Stock_NeurIPS2018_SB3.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yfv52r2G33jY"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/AI4Finance-Foundation/FinRL/blob/master/Stock_NeurIPS2018.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gXaoZs2lh1hi"
      },
      "source": [
        "# Deep Reinforcement Learning for Stock Trading from Scratch: Multiple Stock Trading\n",
        "\n",
        "* **Pytorch Version** \n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lGunVt8oLCVS"
      },
      "source": [
        "# Content"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HOzAKQ-SLGX6"
      },
      "source": [
        "* [1. Task Description](#0)\n",
        "* [2. Install Python packages](#1)\n",
        "    * [2.1. Install Packages](#1.1)    \n",
        "    * [2.2. A List of Python Packages](#1.2)\n",
        "    * [2.3. Import Packages](#1.3)\n",
        "    * [2.4. Create Folders](#1.4)\n",
        "* [3. Download and Preprocess Data](#2)\n",
        "* [4. Preprocess Data](#3)        \n",
        "    * [4.1. Technical Indicators](#3.1)\n",
        "    * [4.2. Perform Feature Engineering](#3.2)\n",
        "* [5. Build Market Environment in OpenAI Gym-style](#4)  \n",
        "    * [5.1. Data Split](#4.1)  \n",
        "    * [5.3. Environment for Training](#4.2)    \n",
        "* [6. Train DRL Agents](#5)\n",
        "* [7. Backtesting Performance](#6)  \n",
        "    * [7.1. BackTestStats](#6.1)\n",
        "    * [7.2. BackTestPlot](#6.2)   \n",
        "  "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sApkDlD9LIZv"
      },
      "source": [
        "<a id='0'></a>\n",
        "# Part 1. Task Discription"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HjLD2TZSLKZ-"
      },
      "source": [
        "We train a DRL agent for stock trading. This task is modeled as a Markov Decision Process (MDP), and the objective function is maximizing (expected) cumulative return.\n",
        "\n",
        "We specify the state-action-reward as follows:\n",
        "\n",
        "* **State s**: The state space represents an agent's perception of the market environment. Just like a human trader analyzing various information, here our agent passively observes many features and learns by interacting with the market environment (usually by replaying historical data).\n",
        "\n",
        "* **Action a**: The action space includes allowed actions that an agent can take at each state. For example, a ∈ {−1, 0, 1}, where −1, 0, 1 represent\n",
        "selling, holding, and buying. When an action operates multiple shares, a ∈{−k, ..., −1, 0, 1, ..., k}, e.g.. \"Buy\n",
        "10 shares of AAPL\" or \"Sell 10 shares of AAPL\" are 10 or −10, respectively\n",
        "\n",
        "* **Reward function r(s, a, s′)**: Reward is an incentive for an agent to learn a better policy. For example, it can be the change of the portfolio value when taking a at state s and arriving at new state s',  i.e., r(s, a, s′) = v′ − v, where v′ and v represent the portfolio values at state s′ and s, respectively\n",
        "\n",
        "\n",
        "**Market environment**: 30 consituent stocks of Dow Jones Industrial Average (DJIA) index. Accessed at the starting date of the testing period.\n",
        "\n",
        "\n",
        "The data for this case study is obtained from Yahoo Finance API. The data contains Open-High-Low-Close price and volume.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ffsre789LY08"
      },
      "source": [
        "<a id='1'></a>\n",
        "# Part 2. Install Python Packages"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Uy5_PTmOh1hj"
      },
      "source": [
        "<a id='1.1'></a>\n",
        "## 2.1. Install packages\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 56,
      "metadata": {
        "id": "mPT0ipYE28wL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "02a8a804-d120-4388-a167-20a81cb33d87"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting git+https://github.com/AI4Finance-Foundation/FinRL.git\n",
            "  Cloning https://github.com/AI4Finance-Foundation/FinRL.git to /tmp/pip-req-build-jjkyf27j\n",
            "  Running command git clone -q https://github.com/AI4Finance-Foundation/FinRL.git /tmp/pip-req-build-jjkyf27j\n",
            "Collecting pyfolio@ git+https://github.com/quantopian/pyfolio.git#egg=pyfolio-0.9.2\n",
            "  Cloning https://github.com/quantopian/pyfolio.git to /tmp/pip-install-i81vvh_0/pyfolio_5ee1cbfbd0264ca78c9515ba6487a8f0\n",
            "  Running command git clone -q https://github.com/quantopian/pyfolio.git /tmp/pip-install-i81vvh_0/pyfolio_5ee1cbfbd0264ca78c9515ba6487a8f0\n",
            "Collecting elegantrl@ git+https://github.com/AI4Finance-Foundation/ElegantRL.git#egg=elegantrl\n",
            "  Cloning https://github.com/AI4Finance-Foundation/ElegantRL.git to /tmp/pip-install-i81vvh_0/elegantrl_8c1ec6a2a32f4f558396f0be5e97acc5\n",
            "  Running command git clone -q https://github.com/AI4Finance-Foundation/ElegantRL.git /tmp/pip-install-i81vvh_0/elegantrl_8c1ec6a2a32f4f558396f0be5e97acc5\n",
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          ]
        }
      ],
      "source": [
        "## install finrl library\n",
        "!pip install git+https://github.com/AI4Finance-Foundation/FinRL.git"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "osBHhVysOEzi"
      },
      "source": [
        "\n",
        "<a id='1.2'></a>\n",
        "## 2.2. A list of Python packages \n",
        "* Yahoo Finance API\n",
        "* pandas\n",
        "* numpy\n",
        "* matplotlib\n",
        "* stockstats\n",
        "* OpenAI gym\n",
        "* stable-baselines\n",
        "* tensorflow\n",
        "* pyfolio"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nGv01K8Sh1hn"
      },
      "source": [
        "<a id='1.3'></a>\n",
        "## 2.3. Import Packages"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 57,
      "metadata": {
        "id": "lPqeTTwoh1hn"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib\n",
        "import matplotlib.pyplot as plt\n",
        "# matplotlib.use('Agg')\n",
        "import datetime\n",
        "\n",
        "%matplotlib inline\n",
        "from finrl.meta.preprocessor.yahoodownloader import YahooDownloader\n",
        "from finrl.meta.preprocessor.preprocessors import FeatureEngineer, data_split\n",
        "from finrl.meta.env_stock_trading.env_stocktrading import StockTradingEnv\n",
        "from finrl.agents.stablebaselines3.models import DRLAgent\n",
        "from stable_baselines3.common.logger import configure\n",
        "from finrl.meta.data_processor import DataProcessor\n",
        "\n",
        "from finrl.plot import backtest_stats, backtest_plot, get_daily_return, get_baseline\n",
        "from pprint import pprint\n",
        "\n",
        "import sys\n",
        "sys.path.append(\"../FinRL\")\n",
        "\n",
        "import itertools"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T2owTj985RW4"
      },
      "source": [
        "<a id='1.4'></a>\n",
        "## 2.4. Create Folders"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 58,
      "metadata": {
        "id": "RtUc_ofKmpdy"
      },
      "outputs": [],
      "source": [
        "from finrl import config\n",
        "from finrl import config_tickers\n",
        "import os\n",
        "from finrl.main import check_and_make_directories\n",
        "from finrl.config import (\n",
        "    DATA_SAVE_DIR,\n",
        "    TRAINED_MODEL_DIR,\n",
        "    TENSORBOARD_LOG_DIR,\n",
        "    RESULTS_DIR,\n",
        "    INDICATORS,\n",
        "    TRAIN_START_DATE,\n",
        "    TRAIN_END_DATE,\n",
        "    TEST_START_DATE,\n",
        "    TEST_END_DATE,\n",
        "    TRADE_START_DATE,\n",
        "    TRADE_END_DATE,\n",
        ")\n",
        "check_and_make_directories([DATA_SAVE_DIR, TRAINED_MODEL_DIR, TENSORBOARD_LOG_DIR, RESULTS_DIR])\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A289rQWMh1hq"
      },
      "source": [
        "<a id='2'></a>\n",
        "# Part 3. Download Data\n",
        "Yahoo Finance provides stock data, financial news, financial reports, etc. Yahoo Finance is free.\n",
        "* FinRL uses a class **YahooDownloader** in FinRL-Meta to fetch data via Yahoo Finance API\n",
        "* Call Limit: Using the Public API (without authentication), you are limited to 2,000 requests per hour per IP (or up to a total of 48,000 requests a day)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NPeQ7iS-LoMm"
      },
      "source": [
        "\n",
        "\n",
        "-----\n",
        "class YahooDownloader:\n",
        "    Retrieving daily stock data from\n",
        "    Yahoo Finance API\n",
        "\n",
        "    Attributes\n",
        "    ----------\n",
        "        start_date : str\n",
        "            start date of the data (modified from config.py)\n",
        "        end_date : str\n",
        "            end date of the data (modified from config.py)\n",
        "        ticker_list : list\n",
        "            a list of stock tickers (modified from config.py)\n",
        "\n",
        "    Methods\n",
        "    -------\n",
        "    fetch_data()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 59,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "h3XJnvrbLp-C",
        "outputId": "3c4dda81-f617-4e9b-f88c-edb502d1500c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'2020-07-31'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 59
        }
      ],
      "source": [
        "# from config.py, TRAIN_START_DATE is a string\n",
        "TRAIN_START_DATE\n",
        "# from config.py, TRAIN_END_DATE is a string\n",
        "TRAIN_END_DATE"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 60,
      "metadata": {
        "id": "FUnY8WEfLq3C"
      },
      "outputs": [],
      "source": [
        "TRAIN_START_DATE = '2009-01-01'\n",
        "TRAIN_END_DATE = '2020-07-01'\n",
        "TRADE_START_DATE = '2020-07-01'\n",
        "TRADE_END_DATE = '2021-10-31'\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 61,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yCKm4om-s9kE",
        "outputId": "24dbccc1-190c-4912-b67a-9fe8fd9afbf2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
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            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
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            "[*********************100%***********************]  1 of 1 completed\n",
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            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
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            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (94331, 8)\n"
          ]
        }
      ],
      "source": [
        "df = YahooDownloader(start_date = TRAIN_START_DATE,\n",
        "                     end_date = TRADE_END_DATE,\n",
        "                     ticker_list = config_tickers.DOW_30_TICKER).fetch_data()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 62,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JzqRRTOX6aFu",
        "outputId": "85539f72-49fe-4418-9a75-5fa715cbfac9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['AXP', 'AMGN', 'AAPL', 'BA', 'CAT', 'CSCO', 'CVX', 'GS', 'HD', 'HON', 'IBM', 'INTC', 'JNJ', 'KO', 'JPM', 'MCD', 'MMM', 'MRK', 'MSFT', 'NKE', 'PG', 'TRV', 'UNH', 'CRM', 'VZ', 'V', 'WBA', 'WMT', 'DIS', 'DOW']\n"
          ]
        }
      ],
      "source": [
        "print(config_tickers.DOW_30_TICKER)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 63,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CV3HrZHLh1hy",
        "outputId": "f1031e65-9b05-4fb4-9128-3b7babd894c5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(94331, 8)"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ],
      "source": [
        "df.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 64,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "4hYkeaPiICHS",
        "outputId": "95d13d1e-3c8d-441e-835e-b9c7c121fd9f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         date       open       high        low      close     volume   tic  \\\n",
              "0  2009-01-02   3.067143   3.251429   3.041429   2.767330  746015200  AAPL   \n",
              "1  2009-01-02  58.590000  59.080002  57.750000  44.523766    6547900  AMGN   \n",
              "2  2009-01-02  18.570000  19.520000  18.400000  15.477426   10955700   AXP   \n",
              "3  2009-01-02  42.799999  45.560001  42.779999  33.941093    7010200    BA   \n",
              "4  2009-01-02  44.910000  46.980000  44.709999  31.942244    7117200   CAT   \n",
              "\n",
              "   day  \n",
              "0    4  \n",
              "1    4  \n",
              "2    4  \n",
              "3    4  \n",
              "4    4  "
            ],
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              "    <div class=\"colab-df-container\">\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>open</th>\n",
              "      <th>high</th>\n",
              "      <th>low</th>\n",
              "      <th>close</th>\n",
              "      <th>volume</th>\n",
              "      <th>tic</th>\n",
              "      <th>day</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>3.067143</td>\n",
              "      <td>3.251429</td>\n",
              "      <td>3.041429</td>\n",
              "      <td>2.767330</td>\n",
              "      <td>746015200</td>\n",
              "      <td>AAPL</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>58.590000</td>\n",
              "      <td>59.080002</td>\n",
              "      <td>57.750000</td>\n",
              "      <td>44.523766</td>\n",
              "      <td>6547900</td>\n",
              "      <td>AMGN</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>18.570000</td>\n",
              "      <td>19.520000</td>\n",
              "      <td>18.400000</td>\n",
              "      <td>15.477426</td>\n",
              "      <td>10955700</td>\n",
              "      <td>AXP</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>42.799999</td>\n",
              "      <td>45.560001</td>\n",
              "      <td>42.779999</td>\n",
              "      <td>33.941093</td>\n",
              "      <td>7010200</td>\n",
              "      <td>BA</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>44.910000</td>\n",
              "      <td>46.980000</td>\n",
              "      <td>44.709999</td>\n",
              "      <td>31.942244</td>\n",
              "      <td>7117200</td>\n",
              "      <td>CAT</td>\n",
              "      <td>4</td>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ],
      "source": [
        "df.sort_values(['date','tic'],ignore_index=True).head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uqC6c40Zh1iH"
      },
      "source": [
        "# Part 4: Preprocess Data\n",
        "We need to check for missing data and do feature engineering to convert the data point into a state.\n",
        "* **Adding technical indicators**. In practical trading, various information needs to be taken into account, such as historical prices, current holding shares, technical indicators, etc. Here, we demonstrate two trend-following technical indicators: MACD and RSI.\n",
        "* **Adding turbulence index**. Risk-aversion reflects whether an investor prefers to protect the capital. It also influences one's trading strategy when facing different market volatility level. To control the risk in a worst-case scenario, such as financial crisis of 2007–2008, FinRL employs the turbulence index that measures extreme fluctuation of asset price."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 65,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PmKP-1ii3RLS",
        "outputId": "9b9facc1-f877-4caa-b66b-a2ce0e023de8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Successfully added technical indicators\n",
            "\r[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (3229, 8)\n",
            "Successfully added vix\n",
            "Successfully added turbulence index\n"
          ]
        }
      ],
      "source": [
        "fe = FeatureEngineer(\n",
        "                    use_technical_indicator=True,\n",
        "                    tech_indicator_list = INDICATORS,\n",
        "                    use_vix=True,\n",
        "                    use_turbulence=True,\n",
        "                    user_defined_feature = False)\n",
        "\n",
        "processed = fe.preprocess_data(df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 66,
      "metadata": {
        "id": "Kixon2tR3RLT"
      },
      "outputs": [],
      "source": [
        "list_ticker = processed[\"tic\"].unique().tolist()\n",
        "list_date = list(pd.date_range(processed['date'].min(),processed['date'].max()).astype(str))\n",
        "combination = list(itertools.product(list_date,list_ticker))\n",
        "\n",
        "processed_full = pd.DataFrame(combination,columns=[\"date\",\"tic\"]).merge(processed,on=[\"date\",\"tic\"],how=\"left\")\n",
        "processed_full = processed_full[processed_full['date'].isin(processed['date'])]\n",
        "processed_full = processed_full.sort_values(['date','tic'])\n",
        "\n",
        "processed_full = processed_full.fillna(0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 67,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        },
        "id": "grvhGJJII3Xn",
        "outputId": "8e48188a-d58d-4d2c-dbed-fecacb74984e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         date   tic       open       high        low      close       volume  \\\n",
              "0  2009-01-02  AAPL   3.067143   3.251429   3.041429   2.767330  746015200.0   \n",
