stat_pairs_backtest/research/notebooks/pt_sliding.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "vscode": {
          "languageId": "raw"
        }
      },
      "source": [
        "# Pairs Trading Backtest Notebook\n",
        "\n",
        "This comprehensive notebook supports both StaticFit and SlidingFit.\n",
        "It automatically adapts its analysis and visualization based on the strategy specified in the configuration file.\n",
        "\n",
        "## Key Features:\n",
        "\n",
        "1. **Configuration-Driven**: Loads strategy and parameters from HJSON configuration files\n",
        "2. **Dual Model Support**: Works with both StaticFit and SlidingFit\n",
        "3. **Adaptive Visualization**: Different visualizations based on selected strategy\n",
        "4. **Comprehensive Analysis**: Deep analysis of trading pairs and dis-equilibrium\n",
        "5. **Interactive Configuration**: Easy parameter adjustment and re-running\n",
        "\n",
        "## Usage:\n",
        "\n",
        "1. **Configure Parameters**: Set CONFIG_FILE, SYMBOL_A, SYMBOL_B, and TRADING_DATE\n",
        "2. **Run Analysis**: Execute cells step by step\n",
        "3. **View Results**: Comprehensive visualizations and trading signals\n",
        "4. **Experiment**: Modify parameters and re-run for different scenarios\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "vscode": {
          "languageId": "raw"
        }
      },
      "source": [
        "\n",
        "# Settings"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Trading Parameters Configuration\n",
        "# Specify your configuration file, trading symbols and date here\n",
        "\n",
        "# Configuration file selection\n",
        "CONFIG_FILE = \"equity\"  # Options: \"equity\", \"crypto\", or custom filename (without .cfg extension)\n",
        "\n",
        "# Trading pair symbols\n",
        "SYMBOL_A = \"COIN\"   # Change this to your desired symbol A\n",
        "SYMBOL_B = \"MSTR\"   # Change this to your desired symbol B\n",
        "\n",
        "# Date for data file selection (format: YYYYMMDD)\n",
        "TRADING_DATE = \"20250606\"  # Change this to your desired date\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Setup and Configuration"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Code Setup"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Setup complete!\n"
          ]
        }
      ],
      "source": [
        "import sys\n",
        "import os\n",
        "sys.path.append('/home/oleg/develop/pairs_trading/lib')\n",
        "sys.path.append('/home/coder/pairs_trading/lib')\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import importlib\n",
        "from typing import Dict, List, Optional\n",
        "from IPython.display import clear_output\n",
        "\n",
        "# Import our modules\n",
        "from pt_trading.fit_methods import StaticFit, SlidingFit, PairState\n",
        "from tools.data_loader import load_market_data\n",
        "from pt_trading.trading_pair import TradingPair\n",
        "from pt_trading.results import BacktestResult\n",
        "\n",
        "# Set plotting style\n",
        "plt.style.use('seaborn-v0_8')\n",
        "sns.set_palette(\"husl\")\n",
        "plt.rcParams['figure.figsize'] = (15, 10)\n",
        "\n",
        "print(\"Setup complete!\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "vscode": {
          "languageId": "raw"
        }
      },
      "source": [
        "## Load Configuration\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Load Configuration from Configuration Files using HJSON\n",
        "import hjson\n",
        "import os\n",
        "\n",
        "def load_config_from_file(config_type) -> Optional[Dict]:\n",
        "    \"\"\"Load configuration from configuration files using HJSON\"\"\"\n",
        "    config_file = f\"../../configuration/{config_type}.cfg\"\n",
        "    \n",
        "    try:\n",
        "        with open(config_file, 'r') as f:\n",
        "            # HJSON handles comments, trailing commas, and other human-friendly features\n",
        "            config = hjson.load(f)\n",
        "        \n",
        "        # Convert relative paths to absolute paths from notebook perspective\n",
        "        if 'data_directory' in config:\n",
        "            data_dir = config['data_directory']\n",
        "            if data_dir.startswith('./'):\n",
        "                # Convert relative path to absolute path from notebook's perspective\n",
        "                config['data_directory'] = os.path.abspath(f\"../../{data_dir[2:]}\")\n",
        "        \n",
        "        return config\n",
        "        \n",
        "    except FileNotFoundError:\n",
        "        print(f\"Configuration file not found: {config_file}\")\n",
        "        return None\n",
        "    except hjson.HjsonDecodeError as e:\n",
        "        print(f\"HJSON parsing error in {config_file}: {e}\")\n",
        "        return None\n",
        "    except Exception as e:\n",
        "        print(f\"Unexpected error loading config from {config_file}: {e}\")\n",
        "        return None\n",
        "\n",
        "def instantiate_fit_method_from_config(config: Dict):\n",
        "    \"\"\"Dynamically instantiate strategy from config\"\"\"\n",
        "    fit_method_class_name = config.get(\"fit_method_class\", None)\n",
        "    if fit_method_class_name is None or fit_method_class_name[-10:] != \"SlidingFit\":\n",
        "        raise ValueError(f\"Only SidingFit is supported, got {fit_method_class_name}\")\n",
        "    \n",
        "    try:\n",
        "        # Split module and class name\n",
        "        if '.' in fit_method_class_name:\n",
        "            module_name, class_name = fit_method_class_name.rsplit('.', 1)\n",
        "        else:\n",
        "            module_name = \"fit_methods\"\n",
        "            class_name = fit_method_class_name\n",
        "        \n",
        "        # Import module and get class\n",
        "        module = importlib.import_module(module_name)\n",
        "        fit_method_class = getattr(module, class_name)\n",
        "        \n",
        "        print(\"Load configuration SUCCESS\")\n",
        "        # Instantiate strategy\n",
        "        return fit_method_class()\n",
        "    except ValueError as e:\n",
        "        print(f\"Error instantiating strategy {fit_method_class_name}: {e}\")\n",
        "        raise Exception(f\"Error instantiating strategy {fit_method_class_name}: {e}\") from e\n",
        "        \n",
        "    except Exception as e:\n",
        "        print(f\"Error instantiating strategy {fit_method_class_name}: {e}\")\n",
        "        raise Exception(f\"Error instantiating strategy {fit_method_class_name}: {e}\") from e\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Print Configuration"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Trading Parameters:\n",
            "  Configuration: equity\n",
            "  Symbol A: COIN\n",
            "  Symbol B: MSTR\n",
            "  Trading Date: 20250606\n",
            "\n",
            "Loading equity configuration using HJSON...\n",
            "✓ Successfully loaded EQUITY configuration\n",
            "  Data directory: /home/oleg/develop/pairs_trading/data/equity\n",
            "  Database table: md_1min_bars\n",
            "  Exchange: ALPACA\n",
            "  Training window: 120 minutes\n",
            "  Open threshold: 2\n",
            "  Close threshold: 1\n",
            "Load configuration SUCCESS\n",
            "  Fit Method: SlidingFit\n",
            "\n",
            "Data Configuration:\n",
            "  Data File: 20250606.mktdata.ohlcv.db\n",
            "  Security Type: EQUITY\n",
            "  ✓ Data file found: /home/oleg/develop/pairs_trading/data/equity/20250606.mktdata.ohlcv.db\n"
          ]
        }
      ],
      "source": [
        "print(f\"Trading Parameters:\")\n",
        "print(f\"  Configuration: {CONFIG_FILE}\")\n",
        "print(f\"  Symbol A: {SYMBOL_A}\")\n",
        "print(f\"  Symbol B: {SYMBOL_B}\")\n",
        "print(f\"  Trading Date: {TRADING_DATE}\")\n",
        "\n",
        "# Load the specified configuration\n",
        "print(f\"\\nLoading {CONFIG_FILE} configuration using HJSON...\")\n",
        "\n",
        "CONFIG = load_config_from_file(CONFIG_FILE)\n",
        "assert CONFIG is not None\n",
        "pt_bt_config: Dict = dict(CONFIG)\n",
        "\n",
        "if pt_bt_config:\n",
        "    print(f\"✓ Successfully loaded {pt_bt_config['security_type']} configuration\")\n",
        "    print(f\"  Data directory: {pt_bt_config['data_directory']}\")\n",
        "    print(f\"  Database table: {pt_bt_config['db_table_name']}\")\n",
        "    print(f\"  Exchange: {pt_bt_config['exchange_id']}\")\n",
        "    print(f\"  Training window: {pt_bt_config['training_minutes']} minutes\")\n",
        "    print(f\"  Open threshold: {pt_bt_config['dis-equilibrium_open_trshld']}\")\n",
        "    print(f\"  Close threshold: {pt_bt_config['dis-equilibrium_close_trshld']}\")\n",
        "    \n",
        "    # Instantiate strategy from config\n",
        "    FIT_MODEL = instantiate_fit_method_from_config(pt_bt_config)\n",
        "    print(f\"  Fit Method: {type(FIT_MODEL).__name__}\")\n",
        "    \n",
        "    # Automatically construct data file name based on date and config type\n",
        "    DATA_FILE = f\"{TRADING_DATE}.mktdata.ohlcv.db\"\n",
        "\n",
        "    # Update CONFIG with the specific data file and instruments\n",
        "    pt_bt_config[\"datafiles\"] = [DATA_FILE]\n",
        "    pt_bt_config[\"instruments\"] = [SYMBOL_A, SYMBOL_B]\n",
        "    \n",
        "    print(f\"\\nData Configuration:\")\n",
        "    print(f\"  Data File: {DATA_FILE}\")\n",
        "    print(f\"  Security Type: {pt_bt_config['security_type']}\")\n",
        "    \n",
        "    # Verify data file exists\n",
        "    data_file_path = f\"{pt_bt_config['data_directory']}/{DATA_FILE}\"\n",
        "    if os.path.exists(data_file_path):\n",
        "        print(f\"  ✓ Data file found: {data_file_path}\")\n",
        "    else:\n",
        "        print(f\"  ⚠ Data file not found: {data_file_path}\")\n",
        "        print(f\"    Please check if the date and file exist in the data directory\")\n",
        "        \n",
        "        # List available files in the data directory\n",
        "        try:\n",
        "            data_dir = pt_bt_config['data_directory']\n",
        "            if os.path.exists(data_dir):\n",
        "                available_files = [f for f in os.listdir(data_dir) if f.endswith('.db')]\n",
        "                print(f\"    Available files in {data_dir}:\")\n",
        "                for file in sorted(available_files)[:5]:  # Show first 5 files\n",
        "                    print(f\"      - {file}\")\n",
        "                if len(available_files) > 5:\n",
        "                    print(f\"      ... and {len(available_files)-5} more files\")\n",
        "        except Exception as e:\n",
        "            print(f\"    Could not list files in data directory: {e}\")\n",
        "else:\n",
        "    print(\"⚠ Failed to load configuration. Please check the configuration file.\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "vscode": {
          "languageId": "raw"
        }
      },
      "source": [
        "## Load and Prepare Market Data\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Loading data from: /home/oleg/develop/pairs_trading/data/equity/20250606.mktdata.ohlcv.db\n",
            "Loaded 782 rows of market data\n",
            "Symbols in data: ['COIN' 'MSTR']\n",
            "Time range: 2025-06-06 13:30:00 to 2025-06-06 20:00:00\n",
            "\n",
            "Created trading pair: COIN & MSTR\n",
            "Market data shape: (391, 3)\n",
            "Column names: ['close_COIN', 'close_MSTR']\n",
            "\n",
            "Sample data:\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>tstamp</th>\n",
              "      <th>close_COIN</th>\n",
              "      <th>close_MSTR</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2025-06-06 13:30:00</td>\n",
              "      <td>251.870</td>\n",
              "      <td>376.245</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2025-06-06 13:31:00</td>\n",
              "      <td>252.175</td>\n",
              "      <td>376.200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2025-06-06 13:32:00</td>\n",
              "      <td>252.490</td>\n",
              "      <td>375.275</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2025-06-06 13:33:00</td>\n",
              "      <td>253.320</td>\n",
              "      <td>376.892</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2025-06-06 13:34:00</td>\n",
              "      <td>252.540</td>\n",
              "      <td>375.675</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               tstamp  close_COIN  close_MSTR\n",
              "0 2025-06-06 13:30:00     251.870     376.245\n",
              "1 2025-06-06 13:31:00     252.175     376.200\n",
              "2 2025-06-06 13:32:00     252.490     375.275\n",
              "3 2025-06-06 13:33:00     253.320     376.892\n",
              "4 2025-06-06 13:34:00     252.540     375.675"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Load market data\n",
        "datafile_path = f\"{pt_bt_config['data_directory']}/{DATA_FILE}\"\n",
        "print(f\"Loading data from: {datafile_path}\")\n",
        "\n",
        "market_data_df = load_market_data(datafile_path, config=pt_bt_config)\n",
        "\n",
        "print(f\"Loaded {len(market_data_df)} rows of market data\")\n",
        "print(f\"Symbols in data: {market_data_df['symbol'].unique()}\")\n",
        "print(f\"Time range: {market_data_df['tstamp'].min()} to {market_data_df['tstamp'].max()}\")\n",
        "\n",
        "# Create trading pair\n",
        "pair = TradingPair(\n",
        "    market_data=market_data_df,\n",
        "    symbol_a=SYMBOL_A,\n",
        "    symbol_b=SYMBOL_B,\n",
        "    price_column=pt_bt_config[\"price_column\"]\n",
        ")\n",
        "\n",
        "print(f\"\\nCreated trading pair: {pair}\")\n",
        "print(f\"Market data shape: {pair.market_data_.shape}\")\n",
        "print(f\"Column names: {pair.colnames()}\")\n",
        "\n",
        "# Display sample data\n",
        "print(f\"\\nSample data:\")\n",
        "display(pair.market_data_.head())\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "vscode": {
          "languageId": "raw"
        }
      },
      "source": [
        "## Print Strategy Specifics\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Analysis for SlidingFit...\n",
            "\n",
            "=== SLIDING FIT FIT_MODEL ANALYSIS ===\n",
            "This strategy:\n",
            "  - Re-fits cointegration model using sliding window\n",
            "  - Adapts to changing market conditions\n",
            "  - Dynamic parameter updates every minute\n",
            "\n",
            "Sliding window analysis parameters:\n",
            "  Training window size: 120 minutes\n",
            "  Maximum iterations: 271\n",
            "  Total analysis time: ~271 minutes\n",
            "\n",
            "Strategy Configuration:\n",
            "  Open threshold: 2\n",
            "  Close threshold: 1\n",
            "  Training minutes: 120\n",
            "  Funding per pair: $2000\n"
          ]
        }
      ],
      "source": [
        "# Determine analysis approach based on strategy type\n",
        "FIT_METHOD_TYPE = type(FIT_MODEL).__name__\n",
        "print(f\"Analysis for {FIT_METHOD_TYPE}...\")\n",
        "\n",
        "print(\"\\n=== SLIDING FIT FIT_MODEL ANALYSIS ===\")\n",
        "print(\"This strategy:\")\n",
        "print(\"  - Re-fits cointegration model using sliding window\")\n",
        "print(\"  - Adapts to changing market conditions\")\n",
        "print(\"  - Dynamic parameter updates every minute\")\n",
        "\n",
        "# Calculate maximum possible iterations for sliding window\n",
        "training_minutes = pt_bt_config[\"training_minutes\"]\n",
        "max_iterations = len(pair.market_data_) - training_minutes\n",
        "print(f\"\\nSliding window analysis parameters:\")\n",
        "print(f\"  Training window size: {training_minutes} minutes\")\n",
        "print(f\"  Maximum iterations: {max_iterations}\")\n",
        "print(f\"  Total analysis time: ~{max_iterations} minutes\")\n",
        "\n",
        "print(f\"\\nStrategy Configuration:\")\n",
        "print(f\"  Open threshold: {pt_bt_config['dis-equilibrium_open_trshld']}\")\n",
        "print(f\"  Close threshold: {pt_bt_config['dis-equilibrium_close_trshld']}\")\n",
        "print(f\"  Training minutes: {pt_bt_config['training_minutes']}\")\n",
        "print(f\"  Funding per pair: ${pt_bt_config['funding_per_pair']}\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "vscode": {
          "languageId": "raw"
        }
      },
      "source": [
        "## Visualize Raw Price Data\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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2mw3PPfcqxoxJatvu97//Fez2WNx005/69Xdjxh8RERERERERERERERENCRUV5bjiikuwfv23uPrq6/DMMy/g3nsfRH7+TNx//90AgM8++wTXXHMlEhIS8dBDj2HVqldw/vkX4ZlnnsQf/3gjpJTdvIrAE0881v+/jA/M+CMiIiIiIiIiIiIiIqIh4b777oIQAv/4xzOIiopquz0rKxunn342WlpacM89f8aCBSfgd7+7qe3+M888B8OHj8ANN/wSH3/8AU4+eUmnr3Heecvw4ourcPHFlyAra1y//j7HYsYfERERERERERERERERDXr19Ufw9ddrcO6553sF/TxiY2Oxbt1aHDlyBBdddEmH+xcsOAFjx6bhww/f6/J1pkyZhuOOW4DHHns4YGP3FzP+aFA5dAgYPjzYoyAiIiIiIiIiIiIiGpqqqgSam33fZ7EI1NUBR44IuN2iz68VHQ0kJnZXdrPd/v1lkFIiLS2j023KykoAAOnpmT7vT0/PQFlZabev9ZOfXIMf/vAibN68EdOmzfB7jH3FwB8NGiUlAjffHIl7723t0Y5ORERERERERERERER919AA/PrXkeisBZ4QgNUKuFy2TrfpCU2TeOSRVsTG+rd9T16z+z5+XcvMzMLSpafhsccewt/+9mSfnqsnGPijQaOiQoOUQF2dYOCPiIiIiIiIiIiIiGiAxcYC997b2kXGn4Zhwyw4csQJt9vs8+tFR8PvoB8AjB07FkIIlJYWd7FNOgCgpGQfpkyZ1uH+4uJiZGb6zgY81hVX/AQXXXQePv/8U/8H2UcM/NGgUVur0oIbGoI8ECIiIiIiIiIiIiKiIaqrxByLRWL4cODQIQm3e+ATeOLihmHOnPl47bWX8b3vXdihz19DQwPmzJmHuLhheOGFZzsE/r788jPs31+KH//4p369XmLiGJx33jL8/e+PIDk5NWC/R1e0AXkVogFw6JAK/DU19b0uMBERERERERERERERDT6//OVvYZoGfvzjH+DTTz9CWVkpiov34eWXX8BPf3oZoqKi8Jvf/B5ffvk57r77duzduwcVFeV4663/4PbbV+Ckk07G4sXf8fv1Lrnkhzh4sAbffruuH3+rdsz4o0Gjrk4F/BobGfgjIiIiIiIiIiIiIqKOUlJSsXLlKvzznyvx8MMPoLb2IOLjh2PChIn41a9uAAAsWnQKRowYiWeeeRJXX/0jOJ1OpKaOxaWXXo5lyy6CEP7HIeLihmH58h/g8ccf6a9fyYuQfe1OOIjU1LBGZDj74x8jUFio4fTTXbjoInewhxP2LBYNw4fH4NChpoDUWiaigcP9lyh8cf8lCk/cd4nCF/dfovDEfZcofHH/7b2EBP+aGbLUJw0anlKfzPgjIiIiIiIiIiIiIqKhiIE/GhQMg4E/IiIiIiIiIiIiIiIa2hj4o0Hh8GEBKYGYGInGxmCPhoiIiIiIiIiIiIiIaOAx8EeDwqFD6mdamsmMPyIiIiIiIiIiIiIiGpIY+KNBobZWfZTT0iQDf0RERERERERERERENCQx8EeDQl2dgNUqkZhoorERkDLYIyIiIiIiIiIiIiIiIhpYDPzRoFBXJzBypERsLGAYAg5HsEdEREREREREREREREQ0sBj4o0GhtlZgxAgJu12l+jU0sNwnERERERERERERERENLQz80aBw6JAK/MXEqMBfY2OQB0RERERERERERERERDTAGPijQaGuzpPxp/7d2MiMPyIiIiIiIiIiIiIiGloY+KOwZ5rAoUPwKvXZ1MTAHxERERERERERERERDS0M/FHYO3IEME2BkSMloqIATZMs9UlEREREREREREREREMOA38U9urqVHbf8OESQgAxMSz1SURERERERERAaanAoUPBHgURERHRwGHgj8Jeba0K8o0cqcp8xsZKBv6IiIiIiIiIhjgpgXvvjcC//20N9lCIiIiIBgwDfxT2Dh0SsFgk7Hb1b7tdoqEhuGMiIiIiIiIiouCqrhaoqxOorOTiYCIiIho6GPijsFdXp/r7if+dx9vtQFMTT+qJiIiIiIiIhrIdO9S0V3U15wiIiIho6GDgj8Jeba3AiBGy7d8q448n9URERERERERDWUGBmvaqrdXgdgd5MEREREQDhIE/Cnt1dR0Df42NQRwQEREREREREQWVlMDOnTrS001ICRw8yAXCRERENDQw8Edhr65OYPjw9sBfbCxLfRIRERERERENZZ7+fiedpFL9qqo4T0BERERDAwN/FNakBA4dUj3+PGJiJJqaBKTs4oFERERERERENGgVFGgQApg/34CuS/b5IyIioiGDgT8KK01N3v+urwcMo2OpTyk7bktEREREREREQ0NBgYb0dBN2O5CQIFFV5d8UWFmZYPsQIiIiCmsM/FHYqKgQ+NnPIrF3b/sqvdpa9XfvwJ/6yXKfREREREREREOPlEBBgY7cXBMAMHq0fxl/u3druPnmCKxcaevvIRIRERH1Gwb+KGwcOiRgmgL//a+17ba6OnXifnSpT7td/b2hYWDHR0RERERERETB5+nvl5trAAASE2W3Pf4OHgT++lcbIiKA9es1HDkyECMlIiIiCjwG/ihsOBzq57ffam2ZfnV1ArouERvbvp0n8NfYyIw/IiIiIiIioqHG099vwgTvjD8pfW/f2grce68NERESK1Y4oGnA559bBnDERERERIHDwB+FjdZWFcizWIAPPtABqMDfiBES4qgYn6fUJwN/REREREREREPPzp0a0tJMxMSof48ebcLlEjh8uOO2UgL33w9UVgr88pdOJCVJzJlj4NNP9U4DhUREREShjIE/Chutrern4sUGPvnEAoejPfB3NJsNsFolm3ETERERERERDTHH9vcDVKlPAKiq6jgN9tprOtauBa65xoW0NLXdokUGqqo0FBRw2oyIiIjCD89gKGw4HIDNJrFkiRvNzQJffaX7DPwBQGwsM/6IiIiIiIiIhpqaGoHa2vb+foAq9Qmo3n9HM03gv/+14KyzgFmz2gOFEyeaGDPGxCef6AMzaCIiIqIAYuCPwobDIRARoU7Y8/MNvPeeBbW1AiNHdgz82e2SgT8iIiIiIiKiIebY/n6AqgwUHy87BP7KygSam4F587yfQwjgpJMMfPONzmpCREREFHYY+KOw0dICREWpIN/SpW4cOKChpkbD8OGdBf4GeoREREREREREFEye/n52u/ftiYkmqqpEh20tFiAnp+PznHCCG1ICX3xh6cfREhEREQUeA38UNjwZfwCQm2ti7Fi1es9XqU+7naU+iYiIiIiIiIaaggLNq7+fx+jREtXV3tNgO3fqyM42YbN1fJ64OGDmTFXuU3acdiAiIiIKWQz8UdhwONAW+BNCZf0B8FnqMyaGpT6JiIiIiIiIhpKaGoGDBzWv/n4eiYnSK+NPSpXxl5vbeVRv0SI3yss17NnD6TMiIiIKHzxzobDR2ioQGdl+Qr5woYHrrnMgM5OlPomIiIiIiIiGOl/9/TwSE9UC4eZm9e+KCoGGBuEzO9AjL89EYqKJ556zwuXqr1ETERERBRYDfxQ2WluByMj2f+s6MGeOCeEjsY+lPomIiIiIiIiGloICDWPHduzvBwAJCSrAV12t5gp27tSgaRLjx3ce+BMC+NnPnCguFnjqKStLfhIREVFYYOCPwobTCURE+HeWHRsr0doq4Hb386CIiIiIiIiIKCR01t8PUBl/AFBVpabCdu7UkJ4uERXV9XOOGydxxRUufP65Be+9pwd0vERERET9gYE/ChutraKtx193YmLUCX1TUz8OiIiIiIiIiIhCQlf9/QBVGSgqSqK6Wvyvv5/e6bbHWrjQwGmnubBqlQ1bt3IqjYiIiEJb0M9WHn/8cZx33nmYMWMG5s+fj6uuugpFRUVe21xyySWYMGGC159bbrnFa5tj758wYQLefvvtgfxVqJ+pUp/+Zvypnyz3SURERERERDT47dyp+vtNnOg7408IYPRoFfirqRGoqxM+ewF25sIL3cjLM/DwwzZUVXGugYiIiEKXJdgDWLduHZYvX44pU6bAMAzcf//9uOKKK/D2228jOjq6bbtly5bhuuuua/t3lI9aDHfeeScWLlzY9u+4uLj+HTwNqJ5k/NntKkDY0CAAsAg/ERERERER0WBWUKAhNdV3fz+PxEQV+Nu5U62DV4E//9bEaxpwzTVO3HJLBJ56yoobbnAGYNREREREgRf0wN/KlSu9/n3XXXdh/vz52L59O2bPnt12e2RkJBISErp8rri4uG63ofDldAKRkf5t6wn8sdQnERERERER0eBXUKBhxoyuM/hGjzaxdq2OXbs0jB3bdZDQl5gYYNkyNx56yNZlP0EiIiKiYAp64O9YDQ0NAIBhw4Z53f7mm2/ijTfeQEJCAhYtWoSrrrqqQ9bfihUrcNNNN2Hs2LG48MILcd5550EI/8svaJqAprFcQyiSEnA4BKKjBSyW7lfjDRumyng0N2uwhNynPDzouub1k4jCB/dfovDF/ZcoPHHfpWBqblY/jyqaNOQcPAgcPKghL8/ocs4gKUmgrk7Djh0CM2easFi0Hu+/xx0n8eabEq++asUf/+hCD6adiCiA+N1LFL64//a/kAqJmKaJO+64A/n5+cjJyWm7/YwzzkBycjJGjx6NXbt24d5778W+ffvw8MMPt21z3XXXYd68eYiKisKXX36JFStWoLm5GZdeeqnfrz9iREyPAoU0cJxOQNeBhAQLhg/37zHx8QDg//bkW1xcx7K6RBQeuP8ShS/uv0ThifsuBcPjj6uFr7/7XbBHEjybNgFWKzB/vgVddX3JyQEsFuDIEWD2bGD48PZ+Ij3Zf3/8Y2DFCmDfPhtmzuzDwImoz/jdSxS+uP/2n5AK/K1YsQJ79uzBc88953X7BRdc0Pb3CRMmICEhAT/84Q9RWlqKtLQ0AMDVV1/dts2kSZPQ0tKClStX9ijwV1fXxIy/EFVfD7hcEXA6XTh0yL9SGjabDZWVJg4dcvfz6AYnXdcQFxeF+voWGAbLlxCFE+6/ROGL+y9ReOK+S8FUXGxDYyNw6NDQ7Tn39dcWJCVpMAwnDh3qfLvISDW3AAApKQ4cOtS7/TcrC8jMtOIf/xDIzHQy648oCPjdSxS+uP/23vDhMX5tFzKBv1tvvRWffvopnn32WYwZM6bLbadNmwYAKCkpaQv8+drm0UcfhdPphM1m82sMpilhmrJnA6cB0dQkICVgtZpwu/07GERHS9TXS7+3J98Mw//3nIhCC/dfovDF/ZcoPHHfpWA4cgSoqxM4csREjH9zQYPO9u0C06cb3e5/w4YBmiaRkCBht5twH7VOuKf773nnuXDHHRFYswaYM4f7PVGw8LuXKHxx/+0/QS+iKqXErbfeig8++ADPPPMMxo4d2+1jCgoKAAAJCQldbjNs2DC/g34U2hwO9TMy0v/H2O0SjY1cdkdERERERESDk5RAQ4P6e2lp0Kd4gqK2VqC6WsPEiUa322oakJQkMWlS3ycZJ00ykZdn4JVXrDA5Z0lEREQhJOgZfytWrMBbb72FRx99FDExMaipqQEAxMbGIjIyEqWlpXjzzTdx4oknIj4+Hrt27cKdd96J2bNnY+LEiQCAjz/+GLW1tZg2bRoiIiLw1Vdf4fHHH8fll18ezF+NAqi1Vf2MiPA/I9Nulzh8mIE/IiIiIiIiGpwcDsDlUte9ZWUCublBHlAQFBSogGdurn/Rt1//2omoqMBUezr/fBf++MdIrFmj4/jjuw88EhEREQ2EoAf+nn/+eQDAJZdc4nX7nXfeiXPPPRdWqxVr1qzBP//5TzQ3NyMpKQlLlizBVVdd1batxWLBqlWrcMcddwAA0tLScMMNN2DZsmUD94tQv2ptVRcyERHdbHgUux3Yv5+BPyIiIiIiIhqc6uvbr3lVxt/QCz4VFGhITTURG+vf9qNGBa7FS3a2RGamiS1bNAb+iIiIKGQEPfC3a9euLu9PSkrCs88+2+U2J5xwAk444YRADotCjKfUZ09W5dntEg0NDPwRERERERHR4OQp85mWZg7ZUp979miYODF4tTYzMkzs3Ts033siIiIKTTwzobDgcPQm40+iqamfBkREREREREQUZJ6Mv7w8A2VlYsj1mjMMoKpKIDU1eL94WpqJ8nIBtztoQyAiIiLywsAfhYXWVkAIwGbz/zF2u+p14MkWJCIiIiIiIhpMPFVuJk0y4XIJVFWFX9WbrVs1rF6to7W15489eFDAMASSkgJXvrOn0tIkDEOgvDz83nsiIiIanIJe6pPIH62tAjabhOjBebTdrk78GxsFIiKCdxFARERERERE1B/q6wWioiSyslTGW0mJhqSk8Ok1JyXw+OM2HD4sYLVK5OebOO44N6ZONWG1dv94T7AtmIE/T7ZhaamGtLTwee+JiIho8GLgj8KCwwFERvbsMbGxnsAfMHJkPwyKiIiIiIiIKIgaGoC4OIm4OCA+XqK0VGDevGCPyn/l5QKHDwtcfrkTTU0Cq1fr+OtfIxAdLTF7toHjjjOQm2tC66ReVUWFBqtVYuTI4AX+YmKAUaNMlJYy44+IiIhCAwN/FBZU4K9nJ/IxMepnY6MAwIw/IiIiIiIiGlzq6wXsdvX3sWNNlJWFV0eXbds06LrEcccZiIwEzjzTjf37VQBwzRodn31mQXy8xI9+5MT06R37+FVUqDKfPakO1B/S0iRKS8PrvSciIqLBi2clFBZaWgQiInr2GE+pT0/PAyIiIiIiIqLBpKFBIC5OXfump5thF3zavl1HTo7pVeEnNVVi2TI37r/fgT/9qRVxcRLvv+973XpFhcCYMcFf6JuWFn7vPREREQ1ePCuhsOBwoMeBv+hoQNMkmpv7Z0xEREREREREwVRf3x74S0uTqK0VaGwM8qD8ZBjAjh0a8vI6ZvIBgBDAuHES06cbKCnxPX1VUaEhOdn34wdSWpqJ+nqBI0eCPRIiIiIiBv4oTDgcoselPoVQwT9m/BEREREREdFgVF/f3t9+7FgVAAu1cp/19fC5ILewUENrq0BentHl4zMyTBw5InDokPftzc3AkSOhkvGnxsCsPwolpgm8+aYFTU3BHgkREQ00npFQWGhthVfpD3/Z7fJ/Pf6IiIiIiIiIBhdV6lP9PSlJQtclSktD5xpYSuC22yLw6KO2Dvdt364hOloiM7PrwF1Ghrr/2Ky/ykr1e4ZCxl9iooTNxj5/FFrKygRefNGKL7/Ugz0UIiIaYDwjobDQ2ioQEdHzVXyxsQibMidERERERERE/nI6VXUcT6lPi0X1x+trxt8rr1jw5pu+e+oBqjxncbF/wcVNmzRUVGjYtElHWZn3Y7Zt0zBpkgmtm+EmJEhER8sOgb+KCvXvUMj4EwIYOza0gq5ElZVqH9mwgYE/IqKhhoE/Cgu96fEHADExzPgjIiIiIiKiwae+Xl3rekp9AqrXXGf98PzR2KhKA778sgUVFR2vpevqgPvus+Gf/7T69XzvvGNBZqaJESMk/vvf9mBiayuwZ4/WbZlPQAXVMjJMFBcfG/gTGDZMIjrar6H0u7Q0kxl/FFKqqtQ+XFCgcVE8EdEQwzMSCgsOBxAV1ZuMPwb+iIiIiIiIaPCpr1c/jw78jR0rsX+/gNnL6perV+uQEoiPB1at6hjcW7XKBodDYM8eve31O1NSIrBjh47TT3dj6VI3Vq/WUVurrs937tRgmgKTJ/s30PR02SHLsLxcQ1JS8Mt8eqSlmSgvF3C7gz0SIqWiQmDkSAnTFNi8mVl/RERDCQN/FBZUqc+eP85uZ6lPIiIiIiIiGnwaGlQgzNPjD1DBJ5dLtGX69NRnn1kwY4aJ5ctd2LRJx9at7dNG27Zp+PprHRdd5ALQffnAd9+1YMQIiVmzDCxa5EZEBPDee/r/nkvHyJHS7zKdGRkmamq8s5YqKwWSkoJf5tMjLU3CMATKy7n4mEJDZaWGnBwDmZkm1q9n4I+IaChh4I/CQmtr70p92u3M+CMiIiIiIqLBpz3w513qE0Cvyn0WFwuUlGg44QQ35swxMHGigX/9ywrDANxu4JlnrMjJMXDaaW6MG2d2Gfg7ckRlD37nO25YLEB0NLB4sRsff2xBU5MKIublGRB+Xq5nZHj/XlKqbKZQCvylpqoxstwnhQpPcHzmTAObN2twubre3uUCnn/ewgX0RESDAM9GKORJCTidApGRPT+ht9slmprUcxARERERERENFvX1AhEREjZb+21xcUB8vERpac8XwH7+uQVxcRLTppkQAvj+912oqNDw8cc63nnHgqoqgR/+0AUhgJkzDWzdqsHh8P1cH35oga4Dixa1171cutQNlwv4z3+s2L9fQ16e/2U6k5IkrFbZFvirrRVwuURIlfqMiQFGjTJ79d4TBVpzs1ocMGaMCvw5HAI7dnQ9Dbxnj4a337ZiyxZmBxIRhTsG/ijkOZ0qcBcZ2fPHxsQApinQ3Bz4cREREREREREFS329d38/j+xsE6tX6z3K2nG5VIbewoUqQw8AMjIkTjjBjVdeseLf/7ZgyRIDaWnq9WbNMuByCa9SoB5Opwr8nXCCAbu9/fbhw4HjjzfwzjvqBSZNMvwen6apUpqePn8VFepncnJorfJNS5PM+KOQUFmp9pExY0ykpkqMHm3i22+7DugVFqrPLoPXREThj2cjFPI8KwgjInp+Qu+5CGK5TyIiIiIiIhpMGhoEYmM73v7977vQ2irw4IM2uN0d7/dl/XodjY0CJ5zgHYw7/3wXDAOIigLOPbe9TuCYMRLJyb77hq1eraOhQWDp0o4vfvrp6raxY00MG+bf2DwyMkwUF6tprIoKAV2XGDUq1AJ/Zq/KrBIFWlWV+hwmJkoIAeTnm9i4Ue+yIta+feoxofwZrqhQCw6++krHf/+r45VX4PdxjohoKLEEewBE3WlpUUG73vT4i4lRZzRNTYEcEREREREREVFwNTQIr/5+HgkJEj//uRN33mnDc89Zceml7QE7KYFNmzRYLEBentnWY+/zz3VkZ5tISfF+vvh44Fe/ciIqSiI62vt18vMNfPqpBabpgva/OEFLC/D66xZMn2747L+XkiKxZIkbyck9L9GZkWHi448tcDiAigoNiYkSeohVJExLM9HQIHDkCHoc2CQKpMpKAbtdIiZG/XvmTAPvvmtBYaHAuHG+o3+FhRp0XYZs4K+oSOCWW9rLgUVEqGNafLyGadP6v+yv2w18/bWO447zvz8pEVGwMPBHIc+T8debHn+e1Y8q4y+0VgISERERERER9VZ9fec97nJzTfzgBy489ZQNY8eaOOkk1ZPv5ZetbVk9OTkGli1zIyFBYutWHZdd5uz0uXyZNcvAW29ZsXu3hokT1TbPPGNFfb3ADTf4fi4AXoHInkhPNyElUFYmUFEhfAYWg81TCrW0VMOUKaHTf5CGnspK1d/PIyfHhN0usX69jnHjOqbIHTmiemfOmWNg3To9JIPX69bpsNslVqxwYNgwiZgYDb/5jQVbt2qYNq3/X3/zZg1/+5sNCQkO5ORw/yai0BaaSziIjuJwqGU0venxZ7ez1CcRERERERENPg0Nvnv8eZx8soHFi914+mkrVqyIwD33RMBiAW680YHf/MYBh0Pgz3+OwG232WCxSMyb53/PPQDIzpYYNky2lftcs0bHl19a8MMfupCYGPig3NixEpomUVysoaJC6zToGUyJiRI2m0RBAafbKLgqKzWMGdO+j+g6MGOG4bM8LwAUFanP7KJFKigYir0qN27UMWOGgcREichIQAhg+nRg27aBGev+/ep1du4MvfeGiOhYPFJRyGttVT97U+rTZgMsFomGhsCOiYiIiIiIiCiYOuvxd7RLL3Vh0iQTLhfwq185cMstDkyaZGLaNBN//rMD117rRGQksHix0aGUZ3dU3zADGzZoqKkReOopK+bNc2PBgp4FEP1ltQKpqRK7d2uorfXOZgoVQqj38o03rPj44xCrQ0pDSlVVx30kP99AebmGysqOi+MLCzXY7RJ5eSYiImRbP83+JqXKNiwuFti8WcO6dZrPPoTV1QIHDmiYMcP7+DJ9OnDggEBdXf+PtaxMvW8M7BNROGCpTwp5nsBfb0p9CgHY7UBTEzP+iIiIiIiIaHBwu4HmZt89/o5msQC/+53vsptCAHPnGpg7t/eBupkzDXzyiQX33GNDdLTEZZe5+rX3VXq6iW++UQG15OTQC/wBwPLlLpgm8OSTNjgcLpx6aseyikT9qbFRVb46NvA3ZYoJq1Xim290nHmm9+eyqEhDVpbq+5mWJlFa2v/zaPv3q6zjY6t0XX21E/Pnex+XNm5U/QePLaHrKfG5fbuOhQv7Z9GBR1mZBotFLT4wDIRcj1EioqNxiQKFPE+pz95k/AGq9AlLfRIREREREdFg4alq013gr79NmqSygyorNVx1lQsxMf37ehkZJlpb1fV9KJb6BFRA9ZJLXDjjDBdWrbLi9de55p4GliejLzHRex+JjASmTzexbp13xEpKFfjLzlbbp6WZA1Lq8+WXrYiKkrj+egduvbUV/+//tSI318AHH3SMqG3YoCM310RUlPftcXFAerrs93KfbjdQUSEwb54Bh0OguJjzjEQU2hj4o5DncACaJmG19u7xMTEs9UlERERERESDR329mnTuqsffQLDZgHPPdeOyy5zIyen/QFxGhvp97XbZbZnTYBICuOACN773PRdefpllP2lgVVaq6V5f5XDnzjWwb58qz+tRXS3Q2CjaAn/p6SYqKgScvpOFA6KoSGD9eh3nnefGzJkmsrIkRo6U+M533Ni9W/cKrDU1qfKa+fm+M/qmTjWxbZvus0RooJSXC5imwMKFBqxWiZ07uU8TUWhj4I9CXmurQEQEel0uJDaWpT6JiIiIiIho8PAE/uLigjwQAKef7sbixf1bYs8jLU2VIgzF/n7HEgI45xw3Zs828MEHzPqjgVNZKTBsmOyQHQcA06apwNXRWX9FRWp6OCurPePPNAX27++/ubSXXrIiOdnEccd5HztmzjQxYoTEhx+27zNbt+owTYEZM3wvLpgyxcSRI/073gMH1HuUnm4iJ8dknz8iCnk8SlHIa23tfZlPQK0EbGhg4I+IiIiIiIgGB881brBLfQ60qChV4jM1NTTLfPqyYIEbZWWa3z3THA6grExg2zYNa9boePddHS++aME//mHFfffZsGJFBDZv5nQeda6yUmDMGN/7SFSUypA7NvA3apTZtpAgNVVCCPRbuc+CAg3btun43vdc0I55CV0HTj7Zja++0tHYqG7buFHD2LEmEhJ8H+8mTDBhsUhs29Z/WXhlZQIjRkjY7cDEiSZ279Zghs9hiIiGIC45opDncACRkb2/mImJkWhqCuCAiIiIiIiIiIKovh6wWmWfFsmGq1/+0ono6PAJeE6bZsJul1i9WkdamrvLbV0u4MYbI1BV1R4NsVolhg2TiItTgd6DBwU++8yCadP6sQ4jhQ0pO1bIqqrSkJbWeVRqzhwDf/ubDQcPCowaJVFYKJCV1b5PRUaq/oAq8BfYbF4pVbZfZqaJ2bN9j3HRIjdee82Czz6z4LvfdWPzZh2LF3e+79hsKvi3bZuGU08N6HDblJVpbQsOJk408eqrAqWloq38MBFRqGHgj0Jea6tAZGTvH2+3A42NzPgjIiIiIiKiwaG+XiA2tvctMcJZOJT5PJrFogItq1dbcMEF7i7/z1av1lFVpeGXv3QgJUUF/I5tffLSSxZ88onFZ8CHhpZDh4Df/jYS117rxNSpKiglpcr4mzOn8/1kxgwDui7xzTc6lixxo7hYw3nneQfW0tKk31mqPbFli4Y9ezT85jeOTj+/cXHAvHkGPvxQR1aWicZG0Wl/P4+8PBP/+Y8Fbrfa5wJt/36BOXPUGLKzVYbhzp0aMjIGpswxEVFPsTYAhTyHA4iI6P2Jvd0u0dwsYPC7mIiIiIiIiAaBhgYx5Mp8hrMFCwzU1Qns3Nn5NJyUwNtvW5CfbyA/30RiokRkZMfgXm6uiYYGgfJyRv2GunXrdLS0CLz8shXyf4eDhgagpUUgMbHzjL/o6PZynwcOCDidoq2/n0d6usr4kwE8zHiy/XJyjLZAZWeWLHGjpkbDs89aERcnkZ3d9UAmTzbgcAjs2RP4qe6WFqCmRkNqqhqDzQaMG2di587+Ky1KRNRXDPxRyGttFX3u8QeA5T6JiIiIiIhoUGhoEIiNZeAvXIwfb2LUKBNffdV5oGDTJg3l5RpOP73rcqDjx5vQNNllEJGGhm++0TFihMS+fRo2bVKfh8pK9bO7zNg5cwzs2aNh/XodQgCZmd6BuLQ0E83NAgcPBi7A/PXXOkpKNJx/fteZrwCQnS2RmWmipETDjBlGt9tnZkrY7RLbtwd+vzhwQL340b1FJ040sXNnYAOjRESBxLMECnmqx1/vH2+3q58s90lERERERESDQX09mPEXRoQAjjvOwLp1OpydtOZ7+20LsrNN5OR0nQkVGQlkZUns2MFso6Hs8GFg1y4d557rQk6Ogddes7aV+QSAxMSujw/5+arc59tvW5CcbHaYd0tPV5/DnpT7rK0V+NOfIlBU1PExLhfwwgsWzJhhIDe368+4x5IlKgg+Y0b3JbyEACZNMrF1a+D3i7IyDUIAKSnt7+nEiaoEqScoSEQUahj4o5Cnevz1/oLGswqSgT8iIiIiIiIaDDw9/ih8HH+8geZm0ZaZdbTCQoGdO3WcfrrLr759EycaKChgttFQpjL1JGbNMvB//+fGvn0aNm/WUFkpMHy47LZyVkyM6ovX2iqQnd0xEDd8uKqgVVrq/9TxRx/p2LtXw9/+ZusQ4P7gAwvq6gQuvNDl9/Mdd5yBn/3MiRkz/AsU5uUZKCrSApqlCAD792tITDRhs7XfNm4cM2+JKLTx6EQhT/X46/3jY2I8gb8ADYiIiIiIiIgoiBob2eMv3KSkSGRkmFizxtLhvrfftiIx0cSsWf4FOCZNMlFfL1BRwQXOQ9W6dTomTTJht6sA3vjxJl591YrKSg1jxvj3OZozR2XSHdvfD1AZdGlppt+BP7cb+PRTC6ZONVBTI/DSS9a2+xobgddft+DEEw2vrLnu6LoKmOt+JvHNnGkgPl7i5psj8O23gZvy3r9fYOxY73H7yryVkm2GiCh0MPBHIa+1FYiI6P0FDUt9EhERERER0WBhGOr6lj3+ws9xxxnYsEHzWphcVSXwzTc6Tj/dDc3PWbqcHJVtVFDAab2hqL4eKCjQ2gJ3QgDnnuvCvn0aNm7Uuu3v5zFrloEpUwxMm+Y7UJiWJv0u9fnttzrq6wUuvtiFZcvcePddC3bsUJ/P11+3wu0GzjvP/2y/3hg2DLjzzlZMnGjigQcisHKlFa2tfX/e0lLNq7+fhyfz9p13LPjrX2342c8i8bOfRQY845CIqDd4hkAhr7VVICqq94+3WIDISMnAHxEREREREYU9T9CIGX/hZ/58N0xT4K9/tWHlSiteecWCZ56xwm6XWLCg+z5mHpGRQGYmA39D1fr1OqQUmDWr/TPjyfpzuYTfgb+YGOB3v3MiIcH39unpJqqqNDQ3d/9cH32kY8IEA6mpEqee6sbEiQb+/ncrSksF3n9fxxlnuBEf79ew+sRuB37+cycuv9yJr77S8Yc/RPg1/s7U1wMNDQKpqR3fo7w8Ew0NAi+9ZEFzM7Bokdq/d+3ifklEwccjEYU8Veqzbxc0drtkqU8iIiIiIiIKe/X1alEre/yFn+HDgQsucCEiAigu1vD55xYUFGg480y3V/8wf+TmGigo0Nnnbwj65hsdEycaiItrv82T9QcAycn+lfrsTnq6ep7i4q6nj8vLBQoKdJxySnsG4pVXutDQILBiRQRiY4FTT3UHZEz+EAJYvNjAzTc7UFGh9SkQV1amHpuW1vE9nTzZxN13t+Lvf2/FTTc5ccEFbowZY2L3bk63E1HwdSwsThRCTBNwOkWfevwBasVPQwMz/oiIiIiIiCi8eQJ/zPgLT2ec4cYZZ/T9eXJzTbz1lkBlpUBSEj8LQ0VjI7Btm4ZLLulYNnPKFBO33OLAuHGBCfyNHSsRHy/x7beqn2BnPvrIgthY6ZWBOHq0xPLlLjz5pA2XXOJEZGRAhtQjmZkSdrtEcbGGGTN6957s3y9gsUiMHt1xHxMCHXoW5uSY2LuXgT8iCj4eiSikORzqZ19PEOx2iaYmBv6IiIiIiIgovHkWtbLH39DGPn9D04YNOkzTu8zn0dTnIjCvJQQwZ46Bdes6zyx1OIAvvtBx4oluWK3e9y1aZODOO1tx4on+l7ENJCGAjAwT+/b1LeMvJUVC1/3bftw4E6WlWkB6CxIR9QXPDiiktQf+WOqTiIiIiIiIqKEB0HWJ6Ohgj4SCKSoKyMiQ2LGDU3tDybp1OsaPNzF8+MC83ty5Bg4fFtizx/fnbO1aHS0tAosWdQzuCaGyBkUQ1+FnZJjdlirtSlmZhtRU/7MFc3JMSAkUFnK/JKLg4lGIQprDoc4O+lrqMyYGaGxkxh8RERERERGFt/p6gdhYBHUynULDpEkGdu5kn7+hoqUF2LpVw5w5A5dBl5NjIj5eYu1a3ylvH35owZQpBhITQ/NDmJEhUVcnUF/f88dKqUp9pqb6/7ulpEhER0uW+ySioONRiEJaS4v62deMv9hYycAfERERERERhT0V+AvNSXYaWLm5Jg4fVn3+KPxICaxbp/kdlNq2TYNhCOTnD1zgr6tynwUFGvbt03Dyye4BG09PZWaqbD1f5T7dbuDjj3Vs3Kjh4EHR9vtJCVRVCXz1lQ6HQyAtzf+MPyGA7Gyz0wxJIqKBYgn2AIi6EqiMP5b6JCIiIiIiosGgoUFg2DAG/ggYP96EEMCmTTqSkkI3+EK+lZYKPPhgBGJjJX78Yyfy87sOMG3erGPMGHPAs+vmzDHw/vsW7NmjISdHjVFK4LnnrMjMNLsddzCNHq0y8IqLNUyb5j3Ob7/V8eSTtrZ/R0VJjBolUV0t2uYj4+NlW/DQX+PHm3j/fQukZGY2EQUPA38U0tp7/PXteex2wOkUcDoBm6377YmIiIiIiIhCjad31EBm/FDoio4GFixw46WXLJg40UBmJgPC4WTLFh02m0R2ton774/AokVuLF/u8jkHJqUK/M2dO/D7/oQJqtzn11/rbYG/r77SsW+fhptvdoR0cEuIzvv8bd6sISXFxG9+40RZmcD+/RpqagQWLJAYO9ZEaqrqpdjT32/8eBOvvaYycZOSuE8SUXAw75hCWmur+hkR0bcvSrtdPb6pqf22xkbg2WetcDr79NREREREREREA6K0VKC2dmBL/VFou+wyF1JTJf761wgcORLs0Qw9xcUCLlfvHrt1q4ZJk0z86ldOXH65E199peOmmyJQW9sx0lRWJnDokMC0aQO/73vKfX79tSr36XAAL71kxezZBiZODN1sP4+MDBP79nm/p1KqwOv06QZGjZKYMcPEmWe6cfnlLpx+uhtTp5oYMaJ3GXvZ2SoTd/duTrsTUfDwCEQhrbVVfcP2PeNPBf4aGtq/sb/+Wse771qwaxd3AyIiIiIiIgp9GzboiIyUYTHZTgPDZgN+8Qsn3G7ggQcieh2EGgqamoCbborA2rV6QJ5v924NN98ciXvvtbUtXPdXayuwa5eGqVMNCAEsXmzgjjscaGoSeOedjgXaNm9W2YHB2vfnzDFw+LDAnj0a3nnHgiNHgAsvDI8PW2amxMGDmlcLoJISgSNHRIfyn4EQHQ2kpprYu5fzjUQUPDwCUUhzOgFdl7D0sSit3a5+Nja2B/42b1YnekVF3A2IiIiIiIgo9G3YoGPqVBNWa7BHQqFk5EiJ6693oqhI4OmnrZCsLujTe+9ZUFKi4cknrair69tzSQk8/7wViYkqwHPPPTY0N/v/+B07NBiGd+BpzBiJE05w44sv9A7VqTZvVtmBwdr3PeU+33vPgjfftGDpUveA9xrsrYwM9R7v29c+/7d5s1pE4SldGmjjxpnM+COioOIRiEJaS4voc7Yf0J7x51nd43IB27erj39hIXcDosHm4491vPQS29gSERER0eBx6JCauGaZT/Jl/HgTl1/uwmefWbrMaPvySx2vv27Bp5/q2LBBQ2GhgDEEPlJNTcC771qwcKEbNhvwxBO2PgVIv/1Ww549Gn74Qxd+/3sH9u/XcOedEV5ZZV3ZvFnH6NFmh+DZyScbaGoSXv+Hzc0qu3D69OD9Rx1d7tNqBc46yx20sfTUmDESkZHSq8/fpk068vLMPicadGb8eBPl5ZpXyyEiooHEiAeFNIcDiIjo+/PExKiTFE/G386dGhwOgenTDezdq3E1HNEgs3mzjq+/Dkz5FiIiIiKiULBxow4hENTJfwptJ55oYNw4E9984/taqKUF+PvfrXjjDQtWrrTh/vsj8Mc/RuL99wf/osn337fA5QKWLXPhRz9yYssWHZ9+2rtrRrcbePFFK6ZMMTBliolx4yRuvNGBmhqB22/3L/i3bZuGKVM6ZpslJkrk5Rn46COL17am2T9lKXti7lx17Pm//3O1VdYKB0J4+vypafDGRmDvXlVmtb+MH29CSiYbEFHw8OhDIa21VSAiou9ROSGAmBiJpiYV+NuyRUd8vMSiRW7U1wufjZOJKHzV1wvU1QkG9YmIiIho0NiwQceECUZYTbjTwJs0ycCOHb4XOO/apQJIt9/uwNNPt+Dhh1uQnW1iz57OpwdXrrRix47wnj5sagLeeceCxYsNDB8OTJ9u4sQT3Vi1yoqamp7PB336qY6qKs2rx11GhsQf/uDAoUMC//xn1/U4q6oEqqo6DzydcoobhYUaiovV2DZv1pGUZCIhIbgXuBMmmLj5ZgeWLAm/xQcZGRIlJer93LZNh5To10DqmDESdrvsct8iIupPPPpQSHM4EJBSn4AK/HlWXW3erGHaNAPZ2epLnitwiAaX+nrA5RJoaAj2SIiIiIiI+q61VbWrmDEjuBk/FPpyc000NgocONAxoLVjh4bhwyUSEyV0HYiPV5lJnoDIserrgU8+sWDVqvDuG/j++xY4ncAZZ7QH6pYvdyEmBvjHP6yoqBDYvVvD+vUaPv9cx9atGo4cgc/fuaUFePVVKxYscCM93XuDlBSJ5ctdWL3agi1bOp9n2rJFg65LTJrke3+eMcPE8OESH31kgZRq8fr06aGx70+caEKE4dr5jAwTVVWq9ObmzRrGjjUxcmT/faiFUH3+ugr8mSawdSurkBFR/xj8ufwU1lpbgcjIwHwD2u2q1GdNjUB5uYbvfc+F+HjVBLuoSGDu3IC8DBGFgIYGdSVSWysQF8ezaCIiIiIKb9u3a3C5BGbODL9MGxpY48eb0DSJnTs1pKZ6f1527NAxaZLhFbjJyDDx7rsWNDWpNilH8yySLinRsGWLFvRSk71xbLafR3Q08OMfO3HXXRH4zW98l/y02yXGjjUxdqxEaqqJ1FSJDRs0tLQA3/ue7x53CxYY+OorA08+acVddzl8LmbfvFlHTo6JqCjfY9Z1YNEiN95+24Ljjzdw6JDo17KUQ0Fmpvrs7tunYfNmHSec0P89CsePN/HWWxaYJqD5iP+tXq3jscdsWLjQjR//2OVzGyKi3mLgj0KawyEC0uMPUCdsDQ0Cmzdr0DSJyZPVl352tsmMP6JBxO0GmpvVlWxdnUBmJgN/RERERBTeNmzQMWaMiTFjeG5LXYuMBLKzJXbs0HHKKe3BosZGFcA75RTvgEd6upobKS3VkJvrHdgrLNRgt6sMwTfesGDaNGf//wIB9sEHKtvvzDNdHe7LyzPx5z+3orlZLRiNjZWIjgYOHhQoKxPYv1/D/v0atm7V8MEHlrbMrDPOcHWaLSYEcNllLtxwQwRefdWK5cu9X9flAgoKNJxzTteBp5NOcuM//7HgiSesiIiQmDgx/IKuoSQpSSIiQuLTT3XU1wtMndr/72dOjomWFoHiYoGsrI6fl02bNMTGSnz1lQ6nU+BnP3PCwpl6IgoQHk4opLW2ArGxgXkuu13i4EHRtrLKs5ItO9vEq69aYBhqVRURhbejy3sePBiGNUiIiIiIiI4ipQr8DUSGCg0OubkGPvlEBao82X27dqmSgp5F0B7JyRJWq0RxccfAX1GRhqwsEyef7MZf/xqBnTu1sApAud0q22/RIu9sv6NlZEgA3kGZpCSJpCSJOXPaf1enE6ioEKiqEt2W3UxMlDj3XDdeesmK+fPdXkGf3bs1OBwCU6Z0ncE3YgQwc6aJb77RkZ9vwNp120DqhqYBaWkSX39tQWSkRE7OwAT+YmIk1q/XkZXlffw2TdVr8OST3UhPN/HIIzY8+KAN117r5P81EQUE05wopDkcImClPmNjgcOHBbZv9y5PkZVlwun0Xf+eiMJPfX37vlxXx/2aiIiIiMJbYaFAQ4NAfn74BFwouHJzTTQ0CJSXt18P7dihY9QoEwkJ3nMsuq4CIsXF3tdOUrYH/vLzTaSmmnjjjfDKH9i/X6CpSWD+/L6XybTZgPR0FQy02brf/rTT3EhLM7FypQ0OR/vtW7dqGDZMdugP6MvixSpYNG0ay3wGQmamCSmBKVPMAcms03Vg+nQD69d3zDIoLNTQ2KiCyHPmmLj+eie2bNHw17/aYPC/m4gCgIE/Cmmqx19gnismRqKqSoPTKbxOmjIzVWNilvskGhw8gb9Ro0zU1jLwR0REREThq6kJ+PBDC+x2ifHjGfgj/3j6/BUUtM9z7NihYdIk35+h9HQTxcXecyI1NQKNjQLZ2WrO5Kyz3NiyRce+feFzjVVcrEEIIC1t4PcdXQeuuMKJsjKBK6+MxO232/Cf/1jw7bc6pkzx7rPYmbw8Ez/9qRMLFjASFAgZGepzMJD9EmfONLB/v4aqKu//8E2bVBnd7Gw1punTPcE/HevXc36SiPqORxIKaQ4HEBERqIw/9TzDh0uMHdv+nJGRQGqqiaIi7g5Eg0FDgzqhzsyUqK3lfk1ERERE4cXpBNau1fHXv9pw1VWR+OorC5YudUPjqS35KTISyMpSff4A1Q6hrKzzwF9Ghonycs0rM80zR5KVpR4zd66BxEQTb74ZPnUIi4o0JCWZAVtQ3lNZWRL33OPAxRe7EBUFvP22BZWVWrelQj2EABYsMBAR0c8DHSLy8gxMmGBg5syBC/xNnWrCYpHYsME762/LFh15eYbXcX3aNBPjx5v44IPwyqwlotDEIwmFtNZWEbATNLtd/Zw6tePKquxskxl/RIPEkSMCVqtEUpKJwkJ+zRERERFReFm50oqvvrIgM9PEhRe6MW+eu9P+ZESdyc018Omnqs+fJ/Pv2B5+HhkZqgRiWZnAuHFqoXRRkcDIkRLDhqltdB04/XQ3nnrKhvJygeTkwCzS7k/79mltgctgGTNGYswYA0uXGjAMoKpKICkp9N+7wWjECOAPf3AO6GtGRqrMzfXrNZx6qrrtyBH12Vy6tGPf1iVL3HjkERvKyoRX0gIRUU8x0kEhLZAZfzEx6nl81UbPyjJRVqahtTUgL0VEQdTQAMTFAaNGSRw+DNbHJyIiIhpk1q7V8cEHHXsmDQZSAps26TjrLBduu82BU09l0I965+g+f9u360hMNDFypO/5lbFjJTRNepX79PT3O9rChQaGD5d45ZXQz/pzu4HSUoHMzNApkavrQHKy9KvMJw0e+fkGdu3S0dCg/r15s/r+8lVydPZsA/HxEu+/z0XMRNQ3QQ/8Pf744zjvvPMwY8YMzJ8/H1dddRWKioq8trnkkkswYcIErz+33HKLz+c7dOgQTjjhBEyYMAH19fUD8StQPzEMwOUSAStpMG6cidNOc/ksqZCdrVa3HVvTnojCT0ODQFycxIgREqYpcPhw36+qnnvOgscfD/2LWyIiIqJAaGxEh35EoWLbNg2PPmrFqlVWNDYGezSBt3+/QFOT6LQkI5G/cnJUn7+dOzUUFHRe5hMArFYgNVWipETNiZim72w5qxX43vdcWLdOx+7doT1/sn+/gGEIZGYya4qCKz9fBfg2blQBv82bdWRmmoiL67itxQIsXuzGV1/pg/I7jogGTtC/pdetW4fly5fjpZdewlNPPQW3240rrrgCzc3NXtstW7YMX375Zduf3/72tz6f76abbsKECRMGYujUzzzZd4Eq9RkVBVx8sRs2W8f7UlMlbDbJcp9Eg8CRIwKxsbJtNWttbfeTVi+8YMETT3Qe2Fu/XsfGjTokrxmJiIhoCHjzTQvuucfHhVOQVVUJPPywDePHmzBNlfk32OzapUHTJMaNY+CP+iYyUvU9X7NGR3l514E/QJX79CyGLi8XcDiEzzKZCxcaSE83sWqVNaSvj4qKNAgBpKdzX6Lgio9XlcY2bNBhGGoBi69qZB6LF7thGMAXXzDrj4h6L+hRjpUrV+Lcc8/F+PHjMXHiRNx1110oLy/H9u3bvbaLjIxEQkJC2x+7p2HbUZ577jk0NDTg8ssvH6jhUz/yNJWOjOz/M0ldBzIz2eePaDBoaBAYNkxl/AFAXV3Xgb+WFuCDDyz4+msdpo9rQrXiXUNjo0B1dWiufCciIiIKpOpqDTU1IqRKpre0APffb0NMjMT11zsxfbqJzz4bfJOiO3dqyMiQAVsAS0Nbbq6BnTv1tr93JT3dRGmpgNvdHjTzVSZTCODii10oLNTw9dehG3zft09DSooZsCpSRH0xa5aBLVtU9m1Tk8D06Z3vj/HxwJw5Bj78kIuPiaj3Qi7K0fC/gsfDPN2D/+fNN9/E3LlzccYZZ+C+++5DS0uL1/179+7Fo48+irvvvhuaFnK/FvWCw6Em2AfqJC0720RRESf1icJdfT0QGysRE6MWDnSX8bdmjQ6HQ6ClRaCsrOO2e/a0f6dwcQARERENBQcPCpim6HYB1UCREvjb32yorRX45S+dsNuBE090Y98+DSUloTHGQJAS2LVLx8SJIRRxpbCWm6sCd8nJJuLju942M1PCMAQOHBAoLNSQlGQiOtr3tpMnm5g+3cCLL1rgcgV2zIFSXKyFVH8/GtpmzjTgdAo8/7wVdrtEVlbXEb0lSwxUVWnYvJlzEETUOyG1PM40Tdxxxx3Iz89HTk5O2+1nnHEGkpOTMXr0aOzatQv33nsv9u3bh4cffhgA4HQ68ctf/hK/+c1vkJycjLKysl69vqYJaNrguWgIdy6XgBCA3S5gsfT/F11ODvDOOxqamzWfdbaHGl3XvH4ShYvGRg3x8SYsFg0JCcChQ1qXx5DPPrNiyhQTO3dqKCy0IDvbe6Jl3z4dw4YBUVESRUU6Tjgh9Jfccf8lCl/cf4nC02Dbdw8fVtdidXUakpL6/9zniy80lJZqWL7c7fP+t97SsWmTjl//2oX0dAFAYOZMYNgw4MsvrcjO9v24cFNdrd77SZMwINfApAy2/fdokyapCkd5ebLbz1RWFqBpQFmZjuJiHePGmV0+5vvfN/C739nw0UdWnHFGaAWr3W5g/34NJ53U9e9A4S2c9t20NCA5WaK0VMNxxxmw2boe88SJQFaWxIcfWjFrVohG14n6IJz233AVUoG/FStWYM+ePXjuuee8br/gggva/j5hwgQkJCTghz/8IUpLS5GWlob77rsP2dnZOPvss/v0+iNGxEAIBv5ChculGkdnZVm6XZkWCLm56vVaWixIT+//1wsXcXFRwR4Ckd9cLvUnNdWC4cOB1FSguRkYPtx36nBREVBWBtx8M/DKK0BpqXrc0crKgKlTVY+MAwc6f65QxP2XKHxx/yUKT4Nh33W71fmT1Qq0tnY8N+oPa9YAu3YBP/5xhM+KL+vXA4sXAyef7D2F8d3vAh9+aME110TAElKzG72zcaN63+fNsyA2NtijGXoGw/57rOHDgRtuAHJyLH5dx6SnAwcOWFBRAZx2WtfXPsOHA2eeCbz9tgXnnIOQ+swWFqqSpNOnD8wxjIIrXPbdE08EXn0VWLjQv/3xe98DHngAaG21ISmp/8dHFAzhsv+Go5A5Nb711lvx6aef4tlnn8WYMWO63HbatGkAgJKSEqSlpWHt2rXYvXs33nvvPQCA/F8B5Hnz5uGnP/0prrvuOr/GUFfXxIy/EFJYqAOwwDQdOHSo/1/PYgFcrggUFbmQmMhyELquIS4uCvX1LTAMvh8UHurq1H4MuHDokInoaAv27dNw6JDT5/b//rcFdruG7Gwn0tMtWL1aQ12dE541IKYJbNsWgXPOcSMiAvjsMwtqahwhP7HE/ZcofHH/JQpPg2nfra4GnE41IVlU5MahQ/2byeN2q/MtlwtYs8aFadO837/6emDnzggsWqTO7442e7bASy/Z8OGHLsydG97vOwB8840FiYka3G7ngFwDkzKY9l9fpkxRP/35TCUlWfHxxxpaWoDRo504dKjrjN/TTgPeeScCr77qxtlnh07W34YNOgzDgvj4gZlPouAIt303P19g9WorsrP9O8ZPngxYLBF44w03li0Lnf2LKBDCbf8NJcOHx/i1XdCnLqWUuO222/DBBx/gX//6F8aOHdvtYwoKCgAACQkJAICHHnoIra2tbfdv3boVN954I1atWoW0tDS/x2KaEqYZ+iXchorKSh0jR5oDtvNHRKh+YFVVgNvNA46HYZh8Pyhs1NUJSAnY7Qbcbon4eBM1NZrPz7DDocpKfec7bkhpYtw4N958MwKVlRIJCeq7oLRUoKUFyMw0YLNJOJ0WFBV1X48/VHD/JQpf3H+JwtNg2HerqrT/nU9JVFb2/7VRYaGA0wlomsSmTQKTJ3u/3ubNOqQEcnPdcB9T0XPMGCAry8THH2uYOTP8y33u2CEwaZIR9p+hcDUY9t++Sk83sHq1Bl2XSEkxOuxzx4qOBnJzDWzZInD66aHz3hUW6khONqHrZre/A4W/cNl3k5KA229XATx/Ppe6Dsyd68ann+o45xwXNFZEpEEoXPbfcBT0wN+KFSvw1ltv4dFHH0VMTAxqamoAALGxsYiMjERpaSnefPNNnHjiiYiPj8euXbtw5513Yvbs2Zg4cSIAdAjuHfrfsons7GzEsVlb2Dp4ULRNvg+UkSMlamuZ9UkUrhoa1P7rKTMzcqREQ4OaTLLZvLf9+msdLS0CJ56oTrxzctSJxu7dGhIS1G1792oQQk0oaZqakCoq0pCVxdV2RERENDjV1anzqZwcEzU1/X9ttHu3DqtVYvZsA1u3dpzV3LJFQ2qq2Wm5vhNPdOOpp2yoqwNGjOjnwfaj+nqgslLDuecySkHBk56uronS0mSH66fOTJ5s4sUXLT6vuYJl3z4NGRmcSKbwd+KJbnzyiQXbt2uYMoWfaSLyX9DXCjz//PNoaGjAJZdcggULFrT9+e9//wsAsFqtWLNmDa644gqceuqpuPvuu7FkyRI89thjQR459beaGgb+iKhn6us9gT917Bg5Uv08dKjjfv3JJxbk5RlITJT/ewyQnGxi1672r8bCQg1paSYiI9VFbHq6RGFh0L86iYiIiPpNba1ATIxEaqqqnNDfdu/WkJVlYsYME/v3a17lz6QEtm7Vu5zsnDvXgMUi8eWXQV/X3Cc7d6r3esIETuxS8HiCZZmZ/n8OJ0824HYL7N4dGtdJLhdQViaQlcV9icJfdrZEcrKJTz8N7+84Ihp4QT9q7Nq1q8v7k5KS8Oyzz/boOefOndvt81Jok1IF/o4/fuADf5zUJwpf9fUCNptEZKT6tyfwd/CgaAvwAcD+/QJ79mi45hrv3n85OabXBeuePRpyc9svGLOyTBQU8BhBREREg1dtrcCIEar0+eHDAi4XYLX2z2tJqQJ/Cxe6kZdnQAgV6DvhBFVdobxc4PBhgSlTOq+2EBMDzJxp4JtvdJx1Vvhmy+3erWPUKLPt/JUoGOx2YMkSN+bN87/CydixErGxEtu3a8jLC36wraxMwDBEj4KXRKFKCODEEw289JIFjY1qHyUi8gdnLykkNTUBra0Co0YN7EXPqFHM+CMKZ/X1QFxc+3FjxAj192P3688+syA2VmLWLO8L2pwcEwcOaGhsVMeh8nIN48a1XzBmZ5uoqNDQ1NSPvwQRERFRENXWCowcqQJ/UnY8jwqkmhqBI0cEJkwwERurygweXe5zyxYdFovsNgsuK0viwAHV6zlc7dqlMduPQsKll7ra2iD4QwiV9bdtm96Po+pISmDtWh133GHD229b2vb/4mINmiaRlhbGBwSioyxY4IaUwOrVPdvHiooE7rjDBqez+22JaPBh4I9CUnW1urgcPXpgL3xGjpRobBRobR3QlyWiAKmvF239/QBVnjM2Vrb1qgHUBeKaNTrmzzdgOSbvfcIEE1Kq3n6e7N/x470Df1KqnhFEREREg1FdXXvGH4B+7fPnqbTgOd/Ky1PBA88E/tatGiZONBER0fXzpKSYcDoFDh4Mz0WcLS0qWMHAH4WrvDwTxcVqAWV/kxLYtEnDzTdH4OGHbWhpEXjhBSvuuMOGmhqBffs0pKT436OQKNQNGwZMn27i88/9L9wnJfDcc1bs2KF7tTMhoqGDez6FJM8FWzB6/AH9u6qViPpPQ4PwyvgDOvbu3LVLw+HDwmf5mtGjJYYNk9i1S8PevRrsdulVIjQ5WSIqSqKoiF+fRERENDjV1amMv5EjJYTo38Dfnj0akpLMttJlU6aYaGgQKClRJUYLCvwrHZicrM7XDhwIz+u4vXs1SMn+fhS+Jk9WCyT7uy2ClMBf/mLDvfdGIDJS4g9/cOC22xz4/e8dqKrScOONEVi/Xmd/Pxp0TjzRjeJiDcXF/n3PbdumYedOHbquyvAS0dDDPZ9CUk2NhogIOeC1qxn4Iwpv9fXdB/6+/lrH8OHSK5PPQwg14bJ7twr8ZWebEML7/qwsE3v38uuTiIiIBh+HA2hsVIE/i0WVTfdUY+kPu3drXiUFc3JM2GwSW7fq2L1bg8vVdX8/j1GjJCIiJA4cCM9ztJ071YIzTwCTKNwkJEgkJpr9Xu5z3z6BLVt0XHmlEzff7GwLlk+aZOLOO1sxY4aB+nqB7GwG/mhwmTbNRFyc9CvrT0rgpZesGDfOxJw5BrZvH9gyvEQUGsLzrJgGvZoagYQE6TXhPhCGD1evycAfUXhqaECXgT/TBNat0zF3rtHp8SUnx0BhoQr8+QoOZmWZKCzUwrqHDBHRYMVjM1HfeMqjexZEJiSY/VY+s6kJ2L/f+3zLagVyc01s26Zh61YNcXH+9ekSQmX9hWPGn5TAN9/omDTJHPDrX6JAmjTJ7PfMog0bdMTESBx/fMfruZgY4KqrXPjzn1tx4ondLxggCicWi+r199VXOtas0btsUfTttxr27dNw/vkuTJ48cGV4iSi0MPBHIengQYFRowZ+5sZiUcE/Bv6IwpPK+PO+bcSI9h5/u3ZpOHJEYN48d6fPkZNjwu0WaG72vVI0O9vEkSPCq28gEREF35df6rjqqsiABSmkBPbv57GehhbP/jNihCfwJ1FT0z/TBkVFvstbTpliYOdODRs26JgypfPFWsdKSTHDMuNv2zYN5eUaTjml8/NTonAwebKJykqtX+dTNm7UMXWqAb2LBKaMDNnl/UThaulSA2PGSDzyiA1XXx2JRx6xYvNm70XJpgm88ooVeXkGJk8228rw7twZft+PRNQ33OspJFVXC4weHZwl28eWBSSi8OByAS0tArGx3seOUaMkWloEmpqAtWt1jBwpkZ3d+fElPV2VihICnQb+AKCwkMcJIqJQsX69hr//3YqGBoGiosAcn7dt03DDDZGoqODxnoYOz8ImT+Bv1CjZbz3+du1S5S3HjPE+L5syxYRhCJSX+9ffzyMlRaK8XIRd5u8771iQnm4iN5elCSm8TZ6sAvX9lfVXWytQUqIhP5/7Cg1NI0dKrFjhwH33teKss9woKdHwl79E4JZbIrBliwoArl6t48ABle0HqAU8o0ebLPdJNAQx8EchR8rgZfwBwMiRJgN/RGGovl7tt8eW+vRMXB08KP5X5tPd5cpxXQfGjTORkmIiOrrj/cOHq+csKuJXKBFRKNi+XcODD9owa5aJmBiJiorAHJ937VLPU1LC4z0F3oYNGu691wZ3iCV51dWpfslWq/p3QoJEfb2AwxH419qzR5X5PPa8LDlZYvhwdf6Wl+d/ub6UFBMtLQKHDgVylP3rwAHVr+y73+36/JQoHMTGAmlpPS/3efiw6t3n+VNW5juAv3GjBk2TmDqVZTxpaEtMlDj7bDfuvtuBG290QNeBe+6JwO232/DKKxbk5xtei50nT+7/MrxEFHq67whKNMAaGgCnUyAhITiruEaNkti3j1+IRKHM7QYMA4iIaL+toUH99NXjDwC++kpHQ4PA3LndXyguX+6C09n5/dnZJvbs4XGCiCjY9u4VuP9+GyZNMvGznzlx++0RAevxVViojvMs90n9YdcuDZs26Xj/fQtOOy10on+1taLt3AlQgT9ALaBKSQncwkzDAPbu1XDOOR1/dyGA/HwDRUUahg/3/zk94ztwQMOIEeGREfTuuxYMGyYxfz4DGTQ45OUZ+PJLC6R0+RXMLi0V+OMfI+ByeW+8bJkLZ53lfXzYsEHHxIkmYmICOWKi8CWE6q35xz86sGmThpdftqKuTuCXv/SezJg0ycQnn1hw6BB69L1KROGNgT8KOZ5SMp6LzIE2cqTEwYNqhRlXXRKFpqeftqKyUuDmm9tPaD0Zf7Gx3tvGx0tomsQnn1gwapSJrKzujy1paV1vM2GCgRdesMLpBGy2no+fiIj67vBh4C9/iUBamsTPf+6E1aoyfkpL+74wQ0q0ZXaHY88wCn0NDeq85dVXLZg/3x0yE3GdBf5qagIb+CsrE3A4BMaP9x2gW77c1eNsyIQECatV4sABgSlTAjDIftbYqHqTnn22GxbOzNAgkZdn4u23BcrLuz9mOJ3AI4/YMGaMxJVXOtrmXz76yII337Rg8WI37HZ1W2uryvC/8MLQWShBFCqEAGbMMDF9ugOHD3cM7k2apBaX7Nih4/jjudCEaKjgVSyFHE/z+GCV+hwxQsIwBI4cCcrLE1E3nE7Vq2/XLh3Nze23d1bqU9eB+HjV/2/ePCMgAf3cXBNut8DevfwaJSIKlm3bdDQ1CfziFw5ERqrbkpMD0+OrslKgqUkgKclEWRlXglHg1dcLTJhgwGYDVq2yBns4bWprRVuZdEBdG2la4Pv87dqlQdclsrJ8B/5sNvgsud4VTVPHgHAJ1n/0kYr2LV7MQAYNHjk5JiIiJG67LQKPPmrFl1/qqK/3ve1zz1lRXS1w9dVOZGZKZGSoP+ef74JpAm+91R4R37pVg2EI5OczaEHUGSF8Z/QNGwakpprYsSM8vh+JKDC4x1PIqakRiI6WbSu7BppnhSv7/BGFps2bNbS2qknd3bvbv8bq6wUiIqRX+U8Pz349Z05gLhTT0iRiYiR27uTXKBFRsOzZo2HMGBPDhrXflpxswuEQqKvr23mcZ2HHCScYqK4WcLn69HREHTQ0CIwZI3HRRS6sXWsJmd47dXXeGX+aphZkBjLwt3atjpdesmLyZDPglROSk82AlfvtT2438MEHFhx/vIG4uGCPhihwIiKAP/7RgUWL3DhwQMNjj9lw1VVReOQRq1f/zY0bNXz4oQUXX+xCaqr3ap24OGDpUjfee8+Cw4c92+tITjaRmBicBeJE4c7T56+vi+OIKHyExtUF0VFqakTQsv2A9kzDgwe5exCFojVrLEhLMzF8uERBwdGBv47Zfh4JCSYSE01kZgbm2CIEMGGC6fX6REQ0sPbu1TqUCUxOVsf58vK+B/7GjDExYYIJ0xSoqAj9QAKFl8ZGIDZWYuFCA+PHm3jmGWuPS1sGWlMT0NrqHfgDVAlNT1WWvjAM4LnnLHj4YRumTzdx3XVdNFTupZQUlfEX6hObX3+t4/Bhge9+l9l+NPikpUlccIEbt9/uwMMPt+CHP3Ri2zYdv/51JN5+24KDBwUef9yG6dMNnHKK74WZp5/uhtUKvP66FVKqwB+z/Yh6b9IkAwcPaqiu5jkt0VDBGUsKOQcPiqD19wOAmBggIkIy448oBLW2Aps2aZg/38DEiQZ27NDb7mtoEB36+3lccIEbv/61M6B9O3NzTezdq8EZ+DkrIiLqRmur6hF2bOAvIUHCYpF9zvgpKtIwbpyJlBT1/Pv387KJAquhQcBuV4uJLrvMicpKgXfeCW6jN0+m7NGlPgG1Xx082Ld9qr4euOsuG95914Lly1245hpnW4neQEpONtHUJDotLRgqPv5YR16e0SHTiWiwiY8HTjnFwL33tuLEE1Wf9F/9KgKaBlx5ZefXZzExKvj38cc6vv5aR0ODwIwZvksDE1H3Jk40IQRCpsIAEfU/7u0Ucqqrgxv4E0KVBWTgjyj0bNyow+kUmDvXQG6uieJira3PX3296DTjb+RIiaSkwB5XcnMNuFwChYX8KiUiGmhFRRpMU2DcOO9JQE0DkpIkyst7f2x2OoGSEvXcMTFAfLzE/v08L6TAcbuB5ub285a0NImlSw28+KIVK1da0dgYnHF5An++M/76tg889ZQNZWUafv97J0491R3QxVhH8wTSQjlYX1cH7N6t47jjmL1EQ0dMDHDppS7cfnsrZs40ce21zm7L3C5d6kZ0NPCPf1hht8sOi32IyH8xMUBWlont2/XuNyaiQSF0z4ZpSJJS9dZLSAjuCR0Df0Shae1aHVlZJkaPlpg0yfTq89dV4K8/pKVJREezzx8RUTAUFmqIjJQ+s2VU4K/353HFxSqomJ2tzkdTU82QDiJQ+GloUD9jY9s/vxdd5MIPfuDE2rWqHN5nn+kDXq6ytlZACBXsPlpCgkRjo2hbbNVTFRUC336rY9kyF3Jz+/c6b/RoCV3v2zGgv61bp0PTJGbOZOCPhp60NInrrnP6dSyIjATOOssNh0Ng2jQDGr+Kifpk0iQDO3aEfjlsIgoMfm1SSDl8GHC5gtvjD1B9/hj4IwotTU3tZT4BIDFRIj6+vc9fQ0PnPf76g6axzx8RUbDs2aMhO9v0OQmYnGz2KeNv714NVqvE2LHqOyU1te+lQ4mO1tCgPk9HB/40DfjOdwz85S+tmDrVwD/+YcODD9oGdFy1tQLDh0voxyQDtPdA791+8N//WhAbK7FgQf8HuiwWYMwYGdLB+q+/tmDqVJVRTERdO/lkN/LyDJx0EgPlRH01aZKJhgbBShZEQ0Tong3TkHTwoPpIBrPUJ6Ay/vrax4KIAmvDBh2GITBnjrroE0KV2/T0+auv77zHX3/JzTWxZ48Gl2tgX5eIaCiTUgX+ji3z6ZGSIlFfL3pdLnHvXg0ZGRIWi+f5TFRXs6crBU574K/jffHxwFVXuXDhhS58+60Oh2PgxlVbKzr09wPQVo2lN+U+jxwBvvhCx9KlbtgGKI6ZkhK6GX+1tQJ79miYO5dBDCJ/2GzADTf4lyFIRF3LyTGh6/1Xtai0VODhh608ZyYKEQz8UUjxXEwGO+Nv5EiJhgbBLyuiELJmjY4JEwyvvjO5uSZKSgTq64HW1oEt9aleX/X5Kyri1ykR0UCprhZoaOjY388jOVnd3tusv8JCDePHt0/Kjx2rSkuHaiCBwo8n8Ge3d37ekptrQEoMaLZpXZ3vwF98PGC19q7P3/vvW6DrKmtnoKSkmDhwIDTPzdat02GxSOTnM/BHREQDKyICyMqSKCjonz5/77xjwdq1Fnz4oaVfnp+IeiY0z4ZpyKqpEbDbJaKjgzsOT2DB0+CeiIKrsRHYtq29zKfHpEkmTFP1jQEGttQnAKSnqz5/oVbu8+BBoLY22KMgIuofe/eqY+748b4Df0lJEkL0LlB3+LDKyMnKav8+SU5Wfw/l0oEUXhobAV3v+ponNVV9jktKBu5zV1srMGpUx/1KCHV9VFPTs7G0tgIffWTBokUG7PZAjbJ7fc367U9r1+qYOtUM+vUuERENTbm5BgoKAt/nr7VVLW6x2yVef90Skt/BREMNr14ppBw8KIJe5hNoD/yx3CdRaFi3ToeUArNnewf+PH3+vv5aBf6O7pUzEDRNlcvor1IZvfX441Y88kiwR0FE1D/27NEwZozZaSDBZgNGjTJ7FfgrLFTH86OzCaOjgREjJPuhUMB4ypOLLj5SERFAYqKJ0tKBOceQsvOMPwAYPbrnGX+ffaajuRlYunTgsv0AlfEHIOSy/mpqBAoLOy5kIyIiGii5uarPX6ArWajy5AK//rUDbjfw1lvM+iMKttA6E6Yhr6ZGBL3MJ6Amd4RQq16JKPh27FC9nIYN875dCGDixPY+f3FxAz+23FwTu3ZpcA/snFaXyssFdu1CwFfxERGFgr17tU6z/TySkmSvSn3u3ashPl56lZUGgNTU0C0dSOGnoUH4tVgpLU2itHRgrkcaGgCXq/PAX0KCRFWV/2MxDFXya+5cY8AXdo4ZI6FpckDLpPpj3TodVqvE9OkM/BERUXCMH29C0wJftejLL3VMnGhg3DiJ005z4913LUymIAoyXr1SSKmpEW3N44PJagWGDZMM/BGFiMpKrW319rEmTTLbAlwDXeoTCL0+fy4XcOiQ6nt48GCwR0NEFFgOB1Ba2nl/P4+UFImKCu/zuNZWFYjoaqFGYaGG7GyzQyaWCvzxvJACo6HBvyoF6ekmysoCX47LF891T2eLMLOyTJSXa36X7lq3TsfBgxpOP33gV0ZZrSpDMdSC9WvX6pg+3URUVLBHQkREQ1VkJJCdLdsWTwdCba3A9u06Fi5UC1tOO82N6GjglVeY9UcUTKF1JkxDmmmGTqlPQJX7ZOCPKPikBCorBcaM8X1syM1Vk78RERI220COTElPl4iKCp0+fzU1om2C0FOyjohosCgq0mCa3Qf+kpNNVFdrcDrbb3vrLQtWrbJ2erw2TXXc9PXcKSmqv5nD0afhEwFQGX/+9LxLSzPR3CwGZMW8p7d5Zxl/kyerhVY7dvh3brF+vQqiZ2QE59ouJSW0yvNWVQns26dh7lxm+xERUXAFus/f6tU6LBbZ1polKgo491wXvvrKgpKS0PkuJhpqOCNIIePQIQHDCI1SnwADf0Sh4sgRoLVVICnJ9yTvmDGqz9+wYcE5dui66vMXKoE/TxmuiAhg377QGBMRUaDs3ashIkJi7Niuj/nJybJt4QigMqzeeUetOt692/excf9+AYdDIDu74/dNaqoKejDrjwKhJ6U+AQxIuc/aWgFdlx3KqnuMGiWRmGhi+3b/MgRKSjRkZQWvkktWlonCQg1GiMTZVq9mmU8iIgoNgezzJyXw+ec6Zs0yEB3dfvtJJxlITDTx/PPWPr8GEfUOZwQpZBw+rH52tsp0oI0cKVmPmigEVFSor6rOMv6EAKZMMYK6aGDSJNXnz+UK2hDaVFcLWK3A9OlAYSGPYUQ0uOzdq7KItG6uYjzloT19/t58UwX9cnIM7Nrl+8EFBRp0XfoMVqSkqO+YUCsdSOGpoUH4VZ58xAgJu12itLT/P3d1daKtz3lnJk0y/cr4a21VZdrT04MX+MvNNdHaKoKeaeB0As8+a8Wrr1qxYIGByMigDoeIiCigff6KigQqKjSccIL3whaLBTjvPDe2bdN71COYiAKHV64UMpqb1RfB0StEgmnkSBO1tWJAemoQUecqKwWEUL1aOnPppS5ce62z0/v72+TJqs/fnj3B/1qtqtKQkCCRk6My/ngMI6LBQkpgzx7fpTiPZbcDdrvq83foEPDBBxaceqobM2ea2LtX89nnb/t2HePHmz4n5iMjgVGjzJAqHUjhSUqgvt6/Hn9CqHKfAxH4q67WujzXAlS5z4oKDXV1XT+Xpy9hRkZwM/5stsD2MOqpkhKBW26JwAcf6Lj4YhcuvzwEVogREdGQFxkJZGUF5jvyiy8siI+XmDy543f+jBkGdF1iw4bgfRcTDWXsskkhwxP4i4oKjVnqkSMlXC6BhgYgLi7YoyEauiorBRISTFi7qBARFTVw4/ElPV2tyN++XcOkScGb5AJUxl9iosT48UBzsyr92Vm2JBFRqDEMddwvK9NQVqZWEDudgNsNOBwC9fUC48f7d5xNSTFx4IDAf/5jhc0GnHqqGxUVAk6nFSUlAtnZ7cdGw1AZf6ed5iMi2PZ8Evv3B3+BB4W31lbAMARiY/3bPi1NYuPGgQj8CWRmdr1vTZqkVvPv2KFjwYLOS1aWlKiyoampwTv/sFjaS7GfccbAvrZpAv/9rwUvv2xBcrLEbbc52sq2EhERhYLcXAOffWaBlOgy278rLhewdq2Ok05y+6zGERmpFg1t3Kjh1FP7Nl4i6jleuVLIaGlRP0Ml48/TiH7LFq5MIQqmigoNiYmhPVkihCp/5W/fm/5UVSUwerTEuHHq30VF/KonovBQWyvw059G4ne/i8TDD9vw6acWNDQAmqbOD0ePNrFkidvvBRbJyRK7dun49FMdZ5zhRkyMOr+zWtXtR9u3T6ClRSAvr/PnTk012eOP+qy+Xn2G/Mn4A1TGX3W1htbW/hyVCvx1l/EXFweMHWti+/auzy2KizWkpEhYgrzMODfXxO7dA9vnr6ZG4I47bHjxRSuWLnXj1lsZ9CMiotAzaZKJ+nqBioren9tu3KihsVF0uRgoP9/Azp0ampp6/TJE1EvM+KOQ0dIiEBEhu+3ZMlASEiQmTjTw5Zddr2glov5VWdn1RGyomDzZwDPPWNHSErwMRCnVhNOYMSZiY1V51KIiDccdx2MYEYW+wkIVfPvVrxzIzjb7XHEhOVnik08Ehg2TWLJEZfJZLMC4caov62mntW+7fbuOyEjf/f08UlIkDh7Ugnqcp/DX2Kh+9iTwJ6Uqn+lvtmtvxtTcLJCQ0P2YJk82sW6dDildnWYIlJQEt7+fR26uiZdfVn3+srL6N/gmJfDVVzqeecaKqCjg9793BL0KBBERUWeO7vOXnNy7+YKPP7YgO9vsMsN/+nQTTz8tsHWrjnnzOC9BNJBCJMRChJCcRFmwwMD27Tpqa7m6mygYTNNTqjL0J04mTzZhmgI7dwbvq7WuTsDtbl+xn5UlsW8fj19EFB4qKzVER0tMn973oB8AJCer746zz3YhIqL99pwcFfg7ugfq9u0acnNN6F0kbnvKIO7dy0so6r2Ghp5l/KWkSGiaRGlp/32fV1er5x49uvvzrcmTDdTVibbHHMvtBkpLRVD7+3lkZZmwWiUKCvpekeGddyx49tnO684//7wFjz1mQ36+gTvvbGXQj4iIQlpkJJCZ2fs+f5WVAtu26TjllM7L5APAqFESaWkmNmzg+TPRQOvTXielRFVVFdzurndyIn80N4uQ6e/nMWeOAYtFYvXq4JfvIxqKDh4UMAyBpKTQOjb4kpgoMWKEDGq5T88knKc0alaWieJiDSbnnogoDJSXq+N9b/uMHGvyZBOXX+7E4sXeq4snTjTR2Nhe2sjhAHbt0jB5cterkFNTJeLjJbZu5cQF9V57qU//trdagaQkiZKS/vvcVVer5+6u1CcATJigMgQ6K/dZXq7O3dLTg3/uZrGo8RYU9O29a20FXn3VgnfftWD9+o7PtWuXhv/+14oLLnDhZz9zISamTy9HREQ0IHJzVRlO2Yuv7I8/1mG3S8yd230WX36+gc2b9QEtvU1EvQz8ffHFF1i2bBmmTJmCRYsWYdeuXQCAP/zhD3jjjTcCOkAaOlpaQqe/n0d0NDBrloEvvtB79UVIRH1TWakmx8aMCf0dUAi1Cr67vjf9qapKQAi0lerKzjbhcAiUlzPrj4hCQ1cLEcrLtbYsvUCwWIDFi40Ofcays00IoSbrAfyvB1j3ZaWFAKZMMdj/mfqkoUEgMlLC2nnyWAdpaSZKS7s+v5ASvb5eqakRiI6WfgWsoqNVRYFt23zvB54AZVpaaKw6mjhRZfj2ZRHU6tU6HA6BceNMPP20Dc3N7fe5XMATT1iRnW3i9NO5IJqIiMLH5MkmjhwRPa4q4HQCn39uwcKFBmy27rfPzzfQ1CSwe3fP5kqkVAv0iKh3ejw7+dZbb+HKK69Eamoq/vjHP8I86gx67NixeO211wI6QBo6WlpCL+MPABYuNFBerrFcHlEQVFQIWCwSI0eG3rHBl8mTTZSVaaivD87rV1cLDB8u206+MzLU+1ZUxOwUIgq+6mqBK66I9LkYQUq12CM5uf+P99HRQHq62Rb4275dw7BhEikp3b/21Kkm9u/XUFfX36Okwaqhwf8ynx5paRJlZaLLwN577+m4+eaIzjfoQnW16u/nb7btpEkGduzwnSFQUqIhMdEMmQWdubkmWloEiot7dy0nJfDRRxZMm2bgmmucaG4GXnyxPWr7xhsWVFUJ/OhHzpDpVU9EROSP3FwTUVESGzb0bFHbunU6GhsFTj7ZvwUvmZkSw4ZJbNzYsy/KTz7RccUVUbjmmkjcfbcNzz1nCepCa6Jw0+O95dFHH8UPfvAD3H///Tj33HO97hs/fjz27NkTsMHR0NLcHHoZfwCQl2ciPl7i888t3W98lMZG4OmnrW0ZS0TUc5WVGhITZdhMpHjKxPW2Tn5fVVVpXv15oqOBMWNMBv6IKCQUFWlwuXz3Qj1yRJV9T0oamCyhnByzbdXx9u06Jk0y/Ap6TJmittu6lVl/1DsNDcLvMp8e6ekqg7+zvnqA6j1ZUqKhtbXnY6quFm1lwv0xebIql+srQ6C4ODTKfHr0tc9fUZFASYmGk092Y9QoifPPd+Hjjy3YvVvDgQMCb7xhwRlnuDF2bOj8zkRERP6wWIBp0wysX9+z78gPP7QgL8/wuzKTEMCMGUaPAoytrcArr1gxdaqBE05ww2oF1q614K67IoK20Joo3PR4JrCsrAwnnniiz/uioqLQ0NDQ50HR0BSqGX+aBixY4MbatTp60s5yyxYdH35owY03RuDjj1kqlKg3KipEWJT59Bg+HEhKMrtdhXbkiPoTaL4m7rKzTezbx8AfEQXfgQMqSOCrV1lFhbptoHq65uSYqK7WsH+/QHGx1m2ZTw+7HcjIMANS7vOLL3Rcd10kmpp69/jCQoGf/zyyy2AQhR4V+Otpxp/6fJaUdP5/7dmHelPeu7paeC0c6s748SqYduxCJymB0lIN6emhUeYTUD0Sx483fS448MeHH1owapSJadPU77RkiYHMTBNPPGHFE0/YkJAgcc45LPFJREThaeZME8XFGg4e9O/8oaREYO9eDYsX9+y7b8YMA5WVWluP7e588IEFTU3AZZe5sGyZG7/8pRO33dYKKcGy+0R+6vHZb0JCAoqKinzet2vXLiQnJ/d5UDQ0tbQAUVHBHoVvCxYYaGwUPUpLr6oSsNsljj/ewJNP2nDffbZ+megnGswqKwcu+yNQJk/uOvBXUKDht7+NxGOP+VEMv4fUxJ33ZGJmpomSEtGjhQtERP3hwAF1bCwu9hX4E9A02aOso76YMEFlaP/731ZIqY7d/po2zcC2bX3rGbZ2rY6//92GujqBqqreBe527NBRWyvw5JNWLjALI70J/MXHq/KgnfX585TKBVSvzJ5wu4Ha2o7nD12x2VTwfNs279eqrhZobhbIyAitc7fc3N71+WtsVPvq4sVGW/UJTQN+9CMnKisF9uzRcMUVLr/6GxEREYWiqVMN6LrEhg3+nT98+KEF8fES+fk9+1LNy1OLhvzJ+mtqAt56y4KTTjKQkNB+fjJsmJrf2LSJC5uJ/NHjPeWMM87AQw89hDVr1rTdJoTA7t278cQTT+Css84K6ABp6GhuDs2MPwBITZXIzDTx5Zf+l/usqlKZSldc4cL11ztQWKjhd7+LxDPPWLF7t++eGETUzukEamu1sMr4A1S5z+pqDTU1HSdy16zRcdddNrjdwL59gT0ONDYCTU0dM/6ysiTcboH9+5kRQkTBVV4uoOuqV9mxE/Dl5RoSEiQsPaus3mvDhwOJiSbWrdORmGhi1Cj/D8hTp5poahIoKurdcXXjRg2PPmrFtGkq+Fhb27vnKSlRi8y2bdPx1Vdc+RwuGhqAuLienwCkp5udBv4OHwYcDvU58mTW+qu2VsA0hdfEmj/mzDGwZYvu1TvPk5EYShl/gAr8NTeLLjMmffniCwtMEzjpJO/VU2lpEj/8oQsXXOBCbm5o/a5EREQ9EROjvif9DcitXq3jpJPcPT5nj4hQC+38Sah45x0LnE7gnHNcHe5TC/D0Pi3AIxoqehz4u+aaazBjxgxcdtllOP744wEAP/7xj3H22WcjLy8PV155ZcAHSUNDS0to9vjzWLDAjY0bNTQ2+rd9VZVqbA+o1Pm77mrFwoUGvvlGx623RuD66yPw+usWBgCJOlFTIyAlwi7wl5trQgh4Zf1JCbz5pgWPPGLDvHkGfvITJxobBerqAheM85R6O7ZUV3q6Gg/7/BFRMBmGyurLzzfhdIoOZX7KywWSkwf2eJ+TY/Y42w9QJZSjo2Wv+vxt367h//0/G/LzTfziF05YrbLX3wWlpRrmzzcwd66BZ5+1gh0XwkNjY897/AGq3KevnnpAe5nPUaPMtsxaf7WfP/Rs/zvxRANJSSZWrWrPOC0p0TBsmER8fI+eqt9lZ6ssg507/d9npQQ++kjH7NkG4uI63r94sYEzz2Q5BSIiCn+zZhnYsUPzWX7eNNX56z/+YcX110fCNIFFi4xevU5+voHdu32/jkd9vQr8LVnixvDhHe+fNk31GS4s5PwGUXd6vJfYbDb87W9/w9NPP41ly5bh/PPPx//93/9h5cqV+Nvf/gbLQC3TpUHFNNUq1VDN+AOA2bMNmKbAtm3+XTBWVnr3Jhs2DFi+3IWHHmrFTTc5MH68iZdftvaqDwfRUOCZFA63Up+e/k8vvGDF738fgd//PgK//W0EXnzRirPPduGnP3UhO1sdG3q68rwr1dXqK/3YibuICCA1lX3+iCi4qqsFDENg/nw1UX5sn7+KCoHk5IE93k+YoF4vL69nkxe6roKFPe0vsnu3hvvvtyE318TVVzthsQAjRsheZfw5nSrYk5Zm4tJLnZASWLXK2uPnoYFlGCrwZ7f3/JonLU3i4EHfk2WVlQJCANOnmz3O+KupUY8dObJnY7JY1LVNQYGOb75R+3NJSWj19/Pw9PlbvVrvdBGnp+Sp588332iorNRw8sm9m9wkIiIKFzNmmDBNgc2bvc9td+3ScN11kbjzzgjs2KHhlFPcuOMOR4/PGTwmT1av01Xf3TfftEIIdLq4JjvbhN0uWe6TyA+9jtLNmzcP8+bNC+RYaJCTEti8WcO0aSr75GgtLepnqPb4A4ARI4CUFBNbt2qYN6/rC8DmZtW/w1efGiFURlBKiok1aywoLxdISQndgCdRsFRUaIiKkj5XWYe6Cy90Yf16ddLsWQV/3nnutmPHiBESdrvq1eOrNr5nUspu9/81q6oEYmKkz8dkZJg+e2oREQ0UT7nhnBwTI0dKlJQIHHecus/pBA4e1JCUNLDZM/n5Bo4/3o0pU3oeqJgyxcDTT1vR1KRKJHVn3z6Bv/zFhsxMT6afun3kSBXM6an9+1VWfHq6iWHDgIsucuGJJ2w4/nijV78PDQxP0K43pT7T0tT/a1mZhokTvf+PKysFRo82kZ5u4qOPVHksf/vOVVcLjBxp9qrM7rRpJqZONfD881bMmOFAcbGGE04IzSy4s89248EHbbjhhkhceaUTU6eq99AwgM8/1/Hvf1s7ZN+mpJgd3msiIqLBZuRI1d7o2291HHecmrM4fBh48EEbRo+W+MUvHMjOlh3mcnsqMVFi1CgT27frmDmz4/drXR3wwQc6zjzT3elciKapRXtbtug4//zQPOcgChU9Pr1fs2YNysvLcd5553W477XXXkNycjIDguRTSYnAvfdG4E9/asW4cd4Xu83N6tsjOjq0A2BTpqheMFK6uvzC66zk3tFiY4GYGPm/0jy8oCQ6lidrtq8nl8EwebLZZek4IdQE3rEZLx5/+5sNQgC//rXT79esrhadlunKyFCr3N1uDFj/LCKio5WXa4iJkRg2TAWrjj7+VVaqINZAl/qMiwN+9rOOvUP8MWWKWrG8fbuGOXO6Po/bv1/g7rsjkJws8atfORER0X7fyJGyrUxjT5SUaBBC9aEGVNnF1asNPPmkFX/5i4PH+hDV0KBOanpT6jM5WULXJUpLBSZO9L6vokJDYqJESoqElGqfSkvzb3+qrtZ6XObzaN//vgs33BCBl16y4vBh/193oE2ebOLOO1vx97/bcM89EfjOd9wYP97Eq69aUFWlYd48NxYsMKAfleyQnBye56FEREQ9NXOmgTfftMDlUsG1Rx9VK4h+/nNHQEt4T55sYscO3+e+n31mgcUCnHpq1wG96dNNPPaYBUeOqOpqRORbj68yH3jgAdTW1vq8r66uDg888EBfx0SDlOdCt76+49VTe8ZfaF4oekyZYqCurmNfmmNVValdq6veZEIASUmyx+V4iIaKykrR1idzMEpPlz579bjdQEGBhoICDUYPqktVVXUe+MvMlDAM0ZZxQ0Q00PbvF22T6J4sZE9GdHm5Om8a6FKffZGQIDFmjNltn7+qKoE774zAiBESv/mNo0N1i96W+iwp0ZCUZLZldQmhyi7W1GjYvJkZ3qHKcx0UG9vzax6LRQV6S0s7/v9WVAgkJcm2fagnff66Wjjkj+RkiVNOMfDOOyranJkZuvvxiBHA737nxCWXOPHppzoefdSGpCSJP/+5Fddc48L06SamTGn/09tSZkREROEmP9+AwyGwY4eGV1+1oKBAxzXXOAPet3fyZBP792s4cqTjfd9+q2P6dAPR0V0/x9SpaqKkp2X3iYaaHl8V7tmzB3l5eT7vmzx5Mvbu3dvnQdHg1NSkLnQbG30F/tRtoVzqE1C9YHRdYuvWrned6mqB6GjZbemn5GSzbbKLiLxVVGhIShq8Ey7p6SaqqjQ0N3vfvm+fBqdTwOEQPgODgCoFemyPH7Vi3/dkW1qaKrHcWYYhEVF/Ky/XkJqqjlHp6SYaG0VbWb3KStXzrCfljUPB1KkmNm/WUVMj0NSkSjtLqc4D16/X8PrrFtx+ewSioyVuuMHh8/cbOVLi8GHRo4UeAFBaKtpKP3qkp0ukpZn46ium+4Wqhgb1szeBP8B3tQDDUJ+5pCQTdjswbFjPFhZWV/tuT9AT557rgt0uERkp+xREHAhCAEuXGrj7bgf+/OdW/PrXTmRkhPaYiYiI+tvYsRIJCSZeftmKN96wYtkyF3JzA7+YZ9IkddK7Y4d30K66WqCkRMOsWd2/ZlycWmi0aRMDf0Rd6fFVoRACDZ4rlmMcOXIERk+vWmnI8ExS+w78qZ+hXuozMlIF/7Zu1bF0aeefdZWp1H1pmKQkiXXrVHkrlpEhatfcrFbFD+bA39G9eiZMaD+53blTQ0SEhGGoZtqZmR2PNQ8+aENNjcBtt6mJZJcLOHSo84m7yEggMVFl2Jx4Ynh/T+/ereGllyw47jgDixeH9+9CNFSYJlBeLrBggTpGeSbZi4sFRo6UKC8Pz+P9jBkG3n/fguuvjwSgzuV0XcLtbi9hn51t4sc/dnbar3bkSFWa8dAhgVGj/HsPpARKSzXMmNGxDNLxxxt4+WWL370HaWA1NgpoWveLAzszdqzE2rUCpqnKcAHAwYMCptl+DpCSYvqd8dfYqFouJCT0bf+z24HLL3fiwAEtbK5pQj1ASURENJCEAGbONPHuuxZMn27gjDP6p39efLxKgti+XcP8+e3X8+vX67BYJKZP9+8af9o0Ax98YIFhwKtMNxG163Hgb9q0aVi1ahWWLFkCcdRZvZQSzz33HKZNmxbQAdLg4Qn4NTZ2vC9cMv4A1dPlP/+xdNkrq6uSe0dLTjbR2ipw+DAwfHhgx0kUzior1TFhzJjQLRfVV55ePSUlAhMmtN9eUKBh/HgTLhewe7eO737X+8S3sVEFB01T4O9/t+H6652oqVELCLo67nhK64Wrw4eBF16w4ssv1YE3NhadBv42b9YwYoTE2LGc1CMKBTU1Ai6XaMv4GzFCwm6XKCnRMHOmiYoKrUP2WjjIyzNx++2tqK8XaGoSaG5WCzHGjJEYO9bE8OHdL+zylBKsrfU/8FddLdDa2jHjDwDmz3fjhRes+PZbPewXegxG9fUCMTG9X/CXlmbC5RKoqmoPlntaEHj+nZIisX27f9/3/vQl95fqdRl++zEREREpJ5/sxqFDApdd5uzXhTx5eSY2bvQ+V/nmGw1TppiIjPTvOaZPN/Cf/1hRWKghJ4fnH0S+9HgG8Nprr8XGjRtx1lln4cknn8Sbb76JlStX4qyzzsLGjRvxi1/8oh+GSYOBJ+PP0+vvaC0t6gI4ImKAB9ULeXmq7vWePZ3vPtXVml+9yVJS1AU6y30SefP0yexr6alQ5unVc3TJLsNQGW25uSZyckzs3NneA8tj2zYdpinwwx86sWGDjv/+14KqKnVc7er9yshQPQXNMDwn/vJLHb/+dSQ2bdJx+eVOLFzo7rIn1jPPWPHCC9YBHCERdaW8XO2vycnqGHVsn79wzfgTQpXXnDLFxLx5Kgt56VID06aZGDHCv+DOiBHq9/aUPfWHpwy0r8DfiBFAbq6Br77qeumzYQDvvqvjz3+2Ye/eMEnRGgQaGkSvy3wCqkwu4F26u6JCg9Uq24LIKSkmKisF3H4s1K+uVs/D7DciIiJKSpK49lpnv5ffnzTJQE2N1rYA6cgRYM8eHbNm+b9oLStLLSTctInzqUSd6fHeMWPGDDz99NOIiYnBvffei9/85je47777EBsbi6effhrTp0/vh2HSYODp8ef5eex9UVHdl8YMBRkZ6sulsz5/TqeavBkzpvsL6IQElfHjWalLREpFhZoYG+xlytLTTZSWth9LSkpUFkduromJE03U14u2k2GPzZtVn6xTTjFwxhkuvPiiBV98YYHFItsmkH3JyDDhcIi2bMqjFReLkD0OtbYCTz1lxdSpJu69txWLFxsYNUp2OklumipzZvt2Da2tAzxYIvJp/34NkZHexyhPr7JDhwCHQyA5OQxXJQRAdLQqCXrwYE8CfxpiYyXi433ff/zxBgoKdNTV+b5/zx4Nf/hDBFatsuHQIYFbb43Aq69a/AoUUd/U1wNxcb0PssXGAvHx0qsHcEWFuu7wXEelpEiYpmhbFNSVmhr/+pITERERBUpurgkhgB071FzI+vU6hJDIz/c/8KdpKjFjyxbW+STqTK/C4jNnzsQLL7yADRs24LPPPsP69evx3HPPYebMmYEeHw0iXZf6DP3+fh5CqC+XrVt9f7l4Jun9yVTSdbUdM/6IvBUUaG2r2gez9HQTZWXtq/ILCnRYrRJZWSbGjVMnwzt3th8fpAS2bNExbZo6IT7/fDdyckysW6cjIaHrxRMZGer9PLbcp5TAQw/ZQjZDbu1aHU6nwPLlrraVhyNHShw54jub4fBhwDAE3G7hd6kzIupf5eUCycnex6iMDInaWoHdu9X5VDhm/AXKiBGyyyzmY5WUqO/Izo75s2YZ0HWJNWu8a9K3tAArV1qxYkUEdB1YsaIV99zjwDnnuPH66xbcemuEz8UhFDgq469vz5GW5r1oqLLSe8FhSor6vj9woPv/y+pq0e35AxEREVEgxcSo+Ylt29T5zLff6pg40ezxOVJenqog4nD0wyCJBoE+zYhFRkYiMTERUeHQmI2CzlPq0xMAPFpLiwiL/n4eU6eqL5eGho73tZfc8y9okZIi20pgEZHahwoKdCxYMPh7E6WlSbjd7dl2O3eq/n4WizoZHjvWxO7d7V/VxcUCR44ITJumji+6Dlx9tRNxcbLbSXO7HRg1ykRxsffxprRUoKpK65BZGCo++cSCKVOMthJmgAr8Sem7NF5trXq/rFaJDRu4+o8oFOzfr7UFIzw8izu+/lqHrkskJAzdwN/IkZ1nMftSWuq7v59HTAyQn296lftsbATuvDMCq1fruPRSJ1ascCArS0LXgXPPdeOW/8/efYfHVV3r43/3OdPUNZJGXXKXm1zBFQdjOgEuBBJIKKGFkDiBhFySfHNDSIAESAKE3J9D4uQCARzgwk0DTAeb4oJtbIOLXCVLsmV1ydKMysycs39/HEayrDaj6dL7eR4e4dHRzNZoTttrr7Xu6YbLBfzsZ9YBF+lRaDidAsnJwX3W+wf+lD49kVNTgeRkiWPHhr/Vb2jwry85ERERUSjNnKlh714VLhewZ4+C004LfP7H10bAnyoHRGORafhNgF/+8pe4+eabkZ+fj1/+8pfDbn/33XcHPTAafXwlPgcO/AEJCfFz01laqkFKYM8eFYsX9z051dUpsFol0tL8e668PB0ffeTXrkg0Jnz4oQqbTWLBgrEQ+Ovt1VNYqGH/fgUXXtibxlZSovcpK/zZZ8Z7c3Lzarsd+MUvuqH6EeMaP172y/jbssX4wfp6ASn960kVKVVVAocPK7jjjr5L+HzlApua+k9Y+rJmli3T8MknKqT0xNTvRDTW+Hr4LVrUd1/Ny5OwWCR27FCQkyNhGsOXQhkZEuXl/q3HdLmAxkYFxcVD1+U84wwvfvc7K6qrBdLSJH79ayuamgR++lMj4HeqyZMl7rmnG7ffbsOGDSouuGD0n4Ojoa0tuB5/gNFX8tVXBZxOo19wc3P/HpkFBbpfGX91dQILF/JvTURERJE1c6aOV18VePVVEzRN4PTTA6/45Fv4VFsrUFwcP3PKRJHi1y32e++9hy9/+cvIz8/He++9N+S2QggG/mhATqfRQ2KgLLmODoHExMiPaaQyMoD8fGNCvn/gz5iI9neiOT/fWOVtBD/DMFiiOKLrwAcfmLBkiQarNdqjCb+kJMDh0FFVJVBUJNDRITBtWu8F77RpOt55x4QTJ4C0NGDnThWlpXq/CXJ/V+uPH6/jtddMPQE+KY1sm5QUifZ2YxIx2BJkofT++yakpEjMn9/3JsCX/TdQhoyvX9HSpRrWrTOhvFxg0iTeBBBFS1OTQHe36JfxpyhAUZHE4cMK8vJGf2nnoWRlSWzd6t+Foy/Ta6iMPwCYM0dHcrLE66+bcOiQAqfTCPoVFQ1+PExLMzIFP/jAxMBfmLS3B9fjD+i7aMiXPXhq4C8/X+LQoaGDyV7vwAtoiIiIiMKtpESHqkq89poJEybofSr8+CslxUgiqatTAIzt+wmigfgd+Bvo/4kC4XQChYUSFRUK3G7AYun9XldX8DfBkTZ7ttFX69Rskrq6vn02hpOfb5ycjh8XA67AJhpL9uxR0NwssHz50JkMo8m4cRKVlQoyMyVUVWLy5N4L1ilTjP/fv1/BjBk6Dh1ScNNN7hG/1vjxOjo6BBobjZ4+x44J1NYquPJKD/7+dzPq64PPRAgVtxvYsEHFWWd5+wU6bTYgKWngnlhNTQKZmUZWZFKSxCefqJg0aex8nohijS/rqKCg/7Fl/HidgT8YixmcToHubgy76KWqSkBVZU9po8GYTMDChRree8+EjAyJn/2s268+imee6cWjj1px5IjA+PGxcT4YLbq7AY8n+B5/ubkSZrNEVZUCu90X+Ou7DxUW6vjwQxWahkErAjQ1Ceg6A39EREQUeVarMd+xb5+KhQtHdr8uhHFdxB7VRAMLqMdfd3c3vvWtb2Hr1q3hGg+NUm63caPr63t3au+QeMv4A4BZszQ0NwtUVvY9wdTVCb/7+wHoCRIePx5Uy02iUeH991Xk5+tjKkNr3DgdlZUK9u1TMXmy3mdRRGamRFaWjgMHVOzerUJK9PT3G+lrAUBFhXHc2rrVKB169tnGhXZDQ+wchz75RIXTKbB8+cBZJxkZAwf+GhvF50FUYM4cDTt2sM8fUTi1tAAnThgZ2wM5dkyB2TxwDz/fMWm4INZo5ytf7E+fv6oqBYWF/pVGveACL+bO1XD33f4F/QDjHJOWJvHBB2O49mqYtLUZf99gF9ioqrGY0ujRa/QMTE7uu01+vtFDuKFh8M+Ur7cvA39EREQUDTNnGvcCp58+8koTOTk6e/wRDSKgGT6r1YqtW7dC01j6hQLjchlffUGuU/v8dXYCiYnxddNZWqojPV1i3breiRGv1+i7EsgNdGIikJ4uUVPDExWNbU4nsG2biuXLtTHVk624WIfTKbBzp9KnzKfP1Kk69u9X8OmnCoqKRlYCw8duB9LSevv8bdmiYv58DWlpxjF4qAnCSFu3TsW0adqgk9WZmUNn/AFGybrqaiXkv9e+fQoaG2PnvSKKlvfeU3H77Qn4zncScMMNCfj2t224+24rdu/uvcU4dkygoGDgEuiTJ+sQYviylaOd75jlz3Glqkrx+/0qKJC46y53QNelqgp84QtebNigwj3yBHMagK/dQSgy64uLdVRVKTh+XCAnp//zFRYan5Gh+vw1NAgIgaCuK4iIiIhG6pxzvPjmN91+L1AbSG6u9DuR4uBBBatWmQddsEg02gS8tP+MM87Ahg0bwjEWGsVcLuOm03dj2t7e9ya0o0PEXX87VTXKIW3apKK723issVFASgx4Az6UggKdGX805m3erELXgTPOGFtlGceNM44XHo8YNPBXWSmwY4eKOXOCX3gzfryOI0eMycLqagULFhjP6XDIntX/0VZXJ7B3r4qzzhr89x0q8JeVZbyns2ZpUBSJnTtDe3xdvdqM119nNgyNbZs3q3jqKQtWrPDizju7cdNNblxwgRc2m8Svf23FK68Y/USPHVP69ffzKS6WeOyxrjFfUtLfjD9NA6qrBYqLw/t+nXmmBpdLYPt2ZkyHku/+JxS9dIuLdRw9KnDs2MClctPTjQU9NTUDn//KyhSsXWtCbm7/vsFEREREkZCaalx3BiM3V+LECYHOzuG33bFDwebNJmzbxvlXGhsCvsy/8sorcc8998DlcmH58uXIzMyEOGUJ78yZM0M2QBodTg38DZTxl5AQf5M+y5drePllMz7+WMWZZ2o96eWBBv7y8iTKynjiobHt/fdNmDtXR3p6tEcSWZmZEklJEp2dvT39TjZ1qg5dF3A6gyvz6TNunI733zdhyxYVVqvsec7s7NjJ+Fu3TkViosTChUMH/k6dJHe5jIUkvsBfUhIwfbqO7dtVnHde6KoVtLcLv0ryEY1Wn36q4PHHzVi61Iubb+7b6/g//gN46SUT/vd/zSgvV1BTIzBv3uDXRcw2Asxmo9f1cMeV2loBr1f0lEgNl/x8o9/sBx+oWLyYlV5CpTfwF4qMPwlNEzhyRAxYHksI4/7i6NG+n6m2NuD558348EMTpkzRccstTOskIiKi+JWba1wX19UN35+6ttaYd331VTMWLOgeU5WmaGwKOPB32223AQCee+45PPfcc32CflJKCCFQVlYWuhHSqODr6edwGCWdfKU/AcDjAbze+OvxBxgBvtJSDevWmXDmmRpqawVMJhnwJFZ+vo5161RompFJSDTWVFUJVFQouPzy7mgPJeKEMIJxbreAzdb/+/n5EsnJEpo2cGAwUOPH63j5ZYF161TMmdPbU9Dh0PHJJ9E/AEkJbNhgwpIlWp9+h6fKyJBwuQS6utDzvvkyAH2BPwCYN0/D88+bP19gEvz4vF6gq4uBPxq79u1T8NhjFsyZo+PWWz39bpgVBbj6ai8mTtSxerUFXV1i0Iw/6pWZKYct9fnWWyaYzTLsgT8AWL7ciyeftPQpn0zBaW4WsFgkrNbgn6uoyPgMDFVppLDQOK8/9pjo2XbfPgVCAN/4hnvMlVYnIiKi0cfXUqq2VsH48UMvWKutFcjJ0VFermDfPgXTp/MehUa3gAN/zzzzTDjGQaOcL+MvJQVISpJ9Mv586djxmPEHAGedpWHVKguOHhWorzf6+wV6E52XZ6zabWzs7dPR3Q3s3q1g3jwdCpMBaZT74AMTUlJkSDLa4tGNN3ogBzkECmEEr4RASMpxTZjg6yWl4Ktf7V3p78v403VE9Zhz8KCClhaBpUuHvmj3TUQ3Nwvk5xv/7wv8ZWb2fo7mzdOxZo3Arl0KFi4M/vPV0WF8bWnhbCmNPe3twKOPWjB5so7bb3cPeUxasEBHQUE33nzTxJtqP2RlDVy+2OfTTxW8+64JN9zgRlJS+MezaJGGZ5+V+OgjFZddNrZKcIdDWZmCf/7ThPnzQ7MvJCf3lrweqNQnACxbpqG1VfTp1bhkiYYrrvAgNTUkwyAiIiKKquRkIDlZ9lRgG4yURuDvyiu9+OADYO1aE6ZPZ+UDGt0CnkKcMmUK7HZ7yAawevVqvPXWWygvL4fNZsO8efNw1113YeLEiT3bXH/99diyZUufn7v66qtx3333AQBaWlpw1113Yf/+/WhtbUVmZibOOecc/OAHP0BycnLIxkoj53IJWK0SJpNxQPZlAAJAZ6dxcI63Hn8+p5+uISVFYv16E+rqBLKzAw9g+iata2qMwJ+UwJNPmrFhgwnz5mlYudIdt+8P0XC8XmDDBhXLlmljts+M7xgwmNtu84TstbKyjNKibjcwd25vcM3hkNB1gaYmAYcjegsxNm9WkZ4uUVIy9OSoL/DX1NQ38Kcosk+52JwciZwcHfv3qyEJ/PkWrrS2GjcPzJagsWTvXhUdHQLf/rZ7yIxcn/x8iZtuCt3xazTLyJDYtWvgVRdOJ/CXv1hQWqrh3HMjU3ozMRFYuFDDBx+o+I//8PJYF4RDhwQeftiCqVN13HZb6CaYiot1NDWpg2b8TZ+uc0KLiIiIRr2cHIna2qEvVltaALdbID9fx8UXS/z5zxYcOyZQUBCfSShE/vBrTb/X68Xvfvc7nHbaaVi6dCnmzJmDH/7whzhx4kTQA9iyZQuuvfZavPjii3jqqafg9Xpxyy23oMO3pP5zV111FT766KOe/370ox/1/hKKgnPOOQd//OMf8eabb+Khhx7Cxo0b8fOf/zzo8VFoOJ3oWZ2cnNzb4wLoLfsZrxl/JhPwhS948dFHKo4dEz1p5oHIyJCwWiVqaoxd8oMPVGzYYMIFF3hRVqbgvvusMdN7iyjUdu1S0N4usGwZMwoiQQigpETHaadpfUqL+hYtRPNYo+vAli0qFi0avvxYRoaRXX1yhkxjo1GS7tSMRbtdor09NGP0Bf50XSAEl0FEceXgQQUOh46MjGiPZPTJyDCyt07N/jYWg1ng8QDf/KY7ogG400/XUFenoLU1cq95ssZGgfXro1+COhiVlQK/+Y0VxcUS3/++fwFzf5WU6MjP1wcsE05EREQ0VuTm6j39+wZz/Ljy+bYSS5dqSE+XWLt2jK48pzHDr0/4008/jdWrV2Px4sUoLS1FdXU1XnvtNei6jkceeSSoATzxxBN9/v3QQw9hyZIl2LNnDxYsWNDzuM1mg8PhGPA50tLScM011/T8u6CgANdcc02/56bocToFkpONmQwj4+/kUp/G/8djjz+fFSs0vPaaGU6nQE5O4MELIYxV8TU1AkePCvz1r2YsX+7F9dd7sGKFF488YsE991hx553uYbNgiOLNhx+aUFysY9y4+Az+x6Pbb+8/eezLoKuvF5gxI7yvv3evguRkieLivn/z/fsVtLYKLF48fEaLyQSkpsoBA3+nSk5Gn/NOME7uUdvcLJCezs8tjR0HDigh6TVK/WVmSnR3C7hcxjHLZ+NGFVu2qPjud90RD7j2LghRYLeHImMa2L1b9esY73YDv/udBZWVCk4/vRPxWMTl+HGBhx6yIidH4q67ukMeoLv4Yi/OO4+LpoiIiGhsy82V+Oyzoe/36+qM6kAOh1GN7sILvXjpJRO+8hUPQljYkCim+JXx989//hPXXHMN/vrXv+Kuu+7C73//e9x77714/fXX0d3dHdIBtX++JD8tLa3P46+88goWLVqESy65BI888gg6fY3hBlBXV4e33367T+CQosuYxBgs8Gd8jdeMP8Do0TdtmjGJMVi5neGfQ8eRIwr++78tyM6W+PrXjdJYhYUS993XjYICHQ8+aEFLS8iGTRR1TifwyScKli2LTOkyMlgsgNnc/zG7XYY9409KYPVqC37/eyOD5WQff6wiI0Ni8mT/Jph9/Y18mpoUZGX1PwYnJcmeXrPBOvn8xT5/FO+cTuDXv7Zg1SozXn7ZhO3blUH7zHV3A0eOCC5ACpOsLON9bW7uff+bm4GnnzZjyRKvX8GyUPMF/urrQ3Os27hRxapVFr/OM888Y0ZVlXGr6quIEU90HfjTnyxISpL48Y+7w9KXUVHAbD8iIiIa83JzJdrbRZ9FuqeqrRU9QT8AWLHCC7MZePNNZv3R6OXXp7u6uho//elP+zx24YUX4u6778bRo0cxadKkkAxG13U88MADmD9/PkpKSnoev+SSS5Cfn4/s7Gzs378fDz/8MCoqKrBq1ao+P/+DH/wA7777Lrq6urBixQr86le/Cuj1FUVAUTiJFw6dnQpSUgCTSUFqqkBFhYDJZNzEu90KhABSUpS47u913nlGD6miIvT8boEoLAQ2bVJgsQC/+pUbSUm9z2G3A3fd5cW3v23F1q1mfPGL4Z/8UVWlz1eicNi6VQUgcOaZ+oj2GxrYSPffnByJpiYlrH+L+nrfxLbAO++YcemlxvFM14Ft20w44wwNZrN/r+9wAC0tveNtbhaYNav/MTglRaCjQ4Tk9+rqMo7Tmga0talxfd6i2BTJ8+/776vYt0/FxIk6PvtMQWcnoKrAz3/uxpQpfYPoBw4ISCkwffrIrnNoaNnZRgWI1lbj7wEA//iHCWazwC23aFF5z5OTgdRUfH5eCH6BXn29CiGAfftU5OUNHkD+4AMF779vws03e/HUUyYcP66GPRM9FE7ed997T0V5uYJf/MKN9HTuL0Sxjve+RPGJ+y4BQEGBcR3d2KgiLW3ga9a6OhV5eb33MWlpxjzu22+bce65ckRtmyg43H/Dz6/pqu7ubiSdskwxISEBANDV1RWywdx77704ePAgnnvuuT6PX3311T3/P3XqVDgcDtx4442oqqpCcXFxz/d+8pOf4Dvf+Q6OHDmCRx99FA8++CB+8Ytf+P36GRlJEOxcHxZerzFBa7dbkJcHbN0K2O1GuomiGGU+s7Pje/b04ouBWbOAceNG9nvMnAn861/A974HzJrV/znsdmDxYmD7dhOuvTbIwQYgNTUhci9GY87WrcCiRcCECfG9/8eqQPffceOA48cBu90aphEBn3xiZBeedRbw6qsm/Md/AOnpwGefAR0dwAUXmPx+/eJiYNs249zi9RrZSxMmmPuV6sjNNcrG+c47wZASyMoy/t/tNrEsCIVNuM+/Xi+wfj1w/vnAHXcYn+3GRuCHPwR27jRh4cK+2x8/bgSBZs829eujScFLSzOyt7q7jeNKdTWwaRNw661AUVH0zpFFRYDLFZpjXVubkW1++LAJV1wx8DZHjgDPPANcdBFw1VUmvPsu0NISX8datzsB//gHcNllwOLFvL4hiie89yWKT9x3x7bp041rzKGuWVtagPnzjbkDn5tuAnbtAh5/3ISHHwas4ZsGoSFw/w0fv+9EPv74Y9TW1vb8W9d1CCHw8ccf49ixY322Pf/88wMeyH333Yf169djzZo1yM3NHXLbOXPmAAAqKyv7BP4cDgccDgcmTZqEtLQ0XHvttVi5ciWys7P9GkNzs4sZf2HS1GTBpEk6Wlq8AFQ0N5vQ3NwNIYCGBhUmkwktLaEtGxsNqakYcSnOyZOBu+8WKCmRgz7H/PkK/vu/zSgrc4d9NYqqKkhNTUBbWyc0jWW9KPRqagR27bLge9/zoKWFn7FQGun+m5Ji9JJqaXGHbWwff2xCXp6CL3/ZjQ0brPjznzXceqsXb71lQlqaAofD7fdx1GZTUVNjnE8aGgC32wqrdaDPk4qWlt7zTjDq600wm42sv+pq33mNKHQidf7duFFBba0Zy5e70dJiXFOYTEZg7/33jX305P3lk0/MKC4GTpzwDPKMFKzkZAsqK43jyp//bEZqqsCiRf4fE8MhNdWMykqgpSX4v3tFhQW6LrB1KwY8Hnd2Avfea0FmJnD11cbv7XCYsX9/aF4/3FRVQUpKAn73Ow9MJuDSS6P7tyMi//Helyg+cd8ln4QEKw4e9GL27P4V0jQNqK62YsUKL1pa+n7/O98RuPtuC377Ww0rV3qDni8g/3H/HTm73b8+An4H/h555JEBH//Nb37T599CCJSVlfn7tJBS4v7778fbb7+NZ599FkVFRcP+jO/5HQ7HkM8LAG63/5OXui6h60ztDQenE0hI0OH16khIEJ+XSNORlAQ4nQpsNuN7Y93EicYK/MHMnq3DYjHho48ELr88Mr1eNI1/GwqP9etNSEiQmD3bO+TnnkYu0P03I0OgpcUEl0sP22q3vXsFTj9dQ0KCji99yY01ayxYvtyDzZsVnHmmN6ALvvR0ge5u4MQJHXV1CqQE7HYNXm/fc7nNJqDrveedYJw4IZGYqCM52ciO4vGRwiWc518pgVdeMWPmTA35+VqfY/C8eV68+aYVhw5JTJgge7Y/cEDgvPO8/MyHkd2uo75eYt8+iS1bFHzrW24IoUf1HJmZqeHgQTXov7umAXV1wOmna9iyRUV5ucS4cX2P1a+9ZkJDA/DAA91QVQmvF8jL0/DBB6a4+dxt2AB88onAHXd0w2qN7t+OiALHe1+i+MR9l7KzdRw7NvD9eV2dgMcDZGVp/b6flwd84xtu/OEPFkyapOHccyPfV3us4/4bPn4F/t59992wDeDee+/Fq6++iscffxxJSUloaGgAAKSkpMBms6GqqgqvvPIKli9fjvT0dOzfvx8PPvggFixYgGnTpgEA3n//fTQ2NmLWrFlITEzEoUOH8Jvf/Abz589HYWFh2MZO/pEScLkEkpKMm/vkZOOr02k81tEhkJgYzRHGD6sVOO00DRs3qrjsMq5EofglJbBhgwmLFmmwWIbfniLD4TCOz42NAgUFoV8I09Ag0NioYPp0I3PjnHM0vPuujkcftaK9XWDRosAusjMz9Z7xNjUZB8SMjP7j9p13Tj4XjZTxHIDdLlFTw3qHFJ8OHlRQUaHgP/+zf7WFadN0JCZKfPKJigkTjKjF8eMCTqdASQlvyMIpM1OiuVnghRfMKCrSsXRp9CcesrON3q9eL4LqadrYKKDrAsuWebFjh4Ldu1WMG9cbFZMS+PBDFQsXasjL6z1OFxZKtLYKuFwIeuFGuDmdwOrVwIIFOhYs4L5CREREFCm5uTqOHRv4/ry2Vny+zcBzAUuWaDh40ItnnzVj3DiJKVN4HUejg1+3bwUFBWEbwPPPPw8AuP766/s8/uCDD+KKK66A2WzGpk2b8Mwzz6CjowN5eXk4//zzsXLlyp5trVYrXnrpJTz44INwu93Iy8vDeeedh29+85thGzf5r6PDuJn33az3TsAa/+7sBBISmGnpryVLNGzcaEJ1tUBxMd83ik979ypoahL4wheiP6lJvbKzjWNKfX14An9lZQqEAKZONS6kTSbg2ms9+O1vrXA49J7sIn9lZhrbNzcLNDYKpKTIATMVexecAH5W/x6U0ylQVKTDbpdoaeHqC4pPb7xhQm6ujrlz+9/UmkzAvHkaPvlExZe/bARmDhww9t1Jk3gTHE6ZmRJbtqjQNIH//M/umOil6HBISAk0NQnk5Iz8vFBfbxwvCwslpk7VsWePgosv7v3+4cMCtbUKbrihb0nP/HzjM3fsmBLzged161R0dAA33hj7ZUmJiIiIRpPcXInt2we+P6+tFTCZJLKyBr+WveYaDyoqFPzxj2Y8+mj8t6IiAgIo9Rku+/fvH/L7eXl5WLNmzZDbLF68GC+88EIoh0Uh5HQaB15flkVKivF4e7sAINHZKZDAPp5+mzVLR3KyxMaNKoqLWT+I4tOWLSocDp0rqWKM3S6hqhINDeEJaJWVKSgqMspk+syZo2PFCi/GjdMDzmJOSwMURaKpycj4G+xC3rfwxOUyzjvBcLmMQKLdbmSsd3ezCThFTnc38Ic/WHDttZ4RB2EaGgS2blVxww3uQfe5007TsGGDCXV1RrDn4EEFhYU6KzSEWWamhKYJlJRoAwZlo8G3IKShIbjAX22tgKoaEy6lpTr+/ncTPB7AbDa+/+GHJmRkSMyc2ff3zs+XEAI4dkygpGTELx8R1dUCEyYAGRlDl+4nIiIiotDKzZVwuQScTvSZbwCA2loFOTlyyPkGkwk45xwv/vQnC+/xadSIgXWkNNr5Mvt8GRcnl/oEgI4OwYy/AJhMwIIFGjZvViH5tlGcOnhQwYwZgQd6KLwUBcjKkqivD8/lQVmZgunT+09m33KLZ0S19BXFKO3Z1CTQ0CB6MgBP5Vt4YgT+guNyCSQnG0FSAMz6o4g6dEjB9u0qtmxRR/wcb71lQmKixLJlg+9zs2frMJsltm0zXufAAYULNSLAV37o6qtjp5x7ZqYxSRLsgpC6OgUOh4SiAKWlGjwegYMHjXONxwNs3qxi6VJvvyxHi8XXsyX2b1trahSwywQRERFR5OXmGvcqvrKeJzt+XPQpJT+Y9HRjm9bWGLkQJwpS7N9BUdzzTbT6Mi6sVsBslnA6jX93dYEryAO0dKmGxkalZ8KEKJ50dQHV1QpLxsUohyM8GX+9/f1CW941M3P4jL+EBCMz0HfeGamTe9b6egk2N/OmgCLn0CHjvF9WNrLzv9MJrF+vYsUKL2y2wbez2YDSUh2ffKLA6QSOH4/9MoujQWmpjt/+tqunHHIsMJmMhQ7Bnhdqa0VPYHPcOInkZIndu43P8Y4dClwugTPPHPj8UFAgcfRobB9rpQRqagQDf0RERERR4KtMcfx4//sk4zp0+Otr3+JeBv5otGDUgMKuN/DXOyGblNQ3489mY+paIKZN05GRYZT7JIo3FRUKpAQmT46diU3qlZ0dnsCfr7/ftGmh/bv7Mv4aGwfP+BPCOO8Em/F3cs9a3hRQNPgW/Bw4oEALMIYuJfDEExYoCnDBBcPXITz9dA0HD6o9WX/M+As/IeDXauRIy87We3r0jZSvbCxg/J4zZ+rYs8f4bH34oQkTJ+rIzx/4dy8oiP2MvxMnjL7lDPwRERERRZ7NZmTsnZrx5/EATU1KzwK0ofgy/ljVh0aL2L6DolHB6TQyLU7u45eSInsCf52dzPgLlBDA4sVefPyxGvDEH1G0HTqkwGqVKCyMvclNMjL+6utFyEsJl5UZPcJOrbcfrMxMiaoqBR6PQGbm4IGJpCTZU3p6pHznreRkCZsNSEiQzPijiJHSOH6Wlmro6hKorAzss7d+vYqtW1Xccosbdvvw28+bZ1xg/OMfZqSlyZ5ebzT2GJngI79t1HWgvl4gJ6f3GF1aqqG8XMHx4wKffqrgC18YPBhdWCjR0iKCPoaHU02N8f4w8EdEREQUHbm5Ompr+16z+uY2/OlVnZhoVKhrbQ3TAIkijIE/CjujLBr69CpJTpZobzcmsTo7BRITOZkUqJkzdbS3C65Eobhz+LCCiRP1fn18KDY4HBJdXYFNsP7lL2Y88ogFdXWDH4/27Ru4v1+wsrKM8fr+fzDJyb2Bu5HyvSe+DHa7XfIYTBFTXy/gdAqcf74XFovE3r3+Z/0fPy7w7LNmnHWWFwsX+rcfpqYCU6dqaG4WKClhT9axLNgS0E1NApom+ky4lJbqkBJ48kkzhACWLBl8JVtBgfGZPX48dj+EtbUCigLk5kZ7JERERERjU26u7Dcn4csAzMsb/h5ICOMen1V9aLQw+bPRtGnTIPy82xdCYO/evUENikYXl6tvmU+gdwK2u9sI/p2cDUj+SUkx3lOnE8jKivJgiAJw6JCCZcuGLzNH0ZGdbVwQ19cLJCcPvyijrk7g/fdNsFolfvxjKy67zIuLL/bCYundpqFBoKFBwfTpnpCP19drDxgu8CdDEPjr27M2I4OBP4ocX5nPkhIdJSU6ysoUXHLJ8D/n9QJ/+IMFGRkS110X2D542mk69u1TWZp5jHM4JNrajOt2qzXwn/dNuJxcYsnhkMjJ0VFWpuL007Uhs8Hz8yWEAI4eVTB5cmyWuqipUZCTI2Hy6+6aiIiIiEItN1di0yaBri709DM/flyBzSaRlubfc6SlsdQnjR5+3Zr8v//3/4YN/K1btw6bN28OyaBodHE6Rc8kqU9yskRjo4KODuPfCQnM+AuU7z1tbxcA+P5RfGhqEmhtFZxEjmG+cn719QomThx+gnXdOhWJiRKPPtqFtWtN+Ne/TPjoIxWXXupFaamOrCyJffuMgEU4Mv58gT+zWSIlZfDtkpIkGhuDu4A/udQnYKwGHKh5OFE4HDyoIDfXKJc7fbqOV181QdMAdZjEv5deMqG6WuDnP+/uuQH218KFGtauNWHOnNgMtlBkOBzGMa+xUaCgIPBrzvp6AUWR/RZnzJypo65u6DKfAGCxGItSjD5/sflZrKkRMdmfkYiIiGisWLhQw9//bsL//Z+5Z8Fjba1Abq70u3oJM/5oNPEr8HfjjTcO+r133nkHjz/+OPbu3YsFCxZg5cqVoRobjRIuV/+skaQkCafTKPMJsMffSPjeU18GClE8OHTICJJMmsTAX6xKSjIWY/hT1s3jAdavN+HMM41sjauv9mLZMg3PPmvGE09YPq+lb5QILCoKfX8/oDfLLzNz6Iv55GTgyJHgS30qiuwJntjtEnv38hhMkXH4sIIpU4xj5/TpOl56yejzN3Hi4MGGujqBtWvNuPpqz5DbDSYzU2LVqq4Rj5lGB1/gr75+ZIG/ujoBh0P2C1IvW6ahtlZgzpzhrwkKCiSOHo3d4+3x4wKLFzPwR0RERBQt2dkSX/6yFy+8YMbSpV5MnCg/D/z5P/+Uni5x9CgX99LoMOJP8htvvIHLLrsMt99+O+x2O9asWYNnn30WS5YsCeX4aBQYqNRnSoqROdHZafzbZuONcqASE40JaKcz2iMh8t/hwwIZGRJ2e7RHQoMRwrhg/uwzBW1tQ2+7ZYsKp1PgnHN6szUKCiT+3/9z449/7MQdd3SjtNTo57hsWXiyNJKSAIulfyZJ/+1k0AslfBnsvgCjUerTKFlNFE7d3UBVlegJ/E2cqMNikSgrGzrdb9cuBYoi++yjRIHKyJBQ1ZH3+autVfr09/MpKdHxX//l9qs8Zn6+L+Mv9rjdQGOjgvx8LmoiIiIiiqYLL/Ri3Dgdf/mLBV6vcR16crn54TDjj0aTgO6epJR49dVXcckll+D73/8+cnNz8cILL+CJJ57A6aefHq4xUpxzOkW/LI/kZInOTvF5mUpm/I2EEMaEt+89JIoHhw8rLPMZBy65xIvKSgV33mnDiy+a4HINvN0775gwY4Y2YHmz5GRg4UIdN93kwW9+042LLw5P4EEIIxvFl5EyGF+meTBBOuN81vsEdruErothA6REwSovV6Droidb2mRCT5+/oXz2mYqSEp3XWRQUIYzs6oaGkQXeamvFgIG/QBQWSjQ3i542AbGktlZASqMXIRERERFFj6oCt9zixrFjAv/4hwmtrSKgwF96ukRHh4DbHcZBEkWIX3dvuq7jX//6Fy666CL88Ic/xPjx4/H3v/8dq1evxpw5c8I9RopzLpfol/Hnmzj19Vtij7+RSUmRPT2niGKd12tMXrPMZ+xbskTDo4924bzzvHj9dRPuvNOG997rm1lUVSVw8KCCs8+Ofr+l73zHjSuu8Ay5TXIy4PUGdwHvcqHPQha73Th3NTfzOEzhdeiQAqtVoqio93pp+nQd+/cr0Ac5pHq9wN69CmbN4jGXgudwjCzjT0qjRGhOTnCfw4IC4+dramLveFtTY9xSM/BHREREFH0TJkhceKEXL79sBgDk5QVW6hMAs/5oVPCrx98FF1yAo0ePYtGiRbjvvvtQUlICAGhtbR1w+/T09FCNj0YBp3PgUp8AUF9v3CgnJER6VKNDcjJLfVL8OHpUwOMRzPiLE8nJwFe/6sWFF3rx0ktmPPmkBbW1Hnzta14IAbz7rglpaRKnnx79wF9x8fCTrb4FJ06ngNU6ssnZUxey+AJ/LS0CEyYM/pz19QKpqb29AYkCdeiQsWhCOWnJnq/P35EjA/f5O3RIQVeXwKxZ0d9HKf45HBLl5YFn/DU3C3i9wWf85ecbfVyPHlUweXJsfaaPHzeywX33N0REREQUXVde6cXWrSoaGgYuOT8Y3z1+a6tAdjYXdVF88yvwV11dDQDYvHkzPv7442G3LysrC25UNGq43YDH07/Up2/itKHBmIBVYrNlR8xLTmapT4ofhw8bvabGj2fgL56kpwO33upBUZGOv/3NgsZGBTfd5MaGDSouuMDrV2+mWOA777hcQGbmyJ7D6USfi/+0NKPX6lCrAQ8dEvjVr6y45BIvrrySfdYocFIaQbzly/t+fiZO1GE2G33+Jk7s/9natUtBcrLE+PG8YaXgZWdLfPxx4NectbXGzwRSYmkgVivgcOhRzfirqxMoK1Nw1ll9A4/Hj4sBS14TERERUXRYrcC3v+3Bhx+q/eakh+LL+Gtp4VwrxT+/pusefPDBcI+DRilfX6j+GX+9gT/2nRm5lBSJ6mpGTSk+HDqkoLhYwmqN9khoJC68UENmZjcef9yCffts6O4WWLEitrIuhuK72He5BICRTdA6nX0zqxTFCP4NVuqzrk7g4Yet8HjEiErkEQFGWfQTJ/pnS5tMwNSpRp+/iy/u/3O7dqmYOVPn4ioKCYfD6HfidCKgyZO6OtHTizVYBQUSR49G7wO9bp2KV181Y+bMrj6/z/HjCoqLuaiJiIiIKJaUlOgoKQnsGi0pCTCZJAYpckgUV/wK/H3pS18K9zholDImWI0D58l8/25oED2rKShwycmyJ7hKFOsOHVIwYwYnxuLZggU6fvrTbjz8sBWnnaYhKyt+jt+9pT5H/hwD9ay12+WAqwHb2oDf/MaC5GSJceN09gigETt0yAh0DFQmefp0HWvXmqDr6BPgczqBigoFZ5/NrvQUGtnZxuevoUH0HE/9UVcnkJWlhyQ7vKBAx8aN0Usz9y2227hRxWWXGVm2Uhp9Bxctip/zIRERERENTAgj648ZfzQacA0whZXTaRwoT50gUFUgMdFYOcz+fiPHUp8UL1wuY0X8pEkM/MW7yZMlHn20C9/+dnwFFHzZ5b4FKYGScuCetRkZsl/GX3c38MgjVnR0CPzoR24UFvbfhmggHg/w3/9txuuvA97Pq3ceOqQgJ0dHamr/7adP19HRIXqCgz67d6uQEpg1i8dcCg1fhlug2ct1dYH1VRnK1Kk6mpsFtm+Pzi1sdbUCIYANG4z9CwBaWoDuboH8fO5rRERERKNBejq4cJdGBb+WTP7yl78M6EnvvvvuEQ2GRh9fNtpAK4OTk32BP66QHSnfe6hpRjCVKFaVlxuTdFOmcGJsNIjHEs2KYiw48S1ICVR3N6Bp/XvW2u0SZWW9k9BSAn/6kwVHjwr89KfdyM6WSE8fug8gkU9lpcCmTQq2bQPsdgu+9CUPDhwYfNHExIk68vN1/O1vZvz85909WX+7dinIz9eRmclrLAqN5GTAapVoaFAA+H8ur6sTAZdYGszcuTpmz9bw179aMG1aV0TPRS6XUdZ56VIvNm40obJSYPx4iZoaY6fLz5cAeJwnIiIiind2O+/faXTwK/D33nvv+f2EQggG/qiHL7NioBtzX7nPeJxAjhW+gKrLhQEzAYhixaFDChITJXJzOQlN0ZOUJEdc6rO3dHX/jL+Ty4Bs3qxi61YVd9zR3dMP0G43Fml0d4M9LmlIVVVGRtGjjwJ//avEn/5kAQCceaZ3wO1NJuAb3/Dg/vutePNNEy66yAspjf5+CxfGTw9Oin1CANnZEvX1/k+CSAnU1gp84QuhOfcLAdx0kwc//rEVL71kxg03eELyvP7w9Ra86CIvdu1SsWGDCePHe1BbK6Cq8vOMSE4QEREREcW79HSJ48dZJJHiX8gDf0QnczoFrFY5YF8PX9AqMZGBgJHyZZ44nQKpqXwfKXYdPy5QWKhDcE6Moig5eeSlPn0Bw1Mz2O12CZdLwO0G3G7gmWfMWLhQw8KFep9tAKNcSKhK3tHoVFmpID9fYsoU4K67PNi3T+L9901YsGDwIF5JiY7zzvPipZdMOO00DV6vkZk0axYDfxRaDocMqNRnSwvg8Qjk5oYu29/hkPjKV7x47jkzli7VIlZJoLpaQFEkiooklizRsGmTiq99zYOaGqOUKStvEBEREY0OzPij0YLhaworpxP9yqL5+CZP2eNv5FJSjPdwpKXriCKluVmw5BxFXVKSHHHgz/dzA5X6BICWFoHnnjND04Drr3cPuA37/NFwqqoUFBf3HisnT5a45RYP0tOH/rmrrvIgJQV48kkzPv1UhapKTJ3K0soUWr7An9MJfPCBit/+1oIf/ciK9vaBt6+rM241Q73g4YILvBg/XscTT5h7emGGW3W1grw8YzHjGWd40doqsGePgpoa8XmZTyIiIiIaDdLTjRYhnsgVlyAKC78y/nzcbjfeeOMNbNu2DbW1tQCA3NxcLFiwABdccAEsFktYBknxy+US/cqi+aSkGF+Z8TdyvuDpYBMuRLGiqUlg8mROQlN0JSWN/Hjpy/gbqNQnAHz4oYoPPjDh5pvdsNv7/uzJGX9Eg5ESqKoSOP30wDP1bDbg5pvd+O1vrThyRMG0aTpstjAMksY0h0NHba0J3/mODbouUFKi4cQJY9HDbbf1nxnZulWF2ewrgxk6igJ84xtu/OxnVqxda8Jll4U/+lddLVBUZFzHTJwokZOjY+NGFcePK1iyJELRRyIiIiIKu5Pv30N9HUsUSX4H/rZt24a77roLdXV1kFIi9fOGYm1tbXjxxRfxu9/9Dr/97W9x2mmnhW2wFH9crv5l0Xx8j3NiauROLvVJFKukZMYfxYbkZIna2pEVO3C5BITo35c2Pd34XP/732ZMm6ZhxYr+QZuEBMBq7dsLkOhU9fUC3d0C48eP7Fg5Z46OM87wYsMGE0pLudCCQm/OHB1792qYOVPDwoUa7HbgvfdUPPmkBWeeqWH69N7P3e7dCt56y4Rrr/UgHGtDx42TuPBCL/71LxMuvtg7YFuBUJHS6PE3Z44R4BMCOOMMDWvXmtDdzYw/IiIiotEkLY2BPxod/Jr9Onz4MG699VYkJSXhN7/5DbZt24YtW7Zgy5Yt+OSTT/Dwww8jMTERt956K8rLy8M9ZoojTqdAUtLA3/NlTTDjb+RMJsBmkwz8UUxrbQU0jYE/ij6j1OfIftbpFEhIkFBOuXJKSDCOw6pqlGQcrI+l0SdgZK9NY0NlpfHhGTdu5EG766/3YMECDYsXs78fhV5ensQPfuDGBRdoPZnNK1YYffaeeqq37KbLBfz5zxbMmKHhwgvDlw03Z44Oj0egvj6818HNzQIdHb0Zf4AR+OvuNl43P5+BdiIiIqLRwlfVp6UlygMhCpJfgb/HH38c48ePx9///ndceumlSD6pwU1SUhIuueQSvPTSSxg/fjz+8Ic/hG2wFH/8KfWZkMBgQDCSkyVLfVJM8/U18108EUVLcvLAGdIPPmjBK68MnS4yVAb7nDk6vvY1D/LyBv+M2+2SPf5oSFVVClJS5LD9/IaSnAx873turkyliBECuOkmN2prBdauNY6jTz9tRlcXcNttgy+GCIXCQiPgdvRoeI+t1dXG8xcV9e5XOTkSkyYZrz/UsZ+IiIiI4ktSEqCqkq06KO75FfjbvHkzbrnlFtiGqMmYkJCAm2++GZs3bw7Z4Cj+dXQMX+ozISGSIxp9UlKMACtRrGpsNE41WVmcGKPoSk6W6OoSPVkpAOD1AmVlCnbtGvqSyFjIMvD3br/dyIAZSno6bxxoaFVVSlDZfkTRUlws8cUvGmU3X3nFhI0bTbjhBk/YM/1TU43jek3NyEo4+6u6WoHNJvtdx1x4oRclJdqg5wYiIiIiij9CAOnpYKsOint+3SW1tbUhNzd32O1yc3PR1tYW9KBo9Biq1KevL1JKCoMBwUhKYqlPim3NzQIWi+TEGEWdLwO9o6P3sePHBXRdoKJCgRzidGScz0Z+vrLb2eOPhlZVJRj4o7h1+eVepKYC//u/ZixcqGHp0vCXmxXCKLMZ7oy/qiqBoiLZL3txyRIN99zjDutrExEREVHkceEujQZ+Bf5yc3NRVlY27HZ79+71K0BIY4OUQEfH4BOlRUUSP/1pN8aNY+AvGCz1SbGuqUkgI6P/hBlRpPmCzycvljh61LgU6uwUqK0d/EM6VKlPf9jtxorBoYKLNHa5XEZ2dHExPyAUn2w24BvfcGPGDA033+yO2Dm/oCD8GX9Hjyo9ZUWJiIiIaPSz2xn4o/jn113Sueeeiz/+8Y84cuTIoNtUVlbiz3/+M84///xQjY3inMtlBP8Gy/IRApg+XWcwIEgpKQP3rCKKFU1NgmU+KSb4AncnHzOrqwUSE43HDx8e/LJoqFKf/rDbJdxu0SfbkMinqsr47DHjj+LZrFk6/uu/3DipHXzYFRToqKkR0MO063i9QE2NQHEx900iIiKisYIZfzQamPzZ6Nvf/jbee+89XHbZZbj88suxYsUK5OXlAQCOHz+O9evX41//+hdyc3PxrW99K6wDpvjh6zsXTIYEDS85maU+KbY1NQkUFXHCjKLPl4HucvU+dvSogkmTdNTXG+U+ly0buDyd0ymCzPgzfra1NbiSoTQ6VVUJmEwSeXkSAM/pRP4qKJDwegUaGgRyckJ/bD1+XEDTBAoLedwmIiIiGivYqoNGA78Cf6mpqfjb3/6GX/ziF3jxxRfx4osv9tvm3HPPxc9//nOkpKSEfJAUnxobjQOkr5cfhYev1KeUYPYkxaTmZoE5c3gcoOgbqNRndbXAggUakpIEyssHz/hzOhFUFovvXNjSIlBQwP2B+qqqUlBYKKGq0R4JUXwpKDAWFh07Fp7An68cNBcwEREREY0d6elGkoXXC5j8ip4QxR6/P7pZWVlYtWoVampqsHXrVtTV1QEAcnJysGDBAuTn54dtkBSfqquN1eu5uZzgDKfUVEDTBLq7jf4qRLHE6wVOnBDIzORxgKLPYgEsFtmT8dfVBTQ0KCgs9CItDfjkEwFNQ7/gi9cLdHcHl6nny/jjqkEaSGWlwlKCRCNgtwM2m8SxYwrmzw/9PlRVJWC3y4iWLyUiIiKi6PIt3G1tZesail8Bx6zz8/Nx2WWXhWMsNMpw9Xpk+ErPtbcL2Gw8GVFsaW4WkBIM/FHMSE7uzfg7etT4WlSko6tLwOMROHZMoLi47+fVFygMJvBnsRjHa/YJoFNpmvFZ/MIXGPgjCpQQRrnPY8fCc2ytrlaY7UdEREQ0xvS26gCysqI7FqKRGrym1UmOHDmCK664Au+///6g27z//vu44oorUF1dHbLBUXyrrubq9UjwTUQ7nVEeCNEAmpqMiTgG/ihWJCXJnh601dVKz6Tx+PE6hMCA5T59gcJgMz7S09kngPo7flzA6+0fcCYi/xQU6Dh2zK/b2oAdPco+xURERERjzcmtOojilV93SE8++SQSExOxfPnyQbdZvnw5kpKS8MQTT4RscBS/NM0o9VlUxEmscPO11Ty5ZxVRrPAF/jIyeCyg2JCUJHsWShw9qiAnR4fFAiQkAHl5+oCBP1+g0JdhPVIM/NFAqqrYQ4woGAUFEjU1RoWBUHK5gMZGhfczRERERGNMSgqgKKzYQ/HNr8Dfhg0bcOWVVw673ZVXXomPPvoo6EFR/Kut9a1e5yRWuPkmohn4o1jU3CyQnCzZf5JiRnIyTsr467tAZdIkHRUVA2X8GV+DKfUJGOVCGPijU1VWCmRl6ewhRjRCBQU6ursFGhtDe3z1ZREyKE9EREQ0tggBpKcz44/im1+Bv7q6OhQVFQ27XWFhIerq6oIeFMU/3+r1ceN4oxxuNhugqpKlPseYY8cENm6M/QaajY2C2X4UU4yMv95Sn4WFveepCRN0VFUJeDx9f8a3fVJScK9tt0u0tgb3HDT6VFYqLPNJFISCAmP/qakJ7cRMVZWAokjk53P/JCIiIhpr0tMlTpxg4I/il1+Bv6SkJLS0tAy7XWtrKxITE4MeFMW/qioBu11y9XoECGFksDDjb2z5xz/M+OMfLaivj+2/e3MzA38UW5KTJVwuoK0NaG/v27tp4kQdmiZQXd13v3K5BKxWCbM5uNf2ZfyFuhwdxbeqKvZEJgpGVpaExSJD3udv+3YVJSV60Md+IiIiIoo/J1fsaWsD1qwx47HHLLyfp7jh191RaWkpXnvttWG3W7t2LUpLS4MeFMU/TmJFVnKyRHt7bAeAKHS8XmDXLgVSAm++aYr2cIbU1CSQmcmrIoodvlKf1dW+Em69n8/iYglFkf36/LlcwWf7AcaNg6YJtLcH/1w0OrS2Am1tLI1OFAwhjKy/Y8dCdy3sdAJ79ihYuFAL2XMSERERUfxIT5eorxd46SUT7rzThnXrVGzbpuLTT0O72IwoXPz6pF5zzTV4/fXXsWrVKmha/5sfXdexatUqvPHGG7j22mtDPkiKP9XVCvthRFBKCkt9jiX79yvo6BCYO1fD+vVqTP/tm5oEsrIY+KPYkZQk4XIJVFUpMJslcnJ6P58WixEIPDXw53SKnn6qwbDbjedgnwDyOXLE+KxNmMDjJFEw8vP1kGb87dihQtMEFixg4I+IiIhoLLLbJWprFbz2mgnnnefF73/fhQkTdLz1VngW4NfVCXzwQey39KH44dcn9ZxzzsE3vvENrFq1Ci+88AKWLFmC/Px8AMDx48exadMmNDY24pZbbsHZZ58d1gFT7HM6jfJ+48ZxEitSWOpzbNmxQ0V6usQ3vuHG979vw7p1Jlx6qTfaw+qnqwvo6GCpT4otSUmAlMCBAwoKCiSUU+aJJ07UcejQqYE/I2AYrPR042trK8+RZKioUJCUJLlAgihIhYUSO3YYpZRFCC6Jt2xRMWWKDrs9+OciIiIiovizZIkGKYGzzvL2XBOed54Xf/6zBbW1Arm5gd/DDXWt+uKLZnz8sQpFcWPZMi4+o+D5vSzyrrvuwurVqzFp0iS8+eabWL16NVavXo0333wTkydPxp/+9Cfcdddd4RwrxYmqKuNjxbJVkcNSn2OHlMCOHQrmzdOQng4sW6bhzTdN8HiiPbL+mpqMzyRLfVIs8WXu7dunoLCw/3lqwgQdR48q6OrqfczlEiHpWZuWJiEEM/6o15EjCiZM0EMSqCAay/LzdXR0CLS2Bv9cHR3AZ5+xzCcRERHRWJaTI/GlL3n7LARbvFhDcrLEO+8EnvX32msm/OxnVgxQTBFdXcZcX0qKxFNPmXH8OG8QKXgB1UNZvnw5nn76aezYsQMbNmzAhg0bsH37djz11FNYvnx5uMZIcaaqSsBkkiNa+UAjk5zMUp9jRU2NQF2dgvnzjSuFiy7yorVVYNOm2CsHwMAfxSJf5l57u+jT389n4kQdUvYuYnG5jAy9UGT8mUxGaWYG/sinokLB+PFcKEUUrIIC4xh99Gjv7W15ucArr5ggAzx879zJMp9ERERE1J/FYmQAvv++2mex8HCOHRP43/814cgRZcAegTt3qnC7BX7yk27Y7RL/3/9ngdsdwoHTmDSiRgiqqiIzMxOZmZlQ1dibbKboqq5WUFgowY9G5LDU59ixfbsKs1li5kxjorigQGLuXA2vvRb4xFa4NTUJCNHb14woFiQl9f7/QBl/hYUSJpPE//yPGXfcYcNttyWgulqBwxGaz7HdLtHczOM1AW1tRmn08eN5jCQKlsNhHLtraozj6759Ch54wIr//V8zNm8O7KZk61YVEyfqLMFLRERERP2ce66Gri6BDRv8u8aUEnjiCTOysiSKi3WsW9c/W3DTJhUTJugoLpa44w43amoE/vY3c6iHTmNM6DqgE32uqkphmc8IS06W6OwU8MZemzcaoc2bVTz8sKVfCc/t21XMmqXDYul97OKLvTh6VMGuXbF1SG9qEkhLkzCFp+8x0Yj4Sn0CQFFR/3OVyQScf74X2dkSS5d6sXKlGw880IX/+I/QHGDtdonWVgb+yCjzCRjlZYkoOKoK5OVJHDtmXA/9+tcWTJyoY948DX/7mxmdnf49T1cXsHMny3wSERER0cCysiROO03DW2/5twB/3ToVBw6ouPlmD845x4udO1U0N/d+3+UCPv1UwZIlxvVncbHE9dd78O67Jnz8MbNqaORia5aY4p6mAUePCgb+IiwlxTjTuFxRHgiFzIcfqti5U8VLL/VGzdrbgUOHjP5+J5s2TceECTpeey22ImxNTYJlPinm2GyAokgkJso+tfpPds01Xtx1lxtf/aoXS5dqKC6WIevBxsAf+VRUKEhMlMjO5nGSKBQKCnTs2KHikUcsmD5dx113uXHDDR64XMA//+nfNdLOnSo8Hpb5JCIiIqLBnXeeF8eOKdi7d+jQSksL8PzzZixf7sXMmTqWLNFgNkt88EHvten27Sq8XoFFi3qvP88+W8PChRqeesocUElRopMx8EchVVsr4PEM3DeJwic52fjKcp+jg9drlKjKz9fx2mvmnky+nTtVSIl+gT8hgIULNRw+HFuH9OZmBv4o9ghhlPssKtJDFswLhN3OHn9kOHLE6O8Xjc8h0WhUUGAcX+fO1XHnnW5YLMaK7C99yYs33jChunr4nW3LFhXjxunIyeH1CxERERENbMYMHQUFOt5+e+jFZc88Y4HJBHzta0Y5r8REYMkSDevXqz3Zgps2qSgp0frMnwlh/ExHB/D++8z6o5GJrVliinvV1cZHatw4ZvxFkq90XXs7Zw9jVXs7cOCAf4fcw4cVdHcL3HabG6WlGlavtqC9Hdixw+g5k57e/2eysoxyrx0doR13MJjxR7EqI0NGra9aerrEiRMCGpNJxryKCoHx43m9RBQqZ53lxbXXevDd77phPqklykUXeeFwSDz9tHnIckxut1FmiWU+iYiIiGgoQgArVnixfbuC7u6Bt9m5U8HWrSpuuMHTk7ABGD/X2Khg924FTiewe3dvmc+TORwSCxdqeP11E+cPaEQY+KOQqqwUsNtlnwMahZ+v1KfTGeWB0KD+7//M+NWvLH3qeA9mzx6j/NuECRK33eaG1wusXm3BZ58pmD9/4LN9RobxGWhujo3gr5RG4M83LqJY8oMfuHHllZ7hNwwDu11CSuDEiai8PMUIpxNobFQwYQKPkUShYrcbQb5TewubzcANN3iwb5+KzZsHXzH92WfGwisG/oiIiIhoODNm6NB1gUOHBg6vfPyxiqIivU8JTwCYNEmisFDHunUmbNmiQsrBrz8vucQIEm7Zwqw/ChwDfxRS1dUK+/tFQVKS8ZWlPmOTphmlozRN4PXXzcNuv3u3gpkzdSiKMYn1zW+6sXOniq4u0a/Mp48vwNbUFBufAacT8HgEMjN5PKDYk5kpkZgYnde22419lX3+xraKCuMSnBl/RJExe7aOBQs0PPfc4Fl/e/eqyMnRkZfHgDwRERERDa2oSCIpSWL//oHDK2Vlxtzeqa0dfNmC27YpePddE2bM0JCWNvBrjB8vUVqqYe1a05CVK4gGwsAfhVRVlYKiIk5iRZqqAomJkqU+Y1RZmYL2doHZszW89546ZGZmVxdw6JCCmTN7A3zz5+v44hc9GDdOR3HxwGd6u11CiNgJ/PnGkZXFKxOik/mC9OzzN7YdOaLAapXIzeUxkihSzj7bi5YWgZqagY+/hw4pmDyZ9zFERERENDwhgClT9AEDfw0NAo2NCqZPH3jx/hlnaFAUoLJSweLFQ1ebuPhiL44cUbB3L8M4FBh+YihknE6jzOC4cZzEiobkZAmXK9qjoIF8/LEKh0PHt77lhq5jyOa/+/Yp0HWBmTP7Tjxdc40X99/f3W+lkI/JBKSlyZgp9ekL/LHUJ1FfKSmAokgG/sa4igoF48f3X/1JROEzebKxzw00OeN2Gy0LGPgjIiIiIn9Nnarj4EEFXm/fx8vKFAhhfH8gycnAwoUaFEViwYKhA3+lpTqKi3W8+urgc4lEA2Hgj0Kmqsr4OLHUZ3QkJYEZfzFI04CtW1UsWqQhNRU46ywNb75pQlfXwNvv2aMiI2PgLBBlmCN2ZqaMqYw/VZWDlisgGquEMALi9fWxsa9SdFRWCvb3I4qwhATjPuXgwf4XVEeOKNA0Bv6IiIiIyH/Tp2twuwUqK/ve35eVGRXxkpMH/9mrr/bizjvdQ24DGHMIF1/sxa5dKqqqOI9A/mPgj0KmslKB2SzZFyNKUlIke/wF4MABBatXD97nJVT27lXgdIqeZr5f/KIXHR3AunUDr9Qx+vtpI8oCycyMnYy/ujoFmZmS2SxEA5g8Wcf+/aFrzv3EE2bs2sVLunjhchnHSPb3I4q8KVN0HDjQ/3h58KBxH1NUxPsYIiIiIvLP+PESZrPEvn197+/37VMwffrQ93uZmRLz5vl3T7hokYaMDIm1a5n1R/7jLBGFTFWVQFGRHDYricIjOVkO2TuO+tq9W8GHH5rQ0BDeyNTmzSpycnSMH29MJDkcEkuXanj9dVO/UgBtbUB1tYLS0pFNBmdkxEbGX2srsH696vcFDNFYM2OGjooKgc7O4J+rudlYSPDee7wBiBeVlcaF0oQJPEYSRdrUqTrq6hS0tfV9/PBhBZMm6TDxUEpEREREfjKZ+vf5a2gQaGgYvL/fSF/nrLO82LEjdAuIafRjiIZC5sgRBePGcRIrWpKTWeozEG1txntVVha+w6DXC2zbZpT5PDnz7dJLvWhuFtiwoe8Je88e498zZozs4sBX6jPcWYzDeeklM8xm4Etf8kR3IEQxavp0HbouBsw6CdTu3cZxY88eBVro7isojCoqFFgsrJBAFA2+Up6nlvs8dEhhmU8iIiIiClhJiVFRwjcXt2+f0d9v2rTQXlvm5Ul0dAi4XCF9WhrFGPijkPB4gGPHBAN/UcRSn4GJROBvzx4FLldvmU+fggKJ+fM1/OMfZhw/3vs3271bQUGBDrt9ZK+XkSHh8UT3IqCiQuCDD0y48krPsHXKicaq3FyJtDQZkuPPnj0KbDbjBqCigueAeFBRITBuHCskEEVDVpaE3S77LLxoahJobhaYNIn3MUREREQUmKlTdTidAjU1xv34vn0KCguH7u83EllZRmQxFip9UXzglAOFxLFjArpuTGRRdPhKfQ6W7dXSAvzjH6aoZ4PFCl+Jp337RnYYPHZM4M03h06x//hjFbm5OoqL+7/p113ngcUi8bOfWfHJJ8bKoD17FMycOfJJp8xM42cbG6NzESAlsGaNGXl5Os4+m6lHRIMRwmgCXlYWXJkOKY2MvxUrNCQmSuzaxbIf8eDIEYVlPomiRAhfn7/e4+WhQ8a1IDP+iIiIiChQU6boUBTZM79YVjZ8f7+RyMoynjPcLYto9GDgj0KistJIYy4q4g1ztKSkALouBu3z9/bbJvzjH2auDPlcW5uAw6GjsVEZ0Unz3XdN+NvfzNAH+cgPVubTJztb4r77ujFrlo7f/c6KJ580o7FRQWnpyANmmZlGgLG5OTp/4y1bVOzfr+L66z1QGX8gGpKvz19X18if49gxgRMnBGbP1jBzpo5du3hZF+u6uoC6OgXjx/N6iShaSko0lJcLuN3Gvw8dUpCVNfKKC0REREQ0dtlswLhxRuCvsVGgvj60/f180tMBVZVRW+xP8YczRBQSlZUKcnJ02GzRHsnYVVJiBJi2b+8fcZHSyD4DopcNFmva2gQWLDDes5GU26uoUKDrAq2tA39/924FHR0CixcPfrJPSADuuMONq67yYP16ExRFBlUDPC0NUBQZlcCf2w08/7wJc+dqmDWLE9pEw5k2Lfg+f7t3KzCZJKZO1TFrloZDhxR0dIRwkBRytbVGH9aCAh4niaKlpESHpgkcOWIcf9nfj4iIiIiCMW2ajv371Z6sv3Bk/AlhlPtsbGQ4h/wT9U/K6tWrceWVV2LevHlYsmQJVq5cifLy8j7bXH/99Zg6dWqf/+65556e7+/btw8/+MEPsHz5csyePRsXXXQRnn766Uj/KmNaVZUYsJwhRU5GhlE6bsOG/oG/ykqBujpjd2fgD9A0wOkUyMuTKC7WAw78aRpw5IjxPg52wt2/X4HdLlFYOPR+IQTwH//hxY9+1I0bb/QgMTGgofShKEafv2j8jd95x4TmZoFrr/VE/LWJ4lFenkRqqsTevcEE/lSUlOiwWICZM41AYjj7llLwfOfinBxeMxFFS3GxhMVi9Pnzeo2+mwz8EREREdFITZ2qoblZ4KOPVBQVhb6/n4/DwYw/8p8p2gPYsmULrr32WsyaNQuapuHRRx/FLbfcgrVr1yLxpBnwq666CnfccUfPvxMSEnr+f/fu3cjIyMBvf/tb5OXlYfv27bjnnnugqiquu+66iP4+Y5GURsbfpZd6oz2UMe+MMzT8z/9Y0NxsBAJ9Nm9WkZwsISWbwAJAe7vxNTVVYvp0Hdu2BTZRfuyYgMdjvI+DvZ8NDQI5OfqAZT4HEqosuYyM6GT8bd2q4rTTdOTlcTKbyB9Gnz8d+/apAAI/f3q9Rrby5ZcbP5uTI5GTo+Ozz4x9kWJTfb1AYqIM240gEQ3PZDL6+R04oGD6dAGvV2DKFB43iYiIiGhkpk41riV371Zx/vnhmx/PypKorORiX/JP1AN/TzzxRJ9/P/TQQ1iyZAn27NmDBQsW9Dxus9ngcDgGfI4vf/nLff5dVFSEnTt34q233mLgLwIaGgQ6OwXGjeMNc7SdfrqGp56S2LTJhIsvNk40vjKfCxZoqKhQuDIERplPAEhLk5g+XcMbb5jQ0CDgcPgXtCovN3pams2Dr7RpaFCQnx/5fSIagT+XCzh8WODGGxn8JwrEjBkann7ajK4uBFwq+/BhBd3dAjNn9pYTLi3VsXs3bwJiWW2tYLYfUQyYPFnHunUmHDxolEweN477JRERERGNTEoKkJ+vo6ZGwbRpoe/v55OVJbFtG+d1yT9RD/ydqv3zVJy0tLQ+j7/yyit4+eWX4XA4sGLFCqxcubJP1t9Az5Oenh7QayuKgKJw5wnU0aNGEGTSJMBk4oRjNKWlAQsWGIG/yy4zgk6HDws0Nio44wwNLhfQ1KT49XdSVaXPV18ppClT4n9ipKPD+Mza7QoKCiQUBThwQEVenn+BuspKFYWFxs+1tAz8fjY1KZg/X4/4PpGdLVBe7t/fOFT271cgpcDcuZLHgBhx6v5Lsam0FJBSoLzchNmzA1soUFamIjkZmDy599pl7lyJ995T0NSkMrgUoxobFeTlDX29xP2XKPxmzABeeUVg0yYzJk6UsNmC39+47xLFL+6/RPGJ+y7FkhkzJI4fN+7zwzU3lpMDuFwCXq8S8OLhWMP9N/xiKvCn6zoeeOABzJ8/HyUlJT2PX3LJJcjPz0d2djb279+Phx9+GBUVFVi1atWAz7N9+3a8/vrrWL16dUCvn5GRBOFvXT7q0dgIOBzAhAkx9XEasy66CPjlL4G2NjPGjQN27QKysoClS03Yvx/Ytg2w2y1+P19qqhFgf/NN4PHHgRdfBKzWcI0+MqQEzGZg/HgTbDZgyhSgstIEu92/n6+pMU7kbW1Gtpvd3vcN6e4GOjqAiRP9f85QGTcOeOcdID3d7HeZ0WCVlxuvO3UqjwGxxrf/UmxKTzfOn5WVJixfHtjPHjwInH46kJnZu9+dcQbwhz8AR46YMG1aaMdKodHSAsyb5995mPsvUfgsXAhYLEB1NXD55YFdGw+H+y5R/OL+SxSfuO9SLLjySmNubNy48M2NTZpkzGd6vZGfbwwX7r/hE1OztPfeey8OHjyI5557rs/jV199dc//T506FQ6HAzfeeCOqqqpQXFzcZ9sDBw5g5cqV+M53voNly5YF9PrNzS5m/I3A3r1m5OUBLS2eaA+FAEycCFitVqxdq+GrX/Xi3XctmDtXR1ubFwkJKo4dM6G5uXvYoJCqKkhNTUBbWyc0TceOHSZ0d6s4dqw77k8uR4+qAEzo6OhGZ6cRoNu2TUFLi3vYn/V4gAMHrFi40AtdFygr6/9zR48KeDwW2GxutLRENuvGalXQ2WnGkSPdCDDpecQ2b7ZgzhwdLS0s9RkrTt1/KXZNmmTG1q3ApZf6fw7t6AB277bixhu9aGnpW0Zk3DgzNm4EFi3iOTnWuN3A8eNWpKR40NIy+H7J/ZcoMnJyLKiuFsjPH3qf9Bf3XaL4xf2XKD5x36VYYrcD551nLPYMF6sV8HisOHjQg+Tk+P7Mc/8dObs9ya/tYibwd99992H9+vVYs2YNcnNzh9x2zpw5AIDKyso+gb9Dhw7hxhtvxNVXX42VK1cGPAZdl9B1lsYKVEWFwJIlXni93EljxYIFXnz4oYq5c40ejKefbvx97HbA7TahpUVHaqp/z6VpOrxeHYcPC0gJOJ0SKSnxvZ+0tChISZE9J5aSEi9ef92K48flsH3+KioEPB5g3DgvOjtV1Ncr8Hj0PoHU2loFUgIZGTq83si+V2lpElKaUV8vkZwc/teuqxOorRX42td4DIhFvv2XYtfUqV4884wZTqfud6mO3bsVaBowY4a33zFm5kyjb2l3tw5VDcOAacRqaozzaFaWf/sl91+i8Jo0SUNVlQkTJmghvV7jvksUv7j/EsUn7rs0VqSkAEJI1NXJUfOZ5/4bPlEvoiqlxH333Ye3334bTz/9NIqKiob9mbKyMgCAw+HoeezgwYP4+te/jssvvxx33nln2MZLfTmdQFOTwLhx8R0IGm2WLdPQ3CywZo0ZKSkS06cbB9CsLOPv1NgYWGZrd7eRxQYAnZ2hHWs0tLUJpKb2fmZ978++fcMfEisqFCiKRHGxRFaWRFeXQEdH320aGgRUVcJuj/x+kZk5sr/xcLxeo6zpqXbvNt4P33tIRIGZPl2HrgscPOj/Jdnu3SqysnRkZ/c/xsyapaOjQ6CighUMYk1dnfE3ycnh8ZIoFixbpmHZMm/PtRMRERERUSxTFGPeL9RzfjQ6RT3wd++99+Lll1/GI488gqSkJDQ0NKChoQFdXV0AgKqqKvzhD3/A7t27cfToUbz77rv48Y9/jAULFmDa5w1sDhw4gK9//es444wzcNNNN/U8R3NzczR/tTGhstL4CI0bx0msWDJlio6sLB2HDytYuFCD8vme7stma2gIbNevqlKg677AX/yfXE4N/CUnA0VFul+Bv/JyBUVFEhaLkbUB9A+yNTQIZGXJnvc9klJSALNZork5tH+nf/3LhP/6Lxs+PzT32LVLxeTJOhITQ/pyRGNGfr5EerrEm2+aIP2Ye/Z6gU8/VVBaqg9YsnniRB02m0RZGdP9Yk1dnQKrVSItLdojISIAmDpVx7e+xbLIRERERBQ/srIk6uujHtKhOBD1Up/PP/88AOD666/v8/iDDz6IK664AmazGZs2bcIzzzyDjo4O5OXl4fzzz+9TyvPNN99Ec3MzXn75Zbz88ss9jxcUFOC9996LzC8yRlVVKTCbJXJzuVI2lggBLF2q4eWXFSxe3Nv/KSkJsFoDXxlSXt67/amBn3h04oRAfn7fYPX06Tq2bzdKdA7V/7C8XMHkycbP+laIn5r12tCgDFsyNFyEADIyQh/4q6lR0NQk8OqrJnz5y0YvP00D9uxR8MUvsrcf0UgJAdx8sxu/+50Vf/977/41mOeeM6OxUeDsswfeTlWB3FzZk11GsaOuTiA7Ww7bY5eIiIiIiIhoIFlZEjU1DPzR8KIe+Nu/f/+Q38/Ly8OaNWuG3Ob222/H7bffHsphkZ8qKwWKi6OT2URDu+ACL6xWYNq03gCXEEawqqkp0MCfgsJCHUePKujqiv8Zy/Z2oxfeyZYs0fDWWyZ8/LHaJ1h6su5u4NgxgfPOM97T9HRAVfsHUhsaBCZMiF4WrJH2H9qdsqXFKF+6dq0JZ52lIStLorxcQWenwKxZA79fROSf+fN1fOUrHrz4ohmFhXLQY9DmzSreesuEG25wY+LEwRcXZGdLNDTE/7F6tKmrE1woRURERERERCOWlSXx6ae836fhMVxDQamsVFBczDKfsSgtDbjsMm+/zIKsrMAz/ioqFEydqkNR5Kjp8ZeS0vexKVN0zJmj4f/+zwRtkDhWZaVR8tQX1BNi4PezoUFELeMPMAJ/oc74a2kBzj5bQ0IC8MILxpqRXbsUJCbKIQMQROSfSy/1YvFiL/78ZzOOHOm//x4/LvCXv5ixeLEX5547dLDd4dAZ+ItBdXWC/f2IiIiIiIhoxBwOibY2Abc72iOhWMfAH42Yx2NkP40fz0mseJKVFVgmSGcncPy48nnfqPjv8ed2A11dol/GHwB85Sse1NYq+OijgXtjVVQImEwSRUW9P3tqdp3LBbhc0Q38hbrUp5RGxl9+vo6rrvJg82YTDhxQsGuXitJSnRm/RCEgBHDrrR7k50v87ndWlJcLeD5vPdXdDfz+9xbY7RK33OIZtlSkw2EsSBhsEQNFntcLNDYqyM7mQgkiIiIiIiIamZPbDhENhdO1NGLd3YDZDEydysBfPHE4Aiv1WVEhICV6An/x3uOvrc343VNT+0++jh8vsXChhn/8w9wz4X6y8nIFxcUSppOKJJ9aOtWX/edwRLfUZ0tL6Cb929sBTROw2yWWL9cwfryOp54y4/BhgdJSRhaIQsVqBe680w1NA+65x4ZbbrHhRz+y4r77rKivF/j+991ISBj+ebKzJXRdhDzzl0ausdE4l+bkMPBHREREREREI+NLNAi0mhuNPQz80YglJwN/+UsXCgs5iRVPsrIkOjoEXK6+j7tcwAcfqJCn/DnLyxVYLBL5+RIJCTLuM/7a2oyvAwX+AODLX/aguVlg3br+WX/l5Uq/3n1ZWX0Df75symiX+pQSaG0Nzd/KFzzIyJAQArjuOg+qq42yp6WlDPwThVJmpsTDD3fhnnu6ceONHsycqSMlReI733H7fb71HX/q6+P7eD2a1NUZfwsG/oiIiIiIiGikfHNzbO9BwzENvwnR4FjiL/6cnBKelNQ7Afn++yY895wZmZndmDmzN5hTXq5g3DgJVcWoz/gDgPx8iWXLvPjXv8w480wNNpvxeEcHUFur4NJLvX22z8yUaG01SvKZzUBDgwKzWSI1Nay/xpAyMnr/xr6/dzBaWoz3zG43nmvaNB2LF3tRXc2ydUThYLMBJSU6SkoAIPCs2qws3gjEmtpao1R0KI7JRERERERENDaZTMb8HDP+aDgM2xCNMVlZRlDv1BNEWZlxOHj11b7rAcrLBSZNMn4mIUGiqyu+Tyy+wF9KyuDbfOlLXrhcwNq1JjQ1CTQ1CezerUBKDJjxB/RmxTU0GP39huvBFU6+wF+oyvw1NwsoikRaWu9jt93mwc9+1h2S5yei0DKbjRsBZvzFjro6JernBiIiIiIiIop/DofOhb40LGb8EY0xdjugqn1Xhug6sH+/guJiHbt2qaisFJg0CXA6jfJkvmBXQoKR+RbPTpwQSEyUMJsH3yY7W+LsszX8859m/POfvRsmJBglT092cm3tnBzZE/iLpqQkwGYLrJfjUJqbBdLT+2b4ms0Y8j0koujijUBsqa8XLPNJREREREREQcvKkqivZz4XDY2BP6IxRgijPOXJgb8jRwQ6OgSuu86NP//ZjLVrTbjjDg2HDhnfnzjRCPzZbBItLfF9YmlvB1JShp98ve46D+bP16CflOCXlWWUPD2Zr/yl7/1saBCYOjX6fe8yMkIX+GtpET2/JxHFB4dDorY2vo/Xo0ltrcDcudE/NxAREREREVF8y8qS2LuXC31paJwRIhqDsrIkGht7d/+9e1VYLBIlJTouusiLzZtVNDQABw8aWX65uUbQJyFhdPT486f/nqoCs2bpmDOn97+Cgv7BL4sFSEszgmxSGoG/WOh7l5kpQ1rq01c+lIjig8PBUp+xQtd95wYG/oiIiIiIiCg4WVkSra0CXm+0R0KxjIE/ojHo1Iy/sjIFJSU6TCZg+XINiYnA66+bcPCgke3n60lks0l0dsb3RHJbm0BaWmiDWL730+kEursFHI7oT+5mZjLjj2gsy86WaGsT6GYrzqhrahLQNJb6JCIiIiIiouBlZUlIiZDN+9HoxMAf0RjkcPQG/jTN6O83Y4avnCdw7rlevPeeir17gQkTeicqR0vGnz+lPgNhZFCKnn5aWVnRn9zNzZU4dkzA4wn+uRj4I4o/J/cfpZFrahL42c+sePdddfiNB1FXZ/wNfNnzRERERERERCPF+33yBwN/RGNQVpaRCeJ2AxUVAl1dAtOn92apnX++F7oOnDgBTJrU+3hCgkRnJyDjeO4yHBl/WVk6mppET2PdWCj1OWuWBrdbYP/+4A7zXV1ARwdLfRLFG9+NAMt9jpzXC6xaZcGxYwJPPWXBCy+YRnT+q68XUBSJzEweR4mIiIiIiCg4vntLXwIC0UAY+CMag3wniKYmgbIyFVarxIQJvQG+1FTgrLM0AEapTx+bDdD10GSRRYOURjAzNTU8pT4bGgQSEiSSkkL69CNSXCyRni7x2WfBHeZbWoyLCGb8EcWXjAwJVWWfv2C8+KIJhw8L/OQnblx7rQdr15qxapUFbndgz1NbK5CVJWEyhWecRERERERENHaYzUB6umTGHw2JgT+iMchXirKxUWDvXgVTp+r9JiS//GUvvvUtwOHofSwhwfi5eC332dEBaJpAamponzcrS0LTBA4dUpCVJXt6IkaTEMDs2Ro++2zk5ekAoLnZ+GWY8UcUX4Qwsv4aGnipNxKffKLgtdfM+NrXvJgyRcdFF3lx++3d2L5dwUMPWeB0+v9cdXVKTGSCExERERER0ejgaztENBjOBhGNQZmZRnCqrk7gwAGlT5lPn9RU4OKL0SeIZbMZXzs74/PE0t5ujDvUPf58GZT79ys95fViwezZOo4eVYJq9suMP6L45XAw428k6uoEVq+24PTTNVx4obfn8YULdfzXf7lRU6PggQesaGvz//lycngMJSIiIiIiotDIytIZ+KMhMfBHNAaZTEZK+NatKrq7BWbM0Pz6OV/GX2dnOEcXPm1txgkx9D3+jOdzOkVMZXWUlmoQAti1a+SH+pYWgcRECas1hAMjoojIzpas+R8gj8fo65ecLHHrre5+GdxTpui4++5utLYK3H+/FS0tQz+flEbgLzc3ds4NREREREREFN+ysyWqqxWcOBHtkVCsYuCPaIzKypLYu1eFzSYxfrx/E5IJCcbXeM34850MQ93jLykJsNmM58zK6p89GS3JycCkSXpQ5T5bWgSz/YjilFHqU0ByF/bbSy+ZUFUlcPvt7kH7tRYWSvzsZ93o7hb45S+tQwZXd+9W4PEIFBbGzrmBiIiIiIiI4ts552hQVeCxx6zweoffnsYeBv6IxqjMTB1SAlOn6lD9jAv5glvx2uOvvV1ACCMgFkpC9Jb7jKVSnwAwZ46G3bsVaP4ldfbDwB9R/HI4JLq6RED96MayXbuMvn5XX+3FhAlDH/fy8ozgn5TA/fcPHPzTNGDNGjNKSjSUljLwR0RERERERKGRmSlx551ulJcL/PWv5pAv+K2oEPjoo5EnElD0MfBHNEb5ylPOmOH/ZKQv46+rKz4z/traBJKTJZQwHPl872esBf5mz9bQ0SFw+PDIfummJoGMjNj6nYjIP9nZxvGd5T6H19YG/PGPFpSWarjoIv+WSzocEnff3Q1Fkfjv/7bA4+n7/XXrVNTUKLjuOk+/kqFEREREREREwZgyRcfNN3uwfr0Jb78d2iDd//6vGX/7mzmkz0mRxcAf0RjlC1BNn+5/KpjVamS3xXOPv1D39/OJ1cDfhAkSyckSO3f2Pdy/8YaKBx6wYPNmdciSAC0tDPwRxSvf8ai+npd7Q5ES+POfLZAS+Na3+vf1G0pGBnDHHW5UVQmsWdN7U+R0Av/3f2Z84QteTJzIYygRERERERGF3vLlGi680Is1a8zYsyc09/6trcCePSra20XczgETA39EY9bcuTouvNDrd38/wAj6Wa0yjnv8iZD39/MpLtaRna33ZEXGCkUBZs3SsGtX78qfjz5SsWaNBa2tAqtWWfCDH9jwyiumfuUAdd3oi8jAH1F8Sk4GEhMlM/6G8c47KnbuVPHNb7qRnh74z0+cKHHDDR68+64JGzcax9p//csMjwf4ylc8w/w0ERERERER0ch97WsezJih4ze/seDZZ81oawvu+TZvVntKh3I+IX6Zoj0AIoqOzEyJ664LfEIyISE+evw5nUBlpYKZM3tLmba3I2yBv7PP1rBs2Qgb6YXZnDk6Nm0yoa0NqK5W8Je/mHHmmV7ceqsH1dUCb75pwt//bsKmTSoeeKC75+dOnAB0nT3+iOKZwyFRX88L9cG0tgLPPWfGeed5MW/eyPvwrVihYd8+L554wgyLReKtt1R8+cte2O2hGysRERERERHRqVQVuPNON954w4S1a01Yv17FBRd4cfHFXiQlBf58GzeaMGmSjsOHFdTXCxQXc14wHjHjj4gCYrPFR8bfhg0qHnzQiiNHesdqZPyF5/WMbMjwPHewZs0yApKvvWbCY49ZMH26UQNcCKC4WOLWWz347nfdqKpScPx47/vV0mL8PwN/RPErO5sZf0NZt84EIYArrwwuM08I4OabPcjMlHjsMSsyMiQuvNC/XoFEREREREREwbBagcsu8+LRR7tw3nlevP66Cfffb+3J3PNXba1AebmCiy7ywmKRbB0Sx/iXI6KA2GzxkfHX2mpMdL/wQm/PpXD2+ItlaWnA+PE6Xn3VjOxsie99zw3TKfnepaU6TCaJnTt7S4I2NxvvIUt9EsUvh0Nn4G8QXi/w7rsmnHGGhuTk4J/PZgO+9z03MjMlvv51DyyW4J+TiIiIiIiIyF/JycBXv+rF977nxtGjCo4eDWw+YNMmFVarxLx5GrKzWUEonjHwR0QBSUyMj4w/p1PAbJbYvVvFrl0KdN14LCVlbAaxli7VkJur4667ugfsQ2izATNm6Ni5s/e00NwsoKoSKSkRHCgRhVR2tkRjo4A+8iqWo9a2bSpaWwXOOy90mXkFBRKPPdaF+fP5hhMREREREVF0zJihw2qV2LFDHX7jz0lpVFBbsECD1QoG/uIcA39EFBCbDejsjPYohtfWJjB9uo4pU3Q8/7wZ7e3GCSxcPf5i3UUXefHb33YP2W9q7lwNZWUKOjqMfzc3G/39BM/xRHHL4ZDQNNGTwUu93npLxbRpWsj7FfCYSURERERERNFksQCzZunYvt3/wF9FhUBtrYKlS42WQQz8xTcG/ogoIDabjItSn+3tRpDvmms8qKpS8PrrRm3LcPX4i3VCDD8ZPXeuDl0X2L3bODW0tIghA4VEFPscDiOoxXKffR05InDggIrzztOiPRQiIiIiIiKikJs3T8Phwwra2vp/76OPVDz0kAVVVb1zBRs3mpCSIjFzplHBxtc6JNA+gRQbGPgjooAkJABdXbE/gdzWJpCSAkyZomPBAg2vvWYE/sZijz9/ZWdL5OfrPX3+jMAf3y+ieJaVZezDo2mVXlWVgNsd3HO8/bYJGRkSp5/OwB8RERERERGNPvPmGfe7vnk+H00DXnrJjLIyBXffbcVLL5nQ3Q1s3qxiyRIN6ueb5+SwglA8Y+CPiAJis8m4KPXpdIqesp5XXeXpeXys9vjz17x5GnbuVCGlUeozI4PvF1E8s1iAceN0vPOOCdooiHFVVgr89Kc2fPCB/+VKTuV0Ahs3qjjnHG/PDQ0RERERERHRaJKaCkycqPfr87d1q4qmJoGf/7wbl1/uxauvmvCf/2lDa6vA0qXenu2ys0ffQuKxhIE/IgpIQgLQ2RnbB3xdB1wugeRk4wSVlyexYoUGm00iISHKg4txc+fqaGsTqKgQzPgjGiVuvtmNI0cUvPqqKdpDCYqUwHPPmSElcPDgyC9h1683QdeBFSu8w29MREREREREFKfmz9fw2WdKn6o5r79uwowZGiZOlLjiCi9+9atuZGZKFBXpmDSpdx7QV0GIrUPiEwN/RBSQhITY7/HndBoTxL6MPwC47joP7r23e9g+d2PdlCk6EhMlNm40obubGX9Eo8GkSRKXXOLBP/5h6lO/P97s3Klgzx4VeXk6ystHdgmr68A77xjlS8Zqz1ciIiIiIiIaG+bP19DdLVBWZtxDHzokcPiwggsu6F0IW1goce+93fjVr/rOm1osgN0umfEXpxj4I6KA2GyA1yvgjeFEifZ244SUktL7mNkMFBQwiDUckwmYNUvHRx8ZZQCY8Uc0OlxxhRd5eRKrV1ti+vg9GE0Dnn/ejBkzNFxyiRe1tQo6OgJ/nh07FDQ2Kjj//Dh8E4iIiIiIiIgCUFgokZWlY/t2Y57vtdfMyMnRMX++3m9bZYBIUXa2jro6Bv7iEQN/RBQQm80IBEU66+/TTxX87GdWvyas29p8gT8GrUZi3jwNTqfxHjLwRzQ6mM3Abbe5UV0t8O9/x1/Jz3XrVBw/ruCaazyYOFGHlMCRI4Ffxr79tgmTJumYOJHHNiIiIiIiIhrdhADmzzf6/DU0CGzbZmT7+VsRLTtbor6eIaR4FH8zP0QUVb4eeZ2dvT30IuH9902oqFCwe7eCuXP7r0o5WXu78ZWBv5GZPVuDEEa5VAb+iEaPCRMkLrvMi3//24S6OoH2doG2NgGXC7jxRs+wx9Zo6egA/v53M844w4vx4yV0HbBaJcrLFcyY4f+Yjx0T2L1bxbe/7R5+YyIiIiIiIqJRYN48DW+9ZcKTT5phswFnnqn5/bPZ2RI7dzLjLx4xXEtEAUlIMAJBnZ2Re02vF9i92zhcbdqkDru90ymgKBJJSeEe2eiUmgpMnKgjOVnCYon2aIgolC67zItFizQ0NCiwWoFJk3R0dAgcOBC7l4Qvv2xCVxfwla8YKd+KAowfH3ifv7ffNiElRWLhQv9vcoiIiIiIiIji2fTpOmw2iV27VJx1lhc2m/8/63BItLeLiFd+o+Ax44+IAuLL+OvqEgAikw128KCCjg6B+fM1bNumorvbA6t18O3b2wWSkuB32jr1d/753pgOBBDRyJhMwHe+4+nzWEWF0tMbNdZ0dQFvvmnCRRd5kZnZe86ZMEFi2zb/j1EdHcCHH6q46CIvzOZwjJSIiIiIiIgo9phMwOzZOrZtU3D++YEthM3ONu7D6+sFiotZFSyecFaXiALiWxUSyYy/nTsVpKZKfO1rHnR3C+zcOXTWX3u7YJnPIJ1xhoabbvIMvyERxb20NIkTJ2Iz8Ld/vwKPR2DZsr43J5Mm6WhsVNDW5t/zfPihCo8HOPtsPxrFEhEREREREY0iX/qSB9/8pgdZWYHNl2ZnG+01GhqGnjN48UUT7r/fgu7uEQ+RQoyBPyIKiM3mK/UZuUniHTtUzJ2rIS9PYsIEHZs3Dx34a2sDUlMZ+CMi8kdqqkRbW2wG/vbsUWC3S+Tl9T2mT5xo3HxUVAx/KSulUebz9NN1ZGSEZZhEREREREREMauoSPZbUOuPtDTAYpGorx/83vuTTxS8/LIZBw6oePJJMySnZGMCA39EFJDeUp+Reb36eoGaGgVz5xonp8WLNezYocDlGvxnjIy/yIyPiCjepaZKtLdHexQD271bRWmp1q90c3a2RFKS9KvP3+7dCmprFZx/PrP9iIiIiIiIiPwlhHH/XV8/8GLhpiaBv/zFgvnzNXz7225s2GDC228PnbBBkcHAHxEFRFEAq1VGLONv504FqipRWmpkdyxerEHTBD75ZPCTSHu7QHIyl5cQEfkjNRUxWerzxAmgqkrBzJl6v+8JAUyYoA8Z+JMSaG0F1q41obhYx9Sp/Z+HiIiIiIiIiAY3WOBP04DHHzfDYgG++U03li7VcMEFXqxZY8aBAww7RZsp2gMgovhjs0Uu42/nThXTpulITDT+nZkpMXWqhk2bVJx55sAp6u3tgqU+iYj8lJoq0dUl4HYDFku0R9Nr715jgUdp6cDH+okTdbz/vglSoicjUErg3/82Yc8eBdXVCpxO4xsrV7r7ZQ0SERERERER0dCysyU+/bR/IO+f/zThwAEVd9/djeRk47Gvfc2DI0cE/vu/LfjlL7uQnh7ZsVIvhl6JKGA2m0RnZ/hfp6sL2LtXwdy5fbM0lizRsGePgra2gX+uvR1ISWHgj4jIH2lpxvEy1vr87d6toLBQH/RGYeJEHSdOCDQ39477ww9V/N//mZGUBJx/vhd33NGNRx7pwtKlgfcyICIiIiIiIhrrHA4dDQ2iT+++vXsV/PvfZlxxhadPdR2TCbj9djcAYPXqGFpZPAYx8EdEAUtIQERKfe7dq8DrFT39/XwWLjT+vWVL/3Kf3d2A280ef0RE/vItlBhsMUU0SAns2qX2lHkeyIQJxrjLy43zkcsFPP+8GYsXe/H977txxRVeLFyoIyeHC0GIiIiIiIiIRiI7W8LrFWhpMf7d0WEE9aZO1XDZZd5+26enA1/9qge7dqmoq4utBcZjCQN/RBSwhAQZkVKfO3aoyMnRkZfXd9I2JQUoLdWxcWP/wF97u3FCYalPIiL/xGLGX22tkck3c+bgmXoZGRJpabKnz99LL5nh8QDXXuuJ1DCJiIiIiIiIRrXsbGPOoL7euPd+/nkznE7gm9/0QBkkurRggQabTeKjj/rP3VJkMPBHRAGz2cKf8Scl8OmnKubNGzjb4/TTNRw8qMLt7vu4L2MlOZmBPyIif/gypGMp8LdnjwJFkZg2bfCMPyGMcp8VFQoqKgTefdeEK6/0wm6P4ECJiIiIiIiIRjGHwxf4E/j0UwXr1plw7bWenoDgQKxWYNEiDR99pPYpEUqRw8AfEQXMZgt/xl9VlZHtMWfOwNkeeXkSUgINDX0nqp1OX8ZfeMdHRDRamExAUpLEiROxE/jbvVvFlCk6EhKG3m7iRB3l5Qr++lcLCgp0nHde/zIjRERERERERDQyFguQni5x5IiC//kfC0pLNaxYMXh1Hp9lyzQ0NCjYv58hqGjgu05EAUtIALq6wjtBfOjQ0NkeubnG47W1fcfhy1hhxh8Rkf9SUiTa26M9CoOuGz1eh+rv5zNxoo6ODoHDhxXccIMHJlMEBkhEREREREQ0hmRn63j7bRO6uoBbb/VA+DEtPG2ajqwsHR9+yHKf0cDAHxEFLCFBorMzvK/hdAokJgJm88DfT08HLBaJ2tq+h7H2dgGLRcJqDe/4iIhGk7S02Mn4Ky8X6OgQmDnTv8AfAJxxhhfTpw+/PREREREREREFJifHqLx23XUeZGb6l2whhJH19/HH/Vs1Ufgx8EdEAYtEj7+ODqP03GCEME46dXV9x9HeDqSmMtuPiCgQqamx0+Nvzx4VNpvsCeoNJSUF+OEPu/H1r3siMDIiIiIiIiKisWfRIg0XXujFmWcOX+LzZF/4goauLoFPPmHWX6Qx8EdEAUtICH+Pv44OI+NvKLm5AwX+BFJSwjgwIqJRKDVVxkzgb/duBTNm6H6X7ZwzR0dSUnjHRERERERERDRWzZ2r47rr/CvxebKcHIkpU1juMxoY+COigNlsgNstoAW2yCMgLpcYMuMPAHJy9H6BP6dTICWFGX9ERIFITY2NUp+aBhw4oLBsJxEREREREdEo8IUveLFrl4qWlmiPZGxh4I+IAmazGV/DmfXX0YFhM/5yciSamhR4Tqrw1tYGBv6IiAKUlibR1gbIKB8+m5oENE2gsJCBPyIiIiIiIqJ4t2iRBlWV2LDBz7I+FBIM/BFRwBITjZnhrq7wZYf4k/GXm2s0lq2v7x0HS30SEQUuJQXQdQGXK7rjaGgwjucOBxdwEBEREREREcW7pCRg/nwdH3ygRn2x8VjCwB8RBcyX8dfZGb7X6OiAX6U+AfQp99nWxlKfRESBSk01jpvt7dEt91lfLyAEkJXF4zgRERERERHRaHDOOV7U1CjYu5fhqEjhO01EAbPZfBl/4XsNI+Nv6G3sdsBslqitNQ5lum78HAN/RESBSUszjpvR7vNXXy+QkSFhYgUQIiIiIiIiolFhxgwdBQU63n6bN/uRwsAfEQUsIcH42tkZngliKf0r9SmEUe7Tl/HX0WH8LAN/RESB8WX8tbVFdxwNDQLZ2ezvR0RERERERDRaCAGcf74Xn3yi9rT4oPBi4I+IApaQYEwQh6vUZ2enEcBLTBx+25yc3sBfW5vxlT3+iIgCk5QEKIrsOY5GS329wv5+RERERERERKPMGWdoSEiQePddNdpDGRMY+COigPl6/HV1hWeCuKPDeN7hMv4AIDdXR22tsb2vNxUz/oiIAiMEkJqKGAj8CWRn8xhORERERERENJrYbMCZZ2pYv94Etzvaoxn9GPgjooCZTEZvvXBl/LlcxtfExOEnf7OzJZqaFHg8vSXqfCXriIjIf2lpMqo9/jo7AaeTgT8iIiIiIiKi0ejcc71wOgU2bmRYKtyi/g6vXr0aV155JebNm4clS5Zg5cqVKC8v77PN9ddfj6lTp/b575577umzzS9/+UtcccUVKC0txWWXXRbJX4FoTLLZIpHxN/y2ubkSUhp9odrbBYTw7+eIiKivlBTZkzkdDb46/yz1SURERERERDT65OZKzJ6t4Y03TJC89Q8rU7QHsGXLFlx77bWYNWsWNE3Do48+iltuuQVr165F4kkNvq666irccccdPf9OSEjo91xXXnklPv30U+zfvz8iYycayxISwpfx19FhfPUn4y8nx9imrs4I/CUlSShRX9JARBR/0tIkmpqiF/irrzdeOztbj9oYiIiIiIiIiCh8LrjAi4cftmLfPiA3N9qjGb2iHvh74okn+vz7oYcewjXsw6MAAQAASURBVJIlS7Bnzx4sWLCg53GbzQaHwzHo89x9990AgObmZgb+iCIgIQHo7AzPBLHLZTzvSbH/QWVkSJjNErW1CtrbBfv7ERGNUGoqUF4ezcCfAotFIjU1akMgIiIiIiIiojCaPVtHTo7E668DN90U7dGMXlEP/J2qvb0dAJCWltbn8VdeeQUvv/wyHA4HVqxYgZUrVw6Y9RcMRRFQlOhNeBHFElVV+nw9VWIi4HYLmEyhT6/r6lJgswE2m3/PnZcHNDQo6OwE0tIQljERxZPh9l+igaSnA06nErVjaHOzgtxcwGwe259b7r9E8Yn7LlH84v5LFJ+47xLFr1tu0eB0mrn/hlFMBf50XccDDzyA+fPno6SkpOfxSy65BPn5+cjOzsb+/fvx8MMPo6KiAqtWrQrp62dkJEEIBv6ITpaaOnCA3W73fbWG5XXtdsBu9+8QNX48cOIEoChATg5gt1vCMiaieDPY/ks0kMJCoLsbSEkxwRSFK8S2NqC4GLDbzZF/8RjE/ZcoPnHfJYpf3H+J4hP3XaL4c+aZvv/j/hsuMRX4u/fee3Hw4EE899xzfR6/+uqre/5/6tSpcDgcuPHGG1FVVYXi4uKQvX5zs4sZf0SfU1UFqakJaGvrhKYN1G/JjJYWoKXFE/LXrq83wWxW0NLi9mv7tDQTPv5YQUoKMH68jpYWb8jHRBRPht9/ifoTQoHHY0ZVVXfP4o5Iqqy0YPZsHsO5/xLFJ+67RPGL+y9RfOK+SxS/uP+OnN2e5Nd2MRP4u++++7B+/XqsWbMGucN0dZwzZw4AoLKyMqSBP12X0HX2ByM6mabp8Hr7H4CtVh0ulzLg94LV3i5hsw38ugNxODQ0NKhwuyVmzvT/54hGu8H2X6KBJCdLSGlGc7OMeL9UKYG6OiArS+Nn9nPcf4niE/ddovjF/ZcoPnHfJYpf3H/DJ+qBPykl7r//frz99tt49tlnUVRUNOzPlJWVAQAcDke4h0dEg7DZgM7O8Dx3R4dAkn+LFwAAOTkSUgKtrQIpKeEZExHRaOc7fra1CQCRDfy1tgJer4DDwQVYRERERERERETBiHrg795778Wrr76Kxx9/HElJSWhoaAAApKSkwGazoaqqCq+88gqWL1+O9PR07N+/Hw8++CAWLFiAadOm9TxPZWUlOjo60NDQgK6urp7g4KRJk2CxsN8XUaglJEh0dYWnNK7LhYAmf3Nze7eNdJYKEdFokZpqHD9PnIh82fP6eqOhd3Y2j+FERERERERERMGIeuDv+eefBwBcf/31fR5/8MEHccUVV8BsNmPTpk145pln0NHRgby8PJx//vlYuXJln+3vvvtubNmypeffl19+OQDg3XffRWFhYXh/CaIxyGYDurrC89xGxp//k78ZGRJms4THIxj4IyIaIasVsFrl5xl/kVVfb7wmM/6IiIiIiIiIiIIT9cDf/v37h/x+Xl4e1qxZM+zzPPvss6EaEhH5wWYzMv68XsAU4iOJyyWQmOj/5K8QRpbIsWMM/BERBSMtTaKtLfKv29AgkJoqYbVG/rWJiIiIiIiIiEYTJdoDIKL4NHmyDkWR+Pe/Q79+oKMDAfX4A4w+fwDY44+IKAgpKYhaxh/LfBIRERERERERBY+BPyIakeJiicsv9+Jf/zLj4MHQHUq8XqC7O7BSnwCQm6sDYI8/IqJgGBl/0Qr86RF/XSIiIiIiIiKi0YaBPyIascsu82LSJB1//KMZnZ2hec6ODuNrIKU+AWDqVB35+TrLxBERBSE1VeLEiWgE/hRm/BERERERERERhQADf0Q0YqoKfPvbbpw4IfDMM+aQPGdHhzHhHGipz9NO0/Gb33RDRH6+moho1EhNlWhvj+xrut1Aa6uAw8HAHxERERERERFRsBj4I6Kg5ORIfP3rHnz4oQkff6wG/Xwul/E10Iw/IiIKni/jT0bwENzYaKzYYMYfEREREREREVHwGPgjoqCdeaaGBQs0PPWUGV5vcM810ow/IiIKXloa4PEIdHdH7jXr6xn4IyIiIiIiIiIKFQb+iChoQgDnn++F0ylQWxtcrU2Xy/h5ZvwREUVeSopx7I1kn7+GBgFVlbDbedwnIiIiIiIiIgoWA39EFBJFRToA4OjR4A4rHR1GIDEhIRSjIiKiQKSmGsG3SPb5q69XkJUlofCqlIiIiIiIiIgoaJxiIaKQSE4G0tMlqquDz/hLSpIQkUs2ISKiz6Wn92b8dXUBn32m4KWXTDh2LHwH5fp6wTKfREREREREREQhYor2AIho9Cgs1IPO+HO5WOaTiChakpONrOtnnzWjpUVA0wQUReKjj0y4//4upKaG/jXr6wVKSvTQPzERERERERER0RjEwB8RhUxhocSOHcEG/gSSkkI0ICIiCoiiAIsWeeF2C1x0kRelpTqsVuBnP7Pi97+34Cc/ccMU5NXj5s0qDh5UcOIE0N4uUFMjcMYZXPBBRERERERERBQKDPwRUcgUFup4800TursBq3Vkz+FyCWb8ERFF0Xe/6+n32J13uvGrX1nw9NNm3HJL/+/7q7xcYNUqC3JydNjtEqmpwDnnaFiyRAtmyERERERERERE9DkG/ogoZIqKdEgJHDsmMHHiyIJ3HR1gxh8RUYwpKdFx000e/M//WDBunI5zzx1ZoO6VV8zIydHx2992Q2GnaSIiIiIiIiKikOOUCxGFTEGBEewLps8fM/6IiGLTWWdpOP98L555xowPP1QhAzxUHzsmsG2biksu8TLoR0REREREREQUJsz4I6KQsdkAh0PH0aNixM9hZPwx8EdEFIuuvdYDpxNYvdqCDz/UcOONHuTn+3fMfuUVE9LTJZYtY1lPIiIiIiIiIqJw4XprIgqpwkIZgoy/EA6IiIhCRlWBlSs9+OEPu9HUJPCTn1jx4osmeIZp+9fQILBxo4ovftELszkyYyUiIiIiIiIiGosY+COikCos1Ecc+JOSGX9ERPFgzhwdDz7Yjcsu8+LVV014662hi0i8+qoJiYnAihXeCI2QiIiIiIiIiGhsYuCPiEKqqEiiuVnA5Qr8Z7u7AV0XSEoK/biIiCi0LBbgiiu8mD1bx2efDX5J2doKvP++iosu8sJmi9z4iIiIiIiIiIjGIgb+iCikCgt1AMCxY4EfXlwuozdgYiIz/oiI4sXMmTr271fgdg/8/ddfN8FkAs49l9l+REREREREREThxsAfEYVUXp6EokgcPSoC/llfliBLfRIRxY9ZszR4vQL79/e/rOzuBt5914Rzz/Uym5uIiIiIiIiIKAIY+COikDKbgdxcierqwA8vHR2+jL9Qj4qIiMKloEAiLU1iz57+x/1PP1XQ1SVw1llaFEZGRERERERERDT2MPBHRCFXWMiMPyKisUIIoLRUw+7dar/vbd5swrhxOnJzeVwnIiIiIiIiIooEBv6IKOSKinRUVyuQAc7zMuOPiCg+zZypo7JSQXt772NdXcCOHQoWL2a2HxERERERERFRpDDwR0QhV1Cgw+kUaGsL7Oc6OgCzWcJsDs+4iIgoPGbO1CElsGdPb9bfzp0qPB6BhQsZ+CMiIiIiIiIiihQG/ogo5IqKjFS/QPv8uVwCSUnhGBEREYVTZqZEXp7ep8/fxx+rmDBBR04Oy3wSEREREREREUUKA39EFHI5ORImU+B9/jo6BBITOUFMRBSPZs3qDfx1dQE7dyrM9iMiIiIiIiIiijAG/ogo5BQFKCiQOHo00Iw/MOOPiChOzZypob5eQV2dwI4dRplP9vcjIiIiIiIiIoosU7QHQESjU2GhPqJSn8z4IyKKT9Om6VAUiT17FHz6qYqJE3U4HDymExERERERERFFEjP+iCgsCguNUp+67v/PdHQASUmcJCYiikdJScDEiRLbtqn49FMFixYx24+IiIiIiIiIKNIY+COisJg6VUd3t8DBg/4fZoyMvzAOioiIwqq0VMNnn6nwegUDf0REREREREREUcDAHxGFxZQpOtLTJbZsUf3+GZdLMOOPiCiOlZYaad6TJ+vIyuLxnIiIiIiIiIgo0hj4I6KwEAJYsEDD1q0qpJ9zvx0dYMYfEVEcmzxZR0qKxBe+4I32UIiIiIiIiIiIxiQG/ogobBYs0NDcLFBeLobdVtOAri5m/BERxTOTCXjssS6cfTbLfBIRERERERERRQMDf0QUNtOmGZkf/pT77Ow0viYmMvBHRBTPrFYj65uIiIiIiIiIiCKPgT8iChtFAU4/3b9yny6XMUuclBSBgRERERERERERERERjUIM/BFRWC1cqKG+XkFl5dDpHy6X8ZWlPomIiIiIiIiIiIiIRoaBPyIKq+nTdSQlDV/u05fxl5gYiVEREREREREREREREY0+DPwRUViZTMD8+Rq2bBm63GdvqU9m/BERERERERERERERjQQDf0QUdgsXaqitVXDs2ODlPltbAUWRSEiI3LiIiIiIiIiIiIiIiEYTBv6IKOxKS3UkJEhs3Tp4uc/du1VMm6ZD4VGJiIiIiIiIiIiIiGhEOMVORGFnNgNz52qD9vlzu4E9exTMmaNHeGRERERERERERERERKMHA39EFBGLFmmorh643GdZmQKPR2DuXC0KIyMiIiIiIiIiIiIiGh0Y+COiiJgzR0diosSGDf2z/j79VEVWlo78fBmFkRERERERERERERERjQ4M/BFRRJjNRtbfxo0q5EnxPSmBnTuNMp+ifzIgERERERERERERERH5iYE/IoqYpUs1NDYqOHCg99BTWytQX6+wzCcRERERERERERERUZAY+COiiJk2TUdmZt9ynzt3qjCZJKZP16M4MiIiIiIiIiIiIiKi+MfAHxFFjBDAkiVefPyxCq/XeOzTTxVMn67DZovu2IiIiIiIiIiIiIiI4h0Df0QUUcuWaXC5BD79VEFXF1BWpmDOHJb5JCIiIiIiIiIiIiIKlinaAyCisaWwUGLcOB0bNpgghBeaJjB3Lst8EhEREREREREREREFi4E/Ioq4pUs1vPSSCYqiIidHR26ujPaQiIiIiIiIiIiIiIjiHkt9ElHELVliZPpt3mzCnDnM9iMiIiIiIiIiIiIiCgUG/ogo4jIygOnTjb5+c+eyvx8RERERERERERERUSgw8EdEUXHOORrS0yWmTWPGHxERERERERERERFRKLDHHxFFxaJFGhYtYrYfEREREREREREREVGoMOOPiIiIiIiIiIiIiIiIaBRg4I+IiIiIiIiIiIiIiIhoFGDgj4iIiIiIiIiIiIiIiGgUYOCPiIiIiIiIiIiIiIiIaBSIeuBv9erVuPLKKzFv3jwsWbIEK1euRHl5eZ9trr/+ekydOrXPf/fcc0+fbWpqavDNb34Tc+bMwZIlS/DrX/8aXq83kr8KERERERERERERERERUdSYoj2ALVu24Nprr8WsWbOgaRoeffRR3HLLLVi7di0SExN7trvqqqtwxx139Pw7ISGh5/81TcNtt92GrKwsvPDCC6ivr8ePf/xjmM1m/OAHP4jo70NEREREREREREREREQUDVEP/D3xxBN9/v3QQw9hyZIl2LNnDxYsWNDzuM1mg8PhGPA5PvroIxw6dAhPPfUUsrKyMH36dHzve9/Dww8/jO9+97uwWCxh/R2IiIiIiIiIiIiIiIiIoi3qpT5P1d7eDgBIS0vr8/grr7yCRYsW4ZJLLsEjjzyCzs7Onu/t3LkTJSUlyMrK6nls2bJlcDqdOHToUGQGTkRERERERERERERERBRFUc/4O5mu63jggQcwf/58lJSU9Dx+ySWXID8/H9nZ2di/fz8efvhhVFRUYNWqVQCAxsbGPkE/AD3/bmho8Pv1FUVAUUQIfhOi+KeqSp+vRBQ/uP8SxS/uv0TxifsuUfzi/ksUn7jvEsUv7r/hF1OBv3vvvRcHDx7Ec8891+fxq6++uuf/p06dCofDgRtvvBFVVVUoLi4O2etnZCRBCAb+iE6Wmpow/EZEFJO4/xLFL+6/RPGJ+y5R/OL+SxSfuO8SxS/uv+ETM4G/++67D+vXr8eaNWuQm5s75LZz5swBAFRWVqK4uBhZWVn47LPP+mzT2NgIAIP2BRxIc7OLGX9En1NVBampCWhr64Sm6dEeDhEFgPsvUfzi/ksUn7jvEsUv7r9E8Yn7LlH84v47cnZ7kl/bRT3wJ6XE/fffj7fffhvPPvssioqKhv2ZsrIyAL1Bvblz5+JPf/oTmpqakJmZCQDYuHEjkpOTMXnyZL/HousSui5H8FsQjV6apsPr5QGYKB5x/yWKX9x/ieIT912i+MX9lyg+cd8lil/cf8Mn6oG/e++9F6+++ioef/xxJCUl9fTkS0lJgc1mQ1VVFV555RUsX74c6enp2L9/Px588EEsWLAA06ZNAwAsW7YMkydPxo9+9CP88Ic/RENDAx577DFce+21sFgs0fz1iIiIiIiIiIiIiIiIiCIi6oG/559/HgBw/fXX93n8wQcfxBVXXAGz2fz/s3ff4XFU1//H31N21astS+694N7ABptmeq8BEgiBkABfCAQIoQUSSAIEAr8QIIGEQCAB0jAlYDA1dBdw772p2XKRrL67M/P7Y+3FsiRbllZaafV5PY8fWzN37pxd+a7sPXvOZdasWfztb3+jqqqK7t27c/LJJ3PddddFxlqWxdNPP829997LxRdfTFJSEueddx433nhjmz4WERERERERERERERERkViJeeJv1apVBzzfvXt3XnzxxYPO07NnT5555plohSUiIiIiIiIiIiIiIiLSoZixDkBEREREREREREREREREWk6JPxEREREREREREREREZE4oMSfiIiIiIiIiIiIiIiISBwwPM/zYh2EiIiIiIiIiIiIiIiIiLSMKv5ERERERERERERERERE4oASfyIiIiIiIiIiIiIiIiJxQIk/ERERERERERERERERkTigxJ+IiIiIiIiIiIiIiIhIHFDiT0RERERERERERERERCQOKPEnIiIiIiIiIiIiIiIiEgeU+BMRERERERERERERERGJA0r8iYiIiIiIiIiIiIiIiMQBJf5ERERERERERERERERE4oASfyIdxEsvvcS0adMYNWoU3/rWt1i8eHG9MQsWLODyyy9n7NixjB8/nksvvZSampoDzltYWMjVV1/NmDFjOPLII3nooYcIhUJ1xgQCAX73u99x/PHHM3LkSKZNm8Yrr7xywHl//etfc/755zNy5EjOOeecA47dtGkT48aNY+LEiQccJ9JRxdv6zc/PZ+jQofV+LVy48OBPhkgHEm9rF8DzPJ599llOOeUURo4cydFHH81TTz11kGdCpOOJt/X7xBNPNPizd+zYsQd/MkQ6mHhbvwCfffYZF110EePGjWPy5MnccMMN5OfnH+SZEOlY4nHtvv3225xzzjmMGTOG448/nr/85S8HeRZEOqaOtH5XrlzJLbfcwrHHHsvo0aM57bTTeOGFF+qNmzNnDueddx4jR47kpJNO4tVXX23isxEf7FgHICIH9/bbb/Pggw9y3333MWbMGF544QWuuuoqZs6cSZcuXYDwi+8PfvADrrnmGu655x4sy2LlypWYZuP5fcdxuOaaa+jatSv//Oc/2bZtG7fffjs+n49bbrklMu7HP/4xO3bs4P7776dPnz6UlJTguu5B477gggtYtGgRq1atanRMMBjklltuYeLEiSxYsOAQnhWRjiGe1+/zzz/PoEGDIl9nZmY24RkR6Rjide3ef//9fP7559x2220MGTKEsrIyysrKDvHZEWnf4nH9fv/73+eSSy6pc+yKK65g1KhRTX1aRDqEeFy/W7Zs4brrruPKK6/kkUceoby8nAcffJAbbriB1157rRnPkkj7E49r95NPPuGnP/0pd999N1OnTmXdunXcfffdJCYmctlllzXjWRJpnzra+l26dCnZ2dn89re/pXv37syfP5+f//znWJYVWZtbtmzhmmuu4ZJLLuGRRx5h1qxZ3H333eTk5HD00UdH6Zlr5zwRafcuvPBC77777ot87TiON3XqVO9Pf/pT5Ni3vvUt73e/+90hzfvxxx97w4YN80pKSiLHXn75ZW/8+PFebW2t53me98knn3gTJkzwdu3a1azYH3/8ce/ss89u9PzDDz/s3Xrrrd706dO9CRMmNOseIu1ZPK7fLVu2eEOGDPGWL1/erHlFOoJ4XLtr1671hg8f7q1bt65Z84p0FPG4fve3YsUKb8iQId5XX33VrPuItFfxuH7feecdb/jw4Z7jOJFjH374oTd06FAvEAg0614i7U08rt1bbrnFu+GGG+oc+9vf/uYdc8wxnuu6zbqXSHvUkdfvXvfee6/33e9+N/L1ww8/7J1xxhl1xtx0003e97///RbdpyNRq0+Rdi4QCLBs2TKOOuqoyDHTNDnqqKMiFXI7duxg0aJFdOnShUsuuYSjjjqKyy67jK+//vqAcy9cuJAhQ4bQtWvXyLGpU6dSUVHB2rVrAfjoo48YOXIkf/nLXzj66KM55ZRTeOihhw5ayt0Us2bNYubMmfziF79o8Vwi7VE8r1+A//u//+PII4/k29/+Nh9++GFU5hRpD+J17X700Uf06tWLjz/+mGnTpjFt2jR+9rOfUVpa2qJ5RdqTeF2/+/vPf/5Dv3791Cpf4kq8rt8RI0ZgGAbTp0/HcRzKy8t54403OOqoo/D5fC2aW6Q9iNe1GwgESEhIqHMsMTGR4uJiCgoKWjS3SHsRL+u3vLy8TheqhQsXcuSRR9YZM3Xq1E61RY0SfyLt3K5du3AcJ1JavVeXLl3Yvn07EC5fBnjyySf51re+xV/+8heGDx/OFVdcwcaNGxude/v27XVefIHI1yUlJZG5582bx5o1a/jDH/7AXXfdxbvvvst9993X4sd155138pvf/IbU1NQWzSXSXsXr+k1OTuaOO+7g97//PX/605+YMGEC119/vZJ/Ejfide1u2bKFwsJCZs6cycMPP8yDDz7IsmXLuPHGG1s0r0h7Eq/rd1+1tbW8+eabXHjhhVGbU6Q9iNf127t3b5577jl+97vfMWrUKCZOnMjWrVt57LHHWjSvSHsRr2t36tSpvP/++8yaNQvXddmwYQPPPfdcnXuLdHTxsH7nz5/PO++8w0UXXXTQe1dUVET9A3ntlRJ/InFgb9/jiy++mAsuuIDhw4dz11130b9/f6ZPnw7AD37wA8aNG8e4ceM444wzmjy353kYhsEjjzzC6NGjOfbYY7njjjt47bXXWvRCec8993DmmWdy+OGHN3sOkXjQEddvdnY2V155JWPGjGH06NHceuutnH322Tz77LPNnlOko+mIa9fzPAKBAA899BATJ05k0qRJ3H///cyZM4f169c3e16RjqYjrt99vf/++1RWVnLeeedFZT6RjqQjrt+SkhLuuecezj33XF555RVefPFFfD4fN954I57nNXtekY6kI67diy66iEsvvZRrrrmGkSNHcvHFF0fiOtC+ZiLxpj2v39WrV3Pddddx/fXXM3Xq1OY9wDhlxzoAETmwrKwsLMtix44ddY7v2LEj8smFnJwcAAYOHFhnzMCBAyksLATg/vvvj7xg2nZ46Xft2pXFixfXuWbvpzn2zpmTk0Nubi5paWl15vU8j+LiYvr169esxzV79mw++uijyKelPM/DdV2GDx/OL3/5S32CWeJCvK7fhowZM4Yvv/wyavOJxFK8rt2cnBxs26Z///515gUoKipiwIABzZpXpD2J1/W7r//85z8cd9xx9T7FLNLRxev6femll0hNTeW2226LHPvtb3/Lsccey6JFixg7dmyz5hVpL+J17RqGwU9/+lNuueUWtm/fTlZWFrNmzQLClbwi8aAjr9+1a9dyxRVXcPHFF3PdddfVOde1a9fIvfa9d2pqKomJiY3OGU/08QSRds7v9zNixIjIPy4g/EmLWbNmMW7cOAB69epFt27d2LBhQ51rN27cSM+ePQHIzc2lb9++9O3bN3Js7NixrF69us6L+5dffklqaiqDBg0CYPz48Wzbto3KysrImA0bNmCaJnl5ec1+XP/61794/fXXI79uvPFGUlJSeP311znppJOaPa9IexKv67chK1asiPzDTaSji9e1O378eEKhEJs3b64TL0CPHj2aPa9IexKv63evLVu2MGfOHH1ITuJSvK7fmpqaetVBe7/eW0Uh0pHF69rdy7IscnNz8fv9zJgxg3HjxpGdnd3ieUXag466ftesWcPll1/Oueeey80331zv/NixY5k9e3adY19++WWn+rCNEn8iHcCVV17Jv//9b1577TXWrVvHvffeS3V1Neeffz4Q/hTSVVddxd///ndmzpzJpk2beOyxx1i/fv0B3xSYOnUqgwYN4rbbbmPlypV89tlnPPbYY1x66aX4/X4AzjzzTDIzM7nzzjtZu3YtX331Fb/97W+54IILDvgJiU2bNrFixQpKSkqoqalhxYoVrFixgkAgAIQ/vTFkyJDIr9zcXEzTZMiQIWRkZETx2ROJrXhcv6+99hpvvfUW69atY926dTz99NNMnz6dyy67LIrPnEhsxePaPeqooxgxYgR33XUXy5cvZ+nSpfz85z9nypQpdaoARTq6eFy/e02fPp2cnByOOeaYKDxTIu1PPK7fY489liVLlvDkk0+yceNGli1bxp133knPnj0ZPnx4FJ89kdiJx7W7c+dO/vGPf7Bu3TpWrFjBr3/9a2bOnMldd90VxWdOJPY62vpdvXo1l19+OVOmTOHKK6+kpKSEkpISdu7cGRlzySWXsGXLFh5++GHWrVvHSy+9xDvvvMMVV1wRvSeunTM8NRQX6RBefPFFnn32WUpKSjjssMO4++67GTNmTJ0xf/7zn3nppZcoKytj2LBh3HrrrUycOPGA8xYUFHDvvfcyd+5ckpKSOO+88/jJT34SKcsGWLduHb/+9a+ZP38+mZmZnHbaadx0000H/AfUd7/7XebOnVvv+IcffkivXr3qHX/11Vd54IEH+Prrrw/2VIh0OPG2fl977TWeeeYZCgsLsSyLAQMGcNVVV3Hqqace4jMj0r7F29oF2Lp1K7/+9a/5/PPPSU5O5phjjuH2228nMzPzEJ4ZkfYvHtev67ocf/zxjX6yWSRexOP6nTFjBn/5y1/YuHEjiYmJjB07lltvvbVe2zSRjize1u7OnTv5v//7P1avXo3neYwdO5abb7653mMSiQcdaf0+8cQTPPnkk/WO9+zZk48++ijy9Zw5c3jwwQdZu3YteXl5XHfddZFkZmegxJ+IiIiIiIiIiIiIiIhIHFCrTxEREREREREREREREZE4oMSfiIiIiIiIiIiIiIiISBxQ4k9EREREREREREREREQkDijxJyIiIiIiIiIiIiIiIhIHlPgTERERERERERERERERiQNK/ImIiIiIiIiIiIiIiIjEASX+REREREREREREREREROKAEn8iIiIiIiIiIiIiIiIicUCJPxEREREREREREREREZE4oMSfiIiIiIiIiIiIiIiISBxQ4k9EREREREREREREREQkDijxJyIiIiIiIiIiIiIiIhIHlPgTERERERERERERERERiQNK/ImIiIiIiIiIiIiIiIjEASX+REREREREREREREREROKAEn8iIiIiIiIiIiIiIiIicUCJPxEREREREREREREREZE4oMSfiIiIiIiItJo77riDadOmxToMERERERGRTsGOdQAiIiIiIiLx4tVXX+XOO+8E4KWXXmLixIl1znuex3HHHUdxcTHHHXccf/rTnyLnKisrefbZZ3nvvffIz88nISGBvLw8Dj/8cH74wx8SDAY54YQTmhTHhx9+SEFBAZdffnnkmGmaZGZmcvjhh/PjH/+YgQMHHtLjAfD7/fTo0YMpU6Zw3XXX0bVr1ybF09YaijsjI4OhQ4dy7LHHcv7555OamtqsuefPn88XX3zB9773PdLT06MVsoiIiIiISFQo8SciIiIiIhJlCQkJvPXWW/USf3PnzqW4uBi/31/neDAY5LLLLmP9+vWce+65XHbZZVRVVbFmzRreeustTjrpJEaNGsXDDz9c57q//vWvFBcX10lyAWRnZ1NQUADAd7/7XUaNGkUoFGLVqlX885//ZM6cObz11lvk5OQ06fHceOON9OrVi0AgwLx58/jHP/7BJ598wltvvUVSUtIBr/3Vr36F53lNuk+07Y07FAqxfft25s6dywMPPMDzzz/PH//4R4YNG3bIcy5YsIAnn3yS8847T4k/ERERERFpd5T4ExERERERibJjjz2WmTNncvfdd2Pb3/y366233mLEiBGUlpbWGf/BBx+wfPlyHnnkEc4666w652prawkGgyQnJ3POOefUOff222+ze/fuesf3NXHiRE499dTI1/379+fee+/l9ddf54c//GGTHs8xxxzDqFGjAPjWt75FZmYmf/3rX/nwww8588wzG7ymqqqK5ORkfD5fk+7RGvaNG+Caa65h1qxZXHvttVx33XW8/fbbJCYmxiw+ERERERGRaNMefyIiIiIiIlF2xhlnUFpayhdffBE5FggEePfdd+sl9gC2bNkCwPjx4+udS0hIaHZbyobsrULce8/mmDx5MgD5+flAeB+/cePGsXnzZn74wx8ybtw4br311si5/ff4c12XF154gbPOOotRo0YxefJkrrrqKpYsWVJn3BtvvMH555/P6NGjOeKII7j55pspKipqdtwARx55JNdddx0FBQX897//jRxfuXIld9xxByeccAKjRo1iypQp3HnnnezatSsy5oknnohUXZ5wwgkMHTqUoUOHRp6H6dOnc/nll3PkkUcycuRITj/9dF5++eUWxSsiIiIiInIolPgTERERERGJsp49ezJ27FhmzJgROfbpp59SXl7O6aefXm98jx49AHj99ddbvS3m3hagLWlTuXnzZgAyMzMjx0KhEFdddRVdunTh9ttv5+STT270+p/97Gc88MAD5OXlceutt3L11VeTkJDAokWLImOeeuopbr/9dvr27csdd9zB5ZdfzqxZs7j00kvZvXt3s2MHIhWSn3/+eeTYl19+yZYtWzj//PO55557OP3003n77be5+uqrI9+Tk046KVLheOedd/Lwww/z8MMPk52dDcA//vEPevbsyTXXXMMdd9xB9+7due+++3jppZdaFK+IiIiIiEhTqdWniIiIiIhIKzjrrLN49NFHqampITExkTfffJPDDz+c3NzcemNPPPFE+vfvz+OPP8706dOZNGkSEyZM4Pjjj6dLly4tiqOyspKdO3dG9vh74IEHMAzjgIm5/VVUVLBz504CgQDz58/nD3/4A4mJiRx//PGRMYFAgFNPPZWf/OQnB5xr9uzZvPrqq3z3u9/l7rvvjhz//ve/H0mwFRQU8MQTT3DTTTdx7bXXRsacfPLJnHfeebz88st1jh+qvLw80tLS6lQ9fuc73+H73/9+nXFjx47llltuYd68eUycOJFhw4YxfPhw3nrrLU488UR69epVZ/yLL75Yp3XoZZddxlVXXcVf//pXLr300mbHKyIiIiIi0lRK/ImIiIiIiLSC0047jQceeID//e9/HH300Xz88cd1El37SkxM5D//+Q9PPfUUM2fO5NVXX+XVV1/FNE2+853vcPvtt+P3+5sVx1133VXn6+zsbB5++GFGjx7d5DmuuOKKOl/37NmTRx55pF4S89vf/vZB53rvvfcwDIMf/ehH9c4ZhgHA+++/j+u6nHbaaezcuTNyvmvXrvTt25c5c+a0KPEHkJycTGVlZeTrfRN2tbW1VFZWMmbMGACWLVsWaZF6IPvOUV5eTjAY5IgjjuDzzz+nvLyctLS0FsUsIiIiIiJyMEr8iYiIiIiItILs7GyOPPJI3nrrLWpqanAch1NOOaXR8Wlpadx2223cdtttFBQUMGvWLJ577jlefPFFUlNTufnmm5sVx/XXX8/EiROpqqri/fffZ8aMGZjmoe368POf/5z+/ftjWRZdu3alf//+9eawbZu8vLyDzrV582a6detWp03o/jZu3IjneY1WJdp2y/8rW1VVVaeasrS0lCeffJK3336bHTt21BlbXl7epDnnzZvHE088wcKFC6murq43hxJ/IiIiIiLS2pT4ExERERERaSVnnnkm99xzD9u3b+eYY45p8r56PXv25MILL+Skk07ixBNP5M0332x24m/IkCEcddRRQLilaHV1Nffccw8TJkyge/fuTZpj9OjRjBo16oBj/H7/IScUG+O6LoZh8Mwzz2BZVr3zycnJLZq/uLiY8vJy+vTpEzl20003sWDBAq666ioOO+wwkpOTcV2XH/zgB03ad3Hz5s1cccUVDBgwILK/n8/n45NPPuH555/Hdd0WxSwiIiIiItIUSvyJiIiIiIi0kpNOOolf/OIXLFy4kN/97neHfH1GRga9e/dmzZo1UYvp1ltv5YMPPuCpp57il7/8ZdTmbao+ffrw+eefU1pa2mjVX58+ffA8j169etG/f/+ox/DGG28AMHXqVADKysqYNWsWN9xwQ50WpBs3bqx37d52pPv76KOPCAQCPPXUU/To0SNyfM6cOVGMXERERERE5MCi83FMERERERERqSclJYV7772XG264gWnTpjU6buXKlXX2sturoKCAdevWRTX51adPH04++WRee+01SkpKojZvU5188sl4nseTTz5Z79zeyrqTTz4Zy7J48skn61XbeZ7Hrl27mn3/WbNm8cc//pFevXpx9tlnAzRYVQjwwgsv1DuWlJQE1G//uXeOfeMtLy9n+vTpzY5VRERERETkUKniT0REREREpBWdd955Bx3zxRdf8MQTTzBt2jTGjBlDcnIy+fn5TJ8+nUAgwA033BDVmK666ireeecdXnjhBW699daozn0wkydP5pxzzuHvf/87mzZt4uijj8Z1XebNm8ekSZO47LLL6NOnDzfddBOPPvooBQUFnHjiiaSkpJCfn88HH3zARRddxFVXXXXQe3366aesX78ex3HYvn07c+bM4YsvvqBHjx489dRTJCQkAJCamsrhhx/OX/7yF4LBILm5uXzxxRfk5+fXm3PEiBEA/O53v+P000/H5/Nx/PHHM2XKFHw+H9deey2XXHIJlZWV/Oc//6FLly4xSbCKiIiIiEjnpMSfiIiIiIhIjJ188slUVlbyxRdfMHv2bMrKykhPT2f06NFceeWVTJ48Oar3GzVqFEcccQT/+Mc/uOaaa0hLS4vq/Afz4IMPMnToUF555RUefvhh0tLSGDlyJOPGjYuMufrqq+nXrx/PP/88f/jDHwDIy8tjypQpB6ye3Nfjjz8OgM/nIzMzkyFDhnDXXXdx/vnnk5qaWmfso48+yq9+9StefvllPM9jypQpPPPMMxx99NF1xo0ePZof//jH/POf/+Szzz7DdV0+/PBDBgwYwOOPP85jjz3GQw89RNeuXfn2t79NdnY2d911V0ueLhERERERkSYzvKbsUi4iIiIiIiIiIiIiIiIi7Zr2+BMRERERERERERERERGJA0r8iYiIiIiIiIiIiIiIiMQBJf5ERERERERERERERERE4oASfyIiIiIiIiIiIiIiIiJxQIk/ERERERERERERERERkTigxJ+IiIiIiIiIiIiIiIhIHFDiT0RERERERERERERERCQOKPEnIiIiIiIiIiIiIiIiEgfsWAfQnpSUlMc6BJF2wzQNsrNT2LmzEtf1Yh2OiBwCrV+RjkvrV6Rj0toV6bi0fkU6Jq1dkY5L67f5cnLSmjROFX8i0iDTNDAMA9M0Yh2KiBwirV+RjkvrV6Rj0toV6bi0fkU6Jq1dkY5L67f1KfEnIiIiIiIiIiIiIiIiEgeU+BMRERERERERERERERGJA0r8iYiIiIiIiIiIiIiIiMQBJf5ERERERERERERERERE4oASfyIiIiIiIiIiIiIiIiJxQIk/ERERERERERERERERkTigxJ+IiIiIiIiIiIiIiIhIHFDiT0RERERERERERERERCQOKPEnIiIiIiIiIiIiIiIiEgeU+BMRERERERERERERERGJA0r8iYiIiIiIiIiIiIiIiMQBO9YBiIiIiIiIiIiIiIiIiLSG+++/l3feeYtzzjmfn/70rjrnHn30IV577T+cdtqZ/Oxn97Jr1y6effZpvvzyc3bt2klaWjqDBg3miit+QCgU4sYbrz3gvR5//GmKi4t44IH7ADAMg+zsbMaMGc911/2YvLy8VnuceynxJyIiIiIiIiIiIiIiInGrW7dcPvzwPW688RYSEhIBqK2t5f33Z5Kb+00y7u67byMYDHL33ffRo0dPdu7cwbx5X7F7dxmTJh3FG2/MjIz9/e8fpbKykrvu+nnkWHp6BsXFRaSkpPDyy9PxPCgqKuDRRx/inntu55lnXmj1x6rEn4iIiIiIiIiIiIiIiMStoUOHUVCQzyef/I+TTz4NgE8++R+5uXn06NEDgPLychYtWsATT/yJceMmAJCX153hw0dG5unSpWvkzwkJCQSDgTrH9jIMI3K8a9eunHnm2Tz22CNUVlaQkpLaao8TtMefiIiIiIiIiIiIiIiIxLkzzjibGTPejHw9Y8Z/OeOMsyJfJyUlkZSUzGeffUwgEIjafXft2smnn36MZVmYphW1eRujij8RERERERERERERERFpFmPrVoyqyiaNNW0TdiZjllVhhtxm3c9LTsHLzT3k604++XT+9Kc/UFxcBMCSJYu4774HWLBgHgC2bfOzn/2Chx66n9dff5WhQ4cyduwETjjhZAYNGnxI96qoqOCkk47G8zxqamoAuPDCS0hKSjrkuA+VEn8iIiIiIiL7MYsKSX7ofip/+QBeekaswxEREREREWmXjPLdpN16I3he08YbBvgskoIOXhOvqcc0Kf/Dn/HS0g/psqysLI48cgpvv/0mnudx1FFTyMzMrDPmuONO4Mgjp7J48QKWLVvK7Nlf8vLLf+P22+/m9NPPanjiBiQnp/Dccy8SCoWYPfsL3ntvJldffd0hxdtcSvyJiIiIiIjsx1qzGnN7CdbGDYRGj411OCIiIiIiIu2Sl5ZO+SOPN7niz7ZN/BnJVJdVEWpJxd8hJv32OuOMc/jd7x4G4JZbbmtwTEJCAocfPpnDD5/MFVf8gN/85lc8++yfDinxZ5oGvXr1BqBfv/4UFBTw6KMPcs89v2pW3IdCiT8REREREZH9mHtav5iFhaDEn4iIiIiISKO83FyaWrvn2iZkpeDuqsRtZuKvJSZNOpJgMIhhGBxxxJFNuqZfv/589tnHLbrvZZd9j4svPpeLLrqUoUOHtWiugzFbdXYREREREZEOyCwuDv9ekB/jSERERERERCRaLMvipZf+w4sv/hvLsuqcKysr5cYbr+Xdd99m7do1FBYW8NFHH/Dyy39n6tRjW3Tf3Nw8jjnmeJ599ukWzdMUqvgTERERERHZj1lcCIBVVBjjSERERERERCSaUlJSGzyelJTM8OEj+de/XqawMJ9QKES3brmcdda5XH75lS2+70UXfYdrr72S5cuXMnz4yBbP1xjDa/buifGnpKQ81iGItBu2bZKVlcKuXZXN7rUsIrHR2devsWMHXmIipKTEOhSRQ9bZ12+74Xmk/+B7eJYJto/yPz4T64ikndPaFem4tH5FOiatXZGOS+u3+XJy0po0Tq0+RUREJK6kPPIgia9Pj3UYItKBGaW7oLaG0OhxGLvLoKIi1iGJiIiIiIiINIkSfyIiIhI/QiHMgnyM7SWxjkREOjCzqAiA0ISJgNp9ioiIiIiISMehxJ+IiIjEDXNrMbguZmlprEMRkQ7MLC4CwyA4eiwYBmZhQaxDEhEREREREWkSJf5EREQkbpgF4Tfnjd1lMY5ERDoyq7gIt2sOpKTgdumKqYo/ERERERER6SCU+BMREZG4YRWFE3+q+BORljCLi3Bz8wBwe/TEUsWfiIiIiIiIdBBK/ImIiEiHYi1b2mjbvb0Vf9TWQE1NG0YlIvHELC7C7d4DCCf+Iq8tIiIiIiIiIu1czBN/L7/8MmeddRbjx49n/PjxXHzxxXzyySeR8yUlJfz0pz9lypQpjB07lvPOO4933323zhylpaX85Cc/Yfz48UycOJG77rqLysrKtn4oIiIi0tpCIZKf+H8kvPLvBk+bRQXh9nyAUaZ2nyLSDK6LuXUrbl53AJwePTBLtkIwGOPARERERERERA4u5om/vLw8br31Vl599VWmT5/O5MmTuf7661mzZg0At99+Oxs2bOCpp57izTff5KSTTuKmm25i+fLlkTluvfVW1q5dy1//+leefvppvv76a37+85/H6iGJiIhIK7GXL8WoqMDasK7+Sc/DKizEOWw4AGZZadsGJyJxwdixA5wQbt6eVp/de4aTgdu2xjgyERERERERkYOLeeJv2rRpHHvssfTr14/+/ftz8803k5yczMKFCwFYsGABl112GaNHj6Z3795cd911pKens2zZMgDWrVvHZ599xq9//WvGjBnDxIkTufvuu5kxYwZbt+o/5yIiIvHEN2cWAGbJNozy3XXOGTt2QG0NocNGhL9W4k+kc/E87EULwPNaNI1VXAgQqfhze/YEULtPERERERER6RDsWAewL8dxmDlzJlVVVYwbNw6AcePG8c4773DccceRnp7OO++8Q21tLUcccQQQTgymp6czatSoyDxHHXUUpmmyePFiTjrppCbf3zQNTNOI7oMS6aAsy6zzu4h0HHG7fkMh/PO/JnTkUdizZ+HftAFn7LjIaWtbEYZhwPDhGLaNXbEb7Dh7DiTuxe36bQPWsqUkPvIbau66B2fU6GbPY5dsxbBtzLxcTMuErEyM1FR8Wwv1miKN0toV6bi0fkU6Jq1dkY5L67f1tYvE36pVq7jkkkuora0lOTmZP/zhDwwaNAiAxx57jJtvvplJkyZh2zaJiYk8+eST9O3bF4Dt27eTnZ1dZz7btsnIyKCkpOSQ4sjOTgm/YSgiEenpSbEOQUSaKe7W7/z5UFuN//JLYeUy/NsKIGvqN+fLtkNyIv6h/SGnC/5gNWSlxC5ekRaIu/XbFkoKwWfhX7kEjjmy+fPs3gm9e+Lvmv7NsYH98Zdu12uKHJTWrkjHpfUr0jFp7Yp0XFq/raddJP769+/P66+/Tnl5Oe+++y633347L774IoMGDeL3v/89u3fv5vnnnycrK4sPPviAm266iZdeeomhQ4dGNY6dOytV8Seyh2WZpKcnsXt3NY7jxjocETkE8bp+E977EKtLDlWZ3Ujs1RcWL6Pm5Mpvzq9eh9m1G9Vl1SQlpeIWbqV2V+UBZhSJPXPtGrzMTLyuOUD8rt+2kLB8NXbQwfv0C6ou/A408wN9iWs3QFZXavZ5/UjIzsFcu4FqvaZII7R2RTourV+RjklrV6Tj0vptvqwmfhi1XST+/H5/pIJv5MiRLFmyhL/97W/84Ac/4MUXX+Stt95i8ODBAAwbNoyvv/6al156iV/+8pd07dqVnTt31pkvFApRVlZGTk7OIcXhuh6u27I9QUTijeO4hEJ6ARbpiOJq/YZCJH01l9ppJxFyPIL9BuD/7JM6jy9hSz6h3B6EQi5OWjrs3BU/j1/ikrm1mOS77wTA7ZqDM/QwvNGj4OzT42v9tpGETRtxcrphbtuKu2Ejbp++B7+oogJSU+seKyoiNH5ineffzO1B4pdfEgo6zU4oSuegtSvScWn9inRMWrsiHZfWb+tpl01UXdclEAhQXV0NgGnWDdOyLDwvnKAbN24cu3fvZunSpZHzs2fPxnVdRo9u/t4eIiIi0n7Yy5diVFQQPGIyAM6AgRiluzB27oiMMQvycXv2BMDLzMQoK4tJrCJNZS9cAJZN1Y9uJnT4JMzCAhKe/iO8916sQ+t4QiGs/HwCJ5yMl5iEb/7XB73EWrKY9Ot+gLV+bZ15zJJtuHl5dca6PXpAbQ3Grp2IiIiIiIiItGcxT/w9+uijfPXVV+Tn57Nq1SoeffRR5s6dy1lnncWAAQPo27cvP//5z1m8eDGbN2/mueee44svvuDEE08EYODAgRx99NHcc889LF68mHnz5vGrX/2KM844g9zc3Bg/OhEREYkG35xZuLndcfv2A8AZEN4L2NqwHgCjohyjfDduj3Diz03PwCgrjUWoIk1mL15IaOgwQpOPpObSy6n81YM4Y8bCRx/FOrQOxywqBCeEM2AgodFjsecdPPGXMOO/4Lr433zjm3lKtoHr4uR2rzN272uLWVAQ3cBFREREREREoizmib8dO3Zw++23c+qpp3LFFVewZMkSnn32WaZMmYLP5+PPf/4z2dnZXHvttZx99tm8/vrr/OY3v+HYY4+NzPHII48wYMAAvve973H11Vczfvx4fvnLX8bwUYmIiEjUhELY874iOGlypMWel52Nl56BtS5cqWMWFgLg9Nhb8ZeFWVYGnlp4SzsVCGCvWE5o1Jg6h0PHHAsrVmAUFcUosI7J2rwJAKdPX0LjJ2BtWHfA6jxzy2bspYsJjRyN7+u5mIXhhJ5ZHH7e3e496ox3c7qBZWMVKfEnIiIiIiIi7VvM9/h74IEHDni+X79+PPHEEwcck5mZyaOPPhrNsERERKSd2L/NJwCGgTNgINb6dQDhN+0NI/JmvZuRCcEAVFdDcnIMohY5MGvVSgjUEhozts7x0MQjIDkZ3+efEjzvW7EJrgMyt2zG7dIVUlIIjR0HhoG9YB7BaSc1OD5h5gy8rGyqbrqVtFt/TMJbb1B99XWYxcXg8+NlZ9e9wLJw8/IiHzIQERERERERaa9iXvEnIiIiciD7t/ncyxk4CGvjevA8rMIC3K454PcD4GVkAGCq3ae0U74li/Ays3B796l7wu+HqVOxP/1EFauHwNq8KfJceqlpOEMPwzd/XoNjjd1l+L74nNqTT4XERGpPOxPfF59j7NiBWVwU3t9vT3XxvpwePTFV8SciIiIiIiLtnBJ/IiIi0q5Za1YTGj2m3hvxzoCBGBUVGNu2YRYWRPbgAvAyMwEwSkvbMFKRprMXLyQ0anSDCSamTcPYXoK1ckXbB9ZBWZs34fTpG/k6OG4C9rKlUFNTb6z/g/fANAkefwIAgWkn4iUmkPDOW5hbi3Dyute7BsLtPy3t8SciIiIiIiLtnBJ/IiIi0q4ZZaW4+7fdA5z+AwCwN6yrl/hz0zP2XFvWNkGKHAJj5w7M/C2ERo9teMDw4Xg53fB/9nEbRtVxGbvLMEp34fTpFzkWmjARggHsZUvqDg4E8L//LoGjj8VLTQsfS0oicNKp+P/3IdamTbi5eQ3ex+3RE6N0F+ae/QRFRERERERE2iMl/kRERKT9CgYxKivxMjLrnfLSM3C75mCtXIFZsg1nn8Qfyclg+9TqU6LOKClpcQtOe/EiMIxwxV+DNzEIHnMsvjmzoba2RffqDMzNmwFw+35T8efmdcft3hPf/K/rjPXN+gKjfDeBU0+vczxw8qnguhjlu3EbqfgLjRiJl92F1J/dRtLj/w9z44YoPxIRERERERGRllPiT0RERNqtvRV7e1t37s8ZMBDfnFngebg9e+1zoYGbmYmhxJ9EkVG+m7Rbf4z/w/daNI+9eBFO/4HfVJw1IDT1GKitwff13G8Oel642kx7/9VhbdkMPn+9Sr3g+AnY8+dhrV6FuX4dZkE+CTNnEBo7Hrd7jzpjvfQMAntafzaW+POysil/9HGqr/wh1ob1pN59O0mP/z8IhVrngYmIiIiIiIg0gx3rAEREREQaY5btAsBtoOIPwOk/EN/c2eExPeq/ka+KP4kma+0acEL4Z7xJ4PgTwbIOfRLXxV62mMBJpx5wmJeXhzNkGL7PPiE44XD8n32M/92ZmMWFVN1wC6FJk5v3IOKQtXkjTq/eYNb9TGPoiEkkzPgvKb+8p87x6suuaHCe2rPOwaiuirQRbpDPR3DaiQSPPR7/ezNJfOkFas86F/dA10izGLvLsJctJXjklFiHIiIiIiIi0qEo8SciIiLtllFaChy44g/AS0uvVz3lZWZqjz+JKmvdWvD5MUu24ftqDsHJRx36HOvXYVRWNr6/3z4CU48h6a/PkHbjtRg1NQQnTgLPw/fVHCX+9mFu3oTbr3+9487AwZQ/8TRGVRUEAhjBINgWzoBBDc7jZWVTfc31TbupZRGcejSJL72AWVKixF8r8M94k4QZ/8VNz8AZMTLW4YiIiIiIiHQYSvyJiIhIu2WUlYJh4KWlN3h+b2VOnTafe7gZGVgbN7ZidNLZWOvWEhoxEkJB/G+9QXDSkWAYhzSHvWgBXnIyzsCGk0/7Ck46Ev+XnxMaNJjAiafgde1KwvR/45/5dri9pK1/yuM4WPn5BI85vsHTXlY2XlZ2q9zaS02DhETMbVtbZf5OzfPwzf4SgMR/vkTlLx845LUmIiIiIiLSWWmPPxEREWm3zLIyvPSMei38IpKTcXv3wenTt94pLyNTrT4lejwPa/1anIGDCJxxNtbGDVjLlx3yNPbiRYRGjm5am9CUFCrvvpfaSy7F69oVgOCEwzGqq7BWLD/ke8cjs6gQnFCDrwGtzjBwu3XDLNnW9veOc9aa1Zg7tlN7wUVYG9aF93IVERERERGRJlHiT0RERNoto7QUt5E2n3tV3v4zar51Sb3jkVafntdK0UlnYm4txqisxBk4iNDI0Th9+pLw1huHNkllJdb6tYRGjWl2HG7ffrhduuKb/3Wz54gn1pbNADi9+8Tk/m6OEn+twTf7S7zMLGrPvYDQ2PEk/Psf4SpXEREREREROSgl/kRERKTdMstK8TIyDzjGy8yCpKR6x930THBCGJUVrROcdCrW2jXAnn0lDYPAGWdjL1mEuXlT0+dYvw48D2fYYc0PxDAIjZ+Ib95XSmoD5qaNeNldIDU1Jvd3u+ViblPiL6ocB9/sL8N7aBoGNZdcilmyDf+H78c6MhERERERkQ5BiT8RERFpt4zSUryMjGZd6+2pFDTKyqIYkXRW1vp1uLndw/u6Ed5/z+3SlYQZbzZ5Dnv9WrzkZNy87i2KJTh+IsbOHZibNrZonnhgbd4Umzafe0Qq/pSEjRprxXKM3WUEjzwKALdXb4LHHEfCa69AVVWMoxMREREREWn/lPgTERGRdssoK8U9SMVfY/YmDI3S0ugFJJ2WtXYNzqDB3xywbQKnnoFv1hcY27c3bY51ayMVgy3hHDYcLzk5XPXXyVlbNsc28detW7iyeNfOmMXQUVirV5Fy3z0QCBxwnG/2F7g53XAGDIocq7ngIoza2kNvrysiIiIiItIJKfEnIiIi7ZPnYZaWhlt5NsPehKG5WxV/0kLBINamjeGk3T4Cx5+Al5xM4vR/HXwOzwsnD/dJZjSbbRMaPQ67k+/zZ1SUY+zaiRvjij8As6Skze9tbi0m6fH/F/570AEqDu1lS7DWrMI364vGB4VC+L6aE2nzuZeX3YXaU04j4d13wHXbIFoREREREZGOS4k/ERERaZ+qqiAYiLTsPGSJiZCQqIo/OSRGWWm9JIq1eSM4IZxB+yXtEhOpveBb+D7/FHPjhgPPu3Mnxu4ynIFRSPwBoYmHY23aiBGDhFN7Ya1bC4DTt1/MYnC75gBgbtva5vf2z3wb39dzSf5/D5N610+xZ89qWVLM8zB27IhegPsxi4sB8L/3TqOJSnvJIozKSoJHTql3zjlsONTWtGqMIiIiIiIi8UCJPxEREWmX9lbqNbfVZ/jajHAiR6QJ7AXzSPvRNfjffafOcWvdWrBsnD796l0TOP5E3NzuJL789wNWXUWSVPtVDTZXcPRYsGx8nbjqz16yGC+7S4v3TGyRxES89IzwPn9tKRDA9+Vn1J5+FpU/uxc3I4PkJ39H6p23Ym4tbtaU/vdnknbLDRilu6IcbJhZXISX3QVr00astWsaHOOb9SVuj564vfvUO+d2yw3P09bPtYiIiIiISAejxJ+IiIi0S3sr9Zpd8Qd4GZmYSvxJE5gb1pP85O/xEhJJeH061NREzlnr1oWryny++hfaNjXfuQx7+VLshfMbnd9avxYvuwteVnZ0Ak5OJnTYcOz5nXefP3vJIkKjRrd4z8SWcnO6tXkyyjf/63Bl3LHH4xw2nKo77qHyvvvB9Ui59+6DVqDWEwiQ8N/XwQlhL1zQKjGbW4sJHDcNNzcP/3sz6w+orcU376twtV8D31O3S1cwjJhUV4qIiIiIiHQkSvyJiIhIu7Q3YeemZzR7Di8jQ60+5aCMHTtIefQhnB49qfzlAxjV1SS8+3bkvLVu7QFbdIbGTSA0fCSJL78IoVCDY+x1awlFqc1n5L4TD8desQIqK6M6b0dg7NyBWZBPaNSYWIeyJ/HXti1XfR9/hDN4KG73HpFjzsDBVP78PtyuOaTcfx/WiuVNns//6f8wykpxu+ViL2qFxF9lJUZFOW73HgROPAXf3Nn1Kgv9n/4PamvC+/s1GKQfLzNLFX8iIiIiIiIHocSfiIhIZ3OAdoTtiVFaCj4/JCc3ew43IxNjT8tQkQZVVZHyyIN4tk3VT27D7dGTwAkn4Z/xX6iogIoKzOLCA+/NZxjUfOe7mMWF+D/+sP5518Vavy5qbT73Co6bCK6Db8G8qM7bEdhLl4BhEBoxMtah4Hbr1qZVaMb27djLlhA49vh657y0dCrv+jnOgAGkPHQ/vtlfhtsdB4ONTxgMkvDf1wlOnkLw2OPxLV504PHNYG0tAsDJzSNw9LFgWfj/981asRctIPHvzxM4/oQ6ycz9ud1yMbcp8SciIiIiInIgSvyJiIh0MklP/p7U22/BWrY01qEckFG6Czcjo0Vt/LzMTExV/EljPI/kP/weY8d2qm69Ay8zC4Das8/FCIZIeOct7A3rAA6c+APcfv0JTj2GhFf+Xa8CzywsgNoanAHRrfjzunTBGTgY39dzozpvR2AvWYzTrz9eWnqsQ8Htlouxa2fUk2WN8X/2MfgTCE46suEBSUlU3XonwfETSHryMdKuv5r0Ky8l/crLSL3tZswtm+sM9332McaundSeez7BseOhtgZr5YqoxmwWh/cddHPzIDWVwJSj8X/0AYRCWOvWkPz7/0do9FhqrvjBAedxc3JU8SciIiIiInIQSvyJiIh0JsFgeG+oslJSHvwlSU/+PvyGdTtklpXiZWS2aA43IyNc8ee60QlK4oq5ZTP2ogVUf/8a3F69I8e9zCxqTzmNhJlvYy+Yj5ecjJvX/aDz1XzrEozaWhLee6fOcWvdWjAMnP4Dov4YghOPwF60sM6ehG0qFhXEnoe9dDGhkaPb/t4NcHO6gedh7tje+jfzPHyffkxw0mRISmp8nM9H9Q03U/mLX1N1y21UX/sjai65FM+2Sbn/Xsz14YQ2oVC42u/wybg9e+H26YuX3QXfAfarbA5za3E4SZuSAkDgpFMwdu3E//abJP/2Nzi9+1B1w81gWQecJ1zxpz3+REREREREDkSJPxERkU7EWrMaggEqb7+b6quvw162hNSf3ozdDiuGjLIyvKysFs3hZWSC52FUlEcnKIkr9qKF4E8gNGFivXOBM8/GMwz8788MV+o1ofLUy+5C4Lhp+N99u04izlq3Nty+sAVtaxsTOmISBAPYixdFfe6DMQsLSLvhWuy5c5p+TVFhy++7aSNG+e72lfiDNklIWcuXYZZsI3DstIMPNgycwUMIjZ9IcOoxBE45jcq7foGb252UB3+FtWolvs8/xdxeQu2550euCY4Zh71wflSTumZxcZ3kudunL86w4ST++x94qWlU/fQOSEg46DxuTrfwhzlqa6MWm4iIiIiISLxR4k9ERKQTsZctwUtNC7clPOY4yn/7GG6//iS88VqsQ6vHKCvFbWHF396KQUPtPqUBvkULwnvE+Xz1znmpaQROPR08D2fQ4CbPWXvG2RhV1fg/fD9yzFq/9qCtQpvLzc3D6dMX39zZrTL/gSTM+C9G6S6Sn3oCa92ag463531F6k9vwlqyuEX3tZcsBn8CzpChLZonWrwuXcA026QFpf/Tj3Fzuzf/saemUnnH3bj9+pPy0P0kvvIvgodPwu3TNzIkNH4C5tZizOKiKEUNZnERbl5enWM155yP038glbf/DC81rUnzuN1yw/Op3aeIiIiIiEijlPgTERHpROxlSwkNH/lN9VJqKoGpx2BtXF9vX7JYM0tL8TIzWzTH3sShUVbW8oAkvlRWYq1eRWjM2EaH1J52Js7goYTGT2jytF7XrgSPPoaEt9+EQAACAazNm1st8QcQOnxSuEKrjfaYAzB27cT3+WfUXnARTr/+JD/yEEZJSeMXOA6J/3wZAP+sz1t0b3vpIkKHDW8wYRsTloXbpeuBH380VFbimzubwHHHt2jvU5KSqPzpnYSGHYZRuovac86vczo0fCT4/Njz57Uw4G+YW4vqtct1Ro2m8lcP4uXkNHked89YJf5EREREREQap8SfiIhIZ1FVhbV+LaGRI+scDg0fGd4za+XyGAXWANfF2F2Gm5HRomm8PdebZaVRCEriib1sKbgOwTHjGx+UkkLlL34VbvV5CGrPOhdjdxn+T/+HtWkjuA6hQ5zjUAQPn4RRU429tGWVdIfC/95MPJ+P2pNPo+rmW/GSkkj57QONfoDA98lHmEUFhMZPDLcWbmqScv9xtbXYq1a1mzafe7k53TC3tW4yKuG/r4HrEpx6TMsn8/upuuU2KvZUfde9UQKh4SOwFy1o+X0AKiowKipwc/MOPvYgvKxssH2t/lyLiIiIiIh0ZEr8iYiIdBL2yuXgujgjRtU57nXrhts1B3v5shhFVp9Rvhs8L9Kqs9n8frykZLX6lHp8i+bj9uh5SNVGTeXmdSc46Sj8b/0Xa/UqsH11WilG/X49e+Hm9cD3VdP32muR6mr8H75H8PgTICUFLz2Dqp/eiVFWSvLjj9ZP1tXUkDj9PwSPOpqai7+DUVWFvXjhge/hefjffJ30qy7H/+brkf3m7FUrIBQkNHpMqzy05vJycjBL9tvjr7IyHPs++z02l7ViOQlvv0nNRd8OJ7+iwbbDe082IDRuPPbKFVGpBLe2hluGOlFI/GEYuF1zVPEnIiIiIiJyAEr8iYiIdBL20iW4XXMieyTtyxkxEnvZkhhE1bC9ibqWtvqE8D5/ZumuFs8jccTzsBctJDRmXKvdovbsczG3l5Dw5ms4ffuBbbfavTAMgocfgT3/a3Cc1rvPHv6PP8KoqaX21DMix9zuPai66afYq1aR8tD9UFEROZcwcwZGRQU137oknKTs3QffrC8bv0EoRNJfnibxXy8TGj6CxH+9TNJfnoZQCHvJYrzMLNwePVvzIR4yt1tuvWRUwnvvkPivl0l55DctS/5VV5P0pz/gDBlG4LQzDj4+CoJjJ4DrhPdTbCGzuBggKhV/AG63bpjbth58oIiIiIiISCelxJ+IiEgnYS9bgjNiZIN7Q4WGj8TM34LRTlpi7m3N6WZktXguvUks+zM3b8Io3UWwFRN/bp++hMZPxKioaNX9/fYKHj4Jo6ICa0Urt+wNhfDPnEHwyCl4XbrUOeUcNpzKO+/BzN9M6n33YGzbhrG7jIQ33yBw0imR6srg5KPwzf+64WRYRQXJDz+A7/PPqL72R1TdcTfV11yP7/PPSH74AewF8wmNGt2yPe5agdstF6Oi4psKOcfB/9EHOMOGY21YT/L/exhqa+tfGAoddO6kF5/HLC+n6urrwGyb/755XbuGE7QLW77Pn7m1GC8tHZKToxDZnraqqvgTERERERFplBJ/IiIinYBRuguzIL/RfbFCw0cAYDeUNPC8SJu9thKp+GvhHn8QrkQyi4paPI/ED3vRAkhIxBk6rFXvU3vu+QA4g4e06n0A3P4DcLvmtHq7T9/c2Zg7tlN7+pkNnneGDqPy3vvBdUj9xV0kPfM0nmVGnguAwOQpEKjFt3B+nWuNslJS770ba/NGKu+4O7KXXfDoY6m8I3zcLC4kNKp9tfmEcDIKiCSk7PlfY+zaSfVl36Pyp3dir11D8u8ehkAAqqvx/e9DUn7xM9J/eAXm+nWNzmvP/xrfJ/+j+rLv4eXWr9ZuTcEx47AXLmhxFalZXIyb1z1KUe2prty2rc1/LomIiIiIiHQUSvyJiIh0AvaypQCERoxs8LyXlY3boyfWnnHfnPBIfuh+0i+/hLRrryL1pzeT8suf45t9gDZ9UWCUleKlpIDP1+K53O7dwxV/bdACUToG36KF4bUQhb9fB+IMGETFrx8iOOnIVr0PAIZBaOIR+OZ91XoJEc/DP+O/hEaOwu3br9Fhbl53Kn/xK9zcPOwF86g961y81LRvpsnNxRkwCN+Xn39zUShE8u//H0Z1FZX33o9z2PA6czqHDafy3vsJnHwawXETov3IWszdU824N/Hn//A9nEFDcPv1xxl2GJW33oG9ahWpd99O+o+uIem5P+OlpODm5pL89JPhhOB+jLJSkp55mtDY8QSPm9amjwcgeNRUjIryut+nZjCLi3DzotPmE/YkWWtrwnvBioiIiIiISD1K/ImIiHQC9rIluL374KU3XkEXGj4Se3ndxJ+1dAn20sXUnnE2gdPOIDRmLEZFOf6332rVeM2yMryMzKjM5eR2ByeEub0kKvNJB1dZibVmFcGxrdfmc19uv/5t1p4xePgkjNJd4b3+WoG5aSPWpo0ETmu42m9fXnoGlXf9nOprf0Tg1Pr70gWPnIK9aGGkNWbiS3/DWreWqh//pNHqMDevOzWXXwlJSS16HK3BS88AfwJmSQlmUSH20iUETjw5ct4ZPoLKn9yOl5BI7ZlnU/7YH6m67S6qfnQT5rZtJP77H3UnrKwk+eEHwDSp/sE1MWlturddbcLrr4LrNnsec2tRlCv+6lZXioiIiIiISF1K/ImIiMQ7z8NeuoTQiFEHHBYaPhJzazHG9u2R6xKn/xtnwCBqL/4OteecT81l36P21NOxNq6H6upWC9koLcXNbPn+fgBuj54AmIUFUZlPOjZ7yWJwXUKj2ybx15acIUMJjR5L0jNPYezYEfX57ZUrwLIJHTaiaRf4/eF2nQ1UVgYnTQbXwTfvK3yffYL//ZnUXH4FzpChUY66jRgGbk4O5rat+D98Hy81leARk+sMcUaOovJXD1J73oWR/RHdXr2puejb+GfO+KbiuqaGlEd+g7m9hMo77saL0mthc9Seez7m1qJmV3kbFeUYlZXhD2BESaSt6jYl/kRERERERBqixJ+IiEicM7cWY+zc0Wibz72c4cPBMCJVf/bihVhrV1N74UV1qk2cw0aA62KvXtl6MZfuOmB14qHwsrPB58csLo7KfNKx+RbNx+3ZC69r11iHEn2GQfV1N4A/geQnfgehUFSnt1atwBk4CPz+Fs/lZXfBGXoYCTP+S9JzzxA89ngC006KQpSx4+Z0wyrIx/fp/wgcd0KTn6fAaWfgDBtO0p//iLG7jOTHHsHavImq2+7C7d2nlaM+MGfAIEJjxpHw+vRmVf3tfd2NZqtPUlLwUlIwVPEnIiIiIiLSICX+RERE4py9bAmYFqFhww84zktNw+nTN7wfoOeR8Mq/cQYPJTRqTJ1xbl53vMwsrBXLWy1mo7QULzMzSpMZuHl5mEWF0ZlPOi7Pw168iNCY+Kv228tLTaPqxluwNqwn8Z8vRXFiD3vlCkJDh0VtyuCRR2EW5OP06k31966KSTvLaHK75WKtWIZRXU3g+BObfqFhUHXN9RiVlaTedjP2yhVU3nIbzsDBrRfsIag970LMwgLsuXMO+VqzuAgANzeKiT/Cz7W5bWtU5xQREREREYkXSvyJiIjEm0AAli3D984Mkv70BxJemx6u0mnCvljOiFHYy5diz/8aa8M6ai68uP6b8YZBaNhw7NZM/O0uxY3SHn8ATveekTegpfMydu7EKCslNOywWIfSqpxBg6n5znfxz5yBPWd2VOY0i4swynfjRPG5C0yeQuDEU6i66daoVBHG2t4WlKHRY/Fycw/pWi8nh5rvfR+jNkDVjTfjHKRCuy05gwYTGjWGxNdfAc87pGvN4qLwfq1R3pfRzemmPf5EREREREQaocSfiIhInEl88vdwxx34//EiZn4+wXHjqbn08iZdGxo+AmPXTpJeeA5n2HCc4Q3v5RUaPhxrw7rW2ecvEMCoqopexR/hNnOWKv46vb37PLo9e8U4ktYXOPlUgkccGbX9/qxVK8NJ/8FR3IMvJYWaK66K7HfX0bndwom/wIknN+v64NHHsvvPfyU0fmI0w4qK2vMuxMzfgv3V3EbH2AvmkXLPnXX+vplbt0a92g/2Vvwp8SciIiIiItIQJf5EREQOprYWampiHUWTmevWwRlnUPnc36n81YPUXHUNzqCmtYwLDT0MTAtj5w5qLrio0dZ7rbnPn1FWBhDdxF/3Hhi7dnao76NEn1VYAJaN2zUn1qG0PsOg+qqrMWprsBctaPF09qoVOH36QnJyFIKLT6Ex46i+7kZCY8c3fxKfL3oBRZEzZCihESNJeP0VcJz6AyoqSHrmaawN60j+3cPhn5uEK/6iur/fHl5ODuaO7Q3HIiIiIiIi0skp8SciInIQSc89Q/IfH491GE1TVYWxcwcMHQq2fejXJyXhDB1GaORonMMa3xPQzeuOl57RKvv8mWWl4XtEsdWn271HeG61++zUzMIC3O7dwbJiHUrbSEnB7dU7XJ3bQtaqlThD47tFaovZNsGjpnb4vQobU/utb2PlbyHxHy/WO5f08t8gFKTqp3diFRaS9Kc/gudhbi3Cyese9VjcbrngulGpZhUREREREYk3SvyJiIgciOdhL16IuW1rrCNpEqso3MqQPn2aPUflTbeG99w6EMMgdNiIVtnnzyjdBUS54m9PxYmlxF+nZhYV4vToGesw2pQzYCDW+pYl/oxdOzG3bQ1XBEun5QwaTM13r8A/cwa+jz+KHLeWLMb36cfUXHIZoTHjqPq/H+GbO4vEvz+PUVWFm9sKib89+ylqnz8REREREZH6lPgTERE5ALOwAKN8d6T9ZHtnFhSEq016tWAPs5QUSEw86DBn7z5/UW6faZaVgmnipaZFbU4vNQ0vNQ1T+/x1alZhQaT6s7Nw+g/A2rIZAoFmz2GvCrf0dYYNi1ZY0kEFTjyFwLSTSPrrX7BWroCaGpKe/RPOYSMIHn8CAKHDJ1F74cX433sHADc3N+pxuF1zwDA6zIdyRERERERE2pISfyIiIgdgr1gGgFFRDq4b42gOzizIx+uaAwkJrX6vUCvt82eUluKlZ4AZ3X+muN17YBap4q/TqqrCKN2F2wkr/nBdrE0bGx7QhMS9tXIFbm53vCi235UOyjCoufxKnCFDSX7sEZKeewazrIzqH1xTp8Vp7TnnE5w8BUwLtxVafWLbeFnZqvgTERERERFpgBJ/IiIiB2CtWBF+M9Pzwsm/ds4qyMdtSbXfIXC792iVff6M0lLcjIyozgngdu+uir9OzCoOf++dzlbx16cfWHaD7T6NrVtJv+b72HPnHHAOe9UKVfvJN2ybqh/fgpecjO/Lz6i54CLc3Ly6YwyD6muvp+L+h5pUQd4cbrdczG1K/ImIiIiIiOxPiT8REZHGeB72yuU4e/a1MnbvjnFAB2cW5LddRZNhEBo2POr7/Jllpa1SWeR074G5tQg8L+pzS/tnFoYTf52t4g/bxunTt8HEn2/+1+CESHrx+cYr/yorMfO3aH8/qcNLTaPqp3dSe96FBE47o+FBto3bu/n7zR6Mm5Ojij8REREREZEGKPEnIiLSCLO4CKOslOARkwDa/z5/NTWY20twe/Vus1s6w4eHEwpR3OfP2LkDLzMzavPt5eZ1x6iqwtjdzr+P0irMwgK87C6tVn3UnjkDBmKtW1vvuL1oPm7vPhi7d5Pw5usNXmuvXgmehzNUFX9Sl9u9B7UXXASWFZv7d8vVHn8iIiIiIiINUOJPRESkEdaK5WCaBCceAYBZ3r4r/syiQvA83J5tV9EU3ufPwV6zKirzGeW7sTZtJDQk+kkGd0+LR+3z1zmZRYU4PTpXm8+9nIGDMIsLoarqm4M1NdgrVhA47gRqTz+ThBlvYmytn0SxVq3Ey8zC7ZbbhhGLHJyb0w2jfHdUP3giIiIiIiISD5T4ExERaYS9cgVO3/54Wdlg+9p9xZ9VkA+A27Nt9viDcNtELy0da9nSqMxnL1oInkdo7LiozLcvNzcPDAOzWIm/zsgqyMft3snafO7hDBgIgLVhfeSYvWwJOCFCY8dRe/Z5eOnpJL30Qr1r7VUrw4l4w2izeEWaYm8yWlV/IiIiIiIidSnxJyIi0pC9+/sdNhwMAy89vX20iPQ87LlzsNasrnfKLMgPtzJMTm67eAyD0PgJ+L/8HBynxdPZC+bj9B+Il5kVheD24/Phds0JV0ZK5xIKYW7d2qbVsO2J26MnJCTW2efPXjgfN69HOCGemEj1dy7Hnv819qIF4QGhENaypVjr1+EMU5tPaX/cnBwA7fMnIiIiIiKyHzvWAYiIiLRHxrZtGDt3EBp2GABuRgbG7ti2+rTWryXxb89jrV2N27sPFQ8+Uvd8QT5Oj7ZPbNSedCq+T/6H/dVcQpOPbP5EoRD2koUETjk9esHtx83rjqXEX6djlmwDJ4TTSSv+ME2c/gOwNuxJ/HkevoULCE76Zr2GJk0m9NFIEl94DqdvP+wlizFqqnG7dCU44YgYBS7SOC8zC3x+zG1K/ImIiIiIiOxLFX8iIiINsFcuD1ezDQ0n/ry0dMwYVfwZpbtI+vMfSfnFzzBqqqk98xzMLZsxtxbXGWcWFOD26t3m8bn9+uMMG07Ce+80aXzSU0+S8O9/1DturVmNUVVFaNz4aIcY4XbvoVafnZBZWACA20n3+INwu8+9FX/m5k0Yu3YSHLNPS13DoObyKzEqyjF37SJw5tlU/PohKh77A16XLjGKWuQADAM3J0cVfyIiIiIiIvtRxZ+IiEgD7JXLcfr0hZQUALyMDMzi4oNcFV3Gjh0kzPgv/v99iJfgp+Z73ydw/IkQDJIw823seV8TOP3M8OBAAHNbcUwq/gBqTz6N5McfxdywHrf/gMYHOg6+ubMBCJxyGl5GZuSUb8E8vPQMnP4DWy1Ot3t3zA/fD7cltaxWu4+0L2ZhIV5iUuu0kO0gnAED8b/9JsbusnA7z4REnD0VzXu5vXpT/ufnYxOgSDO43XK1x5+IiIiIiMh+VPEnIiLSAGvFcpxhwyNfu+kZbbbHn7FrJ4nP/pm0W27A98Wn1J51DuWPPE7gxFPCyarEREKjRuOb91XkGrO4CDwvJhV/AKEJE3G7dD1o1Z+5eRMEAxAM4H+37lh74fxwtZ9htFqcTl4PcEKY20ta7R7S/lhFBbjde7Tq3632LrQnoW6tX4dvwXxCI0eBzxfjqERaxs3ppoo/ERERERGR/SjxJyIish9j+3bM7SWR/f0AvPS22+Mv6bln8H01m5oLL