анализ распределения для каждой выборки\n",
    "analyze_distribution(y_train, \"обучающей выборке\")\n",
    "analyze_distribution(y_val, \"контрольной выборке\")\n",
    "analyze_distribution(y_test, \"тестовой выборке\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Применение методов приращения данных (аугментации)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Распределение Price в обучающей выборке после oversampling:\n",
      "price\n",
      "327      85\n",
      "334      85\n",
      "336      85\n",
      "337      85\n",
      "338      85\n",
      "         ..\n",
      "18791    85\n",
      "18795    85\n",
      "18797    85\n",
      "18804    85\n",
      "18806    85\n",
      "Name: count, Length: 9476, dtype: int64\n",
      "Процент положительных значений: 100.00%\n",
      "Процент отрицательных значений: 0.00%\n",
      "Распределение Price в контрольной выборке:\n",
      "price\n",
      "326      2\n",
      "340      1\n",
      "344      1\n",
      "354      1\n",
      "357      1\n",
      "        ..\n",
      "18781    1\n",
      "18784    1\n",
      "18791    1\n",
      "18803    1\n",
      "18823    1\n",
      "Name: count, Length: 5389, dtype: int64\n",
      "Процент положительных значений: 100.00%\n",
      "Процент отрицательных значений: 0.00%\n",
      "Распределение Price в тестовой выборке:\n",
      "price\n",
      "335      1\n",
      "336      1\n",
      "337      1\n",
      "351      1\n",
      "353      1\n",
      "        ..\n",
      "18766    1\n",
      "18768    1\n",
      "18780    1\n",
      "18788    1\n",
      "18818    1\n",
      "Name: count, Length: 5308, dtype: int64\n",
      "Процент положительных значений: 100.00%\n",
      "Процент отрицательных значений: 0.00%\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from imblearn.over_sampling import RandomOverSampler\n",
    "\n",
    "# Загрузка данных\n",
    "df = pd.read_csv(\".//static//csv//Diamonds Prices2022.csv\")\n",
    "\n",
    "# Выбор признаков и целевой переменной\n",
    "X = df.drop('price', axis=1)  # Признаки (все столбцы, кроме 'Price')\n",
    "y = df['price']  # Целевая переменная ('Price')\n",
    "\n",
    "# Разбиение данных на обучающую и оставшуюся часть (контрольную + тестовую)\n",
    "X_train, X_temp, y_train, y_temp = train_test_split(X, y, test_size=0.4, random_state=42)\n",
    "\n",
    "# Разбиение оставшейся части на контрольную и тестовую выборки\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",
    "# Применение oversampling к обучающей выборке\n",
    "oversampler = RandomOverSampler(random_state=42)\n",
    "X_train_resampled, y_train_resampled = oversampler.fit_resample(X_train, y_train)\n",
    "\n",
    "# Функция для анализа распределения и вывода результатов\n",
    "def analyze_distribution(data, title):\n",
    "    print(f\"Распределение Price в {title}:\")\n",
    "    distribution = data.value_counts().sort_index()\n",
    "    print(distribution)\n",
    "    total = len(data)\n",
    "    positive_count = (data > 0).sum()\n",
    "    negative_count = (data < 0).sum()\n",
    "    positive_percent = (positive_count / total) * 100\n",
    "    negative_percent = (negative_count / total) * 100\n",
    "    print(f\"Процент положительных значений: {positive_percent:.2f}%\")\n",
    "    print(f\"Процент отрицательных значений: {negative_percent:.2f}%\")\n",
    "\n",
    "# Анализ распределения для каждой выборки\n",
    "analyze_distribution(y_train_resampled, \"обучающей выборке после oversampling\")\n",
    "analyze_distribution(y_val, \"контрольной выборке\")\n",
    "analyze_distribution(y_test, \"тестовой выборке\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Выгрузка в датафрейм третьего набора (Заработная плата рабочих мест в области Data Science)\n",
    "\t\n",
