Final_conv_stats.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import mysql.connector\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "\n",
    "from config import config\n",
    "\n",
    "connection = mysql.connector.connect(\n",
    "    host=config.get('HOST'),\n",
    "    user=config.get('USERNAME'),\n",
    "    password=config.get('PASSWORD'),\n",
    "    database=config.get('DATABASE')\n",
    ")\n",
    "\n",
    "cursor = connection.cursor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "start_datee = '2019-09-01 00:00:00'\n",
    "end_datee = '2019-12-31 23:59:59'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "' \\nSELECT tweets.text\\nFROM tweets\\nJOIN hasher ON hasher.id = tweets.id\\nJOIN conversations ON hasher.conversation_id = conversations.conversation_id\\nWHERE conversations.conversation_id IN (1, 2, 3)\\n'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get the text from conversations query\n",
    "\"\"\" \n",
    "SELECT tweets.text\n",
    "FROM tweets\n",
    "JOIN hasher ON hasher.id = tweets.id\n",
    "JOIN conversations ON hasher.conversation_id = conversations.conversation_id\n",
    "WHERE conversations.conversation_id IN (1, 2, 3)\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/_1/64lzdpgn2472v4xdvl_3y1l40000gn/T/ipykernel_12110/2473851308.py:14: UserWarning: pandas only supports SQLAlchemy connectable (engine/connection) or database string URI or sqlite3 DBAPI2 connection. Other DBAPI2 objects are not tested. Please consider using SQLAlchemy.\n",
      "  df = pd.read_sql(conv_length, connection)\n"
     ]
    },
    {
     "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>length</th>\n",
       "      <td>265246.0</td>\n",
       "      <td>2.54693</td>\n",
       "      <td>1.763617</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>76.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           count     mean       std  min  25%  50%  75%   max\n",
       "length  265246.0  2.54693  1.763617  1.0  2.0  2.0  3.0  76.0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Conversation length descriptive statistics\n",
    "conv_length = f\"\"\"\n",
    "SELECT length \n",
    "FROM conversations \n",
    "WHERE conversation_id NOT IN (\n",
    "        SELECT conversation_id \n",
    "        FROM conversations \n",
    "        WHERE start <1500000000000 \n",
    "            OR end <1500000000000)\n",
    "    AND start >= UNIX_TIMESTAMP('{start_datee}') * 1000\n",
    "    AND start <= UNIX_TIMESTAMP('{end_datee}') * 1000;\n",
    "\"\"\"\n",
    "\n",
    "df = pd.read_sql(conv_length, connection)\n",
    "\n",
    "descriptive_conv_length_stats = df['length'].describe() # Compute the descriptive statistics\n",
    "\n",
    "descriptive_conv_length_stats_df = descriptive_conv_length_stats.to_frame().T # Represent it as a data frame\n",
    "\n",
    "descriptive_conv_length_stats_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/_1/64lzdpgn2472v4xdvl_3y1l40000gn/T/ipykernel_12110/2563037356.py:14: UserWarning: pandas only supports SQLAlchemy connectable (engine/connection) or database string URI or sqlite3 DBAPI2 connection. Other DBAPI2 objects are not tested. Please consider using SQLAlchemy.\n",
      "  df_start = pd.read_sql(conv_start, connection)\n"
     ]
    }
   ],
   "source": [
    "# Extract conversation starts time units for all connversations\n",
    "conv_start = f\"\"\"\n",
    "SELECT start \n",
    "FROM conversations \n",
    "WHERE conversation_id NOT IN (\n",
    "        SELECT conversation_id \n",
    "        FROM conversations \n",
    "        WHERE start <1500000000000 \n",
    "            OR end <1500000000000) \n",
    "    AND start >= UNIX_TIMESTAMP('{start_datee}') * 1000\n",
    "    AND start <= UNIX_TIMESTAMP('{end_datee}') * 1000;\n",
    "\"\"\"\n",
    "\n",
    "df_start = pd.read_sql(conv_start, connection)\n",
    "\n",
    "df_start['start'] = pd.to_datetime(df_start['start'], unit='ms') # Convert timestamp to a date time format\n",
    "\n",
    "df_start['hour'] = df_start['start'].dt.hour # Extract the hour when the conv starts\n",
    "\n",
    "df_start['day_of_month'] = df_start['start'].dt.day # Extract the day of the month\n",
    "\n",
    "df_start['day_of_week'] = df_start['start'].dt.dayofweek # Extract the day when the conv starts\n",
    "\n",
    "df_start['month'] = df_start['start'].dt.month # Extract the month when the conv starts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jangalic04/anaconda3/envs/JB1010/lib/python3.10/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/jangalic04/anaconda3/envs/JB1010/lib/python3.10/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/jangalic04/anaconda3/envs/JB1010/lib/python3.10/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"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Monthly distribution of conversation starts\n",
    "\n",
    "monthly_distribution = df_start['month'].value_counts().reindex(range(1, 13), fill_value=0).sort_index() # Count the number of conversations starting on each month\n",
    "\n",
    "plt.figure(figsize=(12, 8))\n",
    "ax = sns.barplot(x=monthly_distribution.index, y=monthly_distribution.values)\n",
    "plt.xlabel('Month of the year', fontsize=12)\n",
    "plt.ylabel('Number of conversations', fontsize=12)\n",
    "plt.title('Monthly distribution of conversations starts', fontsize=14, weight='bold')\n",
    "plt.xticks(range(12), ['January', 'February', 'March', 'April', 'May', 'June', 'July', 'August', 'September', 'October', 'November', 'December'], fontsize = 9)\n",
    "\n",
    "for p in ax.patches:\n",
    "    ax.annotate(format(p.get_height(), '.0f'), \n",
    "                (p.get_x() + p.get_width() / 2., p.get_height()), \n",
    "                ha = 'center', va = 'center', \n",
    "                xytext = (0, 9), \n",
    "                textcoords = 'offset points',\n",
    "                fontsize=11, weight='bold', color='black')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/_1/64lzdpgn2472v4xdvl_3y1l40000gn/T/ipykernel_12110/4204465844.py:15: UserWarning: pandas only supports SQLAlchemy connectable (engine/connection) or database string URI or sqlite3 DBAPI2 connection. Other DBAPI2 objects are not tested. Please consider using SQLAlchemy.\n",
      "  df_duration = pd.read_sql(conv_duration, connection)\n"
     ]
    },
    {
     "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>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>duration_in_hours</th>\n",
       "      <td>29.02089</td>\n",
       "      <td>163.572387</td>\n",
       "      <td>0.000044</td>\n",
       "      <td>0.413893</td>\n",
       "      <td>1.9866</td>\n",
       "      <td>10.864053</td>\n",
       "      <td>4911.677341</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       mean         std       min       25%     50%  \\\n",
       "duration_in_hours  29.02089  163.572387  0.000044  0.413893  1.9866   \n",
       "\n",
       "                         75%          max  \n",
       "duration_in_hours  10.864053  4911.677341  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Conversation length descriptive statistics\n",
    "conv_duration = f\"\"\"\n",
    "SELECT start, end \n",
    "FROM conversations \n",
    "WHERE length > 1\n",
    "    AND conversation_id NOT IN (\n",
    "        SELECT conversation_id \n",
    "        FROM conversations \n",
    "        WHERE start <1500000000000 \n",
    "            OR end <1500000000000)\n",
    "    AND start >= UNIX_TIMESTAMP('{start_datee}') * 1000\n",
    "    AND start <= UNIX_TIMESTAMP('{end_datee}') * 1000;\n",
    "\"\"\"\n",
    "\n",
    "df_duration = pd.read_sql(conv_duration, connection)\n",
    "\n",
    "df_duration['start'] = pd.to_datetime(df_duration['start'], unit='ms') # Convert timestamp to date time format\n",
    "df_duration['end'] = pd.to_datetime(df_duration['end'], unit='ms') # Convert timestamp to date time format\n",
    "\n",
    "df_duration['duration'] = (df_duration['end'] - df_duration['start']).dt.total_seconds() # Compute the duration of conversations in seconds\n",
    "\n",
    "df_duration['duration_in_hours'] = df_duration['duration'] / 3600\n",
    "\n",
    "duration_desc_stats = df_duration['duration_in_hours'].describe() # Compute the descriptive statistics of conversations durations\n",
    "df_duration_desc_stats = duration_desc_stats.to_frame().T\n",
    "df_duration_desc_stats = df_duration_desc_stats.drop(columns=\"count\")\n",
    "\n",
    "df_duration_desc_stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/_1/64lzdpgn2472v4xdvl_3y1l40000gn/T/ipykernel_12110/864047355.py:3: UserWarning: pandas only supports SQLAlchemy connectable (engine/connection) or database string URI or sqlite3 DBAPI2 connection. Other DBAPI2 objects are not tested. Please consider using SQLAlchemy.\n",
      "  df = pd.read_sql(query, connection)\n",
      "/var/folders/_1/64lzdpgn2472v4xdvl_3y1l40000gn/T/ipykernel_12110/864047355.py:3: UserWarning: pandas only supports SQLAlchemy connectable (engine/connection) or database string URI or sqlite3 DBAPI2 connection. Other DBAPI2 objects are not tested. Please consider using SQLAlchemy.\n",
      "  df = pd.read_sql(query, connection)\n",
      "/var/folders/_1/64lzdpgn2472v4xdvl_3y1l40000gn/T/ipykernel_12110/864047355.py:3: UserWarning: pandas only supports SQLAlchemy connectable (engine/connection) or database string URI or sqlite3 DBAPI2 connection. Other DBAPI2 objects are not tested. Please consider using SQLAlchemy.\n",
      "  df = pd.read_sql(query, connection)\n",
      "/var/folders/_1/64lzdpgn2472v4xdvl_3y1l40000gn/T/ipykernel_12110/864047355.py:3: UserWarning: pandas only supports SQLAlchemy connectable (engine/connection) or database string URI or sqlite3 DBAPI2 connection. Other DBAPI2 objects are not tested. Please consider using SQLAlchemy.\n",
      "  df = pd.read_sql(query, connection)\n"
     ]
    },
    {
     "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>length</th>\n",
       "      <td>49646.0</td>\n",
       "      <td>2.863977</td>\n",
       "      <td>2.147621</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>76.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>length</th>\n",
       "      <td>14870.0</td>\n",
       "      <td>2.687223</td>\n",
       "      <td>2.712516</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>46.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>length</th>\n",
       "      <td>10875.0</td>\n",
       "      <td>2.420966</td>\n",
       "      <td>1.737056</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>length</th>\n",
       "      <td>5680.0</td>\n",
       "      <td>2.596127</td>\n",
       "      <td>2.055641</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>37.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          count      mean       std  min  25%  50%  75%   max\n",
       "length  49646.0  2.863977  2.147621  1.0  2.0  2.0  3.0  76.0\n",
       "length  14870.0  2.687223  2.712516  1.0  1.0  2.0  3.0  46.0\n",
       "length  10875.0  2.420966  1.737056  1.0  1.0  2.0  3.0  18.0\n",
       "length   5680.0  2.596127  2.055641  1.0  2.0  2.0  3.0  37.0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Conversation length descriptive statisticss for each airline\n",
    "def compute_descriptive_stats(query, connection):\n",
    "    df = pd.read_sql(query, connection)\n",
    "\n",
    "    descriptive_stats = df['length'].describe() # Compute descriptive statistics\n",
    "\n",
    "    descriptive_stats_df = descriptive_stats.to_frame().T # Convert to a data frame\n",
    "\n",
    "    return descriptive_stats_df\n",
    "\n",
    "british_airways_conv_length_query = f\"\"\"\n",
    "SELECT DISTINCT conversations.conversation_id, conversations.length\n",
    "FROM conversations\n",
    "JOIN hasher ON hasher.conversation_id = conversations.conversation_id\n",
    "JOIN tweets ON hasher.id = tweets.id \n",
    "WHERE (tweets.user_mentions LIKE '%18332190%' \n",
    "    OR tweets.mentioned_airlines LIKE '%British_Airways%' \n",
    "    OR conversations.airline LIKE '%British_Airways%')\n",
    "    AND conversations.start >= UNIX_TIMESTAMP('{start_datee}') * 1000\n",
    "    AND conversations.start <= UNIX_TIMESTAMP('{end_datee}') * 1000;\n",
    "\"\"\"\n",
    "KLM_conv_length_query = f\"\"\" \n",
    "SELECT DISTINCT conversations.conversation_id, conversations.length\n",
    "FROM conversations\n",
    "JOIN hasher ON hasher.conversation_id = conversations.conversation_id\n",
    "JOIN tweets ON hasher.id = tweets.id \n",
    "WHERE (tweets.user_mentions LIKE '%56377143%' \n",
    "    OR tweets.mentioned_airlines LIKE '%KLM%' \n",
    "    OR conversations.airline LIKE '%KLM%')\n",
    "    AND conversations.start >= UNIX_TIMESTAMP('{start_datee}') * 1000\n",
    "    AND conversations.start <= UNIX_TIMESTAMP('{end_datee}') * 1000;\n",
    "\"\"\"\n",
    "lufthansa_conv_length_query = f\"\"\" \n",
    "SELECT DISTINCT conversations.conversation_id, conversations.length\n",
    "FROM conversations\n",
    "JOIN hasher ON hasher.conversation_id = conversations.conversation_id\n",
    "JOIN tweets ON hasher.id = tweets.id \n",
    "WHERE (tweets.user_mentions LIKE '%124476322%' \n",
    "    OR tweets.mentioned_airlines LIKE '%lufthansa%' \n",
    "    OR conversations.airline LIKE '%lufthansa%')\n",
    "    AND conversations.start >= UNIX_TIMESTAMP('{start_datee}') * 1000\n",
    "    AND conversations.start <= UNIX_TIMESTAMP('{end_datee}') * 1000;\n",
    "\"\"\"\n",
    "air_france_conv_length_query = f\"\"\" \n",
    "SELECT DISTINCT conversations.conversation_id, conversations.length\n",
    "FROM conversations\n",
    "JOIN hasher ON hasher.conversation_id = conversations.conversation_id\n",
    "JOIN tweets ON hasher.id = tweets.id \n",
    "WHERE (tweets.user_mentions LIKE '%106062176%' \n",
    "    OR tweets.mentioned_airlines LIKE '%AirFrance%' \n",
    "    OR conversations.airline LIKE '%AirFrance%')\n",
    "    AND conversations.start >= UNIX_TIMESTAMP('{start_datee}') * 1000\n",
    "    AND conversations.start <= UNIX_TIMESTAMP('{end_datee}') * 1000;\n",
    "\"\"\"\n",
    "\n",
    "descriptive_british_airways_conv_length_stats_df = compute_descriptive_stats(british_airways_conv_length_query, connection)\n",
    "descriptive_KLM_conv_length_stats_df = compute_descriptive_stats(KLM_conv_length_query, connection)\n",
    "descriptive_lufthansa_conv_length_stats_df = compute_descriptive_stats(lufthansa_conv_length_query, connection)\n",
    "descriptive_air_france_conv_length_stats_df = compute_descriptive_stats(air_france_conv_length_query, connection)\n",
    "\n",
    "desc_conv_length_stats_per_airline = pd.concat([descriptive_british_airways_conv_length_stats_df, descriptive_KLM_conv_length_stats_df, descriptive_lufthansa_conv_length_stats_df, descriptive_air_france_conv_length_stats_df])\n",
    "\n",
    "desc_conv_length_stats_per_airline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/_1/64lzdpgn2472v4xdvl_3y1l40000gn/T/ipykernel_12110/977993928.py:4: UserWarning: pandas only supports SQLAlchemy connectable (engine/connection) or database string URI or sqlite3 DBAPI2 connection. Other DBAPI2 objects are not tested. Please consider using SQLAlchemy.\n",
      "  df = pd.read_sql(query, connection)\n",
      "/var/folders/_1/64lzdpgn2472v4xdvl_3y1l40000gn/T/ipykernel_12110/977993928.py:4: UserWarning: pandas only supports SQLAlchemy connectable (engine/connection) or database string URI or sqlite3 DBAPI2 connection. Other DBAPI2 objects are not tested. Please consider using SQLAlchemy.\n",
      "  df = pd.read_sql(query, connection)\n",
      "/var/folders/_1/64lzdpgn2472v4xdvl_3y1l40000gn/T/ipykernel_12110/977993928.py:4: UserWarning: pandas only supports SQLAlchemy connectable (engine/connection) or database string URI or sqlite3 DBAPI2 connection. Other DBAPI2 objects are not tested. Please consider using SQLAlchemy.\n",
      "  df = pd.read_sql(query, connection)\n",
      "/var/folders/_1/64lzdpgn2472v4xdvl_3y1l40000gn/T/ipykernel_12110/977993928.py:4: UserWarning: pandas only supports SQLAlchemy connectable (engine/connection) or database string URI or sqlite3 DBAPI2 connection. Other DBAPI2 objects are not tested. Please consider using SQLAlchemy.\n",
      "  df = pd.read_sql(query, connection)\n"
     ]
    }
   ],
   "source": [
    "# Extract conversation starts time units for conversations per airline\n",
    "def extract_time(query, connection):\n",
    "\n",
    "    df = pd.read_sql(query, connection)\n",
    "\n",
    "    df['start'] = pd.to_datetime(df['start'], unit='ms') # Convert timestamp to a date time format\n",
    "\n",
    "    df['hour'] = df['start'].dt.hour # Extract the hour when the conv starts\n",
    "\n",
    "    df['day_of_month'] = df['start'].dt.day # Extract the day of the month\n",
    "\n",
    "    df['day_of_week'] = df['start'].dt.dayofweek # Extract the day when the conv starts\n",
    "\n",
    "    df['month'] = df['start'].dt.month # Extract the month when the conv starts\n",
    "\n",
    "    return df\n",
    "\n",
    "british_airways_conv_start_query = f\"\"\"\n",
    "SELECT DISTINCT conversations.conversation_id, conversations.start\n",
    "FROM conversations\n",
    "JOIN hasher ON hasher.conversation_id = conversations.conversation_id\n",
    "JOIN tweets ON hasher.id = tweets.id \n",
    "WHERE (tweets.user_mentions LIKE '%18332190%' \n",
    "    OR tweets.mentioned_airlines LIKE '%British_Airways%' \n",
    "    OR conversations.airline LIKE '%British_Airways%')\n",
    "    AND conversations.start >= UNIX_TIMESTAMP('{start_datee}') * 1000\n",
    "    AND conversations.start <= UNIX_TIMESTAMP('{end_datee}') * 1000;\n",
    "\"\"\"\n",
    "KLM_conv_start_query = f\"\"\" \n",
    "SELECT DISTINCT conversations.conversation_id, conversations.start\n",
    "FROM conversations\n",
    "JOIN hasher ON hasher.conversation_id = conversations.conversation_id\n",
    "JOIN tweets ON hasher.id = tweets.id \n",
    "WHERE (tweets.user_mentions LIKE '%56377143%' \n",
    "    OR tweets.mentioned_airlines LIKE '%KLM%' \n",
    "    OR conversations.airline LIKE '%KLM%')\n",
    "    AND conversations.start >= UNIX_TIMESTAMP('{start_datee}') * 1000\n",
    "    AND conversations.start <= UNIX_TIMESTAMP('{end_datee}') * 1000;\n",
    "\"\"\"\n",
    "lufthansa_conv_start_query = f\"\"\" \n",
    "SELECT DISTINCT conversations.conversation_id, conversations.start\n",
    "FROM conversations\n",
    "JOIN hasher ON hasher.conversation_id = conversations.conversation_id\n",
    "JOIN tweets ON hasher.id = tweets.id \n",
    "WHERE (tweets.user_mentions LIKE '%124476322%' \n",
    "    OR tweets.mentioned_airlines LIKE '%lufthansa%' \n",
    "    OR conversations.airline LIKE '%lufthansa%')\n",
    "    AND conversations.start >= UNIX_TIMESTAMP('{start_datee}') * 1000\n",
    "    AND conversations.start <= UNIX_TIMESTAMP('{end_datee}') * 1000;\n",
    "\"\"\"\n",
    "air_france_conv_start_query = f\"\"\" \n",
    "SELECT DISTINCT conversations.conversation_id, conversations.start\n",
    "FROM conversations\n",
    "JOIN hasher ON hasher.conversation_id = conversations.conversation_id\n",
    "JOIN tweets ON hasher.id = tweets.id \n",
    "WHERE (tweets.user_mentions LIKE '%106062176%' \n",
    "    OR tweets.mentioned_airlines LIKE '%AirFrance%' \n",
    "    OR conversations.airline LIKE '%AirFrance%')\n",
    "    AND conversations.start >= UNIX_TIMESTAMP('{start_datee}') * 1000\n",
    "    AND conversations.start <= UNIX_TIMESTAMP('{end_datee}') * 1000;\n",
    "\"\"\"\n",
    "\n",
    "british_airways_conv_start = extract_time(british_airways_conv_start_query, connection)\n",
    "KLM_conv_start = extract_time(KLM_conv_start_query, connection)\n",
    "lufthansa_conv_start = extract_time(lufthansa_conv_start_query, connection)\n",
    "air_france_conv_start = extract_time(air_france_conv_start_query, connection)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jangalic04/anaconda3/envs/JB1010/lib/python3.10/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/jangalic04/anaconda3/envs/JB1010/lib/python3.10/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/jangalic04/anaconda3/envs/JB1010/lib/python3.10/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/jangalic04/anaconda3/envs/JB1010/lib/python3.10/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/jangalic04/anaconda3/envs/JB1010/lib/python3.10/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/jangalic04/anaconda3/envs/JB1010/lib/python3.10/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/jangalic04/anaconda3/envs/JB1010/lib/python3.10/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/jangalic04/anaconda3/envs/JB1010/lib/python3.10/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/jangalic04/anaconda3/envs/JB1010/lib/python3.10/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/jangalic04/anaconda3/envs/JB1010/lib/python3.10/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/jangalic04/anaconda3/envs/JB1010/lib/python3.10/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/jangalic04/anaconda3/envs/JB1010/lib/python3.10/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/jangalic04/anaconda3/envs/JB1010/lib/python3.10/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/jangalic04/anaconda3/envs/JB1010/lib/python3.10/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/jangalic04/anaconda3/envs/JB1010/lib/python3.10/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",
