Analysis1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Science Case - F&E"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "from glob import glob\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/mk/f2kz1sjn3s91f64hrrgq01lc0000gn/T/ipykernel_78581/1769563454.py:20: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
      "  bauteile_data = pd.concat(bauteile_data, ignore_index=True)\n"
     ]
    }
   ],
   "source": [
    "def load_data(bauteile_path, verbrauch_path):\n",
    "    bauteile_files = glob(os.path.join(bauteile_path, \"*.csv\"))\n",
    "    verbrauch_data = pd.read_csv(verbrauch_path)\n",
    "    \n",
    "    \"\"\"\n",
    "    Load data from given paths.\n",
    "    \n",
    "    Parameters:\n",
    "    bauteile_path (str): Path to the directory containing bauteile CSV files.\n",
    "    verbrauch_path (str): Path to the verbrauch CSV file.\n",
    "\n",
    "    Returns:\n",
    "    tuple: bauteile_data (DataFrame), verbrauch_data (DataFrame)\n",
    "    \"\"\"\n",
    "    bauteile_data = []\n",
    "    for file in bauteile_files:\n",
    "        house_data = pd.read_csv(file)\n",
    "        house_data['cenarius_product_id'] = os.path.basename(file).replace('.csv', '')\n",
    "        bauteile_data.append(house_data)\n",
    "    bauteile_data = pd.concat(bauteile_data, ignore_index=True)\n",
    "    \n",
    "    return bauteile_data, verbrauch_data\n",
    "\n",
    "# File-paths\n",
    "bauteile_path = './data/bauteile'\n",
    "verbrauch_path = './data/verbrauch/verbrauch.csv'\n",
    "\n",
    "bauteile_data, verbrauch_data = load_data(bauteile_path, verbrauch_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocess the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### I want to extract the information out of the column LongDescBmElement to differentiate between Roof, Window, etc."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bauteile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def categorize_long_desc(element):\n",
    "\n",
    "    \"\"\"\n",
    "    Categorize the long description of building elements.\n",
    "    \n",
    "    Parameters:\n",
    "    element (str): The description of the building element.\n",
    "\n",
    "    Returns:\n",
    "    str: Categorized building element.\n",
    "    \"\"\"\n",
    "    if pd.isna(element):\n",
    "        return 'Unknown'\n",
    "    element = element.lower()\n",
    "    if 'dach' in element:\n",
    "        return 'Dach'\n",
    "    if 'boden' in element:\n",
    "        return 'Boden'\n",
    "    if 'decke' in element:\n",
    "        return 'Decke'\n",
    "    if 'aw' in element:\n",
    "        return 'Außenwand'\n",
    "    if 'wand' in element:\n",
    "        return 'Wand'\n",
    "    if 'fenster' in element or element.startswith('f '):\n",
    "        return 'Fenster'\n",
    "    if 'anbau' in element:\n",
    "        return 'Anbau'\n",
    "    if 'denkmal' in element:\n",
    "        return 'Denkmal'\n",
    "    if 'eg' in element:\n",
    "        return 'EG'\n",
    "    if 'iw' in element:\n",
    "        return 'IW'\n",
    "    if 'at' in element:\n",
    "        return 'AT'\n",
    "    return 'Other'\n",
    "\n",
    "def extract_building_elements(df):\n",
    "    \"\"\"\n",
    "    Extract building elements from the data.\n",
    "    \n",
    "    Parameters:\n",
    "    df (DataFrame): The input dataframe.\n",
    "\n",
    "    Returns:\n",
    "    DataFrame: DataFrame with extracted building elements.\n",
    "    \"\"\"\n",
    "    def get_bauteil(element):\n",
    "        try:\n",
    "            return categorize_long_desc(element)\n",
    "        except IndexError:\n",
    "            return 'Unknown'\n",
    "\n",
    "    def get_floor(element):\n",
    "        try:\n",
    "            parts = element.split(' ')\n",
    "            if len(parts) > 1:\n",
    "                if '-' in parts[1]:\n",
    "                    return parts[1].split('-')[0]\n",
    "                else:\n",
    "                    return parts[1]\n",
    "            else:\n",
    "                return 'Unknown'\n",
    "        except IndexError:\n",
    "            return 'Unknown'\n",
    "\n",
    "    def get_roomnumber(element):\n",
    "        try:\n",
    "            parts = element.split(' ')\n",
    "            if len(parts) > 1:\n",
    "                if '-' in parts[1]:\n",
    "                    return parts[1]\n",
    "                else:\n",
    "                    return 'Unknown'\n",
    "            else:\n",
    "                return 'Unknown'\n",
    "        except IndexError:\n",
    "            return 'Unknown'\n",
    "\n",
    "    df['Bauteil'] = df['LongDescBmElement'].apply(lambda x: get_bauteil(x) if pd.notna(x) else 'Unknown')\n",
    "    df['Floor'] = df['LongDescBmElement'].apply(lambda x: get_floor(x) if pd.notna(x) else 'Unknown')\n",
    "    df['RoomNumber'] = df['LongDescBmElement'].apply(lambda x: get_roomnumber(x) if pd.notna(x) else 'Unknown')\n",
    "\n",
    "    return df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Apply the function to the data\n",
    "bauteile_data = extract_building_elements(bauteile_data)\n",
    "\n",
    "# Drop RoomNumber and Floor columns\n",
    "bauteile_data.drop(columns=['Floor', 'RoomNumber', 'LongDescBmElement', 'Unnamed: 0'], inplace=True)\n",
    "\n",
    "# Onehot-encode the column AdjacentTypeBoolean and call it gegen_aussen\n",
    "bauteile_data['gegen_aussen'] = bauteile_data['AdjacentTypeBoolean'].apply(lambda x: 1 if x == 'Gegen Aussen' else 0)\n",
    "bauteile_data.drop(columns=['AdjacentTypeBoolean'], inplace=True)\n",
    "\n",
    "# Set UUID as index\n",
    "bauteile_data.set_index('UUID', inplace=True)\n",
    "\n",
    "# Replace values outside the range of 0 & 360 with NaN\n",
    "bauteile_data['Orientation'] = bauteile_data['Orientation'].apply(lambda x: x if 0 <= x <= 360 else np.nan)\n",
    "\n",
    "# Group data by 'cenarius_product_id' and 'Bauteil'\n",
    "grouped_data = bauteile_data.groupby(['cenarius_product_id', 'Bauteil']).agg({\n",
    "    'NetArea': 'sum',\n",
    "    'UValue': 'mean',\n",
    "    'DeltaTemperature': 'mean',\n",
    "    'GroupType': 'first'\n",
    "}).reset_index()\n",
    "\n",
    "# Flatten the grouped data\n",
    "grouped_data.columns = ['cenarius_product_id', 'Bauteil', 'NetArea_sum', 'UValue_mean', 'DeltaTemperature_mean', 'GroupType']\n",
    "\n",
    "# Pivot the table to get Bauteil-specific columns\n",
    "pivot_data = grouped_data.pivot(index='cenarius_product_id', columns='Bauteil').fillna(0)\n",
    "\n",
    "# Flatten the multi-index columns\n",
    "pivot_data.columns = ['_'.join(col).strip() for col in pivot_data.columns.values]\n",
    "\n",
    "# Reset the index to make 'cenarius_product_id' a column again\n",
    "pivot_data.reset_index(inplace=True)\n",
    "\n",
    "# Save the pivot_data to a csv file to check the results\n",
    "pivot_data.to_csv('./data/bauteile_pivot.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Check percentage of zero values in each column\n",
    "zero_percentage = pivot_data.apply(lambda x: (x == 0).mean() * 100)\n",
    "\n",
    "# Drop columns with more than 90% zeros\n",
    "columns_to_drop = zero_percentage[zero_percentage > 90].index\n",
    "pivot_data.drop(columns=columns_to_drop, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Verbrauchsdaten"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>cenarius_product_id</th>\n",
       "      <th>verbrauch (kWh/a)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>76abdf0e-bea4-4b70-8922-b49a71933677</td>\n",
       "      <td>18800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>a62da89d-eb83-4657-92d6-b2c75cf3ece9</td>\n",
       "      <td>51200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>30e0f4a5-3530-4ae8-b3e8-a140e563f3ae</td>\n",
       "      <td>29150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>60bc7c62-a3c7-45f3-b781-e39f6cfe88d7</td>\n",
       "      <td>41150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>e1e59983-76bd-493a-ac58-5cf774f82cb9</td>\n",
       "      <td>57700</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   id                   cenarius_product_id  verbrauch (kWh/a)\n",
       "0   0  76abdf0e-bea4-4b70-8922-b49a71933677              18800\n",
       "1   1  a62da89d-eb83-4657-92d6-b2c75cf3ece9              51200\n",
       "2   2  30e0f4a5-3530-4ae8-b3e8-a140e563f3ae              29150\n",
       "3   3  60bc7c62-a3c7-45f3-b781-e39f6cfe88d7              41150\n",
       "4   4  e1e59983-76bd-493a-ac58-5cf774f82cb9              57700"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Handling 0 and negative values for verbrauch data. Set the mean value for NaN values\n",
    "def clean_verbrauch_data(df):\n",
    "    \"\"\"\n",
    "    Clean verbrauch data by replacing negative values and handling NaNs.\n",
    "    \n",
    "    Parameters:\n",
    "    df (DataFrame): The input dataframe.\n",
    "\n",
    "    Returns:\n",
    "    DataFrame: Cleaned dataframe.\n",
    "    \"\"\"\n",
    "    df['verbrauch (kWh/a)'] = df['verbrauch (kWh/a)'].apply(lambda x: max(x, 0))\n",
    "    \n",
    "    # Check if all values are zero or NaN, set mean to 1 to avoid mean of zero\n",
    "    if df['verbrauch (kWh/a)'].eq(0).all():\n",
    "        mean_value = 1\n",
    "    else:\n",
    "        mean_value = df['verbrauch (kWh/a)'].mean()\n",
    "\n",
    "    # Fill NaN values with the mean of the non-zero values\n",
    "    df['verbrauch (kWh/a)'] = df['verbrauch (kWh/a)'].fillna(mean_value)\n",
    "    # Convert to integer type\n",
    "    df['verbrauch (kWh/a)'] = df['verbrauch (kWh/a)'].astype(int)\n",
    "    \n",
    "    return df\n",
    "\n",
    "verbrauch_data = clean_verbrauch_data(verbrauch_data)\n",
    "verbrauch_data.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/simon.berheide/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/simon.berheide/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot distribution\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.histplot(verbrauch_data['verbrauch (kWh/a)'], kde=True)\n",
    "plt.title('Distribution of Verbrauch (kWh/a)')\n",
    "plt.xlabel('Verbrauch (kWh/a)')\n",
    "plt.ylabel('Frequency')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of outliers: 10\n",
      "Data shape after removing outliers: (131, 3)\n"
     ]
    }
   ],
   "source": [
    "# Calculate Q1 and Q3\n",
    "Q1 = verbrauch_data['verbrauch (kWh/a)'].quantile(0.25)\n",
    "Q3 = verbrauch_data['verbrauch (kWh/a)'].quantile(0.75)\n",
    "IQR = Q3 - Q1\n",
    "\n",
    "# Define lower and upper bounds\n",
    "lower_bound = Q1 - 1.5 * IQR\n",
    "upper_bound = Q3 + 1.5 * IQR\n",
    "\n",
    "# Identify outliers\n",
    "outliers = verbrauch_data[(verbrauch_data['verbrauch (kWh/a)'] < lower_bound) | (verbrauch_data['verbrauch (kWh/a)'] > upper_bound)]\n",
    "\n",
    "# Remove outliers\n",
    "verbrauch_data_cleaned = verbrauch_data[(verbrauch_data['verbrauch (kWh/a)'] >= lower_bound) & (verbrauch_data['verbrauch (kWh/a)'] <= upper_bound)]\n",
    "\n",
    "print(f'Number of outliers: {outliers.shape[0]}')\n",
    "print(f'Data shape after removing outliers: {verbrauch_data_cleaned.shape}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/simon.berheide/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/simon.berheide/anaconda3/lib/python3.11/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(verbrauch_data_cleaned['verbrauch (kWh/a)'], kde=True)\n",
    "plt.title('Distribution of Verbrauch (kWh/a) after Removing Outliers')\n",
    "plt.xlabel('Verbrauch (kWh/a)')\n",
    "plt.ylabel('Frequency')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Merge Verbrauch & Bauteile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Assuming verbrauch_data has 'cenarius_product_id' as one of its columns\n",
    "merged_data = pd.merge(pivot_data, verbrauch_data, on='cenarius_product_id')\n",
    "merged_data = merged_data.drop(columns=['cenarius_product_id', 'id', 'GroupType_AT', 'GroupType_Außenwand', 'GroupType_Boden', 'GroupType_Dach', 'GroupType_Fenster', 'GroupType_Other', 'GroupType_IW']) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Explore the merged data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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K4+V3VtCs4wiO/Pw5CYfWlNo/T6JS5bEtfBi52fd5dcQqOvX7jF8PH2fsgm9ZsHIjg17twsLxBb9XDfR0+S54LNu/+JjtX3zMC41ci5R3L/M+07/6vtjn5o15W/3a7V98zJqQiSh1dOjWrds/aoP495DBv9AqpVLJ7NmzWbx4MVeuXCny/IkTJ/D39ycgIIDjx4+zZs0a9u/fz3vvvQfAhg0bqF27NsHBwSQnJ5Oc/OiHXXp6OmvXriUwMJAuXbqQkZFBdHR0kRjz5s3Dw8ODmJgYpk2bVmpMKFghMHPmTOLi4ti4cSMXL14kKCioTG2eNm0a8fHxbNu2jYSEBEJDQ6lRo8ZT9VtmZiaLFi0iMjKS7du3Ex0dTUBAAFFRUURFRREREcHSpUvVA/iyWLZsGS+99BLm5uYEBgYSFhb2VHWqCDd/3suZ6Qu5tnFH6ZlL8P3WX3nNrzWvd2xDvVp2vP9WL2yrW7Jux74nvm72t6vxb+tNY9d6xT6vUCioYVFN4yhJ5OZtvNypPa906aCeFbepXp2N25+8imHeV8vo4tuaRm4lr1KI/OkXXunoy6ud2+NYuyZjBvXDproVP/786xPLnvv1Crr6tMKjmLJnjBlGz24dqV+vDo617flg+Nuo8vM5ciJe6+17qH1TPX45nM3x87kk31KxcscD9PQUNHcrefSfdF3F5v1ZHDuTSzETJAB8tek+fyTkcu22ir9SVKza8YDvV4bTrktPWvj1wraWM68OmIx5dXt+3xVZbBlW1rV4deAUmvu8hqGRWYn1UanyiPxyEl16voeVjYM6/defImjTsQdtOwdgX9uJ3m9PxKK6HXt/+aHYcqrb1OKNQZNo1eEVjIyLj/fH3q106zEEj2Y+HN67lbade9KomQ+xf+yi8xtTqWZpx7E9xd8+6NjeSKpZ2dP5janUsHemSbveeLYJ4I8dy9R5jh9Yz4OMOwSMWEJtl+aYV6+Fg4s3trUb8MfO5TRu9TrXLyfQuc9UXh60CDOrmujqGVCtem2O//a9Rrzjv0ViZmlPh4CpWNk549G6N41aBhCz+1E8B9eWuDTpgpWdMxY16tC0w1vUqOnGXxdi1HmcPDpSr1F7bGs6cuS3bbTt3BMjY1Pu3blNr7cnYVnDjn1P6NPegz6gZftXMTI2LTbPxT/jcHLz4gWfl/hj7xbadAqgdccA7qRcoWPvqZhZ2BG7t/g+jdtX0MaOvadS3d4Zz7a9adw6gMM7l2nkUygUmJpbaxwPeTjAhqh9HDl+Frsmo9gaY4q+vpLOfi0xtXR4PCQArRrqcOGvfPadVJFyF/adVHEhOZ9W7po/91T5cO/BoyMzS7Oce/cLDu/6SnbH5mFuouBicj7f/5qLnhKaOJf887FNIyXn/8pn7/E8Uu4U/Pf8X/m0afRotUHmg0cx7t2H6K0rePX1njRs1RubWs68EjgF8+p2JX4GLa1r8cqAKTRr9zqGRsX//fq+O4/WnftRs25DbGo6ETA4mHyVivPxBwHYt20F3u0DeKFD+cc8dXgnL7TvyTsfRqBAQW2XFyr8/QLw18VYXDw74dy4A5Y1atPohW44N2rLX4knOfBLOE19e9K8fW+sazrTvd8UqlnZcfjX4utkWaM2L/afilfbkttb2P5t4Xi370mLDr3+7s8yfI8OmELzdiV/jzo3bIGHd2dsajkTe2ALLTr0xr5OA9JuJePfdwrVLO04El18/WP2FHyv+fedgnVNZ5r69sarXQAHf37Up3dTk9EzMOIFv/7UqueJR4uXaNzqFf66dBKAnOwHJBz9hc69xmPv9AKOjTrRdcBizGvUJel0NK7NXqV2/XYkX4zRiP3n4fX8MP9FwqZ6smZ+d04d1PwOfNyVM7+Rdv08fn0/pUYtd2q7tmFMv1c5dPJPXmznzet+rXCw/XsyxqIavx4+rv4doqdb9EzcJ8vW0q11Mxq7OBZ5ztzURON3zKGTZzDU15PB/3+IDP6F1vXo0QMvLy+mT59e5Ll58+bRr18/xowZg6urK23atGHRokV89913PHjwACsrK5RKpXoG3s7OTv3ayMhIXF1dadSoEUqlkr59+xY7oO3YsSPjx4/HxcUFFxeXUmMCDBo0iO7du+Pk5ESrVq1YtGgR27Zt4969e6W2NykpiaZNm+Lt7Y2joyOdO3fmlVdeeao+y8nJITQ0lKZNm+Lr60uvXr3Yv38/YWFhuLu78/LLL+Pn58fu3bvLVJ5KpSI8PJzAwEAA+vbty8GDBzXuT/pvlAucvniZlp6as9stPRty/MzFEl+3OfogV66nMLRn9xLz3H+QxSujpvHSyA8Z+2kof168XGy+nJxczpxP5AWvxhrpL3h5cPL02RLL37prL1ev3eDtPj1KzJOTk8uf5xNp4aU5c96iSSNO/Fl0Gd9DW37dx9VrNxj0xmsl5insQXYWuXl5VDM1KbYOFdW+h6pXU2BuosPppFx1Wl4enL+SSz378r3sRF+Zw6lTp3DzbKuRXt+jDZfOxv6jsnf++CUmZpa06PDotj25udkkXUigYZPWGnkbNmnNhT/jnjlWbk42uvr65ObkqMvX1zfg/OlYABwbtuXqhWPFvvbqhVgcG2q2v567D9cunSQvr2BG+Gzcr9Ry8uKX1cEsmtCGb4Nf5sC2r8jJvs+1pFPUcWtBvipPPQtZ160tyRePoatnyNULmjsZX0uMpa6bZry6DXy4kfQoXmH5+fkk/XmQ1BsXqeX8QjFtz+HyhQT0DQzJzrpPvfpNAGjo2ZoLf8aW3nklcG7YlMsXEjifcIzLFxKo7ejGqWP7cPboADy5T/+6WLRPHd19uH5Js43ZWZl8/aEfoVN8Wf/lMK5fLjjhZmYIxgaw/acfsKvrwW8bxrH+s/Yc/uMPLJRJlMTBWodzyZqrXs79paKOtebPvepmCsb31mNsgB69fZVYFjO2szQDM2MFV1NUuDnocORMHnkqSLymoo5NyT8f69jocPaqZh3OXn3Ca/JzOH/mFM6N2mgku3q0Jels8f37LHKyHpCXl4uRiTm5udn8lXgK18aaf6PyiFm47MIxK/L98lBt5+Zc+vN3bl8v+PfuWtJpks4exaVRG5ITT+HSSDOGc6O2XD7/z/s4Nzebq4nxxfTnP/8eLVy+mXkNbiYnqi8lcG7Ulisl1P/q+Vici7S3HcmXTpGXW9Cn5lY1ycnKZP6Y1iz+oBORi4Zz+tguXBu3B0ClytX4XntIqWfAtcQYUq7Gc/3SMWo6PfpeSjj0A4d/XsgL3cbQ+/0oWnQby5FfPudMzI8ltu9GUiyWdq6YVLNVp3m7u5CfD/Y1NC+RTU2/x8qo3Qz6+HN2/hFbpKzNew5x9XoKQwP8S4xX2KboQ3Rt3RRjY+My5a8MCj1FpR1VkVzzLyrF3Llz6dixI++//75GekxMDOfOnWPVqlXqtPz8fFQqFRcvXqRhw6JLlh8KCwtTD2YBAgMD8fX1JS0tDQsLC3W6t7fm9WFliXns2DFmzJhBbGwst2/fRqUq+GGTlJSEu7v7E9s6YsQIevbsydGjR+natSuvv/46bdq0eeJrHmdsbIyzs7P6sa2tLY6OjpiammqklfVShF9++YWMjAy6dy8Y7NaoUYOuXbuybNkyZs+e/VR1eygrK4usLM3po5x8FXoK7Z1jzFBCnkqFlbnmLEJ1czNu3blb7GuSkm+wZPVmls4Yg24J+xk41rTlo+GBuNSpScb9B0Rui2bwjM/4PmQytvXMNfLeSU8vqMNjKwOsLMy5lXan2PIv/3WNryLWsOSTD0usg0bZ5kXLvp12ssSyQ1euI3TW5CeWXVjoynVYW1niXczy/Ips30NmJgX/4KZnal62k56Zj2W18n0/+XneZ3VeHkrD6hrppubVSU978mUMT5J45iiHozcwZvYGjfTM9DRUqjzMzDXjVTOvzt1/EK+hVxt+/SkC21qOqFR53L6ZTNzhaPJVBUsgTKrVIOPuzWJfm3E3BZNqmquRTKpVR6XK5f69VEzNbUhLucylP3+nUYtXeOO9pdy+cYlfIoO5n5lGvioP8+q1qeXUlN+2fon/ACeMTK1IvXGRzHu3sLSuWyRe3Qaa8Yz/jvfgXiom5jYAZN1P59uPfMnLzUaho0PH3tOp20Dzx3zKX3+yZMIbqFR57N+xnqETFmLvUPBdaWbxz/rUu2137t1NZeGMQQWrOL6ZhU/XN2jp/86z9amZZp9a2TnRfcAcrGu5kf3gHjG7v+P7+W8SNHUTNq6OAFw8F8/Nm3uo32Ig7m2GkqtjjH5OMhdPHKVe41eLxDU1KphJL+ze/YL0h66kqNiwv2BlgKkRtPdUMuRFPb7YlMP9Ql/hZkYFn0Mnex2yclBfs3/vAViYlPyjuKAOmp/de/fzMTMqPn8dqzvk5eXxQKHZX6bm1Um/8+x/v8dtX7OAapa2uDRqo/4cmlYr/5iFy34Ys26DNty8+meFvV8sbRwBaNF1KFn30wkL7o6OjpJ8VR4dA8ZQz701KlUeJtWKfs/dO/nP+zgzPbXge+2x8s3Mq3PmH/bng8x0PhnVHpUqj92bv+b1t6fj2rgNGVkF31P3Sij/3t2bmFRrp5FmUq06qrxcMu+lYmZhQ1OfXhgYm3H8wCYeZN4hLeUqunqGNPXtDYCBoSm1nb3Y99OXtOvthJFpDc7HbuVGUsGJ2h8X96J55/do0KK3OsbRXaG0enkS9Ty6AlDNqjap18+T8Psa6jcv/uR3ZvpNjEw1+06lKvgM5f/9X2NDfcb2f42kazf5LTaBFo3qM2Xxd2QPy+XFdgW/a5Ou3eSLNVv4ZtqoMv1be/L8Jc5fSWba0D6l5hVVhwz+RaXw9fXF39+fKVOmaCyfV6lUDBs2jNGjRxd5TZ06dUosLz4+nkOHDnH48GEmTXq0aVNeXh6rV69mxIgR6jQTE83ZzNJiZmRk0LVrV7p27crKlSuxtrYmKSkJf39/9cZ4T9K9e3cuXbrE1q1b2blzJ506dWLkyJHMnz8fHZ2CgUzhvQlycorOfOnpaS51VigUxaY9PClRmmXLlnH79m2NM70qlYpjx44xc+bMZ9rUb86cOXz88ccaaW8qrOivfLpLHMrD4z9L8/Pzi72jRJ5KxYdfhPNOrxepa29b5PmHGrvW07gcoEl9JwKnzOWHn/cwanjRa+oK6qAZr8Q65Kn4+H9fMrhvAHVq2T+hVYULL1p2cfLyVExf+DVD+rxOnZp2xeZ53MqNUezYf4glH0/CQF+Pkt5R5dk+g2rVMavpCMCnLjp8vfnv0cvjzVIUk/YP9OpggLVpwd4eRetefHvKIut+BpGhk+g55GNMzIrf2PTxsvPJL9KnT6P32xNZ9VUwn00bBMCuLRG09nuNg7s3/R0gn6KfjJLrg/o9pfj7YT4mZtXpFjgTHR0ldnU9uHfnBge3L1W//uW3PyXquyl8+5EvKBQolfo0aPYyN64Uc/lISfEKpesbmNB/4kayszK5fOYgezaGUK26Aw6uLdV5LG3qMWra13w27S28WnYk4osPGfPxsoITACW8J8vqzKnDbF//Da/2/z82RnzGq/3+j30/r0Glv4Q2L44s8T1fqJGaTUSzT2vW86JmPS8AnG1h6uDm5OQ8QEehZOepv1+jUmHr0AjPDv8HQLUaShT3FZw/urDYwX9JtSj8sTl79dGjG2lw+WYuYwL0ePEFJQ3qPDq59t2Ogn+LPOvpEHdepXEpzdN+DBUUeks9pkm9gn9vsh+/VOcf/v0K27PlW+J+j2LolBXo6Rtw/+GWPkX/sSi3mMcO/MTxv2Pq6hmU6/sFoJZTM1aE9OBo9Eo6vfEhAKdjooj/YzMvv72AOo7OXLt8mm3fz0ZPr2Bn2iLfO/lFv8v/kWL+bfqn/alvaMLgSd/y1cxAWnR8g63ff4qVjQN2Tq0K6v+k8ot8zzxMLkjX1TPg3PE9vBQ4nZpOnty8epaN307kx28m0H/stwC8NvhTfgqfwqpP2qPQUVKjpjt13Ttx+9qfeHV4hz+2L6BajTq4eL3M/Xu3ybiTzJ51H7J3/UePwqpy0TcsmJTYFjaU5MSCywTMLGrS+/0tBXUq4e+g0ClItzAzpX/3DoRt2oGBvh7De3XnbkYm3239lRfbeRf8nlkSwTs9u1HX3qZMfbsp+hDOte3xcK5beuZKJBv+lS8Z/ItKExISgpeXF/XrP9rAqlmzZpw6dQoXl5KvCdbX1y+ysV1YWBi+vr4sWaK5a2tERARhYWEag//HlRbzxIkTpKSkEBISgoNDwXWWR44cKbV9hVlbWxMUFERQUBA+Pj5MmDCB+fPnqzfPS05OVt/9IDY29qnKflq3bt1i06ZNREZGqnfwh4LBv4+PD9u2bePll19+6nInT57MuHHjNNJ+tSp+p+2KYpIHSh0dbt1J10i/ffceVtWKXlOYef8BCReSOJN4hXnhawFQ5eeTn59Pq/6jWTx5JC94FN2ISEdHB3enuiRdKzqLY25mVlCHx2bBU+/cLTJjD5D54D6nz13k7IVL/O+b7zTq0L7nW3w2faJ6g7yHZd8urmwL82LKfsDp84mcvZjEZ9+u1Cjbp/dg/vfR+3g3frRy5ftN2/hu/RY+nz4BF8firymuiPZ5ezXm9vmCS2i+/M0ZXWXBP/RmJgruFpr9NzNSFFkN8Kx6tjfAw0mX/60xQEdHWWSW/96d25g+NjtfVrduJJF68yorFoxUp+XnF5xGSTgajY6OTpEZ6fQ7tzGzeLZ4AGbmVgyftJD7mfeYEORDr6CJJJ49Tg2bmgBkpN8qMrP4kEm1Gty7o/lezki/jY6OLkamFgCYmlujo9RFR+fRicHqdk7cv3cbhY6Se3dSqOXUlP7vryT9bibRGz4h7eYl8lS5VKteu0i8zMdmQDP/jmdoYqFOU+joYPH3qgGb2g25ff08h3cu1Rj8K3X1qevsgY6OkiYtOnEn9Sa7o1bRb9hHBX36jH9DgC2RX9DC92X8uvdn86rPsa3pyKv9RrPqq2BadxtBZvotjM1K7tPHZ3kzH+vTwpJS4Mc/FOzbHMq9Oym89FbBCiyHeq5k6Tz6IW9iqOBmqoLMO9eKjfv4LD+AiRFk3C82OwA5uXAjNZ+cPAj9qWDAn5eXr/4cVjfX4cjuRye6TQ2LzuwXrYPmj3UTIwX3HhTNa2ECXg0s0fn7PaRRzt3bmFZ79r/fQ3u3LiP6p6UMnrRMvUGnsZlFhcU0NrNAoVBwdN+PvDM1Avs6btxOp1zfL1Dw+bCv25jUG4nqtD0bPqWF/zs09H4JU6N8bB3cSEv5i8O7Vxfb3oy7tzD5B5+Rh4zNCv6Gj6+aKI/+1NHRobZTwWfc2b0VOdlZRP/0DX3/rxWZ6beKrGZ4yLSaNRmPtzf9FjpKXYz+/p6J3rQIz9avqmf6bWu7sX/r11xMOEi+SoVCRwcrmzq8NXElV5IzyXlwD+NqNuxcNRYLG2catnyD+/duEbPjC1y8XlZ/z/v2nImNg+ZdThR/f3f69ppFbk7Bh0FHWTAMMzaz5sbl44+1u/gBb+qddKr/vbqxsYsjm6IPAZB5P4v4i5f589JV5q0oWHH28N/algPf54tJwzU2AHyQlc0vvx9jeE+51v+/Rgb/otI0btyY/v37s3jxYnXapEmTaNWqFSNHjmTo0KGYmJiQkJDAjh071PkcHR3Zu3cvffv2xcDAAHNzcyIiIggODsbDw0MjxpAhQ/j000+Ji4ujSZMmxdajtJh16tRBX1+fxYsXM3z4cE6ePMnMmTPL3M6PPvqI5s2b06hRI7KystiyZYv68gUXFxccHByYMWMGs2bN4uzZsyxYsOBpu/KpREREUL16dXr37q1eefDQyy+/TFhY2DMN/g0MDDAw0LwuTptL/qHgC61BPQcOHT+N3wuP/t5/nDiNb/PGRfKbGBmy+lPNWxet+2UfR+LPEDJmMLWsi/9RkZ+fz5lLV3BxqFnkOT09Xeo7O3I47iTtWz26xORI3EnatSh6KzcTIyO+W6h5qcWG7bs4eiKeWRNGYW/7aEMnPT1d3Jwd+SPuFO1bPjqxcvh4PD4veBXbvoj/ab5XN2z/lZgTCXwyYSQ1bR6VvWrjNsLX/8T/pr1PQ5fiNz2sqPblq1TkqwrWG6fcyQfyuZOhwq2OLldvFgw6lDrgXFuXn/ZnPR7iqfXsYICnsy5frM/kToYuteq5c/bkATxe6KzOc/bkAdybd3ym8q3tnRg7Z5NG2s/rPifrfgavDpjChm+nkHD8d7xadlI/f/r473i+0OGZ4hVmZGxKHSd3EuIO8OeJQzRrXbD0NDHhAK5NOhX7mlpOXpw7rrlfSGLCfuzqeqBUFqwwqu3cjFN/bFH/IAa4fT0RU3NrzCztSUz4DbemXQDQMzAm+eIx6jZox6k/NuDz6gSNsu0cvbh4UjPepT/3Y1PnUbxi5eeTl1t0tZWunh4OTg05ffwg+fn5f99i7WGf+pVcXimysx6go6OjUb5Lw2YUzMnmc+n0AVw8i+/TmvW8OH+iaJ/a1i2+jTl5kJ2Zz/GY36hRqz5pGQWb8HXt9horIgo2C1PqgKOdgl93nsbYvPhVNJdvqnC21+Fg/KM1Oy72OiTdLHlVmFIHapgruHRdxe2/z5sWnF/PJyc3n8ysfK7dzi9UBx1+PpJbYnlJN1S41FRw4NSjNNdaOiTdKFqHZvWV5KiU1HJsxNmTB2jk3UX93LmTB2jY7Nk+gw/t3RrGr5u+YtDEb6jt9Oj3ga6uPjUrKOaBnyMABfU922nELM/3CxT8O3TjcgI1aj2aQMnJeVBkJrzg85qPvWMjzp86QMPmj9p7If4Abl7/rL1Q0J+1HN05d/IAHt6PvkfPnTyA+z/sz8fLp9Bn/EL8AeqXUP9azl6cjdPs0wunfsO+biOUugV9mpN1Hwr9TsnNySb9zo2/V8vka8zF6+kbo6dvTFbmHa6c2U/LF8cXPJGfT15eQX2MzWpgUs2W9FuXcW1a/N5OJuZFVxna1PHi2K9fkXn3BsbVCk72xSScR6GAaymad905dPIM7ZsXvK/+vHSFGhYFJwJMjAyInKN5C9h1O3/jcPxZ5o4Oopa1lcZzOw7FkpObS/e2Jd8q8XlRVa+9rywy+BeVaubMmfzww6PdmD09PdmzZw9Tp07Fx8eH/Px8nJ2d6dPn0fVIwcHBDBs2DGdnZ7Kysli3bh23bt2iR4+i11K5urrSuHFjwsLCWLRoUbF1KC2mtbU14eHhTJkyhUWLFtGsWTPmz5/Pq6+Wbcmlvr4+kydPJjExESMjI3x8fIiMLNj9Vk9PT31ZQpMmTXjhhReYNWsWvXv3LqXUZ7ds2TJ69OhRZOAP0LNnT/r06cP169extS15GXxFUZoYY+Ly6PIO43q1qdakAdm37/DgcvG37Hpcv5c6Mn3Jd7g71aFx/Xr8uOs3rqXcpmdnHwC+WL2Jm6l3+Pjdgejo6BQZwFuZm6Kvp6uR/s26KDxcHXGwsyHj/gPWbI/mzKUrTHr7jWLr0PfV7sz8/CsaONfDw82FzTt2cz3lFq/7F/zw+ypiDTdvpzLt/4ajo6ODU13NWXZL82ro6+kVSQfo+0pXghd9Q0NnRzzcXNi0Y09B2V0LBjmhK9dy83YaH40eio6ODs51ahcp20BfTyN95cYovln9IzPGDMPeuga3Ugtm9Y0MDTA0KboTWEW276E9x3Lo8oI+KWkqbqap6PKCPjk5+cT8+eiymP5dDblzT8WWA49OENhZFbyvdXXA3FSHWjV0yMrJ//ukAvT2M6CZmx7f/nSfB9kFG5r5vRTEqiWTqO3UiDouXvyxey1pt5Jp1angO2Dbms+4m3qDPsND1LH/upQAQFZWJhnpt/nrUgJKXT1sa7mgp2+AnYPm5SBGxgWrIuwcXOn06kBWLJ5KXSd36rk14bcd60lNScana8HnfuOqz0m7dYOg0Z+oX3/54umCeA8yuXcnlcsXT6Orq6e+vv3imeOk3b6BQ70GNGrajq3rvsbE1BzPFh3Y+cNs7qYm09S34P7W0T8uID3tOq+8/SkATX37cjR6FbvWzqFJuze4euEYcb+t59XBj05ENvV9k5jdEez44RO8/QK5feMSB7d/jbffACxt6vLT8oko9QyoYedE0rlY7ty6QuLp/Vja1CPt5iV+XjkR/8CCeJ5t+xK3bxV7fpxD49ZvkJx4jFO/r6f7wEfx/tjxNbYOHljUqENeXjaJ8XtJOLyJjm/MUOf57afPcHT3xUrXiqaturBp1efk5+fTb/h01oV/yu2UZNr93aebVn1O2u3rvDXq0Ymown2afrdonzb2bs+vWyKoXa8BLXxeYv2K+Rz7fQe1XVoQvWEud1OTaeJT0Kd7Nxb06UtBBW1s4tOXY3tW8eu6OTRp+wZ/XTzGiQPreXnQozb+tvULatZrgqWNI1n373E0+jtuXDlN574Fm+GevAx9Arpz5MAvpF9cT2Dfl3nwII/lX32Ce/uCgUdAOyV3M2Hn0YLVcL8nqBjUTZd2HjqcTlLRoI4OTjUVhG17tFrO31vJn5dVpGXkY2qooL2nEgM9OHZec3BuoFewatrYQIF7XR1S7uTToYmSnDyIK5S3l68udzPy+SWmIMbB+DyGvKiHT2MlCUkqGtbRwbmmgqVbNU8YKIBmrkqOnsujXfe3+OGrD6hdz+Pvz+APpN1KpuXfn8Htaz7jbup13hg+V/36h5/B7KxMMu6manwGoWCp/471i+j77nwsa9QiPa1gZl3f0BgDQxN8KjBmG/8BHNyxiv3bwqlVrxF/7Nte4e8XAOfGfvy+/SuqWdakjpMz1y4lcPDncJr69KRmXXc2fDOJmo4eOLh4cWTPD9y5lcwLfgV12rF2AelpNwgY+qi9yUmF2pt+m+SkBJRKPWxqFV0p2a57ED98NYla9RpR18WLQ39/jxbuzzslfI8W9OftIv25e/NSatfzwMrWAc9W3YmKnA/50ClgJL9EzuHO7WSadyio/671BfV/fXBB/Zu378uRX1fxy5o5NPV5g6sXYjm2fz0B78xXx9fV1efwrysxs7ChmqU9B7d/y4PMu7h4tEdHR8mu9Qv4K/EErf0Hk29Qj+N7l3P5dDQmFjWxc2zOn4fXE7d3GR5tH+031bzLe/y2+RP0DE1xcPNBlZvNzSsnybp/F0/ft4v0G0Dt+m2xsHVm95pJtHxxAln377Bp/WZaerixdf8RPOvX43baXY79eYG/Um7T0sONiK27WbVtDw0cawH8/XtG86SgZTVTDPR0i6QDbIr+nfbNG2NhVnRjX1G1yeBfaE14eHiRtLp166p31H/ohRde4JdffimxnFatWhEXp7kr9pPub3/8+KOlVImJicXmKS3mm2++yZtvvqmRVtJ11o/78MMP+fDDD0t8vm3bthp1fLzsh5cLFDZjxgxmzJihkVZc/xbn8ViFBQQEaOw54OjoWOZ2lgfz5h603hWhfuw+v2BW/vJ3Gzg+eHKZyujaujl30jP4dsM2UtLu4uxgz8JJ72L/91nvlLS7XEu5/VT1Ss+8z+xvV3MrLR1TY0PcHGuz9KMxNHJxpLh56E7tWnEn/R7hP2zkVmoa9erUZt6H47GzKVjyeSs1jes3bz1VHR7q3LYld9IzWLZ2M7dS7+BUpxbzp4zFXl32Ha6nPF3ZG7b/Sk5uLlPna142M+iN1xj0Zi+ttu+hXTHZ6OlCLz8DjA0UXLqWR+jG+2QV2hLD0kxBfv6jk1jmJgom9n/0Q6ZTc306Ndfn7JVcvlhfsO65nac+AKN7Fd7Z+GWUeWlsXBPK3bSb2NV25e0JX2NZo+BHVXpaCmkpmiefPp/6aAf/qxdPEXtgK5Y1avLBwp2lts27bTcy0u8QtW4pd1NvYl/HhXenLKG6dcEJp7upKaSmaC7rnjPh0QnQpAvxHN4fhZV1TWaFbgMgJyebnyKXkHL9CgaGxtR1bsSd1Jv8b9ogqtesT+/3lmJevaA99+7c5O7tR+2xqOFA7/eWsmvtHI7uWYWpuQ1d+kylQbNHu0VXs7Knz/8tY9faOYTNfBUzC1u8Ow6klf9QdHSU3L+Xyr4ti7l/r2CWysDYnDr1W9HmpbFEb/iEu6mP4plXd+D1YUvZ8+Mcju9bhYm5DR0CpuLq9ShebnYmu9d+TPqda+jqGWJl44T/gHm4NXtRnSczPYXtKyfy490bGBqbYm1fh/sZ6fzw7SfYO2j26Z3Um0X6NGTio5N3SRfiOfJ3n878cjsA3Xq+AwoFP63+gju3b2BgZEJOdhZJZ37n/r3b9Hy3UJ/evUl6qmaf9nx3Kb+un0Ps3oI+7dR7Km5NH7Ux6/5dfvn+IzLu3sTA0AwbB3f6jluJvWPBcuHjl0BXR8knIfMw0FVw/Phxhr/7DW5t3sPRo2B1lrmJQuM7+vLNfNbuzaVTU106eilJTYcf9uRyJeVRnmrGBQN2Y4OC1QWXb+bzTVQOdzLQ4OmkgyofDp7K49XWuhjqw5Wb+SzfnkN2oXF8QR0ePU66kc+a6Fy6NFPSuZmS2+n5RO7O5cpNzX9LnGsqsDRVEHNGhWerF8m4l8aujV+SnnYT29quBI3/qtBn8CZptzQ/g4s/DFD//9WLp4g7uAWLGjWZ9L+CW47+vms1ebk5rFr0fxqv69RjJJ0D3qvQmL9tXwHA1u8LBqIm1awr/P0C0PmND9n/0+fsXPMxmem3MLOwwbtDH9q/9i66uvpkZqSxZ/MS0u/cxKaWK/3Hfo1FjUffC3du/aXR3q+mP5pU+SvxFCd+34JF9ZqMnV/01rJNWnUn814auzaGkv7392jQ+Effo3fTUor056IPH/sePbgVixo1+eB/Bd+j2Vn32bgimDu3r6Onb4BF9ZpkP8hg96avsK7pypv/9zUWhb/XCtXf0ro2b/7f1/yyJoQju7/HzMKGbm9OpWHzwt9rdty4eoZfIgtOCurqGdLohRd5MXC6usx7aTfZviqYO7evodQ1+Pv77hYbl/SlWvU6tOz+Pg1bPvp+btCiN7p6hsTtXcahqHno6RtjZeeKR7u3ivTZQzo6SroHfc3+jR+zKbQfunoGvNa2MWP6vcam6N/59sefuXH7Dkplwb934/8XRh17a5q41ivx8oAnuZR8g9gzF/li0vCnfq3491Pka/OXvRDiP2WrXtHr5Suaz6EvtBovy7DotfYVSUdV8nLbiqDS0e454lk7Sr6jR0Xxa6PdWxyZGRbd1LMiJaUYajXeg39+ZcZTca2p3YCJNw1Kz1TOkq6UvrlsecrL0+5Pw+YeT7jcowq4na79O2ubGmn3b2igW7YNh8tLRpZ2+/T6Pzuf/dTeqbVNq/HMXnix9EyVZE9Dr0qL3T4httJiVxTtfxsJUcUMHz4cU1PTYo/hw7V/VnXfvn0l1qfwrQGFEEIIIYQQ/x2y7F+Ifyg4OJjx48cX+1y1akV3P69o3t7eFX7HACGEEEIIISqaQikb/pUnGfwL8Q/Z2NhgY1O2e6pqg5GR0RNvlSiEEEIIIYT475Fl/0IIIYQQQgghRBUnM/9CCCGEEEIIIZ47OrLsv1zJzL8QQgghhBBCCFHFycy/EEIIIYQQQojnjkJHZv7Lk8z8CyGEEEIIIYQQVZzM/AshKozPoS+0HnNfy/e0Gq/TqqFajZdz/bpW413+9ahW470wbqdW4wE4WqRoNd6xK9ZajXflWq5W49lZa/enxdm/DLQaT5Wv1XAA6Olrea4mW6XVcLfuard9BnpaDceNlDztBgRMamu3T+9nazfeg2ztzgabGms1HBenf6rVeJ5RL2o13tNQKGWuujxJbwohhBBCCCGEEFWcDP6FEEIIIYQQQogqTpb9CyGEEEIIIYR47sit/sqXzPwLIYQQQgghhBBVnMz8CyGEEEIIIYR47sit/sqXzPwLIYQQQgghhBBVnAz+hRBCCCGEEEKIKk4G/0L8A0uXLsXNzQ0TExOaNm3Knj17KrtKFcbR0ZGFCxdWdjWEEEIIIcR/hI5SUWlHVSSDfy0JCgpCoVAQEhKikb5x40YUirK/uUobgLm5uaGvr8/Vq1eftar/Gd9//z1KpZLhw4c/0+t37NjBiBEjmDhxIidOnCAoKIiAgADS0tLKt6JCCCGEEEII8Q/J4F+LDA0NmTt3LqmpqRVS/v79+3nw4AG9e/cmPDy81PzZ2dkVUo9/i2XLljFx4kQiIyPJzMx86tdv376dNm3aMHjwYJycnPD19SU3N5c//vijAmorhBBCCCHEf4tCqai0oyqSwb8Wde7cGTs7O+bMmVNingMHDuDr64uRkREODg6MHj2ajIwMADp06MClS5cYO3YsCoWiyIqBsLAw+vXrx4ABA1i2bBn5+fkazzs6OjJr1iyCgoIwNzdn6NChpcYEWLlyJd7e3piZmWFnZ0e/fv24ceNGmdqcmppK//79sba2xsjICFdXV5YvXw5AdHQ0CoVCY6Y8NjYWhUJBYmIiAOHh4VhYWLBlyxbc3NwwNjamV69eZGRksGLFChwdHbG0tGTUqFHk5eWVqU4AiYmJHDhwgA8++IAGDRqwbt06jednzJiBl5eXRtrChQtxdHRUP7516xbVq1cHYO7cufTp04cvv/xS43XZ2dlMnDiRWrVqYWJiQsuWLYmOjlY//7B9P//8Mw0bNsTU1JRu3bqRnJwMwIkTJ9DR0SElJUXdnzo6OvTu3Vtdxpw5c2jdujUAeXl5DB48mHr16mFkZISbmxuff/65RjuCgoJ4/fXXmT9/Pvb29lSvXp2RI0eSk5OjznPjxg1eeeUVjIyMqFevHqtWrSpz3wohhBBCCCGePzL41yKlUsns2bNZvHgxV65cKfL8iRMn8Pf3JyAggOPHj7NmzRr279/Pe++9B8CGDRuoXbs2wcHBJCcnqweIAOnp6axdu5bAwEC6dOlCRkaGxiDzoXnz5uHh4UFMTAzTpk0rNSYUDGBnzpxJXFwcGzdu5OLFiwQFBZWpzdOmTSM+Pp5t27aRkJBAaGgoNWrUeKp+y8zMZNGiRURGRrJ9+3aio6MJCAggKiqKqKgoIiIiWLp0aZEB/JMsW7aMl156CXNzcwIDAwkLC3uqOj0uJCSEkJAQ+vfvj42NjTr97bff5rfffiMyMpLjx4/Tu3dvunXrxtmzZzXaN3/+fCIiIti7dy9JSUmMHz8eAA8PD6pXr67eS2Dv3r1Ur16dvXv3ql8fHR1N+/btAVCpVNSuXZsffviB+Ph4PvroI6ZMmcIPP/ygUd/du3dz/vx5du/ezYoVKwgPD9dYLRIUFERiYiK//vor69at48svvyzzCR8hhBBCCCHKg0JHp9KOqki3sivwX9OjRw+8vLyYPn16kQHnvHnz6NevH2PGjAHA1dWVRYsW0b59e0JDQ7GyskKpVKpn4AuLjIzE1dWVRo0aAdC3b1/CwsLw8/PTyNexY0f1wBJg4MCBT4xpaGjIoEGD1PmdnJxYtGgRLVq04N69e5iamj6xvUlJSTRt2hRvb28AjZnzssrJySE0NBRnZ2cAevXqRUREBNevX8fU1BR3d3f8/PzYvXs3ffr0KbU8lUpFeHg4ixcvBgr6aty4cZw7dw4XF5enrh+AlZUVq1at4qWXXsLAwACA8+fPs3r1aq5cuULNmjUBGD9+PNu3b2f58uXMnj1b3b6vvvpK3b733nuP4OBgABQKBb6+vkRHR9OzZ0+io6N56623WLFiBfHx8dSvX58DBw4wduxYAPT09Pj444/V9apXrx4HDhzghx9+4I033lCnW1pa8sUXX6BUKmnQoAEvvfQSu3btYujQoZw5c4Zt27bx+++/07JlS6BgVUnDhg2f2AdZWVlkZWVppmVnY6Cv/0x9KoQQQgghhCg/VfOUxnNu7ty56sFbYTExMYSHh2Nqaqo+/P39UalUXLx48YllhoWFERgYqH4cGBjIhg0bimw+93AQ/jQxjx07xmuvvUbdunUxMzOjQ4cOQMHAvjQjRowgMjISLy8vJk6cyIEDB0p9zeOMjY3VA2MAW1tbHB0dNU482Nralnlm+pdffiEjI4Pu3bsDUKNGDbp27cqyZcueum4PrVy5koMHD+Lt7c3p06cBOHr0KPn5+dSvX1+jf/fs2cP58+dLbJ+9vb1GWzp06KBexbFnzx78/Pzw9fVlz549HD58mPv379O2bVt1/q+++gpvb2+sra0xNTXlm2++KfK3atSoEUqlstiYCQkJ6OrqarxXGjRogIWFxRP7YM6cOZibm2scny2PLGMPCiGEEEIIISqSzPxXAl9fX/z9/ZkyZYrG8nmVSsWwYcMYPXp0kdfUqVOnxPLi4+M5dOgQhw8fZtKkSer0vLw8Vq9ezYgRI9RpJiYmGq8tLWZGRgZdu3ala9eurFy5Emtra5KSkvD39y/ThoHdu3fn0qVLbN26lZ07d9KpUydGjhzJ/Pnz0fl7OU3hvQkKX3f+kJ6ensZjhUJRbJpKpSq1PlCw5P/27dsYGxur01QqFceOHWPmzJkolUp0dHSK7JlQXN0eat26NQkJCQQFBeHn58fZs2dRqVQolUpiYmI0BtqAxomL4tpSOHaHDh34v//7P86dO8fJkyfx8fHh/Pnz7Nmzh7S0NJo3b46ZmRkAP/zwA2PHjmXBggW0bt0aMzMz5s2bx6FDhzRiPKn/HsZ+mrtQAEyePJlx48ZppGXF73uqMoQQQgghhHhIoVM1N96rLDL4ryQhISF4eXlRv359dVqzZs04derUE5ee6+vrF9nYLiwsDF9fX5YsWaKRHhERQVhYmMbg/3GlxTxx4gQpKSmEhITg4OAAwJEjR0ptX2HW1tYEBQURFBSEj48PEyZMYP78+VhbWwOQnJyMpaUlULDhX0W6desWmzZtIjIyUn2JBBQM/n18fNi2bRsvv/wy1tbWXLt2jfz8fPUguLS6mZub89lnn+Hk5ER8fDxNmzYlLy+PGzdu4OPj88x1fnjd/6xZs2jSpAnVqlWjffv2zJkzh9TUVPX1/gD79u2jTZs2vPvuu+q0wqsMyqJhw4bk5uZy5MgRWrRoAcCff/5Z6i0MDQwM1Jc8PHRXlvwLIYQQQgjxXJBl/5WkcePG9O/fX33dOcCkSZM4ePAgI0eOJDY2lrNnz7J582ZGjRqlzuPo6MjevXu5evUqKSkp5OTkEBERwZtvvomHh4fGMWTIEGJiYoiLiyuxHqXFrFOnDvr6+ixevJgLFy6wefNmZs6cWeZ2fvTRR2zatIlz585x6tQptmzZor523MXFBQcHB2bMmMGZM2fYunUrCxYseNqufCoRERFUr16d3r17a/SVp6cnL7/8snofhg4dOnDz5k0+/fRTzp8/z5IlS9i2bVuxZf7222/ExMRw6dIlwsPDMTY2xtXVlfr169O/f38GDhzIhg0buHjxIocPH2bu3LlERUWVuc4Pr/tfuXKl+pILT09PsrOz2bVrlzoNCvr0yJEj/Pzzz5w5c4Zp06Zx+PDhp+ojNzc3unXrxtChQzl06BAxMTEMGTIEIyOjpypHCCGEEEKIf0JHqai0oyqSwX8lmjlzpsbybk9PT/bs2cPZs2fx8fGhadOmTJs2DXt7e3We4OBgEhMTcXZ2xtrams2bN3Pr1i169OhRpHxXV1caN278xJ3sS4tpbW1NeHg4a9euxd3dnZCQEObPn1/mNurr6zN58mQ8PT3x9fVFqVQSGVlwHbienh6rV6/m9OnTNGnShLlz5zJr1qwyl/0sli1bRo8ePdSXHBTWs2dPtmzZwvXr12nYsCFffvklS5YsoUmTJvzxxx8aGyUWFhcXR/fu3WnQoAGbN29m/fr16pUMy5cvZ+DAgbz//vu4ubnx6quvcujQIfUqirLy8/MjLy9PPdBXKBTq1QTt2rVT5xs+fDgBAQH06dOHli1bcuvWLY1VAGW1fPlyHBwcaN++PQEBAbzzzjsadzEQQgghhBBC/Lso8h+/sFkIIcrJ3aM7tB5zX8v3Ss9UjjqtGqrVeDnXr2s13uVfj2o13tFxO7UaD8DDLkWr8Y5dsdZqvCvXcrUaz85au1cU5mq3eagq4VfT7TTtNjInu2x76JQXh1ravUTMQK/0POXpyrW80jOVs3q1tTu/p+3RRGaWdmdl87T8J2y9sINW43lGRWs13tOI7frsl87+U16/VL29q+SafyGEEEIIIYQQzx3Z8K98ybJ/8Y8MHz5c4zZ2hY/hw4drvT779u0rsT6Fd9gXQgghhBBCiP8SmfkX/0hwcHCJ18JXq1ZNy7UBb2/vCr9jgBBCCCGEEKLiKYrZp0s8Oxn8i3/ExsbmudoIzsjI6Im3ShRCCCGEEEKI/yIZ/AshhBBCCCGEeO7INf/lS9ZRCCGEEEIIIYQQVZwM/oUQQgghhBBCiCpOlv0LIYQQQgghhHju6Chl2X95ksG/EKLCZBmaaz1mp1VDtRpvV/9vtBqv2Yk1Wo3n5Oqm1Xj77mv/H/mVO020Gq9RA62Gw8RYqdV4pkb5Wo2nbQZ62m9f8nWVVuPdz8zRajxdpb5W41mZ5Wo1nr6edj+DAEfi7ms1XsumhlqNl3NPu/9WZGVr93Nff3hvrcYT/x0y+BdCCCGEEEII8dyRDf/Kl1zzL4QQQgghhBBCVHEy+BdCCCGEEEIIIao4WfYvhBBCCCGEEOK5o9CRueryJL0phBBCCCGEEEJUcTLzL4QQQgghhBDiuSMb/pUvmfkXQgghhBBCCCGqOBn8C/Ef0KFDB8aMGVPZ1RBCCCGEEKLMFDqKSjuqIhn8/8cFBQWhUCgICQnRSN+4cSMKRdnf9I6OjixcuLDE593c3NDX1+fq1avPWtUqr0OHDigUChQKBTo6Otja2tK7d28uXbpU2VUTQgghhBBC/MvJ4F9gaGjI3LlzSU1NrZDy9+/fz4MHD+jduzfh4eGl5s/Ozq6QevwbDB06lOTkZK5evcqmTZu4fPkygYGBlV0tIYQQQgghxL+cDP4FnTt3xs7Ojjlz5pSY58CBA/j6+mJkZISDgwOjR48mIyMDKJixvnTpEmPHjlXPXBcWFhZGv379GDBgAMuWLSM/P1/jeUdHR2bNmkVQUBDm5uYMHTq01JgAK1euxNvbGzMzM+zs7OjXrx83btwoU5tTU1Pp378/1tbWGBkZ4erqyvLlywGIjo5GoVCQlpamzh8bG4tCoSAxMRGA8PBwLCws2LJlC25ubhgbG9OrVy8yMjJYsWIFjo6OWFpaMmrUKPLy8spUJwBjY2Ps7Oywt7enVatWjBw5kqNHj2rk2bNnDy1atMDAwAB7e3s++OADcnNz1c9nZGQwcOBATE1Nsbe3Z8GCBUXiZGdnM3HiRGrVqoWJiQktW7YkOjpa/fzD9v388880bNgQU1NTunXrRnJycpnbIoQQQgghxD8hy/7Llwz+BUqlktmzZ7N48WKuXLlS5PkTJ07g7+9PQEAAx48fZ82aNezfv5/33nsPgA0bNlC7dm2Cg4NJTk7WGCCmp6ezdu1aAgMD6dKlCxkZGRqDzIfmzZuHh4cHMTExTJs2rdSYUDCAnTlzJnFxcWzcuJGLFy8SFBRUpjZPmzaN+Ph4tm3bRkJCAqGhodSoUeOp+i0zM5NFixYRGRnJ9u3biY6OJiAggKioKKKiooiIiGDp0qWsW7fuqcp96Pbt26xdu5aWLVuq065evcqLL77ICy+8QFxcHKGhoYSFhTFr1ix1ngkTJrB7925+/PFHfvnlF6Kjo4mJidEo++233+a3334jMjKS48eP07t3b7p168bZs2c12jd//nwiIiLYu3cvSUlJjB8//pnaIoQQQgghhKhccqs/AUCPHj3w8vJi+vTphIWFaTw3b948+vXrp94wztXVlUWLFtG+fXtCQ0OxsrJCqVSqZ+ALi4yMxNXVlUaNGgHQt29fwsLC8PPz08jXsWNHjYHlwIEDnxjT0NCQQYMGqfM7OTmxaNEiWrRowb179zA1NX1ie5OSkmjatCne3t5AweqDp5WTk0NoaCjOzs4A9OrVi4iICK5fv46pqSnu7u74+fmxe/du+vTpU6Yyv/zyS7799lvy8/PJzMykfv36/PzzzxrPOzg48MUXX6BQKGjQoAF//fUXkyZN4qOPPiIzM5OwsDC+++47unTpAsCKFSuoXbu2uozz58+zevVqrly5Qs2aNQEYP34827dvZ/ny5cyePVvdvq+++krdvvfee4/g4OAS656VlUVWVpZmWnY2Bvr6ZWq7EEIIIYQQhSl0ZK66PElvCrW5c+eyYsUK4uPjNdJjYmIIDw/H1NRUffj7+6NSqbh48eITywwLC9O4Zj0wMJANGzZoLKkH1IPwp4l57NgxXnvtNerWrYuZmRkdOnQACgb2pRkxYgSRkZF4eXkxceJEDhw4UOprHmdsbKweGAPY2tri6OioceLB1ta2zJciAPTv35/Y2Fji4uLYv38/Li4udO3alfT0dAASEhJo3bq1xqUVbdu25d69e1y5coXz58+TnZ1N69at1c9bWVnh5uamfnz06FHy8/OpX7++Rv/u2bOH8+fPl9g+e3v7J7Zlzpw5mJubaxyff7OizG0XQgghhBBCVByZ+Rdqvr6++Pv7M2XKFI3l8yqVimHDhjF69Ogir6lTp06J5cXHx3Po0CEOHz7MpEmT1Ol5eXmsXr2aESNGqNNMTEw0XltazIyMDLp27UrXrl1ZuXIl1tbWJCUl4e/vX6YNA7t3786lS5fYunUrO3fupFOnTowcOZL58+ej8/cZxsJ7E+Tk5BQpQ09PT+OxQqEoNk2lUpVan4fMzc1xcXEBwMXFhbCwMOzt7VmzZg1DhgwhPz+/yJ4KD+upUCiK7KdQHJVKhVKpJCYmBqVSqfFc4RMXxbXlSeVPnjyZcePGaaTdvXC81PoIIYQQQgghKp4M/oWGkJAQvLy8qF+/vjqtWbNmnDp1Sj0oLY6+vn6Rje3CwsLw9fVlyZIlGukRERGEhYVpDP4fV1rMEydOkJKSQkhICA4ODgAcOXKk1PYVZm1tTVBQEEFBQfj4+DBhwgTmz5+PtbU1AMnJyVhaWgIFG/5VhoeD8/v37wPg7u7O+vXrNU4CHDhwADMzM2rVqoWlpSV6enr8/vvv6hMzqampnDlzhvbt2wPQtGlT8vLyuHHjBj4+PuVWVwMDAwwMDDTSsmTJvxBCCCGEeEY6yqq58V5lkWX/QkPjxo3p378/ixcvVqdNmjSJgwcPMnLkSGJjYzl79iybN29m1KhR6jyOjo7s3buXq1evkpKSQk5ODhEREbz55pt4eHhoHEOGDCEmJoa4uLgS61FazDp16qCvr8/ixYu5cOECmzdvZubMmWVu50cffcSmTZs4d+4cp06dYsuWLTRs2BAomHF3cHBgxowZnDlzhq1btxa7Y35FyMzM5Nq1a1y7do24uDjeffddDA0N6dq1KwDvvvsuly9fZtSoUZw+fZpNmzYxffp0xo0bh46ODqampgwePJgJEyawa9cuTp48SVBQkHo1A0D9+vXp378/AwcOZMOGDVy8eJHDhw8zd+5coqKitNJOIYQQQgghhHbJ4F8UMXPmTI3l3Z6enuzZs4ezZ8/i4+ND06ZNmTZtGvb29uo8wcHBJCYm4uzsjLW1NZs3b+bWrVv06NGjSPmurq40bty4yMaChZUW09ramvDwcNauXYu7uzshISHMnz+/zG3U19dn8uTJeHp64uvri1KpJDIyEihY7r569WpOnz5NkyZNmDt3rsZu+hXpm2++wd7eHnt7e/z8/Lh58yZRUVHqa/Zr1apFVFQUf/zxB02aNGH48OEMHjyYDz/8UF3GvHnz8PX15dVXX6Vz5860a9eO5s2ba8RZvnw5AwcO5P3338fNzY1XX32VQ4cOqVdRCCGEEEIIUdnkVn/lS5FflouEhRDiGdyM/0PrMc1O7tVqvF39v9FqvGYn1mg1nmXi011O80+t0Bmi1XgAZ89najVeowbGWo2Xele7/8zbP91dU/91DPS0/7PpaHyuVuPdzyy6z01F8mhoUnqmcmRtrt3+vPdAWXqmcnYk7r5W47VsaqjVeClp2p2/zMrW7ud+OF9qNZ7hqyO1Gu9pXAh6udJiO4VvqbTYFUVm/oUQQgghhBBCiCpOBv+iSho+fLjGbewKH8OHD9d6ffbt21difQrvsC+EEEIIIYQooNDRqbSjKpLd/kWVFBwczPjx44t9rlq1alquDXh7e1faHQOEEEIIIYQQQgb/okqysbHBxsamsquhZmRk9MRbJQohhBBCCCE0VdWN9ypL1VzPIIQQQgghhBBCCDWZ+RdCCCGEEEII8dyRmf/yJTP/QgghhBBCCCFEFSeDfyGEEEIIIYQQooqTZf9CiAqjo8rVesyc69e1Gq/ZiTVajXe0cR+txvMe10Kr8ax6DNJqPIC+HbO0Gi/6lKFW4/15OlWr8ZSNrbQbT6nVcJw7n6ndgICpmb5W4znUNtZqPDNjlVbjKRX5Wo0XvS9Nq/EApgYkazXesTtuWo1noN2PBA1qPdBqvMxNx7Uaz/BVrYZ7KlX1lnuVRXpTCCGEEEIIIYSo4mTmXwghhBBCCCHEc0c2/CtfMvMvhBBCCCGEEEJUcTL4F0IIIYQQQgghqjgZ/AshhBBCCCGEeO4odHQq7XgWX375JfXq1cPQ0JDmzZuzb9++J+ZftWoVTZo0wdjYGHt7e95++21u3br1TLHLQgb/QgghhBBCCCHEP7BmzRrGjBnD1KlTOXbsGD4+PnTv3p2kpKRi8+/fv5+BAwcyePBgTp06xdq1azl8+DBDhgypsDrK4F8IIYQQQgghxPNHoai0Iysri7t372ocWVkl3x74s88+Y/DgwQwZMoSGDRuycOFCHBwcCA0NLTb/77//jqOjI6NHj6ZevXq0a9eOYcOGceTIkYrqTRn8CyGKcnR0ZOHChZVdDSGEEEIIISrFnDlzMDc31zjmzJlTbN7s7GxiYmLo2rWrRnrXrl05cOBAsa9p06YNV65cISoqivz8fK5fv866det46aWXyr0tD8ngXzxRUFAQCoWCkJAQjfSNGzeiUJT91hulDSbd3NzQ19fn6tWrz1rVKq9Dhw4oFAoUCgUGBgbUqlWLV155hQ0bNlR21YQQQgghhCh3Ch1FpR2TJ0/mzp07GsfkyZOLrWdKSgp5eXnY2tpqpNva2nLt2rViX9OmTRtWrVpFnz590NfXx87ODgsLCxYvXlzu/fiQDP5FqQwNDZk7dy6pqakVUv7+/ft58OABvXv3Jjw8vNT82dnZFVKPf4OhQ4eSnJzMuXPnWL9+Pe7u7vTt25d33nmnsqsmhBBCCCFElWFgYEC1atU0DgMDgye+5vHJ0fz8/BInTOPj4xk9ejQfffQRMTExbN++nYsXLzJ8+PBya8PjZPAvStW5c2fs7OxKXOYCcODAAXx9fTEyMsLBwYHRo0eTkZEBFMxYX7p0ibFjx6pnrgsLCwujX79+DBgwgGXLlpGfn6/xvKOjI7NmzSIoKAhzc3OGDh1aakyAlStX4u3tjZmZGXZ2dvTr148bN26Uqc2pqan0798fa2trjIyMcHV1Zfny5QBER0ejUChIS0tT54+NjUWhUJCYmAhAeHg4FhYWbNmyBTc3N4yNjenVqxcZGRmsWLECR0dHLC0tGTVqFHl5eWWqE4CxsTF2dnY4ODjQqlUr5s6dy9dff80333zDzp071fkmTZpE/fr1MTY2xsnJiWnTppGTk6NR1ubNm/H29sbQ0JAaNWoQEBCg8XxmZiaDBg3CzMyMOnXqsHTp0jLXUwghhBBCiP+KGjVqoFQqi8zy37hxo8hqgIfmzJlD27ZtmTBhAp6envj7+/Pll1+ybNkykpOTK6SeMvgXpVIqlcyePZvFixdz5cqVIs+fOHECf39/AgICOH78OGvWrGH//v289957AGzYsIHatWsTHBxMcnKyxps5PT2dtWvXEhgYSJcuXcjIyCA6OrpIjHnz5uHh4UFMTAzTpk0rNSYUrBCYOXMmcXFxbNy4kYsXLxIUFFSmNk+bNo34+Hi2bdtGQkICoaGh1KhR46n6LTMzk0WLFhEZGcn27duJjo4mICCAqKgooqKiiIiIYOnSpaxbt+6pyn3cW2+9haWlpcbyfzMzM8LDw4mPj+fzzz/nm2++4X//+5/6+a1btxIQEMBLL73EsWPH2LVrF97e3hrlLliwAG9vb44dO8a7777LiBEjOH369D+qqxBCCCGEEGX1b7nVn76+Ps2bN2fHjh0a6Tt27KBNmzbFviYzMxOdx+IolUqAIpOh5UW3QkoVVU6PHj3w8vJi+vTphIWFaTw3b948+vXrx5gxYwBwdXVl0aJFtG/fntDQUKysrFAqleoZ+MIiIyNxdXWlUaNGAPTt25ewsDD8/Pw08nXs2JHx48erHw8cOPCJMQ0NDRk0aJA6v5OTE4sWLaJFixbcu3cPU1PTJ7Y3KSmJpk2bqgfEjo6OZe6rh3JycggNDcXZ2RmAXr16ERERwfXr1zE1NcXd3R0/Pz92795Nnz59nrr8h3R0dKhfv7561QHAhx9+qP5/R0dH3n//fdasWcPEiRMB+OSTT+jbty8ff/yxOl+TJk00yn3xxRd59913gYKVBP/73/+Ijo6mQYMGxdYjKyuryA6oWdnZGOjrP3PbhBBCCCGE+DcYN24cAwYMwNvbm9atW7N06VKSkpLUy/gnT57M1atX+e677wB45ZVXGDp0KKGhofj7+5OcnMyYMWNo0aIFNWvWrJA6yuBflNncuXPp2LEj77//vkZ6TEwM586dY9WqVeq0/Px8VCoVFy9epGHDhiWWGRYWRmBgoPpxYGAgvr6+pKWlYWFhoU5/fFa6LDGPHTvGjBkziI2N5fbt26hUKqBgYO/u7v7Eto4YMYKePXty9OhRunbtyuuvv17iWbuSGBsbqwf+ULDhh6Ojo8aJB1tb2zJfivAkj19PtG7dOhYuXMi5c+e4d+8eubm5VKtWTf18bGys+vKJknh6eqr/X6FQYGdn98S6zpkzR+NkAsCEEYOY9O7gp22OEEIIIYQQKHTKvsF4ZevTpw+3bt1Sr3b28PAgKiqKunXrApCcnExSUpI6f1BQEOnp6XzxxRe8//77WFhY0LFjR+bOnVthdZTBvygzX19f/P39mTJlisbyeZVKxbBhwxg9enSR19SpU6fE8uLj4zl06BCHDx9m0qRJ6vS8vDxWr17NiBEj1GkmJiYary0tZkZGBl27dqVr166sXLkSa2trkpKS8Pf3L9OGgd27d+fSpUts3bqVnTt30qlTJ0aOHMn8+fPVy3MKL8d5/Hp6AD09PY3HCoWi2LSHJyWeVV5eHmfPnuWFF14ACu4Z+nBW39/fH3NzcyIjI1mwYIH6NUZGRqWW+7R1nTx5MuPGjdNIu3fu6NM0RQghhBBCiH+td999V71y9nHFbWw+atQoRo0aVcG1ekQG/+KphISE4OXlRf369dVpzZo149SpU7i4uJT4On19/SIb24WFheHr68uSJUs00iMiIggLC9MY/D+utJgnTpwgJSWFkJAQHBwcADhy5Eip7SvM2tqaoKAggoKC8PHxYcKECcyfPx9ra2ug4OydpaUlUDCTXllWrFhBamoqPXv2BOC3336jbt26TJ06VZ3n0qVLGq/x9PRk165dvP322+VWDwMDgyI7oObIkn8hhBBCCCGeC7Lhn3gqjRs3pn///hr3n5w0aRIHDx5k5MiRxMbGcvbsWTZv3qxxFsvR0ZG9e/dy9epVUlJSyMnJISIigjfffBMPDw+NY8iQIcTExBAXF1diPUqLWadOHfT19Vm8eDEXLlxg8+bNzJw5s8zt/Oijj9i0aRPnzp3j1KlTbNmyRX35gouLCw4ODsyYMYMzZ86wdetWjVn1ipSZmcm1a9e4cuUKhw4dYtKkSQwfPpwRI0ao90lwcXEhKSmJyMhIzp8/z6JFi/jxxx81ypk+fTqrV69m+vTpJCQkcOLECT799FOttEEIIYQQQoiy+Lds+PdvUTVbJSrUzJkzNZa8e3p6smfPHs6ePYuPjw9NmzZl2rRp2Nvbq/MEBweTmJiIs7Mz1tbWbN68mVu3btGjR48i5bu6utK4ceMiGwsWVlpMa2trwsPDWbt2Le7u7oSEhDB//vwyt1FfX5/Jkyfj6emJr68vSqWSyMhIoGA5/OrVqzl9+jRNmjRh7ty5zJo1q8xl/xPffPMN9vb2ODs706NHD+Lj41mzZg1ffvmlOs9rr73G2LFjee+99/Dy8uLAgQNMmzZNo5wOHTqwdu1aNm/ejJeXFx07duTQoUNaaYMQQgghhBBC+xT5FXUfASHEf96tkwe0HlN/94+lZypH97r012q8o42f/c4Qz8J7XAutxtvbY4VW4wHUs7yj1XjRp8y1Gu/P06lajefR2Eqr8f6+K5LWnDufod2AgKmZdi+hqmGlV3qmclTbVrs/RU3080rPVI42/XJPq/EApgZUzD3CS3LsjptW46Xc1e4Hv571fa3Ga7JpjFbjWX34tVbjPY3rkwZUWmzbuRGVFruiyMy/EEIIIYQQQghRxcngX/wnDR8+HFNT02KPh/fi1KZ9+/aVWJ/CtwYUQgghhBDiv0Kho6i0oyqS3f7Ff1JwcDDjx48v9rlq1appuTbg7e1dqXcMEEIIIYQQQlRtMvgX/0k2NjbY2NhUdjXUjIyMnnirRCGEEEIIIYT4J2TwL4QQQgghhBDi+VNFb7lXWaQ3hRBCCCGEEEKIKk5m/oUQQgghhBBCPHcUiqq58V5lkZl/IYQQQgghhBCiipOZfyFEhVHpaP8r5vKvR7Uaz8nVTavxvMe10Gq8I5/9odV41gOytBoPwFQ3Q6vxbKpbajXeJWM9rcZ7kJWv1XgZGblajWdlZajVeAAZGTlajZedo93vbm3fUStXpd2Adetp/y5CR1K1G9NAV6XVeKDUarRclXbnS2/EXdRqPCutRhOVSQb/QgghhBBCCCGeOwrZ8K9cSW8KIYQQQgghhBBVnMz8CyGEEEIIIYR47ii0fV1QFScz/0IIIYQQQgghRBUnM/9CCCGEEEIIIZ4/cs1/uZLeFEIIIYQQQgghqjgZ/AshhBBCCCGEEFWcDP7/A4KCglAoFISEhGikb9y4EYWi7JtoODo6snDhwhKfd3NzQ19fn6tXrz5rVau8Dh06oFAoihy5uRV/n+ro6GgUCgVpaWkVHksIIYQQQoh/SqGjqLSjKpLB/3+EoaEhc+fOJTU1tULK379/Pw8ePKB3796Eh4eXmj87O7tC6vFvMHToUJKTkzUOXd1/z/Yb+fn5WjlZIYQQQgghhCg/Mvj/j+jcuTN2dnbMmTOnxDwHDhzA19cXIyMjHBwcGD16NBkZGUDBjPWlS5cYO3asera6sLCwMPr168eAAQNYtmwZ+fn5Gs87Ojoya9YsgoKCMDc3Z+jQoaXGBFi5ciXe3t6YmZlhZ2dHv379uHHjRpnanJqaSv/+/bG2tsbIyAhXV1eWL18OFD8LHhsbi0KhIDExEYDw8HAsLCzYsmULbm5uGBsb06tXLzIyMlixYgWOjo5YWloyatQo8vLyylQnAGNjY+zs7DSOh5YvX07Dhg0xNDSkQYMGfPnll+rnEhMTUSgUbNiwAT8/P4yNjWnSpAkHDx5U57l06RKvvPIKlpaWmJiY0KhRI6KiokhMTMTPzw8AS0tLFAoFQUFBQMFg/tNPP8XJyQkjIyOaNGnCunXr1GU+7Kuff/4Zb29vDAwM2LdvX5nbK4QQQgghxLNQKHQq7aiKqmarRBFKpZLZs2ezePFirly5UuT5EydO4O/vT0BAAMePH2fNmjXs37+f9957D4ANGzZQu3ZtgoOD1bPVD6Wnp7N27VoCAwPp0qULGRkZREdHF4kxb948PDw8iImJYdq0aaXGhIIVAjNnziQuLo6NGzdy8eJF9aC1NNOmTSM+Pp5t27aRkJBAaGgoNWrUeKp+y8zMZNGiRURGRrJ9+3aio6MJCAggKiqKqKgoIiIiWLp0qcZg+Vl98803TJ06lU8++YSEhARmz57NtGnTWLFihUa+qVOnMn78eGJjY6lfvz5vvvmmeiZ+5MiRZGVlsXfvXk6cOMHcuXMxNTXFwcGB9evXA/Dnn3+SnJzM559/DsCHH37I8uXLCQ0N5dSpU4wdO5bAwED27NmjEXfixInMmTOHhIQEPD09/3F7hRBCCCGEENrz71lrLP6xHj164OXlxfTp0wkLC9N4bt68efTr148xY8YA4OrqyqJFi2jfvj2hoaFYWVmhVCrVM/CFRUZG4urqSqNGjQDo27cvYWFh6pnmhzp27Mj48ePVjwcOHPjEmIaGhgwaNEid38nJiUWLFtGiRQvu3buHqanpE9ublJRE06ZN8fb2BgpWHzytnJwcQkNDcXZ2BqBXr15ERERw/fp1TE1NcXd3x8/Pj927d9OnT58ylfnll1/y7bffqh8PGzaMBQsWMHPmTBYsWEBAQAAA9erVIz4+nq+//pq33npLnX/8+PG89NJLAHz88cc0atSIc+fO0aBBA5KSkujZsyeNGzcGCvrsISsrKwBsbGywsLAAICMjg88++4xff/2V1q1bq1+zf/9+vv76a9q3b69+fXBwMF26dHmq/hNCCCGEEEI8H2Tw/x8zd+5cOnbsyPvvv6+RHhMTw7lz51i1apU6LT8/H5VKxcWLF2nYsGGJZYaFhREYGKh+HBgYiK+vL2lpaepBJqAehD9NzGPHjjFjxgxiY2O5ffs2KpUKKBjYu7u7P7GtI0aMoGfPnhw9epSuXbvy+uuv06ZNmye+5nHGxsbqgT+Ara0tjo6OGicebG1ty3wpAkD//v2ZOnWq+rGFhQU3b97k8uXLDB48WH1JBEBubi7m5uYary88625vbw/AjRs3aNCgAaNHj2bEiBH88ssvdO7cmZ49ez5xlj4+Pp4HDx4UGdRnZ2fTtGlTjbTH/36Py8rKIisrSzMtOxsDff0nvk4IIYQQQohiVdGN9yqLLPv/j/H19cXf358pU6ZopKtUKoYNG0ZsbKz6iIuL4+zZsxqD38fFx8dz6NAhJk6ciK6uLrq6urRq1Yr79++zevVqjbwmJiZPFTMjI4OuXbtiamrKypUrOXz4MD/++CNQtg0Du3fvzqVLlxgzZgx//fUXnTp1Uq880NEpeOsX3psgJyenSBl6enoajxUKRbFpD09KlIW5uTkuLi7qo0aNGurXf/PNNxr9cfLkSX7//fcS6/Rw74WHrx8yZAgXLlxgwIABnDhxAm9vbxYvXlxiXR6+buvWrRpx4+Pji1zK8Pjf73Fz5szB3Nxc4/j8mxVPfI0QQgghhBBCO2Tm/z8oJCQELy8v6tevr05r1qwZp06dwsXFpcTX6evrF9nYLiwsDF9fX5YsWaKRHhERQVhYGCNGjCixvNJinjhxgpSUFEJCQnBwcADgyJEjpbavMGtra4KCgggKCsLHx4cJEyYwf/58rK2tAUhOTsbS0hIo2PCvstja2lKrVi0uXLhA//79/1FZDg4ODB8+nOHDhzN58mS++eYbRo0ahf7fM/CF/4bu7u4YGBiQlJSkscT/WUyePJlx48ZppN29cPwflSmEEEIIIf67FDoyV12eZPD/H9S4cWP69++vMSM8adIkWrVqxciRIxk6dCgmJiYkJCSwY8cOdT5HR0f27t1L3759MTAwwNzcnIiICIKDg/Hw8NCIMWTIED799FPi4uJo0qRJsfUoLWadOnXQ19dn8eLFDB8+nJMnTzJz5swyt/Ojjz6iefPmNGrUiKysLLZs2aK+fMHFxQUHBwdmzJjBrFmzOHv2LAsWLHjarixXM2bMYPTo0VSrVo3u3buTlZXFkSNHSE1NLTKoLsmYMWPo3r079evXJzU1lV9//VXd5rp166JQKNiyZQsvvvgiRkZGmJmZMX78eMaOHYtKpaJdu3bcvXuXAwcOYGpqqrHXQGkMDAwwMDDQSMuSJf9CCCGEEEI8F+RUyn/UzJkzNZa8e3p6smfPHs6ePYuPjw9NmzZl2rRp6mvKoWDDt8TERJydnbG2tmbz5s3cunWLHj16FCnf1dWVxo0bF9lYsLDSYlpbWxMeHs7atWtxd3cnJCSE+fPnl7mN+vr6TJ48GU9PT3x9fVEqlURGRgIFS+dXr17N6dOnadKkCXPnzmXWrFllLrsiDBkyhG+//Zbw8HAaN25M+/btCQ8Pp169emUuIy8vj5EjR9KwYUO6deuGm5ub+naBtWrV4uOPP+aDDz7A1tZWfVeFmTNn8tFHHzFnzhwaNmyIv78/P/3001PFFUIIIYQQorwpdBSVdlRFivzHb8guhBDl5Gb8H9qPOXWyVuM5DXtTq/Hu7N5TeqZydOQz7f4NTY7FajUegJ3RLa3G++NKba3GO3zsrlbj1a795DuxlLeMjFytxtPV1f68SUZG0T1pKlL16galZypHLg5aDYeesuz79JSHk+e1P4ho4KjdeAa62u3Ta2l6pWcqRw7Vs0rPVJ7xZr2i1XgN1v6i1XhP4868UZUW23xCyftm/VvJzL8QQgghhBBCCFHFyeBf/GsNHz4cU1PTYo/hw4drvT779u0rsT6Fbw0ohBBCCCGEKAOFTuUdVZBs+Cf+tYKDg9W37ntctWrVtFwb8Pb2rtQ7BgghhBBCCCFESWTwL/61bGxssLGxqexqqBkZGT3xVolCCCGEEEKIsquqG+9Vlqq5nkEIIYQQQgghhBBqMvgXQgghhBBCCCGqOFn2L4QQQgghhBDi+aMjc9XlSXpTCCGEEEIIIYSo4mTmXwghhBBCCCHEc0ehkA3/ypMM/oUQFWbWjoZaj/nCuJ1ajbfvvnb/UbLqMUir8awHZGk1XkZTL63GAzi+N0Gr8Q4dTtNqvHt3H2g13omb97Qaz9zSWKvxbt+4q9V4AN8ExGo13sUaLbUab8TY01qNF/hee63Gu3UrU6vxABT1DLQa73KKnlbj9Tf/SavxEgx9tBovKvBnrcZroNVoojLJ4F8IIYQQQgghxPNHrvkvV9KbQgghhBBCCCFEFSeDfyGEEEIIIYQQooqTZf9CCCGEEEIIIZ47Ch3Z8K88ycy/EEIIIYQQQghRxcnMvxBCCCGEEEKI549C5qrLk/SmEEIIIYQQQghRxcngX2hFUFAQCoWCkJAQjfSNGzeiUJT9Wh5HR0cWLlxY4vNubm7o6+tz9erVZ61qldehQwfGjBkDwAcffEDDhg01nk9ISEChUDBgwACN9IiICPT09Lh3T7v38BZCCCGEEEL8czL4F1pjaGjI3LlzSU1NrZDy9+/fz4MHD+jduzfh4eGl5s/Ozq6Qevyb+Pn5cfr0aa5du6ZOi46OxsHBgd27d2vkjY6OpkWLFpiammq7mkIIIYQQ4r9IR1F5RxUkg3+hNZ07d8bOzo45c+aUmOfAgQP4+vpiZGSEg4MDo0ePJiMjAyiYsb506RJjx45FoVAUWTEQFhZGv379GDBgAMuWLSM/P1/jeUdHR2bNmkVQUBDm5uYMHTq01JgAK1euxNvbGzMzM+zs7OjXrx83btwoU5tTU1Pp378/1tbWGBkZ4erqyvLly4GCwbRCoSAtLU2dPzY2FoVCQWJiIgDh4eFYWFiwZcsW3NzcMDY2plevXmRkZLBixQocHR2xtLRk1KhR5OXllalOhbVr1w49PT2io6PVadHR0YwcOZL09HTOnTunke7n5/fUMYQQQgghhBCVTwb/QmuUSiWzZ89m8eLFXLlypcjzJ06cwN/fn4CAAI4fP86aNWvYv38/7733HgAbNmygdu3aBAcHk5ycTHJysvq16enprF27lsDAQLp06UJGRobGgPahefPm4eHhQUxMDNOmTSs1JhSsEJg5cyZxcXFs3LiRixcvEhQUVKY2T5s2jfj4eLZt20ZCQgKhoaHUqFHjqfotMzOTRYsWERkZyfbt24mOjiYgIICoqCiioqKIiIhg6dKlrFu37qnKBTAxMeGFF17QmOXfs2cPnTp1om3btur0y5cvc+HCBRn8CyGEEEIIrVEodCrtqIpkt3+hVT169MDLy4vp06cTFham8dy8efPo16+f+np0V1dXFi1aRPv27QkNDcXKygqlUqmegS8sMjISV1dXGjVqBEDfvn0JCwsrMljt2LEj48ePVz8eOHDgE2MaGhoyaNAgdX4nJycWLVpEixYtuHfvXqlL4JOSkmjatCne3t5AweqDp5WTk0NoaCjOzs4A9OrVi4iICK5fv46pqSnu7u74+fmxe/du+vTp89Tld+jQQX3iID4+nvv379O0aVPat29PdHQ0Q4cOZffu3RgYGNCmTZunLl8IIYQQQghR+armKQ3xXJs7dy4rVqwgPj5eIz0mJobw8HBMTU3Vh7+/PyqViosXLz6xzLCwMAIDA9WPAwMD2bBhg8aSekA9CH+amMeOHeO1116jbt26mJmZ0aFDB6BgYF+aESNGEBkZiZeXFxMnTuTAgQOlvuZxxsbG6oE/gK2tLY6OjhonHmxtbct8KcLj/Pz8OHPmDH/99RfR0dG0a9cOpVKpHvxDwZL/Vq1aYWRkVGI5WVlZ3L17V+PIzc16pjoJIYQQQgghypcM/oXW+fr64u/vz5QpUzTSVSoVw4YNIzY2Vn3ExcVx9uxZjcHv4+Lj4zl06BATJ05EV1cXXV1dWrVqxf3791m9erVGXhMTk6eKmZGRQdeuXTE1NWXlypUcPnyYH3/8ESjbhoHdu3fn0qVLjBkzhr/++otOnTqpVx7o6BR8/ArvTZCTk1OkDD09PY3HCoWi2DSVSlVqfYrTtm1b9PX1iY6OZvfu3bRv3x4oOFFy584dzpw5w+7du0td8j9nzhzMzc01jiM7FjxTnYQQQgghhJAN/8qXLPsXlSIkJAQvLy/q16+vTmvWrBmnTp3CxcWlxNfp6+sX2dguLCwMX19flixZopEeERFBWFgYI0aMKLG80mKeOHGClJQUQkJCcHBwAODIkSOltq8wa2trgoKCCAoKwsfHhwkTJjB//nysra0BSE5OxtLSEijY8E/bjIyMaNmyJdHR0ezdu5cJEyYAoKurS5s2bfjuu+9ITEwsdfA/efJkxo0bp5n2jdxRQQghhBBCiOeBzPyLStG4cWP69+/P4sWL1WmTJk3i4MGDjBw5ktjYWM6ePcvmzZsZNWqUOo+joyN79+7l6tWrpKSkkJOTQ0REBG+++SYeHh4ax5AhQ4iJiSEuLq7EepQWs06dOujr67N48WIuXLjA5s2bmTlzZpnb+dFHH7Fp0ybOnTvHqVOn2LJlCw0bNgTAxcUFBwcHZsyYwZkzZ9i6dSsLFlTOTLmfnx+RkZHcv3+fZs2aqdPbt2/PokWL1CcInsTAwIBq1appHLq6BhVddSGEEEIIUUUpdHQq7aiKqmarxL/CzJkzNZa8e3p6smfPHs6ePYuPjw9NmzZl2rRp2Nvbq/MEBweTmJiIs7Mz1tbWbN68mVu3btGjR48i5bu6utK4ceMiGwsWVlpMa2trwsPDWbt2Le7u7oSEhDB//vwyt1FfX5/Jkyfj6emJr68vSqWSyMhIoGA5/+rVqzl9+jRNmjRh7ty5zJo1q8xllyc/Pz/S09Np27YturqPFgS1b9+e9PR02rRpg4GBDOSFEEIIIYT4t1LkP34zdCGEKCf/93m61mO+0OzJd2Aobxn3tXtNmFW1Z9vb4VlZm2p308aMpl5ajQeQsTdBq/H2/HZXq/Hu3X2g1Xh5edp9j5pbGms13u0b2v37AXwTEKvVeBdrPHmlV3kbMfa0VuMFvtdeq/HOXcjUajyAdt7aPWF/PVWp1Xj9zX/SarwECx+txtt32lyr8ca99vxe3565bHqlxTYe9HGlxa4oMvMvhBBCCCGEEEJUcTL4F+IfGD58uMZtAgsfw4cP13p99u3bV2J9Ct8aUAghhBBCCPHfIrv9C/EPBAcHq2/d97hq1appuTYFt+erjDsGCCGEEEIIUe6q6MZ7lUUG/0L8AzY2NtjY2FR2NdSMjIyeeKtEIYQQQgghxH+TDP6FEEIIIYQQQjx/FM/vZoT/RrKOQgghhBBCCCGEqOJk8C+EEEIIIYQQQlRxsuxfCCGEEEIIIcRzRyEb/pUr6U0hhBBCCCGEEKKKU+Tn5+dXdiWEEFXTxsN5Wo/paHFbq/FW7jTRary+HbO0Gs9UN0Or8Y7fqKnVeAAmvg21Gu/K1j+1Gq+6uUqr8Q7Hafc9am9nqNV4N1OytRoPwN5OX6vx7qZr9z1TzUy7c1EH9l7VajxjM+2+RwGcXCy1Gu/2be1+7r2bGGk1noVxjlbjNTbW7r8Tteo31mq8p3F/5exKi20UOKXSYlcUmfkXQgghhBBCCCGqOLnmXwghhBBCCCHE80dHbvVXnmTmXwghhBBCCCGEqOJk8C+EEEIIIYQQQlRxsuxfCCGEEEIIIcRzR6GQueryJL0phBBCCCGEEEJUcTLzL4QQQgghhBDi+SMb/pUrmfkXogqbMWMGXl5elV0NIYQQQgghRCWTwb8oIigoCIVCQUhIiEb6xo0bUSjKfvbN0dGRhQsXlvi8m5sb+vr6XL169Vmr+p9w6tQp3njjDaytrTEwMMDV1ZVp06aRmZmpkU+hULBx48bKqaQQQgghhBDiuSaDf1EsQ0ND5s6dS2pqaoWUv3//fh48eEDv3r0JDw8vNX92dnaF1ON59/vvv9OyZUuys7PZunUrZ86cYfbs2axYsYIuXbpUSr/k5+eTm5ur9bhCCCGEEOI/RqFTeUcVVDVbJf6xzp07Y2dnx5w5c0rMc+DAAXx9fTEyMsLBwYHRo0eTkZEBQIcOHbh06RJjx45FoVAUWTEQFhZGv379GDBgAMuWLSM/P1/jeUdHR2bNmkVQUBDm5uYMHTq01JgAK1euxNvbGzMzM+zs7OjXrx83btwoU5tTU1Pp378/1tbWGBkZ4erqyvLlywGIjo5GoVCQlpamzh8bG4tCoSAxMRGA8PBwLCws2LJlC25ubhgbG9OrVy8yMjJYsWIFjo6OWFpaMmrUKPLy8kqtT35+PoMHD6Zhw4Zs2LCBFi1aULduXXr37s1PP/3EwYMH+d///qfuL4AePXqgUCjUjx+KiIjA0dERc3Nz+vbtS3p6ukacTz/9FCcnJ4yMjGjSpAnr1q1TP/+w7T///DPe3t4YGBiwb9++MvWpEEIIIYQQ4vkgg39RLKVSyezZs1m8eDFXrlwp8vyJEyfw9/cnICCA48ePs2bNGvbv3897770HwIYNG6hduzbBwcEkJyeTnJysfm16ejpr164lMDCQLl26kJGRQXR0dJEY8+bNw8PDg5iYGKZNm1ZqTChYITBz5kzi4uLYuHEjFy9eJCgoqExtnjZtGvHx8Wzbto2EhARCQ0OpUaPGU/VbZmYmixYtIjIyku3btxMdHU1AQABRUVFERUURERHB0qVLNQbXJYmNjSU+Pp5x48aho6P5UW3SpAmdO3dm9erVABw+fBiA5cuXk5ycrH4McP78eTZu3MiWLVvYsmULe/bs0bik48MPP2T58uWEhoZy6tQpxo4dS2BgIHv27NGIOXHiRObMmUNCQgKenp5P1S9CCCGEEEI8NYWi8o4qSHb7FyXq0aMHXl5eTJ8+nbCwMI3n5s2bR79+/RgzZgwArq6uLFq0iPbt2xMaGoqVlRVKpVI9A19YZGQkrq6uNGrUCIC+ffsSFhaGn5+fRr6OHTsyfvx49eOBAwc+MaahoSGDBg1S53dycmLRokW0aNGCe/fuYWpq+sT2JiUl0bRpU7y9vQGKzJ6XRU5ODqGhoTg7OwPQq1cvIiIiuH79Oqampri7u+Pn58fu3bvp06fPE8s