    "https://www.kaggle.com/datasets/henryshan/2023-data-scientists-salary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Для проведения этого анализа был использован набор данных, содержащий релевантную информацию о Data Scientists. Набор данных включает в себя следующие переменные:\n",
    "\n",
    "work_year:Год выплаты зарплаты.\n",
    "\n",
    "experience_level: Уровень опыта работы в течение года.\n",
    "\n",
    "EN > Начальный уровень / Юниор\n",
    "MI> Средний уровень / Средний\n",
    "SE > Старший уровень / Эксперт\n",
    "EX > Исполнительный уровень / Директор\n",
    "employment_type: Тип занятости для должности.\n",
    "\n",
    "PT > Частичная занятость\n",
    "FT > Полный рабочий день\n",
    "CT > Договор\n",
    "FL > Фриланс\n",
    "job_title: На эту должность я работал в течение года.\n",
    "\n",
    "salary: Общая сумма выплаченной заработной платы брутто.\n",
    "\n",
    "salary_currency: Валюта выплачиваемой заработной платы в виде кода валюты ISO 4217.\n",
    "\n",
    "salaryinusd: Заработная плата в долларах США.\n",
    "\n",
    "employee_residence: Основная страна проживания сотрудника в течение рабочего года в виде кода страны по стандарту ISO 3166.\n",
    "\n",
    "remote_ratio:Общий объем работы, выполненной удаленно.\n",
    "\n",
    "company_location: Страна местонахождения главного офиса работодателя или филиала по договору.\n",
    "\n",
    "company_size: Среднее количество людей, которые работали в компании в течение года.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Проблемная область: Данные о зарплатах рабочих мет Data Science\n",
    "\n",
    "\n",
    "Объект наблюдения: зарплаты\n",
    "\n",
    "\n",
    "Пример бизнес-цели:\n",
    "\n",
    "1. Оптимизация процесса найма\n",
    "Цель: Уменьшить время и затраты на рекрутинг, привлекая кандидатов с адекватными ожиданиями по зарплате.\n",
    "Применение: Использовать данные о заработной плате для определения конкурентоспособных предложений о работе, что позволит привлекать высококвалифицированные таланты без чрезмерных затрат.\n",
    "\n",
    "2. Систематизация карьерного роста\n",
    "\n",
    "Цель: Разработка структурированных карьерных треков и программ повышения квалификации.\n",
    "Применение: Анализируя данные о зарплатах на разных уровнях должностей, создать систему повышений и соответствующих компенсаций, мотивируя сотрудников для роста внутри компании.\n",
    "\n",
    "3. Планирование бюджета и прогнозирование кадровых расходов\n",
    "Цель: Более точное планирование бюджетов на оплату труда в отделах Data Science.\n",
    "Применение: Использовать анализ рынка для прогнозирования будущих расходов на зарплату, что позволит компании закладывать соответствующие суммы в бюджет.\n",
    "\n",
    "\n",
    "Актуальность датасета о заработной плате в области Data Science играет ключевую роль для бизнеса и специалистов по работе с данными. Рассмотрим несколько аспектов, подчеркивающих его значимость.\n",
    "\n",
    "1. Актуальные рыночные тренды\n",
    "Изменения в спросе и предложении: Данные о зарплатах могут отражать текущие тенденции на рынке труда, такие как повышение спроса на определённые навыки. Это особенно важно в такой динамичной области, как Data Science, где технологии и инструменты быстро развиваются.\n",
    "Географические различия: Заработные платы могут варьироваться в зависимости от региона (например, наличие IT-центров в крупных городах). Датасет позволяет анализировать, какие регионы предлагают лучшие условия для специалистов.\n",
    "\n",
    "2. Оценка конкурентоспособности\n",
    "Сравнение с конкурентами: Знание о текущих зарплатах позволяет компаниям позиционировать себя на рынке труда. Это поможет избежать проблем с удержанием талантов и привлечением новых сотрудников.\n",