              "1  2009-01-02  AMGN  58.590000  59.080002  57.750000  44.523766    6547900.0   \n",
              "2  2009-01-02   AXP  18.570000  19.520000  18.400000  15.477426   10955700.0   \n",
              "3  2009-01-02    BA  42.799999  45.560001  42.779999  33.941093    7010200.0   \n",
              "4  2009-01-02   CAT  44.910000  46.980000  44.709999  31.942244    7117200.0   \n",
              "5  2009-01-02   CRM   8.025000   8.550000   7.912500   8.505000    4069200.0   \n",
              "6  2009-01-02  CSCO  16.410000  17.000000  16.250000  12.155673   40980600.0   \n",
              "7  2009-01-02   CVX  74.230003  77.300003  73.580002  44.404160   13695900.0   \n",
              "8  2009-01-02   DIS  22.760000  24.030001  22.500000  20.597496    9796600.0   \n",
              "9  2009-01-02    GS  84.019997  87.620003  82.190002  70.735085   14088500.0   \n",
              "\n",
              "   day  macd   boll_ub   boll_lb  rsi_30     cci_30  dx_30  close_30_sma  \\\n",
              "0  4.0   0.0  2.990894  2.660557   100.0  66.666667  100.0      2.767330   \n",
              "1  4.0   0.0  2.990894  2.660557   100.0  66.666667  100.0     44.523766   \n",
              "2  4.0   0.0  2.990894  2.660557   100.0  66.666667  100.0     15.477426   \n",
              "3  4.0   0.0  2.990894  2.660557   100.0  66.666667  100.0     33.941093   \n",
              "4  4.0   0.0  2.990894  2.660557   100.0  66.666667  100.0     31.942244   \n",
              "5  4.0   0.0  2.990894  2.660557   100.0  66.666667  100.0      8.505000   \n",
              "6  4.0   0.0  2.990894  2.660557   100.0  66.666667  100.0     12.155673   \n",
              "7  4.0   0.0  2.990894  2.660557   100.0  66.666667  100.0     44.404160   \n",
              "8  4.0   0.0  2.990894  2.660557   100.0  66.666667  100.0     20.597496   \n",
              "9  4.0   0.0  2.990894  2.660557   100.0  66.666667  100.0     70.735085   \n",
              "\n",
              "   close_60_sma        vix  turbulence  \n",
              "0      2.767330  39.189999         0.0  \n",
              "1     44.523766  39.189999         0.0  \n",
              "2     15.477426  39.189999         0.0  \n",
              "3     33.941093  39.189999         0.0  \n",
              "4     31.942244  39.189999         0.0  \n",
              "5      8.505000  39.189999         0.0  \n",
              "6     12.155673  39.189999         0.0  \n",
              "7     44.404160  39.189999         0.0  \n",
              "8     20.597496  39.189999         0.0  \n",
              "9     70.735085  39.189999         0.0  "
            ],
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              "\n",
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              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>tic</th>\n",
              "      <th>open</th>\n",
              "      <th>high</th>\n",
              "      <th>low</th>\n",
              "      <th>close</th>\n",
              "      <th>volume</th>\n",
              "      <th>day</th>\n",
              "      <th>macd</th>\n",
              "      <th>boll_ub</th>\n",
              "      <th>boll_lb</th>\n",
              "      <th>rsi_30</th>\n",
              "      <th>cci_30</th>\n",
              "      <th>dx_30</th>\n",
              "      <th>close_30_sma</th>\n",
              "      <th>close_60_sma</th>\n",
              "      <th>vix</th>\n",
              "      <th>turbulence</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>AAPL</td>\n",
              "      <td>3.067143</td>\n",
              "      <td>3.251429</td>\n",
              "      <td>3.041429</td>\n",
              "      <td>2.767330</td>\n",
              "      <td>746015200.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.990894</td>\n",
              "      <td>2.660557</td>\n",
              "      <td>100.0</td>\n",
              "      <td>66.666667</td>\n",
              "      <td>100.0</td>\n",
              "      <td>2.767330</td>\n",
              "      <td>2.767330</td>\n",
              "      <td>39.189999</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>AMGN</td>\n",
              "      <td>58.590000</td>\n",
              "      <td>59.080002</td>\n",
              "      <td>57.750000</td>\n",
              "      <td>44.523766</td>\n",
              "      <td>6547900.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.990894</td>\n",
              "      <td>2.660557</td>\n",
              "      <td>100.0</td>\n",
              "      <td>66.666667</td>\n",
              "      <td>100.0</td>\n",
              "      <td>44.523766</td>\n",
              "      <td>44.523766</td>\n",
              "      <td>39.189999</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>AXP</td>\n",
              "      <td>18.570000</td>\n",
              "      <td>19.520000</td>\n",
              "      <td>18.400000</td>\n",
              "      <td>15.477426</td>\n",
              "      <td>10955700.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.990894</td>\n",
              "      <td>2.660557</td>\n",
              "      <td>100.0</td>\n",
              "      <td>66.666667</td>\n",
              "      <td>100.0</td>\n",
              "      <td>15.477426</td>\n",
              "      <td>15.477426</td>\n",
              "      <td>39.189999</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>BA</td>\n",
              "      <td>42.799999</td>\n",
              "      <td>45.560001</td>\n",
              "      <td>42.779999</td>\n",
              "      <td>33.941093</td>\n",
              "      <td>7010200.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.990894</td>\n",
              "      <td>2.660557</td>\n",
              "      <td>100.0</td>\n",
              "      <td>66.666667</td>\n",
              "      <td>100.0</td>\n",
              "      <td>33.941093</td>\n",
              "      <td>33.941093</td>\n",
              "      <td>39.189999</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>CAT</td>\n",
              "      <td>44.910000</td>\n",
              "      <td>46.980000</td>\n",
              "      <td>44.709999</td>\n",
              "      <td>31.942244</td>\n",
              "      <td>7117200.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.990894</td>\n",
              "      <td>2.660557</td>\n",
              "      <td>100.0</td>\n",
              "      <td>66.666667</td>\n",
              "      <td>100.0</td>\n",
              "      <td>31.942244</td>\n",
              "      <td>31.942244</td>\n",
              "      <td>39.189999</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>CRM</td>\n",
              "      <td>8.025000</td>\n",
              "      <td>8.550000</td>\n",
              "      <td>7.912500</td>\n",
              "      <td>8.505000</td>\n",
              "      <td>4069200.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.990894</td>\n",
              "      <td>2.660557</td>\n",
              "      <td>100.0</td>\n",
              "      <td>66.666667</td>\n",
              "      <td>100.0</td>\n",
              "      <td>8.505000</td>\n",
              "      <td>8.505000</td>\n",
              "      <td>39.189999</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>CSCO</td>\n",
              "      <td>16.410000</td>\n",
              "      <td>17.000000</td>\n",
              "      <td>16.250000</td>\n",
              "      <td>12.155673</td>\n",
              "      <td>40980600.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.990894</td>\n",
              "      <td>2.660557</td>\n",
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              "      <td>100.0</td>\n",
              "      <td>12.155673</td>\n",
              "      <td>12.155673</td>\n",
              "      <td>39.189999</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>CVX</td>\n",
              "      <td>74.230003</td>\n",
              "      <td>77.300003</td>\n",
              "      <td>73.580002</td>\n",
              "      <td>44.404160</td>\n",
              "      <td>13695900.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.990894</td>\n",
              "      <td>2.660557</td>\n",
              "      <td>100.0</td>\n",
              "      <td>66.666667</td>\n",
              "      <td>100.0</td>\n",
              "      <td>44.404160</td>\n",
              "      <td>44.404160</td>\n",
              "      <td>39.189999</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>DIS</td>\n",
              "      <td>22.760000</td>\n",
              "      <td>24.030001</td>\n",
              "      <td>22.500000</td>\n",
              "      <td>20.597496</td>\n",
              "      <td>9796600.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.990894</td>\n",
              "      <td>2.660557</td>\n",
              "      <td>100.0</td>\n",
              "      <td>66.666667</td>\n",
              "      <td>100.0</td>\n",
              "      <td>20.597496</td>\n",
              "      <td>20.597496</td>\n",
              "      <td>39.189999</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>GS</td>\n",
              "      <td>84.019997</td>\n",
              "      <td>87.620003</td>\n",
              "      <td>82.190002</td>\n",
              "      <td>70.735085</td>\n",
              "      <td>14088500.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.990894</td>\n",
              "      <td>2.660557</td>\n",
              "      <td>100.0</td>\n",
              "      <td>66.666667</td>\n",
              "      <td>100.0</td>\n",
              "      <td>70.735085</td>\n",
              "      <td>70.735085</td>\n",
              "      <td>39.189999</td>\n",
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              "\n",
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              "          const element = document.querySelector('#df-7b04d272-8549-4e1e-8e70-bf50e3368f2c');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ],
      "source": [
        "processed_full.sort_values(['date','tic'],ignore_index=True).head(10)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-QsYaY0Dh1iw"
      },
      "source": [
        "<a id='4'></a>\n",
        "# Part 5. Build A Market Environment in OpenAI Gym-style\n",
        "The training process involves observing stock price change, taking an action and reward's calculation. By interacting with the market environment, the agent will eventually derive a trading strategy that may maximize (expected) rewards.\n",
        "\n",
        "Our market environment, based on OpenAI Gym, simulates stock markets with historical market data."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5TOhcryx44bb"
      },
      "source": [
        "## Data Split\n",
        "We split the data into training set and testing set as follows:\n",
        "\n",
        "Training data period: 2009-01-01 to 2020-07-01\n",
        "\n",
        "Trading data period: 2020-07-01 to 2021-10-31\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 68,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W0qaVGjLtgbI",
        "outputId": "ac9f2699-65c6-418f-cca4-f0f166973f65"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "83897\n",
            "9744\n"
          ]
        }
      ],
      "source": [
        "train = data_split(processed_full, TRAIN_START_DATE,TRAIN_END_DATE)\n",
        "trade = data_split(processed_full, TRADE_START_DATE,TRADE_END_DATE)\n",
        "print(len(train))\n",
        "print(len(trade))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 69,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "id": "p52zNCOhTtLR",
        "outputId": "14568787-e92d-4d9b-dbce-9e11b3fb1390"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            date  tic        open        high         low       close  \\\n",
              "2892  2020-06-30  UNH  288.570007  296.450012  287.660004  286.754181   \n",
              "2892  2020-06-30    V  191.490005  193.750000  190.160004  190.399994   \n",
              "2892  2020-06-30   VZ   54.919998   55.290001   54.360001   49.750824   \n",
              "2892  2020-06-30  WBA   42.119999   42.580002   41.759998   38.577274   \n",
              "2892  2020-06-30  WMT  119.220001  120.129997  118.540001  115.618233   \n",
              "\n",
              "          volume  day      macd     boll_ub     boll_lb     rsi_30     cci_30  \\\n",
              "2892   2932900.0  1.0 -0.019409  302.845852  270.287372  52.413052 -25.866274   \n",
              "2892   9040100.0  1.0  1.046932  198.399113  184.714235  53.021030 -51.567968   \n",
              "2892  17414800.0  1.0 -0.431679   53.248500   48.123875  48.097045 -51.065541   \n",
              "2892   4782100.0  1.0 -0.082999   42.108876   36.058572  48.830178 -14.542157   \n",
              "2892   6836400.0  1.0 -0.882725  118.955683  113.018228  48.159687 -69.952600   \n",
              "\n",
              "         dx_30  close_30_sma  close_60_sma    vix  turbulence  \n",
              "2892  1.846804    286.997200    280.002895  30.43    12.91868  \n",
              "2892  2.013358    191.146476    181.356466  30.43    12.91868  \n",
              "2892  8.508886     50.378309     50.825242  30.43    12.91868  \n",
              "2892  1.500723     38.675564     38.477854  30.43    12.91868  \n",
              "2892  3.847271    117.276858    119.204112  30.43    12.91868  "
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              "      <th>turbulence</th>\n",
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              "      <td>2020-06-30</td>\n",
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              "      <td>2020-06-30</td>\n",
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              "      <td>191.490005</td>\n",
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              "      <td>2020-06-30</td>\n",
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              "      <td>42.119999</td>\n",
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              "      <th>2892</th>\n",
              "      <td>2020-06-30</td>\n",
              "      <td>WMT</td>\n",
              "      <td>119.220001</td>\n",
              "      <td>120.129997</td>\n",
              "      <td>118.540001</td>\n",
              "      <td>115.618233</td>\n",
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              "      <td>1.0</td>\n",
              "      <td>-0.882725</td>\n",
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              "      <td>113.018228</td>\n",
              "      <td>48.159687</td>\n",
              "      <td>-69.952600</td>\n",
              "      <td>3.847271</td>\n",
              "      <td>117.276858</td>\n",
              "      <td>119.204112</td>\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-04ef9228-b79a-4871-a793-32dcfc64f80b');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ],
      "source": [
        "train.tail()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 70,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "id": "k9zU9YaTTvFq",
        "outputId": "80e64947-4c5c-428f-a98c-6b344d816783"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         date   tic        open        high         low       close  \\\n",
              "0  2020-07-01  AAPL   91.279999   91.839996   90.977501   89.779900   \n",
              "0  2020-07-01  AMGN  235.520004  256.230011  232.580002  238.313568   \n",
              "0  2020-07-01   AXP   95.250000   96.959999   93.639999   91.743042   \n",
              "0  2020-07-01    BA  185.880005  190.610001  180.039993  180.320007   \n",
              "0  2020-07-01   CAT  129.380005  129.399994  125.879997  119.817108   \n",
              "\n",
              "        volume  day      macd     boll_ub     boll_lb     rsi_30      cci_30  \\\n",
              "0  110737200.0  2.0  3.010423   92.570734   80.068735  62.807150  107.494072   \n",
              "0    6575800.0  2.0  3.608528  230.616475  198.678621  61.279625  271.208685   \n",
              "0    3301000.0  2.0 -0.387710  110.012242   87.432133  48.504816  -66.321057   \n",
              "0   49036700.0  2.0  5.443193  220.721139  160.932863  50.925771   24.220608   \n",
              "0    2807800.0  2.0  1.263826  129.720782  112.569084  52.865414   35.633500   \n",
              "\n",
              "       dx_30  close_30_sma  close_60_sma        vix  turbulence  \n",
              "0  29.730532     83.817340     77.609739  28.620001   53.068172  \n",
              "0  46.806139    213.212113    214.276893  28.620001   53.068172  \n",
              "0   3.142448     96.882071     90.357377  28.620001   53.068172  \n",
              "0  15.932920    176.472335    155.614168  28.620001   53.068172  \n",
              "0  14.457404    118.586882    112.860602  28.620001   53.068172  "
            ],
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              "    <div class=\"colab-df-container\">\n",
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              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>tic</th>\n",
              "      <th>open</th>\n",
              "      <th>high</th>\n",
              "      <th>low</th>\n",
              "      <th>close</th>\n",
              "      <th>volume</th>\n",
              "      <th>day</th>\n",
              "      <th>macd</th>\n",
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              "      <th>rsi_30</th>\n",
              "      <th>cci_30</th>\n",
              "      <th>dx_30</th>\n",
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              "      <th>vix</th>\n",
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              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>2020-07-01</td>\n",