      "/Users/jangalic04/anaconda3/envs/JB1010/lib/python3.10/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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qcXtZyjMzQBAEYcGCBVr9zpw5U+LxBd8CPX36VNxmZ2dXbKqkWq0WoqKitLY9axpp0T5FHytNmn3Kmhnw0UcfiftSUlKEOnXqiPu6d+9ertjKeh7o8xwp6JOYmKj1/Bg9erTWcZrfDMtkMvEbnaKPSefOnYXc3FxBEAQhNzdXcHV1FfcV/baxJCqVSnBychKP6dq1q9a3VkWfGxcuXBD3FZ3Kq4vyzgwAoDXd/PPPP9fal5qaKgiCIFy+fFlr+4ABA8RxKXDnzh3xd0PPDDh//ry4zc/Pr9hU4Ly8POHu3bvPvP2KPB6a2z09PcWxEQRBaNeunbhv+PDhpTwq2sr7jZ9EIhHOnz8v7nv99dfFfU5OTuW6rQEDBmjdVnlntVy4cEHruAULFoj78vLyhK5du2rFolKpBEHQ/7V769at4nY3NzfxsTlz5ozW3+vDhw/Fc2l+i7l582at+GfPni3ua9eunbi9aHzPPfdcsTHR9TlXIDk5WThw4IDw9ddfC6tWrRJWrFihNUPp+eef1+qv62u3Zp+qfN/WdzxKos97iUql0tq+ePFirXMuX75c3Ofs7Cw+F4u+Fvj4+AhJSUnlilPTp59+qvUcjIuLEwRB0JrV0L59+2LHlXdmgJ2dnRATE/PMsdJU3pkB5X0fGzZsmLjd399fHENB0P4bBCBcunRJ5zGk2olLCxJVsdmzZ4sVbadNm4bbt28DAGbMmAGFQlHqcQXf8ACAi4sL+vfvL7ZdXV212pp9NclkMsycOVNs+/r6au0v+PZCX7NmzRKXSXNyckKdOnV0Prdm1v7AgQN49OgRAGD37t3ids1vnQxB0GFJOCD/W+CCwmKpqanw8fHB0KFD8fbbb+P777/Ho0eP4OPjU6GYHB0d8eqrr1boHJqzJ+zs7DBo0CCxff78+Qqdu6LOnDkDlUoltidNmqS1X7OtUqlw5syZEs/zyiuvwNzcHABgbm6uNe7lec5FRkaKKwIAwMSJE8UCkiXFVdrfVmXx8PDAkCFDxHZpf7PHjx/X2v7BBx+I41Kg6AomhuTn5wdnZ2cA+d/aN27cGCNHjsSiRYuwY8cOJCUliSstlMVQj8dLL70EW1tbsd20aVPx94q+zhXVtWtXtGvXTmxrPkaGvq2iit5/zfGRyWSYOHGi2H769CkiIyNLPE95X7tHjhwpjuvjx4/FmRmaswICAwPFmVWnT59GXl6euG/q1KlaS7599dVX4r6LFy+WumTi/PnztVZ6AXR/zqnVaixYsACurq4YMGAA/u///g9vvfUW3n77ba0ZSiWt5KCvqnzfNtTfYEnK814SGRkpzlgAgKVLl2o91gsWLBD3PXnyBDdv3izxtl599VU4ODjoHOPWrVvF3/39/eHm5gYAWrPDzp8/L8560dXLL7+MBg0a6HXss5T3faxgxgCQv7SvTCYTx7dz585a5zx58mSlxEo1D5MBRFWsXr16GDFiBADg4cOHAPJf/GfPnl3mcZr/QS54k9Okua20/4C6ublp/YeqaPKhYOqZvry9vbXamucv77lbtWolruWsUqmwZcsWnD59WrxEwMXFpcSKwBVR9D8l9erVe+Yx27ZtQ/PmzQEAjx49wi+//IKVK1di0qRJaNCgAd58880KxdSoUaMSl0HShaurq1Zb8zmSlZWlVbW8QNHESEl9DEHz+Vw0tpLapT2nK/qcM1QclaWs+wcU3sei96OiyagC5X0+WFhY4KeffhL/sxwVFYWff/4ZH3/8McaNG4d69eppraBSGlN5XuiirNsqb6Kx6GtOwTTsZ6nq8bK2tsbo0aPF9rZt26BWq7Fz505xm+YlDkXjK4sgCHjy5EmJ+5o1a1Zsm67PuTVr1mDFihXIzc0tMw5DvuZV5fu2of4GS1Ke9xJdHmsASEhIKHF7SY/1s5w+fVprlQ3NBMCIESO0EqMlLT1YHvrEVV7l/fvTZYxLG1+iolgzgMgIXnvtNa3/PI0YMaLMa9QB7fXZHz9+XGy/5raC67aLKvpNYcE3QYZiqPPPnTtXvL5x8+bNWv9BnDhxYrHbqYiHDx+K16cD+d+6POuxAIDWrVvj2rVruHLlCs6fP49bt27h/Pnz+OOPP6BWq/HZZ59h0KBBCAgI0Csua2trvY7TFB8fr3X9uuZzxMLCQvwPh1RamBcuuB69QEHtAkPTfD4Xja2kdmU9pw0VR2Up7/0rej+io6Ph4uKi123q+3x4/vnnER0djfPnz+PixYu4ffs2Tp48ifDwcOTm5uLtt9/G4MGD0bhx41LPYSrPC10Y4rb69OmDb7/9Vmxv3boVn3/++TOPK2m8Cr4dLmhrMsR4TZkyBZs2bQIA7N27F6NHjxZncNWpU0frW+Oi8b3xxhtlvr6WVheltNdDXZ5zmu+5Hh4e2Lt3L9q2bQu5XI4FCxZgxYoVpcalr6p+3zbE32BJyvNeUvSxnjRpUpm1Y4p+AC6gz3uf5qwAAJg+fTqmT59eYt+QkBAsX75c52S7Id6TS6PL63xBDYUePXpozRorqlu3boYLkGo0JgOIjKBr167o1KmTWOxr3rx5zzymW7duYlGthIQE/PHHH+IUw/j4ePzxxx9afSuq6JtTadM3K8OQIUPQoEED3Lt3D1FRUVi/fr24T5fCWs8SGxuL4cOHIzs7W9xW3m/0L168iLZt26JVq1Zo1aqVuL1Nmza4fPkygPwpiQXJAM3xrKqx/OGHH8QiTampqfjtt9/EfR06dBB/15ySGRkZieTkZDg4OCAlJQXr1q0r9fwVeY507twZMplMvFTgu+++w4ABA8T93333nfi7TCYrNgXSUHx9feHk5CR+4/Ljjz9i5syZ4tR0zTgA0/0PVo8ePbTay5Ytw969e7X+wxsTE1OuacL6PB+ys7MRHR0NPz8/dOzYER07dgSQ/22vo6MjUlJSoFarcenSpTI/iNSUx0NXQ4cOhZeXlzgD6ssvv0Tnzp1LLKh35MgRyOVy9OzZs9j9/+677/Dpp58CyJ9ZpVno1MnJqdgUc310794dTZs2xc2bN5GSkqJ1OdOECRMgl8vFdpcuXbT+zs3NzTF//vxi57x79y4iIyPLvZ48oPtzTjOp3LFjR/E1JTs7W+u1saiKvHZX5fu2of4GS1Ke9xJfX184OzuL45yVlVXiYx0fH48TJ04YrNBqdnY2duzYUe7+8fHxOHDggFicuDopKPAJAHFxcZgxY0axv5msrCzs2rWrxrw2UuVjMoDISL7//nvcuHED5ubmWhWZSzNp0iQsW7ZMfKMdMWIEpk6dCjs7O2zbtg3p6ekA8jPKr7/+eoXjKzpt9dVXX0VgYCDMzMwwePBgrWtwDU0mk2HWrFlYuHAhAIgf1jt27FiuKvWlOXnyJFauXImMjAxcu3YN+/fv1/rmc9CgQXjllVfKda7nnnsOHh4e6NmzJzw8PGBnZ4dLly6JiQBA+0OV5nieO3cOr732Gjw9PSGXy8uVDNLHe++9hxs3bsDLywu7d+/Wup5T81uTTp06ib+npqaiXbt26Ny5M06cOCFeylKSijxHnJ2dMXnyZPEbxp9++gnJycnFVhMA8q/V1Py205CkUineeOMNvP/++wDyr9vt0aMH+vXrhxs3bmhVtQ8ICECbNm0qJY6KatWqFQYMGCBWzd+/fz/atGmDAQMGwMLCAteuXcOxY8e0ngOl0ef5kJycjObNm6NFixbo3LkzPDw8YGlpiePHjyMlJUXs96xrgWvK46ErhUKBrVu3IjAwELm5uVCpVJgwYQK+/PJLBAQEwMbGBg8fPsRff/2FiIgIbNmyBT179kSbNm3Qp08fcTWC5cuXIyoqCi1atMDhw4e1rkN/7bXXtGZ9VMSUKVPE1+fo6Git7ZqcnJwwdepUcdbD8uXLcfbsWXTr1g0WFhZ4+PAh/vnnH1y4cAGTJk1CYGBguWPQ9Tnn6+srzmzZv38/Zs6cCXd3d+zevbvMyzIq8tpdle/bhvobLEl53kukUinefPNN/Oc//wGQ/5oeFRWFvn37wtbWFnFxcTh79ixOnz6NHj16YNiwYRW6vwX27duH5ORksf3888+XOCvq119/Fd/vt2zZUi2TAW+99RZ++eUXCIKA27dvo2XLlhg+fDjc3NyQkpKCK1eu4O+//0ZGRkaFVguiWsZopQuJaoGSVhN4ltJWExCE/PWKHRwctM6p+SOVSoWVK1dqHVNWxfOyKooLgnYFbs2fXbt2lXj/ilaT17wvmpX8y6q+WyAxMVGwsLDQ6rdu3bpnjl9Z96+0H4lEIsyZM0fIzs4u9/goFIoyz+nj4yMkJyeL/S9cuCBIpdJi/aytrcU+5alaLQjlX02gtDWnBw4cqFVpOisrS2jSpEmJfYtWOS/6GFfkOZKenq5Vxbukn+7du4vrfj/rMRGE0qs3lyUvL08YNWpUmXH4+fmJFdILVMVqArqs7pCYmCh06tSp1PtQsC62IJQ9jvo8H2JjY5/5d9a5c2dxrfuybl/fx6O0v4tnjWlp9KkS/qzjnuWvv/4SPDw8njmWmvcvNjZWaN68eZn9R4wYIY69IOj/2l3g4cOHWisOACVXahcEQcjIyBBeeOGFZ94nzXEsz0oluj7nwsPDS1yf3cbGRhg+fHipf8sVfe2uqvdtXcejLEWfwwMHDizxfEXfS1QqlfDSSy89Mw7NMXrWa/qzBAYGisfa2dkJGRkZJfbTjMvc3FxISEgQBKH8qwkUfU0pbaw0lXc1AV3ex9atW1fi87joD1F5sYAgUTXSq1cvXL16FW+99RZatGgBKysryOVyNGjQABMmTMDJkyfx1ltvGez29uzZg2HDhsHJyalSr7ktibOzs9YUWQsLi1LXoNaFVCqFtbU1PD090atXL7zzzjuIjIzE2rVry1zNoaj169djypQpaN26NVxcXGBmZgYbGxu0bt0aCxYswOnTp7Wuf23bti22b9+O9u3bF6uKXVn++OMPvP/++/Dx8YFcLoe3tzc++OAD/Pzzz1qPp4WFBY4cOYLRo0fDwcEBFhYW6NKlC/bu3Yu33367zNuoyHPE2toaR44cwcaNGxEQEAAnJyeYmZnB0dERvXv3xjfffIOwsDDY2Njodf/LSyaT4aeffsKuXbswYMAAuLq6wszMDPb29ujSpQtWrFiBf//9t1y1JIzJ2dkZJ06cwMaNG/HCCy+Iz0tHR0d06NCh3N886vN8cHR0xJdffolx48ahefPmcHJygkwmg52dHTp27Ihly5bhyJEj5bpOt6Y8HvoICAjArVu38PXXX2PgwIGoV68eLCwsIJfL4eXlhVGjRmHXrl0YM2aMeIy7uzv+/fdfrFq1Cl27doW9vT3MzMzg4uKCoKAg7NixA7t3765wQVJNHh4exb7FL22VFysrKxw6dAjbtm3DgAED4ObmBjMzM1haWqJRo0YYOXIkNmzYoHNxO12fcz169MChQ4fQrVs3KBQK2NvbY8CAATh58qTWpV5FVfS1u6retw35N1jUnj17sHTpUjRq1KjM9xKpVIrvv/8ev//+O0aMGIH69etDLpdDoVDAy8sLgwYNwueff661+kRFPHz4EKGhoWJ77NixsLKyKrGv5vNTqVQiJCTEIDFUtdmzZ+PChQuYMWMGmjZtCisrK5iZmcHNzQ29e/fG+++/j0uXLhk7TKpGJIKg45paRERV5JNPPhGnoo4dO9Zg/4EgIiKikm3dulXrwzM/KhDVXKwZQEQmJS4uDhEREYiJicHKlSvF7XPmzDFiVERERERENQuTAURkUg4ePFhsuumoUaPQvXt3I0VERERERFTzsGYAEZkkqVSKBg0a4J133im2nBgREREREVUMawYQERERERER1TKcGUBERERERERUyzAZQERERERERFTLsIBgJVGr1Xj06BFsbW2rfH12IiIiIiIiqn0EQUBaWho8PDwglZb93T+TAZXk0aNH8PT0NHYYREREREREVMvcv38f9evXL7MPkwGVxNbWFkD+g2BnZ2fkaEqnVCpx+PBh9OvXD+bm5sYOp1rgmOmOY6Y7jpnuOGa645jpjmOmG46X7jhmuuOY6Y5jprvqMmapqanw9PQUP4+WhcmASlJwaYCdnZ3JJwOsrKxgZ2dn0k9qU8Ix0x3HTHccM91xzHTHMdMdx0w3HC/dccx0xzHTHcdMd9VtzMpzqToLCBIRERERERHVMkwGEBEREREREdUyTAYQERERERER1TJMBhARERERERHVMiwgSERERERENZJKpYJSqTR2GCZHqVTCzMwM2dnZUKlUxg6nWjD2mJmZmUEmk5WrMGC5z2mwMxEREREREZkAQRAQFxeH5ORkY4dikgRBgLu7O+7fv2/QD5c1mSmMmUwmg6urK+zt7Q0SA5MBRERERERUoxQkAlxdXWFlZcUPvEWo1Wqkp6fDxsYGUimvHC8PY46ZIAjIy8tDamoqYmNjkZWVhbp161b4vEwGEBERERFRjaFSqcREgLOzs7HDMUlqtRq5ubmwsLBgMqCcTGHMbG1toVAokJiYCFdXV8hksgqdj488ERERERHVGAU1AqysrIwcCZHhWVtbQxAEg9TCYDKAiIiIiIhqHF4aQDWRIZ/XTAYQERERERER1TJMBhARERERERHVMkwGEBERERER1VJhYWGQSCTPXIZx8uTJGDp0qNj29/fH66+/XqmxUeViMoCIiIiIiKiGO3XqFGQyGQYOHKi1vVu3boiNjYW9vb1O59uzZw+WLVtmyBCpijEZQEREREREVMNt2rQJc+fOxbFjx/Do0SNxu1wuh7u7e6mF6VQqFdRqdbHtTk5OsLW1rbR4qfIxGUBEREREZCJUagGpucaOgmqa9PR07Ny5E7NmzcLAgQPx3XffifuKXiawdetWODg44Ndff0Xz5s2hUChw7969YucsepmAt7c3/vvf/2Lq1KmwtbVFgwYNsGHDBq1j7t+/j9GjR8PBwQFOTk4YMmQI7t69qxVL586dYW1tDQcHB3Tv3h0xMTEGHQsqxGQAEREREZEJSMlU4oXPj+P9c2b45GCkscOhGuSnn35Cs2bN4Ovri4kTJ2LLli0QBKHU/pmZmfj000+xceNGXLt2Da6uruW6nVWrVqFjx464cOECZs+ejVmzZiEyMv+5rFQqERgYCFtbW4SHh+PEiROwsbFBUFAQcnNzkZeXh6FDh6J37964fPkyTp06hRkzZnCJyEpkZuwAiIiIiIgI2HXuPh4kZQEANp2IwczejeFqZ2HkqKgm2LRpEyZOnAgACAoKQkpKCk6cOIEBAwaU2F+pVOKrr75CmzZtdLqdAQMGYPbs2QCAd955B5999hmOHj0KX19f7Ny5E2q1Ghs3bhQ/4G/ZsgUODg4ICwtDx44dkZKSghdffBGNGjUCAPj5+el7l6kcODOAiIiIiMgEXH2YotX++2aCkSKhmiQyMhJnzpzBuHHjAABmZmYYPXo0fvjhh1KPkcvlaN26tc63pXmMRCKBu7s74uPjAQCXLl3C7du3YWtrCxsbG9jY2MDJyQnZ2dm4c+cOnJycMHnyZAQGBmLQoEH44osvEBsbq3MMVH6cGUBEREREZAIiYtO02mGRCRjV0dNI0VBNsWnTJuTl5cHDw0PcJggCFAoFUlJSSjzG0tJSr+n55ubmWm2JRCIWH0xPT0eHDh0QEhJS7DgXFxcA+TMF5s2bh4MHD2Lnzp147733EBoaiueee07nWOjZmAwgIiIiIjKynDwV7iSka20Lv5WAPJUaZjJO5iX95OXl4fvvv8eqVavQr18/cbtarcbQoUOxfft2NG/evEpiad++PXbu3AlXV1fY2dmV2q9du3Zo164dFi5ciK5du2Lbtm1MBlQSvrIQERERERnZrcfpyFNrF3RLzc7DhfvJxgmIaoT9+/cjKSkJ06ZNQ8uWLbV+Bg0ahC1btlRZLBMmTECdOnUwZMgQhIeHIzo6GmFhYZg3bx4ePHiA6OhoLFy4EKdOnUJMTAwOHz6MW7dusW5AJWIygIiIiIjIyCJiU0vcfvRGfBVHQjXJpk2b8MILL8De3r7YvsGDB+Ps2bO4fPlylcRiZWWFY8eOoUGDBhg+fDj8/Pwwbdo0ZGdnw87ODlZWVrhx4wZGjBiBpk2bYsaMGXj11Vcxc+bMKomvNuJlAkRERERERla0XkCBsMgELAhqVsXRUE3x22+/lbqvQ4cOUKlUkEqlmDdvnrh98uTJmDx5crH+W7du1WqHhYVpte/evVvsmIsXL2q13d3d8d1335UYj52dHfbu3VtqvGR4nBlARERERGRkpc0MuB6bisep2VUcDRHVBiaVDDh27BgGDRoEDw8PSCQS7Nu3T2u/RCIp8WfFihViH29v72L7P/nkE63zXL58GT179oSFhQU8PT2xfPnyYrHs2rULzZo1g4WFBVq1aoUDBw5Uyn0mIiIiotpNEATciCtMBrhZatcO+DuSSwwSkeGZVDIgIyMDbdq0wbp160rcHxsbq/WzefNmSCQSjBgxQqvf0qVLtfrNnTtX3Jeamop+/frBy8sL586dw4oVKxAcHIwNGzaIfU6ePIlx48Zh2rRpuHDhAoYOHYqhQ4fi6tWrlXPHiYiIiKjWepyag6RMpdju6qqGnUXh1bxhN1k3gIgMz6RqBvTv3x/9+/cvdb+7u7tW+5dffkFAQAAaNmyotd3W1rZY3wIhISHIzc3F5s2bIZfL0aJFC1y8eBGrV6/GjBkzAABffPEFgoKC8PbbbwMAli1bhtDQUHz55Zf4+uuvK3IXiYiIiIi0FL1EwNNGQA9bZxy4+hgAEH4rEUqVGuZcYpCIDMikkgG6ePz4MX7//fcSC1B88sknWLZsGRo0aIDx48fjjTfegJlZ/l09deoUevXqBblcLvYPDAzEp59+iqSkJDg6OuLUqVN48803tc4ZGBhY7LIFTTk5OcjJyRHbqan5L+pKpRJKpbK0w4yuIDZTjtHUcMx0xzHTHcdMdxwz3XHMdMcx0w3Hq3yuPkjSantYAe51HcVkQFp2Hs5EJaCzt5MxwjN5RZ9nSqUSgiBArVZDrVYbMzSTJQiC+C/HqHxMZczUajUEQYBSqYRMJiu2X5fX22qbDPjuu+9ga2uL4cOHa22fN28e2rdvDycnJ5w8eRILFy5EbGwsVq9eDQCIi4uDj4+P1jFubm7iPkdHR8TFxYnbNPvExcWVGs/HH3+MJUuWFNt++PBhWFlZ6XUfq1JoaKixQ6h2OGa645jpjmOmO46Z7jhmuuOY6YbjVba/bkpRcPWuk0KAlRmQ+vAqNP+rvuWPM0j04oe2shQ8z8zMzODu7o709HTk5uYaOSrTlpZW8ioWVDpjj1lubi6ysrJw7Ngx5OXlFdufmZlZ7nNV22TA5s2bMWHCBFhYWGht1/xGv3Xr1pDL5Zg5cyY+/vhjKBSKSotn4cKFWredmpoKT09P9OvXD3Z2dpV2uxWlVCoRGhqKvn37wtzc3NjhVAscM91xzHTHMdMdx0x3HDPdccx0w/Eqny9unQCQAQBo4+UCIA4jBvbFjtizuPYo/4PHA7U9BgzoarwgTVjR51l2djbu378PGxubYp8VKJ8gCEhLS4OtrS0kEomxw6kWTGXMsrOzYWlpiV69epX4/C6YoV4e1TIZEB4ejsjISOzcufOZfbt06YK8vDzcvXsXvr6+cHd3x+PHj7X6FLQL6gyU1qe0OgQAoFAoSkw2mJubV4s3v+oSpynhmOmOY6Y7jpnuOGa645jpjmOmG45X6bKVKtx9kiG2m3vYATlxMDc3R4Cvm5gMuBGXhieZKrjb88NtaQqeZyqVChKJBFKpFFIp6yyUpGCae8E40bOZyphJpVJIJJJSX1d1ea2tlo/8pk2b0KFDB7Rp0+aZfS9evAipVApXV1cAQNeuXXHs2DGtaylCQ0Ph6+sLR0dHsc+RI0e0zhMaGoquXZmNJSIiIiLDiYxLg1pjJUFfNxvx94BmLlp9/+aqAkRkQCaVDEhPT8fFixdx8eJFAEB0dDQuXryIe/fuiX1SU1Oxa9cuvPLKK8WOP3XqFD7//HNcunQJUVFRCAkJwRtvvIGJEyeKH/THjx8PuVyOadOm4dq1a9i5cye++OILrSn+r732Gg4ePIhVq1bhxo0bCA4OxtmzZzFnzpzKHQAiIiIiqlWKriTgV9dW/L2tpyPsLQu/5QuLTKiyuIio5jOpZMDZs2fRrl07tGvXDkD+9f/t2rXD4sWLxT47duyAIAgYN25cseMVCgV27NiB3r17o0WLFvjoo4/wxhtvYMOGDWIfe3t7HD58GNHR0ejQoQPeeustLF68WFxWEAC6deuGbdu2YcOGDWjTpg12796Nffv2oWXLlpV474mIiIiottFMBljJZWjgWFh4WiaVoGeTOmL7+P+WGCQiMgSTSgb4+/tDEIRiP1u3bhX7zJgxA5mZmbC3ty92fPv27fHPP/8gOTkZWVlZuH79OhYuXFjsWv7WrVsjPDwc2dnZePDgAd55551i5xo1ahQiIyORk5ODq1evYsCAAQa/v0RERERUu0XEFVYm93W3hVSqXZgswNdV/D0tJw/nYrSXISTSx927d+Ho6Kj1OassEokEwcHBOt2Gv79/lX2ZGhwczEKIejCpZAARERERUW0hCILWzAC/usVXoOrVVLtuAC8VqN22bt0KiUSi9ePq6oqAgAD88ccfFTr3gQMHdP7AXxVUKhU8PDwgkUgqfB9JG5MBRERERERG8DA5C2nZheuEl5QMcLFVoFW9whmxYZEsIkjA0qVL8cMPP+D777/HggULkJCQgAEDBmD//v3lOt7LywuxsbF46aWXxG0HDhzAkiVLSuyflZWF9957zyCx6+qvv/5CbGwsvL29ERISUmKf9957D1lZWVUcWfVXLZcWJCIiIiKq7iJi07TazTWKB2oK8HXBlYcpAPKXGIxNyUJde8tKj49MV//+/dGxY0exPW3aNLi5uWH79u148cUXSz0uLy8ParUaZmZmsLCwgEwmK9ftlbSefVX58ccf0b59e0yaNAmLFi1CRkYGrK2ttfqYmZnBzKzsj7ZqtRq5ublGvS+mhjMDiIiIiIiMoOhKAr7uxWcGAEBvjboBAPA3LxWgIhwcHGBpaan1gfju3buQSCRYuXIlPv/8czRq1AgKhQLXr18vVjNg8uTJWLduHQBoXYJQoGjNgLS0NLz++uvw9vaGQqGAq6sr+vbti/PnzxeL7fr16wgICICVlRXq1auH5cuXl/t+ZWVlYe/evRg7dixGjx6NrKws/PLLL8X6lVQzQCKRYM6cOQgJCUGLFi2gUChw8OBBtG/fHsOHD9fq26pVK0gkEly+fFnctnPnTkgkEkRERAAAYmJi8NZbb8HPzw+WlpZwdnbGqFGjcPfuXfGYqKgoSCQSfPbZZ8ViPHnyJCQSCbZv367zGFYWzgwgIiIiIjICzWSAl7MVbBRmUCqVxfq19XSAg5U5kjPz94VFJmBs5wZVFmdNIQgCspQqY4chsjSX6V30LiUlBYmJiRAEAfHx8Vi7di3S09MxceLEYn23bNmC7OxszJgxAwqFAk5OTsjLy9PqM3PmTDx69AihoaH44Ycfnnn7//d//4fdu3djzpw5aN68OZ48eYLjx48jIiIC7du3F/slJSUhKCgIw4cPx+jRo7F792688847aNWqFfr37//M2/n111+Rnp6OsWPHwt3dHf7+/ggJCcH48ePLMUr5lxj89NNPmDNnDurUqQNvb2/07NlT/EAOAE+fPsW1a9cglUoRHh6O1q1bAwDCw8Ph4uICPz8/AMC///6LM2fOYMyYMfD09MTdu3exfv16+Pv74/r167CyskLDhg3RvXt3cYl7TSEhIbC1tcWQIUN0GsPKxGQAEREREZERaBUPLGVWAJC/xGCvJi749dIjAMDx24nIzVNDbsZJvrrIUqrQfPEhY4chur40EFZy/T6OvfDCC1pthUKBzZs3o2/fvsX6PnjwALdv34aLS2ExyqioKK0+Xbt2RdOmTREaGlpiQqGo33//HdOnT8eqVavEbQsWLCjW79GjR/j+++/F2gTTpk2Dl5cXNm3aVK5kwI8//ohu3brB09MTADB27FjMnj0bCQkJWvenNJGRkbhy5QqaN28ubuvZsyfWrFmDiIgI+Pn54cSJE5DL5QgMDER4eDheffVVAPnJgB49eojHDRw4EP369YOdnR2k0vy/vUGDBqFr1674+eefxfv48ssvY+bMmbhx4waaNWsGAFAqlfjpp58wfPhwWFlZ6TSGlYmvIEREREREVSwjJw8xTzPFdknFAzX5+xZ+8EnnEoO13rp16xAaGorQ0FD8+OOPCAgIwCuvvII9e/YU6ztixIhyfXDWhYODA06fPo1Hjx6V2c/GxkYruSCXy9G5c+diyYiSPHnyBIcOHcK4cePEbSNGjIBEIsFPP/1Urjh79+6tlQgA8pMBAHDs2DEA+R/6O3XqhL59+yI8PBwAkJycjKtXr4p9AcDSsrBOh1KpxJMnT9C4cWM4ODhoTe0fPXo0LCwstIodHjp0CImJiVpjUd4xrEycGUBEREREVMVuxKVBEArbzUopHlig2BKDN+PRtZFzZYRWY1may3B9aaCxwxBZmpeveF9JOnfurFVAcNy4cWjXrh3mzJmDF198EXK5XNzn4+NToThLsnz5ckyaNAmenp7o0KEDBgwYgJdffhkNGzbU6le/fv1il0I4OjpqXZtfmp07d0KpVKJdu3a4ffu2uL1Lly4ICQkRv8EvS0n33c3NDU2aNEF4eDhmzpyJ8PBwBAQEoFevXpg7dy6ioqIQEREBtVqtlQzIysrCf//7X2zfvh0PHz6EoPEHnJKSIv7u4OCAQYMGYdu2bVi2bBmA/EsE6tWrh+eff17sV94xrEycGUBEREREVMWKFg9s/oyZAXVsFGhdX2OJwRssIqgriUQCK7mZyfzoWy+gJFKpFAEBAYiNjcWtW7e09ml+o20oo0ePRlRUFNauXQsPDw+sWLECLVq0wB9//KHVr7TVCjQ/SJem4Jv17t27o0mTJuLP8ePHcerUqXLNLijtvvfo0QPh4eHIysrCuXPn0LNnT7Rs2RIODg4IDw9HeHg4bGxs0K5dO/GYefPmYdWqVRg1ahR++uknHD58GKGhoXB2doZardY6/8svv4yoqCicPHkSaWlp+PXXXzFu3Djx8gKg/GNYmTgzgIiIiIioimkmA2wVZqjv+OwPbP6+rrj8IP8byMjHaXiUnAUPBy4xSPkKigKmp6frdbyuyYm6deti9uzZmD17NuLj49G+fXt89NFH5aoF8CzR0dE4efIk5syZg969e2vtU6vVeOmll7Bt2za89957ep2/Z8+e2LJlC3bs2AGVSoVu3bpBKpWKSYKIiAh069ZNK5nx888/Y9y4cVi5cqX4oT47OxvJycnFzh8UFAQXFxeEhISgS5cuyMzMFGsKaKrMMSwPJgOIiIiIiKrYjbg08fdmdW3L9UHM39cFa44Ufuv7980EjOOqAoT8a9gPHz4MuVwuVr/XlbW1NYD86+UdHBxK7adSqZCeng57+8KZKq6urvDw8EBOTo5et11UwayABQsWiMUDNW3cuBEhISEVSgYAwKefforWrVuL96Vnz55Yv349Hj16hPfff1/rGJlMVmxGw9q1a6FSFV+hwszMDOPGjcO2bdsQERGBVq1aiasUAFUzhuXBZAARERERURVSqwXc0FxJ4BmXCBRoU98BjlbmSPrfEoNHb8QzGVBL/fHHH7hx4wYAID4+Htu2bcOtW7fw7rvvws6ufM+nojp06AAgfzp8YGAgZDIZxo4dW6xfWloa6tevj5EjR6JNmzawsbHBn3/+iX///VerMn5FhISEoG3btiUmAgBg8ODBmDt3Ls6fP6/XMnyNGzeGu7s7IiMjMXfuXHF7r1698M477wCAVr0AIH81gW3btqFOnTpo0aIFTp06hT///BPOziXX7nj55ZexZs0aHD16FJ9++qnWvqoYw/JgMoCIiIiIqArdT8pERm7ht4nlTQbIpBL0auqCXy7mVx8/wSUGa63FixeLv1tYWKBZs2ZYv349Zs6cqfc5hw8fjrlz52LHjh348ccfIQhCickAKysrzJ49G4cPH8aePXugVqvRuHFjfPXVV5g1a5bet1/g/PnzuHHjRrFv5jUNGjQIc+fOxY8//qhXMgDI/7C/a9cureUDO3ToACsrK+Tl5aFLly5a/T///HOo1Wps27YN2dnZ6N69O/78808EBpZclLJDhw5o0aIFIiIiMGHCBK19lT2G5cVkABERERFRFSpaPLC8yQAg/1KBgmRARq4KZ2OeolujOgaNj0zX5MmTMXny5HL19fb2LrVQn7e3N5KSkrRmEchkMqxZswZr1qwp1l/zPHK5HMuXL8fy5cvLvP2wsLASt2/durXM49q3b//MAoNeXl5afYKDgxEcHFxqzCUpaXlCc3NzZGRklNjfwcEBX375Jezs7LQKAd69e7fU2zA3N4e/vz/q1auntb28Y1jZmEYkIiIiIqpC12ML6wVIJYCvW9nLCmrq1cQFmuUFwiK5qgCRKTp79iwuXryIl19+2dihlIrJACIiIiKiKqQ5M8C7jjUs5eVfb97ZRoHW9R3EdlhkvCFDI6IKunr1Kr777jtMnToVdevWxZgxY4wdUqmYDCAiIiIiqkIRehQP1OTf1EX8/ebjdDxMzjJIXERUcbt378aUKVOgVCqxfft2WFhYGDukUjEZQERERERURVKzlXiQVPjh3c+9/JcIFPD3ddFqc3YAkekIDg6GWq1GREQEevfubexwysRkABERERFRFbmhUS8A0G9mQOv6DnCylott1g0gIn0wGUBEREREVEUqspJAAZlUgl5NClcQOHk7ETl5qjKOICIqjskAIiIiIqIqciOuMBlgb2mOuvb6XU/s7+sq/p6Rq8LZu0kVjo2IahcmA4iIiIiIqojmsoJ+dW0h0VwnUAe9mhZdYpB1A4hIN0wGEBERERFVAZVaQGRcxVYSKOBkLUcbrSUGWTeAiHTDZAARERERURW4+yQD2Uq12K5IMgDQXlXgVnw6HiRlVuh8RFS7MBlARERERFQFihYPbF7BZECARt0AgLMDiEg3TAYQEREREVUBzWSATCpBY1ebCp2vVT17OHOJQSLSE5MBRERERERVIEKjeGAjF2tYmMsqdD6pVIJeTQsvFTh5h0sM1gZbt26FRCLB2bNntbanpKSgc+fOsLCwwMGDBxEcHAyJRILExMRSzxUWFgaJRAKJRIIff/yxxD7du3eHRCJBy5YtDXo/yPiYDCAiIiIiqgKaMwOauVfsEoECmnUDMnNV+DeaSwzWRqmpqejXrx8uX76MvXv3IigoSKfjLSwssG3btmLb7969i5MnT8LCQr8lMMm0MRlARERERFTJkjNzEZuSLbYrWjywQK8mLpByicFaLS0tDYGBgbh48SJ+/vln9O/fX+dzDBgwAKGhocVmEWzbtg1ubm7o2LGjocIlE8JkABERERFRJbtepHigX11bg5zX0VqONp4OYvsokwG1Snp6OoKCgnD+/Hn8/PPPGDhwoF7nGTJkCBQKBXbt2qW1fdu2bRg9ejRksopd0kKmickAIiIiIqJKdkOjXgBQ8ZUENGmuKnAnIQP3n3KJwTLlZpT9o8or7JuXW3ZfZVZhX0F49rnVhqvpkJGRgf79++Pff//Frl278OKLL+p9LisrKwwZMgTbt28Xt126dAnXrl3D+PHjDREumSAzYwdARERERFTTadYLcLaWw8VWYbBz+/u6YHXoTbEddjMBLz3nZbDz1zj/9Sh7/6itQIth+b//tRQ4ubb0vh7tgBlh+b9nPgFWNCr73JP2Az49yxtp2aeaNAmPHj3Crl27MHjw4Aqfb/z48Rg0aBDu378PT09PhISEoGHDhnjuuecMEC2ZIs4MICIiIiKqZBFxhckAv7p2kEgkZfTWTUsPe9Sx0Vhi8AYvFagNHj9+DAsLC3h6ehrkfP369YOTkxN27NgBQRCwY8cOjBs3ziDnJtPEmQFERERERJUoT6XGzcfpYttQ9QIKFCwxuOf8QwDAyTtPkK1UVXjpwhpr0aOy98s0Zm08vxjwX1h6X4nGd6tWzs8+t5nhqvJ/8803ePPNNxEUFITw8HD4+vpW6Hzm5uYYNWoUtm3bhs6dO+P+/fu8RKCG48wAIiIiIqJKFJWYgdw8tdg21EoCmvw16gZkKVX49+5Tg99GjSG3LvtHpvF9qZm87L7mloV9JZJnn1tquARN8+bNceDAAWRlZaFv3764f/9+hc85fvx4XLx4EcHBwWjTpg2aN29ugEjJVDEZQERERERUiSKKrSRg+GRAryZ1iiwxmGDw2yDT07lzZ+zbtw/x8fHo27cvEhIq9rj36NEDDRo0QFhYGGcF1AJMBhARERERVSLNZQXNZRI0crEx+G04WMnRroGj2OYSg7VHnz59sH37dty+fRtBQUFITU199kGlkEgkWLNmDT744AO89NJLBoySTBFrBhARERERVaIIjWUFG7nYQG5WOd/H+Td1wbmYJABA1P+WGPR0sqqU2yLTMmzYMHz77beYOnUqBg8ejIMHD4r7Vq9eDSsr7eeBRCLBq6++WuK5hgwZgiFDhlRqvGQamAwgIiIiIqpEmpcJNK+ESwQK+Pu6YpXmEoOR8Xipq3el3R6ZlilTpuDp06eYP38+Ro0ahTZt2gAAPv7442J9ZTJZqckAqj14mQARERERUSVJTM9BQlqO2K6MegEFWnjYaS0xeJR1A2qkyZMnQxAEdOzYsdi+t956C4Ig4LfffsOHH34IQRBK/MnNzQUA+Pv7QxAEjBw5sszbDAsLw9WrVyvl/pDxMBlARERERFRJbmhcIgBUbjJAKpWgd9PCVQVO3klEtlJVabdHRNUbkwFERERERJWk+EoCtpV6e/6+LuLv2Uo1zkRziUEiKhmTAURERERElUQzGeBqq4CzjaJSb69nkSUGuaoAEZWGyQAiIiIiokqiuaxgZV4iUMDBSo72GksM/s26AURUCiYDiIiIiIgqQW6eGncS0sV2VSQDAO1LBaISMxDzJKNKbpeIqhcmA4iIiIiIKsHt+HQoVYLYrux6AQX8fV212mGcHUBEJWAygIiIiIioEhQtHti8imYGNK9rBxfbwtoEYawbQEQlYDKAiIiIiKgSaCYD5GZS+NSxrpLbzV9isPBSgVNRT7jEIBEVY1LJgGPHjmHQoEHw8PCARCLBvn37tPZPnjwZEolE6ycoKEirz9OnTzFhwgTY2dnBwcEB06ZNQ3p6ulafy5cvo2fPnrCwsICnpyeWL19eLJZdu3ahWbNmsLCwQKtWrXDgwAGD318iIiIiqrki4gqTAU3dbGAmq7r/ehddYvCfqCdVdttEVD2YVDIgIyMDbdq0wbp160rtExQUhNjYWPFn+/btWvsnTJiAa9euITQ0FPv378exY8cwY8YMcX9qair69esHLy8vnDt3DitWrEBwcDA2bNgg9jl58iTGjRuHadOm4cKFCxg6dCiGDh2Kq1evGv5OExEREVGNIwgCImLTxLafe9VcIlCgZ2MXyDTWGGTdACIqyszYAWjq378/+vfvX2YfhUIBd3f3EvdFRETg4MGD+Pfff9GxY0cAwNq1azFgwACsXLkSHh4eCAkJQW5uLjZv3gy5XI4WLVrg4sWLWL16tZg0+OKLLxAUFIS3334bALBs2TKEhobiyy+/xNdff13ibefk5CAnJ0dsp6bmZ4KVSiWUSqVuA1GFCmIz5RhNDcdMdxwz3XHMdMcx0x3HTHccM93U5vF6nJqNpxm5Yrupm3W5xsFQY2ZlDrTztMfZmGQA+XUDlMqmFTqnqSo6ZkqlEoIgQK1WQ61WGzM0kyUIgvgvx6h8TGXM1Go1BEGAUqmETCYrtl+X1w6TSgaUR1hYGFxdXeHo6Ijnn38eH374IZydnQEAp06dgoODg5gIAIAXXngBUqkUp0+fxrBhw3Dq1Cn06tULcrlc7BMYGIhPP/0USUlJcHR0xKlTp/Dmm29q3W5gYGCxyxY0ffzxx1iyZEmx7YcPH4aVlVUF73XlCw0NNXYI1Q7HTHccM91xzHTHMdMdx0x3HDPd1MbxikiSACj8j3rK3es4kHSt3McbYszc1IUx3H2Sie9+PgAXywqf1mQVjJmZmRnc3d2Rnp6O3NzcZxxVuyUlJWHp0qX4+eefERsbi/79+yMkJKTU/q1bt4afnx927txZhVGalrS0tGd3qkS5ubnIysrCsWPHkJeXV2x/ZmZmuc9VrZIBQUFBGD58OHx8fHDnzh0sWrQI/fv3x6lTpyCTyRAXFwdXV+2lVMzMzODk5IS4uDgAQFxcHHx8fLT6uLm5ifscHR0RFxcnbtPsU3COkixcuFArgZCamgpPT0/069cPdnZVOy1MF0qlEqGhoejbty/Mzc2NHU61wDHTHcdMdxwz3XHMdMcx0x3HTDe1ebzuH4sGbtwS25OHvgB7y2ePgSHHzDs2Fb9/9Y/YltRriQHPNajQOU1R0THLzs7G/fv3YWNjAwsLC2OHZ3Bbt27FtGnTcPr0aa0vQXUhCALS0tKwe/durF27Fq+99hratWuHBg0a4MGDB9i1axcmTZoEb29vreOkUinMzMxM+vNNZSkYM1tbW0gkkmcfUEmys7NhaWmJXr16lfj8LpihXh7VKhkwduxY8fdWrVqhdevWaNSoEcLCwtCnTx8jRpZ/+YJCoSi23dzcvFq8+VWXOE0Jx0x3HDPdccx0xzHTHcdMdxwz3dTG8boZnyH+7mFvgTp2us0UNcSYtfZ0gqutAvFp+Zeyht9+gmk9G1XonKasYMxUKhUkEgmkUimkUpMqkWYQBfepIvevYJr70aNHUa9ePXz++efivt27d2Pp0qUICAhAw4YNix1bMLa1TcGYGfv+S6VSSCSSUl8jdHndqNaPYsOGDVGnTh3cvn0bAODu7o74eO11VPPy8vD06VOxzoC7uzseP36s1aeg/aw+pdUqICIiIiLSpLmsoF9d43yLKpEUWWLwDpcYJG0JCQlwcHAwdhhkJNU6GfDgwQM8efIEdevWBQB07doVycnJOHfunNjnr7/+glqtRpcuXcQ+x44d0yqsEBoaCl9fXzg6Oop9jhw5onVboaGh6Nq1a2XfJSIiIiKq5rKVKkQlFs4MMFYyAAACmhVeQpuTp8YpLjFY402ePLnY9H4ACA4OFqe33717F46Ojjh69CiuXbsmLtu+detWjBo1CgAQEBAgbg8LC9M61/Hjx9G5c2dYWFigYcOG+P7777X2P336FPPnz0erVq1gY2MDOzs79O/fH5cuXdLqFxYWBolEgp9++gkfffQR6tevDwsLC/Tp00f8wrfArVu3MGLECLi7u8PCwgL169fH2LFjkZKSIvbZsmULnn/+ebi6ukKhUKB58+ZYv369vkNZ45nUZQLp6elaD3p0dDQuXrwIJycnODk5YcmSJeIT4M6dO1iwYAEaN26MwMBAAICfnx+CgoIwffp0fP3111AqlZgzZw7Gjh0LDw8PAMD48eOxZMkSTJs2De+88w6uXr2KL774Ap999pl4u6+99hp69+6NVatWYeDAgdixYwfOnj2rtfwgEREREVFJbj1Oh0otiG1jJgO6N64DmVQixvN3ZAICfF2fcRTVdC4uLvj666/x+eefIz09HR9//DEAoEmTJpg3bx7WrFmDRYsWwc/PDwDEfwHg9u3bGDlyJKZNm4ZJkyZh8+bNmDx5Mjp06IAWLVoAAKKiorBv3z6MGjUKPj4+ePz4Mb755hv07t0b169fFz+bFfjkk08glUoxf/58pKSkYPny5ZgwYQJOnz4NIL9oXmBgIHJycjB37ly4u7vj4cOH2L9/P5KTk2Fvbw8AWL9+PVq0aIHBgwfDzMwMv/32G2bPng21Wo1XX3210se1ujGpZMDZs2cREBAgtgsK8k2aNAnr16/H5cuX8d133yE5ORkeHh7o168fli1bpnWtfkhICObMmYM+ffpAKpVixIgRWLNmjbjf3t4ehw8fxquvvooOHTqgTp06WLx4sbisIAB069YN27Ztw3vvvYdFixahSZMm2LdvH1q2bFkFo0BERERE1ZnmJQIA4FfX1kiRAPaW5ujQwBFn7j4FAByNjEcwWhgtHlOQqcyvtm5pZgmJRIKsvCwIggCFTAGZVIYcVQ5UahXMZeYwl5pDqVJCqVbCTGoGuUyOPHUeclW5kEqksDCzgFpQIzsvGwBgZW5VrtswNmtra4wZMwbbt2+HTCbDxIkTxX3379/HmjVr0LdvX/j7+xc7NjIyEseOHUPPnj0BAKNHj4anpye2bNmClStXAsiv73bz5k2ta+tfeuklNGvWDJs2bcL777+vdc7s7GxcvHhRXPHN0dERr732Gq5evYqWLVvi+vXriI6Oxq5duzBy5EjxuMWLF2ud5++//4alZeGSGXPmzEFQUBBWr17NZEAJTCoZ4O/vL67fWJJDhw498xxOTk7Ytm1bmX1at26N8PDwMvuMGjVKnCJDRERERFRe1zWSARbmUng5WxsxGsC/mYuYDIh5konoxAz41DFuTMbUZVv+5cN/j/kbThZOGLd/HO6k3MHmwM3o5N4JC8MXIjQmFIu6LMK4ZuPw7ZVvsf7SeozxHYP3nnsPR+4dwfy/56OjW0dsCdqCqOQoDPt1GBwVjjg29li5bqM6a968uZgIAPJnGfj6+iIqKkrcpvllrUqlQnJyMmxsbODr64vz588XO+eUKVO0ln4vOH9UVBRatmwpfvN/6NAhDBgwoNSl2zUTASkpKVAqlejduzcOHTqElJQU8TyUr1rXDCAiIiIiMjWaMwN83e0gkxpvGTIA8G+qfVlAWGR8KT2Jnq1Bg+LLUzo6OiIpKUlsq9VqfPbZZ2jSpAkUCgXq1KkDFxcXXL58Wesa/9LOWVDLreCcPj4+ePPNN7Fx40bUqVMHgYGBWLduXbFznThxAi+88AKsra3h4OAAFxcXLFq0CABKvN3azqRmBhARERERVWeCIGglA5ob8RKBAn51beFmp8Dj1PwlBo9GJmBKdx8jR2U8p8fnX4duaZb/LfL2F7eLU/gB4OOeH+PD7h/CXJa/RNv0VtMxucVkmEnzPzr1adAHp8efhlSS/71qQ4eG4jnLexuVqaBIYFEqlWFWkpDJSr7MQXOG93//+1+8//77mDp1KpYtWwYnJydIpVK8/vrr4hJ9up5z1apVmDx5Mn755RccPnwY8+bNw8cff4x//vkH9evXx507d9CnTx80a9YMq1evhqenJ+RyOQ4cOIDPPvusxNut7ZgMICIiIiIykNiUbKRm54ltYxYPLCCRSODf1BU7z94HAPwT9QRZuSpYyo1/7boxFFzXX6DgA3sBhUwBaAyNucxcTAwAgJnUTEwMAIBUIi12zmfdRmVydHREcnJyse0xMTHlOr60ZIIudu/ejYCAAGzatElre3JyMurUqaP3eVu1aoVWrVrhvffew8mTJ9G9e3d8/fXX+PDDD/Hbb78hJycHv/76q9ZMg6NHj+p9ezUdLxMgIiIiIjKQ4sUDjZ8MAAB/Xxfx99w8Nf7hEoM1VqNGjZCSkoLLly+L22JjY7F3795yHW9tnV9PoqSEQnnJZLJiteB27dqFhw8f6nW+1NRU5OXlaW1r1aoVpFIpcnJyxNsEtGcTpKSkYMuWLXrdZm3AmQFERERERAZSNBnQzN34lwkAQPcmdWAmlSDvf0sMHo2MR0AzLjFYnW3evBkHDx4stv2ll17CO++8g2HDhmHevHnIzMzE+vXr0bRp0xKL9xXVtm1byGQyfPrpp0hJSYFCocDzzz8PV9fyP19efPFFLF26FFOmTEG3bt1w5coVhISEoGHDhjrdxwJ//fUX5syZg1GjRqFp06bIy8vDDz/8AJlMhhEjRgAA+vXrB7lcjkGDBmHmzJlIT0/Ht99+C1dXV8TGxup1uzUdkwFERERERAYSEZsm/u7pZAlbC/MyelcdOwtzdPByxOno/FUFwiITIAiCQaaEk3GsX7++xO2TJ0/G3r178eabb2LBggXw8fHBxx9/jFu3bpUrGeDu7o6vv/4aH3/8MaZNmwaVSoWjR4/qlAxYtGgRMjIysG3bNuzcuRPt27fH77//jnfffbfc59DUpk0bBAYG4rfffsPDhw9hZWWFNm3a4I8//sBzzz0HAPD19cXu3bvx3nvvYf78+XB3d8esWbPg4uKCqVOn6nW7NR2TAUREREREBqI5M8DP3TQuESjg7+sqJgPuPc1fYrChi42RoyJdTZ48GZMnTy6zT/369XHlypVi24ODg7Xaf/31F6TS4leOv/LKK3jllVeKbb97926JtxcWFqbVVigUWLlyJVauXFlmv9KWlvf29tba7uPjU6z+QEkGDRqEQYMGFds+ZcqUZx5bG7FmABERERGRAWTm5iH6SYbYNpV6AQU06wYA+bMDiKj2YjKAiIiIiMgAIuPSoPklp6klA5q528LdzkJsH42MN2I0RGRsTAYQERERERmAZr0AAPCraxrFAwtIJBKt2QGno58iK9cwa88TUfXDZAARERERkQFo1guwlsvg6WhVRm/jKLrE4KmoRCNGQ0TGxGQAEREREZEBaCYDmtW1g1RqepX6uzfOX2KwwNEbrBtAVFsxGUBEREREVEGCIOBGXOFlAqZ2iUABWwtzdPR2FNthN+NLrOZORDUfkwFERERERBX0ICkL6Tl5YtvUigdq8vctXC/+/tMsRCVmlNGbiGoqJgOIiIiIiCrousYlAoCpJwO0lxg8eoOrChDVRkwGEBERERFVkGa9AIkkfxk/U+XrZou69oVLDP59k3UDiGojJgOIiIiIiCpIMxng7WwNK7mZEaMpW7ElBqOeIjM3r4wjiKgmYjKAiIiIiKiCImJNv3igpt5NC+sG5KrUOHn7iRGjISJjYDKAiIiIiKgC0rKVuPc0U2z7uZtuvYAC3Rs7w1xWuMRg2E3WDagtlHlqxDzNRHwWkKVUGTscMiImA4iIiIiIKiBSY0lBAGhmwsUDC9hamKOjl5PYDotM4BKDNZBEIkFwcLDWtgfJWUjLzkO2CohKzERieg4f+1qKyQAiIiIiogqIKLaSgOlfJgBoryrwICkLdxLSjRgN6eqrr76CRCJBly5dyn1MllKFtGyl2BYEAY+Ss3D/aRZU6uIJgbCwMEgkkhJ/xo4da5D7QcZjupVNiIiIiIiqgesa9QLsLMxQz8HSiNGUX0AzV3z8xw2xHRaZgMau1SORQUBISAi8vb1x5swZ3L59G40bNy7WJysrC2ZmhR/5EtNySjxXclYuspQqeDlbwcJcVmz/vHnz0KlTJ61t3t7eFbsDZHRMBhARERERVYDmzIBmde0gkUjK6G06mrjawMPeAo9SsgHkJwNe6dnQyFFReURHR+PkyZPYs2cPZs6ciZCQEHzwwQfF+llYFC4hqVSpkZxVOCvATAKoIEFGRjqsrKyRk6fC7fh01He0hIOVXOs8PXv2xMiRI8sVW15eHtRqNeRy+bM7k1HxMgEiIiIiIj2p1YJWzYDm1aBeQAGJRILevoWrCpyJfoqMHC4xWB2EhITA0dERAwcOxMiRIxESElJiP82aAU/Sc/DVqo/RxtMRd27ewH/mvYJeLb0xZXh/sb9aEHDvaSYeJmVCXY46Anfv3oVEIsHKlSvx+eefo1GjRlAoFLh+/Tpyc3OxePFidOjQAfb29rC2tkbPnj1x9OjRUs+xYcMG8RydOnXCv//+W+w2b9y4gdGjR8PFxQWWlpbw9fXFf/7zH60+Dx8+xNSpU+Hm5gaFQoEWLVpg8+bNz7w/tQ1nBhARERER6SnmaaZWRfbqUi+gQICvC7afuQfgf0sM3nmCvs3djBxV5VJnZpa4XaJQQCKTQVCrIWRni9ulVlYAAEGphKBUlnCgBFLL/EtD1Lm5QN7/EipmZpD+79txdVYWoPHhuuCc+goJCcHw4cMhl8sxbtw4rF+/Hv/++2+xqfwFVGoBTzJyxfaCWVPQtHFDfPjhh1AL+QUlNWsJPMnIRaZSBaUq/7mdlpaGxMRErXM6ORUWoNyyZQuys7MxY8YMKBQKODk5ITU1FRs3bsS4ceMwffp0pKWlYdOmTQgMDMSZM2fQtm1brfNt27YNaWlpmDlzJiQSCZYvX47hw4cjKioK5ubmAIDLly+jZ8+eMDc3x4wZM+Dt7Y07d+7gt99+w0cffQQAePz4MZ577jlIJBLMmTMHLi4u+OOPPzBt2jSkpqbi9ddf13vcaxomA4iIiIiI9FS8eGD1mRkAAN0a14G5TAKlKv+DalhkfI1PBkS271Di9gbffQfrLp2hvHcPd4L+9225mRn8rl4BACT99BMeL/uw2HHyxo3QaP9+AED8ypVI+v4HAIDjyy/BfdEiAED0qFHIvX1HPMbvRoTe8Z87dw43btzA2rVrAQA9evRA/fr1ERISUmoyICkzV6tAYOs2rbFpwzews7ODVCqFIAiIT8vB49TCJEhWrgoPkrIAAFOnTi12zujoaPH3Bw8e4Pbt23BxKSxKqVKpcPfuXa3LBaZPn45mzZph7dq12LRpk9b57t27h1u3bsHR0REA4OvriyFDhuDQoUN48cUXAQBz586FIAg4f/48GjRoIB77ySefiL//5z//gUqlwpUrV+Ds7AwA+L//+z+MGzcOwcHBmDlzJiwtq0ddj8rGywSIiIiIiPSkmQyQSoCmbtVrZoCNwgydvLnEYHUSEhICNzc3BAQEAMi/FGDMmDHYsWMHVCpVsf6CICAxXbtw4JxZs7TaEokEbnYW8KljDTNp4UfEgvzBmwsW4vDhwwgNDRV/3N3dxX4jRozQSgQAgEwmExMBarUaT58+RV5eHjp27Ijz588Xi3PMmDFiIgDIr1MAAFFRUQCAhIQEHDt2DFOnTtVKBBTEX3Bff/75ZwwaNCj/ficmij+BgYFISUkp8bZrK84MICIiIiLSk2YyoKGLTYmV2E1dgK8rTt55AgB4mJyF2/HpaFLNkhq68D1/rsTtEoUCAGDeoEGJfRxHj4bDsGElHFhYMNJ1/ny4FkxD16ji77Nrl9ZlAvpSqVTYsWMHAgICtL6Z79KlC1atWoUjR46gX79+Wsfk5KmRm6fW2tawoU+J57e1MEdjVxnuPc1EZm5h/QiPhr5o1LYrPJ2sYC4r/n2yj0/J5/vuu++watUq3LhxA0qNSyxK6l/0A35BYiApKQlAYVKgZcuWJd4WkJ8wSE5OxoYNG7Bhw4YS+8THx5d6fG3DZAARERERkZ4iNJYVrG6XCBTw93XBRwcKp62HRSbU6GTAs67Xl0ilkJTQR2JuDsn/rl0v9dxyOVBCFX2pgaal//XXX4iNjcWOHTuwY8eOYvtDQkKKJQMycwtnCxSkLcqaJi83k6KhizXiUrK1tqfn5OF2fDo8naxgo9D+GFnS+X788UdMnjwZQ4cOxdtvvw1XV1fIZDJ8/PHHuHPnTrH+MlnJiTRdZqqo1flJj4kTJ2LSpEkl9mndunW5z1fT6ZUMOHLkCM6fP4+3335b3LZ582YEBwcjJycH48ePx8qVK0t9QImIiIiIqruUTCUeJmeJ7Wbu1fMDdGNXG9RzsBTvy9HIeEzvxSUGTVFISAhcXV2xbt26Yvv27NmDvXv34uuvv9b6cK5UFc4KKO/MFalEAg8HS7jZKrS2K1VqRCdkwN1egTo2ilKOzrd79240bNgQe/bs0Vpus6QlEMujYcP85+TVq1dL7ePi4gJbW1uoVCq88MILet1ObaJXzYDg4GBcunRJbF+5cgUzZ86Ei4sL/P39sWbNGqxcudJgQRIRERERmZqIOO3igdVpWUFNEokE/r6F13v/e/cp0rnEoMnJysrCnj178OKLL2LkyJHFfubMmYO0tDT8+uuvpZ7DSq7bl7U2FvkzITQvDRAgIDYlGzFPMpGnVpd2qPjFsOY3+6dPn8apU6d0iqGAi4sLevXqhc2bN+PevXta+wpuQyaTYcSIEfj5559LTBokJCTodds1lV7JgIiICHTs2FFs//DDD7Czs0N4eDh27tyJ6dOn4/vvvzdYkEREREREpqa6rySgyd/XVfxdqRJw8nZiGb3JGH799VekpaVh8ODBJe5/7rnn4OLigpCQEABAbp52MUE7C3OYlXC9f3m421nA0Ur78ofUbCVinmSUesyLL76IqKgoDBs2DBs2bMDChQsRFBSE5s2b6xUDAKxZswaCIKB9+/ZYtGgRvv32W/znP/9Bu3btxD6ffPIJ6tatiy5duuD111/Hhg0b8Mknn2D06NHw9fXV+7ZrIr0uE8jIyICdXeGL3cGDBxEUFASr/11b06lTJ/z444+GiZCIiIiIyARpJgMcrczhZlf2tGlT1q2RM+QyKXL/N6X8aGQC+rVwf8ZRVJVCQkJgYWGBvn37lrhfKpVi4MCBCAkJwZMnT5Aj06578Kxp/WWRSiWo72gJa4UMD5OzxW/ic/Py/83MzYMgCFqXA0yePBlxcXH45ptvcOjQITRv3hw//vgjdu3ahbCwML3iaNOmDf755x+8//77WL9+PbKzs+Hl5YXRo0eLfdzc3HDmzBksXboUe/bswVdffQVnZ2e0aNECn376qd5jUBPplQzw9PTEv//+i6lTp+L27du4evUq3nrrLXH/06dPoVBU3xdDIiIiIqJnuRGnXTxQ84NQdWOtMENnHycc/9+MgL8j44t9uCPjKmv6f4EtW7Zgy5YtyFOrcSM2DZfu51fitzSXwVohQ3BwMIKDg6FWq5GamvqMswH+/v5a0/ydrBWwNJch5mkmcvPUqOfZQLyNB0lZqOdgCak0/zkjkUiwcOFCLFy4UOucAwcO1Gp7e3uXWiSwpO0tWrTAnj17yozb1dUVX375Jb788stn3sfaTK95IhMmTMCGDRswePBgBAYGwtHREUOGDBH3nzt3Dk2bNjVYkEREREREpiRPpUZkXPVfSUCTZt2ARynZuBWfbsRoqCKeZuRCrfFB2sVWYbDEjqXcDI1dbWBnob2yQlJmLm4npCNHqSrlSDI1eiUD/vOf/+Ddd9/F/fv30aBBA+zbtw8ODg4A8mcFhIWFlXotCxERERFRdXf3SQZyNNZur2nJAAA4eoPrsVdHakHAk/RcsW0uk8LOsuwlEXVlJpXCy9kKde0tIUFhkiFbqcLt+HSkZOaWcTSZCr0uEzAzM8NHH32Ejz76qNg+JycnxMXFVTgwIiIiIiJTdT02TavtV7d6LiuoqZGLDeo7WuJBUv4Sg2GRCZjZu5GRoyJdpWQptZYTrGMjh7QSLveQSCRwsVXASi7DvaeZ4m2qBAExTzNRJ1cFd3uLSrltMgz9ykkSEREREdVimsUDzaQSNHa1MWI0hlF0icGzMVxisLoRBAGJaTliWyaRwMlaXsYRFWetyL9swEah/T1zYnoOohIykJtX+vKDZFx6zQwA8pcX3LJlC6KiopCUlFSsuINEIsGRI0cqHCARERERkanRTAY0drWBwky39dtNlX9TV/z4T/4a7kqVgBO3ExHIVQWqjYycPGRpXLPvaC2HTFr53/+ay6TwqWONx6k5iE/LFrdn5ubhdnw6PJ0sYWth2EsVqOL0emb88MMPaNWqFdauXYvbt29DrVZDEAStH7WaGSAiIiIiqpk0kwE1oV5AgW6N85cYLBAWWX3rBpRWob4mS9CoFSCBBHVsKndWgCaJRAJ3ewt4O1tDJi28NCBPrUZ0YgYep2bXysfE0Aw5hnrNDAgODka7du3wxx9/oE6dOgYLhoiIiIjI1D3NyMXj1MKp2M3cq3+9gAJWcjN0aeiE8Fv5SwyGRSZUuyUGzczyP+Lk5dWuSxyylSqkZSvFtr2lGeRGmLFiZ2mOJuY2iHmaiazcwlkKj1OzkZmrgqejJcxkvFpdX0pl/mMsk1X8sdXrUXj06BGmTp3KRAARERER1TqaswKAmjUzAAB6Ny2sGxCbko2bj6vXEoMymQwymQypqanP7lyDJKbnaLXr2CqMFAkgN5OhkYsNnK21Y0jLVuJWfDoyWItCL4IgICUlBQqFAubmFb/sQq+ZAa1bt8ajR48qfONERERERNVNTU8G+Pu64sPfI8T20ch4+Faj2Q8SiQSurq6IjY2FQqGAtbV1tZrZoA+lSo2nqRniFHJLcxmk6jxkZ5f8oVutViM3NxfZ2dmQVmJNAWdLCcwhRVxqjhhbbh5wJy4HLjYKOFiZV5vHpqrGrCSCIECpVCIlJQXp6emoV6+eQc6rVzJg9erVGDVqFPr3749u3boZJBAiIiIiouogQmNZwTo2CrgY8RvYytDIxRqeTpa4/7RgicF4/F81W2LQ3t4eWVlZSExMREJCgrHDqXSpWUqkanzwd7aRIzq19GnkgiAgKysLlpaWVfNhXKVGUkYulKrC690fA7CSy+BgZV4tlh+s8jErgUKhQL169WBnZ5gEpF7JgE8//RT29vbo2bMnmjdvjgYNGhS7ZkEikeCXX34xSJBERERERKZCu3hg9fnGvLwkEgn8m7rih39iAABn7yYhLVtZrarBSyQS1K1bF66uruI11jVVjlKFt779BylZ+feznqMVtk7uBKm09A+sSqUSx44dQ69evQwy3bw8MnPzsPrwTRwtUpSyvpMVgl9sAR8X6yqJQ1/GGDNNMpnM4LerVzLg8uXLkEgkaNCgAdLT03H9+vVifarLdA8iIiIiovJSqtS4HV94DX3zGnaJQAF/XxcxGZCnzl9iMKhlXSNHpbuC+gE12c+XYnA9vnA5v1l9vGBlZVnmMTKZDHl5ebCwsKiyD7YWFsCyEe3w4z8xWLr/ujhL4GFaGoZtOIP/DmuF4e3rV0ks+jDGmFU2vZIBd+/eNXAYRERERESm705COnJVhUto17R6AQW6NnKG3EyK3Lz8+xoWmVAtkwE1nVotYFN4tNh2tDLHCBP+QC2RSPBSV2+0qu+AV0PO42Fy/qUo2Uo13vzpEs7GJGHxi81hYV6zEzimwqTWdDh27BgGDRoEDw8PSCQS7Nu3T9ynVCrxzjvvoFWrVrC2toaHhwdefvnlYoUMvb29IZFItH4++eQTrT6XL19Gz549YWFhAU9PTyxfvrxYLLt27UKzZs1gYWGBVq1a4cCBA5Vyn4mIiIio+qjpxQMLWMnN0MXHSWwXLDFIpuXIjXhEJWaI7Zee84Kl3PQ/SLf1dMD+uT3g7+uitX3b6XsY+fVJ3H+aaaTIapcKJQP+/vtvLFiwAGPGjMGYMWOwYMEC/P3333qfLyMjA23atMG6deuK7cvMzMT58+fx/vvv4/z589izZw8iIyMxePDgYn2XLl2K2NhY8Wfu3LnivtTUVPTr1w9eXl44d+4cVqxYgeDgYGzYsEHsc/LkSYwbNw7Tpk3DhQsXMHToUAwdOhRXr17V+74RERERUfWnWTxQLpOioYlf51wR/r6u4u9xqdm4EZdWRm8yhm/Do8Tf5WZSvNTV23jB6MjRWo7Nkzphfr+m0CxvcPVhKgauCcef1x8bL7haQq/LBHJzczFu3Djs27cPgiDAwcEBAJCcnIxVq1Zh2LBh2L59u87XUvTv3x/9+/cvcZ+9vT1CQ0O1tn355Zfo3Lkz7t27hwYNGojbbW1t4e7uXuJ5QkJCkJubi82bN0Mul6NFixa4ePEiVq9ejRkzZgAAvvjiCwQFBeHtt98GACxbtgyhoaH48ssv8fXXX+t0n4iIiIio5tCcGdDEzQbmMpOaaGtQAb4uWLa/sB0WmVBjZ0JUR5cfJONM9FOxPaxtvWq3soVUKsGc55ugXQNHzNt+AU8ycgEAqdl5eOX7s/i/3o0wv19TmNXgvzNj0isZsGTJEuzduxfz58/HW2+9BTc3NwBAfHw8Vq1ahRUrVmDp0qVYtmyZQYMtKiUlBRKJRExGFPjkk0+wbNkyNGjQAOPHj8cbb7wBM7P8u3rq1Cn06tULcrlc7B8YGIhPP/0USUlJcHR0xKlTp/Dmm29qnTMwMFDrsoWicnJykJOTI7ZTU/PfKJRKpUlXMC2IzZRjNDUcM91xzHTHMdMdx0x3HDPdccx0UxPH6/qjwmRAUzcbg983Uxqz+vZyeDpa4n5S/nXdR288xivdGzzjqKpnSmNWlb75+45We1JXz3KPgamNWWcve+yb/Rxe33kZ5+4li9u//vsOzsc8xeejWxs90WFqY1YaXeKTCHpc/OPj4wN/f39s2bKlxP2TJ09GWFhYhQoNSiQS7N27F0OHDi1xf3Z2Nrp3745mzZohJCRE3L569Wq0b98eTk5OOHnyJBYuXIgpU6Zg9erVAIB+/frBx8cH33zzjXjM9evX0aJFC1y/fh1+fn6Qy+X47rvvMG7cOLHPV199hSVLluDx45KnqwQHB2PJkiXFtm/btg1WVlb6DAERERERmZDUXOD9c4XfpQ31UiHAo2ZfR787Sorwx/nfykoh4L+dVLDU6+tEMqSnOcCy8zKokT+/3s9Bjf/zUz/jKNOnUgO/3ZPiaKz2TAA7cwGTmqjQ2N5IgVUjmZmZGD9+PFJSUmBnV/ZMHr3+lGNjY9GlS5dS93fp0gU7duzQ59TlolQqMXr0aAiCgPXr12vt0/xGv3Xr1pDL5Zg5cyY+/vhjKBSVl01auHCh1m2npqbC09MT/fr1e+aDYExKpRKhoaHo27dvjVkio7JxzHTHMdMdx0x3HDPdccx0xzHTTU0br/BbicC582J7+POd0bWhs0Fvw9TGzOpmAsJ/uAAAUEMC60YdENTCzchRaTO1MasKH/8RCTVixPa7QzuhW6PyPxdNecwGATh07THe3XsN6Tl5AIBUpQTrIszw5gtNML2HN6TSql/G3pTHTFPBDPXy0CsZUL9+fYSFheH//u//Stz/999/o379ylnSoiAREBMTg7/++uuZH7S7dOmCvLw83L17F76+vnB3dy/27X5Bu6DOQGl9SqtDAAAKhaLEZIO5ublJP1kKVJc4TQnHTHccM91xzHTHMdMdx0x3HDPd1JTxupmgXeG8VX2nSrtfpjJmPZq4aS0xePz2Uwxqa5pL15nKmFW21Gwlfjr3UGz71bVDL183SCS6f0A21TF7sW19tKzviFkh58U6HWoBWBl6CxcfpGDVqLawtzJO3KY6ZgV0iU2vSgyTJk3CTz/9hP/7v/9DZGQkVCoV1Go1IiMjMWvWLOzatQuTJ0/W59RlKkgE3Lp1C3/++SecnZ+d/bp48SKkUilcXfOroXbt2hXHjh3TupYiNDQUvr6+cHR0FPscOXJE6zyhoaHo2rWrAe8NEREREVUnNzSKB7rbWcDRWl5G75rBUi7DcxqzH8JuxnOJQSPbeea++I05AEzv6aNXIsDUedexxt7Z3TC6o3by6c+IeAxcG44rD1KMFFnNodfMgEWLFuHOnTvYsGEDvv32W0il+TkFtVoNQRAwadIkLFq0SOfzpqen4/bt22I7OjoaFy9ehJOTE+rWrYuRI0fi/Pnz2L9/P1QqFeLi4gAATk5OkMvlOHXqFE6fPo2AgADY2tri1KlTeOONNzBx4kTxg/748eOxZMkSTJs2De+88w6uXr2KL774Ap999pl4u6+99hp69+6NVatWYeDAgdixYwfOnj2rtfwgEREREdUumssK+tW1NWIkVSvA1wXHbiYAAB6n5iAiNg3NPUz3MtiaTKlSY8uJaLHtZqfAi609jBhR5bIwl2H5yDbo6OWE93+5ipz/zVB5kJSFEetP4oPBzTG+c4MamQypCnolA2QyGbZu3Yo333wTBw4cQExM/vUqXl5eGDBgAFq3bq1XMGfPnkVAQIDYLrgGf9KkSQgODsavv/4KAGjbtq3WcUePHoW/vz8UCgV27NiB4OBg5OTkwMfHB2+88YbWtfz29vY4fPgwXn31VXTo0AF16tTB4sWLxWUFAaBbt27Ytm0b3nvvPSxatAhNmjTBvn370LJlS73uFxERERFVbzl5KtxJSBfbtWmJPX9fVyz57brYDrsZz2SAkRy4EotHKdlie3I3H8jNav6ye6M7eaJlPXvMDjmHu0/yL9fJVanxn71Xce5uEj4c1hJWcla21FWFRqx169Z6f/Avib+/f5nTjp41Jal9+/b4559/nnk7rVu3Rnh4eJl9Ro0ahVGjRj3zXERERERU8916nI48deH/RWtTMsCnjjW8nK0Q878PYWE3EjDbv7GRo6p9BEHAxvDCWQFWchnGdza9pR4rS3MPO/w6twcW7LqMg9fixO17LjzE1UcpWD+xAxq52Bgxwuqn5qeRiIiIiIgqKCJWu0J3bUoGAECAr6v4+7l7SUjJMu211mui09FPceVh4XXyozt6Gq2InrHYWZhj/cT2eG+gH2QaKwrcfJyOwWuPY//lR0aMrvopVzJAKpXCzMwMubm5Ylsmk5X5Y2bGaRpEREREVDNo1gtQmEnhU8faiNFUvd6+LuLvKrWAE7cTjRhN7bQxPEr8XSoBpvXwMWI0xiORSPBKz4bYMeM5uNkVruaWkavCnG0XEPzrNXH1CypbuT6xL168GBKJRPyAX9AmIiIiIqoNNGcG+Lrban0rWRt0begMhZlULOB29EY8BrSqa+Soao87Cen4MyJebAe1dIenk5URIzK+Tt5O+H1eT7y24wJO3H4ibt968i4u3k/GugntUc/B0ogRmr5yJQOCg4PLbBMRERER1VSCICAirjAZ4Odeuy4RAPKrundt5IywyPxVBf6+mQBBEPgFYRXZdDxaq/1Kz4ZGisS01LFR4PupXfDFnzex5q/CVeku3k/Gi2vC8fnYdujd1KWMM9RuetUMWLp0Ka5evVrq/mvXrmHp0qV6B0VEREREZCriUrORnFl4jXxtWlZQk7/Gh6r4tBxcL1JHgSrHk/Qc/Hzugdju4OWI9g0cjRiRaZFJJXizny+2TOkEB40aCkmZSkzecgarQ29CpS67EH1tpVcyIDg4GJcvXy51/9WrV7FkyRK9gyIiIiIiMhW1vXhgAX+NIoIAxFkCVLl+/OeeeHkGAEzvWTtrBTxLgK8rfp/XE208HcRtggCsOXILk7ecwZP0HOMFZ6IqZTWBp0+fQi6XV8apiYiIiIiqlGbxQABoVkuTAd51rLUKJ4ZFxpfRmwwhW6nCD//cFdtezlbo29zdeAGZuHoOlvhp5nOY1NVLa3v4rUQMXHMc52KeGiky01Tukv/Hjh1DWFiY2N6zZw9u375drF9ycjJ27tyJVq1aGSRAIiIiIiJj0pwZUM/BEvaWtWs5N029m7ogOjEDAHD+XjJSMpW1bnm7qrTvwkMkpueK7andfWpd8UpdKcxkWDKkJTp4O+Hdny8jM1cFIP9ynzHf/IOFA/wwtbs3611Ah2TA0aNHxan/EokEe/bswZ49e0rs27x5c6xdu9YwERIRERERGZFmMqC2XiJQwN/XBVtP3gWQv8Rg+O0EvNjaw7hB1VBqtYCNGoUD7S3NMapjfSNGVL0MbuOB5nVtMevH87gVnw4AyFMLWLb/Os7FPMWnI1rD1qJ2J7LKfZnAggULkJCQgPj4eAiCgK+//hoJCQlaP4mJicjMzMTVq1fRpUuXyoybiIiIiKjSZStV4jfhANC8lhYPLPBcQ2dYmBd+hGDdgMrz980E3P7fh1gAmNClAazk5f4ulwA0drXFL3O6Y2hb7YTVgStxGPzlCdyIq91FMMv9bLK0tISlZf46jdHR0XBxcYGVVe1e25KIiIiIarbIuDRoFiKv7TMDLMxl6NrQGUc1lhhUqwVIOXXd4L4NjxJ/N5dJMKmbt/GCqcas5Gb4bExbdPR2wtLfriNXlV+MMToxA0PXncCHQ1thZIfaOeNCrwKCXl5eTAQQERERUY3HlQSK01xVIIFLDFaKa49ScPLOE7E9pG09uNlZGDGi6k0ikWDic17YPasr6jlYituzlWrM33UJ7/58GdlKlREjNA6955lcvnwZa9euxfnz55GSkgK1Wq21XyKR4M6dOxUOkIiIiIjIWDSTAVZyGRo48Qsxf18XrXZYZDxa1rM3UjQ108bwaK32K1xO0CBa13fA7/N64M2fLuGvG4WrYez49z6uPEzBVxPaw8vZuowz1Cx6zQwICwtD586dsX//fnh4eCAqKgoNGzaEh4cHYmJiYGNjg169ehk6ViIiIiKiKqW5rKCvuy2nwwPwcrZGQ60lBlk3wJBiU7Lw26VHYrtnkzpo5s4ZKYbiYCXHxpc74u1AX2j+OV97lIoX1x7H4WtxxguuiumVDFi8eDEaNmyIyMhIbNmyBQCwaNEiHD9+HCdPnsSDBw8wevRogwZKRERERFSVBEFARBxXEihJb43ZAefvJSElU2nEaGqWrSfuIk+jUMX0ng2NGE3NJJVK8GpAY/z4ShfUsZGL29Oy8zDjh3P4+EAE8lTqMs5QM+iVDDh//jymTZsGOzs7yGQyAIBKlX+NRZcuXTBz5ky8//77houSiIiIiKiKPUjKQlp2nthmMqCQZt0AtQAcu8XZAYaQnpOHbWfuie1m7rbo2aSOESOq2bo1qoPf5/VEZ28nre3fHIvC+G9PIz4120iRVQ29kgFmZmawtc1fVsXBwQHm5uaIjy+85qJhw4a4fv26YSIkIiIiIjKCG3FpWu3avqygpi4+TlxisBLs/Pe+VgJqWg8fSCS8NKUyudlZYNv0LpjZS3sGxpm7TzFgzXGcvJNopMgqn17JgMaNG+PWrVsA8gsFNmvWDHv37hX3//7773B3dzdMhERERERERlB0JQFfXrctsjCXoVujwm+sC5YYJP3lqdTYfLywcKCLrQKD23oYMaLaw0wmxcIBfvjmpQ6wtSissZ+YnoOJG09j3dHbNfL5rVcyYMCAAdi+fTvy8vKzVm+++Sb27NmDJk2aoEmTJvj1118xc+ZMgwZKRERERFSVNJMBXs5WsFHovRBXjaS5qkBieg6uPeISgxVx8FocHiZnie3J3byhMJMZMaLaJ7CFO/bP7YHmGpcEqQVgxaFIzAy5gIwaVhpDr2TA+++/j0uXLon1AiZNmoTvv/8eLVu2RJs2bbB582a88847Bg2UiIiIiKgqaSYD/DgroBj/pq5a7bDI+FJ60rMIgoBvNZYTtDSXYUKXBkaMqPbycrbGntndMK6zp9b2sJuJWHlFhisPU4wUmeHplQwwNzeHs7Oz1vUrEydOxN69e7F7925MnjzZUPEREREREVW5jJw8xDzNFNssHlhcA2crNHTRWGLwJusG6OtsTBIu3U8W26M61oeDlbz0A6hSWZjL8PHw1lg5qo1WbYynORKM2/gvHteQwoJ6JQNKIggC/vrrL/zxxx9IS0t79gFERERERCbqRlwaBI1LhP1YPLBEmrMDLtxLQnJmrhGjqb6+PRYl/i6RAFO7+xgxGiowskN97J3dHT51CpNer/TwhpudhRGjMhy9kgH/+c9/EBAQILYFQUC/fv3Qt29fDBw4EK1atcKdO3cMFiQRERERUVUqWjyQMwNKFtCssG5A/hKDNbfyemWJTsxAaMRjsd2vuRu8NT58knH51bXDr3O6I6iFG5raqzE3oJGxQzIYvZIBP//8Mzp37iy2d+/ejSNHjuDDDz/E/v37oVKpEBwcbKgYiYiIiIiqlGYywFZhhvqOlkaMxnR19nGCpXlhkTvWDdDd5uPRWrNQpvdsWHpnMgpbC3OsGdMar/iqIZPWnKUe9UoGPHz4EI0bNxbbe/bsQfPmzbFw4UIMGDAAs2bNQlhYmKFiJCIiIiKqUprJgGZ1bbnWeykUZjJ0a+Qstv+O5BKDukjKyMWuc/fFdltPB3TwcjRiRFQaiUQCRQ1b3EGvZICZmRlycnIA5F8icOTIEQQFBYn73dzckJjIKUJEREREVP2o1QJuxBXWwOIlAmXzb1ZYN+BJRi6uPqo51dYrW8jpGGQr1WJ7es+GTDxRldErGdCyZUv8+OOPSEpKwpYtW/DkyRMMHDhQ3B8TE4M6deoYLEgiIiIioqpyPykTmbkqsc1kQNn8m7potcMiuapAeeTkqfDdqRixXd/REoEt3IwYEdU2eiUDFi9ejIsXL6JOnTqYPn06unfvrlVQ8Pfff0enTp0MFiQRERERUVVh8UDdeDpZoZHGEoNHWTegXH65+AgJaTlie2p3H5jJDLbYG9EzmelzUN++fXH+/HmEhobCwcEBY8aMEfclJSWhV69eGDJkiMGCJCIiIiKqKtdjCy8RkEoAXzcuK/gsAb6uuJMQDQC4eD8ZSRm5cLSWGzkq0yUIAjaFR4ttWwszjO7kacSIqDbSORmQnZ2NDRs2oG3btnjttdeK7Xd0dMRnn31mkOCIiIiIiKqa5swA7zrWsJTXsKphlcDf1xUbj+d/uBUE4NitBAxpW8/IUZmuY7cSEfm4MOk0vksD2Cj0+p6WSG86z0OxsLDAO++8g8jIyMqIh4iIiIjIqDSTAbxEoHw6+TjCSq65xCDrBpRlY3iU+LuZVILJ3byNFwzVWnoXELx7966BQyEiIiIiMq7UbCUeJGWJ7eZMBpRL/hKDhQXEj93kEoOliYhNRfitwpXXBrXxQF17SyNGRLWVXsmAjz76CN988w3+/PNPQ8dDRERERGQ0NzTqBQCAX13WCygvf9/CVQWeZOTiykMuMViSjRq1AgDglZ4+RoqEaju9Lkz58ssv4eTkhMDAQPj4+MDHxweWltrZLIlEgl9++cUgQRIRERERVYWiKwk0c+fMgPLSTAYA+asKtPF0ME4wJupxajZ+vfRQbHdr5IwWHvZGjIhqM72SAZcvX4ZEIkGDBg2gUqlw+/btYn0kEkmFgyMiIiIiqkqayQB7S3PUtbcwYjTVS31HKzRxtcGt+HQA+XUDXn+hqZGjMi3fnbwLparw8onpPRsaMRqq7fRKBrBeABERERHVRNrFA235BZeO/H1dxGTApQfJeJqRCycuMQgAyMzNQ8jpe2K7iasNejd1KeMIosqlV80AIiIiIqKaRqUWtJZ740oCuvP3dRV/F4T8QoKUb9fZB0jJUortV3r6QCplsomMR+9kgEqlwo4dOzBz5kwMGzYMV65cAQCkpKRgz549ePz4scGCJCIiIiKqbHefZCBbqRbbTAborqO3I6y1lhiMN2I0pkOlFrDpeGHhwDo2cgxpW8+IERHpmQxITk5G9+7dMX78eGzfvh2//vorEhLys342NjaYN28evvjiC4MGSkRERERUmYoWD+SygrpTmMnQrbHGEoO3EqHiEoMIvR6He08zxfbLXb1hYS4r4wiiyqdXMuDdd9/FtWvXcOjQIURFRUEQCv/AZTIZRo4ciQMHDhgsSCIiIiKiyqaZDJBJJWjsamPEaKovzVUFnmbk4vKDZOMFYyK+1VhO0MJcionPeRkxGqJ8eiUD9u3bh7lz56Jv374lFlVp2rQpiwwSERERUbUSEVtYL6CRizW/udWTZt0AIH9VgdrsXEwSzsUkie0R7euzqCKZBL2SASkpKfDx8Sl1v1KpRF5ent5BERERERFVNe2VBHiJgL7qOViiqVvhrIqwWl5EcGN4lPi7RAJM61H65yiiqqRXMqBRo0Y4f/58qfsPHz6M5s2b6x0UEREREVFVSs7MRWxKtthmMqBiNGcHXH6QjCfpOUaMxnjuPcnEoWtxYrtPMzc0dOHlJ2Qa9EoGvPLKK9i8eTN27twp1guQSCTIycnBf/7zHxw8eBAzZ840aKBERERERJXlepHigc3cbY0USc3g37SwboAgAMdu1c7ZAZtPREOzfuL0npwVQKbDTJ+DXnvtNVy7dg3jxo2Dg4MDAGD8+PF48uQJ8vLyMHPmTEybNs2QcRIRERERVRrNegEAVxKoqI7eTrCWy5CRqwKQXzdgWLv6Ro6qaqVkKvHT2ftiu3V9e3T2cTJiRETa9EoGSCQSfPvtt5g0aRJ2796NW7duQa1Wo1GjRhg9ejR69epl6DiJiIiIiCqNZr0AZ2s5XGwVRoym+pObSdG9cR0cvv4YAHDsZgJUagEyafHi4zVVyJkYZP4vGQIAr/RsWGLxdSJj0SsZUKBHjx7o0aOHoWIhIiIiIjKKosUD+aGt4vx9XcVkQFKmEpceJKN9A0cjR1U1cvPU+O7kXbFdz8ESA1q6Gy8gohLoVTNg9OjR2Lt3L3JyDFsI5NixYxg0aBA8PDwgkUiwb98+rf2CIGDx4sWoW7cuLC0t8cILL+DWrVtafZ4+fYoJEybAzs4ODg4OmDZtGtLT07X6XL58GT179oSFhQU8PT2xfPnyYrHs2rULzZo1g4WFBVq1aoUDBw4Y9L4SERERkWnIU6lx63Hh/xf96rJegCH4+7potWvTEoO/XXqEx6mFn5WmdPeGmUyvj15ElUavZ+SJEycwYsQIuLq64qWXXsL+/fuhVCorHExGRgbatGmDdevWlbh/+fLlWLNmDb7++mucPn0a1tbWCAwMRHZ2YeXXCRMm4Nq1awgNDcX+/ftx7NgxzJgxQ9yfmpqKfv36wcvLC+fOncOKFSsQHByMDRs2iH1OnjyJcePGYdq0abhw4QKGDh2KoUOH4urVqxW+j0RERERkWqISM5CrUottriRgGB4OlvB1K0ys/B0Zb8Roqo4gCPhWYzlBW4UZxnTyNGJERCXTKxnw4MEDhIWFYeLEiQgNDcXgwYPh5uaGadOm4fDhw1CpVM8+SQn69++PDz/8EMOGDSu2TxAEfP7553jvvfcwZMgQtG7dGt9//z0ePXokziCIiIjAwYMHsXHjRnTp0gU9evTA2rVrsWPHDjx69AgAEBISgtzcXGzevBktWrTA2LFjMW/ePKxevVq8rS+++AJBQUF4++234efnh2XLlqF9+/b48ssv9bpfRERERGS6IoqsJMBkgOFozg649CAFibVgicETt5/gRlxhQcqxnT1ha2FuxIiISqZ3AcFevXqhV69eWLt2LY4ePYqffvoJe/fuxZYtW+Ds7IwRI0bg66+/Nlig0dHRiIuLwwsvvCBus7e3R5cuXXDq1CmMHTsWp06dgoODAzp27Cj2eeGFFyCVSnH69GkMGzYMp06dQq9evSCXy8U+gYGB+PTTT5GUlARHR0ecOnUKb775ptbtBwYGFrtsQVNOTo7WZROpqflvKkql0iCzJipLQWymHKOp4ZjpjmOmO46Z7jhmuuOY6Y5jppvqMl5XHySLv5vLJGjgoDBazNVlzMqrZ2MnfHOs8FvyoxFxGNrWw6C3YWpjtuHYbfF3mVSCiZ3rm0xsBUxtzKqD6jJmusRXoQKCACCVStGnTx/06dMHX331FTZv3oz58+fj22+/NWgyIC4uDgDg5uamtd3NzU3cFxcXB1dXV639ZmZmcHJy0urj4+NT7BwF+xwdHREXF1fm7ZTk448/xpIlS4ptP3z4MKysrMpzF40qNDTU2CFUOxwz3XHMdMcx0x3HTHccM91xzHRj6uMVfl2Kggmzrgo1/jx80LgBwfTHrLxUakAhkyFHlV+QcXvYZcgfXayU2zKFMYvNBI7dKvyI1cZRhYsnj+Ki8UIqkymMWXVj6mOWmZlZ7r4VTgYAQGxsLHbt2oWdO3fin3/+AQB069bNEKeuNhYuXKg1myA1NRWenp7o168f7OxMd6qZUqlEaGgo+vbtC3NzTl8qD46Z7jhmuuOY6Y5jpjuOme44ZrqpLuP14ZUwALkAgM5NPTBgQCujxVJdxkwXf6ReRGhEfr2AqEwFAoP8DbrEoCmN2cK91wA8FNvvj+qGlvVM77OAKY1ZdVFdxqxghnp56J0MiI+Px+7du7Fz506cOHECarUanTt3xsqVKzF69GjUq1dP31OXyN09fymOx48fo27duuL2x48fo23btmKf+HjtwiR5eXl4+vSpeLy7uzseP36s1aeg/aw+BftLolAooFAUX4/W3NzcpJ8sBapLnKaEY6Y7jpnuOGa645jpjmOmO46Zbkx5vBLTc5CQniu2m3s4mESspjxmuurj5yYmA5KzlLgWl4EOXoZfYtDYYxaflo1fL8WK7S4+Tmjn7Wy0eMrD2GNWHZn6mOkSm14FBPv06YN69ephzpw5SEtLw0cffYSoqCj8888/eOONNwyeCAAAHx8fuLu748iRI+K21NRUnD59Gl27dgUAdO3aFcnJyTh37pzY56+//oJarUaXLl3EPseOHdO6liI0NBS+vr5wdHQU+2jeTkGfgtshIiIiopqBxQMrX+8iSwzW1FUFfjgVo7UqxfSeDY0YDdGz6ZUMiI+PxwcffIDIyEicP38e77zzDry9vSscTHp6Oi5evIiLFy8CyC8aePHiRdy7dw8SiQSvv/46PvzwQ/z666+4cuUKXn75ZXh4eGDo0KEAAD8/PwQFBWH69Ok4c+YMTpw4gTlz5mDs2LHw8MgvVDJ+/HjI5XJMmzYN165dw86dO/HFF19oTfF/7bXXcPDgQaxatQo3btxAcHAwzp49izlz5lT4PhIRERGR6SieDLAtpSfpq669JZq5F47r0cgEI0ZTObJyVfjxnxix3dDFGs83cy3jCCLj0+sygStXrhg6DgDA2bNnERAQILYLPqBPmjQJW7duxYIFC5CRkYEZM2YgOTkZPXr0wMGDB2FhYSEeExISgjlz5qBPnz6QSqUYMWIE1qxZI+63t7fH4cOH8eqrr6JDhw6oU6cOFi9ejBkzZoh9unXrhm3btuG9997DokWL0KRJE+zbtw8tW7aslPtNRERERMYREVu4BJyrrQLONsUv+6SK8/d1FZfbu/IwBQlpOXCxrTljvfv8AyRlFs48ntbDB1ID1kUgqgwVLiCYnp6OpKQkCIJQbF+DBg10Ope/v3+J5ykgkUiwdOlSLF26tNQ+Tk5O2LZtW5m307p1a4SHh5fZZ9SoURg1alTZARMRERFRtaY5M4CXCFQef18XfP33HbF97GYCRnSob8SIDEetFrD5eLTYdrKWY0T7mnHfqGbTKxmQnZ2NJUuWYNOmTXjy5Emp/VQqld6BERERERFVptw8Ne4kpIttJgMqTwcvR9gqzJCWkwcAOBoZX2OSAX9GPEZ0YobYnvicFyzMZUaMiKh89EoGzJ49G9999x2GDh2Knj17ioX3iIiIiIiqi9vx6VCqCmelsl5A5TGXSdGjSR38cTUOABB+KxF5KjXMZHqVMDMpG8MLZwXIzaR4uauXEaMhKj+9kgF79uzBK6+8gm+++cbQ8RARERERVYmixQObc2ZApfL3dRGTASlZSlx6kIwOXk5GjqpiLt1Pxpm7T8X2iPb1UId1J6ia0CsVJ5FI0L59e0PHQkRERERUZTSTAXIzKXzqWBsxmpqvd1Pt6vpHb1T/VQW+DY/Sak/rweUEqfrQKxkwZMgQ/Pnnn4aOhYiIiIioykTEFSYDfN1sa8SUdVPmbm+hVZch7Ga8EaOpuAdJmeJMBwB4vpkrGrvaGDEiIt3o9Yr3/vvvIyoqCjNmzMC5c+eQkJCAp0+fFvshIiIiIjJFgiBoLSvIegFVw9/XRfz96sNUxKdlGzGaitly4i5U6sKaE6/09DFiNES606tmQJMmTQAAFy5cwKZNm0rtx9UEiIiIiMgUxafl4GlGrthu5s56AVXBv6kL1ocVLjH4d2QCRnX0NGJE+knNVmLnv/fFdgsPO3Rt6GzEiIh0p1cyYPHixZBIJIaOhYiIiIioSlwvUjyQywpWjfZejrC1MENadv4Sg2E3q2cyYMeZe0j/3zKJADC9Z0N+PqJqR69kQHBwsIHDICIiIiKqOlxJwDjMZVL0bFIHB678b4nBmwnVbolBpUqNLSfuiu269hYY2Lqu8QIi0pNB/uqysrKQlZVliFMREREREVU6zXoBHvYWsLcyN2I0tYu/xqoCqdl5uHA/2XjB6OHAlVjEphTWOpjczRvm1SiZQVRA72ftvXv3MGXKFLi5ucHGxgY2NjZwc3PD1KlTERMTY8gYiYiIiIgM6obGzABeIlC1emsUEQSAsMjqs6qAIAjYcKxwOUFruQxjOzcwYkRE+tPrMoEbN26gR48eSE5ORt++feHn5ydu//777/Hbb7/h+PHj8PX1NWiwREREREQVla1UISoxQ2wzGVC13Ows0LyunVi3ISwyAW8HNjNyVOVzKuoJrj0qTCSN6dQA9pacVULVk17JgHfffRdSqRQXLlxAq1attPZdvXoVffr0wbvvvou9e/caJEgiIiIiIkO59Thda0k4JgOqnr+vi5gMuPYoFfGp2XC1szByVM+2MTxa/F0qAaZ09zZeMEQVpNdlAn///TfmzZtXLBEAAC1btsScOXMQFhZW0diIiIiIiAyuaPFAv7q2Roqk9vL3ddVqh91MMFIk5Xc7Pg1/3Si8pKF/q7rwdLIyYkREFaNXMkCpVMLS0rLU/VZWVlAqlXoHRURERERUWTSXFbQ0l8HL2dqI0dRO7Rs4wNaicJLy35GmnwzYdDxaqz29Z0MjRUJkGHolA9q1a4eNGzciJSWl2L7U1FRs2rQJ7du3r3BwRERERESGpjkzwNfdFjIp14evamYyKXo1KSwkeOxW/hKDpioxPQc/n38otjt5O6Ktp4PxAiIyAL1qBixZsgRBQUFo1qwZpkyZgqZNmwIAIiMj8d133+HJkydYt26dQQMlIiIiIqooQRC0kgG8RMB4evu64PcrsQCAtOw8nL+XjM4+TkaOqmQ/nIpBbl5hsuIVzgqgGkCvZMDzzz+PAwcO4O2338Ynn3yita9t27b44YcfEBAQYJAAiYiIiIgM5VFKNlKz88Q2iwcaj3/T4ksMmmIyIFupwg//FC6d7u1shRf83IwYEZFh6JUMAIAXXngBFy5cQFxcHGJi8v84vLy84O7ubrDgiIiIiIgMKeJR0eKBTAYYi6udBVp42IlL9YVFJmBBkOktMbjn/EM8zcgV29N6+PDSEqoR9E4GFHB3d2cCgIiIiIiqhaIrCTRz52UCxuTv6yImA67HpuJxajbcTGiJQbVawMbjUWLbwcocIzt4GjEiIsMpdwHBW7duwcLCAgsWLCiz39tvvw1LS0tER0eX2Y+IiIiIqKrdiEsTf/d0soSthbkRo6GAIksMmtqqAkcj4xGVkCG2J3bxgqVcZsSIiAyn3MmANWvWwN3dHR999FGZ/T766CO4u7tjzZo1FQ6OiIiIiMiQtIoHuvMSAWNr6+kAO40lBsNuxhsxmuK+DS+cFSCXSfFyNy8jRkNkWOVOBhw+fBhjx46FuXnZ2VO5XI6xY8fijz/+qHBwRERERESGkpmbh+gnhd/ysl6A8ZnJpOipUUgw/GYilCayxODVhyn4J+qp2B7S1gOutqZzCQNRRZU7GXDv3j34+vqWq2+TJk3EooJERERERKYgMi4NglDYZjLANGheKpCWk4fzMUlGjKaQ5qwAgMsJUs1T7mSAQqFAenp6ufpmZGRALpfrHRQRERERkaFFxKZptZszGWASehddYvCm8esGPErOwv7LsWK7V1MX+LLYJNUw5U4GNGvWDH/++We5+h45cgR+fn56B0VEREREZGia9QJsFGao72hpxGiogIutAi3rFSZmjt4wft2ArSfvQqUunEYyvaePEaMhqhzlTgaMGTMG+/fvx759+8rs98svv2D//v0YM2ZMRWMjIiIiIjIYzWSAr7stpFwr3mRoXipwIy4NcSnZRoslLVuJ7afvie1m7rbo0biO0eIhqizlTgbMnj0b7dq1w6hRozBr1iycOHECqampEAQBqampOHHiBGbNmoWRI0eiTZs2mD17dmXGTURERERUbmq1oLWsoF9dTvk2Jf6+2pcK/G3EVQV2/nsfaTl5Ynt6z4aQSJg4oprH7Nld8ikUChw6dAiTJk3CN998gw0bNhTrIwgCgoKC8P3330OhUBg0UCIiIiIifT1IykK6xgc8Fg80LW09HWFvaY6ULCUA4OiNBIzp1KDK48hTqbHlxF2x7WanwKA2HlUeB1FVKHcyAAD+n727DpOyah84/n0mtrt36e5GSlK6fN9XsEHERvQnYGOB2I0KYmOAggqohJRS0iIlIbH0BrDdE+f3x7ATLLGz7O7ssvfnuriYc56Ye848O7vPPSfCw8NZuHAhmzdv5pdffmHv3r1kZGQQFBRE48aNGTp0KJ06dSqrWIUQQgghhCiRPU5DBECSARWNXqfRvWEkv+44BcCfB21LDBr1xe7IXCqW7E7kZFquvTyqS228DOUbgxDlxa1kQKEOHTrQoUOH0o5FCCGEEEKIMrEv0ZEM0DTbOHBRsfR0SgZk5pv562gqneqGl9vzK6X4zGk5QT8vPbd3qFVuzy9EeZM0lxBCCCGEuOo5Tx5YO9wfP68SfScmylD385YY/GN/+c4bsOVIKjtOpNvLN7WvQbCfsVxjEKI8STJACCGEEEJc9fYmyOSBFV1koDctqwfby6v3ny7X5//UqVeAToO7rpXlBMXVTZIBQgghhBDiqpaZZ+JYSo693CRG5guoqHo69Q7Yl5hJQnruJfYuPfFnslmxN8le7t8shprhfuXy3EJ4iiQDhBBCCCHEVW2/05KCIJMHVmQ9GkW5lFeVU++Az9cdRilH+Z5udcvleYXwpGIlA95//33+/fffso5FCCGEEEKIUrf3/JUE4iQZUFG1rhFCiNM4/VXlMG9ASnYBP/51wl5uWzOEdrVCy/x5hfC0YiUDxo8fz9atW+1lvV7P7NmzyywoIYQQQgghSssep/kCgnwMxAX7eDAacSl6nUb3Bo6hAn8ePEuB2Vqmz/ntxqPkmRzPca/0ChBVRLGSAaGhoSQlOcbQKOc+NEIIIYQQQlRgzj0DGscGoWmaB6MRl9OzkSMZkJVvZuvRlDJ7rjyTha83HLGXa4b50a9ZTJk9nxAVSbHWVOnZsyeTJk1i+/btBAfbZvj8+uuv2bhx40WP0TSNqVOnlk6UQgghhBBClIDFqlzmDGgq8wVUeN0bRqJp2Mfwr95/mi71IsrkuX7efpIzWQX28l3X1kavk2SRqBqKlQyYPn0648aNY9myZSQnJ6NpGsuWLWPZsmUXPUaSAUIIIYQQwtOOns0m12Sxl2VZwYovIsCbltWC2XEiHbBNIvj0oCal/jxKKT5bG28vB/kYuLF9jVJ/HiEqqmINE4iKimL27NkkJCRgsVhQSvHtt99itVov+s9isVz+xEIIIYQQQpShvQmykkBl5LyqwP6kTE6llf4Sg6v+Pc2B5Cx7+fZOtfD3LtZ3pUJcFUq0tOCXX35Jly5dSjsWIYQQQgghStW+RMd8AToNGkZLz4DKoJfTvAFQNksMfrb2sP2xUa9xZ5fapf4cQlRkJUp9jRo1yv54z549HD16FIBatWrRtGnT0olMCCGEEEKIK+Q8eWDdyAB8jHoPRiOKq2X1EEL9jKTmmADbEoO3daxZauf/51Q6fx48ay8PbRVHdJCsMiGqlhL1DAD4+eefqVevHi1atGDIkCEMGTKEFi1aUL9+fX755ZfSjFEIIYQQQogScR4mIEMEKg+9TqN7Q+clBs+U6hKDnzvNFQBwT1dZTlBUPSVKBixevJhhw4YB8MorrzB//nzmz5/PK6+8glKKG264gd9++61UAxVCCCGEEMId6TkmTjqNNZfJAysX5yUGswssbD1SOksMJqbn8cuOU/Zy1/oRNI2TRJGoeko0TGDKlCm0bNmStWvX4u/vb6+//vrreeihh+jatSuTJ09mwIABpRaoEEIIIYQQ7tjrNF8ASM+AyqZ7A9clBlf9e5ou9a98icGZ649gtip7+Z5uda74nEJURiXqGbBz505GjRrlkggo5O/vz5133snOnTuvODghhBBCCCFKynm+AICmkgyoVMIDvGlZPcReXrU/+YrPmZ1vZvamo/Zyw+gAejSMvMQRQly9SpQM8PHxISXl4t10UlJS8PGRCTiEEEIIIYTnOCcDQv2MRAV6ezAaURI9nW7U/03Kchn2URJztx4nI89sL9/TtS6apl3ROYWorEqUDLjuuuuYOnUqGzZsKLJt06ZNvP/++/Tp0+eKg7uQ2rVro2lakX9jx44FoGfPnkW2PfDAAy7nOHbsGIMHD8bPz4+oqCgef/xxzGazyz6rVq2ibdu2eHt7U79+fWbOnFkmr0cIIYQQQpSN8ycPlJu+yqdX4yiX8pX0DrBYFV/86Zg4MCLAm/+0iSvx+YSo7Eo0Z8Abb7xB586d6dq1Kx06dKBRo0YA7N+/n82bNxMVFcXrr79eqoEW2rJlCxaLxV7evXs3ffv25cYbb7TX3Xvvvbz44ov2sp+fn/2xxWJh8ODBxMTEsH79ehISErjjjjswGo288sorAMTHxzN48GAeeOABZs2axcqVK7nnnnuIjY2lf//+ZfK6hBBCCCFE6TFbrOxPkpUEKruW1YIJ8/ciJbsAgFX7T3N7x1olOtfSfxI5nuLoWTCqcy28DbLUpKi6StQzoE6dOuzcuZP/+7//IzU1lTlz5jBnzhxSU1N55JFH2LFjB7Vr1y7lUG0iIyOJiYmx/1u4cCH16tWjR48e9n38/Pxc9gkKcnz4L1u2jD179vDtt9/SunVrBg4cyJQpU5g2bRoFBbYPmRkzZlCnTh3efvttmjRpwkMPPcTw4cN59913y+Q1CSGEEEKI0hV/JttlKTpJBlROOp1G9waOSQP/PHiGfLPlEkdc3KdrD9sf+xh1jOhUsqSCEFeLEvUMAIiKiuLdd9/16A1yQUEB3377LRMmTHDp9jVr1iy+/fZbYmJiGDp0KM8995y9d8CGDRto0aIF0dHR9v379+/PmDFj+Oeff2jTpg0bNmwoMsyhf//+jBs37qKx5Ofnk5+fby9nZNjGqJlMJkwmU2m83DJRGFtFjrGikTZzn7SZ+6TN3Cdt5j5pM/dJm7nHk+21+0SqS7lBpG+leN/kGiuqW/1wFmy3LQWYU2Bh48HTdKkXbt9enDbbdiyNv4+l2cvD2lQjwEursu0s15n7KkubuRNfiZMBFcGCBQtIS0vjzjvvtNfddttt1KpVi7i4OHbu3MmTTz7J/v37mTdvHgCJiYkuiQDAXk5MTLzkPhkZGeTm5uLr61sklldffZXJkycXqV+2bJnLMIWKavny5Z4OodKRNnOftJn7pM3cJ23mPmkz90mbuccT7bXoqI7CTrA6TXHwr3UcKVGfWM+Qa8whzwQaehS2L/9mLt1CWm1rkf0u1Waf73dcDxqKOgXxLF4cf9H9qwq5ztxX0dssJyen2PtW6mTA559/zsCBA4mLc0z8cd9999kft2jRgtjYWHr37s2hQ4eoV69emcXy9NNPM2HCBHs5IyODGjVq0K9fP5dhChWNyWRi+fLl9O3bF6PR6OlwKgVpM/dJm7lP2sx90mbukzZzn7SZezzZXvO+3gacAaBBVCDXD+lSrs9fUnKNXdjcxE3sOJEOwDFzIIMGXWvfdrk2O5qSw66N6+zlPk2iGTWsdZnHXJHJdea+ytJmhT3Ui6PSJgOOHj3KihUr7N/4X0zHjh0BOHjwIPXq1SMmJobNmze77JOUlARATEyM/f/COud9goKCLtgrAMDb2xtv76LL1RiNxgp9sRSqLHFWJNJm7pM2c5+0mfukzdwnbeY+aTP3eKK99jlNHtg0LrjSvV9yjbm6rnG0PRlw6HQ2SVkmqoe69r69WJt9s/E4SjnK9/WoJ217jlxn7qvobeZObJWos5SrL7/8kqioKAYPHnzJ/bZv3w5AbGwsAJ07d2bXrl0kJzuWJVm+fDlBQUE0bdrUvs/KlStdzrN8+XI6d+5ciq9ACCGEEEKUhZTsApIyHHM5NYkN9GA0ojT0bBTpUl61/3SxjkvLKWDu1hP2cqsaIbSvFVqqsQlRWVXKZIDVauXLL79k1KhRGAyOzg2HDh1iypQp/PXXXxw5coRffvmFO+64g+7du9OyZUsA+vXrR9OmTRk5ciQ7duxg6dKlPPvss4wdO9b+zf4DDzzA4cOHeeKJJ9i3bx/Tp09n7ty5jB8/3iOvVwghhBBCFN/eBNdusrKSQOXXolow4f5e9vKq/cmX2Nth1qZj5Jocqw/c262Oy8TjQlRlbicDcnJyaNeuHTNmzCiLeIplxYoVHDt2jLvuusul3svLixUrVtCvXz8aN27Mo48+yrBhw/j111/t++j1ehYuXIher6dz586MGDGCO+64gxdffNG+T506dVi0aBHLly+nVatWvP3223z22Wf079+/3F6jEEIIIYQomfOTAY1jJBlQ2el0Gj0aOnoHrD909rJLDBaYrXy1/oi9XC3ElwHNYsoqRCEqHbfnDPDz8yM+Pt6jGbV+/fqhnAf+nFOjRg1Wr1592eNr1arF4sWLL7lPz549+fvvv0scoxBCCCGE8Iw9TsmAiABvIgOLzuskKp8ejSKZ9/dJwLbE4Jb4VLo2iLjo/r/sOEVypmO4yF1d62DQV8qO0UKUiRL9NAwYMIClS5eWdixCCCGEEEJcsb0JjskDZb6Aq0f3BpHonL6P/OMSQwWUUny29rC9HOhj4OZrapRleEJUOiVKBjz33HP8+++/jBw5knXr1nHy5ElSUlKK/BNCCCGEEKI8FZitHEx2WklA5gu4aoT6e9G6Roi9fKl5A9YdPMO+RMd1cFuHmgR4V9qF1IQoEyX6iWjWrBkAe/bsYfbs2Rfdz2K59DgeIYQQQgghStPhM1mYLI7hpDJ54NWlZ6Moth1LA2xLDB5PySEmsOhSap+ujbc/Nug07ry2djlFKETlUaJkwPPPPy+zcAohhBBCiApHVhK4uvVsFMk7y/+1l1ftT+aW9tVc9tmfmMmafx1LDw5pGUtssG+5xShEZVGiZMCkSZNKOQwhhBBCCCGunPN8AV56HXUj/T0YjShtzeOCiQjw4kxWAQCr9p8ukgz41GmuAIB7utUtt/iEqExKZTrN9PR0GRIghBBCCCE8zrlnQIPoAIwye/xVRafT6H7+EoMmx31IckYeP28/aS93rhtO82rB5RqjEJVFiT8dt27dyoABA/Dz8yM8PNy+pN+ZM2f4z3/+w6pVq0orRiGEEEIIIYrFORkgQwSuTj0bRdkf55osbD6aai9/teGIy5wR93avU66xCVGZlCgZsH79erp27cqBAwcYMWIEVqvVvi0iIoL09HQ+/vjjUgtSCCGEEEKIy0nOzLN3HwdJBlytujeIcFlicM2/ZwDIKTDz7cZj9vp6kf70bBh1/uFCiHNKlAyYOHEiTZo0Yc+ePbzyyitFtvfq1YtNmzZdcXBCCCGEEEIUl/N8AQBNYgI9FIkoSyF+XrSpGWovrzlgSwbM+/sU6bkme/093eqi08mk50JcTImSAVu2bGH06NF4e3tfcFWBatWqkZiYeMXBCSGEEEIIUVyykkDV0dNp3oDDZ3I4nQtfrj9qrwv39+J/bapd6FAhxDklSgYYjUaXoQHnO3nyJAEBASUOSgghhBBCCHc5JwNignwI9ffyYDSiLPVq7Nr9/4d4HcdScu3lkZ1r4WPUl3dYQlQqJUoGdOrUiR9//PGC27Kzs/nyyy/p0aPHFQUmhBBCCCGEO1wnD5QhAlezprFBRAR428v70x23Nd4GHSM71fJEWEJUKiVKBkyePJmtW7cyePBglixZAsCOHTv47LPPaNeuHadPn+a5554r1UCFEEIIIYS4mDyThUOns+1lGSJwddPpNHo4DRVwdkPb6oQ7JQqEEBdWomRAx44dWbx4MQcPHuSOO+4A4NFHH+W+++7DYrGwePFiWrZsWaqBCiGEEEIIcTEHk7OwWB1Lykky4OrXq/GFkwF3d5XlBIUoDkNJD7zuuuvYv38/f//9NwcPHsRqtVKvXj3atWt3wUkFhRBCCCGEKCsyeWDV061+JDoNnHJA9G4cRf0ombtMiOIocTKgUJs2bWjTpk1pxCKEEEIIIUSJOC8r6GPUUSfC34PRiPIQ7Gekbc1Qth5Ntdfd062uByMSonIpcTIgPz+fTz/9lMWLF3PkyBEAateuzaBBg7jnnnvw8fEprRiFEEIIIYS4JOeeAY2iA9HL+vJVwn/aVLMnA9rVDKFT3TAPRyRE5VGiZMCJEyfo27cv+/fvJzY2lvr16wO2SQR/++03PvzwQ1asWEH16tVLNVghhBBCCCHOp5Rib6LzSgIyRKCquK1DTQpMZjb8vYcXb2klw5WFcEOJJhAcO3YsR48eZe7cuZw8eZLVq1ezevVqTp48yZw5czh27Bhjx44t7ViFEEIIIYQoIjEjj7Qck73cOEaWFawq9DqNOzrVZGgtK5GBsoKAEO4oUc+AlStXMn78eIYPH15k24033si2bdv44IMPrjg4IYQQQgghLkcmDxRCCPeVqGdAYGAgUVFRF90eExNDYKBkZIUQQgghRNlznjwQoLEkA4QQ4rJKlAwYPXo0M2fOJCcnp8i2rKwsvvzyS+6+++4rDk4IIYQQQojL2ePUM6BaiC/BvkYPRiOEEJVDsYYJzJs3z6Xcpk0bFi1aROPGjRk1apR9AsEDBw7w9ddfExYWRsuWLUs/WiGEEEIIIc7jPExAhggIIUTxFCsZMHz4cDRNQykF4PL45ZdfLrL/iRMnuPXWW7nppptKMVQhhBBCCCFc5RZYOHIm215uGitDVYUQojiKlQz4448/yjoOIYQQQggh3PZvUiZW5ShLzwAhhCieYiUDevToUdZxCCGEEEII4TZZSUAIIUqmRBMICiGEEEIIURE4JwP8vfTUDPPzYDRCCFF5FKtnwIWsW7eOL774gsOHD5OammqfQ6CQpmns2LHjigMUQgghhBDiYpyXFWwUE4hOp3kwGiGEqDxKlAx45513ePzxx/Hx8aFRo0aEhYWVdlxCCCGEEEJcklKKvYmOngGNZYiAEEIUW4mSAW+++SbXXnstv/76K8HBwaUdkxBCCCGEEJd1IjWXzDyzvSzzBQghRPGVaM6AnJwcbr/9dkkECCGEEEIIjzl/8kBZVlAIIYqvRMmAXr16sWvXrtKORQghhBBCiGJzni8AoFGM9AwQQojiKlEy4IMPPmDlypW89dZbpKSklHZMQgghhBBCXJZzz4Ba4X4EeJd4bmwhhKhySpQMqFGjBvfffz9PPfUUkZGR+Pv7ExQU5PJPhhAIIYQQQoiytM9p8sAm0itACCHcUqL06fPPP8/LL79MtWrVaN++vdz4CyGEEEKIcpWdb+ZoSo69LJMHCiGEe0qUDJgxYwaDBw9mwYIF6HQl6lwghBBCCCFEie1LzEQpR7mJTB4ohBBuKdGdfEFBAYMHD5ZEgBBCCCGE8IjzVxKQngFCCOGeEt3NDxkyhLVr15Z2LEIIIYQQQhSLczIg0MdA9VBfD0YjhBCVT4mSAS+88AJ79uzhwQcf5K+//uL06dOkpKQU+SeEEEIIIURZcE4GNIkJQtM0D0YjhBCVT4nmDGjUqBEA27dv5+OPP77ofhaLpWRRCSGEEEIIcRFWq2JfYqa93FjmCxBCCLeVeDUByb4KIYQQQghPOJaSQ06B40snmS9ACCHcV6JkwKRJk0o5DCGEEEIIIYpHJg8UQogrJ8sBCCGEEEKISsU5GaDToFG0DBMQQgh3lahnwIsvvnjZfTRN47nnnivJ6YUQQgghhLiovU7zBdSO8MfXS+/BaIQQonIq9WECmqahlJJkgBBCCCGEKBMuKwlcLUMEss/Cggegx0RPRyKEqCJKNEzAarUW+Wc2mzl06BDjx4+nffv2JCcnl3asQgghhBCiisvIM3EiNddebnq1JAOWPwcHlmH4vDdNT34HptzLHyOEEFeg1OYM0Ol01KlTh7feeosGDRrw8MMPl9aphRBCCCGEAGBfQqZLucnVsKxg/FrYPgsATVmIztgJunNDH05shfljwGL2YIBCiKtRmUwg2L17dxYvXlzq5500aRKaprn8a9y4sX17Xl4eY8eOJTw8nICAAIYNG0ZSUpLLOY4dO8bgwYPx8/MjKiqKxx9/HLPZ9cN11apVtG3bFm9vb+rXr8/MmTNL/bUIIYQQQgj3XXUrCZjzYeF4l6odNUaD3gv2LoSZQ2DHbFjyOCjloSCFEFejMkkGbN26FZ2ubBYqaNasGQkJCfZ/69ats28bP348v/76Kz/88AOrV6/m1KlT3HDDDfbtFouFwYMHU1BQwPr16/nqq6+YOXMmzz//vH2f+Ph4Bg8eTK9evdi+fTvjxo3jnnvuYenSpWXyeoQQQgghRPE5JwNC/IzEBPl4MJpS8OdUOHvAXrS2HklKQEPITYUFY8B8brjA1i9g3bseClIIcTUq0QSCX3/99QXr09LSWLNmDfPmzeOee+65osAuxmAwEBMTU6Q+PT2dzz//nNmzZ3PdddcB8OWXX9KkSRM2btxIp06dWLZsGXv27GHFihVER0fTunVrpkyZwpNPPsmkSZPw8vJixowZ1KlTh7fffhuAJk2asG7dOt5991369+9fJq9JCCGEEEIUj3MyoHFMIJqmeTCaK3TmIKx5y1H2i8By3fPwxwbwDYVhn8N3N4Oy2ravnAzBNaDljZ6JVwhxVSlRMuDOO++86LaIiAieeuopl2/bS9OBAweIi4vDx8eHzp078+qrr1KzZk3++usvTCYTffr0se/buHFjatasyYYNG+jUqRMbNmygRYsWREdH2/fp378/Y8aM4Z9//qFNmzZs2LDB5RyF+4wbN+6SceXn55Ofn28vZ2TYflGZTCZMJlMpvPKyURhbRY6xopE2c5+0mfukzdwnbeY+aTP3SZu5p7Tby2JV7E9yzBnQKDqg8r4XSqFfOA6dxfH3o7nvFEyGAOBcm9XphTbgTQxLHnUctmAMFr8IVK2u5R5yRSU/l+6TNnNfZWkzd+IrUTIgPj6+SJ2maYSGhhIYWHaTuHTs2JGZM2fSqFEjEhISmDx5Mt26dWP37t0kJibi5eVFSEiIyzHR0dEkJiYCkJiY6JIIKNxeuO1S+2RkZJCbm4uvr+8FY3v11VeZPHlykfply5bh5+dXotdbnpYvX+7pECodaTP3SZu5T9rMfdJm7pM2c5+0mXtKq72SciHP5PjztSA5nsWLD5fKuctb9ZQ/aXd0rb2cHNiMDUf94JitrRxtFkmT6KE0TPoVAM1qQn13G2sbPEumb/XyDrtCk59L90mbua+it1lOTk6x9y1RMqBWrVolOeyKDRw40P64ZcuWdOzYkVq1ajF37tyL3qSXl6effpoJEybYyxkZGdSoUYN+/foRFFRxJ7YxmUwsX76cvn37YjQaPR1OpSBt5j5pM/dJm7lP2sx90mbukzZzT2m316JdibB9p718c79raRZXcf/OuqjcVAwzHH83Kr03oSO+YFBYvQu3mRqI9ecH0P3zEwBGSw69Tk3HfOcSCIz1xCuoUOTn0n3SZu6rLG1W2EO9OEqUDKgoQkJCaNiwIQcPHqRv374UFBSQlpbm0jsgKSnJPsdATEwMmzdvdjlH4WoDzvucvwJBUlISQUFBl0w4eHt74+3tXaTeaDRW6IulUGWJsyKRNnOftJn7pM3cJ23mPmkz90mbuae02uvf5Gz7Y71Oo3FcCEaj/orPW+6WTIGcM/ai1v0xjNGNXXYp0mb/+wiyk+GIrTeBlnEC49zbYfRi8L4KllcsBfJz6T5pM/dV9DZzJ7ZiJwNatmzpVhCaprFjxw63jnFXVlYWhw4dYuTIkbRr1w6j0cjKlSsZNmwYAPv37+fYsWN07twZgM6dO/Pyyy+TnJxMVFQUYOvmERQURNOmTe37nL8s4vLly+3nEEIIIYQQnrEv0TFfQL1If3wqYyLg6HrY5jQZd0RDuPaRyx9n8Iabv4Uv+sPpfba6xJ3ww51w6/egr7g3J0KIiqnYyYCwsLBizdaamJjI/v37y2Rm18cee4yhQ4dSq1YtTp06xQsvvIBer+fWW28lODiYu+++mwkTJhAWFkZQUBAPP/wwnTt3plOnTgD069ePpk2bMnLkSN544w0SExN59tlnGTt2rP1b/QceeIAPP/yQJ554grvuuovff/+duXPnsmjRolJ/PUIIIYQQovicVxJoElsJhwdYrbDoUde6Ie/abvSLwzcEbv8RPusDWbb5rji4AhZNgKHvQ2VeWUEIUe6KnQxYtWrVJbcnJiby+uuv8/HHH6PX6xk5cuSVxlbEiRMnuPXWWzl79iyRkZF07dqVjRs3EhkZCcC7776LTqdj2LBh5Ofn079/f6ZPn24/Xq/Xs3DhQsaMGUPnzp3x9/dn1KhRvPjii/Z96tSpw6JFixg/fjxTp06levXqfPbZZ7KsoBBCCCGEB6XlFJCQnmcvV8pkgE4HQ96DheMgeQ+0HgG13VwVIKQG3D4XvhwEBVm2um1fQ4sboU730o5YCHEVu+I5A5KSknjttdf45JNPMJlMjBgxgmeeeYZ69eqVRnwuvv/++0tu9/HxYdq0aUybNu2i+9SqVavIMIDz9ezZk7///rtEMQohhBBCiNK3J8F1UqxKmQwAqNkR7l8Dmz6GVreW7ByxreDGr2D2TYCCwW9LIkAI4bYSJwMKewI4JwGeffZZ6tatW5rxCSGEEEIIwd6ETJdyk9hKPGme3ghdHrqyczToA9d/AP4R0FB6sAoh3Od2MiAxMZHXXnuNTz/9FJPJxMiRI3n22WepU6dOWcQnhBBCCCGEy3wB4f5eRAYUc5z91azN7Z6OQAhRiemKu2NCQgKPPPIIdevWZfr06dx6663s37+fzz//XBIBQgghhBCiTJ0/eWBZTFZdJnLTYPWbYMotv+c8sRUyTpXf8wkhKqVi9wyoV68e+fn5tG7dmokTJ1KnTh1SU1NJTU296DFt27YtlSCFEEIIIUTVZbJYOZCUZS9XqiECKyfD1i9g+yzb2P76vcv2+fb+Cj/dA+ENYPRi8KmkcysIIcpcsZMBeXm22Vv//vtvbrrppkvuq5RC0zQsFsuVRSeEEEIIIaq8w6ezKbBY7eVKM3ng8c22RABAajwsfhzGbrLNGVAWdv4A8+4FFCTtgrl3wO0/lN3zCSEqtWInA7788suyjEMIIYQQQogL2lsZVxKwmODXca51Q94p2xvzWp0hMAYyE2zlw3/YYvjPh1BZhlUIIcpNsZMBo0aNKss4hBBCCCGEuKC9iY5kgFGvUS8ywIPRFNOGaZD8j6Pc8hao27NsnzO4uq0nwBcDoeDc6gvbv4WQmtDzybJ9biFEpVPsCQSFEEIIIYTwBOdlBetHBeJlqOB/wqYehVWvOco+IdDvpfJ57pgWcNNXoHP6zm/VK/D3rPJ5fiFEpVHBP0mFEEIIIURV57qSQAWfPFApWPwYmJ1WD+g3BQIiyy+G+r1h6Puudb/+Hxz6vfxiEEJUeJIMEEIIIYQQFdaZrHxOZ+bby00r+nwBe36GA8sc5ZqdofWI8o+jze3Q82lH2WqGOXdA4q7yj0UIUSFJMkAIIYQQQlRYlWrywLx0WOI0Nl9nhCHvgc5Df3L3eBJa3+4oF2TCrJsg/aRn4hFCVCiSDBBCCCGEEBXW+cmAxjEVeJjA7y9BVqKjfO0jENXYc/FoGgydCnV7OeoyT8Gs4bbEhRCiSiv2agJCCCGEEEKUN+fJA6MCvQkP8PZgNJdw4i/Y/KmjHFoHuj/muXgK6Y1w09fw5UBI2m2rS94Dc0bCyPmg03s2PuExQTlH0XZ8Bz4BYPQFgw8Y/cDoAwZfW5293leulauQJAOEEEIIIUSF5Tp5YAUdImAxw8JHAOWoG/y27QaqIvAJsi05+FkfyDgJmg6a/kdu7qq42PRtGBbOL/4Bei/bNT3wDWh1i+u2gytg65fFSCqcVx9aG4KruZ5Lnfs50rQren3i8iQZIIQQQgghKqR8s4WDyVn2coVNBqCgyX/g9L9gyYfmw20z+lckQXG2hMA3N9iGDjQa4OmIhIfprQXuHWApsP27kLOHYN9C94O47rmiPWj2LYS5d5xLGJxLIBh8znt8gWRD7a7QaKDruXJS4Pjm844tTE74Os7hqXk9PEySAUIIIYQQokI6mJyF2er4tr3CLiuoN0KPx6H5DbDiBej/iqcjurDoZvDIDtsNkKhazPlgcB1io1duJgMKGS5w/Zhyi9YVx4V6z5jyQFnBlG37x9nin+/8ZEDyXvju5ssfp/d2TRg0GQL9XnLdpyC7+HFUEpIMEEIIIYQQFdI+p/kCoBIsKxheD27+1tNRXNrFEgEF2eDlX76xiLJnyoVFj0HOGbjlO5dvwPfE3kiNW97BiMm2nznP6f8c2025OddW51wf0bDo8/hHQGzr844999hqvnh8F0oGmEsxsVDcc1nybf8KJ9bMSbnAPqaSxVWBSTJACCGEEEJUSM7zBXgZdNSJkJvVMrH3V1g4Hkb8BLGtPB2NKC0ph23d7RN32crr3nHpkm/R+0BQLBiNV/5cbUbY/l2IxXyBpMK5x6G1i+4f2wp6PXOJhMSFHueC9wV6DpW0x8KFej9chSQZIIQQQgghKqS9iY5kQKPoQAz6CjSuNz8TEnbYxilXZps+hiVPAgpm3QT3rICQGp6OSlyp/Utg3v2Q77SE5OrXodWtRSfsK2t6A+gDL3yzfiGxrUovKVWnBzyw7jJJhVzbdlPOufo8qNGx6Lm0CvT5U0okGSCEEEIIISocpZTLsoIVbr6AP16BjdOh1W22scX+4Z6OyH1WKxxYhn0VhKxEmDUc7loKviGejEyUlNUCf7wMa992rfePghu/LP9EgKf5BEFMi9I711Xm6ktvCCGEEEKISi85M5+UbMcEZxVqJYFT22HTDNvjHbNh1jDHcmiViU4HN86EmJaOutP7YM4I24RzonLJOg3f/K9oIqBmZ3hgbeXvxSJKnSQDhBBCCCFEhbPHab4AqEDJAKsFfn3ENtt5oV7PVN410b0DbUsOBjsNDTiyFn5+qHImOKqq45vh4+4Qv9q1vvNDMOpXCIzxTFyiQpNkgBBCCCGEqHD2np8MiKkgyYDNn0LCdke52f+gQV+PhVMqAmPg9h/BO9hRt2su/D7FczGJ4lHKNu/DlwMh85Sj3isQbvwK+r9sW/pSiAuQZIAQQgghhKhwnOcLiAv2IdivAtzQpJ90vUH2DoIBr3kuntIU1RhumQU6p3Ze+zZs/dJzMYlLy8+Cn+6BJU+4Lt8X2QTu+wOa/ddjoYnKQZIBQgghhBCiwnHuGVBhhgj89iQUZDnKvZ+/urpf1+kG//3ItW7Ro/DvMs/EIy5t1auw+0fXuhY3wr0rIaLBRQ9TSvHqlleZkz2HhYcXku684oCoUiQZIIQQQgghKpQ8k4XDpx033RUiGbB/Cez91VGu1g7a31WmT5ltyi7T819Qyxuh9wuOsrLAD3fCqb/LPxZxaT2ehPD6tsc6Iwx6C274FLz8L3mYpmnUCarDLtMunt/4PD3n9OTdv94th4BFRSPJACGEEEIIUaH8m5SJ1WnuOo8nA/KzYPHjjrKmhyHvgU5fak8Rnx6P1WlSwtS8VK5fcD3vb3sfk9VUas9TLF3HQ7vRjrIpG2bfDKlHyzcOcWk+QXDTNxDeAEYvgQ73XnQiy2xTNmdzz9rLNze8mUaGRgCYlZkovyj7NrPVzNz9c0nOSS7b+IXHSTJACCGEEEJUKPuc5gsAaBIb6KFIzln1KqQfd5Q7PwixLS++v5v+OfMPtyy8hWfWPYPJakIpxeRNk0nOSebTXZ9yx+I7yvfGTNNs3zI36O+oyz4NJ7aUXwzCVUbChZMx0U1h7Caocc1FD911ehfDfxnOk2uetCecMk2ZROgj7Pv0qdnH/nhb0jambJxCnx/6MHLxSPan7C+91yEqFIOnAxBCCCGEEMKZ87KCvkY9tcIv3e25TCXshI1O4+iDa0DPp0vt9IfTDzNmxRhyzDksPLyQtLw06prrsubkGvs+eZY8gp1n+i8PegMM/wJmDoYz/8KNM6Fh/8seJspA/Fr4cbRtfoq7l4PR13X7JXqo/HLoF1748wXMysyJrBPM/GcmdzW/iyCvIPr69OXmTjdzIOMA0f7R9mOWH10OgEKx88xOwn3D7duOZxzHoizUDq5dqi9ReIb0DBBCCCGEEBWK8+SBjWIC0esu3PW5zFktsHCcbdx8oUFvXXZMtjuOph8l0+ToCWFVVuoa6vJm1zcJ8grCS+fF691fx1vvbd8nz5xXas9/Sd4BcNtcGL1YEgGeoBSsew++vt7WMyNxFyx+zK1TtIxoidFpacF5B+ZRYCkAwKAZ6FqtK/e1vM/lmJ1ndtoft41qS4SvowfB57s/Z+iCofzv5//xyc5PSvCiREUiyQAhhBBCCFFhKKUqzkoCuWlg8HGUmwyFRgNK9Sl61ezFtN7T8DX40jqyNW92exODZqB3zd78dP1PvNH9DRqGNrTvv/LYSoYuGMqWxHLqsh8YDXFtyue5hENeOswZASteAKe5JEjYCfmZFz8OXIaU1A6uzdMdbD1ZBtYZyHeDv8NL73XJ42cPms1XA75iRJMR3NDgBnu9xWrhj+N/AHAw7WCRazAhKwGlFKLykGECQgghhBCiwjiVnkdGnmPN9KaenC/APxzuXATbZ8Hq12HgG6Vy2mxTNjpNh6/B1t27S1wXvhzwJdUDquOrc3QBj/GPIcbfsXRhck4yk9ZPIi0/jbuX3s09Le7h4TYPo11k0rgytXch5JyBdneW/3Nf7RJ3w9yRkHLYtb7NSBj0ZtFhAufkmHJ4Y8sbLD2ylB+G/kD1wOoA/Lf+f6keWJ320e2Lda3odXraRrelbXRbl/qdZ3aSkpdiL/et1df+2GQ1MfzX4QR6BdK7Zm/+V/9/1A+tX9xXLDxEkgFCCCGEEKLC2Hsqw6Xc2NMrCWgatBkBLW+xjaO/QgWWAh75/RHyLfl82PtD+1wAzcKbAWAyXXzlgOnbp5OWnwbYxnNblMUziYBNH8OSJ21tExANjQaWfwxXq+3fwcLxYM511Om9YfBb0PaOix6WmJ3Ivcvu5UjGEQCeWvsUMwfMxKAzoGka18RcfILB4mod2Zofh/7I8qPLWXlsJdfVvM6+bUvCFjIKMsgoyODrPV9TP6S+PRlgttqSewad3HpWNDJMQAghhBBCVBjOQwQAGsd4eCWBQqWQCDBbzTy55kk2JW5i++ntjF46mtM5p4t9/BPXPMGwBsMAaBrelIdaP2TfZrKYyqeLdvxaWPIEoGzd13+8C07+VfbPe7Uz59uSAAsecE0EhNSCe5ZfMhEAEOEbQZhPmL288/TOUh9KomkajcIa8VCbh5j/n/kucwksP7bc/liv6elVo5e9/OfJP+n9Q28mrZ/EupPryn+pTHFRkgwQQgghhBAVxt5ERzKgRpgvgT7GS+xdBgpyICfl8vuVQEJWAluTttrLiVmJLt2uL8fP6MekLpN4t+e7vNbtNZeJ4V5Y/wKPrn6U9Pz0Uo25iNpd4Zp7HGVTDsy+GVLiy/Z5r2Zpx+CL/rD1C9f6hgPg/tUQ2+qCh6Xnp9sTQAadgVe7vUqgMZBov2g+7/85neM6l3Xkdnc3v5tH2z1Ky4iWdIjpQIhPiH3b8qPLSclL4acDP/Hw7w+X3wSY4rIkGSCEEEIIISqMvQmOydGaxHhgiMDq12FaB9j5g20291JUI6gGXw38imi/aHz0PnzY+0MahTVy+zx9avWhTnAde3nx4cX8evhXlh9dzrBfhvF38t+lGbYrTbPNndDQaWhA9mmYdWOZJVGuagdWwMfd4ZTTe6bp4Lrn4JbvwDf0goetObGG6xdcz/yD8+11cQFxfND7A366/qdSGRbgjuqB1bmz+Z3MGjyLab2n2etNVpN90kGAzrGdCfRy9PZ5ccOLPL76cZYeWUqOKadcYxaSDBBCCCGEEBVEToGZI2ez7eVyX0kg6R/Y8KHt5nbePbDgwVI5ba5Tt++6wXX5ZuA3vH/d+0UmaCvpuV/b/Jq9fCb3DBplPI+ATg/DP4c4p/jPHoDvbgWTfOvrliNrITfVUfYLhxHzoPtjoLvwrdqbW95k7MqxpOSl8Nrm14hPd/TKaBfdzj4Phac491ixKiuPtH2EzrGd0Wt6l0kHCywFLI5fzG9HfuOx1Y/x7J/PeiLcKk2SAUIIIYQQokLYn5jp8mV8uSYDrFb4dRxYHSsZ0HjwFZ923oF5/O/n/3E046i9LjYgttS6cPsafPmo70fUDqoNwP0t76d1VGv7drPz6ylNXv5w21zbmPZCxzfC/PttbSmK57rnoHY32+Pq18D9a6Ber0seEuUXZX+ca87ls12flWWEV8Rb781NjW7ik36fsPrm1fSv3d++bcOpDWSbHMk/53kGcs25PPL7I8w/MJ+0vLTyDLlKkWSAEEIIIYSoEJyHCAA0Lc9kwLaZcGKzo9xoEDQZckWnXHF0BZM3TOZk1knuWHIH+1L2XVmMF9EsvBlzhsxhXNtx3NvyXnt9en46N/xyA/MOzCubyQUDImHET65d2fcsgOXPlf5zXa30Bhj+BXR7FO5cDMHVi+yilHLpQj+y6Ug6x9qSSaOajuKFzi+UW7hXItg7GD+jn70c4hNC31p98dH7YNAZ6FGjh33b+pPr+f347zy//nl6zu3J5oTNFzqluEKyvoMQQgghhKgQnFcSCPA2UD30wuupl7rMJFg+yVE2+tvGxV8Bi9XCxzs/xqps35Kn5KWw9sRaGoc1vqLzXoyf0Y+7W9xtLyulmLxhMvHp8byw/gXWnVzHpC6TCPIq5QRLRAO49Xv46nqw5NvqNnwIITWh4/2l+1yVmVK2CQKjm0HNTq7bAqKg9/MXPCwlL4Xn/3wei7Iwvfd0NE1Dp+l4uevLHEg9QJdqXcoh+LLRKrIV7/R8hxxTDntT9rpcm8uOLrM/NugMNI9obi9vTtjMv6n/0qdWH2L8Y8o15quN9AwQQgghhBAVgnMyoHFMIDpdGY99L7R0IjjPwt9rIoTUuKJT6nV6Pun7Cc3DbTcxtza+lXta3HOZo0rPmhNrWH7UsdzbgdQDGLQy+h6wZie44RNwnqtgyZOwd2HZPF9lU5AD8x+ARRPghzshq3jLSW5L2sawX4ax+sRq1p1cx+x9s+3bIv0iK3UiwJmf0Y920e1c6kJ9Qu3JgS5xXVx6FMzZP4fXt7xO3x/7cs/Se8pnSc2rlCQDhBBCCCGEx1mtin2JTisJlNcQgYMrYfePjnJMC+j4QIlPZ7Fa7I9DfUL5rP9njG83nqc6PIWmlVNyA+hWvRsT2k3AoDNg0Bl4vfvrLjdUhT0WSk2z/0L/l50qlG3+gKq+wsDZQ/BZH9j5va2cmQA/jgan6+RigryCyCxw/ExM+3uaS/lq9lSHp1h18yo+7vuxS4+XXHMua0+utZeDvYNdfq6WxC/hUNqhco21MpNkgBBCCCGE8LgTqblk5Tsmu2scG3iJvUuJKdf2ba2dBkOm2sZxl0BCVgLDfx3OlsQt9jp/oz93Nb8LnVa+f3brNB2jm49m1qBZPN/peZqGN7VvW3dyHbcvup0j6UdK90k7PehIpBj9bGPh/cJK9zkqk72/wic9IfkfR53OYJuP4iLXQ4GlwP64fmh9Hm//uO1xSH1mDpzpsizf1c6oM9IlrgutIlvZ645nHndpA+fVCbJN2Ty77ln++/N/uX7B9S49Y8SFyZwBQgghhBDC4/Y4DRGAcuoZsOZNSD3iKHe4F6q3u+jul5KSl8J9y+/jSMYRHlj+AG/1eIteNS89K3x5aBre1CURcDb3LM+ue5azeWe5aeFNPN3haf7X4H+l82SaBv1fAXMetB0F1a586cRKyWKG31+EP6e61gfEwI0zoVbRlSSUUsw7MI+PdnzEt4O+tY+Fv6nRTeh0OobWHYqPwaccgq/YGoY2ZPnw5ew6s4uVR1fSrXo3+7a1J9ZSYLUlU+LT49E5fe+dVZDFofRDtIhoUe6JuYpMkgFCCCGEEMLjnOcL0DTbnAFlKnmv681aQAxcV/J1zj/b9RlHMo4AUGAt4KMdH9G9enf0Ov0VBlq63tz6JmfzzgK2Ltf7U/eX7hPo9DB06uX3u1plJsGPd8HRda71tbvBsM8hMLrIIdmmbJ778zn7N9kT103k076fotfp0TSNGxveWB6RVxo6TUeryFYuPQYAtp/ebn/sa/B1mVPhj+N/MHHdRKL9oulTqw9jWo0h2Du4vEKusCQtIoQQQgghPM45GVA73B8/rzL8zspqhYXjweoYlsDA18Cn5DcH49uOt6+hXj2gOtN6T6twiQCwxdkxtiMADUIbML7dePu2Mp+I7fjmYo2Vr7SOboCPuxdNBFw7DkYuuGAiAMBL70VCVoK9vCVxC4vjF5ddnFeppzo8xYL/LGBs67Hc1vg2fA2O1UgKEy1JOUn8fPBnl21ncs9gsprKPd6KQHoGCCGEEEIIj9ub6EgGNCnr+QJSDkHSHke5QT9o+t8rOqVRb+T1bq9TPaA6wxoOI9Iv8spiLCPR/tF80vcTvtnzDV3iuuCt97Zve2njSwR4BfBQ64cw6o2l+8SbPratMNBpDAx4tXTP7WlKwcbpsOw5UE7JDu8g+O9H0GRIkUMsVos9WWTUGXm9++vc+OuNFFgLGNd2HIPrDi6v6K8q9ULqUS+knktdnjmPDac22Mvdq3fHS+9lLz/353PsOrOLXjV6MbjuYDrFnrf041WsUvUMePXVV7nmmmsIDAwkKiqK//73v+zf79q1qWfPnmia5vLvgQdcZ4Q9duwYgwcPxs/Pj6ioKB5//HHMZrPLPqtWraJt27Z4e3tTv359Zs6cWdYvTwghhBCiSsrMM3E8JddebhJTxvMFRDSAh7ZA8+Fg8IVBb9nGJrhBKcWk9ZOYf2C+vU6v0zOu3ThqBF7ZsoRlTafpGNVsFA1CG9jrVh5dydx/5/LF7i8YsWRE6U4uuHIKLHkCOHfTvGF66Z27Iph/v215SudEQHRzuG/VBRMBxzOOM3LJSH499Ku9rmZQTV7p+grfDvqWUc1Gybj2UuRj8OGX//7CE9c8QduotvSr3c++LaMgg40JG0nPT2fBwQUsPOS6HKbJcnX3GKhUPQNWr17N2LFjueaaazCbzUycOJF+/fqxZ88e/P397fvde++9vPjii/ayn59jGRWLxcLgwYOJiYlh/fr1JCQkcMcdd2A0GnnllVcAiI+PZ/DgwTzwwAPMmjWLlStXcs899xAbG0v//v3L7wULIYQQQlQB+xNdl0srl8kDA6Nh+OeQdhxC3Lt5V0rx9ta3+enAT/x04CfS89O5s/mdZRNnOcgsyGTShkn28t6zezmde5rawbVL5wniWgMacG4YwtKJEFwNmv6ndM7vabW6wM45jnKrW2HwO+DlV2TXhYcX8tLGl8g2ZXN402FaR7W2J4961+pdXhFXObEBsYxsOpKRTUe61K8+vhqz03Ah59UJUvNSGTRvEJ3jOtOnZh+6xHThalOpUk6//fYbd955J82aNaNVq1bMnDmTY8eO8ddff7ns5+fnR0xMjP1fUJDjF8qyZcvYs2cP3377La1bt2bgwIFMmTKFadOmUVBgm31yxowZ1KlTh7fffpsmTZrw0EMPMXz4cN59991yfb1CCCGEEFXB3vNXEogrh2RAITcTAQAbTm3gqz1f2ctTt00lPj2+NKMqV4FegUzqPMk+odpdze/imphr7NuveC6BJkNhwGtOFQrm3QfHNl3ZeSuKtqOg9e2g94Ih79mGBlwgEQBwMvMk2aZswDZx4CubXinHQMX5ulXrxuQuk+larSuh3qF0jnOs9PDH8T/IMmWx/Ohynlz7JLvO7PJgpGWjUvUMOF96ejoAYWGu65fOmjWLb7/9lpiYGIYOHcpzzz1n7x2wYcMGWrRoQXS0YwKP/v37M2bMGP755x/atGnDhg0b6NOnj8s5+/fvz7hx4y4aS35+Pvn5+fZyRobtl5rJZMJkqrjdSwpjq8gxVjTSZu6TNnOftJn7pM3cJ23mPmkz9xS3vXafTLc/DvIxEOmnL/02tlpsa7u7ORzgQtpHtue+5vfxye5P0NB4sfOLVPerXioxe+oa6x7XnbkD5zJzz0zua3af/fmzTFk8suoR7m9xPx1iOpT8CdrdjS4lHv3mGbayOQ/13S2Y71wCYfUufexllGubWS22FRPO1+81aHc3xLSE84YfK6XQzl13oxqP4s+Tf/L36b9pF9WOie0neuTzRD7LbPz1/gytPZShtYdSYClAs2r2yQSXxS+z7xfkFUSrsFb8wR8Vvs3ciU9TZT5taNmwWq1cf/31pKWlsW6dY8bOTz75hFq1ahEXF8fOnTt58skn6dChA/PmzQPgvvvu4+jRoyxdutR+TE5ODv7+/ixevJiBAwfSsGFDRo8ezdNPP23fZ/HixQwePJicnBx8fR2zTxaaNGkSkydPLlI/e/Zsl2EKQgghhBDC1Tu79BzNst0s1Q9SPNys9Gecb5Qwj5CceHZWv4Nc79KZ3G9D/gZ06Ojo3bFUzlcR/ZD9AztMO9DQuNb7Wvr49MGglfD7RGXlmiPTiEvbYq/K8opibcPnKTCWY2+QEvIpOMs18R9yOKo/J0MvP8mcVVlZnb+aJEsSN/vdbE8IpFnT2Fmwk67eXWVugApsT8Eedpl2sd+0n2ZezRjmN8zTIRVLTk4Ot912G+np6S495C+k0vYMGDt2LLt373ZJBIDtZr9QixYtiI2NpXfv3hw6dIh69a4s63gpTz/9NBMmTLCXMzIyqFGjBv369bvsm+BJJpOJ5cuX07dvX4zGUp419iolbeY+aTP3SZu5T9rMfdJm7pM2c09x2stiVTy1dSVgBaBz01oMGtS4dAM5cwDDp4vQrCai/30Wa58XsbYb7dYpNiVuYlPiJh5u9bD9pm4Qg0o3TirWNfb78d/ZsXYHAApFvCGe6/pdR4AxoOQnNfXGOnsYuhObAQgoSKZ/ypdYRswHY8m+QCuPNtPiV6NfMB4t5yyhJ7+iVb/bIbLRRfdPyE7g2fXP8nf63wD8p9F/GFbfcTN5G7eVSZzFVZGus4qq8Oc7z5xHtimbIENQpWizwh7qxVEpkwEPPfQQCxcuZM2aNVSvXv2S+3bsaMvUHjx4kHr16hETE8PmzZtd9klKSgIgJibG/n9hnfM+QUFBF+wVAODt7Y23t3eReqPRWKEvlkKVJc6KRNrMfdJm7pM2c5+0mfukzdwnbeaeS7XX8dNZ5Jqs9nLzasGl27ZKwW+Pw7muv5o5F71/GHo3nmPn6Z1MWDOBXHMuaQVpvND5BQy6sv0zuiJcY91rdufGhjfyw78/oNf0vNb9NUL9Qq/spEYj3Po9fN7XtsQjoDv1F7qfx8DN31y4C36xT10GbWa1wrq34feXKZwAUTNlY1zyKNz120WHneRYc9h9dre9/PZfb9OrZi+i/aMvuL+nVITrrKIzGo0E+gbau99X9DZzJ7ZK1S9FKcVDDz3E/Pnz+f3336lTp85lj9m+fTsAsbGxAHTu3Jldu3aRnJxs32f58uUEBQXRtGlT+z4rV650Oc/y5cvp3LkzQgghhBCi9OxNKOOVBHZ8B0edepLWuw6aF7+7b2ZBJg+tfIhcs23pwwUHFzBn/5zLHHV18DX48nzn55naayoT2k2gdVRr+7bNCZuZsGoCaXlp7p/YPxxG/Ah+EY66/Yvgt6dsyZuKIjcVvrsFfn8J+0oIAHFtYNinl5x/onFYY8a1HQeAv9GfF7q8UOESAUJUqmTA2LFj+fbbb5k9ezaBgYEkJiaSmJhIbq7tw/nQoUNMmTKFv/76iyNHjvDLL79wxx130L17d1q2bAlAv379aNq0KSNHjmTHjh0sXbqUZ599lrFjx9q/2X/ggQc4fPgwTzzxBPv27WP69OnMnTuX8ePHe+y1CyGEEEJcjZxXEtBp0DA6sPROnn0Wlj7jKBt8YPDbbk0iGOgVyFMdnrKPk+9RvQc3Nbqp9GKsBK6reR13NLvDXk7LS+PpdU+z/Ohyhv0yjI0JG90/aVhduG0OGJx63W7+BDZ8WAoRl4JT2+HjHnBgqWt9+7vgrqUQUtOl+p+z/zBqySjO5J6x141oOoLRzUbzw9AfGFJ3SDkELYR7KlUy4KOPPiI9PZ2ePXsSGxtr/zdnji076+XlxYoVK+jXrx+NGzfm0UcfZdiwYfz666/2c+j1ehYuXIher6dz586MGDGCO+64gxdffNG+T506dVi0aBHLly+nVatWvP3223z22Wf079+/3F+zEEIIIcTVzDkZUDcyAB9jybuJF7H8echNcZS7P267CXXToLqDeP+697m22rW81eMtjLqK20W4PLyy6RWSc2y9bJNzk1l2ZNlljriI6u1h+OfAueSM0Q/CG5ROkFdi29fweT9IO+qoM/jCf2fAkHfB4BgabFVWvtj9BSMWj2Bb8jae/fNZrMo27EWn6ZjQfgI1At1fvlKI8lCp5gy43MIHNWrUYPXq1Zc9T61atVi8ePEl9+nZsyd///23W/EJIYQQQgj3OCcDSnWIwJF1sP1bRzmyMXT5v2IdmlmQyac7P+XB1g/iY/ABoFv1bnSt1tU+eWBVdleLu9iXuo/49HhqB9XmsfaPlfxkjQfDwDdgzZu2ngLV2pZeoO4y5cLix+Hvb1zrw+rCTd9ATPMih5itZpbEL8FstS0n+OfJP/nx3x+rXO8RUTlVqp4BQgghhBDi6pGeY+JUep693CS2lIYImPNh4XnDO4e8Bwavyx6aZ87j4d8f5st/vmTMijFkFjjmNJBEgE3jsMbMGTKHWxrdwuvdX8fPaRWAd/96lx///fGyX+K56HgfjN3k2URASrytN8D5iYDGQ+C+VRdMBAB46b14vdvr+OhtSaN+tfrRv7b0JhaVQ6XqGSCEEEIIIa4eexNdl8AqtZ4Bf06FM/86ym3vgFrFmwj6+fXP81fSXwBsTdrK02uf5sPeFWQcewXia/DlmU7PuNStObGGL3Z/AcC6k+uY1HkSIT4hxTuhX9iF6y1m0JfxLYvVAt8Os69uAICmg94vwLWPuMwxkWfO462tb9EhpgP9avcDoG5IXSZ2nAjAf+v/V5JGotKQngFCCCGEEMIjnIcIADQtjWTA2UOw5i1H2S8C+kwu9uG3N7mdIC9bHEFeQTzS9pErj6kKSM1L5bk/n7OXVx5byZ6UPVd20k0fwxf9oSD7CqO7DJ0eBr+Ffe4C/0i44xfoOs4lEbA/ZT+3LLyFOfvnMGnDJBKzE+3b/tfgf/yvwf8kESAqFUkGCCGEEEIIj3BOBoT5exEV6H2JvYtBKdvwAEu+o67/Kxf/1vkCWkW2YuaAmdQKqsW03tNoEFoBJrSrBIK9g7mr+V32yRVHNBlBl7guJTuZ1WpbBWLJE3ByK/x4l62HQFmqdx30egZqdIL710KdbkV2WX50OYfSbb0HMgsymbhuonvDIYSoYCQZIIQQQgghPGJvgmM8fpPYwCv/VvXIOoh3mky6Tg9oefmJ3OYdmOeyJFyD0AYs+M8CWke1vrJ4qhCdpmNUs1HMHjyb/rX7M67dOPu2HFMOj656lMPph4t3suxk2DnHUf73N1tioLRuvJP32hIO5+v2KNy5EIJiL3jY/a3up2WEbbnySN9I7m1xr/QEEJWaJAOEEEIIIUS5M1us7E9yJAMax5TCEIE63eDW7yG4Bui9bcvAXeZmbe7+ubyw/gXuWHIHJzJP2OsNOplaqyQahzXmrR5v4a139PJ4Y8sbLDu6jJt/vZkf/v3h8t+mB8bAbXNtSw0W2vq5bS6IK6EUbP4UZnSDdW8X3a7Tgd6xbOS6k+t4ddOr9niNOiOvdX+NgXUG8tP1P9E5rnjzUAhRUUkyQAghhBBClLv4M9kUmB3fzpba5IGNBsKDG23L1IXXu+SuK4+u5KWNLwFwPPM4o5aMIqMg45LHCPesPLqSnw78BECeJY9Pdn5Cjjnn8gdWawvDv7RN5FdoxQuw68eSBVKQDfPuhcWPgdUEv78Mh36/8K6WAt7Y8gZjVoxh9r7Z/Hr4V/u2GoE1eKP7G4T6hJYsDiEqEEkGCCGEEEKIcrcn4fyVBEppWUEA7wCo1+uyuzUJb0KtoFr28s2Nb7ZPHihKR8vIlnSOtX2DrqHxStdX8Df6F+/gRgNg0FuudQvG2IaDuOPMAfi0N+z6walSwb5FF9z9UNohvtv7nb388saXOZ5x3L3nFKISkGSAEEIIIYQod87zBRh0GvWjAso9hriAOGYOmEmTsCaMaDKCe1vcW+4xnM+al4fx7NmrZmK6SL9IZvSdwePtH2dMqzFcE3ONfdvfyX/z9ta3KbAUXPwE19wNXcc7ypYC+P42OL2/eAH8swA+6Qmn9zrqdAYY+EbRRMM5TcKbMLbNWHv5fw3+R5R/VPGeT4hKRAZDCSGEEEKIcue8kkD9qAC8DfqSnWjDdNv/He4r1nr0B1IPYFVWGoU1AiDcN5yZA2biY/Dx6GRw1rw8Ur/9ljOffEqdjAyOzppF8KDBhN93L/rAUuw14QE6Tccdze5wqcssyOSpNU9xKvsUmxI28Vr316gbXPfCJ7jueUg7DrvPDRHIS4dvh8M9KyAw+sLHWEywYhJs+NC1PjAObvoKanSwV6XlpfHetvd4pO0j9u7/o5uNZl/KPq6vdz3dq3cvycsWFZwpKQlrdva5fzlYc7KdyufqsrMJ6NUL/04dPR1umZBkgBBCCCGEKHf7Eh3JgBLPF5ByGFZOBnMe7PgOhk61jTW/iBOZJ7h/+f3kWfKY1nsabaLaAODnPFGdh5wcP4GsP/6wl80nT5E6Zw4RDz/kwajKzsubXuZU9ikA9qbs5et/vmZSl0kX3lmng/9Oh8xEOHpuiED6MZh9I9y5GHTnLUmZkQA/joZjG1zr63SHYV9AQKS9alPCJiaum0hyTjIpeSlM7TUVTdPQ6/S81ePCPQdE+VJWK9acnCI36YU37wE9e7okzDKWLydzyRIs2dmo7BwsOdn4tmxJ7KRJLuc91K8/Kj+fy9GHh0syQAghhBBCiNKQkl1AUobjj/ASzRegFCx6zJYIAEjcCaf3XTQZcCb3DPcvv5/TuacBuG/ZfXzc92PaRl88eVCewkaOcEkGAAT27YPOy8ul7tTTE/Fu1JCggYMwRlferusDag9g/cn1pOanUiOwBo9f8/ilDzB4wy3fwuf94cy5IQIJO2w3/cO/dux3ZB38MNq2PKGzbo9Cr2dA5+iBYrFaeG3zayTn2Pb94/gfzN0/l5sb31waL7HKcty856BMBXhVr+6yPWPpMkwnTpy7obfd2AcNHIB/ly72fXJ37OD42Iew5uSgci494WSdnxegb9TIXi44dJiMxUtc9tEHFk046vz9sRQjGWDNyb7sPpWVJAOEEEIIIUS52ltk8sAS9AzY/RMcWuko1+4GrW696O4+eh9i/GM4lnkMgDrBdWgQ2sD95y0F2evXoyxWArp1tdf5d+mCX+dO5B84yOkmTYg9c5rgwYNdjis4coT0+fMBSH79DfyuuYagwYMJ7NcXQ2jlmt2+Z42e/HT9T0zaMIn7W97vMqngZ7s+o2l4U7rEdXE9yDcURvwIn/WBrCRb3ZF1kLwXlEK34QP44yVQFscxPsHwv49tq0ycR6/T81q317ht0W0UWAuoE1yHlpEty+LlVhrKbCZv3/7zustnEzR4kMu37+kLF5GxcKHLPpYc27f2zjfvhshIGqxd4/IcKV9/Te5ff7nUedWu5ZIMQG/AcuZMsWK2ZrverOv8i05Qef4+hftZUlIuel7NaETn749mMF50n8pOkgFCCCGEEKJcXXEyIDcNfnvaUdZ7weB34BJj/gO8ApjeZzpPrH6CQ+mH+KjPRwR6le9Y/Nxduzn97jtkr9+AsUYN6i1aiOb0zX/cK69g9fNj76pVdBg0CIPB9U/19EVOs98rRc7mzeRs3kzilCkEXHstQUMGE3jddRe8GaqIIv0imdZ7mkvdxoSNTN02FYBRTUfxf23/Dy+9U++IkJpw21z4chB4+dkeR7VAbz2Ibsds10RATEu46WsIqwOAUopfDv1CqE+ofR6ARmGNmNB+AofSDvH4NY/ja/At2xftQcpqJW/3brJWrSZz3Vrqxh/h9PYdxL3wvGOf/HyODB9e5Fjftm1ckgGm48fIWrXqss954ZvwosNyit7QF3/ozvnHGqKi8G7QAJ2/v/2fV+3aRY6LfupJlNlybh8/dP7+6P390fz8bP+f1yvnaiTJACGEEEIIUa6clxWMDPQmIsD7EntfwMrJrt3Au46HyIZFdjNZTfxz5h9aR7UGwFvvzds93yYtP41w3/CShF4i+YfjOT11KplLlzpiO36c1B9/JOy22+x1xthYTCaTvXz+hIb6gAAMMTGYExNdn8BsJmv1arJWr0bz8SGgV0/iXnqp0iQFCqXlpfHM2mfs5a/2fEWXal2K9hCIaw23fgehtSC0NphMWPTemIfNxPhlPzBlQ5uRMOhNMNpu7jMKMpiyYQq/HfmNUO9Qfrr+JyL9bHMH3Nb4No9OHlmWLBkZZP/5J1mrVpO1dq3LN+EGKPLNuObnZ0uqnbeaxfld9XV+xbtZt+bkoKxWNJ1jETv7dWk0ovez3YRrvq5JGENYGKG3347u3HaXf+fVGSIjXI4N6t+PoP79LhtbYO/exXoNVzNJBgghhBBCiHLlvKxg4xg3v50/vhm2fukoh9WDrhOK7GZVVp7/83mWxC9hyrVTGFpvKAAGnYEI34gi+5cFU1ISZz6cRtq8eWCxuG7U6TAnJF74wIsIGzWK0JEjyd22jfRFi8j8bSmW1FSXfVReHvl799lu6ioZH4MP19W8ju/3fw/ATQ1vKpoIKFS3R9G6yEbwnw+gIBvauq5eMHvvbH478hsAqfmpPLPuGWb0nYFO012ViYDU7+eQsXAhOX//XfTac3L+t+qapqHz8ytSbzn/2/e4OHxbtbrkTXph+fzEQtzLL8PrrxeZD8OZPjiYmOeeLe7LFSUkyQAhhBBCCFFuCsxWDiY7kgFN3RkiYDHBr+MAp5uLIe+C0cdlN6UUb2x5g4WHFwIwcd1Esk3Z3NL4liuI3I0w09I4+9lnpHzz7QVnKw/s14/IcY/gXfciS+ldgqbT4de+PX7t2xMzcSLZGzeSsXARmStW2G/gggYPdrnBVRYL8TfeiF+btgQNHoxvm9YV8gbYx+DDM52eoWu1rsz8ZyaPXfOYfVueOY+p26ZyX8v77Mv/XVDzYResvqv5Xfx+7Hf2puzFoBnoENvhgvtVRta8PHQ+rj8DuX9vI2fr1ose41W/Psnh4UT26lVkW/Vp09C8jC439ucvbxnUty9BffuWKN7i9ioQZU+SAUIIIYQQotwcOp2FyeK4mXdrvoCN0yH5H0e55S0X/IZYocgrXGUAW2+AmkE1SxRvSZyc8CjZ69cXqffr2JGoRyfg27J0JqnTjEYCunUjoFs3rHl5ZK1eQ8aiRQSdN/Fgzpat5O/ZS/6evaTOmoUxLo6gwYOIeOCBCjmUoEeNHnSv3t0lYfHOX+/w3b7v+O3Ib7zc9eWL9xg451TWKZYeWcro5qMB8NJ78Xr313l89eNM6jKJ5hHNy/Q1lLWCEydsXf/XrCZn02bq/vIzXrVq2bcH9OhB+s+/2Muajw/+nToR0LMHAd26QVQUuxcvpv2gQUXOfbUuoyeKkmSAEEIIIYQoNyWePDD1KPzxqqPsEwL9XrrgrjpNxwudXyDEO4Qvdn/Ba91eu+zNY2kKv+9el2SAT9OmRD46Af8uXcrsG3mdj89Fx0pnOE88CJhOnSJtwQIix40rk1hKg3M7rTmxhu/2fQfYloh8bt1zLLphET4GnwseuyR+CVM2TCHTlEmsfywD6gwAbCtI/DD0hwrZK6K4lFIcGTacvD17XOqzVq8m7A7H0Aj/rl3xqlUL/65dCejZA79rrnHpPeA8N4WouiQZIIQQQgghyo1zMsBLr6NuZDG+mVYKFj8G5lxHXd8XISDSZbf49HjqBNtmjtc0jXHtxjGwzkAahTWiLCirlcylS9G8fQi8ztHd2r9TJ/yvvZaCE8eJGjeOwP79XSZQK29+7duRH3+Y3K2O5dyCBgxE0+vtZaUUJydMwLdlK4IGDcQYHe2JUC8o2i+aesH1OJR+CIApXadcNBFwJP0IT619CquyAvDihhdpGdmSuIA4oOikjBWZ+fRpcnfudJnoTtM0DJGRRfbNWuWaDNAHBVFv6W/lEqeovCQZIIQQQgghys2+RMd8AQ2iAzDqi3GTvG8hHFjmKNfsbJst3sm6k+t4eOXDjG4+mofbPGy/6SuLRIBSiuw/13P6nXfI27MHY7VqBHS91nWZwNdfQx8cjGb0/Brlwf/5D8H/+Q+mhAQylvxGxqJFBA927R6ev3cvmUt+I3PJbyS/8QZ+7dsTNHgwgf37YQi9xBj9ctAorBHfD/med/56By+dl0svj91ndrMzeSd+yjYOvXZwbe5reR8zdswAoF5IPTQqRwJAWa3k7dp1bmWINeT98w9oGg3+XIchLMy+X0DPHmStXg2APjKCgO7dZWZ8USKSDBBCCCGEEOXGuWdAsYcI1OkBHR+AzZ+AprNNGuj0Tfv25O2M/2M8ZmXm012fkpafxjMdn0Gv01/ipCVjOnWKUxOfIWfjRkfdyZOkzplL2MgR9jpDRPmsWOAOY2ws4XeNJvyu0UW2pTsPJVCKnC1byNmyhcSXXsL/2i4EDx5MwHW90Qd4Zo4BH4MPEztORDnNTJ9jyuGptU9xNOMoXb27MsA6ACNG7m95P5sTNtMpthP3trwXg67i3vJY0tNtS/+tXk3W2nVFlvpDKbLXrSP4+uvtVQE9exKRmkpA9x74NG3i0V4nonKruD8ZQgghhBDiqpKcmceZrAJ7udjJAJ8gGPg6tLwZErZDVBOXzftT9pNncUwYWGApQKeVzQ2SPiSE/IMHXeo0b2+sebkXOaJyMIRHYIiLxXwqwXWD2Uz26jVkr16D5u1NQK9exL1x6WXhypJzN//Xt7zO0YyjAKRZ0+zvuUFn4PP+n1foJIA5JYUT//d/5P69/ZJL/wFkb9jokgwwxsYS+eCDZRyhqAokjSSEEEIIIcrF3oRMl3KT2MCL7HkR1dpC+7uKVN/c+GZe7voyek3PdTWuY1KXSaU2NtySluZS1vn5EfHgGFtBryfkppuot2wpEffeWyrP5ynhd42m/ooV1Jo9i9DbbkXv1C29kMrPx3T8eJFEgDpvHfnyoJSiYWhDvHS2WHabdvNr/K/27RUpEWDNySH/wAGXOn1ICAVHjl40EeDdsCHh995LrW+/IXbKi+URpqiCKs5PiRBCCCGEuKoVWUkgxo1lBc+TlpdGoFegfSjA9fWuJ84/jhaRLUrlRtCcmsrZTz8jddYsan37Db4tWti3hQ4fTsGhw4SOuB3vOnWu+LkqCk2nw69tW/zatiV64kSyN2wkY9EiMleswJqVBVBk2UJrQQHx//kv/p07EzRkML6tW5dLt3VN07i9ye1cE3MNb215i8NJhzHqPD8/Q6GC48fPLf23hpxNmzBERVFv+TJ7kkrT6Qjo3p30efNsZV9f29J/PXoQ0L0bxrg4T4YvqghJBgghhBBCiHLhnAyICfIh1P8SXc13/wThDSC2ZZFN6fnp3LXsLmoF1uL17q/jpbedp31M+yuO0ZqTQ8rX33D2s8/sN8DJb79DzS+/cNzIeXkR89yzV/xcFZlmMBDQrSsB3bpizZ9E1urVZCxaTNCggS77Za9bR0F8PAXx8aTOno0hLpbgQYOIeOghl6XsykrD0IZM6zWNxYsXM7D2wMsfUA5ytm7l6AjXCS5NJ05QEB+Pd9269rqgwYPQ+fkR0KMHfh2uQeftXd6hiipOhgkIIYQQQohy4Tp54CWGCKQdh58fgk96wrJnoSDbvinXnMvDvz/MgdQDrDi2ggdXPki2Kfvi5yomZTKR+t13HOzfn9PvvWdPBADkbNxI7l9/XeLoq5vO25ugfv2oPvU9jDExLtsyFi5yKZtPJZCxbDlaFbixNSUnk/bTT2QsWeJS79OyJZqfX5H9s1atdikHXHstMc8+Q0C3rpIIEB4hPQOEEEIIIUSZyzNZOHTacdN+yckDlzwBphzb4/UfQGQTaHM7YJsscM/ZPfZdj6QfIbMgE39jyWa5V1YrGYuXcPr99zEdO1Zku1ft2kSOG4dvu3YlOv/Vzr9rV8zJyeRs3WqvCxo00GXOBmW1cuLBsfh16EDQoIFFEgqVhbJYyNu1i8zVq8levYa8Pbbr0KdZM4IGOnol6Ly88O/cmayVKzFERuLfozsBPXrg37mzp0IX4oIkGSCEEEIIIcrcweQsLFbHRHMXTQbsXQj7FzvK1TtAq1vtxdZRrfm478c8tPIhDDoDn/T9hBj/kt1cFhw9yolx48nfu7fINkNUFBEPjSXkhhvQDPIn88WE3PA/Qm74H6bERDIWLyFj0SKCz5tXIHfbNrJWrSJr1SqS33wTv3btCBoymMD+/TGEhnoo8uKxpKWRdW7pv+y167CkphbZJ++ffzCfPo0hMtJeF/HgGCLHPoh3kyalNpmlEKVNPtmEEEIIIUSZ23P+5IEXSgbkZ8Lixx1lnQGGvofSNPLNefgYbGPQ20W3Y+aAmZitZuqG1C16nmIyREVhOXPGpU4XFETEffcSevvt6Hx9S3zuqsYYE0P4XaMJv2t0kW3pi5yGEihFztat5GzdSuJLL+PfpTPBgwcT0LsP+oCS9e4oK2nzF5DwzDNgtV5yP11wMPmH412SAb7NmpV1eEJcMUkGCCGEEEKIMrfPaVlBH6OOOhEXuPH74xXIPOUod34IopsxY/tHrDqxio/6fESYj23Ju0ZhjdyOwZSY6NJFXefrS8TYB0mcNBnNx4ewkSMJv+du9MHBbp9bXJwxNg5DXCzmUwmuG8xmstesJXvNWjRvbwKu60W1t95C0+vLNT5rTg7ZGzdijIvDp3Fje71Pk8YXTQR4N2pkm/m/Zw98W7aU3iOiUpKrVgghhBBClDnnyQMbRQei153XdfrUdtg0w1EOqQk9nmTW3llM3zEdgFFLRvFJ30+IDYh167lNCQmc/vBD0hf8TO1Z3+LburXjaYYNw5SQSOhtt2GMjnL3ZYliiLjvXsLvuZvc7dvJWLiIjN9+w5KS4rKPys/HcuZskUSAUqpMutkXnDhB1u9/kLV6NTmbN6NMJkJuuZnYSZPs+3g3aoQhOhpzUhKanx/+nTsT0L07AT26V9p5D4RwJskAIYQQQghRppRS7E10XkngvCECVgv8+ggop29hB79Dtqb4YtcX9qojGUfYmrSVoQFDi/W85tRUzn7yKamzZqEKCoBzywR+/ZVjmUCjkajx40r2wkSxaTodfm3b4te2LdETnyZ74yYyFi0ic/ly+8oNQefNNWDNzubw0OsJ6NmDoMGD8W3TBk1XOouhpc6aTcqXX7rUZa1e45J80DSN6CefQBcUbFv6z+sSS2EKUQnJ0oJCCCGEEKJMJWbkk5ZjspeLJAM2fwoJ2x3lpv+FBn3xN/ozc+BMqgdUB2B8u/EMrVe8RADA6XffI+XLL+2JAICcLVvI27mzJC9DlBLNYCCg67XEvfoKDf5cR/UPPyBo0EAC+/dz2S/z998xnTpF6uzvOHr7CA727kPSm2+iTKaLnNmVKSmZ1B9+4MTDD5N73nse0KNHkf3NZ85gOnnSpS5o0CACul4riQBxVZKeAUIIIYQQokztS8x0KbskA9JPwu8vOcreQVj7v2L/xqpGYA2+Hvg1y44u4/Ymt7v1vOH33Uf6/Pn2m0efli2JmjAe31atSvIyRBnQeXsT2KcPgX36FNmWsXCRS9mckED2n+vRHn+8yL5gW/ovZ9dustasJmv1GpdVIrzq1cO3ZUt72a9tG3T+/uj8/Qno0R3/7t3x79ylwk1iKERZkmSAEEIIIYQoU+cnAxrFBDoKvz0JBY7t+699kKdWPcRbPd6iXkg9ACL9Ii+ZCFBWKxmLFmNJTSXsjpH2eq/q1Qi59Ray164jcvw4Avv2lWXeKpHAfn2x5uSQs3UrKNuylEGDB7nso0wmTtw5mmrp6cS/+hrWtLQLnitr9Rqixo2zlzUvL+r8/DPGanFyTYgqS5IBQgghhBCiTDknA6qF+BLsa7QV9i+Bvb/atx2v1pr7E5ZyNu8so34bxfTe02kZ2fL809kppcheu5bkd94lf98+NB8fAgf0xxjlmAgwatw4tCeekNneK6GQYcNsEzwmJZGxeAkZixYRPMg1GZC9fj15f/2FP3CxBQD1wcF416+PMpnQjEZ7vVf1amUXvBCVgHwqCiGEEEKIMuWcDHAZIuAfBVHNIPkf0PS8Xb0uZ5O3ApCen86nuz7lg+s+uOA5c/7+m9PvvEvOli32OpWXx5np011mhNf5+ZXuixHlzhgdTfjoOwkffWeRbemLFhU9APBu3Ni29F+P7vi2alXuyxUKURlIMkAIIYQQQpSZAgscOZtjLzeNdRoiUL0d3L8aNk6H/EymdH2E1JUPsS15G83Cm/Fat9eKnC//4EGS332PrJUri2zTfHwwhIWXyesQFZN3nToYa9Ui98wZgjt0ILBXTwK6y9J/QhSHJAOEEEIIIUSZScgBq3KUi6wkoDfCtY8AEAjM6DuD9/56jwdaPYC/0TGZm+nUKU5/8CHpP/8M1vM6hBsMhNw4nIgxY1yGCIirX8SYMQTfcw+LFy9m0KBBGJ2GAQghLk2SAUIIIYQQosyczHGdnM05GWCymHhy7ZMMazCMa6tdC4CvwZenOz5d5Dynp08nff78IvVBgwYR+cj/4VWrVilHLoQQVzfd5XcRQgghhBCiZE45JQP8vfTUzPkHTLlYrBaeWfcMy48u56HfH+K3+N8ueZ7IBx9Ec1rr3b9bN+rM+4lq77wtiQAhhCgB6RkghBBCCCHKzKlsRzKgU2QBulnDwC+Mee2Gs+TIEgDMVjMvbniRznGdCdJ8SZ37A9bcHCLuvdd+rDEujtDbbiPn721ETXgU/44dyv21CCHE1USSAUIIIYQQokwopTjlmDuQR8xfQn4G5GfwvxVvsb3DDfxyeiteOi/e6/EOLF3Nofc/wHTiBJq3N8HXX48xOtp+fOSE8WhGo6wLL4QQpUCSAUIIIYQQokycTMsj12K7ce+p+5uW6b/btxliWzOl/yeE/f0+bb3qEfHga5zav9++XeXnc2badGJfnGyv0zkNExBCCHFlJBkghBBCCCHKxL7ETAB8yGeKYSa/+vuRpdNxa1YODH0Pnd7Io+0fRSnFUb+5Lsfqg4PxrlfXE2ELIUSVIMkAIYQQQghRJvaeSwY8YpjHIf8snouMwKJpFPi2YWRUC/tM1pqmEfXoBI6OGInm60vYqDsIv/tu9IGBngteCCGucpIMEEIIIYQQZWJfYiaNtGMM9F7K8KgoQjPg5jUW2u8+xGavN+l0j2MJQb/27Yl+/jmC+vbFEBnpwaiFEKJqkKUFL2PatGnUrl0bHx8fOnbsyObNmz0dkhBCCCFEpbA/IZ1XjJ9TPdvMG4vymPqxhR67FTogfNYyrPn5LvuH3XabJAKEEKKcSM+AS5gzZw4TJkxgxowZdOzYkffee4/+/fuzf/9+oqKiPB3eFcvIM3EkOZPk4/+w7qd4NL0evaYR4+14bammdHIseQAYvLyIrdvKvu2EwUDBqQQ0kwmjzptI32h0uakYAjQSvXLIs9h+wfsUQHSash+XnH8WkzID4KvzJswrBItvBFbvIE6EaZjPXZWBFiuxZ86i0jMASMxPxqIUytcb/9gahHuFOmLZtxWF7Tn8/OIICLKtN6wUJOQcQzPnoc89S2BEHCER1QCwKAvH046gT0oBIMQYiL/ez7atZiwn8hKxegVi9oskMh38823nz8tN5+y+31mXa2uzcK8QfHTeAOQbITHUMcNxrHcUXqfOgMVCrqaRbNADYIqKJDayMV6aDu/UA2SlnSY18Yj9uGivCAw6277ZlhzSTJmOczZog94rgIKQuqQXpJJxcj/6zCwAahWYQNPQAvxJDdaTfu44TSlqnna89+mmDLIsuQDoNQPh0dfYt50N1Mj0Ba/0Ixg1HdWsIahk28EpBankWgtAp6GvWYM4H8cMz0nH9lKQk4U5sBpeOh8ifB3X0ZmkbZw99A/rcuPxDQ4jsnpD+7bjuQlw9ITtvdP7EGoMtsUcE82hQH8s52aMDs7RCM2yYshKRDPncCovicKrKsjgT6AhgMI3/Wi0I88ZagwmOLUAky4Us85AUu5J23scHEREUDAhVgsABfk5JMX/Yz8uzBiEr97X9n5ZTSQXpNi3BTXqhk+EbRxrnjmH5OT9eJ2MR1eQSaRXGF46I5rRSG5MCEn5Z+zHxaUojLZLn1xLLimmDCzeISgvf6J84zDobBd/ji6DFC2Zoyn7+GNLLrV8q6E7kQBApjmbDLPt/TbHRlA7qLZ9Vu0z6ZlkZp17z9ER618dzZSNITvJ9T3X64lr0NYe1+mCs+SePI6Wb8Ko6YnyjsDqFYSlRhOSvXPJMWcD4JuTS/UTpxzva0EK+VYTAN46IxFeYfZtCaFQYLTF5esTQaihDqTY2iIp5xQWcx56Swq+1WoQ6RVuP+7UgW1YLLb3JEDvS7AxyLEtLwmlN2L2i8I/shbBsQ1sb7nVSkL8YvQnkzl7NJ6dptMEeduOs9aI5Xh+ov0c4ZkQkGu7cszASaPj12CIVzh+Rn+M6Ucw6a2cCnP8LEd7R+CbmIYymci3FnCmIBUAS1gQMeG18dH7kB/ehJz0ZNKO7rQfF2u24KWUy3tuP2fdFnh5+QCQYc4kNekYukzbtOsRkdeg0xvAP4D0MF9S889gyDqFzpxHrSQrnHvPM8yZZJptx2gaxHk7fibTAjTS/cAUWB2dwZca+hg4ZftZy0j7l7y8syirlRM5GcTvDkdvsLXF6eP7ycu2xemlGYj0drw/SXo9eTrbcxv9Q4is1x5DThL63LOczEvEeuwEWJX9sx1Ai4rkhJaG+dznfmAehGUozH6RKK9AEnNOYlW29zzQogg79zMJcCwC1LnnCzEGEZJmRmVng1KczE+2vccBvgTUbktAcH3be2LKI2nvOjRTLvq8FJfPdrPVTFLBWfv5Q2NqExBiu/HMtxaQkHIUXYYZi3cwYd5R+Bh8QG8gPzqIs2f+sp3fYib/wF4O/pKNTg85lnxSTKkoq+19DjeEYtCMKAUZgYGciI3BikIpiPauge+hf/HJjCfbnEO6OQsF5EWFEOdfAw0dSimyUhPIPXMKBSgUUfoIzDo/svxrkGVKI8eSiUKhlKJmZAhKgVVZSbOkY04+iSE3Hw0dYbpQUGAKDCA50EquNQerAp3FC9+s2ihl+82ZaT6DyZpHeG48BnQEqyDb56uycjJcR4637fepF0aisox4paaDUqSTTgGK44ENSY2uj68uGqtSWBXUOzwLQ0EON6QcITYhkUP7oog1u34HZU5IJOuPVQQN6I8QQojyJ8mAS3jnnXe49957GT16NAAzZsxg0aJFfPHFFzz11FMeju7KbTh0lvu/+YspB38gZneevd7stE/guX8AyaGKev0T7NtGR3Tk6bnHqZ3s2F8B4dek8eq1AWz2tf2Re9O+PIbPd1xqjtsF1+fTgDdGR5AUkwZAtfRqfL1qN2f32CKIOLff1voap/pl82RKmv0cWT/G4eUcuNM5qzmVj3bMo10d201dmk7HE8ZqvPqV4w/PwlMEjkhifA3HkQ99V43uR44C4At0vshrINrE+Lt87fVfn0rEOCcUS54eo1MsbwzTsdowgbB8X7b6jGFVYjDRq/wveE5vINqpPnp4Isd1NRla8Ape4at4aNsi+v6tXI4JrpPN4gHwaYjtxjokz8In7zoSMv7n/l3I7F51WNfpOAC1C0x8tyWF42tsNwNB5/5l+MLUByzMTkiyH3dwXQw1T1y4s1HkuX8A8fWsdLrGcXP2SPVYXntbQ69cX3etPqf5X5v6YLDdiPZfVYu7Nxyi8N06Px1nfw80xfinjPb6R8+m0mexAdMJXww43oMFnTRSmsbwZo7tD/xjeUaiF7h+G+V8bTq/BzOv+5M5QSMB0PseZmDGDMYusroc5xVkImVYJuNjHJHO/SgPc5rtZ8F43jmd7WsUyus3ZNr6bh2AtQdOkPBjDGC7/gqvsIfv17Ms/bi9i9fv+xrTdXtGkfOZcX3P0/yh3lDHTf0XEWG0W+BDkxOur/vT5kNZ0vMoxsA9ADQ9HMykOY4bqZALPE+h+bdZWFHLliRrnuVN3O89ueefRQDEnNvnn5qw/fo8XjrjSLSc/iWOCKdlyJzP6fyez2lTl5m1HgTAQD51o15h6ieWc+deZj8uengSwxs4fpZnzM/BZ59jNnLnzwfn59RCzYx/wMdeNz0xGb8fAzFlGtHjeO8+GKLj3vBk6ucpGuV/Rae0pbywavkF2+X89zzwv6ep7mNLpswNDODwzmD+s0m5HLs2riVvDayJd9Rv9rq5r5qwXZXgd+7f+c8FcKhrAS93s221mgKpvek23lo7DYDgc/9MevhtnGLsz2/Zj9u/OYY6h3UXPGe40+MdNfwY1e5FnjR8xxjDr0yMi+ax6XoC8lyPq9HjLOOuCeOo0fZzOX5TLp1/tz3WgFhcOT/fsxM0srxtidH7U9O54TeN9CO211TYlkvbaPzVrh5rzzwAQKD5NHMXvn7Rczq/B6d7ZtMqJh2AfV5GZmVF8/g8q8uxZ3yCef2GrhyttdRe9/3PuXDaiBXwAeK4sKy63rx8s+N3TM6/j7Pwp1cB2/tW+Inz5Gg93+afIOhcQuG3+HAab/K+yFkd8ozQZpjjZ/mVsFBqLfWjzSHX6yiiWSbv9TLyS6AtadomwcrTM11f58XsGGbhp4a2WDrl5nLnhmwSt4a47HM4ehmPDm9F3smR9rqvVi8iypYzI52i4/59W7cm6tEJ+F1zTZFtQgghyockAy6ioKCAv/76i6efdoxl0+l09OnThw0bNhTZPz8/n3ynrm4ZGbY/yE0mEyaTqewDLgGL2XL5nYQQQgghSolXvXqE/d//4d+rJ5qmVdi/kTypsE2kbYpP2sx90mbuqyxt5k58kgy4iDNnzmCxWIiOdv3uLjo6mn379hXZ/9VXX2Xy5MlF6pctW4afn1+R+opgZ4oG6D0dhhBCCCEqOR89hPjaeiRoWtHt2QEamQNuIKNdO8jLhSVLyjnCymf58uWX30m4kDZzn7SZ+yp6m+Xk5Fx+p3M0pZS6/G5Vz6lTp6hWrRrr16+nc2dHp/AnnniC1atXs2nTJpf9L9QzoEaNGpw5c4agoCAqotOZ+ew4nsqhxVOJ1WWh04EePbW8HAmQ0+Y0Mq22C0rv7UONph3t23b7+GI9EI8hLx8vnQ8xvrXwykvGK1RxyD+LLKttfHJwjkbdBEd3xJOm0+QrW8bKT/MhxhhGrl8cJu9w9tTUyDvXwzvKCk2OnUA714X4aEESFiyYA3zwrV2XakZHl+6j29eilO05/Pxq4RfYGAArVk5kH0BvzsU7L4nA2FqEx9QGwKws7EvbT8AR23j4CH0wQXpbR+rcJrXZV3CUAu8wcv2qUS9JR3DWubHleSnEH15KaGgYOh3E6MPw09u6E+f4aOx36ndcz6saQQcSwGwhQ2fgmMHW9TajVjWqV++EL0bCkzeQdjaBtGP/2o+raYzCoNlydRmWbM5Y0u3bqrfoDL5hpEW040zeKXIObsXn3JCJZvn5aJqGCgniVJSBZIutj6ZmgdZHHD/qZ8zpZFht3e/1mhehUb3s245H6zkdYCUwbS++aDQuiEB3zNYNNdGUQo7KQ+n1mBrVoqF3Dftxpw78TUFWBjkBtfHR+xHl49iWenodyaePExoahm9IKLH1Wtq37c0/iu+eo4AiUOdHpCHE9t7Vqc76sDDM5/6ojEnXEXfGgl/WEQymTOILEuzzRIToAggznBsjrmBHfad5GwzhRJ/KJVuLxaQzcjLnkO29igwjNjKCaibbz21+Xg4Je7fYj4vShxJwbs6AfKuJk2bHpAvera/HGNcagGxzBonHNhF2eA/GgnRiDRH46rxQ3l6k14kg3uQYWtP0OHgX2GLOtORy2pJKvncEFmMAsX51MWq2iz/TO5UE3xMkJiQQExtLS69aeO23DVNJtWSRarH1PMqsG03zoEb2OQOOp2SSedb286JpOmr4NcBgSscv65jLe64zGKnZoos9rmOmJEyHDqPPLcCoGahhjCLfO4yshp05EpBFpsl2HQVn59Jm7wH7cQmms+QqW/v5at7EGh2dyP+tppF9rpe9r281ImmJ8aRt+MmpnMNYrLl4qdMY69alptExAODozvUoi+3zIVjnT7gh2L4tviABi86bPL8YvGs0JbhOe9t7bjWT9O9n+B9MJjU1hXoRtQgz2rolFzSqxT/mY/Zz1ErWCMu0fVbko3HAyzGkJNw7Fn9DEAFpezAZrexxXMLUMcYSfPg0WoGJXGsBCWbb/Ac5caHUjGyAv96fszHdyDlzlKw9q+3H1TOZ8T332VT4nheKa3oNXt62ZPEZczpnTx3E+6xtPoiwyJ7odN5YQkNJiAsgOe8EflnxGE1ZtD5otb/nKeYM0qy2YzR01PGKsZ//VISOpGBFZnADdIZAGmn18P53LwBZadvJy0/CChwKNHJdnU7o9bbPp4RDO8nPsMXprRldPmsPG73IOffjpQ+OJLzVQPwyDuOXfZx/849j3H8EzWK1f7YDWGvGsdfrDAXnPvcjM3RUP20h1786Jq9QTuYcxHJuzoAwBXGmAvvzba/jmDMgSh9KXJIJLT0Tq1IcOfezVRDij1fjXgSE2ebBMOfnkLLpR/TmbLzzTrt8tpusZo6bHWPbQms1JDjMNlAh15pP/Jl/MSZbMHmHEuFTDT99AMrLi4x6kZxJsg0TsFgVIVsSqBcei06vI8eaT5I55dwNsEYNryh8dEbQNFLCQ9lfrzoa9gc6GwAAV91JREFUoKHRNKQVvts245t+iHRzNmdMaaBBXt1oWkY2w0tnAE0j6eRBUk4esQWpaTT2qwFe/lhDanMm7zSp+WfR0NDpdLRt3tg2VwxwNDeJlAP70WXlYdQZaBRgu4i9YiM5GlTAmYJ0QMPH6kXD/Lr2djieeZysgkzITMBXZ6SOXxzq3Pt8JEojw0eBBkGGABoWBGM6aRsidjjnFHlWEyqyFkEtWtMw1DEfzK4lMyjIycJqtRKfkMzA0c8QGOiY60dcnMlkYvny5fTt2xej0Xj5A4S0WQlIm7mvsrRZRkYGERERpKenX/Y+VJIBF1FQUICfnx8//vgj//3vf+31o0aNIi0tjZ9//vmSx2dkZBAcHFysN8GTTCYTixcvZtCgQRX6oq5IpM3cJ23mPmkz90mbuU/azH3SZu6R9nKftJn7pM3cJ23mvsrSZu7ch8rSghfh5eVFu3btWLlypb3OarWycuVKl54CQgghhBBCCCFEZSNzBlzChAkTGDVqFO3bt6dDhw689957ZGdn21cXEEIIIYQQQgghKiNJBlzCzTffzOnTp3n++edJTEykdevW/Pbbb0UmFRRCCCGEEEIIISoTSQZcxkMPPcRDDz3k6TCEEEIIIYQQQohSI3MGCCGEEEIIIYQQVYwkA4QQQgghhBBCiCpGkgFCCCGEEEIIIUQVI8kAIYQQQgghhBCiipFkgBBCCCGEEEIIUcVIMkAIIYQQQgghhKhiJBkghBBCCCGEEEJUMZIMEEIIIYQQQgghqhhJBgghhBBCCCGEEFWMJAOEEEIIIYQQQogqRpIBQgghhBBCCCFEFSPJACGEEEIIIYQQooqRZIAQQgghhBBCCFHFGDwdwNVKKQVARkaGhyO5NJPJRE5ODhkZGRiNRk+HUylIm7lP2sx90mbukzZzn7SZ+6TN3CPt5T5pM/dJm7lP2sx9laXNCu8/C+9HL0WSAWUkMzMTgBo1ang4EiGEEEIIIYQQVUlmZibBwcGX3EdTxUkZCLdZrVZOnTpFYGAgmqZ5OpyLysjIoEaNGhw/fpygoCBPh1MpSJu5T9rMfdJm7pM2c5+0mfukzdwj7eU+aTP3SZu5T9rMfZWlzZRSZGZmEhcXh0536VkBpGdAGdHpdFSvXt3TYRRbUFBQhb6oKyJpM/dJm7lP2sx90mbukzZzn7SZe6S93Cdt5j5pM/dJm7mvMrTZ5XoEFJIJBIUQQgghhBBCiCpGkgFCCCGEEEIIIUQVI8mAKs7b25sXXngBb29vT4dSaUibuU/azH3SZu6TNnOftJn7pM3cI+3lPmkz90mbuU/azH1XY5vJBIJCCCGEEEIIIUQVIz0DhBBCCCGEEEKIKkaSAUIIIYQQQgghRBUjyQAhhBBCCCGEEKKKkWSAEEJUMjLVixBCCCGEuFKSDBBus1qtng5BiCpp0aJF5Ofno2maJASEEEIIIcQVkWSAcJtOZ7ts9u/fD0hyQIjy8MEHH/B///d/fPzxxxQUFEhCoJikjYrPua0KH0v7XZz87hOVjfM1m5eX58FIKpbCz7n09HQPRyJE+ZNkgCiR5cuX06RJE44cOWJPDojikz8ihbvuvPNOevbsyffff89HH30kCYFi0jQNgKysLA9HUrFZrVZ7W5nNZvLz8wFH+4miCn/3fffddyQlJQGSPLkSO3bsIDk5GYDJkyezbds2D0d09Sm8Zp988kn+7//+D7PZ7OGIKgZN05g/fz4PPPCA/RoUJVP4GZiZmenhSCoPT//ekLs4USJt2rShW7du/PLLL4Dc3Lqr8BfysWPHAM9/EFRE0iY2jz/+ODt37iQwMJAPPviARo0a8d1330lCwA1vvPEGTz/9tKfDqLCsVqv9M+m9995j2LBhXHfddTz88MP2m1xRlFKKtLQ0br/9dr766itAkicltXv3bm677TamTZvGmDFjmDx5Mj4+Pp4O66rh/DtixYoVLF68mLvvvhuDweDBqDyvsF3+/fdfJk6cSL9+/YiIiPBwVJWbpml8//33jB07ltOnT3s6nEpB0zRmzpzJSy+95JHnl2SAuKwL3ehHRETQsGFDvv32WwDpHVACCxYsoGHDhqSkpFT5PyAtFgsACQkJHDp0CJA/qgFWr15NRkYGTZs2BcDPz48PPviAxo0bS0LADREREXz//ffs3bvX06FUSIWf3xMnTuT111+na9euTJw4kWnTpvHwww+Tmprq4QgrrpCQECZNmsTvv/8uiZMr0Lx5c26//XZmzJjBV199xYoVK2jatKn9d4O4MoW/T3/++We+++47+vbtS8eOHat8zwBN09iyZQvz58+nS5cujBgxQn6XllBhu509e5bnnnuOa665hsjISA9HVbEVttmRI0d47LHHPJackzs4cVmFfygePnzY3nUU4NVXX+XkyZN8+OGHngqtUmvfvj2tWrVi/vz5QNXrXfHpp5+yfv16LBYLer2eH3/8kWuvvZZevXrRsWNH/vzzT0wmk6fD9KgePXrw0UcfYTAYmDNnDuvWrSMgIIAPP/xQEgIXcX4bKKXo1q0bDRs2ZMOGDQByg3EBu3btYsGCBcyePZvHH3+cwMBAfHx86NevH6Ghofb9qvI1dv51U3iD1bVrV7Zt28Y///wDVO02cpfVarX/7mvevDl6vZ6aNWuyZs0aTp06hV6vl/YsJWlpabz55pt899139qS7wWCocn97nO+ll17i6aefZsuWLRQUFMg1V0KaprF06VI+/PBDevfuzV133eXpkCo8TdPYuHEj33zzDSNGjOCpp57ySBySDBAX5fwLYvr06fz3v/9l+PDhbNu2jdTUVCIiIrj++uvZsmULVqtVPjwv4UJtExMTQ+3atZkzZw5QtXpXKKWYPHkyd999Nzt27GDnzp089thj3H///XzyyScYjUZGjx7Nb7/9RkFBgafD9YjCRIhOp+PgwYO8+eabvPzyy2zevLlIQmDGjBmSEDin8AYtOzvbXm7QoAFt27blpZdeIi8vD71e78kQK6T09HQMBgO9evViwYIFDBkyhHfffZd77rmH9PR0fv75Z6Bq9tjZuXMnZrPZft0sW7aMdevW2bdfd911DBo0iBdeeIGMjIwq2UYlUTg8RafTcezYMfr378/u3bu54447+OWXX/jggw9ISEiQ9iyh82/yQ0JC+PrrrxkwYAA7duzgyy+/BGy/Y6ry742ff/6ZUaNGcfLkSb7++muys7Pld2kJWK1WNm3axOTJk1m5cmWVTzIVR1paGlOnTuX1118nPj7eXl/u154S4jLefPNNNWXKFDVt2jR1yy23qPDwcHXTTTep+fPnqxUrViiDwaDWrl3r6TArhWPHjimr1WovHzx4UIWHh6uvv/7ag1GVr8LXX1BQoFq3bq3atWunvv76a/Xkk0+67DdgwADVoEED9csvv6j8/HxPhFohvPDCC2r79u1q7ty5asCAAWrIkCFq48aNSimlMjMz1Z133qm6dOmiXnrpJVVQUODhaCuGjz/+WA0fPlytWLHC3iZnz55V7dq1U9OnT1dKKZefw6rmQq89Pj5etWzZUk2aNEkFBQWpGTNm2LetX79edevWTe3YsaM8w6wQJk+erDRNs19Lu3fvVs2aNVNhYWHqwQcfVL/99ptSSqm1a9eqa665Rm3atEkppZTFYvFk2BWec/tMnjxZtW/fXq1atcpeN2nSJNWmTRv17LPPqoSEBKWUUnfeeafauXNnucdaGTm377///qsOHjyoTpw4oZRS6ujRo2rIkCGqV69eavbs2Rc85mpV+Nl36tQplZiYqPbt22ffNnz4cNWsWTP17bffqtzcXJf9RfGkp6erN998U+l0OvX+++97OpxKYeXKlWr48OHK29tbrVy5UilV/tedJANEEc6/EH744QcVERGh9uzZY6/7+eef1VNPPaX8/f3VjTfeqAwGgxo5cqTKycmRD87zOLfl+++/r9q3b69uu+02dejQIZWRkaGUUmrkyJHqwQcfLLL/1azwBq2goEA1bdpUaZqmrr/++iLXz4ABA1TTpk3VDz/8UGVudJ2vgXnz5ilN09SWLVuUUkr99NNPqk+fPkUSAtdff72699575efvnDfffFPdfvvtysvLS912223q/fffVxaLRd18883q1ltv9XR4HuV8fTkn2dLS0tTtt9+u/Pz81Pjx4+31eXl5asiQIeqGG26oMp9P5+vfv7+qXr26WrFihVJKqRMnTqilS5eqDh06qI4dO6oePXqoNWvWqLi4ODVmzBgPR1u5PPHEEyo6Olr98ssvKj4+3mXb5MmTVZs2bVS/fv1Ujx49VFRUlDKZTJ4JtBJx/j3wwgsvqObNm6vGjRur6OhoNW3aNKWUUocPH1aDBg1S1113nfruu+88FWq5KmyXBQsWqI4dO6omTZqohg0bqokTJ9r3ueGGG1Tz5s3V7NmzVU5OjqdCrRQK2zMxMVHt379fnT171v532nPPPac0TVOff/65J0OscArbLDs7W6Wmptrrd+zYoa6//nrVvHlze1K0PP+ek2SAuKgffvhBvfTSS+qNN95QSqkiN2Px8fHqueeeU9dee60KDQ1ViYmJSinJpF7I1KlT1aRJk9Rbb72l+vXrp2JjY9WoUaPUmjVr1Lx585SPj4/atWuXp8MsF4XXR0pKilLKdl117txZRUdHq3Xr1hW54ejcubNq3769yszMLPdYPWn27Nnqww8/VJ988olL/bx581Tfvn1dEgK5ubn2dqtqP3+XukFdvXq1Gj9+vKpWrZoaMmSIuvvuu5WmaerXX38txwgrDudr480331S33nqruv7669Xff/+tlFJq48aNqlOnTuraa69VL730kpo6darq3bu3at68uf3zvyolBJx/5/Xp00dFR0cX6W2ybds2df3116s+ffqosLAwFRQUZE/eiUtbv369ql+/vlq/fr1SypZ4SkxMVPPnz7d/M/vpp5+qRx55RN199932RIDZbPZYzJXJSy+9pCIjI9Xy5ctVTk6OuvHGG1VQUJDavXu3UkqpQ4cOqaFDh6qWLVuq5cuXezja8vHbb78pX19fNX36dLV371717rvvKk3T1KJFi+z7DB8+XFWrVk3NnTvXg5FWbIW/S+bNm6dat26tatasqa699lp11113qdOnTyur1WrvVfXll196NtgKorDNfv31V9W7d2/VpEkT1b9/f/X9998rk8mkNm/erG666SbVsmVLtXr16nKNTZIBogir1aqys7OVv7+/0jRNPfDAA/ZthX8IFv4ytlgsKj8/X3Xo0EHdf//9Hom3InL+g3nWrFkqLCxM7d+/3173zTffqDFjxihvb281atQopdPp1IMPPqjy8/Ov6pu5wte2dOlSNWrUKPsfgSaTSbVo0UI1b95cbd68uUgbHD16tNxj9aQDBw6omjVrKk3T1Ntvv62Usv2hXGjevHmqf//+qnPnzvY/7JSqWjdqSrm+3l9++UV99dVX6uOPP1ZKuQ5HOXv2rHrkkUfUDTfcoDRNs39WVaX2cn6tr776qgoODlb/93//p1q1aqXCw8PVt99+q5Sy3aCNHz9e1ahRQw0YMMDlJqwqfSt7oWujd+/eKjY2Vi1fvrzI0KX169erjz76SPn5+alXX31VKVX1EnOXc357/Prrryo6OlqZzWa1c+dO9eSTT6oGDRooo9GoOnTocMEEcFW6Bq9ETk6OGjhwoPrmm2+UUkrNnz9fhYaG2odJFV6/e/bsUY899liVSbA8+OCD9p4AR44cUfXq1bvg74MRI0aoQ4cOeSTGymLFihXKz89PTZ06VaWmpqopU6YoTdPsw16zs7PViy++qDRNs1+HVd2iRYuUn5+fevHFF9Vff/2l+vXrp+Li4tSff/6plFJq3bp16pZbblE1atRQ69atK7e4JBkgiij8QDx79qxq2rSpql+//gVv0JRy/HKfNGmSGj58eLnGWRksXLhQTZkyRU2dOlUpVbR3xbZt29T48eNVixYtVPXq1e1//FzNf0TOmzfP/mG4YcMGl5u25s2bq+bNm6stW7Zc1W1wvvNfa25urlqwYIFq2bKlateunb3e+QZk1qxZ6pFHHqlSN7TOnF/3k08+qeLi4lSXLl1UZGSk6t+/v9q2bVuRtsnJyVFTp05VPj4+au/eveUdcoVw5MgRdd9996k1a9bY6+68804VHR2tvv76a/u1WDiMqVBVvQlbuHChPWmplC0hEBcXp5YvX37BNpkxY4aqXr26OnnyZHmGWakUDjs8ffq0atSokapfv76KiIhQ9957r5o9e7ZKTExUer1ezZkzx8ORVk5Wq1UlJSWpmJgYtWfPHrVq1SoVEBCgPvroI6WU7XNw4sSJRW52r/aEQEFBgWrbtq2aMWOGSk9PV9WqVVP33Xef/TNv+vTpaunSpR6OsuKzWq3KbDarsWPHqgkTJiillEpKSlI1atRQY8eOte9XUFCgzGazeu2111yGGldFVqtVZWVlqcGDB6vJkycrpWy/Y2vWrOnSZkoptWbNGnXnnXeWazJKkgHikjcTycnJqnr16uraa691+QbyfPfdd59q1aqVys7OLosQKx2r1arOnDmjNE1Tmqapxx9/3L6tsL2de1mkpqaqRo0aFZlE72pz6NAhVb9+fXtypFDhTW7hpILVqlVT27Zt80SI5e78nz/ntli8eLGqVauW6t27d5HtlzpHVfL222+ruLg4tXXrVqWUUt99953SNE1169ZNbdu2zf6HnnPCpVu3bur111/3SLye9M033yhN01Tjxo2LdGUfPXq0io2NVd98841KS0tz2VaVEnPO9u3bp6Kjo9WoUaNc2qswIbBixQqXXnJKKfX333+rVq1aqcOHD3sk5opu7ty5qn379vYu2Pv371cvvfSSWrhwoUpPT1dK2b5R7Ny5s9yYFdPFfj7vuOMO1a9fP+Xn56e++OILe/2pU6dUt27d1FdffXXJ469GL774orrttttUbGyseuCBB+w/t7m5uerOO+9UU6ZMUQUFBVWqTUpqxIgRatq0aerkyZNFEis///yzmjdvnocjrFgsFovq3r272rZtm0pISFCxsbHqvvvus2//+eef7b83ynu+iqqzlpm4oMKlfQA++eQTJkyYwE033cSWLVtITU0lMjKSv/76iyNHjjBmzBj7OsrOjh07xunTp/n888/x8/Mr75dQYYWHh3PgwAHi4uJYsWIFu3fvBhxLCBb+r2kaISEhDB06lISEBI/FWxbUecujnDlzBovFwqBBg1z28fLywmKxYDQa2bRpEzVr1iQ4OLi8w/WIwuvgjTfe4IYbbqBHjx589NFHnDp1ioEDB/LRRx9x/Phx+vfvD4CXl5d92cHzz1EVLFu2jO+//x6wLYd34MAB3njjDdq1a8e8efMYM2YMb7311v+3d+dxNeX/H8Bfp0WWSkq0oE2yJakkW9YoRUIKUbLnm8iMsmQ3jHVoVCRrErLMWNMw9mWskSURspWIRFLd9++Pfvd8u2LGzNe46b6fj8f38f0659zT+36+557lfd6fzwePHz/GuHHjcPHiRRCRzPRk7969w9u3b+X1Fb6aD6d2GjRoEHr27Ilbt27hxo0bMsdRTEwMXFxcMHjwYJw+fVrmc4oytduH5ysLCwusWLECFy5cQEREBM6fPw8ASEpKQuPGjcXpT0tfR6Xn+ipVqnz1+L8FRkZG0NXVxerVq7Fz5040aNAAU6ZMQY8ePaCmpoanT5+if//+KCoqQufOneUdbrknkUjE3+eLFy+QmZkpruvUqRNu3ryJTp06wc/PDwCQm5sLf39/KCsrY+DAgQAq3u+7uLhY/C2/fv0aeXl54jorKyscO3YMderUwffffw8lJSUUFhZi9uzZOHLkCLy9vaGqqlrh2uRLkk4lXqVKFWzduhVt27aFq6sroqKiIAgC3rx5g+3btyMlJQVFRUXyDrdcICIoKSnh/fv3iIiIQLt27dCzZ0+Eh4cDAJ4/f46YmBgcPXoUAL7+9eOrph5YuTVp0iSqXbs2DR8+nFxdXcnQ0JBWrFhBjx8/JqKSEiAjIyOysLD46BuPvLy8rx1yufJnb2Zv3rxJWlpa5OrqSmlpaZ/czsfHhxwcHOjdu3cVJist/R5Hjx6ls2fP0tGjR8nQ0FDmLZt0m0OHDon9phRB6WNm+vTppK2tLQ6Upa2tTQMGDBDfdu/du5caN25M1tbW8gq3XDhx4gQJgkC2trYUGxtLRCXT8jx9+pQuXbpEZmZmYtXJhg0bSBAEaty4scx4HWfPniU1NTWFmibvw1L3bt26kZ6eHh06dKhMafDcuXMrfLnwX5G+oZbavn07NWrUiIYOHUoXLlwQlzdv3px69uwp/rugoIDWr18vDsio6D51Xbx48SK5urpS586dxQqB4uJiWr9+PbVr145atWoldqlT9GPxU2JjY2W6HU6ZMoWaN29O2tra5O3tTb/88gsR/Xc2gWbNmpGHhwfZ29tT8+bNK2T7fvgmeteuXWRra0uNGzemzp07i4MERkdHk7GxMTk6OpKnpyf17t2bdHR0FKYa8e8oKioSf8eZmZn05s0bev78ORERPXr0iOrXr0916tQRq4KLi4tp8uTJVK9ePUpNTZVb3PJUXFwsttnbt2+puLhYvM/dvn076erqynT/JCr5/VpYWJSZUeVr4WQAozVr1pCRkZHMiNKCIFDdunVp8eLF4iwBT548IXd39wp18fgSSt/wxMTE0JQpU2j48OF048YNcQyA69evU/Xq1cnNze2j/YDS0tKoa9eu4sPft650MiMpKYkEQaD9+/dTWloa1apViwICAsokkP7zn//Q2LFjK1Qy5HPcv3+fQkJCxPlliUoGWLSxsSFfX1968+YN5efn07Zt28jb21uhuwTs2rWLBEGg9u3bk5ubG23ZskVct3z5curUqRNlZWURUUlJfEBAAHl5ecmcs3JycsR5yxXB3yl1L02RzvOnTp0SE0ZLly6lsWPHlrkp27p1K+nq6pKPj49MQuDD36Minbs+V0JCQpnR6i9cuEBubm7Url07cXaPkydPUnh4uEIOWPl3HDt2jARBoClTphBRybmvVq1aFBkZSevXrydHR0dycHCg6OhoIiJKTEyk77//nsaNG0fLli2rkO2bnp5OgiCQh4cHERH98ccfpKamRiEhIbRq1Srq2rUrNWrUiJYtW0ZEJQnSGTNmkLu7O82ePZtu3rwpz/DLne3bt8skNRMSEsjKyooaNGhArVu3FmcISExMJC0tLbK1taVu3bqRh4eHwiZW/vjjD/H+g6hkUOM+ffpQ69atafr06WJCfsqUKaSjo0M+Pj40ZcoUGjJkCFWvXl2ubcbJAAWXn59PkZGRFB4eTkQlmdXq1avT+vXrKTAwkKpVq0ZLly6lBw8eyHxOkW4UP9ekSZOoVq1aNGjQIGrdujXVr1+fYmJiKDs7m4hKEgLa2trUunXrMoNLFRUVlRmwqyJ4+PAhRUVF0bx588RlO3bsIGVlZRo5ciQdPnyYLl26RBMmTCAtLS2FG2Rm9+7dJAgC1apVSyYZQES0f/9+qlSpEh05coSIZG/cFDkh4OPjQ46OjuTh4UEdOnQQRy4ODQ2lBg0a0JMnT+jVq1fk6uoqnteIFOec9bGH0a1bt1LTpk1p6NChMgmBLl26UN26dWnPnj0Ke0ylp6eTvb09ubm50ZMnTygmJoZ0dHQoNDSU7t27J7PtjBkzSEtLi/r160cpKSnickU5tj6X9BgsLi6mO3fuUMOGDcnd3b3MdFmXL1+m2rVrU+vWrWUSe0Tcpn9l8+bNVKlSJZo5cybNmzdPnA2EqGRMgJEjR1LLli0/+YBR0dpXIpHQwYMHqXbt2uTt7U2//vorTZ8+XWab//znP9SwYcMy11omKyUlhaytrcnNzY1u375Nd+/eJXV1dZo/fz4tW7aMJkyYQIIgiPd1Dx48oAkTJtDo0aNp/vz5ClkRcOjQIdLU1KQlS5ZQQUEBnTp1iipVqkRBQUHk4+NDXbp0IWNjYzEpGh8fT23atCEnJycaPny43O99ORnAKCUlhR4/fkzp6enUrFkzWrJkCRGVvLHU0NCg6tWrU1xcHBHxW49PiYqKonr16omZ1OPHj5MgCGRubk5RUVH04sULIiJKTk4mJycnhbjxvn//PgmCQJqamjR//nyZdQcPHqT69euToaEh1a9fn5o2baoQpbUfDh756NEjCggIIEEQaN26dUQk+9DfpEkTWrhw4dcPtBySTq24adMmGj58OJ05c4Y8PDzEN4uZmZmkr69P+vr6ZGpqSpaWlmVm71Ak/7TUXRGtWrWKOnbsSP3796fXr19TfHw86enp0aRJk2QqBJYuXUqOjo40ZMgQhTiH/6+kbbR3715q27Yt9enTh37//XeZbbp3707GxsYUHBwsjxC/aZs2bSJVVVUSBEF84y29R8vOziYjIyOaMWOGPEP8qiQSCSUmJlKtWrVISUlJnDKw9DXV0dGRevToIa8QvxmbNm2izp07U79+/SgsLIyCgoJk1q9Zs4YEQZBJQim6wMBAMjMzo59//pkmTpxIc+fOFdddvXqVxowZQ6ampnTu3DmZz5WHxBwnAxTU8uXLxRIzqaNHj1KTJk3EvrQXLlyggIAAWrx4cbk4WMurt2/f0pIlS+jnn38mopJyqurVq9OaNWtowIABpKOjQ6tWraLMzEyZzynCzWRkZCRVqlSJ/Pz8xD5l0puVzMxMunHjBl2+fJmePXsmzzC/iri4OPLz86Nbt27JdJF48uQJDR48mKpUqSLzxuLVq1dkampKkZGR8gi3XDh8+LBY6ir1+PFjMjQ0pJiYGHry5Al5eHhQ27Ztac+ePfTixQtavnw5rVy5skKWwv6ZL1nqrihKJ7fXrl1Lbdq0of79+1Nubi7FxcWRvr4+ff/993T27Fl6//49eXh40LZt22TefLP/Kt0ecXFx5OzsLCbk9u/fTw4ODtS3b18xIfDmzRvy9/enrVu3clt+ho+9jNmxYwdVrlyZvLy86NWrVzLbDBgwgHx8fL5miHJXVFREhw4dInNzc3JwcBBHZZfew86fP58cHBwUOlH8Z0r/DmNjY6l79+5Ur1498vf3J6L/TitIRDR69GhycnKi3NxccZkivjAs/Xw0btw4Mjc3J3Nz8zKzZiUnJ5Ojo6P4gqc8tRknAxTQ69evxQEDS7+xTUhIIF1dXYqPj6fk5GRyc3MjX19fcT0nBEp87Id7+fJlevLkCaWlpVGTJk1o6dKlRFTSX7dKlSpUu3Zt2rVr1yc/X5FFRESQIAi0YMEChfvuUq9evSIzMzPS1dUlS0tL8vf3F/vcEZXcFHt7e1PlypVp3LhxtGDBAnJ1daWmTZsqzMPshw4fPixOzdmtWzeKiIigq1evElHJg4abmxu9fv2arl27Rn369CFHR0dxUEEpRTlncan7P/dnCYFt27aRpaUl6enpUYMGDahJkybi71FRz2WfUvoh4rfffqPhw4eTsrIyDRs2TCYh4OjoSPb29jR48GDq2LEj2dralqmYYmWVbpsPpx3bvHkzqaioUHBwsPjSIT8/n6ysrGj8+PFfNc6v7WO/Q2mFgI6ODnl4eFBeXp64nY+PD3Xu3FmsNGNllb4WbN68mZo3b04GBgZ0+fJlIvpvm8+aNYtatGihsPcopZVOLk2ePJkEQaB+/frJjCFAROTu7k7Ozs5fO7y/pPJ15y5g8lB62iMAUFdXR2BgINTV1REeHg6JRILQ0FB4eHhg8+bNGDVqFNTV1VG7dm0kJCSIn1NWVpZH+OWOdMqZFStWQCKRYNy4cbCysgIAHDhwAMrKynB2dgYAPHv2DL6+vtDT04Orq6vM5ysS+v+p265du4asrCzk5ubC3d0dADBq1CgUFRUhMDAQgiBg4sSJFbIN/ky1atXg6ekJIyMj2NnZ4fDhwxg/fjwSExPRrFkzBAcHY/ny5dDT08OyZcvQp08f+Pj4oFevXlBRUUFRURFUVBTrdF23bl20a9cOKioqKCgowPXr1zF16lRMmzYNEokEb968weXLl9G2bVvMmjULAQEBOHXqFAYMGCDuQ1HOWcbGxvD390dcXByCgoIQHR2NatWqYdy4cZBIJBg1ahSMjY0BANWrV4eVlRWqVq2Khg0bivtQlLb6kCAI4vnL19cXABAdHY3hw4cjIiICVlZWOH/+vDglm4qKCoqLixW2vT5Feo8RHByMo0ePwtbWFjY2NtizZw/y8vKwYcMGdO/eHVpaWti7dy/OnTsHY2NjREVFQUlJqcx9CpMlbZtFixbh/PnzUFVVxaRJk2Bubg5vb29IJBL4+vri2LFjsLCwQG5uLiQSCX788Uc5R/7vkf5uf//9dxw9ehR37txB//790bRpU3Tt2hWbN2/GgAED4OjoCAsLC+jq6mLnzp04ceIE1NTU5B1+uSNtT4lEIp7fvL29oaysjOXLl2PKlCmYN28emjVrBgB4+vQpqlWrhnfv3kFdXV2eocuNtM1UVVXFZXPnzkVhYSFiY2Oxfv16+Pr6ombNmgAANTU11KhRA4WFhTKfkTu5piLYV5WcnCzz70ePHtGsWbOoTp06NGfOHHH54cOH6fjx42J2kLN+Zb18+ZJGjx5NtWrVotWrV4vLN2zYQPr6+rR37166ffs2ubm50dixY8X1FfHtmzRLvGPHDqpTpw5ZWlqSlpYWOTk5UXJysvhGY8WKFaSmpkYzZ86UZ7hys2/fPtLQ0BC74eTn59O0adNIEASysbGhBQsW0L59+2j8+PGkrq4uTrOoyG8wbt26RR4eHuTm5kaHDh2iAwcOkIeHBzk7O5MgCDKzm6Snpyvkm0Uudf8ySrdjTEwMtW3blvr37y9OpStdXxHP4V9KYmIi1axZUxw1u7i4mJYsWULNmzenAQMGiG/PPryn4HuMz7NkyRKqUaMGTZgwgUxNTalRo0a0efNmsQtefHw8qaurk7m5OSUlJSlEV6mEhASqUqUKubu7k4ODAxkYGJCXl5c4UGpiYiI1adKEBEGgo0ePfnQ2J/bf89u+ffuoZ8+eNHz4cIqKihLXb9iwgRwcHEhXV5c8PT1p5MiRpK2trZCzBkhJ2+zIkSMUHBxM4eHhMuMBBAYGkp6eHrm5udHcuXMpKCiINDQ0yjyLlQecDFAQBw4cIF1dXVqzZo3M8ocPH1JQUBBVr169TP8WIr7xkfrYDfOdO3fo+++/JwMDA5mTZocOHUhHR4fq1KlDLVq0qHB90z7WFocOHaIaNWqI/bsvXrxIgiBQp06d6MKFC+JJ88cffyRtbW1xnlpFM2bMGBozZoz478aNG5O7uzsFBwdT9+7dSRAEmj9/Pg0aNIi0tLTEmQQU2c2bN6l79+7k5OREN27coKKiIkpJSSF/f/8yZYtEivlwy6XuX8aHCYH27dvToEGDKCMjQ45RfTs2bdpEBgYG4gw6RES5ubkUFhZGVatWJX9/f/F6KP2d8jH4aR+eyyZPnky7d+8W/927d29q2rQpbdq0Sew6EBMTQ+3atVOI5NW9e/eoYcOGMuPqbN26lbp160YDBgygR48eUVFREe3du5fq169Pjx8/lmO05d+xY8dIXV2dfH19ycnJiYyNjWW6mcTHx1PLli1JT0+PFi1aRA8fPpRjtOXDgQMHSE1NjZycnKhWrVrUrVs3Wr9+vbj++++/J0EQqHHjxhQWFiaO61PecDJAQVy5coVGjRpFTZo0kemrTFQycGC1atVIEIQy65isD3/Id+/epYkTJ5KBgQFFRESIy/fu3UuJiYkVrrpCenOSnp4u3pQUFBRQUFCQOI3P3bt3ydTUlHx9fcnU1JRatWpF58+fFz8rnVlBEUVHR1ObNm3oxYsXZG1tTW3atBFHfc/IyKD4+HgqLCykvLw86tWrFxkaGpbpH6qIUlNTycnJiZycnOjYsWMy6xTx4f9jPpUQePHiBaWmptLmzZspMjJSPBdV5IeE/0XpdlyzZg3Z29uLSXR+cP2vj7XFsWPHqGHDhpSYmCiz/MGDB2RoaEgNGjQgf39/PvY+Q+n2TUxMpJ07d5Kfn59YMSbl4eFBlpaWFBsbKw5MW1Grfj485lJTU8nQ0FCcrk1KOhuIdCrL4uJisXqCfdq2bdto8eLFRET09OlTWrlyJeno6NC4cePEbVavXk39+vXjxMr/CwsLo5UrVxIR0fnz58nb25vatGkj8yw1ZswYatmyZZkZfsoTTgZUQJ+6AKSkpFBAQABZWFjIVAhcvXqVfHx8KD4+ni/SHyjdlvv27SN9fX3auXOnzDZpaWnk7+9POjo6tHHjxjL7qGht+ujRI6pZsyY1atRIHLAtMTGRUlJSKCcnh+zs7GjYsGFE9N9B4GxsbMS3uIrOzs6OBEEgR0fHT1ZIFBYWUnZ2Nj169OgrR1d+paamUvfu3al79+50/PhxeYdTLnGp+5dRuh179OhBvXr1kl8w5dz8+fPFh66nT59SixYtyNXVVRzsk6ikiq5v3740e/Zssra2phMnTsgr3G9C6eNvwoQJpKWlRbVr1yZBECgwMLDMg22/fv1IV1eXDh48+LVD/ddJ78FKn7OuXLlCL168oLS0NDIyMhLvyQoKCsRtLC0tacKECV811m+N9Di7ePEiHTlyhAYOHEg//PCDuD4nJ0dMCJRuy5cvX371WMsLaZvdvn2bMjIyaMyYMfTLL7+I6y9fviwmBEpXCDx58uSrx/p3cDKggil9EVm+fDmNHTuWhg0bJo5oeevWLQoICCATExOaNWsWnTt3jlxcXMjPz49vFD9Qui0PHjxI8fHxNGTIELK0tJQp1SMi2rNnjzjf744dO752qF/VkSNHSElJiezs7Khnz54UFxcnrtuxYwfZ2trS7du3iei//c+sra0Vvq+e9HjauHEjNW3alM6fPy+znP211NRU6tGjB9na2opjLzBZXOr+ZUjbccyYMeTl5SXzoMFK5ObmUs+ePUlJSUl8wE9JSSEDAwNycnKixYsXU1JSEnXp0oW8vb3pxYsXpKGhQUuWLJFz5OVX6d/vhQsXqGvXrnTy5EnKyMggf39/atGiBS1atKhMQmDy5MkV9t4tPT2dHB0diYho586dZGBgIPZV9/b2JkNDQ5lZU96/f0+Ojo60fPlyeYT7TZFOTWliYkLa2trk6ekps/7ly5cUFRVFgiBQSEiInKIsX7Zu3Uq1atUiXV1d0tDQoLlz58qsv3LlCvn4+FCTJk3KzHBUXnEyoAIp/RY7LCyMtLS0qH///mRqakp169YVL9Z37tyhOXPmUJUqVcjCwoLs7e3Ffnz8YFKidFtOnTqV6tSpQxkZGfTHH3/QkCFDqHHjxuJUgUREf/zxBw0aNIjWr19fYS/IpQ0dOpSaN29Offr0oY4dO9KGDRuIqGQaQUNDQ7Ev2eTJkyksLEwh2uRzPXz4kPT19WUy8OzzXb9+nSZMmFDhSmC/JC51/zKePXtGbdq0kXnLrcg+1s//wYMHNHjwYKpUqZJYIXDjxg3q27cvNWzYkMzMzMjR0VHs7uTg4EBbtmz5+sF/Y7Zs2ULdunWjgQMHiu1dUFBAI0eOJDs7u48mBIgq5suclJQUql+/PjVo0IAEQZB5wHr58iV16tSJ9PT0aMOGDZSQkEAhISFUo0YNSk1NlWPU5Zf0eMrJySFHR0fasGEDXb16ldasWUNVqlShgIAAme1fvHhBa9asKbf93b8GaZs9f/6cGjduTFFRUZSQkECDBw+munXrynQTJiqpthg+fDilp6fLIdq/j5MBFVBmZiYNGTJEHNWysLCQXFxcSE9PT6a/bUZGBl2+fFm8wFeUfu1f0sOHD2nMmDG0b98+cdmFCxfI19eXzMzMKDIykq5du0aurq40atSoCldd8eEDl3Rk+71795Kvry8dPHiQPDw8qH379rRz507KyckhQ0NDMjMzozZt2lD16tXp0qVLcoi8fFu+fDnp6OjIzPPO/j5OCHwal7p/Gfn5+fIOodyRdm+SHmMZGRk0aNAgqlSpkniP8fr1a3r+/Dndv39f/FxoaCgZGBh8MzfI8lJYWEjjx48nIyMjsrS0lFn3/v17GjlyJLVq1YqmT5+uMLPNrFixggRBIGNjY3GZ9D7r9evX5OfnRw0bNiRTU1Oys7NT6FHuP0diYiL179+fPD09xf7/hYWFtHXr1o8mBDiJTJSUlETjxo2jgIAAsVIsLS2NgoKCyMLCokxC4FuqJuNkQAUTHR1NmpqaZGtrSzdv3pRZ5+LiQvr6+jLTBkrxTXVZ8fHx4sVHOlWSVHJyMn333XckCAI1aNCAbGxsKlx1hfSYePDgQZmuD1lZWdSwYUMKDw+nrKws8vDwoDZt2tCvv/5KmZmZNHHiRAoNDaXr16/LI/RyLy0tjQYPHsy/O/av4lJ39qXFx8dT1apV6caNG0T032Ps/v371KtXL6patSpduHBB5jOXLl0iNzc3mfJu9l8fuw7k5eXR3LlzycjIiMaNGyczkOz79++pf//+NGzYsApzv/Ex0u/2/v17OnHiBC1evJisra3JyspKHIyt9EuszMxMevjwoUIPUvy5fvvtN1JTU6Nq1arJ3KdJEwLVq1enwYMHyzHC8qWgoIBmzpxJqqqqZGVlJbMuNTWVgoKCqEmTJrR06VK5xPe/4mRABZOZmUmdO3cmFRUVsVtA6QuNm5sbCYLAg7l9hrdv39KgQYNIEATavHkzEZV90L958yadPXu2wlZXPHjwgHR0dEgQBHJxcaH4+HixVOyXX36hdu3aUVZWFl2/fp08PDyoQ4cOtG3bNjlH/W2oaFUkrHziUnf2v5Be26T/febMGerWrRuZmJiILxyk67Zu3UqCIJAgCGXm0v7555/LvKBgsvdnp0+fpt9//12srpA+gNjb21NwcLBMFUBhYWGFnp5R+p0OHTpEU6ZMESsMk5OTqVmzZtSsWTNx9gSikodbRZ2y+J86duwYaWho0KBBg2TasrCwkDZu3EgGBgblfuC7r+nBgwc0d+5cEgRBnHVB6vbt2zRs2DCys7OjnJwc+QT4P+BkwDfsU28Vs7KyqGXLltSoUSNxILfSF4uJEyfyA8gHPvUQX1BQQL169SJdXV06c+aMzLoPL8AV8S3vvXv3yNbWlhwcHKhFixY0bNgwMjIyoqioKIqPjydXV1exC0VKSgp16dKFXF1dy/UUKowpGi51Z/9EXFwc+fn50a1bt+j169fi8kuXLpGzszPVrVtXrBAgIjp+/DiNGDGClixZUuES4/+G0vcQISEhZGJiQs2bNycNDQ0aOHAgpaWlUX5+PoWFhVGrVq3ou+++K/Nbroj3HVIJCQlUuXJlmjNnjswLrKtXr4oJgfPnz9OkSZPI2NiY573/BOlxlpqaSr/99hudOnVKHFA2MTGRqlWrRn5+fjIJgaKiIsrNzZVLvOWBtM2ePn1Kd+7cEdvi3bt3FBYWRurq6rRs2TKZz9y5c4eePn361WP9EjgZ8I0qfQFITk6mixcvypwInz17RjY2NtSkSZOPJgSI+I0kEcnc4BCVvNlYuHAhbdiwQWa08h49elDt2rXLJAQUQWpqKnl4eJC7uzvt2LGDdu7cSR06dCB3d3cSBIHs7e3F8uObN2/yqOWMMfaNe/XqFZmZmZGuri5ZWlqSv78/xcTEiOtv3bpF3bp1Iz09PUpKSqKUlBTq3bs3jR49WtyGEwKf56effqJatWrR2bNniYhozpw5pKKiIlYIvH37lmbMmEGmpqa0YsUKeYb61Vy/fp2MjY0pKirqo+tv3bpF9vb2VLduXTI1NRVn52GypPf9CQkJZGJiQk2aNCF7e3uysrKi06dPE1HJFNDq6uo0bNiwMvfEikjaZjt37iRLS0syMzMTp6rMyMigV69e0cyZM0lDQ6PCzFjByYBvUOlEwLRp08jU1JRMTU1JXV2d1q5dK/aXys7OJltbW2rWrJlM9p6V6N27NwUEBIjtFRISQtWqVaM2bdqQtrY22dvb06JFi8TtpX0eSw/CqChu3rxJzs7O5OTkRLdu3aK8vDw6ffo0ubq60saNG4moYpYqMsaYIioqKqLQ0FCKjIykCxcu0MKFC0lLS4u8vLzoxx9/pMLCQkpJSaERI0aQIAhkZmZGVlZWFW7snK/B19eX5s2bR0QlLyS0tLRo5cqVRETijAF5eXm0evXqCv0SRyKRiMfN/v37qUGDBjKDTX7smDp9+vQ3+zb2azl58iRpamqKx9SOHTtIEASaM2eOuM3hw4dJEAQKCAjg3y6VDBZYrVo1Wrp0Kb1+/ZomT55MlStXFqfSfvr0Kc2ePZsEQSgzcOC3iJMB37CZM2eSvr4+JSYmEhHRoEGDSFNTk3788Uexz0p2djbVq1ePBg0aJMdIy6eIiAgSBIFCQ0Pp9OnTZG9vLw4UeOfOHRo/fjxZW1vTzz//TEQlXQbatm1Lrq6u8gxbblJTU8nJyYmcnJzE8SgYY4xVTPv27SMNDQ2xSi4/P5+mTZtGgiCQnZ0dLViwgG7evElXrlyh06dPiw+qXBHweQoKCqiwsJBsbW1p27ZtdO7cOVJXVxcfLt6/f0+zZs2Smc2IqOJUdUpfbL19+5bevXtH9+/fFwdK3L59O9WuXZtevnxJRLLH1KlTp8QqCvZp0of6JUuWkI+PDxGVzPxRr149mdkCnj17RkRER48eVfgXhxKJhIqLi2nkyJEUGBhIRCUP/sbGxjRmzBhxu6KiIsrOzhbPgd86JbBvxunTp5GamgoAuHbtGo4fP45Vq1aha9eu2L17N/bu3YuOHTti0qRJWL16NZ4/fw4dHR0kJydj3bp18g2+HBo1ahTWr1+P+fPnY+XKlTAwMIC1tTUAwNTUFIGBgWjWrBn27t2L/Px8VKpUCYcPH8bu3bvlHLl8mJubIzw8HEpKSpg9ezZOnDgh75AYY4z9S5ydneHj44OoqCgAQOXKlZGQkIBevXqhffv2SEpKQqNGjXD58mW0atUKysrKKC4uhoqKipwjL5/OnDmDR48eAQBmzJiBpKQkqKiowN3dHd999x3atm2LlStXYtSoUQCAN2/e4Pfff8e1a9dk9qOsrPzVY//SJBIJlJSUcOPGDQwaNAi2trYwMzND69atMWvWLLRr1w7FxcWYMWMGAMgcU1u3bsXhw4dRWFgop+jLNyICAAiCAADIzc1F5cqVcf/+fTg4OKB79+5YsWIFAODAgQNYu3Yt3rx5g/bt26Nhw4Zyi7s8EAQBSkpKyMvLg729PbKzs9GiRQs4OTkhPDwcALBr1y4kJSVBR0cHwcHBsLCwkHPUX4C8sxHs86Snp5O9vT317NmT7ty5Q+/evaPo6GgqKCigY8eOkYGBgdiXzNPTk7S0tCgsLExmAJCKkk3+EkqXQW3cuJEEQSBtbe0yU+EdOnToo7MvVORBe/5Kamoqubq6UqtWrcQ+Z4wxxiqe6OhoatOmDb148YKsra2pTZs24gCxDx8+pLi4OK4E+Ay3b98mOzs78vHxEbtWSGdcOHPmDHXt2pWsrKwoLS2NiIiePHlCzs7O1KpVqwp37ya9/0pOTqbq1atTQEAARUdH044dO6hXr16krKxMffr0odjYWNLR0aGAgADKyMiglJQUCg0NJS0tLYV/g/1Xzpw5Q/Hx8UREFBUVRY0bNyYDAwMaMWKEuE1RURGNHDmSRo8eLTN1paKRHo/Z2dniMj8/P7KxsSFjY2MKCAgQz3Fv374lb29vmj17doU673Ey4BuyatUq6tSpE3l6esoM0jZ8+HDy8/MT++qNHTtWvGhz3x9Zn3qIj4uLI0EQKDAwkB4/fiwuv3r1KllYWPBUjB+4ceMG9e3bl+7fvy/vUBhjjP2L7OzsSBAEcnR0/OT0bRXpxvjfEhMTQ/r6+lS5cmU6ePCgzLq4uDjq3LkzaWpqkpWVFTVv3pzs7OzE+7qKlhDIysoia2trCgkJKbM8PDycNDQ0yNXVlX755RfS09MjAwMDMjMzIwsLC7p48aKcoi7/JBIJFRUVUefOncnFxUVc7uzsTCoqKnT69GnKy8uj3NxcCg0Npdq1ayt0YkX6jLRnzx5q27Yt7dmzh4hKulNYW1uToaGhzLaTJ08mIyMjcWD2ioJrub4BRARBEDB8+HCoqqoiJiYGwcHBmD17Nho0aIBbt27B0tISqqqqAIBHjx5h7dq1aNasGQRBED+v6KRlaQBw7NgxvHjxAoIgoFu3bvDy8kJ+fj78/f3x6tUr9OnTB/r6+pg+fTqqVq0KS0tLOUdfvjRs2BCxsbGoVKmSvENhjDH2L5DeOwQGBmLBggVYvHgxtLW1P3pPwV0DPk1672FkZAR1dXXUqlULW7ZsgYWFBYyMjAAAXl5esLe3x7lz5/D06VPUqVMH7u7uUFZWRlFRUYVr34cPH6KwsBADBw5EcXExlJWVIZFIoKuri4EDB+Lly5dYsGABxo4di6tXr+Ly5cuoVq0aTExMoKenJ+/wyzVlZWXMmzcPLi4uiI+PR//+/REfH4+uXbuif//+UFFRgYmJCW7cuIH9+/crdNcAQRCwe/dueHt7Y/r06dDW1gYA6OnpYerUqQgICECzZs1Qv359EBGOHz+OQ4cOoX79+nKO/MsSiP6/cwkr10pffNetW4d169ahdu3aWLlyJbZs2YLAwED0798fN2/eREFBAa5cuQIVFRVOBPy/0u0wadIk7N69G0pKSqhZsyYePXqEM2fOQFdXF7GxsfDx8QEADB48GO/fv8f69euhqqoqk0xgjDHGFMGjR49gZ2eHwMBAhISEyDucb8aH9ww5OTkAgISEBKxbtw4mJiaYO3cu6tWr98l9SB+UK5p169Zh9OjRyM/PB4Ay96p3795FixYtEBISwsfcX/iw7SQSCfLy8jB69GhUr14dy5cvF5NJ8fHxePLkCerUqYOWLVv+6bGnCJ49ewZnZ2f069cPkyZNkllXVFSER48eYcmSJSgoKEDdunXh6ekJc3NzOUX77+Enm2+E9A0/APj6+sLX1xePHz9GQEAA+vTpg4iICBQXF6Nly5a4fPkyVFRUUFxczIkAAJmZmWI7rFy5EjExMdi4cSOuX7+Ovn37Ij09HadPnwYADBw4ENu3bwcAdOzYEbGxsVBVVUVRUREnAhhjjCkcQ0NDhIaGYtGiRbh+/bq8w/kmlE4EHD9+HKdOnUJqaipq1KiBYcOGwcfHB3fv3kVYWBgyMjIAlNzb/fbbbzL7qYiJAADim9WEhAQAKHOvampqClNTU2RmZgL476B4rCxBEHDu3Dns2LEDAKCkpARNTU1069YN69atw40bN8Rt+/fvj6CgIPTt21fhEwEA8OrVKzx9+hRt2rQBUHKcSY81FRUVGBkZ4aeffkJkZCSmTJlSIRMBACcDvikfJgSGDh2KR48eISgoCL169UJ8fDxWrlwpPrxW1IvI3/HTTz/Bzs4Oz58/BwBcv34dISEhsLOzw65duzB16lRERUWhZ8+eyM3Nxfv37+Hh4YFff/0VAwcOFNu8opXoMcYYY5/LxcUFPXr0UOiS4s9FRGIiYMKECfDw8EDfvn3h7OyMIUOGICcnByNHjsTgwYNx9+5duLq6wtHREQcPHoSjo6Oco/86jI2NoampiQ0bNuD+/fvicolEAqCkiqJKlSqwsbEBUDZZwEoQEV68eIGff/4Zffv2xeDBg7Fp0yYAJdWtrq6umDt3Lt68eSPnSMunSpUqQVVVFXfv3gUg+5x18OBB/PLLL+K2FTkhxcmAb0zpA9XPzw9Dhw7F48eP8d133+Hhw4dQUlLih9f/FxUVhUmTJmHRokXQ0dEBUNJPraCgAPv27YOPjw8WLFiA4cOHQyKRYMOGDVi1ahUkEgl69OgBFRUVFBUV8UWIMcaYQjMzM8O6deugpKSE4uJieYdTbpUu2T5//jx+/fVX/Prrrzhw4ADi4+Oxd+9esZ/8yJEjERwcjF69esHKygoZGRliVWdFV6dOHURERODAgQOYNm0aUlJSAEBMoixZsgSPHz9Gu3bt5BlmuScIArS1tREREYEzZ87g+fPnWLx4MWxsbJCUlISGDRsiNzcXT548kXeoclf6YV6adNLR0YGxsTHWrVtX5hg8cOAAIiMj8fbtWwAVOyHFYwZ8o0pfcGJiYrBq1SqMGDECQ4cO5XECAKxevRpjx45FfHw83N3dxeVTp07F6dOn8ccff2D+/PkYM2YMACA7OxtDhgyBo6Mjvv/+ezlFzRhjjLFvXUxMDH777TdoamoiIiJCXJ6amgobGxuMHj0aP/74Y5nPVdQxAj6muLgY0dHRGDt2LMzMzNCmTRvo6+sjPT0d+/fvx2+//QZra2t5h1nuSO/xb926hfv370NbWxv6+vowNDRETk4OHj9+jGnTpiEzMxPFxcU4d+4cQkNDMXfuXHmHLjfSNktKSsLevXuRkpKCPn36wN3dHe/fv4e9vT0sLS3Rs2dPGBkZYf/+/YiNjcWJEyfQtGlTeYf/r+NkwDes9EO/q6srVFRUsGvXLvkGVQ78/vvv6NSpE2bMmIGwsDBx+dixY1FUVISTJ0/i3bt32LhxIywtLZGdnY3Ro0fj+fPnOHnyJFdVMMYYY+wfyczMxPjx43HgwAF06NBB7MtdUFAANTU1LFq0CLGxsUhKSkKNGjUUfjyis2fP4scff8StW7egpaUFKysr/Oc//+EuKR8hve9PSEjAuHHjoKqqCiJC5cqVER0djbZt24rbJiYm4tKlS1i6dCkSExPRrFkzOUYufzt37sSQIUMwcOBA6OrqIiYmBk2aNMGvv/6KBw8eYOLEibh58yaKioqgr6+P8PBwWFlZyTvsr4KTAd846YkhICAAL168wPr16xV+urfbt2/D398fNWrUwLRp02Bra4s+ffogOTkZ165dQ1ZWFrp06QI1NTU8ffoU9evXh0QiwfHjx6GqqqpQmXnGGGOM/XMfm2nowoULCA8PR2xsLNauXYuBAweK6yIiIhAZGYmTJ09CXV39a4dbLhUXF0NJSQmCIPDMTaWUbgvpFJPnzp1Dly5dsHDhQri6uiItLQ3R0dHYvn07Dh8+DAcHB5l95Ofno0qVKvIIv9zIyMiAq6srRo8ejVGjRoGIUL16dYwePRrz5s2DsrIyCgsL8e7dO+Tm5kJTUxMaGhryDvur4WRABZCdnQ13d3dERkYqRDnL57h9+zYCAwOhrKyMV69e4e3bt0hISICxsTGAkulErl69irS0NJibm6N9+/YVdj5fxhhjjH15pR/WMjIykJubi0aNGkFJSQn37t3D9OnTcezYMcycORN9+/bF69evMWjQIKiqqmLv3r0K36VTqnSlK3d1lXX//n3Uq1cPgiCguLgY69atEytLpMfe06dPERwcjBs3biAxMRE1a9YUP69I7fmp75qRkYFevXrh+PHjePz4MTp27AgXFxesWrUKAHDmzBk0btwYmpqaXzvkcoFTbxVAzZo1kZSUxImAUszNzbF8+XIUFBTg6tWrCAkJERMBRUVF0NXVRadOnTBixAh07NgRysrKKC4u5kQAY4wxxv5S6VkDwsLC4Orqiq5du8LOzg6LFy9GrVq1EBISgvbt28PX1xeNGjVCSEgI3r9/jx07dohvwZns4GyK8uD6OQoKCuDl5QVTU1MQEZSVlZGbm4vLly8jNzcXQMlxqKenhwEDBiA7OxvZ2dky+1CU9pRIJBAEAW/fvkV2djaOHDmCR48e4dWrV1BSUkJWVhbOnTsHZ2dnuLi4IDIyEgCQnJyMn376CXfu3JHzN5AfTgZUEJUrV5Z3COWOubk5IiMj0apVK6xduxYnTpwAUDJ36McKYrhrAGOMMcY+h/Qh64cffsCqVavwww8/ICMjAzVq1MCKFSuQlpaGRo0aYdKkSfDz80OlSpXQrFkzHD16FJUrV0ZBQQGXw7M/ValSJSxcuBDq6upo0aIFiAi9evWCvr4+1q5di5cvX4rHobm5OVRVVcUkgSKRVuikpqZi9OjRaNeuHVxcXNCkSROMGTMGOTk5GDhwIDp37gxra2usWrVK/O1t2bIFd+7cgZ6enpy/hfzwWYhVaGZmZlixYgWICHPnzsXJkycBKE6mlDHGGGNfTkFBgfi/X716hcTERCxcuBAuLi44fPgwzp07h8mTJ6NZs2YoLi5G48aNMW7cOHTs2BGrV68WB3pWU1OT0zdg5dWHlSKCIKB169ZYvXo18vPzYW9vD1NTU/Tu3Rtr167F6tWrkZmZiby8PMTExEBJSUmsglUU0kRAcnIyOnTogKpVqyIkJASXLl3CqFGjcPbsWfTr1w+6urrw8fHB5cuXkZSUhISEBEyYMAHh4eFYvXo19PX15f1V5IbHDGAK4fbt2xg/fjwyMzOxZs0ahR9VlTHGGGN/T2JiIq5cuQJHR0e0bNkSr169QseOHXHw4EFcvnwZHh4eWLhwIUaNGoX8/Hxs2LABHTt2RIMGDXDlyhWsXLkSu3fvRlRUFHr16iXvr8PKEelD7dOnT3Hv3j20atVKXFdYWIhLly7By8sLdevWxdGjRxEWFoadO3ciLS0NzZs3x507d3Dw4EGFmo6xdCLAwcEB48aNw6xZs2S6/G7ZsgVLly6FIAgYNmwYTp06hR07dqBevXqoXbs2Fi9erPDPBNxBmikEc3NzLFy4ENHR0Ty2AmOMMcb+lrVr12LatGno2bMnOnbsCACoXr06qlSpgj59+uDKlStYtmwZ/P39AZQMVBwXFwctLS00aNAAVlZWGDFiBNTU1Pg+hJWhpKSEjIwMWFtb48WLF3B0dISDgwO6dOkCW1tbtGzZEvHx8fD390fbtm1x4sQJjBkzBvv27UONGjXQokULGBkZyftrfFXSNuvcuTN69OiBefPmASgZR0E6DpiXlxdevXqFKVOmgIgQExODyZMnQ19fHxKJRKFmDfgUrgxgComnrmGMMcbY59iyZQv8/f2xdu1adO/eHZqamuLI5fv27UNwcDB0dXVx7NgxAMCbN2/g6emJt2/fIikpSWZMooKCAu4iwD7q/v37cHd3R35+PjQ0NNCkSRPEx8ejYcOGsLS0hKurKwRBQGhoKExNTXHw4EGF7/Z67949eHp6Ql9fH9999x3atm0rris9u0C7du2gq6uLHTt28BTiH+BkAGOMMcYYYx/x7NkzeHp6om/fvggICBCX5+Xl4fbt23jy5AmSk5OxceNGVK1aFXXr1sWzZ8+Qm5uL8+fPQ1VVlR8+2GdLS0vD999/D4lEgtDQUOjr6+PUqVMIDw9HYWEhrl27BjMzM1y7dg29evXCzp07FWr6wI+RTidORJg6daqYECjdLh07doShoSE2bdokz1DLJX41yhhjjDHG2CdkZWXB0NBQ/HdERAT8/PxgY2ODoKAgxMXFYc2aNXBwcEC9evXg7u6OCxcuQFVVFUVFRZwIYJ+tfv36+OGHH/Du3TtMmzYNmZmZ8PLywokTJ3Dw4EFERkbCzc0NzZs3R1hYGAAeFFs6nbggCJgzZ47MYOESiQQPHz5ElSpV0LVrVwD46IxiiowrAxhjjDHGGPuIZ8+eoUWLFujevTu8vb2xcuVKpKamom3btnB3d8erV68wefJkBAQEICgoSOazXBHA/qnbt2/jP//5DwAgNDQUjo6OMuuLiopkBspjn64QCAkJwYEDB7Bnzx7UqVNHzlGWP5wMYIwxxhhj7BN+++039OnTBzo6OtDQ0MCSJUtgZWUFHR0d5OTkoFOnTnB1dcXs2bPlHSqrQEo/3IaFhaF169byDqncK91mP/zwAw4dOoTZs2fjxIkTsLKyknd45RInAxhjjDHGGPsTz549Q15eHkxMTGSW5+TkwN3dHQMHDsSIESPkFB2rqG7fvo0JEyYgOzsbS5culZlykH2ctM3OnTuHnJwcnD59GjY2NvIOq9ziMQMYY4wxxhj7E7q6umUSAc+ePYOPjw8KCgrEKQUZ+5KkU2PXqVMHBgYG8g7nm2Bubo5FixahVatWuHTpEicC/gJXBjDGGGOMMfaZsrOzER0djRMnTiArKwsnT57kWQPYv+r9+/eoVKmSvMP4phQWFkJVVVXeYZR7XBnAGGOMMcbYZ3r48CFOnjyJ+vXr49SpUzxrAPvXcSLg7+NEwOfhygDGGGOMMcb+hpcvX6J69eoQBIErAhhj3yxOBjDGGGOMMfYPEJHCz/POGPt2cTcBxhhjjDHG/gFOBDDGvmWcDGCMMcYYY4wxxhQMJwMYY4wxxhhjjDEFw8kAxhhjjDHGGGNMwXAygDHGGGOMMcYYUzCcDGCMMcYYY4wxxhQMJwMYY4wxxhhjjDEFw8kAxhhjjH2UIAgYO3bsv/538vLyMGzYMOjp6UEQBAQFBf3tfcyYMQOCICA7O/vLB8gYY4xVQJwMYIwxxr6ydevWQRAECIKAEydOlFlPRKhbty4EQYCrq+u/GsupU6cwY8YMvHz58l/9O39m3rx5WLduHUaPHo2NGzfCx8fnT7fdtWvX1wuOMcYYq6A4GcAYY4zJSeXKlbF58+Yyy48ePYqHDx9CTU3tX4/h1KlTmDlzplyTAYcPH0arVq0wffp0DBo0CDY2Np/clpMBjDHG2JfByQDGGGNMTlxcXLBt2zYUFRXJLN+8eTNsbGygp6cnp8i+rqysLGhpack7jG/Ou3fvIJFI5B0GY4yxbxQnAxhjjDE58fb2xvPnz3Ho0CFx2fv377F9+3YMGDDgo5958+YNgoODUbduXaipqcHCwgKLFi0CEclsJ+3vv2vXLjRt2hRqampo0qQJDhw4IG4zY8YMfPfddwAAExMTsevCvXv3ZPb1Z/v4M1lZWfD390ft2rVRuXJlWFlZYf369eL633//HYIgID09HXv37v3k3y/9nd68eYP169eL2/r6+sps8/LlS/j6+kJLSwvVq1eHn58f3r59W2ZfmzZtgo2NDapUqQJtbW14eXkhIyPjT7/PkSNHIAgCdu7cWWbd5s2bIQgCTp8+LS67efMm+vbtC21tbVSuXBm2trb45ZdfZD734sULTJw4EZaWllBXV4empiacnZ1x5coVme2kbbVlyxZMnToVhoaGqFq1KnJzc/80ZsYYY+xTVOQdAGOMMaaojI2N4eDggLi4ODg7OwMA9u/fj1evXsHLywvLly+X2Z6I0LNnTxw5cgT+/v5o3rw5Dh48iO+++w6PHj3C0qVLZbY/ceIEduzYgTFjxkBDQwPLly9Hnz598ODBA+jo6MDDwwOpqamIi4vD0qVLUbNmTQCArq7uZ+/jU/Lz89GhQwekpaVh7NixMDExwbZt2+Dr64uXL19i3LhxaNSoETZu3Ijx48ejTp06CA4OLvP3S9u4cSOGDRuGli1bYsSIEQAAMzMzmW08PT1hYmKCH374ARcvXkR0dDRq1aqFBQsWiNvMnTsX06ZNg6enJ4YNG4Znz55hxYoVaN++PS5duvTJKoUOHTqgbt26iI2NRe/evWXWxcbGwszMDA4ODgCAlJQUtGnTBoaGhggJCUG1atWwdetWuLu7IyEhQfz83bt3sWvXLvTr1w8mJibIzMxEVFQUHB0dcf36dRgYGMj8ndmzZ6NSpUqYOHEiCgoKUKlSpU/+f8AYY4z9KWKMMcbYV7V27VoCQH/88QeFh4eThoYGvX37loiI+vXrRx07diQiIiMjI+rRo4f4uV27dhEAmjNnjsz++vbtS4IgUFpamrgMAFWqVElm2ZUrVwgArVixQly2cOFCAkDp6ell4vzcfXzMsmXLCABt2rRJXPb+/XtycHAgdXV1ys3NFZd/+D3/TLVq1WjIkCFllk+fPp0A0NChQ2WW9+7dm3R0dMR/37t3j5SVlWnu3Lky2129epVUVFTKLP9QaGgoqamp0cuXL8VlWVlZpKKiQtOnTxeXde7cmSwtLendu3fiMolEQq1btyZzc3Nx2bt376i4uFjmb6Snp5OamhrNmjVLXHbkyBECQKampuKxwhhjjP0vuJsAY4wxJkeenp7Iz8/Hnj178Pr1a+zZs+eTXQT27dsHZWVlBAYGyiwPDg4GEWH//v0yy7t06SLz5rxZs2bQ1NTE3bt3Pzu+f7qPffv2QU9PD97e3uIyVVVVBAYGIi8vD0ePHv3sGP6OUaNGyfy7Xbt2eP78uVhOv2PHDkgkEnh6eiI7O1v8j56eHszNzXHkyJE/3f/gwYNRUFCA7du3i8vi4+NRVFSEQYMGASgp/T98+DA8PT3x+vVr8W88f/4c3bp1w+3bt/Ho0SMAgJqaGpSUSm7HiouL8fz5c6irq8PCwgIXL14s8/eHDBmCKlWq/PMGYowxxv4fdxNgjDHG5EhXVxddunTB5s2b8fbtWxQXF6Nv374f3fb+/fswMDCAhoaGzPJGjRqJ60urV69emX3UqFEDOTk5nx3fP93H/fv3YW5uLj7o/lWsX8qH8daoUQMAkJOTA01NTdy+fRtEBHNz849+XlVV9U/337BhQ9jZ2SE2Nhb+/v4ASroItGrVCvXr1wcApKWlgYgwbdo0TJs27aP7ycrKgqGhISQSCX766SesXLkS6enpKC4uFrf5WDcMExOTP42PMcYY+1ycDGCMMcbkbMCAARg+fDiePn0KZ2fnLzayvrKy8keX0weDDf7b+/ia/ipeiUQCQRCwf//+j26rrq7+l39j8ODBGDduHB4+fIiCggKcOXMG4eHh4nrpCP8TJ05Et27dProPaeJg3rx5mDZtGoYOHYrZs2dDW1sbSkpKCAoK+uhMAVwVwBhj7EvhZABjjDEmZ71798bIkSNx5swZxMfHf3I7IyMjJCUl4fXr1zLVATdv3hTX/12CIPz9gD+DkZERkpOTIZFIZKoD/pdYgf89XjMzMxARTExM0KBBg3+0Dy8vL0yYMAFxcXHIz8+Hqqoq+vfvL643NTUFUFJl0KVLlz/d1/bt29GxY0esWbNGZvnLly/FAR0ZY4yxfwOPGcAYY4zJmbq6OiIiIjBjxgy4ubl9cjsXFxcUFxfLvIUGgKVLl0IQBHFGgr+jWrVqAEoePr8kFxcXPH36VCa5UVRUhBUrVkBdXR2Ojo7/aL/VqlX7n2L18PCAsrIyZs6cWaa6gYjw/Pnzv9xHzZo14ezsjE2bNiE2Nhbdu3eXeXCvVasWOnTogKioKDx58qTM5589eyb+b2Vl5TJxbNu2TRxTgDHGGPu3cGUAY4wxVg4MGTLkL7dxc3NDx44dMWXKFNy7dw9WVlZITEzE7t27ERQUVGaavc9hY2MDAJgyZQq8vLygqqoKNzc3MUnwT40YMQJRUVHw9fXFhQsXYGxsjO3bt+PkyZNYtmxZmXEP/k68SUlJWLJkCQwMDGBiYgJ7e/vP/ryZmRnmzJmD0NBQ3Lt3D+7u7tDQ0EB6ejp27tyJESNGYOLEiX+5n8GDB4tjO8yePbvM+p9//hlt27aFpaUlhg8fDlNTU2RmZuL06dN4+PAhrly5AgBwdXXFrFmz4Ofnh9atW+Pq1auIjY0VqwsYY4yxfwsnAxhjjLFvhJKSEn755ReEhYUhPj4ea9euhbGxMRYuXIjg4OB/tE87OzvMnj0bkZGROHDgACQSCdLT0//nZECVKlXw+++/IyQkBOvXr0dubi4sLCywdu1a+Pr6/uP9LlmyBCNGjMDUqVORn5+PIUOG/K1kAACEhISgQYMGWLp0KWbOnAkAqFu3LpycnNCzZ8/P2oebmxtq1KgBiUTy0c80btwY58+fx8yZM7Fu3To8f/4ctWrVgrW1NcLCwsTtJk+ejDdv3mDz5s2Ij49HixYtsHfvXoSEhPyt78QYY4z9XQKV1xGAGGOMMcbKqaKiIhgYGMDNza1Mf3/GGGPsW8BjBjDGGGOM/U27du3Cs2fPMHjwYHmHwhhjjP0jXBnAGGOMMfaZzp49i+TkZMyePRs1a9bExYsX5R0SY4wx9o9wZQBjjDHG2GeKiIjA6NGjUatWLWzYsEHe4TDGGGP/GFcGMMYYY4wxxhhjCoYrAxhjjDHGGGOMMQXDyQDGGGOMMcYYY0zBcDKAMcYYY4wxxhhTMJwMYIwxxhhjjDHGFAwnAxhjjDHGGGOMMQXDyQDGGGOMMcYYY0zBcDKAMcYYY4wxxhhTMJwMYIwxxhhjjDHGFMz/ARLnVdzy+TdFAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Count the number of conversations starting each month\n",
    "british_airways_monthly_distribution = british_airways_conv_start['month'].value_counts().reindex(range(1, 13), fill_value=0).sort_index() \n",
    "KLM_monthly_distribution = KLM_conv_start['month'].value_counts().reindex(range(1, 13), fill_value=0).sort_index() \n",
    "lufthansa_monthly_distribution = lufthansa_conv_start['month'].value_counts().reindex(range(1, 13), fill_value=0).sort_index() \n",
    "air_france_monthly_distribution = air_france_conv_start['month'].value_counts().reindex(range(1, 13), fill_value=0).sort_index()\n",
    "\n",
    "# Concatenate data frames into one data frame\n",
    "airlines_monthly_distribution_df = pd.concat([british_airways_monthly_distribution, KLM_monthly_distribution, lufthansa_monthly_distribution, air_france_monthly_distribution], axis = 1)\n",
    "airlines_monthly_distribution_df.columns = ['British Airways', 'KLM', 'Lufthansa', 'Air France']\n",
    "\n",
    "# Plot the monthly distribution data frame\n",
    "plt.figure(figsize=(12, 6))\n",
    "sns.lineplot(data=airlines_monthly_distribution_df, linewidth=2.5)\n",
    "\n",
    "plt.title('Monthly Distribution of Inclusion in Conversations per Airline', fontsize=14, weight='bold')\n",
    "plt.xlabel('Month of the year', fontsize=12)\n",
    "plt.ylabel('Number of Conversations', fontsize=12)\n",
    "plt.xticks(ticks=range(1, 13), labels=['January', 'February', 'March', 'April', 'May', 'June', 'July', 'August', 'September', 'October', 'November', 'December'],rotation=45)\n",
    "plt.legend(title='Airlines', loc='upper right', fontsize=12)\n",
    "plt.grid(True)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/_1/64lzdpgn2472v4xdvl_3y1l40000gn/T/ipykernel_12110/651757671.py:4: UserWarning: pandas only supports SQLAlchemy connectable (engine/connection) or database string URI or sqlite3 DBAPI2 connection. Other DBAPI2 objects are not tested. Please consider using SQLAlchemy.\n",
      "  conversations = pd.read_sql(f\"\"\"\n",
      "/var/folders/_1/64lzdpgn2472v4xdvl_3y1l40000gn/T/ipykernel_12110/651757671.py:10: UserWarning: pandas only supports SQLAlchemy connectable (engine/connection) or database string URI or sqlite3 DBAPI2 connection. Other DBAPI2 objects are not tested. Please consider using SQLAlchemy.\n",
      "  tweets = pd.read_sql(f\"\"\"\n",
      "/var/folders/_1/64lzdpgn2472v4xdvl_3y1l40000gn/T/ipykernel_12110/651757671.py:16: UserWarning: pandas only supports SQLAlchemy connectable (engine/connection) or database string URI or sqlite3 DBAPI2 connection. Other DBAPI2 objects are not tested. Please consider using SQLAlchemy.\n",
      "  hasher = pd.read_sql(f\"\"\"\n",
      "/var/folders/_1/64lzdpgn2472v4xdvl_3y1l40000gn/T/ipykernel_12110/651757671.py:42: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  responses['in_reply_to_status_id'] = pd.to_numeric(responses['in_reply_to_status_id'], errors='coerce')\n",
      "/var/folders/_1/64lzdpgn2472v4xdvl_3y1l40000gn/T/ipykernel_12110/651757671.py:42: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  responses['in_reply_to_status_id'] = pd.to_numeric(responses['in_reply_to_status_id'], errors='coerce')\n",
      "/var/folders/_1/64lzdpgn2472v4xdvl_3y1l40000gn/T/ipykernel_12110/651757671.py:42: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  responses['in_reply_to_status_id'] = pd.to_numeric(responses['in_reply_to_status_id'], errors='coerce')\n",
      "/var/folders/_1/64lzdpgn2472v4xdvl_3y1l40000gn/T/ipykernel_12110/651757671.py:42: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  responses['in_reply_to_status_id'] = pd.to_numeric(responses['in_reply_to_status_id'], errors='coerce')\n"
     ]
    }
   ],
   "source": [
    "# Compute response times for each airline\n",
    "\n",
    "# Load data from the databases\n",
    "conversations = pd.read_sql(f\"\"\"\n",
    "                            SELECT conversation_id, airline \n",
    "                            FROM conversations \n",
    "                            WHERE start >= UNIX_TIMESTAMP('{start_datee}') * 1000 \n",
    "                                AND start <= UNIX_TIMESTAMP('{end_datee}') * 1000;\n",
    "                            \"\"\", connection)\n",
    "tweets = pd.read_sql(f\"\"\"\n",
    "                     SELECT id, user_id, timestamp_ms, in_reply_to_status_id \n",
    "                     FROM tweets\n",
    "                     WHERE timestamp_ms >= UNIX_TIMESTAMP('{start_datee}') * 1000 \n",
    "                        AND timestamp_ms <= UNIX_TIMESTAMP('{end_datee}') * 1000;\n",
    "                        \"\"\", connection)\n",
    "hasher = pd.read_sql(f\"\"\"\n",
    "                     SELECT hasher.conversation_id, hasher.id \n",
    "                     FROM hasher \n",
    "                     JOIN conversations ON hasher.conversation_id = conversations.conversation_id\n",
    "                     WHERE conversations.start >= UNIX_TIMESTAMP('{start_datee}') * 1000 \n",
    "                        AND conversations.start <= UNIX_TIMESTAMP('{end_datee}') * 1000;\n",
    "                        \"\"\", connection)\n",
    "\n",
    "merged_data = hasher.merge(tweets, on='id').merge(conversations, on='conversation_id') # Merge data into one table\n",
    "\n",
    "merged_data['timestamp_ms'] = pd.to_datetime(merged_data['timestamp_ms'], unit='ms') # Convert timestamp to date time format\n",
    "\n",
    "british_airways_user_id = 18332190\n",
    "KLM_user_id = 56377143\n",
    "lufthansa_user_id = 124476322\n",
    "air_france_user_id = 106062176\n",
    "\n",
    "british_airways_responses = merged_data[(merged_data['user_id'] == british_airways_user_id) & (merged_data['in_reply_to_status_id'] != 0)]\n",
    "KLM_responses = merged_data[(merged_data['user_id'] == KLM_user_id) & (merged_data['in_reply_to_status_id'] != 0)]\n",
    "lufthansa_responses = merged_data[(merged_data['user_id'] == lufthansa_user_id) & (merged_data['in_reply_to_status_id'] != 0)]\n",
    "air_france_responses = merged_data[(merged_data['user_id'] == air_france_user_id) & (merged_data['in_reply_to_status_id'] != 0)]\n",
    "\n",
    "def calculate_average_response_time(responses, merged_data):\n",
    "    response_times = []\n",
    "\n",
    "    # Ensure in_reply_to_status_id is numeric\n",
    "    responses['in_reply_to_status_id'] = pd.to_numeric(responses['in_reply_to_status_id'], errors='coerce')\n",
    "\n",
    "    # Merge responses with initial tweets\n",
    "    initial_tweets = merged_data[merged_data['id'].isin(responses['in_reply_to_status_id'])]\n",
    "    merged_responses = responses.merge(initial_tweets, left_on='in_reply_to_status_id', right_on='id', suffixes=('_response', '_initial'))\n",
    "\n",
    "    # Check for duplicates\n",
    "    merged_responses = merged_responses.drop_duplicates(subset=['id_response', 'id_initial'])\n",
    "\n",
    "    # Calculate response time\n",
    "    for _, row in merged_responses.iterrows():\n",
    "        response_time = (row['timestamp_ms_response'] - row['timestamp_ms_initial']).total_seconds()\n",
    "        response_times.append(response_time / 3600)\n",
    "    \n",
    "    return response_times\n",
    "\n",
    "response_times_british_airways = calculate_average_response_time(british_airways_responses, merged_data)\n",
    "response_times_KLM = calculate_average_response_time(KLM_responses, merged_data)\n",
    "response_times_lufthansa = calculate_average_response_time(lufthansa_responses, merged_data)\n",
    "response_times_air_france = calculate_average_response_time(air_france_responses, merged_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.2719150337923981"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tot = 0\n",
    "for i in response_times_air_france:\n",
    "    tot += i\n",
    "\n",
    "tot\n",
    "avg = tot/ len(response_times_air_france)\n",
    "avg\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/jangalic04/anaconda3/envs/JB1010/lib/python3.10/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/jangalic04/anaconda3/envs/JB1010/lib/python3.10/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/jangalic04/anaconda3/envs/JB1010/lib/python3.10/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"
     ]
    },
    {
     "data": {
      "image/png": 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Sk5PdP9tsNl1zzTXu+4cOHdJPP/3k9Ti33HKLezrwWrVquVsrJOnw4cMl5rFp0yY5HA6veRS873A4tGnTphKPGQj16tXTtGnTCr2O1q5d65FPq1at3FMCR0ZGuq+AS2eu2nsTiNeGYRgeA8pdrS979uxxP75SpUoe/49WiYyMLPT3ulSqVEmjRo2SdOZvSkhIcMfyv37yP+8rV66UzWZzP+89evTw+H2u5/388893vzZff/11nXfeeRo/frweffRRLVu2TJGRkapfv35A/sb876fzzjvPo7Wyffv2Ou+887zum1/NmjV12223BSSfkjz77LOqX7++LrroIt10002aMGGC7rvvPn344YfufVytl9KZltr8pk2bVmgZAG+toi7+fk64vPXWW+6fW7Ro4Z4AxNWiKJ1ppXe1wpTWsGHD1LVr1zI9tiTjx49X9erV3fdbtWrl/rmo1/rPP/+sKlWquF/rjRo18vi8LOozBggWiiIgwH7++Wf3z3a73V3wFMVut2vhwoVq2rSpJGn37t16//339cQTT2jcuHFq3Lixnn32Wb9yOvfcc/16fO3atQt1a8h/Aubq6lFQwQLR1R0n0A4dOuRxv+DJYcH7RZ24FFwIMSoqyv2zq6tTMPLwR82aNfX000/rvvvuc/++P//8UyNGjNBXX31VbL7FcXXdKqisr42Crr32WvdJ1o8//qi1a9dq4cKF7viIESNUr149n/M9W+rVq6fIyP/reV65cmWPWP7nIv9++V8/ZXnee/TooWeffdY9FfO3336ruXPnasqUKRo2bJiaNGkSsAVn8+fnrdDKv62o13Dz5s09/v6yePjhh2WeGfvsccu/NtuSJUt07733Kicnp9hjuWZglDz/vqpVq5b6deXv54Qk/f777+6ukZI8Cu0LL7zQI6f8xVNp+Pu5Xxxfn4NAfMYAwcKYIiCANm/e7B5PJEn9+/f3WKuoKBdeeKH27Nmjb7/9Vqmpqfrll1+0bt06rVmzRqdOndJ9992nv/3tbx7jQUqjWrVqZXqcy8GDBwuNjfrjjz/cP59zzjnun/P/va5+5C75+/AHUsFxVH/88YdHMZo/V+lM4eBNpUqVPO6XdhFZb3kUd7+oPPyRf2rpm266SZ07d9bx48flcDh06623avv27e6T1fz52u12TZs2rcjjFjUepDSvjeJUr15d1157rV588UVJZ1qL8rdUXXfddT4d52wr+BrJz9cioFatWvrzzz8lnVkjqLhW3Pzj6e6++27ddNNN2rBhg77//nvt3LlTy5Yt086dO5WVlaXk5GSPqd3LKv/rouBrtuC2ol7D/n7m+Cr/OMTo6Gh98MEH6tevn+x2u1555RWvrVX5/76//vpLf/75Z6kKI38/J6QzaxPlbyV57LHHilxD7OOPP9bBgwdLvMBW0Nn8P/D1Ocj/XLdr167YxcbLOn4SCBSKIiBAfvrpJ49uD5I0YcKEEh+Xm5urPXv2qE2bNurWrZu7C4VpmqpZs6ays7PldDqVlpbmLoryfyH99ddfAfwrvDt9+rQWLFigq6++WtKZlefzd0HJ350m/0nw1q1bderUKVWuXFl79+71uh6HS8Ev2b/++svnNU0KTsQwZ84cPfXUU5LOdAubO3euO1arVi21bt3ap+OWVo8ePWSz2dwnO3PmzNHw4cM98nKx2WyFukoFWosWLTRx4kRNnTpV0pnX6Lx589zd+PI/b7m5uWrXrp2GDRtW6DgbN270uBKcX2leGyW5/fbb9dJLL8k0Tb377rvulsX69et7PI/lnWuAviTt379fN910k2rUqOGxz4kTJ7Ro0SL3/9Hvv/8um82m+vXr68ILL9SFF14o6cx7zNVF6tdffy3TybO3/FxdO7ds2aLvv//ePbHB9u3bPbpElmW9nEA6ePCg++dmzZrp4osvlnSmtSL/ArX59e3bV9OnT3fff/jhh/XKK694nNhnZGT43W25OKVp/Tl16pTmzZunO++886zlc7bkfy3t27fP3fshv7y8PP3vf/9Tz549rUgRcKMoAspo2bJlysrK0tGjR7V161YtW7bMY1Xy2267zevCgwUdOXJEbdu2Vbt27dSjRw81atRIVapU0ddff63s7Gz3fvmLjfxfKh9//LEmT56sOnXqqE6dOsVeifPH9ddfrzVr1rhnGMu/+OONN97o/rl79+5avHixJOmXX35R165d1aZNG61YscLjBKaggl+UV199tfr06aOIiAiNHz++2PESnTp10qBBg/Tll19KkqZPn67du3erXbt2Wr58uce4h7vuusun1ruyqF27tq699lrNmjVLkrRw4UIdOXKk0OxzkpSUlOT3yasv7rrrLj3zzDPu7kVPPvmkxo8fr4iICI0YMUJt2rTRjz/+KEkaOXKkLr/8crVt21ZOp1O7du3S6tWrlZGRodmzZ6tz585ef4evr42StGrVSoMHD9Znn33m0dVy/PjxfnfFCiX33nuvPvzwQ5mmqV9++UXt27fX5Zdfrvr16ys7O1vbtm3TqlWrdPz4cSUlJUk6M7NZYmKi+vbtqzZt2rjHY3zwwQfu41auXLlUi6MW5bbbbtOrr76qkydPyul0qn///h6zz7m6R1WuXPmsjhsqavY5Se7W0NatW+vzzz+XJH333XcaN26c2rRpo08//VQbNmzw+tjhw4erQ4cO7pbI1157TVu3btWFF14o0zT17bff6s8//9TWrVvPwl8lbdiwQTt27HDf79mzZ6HuaJL05ZdfKisrS5I0e/bsclkU3XHHHXrttdeUm5urQ4cOqXPnzrryyisVGxurnJwc/fDDD1q5cqWOHDmiPXv2nJXWc8BnFk3wAJQ7BWfsKeoWGRlpTps2zWNGKW+Pd834s2/fvhKP2aNHD48ZiPLPGpb/1q5dO/c+vsxQ5+uMW3Xq1ClyDaBbb73V45h//PGHWbt27UL7RUREFFrfI7/c3FyzYcOGXn+Ha1amktYpatu2bbHPY0nrFOWfhck0Cy+E6Atf1ik6//zz3esUuQRq9rmCs1GZpmlOnDjR4/fnX8j2p59+KnadItct/4xfBdewKmqdmIKvDV/+xqVLlxY6zvfff1+q5yO/s7F4a1F/U8FYce/Bl19+udh1igq+xt99990S980/w11xSpp9zjT9X6eotK9hb3n58rzs3LnTrF69eqF4ZGSkmZiYWOTnxa5du8wWLVoUefzi1iny93PiH//4h8fnYkZGhtf9pkyZ4vF7XTMRlmbxVm8CtXhrfsU9bvHixYXWyfN2K/i8AsHGRAuAH2w2m6pXr66EhAQNGjRIU6dOVXp6uh588EGfWyNq1qypl156SePGjVPbtm3d62zUqFFD3bp107Rp0/Tll196XCn/29/+ppdeeklt2rTxGOh9tlSrVk1ff/217rjjDjVu3FiVK1dW69at9fzzz+ull17y2LdevXpatWqVhg0bpujoaFWrVk0XXnihVq5cWah7YX5RUVH65JNPNHjw4EJdiXzRoEEDffPNN3rmmWfUu3dvxcTEKDIyUnXr1tXQoUM1f/58vffee2e9xaFatWr68ssv9cYbb2jgwIGqVauWIiMjVbNmTfXv31+vv/66Vq5c6R4wHwz33nuvR/e3xx9/3D0JRqtWrfTdd99p+vTp6tOnj2rWrOl+XXfs2FE33nijFi9e7O4eV5DdbteKFSt0zz33qEmTJsW+NnwxfPhwj7FzPXv2VNu2bUt9nFB36623auvWrbrpppvUqlUrVa1a1T2DXP/+/TVlyhSP8Yl9+/bVY489phEjRqh58+aqXr26+/U9aNAgv9az8ebKK69Uamqqbr75ZrVo0UJ2u112u13NmzfX3//+d23durXY93OwtGjRQqtXr9bgwYNVtWpVRUdHq3///vryyy910UUXFfm4Zs2aKTU1Vc8++6z69u2rmjVrKjIyUnXq1NH5559fqhbO0sjNzfUYB3XRRRe5J9kp6Nprr/Xo0jd79uyzktPZNnLkSG3fvl0TJkxQhw4dFB0dLZvNptq1a6t379667777tHbtWq+tZUAwGabrmxEAAB888sgj7nFKcXFx7sVfA2Xo0KHuroavvfaa/vGPfwT0+AAAFFRxOmkDAMqtHTt2aO/evdqwYYN7quJzzjlHiYmJFmcGAAgHFEUAAMs9+eSThWYnfOyxx4LazRAAEL4oigAAISMqKkotWrTQPffcoxtuuMHqdAAAYSKkxhTl76fu0rp1a4+pKwEAAAAgkEKupahdu3b64osv3Pcr0toUAAAAAEJPyFUckZGRatCggdVpAAAAAAgTIVcU7dy5U40aNZLdblfv3r31xBNPFDmH/8mTJz1WPXc6nTp06JBq167tMbc/AAAAgPBimqaOHTumRo0albh+ZEiNKfr000+Vk5Oj1q1ba9++fZo6dar27t2r7du3q3r16oX29zYGCQAAAABcMjMz1aRJk2L3CamiqKAjR44oLi5Ozz77rNdZiAq2FGVnZ6tp06bKzMxUjRo1/PrdJ06c0IgRIyRJNWsPkGGEXKMaKgjTzNPhgyslSR9//LGqVKlibUIAAKBMnE6nRo8erezs7EKxmJgYvf/++yW2WCBwjh49qtjYWB05ckQxMTHF7hvSZ/rnnHOOWrVqpV9++cVrPCoqSlFRUYW216hRw++iqFKlSu5JHipViqIowlljmjb3a61GjRoURQAAlGMTJkzQtGnTCm2/9957dc455wQ/Ifg0rCakS9WcnBzt2rVLDRs2tDoVAAAAoFimaWr58uVeY5999plCuINW2AupomjixIlatWqV0tPTtW7dOo0aNUo2m03jxo2zOjUAAACgWOnp6dq4caPX2MaNG5Wenh7chOCzkOoT9ttvv2ncuHE6ePCg6tatq759+2rDhg2qW7eu1akBAAAAxYqLi1NMTEyRY4ri4uIsyAq+CKmiaP78+VanAAAAAJRJRkaG14JIOjMhWEZGhhISEoKcFXwRUt3nAAAAgPIqPj5ePXv29Brr1auX4uPjg5sQfEZRBAAAAASAYRhKTEz0GktMTPRpFjRYg6IIAAAACADTNDVv3rxCxY9hGJo7dy6zz4UwiiIAAAAgAFyzzxUsfkzTZPa5EEdRBAAAAASAa0yRzWbz2G6z2RhTFOIoigAAAIAAMAxDEydOLHI7Y4pCF0URAAAAECCxsbFKSkpyF0CGYSgpKUlNmjSxODMUh6IIAAAACKDk5GTVqVNHklS3bl0lJSVZnBFKQlEEAAAABJDdbtekSZPUoEED3XfffbLb7VanhBJEWp0AAAAAUNH069dP/fr1szoN+IiWIgAAAABhjaIIAAAACLA1a9Zo5MiRWrNmjdWpwAcURQAAAEAA5ebmavr06dq/f7+mT5+u3Nxcq1NCCSiKAAAAgACaM2eOsrKyJElZWVlKSUmxOCOUhKIIAAAACJDMzEylpKTINE1JkmmaSklJUWZmpsWZoTgURQAAAEAAmKapGTNmFLndVSgh9FAUAQAAAAGQnp6ujRs3yuFweGx3OBzauHGj0tPTrUkMJaIoAgAAAAIgPj5ePXv2lM1m89hus9nUq1cvxcfHW5MYSkRRBAAAAASAYRiaOHFikdsNw7AgK/iCoggAAAAIkNjYWF199dUe266++mo1adLEoozgC4oiAAAAAGGNoggAAAAIkMzMTL3zzjse29555x2m5A5xFEUAAABAADAld/lFUQQAAAAEAFNyl18URQAAAEAAuKbkjojwPMWOiIhgSu4QR1EEAAAABIBr6u2C3eRM02RK7hBHUQQAAACcRYZhMJ4oxFEUAQAAAAHgmlChYPc5wzCYaCHEURQBAAAAAcBEC+UXRREAAAAQAK6JFmw2m8d2m83GRAshjqIIAAAACADXRAtOp9NjOxMthD6KIgAAAOAsMk2T8UQhjqIIAAAACADXRAveMNFCaKMoAgAAAALANdGCt3WKmGghtFEUAQAAAAEQHx+vTp06eY117tyZiRZCGEURAAAAcJbRdS60URQBQICtWbNGI0eO1Jo1a6xOBQAQROnp6UpLS/MaS0tLo/tcCKMoAoAAys3N1fTp07V//35Nnz5dubm5VqcEAAgS1ikqvyiKACCA5syZo6ysLElSVlaWUlJSLM4IABAsrnWKitrOOkWhi6IIAAIkMzNTKSkp7n7jpmkqJSVFmZmZFmcGAAiW2NhYJSUluQsgwzCUlJSkJk2aWJwZikNRBAABUNTaFK7tDLAFgPCRnJysOnXqSJLq1q2rpKQkizNCSSiKACAAXGtTOBwOj+0Oh4O1KQAgzNjtdl1yySWKiIjQiBEjZLfbrU4JJaAoAoAAYHAtAMAlNzdX8+fPl9Pp1Pz585l0pxygKAKAAGBwLQDA5fXXX9eJEyckSSdOnNB//vMfizNCSSiKACBAGFwLAMjMzNS7777rse2dd95h0p0QR1EEAAHE4FoACF+maWrKlCleY1OmTGHSnRBGUQQAAWS32zVp0iQ1aNBA9913H4NrASCM7N69Wzt27PAa27Fjh3bv3h3kjOCrSKsTAICKpl+/furXr5/VaQAAguy3334rMd68efMgZYPSoKUIAAAACIA///zTrzisQ1EEAAAABECXLl38isM6FEUAAABAAEREFH9qXVIc1uF/BgACbM2aNRo5cqTWrFljdSoAgCBKSEhQgwYNvMYaNmyohISEIGcEX1EUAUAA5ebmavr06dq/f7+mT5/OKuYAEEYcDof279/vNbZv3z45HI4gZwRfURQBQADNmTNHWVlZkqSsrCylpKRYnBEAIFhmzZrlVxzWoSgCgADJzMxUSkqKe3E+0zSVkpLCKuYAECYuvPBCv+KwDkURAASAaZqaMWNGkdtZxRwAKr7IyOKXAC0pDutQFAFAAKSnp2vjxo2F+os7HA5t3LhR6enp1iQGAAiapk2bFjnDXEREhJo2bRrkjOAriiIACID4+Hj17NlTNpvNY7vNZlOvXr0UHx9vTWIAgKBZt26dnE6n15jT6dS6deuCnBF8RVEEAAFgGIYmTpxY5HbDMCzICgAQTI0bN/YrDutQFAFAgMTGxiopKcldABmGoaSkJDVp0sTizAAAwZCQkKBmzZp5jTVv3px1ikIYRREABFBycrLq1KkjSapbt66SkpIszggAEEzVq1f3uj06OjrImaA0KIoAIIDsdrsmTZqkBg0a6L777pPdbrc6JQBAkKSnpystLc1rLC0tjUl3QhjzAgJAgPXr10/9+vWzOg0AQJDFxcUpJiZG2dnZhWIxMTGKi4uzICv4gpYiAAAAIAAyMjK8FkSSlJ2drYyMjCBnBF9RFAEAAAAB4FqeoeCMo4ZhsDxDiKMoAgAAAAKgqGUYIiIiWJ4hxFEUAQAAAAESGxurxMREj22JiYkszxDiKIoAAAAAhDWKIgAAACBAMjMz9c4773hse+edd5SZmWlRRvAFRREAAAAQAKZpasaMGTJN02O70+n0uh2hg6IIAAAACID09HRt3LhRTqfTY7vT6dTGjRtZvDWEURQBAAAAARAfH69OnTp5jXXu3JkpuUMYRREAAAAQICdPnvS6PTc3N8iZoDQoigAAAIAASE9P144dO7zGduzYQfe5EEZRBAAAAARASRMpMNFC6KIoAoAAW7NmjUaOHKk1a9ZYnQoAIIgSEhKKHVOUkJAQ5IzgK4oiAAig3NxcTZ8+Xfv379f06dPpQw4AYcQwDD344IMyDMOn7QgdFEUAEEBz5sxRVlaWJCkrK0spKSkWZwQACKbY2Fhdc801HtvGjx+vJk2aWJQRfEFRBAABkpmZqZSUFHefcdM0lZKSwirmABBmEhMTPe5fffXVFmUCX1EUAUAAuFYxL2o7g2sBIHzMmzfP4/4777xjUSbwFUURAASAaxVzh8Phsd3hcLCKOQCEkczMTM2dO9dj29y5c+k1EOJCtih68sknZRiG7r77bqtTAYASsYo5AMA0TT366KOFegc4nU6v2xE6QrIo+uabb/T666+rY8eOVqcCAH7jSxAAwkN6errS0tK8xtLS0ug1EMJCrijKyclRYmKi/vvf/6pmzZpWpwMAPuGLEADgdDr9isM6IVcU3XbbbRoxYoQuuuiiEvc9efKkjh496nEDACvEx8erZ8+eiojw/FiNiIhQr1696D4HAGGg4HdAaeOwTkj9z8yfP1/ffvutnnjiCZ/2f+KJJxQTE+O+xcbGnuUMAcA7wzA0ceLEQgvzRUREeN0OAKh44uPjde6553qNtWnThgtkISxkiqLMzEzdddddmjdvnux2u0+PeeCBB5Sdne2+MasHACvFxsYqKSnJXQAZhqGkpCQW7AMAML40xIVMUbRlyxb9+eef6tq1qyIjIxUZGalVq1bphRdeUGRkZKFpbiUpKipKNWrU8LgBgJWSk5NVp04dSVLdunWVlJRkcUYAgGBJT0/Xjh07vMZ27NjB+NIQFjJF0aBBg7Rt2zalpqa6b926dVNiYqJSU1Nls9msThEASmS323XJJZcoIiJCI0aM8LnlGwBQ/pXUGkRrUeiKtDoBl+rVq6t9+/Ye26pVq6batWsX2g4AoSo3N1dLly6V0+nU0qVLlZycTGEEAGEiPj5e0dHRysnJKRSrXr06Y4pCWMi0FAFARTBnzhxlZWVJkrKyspSSkmJxRgCAYMnIyPBaEEnSsWPHlJGREeSM4KuQLopWrlypmTNnWp0GAPgkMzNTKSkp7u4RpmkqJSWFSWAAIEww+1z5FdJFEQCUF6ZpasaMGUVupx85AFR8pmnqt99+8xrLzMzkuyCEURQBQACkp6dr48aNhWbKdDgc2rhxIzMOAUAYWLduXZHd53JycrRu3bogZwRfURQBQADEx8erZ8+ehWbKtNls6tWrF10mACAM9OrVy684rENRBAABYBiGJk6cWOR214KuAICKa8OGDX7FYR2KIgAIkNjY2EJLCLRv315NmjSxKCMAQDD17t1bERHeT68jIiLUu3fvIGcEX1EUAUCAZGZmatu2bR7btm3bxuxzABAm0tPT5XQ6vcacTifjS0MYRREABIBrljlv3eSYfQ4AwsO+ffv8isM6FEUAEADMPgcA6NOnj6pWreo1Vq1aNfXp0yfIGcFXFEUAEADMPgcAMAxDtWvX9hqrVasWk+6EMIoiAAgAZp8DAOzZs6fIcaSZmZnas2dPkDOCryiKACBAYmNjlZSU5C6ADMNQUlISs88BQJjYu3evX3FYh6IIAAIoOTlZderUkSTVrVtXSUlJFmcEAAiWhg0b+hWHdSiKACCA7Ha7Jk2apAYNGui+++6T3W63OiUAQJAw+1z5RVEEAAAABECfPn2KXbyV2edCF0URAARQbm6upk+frv3792v69OnKzc21OiUAQJBkZGQUu3hrRkZGkDOCryiKACCA5syZo6ysLElSVlaWUlJSLM4IABAsv/32m19xWIeiCAACJDMzUykpKTJNU5JkmqZSUlKKnJ4VAFCxlDTbKLORhi6KIgAIANM0NWPGjCK3uwolAEDF1bRpU7/isA5FEQAEQHp6ujZu3CiHw+Gx3eFwaOPGjUpPT7cmMQBA0Hz44Yd+xWEdiiIACID4+Hh16tTJa6xz586Kj48PbkKAxdasWaORI0dqzZo1VqcCBM3IkSMVGRnpNRYZGamRI0cGNyH4jKIIAM4yus4h3DALI8KVzWbTAw884DX2r3/9SzabLcgZwVcURQAQAOnp6UpLS/MaS0tLo/scwgqzMCKcdezY0ev29u3bBzkTlAZFEQAEQHx8vHr27Flo0b6IiAj16tWL7nMIG8zCiHBW1KQ7kph0J8RRFAFAABiGoYkTJ8owDI/tERERXrcDFRGzMCLcuSbd8YZJd0IbRREABEhsbKySkpLcBZBhGEpKSmJdCoQNZmFEuIuLi1NMTIzXWExMjOLi4oKcEXxFUQQAATR27FiPomjMmDEWZwQEj6sbacGWUcMw6EaKsJCRkaHs7GyvsezsbGVkZAQ5I/iKoggAAmjBggVyOp2SJKfTqYULF1qcERA8hmEoMTGxUDc50zSVmJhIN1JUeFwYKL8oigAgQFwDzPNjgDnCiWmamjdvntfY3LlzGVOECs81vtQbxpeGNooiAAgABpgDDDIHXLy1lvI9ENooigAgABhgDpwZZB4dHe01Fh0dzSBzVHimaWrKlCleY1OmTKEwCmEURQAQAK5+5AVXK7fZbPQjR9hIT09XTk6O11hOTg4XB1Dh7d69Wzt27PAa27Fjh3bv3h3kjOAriiIACICi+pEXtX4RAKDi+f333/2KwzoURQAQIKxTBADhrVGjRn7FYR2KIgAIoOTkZNWpU0eSVLduXSUlJVmcERA8cXFxiojwfmoRERHBmCJUeCX1CqDXQOiiKAKAALLb7Zo0aZIaNGig++67T3a73eqUgKBZv369e52ugpxOp9avXx/kjIDgSkhIUKdOnbzGOnfurISEhCBnBF9RFAFAgPXr109LlixRv379rE4FCKqGDRv6FQfKO8MwdMMNN3iN3XDDDbQUhTCKIgAAEBDNmjXTueee6zXWpk0bNWvWLMgZAcFlmqZmzpzpNfbcc88xJXcIoygCAAABYRiGpk2b5jU2bdo0rpKjwtu1a1eR027v3r1bu3btCnJG8BVFEQAACJjY2FiNGzfOY9vVV1/NLIwIC6mpqX7FYR2KIgAIsDVr1mjkyJFas2aN1akAlkhOTva4zyyMCBedO3f2Kw7rUBQBQADl5uZq+vTp2r9/v6ZPn67c3FyrUwKCbsGCBR7rdS1cuNDijIDgKGpKel/jsA7/MwAQQHPmzFFWVpYkKSsrSykpKRZnBARXZmamUlJS3APKTdNUSkqKMjMzLc4MAIpGUQQAAcLJIMKdaZqaMWNGkduZeQsVXUmvcd4DoYuiCAACwHXSV3DhSofDwckgwkZ6ero2btwoh8Phsd3hcGjjxo1KT0+3JjEgSOg+V37xPwMAAeA6GSxY/JimyckgwkZ8fLx69uwpm83msd1ms6lXr16Kj4+3JjEgSOLj44tdq4v3QOiKLMuD8vLy9O233+qHH35w952vU6eO2rZtq65duyoyskyHBYByKy4uTjExMcrOzi4Ui4mJUVxcnAVZAcFlGIYmTpyosWPHFopNnDiRdYoQ1ugxENpKVb188skneuutt/Txxx8XOaOS3W7XpZdequTkZA0bNiwgSQJAqMvIyPBaEElSdna2MjIylJCQEOSsgOCLjY1VmzZt9P3337u3tWnThnWKEBb27NmjHTt2eI3t2LFDe/bsUbNmzYKcFXzhU/e5999/Xx07dtSll16q999/XydOnJBpml5vJ06c0KJFi3TJJZeoU6dOWrx48dn+GwDAcvHx8erUqZPXWOfOnekygbCRmZnpURBJ0vbt25lwBGGBiRbKL59aiq688kr3zzVr1lT//v3VtWtXtWjRQjVr1pRpmjp8+LB++eUXbd26VatWrdLhw4e1bds2XXnllcrLyztrfwAAhDq+BBEuTNPUlClTvMamTJmi2bNn04UOFdrvv/9eYrx58+ZBygal4VNRFBERodGjR+vGG2/UwIEDSxwzlJeXpxUrVui///0vLUUAwkJ6errS0tK8xtLS0pSenk73OVR4dB1CuOvdu7dfcVjHp6Jo9+7datq0qe8HjYzUxRdfrIsvvli//vprmZMDgPLCNevW5s2bPaYjttls6t69O93nEBZ+++23EuMURajINmzYUGK8b9++QcoGpeHTmKLSFESBfCwAlBeuWbeK2k6XIYSDRo0a+RUHyrs+ffooOjraayw6Olp9+vQJckbwVZnXKXI6nR795Dds2KDJkyfrnnvu0fr16wOSHACUJ7GxsUpKSnIXQIZhKCkpiVm3EDb27dvnVxwo7wzDUL169bzG6tevzwWyEFamomjy5MmqVKmSe6alL7/8Uv369dPTTz+tF154QRdccIFWrFgR0EQBoDxITk5WnTp1JEl169ZVUlKSxRkBwUNLEcJdenq6du/e7TW2a9cuFvIOYWUqitauXSvTNDVq1ChJ0iuvvCKHw+GeltvhcGj69OkBTRQAygO73a6OHTtKkjp06CC73W5xRkDwxMfHKyLC+6lFREQEY+tQ4TEld/lVpqLol19+kWEYat++vaQzRZJhGHrvvff097//XZK0efPmwGUJAOXEkSNH3C3lK1as0JEjR6xNCAii9evXy+l0eo05nU6616PCS0hIKHbNOmYhDV1lKooOHTok6UzXkEOHDunPP/9UTEyMLr/8co0dO1aSilzZHQAqsvvvv999Uuh0OjV58mSLMwKCp0+fPoqJifEai4mJYZA5KjzDMPTggw96jT344IOMKQphZSqKqlSpIknatGmTVq5cKUk699xzJUnHjx+XJNWoUSMA6QFA+bFp06ZCaxWlpqZq06ZNFmUEBFdERISmTZvmNfbYY48V2bUOCAd0nQttZfp06tChgyTpgQce0JVXXinDMNSzZ09Jcq9L1Lhx4wClCAChz+l0asqUKV5jU6ZMKbJLEVDR9OjRw32e4NKxY0d169bNooyA4DFNUzNmzPAamzFjBoVRCCtTUXT33XfLMAz3xAqVK1fWTTfdJEn6+OOPJUnnn39+4LIEgBC3bt26IrsNZ2dna926dUHOCLCOa8xxUfeBiio9PV0bN270Gtu4cSOzz4WwyLI8aPTo0frqq6/0v//9T5UqVVJiYqLatGkjSRowYIDOP/98DR8+PKCJAkAoc42l8FYYMZYC4SQzM1MLFy702LZw4UKNGjVKsbGxFmUFBEfTpk0VGRmpvLy8QrHIyEg1bdrUgqzgi1IXRSdOnNCiRYskSVdddZXOO+88j/ikSZMCkxkAlCOusRR33nlnoRhjKRAuXF2HCnYXdTgcmjFjhmbOnMlAc1Ro69ev91oQSVJeXp7Wr1+vvn37Bjkr+KLU39JVqlTRjTfeqOuuu44mQADIp0ePHoVWMq9fvz5jKRA2XF2HCo6bME2TrkMIC71791ZkpPc2h8jISPXu3TvIGcFXZbp06ZpjvXLlygFNBgDKs8zMTB08eNBj28GDB5WZmWlRRkBwxcXFqWrVql5j1apVU1xcXJAzAoLr119/LbalyDUhGUJPmYqiCRMmyDRNvfbaa8yoBAAqesYh13ZmHEI4SE9P119//eU1dvz4cVqKUOHFx8e7Z2QuqFevXoqPjw9uQvBZmSZa2L9/v5o1a6Zly5apRYsWGjp0qOrXr1+on/BDDz0UkCQBINQVNeOQw+FwdxtiJXNUdHv37i0x3qxZsyBlAwSfYRgaPHiw1++DwYMHM6YuhBlmGS5fRkRE+PSf6nA4ypRUWR09etQ9+5O/i8eeOHFCAwcOlCTVqjNIhlGm+hEokWnm6VDWl5KkFStWuBdHRvlimqbuvvtubd682eOzz2azqXv37nruuef4MkSF53A41K9fP6+9SCIiIrRmzRrZbDYLMgOCw+l0avDgwcrJySkUi46O1vLly5l4J4hKUxuU+X/FtUZRUTcACCeGYWjixIlFbqcgQjj49ddfi+xW73Q6GU+BCm/t2rVeCyJJysnJ0dq1a4OcEXxVpuaP2bNnBzoPACj3YmNjdfXVV+vtt992b7v66qvVpEkTC7MCgqeki6JcNEVF17hxY7/isE6ZiqLk5ORA5wEAAMq5hIQEderUSWlpaYVinTt3ZlwdKryEhAQ1a9ZMu3fvLhRr3rw574EQRqdGAAiQzMxMvfPOOx7b3nnnHabkRtgwDEMPPvhgoe6iRW0HwgktpaGtTC1FvswcYxiGdu3aVZbDA0C5U9KU3DNnzuSEEGEhNjZW11xzjUc30vHjx9ONFGFh9+7dXluJ8seaN28e5KzgizIVRenp6UV+ubuqYL78AYQTpuQG/s8NN9yg9957TydOnFDVqlV1/fXXW50SEBS///57iXGKotAU8NnnKIYAhCPXgn0Fpxu22Wws2IewVKlSJY9/gXDQqFEjv+KwTpmKIqfTWeh24MABpaSkqHr16mrbtq0OHDgQ6FwBIGS5pt4uOB2xaZpMyY2wM2fOHB07dkzSmXVCUlJSLM4ICI5mzZopNjbWa6xp06YsXhzCAjbRQu3atXXNNddo8uTJ+uGHH7yu1wEA4Ya12xBuMjMzlZKS4n7dm6aplJQUJhxBWDBNUwcPHvQay8rK4vsghAV89jnXlaElS5YE+tAAELJcEyp4m3VrxowZfBEiLJQ04QjvA1R0a9eu1V9//eU19tdff7F4awgr00QL3gZMOhwO7d+/X1999ZUkKS8vz7/MAKAcKWqiBafTyUQLCBtMOIJwx+Kt5VeZiqK33nqrxP7xw4cPL1NCAFAeuSZa8HZCyEQLCBeu98GmTZs8WoUMw1DPnj15H6DCi4uL8ysO65yV2efGjRunV155JZB5AkBIMwxDgwcP9hq7+OKLmWgBYcEwDCUmJhbqJmeaphITE3kfoMJbvXq1X3FYp0wtRStWrCi0zTAMxcTEKCEhQTVq1PA7MQAoT5xOp1544QWvsRdeeEHDhg1TRETAh3ECIcU0Tc2bN89rbO7cuerWrRuFESq0zz//vMT4wIEDg5QNSqNMRVH//v0DnQcAlGvr1q1Tdna211h2drbWrVunvn37BjkrILiKGlMkiTFFCAuPPPKIe3x9UXGEJr8uW37zzTeaMGGCLrnkEl1yySWaMGGCvvnmmzIf79VXX1XHjh1Vo0YN1ahRQ71799ann37qT4oAEBR9+vRRTEyM11hMTIz69OkT5IyA4IuLiyv2fcB4ClR0kZGRhRbxdrHZbIqMLFN7BIKgzEXRAw88oF69eun555/Xp59+qk8//VTPP/+8evXqpX/9619lOmaTJk305JNPasuWLdq8ebMuvPBCXXbZZfr+++/LmiYABEVERITuvPNOr7G7776brnMICxkZGcW2mGZkZAQ5IyC41q5dK4fD4TXmcDiYkjuElelb+r333tNTTz0lyfuEC08++aTef//9Uh/30ksv1fDhw9WyZUu1atVKjz32mKKjo7Vhw4aypAkAQWOappYvX+41tmzZMtZnQVhwzT7nbb0uZmFEOChpSRqWrAldZSqKXn75ZUlSVFSU7r33Xi1YsEALFy7UvffeqypVqsg0Tb300kt+JeZwODR//nwdP35cvXv39rrPyZMndfToUY8bAFjBl7EUQEVnGIYmTpxY5HYmWUBFd+DAAb/isE6ZOjampqbKMAw98cQTuuuuu9zbr7jiCjVp0kT33HOPUlNTy5TQtm3b1Lt3b+Xm5io6OlqLFy9W27Ztve77xBNPaOrUqWX6PQAQSK4r5Js3b/boOmGz2dS9e3eukCNsxMbGqmPHjkpLS3Nv69ixo5o0aWJhVgBQvDK1FJ04cUKS1KJFi0Ix1zbXPqXVunVrpaamauPGjbrllluUnJysH374weu+DzzwgLKzs923zMzMMv1OAPAXV8iBMzIzM7V9+3aPbdu3b+c7GmFh9OjRRX7eG4ah0aNHBzkj+KpMRZHras9zzz2nw4cPu7cfPnxYzz33nMc+pVW5cmW1aNFC5513np544gl16tRJzz//vNd9o6Ki3DPVuW4AYJXY2FglJSW5vxANw1BSUhJXyBE2TNPUjBkzitzO2DpUdDabTbfeeqvX2O23317kzHSwXpmKouHDh8s0Ta1YsUKNGzdWhw4d1KFDBzVu3FgrVqyQYRgaMWJEQBJ0Op06efJkQI4FAGdbcnKy6tSpI0mqW7eukpKSLM4ICB7X2LqCs285HA7G1iEsmKapr7/+2mts9erVXBgIYWUqiv71r3+pfv36Mk1Tubm5+uGHH/TDDz8oNzdXpmmqQYMG+uc//1nq4z7wwANavXq10tPTtW3bNj3wwANauXKlEhMTy5ImAASd3W7XpEmT1KBBA913332y2+1WpwQEjWtsXcGr4TabjdnnEBbS09M9xtPll5aWxoWBEFamoqh+/fpav369hgwZIsMw3FNxG4ahoUOH6uuvv1b9+vVLfdw///xTSUlJat26tQYNGqRvvvlGn332mS6++OKypAkAlujXr5+WLFmifv36WZ0KEFSuMXROp9Nju2majK1DWCipJYiWotBV5mV14+Pj9emnn+rw4cPauXOnpDOTLNSqVavMycyaNavMjwUAAKHJdfEUqOhiY2P9isM6fi+xXrNmTfXo0UM9evTwqyACAADlm2tCBW+LtzLRAsLB7Nmz/YrDOmVuKdq5c6feeOMN/fLLLzpy5EihDzrDMPTll1/6nSAAACgfilrE2Ol0uidaSEhIsCAzIDiuu+46vfnmm8XGEZrKVBQtWrRIV199daE+wy6u8UUAACB8uCZa2LRpk8fFUsMw1LNnTyZaQIX322+/lRjnwkBoKlP3uQcffFAOh8PdR7jgDQAAhB/DMJSYmFjoXMA0TSUmJnLBFBUeY4rKrzIVRRkZGTIMQxdddJE2bNignTt3as+ePR633bt3BzpXAAAQwkzT1Lx587zG5s6dy4VTVHhLlizxKw7rlKn7XKtWrfT999/rnnvuUY8ePQKdEwAAKIeKGlMkiTFFCAtdunTxKw7rlKml6KGHHpJpmlS7AADALS4uTjExMV5jMTExiouLC3JGQHA1a9ZMDRo08Bpr2LChmjVrFuSM4CufWor+/e9/F9p27rnn6o033tA333yj/v37q2bNmoX2eeihh/zPEAAAlAsZGRnKzs72GsvOzlZGRgYtRajQTNNUTk6O19ixY8eYjCyE+VQUPfLII0X+B6alpSktLc1rjKIIAIDwwexzCHfr1q0rsijKycnRunXr1Ldv3yBnBV/43H2uqJnmmIEOAABIZ4qfiRMnFrmdK+So6Eoaa89Y/NDlU0sRq+8CAICy4oIpwkVxC7e64jfffHOQskFp+FQUJScnn+08AABAOWeapmbMmCHDMAp1n5sxY4ZmzpxJaxEqtHPPPdevOKxTptnnAABFW7NmjUaOHKk1a9ZYnQoQVK4puZ1Op8d2p9PpnpIbqMhYvLX88qkoGjJkiJYtW1bqg3/22WcaMmRIqR8HAOVVbm6upk+frv3792v69OnKzc21OiUgaFwTLRRsDTIMQ7169WKiBVR4ERHFn1qXFId1fPqf+fzzzzVixAjFxsbqzjvv1NKlS/X7778X2m/v3r363//+p7vuuktNmzbV8OHD9cUXXwQ8aQAIVXPmzFFWVpYkKSsrSykpKRZnBASPYRhKTEwsNH7INE0lJibSdQ4VXlxcnKKjo73GqlevzlpdIcynMUXDhg3Tp59+qr179+rll1/Wyy+/LEmKiopSzZo1ZZqmDh8+rFOnTrkf4/pAHD58+FlIGwBCT2ZmplJSUtyff6ZpKiUlRcOGDaPLBMKCaZqaN2+e1zFFc+fOVbdu3SiMUKFlZGQUu04Ra3WFLp9aij7++GOtX79el19+uSpVquSeRSY3N1f79u3T/v37dfLkSff2SpUq6YorrtCGDRu0dOnSs/03AIDlXAPMi9rOzFsIB64xRd5aihhThHDg6kLqDV1IQ5tPLUWS1LNnT7333ns6dOiQPv74Y61du1Y//viju5tInTp11KZNG51//vm65JJLVLNmzbOWNACEGtfJYEEOh8N9MsjVQVR0rhPCb775xmOyhYiICPXo0YMTQlR4hmGoRYsWXr8PWrRoQUtpCDPMCnT58ujRo4qJiVF2drZq1Kjh17FOnDihgQMHSpJq1Rkkw/C5fgRKxTTzdCjrS0nSihUrVKVKFYszQlmYpqm7775bmzdvlsPhcG+32Wzq3r27nnvuOb4MERYyMzM1ZsyYQt3nFi1apCZNmliYGXD25eXlqW/fvkXGv/76a0VGck4ZLKWpDZgCAwACwDAMTZw4scjtFEQIZwXHGAEVlS+LtyI0URQBQIDExsbq6quv9th29dVXc3UcYcM1hq7gtMOuxVspjFDRXXfddX7FYR2KIgAAEBCusXX5u5BKnmPrgIosMzPTrzisQ1EEAAGSmZmpd955x2PbO++8w5cgwoZrogWbzeax3WazMfMWwsLevXv9isM6FEUAEABMyQ0wtg7o3bu3X3FYh6IIAAKAbkPAGbGxsUpKSnIXQIZhKCkpibF1CAvr16/3Kw7rBKQo+v3337Vz585AHAoAyiW6DQH/Jzk5WdWrV5ck1ahRQ0lJSRZnBARHo0aN/IrDOmUuirKzs3XbbbepVq1aio2NVZs2bZSbm6vBgwdr0KBB2rFjRyDzBICQRrchwDu6jiKclPR65/0QuspUFB05ckS9e/fWa6+9piNHjsg0TZmmKbvdLrvdrpUrV2rBggWBzhUAQhrdhoAz5syZo2PHjkmSjh07ppSUFIszAoIjNTXVrzisU6aiaNq0adqxY4dM01TVqlU9YhdeeKFM09SyZcsCkiAAlCdjx471KIrGjBljcUZAcGVmZiolJcV9Rdw0TaWkpDALI8LCJZdc4lcc1ilTUbR48WIZhqHrr7++UPGTkJAgScrIyPA/OwAoZxYsWCCn0ylJcjqdWrhwocUZAcHDLIwIdy+88IJfcVinTEWRa471q666qlA/eVfL0cGDB/1MDQDKF9cV8vy4Qo5wwiyMCHeXXXaZX3FYp0xFUUxMjCR5nXHONdVg7dq1/UgLAMoXrpADZ2Zh7NSpk9dY586dmYURFV7lypX9isM6ZSqKevfuLdM09cADD2j27Nnu7f/+97/1xBNPyDAMnX/++QFLEgBCHVfIgTNOnjzpdXtubm6QMwGCLzY21q84rFOmomjixImKiIjQsWPHNHv2bHcXuqlTp+rkyZOKiIjQhAkTApooAIQy1ikCzlwcKGpJjh07dnBxABXekiVL/IrDOmUqivr166fXXntNlStXdk/H7bpFRUXptddeU+/evQOdKwCELNd6RN66ybFOEcIFa7Qg3HXu3NmvOKwTWdYH3njjjRo+fLgWLVqkn3/+WZLUqlUrXXHFFWrcuHHAEgSA8iI2NlYdOnRQWlqae1uHDh1YpwhhIyEhQZ06dfJ4D7h07tzZPUMtUFFFRBTf3lBSHNYpc1EkSY0aNdJdd90VqFwAoFzLzMzU9u3bPbZt375dmZmZ9CNHWDAMQw8++KDGjBnj0Srk2k6LKSq6pk2b+hWHdfwqio4dO6b09HQdOXLEa5P4BRdc4M/hAaDccM0y51qjyMXhcGjGjBmaOXMmJ4QIC7Gxsbrqqqv07rvvureNGzeOFlOEhQ8//LDE+OjRo4OUDUqjTEXRkSNHdOedd2r+/PmFZlpyMQxDeXl5fiUHAOWFa/a5gkzTdM8+R9chhIuC3/+cDyBcXHrppXr66aeLjSM0lalj480336y5c+cqLy+v0EQL+W8AEC7i4uLca7gVFBMTo7i4uCBnBFgjMzNTixYt8ti2aNEiFjFGWPjoo4/8isM6ZWop+uSTT2QYhux2uy688ELVqVMn0HkBQLmSkZGh7Oxsr7Hs7GxlZGTQUoQKzzRNTZkypcjt+ZfxACoiZp8rv8pUFEVFRen48eN68803NXbs2EDnBADljmudok2bNhUaYN6zZ0/WKUJY2LNnT7HrFO3Zs0fNmjULclYAULIydZ+7+uqrZZqm9u3bF+h8AKBccq1TVPAqeEREBOsUIWzs3bvXrzhQ3m3dutWvOKxTppai6dOnKzMzU/fff79SU1PVrVs31ahRo9B+SUlJficIAOVFbGyskpOTNXv2bPe25ORkZt1C2OjTp49sNpvXSZhsNpv69OljQVZA8NB9rvwqU1F04MAB7d69W6dPn9bbb7+tt99+u9A+hmFQFAEIO8nJyVq6dKkOHDigevXq8TmIsPLrr78WOSutw+HQr7/+ytg6VGj79+8vMd6yZcsgZYPSKFNR9I9//EPfffedDMNgljkAyMdut2vSpEl65plndO+998put1udEhA0rrF13qan79WrF2PrEHCmaSo3N9fqNNxq1qxZYvzEiRNByqZkdrud7t3/X5mKolWrVskwDDVq1EgjR45UrVq1FBFRpuFJAFDh9OvXT/369bM6DSDoDMNQYmKi16IoMTGRky8EXG5urgYOHGh1Gj678cYbrU7Bw4oVK1SlShWr0wgJZSqK6tSpo8zMTL366qu65JJLAp0TAJRra9ascbcUURwhnJimqVmzZnmNvfHGG+rWrRuFEYCQVKaiaNKkSbr99tu1bt06iiIAyCc3N1fTp0/XgQMHNH36dHXv3p0udAgbe/bsUVpamtdYWloaU3Ij4Ox2u1asWGF1Gm5Op1OXXnqp/vrrr0KxqlWr6n//+19I9a7i++n/lHmihebNm+upp57SmjVr1KNHD68ruT/00EN+JwgA5cmcOXOUlZUlScrKylJKSopuuukmi7MCgIrJMIyQ6/715JNP6s477yy0ffr06apWrZoFGcEXZSqKpk6d6m7+XrdundatW+d1P4oiAOEkMzNTKSkp7gloTNNUSkqKhg0bptjYWIuzAwAEQ48ePdSmTRv9+OOP7m3t27dXt27dLMwKJSlz+51pmsXeACCcmKapGTNmFLmdz0WEg4SEBHXq1MlrrHPnzkzHjbDx+OOPe9z39v2A0FKmlqL8CxMCAKT09HSvM245HA5t3LhR6enpnBCiwjMMQw8++KCuvPJKr9uZZAHh4pxzznH/fM0113jcR2gqU1GUnJwc6DwAoFxzrc+yadMmj1YhwzDUs2dP1mdB2IiNjdX48eM9FnYfP368mjRpYmFWgHVuuOEGq1OAD0Jn+gsAKMdc67MU7CZnmibrsyDs5H/NG4ahq6++2uKMAKB4PrUUJSQkKCIiQosWLVLXrl19mk7TMAzt2rXL7wQBoDwwTVPz5s2TYRiFWormzp3L+iwIKwsWLPC4v3DhQmZhBBDSfCqKMjIyZBiGcnNzJZ3pO1/cl7tpmnz5AwgrRY0pMk2TMUUIK8zCCKA88rn7nLcuIcw8BwBnuMYU2Ww2j+02m029evViTBHCArMwAiivfCqKnE6nnE6n+vTp43G/uJvD4TiriQNAKDEMQxMnTvR60jdx4kRazxEWXC2mBc8B8s/CCAChyOeWon//+9/697//rd9+++1s5gMA5VZsbKw6dOjgsa1Dhw7MuoWwQYspgPLK56LokUce0dSpU/Xrr7+ezXwAoNzKzMzU9u3bPbZt375dmZmZFmUEBJerxbSo7bSYAghVTMkNAAHAWArgjNjYWCUlJXlMyZ2UlESLKYCQRlEEAAHAWArg/yQnJ6tOnTqSpLp16yopKcnijACgeD5NyZ3fm2++qS+++MKnfR966KFSJwQA5ZFrLMXmzZs9CiObzabu3bszlgJhxW63a9KkSXrmmWd07733ym63W50SABSr1EXR7Nmzfd6XoghAuHCNmbjqqqu8bmcsBcJNv3791K9fP6vTAACflLr7XHHrE7FWEYBwxlgKAADKp1K3FA0bNkz16tU7G7kAQLmXnJys999/X0ePHlWNGjUYSwEAQDlQ6qLoX//6l3sRVwBA0Wg1Rzh7/fXXNWfOHCUnJ+sf//iH1ekAQLGYfQ4AAmjOnDk6duyYJOnYsWNKSUmxOCMg+I4cOaI5c+bI6XRqzpw5OnLkiNUpAUCxKIoAIEAyMzOVkpLibiEyTVMpKSks3oqwc//998vpdEqSnE6nJk+ebHFGAFA8n4uipKQkJSUlqX79+mczHwAol1yLtLpOBF0cDgeLtyKsbNq0SWlpaR7bUlNTtWnTJosyAoCS+Tym6K233jqLaQBA+eZavLUg0zTdi7cmJCRYkBkQPE6nU1OmTPEamzJlij799FNFRNBJBUDo4ZMJAAIgLi5OMTExXmMxMTGKi4sLckZA8K1bt07Z2dleY9nZ2Vq3bl2QMwIA31AUAUAAZGRkFHsymJGREeSMgODr06ePoqOjvcaio6OZvRZAyKIoAoAAiI+PV8+ePb3GevXqpfj4+OAmBFjAMIwiFyuOjY11L2wMAKGGoggAAsAwDE2cONFrbOLEiZwMIiykp6drx44dXmM//vij0tPTg5sQAPioTEWRw+EIdB4AUCEULH4iIiKYeQ5hw9ViWnAyBZvNRospgJBWpqKoQYMGuvXWW7V69epA5wMA5ZJrSm5vLUJMyY1w4WoxLfg+KGo7AISKMhVFBw8e1Ouvv66BAwcqNjZW9913n7799ttA5wYA5YZrSu6C6xQ5nU73lNxAOIiNjdWYMWM8to0ZM6bIsUYAEArKVBTVrl1bpmnKNE3t3btXzz77rLp3767WrVtr6tSp+umnnwKdJwCEtPj4eHXq1MlrrHPnznQbQljZvn17sfcBINSUqSj6448/tHr1ak2cOFGtW7d2F0g7d+7Uv//9b7Vt21Zdu3bVM888o99//z3QOQNAuULXOYSTTZs2adu2bR7bvvvuO23atMmijACgZGUqiiIiItS3b19Nnz5dP/74o37++Wc9/fTT6tq1q7tASktL06RJkxQfH69bbrlFubm5JR73iSeeUPfu3VW9enXVq1dPI0eOpNUJQLmQnp6utLQ0r7G0tDS6zyEsOJ1OTZkyxWtsypQphbqXAkCo8HtKbofDoZ9++kmbN2/Wjh07ZBiGDMNwF0d5eXn6z3/+o3vvvbfEY61atUq33XabNmzYoM8//1ynT5/W4MGDdfz4cX/TBICzKi4uTjExMV5jMTExiouLC3JGQPCtW7eu2EWM161bF+SMAMA3ZS6K1q1bp9tuu00NGzbU3/72Ny1cuFB//fWXTNNU/fr1df/992vVqlUaO3asTNPUe++9V+Ixly1bpmuvvVbt2rVTp06d9NZbb+nXX3/Vli1bypomAARFRkZGsSeDGRkZQc4ICL4+ffoUe3GgT58+Qc4IAHwTWZYHNWvWzP0F7+orHxkZqeHDh+uGG27Q8OHDZbPZJEmtW7fWggULlJWVVerf4zrBqFWrltf4yZMndfLkSff9o0ePlvp3AEAguNZn2bhxY6EY67MgXEREROjOO+/UtGnTCsXuuuuuQusXAUCoKNOnU3p6urt7XKtWrfTkk08qMzNTS5Ys0aWXXuouiCSpRo0auuCCC3TBBReU6nc4nU7dfffdOv/889W+fXuv+zzxxBOKiYlx32JjY8vy5wCA31zrsHjD+iwIF6Zpavny5V5jn332GZOOAAhZZWopqlKlisaMGaMbbrhBffv2LXZfu92ulStXlvp33Hbbbdq+fbu+/vrrIvd54IEHNGHCBPf9o0ePUhgBCDmcCCJcuNbr8sa1XldCQkKQswKAkpWpKPrjjz8UHR0d6Fzcbr/9di1dulSrV68udrG3qKgoRUVFnbU8AMBXpmnq0Ucf9Rp79NFH9dprr9FahArP1Y1006ZNHhcDDMNQz5496UYKIGT5VBStXr26TAcvbZc50zR1xx13aPHixVq5ciVXkwCUG3v27Cl2Su49e/aoWbNmQc4KCC7DMJSYmFiotcg0TSUmJnJhAEDI8qkoGjBgQKk/yAzDUF5eXqkec9ttt+mdd97Rhx9+qOrVq2v//v2SzsxYU6VKlVIdCwCCqaTPSE4GEQ5M09S8efPcS3O4GIahuXPnqlu3brwXAIQknydacE2sUJpbab366qvKzs7WgAED1LBhQ/dtwYIFpT4WAARTfHy8OnXq5DXWuXNnug0hLLjGFBU8BzBN0z2mCABCkU8tRcnJyWc7D0kMRgZQfhmGoQcffFBjxozx+CyLiIjQgw8+yNVxhAXXmKLNmzfL4XC4t9tsNnXv3p2LAwBClk9F0ezZs892HgBQ7sXGxqp9+/batm2be1v79u2LnTAGqEhcU9NfddVVXrdzcQBAqPJ7FbW9e/dqzZo1WrNmjfbu3RuInACgXMrMzNT27ds9tm3fvl2ZmZkWZQQEX2xsrJKSktwFkGEYSkpK4uIAgJBW5qJo7dq16t69u5o2baoBAwZowIABatq0qbp37641a9YEMkcACHmmaWrGjBlFbqd7MMJJcnKy6tSpI0mqW7eukpKSLM4IAIpXpqLoiy++0KBBg/Ttt98Wmlxhy5Ytuuiii/TFF18EOlcACFkMMAf+j91u1yWXXKKIiAiNGDFCdrvd6pQAoFhlKoomT56sU6dOyTRNVa1aVV26dFHXrl1VrVo1SdLp06f1wAMPBDRRAAhlcXFxiomJ8RqLiYlRXFxckDMCrJObm6ulS5fK6XRq6dKlys3NtTolACiWTxMtFPT999/LMAwNGTJE8+fPV40aNSRJ2dnZGjdunJYtW1aoX315k/9qr2k6itkT8E/+1xddrMqvjIwMZWdne41lZ2crIyODBakRNubMmaOsrCxJUlZWllJSUnTTTTdZnBUAFK1MRVGjRo2Unp6uO+64w10QSWeuht5xxx1atmyZYmNjA5akFfJf1Tp8cKV1iSCs5ObmqmrVqlangTKIi4tTdHS0cnJyCsWqV69OSxHCRmZmplJSUtwXeUzTVEpKioYNG1buzw0AVFxl6j53++23yzRNbd26tVDMte2ee+7xLzMAKEcyMjK8FkSSdOzYMWVkZAQ5IyD4XBOLOJ1Oj+0Oh4MJRwCEtDK1FNWsWVOtW7fWI488oh07dqhnz56SpE2bNundd99Vp06dVK1aNaWkpHg8rjzNPpN/UGjN2gNkGDYLs0FFZpoOd2skg5HLr5JO9jgZRDhwTThSUP4JR+hGCiAUlakouv7662UYhkzT1Lx58zRv3jx3zDRNfffdd7ruuus8HuNap6C8yL/AnGHYZBhleqqAUmFhw/IrPj6+2O5z8fHxwU8KCDLXhCPextcx4QiAUFbmdYry9xXOf/O2LX8MACoius8Bvk04AgChqEzNHw8//HCg8wCAci0+Pl49e/b02nWoV69etBQhLLjeB5s2bfK4GGoYhnr27Mn7AEDIoigCgAAwDEOJiYlei6LExES6RiIsGIahiRMnauzYsR5FUUREhCZOnMj7AEDIKnP3OZdDhw5p3bp1+vzzzwORDwCUS64xlt7MnTuXLsQIG7GxsUpOTvbYlpycrCZNmliUEQCUrMxFUUZGhkaMGKF69eqpX79+GjZsmHJzc9WuXTs1b95cW7ZsCWSeABDSipp1S5J71i0gXIwdO1YREWdOMSIiIjRmzBiLMwKA4pWpKNq7d6/69OmjZcuWyel0uidSsNvt6tixo/bs2aP58+cHOlcACFlNmzZVZKT3HsmRkZFq2rRpkDMCrLNgwQL3WkVOp1MLFy60OCMAKF6ZiqJHHnlE+/btk2mahQZN9u3bV5L01Vdf+Z0cAJQX69evV15entdYXl6e1q9fH+SMAGtkZmYWWqcwJSVFmZmZFmUEACUrU1H06aefyjAM3X///Xr77bc9Yq4i6bfffvM7OQAoL/r06aPo6GivsejoaPXp0yfIGQHBZ5qmZsyY4W4lcnE4HJoxYwZj6wCErDIVRQcOHJAkXXTRRYViNptNkopcpwAAKiLDMFSvXj2vsfr16zPrFsKCa2xdweLHNE3G1gEIaWUqimrXri1J2rx5c6GYaxa6+vXr+5EWAJQv6enp2r17t9fYrl27OBlEWIiLi1NMTIzXWExMjOLi4oKcEQD4pkxFUf/+/WWaph566CE98cQT7u3XX3+9Zs6cKcMwNHDgwIAlCQChrmB3odLGgYogIyOjyJ4i2dnZysjICHJGAOCbMhVF//znPxUVFaW8vDz3+CJJmjNnjkzTVFRUlCZNmhTQRAEglLmmHy5rHKgI4uPj1bNnz0LdRQ3DUK9evQpNzgQAoaJM39IdOnTQBx98oDp16rin43bd6tatq/fff19t27YNdK4AELLi4+N17rnneo21adOGk0GEBcMwNHHixEJFUUREhNftABAqvC+q4YNhw4YpPT1dy5cv188//yxJatWqlS6++GJVrVo1YAkCQHkRFRXldXvlypWDnAlgndjYWCUnJ2v27NnubcnJyWrSpImFWQFA8cpcFElSlSpVdNlll3mNpaWlqVOnTv4cHgDKjfT0dKWlpXmNpaWlKT09XQkJCUHOCrBGcnKyli5dqgMHDqhevXpKSkqyOiUAKFbAO7l/8cUXGjJkiM4777xAHxoAQpZrLIU3jKVAuLHb7brkkksUERGhESNGyG63W50SABSrVEXR//73Pw0ePFht2rTRoEGDtGjRInfs448/VteuXTVkyBB9/vnnLNAGIKwYhqHExESvscTERMZSIKzk5uZq6dKlcjqdWrp0qXJzc61OCQCK5XP3uWXLlmnkyJHu+z///LNWrlypkydP6vvvv9f06dM99memJQDhxDRNzZs3z2ts7ty56tatG4URwsacOXOUlZUlScrKylJKSopuuukmi7MCgKL5XLk8//zzhWaaM01T9913n55++mn3/SpVqui2227Tjh07zmbeABBS0tPTtXHjRq+xjRs3sngrwkZmZqZSUlLcPUZM01RKSooyMzMtzgwAiuZzUfTtt9/KMAx17txZ77//vt577z116tRJf/zxh5xOp6pWrapHHnlEmZmZevHFF9W8efOzmTcAhJSmTZsqMtJ743tkZKSaNm0a5IyA4DNNUzNmzCjUhd7pdHrdDgChwufuc4cOHZIkPfLII/rb3/4m6UwXucsvv1yGYeiDDz7Q4MGDz06WABDi1q9fr7y8PK+xvLw8rV+/Xn379g1yVkBwFdVi6nQ63S2mzMIIIBT53FLkcDgkSXXq1HFvq1u3rvvniy66KIBpAUD50qdPH1WpUsVrrEqVKurTp0+QMwKCLz4+vsjlODp37swsjABCVqnXKXr88cdVr149SdKff/7p3n7jjTd67GcYhmbNmuVnegBQPpimWeQMW7m5uXQbQtjjPQAglJW6KPr000897rtmU5ozZ06hfSmKAISLxYsXF3nSZ5qmFi9erCuuuCLIWQHBxSLGAMqrUs2b7W32uaJuABBOunTp4lccqAhcixjbbDaP7TabjUWMAYQ0n1uKHn744bOZBwCUa3FxcX7FgYrAMAxNnDhRV111ldftrNUFIFRRFAFAAHz44YclxkePHh2kbADrxMbGKikpSW+99ZZM05RhGEpKSlKTJk2sTg0AilSq7nMAAO8uvfRSv+JARZKcnKzq1atLkmrUqKGkpCSLMwKA4lEUAUAA+NJSBIST06dPe/wLAKGMoggAAqB+/fp+xYGKZNasWTpx4oQk6a+//tKbb75pcUYAUDyKIgAIgJIWZ2XxVoSLzMxMzZ0712Pb3LlzlZmZaVFGAFAyiiIACIC1a9f6FQcqAtM09eijjxZamsPpdHrdDgChgqIIAAJg//79fsWBisCXxVsBIBRRFAFAAJS0/grrsyAclNQSREsRgFBFUQQAATBq1Ci/4gAAwDoURQAQABs3bvQrDlQE8fHxio6O9hqLjo5WfHx8cBMCAB9RFAFAADAlNyBlZGQoJyfHaywnJ0cZGRlBzggAfENRBAAB8Mcff/gVByqCuLg4xcTEeI3FxMQoLi4uyBkBgG8oigAgAHr27OlXHKgIMjIylJ2d7TWWnZ1NSxGAkEVRBAAB8OGHH/oVByqC+Ph4NWvWzGusefPmjCkCELIoigAgAOrWretXHKgInE5nkWsR7dmzR06nM7gJAYCPKIoAIAB69+7tVxyoCJYsWVJk4eN0OrVkyZLgJgQAPqIoAoAAeOONN/yKAxVBp06d/IoDgFUoigAgAH744Qe/4kBFUNKYIcYUAQhVFEUAEACjRo3yKw5UBCV1j6P7HIBQRVEEAAHQtGlTv+JARdCuXTu/4gBgFYoiAAiAtLQ0v+JARTB37ly/4gBgFYoiAAgArpADUlJSkl9xALAKRREABMDixYv9igMVQaVKlfyKA4BVKIoAIABM0/QrDlQECQkJRU673blzZyUkJAQ5IwDwDUURAATAFVdc4VccqAgMw9CDDz7oNfbggw/KMIwgZwQAvqEoAoAA2Lt3r19xoKKjtRRAKKMoAoAA2LBhg19xoCIwTVOPPvqo19ijjz5KYQQgZFEUAUAAOJ1Ov+JARbBnz54ip59PS0vTnj17gpwRAPiGoggAAqCkAeQMMAcAIHRRFAFAAPz6669+xYGKwOFw+BUHAKtQFAFAANx7771+xYGKYOvWrX7FAcAqFEUAEAC0FAFSXl6eX3EAsApFEQAEwLfffutXHKgIjh496lccAKxCUQQAAVDSopQsWolwcOONN/oVBwCrUBQBQACMHDnSrzhQEdhsNsXGxnqNNW3aVDabLcgZAYBvKIoAIABYvBWQ0tPTlZmZ6TX266+/Kj09PbgJAYCPKIoAIACYihiQ4uLiFB0d7TVWvXp1xcXFBTkjAPANRREABEBERPEfpyXFgYogIyNDOTk5XmPHjh1TRkZGkDMCAN/wLQ0AAVC/fn2/4kBFYJqmX3EAsApFEQAEwOLFi/2KAxVBQkKCGjRo4DXWsGFDJSQkBDkjAPANRREABACzzwFnxs7t37/fa2zfvn2MrQMQsiiKACAAvvzyS7/iQEXwxhtv+BUHAKtEWp0AAFQEWVlZfsWBiuDcc8/1K47QZJqmcnNzrU6jXDlx4oTXn+E7u90e1IXPKYoAIAD27NnjVxyoCBo3buxXHKEpNzdXAwcOtDqNcmv48OFWp1AurVixQlWqVAna76P7HAAEQN26df2KAxXBH3/84VccAKwSUi1Fq1ev1tNPP60tW7Zo3759Wrx4MYOTAZQLF198sdasWVNsHKjoevXq5Vccoe/Ch1rJVplr6iUxTVPO02emoI+oZAS1G1h55jjl1Ff//tmS3x1SRdHx48fVqVMnXX/99br88sutTgcAfObLRAuDBw8OUjaANdavX19ivF+/fkHKBmeDrXKEIimKfBNldQIojZAqioYNG6Zhw4ZZnQYAlFrDhg39igMVQaNGjfyKA4BVQqooKq2TJ0/q5MmT7vtHjx61MBsA4WzXrl1+xYGKoKQuQnQhAhCqynX75xNPPKGYmBj3LTY21uqUAISpli1b+hUHKoK8vDy/4gBglXJdFD3wwAPKzs523zIzM61OCUCY4go5IH3wwQd+xQHAKuW6KIqKilKNGjU8bgBghd9++82vOFARjB492q84AFilXBdFABAq2rVr51ccqAiaN2+uypUre41VrlxZzZs3D3JGAOCbkCqKcnJylJqaqtTUVElnVoBPTU3Vr7/+am1iAFCCb775xq84UBFkZGTo1KlTXmOnTp1SRkZGkDMCAN+EVFG0efNmdenSRV26dJEkTZgwQV26dNFDDz1kcWYAULxmzZr5FQcqgqZNmyoiwvupRUREhJo2bRrkjADANyE1JfeAAQNkmqbVaQBAqUVFFb9KX0lxoCJYu3atnE6n15jT6dTatWt1wQUXBDkrAChZSLUUAUB5xVTEAACUXxRFABAANpvNrzhQETRu3NivOABYhaIIAAKgevXqfsWBioAWUwDlFUURAATA8uXL/YoDFQGLtwIoryiKACAAqlSp4lccqAji4+P9igOAVSiKACAAzjnnHL/iQEXQqFEjv+IAYBWKIgAIAMMw/IoDFUHDhg39igOAVSiKACAA6D4HSF988YVfcQCwCkURAARARkaGX3GgIqhVq5ZfcQCwCkURAATA8ePH/YoDFQHdSAGUVxRFABAAkZGRfsWBisDpdPoVBwCrUBQBQAAcOnTIrzhQEaSlpfkVBwCrUBQBQADExcX5FQcqgiFDhvgVBwCrUBQBQAAcOXLErzhQEfTr18+vOABYhaIIAALg6NGjfsWBimDdunV+xQHAKhRFABAApmn6FQcqgr179/oVBwCrUBQBQACcPHnSrzhQEezevduvOABYhaIIAALA4XD4FQcqAt4HAMoriiIACIDTp0/7FQcqgoiI4k8rSooDgFX4dAIAAAHRrFkzv+IAYBWKIgAAEBB79uzxKw4AVqEoAoAAoNsQQEsRgPKLb2kACIDmzZv7FQcqAsMw/IoDgFUirU4AAErLNE3l5uZanYaHnTt3lhg/ceJEkLIpmd1u5wQVAUdRBKC8oigCUO7k5uZq4MCBVqdRaqGU84oVK1SlShWr00AFU6dOHb/iAGAVus8BAICA2Lhxo19xALAKLUUAyh273a4VK1ZYnYaHvLw8XXzxxUXGP//8c0VGhs5Hrt1utzoFVEBNmzb1Kw4AVgmdb2gA8JFhGCHZ9eu2227Tyy+/XGj7HXfcoerVq1uQERBcqampJcavueaa4CQDAKVA9zkACJDx48fLZrN5bLPZbEpMTLQoIyC4Tp486VccAKxCUQQAAfTmm2963H/nnXcsygQIvr/97W9+xQHAKhRFABBA+cdMtG/fXnFxcRZmAwTXoUOH/IoDgFUoigDgLHnxxRetTgEIqj/++MOvOABYhaIIAAAExPHjx/2KA4BVKIoAAEBA3HXXXX7FAcAqFEUAACAgZs6c6VccAKxCUQQAAAIiLy/PrzgAWIWiCAAABETBdbpKGwcAq1AUAQCAgOjevbtfcQCwCkURAAAIiI8++sivOABYhaIIAAAERN26df2KA4BVKIoAAEBA7N+/3684AFiFoggAAARElSpV/IoDgFUoigAAQEDs3r3brzgAWIWiCAAABMTx48f9igOAVSKtTgAAAJSeaZrKzc21Og0PDRo00LFjx4qNnzhxIogZFc1ut8swDKvTKBdM03T/7DjltDATVHT5X1/5X3fBQFEEAEA5lJubq4EDB1qdRqns3LkzZHJesWIFY5x8lL/4/urfP1uYCcJJbm6uqlatGrTfR/c5AAAAAGGNliIfmKbD6hTKhTPNnK5mzwi6JfiI1xeAsrDb7VqxYoXVaXjIy8vTxRdfXGT8888/V2RkaJx62O12q1MoN/I/Vxc+1Eq2ylxTx9nhOOV0t0YG+z0aGp9MIe7wwZVWpwAAgAfDMEKy+9dtt92ml19+udD2O+64Q9WrV7cgI/gr/0VOW+UIRVIUIQiCfXGdVzUAAAiY8ePHF7rCa7fblZiYaFFGAFAyWoqKEIrdEkLdiRMnNHz4cEnSJ598EpJXMEMd3TkAVASvv/66kpOT3ffnzJljYTYAUDKKoiKEareE8qJKlSo8fwAQppo2ber+uX379oqLi7MwGwAoGd3nAADAWfPiiy9anQIAlIiWIgBeheLCkOVB/oUpQ2WRyvKEBTUBAFagKALgVXlcGDLUuMbYwXcsqAkAsALd5wAAAACENVqKAJTI7HaRZLNZnUb5YJqS8/8vyBthk+gKVjKHQ8bmL6zOAgAQxiiKAJTMZpNsfFz4rpLVCQAAgFKg+xwAAACAsEZRBAAAACCsURQBAAAACGsURQAAAADCGkURAAAAgLBGUQQAAAAgrFEUAQAAAAhrLDwCAEARTNNUbm6u1WmUOydOnPD6M3xjt9tlsPAzEFQURQAAFCE3N1cDBw60Oo1ybfjw4VanUO6sWLFCVapUsToNIKxQFAHwyjTN/7vjyLMuEVR8+V5fHq87AACChKIIgFf5uwwZm7+0MBOEk9zcXFWtWtXqNLzKG9tfirRZnUb5YJqSw3nmZ1uERFewkuU5FLlgldVZAGGLoggAAF9E2qRKFEUIb45TTqtTKBdM05Tz9JmW74hKBmPEfGTl64uiCIBXdrvd/bPZbZBk4+MCZ4kjz90amf91ByD0fPXvn61OATgrOMsB4JXHVS1bJEURgoKrqQAAK3CWAwAAgCLZ7XatWLHC6jTKlRMnTrhnXvzkk0+YTbAMgt1zgKIIAAAARTIMg5N6P1SpUoXnrxygKAJQMofD6gzKD9OUnP//+YqwMeuWL0L49eUxRXhe6OaJCiDf64up6YHgoygCUCJj8xdWpwBYIv/U9EyXjGAJ5anpgYoqwuoEAAAAAMBKtBQB8IqBtWXD4Fr/hNqU3PnzYfFWnFX5Fm8NtfcBEA4oigB4xcBa/zG4tvzzmCKcxVsRJExNDwQfRREAAL5gogXfmabk+P8r09simHDEF7y+AEtRFAEA4AMmWgCAiouJFgAAAACEtZBsKXr55Zf19NNPa//+/erUqZNefPFF9ejRw+q0AABhhglHyoYJR/zDRAtA8IVcUbRgwQJNmDBBr732mnr27KmZM2dqyJAh+umnn1SvXj2r07OcaZoe62aEkhMnTnj9OdTY7XYGsZZzvA/8x/vAN0w44j8mHMHZFKrfB3wXlD+GGWLLJvfs2VPdu3fXSy+9JElyOp2KjY3VHXfcocmTJxf72KNHjyomJkbZ2dmqUaNGMNINuhMnTmjgwIFWp1GurVixgi/oco73gf94H5R/oXoyKJWPliJOBisGvg/8U9G/C0pTG4RUS9GpU6e0ZcsWPfDAA+5tERERuuiii7R+/fpC+588eVInT55038/OzpZ05gmoqE6cOKG8vDyr0yjXjh49qtOnT1udBvzA+8B/vA/KvxMnTmjEiBFWp1GiwYMHW52CVx9//HGFPhkMF3wf+Keifxe4agJf2oBCqijKysqSw+FQ/fr1PbbXr19fO3bsKLT/E088oalTpxbaHhsbe9ZyRPnXoEEDq1MALMf7AOGO9wAQPu+DY8eOKSYmpth9QqooKq0HHnhAEyZMcN93Op06dOiQateuTZO4RY4eParY2FhlZmZW2C6MQEl4HwC8DwDeA9YzTVPHjh1To0aNStw3pIqiOnXqyGaz6Y8//vDY/scff3itZKOiohQVFeWx7ZxzzjmbKcJHNWrU4AMAYY/3AcD7AOA9YK2SWohcQmqdosqVK+u8887Tl19+6d7mdDr15Zdfqnfv3hZmBgAAAKCiCqmWIkmaMGGCkpOT1a1bN/Xo0UMzZ87U8ePHdd1111mdGgAAAIAKKOSKorFjx+rAgQN66KGHtH//fnXu3FnLli0rNPkCQlNUVJQefvjhQt0agXDC+wDgfQDwHihfQm6dIgAAAAAIppAaUwQAAAAAwUZRBAAAACCsURQBAAAACGsURRXAW2+95dP6TIZhaMmSJT4dszT7loavuQIArPWf//xHsbGxioiI0MyZM73us3LlShmGoSNHjgQ1NyAQeP0iP4qiEHHttdfKMAz3rXbt2ho6dKi+++67Eh87duxY/fzzz+77jzzyiDp37lxov3379mnYsGGBTNurd999VzabTbfddluhWMFcgfLg2muv1ciRIz22vffee7Lb7XrmmWe8xvOLj4+XYRiaP39+oVi7du1kGIbeeuutwCaNsFfS67I4R48e1e233677779fe/fu1U033aQBAwbo7rvvDmiOwNm2fv162Ww2jRgxolCsT58+2rdvn8+Le7qkp6d7nLO5btdcc02g0oYFKIpCyNChQ7Vv3z7t27dPX375pSIjI3XJJZcU+5jTp0+rSpUqqlevXonHb9CgQVCmhZw1a5YmTZqkd999V7m5uR6xknI9derU2U4P8Nsbb7yhxMREvfrqq7r33nt9ekxsbKxmz57tsW3Dhg3av3+/qlWrdjbSBMrs119/1enTpzVixAg1bNhQVatWtToloExmzZqlO+64Q6tXr9bvv//uEatcubIaNGggwzC8PtbhcMjpdBZ57C+++MJ93rZv3z69/PLLhfYxTVN5eXn+/REICoqiEBIVFaUGDRqoQYMG6ty5syZPnqzMzEwdOHBA0v9dmViwYIH69+8vu92uefPmeXRJe+uttzR16lSlpaW5r1y4rkDn7xJ36tQp3X777WrYsKHsdrvi4uL0xBNPeOSTlZWlUaNGqWrVqmrZsqU++uijEv+GPXv2aN26dZo8ebJatWqlDz74wCNesPucq1XrjTfeUEJCgux2u5YuXapzzjlHDodDkpSamirDMDR58mT342688Ub3FZmDBw9q3Lhxaty4sapWraoOHTro3Xffde+bkpKi2rVr6+TJkx65jBw5UuPHj5ckpaWlaeDAgapevbpq1Kih8847T5s3by7x70X4mT59uu644w7Nnz+/VItKJyYmatWqVcrMzHRve/PNN5WYmKjIyJBbMg4VmLduzEuWLHGfGL711lvq0KGDJKlZs2YyDEPXXnutVq1apeeff9793ZKenu5+/JYtW9StWzdVrVpVffr00U8//eSO7dq1S5dddpnq16+v6Ohode/eXV988YXH74+Pj9fjjz+u66+/XtWrV1fTpk31n//8xx0v6Tvr2WefVYcOHVStWjXFxsbq1ltvVU5OTqCeMpRTOTk5WrBggW655RaNGDGiUIt8we5zrvfGRx99pLZt2yoqKkq//vprkcevXbu2+7ytQYMGiomJcR/z008/1XnnnaeoqCh9/fXXAXkfSNJvv/2mcePGqVatWqpWrZq6deumjRs3uuMffvihunbtKrvdrmbNmmnq1KkUZT6iKApROTk5mjt3rlq0aKHatWt7xCZPnqy77rpLP/74o4YMGeIRGzt2rO699161a9fOfeVi7NixhY7/wgsv6KOPPtLChQv1008/ad68eYqPj/fYZ+rUqRozZoy+++47DR8+XImJiTp06FCxec+ePVsjRoxQTEyMrrnmGs2aNavEv/WXX37R+++/rw8++ECpqanq16+fjh07pq1bt0qSVq1apTp16mjlypXux6xatUoDBgyQJOXm5uq8887Txx9/rO3bt+umm27S+PHjtWnTJknSlVdeKYfD4VHU/fnnn/r44491/fXXSzpzwtqkSRN988032rJliyZPnqxKlSqVmDvCy/33369p06Zp6dKlGjVqVKkeW79+fQ0ZMkRz5syRJP31119asGCB+zUIhIqxY8e6T9Y2bdqkffv26fnnn1fv3r3197//3f3dEhsb637Mv/71Lz3zzDPavHmzIiMjPV7XOTk5Gj58uL788ktt3bpVQ4cO1aWXXlroZPOZZ55Rt27dtHXrVt1666265ZZb3MVVSd9ZEREReuGFF/T9999rzpw5+uqrrzRp0qSz+CyhPFi4cKHOPfdctW7dWtdcc43efPNNlbQ8519//aWnnnpKb7zxhr7//nufeuJ4M3nyZD355JP68ccf1bFjx4C8D3JyctS/f3/t3btXH330kdLS0jRp0iR3a9aaNWuUlJSku+66Sz/88INef/11vfXWW3rsscfK9DeEHRMhITk52bTZbGa1atXMatWqmZLMhg0bmlu2bHHvs2fPHlOSOXPmTI/Hzp4924yJiXHff/jhh81OnToV+h2SzMWLF5umaZp33HGHeeGFF5pOp9NrPpLMBx980H0/JyfHlGR++umnRf4NDofDjI2NNZcsWWKapmkeOHDArFy5srl79+5ic61UqZL5559/ehyra9eu5tNPP22apmmOHDnSfOyxx8zKlSubx44dM3/77TdTkvnzzz8XmcuIESPMe++9133/lltuMYcNG+a+/8wzz5jNmjVz//3Vq1c333rrrSKPh/CWnJxsVq5c2ZRkfvnll17jl112WZGPj4uLM5977jlzyZIlZvPmzU2n02nOmTPH7NKli2maphkTE2POnj37LGWPcFXU67Lg57BpmubixYvN/KcEW7duNSWZe/bscW/r37+/edddd3k8bsWKFaYk84svvnBv+/jjj01J5okTJ4rMrV27duaLL77ovh8XF2dec8017vtOp9OsV6+e+eqrr5qmWfJ3VkGLFi0ya9eu7dO+qLj69OnjPmc6ffq0WadOHXPFihXuuOv1e/jwYdM0z7w3JJmpqanFHtd1PlalShX3eVu1atXMb7/91n1M17lQcUr7Pnj99dfN6tWrmwcPHvR6vEGDBpmPP/64x7a3337bbNiwYYm5wDRpKQohAwcOVGpqqlJTU7Vp0yYNGTJEw4YNU0ZGhsd+3bp18/t3XXvttUpNTVXr1q115513avny5YX26dixo/vnatWqqUaNGvrzzz+LPObnn3+u48ePa/jw4ZKkOnXq6OKLL9abb75ZbC5xcXGqW7eux7b+/ftr5cqVMk1Ta9as0eWXX642bdro66+/1qpVq9SoUSO1bNlS0pk+v9OmTVOHDh1Uq1YtRUdH67PPPvO4+vL3v/9dy5cv1969eyWdaSJ3TW4hSRMmTNCNN96oiy66SE8++aR27dpVbM4IPx07dlR8fLwefvjhMnfLGTFihHJycrR69Wq9+eabtBKhwsj/fdGwYUNJcn9f5OTkaOLEiWrTpo3OOeccRUdH68cffyx0hTz/MQzDUIMGDdzHKOk764svvtCgQYPUuHFjVa9eXePHj9fBgwf1119/nZW/F6Hvp59+0qZNmzRu3DhJUmRkpMaOHVtiD5bKlSt7vBaLs2DBAvd5W2pqqtq2beuOFTxXC8T7IDU1VV26dFGtWrW85pOWlqZ///vfio6Odt9crbu8F0pGURRCqlWrphYtWqhFixbq3r273njjDR0/flz//e9/C+3nr65du2rPnj2aNm2aTpw4oTFjxuiKK67w2Kdg9zHDMIodcDhr1iwdOnRIVapUUWRkpCIjI/XJJ59ozpw5xT7O298zYMAAff3110pLS1OlSpV07rnnasCAAVq5cqVWrVql/v37u/d9+umn9fzzz+v+++/XihUrlJqaqiFDhnhM2tClSxd16tRJKSkp2rJli77//ntde+217vgjjzyi77//XiNGjNBXX32ltm3bavHixUXmjPDTuHFjrVy5Unv37tXQoUN17NixUh8jMjJS48eP18MPP6yNGzcqMTHxLGQKFC8iIqJQF6LTp0/7dcz83xeui02uz/2JEydq8eLFevzxx7VmzRqlpqaqQ4cOhSbWKe47p7jvrPT0dF1yySXq2LGj3n//fW3ZssU94J3Je8LXrFmzlJeXp0aNGrnPSV599VW9//77ys7OLvJxVapUKXLihYJiY2Pd520tWrTwmMyq4LlNIN4HVapUKTafnJwcTZ061aNQ27Ztm3bu3Cm73e7T3xTOGN0bwgzDUEREhE6cOFGqx1WuXNk9SUFxatSoobFjx2rs2LG64oorNHToUB06dKjIKxDFOXjwoD788EPNnz9f7dq1c293OBzq27evli9frqFDh/p8PNe4oueee85dAA0YMEBPPvmkDh8+7DHj19q1a3XZZZe5J15wOp36+eefPa7YSGcmZ5g5c6b27t2riy66yKM/vCS1atVKrVq10j333KNx48Zp9uzZpR43gootLi5Oq1at0sCBAzV06FAtW7ZM1atXL9Uxrr/+es2YMUNjx45VzZo1z1KmQNHq1q2rY8eO6fjx4+4Tt9TU1BIf5+t3S0Fr167Vtdde6/48zcnJ8ZikwVdFfWdt2bJFTqdTzzzzjCIizlzrXbhwYamPj4ojLy9PKSkpeuaZZzR48GCP2MiRI/Xuu+/q5ptvDmpOgXgfdOzYUW+88UaR52pdu3bVTz/9pBYtWgQi5bBDURRCTp48qf3790uSDh8+rJdeekk5OTm69NJLS3Wc+Ph47dmzR6mpqWrSpImqV69eaCruZ599Vg0bNlSXLl0UERGhRYsWqUGDBmVeWPXtt99W7dq1NWbMmEJXWIYPH65Zs2aVqiiqWbOmOnbsqHnz5umll16SJF1wwQUaM2aMTp8+7dFS1LJlS7333ntat26datasqWeffVZ//PFHoaLo6quv1sSJE/Xf//5XKSkp7u0nTpzQfffdpyuuuEIJCQn67bff9M0332j06NFleSpQwcXGxmrlypUaOHCghgwZomXLlkmSsrOzC51Y1q5du1Dx3aZNG2VlZTHFMYLC2+uybdu2qlq1qv75z3/qzjvv1MaNG31aJys+Pl4bN25Uenq6oqOjfb6A1rJlS33wwQe69NJLZRiGpkyZUmzvAW+K+85q0aKFTp8+rRdffFGXXnqp1q5dq9dee61Ux0fFsnTpUh0+fFg33HBDoTWIRo8erVmzZgW9KArE+2DcuHF6/PHHNXLkSD3xxBNq2LChtm7dqkaNGql379566KGHdMkll6hp06a64oorFBERobS0NG3fvl2PPvroWfrLKg66z4WQZcuWqWHDhmrYsKF69uypb775RosWLXLPsuar0aNHa+jQoRo4cKDq1q3rMT21S/Xq1TV9+nR169ZN3bt3V3p6uj755BP3VbbSevPNNzVq1CivTc6jR4/WRx99pKysrFIds3///nI4HO6/v1atWmrbtq0aNGig1q1bu/d78MEH1bVrVw0ZMkQDBgxQgwYNvC5YGBMTo9GjRys6OtojbrPZdPDgQSUlJalVq1YaM2aMhg0bpqlTp5YqX4SPJk2aaOXKlcrKytKQIUN09OhRrVy5Ul26dPG4FfUaql27dondIIBA8Pa6nDZtmubOnatPPvnEvYTBI488UuKxJk6cKJvNprZt26pu3brFTlWc37PPPquaNWuqT58+uvTSSzVkyBB17dq1VH9Hcd9ZnTp10rPPPqunnnpK7du317x58wotMYHwMmvWLF100UVeF2UdPXq0Nm/erO+++y6oOQXifVC5cmUtX75c9erV0/Dhw9WhQwc9+eSTstlskqQhQ4Zo6dKlWr58ubp3765evXrpueeeU1xc3Nn4kyocwyzYsRiowAYNGqR27drphRdesDoVAAAAhAiKIoSFw4cPa+XKlbriiiv0ww8/eLQ0AQAAILwxpghhoUuXLjp8+LCeeuopCiIAAAB4oKUIAAAAQFhjogUAAAAAYY2iCAAAAEBYoygCAAAAENYoigAAAACENYoiAAAAAGGNoggAYJkBAwbIMAwZhqH09HSfHxcfH+9+nMvKlSvd26699trAJwsAqLAoigAAZ8XNN9/sLlIMw9CTTz5pdUoAAHjFOkUAgIA7ffq0GjZsqIMHD7q3derUSampqR77bdu2TdnZ2ZKk7t27Kyoqyqfjx8fHKyMjQ5Lk+hrLzs7Wtm3bJEn169dXy5Yt/f0zAABhgqIIABBwn3zyiUaMGFFo+48//qhzzz23zMc9fvy4qlWr5rUoAgCgrOg+BwAIuPnz57t/vuqqq7xul7yPKUpPT3dvGzBggFavXq3evXurSpUquu2224r8nUWNKbr22mvd25cvX66HHnpITZo0kd1u1/nnn6+0tLRCx/ruu+80btw4NWzYUJUrV1bjxo1144036rfffvPY78SJE7rvvvvUsmVLRUVFqVq1akpISNDll1+uxYsXl+YpAwBYKNLqBAAAFUtubq6WLFkiSapbt65mzpyp9957T3l5eZo/f74eeeQRn4+1c+dODRkyRLm5uQHJ7ZZbbtHu3bvd99etW6eRI0dq586diow885X46aefatSoUTp58qR7v99//12zZs3Sxx9/rHXr1ikhIUGSdPvtt+vNN99073fq1Cmlp6crPT1dVatW1ahRowKSNwDg7KKlCAAQUEuXLtWxY8ckSSNHjlT9+vU1YMAASdJPP/2krVu3+nys33//XU2aNNHcuXP1ySefaOTIkX7llpmZqaeeekoffPCBYmNjJZ1pmfrss88kSX/99ZeSk5N18uRJRUZG6rHHHtPy5cs1adIkSdL+/ft16623uo/34YcfSpLi4uL03nvvafny5Zo1a5aSkpJUs2ZNv3IFAAQPLUUAgIDK30XuiiuucP/7xRdfuONdunTx6VgRERFaunSpWrduHZDcbr31VneB8/PPP2vy5MmSpF9++UWStHz5ch04cECSdPHFF+uCCy6QJF166aVauHChu4DKyspSnTp1VKlSJUnSOeeco+bNm6tNmzaKiorS9ddfH5B8AQDBQUsRACBgjh07po8//liSVKtWLV144YWSpMsvv1w2m02StGDBAp8nR2jZsmXACiJJ6t+/v/vn2rVru38+cuSIpDOFksunn36qfv36uW+uMU+maWrHjh2SpBtuuEGSlJaWpi5duqhatWpq27atJkyYoH379gUsbwDA2UVRBAAImCVLlrjH/xw6dEiVKlWSYRiqV6+eHA6HJCkjI0Pr16/36Xj169cPaH75u7S5xhBJpZ/B7vjx45KkadOm6d1339WVV16p1q1byzAM/fjjj3ruuec0ePBg5eXlBSZxAMBZRVEEAAiYd99916f9Cs5CVxTDMPxJp9RatWrl/jk5OVmmaRa6HT9+XEOGDHHvd9VVV2nhwoXasWOHjh075u4yuH37do+WJwBA6GJMEQAgIA4ePKjPP/9cklS9enU9/vjjHvFTp07p3nvvlSQtWrRIM2fODHaKJbr44otVt25dHThwQCkpKapVq5YuvvhiORwOpaena+3atUpLS9MPP/wgSTr//PPVpUsX9ejRQ40bN9axY8fcMUkeM9gBAEIXRREAICBc025L0uDBg3X77bcX2uftt99Wamqq9u/frxUrVgQ7xRJVq1ZNb731li6//HKdPHlSzz33nJ577jmPfeLi4tw///nnn3rllVf0yiuvFDpW27Zt1bFjx7OeMwDAf3SfAwAERP6uc3/729+87nPppZe6f/a1C12wDR8+XJs3b9b48ePVpEkTVapUSXXq1FHnzp01YcIELVq0yL3vAw88oMsuu0xxcXGqWrWqKlWqpPj4eN1888366quv3JNLAABCm2GWdnQpAAAAAFQgtBQBAAAACGsURQAAAADCGkURAAAAgLBGUQQAAAAgrFEUAQAAAAhrFEUAAAAAwhpFEQAAAICwRlEEAAAAIKxRFAEAAAAIaxRFAAAAAMIaRREAAACAsEZRBAAAACCs/T9RWraZ2+6VAwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Box plots for response times for each airline\n",
    "\n",
    "data = {\n",
    "    'Airline': (['British Airways'] * len(response_times_british_airways) + \n",
    "                ['KLM'] * len(response_times_KLM) + \n",
    "                ['Lufthansa'] * len(response_times_lufthansa) + \n",
    "                ['Air France'] * len(response_times_air_france)),\n",
    "    'Reply Time': (response_times_british_airways + \n",
    "                   response_times_KLM + \n",
    "                   response_times_lufthansa + \n",
    "                   response_times_air_france)\n",
    "}\n",
    "\n",
    "response_times_df = pd.DataFrame(data)\n",
    "\n",
    "# Plot the box plot\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.boxplot(x='Airline', y='Reply Time', data=response_times_df, palette='viridis')\n",
    "plt.xlabel('Airlines', fontsize=12, weight='bold')\n",
    "plt.ylabel('Reply Time (hours)', fontsize=12, weight='bold')\n",
    "plt.title('Distribution of Reply Times for Each Airline', fontsize=14, weight='bold')\n",
    "plt.ylim(0, 5)\n",
    "\n",
    "# Optionally, show individual data points\n",
    "#sns.swarmplot(x='Airline', y='Reply Time', data=response_times_df, color=\".25\")\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_count(query):\n",
    "    cursor = connection.cursor()\n",
    "    try:\n",
    "        for result in cursor.execute(query, multi=True):\n",
    "            if result.with_rows:\n",
    "                first_result = result.fetchone()\n",
    "                cursor.fetchall()  # Ensure all remaining rows are fetched\n",
    "        return first_result[0] if first_result else 0\n",
    "    finally:\n",
    "        cursor.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Queries for number of conversations where an airline was mentioned per airline\n",
    "mentioned_ba = f\"\"\"\n",
    "SELECT COUNT(DISTINCT conversations.conversation_id)\n",
    "FROM conversations\n",
    "JOIN hasher ON hasher.conversation_id = conversations.conversation_id\n",
    "JOIN tweets ON hasher.id = tweets.id\n",
    "WHERE (tweets.user_mentions LIKE '%18332190%' OR tweets.mentioned_airlines LIKE '%British_Airways%')\n",
    "    AND conversations.start >= UNIX_TIMESTAMP('{start_datee}') * 1000 \n",
    "    AND conversations.start <= UNIX_TIMESTAMP('{end_datee}') * 1000; \n",
    "\"\"\"\n",
    "\n",
    "mentioned_klm = f\"\"\" \n",
    "SELECT COUNT(DISTINCT conversations.conversation_id)\n",
    "FROM conversations\n",
    "JOIN hasher ON hasher.conversation_id = conversations.conversation_id\n",
    "JOIN tweets ON hasher.id = tweets.id\n",
    "WHERE (tweets.user_mentions LIKE '%56377143%' OR tweets.mentioned_airlines LIKE '%KLM%')\n",
    "    AND conversations.start >= UNIX_TIMESTAMP('{start_datee}') * 1000 \n",
    "    AND conversations.start <= UNIX_TIMESTAMP('{end_datee}') * 1000; \n",
    "\"\"\"\n",
    "\n",
    "mentioned_lufthansa = f\"\"\" \n",
    "SELECT COUNT(DISTINCT conversations.conversation_id)\n",
    "FROM conversations\n",
    "JOIN hasher ON hasher.conversation_id = conversations.conversation_id\n",
    "JOIN tweets ON hasher.id = tweets.id\n",
    "WHERE (tweets.user_mentions LIKE '%124476322%' OR tweets.mentioned_airlines LIKE '%Lufthansa%')\n",
    "    AND conversations.start >= UNIX_TIMESTAMP('{start_datee}') * 1000 \n",
    "    AND conversations.start <= UNIX_TIMESTAMP('{end_datee}') * 1000; \n",
    "\"\"\"\n",
    "\n",
    "mentioned_af = f\"\"\" \n",
    "SELECT COUNT(DISTINCT conversations.conversation_id)\n",
    "FROM conversations\n",
    "JOIN hasher ON hasher.conversation_id = conversations.conversation_id\n",
    "JOIN tweets ON hasher.id = tweets.id\n",
    "WHERE (tweets.user_mentions LIKE '%106062176%' OR tweets.mentioned_airlines LIKE '%AirFrance%')\n",
    "    AND conversations.start >= UNIX_TIMESTAMP('{start_datee}') * 1000 \n",
    "    AND conversations.start <= UNIX_TIMESTAMP('{end_datee}') * 1000;\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Queries for number of conversations where an airline was mentioned and the airline replied per airline\n",
    "\n",
    "replies_mentioned_ba = f\"\"\"\n",
    "SELECT COUNT(DISTINCT conversations.conversation_id)\n",
    "FROM conversations\n",
    "JOIN hasher ON hasher.conversation_id = conversations.conversation_id\n",
    "JOIN tweets ON hasher.id = tweets.id\n",
    "WHERE (tweets.user_mentions LIKE '%18332190%' OR tweets.mentioned_airlines LIKE '%British_Airways%')\n",
    "    AND conversations.airline LIKE '%British_Airways%'\n",
    "    AND conversations.start >= UNIX_TIMESTAMP('{start_datee}') * 1000 \n",
    "    AND conversations.start <= UNIX_TIMESTAMP('{end_datee}') * 1000;\n",
    "\"\"\"\n",
    "\n",
    "replies_mentioned_klm = f\"\"\" \n",
    "SELECT COUNT(DISTINCT conversations.conversation_id)\n",
    "FROM conversations\n",
    "JOIN hasher ON hasher.conversation_id = conversations.conversation_id\n",
    "JOIN tweets ON hasher.id = tweets.id\n",
    "WHERE (tweets.user_mentions LIKE '%56377143%' OR tweets.mentioned_airlines LIKE '%KLM%') \n",
    "    AND conversations.airline LIKE '%KLM%'\n",
    "    AND conversations.start >= UNIX_TIMESTAMP('{start_datee}') * 1000 \n",
    "    AND conversations.start <= UNIX_TIMESTAMP('{end_datee}') * 1000;\n",
    "\"\"\"\n",
    "\n",
    "replies_mentioned_lufthansa = f\"\"\" \n",
    "SELECT COUNT(DISTINCT conversations.conversation_id)\n",
    "FROM conversations\n",
    "JOIN hasher ON hasher.conversation_id = conversations.conversation_id\n",
    "JOIN tweets ON hasher.id = tweets.id\n",
    "WHERE (tweets.user_mentions LIKE '%124476322%' OR tweets.mentioned_airlines LIKE '%Lufthansa%') \n",
    "    AND conversations.airline LIKE '%Lufthansa%'\n",
    "    AND conversations.start >= UNIX_TIMESTAMP('{start_datee}') * 1000 \n",
    "    AND conversations.start <= UNIX_TIMESTAMP('{end_datee}') * 1000;\n",
    "\"\"\"\n",
    "\n",
    "replies_mentioned_af = f\"\"\" \n",
    "SELECT COUNT(DISTINCT conversations.conversation_id)\n",
    "FROM conversations\n",
    "JOIN hasher ON hasher.conversation_id = conversations.conversation_id\n",
    "JOIN tweets ON hasher.id = tweets.id\n",
    "WHERE (tweets.user_mentions LIKE '%106062176%' OR tweets.mentioned_airlines LIKE '%AirFrance%')\n",
    "    AND conversations.airline LIKE '%AirFrance%'\n",
    "    AND conversations.start >= UNIX_TIMESTAMP('{start_datee}') * 1000 \n",
    "    AND conversations.start <= UNIX_TIMESTAMP('{end_datee}') * 1000;\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Queries for number of conversations where an airline was mentioned and the airline did not reply per airline\n",
    "\n",
    "non_replies_where_mentioned_ba = f\"\"\"\n",
    "SELECT COUNT(DISTINCT conversations.conversation_id)\n",
    "FROM conversations\n",
    "JOIN hasher ON hasher.conversation_id = conversations.conversation_id\n",
    "JOIN tweets ON hasher.id = tweets.id\n",
    "WHERE (tweets.user_mentions LIKE '%18332190%' OR tweets.mentioned_airlines LIKE '%British_Airways%')\n",
    "  AND conversations.airline NOT LIKE '%British_Airways%'\n",
    "  AND conversations.start >= UNIX_TIMESTAMP('{start_datee}') * 1000 \n",
    "  AND conversations.start <= UNIX_TIMESTAMP('{end_datee}') * 1000;\n",
    "\"\"\"\n",
    "\n",
    "non_replies_where_mentioned_klm = f\"\"\"\n",
    "SELECT COUNT(DISTINCT conversations.conversation_id)\n",
    "FROM conversations\n",
    "JOIN hasher ON hasher.conversation_id = conversations.conversation_id\n",
    "JOIN tweets ON hasher.id = tweets.id\n",
    "WHERE (tweets.user_mentions LIKE '%56377143%' OR tweets.mentioned_airlines LIKE '%KLM%')\n",
    "  AND conversations.airline NOT LIKE '%KLM%'\n",
    "  AND conversations.start >= UNIX_TIMESTAMP('{start_datee}') * 1000 \n",
    "  AND conversations.start <= UNIX_TIMESTAMP('{end_datee}') * 1000; \n",
    "\"\"\"\n",
    "\n",
    "non_replies_where_mentioned_lufthansa = f\"\"\"\n",
    "SELECT COUNT(DISTINCT conversations.conversation_id)\n",
    "FROM conversations\n",
    "JOIN hasher ON hasher.conversation_id = conversations.conversation_id\n",
    "JOIN tweets ON hasher.id = tweets.id\n",
    "WHERE (tweets.user_mentions LIKE '%124476322%' OR tweets.mentioned_airlines LIKE '%Lufthansa%')\n",
    "  AND conversations.airline NOT LIKE '%Lufthansa%'\n",
    "  AND conversations.start >= UNIX_TIMESTAMP('{start_datee}') * 1000 \n",
    "  AND conversations.start <= UNIX_TIMESTAMP('{end_datee}') * 1000; \n",
    "\"\"\"\n",
    "\n",
    "non_replies_where_mentioned_af = f\"\"\"\n",
    "SELECT COUNT(DISTINCT conversations.conversation_id)\n",
    "FROM conversations\n",
    "JOIN hasher ON hasher.conversation_id = conversations.conversation_id\n",
    "JOIN tweets ON hasher.id = tweets.id\n",
    "WHERE (tweets.user_mentions LIKE '%106062176%' OR tweets.mentioned_airlines LIKE '%AirFrance%')\n",
    "  AND conversations.airline NOT LIKE '%AirFrance%'\n",
    "  AND conversations.start >= UNIX_TIMESTAMP('{start_datee}') * 1000 \n",
    "  AND conversations.start <= UNIX_TIMESTAMP('{end_datee}') * 1000;\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "mentioned_ba_count = get_count(mentioned_ba)\n",
    "replies_mentioned_ba_count = get_count(replies_mentioned_ba)\n",
    "non_replies_where_mentioned_ba_count = get_count(non_replies_where_mentioned_ba)\n",
    "\n",
    "mentioned_klm_count = get_count(mentioned_klm)\n",
    "replies_mentioned_klm_count = get_count(replies_mentioned_klm)\n",
    "non_replies_where_mentioned_klm_count = get_count(non_replies_where_mentioned_klm)\n",
    "\n",
    "mentioned_lufthansa_count = get_count(mentioned_lufthansa)\n",
    "replies_mentioned_lufthansa_count = get_count(replies_mentioned_lufthansa)\n",
    "non_replies_where_mentioned_lufthansa_count = get_count(non_replies_where_mentioned_lufthansa)\n",
    "\n",
    "mentioned_af_count = get_count(mentioned_af)\n",
    "replies_mentioned_af_count = get_count(replies_mentioned_af)\n",
    "non_replies_where_mentioned_af_count = get_count(non_replies_where_mentioned_af)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "replied_percentage_ba = replies_mentioned_ba_count / mentioned_ba_count * 100\n",
    "non_replied_percentage_ba = non_replies_where_mentioned_ba_count / mentioned_ba_count * 100\n",
    "\n",
    "replied_percentage_klm = replies_mentioned_klm_count / mentioned_klm_count * 100\n",
    "non_replied_percentage_klm = non_replies_where_mentioned_klm_count / mentioned_klm_count * 100\n",
    "\n",
    "replied_percentage_lufthansa = replies_mentioned_lufthansa_count / mentioned_lufthansa_count * 100\n",
    "non_replied_percentage_lufthansa = non_replies_where_mentioned_lufthansa_count / mentioned_lufthansa_count * 100\n",
    "\n",
    "replied_percentage_af = replies_mentioned_af_count / mentioned_af_count * 100\n",
    "non_replied_percentage_af = non_replies_where_mentioned_af_count / mentioned_af_count * 100\n",
    "\n",
    "mentioned_convs = [mentioned_ba_count, mentioned_klm_count, mentioned_lufthansa_count, mentioned_af_count]\n",
    "replied_percentages = [replied_percentage_ba, replied_percentage_klm, replied_percentage_lufthansa, replied_percentage_af]\n",
    "non_replied_percentages = [non_replied_percentage_ba, non_replied_percentage_klm, non_replied_percentage_lufthansa, non_replied_percentage_af]\n",
    "\n",
    "replied_counts = [replies_mentioned_ba_count, replies_mentioned_klm_count, replies_mentioned_lufthansa_count, replies_mentioned_af_count]\n",
    "non_replied_counts = [non_replies_where_mentioned_ba_count, non_replies_where_mentioned_klm_count, non_replies_where_mentioned_lufthansa_count, non_replies_where_mentioned_af_count]\n",
    "\n",
    "airlines = ['British_Airways', 'KLM', 'Lufthansa', 'Air France']\n",
    "\n",
    "bar_width = 0.5\n",
    "ind = range(len(airlines))\n",
    "\n",
    "fig, ax = plt.subplots(figsize = (9, 6))\n",
    "\n",
    "p1 = ax.bar(ind, replied_counts, bar_width, label='Replied')\n",
    "p2 = ax.bar(ind, non_replied_counts, bar_width, bottom=replied_counts, label='Non Replied')\n",
    "\n",
    "ax.set_ylabel('Number of Conversations, where mentioned', fontsize= 11)\n",
    "ax.set_title('Number of Mentioned Conversations and Reply Percentages per Airline', fontsize= 12, weight= 'bold')\n",
    "ax.set_xticks(ind)\n",
    "ax.set_xticklabels(airlines)\n",
    "ax.legend(fontsize=10)\n",
    "\n",
    "for i in range(len(airlines)):\n",
    "    ax.text(i, replied_counts[i] / 2, f\"{replied_percentages[i]:.1f}%\", ha='center', va='center', color='white', weight='bold', fontsize=10)\n",
    "    ax.text(i, replied_counts[i] + non_replied_counts[i] / 2, f\"{non_replied_percentages[i]:.1f}%\", ha='center', va='center', color='white', weight='bold', fontsize=11)\n",
    "\n",
    "plt.show()"
   ]
  }
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