    "Формирование предложений: Данная информация поможет создать программные предложения о работе, которые будут привлекательными для нужной аудитории.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['work_year', 'experience_level', 'employment_type', 'job_title',\n",
      "       'salary', 'salary_currency', 'salary_in_usd', 'employee_residence',\n",
      "       'remote_ratio', 'company_location', 'company_size'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "df3 = pd.read_csv(\".//static//csv//ds_salaries.csv\")\n",
    "\n",
    "print(df3.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Атрибуты: \n",
    "work_year - Год работы\n",
    "experience_level - Уровень опыта\n",
    "employment_type - Тип занятости\n",
    "job_title - Должность\n",
    "salary - Зарплата\n",
    "salary_currency - Валюта зарплаты\n",
    "salary_in_usd - Зарплата в долларах США\n",
    "employee_residence - Местонахождение сотрудника\n",
    "remote_ratio - Доля удаленной работы\n",
    "company_location - Местоположение компании\n",
    "company_size - Размер компании"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Проверка на выбросы."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Количество выбросов в столбце 'work_year': 76\n",
      "Количество выбросов в столбце 'salary': 113\n",
      "Количество выбросов в столбце 'salary_in_usd': 63\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1000 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Предполагаем, что ваш DataFrame df уже загружен\n",
    "\n",
    "# Выбираем столбцы для анализа\n",
    "columns_to_check = ['work_year', 'salary', 'salary_in_usd']\n",
    "\n",
    "# Функция для подсчета выбросов\n",
    "def count_outliers(df, columns):\n",
    "    outliers_count = {}\n",
    "    for col in columns:\n",
    "        # Проверяем, чтобы данные были числовыми\n",
    "        if not pd.api.types.is_numeric_dtype(df[col]):\n",
    "            print(f\"Предупреждение: столбец '{col}' не числовой. Пропускаем.\")\n",
    "            continue\n",
    "        \n",
    "        Q1 = df[col].quantile(0.25)\n",
    "        Q3 = df[col].quantile(0.75)\n",
    "        IQR = Q3 - Q1\n",
    "        lower_bound = Q1 - 1.5 * IQR\n",
    "        upper_bound = Q3 + 1.5 * IQR\n",
    "        \n",
    "        # Считаем количество выбросов\n",
    "        outliers = df[(df[col] < lower_bound) | (df[col] > upper_bound)]\n",
    "        outliers_count[col] = len(outliers)\n",
    "    \n",
    "    return outliers_count\n",
    "\n",
    "# Подсчитываем выбросы\n",
    "outliers_count = count_outliers(df, columns_to_check)\n",
    "\n",
    "# Выводим количество выбросов для каждого столбца\n",
    "for col, count in outliers_count.items():\n",
    "    print(f\"Количество выбросов в столбце '{col}': {count}\")\n",
    "\n",
    "# Создаем диаграммы размахов\n",
    "plt.figure(figsize=(15, 10))\n",
    "for i, col in enumerate(columns_to_check, 1):\n",
    "    plt.subplot(2, 2, i)\n",
    "    # Проверяем, чтобы данные были числовыми для построения графика\n",
    "    if pd.api.types.is_numeric_dtype(df[col]):\n",
    "        sns.boxplot(x=df[col])\n",
    "        plt.title(f'Box Plot of {col}')\n",
    "    else:\n",
    "        print(f\"Предупреждение: столбец '{col}' не числовой и не может быть представлен на графике.\")\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "В столбцах присутствуют выбросы."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Количество выбросов в столбце 'work_year': 76\n",
      "Количество выбросов в столбце 'salary': 113\n",
      "Количество выбросов в столбце 'salary_in_usd': 63\n",
      "Количество удаленных строк: 208\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Предполагаем, что ваш DataFrame df уже загружен\n",
    "\n",
    "# Выбираем столбцы для анализа\n",
    "columns_to_check = ['work_year', 'salary', 'salary_in_usd']\n",
    "\n",
    "# Функция для подсчета выбросов\n",