              "      <td>AAPL</td>\n",
              "      <td>91.279999</td>\n",
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              "      <th>0</th>\n",
              "      <td>2020-07-01</td>\n",
              "      <td>AXP</td>\n",
              "      <td>95.250000</td>\n",
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              "      <td>110.012242</td>\n",
              "      <td>87.432133</td>\n",
              "      <td>48.504816</td>\n",
              "      <td>-66.321057</td>\n",
              "      <td>3.142448</td>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2020-07-01</td>\n",
              "      <td>BA</td>\n",
              "      <td>185.880005</td>\n",
              "      <td>190.610001</td>\n",
              "      <td>180.039993</td>\n",
              "      <td>180.320007</td>\n",
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              "      <th>0</th>\n",
              "      <td>2020-07-01</td>\n",
              "      <td>CAT</td>\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ],
      "source": [
        "trade.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 71,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zYN573SOHhxG",
        "outputId": "460cd177-65d2-46f8-eb40-eaeda045c4bb"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['macd',\n",
              " 'boll_ub',\n",
              " 'boll_lb',\n",
              " 'rsi_30',\n",
              " 'cci_30',\n",
              " 'dx_30',\n",
              " 'close_30_sma',\n",
              " 'close_60_sma']"
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ],
      "source": [
        "INDICATORS"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 72,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q2zqII8rMIqn",
        "outputId": "d2e2b678-04f8-44be-96ea-3523903780eb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Stock Dimension: 29, State Space: 291\n"
          ]
        }
      ],
      "source": [
        "stock_dimension = len(train.tic.unique())\n",
        "state_space = 1 + 2*stock_dimension + len(INDICATORS)*stock_dimension\n",
        "print(f\"Stock Dimension: {stock_dimension}, State Space: {state_space}\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 74,
      "metadata": {
        "id": "AWyp84Ltto19"
      },
      "outputs": [],
      "source": [
        "buy_cost_list = sell_cost_list = [0.001] * stock_dimension\n",
        "num_stock_shares = [0] * stock_dimension\n",
        "\n",
        "env_kwargs = {\n",
        "    \"hmax\": 100,\n",
        "    \"initial_amount\": 1000000,\n",
        "    \"num_stock_shares\": num_stock_shares,\n",
        "    \"buy_cost_pct\": buy_cost_list,\n",
        "    \"sell_cost_pct\": sell_cost_list,\n",
        "    \"state_space\": state_space,\n",
        "    \"stock_dim\": stock_dimension,\n",
        "    \"tech_indicator_list\": INDICATORS,\n",
        "    \"action_space\": stock_dimension,\n",
        "    \"reward_scaling\": 1e-4\n",
        "}\n",
        "\n",
        "\n",
        "e_train_gym = StockTradingEnv(df = train, **env_kwargs)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "64EoqOrQjiVf"
      },
      "source": [
        "## Environment for Training\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 75,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xwSvvPjutpqS",
        "outputId": "3645668e-ba1f-4610-9789-bc212eb9b776"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'stable_baselines3.common.vec_env.dummy_vec_env.DummyVecEnv'>\n"
          ]
        }
      ],
      "source": [
        "env_train, _ = e_train_gym.get_sb_env()\n",
        "print(type(env_train))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HMNR5nHjh1iz"
      },
      "source": [
        "<a id='5'></a>\n",
        "# Part 6: Train DRL Agents\n",
        "* The DRL algorithms are from **Stable Baselines 3**. Users are also encouraged to try **ElegantRL** and **Ray RLlib**.\n",
        "* FinRL includes fine-tuned standard DRL algorithms, such as DQN, DDPG, Multi-Agent DDPG, PPO, SAC, A2C and TD3. We also allow users to\n",
        "design their own DRL algorithms by adapting these DRL algorithms."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 76,
      "metadata": {
        "id": "364PsqckttcQ"
      },
      "outputs": [],
      "source": [
        "agent = DRLAgent(env = env_train)\n",
        "\n",
        "if_using_a2c = False\n",
        "if_using_ddpg = False\n",
        "if_using_ppo = False\n",
        "if_using_td3 = False\n",
        "if_using_sac = True\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YDmqOyF9h1iz"
      },
      "source": [
        "### Agent Training: 5 algorithms (A2C, DDPG, PPO, TD3, SAC)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uijiWgkuh1jB"
      },
      "source": [
        "### Agent 1: A2C\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 77,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GUCnkn-HIbmj",
        "outputId": "a2bdd15c-c366-4f89-e6de-6b0572c2ad23"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'n_steps': 5, 'ent_coef': 0.01, 'learning_rate': 0.0007}\n",
            "Using cpu device\n"
          ]
        }
      ],
      "source": [
        "agent = DRLAgent(env = env_train)\n",
        "model_a2c = agent.get_model(\"a2c\")\n",
        "\n",
        "if if_using_a2c:\n",
        "  # set up logger\n",
        "  tmp_path = RESULTS_DIR + '/a2c'\n",
        "  new_logger_a2c = configure(tmp_path, [\"stdout\", \"csv\", \"tensorboard\"])\n",
        "  # Set new logger\n",
        "  model_a2c.set_logger(new_logger_a2c)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 78,
      "metadata": {
        "id": "0GVpkWGqH4-D"
      },
      "outputs": [],
      "source": [
        "trained_a2c = agent.train_model(model=model_a2c, \n",
        "                             tb_log_name='a2c',\n",
        "                             total_timesteps=50000) if if_using_a2c else None"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MRiOtrywfAo1"
      },
      "source": [
        "### Agent 2: DDPG"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 79,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M2YadjfnLwgt",
        "outputId": "211f6ae4-c91e-41df-8906-df246e090f75"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'batch_size': 128, 'buffer_size': 50000, 'learning_rate': 0.001}\n",
            "Using cpu device\n"
          ]
        }
      ],
      "source": [
        "agent = DRLAgent(env = env_train)\n",
        "model_ddpg = agent.get_model(\"ddpg\")\n",
        "\n",
        "if if_using_ddpg:\n",
        "  # set up logger\n",
        "  tmp_path = RESULTS_DIR + '/ddpg'\n",
        "  new_logger_ddpg = configure(tmp_path, [\"stdout\", \"csv\", \"tensorboard\"])\n",
        "  # Set new logger\n",
        "  model_ddpg.set_logger(new_logger_ddpg)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 80,
      "metadata": {
        "id": "tCDa78rqfO_a"
      },
      "outputs": [],
      "source": [
        "trained_ddpg = agent.train_model(model=model_ddpg, \n",
        "                             tb_log_name='ddpg',\n",
        "                             total_timesteps=50000) if if_using_ddpg else None"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_gDkU-j-fCmZ"
      },
      "source": [
        "### Agent 3: PPO"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 81,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y5D5PFUhMzSV",
        "outputId": "3405d353-0a08-4855-ca59-98098968dd11"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'n_steps': 2048, 'ent_coef': 0.01, 'learning_rate': 0.00025, 'batch_size': 128}\n",
            "Using cpu device\n"
          ]
        }
      ],
      "source": [
        "agent = DRLAgent(env = env_train)\n",
        "PPO_PARAMS = {\n",
        "    \"n_steps\": 2048,\n",
        "    \"ent_coef\": 0.01,\n",
        "    \"learning_rate\": 0.00025,\n",
        "    \"batch_size\": 128,\n",
        "}\n",
        "model_ppo = agent.get_model(\"ppo\",model_kwargs = PPO_PARAMS)\n",
        "\n",
        "if if_using_ppo:\n",
        "  # set up logger\n",
        "  tmp_path = RESULTS_DIR + '/ppo'\n",
        "  new_logger_ppo = configure(tmp_path, [\"stdout\", \"csv\", \"tensorboard\"])\n",
        "  # Set new logger\n",
        "  model_ppo.set_logger(new_logger_ppo)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 82,
      "metadata": {
        "id": "Gt8eIQKYM4G3"
      },
      "outputs": [],
      "source": [
        "trained_ppo = agent.train_model(model=model_ppo, \n",
        "                             tb_log_name='ppo',\n",
        "                             total_timesteps=50000) if if_using_ppo else None"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3Zpv4S0-fDBv"
      },
      "source": [
        "### Agent 4: TD3"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 83,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JSAHhV4Xc-bh",