6c+YMAA0bNiz2+YYNG7J//34ArK2tAbCwsCjS3yqVivDwcMzMzAAYMGAAu3bt4pNPPiEjI4PPPvuMX3/9ldatWwMF/bZ//36+/vpr2rdvry4nODiYLl26lFjfrKwssrKyNPsjWxc9fYMntlMIIYQQQoiq4Msvv2TevHkkJyfTqFEjFi5ciI+PT4n5s7KyCA4OZuXKlVy7do3atWszdepUjTFNeZKZf/FEc+fOZcWKFcTHx2ukx8TEEB4ejqmpqfrw9/dHpVJx8eLFJ5YZFhZGYGCg+nFgYCAbNmzQWFIPqAfhTxPz2LFjvPbaa9StWxczMzM6dOgAFAzsSzNixAgiIyPx8vJi4sSJHDhwoNTXPM7Y2Fg98AewtbXF0dFR48SDra1tmS9FeJL8/PwybcDo6OioHvgD2Nvbq+PHx8fz4MEDunTpotGv3333HefPn9co5/G/x+PmzJmDubm5xrE+POSJrxFCCCGEEKJEOjqVdzylNWvWMGbMGKZOncqxY8fw8fGhe/fuTxyHvPHGG+zatYuwsDD+/PNPVq9eTYMGDf5Jjz2RzPyLJ/L19cXf358pU6ZoLJ9XqVQMGzaM0aNHF3lNnTp1SiwvPj6eQ4cOcfjwYSZNmqROz8vLY/Xq1YwYMUKdZmJiovHa0mJmZGTQtWtXunbtysqVK7G2tiYpKQl/f/8ybYzXvXt3Ll26xNatW9m5cyedOnVi5MiRzJ8/X73svvDeBDk5OUXK0NPT03isUCiKTVOpVKXWp379+kBBnxV3u77Tp0/j6upaajlPiv/wv1u3bqVWrVoa+QwMNGfsH/97PG7y5MmMGzdOI+3nE/IVI4QQQgghqr7PPvuMwYMHM2TIEAAWLlzIzz//TGhoaLH7qG3fvp09e/Zw4cIFrKysgGdbefw05Je5KFVISAheXl7qwShAs2bNOHXqFC4uLiW+Tl9fv8jGdmFhYfj6+rJkyRKN9IiICMLCwjQG/48rLeaJEydISUkhJCQEBwcHAI4cOVJq+wqztrYmKCiIoKAgfHx8mDBhAvPnz1cvq09OTsbS0hIouCa/Inl5edGgQQP+97//0bdvX43r/uPi4ti5c6fGF4menl6ZNhIszN3dHQMDA5KSkjSW+D8LAwODIicM9PSfrj5CCCGEEEI8D4q7pLW437tQsO9YTEwMH3zwgUZ6165dS1xNvHnzZry9vfn000+JiIjAxMSEV199lZkzZ2JkZFR+DSlElv2LUjVu3Jj+/fuzePFiddqkSZM4ePAgI0eOJDY2lrNnz7J582ZGjRqlzuPo6MjevXu5evUqKSkp5OTkEBERwZtvvomHh4fGMWTIEGJiYoiLiyuxHqXFrFOnDvr6+ixevJgLFy6wefNmZs6cWeZ2fvTRR2zatIlz585x6tQptmzZor7e3sXFBQcHB2bMmMGZM2fYunUrCxYseNqufCoKhYJvv/2W+Ph4evbsyR9//EFSUhJr167llVdeoXXr1ur9D6Cgv3ft2sW1a9fKfItGMzMzxo8fz9ixY1mxYgXnz5/n2LFjLFmyhBUrVlRQy4QQQgghhCiDSrzVX3GXtJZ0J7SUlBTy8vKwtbXVSLe1teXatWvFvubChQvs37+fkydP8uOPP7Jw4ULWrVvHyJEjy70bH5LBvyiTmTNnaix59/T0ZM+ePZw9exYfHx+aNm3KtGnTsLe3V+cJDg4mMTERZ2dnrK2t2bx5M7du3aJHjx5Fynd1daVx48ZFNhYsrLSY1tbWhIeHs3btWtzd3QkJCWH+/PllbqO+vj6TJ0/G09MTX19flEolkZGRQMGs+urVqzl9+jRNmjRh7ty5zJo1q8xlP6u2bdvy+++/o1QqefHFF3FxcWHy5Mm89dZb7NixQ+PM44IFC9ixYwcODg40bdq0zDFmzpzJRx99xJw5c2jYsCH+/v789NNP1KtXryKaJIQQQgghxHNv8uTJ3LlzR+OYPHnyE1/z+H5cT9qjS6VSoVAoWLVqFS1atODFF1/ks88+Izw8nPv375dbOzTql//4DdaFEKKcbDys/WX/jha3tRpv5c4n74VQ3vp2zCo9Uzky1c3QarzjN2pqNR6AiW/xd9SoKFe2/qnVeNXNS99jpDwdjtPue9TezlCr8W6mlL6HTHmzt9PXary76dp9z1Qz0+5c1IG9V7Uaz9hMu+9RACcXS63Gu31bu5977yYVsyS6JBbGRfd5qkiNjbX770St+o21Gu9pPNi4qNJiG75edJ+xkmRnZ2NsbMzatWs1Jjr/7//+j9jY2CK30AZ46623+O233zh37pw6LSEhAXd3d86cOVOmvb2elsz8CyGEEEIIIYQQz0hfX5/mzZuzY8cOjfQdO3bQpk2bYl/Ttm1b/vrrL+7du6dOO3PmDDo6OtSuXbtC6imDf/GfMXz4cI3b2RU+hg8frvX67Nu3r8T6FL41oBBCCCGEEOL5Nm7cOL799luWLVtGQkICY8eOJSkpST3OmDx5MgMHDlTn79evH9WrV+ftt98mPj6evXv3MmHCBAYNGlRhG/7Jbv/iPyM4OJjx48cX+1y1atW0XBvw9vau8DsGCCGEEEII8a+l+PfMVffp04dbt24RHBxMcnIyHh4eREVFUbduXaDgrmFJSUnq/KampuzYsYNRo0bh7e1N9erVeeONNyp0XzEZ/Iv/DBsbG2xsbCq7GmpGRkZPvFWiEEIIIYQQ4t/j3Xff5d133y32ufDw8CJpDRo0KHKpQEWSwb8QQgghhBBCiOdPCTvli2fz71lHIYQQQgghhBBCiGciM/9CCCGEEEIIIZ4/OjJXXZ6kN4UQQgghhBBCiCpOZv6FEBXGzDBH6zGPXbHWarxGDbQajuhThlqNZ1PdUqvxDh1O02o8AM+tf2o1Xu2X3LQaz+bkIa3GM3nBRKvxzidrNRy9fNK1GxD4/XwNrcazrq7UarzX7bX7Hj1qVE+r8TLTH2g1HsD1a5lajZdxL0ur8X77I1er8XKy8rQa73ZbD63G61dfq+FEJZLBvxBCCCGEEEKI549s+FeuZNm/EEIIIYQQQghRxcnMvxBCCCGEEEKI549C5qrLk/SmEEIIIYQQQghRxcngXwghhBBCCCGEqOJk2b8QQgghhBBCiOePjsxVlyfpTSGEEEIIIYQQooqTwb+oMmbMmIGXl1dlV0MIIYQQQghRHhSKyjuqIBn8iwrXoUMHxowZUyR948aNKBQKFixYgLm5OZmZmUXyPHjwAAsLCz777DMt1PS/Z/bs2SiVSkJCQtRpjo6OKBSKEo8OHTpUXoWFEEIIIYQQz0QG/6LSDRw4kPv377N+/foiz61fv57MzEwGDBhQCTWr+pYvX87EiRNZtmyZOu3w4cMkJyeTnJys/pv8+eef6rQNGzZUVnWFEEIIIcR/iUKn8o4qqGq2SvyrWFtb88orr2gMQB9atmwZr776KtbW1kyaNIn69etjbGyMk5MT06ZNIycnp8Ryi1tx8PrrrxMUFKR+nJ2dzcSJE6lVqxYmJia0bNmS6OjoMtU7PDwcCwsLtmzZgpubG8bGxvTq1YuMjAxWrFiBo6MjlpaWjBo1iry8vDLHvHXrFm+++Sa1a9fG2NiYxo0bs3r16iJtGz16NBMnTsTKygo7OztmzJhRpno/tGfPHu7fv09wcDAZGRns3bsXKPh72NnZYWdnh5WVFQA2NjZF0oQQQgghhBD/HrLbv3guDB48mJdffpmLFy9Sr149ABITE9m9ezdbt24FwMzMjPDwcGrWrMmJEycYOnQoZmZmTJw48Znjvv322yQmJhIZGUnNmjX58ccf6datGydOnMDV1bXU12dmZrJo0SIiIyNJT08nICCAgIAALCwsiIqK4sKFC/Ts2ZN27drRp0+fMsV88OABzZs3Z9KkSVSrVo2tW7cyYMAAnJycaNmypTr2ihUrGDduHIcOHeLgwYMEBQXRtm1bunTpUqa2h4WF8eabb6Knp8ebb75JWFgYvr6+z9aRQgghhBBCiOeazPyL54K/vz81a9YkPDxcnbZ8+XJq1qxJ165dAfjwww9p06YNjo6OvPLKK7z//vv88MMPzxzz/PnzrF69mrVr1+Lj44OzszPjx4+nXbt2LF++vExl5OTkEBoaStOmTfH19aVXr17s37+fsLAw3N3defnll/Hz82P37t1ljlmrVi3Gjx+Pl5cXTk5OjBo1Cn9/f9auXasR29PTk+nTp+Pq6srAgQPx9vZm165dZar33bt3Wb9+PYGBgQAEBgaybt067t69W9buKyIrK4u7d+9qHNnZWc9cnhBCCCGE+I+TDf/KlQz+xXNBqVTy1ltv8f/s3XlcVNX/P/DXMCKyo4Cs5rAIrgiKoaYCluKKhPtHRZQ0c8cFP6YoWommKPnxk+UCQ1aiufYR0EzBENIUUzEpla9IC0guuIGAM/P7wwf3xwjIgOOF8PV8PO7jwZx75r7PPTOg73vOPVcul0OpVEKlUiEuLg7BwcGQSqUAgD179qBXr16wtraGkZERwsPDkZubW+eY586dg0qlgouLC4yMjITtxIkTyM7O1ugYBgYGcHJyEl5bWVlBJpPByMhIraygoEDjmAqFAh999BHc3Nxgbm4OIyMjfPfdd5XO1c3NTe21jY2NEKcmX3/9NRwdHdG5c2cAEC40xMfHa/T+qkRGRsLU1FRt27ltbZ2PR0RERERE2sNp//TSmZiY4N69e5XKCwsLYWJiIryePHkyIiMjcfz4cQBAbm4uJk2aBAA4deoUxowZgxUrVsDPzw+mpqaIj49HVFRUtXF1dHSgUqnUyiquEaBUKiGVSpGRkSFcYChXMXl/Hl1dXbXXEomkyjKlUqlxzKioKGzYsAHR0dHo1KkTDA0NMXfuXJSWltYYuzxOTWJiYvDLL7+gSZP//ydAqVRi+/btmDp1qkbHeNbixYsxb948tbK0q6pqahMRERER1UCHY9XaxOSfXrq2bdsiKSmpUvmZM2fg6uoqvHZycoK3tzdiY2OhUqng4+MjjKqnpaWhdevWWLJkiVD/xo0bz41raWmJvLw84bVCocClS5fg6+sLAPDw8IBCoUBBQQF69+79QueoKU1ipqamYtiwYcKUfKVSiatXr6Jdu3ZaaUNmZibOnj2LlJQUtcX7CgsL0adPH1y6dAkdO3as9XH19PSgp6enVta06eMXbi8REREREb04Jv/00k2fPh2bNm3CjBkzMHXqVOjr6+Po0aPYvn07duzYoVY3JCQEU6ZMAQBs27ZNKHd2dkZubi7i4+PRrVs3JCQkYP/+/c+N27dvX8ybNw8JCQlwcnLChg0bUFhYKOx3cXHBuHHjEBQUhKioKHh4eODWrVs4fvw4OnXqhEGDBmmvE2oR09nZGXv37kV6ejqaN2+O9evXIz8/X2vJ//bt2/H6669Xubhfjx49sH37dmzYsEErsYiIiIiIqGHgPAp66WQyGVJTU5GdnY3+/fujW7dukMvlkMvlGDlypFrd4cOHCyPIgYGBQvmwYcMQGhqKmTNnwt3dHenp6QgPD39u3MmTJ2PixIkICgqCt7c3HBwchFH/crGxsQgKCsL8+fPh6uoKf39/nD59Gq1atdJeBzyjppjh4eHo0qUL/Pz84OPjA2trawQEBGgldmlpKb788ksMHz68yv3Dhw/Hl19+WekWAyIiIiIisakkknrbGiOJ6tmboomItORYpvjT/nNvNRM9pphu39VsXQdtaWku7jXi02cKRY0HAG5uZqLGsx/sWnMlLWp56bSo8W4VGYoaLztP3EmMb7S5LWo8ADiVbSFqvCYizwsNsBH3O7poj4Oo8R4Xi39B3dLGVNR4jx6K+3QffcOmosYrK1GIGq/PG2aixvtXr4ab6BYf31FzpZdEv++Eeov9snDaPxERERERETU8Ek5U1yb2JlE1Bg4cqPY4vorbqlWr6rt51frqq6+qbXeHDh3qu3lERERERFQPOPJPVI1t27ahuLi4yn0VV8lvaPz9/eHl5VXlvmcfD0hERERE1GBx5F+rmPwTVcPOzq6+m1AnxsbGMDY2ru9mEBERERFRA8JLKURERERERESNHEf+iYiIiIiIqMFprI/cqy8c+SciIiIiIiJq5DjyT0RERERERA0PF/zTKib/RPTS5N5qJnrMP/KfiBrP0EAqarzffr0rarwbBuI+IeLh/ceixgMAc1OlqPFaXjotaryCjlU//eNleXP/XFHjmTmNEzWegU7VT4F5mdrYloga78L/NRU13t4/xP2OdnRXiRqvlZW48QBg184cUeO9OdBR1HhGBqKGw18F4v47ocN8l14SfrWIiIiIiIiIGjmO/BMREREREVHDwwX/tIoj/0RERERERESNHEf+iYiIiIiIqOHhAghaxd4kIiIiIiIiauSY/BMRERERERE1cpz2T0RERERERA2Oigv+aRVH/huJiIgIuLu713czXikqlQoffvghHBwcYGRkhF69euHixYv13ayXRiKR4MCBA/XdDCIiIiIiqgMm/y+Zj48P5s6dW6n8wIEDkEgkiIqKgqmpKYqKiirVefz4MczMzLB+/XoRWvrqWbVqFaRSKVavXl2n92/btg2RkZFYv349Ll68iDfeeAMjRoyAQqHQckuJiIiIiF5BEp362xqhxnlW/yBBQUEoLi7G3r17K+3bu3cvioqKMGHChHpoWeMXGxuLsLAwxMTE1On9hw8fRmBgIN5++204Ojqif//+uHr1KrKzs7XcUiIiIiIiohfD5L+eWVpaYujQoVUmoDExMfD394elpSUWLVoEFxcXGBgYwNHREeHh4SgrK6v2uFXNOAgICEBwcLDwurS0FGFhYbCzs4OhoSG8vLyQkpKiUbvlcjnMzMxw6NAhuLq6wsDAACNGjMCjR48QFxcHmUyG5s2bY9asWWoj4TXFvH37NsaOHQt7e3sYGBigU6dO2LlzZ6Vzmz17NsLCwtCiRQtYW1sjIiJCo3aXO3HiBIqLi7Fy5Uo8evQIP/zwg9r+4OBgBAQEqJXNnTsXPj4+am01NzcHALz33nv497//jT179sDa2lqoc+/ePUydOhUtW7aEiYkJ+vbtiwsXLgj7y2/X2LFjB2QyGUxNTTFmzBg8ePAAAPC///0PZmZmUCqVAIDz589DIpFg4cKFwjHeffddjB07Vqv9d/XqVfTp0wfNmjVD+/btcfTo0Vr0LhERERHRi1NJdOpta4wa51n9w4SEhODEiRO4fv26UJaTk4Pk5GSEhIQAAIyNjSGXy3H58mV88skn2Lp1KzZs2PBCcSdNmoS0tDTEx8fj4sWLGDlyJAYMGICrV69q9P6ioiJs3LgR8fHxOHz4MFJSUhAYGIjExEQkJiZix44d2LJlC/bs2aNxzMePH6Nr1644dOgQLl26hKlTp2LChAk4ffq0Wuy4uDgYGhri9OnT+Pjjj7Fy5cpaJajbt2/H2LFjoauri7Fjx2L79u0av/dZhYWF+Oyzz/D5559j+PDhMDExAfB0TYDBgwcjPz8fiYmJyMjIQJcuXfDmm2/izp07wvuzs7Nx4MABHDp0CIcOHcKJEyeEWxH69OmDBw8e4Oeffwbw9KKFhYUFTpw4Ibw/JSUF3t7eALTTf0qlEoGBgZBKpTh16hQ+++wzLFq0qM79Q0RERERE9Y/JfwPg5+cHW1tbyOVyoSw2Nha2trbo378/AGDp0qXo2bMnZDIZhg4divnz52P37t11jpmdnY2dO3fim2++Qe/eveHk5IQFCxagV69eiI2N1egYZWVl2Lx5Mzw8PNCnTx+MGDECJ0+exPbt29G+fXsMGTIEvr6+SE5O1jimnZ0dFixYAHd3dzg6OmLWrFnw8/PDN998oxbbzc0Ny5cvR5s2bRAUFARPT08cO3ZMo3bfv38fe/fuxfjx4wEA48ePx549e3D//n1Nu0+Nnp4eDA0NERsbK4zQA0BycjIyMzPxzTffwNPTE23atMG6detgZmamdkFEqVRCLpejY8eO6N27NyZMmCCci6mpKdzd3YXZESkpKQgNDcWFCxfw4MED5Ofn48qVK8KMBG303/fff4+srCzs2LED7u7u6NOnD1atWlVjP5SUlOD+/ftqW1lpSZ36lIiIiIiItIvJfwMglUoxceJEyOVyKJVKqFQqxMXFITg4GFKpFACwZ88e9OrVC9bW1jAyMkJ4eDhyc3PrHPPcuXNQqVRwcXGBkZGRsJ04cULje9YNDAzg5OQkvLaysoJMJoORkZFaWUFBgcYxFQoFPvroI7i5ucHc3BxGRkb47rvvKp2rm5ub2msbGxshTk2+/vprODo6onPnzgAgJMrx8fEavf9Z+vr6+Prrr7Fjxw74+PggLy8PAJCRkYGHDx8K51G+Xb9+Xa2PZTIZjI2Nqz0XHx8fpKSkQKVSITU1FcOGDUPHjh1x8uRJJCcnw8rKCm3btgWgnf7LysrCa6+9Bnt7e2F/jx49auyHyMhImJqaqm0JX0dq2o1EREREROokkvrbGqEm9d2Axs7ExAT37t2rVF5YWChMDweAyZMnIzIyEsePHwcA5ObmYtKkSQCAU6dOYcyYMVixYgX8/PxgamqK+Ph4REVFVRtXR0cHKpVKraziGgFKpRJSqRQZGRnCBYZyFZP359HV1VV7LZFIqiwrHw3XJGZUVBQ2bNiA6OhodOrUCYaGhpg7dy5KS0trjF1x1P15YmJi8Msvv6BJk///9Vcqldi+fTumTp0KoOb+e5a/vz8uX76MUaNGwc/PDxcuXIBSqYSNjU2V6yiYmZlpfC4+Pj7Yvn07Lly4AB0dHbRv3x7e3t44ceIE7t69K0z5B7TTf8+ed/n+mixevBjz5s1TK4v/Ua/G9xERERER0cvH5P8la9u2LZKSkiqVnzlzBq6ursJrJycneHt7IzY2FiqVCj4+PsKoelpaGlq3bo0lS5YI9W/cuPHcuJaWlsIINPB0RPjSpUvw9fUFAHh4eEChUKCgoAC9e/d+oXPUlCYxy0e2y6fkK5VKXL16Fe3atdNKGzIzM3H27FmkpKSgRYsWQnlhYSH69OmDS5cuoWPHjrC0tMSlS5fU3nv+/PlKSXNFtra2+OCDD9C3b1/8/fff6NKlC/Lz89GkSRPIZLI6t7n8vv/o6Gh4e3tDIpHA29sbkZGRuHv3LubMmSPU1Ub/tW/fHrm5ufjrr79ga2sLAPjxxx9rfJ+enh709NSTfd2mGoclIiIiIlLTWBfeqy/szZds+vTpyM7OxowZM3DhwgVcuXIF//3vf7F9+3a1FduBpwv/7du3D/v37xcW+gMAZ2dn5ObmIj4+HtnZ2di4cSP279//3Lh9+/ZFQkICEhIS8Ouvv2L69OkoLCwU9ru4uGDcuHEICgrCvn37cP36dZw5cwZr1qxBYmKiVvugNjGdnZ1x9OhRpKenIysrC++++y7y8/O11obt27fj9ddfR58+fdCxY0dh69WrF3r06CEs/Ne3b1+cPXsWX3zxBa5evYrly5dXuhhQ7vvvv0dmZib+7//+D19//TVsbW1haWmJt956Cz169EBAQACOHDmCnJwcpKenY+nSpTh79qzGbS6/7//LL78U7u3v06cPzp07p3a/P6Cd/nvrrbfg6uqKoKAgXLhwAampqWoXnoiIiIiI6J+Hyf9LJpPJkJqaiuzsbPTv3x/dunWDXC6HXC7HyJEj1eoOHz5cGD0NDAwUyocNG4bQ0FDMnDkT7u7uSE9PR3h4+HPjTp48GRMnTkRQUBC8vb3h4OAgjPqXi42NRVBQEObPnw9XV1f4+/vj9OnTaNWqlfY64Bk1xQwPD0eXLl3g5+cHHx8fWFtbV3rkXl2Vlpbiyy+/xPDhw6vcP3z4cHz55ZcoLS2Fn58fwsPDERYWhm7duuHBgwcICgqq8n0pKSno06cP3NzckJWVhQMHDkAikUAikSAxMRF9+vTB5MmT4eLigjFjxiAnJwdWVla1aruvry8UCoWQ6Ddv3hzt27eHpaWl2qi+NvpPR0cH+/fvR0lJCV5//XW88847+Oijj2p1DCIiIiIialgkqqpu8CUi0oLYZPFj/pH/RNR4hgbSmitpUdYvd2qupEXNDKq/1eVluH+nSNR4ADB0cEtR47U2q9uTReqqoKOXqPHe3D9X1Hg/O40TNV6LppXX8XnZfn8k7nf0wv+Je8+Wob64C2sVPRb3v76trMT/r/aunTmixntzoKOo8YwMRA2Hvwo0W1dKW1rbivs7MaZnw13c7sGZlzMjWRPG3QbVW+yXhSP/RERERERERI0ck3+q0sCBA9UeT1dx0+SZ7/Xlq6++qrbdHTp0qO/mERERERGRpiQ69bc1Qlztn6q0bds2FBcXV7mv4ir5DY2/vz+8vKqe4vq8lfqJiIiIiIgaMyb/VCU7O7v6bkKdGBsbw9jYuL6bQUREREREL0glabjrEfwTNc75DEREREREREQkYPJPRERERERE1Mhx2j8RERERERE1PI104b36wt4kIiIiIiIiauQ48k9EL83jEvFjWluK+2fNSF8lajxpJ3GftvG4RNzzy/z7oajxAODMBXG/qIbdDEWN9+b+uaLGO/Z2tKjxml8cL2q8+0/EX1T26l96IkcU9/fe1lwharw7D6WixjPTLxU1HgAYNRf374x+M1HD4Y98cb8zd++I+xm6OYmdojXcJ2KpwAX/tIkj/0RERERERESNHJN/IiIiIiIiokaO0/6JiIiIiIiowVFxwT+tYm8SERERERERNXIc+SciIiIiIqKGhyP/WsXeJCIiIiIiImrkOPJPREREREREDY5Kwkf9aRNH/umlioiIgLu7e30345Unk8kQHR1d380gIiIiIqJ6wuT/FeXj44O5c+dWKj9w4AAkEgmioqJgamqKoqKiSnUeP34MMzMzrF+/XoSWvjpkMhkkEgkkEgmkUilsbW0REhKCu3fv1nfTiIiIiIjoH47JP1UpKCgIxcXF2Lt3b6V9e/fuRVFRESZMmFAPLWvcVq5ciby8POTm5uKrr77CDz/8gNmzZ9d3s4iIiIiIRKeS6NTb1hg1zrOiF2ZpaYmhQ4ciJiam0r6YmBj4+/vD0tISixYtgouLCwwMDODo6Ijw8HCUlZVVe9yqZhwEBAQgODhYeF1aWoqwsDDY2dnB0NAQXl5eSElJ0ajdcrkcZmZmOHToEFxdXWFgYIARI0bg0aNHiIuLg0wmQ/PmzTFr1iwoFAqNY96+fRtjx46Fvb09DAwM0KlTJ+zcubPSuc2ePRthYWFo0aIFrK2tERERoVG7yxkbG8Pa2hp2dnbw9fVFUFAQzp07p1Zn79696NChA/T09CCTyRAVFaW2v6CgAEOHDoW+vj4cHBzw1VdfVYpz7949TJ06FS1btoSJiQn69u2LCxcuCPvLb9fYsWMHZDIZTE1NMWbMGDx48KBW50NERERERA0DF/yjaoWEhGDIkCG4fv06HBwcAAA5OTlITk5GQkICgKfJqlwuh62tLTIzMzFlyhQYGxsjLCysznEnTZqEnJwcxMfHw9bWFvv378eAAQOQmZmJNm3a1Pj+oqIibNy4EfHx8Xjw4AECAwMRGBgIMzMzJCYm4v/+7/8wfPhw9OrVC6NHj9Yo5uPHj9G1a1csWrQIJiYmSEhIwIQJE+Do6AgvLy8hdlxcHObNm4fTp0/jxx9/RHBwMN544w3069ev1v3w559/4tChQ2rHz8jIwKhRoxAREYHRo0cjPT0d06dPh7m5uXABJTg4GL///juOHz+Opk2bYvbs2SgoKBCOoVKpMHjwYLRo0QKJiYkwNTXF559/jjfffBNXrlxBixYtAADZ2dk4cOAADh06hLt372LUqFFYvXo1Pvroo1qfCxERERFRrXHBP63iyD9Vy8/PD7a2tpDL5UJZbGwsbG1t0b9/fwDA0qVL0bNnT8hkMgwdOhTz58/H7t276xwzOzsbO3fuxDfffIPevXvDyckJCxYsQK9evRAbG6vRMcrKyrB582Z4eHigT58+GDFiBE6ePInt27ejffv2GDJkCHx9fZGcnKxxTDs7OyxYsADu7u5wdHTErFmz4Ofnh2+++UYttpubG5YvX442bdogKCgInp6eOHbsmMbnv2jRIhgZGUFfXx/29vaQSCRqayusX78eb775JsLDw+Hi4oLg4GDMnDkTa9euBQBcuXIFSUlJ2LZtG3r06IGuXbti+/btKC4uFo6RnJyMzMxMfPPNN/D09ESbNm2wbt06mJmZYc+ePUI9pVIJuVyOjh07onfv3pgwYcJzz6WkpAT3799X28pKSzQ+dyIiIiIienmY/FO1pFIpJk6cCLlcDqVSCZVKhbi4OAQHB0MqlQIA9uzZg169esHa2hpGRkYIDw9Hbm5unWOeO3cOKpUKLi4uMDIyErYTJ04gOztbo2MYGBjAyclJeG1lZQWZTAYjIyO1svLRcE1iKhQKfPTRR3Bzc4O5uTmMjIzw3XffVTpXNzc3tdc2NjZqo+41WbhwIc6fP4+LFy8KifbgwYOFWxSysrLwxhtvqL3njTfewNWrV6FQKJCVlYUmTZrA09NT2N+2bVuYmZkJrzMyMvDw4UPhPMq369evq/WxTCaDsbGxxucSGRkJU1NTte3I7kiNz52IiIiIiF4eTvt/RZmYmODevXuVygsLC2FiYiK8njx5MiIjI3H8+HEAQG5uLiZNmgQAOHXqFMaMGYMVK1bAz88PpqamiI+Pr3QPekU6OjpQqVRqZRXXCFAqlZBKpcjIyBAuMJSrmLw/j66urtpriURSZZlSqdQ4ZlRUFDZs2IDo6Gh06tQJhoaGmDt3LkpLS2uMXR5HExYWFnB2dgYAtGnTBtHR0ejRoweSk5Px1ltvQaVSQfLM9KeK/Vn+87N1KlIqlbCxsalyHYWKFwlqey6LFy/GvHnz1MrkKXrV1iciIiIiep7GuvBefWHy/4pq27YtkpKSKpWfOXMGrq6uwmsnJyd4e3sjNjYWKpUKPj4+wqh6WloaWrdujSVLlgj1b9y48dy4lpaWyMvLE14rFApcunQJvr6+AAAPDw8oFAoUFBSgd+/eL3SOmtIkZmpqKoYNG4bx48cDeJpAX716Fe3atXupbSu/GFE+bb99+/Y4efKkWp309HS4uLhAKpWiXbt2ePLkCc6ePYvXX38dAPDbb7+hsLBQqN+lSxfk5+ejSZMmkMlkWmurnp4e9PTUk33dplo7PBERERERvQBeSnlFTZ8+HdnZ2ZgxYwYuXLiAK1eu4L///S+2b9+OhQsXqtUNCQnBvn37sH//foSEhAjlzs7OyM3NRXx8PLKzs7Fx40bs37//uXH79u2LhIQEJCQk4Ndff8X06dPVElMXFxeMGzcOQUFB2LdvH65fv44zZ85gzZo1SExM1Gof1Cams7Mzjh49ivT0dGRlZeHdd99Ffn6+1tvy4MED5OfnIy8vDz/99BMWLlwICwsL9OzZEwAwf/58HDt2DB988AGuXLmCuLg4bNq0CQsWLAAAuLq6YsCAAZgyZQpOnz6NjIwMvPPOO9DX1xdivPXWW+jRowcCAgJw5MgR5OTkID09HUuXLsXZs2e1fk5ERERERHWhgqTetsaIyf8rSiaTITU1FdnZ2ejfvz+6desGuVwOuVyOkSNHqtUdPny4MKobGBgolA8bNgyhoaGYOXMm3N3dkZ6ejvDw8OfGnTx5MiZOnIigoCB4e3vDwcFBGPUvFxsbi6CgIMyfPx+urq7w9/fH6dOn0apVK+11wDNqihkeHo4uXbrAz88PPj4+sLa2RkBAgNbbsWzZMtjY2MDW1hZDhgyBoaEhjh49CnNzcwBPR+13796N+Ph4dOzYEcuWLcPKlSvVHpUYGxuLVq1awdvbG4GBgcIj/cpJJBIkJiaiT58+mDx5MlxcXDBmzBjk5OTAyspK6+dERERERET1T6J69gZsIiIt2XxY/JhNRL6ZyUhf3D+hBXdEDYfHJeKeX+bP2p9RUxO71s1FjefTTdwvqe+f20SNd+ztaFHjNb8o7owlXR2FqPEA4Ox1M1HjFT0W9/e+jb3ma+Now52H0poraZFdc/GffPPV/kJR4/n4iDt4kFcg7u/h3TulNVfSIl8vcf+dGOihW3OlevL3L6frLbZlB6+aK/3DcOSfiIiIiIiIqJFj8k//KAMHDlR7PF3FbdWqVfXdvGp99dVX1ba7Q4cO9d08IiIiIiJq5LjaP/2jbNu2TVj5/lktWrQQuTWa8/f3h5dX1VOHnn2kHhERERERAXjO46sbok8//RRr165FXl4eOnTogOjoaI2eYJaWlgZvb2907NgR58+ff2ntY/JP/yh2dnb13YQ6MTY2hrGxcX03g4iIiIiIXoJdu3Zh7ty5+PTTT/HGG2/g888/x8CBA3H58mW89tpr1b7v3r17CAoKwptvvombN2++1DZy2j8RERERERE1OCro1NtWUlKC+/fvq20lJdUv4Ll+/XqEhITgnXfeQbt27RAdHY1WrVph8+bNzz3Hd999F//617/Qo0cPbXdfJUz+iYiIiIiIiCqIjIyEqamp2hYZGVll3dLSUmRkZKB///5q5f3790d6enq1MWJjY5GdnY3ly5drte3V4bR/IiIiIiIiogoWL16MefPmqZXp6elVWffWrVtQKBSwslJ/7KWVlRXy86t+jPHVq1fx73//G6mpqWgi0rOqmfwTERERERFRg6OqxwX/9PT0qk32qyN5pr0qlapSGQAoFAr861//wooVK+Di4vJC7awNJv9E9NK0sa3+vqiX5epftfsj/U8jlYob79GjJ6LGM21uIGo8ALCxbiZqvOw8UcPBzGmcqPGaXxwvary7bp6ixvv90G+ixgMAHZFv0nzyRCVqvDsPxf3DZthMKWq8vELx/12aO07cL831uwpR4xVIxU0IdZuK25/5hXwS1D+NhYUFpFJppVH+goKCSrMBAODBgwc4e/Ysfv75Z8ycORMAoFQqoVKp0KRJE3z33Xfo27ev1tvJ5J+IiIiIiIgaHJXkn7FEXdOmTdG1a1ccPXoUb7/9tlB+9OhRDBs2rFJ9ExMTZGZmqpV9+umnOH78OPbs2QMHB4eX0k4m/0REREREREQvYN68eZgwYQI8PT3Ro0cPbNmyBbm5uZg2bRqAp2sI/Pnnn/jiiy+go6ODjh07qr2/ZcuWaNasWaVybWLyT0RERERERA2OCvV3z39tjR49Grdv38bKlSuRl5eHjh07IjExEa1btwYA5OXlITc3t17bKFGpVOLe2EVEr4zvLzb+e/7NjMX9E3r7nqjhUPB3majxbv9dJGo8AGjjYipqPJEW9BV0dXggajyx/6P2KtzzL/Z6V4X3xb0n3spC3Gm9Yt/zX1wi/rRlN9u/RY13/W4LUeNd/V3cX4p798T9t9DZQdz/y0zyFTVcreT9er7eYtu0da+32C/LP+MmCiIiIiIiIiKqM077JyIiIiIiogbnn7Lg3z8Fe5OIiIiIiIiokWPyT/UqIiIC7u7u9d0MeoZEIsGBAwfquxlERERE9ApTSST1tjVGTP6pSj4+Ppg7d26l8gMHDkAikSAqKgqmpqYoKqq8ONfjx49hZmaG9evXi9DSV4dMJoNEIoFEIoG+vj5kMhlGjRqF48eP13fTiIiIiIiogWPyT3USFBSE4uJi7N27t9K+vXv3oqioCBMmTKiHljVu5Y8O+e233/DFF1/AzMwMb731Fj766KP6bhoRERERETVgTP6pTiwtLTF06FDExMRU2hcTEwN/f39YWlpi0aJFcHFxgYGBARwdHREeHo6ysuofl1LVjIOAgAAEBwcLr0tLSxEWFgY7OzsYGhrCy8sLKSkpGrVbLpfDzMwMhw4dgqurKwwMDDBixAg8evQIcXFxkMlkaN68OWbNmgWFQqFxzNu3b2Ps2LGwt7eHgYEBOnXqhJ07d1Y6t9mzZyMsLAwtWrSAtbU1IiIiNGp3OWNjY1hbW+O1115Dnz59sGXLFoSHh2PZsmX47benj59SKBQICQmBg4MD9PX14erqik8++aTSsWJiYtChQwfo6enBxsYGM2fOVNt/69YtvP322zAwMECbNm3w7bff1qqtREREREQvQgVJvW2NEZN/qrOQkBCcOHEC169fF8pycnKQnJyMkJAQAE+TVblcjsuXL+OTTz7B1q1bsWHDhheKO2nSJKSlpSE+Ph4XL17EyJEjMWDAAFy9elWj9xcVFWHjxo2Ij4/H4cOHkZKSgsDAQCQmJiIxMRE7duzAli1bsGfPHo1jPn78GF27dsWhQ4dw6dIlTJ06FRMmTMDp06fVYsfFxcHQ0BCnT5/Gxx9/jJUrV+Lo0aMv1B9z5syBSqXCwYMHAQBKpRL29vbYvXs3Ll++jGXLluH999/H7t27hfds3rwZM2bMwNSpU5GZmYlvv/0Wzs7OasddsWIFRo0ahYsXL2LQoEEYN24c7ty580JtJSIiIiKi+sFH/VGd+fn5wdbWFnK5HCtWrAAAxMbGwtbWFv379wcALF26VKgvk8kwf/587Nq1C2FhYXWKmZ2djZ07d+KPP/6Ara0tAGDBggU4fPgwYmNjsWrVqhqPUVZWhs2bN8PJyQkAMGLECOzYsQM3b96EkZER2rdvD19fXyQnJ2P06NEaxbSzs8OCBQuEGLNmzcLhw4fxzTffwMvLSyh3c3PD8uXLAQBt2rTBpk2bcOzYMfTr169O/QEALVq0QMuWLZGTkwMA0NXVFT4PAHBwcEB6ejp2796NUaNGAQA+/PBDzJ8/H3PmzBHqdevWTe24wcHBGDt2LABg1apV+M9//oOffvoJAwYMqLIdJSUlKCkpUSsrLQWaNtWr87kRERER0auLj/rTLib/VGdSqRQTJ06EXC7H8uXLIZFIEBcXh+DgYEilUgDAnj17EB0djWvXruHhw4d48uQJTExM6hzz3LlzUKlUcHFxUSsvKSmBubm5RscwMDAQEn8AsLKygkwmg5GRkVpZQUGBxjEVCgVWr16NXbt24c8//xQSYUNDQ7X3uLm5qb22sbER4rwIlUoFSYVVST/77DNs27YNN27cQHFxMUpLS4WnKhQUFOCvv/7Cm2+++dxjVmyroaEhjI2Nn9vWyMhItYsOADBh2hIEvRdehzMiIiIiIiJtYvJPVTIxMcG9e/cqlRcWFqol75MnT0ZkZKSw4nxubi4mTZoEADh16hTGjBmDFStWwM/PD6ampoiPj0dUVFS1cXV0dKBSqdTKKq4RoFQqIZVKkZGRIVxgKFcxeX8eXV1dtdcSiaTKMqVSqXHMqKgobNiwAdHR0ejUqRMMDQ0xd+5clJaW1hi7PE5d3b59G3///TccHBwAALt370ZoaCiioqLQo0cPGBsbY+3atcItCPr6+hodt7ZtXbx4MebNm6dWdvJKbc6EiIiIiOj/a6z33tcXJv9UpbZt2yIpKalS+ZkzZ+Dq6iq8dnJygre3N2JjY6FSqeDj4yOMqqelpaF169ZYsmSJUP/GjRvPjWtpaYm8vDzhtUKhwKVLl+Dr6wsA8PDwgEKhQEFBAXr37v1C56gpTWKmpqZi2LBhGD9+PICnFwyuXr2Kdu3avfT2ffLJJ9DR0UFAQIDQlp49e2L69OlCnezsbOFnY2NjyGQyHDt2TOhXbdDT04OenvoU/6ZNS6qpTUREREREYmLyT1WaPn06Nm3aJCwKp6+vj6NHj2L79u3YsWOHWt2QkBBMmTIFALBt2zah3NnZGbm5uYiPj0e3bt2QkJCA/fv3Pzdu3759MW/ePCQkJMDJyQkbNmxAYWGhsN/FxQXjxo1DUFAQoqKi4OHhgVu3buH48ePo1KkTBg0apL1OqEVMZ2dn7N27F+np6WjevDnWr1+P/Px8rSf/Dx48QH5+PsrKynD9+nV8+eWX2LZtGyIjI4UF+5ydnfHFF1/gyJEjcHBwwI4dO3DmzBlhZgAAREREYNq0aWjZsiUGDhyIBw8eIC0tDbNmzdJqe4mIiIiIqGHgCgpUJZlMhtTUVGRnZ6N///7o1q0b5HI55HI5Ro4cqVZ3+PDhwqhvYGCgUD5s2DCEhoZi5syZcHd3R3p6OsLDn3//9+TJkzFx4kQEBQXB29sbDg4OlUanY2NjERQUhPnz58PV1RX+/v44ffo0WrVqpb0OeEZNMcPDw9GlSxf4+fnBx8cH1tbWwki8Ni1btgw2NjZwdnbGhAkTcO/ePRw7dgyLFi0S6kybNg2BgYEYPXo0vLy8cPv2bbVZAAAwceJEREdH49NPP0WHDh0wZMgQjZ+WQEREREQkBpVEp962xkiievYGayIiLfn+ovjT/q/+Je7TBcyMxf0TervyUhwvVcHfZTVX0qLbfxeJGg8A2riYihqvichz7ro6PBA1ntj3Z9518xQ13u+HfhM1HgBIRL7ltfD+i61FU1tWFuL+J9uwmbjnV1wifhLhZvu3qPGu320haryrv4v7S3Hvnrj/Fjo7iPt/mUnauwtU625cE/9vbrnWzq41V/qH4bR/IiIiIiIianC44J92Nc75DPTKGjhwIIyMjKrcVq1aVd/Nq9ZXX31Vbbs7dOhQ380jIiIiIqJ/OI78U6Oybds2FBcXV7mvRQtxp6TVhr+/P7y8vKrc9+wj94iIiIiIiGqLyT81KnZ2dvXdhDoxNjaGsbFxfTeDiIiIiKjBaKwL79UX9iYRERERERFRI8eRfyIiIiIiImpwuOCfdnHkn4iIiIiIiKiRY/JPRERERERE1Mhx2j8RvTQ5f+uJHlOpEjeenq64Aa9lF4kar0WLZqLGu1NwX9R4APB3C31R443o/UDUeAY6VT8B5WW5/0TcxUt/P/SbqPFaDXEVNR4AfL8hQ9R4Zmbi/u0uuC1qOBQWlokaT09P/LG2+Lh8UeO59RD39/7G1QJR4+lIpaLGs7X5Zy5g/TKoJJz2r00c+SciIiIiIiJq5DjyT0RERERERA2OSsWRf23iyD8RERERERFRI8eRfyIiIiIiImpwVByr1ir2JhEREREREVEjx+SfiIiIiIiIqJHjtH8iIiIiIiJqcFTggn/axJF/emERERFwd3ev72YQERERERFRNZj8N2I+Pj6YO3dupfIDBw5AIpEgKioKpqamKCoqqlTn8ePHMDMzw/r160Vo6atDJpNBIpGobfb29qLElsvlMDMzEyUWEREREdGLUkFSb1tjxOT/FRYUFITi4mLs3bu30r69e/eiqKgIEyZMqIeWNW4rV65EXl6esP3888/13aRaUSgUUCqV9d0MIiIiIiKqBSb/rzBLS0sMHToUMTExlfbFxMTA398flpaWWLRoEVxcXGBgYABHR0eEh4ejrKys2uNWNeMgICAAwcHBwuvS0lKEhYXBzs4OhoaG8PLyQkpKikbtLh/BPnToEFxdXWFgYIARI0bg0aNHiIuLg0wmQ/PmzTFr1iwoFAqNY96+fRtjx46Fvb09DAwM0KlTJ+zcubPSuc2ePRthYWFo0aIFrK2tERERoVG7yxkbG8Pa2lrYLC0tAQAqlQoff/wxHB0doa+vj86dO2PPnj3C+1JSUiCRSHDs2DF4enrCwMAAPXv2xG+//SbUuXDhAnx9fWFsbAwTExN07doVZ8+eRUpKCiZNmoR79+4JMw7K211Tv1Ts7/bt20NPTw83btyo1TkTEREREVH94oJ/r7iQkBAMGTIE169fh4ODAwAgJycHycnJSEhIAPA0WZXL5bC1tUVmZiamTJkCY2NjhIWF1TnupEmTkJOTg/j4eNja2mL//v0YMGAAMjMz0aZNmxrfX1RUhI0bNyI+Ph4PHjxAYGAgAgMDYWZmhsTERPzf//0fhg8fjl69emH06NEaxXz8+DG6du2KRYsWwcTEBAkJCZgwYQIcHR3h5eUlxI6Li8O8efNw+vRp/PjjjwgODsYbb7yBfv361bk/AGDp0qXYt28fNm/ejDZt2uCHH37A+PHjYWlpCW9vb6HekiVLEBUVBUtLS0ybNg2TJ09GWloaAGDcuHHw8PDA5s2bIZVKcf78eejq6qJnz56Ijo7GsmXLhIsFRkZGGn8WRUVFiIyMxLZt22Bubo6WLVu+0LkSEREREdWksU6/ry9M/l9xfn5+sLW1hVwux4oVKwAAsbGxsLW1Rf/+/QE8TUrLyWQyzJ8/H7t27apz8p+dnY2dO3fijz/+gK2tLQBgwYIFOHz4MGJjY7Fq1aoaj1FWVobNmzfDyckJADBixAjs2LEDN2/ehJGREdq3bw9fX18kJydj9OjRGsW0s7PDggULhBizZs3C4cOH8c0336gl/25ubli+fDkAoE2bNti0aROOHTumcfK/aNEitT5dtWoVQkJCsH79ehw/fhw9evQAADg6OuLkyZP4/PPP1ZL/jz76SHj973//G4MHD8bjx4/RrFkz5ObmYuHChWjbtq3QvnKmpqaQSCSwtrYWyjT9LMrKyvDpp5+ic+fO1Z5XSUkJSkpK1MrKSvWg21RPo34hIiIiIqKXh8n/K04qlWLixImQy+VYvnw5JBIJ4uLiEBwcDKlUCgDYs2cPoqOjce3aNTx8+BBPnjyBiYlJnWOeO3cOKpUKLi4uauUlJSUwNzfX6BgGBgZC4g8AVlZWkMlkwmh2eVlBQYHGMRUKBVavXo1du3bhzz//FJJZQ0NDtfe4ubmpvbaxsRHiaGLhwoVqt0BYWFjg8uXLePz4caULCKWlpfDw8Kg2vo2NDQCgoKAAr732GubNm4d33nkHO3bswFtvvYWRI0eq9dOzNP0smjZtWum8nxUZGSlcQCo3dMJy+E+MeO77iIiIiIiqwpF/7WLy34iZmJjg3r17lcoLCwvVkvfJkycjMjISx48fBwDk5uZi0qRJAIBTp05hzJgxWLFiBfz8/GBqaor4+HhERUVVG1dHRwcqlUqtrOIaAUqlElKpFBkZGcIFhnIVk/fn0dXVVXstkUiqLCtfmE6TmFFRUdiwYQOio6PRqVMnGBoaYu7cuSgtLa0xdm0WwLOwsICzs7NaWfn7ExISYGdnp7ZPT0995LxifIlEovb+iIgI/Otf/0JCQgKSkpKwfPlyxMfH4+23366yLZp+Fvr6+kKs6ixevBjz5s1TK/sqjaP+REREREQNAZP/Rqxt27ZISkqqVH7mzBm4uroKr52cnODt7Y3Y2FioVCr4+PgIo8VpaWlo3bo1lixZItSvabE3S0tL5OXlCa8VCgUuXboEX19fAICHhwcUCgUKCgrQu3fvFzpHTWkSMzU1FcOGDcP48eMBPE2Mr169inbt2r309pUvpJebm6s2xb8uXFxc4OLigtDQUIwdOxaxsbF4++230bRpU7UFEAHtfhZ6enqVL1Q0faFDEhEREdErTKXiyL82MflvxKZPn45NmzZhxowZmDp1KvT19XH06FFs374dO3bsUKsbEhKCKVOmAAC2bdsmlDs7OyM3Nxfx8fHo1q0bEhISsH///ufG7du3L+bNm4eEhAQ4OTlhw4YNKCwsFPa7uLhg3LhxCAoKQlRUFDw8PHDr1i0cP34cnTp1wqBBg7TXCbWI6ezsjL179yI9PR3NmzfH+vXrkZ+fL0ryb2xsjAULFiA0NBRKpRK9evXC/fv3kZ6eDiMjI0ycOLHGYxQXF2PhwoUYMWIEHBwc8Mcff+DMmTMYPnw4gKfrNTx8+BDHjh1D586dYWBgUC+fBRERERERiY+P+mvEZDIZUlNTkZ2djf79+6Nbt26Qy+WQy+UYOXKkWt3hw4cLI7eBgYFC+bBhwxAaGoqZM2fC3d0d6enpCA8Pf27cyZMnY+LEiQgKCoK3tzccHByEUf9ysbGxCAoKwvz58+Hq6gp/f3+cPn0arVq10l4HPKOmmOHh4ejSpQv8/Pzg4+MDa2trBAQEvLT2POuDDz7AsmXLEBkZiXbt2sHPzw//+9//hKcw1EQqleL27dsICgqCi4sLRo0ahYEDBwr34ffs2RPTpk3D6NGjYWlpiY8//hhA/XwWREREREQkLonq2ZuziYi0ZNsx8WOWlNZcR5tszDVf70EbfjhdJGq8Fi2aiRov68JfosYDAMe2VqLGG9H7gajxDHSKRY13/4mxqPEycsxEjddqiGvNlbTs+w0ZosYzMxN3vRZ9fWnNlbSosLCs5kpapKcn/ljb6eO/ihrPrUfNj2nWphtXNV9oWRt0pOJ+R3v2sau5khbNGChquFr55VpezZVekg7ONvUW+2XhyD8RERERERFRI8fknxqcgQMHwsjIqMqt/LnzDdFXX31Vbbs7dOhQ380jIiIiIvpHUUFSb1tjxAX/qMHZtm0biournqbaokULkVujOX9/f3h5eVW579nHAxIREREREYmJyT81OM8+5/6fwtjYGMbG4t7rSkREREREpAkm/0RERERERNTgNNbp9/WF9/wTERERERERNXIc+SciIiIiIqIGR6XiyL82ceSfiIiIiIiIqJHjyD8RvTS5f5SKHlO3qbjXNPNuKkWNZ2TcVNR4jx6ViRpva+B5UeMBgPzOUFHjncq2EDVeG9sSUeNd/UtP1Hg6Ig9jfL8hQ9yAAN4K7SpqvL5HI0SNt/Syv6jxpCJ/aYqLxf07CgCB4zqJGu/PPHHP8T/NVoga7++JK0WNJz9W9VOvXh59keNpTsl7/rWKI/9EREREREREjRyTfyIiIiIiIqJGjtP+iYiIiIiIqMHho/60iyP/RERERERERI0cR/6JiIiIiIioweGj/rSLI/9EREREREREjRyTfyIiIiIiIqJGjsk/NQoRERFwd3ev72YQEREREZGWqCCpt60xYvJPL5WPjw/mzp1bqfzAgQOQSCSIioqCqakpioqKKtV5/PgxzMzMsH79ehFa+uqQyWSIjo4GAIwZMwYDBw5U25+UlASJRILw8HC18g8++AC2trZiNZOIiIiIiLSIyT/Vq6CgIBQXF2Pv3r2V9u3duxdFRUWYMGFCPbTs1eDr64uTJ0/iyZMnQllKSgpatWqF5ORktbopKSnw9fUVu4lERERE9IpSqST1tjVGTP6pXllaWmLo0KGIiYmptC8mJgb+/v6wtLTEokWL4OLiAgMDAzg6OiI8PBxlZWXVHreqGQcBAQEIDg4WXpeWliIsLAx2dnYwNDSEl5cXUlJSNGq3XC6HmZkZDh06BFdXVxgYGGDEiBF49OgR4uLiIJPJ0Lx5c8yaNQsKhULjmLdv38bYsWNhb28PAwMDdOrUCTt37qx0brNnz0ZYWBhatGgBa2trREREaNTuZ/n6+uLhw4c4e/asUJaSkoJ///vfOHPmjDAjo7S0FD/++COTfyIiIiKifygm/1TvQkJCcOLECVy/fl0oy8nJQXJyMkJCQgAAxsbGkMvluHz5Mj755BNs3boVGzZseKG4kyZNQlpaGuLj43Hx4kWMHDkSAwYMwNWrVzV6f1FRETZu3Ij4+HgcPnwYKSkpCAwMRGJiIhITE7Fjxw5s2bIFe/bs0Tjm48eP0bVrVxw6dAiXLl3C1KlTMWHCBJw+fVotdlxcHAwNDXH69Gl8/PHHWLlyJY4ePVrrPnBxcYGtra0wyv/gwQOcO3cOI0eOhJOTE9LS0gAAp06dQnFxMZN/IiIiIhIN7/nXLib/VO/8/Pxga2sLuVwulMXGxsLW1hb9+/cHACxduhQ9e/aETCbD0KFDMX/+fOzevbvOMbOzs7Fz505888036N27N5ycnLBgwQL06tULsbGxGh2jrKwMmzdvhoeHB/r06YMRI0bg5MmT2L59O9q3b48hQ4bA19dXSKw1iWlnZ4cFCxbA3d0djo6OmDVrFvz8/PDNN9+oxXZzc8Py5cvRpk0bBAUFwdPTE8eOHatTX/j4+AizD1JTU+Hi4gJLS0t4e3sL5eW3Ajg5OVV7nJKSEty/f19te1JWUqc2ERERERGRdjH5p3onlUoxceJEyOVyKJVKqFQqxMXFITg4GFKpFACwZ88e9OrVC9bW1jAyMkJ4eDhyc3PrHPPcuXNQqVRwcXGBkZGRsJ04cQLZ2dkaHcPAwEAtGbaysoJMJoORkZFaWUFBgcYxFQoFPvroI7i5ucHc3BxGRkb47rvvKp2rm5ub2msbGxshTm35+voiLS0NZWVlSElJgY+PDwBUSv779u373ONERkbC1NRUbUs79HGd2kRERERERNrVpL4bQI2biYkJ7t27V6m8sLAQJiYmwuvJkycjMjISx48fBwDk5uZi0qRJAJ5OOR8zZgxWrFgBPz8/mJqaIj4+HlFRUdXG1dHRgUqlUiuruEaAUqmEVCpFRkaGcIGhXMXk/Xl0dXXVXkskkirLlEqlxjGjoqKwYcMGREdHo1OnTjA0NMTcuXNRWlpaY+zyOLXl6+uLR48e4cyZM0hOTsbChQsBPE3+g4KCcOfOHfz444+YOHHic4+zePFizJs3T61s9e7GOWWKiIiIiF6+xrrwXn1h8k8vVdu2bZGUlFSp/MyZM3B1dRVeOzk5wdvbG7GxsVCpVPDx8RFG1dPS0tC6dWssWbJEqH/jxo3nxrW0tEReXp7wWqFQ4NKlS8I96x4eHlAoFCgoKEDv3r1f6Bw1pUnM1NRUDBs2DOPHjwfw9ILB1atX0a5du5fWLicnJ7Rq1Qrffvstzp8/D29vbwBPZxPIZDJERUXh8ePHNd7vr6enBz09PbWyJrql1dQmIiIiIiIxcdo/vVTTp09HdnY2ZsyYgQsXLuDKlSv473//i+3btwsjzOVCQkKwb98+7N+/X1joDwCcnZ2Rm5uL+Ph4ZGdnY+PGjdi/f/9z4/bt2xcJCQlISEjAr7/+iunTp6OwsFDY7+LignHjxiEoKAj79u3D9evXcebMGaxZswaJiYla7YPaxHR2dsbRo0eRnp6OrKwsvPvuu8jPz38p7anI19cXn376KZydnWFlZSWUe3t74z//+Q8cHR3x2muvvfR2EBERERGVU9bj1hgx+aeXSiaTITU1FdnZ2ejfvz+6desGuVwOuVyOkSNHqtUdPny4MHocGBgolA8bNgyhoaGYOXMm3N3dkZ6ejvDw8OfGnTx5MiZOnIigoCB4e3vDwcGh0sh1bGwsgoKCMH/+fLi6usLf3x+nT59Gq1attNcBz6gpZnh4OLp06QI/Pz/4+PjA2toaAQEBL6095Xx9ffHgwQPhfv9y3t7eePDgAVf5JyIiIiL6h5Oonr0xmohIS5bFiT/tX7epuNc0S0oa67Xhp8pKFaLGC3er/SMrX5T8zlBR40l1xL1/sY2tuE/duPqXXs2VtEhH5GGMX688FDcggLdCu4oar+/RCFHjLb3sL2o8qchfmrIn4v4dBYAO7TRbv0hb/swrq7mSFs24Mk3UeH9PXClqPPmxFqLGWzNVX9R4tXHq18prh4mle1vTeov9svCefyIiIiIiImpwuOCfdnHaP1EVBg4cqPY4vorbqlWr6rt51frqq6+qbXeHDh3qu3lERERERFRPOPJPVIVt27ahuLi4yn0tWog7Fas2/P394eXlVeW+Zx8PSERERETUkKnAkX9tYvJPVAU7O7v6bkKdGBsbw9jYuL6bQUREREREDQyTfyIiIiIiImpweM+/dvGefyIiIiIiIqJGjsk/ERERERERUSPHaf9ERERERETU4HDBP+1i8k9EL41CoRI/aKlS1HDFRWWixmtlbyBqvNIycf+ZuG5R9dMqXqb7N8T9zliaS0WNd+H/mooaDxD39/7JE3HjmZnpiRoPAPoejRA13vF+4sYz3DZc1Hh6euL+Dv7x+wNR4wGAqaG4f9cKDcTt07J/zRI1nkP2d6LGa9ZsvKjx6NXB5J+IiIiIiIgaHGU9jCM1Zrznn4iIiIiIiOgFffrpp3BwcECzZs3QtWtXpKamVlt337596NevHywtLWFiYoIePXrgyJEjL7V9TP6JiIiIiIiIXsCuXbswd+5cLFmyBD///DN69+6NgQMHIjc3t8r6P/zwA/r164fExERkZGTA19cXQ4cOxc8///zS2shp/0RERERERNTg/JMW/Fu/fj1CQkLwzjvvAACio6Nx5MgRbN68GZGRkZXqR0dHq71etWoVDh48iP/973/w8PB4KW3kyD8RERERERFRBSUlJbh//77aVlJSUmXd0tJSZGRkoH///mrl/fv3R3p6ukbxlEolHjx4gBYtWrxw26vD5J+IiIiIiIgaHJVKUm9bZGQkTE1N1baqRvAB4NatW1AoFLCyslIrt7KyQn5+vkbnGhUVhUePHmHUqFEv3G/V4bR/IiIiIiIiogoWL16MefPmqZXp6T3/ca8SifptCiqVqlJZVXbu3ImIiAgcPHgQLVu2rH1jNcSRfxJdREQE3N3d67sZr4Tg4GAEBATUdzOIiIiIiGpNpaq/TU9PDyYmJmpbdcm/hYUFpFJppVH+goKCSrMBnrVr1y6EhIRg9+7deOutt7TWd1Vh8k8CHx8fzJ07t1L5gQMHIJFIEBUVBVNTUxQVFVWq8/jxY5iZmWH9+vUitPTVkp6ejkGDBqF58+Zo1qwZOnXqhKioKCgUCqFOTk4OJBIJzp8/X38NJSIiIiJ6BTVt2hRdu3bF0aNH1cqPHj2Knj17Vvu+nTt3Ijg4GF9//TUGDx78spvJ5J80FxQUhOLiYuzdu7fSvr1796KoqAgTJkyoh5Y1Xvv374e3tzfs7e2RnJyMX3/9FXPmzMFHH32EMWPGQKVSid4mhUIBpVIpelwiIiIiooZq3rx52LZtG2JiYpCVlYXQ0FDk5uZi2rRpAJ7eRhAUFCTU37lzJ4KCghAVFYXu3bsjPz8f+fn5uHfv3ktrI5N/0pilpSWGDh2KmJiYSvtiYmLg7+8PS0tLLFq0CC4uLjAwMICjoyPCw8NRVlZW7XGrmnEQEBCA4OBg4XVpaSnCwsJgZ2cHQ0NDeHl5ISUlRaN2y+VymJmZ4dChQ3B1dYWBgQFGjBiBR48eIS4uDjKZDM2bN8esWbPURtNrinn79m2MHTsW9vb2MDAwQKdOnbBz585K5zZ79myEhYWhRYsWsLa2RkREhEbtfvToEaZMmQJ/f39s2bIF7u7ukMlkeOeddxAXF4c9e/Zg9+7dAAAHBwcAgIeHByQSCXx8fNSOtW7dOtjY2MDc3BwzZsxQ+zxqOs+K/de+fXvo6enhxo0bGp0DEREREVFdKSGpt622Ro8ejejoaKxcuRLu7u744YcfkJiYiNatWwMA8vLykJubK9T//PPP8eTJE8yYMQM2NjbCNmfOHK3137O44B/VSkhICIYMGYLr168LCWdOTg6Sk5ORkJAAADA2NoZcLoetrS0yMzMxZcoUGBsbIywsrM5xJ02ahJycHMTHx8PW1hb79+/HgAEDkJmZiTZt2tT4/qKiImzcuBHx8fF48OABAgMDERgYCDMzMyQmJuL//u//MHz4cPTq1QujR4/WKObjx4/RtWtXLFq0CCYmJkhISMCECRPg6OgILy8vIXZcXBzmzZuH06dP48cff0RwcDDeeOMN9OvX77lt/u6773D79m0sWLCg0r6hQ4fCxcUFO3fuxOjRo/HTTz/h9ddfx/fff48OHTqgadOmQt3k5GTY2NggOTkZ165dw+jRo+Hu7o4pU6Zo3LdFRUWIjIzEtm3bYG5u/lIXIiEiIiIi+ieaPn06pk+fXuU+uVyu9lrTgUxtYvJPteLn5wdbW1vI5XKsWLECABAbGwtbW1vhuZZLly4V6stkMsyfPx+7du2qc/KfnZ2NnTt34o8//oCtrS0AYMGCBTh8+DBiY2OxatWqGo9RVlaGzZs3w8nJCQAwYsQI7NixAzdv3oSRkRHat28PX19fJCcnY/To0RrFtLOzU0vMZ82ahcOHD+Obb75RS/7d3NywfPlyAECbNm2wadMmHDt2rMbk/8qVKwCAdu3aVbm/bdu2Qh1LS0sAgLm5OaytrdXqNW/eHJs2bYJUKkXbtm0xePBgHDt2DFOmTNG4b8vKyvDpp5+ic+fO1ba3pKSk0rNPn5QBTXSfvyoqEREREVFVVKraj8BT9Zj8U61IpVJMnDgRcrkcy5cvh0QiQVxcHIKDgyGVSgEAe/bsQXR0NK5du4aHDx/iyZMnMDExqXPMc+fOQaVSwcXFRa28pKQE5ubmGh3DwMBASPyBp8/clMlkMDIyUisrKCjQOKZCocDq1auxa9cu/Pnnn0Lya2hoqPYeNzc3tdc2NjZCHE1Ud1+/po8O6dChg/DZlMfPzMwEoHnfNm3atNJ5PCsyMlK4IFSul/8S9BkWXmMbiYiIiIjo5WLyTwITE5MqF5goLCxUS94nT56MyMhIHD9+HACQm5uLSZMmAQBOnTqFMWPGYMWKFfDz84OpqSni4+MRFRVVbVwdHZ1KCW7Fe9KVSiWkUikyMjLUklgAasn78+jq6qq9lkgkVZaVL2SnScyoqChs2LAB0dHR6NSpEwwNDTF37lyUlpbWGFuTBfPKE/KsrKwqVwn99ddf0b59+xqP86LnCQD6+vo1Xmio6lmoH+2spjIREREREYmKyT8J2rZti6SkpErlZ86cgaurq/DayckJ3t7eiI2NhUqlgo+PjzCqnpaWhtatW2PJkiVC/ZoWh7O0tEReXp7wWqFQ4NKlS/D19QXwdBE7hUKBgoIC9O7d+4XOUVOaxExNTcWwYcMwfvx4AE8T6atXr1Y7Tb+2+vfvjxYtWiAqKqpS8v/tt9/i6tWr+OCDDwBAuMe/4oKFmtBm3+rp6VV69mkT3ZJqahMRERERPV89PNiqUeNq/ySYPn06srOzMWPGDFy4cAFXrlzBf//7X2zfvh0LFy5UqxsSEoJ9+/Zh//79CAkJEcqdnZ2Rm5uL+Ph4ZGdnY+PGjdi/f/9z4/bt2xcJCQlISEjAr7/+iunTp6OwsFDY7+LignHjxiEoKAj79u3D9evXcebMGaxZswaJiYla7YPaxHR2dsbRo0eRnp6OrKwsvPvuu8jPz9daGwwNDfH555/j4MGDmDp1Ki5evIicnBxs374dwcHBGDFiBEaNGgUAaNmyJfT19XH48GHcvHlT40eE1EffEhERERGR+Jj8k0AmkyE1NRXZ2dno378/unXrBrlcDrlcjpEjR6rVHT58uDDSGxgYKJQPGzYMoaGhmDlzJtzd3ZGeno7w8Off8z158mRMnDgRQUFB8Pb2hoODgzDqXy42NhZBQUGYP38+XF1d4e/vj9OnT6NVq1ba64Bn1BQzPDwcXbp0gZ+fH3x8fGBtbY2AgACttmHEiBFITk7G77//jj59+sDV1RXr16/HkiVLEB8fL0zFb9KkCTZu3IjPP/8ctra2GDZsmNbOk4iIiIioPqggqbetMZKoqltNjIjoBS2JEX/av1Qq7h/rR4/Kaq6kRa3sDUSNV1om7j8RAzv/LWo8APhfhoWo8SzNpTVX0qL7Dxv3P/NPnoh7fo8f1+72Km0Is98jarzj/SJEjXd6W6ao8fT0xP0d/OP3B6LGA4C+veu+0HJd3PhL1HAY1/6iqPGa5/4sarzI/PGixlsRpFtzpXry3YXSmiu9JP07N6250j8MR/6JiIiIiIiIGjkm//SPN3DgQBgZGVW5lT+nviH66quvqm13hw4d6rt5RERERET1Sqmqv60x4mr/9I+3bds2FBcXV7mvRYsWIrdGc/7+/vDy8qpy37OP5yMiIiIiInoRTP7pH8/Ozq6+m1AnxsbGMDY2ru9mEBERERE1SCpV41x4r75w2j8RERERERFRI8eRfyIiIiIiImpw+Fw67eLIPxEREREREVEjx+SfiIiIiIiIqJHjtH8iemm6dhT/qQW374t7TbOJtKmo8YwNlKLG0xF5nZ33Qn8VNyCAMe/1ETVegM1pUePt/aPqp4q8LLbmClHj3XkoFTVewW1RwwEAll72FzWe4bbhosbzeqeTqPGKT2aJGq+kxEDUeABQ9kTcP95O9uL+27TmqLOo8SQSceP19BT371pDpgQX/NMmjvwTERERERERNXIc+SciIiIiIqIGhwv+aRdH/omIiIiIiIgaOSb/RERERERERI0cp/0TERERERFRg6NSccE/beLIPxEREREREVEjx5F/IiIiIiIianCUXPBPqzjyX08iIiLg7u5e380gIiIiIiKiVwCT/1oKDg6GRCKBRCKBrq4urKys0K9fP8TExECpVL7QcQMCAmr1Hh8fH6EtVW0ymazO7WmoeNFEu77++mtIpVJMmzZNKHsVv1dERERE1PCoVPW3NUZM/utgwIAByMvLQ05ODpKSkuDr64s5c+ZgyJAhePLkiWjt2LdvH/Ly8pCXl4effvoJAPD9998LZWfOnBGtLS+qtLRU1HgqlUrUz6qhiomJQVhYGOLj41FUVASgcX2viIiIiIjoKSb/daCnpwdra2vY2dmhS5cueP/993Hw4EEkJSVBLpcDAO7du4epU6eiZcuWMDExQd++fXHhwoUqjxcREYG4uDgcPHhQGF1NSUkBACxatAguLi4wMDCAo6MjwsPDUVZWBgBo0aIFrK2tYW1tDUtLSwCAubm5UPb3339j0KBBMDIygpWVFSZMmIBbt24JcX18fDBr1izMnTsXzZs3h5WVFbZs2YJHjx5h0qRJMDY2hpOTE5KSkoT3pKSkQCKRICEhAZ07d0azZs3g5eWFzMxMtXNKT09Hnz59oK+vj1atWmH27Nl49OiRsF8mk+HDDz9EcHAwTE1NMWXKlBrPVy6XY8WKFbhw4YLQT3K5HDk5OZBIJDh//rxw/MLCQrV+LG/3kSNH4OnpCT09PaSmpkKlUuHjjz+Go6Mj9PX10blzZ+zZs0ej70HFY3p4eEBfXx99+/ZFQUEBkpKS0K5dO5iYmGDs2LFCYg2gxpgKhQIhISFwcHCAvr4+XF1d8cknn6jFLp8psm7dOtjY2MDc3BwzZswQ+koTOTk5SE9Px7///W+0bdtWaENN36vyMiIiIiIi+udg8q8lffv2RefOnbFv3z6oVCoMHjwY+fn5SExMREZGBrp06YI333wTd+7cqfTeBQsWYNSoUcKMgry8PPTs2RMAYGxsDLlcjsuXL+OTTz7B1q1bsWHDhhrbk5eXB29vb7i7u+Ps2bM4fPgwbt68iVGjRqnVi4uLg4WFBX766SfMmjUL7733HkaOHImePXvi3Llz8PPzw4QJE9SSVwBYuHAh1q1bhzNnzqBly5bw9/cXEs/MzEz4+fkhMDAQFy9exK5du3Dy5EnMnDlT7Rhr165Fx44dkZGRgfDw8BrPd/To0Zg/fz46dOgg9NPo0aM1/ISeCgsLQ2RkJLKysuDm5oalS5ciNjYWmzdvxi+//ILQ0FCMHz8eJ06c0PiYERER2LRpE9LT0/H7779j1KhRiI6Oxtdff42EhAQcPXoU//nPf4T6NcVUKpWwt7fH7t27cfnyZSxbtgzvv/8+du/erRY3OTkZ2dnZSE5ORlxcHORyuXDxSRMxMTEYPHgwTE1NMX78eGzfvl3j9xIRERERvWwqSOpta4y42r8WtW3bFhcvXkRycjIyMzNRUFAAPT09AMC6detw4MAB7NmzB1OnTlV7n5GREfT19VFSUgJra2u1fUuXLhV+lslkmD9/Pnbt2oWwsLDntmXz5s3o0qULVq1aJZTFxMSgVatWuHLlClxcXAAAnTt3FmIsXrwYq1evhoWFhTASv2zZMmzevBkXL15E9+7dhWMtX74c/fr1A/D0AoK9vT3279+PUaNGYe3atfjXv/6FuXPnAgDatGmDjRs3wtvbG5s3b0azZs0APL1gsmDBAo3PV19fH0ZGRmjSpEmlftLUypUrhXY/evQI69evx/Hjx9GjRw8AgKOjI06ePInPP/8c3t7eGh3zww8/xBtvvAEACAkJweLFi5GdnQ1HR0cAwIgRI5CcnIxFixZpFFNXVxcrVqwQju/g4ID09HTs3r1b7eJN8+bNsWnTJkilUrRt2xaDBw/GsWPHhM/ueZRKJeRyuXBRYsyYMZg3bx6uXbsGZ2dnjc77WSUlJSgpKVErKyvVhW5TvTodj4iIiIiItIfJvxapVCpIJBJkZGTg4cOHMDc3V9tfXFyM7OzsWh1zz549iI6OxrVr1/Dw4UM8efIEJiYmNb4vIyMDycnJMDIyqrQvOztbSP7d3NyEcqlUCnNzc3Tq1Ekos7KyAgAUFBSoHaM8cQWeThN3dXVFVlaWEPvatWv46quvhDoqlQpKpRLXr19Hu3btAACenp5aO19NVYx5+fJlPH78WLgYUK60tBQeHh4aH7NiH1pZWQm3LFQsK793XtOYn332GbZt24YbN26guLgYpaWllRY67NChA6RSqfDaxsam0u0X1fnuu+/w6NEjDBw4EABgYWGB/v37IyYmRu2CUW1ERkaqXbQAgFHvLMPoKcvrdDwiIiIierXxUX/axeRfi7KysuDg4AClUgkbGxvhfvOKzMzMND7eqVOnMGbMGKxYsQJ+fn4wNTVFfHw8oqKianyvUqnE0KFDsWbNmkr7bGxshJ91dXXV9pU/xaDi6/Lj1aRi3XfffRezZ8+uVOe1114TfjY0NFTbV9fz1dF5eveKqsKynNXd+14xZvk5JSQkwM7OTq1e+YwNTTzbX1X1aXksTWLu3r0boaGhiIqKQo8ePWBsbIy1a9fi9OnT1cZ9Nk5NYmJicOfOHRgYGAhlSqUSP//8Mz744AO1iwqaWrx4MebNm6dWlnRRt5raREREREQkJib/WnL8+HFkZmYiNDQU9vb2yM/PR5MmTTR+LFrTpk2hUCjUytLS0tC6dWssWbJEKLtx44ZGx+vSpQv27t0LmUyGJk20/zGfOnVKSOTv3r2LK1euoG3btkLsX375pdbTxzU536r6qXwBury8PGH0vOLif9Vp37499PT0kJubq/EU/xelSczU1FT07NkT06dPF8pqO2PkeW7fvo2DBw8iPj4eHTp0EMqVSiV69+6NpKQkDBkypNbH1dPTq3TRRLdp3R9/SURERERE2sPkvw5KSkqQn58PhUKBmzdv4vDhw4iMjMSQIUMQFBQEHR0d9OjRAwEBAVizZg1cXV3x119/ITExEQEBAVVOd5fJZDhy5Ah+++03mJubw9TUFM7OzsjNzUV8fDy6deuGhIQE7N+/X6M2zpgxA1u3bsXYsWOxcOFCWFhY4Nq1a4iPj8fWrVvrNLJb0cqVK2Fubg4rKyssWbIEFhYWCAgIAPB0xf7u3btjxowZmDJlCgwNDZGVlVVp4btnaXK+MpkM169fx/nz52Fvbw9jY2Po6+uje/fuWL16NWQyGW7duqW2dkB1jI2NsWDBAoSGhkKpVKJXr164f/8+0tPTYWRkhIkTJ75QH9U1prOzM7744gscOXIEDg4O2LFjB86cOQMHBwettGHHjh0wNzfHyJEjhVkT5YYMGYLt27fXKfknIiIiItImFaf9axVX+6+Dw4cPw8bGBjKZDAMGDEBycjI2btyIgwcPQiqVQiKRIDExEX369MHkyZPh4uKCMWPGICcnR7iH/llTpkyBq6srPD09YWlpibS0NAwbNgyhoaGYOXMm3N3dkZ6eLqyKXxNbW1ukpaVBoVDAz88PHTt2xJw5c2Bqalop4auL1atXY86cOejatSvy8vLw7bffomnTpgCe3gN/4sQJXL16Fb1794aHhwfCw8PVbjeoiibnO3z4cAwYMAC+vr6wtLTEzp07ATydxl5WVgZPT0/MmTMHH374oUbn8cEHH2DZsmWIjIxEu3bt4Ofnh//9739aS7TrEnPatGkIDAzE6NGj4eXlhdu3b6vNAnhRMTExePvtt6v8HgwfPhyHDh3CzZs3tRaPiIiIiIjqn0Sl4vUU0lxKSgp8fX1x9+7dWq1fQK+mfT+JP+3/9n1xr2k2ebFJNLVmbCBun+qI/KSb9R+kihsQwJj3+ogab3Trn0SNt/cPL1Hj2Zoraq6kRXceivtLWHBb/L9rN28WixrP0FDc9Vq83ulUcyUtKj6ZJWq8i78+ETUeALR1Evcz1Bf5Nr8Tp8X9nZCI/G9hT0+Dmitp0cjuDXc8ePeP9XcL6ageDbdf6qrxnRERERERERERqWHyT1SNadOmwcjIqMpt2rRp9d28aqWmplbb7qoe/UhERERE1BApVZJ62xojLvhHteLj44NX5U6RlStXYsGCBVXuMzExEbk1mvP09NToaQdERERERPTqYPJPVI2WLVuiZcuW9d2MWtPX16/1YxaJiIiIiKhxY/JPREREREREDc4rMuFYNLznn4iIiIiIiKiR48g/ERERERERNTgc+dcujvwTERERERERNXIc+SeiRkVPV9x4LYyfiBpPKhH3EvgTpbiPuhk/01vUeADwQ/IfosY7p+8garyO7uJ+Z+48lIoaz7CZUtR4hYVlosYDAKmOuGM1enrifobFJ7NEjaffq52o8Tx/vCxqPADoI00WNd7VWe+LGq/dslOixrNqrhA1Xpm44egVwuSfiIiIiIiIGhwlp/1rFaf9ExERERERETVyHPknIiIiIiKiBkelEvf2w8aOI/9EREREREREjRxH/omIiIiIiKjB4aP+tIsj/0RERERERESNHJN/IiIiIiIiokauXpL/iIgIuLu710doIq3asmULXF1dYWhoCA8PD5w4caK+m/TSyGQyREdH13cziIiIiOgVoVTV39YY1Sr5Dw4OhkQigUQiga6uLqysrNCvXz/ExMRAqVTWuRHBwcEICAio1Xt8fHyEtlS1yWSyOrenoeJFE+36+uuvIZVKMW3atDq9/+jRo3jvvfcQFhaGzMxMBAcHIzAwEIWFhdptKBERERER0Quq9cj/gAEDkJeXh5ycHCQlJcHX1xdz5szBkCFD8OTJk5fRxirt27cPeXl5yMvLw08//QQA+P7774WyM2fOiNaWF1VaWipqPJVKJepn1VDFxMQgLCwM8fHxKCoqqvX7Dx8+jJ49eyIkJASOjo7o06cPnjx5InwfiYiIiIio7lSq+tsao1on/3p6erC2toadnR26dOmC999/HwcPHkRSUhLkcjkA4N69e5g6dSpatmwJExMT9O3bFxcuXKjyeBEREYiLi8PBgweFUfuUlBQAwKJFi+Di4gIDAwM4OjoiPDwcZWVlAIAWLVrA2toa1tbWsLS0BACYm5sLZX///TcGDRoEIyMjWFlZYcKECbh165YQ18fHB7NmzcLcuXPRvHlzWFlZYcuWLXj06BEmTZoEY2NjODk5ISkpSXhPSkoKJBIJEhIS0LlzZzRr1gxeXl7IzMxUO6f09HT06dMH+vr6aNWqFWbPno1Hjx4J+2UyGT788EMEBwfD1NQUU6ZMqfF85XI5VqxYgQsXLgj9JJfLkZOTA4lEgvPnzwvHLywsVOvH8nYfOXIEnp6e0NPTQ2pqKlQqFT7++GM4OjpCX18fnTt3xp49ezT6HlQ8poeHB/T19dG3b18UFBQgKSkJ7dq1g4mJCcaOHauWWNcUU6FQICQkBA4ODtDX14erqys++eQTtdjlM0XWrVsHGxsbmJubY8aMGUJfaSInJwfp6en497//jbZt21Y676pmWURHR6vNKLl9+zbMzc0BAGvWrMHo0aPx6aefqr2vtLQUYWFhsLOzg6GhIby8vITPBXj6uZqZmeHIkSNo164djIyMhAtsAJCZmQkdHR3hu3v37l3o6Ohg5MiRwjEiIyPRo0cPrfZfQUEBhg4dCn19fTg4OOCrr77SuG+JiIiIiKjh0co9/3379kXnzp2xb98+qFQqDB48GPn5+UhMTERGRga6dOmCN998E3fu3Kn03gULFmDUqFFCwpOXl4eePXsCAIyNjSGXy3H58mV88skn2Lp1KzZs2FBje/Ly8uDt7Q13d3ecPXsWhw8fxs2bNzFq1Ci1enFxcbCwsMBPP/2EWbNm4b333sPIkSPRs2dPnDt3Dn5+fpgwYUKlUeGFCxdi3bp1OHPmDFq2bAl/f38hccrMzISfnx8CAwNx8eJF7Nq1CydPnsTMmTPVjrF27Vp07NgRGRkZCA8Pr/F8R48ejfnz56NDhw5CP40ePVrDT+ipsLAwREZGIisrC25ubli6dCliY2OxefNm/PLLLwgNDcX48eNrdd96REQENm3ahPT0dPz+++8YNWoUoqOj8fXXXyMhIQFHjx7Ff/7zH6F+TTGVSiXs7e2xe/duXL58GcuWLcP777+P3bt3q8VNTk5GdnY2kpOTERcXB7lcLlx80kRMTAwGDx4MU1NTjB8/Htu3b9f4vVVZvXo1Vq9ejXHjxqFly5ZC+aRJk5CWlob4+HhcvHgRI0eOxIABA3D16lWhTlFREdatW4cdO3bghx9+QG5uLhYsWAAA6NixI8zNzYX++eGHH2Bubo4ffvhBeH9KSgq8vb0BaK//goODkZOTg+PHj2PPnj349NNPUVBQ8EJ9RERERERE9aeJtg7Utm1bXLx4EcnJycjMzERBQQH09PQAAOvWrcOBAwewZ88eTJ06Ve19RkZG0NfXR0lJCaytrdX2LV26VPhZJpNh/vz52LVrF8LCwp7bls2bN6NLly5YtWqVUBYTE4NWrVrhypUrcHFxAQB07txZiLF48WKsXr0aFhYWwkj8smXLsHnzZly8eBHdu3cXjrV8+XL069cPwNMLCPb29ti/fz9GjRqFtWvX4l//+hfmzp0LAGjTpg02btwIb29vbN68Gc2aNQPw9IJJeYKnyfnq6+vDyMgITZo0qdRPmlq5cqXQ7kePHmH9+vU4fvy4MGrs6OiIkydP4vPPPxeSyZp8+OGHeOONNwAAISEhWLx4MbKzs+Ho6AgAGDFiBJKTk7Fo0SKNYurq6mLFihXC8R0cHJCeno7du3erXbxp3rw5Nm3aBKlUirZt22Lw4ME4duyY8Nk9j1KphFwuFy5KjBkzBvPmzcO1a9fg7Oys0Xk/q0WLFvjqq68wePBg4XufnZ2NnTt34o8//oCtrS2Apxe7Dh8+jNjYWOH7WVZWhs8++wxOTk4AgJkzZ2LlypUAAIlEgj59+iAlJQXDhw9HSkoKJk6ciLi4OFy+fBkuLi5IT09HaGgoAGil/65cuYKkpCScOnUKXl5eAIDt27ejXbt2z+2DkpISlJSUqJWVlepCt6lenfqUiIiIiF5tjXX6fX3RWvKvUqkgkUiQkZGBhw8fCtOhyxUXFyM7O7tWx9yzZw+io6Nx7do1PHz4EE+ePIGJiUmN78vIyEBycjKMjIwq7cvOzhaSfzc3N6FcKpXC3NwcnTp1EsqsrKwAoNKIZ3niCjxN+lxdXZGVlSXEvnbtmto0aZVKBaVSievXrwsJlKenp9bOV1MVY16+fBmPHz8WLgaUKy0thYeHh8bHrNiHVlZWwi0LFcvK74HXNOZnn32Gbdu24caNGyguLkZpaWmlKfgdOnSAVCoVXtvY2FS6/aI63333HR49eoSBAwcCACwsLNC/f3/ExMSoXTCqjS+//BLDhw+Hp6cnvvnmG7Rt2xbnzp2DSqUSvm/lSkpK1H4/DAwMhMS//Fwqfud8fHywZcsWAMCJEyfwwQcf4Pr16zhx4gTu3buH4uJi4QIM8OL9l5WVhSZNmqh9X9q2bQszM7Pn9kFkZKTahQcAGPXOMoyesvy57yMiIiIiopdPa8l/VlYWHBwcoFQqYWNjo3Zfc7makoeKTp06hTFjxmDFihXw8/ODqakp4uPjERUVVeN7lUolhg4dijVr1lTaZ2NjI/ysq6urtq/8KQYVX5cfryYV67777ruYPXt2pTqvvfaa8LOhoaHavrqer47O0zs3VBUui1V373vFmOXnlJCQADs7O7V65SPXmni2v6rq0/JYmsTcvXs3QkNDERUVhR49esDY2Bhr167F6dOnq437bJyaxMTE4M6dOzAwMBDKlEolfv75Z3zwwQeQSqXQ0dFR61Og+n4Fnl4QysrKQnBwMHx9fXH16lUolUpIpVJkZGSoJdoA1C5MVXUuFWP7+Phgzpw5uHbtGi5duoTevXsjOzsbJ06cQGFhIbp27QpjY2MA2um/8tjl32lNLV68GPPmzVMrS7qoW01tIiIiIqLna6yP3KsvWkn+jx8/jszMTISGhsLe3h75+flo0qSJxo/ba9q0KRQKhVpZWloaWrdujSVLlghlN27c0Oh4Xbp0wd69eyGTydCkidaubwhOnTolJPJ3797FlStX0LZtWyH2L7/8Uuvp45qcb1X9VL7YYV5enjB6XnHxv+q0b98eenp6yM3N1XiK/4vSJGZqaip69uyJ6dOnC2W1nTHyPLdv38bBgwcRHx+PDh06COVKpRK9e/dGUlIShgwZAktLS+Tn5wszWoCa+9XU1BTr16+Ho6MjLl++DA8PDygUChQUFKB37951bnP5ff8ffvghOnfuDBMTE3h7eyMyMhJ3795V60tt9F+7du3w5MkTnD17Fq+//joA4LfffqvxEYZ6enqVLhzpNq37I0CJiIiIiEh7ap0Zl5SUID8/HwqFAjdv3sThw4cRGRmJIUOGICgoCDo6OujRowcCAgKwZs0auLq64q+//kJiYiICAgKqnO4uk8lw5MgR/PbbbzA3N4epqSmcnZ2Rm5uL+Ph4dOvWDQkJCdi/f79GbZwxYwa2bt2KsWPHYuHChbCwsMC1a9cQHx+PrVu3VhqFra2VK1fC3NwcVlZWWLJkCSwsLBAQEADg6Yr93bt3x4wZMzBlyhQYGhoiKyur0sJ3z9LkfGUyGa5fv47z58/D3t4exsbG0NfXR/fu3bF69WrIZDLcunVLbe2A6hgbG2PBggUIDQ2FUqlEr169cP/+faSnp8PIyAgTJ058oT6qa0xnZ2d88cUXOHLkCBwcHLBjxw6cOXMGDg4OWmnDjh07YG5ujpEjRwqzJsoNGTIE27dvx5AhQ+Dj44O///4bH3/8MUaMGIHDhw8jKSmpytsw0tLS0KxZM1hYWEAul8PAwABt2rRB8+bNMW7cOAQFBSEqKgoeHh64desWjh8/jk6dOmHQoEEatbn8vv8vv/xSuLffzc0NpaWlOHbsGObMmSPU1Ub/ubq6YsCAAZgyZQq2bNmCJk2aYO7cudDX19f4GEREREREL4r3/GtXrVf7P3z4MGxsbCCTyTBgwAAkJydj48aNOHjwIKRSKSQSCRITE9GnTx9MnjwZLi4uGDNmDHJycoR76J81ZcoUuLq6wtPTE5aWlkhLS8OwYcMQGhqKmTNnwt3dHenp6cKq+DWxtbVFWloaFAoF/Pz80LFjR8yZMwempqaVEr66WL16NebMmYOuXbsiLy8P3377LZo2bQrgaVJ24sQJXL16Fb1794aHhwfCw8PVbjeoiibnO3z4cAwYMAC+vr6wtLTEzp07ATydxl5WVgZPT0/MmTMHH374oUbn8cEHH2DZsmWIjIxEu3bt4Ofnh//9739aS7TrEnPatGkIDAzE6NGj4eXlhdu3b6uNYr+omJgYvP3221V+D4YPH45Dhw7h5s2baNeuHT799FP897//RefOnfHTTz9VWqCx3IULFzBw4EC0bdsW3377Lfbu3YvmzZsDAGJjYxEUFIT58+fD1dUV/v7+OH36NFq1alWrdvv6+kKhUMDHxwfA0wsC5bMJevXqJdTTVv/FxsaiVatW8Pb2RmBgoPDoTiIiIiIi+meSqJ69sZmqlZKSAl9fX9y9e7dW6xcQvar2/ST+tP+HxVp5gqnGWhg/ETWeVCLun+wnytqt/fCi/ryl/Vu1avJD8h+ixmum31TUeB3dLUWN18JU3O+MYTNx/878/Ev167+8LE/KxD1HUzNxv6OuDuJ+Z/R7Pf/pNdqm+PGyqPEAoI9U88c2a8PVWe+LGu/cslOixrNqrqi5khaVKcT9nRjZXdz/O9XG1u/rL/aUt+ov9ssi/v+yiIiIiIiIiGqg4XrepKGGe5mH6tW0adNgZGRU5TZt2rT6bl61UlNTq213VY9+JCIiIiIiehVw5L8WfHx8Kj3+rbFauXJltfe4V7XoXUPh6emp0dMOiIiIiIioYXtFUi/RMPmnKrVs2fIfucCbvr5+rR+zSERERERE1Nhx2j8RERERERFRI8eRfyIiIiIiImpwOO1fuzjyT0RERERERNTIceSfiIiIiIiIGhwlR/61isk/Eb00dx6IP7mo4JZC1HhNdaWixktJLRQ1XmsHcZ/ucft2kajxAMDAuJmo8YoePBY1Xisrcf/nZKZfKmq8vEI9UePp6Yn/d624uEzUeH/8/kDUeCUlBqLG8/zxsqjxpD3aixoPAH4Q+Rwfrv5R1HitjcT9O/NEKe7v/ZVciajx6NXB5J+IiIiIiIganPp9zHrjuwjDe/6JiIiIiIiIGjkm/0RERERERESNHKf9ExERERERUYPDR/1pF0f+iYiIiIiIiBo5jvwTERERERFRg6NU1ncLGheO/BMRERERERE1ckz+K4iIiIC7u3t9N4NI63x8fDB37tz6bgYREREREdWTRpH8BwcHQyKRQCKRQFdXF1ZWVujXrx9iYmKgfIG5IsHBwQgICKjVe3x8fIS2VLXJZLI6t6eh4kUT7aj43dHR0YGVlRVGjhyJGzdu1HfTiIiIiIhEp1LV39YYNYrkHwAGDBiAvLw85OTkICkpCb6+vpgzZw6GDBmCJ0+eiNaOffv2IS8vD3l5efjpp58AAN9//71QdubMGdHa8qJKS0tFjadSqUT9rBqiKVOmIC8vD3/++ScOHjyI33//HePHj6/vZhERERER0T9co0n+9fT0YG1tDTs7O3Tp0gXvv/8+Dh48iKSkJMjlcgDAvXv3MHXqVLRs2RImJibo27cvLly4UOXxIiIiEBcXh4MHDwqjsSkpKQCARYsWwcXFBQYGBnB0dER4eDjKysoAAC1atIC1tTWsra1haWkJADA3NxfK/v77bwwaNAhGRkawsrLChAkTcOvWLSGuj48PZs2ahblz56J58+awsrLCli1b8OjRI0yaNAnGxsZwcnJCUlKS8J6UlBRIJBIkJCSgc+fOaNasGby8vJCZmal2Tunp6ejTpw/09fXRqlUrzJ49G48ePRL2y2QyfPjhhwgODoapqSmmTJlS4/nK5XKsWLECFy5cEPpJLpcjJycHEokE58+fF45fWFio1o/l7T5y5Ag8PT2hp6eH1NRUqFQqfPzxx3B0dIS+vj46d+6MPXv2aPQ9qHhMDw8P6Ovro2/fvigoKEBSUhLatWsHExMTjB07FkVFRcL7aoqpUCgQEhICBwcH6Ovrw9XVFZ988ola7PKZIuvWrYONjQ3Mzc0xY8YMoa80YWBgAGtra9jY2KB79+6YMWMGzp07p1bnxIkTeP3116GnpwcbGxv8+9//Vrto8ujRIwQFBcHIyAg2NjaIioqqFKe0tBRhYWGws7ODoaEhvLy8hM8FePq5mpmZ4ciRI2jXrh2MjIyEC2xERERERGJQqupvq4tPP/0UDg4OaNasGbp27YrU1NTn1j9x4gS6du2KZs2awdHREZ999lndAmuo0ST/Venbty86d+6Mffv2QaVSYfDgwcjPz0diYiIyMjLQpUsXvPnmm7hz506l9y5YsACjRo0SEp68vDz07NkTAGBsbAy5XI7Lly/jk08+wdatW7Fhw4Ya25OXlwdvb2+4u7vj7NmzOHz4MG7evIlRo0ap1YuLi4OFhQV++uknzJo1C++99x5GjhyJnj174ty5c/Dz88OECRPUklcAWLhwIdatW4czZ86gZcuW8Pf3FxLPzMxM+Pn5ITAwEBcvXsSuXbtw8uRJzJw5U+0Ya9euRceOHZGRkYHw8PAaz3f06NGYP38+OnToIPTT6NGjNfyEngoLC0NkZCSysrLg5uaGpUuXIjY2Fps3b8Yvv/yC0NBQjB8/HidOnND4mBEREdi0aRPS09Px+++/Y9SoUYiOjsbXX3+NhIQEHD16FP/5z3+E+jXFVCqVsLe3x+7du3H58mUsW7YM77//Pnbv3q0WNzk5GdnZ2UhOTkZcXBzkcrlw8am27ty5g2+++QZeXl5C2Z9//olBgwahW7duuHDhAjZv3ozt27fjww8/FOosXLgQycnJ2L9/P7777jukpKQgIyND7diTJk1CWloa4uPjcfHiRYwcORIDBgzA1atXhTpFRUVYt24dduzYgR9++AG5ublYsGBBnc6FiIiIiKgx27VrF+bOnYslS5bg559/Ru/evTFw4EDk5uZWWf/69esYNGgQevfujZ9//hnvv/8+Zs+ejb179760NkpUqn/+HQ3BwcEoLCzEgQMHKu0bM2YMLl68iE2bNuHtt99GQUEB9PT0hP3Ozs4ICwvD1KlTERERgQMHDgij1c87bkVr167Frl27cPbsWbXynJwcODg44Oeff4a7uzuWLVuG06dP48iRI0KdP/74A61atcJvv/0GFxcX+Pj4QKFQCFeJFAoFTE1NERgYiC+++AIAkJ+fDxsbG/z444/o3r07UlJS4Ovri/j4eCHxvnPnDuzt7SGXyzFq1CgEBQVBX18fn3/+uRD75MmT8Pb2xqNHj9CsWTPIZDJ4eHhg//79tTrfZ/utqnMHno78N2/eHMnJyfDx8RHafeDAAQwbNgzA01FrCwsLHD9+HD169BCO984776CoqAhff/31c9tWfszvv/8eb775JgBg9erVWLx4MbKzs+Ho6AgAmDZtGnJycnD48OE6x5wxYwZu3rwpzBAIDg5GSkoKsrOzIZVKAQCjRo2Cjo4O4uPjn9tu4Omsj/T0dDRt2hQqlQpFRUVwcXHBkSNHhLUilixZgr179yIrKwsSiQTA0yuMixYtwr1791BUVARzc3N88cUXlb4LU6dORXR0NLKzs9GmTRv88ccfsLW1FeK/9dZbeP3117Fq1SrI5XJMmjQJ165dg5OTkxBn5cqVyM/Pr7L9JSUlKCkpUSv7Kk0Puk31qqz/shTcUogaz7GVuNdQU1ILRY3X2sFE1Hi3b5fUXEnLCu8U1VxJi4oePBY13tsBtjVX0iIzfc1nO2lDXqG4f2Ou/y7u+QFAYaG4vxePi8W9Bc+ypYGo8Tw7SEWNJ+3RXtR4AKD48bKo8R4+FvffwhZG4v4ePlGKe36Xr4sbL2x4wx0PXn+w/lLVGQNKK/3fVk9PTy2XrMjLywtdunTB5s2bhbJ27dohICAAkZGRleovWrQI3377LbKysoSyadOm4cKFC/jxxx+1dBbqGu4nrSUqlQoSiQQZGRl4+PAhzM3NYWRkJGzXr19HdnZ2rY65Z88e9OrVC9bW1jAyMkJ4eHi1V3QqysjIQHJyslr8tm3bAoBaG9zc3ISfpVIpzM3N0alTJ6HMysoKAFBQUKB2/IqJa4sWLeDq6ip8mTIyMiCXy9Vi+/n5QalU4vr168L7PD09tXa+mqoY8/Lly3j8+DH69eun1tYvvviiVp9TxT60srISblmoWFbef5rG/Oyzz+Dp6QlLS0sYGRlh69atlfqhQ4cOQuIPADY2NpU+p+cZN24czp8/jwsXLuDkyZNwdnZG//798eDBAwBAVlYWevToIST+APDGG2/g4cOH+OOPP5CdnY3S0tIqvwvlzp07B5VKBRcXF7XzPXHihNr5GhgYCIm/JucSGRkJU1NTtS1pZ+U/dEREREREmqjPBf+q+r9tVUk88PSW2oyMDPTv31+tvH///khPT6/yPT/++GOl+n5+fjh79mytbhuujSYv5agNSFZWFhwcHKBUKmFjY6N2X3M5MzMzjY936tQpjBkzBitWrICfnx9MTU0RHx9f5X3Vz1IqlRg6dCjWrFlTaZ+NjY3ws66urtq+8qcYVHxdfryaVKz77rvvYvbs2ZXqvPbaa8LPhoaGavvqer46Ok+vK1WcWFLdl7hizPJzSkhIgJ2dnVq96q6yVeXZ/qqqT8tjaRJz9+7dCA0NRVRUFHr06AFjY2OsXbsWp0+frjbus3E0YWpqCmdnZwBPZ6Vs374dNjY22LVrF9555x3hYlZF5X0skUigyUQepVIJqVSKjIwMtQsVAGBkZPTcc3ne8RcvXox58+aplX2VJu6IHBERERGRNlT1f9vq8pFbt25BoVAIg7TlrKysqp01m5+fX2X9J0+e4NatW2r5obY06uT/+PHjyMzMRGhoKOzt7ZGfn48mTZpo/Li9pk2bQqFQn0KclpaG1q1bY8mSJUKZpo9i69KlC/bu3QuZTIYmTbTf9adOnRIS+bt37+LKlSvCzIIuXbrgl19+ERJLTWlyvlX1U/lih3l5efDw8AAAtdsCqtO+fXvo6ekhNzcX3t7etWprXWkSMzU1FT179sT06dOFstrOGKmL8uS8uLhYaOvevXvVLgKkp6fD2NgYdnZ2aN68OXR1dav8LpSfm4eHBxQKBQoKCtC7d2+ttbWqaVC6TbV2eCIiIiJ6xajquvKeFjxvin91qhqke7aspvpVlWtLo0n+S0pKkJ+fD4VCgZs3b+Lw4cOIjIzEkCFDEBQUBB0dHfTo0QMBAQFYs2YNXF1d8ddffyExMREBAQFVTneXyWQ4cuQIfvvtN5ibmwujsrm5uYiPj0e3bt2QkJBQ4z3y5WbMmIGtW7di7NixWLhwISwsLHDt2jXEx8dj69atlUZha2vlypUwNzeHlZUVlixZAgsLCwQEBAB4ek9J+erxU6ZMgaGhIbKysiotfPcsTc5XJpPh+vXrOH/+POzt7WFsbAx9fX10794dq1evhkwmw61bt7B06dIaz8HY2BgLFixAaGgolEolevXqhfv37yM9PR1GRkaYOHHiC/VRXWM6Ozvjiy++wJEjR+Dg4IAdO3bgzJkzcHBw0GpbioqKhKuDN2/exIcffohmzZoJU4KmT5+O6OhozJo1CzNnzsRvv/2G5cuXY968edDR0YGRkRFCQkKwcOFCte9C+UwMAHBxccG4ceMQFBSEqKgoeHh44NatWzh+/Dg6deqEQYMGafWciIiIiIgaMwsLC0il0kqj/AUFBZVG98tZW1tXWb9JkyYwNzd/Ke1sNPf8Hz58GDY2NpDJZBgwYACSk5OxceNGHDx4EFKpFBKJBImJiejTpw8mT54MFxcXjBkzBjk5OdV+IFOmTIGrq6twn3daWhqGDRuG0NBQzJw5E+7u7khPTxdWxa+Jra0t0tLSoFAo4Ofnh44dO2LOnDkwNTVVS87qavXq1ZgzZw66du2KvLw8fPvtt2ja9OnQq5ubG06cOIGrV6+id+/e8PDwQHh4eI3TSTQ53+HDh2PAgAHw9fWFpaUldu7cCQCIiYlBWVkZPD09MWfOHLUV6Z/ngw8+wLJlyxAZGYl27drBz88P//vf/7SeaNcm5rRp0xAYGIjRo0fDy8sLt2/fVpsFoC1bt26FjY0NbGxs4Ovri7///huJiYnCPft2dnZITEzETz/9hM6dO2PatGkICQlRu7Cydu1a9OnTB/7+/njrrbfQq1cvdO3aVS1ObGwsgoKCMH/+fLi6usLf3x+nT59Gq1attH5ORERERER18U951F/Tpk3RtWtXHD16VK386NGjwhPjntWjR49K9b/77jt4enpWuv1WWxrFav+vuvIV7u/evVur9QuIXrZtx8SPydX+tYur/WsfV/vXLq72r31c7V+7uNq/9nG1f+1qyKv9f7xX87WztK22/bJr1y5MmDABn332GXr06IEtW7Zg69at+OWXX9C6dWssXrwYf/75p/AEt+vXr6Njx4549913MWXKFPz444+YNm0adu7cieHDh7+MU2o80/6JiIiIiIiI6sPo0aNx+/ZtrFy5Enl5eejYsSMSExPRunVrAE/XQqv4pDAHBwckJiYiNDQU//3vf2Fra4uNGze+tMQfYPJP/zDTpk3Dl19+WeW+8ePH47PPPhO5RZpJTU3FwIEDq93/8OFDEVtDRERERNTw/dPmqE+fPr3aW4PlcnmlMm9vb5w7d+4lt+r/Y/LfCPj4+Gj0iLfGYOXKlViwYEGV+0xMxJ2eXBuenp4aPe2AiIiIiIjoZWDyT/8oLVu2RMuWLeu7GbWmr69f68csEhERERG9ypT1+Ki/xqjhru5ARERERERERFrB5J+IiIiIiIiokeO0fyIiIiIiImpwXpFlzUTDkX8iIiIiIiKiRo4j/0T00hjpi3+51tBe3GuaZy8UixpvSWCeqPHO3hX3KRoSBz1R4wHA5f8TN+bN/CJR4+3amSNqPKPmhqLGmztO3N/5+Lh8UeMBQOC4TqLGMzVUihqv7IlE1Hh9pMmixvvhx8uixgMAaY/2osbzufSNqPGySl1Ejeds/Keo8f5u4SBqvIaMI//axZF/IiIiIiIiokaOI/9ERERERETU4Cg59K9VHPknIiIiIiIiauSY/BMRERERERE1cpz2T0RERERERA2OStz1Rxs9jvwTERERERERNXIc+SciIiIiIqIGR8UF/7SKI/+1FBERAXd39/puBtFLJ5PJEB0dXd/NICIiIiIiLXhlkv/g4GBIJBJIJBLo6urCysoK/fr1Q0xMDJTKut9MEhwcjICAgFq9x8fHR2hLVZtMJqtzexoqXjTRjorfHT09PdjZ2WHo0KHYt29ffTeNiIiIiIgasFcm+QeAAQMGIC8vDzk5OUhKSoKvry/mzJmDIUOG4MmTJ6K1Y9++fcjLy0NeXh5++uknAMD3338vlJ05c0a0tryo0tJSUeOpVCpRP6uGaMqUKcjLy8O1a9ewd+9etG/fHmPGjMHUqVPru2lERERERFqjVNbf1hi9Usm/np4erK2tYWdnhy5duuD999/HwYMHkZSUBLlcDgC4d+8epk6dipYtW8LExAR9+/bFhQsXqjxeREQE4uLicPDgQWE0NiUlBQCwaNEiuLi4wMDAAI6OjggPD0dZWRkAoEWLFrC2toa1tTUsLS0BAObm5kLZ33//jUGDBsHIyAhWVlaYMGECbt26JcT18fHBrFmzMHfuXDRv3hxWVlbYsmULHj16hEmTJsHY2BhOTk5ISkoS3pOSkgKJRIKEhAR07twZzZo1g5eXFzIzM9XOKT09HX369IG+vj5atWqF2bNn49GjR8J+mUyGDz/8EMHBwTA1NcWUKVNqPF+5XI4VK1bgwoULQj/J5XLk5ORAIpHg/PnzwvELCwvV+rG83UeOHIGnpyf09PSQmpoKlUqFjz/+GI6OjtDX10fnzp2xZ88ejb4HFY/p4eEBfX199O3bFwUFBUhKSkK7du1gYmKCsWPHoqiotbAXDgABAABJREFUSHhfTTEVCgVCQkLg4OAAfX19uLq64pNPPlGLXT5TZN26dbCxsYG5uTlmzJgh9JUmDAwMYG1tjVatWqF79+5Ys2YNPv/8c2zduhXff/+9UO95n0m5b7/9Fp6enmjWrBksLCwQGBiotr+oqAiTJ0+GsbExXnvtNWzZskXjdhIRERERUcPxSiX/Venbty86d+6Mffv2QaVSYfDgwcjPz0diYiIyMjLQpUsXvPnmm7hz506l9y5YsACjRo0SZhTk5eWhZ8+eAABjY2PI5XJcvnwZn3zyCbZu3YoNGzbU2J68vDx4e3vD3d0dZ8+exeHDh3Hz5k2MGjVKrV5cXBwsLCzw008/YdasWXjvvfcwcuRI9OzZE+fOnYOfnx8mTJiglrwCwMKFC7Fu3TqcOXMGLVu2hL+/v5AQZmZmws/PD4GBgbh48SJ27dqFkydPYubMmWrHWLt2LTp27IiMjAyEh4fXeL6jR4/G/Pnz0aFDB6GfRo8ereEn9FRYWBgiIyORlZUFNzc3LF26FLGxsdi8eTN++eUXhIaGYvz48Thx4oTGx4yIiMCmTZuQnp6O33//HaNGjUJ0dDS+/vprJCQk4OjRo/jPf/4j1K8pplKphL29PXbv3o3Lly9j2bJleP/997F79261uMnJycjOzkZycjLi4uIgl8uFi091NXHiRDRv3lxt+n9N38GEhAQEBgZi8ODB+Pnnn3Hs2DF4enqqHTcqKgqenp74+eefMX36dLz33nv49ddfX6itRERERESaUKlU9bY1RlztH0Dbtm1x8eJFJCcnIzMzEwUFBdDT0wMArFu3DgcOHMCePXsqTas2MjKCvr4+SkpKYG1trbZv6dKlws8ymQzz58/Hrl27EBYW9ty2bN68GV26dMGqVauEspiYGLRq1QpXrlyBi4sLAKBz585CjMWLF2P16tWwsLAQRuKXLVuGzZs34+LFi+jevbtwrOXLl6Nfv34Anl5AsLe3x/79+zFq1CisXbsW//rXvzB37lwAQJs2bbBx40Z4e3tj8+bNaNasGYCnF0wWLFig8fnq6+vDyMgITZo0qdRPmlq5cqXQ7kePHmH9+vU4fvw4evToAQBwdHTEyZMn8fnnn8Pb21ujY3744Yd44403AAAhISFYvHgxsrOz4ejoCAAYMWIEkpOTsWjRIo1i6urqYsWKFcLxHRwckJ6ejt27d6tdvGnevDk2bdoEqVSKtm3bYvDgwTh27Jjw2dWFjo4OXFxckJOTI5TV9B386KOPMGbMGLU2d+7cWe24gwYNwvTp0wE8nUmwYcMGpKSkoG3btpXaUFJSgpKSErWystKm0G2qV+fzIiIiIiIi7WDyj6dXlCQSCTIyMvDw4UOYm5ur7S8uLkZ2dnatjrlnzx5ER0fj2rVrePjwIZ48eQITE5Ma35eRkYHk5GQYGRlV2pednS0k/25ubkK5VCqFubk5OnXqJJRZWVkBAAoKCtSOUZ64Ak9vP3B1dUVWVpYQ+9q1a/jqq6+EOiqVCkqlEtevX0e7du0AoNLo8Iucr6Yqxrx8+TIeP34sXAwoV1paCg8PD42PWbEPrayshOnxFcvK12TQNOZnn32Gbdu24caNGyguLkZpaWmlhQ47dOgAqVQqvLaxsal0+0VdlH+Py9X0mZw/f77GCw4V+0gikcDa2rrSd6pcZGSk2oUEABg+eRlGhETU4WyIiIiI6FWnbJwD8PWGyT+ArKwsODg4QKlUwsbGRrjfvCIzMzONj3fq1ClhRNXPzw+mpqaIj49HVFRUje9VKpUYOnQo1qxZU2mfjY2N8LOurq7avvKnGFR8XX68mlSs++6772L27NmV6rz22mvCz4aGhmr76nq+OjpP7zqpOK2munvfK8YsP6eEhATY2dmp1SufsaGJZ/urqj4tj6VJzN27dyM0NBRRUVHo0aMHjI2NsXbtWpw+fbrauM/GqSuFQoGrV6+iW7duADT7TPT19Ws8bm3aunjxYsybN0+t7OC5prU9FSIiIiIiegle+eT/+PHjyMzMRGhoKOzt7ZGfn48mTZpo/Li9pk2bQqFQqJWlpaWhdevWWLJkiVB248YNjY7XpUsX7N27FzKZDE2aaP/jOXXqlJDI3717F1euXBGmcHfp0gW//PILnJ2da3VMTc63qn4qX+wwLy9PGD2vuPhfddq3bw89PT3k5uZqPMX/RWkSMzU1FT179hSmyQOo9YyRuoqLi8Pdu3cxfPhwAJp9Jm5ubjh27BgmTZqklTbo6elVuvii25SXa4mIiIiIGoJXKvkvKSlBfn4+FAoFbt68icOHDyMyMhJDhgxBUFAQdHR00KNHDwQEBGDNmjVwdXXFX3/9hcTERAQEBFQ53V0mk+HIkSP47bffYG5uDlNTUzg7OyM3Nxfx8fHo1q0bEhISsH//fo3aOGPGDGzduhVjx47FwoULYWFhgWvXriE+Ph5bt25Vmy5eFytXroS5uTmsrKywZMkSWFhYICAgAMDTe7q7d++OGTNmYMqUKTA0NERWVlalhe+epcn5ymQyXL9+HefPn4e9vT2MjY2hr6+P7t27Y/Xq1ZDJZLh165baferVMTb+f+zdd1QU1/838PcuUpbei1gAFQELRaIUlWLBFmPvBTXGGgmKvYI9lthiL6BRUbEH0agsCJaIKIpCFFDU5EuxF1BB9j5/8LA/VlAXnZ0F/LzO2XN0Zpn3zO7O3r0zt+ggKCgIgYGBkEgkaNmyJV6+fIkLFy5AW1sbQ4cO/arX6Esz69evj507d+LUqVOwtrbGrl27kJCQAGtra073JT8/H9nZ2Xj//j3+++8/HDp0CL/99hvGjBkDHx8fAPK9J3PnzkWbNm1Qr1499OvXD+/fv0dUVNRnx6UghBBCCCGED4za/XPqmxrt/+TJk7CwsICVlRU6dOgAsViMNWvW4OjRo1BRUYFAIMCJEyfQunVrDB8+HLa2tujXrx8yMzOlfeg/NHLkSDRs2BCurq4wMTHB+fPn8cMPPyAwMBDjx4+Hk5MTLly4IB0V/3Nq1qyJ8+fPo6ioCH5+fmjcuDECAgKgp6cnbSb/NZYsWYKAgAA0a9YMWVlZOHbsGNTUiptmN23aFLGxsUhLS0OrVq3g7OyM2bNny3Q3KI88x9uzZ0906NABPj4+MDExwd69ewEUD2ZYWFgIV1dXBAQEYMGCBXIdx/z58zFnzhwsXrwY9vb28PPzw/HjxzmvaFckc/To0ejRowf69u2LFi1a4MmTJzKtALiyZcsWWFhYoF69eujevTtSUlKwb98+rF+/Xvoced4Tb29vHDhwAMeOHYOTkxN8fX3LdFEghBBCCCGEVA8CVl3nMSAyYmJi4OPjg2fPnlVo/AJCvkb4Bf6/Xvj+Rku88YbXvIC2mbzmXXnWkNe8UmNW8iblLr95Odn5n38Sh/67+4jXPG0Drc8/iUO/DOT3PsaMxdm85gFAj4FNPv8kDulpfd04NBVV+J7fE7+TlpjXvHNF/HRRLE3F3YHXPOebB3jNSy2w5TWvrmYWr3lxDxR3M6s8w3x4jauQGdveff5JCrJoRPWbseqbuvNPCCGEEEIIIYR8i6jyT6qd0aNHQ1tbu9zH6NGjlb17HxUXF/fR/S5v6kdCCCGEEEIIkdc3NeDft8zb2xvfSg+PkJAQBAUFlbuu9Dz3lY2rq6tcsx0QQgghhBDyLZDQgH+coso/qXZMTU1hamqq7N2oMJFIVOFpFgkhhBBCCCFEHlT5J4QQQgghhBBS6XwrLZf5Qn3+CSGEEEIIIYSQao7u/BNCCCGEEEIIqXQYvzOPVnt0558QQgghhBBCCKnm6M4/IURh1Gvwf7n2TQG/1zRbOGvwmnftRUNe8/h+Dx8+VuU1DwCePs3nNS/v9Tte89p0tOE1T8TvKYF7z4p4zWvqrsNrHgD8l1XIa95zTRVe8+rV4vd7Ju3nGbzmvV5ykdc8APC+eYDXvGuNe/Oad/fIP7zmPTKw4jXPWPc9r3lUJfx20DtNCCGEEEIIIaTSkdCAf5yiZv+EEEIIIYQQQkg1R3f+CSGEEEIIIYRUOjTVH7fozj8hhBBCCCGEEFLNUeWfEEIIIYQQQgip5qjZPyGEEEIIIYSQSkcioWb/XKI7/4QQQgghhBBCSDX3zVX+582bBycnJ2XvBiGEEEIIIYSQT2BMeY/qqMpU/v39/SEQCCAQCKCqqgozMzO0a9cO27dvh0Qi+artduvWrUJ/4+3tLd2X8h5WVlZfvD+VFV004cbHPjvv379XeHZMTAwEAgGeP3+u8CxCCCGEEEJI5VJlKv8A0KFDB2RlZSEzMxNRUVHw8fFBQEAAunTpwkvlqcShQ4eQlZWFrKwsXL58GQBw5swZ6bKEhATe9uVrFRQU8JrHGOP1vaqMRo4cKf2slDxq1Kg6w2/Qe0gIIYQQQvjAJExpj+qoSlX+1dXVYW5uDktLS7i4uGDGjBk4evQooqKiEBoaCgB48eIFfvrpJ5iamkJXVxe+vr64fv16udubN28ewsLCcPToUekd2JiYGADA1KlTYWtrC01NTdjY2GD27NkoLCwEABgaGsLc3Bzm5uYwMTEBABgZGUmXPXr0CJ06dYK2tjbMzMwwePBgPH78WJrr7e2Nn3/+Gb/88gsMDAxgZmaGzZs3Iy8vD8OGDYOOjg7q1auHqKgo6d+U3LWNjIyEo6MjNDQ00KJFCyQnJ8sc04ULF9C6dWuIRCLUrl0bEyZMQF5ennS9lZUVFixYAH9/f+jp6WHkyJGfPd7Q0FAEBwfj+vXr0tcpNDQUmZmZEAgESEpKkm7/+fPnMq9jyX6fOnUKrq6uUFdXR1xcHBhj+PXXX2FjYwORSARHR0dERETI9TkovU1nZ2eIRCL4+voiNzcXUVFRsLe3h66uLvr374/8/Hzp330us6ioCCNGjIC1tTVEIhEaNmyI1atXy2SXtBRZvnw5LCwsYGRkhHHjxklfK3loampKPysljxI7duyAvb09NDQ0YGdnh/Xr10vXlbzehw4dgo+PDzQ1NeHo6IiLFy9Kn3P//n18//33MDAwgJaWFho1aoQTJ04gMzMTPj4+AAADAwMIBAL4+/vL9bp87D0khBBCCCGEVB1VqvJfHl9fXzg6OuLQoUNgjKFz587Izs7GiRMnkJiYCBcXF7Rp0wZPnz4t87dBQUHo06ePtEVBVlYWPDw8AAA6OjoIDQ1FSkoKVq9ejS1btuC333777P5kZWXBy8sLTk5OuHLlCk6ePImcnBz06dNH5nlhYWEwNjbG5cuX8fPPP2PMmDHo3bs3PDw8cPXqVfj5+WHw4MEylVcAmDx5MpYvX46EhASYmpqia9eu0opncnIy/Pz80KNHD9y4cQP79u1DfHw8xo8fL7ONZcuWoXHjxkhMTMTs2bM/e7x9+/bFpEmT0KhRI+nr1LdvXznfoWJTpkzB4sWLkZqaiqZNm2LWrFnYsWMHNmzYgFu3biEwMBCDBg1CbGys3NucN28e1q1bhwsXLuDhw4fo06cPVq1ahT179iAyMhKnT5/G2rVrpc//XKZEIkGtWrWwf/9+pKSkYM6cOZgxYwb2798vkysWi5GRkQGxWIywsDCEhoZKLz59jS1btmDmzJlYuHAhUlNTsWjRIsyePRthYWEyz5s5cyaCgoKQlJQEW1tb9O/fX3onfty4cXj37h3OnTuH5ORkLF26FNra2qhduzYOHjwIALh9+zaysrKkFzbkfS8+fA8JIYQQQgghVUfVaWv8CXZ2drhx4wbEYjGSk5ORm5sLdXV1AMDy5ctx5MgRRERE4KeffpL5O21tbYhEIrx7907m7itQXCEqYWVlhUmTJmHfvn2YMmXKJ/dlw4YNcHFxwaJFi6TLtm/fjtq1a+POnTuwtbUFADg6Okozpk+fjiVLlsDY2Fh6J37OnDnYsGEDbty4ATc3N+m25s6di3bt2gEovoBQq1YtHD58GH369MGyZcswYMAA/PLLLwCABg0aYM2aNfDy8sKGDRugoaEBoPiCSVBQkNzHKxKJoK2tjRo1apR5neQVEhIi3e+8vDysXLkS0dHRcHd3BwDY2NggPj4emzZtgpeXl1zbXLBgATw9PQEAI0aMwPTp05GRkQEbGxsAQK9evSAWizF16lS5MlVVVREcHCzdvrW1NS5cuID9+/fLXLwxMDDAunXroKKiAjs7O3Tu3Blnz56Vvnefs379emzdulX6/1GjRmHFihWYP38+VqxYgR49ekjzU1JSsGnTJgwdOlT6/KCgIHTu3BkAEBwcjEaNGiE9PR12dnZ48OABevbsiSZNmkiPsYShoSEAwNTUFPr6+gAq9l6Ufg/L8+7dO7x7905mWWFBDaiqqcv1uhBCCCGEEFKapLqOvKck1aLyzxiDQCBAYmIiXr9+DSMjI5n1b968QUZGRoW2GRERgVWrViE9PR2vX7/G+/fvoaur+9m/S0xMhFgshra2dpl1GRkZ0sp/6TunKioqMDIyklbYAMDMzAwAkJubK7ONkgoaUFyZa9iwIVJTU6XZ6enp2L17t/Q5jDFIJBLcu3cP9vb2AABXV1fOjldepTNTUlLw9u3bMhXJgoICODs7y73N0q+hmZmZtMtC6WUlYzLIm7lx40Zs3boV9+/fx5s3b1BQUFBmoMNGjRpBRUVF+n8LC4sy3S8+ZeDAgZg5c6b0//r6+nj06BEePnyIESNGyFxEeP/+PfT09D563BYWFgCKPyd2dnaYMGECxowZg7/++gtt27ZFz549P3mXviLvRXmfm9IWL14sc/EEAPr8OBv9Rs795N8RQgghhBBCFK9aVP5TU1NhbW0NiUQCCwsLaX/z0krudMrj0qVL6NevH4KDg+Hn5wc9PT2Eh4djxYoVn/1biUSC77//HkuXLi2zrqSiBgCqqqoy60pmMSj9/5LtfU7p544aNQoTJkwo85w6depI/62lpSWz7kuPVygs7jXCSl2R+1jf99KZJccUGRkJS0tLmeeVtNiQx4evV3mvaUmWPJn79+9HYGAgVqxYAXd3d+jo6GDZsmX4+++/P5r7YY489PT0UL9+fZllOTk5AIqb/rdo0UJmXekLDR/mf/g5+fHHH+Hn54fIyEj89ddfWLx4MVasWIGff/653H2pyHvx4efmQ9OnT8fEiRNllp28US2+YgghhBBCiBJU14H3lKXK/zKPjo5GcnIyAgMDUatWLWRnZ6NGjRpyT7enpqaGoqIimWXnz59H3bp1Ze7O3r9/X67tubi44ODBg7CyslLICO6XLl2SVuSfPXuGO3fuwM7OTpp969atMhXLz5HneMt7nUoGO8zKypLeJS49+N/HODg4QF1dHQ8ePJC7if/XkiczLi4OHh4eGDt2rHRZRVuMfCkzMzNYWlri7t27GDhw4Fdtq3bt2hg9ejRGjx6N6dOnY8uWLfj555+hpqYGADLvI5fvhbq6epkLBqpqRR95NiGEEEIIIYRPVary/+7dO2RnZ6OoqAg5OTk4efIkFi9ejC5dumDIkCEQCoVwd3dHt27dsHTpUjRs2BD/+9//cOLECXTr1q3cZstWVlY4deoUbt++DSMjI+ld2QcPHiA8PBzfffcdIiMjcfjwYbn2cdy4cdiyZQv69++PyZMnw9jYGOnp6QgPD8eWLVvK3MWtqJCQEBgZGcHMzAwzZ86EsbExunXrBqB4xH43NzeMGzcOI0eOhJaWFlJTU8sMfPcheY7XysoK9+7dQ1JSEmrVqgUdHR2IRCK4ublhyZIlsLKywuPHj2XGDvgYHR0dBAUFITAwEBKJBC1btsTLly9x4cIFaGtry/Rv54o8mfXr18fOnTtx6tQpWFtbY9euXUhISIC1tTXn+1OeefPmYcKECdDV1UXHjh3x7t07XLlyBc+ePStzR/1jfvnlF3Ts2BG2trZ49uwZoqOjpd096tatC4FAgD///BOdOnWCSCRSyntBCCGEEEII4V+VGu3/5MmTsLCwgJWVFTp06ACxWIw1a9bg6NGjUFFRgUAgwIkTJ9C6dWsMHz4ctra26NevHzIzM6V96D80cuRINGzYEK6urjAxMcH58+fxww8/IDAwEOPHj4eTkxMuXLggHRX/c2rWrInz58+jqKgIfn5+aNy4MQICAqCnpydtJv81lixZgoCAADRr1gxZWVk4duyY9I5u06ZNERsbi7S0NLRq1QrOzs6YPXu2THeD8shzvD179kSHDh3g4+MDExMT7N27F0DxYIaFhYVwdXVFQEAAFixYINdxzJ8/H3PmzMHixYthb28PPz8/HD9+XKEV7c9ljh49Gj169EDfvn3RokULPHnyRKYVgKL9+OOP2Lp1K0JDQ9GkSRN4eXkhNDS0Qq9JUVERxo0bB3t7e3To0AENGzaUThdoaWmJ4OBgTJs2DWZmZtJZIJTxXhBCCCGEEPI5TMKU9qiOBIzREIpVQUxMDHx8fPDs2bMKjV9AiDIdvsx/s/83Bfxe01Stwe9XqDrPeTWE8o9nwYWHj1U//ySOpWXkf/5JHHr+lN+8Zs2Mec0TafAaBz1Nfr9n/r5e/tg2iiQSfV2rwYrS0uQ3r14tfr/Xak305DUvbclFXvMAwNtE/oGIuXCtcW9e8+4e+YfXPBMDXuOgpc7v99r3zSpvY/Bxy58rLfv3IH2lZStK5X2nCSGEEEIIIYR8s6rpDXilqVLN/sm3YfTo0dDW1i73MXr0aGXv3kfFxcV9dL/Lm/qREEIIIYQQQvhCd/6rCG9vb3wrPTRCQkIQFBRU7jpdXV2e90Z+rq6ucs12QAghhBBCCPm86tr3Xlmo8k8qHVNTU5iamip7NypMJBJVeJpFQgghhBBCCOEDNfsnhBBCCCGEEEKqObrzTwghhBBCCCGk0vlWuj3zhe78E0IIIYQQQggh1Rzd+SeEEEIIIYQQUulIaMA/TlHlnxCiMHnv+G9c9LZAwGte4Wt+89TVeI0DoMJr2kC947zmAcCfjl14zTt/+T2vedqavMbh3+wiXvNyVfg9B++n5fKaBwBrNYJ5zSsc8DOveUtP8ztYrv2cS7zm1dUu4DUPAFILbHnNu3vkH17zbLrZ8ZrX+vxKXvPCn3XmNY98O6jZPyGEEEIIIYQQUs3RnX9CCCGEEEIIIZUODfjHLbrzTwghhBBCCCGEVHN0558QQgghhBBCSKXDaMA/TtGdf0IIIYQQQgghpJqjyj8hhBBCCCGEEFLNUbN/QgghhBBCCCGVDjX75xbd+VeSefPmwcnJSdm7QQghhBBCCCHkG0CV/wry9/eHQCCAQCCAqqoqzMzM0K5dO2zfvh0SieSrttutW7cK/Y23t7d0X8p7WFlZffH+VFZ00YQb3t7e+OWXXwAA06ZNg729vcz61NRUCAQCDB48WGb5rl27oKqqitevX/O1q4QQQggh5BslYUxpj+qIKv9foEOHDsjKykJmZiaioqLg4+ODgIAAdOnSBe/fv+dtPw4dOoSsrCxkZWXh8uXLAIAzZ85IlyUkJPC2L1+roKCA1zzGGK/vVWXm4+ODf/75B9nZ2dJlMTExqF27NsRiscxzY2Ji0Lx5c2hra/O9m4QQQgghhJCvQJX/L6Curg5zc3NYWlrCxcUFM2bMwNGjRxEVFYXQ0FAAwIsXL/DTTz/B1NQUurq68PX1xfXr18vd3rx58xAWFoajR49K79rHxMQAAKZOnQpbW1toamrCxsYGs2fPRmFhIQDA0NAQ5ubmMDc3h4mJCQDAyMhIuuzRo0fo1KkTtLW1YWZmhsGDB+Px48fSXG9vb/z888/45ZdfYGBgADMzM2zevBl5eXkYNmwYdHR0UK9ePURFRUn/JiYmBgKBAJGRkXB0dISGhgZatGiB5ORkmWO6cOECWrduDZFIhNq1a2PChAnIy8uTrreyssKCBQvg7+8PPT09jBw58rPHGxoaiuDgYFy/fl36OoWGhiIzMxMCgQBJSUnS7T9//lzmdSzZ71OnTsHV1RXq6uqIi4sDYwy//vorbGxsIBKJ4OjoiIiICLk+B6W36ezsDJFIBF9fX+Tm5iIqKgr29vbQ1dVF//79kZ+fL/27z2UWFRVhxIgRsLa2hkgkQsOGDbF69WqZ7JKWIsuXL4eFhQWMjIwwbtw46WtVES1btoSqqqr0tSo5tnHjxuHVq1dIT0+XWe7j41PhDEIIIYQQQiqKSZjSHtURVf454uvrC0dHRxw6dAiMMXTu3BnZ2dk4ceIEEhMT4eLigjZt2uDp06dl/jYoKAh9+vSRtijIysqCh4cHAEBHRwehoaFISUnB6tWrsWXLFvz222+f3Z+srCx4eXnByckJV65cwcmTJ5GTk4M+ffrIPC8sLAzGxsa4fPkyfv75Z4wZMwa9e/eGh4cHrl69Cj8/PwwePFim8goAkydPxvLly5GQkABTU1N07dpVWvFMTk6Gn58fevTogRs3bmDfvn2Ij4/H+PHjZbaxbNkyNG7cGImJiZg9e/Znj7dv376YNGkSGjVqJH2d+vbtK+c7VGzKlClYvHgxUlNT0bRpU8yaNQs7duzAhg0bcOvWLQQGBmLQoEGIjY2Ve5vz5s3DunXrcOHCBTx8+BB9+vTBqlWrsGfPHkRGRuL06dNYu3at9Pmfy5RIJKhVqxb279+PlJQUzJkzBzNmzMD+/ftlcsViMTIyMiAWixEWFobQ0FDpxaeK0NLSwnfffSdzlz82NhZt2rSBp6endPnDhw9x9+5dqvwTQgghhBBSBdFo/xyys7PDjRs3IBaLkZycjNzcXKirqwMAli9fjiNHjiAiIgI//fSTzN9pa2tDJBLh3bt3MDc3l1k3a9Ys6b+trKwwadIk7Nu3D1OmTPnkvmzYsAEuLi5YtGiRdNn27dtRu3Zt3LlzB7a2tgAAR0dHacb06dOxZMkSGBsbS+/Ez5kzBxs2bMCNGzfg5uYm3dbcuXPRrl07AMUXEGrVqoXDhw+jT58+WLZsGQYMGCDtU96gQQOsWbMGXl5e2LBhAzQ0NAAUXzAJCgqS+3hFIhG0tbVRo0aNMq+TvEJCQqT7nZeXh5UrVyI6Ohru7u4AABsbG8THx2PTpk3w8vKSa5sLFiyAp6cnAGDEiBGYPn06MjIyYGNjAwDo1asXxGIxpk6dKlemqqoqgoODpdu3trbGhQsXsH//fpmLNwYGBli3bh1UVFRgZ2eHzp074+zZs9L3riK8vb2lrQ9SUlLw5s0bODs7w8vLCzExMRg5ciTEYjHU1dWlF6Y+9O7dO7x7905mWWGBGlTV1Cu8P4QQQgghhBBuUeWfQ4wxCAQCJCYm4vXr1zAyMpJZ/+bNG2RkZFRomxEREVi1ahXS09Px+vVrvH//Hrq6up/9u8TERIjF4nL7ZmdkZEgr/02bNpUuV1FRgZGREZo0aSJdZmZmBgDIzc2V2UZJxRUo7n7QsGFDpKamSrPT09Oxe/du6XMYY5BIJLh37550cDlXV1fOjldepTNTUlLw9u1b6cWAEgUFBXB2dpZ7m6VfQzMzM2mXhdLLSsZkkDdz48aN2Lp1K+7fv483b96goKCgzECHjRo1goqKivT/FhYWZbpfyMvHxweLFi3C//73P8TExKBly5ZQUVGBl5cX1qxZA6C4yb+bmxtEIlG521i8eLHMRQsA6O4/Bz2Gz/uifSKEEEIIId82Vk0H3lMWqvxzKDU1FdbW1pBIJLCwsJDpQ11CX19f7u1dunQJ/fr1Q3BwMPz8/KCnp4fw8HCsWLHis38rkUjw/fffY+nSpWXWWVhYSP+tqqoqs65kFoPS/y/Z3ueUfu6oUaMwYcKEMs+pU6eO9N9aWloy6770eIXC4t4rpb8cPtb3vXRmyTFFRkbC0tJS5nklLTbk8eHrVd5rWpIlT+b+/fsRGBiIFStWwN3dHTo6Oli2bBn+/vvvj+Z+mFNRnp6eUFNTQ0xMDMRisbTVg6urK168eIE7d+5ALBbD39//o9uYPn06Jk6cKLPsYILaF+0PIYQQQgghhFtU+edIdHQ0kpOTERgYiFq1aiE7Oxs1atSQe7o9NTU1FBUVySw7f/486tati5kzZ0qX3b9/X67tubi44ODBg7CyskKNGty/zZcuXZJW5J89e4Y7d+7Azs5Omn3r1i3Ur1+/QtuU53jLe51KBjvMysqS3j0vPfjfxzg4OEBdXR0PHjyQu4n/15InMy4uDh4eHhg7dqx0WUVbjFSUSCRCixYtEBMTg3PnzmHy5MkAgBo1asDDwwM7d+5EZmbmJ/v7q6url7looqpGV2sJIYQQQsiXkVTTgfeUhSr/X+Ddu3fIzs5GUVERcnJycPLkSSxevBhdunTBkCFDIBQK4e7ujm7dumHp0qVo2LAh/ve//+HEiRPo1q1buc3drayscOrUKdy+fRtGRkbQ09ND/fr18eDBA4SHh+O7775DZGQkDh8+LNc+jhs3Dlu2bEH//v0xefJkGBsbIz09HeHh4diyZYtMc/EvERISAiMjI5iZmWHmzJkwNjZGt27dABSP2O/m5oZx48Zh5MiR0NLSQmpqapmB7z4kz/FaWVnh3r17SEpKQq1ataCjowORSAQ3NzcsWbIEVlZWePz4sczYAR+jo6ODoKAgBAYGQiKRoGXLlnj58iUuXLgAbW1tDB069Kteoy/NrF+/Pnbu3IlTp07B2toau3btQkJCAqytrTnfn9J8fHykgyu6uLhIl3t5eWHp0qXSCwSEEEIIIYSQqodG+/8CJ0+ehIWFBaysrNChQweIxWKsWbMGR48ehYqKCgQCAU6cOIHWrVtj+PDhsLW1Rb9+/ZCZmSntQ/+hkSNHomHDhnB1dYWJiQnOnz+PH374AYGBgRg/fjycnJxw4cIF6aj4n1OzZk2cP38eRUVF8PPzQ+PGjREQEAA9PT1pM/mvsWTJEgQEBKBZs2bIysrCsWPHoKZW3MS7adOmiI2NRVpaGlq1agVnZ2fMnj1bprtBeeQ53p49e6JDhw7w8fGBiYkJ9u7dC6B4MMPCwkK4uroiICAACxYskOs45s+fjzlz5mDx4sWwt7eHn58fjh8/rtCK9ucyR48ejR49eqBv375o0aIFnjx5ItMKQFF8fHzw6tUreHp6yrQW8fLywqtXr+Dh4VGh7hCEEEIIIYSQykPAaBQFUgEl87w/e/asQuMXkG/TH3H8f728LRDwmveR4SUURr2aD6PQR+s475l/FnXhNe/85Ve85jV34W7QVHn8m130+SdxSEWF33P+2t//8poHAGs1gj//JA4VDviZ17ylpyvWTfBr2TcsOxiyItU1LeA1DwBUVb5sDKAvlfKA35sDNt3seM1rfX4lr3nhLzvzmjeyLa9xFTJw+n9Ky9692PLzT/oCz549w4QJE3Ds2DEAQNeuXbF27dqP1q0KCwsxa9YsnDhxAnfv3oWenh7atm2LJUuWoGbNmhXKpjv/hBBCCCGEEEIIDwYMGICkpCScPHkSJ0+eRFJSEgYPHvzR5+fn5+Pq1auYPXs2rl69ikOHDuHOnTvo2rVrhbOpzz8hHzF69Gj88ccf5a4bNGgQNm7cyPMeyScuLg4dO3b86PrXr1/zuDeEEEIIIYR8merWSD01NRUnT57EpUuXpGNpbdmyBe7u7rh9+zYaNmxY5m/09PRw+vRpmWVr165F8+bN8eDBA5nZ1D6HKv+kQry9vavdSfgxISEhCAoKKnedri6/zWgrwtXVVa7ZDgghhBBCCCHle/fuHd69eyezrLzZrSri4sWL0NPTkxlE283NDXp6erhw4UK5lf/yvHjxAgKBoMLdsKnyT8hHmJqawtTUVNm7UWEikajC0ywSQgghhBBS2TAJv+NXlLZ48WIEB8uOuTJ37lzMmzfvi7eZnZ1dbv3C1NQU2dnZcm3j7du3mDZtGgYMGFDhG5LU558QQgghhBBCCCll+vTpePHihcxj+vTp5T533rx5EAgEn3xcuXIFACAQlB2oljFW7vIPFRYWol+/fpBIJFi/fn2Fj4nu/BNCCCGEEEIIIaVUpIn/+PHj0a9fv08+x8rKCjdu3EBOTk6ZdY8ePfrolPAlCgsL0adPH9y7dw/R0dFf1A2ZKv+EEEIIIYQQQiodiaRqjDVmbGwMY2Pjzz7P3d0dL168wOXLl9G8eXMAwN9//40XL17Aw8Pjo39XUvFPS0uDWCyGkZHRF+0nNfsnhBBCCCGEEEIUzN7eHh06dMDIkSNx6dIlXLp0CSNHjkSXLl1kBvuzs7PD4cOHAQDv379Hr169cOXKFezevRtFRUXIzs5GdnY2CgoKKpRPd/4JIQqT84T/TG1NfvPeFfB7RdrO8i2vee8l/F4jTtVoxWseAOi/KeQ1r/BdEa95/8vld7CkZ08r9kPka6mq8fsZFaqo8JoHAI+GhvCaZ53xF695AgG/g9SaGfB7DvL9PQoA9XX+4zXvkYEVr3mtz6/kNe+c50Re89TPdOY1rzKrjrOM7d69GxMmTED79u0BAF27dsW6detknnP79m28ePECAPDvv//i2LFjAAAnJyeZ54nFYnh7e8udTZV/QgghhBBCCCGEB4aGhvjjjz8++ZzSFz2srKw4uwhCzf4JIYQQQgghhJBqju78E0IIIYQQQgipdFgVGfCvqqA7/4QQQgghhBBCSDVHd/4JIYQQQgghhFQ6dOefW3TnnxBCCCGEEEIIqebozj8hhBBCCCGEkEpHwvidrra6ozv/nzFv3rwy8ykSUlXQ55cQQgghhBACVOPKv7+/PwQCAQQCAVRVVWFmZoZ27dph+/btkEi+/AqSv78/unXrVqG/8fb2lu5LeQ8rK6sv3p/Kiiqd3Ll16xb69OkDExMTqKuro0GDBpg9ezby8/NlnicQCHDkyBHl7CQhhBBCCCGkUqu2lX8A6NChA7KyspCZmYmoqCj4+PggICAAXbp0wfv373nbj0OHDiErKwtZWVm4fPkyAODMmTPSZQkJCbzty9cqKCjgNY8xxut7VdlcunQJLVq0QEFBASIjI3Hnzh0sWrQIYWFhaNeuHe/vB0DvCSGEEEII4QeTMKU9qqNqXflXV1eHubk5LC0t4eLighkzZuDo0aOIiopCaGgoAODFixf46aefYGpqCl1dXfj6+uL69evlbm/evHkICwvD0aNHpXftY2JiAABTp06Fra0tNDU1YWNjg9mzZ6OwsBAAYGhoCHNzc5ibm8PExAQAYGRkJF326NEjdOrUCdra2jAzM8PgwYPx+PFjaa63tzd+/vln/PLLLzAwMICZmRk2b96MvLw8DBs2DDo6OqhXrx6ioqKkfxMTEwOBQIDIyEg4OjpCQ0MDLVq0QHJysswxXbhwAa1bt4ZIJELt2rUxYcIE5OXlSddbWVlhwYIF8Pf3h56eHkaOHPnZ4w0NDUVwcDCuX78ufZ1CQ0ORmZkJgUCApKQk6fafP38u8zqW7PepU6fg6uoKdXV1xMXFgTGGX3/9FTY2NhCJRHB0dERERIRcn4PS23R2doZIJIKvry9yc3MRFRUFe3t76Orqon///jJ30z+XWVRUhBEjRsDa2hoikQgNGzbE6tWrZbJLWoosX74cFhYWMDIywrhx46Sv1acwxjBixAjY29vj0KFDaN68OerWrYvevXvj+PHjuHjxIn777Tfp+wQA3bt3L7c1ya5du2BlZQU9PT3069cPr169kvs4P/aeEEIIIYQQQqqOal35L4+vry8cHR1x6NAhMMbQuXNnZGdn48SJE0hMTISLiwvatGmDp0+flvnboKAg9OnTR9qiICsrCx4eHgAAHR0dhIaGIiUlBatXr8aWLVukFbNPycrKgpeXF5ycnHDlyhWcPHkSOTk56NOnj8zzwsLCYGxsjMuXL+Pnn3/GmDFj0Lt3b3h4eODq1avw8/PD4MGDyzQFnzx5MpYvX46EhASYmpqia9eu0opncnIy/Pz80KNHD9y4cQP79u1DfHw8xo8fL7ONZcuWoXHjxkhMTMTs2bM/e7x9+/bFpEmT0KhRI+nr1LdvXznfoWJTpkzB4sWLkZqaiqZNm2LWrFnYsWMHNmzYgFu3biEwMBCDBg1CbGys3NucN28e1q1bhwsXLuDhw4fo06cPVq1ahT179iAyMhKnT5/G2rVrpc//XKZEIkGtWrWwf/9+pKSkYM6cOZgxYwb2798vkysWi5GRkQGxWIywsDCEhoZKLz59SlJSElJSUjBx4kQIhbKnqqOjI9q2bYu9e/cCgLT1yI4dO8q0JsnIyMCRI0fw559/4s8//0RsbCyWLFki93GW+PA9IYQQQgghRJHozj+3vsnR/u3s7HDjxg2IxWIkJycjNzcX6urqAIDly5fjyJEjiIiIwE8//STzd9ra2hCJRHj37h3Mzc1l1s2aNUv6bysrK0yaNAn79u3DlClTPrkvGzZsgIuLCxYtWiRdtn37dtSuXRt37tyBra0tgOLKXknG9OnTsWTJEhgbG0vvxM+ZMwcbNmzAjRs34ObmJt3W3Llz0a5dOwDFFxBq1aqFw4cPo0+fPli2bBkGDBiAX375BQDQoEEDrFmzBl5eXtiwYQM0NDQAFF8wCQoKkvt4RSIRtLW1UaNGjTKvk7xCQkKk+52Xl4eVK1ciOjoa7u7uAAAbGxvEx8dj06ZN8PLykmubCxYsgKenJwBgxIgRmD59OjIyMmBjYwMA6NWrF8RiMaZOnSpXpqqqKoKDg6Xbt7a2xoULF7B//36ZizcGBgZYt24dVFRUYGdnh86dO+Ps2bPS9+5j7ty5AwCwt7cvd729vT3i4+MBQNqiRF9fv8xrLpFIEBoaCh0dHQDA4MGDcfbsWSxcuLBCr23p96Q87969w7t372SWvS9UQw1V9U8eJyGEEEIIIUTxvsnKP2MMAoEAiYmJeP36NYyMjGTWv3nzBhkZGRXaZkREBFatWoX09HS8fv0a79+/h66u7mf/LjExEWKxGNra2mXWZWRkSCv/pe+0qqiowMjICE2aNJEuMzMzAwDk5ubKbKOkQgcUdz9o2LAhUlNTpdnp6enYvXu39DmMMUgkEty7d09a6XR1deXseOVVOjMlJQVv374tU/EsKCiAs7Oz3Nss/RqamZlJuyyUXlYyJoO8mRs3bsTWrVtx//59vHnzBgUFBWUGOmzUqBFUVFSk/7ewsCjT/eJLlHyOP8fKykpa8S/JL/mcVOS1Le9zUNrixYtlLoYAQLu+c+DXb95n95EQQgghhBCiWN9k5T81NRXW1taQSCSwsLCQ9jcvTV9fX+7tXbp0Cf369UNwcDD8/Pygp6eH8PBwrFix4rN/K5FI8P3332Pp0qVl1llYWEj/raqqKrOuZBaD0v8v2d7nlH7uqFGjMGHChDLPqVOnjvTfWlpaMuu+9HhLmq4z9n/NaD7W9710ZskxRUZGwtLSUuZ5JS025PHh61Xea1qSJU/m/v37ERgYiBUrVsDd3R06OjpYtmwZ/v7774/mfpjzKSUXflJSUsqdOeGff/5BgwYNPrudrz3OEh9+Dj40ffp0TJw4UWbZhlNqn90/QgghhBBCylO63kC+3jdX+Y+OjkZycjICAwNRq1YtZGdno0aNGnJPt6empoaioiKZZefPn0fdunUxc+ZM6bL79+/LtT0XFxccPHgQVlZWqFGD+7fj0qVL0or8s2fPcOfOHdjZ2Umzb926hfr161dom/Icb3mvU0nT9KysLOld5dKD/32Mg4MD1NXV8eDBA7mb+H8teTLj4uLg4eGBsWPHSpdVtMXIpzg5OcHOzg6//fYb+vXrJ9Pv//r16zhz5gwWL14sXaaqqlrmNf8cLl9bdXX1MhcMaqjSFzYhhBBCCCGVQbWu/L979w7Z2dkoKipCTk4OTp48icWLF6NLly4YMmQIhEIh3N3d0a1bNyxduhQNGzbE//73P5w4cQLdunUrt5mzlZUVTp06hdu3b8PIyAh6enqoX78+Hjx4gPDwcHz33XeIjIzE4cOH5drHcePGYcuWLejfvz8mT54MY2NjpKenIzw8HFu2bJFpLv4lQkJCYGRkBDMzM8ycORPGxsbo1q0bgOIR+93c3DBu3DiMHDkSWlpaSE1NLTPw3YfkOV4rKyvcu3cPSUlJqFWrFnR0dCASieDm5oYlS5bAysoKjx8/lhk74GN0dHQQFBSEwMBASCQStGzZEi9fvsSFCxegra2NoUOHftVr9KWZ9evXx86dO3Hq1ClYW1tj165dSEhIgLW1NSf7IBAIsHXrVrRv3x49e/bE9OnTYW5ujr///huTJk2Cu7u7dLwGoPg1P3v2LDw9PaGurg4DAwNOjpMQQgghhBBlkKe1LJFftR7t/+TJk7CwsICVlRU6dOgAsViMNWvW4OjRo1BRUYFAIMCJEyfQunVrDB8+HLa2tujXrx8yMzOlfeg/NHLkSDRs2BCurq4wMTHB+fPn8cMPPyAwMBDjx4+Hk5MTLly4IB0V/3Nq1qyJ8+fPo6ioCH5+fmjcuDECAgKgp6dXZoT3L7FkyRIEBASgWbNmyMrKwrFjx6CmVtwUu2nTpoiNjUVaWhpatWoFZ2dnzJ49W6a7QXnkOd6ePXuiQ4cO8PHxgYmJiXRU+u3bt6OwsBCurq4ICAjAggUL5DqO+fPnY86cOVi8eDHs7e3h5+eH48ePc1bR/pLM0aNHo0ePHujbty9atGiBJ0+eyLQC4IKnpycuXboEFRUVdOrUCfXr18f06dMxdOhQnD59WuZO+4oVK3D69GnUrl27QmMhKOO1JYQQQgghhPBLwKgjRbUUExMDHx8fPHv2rELjFxDCpRVH+P960db8/CCIXHrzlt9jdLR+y2veewm/14j1Nd7wmgcAT95o8pp35FQer3nW9fR4zXvy+N3nn8QhVTV+P6OZaY95zQOAWT/ym2ed8RevedPv9OY1z6uFiNe8Gir8l4V2eg95zbuSY8VrXucaJ3jNO+c58fNP4tCTM7d5zRvCT6/aL9JlZIrSsv/c4qC0bEWp1nf+CSGEEEIIIYQQQpV/Ug2MHj0a2tra5T5Gjx6t7N37qLi4uI/ud3lTPxJCCCGEEELIl6rWA/59y7y9vb+ZqTFCQkIQFBRU7jpdXV2e90Z+rq6ucs12QAghhBBCyLeIMRrwj0tU+SdVnqmpKUxNTZW9GxUmEokqPM0iIYQQQgghhHwJqvwTQgghhBBCCKl0mOTbaMnMF+rzTwghhBBCCCGEVHNU+SeEEEIIIYQQQqo5avZPCCGEEEIIIaTSoWb/3BKwb2VIeEII714lnOA9897cX3nNsx3dm9e8/Bs3eM3LvX6P17wTg07xmgcAfe1v8poXm9uY1zwhz2389ETvec3Lfq7Ka17+W17jAAAPHr7hNU9Dg997Q41tVXjN49u9//jPNDHk98Q31uX5vH/G72dUnd+vGRi1bchrXufC27zmVURHf35/95QWFdpUadmKQnf+CSGEEEIIIYRUOhKa6o9T1OefEEIIIYQQQgip5ujOPyGEEEIIIYSQSof6/HOL7vwTQgghhBBCCCHVHFX+CSGEEEIIIYSQao6a/RNCCCGEEEIIqXSYhAb84xLd+SeEEEIIIYQQQqo5qvyTSiUmJgYCgQDPnz9X9q6Uy9/fH926dfuiv23dujX27Nkj/b9AIMCRI0e42bH/LzMzEwKBAElJSZxuNzc3FyYmJvjvPyVMVkwIIYQQQr5JTMKU9qiOqPJPCA/+/PNPZGdno1+/fnI9f9q0abC3t5dZlpqaCoFAgMGDB8ss37VrF1RVVfH69esK7VNMTAwsLCzA2Oe/3ExNTTF48GDMnTu3QhmEEEIIIYSQyoEq/6TSKCws/KK/KyoqgqSS9wdas2YNhg0bBqFQvlPOx8cH//zzD7Kzs6XLYmJiULt2bYjFYpnnxsTEoHnz5tDW1q7QPh07dgxdu3aFQCCQ6/nDhg3D7t278ezZswrlEEIIIYQQQpSPKv+kwjZt2gRLS8syFe6uXbti6NCh0v8fP34czZo1g4aGBmxsbBAcHIz3799L1wsEAmzcuBE//PADtLS0sGDBAum68+fPw9HRERoaGmjRogWSk5Ol60JDQ6Gvr48///wTDg4OUFdXx/3795GQkIB27drB2NgYenp68PLywtWrV6V/V16T+OfPn0MgECAmJka67NatW+jcuTN0dXWho6ODVq1aISMjQ+ZYly9fDgsLCxgZGWHcuHGfvHDx+PFjnDlzBl27dv3k6xoSEgIzMzMkJSWhZcuWUFVVldmvmJgYjBs3Dq9evUJ6errMch8fH5lt3b17Fz4+PtDU1ISjoyMuXrxYJq+k8g8AJ0+eRMuWLaGvrw8jIyN06dKlzDE3adIE5ubmOHz48CePgxBCCCGEEC4wJlHaozqiyj+psN69e+Px48cyd6CfPXuGU6dOYeDAgQCAU6dOYdCgQZgwYQJSUlKwadMmhIaGYuHChTLbmjt3Ln744QckJydj+PDh0uWTJ0/G8uXLkZCQAFNTU3Tt2lWmgp2fn4/Fixdj69atuHXrFkxNTfHq1SsMHToUcXFxuHTpEho0aIBOnTrh1atXch/bf//9h9atW0NDQwPR0dFITEzE8OHDZS5aiMViZGRkQCwWIywsDKGhoQgNDf3oNuPj46GpqVmmGX8JxhgCAgKwbds2xMfHw8nJCVpaWvjuu+9kXuPY2Fi0adMGnp6e0uUPHz6UVvRLmzlzJoKCgpCUlARbW1v0799f5hhu3bqF7OxstGnTBgCQl5eHiRMnIiEhAWfPnoVQKET37t3LXOBp3rw54uLi5HsxCSGEEEIIIZUGTfVHKszQ0BAdOnTAnj17pJXHAwcOwNDQUPr/hQsXYtq0adKWADY2Npg/fz6mTJki0298wIABMpX+e/fuASi+KNCuXTsAQFhYGGrVqoXDhw+jT58+AIq7CKxfvx6Ojo7Sv/X19ZXZz02bNsHAwACxsbHo0qWLXMf2+++/Q09PD+Hh4VBVVQUA2NrayjzHwMAA69atg4qKCuzs7NC5c2ecPXsWI0eOLHebmZmZMDMzK7fJ//v37zFkyBBcuXIF58+fR61ataTrvL29ERERAQBISUnBmzdv4OzsDC8vL8TExGDkyJEQi8VQV1eHh4eHzHaDgoLQuXNnAEBwcDAaNWqE9PR02NnZAQCOHj0KPz8/aGhoAAB69uwp8/fbtm2DqakpUlJS0LhxY+lyS0tLXLt2rdzjfPfuHd69eyezrKCgEOpqquU+nxBCCCGEkE+RVNOB95SF7vyTLzJw4EAcPHhQWtnbvXs3+vXrBxUVFQBAYmIiQkJCoK2tLX2MHDkSWVlZyM/Pl27H1dW13O27u7tL/21oaIiGDRsiNTVVukxNTQ1NmzaV+Zvc3FyMHj0atra20NPTg56eHl6/fo0HDx7IfVxJSUlo1aqVtOJfnkaNGkmPEwAsLCyQm5v70ee/efNGWsn+UGBgIC5evIi4uDiZij9Q3O//zp07+N///oeYmBi0bNkSKioq0so/UNzk383NDSKRSOZvS782FhYWACCzj0ePHpXphpCRkYEBAwbAxsYGurq6sLa2BoAyr51IJJJ5/0pbvHix9HUveawI3f/R14UQQgghhBDCH7rzT77I999/D4lEgsjISHz33XeIi4vDypUrpeslEgmCg4PRo0ePMn9buiKspaUld2bpgelEIlGZger8/f3x6NEjrFq1CnXr1oW6ujrc3d1RUFAAANI776VHt/+wr/6HlejyfHhhQCAQfHLAQWNj448OkteuXTvs3btXpstECU9PT6ipqSEmJgZisRheXl4Aii+YvHjxAnfu3IFYLIa/v/8n97HkdSrZx+zsbFy9elXaMgAofj9r166NLVu2oGbNmpBIJGjcuLH0tSvx9OlTmJiYlHss06dPx8SJE2WWFSSLy30uIYQQQgghn8Mq+aDeVQ1V/skXEYlE6NGjB3bv3o309HTY2tqiWbNm0vUuLi64ffs26tev/0Xbv3TpEurUqQOgeDyBO3fuSJusf0xcXBzWr1+PTp06ASjuD//48WPp+pJKa1ZWFpydnQFAZvA/oPiOeVhYGAoLCz95978inJ2dkZ2djWfPnsHAwEBmXdeuXfH9999jwIABUFFRkZkKUCQSoUWLFoiJicG5c+cwefJkAECNGjXg4eGBnTt3IjMzs0x//885duwY3N3dYWxsDAB48uQJUlNTsWnTJrRq1QpA8TgF5bl58ya8vb3LXaeurg51dXWZZa+oyT8hhBBCCCGVAjX7J19s4MCBiIyMxPbt2zFo0CCZdXPmzMHOnTsxb9483Lp1C6mpqdi3bx9mzZol17ZDQkJw9uxZ3Lx5E/7+/jA2Nka3bt0++Tf169fHrl27kJqair///hsDBw6UuZMvEong5uaGJUuWICUlBefOnSuzP+PHj8fLly/Rr18/XLlyBWlpadi1axdu374t34tSDmdnZ5iYmOD8+fPlru/evTt27dqFYcOGSfv4l/Dx8UF4eDjevHkDFxcX6XIvLy+sWbNGeoGgIo4dO4YffvhB+n8DAwMYGRlh8+bNSE9PR3R0dJk7+EDxIIuJiYlo3759hfIIIYQQQgghykeVf/LFfH19YWhoiNu3b2PAgAEy6/z8/PDnn3/i9OnT+O677+Dm5oaVK1eibt26cm17yZIlCAgIQLNmzZCVlYVjx45BTU3tk3+zfft2PHv2DM7Ozhg8eDAmTJgAU1PTMs8pLCyEq6srAgICZKYXBAAjIyNER0fj9evX8PLyQrNmzbBly5avagWgoqKC4cOHY/fu3R99Tq9evRAWFobBgwfj0KFD0uU+Pj549eoVPD09UaPG/zXU8fLywqtXr+Dh4VHmbvun5OXl4ezZszL9/YVCIcLDw5GYmIjGjRsjMDAQy5YtK/O3R48eRZ06daStAwghhBBCCFEkJmFKe1RHAla6AzQhRCFycnLQqFEjJCYmyn0BRBEOHTqEWbNmISUlpcJ/27x5c/zyyy9lLvR8yquEExXO+Vr35v7Ka57t6N685uXfuMFrXu71e7zmnRh0itc8AOhrf5PXvNjcxp9/EofKmWhEofRE7z//JA5lP+e3e1H+W17jAAAPHr7hNU9Dg99eoY1tVT7/pCrs3n/8Z5oY8nviG+vyfN4/4/czqs5zL0ajtg15zetc+OUtXBXNq8cFpWXHHvL4/JOqGOrzTwgPzMzMsG3bNjx48ECplX9tbW0sXbq0wn+Xm5uLXr16oX///grYK0IIIYQQQspijAb84xJV/gnhSel+9srypf31TU1NMWXKFI73hhBCCCGEEMIX6vNPCCGEEEIIIYRUc3TnnxBCCCGEEEJIpVNdB95TFrrzTwghhBBCCCGEVHN0558QQgghhBBCSKXDJDTgH5fozj8hhBBCCCGEEFLNUeWfEEIIIYQQQgip7hghhFQyb9++ZXPnzmVv376lPMqrdHnKyKS8qp2njEzKo7zKnkl5VTuPVE0CxhgNoUgIqVRevnwJPT09vHjxArq6upRHeZUqTxmZlFe185SRSXmUV9kzKa9q55GqiZr9E0IIIYQQQggh1RxV/gkhhBBCCCGEkGqOKv+EEEIIIYQQQkg1R5V/Qkilo66ujrlz50JdXZ3yKK/S5Skjk/Kqdp4yMimP8ip7JuVV7TxSNdGAf4QQQgghhBBCSDVHd/4JIYQQQgghhJBqjir/hBBCCCGEEEJINUeVf0IIIYQQQgghpJqjyj8hhBBCCCGEEFLNUeWfEEIIIYQQQgip5qjyTwgh5KsVFRUhNjYWz549U/auKER1Pz7CPcYY7t+/jzdv3ih7VxSiuh8f4d638D3K5zG+ePECoaGhGDFiBNq0aQN3d3d07doVc+fOxYULFxSeT6omqvwTQpTKxsYGT548UfZukK+koqICPz8/PH/+XOFZ+fn5Cs/4EJ/H5+vry0sOUSzGGBo0aIB///1X2buiEHwfH5UVVV91LycAfo4xKysLI0eOhIWFBUJCQpCXlwcnJye0adMGtWrVglgsRrt27eDg4IB9+/YpbD9I1VRD2TtACPm2ZWZmoqioiJesNWvWyP3cCRMmcJqdk5ODoKAgnD17Frm5uWCMyaxX1Gtw584dxMTEIDc3FxKJRGbdnDlzOM1q0qQJ7t69C2tra063+yF9fX20aNECPj4+8PHxgYeHB9TV1RWaCfB3fDExMSgoKFBoRoljx47J/dyuXbtynp+Xl4clS5ZIz4sPP6N3797lPPP58+e4fPlyuXlDhgzhLEcoFKJBgwZ48uQJGjRowNl2y2NpaQlfX1/pOaHozyjA7/EBVFaUUMRrQOUEtxR9jI6OjhgyZAguX76Mxo0bl/ucN2/e4MiRI1i5ciUePnyIoKAghewLqXoE7MNvFUII4ZFQKER2djZMTU0VnvVhQfzo0SPk5+dDX18fQHGlQFNTE6amppxXOjp27IgHDx5g/PjxsLCwgEAgkFn/ww8/cJoHAFu2bMGYMWNgbGwMc3NzmUyBQICrV69ymvfXX39h6tSpmD9/Ppo1awYtLS2Z9bq6upzk7Nq1C7GxsYiJicHdu3ehoaEBNzc36Y+8Fi1aQFVVlZOs0vg6Pj7PCaFQtgGgQCCQqWyU/swootLRv39/xMbGYvDgweWeFwEBAZzmHT9+HAMHDkReXh50dHTKnBNPnz7lNC8yMhJLlizBhg0bPvojnQvz589HbGwsLl68iLdv36J27doyFwNq1aqlkFy+jg+gsqIE12UFlRPcU/QxPnr0CCYmJgp7PqneqPJPCFEqoVCI6OhoGBoafvJ5TZs25TR3z549WL9+PbZt24aGDRsCAG7fvo2RI0di1KhRGDhwIKd5Ojo6iIuLg5OTE6fb/ZS6deti7NixmDp1Ki95pSuSpX9AMsYgEAgUUnn8999/ER0djdjYWIjFYty/fx8ikQienp44deoUp1l8HZ9QKERaWtpnf6xx9SO5xJkzZzB16lQsWrQI7u7uEAgEuHDhAmbNmoVFixahXbt2nOYBxXfnIiMj4enpyfm2y2Nra4tOnTph0aJF0NTUVHiegYEB8vPz8f79e6ipqUEkEsms5/piQ2FhIS5evIiYmBjpxYB3797BxsYGvr6+2LRpE6d5fB4flRWKQeUEt+UEoJxjJEReVPknhCiVUCgsc7exRMlyRRSW9erVQ0REBJydnWWWJyYmolevXrh37x6neQ4ODti9e3eZPEXS1dVFUlISbGxseMmLjY395HovLy+F5qelpWHnzp1Ys2YNXr9+zflnhq/jKzknPkZR50Tjxo2xceNGtGzZUmZ5XFwcfvrpJ6SmpnKaBxTfYT1x4gTs7e0533Z5tLS0kJyczNs5ERYW9sn1Q4cOVWj+s2fPsGLFCqxdu1Yh5wSfx0dlhWJQOcF9RVwZx5iSkoIHDx6U6TKmiO5apGqjPv+EEKX7+++/eW+SlpWVhcLCwjLLi4qKkJOTw3neqlWrMG3aNGzatAlWVlacb788vXv3xl9//YXRo0fzkqfoH20funv3LsRiMWJiYhATE4MXL17Aw8MDU6dOVci