    "def count_outliers(df, columns):\n",
    "    outliers_count = {}\n",
    "    for col in columns:\n",
    "        Q1 = df[col].quantile(0.25)\n",
    "        Q3 = df[col].quantile(0.75)\n",
    "        IQR = Q3 - Q1\n",
    "        lower_bound = Q1 - 1.5 * IQR\n",
    "        upper_bound = Q3 + 1.5 * IQR\n",
    "        \n",
    "        # Считаем количество выбросов\n",
    "        outliers = df[(df[col] < lower_bound) | (df[col] > upper_bound)]\n",
    "        outliers_count[col] = len(outliers)\n",
    "    \n",
    "    return outliers_count\n",
    "\n",
    "# Подсчитываем выбросы\n",
    "outliers_count = count_outliers(df, columns_to_check)\n",
    "\n",
    "# Выводим количество выбросов для каждого столбца\n",
    "for col, count in outliers_count.items():\n",
    "    print(f\"Количество выбросов в столбце '{col}': {count}\")\n",
    "\n",
    "# Выбираем столбцы для очистки\n",
    "columns_to_clean = ['work_year', 'salary', 'salary_in_usd']\n",
    "\n",
    "# Функция для удаления выбросов\n",
    "def remove_outliers(df, columns):\n",
    "    for col in columns:\n",
    "        Q1 = df[col].quantile(0.25)\n",
    "        Q3 = df[col].quantile(0.75)\n",
    "        IQR = Q3 - Q1\n",
    "        lower_bound = Q1 - 1.5 * IQR\n",
    "        upper_bound = Q3 + 1.5 * IQR\n",
    "        \n",
    "        # Удаляем строки, содержащие выбросы\n",
    "        df = df[(df[col] >= lower_bound) & (df[col] <= upper_bound)]\n",
    "    \n",
    "    return df\n",
    "\n",
    "# Удаляем выбросы\n",
    "df_cleaned = remove_outliers(df, columns_to_clean)\n",
    "\n",
    "# Выводим количество удаленных строк\n",
    "print(f\"Количество удаленных строк: {len(df) - len(df_cleaned)}\")\n",
    "\n",
    "# Создаем диаграммы размаха для очищенных данных\n",
    "plt.figure(figsize=(15, 6))\n",
    "\n",
    "# Создаем подграфики для каждой переменной\n",
    "plt.subplot(1, 3, 1)  # Изменено на 1 строку и 3 столбца\n",
    "sns.boxplot(x=df_cleaned['work_year'])\n",
    "plt.title('Box Plot of Work Year (Cleaned)')\n",
    "plt.xlabel('Work Year')\n",
    "\n",
    "plt.subplot(1, 3, 2)  # Изменено на 1 строку и 3 столбца\n",
    "sns.boxplot(x=df_cleaned['salary'])\n",
    "plt.title('Box Plot of Salary (Cleaned)')\n",
    "plt.xlabel('Salary')\n",
    "\n",
    "plt.subplot(1, 3, 3)  # Новая диаграмма для salary_in_usd\n",
    "sns.boxplot(x=df_cleaned['salary_in_usd'])\n",
    "plt.title('Box Plot of Salary in USD (Cleaned)')\n",
    "plt.xlabel('Salary in USD')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# Сохраняем очищенный датасет\n",
    "df_cleaned.to_csv(\".//static//csv//ds_salaries.csv\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Выбросов стало меньше.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Количество пропущенных значений в каждом столбце:\n",
      "work_year             0\n",
      "experience_level      0\n",
      "employment_type       0\n",
      "job_title             0\n",
      "salary                0\n",
      "salary_currency       0\n",
      "salary_in_usd         0\n",
      "employee_residence    0\n",
      "remote_ratio          0\n",
      "company_location      0\n",
      "company_size          0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Проверка на пропущенные значения\n",
    "missing_values = df.isnull().sum()\n",
    "\n",
    "# Вывод результатов\n",
    "print(\"Количество пропущенных значений в каждом столбце:\")\n",
    "print(missing_values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Пропущенные значения отсутствуют. Переходим к созданию выборок.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Размер обучающей выборки: (2253, 10) (2253,)\n",
      "Размер контрольной выборки: (751, 10) (751,)\n",
      "Размер тестовой выборки: (751, 10) (751,)\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Разделение на признаки (X) и целевую переменную (y)\n",