        "outputId": "802fef04-8df5-4df2-f710-e5b08941842f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'batch_size': 100, 'buffer_size': 1000000, 'learning_rate': 0.001}\n",
            "Using cpu device\n"
          ]
        }
      ],
      "source": [
        "agent = DRLAgent(env = env_train)\n",
        "TD3_PARAMS = {\"batch_size\": 100, \n",
        "              \"buffer_size\": 1000000, \n",
        "              \"learning_rate\": 0.001}\n",
        "\n",
        "model_td3 = agent.get_model(\"td3\",model_kwargs = TD3_PARAMS)\n",
        "\n",
        "if if_using_td3:\n",
        "  # set up logger\n",
        "  tmp_path = RESULTS_DIR + '/td3'\n",
        "  new_logger_td3 = configure(tmp_path, [\"stdout\", \"csv\", \"tensorboard\"])\n",
        "  # Set new logger\n",
        "  model_td3.set_logger(new_logger_td3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 84,
      "metadata": {
        "id": "OSRxNYAxdKpU"
      },
      "outputs": [],
      "source": [
        "trained_td3 = agent.train_model(model=model_td3, \n",
        "                             tb_log_name='td3',\n",
        "                             total_timesteps=30000) if if_using_td3 else None"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Dr49PotrfG01"
      },
      "source": [
        "### Agent 5: SAC"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 103,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xwOhVjqRkCdM",
        "outputId": "3c848ff6-587e-43d1-b50d-a7d521a14b4f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'batch_size': 128, 'buffer_size': 100000, 'learning_rate': 0.0001, 'learning_starts': 100, 'ent_coef': 'auto_0.1'}\n",
            "Using cpu device\n",
            "Logging to results/sac\n"
          ]
        }
      ],
      "source": [
        "agent = DRLAgent(env = env_train)\n",
        "SAC_PARAMS = {\n",
        "    \"batch_size\": 128,\n",
        "    \"buffer_size\": 100000,\n",
        "    \"learning_rate\": 0.0001,\n",
        "    \"learning_starts\": 100,\n",
        "    \"ent_coef\": \"auto_0.1\",\n",
        "}\n",
        "\n",
        "model_sac = agent.get_model(\"sac\",model_kwargs = SAC_PARAMS)\n",
        "\n",
        "if if_using_sac:\n",
        "  # set up logger\n",
        "  tmp_path = RESULTS_DIR + '/sac'\n",
        "  new_logger_sac = configure(tmp_path, [\"stdout\", \"csv\", \"tensorboard\"])\n",
        "  # Set new logger\n",
        "  model_sac.set_logger(new_logger_sac)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 121,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K8RSdKCckJyH",
        "outputId": "4d65a11b-32b0-4c3f-b0da-e1b59fc6e194"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "day: 2892, episode: 30\n",
            "begin_total_asset: 1000000.00\n",
            "end_total_asset: 2919458.30\n",
            "total_reward: 1919458.30\n",
            "total_cost: 3183.10\n",
            "total_trades: 44149\n",
            "Sharpe: 0.565\n",
            "=================================\n",
            "-----------------------------------\n",
            "| time/              |            |\n",
            "|    episodes        | 4          |\n",
            "|    fps             | 23         |\n",
            "|    time_elapsed    | 493        |\n",
            "|    total_timesteps | 11572      |\n",
            "| train/             |            |\n",
            "|    actor_loss      | 89.1       |\n",
            "|    critic_loss     | 4.98       |\n",
            "|    ent_coef        | 0.00469    |\n",
            "|    ent_coef_loss   | -125       |\n",
            "|    learning_rate   | 0.0001     |\n",
            "|    n_updates       | 46371      |\n",
            "|    reward          | -1.9832951 |\n",
            "-----------------------------------\n",
            "-----------------------------------\n",
            "| time/              |            |\n",
            "|    episodes        | 12         |\n",
            "|    fps             | 23         |\n",
            "|    time_elapsed    | 1458       |\n",
            "|    total_timesteps | 34716      |\n",
            "| train/             |            |\n",
            "|    actor_loss      | 36.5       |\n",
            "|    critic_loss     | 3.4        |\n",
            "|    ent_coef        | 0.00129    |\n",
            "|    ent_coef_loss   | 2.35       |\n",
            "|    learning_rate   | 0.0001     |\n",
            "|    n_updates       | 69515      |\n",
            "|    reward          | -0.9170374 |\n",
            "-----------------------------------\n",
            "day: 2892, episode: 40\n",
            "begin_total_asset: 1000000.00\n",
            "end_total_asset: 3095382.62\n",
            "total_reward: 2095382.62\n",
            "total_cost: 1615.56\n",
            "total_trades: 42233\n",
            "Sharpe: 0.576\n",
            "=================================\n"
          ]
        }
      ],
      "source": [
        "trained_sac = agent.train_model(model=model_sac, \n",
        "                             tb_log_name='sac',\n",
        "                             total_timesteps=40000) if if_using_sac else None"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f2wZgkQXh1jE"
      },
      "source": [
        "## In-sample Performance\n",
        "\n",
        "Assume that the initial capital is $1,000,000."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bEv5KGC8h1jE"
      },
      "source": [
        "### Set turbulence threshold\n",
        "Set the turbulence threshold to be greater than the maximum of insample turbulence data. If current turbulence index is greater than the threshold, then we assume that the current market is volatile"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 122,
      "metadata": {
        "id": "efwBi84ch1jE"
      },
      "outputs": [],
      "source": [
        "data_risk_indicator = processed_full[(processed_full.date<TRAIN_END_DATE) & (processed_full.date>=TRAIN_START_DATE)]\n",
        "insample_risk_indicator = data_risk_indicator.drop_duplicates(subset=['date'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 123,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VHZMBpSqh1jG",
        "outputId": "73edde3b-1414-4a0d-f876-6460de2ff8e1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    2893.000000\n",
              "mean       18.824245\n",
              "std         8.489311\n",
              "min         9.140000\n",
              "25%        13.330000\n",
              "50%        16.139999\n",
              "75%        21.309999\n",
              "max        82.690002\n",
              "Name: vix, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 123
        }
      ],
      "source": [
        "insample_risk_indicator.vix.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 124,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BDkszkMloRWT",
        "outputId": "c5df1f2f-ed52-4107-a3af-a358767ae155"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "57.40400183105453"
            ]
          },
          "metadata": {},
          "execution_count": 124
        }
      ],
      "source": [
        "insample_risk_indicator.vix.quantile(0.996)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 125,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AL7hs7svnNWT",
        "outputId": "ab5a1015-3f83-41b7-ba65-1487f3188588"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    2893.000000\n",
              "mean       34.567961\n",
              "std        43.790804\n",
              "min         0.000000\n",
              "25%        14.962878\n",
              "50%        24.123915\n",
              "75%        39.162711\n",
              "max       652.503481\n",
              "Name: turbulence, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 125
        }
      ],
      "source": [
        "insample_risk_indicator.turbulence.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 126,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N78hfHckoqJ9",
        "outputId": "2c8a54d6-022f-4716-dc6d-22a450f5b9ab"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "276.4524426976551"
            ]
          },
          "metadata": {},
          "execution_count": 126
        }
      ],
      "source": [
        "insample_risk_indicator.turbulence.quantile(0.996)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U5mmgQF_h1jQ"
      },
      "source": [
        "### Trading (Out-of-sample Performance)\n",
        "\n",
        "We update periodically in order to take full advantage of the data, e.g., retrain quarterly, monthly or weekly. We also tune the parameters along the way, in this notebook we use the in-sample data from 2009-01 to 2020-07 to tune the parameters once, so there is some alpha decay here as the length of trade date extends. \n",
        "\n",