+8Hl8ERERn73DybWMjAzo6emVWa6np4fMzEyFZM6fPx9z5sxBWFgYL3fi/fz8cOXKFd4qOoqu3H/o7du3OH/+vPScSEhIgJWVFfr27auQzy/fx0dlBfeonOAen8d49+5ddO/eHcnJyTIXx0ouIFMrA/IhuvNPCFEqPvtxlvb999/jwYMH2LZtG5o1awaBQIArV65g5MiRqF27doUGQpNH6eaxmpqaZfoactU8tvRAVXl5eVi5ciU6d+6MJk2alMnkeqAqoPgu8aZNm3D37l0cOHAAlpaW2LVrF6ytrcvcUf5SQ4cOhVgsxqtXr+Dp6YnWrVvDy8sLrq6uUFFR4STjY/g4PmWdE61bt4aqqir++OMPWFhYAACys7MxePBgFBQUfPZu1pdwdnZGRkYGGGOwsrIq8xnlor9x6XP50aNHCAkJwbBhw8o9JxRxlywjIwM7duxARkYGVq9eDVNTU5w8eRK1a9dGo0aNOMmYO3cuxGIxEhISYGNjAy8vL+nD3Nyck4yP4eP4ACorSnBRVlA5odhyAuDnGIHiz6eKigq2bNkCGxsbXL58GU+ePMGkSZOwfPlytGrVirMsUj1Q5Z8QolTy/KBTxGA1jx49wtChQ3Hy5Enpj53379/Dz88PoaGhnP/A5Kt5rLyjCwsEAs4Hqjp48CAGDx6MgQMHYteuXUhJSYGNjQ3Wr1+PP//8EydOnOAkRygUok6dOhg3bhzatGkDZ2fnTzaT5wqfx/e5c+L9+/eoUYPbxnvp6eno3r07bt++jTp16gAAHjx4AFtbWxw5cgT169fnNA8AgoODP7l+7ty5X53x4aCGH6OIJuOxsbHo2LEjPD09ce7cOaSmpsLGxga//vorLl++jIiICE5ySs6JadOmoXfv3jAyMuJku5/D1/EBVFaU4KKsoHJCsfg6RgAwNjZGdHQ0mjZtCj09PVy+fBkNGzZEdHQ0Jk2ahGvXrnGWRaoJRgghSuTt7c2ePXtWZrlEImGRkZGse/fuTE1NTWH5t2/fZkePHmVHjhxht2/fVljOt8DJyYmFhYUxxhjT1tZmGRkZjDHGrl27xszMzDjLSU1NZRs2bGB9+/Zl5ubmTF9fn3Xp0oUtW7aMJSQksKKiIs6ySuPr+KysrNjjx4/LXXfr1i0WGBjITE1NOcsrTSKRsFOnTrHVq1ezVatWsb/++otJJBKFZH0L3Nzc2IoVKxhjsp+Zy5cvs5o1a3KWExUVxaZOncpatGjB1NTUWOPGjdn48ePZgQMHWG5uLmc5H+Lr+BijsqK6qO7lBGP8HSNjjOnr60u3b2Njw6KjoxljjKWnpzORSMRpFqkeqPJPCKlUMjIy2MyZM1mtWrWYvr4+GzhwIDt06JCyd4sT6enpbObMmaxfv34sJyeHMVb8o/3mzZtK3jNuiEQidu/ePcaY7A+ejIwMpq6urrDcW7dusfXr17PevXszc3Nzpqenxzp37sx5jrKO79WrV2zLli3Mzc2NqaioME9PT7Zy5UqF5fHt2bNnbMuWLWzatGnsyZMnjDHGEhMT2b///qvkPft6Wlpa7O7du4wx2c/MvXv3FPaZefnyJYuMjGRTpkxh3333HVNTU2MODg5s3LhxnGcp4/hKUFlRNVX3coIxfo+xZcuW7PDhw4wxxvr37886dOjA4uPj2ZAhQ1ijRo04zSLVAw34RwhRurdv3yIiIgJbt27FpUuX0K5dO2RlZSEpKUlhc0cXFRUhNDQUZ8+eRW5uLiQSicz66OhoTvM+bB67cOFCmJqa4saNG9i6dSunzWNL9OrVC66urpg2bZrM8mXLluHy5cs4cOAAp3kWFhZIT08vM0hVfHy8QgdYc3BwgKGhIQwMDGBgYIDw8HBERUVxnsP38cXHx2Pr1q04ePAgrK2tkZKSgtjYWIVOi3f27NmPnhPbt2/nPO/GjRto27atdFDBkSNHwtDQEIcPH8b9+/exc+dOTvMmTJiA+vXrl+nHvG7dOqSnp2PVqlWc5unr6yMrK6tMM+tr167B0tKS06wSOjo66NSpE/z8/HD58mUcO3YM69evx4YNG7Bu3TpOs/g+PioruC8rqJzgHp/HOGvWLOTl5QEAFixYgC5duqBVq1YwMjLCvn37OM0i1YN8HeEIIURBxo4di5o1a+L3339H79698d9//+H48eMQCARy99X9EgEBAQgICEBRUREaN24MR0dHmQfXpk2bhgULFuD06dNQU1OTLvfx8cHFixc5zwOKf0R27ty5zPIOHTrg3LlznOeNGjUKAQEB+PvvvyEQCPC///0Pu3fvRlBQEMaOHctpVm5uLvbv348xY8bA3t4elpaWGDZsGP755x8EBgZy/oMc4O/4fv31V9jZ2aFfv34wMTFBfHw8bty4AYFAAAMDA85yPhQcHIz27dvj7NmzePz4MZ49eybzUISJEyfC398faWlp0NDQkC7v2LGjQj6jBw8eLPfiiYeHh0IuwA0YMABTp05FdnY2BAIBJBIJzp8/j6CgIAwZMoTTLIlEgsuXL2Pp0qXo2LEjDAwM0LJlS+zZswfdu3dXyMUbPo+PygrFlBVUTnCPz2P08/NDjx49AAA2NjZISUnB48ePkZubC19fX06zSDWh7KYHhJBvm4qKCpsxYwZ7+fKlzPIaNWqwW7duKSzXyMiIRUZGKmz7H1JG81gNDQ32zz//lFmemprKNDQ0FJI5Y8YMJhKJmEAgYAKBgGloaLBZs2ZxmmFvb8+EQiFTU1Njnp6ebObMmezMmTPszZs3nOaUh4/jKzkn3r9/L7Nc0eeEubk527lzp8K2Xx5dXV2Wnp7OGJM9LzIzMxVyXqirq7O0tLQyy9PS0hSSV1BQwAYMGMCEQiETCARMVVWVCYVCNmjQoDLv79fo2LEj09XVZQKBgFlaWrKBAweyrVu3Sl9PReHr+BijskJRZQWVE4qh6GO0sLBgo0ePZidOnGDv3r3jbLuk+qNm/4QQpdq5cyd27NgBCwsLdO7cGYMHD0aHDh0UnqumpqaQ0cs/RhnNfxs3box9+/Zhzpw5MsvDw8Ph4OCgkMyFCxdi5syZSElJgUQigYODA7S1tTnN+OGHH+Dj44OWLVvyMjd8aXwcX0hICEJDQ7Fr1y70798fgwcPVliT5tIKCgrg4eGh8JzSNDQ08PLlyzLLb9++rZD53OvXr4+TJ09i/PjxMsujoqIU0uRYVVUVu3fvxvz583H16lVIJBI4OzujQYMGnObo6elh2bJl8PHx4Xzbn1JyfCEhIbh27ZrCjg+gskJRZQWVE4qh6GPcs2cPjh8/jgkTJiAnJwd+fn7o2rUrOnfuDENDQ85ySDWk7KsPhBDCWPFdjTlz5rA6deowY2NjJhQK2YEDBxSWt3z5cjZ27FjeRjKfPHkya9myJcvKymI6OjosLS2NxcfHMxsbGzZv3jyFZB49epTVqFGDDRkyhIWGhrLQ0FA2ePBgVqNGDekAQVwaNmxYmbtyjDH2+vVrNmzYMM7zqruYmBg2ZMgQpqWlxZo2bcpUVFRYfHy8wvKmTJnCQkJCFLb98owcOZJ169aNFRQUMG1tbXb37l12//595uzszAICAjjP27ZtGxOJRGzOnDksJiaGxcTEsNmzZzNNTU22efNmzvOCg4NZXl5emeX5+fksODiY87xvAZUV3KJyouq7efMmW7RokXS2Dy8vL7Zy5UppqypCShMwxpiyL0AQQkgJxhhOnTqF7du349ixYzA2NkaPHj2wZs0aTnO6d+8OsVgMQ0NDNGrUSDp/c4lDhw5xmldYWAh/f3+Eh4eDMYYaNWqgqKgIAwYMQGhoKFRUVDjNKxEZGYlFixYhKSkJIpEITZs2xdy5c+Hl5cV5loqKCrKyssrMe/348WOYm5vj/fv3nORMnDhRruetXLmSk7wSeXl5WLJkyUcH/uJ6PuwSr169wu7du7Fjxw4kJiaiefPm6NWrl9yvg7wCAgKwc+dONG3aFE2bNi1zTnD9egLAy5cv0alTJ9y6dQuvXr1CzZo1kZ2dDXd3d5w4cQJaWlqcZ27YsAELFy7E//73PwCAlZUV5s2bx3kfdeDj58STJ09gamqKoqIiTnLk/X78cKDDr8X3YHilUVnBHSonuKWssgIAsrOzcfz4cRw7dgxnz56FjY0Nli5dWu64DuTbRJV/Qkil9fTpU2lTz+vXr3O67WHDhn1y/Y4dOzjNK5GRkaHw5rF8e/nyJRhjMDAwQFpamkxz7aKiIhw/fhzTpk2TVra+lo+Pz2efIxAIOK949O/fH7GxsRg8eDAsLCwgEAhk1gcEBHCaV57k5GRs27YNe/bsQW5uLqfb/tTrqojXs7To6Ghps3gXFxe0bdtWYVklHj16BJFIxHlz49KEQiFycnLKdGGIjo5G37598ejRI05yPmwiXh6BQMB5pWP8+PEIDQ1F586dyz0nfvvtN07zPobKisrvWykngMpRVgBAfn4+Tp06BR0dHV6+U0nVQJV/QkiVoquri6SkJIVOCUQqRigUlvlxU5pAIEBwcDBmzpzJ415xT19fH5GRkQqdak9ehYWF0juQTZo0wYkTJ1C7dm0l7xUpYWBgAIFAgBcvXkBXV1fm/CgqKsLr168xevRo/P7770rcy69nbGyMnTt3olOnTsrelTKorKhcvpVyAuC/rJBIJEhPTy/TykAgEKBVq1a87AOpOmjAP0JIlVKVrldWpFm2IpoeFhUV4bfffsP+/fvx4MEDFBQUyKx/+vQpJzlisRiMMfj6+uLgwYMygw2pqamhbt26qFmzJidZX4KrSoCBgUGlGUipdNPjzMxMFBYWKnFvKqYizbK5bqYOABERER89J65evcpJxqpVq8AYw/DhwxEcHAw9PT3pOjU1NVhZWcHd3Z2TrC/B1QUjvgfDqwgqK+RD5UQxLi8W8VlWXLp0CQMGDMD9+/fLfOYFAgFnXYtI9UGVf0LIN0vRlYBr167J/D8xMRFFRUVo2LAhAODOnTtQUVFBs2bNvjqrPMHBwdi6dSsmTpyI2bNnY+bMmcjMzMSRI0fKjOz8NUr6hd67dw916tT55N0dZeCqEjB//nzMmTMHYWFhShk9mg8JCQk4cOBAuecEV32bP2wK/ujRI+Tn50NfXx8A8Pz5c2hqasLU1JTzyv+aNWswc+ZMDB06FEePHsWwYcOQkZGBhIQEjBs3jrOcoUOHAihuju/p6YkaNSrXzy2uLhhNmjQJq1evxrp16yrdec+l6lxWUDlRjMuLRXyWFaNHj4arqysiIyPL7WJAyIcqV2lECCE84aMSIBaLpf9euXIldHR0EBYWBgMDAwDAs2fPMGzYMIU1y9u9eze2bNmCzp07Izg4GP3790e9evXQtGlTXLp0ifOKVWpqKh4+fIiWLVsCAH7//Xds2bIFDg4O+P3336XHXZU4OzvL/JhKT0+HmZkZrKysygz8xdVdY2UJDw/HkCFD0L59e5w+fRrt27dHWloasrOz0b17d85y7t27J/33nj17sH79emzbtk1a0bl9+zZGjhyJUaNGcZZZYv369di8eTP69++PsLAwTJkyBTY2NpgzZw5ndzhL09HRQWpqKpo0aQIAOHr0KHbs2AEHBwfMmzcPampqnGcqWo8ePWT+Hx0djaioKF4Gw1OG6l5WUDnBDWWVFWlpaYiIiKi0LXBI5UN9/gkhVYqOjg6uX7/+1U3z7OzsMHfuXPTv319mmyWVgHXr1nG0x8UsLS3x119/oVGjRjLLb968ifbt23M2yFFpWlpaSE1NRZ06dWBhYYHIyEi4uLjg7t27cHZ2xosXLzjNa9KkCZYuXYpOnTohOTkZrq6umDRpEqKjo2Fvb6+wgbE+52s+M8HBwXI/d+7cuRXePhe4OieaNm2KUaNGYdy4cdJtWltbY9SoUbCwsKjQayGvevXqISIiAs7OzjLLExMT0atXL5kLBVzQ1NREamoq6tatC1NTU5w+fRqOjo5IS0uDm5sbnjx5wmned999h2nTpqFnz564e/cuHBwc0KNHDyQkJKBz585YtWoVp3ny+prPzOcGwCtNWec8QGWFvKicKPa1nxdllRW+vr6YMmUKOnTowNk2SfVGd/4JIVUKV03aHjx4AA8PDwCASCTCq1evAACDBw+Gm5sb5z/oXr58iZycnDI/6HJzc6XZXKtVqxaysrJQp04d1K9fH3/99RdcXFyQkJAAdXV1zvPu3bsHBwcHAMDBgwfx/fffY9GiRbh69WqlHBBMHsqq0CtDRkaGdDoodXV15OXlQSAQIDAwEL6+vgqp/GdlZZXb/LyoqAg5OTmc55mbm+PJkyeoW7cu6tati0uXLsHR0RH37t1TSB/xO3fuwMnJCQBw4MABeHl5Yc+ePTh//jz69euntMr/11Bmhb4iqKyQD5UT3OCzrLhx44b03z///DMmTZqE7OxsNGnSpEwrg6ZNm/K2X6RqECp7BwghpCK4+oFeUgkAIK0EAFBYJaB79+4YNmwYIiIi8O+//+Lff/9FREQERowYUaYZLZeZZ8+eBVA8tdDs2bPRoEEDDBkyBMOHD+c8T01NDfn5+QCAM2fOoH379gAAQ0NDvHz5kvM8eXFVCbCxsSn3zvDz58+rxYjihoaG0sqFpaUlbt68CaD4+EreV661adMGI0eOxJUrV6Tn3ZUrVzBq1CiFTE3l6+uL48ePAwBGjBiBwMBAtGvXDn379uW0a0MJxph09O0zZ85IKze1a9fG48ePOc/jm6+vL54/f15m+cuXL+Hr68v/DpVCZYX8eVROcFdOAIovK5ycnODs7AwnJyf07NkTqampGD58OL777juZdR+2qCIEAMAIIaQKiYuLY2/fvv3q7YwYMYLNmzePMcbYhg0bmEgkYm3btmX6+vps+PDhX739D+Xl5bExY8YwdXV1JhQKmVAoZGpqamzMmDHs9evXnOeV5+LFi2zFihXs6NGjCtn+999/z/z8/FhISAhTVVVl//77L2OMsVOnTrEGDRooJFMe2traLCMj46u3IxAIWE5OTpnl2dnZTFVV9au3/6V2797NyWeof//+bMWKFYwxxhYsWMBMTEzYjz/+yOrWrcu6d+/+1dsvT25uLuvYsSMTCARMTU2NqampMaFQyDp27Fjua/21ioqKWGFhofT/+/btYz///DNbvXo1e/fuHed5Pj4+bMiQIWznzp1MVVWVpaWlMcYYi4mJYXXr1uU8T16KPidycnJYjRo1vnr7X4PKii9D5cTXU3RZkZmZKfeDkA9Rn39CSKXAGENERATEYnGZuWoB7geOkkgkkEgk0lG49+/fj/j4eNSvXx+jR49W2EBceXl5yMjIAGMM9evXh5aWlkJylOHBgwcYO3YsHj58iAkTJmDEiBEAgMDAQBQVFVVoijcuxcfH47vvvvviJqzHjh0DAHTr1g1hYWEy07YVFRXh7NmzOH36NG7fvs3J/pZ29uxZnD17ttxzYvv27ZxmPX36FG/fvkXNmjUhkUiwfPly6Tkxe/ZshQ7EdefOHaSmpgIA7O3tYWtrq7AsPt24cQMDBw7EgwcPMHHiRGnT4J9//hlPnjzBnj17lLJfe/bswQ8//PDF3z8lzY6dnJwQHR0tM61ZUVERTp48iU2bNiEzM5OL3ZVRVFSE0NDQj54X0dHRnOZRWcGt6lpOAPyWFfn5+dV21hmiWFT5J4RUChMmTMDmzZvh4+MDMzOzMk3wqko/U3n8+++/EAgEsLS0VGhOdHQ0Dh06hMzMTAgEAlhbW6NXr15o3bq1QnP5wtcFI6GwuIecQCAo08xXVVUVVlZWWLFiBbp06cJJXong4GCEhITA1dW13CmcDh8+zGmespW8toqcqiotLQ1Hjx6VnhM2Njbo1q0brK2tFZZZnrdv30JFRaVM/1wu8HHBSCgUSt+n8n5GikQirF27ViHNxsePH4/Q0FB07ty53PPiw6kkqzI+yorqXk7webGIz7JCTU0NLVq0gI+PD3x8fODh4aGQMRpI9UOVf0JIpWBoaIg//viDtwF/PD094eXlBW9vb3h6eir8ropEIsGCBQuwYsUKvH79GkDx6MKTJk3CzJkzpT8auDJ69Ghs3rwZBgYGsLW1BWMMaWlpeP78OcaOHYu1a9dymldCIpEgPT293B9ZXP+Y5PuCkbW1NRISEmBsbMzpdj/GwsICv/76KwYPHsxL3sCBA+Ht7Q0vLy9e77zv3LkTy5YtQ1paGgDA1tYWkydP5vy4Fy9ejDlz5kAikcDU1BSMMTx69AgqKipYtGgRgoKCOM0rraCgoNxzok6dOpzm8HXB6P79+2CMwcbGBpcvX4aJiYl0nZqaGkxNTaGiosJJ1oeMjY2xc+dOKis48C2UE8q4WMRHWbFr1y7ExsYiJiYGd+/ehYaGBtzc3KQXA1q0aKGQi4ukGuC7nwEhhJTHysqKpaam8pa3aNEi5ufnx3R0dJiqqipzc3NjU6dOZVFRUezVq1ec502bNo2ZmJiw9evXs+vXr7OkpCT2+++/MxMTEzZjxgxOsw4dOsTU1NTYjh07mEQikS4vKipi27ZtY2pqagrpz3nx4kVmbW3NhEIhEwgEMg+hUMh5noGBAYuMjOR8u5WFoaEhS09P5y3vp59+Yg0bNmQCgYBZWFiwfv36sQ0bNij0vFyxYgXT1NRkU6ZMYUePHmVHjhxhkydPZpqammzlypWc5URHRzOhUMjmzp3Lnj59Kl3+5MkTNnv2bKaiosJiY2M5yytx+/Zt1rJlS2nf7ZKHos4Jc3NztnPnTs63W5lYWFiw27dv85ZXXcuKb6WcMDIyqtblBGOMPXz4kIWFhbHhw4dLX1stLS3Wvn17Ze8aqYSo8k8IqRRCQ0NZv379WH5+Pq+579+/ZxcvXmSLFy9mfn5+TFVVlampqXGeY2FhUe4PqSNHjrCaNWtymvX999+zadOmfXT9lClTWNeuXTnNZIwxR0dH1rt3b5aSksKePXvGnj9/LvPgGp8XjAoLC9mvv/7KnJ2dmZaWFtPW1mbOzs5s2bJlrKCgQCGZU6ZMYSEhIQrZ9qdkZWWxvXv3slGjRjE7OzsmFAqZubm5QrKsrKxYWFhYmeWhoaHMysqKs5w+ffqwn3766aPrR44cyfr168dZXgkPDw/WunVrduLECXbt2jWWlJQk8+Aa3xeM9u/fz7p3784aNWrEGjduzLp3784OHDig0Mzly5ezsWPHylRY+VDdyopvpZzg+2KRMsqK0u7cucNmzZrFdHV1FXIxhVR9NZTd8oAQQgCgd+/e2Lt3L0xNTWFlZVWmudrVq1cVkpuWlobr16/j+vXruHHjBnR1ddGqVSvOc54+fQo7O7syy+3s7PD06VNOs65evYpZs2Z9dH3Pnj0VMmVUWloaIiIiUL9+fc63XZ558+YhODgY27dvh0gkUljOmzdv0K5dO1y8eBFt27ZF69atwRjDP//8g6lTp+LYsWP466+/oKGhwWnu27dvsXnzZpw5cwZNmzYtc06sXLmS07wSOjo6MDAwgIGBAfT19VGjRg2Ym5srJCsrK0s6h3ppHh4eyMrK4izn8uXL2LVr10fXDx48GEOGDOEsr0RSUhISExPLPfcV4ccff8SePXswe/ZsheZIJBL0798fBw4cgK2tLezs7MAYw61bt9C3b1/p97kixm+Ij4+HWCxGVFQUGjVqVOa84Hpw2BLVraz4VsqJSZMmYfXq1Vi3bp1CxxMBlFNW3L17F2KxGDExMYiJicGLFy/g4eGBqVOnwsvLi7McUn1Q5Z8QUin4+/sjMTERgwYNKrf/Ntf69u2Lc+fOQSKRoHXr1mjdujWmT5+Opk2bKiTP0dER69atKzOS8bp16+Do6Mhp1uPHjz85QJSlpWW5cxB/rRYtWiA9PZ23H3V8XTBavHgxHj58iGvXrpX5fFy/fh1du3bFkiVLMG/ePE7ySty4cQNOTk4AgJs3b8qsU8T5MXXqVMTGxuL69eto3Lix9Jxo3bo19PX1Oc8DgPr162P//v2YMWOGzPJ9+/ahQYMGnOXk5OTAysrqo+utra2RnZ3NWV4JBwcHPH78mPPtfgxfF4xWrVqFM2fO4NixY2UGLzt27BiGDRuG1atX45dffuEkrzR9fX10796d8+1+THUtK76VcoLPi0V8lhVDhw6FWCzGq1ev4OnpidatW2P8+PFwdXVV2HgbpHqgAf8IIZWClpYWTp06hZYtW/KSJxQKYWxsDH9/f/j4+KBVq1bQ1tZWWF5sbCw6d+6MOnXqwN3dHQKBABcuXMDDhw9x4sQJTu8gCYVC5OTkyAzCVVpOTg5q1qyJoqIizjKB4sHEZs2ahcmTJ6NJkyZlfmRx/WO5T58+EIvF6NWrV7kXjEqmVftatra2WLx4MXr27Fnu+gMHDmDmzJm4c+cOJ3nKIhQKYWJigsDAQPzwww+wt7dXeObBgwfRt29ftG3bFp6enhAIBIiPj8fZs2exf/9+zip5QqEQ2dnZMDU1LXe9os6J6OhozJo1C4sWLSr3nNDV1eU0z8fH56PrBAIBZyObN23aFL/88stHR/Pftm0bVq1aheTkZE7ylKm6lhXfSjkxbNiwT67ncmBYPssKoVCIOnXqYNy4cWjTpg2cnZ0VftOEVA9U+SeEVAp2dnbYv3+/wu6mfOj58+c4d+4cYmJiEBsbi1u3bsHR0RHe3t7w9vZGx44dOc/83//+h99//x3//PMPGGNwcHDA2LFjUbNmTU5zhEIhfvrpp4/OAZyfn48tW7Zw/qOuvFGoS6Y8EggEnOfxdcFIQ0MDaWlpqF27drnrHz58iAYNGuDt27cK3Q9Fu379unT06Li4OKioqEhHOff29lbYxYDExET89ttvSE1NlZ4XkyZNgrOzM2cZQqEQCxYs+Gil7dWrV5gzZ47CzokPf5Qr6pzgi0gkwu3btz86W8H9+/dhZ2eHN2/e8Lxn3KuuZcW3Uk7wic+y4p9//pE29Y+NjcXbt2/RsmVL6Xe2i4sL57MIkeqBKv+EkEohMjISa9euxcaNGz/ZPFdRMjIysGDBAvzxxx+QSCRV+geIt7e3XHcAxGIxp7n379//5Pq6detymsfXBSNTU1NERUWhWbNm5a5PSEhA586dkZuby3l2QkICDhw4gAcPHqCgoEBmnaL6Npe4fv06Vq1aVS3OCSsrK7nOiXv37nGaGxsb+8n1VbVPrqGhIWJiYj567iUnJ8PLy4vz8UxKREREYP/+/eWeF4oaH6ZEdSkrvpVygk/KLCtSUlIQGxsLsViMuLg4vHnzBi1btsSff/7JeRap2qjyTwipFAwMDJCfn4/3799DU1OzTFNArn9EPn36VHqXMyYmBrdu3YKhoSFat24NHx8fjBs3jtO80vLy8rBv3z68efMG7du357Rv87eErwtGffv2xfv373Hw4MFy1/fs2RMqKirYv38/p7nh4eEYMmQI2rdvj9OnT6N9+/ZIS0tDdnY2unfvzmlz1RLXrl2TnhNxcXF4+fIlnJyc4OPjg2XLlnGeVxpjDGKxGG/evIGHhwcMDAwUmldd8XHBqKRZ+oYNG8pdP3r0aDx8+BCRkZGc5JW2Zs0azJw5E0OHDsWWLVswbNgwZGRkICEhAePGjcPChQs5zaOyourj62KRssqKEtnZ2YiJiYFYLEZ4eDhev35dZS9OEQXie3oBQggpT2ho6CcfXBMKhczU1JT17NmTrV27liUnJ3OewRhj9+/fZ61bt2ba2tqsbdu27P79+8zW1lY6r7GmpqZC5hevCB0dHZaRkcHJtnbu3Mk8PDyYhYUFy8zMZIwx9ttvv7EjR45wsv3S9PX1mZqaGhMKhUxbW5sZGBjIPLhy69Ytpq2tzVq0aMH27dvHrl+/zq5fv8727t3LmjdvzrS1tdnNmzc5yyvRpEkTtm7dOsYYY9ra2iwjI4NJJBI2cuRINmfOHM7z9PX1WY0aNVizZs3YpEmT2PHjx9mLFy84z2GMsWfPnrEhQ4awxo0bsx9//JG9ePGCeXp6Ss8LU1NTdv36dYVky6Nx48bswYMHnGzr3LlzbODAgczd3Z39+++/jLHi8yQuLo6T7Ze2d+9epqqqyjp37szU1NRYly5dWMOGDZmenh7z9/fnLOf8+fNMVVWV9e7dm/3999/sxYsX7Pnz5+zixYusV69eTFVVlcXHx3OWV1rDhg3Znj17GGP/d14wxtjs2bPZuHHjOM+jsqJYVS0nVq9ezbS1tdm4ceOYmpoaGzVqFGvbti3T09NjM2bM4DSL77IiJyeH7du3j40ePVo6LauGhgZr3bo1mzt3LouJieEsi1QfVPknhHyTFPUD7kO9e/dmbm5ubNeuXaxr167Mzs6Ode7cmWVnZ7Pc3FzWq1cv5uPjw8u+fEzpH9BfY/369czY2JgtWLCAiUQi6TZ37NjBvL29v3r7H+LzgtHFixeZg4MDEwgETCgUMqFQyAQCAbO3t2fnz5/nNKuEpqYmu3fvHmOMMSMjI3bjxg3GGGMpKSnM3Nyc8zxFVvY/NGLECNagQQM2f/581qJFC+bu7s7c3NzYpUuX2OXLl5m3tzfr0qULL/tSHq7OiYiICCYSidiPP/7I1NXVpdv8/fffWceOHb96+x/i84LRoUOHmLGxsfR8KHkYGRmxiIgITrNKE4lE0gqjiYkJS0pKYowVz29uaGjIeR6VFcWqajnB98UivsoKe3t7JhQKmZqaGvP09GQzZ85kZ86cYW/evOEsg1RPVPknhFQK9+/f/+SjqjIzM2N///03Y4yxJ0+eMIFAwC5cuCBdn5SUxIyMjJS1e4wx7n7U2dvbs8OHD5fZZnJystKPkSvXrl1j+/btY/v27WPXrl1TaFatWrWkFf6mTZtKf8BeuHCB6erqKjRb0WrWrCm9K/Xvv/8ygUDAxGKxdP3ff//NzMzMlLR33J0TTk5OLCwsrMw2r127ppDj4/uCUV5eHjt06BBbunQpW7p0KTt8+DDLy8vjPKc0a2trlpiYyBhjzNXVlW3cuJExxtipU6c4bfHDt8peVlTVcoLvi0UlFF1WTJs2jZ06dUrh5xupfmoou9sBIYQAnx+Qi+t+azk5OQgKCsLZs2eRm5sL9sHwJ1zlPXr0SDqAkaGhITQ1NWFmZiZdb25ujmfPnnGSpWz37t0rd4R2dXV15OXlcZ734MGDT67/2EjkX8PJyQlOTk6ffI6uri6SkpJgY2PzVVmtWrXC6dOn0aRJE/Tp0wcBAQGIjo7G6dOn0aZNm6/adnny8vKwZMkS6TkhkUhk1t+9e5ezrJycHNja2gIonk9cQ0NDZoTsOnXq4NGjR5zlKcvt27fRunXrMst1dXXx/PlzzvMMDQ3x6tUrAMWv682bN9GkSRM8f/4c+fn5nOdpamrKNR1jkyZNcOLEiY+Ogl4Rvr6+OH78OFxcXDBixAgEBgYiIiICV65cQY8ePb56+x+isoJbfJcT5ubmePLkCerWrYu6devi0qVLcHR0xL1798q8l1xSdFmxePFiAMCNGzc+OvDmkSNH0K1btwpvm1RvVPknhFQK165dk/l/YWEhrl27hpUrV3I+gBMA+Pv748GDB5g9ezYsLCwUNj8u+//TF5WozvPwWltbIykpqcxozVFRUXBwcOA8j+8LRvLi6gflunXrpFNCTZ8+HaqqqoiPj0ePHj0we/ZsTjJK+/HHHxEbG4vBgwcr9JwAAIlEAhUVFen/VVRUquV5YmFhgfT09DIDUsbHx3/1xaHy8H3BSF6ZmZkoLCzkZFubN2+WXpgaPXo0DA0NER8fj++//x6jR4/mJKM0Kiu4xXc5wffFoorgoqzw8/PD+fPny3yfHDx4EEOGDFHIBRVStVHlnxBSKTg6OpZZ5urqipo1a2LZsmWcF9Lx8fGIi4v77JV5LsyZM0c6l3JBQQEWLlwIPT09AFDI3biK4upH5uTJkzFu3Di8ffsWjDFcvnwZe/fuxeLFi7F161ZOMkrj+4IR3wwNDaX/FgqFmDJlCqZMmaKwvKioKERGRsLT01NhGaVt3boV2traAID3798jNDQUxsbGACC9e13VjRo1CgEBAdi+fTsEAgH+97//4eLFiwgKCsKcOXM4z+P7gpEyCIVCmfnL+/Tpgz59+igsj8qKYlW1nOD7YhHfxowZgzZt2uDChQuwsLAAAOzbtw/Dhw9HaGiocneOVEo01R8hpFJLS0uDk5MT51evHRwcsHv37nKbH3JJWXMpV4SOjg6uX7/OyZ3ILVu2YMGCBXj48CGA4qbH8+bNw4gRI7562/KKjIzEsmXLEBMTw1tmaVy+nhkZGdixYwcyMjKwevVqmJqa4uTJk6hduzYaNWrEwd7+H2tra5w4cQL29vacbrc8n2u1UeLevXsK35fycPkezpw5E7/99pu0Uq6uro6goCDMnz//q7ddVXD5egJAXFwcNm3ahIyMDERERMDS0hK7du2CtbU1WrZsyUlGCSorilW3cqIy4Oo1DQgIwJkzZxAXF4eTJ0/ixx9/xK5du9CzZ0+O9pRUK0oaa4AQQmS8ePFC5vH8+XOWmprK+vbtyxwdHTnPO3XqFGvfvr10cKxvWVxcHHv79u0X/31RUVGZZY8ePWI5OTlfs1tf7M6dO0xTU1Mp2YxxNzBWTEwME4lErG3btkxNTU26zaVLl7KePXt+9fY/tGvXLtarVy8aQIoxtnv3bvb69WvOtpeXl8cSEhLY33//zV69esXZdsuTnp7OZs6cyfr16yc9B6OiohQyHaW8uDonGON/BgUqK4pV5XKiZLpNNzc3hU+3WRFcnheDBg1iDRo0YJqamgqZMpFUH3TnnxBSKQiFwjJ3PRhjqF27NsLDw+Hu7s5pnoGBAfLz8/H+/XtoampCVVVVZv3Tp085zZMXV4PFAcWvX0REBMRicbkDuB06dOirM4Di/tpZWVkwNTUFUNysc/r06TLN1hXh5cuXMv9njCErKwvz5s3DP//8g6SkJIXmfwxX76G7uzt69+6NiRMnytwhSkhIQLdu3fDff/9xtMfFnJ2dkZGRAcYYrKysypwTV69e5TSvIrgcMO7s2bMfHdRw+/btX719oHjAwmvXrsHIyAhAcXP8IUOGQFdXl5Ptf0xsbCw6duwIT09PnDt3DqmpqbCxscGvv/6Ky5cvIyIiQqH5H8PlXWNnZ2cEBgZiyJAhMttNSkpChw4dkJ2dzcEe/5/qXlYUFRUhNDT0o+dEdHT0V22/hLLKiYMHD2Lw4MEYOHAgdu3ahZSUFNjY2GD9+vX4888/ceLECYXmf8qXvofHjh0rs6ywsBCBgYFo3749unbtKl1e+t+EANTnnxBSSXzYlFEoFMLExAT169dHjRrcf1WtWrWK821ygcvrsQEBAdi8eTN8fHxgZmam0IGqStu0aRPGjBmj8B91+vr6n7xgpCxcvYfJycnYs2dPmeUmJiZ48uQJJxmlVeZRobkaMC44OBghISFwdXVV6OBt//77r8yAkzNmzECnTp0UXvmfNm0aFixYIL1gVMLHxwerV69WaDZf+J5BobqXFQEBAQgNDUXnzp3RuHHjaldOLFiwABs3bsSQIUNkygUPDw+EhIQoNPtzvvQ9/NR39fbt26UXMQUCgdIGviWVF1X+CSGVgpeXF695Q4cO5TVPGf744w8cOnQInTp14jWXrwZlfF8wkldUVBQsLS2/ejv6+vrIysqCtbW1zPJr165xsv0PzZ07l/NtVjYbN25EaGgoBg8ezGsuX+cE3xeM5LVp0yaZaeu+Bt8zKFT3siI8PBz79++vtuUE3xeLKuJLy4oPW2cQUhFU+SeEVAphYWEwNjZG586dAQBTpkzB5s2b4eDggL1795aZFogLfA6mpgx6enoK+TFcWfB9wUjebhRcDTg2YMAATJ06FQcOHIBAIIBEIsH58+cRFBSEIUOGcJLxoefPnyMiIgIZGRmYPHkyDA0NcfXqVZiZmSnkggPfCgoK4OHhoezdUBi+LxgB8nWjGDBgAGd5fM+gAFTvskJNTQ3169dX9m4oDN8XiwD5u1JwPTglIfKgyj8hpFJYtGgRNmzYAAC4ePEi1q1bh1WrVuHPP/9EYGAgZ/3TS3zYN3bhwoUwNTXFjRs3sHXrVqX1jeXSvHnzEBwcjO3bt0MkEik061NTVJVYuXIlp5l8XzDiqxtFiYULF8Lf3x+WlpZgjMHBwQFFRUUYMGAAZs2axXnejRs30LZtW+jp6SEzMxMjR46EoaEhDh8+jPv372Pnzp2cZ/Ltxx9/xJ49e3iZ9u5TUxmWmDBhAqeZfF8w4qsbRWlTpkzBixcv4OPjg7dv36J169bSGRTGjx/PeV51LysmTZqE1atXY926dQp//5RRTijjYpGiu1KEh4ejX79+cj334cOHePDgAW9TuJLKjwb8I4RUCpqamvjnn39Qp04dTJ06FVlZWdi5cydu3boFb29vPHr0iNM8vgdTkxeXA/7l5+ejR48eOH/+vEIHcJNniiqBQMDZwFElGjZsiA0bNsDX1xcXL15EmzZtpBeMatSowfkFI0NDQ/zxxx+8N4/NyMjAtWvXIJFI4OzsjAYNGigkp23btnBxccGvv/4qc05cuHABAwYMQGZmpkJy5cHllFg7d+5E06ZN0bRp0zLnBFcVD3mmMhQIBLh79y4neSUKCwvh7++P8PBwMMZQo0YN6QWj0NBQqKiocJpnYWGBX3/9lfduFEDx91tKSgokEgkcHBykF1q4Vt3Liu7du0MsFsPQ0BCNGjUqc05w9T2qrHIC4H+6TWNjY+zcuVNhZYWXlxdycnIwbNgwdO3atcz0rC9evMD58+fxxx9/4MyZM9i2bRu+//57hewLqXrozj8hpFLQ1tbGkydPUKdOHfz1118IDAwEAGhoaODNmzec51XWvrFcXo/19/dHYmIiBg0apNA71TExMQrZ7uc8fPhQ2lz1yJEj6NWrF3766Sd4enrC29ub8zxldaOoV68e6tWrp/CchIQEbNq0qcxyS0tLzkdQV5YbN27AyckJAHDz5k2ZdVyeH8q6UKKqqordu3cjJCSElwtGyuxGoampCVdXV4XnVPeyQl9fH927d+dkW5+irHICKG5FNXPmTF4uFgGK70oRGxuLP//8E2vXrsWMGTOgpaUFMzMzaGho4NmzZ8jOzoaJiQmGDRuGmzdvSmdYIASgyj8hpJJo164dfvzxRzg7O+POnTvSpty3bt0q01ePC8roGysPrgaLA4DIyEicOnWq0vUr5OqOFd8XjPjsRgHwN1VjCQ0NjTLTJwLFA2aZmJhwmlVRXA0Y9+EgkZUFl1MZAvxdMOKzG0WJt2/fYu3atR89L7iekrK6lxU7duzgYG+4x2UrOIC/i0UAP10punTpgi5duuDJkyeIj49HZmYm3rx5A2NjYzg7O8PZ2RlCoVAh2aRqo8o/IaRS+P333zFr1iw8fPgQBw8elM6PnZiYiP79+3Oex3ffWL4HiwOA2rVrK3xqsS/B1R0rvi8Y9e7dG3v37oWpqalCu1GU4HuMgR9++AEhISHYv38/gOI74Q8ePMC0adPQs2dPheXyPWBcZcTVVIZ8XzB6+/YtNm/ejDNnzii0G0Vpw4cPx+nTp9GrVy80b95c4ecF32UFDRZXjKtygu+LRUDxYIJisRhRUVEK7UoBAEZGRvjhhx842x6p/qjyTwipFPT19bFu3boyy4ODg2X+P3bsWISEhJQZOKui+B5Mje+KHACsWLECU6ZMwcaNGxVSGVY2vi8Y8dWNogTfUzUuX74cnTp1gqmpKd68eQMvLy9kZ2fD3d0dCxcuVEimMgaMS0hIwIEDB/DgwQMUFBTIrOO6csw3vr9n+OpGUVpkZCROnDjB2wBmyigrFDlYXHkiIiKwf//+cs8JRVSO+cT3xSKAv64UhHwJGvCPEFKlcN0U8O7du7h69arC+8YqY7A4AwMD5Ofn4/3799DU1Cxz9+Hp06e87UtpXA3eJi+uLhhpaWnx2o3C2toaUVFRsLOz4yWvRHR0tPSccHFxQdu2bRWWxfeAceHh4RgyZAjat2+P06dPo3379khLS0N2dja6d++utCbQXJ0TyhqUkk8ODg4IDw9H06ZNec3lq6xQ9GBxH1qzZg1mzpyJoUOHYsuWLRg2bBgyMjKQkJCAcePGKezC3+dwdU7o6enxerGIkMqO7vwTQqoUrq9X2tjYwMbGRjpy9IsXL8pMPcQFZQwWt2rVKl7zKqs//vgDQUFBX13557sbBd9jDJTw9fWFr68vAODx48cKzeJ7wLhFixbht99+w7hx46Cjo4PVq1fD2toao0aNgoWFBW/7oSjKGpSSTytWrMDUqVOxceNGzqfz/BS+ygpFDxb3ofXr12Pz5s3o378/wsLCMGXKFNjY2GDOnDlKu0DMJUtLS+jo6Ch7NwipNOjOPyGkSvnauwEvX77E5cuX4eTkJK0M3r59G76+vsjKyoKWlhYOHTqEdu3acbnbCAsLw8mTJ3mvyFVGXLfe+Byu7iBFRkZi7dq1vHWj4GuqRsYYMjIyYG5uLh0BOycnB35+fkhOTkbNmjVx5MgRNGvWjJO80qZOnQptbW3eBozT0tKSjglhbGwMsViMJk2aIDU1VfodoAxcfUaV8T3DdzeKR48eoU+fPjh37pxCWzQpq6xYsWIF7t69q9DB4krT1NREamoq6tatC1NTU5w+fRqOjo5IS0uDm5ub0mY04KqciIqKwpo1a3i/WFSdu1KQqo3u/BNCvhnnzp1Djx49UFRUhA0bNqBfv34oKChAnz590KJFC6xcuRI7duzAtGnTOP9Bx/dgcQDw4MGDT66vU6cO55nyqKrXnAcNGoT8/HzUq1ePl24UfIwxkJycjF69eiE9PR0HDx5Et27dwBhD3759oaOjg7Nnz2L37t0IDAzEuXPnOM/ne8A4Q0NDvHr1CkDxHcGbN2+iSZMmeP78OfLz8znNUga+v2c+141CEfr374///vsPixYtUth5ocyygs/B4gDA3NwcT548Qd26dVG3bl1cunQJjo6OuHfvnlK/q7nKdnV1xdu3b2FjY8Nb97fSXSmOHj1apisFIcpElX9CyDcjMDAQc+bMQatWrdC/f39oaGhg9+7dePz4MeLj46Gjo4MRI0Zg+fLlnGfzPVgcAFhZWX0yp6ioSOH7UB4upzPkE9/dKPiYqnHcuHHo3r072rVrhxkzZsDa2hrbt2/H9evXkZqaCnNzc9StWxeOjo4Kyed7wLhWrVrh9OnTaNKkCfr06YOAgABER0fj9OnTaNOmDed58uJqKkO+v2eU0Y3iwoULuHjxosI+k4Byywq+B4vz9fXF8ePH4eLighEjRiAwMBARERG4cuUKevTowdt+fIircoKPi0Uf4rMrhbyzQxBSgir/hJBvxu3bt9G1a1dYWVlh2LBhGD58OAwNDXHw4EFpn8DHjx9Lmz5ziY+K3IeuXbsm8//CwkJcu3YNK1euVMggTsqYzpBPQ4cO5TWPjzEGrl27hp07d8LKygouLi5wdnaGlpYW9uzZA3NzcwBAXl4eatRQzM8FsViskO1+zLp16/D27VsAwPTp06Gqqor4+Hj06NFDYV0P+JzKkO/vmYyMDOkUm+rq6sjLy4NAIEBgYCB8fX3LzNbCBTs7O7x584bz7ZamzLKC70EnN2/eLP1cjh49GoaGhoiPj8f333+P0aNHc57H91SGfFws+tCDBw+kY5mIRCJpa6PBgwfDzc2t3JmNvpQyZocgVRtV/gkhVcqgQYO+uEJUq1YtxMTEwN/fH1OnTsXUqVPLPOft27cK+cHK92BxAMr9sePq6oqaNWti2bJlnN/VUcZ0hnziuxsFH1M1mpiY4MaNG7CyssKGDRuwZMkSqKurQ0NDQ/qcnJycatNU1dDQUPpvoVCIKVOmYMqUKQrL43sqQ76/Z5TRjWLJkiWYNGkSFi5ciCZNmpRpxs3F8SuzrOCbUCiEUCiU/r9Pnz7o06ePwvL4rqzycbHoQ3x2pQgPD8f+/fur9QwfhFs04B8hpFLJz88vd4AcLqZ12rx5M8aOHQtPT0/pnPAfw3W/Sr4Hi/uUtLQ0ODk5IS8vj9PtVtZpxsaMGYP58+d/9Wj/QqGQ124UfEzVuGDBAixduhQ9evT47DnBdf/7EnwPGJeRkYEdO3YgIyMDq1evhqmpKU6ePInatWujUaNGnGbxPZUh398zAwYMgKurKyZOnIiFCxdi9erV+OGHH3D69Gm4uLgo5P0rqah+eC4yxiAQCDg5D5VZVgD8DxYXFxeHTZs2ISMjAxEREbC0tMSuXbtgbW3NeSsSvqcy/OuvvxAcHKzQi0Uf+vHHH1G7dm3MnTsXGzduxMSJE+Hp6SntSrFt2zbOsmrWrImYmBjY2tpytk1SvdGdf0JIpfDo0SMMGzYMUVFR5a7n4gfdTz/9BGtra5w6dYr3EYz5HiwOKB6tujTGGLKysjBv3jyFzFGtrGnGPnfBaMOGDZzk8N2Ngo8xBmbNmgUzMzOcOnXqky0bFHV3ju8B42JjY9GxY0d4enri3LlzWLhwIUxNTXHjxg1s3boVERERnObxPZUh398zyuhGwUdXEWWWFXwPFnfw4EEMHjwYAwcOxLVr1/Du3TsAwKtXr7Bo0SKcOHGC0zy+pzLs0KEDAJQZ04PLi0Uf4rMrxaRJk7B69WreZocgVR/d+SeEVAoDBw5EZmYmVq1aBR8fHxw+fBg5OTlYsGABVqxYIe1XWlWFhYV9cr0i+pOXd6eaMYbatWsjPDwc7u7unObxPc0YHxeM5BEZGYlly5YhJiaGl7wPLVmyBKNHj4a+vr5S8r9G06ZNMWrUKOmAcdevX5cZMI7rZtXu7u7o3bs3Jk6cKDO9XkJCArp164b//vuP0zy+pzJUxvdMZTV27FiEhIR8dYsfvtnZ2WHu3Lno37+/zGe0ZLA4LvuLA4CzszMCAwMxZMgQmbykpCR06NAB2dnZnObxPZVhbGzsJ9d7eXkpfB+49mGXvejoaBgaGvIyOwSp+qjyTwipFCwsLHD06FE0b94curq6uHLlCmxtbXHs2DH8+uuviI+PV/YuVjkf/ugRCoUwMTFB/fr1FTKAG1/z0peoLBeMFNWNQl5czYetDFpaWrh16xasrKxgbGwMsViMJk2aIDU1VTqfOpe0tbWRnJwMa2trmYpOZmYm7OzspHexuRIQEICdO3eiadOmvExlKC8uLxjx2Y2iIqrqeaGpqYnU1FTUrVsXpqamOH36NBwdHZGWlgY3NzfOWyJoamoiJSUFVlZWMufE3bt34eDgwPk50b17d4jF4kpXWeX6YpEiu1IMGzZM7ufyPYAkqfyo2T8hpFLIy8uDqakpgOK+448ePYKtrS2aNGmikD6O1tbWn7zrcPfuXU7z+B4sDuD/jgbf04xFR0fj6NGj+O677yAUClG3bl20a9cOurq6WLx4MeeVf767UciLq2v4Pj4+n3zPFDFlFN8Dxunr6yMrKwvW1tYyy69du6aQ6Sf5nspQXosWLUKfPn2+uvLPdzeKiuDqvOC7rOBzsDig+MJ7enp6mTEi4uPjFXLhhO+pDOX1xx9/ICgoiJPKv6K7UlCFnnwNqvwTQiqFhg0b4vbt27CysoKTkxM2bdoEKysrbNy4USHzRf/yyy8y/y/pv33y5ElMnjyZ8zwrKyteB4sDipsAGxsbSyvBU6ZMwebNm+Hg4IC9e/eibt26nObxPc0Y3xeM9PX1P9mNoqorqaSWKCwsRFJSEm7evKmw5uKtWrXC6dOn0aRJE/Tp0wcBAQGIjo7G6dOny/TR5cKAAQMwdepUHDhwAAKBABKJBOfPn0dQUBCGDBnCeR7fUxnKi6tK5LRp07BgwQJpN4oSPj4+WL16NScZysZ3WeHr64vjx4/DxcUFI0aMQGBgICIiIqSDxXFt1KhRCAgIwPbt2yEQCPC///0PFy9eRFBQEObMmcN5XmWtuHJ5YWXBggXYuHEjhgwZIlM2eHh4ICQkhLMcALh37x7ev39f5gJ0WloaVFVVlT7AMKl8qPJPCKkUfvnlF2kT37lz58LPzw+7d++GmpoaQkNDOc8LCAgod/nvv/+OK1eucJ7H92BxQPHdvZLB7i5evIh169Zh1apV+PPPPxEYGMh580q+pxnj+4LRhxU5RXej4Ntvv/1W7vJ58+bh9evXCsnke8C4hQsXwt/fH5aWlmCMwcHBAUVFRRgwYABmzZrFeV51l5ycjD179pRZbmJiwvtAeYrCd1nB52BxQPFF4RcvXsDHxwdv375F69atoa6ujqCgIIwfP57zvG/B7du30bp16zLLdXV18fz5c06z/P39MXz48DKV/7///htbt25V2lg0pPKiPv+EkEopPz8f//zzD+rUqcPrgE13796Fk5NTmSbeiqLIweI0NTWlr+HUqVORlZWFnTt34tatW/D29sajR484zeN7mrHdu3ejsLAQ/v7+uHbtGvz8/PDkyRPpBaO+ffsqfB8qg9L9dBUhPT0dzZs3V8iMFMqSkZGBa9euQSKRwNnZWaHdNvieylAeXH1matWqhf3798PDw0Nmm4cPH0ZQUBAyMjI42uOKU/R5wXdZoWj5+flISUmBRCKBg4MDtLW1FZbF91SG8uDy81KvXj1s2rQJbdu2ldnuzp07sWTJEqSkpHCwx8V0dXVx9erVMjMopKenw9XVlfOLDaTqEyp7BwghpLSCggLcvn0bampqcHFx4X2k5oiICBgaGvKWZ2tri4SEBIVsW1tbW3r37a+//kLbtm0BABoaGnjz5g3neYMGDYJYLEa9evWgo6MDQ0NDmQfXBg4cCH9/fwDFI1ZnZmYiISEBDx8+VEjFPywsDJGRkdL/T5kyBfr6+vDw8MD9+/c5z6ssLl68CA0NDYVtPyMjA7NmzUL//v2Rm5sLADh58iRu3bqlsMx69eqhV69e6NOnj0Ir/uHh4fD09ERKSgoOHz6MwsJCpKSkIDo6Gnp6egrL5UtJN4rs7GxeulFUJoosK+Li4jBo0CC4u7tLZ6DYtWuXQge+1dTUhKurK5o3b67Qiv+aNWswbNgwmJqa4tq1a2jevDmMjIxw9+5ddOzYUWG5fCrpSvH3339Lu1Ls3r0bQUFBGDt2LKdZAoFAOm5KaS9evOBtxhtStVT9doqEkGohPz8fP//8s3Sqqjt37sDGxgYTJkxAzZo1MW3aNE7znJ2dZfpvM8aQnZ2NR48eYf369ZxmAcoZLK5du3b48ccf4ezsjDt37kj7/peMrs41PualL09BQQHu3buHevXqwcXFRWE5fHejkFerVq04mVrxw/7EJZ/RK1euKGyqOr4HjGOMISIiAmKxGLm5udLm1SW4fg8XLVqE3377TTqV4erVq2WmMqzqKnM3ikGDBnHSDYnvskLRg8V96O3bt1i7du1Hzwmu78SvX78emzdvRv/+/REWFoYpU6bITGVYHfDZlaJVq1ZYvHgx9u7dCxUVFQDFYwgtXryYt/F3SNVCzf4JIZVCQEAAzp8/j1WrVqFDhw64ceMGbGxscOzYMcydO7dMn/mv9eH84SX9t729vWFnZ8dpVsn2PzVYnLu7O+eZz58/x6xZs/Dw4UOMGTMGHTp0AFA8poKamhpmzpzJeSaf+L5gxHc3CoDfadQ+nD6q5Jzw9fVF+/btOc0q4e7ujt69e0sHjCtpHpuQkIBu3bpJ73pyZcKECdi8eTN8fHzKnZGC68HI+J7KUF6dOnXCtm3bOLsAwWc3CkCx06h9iO+ywtnZGYGBgRgyZIjMOZGUlIQOHTogOzub07wBAwbg9OnT6NWrV7nnxNy5cznN43sqQ3mNGTMG8+fP57S1IR9dKVJSUtC6dWvo6+ujVatWAIrPj5cvXyI6OhqNGzfmPJNUcYwQQiqBOnXqsIsXLzLGGNPW1mYZGRmMMcbS0tKYjo4Or/uSm5vL+TZjYmJkHufOnWOpqamssLCQ86yKGjNmDHv06NFXb+f+/fuffHBtwoQJrFmzZiwuLo5paWlJPzNHjx5lTk5OnOeZmJiwq1evMsYYc3JyYmFhYYwxxtLT05mWlhbneTExMUwkErG2bdsyNTU16fEtXbqU9ezZk/O8T1HU51RLS4vdvXuXMSZ73t+7d4+pq6tznmdgYMAiIyM53+7H1KpVi924cYMxxljTpk3Znj17GGOMXbhwgenq6iokMz09nc2cOZP169eP5eTkMMYYi4qKYjdv3