    "X = df.drop(columns=['salary_in_usd'])\n",
    "y = df['salary_in_usd']\n",
    "\n",
    "# Разбиение на обучающую и остальную выборку (60% обучающая, 40% остальная)\n",
    "X_train, X_rem, y_train, y_rem = train_test_split(X, y, train_size=0.6, random_state=42)\n",
    "\n",
    "# Разбиение остальной выборки на контрольную и тестовую (50% контрольная, 50% тестовая)\n",
    "X_valid, X_test, y_valid, y_test = train_test_split(X_rem, y_rem, test_size=0.5, random_state=42)\n",
    "\n",
    "# Вывод размеров выборок\n",
    "print(\"Размер обучающей выборки:\", X_train.shape, y_train.shape)\n",
    "print(\"Размер контрольной выборки:\", X_valid.shape, y_valid.shape)\n",
    "print(\"Размер тестовой выборки:\", X_test.shape, y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Анализ сбалансированности выборки:\n",
      "\n",
      "Обучающая выборка:\n",
      "count      2253.000000\n",
      "mean     137387.371505\n",
      "std       62741.721382\n",
      "min        5132.000000\n",
      "25%       95000.000000\n",
      "50%      135000.000000\n",
      "75%      177000.000000\n",
      "max      450000.000000\n",
      "Name: salary_in_usd, dtype: float64\n",
      "\n",
      "Контрольная выборка:\n",
      "count       751.000000\n",
      "mean     138156.014647\n",
      "std       64747.578329\n",
      "min        5409.000000\n",
      "25%       95000.000000\n",
      "50%      132000.000000\n",
      "75%      175000.000000\n",
      "max      430967.000000\n",
      "Name: salary_in_usd, dtype: float64\n",
      "\n",
      "Тестовая выборка:\n",
      "count       751.00000\n",
      "mean     137533.82024\n",
      "std       62357.85690\n",
      "min        5409.00000\n",
      "25%       97600.00000\n",
      "50%      133300.00000\n",
      "75%      174750.00000\n",
      "max      423834.00000\n",
      "Name: salary_in_usd, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Разделение на признаки (X) и целевую переменную (y)\n",
    "X = df.drop(columns=['salary_in_usd'])\n",
    "y = df['salary_in_usd']\n",
    "\n",
    "# Разбиение на обучающую и остальную выборку (60% обучающая, 40% остальная)\n",
    "X_train, X_rem, y_train, y_rem = train_test_split(X, y, train_size=0.6, random_state=42)\n",
    "\n",
    "# Разбиение остальной выборки на контрольную и тестовую (50% контрольная, 50% тестовая)\n",
    "X_valid, X_test, y_valid, y_test = train_test_split(X_rem, y_rem, test_size=0.5, random_state=42)\n",
    "\n",
    "# Функция для анализа сбалансированности выборки\n",
    "def analyze_balance(y_train, y_valid, y_test):\n",
    "    print(\"Анализ сбалансированности выборки:\")\n",
    "    \n",
    "    # Описание для обучающей выборки\n",
    "    print(\"\\nОбучающая выборка:\")\n",
    "    print(y_train.describe())\n",
    "    \n",
    "    # Описание для контрольной выборки\n",
    "    print(\"\\nКонтрольная выборка:\")\n",
    "    print(y_valid.describe())\n",
    "    \n",
    "    # Описание для тестовой выборки\n",
    "    print(\"\\nТестовая выборка:\")\n",
    "    print(y_test.describe())\n",
    "\n",
    "# Анализ сбалансированности выборки\n",
    "analyze_balance(y_train, y_valid, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Объедините выборки в один DataFrame для визуализации\n",
    "data = pd.concat([y_train, y_valid, y_test], axis=1)\n",
    "data.columns = ['train', 'valid', 'test']\n",
    "\n",
    "# Визуализация\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.boxplot(data=data)\n",
    "plt.title('Сравнение распределений зарплат')\n",
    "plt.ylabel('Salary in USD')\n",
    "plt.xticks(ticks=[0, 1, 2], labels=['Обучающая', 'Контрольная', 'Тестовая'])\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "aimenv",
   "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
}