        "Numerous hyperparameters – e.g. the learning rate, the total number of samples to train on – influence the learning process and are usually determined by testing some variations."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 127,
      "metadata": {
        "id": "cIqoV0GSI52v"
      },
      "outputs": [],
      "source": [
        "e_trade_gym = StockTradingEnv(df = trade, turbulence_threshold = 70,risk_indicator_col='vix', **env_kwargs)\n",
        "# env_trade, obs_trade = e_trade_gym.get_sb_env()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 128,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "id": "W_XNgGsBMeVw",
        "outputId": "56d2af8e-7ee7-4248-ce7a-3132625c61b1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         date   tic        open        high         low       close  \\\n",
              "0  2020-07-01  AAPL   91.279999   91.839996   90.977501   89.779900   \n",
              "0  2020-07-01  AMGN  235.520004  256.230011  232.580002  238.313568   \n",
              "0  2020-07-01   AXP   95.250000   96.959999   93.639999   91.743042   \n",
              "0  2020-07-01    BA  185.880005  190.610001  180.039993  180.320007   \n",
              "0  2020-07-01   CAT  129.380005  129.399994  125.879997  119.817108   \n",
              "\n",
              "        volume  day      macd     boll_ub     boll_lb     rsi_30      cci_30  \\\n",
              "0  110737200.0  2.0  3.010423   92.570734   80.068735  62.807150  107.494072   \n",
              "0    6575800.0  2.0  3.608528  230.616475  198.678621  61.279625  271.208685   \n",
              "0    3301000.0  2.0 -0.387710  110.012242   87.432133  48.504816  -66.321057   \n",
              "0   49036700.0  2.0  5.443193  220.721139  160.932863  50.925771   24.220608   \n",
              "0    2807800.0  2.0  1.263826  129.720782  112.569084  52.865414   35.633500   \n",
              "\n",
              "       dx_30  close_30_sma  close_60_sma        vix  turbulence  \n",
              "0  29.730532     83.817340     77.609739  28.620001   53.068172  \n",
              "0  46.806139    213.212113    214.276893  28.620001   53.068172  \n",
              "0   3.142448     96.882071     90.357377  28.620001   53.068172  \n",
              "0  15.932920    176.472335    155.614168  28.620001   53.068172  \n",
              "0  14.457404    118.586882    112.860602  28.620001   53.068172  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b6703519-151f-40fb-bfc5-3fd4ec4249c0\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>tic</th>\n",
              "      <th>open</th>\n",
              "      <th>high</th>\n",
              "      <th>low</th>\n",
              "      <th>close</th>\n",
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              "      <th>cci_30</th>\n",
              "      <th>dx_30</th>\n",
              "      <th>close_30_sma</th>\n",
              "      <th>close_60_sma</th>\n",
              "      <th>vix</th>\n",
              "      <th>turbulence</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2020-07-01</td>\n",
              "      <td>AAPL</td>\n",
              "      <td>91.279999</td>\n",
              "      <td>91.839996</td>\n",
              "      <td>90.977501</td>\n",
              "      <td>89.779900</td>\n",
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              "      <td>2.0</td>\n",
              "      <td>3.010423</td>\n",
              "      <td>92.570734</td>\n",
              "      <td>80.068735</td>\n",
              "      <td>62.807150</td>\n",
              "      <td>107.494072</td>\n",
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              "      <td>83.817340</td>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2020-07-01</td>\n",
              "      <td>AMGN</td>\n",
              "      <td>235.520004</td>\n",
              "      <td>256.230011</td>\n",
              "      <td>232.580002</td>\n",
              "      <td>238.313568</td>\n",
              "      <td>6575800.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.608528</td>\n",
              "      <td>230.616475</td>\n",
              "      <td>198.678621</td>\n",
              "      <td>61.279625</td>\n",
              "      <td>271.208685</td>\n",
              "      <td>46.806139</td>\n",
              "      <td>213.212113</td>\n",
              "      <td>214.276893</td>\n",
              "      <td>28.620001</td>\n",
              "      <td>53.068172</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2020-07-01</td>\n",
              "      <td>AXP</td>\n",
              "      <td>95.250000</td>\n",
              "      <td>96.959999</td>\n",
              "      <td>93.639999</td>\n",
              "      <td>91.743042</td>\n",
              "      <td>3301000.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>-0.387710</td>\n",
              "      <td>110.012242</td>\n",
              "      <td>87.432133</td>\n",
              "      <td>48.504816</td>\n",
              "      <td>-66.321057</td>\n",
              "      <td>3.142448</td>\n",
              "      <td>96.882071</td>\n",
              "      <td>90.357377</td>\n",
              "      <td>28.620001</td>\n",
              "      <td>53.068172</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2020-07-01</td>\n",
              "      <td>BA</td>\n",
              "      <td>185.880005</td>\n",
              "      <td>190.610001</td>\n",
              "      <td>180.039993</td>\n",
              "      <td>180.320007</td>\n",
              "      <td>49036700.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5.443193</td>\n",
              "      <td>220.721139</td>\n",
              "      <td>160.932863</td>\n",
              "      <td>50.925771</td>\n",
              "      <td>24.220608</td>\n",
              "      <td>15.932920</td>\n",
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              "      <td>2020-07-01</td>\n",
              "      <td>CAT</td>\n",
              "      <td>129.380005</td>\n",
              "      <td>129.399994</td>\n",
              "      <td>125.879997</td>\n",
              "      <td>119.817108</td>\n",
              "      <td>2807800.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.263826</td>\n",
              "      <td>129.720782</td>\n",
              "      <td>112.569084</td>\n",
              "      <td>52.865414</td>\n",
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              "      <td>14.457404</td>\n",
              "      <td>118.586882</td>\n",
              "      <td>112.860602</td>\n",
              "      <td>28.620001</td>\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 128
        }
      ],
      "source": [
        "trade.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 129,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eLOnL5eYh1jR",
        "outputId": "d31d2209-05ef-41df-fd8f-ee081d427949"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "hit end!\n"
          ]
        }
      ],
      "source": [
        "trained_moedl = trained_sac\n",
        "df_account_value, df_actions = DRLAgent.DRL_prediction(\n",
        "    model=trained_moedl, \n",
        "    environment = e_trade_gym)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 130,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "ERxw3KqLkcP4",
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      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(336, 2)"
            ]
          },
          "metadata": {},
          "execution_count": 130
        }
      ],
      "source": [
        "df_account_value.shape"
      ]
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      "outputs": [
        {
          "output_type": "execute_result",
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            "text/plain": [
              "           date  account_value\n",
              "331  2021-10-22   1.489957e+06\n",
              "332  2021-10-25   1.490605e+06\n",
              "333  2021-10-26   1.486047e+06\n",
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              "            AAPL  AMGN  AXP  BA  CAT  CRM  CSCO  CVX  DIS  GS  ...  MRK  MSFT  \\\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-6f4e1bb8-db3b-41d6-b92c-2d54042399ce button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-6f4e1bb8-db3b-41d6-b92c-2d54042399ce');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 132
        }
      ],
      "source": [
        "df_actions.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W6vvNSC6h1jZ"
      },
      "source": [
        "<a id='6'></a>\n",
        "# Part 7: Backtesting Results\n",
        "Backtesting plays a key role in evaluating the performance of a trading strategy. Automated backtesting tool is preferred because it reduces the human error. We usually use the Quantopian pyfolio package to backtest our trading strategies. It is easy to use and consists of various individual plots that provide a comprehensive image of the performance of a trading strategy."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Lr2zX7ZxNyFQ"