6b8sT9Se/63IDW1zpjghMOxVq+MJCOtgnwghq0MLYvAiafg+/KLAyZI7XVrwLIJnHAy/g/ehaoqAMytxZiFBQTHtl6bTwi3+gTU7rOTMQsLcWNUDdteeN264aWmYi9ehLV2dauvNZG24HbLxSwpAc+LdSgiIiIiIiLthhJ/IiIi+7FXLgeo0wbPS0/HqK6CQKDV729t3EBg2kkEzjoHkpIaHBMaPyEc6/x5AJgF+XgZmXipaa0eX2OCx08D08T/0QeNjrHWrMHp05fac87DqA1ExtoLF4BlExo5ulVj9Lp0AZ8fs7CwVe8j7YjnYRYVdOr9/QAwDJz+A/H/70PwPEJjxsY6IpEWc3NyIFDbZhX5IiIiIiIiHYESfyIiIvuxVyzD7dUbLy09csxLD/+51av+Kiowdu3E7d3ngMO89AycIcMi7T7Ngnycnr1aN7aD8FLTCEw9Gv8H70Eo1OAYa+0anMFD8LK7EJx6NAnvvAWBAPaCeYQOO6zRRGfUGAZuXp4q/joRo3w3RmUlTvfOXfEH4X3+CAZw+vbDy+4S63BEWsztlgsQrvoTERERERERQIk/ERGRujwPe8niepVnbnoGAObu0la9/d6WnU1J4gUnHI69ZDHU1GAV5OPGOPEHEDjpVIzSXfi+mlPvnFG+G3NrEc6gwQDUnnkOxu4y/B+8h71iOaGxE9okRrdbLubW4ja5l8TAfi3/zIICANyeSvw5A8L7/IXGjItxJCLR4eZ0A8DctjXGkYiIiIiIiLQfSvyJiIjswywswNi5g9DoMXWOe3sSf61d8WcWbAHTjOxFdyDBCYdDKIhvwTzM4uJ2kdhw+/TFGTYc3/8+rHfOWrsGgNCgIeGx3XsQPHwSif96GZwQoXFts+eYm5uHWbKt2dcbpbugtjaKEUnU1NaSeuuPSXj1P5EEoFlUEF5TuXkxDi72nCFDcXO7E5x8VKxDEYmOpCS81LQWvaaLiIiIiIjEGyX+RERE9mEvWgg+P6Fhw+scb6tWn9aWLbh53cHnO+hYLzcXt3cf/DP+C66D07N3q8bWVIGjpmKvWBZOkO3DWrsGLy0dLyfnm7Fnng1OCLd7zzZLzLi5ueG2cI20Iz2YlIfuJ+HN16MblESFvWwJ5tZiEl79Dwn/fAk8D6uwELdbHth2rMOLOS8tnYpHf4/bp2+sQxGJGjenmyr+RERERERE9qHEn4iIyD7sxQsJDTsM/P66J3w+vORkjLKyVr2/lb8Fp9eB9/fbV3DC4VgbNwDtp5Vh6IhJYJj45s6uc9xaF97fD8OIHHMGDCJ4zHEETjipzeJzuuWB62Du2N6s681t2zC3qbqkPbLnf42b14Oay64gYcZ/Sfz785gF+bg9Dl5BKyIdk9stF0N7/ImIiIiIiEQo8SciIrJXbS32yhWERo9t8LSXnolZ3sqtPvO34PZueuVecOIRAHipaZF2pLHmpaYRGj0G36wvvznoulhr1xIaPKTe+OqrryNw6ultFp/bLRdo5p5QgQDU1tSrZpR2wPPwzZ9HaPwEAqeeTvX3f4j/vXewlyzC6dE+kuIiEn1uTo4q/kRERERERPahxJ+IiMge9srlEArW299vLy89vVUr/ozdZRjluw+pZafbtx9ul664vdpHm8+9gpOPwlqzKlKFYRbkY9RU4wwcHOPIwOvaFUwLc2vxIV9rlJcDYO5S4q+9sdatxdhdRnD8RACC006i+urrwDBw+g2IcXQi0lrcbt3CFdzNbN8sIiIiIiISb7TZiYiIyB724kV42V1wG6kOcjMyMHa3XuLP3LIlfJ9DqPjDMKi54io8++B7Aral4PiJJPn8+ObMInDm2Vhr14QTMAMGxjo0sKxwhcjWQ68QMcpK9/yuxF97Y8//Gi81NdxOdo/gMccRGj0GLyMzdoGJSKtyc3LB8zB3bG+zvWJFRERERETaM1X8iYiI7GEvXkRwzNg6e9Dty0tLb9XEn5W/BWxfpBVlU4XGTcAZNbqVomqm5GSCY8fhm/0FAPba1Ti9+0BiYowDC3O75Tar4m9vq1ejqirc9lPaDd+CeYTGjgfLqnPcy8xqdE2LSMfndesGgKG9V0VERERERAAl/kRERAAwSkowiwoIjRrb6BgvPR1zd+vt8Wfmb8Hp0aNe4qKjCh45FWvjBsyiQqy1a3AG1d/fL1bc3Lxm7Qm1t9UngKF2n+2GsW0b5pbNBMcfHutQRKSNuV26gmFglmifPxEREREREVDiT0REBAB7yUIwTUIjRjY6xsvIwNi9GzyvVWKw8rfg9urTKnPHQmjsOEhIxP/RB5iFBTiDYr+/315ubl641echfi/33ePR3NP2U2LPt2AeWDah9lb5KiKtz7Zxu3TFVMWfiIiIiIgIoMSfiIgIAL7Fi3AGDoaUlEbHuGkZ4ISgqir6AXheuOLvUPb3a+/8foITj8D/3kzwvDp7r8Wa260bBAMYpYdWtWeU78bb83fE2LmzNUKTZrDnf0Vo+AhISop1KCISA263XCX+Ojhj107MgvxYhyEiIiIiEheU+BMREQmFsJYuITRm7AGHeRkZAJitsM+fsXMnRnUVbq84SvwBwclHghPCS0nBzese63Ai9sZibj201nBm+W7c3O5g+1Tx115UVmKvWEFowsRYRyIiMeLl5GCWKPHXkSU99SQpD93fal0VREREREQ6k5gn/l5++WXOOussxo8fz/jx47n44ov55JNPAMjPz2fo0KEN/nrnnXcicyxevJjvfe97TJw4kcMPP5yrrrqKlStXxuohiYhIB2OtXYNRU01o9JgDjvPS0wHC7T6jHUP+ZgCcnvGV+AuNGoOXkhKupjSMWIcT4eZ0Azjkff6MsjK89HTczEyMXar4aw98ixeC6xAcp8SfSGfldsvVHn8dmLVuDfbypRg7d2CtWxvrcEREREREOjw71gHk5eVx66230rdvXzzP4/XXX+f666/ntddeY8CAAXz++ed1xv/rX//i2Wef5ZhjjgGgsrKSH/7wh0ybNo1f/OIXOI7DE088wVVXXcXHH3+Mz+eLxcMSEZEOxF68EC81Daf/wAOO21vxt+8+b9Fi5udDQiJeTk7U544p26b6muvx0jNiHUldfj9eVjbm1uJDuswoL8ft2RMvM0sVf+2EPX8eTt9+eF26xDoUEYkRN6cbRkVFuBV3cnKsw5FDlPDmG7h5PTAqK7C/mtOu9gQWEREREemIYl7xN23aNI499lj69etH//79ufnmm0lOTmbhwoVYlkVOTk6dXx988AGnnXYaKXv211m/fj2lpaXceOONDBgwgMGDB3P99dezfft2CgsLY/zoRESk3fM8fHNmExo77qAVaV5KKpgmZnn0E39W/macnr3aVVVctITGT2yXb+K5uXmH3OrTKN+Nm56Bm5WFsevQ9geUVuC62IvmExo3IdaRiEgMud1yAdTuswMyC/Kxv55L7ZlnE5x4OL6v5qjdp4iIiIhIC8U88bcvx3GYMWMGVVVVjBs3rt75pUuXsmLFCi688MLIsf79+5OZmckrr7xCIBCgpqaGV155hYEDB9KzZ8+2DF9ERDoga8M6zK1FBKccffDBhoGXlt4qrT7N/Hzc3vHV5rO9c3NzD7nizywrw0tLV8VfW3FdEp9/FnPL5gZPm/lbMKqqCI0Y1caBiUh74u6pllfir+NJmPFfvMwsglOOJnT4JMxtWzE3bYx1WCIiIiIiHVrMW30CrFq1iksuuYTa2lqSk5P5wx/+wKBBg+qN25vQGz9+fORYamoqf//737n++uv54x//CEDfvn159tlnse1De3imaWCa8VdpIdIclmXW+V0kXvlnfQFZWTB6NHZT/r5nZmBV7Ma2o7g2PA+7YAuBqVOjMq/WbxPl5WHP/7rpz3kggBGoxczKwHBDWKW7GrzW/vJz3F69cfv0jXLAnY81Zy4JH76HmZxEoP9l9c7b69diWBbGkMHRXZMxpPUr0gzZWRgJCfh2lECMXgu0dg+dsWM7/i8/J/DtS7GTEmD0aIyUFBIXfE1g0IHbr4tEk9avSMektSvScWn9tr52kfjr378/r7/+OuXl5bz77rvcfvvtvPjii3WSfzU1Nbz11ltcd911da6tqanhZz/7GePHj+fRRx/FdV2ee+45rrnmGl555RUSExObHEd2dgpGHLZYE2mJ9PSkWIcg0nocB+bNgROnkdA1vWnX5OZAsIaUrJToxVFUBLj4RwyFKM6r9XsQg/tDbTV+nwepqQcfv70afBb+XnmQkhC+Ni0B9v2gkefB3/8K2dnw+ON1z8mh8Tx4b0b4Od+0ruE1V7ARhgzCn5fd5uG1Nq1fkUPUuyf+yrKo/hxtDq3dQzD9H5Ceiv+CcyBpz/M29Sj8C78m5erv1x0bCkFtLaTE9vsr8U3rV6Rj0toV6bi0fltPu3g3yu/307dv+FPxI0eOZMmSJfztb3/jl7/8ZWTMzJkzqamp4dxzz61z7ZtvvklBQQH/+te/MM1whviRRx7hiCOO4MMPP+SMM85ochw7d1aq4k9kD8sySU9PYvfuahzHjXU4Iq3CWrSQxJIdVI89HHdXZZOuSfAlYRSXUNPE8U2KY+kqEoMOVRld8aIwr9Zv05jJGSQFHapXrsMdWL/TQL3xmwrD4/FhWInh79mGfLyuOZExRlkpyaW7oXQ3gX++QvCMs1rzIcQ1a+kSEpevxBk3HmvJIiq37gK/v86Y5MVLCY0YSSCK6zHWtH5FmicxIxs2bI7qz+dDobV7iMrLSfnvWwTPPJtAjQs14e+bNWo8ie99QNXS1Xh7t+4IhUj61b0QClF9/29iF7PELa1fkY5Ja1ek49L6bb6sJn7QsV0k/vbnui6BQKDOsenTpzNt2jSys+t+orumpgbTNOtU6u392nUP7S+N63q4rjYSF9mX47iEQnoBlvjk+/RTnNzuBPr0hyb+PbfT0rHXr4/qurA2bcJNTiaYmtHkOJpC6/cgunQj0fPwiooJ9R1w0OH2rlI8zyOYnIbhGSR4Hu72nTiZXSJjrM35eJ5HaMRI7P/8m5pJR+FlZrXmo4hb/tdfJdSnL9XnX0Tq/Hl4q9fgDDssct6oKIeCAoJnnReXf8+1fkUOTahLDvbSxTFfN1q7TZM4/RU816P6hJPx9nm+QsNHkeBPwJw9i9pzzg+PffHvGKtWAuCUbMfLir8qb2kftH5FOiatXZGOS+u39cS8ieqjjz7KV199RX5+PqtWreLRRx9l7ty5nHXWN5+Q37RpE1999RUXXnhhveuPOuooysrKuO+++1i3bh1r1qzhzjvvxLIsJk2a1JYPRUREOpLaWnxfzyU4ZSocQptnNz0do7wsqqFYa1bj9ux9SHFIFKSk4KWmYm4tbtJwo7wcAC89HS8rnMwzSnfVGWMWFYBpUv1/N4Btkfivl6MbcydhrV+LvXQJtWedh9unL15iEtbqlXXHrFsLgDN4SCxCFJF2xu3ZE7OokKQnf4+1amW4XbC0S76PP8I/cwY1F16Ml55R96TfT3DMOOyv5gBgz5mNf+YMas+9AAwDe8niGEQsIiIiItKxxDzxt2PHDm6//XZOPfVUrrjiCpYsWcKzzz7LlClTImOmT59OXl4eU6dOrXf9wIEDefrpp1m1ahUXX3wxl156Kdu2beMvf/kL3bp1a8uHIiIiHYhv/tdQW0PwqPo/Ww7ES8/AqKgI7zUTBfb8r7EXzidw3LSozCeHxu2W1/TEX1kZJCSC34+Xlg6mhblf4s8qLMTN6YaXmUXNRd/G99knWGtWt0bocc3/5hu4uXmEjpgEpokzeAj2qv0Sf2vX4KWm4nbLjVGUItKeBI6dRs23v4u1YT0pv/o5qXfeiu+Lz2IdluzHWrqEpOeeIXD8iQROP7PBMaEjJmFt3IC9ZBFJzzxF8Igjqb3gIpx+A7AXL2rjiEVEREREOp6Yt/p84IEHDjrmlltu4ZZbbmn0/JQpU+okCkVERA7G98VnOAMH4+bmHdJ1Xno6AEb57ha3mjIqykl69s+Exo4nePSxLZpLmsftlou5bVuTxhrlu3H3fP8xDLzMTIxd+1f8FeJ27wFA8LgT8H/0AYl/e47K+x4AM+aft+oQzKJCfF/PpfrKH0aeM2foMPxvvwmuGzlmrV2DM2iIKmVFJMy2CZx+JoHTzsBauoSEd94i6akncPoPwO3RM9bRCWAW5JP8+KOERoyk5oqrGn39Do4ZR5LtI/mRh3BzulH9w2vBMAiNGo3/ow/C1Zx67RcRERERaZTegRIRkfhTUxPe/6sRRvlu7MWLwm0+D9HellTG7t3NDm+vxL+/AMEA1d//od7AihE3N7fJFX9m+e46LcncjMx6FX9mUeE3bzCbJjWXfx9rw3p8X34etZjjXcJbb+BlZNZJhoeGDMOoqsLM3xI+4HlY69bgDBocoyhFpN0yDJxRo6m6+ad4GZn435kR64gEMMpKSf7tg3hdulJ1w81gWY0PTkoiNHoMmCZVP74FkpIACI0ag1FRjrlhfRtFLSIiIiLSMSnxJyIicSfxxedJfuShRs/75swCPIKTjzrkud2MTKDliT97/tf4vviUmsuuwMvu0qK5pPnc3DyMXTshEDjoWKOsDC8tLfK1l5WFUVr6zYBAALNkG86eij8AZ8hQ3N59sNauiWbY8SsUwvf5Z9SefCr4fJHDzsBBYFrYe/b5MwsLMKqqCA3S/n4i0gifj8DJp+L//FOM3dHdm1cOXcKbb2DUVFN5652QnHzQ8dXf/yEVv3wAt3efyDFn8BBISMS3RO0+RUREREQORIk/ERGJO/a6tVjr1kBFRcPnv55LaPjIOtVbTbU38WO24E1EtfhsP/a2ejW3bT3oWKO8PNLqFcJJYGOfij9zazF4XqTVZ2RcVna9ykBpmLltKzihcAvPfSUk4PQfgLUynPiz1q4JV/UMGBiDKEWkowiccBIYBv4P3ot1KJ2eWVyEM2gIXpemfdjJy8yqk/QDwLYJjRiJvWRxK0QoIiIiIhI/lPgTEZH4EgphFhaC52GvXF7/fCCAvXIloTHjmjd/YiIkJLao4i/hP/9Si892wu3WDaBJ7T7De/x9kyz2srMx99njzywqCs/Zo27iz8vKqrcXoDTMLCoEwO3evd45Z+iwSMWftXY1bs9eTaoaEZHOy0tNI3DMcfjff7dJld3Seswd23G7dG3xPKHRY7BWr4Lq6ihEJSIiIiISn5T4ExGRuGIWF4ETAsPAXra03nl71QoIBQmNGt3se7jp6Rhlpc27OBTCN+tzAiedqhaf7YCXmQX+BMxt2w461iwrw0vbr+KvfDc4DgBWUQFeSkqdMQBuZhbmrp3RDTxOmUVFkJAY/r7sJzR0GMbOHRjbt2OvXUNI+/uJSBMETjsDo6Ic3+efxjqUTs3YsR23axQSf6PGgOtgr1gWhahEREREROKTEn8iIhJXrC2bAQhOPAJ72ZJ65+0li8Pto3r2avY9vPQMzJ07sZYsJvHFF0i99SaSH30okgA6EHvZEoyqKoJHTG72/SWKDAOnbz/8//vgwNUDgQDU1tRp9ellZYHnRfaOMgsLcbv3rFfFGdkL0PNa4xHEFauoAKdHjwYrYZ3B4faf9uIFmPlb6rcDFRFpgJubR2jC4SS8M0Ovw7FSVYVRVYXXNafFU7m5ebjdcrEXa58/EREREZHGKPEnIiJxxdy8CS8rm9ARkzELC+rswQZgL1kUrvZrQYtNLz0d35efkfLQr/HNnY0zYCD2wvkk/OefB73WN3d2+E2rPn2bfX+JruofXouxcydJzzzV6JvCRnk5QL09/oBIG0+zsKDBFpVuVja4TiRBKI0zi4txc+s/hxBOuLvde5LwztvgeZFEoIjIwdSefhZmUQH2wvmxDqVTMnfuAIhKq0+A0MjR2EuU+BMRERERaYwSfyIiElesLZtxevchNHwEAPaKb/b5M3btxNyyOdwmqgVqzzyHmksuo+KB31L++z9Sfd0N1Hz7uyS89Qb27FmNXxgKYc/7iuDhk7S3Xzvi9uhJ9bXX45s7G/9bbzQ4Zm9rVzdtv4o/wCwrBc/DLC7C6dGz3rV7x2mfv4NrLHm6V2joUMyiArykZNwGnmsRkYY4g4fgDByM/50ZsQ6lUzJ3bAeimPgbPQZzazHG1q1RmU9EREREJN4o8SciInHFzN+C07sPXkYmbs9eWPu0+9y7519o5KgW3cMZOozAmWeHq/b2JPACp51BcPIUkv/8R8zNmxq8zlqxHKOiQm0+26HQxCOoPfs8Ev/9jwarCMzy3QB4e6r8IFyBhmFg7NqFUboLo7oKt3uPete6e/ar0z5/B1FZiVG++4AJPWfIsPDvAwcqeS4iTWcYBE44CXv5UoyK8lhH0+kYO7aDaeJlZkZlvtBhI8A08S1V1Z+IiIiISEOU+BMRkfhRWYm5vQS3dx8AQiNGYS9fFjltL1mM07dfOGETbYZB9Q+uwc3NJfn3j0JFRb0hvrmzcbvm4PYfEP37S4vVXngxoVFjSHryMYxt2+qci7T6TEv75qBlhfd7LN2FWVQE0GDSysvIjCQIpXFWcSEATm5eo2NCQw8Lj9H+fiJyiJyh4Q8OWGvXxDiSzsfcvj3c9tqyojNhSgrOoCH4PvkYo6QkOnOKiIiIiMQRJf5ERCRuWAX5ADh7E3/DR2Bu2xp+U8jzsJcubnGbzwNKTKTypp9iVJST/NQTdfeLc118X88ldMRkVSq1V6ZJ1XU3gj+BhDdfr3PKKCuDhETw++scdzMzMXbtwioqANPE7ZZbf959EoTSuEjytIGqyb28bt2oPfcCAkcf10ZRiUi8cLvl4qWlY61e1To3cBwoVzVhQ8wdO/Ci1OZzr9pzzsMs2UbarT8m8a9/wdixI6rzi4iIiIh0ZEr8iYhI3LC2bAonX/ZUXYWGDQfDwF62BHPLZoyyUkIjR7dqDF5uLtXX3Yi9eCEJ/33tm9hWrsAo3602n+1dairBseOxV62oc9go342bUb9S1MvMwiwrxSwqwu2WB7bd4LRuVpYq/g7CLCrEy8yCpKTGBxkGtRdejJfbQIJVRORADANnyNCmV/xVVh5SktD339fhmmugsrJ58cUxc8f2qO3vt1dozDjKf/ckNRdejG/Ol6TdcgP+d9+J6j1ERERERDoqJf5ERCRumJs34+Z1B58vfCA1Fadff+zly7CXLAafH2fI0FaPIzRmHLXnnE/CK//C2rOvoG/ubLzsLjgDB7X6/aVlnKHDMAsL6uwDZZbvxktLrzfW21PxZxYW4Hbv3uicXmaW9vg7CLOo6IDVfiIiLRUaPBR77Zpwdd5BJLz7Nim/vrdpiTzPw/fJ/6C8HN/Mt5sWTCCA0Ul+LhjbS3C7dIn+xImJBM46h/Lf/YHQmLH4P3gv+vcQEREREemAlPgTEZG4YeVvwendt86x0PCR2MuXYi9ZRGjYYfVaNbaW2vO/RWj4CJKffAxjxw58X80JV/upzWe7t3cfuX0rPYyysrr7++3hRir+CnEa2N8vMi4rG0OtPg/I3FqEk9d48lREpKWcIUMhUIu5ZfNBx1pr14Dr4Fuy6OBj163F2LoVBg/G/84MqKo66DX+d98m9We3120LHo9cF3PnTryu0a34qyMpidDQwzB37oj/51NEREREpAmU+BMRkfjgeZhbNuH26VPnsDNiJEbpLuxlS1p3f7/9mSbV1/8YLIuUX/8Co6xUbT47CK9rV7ys7LqJv/JyvIZafWZlYZTuwtxecuC96bKzMdXqs3Geh6WKPxFpZU6//mDZ2GsO0sLT87DWrwPAXjD/oPP6vvwcLysL7rwTamvwf/DuQa+xNm/G2F2GuWd/4nhllO4C14l6q8/9eV26QG1Nk5KuIiIiIiLxTok/ERHpcMwN67EX1n0jzti5E6OqCqd33cRfaMgwMC3wPEIjR7VlmHjpGVTdcDPmju14mVk4g4e06f2lmQyD0JBh2Cu/2efPKN+N20CrTzczC1wXPC+yt2RD3MwsjN1lTWov1xkZO3dCoPaA7VJFRFrM78fp1x9r9eoDDjO2b8eoKMfp2w970fwDv3Y7Dr5ZXxCafBTk5BA8/gQS3n4TamoOeA+zuAgAa/XKQ34YHYm5YwdAqyf+3OxwK1Fz545WvY+IiIiISEegxJ+IiHQ4ia9PJ/nx34UTKXtYWzYB1Gv1SWIizqDBeBmZuPslBduCM3QYVT+6merLv682nx2IM3Qo1ob1EAgAYJaVNbLHX1bkzwfc4y8rCzwPo6w06rHGA7OoEEAVfyLS6pzBQ7AOUvFnrVsLhNt2GxUVWGsaTxTaSxdjlO8mdPQxAATPPhejuubA+815XiTxZx9g7nhg7tgOKPEnIiIiItKWlPgTEZEOx9y8CQK1JLz132+ObdmMl5jU4B4yNRdcFNPEW+iISYSOmBSTe0vzhIYMAycUfvM3EIDamgZbfbp7En9eamqDicH9x5na569BVnEhmBZu15xYhyIicS40eCjm9hKMXTsbHWNtWIeX3YXQ+Il4aen1ugzsy/flF7jde+L26w+A1zWHwDHHkTDjv1Bb2+A1xu4yjOoq3C5d67SVjkfG9u14ScmQktKq9/EyM8EwwhXkIiIiIiKdnBJ/IiLSsVRVYZZsw83rgf/9dyMVVNaWLbi9ejeY3HNGjCQ0SfvrSdO5ffriJSZhr16JUV4OgJeWVm+cl5kZHt+98TafEN7jD8DQPn8NMouKcLvlgm3HOhQRiXPOkHDbbWvNmkbH2OvXERo4KNz6edx4fPO/bnhgTQ2+r+cSnDK1zr8/as8+D6OyEv9HHzR4mVlcDEDw6GMxtxbHdTW4uWN7eP+91mbbeBmZqvgTEREREUGJPxER6WCs/C0AVF/5AzzbjlT9WVs21dvfT6TZTDPcDm7Vysgbsg3t8Ydt46Wm4vY4cItKLy0dTAvzABUmnZlZVKj9/USkTXhZ2bhdumI31u7T8zA3rMcZMBCA4LgJmIUFmFuL6w31zf8aamsIHDm17hQ5OQSnHk3CO2+B59W7ziwqBMMgODXcHjSeq/7MHdtbvc3nXm52FyX+RERERERQ4k9ERDoYa/NGMC2cIUMJnHIa/g/ew9ixA7OwELePEn8SPc7QYVhrV2Pu2UvSy8hscFztORcQOOb4A09mGHiZmWpB1gizuEj7+4lIm3EGD2103z6zsACjphqn3wAAQqPGgGVjL6jf7tP3xWc4g4bg5ebWOxccNwFj5w6MBlo8m8VFuF264uZ1x8vuEtf7/Bltmfjr0gVjhxJ/IiIiIiJK/ImISIdibtqE27Mn+HzUnnYmns8m6Zk/ghPC6aXEn0RPaOhhGFVV2MuXAQ23+gQInHYGztBhB53PzcrWHn8NCQYxS7bh5KniT0TahjN4CNaG9eE9XPdjrV8XHrOn4o/ERELDR2AvqNvu09hdhr1kUbjNZ0P32JM4tDZuqH+P4iLcvDwgvKds3Ff8NbD/cmvwsrJV8SciIiIighJ/IiLSwVhbNuP07hv+IiWFwKlnYC9dAqBWnxJVzoCB4SqPr+ZAQiL4/S2az8vK0h5/DTC3bQXPU8WfiLSZ0OAh4IQaTsqtX4eb2x1SUr4ZP34C9ooVUFUVPlBbS+JLfwcgOOnIBu/hde2Kl5KCtWljvXNmcRFuXvg1zxnSeBKyw6upwaiowGvDVp/6OSsiIiIiosSfiIh0JJ4XTvz16Rs5VHvK6XjJyXhZ2ZCaGsPgJO4kJOD0649Zsg03I6PF07mZWar4a4BZVAigPf5EpM24ffqCP6HBdp/W+nXfVPvtERw7AVwHe/EizM2bSL3nDnxzZ1P9/avx0hv5+WAYOH37Y+6f+PM8zOJi3D1VzqHBQ8NJyA3ro/HQ2hVzx3aANmv16XXpglFd9U2CVkRERESkk7JjHYCIiEhTmdu2Qm1N3b38UlKoufz7GKWlMYtL4pczdBjWujV4aektnitc8ac9/vZnFhXiJSY1uoeiiEjU2TbOgIHYa1YR4KxvjodCWJs3EZx8VJ3hXk4Obu8+JL76b8xt23C7d6fiVw/i9up9wNu4ffthfz23zjFj+3YIBSMfdnD79IWERKzVK5vUNroj2dt2s832+MvuEr7vrp24ycltck8RERERkfZIFX8iItJhmJs3AfVbeganHkPgzLNjEZLEudCeN2Eb29/vULhZ2RgVFRAMtniujsyeOwdzzx5asGevq+49wDBiGJWIdDahwUOwVq6AmprIMTN/CwQDOAMH1RsfnHA4ZmEBgeNPoOK+Bw6a9ANw+vbDLNkGlZXf3KO4CCBS8YdlERo0CLuB6sOOzti+HQwDLyurTe4XSfxpnz8RERER6eRU8SciIh2GtXkTXlo6XmbbvIEk4gweAoAXjVafWdkAGKWleDk5LZ6vIzLKd5P8+KMAhIaPJHDGWZiFhd+8AS4i0kaCxx5Pwsy3SfznS9RccRUA1vq1YJo4ffvVG1979nkEj5gcrtBrIqdf//C8mzbiDB8R/nNxIVg2btdvfg44g4fi/+A98Ly4+hCEuWN7uBW73TZvO+xNMBo7lPgTERERkc5NFX8iItJhhPf36xNXb4pJ++alZ+AMGYbTq8/BBx9srj1vSJqduN2ntWolADWXX4lRXU3ybx/EWrMKt0ePGEcmIp2Nm9edmm9fiv+Dd7GXLALC+/u5PXtBQkL9C/z+Q0r6AeFqZp8fa599/sziYtycbmBZkWPO0GEYFeWRasB2IRSqUw3ZHOaO7W3W5hMA28ZLz+jUP2dFRERERECJPxER6UDMzZtw+/SLdRjSyVTecx+B085o8TzunkpVY9euFs/VUdkrV+B26Urg5NOo/OUDVP7sXoJHH0tw4hGxDk1EOqHASacSGjmKpD8/BRUVWOvX4QwYGL0bWBZO7z5YGzdEDplFhZH9/fYKDRgEhhH5cER7kPDqf0i78VqsZUubNN7//kySH30oXLW4h7FjR9sm/gC3SxcMtfoUERERkU5OiT8REekYqqsxt22tt7+fSKuLVoVpSgr4/JilnTfxZ61aiTP0sPAXhoFz2HCqr7keV+taRGLBMKj+4XVQW0PSs3/Cyt9CqIH9/VrC6dsPa9M+ib+txeFKwH2lpOD26o29ZlVU790S9qIFGLUBUh5+AN/nnx54sOfhf3cm9oJ5+D77JHLY3LEdt2vbJv687C6YavUpIiIiIp2cEn8iItIhWPlbAHAOsc2WSLthGLhZWRhRbkGW9NST+GZ9EdU5W0V1NdbG9YSGDYt1JCIiEV6XLtR87yp8X80B18XpH8WKP8Dp3x+zsAACAQiFwh9iysurNy40ZBjWyhV1KuZiprISa/Mmqr93JcGjppD09JMkvPZKo7GZ+Vswiwtxc/NI/OdLUFUFnoe5fTtely5tGrqb3QVTFX8iIiIi0skp8SciIh2CtXkjmGZ47x2RDsrLzMKMZqvPmhp8X35G0l/+hLF1a/TmbQX22tXged9U/ImItBPBo6YSPOJI8CdEvQLZ7dsPXBcrfzPmtq3gebh59fc1DU08HHNrMdaa1VG9f3PYq1eC5xEaMZrqq6+j9oKLSJj+bxJffKHB8b65s/GSk6m67S6MmhoSX5+OUVYKTqjtW31mZ6vVp4iIiIh0ekr8iYhIh2Bu3hxujeXzxToUkWZzM6Nb8WcWFUYqMJL/9Adw3ajNHW3WiuV4qWm4PXrGOhQRkboMg+prr6fivvvBtqM6tdO7L5gm5sYNmMVFALgNVfyNHI2bm4f//Xejev/msFcsx8vKxuvWDQyD2vMupOaSy/C/907kMezLN2cWoXETcXPzqD3nPPwz38ZeshigzRN/XnYXjKoqqKlp0/uKiIiIiLQnSvyJiEiHYG3ehNOnX6zDEGkRLzs7qhV/VkE+AFU/+jHWmlX4Z7x54Auqq0l441XMzZuiFkNT2atWEjpsePT2TBQRiSa/v3X2G/X7cbv3wNq4EbOoCPwJeFnZ9ccZBoETTsY3dzbG7rLox3EIrBXLCQ2r+3odOPlUvNQ0/O/MqDPWLMjHLCwgeMQkAGpPOxO3SxcS//5XoO0Tf252uLWoGeW22iIiIiIiHYkSfyIi0v55HuaWzTh9WuENOZE25GZmYZRGL/FnFuTjZXchNG4CtaefReIr/2o0qWcvmEfa7beQ8J9/kvz7R6G6OmpxHFQggLVuLc4Q7e8nIp2P028A1qaNmMVFON27N/oBiOAxx4Jp4vv4ozaOcB/V1VibNhA6bL+2zH4/gZNOwf/pxxgV5ZHDvrmz8RKTCI0aExlXc9n3wlV3CYmQktKGwX+T+DO2b2/T+4qIiIiItCdK/Enbqq2NdQQi0gEZJSUYNdW4ffrGOhSRFvGysjCqo9eCzCrIx9nTOrP2gotwu3cn+aknwm/KVlZCZSVGSQlJTzxG8qMP4fTsReUd92CWlpL04vNRiaFJca5fB6EgoWHa309EOh+nbz+szZuwCgtwc7s3Os5LTSN45FH4P/ogZq2b7dUrwXVxDhtR71zgxJMB8H/4fuSYb+5sQuMmgN8fORYaP5HQqDE4eXltXuXtZWUBqvgTERERkc5NiT9pM0ZJCelXXxnej0hE5BBYmzcC4CjxJx2cm7nnDckoVf2ZBQW4vXqHv/D7qbr2R5iFhaRdexXp11xJ+jVXknbz9djLllD9fzdQddtdOCNHUf3dK/B98j/suXOiEsfB2KtW4CUm4fbt1yb3ExFpT5y+/SAYwFqzqsH9/fYVOPFkzO0l2IsWtE1w+7FWLMdLz8DNq5+g9NIzCEw9Bv97MyEYxCwqxNyyOdLmM8IwqLrxFqpuub2Not6H34+Xlo65c0fb31tEREREpJ2I7s7lIgdgbi8BJ4S5tRi3e49YhyMiHYi1ZTNeairenqSJSEflZYf3dTJ27YIG3lQ9JIEA5rbiSMUfgNuvPxW/ehCzcJ8P2ZgmzvDheKlpkUPBY4/HXrSApGefpmLQILw9rdFai7VyBc6QoWDqM2ci0vk4ez/04LoH/X+QM2AQTv+B+N9/N1xJ18bslSvq7e+3r8BpZ+D/3wf4vvw8/CGWhERCY8bVH5iUhJeU1MrRNszNzsbYocSfiIiIiHReevdF2oxRVRn+fffuGEciIh2NtWF9+E2zNm4XJRJtTan4S/j3P7CWLT3oXGZxEXjeNxV/e+/Rpy+hyUd+8+uISXWSfgAYBjVXXQ0+P0l/+gN43qE/mKZyHKw1q3GGan8/EemkUlNxu+YANOkDkIGTTsFesghza3FrR1ZXTQ3W+nU4w4c3OsTt0ZPQuAkkvPMW9tzZBMeOr9Pmsz3wsruo4k9EREREOjUl/qTNGJV7En9lZTGOREQ6FM/DWrsGZ+DgWEci0nJJSXiJSRg7G957yCjfTcKbr5P0wrMH3d/JKsgHwO3RvCp6LzWNqmt/hL1sKb4vPmvWHE1hbtqIUVMdriAREemk9rY6PlirT4Dg5KPwkpPxffRBK0dVl7VmNbjOQV+va08/CzN/C9amjQSPmNxG0TWdq8SfiIiIiHRySvxJm9mb+DPLVfEnIk1nbN+OsbsMZ+CgWIciEhVu9x5YG9Y3eM5ethQ8D7Ow4KD775kF+XgZmfWr+Q6BM3IUbs9eWOvWNnuOg7FXrQTbhzNgYKvdQ0SkvQsNGYaX3aVpr9l+P8Fjp+H/3wdQUdH6we1hr1yOl5qG27PXAcc5ww7D6T8AfH5CY8a2TXCHwO3SBWNXwx+wERERERHpDJT4kzbzTatPVfyJSNPtTUg4g1TxJ/EhNGYs9uIF4Dj1ztlLFoXbqI0aQ+LrrxywBadZkI9zkDdnm8Lt3gOzqKDF8zTGXrUivH59vla7h4hIexc45TQqHni4yeNrTz8Tw3FJeOuNVoyqLnvlCkKHNb6/X4RhUH3lD6m++v8gMbFtgjsEXnYXjIoKqK2NdSgiIiIiIjGhxJ+0mUirT+3xJyKHwF63BrdrDl5GZqxDEYmK0PgJGFVVWKtX1T3hedhLFhMaPZba8/4/e/cdX1dB/3/8dcYdWW2aNLN7T+iiLbNsZAoqbr8ozp+4EEFQERQVQVkKuBEFBFGBIiB7z7Z0QAsFOpOmzWjTlXnvPeP3x03Tphm9SW5yM97Px4NHyZmf2+Yk957P+Xw+52OWbcFetrTd41hbyw5ZlZEIt7AIsyLBOVK+T/oN12GvWpHgwV2s99biTJ7S9QBFRAYC2+5UhbafPYzIGWcReuJ/GDt29GBgTaLReGv1BNsye+MnEDvqmB4Oqmu8YTkAmKr6ExEREZFBSok/6TWq+BORrtB8Pxlo3HET8IcMbZU8M8u3YeysxjnscNzJU3BmzCTUXtWf42BWVOCNGNHteLyiYszqHRCNHnJbs2Qz9qoVhP79zw6rEfexVy7HqK3pkzOgRET6ushZH8ZPSyP8wP09fi5r/TpwHZyp03r8XD3NHz4cAKNac/5EREREZHBS4k96jVFfD4Cpij8RSZTjYG3epDafMrAYBs7sOQRWLm+x2F79Nlg2zpT4TdfIeedjlZZgr3iz1SHMygrwXNwRo7odjldUHJ8rWFF+yG0DK94E08Qq2Yz17juH3D747NO4EybhjR3X7ThFRAadtDQiHz2fwCsvYZaW9Oip7PfX4mdk4I0e06Pn6Q3NFX87lfgTERERkcFJiT/pNS1afSZQJSAiYm4phVgUd8LEVIciklSx2fMwt23FqKpqXmavfgtnytTmeUnutOm4U6cTWvxAq9+bZlkZQHIq/goL48dMoN2nveJNYvOPxBs1mtD/HulwW7OyAnv1W0RPPrXbMYqIDFbRE0/Byy8k/M9/dLid/fYqwnf+pc35sYmw3n8Pd9KUQ8/36w+CQfzMTCX+RERERGTQUuJPeo1RX4efNQScGDQ0pDocEekH7A3rwLRwVS0kA4xz2OFg2QRWNVX9OQ722ndxZh7WYrvGj5yPtWkj9lsrWyy3tpXhZ2bhDxna7Vj8rCH4GRmYFds63M6orsbavAnniPlEzjgb+62VmFvL2t0+8Nwz+BkZxI48utsxiogMWrZN4yc+jf32Kqx31rS9jecR/vudBJ99ivA/7ur8OXwfa+N63EmTuxdrH+Ll5GLs1Iw/ERERERmclPiTXmPU1eEVFgFg1qjdp4gcmrV+He6YMRAMpjoUkeRKS8OZNg17ZXzOn7XuA4g04sw8vMVm7vQZuJOmEHqoZdWfWVaGN7L7bT4BMAy8wmKs8o4Tf4GVb4JpETtsFrGjj8XPHkbwicfa3jgaJfjic8SOO0HXr4hINzkLFuJOmET4vrvB81qtt5cuwawsJ3ryaQSfepzAc0936vjmtq0Y9fU4A2imsp+Tq4o/ERERERm0lPiTXmPU1eEVxRN/hub8iUgCrPXrccerzacMTM6sudjvvgONjdir38LPzMQbN77lRoZB5KPnY21Yh73m7ebF1rYy3OLut/ncxysswizveMafvWI5ztRpkJkJtk3ktNMJvvwSxt49rbYNLFuCUVurNp8iIslgGDR+9gKszZsIPv9My3W+T+jRxTgzZtJ44ZeJnvIh0v72V6y17yZ8eGvdB2AYA6q1upc7HHPH9lSHISIiIiKSEkr8Se9wXYg04hYVA7R5k1BEpIXaWsyKbbgTB87T5yIHcubOA9fBfmc19prVODMOb3O2kjPzcNwJkwg9+J941Z/rYm7bhjdiZNJi8YqLO2712diI/c4anLlHNC+KnXQKmCbBp59stXnwmadwps/Ea/q9LyIi3eNOnkLs+BMJ3X9vi89S1prVWJs3ETn7PAAaP/d5nGnTSP/NDRiVlQkd2173fryKPC2tJ0JPCS+/ALOqSrPlRURERGRQUuJPeoXRUA+Al1cAhoGxR4k/EemYvWkDgBJ/MmB5BYV4hcUEXnkJa9OG+Ny/thgGkY98DGvd+1jvvoNZVQmug5vExJ9bUIRRW4tRW9Pmevvtt8B1iM2Z17zMz8wievyJ8cRfNNq83CwtwVr3PtGTT0tafCIiAo2f+iyYJuH77mleFnpkMe648bj7ZsTaNg3f+i5+RiYZv7wGe+XyQx7XWvcBzsSBM98PwMvPh0gjhkZMiIiIiMgg1OXE34YNG1i8eDF/+MMf2L493kKjpKSE2trapAUnA4dRVweAn5WFn5mlGX8ickjW+nX46enNs0FFBiJnzlwCy5aA7+McNqv97WbNwR03nvBD/8HcthUAb0QSW302teJur91nYOWbeCNH4RcUtFgePf1MjLpaMn/4fdL+9DsCzz1N6L+L8bOH4cw7os1jiYhI1/hZQ2j8xGcIvPwi1ntr422g311D5JyPtKgY9zOzqPv+j/DyC0i/8XrSf/1LzPbmuNbVYW7bijtpoCX+4r+vzASrHkXaFI0SfOZJMi+9mIxf/BRr9duqIhUREZF+we7sDg0NDVx55ZU8/vjjGIaB53kcd9xx5OXlceONNzJy5Ei+//3v90Ss0o8ZdfGEsJ+RgZ+VpRl/InJI1oam+X5ttD4UGShis+cSfPxRvKIR+Lm57W9oGETO+xjpN/+aYCCAn56Onz0saXHsS7Cb5dta3/z1POyVy4me1Hpen1dQSP1lP8B+cxnWhnWkvfISeB6R8z4GdqffZoqIyCHETjyZ4IvPk/a3v+Dl5eMVFuMcMb/Vdn5BAfU/+DH2sqWE772LzCsupfH8TxA957wW29kb1gEM3MRfVeWAe23SCxobCT73DKHH/ouxdw+x+Udibq8i4/qf406YROTcj+DMmafPKSIiItJndfqOzPXXX88bb7zBn/70J4444ghmz57dvO7444/nb3/7mxJ/0lpdvNWnn5GJPzRbrT5FpGO+j7V+HdFT1CpQBjZ3ylT89HScw9uv9tvHmXsE7ugx2Kvfwp00Jbk3m0Ih/JxczIrWFX/Wug8wamtbzPdrEdfhs3EOnx3/orERa+sW3NFjkxebiIjsZxg0fOFLZF71A8yyLTR8+f+B2U4jH8PAWbCQ2tlzCN93D+F/3Ufs6ONaPGhirfsAPzNz4HVYSEvDzxoSb48t0hmuS+ZPr8TcupXYcYuInHNe/PrwfezVbxFa/CDpN/2KyPmfjD/oJCIiItIHdbrV55NPPsmll17KscceSyAQaLFuxIgRbN26NWnBycDR3OozPR0vawhGjRJ/ItI+o6oKo7ZG8/1k4LNt6q76GY0f/fihtzUMIuedD4CbxDaf+7hFRW22grNXvImfNQR3wsRDHyQcxp0wCQ56jygiIsnjjRtP5Mxz8AqKiB1z3KF3CAZp/MSn8UNhgs893WKVte4D3ImTB2TlkpdfgFlZkeowpJ8JvPoy5pZS6q66hoavfH1/UtwwcA6fTd1V1xA77ngCL72gtp8iIiLSZ3U68VdfX09eXl6b6xoaGrodkAxMRn1d/MNkejr+0KGYavUpIh2wN64HSCzRINLPeSNHQUZGQts68xcQm78w3l4q2XEUFmMdXPHn+wSWv4kzV+2sRET6ksinP0ft9Tcm/qBFWhqxYxcRfP5ZcJz4Mt/H2rh+wLbC9PILMKuqUh2G9CeOQ+jBfxObv7DDBxCjRx+HWVWJuXlTLwYnIiIikrhOJ/6mTJnCU0891ea6F154gZkzZ3Y7KBl4jLo6/LQ0MAz8IUM0409EOmRtWI+XX4CfNSTVoYj0LYZBw3e+hzOv9Tyn7vKKijErKlo8vW5t2oBZsY3YgiOTfj4REemmTs5SjZ5yGsbePQTeXAqAubUMo74eZ+IATfwVFKjVp3SK/fxzmNU7iHzsEx1u506fgZ+ZSWDJ670UmYiIiEjndDrxd9FFF/Gf//yHyy67jBdeeAHDMHj77be5/vrreeCBB/j617/eE3FKP2fU1+Gnx6sZ/CFDMGr2qi2GiLTL2LkTb3jb1eUi0jO8oiKIRTF27GheFnjhefxhOTiHHXoGoYiI9G3eyFG402YQfCb+IK+17gMwjAHbYcHLL8DYvQsikVSHIn2MvWwJ5qaNLRdGowQf+g+xo46Nd2PoiGURO2IBgaVv6L6GiIiI9EmdTvydcMIJ3HTTTSxfvpxvfOMb+L7PT3/6Ux5//HFuuOEGjjrqqJ6IU/o5o74ev6mNmZc1FDwPo642xVGJSF9l1NXgZ2alOgyRQcUtLAbA3NfuMxIh8PqrRI87HsxOv2UUEZE+KHLyaVjvvYu5pRR7/Qe4o0ZDOJzqsHqEl18AgLld7T4Hm8DzzxJ45aXWSTnPI3z330j/zY1k/uRKAgfOvHz8cYw9e4h89PyEzhFbeLTafYqIiEif1bneIE1OP/10Tj/9dDZt2sSuXbsYOnQoEyZMSHZsMoAYdXX4GZkA+EOHxpft3asb+yLSJqO2Fq+gKNVhiAwqfl4eWDZWxTbcww4n8OZSjIZ6YsefmOrQREQkSZwj5uMPzSb4zFNY6z7AmTo91SH1GK+gKfFXWXHoCi4ZMIzKStLu/DN4HsEXnqPhS1/FKyqG+nrSb/8N9uq3aPy/CzG3bSXtr3/G2rQR5zOfg3//G2fR8XiFiX0GObDdZ2Tc+B5+VSIiIiKd06XE3z7jxo1j3LhxyYpF+qNoFILBQ25m1NdBejoQb/UJYOzZA8UjejQ8EemfzJoanMzMVIchMriYZnweUnm84i/w4vO4U6fjFRSmODAREUka2yZ64smEHn8MIo2455yX6oh6jJ89DAJBzCpV/A0moUcews/MouEr/4/wPX8n84pLiZx1DoEVb2JUV1N/6RU4h88GwB03nrQ7/0LozaXgxogmWO0HtGj3GfnkZ8AweuYFiYiIiHRBp/s23XzzzVx11VVtrrvqqqv4zW9+0+2gpJ9obGTIV75AxtU/IvDGa+A47W4ar/jb1+qzKfG3d2+vhCki/Y9RW4vf9LNCRHqPV1SMWb4No6oK+901RFXtJyIy4ERPPAViUQDciZNSHE0PMgy8vDzMqspURyK9xNi+neBLLxI568M4c+ZR+8sbiJx1DqFH/wuRCHU/+Xlz0g8gdsJJ1F19DX44DT78Yfy8/E6dT+0+RUREpK/qdOLv0UcfZe7cuW2umzdvHo899li3g5J+Ihym/pLL8EMh0m67hazvfpPgY4+0OdzaqKvDT48n/sjIANPCrNnTywGLSL8QjUI0gq+KP5Fe5xYVY1aUE3zpefxwGrH5C1MdkoiIJJmfm0ts3nz8zKyE2xr2V15+gRJ/g0jokcX46elETz41viAYJPKJT1Nz42+p/fn1eCNGttrHHT+R+t/eDl/4QqfPd2C7TxEREZG+pNOtPquqqigqavvDQWFhIRUVFd0OSvoPZ9YcnFlzMEtLCD32COH77sadNBl38pQW2xn1dfhNrT4xDPwhQ1TxJyJtMmprAJT4E0kBr7AIc8d2gi8+T+yooyEcTnVIIiLSAxovuBBzZ/WAb0/oFRRiv7Uq1WFILzCqqwm++DyN53+i1fsXPy/vEDsbXbsW1O5TRERE+qhOV/zl5OSwbt26NtetW7eOoUOHdjso6X+80WNouPDLAJjbW89QMOrrm1t9AnhDhsRn/ImIHMSorQVQq0+RFPCKisH3MXbtJHb8SakOR0REeog/LAd3wgBu89nEyy+Ifz5toyuNDCyhRx/GTwsTPeVDvXpetfsUERGRvqjTib9TTjmFW2+9lbfffrvF8rfffpvbb7+dU089NWnBST8TDuOnp2PsrG65PBoFJ7a/1SfEK/5qVPEnIq0ZNar4E0kVr6mrg1c8AnfCxBRHIyIi0j1efj64DsbOnakORXqQsWsnweefJXrG2ZCW1qvnjrf7zCL0yGIlmEVERKTP6HSrz4svvpgVK1bwyU9+kgkTJpCfn09VVRUbNmxg2rRpfPe73+2JOKWf8HOHY1a3TPwZdU3VOxkHJv6y461lREQO0lzxp8SfSK/zs4bgDc8jetrpalclIiL9nldQCIBZWYGbm5viaKSnhB57BD8YINLL1X4AWBYNF36F9FtvwvvP/UQ+/qnej0FERETkIJ1O/GVlZXH//fezePFi3njjDXbv3s3kyZP5/Oc/z7nnnkswGOzU8e69917uu+8+tm7dCsCkSZO46KKLOP744ykrK+Pkk09uc79bbrmFM844o/nrBx98kDvvvJPNmzeTmZnJ6aefztVXX93Zlyfd5OXktkroGfX1APgZ+2/i+0OGYGze2KuxiUj/YNTVxGeBZijxJ9LrDIPaG38LZqebQoiIiPQ53vA8MAzMqkrc6TNSHY50kbmlFD8rCz97WOuVjkPgpeeJnnwaHPCwcW9yFh5JY9VnCd//D7z8AmLHn5iSOERERET26XTiDyAYDPKJT3yCT3ziE90OoLCwkEsvvZQxY8bg+z6LFy/mG9/4Bg899BDjx4/nlVdeabH9/fffzx133MGiRYual91555389a9/5fvf/z6zZs2ivr6+OZEovcvLycXauL7FMqOuDgA/PX3/dkOGYOzVjD8Rac2sqYlXCKvaSCQ1LCvVEYiIiCRHMIg/LAezqjLVkUgXBV54jrS//hln1mzqv3d5q/X2u2sw6uuJHXl0CqLbL3r2hzGrKki74094ucNxZx6W0nhERERkcOtS4i+ZTjrppBZff/e73+W+++5j1apVTJo0iby8vBbrn3nmGc444wwymp7k2rNnD7fccgt/+MMfOOqoo5q3mzp1as8HL634ubmYby5tscyob0r8tWj1OTTezs91dYNRRFowamvxM4ekOgwRERERGQC8/ALMqqpUhyGd5fuE/nM/oYcfxBsxEvutVRg1e/GzWn5OsJctwcsvwBs9JkWBNjEMGj//Jczt20n/7Y3UX3I57tRpqY1JREREBq2EEn9z587lrrvuYubMmcyZMwejgyoMwzBYvnx5l4JxXZcnnniC+vp65syZ02r9mjVrWLt2LVdddVXzsldffRXP86isrOSMM86grq6OOXPmcMUVV1BUVNSp85umgWmqwqQ7jLzhmLU12J4DTW1f7cZ6DMPAGpIFdrx1mDksG8MwCDTUtt2uQ1LOsswWf4r0Fqu+DmNIFrat772u0vUr0n/p+hXpn3Tt9mGFBdhbtui9ZX/iOIT+9HvsV14i+pnPETvhRDK+/lVCby7BOfWAOX6eR3DFmziLTsAOdP2B4qRdv3aQ6CWXkvaLa8j8xU/wJk4iduZZOPMXgp3y5+5FBhz97hXpv3T99ryE3nl88YtfbK68++IXv9hh4q8r3n//fT71qU8RiURIT0/n9ttvZ+LEia22+89//sOECROYO3du87KysjJ83+cPf/gDP/rRj8jKyuKWW27hwgsv5L///W+nZg7m5GQk/bUNOuNGQcAi6DXCsKaEnulBWohgwbD9rftGFca3MxwYlpo+/JKYIUPSUh2CDDZOI+TlENbPhm7T9SvSf+n6FemfdO32QePHwOpVem/ZHzgOvPYaPPAAlJbCD68guG/My4IjCL75Bnzio/u3X7MGGuoInXoiGUn4903K9TssA277Dbz5Jjz8MOHf3wr/uhcuvRRmzuz+8UWkFf3uFem/dP32nIQSf9/85jcB8H2f//u//yM9Pb1TCbVDGTduHIsXL6ampoYnn3ySyy+/nHvuuadF8q+xsZFHH32Uiy66qMW+nucRi8W48sorOfbYYwG46aabOOaYY1iyZAnHHXdcwnHs3Fmnir9uMux00mMuDRtL8cLxFhyBymoCgRD1u+v3b+cHSI+5NG6pwB2a197hJIUsy2TIkDT27m3Adb1UhyODSNr2nXhFxUR21aU6lH5L169I/6XrV6R/0rXbd9mZ2YR27qaurAoylPzrk2pqCDz7NIGnn8TYuRN3xgyiV/wYb/IUaPpMYM9bSOj2W6l/fyN+fgEAwaefx84aSn3eiObtuqJHrt9JM+DSGZilJYR+dxv86Q4afvrz5BxbRAD97hXpz3T9dt2wBB926lSvgVgsxtFHH83vfvc7TjjhhK7E1aZgMMiYMfF+7DNnzmT16tXcddddXHPNNc3bPPHEEzQ2NnLeeee12HdfJeKBScKcnByGDRtGeXl5p+LwPB/P87v4KgSAocPwfR+/agfO5PhFa9fU4qVl4DgHXMQZWfi+j7drd8vl0ue4rqd/I+lV/t4a3AkZ+r5LAl2/Iv2Xrl+R/knXbt/j5+YR9H28beV448anOhw5mO+Tec3VmBUVRI85luhpZ+yf13fAteTMPoJgIIj58stEzv0o+D5pS94gOn8hjusD3b+X0yPXb/EovLPPI/22m/FKSvFGjEzu8UVEv3tF+jFdvz2nU01Ug8EghYWFuK7bU/EA8Sq+aDTaYtkDDzzASSedRE5OTovl+9p+btq0qXnZ7t272bVrF8XFxT0ap7QhFMLPzMTYWd28yKirxU9Pb7UdgSDG3r29HKCI9HVG7V78zMxUhyEiIiIiA4DXVB1mVlWlOBJpi73iTcwtpdRdcSWNX/5/+5N+BwuHiR2xgMCrL4PvY21Yj7FrJ7H5C3s34C5w5h2Bn5lJ8MXnUx2KiIiIDBKdnp74mc98hr/97W9EIpGkBHDjjTeybNkyysrKeP/997nxxhtZunQp55xzTvM2JSUlLFu2jPPPP7/V/uPGjePkk0/mF7/4BStWrOCDDz7giiuuYPz48Sxc2PffAA5E/rAczOr9iT/q6/EPbqliGHhDhmDs2d2rsYlIH+f7GHV1eFlZqY5ERERERAYAPzMLPy0ds6oi1aHIwXyf0H8X406Zhjtl6iE3jx1zLOa2rZglm7GXLcHPGpLQfikXCBA79ngCL78Yn2MoIiIi0sM61eoToLy8nE2bNnHCCSewYMEChg8fjmG0nIt35ZVXJny86upqLr/8cqqqqsjKymLKlCnccccdHHPMMc3bPPDAAxQWFjbP8DvYr371K6699lq+9rWvYZom8+fP5y9/+QuBQKCzL0+SwMsdjlm9o/lro64OP2tIq+38IUMxalTxJyL7GXW14Pv4ma1/ZoiIiIiIdJph4OXnY1ZVpjoSOYj13lqsDeuov/SKhLZ3Zh6OnzWE4GuvYC9fRuyI+WB2+nn2lIiecBLBJx7DXrEcZ4EeUhcREZGe1enE3/PPP08wGARg9erVrdYbhtGpxN+11157yG0uueQSLrnkknbXZ2Zmcu211yZ0LOl5Xk4u1oZ1zV8b9XV4hUWttvOHDMHcs6c3QxORPs6orQVQq08RERERSRovvwCzUom/vib0yGK8UaNxZs1JbAfLInbk0QSffRoijTif/2LPBphE3shRuBMmEXzhWSX+REREpMd1OvH33HPP9UQcMoD4ubmYS19v/tqor28944+mxF+F2q2IyH5K/ImIiIhIsrnjxhN++CFwXbCsVIcjgLl5E/bbq2j4xnfgoC5SHYkdcxzBp5/AT0/HmT6zByNMvuiJJ5N2xx8xduzAHz481eGIiIjIAJZwT4RIJMJTTz3FX//6Vx577DF27tzZk3FJP+bl5MZv3jfNgTTqalvP+AO8IUMx9qriT0T229f+19eMPxERERFJEnfqdIg0YpVsSnUo0iT0yMN4efnEFhzZqf3cCRPxikbgHLEA7E4/y55SsSOPhmCI4EvPNy8zy7eR9ofbCN/zd8zSkhRGJyIiIgNJQu+Stm3bxoUXXkhpaSm+7wMwdOhQbrvtNubPn9+jAUr/4+XkAmDu2olXUBiv+Gsj8ecPGYqxVzP+RGS//RV/SvyJiIiISHK44ydAIIj13nu44yd2ev/Qv+7D3FlNw//7Zg9EN/iYlRUElr5O4+e/2PkKTMOg7qqf4geCPRNcTwqHiR51NIEXnydy2hmE//sQwScfxx86FGIxgk88hjtuAtETTyJ23AkQCKQ6YhEREemnEqr4u/HGG9mzZw/XXXcdjz32GH/84x8ZPnw4V199dU/HJ/2QnxtP/BnV1dDQAL6Pn966bZ8/ZAhGQz3EYr0dooj0UUZtLYTC+pArIiIiIslj2ziTJmO/927Xdl/9FoHXX4Omh9SkG3yf0L/vx8/MIrroxK4dImsIhMNJDqx3xE44GbN6B1kXf4PgM08R+cjHqLnhN9Tc+gfqv/M9/KFDSbvzLwQffzTVoYqIiEg/llDF34oVK/jud7/LueeeC8CECRPIzc3l4x//ODt37iQnJ6dHg5T+pbnib2c1Xn4BQDsVf0MAMPbubU4WisjgZtTsxdN8PxERERFJMnfKVIJPPwG+36mZcngeVlkZuA6BFW8SW3RCj8U44DU2kv77W7FXvEnDV74OwX5YtddN7oSJOLPn4qdn0PjJz7S4F+LMX4gzfyHp116DtXlz6oIUERGRfi+hir+KigomT57cYtmUKVPwfZ+qqqoeCUz6sWAQPzMTc2c1Rn1dfFlGeqvNvKHZQLwlqIgIxCv+fCX+RERERCTJnKnTMWprMbeUdmo/s6oSYlEIhQksfSNJwTgQjSbnWP2EUVVF5k+vxHpnDfWXfH/wJlANg/pLr6Dhom+1+wC0V1CIWbGtlwMTERGRgSShxJ/v+1gH9V03zfiunuclPyrp97zc4Rg7dmDUxRN/fnrrij9vxMj4nIV1H/R2eCLSRxm1tfhZmu8nIiIiIsnlTpwElo39/tpO7bcvURg59UPYq9+Gps+43ZH25z+QddFXCN91J2Z59xM8ZmUFxvbt3T5OT7HeW0vmVVdANErdT3+BM2deqkPq07yiYqyKinh1qoiIiEgXJNTqE+D6668nq42bsddeey2ZB1RnGIbB73//++REJ/2Wn5OLuWtnc8VfW60+CQRwJ07Cfu9domec1csRikhfZNTWxGd2iIiIiIgkUyiEO34C1tq1cOrpCe9mlW3Bz8wkesqHCD36MIFVK4gdc1zX4/B97DVv4xUVEXj9VYJPPY5z2CwaP/kZvLHjOn+8hgYyfv4T3BEjqb/iyq7H1UPMzZvI+PUvcSZMpOHb38XP1EN+h+IVFUE0grFzp8aiiIiISJckVPE3f/58TNOkrq6uxX/z58/HMIwWy2o17FqIz/kzq3dg1NcD4Ke1bvUJ4EyZivX+2n7xJJuxs5r0G67TQHeRHmTU1qjVp4iIiIj0CGfK1HjFXyc+f5pbtuCNHI0/fDjuhEndbvdp7NiBsWc3kY+cT81vfkfDVy/CrKok7Y4/del44Qf/jbFrJ9bG9X3uc7VRXU3GDdfhFo+g/pLvK+mXIK+oGCAp1aAiIiIyOCVU8Xf33Xf3dBwywHi5uRg7q+OtPkNhsNv+VnOmzSC0+AHMsi14o0b3cpSdY61fj71qBcEXnyd61jmpDkdkQDJra3HU6lNEREREeoA7bTrGow9jVpQ3J1cOxSorxZk+E4DYgiMJ//uf0NAAaWldisFeHx914U6cBMFgfNad45B255/jbUTb6pbTDrO0hOCT/8OZeRj2mtWdel09rqGB9Buvw7cs6r/3fQiHUx1Rv+Hl5YNpYVVsw515WKrDERERkX4ooYo/kc7yc3Ix6uowd+1su81nE3fiJDCtTs9ZSAVz724Ags88CZptKdIjjJoaPQksIiIiIj3CmTQFDAPrvQQ/f8ZimBUVeKNGxb+cvxCcGIG3VnY5Bmv9OryCQvwhQ5uXuTNmxluAfvBe4gfyfdL++me8wiIaLvp2/Ngb1nc5rqRyXdJvvRlz+3bqL/sBfvawVEfUv1gWXn4BZnl5qiMRERGRfkqJP+kRXk68D725pRQ/ve02n0DLOQt9nLFrF1g25vYq7G580BORdkQiEIuq1aeIiIiI9Iz0dNyx47ATTPyZ5dvAc3FHxrvT+Pn5uOPGY3ej3ae1fh3uhEktlnn5Bfg5udjvvpPwcQIvPIe1/gMavvBl/CFD8QqL+0ziL/zPf2CvWU39ty/BGzkq1eH0S15RkVp9Jom1cT3EYu2uN0tL4lW8IiIiA4gSf9Ij9g2gtraU4md0fBPfmToN+713+9w8goMZe3bjjh6DO24CwaeeSHU4IgOO0TQ/01erTxERERHpIe6UafE58wmwtpbF9xkxsnlZbMGRBFathMbGzp88GsXavAl34sSWyw0DZ/oMrHfXJHQYo2Yv4fv/QezYRbjTZ8RjnDCxTyT+jOpqgk8+TuP5n8A97PBUh9NveUXFmBU9U/Fn1NaA6/bIsfsaY/t2Mq7+EcFnnmp7/a6dZF55OVkXX0ToP/dj1Ozt5QhFRER6hhJ/0iO8YTkAGLt3dVzxB7hTp2Hs2Y1ZWdEboXWZuXs3/rBhRE87HXv1W3r6TiTJ9n3IUsWfiIiIiPQUZ+o0zB3bMbZvP+S25pZS/GE5cMD7U2f+QohGsN9+q9Pntko2g+vgTJzcOq7pM7FKS+JJmY74PuG7/ga+T+Nn/q95sTthQvz4HVQ29YbQ44/ip4WJnnp6SuPo79zCIsztVcn/93QcMi/7LsH/PZLc4/ZRgddfibfRXbW8zfX2W6vA94kddSyh/z1K1ne+Qfiev3cusd/HH2IXEZHBSYk/6RnBIH7WEAD89I6Hk3d6zkKKGHt24w3NJrbwKPzMrHafGBORrmmu+NOMPxERERHpIe6UqQAJzZm3yrbgjhrdYplXWIQ7egyBLrT7tNavg0AQb/SYVuucaTPA97HWvtvhMYLPPEng9VdovOCLLecETpgIroO1paTTcSWLUVtD8Lln4km/tLSUxTEQeEXF4PuYVZVJPa69+i2Mmr0Elrye1OP2Sb5P8NWXIRTGfu+9Ntt52m+txJ04mcYvfIma39xO5MyzCT77NOEH/tXuYc3SEoJPPk7aH28n8weXMuSL/4e9uvMPAoiIiPSkpCf+li1bluxDSj/l5cSr/vyMjhN/ZGTgjhkbb/fZh5m7duFnZ0MwSPTEkwm89ELX2ruISJv2Jf48Jf5EREREpIf4WUPwRozEXr4MHKfDbc0tpW3OqHMOn92lcRXWug9wx40H224dV14eXl4+dgeJP+uD9wnf/Xeip51B7JjjWqxzR48Fy05pu8/gk4+D7xM9TdV+3eUVFQEkvdNQYMnr8QevN2/CqK5O6rH7GrO0BHNrGY2f+DS4Dvaat1tu4DjYq9/GmT0HiP9siJz/SSJnf5jgM09h7Gz992NtWEfmj75P+N67McvKcCZOwsvJIfD8s73xkkRERBKWtMTfM888w6c+9SkuuOCCZB1S+jk/d3j8z0O0+gRwp07v2xV/vo+xd0888QdETz4No7GB4KsvpTYukQHErN0LpgkJ/MwQEREREemq6HEnEFi2hKyLv0Ho4Qcx9u5pvVFjI+b2Ktw2En/u5CkYu3dh7NjRqfPa6z/AmdS6zWfzcafPwG5nzp+xayfpv7kRd9LkFi0+mwUCuKPHpC7x19hI8OkniJ54cotKROkaf2g2fjgNszyJc/5iMewVb8YrMk2LwIqB/eB+4LVX8DOziJ58Kl5hMfZbK1ust95bi9HYQGzWnBbLI6efhR8KEnr4oZYH9DzCd/4Fd/QY9t5xF3U/+yWNX/oa0RNPIbByBdTXtw7CcQjf+ZeEWguLiIgkU0KJP8/z+POf/8wZZ5zB7NmzOfvss3niiScAeOWVVzjnnHP41re+xY4dO7jmmmt6NGDpP7ycXAD8jEPP63KmTE14zkIqGDV7wfPwsocB4A8fjjNvPsGnnlA/d5EEGHv3YG7b2vE2tbXxnxeG0UtRiYiIiMhgFD37w9RedyOxOfMILX6QrG9fRPCRxS22sbZuAWg78deUvLM/eC/hcxo7qzF2VuNOmNTuNs70mZhlW1onIh2H9N/eDIZB/bcubrNiEOLtPq0NGxKOqT32ijcJPPd08wzuRASffxajvoHIGWd3+/wCGAZeUTFWRfIq/uw1b2PU1xM94SSc6dPjVa8DlecRfO0VYkceDbaNM2cugVUrW9y/Cby1En9YDt6YsS33zcggctaHCb7wHEZVVfPi4HNPY5VspvHCr7S4BmNHHg2xKIE2/j4Db7xG8NmnCD7b/qgYa90H4Lpdf60iIiJtSCjxd88993DjjTdiGAYnnngiGRkZXHLJJdx888185StfYc+ePVxzzTU88cQTfPzjH+/pmKWf8HL3Jf4O0eoTcKdOAxKbs5AKxu7dQPypu32ixx2PubWs0095igxG4X/cTcYvftrhBxqjtlbz/URERESkV3gjR9H4pa9Sc+vviS46gfC/729xk9/csiWefBkxstW+ftYQvMJirA/eT/h81vp4JZ47qaPE34z4tu+2bPcZvufvWBs3UP+d7+E3PYzaFnfCRMzyrVBXl3BcB7NXrSD95l+T9tc/k/WNr5J+3c8JvPBcm/PR9gfuEHr8UWJHH4Ofl9flc0tLXmFRUiv+AktexysagTdqNM7cI7DXru3W90pfZq19F2PXzuaWuLFZczB278Is2dy8jb1yBbHZc9p88DR66un4GRmEF/8HAGPPbkL/uo/oCSfhTmx5DfvDh+NOmUbgtVdaHsT3CT7xGEB8JmgbD42bmzeR8dMrsZe/2Z2XKyIi0kpCib9///vfnH322Tz22GPcfPPN3H///Xzve9/jj3/8I/PmzWtO+NntPHUmg5O/r+IvgbZ9++YsWH008Wfu3gXQXPEHNLcvMRo7+AAkIuD72O+sxtizG/vtVe1uZtTU4GceukJYRERERCRZ/MwsGj97AX5mJuGHH2hebpVtwcsvgFCozf3cKVOw1rWd+LPeWYO9dEmLZfb6D/BzcvGH5bQfy7CceEvCA9p9Bl56geAzT9J4wReaKw3b406YGD//po0dbtcec/Mm0m+9BWfOPGpu+yONn/8ieB5pd/yRzGt+3G6SKPDaKxg7q4mcfW6Xzitt84qKkjfjr6nNZ2zhkWAYxObOB9ch0MHns32CzzzZqSR3XxB87WW8/ILmJJ07dRqEwgSa2n2alRWYFdtwDmrz2SwcJnLuRwm8/CJm+TbC994Dpknkk59pc/PY0cfEP/MeUK1rvf8e1uZNRM84G7OqEnPzptZxvvRCfNttZd14tSIiIq0llPgrKyvjIx/5CMYBT8F87GMfA+CrX/0q6ZrHJG3oTKtPAGfq9PgTZ33Q/oq//bMK/EAgvi4WTUVIIv2GWVGOsXsXBIIEmj7YtMWorcHPUsWfiIiIiPSyUIjIOefFb/JXVgBglpXitdHmcx9n0hSsLaVtzvVK+9sdpN96E/ay/ck/a/06nIkdJ+4AnGnTsd99Jx7Dpo2k3fkXYotOIHrSqYfc1ysqxk9Lx9p4iDl/nhf/7wDGjh1k3HAdbvEI6i/6Nn72MKInn0b9D6+i9tpfY+ysJuPmX0O05edf6/33CN97F868+R3+fUnneUXF8XarSajK29fmM7bwKKCpSm3M2ENWmpmVFYT/dgcZP/8JwScf7x+jTqJR7GVLiB197P5qPtvGOexw7FXxxJ+9aiVYNs7Mw9s/zIkn4w/LIe22Wwi8+hKNn/pcux1qYguOBAwCS15vXhZ88n94RSNo/ORn8DMzW6wDwHEIvPYyQHJnOYqIiJBg4q+hoYGsg27G7vs6t6mdo8jB3HHjiS06AXf0mMS2nzoNs2JbPEHQxxh7dscrkZqSfcD+Jz8jSvyJdMR+d0386cgPn0dgxXKM2po2t4u3+lTFn4iIiIj0vujJp+IPGUpocbzqzyora3O+3z7u5Cng+1gbWibZzK1lmOVb8YqKSf/drVgb1oHjYG3aeMiKPQB3xkzMim2YpSWk/+ZG3JGjaPjClxObg20YuOPGY2/oIPEXiZDx0x+T9f++SNpvbyLwwnOYW8vIuOGX+LZN/aWXQzjcYhdv1Gjqv3cF1ob1pP3htuakYeCN18j45c/wRo2h/itfP3R80iluYRFAUub8Nbf5POB72pk3H/utleA47e/36ssQChM95TTCd99J2u9uhUik2/H0JHvVyniSs6nN5z6x2XOx1n+AUVuDvXI5zrRprb7XWwgGaTzvo1glm3EnTiZ2/IntbupnDcE57HACr70KgFFVReDNpUTOOBNsm9gRC1q1+7RXLseorcUdNyF5lZ2Dje/DptaVlCIikmDiD2Djxo288847Lf7raLkI4TANX70IEpjxB+COnwA0zVLoY8zdu5tbe+7jB+OJPyPat9/0iqSa9c47uOMnEj35VPD91rMPmhi1NZrxJyIiIiKpEQzGq/5eeQlr/TqM3bvwRo1ud3OvqBg/MxP7oHafgaVv4IfTqL3657hjxpJ+w/XYK5ZDLNpqNlhbnGnTAcj45c8wGhup/873IBhM+GW44ye0SkY2833S/vwHrC2lRE86FbO6Ot7G8/JLMHZWU3/ZD1rMtW9x3MlTqP/mdwgsW0L4rjsJPrKYtNtuIbbwKOq+/0PQA3xJ5xUVA0moBjuozWfz4rlHYDTUY619t+39fJ/Aq68Qm7+AxgsupOEb3yGwfBmZP/kRxvbt3YupBwVfexl33Pjmv799nFmz42Molr6BvfZdnNnzDnms2KITiZ55Dg1fu+iQyffY0cdirXsfY/t2Qk89jp+RQeyYRfF1C49u1e4z+PKL8QfmFyzErNjWP6op+xCjtobw9dfCt7+NtWxpqsMREelzEh7K94Mf/KDN5ZdddllzC1Df9zEMg7V9tF2j9G1e7nAwDMztlbipDuYgxq5deMMOGqIebKr+U8WfSPt8H3vtO/E2KUOG4syeQ+DlF4medkarTY3aGjy1+hQRERGRFImeeDKhRxaT9ofbATqs+MMwcCdOxvrgvRaLA0vfwJkzDzIyqL/kMjJ+ciXpt/8GLBt3zNhDxuAPzcYbMRJz21bqrvgx/vDhnXoN7oSJGI8+jLGzGj+nZYem0H8fIvDGq9R/+3s4CxYSIf4e3FqzBq+oCG/EyA6P7cybT8OFXybtr38GIHLex4h87BOJVSNK54XD+NnDul0NdnCbz328MWPxhucRWL4M97DWLS+tTRswK8tp+MKXAIgddQzuiJGk3/xrMq77GXVX/6zVA9KpZuzaib1yBY2f+myrdf6wHNwxYwk/8G9wHZzZ7cz3O5Bt0/iZ/0vo3LF580kLBAk+9zSBF54jeuqHmjtFudNnNLf7jIwbj7FnN/aqFTT+3xfwhg7DqK/HqNnbI3+f1tp34xXKltV6pe9jvbMGd+y4fpW8NzdtJP03N2JFGmH0aIIP/JvI7Hn6WSQicoCEEn933XVXT8chAraNlzscs6oq1ZG0Yu7d0zyzcJ/mij/N+BNpl1m2BaNmL860GQBEF51A+i03YJaW4B3YBtjzMOrrVfEnIiIiIqkTDBL58HmE77oTLBuvqdVie5zJUwj9d3G89aVpYlaUY24ppfEjHwfAHzKU+kuvIOOnV+IVFidcudf4mf+DSBR3xsxOvwR3wkQArA3rcQ74DGsvW0Lo3/8k8rFP4CxY2Lzcz8zCOfKoVsdpT+ykU8EwIS2N2JFHdzo+6RyvqLh1xZ/vJ5bg8H2Mmr0EXn4Jr3hE6xmMhoEz9wgCby6l8fNfbHXMwKuv4A/NbvF96I0eQ90VPybzmh+T/utfUvfDqyEtrasvL+lCjz2CHwoSXXRCm+ud2XMJPfwgXkHRIa/vTguHic09gtCjD4NhxhN/+1hWc7vPyCc/Q+DVV8AwiR11DMau+Lgbs6ICN8mJP2vjejJ+8RMaP/t5omec1Wq9vWoF6TdeD8EQ0eOOJ3r6ma0qJfuawAvPkfa3O3BHjab+6p8SbKzB/P4V2KtWxB+66MlzP/c04X/dhx8K4w/Nxs/OxissovEj5/ep60BEBBJM/BmGwfTp08lIsGWjSFd5+QXtJv6M7dvJ/NlV1P7k562eXOxpxq5d+E2tSJvt+9AWVeJPpD32u+/En26ePAWIf9DyM7MIvvJSiycnjbpa8H3N+BMRERGRlIqecDKhRx7Gz8gAu+NbJu6kKRiNDZhlW/BGj8FetgSCoXhLwSZe8Qjqrvll81y8RDizEqhEaoefk4ufPYzgyy9iRCL4aWkQc0j/0++ILTiKyHkf6/Kx94mdeHK3jyGJcQuL4nMimxi7dpLxy5/FkzTnnNfGDi5pv78N+/21GHv2gBfvp9ReZWZs7hEEn3occ/MmvHHjWxwn8PqrxI4+FsyWU4L8ggLqLvsBGb/4Kem/uYH6S39wyGulNxh7dhN89mkiZ3+43ZEz+xJ/zpy5PRJD7OhjCCx5jdiRR7W6b+UsPIrgC89hbt5E8KXnic07Aj8zK/5QuWFglm9r/tycLPabywAIPfbf+OiNAx8+8H1CDz2AO2ESzqzZBJ9+kuCzT+HMmkPD1y7qc9WcANa6D0j7yx+InngKjRdciJ0WguyxeFOmEnrwPziz5/ZM1Z/jEL77bwSffYrYccfjDcvB3L0LY3f8e87cvIn6S6/oVFtmEZGeltCMvwsuuIANGzb0dCwi+Hl5mNsr21xnb1iHsbMaq6z3ZwAae3bjHTzrwDDADmAo8SeCveJNQv+6r/Xyd9fgTprc3OIE2yZ2zHEEXnmpxRB5o7YWQBV/IiIiIpJawSD1F32bxk9/7pCbuuMngGlhN7X7DCxdQmz2nP3vfZt4BYW9WkUTm78Qe+Vy0v5wG+k3/5r0227GHTEioTll0rd4RcVYFRXxKr+6OjJ+dS1mZSXhf9/fYl7cPqH/PkRg6etEjzmOxgu+QP3Fl1L3k18QaStJCLhTp+FnZhJ+4F8tZszZ767B2LuH6NHHth3X2HHUf/cy7LVr461x+8B8uuD/HsW3LCIfOrPdbdwJE4kddzzRE07qkRicWXOIHX0ckY+0TrA70+LtPsP/ug+zbAux445vCjwY737VzZaubQm8uRR3yrR4UvTF51qss1e/hbVxPY3nf5LIRz9OzW9/T8NXL8LasI7w3X9PeizJEHjjNfxhOTR+8SsQaBq/YxhEP/ZxrE0bsN9elfRzGrU1pP/qWoLPP0vDF79Kw9e+QeQTn6bhqxdR//0fUnfpFdjrPiD9tlta3OMQEUm1hBJ/fh/4BS6DQ7zir+3E3743QUZ1dW+GBI2NGI0N+AfP+AP8UBCikd6NR6QPCix5g9B/H8J6Z83+hZ6H9d67ONOmt9g2uugEjL17sFe/1bzMqKkBwNeMPxERERFJMXfa9MSq7kIh3LHjsNatw9i+HWvTBpz5Cw+9Xw9r/PwX2XvXP9n713uo+d2fqbnxVuqu/nmrhKT0fV5REUQjmJUVZNz0K4yd1dRecy3uyJGk//H2FokGa+N6Qg89QOTDHyHyqc8SPeVDOEcswJ04qf2KPNum4WvfwH5rZYsHOQOvvIxXWNyyCvAg7vQZ1H/jOwSWvEbg5Rfb3S74yOIeSWodyNi7h9DTTxI97fSOZ9WZJg1f+0brtqfJYts0XPQtvOIRba6LHbEAe/Vb+EOzcQ6f3bzKKyzErKxIaihm+TbMbVuJnHEWsaOOJfTIwxCLxVf6PqEH/4M7YRLuzMPiywIBYotOoPGznyfw+istPq/3Cb5PYNkSYvMXtnqAwZ15GO6ESYQe/E+LJLS1YR3hO/6EsXdPl05pVlaQcdUPsUpLqPvBj4mddEqrbdxp06n/ziXYq1aS9uc/9IkkuIgIJJj4E+ktXn4BRl0d1NW1Wrevr725s3cTf+ae3QCtK/4AAkGMiBJ/Imb1DgDC997V3MbILNmMUVeHM73lbBJvzFjcMWMJPvFY8wdVo7Yp8adWnyIiIiLSj7iTp2B98B6BZUvADhCb3TMtBDvNMCAYxB8yFL+goE+0YpTO21cpmn7D9VgbN1D/vSvwxoyl4asXYW7dSmjxA/ENo1HSfn877ujRnW7n6syZR+OnPkfokcUEXn0ZIhECby4ldsyxh6wQdRYsjFcltlF9CEB9PeH77yXwxmudiqmzgo8/Fq/8OvPsHj1PdzkL4/M0o8cuAstqXu4VFmMlOTlqL38TAkGcw2YROfcjGLt2Enj5BQCsd9Zgrf+AyEfPb/VvHDt2Ec70mYTv/Av0oftd1ob1GDuriS04svVKwyDy0fOxNqzDWrM6/n3397+S8ZMrCb7wLOk337A/6Zkgo7KSjF/8FEyL2p9dh3vQA80HcmbPpf6ibxN47WXCf/9rZ1+aiEiPSPid35IlS6ioSOzpk9NOO63LAcng5uXnA2Bur8LLGNdi3b4nxHo78Wfs3g2An53dap0fCnX6zYPIQGRU78CdNgNr7TsEXnqB2Aknxef7BYLxJ0wPEvnYJ0j/zU2k33Ad9d++RK0+RURERKRfciZNIfjEYwSfewbn8FmQlpbqkGQA8YbngWlhVlVQf/GlzTPgvLHjiJz7EUIPP0TsiAUEX3kJc3sVtb+4vktJ3uiZZ2OVlZL25z/Ek3iRxvh8v0RiLCxqt6LPrGh6gLudzk7JYNTWEHrqCSKnfqjPf550ps8kctaHiZ52RovlXlEx5gvPxavFktSON7B8Gc5hh0MohDdiJLEFRxH672Jii04kvPgB3HETWlQdNjMMGi/8Mpk/uIzQ4geIfPIzSYmnuwJLXscfmt3uHETn8Nm44yaQdvedGA0NGPX1NH7mAtwJE8m49hrS7vwzDV/5esu/X9/H2LMbf2h2i+XG9u1kXPtT/ECAuh/+uNW8xjbPf+RRNO7ZTfjuO4l+6Ixebe8sItKWhN8N3HTTTQm1/DQMg7Vr13YrKBm8vPwCAMyqKryxByT+fP+AVp87ejWmfYm/Niv+gkHN+BPxPMydO2k86xy8odmE//1PYkcejb32HZzJk/f33j+AM/cI6r7/Q9J/cwOZP7sKZ8Zh+OE0PYksIiIiIv2KO3kyAGbFNiLnfiTF0ciAY9tETz4Vd9JknLlHtFgVOfej2MuXkX7zrzGrd9D42c/jjRjZtfMYBg1f/CpmeTnBxx/FnTgZr6AwoV3domICS99oc521L/FX2XOJv+AT/wPf7/PVfgBYFpE25od6hYXgxDB27MDPy+v2aYzdu7DWfxBPdDWJnPdRMn9wKWl3/BHrvXepv+T77SYZvaLieGL5oQeIHX0s3qjR3Y6pW3wfe+kbxI5YAGY7zesMg8jHPk76DdfhzJ5Lwxe+jD98OAANX/4aaX+4DXfUGKJnnAWAubWM8N13Yq9ZjVc8gugJJxM79jiIxsi49qdgWdT98OqEkn77xBYsJHz3nZjbtirxJyIpl/Ad1ltvvZVp06b1ZCwi+JlZ+OG0Vk+DGXt2YzQ24A3Pw9y5s1djMnfvAjsAGRmt1vnBEIZm/MkgZ+zZDa6Dlzuc2Cc/Q9b3v0to8QNY760les657e7nzphJ3VU/I+NX1xJ84rH406wiIiIiIv2IPywn/jl11y5ic+alOhwZgBo//8W2V9g2DV+9iMyrfog7bQbR08/s3okCAeq/eynpv7qWyOlnJbybV1iEuWN7vBvSQQ99mtu2xv/sqYq/aJTgU48TPflU/CFDe+YcvcAtjCeJzIpy3E4m/syK8niS9oAknr1iOQDO3P0/k7xRo4nNX0jg5Rdxx4zFOcTPq8jZ5xJ47VXS/vpn6q66JmmViAczduzAz83t8PjWpg2Y1TvabvN5AGf2XGp+83v8nJwWx4sduwizbAvhe+/Cz87GWvcBwWeexMvLp/ELX8J6913C999L+P578dPS8MNh6n70k3hcneBnD4NQGDPBjnkiIj0p4cRffn4+I0a0MZxWJJkMAy8/v9Wbwn3z/dwZMwm88XpS2x8cMqQ9u/Gys9s+XzAIEVX8yeBmVsfb73o5ufh5eUTOOIvQI4vB91vN9zuYN3IUtT/9Bek3XNevP6iJiIiIyODlzD0CY88e0Lxq6WXe2HHU/vQX8e5JSbhH4g/Npu4Xv+pcDEXF8S5NVZWtKg73JUCM3bsgGo3fQ0ki+53VGPX1RE84KanH7W1+Xh5YNlbFNtzDDk94P6O6mszLLiZ6+lk0fvaC5uWB5ctwp05v1fo0ct7HCLz9FpGPfeLQ3y+BAA0XfpmMa3+KveZtnMNmdeo1JWJfxWrjZy7osGLTXroEP2sI7tRDF6S0l6yLfOLTWGVbSLv9NxAK0/jxT8eT5YEAnPIhGmv2EnjlZez319Lw2c83Vwt2imHgFhQ0t7jtEzyv/SpJERnQdOVLn+PnF7SR+NsKhoEzbQZEGqG+PvknjsUI7OupfuC5d++K9/tuK9ZAACOmxJ8MbsaOePtdLzf+xjhyznn4WUMgFMYdN/6Q+/vDcqj7+fXUf/eyHo1TRERERKQnNF5wIQ3fujjVYcgg5Y0b32aHol47f1ERQJtz/szyrXjF8SKCnqj6s1csxysobD5Hv2WaeAUFzQ+9J8ratAF8n+DjjxJ85sn4woYG7DWric2b32p7b8xY9v7+L63axrbHnTYdP2sI1tp3OxVXQhyH8H3/wE9PJ/zPe7DeWdP2dr5PYF+bT8vq+vlMk/pvfIfGz36emhtuiXcnOqBC1c8aQvSMs6i/+NJutVv1CosxK/tGxZ/13lqyvv4lrHUfpDoUEUkBJf6kz/HyCzC3V7VYZlVU4OUX7J8BuLM66ee1V64g7S9/wHqv5YxKY/du/OzsNvfxQyGIqNWnDG7mzmoIhfd/2ExLo+FrF9H4iU8nPrPPMJL+9KeIiIiIiIj0LH/IUPy09NZJK9/HqqjAmTUHIPnJEN8nsHJ5vGVlL3WE6kleYVGbydOOWCWb8TOziH7oTMJ//yv2qhXYb60C12kz8Qd07nO3YeBMm4590H2yZAg+/wxmZTl1P7waZ9p00m+9ufmh4gOZJZsxqyoP2eYzIWlpRM84C39YTveP1Q6vsLB5tmVnmVtKyfjR9zFqa5ISi71qBUZdHek3/xqjOvn3UUWkb0so8ffss89qvp/0Gi8/H3P79ng5ehNz29b4U1xNQ3V7IvFnbdoAgP3+QYm/Pbvxsoe1vVMwhBGLJT0Wkf7ErN6Bd1BPfmfWHKIfOiOFUYmIiIiIiEiPMwy8wiKsipZJK2PXTog04kydBoEgZlVVOwfoGmvTBozdu4glWL3W17lFxZ1uEWmVluCOGUPjZy/AmTOP9FtvIfTEY7hjxnaraq1FXFOmYW1YH2/Vmix1dYQe+DexRSfgjR1Hwzcvxg+HSf/NDa3OE1j6Bn5GBu606ck7fw/yCoswdlZ3qUggsOR1rJLN2G8uS0os9ob18ZavwSDpN10PjY1JOa6I9A8JJf4cx+Hll19utfzll1/mox/9KLNnz+aUU07hnnvuSXqAMvh4efnguS2eRjErKvCKR+APGwaG0SNPquxL/FnvtWxhYHZU8RcMQlQVfzK4xRN/Xeh/LyIiIiIiIv1evFqtZdJq39de8Qi8vLykt/q0VyyPJ4QmT0nqcVPFKyzC3LEdOvFwuVlagjd6bLyN5UXfxi0uxlr/Ac4RC5IWlzN1GrhOPPmXJKFHFmNEozR+7BNAvM1m/cWXYpWVkfaXP2KWlmBUVWHs3UNg6RKcefMT7yaUYl5hU+vbLlS42k3tTgNLXu9+IK6LtWE9sdlzqb/k+1gVFaT98fZW441EZOBKKPF32223cccdd7RY9v7773PRRRdRUlLCokWLSE9P5xe/+AXPPPNMjwQqg0dzO899bwodJz4kurAQLAs/e1jyK/58H2vzJvzMTOx168BxmoLxMPbuwWtnxh/BIIZafcogZ1RXxyv+REREREREZNDxilq3qbTKt4Jp4eXl4xUUJr3Vp73iTZxZc/tNQuhQvKJi8P3EE6R1dZjbq3BHj4l/HQ5Tf8n3iR19HNFFJyYvrlGj8dPTW3XH6ipj+3ZCT/yPyJln4+fsv4/gjR1Hw5e+SuC1l8n84WVkXfJNsi76CmbFtuS0+ewlXmEhEC9g6JT6eqwN63DHjMV+Z3W3232aW0ohGsGdNBlv9Bjqv/5NAsuWEHrw3906roj0Hwkl/t566y3OOKNly7Z77rkHz/P4xz/+wW9/+1sefvhhjj/+eP7+97/3SKAyeHjD88Awmuf8mdurwHNxi+LDmr2c3KQn/owdOzBqa4mefBpEGrE2b4ov37sHfL/9ir9AoFNPY4n0V9b77xF89qk215k7tuOr4k9ERERERGRQ8oqKMWr2Ql1d8zKzoiL+YLdt4+UXJLXVp7FjB1ZpCc7ceUk7Zqp5RU2VYgnO+bO2lALgjhnbvMwflkPDRd/CT+aDuaaJO3lqq+5YXRX+1334GRlEzj631brYsYuoufl26n7yC+qu+DH1F19K/Xcva54T2R/4WUPw09MxKzvXttV+713wPBov+CL4frfbfdob1oFp4Y4dB4BzxAIi532M0OIHoLa2W8cWkf4hocTf9u3bmTBhQotlL7zwAocffjhTp04FwDAMzj//fDZt2pT8KGVwCQTwh+U0P+W0703PvjdBXm5uvF92Elkb420+oyedCsEQVtPgYmP3boB2E3+EQqr4k0Eh+PSThO6/t3VbiGgUo2avWn2KiIiIiIgMUm5BvMrJOiDZYW7buv8+Tn5B00PdXlLOF1j5JpgWscNmJeV4fYE/ZCh+WnqrlqntsUo3g2XjFY/o2cAAZ8q0lt2xusjcWkbg9VfiLT7D4Ta38fPycCdOwp15GM4RC+JtPg2jW+ftVYaBV1CE1cl5jfY7a/CG5+FOnoI7dXq3231a6z7AHTMGQqHmZc7hs+NVpbt2duvYItI/JJT4C4VCRA8Yrrp161a2b9/O/PnzW2yXnZ1NTU33SpFFgBZPg5nl5RAK4w/LAcDPycWsTu4vKWvTBvxhOfi5uTiTJ8eftAHMPbvj8Qwd1uZ+fjCkij8ZFKytWzDq6zF2trz29r1hVOJPRERERERkcGqea3ZA0sqsKI+3rwS8ggJwnVafJ7vKXv4mzvTpkJGRlOP1CYaBV1iEVZFYxZ9ZWoI7YkSvtDp1p02Pd8cq6V6xR+D1V/HT04kdd3ySIuubvILChBO4+9hr3sadMRMMg9jCI7vd7tNavx534uSWcWXH723uK3IQkYEtocTfpEmTePLJJ5u/fvrppzEMg+OOO67Fdtu2bWP4cN38le7z8vP3t/qs2IZbWNj8hE9zq88kDqS1Nm/EHTceAHfKNKwP3ovP99u1CwB/6NC2dwxpxp8MAq6LuS3+4cPaUtJilbFjBwC+fvaLiIiIiIgMTunp+EOz97epjMXi8+eaE39Nc8+SMeevvh577bs4c47o/rH6GK+wKOHZcFZJCd6++X49zB07rqk71ntdP4jvE3jj9XgFXyCQvOD6IK+oCLMTFX/Grp2YW8twZh4OQOyIBd1q92nU1sTvpU6c1GL5vnub+4ocRGRgSyjx9+Uvf5kHH3yQL3zhC1x22WXcdNNNzJgxo1XF3/PPP8+MGTN6JFAZXLy8/OZWn1Z5OV5h8f51ObkQaWzRO75bfB9r0/7EnzN1OkZ9PWZpCeae3fiZWe0+QeUHguC54LrJiUWkDzIrK8CNt/Qwm+YINK+rjif+vKaKXBERERERERl8vKLi5qSVWVUJvt9cCegNzwPDaH7Auzvs1W+D6xCbOwATf0VFic34c12ssi24Y8b1fFAAto0zaVJzd6yuMEtLMCu2ETvy6CQG1jd5hUUYe/dAfX1C29vvrAHAmTETAD97WLfafVrr1wG0SvwRCuGnpaviT2SQSCjxd/zxx3PTTTcRjUZZu3YtZ5xxBrfffnuLbaqrq9m8eTOnn356jwQqg4uXXxAfDN3Q0KIvPNA8pNhM0pw/Y/t2jLq6/RV/EyaCZWO//x7G7t3tz/cDCAbjfx7QCldkoDHLygDifepLWlb8mdU78LOG7L8WREREREREZNBxi4owm9pUmtu2AuAVNz3EHQjgD8tJSsVfYMWbeKNG4+fldftYfY1XVBxPGB3iQXezohxiUdxeqviDA7pjdbH7VmDJ6/gZGTgzDktyZH2PV9i5Clf7ndXx7+kh+7uNdafdp7V+HX5mFl5+Qat1/tBsVfyJDBIJN4I+88wzOfPMM9tdn5uby0MPPZSUoET2/XKySjZj7N3TYlixl7M/8ZeMtgbWpo0AzYk/gkHciZOw3ns33mO9g8Sf35TsMKIR/LS0bsci0hdZZaX4mVk4M2Zif9CytYdRvSP+9KaIiIiIiIgMWl5hMcHXXgXfx6wox09Pb5HI8AoKMSsru3cS38d+awXRk07tZrR9k9vUEtWqLMcdP7Hd7azS+AO53pjeS/w5U6YRevDfmFtKO38vzvcJLHkd54gFvTKTMNXcgnjxglVRjrfvXmN7fB/7nTXEFh7VYnHsiAWE//5X7DeXETvhpE6d31r/Ae6kyc0jk1qcLjtbFX8ig0RCFX8ivc3LzwfAfntV/Oump2Ug/ksKw8BIUsWftWkDfk4u/tDs5mXOlKnY763F3LULf+iwdvf1g6H4/0RjSYlFpC8yy8rwRo7CGz06PuvPcfavq65ursIVERERERGRwckrLIRII8buXVjl2/AKilokHrz8/G63+jQrKzBqa3GmDcwxQ17xCAgEsd55p8PtzJLN+MNy4qNpeok7cVJTd6y1nd7X3LwJs7JiULT5BCAzEz8zK6E5f2ZFOcbOapyZLSshu9zu0/exNmxo3eaziTc0G0MVfyKDQkKPWfzgBz9I+ICGYXDttdd2OSARIP5UWDDUnPhzD5jxh2XFW0RUJynxt3ljfFDxAdxp0zH++xBWQwPOlKnt7xyKJ/6MaISuNTsQ6fusslKc6TNxR48Fz4233216ws+s3oFz2KzUBigiIiIiIiIp5RXF79uYFRWY5eUtOjdBvLOT/ebSbp3D3Lw5fqxerHTrVeEwsblHEHzlRaJnf7jNii2IV/y5Y8b2bmyhEO74CVhr18KpTWOefB+zqjLetaudWKGpzWdmJs70mb0UbOp5hUXNMy87Yq95GywbZ8q0VutiC48kfNedBF57hdhRx3T4d7yPubUMo6EeZ+LkNtf72dlYW0raXCfSqzwPGhtTHcWAllDi76GHHiIjI4PRo0fjH6KXs5HADyGRQzIMvPx8rM2b4pV46ektVns5ua1m/Bl7dhP+2x00fOlrkJmZ2Hl8H2vTRqKnn9VisTNxMpgmOLEWlYCtdg8E4v8TiSR2PpH+xnEwKyrwTjsdd8RIIP4hwxs9Jv4mv7q6uf2uiIiIiIiIDE77kj9m+TbM8m04s2a3XF9QiFFXB7W1id+zOYhVuhk/e1iLFqIDTWzR8QR+/UvMzZvabRNplZYQXXRC7wZGvDtW8OUXMbZvJ/jyCwRefhFzexWNn/wM0XPOa3sn3yfwxmvE5h8JltWb4aaUV1iIWX7oij97zep4W85wuNW66KITsd57j7Tf/RZ72RIaL/xy8/e+sWMHwZeex9q0kYbPfwl/+HAgPt8Pw8AdP6HtuLJV8Sd9gOsSvv6XMGwIXHRxqqMZsBJK/M2ePZu33noL13U5++yzOeussxgxYsShdxTpBi+/ALNsS/NTYy3W5eS2avUZeON1AsuW4MyZRyzBN0BGVRVGXV3rX4hpabhjx2NtXB9vLdqefTP+Ymr1KQOTWb4NPBd35GjIyMAbnoe5pTS+sq4OIo14ucNTG6SIiIiIiIiklm3Hq/o2rMOorWnZuYn9I12sqgrczPbn13XEKtnc+5VuvcyZeTj+0GyCL79IYxuJP2PvHozdu+IdeXqZO3UaxqMPk/Xdb0AoTOzIo3Ds2YTvvxcvrwDnyKNa7WNtXI+5Y/vgafPZxCsswl614hAbeVhr3yF6xtltrw8Gafjmd4gdsYC0v/2ZzMu/R+SsD2O/szpeKRgM4YdCZPz8aup+cBV+QQHW+g/wRo6CtLQ2D+kPzY4n4GMx2FfMINITIhGMxoY2C2rC99+L9e478Kvrej+uQSShGX///Oc/efbZZznnnHN47LHHOOWUU/j0pz/NP/7xD3bu3NnTMcog5eUXAOC2kfjzc3Mxq1t+79krl8f/PNQv1gNYmzbGz9HGmyl3arzM3uuo4i+4v9WnyEBkbS0DwBs5sunPUVhNiT+zegdA85NlIiIiIiIiMnh5BYXYK+P3ZLyiopbrmu7xGFXbu3z8wZD4w7KIHXMcgddeAcdptdosibdpTEW7U2f6TKKnfIiGr17E3tv/RMNXvk7jF75E7MhjSP/j7fFqs4MElryBnzUEd9r0Xo83ldzCYozaWozamna3sd9ZjVFfjzPjsHa3AXCOPIra62/CnTyF8D/vwWhooOFLX2PvbX+k9mfXgWWT+fOrMcu3Ya/7oN02n0BzcYOxZ0+XXpdIosL3/J2si79J4KUXWiwPvP4qwf89QvRzF8DMwdP+NxUSSvwBFBcX85WvfIWHH36YRx55hIULF3LXXXexaNEivvzlL/Piiy/2ZJwyCHl58afBvMKi1uv2tfrc13q2sRF77bt4ucOx336rzTdHbbE2b8TPyW2zTYQzI/7Dxxue1/4BQvGKP6LRhM4n0t+YW0rxh2Y3Dw13R4/BKo1/0NjXbtfLVatPERERERGRwc4rKsbYG08oeAWFLdb5mVn46emYVYeee9aWVFa69bbosYswamuw31rZap1VWgLBUKu/314RDMYTfYtO2N+a0jBo+OrXcceOI/3G6zG270/sGjV7420+FxwZH6cziHiF8X+f9ub8Gbt2kvaH23GnTMOdOOmQx/OHZlN/8aXs/eOd1P3k58ROOAnS0vBzc6m78mr8tDQyfnY15ratHR5vX3GDuXtX51+USKIch8CS1/CGDiXtT78jfMefIBrFLNlM2p9+T+zo44idfmaqoxzwuvRTd+LEiVx88cX897//5YILLuC1117j3//+d7Jjk0FuXxsIr7jtVp9EIxh1tUDTMFzXofGzn8dobMB6/72EzmFt2tBmtR+Ac/hsan9+PX5THG3ZX/GnxJ8MTNbWMtxRo5q/9kaPwdi9C6O2Jl7xZ9n42cNSGKGIiIiIiIj0Bfuq/Pyc3DZnlnkFhViVXUv8NVe6jR3b5fj6C2/0GNwxYwm88lKrdVZpCe6o0X0rkRYMUv/dS+PJp+t+Rvr1vyDrm18j6+tfxti9i9hxx6c6wl63LzFrVrQx589xSP/tzWAY1H/r4sT/LQ0DMjJaLfaH5VB35U/whw4F34/PDGzH/oq/3YmdU6QL7NVvYdTXU/+9y2n48v8j+PKLZPzsatJvuQG3uJiGL301/v0sPSqhGX8Hcl2XV155hf/97388++yzWJbF+eefz/nnn98T8ckg5k6YiDPzsDafVPGbKoyMnTvxM7MIrFyOV1iMM38B/rAcAiuX4844RLmw72Nt2kj0zHPaXm8YeGPHdXyMphl/RNTqUwYms6ysxVB2d9To+PLSUowdO/CGDdMvaxEREREREcEtiCf+3DYe4Abw8gpaVIR1hlWyGULh1FS6pUDs2OMJ//MfNNbWNHfgAbBKN3fYyjFV/CFDqb/0CtL++mcIBIgefyLuqDG448bjFxSkOrzel5aGPzS7zcRf+O6/YW3cQN2Pf5q0B6n9IUOp/dFPsN9fi1c8ov3tsoaAYSjxJz0q8MbreMUj8EaNxhs1Gnf0GNJ/exNGYwN1P7waQqFUhzgoJJz4W7ZsGY8++ihPPPEE0WiUk08+mRtuuIFjjz0W2+50/lDkkPwhQ6m/4sdtrvNycoD4jDFv1Gjst1YRO/pYMAxis+dir1oJn/t8h8c3qyox6uvbrfhLiGmCZWPEVPEnA1A0illZjjtyf3LcKywCy8baUopZXY3fUStcERERERERGTT2JRy8gtYjWyDe2SmwofUcuERYmzfhjh4zaB48jR19DOH77ibwxmtET/kQAObWMsytW/FOPjXF0bXNKx5B3ZU/SXUYfYZXVNyq1WfghecIPvsUDV/8akItPjslMxNn3vyOtzHNeEJy9+7knltkn2gUe/kyomcdcC9x/ARqr7sRo7FBXcN6UUIZu+OPP55du3axaNEifvKTn3DSSScRUmZWUsjPHgamibmzGnPTxnjrgFlzAHBmzyH4/DOY5dvwitp+ygzALNsCxGeWdSuWUBAiSvzJwGNu2wq+jzdyf6tPLAt35EjMLSXxxHte+61wRUREREREZPDwc3Lws4bgjp/Q5nqvoDA+Kz4Wg0CgU8e2SjfjTD9EZ6cBxB+ajXP4bAIvv4QzcTKhhx8k8OZS/GE5xGbPS3V4kgCvoACzNN6iloYGgs88RfiBfxE98WRiJ52SuriGDsXYpRl/0jPst1dhNDYQW3hUyxXhMH4bLaCl5ySU+KusrMS2bV599VVee+21Drc1DIPly5cnJTiRdpkmfvYwjOpqAnv34qel406ZCoAz4zCwA9irVhLtKPFXXo4fTuv+kwaBoCr+ZECy9iXHD2oT4Y0ajVVairF3D17TdSciIiIiIiKDnGFQc8NvID29zdVefgH4PuaO7fsf1HYcOFQnsUgEs3wb7hlnJzngvi167PGk33YzmVdejpdfQMMXvxqfl6fOa/2CW1iEveQNQg/+m+CT/8NojBA94SQaD9GhrKf5Q7Mx1epTekjgjdfjLT47aDkrvSOh3xTf/OY3ezoOkU7zcofHK/62bsU5bNb+Nz7hMM70GdirlhM946x297fKt8YHT3ezTYQfCmnGnwxIZtmW+FD2g4ZHu6PGEFi6BJwY/vDhKYpORERERERE+pyDPj8eyMuPz3oLPv0kRqQRa90HmOXbiJ58Go2f/2K792esstJ4N5ox3evY1N84844geuIpuFOmEjvqGLCsVIckneAVFmE0NhB65GGiJ51C5Mxz8HNzUx0WfnY25tatqQ5DBqJIhMDK5UQ+fF6qIxGU+JN+zMvJxdq4AXPbVqKnnd5inTNnLuG7/w719e0+aWZWVLTbd75TAgGMqCr+ZOCxtpbhHtjms4k3ejRE48luL1eJPxERERERETk0PzcXPz2d4NNP4I4ajTNtBv68+YQeWQyGQeMFF7aZ/DNLNoNp4o4c3esxp1QgQOOXvprqKKSLnNlzafjiV3HmHYE/NDvV4TTzsodhvftOqsOQAcheuQIijcSOPDrVoQgJJv5E+iI/NxdzyWtgGDizZrdYF5s9j/Df/4q9+m2chUe2ub9Zvg1nRvf7w/vBEKjVpwxAZtkWnPkLWy13R+3/sOXlpP5pNREREREREekHTJPa62/CD4VbPKTt5eWR9tc/g2XR+NkLWiX/rJKSeGvQYLC3IxbpukAgpbP82uMPzcbctQt8v9td0EQOFFjyGu648XgFhakORQAz1QGIdNW+hIM7fiL+kKEt1vl5eXgjRhJY1c68ybq6+HyyDmYAJiwUwogo8ScDTGMj5vaqNiv+/KHZ+FlDAFX8iYiIiIiISOL8YTmtOjPFTjqVxi98ieATjxG+7554QuIAVslm3DHjejNMkQHLG5oNrgN1dakORQaSxkYCq1YSW3BUqiORJkr8Sb+1L/HnzJ3X5vrYnHnYq1a2esMIYFWWA+Am4QkEPxBQxZ8MONa2MgC8kSNbrzQM3FGj8cNpHc5vEBEREREREUlE9JQP0fh/FxL83yME//fo/hWeh7WlFHfM2JTFJjKQ+NnZAJh7dqc0DhlYAiuXQyyqNp99iBJ/0m95I0eCZRM7YkGb651ZczBq9sZ7wR/ELI8n/pJX8Rfp/nFE+hCzLJ74c4vbSPwB7uQpeKMG2XwFERERERER6THRD51B5KwPE77/Xqz16wAwKysg0og7ekyKoxMZGPxhwwAwdu9ObSAyoNhr3sYdPQY/Ly/VoUgTJf6k3/KKR7D3T3fijWgnMTF+Apgm1sb1rdaZ5dvws4dBWlq34/ADQYyoKv5kYLE2b8LLL4BwuM31kY9+nLofXd3LUYmIiIiIiMhAFvn4p3DHjSfttlugrg6zpAQAb+zYlMYlMlB4Q7MBVfxJcplbtuCpMrtPUeJP+rdQqMN17shRWBs3tFpllpfjFRYlJ4ZgAJT4k4HE97FXvIlz2Kz2tzFNsO3ei0lEREREREQGPtum/psXY9TXkXbHH7E2b8TPyW2eMy8i3RQOQyisij9JHt/H2lqGO1KdwfoSJf5kQPPGjW878VexDTcZbT4BPxTGiKrVpwwc5qaNmDu2E5u/MNWhiIiIiIiIyCDj5+XR8OWvE1j6BsHnnlabT5Ek87KzMVTxJ0libN8OkUa8UaNSHYocQIk/GdCcCROxyrbAgTP4fB+roiJpFX9+MAixWFKOJdIXBJYtwc/MxJ02PdWhiIiIiIiIyCDkLFhI9OTTMOrrcdU+TiSp/KHZmKr4kySxykoBcEcq8deXpLxP27333st9993H1q1bAZg0aRIXXXQRxx9/PGVlZZx88slt7nfLLbdwxhlntFi2a9cuzj33XCorK1m2bBlDhqgNwGDnjpsAnodVshl38hQAjF07408hFCWr1WcQI6KKPxkgfJ/AsqU48+aDZaU6GhERERERERmkGj97AUY0om40IkkWr/jbleowZIAwy7bgp6fjD8tJdShygJQn/goLC7n00ksZM2YMvu+zePFivvGNb/DQQw8xfvx4XnnllRbb33///dxxxx0sWrSo1bF+9KMfMWXKFCorK3srfOnjvFGjwQ5gbdzQnPgzy8vj65LV6jMQBLX6lAHC3FqGWbGNxs/+X6pDERERERERkcEsGKTha99IdRQiA44/NBtr29ZUhyEDhFVaijdyNBhGqkORA6Q88XfSSSe1+Pq73/0u9913H6tWrWLSpEnk5eW1WP/MM89wxhlnkJGR0WL5vffeS01NDRdddBEvvfRSj8ct/YRt444Z22LOn1WxDUwTLy8/OecIBTGiavUpA0Ng2RL8tHScmYenOhQREREREREREUkyPzsbQ60+JUmsrVtwJk1JdRhykJQn/g7kui5PPPEE9fX1zJkzp9X6NWvWsHbtWq666qoWy9evX8/vfvc7/vWvf7Fly5Yun980DUxTmemBxp80kcDbbxGz4yMt7coKKCjADgeTcnwzLQ3DdbBNwBw4YzMty2zxpwwOwTeX4M2dh50WSnUo0g26fkX6L12/Iv2Trl2R/kvXr0j/pGu364ycHMy6Wmw8sPtUekD6G8fBqtiGc+pp2Hbi16Ku357XJ67s999/n0996lNEIhHS09O5/fbbmThxYqvt/vOf/zBhwgTmzp3bvCwajXLJJZdw2WWXUVxc3K3EX05OBoZKUgeeWTPhuacJBYGMDNhTDePHEhqWcchdE5I7BAIWwYwAhMPJOWYfMmRIWqpDkN5SXg7lW+ELF5CerOtDUkrXr0j/petXpH/StSvSf+n6FemfdO12weii+L1MIwbDhqY6GunPtmwBA4IzpkAX7iXq+u05fSLxN27cOBYvXkxNTQ1PPvkkl19+Offcc0+L5F9jYyOPPvooF110UYt9b7zxRiZMmMC5557b7Th27qxTxd8AZOSNID3m0rhiNe7Mw0jfsAln3nyiu+qScnwr4hGOudRV7oQhA+eXpWWZDBmSxt69Dbiul+pwpBcEnnqOICZ146ZAkq4PSQ1dvyL9l65fkf5J165I/6XrV6R/0rXbdaYZIi3m0rB5K56lxIt0nbXmvfh98azcTt1L1PXbdcMSTLD2icRfMBhkzJgxAMycOZPVq1dz1113cc011zRv88QTT9DY2Mh5553XYt833niDDz74gCeffBIA3/cBOPLII/l//+//8e1vfzvhODzPx/P8br4a6XMKivBCYfx163AmToGqKmIFhThOkn6oWDa+7+PWR/DTB94PKtf1kvd3JX1a6I03iM6ag2MHQf/mA4KuX5H+S9evSP+ka1ek/9L1K9I/6drtPCNzCGHfx9u5C2eM/u6k66zNJXhDhuKkZ3bpXqKu357TJxJ/B/M8j2g02mLZAw88wEknnUROTk6L5bfeeiuNjY3NX69evZof/vCH/OMf/2D06NG9Eq/0cYaBN2481sYNmNurwPPwCoqSdng/GJ+FZsSiKG0s/ZVRXY21cT3R089MdSgiIiIiIiIiItJD/CFDwTAwdu1KdSjtMnbvwl6zmtixi1IdinTAKtuCO2pUqsOQNqQ88XfjjTeyaNEiioqKqKur49FHH2Xp0qXccccdzduUlJSwbNky/vSnP7Xa/+Dk3q6mH1gTJkxgyJAhPRu89BvO+AkE3ngNc9tWALzi4uQdPBiI/xmJJO+YIr0s8OZSsGxis+akOhQREREREREREekploWfmYW5Z3eqI2mb75P2h9uw16zGmT0HPzMr1RFJO8wtW3DmzE11GNKGlCf+qqurufzyy6mqqiIrK4spU6Zwxx13cMwxxzRv88ADD1BYWMixxx6bwkilP3PHTSD02H+xPngfQmH87GFJO3Zzxd9BVaoi/Ym9+i2cadMgo/ODeEVEREREREREpP/ws7Mxdu9OdRhtCrz8Ivaa1QCYJSW4M2amOCJpUzSKWVWBO1IVf31RyhN/11577SG3ueSSS7jkkksSOt7ChQt5//33uxuWDDDu+AkABF9/FbewEAwjacfel/hTxZ/0W46DvfZdGs/7aKojERERERERERGRHuYNzcbYuyfVYbRi7NlN+B9/J3bkMQSWL8MqVeKvrzK3loHv4ynx1yeZqQ5ApDf4eXn4mZkYO6vxikYk9+BNrT6NmCr+pH+yNm2ESCPu9BmpDkVERERERERERHqYP2wYZh+c8Rf++51gmDR+/kLckaOwSjenOiRph1W2BQB3xMgURyJtUeJPBgfDwB0/EQCvsDCph26u+IvGknpckd5iv7sGPy0dd+z4VIciIiIiIiIiIiI9zBuajdHHZvzZby4lsPR1Gj//RfysIbhjxmCWlqQ6LGmHWbYFLy8f0tJSHYq0QYk/GTT2tfv0ioqTe+BgEAAjqlaf0j9Z76zBnToNLCvVoYiIiIiIiIiISA/zh2Zj7t4Nvt+l/Y3qakIP/As8LzkB1dWR9rc7cGbPJXbk0QB4o8dglZWB4yTnHJJU1pZStfnsw5T4k0HDnRCv+HOTnfizbTBNJf6kf4pGsT94H2e6+qWLiIiIiIiIiAwG3rAciEUxavZ2af/QY/8l9NB/CLzyUpvrjV07CT79RMKJxeArL2HU1NBw4VfAMABwR48F18HctrVLMUrPssq24Crx12cp8SeDhjNrDvXfugRvXPLbGfrBEEQ040/6H2vdB+DEcDQoWURERERERERkUHCnTAHAXv12m+vNrWWk3fYbiLZxvzMaJfDqSxAIEv73P1tv4/uk/fF2wn//K2bTHLhDsTasxx07Dj83d3+Mo0bH16ndZ99TV4exsxqv6d9I+h4l/mTwME2chUc2PzWSVIEARkyJP+l/7HfX4Gdm6Re1iIiIiIiIiMgg4Q/LwR0zFnvVyjbXB599msAbr7ZZ0RdY8SZGXR3137kEY+9egk881nL9Sy9gr1kNlk3gzaUJxWNtWNfcra1ZRgZeXr4Sf32QtbUMQBV/fZgSfyJJ4IdCbT8BI9LH2e++E2/z2RMJcRERERERERER6ZOc2XOxV69qPafP9+MJO8Mg9NgjrdYHXngOd9IUnNlziZ5yGqH/LsbYuwcAY/cuwv/4O7FjFhGbvxA7gcSfUVuDWVnROvFHfM6fWbq5qy9ReohZVgqmiZfskVqSNEr8iSRDMIihxJ/0Nw0N8VYKM2akOhIREREREREREelFzpx5GLW1WOvXtVhubVyPsbOaxk9+FrOyHHvZ/uSdsX079juriR5/IgCRj3wMTIPQQw8AEL7rTrBsGj93AbEjFmCVbMaoquowDmvDegDciZNarXNHj8EqKUl4VqD0DqukBK+wCAKBVIci7VDiTyQJ/GAQIpFUhyHSKfb7a8FzcWYclupQRERERERERESkF7njJ+BnZmGvWtFiub1sKX7WEKJnno0zfSahRxc3J96Cr7wIwRCxhUcB4GdmETnnIwSffZrgIw8TWPoGjRdciJ81BGf2HLADh2z3aW1Yj5+ZiZdf0DrG0WMwavZi7N6VnBctSWFt3IA7fkKqw5AOKPEnkgzBkCr+pN+x330HPycXr6Aw1aGIiIiIiIiIiEhvMk2cWXNaJv58n8CyJcSOmA+mSeSc87A2bcR6Z0183YvPEzvyKEhLa94l+qEz8LOzCd//D5zZc4kdeXR8RTiMc9jhCSX+3PET2xxD444ZF99Gc/76jmgUq7SkzQpN6TuU+BNJAj8YgJgSf9K/WO+sxpk+Q/P9REREREREREQGIWf2HKzSEozqagDM0hLMygqcIxYA4M48DHfMWEKPLsZ6Zw3mju1Ejz+p5UGCQRo+9wW8/AIaLvxKi/tMsSMWYK17H2PP7rYD8H2sDevanO8H4Ofl4aelYyrx12dYpSXgOqr46+OU+BNJBlX8ST9j1NZglZbgTJ+Z6lBERERERERERCQFYofNAsPAfite9RdYtgQ/PX3/WBjDIHLOR7DXrCZ83914hcW4kya3Oo6zYCG1N/4WPze35fK58+LHX76szfMbVVUYtbXtJv4wDLxRo7E2b+7ya5TksjasB8vGHT021aFIB5T4E0kCPxgCJf6kH7HefRd8X4k/EREREREREZHBKjMTd9IUAqtWAvHEnzPnCLDt5k2cBQvxCgqxSjYTPeGk9jtHtbHczxqCM3U6gWVtt/u0N64HaD/xR3zOn1p99h3WhvW4Y8e1+B6RvkeJP5FkCAYwIpFURyGSMHv9B3jD8/CHD091KCIiIiIiIiIikiKxOXOx31kTb/O5tYzY/AUtNzBNIh/+CASCxI49rtPHd46Yj/3uO1BX12qdtWE9Xn4BftaQdvd3x4zFrNimoos+wtqwvsNErfQNSvyJJIEfDGFElfiT/sOsrMArHpHqMEREREREREREJIWcWXMg0kj4nr9DMIRz+OxW28QWncDe2/6Inz2s08ePzVsArkPgrZWt1lnr1+GO7ziJ5I0ZE58FWFba6XNLchm1NZiV5Ur89QNK/IkkQzAI0ViqoxBJmFlViZeXn+owREREREREREQkhbxRo/FzcrHfXUNs9pz4fc6DGQZkZHTp+H5uLu64CdjLlrRc4ThYmzfhTpzU4f7uyNFgGJgDaM6fWbaF4P8e7XdVjNbGDUDHrVmlb1DiTyQJ/GAQI9a/flDLIOb7mFVVePkFqY5ERERERERERERSyTCIzZoDgDN/YY+cIrZgIYG3VmFUVzcvM0tLwIkdOokUDOIVFreY82duLcN6Z02PxNrj6upIv/F6wvfeReYPLsVa/XaqI0qYtWE9fno6XkFhqkORQ1DiTyQJ/FAI1OpT+gmjZi9EGlXxJyIiIiIiIiIixI5dhFdYTGz23J45/okn42dmkn7bLeC6ANgb14Np4Y4Ze8j93dFjsDasJ/DCc2T85EoyL7+EjOt+BvX1PRJvj/F90u74E0ZdLfWX/wg/J5eM639O2u9vw9i7J9XRHZK1YX28NathpDoUOQQl/kSSIRDEiHSu4s+srCD9up/1u5Ju6f/MqioAvHwl/kREREREREREBjt3ylRqb7gF0tJ65Ph+Zhb137wYa8M6Qg/8C2hKIo0Z03Zr0YPjGzsWa9MG0u74I35aGo2f+lx87l/J5h6Jt6cEnn+GwNLXafjy13EOm0XdD6+i4Stfx35rBRk//gE0NKQ6xPb5fvzf7BCtWaVvsFMdgMhA4AeDEIuC7yf8xIP99irsNasxq3fgFRX3cIQi+5nbmxJ/qvgTEREREREREZFe4E6eQuRjnyD0n/txps3AWr8eZ/qMhPaNHX8iBEPE5s3HHz4cPI/wg//G2rgBd9r0Ho48OczSEtLu/jvRk0/DWdDUUtUwiB1/Is7U6WRd8T1CDz9I5FOfTW2g7TB27MCo2av5fv2EKv5EkmHfkymxWMK7mKWlABi1tT0RkUi7zKpK/MzMLg9lFhERERERERER6azIhz+CM2Mm6b/7LWb51oSTSP6QoUQ/dEY86Qdgmrhjx2Ft3NCD0SbOXr4s3tnN99veoLGR9FtvwSsspPGzF7Ra7RcUEDnnXEKPP4ZZvq2Ho+0aa8N6ANzxE1IciSRCiT+RJPCDIQCMTsz5s7bsS/zV9EhMIu0xqyrx8gpSHYaIiIiIiIiIiAwmhkHD//tm85fdaRvpjp+AtXljMqLqNnv129hrVmNtajsRGXr8Ucwd26n/5sXttjaNnH0u3rBhhO/+W/sJxBSyN67HG56HPzQ71aFIApT4E0mGUNMP7ETn/Pn+AYk/VfxJ7zKrqvDylfgTEREREREREZHe5WcPo/7blxBbcFS3xh+548ZjVlZAH7i3albEq/TspUtar/R9Aq++QuzIo/BGjGz/IMEgjZ/7fHw81Io3eyjSrrM2rMcdrzaf/YUSfyJJ4AfiiT8jlljiz6iqgkhj/P/7wC8nGVyMqkq8fM33ExERERERERGR3udOm07Dt78LhtH1YzS1nLQ2pb7qz9q2DQyDwLKlrar1zJLNmBXbiB159CGP48ybjzPzcML/uAuiCRaY9AbXxdq0UfP9+hEl/kSSobniL7FWn1ZZvNqPQFCtPqV3OQ5iHHLVAABCPUlEQVTmzmpV/ImIiIiIiIiISL/lFRbhh9NS3+4zEsHYWU1s/pGYleWYTV3e9gm88Rp+ZibOjMMOfSzDoPGCCzF37CD02H97KODOM8u2QDSixF8/osSfSBLsr/iLJbS9VVqCn5mJV1ioij/pVeaO7eD7SvyJiIiIiIiIiEj/ZRh448ZjbWx7rl5vMSvKAYiechp+WjqBZQe0+/R9Am+8RmzBUWDbCR3PKx5B5MyzCT3wL8J33ZlwoUlPsjasB8PAHTsu1aFIgpT4E0mGUCj+Z4I/iM0tW3BHj8XLGqLEXz9lrV9H+N67Ux1Gp5lVlQBK/ImIiIiIiIiISL/mjJ+QUOLP2rie8B1/As9LegxmeTzx540ejTNnHoE3l+4/7/p1mDu2EzvqmE4dM/LJz9D42c8TfP5ZMn/4fawP3k9qzJ0VWLUCd+x4CIdTGockTok/kSTwg00Vf9EEW32WluCNHIWfmYlRp1af/VFg6RsE//cIxvbtqQ6lU8yqSjAt/JycVIciIiIiIiIiIiLSZe64CZjVOzD27ulwu+CzTxN8/hkCr7yU9Bisim34mZn4mVnE5i/A3FLaXAUYeP1V/OxhuFOmdu6ghkH0jLOovfZX+JmZZPzsKoKPLE567AmFsrMae9UKYsefkJLzS9co8SeSBH6wqeIvmkCrz2gUs7Icd/QY/IxMjLq6ng1ugDBqawg+9TjBJ/7X/J+94s2UxbOvci6wannKYugKs6oKLy8PLCvVoYiIiIiIiIiIiHSZO248ANamDub8+T72qpVg2YT/dV/SW2ea5eV4hcUAOIfPhmAIe9kS8DwCS14nduTRYHYtDeMVFVN31TXEFp1AePGD4PtJjDwxwRefh0CQ6NHH9fq5peuU+BNJhk5U/JllW+Iz1kaNws/IwKhRxV8iAi88T/iuOwn/6774f/fdQ/otN4DjpCQes7ICAHvlipScv6uM7U2JPxERERERERERkX7Mz8/Hz8josN2nuXkTxp7dNFz4ZYyaGkJPPNZqG2v9OkIP/adLMZjl2/CKiuJfhELEZs0msHQJ1tp3MfbsJnbU0V067v7gLGKHzYZII0ZdL4+M8jyCzz9L9KijIT29d88t3aLEn0gy2DYYBsSih9zU2lIaH4Y6YhR+1hBV/CXIKi3BnTiZvX+9m71/vZu6y34AnodZvaP3g/H9eOVc7nDsd9+Bxsbej6GLzKpKvDzN9xMRERERERERkX7OMHDHje+w4i/w1kr8cBqxYxcRPfVDhP67GGPP7ub11vp1pF/3c0IP/Auih76324LvY1Zswy0e0bzImb8Qa9MGQo/9Fy8vH3f8xM6+qtanyRsOgLGjd++D2m+txNhZTeykU3r1vNJ9SvyJJINhQDCEEUks8eflF0I4HK/4a6hPWdVaf2JtKcEdNbr5a7+pas2oqur1WIy9eyDSSPSUD4ETw35nda/H0CX7Epb5+amOREREREREREREpNvccRM6rPizV67AOexwsG0i530U3zIJPRiv7jM3byL9V7+AcDj+dScLDIy9ezDq6/EKipqXxWbPBcvGfntVvM2nYXThVbXkDY/fBzV3bO/2sToj+OzTuGPHJSV5Kb1LiT+RJPEDgcRafW4pwR0dT2D5WVkAGPWq+uuQ42Bu24Y3en/iz8sdDoaBub33E39mZXy+nzNrNl5hMfaq1Lf7NLZvx9qwruNt6moxGupV8SciIiIiIiIiIgOCO24Cxu5dGLt2tlpn1OzF2rgeZ/ZcAPzMLCLnfozg889gL3mDjOt+hldQFO8sRucTa2Z5OQBecfH+henp8UQjEDvqmK68pFb8IUMhEMTsxYo/o7oa+62VRFXt1y8p8SeSJH4olFA5uFVaitdUueZnZAJg1PZyf+Z+xizfBq6DO2rM/oW2jT8sp9efdIF4u0wALy8fZ/YcAitXpGS47j7WmtVk/ugy0m/6dYfbNcetij8RERERERERERkA3PETANqs+rPfWgW+jzNrdvOy6Gmn4+Xkkn7rTXg5udRf/kO8ESPBMDA6WWBglm8Dw8ArKGyxPPKhM4kdd3zzPeBuMwy83OE9dh/U2LUT++1VLe5vBp9/BoIhYkcd2yPnlJ6lxJ9IsgSCGIdI/Bl7dmPU7MUd2ZT4y1TiLxHWllIA3JGjWiz38gswU9Dq06yqxM8aAmlpxObMw9i9C7Nkc6/HARB47mkyfvWLeKvZPbuhoaHdbY2q+JsDL18VfyIiIiIiIiIi0v/5ubn4WUPanPNnr1qJO248fvaw/QsDARov/DLO4bOpv+JK/MwssG28nNxO32c0K8rjbTgDgRbL3cMOp+Fr30hKm899vOE9l/gL//Ne0n91LZmXX0Lg1ZchFiP4wnNEjz4W0tJ65JzSs5T4E0kSPxQ8ZMWfWRpPYHlj4pVr+xN/NT0bXD9nlpbg5+RC09/XPj35C6/DeCorm5/kcadMxQ+nEejtdp+uS/iuO0n765+JnvIh6r95cTy2Dp5MMrdX4qent/p7FBERERERERER6ZcMA3fc+NYVf66LvXoVzqw5rXZxZs2h/vs/jLfQbOLn5Xe6laZVvg2vqPjQGyaBl5eH0RP3QR0He+WbxI48Bi+/gLTf30rWt7+OsXuX2nz2Y0r8iSRLMHTIGX9WaQkEQ80VV35m04w/Vfx1yNpSijt6TKvlXl5+amb8Ve1P/GHbOIfPwl7Zu4m/4BP/I/jMkzR+4Us0XnBhcy9xs7Ki3X3MqkpV+4mIiIiIiIiIyIDijp+AtWEdxt49zcusDesx6uqa5/sdipeX1+n7jOa23kv8+cPzeqQAwnpvLUZ9PZGzzqH+0iuo/fn1ONOmE1twFN648Uk/n/QOJf5EksQPBiDSccWfVVYab1e5r8zbtiEUVuLvEKwtpa3afEI88Wfs2Z3QbMVkiifQ9s/Jc2bPxdq4vsWbi55mbViHM3U60VM+BBBvPRoKd9iSQIk/EREREREREREZaGLHHQ+WTcbPfoKxsxoAe+Vy/Mys5hmAh+J1NrHmuvF7bUVFXQm507zc4fF7yI2NST1uYPky/JxcvLHj4ucZO46Gb19Cw7e/m9TzSO9S4k8kWYIhjNihWn2W4I5uOdTVy8xUq8+O1NZi7KzGGzO21SovL55869V2n42NGHt2txjau29AsL1qZa+FYZVtiQ8e3scw8PLzMasq293HrKpS4k9ERERERERERAYUr6CQuh//FCPSSMbPrsaorMR+a2W8zaeZWAqkswUG5vYq8Fzcwl5q9Tk8L37e6s61I+2Q7xNYvozYEQuSOo9QUk+JP5Ek8QNB6KjVp+tibd2KN6ply0o/MxOjrq6Ho+u/rC3xuYhtVvzt+4XXi+0+9yXWDkyg+UOzccdN6L3En+NgVlTgjRjRYrGXX9B+4s9xMKt3tKhUFBERERERERERGQi8omJqr/oZmBaZP/kRVmkJzuzW8/3a3b+TBQZm+bbm8/aG5vugCcRnlmwm8Pyzh95u00aMndXE5s3vdnzStyjxJ5IsoSBGNNbuarOiHJwY7qiWFX9+ZiZGjSr+2mOVlYJlt/lL1M/JAdNKUeKvZQLNmTMX++1VvdJ21KysiD9RNKJlMtQrKGw38WdUV4Pn4eWp4k9ERERERERERAYef/hw6q76KX52NpgWscNmJbxvZwsMzPJyCIbi9yd7gT9sGJhmQom/4DNPkXbnnzFq9na4XWD5MvyMDNwpU5MVpvQRSvyJJIkfDHVY8Rd4/VUIBHEPalnpZ2Rh1GvGX3vM0lK84uL4PMSDWRZebi7GjiSWuB8qnspKCIXxh2a3WB5beBRGY0M8+dfTMWzdCtBGxV8+5vbt4Hmt92lKCPqq+BMRERERERERkQHKH5pN7Y+vofZnv4TMzMT3y8lJOLEG8Yo/t6io91pkWhZeTi7G9kPHZ5VvA88jsPSNDrcLLF+GM2de2/ddpV9T4k8kWYJBjEjb1V5GzV6CT/yP6KkfgoyMFuv8jIz4YFZpk1Vagjt6bLvrvbx8zKrerfjz8vNb/VL3ikfgjRpN4I3XezwGa+sW/Mws/CFDW8aQXwCeG6/uO4i5vTI+BzB3eI/HJyIiIiIiIiIikjLp6XgHFV8cUicSawBWRTleQVHnY+sGf3geZgIFEObWMgACr7/W/jaVFZhlW9Tmc4BS4k8kSfxgEGJtJ/6Cjz2C4ftEzv5w6/2yspT4a4/vY5VtadUetcUmeXm93urzwPl+B4odeTSBlcsh0sGsx2TEsHUrXlszD5viaqvdp1VREU/66QkeERERERERERGRVrz8goQLDMzybfEuZb3IGz78kBWJRm0NRs1enMNnY72/FmNn6wIBAPvNZRAI4nSiHar0H0r8iSRLMIjRxnw3Y9dOQk8+TuT0M1tVaAH4GZlK/LXDqKqCSCPeqNZJrn284SlI/BUUtrkuduTREGnEXrWyR2Owtm7BbWPmoTc8DwwjPgPwIObmTXhjx/VoXCIiIiIiIiIiIv2Vn0BiDYD6eozdu/DauD/Xk7zheYeMb9+IoMiHPwKmRWBJ2+0+A28uxTnscAiHkx6npJ4SfyJJ4gdDbSb+Qo88jB+wiZx5Ttv7ZWVhNDaA4/R0iH2KUV1N8LFHOtzGKisFwB09pt1tvLx8jNoaaGxManxtcl3M7dvjrT7biqWgEHfsOAJL2imj9/3kxFBe3mbFH7aNlzu8dcWf72Nt2ogzfkL3zy8iIiIiIiIiIjIAeXn5CRUYmBXl8e2LerfVpzc8D2PP7g7vI5tby8AwcMdPwJk1m8Abre9TGnt2Y63/QG0+BzAl/kSSxA8FIRppkdwxduwg+OzTRM/6cKvZfs37NS036gZX1V/gtZcJ33c3xt497W5jlZbgZ2biZw9rdxsvL56ES3TwbncY1dXgue22+gSILTyawMoVrRKRof/cT8aVl3c7BnN7FTgx3OIRba5vqyWBWb4No6Eed5wSfyIiIiIiIiIiIm3xhudh1Ow9ZIGBVb4NALewlyv+coeD72NWtz/nz9q2NX7vMhgkdtQxWBvWxbuqHSCwNF4F6Myd16PxSuoo8SeSLIF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            "text/plain": [