lRIHt8iIiKYSCRiP/74I1NXV5d+Zn7//XfWsWNHXvdFEWWFSCRi9+7dY4zJnhMZGRkKOSd0dXVZfHw859v9GGtra5aYmMgYY8zV1ZVt3LiRMcbYqVOnmIGBgUIyz507xwYOHMjc3NzYv//+yxhjbOfOnSwuLk4heXz777//2PTp01mnTp1Yz549WXBwMHvy5Imyd4tUUtTsnxBSKTx69Eg6bVtpeXl5vMyHzRhDVFQUtm7disjISGlTS654eXlxuj0ucTW/Md/TGR45cgT79u2Dm5ubTK6Dg4NCBhrjuxtFZZhGLSUlBdu2bcMff/yBnJwczrevr6+PrKwsWFtbyyy/du0aLC0tOc/T09NT2ABw5eF7KsPq3o0C4HcatfIouqywsLBAenp6me+U+Ph4hXx2LS0tZb5fFI3vqQz57kYB8N+VombNmli0aBGn2yTVF1X+CSGVwnfffYfIyEj8/PPPACCtzG3ZskUhTeJL3L17F9u3b0dYWBhev36Nzp07K2TO9rCwMBgbG0srjFOmTMHmzZvh4OCAvXv3om7dupxnyotx1PuL7+kM+b5g9Pvvv0u7URw8eBBGRkYAgMTERPTv35/zPGVNo/b69WuEh4dj27ZtSEhIgJubG+ddKEqUDBh34MABXgaMmzdvHoKDg7F9+3ZOxkn4HL6nMuT7glFAQMAnu1EoAp/TqJXGV1lRMljc9u3bpYPFXbx4EUFBQZgzZw7neStWrMDUqVOxceNGXsohvqcyVMbFouHDh0u7UjRv3lyh58W5c+c+ub68c4V845Tb8IAQQoqdP3+e6ejosNGjRzMNDQ0WEBDA2rZty7S0tNiVK1c4zXrz5g3btWsX8/LyYurq6qxLly5MRUWFJScnc5pTmq2tLTt79ixjrLjJr0gkYps2bWLff/896969u8Jy5VG6aaki/Pnnn8zLy4vz7bZu3ZqtWbOGMVZ8DCXNx8eNG8f8/Pw4z5MXV90oLC0t2fnz5xljsu/RoUOHmI2NzVdv/0NxcXFs6NChTFtbmzVp0oSpqKgovDlwQUEBGzBgABMKhUwgEDBVVVUmFArZoEGD2Pv37znPy8vLY35+fkxbW5s1btyYOTs7yzyquurejYIxxmxsbNjp06cZY7LHGBYWxuzt7TnNUkZZwRhjM2bMYCKRiAkEAiYQCJiGhgabNWuWQrJyc3OZt7c3EwqFTFtbmxkYGMg8qjq+u1Ewxm9XipLPSOmHUCiUPgj5EN35J4RUCh4eHrhw4QKWLVuGevXq4a+//oKLiwsuXryIJk2acJYzduxYhIeHo2HDhhg0aJD0Dq6qqiqEQsXNfvrw4UPpPLxHjhxBr1698NNPP8HT0xPe3t4Ky60MFDWd4eLFi9GhQwekpKTg/fv3WL16NW7duoWLFy8iNjaW8zx5cdWNgq+74r/++iu2b9+O169fo3///oiPj4ejoyNUVVVhYGDAWU55VFVVsXv3boSEhPAyYJy/vz8SExMxaNAg3u5U8zloY3XvRgHwd2dcWWUFUDyLwsyZMxU+WBwA9O/fH//99x8WLVrE2znB54CNfHejAPjtSvHs2TOZ/5e0uJs9e7ZCWtyRakDZVx8IIaSgoID5+/sr9O5zCRUVFTZjxgz28uVLmeU1atRgt27dUlgu34PFVQRXd/5fvHgh83j+/DlLTU1lffv2ZY6Ojl+/o+VITk5mQ4YMYY0aNWL29vZs4MCB0gHWlIWr15Ovu+Il58SH21T0OaEMmpqavA7yxfegjZMnT2YtW7ZkWVlZTEdHh6WlpbH4+HhmY2PD5s2b9//au++oqK7ub+DfGRQBaZqA2AABJWpQ0RhQEyxggBhrLI+oiC3FWB54jGIMKBqw92RFf6IgsURFRQ0+YGIBpViRscWCoEkECyoaAYHhvH/4Mg8jgyWcey7C/qw1azn3su4+4AzMOffsvbnHi4yMZP/6179Yfn4+92u/iIg743L9rRDN0NCQnT17Vlg80QUbFy5cyNq0acNSU1OZiYkJO3r0KNu0aROzsLBgq1ev5h6PMcb279/PvLy8WFZWliTXfxUJCQmsY8eOssUn1RdN/gkh1YKZmZmQyf/mzZs16QRDhw5l+/btY8XFxZJ/oPPx8WEdO3Zk48aNY0ZGRuzevXuMsWeV6du2bStZ3FfBa7L6/HbDskmrtbU1S05O5jDS/xG5YPS6eKdRXLt2je3YsYNt27aNXblyhdt1y4SGhrKWLVuy5s2bs+nTp2u2NIuY5JSWlrLt27ezL7/8kn366ads4MCBWg/eHB0dWXp6OvfrVsbV1ZUtXbqUMab9ujhx4gRr0qQJ93g1PY2ipKSEHTlyhOXm5rInT56wkydPsuPHj7PHjx9zjyXX34qCggK2aNEi5u3tzTp16iT5z9TZ2VnTaUeE8ovf5d8TaWlprFGjRpLEFJlGwVj1SKW4ePGi7DcWSPWkYIxTpSdCCKmCMWPGwMnJCQEBAULiZWVlISIiApGRkcjPz8f9+/exbds2DB48WJJ4Dx8+1BSL+/LLL+Hl5QXgWQ9lfX19zJo1S5K4r4JXf+Pnt9qX9cN2cHBAnTr8s8zMzc1x5swZ4duOX6Z8b+43SUJCAjZs2ICdO3fC3t4eFy5cQEJCArp16yZZzClTprywYFxERATXeLGxsVi9ejXWrFkjSYeG5xkbG+PcuXNo0aKF1usiKysL77zzjqYYIG8ZGRlC0iiGDh2Kw4cPC+sRDwAGBga4dOlShdQGqYj+W+Hj46MpFifiZ3rgwAGEhIQgNDQUTk5OqFu3rtZ5U1NTrvGMjIxw8eJF2Nraar0nrl+/jjZt2nB9T6jVahw7dgxOTk4wMDAQkkYBAB4eHrh58ybGjRun8/9w9OjR3GKpVCqt54wxZGdnY8GCBSguLkZSUhK3WKRmoMk/IaRaCA0NxZIlS+Du7o5OnTqhfv36WuenTJkiSVzGGOLj47Fhwwbs3bsXb7/9NgYNGoRVq1ZJEu9lJk6ciLlz51Z5Il5efn4+bt68iaKiIq3j7dq14xZDDqIXjF4Vr8n/2LFjX3h+w4YNVbp+ZR4/fozNmzcjIiICp0+fxvvvv4/BgwdL8nNu2LAhNm3ahI8//pj7tXVp0KAB8vPzUVJSAiMjowoTnfv373ON16xZM2zfvh1du3bVel3s3r0b06ZNk6QlpUj169dHfHw89zztF+ncuTMWLFggSavEFxH1t8LMzAz79++XdNGtvLL6Bc9PUBljUCgU3Fu02tvbY+3atfDw8NB6T0RFRWHBggW4ePEi13iiF4uAZwscKSkpaN++veSxlEolFApFha49rq6u2LBhA9555x3Jx0DeLFTwjxBSLYSHh8Pc3BynT5/G6dOntc4pFArJJv8KhQJeXl7w8vLC/fv3ERUVxf1u4+vgVSwOeNYKb8yYMfjvf/+r8zzvD3Wi2xk6ODhg3rx5SE5OFrpgJIquQk7nz5/Hw4cP0atXL8nimpiY4IsvvsAXX3yBc+fOYf369ViwYIEkk3/RBeNWrFghLBYgvpWh6AWj5s2bc78z/DKhoaGYNm0a5s2bp/N9L9V4RP2tEFksDgAOHz4sLBYgvpWhk5MTrl+/LnTy/84776CgoEBIrMzMTK3nZTvuDAwMhMQnbyDZEg4IIeQNYGJiIjSvnGe+uI+PD+vatSs7ceIEq1+/Pjtw4AD76aefmKOjI/vll1+4xChPdDtDW1vbSh8tWrTgHu9VffHFF1xa/emiVqvZ559/zhYuXCjJ9StTVFSk+fe7777Lbt68yeW6chWMe5n58+ezBw8eVPk6onPwBwwYoPXo06cPs7GxYWZmZpK8B3/55Rfm6empaaUmQmUtzcqey4XX34rqUCxOF14tTBkTm4MfHx/POnTowPbt28du3bpVoTCtVDG7du3KDh8+zO7duyckJiGvirb9E0KqtbI7j6Lv2JURnb/NM17jxo2xZ88evP/++zA1NcWpU6fQqlUr7N27F4sWLcKxY8c4jPh/jIyM8Pvvv8Pa2hozZsxAdnY2oqKicOHCBfTo0QN3797lGk8ucqdRXL58GT169EB2draQeM/j+RrNz8/HoEGDkJSUBFtb2wrb8M+cOVPlGP+Eqakpzp49y+19LyoHX5fS0lJMnDgRdnZ2mD59Otdri06jACrWFnle9+7ducd8FbzeF3fv3sXQoUORmJgo7Gf6Kni/J/Lz84Xk4Jdvy1g+tYFJlNZQPqaoVIonT54gISFB59+lN30HHOGPtv0TQqqdR48eYevWrVi/fj1OnTr1xuemy+XJkyewtLQE8Cy3+u7du2jVqhWcnJwkmVQZGxsjNzcX1tbWOHDgAPz9/QE8y7kUtQUSkG7BSHQaRWUyMjJQUlIiJJbU/Pz8cPr0aYwcOVJYj/FXwfu+iL29Pezt7ble81UplUr4+/ujR48e3Cf/cizKyjW5F2X48OH466+/EBYWVqPfE0ZGRnjvvfe4XlMX0WkNomOmpaXh448/Rn5+Pp48eYKGDRvi3r17MDIygqWlJU3+SQU0+SeEVBsJCQlYv349du7cicLCQnz99dfYsmULHBwc5B7aG8nR0RGXL1+Gra0tOnTogLVr18LW1hZr1qxB48aNucfr3bs3xo8fD2dnZ1y5ckWT+3/hwgXJK6uLWDD697//jQcPHiA1NRU9e/bE7t27cfv2bXz33XdYunQp93jP59iz/1/FOTY2lmu1aDnFxsYKLxgnEmMM0dHROHz4MO7cuYPS0lKt87t27RIyDqkWjF71dbhgwQJ88cUXMDc3r3LMxMTEF553c3Orcgw5JScnCysWJ4fCwkKsXr260vcE74VpORaLXjUmjwK//v7+6Nu3L3788UeYm5sjNTUVdevWxciRIzF16tR/fF1Sc9HknxAiq+zsbERERGDDhg148uQJhg8fjoSEBHTp0gW+vr408a+Cf//735qt4bNnz4anpyc2b94MfX19REZGco/3ww8/aNoZ7ty5E2+99RYA4PTp0xg+fDj3eIDYBaNDhw5hz5496Ny5M5RKJWxsbNC7d2+Ymppi/vz5msUOXtLS0rSelxVyWrp06UsLu70p5CgYJ9LUqVNf2MqQt+q6YBQWFoahQ4dymfz36NGjwrHyP1dRO3CkIrJYnBzGjh2raWX4/vvvS/6eqM6LRTwK/J49exZr166Fnp4e9PT08PTpU9jZ2WHRokUYPXo0Bg0axHHEpCagyT8hRFYtWrTAkCFD8MMPP6B3795a+XnVgegtlyNHjuQ2GRoxYoTm387OzsjKytLk5PNsJVjG3Nwc33//fYXjISEhWs+rerdDrgUj0WkUcmxXFW3p0qWYPn061qxZI/nuEDls2rQJu3btEtbKsLouGPHcMq6rC0ZaWhqCgoIQGhrKLc7r4vW3YsGCBfjPf/6D0NBQODk5Vcj5f9MXy2JjY4W2MqzOi0U83hd169bVfD+NGjXCzZs30bp1a5iZmeHmzZtVvj6peWjyTwiRlY2NDY4dOwZra2vY2NhUu560vPMcX1Ys7scff+QaDwCKioqQmZkJe3t7dOzYkfv1X1dV73bItWAkOo2iNhg5ciTy8/Nhb29frYqb8SK6lWFtWDAyMzOrcKx3796oV68e/P39K7SKFYXX3wovLy8AgLu7e4XrS1WgTiTRrQyr62IRL87Ozppivj179kRwcDDu3buHn376CU5OTnIPj1RDNPknhMjq8uXLSEpKwvr169G5c2e0atUKI0eOBCD+rrsu//3vf9G0adMqX0eOYnH5+fmYPHkyNm7cCAC4cuUK7OzsMGXKFDRp0gSBgYHcY76Kqn5IlmvBSEQahbOz8yu/7uWqhL927Vo0atSIy7Xk6uLxMh9++CEMDQ2rfJ05c+YgJCQEGzZs4HI9UjkLCwtcvnxZtvi8/lZU1wUcXrvSli5dihkzZmDNmjWwsbHhMLIXq66LRbyEhYXh8ePHAIB58+Zh9OjR+PLLL+Hg4ICIiAiZR0eqI2r1RwipNv7++29s3boVGzZswPHjx9G9e3f4+PhgwIABsLCw4BpLdCGuESNGICsrCytWrNBZLI53vjjwLN84KSkJK1asgJeXF1QqFezs7LB3717Mnj27whZhUXi0xCpbMNqxY4dmwWj69OlQqVRo3bo1x9FWLj8/n3saxfMpEi8ye/ZsLjHLO3jwIA4ePKjzPbFhwwbu8V4Vz4JxGRkZiIiIQEZGBlauXAlLS0vExcWhefPmaNu2bdUHW46IVoZvwoIRz/aQKpVK63lZXYMFCxaguLgYSUlJVY5RnlqtRmRkZKXvi0OHDnGN96p4FIsrc/ToUaxduxYZGRmIjo5G06ZN8dNPP6FFixbci3FWl1aGly5dQufOnfH3338LiadLVd8XjDHcvHkTlpaWtLhIXhnd+SeEVBvGxsaYMGECJkyYgEuXLmH9+vX49ttvMXHiRBQXF3ONJboQl+hicQAQExODbdu2wdXVVev7a9OmDTIyMrjHE6lbt27o1q0bVq1apVkwUqvVmDhxomQLRmWkTKOQYkL/qkJCQjB37ly89957aNy4cbXYeVOGV8G4hIQEeHt7o1u3bkhMTERoaCgsLS2hUqkQHh6O6OhoPgP+/0S0MhwwYAD3a1ZnHTp0gEKhqLCDyNXVVZIFqqlTpyIyMhJ9+vTBu+++W23eFzyKxQHAzp07MWrUKIwYMQJpaWl4+vQpAODx48cICwvD/v37eQxXQ3QrwxctFr3pHRUYY2jZsiUuXLiAli1byj0c8qZghBBSjRUXF7OdO3dyv26DBg1YbGws9+tWxsTEhGVmZjLGGLOxsWHHjh1jjDF2/fp1ZmhoKElMQ0NDlpGRwRhjzNjYWPPvs2fPMlNTU0livoryY+Hp4sWL7D//+Q+ztLRkderU4X79J0+esLFjxzI9PT2mp6en+R4mT57M5s+fzz2eaFZWViwqKkruYejE6zXj6urKli5dWuGaJ06cYE2aNKny9Z9nZGTEjh49yv26bxpvb29269YtLtfKysrSety8eZMVFBRwubYub731ltC/Fa+K13uiQ4cObOPGjRWumZaWxho1alTl6z/P0NCQnT17lvt1K6NQKJhSqWQKhULr0aVLF3bp0iVh49Dliy++YHfv3q3SNdq0acNSUlI4jYjUBtWrrDYhpNbS09PDnTt3KhzPy8vDkCFDuMcTXYirrFgcAE2xuL/++kvSYnGdO3dGbGys5nnZHZZ169ahS5cuksSUU+vWrbFkyRL89ddf2LZtG/frz5w5E+np6Thy5AgMDAw0xz08PCSJp1arsWTJErz//vuwsrJCw4YNtR68FRUVoWvXrtyvW52cO3cOAwcOrHDcwsICubm53OPV9FaGwLM0im+//RbDhw/X/A6Pi4vDhQsXNF+zf/9+br/nEhISYGVlBRsbG9jY2KB58+YwMDBAUVERoqKiuMQoT19fv0a3nL18+bLOdnempqZ4+PAh93iiWxlmZmbi+vXryMzMRGZmJm7cuIH8/HwkJydLWi/m6NGjGDlyJLp06YK//voLAPDTTz/h2LFjmq/58ccfq7xzY9GiRfj6669x/vz5Kl2H1B40+SeEVAuskvIjT58+hb6+Pvd4ZYW4RH0Ieb5YXFxcHKytrbFq1SqEhYVJEnP+/PmYNWsWvvzyS5SUlGDlypXo3bs3IiMjZa1yzKtwlOgFo5iYGHz//ff44IMPhKRRhISEYNmyZRg6dCjy8vIQEBCAQYMGQalUYs6cOdzjjR8/Hlu2bOF+3erE3Nxc8z4sLy0tjUuxtueVtTLMysrifm1dRC8YJSQkwMnJCcePH8euXbs0+dMqlUqyFJYxY8YgLy+vwvHHjx9jzJgx3OP95z//wcqVK7l3fqkuGjdujGvXrlU4fuzYMUkWyMtaGR45cgS5ubl49OiR1oM30YtFwLNUCk9PTxgaGupMpeBp5MiROHHiBNq3bw9DQ0PJ3/PkzUc5/4QQWa1atQrAs7vS4eHhMDY21pxTq9VITEyUZHV+yJAh2Lp1KywtLSUrxFXeiBEjNP92dnZGVlYW92Jxz+vatSuSk5OxePFi2Nvb48CBA+jYsSNSUlIkbQEkqp2h6AWju3fvwtLSssLxJ0+eSJK3unnzZqxbtw59+vRBSEgIhg8fDnt7e7Rr1w6pqamYMmUK13iFhYX4v//7P/z2229o165dhffEsmXLuMaTg4+PD2bMmIEdO3ZAoVCgtLQUSUlJmDZtGnx9fbnHE93KMCQkBOHh4QgICEBQUBBmzZqFrKwsxMTEIDg4mGssAAgMDMR3332HgIAArfZtPXv2xMqVK7nHA/7X8u55f/75p87K7lV17NgxHD58GP/973/Rtm3bCv+HvIvDivb5559j6tSp2LBhAxQKBW7duoWUlBRMmzZNkteM6FaGY8aMgZeXV4Xf3WWLRVK877/77jusWbMGvr6++PnnnzXHu3btirlz53KNVV07ppDqiyb/hBBZLV++HMCzP/xr1qyBnp6e5py+vr6mjzpvIgpx6SJlsbjyiouL8dlnnyEoKEjT6k9qotoZyrVgVJZGMXnyZE18QLo0ipycHM0ijbGxseZu5yeffIKgoCDu8VQqFTp06AAAFbaQVpciZ1UVGhoKPz8/NG3aFIwxtGnTBmq1Gj4+Pvj222+5xxP9wVz0gtG5c+d07haRIo2irKuBQqGAu7s76tT530dYtVqNzMxMzcSSJ3Nzc52pIjXF9OnTkZeXh549e6KwsBBubm6oV68epk2bhkmTJnGPJ7qVoejFIkBsKsXo0aO5Xo/UfDT5J4TIKjMzE8CzO0W7du1CgwYNhMSNjY1FfHw89zZGlcnPz8fkyZM1E/ErV67Azs4OU6ZMQZMmTRAYGMg1Xt26dbF7925JJomV+fe//40HDx4gNTVVZztDXuRaMJo/fz68vLxw8eJFTRrFhQsXkJKSgoSEBO7xmjVrhuzsbFhbW8PBwUGzc+PkyZOoV68e93jVtb84AHz44YdcWlnVrVsXmzdvxty5c5GWlobS0lI4OztLVin7VT+Y82plKHrBqCyNokWLFlrHpUijKOtqcPbsWXh6emot+pW97z/99FOuMQFU217pPNKn1Go1jh07hv/85z+YNWsWLl68iNLSUrRp00br58tT9+7dX+nrqtrKUK7FIuB/qRS2trZax6VKpVCr1di9ezcuXboEhUKB1q1bo3///lrfMyEaspUaJIQQHZ4+fcp+//13VlxcLGkcR0dHlp6eLmmM8qZMmcI6derEjh49yurXr6+pqLxnzx7WoUMHSWL6+flpKpuLYGVlxY4fP84Ye9bd4PLly4yxZ99jt27duMfr0aMHu3//Pvfrvsi5c+eYr68va9u2LWvdujUbMWIEU6lUksSaMWMGCw0NZYwxtmPHDlanTh3m4ODA9PX12YwZMySJKYdr166xWbNmsX/961/s9u3bjDHG/vvf/7Lz58/LPDJxTExMuFRub9WqFUtNTWWMMfbBBx9oulD8/PPPzMLCosrXf97XX3/NPvjgA5adnc1MTEzY1atX2bFjx5idnR2bM2cO93iMMRYZGSlpdf/qIDExkY0YMYK5urqyP//8kzHGWFRUlCSdI+rVq8euX7/O/bpVVdX3xJw5c9icOXOYQqFg06ZN0zyfM2cOCwsLY1u2bGFPnz7lOOL/WbhwIWvTpg1LTU1lJiYm7OjRo2zTpk3MwsKCrV69mmusc+fOMTs7O2ZkZMScnZ2Zs7Mzq1+/PrO1tZXsbxN5s9GSECGkWigoKMCkSZOE3RkvK8S1Zs2aCqvzUoiJicG2bdvg6uoqpFgcADg4OGDevHlITk5Gp06dUL9+fa3zvLcAP3nyRJNX2bBhQ9y9exetWrWCk5MT9xoKwP/uVJdPpZDqTofINIoVK1bA19cXCxYs0BwbPHgwmjVrhuTkZDg4OKBfv36SxD558iR27Nihs2aDFLnNCQkJ8Pb2Rrdu3ZCYmIjQ0FBYWlpCpVIhPDwc0dHRXOONHTv2heel6BP/KhinYnIDBw7EwYMH4eLigqlTp2L48OFYv349bt68CX9/fy4xyhOdRgHIs805Ojoa27dv1/m+4P27befOnRg1ahRGjBihs1jc/v37ucZzcnLC9evXK+zekFtV3xNlBSdtbW0xbNgwrQ4tUhOZSjF+/Hi0bdsWp06d0uycfPDgAfz8/PDZZ58hJSWFazxSA8i8+EAIIYwx8XfGzc3Nmb6+PlMqlczY2Jg1aNBA68GboaGh5nsq30v57NmzzNTUlHs8xhiztbWt9NGiRQvu8d577z0WFxfHGGOsf//+bNSoUezPP/9k06dPZ3Z2dtzj5efns7FjxzI9PT2mp6en+ZlOnjxZc8eTJzMzMy53Z1/G3Nyc1atXjw0dOpTFx8ez0tJSyWMyxtjWrVtZ3bp1WZ8+fZi+vj775JNPmKOjIzMzM2N+fn6SxHR1ddXsTin/vjhx4gRr0qQJ93gDBgzQevTp04fZ2NgwMzMzNnDgQO7xXlVVe7YvX76c5ebmVjiekpLCli5dyvbs2VOV4b3UtWvX2I4dO9i2bdvYlStXJI1VUlLCFi9ezDp37swaNWok+e/ulStXMmNjY/bVV18xfX199vnnnzMPDw9mZmbGvvnmG+7xOnTowDZu3MgY035dpKWlsUaNGnGPFx8fzzp06MD27dvHbt26xfLy8rQecqnqe0IuJSUl7MiRIyw3N5c9efKEnTx5kh0/fpw9fvxYkngGBgY6d0mdO3eOGRgYSBKTvNlo8k8IqRasra1ZSkoKY0z7j/7Vq1eZiYkJ93iRkZEvfPDm5ubGVq1axRh79v2VbbP86quvmKenJ/d4cti0aROLiIhgjDF25swZZmFhwZRKJTMwMGA///wz93iiF4xEpVEUFhayTZs2MXd3d6ZUKlnz5s1ZcHCw5FtznZyc2Pfff88Y+997sLS0lE2YMIEFBwdLErN+/fqa76v8+z4zM5PVq1dPkpjPU6vV7PPPP2cLFy4UEk+Xqk505FowkkNQUBBr3LgxW7x4MTMwMGDz5s1j48aNY2+99RZbuXIl93iOjo5sy5YtjDHt/6egoCD21VdfcY9naGjIMjMzK8TLyMiQ5D2hUCg0D6VSqXmUPZcLr8m/6MUixsSmUrRv354dPHiwwvGDBw+yd999V8gYyJuFtv0TQqoF0W3URG8dFV0s7kXOnTuH9evXc69ELrqdoehUClFpFPXq1cOIESMwYsQIZGVlISIiAlFRUQgNDUWPHj0wbtw4DBo0iHvRv4yMDPTp00czhrL3nr+/P3r16oWQkBCu8QCxBeMqo1Qq4e/vjx49emD69OlCYvKWk5OD6OhoREREwNvbG02bNsWYMWPg5+cn6XZuOdIoRHc0uHnzJrp27QoAMDQ0xOPHjwEAo0aNgqurK77//nuu8UQXi6vOhT55EN3+EpA+leLRo0eaf4eFhWHKlCmYM2cOXF1dAQCpqamYO3cuFi5cKEl88oaTe/WBEEIYE39n/MaNGy98SEFksbjn5eXlsTVr1rDOnTszhULB2rdvL1ksUUUbRadSiE6jeN6vv/7KfHx8mJGREWvYsCH36zdr1kzzemzXrp3mbmdycrJkqSlyFIzTJTY2lr399tvC4j2P5xbnzMxMFhwczGxtbZmenh5zd3dnW7ZsYYWFhVyuX54caRRGRkaa39FWVlbs9OnTjLFnd8aleJ22aNFCE+O9995ja9asYYw92y4vxZ1jkcXiqjNe7wk7Ozv2yy+/aK557do1xtizdI7hw4dX+fq6SJ1KoWuXRvlj5Z8T8jy6808IqRZE3xm3tbV94Y4CXj3pAbHF4p6XkJCA9evXY+fOnSgsLMTXX3+NLVu2wMHBgXss0e0MO3fujNjYWEyePBnA/3rRr1u3Dl26dOEaC/hfW0q5KJVKKBQKMMZQWlrK/foffvghfv31Vzg5OWHo0KGYOnUqDh06hF9//RXu7u7c4wHiC8YFBARoPWeMITs7G7GxsbL2y+bVyhB49rstJCQEISEh+O233xAREYHx48dj0qRJyM3N5RKjzO7duyscKy0txcSJEyW5Sw2Ib4HZq1cv7Nu3Dx07dsS4cePg7++P6OhonDp1CoMGDeIeT2SxOABITEx84Xld/epF4NHKEBDf/hKApoVgv379tD5nMMagUCiq/Pmipu/WINJSMMapxCwhhFTRuXPnsGTJEpw+fRqlpaXo2LEjZsyYofnDzVN6errW8+LiYqSlpWHZsmUIDQ3l/qHO3NwcZ86ckewDcXnZ2dmIiIjAhg0b8OTJEwwfPhw+Pj7o0qUL0tPT0aZNG0niTp06FUlJSVixYgW8vLygUqlgZ2eHvXv3Yvbs2UhLS+MaLzk5GV5eXhgxYgQiIyPx+eefay0YderUiWu8ykiVRgEAN27cQGRkJCIjI/HHH3/Azc0N48aNw6effsq9evX9+/dRWFiIJk2aoLS0FEuWLMGxY8fg4OCAoKAgTSVpKWRkZCAtLQ2lpaVwdnZGy5YtJYnTs2dPredKpRIWFhbo1asXxo4dK0m3iIyMDERERCAjIwMrV66EpaUl4uLi0Lx5c7Rt25Z7vPIOHTqEDRs2YNeuXahXrx4ePHggabwyly9fRo8ePZCdnc392oGBgTA1NcU333yD6OhoDB8+HLa2tpqOBuW7ZPBQWlqK0tJSzWtj+/btmvfFF198AX19fW6x1Go1jh07BicnJxgYGODixYsoLS1FmzZtYGxszC1OeUqlssKx8hNWngvhZY4ePYq1a9ciIyMD0dHRaNq0KX766Se0aNECH3zwAddYjo6OiIqKgouLCz788EP06dMHgYGB2LZtGyZPnow7d+5wjQfgpTcsunfvziVOSUkJQkNDMXbsWDRv3pzLNUktIOe2A0IIqW5++eUX1r17d+7XFVUsjrFnxYZGjhzJ4uLimFqt1hyvU6cOu3DhgmRxRRdtZIwxlUolSyqFlGkUBQUFbNOmTaxXr15MT0+PNWvWjM2aNeuNrHxd2x05coQZGhoyDw8Ppq+vr/k/XLhwIfv0008liZmVlcXmzJmj2fbfs2dPtmnTJlZQUCBJPF1EplGkpqYK6WggishicYwx9vDhQ63H3bt32YEDB5iLiwv77bffuMeLjo5mhoaGbPz48axevXqa98QPP/zAvL29ucebMWMGCw0NZYwxtmPHDlanTh3m4ODA9PX12YwZM7jHE83Y2FhTIJKQV0Hb/gkhsirbyvwiCoUCJSUlQsbTqlUrnDx5kvt1RRWLAwAbGxscO3YM1tbWsLGxwTvvvMPt2i8iumgj8KywkshUChFpFFZWVigoKEDfvn2xb98+eHp66rw7JwXRd6nlKBgnUmBgIL777jsEBATAxMREc7xnz55YuXIltziFhYXYuXMnNmzYgISEBDRu3BijR4/G2LFjJd1tJDqNonwKVdn35eLiAhcXF+6xyhN5p1rqYnHPMzMzq3Csd+/eqFevHvz9/XH69Gmu8b777jusWbMGvr6++PnnnzXHu3btirlz53KNBUBrJ8jgwYPRvHlzJCUlwcHBAf369eMeDxCbSuHu7o4jR47Az8+P2zVJzUaTf0KIrHTljJZJTk7G6tWrwSTITipfLRf434fWOXPmSLLlODw8HObm5jh9+nSFD1MKhYLr5P/y5ctISkrC+vXr0blzZ7Rq1QojR47UxJKKqBx80QtGutIoEhIS0KVLF/j6+nKvnxAcHIzRo0fjrbfe4nrdl0lISIC3tze6deuGxMREhIaGwtLSEiqVCuHh4YiOjuYe8/lt6MXFxTh//jwePnyIXr16cYnh7Oz8yq/7M2fOcIlZ5ty5c9iyZUuF4xYWFlzz7+VaMHo+lacsjWLp0qUvXdj5J+rWrYvdu3dLlquty86dOzFq1CiMGDECaWlpePr0KQDg8ePHCAsLw/79+7nGCw0NxbRp0zBv3jydC8U88uBfhYWFBS5fvsz9upcvX9Y5+TU1NcXDhw+5xpJrsahHjx4VjkmVSuHt7Y2ZM2fi/PnzOl8vUi1wkDcXTf4JIbLq379/hWO///47Zs6ciX379mHEiBGYN28e97jm5uYVJgSMMTRv3lzrbgQvoovFdevWDd26dcOqVauwdetWbNiwAWq1GhMnToSPjw8GDBgACwsLrjFFFW0UvWDUokULDBkyBD/88AN69+4t+aRq2rRp+PrrryscNzU1haOjI6ZPny5JoTFRd6nLE1EwbsCAAVyu80+IamUo14KRHIXHBg4ciJiYmAq7DqQi+k611MXinqdSqbSely2EL1iwAO3bt+caCxDbylCOxSJA96JmWloagoKCEBoayjXWl19+CQBYtmxZhXNSvF7Im48K/hFCqo1bt25h9uzZ2LhxIzw9PTF//ny8++67ksR6fjJadsfKwcFBkqJflZGyWNzzLl26hPXr1+Onn37C/fv3UVxczD3G+fPnsXjxYiFFG8vTtWBkbW3N5dqOjo4oKiqCj48PRo0apUmjqFu3riQFFPfs2aPz+MOHD3HixAlERERg48aNGDJkCNe4xsbGOHfuHFq0aAETExOkp6fDzs4OWVlZeOedd1BYWMg13otIWTBOpOnTpyMlJQU7duxAq1atcObMGdy+fRu+vr7w9fXF7NmzucSpbDeM1AtGcggNDcWSJUvg7u4ueQoVABgZGeHixYuwtbXVel9cv34dbdq04f6+EFUsrkz5LiLlubq6YsOGDdzTxhYtWoSNGzdiw4YN6N27N/bv348bN27A398fwcHB3DsajBkzBk5OTsIWi14kMTFRklQKQl4H3fknhMguLy8PYWFhWL16NTp06ICDBw/iww8/lDQm7w9Qr+PRo0fYunUr1q9fj1OnTqFdu3ZC4rZu3RpLlizBggULsHfvXq7Xlqud4fMLRmfPnuW+YCQ6jULXbpgyo0ePRps2bbBkyRLuk39Rd6lfRUZGhrA6H1IS1cqwst0wZQtGI0eO5LZgJGcaBSA2hQoQe6caEP+36fldaWUL4by7iZQR3cpQZL2dl5EqlaJMYWGhZP9vpOagO/+EEFktWrQICxcuhJWVFcLCwl448eFp48aNePvtt9GnTx8Azz6Q/N///R/atGmDrVu3wsbGhntMXcXixo8fzz1nvIyenh6ys7MrFOHLzc2FpaUl9+2AItsZPr9gtHDhQskXjADg77//1qRRHD9+HN27d5csjaIyV69exfvvv8+9bZuou9Tlvaxg3Pfff881nlqtxvLly7F9+3bcvHkTRUVFWufv37/PNV4ZUa0MK/PDDz8gKioKx48fr/K1QkJCXvlrpXjNiCb6TrXIYnEAEBUVhWHDhqFevXpax4uKivDzzz/D19eXWyw5Whm+qHCiQqHA9evXucd8USpFcXExkpKSuMVSq9UICwvDmjVrcPv2bVy5cgV2dnYICgqCra0txo0bxy0WqSFk6DBACCEaCoWCGRkZsX79+rGBAwdW+uCtVatW7ODBg4wxxpKTk5mhoSFbu3Yt69u3L9d4t27dYqGhocze3p5ZWVkxf39/dvLkScnb7jH27Gd7+/btCsf/+usvZmBgwD2eqHaGCxcuZA0bNmRt2rRhMTExkserzMWLF9l//vMfZmlpyerUqSMsbnp6OrOysuJ+3aKiIubj48OUSiVTKBSsbt26TKlUspEjR7KSkhLu8RhjrEePHlqPXr16sWHDhrG1a9ey4uJi7vGCgoJY48aN2eLFi5mBgQGbN28eGzduHHvrrbfYypUrucerLq5cucLMzc3lHgZXT58+Zb///rskr5PnffPNN8zQ0JApFAqmUCiYgYEB+/bbbyWJVRaj/EOpVGoevCmVSp1/J+7duydJPNGtDOVQ9n/2/P9jly5d2KVLl7jGCgkJYXZ2dmzTpk3M0NBQ0zpx27ZtzNXVlWssUjPQnX9CiKz8/PxeaQtpREQE17hGRkb4/fffYW1tjRkzZiA7OxtRUVG4cOECevTogbt373KJY2BggCFDhmDkyJFaxeKkyhcHgFWrVgEA/P39MW/ePK07Kmq1GomJicjKyqpQqbuqROXiKpVKGBoawsPDA3p6epV+3a5du7jEe5mSkhLs3btXWE715MmTkZGRwb3KeBm571JLyd7eHqtWrUKfPn1gYmKCs2fPao6lpqbqrMxfFdWllaFKpYKnp+cbX0MBAPLz8zF58mRNelHZnc4pU6agSZMmCAwMlCyuiDvVeXl5Ws+fLxbn7u7ONZ5SqcTt27cr7FxKT09Hz549ue+G6dy5MxYsWMD9+3iZoqIiZGZmwt7eXvK6Pjdu3NB6LmUqhYODA9auXQt3d3etmhS///47unTpwn2HGHnzUc4/IURWkZGRssQ1NjZGbm4urK2tceDAAfj7+wN4NlkvKCjgFsfGxgbHjh2DtbU1bGxsuBdP0mX58uUAnm01XLNmjdYEWV9fH7a2tlizZg33uKJycX19fSVtWViZytIo8vLyMGTIEG5pFJUVpsrLy8OpU6eQkZGBo0ePcomli729Pezt7SW7vpxycnI0xSeNjY01E61PPvlEkorgIloZvop169bB2dmZ+3XlSKOYOXMm0tPTceTIEU1lfADw8PDA7NmzJZv8GxkZ4b333pPk2uWZmZlVONa7d2/Uq1ePa7G4stoNCoUC7u7uWhNitVqNzMxMrZ8vL6JbGcqxWJSQkCAsleKvv/7SmTpYWloqSVFf8uajyT8hpFbq3bs3xo8fD2dnZ1y5ckWT+3/hwoUKhZ2qQnSxOOB/BZx69uyJXbt2oUGDBpLEqSyu1ORaMKpso9zTp0+hr6/PLU5lOzJMTU3h5eWFiRMnSlKTgjGG6OhoHD58GHfu3EFpaanWeV47KeQsGNesWTNkZ2fD2toaDg4OOHDgADp27IiTJ09W+KDOg4hWhoB8C0YhISEIDw9HQEAAgoKCMGvWLGRlZSEmJgbBwcHc4wFATEwMtm3bBldXV63XUZs2bZCRkcE9XmFhIVavXl3p+0KKooa68C4WV9YC8+zZs/D09NTayVC2SPzpp59yi1dGdCtDORaLxowZAy8vrwoLxY8fP8aYMWO4Tv7btm2Lo0ePVvibsGPHDkkW/Mibjyb/hJBa6YcffsC3336LP/74Azt37tT0xz59+jSGDx/ONVa3bt3QrVs3rFq1SlMsTq1WY+LEiZIWiyvrwS1yu6MuItsZSqEsjUKhUCA8PFxnGgXPHR1y9E4HgKlTp+L//u//0LNnTzRq1EiyxamySYccBg4ciIMHD8LFxQVTp07F8OHDsX79ety8eVOz+0dqSqUS/v7+6NGjB6ZPn87lmnItGG3evBnr1q1Dnz59EBISguHDh8Pe3h7t2rVDamqqJJXU7969W2FSBQBPnjyR5DU7duxY/Prrrxg8eDDef/99yXcdvahYXPv27bnFKSvGaGtri2HDhgmrEi/695voxSLgfwsZz/vzzz917uyoitmzZ2PUqFH466+/UFpail27duHy5cuIiorCL7/8wjUWqSHkKzdACCHV35dffsnu3r3L/boiisXl5+ezsWPHMj09Paanp6cpBDR58mQ2f/58SWKWycvLY2vWrGGdO3dmCoWCtW/fXtJ4UrK1tWW2trZMoVCw5s2ba57b2tqyVq1asY8++oilpqbKPcwqa9CgAYuNjZV7GJJYvnw5y83NrXA8JSWFLV26lO3Zs0foeGJjY9nbb78tNKYUjIyM2I0bNxhjjFlZWbHTp08zxhjLyMhgpqamksR0c3Njq1atYowxZmxsrCke99VXXzFPT0/u8UxNTdmxY8e4X7cyIovF1Qbli+AZGxtr/n327Fnur9EOHTowZ2dnplQqmZOTE3N2dtY82rVrx0xMTNiQIUO4xmSMsbi4OObm5sbq16/PDA0NWbdu3Vh8fDz3OKRmoDv/hBDyAps2bcK0adPw9ttvc71u69atsWTJEixYsAB79+7leu0ygYGBwrc76mpnuGXLFsnaGYogVxqFaGZmZkLaNMohJCQEgYGB6N+/P8aNG4fevXtDoVDA1dUVrq6uksV9WSvDN53oNAoAmD9/Pry8vHDx4kWUlJRg5cqVuHDhAlJSUpCQkMA9XtOmTWFiYsL9upV5Pn1KymJxgPi6DaJbGXbu3BmxsbGYPHkygP+l261btw5dunThGkuuVApPT094enpyvy6poeRefSCEkOqs/J2CqhDdTokxxqytrVlKSgpjTPv7uHr1KjMxMeEWR852hnIQ2WJMpMjISPavf/2L5efnC4tZUlLCFi9ezDp37swaNWrEGjRooPXgpbCwkG3atIm5u7szpVLJmjdvzoKDgyVvOSa6laFoM2bMYKGhoYwxxnbs2MHq1KnDHBwcmL6+PpsxY4ZkcVUqFfP19WVt27ZlrVu3ZiNGjGAqlUqSWPv372deXl4sKytLkus/b+PGjaywsLDC8adPn7KNGzdyjye6/aXoVoZJSUnMxMSEffHFF8zAwIBNnTqVeXh4sPr167NTp05xj8fYs9+lBQUFklz7eX5+fuy3335jpaWlQuKRNx9N/gkh5AV4Tf4VCoXOyf9ff/3FDAwMqnx9XURtd6xXrx4bOXIki4uLY2q1WnO8pk3+5UyjEOHJkyfM09OTGRsbs3fffVdry6qzs7MkMUVPPBhjLDMzkwUHBzNbW1ump6fH3N3d2ZYtW3ROuIhu1S2NQkp37txhPXr0YEqlkhkbG0u2QFVG9EKxnZ0d++WXXxhjz/5OXLt2jTHG2MqVK9nw4cO5x3v48KHW4+7du+zAgQPMxcWF/fbbb9zjMSZ2sUi0vn37snr16rEmTZqwgIAAdubMGbmHRKo5BWOVlC8mhBCi1Tf3nygrFufv74958+bpLBaXlZVVacGuqujevTsGDx6MyZMnw8TEBCqVCi1atMCkSZNw7do1xMXFcYnj6OiIoqIi+Pj4YNSoUZrid3Xr1kV6ejratGnDJY7cpk6diqSkJKxYsQJeXl5QqVSws7PD3r17MXv2bEn+D0UaOnQoDh8+jMGDB+ss+FdWIIwne3t7rFq1Cn369IGJiQnOnj2rOZaamootW7Zwj1neb7/9hoiICMTExMDAwAC5ubmSxqspGjRogIKCggppFKKo1Wrs3r0bly5dgkKhQOvWrdG/f39JCpp6eHjg5s2bGDdunM73Be/0DaVSidu3b1coApueno6ePXty34Zfv359XLp0CdbW1mjcuDFiY2PRsWNHXL9+Hc7Ozpp2mFJLTEzk2spQTqJTKR4+fIjt27djy5YtOHr0KBwdHTFy5Ej4+Phw7V5EagbK+SeEEAktX74cwLNc3zVr1kBPT09zriwHcM2aNZLEFpUbK0c7QznIUTVapNjYWMTHx+ODDz4QFjMnJwdOTk4AAGNjY81E45NPPkFQUJDk8ZVKJRQKBRhjFVq4/VNytjIUJScnB9HR0YiIiIC3tzeaNm2KMWPGwM/PDy1atJA09vnz59G/f3/k5OTA0dERwLPe7RYWFti7d6/m9cRLcnIyUlJSuFba16XsdaNQKODu7q61kKFWq5GZmalVu4UXOeo26MK7lWF5IheLAPEtMM3NzfHZZ5/hs88+w59//qnpKhQcHIySkhLu8cibjSb/hBAiITmLxXXt2hVJSUlYsmQJ7O3tNR/qUlJSuH9AlqudoUiiW4yJ1rx5c5iamgqNKcfE48aNG4iMjERkZCT++OMPuLm5Yd26ddwKccnZylCUevXqYcSIERgxYgSysrIQERGBqKgohIaGokePHhg3bhwGDRokyf/h+PHj0bZtW5w6dUrz+/TBgwfw8/PDZ599hpSUFK7x3nnnHRQUFHC9pi5yFYsT3f5SVCvDMqIXiwB5WmACQHFxMU6dOoXjx48jKysLjRo1kiQOecPJmnRACCHV3BdffMG11V9NLRani4h2hiKJbjEm2i+//MI8PT1ZZmamsJiiCsYVFBSwTZs2sV69ejE9PT3WrFkzNmvWLC71PMj//Prrr8zHx4cZGRmxhg0bShLDwMCAnT9/vsLxc+fOSVI/JT4+nnXt2pUdPnyY3bt3j+Xl5Wk9eBNZLE6X1NRUSes2iG5l6OLiwvr27cvu37+vOXb//n3Wr18/5urqyj0eY+JbYB46dIiNHz+eNWjQgJmamjI/Pz/266+/atXgIaQM3fknhNRq+fn5OnPy2rVrBwD48ccfucQpKCjApEmTsHHjRgDP7jzY2dlhypQpaNKkCde2e2VbmV9EoVBIvh1QRDtDkUS3GBNt5MiRyM/Ph729PYyMjFC3bl2t8zzzVFesWAFfX18sWLBAc2zw4MFo1qwZkpOT4eDggH79+nGLZ2VlhYKCAvTt2xf79u2Dp6cnlEolt+uTZ6RIo3ieo6Mjbt++jbZt22odv3PnjiQtRcu22ru7u2sdZ4xBoVBArVZzjSeyBWRxcTE+++wzBAUFaerauLi4wMXFRbKYolsZpqena+0SAZ7VrAgNDUXnzp0liSlyR1OzZs2Qm5sLT09PrF27Fn379pXsZ0lqBpr8E0Jqpbt372LMmDH473//q/M87w90gYGBSE9Px5EjR7TyNj08PDB79myuk//du3dXei45ORmrV68Gk6DWq56eHrKzsytsjc/Ly8OQIUO4/0xFE5lGIYcVK1YIixUSEoLAwMAKBeNcXV3h6urKPV5wcDBGjx6Nt956i/u1X0R04S85SJ1G8bywsDBMmTIFc+bM0bxWUlNTMXfuXCxcuBCPHj3SfC2PNJbDhw9X+RqvQ+Rrpm7duti9e7eQ+hplEhISMGzYsAqT4KKiIvz888/w9fXlGk/0YhEgNpUiODgYQ4YMEZpSSN5wMu88IIQQWfj4+LCuXbuyEydOsPr167MDBw6wn376iTk6OmraHvFkbW3NUlJSGGPabfeuXr3KTExMuMd73qVLl9iAAQOYnp4e8/X11WxJ5EmOdoZEvPnz57MHDx5U6RqFhYVs06ZNzN3dnSmVSta8eXMWHBysSaXg7fle4mUPc3Nz5uLiwnbu3ClJXDlaGYogZxqFrv7wup5L0RbvRb788ksuKWKiXzN+fn5s6dKl3K9bGdGtDGNjY1nbtm3Zjh072B9//MH++OMPtmPHDubk5MRiY2MlTeEoI3UqBSGvg1r9EUJqpcaNG2PPnj14//33YWpqilOnTqFVq1bYu3cvFi1ahGPHjnGNZ2RkhPPnz8POzk6rfWB6ejrc3Nwka6d069YtzJ49Gxs3boSnpyfmz5+Pd999l2sMOdsZilBd0iiqC1NTU5w9e/Yft798XvmCcX/88YckBeP27Nmj8/jDhw9x4sQJREREYOPGjRgyZAiXeGXkbmUoFXNzc00axbhx44SmUbxOik337t0lHIk2Xu8L0a+Z0NBQLFmyBO7u7ujUqRPq16+vdZ53cTrRrQzLvy7Lfo+XTX3KP+eVwqErlYKQ6oQm/4SQWsnU1BQqlQq2trawtbXF5s2b0a1bN2RmZqJt27bIz8/nGq979+4YPHgwJk+eDBMTE6hUKrRo0QKTJk3CtWvXEBcXxzVeXl4ewsLCsHr1anTo0AELFy7Ehx9+yDVGmbLWXjdu3ECzZs10tjOcO3eupHmkUqps4ghop1GIqAheHZRfvOLtt99+Q0REBGJiYmBgYIDc3FzuMXT54YcfEBUVhePHj3O9bnXpoc7bsmXLZEmjqM54vS9Ev2Ze1JpRoVDg+vXrXOKUtTJMT09H27ZtK21luH37di7xysixWGRubo4zZ87Q5J9US5TzTwiplRwdHXH58mXY2tqiQ4cOWLt2LWxtbbFmzRo0btyYezyRxeIWLVqEhQsXwsrKClu3bkX//v25Xv95crYzFEHXz+/333/HzJkzsW/fPowYMQLz5s2TYWQ1j4iCcbp89NFH+Pbbb7lft7r0UOdt2rRp+PrrryscNzU1haOjI6ZPn45BgwZJFr+wsBAqlQp37typ8DrhWShSDqJfM88X4JOKXK0MRe7+KDNw4EDExMQgICBAeGxCXoYm/4SQWunf//43srOzAQCzZ8+Gp6cnNm/eDH19fURGRnKPJ7JYXGBgIAwNDeHg4ICNGzdqOgw8b9euXVzjlhXGKioqQmZmJuzt7bXu7tQEz6dRnD17lnsaRW0jumCcLgUFBZJUyBbdQ12UyoqKlqVRjBw5UpI0CgCIi4uDr68v7t27V+GcFNX3RZPrNSP17+3Zs2cDAGxtbTFs2DChFelFLxY5ODhg3rx5SE5OFpJKQcjroG3/hBCCZy3/fv/9d1hbW+Ptt9+WezhV4ufn99IcdQCIiIjgGldkO0PRRKZRVHc8tjcXFhZi586d2LBhAxISEtC4cWOMHj0aY8eOlWWr7OTJk5GRkYH9+/dzuV5ZK8OGDRtqHU9NTZWklWF1I1UaBfBsYuXp6Yng4GA0atSI+/X/KanSYY4fP46kpCTJXjP5+fmYPHlyjfy9DcizWCQqlYKQf4Im/4SQWk3qux21qVjc1KlTkZSUhBUrVsDLywsqlQp2dnbYu3cvZs+e/cYW/CufRhEWFiZ5GkV1x2OSI7pgXGXbb/Py8nDq1ClkZGTg6NGjcHZ25hKvQYMGKCgoqNDKsLa4evUq3n//fTx48ID7tU1NTZGWlgZ7e3vu164KHu8LOYrFif69Lbr9ZXVdLCJELjVrPyYhhLwiUXc7KtseC2gXi6sJYmJisG3bNri6umpNdNq0aYOMjAwZR1Y1cqVRVFcffvghDA0Nq3SN4OBgoQXjKpvAmJqawsvLCxMnToSNjQ23eDk5OYiOjkZERAS8vb3RtGlTjBkzBn5+fi+8K1hTSJVGAQCDBw/GkSNHqt3kf+TIkTA1Na3SNerWrYvdu3cjKCiI06heTvTv7ZCQEISHhyMgIABBQUGYNWsWsrKyEBMTg+DgYO7x7ty5g4CAAFkm/jU5BY68uejOPyGkVpLzLrWuYnHW1taSxRNFrnaGUpMrjUIOGRkZiIiIQEZGBlauXAlLS0vExcWhefPmaNu2Lbc4le2IEVUwTiQRrQyrG95pFOXl5+djyJAhsLCwgJOTE+rWrat1Xop86qNHj2Lt2rXIyMhAdHQ0mjZtip9++gktWrTABx98wDXWmDFj4OTkJKxYnOjf26JbGY4dOxbdunXDuHHjuF73RWp6KgV5s9EyFCGkVpLjLnVNLxbXuXNnxMbGYvLkyQD+10N53bp16NKli5xDqxIpCkBWRwkJCfD29ka3bt2QmJiI0NBQWFpaQqVSITw8HNHR0dxiyVkwTjRbW1uEhIQgJCRE08pw/PjxmDRpkrBWhry9ahqFFLZs2YL4+HgYGhriyJEjWr+/FQoF98n/zp07MWrUKIwYMQJpaWl4+vQpAODx48cICwvjvsAhulic6N/bOTk5miK3xsbGmsWFTz75RJIdD99//z2GDBmCo0ePClssmjlzJtLT03HkyBF4eXlpjnt4eGD27Nk0+Seyosk/IaRWunv3LiwtLSscf/LkCffc3OeLxR08eLBGFosT2c6Q8BcYGIjvvvsOAQEBMDEx0Rzv2bMnVq5cyTXWi+omjB49Gm3atMGSJUtqxOS/PLlaGfImOo2ivG+//RZz585FYGCgpHUiynz33XdYs2YNfH198fPPP2uOd+3aFXPnzuUeLzw8HObm5jh9+jROnz6tdU6KxQ3Rv7dFtzIUvVgE1NwUOFIz0OSfEFIribrbUb5Y3NatW2t0sTiR7QwJf+fOndO55dbCwkL4HeqPPvoI3377rdCYUqkOrQx5K2vrKYeioiIMGzZMyMQfAC5fvgw3N7cKx01NTfHw4UPu8TIzM7lf80VE/94W3cpQ9GIRIPbmAiGviyb/hJBaSdTdjtpWLM7JyanS75FUb+bm5sjOzq5QkC4tLQ1NmzYVOhYpC8aJUN1aGdYko0ePxrZt2/DNN98Iide4cWNcu3YNtra2WsePHTsm6f+lyGJxIn9vL1iwQPPvwYMHo3nz5pK2MhS9WATU3BQ4UjPQ5J8QUit17doVycnJWLx4saR3O3x9fWv8Sn9tamdYk/n4+GDGjBnYsWMHFAoFSktLkZSUhGnTpsHX11foWNatW8et7Z4crKysNK0M9+3bJ3krw9pErVZj0aJFiI+PR7t27SrkcC9btoxrvM8//xxTp07Fhg0boFAocOvWLaSkpGDatGmSVKeXo1icWq3G7t27cenSJSgUCrRu3Rr9+/fnvuigq5Whi4sLXFxcuMYpT/RiEUApcKR6o2r/hJBaR45eyjXZnj17Kj1Xvp1hQUGBwFGR11VcXAw/Pz/8/PPPYIyhTp06UKvV8PHxQWRkJPT09LjFetWCcW/qAsCyZcuEtjKsTXr27FnpOYVCgUOHDnGPOWvWLCxfvhyFhYUAgHr16mHatGmYN28e91iiO9GcP38e/fv3R05ODhwdHQE8W3CwsLDA3r17uW/9Nzc3x5kzZ4T97Z0yZQqioqLQvn17IYtFZc6dO4clS5bg9OnTKC0tRceOHTFjxgxKgSOyo8k/IaRWEv0BpLapqe0Ma4OMjAykpaWhtLQUzs7OaNmyJfcYlU3gTE1N8c4770haME6E2tTKsCZTq9U4duwYnJycYGBggIsXL6K0tBRt2rSBsbGxJDFtbGw0xeLKt967du0aOnbsiEePHnGN5+rqCktLS2zcuBENGjQAADx48AB+fn64c+cOUlJSuMYT3cpQjsUiQqozmvwTQmol0R9Aaovn2xnOnz+/RrUzJORVVLYbpqyVYURERI1pZSiXa9euISMjA25ubjA0NARjTJIUKwMDA1y6dKlCLQypGBkZ4fz587Czs9Oa/Kenp8PNzU3TGo8XQ0NDnDp1Cm3bttU6fv78eXTu3Jn7jq3Q0FAsWbIE7u7uQloZykVUKgUhr4tegYSQWkl0L+Warra0M6zJxo4d+8LzGzZsEDSSN19tbWUoQm5uLoYOHYrDhw9DoVDg6tWrsLOzw/jx42Fubo6lS5dyjefk5ITr168Lm/yLLhbn6OiI27dvV5j837lzBw4ODtzjiW5lWEbUYhEgPpWCkNdBd/4JIbXSiz7IKRQKXL9+XeBo3mzl2xmGhYXV6HaGNdnAgQO1nhcXF+P8+fN4+PAhevXqVWM6UlQHV69exfvvv48HDx7IPZQ3jq+vL+7cuYPw8HC0bt1ac2f8wIED8Pf3x4ULF7jGO3DgAGbMmIF58+bpXCg2NTXlGi85ORleXl4YMWIEIiMj8fnnn2sVi+vUqRPXePv378f06dMxZ84cuLq6AgBSU1Mxd+5cLFiwAB988IHma3l/ryJUtlg0btw4SRaLAPGpFIS8Dpr8E0IIqRKlUglDQ0N4eHi8sCgcTR7fPKWlpZg4cSLs7Owwffp0uYdTY6hUKnh6eiI7O1vuobxxrKysEB8fj/bt22tti8/MzISTkxP+/vtvrvHKd2kof6e47M6xWq3mGg8QWyxO1/dXNjUo/5z39yqqlaHoxSJAfCoFIa+Dtv0TQkg5586dw/r167FixQq5h/LGqA3tDGsrpVIJf39/9OjRgyb/HL3prQzl9OTJExgZGVU4fu/ePdSrV497vMOHD3O/5ss4OTlpWv1JTfT3J7qV4YEDBxAfH49mzZppHW/ZsiVu3LjBNVYZ0akUhLwOmvwTQmq9R48eYevWrVi/fj1OnTqFdu3ayT2kN0pkZKTcQyASysjIQElJidzDeKO8aitD8vrc3NwQFRWlabOnUChQWlqKxYsXv7Cy+z/VvXt37td8GZHF4kR/fzNnzkR6ejqOHDkCLy8vzXEPDw/Mnj2b++Rf9GIRAISFhWHKlCk6UykWLlyo1bHhTUylIG822vZPCKm1EhISsH79euzcuROFhYX4+uuvMX78eFqZJ7XS8xNWxhiys7MRGxuL0aNH4/vvv5dpZG+emt7KUA52dnY4efIkbt++jR49eqBTp044dOgQ+vXrhwsXLuD+/ftISkqCvb0917iJiYkvPO/m5sY1nhzF4goLC6FSqXDnzh2UlpZqnevXrx/XWKJbGfbp0wcdO3bEvHnzYGJiApVKBRsbG/zrX/9CaWkpoqOjucYD5EulIORV0OSfEFKrZGdnIyIiAhs2bMCTJ08wfPhw+Pj4oEuXLkhPT0ebNm3kHiIhsnh+wqpUKmFhYYFevXph7Nix1KKKyEqpVCInJweWlpbIycnBjz/+qJUT/9VXX6Fx48aSxH1e+TQn3pM30cXi4uLi4Ovri3v37lU4J8XkVFQrQ7kWi4BnNxZelRw7S0jtRpN/QkitYmBggCFDhmDkyJHo3bu35oNd3bp1afJPCCHVVPnJv0jPT0aLi4uRlpaGoKAghIaGwt3dnWs80cXiHBwc4OnpieDgYDRq1IjrtXXp3r07Bg8ejMmTJ2vuxLdo0QKTJk3CtWvXEBcXxyWOXItFhFR3tIxPCKlVbGxscOzYMVhbW8PGxgbvvPOO3EMihBDyCi5evIicnJwXfg3vmi1mZmYVjvXu3Rv16tWDv79/hV71VSW6WNydO3cQEBAgZOIPAPPnz4eXlxcuXryIkpISrFy5UquVoRSsrKwQEhIiybUrIzKVgpDXQZN/QkitcvnyZSQlJWH9+vXo3LkzWrVqhZEjRwIAVawntY6zs/Mrv+7PnDkj8WgIeTF3d3e8aMOqyBxqCwsLXL58mft1RReLGzx4MI4cOSLJ9nddunbtiqSkJCxZsgT29vY4cOAAOnbsiJSUFO71DORYLALEp1IQ8jpo2z8hpNb6+++/sXXrVmzYsAHHjx9H9+7d4ePjgwEDBsDCwkLu4REiude5GzZ79mwJR0LIiymVSpw4ceKlv5t5F1JUqVRaz8sKYS5YsADFxcVISkriGk90sbj8/HwMGTIEFhYWcHJyQt26dbXOT5kypcox5KBUKqFQKGRZLBKdSkHI66DJPyGEALh06RLWr1+Pn376Cffv30dxcbHcQyKEEPL/yZXzX9kk0tXVFRs2bOCeOia6WFx4eDi++OILGBoa4q233tLaCaRQKHD9+vUqx3ieiFaGci0WAc92ZKSlpQnbTUHI66DJPyGElFNSUoK9e/di0KBBcg+FEELI/yfX5P/GjRsVxmFhYQEDAwOh45CKlZUVpkyZgsDAQJ2dDXgT1cpQrtcLAIwdOxbdunXDuHHjhMcm5GVo8k8IqZX09PSQnZ1d4YNBbm4uLC0tKSeP1DpqtRrLly/H9u3bcfPmTRQVFWmdv3//vkwjI+RZK8rdu3fD3NxcaNyoqCgMGzYM9erV0zpeVFSEn3/+Gb6+vtxjiiwW17BhQ5w8eVLYXWpRrQzlnPzX1FQKUjPQ5J8QUitV9sHg1q1bsLe3595OiZDqLjg4GOHh4QgICEBQUBBmzZqFrKwsxMTEIDg4mD6wklpJ9EKx6GJx/v7+sLCwwDfffMP1upUR1cpQrsUiQJ5UCkJeFVX7J4TUKqtWrQLw7A9weHg4jI2NNefUajUSExOp/R+plTZv3ox169ahT58+CAkJwfDhw2Fvb4927dohNTWVJv+kWhC9Q6WsuN7z/vzzT51tAKtq0qRJGDJkiLBicWq1GosWLUJ8fDzatWtX4S71smXLuMYT1crw8OHD3K71ur799lvMnTtXWCoFIa+DJv+EkFpl+fLlAJ59oFuzZg309PQ05/T19WFra4s1a9bINTxCZJOTk6PJtzU2NkZeXh4A4JNPPkFQUJCcQyNEIyQk5IU7VHgpa4OpUCjg7u6uVYxOrVYjMzMTXl5e3OKVuXPnDgICAoRViT937hycnZ0BPLv7Xp4U7W9FtzKUI52pqKgIw4YNo4k/qZZo8k8IqVUyMzMBPNsSuGvXLk3OISG1XbNmzZCdnQ1ra2s4ODho+m+fPHmyQr4zIXIRtUNlwIABAICzZ8/C09NTa5dY2ULxp59+yiVWeYMHD8aRI0eE5eCLvkP+ySefAACGDh1aoZVh3759Nc95pTiIWiwqb/To0di2bZuwVApCXgfl/BNCarWioiJkZmbC3t6ea5shQt40gYGBMDU1xTfffIPo6GgMHz4ctra2uHnzJvz9/bFgwQK5h0gI6tevj0uXLsHa2hqNGzdGbGwsOnbsiOvXr8PZ2VmzY4WXjRs3YtiwYcKq+8tVLO7atWvIyMiAm5sbDA0NK013qCrRrQzt7e2xatUq9OnTByYmJjh79qzmWGpqKrZs2VLlGM+bMmUKoqKi0L59eyGpFIS8Dpr8E0JqpYKCAkyaNAkbN24E8KzVkJ2dHaZMmYImTZogMDBQ5hESIsaKFSvg6+uLhg0bah1PTU1FcnIyHBwcuFcYJ+SfcnR0RFRUFFxcXPDhhx+iT58+CAwMxLZt2zB58mTcuXNH7iFWiehicbm5uRg6dCgOHz4MhUKBq1evws7ODuPGjYO5uTmWLl3KNZ5ooheLgGc7CyujUChw6NAh7jEJeVV0m4sQUisFBgYiPT0dR44c0crb9PDwwOzZs2nyT2qNkJAQBAYGon///hg3bhx69+4NhUIBV1dXTU4uIdXFwIEDcfDgQbi4uGDq1KkYPnw41q9fr9mhwpvonHHRxeL8/f1Rt25d3Lx5E61bt9YcHzZsGPz9/SWZ/ItsZShHOpOcxQYJeRma/BNCaqWYmBhs27YNrq6uWndW2rRpg4yMDBlHRohYOTk5iI6ORkREBLy9vdG0aVOMGTMGfn5+aNGihdzDI0RL+fSTwYMHo3nz5khKSpJsh4ronHHRxeIOHDiA+Ph4NGvWTOt4y5YtcePGDe7xRLcyFL1YVJ6oVApCXgdt+yeE1EpGRkY4f/487OzsYGJigvT0dNjZ2SE9PR1ubm6SbAUkpLrLyspCREQEoqKi8Mcff6BHjx4YN24cBg0aREX/SLWQmJiIrl27VqjRUlJSguTkZLi5uXGNJzpn3N/fHxYWFsKKxZmYmODMmTNo2bKl1t/CkydPwsvLC7m5uVzjOTg4wNPTU1grw+cdP35c0sUioOanUpA3G/WgIITUSp07d0ZsbKzmedlq/Lp169ClSxe5hkWIrGxtbRESEoLMzEzExcWhUaNGGD9+PJo0aSL30AgB8CyfWtdW+7y8vBfmWv9TL2qBWf5vCC9qtRqLFi1C9+7dMXnyZAQEBGg9eHNzc0NUVJTmuUKhQGlpKRYvXizJz1N0K8PExESUlJRonru4uCAgIAAff/wxEhMTJYlZPpXCyMhIc3zYsGGIi4uTJCYhr4q2/RNCaqX58+fDy8sLFy9eRElJCVauXIkLFy4gJSXltaoRE1JTKZVKKBQKMMYq5OUSIpfKtk7n5uaifv363OOJzhk/d+4cnJ2dAQDnz5/XOsdzy3jZ3f3FixejR48eOHXqFIqKijB9+nRcuHAB9+/fR1JSErd4ZUS3MuzZsyeys7NhaWmpdbxssYh3mgEgPpWCkNdBk39CSK3UtWtXJCUlYcmSJbC3t9d8oEtJSdHc5SGktrlx4wYiIyMRGRmJP/74A25ubli3bp0k/cwJeR2DBg0C8GwC7OfnpzXxVqvVUKlU6Nq1K/e4onPGRRWLy8rKglqtRps2baBSqfDjjz9CT08PT548waBBg/DVV1+hcePG3ON+//33GDJkCI4ePSqklaHoxSIAePLkidYd/zL37t2j9CkiO8r5J4QQQmqxwsJC7Ny5Exs2bEBCQgIaN26M0aNHY+zYsbCzs5N7eIQAAMaMGQMA2LhxI4YOHQpDQ0PNOX19fdja2mLChAl4++23JR2HiJxxQPpicUqlEjk5ORXuiEtNVCvDssWiPXv2wMvLS+dikaOjoyTb8Pv06YOOHTti3rx5MDExgUqlgo2NDf71r3+htLQU0dHR3GMS8qrozj8hpFYp28r8IgqFQitHkJCazMrKCgUFBejbty/27dsHT09PYZXGCXlVERERAJ7VpZg2bZpkd23LKy4uxmeffYagoCDNQpiLiwtcXFwki1lZsbjx48dzLxZ38eJF5OTkvPBr2rVrxy0eIK6VoZmZGYBnd/5NTEwqLBa5urpiwoQJXGPKlUpByOugO/+EkFplz549lZ5LTk7G6tWrwRhDQUGBwFERIp9ly5Zh9OjReOutt+QeCiHVjrm5Oc6cOSNsF4yvry/u3LmD8PBwtG7dWlN9/8CBA/D398eFCxe4xClf06MyUrTea9iwIU6ePCks5z8kJETYYlH53RQ5OTn48ccfcfr0aZSWlqJjx46SpVIQ8jpo8k8IqfV+//13zJw5E/v27cOIESMwb948WFtbyz0sQoSobDeMqakpHB0dMX36dM0WWkLk4Ozs/Mpb3s+cOcM19pgxY+Dk5CRJpX1drKysEB8fj/bt22u13svMzISTkxP+/vtvLnGUSiVOnDgBCwuLF36djY0Nl3hlRLcyFEmuVApCXgdt+yeE1Fq3bt3C7NmzsXHjRnh6euLs2bN499135R4WIULt3r1b5/GHDx/ixIkTGDlyJDZu3IghQ4YIHhkhzwwYMEC22A4ODpg3bx6Sk5PRqVOnCneQeReoE1ksztraWvhEtayVYXx8PNq1a1eh4N+yZcuqHEPOxSI5UikIeR10558QUuvk5eUhLCwMq1evRocOHbBw4UJ8+OGHcg+LkGrphx9+QFRUFI4fPy73UAgRrkWLFpWe41mgroyoYnFy3aXu2bNnpecUCgUOHTpU5RghISGv/LWzZ8+ucrwycqVSEPI6aPJPCKlVFi1ahIULF8LKygphYWHo37+/3EMipFq7evUq3n//fTx48EDuoRAC4NmulOjoaGRkZODrr79Gw4YNcebMGTRq1AhNmzaVe3j/SFmxuNu3b6NHjx7o1KkTDh06hH79+mkVi+OVK9+zZ0/s3r0b5ubmXK5H5EulIOR10OSfEFKrKJVKGBoawsPDA3p6epV+3a5duwSOipDqS6VSwdPTE9nZ2XIPhRCoVCp4eHjAzMwMWVlZuHz5Muzs7BAUFIQbN24gKipKkrhFRUXIzMyEvb096tThnzVb24rFSd3KsDxRi0WU80/eBJTzTwipVXx9fSX7gEFITbRu3To4OzvLPQxCAAABAQHw8/PDokWLYGJiojnu7e0NHx8f7vHy8/MxefJkbNy4EQBw5coV2NnZYcqUKWjSpAkCAwO5x7SysnqtretVoVarsXz5cmzfvh03b95EUVGR1vn79+9zjSeylSFQcbFowoQJaNiwIXbv3i3pYhEh1RVN/gkhtUpkZKTcQyCkWqmsinleXh5OnTqFjIwMHD16VPCoCNHt5MmTWLt2bYXjTZs2fWmhtX9i5syZSE9Px5EjR+Dl5aU57uHhgdmzZ3Od/MtRLC4kJATh4eEICAhAUFAQZs2ahaysLMTExCA4OJhrLOBZtf+6devi5s2baN26teb4sGHD4O/vz33yL3KxqHv37tDX1+d6TUJ4o8k/IYQQUoulpaXpPG5qagovLy9MnDiRclRJtWFgYIBHjx5VOH758uWX5lr/EzExMdi2bRtcXV21do21adMGGRkZXGO5u7sLLxa3efNmrFu3Dn369EFISAiGDx8Oe3t7tGvXDqmpqdy7GRw4cADx8fFo1qyZ1vGWLVvixo0bXGMBYheLDh8+zPV6hEiBJv+EEEJILUYfWMmbpH///pg7dy62b98O4NmE+ObNmwgMDMSnn37KPd7du3d15nA/efKEewrZ8ePHJVnAeJGcnBw4OTkBAIyNjZGXlwcA+OSTTxAUFMQ9nshWhoD4xSJAfCoFIa9DKfcACCGEEEIIeRVLlizRTMgLCgrQvXt3ODg4wMTEBKGhodzjde7cGbGxsZrnZRP+devWoUuXLlxjWVtbw8bG5oUP3po1a6Yp5ung4IADBw4AeHbHXIrJuJubm1aevUKhQGlpKRYvXvzCNoD/VNliUXFxsSaelItFwLNUimXLlmHo0KHIy8tDQEAABg0aBKVSiTlz5kgSk5BXRdX+CSGEEELIG+Xw4cNa1fA9PDwkiZOcnAwvLy+MGDECkZGR+Pzzz3HhwgWkpKQgISEBnTp14hJHrkrxgYGBMDU1xTfffIPo6GgMHz4ctra2uHnzJvz9/bFgwQIucUS3Mizz6NEjfPzxx7hw4QIeP36MJk2aICcnB126dMH+/ftRv359rvEAwN7eHqtWrUKfPn1gYmKCs2fPao6lpqZiy5Yt3GMS8qpo8k8IIYQQQqq90tJSREZGYteuXcjKyoJCoUCLFi0wePBgjBo1SrJOLufOncOSJUu0FhtmzJih2S7PQ8+ePbF7926Ym5tzu+Y/cfz4cSQlJcHBwQH9+vXjdl25WxmKWiwCgPr16+PSpUuwtrZG48aNERsbi44dO+L69etwdnbWpFYQIgfK+SeEEEIIIdUaYwz9+vXD/v370b59ezg5OYExhkuXLsHPzw+7du1CTEyMJLGdnJw0rf6kIlftjcTERHTt2hV16jybEri4uMDFxQUlJSVITEyEm5sb95iiWhlWtlhkZWUFxphki0VlqRTW1taaVIqOHTtKlkpByOugyT8hhBBCCKnWIiMjkZiYiIMHD1bIDT906BAGDBiAqKgo+Pr6co+tVquxe/duXLp0CQqFAq1bt0b//v01E2besUQWi+vZsyeys7MrpBvk5eWhZ8+eXLsLiGxlKOdi0cCBA3Hw4EG4uLhg6tSpGD58ONavX69JpSBETrTtnxBCCCGEVGsfffQRevXqhcDAQJ3nw8LCkJCQgPj4eK5xz58/j/79+yMnJweOjo4AgCtXrsDCwgJ79+7luvUfAIKDgxEeHo6AgAAEBQVh1qxZyMrKQkxMDIKDg7m33lMqlbh9+3aFyvdXrlzBe++9p7NS/j+No1AohLUyjIiIwNSpU7Fnz55KF4u+//57SRaLnidVKgUh/wRN/gkhhBBCSLVmZWWFuLg4dOjQQef5tLQ0eHt7c+/d7urqCktLS2zcuBENGjQAADx48AB+fn64c+cOUlJSuMYTVSxu0KBBAIA9e/bAy8tLazu6Wq2GSqWCo6Mj4uLiuMRTKpU4ceLES9vr8epoINdiEVAxlaJMSUkJkpOTJUmlIORV0bZ/QgghhBBSrd2/fx+NGjWq9HyjRo3w4MED7nHT09Nx6tQpzcQfABo0aIDQ0FB07tyZe7ycnBzNbgJjY2NNcbhPPvkEQUFB3OKYmZkBeLY93sTEBIaGhppz+vr6cHV1xYQJE7jFA561MhTVzUClUmHRokWVnvf29saqVaskiS0ylYKQ10WTf0IIIYQQUq2p1eoX5tjr6emhpKSEe1xHR0fcvn0bbdu21Tp+584dODg4cI8nqlhcREQEAMDW1hbTpk2TpOWdnORaLAJQaTHB3NzcGvdzJm8emvwTQgghhJBqjTEGPz+/SifAT58+lSRuWFgYpkyZgjlz5sDV1RUAkJqairlz52LhwoVaOfGmpqZVjie6WNzs2bO5X1OX7t27Q19fX0gsQJ7ForJUCoVCUeG1WpZK0bVrV64xCXldlPNPCCGEEEKqtTFjxrzS15Xd0eZFqVRq/l12N7fso3P55zyL1ZUnRbE4Z2fnV25zd+bMGS4xRVMqlfD29n7hYlFcXBzX/7Oy1+jGjRsxdOjQCqkUtra2mDBhAt5++21uMQl5XXTnnxBCCCGEVGu8J/Wv6vDhw0LjPV8szsXFBS4uLigpKUFiYiKXYnEDBgyo8jX+KVGtDEePHv3Sr+Fd6b+mp1KQmoHu/BNCCCGEEFIN6Onp6SwWl5ubC0tLyze+WJzoVoaEEG00+SeEEEIIIaQShYWFUKlUuHPnDkpLS7XO8e7brlQqcfv27Qot8a5cuYL33ntPq8YALw8fPkR0dDQyMjLw9ddfo2HDhjhz5gwaNWqEpk2bco0lqpWhaLUhlYLUDLTtnxBCCCGEEB3i4uLg6+uLe/fuVTjHM89frmJxKpUKHh4eMDMzQ1ZWFiZMmICGDRti9+7duHHjBqKiorjGE9XKUDQ5UykIeR00+SeEEEIIIUSHSZMmYciQIQgODn5h67iqMjMzA/CseKCJiUmFYnGurq6YMGEC97gBAQHw8/PDokWLYGJiojnu7e0NHx8f7vFEtTIUTVTXBEKqiib/hBBCCCGE6HDnzh0EBARIOvEH5CsWd/LkSaxdu7bC8aZNmyInJ4d7PNGtDOUiMpWCkNdBk39CCCGEEEJ0GDx4MI4cOQJ7e3sh8UTfQTYwMNBZR+Dy5csV6g7wsGDBAs2/Bw8ejObNm3NvZSg30akUhLwOKvhHCCGEEEKIDvn5+RgyZAgsLCzg5OSEunXrap3nUZ1ezmJxn332Ge7evYvt27ejYcOGUKlU0NPTw4ABA+Dm5oYVK1Zwjfd8K8MyJSUlSE5O5tLKUG4eHh7o2LGjJpUiPT0ddnZ2SE5Oho+PD7KysuQeIqnFaPJPCCGEEEKIDuHh4fjiiy9gaGiIt956S2uSrlAocP369SrHCAkJeeWv5b0z4NGjR/j4449x4cIFPH78GE2aNEFOTg66dOmC/fv3c08/qOmtDIFn9RvOnDkDe3t7rcn/jRs34OjoiMLCQrmHSGox2vZPCCGEEEKIDt9++y3mzp2LwMBAKJVKSWLIWSzO1NQUx44dw+HDh3H69GmUlpaiY8eO8PDwkCQeY0znLofc3FxhdQ6kJjqVgpDXQZN/QgghhBBCdCgqKsKwYcMkm/jrIqpYXGlpKSIjI7Fr1y5kZWVBoVCgRYsWsLKyqnSS/k/J1cpQDv3798fcuXOxfft2AM++55s3byIwMBCffvqpzKMjtZ2432SEEEIIIYS8QUaPHo1t27YJi6dSqdCqVSssXLgQS5YswcOHDwEAu3fvxsyZM7nFYYyhX79+GD9+PP766y84OTmhbdu2uHHjBvz8/DBw4EBusYBnW+HNzMw0rQzLnpuZmcHKygqfffYZNm3axDWmXJYsWYK7d+/C0tISBQUF6N69OxwcHGBiYoLQ0FC5h0dqObrzTwghhBBCiA5qtRqLFi1CfHw82rVrV6Hg37Jly7jGCwgIgJ+fn6ZYXBlvb2/4+PhwixMZGYnExEQcPHgQPXv21Dp36NAhDBgwAFFRUfD19eUST65WhnIQnUpByOuggn+EEEIIIYTo8PzEuDyFQoFDhw5xjSeqWNxHH32EXr16ITAwUOf5sLAwJCQkID4+nku82qKyVIrBgwdj1KhRXFMpCPkn6M4/IYQQQgghOhw+fFhoPFHF4lQqFRYtWlTpeW9vb6xatYpLLDlbGYpUlkqxf/9+tG/fHk5OTmCM4dKlS/Dz88OuXbsQExMj9zBJLUeTf0IIIYQQQl7g2rVryMjIgJubGwwNDbkXxCsjqljc/fv30ahRo0rPN2rUCA8ePOASa8CAAVyuU92JTqUg5J+gbf+EEEIIIYTokJubi6FDh+Lw4cNQKBS4evUq7OzsMG7cOJibm2Pp0qVc4z169Agff/wxLly4gMePH6NJkybIyclBly5dsH//fm658np6esjJyal0N8Ht27fRpEkTqNVqLvFqA0qlIG8CmvwTQgghhBCig6+vL+7cuYPw8HC0bt1ak4N/4MAB+Pv748KFC5LElbpYnFKphLe3t1bLvfKePn2KuLg4SSb/oloZimZlZYW4uDh06NBB5/m0tDR4e3sjJydH7MAIKYe2/RNCCCGEEKLDgQMHEB8fj2bNmmkdb9myJW7cuME1VmXF4qysrLinGYwePfqlXyPF9nSVSgUPDw+YmZkhKysLEyZMQMOGDbF7927cuHEDUVFR3GOKIjKVgpB/iib/hBBCCCGE6PDkyRMYGRlVOH7v3r1K75r/E6KLxZW13hNNVCtDOajVatSpU/nUSk9PDyUlJQJHREhFNPknhBBCCCFEBzc3N0RFRWHevHkAnhXgKy0txeLFi1/YBvB11ZZicSdPnsTatWsrHG/atOkbvx2eMQY/P78XplIQIjea/BNCCCGEEFKOnZ0dTp48icWLF6NHjx44deoUioqKMH36dFy4cAH3799HUlISt3hbt27FN998o3NBoayI3ObNm9/4yb+oVoZykCuVgpDXQQX/CCGEEEIIKUepVCInJweWlpbIycnBjz/+qFWA76uvvkLjxo25xastxeI+++wz3L17F9u3b0fDhg2hUqmgp6eHAQMGwM3NDStWrJB7iITUaDT5J4QQQgghpJzyk38R9PX1cePGjUoXFG7duoUWLVq88VvHRbUyJIToRtv+CSGEEEIIec7Fixdfeqe9Xbt2XGLVlmJxpqamOHbsmOStDAkhutHknxBCCCGEkOe4u7vjRRtkFQoF1Go1l1i1oVicyFaGhBDdaNs/IYQQQggh5SiVSpw4ceKlRehsbGy4xBszZswrfZ1cLfqqijGGvn37aloZvvPOO5pWhufOnUO/fv24tjIkhOhGk39CCCGEEELKEZ3zX9NFRERg6tSp2LNnT6WtDL///nuqhk+IxJRyD4AQQgghhBBSc71qK0NCiLRo8k8IIYQQQkg53bt3h76+vtzDqDFUKhW8vLwqPe/t7Y309HSBIyKkdqJt/4QQQgghhBDJ1JZWhoRUd1TtnxBCCCGEEB3UajWWL1+O7du34+bNmygqKtI6f//+fZlG9mapLa0MCanuaPJPCCGEEEKIDiEhIQgPD0dAQACCgoIwa9YsZGVlISYmBsHBwXIP741RG1oZEvImoG3/hBBCCCGE6GBvb49Vq1ahT58+MDExwdmzZzXHUlNTsWXLFrmH+Eao6a0MCXlT0OSfEEIIIYQQHerXr49Lly7B2toajRs3RmxsLDp27Ijr16/D2dkZeXl5cg+REEJeGVX7J4QQQgghRIdmzZohOzsbAODg4IADBw4AAE6ePFnpFnZCCKmuaPJPCCGEEEKIDgMHDsTBgwcBAFOnTkVQUBBatmwJX19fjB07VubREULI66Ft/4QQQgghhLyC48ePIykpCQ4ODujXr5/cwyGEkNdCk39CCCGEEEJ0SExMRNeuXSu0qSspKUFycjLc3NxkGhkhhLw+mvwTQgghhBCig56eHrKzs2Fpaal1PDc3F5aWllCr1TKNjBBCXh/l/BNCCCGEEKIDYwwKhaLC8dzcXNSvX1+GERFCyD9X5+VfQgghhBBCSO0xaNAgAIBCoYCfn59WZX+1Wg2VSoWuXbvKNTxCCPlHaPJPCCGEEEJIOWZmZgCe3fk3MTGBoaGh5py+vj5cXV0xYcIEuYZHCCH/COX8E0IIIYQQokNISAimTZtGW/wJITUCTf4JIYQQQgghhJAajrb9E0IIIYQQ8v85OzvrLPKny5kzZyQeDSGE8EOTf0IIIYQQQv6/AQMGyD0EQgiRBG37J4QQQgghhBBCajil3AMghBBCCCGkunr48CHCw8Mxc+ZM3L9/H8Cz7f5//fWXzCMjhJDXQ3f+CSGEEEII0UGlUsHDwwNmZmbIysrC5cuXYWdnh6CgINy4cQNRUVFyD5EQQl4Z3fknhBBCCCFEh4CAAPj5+eHq1aswMDDQHPf29kZiYqKMIyOEkNdHk39CCCGEEEJ0OHnyJD7//PMKx5s2bYqcnBwZRkQIIf8cTf4JIYQQQgjRwcDAAI8ePapw/PLly7CwsJBhRIQQ8s/R5J8QQgghhBAd+vfvj7lz56K4uBgAoFAocPPmTQQGBuLTTz+VeXSEEPJ6qOAfIYQQQgghOjx69Agff/wxLly4gMePH6NJkybIyclBly5dsH//ftSvX1/uIRJCyCujyT8hhBBCCCEvcPjwYZw+fRqlpaXo2LEjPDw85B4SIYS8tjpyD4AQQgghhJDqprS0FJGRkdi1axeysrKgUCjQokULWFlZgTEGhUIh9xAJIeS10J1/QgghhBBCymGMoW/fvti/fz/at2+Pd955B4wxXLp0CefOnUO/fv0QExMj9zAJIeS10J1/QgghhBBCyomMjERiYiIOHjyInj17ap07dOgQBgwYgKioKPj6+so0QkIIeX10558QQgghhJByPvroI/Tq1QuBgYE6z4eFhSEhIQHx8fGCR0YIIf8ctfojhBBCCCGkHJVKBS8vr0rPe3t7Iz09XeCICCGk6mjyTwghhBBCSDn3799Ho0aNKj3fqFEjPHjwQOCICCGk6mjyTwghhBBCSDlqtRp16lReGktPTw8lJSUCR0QIIVVHBf8IIYQQQggphzEGPz8/1KtXT+f5p0+fCh4RIYRUHU3+CbCIJOMAAACBSURBVCGEEEIIKWf06NEv/Rqq9E8IedNQtX9CCCGEEEIIIaSGo5x/QgghhBBCCCGkhqPJPyGEEEIIIYQQUsPR5J8QQgghhBBCCKnhaPJPCCGEEEIIIYTUcDT5J4QQQgghhBBCajia/BNCCCGEEEIIITUcTf4JIYQQQgghhJAa7v8Bz86KA8b+CogAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot correlation matrix\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "corr = merged_data.corr()\n",
    "plt.figure(figsize=(10, 8))\n",
    "sns.heatmap(corr, annot=True, cmap='coolwarm')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ML-Models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Separate prediction target and features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Separate features and target\n",
    "X = merged_data.drop(columns=['verbrauch (kWh/a)']) \n",
    "y = merged_data['verbrauch (kWh/a)']\n",
    "\n",
    "# Split data into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preprocess data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the preprocessor and the pipeline\n",
    "numeric_features = X.select_dtypes(include=['int64', 'float64']).columns\n",
    "categorical_features = X.select_dtypes(include=['object', 'category']).columns\n",
    "\n",
    "numeric_transformer = Pipeline(steps=[\n",
    "    ('imputer', SimpleImputer(strategy='mean')),\n",
    "    ('scaler', StandardScaler())\n",
    "])\n",
    "\n",
    "categorical_transformer = Pipeline(steps=[\n",
    "    ('imputer', SimpleImputer(strategy='constant', fill_value='missing')),\n",
    "    ('onehot', OneHotEncoder(handle_unknown='ignore'))\n",
    "])\n",
    "\n",
    "preprocessor = ColumnTransformer(\n",
    "    transformers=[\n",
    "        ('num', numeric_transformer, numeric_features),\n",
    "        ('cat', categorical_transformer, categorical_features)\n",
    "    ])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train XGBoost, Linear Regression & Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Model</th>\n",
       "      <th>MAE</th>\n",
       "      <th>RMSE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Random Forest</td>\n",
       "      <td>8445.920000</td>\n",
       "      <td>13822.502169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Linear Regression</td>\n",
       "      <td>11309.853552</td>\n",
       "      <td>15499.300681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>XGBoost</td>\n",
       "      <td>9299.009062</td>\n",
       "      <td>14947.723985</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Model           MAE          RMSE\n",
       "0      Random Forest   8445.920000  13822.502169\n",
       "1  Linear Regression  11309.853552  15499.300681\n",
       "2            XGBoost   9299.009062  14947.723985"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from xgboost import XGBRegressor\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "# Define the preprocessor and the pipeline for Random Forest\n",
    "rf_model = Pipeline(steps=[\n",
    "    ('preprocessor', preprocessor),\n",
    "    ('regressor', RandomForestRegressor())\n",
    "])\n",
    "\n",
    "# Train the Random Forest model\n",
    "rf_model.fit(X_train, y_train)\n",
    "\n",
    "# Make predictions and evaluate\n",
    "y_pred_rf = rf_model.predict(X_test)\n",
    "mae_rf = mean_absolute_error(y_test, y_pred_rf)\n",
    "rmse_rf = np.sqrt(mean_squared_error(y_test, y_pred_rf))\n",
    "\n",
    "# Define the preprocessor and the pipeline for Linear Regression\n",
    "linear_model = Pipeline(steps=[\n",
    "    ('preprocessor', preprocessor),\n",
    "    ('regressor', LinearRegression())\n",
    "])\n",
    "\n",
    "# Train the Linear Regression model\n",
    "linear_model.fit(X_train, y_train)\n",
    "\n",
    "# Make predictions and evaluate\n",
    "y_pred_linear = linear_model.predict(X_test)\n",
    "mae_linear = mean_absolute_error(y_test, y_pred_linear)\n",
    "rmse_linear = np.sqrt(mean_squared_error(y_test, y_pred_linear))\n",
    "\n",
    "# Define the preprocessor and the pipeline for XGBoost\n",
    "xgboost_model = Pipeline(steps=[\n",
    "    ('preprocessor', preprocessor),\n",
    "    ('regressor', XGBRegressor())\n",
    "])\n",
    "\n",
    "# Train the XGBoost model\n",
    "xgboost_model.fit(X_train, y_train)\n",
    "\n",
    "# Make predictions and evaluate\n",
    "y_pred_xgboost = xgboost_model.predict(X_test)\n",
    "mae_xgboost = mean_absolute_error(y_test, y_pred_xgboost)\n",
    "rmse_xgboost = np.sqrt(mean_squared_error(y_test, y_pred_xgboost))\n",
    "\n",
    "# Store loss functions in table\n",
    "loss_functions = pd.DataFrame({\n",
    "    'Model': ['Random Forest', 'Linear Regression', 'XGBoost'],\n",
    "    'MAE': [mae_rf, mae_linear, mae_xgboost],\n",
    "    'RMSE': [rmse_rf, rmse_linear, rmse_xgboost]\n",
    "})\n",
    "loss_functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               Model           MAE          RMSE\n",
      "0      Random Forest   8445.920000  13822.502169\n",
      "1  Linear Regression  11309.853552  15499.300681\n",
      "2            XGBoost   9299.009062  14947.723985\n",
      "3      Mean Baseline  20311.191919  23075.523793\n",
      "4    Median Baseline  15522.000000  19814.305438\n"
     ]
    }
   ],
   "source": [
    "# Compare to Baseline-Models\n",
    "from sklearn.dummy import DummyRegressor\n",
    "\n",
    "# Define the mean baseline model\n",
    "mean_baseline = DummyRegressor(strategy='mean')\n",
    "\n",
    "# Train the mean baseline model\n",
    "mean_baseline.fit(X_train, y_train)\n",
    "\n",
    "# Make predictions and evaluate\n",
    "y_pred_mean_baseline = mean_baseline.predict(X_test)\n",
    "mae_mean_baseline = mean_absolute_error(y_test, y_pred_mean_baseline)\n",
    "rmse_mean_baseline = np.sqrt(mean_squared_error(y_test, y_pred_mean_baseline))\n",
    "\n",
    "# Define the median baseline model\n",
    "median_baseline = DummyRegressor(strategy='median')\n",
    "\n",
    "# Train the median baseline model\n",
    "median_baseline.fit(X_train, y_train)\n",
    "\n",
    "# Make predictions and evaluate\n",
    "y_pred_median_baseline = median_baseline.predict(X_test)\n",
    "mae_median_baseline = mean_absolute_error(y_test, y_pred_median_baseline)\n",
    "rmse_median_baseline = np.sqrt(mean_squared_error(y_test, y_pred_median_baseline))\n",
    "\n",
    "# Add baseline model performance to the loss functions table\n",
    "loss_functions = pd.DataFrame({\n",
    "    'Model': ['Random Forest', 'Linear Regression', 'XGBoost', 'Mean Baseline', 'Median Baseline'],\n",
    "    'MAE': [mae_rf, mae_linear, mae_xgboost, mae_mean_baseline, mae_median_baseline],\n",
    "    'RMSE': [rmse_rf, rmse_linear, rmse_xgboost, rmse_mean_baseline, rmse_median_baseline]\n",
    "})\n",
    "\n",
    "print(loss_functions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hyperparameter Tuning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                Model           MAE\n",
      "0       Random Forest   8445.920000\n",
      "1   Linear Regression  11309.853552\n",
      "2             XGBoost   9299.009062\n",
      "3  Best Random Forest   9142.280000\n",
      "4        Best XGBoost   8964.422656\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from xgboost import XGBRegressor\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "# Define the preprocessor and the pipeline for Random Forest\n",
    "rf_model = Pipeline(steps=[\n",
    "    ('preprocessor', preprocessor),\n",
    "    ('regressor', RandomForestRegressor())\n",
    "])\n",
    "\n",
    "# Define the parameter grid for Random Forest\n",
    "param_grid_rf = {\n",
    "    'regressor__n_estimators': [100, 200, 300],\n",
    "    'regressor__max_depth': [None, 10, 20, 30],\n",
    "    'regressor__min_samples_split': [2, 5, 10]\n",
    "}\n",
    "\n",
    "# Perform Grid Search with Cross Validation for Random Forest\n",
    "grid_search_rf = GridSearchCV(estimator=rf_model, param_grid=param_grid_rf, cv=5, n_jobs=-1, scoring='neg_mean_absolute_error')\n",
    "grid_search_rf.fit(X_train, y_train)\n",
    "\n",
    "# Get the best parameters and best score for Random Forest\n",
    "best_rf_model = grid_search_rf.best_estimator_\n",
    "y_pred_best_rf = best_rf_model.predict(X_test)\n",
    "mae_best_rf = mean_absolute_error(y_test, y_pred_best_rf)\n",
    "\n",
    "# Define the preprocessor and the pipeline for Linear Regression\n",
    "linear_model = Pipeline(steps=[\n",
    "    ('preprocessor', preprocessor),\n",
    "    ('regressor', LinearRegression())\n",
    "])\n",
    "\n",
    "# Train the Linear Regression model\n",
    "linear_model.fit(X_train, y_train)\n",
    "y_pred_linear = linear_model.predict(X_test)\n",
    "mae_linear = mean_absolute_error(y_test, y_pred_linear)\n",
    "\n",
    "# Define the preprocessor and the pipeline for XGBoost\n",
    "xgboost_model = Pipeline(steps=[\n",
    "    ('preprocessor', preprocessor),\n",
    "    ('regressor', XGBRegressor())\n",
    "])\n",
    "\n",
    "# Define the parameter grid for XGBoost\n",
    "param_grid_xgb = {\n",
    "    'regressor__n_estimators': [100, 200, 300],\n",
    "    'regressor__max_depth': [3, 6, 9],\n",
    "    'regressor__learning_rate': [0.01, 0.1, 0.2],\n",
    "    'regressor__subsample': [0.6, 0.8, 1.0]\n",
    "}\n",
    "\n",
    "# Perform Grid Search with Cross Validation for XGBoost\n",
    "grid_search_xgb = GridSearchCV(estimator=xgboost_model, param_grid=param_grid_xgb, cv=5, n_jobs=-1, scoring='neg_mean_absolute_error')\n",
    "grid_search_xgb.fit(X_train, y_train)\n",
    "\n",
    "# Get the best parameters and best score for XGBoost\n",
    "best_xgb_model = grid_search_xgb.best_estimator_\n",
    "y_pred_best_xgb = best_xgb_model.predict(X_test)\n",
    "mae_best_xgb = mean_absolute_error(y_test, y_pred_best_xgb)\n",
    "\n",
    "# Store loss functions in a table\n",
    "loss_functions = pd.DataFrame({\n",
    "    'Model': ['Random Forest', 'Linear Regression', 'XGBoost', 'Best Random Forest', 'Best XGBoost'],\n",
    "    'MAE': [mae_rf, mae_linear, mae_xgboost, mae_best_rf, mae_best_xgb]\n",
    "})\n",
    "\n",
    "print(loss_functions)\n"
   ]
  }
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