              "<Figure size 1800x1000 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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VFRVhmmar4APEMlvS0tLaZOB0lGEEsQDM9loCJTtmlqWmpuI4Dg0NDfFyhgsWLOCee+5h0aJFBAKBVuMbGhpaBd4667333uO+++7jsssu4/jjj49vbwleXHDBBe0el5KSAkBxcTEAY8aMaTNm3Lhx7QaQ2jNy5Eh++9vfYhgGPp+PsWPHkp2d3Wbczl7bFtXV1TQ2NjJp0qSdjtu0aROrVq1qk/W1/Twdabn+pqamXa4HYq/TPvvs02b7+PHj4/tbSnDC7r1PoP2/j7FjxxIIBKiuriY3N5dgMMgDDzzAc889R1lZWav+eu0Fkjvz2s+ePZvjjz+ee++9l0cffZTZs2dz7LHH8o1vfCMeJCoqKiIvLy/+GraYMGFCfP/22su6TE9Pp76+fpfrAdr0DWyRlJQU7z0HcPjhh7P//vtz2mmn8eCDD3LdddcBdPp1uuqqq/j+97/P8ccfz+TJk5k7dy4nn3wyU6dOBWDz5s24rstdd93FXXfd1e6aqqqqdlpyeP/99+/UNfe2lmDojv0lAUKhUKtgaQvXdTvda1FEREREpK8o8CciIiIisgfaPuvv0ksv7XBcZz+0bu9D7xbbl3PszPaWIMPmzZu58MILGT9+PNdddx3Dhw/H6/Xy7rvv8uijj+I4TqfWtr3CwkJ+9rOfccghh/CjH/2o3fPefvvt7Zbz60xfu67YMQjTkZ29tl3lOA6HHnoo3/ve99rd31L+sz0tAbtVq1Zx7LHH9tiaWnT3fdIVv/nNb3juuee44IILmDVrFqmpqRiGwY9//ON25/P7/buc0zAM7r77bhYtWsTbb7/N+++/zy9+8QseeeQRnnrqqU5nSG6vo/farq65JRDa2QAhwPTp00lNTW2V7dnZ1+nAAw/kjTfeYP78+Xz44Yf83//9H4899hg333wzp59+evweveiiizrMCN7x4YIdVVdXd6rHX1JSUrde685q+Z5QUVHRJjBbUVHBzJkz2xxTX1+/056MIiIiIiL9QYE/EREREZE91BVXXMF//vOfeP+07Y0cORLHcdi0aVM8KwmgsrKS+vr6Tvex2x1vvfUW4XCY+++/v1U2WEvJza4KBoP88Ic/JDU1lT/96U9tAkoFBQUAZGdn7zQg17KWTZs2tdm3YcOGbq1td2VlZZGSksKaNWt2Om706NE0Nzd3KuC4o/3335/09HRefvllLr/88l0GQkeMGNHu69HSh27HDL/d1d7fx8aNG0lMTCQrKwsg3p+uJbMNYtlau1M2tsWsWbOYNWsWP/7xj3nppZf46U9/yiuvvMLpp5/OyJEj+fjjj2lsbGyV9dfyWvTU/dQSnN2yZUuXjrNtm+bm5vjXXXmdMjIyOO200zjttNNoamri3HPP5Z577uH000+P31Ner7db7zmAb3/72wOix99ee+0FwNKlS1sF+crKyigtLeWMM85oc8yWLVvi2Y8iIiIiIgOFevyJiIiIiOyhRo8ezTe/+U2eeuopKioqWu074ogjAHjsscdabX/kkUda7e9NLYGlHcsMPvvss92a78Ybb2Tjxo3ce++9pKent9l/2GGHkZKSwgMPPNBur7aWMph5eXnstddePP/8860CIR9++GGbvnV9xTRNjj32WN5++22WLl3aZn/La3jiiSeycOFC3n///TZj6uvr2/TN215iYiLf+973WLduHXfccUe72WcvvvgiS5YsAWLvkSVLlrBw4cL4/ubmZp5++mlGjhzZpX6InbFw4UKWL18e/7qkpIT58+dz6KGHxt9L7QUr//nPf3Yqo6wjdXV1bV6LliBRS1nIww8/HNu2efzxx1uNe/TRRzEMg8MPP7zb599efn4+w4cPZ9myZZ0+5pNPPqG5ublVgKqzr9OO/euSk5MZPXp0/Lqzs7OZPXs2Tz31VLv9RHdWWrZFf/T4i0QirFu3rtWaJ02axPjx43n66adbvQ5PPPEEhmFwwgkntJqjoaGBzZs3s++++/bYukREREREeoIy/kRERERE9mCXX345L774Ihs2bGjVH27q1KmccsopPPXUU9TX13PggQeydOlSnn/+eY499lgOPvjgXl/boYceitfr5fLLL+ess86iqamJZ555huzs7DaByl155513eOGFFzj++ONZtWoVq1atiu9LTk7m2GOPJSUlhZtuuolrr72WU089la9//etkZWVRXFzMu+++y3777cevf/1rAK655houu+wyzjnnHE477TRqa2v517/+xaRJk1plTvWla665hg8//JDzzjuPM844gwkTJlBRUcFrr73Gv//9b9LS0rj44ot56623uPzyyznllFPYe++9CQQCrF69mtdff5358+fHs+Pa873vfY+1a9fy8MMP8+mnn3L88ceTk5NDZWUlb775JkuWLOHJJ58E4NJLL+Xll1/mkksu4bzzziM9PZ0XXniBLVu2cM8993RYwrO7Jk+ezMUXX8x5552Hz+fjiSeeAGiVBXbkkUfy4osvkpKSwsSJE1m0aBEfffQRGRkZ3T7v888/zxNPPMGxxx7L6NGjaWpq4umnnyYlJSUe0Dv66KM56KCDuPPOOykqKmLKlCl8+OGHzJ8/nwsuuGCX5S674phjjuGNN95ot79cQ0MDL774IhDL8tuwYQNPPPEECQkJrUr+dvZ1mjdvHrNnz2bvvfcmIyODpUuX8vrrr3PuuefGx9x4442cc845fOMb3+CMM86goKCAyspKFi1aRGlpKf/5z392ej092ePvq6++4q233gJiGaINDQ385S9/AWLf844++mgglsX39a9/nVNOOYXbbrstfvy1117LFVdcwUUXXcS8efNYvXo1jz/+OKeffnqrzGiAjz76CNd1OeaYY3ps/SIiIiIiPUGBPxERERGRPdiYMWP45je/yfPPP99m329/+1tGjRrF888/z5tvvklOTg6XXXYZV155ZZ+sbfz48dx99938+c9/5ve//z05OTmcffbZZGVl8Ytf/KJLc7VkFr3++uu8/vrrrfaNHDky3rPuG9/4Bnl5eTz44IM89NBDhMNh8vPzOeCAAzj11FPjxxx++OHcdddd/PnPf+aPf/wjo0eP5tZbb2X+/Pl89tlnu3nl3ZOfn8/TTz/NXXfdxUsvvURjYyP5+fkcfvjh8T6BiYmJ/POf/+SBBx7gtdde44UXXiAlJYWxY8fGy6DujGma3H777RxzzDE8/fTTPPzwwzQ2NpKZmcmBBx7Iz372s3iGU05ODk8++SR/+MMf+Ne//kUoFGLKlCn89a9/5cgjj+zx6z/wwAOZNWsW9913H8XFxUycOJFbb721VSbbL3/5S0zT5KWXXiIUCrHffvvxyCOPdNjzsDNmz57N0qVLeeWVV6isrCQ1NZWZM2dyxx13xEtdmqbJ/fffz913380rr7zCc889x8iRI7n22mu56KKLdvvat3faaafxr3/9iwULFnDAAQe02ldaWsq1114LxHoTpqenc+CBB3LllVfGsxSh86/Teeedx1tvvcWHH35IOBxmxIgR/OhHP+Liiy+Oj5k4cSLPPvss9957L88//zy1tbVkZWUxbdo0fvCDH/Tote/KihUruOuuu1pta/n6lFNOiQf+OnLUUUdx7733cu+99/Kb3/yGrKwsLrvssnav47XXXmP//ffv0aCuiIiIiEhPMNzudEwXERERERER6SNTpkzhO9/5Tjwjc6i74IILyMvL4w9/+EN/L2VIqqio4JhjjuFPf/pT/KECEREREZGBQj3+RERERERERAaRa665hldffZWioqL+XsqQ9NhjjzF58mQF/URERERkQFKpTxEREREREZFBZJ999mHZsmX9vYwh66c//Wl/L0FEREREpEPK+BMRERERERERERERERHZA6jHn4iIiIiIiIiIiIiIiMgeQBl/IiIiIiIiIiIiIiIiInsABf5ERERERERERERERERE9gAK/ImIiIiIiIiIiIiIiIjsATz9vYChoKKiob+XINLvTNMgKyuZ6uomHEetRUUGMt2vIoOL7lmRwUP3q8jgontWZHDRPSsyeOh+7Z7c3NROjVPGn4j0CdM0MAwD0zT6eykisgu6X0UGF92zIoOH7leRwUX3rMjgontWZPDQ/dq7FPgTERERERERERERERER2QMo8CciIiIiIiIiIiIiIiKyB1DgT0RERERERERERERERGQPoMCfiIiIiIiIiIiIiIiIyB5AgT8RERERERERERERERGRPYACfyIiIiIiIiIiIiIiIiJ7AAX+RERERERERERERERERPYACvyJiIiIiIiIiIiIiIiI7AEU+BMRERERERERERERERHZAyjwJyIiIiIiIiIiIiIiIrIHUOBPREREREREREREREREZA+gwJ+IiIiIiIiIiIiIiIgA8Jvf/Ip//OPh/l7Gbpk79wDee+8dAEpKipk79wDWrFnVa+d75ZWXOOGEI+Nfv/DC/3HttT/utfPtjAJ/IiIiIiIiIiIiIiIifaSqqpI777yd008/maOOmsOpp87j2mt/zBdffNZq3NKli/npT6/ihBOO4uijD+H888/kySf/hW3brcZtH+Rq+froow+htLSk1bjrr/8Jt9xy007XtmbNaj7++CO+/e2zdusaB5K8vHxefPE1xo2b0GfnnDfvZFav/orFixf22TlbKPAnIiIiIiIiIiIiIiLSB0pKirn44vNYsOALfvCDq3jssSe544672W+//fnTn34fH/fuu29z5ZWXkpubzz33/JXHH/8/Tj/9bB577GFuvPEXuK67izMZ/P3vf+3y+p599imOOuoYkpKSunxsV4TD4V6df3uWZZGdnYPH4+mzc3q9Xo477gSeeebJPjtni767ShERERERERERERERkSHsj3+8DcMw+NvfHiMxMTG+ffz4CcybdzIAgUCA22//LXPnHs7Pf/7L+JhvfONbZGZmcd111/DWW29wzDFf6/A8p512Bk899TjnnHMe48dP7NTabNvmnXfm8+tf/7bV9m9/+xt885unsGVLIW+/PZ/U1FQuuOBiTj751PiYdevWctddd7Bs2VISEhI44oij+eEPfxwPIN5yy000NjYwdeo0nn/+Gfx+P/fc8wCnnnoSN998K88++xRffbWS8eMn8Otf/4ampkbuuOM2Nm/eyMyZ+3LDDTeTmZkJwMqVy3nggftYs2YV0WiUSZOm8MMfXsOUKVPbva6SkmJOP/2bPPLI40yaNIVbbrmJV1/9b5txd9/9V/bb7wDC4TAPPvgX3nzzdRobGxg3bgJXXPFD9tvvgPjYV155ib///a/U1dUye/YcZs6c1Wa+Qw89jB//+AeEQkH8/oRO/R30BGX8iYiIiIiIiIiIiIiI9LL6+jo+/fRjTj319FZBvxapqakAfPbZJ9TV1XH22ee1GTN37uEUFIzmzTdf3+m5ZszYh0MOmctf/3pvp9e3bt0aGhsbmTp1rzb7nnzycaZOncYjjzzOKaeczh//GAvKQSxQec01V5Kamsrf//4Yv/nNbXzxxWfceeftreb44ovP2bx5E3fffT8PPPBAfPvDDz/ABRdczMMP/wvLsrj55hv4y1/u5uqrf8J99/2NoqJCHnpoW/Zic3MzJ554En/5y0M88MCjjBpVwM9+djXNzU2dus6rr/4pL774Wvy/008/m8zMLMaMGQvAnXfezvLlS7j55t/x2GNPctRRx/LTn15FYeFmAJYvX8Ztt/2G0047g0ce+Tf77XcAjz32UJvzTJ06Ddu2Wb58WafW1VOU8SciIiIiIiIiIiIiInsEo6ERIxzps/O5Pi9uakqnxm7ZUojruowePXan4woLNwEwZsy4dvePGTM2HoTamcsuu5ILLzybxYsXss8+++5yfGlpKZZlkZmZ1WbfnDmHcOqppwNw7rkX8PTT/+bLL79g9OixvPHGa4TDYW644f/FA5rXXPMzfv7za7jiih+SlZUNQGJiItdd9ysSE/1kZiazfPkaAM4++1wOOmgOAKeffhY33fRL7rrr/ngW3bx5J/Pqqy/F17L//ge2Wtu11/6SE044ioULv+TQQw/b5XWmpKSQkhL7O3v33bd48cXn+POf7yM7O4fS0lJeeeUlnn32v+Tk5AJwzjnn8emnH/PKKy9x2WU/4JlnnuCgg+bwne9cAMDo0WNYtmwxn376cavzJCQkkJycQllZ6S7X1JMGReDv888/56GHHmLZsmVUVFRw3333ceyxx+70mE8//ZTbbruNNWvWMHz4cK644gpOPXVb2mln5rzuuut4/vnnW22bO3cuDz3UNnIrIiIiIiIiIiIiIiL9KBgi8dnXYZf973qQYdB81kmQ4N/l0K4ua9d9/HZu3LjxHH/81/nrX+/h/vsf3uX4UCiI1+vFMIw2+yZMmBT/s2EYZGVlU1NTA8CmTRuYOHFSqyzGGTNm4TgOmzdvigf+xo+fgNfr3enc28ZO3G5bVvxcANXVVfztb/ezcOECamqqcRyHYDDY5QDb6tVf8Zvf/JprrvlZPMi4fv1abNvm7LNPbTU2HA6Tnp4ev97DDz+q1f69957ZJvAH4Pf7CQaDXVrX7hoUgb/m5mamTJnCaaedxpVXXrnL8YWFhVx22WWcddZZ3HHHHXz88cfccMMN5Obmcthhh3VpzsMOO4xbb701/rXP59v9CxIRERERERERERERkZ6V4Cdw2vF9nvHXmaAfQEFBAYZhxEtkdjxuDBALMM2YsU+b/Rs3bmTcuPazAXd08cWXcfbZp/Hee+/scmxGRgbBYJBIJNImQOfxtA4nGYaB4zidWkOL9sqbtjf3jtsMw8B1t53rt7+9ifr6Oq6++ifk5w/H5/Nx+eXfJRrt/N97VVUl1133E0466VucdNK34tsDgWYsy+Khh/6JaVqdWv/O1NfXk5GR0eXjdsegCPwdccQRHHHEEZ0e/+STTzJq1Ciuu+46ACZMmMCCBQt49NFH44G/zs7p8/nIzc3t3sJFRERERERERERERKTPuKkp9GG+X5ekpaUze/YcnnvuGb797bPaBJIaGhpITU1l9uyDSUtL58kn/9Um8PfBB++yZctmLrnk8k6dMz9/GKeddgYPPngfI0aM2unYiROnALBx43omTZrS6esaM2Ycr7zyXwKBQPyali5dhGmajB49ptPzdNbSpYv5yU9+zpw5cwEoKyultra208eHQiGuu+4njB49hh/+8Met9k2aNAXbtqmpqemwPOqYMeNYsaJ1377ly5e2GVdUtIVwOMTkyVM7vbaeYPbp2frIokWLmDNnTqttc+fOZdGiRV2e67PPPmPOnDkcf/zx3Hjjja3SSUVERERERERERERERDrrmmuuxXFsLrnkAt55Zz6FhZvZuHEDzzzzJJdf/l0glln2s59dzwcfvMfvf38La9euoaSkmP/+9wVuueVmjjzyGI4++rhOn/O88y6ksrKCL774bKfjMjMzmTx5KkuWLOrSNX3tayfi8/m45ZYbWb9+LV9++QV33vkHjj/+6/HSnT2poKCA119/hY0bN7B8+TL+3//7FX5/57IuAf7wh99RXl7Gj370M2pra6iqqqSqqpJIJMLo0WP42tdO5Le/vZF3332L4uIiVqxYxj//+QgfffQBAN/+9ll8+unH/Pvf/6SwcDPPPvtUu2U+Fy9eyIgRIxk5cucB1542KDL+uqqyspKcnJxW23JycmhsbCQYDJKQkNCpeQ477DCOO+44Ro0aRWFhIX/605+45JJLeOqpp7Asa9cTbGWaBqbZtiauDHzmpmJwHJxxfXtj7oksy2z1fxEZuHS/igwuumdFBg/dryKDi+5ZkcFF96wMFmPGjOaxx/7No48+xL33/pmqqkoyMjKZOnUvrr32F3g8sffwccd9jdzcHB599CF+8INLCIdDFBSM5sILL+ass85pE6OwLCN+7I5fZ2Vlct55F3L//fdiGK3H7ejkk0/h1Vf/y5lnnt1qu2m2Ps4wtm1LSUnirrvu4847/8All1yA35/AUUcdzdVX/yR+jGEY8XNvu1+Nrf834+Na9nk827a1xFdavv7lL2/kttt+y8UXn0teXj5XXHEld999Z5s1trwG239/8HhMFi36kqqqSs499/RW13jffQ+y//4H8Otf38Qjj/yde+/9MxUV5WRkZLD33jM47LDD8XhMZs3ah+uvv4G//e0BHnroAQ48cDbf/e73eOSRv7U6//z5r/Otb52609e7Nxju7naH7GNTpkzhvvvu49hjj+1wzPHHH8+pp57KZZddFt/27rvvcumll7J48eI2gb/OzAmx3oHHHnssjz76aJuMwp1xXbfdZpgy8EWefh1w8Z5xQn8vRURERERERERERESkVwWDQU444QTuvPNO9t23/VKXsmtr1qzhggsu4PXXXyc1NbVPz71HZvzl5ORQWVnZaltlZSUpKSmdzvZrT0FBAZmZmWzatKlLgb/q6iZl/A1Svqo6XI9FY01Tfy9l0LMsk7S0ROrrA9h215q+ikjf0v0qMrjonhUZPHS/igwuumdFBhfdsyI954YbbqawsISxYyf3yvxD4X5dv76QX/3qZqJRk5oeii9kZiZ3atweGfibNWsW7733XqttH330EbNmzdqteUtLYw0ic3Nzu3Sc47g4zqBKrBQA18XX2Ax+H9HonvnNpz/YtqPXU2SQ0P0qMrjonhUZPHS/igwuumdFBhfdsyK7b5999gPo9XtpT75f99vvQKD3X8P2DIqCx01NTaxcuZKVK1cCsGXLFlauXElxcTEAf/zjH7n22mvj48866ywKCwu5/fbbWbduHY8//jivvvoqF154YafnbGpq4ve//z2LFi1iy5YtfPzxx3z/+99nzJgxHHbYYX105dKfjOYAuC5GKAyDqyKuiIiIiIiIiIiIiIgMQYMi42/ZsmWcf/758a9vvfVWAE455RRuu+02KioqKCkpie8vKCjggQce4NZbb+Uf//gHw4YN47e//W2rgN2u5rQsi9WrV/PCCy/Q0NBAXl4ehx56KFdffTU+n6+3L1kGAKOxOfYH14VQGBL8/bsgERERERERERERERGRnTBcV6lMva2ioqG/lyDdYK3bhP+9zwEInHo8bnrfNuDc03g8JpmZydTUNO2x6dsiewrdryKDi+5ZkcFD96vI4KJ7VmRw0T0rMnjofu2e3NzOxSgGRalPkf4Qz/gDjGCoH1ciIiIiIiIiIiIiIiKyawr8iXTAbGzGTU4EFPgTEREREREREREREZGBT4E/kQ4Yjc04WRmxPyvwJyIiIiIiIiIiIiIiA5wCfyIdMJqacNJScBN8oMCfiIiIiIiIiIiIiIgMcAr8ibTHdTEbA7jJibh+P0Yo3N8rEhERERERERERERHpcz/4wSX873+v9fcyuq2kpJi5cw9gzZpVAHz55RfMnXsADQ0NvXbOhx56gAsvPCf+9f3338Odd97ea+fbnqdPziIy2ARDYNu4Kcm4CX6V+hQRERERERERERGR3XbLLTfx6qv/5eSTT+VnP/tFq31//OPvef75ZzjxxJP45S9vAqCmpoaHHvorH330ATU11aSmpjFx4iQuvPB7RKNRrrrq8p2e7+67/0ppaQm/+93NABiGQVZWFvvssx/f//7VDBs2bKfHf/DBu1RXV3HssV/r/kUPMDNm7MOLL75GSkpKn53z7LPP44wzTuaMM85h5MhRvXouBf5E2mE2NgPgpiSBAn8iIiIiIiIiIiIi0kPy8vKZP/9/XHXVNfj9CQCEQiHeeOM18vNbB+JuuOFaIpEIN9xwMyNGjKS6uooFCz6nvr6Ogw46hBdf3JaJd9ddf6SpqYlf/OLX8W1paemUlpaQnJzMv//9LK4LJSVF/PGPv+dXv/o5f/vbYztd6zPPPMXXv/5NTLN3C0hGIhG8Xm+vnqOF1+slOzunT87VIiMjg9mzD+aFF57lBz+4ulfPpcCfSDuMrYE/JyUZN8GHWdnUzysSERERERERERERkT3BlClTKSrawrvvvs3XvnYiAO+++zb5+cMYMWJEfFxDQwOLFy/knnseYN999wdg2LDhTJs2PT5m+wCW3+8nEgm3G9QyDCO+PScnh5NO+iZ//vMdNDU1kpzcfuZbTU0NX375OVdf/ZNW2+fOPYCf//wGPvroAz777GNyc/O48sofMXfuEfExCxcu4C9/uYu1a9eQlpbGCSecxCWXXIHHEwtLnXfeeYwePRbDsPjf/15h/PiJfPe7l3DVVZfzxz/ew1//eg+bNm1i+vQZ3Hzz7/jqq5Xce++dVFRUcMghc7nuul+RkBALmn7yyUc89thDbNiwDtO0mD59Bldf/dMOM+u+/PILrrrqcl599W1SU1O58spLWbToyzbjnnnmPwwfPoKGhgbuu+/PfPDBu4TDEaZO3Ysf/vAaJk2aHB/7z38+ytNP/5tgMMjRRx9LRkZmm/kOPfQw/va3+3s98KcefyLtMBqbcL0e8HljPf6C6vEnIiIiIiIiIiIiIj1j3rxv8vLLL8W/fvnl/zBv3jdajUlMTCQxMYn333+HcLjnPqOuqanmvffewbIsTNPqcNySJYtISEhg7NhxbfY98sjfOProY3nssSc5+OBDufnmX1FfXwdARUU5P/vZ1UydujePPvoEP/nJ9bz88os89thDreZ45ZX/4vV6uP/+h/jZz66Pb3/44Qf58Y+v5a9/fYjy8jJ+9avreOaZJ7jxxt/yhz/8mc8//4T/+78n4+ODwQBnnfUd/v73f3LXXX/BMAx+8Yuf4jhOp16P3/3uD7z44mvx/4444ihGjx5DVlYWAL/61c+pqanmjjvu5qGH/snkyVP50Y+uiF/v/Plv8MgjD3LZZd/noYf+QXZ2Ds8//39tzjNt2nTKy8soKSnu1Lq6Sxl/Iu0wm5pxk5PAMNTjT0RERERERERERGSQiAQqsKPNfXY+y5OENzG3y8d97Wtf54EH7qO0tASApUsXc/PNv2PhwgXxMR6Ph1/+8kZ+//tbeOGF55gyZQqzZu3PMcd8jYkTJ3XpfI2NjRx33GG4rkswGATg298+i8TExA6PKSsrITMzq90ynyeeeBLHHXcCAJdd9gP+7/+eZMWK5Rx88CE899wz5OXlc80112IYBmPGjKWysoL777+H7373Elpy0kaNGs33v78t+62yshKASy65gpkzZwEwb97JPPDAvTz11AvxDL4jjzyGL79cwLnnXhj/envXX38jJ510LBs3rmf8+Im7fG3S0tLjf37qqcdZsOALHnzwUfz+BBYvXsTKlct56aU38Pl8AFx55Y94//13ePvt+Zx88qk888wTzJt3Mied9C0ALr30+3zxxWdtgrU5ObGMy9LSEoYPH0FvUeBPpB1GY3Osvx/gJvjAtiESBa9uGREREREREZE9gbVmI2ZVDZGD9+3vpYiISA+xww1s+ORX4HYu06tHGCYTDv0Dli+1S4dlZmYyZ86hvPLKS7iuyyGHHEpGRkabcUceeQxz5sxlyZKFLF++jE8++Yh///sf/PznN/D1r3+j7cQdSEpK5uGH/0U0GuWTTz7kf/97jUsv/f5OjwmFQvh8/nb3TZiwLfCYmJhIcnIyNTXVAGzatJHp02diGEZ8zIwZ+xAINFNeXs6oUbGg19Spe+1y7qysLBISElqV7czKymblyuXxrwsLN/P3v/+VFSuWU1dXi7v177+srLRTgb8WH3/8IX/96738/vd3Mnr0GADWrl1NIBBg3rzWwcVQKERR0RYANm7cwMknn9pq//TpM/jyywWttrX0c2wJvPYWRTFE2mE0NuPkZwPgJsS+sRmhUKz8p4iIiIiIiIgMet5V6zHqGogcNAu2+2BSREQGL8uXyriDf9PnGX9dDfq1mDfvZO6883YArrnm2g7H+f1+DjzwYA488GAuvPB73Hbbb3jooQe6FPgzTYNRowoAGDt2HEVFRfzxj7fyq1/9psNj0tMzaGiob3dfS6++FoZh4Lpup9cDkJiYsMu5DcNocy4Ax9l2rp///McMGzacn//8l+Tk5OI4DueffyaRSLTTa9mwYT033fQLLr/8SmbPPji+PRBoJjs7h3vueaDNMSkpXft7bykNmpnZtv9fT1IUQ6QdRmMTzoTRwHaBv2AINyW5P5clIiIiIiIiIj0hFMasrAHXhWAIOvjgUUREBh9vYi7e/l5EJx100BwikQiGYTB79pxOHzd27Djef/+d3Tr3uedewJlnfoszzvgOU6ZMbXfM5MlTqK6uor6+nrS0tE7PPWbMWN599y1c141n/S1dupikpGTy8vJ2a907qqurZfPmTfz85zewzz6xLP7Fixd1aY7a2lp+/vMfc8QRR3Pmmd9ptW/KlKlUV1dhWVaH5TnHjh3HihXLOfHEk+Lbli9f1mbc+vXr8Hg8jBs3vkvr66q2hVlFhrpQGCMSjfX4A9ga+CPYc81TRURERERERKT/WMXlsaAfYNY39vNqRERkqLIsi8cff4Z//etpLMtqs7+urparrrqc119/hbVr11BcXMRbb73Jv//9T+bOPWK3zp2fP4zDDz+Khx76a4djJk2aQnp6BkuXLu7S3Keeejrl5WXceeftbNq0kffff4eHH36AM888p91+gbsjNTWN9PR0/vOf59iypZAFCz7n3nv/1KU5brjhWhISErjoosuoqqqM/2fbNgcccBB77z2D66//KZ999gklJcUsXbqYBx64j6++WgHA6aefxcsv/4eXX/4Pmzdv4qGHHmDDhvVtzrN48UL22WffeMnP3qKMP5EdGI2xNPBtPf62ZfyJiIiIiIiIyOBnFZfhpCZjNjRh1jXg5Of095JERGSISk5O6XBfYmIS06ZN56mn/k1x8Rai0Sh5efl84xvf4vzzv7vb5z7jjHO4/PLvsmLFMqZNm95mv2VZfP3r3+B//3uVQw89rNPz5ubm8Yc/3MVf/nIXF154NmlpacybdzIXXHDxbq95R6ZpctNNv+Ouu+7g/PPPpKBgDD/60U/54Q8v6/QcixZ9CcC3v31Sq+3PPPMfhg8fwR133MWDD/6F3/3uZmpra8jKymbWrP3IzMwC4JhjvkZR0Rbuv/9uQqEwRx55NN/61ml89tknreabP/9/XHTRpbt5xbtmuF0tuipdVlHR0N9LkC6wNhfjn/8RgTPn4SYlApD4rxeIzJpGdPrkfl7d4OXxmGRmJlNT00Q02ofNdUWky3S/igwuumdFBg/dryIDhOuS+MwrRMeMxLO5mOjYUUQOnNlmmO5ZkcFF96xI76iqquS8887k4Yf/xbBhw3tkzqF4v3788Yfcd9+fefTRJ9rtWdgZubmd6ymoUp8iOzAam8E0cbev7+/3KeNPREREREREZA9g1DVgNAWwRw7DSU9TqU8REZGdyM7O4brrfkVZWWl/L2VQCwYDXH/9jd0O+nWFSn3KkGbU1GM2NGKP3taU02hswklJgq1NRyFW7tMIqcefiIiIiIiIyGBnFZWBaeLk5+AUlca+3hXXxbNiDW5iIvb4gt5fpIiIyABy+OFH9vcSBr2jjjq2z86ljD8Z0rxfrcP/zicYzYH4NrOxGTc5qdU4N8GvjD8RERERERGRPYBVXIadnwNeD25aKmZDIzg7LzPmWboK32dL8H26EGy7j1YqIiIi0nUK/MnQFomA7eBZtjq+yWhqxk3ZIfCnUp8iIiIiIiIig59tY5ZWYI/MB8BJTwHHjbX96ID51Xp8C5YRnTwOIxjGWr+5r1YrIiIi0mUK/MmQZkSiAHhWrYetgT2jsZ3AX4I/vl9EREREREREBiezrAojamOPiAX+3PTU2Pa6hnbH22s24f1wAdG9JhA+ZD/sguF4l68F1+2zNYuIiIh0hQJ/MrRFItjD8zBc8K5cC9EoRjCEk5Lcalisx58CfyIiIiIiIiKDmVVUipvgx81KB8BNSsT1WBjtBP6MknKir7yPPW4U4YNmgWEQmTYJs6YOs7Sij1cuIiIi0jkK/MmQZkSiuKnJRKaMw7NiLWZNPUCbjD8S/BihyC5r/ouIiIiIiIjIwGUVl8XKfBpGbINh4KalYNa3Dfx5vliGmZ9N9IjZ8fHO8FyczHS8y9f05bJFREREOk2BPxnaIlFcr4fo9MkY0SjeBUuBtoE/N8Ef+0Mo3NcrFBEREREREZGeEAhiVtfFy3y2cNJTMeoaW48NRzDLqzD3Gg+WtW27YRCdNhGrsASjfodjRERERAYABf5kSDMiUfB6cJOTiE4ci1VSEXvaLymx1biWwJ+hPn8iIiIiIiIig5JVVAYQy/jbjpuW2qbHn1VaAa6LOWZEm3mi40fjJvjwrFzbe4sVERER6SYF/mRoi0RwPZ7YH2dM2Rb0M1vfGq7fByjwJyIiIiIiIjJYWcVlOFnpkJjQaruTloIRCEIk0mqsm5KMkZHadiKPRXTKeDxrNkI40na/iIiISD9S4E+GLtfFiNrg88a+TEshOmksTk5G26HxjD+V+hQREREREREZdFwXq6gMe+Swtru2BvfM7cp9msXlOKPy24xtEZ06AcO2lfUnIiIiA46nvxcg0m+iNrhuPOMPIHzIfu2P9XnBMJTxJyIiIiIiIjIIGdV1GMFQmzKfAE5aLPBn1DVATiZGUzNmXQPRA6Z3OJ+blEhkr4n4Fq7AzUzHHt22JKiIiIhIf1DGnwxdkWjs/97t4t+GEftvR4aBm+BX4E9ERERERERkELKKSnE9Fk5edtudPi9ugh+zPpbxZxbHegE6I/J2OmfkwJlEx4zA/84nmKUVPb5mERERke5Q4E+GLGNr7X7X6+3UeDfBjxFS4E9ERERERERksLGKy3CG5YJltbvfyUjFqKvfOrYcJycTtrb96JBhED58NnZeNv75H2FU1/X0skVERES6TIE/GbKMaDsZfzvhJvhAGX8iIiIiIiIig0skilVW1W6ZzxZuWmqsx5/rYhWXYw/febZfnGUROvoQ3JQkEt54H6OxqYcWLSIiItI9CvzJ0LW11Kfb6cCfSn2KiIiIiIiIDDZWWQU4DvbIYR2OcdJSMOobtvUCHNFxkLANn5fgcYeBaeL7YAG4bg+sWkRERKR7FPiTIaul1GdnM/5I8GMEw723IBERERERERHpcWZRGW5yEm5aSodj3PRUjKiNZ90msCyc/HZ6Ae5MUgLhg2dhlZRjFZbs5opFREREuk+BPxm6uprx51fGn4iIiIiIiAwuRl0DVmFxfy+jX1lFZbEyn4bR4RgnPRUAz5qN2MNyOuwFuDP2qOHYI/PxfrYYbLvb6xURERHZHQr8yZBlbA384el8jz8jFFLJDhERERERERk0PF+tw/fuZ0P2d1mjsRmzrmGn/f0A3NRkMAyMcKTz/f3anMwgfOA+mI3NeFas7d4cIiIiIrtJgT8ZuiLRWLbfTp74256b4AfHjWcKioiIiIiIiAx0RiCIEYli1Df291L6hVVcBoax62CeaeKkJgN0rb/fDtzMNCJTx+NdvBKag92eR0RERKS7FPiTIcuIRDvf34+tgT9QuU8REREREREZNIxALPhkVtX270L6iVlUipObBX7fLse66am4CX7crPTdOmdk373BNPB9uWy35hERERHpDgX+ZOiKRDrd3w9iPf5AgT8REREREREZPIxA7HdYs7K6n1fSD1wXq7i80xl8kWmTCB+0T6crA3XI7yOy79541mzErKrZvblEREREukiBPxmyYhl/3k6PV8afiIiIiIiIDDZGcOhm/JkV1bGefbvo79fCGZGHPX50j5w7OmU8blIi1rrNPTKfiIiISGcp8CdDVxcz/kjYWhYkpMCfiIiIiIiIDAK2jRGK4KSnxDLPXLe/V9SnzNIKXK8HJyezH05uYudnY1Yq409ERET6lgJ/MmQZURs8XQj8mSauzxsvkyIiIiIiIiIykLVUrLFHDceIRDHqG/t5RX3LrKnHzUwHs38+/nKyM4dkwFVERET6lwJ/MnRFIrhdKPUJsXKfRijcSwsSERERERER6TlGIFbm0x41DGDwZZ8Fgvjf/BDv4pUYNfVdDqCZtXU4GWm9tLhdc7IzMaI2Rl1Dv61BREREhp4upDuJ7FmMSLRrpT4BEvzq8SciIiIiIiKDQkvgz01PxU1JwqyqwZ7QMz3s+oJn4xasLaWYpRV4v1yOk5aCPXoE9piROLlZYBgdH+y6mHUNRCeO7bP17sjJzgBiAVe7HwOQIiIiMrQo8CdDVzcCf67fp8CfiIiIiIiIDAotrSrcxATsnKxY2clBxCoswR6WS+i4Q7GKy7E2FeFZuwnvstWxaxo9gujEMTh52W2ONeobwXZwMvsx4Ob34aTF+ivaE8f03zpERERkSFHgT4YsIxKFrgb+EvyYNXW9tCIRERERERGRHhQI4vp9YJo42Rl4l3wVK5e5s0y5gSISxSqtILz/dLAs7ILh2AXDY5l8ZZVYm4vxbCrCs2YDzWfMg8SEVoebtfUAuP2caedkZwy+EqsiIiIyqKnHnwxdkUjXM/5U6lNEREREREQGCSMYwt0aEHNyMjEi0UHTb84qKQfbwR41vPUOw8AZlktk9j4ETzwCHBerorrN8UZtPa7fG7/+/uJkZ2JW13a5P6GIiIhIdynwJ0OT62JEbfB6u3ZYgh9C4V5alIiIiIiIiEjPMQJB3EQ/EAtAAQOv3Gck0u5mq7AEJy0FNz21w0Pd5KRYZZ52An9mbT1ORlq/Zzc6OZkYURujdnAEXGUPF4lgrd+sQLSIyB5OgT8ZmiJRgO5l/EWiYNu9sSoRERERERGRHhML/G3NePP7cFKTMatq+3VN2zOq60h64iWs9YWtd7gu1pbSWGnPnU5g4ORmtR/4q6nv9zKfMIADrjIkWRuL8L/7md6PIiJ7OAX+ZEgytj5R6HYx448EX+x4lfsUERERERGRAc4IhFqVunRyMne735zvvc/wLF3V/k7XxbNyLUZ9Y6fm8i5ZCbaDd+FycJxt666uw2gOYI8atss57NysWBBj+wwmx8Gsa8DJSO/UOnqVz4uTloJZ2TY4KdLXWnpftgm2i4jIHkWBPxmatmb80Y2MPwAU+BMREREREZEBzggEt/0ey9Z+czsGyboyX1UNnnWb8S1YhtFO5qC1fjO+TxbhXbhi13PV1uPZsIXo1PGY9Y1Y6zbH93m2lOB6PTj5Obucx8nNwghHWvUuNOobwXFipT4HACcnE2sAZVrK0GVuvU88G7ao3KeIyB5MgT8Zkozo1lKfni4G/vyxX5iMoPr8iYiIiIiIyABm2xjhSOuMv+yt/ebqutdvzrtyHW5SIk56Kv6PFrQOHDQH8X26CNfnxSoshujOW2R4l3yFm5RIePY+2GNG4F28Mp71ZxWW4IzIB8va5ZqcnCyAVuU+W7KanMwBEvjLzoyVWN0uq1GkPxi19bG+k80BzPKq/l6OiIj0EgX+ZGgKb83483U140+lPkVERERERGTga/m9tXXgLwOge+U+Q2E86zcTmTqe8KH7Y1bW4Fm5NrbPdfF//CUYJqHj5mJEolhbSjpeW30jnvWFRGZOAcsiPGsaZkMTnrWbIBDCrKgmuqv+fi18Xpz0VKzK1oE/1++D7bId+5OTnRkLxHYz4CrSI6I2ZkMT0cnjcJMS8WzoRLlPx4GQHn4XERlsFPiTIWlbxl8Xe/x5PGCZCvyJiIiIiIjIgGYEggC4idsFv/y+WL+5qq4H/jyrN4AL0cnjcPKyiU6dgHfBMozGZqz1m7E2FxM+ZD+cvGyc7AysnQQVvEu+wvX7iE4aF1tjVgbRsSPxLl6Jp7AYoFP9/Vo4uVmYFduuyaitj2X7GUaXr7M37FbAVaSHtASencx0ouNGYXWi3Kdn6SoS//OmyoKKiAwyCvzJ0BSOxP7fxYw/DAM3wY8RUuBPREREREREBq6WwB/bZfxBLAhlFZVCJNr5yVwX71friI4bFZ8vvP908HnxffA5vk8XER1fgD1mJADRcQV4CksgEmm7rsZYZl9kxhTwbCvlGZk1DaOxGe8XS3ByMtuse2ec3CzM6tp4eVGzph43I73z19fbfF6ctBSsbgRcRXpKS38/Jz0Ve1wBRjCEWVKx02Os4nKMxmaM5kBfLFFERHqIAn8yJBnRaOzJP7Prt4Dr9yvjT0RERERacV2b+pKPcF31bxKRgcEIbC31uUO5y8iMKRjNQfxvf9LpnnNWYQlGYzPRaRO3bfR5CR+8L1ZJBRgm4YNmxXfZ40aB7WBtblvu07t0Fa7fS3TK+Fbb3a1ZSEYogt3ZMp9bOTlZ4Lqx4J/jYNY34GQMjP5+LZycTGX8Sb8y6+pj3w/8PpycTNyUpJ2X+3QcrK29MwfsezccwaitxyyrxNpUhLl2E66tn8VERLqY7iSyh4hEcb2ebpX9cBP8GEHVNxcRERGRbQI1qyld+SjepGEkpo/f9QEiIr0tEIz1udvhgVc3O5PQUXNIePMDfB98QfiwA7f9buy6mEVlADgj8+PbPSvX4eRkxgJs27HHjCS8/3ScvOxW/fTclGScvGw8GwqxJ4yObzeqavCs3kh41l7gbfuRVGTfvbHKqoiOHdWlS3Wy0sEyMSuqcL1ecNxYqc8BxMnOxLOpOBZs7cZDyCK7y6jdLiBuGETHj8azah3M2bfd96RZVQu2DYaBWVkTz+gdMAJBEv/vVYytmb4Q+5blpCTAsPxeP73R2IzR2IQzLLfXzyUi0lUK/MmQZEQi7f6S0Rlugh+jubmHVyQiIiIig1k4ECuVFQmUK/AnIgOCEQjidlAu0xmZT+iw2fjf/RQ3MYHI/tOx1hfiXboKs7Y+NiYznciMKThZGVjFZYQOO7DduaIzp7a/fdwofJ8vgVAY/D4Ihkh46+NYf7G9J7d7jJueSuDMeV2/WNPEyc7ErKjGTUqKrX8AZvxh2xi1DbhZA6gMqQwZZl09Tn5O/Ovo2FF4l3yFWVyO005PTbO8EiwTe1geZmV1Xy61UzybijBsh+Dxh+EmJeIm+El8432cDVv6JPDnXbISa30hgXO+qWC+iAw4+q4kQ1MkGnsKsBvcBJ9KfYqIiIhIK5FgJQDh5rJ+XomISIwRDOEm+jvcb48vIDx7H7zLVpP41Mv43/8cNyWJ4NePJHjiEbhJifjf+4zEl97ETfDHynd2gT12FDgu1uYicF38734K0Siho+e06u3XU+zcLKyKaszareUMEzq+9v7gZGcA4Fm3CVy3fxcjQ4/jYNY14qRvC4i7Wek46al41m9u9xCrrAo7JwsnLwuzqmbAvW+tjVuwh+fijMjHzUiDBD/O6BG4G4s7XcZ4d5hVtRiR6MAtgyoiQ5oy/mRIMiLR3cv4U+BPRERERLYTacn4ay7v55WIiMQYgSBuUuJOx0T3ngS2jVlbT2Tvybhbg1MAoWG5GFU1eJeviZWys7oWrHOTErGH5+JZvwWzrhGrpILg1w7DTUnqzuXskpOThbF8DWZp+YAr8wmA10tk32l4F67AiEQIH9x+eUWR3mA0NIHj4GSkbrfRwB5XgGf5aojarQPyrotZXkV04hjsnCy8oQhGQxNuWkrvrrOqBqu8CiMQwggGIRQmOmNKmzLDBIJYpZWE5+zbarNTMBx3yVcYZVWQm917C3UczJo6AKyisli5YxGRAUQ/YcjQFInieroZ907wY4QiA+5JJxERERHpP5HtSn2KiAwERiDUYanP7UVnTiV8+OxWQb8WbnYm4cNnE508rltrsMcVYBWX4V26KtYLcERet+bpDCc3FhiwSitj2T8DUGTWNEJz98ezeiP+Nz+CSKS/lyRDhFnXAMTK6W4vOr4AIxLFKixutd1obMIIBHHyc3CyM2Nz9HJmm9HQSOLLb+P7bDGeNRsxK2uwyqrwfrG0zVjPpqLY+nfoO+jmZmEkJWLucD09vta6BrAd3KQErGJVexCRgUeBPxmSjEgUfN3M+PP7YkG/ULiHVyUiIv3NdV0Cdev7exkiMghFApVY3mTCgXJcPSAmIgOAEQjGSl72o+iYkWCZRMeNIjq9/b5+PcVNSYpf70Dr77c9e9I4gsfNxSyvJOGVdyCgikLS+4zaelyvp00WsJueipObhWdd63KfZlkVAHZeNiT6cVOSYuU+e5Hv86W4fj/N55xM4Mx5BL95LOGDZ2GVVGBWtO4x2FLms01JX8PAHDcSc3NJr67VrKoFIDJtUmxtYQXxRWRgUeBPhqZIpNsZfy2/SKjcp4jInidQ8xWFC26LZ+6IiHSGHWnCiTaTlDkNJxrAjjT095JEZKizbYxwpFMZf70qwU/glOMJHz4bDKN3z2UYODmxzKSBHPgDcEbmE/z6URiNzXiXr+7v5cgQYNY1xLL92rkPoxPGYG0phUAwvs0qr4zdR34fAE52JmZldZtje2x9JRVYm4oIHzC9VWsee8xInLQUvEtXbRu8tcynPbb9vqPG+FGYtfWx8qa9td7qWpzU5NgaXBerRBUfRGRgUeBPhqRYxp+3W8cq8CcisucKNcVKwkRDtf27EBEZVFoeFkjK3rvV1yIi/aXl99V+D/wBbmpyn/Wyayn36WSm98n5doeblU504hg8azaCbXfyIBeCIYzaeszSWKDEs2o9nsUr8X62OFYSUVnn0g6ztr7DgHh03CgwwLOhcNv4sqpWfevsnMxYlltvvL9cF9+ni3Bys7DHj269zzCIzpiCtakoVl6Tjst8xtc+ejiYJtaW3sv6M6tqcLIzcFOTcVKTsYoV+BORgaWbTc5EBjdDGX8iItKOlt5c0bCydUSk8yKBSgCSs6bFvm4uIzF9Qn8uSUSGOGNr5o6b2L+lPvtaZPL4WCnDrVlKA1108ji8K9ZiFZZ0mL3UwqiuJeHVdzHaKSnoJvhwvV7Mhibs0SNaBWxkCHLd1pl9rotR14DTQaCMBD/2qOF41m0mOm0ShMKYtfVEZkyJD3FyMjEiUYy6hh7voelZvQGzpo7gSUd3kJE4Gu+Xy2O9Quce0HGZz60Mvw9nWC5WYQnRvSb26FoBcF3M6joiW8sXOyPyMdXnT0QGGAX+ZGiKRnG93Xz7+31gGBjq8ScisscJN8d+YbMjjf28EhEZTCLBCixvMh5/Bh5/JuFmPfUtIv2rJfDHAMj461NJCUQnj+vvVXSam5mOk5eNZ9WGXQb+fF8ux/X7CM89ADfBj+v3xR5M3voZBY5D4hMvYRWVKfA3hBlVtSS88jahE46IZ8AazQGMSHSnAbvohNH43/4klk3aGCuRuf37yMmOldE1K2uwezLwFwrj/XIZ0Qmj4+ttw7KI7D0J35fLiO41Aau0kvCcfXc6rVMwHOuzJRCJgLd7Fb86YjQ2YYQj8dfEHpGHZ9V6jIamWIaziMgAoFKfMiQZkWj3/+E3DFy/D5TxJyKyx4ls/bDeDtf380pEZDCJBCrxJuQA4E3Mi2cPi4j0FyOwtdRnBxkxMnBEJo/FKi7baT8ys7IGq7CEyKxpsZ5n+TmxIE6Cf1uGlGniDM/DUubRkOZZuxEjauP9fHG8LGdLiUwnPbXD4+yC4bg+L551mzHLqmLB5e2DWH4fTloKZlVNj67Xu3glRG0iB8zY6bjolPG4Hg/++R/Hvu4oe3ErZ/RwcJxeKcFpVtXGzpGVAYA9PA8MQ/eeiAwoCvzJ0OM4YDvdz/gjVkZDpT5FRAYn14l2uD0SijWsV8afiHRFJFCBJzEW+PMl5RJW4E9E+lsgiJvg67PeetJ99tgCXK8Hz+oNHY7xLlyOk56CPb5g53ONzMesqAZVKBqaHAfP+kKc3CyssiqszbH+5WZtA1jmzrPRLAt73Kitgb8KnPycNmU3nZxMrMouBv6CIWinPC2AUVOHd8VaIjOnxkr07ozPS3TqBIym5p2W+WzhpqfipKdgFfZ8nz+zqjbWPzVpa0a134eTk4mpPn8iMoAMip8AP//8cy6//HLmzp3LlClTePPNN3d5zKeffsopp5zC9OnTOe6443juuee6PKfrutx1113MnTuXmTNncuGFF7Jx48aeuizpL5GtH/juVuDPr8CfiMggFAlUsPa9HxFs2NTuPlwHw/Riq8efiHRBJFCJLzEXAG9iPpHmctytT9mLiPQHIxDETRhiZT4HK68He8JoPGs3xh5U3oFZUYW1pZTIrGm7DOTaI/PBdbFKFIAYiqyiMoxgiNCc/bBH5OH9Yik4DmZdPU5a6i7fP9EJYzCamrHKqrDz25aLdbIzY9lu7bxP2+W6JLz+HgkvzY+V3Nxhn+/jL3HSkolu7ZW3K5FpE3G9HqITxnRqvD1qONaW0njmY08xq2vj2X7xc43Ixyop6/FziYh016AI/DU3NzNlyhRuvPHGTo0vLCzksssu46CDDuLFF1/kggsu4IYbbuD999/v0px/+9vf+Oc//8lNN93E008/TWJiIhdffDGhkAI+g5mx9YeN3cn4w+/HCOoJOhGRwaaxYiGuEyZQu7bNvpYMnYS0cdgRlfoUkc5pyRb2JsQCf76kPBw7iB3RAwQi0n+MYAg3UWU+B4vI5HEYzcFYkGIH3oUrcTLSsMftPNsPwE1JjmU5qeTgkGSt24STkYablU74gJmY9Y14Vm3AqG3Ayei4zGcLJy8bZ2tWoJOX03Z/TibYNkZt535XMovKMKvrMBub8X24oFVQzFq7CausivDB+4Jlde4CExMInHkS9oTRnRpujx6BEQji+2RR28DjbjCranGyM1qfa2Q+RijS46VQRUS6azciH33niCOO4Igjjuj0+CeffJJRo0Zx3XXXATBhwgQWLFjAo48+ymGHHdapOV3X5R//+AdXXHEFxx57LAC33347hxxyCG+++Sbz5s3bjSuSfhXP+Ot+c183wYe5k/r7IiIyMDVWLgEg1FjYZl+kuQzT8uNPGUlzzeq+XpqIDFKRYDW4Dt7EbT3+IPY9xeNL68+licgQZgSCuy6dJwOGm52Jk5OJZ9V67NEj4tvN8iqsolJCRx7UpuxiR+wR+XgKS2JBlk4eI3uAcATP5mLCs6aBYeBmZxCdOAbvohXgukSHTdz1HIaBPXEMxvI1bQJbQHybWVWDndV2/468y1bj5GQS2Xsy/nc/xRmWS3TqBAiF8X2xhOj4ApwR+V27zi48xO/k5xCevQ/eL5eRWFhC6JD9cEYN69r5dtQcxAgE27w+Tm4WrteDtbkEbCd271ZUY+dldzqjUUSkJw2KjL+uWrRoEXPmzGm1be7cuSxatKjTc2zZsoWKigoOOeSQ+LbU1FT22WcfFi5c2FNLlX5gbA387V6PP5X6FBEZbOxII4G6tZieREINbQN/4eZyvIl5WL40ZeqIDBEVa54mULdut+aIBCsA8MZLfcb+H25WmTUR6T9GIBjrQSWDRnTyOKyiMjyLV+JZvQFrczHeBUtxMtOwx47q9DzOyGEYjc0Y9epZPZRYm7aA7bTKhovstzdGJIIRCuOk7zrjDyAycyrBk49rvyyo14uTkdapPn9GVS1WSTmRvSdjjy8gOnUCvk8XY1bV4FuwFGyHyIEzO3193WIYRPeeRPBbX8NJSyHhjQ/wvf/5bpXjNKtrAdoGRk0TZ1gu3sUrSXjlHbyLVmBWVONdukrlP0WkXwyKjL+uqqysJCendUp6Tk4OjY2NBINBEjpR576iIvYLfHZ265rW2dnZVFZWdmk9pmlgmnrKaqAwHBvDACvBB57uxb7NpESMcBiPZegJuk6yLLPV/0Vk4NpT79emiuUYuGSPPoaqja9hmQ6Gue1HoWioAn9KPr6ENJxoI5YFhrFnvQayZ9pT79ne5kSD1G6ZjxNtIDV7UvfnCVdhmBYJydkYpgmeBLyJWdihCjzd/FlT9ly6X6WvmMEwbnKivg/tpj69ZyePwd1YiG/FGozQttYikWMPxePtZClEgFF5GJaJr7QcOzu9FxYqA5F3fSHuyDys9JRtG9NTsGdMwbN4JWZ2Okanvh+Y4EvpeHduJlZF9S6/t3hWroHUZIyJBXhME+eQWbhV1STM/wijOUh0ziystOTOXVwXtHvPZqZizzsCVqzF+/FC3KnjcYfndm/+2lrwe7EyUtt8HugctA/umBG4uVm4WekYxeX4XnsPb2MTbqaqQIjsSD8X9649MvA30GRlJWMoODRg2JUeopZJRl46RkL3eh7YOWlEcUlK8WP4ul8ydChKS1O5GZHBYk+7XytXLSc9dyLDx8+mZvOr+K1akjO2NYbfGK4kY/hUkjNyqTANUpPB6+/5X0ZFesueds/2tsaaMizLJFy/ioyMxG4H+hu21JOclkdW9rYPdFIzR2I6NWRm6nuItE/3q/QmN2oTtqMk5KZj6ftQj+ize/bckwBwHQcCIYhGSepkptb2IqOH4a2swps5q4cXKAOR29BEuKIKz3FzSNnhnnePOgBnVC6JE0b0yGeT9rTxRF9+l2RsjA6CWW5DE+FNRXgO25/k7G3vX/eUYwg//l+M4dkkHjIz9sBUL2nvnnUPmUl44XJSmxqxMsd2a95IYxMMzyE5q53gaGYyjB++7XypCYTftEhtqMfabruItKafi3vHHhn4y8nJaZOVV1lZSUpKSqey/QByc2NPflRVVZGXlxffXlVVxdSpU7u0nurqJmX8DSBmdQNe26G2KQyBaLfmMGwXn+1QW1oDqfpFqjMsyyQtLZH6+gC27fT3ckRkJ/bE+9WxI1QVLSJ77ImEnSxs26V8y1dkuDlb94cINFYSJYNA2IdtO1SVl+JP0ZNnMvAEGwoJN5eTnDUVy5u8R96zfaG2eB227WA311GyeSWJaWO7NU9dVRFYmdTUbOv/7JpZ1NdsaLVNBPbMf2NlAGpswm87BG1w9H1ot/T/PWtCN/4OrdxsrIUraaysB6sL2YIyKFmLv8JyoTk3t/33y/BhUNvcMyfLzsJnWYS+WIl9wPT21/PZYizDoLlgxA7rMeGko2N9+uoCPbOeHc+9i3vWm5FOeGMJ0YnjujW/r6gCZ8wIGjt5X3qzMgivLSQ6uvPlekWGiv7/N3Zw6uzDpXtk4G/WrFm89957rbZ99NFHzJo1q9NzjBo1itzcXD7++GP22msvABobG1m8eDFnn312l9bjOC6Oo3rOA4UnGME1TaIO4HTvm4rh8eF1wWkM4CTqqYSusG2HaFTfzEUGgz3pfm2qWokdDZKYNRMHH97EXJrrNpOSH7u+UGMZruti+XLBTMZ1XUKBOqyE3Wz+LtILipY+TKixEDBISBtDSvZeJMw4Cdv27zH3bF8I1Bdh+TJwogHqy5fhTRq9y2Oi4Xo8vtZPt4eayklIG9fqtbcScgmXfEIkYqvyh7RrT/o3VgYeszGA60LE58PV+6xHDLZ71h6WR2JkKU5xJU43SxrKIOG6eFZvJFownKhpQa+/Tw3MsaOwVm8ktM9ebdvfhCN4V6wjMnUCUaOd9SQlxf7fy+vs6J41crLwrNvcvfs5HMFX30gkIx27k8cbeTl41mwkGrHVKkikA4Pt39jBYlA8xt7U1MTKlStZuXIlAFu2bGHlypUUFxcD8Mc//pFrr702Pv6ss86isLCQ22+/nXXr1vH444/z6quvcuGFF3Z6TsMwOP/887n//vuZP38+q1at4tprryUvL49jjz22j65cekUkguvdzZh3gg8AIxTqgQWJiEhva6xcjDchB1/yCAD8KQWEGrfE94ebywHwJuVjeWPlaOxIQ98vVGQX7EgjocZCciacRv7U8/Em5lGz5T02fPn3/l7aoBNuKsafMoqkzKk0Vy/f5fhQUwnrP7yWhvIv4ttc1yUSrMSb2PpDVV9iHo4dwg7X9fi6RUR2xQgEY39I7FzFI9nzuFkZuAl+rKLS/l6K9DKjpg6zth57wphdD+4h0QljMJqaMUsr2+zzrN6AYdtE95rQZ+vpCic3C6M5gNHU9QxIs7o2Nkd2RqePsYflYgRDGPWNXT6fiMjuGBQZf8uWLeP888+Pf33rrbcCcMopp3DbbbdRUVFBSUlJfH9BQQEPPPAAt956K//4xz8YNmwYv/3tbznssMM6PSfAJZdcQiAQ4Ne//jX19fXsv//+/P3vf8fv715fOBkYjEgUPLv31ndbegMGFPgTERnoXNelqXIJKXn7xTNv/KkFNG16Ddd1MAyTSKAc05OI5Y31ajAMCzuswJ8MPM01qwFIzT8Qb0IW6SMOpb7kHarWPUP2xEYwk/p5hYNHuKmElLz98SZmU7H6KexoAMvTcSWHuqJ3wXWoKZxPat4BADjRJpxooE3gz5sUaxUQbi7H48/otWsQEWmPsfX3VLebPe1lD2AY2CPysYrLiDCjv1cjXeG6+F97Fycrg8h+e4PXu9Phnk3FuF4P9oi8nY7rSU5eNk5qMp51mwhvn1EaCOJdspLoxLG4yQPzZ1InNwsAs6Iae4c1mqUV+N/7HCclCTc9FScjFdfvx6xvwKxtwKyqAcvE7ULfTSc/GwwDq7SCaDf6dYqIdNegCPwddNBBrFq1qsP9LYG6HY954YUXuj0nxLL+rr76aq6++upOr1UGPiMaxd3FD067ZFm4Xg9GUIE/EZGBLtS4mWiohpScfeLb/CkFONEA0WAV3sRcws1l+JLy44FBy5eKHdFTmTLwBGpX4U3Mw5uQFd+Wlrc/VWufoaFiISn5h/bj6gYPxw4RCVbiSx5OUsYkyl2bQM1XpOTu2+H4+tJP8KeOJli3jmD9RhLSxhIJxJ509ybmtBofCwQaRALlkDm5ty9HRKQVo64BNzEBzEFR5El6iT16OJ53NmOt2Yg9aWx/L0c6ySyrxCqtxCqvwrOxiPCcfbFHj+hwvLWpCLtgeN/2cjQM7Alj8CxfDQfNivXsA3yfLwEMwvu33/tvIHCTEnFTkjDLq7DHtu6751m9AVwXNzkRs7IGz7rNYNu4SQk46anYBcOxh+d17Xur14uTnYFZWgFTxvfw1YiIdGxQBP5EelS4B0p9Avh9GKFwq01mWSVuStKAfbJJRGQoaqpYjOlJIjF9YnybP6UAgGBDId7EXCKBcryJ256StbypRMP1fb5WkV1prvmKpMwprbZ5/Omk5kylvvRzBf46KdwUqxbiTx6BNzEXb2IeTVXLOwz8NZQvwIk2M3zvSyhadBe1W95i2LSLiAQqgLaBP9P04k3IIhwo790LERHZketibS7GHqU+xUOdPXYU0cnl+D9cQCjBHwsOSd+w7VhwqBs93azNxbiJCQTnHYXv44X453+EPWYkoUP3B7+v1VijoRGzpo7IrL16auWdFp04Gu+iFVibi7AnjMEsLsOzbjOhufvDAM82tnOzMCuqW290HKwtJUSnTCDSErh0XYja8cBmt883LDcWRHRd9fkTkT6jx79kyDGi0V2WSugMN8HfOuPPtvG/8QHehSt2e24REek5jZWLSc6ejmFu+4XN40/H8qUTaiwEYuX4fEn58f2WL1WlPmXAiYbqCDeVkJQ5tc2+7FEH01TzFVG9bzulJfDnS4p9MJ6cvTfN1StwXbfd8XVF75KUtTe+pHwyRh1JQ9nnsb+PQAWWNxnL0/ahL29iHpFmBf5EpG8Z1bWY9Y1Ex43a9WDZsxlGLFusYDj+tz/BLK/q7xUNWOaWUnzvfhYL2O0uxyHh5XdIePHNrveRawncjx6Om5pM6LhDCR1xEGZxGd4lX7UZbm0qBsvEHpnfzmS9y01Nwc7PwbM2lhXn+3gh9rAc7Ilj+3wtXeXkZmFV1bT6+zZLKzBCEewxI7cNNIzdDvoB2MPzMAJB9fkTkT6lwJ8MPeFoj2T87Rj4M0sqMCJRzLK2zY1FRKR/2JFGQo2FJGe37W2SkFpAqLEQJxrEDte1yfhTqU8ZaJprY2XqEzPalo7MHBnrOddYsbBP1zRYhZqK8SZkY3oSAEjK2ptIsDJWmnPHsQ2FBOs3kDHycADShh8KpkVd8ftEA5Vt+vu18CbltTufiEhv8mzYguv34Qzvu35fMoCZJqEjDsLOycT/5gcYtapo0YZt4//oSzzrN+P9fMluT+dZuQ6zqgYjFCbhpbcwqmo6faxR24DZ0LSttKdhYI8vIDplfKwMZSTSary1qQh7RH6PPNzeHfbEMVgl5fg+WYTZ2ER4zn6DIqPNyc0G28GsqYtv82wuxk1OwsnO6Pnz5W3r8yci0lcU+JMhJ5bx10OBv+1KfXo2FQFg1jdCILjb84tI/zKLy/C/8k6sHIcMWtFQ7Je5HcvwAfhTRhFqKCQcKAPAl7TtAzKPLxVbpT5lgAnUfIUveQQef3qbfV5/GsmZU2ks/6IfVjb4hJtL8CVvK3mWlDEZw7Boql7eZmxt8Xt4/BnxBwgsbzJpw+ZQW/Qu4eZSvAntB/58iXmEA+UdZhGKiPQ418XauCWWsaL+ftLCYxE65hDcpET8b37Y4e83ZnkVvg++wPv5EjxLvsKzesOQCBR6VqzFaA4Q2XsS3pXrsDZu6fZcRnMA78LlRKeMJ/CNY3CTE0l45R2sLSWdOt7aXITrsWJ95LYTnTYRIxKNBf9aBIJY5VVEx3Tc/6+3RceOAtPAs3oDkelTcDPS+m0tXeFkZ4Bpbiv36bpYm4pjr2VvBC592/X5ExHpI/pJUIaeSM9l/NGS8be1HEN0fKxnlKUSGiKDnlVehVVWCTv08pTBpaVcp+VNbbPPnzKaaKiGYN16gHYy/lQyUQaW5ppVbfr7bS9t2IE0165Wf8pOCDcW40ve9kGZ6UkgMWMizVWtA39ONEhD6aekD5/bqlxwxqijsMN1BOrWtvtgAYAvZQSuHaZs5SNEAqoIISK9z6yqxWxoUplPacvvI3zwvpgNTZjVte0O8S75CmtzEdbmYrzLVuH7cAH+tz/peE7XHfwPSQZDeJd8RXTKeCIHziQ6diS+DxdgNHSv8of38yVgmYT3nw5JCQRPOAJnRD7+Nz/CWrdpl8d7NhfjjBwGltVqu5ucRHR8Ad7la8Fx4mMxDOyC/gv84fMSHT8aJy2FyD5932ew2ywrFogrjwX+zMoajOYA9uiRuziw++xhuVillYP/nhGRQUOBPxlyjEik5zL+tgb+zPIqjGCI6F4TcVOSVO5TZA9gBLbe341N/bwS2R0twTuPr+3Tp/7U2MMaDeULsLwpWN7k+D7Ll4odacJ1e6DPhwx5oaYSqje9ultzRIJVRAIVJGZ0HPhLzd0XA4PG8i9361x7OscOEQlW4Usa3mp7UtZ0mmtX4TjbymjVl32G44RJGzG31Vh/8giSMmMfcHUU+EvKnEbe5HNoql7Bxk9+RfnqJxWUFZFeZW0oxE3w4QxrPxNZhjYnNwvX68EsKmu707YxS8qJTJ9C8LQTCJxzMqG5B2DW1nf4IKTv3U/xv/VxL6+6d3kXrwTXJTxrr1hPxEMPwPX78L/zaZf7/Zkl5XjWFxI+YAb4fVtP4CF09Byi4wvwffQlRl3HDxYazQHMyhqio9sP5EX2nozR1Iy1MVZtytpUjJ2fAwn+Lq2zp4UP2Y/gyceBx9r14AHEzs3CrIg9tG9tKoqVSM7P7rXzOcNyMZoDGA36fEFE+oYCfzL0RKK4PVD/3E3wYYQj4DixHxISE3Bys7DzcmJZQiIyqBlbS/YajV1syC4DSjRcj2F6May2vxB7E3MxLT+B2jV4k/Jb7bN8sQxBO6w+f7L76ks+pHLd8wTrN3Z7jkBNrL9fUmbb/n4tLF8KSVl70bBduc9oqJaqjS8Taizq9rn3NOGmUsDFn9L6g7XkrGmxDL0Vj1Kx9hkq179IbeEbJGfPwJuQ1WaejIKjAfAm5rfZB2AYBhmjjmTcnFvIHvdN6ks/YdNn/w/HVia5iPQC18WjMp+yM5aFMywXq6i0zS6zrBIjamOPHBbf5uTG/u0zK9vpUee6WEVlWJuLB235QqO+Ee9X64jMmAKJsZ6/+LyEjzwYs7oO/xsf4HvnE/yvvUfCi2+S8NJ8fO9+hnfxSqyNWzBq6rcFBx0H3ycLcfKysSeO3eFEBuE5+8VKrb73WTxjb0dWYcnWDL7h7e53szOwh+fhXbYKwhGskjLsfizzGWeagy7oB7E+f2ZDEwRCsV6Jo0f06vdOOz9nl33+vJ8vwbtwhbICRaRH6KdBGVpcFyPSMz3+8Mc+RDaCITwtPyQYBk5+NmZVLUSiu38OEek/LYE/PZE3qNnhBixfKkY7vRoMw8SXMgpw8SW27qPRUhpU5T6lJ4QaNgNQW/ROt+dorlmFP6UAy5uy03EpeQcQqF1DoHYNZaseZ8PHv6Bq/YvUbH692+fe04SbY312dsz486WMIjXvQMLNpTRVLqG+5GPsaJDMguPanSc5eyaj9v0JiRmTdno+0/KTNfZERsy4HDtcTzSokvA9raHsc6o2vNTfyxDpV2ZVDUZjM9GxBf29FBnA7JH5sdYkkUir7VZRGW5iAm7Wtj7Cbnoqrs+LWVndZh6jth4jHMH1efEtWDYoAxXeBctwExKI7t3633EnJ5PQ3P0xwlGMYDiWRZubhZOZjtHYhGf5avxvf0LiC/8j6Z8vkPB/r5Lw8tuYdY2EDt63/R5xXg/hw2djVtXiXbSi3fVYm4qxh+VsyxZsR2T6ZMyqWnxfLAHH7dXSlHu6lsC2Z+1GzPrG3g+itvT5Kylvf38whHf5GryLVuDrRsapiMiOeiD6ITKI2A64Lq6nh3r8AWZxeewXrK0/JNj5OeC6mJXVODs0ZBaRwaMl489Uxt+gZkca2i3z2SIhpYBg3Tq8Sa2/X3viGX8K/MnucV2XYMNmPL50Gso+J3fCafGM0q7M0VyzitS8/XY5NiVnH8oNk8Iv/4DlSyV73DeIBmtoqFiA6zoYhp77CzUV40nIwvQktNpuGAbDp1/S6XkMw9hpz8UdeRNipfciwWp8ye0/zS/dU73pFUKNxaTk7oc/RR+CytBkrS/ETfDjDGu//LAIEMvocxZhlVTEHl7eyioqxR6Z3zpoZRg4OZlYFdXs+FizVV61tTTm/vjf/gRrS0n/9prrIrOyGs/GLYTm7g/tfD5kTxiDPWFMxxMEQ5h1DZh1DRi19Zh1DYT3n46bndHhIU5uFpF9p+FduAJ75DCc/O3u1UgEq6Q8ViZ0J5yR+TiZaXhWbcDJzsRNSdrVpUoH3JQk3AQ/3qVf4Xo92CPar+DQk+xRw/EsXx0L6u3Qx9EqLImVnZ2zL77PFmO+9h7BYw7p91KuIjJ46Td/GVpanmrz9USpz9g/vp7V63F93ngfBTcjDdfnVblPkUFuW6lPZfwNZna4IZ69156WPn++HUr1tRwTVcaf7KZosAon2kzOhFMBqCv5qMtzRAIVREPVJHYiyGR5k8mdfBZ5k89m3JzfkTXmRFLyDsAONxBqKOzyufdE4aYS/Ml9/+Gkx58Ohkk02DZzQrov3FxOqLEIwzCp3vhyfy9HpH+0lPkcO0plPmWn3NRk3JQkrOJtff6MpmbMmvpWZT5bOLlZmBXVbTL6zLJKnKwM7DEjsYfl4F2wfFBl/VkbY+1a2pTl7KwEP05+DtHJ44jM3ofQcXOJztj1z2mRGVNwcrNiJT/D27IurS1l4DitgrHtMgwie8fKvkcHQpnPwcwwcPKyMUKR2Hvf6v1ypdFxozAiUawtbcvtejYV4eRlE506geCJR2DUN5Lw8tuqQCQi3aafCGVIMbaW3+zJjD+rrAp71PBtPyRs/eHBVOBPZPCKRDCiNm6CX4G/QS4aacDaWcZf+gQMw4oHAFsYlh/D9CrjT3ZbcGuZz6SsaaTmHUBd0Tu4bvu9XToSqFkFhrnLkpItMkYeQcaoozC39rZMTB+PaSXQVL28a4vfQ4Wbivsl484wPXh86URU6rNHNVUuwjC95Ew8jYbyBYSaivt7SSJ9y7ax1m7CaAoQHTeqv1cjA51hYI8chlm0LfBnFZVt3d4248nJzcIIhtr0PTfLq7Dzs2OBqP1nYNbUYa0fPA8YmRVVOHnZ7Zfl7NUTm4QOPxBCYRKf+i/+197Fu3AFnlXrcTLTcVOTdzmFPb6AyPTJRCeN64MF79nsreU++6pXopuRhpOVjrVhh3slEsUqLiM6Jla1wMnNJnjSURiOg+/TRX2yNhHZ8yjwJ0NLS9+9nujx5/WAGfshcccfEuz8nNhTcR00bRaRgc0IhICtT7g2NA+qp1elNTtcv9Oyiv7kEYw/7I/4klp/0GEYBpYvVYE/2W2hrWU+Pf50MkYdRSRYRVPV0i7N0Vy7ioTU0Vie7pVzMkwPSVl70Vy1rFvH70kcO0QkUIkvqX+ekvcmZBMNKeOvJzVWLCIpaxoZI4/Ek5BJ9QZl/ckQ4LpYG7fge+cTEp94Cf8HX+DkZbcuHSjSAXtkPmZ9YzyTyCwqjfU7a6e3nJ2THRtTsd2/Xc1BzIamWOAMcPKysQuG4124fHB8BuI4mJU18aBPX3NTUwh+4xgis6aBx4Nn5RqsknLssZ0sVW1ZRA6cCUkJux4rO2UXjIi9f0f13QNh0XEFsbKekW0FdK2iUrCdVp8tuqkphGdNwyoswaip67P1icieQ4E/GVKMraU+3Z4I/BkGrt8PltmmJIaTn4MRiWLqH2eRQamlzKedmxWrvx8M9fOKpLvscAOenZT6BDoMpni8qdgq9Sm7KdS4GX/qaAAS0saSkDaO2i1vd22Ohs0kpO3eU91JWXsTqF+PHRnaWczh5lLA7bcee56ELCIq9dljouF6AnXrSMmZhWF6yBrzdRrKv1DWn+zxrLWb8L/9CWZdA9HpkwmcfCzBrx/Z99lLMijZw/PAMGLlPh0Hq7i83Ww/ABL9uClJmJXb/u2yymPVjVoCfwDh/aZjNjThWb2hV9feE8yaOoyo3Wr9fc1NTyU6YwqhYw8lcPY3CZx2ApFOlAqVnuVmphGcd1SPtAPqLHtcAUbUjgX/trI2FW3N+ExpPXZ8AW5yIt6lq/psfSKy51DgT4YUI9qDGX+Am5QQC/rtMJ+TkwmWqXKfIoNUS+DPyd36hGsPlPs06hsxGhp3ex7pPMcO49jBnWb87YzlU+BPdo/rugQbtgX+ADJGHUVz9QrCzWU7OXIbJxok3FyGP2X0rgfvRHL23uA6NNd8tVvzDHbhptiHLP3R4w/Am5ClHn89qLFiERgGKTkzAUgffggev7L+BHBdfB8vxP/6e3tk5QZra4ZW8OTjiMyahpuVoaCfdJ7Pi52bhVVUhllRjRGOtNvfr4Wdm4W1XcafWV6Fm5KEm7zt4Tk3K53oxDF4v1w24B+aNMurwDRwsjP7eykxhoGbltInPeak/7mpyTi5WXhayn3aNtaWUuwx7WR8WhaRvSfjWV+oFiQi0mUK/MnQEt7a48/bM0/zhA6bTfjgWW13WBZ2TiZmmXq4iAxGRiC09ZfBjNjXDc07P4BYYM8s7/ie973/Ob4PF/TUEqUTWoJ23Q78eVOJqtSn7AY7XIcdridhu8BfSt7+WL5Uaove6dQcocYtgNtqju7wJmTjSx5B0xAv9xluKsbjz8L09E95LE9CFtFQTZf7PA41oaZiKte9gLuLgE1T5SIS0yfGv88bpoessSduzfor2emx0jO8ny7CqKrt72W04V2wDM9X67CKy3f689mg5LqxsoAj8vp7JTKIOSPzMUvKsQpLcP2+2MPLHY3NzcKsqomX8bTKKrHbyZYLHzADAN8XXStp3tfM8mqcrAzwKNAm/SM6bhTWllIIRzBLKjDCEaKj238oLTp5HK7Pg2fZ6j5epYgMdgr8yZDS4xl/mWmtnnLbnpOXg1VWuUc+YSqypzMCQdyEBPD7cH3eTj1d5/vgc/xvf9L+PR+NYlVWY5VXx0qHSp9o6c/n8aZ163j1+JPdFWzYDNAq4880vaQPP4z64g+xI7vOAg42bsYwrB4pTZmctTfN1ct3GUzZk4WaSvCn9E+2H8QCsK5rEw31fTn4QN16gvUDvwQbQGP5Aqo3vUKoYVOHY+xogOaar0jJ3bfV9rStWX81m1/v7WVKOIJ3xVo8mwdWaVXP8tV4l64ifOBMnLQUPCvX9veSepRRXYcRDGMP76A0o0gn2CPzMcIRPF+ti5X53EnGqJObDbaDWV0H0ShmVS1OXjv9JBMTiOw/A8+ajZilFb24+t1jVlS1G7gU6Sv22FGxMrubivBsLopl0Galtz/Y6yG61yS8qzdCYGBn04rIwKLAnwwtkQiux+qTMihOfg5GIKh0fJFByAgEcbc2S3dTkndZ6tOsrMYqq8JoDmDUt/0g3yyvBscF28asru2NJUs7eiLjT6U+ZXeEGjZjeVPw+LNabc8oOAZwqd78v07MUYgvZSSGufsPLSVnTycaqiXcVLTbcw1W4cYifEn9098PiL8XosG+zUByXZvipX9h8xe3UrTkPkKNA/s9EAnEyuXXFX/Q4ZjmqmW4TpSUnFmttpuml+ScGTsNGkrPMBqatv5/4JQyt9ZvxvfZEiLTJxOdPpnoXhPwbCzCaNp19YbBwiopB8vCycva9WCRDjg5Wbh+L0YkutMyn0AsO84wMCuqMStrwHU7DJxFJ4/Dyc3C9/HCgfnAYyCI2dAUb+kg0h/c5CTsYTl4NhRibS4hOmbkTj+njOw1AdcA78o1fbhKERnsFPiTIcWIRMHTM9l+u9Lyg7BZqj5/IoONEQjiJm4N/KUm7bLUp2fFWtzkRDCM2IcxO7DKKnD9XlyPpe8JfailTKflTdnFyPZZvlScaADHifTksmQPFKzfSOmKR3DscKvtoa39/YwdfpH3+FLJGHUMtVveIhqu3+ncoYbNJOxmf78WCRkTMSwfTVXLe2S+wSYSrCESrCQhfXy/rcGTEPugPhLq2z5/zdVfYYfryR73TcJNxWz67P9RuuJhIsGabs3n2CFKlj1IuLntv3k9IRKoAAwayj/Hsdt/ur2xYiH+1NF4E9t+eOtNyCaiXoq9ztwaTDPbeeipNxgNTXgXreiwoopZWoH//c+JThhNZGvJwejEsbgeC8/qwZHt2hlWcTn2sBz1A5PdYxjxrFF75C6yRz0WTlYGZkUVZlklrtfTcXaSYRCasx9mXQOe5QMvSNHSq1CBc+lv9rgCrKIyjECw/f5+20vwE50yHs/KdRDR76Yi0jkK/MmQYjQHcJMS++Zkfh9OajJm7c4/0BORgSdW6tMPgJOSvNPMXaM5gGdDIZG9J8X6X7QT+DNLK3Hyc3Fys7D2tD4zA5gdbsDyJnc7U8qzNVPQDg+cTAYZeKLheoqX3k996cfUl3zYal9wa+CvPZmjj8MwLGo2vdbh3I4TIdxU3OEcXWWaXpIyp9JcPTT7/AVqY71REjMm9dsaLE8ipiepzzP+Gso+w5c0jKyx8xh70M3kTTmHpuqVbPnyD0QCXV9LfcnHNJR/QUPZZ72wWogEK0nNn40TDdBY/mWb/Y4ToalqWZsyny28Cdk40QB2dM/J8hqI+jrjz7N6A96FKzBq2v/9yrNyHU56GuG5B2zLnPB5sSeMwfPV+oGZfdRVto1ZVoE9XP39ZPdFp00kMnMqJO66762TmxWrclJeFcuW20l2kpudQWTaRHyLVsa/TwwUZnkVbmJChy1bRPpKS5afm+DH6UTp2ejekzCiUTyr9pwHWUSkdynwJ0OKUd+Ik9a9zI/ucFOSMQfYD7oismtGILQt4y8lGbOxucOnyz1frcO1LKKTxmEPz8UqqWg91raxKqqwh+Xg5Odgqvdnn7Ej9Vje7pX5BOLHqtyndMR1opQsexDXtUnK2puazf/DdWL9hKPhBqKh6g6z9SxvMpkFx1Fb9C7RUG27Y8JNxbiujT+1oMfWnJy1N4HatTjRYI/NOVgEalfjSx6Bx9e9vp89pa+z0Rw7TGPFQlLzD8QwDAzTQ8bIIxh9wPVgGGxZ+McuBf9c16Gm8A0AmqtX9Px6nQjRUC1JWXuRlDmFuh0C6gCBmq9w7GCbMp8tWjIro8r661Ut5TONYBhC4V2M3n3W1p5hVmE7PQVtG6uoFHvsSDBbf8wRmToBIxjC2jSwS9x2hllRjRG1sUco8Ce7z8nPIbL/9E6NtXOzMOsaMcsqcfJ3HaSI7DsN1+fFu/Sr3V1mjzIrqmJBlj5o/yKyU4kJRMcXEJ08rlPvRzc5iejYAjxfrdPnCSLSKQr8yZBi1jfh9mXgL3XnmUIiMgC5busef6lJ4DgYgXY+JI/aeFatJzppbOyJ8uF5GKEwRnVdfIhZWQO2g5Ofi52XE9tfp0BSX7DDDd3u7wfbBf7C+vuS9lWue45g3TqGT7+M3ImnEQlW0VD+BQChxs0AO83Wyyg4BtPyUb3p1Xb3hxo2g2HiTxnVY2tOytob17VprhlYH8T1hUDtapIyp/T3MvAkZPVpQKqpaimOHSQ1f3ar7d6ELEbtew1ALPjXySzEpsrFRAIVpA0/hGD9hh4PIke39vfzJuSQNnwugdrVhJvL4vsdO0zVhpfwJQ/Hlzyi3Tlaeil29pqke8yGJtwEX/zPvSoSwayoBsvEU1jSdi2llRiRKNGCtu8JNzMNe3gu3pXreneNfcAqLsf1+3CzMvp7KTLEOLmx76tGJNphf79WvF6i4wuwCksHTpDCcTAra7BzVeZTBobw4bM7HXwHiO41AbOhCauotBdXJSJ7CgX+ZOiwbYymZpy05D47paPAn8jgE46A4+AmxAJ/Tkrse4bR2LZcmGf9ZoxgmOheE2Nj87LBMrFKt5X7NEsrcL0enOyMWC8Jwxj05T4dJ0KoqZ2n7QeYaLhh9zL+fLEHRZTxJ+2pL/uMmsI3yZl0OkkZk/CnjCI5ewbVm17FdR1CDZsxPYl4E3M6nMPyJJI5+mvUFb/fboAi1FCILykf0/L32Lp9SXn4kvJpqhpa5T4jwRrCzWUkZkzu76XgTcjq04y/hrLPSEgbiy+pbQ8nb0I2o/b7CQBbvvwj9SUf01i5hEDdOsLNZbjtfFhbs/kNEjMmkTXm67Egcu2qHl1vJLg18JeYQ0ruvpieROpLPgLAdV3KvvoHocZihu11UZv+mS08/nQMw1LGXy8zGpviJSeNXu7zZ5ZVgesSmTYpFgDc4YEsq7AYNzmxw75j0b0mYpZXxR7IGsSskvLYa65sJeljbloKrs8LhhEPAu6KPXIYRnOg1UOR/cmsqcOI2p0qqygyEDm5WTjZmXhWDP4HWUSk9ynwJ0NGS215N7VvS30aoUiflL4RkZ7RktnnJsY+aHdTYv0f2vSvcV08K9ZgFwzflklsWdh5OVjF2wJ/VlklTl5O7AMarxcnKz1W7nMQq9n8Bps+vYnSlY9hRwZu/yQ7snsZf6blx7QSlPEnbUQClZSt/Adpww4mY+RR8e1ZY04k3FRCU+XiWH+/lAIMY+c/bmeMPArTk0j1xlfa7IvN0TP9/baXnD2Dpqol7QZ19lQDob9fC48/i2iobwJSdqSZpqplbbL9tudNyGbUvj/BsLyUrnyE4iX3Urjg92z85FeUrXwU13XiYwN16wnUrSWz4Di8ibl4E3J6vNxnJFCJYXrw+DMwLR9p+QdRX/IRrmtTs/k1Gso+Y9heF5CQNqbDOQzDxOPP7NMA61BkNDbjZGXg+n2Yvdznzyopx01MILL35NhDVFu2y/pzXTyFJdgFIzoMiNkFw3GTE2Ml0garcCzr0VGZT+kPhoGTm42TlQFeb6cOcYbl4HqsAZOdZJZXgWng5GT291JEuscwiOw1AauoVFWERGSXFPiTIcPc+hRq35b63BowUNafyKBhBEIA8R5/eL24Cb42GX9mSTlmTT2Raa0/RLaH58YCe44TKydTHuvvF9/f0udvEAvUfIU3MY/G8gVs+uwmGiuX9PeS2mWHG3a7l5flTSGqjD/ZQWPFQsAlb8p3WmUcJWZMJDFjEtWbXiPUsJmEnZT5bGF6Esgs+Br1pR8TDW17It51HUKNW0jowf5+LZKzZxIN1RJqLOzxuTujpQ9iXxoo/f0gFmhzooE+eXCisWIhrhMlNe+Ana8pMZuxB93MxCPuZfyhtzNm9o3kTTmH+tJPKF/173iQuKbwDXxJ+STnzMQwDJKyptFcvbJH1xwJVOJNyI4HzdNGHEo0XEfF6qeoXPcCWWPnkZp/4C7n8SRkE1Wpz94TCmOEI7gpybhpKb2e8WeVVsR+nkr0Y+dm4dm8LfBn1NRjNDYTLRje8QSmSXRcAVZJecdjBjirNNZHuiXLUqSvhQ+eRejwXX//jbMsnBH5WO2U5+1tRnMAa+MWsO34NrO8Oha4tKw+X49IT7HHFeD6fXhWre/aga4buyccZ9djRWSPoMCfDBlGQyNY1rYP8/uAkxILMvZ6zwsR6THxjL+kbd8r3JTkNvexZ9V6nIw0nOG5rbY7w/MwIlHMyhrM6lqMSBRn2LYxTl4OZkMTRnOgF6+i97hOlEDdejJGHs6Yg27CnzKK4iX3UrHm6f5eWiuu62KH63er1CeA5UtVxp+00VyzksSMSe2W4MwacyLB+g1EAhU77e+3vfQRh2EYFrVF78S3hZtLcZ1wp+foisSMiZieRJr6IWhfW/Qe6z/8GeGmzn0IaEeaeyRrK1C7mqQBUOYTYj3+gD7J+mso+4ykzMl4/BmdGm9aPjz+DPwpI8kYeST5U8+nrvg9KtY+TSRQQWPFQjIKjo0H5ZKy9iLcXNqjmXWRYAWehG0PzCSkjsGfUkBt0Tuk5M4ie9w3OjVPX5dUHWpaHohyU5NwejvwF45gVtXibA142QXDMYvL4h/oW4XFsbLqO/xMtiMnKz227kFajcUsKcdNScJN7bvWFSLbc9NScDO69gCNXTAcq6IagqFeWlVrRn0jvg8XkPjMq/jf/oSEF9/ELK0AwKyo6lx/QpGBzGMRnTwOz5qNEIl0/rDVG/C//QnW5oHfskNEeoYCfzJkmA1Nsf5+fdkPIcGH67Ha7Q0mIgOTEQjGngL1eOLbnJTk1vdxKIxncwnRSWPbfE9xcjJxvZ5YRmBJBa7HwsnO2LY/P/bLpjlI+/wFGzbhOmESMybjTchixMwfkjXmRGq3vI1j980v9J3hRAO4rr1bpT5ha+BPGX+yHdeJEqhdQ1Lm1Hb3J2XtHQ/WdTZoZ3mTSBt+KHVF7+I4sV/gQw2xbDx/Ss9n/Bmmh6SsaTRVLe3xuXfGdR1qNv8PO9JE8dL7caLBXR5TuuLvFC26c7fKkkZDtbH+fpkDI/Dn3Rr4a6+vY0+Khmpprl210zKfu5I+4lDyJp9DbeF8tiy8E8uTRNqwg+P7Y/eB0aNZf5FAVZvemNnjTiI5ewbD9vruLsvntvAq469XmVsrmjgpybipKfHqKr3BKquMZbptfZDKLhiBEbXj2XuewhKcEfm7zOJxMjNia68ZGP3GusoqVn8/GXzskfmxTKPist49USSK791PSXzudazNxYT3nUZw3lHg95Hw6rv43vss9plQrgJ/MvhFp47HiETxrO9k9Y5QGO+Xsf7e1paBUXpXRHqfAn8yZBj1jX3a3y92UgM3NTn+i7GIDHxGIBjr77fdhypuSlKrkr2e9YXgukQntPOhvmniDMvFKimP9ffLzW71QZSblIiTmjxoy30GatdiWn78W8sPGoZBav5sXNcmWLehn1e3jR2pB9j9jD+vMv6ktWD9Bhw71GHgzzAMcsZ/i4S0cfiS8js9b8aoo7EjTTSUfgpAqGEz3oQcLG/vZHakZM8gWL+RaLi+V+ZvT3P1SiKBcvKnXkA0VEvpykd3GtAL1K2nqWoZ4eYywk1F3T9vvL/fwAj8Wb40DMMi2svZaA3lCzAwSMndb7fmyRh1JLkTTycSrCRj5JGtMl0tbzIJaWN7rM+f67pEAhV4E1oH/lJy92XkPj/E9HS+cocnIYtouC4eTJeeZTQ2gWVCgh8nLRkjGOpS5kFXmCXluEmJ8ZYNbkYqTmpyLGshEMSsqN55mc+t3PQUMI1BGfj7/+z9eXhk12HeCf/OXWrfsK+9781mcxcXkaIoiZKiyLKWWMqM7dixvxnHdma+eLLOM/mcsSffyPHYzmcnccb5MuNYYzv2xJK1WJIlURQlUiRFsrl0s/e9G0Bjr32725k/LlANNApAAajC0ji/5+lHYtVdTgGoW3XPe973FaUyWiaH29/454pCsRmQ0QheexL9ZmvFBuPKDYyrQ1iP3kf5Jz6Gc/wwXncHlY+9H+vxB2ouJ085/hR3ATIWxd3Rh3H2EjSwOM585yw4Ls7eHb7wt416vhWK7YwS/hTbBpEr4K1jv98sMhZFqKhPhWLLIMoVZCQ87zFfwC/VviAbl67hDvbCItHBbl8X+vgU2tgkXm/ngue97g5/9foWpJy5QCi5DyFui5mBaB+6GaWcvbSBI5uPMyPWGWt0/Bkq6lNxB6X0OXQzuqSbL9pxjJ0P/4/z3ifLEYh0E+08Tvrmc0gpqRZutCTmc5ZIxzEAilPvtuwcd5IZfoFgbAeJvifoPfp3KUy8SfrGtxbdfvraXxGI9KIZYQoTb6/6vOX0BQLRvvr9fq677pMfQmgYLY6h9Dyb7PALRDvubYp43LbzWXY89M9o3/2xBc9F2o5QSp9FyrV3xnh2Ac+tYIaXjmxsBDPkT+46lfSaj6VYiCiU8GJ+msqsICdyrbnn0UcncPu6bi/KEgJ3Zz/6zVGMme4wd3B54Q9dx0sl0Ka3nvCnX73pv+5l4kwVis2IO9iHPtxasUG/NoTb24lzZD8Yc75/CYFzeB+VT3+E6oeeQMYiLRuDQrGe2Ef2oaVzmCfeRaRzi76/RCaHeeYS9n1HcA7sQZQriC34OahQKFaOEv4U2wPPQyuUajel63rqeHSeU0ihUGxuRLmKDM3v7fJiUZASUSojMjm0yTTO/l2LHsPt7QbXQ1h2LZZq3vF6Ov1JpxatjG8VUnqUs5cIpw7Me1wIjVByH+XMxQ0a2UJmxTq93kT/CtBNFfWpmE9p+izh1KGG4wZXQtuOD2EVRyilz1DJ32yp8GcEEoQSe9at588uT1KcPElq4P0IIYh1PUD7ro8xeeXLFOu4xSq5qxSn3qVjz48RbT9GYeKtZc/huVVunf6PjF/883kiVDlzvq7bTxudIPyfv4ZxoUluZSnRrw41tvI61L7A8ed5NtXC6p2Nc0lf/2vs8iQd+z7ZlOMBhJN7EZqx4PFI+1Fcu0C10GDc1BLMxp/eGfW5GmpdiqrnryVohRIy5ovKXny217wFcZ9VC20qs0Dwcgf7EKUyxqnzvoMnvLBztR5eW3LrOf5cF/PUBT9pYh376hWKZuHu6ENULbSJFl2PK1X0WxO4uwcX3URGwrg7+ltzfoViA/D6unEO7cU4e4nwl79N6Evfwnz95ILO3cDrJ5HRMM49B/B6OpCmgT7UWNe2QqHY2ijhT7EtEDNOHW8DitBlLIKWLykrvUKxRfCjPudPqsyuDBX5Isal68igibtEpJRsTyJDAT/2s6t9wfNuTydIiTa+tSYjrcIwnlMmnDyw4Llwcj+V3BWkdDdgZAtx7RxC6GhGePmNl0APxPHc6qbqL1RsHJ5ToZK7umjM51oJpw4SjO1g8tJf4DklQvHm9/vNJdp5L6XpM0jPael5ADIjP0AzQsR7b/fNdez9BJG2w9x69w8oZ6/M237q6l8RiPYR636IWNcDVAs3scuLO6Vdu8Tw279LYfJtMkPf49bp/z+eZ9f6/SKpQ/O210bGCH7nJYTtNC16WR+6RfCFVxvqcDWC7Qs6/qaufIXrr/8vDYmcS1Et3mL62jdo2/VRgtHWT3KGk3vR9GBTev7s8gTAgqjP1WCsU5diy5ESysv3Ya43olC87ZwJBpABc8FkYzOYTUjwervnPe71diIDJlqu0FDMZ22/tiQind1S92bGhWuIShX7eGs+exSKVuN1tSODAfSbrREbjOv+ohln10BLjq9QbEqEwHriQcr/9SeofugJvJ5OjItXCX/pWwS+/yPEdAZtaBR9aBTrkeN+/Yiu4/V1+w5chUJx16OEP8W2YPYmdCMcfzIe82OkymrSWKHYCtQX/vxFAyJfwLh8HXfPznm9fQsPInAHenF7O+tuJ5Nx/+Z3i8V9lrIXEZpBKLlnwXPh1AE8t0o1v3bHRzNwrTy6GVuzK8sIpgDlGFH4lLOXkNIl0t6ayVchBG07PlhzfQVjrXP8AcQ6juO5lZa7dT3PJjfyEsm+987rhxNCo+/YLxCMDjD89r+mlD4PzLr9TtGx++MIoRHtOIbQTAqTb9c9vmPlGHrrt7FKtxi8/3+g/9gvUJx8h5GT/64WZRpuu+3402/eIvTcD/F6unD27Wya+0cb9a/p2nRm2W3NUAdO9fZ1RXoOudFX0fQgt07/R0qr/J1I6TF+/o8xw511YzlbgdAMwqmDTRL+JtHNKLq59ig2TTPRA4l5P+etiH75BpH/8g0/xmsT4Ud9zvyeZuI+tRYIf9qtcWQsgrxzAaem4Q74fXcrcfF47SmE47ZEpGwJrot56hzOnkFkcm3x5QrFhjFzb7Qil5HnYb5zlsArb9X+mW+cgqq1YNPZmE/liFVsS3Qdd0c/1pMPU/7sx7EevQ99fIrwV54j+MKruH1duHNEcXewF318uu57SaFQ3F0o4U+xLdDyBdAEMrr+ee6zN8SaivtUKDYXpYp/8+jN6SSSElGpIiN33DQaOjIcwrxwDVGq4BxYPOZzFuuJh6h+4PH6TwqB19uJNjq+hhew/pQzFwnFd6Np5oLngvGdCM3cND1/rpVfc8wnUOuZsmZcKIrtTWn6LEawDTPc07JzxHoeQQ8k0ANJjGCyZecBCMQGMYJtFKdOtfQ8hbE3cO0CyYGnFzynG2EG7v9/E0ruY/id36M4dcp3+0V6iXU/BIBmhIi0Ha7b82dXphh683/DsXIMPvAPCSf3Eut6gIHj/z2V7BXGz/8JgUhvrd9Pvz5M8PmXcQd6qX7wcbzONrRsfv5nwSqZXczRSH+YEWrHqWZrbsvi1Lu4Vo7B+3+FUGIvIyf/3bzYT8+pkBn6HpnhHyx53NzIDylnLtJ96CfrXqtbRaT9KOXspTW7o+3KZFPcfrOYoY6WdimuB1q+CK5H8KXXm/J32hSqFsKyawujYKbeoAVRn/qtibqx6QDOkf04B3YjU40LYl67f13dKnGfxuUbiGIZ+/iRjR6KQrEm3B29aNNZRLHU0PbmG6cw3zqDNj5V+2ecvUTgjTu+szQQ86lQbBsMHefIfsqf+SjVpx7B6+7AevSB2x25+MIfUqIPj23gQBUKxXqghD/FtkDki37M55wPu/VidnVqK26EFQrF6jHPXsI8dX5+JFvFAikXdPzBTGzv+BReKo7X0bb8CQwdzMUnXd3+Hn+lnbU1ev6klJQzF+v2ZIHvrAgl9tR1DlXyN5i+/i2yIz+kMHmSSu5qy6MzHTuPHlj7yngj2IbQjFr8nGJ7U0qfI9J2GNHC7xOaZtK1/ydo2/lsy84xixCCaMe9FFrc85cZ/h6R9qMEIvUFU00P0n/8l4m238PIyd+nOHWK9j0fn+fYjXXdTzl7Cce63bnpuVWG3/k9pOey48F/TDB2e9Iv0n6YwQd+Bc0IEe24139QSgI/PIE72Ef1mcf8uKO2pN/Julb3j+2gTaZB1xp2/IHEqaYByI2+TDC+k1BiN/3HfxEz1MHwO79Hafoc4xf+M1d++E8Yv/CfGT//x4s6NJ1qlonLXyTR996WxdEuRrT9KNKzaw7L1WKXJzGa0O83ixFqx9niUZ+iUkUGTbSpDMa7FzZ6OMBMjQIg47cXVfqOvyYvdKxU0dJZ3L7uuk97PZ1YTz68snu8cAgZCjYk0G84nodx8hzurgFk29oXMykUG4k70AtCEPjRO+jXh5d0G+mXb2Cevoj16H1UfvxDtX/2w/diXLiKmErXtlUxnwpFHTQNd/8uqh9+asHnh4xG8NqSKu5TodgGKOFPsS3QcgU/cnMjME1kMIBWaGxlm0KhWAekxLh0HWDeSjcx06FzZ9QnUOsIdfbvbsoiAnegx19pd2truP7s8hiulSOcWtjvN0s4uX8mCvF2b46UHqNn/k+mrnyZsXN/xMjJf8uNNz7PrdP/saXj9aM+1y78CaFhhjqV8KfAsfJUCzeJtLfedZHofZT2nR9u+XnA7/mzy+NYpdas+q3krlPJXSM18P4lt9M0k75j/y3x3kcJJfYS7374jnHeB0Bx8p3aYxMX/xy7MkX/ff8dgchCYSCU2MOeJ36Dzn2fAkCks4iqhX10P2j+bZDX1hz3jzYxDVLi7J2JDl3GmXW7f24ax8pRnDxFou8JAHQjwsB9/x1C6Ay9/Tvkx98gNfgB9jzxeUKJPYyd/5MFvYxSeoyd/78Qmk7X/s+s6bWshkC0j3DqIOmbz63pOHZ5osmOv/Yt7/gT5QpeRxv2PQcIvHVmU0R+ziaZePMcfzFEqQxOkzpDLZvAq37fpddX3/G3Wrz25JZw/OlXbqLli1j3Kbef4i4gGMB66BjaVJrg868Q+dOvEvrKdzDOXp73mSmmMgR/eAJn306cw/vmHcI5tBcvlSDwo3dqPZ0q5lOhWDnuYC/60OiW6rtVKBQrRwl/im2ByBXwNqDfbxYZjyLyKupTodgsaMNjiFIZL5VAH2lM+JMx3zXs7mtO55aMx/ASsXnn38yUMxdBaIST+xbdJpzaj2vlscu3X1Nx6hRWcYTBB/4hB97/++x972/StvNZypkLSNm6yDLXymE0wfEHftynEv4U5fQ5gHV3UrWaSNthNCPM9LVvtOT4xenTaEaYaOfxZbcVmkHvkZ9lx0P/dEE/pxFIEE7uozDhCwH5sdfJjrxE94G/TTDat+gxNT2I0AwA9NFJ0DS8ro7bG4SCyHBozSKAPjaJDJo4e3c05CA0gr5z3KlMkR97DYQg0fOeOc+nGHzgH9J37BdmxMtPYoY66Dn809ilMaZvfGve8SYv/QXFqXfpPfKz6ObGfOdt2/kslexlytnLq9pfShe7Ol2LWG4GxkyXYis/b1pOxe8eth+8By8e3RSRn6JQ9DuM5yQkzHapN+OeRxubJPyV76APjVJ933uaXtfgtSUbcuZuKFJinjyLu6MP2ZHa6NEoFE3BufcQ5Z/4GOWf+BtUn3wYLxkn8OpbhL70LfQrN6FSJfT8y3jJONYTDy1cbKlpWO85jj42iX5tWMV8KhSrxB3sRVSqaHPcswqF4u5DCX+Kux8p0fLF2s3oRuDFo/4NskKh2BQYl67hpRLYxw760WwVP3ZyKeHPObjHn3yKhJs2Dq+/B22LZOuXMxcJxXagGYuvpg0l94HQKGdu9/xNX/9rwsn9hFP7EZqBEUwR7TiO55SxiiMtG6/bpKhPUMKfwqeUPkcg2ocRTG30UJqKpgfpPvA5cqOvkB8/0fTjV3JXCCX2LBDylmKxKNVY1wOU0mepFoYYO//HxLsfJtH33oaPq49O4Ha1+VHMc/DaEmhrdFFpY5N43Z21KOjlRAVND6AHEtiVaXK3fki0874Fgp0Z7iDe/dC8rr5gbJDUzmeZvvaNmkszM/Q90jefo/vA527Hmm4A0Y57CUR6SN/4zqr2dyppkB5muGP5jRvEDLYjPQfX2niX3GoR5aofQa7rWE897Ed+njq/sWMqlPDikXmT8rOLLLW1xOZKifnWaULf/D4yEqby4882bcHVXLz2lB9Xuonj1vWbt9CyBez77q7FJgoF+Asq3QO7sd7/GOUffxaZShD8/o8I/8U3wXb8nvQ7Pqtn8QZ6cXf0EXjjJMaVm4CK+VQoVorX3YE0Dd/1p1Ao7lqU8Ke46xHFEnge3kZFfeJ/sdWU40+h2BxULYzrIzj7d+EN+H1T+ogftynKFaRp1L3RlPEo7t4dTR2KO9CDli+uvVtqHfD7/RaP+QTQjTDB6ECtf6qcuUgle5m2XR+Zt10osdsXCLNXWjJW6Tm4dhHdbE4fjhnuwq5MbW3HiGLNzPb73Y3Eex8n1vUg4+f/GKeaadpxpZRUslcIJ/Y25XixzvuRnsPQW7+NbkToPvRTjfctSumLcz0L3WReWxKxFsef56FNTOH2dEIwgIyG0aYyy+5mhtopTL5NtTBMcibmsxE6dv9NjGCK8fN/TGHyJOMX/5y2HR8iNfjM6l9DExBCI7XjWQoTb2GVVh5jPbvAwgw1z/HndymypeM+RaVaW5DkdXVgHztI4M3TBJ/7ISKzMYKmli8io9H5D4aCSNNY03ca48JVzLfPYt9/hMrfeLrWld5smhXx20qMS9fw2pPzHcoKxV2IbE9S/dB7qXzs/Xh93VSfeXzZ9771yHFEqUzgjZMq5lOhWA2ahjvQo4Q/heIuRwl/irue2ZvPjXT8yXgUUSxveCyPQqEA48oNv4dp3y5kJDyv2FqUK3Xdfq3C7e0CITZ93KddmcKuTC0r/AGEUwcoZ33H3/SNbxGI9i9woGh6kFB8J5XspXqHWDOu7V/3m+n4k57dVEFEsbWwy5PY5QkibXdnz5IQgp7DP4UQBqNn/1PTRG67PIFrFwglmyP8meFOgrEduE6Z3nv+X+hm4/F/IptHVKr+BOEdeG1Jf4GWvbpuMm0qjXBcvJlje+2phgQFI9hONX8DI5Ak0n608fPpQboP/SSl9HlGTv0+sY7jdO7/W6sae7NJ9D6GbkbJDH13xfvalUlAYM70HzaD2S5FZ6sKf66LsGzf8TeD/dAxqk8/ipbOEv7ydwi8fAJmEgvWC1EoIeN3vP+EQMajaPlVCn+ui/nOWZy9O7DvP1rr4WwFMhUHITav8Fepot+8hbN/10aPRKFYN7yeTqoffKKhTk+ZjGMf2Q+up2I+FYpV4g72+h3VM+lHCoXi7kMJf4q7Hi1f9G9EY83thlgJXiwCUvruQ4VCsaEYl67jDvZCxBf43P5uX3iT0l9VH1nHFaMBE7e7A30Txn2WckNkhl9i7NwfM/zO7wEQTu5fdr9waj92eYLi9GmKkydp3/mRuhF/ocTeVfdALYdr5QGa1vEXmOmbsssrd7Ao7g6qxWFgxq16l6KbMXqO/Cyl6TNkhr7XlGNWcleB5v7cug/+bfrv/cUl+0broY9OgBB43QvdM3LW/bNK95Q2Ook09FrMp9eeatjxBxDvexwh6keaLUa0/SjJgacJJ/fRe8/PryhKtZVoeoDU4DNkb/0Q115Z2oVdnsQItdU6GZsyHiOCpoewK1NNO+Z6ImYm4+YtShICd+8Oyp/+CNbD96JfHSL0rRfXb1BSIgpFvNhCR46XiCFyq0s5MS5cQ5Qq2PetwwILXcdLJTZtz59x9SZIcPY2P+ZUobhbsO8/in3PAfU+UShWiTfQC9C6RchSgr15I7UViu3A5rhDVChaiMgVfOGthatGl0PO3Bg3o+xeoVCsHpHOok2mcQ7srj3mDvQiShVEOocoVZCh9Y2KcQd60G6NbypHcGHiJKe+88+4deYLlLOXCCX20nfPf9uQg25WHBw7+0cYwXbiPY8sup1dnsBpQe+SY/vCn242R/gzwp2AUD1/2xi7NI7QA+iB5EYPpaVEO+4hNfgBJi9/aVVRjXdSyV0hEOlZ0F23FsKpA8Q671vxftroBF5nG5jmgue8VGJN7h99bNKP45v5rum1J33BprS0C8uYiaFM9j6+qvP2HPpJBh/4R2h6cPmN15HkwNMgJZnh7y+6TSlzkRtvfB67fFuQs8uTTY35BN/NaoTacapb0/EnyjOr8MN1fse6jnPsINZjD/h/u+u1Yt+yEbZTd1GljMdW5/hzXcyTvttPppoT070cXltyzd2ercK4OLNITcUXKhSLEzCx33MfBBZ+risUiuW5nX7UGuEv8MpbhL/8HSX+KRQbiBL+FHc9Wr6A3MB+P6B2Y6wVlONPodhIjIvXkKGAP5kyg9fbCbqOPjKGqFSQ9SbXWog30IOwHT9mY5NQyd/ACEQ59MzvsfvR/5neIz9DvOfhhvY1ginMcBdONUPbzmcXdW6EZtw6lRa4/mYdf82K+tQ0EyPYpoS/bYxVHicQ7m68T24L07nvU4CkOHVqzceq5K4SSuxZ+6DWipToo5N+B189DB0vHl1dz5+UaOOTeHOO7bWnAJZ1EyV6H6Xv2N8jEO1b+Xln2Ix/k0YgQaL3cTJDz+N5Cyd7pOcwfv6PqeSucuv0f0B6fsSqXZnEDC/yO1oDZqhjy3b8iYovHs+N+rwTr9N3mmqT6fUZU8FfyCjrOP5kIoYolMB1V3RM4/wVRLm6Pm6/Gbz2JGI64zsSNhEinUObmr9ITaFQKBSKVjA3/aipVC2MS9cRhRLm22ebe2yFQtEwSvhT3PWIXHFD+/0A0HVkJKwcfwrFRuJ5GJdv4OzbBfqcSDVdx+3tQh8ebWnH39i5LzB+4c8WDqujDRk0N1XcpzV+lcAtC02uLm4tnDqIbkZJ9j+56DZmqA0j1N6SuE/XzqPpwaa6YAKRLiwl/G1b7NI4Zrh7o4exLmh6kEC0n2phaE3H8VyLav4moURz+v3WgsgXEeUKXu/ibjLZllyV409kcoiqPU9UlPEo0jSWFf50M0a8+8EVn3Mr0LbzWTynxOSlLy54LjP0PazSGD2Hfppq/gaTl78E+J2QrRD+jFA7zhaN+mTG8beU8CcTMWTARJtaH+FPm7mfWSzqE1aYcuI4mCfP4+zbiUw2Z8FOI3htSYTjbrr7M+PSNWRw/iI1hUKhUChaQS39aJVx94thXLoGnod9ZB/m6YuITeqwVyjudpTwp7i7kRItV8BLLLwxXW+8eLS2QlahUKw/2tgkolLF3bewB8Id6EYfm0RUrJbEKkkpKUyerD+RLgRufw/68GjTz7tarMxNgtUwYpUuxM59n2bwwX+yrPAWTuxrjfBn5ZoW8zmLGe5Sjr9tjF0eJxDZHsIfQCi2k2r+xpqOUc3fQEqXUHLjHX+z/X6LOv5YfeyfPjY50x3YfvtBIfDak5u2P2w9CER66Nr/E2SGnic//kbtcaeaZerq10gNPE1y4Ck69/8t0jefI3frZVy7gBlqheOvfes6/soVZMCcv2BpwUYCryOFvk7CnyiUkIYOocCC52Tcv+fSco3HfRrnriAq6+v2A5DtM92eq4z4bQmzi9T27lj6d65QKBQKRRPwejpB15q7CFlKjPNXcHYPYD9yHC8eJfDqW5vOYa9QbAeU8Ke4qxHlCrjuhkd9gn8jrG2yFaUKxXZCS+dA0/DaFnZ0uf294Pode61w/NnlCVwrh2vXnwhzB3r8iK6q1fRzrxQpJVZ5nKCMo41PruoYRiBOsIHoulBqH9X89bpRcGvBsfJNi/mcZVb4k+qGZdvheTZ2ZRoz3LPRQ1k3gvEdWMWRWgTjaqjkriC0AMHoQBNHtjq00Qm89uSSPUBeW8Lv5SvP6eWbmbhY6tqsjU36UYvGfIe0bE+hTW8iQWEDSA68n3j3w4yd/UKtM3Ly8pcQmkHHnk8AkBr8ALGuBxk7938BtMjx14HnlHCdctOP3WpEpdpQBLnX0bauUZ8yFoU6MbMyEkaGQ2hjDX5/sB3MU+dx9u9a94QWGQ4hQ4FN9T7VRsYR5Qru/t0bPRSFQqFQbAeM2fSj5gl/2ugEWraAc2gf6DrWo/ejj06gX73ZtHMoFIrGUMKf4q5GzKw29TY66hO/B2OzRckoFNsJLZPDS8ZBW/jRJ1NxZCTs//8WCH+V3BWARYU/r98XFPSRjY/7dO08nlMm5MXRxlobjRZO7kN6DtXc9aYe17Xz6IFEU49phrvwnDKeo67j2w3f6Skxw4vHRN5tBGM7kNLFKt5a9THKuauEErsW7flcT/TRSdwlYj6B2qKQue4f/eoQgZffxLh4rf5OS3QHeu0p3/XkrF483eoIIeg+/NMYwSS33v0DStPnyI2+QsfeT6Kb0do2PUf+DkbId0y24n1mBv1jb8W4T1GuIkPLfy/xOtsQxfJ84bpFaIVSrb98AULgDvaiDzWWYqCPTvhuv2MHmzjCBhECry21qZy5xqVreG0JvI7URg9FoVAoFNsEt78HfWwCnJX18y6Gce4KXjKO1+t/P/YGe3F3DRB47SRYzV3wq1AolkYJf4q7GpH3J9lnY2c2Ei8e9VeSb+MJIIViIxHZHF5qEReYELgDvvjWyMr6lTIbZ+naBaT0FjwvoxG8VGJT9PxZpTFwPYJGO2J8qqWRHMHYIEIPND3u07XyGC2I+gRU3Oc2ZPZ3vp2iPoOxQUBQKSxcmes5FcYv/jmuvbQIXsle3Tz9fsXSkv1+4Pekoeu3hT/HIfDGSQD0kfH6xy4UEaWyH5N0B1570o+c3+adJroRpu+e/xarNMrwO79LKL5rQf+rbkToP/7LtO36aNNjmsF3/AFbMu7T7x5uwPHX2QawLnGfolDyHX+L4A72omVyDS141CankaHAunb7zcXr6UQbndgU8WMincO4MeJ3UddxUyoUCoVC0QrcAT/9SBtrwn1uqYJxfRjn0N55n2XWo/eBbWO+fWbt51AoFA2jhD/FXY0oVZCh4KboSJhdGSvypQ0eiUKxPdEyeWRqcReYs3cHXiLWGsdf9gq6GQPp4S0SNVZbIb/Bk092dgikJHLwGKJqIbL5lp1LCJ1wYg+VFgh/rYj6BGpxdYrtg10aQ9OD6IGFMcF3K5oRIhDprtvzV5x6l8zN7zJ97euL7m9X0jjVaUKJje/3m40cdLs7lt5QCLxUoibUmacuIMoVnIN7/IkQd+EqaH1oDDRR103opZIgxKZyE20UwfgOug/+VyA0ug7+Vwix8BY0GO2na9+nES0QPIxgEiF0nK0o/FWq0IDjT8aiyKDZ+rhPKRGFIt5ijj985wBCNNRdrE2k8TrbN0zocgd6EJaNNrmBfxuui/nmu4S/+h28WBTnwO6NG4tCoVAoth2z6UfNWIRsXLwKmoazf9f8c0QjOEcP+Ckadb5TKxSK1qCEP8VdjbBs5BJ9LutJrey+0HjZvUKhaBLlKqJSxVtC+PP6e6h85qN1o0DXguuUqRaHiXbc6//3Yj1/g72IcgWxwZPEdvomAS+CcfSQP3E33tpotFByP+Xc5aZ25zl2runCn25E0M2YcvxtQ6zSOGa4uyWCxGYmEBukWhha8HgpfRaAzPAL2OX6PV6V3FUAwsl1dvxJiSiWENMZtMlptLFJjOvDfoxnqAHXVFsCLZ1FFEuYp85jHz2Ac2gPwnHrCir60C0/5rPed01Dx0vGN1V/2EaS7H+SfU/9zvr/TQBCaBjBNuwtGfXZmOMPIdal50+UKwjbWdqhFzBxezqWj/uUEm1yCq+rvbmDXAFeZxsyYG5Y4oJ2a5zQl7+DeeoC9vEjVH78Qw1dqxQKhUKhaBoz6Udrrh2REvP8VZy9OyAYWPC0s3cHwrIXTdJQKBTNRwl/irsaYdn1J2M2ABkJ+xFSagJIoVh3tKzv4PCSze19a4Rq7hpIj2jncWCJnr/uDqRpNNyL0yrs3DBBGUd0t+O1JdFaLPyFk/twrXzTBDW7kka6Fkaw+ROJZrhLCX/bELs8jrmNYj5nCcV2Ui3cnBdPLKWkNH2WZN+TaEaUqatfrbtvJXcVI9iOEUy1fJzayBiBH7xG6GvfJfwnXyH8f3+D8FeeI/S15wl94wX0GyO1KOfl8NqSaOkc5usnkaaBfd8RvI4ZYeDOSQrHRb81jjvYt/jx2pPK8TcHTd84QcMItW89x5+UiKrlp5c0gNfZ3nrhr8H+dHewD+3W+JKr+kW+iKjauJ0bJ/yhaXh93WjrLfxVqgReeoPQX/8AGQ5R/vEPYT9wdFOk1CgUCoVi++H296Clc4ji6hPK9KFbiGIJ53D9RV4ylcBLxtGvLVxYqFAoWoOx0QNQKFqKZSEDC1eabAhC4OwewLhwFfv4YdXdoFCsI1omB0IgE+vf91nOXkY3o4ST+4HFhT90Ha+/G2NoFOe+I+s4wvnYpXHiRjsiYCJ7OtCGW7sib7YDrJK91JQOtXL6LCCIpA6s+Vh3ooS/7YlVHieReHSjh7HuBOM78JwyTmVqXselXZkk2nUfwcRuxs//CW07niUY3zFv30ruCqHk+sR8Bt46gyiUcAd68HYPIpNxZDiE1DTQ/X9L9ZHNxWtLgutiXB2i+uRDtcVjXl+XL2I8cLS2rT46Dq6HO9i7+PHaU+g3RvwIZ/W9b0MxQx1Y5S22wrxigZQNR5B7HSnEyQqiVPYXHLYALZv3v08t05/uDvQSeOMU2ugE3kD998hsvOZsP+FG4Q70EHjlLViPBaNSol++QeD1d8CTWO99yI/2VNcHhUKhUGwgbr9/H66NjOOuMnLaOH0Jr7PNj/CuhxC4uwcxzl7yFwapxS4KRctRjj/FXY2o2sjg5nD8ATiH9yEKpfVfVapQbHNEJuevTt+AL5eV3BVCib3oAX91vGstHvfrDvahTUxDpbpew5uHlC5WdZJg2HfHeN0d/iRfC8ejmxEC0X5KmYtNOV5x+iyh+M6mR32CEv62I55n41TSmOHt5/gLxnwxr5K/WXuslD4HQiOcOkiy7wkCkW4mLn9p3n7Sc6jkrhNOrEOko5SIdBb7yD6sJx/GufcQ7s5+vK52ZEcKmUog47GGJ9W9Nr/H0etI4e7fXXvc7etGn5gC26k9pt8cRcYiS0Yeeh0phOMi0irtYaPxHX9bK+pTzHz2rsTxB7TU9SdyBWQ0vOz3KdmW8PuClkgx0CbTePHohkdbuv09viB3a43CsJQY75xFTC3y83dcgt/5IcEXX8ft66b8qY/gHNyjRD+FQqFQbDyhIF5nW0P9vPUQUxn0W+PY9xxccjtnz6CK+1Qo1hEl/CnuaoS9eaI+Abyudrz2JOa5yxs9FIViW6Fl8sgl+v1ahZQe5exlQsm9CKGjm1FcO7/o9u7AzOTTWvP1l6FaHGHy8pfxXGve43Z5ClybQKIfAK+nEwB9orXxaLHO+ylMvInnrk1glNKjlD5LpL01jslAuAvHyq55nIqtgy/0ym0Z9WkEk+iBBNXCHOFv+gzhxF50I4zQDDr2forS9GlK037vn2PlSQ89j/QsQuvQ5SaKZb9vbEawWzPhIPY9B6i+9+F5k/FuXzd4Em1sptNQSr/fb0ffkpP2Xk8n0jQwbt5qzvgUq8YMteNUs0jPWX7jTYIoVwAadvzJaBgZCrZU+NNyBbyl+v1mEQJ3R++Swp8+Mb2h/X6zyHgULxFb9WTnLNrYJIE3TxN6/hXfPXgH5olT6KPjVD/0Xqz3PwaRxn6vCoVCoVCsB25/jy/ISbnifc3TF5CxCO7ugSW3U3GfCsX6ooQ/xd2NZSPNzSP8IQTO4X3oQ6OIwuqzsxUKxcrQMjm8VPMdYMthlUbxnDLhxD4AdDO2eNQnIKMRvPZky3r+PM9m8spXuPHa/8L09W9QnHp33vN2aRRcDzM1E9sXj/qTiC3u+Uv0PYHnlCmMn1jTcazCMK6VI9J2dPmNV8HtuMPJlhxfsfmwS/5q1MA2dPwBhOI7qeZvADMLGTLn5wnrsa4HCCX2Mn7hT7n55m9x5Yf/mMlLXyTaeZxQfFdzBuG6dSfRwXdzA3htTVrYIQT2e+5DdqTmPTwbHzrrCBKZPKJQwlmi3w8AXccd6PHjPhUbihHqACROtbUdeM1kpY4/hMDraFte+LOdee7VFY0pl1+2328Wd6AXLVeo9QLOw/PQptIbHvM5izfQgz48tqrJzlnMdy/4DsaqReDVt+Y9pw2PYp65hPXwvf6CAYVCoVAoNhnuYC+iaq343l8USxhXbmIfPQDaMjKDELh7Bv3vxkv0ANelUkWUymv6rFYothtK+FPc1QjLQgY3ScffDM7eHUhDx7hwdaOHolBsD6oWolzB2wDHXyV7GYRGKLEbAG0Z4Q/8uE99eLTpX2hL6QvceO3XSV//a9p3/Q0CkR5Kdwp/0zcRUsPo8B1/CIHX04E20VrhLxDpJtJ2hOzISw1tP/TW75C99fKCx4vpswgtQCi1r9lDBJjXc6bYHljlcTQ9hB5Y/+vHZiAYG6Ra8FfkVvM3cO0ikbbDteeFEHQd+AkcK4+mB+g5+JPsffJ/Y+D430dozakSN0+8S+jbL9Z9TktnkaaBjEaacq5FEQK3v7sWS6QP3fJ7WXu7lt3V3dGPNpn2JyoUG4YZ6gDA3kJxn6Jc8XsqzcbfS15nG9pUesnvEIEfvkHgpddXPiAp0XKFJeNt5+L2d4Mm6i5m0tJZcD28ro6Vj6MFuAM9iEKpvkjZACKbR795C/u+w1iPPYBx+Qb61Rm3dKVK8MU3cPt7cI7sb+KoFQqFQqFoHl53BzISxrh6c/mN52CcuYQ0DZyDuxva3tm9irhPyyb85e8Q/vOvE/7TrxL6q+cJvPQGIl9c0VgViu2GEv4Udy9SIqqbK+oTANPE3bfTF/48r+HdRKlM6C+/jXH6glrholCsAC3rR2t6yfWfuC9nrxCMDqAZfpzTco4/mFlpV7GaFtVVLY4wcur3GXrrt9DNODvf8/+hY+8niHYcpzj9LnLO9cTKDBH04si21O3xdHX4vYMruF6thmT/k5Szl6gWl3bGOFaeUvoc09e+jpTzx1SaPkOk7SCa1prrvh5IIvQAdll1EmwX7NI4ZqQbsU07mIKxnTjVtP++mz6LpocIJfbM2yac3Mf+9/3/GLjvvyc58BRGk0VSfXzKvx7WcShp6awf47wOvx+3rxttOgOVKvrQqC9qGMv3xs7GgTbF9Ve1EIXVT3CIXAFm4iO3G0bQd5Y5ldZGVzcTUan6MZ8r+Pv2OtsQsyviF0GbTKOPr/znIApF8GTDjj9ME7enq77wNzHtLy5qT614HK3A7e3yRcpVRq2bpy8gwyHcvTtx9+3E2T1I4OU3EcUSwZffBM/Deuph1eenUCgUis2LEDh7BtGvDjU+52jZGOev4BzaCw2mra0m7tM8eQ5h2VSfegT73kN+RPeNEcy3Tjd8DIViO6KEP8Xdi+OAlMjNJvwB9qF9iHJlRZNA2vgUWiZH4LWThP7q+cWL4xUKxTy0mSi4RleoN5NK9jLh5G33mW7GcK2lhT+vqx0ZMJeO+6xamK+9g8gu3hdolycZPfOHXH/t16nmh+g98ncZfPAfEYz6br5oxzGcagarcPsLt10YIUgCYrfdM15PJ8Jx/dX5LSTadT+6GSM38sMlt6vmr/tjLU9QnDp1e5yeTTlzkUhba/r9wHc3maEuLOX42zbY5bGa03M7Eoz7sb/Vwk1K6bOE2w42zcnXEFIi0lnfaVTne48f47w+izq8fj/u1bgxgj42iTvY29iOwQBuTyf6jbX1/IlcgfBXniP43EK3c6MEv/syoW+9uPJopbsATQ+gBxLYW0n4K1caj/mcYTY6c9HFQ46Dli/6wmDVqr/NYuPJ+t9fZKLx71PuYC/66Lh/XzYHbTKN15ZsSDxfF0wTt7vTj/tcKeUKxqXr2Ef3g66DEFhPPAiGQejr30O/Poz1xIPISLj541YoFAqFoom4e3YgKlW0W43d7xoXriJcF+fIChJ3Vhj3KXIFzNMXse89hLt/F87xw1jvew/O4b3oN2+1fIGyQrGVUcKf4q5FzPTByMDmivoEkO1J3O4OjHNXGt5HZPPIoEnlbz4Drkv4a8/7E/8zooZCoaiPyOT8zpV1nlxy7QJWaZRQcm/tMd/xt7hYB4Cm+Z1QQ/UnicVUmvBXn8M8fRHj0vVFzl3k+mu/TnH6NN0HPsfux36dRN/jCHH7Yz+U2o+mB+f1/NnlcQKBznkr0r2OFGhay3v+NM0k0fs4udFX8Lz6fV4Alfx1NCNCKLGHzM3nbz+euYT07Hn9Y60gEOlSUZ/bCKs0QSDcs9HD2DDMcBeaHqKSvUw5e6mlwno9RL6IcPwJgQVChpS+8Nesfr9lkNEIXiKG+fYZkLJx4Q9wd/b5/YD24te2pRDpLKFvvIAoldFy+dUlP1g2WiaHls5ivrk9V0ebofatFfU56/hbATISRoZDiwp/cxcMadMrW9Cj5QqgachY49G67mAvuB76teH5x5qYxutqX9H5W4070IM2OrFiYdw8dxkphO92mCUYoPrUw4hiGefAbtzdg00erUKhUCgUzcfrbEPGIo3FfXoe5umLOHt3rjh2fyVxn4HXTyLDQex7D8573N01gLBs/7NboVDURQl/iruXGeFv00V9zuAc2ot+a3xJx85ctGwemYjjdXdQ+cSHsB68B+PCVcJ/+W1CX/przNdPIqYyrR20QrEF0TJ5PwpunSlnfWF/geNvmahP8Hv+tMn0gkg2/dJ1wl//HjIYwOvuQJus71yoFobw3AqDD/wPpAafqevQ0TSTcNthitO+8Oe5VWw7SyB6x2S2rvudQS0W/gAS/U/i2gWKE28vuk01f51QfBepwQ9QSp+tRYMWp8+gB5IEogMtHaMZVsLfdsFzLZzqNGake6OHsmEIoRGMDZId/gHSc1ourN+JNp0BwEvEFlzvRK7gd4S1JddtPF5fN6JYxkslkLFow/u5O/vB81blJtImpwl98wVkOOi7iFzP735b6XFmfpbO/l2Y715AW0mvyl2CEWzfWlGfq3D8wUzP3yLCn5aeWTCoidrfRKNoubwf87mCuEqZSvixl6+fvO0wtH0R2u1qW9H5W4030IOwHT+GtFEcB+PsZZyDe+COXnmvv4fyJz+M9fgDTR6pQqFQKBQtQgicvTvRrw8t7qSzHfRrQwRfeBVRKmPfc7D+dkvQaNynNjKGfmME6+HjYMyf0/DaU75Ieb0JcfoKxV2KEv4Udy23HX+bU/hz9wwiTQPjSmPFuVo2jzcbVahpOMcPU/7bP0b1Q0/gdXdiXLpO+OvPQ7nawlErFFuP9YyCm0slewU9kMAIddYe080YnlNGegu7qubiDvgOo+CLbxB4+QSBl08Q/O7LBF98HWfPDiofe8ZfmT6Vruv8sEqjCKETCC8tWEQ7jlHOXsa1S9ilcYTrYsT7F46nqx19rPXCXzDaRzi5n+zIS4tuU8ldJ5jYRbz7IYxAsub6K02fIdp+pOVdbGa4C7syhZTbLypvuzEr8Aa2cdQn+HGfjpXFCCQJRPrW9dxaOosMBXF39KHfMRk/G+PspdZP+HP7/GvqStx+ADIew2tLrLjnTxudIPjXP0AmE1Q++jRup++QEoXSio4DvsNKGjrWex/C7esi+OLrK4563OqYoQ6c6hYS/lbh+IMZ4W9quu73Ay2bR0bCeG2pFUd4i2wB2Wi/3xzsR+8Dz/PFP0CbzMyMs2PFx2olXnsKGQpiXLnRsKvWuHQdYdk4Rw/UfV62Jfz4T4VCoVAotgjO7kFE1V6wSExk8wS/+zKR//w1gt97FZErYD12P7J9Fd/FhcDdNeCnHC32met5BF57B7e7A3dPHee8EDg7+9FvDK8uDUOh2AYo4U+xtfG8RR0vwvInMzZj1CcAuu73XtxsoPNFSsRc4W8WQ8fd0Y/15MNUPvZ+cD20dKYVo1Uotia2jSiWNkT4K2cuEE7unydE6QF/wsy1i0vvHA7hHNnn5+tPZtAmM4hSGeuJB7GefNh/73e2I6o2Ir/wWFZpFDPQvmwXV7T9GEiPUvoMVn4YXA+zfeeC7bzuDkSx5HcCtZhk/1OU0mexSgvdKI6Vw6mmCcV3IjSD5MDT5EZfxSqNUi3cJNJ+tOXjM0OdID2ciupZvduxy/7foBnZvlGfAMGY3/MXWQdh/U606SxeexKvs90Xu+Y43bRMDhkMQHjljqjV4vZ346USOHsXXieX3XdHvz+50WAPiTY0SujbL+F1tlH58JMQDCDjfoySyC/vHF9wvKk0XnsKNA3rqUfAcQi88ua2migxQu3YlWmk3AJdMFIiylVkaOX3MV5nm//9oI5APLsYymtPrlj4qzn+VoiMhLEfvhfj4jW0W+Nok9NI00Cm1r97eUmEwDmyD+P8VYJ//YP5qSyui3H+CqEv/jWRL3yp9i/w6ts4uweQ8cYdwAqFQqFQbGZkexIvGfcXwsxSqRL8zkuIdBbrgaOUP/NRKp/8MM6R/as+jzvYi6hYi6YUGBeuoqVz2I/ev2jagLtrAFGqLDovrFBsd5Twp9jS6DdGCP3V927Hes6lOvNYcHM6/gDcHX1oU+nlJ9PLVYTtIO8U/uYgEzHQtRXfxCsUdzPazKTNek8ueW6VSu4qkbZD8x7XTX8cjcR9Wo89QOUTH7z978c+6PfHzHzp9Tr9iKx6X3LtzE0iN4poQ6NLnsMMdxCI9lOcOo09fRODIFr7QpfgbA/PiuKvVkms+0E0I0zu1ssLnqvm/U7DUHw3AMmB9wEeo2f+EIBI2+GWj88M+w7OrdQTpVgeKSWFibdw7dsT5VZpHE0P1d6325Wa8LfO/X7gx1N6bcnaNUifMzEg0lm/3289xchggMqnPozsSK14V3dXv796uoHYZP3aEKHv/hC3v5vqs0+COfNd1jSRwcDqHH+T6drPUUYjWI8/iHF1CP3q0hFLdxNmqB3p2Q19Bm84jgOuuyrHn9sx+/1g4USamOnF9NoS/j1Dg0I0rosolJa8F1kK5+Ae3J5OAi+/iT46gdfRtr7v3Qax7z9K5SNPIYolwl/5DubbZzBOnSf8X75J4OU38dpTWA8fx3pk5t+j92G/576NHrZCoVAoFM1DCNw9O/ykCtcF1yX4/MsI26H6kffh3HtoVQkAd+J1tSMDJnq9OQspMU+ew9m3szbvUfcYPZ3IUABdxX0qFHVRwp9iSyNKlZkVsQu7ToRtg65t6ngVd7APhFjW9TcrXixw/M1FCLxU4nZ3h0KhQMy8H5Z877SAcvYSUrqEFwh/s46/xro9lyQURMYidSf27PRNgl4cfXhp4Q/8uM/i1LvY2SECXrzuan4ZCSPDoXUR/jQ9SKzrQQoTby54rpK7jm5GMUJ+PJgRSBDvfoRK7iqB6ABGMNXy8RlBf+J8tcJfOXOJ/NjrzRySoglU8zcYOfXvGT75b/Ac/zuFXR7HjHSvu8ttsxGM76Tn8M8Q63l4fU9s+Y6l2f4OGQrMuwZp6RxyHWM+14rX0YYMh5aN+9QvXSf4wo9wdg1Q/cDjC77HylgUbaXCX9VCyxfx5giW7t4duAO9mO9eWNmxtjCznx3OFli4IWai+1fT8Uc4hIyEFy4Mcl20fNHv1WlP+X2RucZEUJHz0wVW4/jzDyCwnngQrVBCHxpdchJvo/H6e6h88sPY9xzEfPssgTffxR3spfzpj2A98xjO0f04R27/k5HwRg9ZoVAoFIqm4uwZRNgO+tAogR+eQJ9MU/3gE811uGsa7kCPn4hx51Njk4hi2V/4vBRC+Kka11Xcp0JRDyX8KbY0ourfFIvKwl47YdlIc/O6/QAIBnB7OpadBNKyORBi2Q9ZX/hTjj+FYhYtm0dGI7fdEutEOX0ePZBY0Id1W/hrjtvA7WpfMLHnuVXs8iRBL4E+sjAu09/RxTh3GRyHaPsxXCtLIXuGoEhBvUlGIfDqnKtVxLoe8OM7i/NvAir56wTju+YJManBDwAQbW/QjSRl3XjURtH0AHogueqJ4+zID5i69lerPr+iNRSnTyP0AFZhmJFT/x7Ps7FK48v2ZG4HhBAk+9+Lpq3vdXT2+4zXlvSvQZ1zrkGu63cft61/jPOqEQJ3Zx/G1SGM81fQr9xEGxpFGxrFOH2RwMtvEvzmC36X64HdWE8/CtrCWzUvHlnxNWx2gYg30xE4i3N4L9pU2u+L3QaYM8LfVnBsz97brMbxB7M9f/N/ryKbByn9qM82XzRv9L5By80sQkysfiGVTCWwj/vO/Fn36abF0LEfOkb5b/0Nyj/xMawnH16121GhUCgUiq2GnIkFD7z8JsblG1SffBivu/ndvO5Ar/899Q4zh3HlBjIWaeic7q4BtFwBkWnC4mqF4i5DCX+KLY2o+j1+9YQ/qhZyE8d8zuLu6Ee/Ne5H+iyCyBXw4tFl3YteWxKRyamVLgrFDLNdNutNKX2eSOrQAqeQZoRBaE0T/ryONrSpzLyoLrs4hrBtAtE+tEwOSgsd0frVIQKvvEXwB68TTu5D00N4TmlJkcMXGdPrcn2JtB9B04MUJt6a93g1f51QfNe8x0KJXXQf/K9qAuBSaONTBL/5AuG/+GZDcXuLYYY7scuTq9rXtQu4lnJmbzZK06eJth2l//jfp5y9xK13/wN2aRRTCX8bhpbO+oueZqKaa4sPpPRdSjMCxlbC2b8bXNcX+b7/I0LfeYnQd14icOIU2vgUMhzCeux+rCceXDQGUcaiiMIKhb+ptN+pdodbyx3sRUZCGOevrvYlbSk0I4KmB3Eqm78HZjbNZFWOP8DtnPl+MOczW8vMSRAJBZGRENp0Y8KfyBWQprHmTk37+CGsR+/zU0+2ADIWUY4+hUKhUGxLnD07EJUq9v1HcVfRb90I7mAvAPrw2JwHXfRrQzh7djQUC+72dyNNA+PGcEvGqFBsZYyNHoBCsSZmBL9ZAXAuwrIhEFjvEa0Yd0cfvH4SfWQcd2d/3W20TL6hVaZeWxLhuIhCERlfe+a2QrHVEZncou+rVuE6JSr56yT7nlw4HiHQzRiu1SThr6vdf89n8sh2f/W+PXoRPIn24KPw/XfQb43j7pv/Rd24PuxHzl0fJvB2nEj7YUrTL2Imepc+l+0gMjlkW2vj9TTNJNJxjOLk23Ts/hgATjWDU80QvEP4A0gNPrPk8UQ6R+DEKfSbt/Bmfk5aJld3BaHI5gl9+0Uqf/OZRSf7zFDHqh0jrl3AtYtIz0Fo6mvYZsB1ylSyV+g6+DkibQfpv/cXGTn575DSJRBRwt9GoU1nfWFvZtGT29mOWbUR+WIt1txr8bWo2XjdHZT/60/4Yozt+LH0nkTGIg33nclYBK1Y8o/R4D765LQfrXjn9pqGc2A3xplL8MhxMJe4JkmJNjbp/8yDm//7dT2EEBihDuytIPxVqv7va5XCn9fRhrD898us4Ktlsr6DcOb357WlVuD4K/jHWWv0sa7jHD2wtmMoFAqFQqFoOc7R/chEDHfXQOtOEg7hdbShD93C3e/f5+sjY4iqjdOo2KjruIO96NeHse9b/05yhWIzoxx/ii2NqMwIfotFfQY2v+NPJv1OraV6/rRsrqGOMjkTeaV6/hQKwLndZbOelDOXQHoL+v1m0c14Ex1/KQC0qduTmM7oZQwRht378NoSvqN4LraDPjyGfc8BrEeOY548R7zaA66LmRpc/FwzfTx6vZ4/Kf3i7yYS67yfSu5abYK2kr8BsMDxtxwiXyT8tefQ0lmqT7+Hyic+hIyEF3XMaBPTiEIJ/erNRY/pC3+rdPzNiL6OpaJINgvl9DmkdIm23wP4vZe99/w3CM0kGG/N6lbF8mjp7Dxhb/YapE1M+wJGJLRlBSiEgICJjEb8GPcViCkyHgVPIkrlhvfRJtN4HfU71ZwDe/wOl2uLX/O04TFCX3mO0De/T/hL30K/cnPLpkuYofatEfVZriCDgVULbbX3y5weYC2Tn+eS9dqTK3D85Vff76dQKBQKhWLrYRi4uwfXvuhnGdzBXvSRsdp3S/3KTby2RG1hc0PH2DmANpVZcSqGQnG3o4Q/xZZmqY4/tojwB+Du7Pd7/upNojguolDCSzUg/EXCyICJUD1/CkWtC2q9o+DK6fMYwXbMcFfd53Uz1jThD9PESyXQJ25P7FnpGwTCPX5Zdl+3L/zNubbow6Pgurg7+3HuOYBzcA9d77oMlh/BbN+99LnaEmh1hD/znbOEvvZ8c17TDNHOexFCpzD5NuDHfOpmDCO0sl4g4+wlpKFT/uSH/YgSIfBiEUS+VHd7baY7y7i2eFSIEerAqWaQ3uIRzYsx+7t3bbVAY7NQnD6DGe6e956Ndz/I/vf9LsHY4mK4ooVIiUhnaw5dAEJBvHgUbXIaLZ3DS20tt1+z8KJ+33PDPX/lCqJYrglBdyLjUdyBHsw6cZ8inSX47ZcIfftFpGlQffa9uL2dBL//I4LP/XBLTq4YwfatEfVZqa663w/wozxjkXndvCKTm3c/4bUnEcUS1ElOuRMtW1AddwqFQqFQKJqOO9iLqNr+PINto98YadztN+cYCIE+NNqiUSoUWxMl/Cm2NLOOv3rCnx/1uUWEvx19iEp13qrcWUTOnySWiQZutoXAa0s2HNujUNzNGOev4sWjeF0rE4rWSil9jkjbwn6/WfRAE4U//FX9tWuHbWNVxjGTO/zn+roRhdK8CWL9xoi/gi4ZByGwHrsf2dtDu7MHucxEutfZvlD4kxLjwlX/utNE159uRAi3HaY40/NXyV8nGN+16M+1LraNcfEazoE98yLsZHzxjiyR93832viUPyFaBzPcCdLDqS68Zi+F9Bw81+9tcpXjb1MgpaQ0dZpoxz0LnlNRrBuHKBQRtrMgytPrakefTPsCRtvW6vdrFjIeAUAU6l+f7kSf+XzwOhf/LHQO7vHdznPcX/rl64S/+hwiX6D6zGNUP/Z+3ME+rGcep/rBJ9Cms4T+8tt1v7tuZsxQB0518wt/lCur7vebxe/5m/n9uK4f1znXRduWAlj+vsGyEeWKcvwpFAqFQqFoOl5XOzIYQL95C/3GLYTj4u7ZsbKDBEy8ZFylnykUd7AlhL/XX3+dv/f3/h5PPvkkhw4d4rnnnlt2nx/96Ed86lOf4tixYzz77LN86UtfWrDNn/zJn/CBD3yAe++9l5/4iZ/g5MmT857/6Z/+aQ4dOjTv36/+6q827XUp1s5st199x5+F3AIdf+B3vsigiX5zZMFzWtafHG4k6hP8uE8toz7sFNucUgXj2k2cw/taHk0xF9cuUC0MEVkk5hOa7PhjRvhLZ/xJvZExqiKH2bPXH09vl7/ybTbu0/PQb97C3Tknp1/XqT7zONYTDyKXcRZ7Xe3+9cW2a49po5OIoh85J3LNdX/Euu6nlLno/1xz11cc82lcuoGwHZwj++c9LmMRtMWEv0LR717VNPRrQ3W3MUN+N6BdXllc3Nzfu2Op6/RmwC6PY1cmibQvFP4UG8ds/OCdET/ejJCh5QrbVvjDMJChYM2dvBzaZBoZNP0ewUVwd/YjQ0GMC1dASsx3zhL8wes4e3dS+eSzC2Ke3J39lD/9YWQkjPHu+TW/pPXECLXj2kU8p7LRQ1kS3/G3NuHP62hDm8r4DtpcEaScdz8hkzHQtGXjPrWZBTHK8adQKBQKhaLpCIE70IM+NIpx5QZud4cfbb9CvFQckVX32ArFXLaE8FcqlTh06BD/4l/8i4a2v3nzJr/wC7/Ao48+yle+8hV+5md+hn/+z/85L774Ym2bb3zjG3z+85/nl3/5l/nLv/xLDh8+zM///M8zNTV/Eu+zn/0sL730Uu3fP/kn/6Spr02xBhwHXBdp6Is6/rZK1CeahjvQV7fnT8vm/I6PBlf9em1JtEy+6X1bCsVWwrxwBYSGc2D3up63lL4AyEX7/WBG+LOaKfy1gyfRprN4Q5eQusTs2OM/GTD9ifIZ4U+7NYGwbJxd/fMPEgzgHNq7rEjqdrWDlP5E4gzG5evIkL/IQss118UW67wPpCQ7/CKOlSWUWIHwJyXGmYs4u/oXTHh78SiiVKl7ndTyRbz2FO5Az6Jxn37cqFhxT5Rr356od5XwtykoTp9GCJ1I6uBGD0UxB206iwwFFkQdel0d4HoAyzqU72aWci3fiTaVxutoX/r6rvmfl8blGwReegPzzdPYDxzFevJh0PX6+5gmzuF9GNeGV9Q3uNGYM3HR9iZ3/YlyZW1Rn/hCubAdRDZfWxQ4L/5c0/BSiWUdfyLrf2fx4srxp1AoFAqFovm4g71o0xn04TG/nmMVyGTCnwtVKBQ1toTw9/TTT/Mrv/IrPPvssw1t/2d/9mcMDg7yz/7ZP2Pfvn381E/9FB/5yEf4T//pP9W2+cM//EM++9nP8pnPfIb9+/fza7/2a4RCIb74xS/OO1YoFKKrq6v2LxZTNzybhVm3n0wmapGfNaTcUlGfAO7OPrTp7IKJHJEtNOz2g5kbeilrEaEKxbbD8zDOX8HZtxOC6+v6LWfOY4a7ao6wejTd8deeBE2gTU5jj11CmiaBSG/t+bk9f8aNYWQsgmxPrepcsi2JNPTbcZ+Oi35tCOfwPqRpNP26YwRThJJ7Sd/8NgDBFTj+tOExtFwB5+iBBc/J2Y6sO6PyXBdRquDFozi7BxeN+9Q0EyOYwq5MruDV3Hb8Cc1Qwt8moTR1mnDqAJqxtgl2RXPR0hk/hvAOscprv/1YI93HdyteLNJY1KeUaBPTi/b7zcU5uAdh2RhXblB96hHs+48uuxjEObAbqWsYdfoBNyvGzOezs8KFG+uNqFTXHPXpdfi/d20yjZbJ+ce745heexJtOrPkcbRc3t93nb9TLYas14muUCgUCoViy+IO3J6/cHavrmPdS8UR5UpD3cUKxXbhriwvefvtt3n88cfnPfbkk0/yv/6v/ysAlmVx+vRpfuEXfqH2vKZpPPHEE7z11lvz9vva177GV7/6Vbq6unjmmWf4pV/6JcLh8IrGo2kCTVu/qLntgrBtfz6iPYG4OoRhzNGxbRuBRAsH5z++mdnVB4ZO4OpN3AeO1h7W83lke6rx19HVhhBg5vJ4XctP9KwXuq7N+1+FolVoV4bRyhWcYwfW/f1fyVwg1nF4yfMGQgmQNho2mrG2ST0ADA3ZkcK8MUKuNIbWHSEU60CImTHs6EE7dQ4zl8e4eQtv7w4McxEHxwxLvl+72jGm0mBoaDeG0RwHDu6Gm7cwikVo8s882fMA4xe/iBFMEIp2NNzxZ567hOxsQ+vvQrtzn1QMIcAol5Edt11DolBACNBScWRHEvHyCQI3RnDvXegGC0Q68Kzplf2NeSWEEASjvXhOfut8Pt2leK5NOXuBrr0/tuV/F1vqMzZXQL98A0zTd/WFgn6E4JxIHz2dw9vVv/D3Ymj+e7NqY6wxBnErI5Ix9Mn08n+3xRJatYro6Vh+2/YEzpMPIdsSiN6uxm4SjSDy4G7MC1eQDx5Z3B24idCj7QhNw7Ma+Pm1agzLvV9dF82y0aLhtY3RCEEyhpnOQKkC7YkFxxOdKbRrQxi6WFTo1fNFSMU3xXUyO/oaY+f/jP1Pfh5N377XAMX6sqU+YxUKhXrPbkViYWRvJwQDGPGVzbnPIjpS/lxooYCMdi6/Q7EE0cWj8BXrg3q/tpa7UvibnJyks3P+m7yzs5NCoUClUiGbzeK6Lh0d8x0ZHR0dXLlypfbfH//4x+nv76e7u5vz58/zW7/1W1y9epV/+2//7YrG094ebXiiUtE4Xi6HrWsE+jtxr94kEg0gZhx+Ml/E0jVCHQm0tpVnQ28MUZzjB/AuXsV8+kGEriOlxCqWMI7tR2/4dUSxknEC1QrGJnzticTqPsQVikaxL1+DXX1E9g8su21Tz1vJ4lRG6dn5adqWeO9pVhdjukYs4hKMNuc96uzsxX3nPJZZINq1i/b2OR0+8V1Y3zWJnruEV61i3ruv4etivfers6sP79xV4m1R7JvDMNhDZHcvdk87lCuYTb7uhI0nmLryl6S69tHe3pjr3pvOYt8ax/jok8Tq7CMTYSxDJy7deddWL5PG1jWSO7oQ8Sj2/h2YI6OY73tgwTGm2/qoliaX/F3fiZ120C2HWFFHxssr2lfRfLLj76Lh0LfnYaKpu+N3sdk/Y72pDM5ffx9pOSA9cGbidjUN470PoD10DzgOVqmEsbOn7ncf98EjyHKF6DZ+/7i9HTinLxJJhhHa4jfJ7tQUjq4R3j+AaKQr5YnjKx6L9/hx7AtXCU1NoR/as+L9N4JIrAtTK2z4NXix92vtPqY7teb7GHuwB3J5ZNVC29FL7I7jebv6sN84RUR4iDq9mbJqYefzaL2dC/Zdb6SUDJ34DrhFdPs6qc6Fn80KRSvZ7J+xCoViPuo9u7WQP/EsCIFYZeKBjAf9e3zHWnb+VGbzWP/lmxgfex/6od2rOp+iuaj3a2u4K4W/ZvG5z32u9v8PHTpEV1cXP/uzP8uNGzfYubPxzOHp6aJy/LUAbSKD6XpUzCCm65EZTddWi4vpDAHXo1x1kenGOlA2A2LvLgJvnaN84hzegd1QLBEsV6mYQbwVvA4zHoWhcexN9Np1XSORCJPLlXFn+nkUimYjpjIErt/C/sDjK3rPNIPc6Ju4rodn7ia9xLkrFR3X9ZiaHCdsNWeFmRaNYboe5UQVLbhvwfnNjja0s1eQoSClSAyW+dks9X7VYjHMbJ7SpWECl27iPHY/hXQRPRhEv3GLQtN/7glCyUMY8SNL/lznYrz8DlogQKm7a9HXGggFqY5O4+64/bw+PIkhIWNLSBfR+nowv/8ahZsTcEdPoEuSQubdhscEkE1PIiyByNsUolMr2lfRfMavnkDoMapeB9YW/11shc9YMZXB/Ob3keEQ9mc+COEQ2A5UquhnL+O+8Abe+Wu4R/b717NAqP53uF07/P/d4r+ztaAJHdNxyQxNznNK3ol+dQQ9EKA0c01rzWBMzO5O7B+9i93d3ZpzNBsjRXb6FrEN+hta7v0qJtP+fYwj13wfo8di6OevIaTE3r974XcjM0DQ9chdHcFjjmPT89DPXkZ/6wzYDs4D96z796o7KaXPk5++htAMRq6+hgypblbF+rAVPmMVCsVt1Ht2i1N2Vr1rIBSiOjSBO9C/5HbatRFMx8X5/gmsrs5l4+0VrUO9X1dHowsY70rhr7Ozk8nJ+b07k5OTxGIxQqEQmqah6zpTU/O7HaamphY4Bedy3333AXD9+vUVCX+eJ/E81UXQbIxSBSkEdiSMIcErlvFmYli1UhUpwTEMpLOFLhzxOFpfD9qpi1i7d6BNZZES7Fh0Ra9DJBMY14ZwNuFrd11vU45LcXcQOH0RLxzCGuyDdf47y02cxoz0gh5f8m9cahHfzVvOY0aaM0bRnsKQUDFLJMI9C8/f00VgeBxnRz+OK4HGPpPqvV9nz6W98hZSQnXXIDgeMhpBL5RxKhYYzf160X/fPwBo7Nph2ZgXrmEfO4QjxaJ/B3o0gszm5x1TZAu40cjtn9FAH4bQ4PINnHvmTzBqgXbsSgbLqqJpjfXJ2tU8uhfAqGq+Q1RdCzeU/OS7hNuO4q7gPbHZ2ayfsdrkNMFvv4gXi1H58JNgBvz3ptAgHMZ58Bh2XzfBH7yO8d1X/O938di6X8e3CiI88903k699912A62JcG8HtaFvRdX81yMP7CD7/Cu7YFLJj88TML4YeaKNamtjw98pi71etUPbvY8zAmu9jvPYUuuMiAScex7vzeGYAMxRCnL8GhcrMTi7G+Sto2QLOgd3YDxxFRiMb/n6cvPZdzEgvkbYj5MffpnO/q1J1FOvKZv2MVSgU9VHv2e2HnozD9PL32cZUFik0yOSQF67h7tu1TiNULIZ6v7aGuzJA9f777+fVV1+d99jLL7/M/fffD0AgEOCee+7hlVdeqT3veR6vvPIKDzyweGTI2bNnAejq6mr+oBUrRlQsZHBOSX3ldoGrsGwAZKCxydjNhH3PAbSpNNr4FFq2AJpANhLPNAcvlUAUSmDbtx90XUQm1+TRKhSbiKqFfvkGzqG9sET0WSuQ0qU4+Q6xzvuW3VY3/ehJ1y407/ypBNVDA9imQyDSu+B5b6Ys29m19vhTGY0gI2H0WxO4g721a7BM+PGiIrexjgDj2hDCcXEO7l5yOxmLohVK8x7T8sX519uAiTvQg3FteMH+ZqgDkDiVdMNjc+0ihmdieEHcag4p3Yb3VawcKRcXOlwrT7UwRKT96KLbKJqDyOQI/vUPkIk4lY88dft72x14fd2UP/kszt4duDv7tkRf3EYhZxzIorD49dZ84xRaNod935GWj8fd0YeMhjHPXm75uZqBEWzHqUxv9DDqU64QOHHK775sQo+l1566/f9T8brbuIO96MOjBF572//3xilkNEL5Ex/CevJhX/TbYOzyFIXJt0kNPkO08zhOdRqruPCzWaFQKBQKxfbFS8YR2fyy22mZPF5nG+6OPsy3z4KnBCfF3cmWcPwVi0Vu3LhR+++hoSHOnj1LMpmkv7+f3/7t32ZsbIzf/M3fBOBv/+2/zZ/8yZ/wm7/5m3zmM5/h1Vdf5Zvf/CZ/8Ad/UDvG3/27f5d/+k//KceOHeP48eP80R/9EeVymU9/+tMA3Lhxg6997Ws8/fTTpFIpzp8/z+c//3keeeQRDh8+vL4/AEVdRLWKDAWQMxNIolK9/eSM8McWFP68gR68ZAzjzEVkOIQXj61YxJDtSQC0TA6vqwOkJPi9V9Fv3sK+9xD2g/esuzCiULQa4/J1hOvhHFz/jqFy+gKuXSDW9dCy22p6EKEHcO3lv5A2jBAU7umBE1p94a+zjfInn0WmFvb3rAavqx39+jDOnJVxXsIXNLVcHnfmGrQR6Jeu4/Z3LztRKWNRxPDYvMdEvojXM7//19k1QPClN6BqQTBQe9wM+wkBTmWKQKSxeDvXLmA6JqYMgefhWnmMYKqhfRUrQ3oO1370L2jf+VGSA08teL6S979XhhK713lk2w/jyg3QNF/0M5f5XhYMYD396PoMbCuj68hICC1fot7yAf3KTcwzl7Aeux+vq73149E07MP7CLx9BuvhexcVdzcLZqgDx8oiPQehbZ7bYVEsEfzWDxCWQ+Wj72uO+B0w8ZIxRMVa9PdiPfkw1pMPr/1cLSQz/AKaHiTR+zgIDU0PUZg8STA2uNFDUygUCoVCsUnw2hKYpy/6PeLG4t+jtGwOrz2FfWQf4a9+F/3yDdwDu9dvoArFOrF57nSW4N133+Xv/J2/U/vvz3/+8wB86lOf4jd+4zeYmJjg1q1bted37NjBH/zBH/D5z3+eL3zhC/T29vIv/+W/5Kmnbk/8fOxjH2N6eprf+73fY2JigiNHjvAf/+N/rEV9mqbJK6+8whe+8AVKpRJ9fX18+MMf5pd+6ZfW6VUrlqU64/jTdaRpzBP+hGWDrm3N1eJC4Bw9QODVt/GScWSy/urcpfCS/uS+lvaFP/PEu+hDozgH92C+ewFtfBLr6Uc3xQpehaIpSIlx/grOrgFkZP1LgfMTb2KGOgnGG4uB1s0YrtU8xx+AVRoFBOYiIpRsa54Y5/Z1oY1N+q6cWUJB/1qca+7rWgkiV0Afm6T6vvcsu60XiyDKlds3BVIiCkW8ffN/h7MOQFGpIucIf0awDYSGXZ4fLb4UrpUn5OgYMoRw6wt/6ZvPgZS07Xy24eMqFpIfP4FdnqA4fXoR4e86mhHGDKsUh1aj3ZrA7etaXvRTrAgZi9Z1/IlMjsDLJ3D2DOIc3rdu43H37YQT76KPT+HuXLpXpRVow2OIStUfxzIY4XaQHk41U1vEsdGIXIHQt34AEiofez9yZjFNM/B6uxDF8pbtr/HcKrlbL5HsfxJN98XLSPtRilOn6Nj9sQ0enUKhUCgUis2CnJkLFdk8siO1yEYSLZPH2bsT2dGGu6sf852z/ndIZZBQ3GVsCeHv0Ucf5fz584s+/xu/8Rt19/nyl7+85HF/6qd+ip/6qZ+q+1xfXx9//Md/vKJxKtYXUanW3BcyFLxD+LOQgcBiu256nH27ME+8i5bJYe/oW36HOzF0vEQMkcmhX7qOeeo81iPHcY4dxNm/i+ALPyL01eeovu9RvIGe5r8AhWKd0UYn0TJ5rMcWj2tuFVJ6FCbeItH7aMNdM7oZa2rUJ4BVvIUZ6qhNirUS5/A+3+03d3GFEMhEDG0dhD/jnbN4fd143fPdecbl60jTwN21/KRzTdArFH0nZNVC2M6CaOW5rvK5CzGEZmAEUtiV+X3BS+FaBQwvgCGD4Lk4Vo47f1u5W69gFUeIdd2vRKk1kBl6HoBK7mrd56v564TiuxBC3dytCs9r7MbYdtAnp7Hes3wMsmJlePWEP9sh+L1XkdEw1nsfWlehR0bCSENv2uIPkc5hXBvCfmD5OF5tcprQd3+IDAUpNyD8+VHNYFemN4XwJ/IFQt94AWkaVD/6vqYvzLMevb+px1tvcqM/wrVLpAaeqT0W7biXsXNfwLXy6IHbn82uXcSuTBFqcCGWQqFQKBSKuwdv5n5dy+ZwFxH+RKEEros3k4Zk3X+U8Feew7h0fUPSoxSKVqJmOxRbFlG1kKEZcS8YWBD1uRX7/WqYRu0Dx1uF4w98d49+Y4Tgyydw9u/CueeAf7yeTso//iG8thTB770yvwdQodiiGOcv4yVjeL3rL5SUM5dwrRyxrgcb3qclwl9plEB0YcxnSxCibpSyl4gh8i0W/spVAm+eJvj9H4Ht3H5cSoxL13H37ABj+XVNMjYr/Pk9f1ren0D37hT+ZhaYiKrFnZjhDuzKyhx/hgxiyPBM1Of83lUpJXZ5AildJq98peHjKuZTzl6hkrtKou+9ONU0TjWzYJtK/jrBuCpxXw3aVJrIF/6S8H/+GsFvvkDg5RPoF69BnU5FbXwKPInb21gcrqJxZCyClp/fUxp47R1EoUj1mcfX32HZ5MUfxtUbmG+fWVZIFIUSwedeRmqa72qrc61ecOxgG+BHNW84nkfwB6+DrvlOv1akcej61kxBwf9czAw9T6zzvnkibbTjGCApTp+es63HyKl/z9Bbv606dBUKhUKh2I4EA8hwCC2zeK2KlvXvweVM97FsT+HsHsB85yy46vuD4u5CCX+KLYuozER9Us/xZ2/Jfr+5OEf346XiCxwtjeK1JdDyRbyONqwnHpy/6jsUxHryYYTtYFwdatKIFYoNolzBuD6Mc2jvhsRYFSbexAi2EUo0vjpMN+NNF/7s0mjdfr/1ZD0cf/otv5dPlCuYb92e8NNGJxGFEs7+xsQcGQmBEGgzjhkxI/zJ+B3xarPxntUqd2KGOht2/EnPwbNL6DKICEXQPQPnDuHPtXJ4boVox73kx16r9dApVkZm6HnMcBcde34MWOj6c6w8TmWakBL+VoX59lm8WATnyD7/xnp8muBLb6CNjC3YVh+dQIaCtRtrRfOQ8SiiVK5NUGjjUxgXrmI/crxpfa4rxUvEmuf4y/oTNvrQrcU3sm2Cz/0QdI3qB58AQJvOLntsTQ+iB+Ircmy3CvPkObSJaapPPwrh0EYPZ9NRnHwbqzhCavAD8x43gklCid0UJ0/WHkvf+A7lzAU8p0xVfX4qFAqFQrEt8VJxRDa36PMinUMa+rzFVvb99yBKFQI/PFF3MaNCsVVRwp9i61Kt1hx/MhSc58YQlr2loz4BZDRC5VMfWVXHH4A70Ivb00nlA4/XXeUrYxHcgR6Mi9fWOFKFYmMxLlz1uzH37173c8/GfMa6HlhRZKAeaK7jz3VKWOUJzA0W/rxEHFGqzHfiNRl9eAyvLYl1/1HMM5fQptIAGJeu4cWjjS+W0DRkNFwT/ES+gAyaCxeNaBoyaCIqdRx/oQ6cBjv+XKcEnkSXAbyONgw3iGvPvyGxy+MAdO79JIFIL5OXv9TYa1HUcKoZCuMnSA0+gxlqxwi2Ub5D+KvmrwM03MmpuI2YzqDfGMG+7wj2/Uex3v8YlR//EF4iVnchkT460++3RbvFNjNezJ+sEMUySEngR2/jtaf8RTAbhIzH0Jrk+tZmhb+bo4ucTBL8/muIQpHKh96L19MJuoY2nWno+Ga4m+zIi6SHnsdzKk0Z80oR41OYb5/Fvu/wqhf63c1YpXFGz/4R0c7jhNsOLXg+2nEvxenTSM+hkr/B1JUv07bjQwg9QCl9YQNGrFAoFAqFYqORycQyjr+8v0huzv2JbEtQffJhjMs3fOefQnGXoIQ/xdbEdRGOO8/xx7yoTwsZ2BIVli3D6+6g+rH3L7l62DmwB218CpFZfDWMQrGpkRLzwlWcPTtuO7PWkUruGk41vaKYT2h+1OfUla+iaSbRjnubdszVIBO+W65Zjo+FJ5DoI2O4/d04xw7itSUIvHQCLBv92hDu/l0rEhi8ePR21GehhIzF6m4ng/Nd5bMYoQ4cK4vnLR+Z7NoFhPQwZBCvI4XhmLjW/BsSqzwBgBnppnPfpyhNn6E4fabh16OAzPD3EZpJos93/4QSe6hk5wt/lfx1NCOiOhRXgXnynL9waG6PmhC4e3agXx+eH49j22gT0xsSwbwdqMUV54sYF6+hTaaxHrt/Q0VWLxmr9aasCSnRsgW8RAx9dKJuLL1x6jz60CjV9z+GbEuCpuGlEmjp5R1/AL1HfoZwcj8TF/9vrr7yPzJ5+Uu4dnH5HZuEtGzMF36E15HCvu/Iup13q+C5VW69++/RzRi9R36ubodytOM4nlOmlD7H6Jn/k0C0n459nyKc3E85c34DRq1QKBQKhWKj8VJxtFze7ySvg5bJ1a1UcvftxH7wHsy3zqBfut7qYSoU64IS/hRbktoE7FzH34Koz63t+FsP3J19yGDAd0wpFFsQfWjUj3c8vG9Dzl+YOIEeSBBO7V/RfrPCn5T1v4yuhEr+OpnhF+jY82OYobY1H28teDMxmc1yfNyJyOQQpQruQC9oGtZ7H0KbzhD8zksIx2045nMWGYvOifosLOj3q3GHq3yW2b4hpzK97Llcq+ALl1oYLxnHdE2cSmbeNnZpHCPYhqYHiXbeTyi5j8nLX2rK38l2wPNssiM/INH3OLrhu6FCiT1U89fn/Qyr+euE4rvqTiQrFkdkchhXh7CPHwZt/i2Es3cHwrLRh267s7SxKZASVwl/LUFGw35c8XQG88QpnH07fdfbRo5p5jNg1km9WkS+CJ6Hc+wgeB76yPj8DTwP8+wlnIO78QZvO9299lTDjr9ApJe+Y/8Nex77/5LofYLM0AuMn//TNY17JbjffwNKFarve8+C99N2R0rJ2Ln/C6s8Qf+9v4hu1u89DMZ3YASSjJ75P7DLE/Te8/Nomkmk7RDlzCWk17r0AYVCoVAoFJsTLxkHT9b/PiolIpPDWyQW3z5+GGf/LoI/fANtdKLFI1UoWo+6y1BsTWYi1247/gK+2De7wtiykVu8429d0HWcfTsxLt9QJbaKLYlx7jJeewqvc/0FLyklhfE3VxzzCb7wh/TwnPIax+Axfv5PCUb7FvTfbAihADJgtszxpw+Pga7h9fqT215nO/bR/ejjU7h9XTUHTKPIWOS24y9fRC4i/MlgoK7jzwz50Wx2A3GfnlP0oz4DMWQsguGFcEvpedvY5QnMcDcAQgi69n2aav4G+fE3VvS6tiuFsTdwrTypwWdqj4USe/DcClZxpPZYJXedYEL1+60U8+Q5ZCRcV2CXqQReexL96s3aY/roBDIcWnVkuWIZdB0ZCWG+fQZcD/vhjXV8w23X91q7XrWZXhZ3sBcvGUe/Ob/nT795C1GqYB+av+jHa0uiZXKLrvCuhxnuoOvAT9Cx9xMUJt7EqTbmGFwLYiqDe+oCzqP3qfdHHTJDz5Mfe43ewz9DMDaw6HZCaEQ77sW1i3Tu+zTBaD8A4dQhPLeienIVCoVCodiGzHZdz8bGz0WUKwjbWbwPWwisJx7E7ekk+PzLYC2f7KNQbGaU8KfYkoiqPwErZ6P9QsGZx31BUFiWEv4axDmwB1GpLphUUSg2PeUq+tAozuG9LYk2K6XPM/zO7yEXKXeu5m9gV6aIrzDmE0A3/Ym+tcZ9ZkdeopK7SvfBn0RomyDeWAhkIrbmSd/F0IfHfPfQnN5S+8F7cHu7sI8t7P9ZDi8W9QW9qoUolpcW/uo4/oxQGwgNuzK17LlmHX9aMI6MRjBkCLcyf4LZLo8TiNx2R4VTBwgl9lKcPLnCV7Y9yYz8gEj7PQTmdF2G4jtBaFRmev4cK4dTTfuPbyMCL59AH1r957zIFTCu3MQ+fqhubzCAs2cHxo1btVhGfXQCt7dT9fu1EC8eRTgu9n1HkJHwRg/HF3oNfc2LP0QmjzR0ZCSMO9jrO0nnfBYb5y7jdbUjO1Lz9vPaU+B6iOzKz5/ofRyETvbWD9c09kYQE9MgBN7B3S0/11bDKo0yeekvaNv5LPGeR5bdPrXjA3Ts/vj8BR/xnWh6iHJaxX0qFAqFQrHdkOGQvxi5TqXR7GP1oj5r6DrWI/chqnZtMZpCsVVRwp9iSzI7AStDwXn/S6XqW7ctZ0P6vrYisj2J19mGcfHaRg9FoVgR+pgfveDOiflqJsWpdylOvYtVGq37fGHiLXQzSrjt4IqPrQd8V8RahD/HyjF15S9J9L13xVGjrcRLxBC5xcu0V43joo9N4A70zH/cNKn+jafnxb01yqzQp49NgpR4izgGF/TIziCEjhlsx6ks7/hznQK6NCEUQkZCvvBn52sRlFJKrPJ4zfE3ixnqwLVa70DZ6kjpUc3fINpxz7zHNSNEMNpfE/6qeb+vIRTfvd5D3DjKVYzzVzHOXVn1IcyT55DBAM6BPYtu4+7ZAa6LfmPE7/ebTOP1dS+6vWLtyFQCLxXHObpJPgOatPhDy+aRyYTfH7mjH1GuoE1l/FNk8+gj49h1Ir699qS/f4Nxn3PRzSjxnkfIDv+g5fHKWjqLSCUWFdG3M5X8DaR0ad/98Ya2D8YG6dj7iXnJC0IzCKf2U1I9fwqFQqFQbD+EQCbjfgrEHWiZPOjaogt+Z5FRf0GdKK4toUmh2GiU8KfYkoiK5a8gN32Hy6zwJypVsB2QEmkqx1+jOAf3oA+PIYqljR6KQtEw2vgUMhZBRut3v6yV2WjAcuZi3edLmfNE2o4gxMon7nRz7cLf5KUvAtC179OrPkYrkIkY2ircFsuhjU6A6+H2N0/olbHI7WPDMo6/hcIfgBHqwC434PizCxgyiAwFQNfRg3Fw3drfgGsX8JzyAuHPCCZxLLXScDmcahbp2Qt+fuDHfZazvvBXyV1HN6MYMzGt24HZRRL+e2jlsd5iKoNx6Tr2vYfAWPx6J+NRvO4OjCs3a2K66vdrLdZ77qPy8Q9sKgHJX/yxRsdfNl9bie31dCADZs2xaly4igyauLsHF+4YDCCj4VUJfwCpgadxqtMUp06tdugNIdI5RGeqpefYqjiVNJoeQjfW5mANpw5RzqqeP4VCoVAotiNeKlE36lPL5PAS8eX7lYP+PbsS/hRbHSX8KbYkolr1Yz5n4qNmu/5ExfK7/kBFfa4AZ88O0DT0S9c3eigKRcPoo5O4PZ0tO/6s06+SvbTgOc+tUs1dI5xaudsPQDd8wcm1Vjc56lSz5EZfpWPPJ9ADm6sfyIvHZhZhNDcPXx8ZQ0bCyFTzXq+MhEET6CPj/srA2CIiciiIqNp1e6PMcAd2I44/q4DhmrVoaiPUBp6HOyPq2eVxAALh+UKJHkjUtlEsjl32xS0zvFBoCiX2YBVH8JwKlfx1gvFdiG0UP6mNTYKuIWwHbWJ6ZTtXLULPv4zXlsSp47C6E2fPDvThMfRrw37Mzkznm6JF6DpssoVuMh5DyzfB8Td7rdc03IEeP5LedTEuXsPZv3tREdprT6GlV+eSDiV2E0rsJjv8/VWOvDFEOquEv0VwrIz/+bhGIm2HkK5FJa/ubRQKhUKh2G54yTgim58XFQ8gsjm8RuYThMCLhhElJfwptjZK+FNsTarW7XhP8J1/muZPNlszPUwq6rNxAibO7gGMa0MbPRKFojEsG206g9ci4c9zLezyJLoZo5xZKPyVs5eR0l1VzCf4MVSaEam5vaT0KGev4FiNRWQWp06BEMS7H17V+VuJTPoT/StxfGjjU2gjYwu+mM9FHx7zYz6bKdgIgReLoqWzfpzHIiv/an2y1YViphnqxC5PLOsqcJ0iumvWFqpo0Q7wvJqbryZcRe5w/AUSuHYRz1PF4kvh//wEZnjhNSGU2ANIKvlrVPPXCcV3rfv4NhJ9dAJn9yAyFEAfHmt8RykJfv9HYDtUP/D4km6/WZw9vgvLuHgNt69L9fttQ7xkDFEorcpdCkCliqha87pX3ME+tMk0xtnLiKqFc2jv4udvS6JNrz4eOdn/NMWp01il8VUfY0nKVUS5guhYu7h1N+JUMxiB1JqPE4zvQNNDlFTPn0KhUCgU2w6ZiiNsZ4Fwp2Vm4uQbOUY0rFLRFFuelgp/ly9f5stf/jL/+//+vzMx4U9oXb9+nUKh+RFgiu2FqFh+XFrtAYEMBRGVqnL8rRIvlVA2dsWWQRuf8mPkWiT82aUxQJLoewK7MoldSc97vpy+gB6IE4j0rfocvqh4gbFzf8yVH/4Tbp74DYbf+d2GBJ7i5EnCib2bzu0HvuMPaLzjyXUJfvdlQt96keC3XkTM9DjNRRRLaJkcbn/Pwv3XyKzLz1si578WJ10n7jPaeS+uU2Lq6l8teR7XKmA4Ru2zS4+1I9w5jr/SOEYgiaYH5+2nB5Iz+7egN/Euwi5PYARTaNrCz/5AtA9ND1GYfAenmiG4nYS/qoU2ncXt68Lt71mR8Ge+eRp9ZJzq+x9dtgejRjhUi/dU/X7bEznzGSDyxVXtPxvLNE/4G/AjngMn3sXt60YmF//s89pTiHIFypVVnT/e8zCaESY78uKq9l+O2b4Z0ZFsyfG3Ok4l3RTHnxA64dQBykr4UygUCoVi2+HNiHsiPSc5p1JFVKp4qQaFv4hy/Cm2Pi0R/srlMv/wH/5DfuzHfoz/6X/6n/jd3/1dxsf9VZO//du/ze///u+34rSKbYSoViE4f3JUhgJK+FsLoSCiatWNslMoNhv62CQyFFxy8m8tVGf6/ZL9TwEL4z5LmfOEUwfXFBdoBFMUp05RSp8l0fsYvUd/HqswzNTlLy+5n+fZFNNniHbeu+pzt5RQEBk0G3b8GZdvICpVrMceQJRKhL/6HIEfvIaYStccgNqIL1a4/c0XEmQsOu9/624z4/gTVWvBc6H4Ljr3/DjT179JafrcosfwrDyGF7gdTR1PonkabtW/GbHK4wvcfuA7/gAV97kMdnmibr8fgBAaocQucrdeBthWjj993I+h9Xp84U+bSkO5fl/lvP2uD2OePIf10DG8FQruzr6dAKrfb5syG+/a8OKPO9CyeX9B39yY2HAQr6vdd0kfXtztB+C1+4Laal1/mh4k2fcEuVs/bInTWstk/ZSSBiedthvNcvyBH/dZzl5WjnmFQqFQKLYZMh5FxiKYJ8/enlOYWXzVsPAXjaApc4Rii9MS4e9f/at/xauvvsp/+A//gRMnTiDnRHc9/fTTvPhia1ZQKrYPomLdjl6bQYaCUK3CjPCHEv5WRC06tc7EtkKx2dDGJ/2YzxbFyFmlUYxAkkCkh0Ckh/Ic4W+23y+yyn6/WXqP/F12vedfsPuxf0nX/r9FovdROvd/hvTN71CcenfR/crpc0jXItp535rO30pkPNbYpK+UGO9ewN3Zj3NkH5VPfhjr8QfQh8cIf/W7hP/kKwS//RLmmUt4nW21frxmMuv0a8jxV6kvmLTt+giRtkOMnvk/FnXmudU8ugzUPrtkNILhBXFKfueaXR6vK1zpM8KfY60+um47YJfHF/QjziWU2IvnlNHNGEaofR1HtrFoo5PISAgZj+IN+AKefmsZ11/VIvDi6zi7B3GOrfw65+7fRfkTH1L9ftsUGQ4hDX1Fcc9zEZkcXizi9xfOwdmzAy8exd3Zv/T5EzGkoaNNZ1Z1foDkwPtw7QKF8TdXfYzF0NJZP2VDV40bdyKl17SOP4Bw6hDSs6jkrjXleAqFQqFQKLYIQlB96hH08WmMk/7i3NuLyxpLMqk5/paoI1EoNjstueP41re+xT/6R/+IJ598EvOOwvmBgQGGh4dbcVrFNkJUq/M7/mBO1KflTxboy3fRKG6z3MS2QrFpcF30iWncno6WncIq3iIQ9ScXQ8n9lDMXa89Vsldm+v0OrekcZriDYGxgnmswNfhBoh33Mnr2D3Gqmbr7FSZPYoY61xQz2mq8RBxtbLJubOdc9KFbaNk89qy4oGk4h/dR/uzHqPyNp7HvPQRIRKGEs2dHS8Y6G/W5ZJThEo4/8B1lvUd/DildRs/90bwFTwBSunh2EUMGa1GfMhrBkCG84qzwN4FZR7jSA3EQt52BivpYi/z8ZvF7/iAY37Ump+5WQx+b9J13QiAjYby25LJxn/rwGMJ2sN9zfHWLK4RAdqRWN2DF1mfGrbcWx189N79zdD+VT39k0S7WeedvS6KlV79YIhDpJRjfSWn6zKqPsRginUO2KbdfPVwrB9LDCKaacrxgfAeaEVFxnwqFQqFQbEO83i7sew8ReOsM2uS0v7gsEW14rlhGw+DJhtJSFIrNSkuEv1KpRFdX/cmXclnZZBVNoFrf8ScqFsKyVcznKqhF2SnhT7HJ0SbT4Hp4Pa2LkbNKtwhE/U6hcHI/1cIwru0XO5fS59HNWEuENyEEPUd+BtAYPfOHSDk/eldKSXHyFNHO45tavHCO7ENISfirzxH8zktooxN1tzPevYjX1Y7XfYeIq+t4vV049x2h+uGnKP/kj6/KedQIMuFPMC8ZGysEMmjCEtdHI5ii58jPUpw8SWbo+XnPuXYJ6Ul0Gai5Fr0Z4c8tZ3DtIq5dJFAn6lMIDd2MKcffErh2Ec8pNST8baeYT2wbbTLtu6NncAd60EfGlly5qt8cwetIIaOR9Ril4i5ExmOrd/xl8/P6/W4/IZYX/Wbw2pOrjvqcJRTfTSV/fU3HWICUaJkcsk31+9VjdsFTs4Q/ITTCqQPkx09gl6eackyFQqFQKBRbB/v+I3jtKQLffw1tMo1cQdS6jIYB0EqlVg1PoWg5LRH+Dh06xLe//e26z73wwgscO3asFadVbBdcF2E7C4Q/Zhx/WLY/QatYEcrxp9gqaGOTSNOo9fg0G+k52KVxAlFf2AunDgCSSu4yAOXMBcJth1omvBmBBH1Hf45S+hzpG9+Z95xVGMKpThPbxDGfAF53B+XPfJTqU48gCiVC3/w+wW+/NE840yan0UcnfLffBoqYXmcblR/7IF7H0tFiMhhc1PE3S6zzOMmBp5m6+tV5oq1nFxDS8x1/s59doQAmIZxKBrvs9yAv1lFnBJKq428J7LIvLJuRxYU/I5ikY+8nSfQ9vl7D2nC08WmQEnfOIgm3vwdRqiAyi/w9eR760CjujqXjFBWKpfASMbT8KoQ/10UrlFY0KVP3/G0pv8fFdVd9jFBiN1bxFp5TWdNY5iJKZX+BYltywcIeBTjVNABGsDlRnwBtO57FtfNc+9GvMnHpi7VFXAqFQqFQKLYBuk716fegFcvo41N4yca/Y3oRfxGkUD1/ii1MS4S/X/qlX+Iv/uIv+Mf/+B/zwgsvIITg5MmT/Kt/9a/44he/yC/+4i+24rSKbUJt4jVUx/FXtfznA4E6eyqWJGCCJpTwp9j06KOTvkOswZX/K8UqTyClW3P0meEu9ECCcuYSnlulkru65n6/5Yi0H6Ft54eZuvJlKvkbtccLk++gGeEZMXKTo2m4+3dR+eSzVJ95DG1qmvBXv+s7NgHj3Qt+X9OugQ0eqC/+Lcvs4pJliHc9iOeUsUqjtcdcuwCeRNPDYBj+g0Kgzwh6VmlW+FsoXBnnLmPYBo4S/hbFKi/+85tLx+6PEYj0rMeQNgX66AQyFECmbrunvN5O0LVF4z610UmEZePu3LxRworNj0zEEIXSioU3kSuAlPUdfyvAa0+ClIhM/c7VRggldgNy3mfwUtjlqQUxz3ci0v51vGRmOfHV/4ZqYWTV47sbcaoZhNDRzeb1g0baDrLnsX9J+86PkBn+Htde/efkW9DdqFAoFAqFYnMik3Gs9xwHwEut4DtmKACa5vf8KRRblJbMmr7//e/nd37ndzhx4gS//Mu/jJSSX/u1X+Ob3/wmv/Vbv8Xjj2+f1daKFlDxhT8ZvKPjLxjwb/ILRaSpHH8rRgjf0VJZ2tGiUGwoUqKNT+LOia5rNlbxFkDN8SeEIJw6QDlz8Xa/X4uFP4COvZ8gEO1n9Mz/ief678vi1Eki7UcRmtHy8zcNIXB3D1L5xIeQ4SChb3wP4+Q5jGvDOPcc2FC330qQwcCyjj+AYGI3IKhkr9Yec+2i31sUmD+ZqQeTuE4BuzyOHkigG+F5z5vvnCXwylsEMo5y/C2BXZ5AN2PohoqmnIs2NulHIs99j+k6bm/XosKffnPE7wJsT63PIBV3JV7Cv9aJfHFF+2lZX6hrivAHaNOZVR8jEO1DaAEq+WvLbutaea69+s8pjL/hP1CpEv6zv0Ibnx8vqWVySEMnm38H16kydf1bqx7f3YhTTWME2xCiuVMUmhGiY+8n2PPYvyQQ7WP62teaenyFQqFQKBSbG+fQXqofeBx312DjOwmBFw0rx59iS9MauwTw0Y9+lOeff55vfvOb/Omf/ilf//rXeeGFF/joRz/aqlMqtgmi6jsu6nX8AWjZgor6XCWyQUeLQrFRiOkMwnbmdVY1G6s0im5G0c3bE4/h5H4q+WsUp0/7/X7R1rthNM2k956fxy5PMHnlL3GqGSq5a5s+5nMxZDRC5WPvx9m7k8CJd5EBA2f/7o0eVsM0en3UjTCBaB+V3Fzhb8bxF5gfLaJHUuC5VHJXF7jVjFPnMd88jQwFMW1DdfwtgV2eWNbtt+1wXfSJadzehddKd6AHfWwCnDvcWFJi3Bjx3X5bRJBXbE7kjPCnrbDnT8vm/e/3oeDyGy+FaeLFo2jp1V83hdAJxXdSzV1bdttK4SZSuhQmTwKgT04jyhWMMxfnbaels3jJOPnxtzACMXK3flTrtVuK4tRprr/2v9z18aBONdO0fr96GMEUkfZ7cKrq81ShUCgUim2FEH7SkKGvaDephD/FFqdlwt8se/bs4cEHH2Tfvn2tPpVimzDruJB3TArUOurKFT+2UrFiZCg4r4NLodhs6GOToGl4Xe0tO4dVHCEQ6ZvX4RdO7Ud6DtmRFwmnDjZ9NfpiBKP9dO77NJmb32Xi0l+A0Ih2bOGeXF3HevJhqk+/B+vJR8DcOs5FGQxAA44/gFBizx3CXxGdAIRD87bTox3gepSzlwnM6fczTl8g8MYp7PuO4Bzei2kZyvG3BEr4AzE8hnvxOsxEDWoT0+B58/r9ZnH7e8H10EYn5h8jk0cUSjiq30+xRmQ4hDR0P7pzBYhMfmURTEvgdbSh37y15p6/SgPCXzV/E4BS+ixSemhTGQCM6yPzu20zOUqJCnZlmj0P/jxCM8gMvbDs8cuZi1QLN+dFSN+NzDr+WokRSOLaBTzPbul5FAqFQqFQbH1kJIwoqX5gxdalJTOX//pf/2t+9Vd/te5zv/qrv8rv/u7vtuK0im2CqFT9leh3iHtzhUCpOv5WhQwrx59ic6ONTeJ2tYG+spVai1Fv9bxVGl3g6AvGBtH0EJ5TJtJ2qCnnbpTU4DNE2o6QH3uNcGJvU7tvNgp3707cnVtLXPB7ZBu7PoYSe6gWh/Fcf3vXLqB7gQULVrR4B3genl3GjPjCn3H+CoHXTmLfewj7gaPIaASzquM5VTyn0twXdZdgl8a3vfBnvvgGztdeIPAXf41+8Sr6yBgyYCJnIg/nIlNxvLYEgROn5oki+s0RpKHj9W3vn6WiCQiBjMfQ8qtw/CUTy2/YAPb9R9CKJcw3Tq36GMH4LuzKJK61dFdgtXATTQ/6na2FYbSpdC1u1Lh03d9ISrR0jqw+hG5Gaet7kNTAU2RHvl/7rFj0tZR9kX5uhPTdSKsdf0Dt+G5VLaZRKBQKhUKxNDKiHH+KrU1LhL+/+qu/4sEHH6z73EMPPcTXv/71VpxWsU0QVct3XtwZQzXnMakcf6tCRX0qNjVSoo/OdFY1gXL2Mpdf/BXs8u0OHik9rOJC4U8InVByL8C69PvNP7dGz5GfRQ/EiXU/tK7nVswhGEBUbfCWj1oLJ/aA9KjkbwC+8Gd4Af9zag56bOZv2fMIhLsRUxkCr76Nc3gv9kPH/F6BSBhDBsHzcJTrbwGeW8WxsgS2s/BXqSIKRfRH7sVLxQm+dALznXN+JHK9yE4hqD71CFomh3ni3drDxo0RvIHepi2sUGxvZCKGyK5A+JMSkc2tud+vdri2JNZD92KeuYQ2vDqnXCixG4BK/vqS21XzN4j3PIKmBylOn0abyuD29+Ds6se4cNV/bfki0nXIOZeIdz+A0HTad34Q1ymTu/Xykse3KzPCXwN9g1sVKSV2NY0RarHjL+gLso6Vael5FAqFQqFQbH1kNIJWLNdSVRSKrUZLhL/x8XH6+ur3H/X29jI6enfHlChaTNVChuo4+oS43e2nhL/VoYQ/xWamYiEqVbyOVFMOV85cwnPKpIeeqz3mVKaRnkUg0rtg+2jHMYxg+7r0+92JGWpjz+OfJzX4gXU/t8Kn5tZrIO4zEOtH6IFa3KdrFzBcAxmc7/gTyTY0DPA8zEA7wR+8hpeMY73nvtsLWaIRDC/sxzaqnr8F2OVJgG3t+NMm0wDox/bjPPsk5U9+GOfQXuwj+xfdR3a0+aLI6Ytow2NQqqBNTOPsWP/rm+LuxEvc4fiTEuzF4xVFqYxwXGSThD8A5+h+3P5ugi++saooezPcjWZEloz79NwqVmmMUGIP4dRBShOnEIUSXkcK5+BetGwebXwKLZOjomWw3Azx7gdnjt9JvOsh0jefW7K/z7/OiYb6BrcqnlNGuhZGINXS88w6/lTPn0KhUCgUiuWQUf8+nEpjlR8KxWajJcJfe3s7Fy9erPvcxYsXSSYXxg4pFI0iKlXf8VeH2YlZ5fhbHX6UnaVWsyg2JVqxCICMR5tyPKt0C4DsyIu4tj85WS2OANQV91KDH2D3o//zuvX73YmmB+b1DirWl9nPHdHAl34hdELxXbeFPyuP4egLFq3IqO/mE55H5PwkWr5A9en3zHNcyVgYQ4YQyvFXF7s8DvgT9NsVbSrtf++Z6UaTbQmsJx7EG+hZcj/nngMzosjrGJeuAeAOKuFP0RxkIoYolNCGxzBfe4fQF/+ayJ98FW1ssu72IuPHaTbL8ecfVGA99Qh4LsGX31zx91shhH8tX8LxVy0MA5JgbCeR9qOUpy/g4uB1tOH1deHFoxjnryDSWTLBW2iBGNH2I7X923Y+i12eoDDxdt3ju04J1y4QTh2gWhi6a7vpnKq/gKHVUZ+aEUVohnL8KRQKhUKhWBYZDTTGxzcAAQAASURBVAOonj/FlqUls5cf+tCH+Df/5t9w8uTJeY+fPHmSf/fv/h3PPvtsK06r2CaIigV3uCZmUcLf2pDBgD8p0oCjRaFYb0TeF/68WJOEv+II0Y57QUoyQy/4j5VG0fQgRrB94fmFhmaEmnJuxdZj9vNlJT1/s31MnlVA94ILHH8YBoYWxahqhM7exHroXmTbHYujTBMtEEF4AlcJfwuwyxNoehA90JxesK2IPplGdravfGFATRTxCJx4F7e7A8L1v18pFCvFS/h9tKFvv4hx9SZefzdeR4rAi68vdP55HuY7Z5CRcNMW98wiI2GsJx5Cvz6MfmnpyM56hBK7qeSuIRcRDauFmyA0ArF+oh33gF2lGJxGJmIgBM7BPRjXhtDHpsiGRol13ofQjHnHD6cOkr75nbrHn+33i/c8jJQu1fzNFb+GrYBTzQBgBFsb9SmEwAikcGfOp1AoFAqFQrEYXiQC4Md9KhRbkJYIf//gH/wD+vv7+dznPsfHP/5xfu7nfo6Pf/zjfO5zn6Ovr49f+ZVfacVpFdsEUV3c8VcTBBd7XrEktYltFfep2ISIQglpGk2J8vW7/G4RbjtEsu+9ZIaex3MtrOItAtE+5axTLKDm+GtwYUQosQenOo1TzfiOPxmoG1NtGHGClQBufzfO0UWiGaMRDMLK8VcHqzyBGe7c1u9ZbTKN17W6yXIZCWM9+TAA7s7+Zg5Lsc3xejqpPvkQlR/7IOXP/k2sJx6i+vSjiHKFwOvzF4eab5xCH5+m+v5H6/dSrhF39yDuQG/N2boSQonduFa2JkzdSTV/k2C0D00zMcM9mF6EXCxdex3O/t3gSeyR81S1HLGZmM+5pAY/QCV7uRZdPJfZx2KdDyA0o+Ykv9u47fhrfTKQHkyqqE+FQqFQKBTLEw6CEIiSEv4UW5OWCH/xeJw///M/59d+7dc4ePAgAAcPHuTXf/3X+bM/+zPi8SZGuCi2H1XrdtfSHdQcf6Zy/K0GJfwpNjNaoYiMRZsyKehU0nhulWC0j7adz+I6JXKjL/vCX51+P4WCWtTn/OujyOYxTl9YsHkosReAcvYKnl1El3Ucf0BX7Cn65Xt859Uif9syEsHwgqrjrw52eWJb9/uJUhlRKiM7F7qUG8Xd2U/l4x/AuedAE0em2PYIgXtgD15n2+3O0kQM+5HjGOevog35ne/6tSHM0xexHrkXr6ezZcNxezrQ0tkVx30G47sBqOav1X2+WrhJMLYD8N1kcauLgjF+e4NICHdnP1ljCM0ME2k/uuAYocSemWMNLXjOLk+gGWH0QIJgbMeSfYNbGaeaQQ8k5rkhW4URSOKoz1OFQqFQKBTLIQQyGkYox59ii9Kyb9aBQIDPfvazfPazn23VKRTblKU7/vzHZUA5/laDEv4UmxmRLzWx32+myy/SjxnuIN71EOkb38G1C8S67mvKORR3GUL4nz13OP6Mi9cwT53H2btrXkyiGWrDCKYoTZ9Buh6GDEIdx5/22NPgeMhIeNFTy1gYsxhQjr862OUJYl0PbPQwNgxt0nfJeJ1ri8fzulYvHCoUK8E5tBf9xgjBH75B9QNPEHjpDdxdAzhHWys8y7Ykomr7Qnk00vB+RjCFHkhSyV1bcK2R0qVaGCLe84j/gGWTKLYzHbmJXZnGDPnvK/vgHrLjQ0Q77kXTFi5ONIIpdDNKtThMrOv+ec/NLm4QQhBK7KE0fXplL3yL4FTTLe/3m8UIpiilz63LuRQKhUKhUGxtvEgYUVQdf4qtSUscfwpFy/A8hO3UjUsDkKGZ/q1A61eL3pUEA76NXQl/ik2IKBTxYo1P1i1FtXjL7/IL+ZPlbTs/jF2ewHPKBCJ9TTmH4u5DhgILro/adAYAfWJqwfahxB6KU6cQ0kOXgbqOPxmPIduW7qeT0QimbaiOvzuQnoNdmdrWjj9tKu0v2mnStVGhaDlCYL33IXBcQl//HjIcovrkwy2J+JyL1+5HSGrTK3N6+YLb7rpOO6s0hvRsgrHBmWNniLo9YJiUps/UtstoVyi1e0R3v3fRcwSiA1iF4QXP2eXJ2jUulNiNVRrDtYsreg1bAaeaaXm/3yxGMKkc9AqFQqFQKBpCRsMq6lOxZWmaOvLggw/yhS98gWPHjvHAAw8s2bUihODEiRPNOrViO1GZcVrUmTwFcHYPIk0ddH0dB3UXMeNoUcKfYtMhJVqhiBNrkuOvODLT5eevfwkldhFpO0IpfZZAVAl/ivrIYHB+x5+UaFMZALTxqQUdaaHEHgoTbyE8ia6HkcbqPpu8SBjDNnEqmVWO/O7ErkyD9La38DeZxutoa7loolA0ExmNYD3xIIEfvUP1mceb0t3byDllwESks7BjZZ/zofgu0jefQ0o57x63mr8JUIv61KbSGHqIYGovpekzJHofY+LSfyEz9D0SO55a4OabSzA2UNeFZpcniMUf8scxEwlayV8nWicydCvjVNOEkvvW5VxGIIVrF/FcC01XKTEKhUKhUCgWR0bCtXt+hWKr0TTh7+d+7ufo6uqq/f+lhD+FYrWIqi9ILdbxRySEe2DPOo7o7kOGgqCEP8Vmo1wF12te1Gfx1gJnX8feH0dc0zHDresYUmxx7lgYIUplP346YKKN13f8ASA99EACZ5WnldEIphfCrdxCSq8mWG937MoEAIHtKvxJiTaZxjm0d6NHolCsGHfPDsq7B9dPtBYCry1Zc2mvhFBiN55Twi6PE4j01B6vFm5ihjrQTf+7iTaVwWtLEuk4Snb4BYbe+h0quat0H/pJkv3vW/L+OBAdIDP8fTzPrsWBSs/Brk7XFjeY4S40I0Ild+0uFP7W0/GXAsC1smjb9fNDoVAoFApFQ8hoBK1Y9nuildah2GI0Tfj7+3//7wMgpeSnf/qniUQiBFTPmqLJzDotFuv4U6wdGQoqx59i06EV/FgrbwW9PIshpcQq3iLW9eC8x8PJvQzc99+v+fiKuxcZCqJlbsdtzk4gOwd2Y567DK47z3Eeiu8CoYEn0YKx1Z83GsaQIaRr4zkldHP1x9rKuHYBoPb67dIEQugYoe3ZTyeKvvDsdbahbkEVW5J1njyR7Um0W+Mr3i8U3wVAJXd1vvCXv0kwvqP239p0Bq+7k2j7LqavfR27Msngg/+YcHJ5cT4YGwDpYRdHa8ecdTXPLm4QQiMU30Uld3XFr2Ez47kWrl3AXKeOPz3ox7461cy2dowrFAqFQqFYHhkN+/f5lu3XI/0/7N13nGRndeD9302VQ+c8OWtmJI0yI6EACNmAEVg2YMBgzDqAsf3avOvFLGsM2ATveo1tDIYXY2yMjTEGk0QSEgKkEQqjyTl193TOlatueN4/qrtnWp27qzrN+X4++ki68anurqp773nOOUKsIiWfMm7bNvv37+fJJ58s9aGFQB9JFstRhoPLPZQ1qxj4K8y+oRBLSBsN/JUi48/JD+K5OSnpKeZN+X0TMqL1gWGU38Ld1AKuNymTRDcD+MONGMpCCyw8aK3CIUwVQPM8nGu4z1/n0b/nwhPvoe/cf+AUEtjZPsxANZp2bZb31vsHAfBqliZLRojVzquMo4+kwHHntZ/hixKs2MbgpYfxPBsoTiLKp9rxR9YXN3Ic9OEkXnUFgfhm6ne+lfW3/M85Bf0AfOFiqeh8+kqfPztbzGq+OjgViG0il7iIUmper2Elc/LDAEuX8ecbDfxJnz8hhBBCzGLs+bOWzizzSISYv5IH/nw+Hw0NDbju/G6ohJgLvX8IryIGZsmSVcULScafWIG0ZBrlt0rSB6iQ7gLAH26aZUshJlKBiT3+9MFhvKpKvKoKMPRpyn1uwVR+VGARswNNA9MXBc/DzV+bDyo9zyaXuEAgtpGRjp9w8cB7SfY+c01na+j9Q6hQABWSyVBCzIVXFS+WyB2e/wSKuu1vxM72MdT6XaAYrHLt1Hh2nj40AkrhVVegaTrxpjsxRzPL5sIwg5iBKvKpqwJ/ubGs5isBsUBsI24hgZMfmvdrWKmuBP4qluR8uhlC0y2ca/T7VAghhBBzN3avpWeyyzwSIeavLE1i3vjGN/L5z3+efF6CB6K09IEhmdleZlLqU6xEeiqDipSqv18nmuG7ZssDioVTAR9awQbPA0b7OVVXgGHg1lRNGfir3vxq1ql7Fl2i2ghWgXvtZvzlk+0oz6F26y+xaf9HqFx3P56TGy/Bdy3S+4fwauRzTIi58iqKgThtaHje+/ojzVSuf4DB1u9QSHeRT7WNLh8N/A0Mj/cRXCh/uJnCCzL+XpjVHIhtBCCXuLTg85SSUoq+c18hPXB8jtt7jHQ+gedemUQzFsRcqow/TdMw/RXjAUchhBBCiOmoUBA0DS0tgT+x+pQlbaqrq4uLFy9y7733ctttt1FTUzOpmfn73ve+cpxarGWuiz40grNt43KPZE0rZrTkpXGtWFG0VLpkgb98ugt/uAlNK8vcF7GW+f3Ff+cLoOtoqUwx2w/w6qoxz7dO+uw0fTGC+ShOwL+oU2uRGEbKwL1GA3+5xAU03cIfaUHTTWo2P0j1xlcWeyhei5RCHxjC2bN9uUcixOphmXixCPrgCAupTVO18RUke5+l5/S/EKrciWGFx4NV+sAwXmVsQp/X+fJHmkl0/2z8/+1sH1awZsI2pr8C019JLnmJaN1NLzzEknNygwy1fZ/h9h/SsPu/Ea27ecbtM0Mn6Tn1Tzj5Iao3vap4jPwwuhlENwNLMWSgWO7TlVKfQgghhJiNpqGCASn1KValsgT+HnvsMXy+4sz2o0ePTlqvaZoE/sS86UMj4CnJ+CszFfCDp6RxrVhRtFQGd11pevIV0p34QtLfT8yfGg3eabkCWjYHUMz4oxj4046eRku/IDtVKbRcHuVfXOBPRUKYCX/JM/4KmV6sYO2kCVorTW7kIoHoBjT9yqXr1f99rdGSabSCjVst10RCzIeqjBfvKRZAN3zU73gzlw/9XwrpTvyR9eOfnfrAEN4i34++cDNOfgjXTmNYYexsP4H4lknbBWKbyCcuLupcpZJNnAcgXL2XruP/H8rNE2vcP+326f4jAAy1fZ948z2YvihOfmjJynyOkYw/IYQQQsyVCgcl40+sSmV5YvLoo4+W47DiGqf3D42W0KlY7qGsaVcebOcXXZpOrAK5PEZnD+7m9cs9kukphZ5K40QXn/GnlKKQ7iJSu/yz5MXqM/aZqOXz6ANDKNNAxaMAuHXVAOi9A7hXB/5sB5RaXI8/wAsFMR1fSTMU7NwAl576X9Ruex2V615asuOWQzZxftZMkmuJ3l8sjSeToYSYH68qjnni3IIrW4SqdhJreBGJ7gP4oy3FhSWqSuKPNAOQT3cSjG/FzvYRbbhj0naB2CYGLn0Tz8ktaZbcVHIj5/GF6mnc+9v0nv4i3Sc/j+cWqGi5d9K2SinS/UeI1t1KevAYg60PU7ft9Tj54SUr8znG8MfJpy4v6TmFEEIIsTp5EvgTq1RJ6yPl83m+//3v87nPfY5vf/vbDA4OlvLw4ho3XkLHXHgJHTG78cBfvjDLlmItsI6fxf/40+jdfcs9lGlpmSx4qiSlPp38EJ6bwxeWjD8xf1dPjNAHh1FVFVceHAf8eLEIxgv6/I31TF3sRAoVDmG5Ppzc0KKOc7VCugtQDFz4L+zc5P6EK4WTH8bJDRKIbV7uoawYev8gKhKCRZaQFeJa41VVoOULxWuLBard+kv4wk2Eqq4DwGjvAk/hNtYtamy+UAOaZlBIdeDaSTw3jy9YO2m7aN0tKNcm2XdwUecrhdzIBQKxzWiaTt2ON1O57mX0nvlXUqOZfVcrpDuwcwPEGvdTuf4BRjoex872L0/Gn68CR0p9CiGEEGIOVDg0fu2opTNYTx/G/90fg7OQ4vFCLJ2SZfx1dnbytre9jba2NpRSAMTjcT7xiU9w6623luo04hqm9w8uuoSOmN1YVsrYw2qxhimFcak429k6dJL8z01+uLQSaKliLXWvBIG/QroTAH+4adHHEtcgn1Vs7J0voA8O4zVMfMjr1VWj97wg8Dc6iWLRpT7DQUwvQCYzvKjjXK2Q6UbTLXQzSO+ZL9G0950rsuRnbrSknQT+rtD7h3Al20+IefMq4wBoQwlUOLSgYxi+KBtv/9Px/zfPt+HVVKIqYosam6abWKEG8ukO/Nl1AFhTBP6sYDWhyp0kup4kPkNZzXLz3Dy5VDuxphcDxXYeNVt/mVziEsPtjxCpuX7C9qn+I+hGgGDldoIVWxm+/BgDF7+Bkx8mVLlrScdu+uN4ThbPzaMbMoFCCCGEENNToSBaOoPviecwz7WiTAPNdjDPXcLZObksuxArRcky/v7yL/+SkZERPvrRj/Ltb3+bT3/609TU1PD+97+/VKcQ1zLXRR9KSEmrpXBVRotY27TBEfRECmfHJoyu3hWb9acl0wCoSHDRxyqku9AMH2agatHHEtcgTUP5LbRUBn0kNd7fb4xXV13sHWXbV3bJj36WLrLUpwqHsFQANze8qONcrZDpwReqo3bbG0j3HybV9/yM26cHjtN94h9xnaUtc5IduYDpr8QKyDUAAJ6H0T+EV1u93CMRYtVRkRDKMtEHh0tzwFwe43IXzpbSlEz3h5sopDqws8VrMitQM+V2scb9ZIfPUMj0luS8C5FLXALlEYxfmZShaRrx5nvIDJ0azSq/It1/mFDVdei6hW74qd70KhLdPyuW+lziz3fTVwGAk5esPyGEEELMTEVCaI6L0dZJ4abdZH/5FTgbm7GOngbPK/n5zKOnsQ6fLPlxxbWnZIG/gwcP8gd/8Ac8+OCDbNmyhXvuuYePfexjXLx4UUp+ikXTB0dAKcn4WwqaVixJJ4G/Nc+8dBnltyjcfiNeVRzr0InlHtKU9FS6WGLRshZ9rHy6E3+oEU0raaVrcQ1Rfj9GV+/od1LFhHVuXU2xJ2XfVeU4c6MZf4ssyajCQUwVwLWTKFWakiJ2pgcrWE+kdh+RmhvpO/NvuE5m0naem6f3zL/RcfivSXQfoP/8V0ty/rnKJS4QiEu23xhtaARcF69WJjAIMW+ahlcZL07SKAHz0mVQ4GxaV5Lj+SLN5NPFwJ/hi03bwy9SeyO6GSTRfaAk512I3MgFdDOI7wVVFCJ1N2H4ogx3/nh8mZMfIZe4NCELMN54J75QHaCWvtSnPz46ruElPa8QQgghVh93fRP5l91ZDPjt3QE+C3vvDrRUZryK1nxoyTR6/zSxEtvGOnQC6+BxzNMXFjlyca0r2ZPP7u5utm/fPmHZjh07UErR27t8MxHF2qAPDBVv1Kviyz2Ua4IK+CXjb60bLfPprm8Gw8C+8TqMrr4VmfWnpTLFXlYlUEh3SX8/sSgq4EPvH/1OekFZN1URRfksjL4r5T61XB5lmWAssj+trmP4YuB6uIXk4o41qpjx14CmadRufwOem6f//NcmbJNLtNL2zJ8z0vlTare/gdrtb2Ck43EyQ2dKMobZKM8hl2glKGU+xxl9g8W/P5kMJcSCqKqK4qTCq+UL4M5/UoV5vg23uR6CUwfo5ssfacZzsmSHz0yb7QegG36idbeS6D6AUqWfaT4X2cR5ArFNkyZT6bpFvPFOEl1P4rnF+4n0wFEAQtV7x7fTdJPqza8Bpi5pWk7GaKDRlT5/QgghhJiNruOuawTzyj29qq7Eba7HOnIaRluezUnBJvDdx/F//yfgOJNWG5cuozkuzsYWfAeeX5HP6MTqUbLAn1IK4wUPtXS9eHivDGmv4tqi9w/hVcYmPTjNDJ/l/E/+AM8tLNPI1iYV8KNlJfC3lo2X+dzYAhRnMK3UrD8tlcaLLr6/n1KKQqZr0sx0IebF7y9m+03xnYSm4dVWY7R2Yh4+ie/HT2OdPAf+xZX5HGMGK8DzcAqJRR/Lc/M4+aHRbAuwAlVUb3kNIx2Pc+bR3xz/p+3ZP0czfKy/9X1UtryEiuZ7Cca30nv6C0vy3ZtPdaC8gmT8XUXvHShmm5qLDCYLcY3yquLoI8nxQJ/e2UPwPx7G/9hT83pwoyVS6L0DOFs2lGxs/nAzULzHmS0YFmvcj5MbJDt0umTnnyulFLmRCwTjU/e1iTfdjefkSPY8A0C6/wjB+BZMX3TCdtG6m9l4x58tee9l3QigG37J+BNCCCHEgtl7d6IPjaB39Mx5H99Tz0O+gFZwMM+3TVpvnr2E21RH4Z7bcBtq8D92YLz9jRDzZZbyYB/72MeIRqOTln/4wx8mEomM/7+maXzqU58q5anFGqcPDE05s93OdOPaadxCEj0ovW5KRjL+1o58AevEWew92yeUyhwr8+k1FR/6o2nYN16H/9ED6N19eA1LO/N6JnoyjVOCzBYnP4znZCXjTyyKGg3iTZdt5TbV4XvmCFYqhYrHcBvrcNeX5oGmEaqGlIdbgsCfPdoXygrVjy+raL4Xy1+Ja6fGl+lGgEjtPjS9eMmoaTr1u95C69MfYuDiN6nd+tCixzKTXOICmmbgj5amf9ZaoPcN4jXXz76hEGJKXmUclEIbTqAPDuN/4iBeZRyjvQvjfBvu1rkF8szzbSjLxF1fuusKM1CNbgTw3By+WQJ/gdgmfKEGRrqeIFS1q2RjmAs724trp6adlGEFawhX72Gk43GiDbeTHjpB9cZXTbnt2ASUpaRpGoYvjlMYXvJzCyGEEGJt8Bpq8GqrsI6eIt/SMOv2xoV2zPNt5F98K2ZbB+aJszjbN4GmAaCNJDF6BsjfcxvoOvl77yD4zR/i/+GT5F55b0na34hrS8kCf7feeisA6XR6TsuFmDPHRR9K4OyYfGPpOVkAXDuFJYG/klEBH7rMKFkTzPOtWIdOog0nKNx7R/GC4uoyn/qVxO+rs/7yP3fPMo76Kp6Hls6iSpDxV0h3AFdm0wuxEGO9+ryqiinXO7u34WzbCD5r/AK+VPRoNVrCw8lPX5os2XuQQGwTVmDmYHkhU5yV6AtduUHRNJ1I7b5Zx+ELNVC96VX0X/g60bqbCcQ2zu0FLEB25AL+6Hp0XW5yAMjl0RMp7BuX9iG/EGuJV1ks0+x/6lAxY2/HJgp37MP302fx/ewQuaY6VCg480GUwjjfiruhGczSzaXVNA1fpJncyHms4PSlPse2jTXeycDFb+DaGQyrNGXR5yI7ch7QCMQ2TbtNvPkeOo98gqHW76LcAuGr+vutBKa/YsbvUyGEEEKIGWka9t4dxQn0vQN4ddM/l9bSGXwHDuJsasHdsh4VCRH4zuPonT14zcV7cvNcK8pnFZ/VAQT85F52J4FvP4bv6SMU7rx5KV6VWENKdpfyhS98oVSHEmICfWi4WFatZvJDTPeqwJ8oHenxt3YY7d2oUBDzUgfesTM4e3eMl/ks3H7jxI01DWfHZnxPHSqWv1psT7IS0DLZ4vs/svjAXy7Zhm6GMAMySUAs3JWMv4qpN9C0kpX2nCQcwXDNaXsSuU6GruOfIVp3C427/9uMhypkujGsCIa1sPdW5br7SfY+R8+pL7D+1v85qcdTqeQSFwhf1RPqWqf3DwHg1crnmBALZll40TB67wCFm/fg7N0Bmkbh9hsJdvTge/Ig+Zfun3Hyht43iJ5MU9hf+gcw/vBY4G/26guxhtvpv/A1kr3PUtF8d8nHMp3cyHl84SYMc/pgY7h6D1agmoFL38YK1uILrayKC6YvLoE/IYQQQiyKu74JryKKdeQU+ZfdOfVGSuH7yTNgmhRedFOxRUh9DV5VBdbxc+SbG0ApzHOtuJvWTewlWBnH3ncdvmeOYu/dgYpFpj6HEFMoz1MaIUpI7x8CXSuW5XkBTwJ/ZaECfrR8fn4NasXSUArz5DnIz6G3lm1jdPdh79mGff1OfM8dQ+/omVzm8ypeNFIsf5XJlmHw86clMwCoyOJnseeTrQSi69FKnIUlri0qEgbDwKua/J1U9nOHg1huEDvVPeX67PA5UB7J3mexcwMzHsvO9k4o8zlfmm5SvfFV5FPtZeuR5BQS2Nm+aXtIXYuMvgFUwFeSLGghrmX2bTeQf9mdONfvvBLg8/so3HnzeMnPmZjn21ChIF5j6Uuj+yPFWd5zCfyZ/grCVbtJdP0UtYTX7dkZ+vuN0TSdePPdoDwiNTesuOsv01+BK6U+hRBCCLEYmoZz3TaM9q5pn9OZpy9gdPWRf/GtVyYJaxr27m0YHd3F8vMdPWiZbLF60As4O7agAn6sQyfK+ELEWiSBP7Hi6QPDeBXxKbOPJPBXHirgB9cDx1nuoYgX0Dt78T11aE5f+EZXH3gebksj9k27cZvq8D/+FOb51kllPseMBdjGAm7LTUsVS86qkmT8teKPzq1vjxDTcTc2k33ogWWpr6/CIcJuLZmBk1M+4M0On8XwxTHMIMPtj854rEKmG98iAn8AvnCxJImd7VvUcaaTS1wEmLGU3LVG7x3Eq6kqeRlZIa417vom3HWTM9Dc9U04m9fhe/rQ9JOglMK42I6zeV1Z3ovR+tuo2/FmDN/cJphUtNxHLnGJzNDJko9lKq6doZDuJDhNf7+rxRrvwheqJ1p/2xKMbH5Mv2T8CSGEEGLx3NFSnUZX75TrjYvtuC0Nkybfu5taigG9k+cwz13Cq4hNWe0O08C+YSfmhXa0kWTJxy/WLgn8iRVP7x+a+oMP8FwJ/JXDWA8rKfe58pjnLgFgnbqAlpo5OGdc7sKLRVDxKGga+XtuR/l8aOkszsaWKfcZD/ytkL6seiqDCgYmlDpYCLeQxMkNEoiuL9HIxDVL01DhpeujdDUVDhJ163Gy/VMG27LDpwlV7iTedDcjnT/Bdab+jFBKUcj0LDrwVyybq8058OfaafLpzjkfPzdyEcMXl/K8Y5RC7x+csXeEEGLxxkqhm8fPTrleHxhGyxdw1zeV5fyGFaai+e45Z8iFqnYTiG1i8OK3liTrrzgpQxGYQza26Yuy8Y4PlbUX7EIZ/go8N4fn5JZ7KEIIIYRYxVQkhBeLTB34s22M3gHclobJ6wwDZ+cWjHOtmG2dOFs3TDupzNm+CRUMSNafmBcJ/ImVzXHQhxPTB/4k468sJPC3QhVszNZO7Ot3onwm1uEZZnYrhdHePXE2u99H/qV3Yu/eNmWZTwAMAxUKoq+UjL9kqiQl7XKpYskuvwT+xCqmQkHCXj14kBmaeMHvOllyyTZCFdupaLkP5dmMdP50yuO4dhLPyWIFFxf403ULM1A558DfwMVv0P7cx+b8kDUzeJxgfMuKKw+3XLSRJFrBxq2tWu6hCLG2Bfy4zY3FyglT0Hv6wNCnvT9ZapqmUb3pF8iOnCM7dKrs58slLmBYEazgNNeSq4Q5mlHpSLlPIYQQQiyS11iH3jk58FesxKVwm6a+97Z3bkbzFHgKZ8sMFaqMq7L+hhOlGrZY4yTwJ1Y0fXAElMKrrphyvSuBv/KQwN+KZF66DK6Ls3Mz9vU7Mc9emjbNXxscQctkx0sOjFGVMezbbpiyzOcYLxIaL7G53LR0Bq8k/f3a0I3AnPrlCLFiaRp6KErIaCAzODHwnxsp9vcLVm7H9FcQbbid4fYforzJJZsLmR7gSqnOxfAF6+YU+FNKke4/iudkSfQ8Pev2+VQHuWQrsYbbFz3GtULvK/Zt9Gok8CdEuXmNteiDw1P2ajG6+3Frq6dsQ7Bcill/Gxm4+M2yZ/1lR84TWAOTMkx/BYCU+xRCCCHEorlNdeiJ1KTKXEZnDyoSQsUiU+8YDGDv2lLs7RcKzHgOZ9tGVDgkWX9iziTwJ1Y0vX8QdB2vcuoeF+MZfwUJ/JXSWMYfEvibE72nH727PD2urmacu4TbVI8Kh3B2bpkxzd/o6EaZBl5DzbzPoyLhWcuILhU9mS5Rf782/NH1aJp87YnVzauqIFKoITN0CqXc8eWZoTOY/orxDIzKdffj5IdI9j436Rh2pgfQsAI1mMfPgG0veDxWsJZCdupeBhPOme3FzvVj+KKMdDw+64PpRPcBDCtCuHrvgse21hh9g3gVMfAtfX9JIa41bmNtsXpCT//EFUqh9/ThNaysiURLlfWnlEcucXFN9F4dz/jLDy/vQIQQQgix6rmNxftwvatnwnK9owe3uX7GvtD2bTdQuPPm2U8ylvV38TLaoExcErMzS3WgT3ziE/Pa/l3velepTi3WML1/CK8qPu2MWin1WSa6jvJZaLnJs5zFZNbzJ9ByeXKvub9s59ASKYyeAfJ331ZcYBjYN+7C9+RB7L07UFUVE7Y32rvwmuoXNBtdRUIYPeUPZM7GaO1AS2dL0s8qn2wjUnNDCUYlxPJymxuIPROhJ5Qhl7hEcLTHUnb4DMGK7eMZGP5IM6Gq3Qy1/4Bo/W0TMjMKmR6sQDVm7zC+p4+M9xZYCCtYS7L3WZRSM2Z/ZAZPoGkGddt/ha5jnyGXuEgwvnnKbZVySXQ/VRy3XrJL1VVP7xvEkzKfQiwJFQmjwiH07r4Jvfy04QRa3satn//EqnILVe0Zz/oLVu6c9jO559S/4NopGvf85rwnRNnZXjwnS3ANBP50M4BuBHAK8uBMCCGEEIvk9+FVV2J09uJuK14nack0eiKFffOekp3G2bYR69BJzLMXsUf7UgsxnZI9Tfmnf/qnCf9v2za5XLGHi9/vJ58vZg4FAgF8Pp8E/sSc6H2DeM1T10FWSuE5WQxfTAJ/ZaACfin1OUdaLoc+lEDLZFGhYFnOYZ5rRVkm7oYrD5+cbRsxj57Gd/A4+ZfdeWXjXB6jb5DC/psWdC4VDaOls+C6y1fGqmDje+p53HWNUzdBngfXTmNn+/BHZ6iXLsQq4TbXE3qqEsM1yQyeJBjfMtrfr5VY450Ttq1cfz8dhz5Odug0oaqd48vtTA9WqB6jvQsAo61rUYE/z8niOWkMa5ryJUB68DiB+FYitTdhBWoY6fjRtIG/9MBx3EKCWOP+BY1pTbJt9KEEzq6tyz0SIa4NmobbWIvR1cvVOdFGdx/oGl7dygvCa5pG1cZX0XnkE2SHThGq2jVpGzs3wEjXT0F5DF56mOpNr5rXOXKJVmDt9Ew2/RW4kvEnhBBCiBJwm+owz14CpUDTMDp7iteU0/T3WxBdx6uuQB+R5+BidiWrefbMM8+M//O5z32O6upq/vzP/5xnn32Ww4cP8+yzz/Jnf/ZnVFdX89nPfrZUpxVrWb6AnkjhVldOuVp5Nkq5xYeOdqrs/SyuORL4mzMtW5zkoHf0zLLlAimFeb4Vd9M6MK+ar6Hr2Puuw2jvwjx6unhxARgdPaBUsZzAAoz11NPS2UUPfaF8zx0D26Hwon0zlkSYi3yyHVg7D6nEtU3FIqholIhqIDNYLPWbGzkPyiNUuWPCtqHKXfgjLQy1/2DC8kKmG1+oHuNyF8oyMbp6F1zuc6xv5kx9/jzPJjN0inD1bjRNJ958D8ne56adtJPoPoA/0oI/sm5BY1qL9P7h4ue6ZPwJsWTchtpiv/Grrof1nv5in01zZWYjh6v3EohtpP/iN6a8NxrueBzd8FO5/gEGLn5zUr/Y2eSTrVjBWgxr8WXYVwLTH5cef0IIIYQoCbexDi2XRxtOAMVnc25tVclbNXixCFpSAn9idmVpdvShD32It7/97Tz00ENEIsXZ35FIhF/6pV/i13/91/ngBz9YjtOKNUYfGAIo3lxPYazMpy9Yi1IunptbsrFdC2bL+DOPnMI8eX4JR7RCue54SVSjc2GBP98Tz6F3Td8jS+/uQ0tlcLZOzlhzN6/H3rMd37NH8X//J2iZLMblLryqOCocWtB4xnrqaan0gvZfLL13APPUeeyb9iz4NVwtl2xFN/z4QiWcZSXEMvKa6omk4mQTF3CdLJnhM5i++Hh/vzGaplG57n7SA0fJpzuBYhlNO9uHT4ujj6Swb7wOPA+jc/Y+fVMZC/wVZgj85YbPo9wC4ardAKOZfIqRricnbevaKdL9h4k17p+xdOi1Ru8bQFkmqiK23EMR4prhjfZqMcb6OCuF0d2/Ist8jtE0jerND5IbOU+6/9CEdZ6bJ9H5E+KNd1Gz5bWEKnfSdeIf5tXjLpe4RCC6saRjXk6mvxI7P7jcwxBCCCHEGuDV14ChF++tPQ+9q3faKnaLoaJh9FR6fPK/ENMpS+Dv1KlTtLS0TLlu3bp1nD17thynFWuM3j80+pArOuV6zy0G/sYeOnpS7rOkVCiINpKc+ovEtrEOn8S40Lb0A1thxoKjXlVFMfA33y/ebA7zzMXxkntTMc+14kXDU/e60zTsW68n9/IXow8lCHz9BxjtXbgtjfMbx1VUuFiuVE9lFnyMBXNdfE88h1dTibNrYaUHXyifbMMfWTfvPjZCrFRucz2xZAU4DtnhM2SHTk/o73e1aP2tmP4KhtseAcDODqCUS3DEAF3H2bEJryKK0da5oLEYZhDDisyY8ZcePI7hi+GLNANg+qJEam9mpONxlPImbJvseQaUIlp/24LGs1YZ/UN4NZWLzoAWQsydioTwouHxwJ+WSKFlc7gNtcs8spmFq3YTqtpN37n/RHnO+PJk99O4doaKlnvRNJ2G3W9HQ6Pr+GdRyp31uEp55FJtBGJrp3S66a/CyUngTwghhBAlYBq4ddUYXb3o/UNoBXvBlbhm4kUj4Cm09DI8sxOrSlmegjY3N/OlL31pUnkRpRT/+q//SlNT0zR7CnGF0T+EVz39Q66xjL+xwJ/0+SstZ2MzejKN3tM/aZ1xqQPNcdGTy5MRtpKMlfl0tm5AyxXQB4bntb/RV3zYMO3P0nUxLl3G3bphxge+XnM92Qfvx6utRrOdCb0A580wioHfZcj4M4+dQR9Jkr/z5pI94M6n2qTMp1hT3MY6/ETxqTCpvufJJVsJvqDM5xhNN6loeQmJnqdw8iPY2WJmcqDPxm2sBcvCXdeEcbl7wTMGrWAtdmb6jMHM4HHCVddNCL5XNN+Dne2bVGYu0X2AUPUeTJ9ktl1NG0lItp8Qy8BrqEXvKgb+jJ5+0LSpJ2KtMLVbfwk7189wx+NA8T586PKjRGpuGL93Mn0xGnb/BtmRc/Sc/GdcZ+YS74V0F8ot4F9DGX9WsBonPzwhQCqEEEIIsVBuYx16dx9GexfKb01bxW4xVHS0Spc8kxWzKEvg793vfjePP/44L3/5y/nwhz/Mpz/9aT784Q/z8pe/nJ/85Ce8+93vLsdpxRqj9w8WZ7dPw5XAX1l5DbV4sQjmmYuT1pnnWkHTikEv59q+UR4L/LkbmlGWid7RPa/9xwKr031ha6kMmuPi1s9hdnnQT/6l+8n+0s8v+uLCi4bQljrjz3Wxjp7Gvm4rqqqiNId0shQyPQQk8CfWEp+FW1dF1Kkn0f3UaH+/7dNuHm+6G00zGe74EYVMD5pmEujNjmcGu+sa0XJ59L6FZT1YwbppM/6c/DD51GVCo2U+xwTiW/BHWug79x8MtX2fXOIi+WQ7ucQl4o37FzSONcvz0BMpvPjUFRCEEOXjNtaiDycgm0Pv7sOrrih5n5Zy8EeaiTfeyeClb+HaabLDpymkO6hoecmE7UKV26nf+RZSfQdp/dmfkuo/PO0xc8lWQMMfXTv9V61ADaCwJetPLJCd7aPn9BfpP/9fuLY8gBVCiGud11SPZjuYp87jNtaXpWKLioRA0yQZQ8yqLF3JX/ayl/GVr3yFz3zmM/zwhz+kr6+P2tparr/+ev7mb/6GXbt2leO0Yi3J5NDS2RmDF+MZf4Finw23IIG/ktI0nG0b8R06QeH2G8HvKy5OpjG6+3C2b8I8cxEtmUFVXrtZCFqmGPhToQBeQy1GZw/ODXP/jNP7BorHSaaK2TYvuCjQE8W/axULz3FA2vjsn8VQkfCSZ/zpXX1otoOzdWPJjplPtgPgj66dslRCAHjNDUSPRxgIeBi+OFZw+hIihhUi1nQnIx0/Ily9Fz9xNA/cdQ3FY9VVowI+jLbOBWWy+IK1ZIdOTbkuM3gC0AhVXTdhuaZp1G3/FfovfIP+C99AeQVAw7AihKv3znsMa5mWTIOnJPAnxDK4us+f0d2Ps7F5mUc0d9WbXk2y52kGW7+DnenFF26aMjs83rifUMUOes98kc4jf0ek7mbqtr8R0zfxMyefbMUXqscwg0v1EsrODBS/85zcAL5Q3SxbC3GFkx9m8NLDjHT+BN2KoNwcwx2PUbn+ASrXvRTd8C/3EIUQQiwDr6YS5bPQCnZZ+vsBxSpd4SBaQp6Di5mVJfAHsGvXLv7qr/6qXIcXa5zRX5x1OVPG31jgz/BF0Y2AZPyVgbNtI76DxzHPt+Jctw0A83wryjKx92zHPHMRPZnCXUjgL5MDvwWGUeJRLy0tm0MF/KDruM0N+H52CGwbrDnMBnddjP4h3IYajO5+yOUhGJh4/EQKDB0VWtqHLCoSwuiavmdXOZiXu1CRUEkDyflUG5ruwxduKNkxhVgJ3KZ6Igdr0ByXUN3U/f2uVtnyUoYvP0ai52ni+Qa8iigqGimu1DTc5gaM9i7sW+YfdLOCtTiFETw3P+lBV3rgOIHo+kkPkAGCFdtYd9O7UZ5DLtlGduQcvlA9ml62y9NVSU8kAVAS+BNiyalQsFgB42wrWjqDt8L7+13N9MepXP8Ag60PozyXuh1vmva7wgpW03T975LsfYa+M/9G35l/o3HPb07YJpe4RCC2cQlGvnTMQPFeUzL+xHwMdzxO39n/QDcsqje/hoqWe/HcPIOXHmbw4jcZvvwojbt/c8ZqDEIIIdYoTSsmBbR14jaVKfAHeNGwlPoUsypLqc+rdXV1cfDgQTIZaTgp5k7vH0IF/MX05Wl4ThbdCKBpOoYVkcBfOQQDuOubiuU+lQKlMM61FstaxiJg6Av7ovE8gv/1vWLJ0FVOy+ZRo8E6t7m++DOaY8BMHxwG18PdXCxDOVWavp5MFRv3lqE8wExUJIyWyYLrLtEJFUZbF+66xpK+1lyiFX+kBU1b3QFmIV7Iq6nE8Iep9d9BRcu9s25vBWuI1t4EyiOQMMbLfI5x1zehDyeK2cfzNFZy285O7AmrlEdm6OSkMp8vpOkmwfhmqta/nEjNDfM+/1qnDSdRprHkE0CEEEVeYy3GaCl3t75mmUczP5Xr78ewIsXM74bbZ9xW0zRi9bdRteEVpPoPTbi3Up5DPnV5zVVQ0HUL01+BnZvc01yIqTiFJH1n/4No3U1svOPPqdrwALrhx/TFqNv+Bjbe8SFMX5yhtu8u91CFEEIsE2f7Jpwt62d8pr1YKhpBX8C9u7i2lC3w9+///u+8+MUv5r777uNNb3oTFy8W+4T9zu/8Dv/0T/9UrtOKNULvHypm+80QAPCcLPpoqRnDJ4G/crG3b0IfSqD3DaL3DqAn0zjbNhZnsUTDC3pIrA+NoOVttHS29ANeYlo2hwoVA38qFsGLhjE6e+a0r947AIaOs6FYNmqq0ppaIl0Msi4xLzLaLHiJfkfa0AhaOoOzrqmkx82n2qS/n1ibNA23qZ6G5BaCFdvmtEvl+peD4xLIh4pB9qu4zfWg6xhtXfMeypXA38RJD/lkG66dIlQ9c+BPzExPpIrZfks8AUQIUeQ2FEtAepXx8dL3q4Vu+Gna+zs07v6tOZcejDbcDkqR6Hl6fFk+3Yny7DWX8QfFcp9ObmC5hyFWieHLj6JpGrXbXodhTX6gawVriDXuJzN0Cs/JLcMIhRBCLDd3XSOFu28r6znGM/6UKut5xOpWlsDf5z//eT70oQ/xmte8hs997nOoq/4Ib7vtNr77XZn9JGagFHr/4Iz9/QBc96rAn2T8lY3XXI+KhDDPXMQ814qKhPBGZzsXZ5jMP+NP7x29ubbtUg51WWjZ3HjGHxR/XnrH3AN/bk0VBPyogG/K7EktkcRbhsCfihZvZJeqz5/Z3oWyTLyG0s2k95wchXT3mpudLsQYt7kevX8I8oU5bR+IbWRj7JeJ6Zsm9/KzLNyGWoz2+Qf+DF8M3fBjZ3snLE/3H0E3gwRjm+Z9THGFNpKQ/n5CLCO3sTi5oZTXKEspENtAqGrnnLc3fTHCNXtJdD05viyfaAVNxx9pKccQl5UVqJZSn2JOXCfL8OVHiTfdjWFNf38WqbkB5TmkB48v4eiEEEJcS1Q0glaw5/wsQFybyhL4+5d/+Rfe+c538u53v5vbb59YUmTTpk3j2X9CTEVLpdHyhRn7+8ELMv4k8Fc+moazfRPmhXaMi+04WzeMZx140fCCAkNjgT+tsAYCf5nshMCf21SPnkjNngmpFEbvwPjDdxUJTw6ieh56KrMsGX8qFARNQ0+Vtkyz3t2HNjg8abnR1lnMOCphz8dCtgdQ+COlzSIUYqXwmkbLC89xsgFArM9CNTeBPvkS0F3fiNHdB/P8bNY0DStYQ+GqjD+lFMneZ4jU3Cg9+xZJH0lKfz8hllMwQOGWvdg7tiz3SJZMrPFO8sk28sl2AHLJVvzhxjlnDa4mxcCflPoUsxu+/DjKLVCx7mUzbmcFa/BHmkn3H16ikQkhhLjWqFixStdCkjHEtaMsgb+enh727ds35TrLsubd7++ZZ57ht3/7t7nrrrvYsWMHjzzyyKz7/OxnP+O1r30te/bs4f777+erX/3qpG2++MUv8pKXvIS9e/fyy7/8yxw5cmTC+nw+zwc+8AFuv/129u3bx+/+7u/S3y83BeWm9w0B4M4h8GdI4G9JOFs3gOeh2Q7OlivZU+PBqnmmlhtjgb/VnvGn1GiPvysPQdzGOtC0WR/Ea+kMWiY3HvibqjGvlsqAUnjRcOnHPhvDQIUCJW8W7HvqEIFHnpyY7ZnJofcP4Za4zKdrF8duWPLAXKxNKhzCra/G98zhOU3C0C93j77XGqdc744GEvX++Wc+WMHaCaU+88k2Cpkeog3lLXGy5uXyaLmCZPwJscycvTtQlbHlHsaSCVftxvDFGOkuZv3lkpfwRzcu76DKxAxU4+SHUZ6z3EMRK4BSiqG279N37j8m/E14rs1g6w+INb4IKzDzcwqAcPUNpAeOotQS9UsXQghxTfGixQSBhbRfEteOsgT+mpqaOHr06JTrDh8+zMaNG+d1vEwmw44dO3j/+98/p+3b29v5rd/6LW6//Xa+/vWv89a3vpX3ve99/OQnPxnf5uGHH+YjH/kIv/M7v8PXvvY1du7cydvf/nYGBq7U9//whz/MY489xsc//nG+8IUv0Nvby7ve9a55jV3Mn94/WGyAelUW1VQ8J4tuFAN/ugT+ykqFQzgbmnAb6yZkn6lYGFwPLTOPPnDpDFoqgzKNeWeVrDi2A66LCgavLPNZuPXVmOdaZwyI6r3FB+tu7WjG3xRlU7VE8W96OTL+oNjnTytxxp+WSqOlM1jPnxhfZl7uKvYra2ko6bk8uzh2fYr+G0KsFfl7XwS6jv/7P4FcftrttKER/D96CnddI+7mqfteqmi4mOmbmP/3qRWsmxD4S/Y8jeGLEaqce3k5MdnY78KLSeBPCLF0NN0k1nAHye6fFUunpzoIrNHS6VagGpSHkx9e7qGIJZLsPUjHkb+jkJlYolwpj76z/07fua8w3P4oHYf/Fnf0fqKv9cc4dpLK9Q/M6RyR2htw7TTZ4fMlH78QQgiBz0L5fZLxJ2ZUlsDf6173Oj71qU/xH//xH6RSxQcWjuPwox/9iH/4h3/g9a9//byOd8899/AHf/AH3H///XPa/ktf+hItLS285z3vYcuWLbz5zW/mgQce4POf//z4Nv/4j//I6173Oh566CG2bt3KBz7wAQKBAP/5n/8JQDKZ5D//8z95z3vew4te9CL27NnDhz/8YZ5//nkOHTo0r/GL+dEHhmbN9oOpSn2mUcor9/CuWYW7byN//50TlnmRsRkmc/+iGSvz6TXWodmre2atli02bFehiUFq+8br0PsGMS60Tbuv3ttf7N03mi2oomG0dAbcK7NC9WQKdB0VXp7AlYqEStvjr2Cj2Q5ebRXWiXPjWUVGWyduXbHXYSm5TgY0fXyCgBBrUihA7oG70fI2gR/8tDgh4YWyOQKPPIGKhMnfc9t4ueZJdL2YfbygwF8tTm4QpVyU8kj2PkO07mY0rXTle69F2kgCABVfngkgQohrV6xxP66dYqD1YZRyCcTWaOAvWJyEZ+cGZtlSrAVKefSf/0/S/UdofeaDDHc8jlIK5Tl0n/gcw5cfo27Hm2m+8Q/Ip9poP/gXFDK9dJ35FrG6m/GF6ud0Hn90A4YvTnpAyn0KIYQoDzVF5TAhrlaWwN/b3/52HnroIf7kT/6EF73oRQD8yq/8Cu985zt58MEHedOb3lSO0447dOjQ+HnH3HXXXeMBu0KhwPHjx9m/f//4el3X2b9/P88//zwAx44dw7btCdts2bKFpqYmCfyVk1LQ34dXUzXrpu4LAn8oD8+ZR+aZmB/DmNR/TY2WoJxX4K9nABUJFcuWrfKMv/HA3wuyU73GOtwNzfiePTb1Q3jA6B0cL/MJxew6YEKGnZZIFct8TveQvsxUJFzSHn9jr61wy168yhi+J54D28bo7C15mU8Az05jmCG0Zfr5CbFUVCxC/v670EaS+B97auLnjusSePQAOC75l90JljXzsaKRBWb81aKUi5MbJDt8Fic/TLReynwulj6cLFZBMKVPohBiafnDTQRimxhu+wGaZuCLtCz3kMrC9BfvOyXwd21I9R3CzvbRsu8PiTW8iN7TX6Tj8N/QefSTpPoO0rjnN6lovptQ5XbW3fRHKLfAhQN/Sj7dR/XGn5/zeTRNJ1JzPam+w6h5tsUQQggh5sKLRaTUp5hR2Z4ivO997+Otb30rTz75JENDQ8TjcV70ohfNu8znQvT391NTUzNhWU1NDalUilwux8jICK7rUl1dPWGb6upqLly4MH4My7KIxWKTtunr62M+dF1D1+XB81x4qRFO+L9CIHWG6uHXEq7ePe1De+XlsHwhTFPHH4yiaRqal8E0pRzWkjF1CAcxMxk0c+Z5BIZRXK/3DuA11KIHfOiOgznLfiuZns+jaWBEg8WfxVXcO27A/Mp38Z84g3vznok72g7G0DDers1XXn9lFE0DK5vFq44DYKbSUBFdtp+RFo+gZ3OYmpoU9F0IPZdF00CvjOLefSu+b/yQ4I+eQvNctI3NpX+dXhbTF17Vf2PLZez9OvZvsQo0VOPcfye+7/0E84tfR0VCqIooOC7a4DD2K+/FqJg9a0yrjKJf7pn3+yYYqUfTNLzCAOm+Z/GFaohUbZXA+yIZqRRUxmb9fch7VojVYzW9Xytb7qT75CUC0fX4fKWtzLBimAFMfwyvMCjXjNeAkcuPEK7aQax2F7HaXcTrb6TrxD/hOTnW7/s9wtXXjW9rxpvZdPt76Tz694Ri1YQrN+K6c68wFKu/kUTXT/HyPfgjpZ/kKISY2mr6nhViMbR4FKO3f1Vfv8j7tbzKOn143bp18y7ruRZVVYXlwddcVYbRf+59dLZ9l84jf0u4YiPNu15LZdPNkzbVVZ5YRQWVlWH8ei2XDZ1Q0CVaGV6GgV+77Jo4FApYL/i5e+faIBZBr7uSvakcB3NoBPOG7aAUjucSXsW/L1dXOH6LirqKye/xyjDO7XsxDp7Ad9tutKv69Hnt3di6RmD7OvTR16/iQQqWid9zMEaXFbJZ9E0tRJbpZ+Q1V2MbGhUGaCUYg9vm4VgmFU01aLqGc+tu3OdOoFXHCW5qKPnn5JBZIBiOUbmK/8aWWywmZVJXlcrNqKZqvK4+1MAIamgElUxj/MI9hLdN3dfvhdymGpyzlwjFQ2jzmLSk4gFaLROTAbKDh2jY/FKqqqQ85WIV0ln0TU1z/h6Q96wQq8dqeL9Gw/cycP4rVNZvX9PXU5F4AybJNf0aBSQHzlJIXWT7i/5w/HddWXkHjRuvx7Wz+EPVU+wVprbhAyil5n2vEo/dQs/JAF7mFJXrtpXgFQgh5mM1fM8KsRhuYxXO0VOEon60VV4hRt6v5VGWv4pdu3axadMm/u7v/o5NmzZNWHf48GHe8IY3cPLkyXKcGihm9/X3909Y1t/fTyQSIRAIoOs6hmEwMDCxnMfAwMB4pmBNTQ22bZNIJCZk/Q0MDFBbWzuv8QwOpiXjbz6qbqCx8noygyfpv/htTv70L9my/8/wha/U0/dcG9sukM3rDA2lcfIGrusxNNCLo8tsuqVk+gNofUOkhq4q9+l5+L71Y1Q0jP3a+0HTMAydSDKBa7tkwxG0wRGsvM1Qf6Ik2WTLwegfwbB8ZIanKYe5fTO+Q6cpfP8pnJdeKT9snGvH0A0yugVX/dx8gQD57gHc9WnwPPyDCeztPryhZarZrQz8rsdIRx+Kxf+OjJ5BDL+fzMjoz2vXNnwnL+G2NJGe7me4COnkCJ7nZ2i5fn6rmGHoxGJBEonsvGY2ixVAM6GpsfjP1eb4PtANC6tgM3y5D6LzewCqmZVcPv19CtkkVmyfvPcWy/PwDwxjb9806/eAvGeFWD1W2/u1Yc878IXq1/RnutIrSAx1runXKODy0a9j+GtQgW1T/K4DZPJT//4X854NxHbSc+lpgnUvWeCohRDztdq+Z4VYKE238LkeI229qMr4cg9nQeT9ujBznaxWlsCfUgrbtvnlX/5l/s//+T/ce++95TjNtG688UZ+/OMfT1j25JNPcuONNwLg8/nYvXs3Bw4c4GUvexkAnudx4MAB3vzmNwOwZ88eLMviwIEDPPDAAwBcuHCBzs7O8ePMlecpPE/qus+XP76Tup21XHzyj8mmetD9VwKuTiFdbMKtBXAcD6UFUUpRyCZxHPmgWEpaOITZ1jXh56739EPBRhsYRp1vx91Y7AmiOvtQpoEdi2KMpDAVOJkCBFdn6SA9lcULBqb/m9MM1E178P/0WaiIgd9XXHyhA7e2CsdVwJXPBiMSguEUjuOhJdMo18MJh/CW62/a78eHhhpO4dQvfgx6Io0bDl75eWkGzmvuB12HMrxGO5/C9MflM2ERXNeTn981RguHMBV4gyN4wfnNujMDNWQGT+CPtGAEGuRvZ5G0kSTKUzjRyJy/B+Q9K8TqsVrer4H4LoBVMdaF0v1VFIYvrOnXeK0rZHpJ9BykbsebcF2A+f+uF/KeDVZdT8+pfyaXGcb0xWbfQQhRMqvle1aIhdJCQSwF3lASN7q6217J+7U8ylZA9WMf+xivfvWreec738knP/nJRR0rnU5z8uTJ8SzBy5cvc/LkSTo7OwH4y7/8S/7oj/5ofPs3vOENtLe38xd/8RecP3+eL37xi3znO9/h137t18a3edvb3saXv/xlvva1r3H+/Hn+9E//lGw2yy/+4i8CEI1Geeihh/joRz/KU089xbFjx3jve9/Lvn375h34Ewtn+oozFpz8yITlnpMFQDeKDyU13UQ3g7i2NDVdal4kjJbLg22PLzMud6P8PtymOqznT8BoQ3OvsxdVVwW6jrIsALSr9ltttGwONUvQ0t26Abe5Ad/zJ/A9dQjfU4fQB4dx10/OTFXRMFqyONNUSxT/llVsGUvl6ToqFERLlSYbT0tnUOHQxIWGAWUqhew5GQwzNPuGQohxKhwCXUNPzP/71AoWJ+hE628t9bCuSfpIEgAVX903cUIIsdJZgSqc/BBKucs9FFEmw5d/iGGFiTXcsaTnDdfsBSDV+9ySnlcIIcTap0JBMHS0pDwLF1MrWwFY0zT5kz/5E3bu3MmHPvQhTp06xUc/+tEFHevYsWO85S1vGf//j3zkIwC89rWv5aMf/Sh9fX10dXWNr1+3bh2f/vSn+chHPsI///M/09DQwJ/92Z/x4he/eHybV7ziFQwODvI3f/M39PX1sWvXLj772c+Ol/oEeO9734uu6/ze7/0ehUKBu+66i/e///0Leg1iYTTdxPDFcArDE5aPBf4M80o2gmFFJPC3DMYCU1oyjaqqAMDo7MFtrse5bhuBbz2KcbEdtm1AdfXhbdlY3NE3Gvgr2KzWfFgtm8Orm6oXxNUbaeRfftecjudFwhgX2kEp9GQKNG1yoGyJedEQWqo0ZY/0VBanfn6lkhfDddLoEvgTYn50HS8aHp98MB++ULEkd7ROAn+loI0kUZaJCgaWeyhCCLGmWYEalHJx8sNYgVmu7cWq49ppRrqeoGr9A+iGb0nPbfpixBpup//8VwlWbMcfaV7S8wshhFjDNA0vGkFPSqlyMbWyd3583etex+bNm/n93/99Xv/61/Mbv/Eb8z7G7bffzunTp6ddP1VA8fbbb+e//uu/Zjzum9/85vHSnlPx+/28//3vl2DfMjN9cZz88IRl4xl/Evhbdt5oDyg9mcatqoBsHr1/CHvnFrzaKtx1jVjPn8Ctq0Jlcqj64s20Gg38sZoz/jK5kj6QVdEImu0Uy6QmUsWfrV62xOy5jSkcKs1FhOehZbKoyNIF4jw7g2HNr0eZEKL4WbSQjL9Yw4vwheqxgjWzbyxmpY8ki9l+ZcqKFkIIUWSOBvvs7IAE/tagRPcBUIp48z3Lcv66HW8in2qn8+inWH/LezEsmZgohBCiNFQ0jJaQwJ+Y2pI8Ub7lllv4yle+gmmavPe9712KU4o1xPRX4L6g1Kcrgb+VI+BHmcZ4iUqjswcAr7mY+WHvuw49kcI88Hxx+WiG3Hipz8IqDfwphZYvlDjwNxZETaEnUstb5nOUikXQRhLj5VoXSsvkQCm8JcpgVJ6D5+Yk40+IBfDi0QVl/BlWmHD13jKMaPXRBoaxnj4M+cKCj6GPJPGkzKcQQpSdFagCwMkPLvNIxEKk+p6n48jf4XlT31em+g4Tqtq1bD32dMNP45534Nopuk9+DqWkh5EQQojS8GIRtJQ8CxdTK0vg713vehf19fUTljU2NvJv//Zv/OIv/iK33HJLOU4r1ijTH5+21KduXgm6SOBvmWhaMTtkLPDX0Y1XFS/Wmga86krcDU3oHT1o1RXgHy2v4htNOF6lgT8tWwxklTLwN5Y9qSXTaMkU3goI/LlN9Wh5G713YFHH0dLFPoFLVbrUdYrnk4w/IeZv/DPdkwdTC6GlMwQe+SnW8bMEvvlDtMHhhR1nJCGBPyGEWAK64cfwRbGz/cs9FDFPie6n6Dz2adL9h8kMnpi03rUzZEfOLvvEJF+ojsbdbyfdf5TBSw8v61iEEEKsHSoaRk9mFj1ZX6xNSxb4AwgEAnzwgx/kC1/4QjlOK9Yo0185udSnm0U3/GiaMb7M8EXwJPC3LFQ0XGwmqxRGRw9uc8OE9YUbrwNAb7qqv5thFJvQrtZSn9kcACpUwt5Lfh/KZ6ElUuiJNCq6/IE/r7YKFfBjtHUu6jhaaizwF5xly9LwRgN/kvEnxPx5sUgxq3n0fSvmwXbw//BJQCP3yvvAMgl+6zGM823zO04uj5a3i6U+hRBClJ0VqMbOLW6im1hawx0/pvvEPxZLjYcbSfUenLRNZvA4KG/ZA38A4eq9VG/6BQYufpOBi9/EteU6SwghxOJ40UixtU46u9xDEStQyXr8HT9+nC1bthAIBDh+/Pis2+/evbtUpxZrnOGL4xQSKOWhacVYtedkJ5T5BMn4W05eNIzR3oU2OIyWy+M2Twz8q6oK7BffSmBby8TlllXsabcKadk8QEkz/qAYRDV6+sHzVkTGH5qGu6EJo7UD+5a9C+41paUzxb6OY70dy8y1RzNQJeNPiHkbKzOsrZCSw6uGUvh/8gzaSJLcK+5DVVeQe+V9+J48iP/HT2MPDGHfev2cPkf1kSSAZPwJIcQSMQPVODkp9blaDLV9n75zX6Gi5SXUbnsdgxe/xdDlH6I8B02/8pgrPXAUf2TdeDnX5Va18RV4bpbB1u8y1PYDKlrupWLdy5atDKkQQojVTY1XDkuhIjLxXUxUssDfQw89xJe//GWuv/56HnroIbRpHmoopdA0jZMnT5bq1GKNM/0VoDzcQqL438wU+MuglDshE1CUn4qG0VNpjMvdKNMY7+N3NW/HJvTKMAxdaTqrfNYqLvVZnE2jAv6SHteLhjHbu4vHjq6MoJWzvpnA6Ytow0lU5cJuSvV0ZsnKfIJk/AmxGCoSAl1HT6SQYp9zZx08jtHaQf6l+1HVFcWFpknhxbfi1VThe/oweiJF/p7bwJp5EoQ+kiyW0pbAqxBCLAkrUE0qOc/sbLGklFJkhk4y1PodMkOnqdrw81Rvfg2aphGpu4mBS98iM3SKcPWe0e090gPHiDfdvcwjv0LTdGq3/jKV617OUPsjDF/+EUPtP6Tlxv+HYMW25R6eEEKIVWYs2Kcn03iNyzwYseKULPD3z//8z2zZsmX8v4UoFdMfB8DJD48H/lwni25MDvyBwrUzmD6ZIb+UiqnlCvPsJbzGumIZz7nwWWirNfCXyaH8vrm/1jlSkTC47mjvxJUR+PMaa1GWidHWgbPAwJ+Wyizp7KOx0jmGJYE/IeZN0/BiYbREcrlHsmoY51uxjpyicMte3PVNE1dqGs51W/FiEfw/eorAdx4n/7I7x3vhTkUbTuBFQiX/jhFCCDE1azTj7+oqM2LlSPUdYrD1YXKJSwSiG2ja+w4itfvG1/vCzfhC9aR6D44H/nKJi7h2inDN8pf5fCHTH6d260NUbfg52g/+BcOXfySBPyGEEPNnGHiVMcyT53A2tixZlS2xOpQs8HfbbbdN+d9CLJbpqwDAKYyML5sq40+3irPiPTsFEvhbUmMBKj2ZprB7+9z3s0xYpT3+tGyu5GU+4crP0hvNuFkRDAO3pQGzrRPnhl0LOoSWzuLV15R4YNNznTSabqHpviU7pxBriYpG0BPp2TcUaAND+J84iLNlPc6e6b8DvZYGcj9/L4FHniDwrcfI3X/XlFnUWjqDeeYiztYN5Ry2EEKIq5iBapRycfIjWIHK5R6OuEp64BidRz9JsGI7zTf+PqHK6yZVmNI0jUjtTYx0/oQ69SY0zSDdfwTDihCIbVqmkc/OsMLEGvYzcPEbuE4Ww1yafuhCCCHWjvzdtxP4zo/wP/ok+fvvKv3kUduetWKNWJlWyFNlIaZn+KKg6Tj54fFl3hQXxcZo4E/6/C29qzO5Xtjfb0Y+C62wenv8lSPw540G/lZaeTd3fRN6/xBaemFN6LV0Bi+8dDeynp1BN0PTlp0WQszMi0XQkvJ9OqtcnsCjB/AqohT23zxr/z5VXUHuVfehfBaB7zyGlnjBz1gpfAeeB9PEvmlPGQcuhBDialag2KrAyQ0s80jECw21P0Igtol1N/2/hKt2T3t9H6m9CddOkR06A0B64Ajh6r0rPoMzWn8rynNI9z2/3EMRQgixCqmqOPmX7sfoHcD342dAqZIdW+/uI/Sv30BLyqTg1ahkGX/79u2b1wPWgwcPlurUYo3TNB3TF58Y+HOzWMGJ2UMS+FtGhoEKB1GGMa+AlbIs9Ozq/H1p2VxZetapaPHnt+ICf80NoGkYbZ04u7aOL9c7utEHR3C3rJ++bF3BRivYxTKmS8R10hjWyiiVKsRqpOJR9BPnwPNWTvbxSuN5+H/0M3Ac8j9/D5hzm1mpwiFyP38PgW89iv8HPyX3qpeAv5idbLR2YLR3kb/vDinTIoQQS2gs8GfnBgiydZatxVLJpzvJDJ6g4bq3z7qtP7oeK1BDsu8gVrCOfKqDqo2vXIJRLo4VqCJYsZ1E98+INe5f7uEIIYRYhbyGWvL33I7/safgZ4co3H7jrJNS58I8dR48hT40grtC2hGJuStZ4O/Xf/3XJbNClI3pr8DNz1zqs9jLS5PA3zJxWxqL5SnnQfksWK09/rI5vJrSlwFS4SAYOl58hZWr9ftwG2snBP6M9k78jx4AT8Fzx3DXNeJs34Tb0jDhAmMsS3Ape/x5dgbDlP5+QiyUFw2DUmjJNGqlfR6tENZzxzC6+8g98OL5T2zw+8i/7E4C334U/2MHyL/8xWA7+J46hLu+CXdDc3kGLYQQYkq6GcCwItiS8beiDF9+DMMXJ1p386zbappGpG4fie6f4Q83oWkGoarrlmCUixdruI2e01/EyQ9j+iuWezhCCCFWIXdDM4UX7cP35EG0VAb7hp14tdULP2Auj9naCYA2kizRKMVSKlng73d/93dLdSghJjH9cZzC8Pj/u1ME/jTNwLBCEvhbJoX9N81/J8tCWwWBPy2dGQ30VV1Zls1Nn+G2GIZB7pX3rbzAH+Cub8b3s0OQL6D3DeJ/9CncdY3k99+MefEy5pkL+B95Amfzegr3XOn1qo8F/paw1KfrpNEl40+IBVOx4meQnkzhrsDPo+Wmd/VhHTtD4dbr8RrrFnQMFY+Sf8l+At/7Mb6nRst7OQ6FO24syexMIYQQ82MGqnBy/cs9DDHKtTMkug9Qtf4BNH1uj64itTcz1PYDBlu/Q7Bi66qZCBipvZneM/9GsucZKtffv9zDEUIIsUo5OzajLAvr0HEC33oMt7EW+/qdxXvWed5jmufbAIWKhNBf2KJCrApSu0msCqavYlKPvxcG/qBY7lMCf6uH8lnFJrErnHX4FP7vPH6lv51to9kOKugvy/m86kowSzYvo2Tc9Y3F/lPPHSXw6JO4zfXk77kdAn6cXVvIvfplFG7eg3np8oRMTi2VAU0rT6B0GpLxJ8TijGUfayPynToV6/BJvOoKnN3bFnUcr6GW/P6bME9fxDx9EfvmPWUpIy2EEGJ2vlAj+VTHcg9DjEp0PQGeS7zp7jnvE4htxPRX4uSHCVdfX8bRlZZhhQhXX0+i52fLPRQhhBCrnLt5HbnXPkD+vjvQCjaB7/0E43zb/A6iFObZizjrm3Brqyb3pherQtmeLLe2tvLVr36VS5cukc/nJ63/+7//+3KdWqxBhv9K4E95DsqzMYzJQQTdiuAW5MNo1fBZaLZTbDy7grMbtEwWzXGxnj5C4b470LLFzzQVDCzzyJaWCofwaioxT18sBv3uuwOMq3paaRrulvXw3DGM9q7ifwNaOlsMIizh79h1MvgteXguxIJpGl40gp6U79QX0vsHMbp6yd97R0k+19xtmyhkchgDwzg7t5RghEIIIRYiGN9MqvdZPM9G16XP6nJSymP48mNE62/F9MfnvJ+m6URq9zF8+VHCNasn8AcQa7idzqOfIp/uwh9uBEApl+zQGfzRDaOtTYQQQog50DTcjS24G5rxf/fHmOdacbdumPPuet8g+lCCwq3XY/QMYPRIRYTVqCyBvyNHjvCrv/qrNDU1cenSJXbs2EEymaSjo4OGhgbWr19fjtOKNcz0V+DaKTzPxnNyAJLxtwYoa/SGumCD37e8g5mBlsujggHMS5dxOnrALAa7VHDpMthWCnvPdoz2Lgr7b54Y9Bs1HhxsvXwl8JfK4C1xBotrpzFMKfUpxGKoWEQy/qZgHTmNF4vgbixdHz7nhl04JTuaEEKIhQjENqOUSz7ZRjA+/4kYyZ5nCMS3YgVK3wf8WpPuP4Kd66ex5TfnvW/lupdh+ivxherLMLLyCVXvQTdDJLt/hn/La8glWuk5/QXyyTY0w0es/g4qWu7FH2lZ7qEKIYRYLTQNd1MLvqcOQS4PgblVLjPPXERFQnhN9WjZPFomV6zYZsnEqNWkLKU+//f//t/8/M//PN/61rdQSvHnf/7n/PCHP+Rf//Vf0TSN3/iN3yjHacUaNjbLz82P4DlZQAJ/a4GyinMPtBVe7lPL5nC2rMdtqMH31PPF0pVQtlKfK5m7aR2Fu28bD35OxdnYgnG5B+ziY2wtnVnS0nVKKTwngy6zYoVYFC8WQU9IE++raSNJjNYOnD3bV3SmuhBCiPnzR5rRdB+5kfPz3tfzbLpO/ANDbd8rw8iuPcOXHyUQ30IgtnHe+1rBGqo2PFD6QZWZrltE624m2fMz+s7+B23PfQSUovnG36dq/c+R7j9M69Mf5PLzf4XnTq6qJYQQQkzF2VCcsGq2TlHOXCn0rt6JbZhsG+NiO862jcW2PfEIgJT7XIXKEvg7ffo0r3zlK9H14uHHSn3edNNNvOtd7+Iv//Ivy3FasYaZvgoAnIIE/tYU31jG3wrPcxjN+CvcsQ89mcY6dAJ0bUVnKS4nd0MzuC5GRzcAejqDiixh4M8rFMsBS8afEIuiYhG0dBZcd7mHsmJYR0+jggGceZRJEUIIsTpoukkgtpHsyIV572tnekB5pPuPoJQqw+iuHSOdT5AZOkVly0uWeyhLLlp/O3ZugOGOx6nZ/FrW3/LHhKt2U73pVWza/xHqd76VzNBJsgsITgshhLhGBQO4DTUYFy9PWqV39BD47o8JfvV7GOdbi739Ll5Gc9xi4A/wYtHithL4W3XKEvjTNA3LstA0jerqajo7O8fXNTQ0cOnSpXKcVqxhpr8CACc/PB74MyTwt+qp0cDfis74sx00x0UF/ajKOPZ1W9ETqWJ/P8n2mJKKRfCq4hiXLoNSV3r8LRHXLmZkSh8MIRbHq4iBUhgdPcs9lMXLFwh8/QcY7V0LPoSWzmCeb8XevW3KUsdCCCFWv0B8M7nEhXkH7wqZ4oQ3O9dPIbPw75prmVKKgUsP03Pqn4g330Ok7ublHtKSC1ZspW7Hm9lw259QteEBNP1Kdx5NN4k13A6ajp3tW8ZRCiGEWG3cjS0Y3X3Fcp9jlMI6fBKvugKvthr/j58h8PCPMI+fxW1uuFK5y+9DBXwS+FuFyhL427JlC+3t7QDceOONfO5zn+PMmTNcuHCBz3zmM6xbt64cpxVrmG6G0HSrGPhzp8/4M/0VeE4W104v9RDFQowF/gorN/Cn5Yo9JVUgAIC97zpUKFAM/IlpuRtbMC53F0sBKLWkGX+eUwz86ZLxJ8SieHXVuM0N+J54Fi2dWe7hLIrv+ePogyMYbZ2zbzwN8/hZlGni7NhcwpEJIYRYSYKxzTj5YZz84Lz2K6S7MawwmuEj3X9k0eNQyiMzeBLPLSz6WKuBUh59Z/6NgQv/RfXmB6nb/kY0rSyPq1Y0TdOpaL4bX6hu6vW6iRWolsCfEEKIeZmq3Kfe3YfRO4C9bzf5l7yI3AN3Q8FGH07g7Ng0YX8Vi6KNSBuQ1aYsV1Kve93r6OsrXoj84R/+IQMDAzz44IO88pWv5OjRo/yP//E/ynFasYZpmobpr8DJD+OOlfo0JgdexnoA5BIXl3J4YoHGevyxkgN/2eJsmPF+fpZF/qX7Kdx6/TKOauVzNjSj2Q7mmeJ70VvCHn9jgX/J+BNikTSN/N23gm7g+/HT4HnLPaIF0fsGMU+eRwX86L0DCztIvoB55iLOzi1XylQLIYRYcwLx4uSO3DzLfRYy3fhCjYQrrytJ4C8zeILLh/6Kiwfey8Clhxc9sTWf7iLVd2jR4yoH5Tl0HfsMw50/pn7nW6je+Eo0qawyLStYK4E/IYQQ8zNFuU/r8Em8qgrclgYAvKY6cg++jNwr78Nd3zRhdy8WkYy/VcicfZP5e81rXjP+31u2bOHhhx/m+eefJ5/Pc+ONN1JdXV2O04o1zvTFcQsjeP4KNMM3oezFGCtYh2FFyI6cJ1y9ZxlGKebFNEHTlr7Up1LFf/TZ5z5oubHA35VAs1dTVbahrRWqIoZXER0P/KklDPxdyfiTwJ8Qixbwk7/nNgLf/THWoRPYN62y71bPw/fkQbyqYqlm/0+fg3xh3j1arZPn0DwP+7qtZRqoEEKIlcD0xbCCtWQTF4jW3zrn/QqZbgLRDQRim+g5/S+4hSSGL7rgceQSlzCsMJHafQxe+jZDrd+lYt1Lqd706nkHxZTy6D7+WezcAFtq/mrFBdX6z3+VdP9hmva+g0jNDcs9nBXPCtaSkx5/Qggh5sndtA7fgechl0cfSWJ09ZG/746JbYx0Ha9uctzGi0Uw2hdePUcsjyWpnRAOh7nrrrt46UtfKkE/sWBjGX+ek8Uwpu4XpmlasS/DAhqyLzWlPDoO/+2CmsevGZqG8plLnvFnnG8j+O/fBtuZdVstWyz1Od+HxALcDc1oBbvYy3EJM2TGM/4k8CdESXgNtRT2XYd1+BR652i/P6Ugmy8G0VYw8+R59MFhCvtvxquvBcDom2fWn+1gnjiHs30TSJlnIYRY8wKx+d1PKuVRSHfjCzUQrt4LyiM9cGxRY8glW/FHN1C/401s2v8RYo37Gbz0bQqpy7Pv/AKJrgPkU+14TgYnt8DM9zJJdP+MofZHqN32Ogn6zdFYxt98+1AKIYS4tjmjWXxmawfW4VN4lTHc0RKgs1HxKFrentgjUKx4Zcn4A8hmsxw4cICuri4KhYkPhTRN49d+7dfKdWqxRpn+CvKpy3hOdsr+fmMCsc0MtX4XpbwV3RfAtVOkB46iGT6C8d9a7uEsH8tCm0MArpTMc61ouTzGpcu42zbOuK2Wy6MC/jllB4qJnA0tWIdPLWm2HxQz/nQjMGVWsBBiYZzrd2J09xN45EmUaaCNBvyUaZD9lVeDaSzzCCfT0hms54/j7NyCV1tV7Dca8KP3DOC2NM75OObZS2gFG3v39jKOVgghxEoRjG8m1fssnmej67NPXnNyQyivgC/ciOmPE4htIjVwhFjjixY8hnyylVhDcX/TF6Nmyy8y0vE42ZFz+KPr5nwcz8nRf+G/CFXuIjN0klyyDStYs+BxlVI+dZmeU18g1nAH8eZ7l3s4q4YvWIvn5nELCUx/fLmHI4QQYrUIBnAbajGPnkZPpsnfc/vEbL8ZeNEIAHoihRfwl3OUooTK8lT06aef5nd/93cZGRmZcr0E/sRCGP4KnMLIrIG/YHwzA26OQroTf6RlCUc4P24hAUC67xBOIYm5iFIwq5myLLSlzPjL5TG6+8DQMc9emj3wl81d6e8n5kVVxfGiYVR4+vdrObh2Gl36+wlRWppG/p7bsE6eRxk6BPxomSzW8yfQslnU6I3ASmI9fQRMk8JNu4sLNA2vvnp+ff48D+vYGZzN61DRcHkGKoQQYkUJxDejlEs+0UqwYvYSz4VMFwC+ULFHTrjmeoZav4fynAVNRHPywzj5YfzRDePLdMOHP7qB7Mh5Klrum/Oxhtq+j+dkqN/5q7Q99zHyyTaidTfNe0yl5tppOo9+Cl+onrodb1px5UdXMitYrGBgZ/sk8CeEEGJe3E0txVYYsQjuprk/M1ex4v2+lkjBFKVAxcpUlhSWD37wg+zYsYNvfvObHD16lFOnTk345+TJk+U4rVjjTF8cz8niFIZnzviLbgRNX/ElNN1CEgClXJI9P1vm0Swf5bNgCXv8Ge2doBSFm/dg9PSjjSRn3mEs40/Mn6ZRuOc2CkvcE8xzMhimPKAXouQCfux91+FcvxNn+yacDcUbBS2TW+aBTSGXx7x0GfvGXRNKNbu11Rh9g+B5czqMcaEdLZ3B3rOjXCMVQgixwvgjLWiGj2xibveThUw3mm5hBop9wCPV1+O5OTLDZxZ0/lyyDYDAVYE/gGDFVrLD5+Z8HDs3xGDb96lc91KsYA2B6HryqbYFjamUlFJ0n/gcnpOhae870A2515qPqwN/YuGUUnje9M8hlFJSTlUIseY465tRlom977o5Z/sBYJmoUBB9lmeoWjqDeeZisTWIWHZlCfx1dHTw27/922zbtg3LWrq+TmJtM/0VABTSXTMG/nQzgD/cTG6ON2rLxRnN+AtX72Wk86fX7kWlb2kz/sxLHbj1NTg7tqB8Fua51hm3L2b8SU+nhfJqq1HVFUt6TtfJoEt/PyHKToWKn43jvVBXEONyN1C8sbmaV18Dros+ODz7QZTCOnoad10jqkpm1AshxLVC0wwC0Y1z7vM31t9vrM2EL9KC6a8i3X9kQefPJ1sxrMh4IHFMML4VJz+IPcc+fQMX/gvd9FO54ecB8EfWkUu2L2hMpZQdPk164Cj1O9+6YsqOria64cfwxSXwt0jJ7gNcfOJ/TBv8G2r/Aed/8gcMXHoYz1l517pCCLEgQT/ZN/wC7ub1897Vi0eKGX/TsW383/8pvieewzp0YhGDFKVSlsDfTTfdxMWLF8txaHENGwv82bkBjBkCf1AszzKfhuzLwbWTaIaPinUvoZDuJJe4Nt8zylrCjL+CjdHZU2xeaxq4m9djnr00Y+aHJhl/q45rpzEsyfgToux8Fuj6Cg38deHVVEJo4sQNr7oCDH1O5T6N9i704QT2Xsn2E0KIa00wvpncyPk5Tc4sZLrHy3xCsbVJuGYv6f4jC5rcmUu24o9umFT+MhDfAkB25Pzsx0hcItF9gOpND47fO/uj63ELIzj54XmPqZSGLz+KL9xEuOaGZR3HauYL1lIoQ+Av0fM0Pae/WPLjrkSJ7qdw7RS5ad5Pqd6D6EaAwUvf4uJT72Oo/ZEZMwSFEGLVMI0F7aZiEfTENBl/SuH/8TNo6QzOri1Yh05inL208DGKkihbqc+vfe1rfPnLX6a9vZ3h4eFJ/wgxX6bvymz7mTL+AILxLRQy3bh2utzDWjC3kMC0YoQqd2EGqkh0PbHcQ1oePnPJMv6M9k7wFO7GYgaIs30jWjaH0dEz7T6S8bf6FEt9SsafEGWnaahgYOWV+vQ8jI4e3JbGyesMA7e6cvbAXy6PdeQUbn11MUtQCCHENSUQ34JTGMHJDc66bSHTjS/cMGFZpOYG7Fz/eP+/uVJKkUu0TirzCWD6ovhCDeRmKffpuXm6T34ef6SFeNOd48sD0eLs/nxq+bL+7Gw/qf4jVLS8RPr6LYIVrC1Lxt/w5ccY6fgxrj1DRsca4BSS46V4M4OTWxG5doZc8hLVG1/Jxts/RKTmevrOfYXLz//fGYP5Sins3CDJ3oP0nf8q6UHJeBFCrB1eLFrM+Jvic9A6dAKjrZPC3bdRuP1GnB2b8D/xHHrn9M9bRfnNv9P0HMRiMZqamviTP/mTaS/mpM+fmC/dDKCbQTwni27MkvEX2wxALnGRcPXS9hebK6eQxPBF0TSdeMN+htofoXbb6665HgfKWrpSn2ZrB15tFSpcDAp5VRV4VXHMs5dw103xgNjz0PI2Knht/U5WO9dOo0vGnxBLQgX9Ky7jT+8dQCvYuOsaplzv1VVjXnjBQ0/XxXruGHrfIPpIEi1fACB3/13lHq4QQogVKBDbBEAucQErWD3tdq6dxi0kJmT8AQQrd6DpFpnBE/jDTXM+r5Mfxi2MEIhNDvxBsdxndmTmwF/fua9gZ/tYf+v/RNOuzOo3A9XoZohcsp1w9d45j6mUhjt+hGEGiTXcviznXyusYC3pwWMlPaZTSIxWTVKkB08Qq7+tpMdfSdL9hwAIVe4iM3QSeO2E9dnh06A8QlW7sILV1O98C5G6m+k49NdkBo9P+YxpqP2HDLZ+F7cwAoCmm2QGjhO+7bpyvxwhhFgSKh5Bc1y0THb8uSqAceky1qGTFG7ajbu+eM1TuGMfWiqL/9ED5F55H6pSWmcsh7IE/v77f//vHDx4kLe97W1s2rRJ+vyJkjF9cQpOdtaMPytYi2FFyI6cX7GBP7eQwPDFAIg17mfg0rdJ9j5HvHH/Mo9saSnfEpX6tG2My90U9u2+skzTcLZtxPf0Ecjm4AWZfVouX/wPKfW5qkjGnxBLR4WCKy7wZ7R3oYIBvOrKKdd79TVox86gpTKoSPGzwjp0EuvEOZxNLTjN9XgVMbzKOCoeXcqhCyGEWCFMXwwrWMdg63fRrTChyl1TTmouZIo9ZV8Y+NN1q9hTL3FpXufNp9oA8E+R8QfFTMSRridw7QyGNfl6N9X3PCMdj1O3402TAo6aphGIriOfbJvXmErFc/OMdP6UeNOLr7nJrqVmBWtxC0lcJztrG5S5SvcfAU3D8leTGTi+pgN/yd6DBOPbiNbfRs+pf57UKiIzeBIrWDuhB2Wo8joCsU0Mtn530jOmQqaH/nNfIVJ7E9GG2whEN5AdOUfXsc9QyPTiC9VNGoPn2WiaMd4bVAghVjovVrw31hKp8cCfNjSC7yfPFO+jr995ZWNdJ3/f7QQe/hH+Hz5J7hcfAF0+75ZaWQJ/Tz31FB/4wAd48MEHy3F4cQ0z/RUUMt2zXtxqmkYgvmVF9/lz7ST+SAsAVrCGUOVOEp0/veYCf/gstIJTTBUvY7kX43IPuN54mc8xzub1+J45inm+DWfP9ok7jT7MllKfq4dSHq6TkYw/IZaICvrR+4eWexgTGJe7cFsapv1OcWurgGJmoBsJofcPYh09jX3jLuwbZVa2EEKIovpdb6X/7JfpOPRxArGNVK7/OSK1N054UF9IdwMaVqh+0v6B2EYyg8fndc5c4hKGL4rpn3rySrBiK6DIJS5MCj7YuUF6Tv0zkdp9xJvunnJ/f3Q9qd7n5zWmUkl0P4Xn5qhovndZzr+WWMFaAOxsH8ZoCdfFSvUfJhjfQiC2mUT3AZTy1mRQyrXTZIdOUbvtdYSqdgKKzNBponU3jW+TGTpJqGriNaGmaVRt+Hk6j36S7PC50fdiUf/5r2H44tTveiu64QMgXLUHTbdI9R+iav3LJxxLKY+2pz9ItP42qjf9QvlerBBClJCKhEDT0BMpvMY6sG38jz2FikYo3HnL5Ptvy6Jw1y0EvvFDjMvd49mAYumU5Vu8vr6eaFRmSIvSM/0VwOw9/gCCsc3kEhdRyivzqBbGKSQwrNj4/8eb7iI7co5C5tqqf6wssxj0c9yynsdovYxXFUdFIxNXBPw4G5owz7VO2kfLFjP+lGT8rQhKKfovfH3GfhaemwPlScafEEtEBYMrqseflkyhDyenLt88JhjAi0UwevvBdfH95Fm8yjj21TMUhRBCXPNCFdtYd8t7ab7x99F0P13H/p7+81+bsE0h04UVqB5/2H+1QGwjhUzPvPrO55NtBKIbpm2ZYgXrMHwxsi/o86eUR/eJz6HpPup3vmXa/f2R9di5/nmN6YWUcuk6/lk6j36KVN/zKM+Zwz6K4cuPEqm5ccbSqWJufFcF/krBc/NkBk8SrrmBcPUe3EJiWXtBllO6/zBKeURq92EFqvGF6kfLfRbZuQEKmR5Clbsm7RuuuR5fuInB1u+ML8sOnyXVd5CaLa+Z8DmgmwHCVbtJ9U0OtKcHjlHI9JDsfXbKMSqlGLj07Wvu2ZAQYoUzDLxoGG0kCYDvqUNo6Qz5e28Ha+rcMq+6Eq+mEvPU+aUcqRhVlsDf7/3e7/GZz3yGkZGRchxeXMMMX7Em8FwCf4H4Zjw3RyHdWe5hzZtSCne0x9+YUFXxwjKf6liuYS0PX7EUsFbOcp+uW5xdsrFl6tXNDehDI+BMvGkdK/UpGX8rg53tZfDStxlqf2TabTw7A4A+RekjIUTpqVCg+Fk5RYPv5WC0d4Ou4TZNLql0Na+uGr13AOvQSfREkvyLb5HSI0IIISbRNI1w1W7W3fRuqja8guGOx3Dt1Pj6QqYbX3jqySbjfQKTkycYTkUpRS7ZOm2Zz7HxBONbJvX5G2z9DtnhszRc9/YJJQtfyB9dB7CooE7/+a+R7H0WO9tP59FPceGJP6L3zL9h56avAJAdOkUh3UVFy0sWfF5xhW5F0M1gyQJ/maFTKK9ApOYGgvEt6EaA9MD8slVXi2TfQYLxLeOTyoOVO8kMXgn8ZQZPARqhyh2T9tU0naoNP0d64Cj51GWU8ug79xUCsY1EpyiNGqndR27kPE5+eMLykc6foOk+CumuKYN7hXQHAxe+TrL3uUW9ViGEKDUVi6AnUhhnL2Gea6XwoptQFbEZ97F3bsHo6EFLpGbcTpReWZ5wfPOb36Szs5P77ruPN73pTfz2b//2hH/e8Y53lOO04hown4y/QGwjaDrZFVju03NzKM/G9F35cNT04uww5RWWa1jLQo31AC2UL/Cnd/Wh2Q7O+uYp14/1cNJGJn4JadlcMSPRMMo2NjF3YzObk73PodTUGaKuU5y9bJhS6lOIpaCCgWLQb6wn6jIz2rtwG2phlv7SXl01+uBIscTnDbtQVRVLM0AhhBCrVsW6l4JSDHc8Pr6skO6e1N9vjBWsRTeDU/b585wc3Sf+kfxVk1Sd/BBuIUFghsAfQDC+lVzi0nimXXbkPAMXv0nVxlcQqtw+476+UAOa7ltwn79kzzMMtX2f2q2/xIbb/hcbbns/scY7SfY+S8ehv8J1MlPuN3T5h/gjLQQrti3ovGIiTdOwgrUlC/yl+w7jCzXgC9Wj6Sahqp1zKlM7fPkxBi58Y8VWWXoh18mSGTxB5KqynuGq67CzvdjZfgAygycIxDZMG0CP1t2CFahmsPW7JHufJZe4SM2WX5qyLGq45no0zSDVd2h8mZ0bJD1wlJrND6LpFun+w5P2S/YUMwFL9fsVQohS8WIR9L5BfE89j7NtI+7Wma9ZANxN61A+C/P0yns+v9aVJfCXTqfZsGEDu3fvRtd10un0hH9SKYnwioUZD/wZswf+dMOPP9JCLrHyPljcQjEt2rCuZPxpejEtWnllzHxbgdQSZPzp6QxoGqpi6hLE3mjgTx9NVx+j5fKS7beC5EbOo5tB3EKC7NCZKbcZy/gzJONPiCUx9hm5Isp92jZGdx9uywxlPke5dTWgFF6VlPgUQggxN6YvSrzxToYvP4rnFvA8GzvXP23gT9N0AtGN5BIXJ61LDxwj0X2AziOfxB29fs2PZgbOlPEHxT5/yiuQS7bh2hm6j3+WQGwT1ZteNetr0DS9eI+cnDnjT3kO3gvuS/PJdrpP/hPR+tupaHlpcayRZmq3PsS6m/47TmGE7uOfnRAEUkoxcPFbpPuPULn+5dOWIBXzV6rAn1IeqYEjhGtuGF8WqtpDduT8+N/mlPt5DgMXv8HApW/RefRTeM4KuBacRbr/CMpziNTuG18WrNgOml7MelQemeFTU5b5HKPpJpXrX06y91n6z/4HkZobpw24G1aYYOWOCeU+E11PoOsWsaa7CFXtItV/ZMI+SimSvc8AEvgTQqw8KhYtPiuNhCncfuPcdjINnG0bMc9dAre8bZ7ERFMXYF0EpRR/+7d/SzAYxO+XvliitEKVu6ja+EqsYM2ctg/GNpMZOlXmUc2fa48G/vxXZfxpOppuTbrBWvOWIOOPfAHltyY3mh3j96GCAfSRBFd/BWnZnPT3W0GyI+eI1t9GZvAEyd5nxsvjXm0s40+XjD8hlsR44C+bY7mLfRqdveB5M/f3G6Uqoti7t+Fs3yQlPoUQQsxZ5fr7Ge78MYnuJwnGt4Hy8IWnDvxBsQrNSNeTKKUmBL3SA0ewAjW4doruk5+jae87ySVaMXyx8cmu0/FH1qHpPrIj5xhufwTXydCy791o2tyqlASi68kMn552vefZXD74v8mnOglV7SJScyPB+GY6j34KX6ie+p1vnhTA84UaaNzzm3Qc/lv6z3+N2q0PFftzn/8qQ23fo3rza4g13DGn8Ym58QVrSUwRVJ6vXOISbiFB5KrAX7hqNyiPzNApoldlx10tM3wG105Ts/m1DLZ+h/bn/w/N179r1r/f5ZTqe55AbBNW4EqfScMKE4huIDN4kkB0A24hOeV95tVijXcycOnbuHaKmq0PzbhtpPYmes/8K66dQjeDjHT+lGj97RhmkEj1DfSc+SKuncKwIgDkk5ews30E4lsk8LcASinyqU5UxdblHooQa5JbV4UKBcnfe8e0ff2m4uzYjHX8LMalDtwt68s4QnG1kj/psG2b/fv3c+DAgVIfWggMK1QsiTBFGYWp+CItFLK9c2o4vpTcQgKYmPEHxdljyr22An9XMv7K9zvScnmUzzfjNl48Ot6gdny/bB4k429FcApJCplughXbiNbdSqrv+Snf18VZqRq6Kb83IZaCChYnR2jZ5Z/lbVzuxotHULHI7BtrGvZtN8zaj0AIIYS4mhWsJVp7M0NtPxjvJT9dxh8UA39uYWRCjy+lPNIDx4jW30bj7v9Guv8og5ceJp9qIxDdMGtWnKabBGKbGGr7HsneZ6nf8eY5T4yFYp+/QqYHz526THf/ua+QT12mcv39eHaantNf4NLP3o/nZmna+w50Y+qJkeGq3dRu/SWG2r5HoutJes/8K0Nt36N22+up3viKOY9PzI0VrMXJDS164nC6/zCGFSEQ33zVsavxhRvJDBybdr9U70GsQA2VG36OdTf9d9xCkrZnP0I+dXlR4ykXz82THjhGpHZyIDNUuYvM8CnSgyfQdB+B+JYZj6UbPup3voW6HW/GF6qfcdtIzQ2gFKm+w6QHjuHkh4g3vxiAcM1eUB7p/qPj2yd7nsHwxYg33omTH8Jzr61WMIuhPIfe01/kwoH3M9D20+UejhBrkqquJPv6V6Iq53cfreJR3MZazNPnyzQyMZWSB/58Ph8NDQ24kropVgArUA3Km9RMebk5hSRo+qS68Zruu+ZKfTIW+Ctjxp9WsME/c+BPxaPowy8s9SkZfytFbqTY3y8Y30K0/hZcO0168MSk7TwnjWGF5jw5QAixSIaB8lsrI/DX2YPbPP3DVyGEEKIUKte/HDvbx2DrdzGsCIZv6nYCAIHYJqCYxTMmN3IB104RrtlLuHoP1Zt+gYGL3yQzdLrYp34OghVbcQtJYo13Eq2/dV7j90fXg/KmDNAke55h+PJj1G57PTWbH2TdzX/E5jv/Nw3X/TrNN/4/swYYK1peSqzxTrpPfp6Rjh9Tv/MtVK576bzGJ+bGCtYCCic3sKjjpPoPj/aim3j/FK7aTXrwOEpNrumglEeq/3kidTejaRr+6DrW3/LHGFaEjkN/jZ0bWtSYyiHVdwjlFSaU+RwTqtqFW0gy3PEYwYpt6PrMvaIBIjXXE2+6c9btTH+cYHwLqf7nGen4MYHYxvE+nqa/gkBsE6nRPn9KeSR7ny32EQzWAWDn+ufzMudEKTXjMzK1Ap+hzca103Qc/hsSXU/gC9XRdfbhKf92hRDLx9mxBaNnAG1wZLmHcs0oy5PRN77xjXz+858nn596BpkQS8UKVAHFBsoriVtIYFiRSRfXum6hvGtsRpemoSyzrKU+tXwBNUvgz6uIoieScNXFoZaVHn8rRXbkPKa/CitQjS/cjC/cRLLnmUnbuXYG3ZT+fkIsJRUMLn/gz3XRUhm8qvjyjkMIIcSaF4htIFS5i3yqfcZsPyg+2Df9FRP6/KUHjmD4ouNBwaqNryBccz3Ks4tBuTmI1t9KtP426ra9ft7j94Wb0DSD/Av6/BUyPfSc+gLR+tuIN9195TX4osQa7hgPVsxE0zTqdryReNNdNO75TeJNd817fGJuioG/xfWBK2R6KaQ7J5T5HBOq3o2THxrPbL1advgsbiE5IYhm+itovuH3QDPoOvrJaTNKl4NSiuHLPyRUuQtfqG7S+kB8M5ruw8kNzlrmcyEidTeRGThOeuAY8aYXT1gXrrmBzOBxPM8mO3wOJz9MtP7Wkvx+p5PqO8jFJ/+YQqZnyvWDl77NhSf+iM4jnyQ7cqHk5y+1QqaX9uc+Sj7VTvONf0DDrl8lM9I+5UThUhvueJz2g39J17H/j75z/8FQ+yOTPluFEEXuhiZUMIB1ZuV/rqwVJe/xB9DV1cXFixe59957ue2226ipqZlUruJ973tfOU4txATmaOBvullw6YFjFNKdVK5/+VIOC9dOYvomp0VrhrXiypIuCctEs8vc4y8yczDIi0fB9dBSaVQ0AkoVS4RKxt+KkB0+R3C0Tr+maUTrb2Wo9Xt4bn5CuSHPyUzKpBVClJcKBpY98Kcliv09VXQOZT6FEEKIRarc8HIyQydn7O83JhDbSC7ROv7/qf4jhKv2jE8C1TSdhuvexnDbI4Qqd87p/P5wE427/9uCxq7rFr5wE7nEBTznDjTDh/Icuo79PaY/Tv2OyT385nv8+p1vWfD+Ym5MfyWablHI9jF295NPtjPcUczYnKokq50bZLj9EezcIE5+CDs3gKZbhKqum7RtsGI7mu4jPXAMf6R5wrpU30FMf9V48PrKmOI0X/87tB38GD0n/4mG3b+xqL+lUskOnyWXuFQMTE5B1y2CFdvIDB4nXFmGwF/NPvrOfhndCBCtu/UF625g4MJ/kR06Tar/MGbgys9V0y3sbOkz/lJ9h1DKZaj9Eep3vGnCOs/NM3z5MUKVuyhkuml/7qOEKndSvenV4/fjK4nrZGk/+DEMM8S6m9+DL1SPYWiEKzYw2Pp9mq4v/e8TisHk/gtfY6j1u4SqrsMpjJBLto5mSipa9r2b4FXlc4UQgK7jbN+EeeIs7Ns9a2U2sXhlCfw99thj+Eb7aR09enTSek3TJPAnloRu+DF8UexpAn+JrgNkR84veeDPKSQm9feD4oXdtVjDXfksKGePv3wBr7pi5jHEi4FYfSSJG41AvgBKjfevEksjO3IeK1iHeVXJJM/Nk0+2Emu4Y3xZtO4WBi58vdgfpe7m8eWunZaMPyGWmAoG0DKZZR2DniiWavZi05dbE0IIIUolVHkd0frbCdfcOOu2/uhGhtq+VyyflxukkO6ketOrJ2xjmCGqN796miOUXiC2kZHOn5DofgrQ0PTio6H1t/yx9MpeJTRNxwpUj2eEOfkROo58Aic/hG5FqN3yixO2V8qj+/hnKWS68Ec34As3EqrcRbBi65RBQl23CFXuINX7LJXr7x8PVCvlkeo9SKT+limDev7oOhp2/Tpdx/4eX7iR6k2/UIZXPz9D7T/AF24iVLV72m2itTdhZ/vwvSDIWQpWsJpgxQ4CsQ2T3l++cBNWoIZU30FSfYeINd45/rO2grUlz/hTyiMzeBzDFyPR9STVm1494d470X0A18lQv/NXMQNVpPqeZ/DSw1w+9FdsvOODxXY6K0hm8DhuIcn6m98zniWpaRoN217Jmac+QT51GX+kpaTnVJ5Dz6kvkOg+QO2211G57mXj6zzP5vLz/5fOo58cHdPc+68KcS2wd23BOnYa69R57BvKE5gXV5Ql8Pfoo4+W47BCLIgVqJ428Gfn+nALIyjlLWlPMLeQxPRXTlqu6da11+MPwLLK2+MvX5i9x184iDINtOEktDSiZYulSaTU59LqPPJ3BONbabr+nePLcolWlHInzDD0heoJRDeQ7HlmQuCvmPEnD/6FWEoqFEDvX96S2loiVSwbLZM1hBBCLAFN02jc/fY5bRuMbWLAyWJnesgMnULTjLKUE5yPmi0PEa65AeXm8ZwcnpslEN9S8gfkorzGAkOeZ9N59FOgPCpa7mO47QfEGu7AH24a33ak86dkR87Rsu//JVS5fU7Hr1z/AJcP/V8GLn6Tms0PApBLXMQpjBCtvWna/aJ1N1HY/BoGLvwX/uj6KUuJwuiEaDM0Hnguh0Kmm3T/Yep3vnXG7MN484uJN7942vWL1bLvD6dcrmka4dobGL78GCiPaP0t4+vKEfjLJS7h2ima9r6TruOfZaTjcao3vQooBgWH2h4hUrtvPGAVrbuZcNVuLj71v+g/91Ua9/xGScezWOn+I/gjzeNBvzFVLbdh+isYavsBDde9bXy5Ul4xwBuqX9D5PCdH57FPkx06RePu35jUY1XXLZr2vpP25z5Kx5G/Zd1NfyQViYS4WjCAs62Y9Wfv3gamZP2V09JFOoRYJqa/CmeaHn92tg+lXFw7taRjcguJCbOqxui675oM/ClfGQN/Ss2pxx+ahopH0UeKWSNabjTwJ6U+l4znFnDtFKn+QxP6oGRHzqGbQXxX3bgCROpvJT1wFNfJji9z7TS6JRl/QiyllVDqU0+kULEIrIByUkIIIcTV/KO98XLJVlL9hwlWbMcwg8s6JsMKEam5nmj9rcSbX0zl+pcTjG9Z1jGJ+bOCtdiZXnpPfYF8qp2m63+Hmq2/hBWsoff0v6JG+9c7+WH6z/8n8ca75hz0AwhVbqd606sZvPQw6YHjAKR6n8P0xQnM8vdSteHn8UfXk+p9bsr1Silan/4gI10/nfN4FmKo7REMX5xow21lPc9sNE2bNvAYqb4BlIcvVI8/cqXPp68Mgb/M4HF0M0S4Zi/xxv0MdzyGN/oMKt1/GDvbS+W6+yfso5sBaja/hmTvM2SHz5V0PIuhlEd64Bjh6smBZV03qdpwP8mep0fLb4KdG6Dj0Me59NT/Gs12np9igP2T5EbO03zD700K+o0xfVGab/hd3EKCrmOfvjbb+QgxA3vPdrS8jXn20nIPZc0rW+Cvp6eHj33sY7z+9a/ngQce4PWvfz1/8Rd/QU/P1M1jhSiX6TL+XDuNaxd7Ao1dCCwV105iTBH40wxr/KLrWqIsC8oV+HNc8DyUb/ZZJF48ijaSABh/iC0Zf0tn7H2o6Rb9F74+vjw3co5gfMukrNxo3S0o5dJ35t9QygVGM/5MmVEnxFJSQT+a7ZS1ZPNstEQSLyb9/YQQQqw8hhXCF6onM3SK7PAZwjXXL/eQxBphBWspZLpJdD9F/a63EohtRNct6ra/kezwGZI9PwOg9+y/o+kWNVsfmvc5qjb8HOHq3XSf+Afs3CDJvoNEavfNWjFJ0zT84aZpe9S5hQRuIUEh3TXvMc2VU0iQ6D5ARct96LpVtvMsVrBiK4YvRrThjgnBQStYi53rRymvZOdKDxwlXHUdmmZQse5luIUUya5iEGyo7QcE41un7E0Xa3wRgegG+s7+e0nHsxi5kQu4dmraz9SKprvQdIvhy4+S6DpA69MfpJDpIVy9h57T/0I+3Tnncynl0XPiH8mOnKfp+nfNmrXtCzXQuOcdZIfP0n/+a/N6XUKsdSoaxtm0DuvYGfBWxufJWlWWwN+ZM2f4hV/4Bb70pS9RW1vLHXfcQW1tLV/60pd49atfzdmzZ8txWiGmZAarcfKDky5Orp45tZSBP+U5uHYaw4pNWnfNlvr0WWh2eV63Vij2TJw14w8mZ/zpOljlKzsiJhp7H1ZtfCWZwRNkhs6glEd25DyB+ORG4lagisbr3k6i52m6jv1/xfeWk8GQjD8hlpQKFrMWljPrbzzjTwghhFiBArFNJLt/hvIcIhL4EyUyVq6wasMriNVfyWgLVe0iWncrfee+QqL7KVK9z1G77XULKjmoaToNu96Gpltcfv4vcXKDROqmL/N5NStYS2GajDU721v89zRtWUphpONx0DQqmu8p2zlKQdNNNt7+Aao2/NyE5VawBuU5OPmhkpzHKSTIJVoJV+8BwBeqI1J7A0PtPyA7coHsyDkq198/5b6aplO77XXkkq0LypYrh9TAEQxflEBs45TrDStEvPnFDLZ9n+6T/0ik5kY23PZ+Gvf8Flaghq5jn8Zz8xP2yae7GGp/BDt75e9SKUXf2S+T7DtIw3Vvn3PWbKhyO7Gmu0gPHl/waxRirbL37kBLZdDPty33UNa0sgT+Pvaxj7Fu3Tp+9KMf8YlPfIIPfOADfOITn+Cxxx6jpaWFj33sY+U4rRBTsgLVxYBAITFh+dWBPzc/smTjcQrFwNJUGX+6bqHcwpKNZaVQllm+jL/c6M8zMJeMvxhargC5PFo2hwr6pWzcEhq7oalseQn+6HoGLn6dQqoDz8kSnCLwBxCtv5WmPb9NeuAInUc/iedk0SXjT4glNZYZvWyBP9tGy+TwYtLfUwghxMrkj25AKRdfuGlSLyohFipUuZOm63+H6s2vnrSudtsvo9wC3Sc+R6hqN9G6qcsSzoXhi9K45zdxcoMYvijBim1z2s8K1uIWRiYFV+DK85irAyyl5LkFhi8/RrzxzlXRY82wwmiaMWHZ2GdFqcp9ZgZPAIrQaOAPoHLdyylkuuk+/ll8oXrC0/RjBAhWbCNadwv957+G5yxvmX8o9vcLV++dMfu0ouVlhCq207T3HTRc9zYMK4Ru+Gnc81s4uUF6T38RpRSeZzNw4Ru0Pf1B+s5+mYtP/U86j3ySzOBJhtq+y/DlR6nb/itE5xj0HuML1WNn+8fL7gohilRVHLelAePwKZQn749yKUvg7+DBg7zjHe8gHo9PWB6Px3nHO97Bc89NXeNbiHKw/FUA2C/o81fI9mFYYUxfHKcwvGTjce1i4M/0TZPxp67B+t9l7PGn5Ucz/uZS6rOi+NBYH0mi5fJS5nOJOflhdDM42kPgQbLDZxm4+E00zSAQ2zDtfpHaG2m6/l1khs8ASMafEEtMhZY38KclimW7JeNPCCHEShWIbQIgXL13mUci1hJNN4nU3DBl4MP0V1Cz9SEMK0z9jjdN219uroLxLTTs/m/Ubnv9pADVdGYKXI1lAjq5gbIERdL9h3HtFBUtLy35sZeKGagGtHkH/pRySYxmGF8tPXCMQHTDhGdRgfgWArFN2Ll+Kta9dNYSrjVbHsJzMgy2fmdeY5qPzNAp7NzMWY52to9CupNw9cwZ1FagkpZ9f0ikdt+E5f5wI3U73kyi+yn6z3+Ftqc/xGDrd6jc8HNsefH/pW77Gylk+7h86K/oP/81qje+akGZo1awFuUVJiUiCCHAvn4n+nACdfHycg9lzSpLDTvDMCgUps5aKhQKGMbcLhKEKIXixVLxgpKrapXb2T6sYB0oD2cJM/7cGTL+NN3CuyYz/lZIqc9YBDStGPjL5lABf1nGJKbm5Icw/RUAhKr2EIhvIdV/iEBsM7ox8+8iXHUdLTf8Pj2nvoAv3LgEoxVCjPNZoOvLFvjTEykA6fEnhBBixfJH1xGuuYFY4/7lHoq4hlQ030O88U40vTSP/qJ1N89r+6sDf/5Iy4R1Y8Esz83hOWkMq7TXcenB4/jCzfhCdSU97lLSdQszUDltn8TpZIfO0n3iH8glLlC3/VeAYo+6zOBx4s33TthW0zSqN72agYtfJ9bwolmPbQWrqWi+h5GuJ6je/JpFB5RfqJDp5fKhj+MPN7LulvdO25sx1X8ETTcJV1234HPFGm4nO3yWobYfEIhvYf3e38YfbgKgovlu4k0vJjt8FjvbS6zxzgWdwwrUAGDn+jH98Vm2Lj87N4QVqFzuYQgBgFdfg1dfg3vsLNx9+3IPZ00qS8bf/v37+fjHP87FixcnLL906RJ//dd/zf79crErlo5hhdDN4KSMPzvbixWsxfBXlKxm+gslug5QyPRMWDY208eYKuPP8F2TPf6UzwLXA9ct+bG1fKFYrtM3h2behoEXCaENJyTjbxk4+WFMXwVQvAGp2fwgUGx2PhfBim1svOOD+EIN5RqiEGIqmoYKBtAyy5Xxl0T5LZDJGkIIIVYoXbdovv538MsENbHEShX0WwjDiqIb/ikz1uxsH75w8+h/l7bcp1KKzMBxwtW7S3rc5eAL1s474y+f7gBg+PJjJLoOAJBLXMS10+P9/a4Wrt7N+lveO+tk2zHByp24hQRObn4BybkYvPRtDDNEIdNL/7mvTLtduv8IwYod6ObintnUbX8DLfv+kHU3/ffxoN8YTdMIVW4n3nTXggOcVnA08Feicq2LUUh3cfHJ/8Hw5R8t91CEGGffdwfG7dL7uFzKEvh7z3veg+M4vPKVr+TBBx/k7W9/O695zWt4xStegeM4/PEf/3E5TivEtKxAVTHj7yrFjL/a0VKfpc/4U55Dz6l/Zqj9kQnLHTtZLGc4xcwlXbeuycDfeFDOLkOZ03yhGFic44Waikcl42+ZOPnh8Yw/KPasqN32euJNdy/foIQQc6JCgWXN+FPS308IIYQQYkXRNA0rWDNe1vNqdraXcNWu4n/nShv4K6Q7cAojhKtWf+DPWkDgr5DuxB9ZR6zxTnpO/wu5ZCvpgWMYVni87PBiBEcraWVHLiz6WFcrZHpI9PyM6k2/QO3WX2L48mOk+g5N2s51MmSHzxCpWXywQNNNQpU7Zy1xulC64cfwxRYc+Os48glGOn9akrGM/b76zn2ZXOJSSY4pxKJFQugNNcs9ijWrLJ9sTU1NfPOb3+Q973kPGzduxPM8Nm7cyB//8R/zjW98g8ZGmeUmlpYZqJ5wMem5eZz8ML5gLaa/Aic/XPJzFjLdKOWSe8HFkFtITNnfD0Z7/F2DgT9lFQN/5ejzp+ULcyrzOcariKGNJCGXRwUl8LeUioG/iWUnKte9dFWXZxHiWqGCyxv4kzKfQgghhBArz1SBK9fO4NppArFN6IZ/0iTtxUoPHEfTfQTmWDlmJbOCtVMGTmeST3fiCzdRt+ON+MNNdB39e1J9BwlV7S5JgMuwIvhC9eQSpQ38DVz8JqYvTqzpLuLN9xKpuZGeU/80qd9fZuAESrmz9vdbKYrvgflnRzqFBOn+IyUL/OWTrVjBOvyRFrqOfQbXTpfkuEKIlas8UxqAcDjMW97yFv76r/+af/zHf+TjH/84v/qrv0o4HC7XKYWYlhWonlDqc+xL1xoN/LmF5KTGx4uVTxWbk+bTHXjOlYehbiGJYU2dmVDs8XftBf7wjZYfKUOfPy1fgHkE/lQ8ip5MozkuKiClPpeKUh7/P3t3Hh9XXe+P/3X22ZJM9qRN0oXSja4sKlCoIIiAX1QQ5CryFVFx33fRK/eHl2vlKip6r19x4SpuV8TtIuLyFeFLWQRaoC2U7mmbdSbL7Gf7/P6YZNrpTJJJMsksfT0fDx6055w55zPTfpL0vM77/XHMEaieYKmHQkQzUNpWn9H0Gq1EREREVFbyBX/jv9e8zTkPaRdDPLwTvvrlE64PV0k0TzNcO15wSCOEGKv4WwBZ1tC+9t1wnRTMWE/eNp8z5aldmvOQ+2ykYkcR6XsSDYsvgyxrkCQJrauuhyTr6N35vaz7dbHQszACHdC8jUW7/lzSPE0zqvhLDO8BkG7Taqdm36UsGTkIb91StJ/2Ljh2HH277oYQYtbnJaLyNWfBH1E5UY10q8/xb2pZP2gaQQACthkp6jVT0W5IsgYIF8nIgcx22xzNu74fMF7xZ55033yPVfwVv9WnlDIh9GlU/NUdC2VZ8Td/HDMCIZycij8iqgwlq/hLmZCSKVb8EREREZUhzdsMOxmGEE5mm5Xoz+zTihz8uU4KiZGX4KuCNp9A+jMCCl8jzk4Nw7UT0MfWq9M8jWhf8y546k4pbvBXtxSp6GG4Tqoo5wvv/z00TwPq2s/NbFO0ANpW34jEyB689Lf3Ys9DH8DeRz6BSN+TFVPtB4yt0ziD9RATIy+l7x1KEmKh52Y1BuHaSEUPw6hZBM3bhLbVNyA6uA3Dh/8yq/MSUXkr2iq/F154YcGLnUqShD//+c9TH0hUJJqnEa6ThGvHoWh+WIl+SIoORa+DqqcDPzs1BM1TvNAhFTkMX8NqJIZeRGJkH3z1KwEAjhXJLPB7IllJB1RC2JCkyn86rVBibI0/aQ4q/mCaED5fwYdnBX+s+Cs6M9YDWfVmreUHINNuV9WDOa8hovInfB5IyRQgRMFrqhaDHImmr881/oiIiIjKjuZthhAO7ORQ5j6ImRiAovmhaH5onkYkRl4q2vXiQy9CuDb8jdUX/HlqF095vBk7CgAwxoI/APDVr0TXGSuLOi5v7dL00jaRQ/AFT53VuVLRw4j0/wOtK6+HJGffpvbVL0fn6Z+EGTs6dk8vCeGaCHa8clbXnE+atykdyDpm5p5fIRJDu+FvXAMrMYDo4HbULdg04zGYsR4I14KnZhEAINC0HvVdF2Nwz70INJ9R1HuhRFQ+ihb8vepVr5oy+HvxxRfx+OOPFxwQEhWL6mkAANjJMBTNDzMxAN3bDEmSoIwFEI45+9L5cUIIpGKHEVz4SgjHzOp97pgRqBO2+kxPSeFYQBW0pSjYePAXSxT91FLKhNsQLPwFHgPCY0DiGn9zomfHXTACC9G2+u1Z2+1Uum//iYEgEVUG4fWkQ79kCvDO30MT0kg6+GPFHxEREVH50TzHgqvx4C/96/R21dMIq+9xCCGKcq8wHt4JzdMIzds663OVA0XzZe5hFcKMHYWk6FA9c9sGUw8sgKwYSI7sm3XwF9r/e2jeZtS2vSLvfm/dUnjrls7qGqWkjoe3yRAMf3tBr3GsOFLRwwh2XAjdvwChfb+F66QgKzO7R5WMHAAkGUZNZ2Zb3YLzMHToT+n5yOCPqCoVLfj73Oc+N+G+Xbt24Vvf+haeeOIJdHV14V3velexLktUEG3shx4rGYJR0zn2g2YLgHT7AElSMhVHxeCYI3DMCIxAByAEho/8LdO+05m01edYxZ9rAii8Sq3iKQqcjjZozzwPp6MNoqZ4a4FKKROYRqtPIF31p6RMwMPgr5iEcGHGe/Pus1PDkCQFis6qHaJKJMbCPimezPx6PsijUQiPkXmAhIiIiIjKh+ZpACQZZmIAPqwCgKz7MZqnEa6dyHRnmq14eAd8DadVVcFBvnUSJ5KKHYXhXwBJmtuVnSRJgad2cdZD7jPh2HHEBrejadkbc6r9qoXmORZ4Fxr8JUb2ABDwBk8FIDC455eID72AQNP6GY0hGTkE3deWFRwqYwUJjlXcZY+IqHzM6XeC5557Du9+97tx5ZVXYu/evfi3f/s3PPDAA3jjG984l5clyqHoNZBkLdM7/vgnzCRJgmoEi7JY7rhU9DAAwAh0wFO3FI4VhZXoh2vHIYQzYbgxvvi0cOeg5WWZS53/MkDTYPzfxwDHmfoFhRAivcafMb3gT9TVpF9TRf9YKAd2MgThWjDjvRDCzd6XGoai1835P1CIaG4I31jwN8/r/EmjEbh1rPYjIiIiKkeSrEIzGrKCq+Pvx2jeYw9pz5aVGIQZ76uaNp/jNE/hwZ8ZOwrdV1i4NFue2qVIjOzLPOQ+E/HQDgjhINC8oXgDKzOqUZe+H1ngnyEAJIZfgmoEoXmboftaofvaEBvYPuMxpCIHM20+x8mqD5KkwDEZ/BFVqzm5w7pt2za84x3vwDXXXIMjR47g3//933H//ffjda97HWSZN3Vp/kmSDM3TMBY82LCSocwPmgCgGEHY5nDRrpeKdkNWPFA9jfDULgEAJEf2wTFH09ebsOIvHfy5J2HwB0NH6sKzIQ+PQH9sW3HO6TiA40IY06sEsVYuhXlm8Ra+pjQz3gcgHWzbJ/zDzk4Nsc0nUQUTYxXS8x38yaNRru9HREREVMaOr1hzHRN2aiir1SeAnH8fzkQsvAOQZHjrV8z6XOWk0Io/IQTMWA/049b3m0ueuqVwzJFZ/dlFB7bBqOnKdOmqRun7kY2wkoMFvyYx/BK8dadmKlf9TesRDT2b8wB1IYRrIxU9DKM2O/iTJAmKXpO5T0lE1aeoKdyTTz6JG264Addeey3C4TC+8Y1v4He/+x0uu+yyqiqzp8qkGg2wkmFYyTAgXOhjrSUAQNXritrqMxU5DCPQAUmSoWh+6P52JEb3wR57kkadoOJPUsYq/pyTMPgD4DbWI3X2Rqi790PdvX/W55NSJgBAGNNr2Ska6+GcumTW16ds6Taf6e8FqVhP1j47Nczgj6iSKQqEoc1v8CcEpNEo1/cjIiIiKmPHB1fj4Yc+FvwpWg0kWS9KxV88vAPe2qVQ1OpaNkXzNsNODU/5gLidDMN1kjAC8xT8jT/kPjqzezfCtRELP49A04Yijqo8pedAYcGf66SQihyEt355ZlugaT0ccxTJ0QPTvnYqdgTCtXMq/oD0/LPZ6pOoahUt+HvrW9+K66+/HvF4HN/5znfwq1/9ChdffHGxTk80a5q3EXYylPmB8/iKv3Srz+GiXSsVSwd/4zy1S9MVf9ZYxZ82ecXfydjqc5xz6hLYK5ZAf+wZSMOzfPJoLPjDNFt90twwY70wAh2QFQ/ME4M/cxgqF5QmqmjC653f4C9pQjItCAZ/RERERGVrPPgTQuTcj5EkCZqnYdbBn3BtxMMvwNdYfZ170p+VmDI4SsWOAMC8Vfypei00bzMSI3tn9Pr48Itw7cSM162rJJq3qeBWn8mRfRDCGVvfL81TtxSKFkBscPrtPlOjhwBJzrpHOU7Ra9nqk6iKFW3l1CeffBIAsHv3bnzkIx+Z9FhJkvDUU08V69JEBVE9jYgObIOVGIAkKVkhg2oE4ZjFWePPdUyY8T4EO16V2eatW4rR3q3pa8sqZNWb97WynA6oTubgDwDMl2+AuucQlMO9sIP5Q9JCSKn05zjdNf5obpjxXui+NkiyCjOep+JPD5ZmYERUFMLrmdfgT45EAYAVf0RERERlTPM2w3WScKwIrHg/JEWHotcdt78JdmJ2wV8ychCuk4S/YdVsh1t2xkNSK9EPwz/x+n1mrCe95IzRMF9Dg7fuFCRH983otdGBbdA8jdDzBFLVRvM2wzr6/yCEmLIjXnx4NxQtkLVWoyTJ8DetQ3RwO5pOecO0rp2MHITua4es5HbCUvWagisRiajyFC34e//731+sUxHNCc3TCMeKIhU9DM3bBElSMvtUIwjHisF1TMjK7EIiM3YUEG52xV/dKYBwERt8Lt3KYoJv9MfW+DNnNYaKpyhwG+ogh4dndRoplQLA4K9cmPFe+OpXQFK0rIo/107CtRNs9UlU4YTPAykan7frSSPpp1NZ8UdERERUvvRMcDUAKzEA3ducdU9E9TQiOTKz8GhcKnoEkOSqDJFUow6qEUQ8vGvS6jgzdhS6v31el1ry1C5BpO/Jad9LE0IgNvgsAi2nnxRLQ2neZgjXhGOOTHnfIzH8ErzBU3M+l0DTeoz2PAoz3g/d1zLBq3OlIgfhqc1t8wkAil4z41atRFT+GPzRSWP8qafE8ItZbT6B9Bp/QLrqaDrfQPNJRQ8DkGAEFma26b42yKoXidF9ecvrx7HV5zFuQxBy3+yePBpf4w+6VoQR0Ww4VgyOOTpW8acj2v905mk3OzUEAFANtvokqmTC64E8EAYSSSj9ofTXcFmGdfppgFzUZaUBAPJoFMLnBdSi/ThLREREREV2rGJtEGaiH5o3+56L5mlEpO+JWV3DjB2B7muFLFffv/0lSUag5QxE+/6B5lOvgSTl/7k6FTsCI9A5r2Pz1C2FEA5SkUPwBpcBSN9XgyRD1Sfu3pSKHISdGjop2nwCgOZpApCeA5MFf65rITm6P29Vn69hNSRZx8CeX6Bt1duhaFOvZem6FlLRw6htPyfvfkWrhW3OcokdolkQrg3bjAKo/gcASqH4d2GIypTmaQQAmPG+nOBPGfvGa5/Q7nP4yN8w1P2XaV0nFT0M3deSVUYvSXJ64WPhTvrDj6SMBX8Ogz+3MQh5JALYzsxPkjIhDA04CZ4gK3dmvA9AOgTX/e1wnWQm8BtfX5MVf0SVTXg9kEej8P3s9zD+uhXqwSPQduyG/sg/ACGKeCEBWBbkoRG2+SQiIiIqc7LqgaLXZCr+Trwfo3ka4doJONbMO0ekokfnbW27UqhpORO2OYLE8J68+4VwYcZ6sx5Anw9GoAOSrCMxshex0A4cffbb2Pfop3HoyX+ddO246MA2KJo/ax27aqZ5x4K/5BTrNI4egHCtvJ+LrBhoX/NOJIb34NCTtyJxXJWs61oY7XkUR7Z/A/GhFzLbzehRCOHAqJm44s+1E3BZfEAlIISLQ09/Dfv+8X9KPZSqxUek6aSheoKAJAPCza34M9IVf85YAAGkWw8MHXwQQtgIdlxYcPuBVPRw3qesvHWnIB7eCUWrmfC1kqQCkPhNF+ngD0JAHh6B2zSzHvVSyoQwcvuY0/wz470AAM3XAln1pLfFeqB5Ghj8EVUJZ9FCWIkk3MYg3NYmCL8Pyr5uGA89DuH1wDpr3azOrz31PNQ9B9NtnB0XAGCvOqUYQyciIiKiOaR5mmHGe2EnwzkVf+rYQ9p2MlRQFdOJhBAwY0cQrL+wKGMtR57apVA9DYj2/wO++uU5+63EIIRrQp9kDcC5IEkKPLWLMbjvvsySN83L3ojwwT+gZ8ddWLjhQ3krFKOD2+FvXJu1BE81kxUDil4LKzEw6XHx4Zcgq94JKzcDTeux6Kyb0bPjuzj89FfQsOR/QThJjBx9BI4VhWo04Oiz38LCDR9Or78YOQhI8oSdx8YLExwzCtnDDkxUfEIICGHnrcYOH/wD4kMvYemGmzGLkg+aBIM/OmlIkgLVCI79oJkd/MmKF7JiZFX8WYmBzNM4ZrwHRgFPjwkhkIp2w9d1Sc4+T+1SAIAyWcWfJEGSNbb6BOAG6wBJghwannnwZ1ps81kmrHgvVE8DZMWA5GmEJOsw4z3wN54GOzUMRfPPen1NIiotUePPCfecpZ0wk0noj2+H8Hpgr8m9UVEIKTQE7dkXYC9bBLexHsKjQxgG3JaZfX8gIiIiovmjeZuRGHoBQjiZNf8y+8aCPysZglEz/VaVjjkKx4pWdcWfJEmoaTkDo72Po3n5m3ICMzPeAwDQffP/GQQ7LoTubUHtgnPhqV0KSZJg+DtwePsdCO37LZpOef0JY+2HGTuCxiX/a97HWkqatxlWYvKKv3h4F7x1yyZs55o+TxM6T/8EBvf9BqF9v4aselHXfi7qFm6GagRxZPs3cGT7N9C58eNIRQ7C8C+Y8F7LeGGCY41CY/BHc2D48F8R2vcbtJ32DgSajt0rSAzvQWj/79C85HLUNq/C0FCshKOsXgz+6KSieRrzPmEmSVI6FDyu4i8e3pn+YUqSEQ/vLCj4s5NhuHYi79M0ntolAKRJW30CgKxoEK5Z0PupaqoCt64Gcmh4xqeQUilW/JUJM94L3dcGIN36Vve3wYyl/3Fim8NQ9GAJR0dEc8lefSqkeBL6k89CGBqcU5dM+xz6Uzvg1gZgnnvGnKwXSERERERzR/c2I9L3OADkLr2i10KSNVjJ0IzObcaOAsC8t7mcbzUtZ2Ho0J+QGHoJvoaVWftS0SOQVV9JuujUtJyOmpbTs7b5GlaiaekbMLj3XnhqFyPQvCGzLza4HZKswd+wep5HWlqap2nSir/R3seQGH4R7ae9a8pzSbKK5mVXIbhwMxQtkOmqBAAL1r0fR7Z9DYe33wFZ1uGtXznheRR9LPibpC0r0WxE+p6AEC6OPvdtNC+7GsGOC+HacfTsvAve2qVoWvraUg+xqjH4o5OK5mlEAnsy/bWPp+h12cHf0E546pZCklTEw7tQ33nRlOdPxQ4DQN6yfEXzoX3NTZkFjyeSrvizp7zWycBtDEIOD8/8BCkTwuct2nho5sxYL3zH/WCv+44L/lJDfLqMqMpZZ6yBlEzBeOQpOPsOwzxrLURDsKDXyr0DUI70IvXKlzP0IyIiIqpA42GfJClQT/i3nyRJYw9pzyz4S8WOQJK1nECx2hg1i6B5mhDp/0dO8GfGjsLwLyh4iZr5UN/1aiRH96N31w/QbF0DM9GHVOQQkiP74KtfmRVWnQw0bxMSwy/m3WfG+9D/4k9Q23Y2alrPnNY5T6SoXixc/0F0P307zNhR1E+wvh9wfPA3WvA1iQplp4aRHN2PtlVvQyrWg4GXfg4z3gvHjMC1k2jbeCMk+eRo91sqFXP35J577sGFF16ItWvX4uqrr8azzz474bGWZeHOO+/ERRddhLVr1+KKK67A3//+96xjotEovvSlL+GCCy7AunXrcO211+ac89Of/jRWrFiR9d+NN944J++P5ofuXwjd35a3t/DxFX9COIgPvQhf/Sr4GlYjPvxiQevupSLdUDT/hE9Z1bScPmXFnyRrcB1W/AGA2xCENDQCCDGj10spCzDYPrLUhGvDSgxkKv4AQPcvgBnvgRACdmoYKiv+iKqbJME89wykLjwbUjQG72/+DP2Rf0CKJyZ/nRDQnnoebkMQzuL8a1MQERERUXkbD+U0b1Pedd1UT8PMK/6iR6H72ydtj1gNJElCoPVMRAeehhDZK2KZsZ6ya3UqSRJaV/1vqEYQfS/cjUjv45BkDfVdF6NlxXWlHt68073NsFPDOff7hGujd8ddUPRatCz/p6JcS9EC6NjwYdS2n5tVbXkiWdYgq17YrPijORAd3A5IMvxN69G87Cq0rrweo0cfQXTgabSuvB6at7HUQ6x6FVHxd//99+O2227DLbfcgvXr1+Puu+/GjTfeiAceeACNjbl/Se644w789re/xa233oqlS5fi4Ycfxvvf/3787Gc/w+rV6YqTm2++GS+99BK2bNmClpYW/Pa3v8UNN9yA+++/H62trZlznXfeebjtttsyv9d1hgiVrL7rIgQXbs67TzXqkIocBAAkRw/AtRPwNayGJKsY3Hvv2FNJKyY9fyp6GEagY1ZPWXGNv2PchiAk24E0EoEITh6Y5iOlTLb6LANWMpRey+G44M/wt8OxYnCsCOzkEPwNa0o4QiKaF5IEZ9FCOJ3tUF/cB23bTsihISRfd/GEL1EO90DpDyF58SagjJ5gJiIiIqLCjVcmTVSVp3makIwcmNG5U7EjMPzV3eZzXE3LGRg6+ADiQy/A33AagPSD62a8B7Xt55R4dLkU1YtFZ30ejp2AOlZddrJSx/7uW8nBrKWEBvfdh1T0MDrP+HRRqyBVI4i2Vf976uP0WjgWgz8qvtjgdviCp0LR/ACAugWboPtaYcb7ctoD09yoiMdhfvCDH+Caa67BVVddhWXLluGWW26Bx+PBvffem/f43/zmN3j3u9+NzZs3o7OzE29+85uxefNmfP/73wcAJJNJPPjgg/jEJz6Bs846C4sWLcIHPvABLFq0CD/5yU+yzqXrOpqbmzP/1dXVzfn7pbkjScqE30hVox52ahhCCMTDuyCrXnhqF8EIdEDRaxAP75ry/GbsCPQ86/tNh6zoDP7GuI1BAJjxOn/p4C+3upPmlxnvBQDo/uMr/toBpMNy24pAKcFaBERUIrIMe9UymOecATk8AikSzX+cENCe2gGnrQnuwtb8xxARERFR2VP0OkiKPknwN7NWn0K4ZVntNleMQBc0bwuifU8BAFKxo+h5/jsQrg3PJC0dS0mS1ZM+9APS4TYAWInBzLbo4LMYOvQnNC27Cp7a0vz5KVoN1/ijonPsBOLhXfA3bcja7g2eiroFm0ozqJNQ2Vf8maaJHTt24Kabbspsk2UZ55xzDp555pm8r7EsK6cyzzAMPP300wAA27bhOA6MEyqBjj9m3BNPPIGzzz4btbW1eMUrXoEPf/jDqK/nWlTVSNXr4DopuE4S8fBO+OpXZVpQ+OpXIT60E8DrJ3y9EA6sxCB07+xuTrLi7ziGDuH3QQ4Pwzmla3qvtR3AcSBYpVtyZqwXsuKBoh97cELzNkOSFCSGXgCEW5JFyImotJwFLYAsQenuhb06d/1bZV835KERJC+/gNV+RERERBVMkiQ0nXIlvLWn5N2vehvTHWHsOBTVV/B57WQYrpOEETg5Kv4kSUJNy5kYOfoQxC6B0d6t0IwGtK1+O7zB3J+nqXyoRh0kWYOVGICVGMDgvt8i0vcE/I1rEey4sGTjUvQaVvxR0cVDOyCEg8AJwR/Nr7IP/oaGhuA4Tk5Lz8bGRuzbty/vazZt2oQf/vCHOOuss9DV1YWtW7fiT3/6Exwn3QM7EAhg48aN+Pa3v42lS5eiqakJv//977Ft2zZ0dR0LF8477zxcfPHF6OjoQHd3N7761a/ine98J37+859DUQpffFKWJcgyb1iVO8NXD0mS4Kb6kYzsQ9vKN0NV00WxNU2r0bPzH5DcOBQ9kPf1ZiIMwIUn0JJ53UwoigYIa1bnKEeKImf9v1CiOQh1aARiup9HKglJAhS/B1KVfZaVxk72wQi0Q9OO/7opw/C3ITH8IiRJgsffWHV/5yvZTOcr0bSoBsSCFmhHeoF1y3N2azt2Q3S2QV7QXBktKkqIc5aocnC+ElUWztniaV580YT7PP5mSJIEYQ1BMXwwYz1IRY/A37QWiuqd8HWJZA8kSYKvruOk+fdkcMHLMHToD4iHn0PbijchuPB8yAo7HY0r3zkrQ/c1YeTIXzC495dQ9Rq0r3oLggs3QZJLd3teM2qRHN1/0swfmh/x8HZ4arvgrclf5T2ufOdrdSj74G8mPve5z+Hmm2/GpZdeCkmS0NnZiSuvvDKrNeiWLVvw2c9+Fueffz4URcHq1atx+eWXY8eOHZljLr/88syvV6xYgRUrVuCiiy7KVAEWqqHBP6s132h+eLUFOKzIsEaehiJLWLDkTHgC6T7Efs9Z6H/xR5CsA6hvfXne149Y+6EoMprbF2VeNxMDPj+EcFFfP/NzlLPa2ol/aM/H7myFu+0F+IO+ac0j1zZhKTI8TXWQq/SzrBRH7UHUNXbm/J2uberC0JEnoSgymloXQDP451RupjtfiabLWbkY9sNPw+/XIenHbli4/WFYI6PQXnkmv4ZPA+csUeXgfCWqLJyzc8vv6cRhRUZ470+QjPbDNtOt4GubVmLFpk9NGGwl+gehe/xobus4ee671a9ATeBf4K1ZCEXj38uJlOOcDTYtxUj/c1i87k1oPeXVUFRj6hfNsVh9M1IjO6v2HiTNP9exsHd4J9qWvabgv1flOF+rQdkHf/X19VAUBaFQdq/vUCiEpqamvK9paGjAt7/9baRSKQwPD6OlpQW33347Ojs7M8d0dXXhxz/+MeLxOKLRKFpaWvDhD38465gTdXZ2or6+HgcPHpxW8BcOx1jxVwFcR4fjuOjd9whkvQEJK4DEUGxsrwHF04K+g09D9q/J+/qhvkNwXIF4youEFct7TCFMC3CsBIaGZn6OcqQoMmprvRgdTcBx3IJfJ3v90KIJxA4PAoHCW35IAyPQHRcJ04Goss+ykgghEB3qhl6zKufvtFCaYdsOJFlFJCZDivPPqVzMdL4STZfU0ADdtDC6Yz/cxcdaNKlP7YKsaYgHgwC/hk+Jc5aocnC+ElUWztn5IYQKPbAIli2jpu18+OpPBQB0b/smdjz8dSxcdxMkKbciJNy3H4rRhuHh+HwPubSkdlhRFwB/Tj5ROc/Z+lPegvpTBGTFwGjEBmCXekhI2TqSsSGEw9GTJzynORUL7YSZjEIJrJ7y3nY5z9dyVmigWvbBn67rOO2007B161ZcdFG6LYDruti6dSuuu+66SV9rGAZaW1thWRYefPBBXHrppTnH+Hw++Hw+jIyM4JFHHsEnPvGJCc/X29uL4eFhNDdPXqZ6ItcVcF0xrddQKaiQVR9sM4K6po2w7ewvON7gKkQHt8OynLzfDJPRfqhGIxxXBtyZf7ESUGHbqZzrVwvHcaf13qS6WqgCEP1hOB5Pwa9T4kkIAViKClTpZ1kJbDMC24xB8bTk/LmrnjYIIaBqdXAcAYBfJ8vNdOcr0bT5/VBqA8DBo7A72tPbXBfaSwdhndKV/vI9i++pJxvOWaLKwflKVFk4Z+dex+mfztnWdtq7cPS5/4C88ydoXv5POfdikpEj8NQu4Z8N5SjPOZu+De+W0bgkJQDXtWEmY1C0wh+2J5rISN8zUI0GKJ4FBc/B8pyvla/sgz8AuOGGG/CpT30Ka9aswbp163D33XcjkUjgyiuvBAB88pOfRGtrKz72sY8BALZv346+vj6sWrUKfX19+OY3vwnXdfGOd7wjc86HH34YQggsWbIEhw4dwpYtW7B06dLMOWOxGO68805ccsklaGpqQnd3N77yla9g0aJFOO+88+b/Q6B5oeh1cKwYfA2rc/b5GlZj+PBfYSX6oftac/ZbiX5o3umFwvlIig7hWrM+T7UQfi+EoUEOD8PpWlDw66SUmf6Foc/RyKgQZrwXAKD72nL26f70NtUIzueQiKjMOB3tUPd1A0IAkgTlSC+kZAr2KYtKPTQiIiIiKqFA03q0rngL+l74ERSjDo2Ljy3JI4QDM96D2gXnlnCERJVN0WoBAI41yuCPZk0IgejANgSaN7KCtAxURPB32WWXIRwO4xvf+AYGBgawatUq3HXXXZlWnz09PZDlYyX/qVQKd9xxB7q7u+Hz+bB582Zs2bIFtbW1mWMikQi++tWvore3F8FgEK9+9avxkY98BJqW7huuKAp2796NX//614hEImhpacG5556LD33oQ9B1BgnVSjWCMOO98NWvyNnnCy6HJCmIh3dNEPwNwFN3yqzHIMsag7/jSRLchnrIoeHpvS5lQugawG80JWXFewFJhuZrydmn+VoBSYZq1JdgZERULpzOdmg7XoIcHobbWA/1pYNwG+ogGoOlHhoRERERlVjdgvNgm6MI7fsNdF87alpOBwCY8X4I14bhL/wBYSLKpuo1ANLdmvI9sE00HanIIdipIQSaNpR6KIQKCf4A4LrrrpuwteePfvSjrN+/7GUvw/333z/p+S677DJcdtllE+73eDz43ve+N/2BUkXTfW0QrgNFy+2VK6seeGqXID78IoIdr8zaJ4SAlRhATdsrZj0GSdYgHAZ/x3Mb6qAePDKt10gpE4LVfiVnxnuheRohy7mLscuyBk+gM1P5R0QnJ7e1CUJToXT3wA34oXT3wDwz/3q6RERERHTyaVh0GVKRg+h/8cfwBpdB1WthxtL3CHT/wileTUQTUfSxij8zUuKRUDWID78ISdHhDS4r9VAIQO7KuEQnsaZlV2Hh+vdPuN9TuxipyKGc7Y4VgeukoBej1aeswWXFXxa3MQgpGgfG23cWQEqZbPNZBsxYz6RPjXVs/BgaFuWuv0pEJxFZhrOwFUp3T6blp720q9SjIiIiIqIyIUkSWlakiwH6X/wJhBAwo0eh6LWZiiUimj5Z9UKSFDjWaKmHQlXAivVC97VBkium1qyqMfgjOo4sa5AVY8L9RqATVmIAjp3I2m4lBgCgOGv8yRqEW3jAdTJwG9OtIOWBcMGvYcVf6bmOifjwbniDp054jKx6+AMBEcHpaIc8OAR150twOtoAr6fUQyIiIiKiMqLqtWhZ8RZEB55GpO8JpGJH2eaTaJYkSYaiBVjxR0VhxnvZMraMMPgjmgajphMAkIoeztqeCf48TbO+hqzoXOPvBKKuBm6NH+qBw1MfPM5k8FdqieHdEI4Jf9O6Ug+FiMqc05H+x4E8GoW9bFGJR0NERERE5aim5QzUtJyFgZd+huToPugBtvkkmi1Fr4VtsuKPZkcIATPeA93fXuqh0BgGf0TTMF6unIp2Z223EgNQ9FrI6uwrFCRZhXBtCOHO+lxVQ5LgnLIIyoHDgO0U9pIkW32WWiz0HDRPE3Qfv+kT0RS8HrjNDRCGBqeTXzOIiIiIKL+WFf8ESCrs1DAr/oiKQNFrWPFHs+ZYUThWjBV/ZYTBH9E0SLIK3b8AqUhu8FeMNp/pa6TDKlb9ZbOXdkKybCiHjhZ0vJQyIXQGf6UihEBs8Fn4m9ZCkqRSD4eIKoB55lqYm84CFKXUQyEiIiKiMqVoAbSuvA6ABE/N4lIPh6jiqXotHIvBH82OGe8FAAZ/ZYTBH9E0eQJdORV/ZrwfetGCPw0AIFy7KOerFqKuBm5zA9R9hwp7AVt9lpQZOworGYK/cW2ph0JEFcJta4bTxae2iYiIiGhygab1OOW8r2aWYyGimVM0VvzR7JmxHkCSoflaSj0UGsPgj2iajJpOmNEjWcGclRyA5i3OFzZZSQd/rmMW5XzVxD6lC8rhXiCRmvxAx4FkOwz+SigWehaSosNbv6LUQyEiIiIiIqIqo2j+Ug+BqCooeg0r/mjWrHgvNE8T5LGCFio9Bn9E02QEOiGEk36SAYBrJ+GYkSK2+hyv+GOrzxPZi9NP86kHuic9TkqNhaYM/komNvgc/PWr+Q2fiIiIiIiIiKhMKVoNHCvGzmM0K2a8F7q/vdTDoOMw+COaJiPQAUDKtPu0EgMAwOBvPngNOB1tUPdO0e5zLPhjxV9pOFYUidF98DexzScRERERERERUblSjVoAgM12nzQLZqyX6/uVGQZ/RNMkqx7ovhYkx4I/s8jBnyynwyoGf/nZS7sgD4QhjUz8A4nE4K+kYqEdgHC5vh8RERERERERURlTtBoAYLtPmjHXScFKhhj8lRkGf0QzoAc6kIocq/iTFQ8ULVCUc0tc429STtcCCE2Fum/idp/y0AggSRA+zzyOjMbFQs/BqOmCagRLPRQiIiIiIiIiIpqAoo8Ff6z4oxmy4v0ABHQ/g79ywuCPaAY8gS6kot0QQsBKDEDzNkOSpKKcW5LGWn0KVvzlpSpwFndA2XsQECLvIcrBo3DaWwCN68vNNyEcxELPI9C4rtRDISIiIiIiIiKiSSh6utWnY46WeCRUqcx4LwCw4q/MMPgjmgGjphOunYCdDMFK9BetzSdwrOJPOAz+JmIv7YIciUHuD+XuTKag9A7AWbRg/gdGSAzvhWvH4W9i8EdEREREREREVM5kWYOsemGXcavPWHgHrLGllqj8mPFeKHotFM1f6qHQcRj8Ec2AEegEAKSi3ZmKv2KR5LHgj2v8Tchtb4YI+KDu3p+zT+nuAYSA08XgrxRioeeg6LUwarpKPRQiIiIiIiIiIpqCotWUbatPM9aDo9vvRPjgH0s9FJqAGethtV8ZYvBHNAOqUQdFr0VidD+s1FBRgz95LPhzGfxNTJJgLV8Cdf9hIJW9FqJ68AiclkYIn7dEgzu5JYZegL9hNSSJ316IiIiIiIiIiMqdoteUZatPIQT6X/o5hHBgxo6Wejg0ATPey+CvDPHOLNEMeWq6EBvYBggXelEr/lRAkiFcc+qDT2LOqYsB14W6r/vYRsuCcrQPzqKFJRvXycy1k0hGu+GpW1bqoRARERERERERUQFUvQZOGbb6jA1uQzy8E966ZUjFjkIIUeoh0QmEcGHG+6D720s9FDoBgz+iGTICHZnFSzVvS1HPLcsahGsX9ZzVRvi8cDrboe7eB4x941cO9wGOy+CvRBKj+wDhwhs8tdRDISIiIiIiIiKiAihaLewyq/hznRQGXvoF/I1rUd/1arh2HI45Uuph0QnsZAjCtVjxV4YY/BHN0Pg6f5KkQPXUF/XckqzBdVjxNxV7xRLI4RHIoSEAgHLwMNyGOogaLiZbConhl6BoAX6zJyIiIiIiIiKqEOlWn+VV8Td06EHY5giaT70Gun8BACDFdp9lZ7woRvfzXmC5YfBHNENGTRcAQPM2FX09M0nWILjG35SchW0Qfi/UF/cDjgPlcC+r/UooObIH3rplkCSp1EMhIiIiIiIiIqICqHotHCsy41aaQrhwnVTRxmMlBhE++ADqOy+G7mtN33uVda7zV4bMWC8kRYdqFLcohmaPwR/RDGneZsiKAa2I6/uNkxWdwV8hJAn2qUug7DsE5eBRSJYNe1FHqUd1UhKujcTIfniDXN+PiIiIiIiIiKhSKFoNhGvDdRIzen34wP/gwGP/XLTuZYN774OiBdCw6FIAgCTJ0P1tMKMM/sqNGe+B7msrelEMzR7/RIhmSJJk+Js2wFu/svjnZsVfwexTF0NyXOiPb4NbG4AI1pR6SCelZOQQhGvCU8fgj4iIiIiIiIioUmi+dFFDKnok734hBBw7PsE+F6M9j8JOhTFy9O95jxl46Rfo2XFXQWMRQiA+tBN1CzZBVj2Z7YZ/AVt9liEz1sslf8oUgz+iWWg/7UY0dL266OeVZA3CYfBXCBHwwVnYCimZSrf5ZJvJkkiM7IGk6PCMtcAlIiIiIiIiIqLyZwQ6oWh+JMK78u4f7XkU+//fp/OuA5gc2QsrGYJR04XwwQdyWn4mR/djqPvPiPY/BceeuqLQTobgWDEYNYuytuv+hTBjR2fcjpTmhhln8FeuGPwRlSFJ1uC6xSmPPxnYK5YCAJzFbPNZKsnhPfDWLoUkq6UeChERERERERERFUiSZHjrVyI2lD/4iw48DddJYqTnkZx9o31PQDUasGDNTXCtGEaOPJTZJ4SL/t0/heZpghAO4hMEi8dLRg4BQM6D5bq/Ha6ThJ0KT+et0RxyzAgcKwrd317qoVAeDP6IylC61add6mFUDKdrARJXXgK3iQvJloIQLhIje+Blm08iIiIiIiIioorjq1+F5Oj+nJaerpNCfOgFyKoXw4cfghBOZp9wbUT7n0JN61nQvM2obT8H4UN/zFT9jfY8iuToAbStvgG6rw3x0PNTjiMVOQRFr4NqBLO2G/4FAAAz1jPLd0rFYsZ7AYAVf2WKwR9RGZIVDYIVf9Mi6ri2X6mY8V44VhTe4KmlHgoREREREREREU2Tv2EVIFwkhl/K2h4fehHCtdC64jrYqTCiA9uP27cLjhVFbevLAAANiy6Da8UxfPhvcKwYBvfdh5rWl8MbPBX+xrWIhZ6bslVnKnoo7zIyqqcBsmJwnb8ykoodhSQp0HwtpR4K5cHgj6gMSbIOl2v8UYVIDO8BJBme2iWlHgoREREREREREU2T5m2G5mlCPLwza3ts8Flo3mYEWs6EN3gqhg//NbNvtPdx6P4F0AMdY+doRG37uRg69EcM7PklhGOhedlVAAB/41rY5ghS0e4JxyCEQDJyCEae4E+SZOj+BTCjR4rxdqkIUpFD0P3tkGWt1EOhPBj8EZWhdKtPBn9UGZIje+Cp6YKseko9FCIiIiIiIiIimgFfw+qsdfiEEIiFnoO/aR0kSUKw40IkhncjFemG66QQHdyGmtaXQZKkzGsaFl8K105gtOf/oWHx5ZmWnd7gMsiKB7HQcxNe3zFH4JijeSv+AKSDvzhbfZaLVLQ7b0hL5YHBH1EZkhn8UQVJDHN9PyIiIiIiIiKiSuZrWAkz3gsrOQQAMKOHYaeGEGhcBwAING+AatRj+PD/RXRwO4Rjorb1rKxzaJ5GBDsvglHThfrOV2W2S7IKX8NqxCZZ5y8ZOQQAMAL5wyTD345U7CiEcGf1Pmn2hGsjFT3M4K+MMfgjKkOs+KNKYSWHYCUH4WHwR0RERERERERUsXz1KwFIiA+lq/6ioecgKx54g6cCACRJQXDhKzHa9zhGDv8NnrpToHmbc87TvOwqdJ35WUiymrXd37gWyZF9cMxI3uunIoegaH6onoa8+3X/AgjHhJ0Mz+JdUjGYsR4I14Yn0FnqodAEGPwRlSFJ1uC6ZqmHQTSl5MgeAOmWDUREREREREREVJkULQBPTVem3Wds8Fn4GlZnBXh1CzYBABIje1Db+rIJzyVJubGDv/E0AAKxE9YRHJeKHIIR6MpqHXo83b8wfVyM6/yVWjLaDUCCweCvbDH4IypDkqKz4o8qQmJkL3RfK1S9ttRDISIiIiIiIiKiWfA1rEJ8aBdscxTJ0f3wN67N2q/oNahpfRkgyQi0nDGtc6tGEEZN14Tr/CUjhyZtHakaQciqF2bs6LSuS8WXihyC7muFrHpKPRSaAIM/ojIkyyqEw+CPyp+V6Ifuay/1MIiIiIiIiIiIaJZ89avgmKMYOvQgAAF/09qcY5pOeQMWrvvAjB4C9zeuRTy8I2edPtuMwE6F4Zkk+JMkCbp/AcxYz7SvS8WVrs5ktV85Y/BHVIYkmRV/VBmsZGjC3utERERERERERFQ5PMFlkGQNw91/gad2Sd5wT9Vrx9p2Tp+/cS0cK4bkyL6s7anoIQCYtOIPAAx/O1LR6mn1KYSbE4KWOyFcpKLdU/5ZUWkx+CMqQ5KsQQgHQjilHgrRhIQQsJIhaJ7GUg+FiIiIiIiIiIhmSZY1eIOnQggnp81nMXhqF0PRAjntPlORQ5AVDzRv86Sv1/0LYcZ7Ky4sy0cIgaPP/QcOPPYFJIb3lHo4BbMS/XCd1KTVmVR6DP6IypCkaADAdp9U1lwrCuGYUBn8ERERERERERFVBV/9KgDI2+ZztiRJRqD5dIwc/Tvs1HBmeyqSriCTpMnjCsO/AMK1YCUG4LoWRnu24vC2OyoqOBs3evQRxAa3Q5I1dD9zOwb2/gpuBXSAS0YKq86k0lJLPQAiyiXL6eDPdS3IKHyRVMeOQ1F9czUsoixWMgwA0Njqk4iIiIiIiIioKtQtOBeSrMIIzE2w03TK6xEd3Ia+F3+MBWvfB0mSkIwcQqBp3ZSv1f3tAICBPf+N5Mg+OFYUkqQg6l8Ab3DZnIx3LliJQQzs+W/UtW9Cy8rrMHToQYT2/Qbx0HNoO+2dMPwLSj3ECaUi3dA8jVA0f6mHQpNgxR9RGZLGgr/prPMXD+/Cvkc+CddOztWwiLJYyRAAsOKPiIiIiIiIiKhKKFoA9Z2vgiRJc3b+1pVvRWzwWYz2PArHjsNK9BdUQabodVCNIBLDL6Gm9WVY/Ip/gb9pPVLR7jkZ61wQwkXfC3dD1nxoOvVqSJKMhkWvQddZn4MQAj3P/QeEa5d6mBNKRQ6x2q8CMPgjKkOSrAOYXvCXGN0P4ZqwzdG5GhZRFjsZgiTrULRAqYdCREREREREREQVItC0HrVtZ2Ngzy8QG3wWQGGtIyVJQteZn8XSc76MluXXQve1wQh0IBU9DCHEXA+7IFOtPzhy5CHEh15E28r/DUX1ZrYbgQ60r74RZrwfIz2PzvUwZ0QIgVT0EIxAZ6mHQlNg8EdUhmRl+hV/ZqwHAODa8TkZE9GJrGQImqdhzp4AIyIiIiIiIiKi6tR86psgKx70vfBjSLIO3dda0OtUIwhZPbY0klHTCceKwU4NzdVQCzZ85O84+PgXJwwhzXgfBvbci+DCV8LXsCpnv1HTidq2lyO0/3dwndRcD3fa7GQYjhVjxV8FYPBHVIbGW326jlnwa8x4Ovhz7NicjInoRHYyDM3bVOphEBERERERERFRhVE0H1pXXg/hmjACHZAkZUbnGa8+K4d2n8nR/TDjvUhFD+XdH9r/O6h6LZpOuXLCczQuuQKuHcNQ95/napgzNv6+PAz+yh6DP6IyNN01/oRwj1X8WYk5GxfR8axkCKrRUOphEBERERERERFRBfI3nobGpa9HcOH5Mz6HatRD0fxIRQ8XcWQzYyUGAACxwedy9rmuhdjgs6htPzurYvFEmrcJwYUXYOjgH2GbkTkb60wkI4eg6LVQjWCph0JTYPBHVIamG/zZyVDmWIetPmme2KkwNE9jqYdBREREREREREQVqnHxZahtP2fGr5ckKb3OX6T0FX+Z4C/0bM6+xNCLcJ0kAs0bpzxPw+JLAUlC+MD/FH2Ms5GKHGK1X4Vg8EdUhuRpBn+psWo/SDJctvqkeeDaSThWDKqHFX9ERERERERERFQ6RqCz5K0+XceEnRqCr34FkqMHYJujWfujA9ugeZuh+xdOeS5FC6Ch6zUYOfJQJkwsB6nIIRgBBn+VgMEfURmSFB1AugS8EGasB7LigeZphGOx4o/mnpUMAQAr/oiIiIiIiIiIqKSMQAesxAAcu3RLIFnJQQBA3cILAEhZ7T6FcBEd3IZA8wZIklTQ+YKdF0LRa3D0uf+EGe+fiyFPi50agW2OwGDFX0Vg8EdUhjKtPp0Cg794D3R/OxTND5etPitOYngPHKuyKjXHgz+VwR8REREREREREZWQEegEAJjRI5MeJ4RAdHA7hGsXfQzjlXneuqXw1C1FLHQs+EuO7odjjiLQtKHg88mKgYXrPgDXSeHQk7ditO+JYg95WhIjewGArT4rBIM/ojIkSTIkSYFwzYKON2Pp4E9WfVzjr8IIIXBk+zcQPnB/qYcyLXYyDElSoBp1pR4KERERERERERGdxHR/OyRJmbLdZ3J0P44++y0MH/6/RR+DFe+HJOtQ9Dr4G9ciHt6Z6eYWHXgGil4LT90p0zqnUdOJrrM+B3/TOvTuuAt9u/4LrpMq+tgLEel7AkZNFzRvU0muT9PD4I+oTEmyVtAaf0KIdPDna4ei+uCy1WdFca0oXCeJWHhnqYcyLVYyBNWohyTx2wgREREREREREZWOJKvQ/QumDP7iY/ffwof+WPQAzUoMQPM2Q5IkBBrXwnWSSA7vSVcZDjyDQNP6Gd1HU1Qv2lbfiNaV12O073GED/yhqOMuhGPFEBvcjtq2V8z7tWlm1FIPgIjykxS9oDX+7NQQXCcJ3d8OKzFQVgu+lqt4eBd6dn4fgJvZZgS60LHhQ/M+lvGWmWbsCOzUMFQjOO9jmAk7GWabTyIiIiIiIiIiKgtGoAOp6OFJj4kPvQBP7WKkIt0YPvw3NCy6JOcY4dqQ5OnHJmZiALq3GQCgBzqgGvWIhp6FotfASgwgsPyfpn3OcZIkoW7BJkT6/wEz3jvj88xUpP9JCAjUtL5s3q9NM8NSDaIyJctaQWv8mfEeAIDhb4eisdVnIWJDuwDhINjxKgQ7XgVvcDni4Z1z0t97KuPBH5D+4aNSWMkQNE9DqYdBREREREREREQEo6YTqegRCOHk3e/aSSRH9qK27RzUtp+LoUN/hGsns44ZOfoI9jz8YdipkWlf30r0Q/Olgz9JkuBvWofY4LOIDmyDrHjgrV8x/Td1AtWoh50amvV5pmu05zH4G9dA1Wvn/do0Mwz+iMpUoa0+zVgPJFmH6mmErPrgMvibkhnrgad2MRoXX4bGxZehbsF5AATs1PC8j8VKhiArHhiBzky7gUpgJ0PQWPFHRERERERERERlwAh0QrgWzHhf3v2JkZcghANfwyo0LL4Urp3A8JFja/3FQs+h78UfQzjmhOeYiBAO7GQY2ljFHwD4G9fCSgxg+MhD8DeuhSxrM3tjx9GMetjJ8KzPMx1mvBfJ0X1s81lhGPwRlSlJ1uC65pTHmbEe6P42SJIMWfXBseIQwp3ydSczM3YUuq898/vxyjVrnr9xAukATfU0wtewGrHwLggh5n0M0+W6FmxzhK0+iYiIiIiIiIioLBiBDgBAKpJ/nb94eBdUowGatwWapxF1C87D0KEH4dgJJEcP4Ojz34G/YTUAwE5N7x6hnQxDCAeatyWzzVe/ApKswTFHEGjeMLM3dQLVUw/bHJ3XrmWjPY9BVn3wN62ft2vS7DH4IypT6Yq/qb+Im7Ee6L42AICi+QCInDJ1OsZ1TFiJQej+Y8GfatQDmP439WKwEoPQvI3wN6yGY47AjB2Z9zEcLxk5hJGeRyc9xk6mWwqw1ScREREREREREZUDRfND9TRMuM5fLLwLvoZVkCQJAFC/6FK4TgqDe+7FkWfvhOHvQPuam6Bo/sy9r0KZiX4AyKzxBwCyYsBXvxKSrMLXuGaG7ypb+h6mmFEr0pkQwsVo32OoaTmzKBWLNH8Y/BGVKVnRIJzJK/6EEDDjPdD9CwAAiuoHALb7nIQV7wMgsoI/WTGgaIGSVPxZYy0zPcFlkGQN8fCueR/DuFh4Jw4//RX0vfBfk4bO9ti6hKz4IyIiIiIiIiKicmEEOpGK5lb82an0w/a+hlWZbZqnHnULzsfI0b9DUb1YuO59kBUDqtEAa5rFAVZiAJKkQD3hIfmGxZej+dRroajemb2hE4yffy7W+RPCwciRh2EdF3omhnfDToZR23520a9Hc0st9QCIKD9J1qdc488xR+FYMRhjIZas+tLb7Tj4DEZ+qXgPAGS1+gTS3zjnu0e2ECLd/9vTCFnW4A2eivjQLtR3XTyv4wCASN+T6N35faieBriJAdipYWjeprzHpgNSiRV/RERERERERERUNjyBTgwffQhCiExlHwDEh14AAPjqV2Yd37D4Mrh2HA2LXwtFrwEws3uEVnwAqqcRkqRkbffWLYW3bulM3kpeqhEEMDfBXzy8E30v/giSrCHY8Uo0dL0Go72PQfO2wFNbvPdA84MVf0RlKr3G3+TBnzkeYo0Ff+lWn4BrseJvImasB6pel/msxmlGw7y3+nTtGFwnCdWTDth8DasRH9o95Z97sQ0f/r/o2XEXalrPwoK17wWQrkSciJ0MQTXqIMl8doSIiIiIiIiIiMqDHuiAY0bgmNmtMOPhnTACHVD12qztql6LttVvh+47tjafZtRP+x6hlRiAdlybz7miqD7IigfWHAR/sdDz0DyNaFj0GowceRj7t34Okb5/oLbtFVkhKlUGBn9EZSq9xt8UwV+sB5KkZL6xHF/xR/mZsWOtUY+neurnvdWnlUiHa9pYy0x//SoI10RyeO+8jSEWeh79u3+K+q6L0LrqbZm/S5MFf1YyxDafRERERERERERUVjw1nQCAxMi+zDYhBOJDL2S1+ZyM6mmY9j1CMzGQFR7OJdVTX/SuZUIIxELPw9+4Fo1L/heWnP0l1C04D6peyzafFYrBH1GZkgsM/jRfa6aMXB7rF+3asTkfX6UyY0ez1vcbpxoNc1ImP5nxcE3zpkM0PbAQil6L+NDOeRtDYmQvVL0OTae8EZIkQ1Z0KHpdZh2/fKxkCJrBNp9ERERERERERFQ+VE8TvHXL0PfCfyEVSa/1Z8V7YaeG4KsvMPgz6uHaCTh2oqDjhRDzVvEHpMdnp4aLek4r0QcrMQBf4xoAgKLXoPnUq7HknH/NFCxQZWHwR1SmJEWDcM1Jj0nFerJCLEmSIateOGz1mZdwbViJgbzBn+ZpGPumPn+fnZ0chKx4IKt+AOk/P1/9SsTDu+ZtDFa8D5qvLatkX/M0TNHqMwzVy2/6RERERERERERUPiRJwoL174fua8HhbV9DKnoE8aEXIEkKvMFTCzqH5kk/7F5ogYBjjkC45jwHf8Wt+IuFnocka/DVryjqeal0GPwRlSlJ1uE6U6/xZ5zQtlJR/XDZ6jMvM9EPIRzovjwVf+Pf1Oex3ed4y8zjQzd/w2okI4fgmJF5GYMZ74Pua83apnmaMm1ITySECzs1lPkhiIiIiIiIiIiIqFwoqg8L138IqlGPw9u+htHerfDUnQJZMQp6/XTvEZqJAQCYt+BP8zTAThYWSlqJQQjhTnlcLPQ8vMHlBX9GVP4Y/BGVKUlWJ2316VhROOZoToglaz6u8TcBM9YDABO2+gQwr+v8WclQps3nOG/9KgDp3uNzTQgXZiI3+FO9jRO2+rRTwxDCYZk/ERERERERERGVJUXzY+GGD0PRAkiOHih4fT8AUI0gIMkF3yO04v0A0g/SzwfVCMI2RyFce9LjXNfCgce/iOEjf5v8OCeFxPBu+MfafFJ1YPBHVKZkWZ80+EuOHgSQG2IpqhcuW33mZcZ6oGgBqHpNzj7VqAMkeV7X+bMSoZwATfPUwwh0Ijr47Jxf304NQzgmtJyKv/R6h0I4ua8ZCwRVBn9ERERERERERFSmVL0GHRs+gtq2s1Hb+oqCXydJClS9ruB7hFZiAKpRD1nRZzrUaUkXLwjYqZEpxyVcE9H+pyY9Lj70IoRrM/irMgz+iMpUeo2/iYO/4cN/gRFYCP2EVp+y6mfF3wTMWE/O5zVOkmRoRv28tfoUQsAea/V5okDTesRCz0355M5sWfE+AMjb6lMIJ+9CweNPO2kGW30SEREREREREVH5Uo06tK2+Iafj1pSv8zQUfI/QSgzMW5tPAFA99QAw5Tp/4/f9EsN7YJujEx4XCz0HzdsMzdtSvEFSyTH4IypTkqzBdS0IIXL2JSOHEAs9j/qu12StDwek+1hzjb/8zHhP3jaf41RPw7y1+nTtGFwnmbdlpr95A1w7jsTwS3M6BjPeB0lSoHmzWxGMh5H5PgsrGYKi+SGrnjkdGxERERERERERUSloRsOUwdo4KznPwZ8xHvwNT3qcGe+HJGuAJCE2uD3vMUIIxEM74G9ck3OPmSobgz+iMiXLGiBcIE+7xaGDD0DzNqOm9czc12kM/vIRwoEZ75s8+JvGN/XZssZaZuYL/oxAJ1RPA6KD2+Z0DGaiD5q3GZKkZG3XMosY567zZ8X75vWHGSIiIiIiIiIiovmkeuoLX+MvMQB9HqvlFNULWfFMOT4rkb4P6q07BdGBbXmPMeO9sJKD8DeunYORUikx+CMqU5KsAUgvxHo8M96HSP9TqO+6JCewAQBZ9cGxYvMyxkpiJQYhXAuGb+LgT/PUw07Ozxp/VmLi4E+SJASaNiA6sD1vxWfRxhDvy1nfDwBkxYCi18BKDObsS0W7YQS65mxMREREREREREREpaR6GmCnhiGEO+lxjhWDY8Xm/SH59Pgmv4dpxvuhe1sRaN6I+NAuuHYy55h46HlIsgZvcPlcDZVKhMEfUZmSxhaEPXGdv6GDf4Si16K2/ey8r1M0Hxw7PqeBUbmxzVEcfe4/834DG2fGegBgylafdmpoym/qxWAnQ5AVA7IWyLs/0LQBdiqMVPRQzj7Hihblz9eM9+Ws7zdO8zTm9DJ3XQtmrAdGTcesr01ERERERERERFSONKMBwrXgWNFJj7MSA+njffMc/BnBKYM/a+y+X6BpA4RrIxZ6PueYWOh5+OpXQB67D03Vg8EfUZkar/gTzrHgz0oOYbR3K+o7L0q3As1DVn2AcCGc1LyMsxwkhncjOvA0kpEDEx5jxnogq14oet2Ex6hGA4RwYKdG5mCU2axkCKqnccL+2d7gMsiqD7GB7B7ciZG92PvIx5EYfnFW13ddC1YyNGHwp3oaM+1Ix5mxoxDCgRHonNW1iYiIiIiIiIiIypXqGVtHb4p2mma8FwDmveIvvQbhxMGfaydhmyPQfC3QvE0wAp2IDjyTdYxtjiIx8hJ8jWvmerhUAgz+iMpUJvhzzcy24e4/QVIMBBdunvB1iuoDADgn0Tp/Zrw/6//5j+mB7l8w6UK1mbXt5mGdPysZytvmc5wkqwg0rcta5891LfTtuhsQ7qTvtaDrJwYA4ULzTlzxZyWzW32mIocBSDACrPgjIiIiIiIiIqLqpBnpe4STraOXivVgYM8v4aldnLkfO1/SaxBOHPyZifR9w/EH/gPNGxELPZdZUkoIB7077oKselHTcubcD5jmXcUEf/fccw8uvPBCrF27FldffTWeffbZCY+1LAt33nknLrroIqxduxZXXHEF/v73v2cdE41G8aUvfQkXXHAB1q1bh2uvvTbnnEIIfP3rX8emTZuwbt06vO1tb8OBAwfm4u0R5ZCPW+PPNkcxcuRhjBx9GMGOCyCrnolfN/aNxj2Jgj9r7OkaK9434TFmrGfS9f2AdMUfgHlZ589ODkLzNk16jL9pPVLRw5m19sL7fwcrOQhZ8cBJDc/q+uOfle5vy7t/vNXn8W1PU9FD0H2tkBVjVtcmIiIiIiIiIiIqV7IWgCRrExYHpGI9OPzMv0PVa7Bg3QfmeXSAatTDMUchXDvv/vH7fpq3BQAQaN4A10kiMZTuIDa499eID+9G+2nvgqrXzs+gaV5VRPB3//3347bbbsP73vc+3HfffVi5ciVuvPFGhEKhvMffcccd+PnPf47Pf/7zuP/++3Httdfi/e9/P3bu3Jk55uabb8ajjz6KLVu24He/+x3OPfdc3HDDDejrOxYcfPe738WPfvQjfPGLX8QvfvELeL1e3HjjjUilTp4WilQ64xV/vTvuwr5HPoG+3ffAU3cK6jsvmvR1iuYHADjWyRP8mWPfzMxE/uBPCBepeM+EIdc4WfVCVjyw5rjiTwgBK5Fu9TkZf8NpkGQN0cFtSI4eRPjQg2hYfDl0fxvsWQZ/Zrwv3fpUq8m7X/U05rQ9TUW62eaTiIiIiIiIiIiqmiRJ0DwNeYsDzLHQT9FqsHDDR6Hq+e+tzaV0K1Ix4f1BM9EPRQtk7hPr/oXQvM2IDmxDpP8fGDr0RzQveyN89Svmb9A0ryoi+PvBD36Aa665BldddRWWLVuGW265BR6PB/fee2/e43/zm9/g3e9+NzZv3ozOzk68+c1vxubNm/H9738fAJBMJvHggw/iE5/4BM466ywsWrQIH/jAB7Bo0SL85Cc/AZC+Mf9f//VfeM973oOLLroIK1euxJYtW9Df348///nP8/be6eSlGvXQvM3QfC1oXXk9lp77FXRs+HDmC/ZEjlX8xeZjmCUnhIAZ74MkKRNW/NnJMIRjQvcvmPRckiRB9TRM2b97tlw7DtdJTtrqEwBk1QNf/UpE+59G3wt3w/AvREPXJVD1IGxzeFZjMMcW+J2o9en42Oyxdf6EcJGKHoZRw+CPiIiIiIiIiIiqm2o05BQHWIlBdD/zVShaDTo2lib0S49tbA3CCdb5s+J90HzHlveRJAmB5g2IDjyFvl13o6blLAQ7XjUvY6XSUEs9gKmYpokdO3bgpptuymyTZRnnnHMOnnnmmbyvsSwLuq5nbTMMA08//TQAwLZtOI4DwzAmPObw4cMYGBjAOeeck9lfU1OD9evX45lnnsHll19e8HuQZQmyPPG6YkT5qGoAp55327Rfp8j+dJjjJqCq5ZPtK4qc9f9isc0IhJOAv2EVYkMvQpFdSHL2l7Zkqg+SJMFXu2DKz0T3NsIxw3P62dnxMCRJgsffPOV1als3onfXjwBJwpKX3QxN16F76xEf3jOrMdrJPhj+tgnPIQeaIUkSXGsIqirDjA9AuCn4govK6u8VzY25mq9ENDc4Z4kqB+crUWXhnCWqLJyzVEy6rxFm7GjWfbBQz0OQ4GLxmR+DapSuRabsb0rft7NH8t6ns5MD8Phbs/bVtZ2B4e4/wwgswMI1b4OsKvM55Bycr3Or7IO/oaEhOI6DxsbsypjGxkbs27cv72s2bdqEH/7whzjrrLPQ1dWFrVu34k9/+hMcxwEABAIBbNy4Ed/+9rexdOlSNDU14fe//z22bduGrq4uAMDAwEDmOided3BwcFrvoaHBP2FVDdFc0A0vvIaL+vrJqwNLobbWW9TzRQYPQ1FktC5+GQ6OvAifHoenJnstv0RfP3SPH83tnZCkyb+ZDNe3ITa0b04/u3A8CkWR0dzeCc2Y/Dp+79kY3PNTtC9/LdoXrQIAxBtaEQ89nXeMrmujf++foBo18NYshCfQBkXL/cz3myHUd26c5H36YXhroMkR1Nf7EYoNQFFktHWsgOYpv79XNDeKPV+JaG5xzhJVDs5XosrCOUtUWThnqRhiDW0YGNmVuXcmhMDB8DNoXXw2mtvap3j1XPND9/ihK7G89/b2mQMILjora18wuBaS9WY0Lnw5DH/DfA52Upyvc6Psg7+Z+NznPoebb74Zl156KSRJQmdnJ6688sqs1qBbtmzBZz/7WZx//vlQFAWrV6/G5Zdfjh07dhR9POFwjBV/NK9cGBgdHoIxVD7tPhVFRm2tF6OjCTiOW7TzDvcegOMISN5T4Tgu+nv2o8bOfuIm1PcSVO9CDA8npjyfLQKIjfZjaA4/u/DAEbhQEYnJkOJTXUfDopf9CzRvY2ZMpuNFKjGK0OAwZEXLOjoWfgGHnvlR1jYjsBBdp3808ySSY8WQjA/DRv2k71NS6zES6oFvKIbBnpcgqbWIJlQgUT5/r2huzNV8JaK5wTlLVDk4X4kqC+csUWXhnKVislw/ErEhhEMjkGQV8eG9iEcG0LR8/ZzeNyyUpNRhJNQL7wljccwoUokIbARzxultvgBxE4ibpR8/5+vMFFqsUvbBX319PRRFQSgUytoeCoXQ1NSU9zUNDQ349re/jVQqheHhYbS0tOD2229HZ+extam6urrw4x//GPF4HNFoFC0tLfjwhz+cOaa5uTlznZaWlqzrrly5clrvwXUFXFdM6zVEsyGrPlhmFLZdfl80Hcct6rgSkR6oRgMkrQmQNSQjvfDWr80+ZuQgAk3rC7qurNfDNiMwUwnIijHl8TORig1CNRrhOALA1F8bZL0R6YLl9PgltQ5CCKTiQ9C82V8Hk9EBCCFwyqZ/h5UMwYz1oH/3TzGw/49oXnYVACAx2gMhBGSjZdLPRDEakYoPwLZdJEYOQvd3lOXfKZo7xZ6vRDS3OGeJKgfnK1Fl4Zwlqiycs1QMklYPIVwkY2Fo3iaM9DwJWauFXnNKWfz9kvUgzEQ4ZyyJSC+EEFD05rIY51Q4X+dG2TdQ1XUdp512GrZu3ZrZ5routm7dio0bN076WsMw0NraCtu28eCDD+JVr8pdsNLn86GlpQUjIyN45JFHMsd0dHSgubk567rRaBTbt2+f8rpEpSarfjhWvNTDmBdWog+arwWSJEH3tsKM92Xtd+w4rMQAjJqugs6nGulS94kWx83HdUy4TqrwMSdD0DyNUx84AdUIAgDs1HCecw9CNYJQ9Bp4ahejtv1sBDteiZEjD8Gxouljxj4j3duS8/rjaZ4G2Mn0QxepSDeMQOekxxMREREREREREVUDzagHAFjJMIRwEel/CjUtp0+5jNB80Tz1ee9fjt/307zN8z0kKiNlX/EHADfccAM+9alPYc2aNVi3bh3uvvtuJBIJXHnllQCAT37yk2htbcXHPvYxAMD27dvR19eHVatWoa+vD9/85jfhui7e8Y53ZM758MMPQwiBJUuW4NChQ9iyZQuWLl2aOackSbj++uvxH//xH1i0aBE6Ojrw9a9/HS0tLbjooovm/0MgmgZF9cG1T47gz4z3wVefXvtO9+UGf6nIYQAoOLTSPOlv6nZyCLqvraDX9L/4Y7iuhQVrbiroeDs5CG/dqQUdm4+qB9PnyRP82ckQNE92FWCw82IMdf8VQ91/QdPS18GM90E16iGrnsmv42mElQzDTo3ANkdg1DD4IyIiIiIiIiKi6qd6xosDwkiO7IWdGkJNy5klHtUxqlGP6OBzOdvNRD9UIzjlfT+qbhUR/F122WUIh8P4xje+gYGBAaxatQp33XVXptVnT08PZPlY0p5KpXDHHXegu7sbPp8PmzdvxpYtW1Bbe2zdr0gkgq9+9avo7e1FMBjEq1/9anzkIx+Bph1bL+ud73wnEokEvvCFL2B0dBRnnHEG7rrrLhjG3LT/IyoWWfPlBGDVSAgXVrwf+sJXAgA0XysSI3uyjklFD0GSNej+wkI8xQgCkGAlwwWPw4z3wS7weOHaMON9qG3fVPD5TySrXkiyDtscztlnJQahnlBNqOo1CC48H8OH/4r6rothJvqg+1qnvI7maYRwLcTD6bVPWfFHREREREREREQnA1kxoGh+WMkwkqP7oRr18NSdUuphZahGAxxzFMK1IcnHYh4r3gfNO/V9P6puFRH8AcB1112H6667Lu++H/3oR1m/f9nLXob7779/0vNddtlluOyyyyY9RpIkfOhDH8KHPvSh6Q2WqMROloo/KzEIIRxovnTLSt3XCjs1DNdOZp5qSbeo7IAkKQWdU5Y1qHot7FThwZ9tpivi7NQIVKNu0mPNWA+Ea8NTYOvRfCRJgmoEJ2j1GYI3uDxne33XqzF85CEMH/4brHhfQT+ojLcjjQ5ug6x4ctYTJCIiIiIiIiIiqlaqkV4GJzr4LGpbzyqbNp8AoHqCAATs1HDWPTsz0Q9PzeJSDYvKRPn8TSWiopFV30mxxp+V6AcA6N50NZ82tmadObYdAJKRQ9OuVFM9DQVX/Akh4JgRAEAq2j3l8cloNwBp1tVz+YI/4dpj3+xz1w9UjSDq2s/FcPefYcb7C6r4G68cjIV2joWn/JZBREREREREREQnB9XTgOjAM3DMEQTKqM0nkA4lAcA6bp0/IUS64m+sSIJOXryLS1SFZO3kqPgz472QZA3q2Lp842HW+CK2rmPCjPfAmGZ1neppKLh1p+skIFwLQDpknEoqcgi6r3XWfbbzBX/psFLkrPE3rr7rErh2AsI1Cwr+FM0HWfVCuOa0P0MiIiIiIiIiIqJKpnka4FhRqJ4GeGqXlHo4WTQjfT/UPi74c8wRuE6qoPt+VN0Y/BFVIUX1QbgWXMcs9VDmlBnvg+5ryVSiKZofil6TWd/QjB0BhDvttpqapwFWMlTQsePVfpKsI1Vg8FeMtfLyBX/22JhPXONvnOZtRE3bK8Z+XdgPAOMhItf3IyIiIiIiIiKik8l4VV1Ny5mQJKnEo8kmqx7Iqhdm9GhmmxlPd0Eb74pGJy8Gf0RVSFZ9AFD1VX/5FqvVva2Z4C8Z6QYkGXpg4bTOq3lbYCdDEK495bF2agQA4KtfMWWrTyFcpKKHi1I9Nx78CSEy26zkIAAJmqdhwtc1LX0dGpdcUfB6fePr/Bk1DP6IiIiIiIiIiOjkMX6PrabM2nyOq217BYYO/RGx0PMAxrqgSXLB9/2oejH4I6pCyljw51R58Jeu+GvL2qb5WmEl0sFfKnIIhr8dsqxN67y6rx1CODATA1Me61ijAAB/4xpYiYFJ11a0EgNwneS0KxDzUfUghGvCtRPHnT8E1QhCktWJX2cE0bjktQWv16d6GiBJCnR/+6zHTEREREREREREVCn8TevRvubdMGoWlXooeTWfeg18jaeh5/nvIDl6AGaiH5rRMO17oVR9Jr47TEQVS9bGKv6sWGZbdHA7Bnb/DIoRhGY0QPM2whtcDn/jmlINc1ZcJwU7NQT9hMVqdV8LogNPQwiBVPQQjMD0Qzbdnw4TrXgvjCkCL8eMQJJVeOtXAABS0W74xn59ovFWoMWonlONIADANoehjP15W8nBoj/RU9P6MqhGHX9gICIiIiIiIiKik4qs6KhpOb3Uw5iQJCloP+2dOLztDhzZ/g1onkZoXN+PwIo/oqp0rOLvWDXYyJGHIMkqdG8LbHMEIz1bcfS5/yyonWU5ssZ7Vp9Y8edthWsnYKeGkYoemVFbTUWrgaL5YcZ6pzzWTo1A0Wqh+9qmXOcvGTkE1dMARQtMe0wnygR/x63zZyVDmdacxeKtW4qGRZcW9ZxEREREREREREQ0e7JiYOG690HRa5CMHMwpkqCTE4M/oioka34Ax9b4c6wY4uFdCHZciLbVN6Dz9I9jwdr3QLgmkpMEVeXMjKdDOf2Ep1jGq/Vig9shXGtGwZ8kSdB8bTDjPVMe61ijUI1aSJIMI9Ax6Tp/qcihorT5BADFqAOQHfzZyRDUIgd/REREREREREREVL4ULYCF6z8I3dcKb92ppR4OlQEGf0RVSJY1SLIGZ6zVZ3RgG4RwEWg+VpruqemCJOtIjuwp1TBnxYz3QdECUMZCznGatxmAhEj/PwAARqBjRufXfW2ZcHEythmBotemr1XTOWGQOpvWo/nIsgZFC2SCP9e1YKeGoXm4eC8REREREREREdHJRPM0YvEr/j/UtJ5Z6qFQGWDwR1SlFNWXqfiL9P8D3uByqGNVYgDS69LVLUGigoO/fD2rZVmD5mlEYvgl6L5WKKp3RufXfW0wY70QQkx6nGOOQNXSwZ+npgtmvBeuk8o5zk6F4VixGVUgTkQ1gnDGgj87GQYAaF5W/BERERERERERERGdrBj8EVUpWfPBseNwrCjiQ7tQ03pGzjGeumVIDO+ZMtwqR1aiL6fN5zjN1wJAwAh0zvj8ur8NrpPMaqWZj22OHqv4C3QBwkUqeiTnuPG1/2YzphOpRjAzPis5CACs+CMiIiIiIiIiIiI6iTH4I6pSsuqDa8URHXgGALLafI7zBpfBsaKwCmhpWU6EEDDjEwd/49tnU12n+9JrBU722Qgh4JgRKEY6+NMDCyBJSibkO14q0g1Fr4VqBGc8phOpehC2OZweZyIESHJRz09ERERERERERERElYXBH1GVUtR0xV+k/yn4gsuhjlWlHc9TuxSQ5Ipr9+lYEbh2App3ooq/8eBv5tV1mrcJkqxOus6f6yQgXCvT6lOWNej+BUhFc4O/ZPRQel1FSZrxmE50fMWfnRyEagQhyWrRzk9ERERERERERERElYXBH1GVUjQ/rEQ/4kMvINCSf1FXRfXCCHQgMVxZwZ+VGAAA6L6WvPu9tadA0WvhqVk842tIkgLN2zJp8OeYowCQafUJpKsMkxNU/BWzzScAKEYQtjkKIVxYiRDbfBIRERERERERERGd5Bj8EVUpWfXBjPUAAALNGyc8zlu3rOIq/qxEej071dOYd7+ndhFO2XQ7FM0/q+vo/vbMZ5iPnUoHf6pxfPDXCTN2FMK1jx1njsJODc2q9Wg+qhEEhAvHHIWVDEGb4PMgIiIiIiIiIiIiopMDgz+iKqWoPgCAL7gCql4z4XHe4DJYiYFMy8hKYCfDkFUfFNU7p9fRfW2TV/xZYxV/2rHgzxPognDtrMAwFekGgKJX/KlGHQDATg2ngz8vK/6IiIiIiIiIiIiITmYM/oiqlKylg7+aCdp8jvPWLQOAiqr6m6/qNt3XBjs1DMdO5N1vm6OQZBXycQGkEegAIGW1+0xFDkFWvdC8zUUdn2rUA0i3PnXMEVb8EREREREREREREZ3k1FIPgIjmhmo0QJJ1BJo3THFcEJq3GYnhPVOGhOXCToXnJ/jztwEArHgvlNolOfud1CgUrRaSJGW2yaoHuq8VQ4ceQGJ4NxS9FomhF2AEOrOOKwZFC0CSFCRG9gIAVK7xR0RERERERERERHRSY8UfUZUKNG/AknP+FcokbT7HzWSdv4GXfoHQ/t/PdHizYiVDUD0Nc34d3dsKADBj+dt9OtZo1vp+4xqX/C/o/oWwEgOIDjwNM94Lf+Oaoo9PkmQoeh2SY8EfK/6IiIiIiIiIiIiITm6s+COqUpIkQ9VzQ6l8vMFlGO17HI6dKGjdPMeKY/jIQ/DULkHjktfOdqjTIoSAlQhBa5/7kEtWPVCNhgnX+bPNUSh5PuOa1rNQ03rWXA8PQLpiMxk5AEgyVE9wXq5JREREREREREREROWJFX9EBG/wVEC4SI7uK+j4aP9TEK4FxxyZ45HlcqwohGtCnafqNt3fBjPek38s5ihUrbBwda6oRhAQLjSjAZKklHQsRERERERERERERFRaDP6ICJq3FYoWQGK4sHafo71bAQB2av6DPzsZAgBo89DqEwB0X9uErT4nqvibT6oRBABoXrb5JCIiIiIiIiIiIjrZMfgjIkiSBG9wWUHBn5UYRGJkD/yNa+E6SbhOah5GeNz1k2EA87eene5vh5UYgHDtrO1CCDhmBEqeNf7m03jwp3qaSjoOIiIiIiIiIiIiIio9Bn9EBAAwAl0wY0emPG609zHIioG6hecDmP+qPzs5CEnRIWuBebme7muDEA6sxEDWdtdOQLhWebT6xPwFoURERERERERERERUvhj8EREAQPM2wbGicO3khMcIIRDpexyB5tOheZsBYN7X+bOSYWieRkiSNC/X031tAAAznt3u07FGAaD0rT71IID0nx8RERERERERERERndwY/BERAEAbaxVpJQcnPCY5uh9mvA+1ba+AqtcBAOx5D/5C81rdpui1kFVfzjp/diod/KklbvWp+VogSQoM/8KSjoOIiIiIiIiIiIiISo/BHxEBOFYxZiVDEx4T6X0MqhGEt34FZNUHSdZK0OozDHUegz9JkqD72nIr/syxir8St/rUPA1Yet5XYdR0lnQcRERERERERERERFR6DP6ICEC6sk2SNViJ/BV/wrUR6X8SNa0vhyTJkCQJql5Xglafg9A8DfN6Td3fjtQJ6x/a1igkWYWseud1LPkoZTAGIiIiIiIiIiIiIio9Bn9EBACQJBmapwH2BBV/sdDzcKwYattekdmmGHUTVvyZ8V44ZqSoY3TsOFw7Ma8VfwDgDZ6KVKQb9liVHwA4qdF0WDpPaw0SEREREREREREREU2FwR8RZaiepgkr/qIDT8MIdMAIHFtLTtXrJlzj7+iz30b44B+KOj47GQaAeV3jDwD8jWsAALHB5zLbHHMUql7aNp9ERERERERERERERMdj8EdEGZq3CVYyf/CXinbDU7s0a5uq1+Zt9SmECysxMOl6gTMxfr75Dv5UvRaeuqWIhbZnttlWuuKPiIiIiIiIiIiIiKhcMPgjogxtrOJPCJG1XQgHZrwfur89a7ti1GW1vxxnJ4cghAM7NVzU8dnJMCRZLUngFmhch1h4J1zXAjBW8acx+CMiIiIiIiIiIiKi8sHgj4gyNG8jXCcJ145lbbcSgxCuBcO/IGu7qtfBMSMQrp19fHIAACZc/2+mrOQgVKMekjT/X7r8TesgHBOJoRcBALbJij8iIiIiIiIiIiIiKi8M/ogoQ/M0AQCsRHaLTjPWAwA5FX+qUQcAsM1I1vbx1zvmCIRwizY+Kxme9zaf43T/AmieJkQHt0MIAccchWIw+CMiIiIiIiIiIiKi8sHgj4gyxkO1E9f5M2M9kFUvFL0ua/v4709c589KpCv+hHDgWNGijc9OhqCWKPiTJAn+pnWIDT4H145DuDZbfRIRERERERERERFRWWHwR0QZshaArHhgJU4I/uI90H3tkCQpa7uqj1f8nRD8JQchK570viKu81fKij8g3e7TToURD+8EALb6JCIiIiIiIiIiIqKywuCPiDIkSYLmbcxb8Xdim08AUPQaQJJz1vKzEoPw1C0FULzgz3VScMxRaJ6GopxvJnzB5ZAVD0aOPgIAUNnqk4iIiIiIiIiIiIjKCIM/IsqieppgJ4+t8SeEi1S8B0ae4E+SZChaTW6rz+QgPLVLAEmGc0IoOFN2Mjw2vtJV/EmyCl/jaYgP7QIAKGz1SURERERERERERERlhMEfEWXRvE1ZrT7tZBjCMaH7F+Q9XjXqsir+xivzdF8rVK0GtjlclHFZY8FfKVt9AkCgcR2AdAgoq96SjoWIiIiIiIiIiIiI6HgM/ogoi+ZpgpUMQQgBADDjvQAA3deW93hVr8ta4288NNQ8TVA99TltQGfKSoYASYZqBItyvpnyN64BIEHRa3PWPCQiIiIiIiIiIiIiKiUGf0SURfM0QrhWpn2nGTsKSdGhTrC2nqrXZbX6HF8fUPM2QdHrilbxZyfDUPUgJFktyvlmStFr4KlbClWvK+k4iIiIiIiIiIiIiIhOVNo76ERUdjRvE4B0hZ1qBJGK9cDwtUOS8j8noBh1sMM7Mr+3EoOQZA2KXgvVCCI5ur8o47KSg9AmCB/nW8vya+HaqVIPg4iIiIiIiIiIiIgoC4M/IsqiecaCv8QgvHWnwIz1QPe3T3h8uuJvFEK4kCQZVmIAmqcRkiSn24CmhosyLjsZhlri9f3GeWoWlXoIREREREREREREREQ52OqTiLLIqgeKFoCVHIQQAmZ88uBP0WshhAPHigFIVwpq3mYAgGoE4ZgRCNee9jiiA9uQHD0AIdxj5y2T4I+IiIiIiIiIiIiIqByx4o+IcmjeJliJQTjmCFw7Ad03ScWfkV7rzjFHoOo1sBID8NWvGNsXBCBgmxFoeuGhnZ0awdHnvg0AUPQ6BJrWwjZHGPwREREREREREREREU2CFX9ElEP1NMJODsKM9QDAlK0+gXRYJ4QYW4sv3S40HfwBjjk8retbyUEAQMuK61Db+jIkhl8ChAs9sHCa74SIiIiIiIiIiIiI6OTBij8iyqF5mhAdPYhU7CgkWYPmbZrwWOW4ij/HikA4Zub4Y6Hg8LSubyXSwV9N61lQVC+aT70ajhWDovln8G6IiIiIiIiIiIiIiE4OrPgjohyatxFWKgwzdgS6rxWSpEx4rCxrUDQ/bHMEVmJg7PXpNf5kLQBJUqYf/CVDUDQ/FNWb2cbQj4iIiIiIiIiIiIhocgz+iCiH5mkChItYeOek6/uNU/Q62KmRTKXeeKtPSZKgGkHYqZFpXd9ODkLlen5ERERERERERERERNPC4I+Icoy36rST4UnX9xun6rXpir/kIBQtAFn1ZPYpRhD2dNf4S4Qy4SERERERERERERERERWGwR8R5Ti+2q6Q4E/R6+CMVfyduB6gqtfNoNXnIDQvK/6IiIiIiIiIiIiIiKaDwR8R5ZBlDaoRBFBY8KcadWNr/A1C8zSfsG96rT6FcGEnw6z4IyIiIiIiIiIiIiKaJgZ/RJSX6mmEJCnQvS1TH6uPBX/JgdyKPyMIZxqtPu3UCIRwuMYfEREREREREREREdE0qaUeABGVJ93bDNdOQJKn/jKhGHUQjgnbya3UU40gHCsG1zEB+Kc8l5UcBICcAJGIiIiIiIiIiIiIiCbH4I+I8mpccgUcO17Qsapel/l1vjX+AIy1+6yf8lx2MpQ+Dyv+iIiIiIiIiIiIiIimhcEfEeWleZugFXisahwf/GWv8aeMrRVop4YLOpeVGISi10BWjAKvTkREREREREREREREANf4I6IiUPTa9C8kGaonu6pvPBQsOPhLhnLahRIRERERERERERER0dRY8UdEsyYrXkiyDlWvhSQpOftkxSg4+LOTg2zzSUREREREREREREQ0A6z4I6JZkyQJqlGXs77fsX1BWAW3+gxBzXMeIiIiIiIiIiIiIiKaHCv+iKgoPDWLoHlb8u5T9LqCKv6EcGClwqz4IyIiIiIiIiIiIiKaAQZ/RFQU7WveNeE+1QgWFPzZySFAuFzjj4iIiIiIiIiIiIhoBtjqk4jmnGoUVvFnJUMAAM3Lij8iIiIiIiIiIiIiouli8EdEc07Vg7BTI1MeNx78qWz1SUREREREREREREQ0bQz+iGjOqUYQrpOCYyUmPc5ODELV6yDL2jyNjIiIiIiIiIiIiIioejD4I6I5pxhBAICZHJr0OCsZgurl+n5ERERERERERERERDPB4I+I5pyq1wEAzMRUwd8gNLb5JCIiIiIiIiIiIiKaEQZ/RDTnVCMd/FlTVfwlQtA8rPgjIiIiIiIiIiIiIpqJign+7rnnHlx44YVYu3Ytrr76ajz77LMTHmtZFu68805cdNFFWLt2La644gr8/e9/zzrGcRzccccduPDCC7Fu3TpcdNFF+Na3vgUhROaYT3/601ixYkXWfzfeeOOcvUeiaiUrBmTVh1Q8NOExwrVhm8PQvKz4IyIiIiIiIiIiIiKaCbXUAyjE/fffj9tuuw233HIL1q9fj7vvvhs33ngjHnjgATQ25oYEd9xxB37729/i1ltvxdKlS/Hwww/j/e9/P372s59h9erVAIDvfve7+OlPf4ovf/nLWLZsGZ5//nl85jOfQU1NDa6//vrMuc477zzcdtttmd/ruj73b5ioCvkbV2HgwEPoan4l8j1zYCXDgHBZ8UdERERERERERERENEMVUfH3gx/8ANdccw2uuuoqLFu2DLfccgs8Hg/uvffevMf/5je/wbvf/W5s3rwZnZ2dePOb34zNmzfj+9//fuaYZ555Bq961avwyle+Eh0dHXjNa16DTZs25VQS6rqO5ubmzH91dXVz+l6JqlXTktciFevHSM9jeffbyXQ1oMo1/oiIiIiIiIiIiIiIZqTsgz/TNLFjxw6cc845mW2yLOOcc87BM888k/c1lmXlVOYZhoGnn3468/uNGzfisccew/79+wEAL7zwAp566imcf/75Wa974okncPbZZ+OSSy7BP//zP2NoaPI1yogoP09NBxoWnoXB/f8D4do5+63kIAAJmqdh/gdHRERERERERERERFQFyr7V59DQEBzHyWnp2djYiH379uV9zaZNm/DDH/4QZ511Frq6urB161b86U9/guM4mWPe9a53IRqN4tJLL4WiKHAcBx/5yEdwxRVXZI4577zzcPHFF6OjowPd3d346le/ine+8534+c9/DkVRCn4PsixBlqVpvnOi6qIoMhauegMGuj+N2OCTCC44N2u/Y4aheYLQ2E6XqOQURc76PxGVN85ZosrB+UpUWThniSoL5yxR5eB8nVtlH/zNxOc+9zncfPPNuPTSSyFJEjo7O3HllVdmtQb9wx/+gN/97nf493//dyxbtgy7du3CbbfdhpaWFrzhDW8AAFx++eWZ41esWIEVK1bgoosuylQBFqqhwQ9JYvBHBHShufPlGD38ABavehUk+ViAHsYoAsF21Nf7Szg+Ijpeba231EMgomngnCWqHJyvRJWFc5aosnDOElUOzte5UfbBX319PRRFQSgUytoeCoXQ1NSU9zUNDQ349re/jVQqheHhYbS0tOD2229HZ2dn5pgtW7bgXe96VybcW7FiBY4ePYrvfOc7meDvRJ2dnaivr8fBgwenFfyFwzFW/NFJT1Fk1NZ6UdvxGgwcugUHdv0VwQXHWviOhnug+5oxNBQr4SiJCDg2X0dHE3Act9TDIaIpcM4SVQ7OV6LKwjlLVFk4Z4kqB+frzBRaNFP2wZ+u6zjttNOwdetWXHTRRQAA13WxdetWXHfddZO+1jAMtLa2wrIsPPjgg7j00ksz+5LJZE4VnqIoEEJMeL7e3l4MDw+jubl5Wu/BdQVcd+LzEp1MdH8H/E0bMLD3d/A3nQnXScJODSMV74cnuAK2zS/0ROXCcVzOSaIKwjlLVDk4X4kqC+csUWXhnCWqHJyvc6Psgz8AuOGGG/CpT30Ka9aswbp163D33XcjkUjgyiuvBAB88pOfRGtrKz72sY8BALZv346+vj6sWrUKfX19+OY3vwnXdfGOd7wjc84LLrgA//mf/4kFCxZkWn3+4Ac/wFVXXQUAiMViuPPOO3HJJZegqakJ3d3d+MpXvoJFixbhvPPOm/8PgaiKNC5+LQ4++f9hz0MfgBDH1t40Ap2TvIqIiIiIiIiIiIiIiCZTEcHfZZddhnA4jG984xsYGBjAqlWrcNddd2Vaffb09ECWjy0CmUqlcMcdd6C7uxs+nw+bN2/Gli1bUFtbmznm5ptvxte//nXccsstCIVCaGlpwZve9Ca8733vA5Cu/tu9ezd+/etfIxKJoKWlBeeeey4+9KEPQdf1+f0AiKqMUdOJttU3wrGiUI16qEYQqlEPzVNf6qEREREREREREREREVUsSUzW25KKYmAgUuohEJWcqsqor/djaCjG8m2iMsf5SlRZOGeJKgfnK1Fl4Zwlqiycs0SVg/N1Zpqbawo6Tp76ECIiIiIiIiIiIiIiIiIqdwz+iIiIiIiIiIiIiIiIiKoAgz8iIiIiIiIiIiIiIiKiKsDgj4iIiIiIiIiIiIiIiKgKMPgjIiIiIiIiIiIiIiIiqgIM/oiIiIiIiIiIiIiIiIiqAIM/IiIiIiIiIiIiIiIioirA4I+IiIiIiIiIiIiIiIioCjD4IyIiIiIiIiIiIiIiIqoCDP6IiIiIiIiIiIiIiIiIqgCDPyIiIiIiIiIiIiIiIqIqwOCPiIiIiIiIiIiIiIiIqAow+CMiIiIiIiIiIiIiIiKqAgz+iIiIiIiIiIiIiIiIiKoAgz8iIiIiIiIiIiIiIiKiKsDgj4iIiIiIiIiIiIiIiKgKMPgjIiIiIiIiIiIiIiIiqgIM/oiIiIiIiIiIiIiIiIiqAIM/IiIiIiIiIiIiIiIioirA4I+IiIiIiIiIiIiIiIioCjD4IyIiIiIiIiIiIiIiIqoCDP6IiIiIiIiIiIiIiIiIqoAkhBClHgQRERERERERERERERERzQ4r/oiIiIiIiIiIiIiIiIiqAIM/IiIiIiIiIiIiIiIioirA4I+IiIiIiIiIiIiIiIioCjD4IyIiIiIiIiIiIiIiIqoCDP6IiIiIiIiIiIiIiIiIqgCDPyIiIiIiIiIiIiIiIqIqwOCPiIiIiIiIiIiIiIiIqAow+CMiIiIiIiIiIiIiIiKqAgz+iIiIiIiIiIiIiIiIiKoAgz+ik9Q999yDCy+8EGvXrsXVV1+NZ599NueYZ555Btdffz02bNiA008/HW95y1uQTCYnPe/Ro0fxrne9C+vXr8fZZ5+NL3/5y7BtO+sY0zTxta99DRdccAHWrFmDCy+8EL/85S8nPe+tt96KK6+8EmvWrMHrXve6SY89ePAgNm7ciDPPPHPS44gqRbXN18OHD2PFihU5/23btm3qD4OoAlTbnAUAIQS+973v4ZJLLsGaNWtw3nnn4T/+4z+m+CSIyl+1zddvfvObeb/HbtiwYeoPg6gCVNucBYCHH34Y11xzDTZu3IhXvOIV+MAHPoDDhw9P8UkQlb9qnK/3338/Xve612H9+vW44IILcNddd03xKRBVjkqasy+88AI++tGPYvPmzVi3bh0uvfRS3H333TnHPf7443jDG96ANWvW4OKLL8avfvWrAj+NyqeWegBENP/uv/9+3Hbbbbjllluwfv163H333bjxxhvxwAMPoLGxEUD6C/k73vEO3HTTTfj85z8PRVHwwgsvQJYnfl7AcRzcdNNNaGpqws9+9jP09/fjU5/6FDRNw0c/+tHMcR/60IcQCoXwpS99CV1dXRgYGIDrulOO+6qrrsL27dvx4osvTniMZVn46Ec/ijPPPBPPPPPMND4VovJUzfP1hz/8IZYtW5b5fTAYLOATISpv1Tpnv/SlL+GRRx7BJz/5SSxfvhwjIyMYGRmZ5qdDVF6qcb6+/e1vx7UZCloAAAEAAElEQVTXXpu17W1vexvWrl1b6MdCVLaqcc52d3fjve99L2644QbcfvvtiEQiuO222/CBD3wA99133ww+JaLyUI3z9aGHHsInPvEJ3Hzzzdi0aRP27t2Lm2++GR6PB9ddd90MPiWi8lFpc/b5559HQ0MDvvKVr6C9vR1PP/00vvCFL0BRlMx87O7uxk033YRrr70Wt99+O7Zu3Yqbb74Zzc3NOO+884r0yZUxQUQnnTe+8Y3illtuyfzecRyxadMm8Z3vfCez7eqrrxZf+9rXpnXev/3tb2LlypViYGAgs+0nP/mJOP3000UqlRJCCPHQQw+JM844QwwNDc1o7N/4xjfEFVdcMeH+LVu2iI9//OPi3nvvFWecccaMrkFUTqpxvnZ3d4vly5eLnTt3zui8ROWsGufsnj17xOrVq8XevXtndF6iclWN8/VEu3btEsuXLxdPPvnkjK5DVE6qcc7+4Q9/EKtXrxaO42S2/eUvfxErVqwQpmnO6FpE5aAa5+tHP/pR8YEPfCBr23/913+J888/X7iuO6NrEZWLSp6z4774xS+Kt771rZnfb9myRVx++eVZx3z4wx8Wb3/722d1nUrBVp9EJxnTNLFjxw6cc845mW2yLOOcc87JVMiFQiFs374djY2NuPbaa3HOOefguuuuwz/+8Y9Jz71t2zYsX74cTU1NmW2bNm1CNBrFnj17AAB//etfsWbNGtx1110477zzcMkll+DLX/7ylGXhhdi6dSseeOAB/PM///Osz0VUDqp5vgLAe97zHpx99tn4p3/6J/zlL38pyjmJSqla5+xf//pXdHR04G9/+xsuvPBCXHjhhfjc5z6H4eHhWZ2XqJSqdb6e6L//+7+xePFitsCniletc/a0006DJEm499574TgOIpEIfvOb3+Ccc86BpmmzOjdRqVTrfDVNE4ZhZG3zeDzo7e3FkSNHZnVuolKqljkbiUSyOklt27YNZ599dtYxmzZtOmmWmWHwR3SSGRoaguM4mTLtcY2NjRgcHASQLoUGgDvvvBNXX3017rrrLqxevRpve9vbcODAgQnPPTg4mPWFHEDm9wMDA5lzP/XUU3jppZfwrW99C5/97Gfxxz/+Ebfccsus39dnPvMZ/Nu//RsCgcCszkVULqp1vvp8Pnz605/G17/+dXznO9/BGWecgfe9730M/6jiVeuc7e7uxtGjR/HAAw9gy5YtuO2227Bjxw588IMfnNV5iUqpWufr8VKpFH73u9/hjW98Y9HOSVQq1TpnOzs78f3vfx9f+9rXsHbtWpx55pno6+vDHXfcMavzEpVStc7XTZs24U9/+hO2bt0K13Wxf/9+fP/738+6NlElqoY5+/TTT+MPf/gDrrnmmimvHY1Gi/6wXTli8EdEOcZ7KL/pTW/CVVddhdWrV+Ozn/0slixZgnvvvRcA8I53vAMbN27Exo0bcfnllxd8biEEJEnC7bffjnXr1mHz5s349Kc/jfvuu29WX3Q///nP47WvfS3OOuusGZ+DqBJV4nxtaGjADTfcgPXr12PdunX4+Mc/jiuuuALf+973ZnxOokpRiXNWCAHTNPHlL38ZZ555Jl7+8pfjS1/6Eh5//HHs27dvxuclKneVOF+P96c//QmxWAxveMMbinI+onJXiXN2YGAAn//85/H6178ev/zlL/HjH/8Ymqbhgx/8IIQQMz4vUbmrxPl6zTXX4C1veQtuuukmrFmzBm9605sy45psjTOialDOc3b37t1473vfi/e9733YtGnTzN5gFVJLPQAiml/19fVQFAWhUChreygUyjwF0dzcDAA45ZRTso455ZRTcPToUQDAl770pcwXX1VNfylpamrCs88+m/Wa8SdDxs/Z3NyM1tZW1NTUZJ1XCIHe3l4sXrx4Ru/rsccew1//+tfM01ZCCLiui9WrV+Nf/uVf+KQzVaRqna/5rF+/Ho8++mjRzkdUCtU6Z5ubm6GqKpYsWZJ1XgDo6enB0qVLZ3ReolKq1vl6vP/+7//GK1/5ypwnnYkqUbXO2XvuuQeBQACf/OQnM9u+8pWvYPPmzdi+fTs2bNgwo/MSlVK1zldJkvCJT3wCH/3oRzE4OIj6+nps3boVQLp6l6hSVfKc3bNnD972trfhTW96E9773vdm7Wtqaspc6/hrBwIBeDyeCc9ZLfg4AtFJRtd1nHbaaZkfToD0Uxtbt27Fxo0bAQAdHR1oaWnB/v37s1574MABLFy4EADQ2tqKRYsWYdGiRZltGzZswO7du7O+UTz66KMIBAJYtmwZAOD0009Hf38/YrFY5pj9+/dDlmW0tbXN+H39/Oc/x69//evMfx/84Afh9/vx61//GhdffPGMz0tUStU6X/PZtWtX5oc+okpVrXP29NNPh23bOHToUNZ4AWDBggUzPi9RKVXrfB3X3d2Nxx9/nA+/UdWo1jmbTCZzKoXGfz9eXUFUaap1vo5TFAWtra3QdR3/8z//g40bN6KhoWHW5yUqlUqdsy+99BKuv/56vP71r8dHPvKRnP0bNmzAY489lrXt0UcfPWkeqmHwR3QSuuGGG/CLX/wC9913H/bu3YsvfvGLSCQSuPLKKwGkn2K68cYb8aMf/QgPPPAADh48iDvuuAP79u2b9ObBpk2bsGzZMnzyk5/ECy+8gIcffhh33HEH3vKWt0DXdQDAa1/7WgSDQXzmM5/Bnj178OSTT+IrX/kKrrrqqkmftjh48CB27dqFgYEBJJNJ7Nq1C7t27YJpmgDST4IsX748819raytkWcby5ctRV1dXxE+PaH5V43y977778Pvf/x579+7F3r178Z//+Z+49957cd111xXxkyMqjWqcs+eccw5OO+00fPazn8XOnTvx/PPP4wtf+ALOPffcrCpAokpTjfN13L333ovm5macf/75RfikiMpDNc7ZzZs347nnnsOdd96JAwcOYMeOHfjMZz6DhQsXYvXq1UX89IjmVzXO13A4jJ/+9KfYu3cvdu3ahVtvvRUPPPAAPvvZzxbxkyMqjUqbs7t378b111+Pc889FzfccAMGBgYwMDCAcDicOebaa69Fd3c3tmzZgr179+Kee+7BH/7wB7ztbW8r3gdXxiTBpuFEJ6Uf//jH+N73voeBgQGsWrUKN998M9avX591zP/5P/8H99xzD0ZGRrBy5Up8/OMfx5lnnjnpeY8cOYIvfvGLeOKJJ+D1evGGN7wBH/vYxzIl3gCwd+9e3HrrrXj66acRDAZx6aWX4sMf/vCkP4C99a1vxRNPPJGz/S9/+Qs6Ojpytv/qV7/Cv/7rv+If//jHVB8FUdmrtvl633334bvf/S6OHj0KRVGwdOlS3HjjjXjNa14zzU+GqDxV25wFgL6+Ptx666145JFH4PP5cP755+NTn/oUgsHgND4ZovJTjfPVdV1ccMEFEz79TFTJqnHO/s///A/uuusuHDhwAB6PBxs2bMDHP/7xnHZqRJWm2uZrOBzGe97zHuzevRtCCGzYsAEf+chHct4TUaWqpDn7zW9+E3feeWfO9oULF+Kvf/1r5vePP/44brvtNuzZswdtbW1473vfmwkzqx2DPyIiIiIiIiIiIiIiIqIqwFafRERERERERERERERERFWAwR8RERERERERERERERFRFWDwR0RERERERERERERERFQFGPwREREREREREf3/7N11nFT1/sfx18xsdzfdnVIiICGhGNiKKFdFvXLtn9iKxbUvCgoWxjWuHYiIhCASUpJLwxbb3TEzvz+WHRl2l+1i38/Hg4dyzvec7+fM7tll5nM+n6+IiIiIyFlAiT8RERERERERERERERGRs4ASfyIiIiIiIiIiIiIiIiJnASX+RERERERERERERERERM4CSvyJiIiIiIiIiIiIiIiInAWU+BMRERERERERERERERE5CyjxJyIiIiIiIiIiIiIiInIWUOJPRERERERERERERERE5CygxJ+IiIiIiIiIiIiIiIjIWUCJPxEREREREREREREREZGzgBJ/IiIiIiIiIiIiIiIiImcBJf5EREREREREREREREREzgJK/ImIiIiIiIiIiIiIiIicBZT4ExERERERERERERERETkLKPEnIiIiIiJnlYceeoixY8c2dRgN6o033qBbt25NHUaVnnrqKWbOnNnUYdTYunXrGDBgAGlpaU0ax65du+jduzdxcXFNGkd9SE9Pp3///qxdu7apQxEREREROasp8SciIiIiIs3GN998Q7du3Wx/+vTpw8SJE3n66adJSUlp6vAqdXrcPXv25LzzzuOhhx4iMTGxVufMz8/njTfeYPPmzfUcbXllicSyP/369WPKlCm89tpr5OTk1OqcMTExfPXVV9x2223l9uXk5LBgwQIuvvhiBgwYQN++fbnooot46aWXKny91qxZw80338zQoUNt3xMvvPAC6enp5cY+9NBDDBgwwG7bDTfcQLdu3bj99tvLjY+NjaVbt2689957tm2jRo2ibdu2LF68uNLrW716Nd27dyc5Odl2jrI/PXr0YMyYMdx5551ERkae8XU6k9dee40LL7yQ8PDwctdywQUXVHjMH3/8YYtj+fLldvsOHDjAXXfdxfnnn0+fPn0477zzmDlzJh9//DFQ/vugsj833HADUPpan7q9d+/eTJw4kfnz51NYWGg3t6+vL1dccQXz58+v9eshIiIiIiJVc2jqAERERERERE531113ERERQVFREdu2beOzzz5j7dq1LF26FFdX1zMe+8wzz2C1WhspUnunxv3XX3/x7bffsm3bNpYuXYqzs3ONzpWfn8+CBQuYPXs2Q4cOtdt3xx13MGvWrPoMHSit0HNzcyMvL48//viDRYsWsXnzZj777DMMBkONzvXRRx8RHh7OsGHD7LbHxMRw0003ER8fz6RJk7j66qtxdHTkwIEDfPXVV6xcuZJffvnFNv6FF17g/fffp3v37txyyy34+Piwd+9e/vvf//LTTz/xwQcf0LFjx2rFtGbNGvbs2UPv3r2rHHv11Vfz4osv8q9//QsPD49y+3/77Td69epFYGAgsbGxAFx00UWMGjUKi8XCkSNH+Oyzz1i3bh1ffPEFPXr0qFaMZSIjI9mwYQOff/55uX3Ozs5ERUWxa9cu+vbta7fvxx9/xNnZuVzibfv27cyYMYOwsDCuvPJKAgMDiY+PZ+fOnXz00UfccMMNTJgwgbZt29qOycvL46mnnmLChAlMmDDBtj0gIMD2/05OTjz77LNAaUJ31apVvPnmm0RHR/PKK6/YxXDttdfy8ccfs3HjRoYPH16j10NERERERKpHiT8REREREWl2Ro0aRZ8+fQC48sor8fHxYcmSJaxatYqLLrqowmPy8vJwc3PD0dGxMUO1c3rcvr6+vPPOO6xatYopU6bU2zwODg44ONT/27mJEyfi5+cHlCZp/vWvf7FixQr++uuvclV0Z1JcXMyPP/7INddcY7e9pKSE2bNnk5qaykcffcTgwYPt9t9777288847tr8vXbqU999/nylTpvDyyy9jMpmA0td22rRpzJgxg7vvvptvv/22ytcjLCyM3NxcFixYwKJFi6q8hokTJ/Lss8+yfPlyrrjiinL7161bx+WXX263rWfPnlxyySW2vw8cOJA77riDzz77jKeffrrKOU/19ddfExYWRv/+/cvta9u2LSUlJSxdutQu8VdYWMivv/7KmDFj7JKnAIsWLcLT05OvvvoKLy8vu32pqakAdO/ene7du9u2p6Wl8dRTT9GtWze76zqVg4OD3b7rrruOa665hp9++omHH37YLknYqVMnunbtyrfffqvEn4iIiIhIA1GrTxERERERafbKqsbKKqvK2jlGR0dz6623MmDAAB544AHbvtPX+LNYLHz44YdMnTqVPn36MGzYMG6++WZ2795tN+77779n2rRp9O3blyFDhnDvvfcSHx9f67jLElsxMTG2bUVFRcyfP59p06YxaNAg+vfvz3XXXcemTZtsY2JjY22JkQULFthaKb7xxhtAxWv8lZSUsHDhQsaPH0/v3r0ZO3Ysr776KkVFRbWO/9TXvaCggEmTJjFp0iQKCgpsYzIyMhg5ciTXXHMNZrMZgG3btpGens6IESPszrdixQr279/P7bffXi7pB+Dh4cG9995r+/uCBQvw9vbmmWeesSX9yvTt25dbbrmFgwcPlktyVcTd3Z0bb7yRNWvWsHfv3irH+/v7061bN1atWlVu34EDB4iPj2f06NFnPMfp37evv/463bt3Z+PGjXbjHn/8cXr37s3+/ftt21atWsWwYcMqrbS86KKLWLZsGRaLxbZt9erVtq/T6aKjo+ncuXO5pF/ZtdYXg8HAwIEDsVqtdt/3ZUaMGMGaNWuarCpXRERERORsp8SfiIiIiIg0e9HR0QD4+PjYtpWUlHDzzTfj7+/PnDlzKl3zDODRRx/l+eefJyQkhAceeIBZs2bh7OzMzp07bWPeeust5syZQ7t27XjooYeYMWMGGzdu5PrrrycrK6tWccfFxQHYJVtycnL48ssvGTJkCA888ACzZ88mLS2NW265xbYenJ+fH0899RQAEyZM4MUXX+TFF1+0a7d4uscee4zXX3+dnj178vDDD3POOeewePFiu0RaTZ36uru4uPDCCy8QHR3Na6+9Zhvz9NNPk52dzbx582zJuR07dmAwGOjZs6fd+cqSaJVVj53q+PHjHDt2jHHjxlXYahPg0ksvBUpbeFbHjTfeiLe3ty2BWpVevXqxY8eOctvXrl2Lv7+/rbqzMqd/395xxx306NGDRx991LZ24u+//84XX3zBP//5T1u1XWJiIidOnCj3+p3qoosuIjk52W4NyKVLlzJs2LAKE3nh4eHs3buXgwcPnvmi60FF3/dlevXqRVZWFocOHWrwOEREREREWiO1+hQRERERkWYnJyeHtLQ0ioqK2L59OwsXLsTFxYXzzz/fNqaoqIhJkyZx//33n/FcmzZt4ptvvuGGG27gscces23/xz/+Yas6iouL44033uCee+7h9ttvt4254IILuOyyy/j000/ttlcn7p07d7JgwQKcnJzs4vb29mb16tU4OTnZtl111VVMnjyZjz/+mOeffx43NzcmTpxYZZvFMvv37+fbb7/lyiuvtK23dv311+Pn58f777/Ppk2byq21V5HMzEwA2xp/n376KQEBAbbqvH79+nHLLbfwzjvvMGHCBFJSUvjpp5945JFH6NChg+08R48exdvbu1zC7ujRo3h6ehIaGlplLIcPHwYoV9l4qoiICDw8PDh69GiV54PSisIZM2bwxhtvsHfvXnr16nXG8W3atCE9PZ3U1FS7ZNratWsZNWpUuWq8/Px80tLSsFgsHD16lHnz5gHYKvAcHR154YUXmDZtGv/+97958MEHefTRR+ndu7fdmo1l1xMREVFpbO3bt6d3794sXbqU4cOHk5WVxdq1a21f/9P94x//4NZbb+XSSy+lb9++DBo0iOHDhzN06NA6t8dNS0sDSr//V65cyYoVK+jatWuFay+2adMGKP36du3atU7zioiIiIhIeUr8iYiIiIhIs3PTTTfZ/T08PJyXX36Z4OBgu+3XXnttledasWIFBoOB2bNnl9tXlrj59ddfsVgsTJ482ZbEAAgICKBdu3Zs3ry5Wom/iuJ+6aWXCAkJsW0zmUy2yjiLxUJWVhYWi4XevXuzb9++KueoyNq1awGYOXOm3fZ//OMfvP/++6xdu7Zaib/TW0R26dKFf//737i6utq2zZ49mzVr1jBnzhzy8vIYMmQIM2bMsDsuIyMDb2/vcufPycnB3d29WteUm5sLUOV4d3d3W/Vcddx444189NFHLFiwgLfeeuuMY8sq1tLT022Jv6ysLP766y+mT59ebvwbb7xhV03o4eHBAw88YFeN2rVrV+666y5eeeUVDhw4QHp6Ou+//77dGoXp6el281dm6tSpvPnmmzz55JP88ssvmEwmxo8fX2Er03PPPZfPP/+ct99+m/Xr17Njxw7effdd/Pz8ePbZZxk3btwZ56pMXl5eufX6Bg0axAsvvFBhm9JTX1MREREREal/SvyJiIiIiEiz88QTT9ChQwdMJhMBAQF06NABo9F+pQIHBwe7hFploqOjCQoKsmsTerrjx49jtVorbRd6alKmOnFnZ2fz9ddfs2XLFrvKvjLffvst77//PseOHaO4uNi2/UwVXmcSFxeH0Wikbdu2dtsDAwPx8vKytV6syhtvvIGHh4fttT39fABOTk48//zzXHHFFTg7O/P8889XmOCpaA03Dw+PCtd9q0hZwq8sAViZ3NzcGq1R5+npaav627dv3xmTa2XXcOr1rV+/HoCRI0eWG3/11VczadIkDAYDXl5edOnSpcKv/80338xPP/3Erl27uO++++jcufMZ56/MlClTeOGFF1i3bh0//PADY8aMqbQtKpSui7hgwQKKiorYv38/K1eu5IMPPuDuu+/mu+++qzSOM3F2dmbRokUAJCQk8O6775Kamoqzs/MZj6ts7UIREREREakbJf5ERERERKTZ6du3b5Xrpzk5OZVLBtaWxWLBYDDwzjvv2KrxTuXm5lat85wa9/jx47nuuuu4//77Wb58uS2R9f333/PQQw8xfvx42xqFJpOJxYsXVzspVpm6JlMGDx6Mn59flePKkl+FhYVERUXZ2jeW8fHxqXBdxI4dO7Jv3z7i4+OrbPfZqVMnAA4cOFDpmLi4OHJycmxjq+vGG2/kww8/ZMGCBTzyyCOVjiu7Bl9fX9u2tWvXMnDgQDw9PcuNb9euHSNGjKhy/piYGKKiogAqXHOvbL6q1pYMCgpiyJAhLFmyhO3bt1d77UInJyf69u1L3759ad++PQ8//DDLly+vsCq2KiaTye6aR44cyeTJk3niiSdsCcFTlbWTPfU1FRERERGR+lM/75JFRERERESaqbZt25KUlERGRsYZx1itViIiIhgxYkS5P/3796/xvCaTifvuu4+kpCQ++eQT2/ZffvmFNm3asGDBAi699FLOO+88RowYQWFhod3xNUnihYeHY7FYbMmkMikpKWRlZREeHl7j+Cuzf/9+Fi5cyLRp0+jZsyePPfYY2dnZdmM6duxIZmZmue1lax3+8MMPVc7ToUMH2rdvz6pVqypt5fndd9/Znbe6PD09ufHGG1m1ahWRkZGVjouNjcXX19eWDLVarfz++++MHj26RvOdymKx8NBDD+Hh4cHtt9/O0qVLWbFihd2YsrXxYmNjqzzfRRddxNatW/Hw8GDUqFE1jqd3794AJCUl1fjYigQFBXHTTTexZs0a/vrrr3L7y66ppslaERERERGpHiX+RERERETkrHbBBRdgtVpZsGBBuX1lrRQvuOACTCYTCxYsKNde0Wq11no9sqFDh9K3b18+/PBDW2KvrKLw1Hl27txZLklStq5eVVVfgC0R9eGHH9ptX7Jkid3+uiouLubhhx8mKCiIRx99lHnz5pGSksLzzz9vN65///5YrVb27Nljt33ixIl07dqVRYsWsWPHjnLnz8nJ4bXXXrP9/c477yQzM5Mnn3wSs9lsN3bPnj28++67dO3atdIWrWdy44034uXlxcKFCysds3fvXruk7+7du0lNTWXMmDE1nq/MkiVL2LFjB08//TR33303AwYM4KmnnrJbWzI4OJjQ0NByr19FJk2axOzZs3nyyScrbCtaZtOmTRW2Di1bH7Is2Vgfpk+fjqurK2+//Xa5fXv37sXT05MuXbrU23wiIiIiIvI3tfoUEREREZGz2rBhw7jkkkv4+OOPiYqK4rzzzsNisbBt2zaGDh3K9OnTadu2Lffccw+vvPIKcXFxjB8/Hnd3d2JjY1m5ciVXXXUVN998c63mv/nmm7n77rv55ptvuPbaaxkzZgwrVqzgzjvvZMyYMcTGxvL555/TuXNn8vLybMe5uLjQuXNnfv75Z9q3b4+Pjw9dunSha9eu5ebo3r07l112Gf/73//IysrinHPOYffu3Xz77beMHz+eYcOG1fr1O9Vbb71FZGQkH3zwAR4eHnTv3p0777yT//znP0yaNMmWYBw0aBA+Pj5s3LiR4cOH2453dHRkwYIFzJw5k+nTpzNp0iQGDhyIo6Mjhw4dYunSpXh5eXHvvfcCcPHFF7N7924++ugjjhw5wtSpU/Hy8mLfvn18/fXX+Pj4MH/+fBwdHWt8LWVr/VWUEAZITU3lwIEDXHfddbZtv/32G+Hh4bVaCw/gyJEjzJ8/n2nTpjF27FgA/v3vf3PppZcyd+5c5s+fbxs7btw4fv31V6xW6xmrPz09PfnXv/5V5dzPPvss+fn5TJgwgY4dO1JcXMz27dv5+eefCQ8PZ9q0abW6por4+voybdo0Pv30U44cOWJX3bdhwwbOP/98rfEnIiIiItJAVPEnIiIiIiJnvXnz5vHggw8SGxvLiy++yOLFiykoKGDAgAG2MbNmzeKNN97AaDSycOFCXnzxRVavXs25555rS9LUxgUXXEDbtm15//33MZvNTJs2jfvuu48DBw7w7LPPsn79el566SVby8VTPfvsswQFBTFv3jzuu+8+fvnll0rnefbZZ/nXv/7F7t27mTdvHps2beK2226zq6Cri71797J48WKmT59ul0icNWsWffr04bHHHrNVJzo5OTF16lSWL19e7jzt2rXju+++47bbbuPgwYO89NJLPPvss2zatIkrr7yS//73v3bjH330URYuXIifnx+LFy/m6aef5o8//uD666/nu+++q1Ol2o033ljhWn0AK1aswMnJicmTJ9u2rV27ttbVk2azmTlz5uDr62u3rmD79u257777WL58OcuWLbNtv/zyy0lMTGTbtm21mu90Dz74IEOHDmXt2rXMmzePefPmsXv3bq677jq+/PJLvLy86mWeMjNnzsRoNPLOO+/Yth05coSDBw/Wa5JRRERERETsGawV9foQERERERERqYOYmBgmT57MO++8Y1f111JceumlDBkyxJakS0lJYeTIkSxevLjeWqdW5cYbbyQoKIiXXnqpUeZraM899xxbt27lm2++UcWfiIiIiEgDUcWfiIiIiIiI1Ls2bdpw+eWXV7jOW3O3bt06oqKiuO2222zbsrOzufPOOxk6dGijxXHffffx888/ExcX12hzNpT09HS++uor7rnnHiX9REREREQakCr+RERERERERERERERERM4CqvgTEREREREREREREREROQso8SciIiIiIiIiIiIiIiJyFlDiT0REREREREREREREROQsoMSfiIiIiIiIiIiIiIiIyFlAiT8RERERERERERERERGRs4ASfyIiIiIiIiIiIiIiIiJnAYemDqA1SE7ObuoQRJqc0WjAz8+dtLRcLBZrU4cjImeg+1WkZdE9K9Jy6H4VaVl0z4q0LLpnRVoO3a+1ExjoWa1xqvgTkUZhNBowGAwYjYamDkVEqqD7VaRl0T0r0nLofhVpWXTPirQsumdFWg7drw1LiT8RERERERERERERERGRs4ASfyIiIiIiIiIiIiIiIiJnASX+RERERERERERERERERM4CSvyJiIiIiIiIiIiIiIiInAWU+BMRERERERERERERERE5CyjxJyIiIiIiIiIiIiIiInIWUOJPRERERERERERERERE5CygxJ+IiIiIiIiIiIiIiIjIWUCJPxEREREREREREREREZGzgBJ/IiIiIiIiIiIiIiIiImcBJf5EREREREREREREREREzgJK/ImIiIiIiIiIiIiIiIjUwbJlPzJp0pimDgOHpg5AREREREREREREREREKpeamsJHH73Phg1/kJKShK+vH507d+Wqq65l8OAhtnG7d+/kww/fY8+e3RQVFRIR0YYpU6Zy5ZXXYjKZbONGjhzM88+/zKhRY2x/d3Jy4tNPvyYkJNQ27uGH78fDw5NHH33KLp7nn59LQEAgs2b9k5EjBwOwaNESevfuYxtTVFTEpZdOJisrk9dfX8TAgaXjtm/fxocfvktkZCSFhYUEBgbRu3df5sx5jBdffI6ff15a6esQEhLKV1/9yOzZs/jrr+0AODk5ERQUwoUXTmX69JswGAyVHl/b4053xRVTueqqa7nqquts28aNm8Dw4edW+xwNRYk/ERERERERERERERGRZio+/gR33HEzHh6e3HnnXXTs2JmSkhL+/HMjr776Ap9++jUAa9eu4YknHmLKlIt544078fDwZOvWP3nzzdfZs2c3zzzz7yqSWwbefXcRjz0294zxmM1mNmz4nRdf/I9tW1BQMMuW/WCX+Fu3bg2urq5kZWXath07dpR7753N9OnTueuu+3FwcCImJpq1a1djsZi5++4HuP322bbxl1wyiUceeZKhQ4cDYDT+nbycOvUybrnlNoqLi9m2bQsvvvgcHh6eXHbZFWeMv7bHVcXZ2QVnZ5c6naM+qNWniIiIiIiIiIiIiIhIM/XKK6UJu3fe+ZAxY8bRtm07OnbsxDXXTGfx4g8AyM/P58UXn2XkyFHMmfMoXbp0IzQ0jKlTL+XRR5/it99WsXr1r2ec5/LLr2LFip85evTwGcft2bMLk8mBHj162bZNnnwRq1atoLCwwLbtp59+YPLki+yO3bJlE35+/jz44IN06tSZ8PAIhg0bwZw5j+Hs7IKHhwf+/gG2PwAeHp62v/v6+trO5eLigr9/ACEhoVx44cV06tSFLVs2V/l6VnVcXFwsDz10H1OnXsCECedxyy0z7PbPnj2LhIR4Xn/9VUaOHGyreKyo1ee3337FVVddwpgxw7j22mksX/5TlfHVlSr+RERERERERERERESk1YrPOUFOcU6jzefh6EGoR1i1xmZlZbJ580Zmzfonrq6u5fZ7enoC8Oefm8jMzOTaa28oN2bkyFG0adOWlSt/Ydy4Cyqdq0+ffsTERLFo0QK7ar7TrV+/jnPPPc+uerBbtx6EhITx22+rmThxCgkJCezcuYP77pvDBx+8axvn5+dPamoKW7ZsoXPnntV5CapktVrZtesvoqOP06ZNmzofl5eXx7Bh5zJr1j9xdHRi+fKfmDPnvpNtUEN4/vmXuOmm67j44suYOvXSSs+/du0a5s9/mbvuup/Bg4ewYcPvzJv3NEFBwba2pw2hWST+PvnkE9577z2Sk5Pp3r07jz/+OH379q10fFZWFq+99hq//vorGRkZhIeH88gjjzB69GigtMz0jTfe4IcffiAlJYWgoCAuu+wy/vnPf2IwGCguLuY///kP69atIyYmBg8PD0aMGMH9999PcHCwbZ6xY8cSFxdnN/f999/PrFmzGuaFEBERERERERERERGRRpNZmMFNy6/DYrU22pxGg4Evpn6Ht7NPlWNjY2OwWq20bdv+jONiYqIAaNeuQ4X727VrT0xMdJXz3XbbbG666Vp27txBv34DKhzz++9rueuu+8ptv/DCi/nppx+YOHEKP//8I8OGnYuPj6/dmPPPH8+WLZuYPn06/v4B9OzZm8GDz2HSpAtxd/eoMr5Tffvtlyxd+h3FxcWUlJTg5OTMFVdcU+fjunTpSpcuXW1/v/XWO1i3bg1//LGWyy+/Gi8vb4xGI25ubraqxIp8/vnHTJ48lWnTrgSgbdt27N27h88++/jsTvwtW7aMefPmMXfuXPr168eHH37IzTffzPLly/H39y83vqioiJkzZ+Lv78/8+fMJDg7mxIkTeHl52ca88847fPbZZ7zwwgt07tyZPXv28PDDD+Pp6cmMGTMoKChg37593HHHHXTv3p2srCyee+457rjjDr755hu7+e666y6uuuoq29/d3d0b7sUQEREREREREREREZFG4+3swweTPm30ir/qJP0AapqPtNYxgdmhQ0cmTpzCokVv8NZb75fbf/z4MVJTkxk06Jxy+yZOnMyiRQuIi4tl2bKl3HPPA+XGmEwmHn98LnPm/B+rVv3G7t27+eijJXzyyUe8/faHBARUnkg73QUXTGbGjH+QnZ3Fe+8tpnfvvvTp06/Ox+Xl5fH++2+zceN6UlNTMJvNFBYWkpiYUO3YAI4fP87FF0+z29anTz++/PLzGp2nppo88bdkyRKuuuoqLr/8cgDmzp3Lb7/9xtdff11hZd3XX39NZmYmn3/+OY6OjgBERETYjdmxYwfjxo1jzJgxtv0//fQTu3btAkpLX5csWWJ3zOOPP86VV17JiRMnCAv7u8TW3d2dwMDAerteERERERERERERERFpPqrbdrMptGnTBoPBQHT08SrGtQMgKupYhcmv48eP06FDxdWAp7v55tu49trLWbfut3L71q9fy+DBQ3F2di63z9vbhxEjRvLvfz9DUVEhw4aNIC8vr8I5goODmTz5IiZMmMItt9zBdddN4/vvv+bmm2+rVowA7u4eRESUtuh8+ul/c801l9GrVx/OOWdonY5buPA/bNmymTvvvIeIiDY4Ozvz2GNzKC4uqXZsTcnYlJMXFRWxd+9eRowYYdtmNBoZMWIEO3bsqPCY1atX079/f55++mlGjBjBRRddxKJFizCbzbYxAwYMYNOmTRw7dgyA/fv3s23bNkaNGlVpLDk5ORgMBrvKQSitHhw6dCiXXnop7777LiUlLeMLKyIiIiIiIiIiIiIiLZuXlzdDhgznm2++JD8/v9z+7OxsAIYMGYaXlzeff/7fcmPWr19LbGw048dPrNacwcEhXH75Vbz99kLMZstp51rHeeeNrvTYCy+8mB07tjFp0oWYTKZqzefl5YW/f0CF11ddbm5uXHnlNSxcOL9GVY8VHbd7906mTJnK6NHn06lTZ/z8/ElIOGF3nIODY7nX5nTt27dn166ddtt2795Z7QRsbTVpxV96ejpms7lcS09/f3+OHj1a4TExMTFs2rSJqVOn8vbbbxMdHc3cuXMpKSlh9uzZAMyaNYucnBwmT56MyWTCbDZz7733cvHFF1d4zsLCQl5++WUuvPBCPDz+7iF7ww030LNnT7y9vdmxYwevvvoqycnJPPzwwzW6TqPRgNFoqHqgNDvHM49hsVro6NOpqUNp8Uwmo91/RaT50v0q0rLonhVpOXS/irQsumdFWhbds3I2e/DBh5g16x/MmnUjt956B507d8FsLuHPPzfzzTdf8r//fYOnpzsPPfQojz/+MC+99BxXXHE17u4ebN36JwsW/IexY8dzwQUTMRj+zlWYTAYcHIwV/v2mm/7Bjz9+S3z8CcaNuwAHByNpaWns37+Pl19+ze64U48dOXIky5evwt3dAwcHo22cyVT6/99++xWHDx9iypRJ+PkFkZ9fwLJlSzl27Cj33z+n3HkrihPAYDBgNGK3fdq0K/jgg/f4/fc1jB07vsLXsjrHtWnTlnXr1jBq1GgMBgOLF7+JxWK1Oy4sLIxdu3YwceIknJwc8fHxteWBysZMn34jjz46h+7du3POOUNZv34d69at4fXX36rwOutLk7f6rCmr1Yq/vz/PPPMMJpOJ3r17k5iYyHvvvWdL/P3888/8+OOPvPLKK3Tu3JnIyEjmzZtHUFAQl112md35iouLufvuu7FarcydO9du38yZM23/3717dxwdHXnyySe5//77cXJyqnbMfn7udjeTtBxz1r+OyWDirYveaupQzhpeXq5NHYKIVJPuV5GWRfesSMuh+1WkZdE9K9Ky6J6Vs5Gvbze+++5bFi1axMKF/yEpKQk/Pz969erFM888ja+vOwCXX34J7dqF89Zbb3HHHbdQWFhI+/btueOOO7jxxhvLVeB5eLjYjj39776+7syaNYtXX30VZ2cHfH3dWblyGX379qVDB/vl104/1s/v7wIrk6m0W6OnZ+n+YcPOITJyD08++SRJSUm4ubnRpUsXFi5cyNixFXdtPD1OAEdHE87OjnbbfX3dufTSS1iy5B0uu2wqRmP55Fp1jnviicd45JFHmDVrJr6+vtx6660UFRXYHXf//ffyxBNPcMUVF1NUVMSBAwdwd3fGYDDYxlx66UXk5WXx/vvv85//vEx4eDjPP/8848dXXjFZHwzWuq70WAdFRUX079+f119/nfHj/86+zpkzh6ysLN56q3yyZfr06Tg4OPDBBx/Ytq1du5ZZs2axe/dunJycGD16NLNmzeL666+3jXnzzTf54YcfWL58uW1bcXEx99xzDzExMXz44Yf4+vqeMd5Dhw5x0UUX8fPPP9OxY8dqX2dqao4q/logs8XMRV9PItAtgI8u/Kypw2nxTCYjXl6uZGXlV1kCLSJNS/erSMuie1ak5dD9KtKy6J4VaVl0z4o0vAceuId+/fpzww031ek8ul9r5/TkZ2WatOLPycmJXr16sXHjRlviz2KxsHHjRqZPn17hMQMHDmTp0qVYLBZbtvb48eMEBgbaqvAKCgrKVdiZTCa7vq5lSb+oqCg++uijKpN+AJGRkRiNxnKtSatisVixWJosvyq1dCTjMAUlBSTkJFJcbFbVZj0xmy2UlOiHuUhLoPtVpGXRPSvScuh+FWlZdM+KtCy6Z0UaTp8+/Rg79oJ6u8d0vzaMJm/1OXPmTObMmUPv3r3p27cvH374Ifn5+UybNg2ABx98kODgYO6//34Arr32Wv773//y3HPPMX36dKKioli8eDE33HCD7Zznn38+ixYtIiwszNbqc8mSJVx++eVAadLvrrvuYt++fSxevBiz2UxycjIA3t7eODk5sWPHDnbu3MmwYcNwd3dnx44dzJs3j4svvhhvb+9GfpWkKexL3QdAsaWY7KIsvJz1dRcRERERERERERGR1un6629s6hCkGpo88TdlyhTS0tJ4/fXXSU5OpkePHrz77rsEBAQAEB8fb9eHNTQ0lPfee8+WhAsODmbGjBnceuuttjGPPfYY8+fPZ+7cuaSmphIUFMTVV1/NnXfeCUBiYiKrV68G4JJLLrGL56OPPmLo0KE4OTmxbNkyFixYQFFREREREdx000126/7J2W1/2j4cjY4UW4pJzk9S4k9ERERERERERERERJq1Jl3jr7VITs5u6hCkFv6x/AYiPNuw8cQfPHPuPIaFjWjqkFo0Bwcjvr7upKfnqnxbpJnT/SrSsuieFWk5dL+KtCy6Z0VaFt2zIi2H7tfaCQz0rNY4Y9VDRFqf7KIsYrKjGRE2EqPBQHJ+clOHJCIiIiIiIiIiIiIickZK/IlU4EDafgB6B/TB3yWA5PykJo5IRERERERERERERETkzJT4E6lAZNo+PJ08CfeIINAtiOQ8Jf5ERERERERERERERKR5U+JPpAL7U/fR3a8HBoOBQNcgUvJTmjokERERERERERERERGRM1LiT+Q0VquV/WmRdPfrCUCAW4Aq/kREREREREREREREpNlT4k/kNCdy4sgqyrIl/gJdg0jOT8JqtTZxZCIiIiIiIiIiIiIiIpVT4k/kNJFpewHo4d8DgEC3IIrMReQUZzdlWCIiIiIiIiIiIiIiImekxJ/IaSLTIgn3iMDTyQuAANdAAJLyEpsyLBERERERERERERERkTNS4k/kNPtT99HDv6ft74GuQQAk56c0VUgiIiIiIiIiIiIiIiJVUuJP5BSF5kKOZByip38v2zZ/V3+MBgMpeclNGJmIiIiIiIiIiIiIiMiZKfEncorD6YcwWy109/u74s9oMOLn4k9Svlp9ioiIiIiIiIiIiIhI86XEn8gpItP24mRyooN3R7vtQW7BqvgTEREREREREREREZFmTYk/kVMczjhEZ58uOBgd7LYHuAaSnK/En4iIiIiIiIiIiIiINF9K/ImcIiEnnlCPsHLbA90CSc5LaoKIREREREREREREREREqkeJP5FTJOYlEOwWUm57oGsQKfnJWK3WJohKRERERERERERERESkakr8iZxUbC4mNT+lwsRfgGsgheZCcoqzmyAyERERERERERERERGRqinxJ3JSSn4yViDYPbjcvkC3IAC1+xQRERERERERERERkWZLiT+RkxJy4wEqbfUJkJyf0qgxiYiIiIiIiIiIiIiIVJcSfyInJeYlAhDkVr7iz8/FD6PBoIo/ERERERERERERERFptpT4EzkpMS8BPxc/nExO5faZjCb8XPxJzlfiT0REREREREREREREmicl/kROSsxNINi9fJvPMoFuQaTkJzdiRCIiIiIiIiIiIiIiItWnxJ/ISYl5iRWu71cmwDVQrT5FRERERERERERERKTZUuJP5KSkvASCK1jfr0ygaxDJqvgTEREREREREREREZFmSok/EcBsMZOcl1RFq89AUvKTsVqtjRiZiIiIiIiIiIiIiIhI9SjxJwKkFKRgtloIdgutdEygaxAFJQXkFGc3YmQiIiIiIiIiIiIiIiLVo8SfCJCUmwBAkFtQpWMCXAMB1O5TRERERERERERERESaJSX+RIDEvNLE35lbfZYmBZPzlPgTEREREREREREREZHmR4k/ESAxNxEvZ29cHVwrHePv4o/RYCA5L6kRIxMREREREREREREREakeJf5EKK34C3GrvNoPwGQ04efiT4pafYqIiIiIiIiIiIiISDOkxJ8IkJAbT5BbcJXjAlwDSc5XxZ+IiIiIiIiIiIiIiDQ/SvyJAEl5SQS7V534C3EPJSE3vhEiEhERERERERERERERqRkl/qTVs1gtJ1t9hlY5NtQjjBM5cY0QlYiIiIiIiIiIiIiISM0o8SetXnpBOiWWEoLdz7zGH0C4RwQp+SkUmgsbITIREREREREREREREZHqU+JPWr2y1p3B1VjjL8w9DID4nBMNGpOIiIiIiIiIiIiIiEhNKfEnrV5SXiIAQdWo+AvziAAgPleJPxERERERERERERERaV6U+JNWLzEvAQ9HDzwcPaoc6+fih7PJmbic2EaITEREREREREREREREpPqU+JNWLzE3gWD3qtt8AhgMBsI8wjihVp8iIiIiIiIiIiIiItLMKPEnrV5iXgKB1Vjfr0yoezgnVPEnIiIiIiIiIiIiIiLNjBJ/0uol5iUS4hZa7fHhHuGc0Bp/IiIiIiIiIiIiIiLSzCjxJ62a1WqtUatPgFCPcBJz4ymxlDRgZCIiIiIiIiIiIiIiIjWjxJ+0apmFGRSaCwl2C6n2MeEe4VisVpLyEhswMhERERERERERERERkZpR4k9atcSTybuaJP7CPMIBiNM6fyIiIiIiIiIiIiIi0owo8SetWmJeAgAh7tVP/AW6BuFgNBGfo3X+RERERERERERERESk+VDiT1q1xNwEXBxc8HTyqvYxJqOJYLdQ4nLiGjAyERERERERERERERGRmlHiT1q15PxkgtyCMRgMNTouzCOME7lK/ImIiIiIiIiIiIiISPOhxJ+0apmF6fg4+9b4uFCPcLX6FBERERERERERERGRZkWJP2nVMgsz8Xb2rvFx4R7hnMiJw2K1NEBUIiIiIiIiIiIiIiIiNafEn7RqmYWZeDvVPPEX5h5OsaWY1PzUBoiqfhzLPMq//3yWYnNxU4ciIiIiIiIiIiIiIiKNQIk/adWyijLxqkXFX5hHOADxjbzOX1JeUrXH7kjaxqqoX1l27McGjEhERERERERERERERJqLZpH4++STTxg7dix9+vThyiuvZNeuXWccn5WVxdy5cxk5ciS9e/dm4sSJrF271rbfbDbzn//8h7Fjx9K3b1/Gjx/PwoULsVqttjFWq5X58+czcuRI+vbty0033cTx48ft5snIyOD+++9n4MCBDB48mEceeYTc3Nx6vXZpWrVt9RniHooBiMtpvMTf0YzDXP/TlexN2VOt8an5KQD8d9+H5JfkN2RotfL1wS+IyY5u6jBERERERERERERERM4aTZ74W7ZsGfPmzePOO+/k22+/pXv37tx8882kplbcQrGoqIiZM2cSFxfH/PnzWb58Oc888wzBwcG2Me+88w6fffYZTzzxBMuWLeOBBx7g3Xff5eOPP7Yb8/HHH/PUU0/xxRdf4Orqys0330xhYaFtzAMPPMDhw4dZsmQJixYtYuvWrTzxxBMN92JIoyooKaDQXFirVp9OJicC3YI40YiJvyMZhwFYFb2iWuNTC1IJcQ8hpzib7w59XeX4Fcd/5vXtr9YpxuoqMhexeOdC1seua5T5RERERERERERERERagyZP/C1ZsoSrrrqKyy+/nM6dOzN37lxcXFz4+uuKExVff/01mZmZLFy4kEGDBhEREcGQIUPo3r27bcyOHTsYN24cY8aMISIigkmTJjFy5EhbJaHVauWjjz7ijjvuYPz48XTv3p0XX3yRpKQkVq5cCcCRI0f4/fffefbZZ+nXrx+DBw/mscce46effiIxMbHhXxhpcFlFWQB41iLxBxDqHt6oib/ok9Vxa2N/w2wxVzk+LT+VLr7duLDjJfzvwKdkn7zeimQVZvLWXwtYdvRHCs2FlY6rLydy4rACeSV5DT6XiIiIiIiIiIiIiEhr0aSJv6KiIvbu3cuIESNs24xGIyNGjGDHjh0VHrN69Wr69+/P008/zYgRI7joootYtGgRZvPfiZABAwawadMmjh07BsD+/fvZtm0bo0aNAiA2Npbk5GS7eT09PenXr59t3h07duDl5UWfPn1sY0aMGIHRaKyyFam0DFmFmQC1avUJEO7RuIm/mOxoAlwDyCrMZHvS1irHpxWk4efiz/U9bsBsNfO//Z9WOvaTyI/JL8nDbLVwMG1/fYZdodicGECJPxERERERERERERGR+uTQlJOnp6djNpvx9/e32+7v78/Ro0crPCYmJoZNmzYxdepU3n77baKjo5k7dy4lJSXMnj0bgFmzZpGTk8PkyZMxmUyYzWbuvfdeLr74YgCSk5Nt85w+b0pK6bpoKSkp+Pn52e13cHDA29vbdnx1GY0GjEZDjY6RhpdjzsJgAD83Xxwcap4Dj/COYG3sakwmAwZDw399Y3OiOa/NKLYm/Mm6uDUMjxh+xvHphakEuQcS6BHAld2u4n/7P+eK7lcS4BZof97sWH448i039vkHn0X+l/0Z+xgQOqDe4zeZjLb/xufFYTBAvjm3Vq+9iDSsU+9XEWn+dM+KtBy6X0VaFt2zIi2L7lmRlkP3a8Nq0sRfbVitVvz9/XnmmWcwmUz07t2bxMRE3nvvPVvi7+eff+bHH3/klVdeoXPnzkRGRjJv3jyCgoK47LLLGj1mPz/3RkkMSc1YUgsxmYy0DwnDzdGtxsd3D+1M4Z4CjG4l+Lj41H+ApyixlJCYH0/P0G6E+ATy6Z5PcfdyxMnkVOH4wpJC8sy5tAsMx9fXnduG38JPx3/gy6Of8sh5j9iN/ffW9wn2CuS2YTcTmbGbw9n78fV1b7Br8fJyJaU4AZPJiMVY3KBziUjdeHm5NnUIIlIDumdFWg7dryIti+5ZkZZF96xIy6H7tWE0aeLP19cXk8lEamqq3fbU1FQCAgIqPCYwMBAHBwdMJpNtW8eOHUlOTqaoqAgnJydefPFFZs2axYUXXghAt27dOHHiBIsXL+ayyy4jMDDQNk9QUJDdvGVrBQYEBJCWlmY3d0lJCZmZmbbjqystLVcVf81QbGoCRqsDBdkWCg25NT7e0+qH2Wxhb+xBevr3aoAI/xabHUNhcRF+pmC6BPRkUf5iftm3ipERoyocH58Tj9lswdnsQXp6LmDg2m438OaON0jNSmf2wLvxcfFlb8oeVhz+lYeHPUZedgmdPbvx45EfSEvLqfdktclkxMvLlaysfA4lHcVstpCWk3EyPhFpTk69X81mS1OHIyJV0D0r0nLofhVpWXTPirQsumdFWg7dr7VT3SKaJk38OTk50atXLzZu3Mj48eMBsFgsbNy4kenTp1d4zMCBA1m6dCkWiwWjsbQM9Pjx4wQGBuLkVFr9VFBQUC5pYTKZsFqtAERERBAYGMjGjRvp0aMHADk5OezcuZNrr70WKF0nMCsriz179tC7d28ANm3ahMVioW/fvjW6TovFisVirdEx0vDS8zPwcvLCbLYCNf/6BLmEYrVCTEYMXb171H+ApziWfhyrFcLc2hDgGkBH706sPLaSYSEjKxyflJOM1Qrejr6UlJT+4Lyk4+V4Onix8K/XuXHZDczufzffHv6KTt5dGB0+jpISC919e/NxwUdEZ8QQ7hnRINdiNluIyYrGaoWcolxbfCLS/JjNFt2jIi2I7lmRlkP3q0jLontWpGXRPSvScuh+bRhN3kB15syZfPHFF3z77bccOXKEp556ivz8fKZNmwbAgw8+yCuvvGIbf+2115KRkcFzzz3HsWPH+O2331i8eDHXX3+9bcz555/PokWL+O2334iNjeXXX39lyZIltuSiwWBgxowZvPXWW6xatYoDBw7w4IMPEhQUZBvTqVMnzjvvPB5//HF27drFtm3beOaZZ7jwwgsJDg5uxFdIGkpmYSbezt61Pt7N0Q0fZx9O5J6ox6gqFp0VhauDK/4upetSjmkzjo3xf5BXnFfh+NSC0rUqy8ZD6ff9uHYX8N7Ej+gfOIDnNz9NZOo+buv3T4yG0h8FPfxLE5j7Uvc02LXkFOWQUZhBgGtApfGLiIiIiIiIiIiIiEjNNfkaf1OmTCEtLY3XX3+d5ORkevTowbvvvmtr9RkfH2+r7AMIDQ3lvffeY968eVx88cUEBwczY8YMbr31VtuYxx57jPnz5zN37lxbO8+rr76aO++80zbm1ltvJT8/nyeeeIKsrCwGDRrEu+++i7Ozs23Myy+/zDPPPMONN96I0Wjkggsu4LHHHmuEV0UaQ1ZhJl5OtU/8AbT1asexzKP1FFHlYrKjaePZ1lbJOqbNWN7b/Tab4v9gbNsJ5can5afiYHTA08mr3D5fFz8eHz6XP+J+Jy4nlv5BA237PJ28aOvVjr2pe5jQflKDXEtsdgwAXXy7EZm6t0HmEBERERERERERERFpjZo88Qcwffr0Slt7fvzxx+W2DRgwgC+++KLS83l4ePDoo4/y6KOPVjrGYDBw9913c/fdd1c6xsfHx67aUM4umUWZ+Dj71OkcPfx6sjJ6BVartd7XxDtVTHY0bbza2v4e4h5KD/+erIlZXWHiL7UgFX8X/zPGdG74eRVu7+nfq0ETcrHZsQB09e3GtsQtDTaPiIiIiIiIiIiIiEhr0+StPkWaSlZhBl51aPUJ0CugD6n5qSTlJ9VTVOVZrVZisqNp69nObvv5bcaxNWEzOUXZ5Y5JzU/B3zWgVvP18u/Dscyj5Bbn1ur4qsTmxODj7EOgWxBF5iJKLCUNMo+IiIiIiIiIiIiISGujxJ+0WplFmXjXsdVnD7+eAOxN2V0fIVUoozCd7KJs2ni2tdveL2gAJRYzx7KOlTsmrSAVv1PW96uJnv69sAL70/bV6viqxGXH0MazLW4ObgDkl2idPxERERERERERERGR+qDEn7RKVquVzMJMvOtY8efj4ku4RwR7U/fUU2Tlla2Jd3riL9wjAoC4k60zT5VWkIqfa+0SfxGebfB08mRvSsNcU2x2LGEe4bg7ugM0WGWhiIiIiIiIiIiIiEhro8SftEr5JfmUWErwqmPFH5RWyO1roCQZQHR2NEaDgTCPcLvtziZngtyCiMuJKXdMan7pGn+1YTQY6eHXk8i0+l/nz2q1EpMVTYRnG9xOJv7ylPgTEREREREREREREakXSvxJq5RVlAlQ54o/gJ7+vTmaeZi84oZpWRmTHUWIexhOJqdy+8I9Iog9reKv2FxMVlFWrVt9QunahftS92KxWmp9joqkF6STV5JXmvg72eozV60+RURERERERETOOkXmIorMRU0dhohIq6PEn7RKmYX1l/jrHdAHi9XKwfT9dT5XRWKyoml7WpvPMhGebcpV/KUVpgHUKfHX078XecV5RGUdr/U5KhKVEQVAhMepFX9K/ImIiIiIiIiInG3mbniMh3//v3p/sFxERM5MiT9plcoSf15OPnU+V1uvdrg7urMvtf5bYwLEZEeXW9+vTFnF36n/gErNTwEgoJZr/AF09e2O0WCo93X+YrJiMFDatrSs4k+tPkVEREREREREzi75JflsT9rGruS/+PnYT00djohIq6LEn7RKWUUZAHg5e9X5XEaDkZ7+vdibsrvO5zpdkbmIhNx42ni1q3B/uGcbii3FJOcn27alFaQCdav4c3N0o6N353pf5y8qI4pg9xCcTE64OrhiAHKV+BMREREREREROavsSt5JiaWEfoEDeGfXW6TmpzZ1SCIirYYSf9IqZRZm4uLggrPJuV7O19O/N5Fp++q9dUFsTgxWqLTiL8IjAoC47L/bfabmp+BgNOFVxzam3fy6cyTjUJ3OcbrozGgiPEtjNhgMuDm6k1eixJ+IiIiIiIiIyNlkW+IWgt2CeWL4XByMjrz51+tNHZKISKuhxJ+0SplFmXg71X19vzI9/XuRXZRNdFZUvZ0TStf3Aypd4y/EPRSTwUhcTqxtW2pBKr7OfhgNdbu9g9yCSc5LrnpgDURlRhHh2cb2dzcHN63xJyIiIiIiIiJyltma8CeDgs/By9mbf/afzbrY39h44o+mDktEpFVQ4k9apazCzDpXxJ2qu19PjAZDva/zF5MdjZezd6WxOhgdCHEPIzb778RfWn4qfnVY369MoGsgWUVZFJoL63wuAIvVQmxWLOEnK/6AkxV/SvyJiIiIiIiIiJwtEvMSicmOZlDIOQCc32Y854QM4Y3tr+kBcBGRRqDEn7RKmYWZeNdj4s/N0Y0O3h3Zl7qn3s4JEJMdVWm1X5kIzwjicv5u9ZlWkIq/S0Cd5w5wDQQgpZ6q/hJzEykyF9lV/Lk7upOnNf5ERERERERERGosOS+Zm3+ZQUx2dFOHYmd74laMBgMDggYCpcu93DXwPrKKsvhw7/tNHJ2IyNlPiT9plbLqudUnQK+Avuyt58RfdFZ0pev7lQn3aGNf8VeQin99VPy5BQGQkl8/ib+ydqSnXo+boxu5SvyJiIiIiIiIiNTYyqhfiM6K4reY1U0dip2tCX/Sza8Hnk5etm0h7qFM73kj3x3+iuOZx6p1nrSCVCxWS0OFKSJy1lLiT1qlzMJMvJx96vWcvfx7EZsdQ2ZhRr2cz2K1EJNddeIvwjOC+Nw4SiwlAKTmp+LnUvfEX1nFX3J+Up3PBaXrFZa2Jg2xbXNzcCevRIk/EREREREREZGasFqt/Br1CwCbTmxo4mj+ZrFa2JG0jUHB55TbN63LlYS6h7Pwr9exWq1nPE9MdjTTf7paFYIiIrWgxJ+0SpmFGfVe8dfTvzcAkan76uV8qfmpFJoLq1HxF4HFaiUhN54SSwkZhRn4u9a91aeLgwueTp6k5KfU+VwAsTmxtPFug9Hw948dN0c39XYXEREREREREamhg+kHiMmOZly7CRxMP0BaQWpThwSUxpVdlM3g4CHl9jmZnPhn/7v4K2k7v8etrfQcFquFV7e+SLGlmO8Pf6NuUSIiNaTEn7Q6VquVrKJMvOpxjT+AYLcQfF18iUyrn8RfTnE2gF1bhIpEnEwMxubEkl6QDlAvFX8Aga6BJNdXq8/sGNp62ScxSyv+lPgTEREREREREamJFVHL8Xf157a+/8RoMLA5flOVx2QXZfHFgc/IKc5psLi2JvyJm6Mb3f16VLh/SOhQhoWNYPHOhRSUFFQ45scj37EnZTcPD32cQnMBy47+2GDxioicjZT4k1YntzgHi9WKVxUJtZoyGAyEuoeRlJdYL+crMhcB4OLgfMZxAa4BOBodicuOsT3dFVAPa/yVnieQ5Lz6afUZmx1LW2/7xJ+7ozt5empLRERERERERKTais3FrIleybi2E/B18aOHX69qtfv89tDXvLNrEbetmMnOpB0NEtu2xC0MCBqEyWiqdMwd/WaTXpDO5/s/KbcvITeed3cvZmqnSxjbdjxj207gm0NfUmwubpB4pWFU1cpVRBqWEn/S6mQWZgLgXc8Vf1BaaVdfrRUKzaVPPTmZzpz4MxqMRHhGEJcTS+rJtpz1VvHnFkRKPVT8WawWEnITCPMMs9vu5uimdg0iIiIiIiJnkczCDMwWc1OHIXJW+zNhE9lF2YxvNxGAYWEj2J601fYQeUWsVisro1cwNHQYIe6h/N/ae1i8c+EZj6mpnOIc9qXuqbDN56nCPMK5otvVfHHgM45nHrOL8bVtL+Hp6MnNfW4D4Mqu15CSn8LqmJX1Fqc0rP/u+5B718xu6jBEWjUl/qTVySwqTfzVd6tPAD9Xf9Ly0+rlXGX/8Koq8QcQ7tGG2OwYUgtSMRoMeDv71EsM9VXxl5qfisVqJtQz1G67m4M7+SX5WKyWOs8hIiIiIiIiTavQXMg/lt/AN4e+bOpQRM5qv0b9QmefLnTw7gjAsNARFJQUsDO58iq+/WmRxOecYFqXK3lp9H+4pe/tfHf4G+5dM/uMn8vUpHJrZ9IOLFYrg0POqXLstd2n4+/qz60rbuLWX27irb8W8P6ed9ieuI17Bv0f7o7uALT37sCwsBF8eeDzFv35UUJuPAm58U0dRqPYkrCZval7SM6rn+WDRKTmlPiTVierrOLPyafez+3v4k9qQUq9nKss8edsdKpybIRnBLEnW336ufhjNNTPrR3gGkhGYUad2ymUtT8N9Tgt8efoBkB+SX6dzi8iIiIiIiJNb3P8RrKKstia+GdThyJy1soqzGRz/AYmtJ9o29bOqz0h7iFsit9Y6XEro1fg5+JH/6CBGA1Grup2LY8MfYKD6Qc4kRNX4TE/HP6WWStmVjv593vcWsI8wglxD61yrKuDK2+Of4eHhz5ON7/u/B77G5/v/4Tx7S5gSOhQu7FXd7uOqKzjbD7D9TV3/978LC/++XxTh9Hgis3FHEo/CJQmAEWkaTg0dQAijc1W8VfPa/wB+LsGkF2UTZG5CCdT1Qm7Myk0FwLVr/hLzk8mPieu3tp8AgS6BQKQWpBSrX+0VSYxLwGAEI8Qik7p7OnmUPr0Vl5xnu1JLhEREREREalfxeZidqfs5M+ETfwZvxk/F3+eOvdZPBw96nWeVVG/ArA3ZQ8llhIcjPrYSaS+rYlZhdVq5fw242zbDAYDQ0KHs/nEBmb3vxuDwWB3TImlhLUxa5jQ7gK7h8X7BPQF4HDGISI825Sba1viVo5nHeNIxmE6+3Y5Y1y/Hl/OqqhfuWvgvdW+Fk8nr5Pr+I3HarWSmJeAv0tAuXG9A/rQy783Xxz4jOFh51b7/M1FTnEOkWl7bf9f3z97m5OjmUcothTj4ejBloTNTOl4UVOHJNIqqeJPWp2swkzcHN1wNDnW+7n9XPwASC+oe7vPssSfczUSf2X/ONudsgs/1/pL/AW4lib+kuu4zl9SXiIejh64O9kn98oq/nKLc+p0fhEREREREanYutjfuPyHqcxZdz+/xaymp38vjmQc4sk/HqnXtb2yi7L4M2ETY9uOo9BcyIG0/fV2bhH5269Rv3BO6DB8T34GVWZY6HAS8xI5nnWs3DHbEreSWZjBuLYX2G33cfEl0DWQwycrtE53ML30Pt5wYv0ZYzqQtp/Xtr3MxPaTuajjJTW5HBuDwUCIe2iln9dd3f069qTs5pN9H9XL526NqawFqsVqZUfitqYOp0HtT9uHg9HE1M6Xsj1pKyWWkqYOSaRVUuJPWp2soky8nep/fT8obfUJkFqQWudzFZkLMRmMmIymKseGe4QDkJSXZIuhPgS6BgGQUsee3Im5CQS7h5TbXpb4yyvJq9P5RUREREREpGLbErbg4+LLognv8tmFX/PAOQ/x9Mh5RKbu49lNT2G2mOtlnt9j12Gxmrm5z+24OriyK/mvejmviJSyWC28u2sRB9L2M7H95HL7+wUOwMXBpcJ2mKuiVtDOqz2dfDqX29fJtwuHMw6V255ekEZKfgqeTp5sOPF7pXGlFaTy1IZH6eTTmbsG3leu2rC+DA0dzpQOF/HfyA+5dunlzN3wOH/Gb67RGoRNZVviVkI9wgj3iGBb4pamDqdBRabto6N3Z84NO4+84jz2pe6p9zl+j13batZLFKktJf6k1ckszMTLuWESf2VtNlPz677OX6G5sFptPgF8nH1trTLrs9Wnm6Mbbo5uJOcn1ek8SXmJBLkFl9vubmv1mVtun4iIiIiIiNRdSn7yyQ/8u9g+kO8T0JcnRjzD5vgNvLbtpXr54HxV9K/0CxxAkFsQvQP6sDN5R53P2dAsVkud17QXaQw5xTk88cfDfHnwc27r90/ODTuv3BgnkxODgs9h04kNdtvzivP448TvjGs7ocKkXBefrhzKOFTu58DBk1WAV3W7liMZRypMtBSbi3l6wxNYrBaeHPFsnZe9OROjwci9g/+P/130Dbf1u5O4nBgeXf8gT298grzi5v1A+fbErQwKGszgkCFsSWgZycra2p8aSXf/nnTx7YqPsw9/xm+q1/NbrBbmbX6Gz/d/Uq/nFTnbKPEnrU5mYcNV/Hk5e+NgNJFWLxV/RdVq8wml7RDCPSKA0nUG61OgaxApdUxkJuYlquJPRERERESkCaQWpBB4chmHUw0LHc7/nfMwvxz/mUU7F1brg+i84jz+b+29vLPrLbvtyXnJ7E7+i7FtJwCllUd7U/c0+xZvH+x9jwfXVX89MpGmEJsdw12r7mBPym6eOfcFruh6daVVdcNCRxCZtpftiVtt2zac+J0icxFj246v8Jguvl3JKsws99nPofQDeDp5MrXTpTgYTWw88Ue5Yz/Y+y4H0vfz5IhnCajnz6Mq4+XszWVdrmDxhCU8MfxptiVu4e7V/+RETlyjzF9mX+pevj/8TZXjEnMTiMuJZWDwYM4JGUpSXhIx2dGNEGHjyy7KIi4nlh5+PTAajAwKOYctCZvrdY74nBMUW4rtvsdFpDwl/qTVySpquIo/o8GIr7NfvbT6LLQUVjvxBxDheTLxV48VfwABrgEk59W+4s9qtZKUl0hwBRV/braKPyX+REREREREGkJyfopt/fbTjW83kdkD7uabQ1/ywpbnzpioM1vMPLvpSfak7OKLA5+z/Ngy2741MStxMDoyMmIUAH0D+1NQUsDB9AP1ezH1bFvCFo5kHG6R1TcWq4XdyTtbZOxSfUXmIu5dMxsrVt4Yt4ghoUPPOH5kxCi6+fVgzrr7eWz9HI5nHmNV9K/0Cehb4QPZAJ19ugJwOMN+nb8D6fvp7NMFd0d3+gcNZMNpib+kvCS+OfQV1/W4gZ7+vepwlbVjMBg4L2I0r499iyJLEbNX3cbWhMZro/ntoa9YtHMhBSUFZxy3LXErRoOBAUED6RvYDwejA1sT/yw3bnfKLuJzTjRUuI0iMjUSgB5+pd8PQ0OGczTzKMl1XELoVNHZUQDE58a3+NdLpCEp8SetTmZhJt4NlPgD8HP1Jy2/ftb4q26rT4Bwjzal89d74i+QlPza/4LOKc4mvyS/wn9gmowmnE3O5Bbn1CVEERERERERqUCRuYiswswzdoa5pPM0Hhn6BGtjVvPY+jkVPphptVr5z/aX2Z64ledGvsCFHacyf/srRKbuA2B19EqGhY3Aw9EDKK0gau7r/BWaCzmScYj8knyyijKbOpwa+ytpO/f9dhef7f9vU4ciDSghN56MwgzuGXg/bTzbVjnew9GD+ee/yWPDniI6O5rbfp3JtoQttmrcigS4BuDl7M2hdPvE3+H0g3Tz6w7AiLCR7EreQXZRlm3/p5Ef4ebgxuVdrqrl1dWP9t4dWDBuEd39evDQ2gdYdXRVo8y772RV867knWcctz1xK119u+Ph5Imrgyt9AvqyNcE+8ZeSn8LD6x7gw33vN2TIDW5/2j68nLwI8wgHYHDIORgNhgoTnbUVlXUcFwcXjAYD21T1J1IpJf6k1cksysTbyafBzu/n4l9vrT5r0hu9rVc7AALdKn6Ss7YC3YLqlPhLzE0EINi9fMUflLb7VKtPERERERGR+lf23rSqFnzntx3H8yNfYl/qXh5YezcJufF2lWT/jfyQ5ceW8cA5DzEweDD/7H8XXX27MXfjY/yVtJ0jGYft2gg6GB2a/Tp/B9P2Y7ZaAEjITWjiaGouLicWgCV73uX32LVNHI00lLKvc7hnm2ofYzAYGN3mfN674CNu7zebAcGDGN1mzBnHd/HpwqFTKv7SClJJyU+hi283AIaFnovFarWt1xafc4Llx37i6u7X2ZZxaUqeTl48O/IFRrc5n8fXPM6+1L0NOl9yXjJJJ7tjbU+qvMrQYrWwPWkrA4MH27YNDhnCzuS/KDQX2rZ9vHcJheZCDqcfarigG8H+tH108+tua0Xr6eRFd7+e9brO3/GsY3T07kR3v55sT1LiT6QySvxJq2KxWsgpymqwVp9Q+oaqPhJ/heaatfocGT6KV8bMx9fFr85znyrANZC0glTMFnOtjk/KK038hVTSUsLNwZ284txaxyciIiIiIiIVSz75EKe/S9Vrbw0IHsRr579Ban4KNyy7hit+uJj7f7uL5zbN5aO9S5jZ+xbGt5sIgJPJiSeGPwPAI78/iLujO0NChtmdr29gf/amNN91/vam7sHB6ABAfG7LaxeXkBtPqHsoY9qM5d9/PsuBtP1NHZI0gBM5cTiZnGq1rIujyZHLulzBC6NexdPJ64xjO/t04cgpSaeDJ6v/up5M/AW6BdLVt5ut3edH+5bg5ezNxZ0uq3FcDcVoMDJn6CN0D+jOY+seJiE3vsHmikwrTSwOCh58xrXmDqUfJLsom0GnJP7OCRlKkbmI3ScrBY9nHmP58Z/o5NOJmOwou4RgS2K1WtmfFkmP09q+nhMylO1JW+1+F2QVZtpVj9ZEdFYUbb3aMTB4MH8lbcdy8gEOEbGnxJ+0KjlF2Vis1oZt9eniT2q9tPosqlGrTwejA30D+9d53tMFugZhsVpJK0ir1fGJeQk4Gh3xdvapcL+7o7vW+BMREREREWkAKSfXVQqoZmeYTj5deOeCD3hi+NNc1uUKfJ39iMo6xjXdr+Pa7tPtxvq7+vPk8GexYmVUxJhyHWv6BvYnvyS/XPvA5mJf6l56+ffBzdGtQRMEDSU+N54Q91AeOOchOnp34ok/Hq7XdbSkeYjLiSPcI9xWQdVQOvt2JTk/mczCDAAOpR/A08mTYLe/H+I+N/w8tiRs5kjGIVZH/8r1PWbg4uDSoHHVlJPJiVcueAVXB1ceW/8QOQ20tMy+1D2EuIcwod1EjmUeq7QAYHviVlwcXGxr3gG09+pAgGuArf3le3veJsgtmDsH3IPFauVY5tEGibmhxeXEkl2UTXe/HnbbzwkZSl5xHvtS95BWkMpbfy3gmqWXM2fd/TVeo9RitRCdFUU7r/YMDBpEdlF2s/0dI9LUlPiTViXzZN9+b6eGTfxlFmbU+anGAnMBzjVo9dlQAtxKnwxNzk+q1fGJeQkEuQVjNFT848bN0V2tPkVERERERBpAakEKLg4uuDu4V/sYL2dvzosYzfSeN/LY8Kd4+4IPuLnPbRUmHnr492TxhPe5vd/scvu6+nbDxcGlWbb7tFqtRKbupWdAL0LdQ0lsga0+43NOEOoehrPJmbnnPoeD0YHH/3iIYnNxU4cm9SguJ4Ywj4gGn6ezTxcADmeUVv0dTD9AV99udvf98LBzyS/J56kNjxHgGsjkDhc1eFy14evqy7zRL5Kan8IzG5+odQerM9mXupcefr1sLTwrq/rblriVfoH9cTQ52rYZDAYGBZ/D1oQt7Er+i00nNvCP3rPo6tsNo8HQYtt97k8rXfP19MRfF9+u+Dj7sHDHfG5Ydg3Lj//E+W3HcSj9IJsTatYCNCkvkUJzIW0929PDvxeuDq5nrLgUac2U+JNWJbOwNPHXkK0+/Vz9sQLpBel1Ok+RubBGFX8NJdC19MnQqtb5yyrMrLByLzE3kSC3oEqPc3NwI7eBnsASERERERFpzVLyk/F3CWjQaqG2Xu0qXOPLwehAL//e7Er+q8Hmrq2E3HgyCjPo6d+bYLdQEvJaXsVfYl4CIe6hQOkDyE+NeI4jGYf5NeqXJo5M6tOJkxV/DS3MIxxXB1db9dSh9AO29f3KtPfqQKh7KAm5CUzveVO5Kt/mpK1XO54Y/jR/JW3n430f1Ou5i8xFHEo/SK+A3vi6+NHRuyPbKkg+FZQUsDd1t936fmUGhwwhKus4/9n2Cl19uzG6zfk4m5xp49mOIxktM/EXmRZJuEdEubayRoORc8PPIyEvgau7Xcd/p/yPBwY/RE//Xnyy78MaVf0dzzoOQDvv9jgYHegX2J/tSdvq8zJEzhpK/EmrklXY8BV/ASfXTqjrOn+lrT6b/h9RHo6eOJucSc47c8XfnHX38+Zfr5fbnpSXSHAl6/sBuDm6qdWniIiIiIhIA0jNT8Xfter1/RpKv8AB7EnZ3SAVN3WxN3U3AD39ehLiHkp8TstK/OUU55BdlG1L/EFpVc15EaP5bP/HzXZdRamZYnMxSXmJjVLxZzQYS9f5yzhMan4qqfmptvX9yhgMBs5vO552Xu2ZcHK9z+ZsQPAgZvT8B5/t/7heH0A4lH6QEkuJrX3nwJPr/J2ewNqVvJMSSwkDg8on/gYFD8ZoMBCTHc2tfW+3dcnq7NvFVnXZnCXmJfLjke/tKoz3p+6jh3/PCsfPHnAPX079nhm9ZuLp5IXBYOD6HjeyPy2SHTVI3EVlHsPVwZUg19ICgwHBg9iTsouCkoJqn6PQXMhrW1/iyT8e5ZHf/4/7f7ubOevuI72WSxyJNFdK/EmrklmUiQGqXNS4Lvxc/YD6Sfy5mJq+V7rBYCDANfCMFX+x2TEczjjEX0nby+1LykskyC240mNLW33m1kusIiIiIiIi8reU/GQCmzDxZ1vnL6N5rcG0L3UfEZ5t8HL2JsQ9hMS8BCxWS1OHVW2JJ9ckPDXxB3B9jxtIyE1gdfSvTRGW1LOEvHgsVmujVPxB6Tp/h9IPcij9AEC5xB/ATb1uZtGE93AwOjRKTHV1bY/p9PLvw7zNz5BdlFUv59yXugdnkzMdfToBpYm/tII0ok5Wo5XZnLARf1d/2nm1L3cOTycvegf0Y1jYCPoHDbRt7+zThaOZR5r9z6OP9y7h9e2v8q/Vt3M88xiF5kKOZByih1/FiT8Ho0O54oZzQobQ1bcbn0R+VO15o7OjaOvVzlbFPjBoMCWWEnan7Kz2OdZEr+LnY0spsRTjbHIhwNWfvSl7WHr0h2qfQ6QlUOJPWpXMwgw8nDwxGU0NNoePsy9Gg4HU/Lol/gqbSatPgEC3IFLyUyrd/0fc70DpEz+nLiZeaC4kozCD4DMk/twd3VXxJyIiIiIi0gBS8pObtOKvm193vJ19eGfXIorMRU0Wx+n2pe6hp39ptU6IeygllpI6v4dvTCdyTgAQelrir5NPF4aHncunkf9t9okDqVpcThwA4R5tGmW+zj6dicuJ5a/k7Xg5eVX4ELfBYGgxST8orWScM/QxCkoKeG3byzVqK1mZfal76ebX3fY69Anoh4PRgW2JW2xjDqUf5Kcj3zOp/YWVtlp+buQLPD5srt22zj5dKDIXEZ0VVec462J3yi7mb3ulwjVDC82FrIv9jbFtx1FsKeafK29l4Y75mK0WuleS+KtIadXfDHYl76x2RWZU1nG7RGo7r/b4u/qzI7H6VYM/HvmOwSFDeO68F3lyxDM8PPQJxre7gJ+O/qBqaTmrKPEnrUp2URYeTp4NOofRYMTXxY/UgsoTZdVRZC7EyehY9cBGEOAacMZWn+vj1tneNJW1TIHSaj+AYLcztPp0cCOvRIm/umiJC9GLiIiIiEjDslqtpOanEnBy3fam4GB04MkRzxCZupeXtswrl4xaHb2Sq368lH2pexstprziPI5lHqGnf2/g76q5xLzy76ssVku9JArqW0JuPC4OLng7+5TbN73HjcTlxPJbzOrGD0zq1YmcWByNjvi7+jfKfJ19uwKwMupXuvp1a9C1QRtTsFsw9w7+P36PXcvy48vqdC6r1cre1N22nx8ALg4u9A7oy/aT6/wVmYt44c/naO/dget7zKj0XC4OLuWq4Dr5dAZo0nX+1setY87a+1h69Ad+j1tbbv/GE3+QX5LPDT1n8ub4d5ja6VJ+PvYTjkZHWxVkdQ0PO5eO3h35774PqxxrtVqJzoqirWc72zaDwcCAoEFsTyq/xmJFDqTt52D6AaZ2utRu+8WdLiU1P5UNJ9bXKH6R5kyJP2lVcotz8XRs2MQflC6snXbWVfxV3OozOS+Z/WmRXNzpUkLdQ9mT8nfirywhdaY1/kor/nKb5ZupliA+5wQ3LLuaA2n7mzoUERERERFpRrKKMim2FDdp4g+gT0BfHhr6GGtjVvPe7sUAmC1m3vprAfM2P0NWYSYrjv/caPEcTN+PxWq1Pbxa9qBqQu6JcmP/s/UVbvr+JnKKchotvupIyEsgxC20wsRMV79uDAkZyqeRH6vqr4WLy4kjzCPctv5bQ2vr2Q5HoyOZhRl0qaDNZ0s2KmIMkztcyMId8+u0lltiXgLpBem2nx9lBgUPZlfKTorNxby/523icmKZM+QxHE01e6Df08mLEPeQJlvnb+mRH3hm4xOMCBtJ38B+fHf463JjVkX/Sne/HkR4tsHZ5Mwd/Wfz8uj/8MA5D9W4GtRgMHBdjxnsSNpe5QMgSflJ5Jfk0867g932QcGDOZJxpFpf15+O/kCgayBDQ4fbbe/o05neAX344fB3NYpfpDlT4k9alZyiHNwd3Rt8Hn8X/3pZ48+5uST+Tq7xV9Gbhg0nfsfBaGJo6HB6BvRmb8qpFX9JGOCMbzTdHNwwWy0UWZpP25eWJCY7BisQmx3d1KGIiIiIiEgzknpyuYambPVZZlTEGG7vP5svDnzOJ/s+4sF19/H94a+5s/9dXN71Kn6PXYvZYm6UWPal7sXN0c3WLs7N0Q1vZx8SKuiksjl+E3uT9vLY7w9RaC5slPiqIyHnBCEeoZXuv77njURlHWd93LpGjErq24mcWMIaaX0/KK3Q7eDdEah4fb+W7pY+t1FsKWLjiQ3VGm+1Wst9DlaWnDo98TcwaDAFJQV8tv+/fHPwS/7R+1bba1lTnXy6cDjjcK2OrS2r1cpHe5cwf/srTO10GY8Me4Irul5NZOo+9qdF2sZlFmawJX4T49tdYHd8v6ABjG07vlZznxcxmjaebfnf/k/POC4q8zgA7U9bM3Fg8GAAfj720xmPzynOYXX0SqZ0nFphMn1qp0vZmbyD45nHqh+8SDOmxJ+0KjnF2Q3e6hNKK/5S65j4KzQXNpvEX4BrIGarhYzC9HL71sWuZUDQIDycPOnt35ejmYdta/Yl5iXg7xpwxid+3E4mYvOKcxsm+LNcYl7pou7JlVRkioiIiIhI65RysgtNU1f8lZnW5Uqu6HoVH+x9j+is47w4+jUu7XI5Y9qMI6soi7+StzdKHPtS99DDr6fdB78h7iEk5MbbjUvNTyU5L4nr+1zP/rT9PLvpqUZLTlalrOKvMj39ezEweBCf7PvwrOiuU2Qu4vvD37Au9rc6VWu1NHE5cUR4RDTqnF1Otvs82yr+ALycvenp35tN8ZUn/nKLc/kj7nf+s+1lZvx8DVf/eBkH0w7Y9u9N3UO4R0S5Nrudfbvg5eTFx/s+oE9gPy7velWt4+zs04UjGYca9d7dnbKTj/d9wMzet3Bn/7swGowMDR1OiHuIXdXfutjfsGJldMT59Ta30WDksi6Xsyn+D5LzKv9sKzr7OM4m53JrT/q5+DOty5Us2fMub/21oNKf06uiVlBsKWJyh4sq3H9e+Gh8nH348ej3tb8YkWZEiT9pVXKKc/Bw9GjwefxdA2xPV9aGxWqh2FLcfFp9ugYBlPsFnFGQzp6UnYwMHw1A74A+WKxW9qftA0oTfxUtBn2qsgrMXCX+aqWsnaoSfyIiIiIicqqU/GQMgJ+LX1OHYnNr3zu4f/Ac3hz/Ln0D+wOlH3KHuoeyNmZNg89vtVqJTN1ntz4XQIhbKPGnJf4Oppcup3Btn2t56txn2BK/iVe2vtDk7TOtVivxOScIPUPFH8DV3a7jaOZRu2qdlshqtfLqthdZuGM+z2x8kqt+vIxbfrmRRTsXUGQ+ezsHlVhKSMyNJ6yRE3/DQkfQN7A/QSc/BzrbDA0dzvbErRV+7/yVtJ0rfriYpzY8xl9JOxgaOoIQ91DmrLvPlvyLTN1brtoPSpNXA4MH4+rgyv+d83Cd2rN29ulCdlE2SXmJtT5HTUVnRWM0GLi623W2FsJGg5FLOk9jbcxqW1ezlVErOCdkKD4uvvU6/7i2F+BscmHZsR8rHROVdZy2Xu0qfG3v6D+b2QPu5rvDX/H4Hw+RU2zfntlqtfLjke8ZEXZepWtmOpocmdJxKr8eX24raBBpyZT4k1Ylp6hxEn9+Lv5kFKbX+g1B2T9AnE9b5LepBJxsDXP6On8bTz4lNTxsBABtvdrh4ejB3tQ9ACTnJdnWS6iMm0NZxZ9+qdZG4sl/CKac4akoERERERFpfVLyk/Fx8a3xmksNyWgwMqnDFALd/q5CNBgMjG5zPuvj1lFiKWnQ+eNyYskqyqKHf0+77SHuISSelviLTNuHj7Mvwe7BDAsbzoNDHuHXqF/4fP8nDRpjVdIK0ii2FBPqHnbGcf2DBuLv6s/K6BWNFFnD+CTyI1ZF/cpDQx/n0wu/4uGhj9HTvxffHPyS32JWNXV4DSYhNx6L1UqYx5m/zvVtWNgIXhkzv8L1I88Gw0JHUGgu5K+kHeX2fX3wC9p5tePDyZ/yweRPmD3gbuaNepk2Xm2Zs+4+difv5EjGoXIPDpS5vd9s5o99kxD3Myflq9LJpwtAo67zl5SfSIBrICajyW77pPZTMBkcWHrkB+JzTrAvdS/j2l5QyVlqz83RjbFtx7Ps6I+V/h6IzoqirVe7Ss9xSedpPH/eS+xL3cvdq//JtsQtts9l96TsIirrOFM7XXLGOC7seDGF5gJWtfCfmyKgxJ+0MrnFOY3S6tPf1R+L1Vpha8zqKD653l1zqfjzdvbBwehQLvG3PnYtvQP64nvyCVKjwUhP/162df4ScxMIdj9zxZ+boxsAeSWq+KuNxLzSir/TvzYiIiIiItK6peanNJs2n1UZHTGW7KJsdiRta9B59qbuwQD08Ds98RdGcn6SXYu4A2mR9PDvaUuAjG07gamdLuH7w980adVfWUvSYPczP2RrNBg5v8041sasafCEakNZHf0rH+59nxt7/YOxbccT6BbI2LYTuG/wg/QO6NsoVaJN5UTOCQDCPds0cSRnl3Ze7QlxD2FT/B9221PyU/gzYRMXdrzEbl1FD0cPnj/vJdp4teX/1t6DxWqlZ0D5ij8o/Sywtuv6nSrANQBvZx+7xJ/FamHJnnc5kLa/zuevSFJuxR27PJw8uaD9JJYe/Z7lx5fh6uDK8LBzGySGqZ0uIa0gjY0n/ii3z2q1cjzrGO29OpzxHIOCz+GNcYtwNDrw0LoHuPHna/k08mO+OPg54R4R9A8aeMbjg9yCGB42ku8Pf0t+SX6drkekqSnxJ62G1WolpzjH1lqyIfm5lJaNp+XXrvd8QUnpouHOJpd6i6kuDAYDga6BJOcl2bblFOewPWkbI8NH2Y3tFdCHfal7KTYXk5yfVGWrTzeHk4k/VfzVSmJuAo5GR5Lzk6oeLCIiIiIirUZKfjL+J7u3NHedfDoT5hFeLpGTV5zHj0e+q5f3i/kl+XwW+V96B/Qt90BwiHsIFqvV9r7KarVyMO0A3f172I0b1/YC0grS2JOyq87x1FZCbmlC6Exr/JUZ1/YCMgsz2J7YsAnVhrA7ZRcvbfk349tdwPU9ZpTbPypiDNuTtpJdlNUE0TW8EzmxOBodCWwhyfuWwmAwMDR0BJtObLBbQ29l1C+YDA6c33ZcuWPKkn9d/brj5exdZfKpPmLs7NPZLvH33u7FfBr5Mf/+81mKzcX1PmdiXiLBlXx+d3Gny0gvSOeLA59ybvh5uDg0zGeVnXy60NO/Fz8e+a7cvpT8FPKK82jn1b7K87TxbMtb49/jtfMX0DewP59EfsSmExu4sOPUarVgvbzrVcRkR3HlD5fw7Man+D12LYXmwlpckUjTUuJPWo0iSxEllpJGa/UJkFJQu3X+ik7+QnEyOdZbTHUV6BbEwfQDHEjbT0p+CptO/EGJpYRzT0v89Q7oQ35J/smSeitBVbX6PJmIVcVfzRWZi0grSKOrbzcyCtIb5B9/IiIiIiLSMqUWpLSYpEFZu88NJ9bbqtOKzEU8ueFRXt/+Gnev/qet0q223t75Jsn5Sdw7+P/K7StrzRd/ssoqLieWnOIcuvvZJ/56+PckyC2INdFN12IyITcBL2dvW/ecM+nk05l2Xu1Z3cLa1h3PPMZTfzxKT//e3DfowQrbTo6MGI3ZYmZDBdVBZ4PYnFhCPcLqtFacVGx42AiS85M5lnkEKE30Lz+2jFFtxlT6maGHowcvj57PovHvNcrXpLNPFw6nHwRKK1+/OPA5UztdwomcWL459GW9z5eYm0BQJVXE7b07MDB4ECUWM+Pb1X+bz1NN7XQJO5K2E5sdY7c9Ovs4AG09K2/1eSqDwUDvgD783zkP87+p3/LE8Ke5pPO0ah3bJ6AvH0z6lOt7zCA2J5qnNz7BDcuuJqsws0bXItLU9NtDWo2cotKFXd0bodWnn4sfBiAtP7VWxxdayhJ/zaPVJ5Q+ebMjaTuzV93GtUsv54U/n6erbzeC3OwXfO7q2x0Ho4nV0SsBKn1iqIyTyQkHowO5xUr81VTZQs+9AnpjBdtiyyIiIiIiIsktqNUnwOiIMWQXZbM9cRtmi5nnNs1lb8pu7hv8IIXmAu5cdRu7kv+q1bk3xW9k6dEfuL3fbNp4ti23P8gtGAOQcHIphQNpkQB08+tuN85oMDKmzVjWxa1tsvaZ8bknCKniAdsyBoOBsW3Hsz7u9xbTZedEThxz1t2Hv6s/T414BsdKHogOcA2gT2A/1p2l7T5P5MTatZyU+tMnoB+uDq5sit8IwJ7U3cTlxDKp/ZQzHudkcrJbn7QhdfLpQkp+ClsSNvPylhcY3+4C/jXgXi7pfDn/jfyQ5Lz6W+7FbDGTWpBCkGvln9/d0HMmw8POZUDQoHqbtyKjIs7H08mTpUe/t9selXUcR6MjobVY89LD0YPzIkbjZHKq9jGhHmFc22M6iya8z+IJ75FVmGn7nFOkpVDiT1qNnOJsgEap+DMZTXg7+9Q6EVNkLl3jz7kGv5Qa2h39ZvPF1G95c/w7PHPuPO4eeD/3D36w3DgXBxc6+3Rlw4n1AFW2+oTSqr+W8iakOSlL/PUO6AtAstb5ExERERERSt9TZhVmtphWnwAdvTsT7hHBbzGreHXbi2yO38ATI55hcocLWTBuMR28OvLg2ntZeuSHGp03vSCNV7b8m6Ghw7io48UVjnEyOeHvGkD8yTaa+9P2E+4RgZezV7mxY9qMI6sws8HXI6xMfG58jRJCY9uOp9BcyMaT79Gbs6S8JB5cey+uDm68MOpVPJ3Kv/6nGhUxhm2JW87Kdp8nck4QrsRfg3AyOTEo+Bw2xW8AYPmxnwh1D6VvYP+mDewUXXy7AvDkH4/Swbsj9w76PwwGAzN6zcTF5MLbu96st7lS8pNPduyq/PO73gF9ePrc5xu82tHJ5MTE9pNZcXy5XXvN6Kwo2nq1bZIK2I4+nRkaOoLlx5c1+twidaHEn7QaZRV/jZH4g9JFfWuf+Gtea/xB6ZOCvi5+dPHtyrCwEVzU6WI6+nSucGyvgN4UmgvxdPKsVvsRdwc3tfqshcS8RLuF6U9dg1FERERERFqvsveiAS0o8VfW7vPXqF/49fhyHhzyKMNChwPg5ezNv0e9zIWdLmH+9leq3WrTarXy6raXsGDlvsEVt4wsE+IeSlLuyYq/9Ei6n1btV6azT5eTCcrVNbzC+pGYG09IJS35KhLiHkov/96siv61AaOqu7SCVB5cey8AL45+DV8XvyqPGRk+GovVwh9xzT+pWRNmi5mE3BOEe0Q0dShnrWFhI9ifuo8TOXGsjVnDxPZTmlVb1TCPcFwdXPF08uSpEc/ZqtU8HD24te/t/Bazmr+SttfLXIknK52D3at+cL8xXNjxYrKLsnl5y79ZtHMBb+z4D3/Gb6rW+n4NZVKHKRzJOMyRU9ZdFGnums9PNJEGVtZK0r2REn9+Lv6k5Ndujb+CkubX6rMmevn3ASjXBrQybo5u5Krir8YScuPxdw3A29kHVwdXUlTxJyIiIiIi/N0NxN+l5ST+oLQ6zcXBhdkD7mFs2/F2+xyMDszufzfntxnHa9teKrcGVEXWxKxi04kN3D/oQfxc/M84Ntg9hPjceIrNxRxOP0TXShJ/BoOBMW3Gsj5una1bT2W2J26t1yUZSiwlJOcnEewWWqPjxrWbwLbELaQXpNVbLDW1MuoXjmceq3Cf1Wrl0d/nkF+Sx4ujX6v2Zwn+rv6l7T5j69bu82DaAT7au6RO56hPiXkJmK0WtfpsQENChgLwytYXKDIXMqH9pCaOyJ7RYOTeQf/HvFEvl2svOr7dRHr692LBjvn10nK4rJtUdTp2NYYIzzZM7nAh+9P28Wf8ZvYk78TXxY8xbcY1WUxDQobh4+zD8mM/N1kMIjWlxJ+0GrZWn06Nl/ir7T/wiy3Nr9VnTfQOKEv8Ve8pRHdHD1X81UJiXgLBbiEYDAYCXANrnWgWEREREZGzS8rJ9Z8CGmk9qvrSzqs931y8lIs7X1bhfoPBwD2DHsDfNYCnNz5u1wquIj8c/paBwYMYET6yyrlD3cNIyI3nWOZRii3FdPPtUenYMW3Gklecx9aEPysdczj9EA///gCfRX5S5dzVlZyXhMVqJdSjZom/0RHnY8DAutjf6i2Wmig0F/LK1hdZdmxphfszCtM5nHGI2/vNrnGya1TEGLYnbq1Tu8+V0Sv4774Pms0SJHE5sQCq+GtAvi5+dPPrwa7knQwOGVLtZHNjOr/tODp6dyq33Wgw8q8B9xKTHcULfz5Hfkl+uTH5JfmsjPqlyocToLTFrpeTF64OrvUSd324b/CDfDzlf7w/6WMWX7CEhePfZnjYuU0Wj8loYkK7iayO/rVar6lIc6DEn7QaucU5mAxGXBqpfaafqz9p+bVL/JW9eWmpFX++Ln6082pPO6921Rrv5uBmq8iU6kvKTbS1YghwDSA5X60+RUREREQEUgtScHFwwd3BvalDqTFHk+MZ97s5uvH48LnE5cSxYMd/Kh0Xkx3N3tQ9TO5wUbXmDXEPJa0gjd0pOzEZjHT27VLp2PbeHejg3aHSdp8Wq4X521/BYrXW61qAZWsQhtSw4s/L2ZtzQoexMmpFtY/ZnxbJH3G/12ieyhxIi6TEUkJCbnyF+xNOtlht49m2xuc+L6Lu7T6PZx7FChzLPFrrc9SnEzlxOBhNzaYC62xVlkia2H5KE0dSc519u/Dw0CfYFL+Bf626neisKNu+jSf+4JZfZvDCn8/za9QvVZ4rMTdB32vVcEH7yWQVZdnWhhRp7ppN4u+TTz5h7Nix9OnThyuvvJJdu3adcXxWVhZz585l5MiR9O7dm4kTJ7J27Vrb/rFjx9KtW7dyf+bOnQtAbGxshfu7devGzz//XbZb0f6ffvqpYV4EaVA5RTm4O3mesad/ffJ38Se9MA2L1VLjY8ueHnFuoYk/gFfHvM70njdVa6ybo1uzebKuJUnMSyDYvfQNX6BbkFp9ioiIiIgIACn5yfi7BDTa+9/G1tG7E3cNvI/lx5ax4njFrdeWH/sJTydPRoRVXe0HEHKyY8262N/o4N2pyvfjY9qMY8OJ9RVW2yw7upT9aZFc3PkyorKO11u7z/jceAzUriXf6Igx7E+LrHa7z/d3v82rW1+s1Wcap9udXPoZX2Klib/S7TVZu7CMn0vd232WJfwOZxys9TnOZEfiNqKyjld7fFxOHCHuYc1qzbmz0YR2k7i482VNWklWF2PajGXBuMVYsTJ71W0sPfIDT/zxCE/88QjtvNrTyacTm+M3VnmexDwl/qqjvXcHuvv14Jdjy5o6FJFqaRa/QZYtW8a8efO48847+fbbb+nevTs333wzqakV/8OoqKiImTNnEhcXx/z581m+fDnPPPMMwcF//5D66quvWL9+ve3PkiWlvbonTSrt2RwaGmq3f/369fzrX//Czc2NUaNG2c03b948u3Hjx9v3mZeWIac4G49GWt8PwN81gBKLmazCzBofW2guwMFoatH/yPNy9q524tLNwZ08VfzViNliJiU/mSDXsoq/QJLzVPEnIiIiIiKQmp9KgGvLavNZUxPbT+aC9pOYv/3VcpVkJZYSVhxfzti2E3Cq5hIaIScfqtyXupfufpW3+Swzps1YCs2FbDphX/2RXpDGe7sXM7H9ZKb3mAHAX0nbqxVDVRJy4wl0C6qyKrIi/YMGnYxlR5VjC0oK2J2yi6yiLCJT99V4rtPtSvkLKE1cWq3WcvsT8xJwd3THw8mzVucfHXF+rdt9ZhSkk1GYAcCRjMO1mv9M8orzeHLDoyzZ8261jzmRE0uE2nw2uEC3QP414J5q/4xojtp5teeNsYsYFjqC+dtf4WD6fh4fPpfnRr7ImDbj2JG0rcrWlEl5SQTXIuneGk1sP4WtiX9qqR1pEZpFVmHJkiVcddVVXH755XTu3Jm5c+fi4uLC119/XeH4r7/+mszMTBYuXMigQYOIiIhgyJAhdO/+98LLfn5+BAYG2v6sWbOGtm3bMmTIEABMJpPd/sDAQFauXMnkyZNxd7dvheHl5WU3ztm55VZhtWa5xbmNmvgrWzi8Nk/2FZoLW2ybz9pwc3Qjr0QVfzWRnF+6tkNZq89A1yDSClIxW8xNHJmIiIiIiDS1lPxkAtwCmjqMBndn/7vxcvJiwY7/2CWUtiRsJqMwg8kdqt/CL9AtCNPJh2+7VSPxF+YRTt/Afry0ZR4f7V1iW7Lj7V1vYTAYuLXv7fi6+NHeqwPbE+un3WdCbrwtQVlTAa4BtPVqx87kqhN/u5J3UmIpwcHowJ8Jm2o1X5kSSwn7UvfSxbcr+SX55BRnlxtTel21TzycGz4Ks9VSreqm05VV4nX369Egib+VUb+QX5LP7uSd1aqetFqtxGbHEqbEn1STm6MbDw99nJdGv8Z7Ez9mVMQYDAYDQ0KHUVBScMZ73mq1lnaTUsVftYxpOxYHoyMrq9FCVaSpOTR1AEVFRezdu5fbbrvNts1oNDJixAh27Kj4B9Pq1avp378/Tz/9NKtWrcLPz4+LLrqIW2+9FZPJVOEcP/zwAzNnzqy0zcWePXuIjIzkiSeeKLdv7ty5PProo7Rp04ZrrrmGyy+/vEbtMoxGA0bj2dleoyXJN+fh6eyBg0Pj5LuDPAIxGCCjOL3Gc5ZYi3E2OTdarI3BZDLa/fdUns6e5JfknVXX29BSC5MxGCDCKxwHByMhnsFYsZJVkk7gGRalLigp4HjmMbr7V/1GVlqvM92vItL86J4VaTl0v0pjSS1IoXdg77P+PZaXgwf/GnQ3T/3xOJsS1nNem9EA/BK1jK5+XekW0K0GZzMS7B5CfO4Jegf1wsHBWOU9O2/0i3yy7yM+P/AJv0YtZ1LHKayKXsED58zB390PgMGh5/B77FpMJkOdW68m5sXTzrt9rb+ug0IGsSV+c5XH70jeSpB7EH0C+rAlcRO39p9Vq/kADqUeptBcwKSOkzm8/SBJBQn4uvnYjUnOTyTUI6zW1xXsGUgP/x5sTtjIpE6Ta3RsdM5xHE2OjGl7Pu/vfheD0YrJWP6zxdqwWq38cPRbgtyDSM5LIi43mg4+HcuNK7GUsCt5Jxvi/mBD3HoScuPp6NvhrL9/G0Jr/j07OGyw3d+7+HUmyD2ILYmbGR4xvMJjMgrSKbYUEeoZou+3avBx8GJUm9GsiFrG9b2mn7XttBtLa75fG0OTJ/7S09Mxm834+/vbbff39+fo0YoX1Y2JiWHTpk1MnTqVt99+m+joaObOnUtJSQmzZ88uN37lypVkZ2dz2WWXVRrHV199RadOnRg4cKDd9rvuuothw4bh6urK+vXrmTt3Lnl5ecyYMaPa1+jn564fBM1AsaGAAE8/fH0bZ3Fzd682mExGiky5NZ7TwRk8Xd0bLdbG5OXlWm5bkI8fhdb8s/J6G0pucgYmk5Fu4R1xdnCms6UdJpORQoccfH07VHhMSl4KD/52L5HJkfxw7Q+EeYY1ctTS0lR0v4pI86V7VqTl0P0qDclqtZJZnE77wDat4j3WxT5TWB23gsV7FjK+xxjyi/PZkriJB0Y8UOPrb+/flhxzFv3a9bRbeqOye9YXdx4Mup9rBlzJqxtf5b+RHzAgrD/XDrrSdvzozufy3ZGvyDWl08a7Te0vFEgpSmJs4Jhaf13P6zSCH49+R6FjNiEelVfY7UzbxqgOIxkUOojH1zxOiVMege61ax17NOYAbs6uXNx7Cm/tfINcQ0a5+FOKkhjZdmSdvl8ndB3HB399gLuXY41aNyYUxdIloBNDOgzk3T0lZBqS6eTbqdZxnOrPuD+Jy41h/qT53LfiPg7nRTKwQx+7MRarhenfTOdg6kGC3IMY3XEUo9qNYljEsBa9/EtT0+/ZUud3HM2muE34+LhV+Nl0QnI0JpORLqEdWsXvi/pweZ9L+dfPq0i1xtPFr0tTh3NW0P3aMJo88VcbVqsVf39/nnnmGUwmE7179yYxMZH33nuvwsTf119/zahRo+zWADxVQUEBS5cu5Z///Ge5fXfeeaft/3v27El+fj7vvfdejRJ/aWm5qvhrBlJz0vHy9iU9vfHWkvN08OZgwhHSg2o2Z3p2NgaLqVFjbWgmkxEvL1eysvIxm+3bW1iLTOQXFZCUklGrtQqaG6vV2uDJ/kOJx/B08CYvu4Q8SnAq9sBstnAkMYoIp/JPEB7JOMwj6+ZgsVqwWuC3g+uZ3PHCBo1RWq4z3a8i0vzonhVpOXS/SmPIKswkv6gAV4vnWfWe8kxm9Z7NzGU38Pr6hfg4+4LFyFD/82p8/V08u+OCO5kZ+UD171lP/Hly6HPs7riLcI9w2/EAHVy7YbXAmoO/M7XzJbW7QErXikvJScXb6F/rr2sn1+5YzFZ+O7ieiR0qroxLykvkcMoRrut6Iz08+2IxW1kRuZopnS6q1Zwbjm2mm08PTIWuOBqcOJRwjIG+f8dvsVqIzTiBV3u/On2/9vMZTHbBAtYcWM+Q0KHVPm5fwgHC3NsQaAzHbLawLWonfob6We/so22f0MajHT08+tHZuxsbjm1mQpj9+/D9qZFEJu3nseFPcX7bsbbPEk79HpLq0+9Ze/18B/HFni/ZFR1JW6925fYfjD+K2WzBtcSr1fy+qKuOrt1xMjjzc+SvBPTSA/V1ofu1dqqbpG/yxJ+vry8mk4nUVPt10FJTUwkIqLgffWBgIA4ODnZtPTt27EhycjJFRUU4Of39ZE9cXBwbNmzgjTfeqDSG5cuXU1BQwKWXXlplvP369ePNN98sN8+ZWCxWLJbyixdL48oqzMbN5E5JSeP9IOkb0J/NJzYzo+fNNTquoLgAR4NTo8baWMxmS7nrcja6YrVCdkEOXs7eTRRZ/bBarTyx4RECXYO4a+C9DTZPfHY8QW7BttfS1eiOo9GJhOzEcq/vn/GbeXbTk4R5hPHMuS/w1IZH2Ra/lQlta9YCRVqfiu5XEWm+dM+KtBy6X6UhJWQnYrWCj5N/q/k+C3IJ4foeM/ho7/v4uvgxImwkbiaPGl//9B4zAcodV917todv73LHOxtc6ebbg63xW5ncfmqN4jlVVEY0VisEuATX+uvqZvKkg3cntsZvZVybiRWO2Rz3JwYM9AsYgLvJi+5+PdkYt5EL2lV/vcQyFquF3cm7uLTz5ZjNVoJdQ4nLPmEXf0p+CkXmIoJcQur0/drGvT3BbiGsj1nPwMBzqnWM1WrlWMZRhoYMx8XoRrBbCAdTD3J+xIRax1EmMTeBDXHr+deA+zCbrfTx78vyY8soLjbbPSi8Nnotno5enBs6CrPZCujzw/qg37Ol+vgPwMHgyB8xfxDWrXzFcXx2Ao5GJ9xNXnq9qsmIA4ODh7I+5neu7XZDU4dzVtD92jCavGbcycmJXr16sXHj3wvwWiwWNm7cyIABAyo8ZuDAgURHR2Ox/P0Ncfz4cQIDA8sl47755hv8/f0ZM2ZMpTF8/fXXjB07Fj8/vyrjjYyMxNvbu9pJP2k+coqy8XDybNQ5h4UN52D6AVLyU2p0XKG5EGeTcwNF1fy4ObgBkFeS18SR1N2amJVsOrGBH498x56U3Q02T1JeIsFufz+FaDAYCHQNIiU/2W7c4fRDPP7HHPoF9ufVMQsIdAukf9AAdiRtx2rVGwoRERERkbNNSn7pg9UBrrVrzdhSXdH1akI9wknJT2FSh5onqRpS/6CB/JW0HYu19h9sroxagZeTF918u9cplgEnY6ns/eDWhD/p6tsdTycvAIaEDGNb4haKzcV24yxWS5XXE5V1nOyibPoE9AUgxCOUxNx4uzGJuQkABLvXrcrOYDAwPGwkG0+sr/Z73eT8ZHKLc2nvVbpcRmefrhzOOFzlcVmFmSTlJZ1xzNKj3+Pq4MbYtuMB6BPQn4zCDGJzYuzGbTixnmFhI+ptXUGRU7k4uNA/aACb4zdVuD8xL5Egt2AtUVVDI8LOrdXnvSKNqckTfwAzZ87kiy++4Ntvv+XIkSM89dRT5OfnM23aNAAefPBBXnnlFdv4a6+9loyMDJ577jmOHTvGb7/9xuLFi7n++uvtzmuxWPjmm2+49NJLcXCouLgxKiqKLVu2cMUVV5Tbt3r1ar788ksOHjxIVFQUn376KYsXL2b69On1ePXSWHKLc3F3bNx+1UNDh2M0GNgcv7HqwacoMhfVqCd9S+d28uuSW5zTxJHUTW5xLot3vsnI8FF09e3Ggh3/qdMbuzNJyEsg2M2+fXGAa2C5f3T8ceJ33B09eGrEc7g5liZY+wcNJK0gjZjs6AaJTUREREREmk5KfjIGwM+l6oebzyZOJiceGvIYUztdyoCgQU0djp1BwYPJKsriaMaRWh1fZC7i16jlXNB+Up0/K+gfNIiU/BTicmLL7bNYLexI2sbgkCG2bUNDh5Nfks+elF128dy7Zjb3rplNXnHlD/DuTt6JyWCkh38vAILdQkjIS7Abk5hXmggMcQut03UBDA8dQUp+CoczDlVr/PHMYwC09y5N/HXy6cyRjENnTBxarVYeXT+He1b/s1wytEyhuZBlR5cysf0U2/vw3gF9MBoM7Ez6yzYuJjua6Kwozg07r1rxitTGsNAR7EnZSU4Fn7mVPlRe8dJYUrkhocMwGgxsOrGhqUMRqVSzSPxNmTKFOXPm8Prrr3PJJZcQGRnJu+++a2v1GR8fT3Ly31UsoaGhvPfee+zevZuLL76YZ599lhkzZjBr1iy7827YsIETJ05w+eWXVzr3119/TUhICCNHjiy3z8HBgU8++YSrr76aSy+9lP/973889NBDFa4jKM1bkbmIYksxHo4ejTqvp5MXvQP6sunEHzU6rtDSSiv+zvCGoSX4774PyCnO4fb+s7lzwN0cyTjMz8d+qvd5LFYLyXlJ5Z6IDHALIPm0pw63J26lf9BAu6cHewf0xWQw8lfS9nqPTUREREREmo7VamV93FrCPdvgYGzy1V0aXTe/7tw18F6MhmbxcZdNd7+eOJmc2J60tVbHr4tdQ3ZRNhd2vLjOsfQ5+X5wRwXvBw+k7Se7KJvBwX8n/jr5dMbPxY/NCX8/0PzmX69zKP0gx7OO8cQfj1BkLqpwrt0pu+jq1x0XBxcAQt1DSciNt0usJeQm4OXkZUuQ1enaAvvh7ujOxmp+BnMs8wiuDq4EnUx8dPLpTHZRNsmnddI51fakrexPiyQ5P5kVUcsrHPNbzGqyirK4uNOltm1ujm509unK7pS/bNv+iPsdZ5Mzg0Kq15pUpDaGhg7HbLWwPbH8z5/E3AS7blJSPaWf9/ZjU3zNPu8VaUzN5l9C06dPZ82aNezZs4cvv/ySfv362fZ9/PHH/Pvf/7YbP2DAAL744gt2797NypUruf322+3W/AMYOXIkBw4coEOHDpXOe9999/Hbb79hNJZ/KUaNGsV3333Hjh072LFjB99//z3XXHNNhWOlecspzgbAvZETf1D6C3Zb4lYKSgqqfUxhSQFOrSnxV1bx10JafW5J2MxXB/9HobnQtu145jG+PfQV1/eYQbBbMD39ezGu3QTe3/MO2UVZ9Tp/WkEaJZb/Z+/O4+Oqq/6Bf2bft8xMksnWNknbpE3bdC+llFJAkE2WB1BZpG64ID6ioj83qKiIPriA+ICCiMIjggVFBBQoFEpbuu9Jm6ZL9mQyySSzr/f3R5KhIXsykzuTfN6vFy/tvd9770mT28zMueec6IAXZx9u9emNeFHdcRRLspf1W6eRa1CWNQ/7nfuSGhcREREREaVeKBbCQef+QfdtbXwHu1p24nMLvjC5QdGwlDIlFtgWjvvhy3+d/Ccqs5egwDBwRtdYaRVazM0qHzSW3a07oVPoUJZVntgmkUiwwrEKO5vfBwD8+/Sr+NfJf+IrS76GH615AFUdR3DfjnsQjUf7nUsQBBxqP5Bo8wkAuToHwrEw3KHOxLYWX/OE23z2kUvlWJG7Ctuato5q/enuU5hhnJlIFJea5wAAaoepGHzm6J8wxzIX5xWcj2ernx7wdYdiIfyl6mksz12BfENBv32L7JU46DyQSHy+1/guluWumFYPftPky9HlYoZx5qDVaW29rT5p7FbnnYu9rXsQiAbEDoVoUMxg0bTgDfeUs+uUk5/4W523BpF4BPva9oz6mGnX6jNR8ecTOZKRecMe/HTnj/HYgd9iw6s3YXPd64gLcTyy/yHk6Bz4rzk3JtZ+dsEXEImF8eejTyU1hr4ZCNm6/i/O7Bo72gPORHvRQ84DiAsCluYsG3COyuwlONC2L2WtSImIiIiIKDW21G/G19/+Kv5es6nfdn/Ej0f2/xqr8lZjdf7ArkYkrsXZS3HQeWDI9pBDOd11CofbD+Hy4iuTFktl9hLsdw58P7inZRcWZy8dMG9uRe4q1Hvq8G7DFvx6z4O4dNZl+Oisy7HAthD3rv4Rdre8j5/t/Em/8zX7muAKuLDAXpnYltub4Gs+a85fi685qRVH5+Sdi1r3iRFn8AE9f7d9bT4BwKaxwag0Dtkq9KBzPw61H8TN8z6Fm8pvQYuvBW/Wvd5vzVOHn0CrvwWfX/ilAccvtFeiPdCOZl8T2gPtqO6owpp8tvmk1FvpWIVdLe/3u0cD0QC6w93I0THxNx7n5J2LSDwyaCUlUTpg4o+mhb4+1pPd6hMACgyFyNcXjLrVBNDzhNh0euJLI9dALpUlvTIuFf5S/TRC0SAeXPdrzM0qx/3v/wiffu0W7G/biy9V3tkvYWvT2PDJ8lvwjxObErMDkqG1dybCgIo/bTZiQjzx9OTu1l3I1eXCoc8bcI7F2UvQHe7Gqa7xzZggIiIiIiJx9CVNfrv/Ibxdvzmx/akjf4A37MUdlV8VKzQaxpKcZQjFQtjdunPINbF4bMC2f538J8wqc1LnwC3OXoLuUBdOd51MbPNGvKjqOIKlOQPbTi7JWQa5VIYf7bgHM02z8JXFX0vsW567Et9ZeQ+2NGzG3Vvuwo7m7YgLcRx0HoAEQIW1IrE2R9czx6/l7MSfvwUO3cTn+30QzwrIJNIRR67EhTjOdJ/GTOMHiT+JRNI75+/EoMc8ffQplJhLsMqxGiXm2VidtwZ/qXo68X070n4Yfzv+V9w2/zP9Eop9KmwLIAFw0HkA25veg1QiwUrHOeP/YolGaZVjNdwhN460H0psa/O3AgAr/sYpT5+PGcaZY/q8l2gyMfFH00JfxZ9eYRDl+qvzzsWO5m2jrq6KxCNQydQpjip9SCQSZKmtcAVdYocyrBZfM16o+RtumPsJLLRX4p7V9+HBdb+GQWnAhUUXYaVj1YBjrp19PeyabPz9xKZBzjg+rb4WGJQG6HpbpPaxaewAAKe/p93nvtY9g75pA4By63wopArsb2O7TyIiIiKiTNLmb8XcrDKsL7oID+z8Mfa37cWJzhr8/cTfcMu825LWNpGSq8RcihnGmfjBe9/BXW99BW+c+TdCsRCavI147thf8NXNX8JlL1yIX+7+eWKsRCgWwhtn/o1LZn4UCpkiabHMs1ZAIVUk5vwJgoBtje8iLghYNsi8OZ1ChwW2RdAp9PjBOT8c0KHovILzsXH1TxCI+vH9rd/Gba9+En8/8TcUm0ugV37wOYxeoYdBaUh0semZX9+a1J9ZvdKAhfbKEdt9NnkbEYlHMMtU3G97qXn2oK0+j7Qfxr62vfhk+a2QSCQAgJvKb0WjtwFv17+JYDSIn++6H2XWebh+7seHjK3YXIoDzn14r/EdLLQvhkFpHOdXSjR6820LkK8vwAs1f0tsa/Ux8TdR5+StTjzsQJRupt+kZ5qWfL0Vfx9OlEyWVXmr8fzxv+J457F+vfKHEooFp1WrT6AnadXuH3qAdjp44tDvYFKZ+r2IX2ivxMMXPjrkMUqZEgvtlajpPJ60OFr9LYO+MLNpbACA9oATWX4r6j11+NT8Tw8ZV4VtAfY79+G6OTckLTYiIiIiIkqtVl8rcrUOfH3Zt+EOuXHPtu8iW5ODIsNMvrZPY1KJFL+96PfY1rQV/zr5Tzyw8yf45Z7/QTgWhkKqwLLcFfh42c14/tizOOo6gu+dcy+OdVTBG/Hi8uKrkhqLUqbEfNsC/Of0q6juqMIB5z50BjsxN6sMuUNU39294ruIxiND7l+Vtxqr8lajynUU/6h9AVvqN+Pa2dcPWJetzUlU/LUH2hGNx5CrG9ilZiJW563BYwcfgTfiHbLz06neascPJ/5KzKV4/vhf4Q17+iUtn6l6CjOMM7Emf21i25ysuVjpWIX/q34aVR1VaPO34r419ydmBg5mkX0x3qp/A55wN76w6I6JfJlEoyaVSPFfc27EQ3sfRJO3EXn6fLT5WyGVSBIPkdPYrXKci2er/w9VrqOYb6sY+QCiScSKP5oWvBEvpBIJNHKNKNefb10Ag9Iw6vLvUCw07RJ/PRV/7WKHMaQj7Yfxdv1m3Db/s2P+OSoxl+J096mkPQHU5m8ddAaCSWWGXCpHe8CJvW27IUFPC5ehVGYvwUHn/kHbyRARERERUXpqC7QiW5sNhUyBH5xzH/L1BTjdfQp3Lr0Lcimf705nSpkS6wrX4+fn/xJ/vPQZ3Fz+KfzgnB9i08f+iR+e+xNsqPgsHrnod4gjji+/8Xn86ciTWJqzbNDxDRO1Ou9cnO4+hRZfMz4y41L8eM3P8PPzfzXkepvGNmTS72zl1nn49orvYdNVL2NDxecG7Hfo8tDsawIAtPYmAHOSXHG0Ov88ROOxYdt9nu4+BaPKBLPK0m97iXk2AODkWWMxjnVUY1fLTnyy/OYBSb2byj+Fuu4z+MeJF/DpBZ9DoaFo2NgW2hehM9iJaDyGc/I4i5Mmz8UzL4FBZcKmmucB9Pwusapt/L0xAeXWeTCrzNg+QoUxkRiY+KNpwRfxQq8wJNoxTDaZVIYVjlV4v3nbqNaHY2EopdMr8WfV2OAKpGerT0EQ8NjBR1BiLsFHZl465uOLTSUIx8Jo8NQnJZ4WX8ugrVCkEilsGhuc/jbsa92NUsscGFWmIc+zyL4Y/ogfNe7kVSMSEREREVHq9LRGbEu8H9AqtHhg7YN4cN2vscC2UOToaCzyDQX4RPnNOK/g/H4Pl84yFeM3Fz6G8wsvQKu/FVeVXJOS618z+7/wr2vfwMMXPorPLvwCVjhWJvVhaa1CO2hCIVeXi9be2WJ9lX+jSSiORbY2G/Os87Gl4e0h15zuOoWZxlkDPicqNBRBIVXghLsGcSGOV0/9C//v3W+g0FCEdYUXDjhPuXUezs0/D5XZSwatcPywvvt0tmUOsrXZY/vCiCZAJVPhYyXX4N+nXoEn3I023+DdpGj0pBIpVjrOwfZRft5LNJmY+KNpwRv2QKccvL3DZFnlWI1ad22il/1wQrEQ1PLpM+MP6Hl60BVIz4q/dxreRpXrKD6/8EvDtuwYSrG5BACGHBA+FoIgoNXfMuQTkXZNNpyBNuxp3Y2lOcuGPdfcrDJo5Brs753rQERERERE6a2nUija78Nag9KIhfZK8YKipNPINfjm8v+HP1/2LFbnp64qTIxKn1ydA23+FsSFOFr8LTCrzCn5/GNtwTrsbtkJb9gz6P7T3acw0zRrwHaZVIZZpmJsb9qGr799J36x+2dY4ViFB9f9esjPA35wzg/xwNoHR/V5gVFlwpr8tbii+GNj+4KIkuDKko8hLsTxz9p/oNXfihwdE38TNc9agfruM+ymRWmHiT+aFobr6z5ZluWugFQiwZ7W3cOuiwtxRONRKGWqSYosPdg0NngjXgSjQbFDGWBf2x7MMs3CkhESaUMxqcywaqz9WoWM12HXIYRjYeTp8wfdb9PYsbd1D9whNxZnLx32XHKpHAtsC7Gvbc+E4yIiIiIiotRr9fc8SMpKoekh2ZVw6SBH50A0HkN7oB0tvuaUfY1rCy5ANB4ddORKT0eeOswyFg9yJFBqno39bXvhDrnx8/N/iW+v+B4s6qwhryWVSMf0kPA9q+/DZcVXjHo9UbJY1Fn4yMxL8WLN39DkbUT2IGNkaGwsagsEAF1ht9ihEPXDxB9NC76IT/TEn16hh1FpQmewY9h1oVgIAKCaZjP+rGobAKRl1Z8v4oPpQ33/x6rYVIKTE6z4a/W14Ifbvo/51goszVk+6Bq71g53yA2FVIGKUbT6qcxegsPthxI/d0RERERElL7aelsksj0bZarc3kRDq68ZLb6WlCX+7Fo7KmwLsKXhrQH7Gjx1iAvCoBV/APBfc2/EnUvuwmMX/wGV2UtSEh+RWK6bcwPcITc6gh3IYeJvwvrmhLqDnSJHQtQfE380LXgjHuhETvwBPS1YusPdw64J9yZgplvFn1XTm/gLpl/izx/xQSvXTugcJebSCVX8+SI+fG/rt6GWq3Hv6h9BOURi2KaxAwAW2BcOueZs5+Sdi3AsjN0tO8cdGxERERERTY5WXws0cg30CoPYoRCNS998yhZfM9qGGWORDOcXXIA9rbvg+dDnMFUdVQCAmcaZgx5XaCjClSUfG9V7aqJMU2gowqq81QD4EEkyWNS9ib+QW9xAiD6EiT+aFrxhL/Qiz/gDAIPSMOAF54eFYxEAgFI6PRN/7WlY8eeP+qFRTGzIebGpFO2BdnSHusZ8bFyI4yc7NqIt0Ir7zv0pzOqhqw/tvS1/lmSPri1pgaEQs0yz8O4wQ8+JiIiIiCg9tAXakK3NgUQiETsUonHRyDUwq8xo8Dagzd+KXF1eyq61puB8xOIxbDur3ac72Ik/Hn4cq/JWQ69kAp2mp4/PvQlKmRIzTDPFDiXjmVRmAIA7xIo/Si9M/NG0kA4z/oDexF9k8MHSfUKxnhl3063Vp06hg0auSctWn/6Ib8IVo8XmEgBA7TjafT524LfY3boT31t175CtSPoUGWZAJpFipeOcUZ//vIJ12Nb0HsKx8JhjIyIiIiKiydPmb01phRTRZMjVOXC4/RDigoAcXep+nm0aGxbYF2FL/WYAgCAI+NXeBxGHgLuWfjNl1yVKd/NtFXjp6tf4+yQJNHIN1HI1Otnqk9IME380LXjDnrRohaJXGuAND5/4m66tPgEgS21Nz1afUf+EW30W6AuhkCrG3O7TH/HjhZrn8an5n8Hy3JUjri8yzsCmj708YoLwbOfln49ANIC9rbvHFBsREREREU0up7+Vrdko4+XqHKhyHQEAOFJY8QcAawvWYW/rbnjC3Xj9zGt4r/Fd/PeSr8OizkrpdYnSnUwqEzuEKcOsMrPij9IOE380LfgivrRo9WlUmuAZIfEX6q26mm4Vf0DPfDpXwCV2GAP4I37oFLoJnUMmlWGWqXjMFX8dwZ6/jwrbglEfM9ZYZ5pmocg4A++w3ScRERERUVpr87clZqQRZapcXS4i8Z4xJ6lOZJ9XcD7iQhwv1mzCI/sfwkUzPoLzCs5P6TWJaHoxqyys+KO0w8QfTXnhWBiReGTCiZtk0Cv0o5jxN30r/mwaa9q1+hQEAf6ob8IVf0BPu8+TXeNL/KX6acTz8s/H9qb3EOmdMUlEREREROnFH/HDE/Ygu3euN1GmytE6AABWjRXKFD/0nKW2YoG9En8++kfo5Dp8efFXU3o9Ipp+zGoLukJuscMg6oeJP5ryvL0z9dKh1adRaYQn7IEgCEOu+aDiTz1ZYaUNq8aG9oBT7DD6icQjiMZj0ComnvgrMZXiTPcZROPRUR/TEewAAFhUlglffzhrC9fBG/FiX9velF6HiIiIiIjGp83fCgDI1rLijzJbbm/Vas4k/SyvL7oIAPDN5f8PeoX43aCIaGoxq8zoZKtPSjNM/NGU5w17AQC6NGj1aVAaEIlHEOqt6htMKBYEMD1bffYk/tqHTYxONl+k5+dHK594xWixuQTReBR1njOjPsYd7IRCqoAuxW9OZhmLka8vwDsNb6X0OkREREREND6ticQfZ/xRZsvV9VT8OXr/N9Uum3UF/nzZs1ics3RSrkdE04tFncWKP0o7TPzRlOftTdykw1NdeqURAIad8xeJ91T8TcdWn1a1DZF4JFGlmQ78ET8AJKXir9hUAgA4OYY5fx2hDmSpsyCRSCZ8/eFIJBKsLVyHbU1bx1SRSEREREREk6PN3wqpRAKr2ip2KEQTkq3NgQRA9iTNq5RIJIlkIxFRsplVZs74o7TDxB9NeX0Vf+nQ6tOg7InBExl6zl9fq89U97lPRzaNHQDQnkZz/vzR3sRfEir+9EoDcrQ5OOmuHfUxHQFXyuf79Vmbvw6esAcHnPsm5XpERERERDR6bf4W2DXZkEllYodCNCFKmRK3zv801hVcIHYoREQTZlZZEIqFEsUDROlALnYARKnW16pRp5h44maijL0Vf95hKv7CsRDkUjmkkumXl7dqep5cdQXaMctULHI0PQLR5FX8AcAscwlqu0Zf8dcZ7Ji0xF+JuRQOnQP/Of0qQtEQqjqO4KjrKOwaG7698vuTEgMREREREQ2uzd/KNp80Zdw871Nih0BElBRmlRkA0BVyJ+3zQ6KJmn6ZBZp2vBEvpBIJNHKN2KEk2o12h4eu+AtGg1BNwzafAJDV27ImnSr+fBEfgOQljktMpWOr+Av2tPqcDBKJBGsL1mFz3Zu4Z9t38Z/Tr6Er5MaWhrcRF+KTEgMREREREQ2u1deKbB0Tf0REROnErLYAADpDbPdJ6YMVfzTl+SJe6BWGlM9IGw1DouLPO+SacDw8Ldt8Aj3tPowqEzqCLrFDSfD3Jv60yUr8mUvhDrnREXQlEp3DcYc6J63iDwA+Xn4z5tsWoMQ8G3aNHe81vYuN276PrpB7UuMgIiIiIqL+2vytWJhdKXYYREREdBaLqifx1xVyixsI0VlY8UdTnjfsgU6pFzsMAIBMKoNWoUV3uGvINeFYaNpW/AGATW1Nq4o/f9QPqUQCpTQ5ydhiUwkAoNY9crvPuBBH5yRW/AE9Vann5J2LbG02JBIJ7JpsAIDT75y0GIiIiIiIqL9oPApXsB3ZGlb8ERERpROj0gQJgM7gwIq/v9dswh1v3o5YPDb5gdG0xsQfTXneiDfRYjMdGJVGeIaZ8ReKhaGSqScxovRi1djQHkifJJMv4oNOoU9axahDnweNXDOqxJ8n3I2YEBe10i5b25v4C7SJFgMRERER0XTnCrQjLgic8UdERJRmZFIZjCoz3IO0+jzcfgjHOqrxnzOviRAZTWdM/NGU54v40irxp1Po4R0m8ReOhaZtq0+gJ/HnSrOKP608eYN5pRIp5maV40j7oRHXdgQ7AAAWlXiJP5PKDLlUDqefiT8iIiIiIrG0+lsAADmc8UdERJR2zCrzoBV/Tb5GAMCfjvwBwWhwssOiaYyJP5ryvBEPdGmU+Bu54m+at/rU2OEKpk/izxfxQatIXuIPABZnL8EB5/4Ry/w7exN/k9nq88OkEilsGhsr/oiIiIiIRNTmbwUAVvwRERGlIbPKMmjFX7O3CR+ddTk6gx34+4lNIkRG05Vc7ACIUs0b9sJmsosdRoJBaRxhxl8YCqliEiNKL1a1DZ3BDsSFOKQS8Z9NCET80Mp1ST1nZfYSPHn4cRzrrMY86/wh1/Ul/sxqS1KvP1bZ2hzO+CMiIiIiElGbvw1GpREauUbsUIiIiOhDLGoL3CF3v22ecDe8ES+W5iyHQqbEs9XP4PLiK2FQGpN23VdOvoyjrsOICTHEhRgEAfh42SdRbC5N2jUoM4n/qTpRiqXbjD+D0gBvxDvk/nAsBJV8Os/4syIuCIOWx4vBH/UnveJvrqUMGrkG+9v2DruuI9gBjVwj+pt7u8bOij8iIiIiIhG1+Vth752/TUREROnFpDLD/aHPMpu8TQCAPH0+bi6/FXEhjr9UPZ20ax51HcEv9/wcJ9zH0eprgSvQgfea3sXbDW8l7RqUuZj4oynPG/ZArzCIHUaCQWmAJ9w95P5QLASVdHq3+gSA9kB6VJj5I76kV/zJpDIssldib+ueYdd1BjuQpbYm9drjYddmc8YfEREREZGIWv0tyNHmih0GERERDcIySKvPJm/PfD+HzgGLOgvXz/04/n7iBbT2tu8eiSAIeKvuTRx1HRl03+8O/BYl5hL89qLH8YsLHsb/rPsV5lrK0OprnvgXRBmPiT+a8nwRH3SK5CZuJkKvMIw4408pU05iROnFqulJdHUEXSJH0iMVM/6AnnafR12HEYqFhlzTEeqAReQ2n0BPMrY94ERciIsdChERERHRtNTmb+N8PyIiojRlUpnRHe7q99lZs68JBqUBemVPQcp1s2+ATqHDn488OeL5nH4nvrv1bvzk/R/iu+/enUgi9tna+A6OuA7j8wu/1G9UUo4uFy2+liR9VZTJmPijKS0cCyMSj0CvTKdWn0b4Ir4hkyihWAgq2fSt+DOrLJBKJOlT8ZeCVp8AsDhnKSLxCI60HxpyTWewAxZ1VtKvPVbZ2hzEhHjatF8lIiIiIsok4VgYd731FdR0Hh/X8YIgoNXXgmy2+iQiIkpLWeosxAUB3aGuxLYmbyMcurzEn7UKLa4suRpbG98Z8nNhQRDw+unX8Ln/fAq17hP4zsofwKgyYeO27yEYDQIAIrEIHj/0GJbnrsCSnGX9jnfo8gYkCWl6YuKPpjRvpKeyTpdmM/6AnhakgwnHwlBO48SfVCJFltqK9kC72KEASE2rTwCYaZwFk8qM/c59Q65Jl8SfXdPzAQPn/BERERERjV1d9xkcaj+If9b+fVzHe8LdCMVCyNGx1ScREVE6MqnMAIDOs9p9NvuakKfP77ducfYS+CI+nOisGfQ8jx96FD/bdT9W5a3G45c8hQuKLsQ9q+9Do7cRv9r7PxAEAS+f/AdafE343MIvDjjeoXPAHXInkoQ0fTHxR1OaN+wFAOjTKPFnVBoBAN1DzPmLxMNQyadv4g/oaS3pSpPEXyAaSEmrWKlEisXZS7BvmDl/HcFOWNNixl/P3EXO+SMiIiIiGrt6Tx0A4N2GLQjHwmM+vq33dThbfRIREaWnvlE97rO6ZTV5G+HQ5/VbV5Y1D0qZEvudewecIy7E8eqpf+G6Odfj2yu+B0PvZ8jFphLctexuvHnmdfyl+mn8+egfcenMyzHLVDzgHLk6BwCghXP+pj0m/mhK80Z6En+6tGr12VPxN9Scv2A0CKV0+s74AwCrxgZXUPzEX1yIIxANQJuiGZGL7ItxvLM68XN6tlg8hu6QOy0q/oxKExRSBSv+iIiIiIjGod5TB4VUAW/Ei90tOwfsj8Vj2Fz3OqLx6KDHn3D3VAU4ej/MIyIiovRiVvUm/kJuAD2jnNoD7cjT9a/4U8gUqLAtwIG2gR3AqlxH4Ql7sLbgggH71hddhKtLr8OThx9HJB7BrfM/PWgcub2tRZn4Iyb+aEr7oOLPIHIkH9D3Pq0xVOIvHA9DKWPiLx0q/vxRPwBAI9ek5PxLcpYiLgg45DwwYJ875IYAwNL7wkFMEokEdm02K/6IiIiIiMah3lOHcus8FJuK8Vb9mwP2v3rqX7j//R/hrbo3Bj3+9TOvoTJ7SaKNGBEREaUXjVwDhVQBd2+rz77EW96HKv4AoNK+BIfaDyIWj/XbvrNlB4xKI8qyyge9xu2LvoTzCy7A5xd+CVbN4B3CrBorFFIFmn1NE/lyaApg4o+mNF8k/Vp9Jmb8RQZv9RmOhaCaxjP+AMCmtqXFjD9/pCfxl4oZf0DPwN0cbQ72D/KUT2ewAwDSouIP6Jnz5/Q7xQ6DiIiIiCjj1HvOoNBQhAuKLsK2pq2J9xlAz4z3/6v6EwDgpUFmADZ6GnDQeQCXzvzoZIVLREREYySRSGBRWxIz/pq9PYk3x4cq/gBgUfZiBKIBHO881m/7zuYdWJ67AlLJ4CkbuVSO751zL64s+diQcUglUuToctHiaxnvl0JTBBN/NKV5I15IJZKUVWyNh1qmhlwqH7TiLy7EEY3HoJKpRYgsfVg1VnjCnnHNv0gmf8QHACmZ8Qf0vCiozF6CfW27B+zr6E38ZaXBjD+gZ84fW30SEREREY1NXIij3lOPAkMhLii8EOFYGNua3k3sf+3Uv+AKtuOzC25HdUcVjnf0/xDw32dehU6hw5qC8yc7dCIiIhoDs8qCrqAbANDka4RCqhi0Mm+OZS40cg0OOD8oBHAFXDjhrsHy3JUTjsOhc7Dij5j4o6nNF/FCp9BDIpGIHUqCRCKBXqEfNPEXjAYBYNq3+rRp7ACAjqBL1Dh8vYk/rUKbsmsszl6CU12nEhV+ffq+dnOatPNhq08iIiIiorFzBpwIx8IoNMxAji4XFbYF2Nzb0jMcC+Mv1U/jgsILcf3cjyNbm42Xal9MHBsX4nj99Gu4oPDCad8VhoiIKN1Z1BZ0hHo+32vyNiFPnz9o9Z5cKkeFbQH2t+1NbNvV8j4kAJblrphwHLm6PLQw8TftMfFHU1ogGkirar8+RpVp0MRfOBYCgGn/pq6vys0ZELe1ZKB3xp82RRV/ALAoewkADGj36Q51wqA0QCFTpOzaY2HXZMMVbEdciIsdChERERFRxqjvPgMAKDQUAgDWF12EPa274A524pWT/0RH0IWb590GqUSKy4uvwua6N+AJ94yF2N2yC+2Bdlw663LR4iciIqLRMass6Aq5AQDN3kY4dI4h1y6yL8bh9kOIxCIAeub7lVnnJWWeb25vq09BECZ8LspcTPzRlBaMBqGRp65aa7wMCgO6w10DtofiPa0tp3vFn12bDQBoE7kftb838adL4c+QTWNDkXFGv/J+oKfVZ7q0+QR6vidxQYArIG4VJhERERFRJqn31EEulSO398O/tQXrIIEEb9T9B3+pfhrriy5GQW9S8KOzLoeAOP5z+jUAwGun/4VZplmYY5krWvxEREQ0OmaVGe5gz4y/Jl8THPqB8/36VGYvQSgWQnVnFaLxKPa07sKK3FVJicOhy0MgGkgkIWl6YuKPprRA1A+1PP3m5RlURngHqfiL9M60m+4VfzqFDjnaHNS6T4gahz/Sk/hLdfJ4kb0SB5z7+23rDHbAos5K6XXHIru3/Wqbv1XkSIiIiIiIMke9px4F+sJEqy+TyowlOcvwxKHfwR3qxM3ln0qstaizsLZgHf5Z+w+4g53Y3rQVl8y8LK1GVxAREdHgzGoL3CE34kIcLb5m5Onyhlw72zIHOoUOB9r24ajrMPwRf1ITfwDQ4he3oILExcQfTWk9rT7Tr+JvqBl/oVjfjL/pnfgDgFLLHJxw14gagy/ihUqmgkwqS+l1Ftor0eCpR3ugPbGtI+hCltqS0uuORV8VZrvI7VeJiIiIiDJJvacuUdHXZ33RRYjGo7h4xqXINxT023dlyTVo9Dbgwd0PAAAuLLp40mIlIiKi8TOrzAhEA2j0NiAajw5b8SeVSLHAvgj72/bh/ebtMKvMKLXMTkocubpcAECzl3P+pjMm/mhKC0YDaVnxZ1Sa4IkMlvhjq88+pebZOOGuEbUftT/qh1aR+sTxInslAODgWVV/ncHOtKr40ysMUMlUcAbaxA6FiIiIiChjNHjqUGgs6rft3Py1uHjGJbh1/qcHrJ9vrUCJuQQ7mrdjleNcmNPoYUAiIiIamlnV8zv7qOsIACBPP3TFHwBU2hfjqOswtjW9h+W5KxPdASZKrzTAoDSgxdeclPNRZmLij6Y0fzQArVwjdhgDGJQGdIcGzvgLx0IAAJWUFX+l5tnwhD2itpb0R3zQynUpv45FnYUZxpkfSvx1wKJKn8SfRCKBXZsNp58Vf0REREREo+GP+NEeaEeRoX/iTyPX4O4V30F2b1eNs0kkElxRfDUA4NJZl09GmERERJQElt6HdapcRyABkKPNHXZ9ZfZiROIRNHjqscKRnDaffXK0uUz8TXNysQMgSqVgmrb6NCgN8Ea8EASh37yGDyr+mPgrtcwBANS4jyNHN/wvylQJRAPQKVKf+AN62n3ubd0NAAjHwvBGvMhKo4o/ALBr7JzxR0REREQ0SvWeOgBAoWHGmI67dNZlMKlMWJ67IhVhERERUQqYex/gP9J+GHZt9ogd3WaZSmBQGuCLeLE0Z1lSY8nVOdDsY6vP6YwVfzSlBdK01adBaUA0HkWwd6Zfn76KP7WciT+r2gqzyowTneLN+fNFfNBOUuJvkb0Sjd4GOP1OdAY7ACCtWn0CQLY2hzP+iIiIiIhGqaE38ffhGX8jkUvlOK/g/KS1/CIiIqLUM6lMAIAz3afg0A0936+PVCLF8twVWGRfDIPSmNRY8vR5rPib5ljxR1NaIOpPy4o/vaLnH3NP2APNWa1IQ72JQFb89bS4mW2Zg1q3eIk/f8TX7/uTSgvtiwAAh9r3I09fAABpV/Fn09ixu3Wn2GEQEREREYnutVOvoCyrHDNNs4ZcU+epQ5Y6a9K6iBAREZF45FI5DEoDPGHPiPP9+nxt6d2IC/Gkx5Krc6DN34pYPAaZVJb081P64+NjNKUFogGoZelZ8QcA3nB3v+3hWAQAoJQOXwo+XZSYZ6PGfVy06/ujfmgVk5M47pvzt79tX9pW/Nm12egIuBCNR8UOhYiIiIhINA2eevxi9wN4seZvI64ba5tPIiIiylxmVc+cvzz9yBV/AKCWq1Py2WOuzoGYEIcz0Jb0c1NmYOKPpixBEBCI+ietVeNY9CX+PGFPv+3hWAgKqaLf3L/pbLZlDlwBVyIRNtn8ER90Cv2kXW9R9mIccO6HK+CCVCKBSWWetGuPhl2TDQGAK9AudihERERERKJ58cQmCACOuA4Pu67ecwaFY2zzSURERJnLou5J/OXqHKLG4dD1VByy3ef0lTaJv2eeeQbr16/HggULcP311+PgwYPDru/u7sbGjRuxZs0aVFRU4JJLLsGWLVsS+9evX4+5c+cO+G/jxo2JNbfccsuA/T/4wQ/6XaepqQmf//znsWjRIpxzzjl44IEHEI2y2iUTROIRxAUBmjSc8Wfs7dvc/aGKv1AsBBXbfCaUmmcDAGpEmvPnj/qhncRWsYvslWjyNqKm8xhMKnPazfTI1mYDAJyc80dERERE05Qn3I1/n3oFM4wzcab7NDwfek/XJy7E0eBpQKGxaJIjJCIiIrH0PcSfN4oZf6mUrc2BBEAzE3/TVlrM+HvllVdw//33Y+PGjVi0aBGeeuopfOYzn8Frr70Gq9U6YH04HMaGDRtgtVrx61//Gjk5OWhqaoLR+MEQzL/97W+IxWKJP9fU1GDDhg249NJL+53rhhtuwJ133pn4s0bzwTyvWCyG22+/HTabDc8++yza2trwrW99CwqFAnfddVcy/wooBQJRPwCk54y/vlafEW+/7aFYCEoZ23z2ydU5oFVoUeuuwQrHykm/vj/in9R5HAttPXP+3m3ckkiypROb1g4AcPrZJoCIiIiIpqd/nfwn4kIcdy27G1/d/CUccR3BKsc5A9a1+VsRiUdQoGfij4iIaLr4oNXn6Gb8pYpSpoRVY0Ozr0nUOEg8aVFO8uSTT+KGG27Addddh9LSUmzcuBFqtRqbNm0adP2mTZvQ1dWFRx55BEuXLkVBQQFWrFiBsrKyxJqsrCzY7fbEf2+99RaKioqwYsWKfudSq9X91un1H7T127p1K06cOIGf//znKC8vx/nnn4+vfvWreOaZZxAOh1Pzl0FJE4gGAAAauWaElZNPKpFCp9ChO9TVb3uYFX/9SCVSlIo0508QBPijvkn9+TGrLZhpnAVP2IMs9cCHHsSmV+ihkWvQ5m8VOxQiIiIiokkXjUfxjxMvYH3RRSjPmgeL2oIj7YcGXVvXXQcAKGLFHxER0bSRo82BVWNNFH2IyaHLQ4uXFX/TleiJv3A4jCNHjmD16tWJbVKpFKtXr8a+ffsGPWbz5s2orKzED3/4Q6xevRpXXHEFHn300X4Vfh++xksvvYTrrrtuwOy0f/7zn1i5ciWuuOIKPPjggwgEAol9+/fvx5w5c2Cz2RLb1qxZA6/XixMnTkzky6ZJ0Jf4U6dh4g/omfPnjfSf8ReKhaFk4q+fUvMcnHBPfqvPSDyCaDw2qRV/ALAwuxIAYFFnTep1Rytbm8NWn0REREQ0Lb3T8BbaA+24ds71kEgkmG9dgKNDzPmr95yBQqpAtjZnkqMkIiIisVw9+zo8tP5RscMA0NNJrcU/dOIvGo/i3YYtiMY51mwqEr3VZ2dnJ2Kx2ICWnlarFSdPnhz0mPr6euzYsQNXXnklfve736Gurg4bN25ENBrFHXfcMWD9G2+8AY/Hg2uuuabf9iuuuAJ5eXnIzs7GsWPH8D//8z84deoUfvOb3wAA2tvb+yX9ACT+7HSO/oNvqVQCqVQy8kJKqogQhEQCGFQ6yOWi57gHMKlM8EW9/WKLIgyNQp2W8U6UTCbt97+jVWabixdPPI9g3A+9Uj/yAUniifp7f370k/r9WJq7BP+sfRFWbVZa/hzk6LLREWpPy9goecZ7vxKROHjPEmUO3q+ZSxAEvHjib1iauxRzrD2zyBflLMLvDzwGQRKDQqbot77RV48iUxGUCtE/dqEJ4D1LlFl4z5LY5HINdKr0KELJM+Zhd+vOQT/D6wx2YON7P8BB5wF8Z9UPcNHMiyc9Pt6vqZWRr0AFQYDVasV9990HmUyGiooKtLa24oknnhg08bdp0yasXbsWOTn9n7S78cYbE/9/7ty5sNvtuO2221BXV4eiouS148jK0g2oNKTUU/glkMmkyLVZYTFMbtXWaFgNFkSkQVgsH8QmVQgwaHT9tk01RuPYfvktFRZBtksKZ7wRhZYlKYpqIG9XR8/Pj9U2qd+P8zXnQrZDiiJrflr+HBRZC3DcdTwtY6PkG+v9SkTi4j1LlDl4v2ae/S37caLrOH596a8Tr4VXF6/AYwcfQWusHgtsC/qtbw01YY69lK+bpwjes0SZhfcsETA3twSeqi6o9VJoFB/cE9Xt1fj6W19HJBZBtsGOWl81rrdcLVqcvF9TQ/TEn8VigUwmg8vl6rfd5XINqLbrY7fbIZfLIZPJEtuKi4vhdDoRDoehVCoT2xsbG7Ft2zY8/PDDI8ayaNEiAMCZM2dQVFQEm82GgwcP9lvT3t6eiGG0Ojp8rPgTQWuHC7FYHGGfgM6oT+xwBlAJWji7Xejs/CC2Lp8XiEn7bZsqZDIpjEYNursDiMXioz7OJNghFeTYc+YAZqnnpjDC/po6nIjF4ogGJJP8/VBg4+ofo8K2IC1/DozSLNR11qdlbADQ4mtBjjaHD1tM0HjvVyISB+9ZoszB+zUzxYU4/nfHY3Bo81GmX5h4LZwtK4AMCmw7uRMFyuJ+x5xoP4mykvlp+7qZRof3LFFm4T1L9AG9YEEsFkd1Yy1mmmbBH/FjS/1b+NXuX6DYXIwfnv9j/PHwH7Czbrcor1d4v47PaB8qEz3xp1QqMX/+fGzfvh0XXXQRACAej2P79u24+eabBz1myZIlePnllxGPxyGV9pSCnj59Gna7vV/SDwBeeOEFWK1WrFu3bsRYqqqqAHyQ1KusrMSjjz4Kl8uVaEW6bds26PV6lJaWjvprjMcFxOPCqNdTcnhDPggCoIAa0Wj6/eOhk+tR113XL7ZgJAiZRJGW8SZLLBYf49cnwSxTMapd1ZP699L386OSaCb9+7Eip2fmaTr+HORocuEOdqE74IVWoRU7nH7OdJ/G5/79Kfzygkcw31YhdjhTwtjvVyISE+9ZoszB+zVzxIU4Htz9AHY2vY8frL4P8RgQR9/3Toq5lnIcbDuIa0tvSBxT3VGFjkAHSoxz+H2eInjPEmUW3rNEgE2VA0EAfrnrQbhDbtR3n4EA4MIZF+NrS78JlUyFeVkL8Grtv+D2d0GvNPQ7XhAEnO4+hVmm4sEvkCS8X1MjLRqobtiwAc899xxefPFF1NbW4t5770UgEMC1114LALj77rvx4IMPJtZ/4hOfgNvtxo9//GOcOnUKb7/9Nh577DHcdNNN/c4bj8fxwgsv4Oqrr4Zc3j/HWVdXh0ceeQSHDx9GQ0MD3nzzTXzrW9/C8uXLUVZWBgBYs2YNSktLcffdd6O6uhrvvvsufvWrX+Gmm24akGCk9BOIBiCVSKCQKkZeLAKDyghfxNtvWygWhlqmFimi9FVqno3azhOTek1fpOdJl3RLboktV5cHAGjxNYkcyUDvN2+HAKDN3yp2KEREREQ0BcSFOH626yd448y/cfeK72BN/toBa+bbKnCk/RAE4YOHff985EkUGoqwKm/1ZIZLRERElGDVWDE3qwyhWAiV2Uvw9eXfxu8/8kd8a/l3oZKpAAAV1gUQABx1HR1w/K6Wnfj8fzagwVM/yZFTMohe8QcAl112GTo6OvDQQw/B6XSivLwcjz/+eKLVZ3Nzc6KyDwAcDgeeeOIJ3H///bjqqquQk5ODW2+9FZ/73Of6nXfbtm1oamrCddddN+CaCoUC27dvx5/+9Cf4/X44HA585CMfwZe+9KXEGplMhkcffRT33nsvbrzxRmg0GlxzzTW48847U/Q3Qcnkj/ihkWvTtuWfXqFHd7i737ZwLASljEnlDys1z8Grp15GKBZK/GJKNX9f4k/OmRxnc+gcAIBmXzOKzaOvfJ4Mu1p2AgC6w10iR0JEREREmS4Wj+Fnu36Mt+s349srvo8Lii4cdF2FdSH+UvU0mryNyDcUoLqjCjtb3sf/W/l9SCVp8aw1ERERTUNSiRS/ufCxYdfk6fNhVplx2HUQKxwr++3b2rgFANDia0aBoTBlcVJqpEXiDwBuvvnmIVt7/vnPfx6wbfHixXjuueeGPeeaNWtw7NixQfc5HA48/fTTI8aVn5+P3//+9yOuo/QTjAWhkafvcFCj0gR/xI9YPAaZtGdeZSgWnLTEViaZbZmDuCDgVNdJlGWVT8o1/VE/pBIJvx8fYlZZoJKp0JxmFX/+iB+H2w8AALpCTPwRERER0cT89sDDeLt+M76z8h6cX3jBkOvmWedBAuCw6xDyDQWJar91hesnL1giIiKicZBIJKiwLcSR9kP9tseFOLY3vQcA6Ai6xAiNJoiPn9GUFYj2VPylK0Nv32RvxJPYFoqFWfE3iDx9T3vJyWzh6Iv4oFPo07ZiVCwSiQQOXR6afc1ih9LPAec+ROMx6BV6dLHij4iIiIgmIBaP4Y0z/8Ynym4ZNukHAHqlATOMs3Ck/VCi2u/meZ9itR8RERFlhArbAlS5jiIcCye2VbmOwh1yAwBcASb+MlHaVPwRJVswmt4Vf32JP0/YA5PKDKCv1ScrzD5MrzBAJpHCHeyctGv6o35o0zhxLKZcvQPN3kaxw+hnV8v7cOjzkKPNRTcr/oiIiIhoAk64a+CP+LEsd8Wo1s+3VeBQ+0G4Au2s9iMiIqKMUmFbiEg8gprO45hvqwAAbGt6F2aVGSaVGe3BdpEjpPHgI2g0ZQWifqjTOPGn7038nT3nLxwLQy1TixVS2pJIJDCrLYknTSaDP+KHVsHE32DydPlo8bWIHUaCIAjY1fI+lueuhElpQtck/pwQERER0dRz0LkfSpkScy1lo1pfYVuAuu4zrPYjIiKijFNqng21XI3D7QcB9HzO9l7jVqzKWw271o4OVvxlJL4apSnLHw1Am8aJP4PCCADwhr2JbaFYCAqpQqyQ0ppZZYY7NIkVfxFfWreKFVOuLhctvmbEhbjYoQAA6j11aPG1YHnuShhVJnSz1ScRERERTcBB537Mt1ZAIRvde7MK20IAYLUfERERZRyZVIbyrHk47OqZ81fnOYNGbwPOzTsPWWorXKz4y0hM/NGUFYwG07riz6gyQi6V4ZVT/4Q/4gfQ0+pTxVafg7Kos9A5ya0+dQrdpF0vkzh0eYjEI2nT43tXy/tQSBWotC+GSWVCF1t9EhEREdE4xYU4DrYfwCL74lEfk6PNxZr8tfjCojtY7UdEREQZp8K2EEfbDyMuxLG96T2oZCoszlkKq8YGV4CJv0zEV6Q0ZQWi/rSe8aeSqXD38u9ib+tufOGNz6DKdRQxIc4Zf0MwTaDizxPuxmunXoEgCKM+xh/xQStn4m8wuToHAKDF1yRyJD12tbyPhfZFUMvVva0+u8b0vSYiIiIi6lPrPgF/xI+F9kWjPkYikeCe1fdhhWNlCiMjIiIiSo0K2wJ0h7tR76nDe43vYnnuSqhkKljVVnQEXfycLQMx8UdTVjAaTPtWjRcUXYhHL34CBoUB//3WlwCAFX9DsKgs4674e7fhHTy4+wGc6qod9TG+iI8z/obQl/hrToPEXzAaxEHnASzP7fmQxagyIhKPIBgLihwZEREREWWiA859UEgVmJtVLnYoRERERJOiLGsepBIJttS/heqOKqzOOxcAYNXYEI3HOFYnAzHxR1NWIOqHWq4WO4wR5enz8asLHsG1s68HANg0NpEjSk9mlQVdIfe4jm0POAEA25reG/Ux/qifib8hqOVqWNQWtPhaxA4FB5z7EYlHPkj8KU0AgG62+yQiIiKicTjg3I951gooZUqxQyEiIiKaFFqFFiXm2fjb8b9CKpFgpeMcAIBV3fM5Ndt9Zh4m/mjKCkQDad3q82wKmQK3L/oyNl31EhaMoaXMdGJWW+CNeBGJRcZ87AeJv62jPiYQ9bPV5zAcujw0+RrFDgO7Wt5HjjYHhYYiAD0tYQFwzh8RERERjVlciOOw8yAWZVeKHQoRERHRpKqwLUQgGsACWyWMqp4H6629BSrtAZeYodE4MPFHU1ZP4iazKrb6/lGlgSyqLACAexxVf+0BJxRSBWo6j6PN3zaqY/wRVvwNx6HPQ4u3WewwsKvlfSzPXQmJRALgg3uoK+wWMSoiIiIiykQn3bXwRrxYZK8UOxQiIiKiSVVhWwAAWJ1/bmKbRWUBAHQEmfjLNEz80ZQUiUUQjccypuKPRmbureRyh8Y+58/pd2Jt4TrIpTJsH0XVX1yIIxANsOJvGA5dnugz/jqCLjR5G7EkZ1lim4mtPomIiIhonPrm+5VlzRM7FCIiIqJJtSR7Kc4rOB8XFF6Y2KaQKWBSmdnqMwMx8UdTUjAWAAComfibMszqnidMOoNjT/y5gu2YaZyFhfZKvNf47ojr/VE/ALDibxgOnQMdwQ6EYiHRYmj09rQa7WvzCfTMH1TKlOji0GEiIiIiGqMDzv0ot87nfD8iIiKadvRKA35wzg9hUWf1227TWOFixV/GYeKPpiR/tCfxx4q/qcOcmN02tsRfIBqAJ+yBTWPD6rw1OOjcD2/YM+wx/khv4o8Vf0PK1TkAAC0+8dp9tnib+sXSx6Q0ccYfEREREY1JXIjjcPtBtvkkIiIiOkuW2sqKvwzExB9NSYHexI0mw2b80dCUMiW0Ci06x5j46/vFZNPYcU7eGsSEOHa27Bj2GH/EBwDQKZj4G4pDlw8AaBYx8dfka0KWOgtqubrfdqPKxFafRERERDQmp7pq4Ql7mPgjIiIiOotVY4MryMRfpmHij6akYCwIANB8KCFAmc2iykJXyD2mY9oDTgA9ib9sbTZmW+ZgW+N7wx7DVp8js2qskEvliao7MTR7G5Gnzx+w3aQysdUnEREREY3JAed+yKVylFk534+IiIioj1VtY8VfBmLij6YkVvxNTWaVGR3BjjEdc3biDwDOzTsPO1t2IBwLD3lMX8Wflj8/Q5JKpMjVOUSv+HPo8gZsNynNbPVJRERERGNy0HkA5VnzoJKpxA6FiIiIKG1YNVZ0BF2IC3GxQ6ExYOKPpqS+ir8PtwCkzGZWW8Zc8ef0O2FQGhI/C+fkn4tANID9bfuGPIYVf6Pj0DnQ5GsU7fpN3qZBK/7Y6pOIiIiIxkIQBBx1HcZ82wKxQyEiIiJKK1a1DXFBGPNnsiQuJv5oSgpEWfE3FZlVZnQGxzbjrz3YDpvGlvjzLGMxcnW52N60dchj/L0Vo1o5Z/wNJ1eXh1aRKv78ET+6Qm44dI4B+3pafbonPygiIiIiykhtgTZ0BjtRnsU2n0RERERns/Z+rtoRdIkcCY0FE380JfkjAUgAtmmZYswqC9yhsSX+nP62RJtPAJBIJFiddx62NW0dskTdF/FCJVNBJpVNKN6pztHb6lMQhEm/dnNvpaFjsBl/yp6KPzHiIiIiIqLMU+U6AgAos5aLHAkRERFReslSWwEA7QEm/jIJE380JQWifqjlGkgkErFDoSSyqC1wh9xjSui0B5z9En8AsLZgHTqCHdjftnfQY/xRP9t8joJDn4dgNDjmZGwyNHmbAAB5g8z4M6pMiAnxRMtWIiIiIqLhVHccRa4uN/HBFhERERH1yFJnQQLAFWgXOxQaAyb+aEoKRANM3ExBZpUF0XgUvoh31McMlvibZ52PfH0BXj/z70GP2d+2F4WGGROKdTroa7PZLEK7zyZvIzRyDUwq84B9JqUJADjnj4iIiIhGpcp1FGVs80lEREQ0gEwqg1ltgSvIxF8mYeKPpqRgNAC1TCN2GJRkFrUFAOAe5TDZSCwCd7ATdm12v+0SiQQXz7gEWxvfSczz63O66xQOOg/giuKrkhLzVJbbW23X4mua9Gs3+5qQp88btKrXpOpJ/HWFmfgjIiIiouFFYhHUdB5HWRbbfBIRERENJkttZcVfhmHij6akQDQAjZyJv6mmr7qrc5StJTuCLggArGrbgH0XzfgIgtEg3mt8p9/2l2r/DrPKjDX5ayca7pSnU+hgVBrR4muZ9Gs3eRvh0A2c7wcAxt6fky5W/BERERHRCE521SISj7Dij4iIiGgINo0NriBn/GUSJv5oSgrE2OpzKrKoeiv+gqNL/LX3Poli19oH7MvR5WKhvbJfu09/xI83zvwblxdfBYVMkYSIp75cnQNN3sZJv26Lrxl5+oHz/QDAqDQCALpHWRlKRERERNNXdcdRyKUyzLbMETsUIiIiorRkVdvQEWDiL5Mw8UdTUiASgFqmFjsMSjK90gCZRDrqVp/OQBsAwK4ZmPgDgItnXIL9bXvR5u9Z98aZfyMUC+JytvkctTx9PponudVnNB5Fq79lyIo/pUwJjVzDVp9EREREKXaiswbBaFDsMCakquMoik2lUMqUYodCRERElJasGhtn/GUYJv5oSgpE/dDIWfE31UglUphUZnQGO0a1vj3ghEqmgk6hH3T/2oJ1UMiUePPMfyAIAl6q/TtW5503aIUgDW6WqRi17hOIC/FJu2abvxVxQRiy4g/omfPHVp9EREREqeMNe/CVzbfj1VMvix3KhFS5jqLcOl/sMIiIiIjSVpbais5gx6R+/kcTw8QfTUnBWBBqOSv+piKL2jLqir/2gBM2jR0SiWTQ/VqFFuflr8V/zryGQ+0HcKb7NK4s+VgSo5365ljmwhfxTWq7z75rOXRDJ/6MShO6mfgjIiIiSpl9bXsRjcdwuuuU2KGMW3eoC03eRpRnlYsdChEREVHasmlsiAsCOkc5fonEx8QfTUmBSIAVf1OUSWWGOzTKGX/+dti12cOuuXjmpWjw1OOhvb9EgaEQi7OXJiPMaWNuVhkA4FhH1aRds9nXBJlEimxtzpBrTCoTW30SERERpdDulp0AgHpPnciRjF9V72tYVvwRERERDS1LbQUAuAJs95kpmPijKSkYC0Cj0IgdBqWARWVB1xhm/Fk11mHXLM5eCpvGhjPdp3FVydVDVgfS4AxKIxz6PBzrPDZp12z2NiFblwuZVDbkGqOKFX9EREREqSIIAna37oRcKkddBif+qjuOwqgyDdtJgoiIiGi6s2psAICOoEvkSGi0mPijKckf8UMjY+JvKjKrLegYw4w/u2b4ij+pRIqLZlwCjVyDi2demowQp525lrJJrfhr8jUhb4QPZ0xKMyv+iIiIiFKk3lOHNn8b1hddhK6QO2MfuKpyHUF5Vjkf/iMiIiIahkVtgVQiQTsr/jIGE380JQWiAWjkTPxNReZRVvzFhThcgXbYNfYR194y7zb8/iN/hF6hT0KE08/crDKccNcgFo9NyvWavY0jPpVtUpnQlaEfQBERERGlu75qv8uLrwKAjKz6iwtxHOuoRlnWPLFDISIiIkprUokUFnUWK/4yCBN/NOVE41FE4hFoFJzxNxWZVRZ4wh5EYpFh17lDnYgJcdhGkfhTypTI0eUmK8RpZ66lDOFYGKe7T6b8WoIgoMnXBId++MSfUWmCJ9yFuBBPeUxERERE083ulp2osC1EqXk2pBIJ6rrPiB3SmDV46uGNeFFuZeKPiIiIaCRWtY0z/jIIE3805QSjAQBgxd8UZVZbAGDENo5OvxMARpX4o4kptcyBVCLBsY7Uz/lzhzoRjAaRp88fdp1JZUJcEOCLeFMeExEREdF0Eo6FccC5H8tylvc+QOdAvSfzEn9VHUchATA3q1zsUIiIiIjSXpbGCleQib9MwcQfTTmBaBAAoOaMvynJoupN/IU6h13X9wSKXcvEX6pp5BoUGWbieGd1yq/V5G0CgJFn/KlMAMB2n0RERERJdrj9IMKxMJblLgcAFBmKMrLir9p1FIXGGWz3T0RERDQKVrWVM/4yiHyiJ+jq6sLBgwfR1dUFk8mEhQsXwmQyJSM2onEJRP0AAI2Cib+pqK/iryPYgZJh1jkDbZBLZTCpzJMS13Q3N6sM1R1VKb9Os68RAJA7QuLPqPwg8VdgKEx5XERERETTxZ7WXbCoLSg2lQIAigwzsLXxHZGjGruqjiMo53w/IiIiolGxamyc8ZdBxp34EwQBP//5z/H0008jHA4ntiuVStxyyy345je/mZQAicYq0NvqU8tWn1OSuTeR1xVyD7uuPeCEVW2DVMLC5skwN6sMb5z5N8KxMJQyZcqu0+RtglllhnaEGZ59FX/dI7SEJSIiIspkXSE32gNOlJhnT9o1d7fsxNKc5ZBIJACAQuMMtBxvRigWgkqmmrQ4JiIQDeBU10lcUXy12KEQERERZQSr2gZ3sBOxeAwyqUzscGgE4/5E/NFHH8VTTz2FDRs24O9//zu2bt2Kv//979iwYQP++Mc/4rHHHktmnESj1jfjj60+pyaVTAWNXIPO4PCtPp0BJ+f7TaI5ljLEhDhOuGtSep0mX+OI8/0AwKA0AmCrTyIiIprafr33F/j6219FOBYeeXESuAIunOw6ieW5KxLbigwzIABo9NRPSgzJUNN5DHFBQLmV8/2IiIiIRsOutUMA0OpvETsUGoVxV/w9//zz+OIXv4g77rgjsc1ms6GsrAwKhQJ//etfcfvttyclSKKx6Kv4Y6vPqcustsA9woy/dn877NrsSYqIik0lkEvlON5RjXnW+Sm7TrO3CQ798G0+AUAulUOv0I9YGUpERESUqRo9DdjasAUCgL2tu7Eqb3XKr7mndScAYHH20sS2wt626nWeOhSbS1MeQzJUuY5CJVNhprFY7FCIiIiIMkJZVjkkAA61HxzVQ/kkrnFX/DmdTixZsmTQfYsXL4bT6Rx3UEQTEWDF35RnUVngHk2rT411cgIiKGQKFJtKUN2Z2jl/Td5G5OlG9+LCqDKx1ScRERFNWX87/leYVGbk6wvwdsPmSbnmntZdKDXPhkWdldhmVJlgUplR131mUmJIhuqOKszNKmObKiIiIqJRMiiNKDaX4kDbPrFDoVEYd+IvPz8fb7/99qD7tmzZgvx8Zn1JHP6oHwCglqtFjoRSxayyoDPYMeR+QRDQHnDCrmHF32Sam1WO4x3HUnZ+d7AT7pAbBb1PlY/EpDKx1ScRERFNSZ3BDvz79Ku4ZvZ/4YKiC7G96b2Ut/v0R/zY1bITyx0rB+ybYZyBek9dSq+fTNUdR1GWxTafRERERGOxyL4YB537xQ6DRmHcib/bbrsNf/rTn/CNb3wDb7zxBvbt24c33ngD3/jGN/DnP/8ZGzZsSGacRKMWjAaglqshlYz7x5vSnEU9fMWfN+JBKBbijL9JNjerDA2eOvgivpScf2/bHgA9LzJGw6Q0oYsVf0RERDQF/f3EC5BJZbiy5GM4v+AC+CN+7G3dndJrvlDzPALRAC4vvmrAvkJDEeo9mVHx5/Q70R5oR3kK29MTERERTUWV2YvR6m9Fi69Z7FBoBOOe8ffxj38ckUgEv/3tb/Hyyy9DIpFAEARkZWXhu9/9Lm688cZkxkk0aoFoAGoZq/2mMpPKDHdw6Bl/zkBPq2Em/ibXXEsZBAA1ncdQmT14K+iJ2NO6C7NMs0bdwtWgNKLR25D0OIiIiIjE5I/48c/av+OyWVfCoDTCoDSiyDgDWxreStmcv+5QF54//iyuKrkGOdqcAfsLDUX4z+nXEBfiaf8AZnXHUQBAWdY8kSMhIiIiyiwVtgWQANjftg+XznKIHQ4NY9yJPwC45ZZbcNNNN+HkyZPo6uqC2WzGrFmzIJWm9wt9mtoCUT+0Cp3YYVAK9c34EwQBEolkwP52fzsAwKaxTXZo01qRcQY0cg2OdVSPOvG3r3UPcnUOOPR5w64TBAF7W3djXeH6UcdjUplw1HUk8eeTXbXYdPw5fLnyq9AqtKM+DxEREVE6efXUy/BHfLh2zvWJbWsL1uGFmucRjoWhlCmTfs2/Hvs/xIU4Pl72yUH3FxpmIBKPoM3filxden8IVOU6ArvGzvcKRERERGOUmPPn3IdLZ10mdjg0jAln6KRSKUpLS7F06VKUlJQw6UeiC0SD0HC+35RmUpkRiUcS8xw/rCPoAgBkqUdXGUbJIZVIMdsyB8c6q0e1XhAE/Oj9jXi2+pkR157pPo32QDuW5CwbdTwmlRndva0+Gzz1+NaWu/Cf069hZ8uOUR1/uusUnjz8+KivR0RERJRq0XgUm44/h3VFF/arvFtbsC5l7T7bA+14sWYTrpt9AyzqrEHXzDDOAADUdaf/nL+qjqMos7Laj4iIiGg8+ub8CYIgdig0jDFV/D355JO48sorYbPZ8OSTTw67ViKR4LbbbptIbETjEoj6oZGzmmcqy+r9wKEz2AHdINWdHUEXjEojFDLFZIc27c00FeNA275RrW31t6A71IV6z8gfEO1t2w2FVIEFtkWjjsWoNMEb9qDJ24hvbvlvmFRm6BR67Gp5f1SVg6+feQ3PHXsWl868bMSKRCIiIqLJ8E7D23AGnLhh7if6bZ9pnIVCQ1FK2n0+c/QpqOVq/Nfcocd52LXZUMqUqPOcxgrHyqRef7xeOfkydrbswD3n3JfoEhKNR3G88xhum/8ZkaMjIiIiykyV2YvxQs3zaPW3pH2nh+lsTIm/Bx54AEuXLoXNZsMDDzww7Fom/ihVhmrv2CcYDUIj10xiRDTZzGoLgJ7EX4GhcMB+V9DFaj+RFOoL8erJl0c136XK1TNfpW4Uib89LbtQYVsA9RiqeU0qEwQAd739FShlKvx07YN4seZ5vH7m36OKr7qjCgCwt20PLmfij4iIiNLAuw1bUG6dh2JTSb/tEokEawvW4e8nNg3b7vOhvb8EANy55Gujul6TtxGvnnoZGyo+B71CP+Q6qUSKQkMh6tOo4u+9xnews+V9bG18B+cVnA8AONV1EuFYGOXW+SJHR0RERJSZOOcvM4ypL2d1dTUWLlyY+P/D/VdVVZWSgGl6q3XX4LqXrkRnsGPINYGoH2om/qY0a+88jvZA+6D7OwIuZGkGb0NEqZVvKEjMdxnJsc6e3xNdITe6Q11DrgvHwjjg3I+lOcvHFItRZQIASCHFz9f+EjaNDctzV6Iz2ImT7tphj40LcRzvPAagZw4hERERkdjCsTD2tO7CKsfgFX1rC9fBF/Fhb9vgr13iQhxb6jfj3Ya3R92a6U9Hn4RZbcHVs68bcW2RYeaoOjl8OKZUOeGugQTAHw7/HrF4DABQ3XEUst729EREREQ0dmfP+aP0Ne6BfLt27YLP5xt0n9/vx65du8YdFNFQ6rrr4Al7sH+YVoKBaIAVf1OcTq6DSqZKzPL7sA5W/Imm0FAEoGem3kiOdVQn1tcPs77KdQShWGjMib9ZpmKsK1yPn53/S+TocgEA860LoJFrRpzzd6b7NILRIBbYFmJf256UfihFRERENBqH2g8gEA1gleOcQffPMhb3tPus3zzo/lNdtegOd8MdcqPZ1zTi9fwRP7bUv4VrZ18PlUw14vpCYxHOeM6MuK5Pg6ceV754CVp8zaM+ZrQ6gx3oCHbg4+U3o8FTj9dOvwIAOOo6gmJz6ai+HiIiIiIaXGU25/ylu3En/m699VbU1g5eMXHy5Enceuut4w6KaCjeiBcAcNh1aMg1wWiAFX9TnEQigU1jR3vAOej+nsQfK/7EkK3NgVwqR6O3Ydh1sXgMxzuP4cKiiyEBhn06fHfrLphUZhSbS4ZcMxi9Qo/vrrqnXztYhUyByuwl2NXy/rDHHuuohgTAjWU3oTvcjVr3iTFdm4iIiCjZdjRth11jxyzT4K+JJBIJzis4HzuatiEajw7Yv69tLxTSnhnYR9qHfj/VZ0/rLkTjUazJXzuq+IoMM9Ad6kJXyD2q9cc7jyEcC+NU18lRrR+Lms4aAMClMy/DBYUX4s9Hn0QwGkR1RxXKsuYl/XpERERE08ki+2K0+dvQ6m8ROxQawrgTf8NlcwOBANTq0c9hIhotb9gDADjsPDDkGn/UDy0Tf1OeVWMbtNWnIAhwBVjxJxapRIp8fcGwFXwAcLq7Z77KQvsi5OhyUT/M0+F7W3djSfbSEWfyjdaK3FU46jqc+PdkMNUdRzHDOAtLspdCLVdjb+vupFybiIiIaDwEQcCO5vewKm/1sPPOz807D96IF4cGeb+0v20vKmwLMMM4E0dch0e85o7mbZhhnIk8ff6oYiw09nRyGM38ZuCDDhFN3sZRrR+LWncNtAotcnUObKj4LLpCbjx99I9o8NRjnpWJPyIiIqKJWGBbmJjzR+lJPpbF+/fvx759H3wz//nPf2LPnv7zA0KhEN58800UFxePKZBnnnkGTzzxBJxOJ8rKyvD9738/MU9wMN3d3fjlL3+J119/HW63G/n5+fjOd76D88/vGdq9fv16NDYOfAPxyU9+Evfccw/cbjcefvhhbN26Fc3NzcjKysJFF12Er371qzAYDIn1c+fOHXCOX/ziF7j88svH9PVRcngjPR/Un+o6CW/YA73SMGBNIBqERq6d7NBoktk01kETf76IF5F4hIk/EeXrC9DoHT7xV91RDalEglLLHBQaiob8gKg71IWazmO4qvSapMW33LES8b0C9rTuxvmFFwwR31HMzSqDQqbAQtsi7GndhRvLPpm0GIiIiIjGos5zBi2+FqwcYr5fn9mWObBpbNjW9B4W5yxNbI/GozjoPIBPlt+CFl8zDo9Q8RcX4tjRvB2XzRr9+94CfSEMSgP+ePhx/HjNz6CWD/8wcN/rxRZf8p8UP+GuQal5NqQSKRz6PFxWfBX+euwvAMCKPyIiIqIJ0isNKDHPxgHnPlw66zKxw6FBjCnxt3XrVvzmN78B0NNG5M9//vPAE8rlKCkpwT333DPq877yyiu4//77sXHjRixatAhPPfUUPvOZz+C1116D1Trww/twOIwNGzbAarXi17/+NXJyctDU1ASj0ZhY87e//Q2xWCzx55qaGmzYsAGXXnopAKCtrQ1tbW341re+hdLSUjQ2NuLee+9FW1sbHnrooX7Xu//++3Heeecl/nz2dWhyecNeGJVGdIe7cdh1eND5FoGof8Q3mZT5bBo7qjqqBmzvCHYAAKwaJv7EUmAowDsNbw+75nhHNWYaZ0Ej16DIOAM7mrYPum5v2x4IAJZkL0tafDnaHMwwzsSulvcHTfwFo0Gc6jqJK4qvBgAsyVmGJw79DqFYiPNgiIiISBQ7mrZBKVOiMnvxsOskEgnOyVuDbU3v4kuVX0lUBx7rqEYgGkBl9hLUd5/Bv07+E55wNwzKwd/bVrmOojvUhVWOc0cdo1KmxH3n/hTfeucu/HD797Fx9U+gkCmGXN/g6WkN3zKKeYNjdcJdg5VnvVe8ufxW/Of0q1BIFcjXFyT9ekRERETTzaLsSrzbsAWCIAzbkYLEMaa+aXfccQeqq6tRXV0NQRDw3HPPJf7c99/hw4fxj3/8A0uWLBn1eZ988knccMMNuO6661BaWoqNGzdCrVZj06ZNg67ftGkTurq68Mgjj2Dp0qUoKCjAihUrUFZWlliTlZUFu92e+O+tt95CUVERVqxYAQCYM2cOHn74Yaxfvx5FRUU455xz8N///d/YvHkzotH+8xCMRmO/c6lU/OBXLJ6IByXmUljUlkHbfcaFOMKxMCv+pgGrxoZ2v3NA2+GOoAsAWPEnogJDEVp9LQjHwkOuOdZZhblZ5QCAQsMMNPsaB12/p3UXZhhnwq61JzXG5bkrsKvl/UHbVp9w1yAuCCjrjW9xzlJE4pFRzcIhIiIiSoWdLTuwJGfZqB5COjd/Ddr8bTjZ9cGM4gPOfdAqtJhtnoP5tgUAgCOuI0OeY0fzezCqTCgfY1vM+bYK/PDcn2Bf2178dOePEBfig64TBAGN3gZIJRI0+5rHdI2R+CI+NHkbUWouTWyzqLNw+8Iv4+rS6/jBFBEREVESLLAtQpu/bdCObCS+MVX8na26ujopAYTDYRw5cgS33357YptUKsXq1av7tRU92+bNm1FZWYkf/vCHePPNN5GVlYUrrrgCn/vc5yCTyQa9xksvvYQNGzYM+yLf6/VCr9dDLu//17Jx40Z897vfRWFhIT7+8Y/juuvG9mZBKpVAKuWbi2TwR70wqU1YpK7E4Y5DkMv75659kQAkEkCv0g3YR+KSyaT9/neisnXZiAoRBOI+GFUfPKnsDndCIgGy9Xb+DIhkhqkIkABtwWbMNM0asD8QDeB09ylcPedayOVSzDLPhABhwHpBELCvbQ/WFKxN+vdyVf452FTzHM54T6LUMrvfvhNdx6CSq1BqLYFcKsXsrJ6HDQ6078WK/BVJjSNdJft+JaLU4j1LlDnGc792h7pxxHUIX11616heEy3JXQKdQovtLe9hrq1ndMV+5x5UZi+GSqlAoaIAFrUF1R2HsaZw8Iq+Hc3bsDp/NZSKsX9ksCJ/Be4994e4573v4Vd7f45vrPjWgFnN7mAn/FEfKmwLcbzzGGQySdIScmc6TkIiAeba5vb7+7p67tVJOT9NL/wdS5RZeM8STZ65trmQSIAz3pNwGHPGfDzv19Qad+KvTygUQn19PUKh0IB98+fPH/H4zs5OxGKxAS09rVYrTp48Oegx9fX12LFjB6688kr87ne/Q11dHTZu3IhoNIo77rhjwPo33ngDHo8H11wz9Iyojo4O/Pa3v8WNN97Yb/udd96JVatWQaPRYOvWrdi4cSP8fj9uvfXWEb+2PllZOj5VmCRhSRDZplkosZTglzt+Ca1BDpX8g6deoz4/ZDIpcixZsFh0IkZKQzEaNUk5T0m4CDKZFFGlHxaLI7E9VO+FQa1HfnZyK8Ro9BaoyyCTSeEWnLBYKgbsP9NSA4kUWDlrKSwWHRaqyyGTSdEptGHxWevPuM/AFXJi/ey1Sb+f1xrPgX6HDke692N5cWW/fad8NajImQe71ZTYtmbmahzq3D/t/l1J1v1KRJOD9yxR5hjL/bqr9j1IpMCl8y6CRTe61yLnF6/Frrbt+JrlKwhFQzjmrsJXVnwl8VpmeeFS1HiqB31t09DdgAZfHe5cfce4X/tcYbkUUnUcP3jrB7h83qVYU7Sm3/66lhOQyaRYV3IeqnYfhqAOIUubnI4dzY110CjVqJwxH3LphD/yIALA37FEmYb3LFHqmc3FMGtNaA7XwWK5aNzn4f2aGuN+FRwOh3HvvffipZde6jdL72xVVQPnbyWDIAiwWq247777IJPJUFFRgdbWVjzxxBODJv42bdqEtWvXIidn8Myz1+vF7bffjpKSkgHHf/nLX078/3nz5iEQCOCJJ54YU+Kvo8PHir8kcXk6IbMoUaydi1AkjO21u7EouzKxv8XjQiwWRyQAdHb6xAuUBpDJpDAaNejuDiAWG7zlz1goIjrEYnGcaDmDLEluYntdexNMCjO//2ISlFBJ1KhqrkGleWCF3M5TeyGDAlnIRWenD4KggFamw5HGY1hsXplY9+bxLUBcilmauSn5fi7IqsRbJ7bgYzOu77d9X+MBnJt/Xr9rzjMtxL+OvYIzLU0wqkwfPtWUk+z7lYhSi/csUeYYz/36n2NvYqahGIqwDp3h0b0mWmpdhVePv4aqhlo0eRsRCAcxRz8/8fqmVF+Gx0/+Dm3t7gFz+F499jokggxzdQsm9BpslXUtDHITdp7eg/mG/rMJjzQeQywWR7lxIWKxOKoaT2C+LTlz2vc3HEKhbgY8XSEAAx9QJhoL/o4lyiy8Z4km1wz9LBxsPILOmWN/zcj7dXxG+2DeuBN/jzzyCN577z389Kc/xTe+8Q384Ac/gFarxUsvvYS6ujp8//vfH2WgFshkMrhcrn7bXS4XbDbboMfY7XbI5fJ+bT2Li4vhdDoRDoehVCoT2xsbG7Ft2zY8/PDDg57L6/Xis5/9LHQ6HR555BEoFEMPHweARYsW4be//e2A6wwnHhcQjw+cI0Vj5wl7oJXpUaSfBY1ci/0t+zE/a+EH+4M+CAKglKgRjfIfjHQUi8WT8r0xyS0QBKDN6+x3vna/E2ZlFr//IsvXF+KM+8yg34ej7Ucx2zwHQlyCaLxnf4G+CKe7Tvdbv7N5J+ZZK6CAKiXfz2U5K/Gbfb9EU3cLsrXZAHraTjV7mzHHXNbvmgttSxAXBOxu3oO1BeuSHku6Stb9SkSTg/csUeYY7f0ai8fwftMOXFly9Zju76XZKyCTyPBu3TvoDHXCqDSjQDcjcY4yy3yEY2FUtx8bMMfvvYb3UGlfnJT3VKXm2ahurx5wnrquetg12cjXFkEQgIauBsw1j22e4FBqOo5jjqWM/x5SUvF3LFFm4T1LNDmKjbPxfvP2Cd1vvF9TY9wNVF977TXccccd+OhHPwoAWLhwIa6++mr84Q9/wNKlS7F58+ZRnUepVGL+/PnYvn17Yls8Hsf27duxePHiQY9ZsmQJ6urqEI9/8ANx+vRp2O32Acm4F154AVarFevWrRtwHq/Xi8985jNQKBT43//9X6hUIw9Kr6qqgslkGnXSj5InLsThDXugVxgglUgx31qBQ+0H+q0JRP0AAI2cJcJTnUKmgEllhutDA2RdgQ5kqZPTJojGr9BQiEZvw6D7qjuOYm5W2YfWF6G+uy7x52g8igNt+7AsZ3nKYryg6EJkqa34xe4HIAg9D2cc6zwGACjLKu+3NlubjQJDIfa27k5ZPEREREQfdrTjCDxhD1Y5Vo/pOJ1Ch8rsJdjW9B72t+3F4uwl/ebslZpnQylT4ojrUL/jvGEPDjn3j/l6Qym1zMHxzmOJ11p9Gjz1yNcXQKvQwqQyo9nXnJTrhWNhnO46hVLz7JEXExEREdGElJhL0ORtgD/iFzsU+pBxJ/5aWlowa9YsyGQyqFQqdHd3J/ZdddVVeO2110Z9rg0bNuC5557Diy++iNraWtx7770IBAK49tprAQB33303HnzwwcT6T3ziE3C73fjxj3+MU6dO4e2338Zjjz2Gm266qd954/E4XnjhBVx99dWQy/sXN3q9Xnz605+G3+/Hj3/8Y3i9XjidTjidzkTr0s2bN+P555/H8ePHcebMGfzf//0fHnvsMdx8881j/vuiifNH/RAA6JV6AMAC2yIcdR1BLP5Bq9lANAgAUDPxNy3YNFa0B5z9tnWGOpClYeJPbPmGQjR46gdsdwc70eJrwVxL/8RaoaEI9Z66xIdCVa4jCEQDWJKzLGUx6hV6fG3p3djTuhuvnHoZQE9S0qgyIVfnGLB+Sc4yvN+8HcHef2eIiIiIUm1Pyy4YlcYBD02Nxuq8NTjo3IdjHVVYZO//UK1CpkBZVjkOt/dP/O1q2YmYEMeqvHMnFHefOZa5cIfccAX7d/hp8jagwFAIAHDoHGj2NSXleme6TyMmxFHCxB8RERFRypWYSyEAONV1UuxQ6EPGnfiz2+2JZF9BQQHef//9xL7Tp0+P6VyXXXYZvvWtb+Ghhx7Cxz72MVRVVeHxxx9PtPpsbm6G0/nBh/sOhwNPPPEEDh06hKuuugo/+tGPcOutt+Lzn/98v/Nu27YNTU1NuO666wZc88iRIzhw4ACOHz+Oiy++GGvWrEn819zc87ShXC7HM888gxtvvBFXX301/vrXv+Lb3/72oHMEKfW8YQ8AwKA0AAAq7AsRiAZQ6z6RWMOKv+nFqrahPdi/4q8j4EKWOkukiKhPgb4Q7pA7cd/26auo+3BLqSLjDASiAbT3VnDubu35kGu2ZU5K41zhWImPzrocjx14BK2+FhzrqEKZpQwSycC5rB8ruQbd4W48euA3KY2JiIiIqM+h9gNYYF/Ur1pvtFY5zkVcEBAXBCzOXjJg/3zbAhxpP5R48CouxLG5/g2UmEsSbdAnarZlLgCgpvc1YN91GrwNyNcXAABydQ60JKni74S7BhIAxeaSpJyPiIiIiIZWZJgJuVSGWneN2KHQh4x7xt+KFSuwe/durF+/Htdffz1+9rOf4eTJk1AoFHj99ddx5ZVXjul8N99885CVdH/+858HbFu8eDGee+65Yc+5Zs0aHDt2bNB9K1euHHJfn7Vr12Lt2rXDrqHJ4w17AQB6RU/ib66lDAqpAofaD2BO1lx4wt3YUv8WpBIJE3/ThE1jR437eOLPoVgI3ogXVrb6FF3fE9yN3sZ+T6gf66iCUWVCjja33/pCQxEAoN5zBnatHXtbd2Nx9tJxfcg1Vrcv+jJ2t+zE/+x+ALXuGlxdOvBhEaAnOfnFRV/Br/c+iGW5K7Amn78fiIiIKHXCsTCqXEfx2YW3j+t4u9beW3HXiTx9/oD9860L8Jeqp9HkbUQccfzPrp/iqOsIvrb0mxMNPSFbkw2j0oiazuM4p7eKsD3QjnAsjPy+ij99Ho58qPJwvE64a1BgKOL7QSIiIqJJoJApMMM4EyeY+Es74078fe1rX0NnZycA4LbbbgPQM/cvFArhU5/6FL7whS8kJUCiPp5wT4WpXtHT6lMpUyba01jUFvzv/t8gFAvha0vvnpRkAYnPqrFhe/N7iT93BjsAgK0+00DfE9wNnrp+ib+jrsODVtQ5dHmQS2Wo99RhtmUOjndW47LisT1AMl46hQ7fWP5tfOudrwMAyrLmDbn28uIrsad1F36x+2eYaymHXWuflBiJiIho+qnuOIpIPIJF9spxn+MLi74Mb8Q7aDeDeb0dGH67/yHsa9sLuzYbv1j3EBbYF437eh8mkUgw2zKn38N6jb3t4At6Xy86dHloDzgRjoWhlCkndL0Tncc534+IiIhoEpWYZ/fryEfpYUKtPufM+aAF22233YZnn30Wv//97yGRSHDBBRckJUCiPt5IT8VfX6tPoKfd59bGd3D/+z/CQnsl/nDp07h01mVihUiTzKqxwR3sRDQeBQC4Aj2zQ7JY8Sc6rUKLLHUWGrwNiW2Nngbsa9sz6MwYmVSGfH0h6jx12N+2D3FBSOl8vw9bkrMMV5Z8DFKJBGXDzNCRSCS4a9k3oZKp8NOdP0JciE9ajERERDS9HHDuh16hxyzT+NtWLrAvSlTafZhBacRM4yzsankfV5Vejccu/kNSk359Zlvm9Gv12eBtgEwiTcxUztXmQgDQ5m+d0HXiQhwnu2pRYi6d0HmIiIiIaPRKzKU41XUSsXhM7FDoLGOu+Nu/fz9efPFFNDc3o7CwELfccgtmzpyJ9vZ2PPLII3jhhRcQjUZx2WVMvlBy9SX+9Gcl/tYVXIDDzoP4eNnNWOFYKVZoJBKbxg4BQGewE3atHR3BvsQfZ/ylgwJDIRp6n+gGgOePPwuj0oRLZn500PWFhiLUd59BLB5FgaEQOdqcyQoVAPDFRV/BJTMvg1FlGnadQWnEt1d8D9/c8t94pupPuGXebZMTIBEREU0rB50HUGFfmNJuJt9e+V3EBSGlc5VnW+bi2er/Q0fQhSy1FY3eeuTq8iCX9nwc4dDnAQCafU2JdvHj0ehtQDAaTPmMaCIiIiL6QKl5NiLxCOo9dZhpmiV2ONRrTIm/LVu24Itf/CIEQUBWVha2bduGl19+GT/72c9w9913w+Px4PLLL8eXvvQlzJrFbzIllyfcDa1C2++Nb7G5FL+44GERoyIx2XpberYHnInEn1wqg1E5fOKGJkeBvhDHOqsB9FRj/vv0q/jU/E8P2cKp0FiE/5x+Fc2+JqxwnDOZoQLo6Us+d5hqv7Mtyl6Mm+fdhj8deRKesAe3L/wSZFJZiiMkIiKi6SISi+Co6zBum/+ZlF6nZBLaYs6xzAUA1HTWYKXDikZPAwoMBYn9dk02ZBIpWnzNE7rOic6e2TJs9UlEREQ0eYp7u1PUumuY+EsjY3p08LHHHkN5eTnefvttvPfee3j//fexevVqfPnLX4ZWq8Vzzz2Hn//850z6UUp4I14YFIaRF9K0YVXbAPQk/gDAFXTBosoadIYJTb58QwEavQ0QBAEv1DwHpUyJK0o+NuT6IkMRXAEXWnwtWJazfBIjHZ9b5t2GOxZ/FS+deAHffvcb6A51jen43S07UeU6mqLoiIiIKJMd66xGOBbGIvtisUOZsBxtLgxKQ6LdZ72nHvn6Dyr7ZFIZsrU5E078bWvaCoc+b8TuDURERESUPHqlAbm6XM75SzNjSvzV1tbii1/8InJyetqv6XQ6fPOb30Q0GsXXv/51VFRUpCRIIgDwhr3QK/Vih0FpxKQyQy6VJ1p8dgRcyNJwvl+6KDQUIRgN4kz3afyz9h+4quRq6BVD38OFhhkAAJlEmhEfckkkEnys9Fr87Pxf4mRXLb785udxsqt21Mc/fuhRPHHodymMkIiIiDLVQed+aBValFoyv3pNIpGg1DwbNZ3HEYvH0OJr6lfxBwC5OgeavE3jvsbJrlpsqd+MG+Z8YqLhEhEREdEYFZtKccJdI3YYdJYxJf66urqQnZ3db1tfEnDGjBnJi4poEN6wBzpW/NFZJBIJbBpbouKvb24IpYd8fc8HOv974GFE41FcM/u/hl1faCgCAMyzVkCr0KY8vmRZaK/Eby/8HVQyNR7c9cCoj2vzt+Go6zDCsXAKoyMiIqJMdMC5DxXWBSmd7zeZZlvmoKbzGFp8zYgJ8cTrxD55+nw0+8af+Hvq8B+Qq3Pg0lmXTTRUIiIiIhqjEnMpartqIQiC2KFQr6S9i5DJONuIUssb8cCgZOKP+stSW+E8K/FnZeIvbTh0eZBKJNjbugeXzPzoiElZrUKLmcZZWFOwdpIiTJ4cXS4unnEJGr0No1ofiAbgCXsQiUdwvLftFREREREARONRHHUdwUJ7pdihJE2peQ6cASeOug4DAPINhf325+ocaPW1jOvcxzqqsa1pK26ZdxvkUvmEYyUiIiKisSk1z0Z3qAvtgXaxQ6FeY35V/KlPfWrQ+Vk33XRTv+0SiQR79uyZWHREZ/GEPZhhZFKH+rNp7OgI9LT6dLHVZ1pRyBTI0TnQ6mvGDXNH13bpsY/8ARJk5ozGHF0ufBEfvGEP9CM8pOD0tyX+/0HnflTYFqQ6PCIiIsoQxzuPIRgNTqnE3xzLXADAW/VvQiFVwK6x99ufq3PAG/HCE+6GQWkc07n/eORxFBqKcOGMjyQtXiIiIiIavRJzKQCgtusE7Fr7CKtpMowp8XfHHXekKg6iEXkjnPFHA9k0dpzqOolYPIaukJutPtPMYvsSSLIlcOjzRrU+k9tZ5eocAIBWf8vIib9AT+Kv0FCEg879+GT5LSmPj4iIiDLDQed+qOVqzLbMETuUpHHo86BVaLG3dTeKjDMGvOZz6HpeKzZ7m2HIGn3i75DzAHa37ML3Vt2b0a8jiYiIiDJZtjYHeoUeJ90nsMpxjtjhEJj4owziDXtgUIzt6U+a+rLUWWgPONEZ6oTQ+2dKH19b9k2xQ5g0OdqembctvhaUmGcPu7bN3wYJgPVFF+Gvx/4P0XiUramIiIgIQE/ib761Ykq9NpBKpCg1z8FB537k6wsH7Hf0PkDV4m/GnKy5ozqnIAj445EnUGIuwXkF5yc1XiIiIiIaPYlEgmJzKU64a8QOhXpNnXcSNKUJggBvxMOKPxrArrUjEA2gqXe2Giv+SCxmlQVKmRItvuYR1zr9bbCos7AkZxmeOvIH1HQeR7l13iRESUREROksFo/hcPshfLzsJrFDSbo5lp7EX4GhYMA+g9IIrUKLZm/TkMeHYiFsqd8MV8AFd8gNZ6ANB50HcN+597Paj4iIiEhkpebZ2NG8TewwqBcTf5QRgrEgovEYDCO0z6Ppx9Y7H6Sm8zgAJv5IPBKJBLk6B1r9rSOubfO3wq7NxhzLXKjlahx07mfij4iIiHDCXYNANDCl5vv16WtdOljFn0QigUPnQLNv6MTfI/t+jVdP/QsGpQEmlRlmlRk3zv0EVrKdFBEREZHoSs2leKHmeXjDnhFH4FDqMfFHGcEb9gIAdAr+o0H99SX6jndWQwK2+iRx5WhzRlXx1+ZvRbY2B3KpHPOs83HQuR83ln1yEiIkIiKidFbTeRxSiQRzLKNrd5lJ5lkroJAqMGeI2YW5urwhX0cddR3Bq6f+hTsWfxUfK702lWESERER0TiUW+cDAKo7qrAsd4XI0RD7YVBG8EY8AAC9gq0+qb++ir9jHcdgUpkhk8pEjoims1ydA23+lhHXtfnbYNdkAwAW2RfjsOsQ4kI81eERERFRmmv01iNXlwelTCl2KEmXq3Pgxav/hWJz6aD7eyr+Bib+4kIcD+39BUrMpbiy5OoUR0lERERE45GvL4BBacBR1xGxQyEw8UcZwhvuSfyx1Sd9mFquhl6hR6O3AVYN23ySuHK0uWjxDZ/4EwQBzkAbsrU9ib8F9kXwR/w40ckByERERNNdg7cB+fp8scNIGZVMNeQ+hy4Pbf6WAQ9DvVz7D9S6T+DOJXdxlh8RERFRmpJIJCjPmofqjqNih0Jg4o8yhDfS0+qTiT8ajFVjA8D5fiS+HF0ufBEfPOHuIdd0h7sQjoWRrc0BAMy1lEEhVeBg+/5JipKIiIjSVaOnYdAZeNNBkXEGovEYfrnn5+gMdgAAOoMdePLw4/jorMsxr7d9FBERERGlp3LrfFR3VLGrVRpg4o8yQl/Fn46tPmkQfZV+TPyR2HJ1DgBAq691yDVOvxMAEok/pUyJedYKHHQeSH2ARERElLbiQhzNviYUGArEDkUUi+yL8eXKO7G14R3c9tpNeP7Ys3js4G8hkUjwmQWfFzs8IiIiIhrBPOt8eMIeNHjqxQ5l2mPijzKCJ+KBUqackrMuaOL65vxZ1FkiR0LTXU5vMq/FP3A+TZ82f09S0K61J7YttC/C4faDfCKKiIhoGmvztyIajyJfPz0TfxKJBFfPvg5//OgzuLDoI3j80KN488zr+HTF52FSmcUOj4iIiIhGMCerDBIAVWz3KTom/igjeMNetvmkIfW1+rSy4o9EZlZZoJQp0TrMnD9noA1yqRxmlSWxbaF9ETxhD053nZyMMImIiCgN1XvqAAAFhunZ6rOPSWXGnUu+hkcv/gO+sOjLuKz4CrFDIiIiIqJR0Cv0KDLORJXriNihTHtysQMgGg1P2AO9gok/GpxN3TvjT8PEH4lLIpEgV+dAyzCJvzZ/K+waO6SSD569KbfOh1wqx0HnARSbSycjVCIiIkozTd5GyKXyRDvw6W6WqRizTMVih0FEREREYzDPOp+JvzTAij/KCN6IB3rO96Mh9LX65Iw/Sgc52pwRWn22wf6hD/RUMhUqsxfjP2degyAIqQ6RiIiI0lCDpwF5+vx+DwcREREREWWScut8nO4+BX/EL3Yo0xrfUVBG8EbY6pOGNtsyF/OtFZhhnCF2KETI1TnQNlLF31nz/fpcN/sG1HQex762PakMj4iIiNJUo7ceefp8scMgIiIiIhq38qx5iAsCjndWix3KtMbEH2UEb9gDnZIVfzQ4u9aOX61/BAalUexQiJCjzUWLv2XIyj2nv23QFl5Lc5ajxFyKvx77vwH7wrEw7t32PexqeT/p8RIREVF6aPA2oEBfIHYYRERERETjVmScAa1Ci6Ns9ykqJv4oI3jCHhgUTOoQUfrL0eXCH/HDG/EM2BeLx+AKtiNbMzDxJ5FIcOPcT2Jv6x4c7zjWb99TR57Ae43v4v+q/pyyuImIiEg80XgUrb5m5OsLxQ6FiIiIiGjcpBIpyrLKUdVxVOxQpjUm/igj+CJe6FnxR0QZIFfnAAC0+loH7HMFXYgLwqAVfwCwtmAdHPo8PHfsL4lth9sP4fljz2KhfREOtx9Cg6c+NYETERGRaFp8zYgLAgoMrPgjIiIiosxWnjUfVa6jQ3bDotRj4o8ygifsgV7BxB8Rpb+c3qRei795wL42f08ycLAZfwAgk8pw/ZyP493Gt9HoaUAgGsDPdv0E5db5+NGaB6BX6PHv06+mLngiIiISRd+DPaz4IyIiIqJMV26dj66QG82+JrFDmbaY+KO0F4lFEIqFYFAaxA6FiGhEZpUFSpkSrb6WAfuc/jYAGLLiDwA+MvNSGJUmPH/8WTx+6DG4Au345vL/B41cg/UzLsbrZ15DLB5LWfxEREQ0+Rq9DVDKlLBqrGKHQkREREQ0IeVZ5QCAKs75Ew0Tf5T2+uZkGZSc8UdE6U8ikSBX50DLIIm/Nn8rtAotdArdkMerZCpcO/t6vHbqX3jpxIv4/MIvosDQ8/T/JTM+ClfAhd2tu1IWPxEREU2+Rm8DCvQFkEr4Fp2IiIiIMptRZUK+vgBHOedPNHxXQWnPG/ECwLAflBMRpZMcbc7grT4DbbBrskc8/sqSj0ElV6MyewmuLLk6sX22ZQ6KTcX49+lXkhkuERERiazBU488Pef7EREREdHUUG6dx4o/ETHxR2nPE+6p+NOz1ScRZYhcnQNtQ1T8ZWtHTvzplQb870WPY+PqH/d78l8ikeCSWZdhe9NWdIXcyQyZiIiIRNTobUCBgYk/IiIiIpoalueupcgbngAAffhJREFURE3ncRx07hc7lGmJiT9Ke95wT8WfQcFWn0SUGXK0uWjxt0AQhH7bnf62Yef7nS1Pnw+tQjtg+4VFFwMA3qx7feKBEhERkejCsTCc/jbk6wvFDoWIiIiIKCnWFa5HWVY5Ht77K0TjUbHDmXaY+KO05410AwD0Sr3IkRARjU6OLhf+iD8xo7TPWBJ/QzGpzFjlOBf/PvXKgMQiERERZZ4mbyMEAPms+CMiIiKiKUIqkeLOJXfhTPcp/OPEC2KHM+0w8Udpzxv2QiaRQi1Tix0KEdGo5OocAIBWX2tiWzAaRHe4G3aNfcLnv3TW5TjZdRI1nccnfC4iIiISV4O3HgBQwBl/RERERDSFzLbMwRUlV+OpI39Ae6Bd7HCmFSb+KO15I17olUZIJBKxQyEiGpWc3qq+Fn9zYpsz0AYAE674A4BlOcuhkWuwr23PhM9FRERE4mr0NEAj18CssogdChERERFRUm2o+AyUMhV+d+C3YocyrTDxR2nPE+6GXsE2n0SUOcwqC5QyJVp9LYltTn9P4s+uzZ7w+WVSGQoMhWj0Nkz4XERERCSuRm8DCgyFfNCRiIiIiKYcg9KIzy38At6qfxP72/aKHc60wcQfpT1P2AOD0iB2GEREoyaRSJCrc6DlrMRfW2/iz5aEVp8AUGgoRIOnPinnIiIiIvE0ehuQzzafRERERDRFXTzjEsyzzsejBx4RO5Rpg4k/Snu+iA96JSv+iCiz5GhzsK9tD/a07kIsHkObvxVmlRlKmTIp58/XF6LeU5eUcxEREZF4Gjz1TPwRERER0ZQllUhx7ezrUes+kXgwnlKLiT9Ke56wB3oFK/6IKLNcN+cGRONRfPudb+DjL1+LN+r+k5T5fn0KDUVwh9zwRrxJOycRERFNLn/Ej45gBwoMTPwRERER0dS1JGcppBIJ9rbuFjuUaYGJP0p73kg3W30SUcZZmrMcT176NH5z4WNYX3QxwrEQyqzzknb+AkMhAKDRwzl/REREmarZ1wigp5KfiIiIiGiqMiiNmGMpw+6WnWKHMi3IxQ6AaCTesBd6Jv6IKANJJBLMzSrD3KwyfLHyjqSeu68lWL3nDOZmlSX13ERERDQ5ajprIAFQaCwSOxQiIiIiopRamrMcL9W+iLgQB2vSUot/u5T2vBEvdHKd2GEQEaUVrUILq8aKBlb8ERERZay9rbsx2zIXegVnmhMRERHR1LYsdwU8YQ+Odx4TO5Qpj4k/SmtxIQ5fxAeD0ih2KEREaSdfX4gGT73YYRAREdE4xIU49rbtwZKcZWKHQkRERESUcmVZ5dAqtNjTskvsUKY8Jv4orfkiXgDgjD8iokEUGgrR4K0TOwwiIiIah1NdtegKubGUiT8iIiIimgbkUjkq7Uuwp5WJv1Rj4o/SmifsAQC2viEiGkSBoRANnobe3uhERESUSfa27oFSpsQ8a4XYoRARERERTYpluStw1HUYvohP7FCmNCb+KK0lEn+s+CMiGqDAUIRQLIT2QLvYoRAREdEY7WvbgwW2hVDKlGKHQkREREQ0KZblLEdMiGN/616xQ5nSmPijtNbg6Wlh59A5RI6EiCj9FOgLAHzwbyURERFlhnAsjAPO/ZzvR0RERETTikOfB4c+D7tadoodypTGxB+ltZNdtcjR5rDij4hoEA5dHmQSKRo89WKHQkRERGNw1HUE4ViY8/2IiIiIaNpZlrMCu1s45y+VmPijtHayqxazzCVih0FElJZkUhkc+nw0eBvEDoWIiIjGYE/LLhhVJswy8b0OEREREU0vy3KWo8nbiIZufp6VKmmT+HvmmWewfv16LFiwANdffz0OHjw47Pru7m5s3LgRa9asQUVFBS655BJs2bIlsX/9+vWYO3fugP82btyYWBMKhbBx40asXLkSixcvxle+8hW0t/efk9TU1ITPf/7zWLRoEc455xw88MADiEajyf3iaUi17hMo5pthIqIhFRgK2eqTiIgow+xp2Y0l2UshlaTNW3IiIiIiokmxKHsxpBIZdjTsEDuUKUsudgAA8Morr+D+++/Hxo0bsWjRIjz11FP4zGc+g9deew1Wq3XA+nA4jA0bNsBqteLXv/41cnJy0NTUBKPRmFjzt7/9DbFYLPHnmpoabNiwAZdeemli209+8hNs2bIFv/rVr2AwGHDffffhjjvuwLPPPgsAiMViuP3222Gz2fDss8+ira0N3/rWt6BQKHDXXXel8G+EAKAz2IHOYCdKzKVih0JElLYK9YXY2viO2GEQERHRKHWHunG88xg+OutKsUMhIiIiIpp0OoUO820V2NGwAxc6Pip2OFNSWjxe+OSTT+KGG27Addddh9LSUmzcuBFqtRqbNm0adP2mTZvQ1dWFRx55BEuXLkVBQQFWrFiBsrKyxJqsrCzY7fbEf2+99RaKioqwYsUKAIDH48GmTZvw7W9/G+eccw4qKirwk5/8BPv27cP+/fsBAFu3bsWJEyfw85//HOXl5Tj//PPx1a9+Fc888wzC4XDK/16mu5NdtQDAij8iomEUGIrQ6m9BOMbfS0RERJlgT9MexIU4lmQvFTsUIiIiIiJRfKL8k1iet1zsMKYs0RN/4XAYR44cwerVqxPbpFIpVq9ejX379g16zObNm1FZWYkf/vCHWL16Na644go8+uij/Sr8PnyNl156Cddddx0kEgkA4PDhw4hEIv2uW1JSgry8vETib//+/ZgzZw5sNltizZo1a+D1enHixImJfuk0glr3CajlauTp88UOhYgobRUYChAXBDR5G8UOhYiIaNrzR/z4781fRv0wbbjfb3wf+fp85OhyJzEyIiIiIqL0sSpvNW6suFHsMKYs0Vt9dnZ2IhaLDWjpabVacfLkyUGPqa+vx44dO3DllVfid7/7Herq6rBx40ZEo1HccccdA9a/8cYb8Hg8uOaaaxLb2tvboVAo+rUH7buu0+lMrDk76Qcg8ee+NaMhlUoglUpGvZ56nPacRLG5BEqF6D+mlAQymbTf/xJRcsyyzIREAjT7G1BqTU6FNO9XoszCe5YofTR3N+Box2G83fAGNiz47ID9MpkUOxt3YpljBeRy3rNE6Y6/Y4kyC+9ZoszB+zW1MjKjIggCrFYr7rvvPshkMlRUVKC1tRVPPPHEoIm/TZs2Ye3atcjJyREhWiArS5eoNKTRq/edxkLHQlgsOrFDoSQyGjVih0A0pZgFLYwaAzrjzqT/e8n7lSiz8J4lEl+oywuZTIq97btwl+WrA/Y3e5pR11WHO1bcwfc5RBmEv2OJMgvvWaLMwfs1NURP/FksFshkMrhcrn7bXS7XgGq7Pna7HXK5HDKZLLGtuLgYTqcT4XAYSqUysb2xsRHbtm3Dww8/3O8cNpsNkUgE3d3d/ar+XC4X7HZ7Ys3Bgwf7Hdfe3p6IYbQ6Onys+BujcCyMmvZaXFp0BTo7fWKHQ0kgk0lhNGrQ3R1ALBYXOxyiKcWhyUd1S03S/r3k/UqUWXjPEqWP2tYziMXiONJ6FCeazsCq6f+e9pWaf0MhU2CuoYLvc4gyAH/HEmUW3rNEmYP36/iM9uFB0RN/SqUS8+fPx/bt23HRRRcBAOLxOLZv346bb7550GOWLFmCl19+GfF4HFJpTyno6dOnYbfb+yX9AOCFF16A1WrFunXr+m2vqKiAQqHA9u3bcckllwAATp48iaamJlRWVgIAKisr8eijj8LlciVakW7btg16vR6lpaWj/hrjcQHxuDDq9QSccp9CLB7DDEMxolHe+FNJLBbn95QoyfJ1Bajrqkv6vcX7lSiz8J4lEl+rtxUGhRGecDe2NWzHR2dd3m//66f+gzWFa6CV6Xi/EmUQ/o4lyiy8Z4kyB+/X1EiLBqobNmzAc889hxdffBG1tbW49957EQgEcO211wIA7r77bjz44IOJ9Z/4xCfgdrvx4x//GKdOncLbb7+Nxx57DDfddFO/88bjcbzwwgu4+uqrIZf3z3EaDAZcd911+OlPf4odO3bg8OHD+M53voPFixcnEn9r1qxBaWkp7r77blRXV+Pdd9/Fr371K9x0000DEoyUXLXuEwCAYlNy5lUREU1lBYYi1HvrxQ6DiIho2msPODHTNAvl1vl4v3l7v30Nnnoc66jGpaWXihQdERERERFNB6JX/AHAZZddho6ODjz00ENwOp0oLy/H448/nmj12dzcnKjsAwCHw4EnnngC999/P6666irk5OTg1ltvxec+97l+5922bRuamppw3XXXDXrd73znO5BKpbjzzjsRDoexZs0a3HPPPYn9MpkMjz76KO69917ceOON0Gg0uOaaa3DnnXem4G+BznayqxYOnQNahVbsUIiI0l6+vgDdoS54wt0wKI0jH0BEREQp4Qw4YdPYMMM4C89WP4NwLAylrOeh0c11b0Aj12BN0Rr4PVGRIyUiIiIioqlKIggCe1CmmNPpETuEjHP3lq9Bq9Dh3tU/EjsUShK5XAqLRYfOTh/Lt4mSrNZdgy+8/ln8ev1vMc86f8Ln4/1KlFl4zxKlj1tf+TjOKzgf64suwhde/yweWPsgluQsgyAI+PS/b8F823w88NGf8H4lyhD8HUuUWXjPEmUO3q/jY7cbRrUuLVp9Ep1NEATUdtWyzScR0Sjl6Qsgl8pQ5ToidihERETTliAIcAacyNbmoNhUCpvGhvebdwAATrhr0OCpx/oZF4kcJRERERERTXVM/FHacQVd6A51ocRcKnYoREQZQSPXYHnuKmyue0PsUIiIiKYtd6gT0XgUNo0dEokEK3JXJeb8ba57HSaVGUtzlokcJRERERERTXVM/FHaOemuBQAm/oiIxuCiGR/B8c5jqPfUiR0KERHRtNQeaAcA2DR2AMBKxzlo9Dag3lOHt+s34/zCCyCTysQMkYiIiIiIpgEm/ijtnOw6AY1cgxxtrtihEBFljFWO1dAqtHjzzOtih0JERDQttQecAIBsbTYAoDJ7CeRSOR4/+BjaA+1YX8g2n0RERERElHpM/FHaqXWfQLGpBBKJROxQiIgyhlKmxNqCdXiz7j8QBEHscIiIiKadNn8r5FIZTCozAECr0GKRvRLbmrYiV5eLedb54gZIRERERETTAhN/lHZOdtWimG0+iYjG7KKij6DF14KjriNih0JERDTttAecsGnskEo+eJu90nEOAOCCoov4YCMREREREU0KJv4orYRiITR46lBsKhE7FCKijLPAvgg2jQ1v1v1H7FCIiIimHae/DXZNdr9t5+avRYGhEB+ZcalIURERERER0XTDxB+llXAsBK1chwX2hWKHQkSUcaQSKS4suhhv129GJBYROxwahRZfMxo89WKHQURESeDsrfg7W7Y2G09e+jQKDIUiRUVERERERNMNE3+UVgxKI1742MuYYZwpdihERBnpwhkfgSfswe7WnaM+Ji7EcaT9MA63H8KxjmqcdNeiI9CRwiipz493bMT/7Pqp2GEQEVESOANO2LX2kRcSERERERGlkFzsAIg+jLMviIjGb5apGCXmErxx5j84J+/cUR2zue51PLDzJ4k/SySARqnGs1e8AI1Ul6pQp72TXbWo7qiCRq6BIAiT8vtvb+tuVNgWQilTpvxaRETTiSAIaPc7B7T6JCIiIiIimmys+CMiIppiLiz6CLY3vQdvxDuq9S/WbMLi7CV4/JKn8L8XPY6N5/4I4VgYxzqqUxzp9PbaqVcAAIFoAG2BtnGdQxCEUa9t8NT///buOzzKKu3j+G9mkknvGUJ6aAm9Ix0Eu2JfsBfW7qprX3V1lVWXde2K71pw1bUuirhWLLCwKl2qdEJ6nfSeSTLz/gGMZhMgfZLJ93NdXJrznHOe+xk4affc5+gP/71LX6V83qZ7AQCOrrS2RHX2OoVT8QcAAADAxUj8AQDgZk6MPUl19jptzFl/3L57inZrX/FeXTBoruIDEzQwZJCmxkyXv9lfewp3d0G0vZOtwabv0r7WzJhZkqTU0pQWjXtv99u6dcWNuvqry3Thp+fozI9P0ufJn7Zo7MbcQ/8e1uesbVvQAICjKqgukCQq/gAAAAC4HIk/AADcjMXXoki/SO0u2nXcvp8cWKpIv0idEDnJ2WY0GDXUMlS7C48/Hm3zY9b3KreV68ph8+Xj4aPU0oPHHbOvaK/e+Hmxgr1DNCVqqn4zaJ6i/WO1OX9Ti+65MXe9jAaDtuRvVlVdVXsfAQDwK9bDlduc8QcAAADA1TjjDwAANzQ0bJh2Ff58zD7FNUVanfEf/Xb4dTIaGr8XaHif4fro54+77Oy53ubLlM81Inyk4gLjlRDUTyllx0/8vblzsWID4rRgyuPOv6+S2hKtzfnxuGNrG2q1NX+Lzup/jj5L/re25P+kqdHT2/0cAIBDrFX5MhmMCvYKcXUoAAAAAHo5Kv4AAHBDQ8KG6UDxPtU21B61z1cpX8hoMOr0fmc2uTbMMkzFNUXOrcvQcbIrsrQ1f7PO7D9HkpQQ2O+4W33+XLBDG3M36Mph8xslaQcGD1RORbYq6yqPOX5b/lbV2et0zoDzFRsQp3U5a9r/IAAAJ2t1vsJ9LE3eSAMAAAAAXY2fSgAAcEPDwkaowWHXvuK9zV6vt9frs+RPdHLcqQowBzYd32eYJGlPC7YLRessT/1Sfp5+mhY9U5KUENRP6WVpsjvszfZ3OBx64+fF6h/UXzNiTmx0bUDIIEnSwZIDx7znhtx16uPbR/GBCZocNUXrc9Ye9X4AgNazVltl8eV8PwAAAACuR+IPAAA31C+ov7w9vLWroPntPtdk/6CC6gKdM/C8Zq+H+4Yr3MeivUW7OzHK3qfB3qCvU77U7LhT5O3hLelQxV+dvU7ZFVnNjtmS/5O2W7fq6uHXNqkkiQuIl6fRU8nHSfxtzF2vCX0nymAwaGLkZBXXFGt/8b6OeSgAgKxVVoX7cL4fAAAAANfjjD8AANyQyWhSYshg7T5Kxd6/D3ysEeEjNSB40FHnGBI2VHuK9nRWiL3C16lf6b3dbyvaP1qxAfEyGKSimiKd2W+Os0+/oP6SpJTSg4oJiG003uFw6M2dryspdLAmRU5pMr+H0UMJQf10oGT/UWPIKs9UdkWWbhh5s6RD1aD+nv5al7NGSaGDO+IxAaBX+U/6Co2NGKcgr2Bnm7U6X0khSa4LCgAAAAAOo+IPAAA3NTRsmHYW7JDD4WjUnlJ6UNut23TuwAuOOX5w2BDtK97DlpDt8H3mKjkcdpmMHlqfs1bL9n+koWHDNDDkl4RrsFeIAr2ClFrW9Jy/9bnrtLtwl+YPv1YGg6HZewwIHnjMxN/G3PXyMJo0us9YSYeSwhP6TtS6bM75A4DWKreV6S/r/6z3dr/jbHM4HCqoYqtPAAAAAN0DiT8AANzUsLDhKqktUV5VbqP2r1K+ULBXsKZGTz/m+MGhQ1RdX630srTODNNtORwO7S3aoxPjTtKjUxfqzTPe1efnf6unT3yhUT+DwaCEwH5KKT3YZI73d7+tEeEjNbbP+KPeZ2DwIKWVpaiuoa7Z6xty12l4+Cj5evo62yZFTdaBkv2yVlnb+HQA0Dullh56k8Z3aV87P++W2UpVZ69jq08AAAAA3QKJPwAA3NSQsKGSpF2Fv5zzZ2uw6bu0r3VK/GnyMB57x+/E0CQZJO0tZrvPtiioLlBJbYmSQn7ZTtPT5Nns694vqL/zl8lH5FbmaFfhTs0ZcM5Rq/0kaUDwINXbG5RentrkWm1Drbbmb9GEvic0ap/Qd6KMBoM25K5r5VMBQO+WWpYig6QyW5nW5vwoSc43UVDxBwAAAKA7IPEHAICbCvIKVrR/jHYW7nS2rcn+QeW2cp3e76zjjvfz9FNcYIL2Fu3uzDDd1v7ivZKkxJDjn6OXENhPWRUZsjXYnG2rM/4jT6OnJkVOPebY/kEDZJB0oORAk2vb8reqzl6nE/pOatQeYA7U8PCRWpfDdp8A0BqpZamKC0zQkLChWp7yhaRD5/tJksWHxB8AAAAA1yPxBwCAGxsaNky7Cn6p+Fue8oWGhg1TXGB8i8YnhQ7WHhJ/bbK3eI+CvYIV7hN+3L79gvrL7nAoo/yXbVVXZ/5HEyMnN9qiszm+nr6K8o9RcjOJv42562XxsSg+MKHJtRP6TtLmvE2qbag9/sMAPZitwabC6kKllqbo54Idqq6vdnVI6MFSSw8qIbCfTk84S5tyNyi/Kl/WqnyZDEaFeIe4OjwAAAAAIPEHAIA7Gxo2XAdLD6i6vlp5VXnanLdJZ/Sb0+Lxg0OH6GDJgUaVaGiZ/cV7lRQ6+JjbdB4RH5Qg6Zezo7LKM7W/eJ9mxs5q0b0GBg9Scsn+Rm0Oh0Mbc9frhMhJzcYwOWqqbA02fZ+5qkX3ALqjcluZKmzlzV4rrinS7767Xmd9fIou/vwCXffN1brjP7fovd1vd3GUcBcOh0OppSmKD0rQzNhZMpu89G3qclmrrQrzCZfRwI/XAAAAAFyPn0wAAHBjQ8OGyu5waH/xXn2T+pW8PXw0I+bEFo9PChmiBoddB/4nqYRDkkv269Vt/yeHw9Go3eFwaG/x3hZt8ylJ/p7+svhYlFp2KPH338xV8jJ5aWLk5BaNHxA8UAdK9jeKY7t1q7IqMjUtekazY+IC4zUlapre+HkxVX/osZ7Y8LiuWn6ZtuT91Ki9qKZQ96y+Q9bqfN01/g96dOpCPTfrJY3pM5bti9FmJbXFKrOVKSGwn/w8/TQzdpaWp34ha1Ue23wCAAAA6DZI/AEA4MYSgvrLx8NHPxfs0DepX2lm7Kzjbh35a/2C+svT6Mkvyo9iQ856fbjvX0orS23Unl+dr7LaUg0KSWzxXAlB/ZwVf6szV2py1FT5ePi0aOyA4EGqqqtSbmWOs+39Pe9oQPAAjYuYcNRx1428UYXVBfp434ctjhPoLuwOu7Zbt8nhsOu+7+/Skr3vy+FwqKC6QHet+r0q6sr19Ikv6PR+Z2pS1BQNCx+uEZZRTZLkQEullB6UdOhroySd3u8s5Vbmal3OWll8SfwBAAAA6B5I/AEA4MaMBqMGhw7RJweWKrcyV6cnnNWq8Z4mTw0MGUTi7yjKbKWSpB+y/tuofV/RHknSoJCkFs/VL6i/UstSlFGeruSS5FZVZg4MGShJzsrMfUV79VPeJl2UdNkxtxqNCYjVOQMv0Pt73lFxTVGL7wd0B+llaaqur9aDkx7R3MSL9dr2l/Xouod116rbVFtfo6dnvqDYgLhGYwYFJ6rcVq78qjwXRY2eLLUsRZ5GT0X5R0uShoeNUExArCrrKlt0nisAAAAAdAUSfwAAuLmhYcNVXFOs2IA4DQ0b1urxSSFDtOdwIssV3tv9ttZm/+iy+x9Lma1MkvRD1upG7fuK9yrMJ6xVvwhOCOyn3MpcLU/5Qj4ePjohclKLx4Z6hynEO8SZ+Ptg77uK9I9q0RmBVwy9Sh5GD7218x8tvh/QHewu2iWjwaDBoUN17cgb9dDkBdqYu14N9no9M+tFRQfENBkzIHiQJGl/yb6uDhduIK00VXGBcc6z/AwGg05POFOSqPgDAAAA0G2Q+AMAwM0NDRsuSTq935nHrP46mgHBA5VVkSlbg62jQzuu4poivbXzdT2+boHSy9K6/P7HU2Yrk6fRU8klycqpyHa27yve06pqP+nQtqyS9O8DyzQlaqq8TF6tGj8weJAOlhxQRnm6fshcrXmJlzh/OX0sAeZAXTbkSn2V8rlzGzugJ9hduFMJgf2c2xfPiDlR/zjtHf39lMXq6xfZ7Jhwn3AFewXrQDHnlqL1UstSnJ+rjzgl4TT5evqqX2D/o4wCAAAAgK5F4g8AADc3us8YzUu6WGf2m9Om8X0OVzEUVFs7MqwWObKFZriPRY+ve6TZ5OPW/M0qP1x519XKaks1MXKyPI2ezlgdDof2F+9TUsjgVs0VFxgvg6TahlrNjJ3d6lgGBA/SgZL9WrL3fYV4h+rUhNNbPPacAeerr1+UXtv+91bfF3CV3YW7NOR/qpgtvhYFmAOPOsZgMGhgyCAdoOIPreRwOJRSelAJgf0atYd6h2npOZ9pTMQ4F0UGAAAAAI2R+AMAwM2ZTWZdN/Im+ZsD2jS+j2+EJMlald+RYbXI6oxVGtNnnP40eYEyyjP0yvb/c16rqa/RkxsX6p7Vd+ie1be7JPlXZitThF+ExvWd4Ez85VbmqNxWrsRWJv68TF6K8o+Rn6efxkVMaHUsA4IHqqC6QN+mLtcFg+bKbDK3eKynyVPXjLheG3M3KLmESih0f5V1lUorS9Hg0KGtHjswONG5La6rORyOFvc9ULxfezhv1WXyq/NVXV/dJPEnSR5GDxdEBAAAAADNI/EHAACOKdzHIkmyVndt4q+4pkg7CrZqRsws9Q8eqJtG36JPDyzTj1nfK6M8XbeuuFGrM/6ja0Zcr/yqfN3//T2qrKvs0hjLbGUKNAdpevQM7SrcqcLqQu0r3itJGhQyqNXzTY+ZqXMHXtCqpN0RAw+fXebj4auzB5zX6vFHko1pZamtHgt0tb1Fu+WQNDh0SKvHDgpJVGF1oYprijo+sFZYl71Gl30xV1V1Vcfsl1meocfWPqKbvrtWf1m3oIuiw/9KK02VJCUENU38AQAAAEB3wlsTAQDAMXl7eCvAHCBrVddu9Xmkgm5q9DRJ0pz+5+qnvE16cuNC2R12hftYtOikV5QQ1E/jIibontW368Ef/qC/TH9SPh4+nR6f3WFXxeHE3+SoqTIZjFqT/b1yK3Nk8bEoxDu01XNeM+L6NscT5R+tIK9gnTPgPOeZZ63h5+mnIK9gZVVktTkGoKvsKdotX09fxQXGt3rskST5/uL9OiFyYkeH1mL7ivfKWm3VfzNX6fR+Zza5Xm4r0xs/L9aXBz9TiHeoZsScqO8zV6m2obbVZ4Ci/VJKk+Xj4eOsggcAAACA7oqKPwAAcFwWH4usXXzG3+qMVRrdZ6yCvIIlHTqb667x9yrMJ1xToqc5k37SoQqev0x/UsklB/SnHx9QbUNtp8dXWVchu8OhQK9ABZgDNarPGH2fuVr7ivcpMbR123x2BKPBqNdPe0uXD72qzXNE+0crqyKzA6MCOsfuol0aHDpERkPrf5yJ9IuSn6efy8/5y6/KkyR9mfJZs9df2vK8VqR/q9+OuF5vnvGeLhg0Vw5JWeUZXRgljkgtS1FcYHyb/s0BAAAAQFfipxYAAHBcFt8+XbrV56+3+fy1AHOgFp/6lu474cEmVW1Dw4bp8WlPaGv+Zn2fuarTYyyrPXSmYKA5UJI0LXqmtlm3aG/RbiWGJHX6/ZsT5BXcrl9KR/pHKaciuwMjQk9na7Bp0ZbnVVBd0Oz14poi/d/WF7sk2X6Ew+HQ7sJdGhI6rE3jDQaDBgQP0v5i1yf+/D39tbtwl1JKDza6llORrf9krNBvh1+neUmXyMvkpdiAWElSBok/l0grS232fD8AAAAA6G5I/AEAgOMK97HIWtV1ib8fsr6XJE2Lnt7kmsFgOOq4EZZRCvQKUm5lbqfFdkSZrXHib0rUNDkcDlXXV7ss8dde0f4xymarT/zKwdJk/fvAx3p12/81e/217S9r2f6PtCFnXafc/0Dxfv14+PPBEbmVOSqtLdGQsLYl/qRD230ml+xvb3jtkl+Vr9nxpyjYK1hfpXzR6Nq/9r6nQHOQTu93lrMt0CtIgV5ByihP7+pQez27w660slT1C+rv6lAAAAAA4LhI/AEAgOOy+PQ5asVPZ/hvZuNtPlvD4hOugi7YlvRI4i/AHCRJCvMJ09Cw4ZLUYxN/UX5RKqktUWVdpatDQTeReTjJ9J+MFdpRsL3RtT1Fu/Vt2tfyMJq0JvuHTrn/P35+VY+u/ZNSS1OcbbuLdkqShoQOafO8g0IGKacyRxW28nbH2BYOh0P5VXmK9o/WqQmn67u0r2VrsEmSCqoL9HXqV7pg0NwmZ/nF+scqozzNFSH3ajkV2bI12BQfmODqUAAAAADguEj8AQCA47L4WlRaW+L8xXRnKq4p0nbrlibbfLZUmHe4CmsKOziqpsptpZKkIK8gZ9ucAedoXMR4Bf6qrSeJ8o+RJLb7hFNGeYZCvEOUGJKkv299UXaHXdKhCqiXtjyvAcEDdOGgedqQs04N9oYOvbetwaZt1q2yO+x6fvPTznvvKtylaP+Ydq2zgcGJkqTkkgMdEmtrldQWq85epz6+ETqj3xyV28r1Q9ZqSdLH+5bIbDLr7IHnNRkXGxjHVp8ukFp2KPGcQMUfAAAAgB6AxB8AADiucB+LJHVJJd2PWYcqh5rb5rMlwn0sKqjq/DhLa0vlZfKS2WR2tp0cf5r+OuPpTr93Z4n2j5YkZVey3ScOySrPVGxAvG4efZv2F+/TN6nLJUkr07/VnqLdunn0bZoaPUNltjLtKvy5Q++9o2CbbA023Tj6Fv1csENfp34lSdpduFODw9pe7SdJsQFxMpvM2l/imnP+8g9vndzHJ0IxAbEaaRmlLw9+oXJbmT47+G+dO/AC+Xv6NxkXGxCnjPJ0ORyOrg65V0srS1WAOUBh3mGuDgUAAAAAjovEHwAAOK4+vhGS1CXn/P0n4zuNsoxp0zafkhTWRVt9ltpKnef7uYsAc6D8Pf055w9OmRXpig2I1bDw4ZoVe5Je3/GKCqsLtXjHK5oeM1MjLaOVFDpYwV7BWpv9Y4fee1PuBoV6h+r8gb/RSfGn6LXtf1d+Vb6SS/ZrSOjQds1tMpo0IHigDhS7KvGXJ0nq49tHknRmvznaZt2il7a+oAZ7g84feGGz42ID4lRTX9OlWy9DSik9qPjAhGOeMQsAAAAA3QWJPwAAcFxhPuGSJGt15yb+dhb8rO3WbZoz4Nw2zxHuc2hb0np7fQdG1lR5bZkCvdwr8WcwGBTlH62sikxXh4JuwOFwKLM8U9GHt4C9duSNqqqv0q0rblBZbZmuH3mTJMloMGpy1FSt6fDE30aNi5ggg8GgG0beLEn604/3qcFh15DQYe2ef2DwIB1w0Vaf+VV5MpvMzjc4TIuZKX9Pf61I+1Zn9j9bId6hzY6LDYiTJM7562KppSlKCOzn6jAAAAAAoEVI/AEAgOPy8fBRgDmg06tM3t39luIDEzQtekab5wj3scghdfo5f2W2MgWae+ZZfscS5R9NxR8kSQXVBaptqFVMQKykQ9VpFw++TNZqq+YmXay+fpHOvpOjpiqrIlMZ5ekdcm9rlVWpZSka3/cESVKId6iuHXGjkkuS5Wn0VP/gAe2+x8DgRGWUp6mmvqbdc7VWflW++vhGOCvIvExeOin+VJkMRs1Nuvio4yL9ouRhNHXY64xjczgceuPnxUotS9FIy2hXhwMAAAAALULiDwAAtIjFx+I8l6oz7CnarY25G3TZkCtlNLT9W5Rwn0NnMHX2OX/umviL9I8i8QdJUubh5NKRKjNJuijpUv1+7F26ZPDljfqO6TNOZpNZaw6f0dlem/M3ySBpXMR4Z9vp/c7UiPCRGh4+Qh5Gj3bfY1BIouwOhw6WJrd7rtbKr8pzbvN5xNXDr9Fzs/9PEYe3Vm6OyWhSlH+MMsozOjvEXs/usOvFLc/pvd1v69oRN2hW3EmuDgkAAAAAWoTEHwAAaJFwH0unbvX57q63FBsQp5mxs9o1T7iPRZJUWNO51YnltlK32+pTkqL9Y5yVXujdMisyZTQYGlX2mU1mzRlwjrw9vBv19fbw1tiI8VqXs6ZD7r0pd4MGhSQ1OuvTaDDq8el/05+mPNoh94gPTJCH0aTdhTs7ZL7WOJT4a5zg8/f01+DQIccdGxsQx1afnazeXq+/rn9MXxz8t+4Yd48uGnypq0MCAAAAgBYj8QcAAFrE4ttH1k6q+NtXtFfrctbqsiFXtKvaT5ICzIHyNHqqoLpzK/5Ka0sVYHa/xF+Uf7QkKaci28WRwNUyyzMU6Rfd4uq6KVHTtKvwZ5XUFLfrvnaHXZvzNmlc3wlNrvl4+Mjf079d8x9hNpk1KXKqPkv+t+wOe5PrDodDXx78XOW2sg653681l/hrqdiAWGWUsdVnZ6mpr9HDPz6g77NW6Y+THtGZ/ee4OiQAAAAAaBUSfwAAoEUsPn067Yy/d3a/pWj/GM2KO7ndcxkMBoX7hKuwk88jPLTVpxsm/vwOJf7Y7hNZFRmKCYhpcf9JkZPlcDi0IXddu+67v3ifymxlmhBxQrvmaYl5SZcoqyJTa7KbblH638xVevanJ7U6Y1WH3tPWYFNJbUk7En9xslZbVV1f3aFxQaqwlev+7+/W9oJtemzaE5oRc6KrQwIAAACAViPxBwAAWiTc16LS2hLZGmwdOm9yyX6tzf6xQ6r9jgj3sXRqxV9tQ61sDTYFebnfGX+h3qHyMnkpqyLT1aHAxTLLMxXtH9vi/iHeoRocNlRrs9u33eem3A3y8fDRkLBh7ZqnJYaEDdVIyyj9a897cjgczva6hjq9/vOrkqSM8qNX1+2wbms0riWOVE738elznJ7Nizl85mJWeevWaFFNoSrqKtp0z7qGOr2w+dljvhY9XVFNoe5e/XullaXqiRnPaFxE04pTAAAAAOgJSPwBAIAWsRw+O6+jE2of7v1AkX6Rmh13SofNGeYT3mnViZJUVnto678As/sl/gwGg6L9o5VTyVafvVldQ51yKrMUezjJ1FKTI6dqU94G7S/e1+Z7b8rboDF9xrV4i9H2mpt0ifYU7daOgm3Ots8OfqK8yhzFBcYf9Ty95JL9unPVba0+1zC/Kk+S2rXVpySlt+Kcv7qGOt264kbd9O01yizPaPU9l+5fos+SP9EnBz5u9dieILcyR7f/5xYV1xTr6RNf0NAuSDoDAAAAQGch8QcAAFrE4nuoOqWjE397ivZoctQ0mYymDpszzCescxN/thJJcsutPiUpyj+Gir9eLrcqR3aHo1VbfUrSaf3OUJRflG5Zcb1e2vKCKusqWzW+oq5Cuwp/1vi+nb/N5xET+05SQmA/Ldn7/qEYbOV6Z9dbOi3hTE2KnHzUKrfkkgOSpLXZP7bqfvnVhyr+jnxOba0Ac6CCvYJblcBbkf6t8g9XGt7xn1talZjNq8zVO7veUoA5QP/N+I8a7A2tjrk7q7CV657Vt0uSnp/9f+oX1N+1AQEAAABAO5H4AwAALRJ+uOLvSLXKEdvyt+itnf9o05z19nrlVGYpJqDl2wm2xJGtPlu7BV9LHan4c9/EXxRn/PVyR5JKrdnqU5JCvcP00smv6doRN+qrlM91zddX6PvM1S0aa3fY9X3matkdDk3owsSfwWDQvKSLtT5nnVJKD+r9Pe/I1mDTVcOuUWxAvPIqc1XbUNtkXGppiiRpfc5a2R32Ft8vvypPwV7BMpvMbY75UCViy7bdtDvs+mDPu5oaPV2LTnpFfXwjdNeq27Q1f3OLxi/a+rwCzAH60+Q/q6S2RNusW9ocd3fjcDj09Ka/qdxWrr/NeEZ9/SJdHRIAAAAAtFu3SPy9++67mj17tkaMGKG5c+dq+/btx+xfVlamBQsWaNq0aRo+fLhOO+00rV7d+BcKeXl5uvvuuzVx4kSNHDlSZ599tnbs2OG8npSU1OyfxYsXO/vMnj27yfVXX321Yx8eAIAewsfDRwHmgCaVdB/tX6J3dr2l3MqcVs+ZW3m4qsi/dVVFxxPmHS5bg02VbTzP6njKbIcTf17umfiL9ItWflWe6hrqXB0KXCSzPEPeHt4K9wlv9VgPo4fmJl2s109/W4NCkvTntX/S5rxNzfYtqy3Vkr3v66Ef79eF/z5bz2z6mwaHDunyBMyJsScp3Cdcr2x7Scv2L9XcpIsV5hOmmIBYOSRlNVNdl1qWoiCvYBXVFLWqgi6/Kq/N23weERsQd9QtSP/XfzNXKasiU5cMvlxBXsF6cuZzGhI2VPd/f4825Kw/5tg1WT9oXfYa3TT6Vo2yjFGkf5T+k7GiXbF3J58f/Ld+yPqv7hr/B5J+AAAAANyGyxN/X375pRYuXKjf/e53WrZsmQYPHqxrrrlGhYWFzfa32WyaP3++srKy9Pzzz2v58uV69NFHFRHxyw/PpaWluuSSS+Tp6anXXntNX3zxhf7whz8oKOiXc3h++OGHRn/+8pe/yGAw6LTTTmt0v9tuu61Rv8svv7xzXggAAHqAcJ9w53ZxkmRrsDmrRlamf9fq+Y5UFcW08hyx4wn3PXIeYeds91lmK5PRYJCfp3+nzO9q0f7RsjscyqvKdXUocJGsikxF+8fIYDC0eY4I3wgtmPK4RvcZqyc3LlSFrbzR9dqGWt3//T168+fXVVtfowsS5+qpmc/p6RNfaG/4reZp8tRvEi/ST3mb5Ofpp7mJF0uS4g5/bkpvproutTRFp8SfKn9P/1ad89cRib+YgFhllGcct9LQ4XDogz3vaGzEOCWFDpYk+Xr66rGpT2hMn7F6atNClR9+I8P/qq6v1ktbn9eEvidoevRMGQwGnRg7Wz9k/tct3hSQXLJff9+6SGcPOE/TY2a6OhwAAAAA6DAuT/y98cYbmjdvni688EINHDhQCxYskLe3t5YuXdps/6VLl6q0tFQvvfSSxo0bp5iYGJ1wwgkaPHiws89rr72mvn37auHChRo5cqRiY2M1bdo0xcX98ktFi8XS6M+KFSs0ceJExcY23s7Iz8+vUT9fX9/OeSEAAOgBLD59ZK3+JfH3c8F21dTXaEDwAK1I/7bVW2tmVmTIy+SlMJ+wDo7zSOKvY88jPKLcViZ/c6CMBpd/K9Upog6f65Zdke3iSOAqGeXpiu2AhLzRYNTdE+5TVX2VXtr6S0LP4XDo2U1/U0rpQT07a5H+NvNZXTH0ao3qM6ZdW2C2xxn95ig+MEE3jvqdfD0Pfc8f6BWkQK+gJufpVdZVylpt1YDggRrf9wSty2554i+vsiMq/uJla7DJ+qs3YjRnQ+56JZck69LBVzRq9zR56o5x96q2oVavbX+52bHv7HpTxTXFumXM7c4E8KzYk1RRV6Gf8jY26mtrsGnJ3vdVXV/djqfqOlV1VXps3QLFBcbpxlG/c3U4AAAAANChPFx5c5vNpp07d+qGG25wthmNRk2ZMkVbtjR/dsTKlSs1evRo/fnPf9aKFSsUGhqqOXPm6LrrrpPJZHL2mTZtmm677TZt3LhRERERuvTSSzVv3rxm5ywoKNDq1av117/+tcm11157TX//+98VGRmpOXPm6Oqrr5aHR+teNqPRIKOx7e+WBtyByWRs9F8A3dex1muEf4T2FO2Wh8ehaz/lb1S4b7iuHXW9HvjvH5RakaxBIYktvld2ZaZiA2Nl9uzYb0n6+FtkMEjFtkJnrB2pvK5MQV6BnTJ3dxAZECGzyazc6iy3fUZ30hlfY7MqMjU6YnSH/P1HB0bq9+Pu0F/XP67psdM1I/ZEvb/7Xa3M+E4PTn5Ew/oM7YCI2y/Qw19vnPnPJlWO8YHxyqpMb/RaZJakyWCQBoYOlIfJQ39Z92cV2wpk8e3j7FNTX6MteT9pUtQU55wOh0PW6nxFBvRt12vbLyRBBoOUXZWp6KAoSVJdQ53sDru8PLyc93p/79saZhmusZFjmzxXZGCEbhx9s57d9JRO6XeqxkSMdV77IvkzfbTvX5o/4lrFBf/yxshBYQOVEJSg1VkrNS1umrP9/7b9XZ/s/1h9/ftqdvxJbX6urrJ4y99VWGPVK6e+Ll8v7y69N98TAz0LaxboWVizQM/Beu1cLk38FRcXq6GhQWFhjd/lHxYWpoMHDzY7JiMjQ+vWrdPZZ5+tV199Venp6VqwYIHq6+t1yy23OPu8//77mj9/vm688Ubt2LFDjz32mDw9PXX++ec3mXPZsmXy8/PTqaee2qj9iiuu0NChQxUUFKQtW7bomWeekdVq1f3339+q5wwN9WvXNkmAOwkM9HF1CABaqLn1mhAeq3V5PyokxE+StKVwo2b0m6ZThszSM5vDtCZ/tU7oP6bF97DacjXQ0t85X8fxU5hfqKoN5e2au7i6WB/u+lDXjr22UXVfnbFaloCwToi7+4gPjVVxg9Wtn9HddNTX2Kq6KpXWFWtw5KAO+/ufF3yBNhWs04tbn5XM9Xpj52u6bvy1unD0OR0yf2dKihioPQV7Gr0W1txseXp4aFT8UCXV99eTm/6in8u26ILoC5x9nvzx//Svnf/SK3Ne0biocZIOfU6xG+o1ICK+Xa9tUPAA+Zi9VWTPU1Cwjz7d+6n+vunvKqst05i+YzQldopCvEO0t3iXnj3tWYWGNr8t8eXjL9b3Of/Ri9ue0QcXfiAvDy998PMHem7zU7poxDz9buoNTSqbzx5ylt7a9pZ8Akzy9vDWypSV+uzgJ/L08FBmTUq3/5xRaavUioxvdN34azUyfojL4uB7YqBnYc0CPQtrFug5WK+dw6WJv7ZwOBwKCwvTo48+KpPJpOHDhysvL0+vv/66M/HncDg0fPhw3XnnnZKkoUOHav/+/frggw+aTfwtXbpUZ599try8vBq1z58/3/n/gwcPlqenpx5++GHdddddMptbvgVRUVElFX/o9UwmowIDfVRWVq2GhmOfRwPAtY61Xn0dgSqoKFReQbGKaop0oCBZlyVdpfLSWk2LnKnPdn+hKxKvafEWmMkFKTqt3+kqLq7s8OcI9gxVWkFmu+b+OmWF/r7hZZ0QNlXxQQnO9ryyAnnLr1Pi7i7CzRE6YE1x62d0Fx39NXZf0V41NNgVYujToX//N4/4vX6beZUe+c8CTY2erosGXNEj/n1ZPCP1ecGXKiwqd35u+zl7jyJ8+qqyrE6SSUNChunbfSs1q++h88J3F+7S+9s/kMFg0Mc7/q3+PoeOJdhbdFANDXb5NAS2+9kjfCL11Z6v9cG2JUorS9XsuJM0JGyoNuZu0AtrF6nOblP/4AEaFjDmmPe6ddSdumb51Xr+h0XyNwfotW0va97gi3Xd0JtVWtJ0684TwqdqUc1LWr7rOyWGDNafVjysKZHTJUlbsrZ1+7/Tb1O/VrWtRlMsJ7okVr4nBnoW1izQs7BmgZ6D9do2LX2jpUsTfyEhITKZTCosLGzUXlhYqPDw8GbHWCwWeXh4OLf1lKT+/fvLarXKZrPJbDbLYrFowIABjcb1799fX3/9dZP5Nm3apJSUFD333HPHjXfUqFGqr69XZmam+vfv34InPMRud8hub92ZR4C7amiwq76eT+ZAT9Dceg31CpfDIeWW5+mnvE0yyKBRYWNVX2/XrJhTtGzfx9qUvUljI8Yfd/6quipZq6yK8o3plM8LoV5hyq+ytmvuvIp8ORzSgaJkRfv9ct5ZaU2pYgPi3PrzWaRvtDbkrnPrZ3Q3HfU1NrUkTQ6H1NcnqkP//v09gvSHE/6orw5+oTvH3yt7g2RX9//3FeUXq5r6GuWW56vP4a08DxYnKz6gv/P1mRAxSW/t/IcqaqrkafTU0xv+pv5BAzW+7wR9lvxv3Tzq9zKbzMopz5XDIYV5Wdr92sb6x+u/mas00jJKL85+RUmhh5KL5w74jWrqa7SjYJui/WPU0OCQdPSfRSJ9Y3T5kKv05s+L5ZB0xdCrdcXQq486rq9PtAYGJ2r5weX6oOZ9+XsG6I6x9+jzg5/qvd1vy1ZX363PP/025RsNCxvRIX8H7cH3xEDPwpoFehbWLNBzsF47h0t/IjObzRo2bJjWrl3rbLPb7Vq7dq3GjGl+m7CxY8cqPT1ddvsv/xhSU1NlsVicVXhjx45VSkpKo3GpqamKjo5uMt9HH32kYcOGafDgwceNd/fu3TIajU22JgUAoLc4cn5VQbVVG3LXaVjYCPmbAyRJg0OHKNI/SivTv2vRXDmVWZKk6IDY4/Rsm3AfiwqqrO2aI78qT5KUWtb4+4qy2jIFmgPbNXd3F+UfpdzKbNkdfAPe22SWZyjYK9i5tjvSuIgJenDyI/L19O3wuTtLrP+hz1GZ5enOttSyFMUHJjg/nhw1VXX2Om3N36yP93+olNKDumPcPTol/nRV1lVqfc6hn3fyq/LkafRUsFdIu+O6ZsT1+tuMZ/TUzOedSb8jvD28NaHvREX5N/35pzlzEy/WpKipunHU73TlsPnHPabgxNjZWpe9RvuL9+qBiQ/L3xygxJAkVddXK6sis83P1NlKaor1U95GzYrt/ucQAgAAAEBbufytmPPnz9eSJUu0bNkyJScn65FHHlF1dbUuuODQ+Rj33nuvnn76aWf/Sy65RCUlJXr88ceVkpKiVatW6ZVXXtFll13m7HPVVVdp27Ztevnll5WWlqbPPvtMS5Ys0aWXXtro3hUVFVq+fLnmzp3bJK4tW7bozTff1J49e5SRkaFPP/1UCxcu1DnnnKOgoKBOejUAAOjewn0skqTsimxtzd+sCX0nOq8ZDAadHHeq/pu5SrUNtcedK6M8Q5IU20mJvzCfcBVUty/xd2R8aun/JP5spQo0u/f3A1H+0aq3Nyjz8N8Teo+sigzFdNK67In6+kXKw+ihjMOJv7LaUhXXFCshqJ+zT2xAnCL9o/RZ8id6a+c/dO7AC5UYmqS4wHgNCknUivRvJR1K/PXxjeiQ87+j/KM1JmJch8zlafLUn6f+RRcmzmtR/xNjT5K3h7euHXmjhoQNlSQNCkmUJO0r2tPueDrL91mrJUkzYk50bSAAAAAA0IlcfsbfmWeeqaKiIr3wwguyWq0aMmSIFi9e7NzqMycnR0bjL/nJyMhIvf76684kXEREhK688kpdd911zj4jR47UokWL9Mwzz+ill15STEyMHnjgAZ1zzjmN7v3FF1/I4XBozpw5TeIym8368ssvtWjRItlsNsXExOjqq69udO4fAAC9jY+HjwLMAVqZ/q1q6mt0QuTERtdnx52st3e9qXXZazQzdtYx58quyFKgOVABnVQ5F+5jUWltiert9fIwtu1bniOJv5TSg842u8OuClt5p8XdXQwLG6EAc4A+Tf5Et4z5fZPrDodDhTWFCvdpfnt29FyZ5ZnqHzzg+B17CZPRpGj/GKUfTvwdqQBOCPwl8WcwGDQ5cqo+3v+hwn3CdfWwa5zXToo7RYt3vKJyW5nyq37ZLrQn6+PbR0vO/kQ+Hj7OtgBzoCL9o7SveJ9Oij/VhdEd3X/SV2hsxHgFe7e/4hIAAAAAuiuXJ/4k6fLLL9fll1/e7LW33367SduYMWO0ZMmSY845a9YszZp17F84XnTRRbrooouavTZs2LDj3gMAgN4o3CdcW/I3K9Q7VP2DBja6FhMQq8SQJH2X/s1xE38Z5emdWlUU7mORQ1JhTaEifCPaNEd+Vb4i/SKVU5ml2oZaeZm8VGErl0NSkJd7V/z5evrq/EG/0fu739FlQ65QiHdoo+tL9r6vN3e+rvfO+rDJNfRcDodDmRUZVET9j9iAOGWUpUk6VAHscTgZ+GtTo6fp4/0f6tYxdzTaynRW3El6dfv/aXXGKuVX5TVKGPZkv076HZEUMlj7irtnxV9+Vb52FGzXPRPuc3UoAAAAANCpXL7VJwAA6FksPoeqVSb0ndjsFnOnxJ+mjTnrVFJTfMx5sioym/zivCOF+RxKRrX1nL+a+hqV28o1IXKS7A6HMsoOb/NnK5Mktz/jT5LOG3iBPIwe+nj/R43a86vy9fauN1Vvr9eG3PUuig6doaC6QFV1VYoNiHN1KN1KbGCcc6vP1LJURfvHytPk2ajPSMtovXPWEk2JntaoPdQ7TGMjxmtl+rfOrT7d1aCQRO0v3tctzwZdlbFCnkZPTY2e4epQAAAAAKBTkfgDAACtYvH9JfHXnFlxJ8lgMGhlxnfHnCezvHPPETtyHmFRTWGbxh/Z5nN8xARJUmrZoe0+S2tLJUkBXu6f+AswB+rsAefq0+RlqrCVO9tf3rZIvp6+GhA8QOuy17gwQnS0I9Vag0KSXBxJ9xIXEOdMiqaVpahfUP9m+x2tuvikuFO0o2C7imuK3TrxlxQyWLUNtUo/XB3ZnaxM/04TIyfLz9PP1aEAAAAAQKci8QcAAFqlj2+EjAaDxkWMb/Z6kFewJkVO1TepXx11jrLaUpXbyjs18RdoDpKn0dOZwGsta1W+JCkuIF59/fo6z/krd1b8ufdWn0dcmDhPdQ11+jT5E0nSptwN+j5ztW4c9TvNiJmln/I2ytZgc22Q6DB7i/coxDuEsxv/R2xAvKRDWxSnlqa0ervOKVHT5WXykiS3OOPvaAaGJEpSt9vuM6M8XcklBzQ77mRXhwIAAAAAnY7EHwAAaJUz+p2lx6Y9IX9zwFH7nJpwupJLkpVcsr/Z6xnlGZKkaP/oTolRkgwGg8J8wtqe+Ks+lPgL97UoIbCfUktTJElltkMVf71hq0/p0DaFp/c7S0v3f6hyW5kWbXleIy2jNCv2ZE2KmqLq+mpts25xdZjoIHuLdispdEiz2/j2ZkfepLDdulVltjLFByW0aryvp6+mHt4C1J0r/vw8/RQTEKu9xXtdHUoj/0lfIR8PH50QOcnVoQAAAABApyPxBwAAWiXEO/So23weMaHvRAV5Beub1K+bvZ5VcSjxF9WJZ/xJh7b7LKwuaNPYguoCBXoFycvkpYSgfkotO5L4K5O3h7fMJnNHhtqtzUu6WBW2Mt2z+nblVGbp1jF3yGAwqF9gf/Xx7aN1OWtdHSI6gMPh0P7ifUoKGezqULodP08/hfmE6Yes/0pSqyv+JOmcARcoMSTJrRN/kpQUkqT9/5P4s1ZZ9eymJ1VTX+OSmLbk/6QJfSc6qy4BAAAAwJ2R+AMAAB3Ow+ihk+JO0Yr0b1Vvr29yPbMiU+E+4fLx8OnUOMJ9LLK2MfGXX5Uny+HtDhMC+ym/Kl+VdZUqs5X1mmq/I/r6RWp23ClKLknWBYPmKiHoUNLDYDBoUtRUrc9eI4fD4eIo0V7ZFVkqt5UrkcRfs2ID4rWrcKc8jZ6KakO18rDw4Xrp5Ffd/k0Dg0KSdKB4f6PP/S9tfV5fpnyuDbnrXBJTVkWm4gLjXXJvAAAAAOhqJP4AAECnOC3hdJXWlmhj7vom1zLLMxQTENfpMYT5hLWj4s8qy+HKnCOJrtTSFJXVliqglyX+JOmqYb/Vmf3m6PKhVzdqnxQ5WXlVeUopO+iawNBh9h4+ly0pNMnFkXRPsYc/Z8UFxsto4Meoo0kMHaw6e53SDldJb8hZrx+zvpe3h7fWZP/Q7JjimiIt2/+R7A57h8dTVVel4priTt1aGgAAAAC6E35iBQAAnaJ/8EANCB6ob1KXN7mWVZ6hmE7e5lM6VPFXUG1tUzWatSpfFh+LpEOVPkaDQallKb2y4k86VPV3x/h75Ofp16h9lGWMfDx8tC57jYsiQ0fZW7RHff36Ksgr2NWhdEtxhxN/R94IgOYNCBooo8GgfcX7ZGuwadHW5zS6z1hdOGie1mevbbYK/P097+r/tr6o17b/vcPjyanMktT5W0sDAAAAQHdB4g8AAHSaUxNO17qcH1VaW+JsszvsyqzIVExAbKffP8w7XLUNtaqsq2j1WGu1VeGHE39mk1lR/jGHK/7KSIz8itlk1riICVrPOX893r7iPUoKGeLqMLqt2MDDib82nO/Xm/h6+io2IF77ivboX3vfk7UqT7eOuV1Toqapoq5CO6zbGvWva6jTivRvFRMQq4/2LdE3qV91aDxZFYcSf1T8AQAAAOgtSPwBAIBOMzv2ZDkcDv0nfYWzraC6QLYGm6K7IPEX7mtx3rM1auprVG4rVx/fPs62hMB+Si1LUbmtd271eSwTIydrd+FOFdcUuToUtFGDvUH7i/cpkW0+jyohsL9MBqOSOAPxuBJDkrQpb4Pe3/2OLhw0T3GB8RoUkqhwn3D9+D/bfW7MXa+y2lI9NHmBTks4Q8/+9JR2Fvx8zPkzyzP0U97GFsWSXZElP08/BZqD2vw8AAAAANCTkPgDAACdJtg7RJOjpuntXW86t4LMLE+XpC7Z6vPIVp35VfmtGldQbZUkZ8WfJPUL6q/U0oMqtZX2yq0+j2Vi5CRJ0oZmznNEz5BWnqrahloNDqXi72jCfML0zlkfanSfsa4OpdtLCh2s3MpcBXsF67KhV0mSDAaDpkRP19rsHxptv/xN2nINCB6o/kEDdNvYO5UUMliPrPnjUT9vF9cU6Z7Vt+vBH+6Ttcp63FiyKjIV5R8tg8HQMQ8HAAAAAN0ciT8AANCpbh93l4aEDdVDP96vl7a8oJTSgzIZjOrrF9np9+7jG6FQ71BtyF3XqnHWw79wtvj8quIvqJ9KaktUVF2oQC8Sf78W4h2qwWFDOeevB9tXtFcGSQODE10dSrcW7hNOAqkFhoWNkCTdNPpW+Xj4ONunRE1VflW+kksOSJJKa0u0PmeNTks4Q9KhrYMfnvKozCaz/vTjfSqqKWw0b4O9QY+tW6B6e718PHz0r73vHTeW7IosRXO+HwAAAIBehMQfAADoVEFewXp06l/1u9G36YuDn+rV7f+nvn5R8jB6dPq9jQajZsedrFUZK1Vvr2/xOGv1ocTfka1CpV/O9XJIVPw1Y1LkFP2Ut1E19TWuDgVtsLdot+ICE+Tr6evqUOAGBoYM0jtnLdH0mJmN2keGj5afp5/WHN7u8z/pK+RwODQr9iRnnxDvUD067a8qqS3RLd/doOSS/c5ri3e8rJ8LtumhyQt04aB5+vLgZ8fdyvlIxR8AAAAA9BYk/gAAQKczGAw6b9CFWnTSy4oLSNCw8OFddu+T409VaW2Jfsrb1OIxBdUFCvQKkpfJy9kW7R/jTFYGmoM7Oswe78TY2aqur9aa7O9dHQraYG/xHiWGcL4fOk6Eb0STNk+Tp07oO8n5eeKbtOU6IXKygr1DGvXrHzRAL85+RUFewfr9yt/px6zv9Z/0Ffpo3xLdMOp3GmkZrXMHXSAvk5eW7H3/qDFU11ersLpQ0ST+AAAAAPQiJP4AAECX6R88UK+d9qbuHn9f190zaKDiAxO0Iu2bFo/Jr8qTxSe8UZvJaFJ8YLwksdVnM6L8ozUifKS+SV3u6lDQSrYGm1JKk5UUOtjVoaAXmBw1VcklyVqXs1b7i/c5t/n8XxZfi56dtUgnRE7SI2se1JMbF2p23Ek6f+BvJEn+nv46f9Bv9Hnyv1VYXdjsHDmV2ZKkaP/YznkYAAAAAOiGSPwBAIAu15VnZBkMBp0cf6p+zP5eVXVVLRpTUG2VpZlqlSPbfbLVZ/NOSThdm/M2yVpldXUoaIWDpcmqtzcoKXSIq0NBL3BC5CR5GE16ZtMTCjQH6oS+k47a19vDWw9OekRXDpuv0X3G6PZx9zT6+nHBoN/IbDIfteovuyJLkhTlH9WxDwEAAAAA3RiJPwAA4PZmxZ0sW4NNP2b9t0X9rVX5svhYmrQnBPWXROLvaGbEnChPk1kr0lteXQnX21u0Wx5Gk/oHDXB1KOgF/Dz9NLrPWBXXFGtW3MnyNHkes7/RYNQVQ6/WX6Y/KR8Pn0bX/M0Bh6r+Dv5bxTVFTcZmlWfKx8NHwV4hTa4BAAAAgLsi8QcAANxehG+ERlpG6bsWJqSs1VZZfPo0aZ8ZM0vzki6Wn6d/R4foFvw8/TQterq+SV0uh8Ph6nDQQnuL9qhf0ACZTWZXh4JeYkrUdEnSqfGnt3uuCwb9Rh5Gj2ar/rIrshTlH92lVeYAAAAA4Gok/gAAQK8wO+4Ubc3ffNSzoI6oqa9Rua1cFt+mFX+R/lG6buRN/BL5GE5NOEMZ5enaU7Tb1aGghfYV71VSCOf7oeucnnCmnpjxtBJDk9o9V4A5UGf0O0vfpX0ju8Pe6FpWRZZiON8PAAAAQC9D4g8AAPQKM2JmymgwaVXGimP2K6g+dD5dcxV/OL4xfcYp3Cdc36Ytd3UoaIGcimyll6VqSNhQV4eCXsTT5KmxEeM7bL7JkVNVUluiA8X7G7VnV2QqKiC6w+4DAAAAAD0BiT8AANArBJgDNSlyir5LO/Z2n/lVeZKk8GbO+MPxGQ1GnRx/qlZlrJStwebqcHAc7+7+p4K8gjUzdrarQwHabGjYcPl4+GhT3gZnW21DrazVVkX5kfgDAAAA0LuQ+AMAAL3GSfGn6EDJfqWWphy1j7Piz5eKv7Y6Jf50ldvKtTb7R1eHgmPIrczRd2lf66LBl8rL5OXqcIA28zR5akyfcdqQs87ZllORLUmK9ifxBwAAAKB3IfEHAAB6jRP6TlKAOUDfpX191D4F1QUK9AqS2WTuwsjcS1xgvAaHDmG7z27uvd1vK8AcqDn9z3V1KEC7Teg7UbuLdqrcViZJyq7IkiRFB8S4MiwAAAAA6HIk/gAAQK9hNpl1YuxJ+i79G9kd9mb75FflycI2n+12Yuxs/ZS3ke0+u6ncyhx9k/qV5iZdLG8Pb1eHA7TbhMiJsjsc2pz3kyQpqyJT3h7eCvEKdXFkAAAAANC1SPwBAIBe5dSE01VYXajNeZuavV5QbWWbzw4wNGy46u0NSik96OpQ0IwP9rwrf3Ogzh5wnqtDATpEhG+E4gMTtDF3vaRDib9o/2gZDAYXRwYAAAAAXYvEHwAA6FWSQgYrNiDuqNtQWqvyZfEh8ddeA4IHymQwam/RbleHgv+RV5mrr1O/1NzEi+Tj4ePqcIAOM6HvCdqYu14Oh0PZFVmK8mebTwAAAAC9j4erAwAAAOhKBoNBp8Sfpnd2v6XKukr5efo1um6ttrLVZwcwm8zqFzRAe4v3uDoUSYfO+7L49JGnydPVoXS5f/z8mnIqsmXxsSjc16Jt+Vvl6+lPtR/czoS+E/XRviU6WHpA2RVZmhV3kqtDAgAAAIAuR8UfAADodU6OP011DTZ9n7m6UXtNfY3KbeWy+JL46whJoYO1t8j1ib96e71u+Pa3+jbta1eH0uVKa0v0we53lFJ6UD9m/6DF21/RmuwfdHHSpfL19HV1eECHGh4+Ul4mL63J/lH5VXlU/AEAAADolaj4AwAAvY7F16LRfcbq27TlOr3fmc72gmrroets9dkhkkKH6KuUz1VVV+XSJFN2RZZq6muUU5nlshhcZXPeT3JIWjj9KVl8LXI4HKqsr5Sfh99xxwI9jdlk1pg+Y/Xlwc/kkBTtH+3qkAAAAACgy1HxBwAAeqVTE07Xdus25VbmSJIq6yr1+o5XZTQYqBLpIEkhSbI7HDpQss+lcWSUp0s6tI1rb7M5f5PiAuOdVawGg0H+nv4yGAwujgzoHBP6TlRBdYEk8bkcAAAAQK9E4g8AAPRKU6NnyMfDR9+kLtfB0mT97rvrtTl/kx6ctICtPjtIfGA/mU1ml2/3eSTxV1Rd6NI4uprD4dDmvE0a22e8q0MBusyEvhMlHar+C/MOc3E0AAAAAND12OoTAAD0Sj4ePpoRc6L+nbxM/9r7nmIDYvV/015TdAAVIh3FZDRpUHCi9hXvdWkc6WVpkuSsAuotsioylV+Vr3ERJP7Qe0T6RynaP0ZmkyeVrQAAAAB6JRJ/AACg1zq931n6OvUrnZZwhm4de4e8TF6uDsntJIUO0drsH1waw5GKv4JettXnT3kbZTIYNdIy2tWhAF3q8qFXqrq+xtVhAAAAAIBLkPgDAAC91vDwEfrX2R8rlO3gOk1SaJI+3v+hympLFegV1OX3dzgcyihPV/+g/jpYelBVdVXy9fTt8jhcYXPeTxoaNrzXPC9wxMnxp7k6BAAAAABwGc74AwAAvRpJv86VGDJYkrS3uPE5f4u3v6z7v7+70+9fXFukyrpKje4zTpJUWNM7tvtssDdoq3WzxrLNJwAAAAAAvQq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            "text/plain": [
              "<Figure size 1800x1200 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "Price Statistics:\n",
            "  COIN: Mean=$253.13, Std=$1.12\n",
            "  MSTR: Mean=$375.94, Std=$2.37\n",
            "  Price Ratio: Mean=0.67, Std=0.00\n",
            "  Correlation: 0.1164\n"
          ]
        }
      ],
      "source": [
        "# Plot raw price data\n",
        "\n",
        "# Get column names for the trading pair\n",
        "colname_a, colname_b = pair.colnames()\n",
        "price_data = pair.market_data_.copy()\n",
        "\n",
        "# # 1. Price data - separate plots for each symbol\n",
        "# colname_a, colname_b = pair.colnames()\n",
        "# price_data = pair.market_data_.copy()\n",
        "\n",
        "# Create separate subplots for better visibility\n",
        "fig_price, price_axes = plt.subplots(2, 1, figsize=(18, 10))\n",
        "\n",
        "# Plot SYMBOL_A\n",
        "price_axes[0].plot(price_data['tstamp'], price_data[colname_a], alpha=0.7, \n",
        "                    label=f'{SYMBOL_A}', linewidth=1, color='blue')\n",
        "price_axes[0].set_title(f'{SYMBOL_A} Price Data')\n",
        "price_axes[0].set_ylabel(f'{SYMBOL_A} Price')\n",
        "price_axes[0].legend()\n",
        "price_axes[0].grid(True)\n",
        "\n",
        "# Plot SYMBOL_B\n",
        "price_axes[1].plot(price_data['tstamp'], price_data[colname_b], alpha=0.7, \n",
        "                    label=f'{SYMBOL_B}', linewidth=1, color='red')\n",
        "price_axes[1].set_title(f'{SYMBOL_B} Price Data')\n",
        "price_axes[1].set_ylabel(f'{SYMBOL_B} Price')\n",
        "price_axes[1].set_xlabel('Time')\n",
        "price_axes[1].legend()\n",
        "price_axes[1].grid(True)\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "    \n",
        "\n",
        "# Plot individual prices\n",
        "fig, axes = plt.subplots(2, 1, figsize=(18, 12))\n",
        "\n",
        "# Normalized prices for comparison\n",
        "norm_a = price_data[colname_a] / price_data[colname_a].iloc[0]\n",
        "norm_b = price_data[colname_b] / price_data[colname_b].iloc[0]\n",
        "\n",
        "axes[0].plot(price_data['tstamp'], norm_a, label=f'{SYMBOL_A} (normalized)', alpha=0.8, linewidth=1)\n",
        "axes[0].plot(price_data['tstamp'], norm_b, label=f'{SYMBOL_B} (normalized)', alpha=0.8, linewidth=1)\n",
        "axes[0].set_title('Normalized Price Comparison (Base = 1.0)')\n",
        "axes[0].set_ylabel('Normalized Price')\n",
        "axes[0].legend()\n",
        "axes[0].grid(True)\n",
        "\n",
        "# Price ratio\n",
        "price_ratio = price_data[colname_a] / price_data[colname_b]\n",
        "axes[1].plot(price_data['tstamp'], price_ratio, label=f'{SYMBOL_A}/{SYMBOL_B} Ratio', color='green', alpha=0.8, linewidth=1)\n",
        "axes[1].set_title(f'Price Ratio Px({SYMBOL_A})/Px({SYMBOL_B})')\n",
        "axes[1].set_ylabel('Ratio')\n",
        "axes[1].set_xlabel('Time')\n",
        "axes[1].legend()\n",
        "axes[1].grid(True)\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()\n",
        "\n",
        "# Print basic statistics\n",
        "print(f\"\\nPrice Statistics:\")\n",
        "print(f\"  {SYMBOL_A}: Mean=${price_data[colname_a].mean():.2f}, Std=${price_data[colname_a].std():.2f}\")\n",
        "print(f\"  {SYMBOL_B}: Mean=${price_data[colname_b].mean():.2f}, Std=${price_data[colname_b].std():.2f}\")\n",
        "print(f\"  Price Ratio: Mean={price_ratio.mean():.2f}, Std={price_ratio.std():.2f}\")\n",
        "print(f\"  Correlation: {price_data[colname_a].corr(price_data[colname_b]):.4f}\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "vscode": {
          "languageId": "raw"
        }
      },
      "source": [
        "# Run"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Analysis"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Running SlidingFit analysis...\n",
            "\n",
            "=== SLIDING FIT ANALYSIS ===\n",
            "Processing first 200 iterations for demonstration...\n",
            "***COIN & MSTR*** STARTING....\n",
            "********************************************************************************\n",
            "Pair COIN & MSTR (0) IS COINTEGRATED\n",
            "********************************************************************************\n",
            "69\r"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "COIN & MSTR: current offset=272 * Training data length=119 < 120 * Not enough training data. Completing the job.\n",
            "OPEN_TRADES: 2025-06-06 17:01:00 open_scaled_disequilibrium=np.float64(2.134862885140155)\n",
            "OPEN TRADES:\n",
            "                 time action symbol   price  disequilibrium  \\\n",
            "0 2025-06-06 17:01:00   SELL   COIN  253.02        1.258356   \n",
            "1 2025-06-06 17:01:00    BUY   MSTR  377.33        1.258356   \n",
            "\n",
            "   scaled_disequilibrium         pair status  \n",
            "0               2.134863  COIN & MSTR   OPEN  \n",
            "1               2.134863  COIN & MSTR   OPEN  \n",
            "CLOSE TRADES:\n",
            "                 time action symbol    price  disequilibrium  \\\n",
            "0 2025-06-06 18:17:00    BUY   COIN  253.925       -3.174396   \n",
            "1 2025-06-06 18:17:00   SELL   MSTR  375.010       -3.174396   \n",
            "\n",
            "   scaled_disequilibrium         pair status  \n",
            "0               0.989361  COIN & MSTR  CLOSE  \n",
            "1               0.989361  COIN & MSTR  CLOSE  \n",
            "OPEN_TRADES: 2025-06-06 19:41:00 open_scaled_disequilibrium=np.float64(4.316094374910605)\n",
            "OPEN TRADES:\n",
            "                 time action symbol    price  disequilibrium  \\\n",
            "0 2025-06-06 19:41:00    BUY   COIN  252.005       -1.095798   \n",
            "1 2025-06-06 19:41:00   SELL   MSTR  373.290       -1.095798   \n",
            "\n",
            "   scaled_disequilibrium         pair status  \n",
            "0               4.316094  COIN & MSTR   OPEN  \n",
            "1               4.316094  COIN & MSTR   OPEN  \n",
            "COIN & MSTR:  *** Position is NOT CLOSED. ***\n",
            "COIN & MSTR: NO CLOSE SIGNAL FOUND - Position held until end of session\n",
            "  Open: 2025-06-06 19:41:00 | Last: 2025-06-06 15:30:00\n",
            "  COIN: BUY 3.97 shares @ $252.00 -> $251.94 | Value: $999.72\n",
            "  MSTR: SELL 2.68 shares @ $373.29 -> $376.41 | Value: $1008.36\n",
            "  Total Value: $2008.08\n",
            "  Disequilibrium: -1.4396 | Scaled: 0.9242\n",
            "***COIN & MSTR*** FINISHED ... 6\n",
            "Generated 6 trading signals\n",
            "\n",
            "Strategy execution completed!\n",
            "\n",
            "================================================================================\n",
            "BACKTEST RESULTS\n",
            "================================================================================\n"
          ]
        }
      ],
      "source": [
        "# Initialize strategy state and run analysis\n",
        "print(f\"Running {FIT_METHOD_TYPE} analysis...\")\n",
        "\n",
        "# Initialize result tracking\n",
        "bt_result = BacktestResult(config=pt_bt_config)\n",
        "pair_trades = None\n",
        "\n",
        "# Run strategy-specific analysis\n",
        "print(\"\\n=== SLIDING FIT ANALYSIS ===\")\n",
        "\n",
        "# Initialize tracking variables for sliding window analysis\n",
        "training_minutes = pt_bt_config[\"training_minutes\"]\n",
        "max_iterations = len(pair.market_data_) - training_minutes\n",
        "\n",
        "# Limit iterations for demonstration (change this for full run)\n",
        "max_demo_iterations = min(200, max_iterations)\n",
        "print(f\"Processing first {max_demo_iterations} iterations for demonstration...\")\n",
        "\n",
        "# Initialize pair state for sliding fit method\n",
        "pair.user_data_['state'] = PairState.INITIAL\n",
        "pair.user_data_[\"trades\"] = pd.DataFrame(columns=pd.Index(FIT_MODEL.TRADES_COLUMNS, dtype=str))\n",
        "pair.user_data_[\"is_cointegrated\"] = False\n",
        "\n",
        "# Run the sliding fit method\n",
        "# ==========================================================================\n",
        "pair_trades = FIT_MODEL.run_pair(config=pt_bt_config, pair=pair, bt_result=bt_result)\n",
        "# ==========================================================================\n",
        "\n",
        "if pair_trades is not None and len(pair_trades) > 0:\n",
        "    print(f\"Generated {len(pair_trades)} trading signals\")\n",
        "else:\n",
        "    print(\"No trading signals generated\")\n",
        "\n",
        "print(\"\\nStrategy execution completed!\")\n",
        "\n",
        "# Print comprehensive backtest results\n",
        "print(\"\\n\" + \"=\"*80)\n",
        "print(\"BACKTEST RESULTS\")\n",
        "print(\"=\"*80)\n",
        "\n",
        "assert pair.predicted_df_ is not None\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "vscode": {
          "languageId": "raw"
        }
      },
      "source": [
        "## Visualization\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "        <script type=\"text/javascript\">\n",
              "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
              "        if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
              "        </script>\n",
              "        <script type=\"module\">import \"https://cdn.plot.ly/plotly-3.0.1.min\"</script>\n",
              "        "
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "=== SLIDING FIT INTERACTIVE VISUALIZATION ===\n",
            "Note: Sliding strategy visualization with interactive plotly charts\n",
            "Using consistent timeline with 391 timestamps\n",
            "Timeline range: 2025-06-06 13:30:00 to 2025-06-06 20:00:00\n",
            "Symbol_A trades:                  time action symbol    price  disequilibrium  \\\n",
            "0 2025-06-06 17:01:00   SELL   COIN  253.020        1.258356   \n",
            "2 2025-06-06 18:17:00    BUY   COIN  253.925       -3.174396   \n",
            "4 2025-06-06 19:41:00    BUY   COIN  252.005       -1.095798   \n",
            "\n",
            "   scaled_disequilibrium         pair status  \n",
            "0               2.134863  COIN & MSTR   OPEN  \n",
            "2               0.989361  COIN & MSTR  CLOSE  \n",
            "4               4.316094  COIN & MSTR   OPEN  \n"
          ]
        },
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      ],
      "source": [
        "# Interactive Plotly Visualization\n",
        "import plotly.graph_objects as go\n",
        "from plotly.subplots import make_subplots\n",
        "import plotly.express as px\n",
        "import plotly.offline as pyo\n",
        "from IPython.display import HTML\n",
        "\n",
        "# Configure plotly for offline mode\n",
        "pyo.init_notebook_mode(connected=True)\n",
        "\n",
        "# Strategy-specific interactive visualization\n",
        "assert pt_bt_config is not None\n",
        "assert pair.predicted_df_ is not None\n",
        "\n",
        "print(\"=== SLIDING FIT INTERACTIVE VISUALIZATION ===\")\n",
        "print(\"Note: Sliding strategy visualization with interactive plotly charts\")\n",
        "\n",
        "# Create consistent timeline - superset of timestamps from both dataframes\n",
        "market_timestamps = set(pair.market_data_['tstamp'])\n",
        "predicted_timestamps = set(pair.predicted_df_['tstamp'])\n",
        "\n",
        "# Create superset of all timestamps\n",
        "all_timestamps = sorted(market_timestamps.union(predicted_timestamps))\n",
        "\n",
        "# Create a unified timeline dataframe for consistent plotting\n",
        "timeline_df = pd.DataFrame({'tstamp': all_timestamps})\n",
        "\n",
        "# Merge with predicted data to get dis-equilibrium values\n",
        "timeline_df = timeline_df.merge(pair.predicted_df_[['tstamp', 'disequilibrium', 'scaled_disequilibrium']], \n",
        "                                on='tstamp', how='left')\n",
        "\n",
        "# Get Symbol_A and Symbol_B market data\n",
        "colname_a, colname_b = pair.colnames()\n",
        "symbol_a_data = pair.market_data_[['tstamp', colname_a]].copy()\n",
        "symbol_b_data = pair.market_data_[['tstamp', colname_b]].copy()\n",
        "\n",
        "print(f\"Using consistent timeline with {len(timeline_df)} timestamps\")\n",
        "print(f\"Timeline range: {timeline_df['tstamp'].min()} to {timeline_df['tstamp'].max()}\")\n",
        "\n",
        "# Create subplots with price charts at bottom\n",
        "fig = make_subplots(\n",
        "    rows=4, cols=1,\n",
        "    subplot_titles=[\n",
        "        'Testing Period: Scaled Dis-equilibrium with Trading Thresholds',\n",
        "        'Trading Signal Timeline',\n",
        "        f'{SYMBOL_A} Market Data with Trading Signals',\n",
        "        f'{SYMBOL_B} Market Data with Trading Signals'\n",
        "    ],\n",
        "    vertical_spacing=0.06,\n",
        "    specs=[[{\"secondary_y\": False}],\n",
        "            [{\"secondary_y\": False}],\n",
        "            [{\"secondary_y\": False}],\n",
        "            [{\"secondary_y\": False}]]\n",
        ")\n",
        "\n",
        "# 1. Scaled dis-equilibrium with thresholds - using consistent timeline\n",
        "fig.add_trace(\n",
        "    go.Scatter(\n",
        "        x=timeline_df['tstamp'],\n",
        "        y=timeline_df['scaled_disequilibrium'],\n",
        "        name='Scaled Dis-equilibrium',\n",
        "        line=dict(color='green', width=2),\n",
        "        opacity=0.8\n",
        "    ),\n",
        "    row=1, col=1\n",
        ")\n",
        "\n",
        "# Add threshold lines to first subplot\n",
        "fig.add_shape(\n",
        "    type=\"line\",\n",
        "    x0=timeline_df['tstamp'].min(),\n",
        "    x1=timeline_df['tstamp'].max(),\n",
        "    y0=pt_bt_config['dis-equilibrium_open_trshld'],\n",
        "    y1=pt_bt_config['dis-equilibrium_open_trshld'],\n",
        "    line=dict(color=\"purple\", width=2, dash=\"dot\"),\n",
        "    opacity=0.7,\n",
        "    row=1, col=1\n",
        ")\n",
        "\n",
        "fig.add_shape(\n",
        "    type=\"line\",\n",
        "    x0=timeline_df['tstamp'].min(),\n",
        "    x1=timeline_df['tstamp'].max(),\n",
        "    y0=-pt_bt_config['dis-equilibrium_open_trshld'],\n",
        "    y1=-pt_bt_config['dis-equilibrium_open_trshld'],\n",
        "    line=dict(color=\"purple\", width=2, dash=\"dot\"),\n",
        "    opacity=0.7,\n",
        "    row=1, col=1\n",
        ")\n",
        "\n",
        "fig.add_shape(\n",
        "    type=\"line\",\n",
        "    x0=timeline_df['tstamp'].min(),\n",
        "    x1=timeline_df['tstamp'].max(),\n",
        "    y0=pt_bt_config['dis-equilibrium_close_trshld'],\n",
        "    y1=pt_bt_config['dis-equilibrium_close_trshld'],\n",
        "    line=dict(color=\"brown\", width=2, dash=\"dot\"),\n",
        "    opacity=0.7,\n",
        "    row=1, col=1\n",
        ")\n",
        "\n",
        "fig.add_shape(\n",
        "    type=\"line\",\n",
        "    x0=timeline_df['tstamp'].min(),\n",
        "    x1=timeline_df['tstamp'].max(),\n",
        "    y0=-pt_bt_config['dis-equilibrium_close_trshld'],\n",
        "    y1=-pt_bt_config['dis-equilibrium_close_trshld'],\n",
        "    line=dict(color=\"brown\", width=2, dash=\"dot\"),\n",
        "    opacity=0.7,\n",
        "    row=1, col=1\n",
        ")\n",
        "\n",
        "fig.add_shape(\n",
        "    type=\"line\",\n",
        "    x0=timeline_df['tstamp'].min(),\n",
        "    x1=timeline_df['tstamp'].max(),\n",
        "    y0=0,\n",
        "    y1=0,\n",
        "    line=dict(color=\"black\", width=1, dash=\"solid\"),\n",
        "    opacity=0.5,\n",
        "    row=1, col=1\n",
        ")\n",
        "\n",
        "# 2. Trading signals timeline if available - using consistent timeline\n",
        "if pair_trades is not None and len(pair_trades) > 0:\n",
        "    # Separate trades by action and status for different colors\n",
        "    buy_open_trades = pair_trades[(pair_trades['action'].str.contains('BUY', na=False)) & \n",
        "                                    (pair_trades['status'] == 'OPEN')]\n",
        "    buy_close_trades = pair_trades[(pair_trades['action'].str.contains('BUY', na=False)) & \n",
        "                                    (pair_trades['status'] == 'CLOSE')]\n",
        "    sell_open_trades = pair_trades[(pair_trades['action'].str.contains('SELL', na=False)) & \n",
        "                                    (pair_trades['status'] == 'OPEN')]\n",
        "    sell_close_trades = pair_trades[(pair_trades['action'].str.contains('SELL', na=False)) & \n",
        "                                    (pair_trades['status'] == 'CLOSE')]\n",
        "    \n",
        "    # Create y-values for timeline visualization\n",
        "    trade_indices = list(range(len(pair_trades)))\n",
        "    \n",
        "    # Add trading signals with different colors based on action and status\n",
        "    if len(buy_open_trades) > 0:\n",
        "        buy_open_indices = [i for i, (_, row) in enumerate(pair_trades.iterrows()) \n",
        "                            if 'BUY' in row['action'] and row['status'] == 'OPEN']\n",
        "        fig.add_trace(\n",
        "            go.Scatter(\n",
        "                x=buy_open_trades['time'],\n",
        "                y=buy_open_indices,\n",
        "                mode='markers',\n",
        "                name='BUY OPEN',\n",
        "                marker=dict(color='red', size=10, symbol='circle')\n",
        "            ),\n",
        "            row=2, col=1\n",
        "        )\n",
        "    \n",
        "    if len(buy_close_trades) > 0:\n",
        "        buy_close_indices = [i for i, (_, row) in enumerate(pair_trades.iterrows()) \n",
        "                            if 'BUY' in row['action'] and row['status'] == 'CLOSE']\n",
        "        fig.add_trace(\n",
        "            go.Scatter(\n",
        "                x=buy_close_trades['time'],\n",
        "                y=buy_close_indices,\n",
        "                mode='markers',\n",
        "                name='BUY CLOSE',\n",
        "                marker=dict(color='pink', size=10, symbol='circle')\n",
        "            ),\n",
        "            row=2, col=1\n",
        "        )\n",
        "    \n",
        "    if len(sell_open_trades) > 0:\n",
        "        sell_open_indices = [i for i, (_, row) in enumerate(pair_trades.iterrows()) \n",
        "                            if 'SELL' in row['action'] and row['status'] == 'OPEN']\n",
        "        fig.add_trace(\n",
        "            go.Scatter(\n",
        "                x=sell_open_trades['time'],\n",
        "                y=sell_open_indices,\n",
        "                mode='markers',\n",
        "                name='SELL OPEN',\n",
        "                marker=dict(color='blue', size=10, symbol='circle')\n",
        "            ),\n",
        "            row=2, col=1\n",
        "        )\n",
        "    \n",
        "    if len(sell_close_trades) > 0:\n",
        "        sell_close_indices = [i for i, (_, row) in enumerate(pair_trades.iterrows()) \n",
        "                                if 'SELL' in row['action'] and row['status'] == 'CLOSE']\n",
        "        fig.add_trace(\n",
        "            go.Scatter(\n",
        "                x=sell_close_trades['time'],\n",
        "                y=sell_close_indices,\n",
        "                mode='markers',\n",
        "                name='SELL CLOSE',\n",
        "                marker=dict(color='purple', size=10, symbol='circle')\n",
        "            ),\n",
        "            row=2, col=1\n",
        "        )\n",
        "\n",
        "    fig.add_trace(\n",
        "        go.Scatter(\n",
        "            x=symbol_a_data['tstamp'],\n",
        "            y=symbol_a_data[colname_a],\n",
        "            name=f'{SYMBOL_A} Price',\n",
        "            line=dict(color='blue', width=2),\n",
        "            opacity=0.8\n",
        "        ),\n",
        "        row=3, col=1\n",
        "    )\n",
        "\n",
        "if pair_trades is not None and len(pair_trades) > 0:\n",
        "    # Filter trades for Symbol_A\n",
        "    symbol_a_trades = pair_trades[pair_trades['symbol'] == SYMBOL_A]\n",
        "    print(f\"Symbol_A trades: {symbol_a_trades}\")\n",
        "    \n",
        "    if len(symbol_a_trades) > 0:\n",
        "        # Separate trades by action and status for different colors\n",
        "        buy_open_trades = symbol_a_trades[(symbol_a_trades['action'].str.contains('BUY', na=False)) & \n",
        "                                            (symbol_a_trades['status'] == 'OPEN')]\n",
        "        buy_close_trades = symbol_a_trades[(symbol_a_trades['action'].str.contains('BUY', na=False)) & \n",
        "                                            (symbol_a_trades['status'] == 'CLOSE')]\n",
        "        sell_open_trades = symbol_a_trades[(symbol_a_trades['action'].str.contains('SELL', na=False)) & \n",
        "                                            (symbol_a_trades['status'] == 'OPEN')]\n",
        "        sell_close_trades = symbol_a_trades[(symbol_a_trades['action'].str.contains('SELL', na=False)) & \n",
        "                                            (symbol_a_trades['status'] == 'CLOSE')]\n",
        "        \n",
        "        # Add BUY OPEN signals\n",
        "        if len(buy_open_trades) > 0:\n",
        "            fig.add_trace(\n",
        "                go.Scatter(\n",
        "                    x=buy_open_trades['time'],\n",
        "                    y=buy_open_trades['price'],\n",
        "                    mode='markers',\n",
        "                    name=f'{SYMBOL_A} BUY OPEN',\n",
        "                    marker=dict(color='red', size=12, symbol='triangle-up'),\n",
        "                    showlegend=True\n",
        "                ),\n",
        "                row=3, col=1\n",
        "            )\n",
        "        \n",
        "        # Add BUY CLOSE signals\n",
        "        if len(buy_close_trades) > 0:\n",
        "            fig.add_trace(\n",
        "                go.Scatter(\n",
        "                    x=buy_close_trades['time'],\n",
        "                    y=buy_close_trades['price'],\n",
        "                    mode='markers',\n",
        "                    name=f'{SYMBOL_A} BUY CLOSE',\n",
        "                    marker=dict(color='pink', size=12, symbol='triangle-up'),\n",
        "                    showlegend=True\n",
        "                ),\n",
        "                row=3, col=1\n",
        "            )\n",
        "        \n",
        "        # Add SELL OPEN signals\n",
        "        if len(sell_open_trades) > 0:\n",
        "            fig.add_trace(\n",
        "                go.Scatter(\n",
        "                    x=sell_open_trades['time'],\n",
        "                    y=sell_open_trades['price'],\n",
        "                    mode='markers',\n",
        "                    name=f'{SYMBOL_A} SELL OPEN',\n",
        "                    marker=dict(color='blue', size=12, symbol='triangle-down'),\n",
        "                    showlegend=True\n",
        "                ),\n",
        "                row=3, col=1\n",
        "            )\n",
        "        \n",
        "        # Add SELL CLOSE signals\n",
        "        if len(sell_close_trades) > 0:\n",
        "            fig.add_trace(\n",
        "                go.Scatter(\n",
        "                    x=sell_close_trades['time'],\n",
        "                    y=sell_close_trades['price'],\n",
        "                    mode='markers',\n",
        "                    name=f'{SYMBOL_A} SELL CLOSE',\n",
        "                    marker=dict(color='purple', size=12, symbol='triangle-down'),\n",
        "                    showlegend=True\n",
        "                ),\n",
        "                row=3, col=1\n",
        "            )\n",
        "                  \n",
        "          # 4. Symbol_B Market Data with Trading Signals\n",
        "    fig.add_trace(\n",
        "        go.Scatter(\n",
        "            x=symbol_b_data['tstamp'],\n",
        "            y=symbol_b_data[colname_b],\n",
        "            name=f'{SYMBOL_B} Price',\n",
        "            line=dict(color='orange', width=2),\n",
        "            opacity=0.8\n",
        "        ),\n",
        "        row=4, col=1\n",
        "    )\n",
        "    \n",
        "    # Add trading signals for Symbol_B if available\n",
        "    # if pair_trades is not None and len(pair_trades) > 0:\n",
        "        # Filter trades for Symbol_B\n",
        "    symbol_b_trades = pair_trades[pair_trades['symbol'] == SYMBOL_B]\n",
        "    \n",
        "    if len(symbol_b_trades) > 0:\n",
        "        # Separate trades by action and status for different colors\n",
        "        buy_open_trades = symbol_b_trades[(symbol_b_trades['action'].str.contains('BUY', na=False)) & \n",
        "                                            (symbol_b_trades['status'] == 'OPEN')]\n",
        "        buy_close_trades = symbol_b_trades[(symbol_b_trades['action'].str.contains('BUY', na=False)) & \n",
        "                                            (symbol_b_trades['status'] == 'CLOSE')]\n",
        "        sell_open_trades = symbol_b_trades[(symbol_b_trades['action'].str.contains('SELL', na=False)) & \n",
        "                                            (symbol_b_trades['status'] == 'OPEN')]\n",
        "        sell_close_trades = symbol_b_trades[(symbol_b_trades['action'].str.contains('SELL', na=False)) & \n",
        "                                            (symbol_b_trades['status'] == 'CLOSE')]\n",
        "        \n",
        "        # Add BUY OPEN signals\n",
        "        if len(buy_open_trades) > 0:\n",
        "            fig.add_trace(\n",
        "                go.Scatter(\n",
        "                    x=buy_open_trades['time'],\n",
        "                    y=buy_open_trades['price'],\n",
        "                    mode='markers',\n",
        "                    name=f'{SYMBOL_B} BUY OPEN',\n",
        "                    marker=dict(color='red', size=12, symbol='triangle-up'),\n",
        "                    showlegend=True\n",
        "                ),\n",
        "                row=4, col=1\n",
        "            )\n",
        "        \n",
        "        # Add BUY CLOSE signals\n",
        "        if len(buy_close_trades) > 0:\n",
        "            fig.add_trace(\n",
        "                go.Scatter(\n",
        "                    x=buy_close_trades['time'],\n",
        "                    y=buy_close_trades['price'],\n",
        "                    mode='markers',\n",
        "                    name=f'{SYMBOL_B} BUY CLOSE',\n",
        "                    marker=dict(color='red', size=12, symbol='triangle-up'),\n",
        "                    showlegend=True\n",
        "                ),\n",
        "                row=4, col=1\n",
        "            )\n",
        "        \n",
        "        # Add SELL OPEN signals\n",
        "        if len(sell_open_trades) > 0:\n",
        "            fig.add_trace(\n",
        "                go.Scatter(\n",
        "                    x=sell_open_trades['time'],\n",
        "                    y=sell_open_trades['price'],\n",
        "                    mode='markers',\n",
        "                    name=f'{SYMBOL_B} SELL OPEN',\n",
        "                    marker=dict(color='blue', size=12, symbol='triangle-down'),\n",
        "                    showlegend=True\n",
        "                ),\n",
        "                row=4, col=1\n",
        "            )\n",
        "        \n",
        "        # Add SELL CLOSE signals\n",
        "        if len(sell_close_trades) > 0:\n",
        "            fig.add_trace(\n",
        "                go.Scatter(\n",
        "                    x=sell_close_trades['time'],\n",
        "                    y=sell_close_trades['price'],\n",
        "                    mode='markers',\n",
        "                    name=f'{SYMBOL_B} SELL CLOSE',\n",
        "                    marker=dict(color='blue', size=12, symbol='triangle-down'),\n",
        "                    showlegend=True\n",
        "                ),\n",
        "                row=4, col=1\n",
        "            )\n",
        "  \n",
        "    # Update layout\n",
        "    fig.update_layout(\n",
        "        height=1200,\n",
        "        title_text=f\"Sliding Fit Strategy Analysis - {SYMBOL_A} & {SYMBOL_B}\",\n",
        "        showlegend=True,\n",
        "        template=\"plotly_white\"\n",
        "    )\n",
        "    \n",
        "    # Update y-axis labels\n",
        "    fig.update_yaxes(title_text=\"Scaled Dis-equilibrium\", row=1, col=1)\n",
        "    fig.update_yaxes(title_text=\"Signal Index\", row=2, col=1)\n",
        "    fig.update_yaxes(title_text=f\"{SYMBOL_A} Price ($)\", row=3, col=1)\n",
        "    fig.update_yaxes(title_text=f\"{SYMBOL_B} Price ($)\", row=4, col=1)\n",
        "    \n",
        "    # Update x-axis labels and ensure consistent time range\n",
        "    time_range = [timeline_df['tstamp'].min(), timeline_df['tstamp'].max()]\n",
        "    fig.update_xaxes(range=time_range, row=1, col=1)\n",
        "    fig.update_xaxes(range=time_range, row=2, col=1)\n",
        "    fig.update_xaxes(range=time_range, row=3, col=1)\n",
        "    fig.update_xaxes(title_text=\"Time\", range=time_range, row=4, col=1)\n",
        "    \n",
        "    # Display using plotly offline mode\n",
        "    pyo.iplot(fig)\n",
        "\n",
        "else:\n",
        "    print(\"No interactive visualization data available - strategy may not have run successfully\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "=== NORMALIZED PRICE COMPARISON WITH TRADING SIGNALS ===\n",
            "Interactive visualization showing normalized price movements with BUY/SELL signals\n"
          ]
        },
        {
          "data": {
            "application/vnd.plotly.v1+json": {
              "config": {
                "plotlyServerURL": "https://plot.ly"
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              "data": [
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                  "line": {
                    "color": "blue",
                    "width": 2
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                  "name": "COIN (Normalized)",
                  "opacity": 0.8,
                  "type": "scatter",
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          "metadata": {},
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        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "Chart shows:\n",
            "- COIN and MSTR prices normalized to start at 1.0\n",
            "- BUY signals shown as green triangles pointing up\n",
            "- SELL signals shown as orange triangles pointing down\n",
            "- All BUY signals per symbol grouped together, all SELL signals per symbol grouped together\n",
            "- Hover over markers to see individual trade details (OPEN/CLOSE status)\n",
            "- Total signals displayed: 6\n",
            "- COIN signals: 3\n",
            "- MSTR signals: 3\n"
          ]
        }
      ],
      "source": [
        "# Normalized Price Comparison with BUY/SELL Signals (Plotly)\n",
        "import plotly.graph_objects as go\n",
        "from plotly.subplots import make_subplots\n",
        "import plotly.express as px\n",
        "\n",
        "print(\"=== NORMALIZED PRICE COMPARISON WITH TRADING SIGNALS ===\")\n",
        "print(\"Interactive visualization showing normalized price movements with BUY/SELL signals\")\n",
        "\n",
        "# Get column names for the trading pair\n",
        "colname_a, colname_b = pair.colnames()\n",
        "price_data = pair.market_data_.copy()\n",
        "\n",
        "# Calculate normalized prices (base = 1.0)\n",
        "norm_a = price_data[colname_a] / price_data[colname_a].iloc[0]\n",
        "norm_b = price_data[colname_b] / price_data[colname_b].iloc[0]\n",
        "\n",
        "# Create the main figure\n",
        "fig = go.Figure()\n",
        "\n",
        "# Add normalized price lines\n",
        "fig.add_trace(\n",
        "    go.Scatter(\n",
        "        x=price_data['tstamp'],\n",
        "        y=norm_a,\n",
        "        name=f'{SYMBOL_A} (Normalized)',\n",
        "        line=dict(color='blue', width=2),\n",
        "        opacity=0.8\n",
        "    )\n",
        ")\n",
        "\n",
        "fig.add_trace(\n",
        "    go.Scatter(\n",
        "        x=price_data['tstamp'],\n",
        "        y=norm_b,\n",
        "        name=f'{SYMBOL_B} (Normalized)',\n",
        "        line=dict(color='red', width=2),\n",
        "        opacity=0.8\n",
        "    )\n",
        ")\n",
        "\n",
        "# Add BUY and SELL signals if available\n",
        "if pair_trades is not None and len(pair_trades) > 0:\n",
        "    # Define signal groups to avoid legend repetition\n",
        "    signal_groups = {}\n",
        "    \n",
        "    # Process all trades and group by signal type (ignore OPEN/CLOSE status)\n",
        "    for _, trade in pair_trades.iterrows():\n",
        "        symbol = trade['symbol']\n",
        "        action = trade['action']\n",
        "        status = trade['status']\n",
        "        \n",
        "        # Create signal group key (without status to combine OPEN/CLOSE)\n",
        "        signal_key = f\"{symbol} {action}\"\n",
        "        \n",
        "        # Find normalized price for this trade\n",
        "        trade_time = trade['time']\n",
        "        if symbol == SYMBOL_A:\n",
        "            closest_idx = price_data['tstamp'].searchsorted(trade_time)\n",
        "            if closest_idx < len(norm_a):\n",
        "                norm_price = norm_a.iloc[closest_idx]\n",
        "            else:\n",
        "                norm_price = norm_a.iloc[-1]\n",
        "        else:  # SYMBOL_B\n",
        "            closest_idx = price_data['tstamp'].searchsorted(trade_time)\n",
        "            if closest_idx < len(norm_b):\n",
        "                norm_price = norm_b.iloc[closest_idx]\n",
        "            else:\n",
        "                norm_price = norm_b.iloc[-1]\n",
        "        \n",
        "        # Initialize group if not exists\n",
        "        if signal_key not in signal_groups:\n",
        "            signal_groups[signal_key] = {\n",
        "                'times': [],\n",
        "                'prices': [],\n",
        "                'actual_prices': [],\n",
        "                'symbol': symbol,\n",
        "                'action': action,\n",
        "                'status': status\n",
        "            }\n",
        "        \n",
        "        # Add to group\n",
        "        signal_groups[signal_key]['times'].append(trade_time)\n",
        "        signal_groups[signal_key]['prices'].append(norm_price)\n",
        "        signal_groups[signal_key]['actual_prices'].append(trade['price'])\n",
        "    \n",
        "    # Add each signal group as a single trace\n",
        "    for signal_key, group_data in signal_groups.items():\n",
        "        symbol = group_data['symbol']\n",
        "        action = group_data['action']\n",
        "        status = group_data['status']\n",
        "        \n",
        "        # Determine marker properties (same for all OPEN/CLOSE of same action)\n",
        "        if 'BUY' in action:\n",
        "            marker_color = 'green' if symbol == SYMBOL_A else 'darkgreen'\n",
        "            marker_symbol = 'triangle-up'\n",
        "            marker_size = 14\n",
        "        else:  # SELL\n",
        "            marker_color = 'orange' if symbol == SYMBOL_A else 'darkorange'\n",
        "            marker_symbol = 'triangle-down'\n",
        "            marker_size = 14\n",
        "        \n",
        "        # Create hover text for each point in the group\n",
        "        hover_texts = []\n",
        "        for i, (time, norm_price, actual_price) in enumerate(zip(group_data['times'], \n",
        "                                                                  group_data['prices'], \n",
        "                                                                  group_data['actual_prices'])):\n",
        "            # Find the corresponding trade to get the status for hover text\n",
        "            trade_info = pair_trades[(pair_trades['time'] == time) & \n",
        "                                   (pair_trades['symbol'] == symbol) & \n",
        "                                   (pair_trades['action'] == action)]\n",
        "            if len(trade_info) > 0:\n",
        "                trade_status = trade_info.iloc[0]['status']\n",
        "                hover_texts.append(f'<b>{signal_key} {trade_status}</b><br>' +\n",
        "                                  f'Time: {time}<br>' +\n",
        "                                  f'Normalized Price: {norm_price:.4f}<br>' +\n",
        "                                  f'Actual Price: ${actual_price:.2f}')\n",
        "            else:\n",
        "                hover_texts.append(f'<b>{signal_key}</b><br>' +\n",
        "                                  f'Time: {time}<br>' +\n",
        "                                  f'Normalized Price: {norm_price:.4f}<br>' +\n",
        "                                  f'Actual Price: ${actual_price:.2f}')\n",
        "        \n",
        "        fig.add_trace(\n",
        "            go.Scatter(\n",
        "                x=group_data['times'],\n",
        "                y=group_data['prices'],\n",
        "                mode='markers',\n",
        "                name=signal_key,\n",
        "                marker=dict(\n",
        "                    color=marker_color,\n",
        "                    size=marker_size,\n",
        "                    symbol=marker_symbol,\n",
        "                    line=dict(width=2, color='black')\n",
        "                ),\n",
        "                showlegend=True,\n",
        "                hovertemplate='%{text}<extra></extra>',\n",
        "                text=hover_texts\n",
        "            )\n",
        "        )\n",
        "\n",
        "# Update layout\n",
        "fig.update_layout(\n",
        "    title=f'Normalized Price Comparison with BUY/SELL Signals - {SYMBOL_A} & {SYMBOL_B}',\n",
        "    xaxis_title='Time',\n",
        "    yaxis_title='Normalized Price (Base = 1.0)',\n",
        "    height=600,\n",
        "    showlegend=True,\n",
        "    template=\"plotly_white\",\n",
        "    hovermode='x unified'\n",
        ")\n",
        "\n",
        "# Add horizontal line at y=1.0 for reference\n",
        "fig.add_hline(y=1.0, line_dash=\"dash\", line_color=\"gray\", opacity=0.5, \n",
        "              annotation_text=\"Baseline (1.0)\")\n",
        "\n",
        "# Display the chart\n",
        "fig.show()\n",
        "\n",
        "print(f\"\\nChart shows:\")\n",
        "print(f\"- {SYMBOL_A} and {SYMBOL_B} prices normalized to start at 1.0\")\n",
        "print(f\"- BUY signals shown as green triangles pointing up\")\n",
        "print(f\"- SELL signals shown as orange triangles pointing down\")\n",
        "print(f\"- All BUY signals per symbol grouped together, all SELL signals per symbol grouped together\")\n",
        "print(f\"- Hover over markers to see individual trade details (OPEN/CLOSE status)\")\n",
        "\n",
        "if pair_trades is not None and len(pair_trades) > 0:\n",
        "    print(f\"- Total signals displayed: {len(pair_trades)}\")\n",
        "    print(f\"- {SYMBOL_A} signals: {len(pair_trades[pair_trades['symbol'] == SYMBOL_A])}\")\n",
        "    print(f\"- {SYMBOL_B} signals: {len(pair_trades[pair_trades['symbol'] == SYMBOL_B])}\")\n",
        "else:\n",
        "    print(\"- No trading signals to display\")\n"
      ]
    },
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      "cell_type": "markdown",
      "metadata": {
        "vscode": {
          "languageId": "raw"
        }
      },
      "source": [
        "## Summary and Analysis\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "================================================================================\n",
            "PAIRS TRADING BACKTEST SUMMARY\n",
            "================================================================================\n",
            "\n",
            "Pair: COIN & MSTR\n",
            "Fit Method: SlidingFit\n",
            "Configuration: equity\n",
            "Data file: 20250606.mktdata.ohlcv.db\n",
            "Trading date: 20250606\n",
            "\n",
            "Strategy Parameters:\n",
            "  Training window: 120 minutes\n",
            "  Open threshold: 2\n",
            "  Close threshold: 1\n",
            "  Funding per pair: $2000\n",
            "\n",
            "Sliding Window Analysis:\n",
            "  Total data points: 391\n",
            "  Maximum iterations: 271\n",
            "  Analysis type: Dynamic sliding window\n",
            "\n",
            "Trading Signals: 6 generated\n",
            "  Unique trade times: 3\n",
            "  BUY signals: 3\n",
            "  SELL signals: 3\n",
            "\n",
            "First few trading signals:\n",
            "  1. SELL COIN @ $253.02 at 2025-06-06 17:01:00\n",
            "  2. BUY MSTR @ $377.33 at 2025-06-06 17:01:00\n",
            "  3. BUY COIN @ $253.93 at 2025-06-06 18:17:00\n",
            "  4. SELL MSTR @ $375.01 at 2025-06-06 18:17:00\n",
            "  5. BUY COIN @ $252.00 at 2025-06-06 19:41:00\n",
            "  6. SELL MSTR @ $373.29 at 2025-06-06 19:41:00\n",
            "\n",
            "================================================================================\n"
          ]
        }
      ],
      "source": [
        "print(\"=\" * 80)\n",
        "print(\"PAIRS TRADING BACKTEST SUMMARY\")\n",
        "print(\"=\" * 80)\n",
        "\n",
        "print(f\"\\nPair: {SYMBOL_A} & {SYMBOL_B}\")\n",
        "print(f\"Fit Method: {FIT_METHOD_TYPE}\")\n",
        "print(f\"Configuration: {CONFIG_FILE}\")\n",
        "print(f\"Data file: {DATA_FILE}\")\n",
        "print(f\"Trading date: {TRADING_DATE}\")\n",
        "\n",
        "print(f\"\\nStrategy Parameters:\")\n",
        "print(f\"  Training window: {pt_bt_config['training_minutes']} minutes\")\n",
        "print(f\"  Open threshold: {pt_bt_config['dis-equilibrium_open_trshld']}\")\n",
        "print(f\"  Close threshold: {pt_bt_config['dis-equilibrium_close_trshld']}\")\n",
        "print(f\"  Funding per pair: ${pt_bt_config['funding_per_pair']}\")\n",
        "\n",
        "# Strategy-specific summary\n",
        "print(f\"\\nSliding Window Analysis:\")\n",
        "training_minutes = pt_bt_config['training_minutes']\n",
        "max_iterations = len(pair.market_data_) - training_minutes\n",
        "print(f\"  Total data points: {len(pair.market_data_)}\")\n",
        "print(f\"  Maximum iterations: {max_iterations}\")\n",
        "print(f\"  Analysis type: Dynamic sliding window\")\n",
        "\n",
        "# Trading signals summary\n",
        "if pair_trades is not None and len(pair_trades) > 0:\n",
        "    print(f\"\\nTrading Signals: {len(pair_trades)} generated\")\n",
        "    unique_times = pair_trades['time'].unique()\n",
        "    print(f\"  Unique trade times: {len(unique_times)}\")\n",
        "    \n",
        "    # Group by action type\n",
        "    buy_signals = pair_trades[pair_trades['action'].str.contains('BUY', na=False)]\n",
        "    sell_signals = pair_trades[pair_trades['action'].str.contains('SELL', na=False)]\n",
        "    \n",
        "    print(f\"  BUY signals: {len(buy_signals)}\")\n",
        "    print(f\"  SELL signals: {len(sell_signals)}\")\n",
        "    \n",
        "    # Show first few trades\n",
        "    NTRADES_TO_SHOW = 6\n",
        "    print(f\"\\nFirst few trading signals:\")\n",
        "    for ii, (idx, trade) in enumerate(pair_trades.head(NTRADES_TO_SHOW).iterrows()):\n",
        "        print(f\"  {ii+1}. {trade['action']} {trade['symbol']} @ ${trade['price']:.2f} at {trade['time']}\")\n",
        "    \n",
        "    if len(pair_trades) > NTRADES_TO_SHOW:\n",
        "        print(f\"  ... and {len(pair_trades) - NTRADES_TO_SHOW} more signals\")\n",
        "        \n",
        "else:\n",
        "    print(f\"\\nTrading Signals: None generated\")\n",
        "    print(\"  Possible reasons:\")\n",
        "    print(\"    - Dis-equilibrium never exceeded open threshold\")\n",
        "    print(\"    - Pair not cointegrated (for StaticFit)\")\n",
        "    print(\"    - Insufficient data or market conditions\")\n",
        "\n",
        "print(f\"\\n\" + \"=\" * 80)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Performance Results"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "Detailed Trading Signals:\n",
            "Time                 Action          Symbol     Price        Scaled Dis-eq   Status    \n",
            "------------------------------------------------------------------------------------------\n",
            "2025-06-06 17:01:00  SELL            COIN       $253.02      2.135           OPEN      \n",
            "2025-06-06 17:01:00  BUY             MSTR       $377.33      2.135           OPEN      \n",
            "2025-06-06 18:17:00  BUY             COIN       $253.93      0.989           CLOSE     \n",
            "2025-06-06 18:17:00  SELL            MSTR       $375.01      0.989           CLOSE     \n",
            "2025-06-06 19:41:00  BUY             COIN       $252.00      4.316           OPEN      \n",
            "2025-06-06 19:41:00  SELL            MSTR       $373.29      4.316           OPEN      \n",
            "\n",
            "====== OUTSTANDING POSITIONS ======\n",
            "Pair            Symbol     Side Shares     Open $   Current $  Value $      Disequilibrium \n",
            "----------------------------------------------------------------------------------------------------\n",
            "COIN & MSTR     COIN       BUY  3.97       252.00   251.94     999.72                      \n",
            "                MSTR       SELL 2.68       373.29   376.41     1008.36     \n",
            "                PAIR TOTAL                                     2008.08     \n",
            "                DISEQUIL                                       Raw: -1.4396 Scaled: 0.9242\n",
            "----------------------------------------------------------------------------------------------------\n",
            "TOTAL OUTSTANDING VALUE                                                          $2008.08     \n",
            "\n",
            "====== GRAND TOTALS ACROSS ALL PAIRS ======\n",
            "Total Realized PnL: 0.00%\n",
            "\n",
            "================================================================================\n"
          ]
        }
      ],
      "source": [
        "from datetime import datetime\n",
        "\n",
        "if pair_trades is not None and len(pair_trades) > 0:\n",
        "    # Print detailed results using BacktestResult methods\n",
        "    bt_result.print_single_day_results()\n",
        "    \n",
        "    # Print trading signal details\n",
        "    print(f\"\\nDetailed Trading Signals:\")\n",
        "    print(f\"{'Time':<20} {'Action':<15} {'Symbol':<10} {'Price':<12} {'Scaled Dis-eq':<15} {'Status':<10}\")\n",
        "    print(\"-\" * 90)\n",
        "    \n",
        "    for _, trade in pair_trades.head(10).iterrows():  # Show first 10 trades\n",
        "        time_str = str(trade['time'])[:19] \n",
        "        action_str = str(trade['action'])[:14]\n",
        "        symbol_str = str(trade['symbol'])[:9]\n",
        "        price_str = f\"${trade['price']:.2f}\"\n",
        "        diseq_str = f\"{trade.get('scaled_disequilibrium', 'N/A'):.3f}\" if 'scaled_disequilibrium' in trade  else 'N/A'\n",
        "        status = trade.get('status', 'N/A')\n",
        "        \n",
        "        print(f\"{time_str:<20} {action_str:<15} {symbol_str:<10} {price_str:<12} {diseq_str:<15} {status:<10}\")\n",
        "    \n",
        "    if len(pair_trades) > 10:\n",
        "        print(f\"... and {len(pair_trades)-10} more trading signals\")\n",
        "    \n",
        "    # Print outstanding positions\n",
        "    bt_result.print_outstanding_positions()\n",
        "    \n",
        "    # Print grand totals\n",
        "    bt_result.print_grand_totals()\n",
        "    \n",
        "else:\n",
        "    print(f\"\\nNo trading signals generated\")\n",
        "    print(f\"Backtest completed with no trades\")\n",
        "    \n",
        "    # Still print any outstanding information\n",
        "    print(f\"\\nConfiguration Summary:\")\n",
        "    print(f\"  Pair: {SYMBOL_A} & {SYMBOL_B}\")\n",
        "    print(f\"  Strategy: {FIT_METHOD_TYPE}\")\n",
        "    print(f\"  Open threshold: {pt_bt_config['dis-equilibrium_open_trshld']}\")\n",
        "    print(f\"  Close threshold: {pt_bt_config['dis-equilibrium_close_trshld']}\")\n",
        "    print(f\"  Training window: {pt_bt_config['training_minutes']} minutes\")\n",
        "    \n",
        "    pass # TODO: Implement sliding fit cointegration check\n",
        "print(\"\\n\" + \"=\"*80)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "vscode": {
          "languageId": "raw"
        }
      },
      "source": [
        "# Conclusions and Next Steps"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "\n",
        "This notebook demonstrates a comprehensive **SlidingFit pairs trading backtest framework** with advanced interactive visualizations and detailed analysis capabilities.\n",
        "\n",
        "### Current Implementation Features:\n",
        "\n",
        "#### SlidingFit Strategy Analysis:\n",
        "- **Adaptive cointegration modeling** using sliding windows (120-minute default)\n",
        "- **Dynamic parameter updates** every minute based on market conditions\n",
        "- **Real-time dis-equilibrium tracking** with configurable open/close thresholds\n",
        "- **Comprehensive signal generation** with detailed trade tracking and status\n",
        "\n",
        "#### Advanced Visualization Suite:\n",
        "- **Interactive Plotly charts** for comprehensive market analysis\n",
        "- **Normalized price comparison** with overlaid BUY/SELL signals\n",
        "- **Multi-panel analysis** showing dis-equilibrium, thresholds, and trading timeline\n",
        "- **Clean legend grouping** with detailed hover tooltips for individual trade information\n",
        "- **Unified signal visualization** combining OPEN/CLOSE actions per symbol\n",
        "\n",
        "#### Configuration-Driven Framework:\n",
        "- **HJSON configuration files** for easy parameter management\n",
        "- **Multi-asset support** (equity, crypto configurations available)\n",
        "- **Flexible symbol selection** with automatic data file construction\n",
        "- **Configurable thresholds** for dis-equilibrium open/close levels\n",
        "- **Funding and position sizing** parameters\n",
        "\n",
        "### Key Analysis Capabilities:\n",
        "\n",
        "1. **Market Data Processing**: Automated loading and preprocessing of OHLCV data\n",
        "2. **Cointegration Analysis**: Dynamic sliding window cointegration testing\n",
        "3. **Signal Generation**: Automated BUY/SELL signal generation with precise timing\n",
        "4. **Performance Tracking**: Comprehensive backtest results with P&L analysis\n",
        "5. **Interactive Exploration**: Rich visualizations for strategy analysis and debugging\n",
        "\n",
        "### Current Notebook Usage:\n",
        "\n",
        "1. **Configure Parameters**: Set `CONFIG_FILE`, `SYMBOL_A`, `SYMBOL_B`, and `TRADING_DATE`\n",
        "2. **Load Configuration**: Automatic HJSON config loading with path resolution\n",
        "3. **Process Market Data**: Automated data loading and trading pair creation\n",
        "4. **Run Analysis**: SlidingFit strategy execution with signal generation\n",
        "5. **Analyze Results**: Multiple visualization panels and detailed trade analysis\n",
        "6. **Interactive Exploration**: Plotly charts with hover details and zoom capabilities\n",
        "\n",
        "### Implemented Visualizations:\n",
        "\n",
        "- **Raw Price Charts**: Individual symbol price movements over time\n",
        "- **Normalized Price Comparison**: Base-1.0 normalized prices with trading signals\n",
        "- **Dis-equilibrium Analysis**: Raw and scaled dis-equilibrium with threshold overlays\n",
        "- **Trading Signal Timeline**: Comprehensive signal tracking and status visualization\n",
        "- **Interactive Price Charts**: Symbol-specific price movements with signal overlays\n",
        "\n",
        "### Recommended Next Steps:\n",
        "\n",
        "#### Framework Enhancement:\n",
        "- **Transaction cost modeling** with realistic bid-ask spreads and fees\n",
        "- **Position sizing algorithms** based on volatility and risk parameters\n",
        "- **Stop-loss and take-profit** mechanisms for risk management\n",
        "- **Portfolio-level analysis** across multiple trading pairs\n",
        "\n",
        "#### Analysis Expansion:\n",
        "- **Multi-timeframe analysis** (1-min, 5-min, 15-min windows)\n",
        "- **Cross-validation** on different market periods and conditions\n",
        "- **Parameter optimization** routines for threshold and window selection\n",
        "- **Regime detection** for adaptive strategy switching\n",
        "\n",
        "#### Production Implementation:\n",
        "- **Real-time data integration** for live trading signal generation\n",
        "- **Alert system** for threshold breaches and signal generation\n",
        "- **Performance monitoring** with real-time P&L tracking\n",
        "- **Risk management dashboard** with position and exposure monitoring\n",
        "\n",
        "### Strategy Validation:\n",
        "\n",
        "Test the current implementation with:\n",
        "- **Different symbol pairs** to validate cointegration relationships\n",
        "- **Various market conditions** (trending, sideways, volatile periods)\n",
        "- **Multiple time periods** to assess strategy consistency\n",
        "- **Threshold sensitivity analysis** to optimize entry/exit parameters\n"
      ]
    }
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