      },
      "source": [
        "<a id='6.1'></a>\n",
        "## 7.1 BackTestStats\n",
        "pass in df_account_value, this information is stored in env class\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 133,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Nzkr9yv-AdV_",
        "outputId": "216032a2-5566-4dba-db83-7d7d880b1e88"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "==============Get Backtest Results===========\n",
            "Annual return          0.342443\n",
            "Cumulative returns     0.480906\n",
            "Annual volatility      0.173815\n",
            "Sharpe ratio           1.786679\n",
            "Calmar ratio           4.234423\n",
            "Stability              0.890528\n",
            "Max drawdown          -0.080871\n",
            "Omega ratio            1.341596\n",
            "Sortino ratio          2.868776\n",
            "Skew                        NaN\n",
            "Kurtosis                    NaN\n",
            "Tail ratio             1.356559\n",
            "Daily value at risk   -0.020666\n",
            "dtype: float64\n"
          ]
        }
      ],
      "source": [
        "print(\"==============Get Backtest Results===========\")\n",
        "now = datetime.datetime.now().strftime('%Y%m%d-%Hh%M')\n",
        "\n",
        "perf_stats_all = backtest_stats(account_value=df_account_value)\n",
        "perf_stats_all = pd.DataFrame(perf_stats_all)\n",
        "perf_stats_all.to_csv(\"./\"+RESULTS_DIR+\"/perf_stats_all_\"+now+'.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 134,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QkV-LB66iwhD",
        "outputId": "7231e720-1598-45ac-ff49-9f8d29f296fa"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "==============Get Baseline Stats===========\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (335, 8)\n",
            "Annual return          0.273520\n",
            "Cumulative returns     0.379084\n",
            "Annual volatility      0.139248\n",
            "Sharpe ratio           1.811893\n",
            "Calmar ratio           3.062662\n",
            "Stability              0.918651\n",
            "Max drawdown          -0.089308\n",
            "Omega ratio            1.351851\n",
            "Sortino ratio          2.684720\n",
            "Skew                        NaN\n",
            "Kurtosis                    NaN\n",
            "Tail ratio             1.051856\n",
            "Daily value at risk   -0.016542\n",
            "dtype: float64\n"
          ]
        }
      ],
      "source": [
        "#baseline stats\n",
        "print(\"==============Get Baseline Stats===========\")\n",
        "baseline_df = get_baseline(\n",
        "        ticker=\"^DJI\", \n",
        "        start = df_account_value.loc[0,'date'],\n",
        "        end = df_account_value.loc[len(df_account_value)-1,'date'])\n",
        "\n",
        "stats = backtest_stats(baseline_df, value_col_name = 'close')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 135,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "qg1kvfemrrQH",
        "outputId": "fc34eb83-c616-42b3-98c7-8f8318644061"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'2020-07-01'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 135
        }
      ],
      "source": [
        "df_account_value.loc[0,'date']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 136,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "tt1bzL5OrsTa",
        "outputId": "59ed1fd0-5a77-40e2-8def-229148fec9db"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'2021-10-28'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 136
        }
      ],
      "source": [
        "df_account_value.loc[len(df_account_value)-1,'date']"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9U6Suru3h1jc"
      },
      "source": [
        "<a id='6.2'></a>\n",
        "## 7.2 BackTestPlot"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 137,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "lKRGftSS7pNM",
        "outputId": "db260938-69a7-4417-fed7-25679d43b0f6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "==============Compare to DJIA===========\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (335, 8)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\"><th>Start date</th><td colspan=2>2020-07-01</td></tr>\n",
              "    <tr style=\"text-align: right;\"><th>End date</th><td colspan=2>2021-10-28</td></tr>\n",
              "    <tr style=\"text-align: right;\"><th>Total months</th><td colspan=2>16</td></tr>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Backtest</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Annual return</th>\n",
              "      <td>34.244%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Cumulative returns</th>\n",
              "      <td>48.091%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Annual volatility</th>\n",
              "      <td>17.381%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sharpe ratio</th>\n",
              "      <td>1.79</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Calmar ratio</th>\n",
              "      <td>4.23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Stability</th>\n",
              "      <td>0.89</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Max drawdown</th>\n",
              "      <td>-8.087%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Omega ratio</th>\n",
              "      <td>1.34</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sortino ratio</th>\n",
              "      <td>2.87</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Skew</th>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Kurtosis</th>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tail ratio</th>\n",
              "      <td>1.36</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Daily value at risk</th>\n",
              "      <td>-2.067%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Alpha</th>\n",
              "      <td>0.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Beta</th>\n",
              "      <td>1.13</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>Worst drawdown periods</th>\n",
              "      <th>Net drawdown in %</th>\n",
              "      <th>Peak date</th>\n",
              "      <th>Valley date</th>\n",
              "      <th>Recovery date</th>\n",
              "      <th>Duration</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>8.09</td>\n",
              "      <td>2020-10-12</td>\n",
              "      <td>2020-10-28</td>\n",
              "      <td>2020-11-09</td>\n",
              "      <td>21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>7.52</td>\n",
              "      <td>2020-09-02</td>\n",
              "      <td>2020-09-23</td>\n",
              "      <td>2020-10-12</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>7.10</td>\n",
              "      <td>2021-08-11</td>\n",
              "      <td>2021-09-21</td>\n",
              "      <td>NaT</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>6.57</td>\n",
              "      <td>2021-06-04</td>\n",
              "      <td>2021-06-18</td>\n",
              "      <td>2021-08-10</td>\n",
              "      <td>48</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>6.53</td>\n",
              "      <td>2021-01-14</td>\n",
              "      <td>2021-01-29</td>\n",
              "      <td>2021-02-16</td>\n",
              "      <td>24</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>Stress Events</th>\n",
              "      <th>mean</th>\n",
              "      <th>min</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>New Normal</th>\n",
              "      <td>0.12%</td>\n",
              "      <td>-3.25%</td>\n",
              "      <td>5.70%</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1008x5184 with 13 Axes>"
            ],
            "image/png": 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77yXs9Xbs2IHi4lBVrbFZv349nnjiibhci4iIiMjlccHhcgAAdEKHqpwq3LrwVhh1RgDAoGcQ/3Pof/DK0Vfg8riSuVRKESkdxEkpW6SUR6WUXinlWQAPAbh16GH/8LIs1VOyAQQviAZ+DqAm4M/a+K96/Pz85z+HzWZDd3c3vvKVr+Dv/u7vIKVM9rKIiIiIxpW6lDLTnAkhBJaWLMX9F9+PAkuB8tjehr347b7f8vsSJVxKB3FBSAACAKSUXfA1SFmienwpgMNBnyhlt5SyTv0HQEOC1zsudDod7rrrLrS1taGtrQ0ff/wx1qxZg5ycHJSUlOCb3/wmXK7h3zodO3YM11xzDfLz81FUVIR//ud/Dnrd733ve1i+fDnq6+uxYcMGtLa2wmq1wmq14syZM/B6vfjJT36CmTNnIj8/H7feeiva2toAAAMDA/jiF7+I/Px85OTkYMWKFWhubsYjjzyCXbt24dvf/jasViu+9rWvjctnRERERFOXupQy0zxclFWSWYKvr/46FhUvUo6d6z6Htv62cV0fpZ4pGcQJIQxCiDQAegB6IUSaEMIY5LwrhBBVwqcCwL8C+IvqlKcBfFcIUSCEqALwIIDfjsNbmFDcbjeeeeYZzJw5EwUFBdDr9Xj88cfR3t6O999/H9u2bcOTTz4JAOjr68NVV12FK6+8Eg0NDairq8ONN96ouZ6UEt/4xjewY8cOvPPOO6iqqsLrr7+OoqIi2Gw22Gw2TJ8+Hb/4xS/w8ssv4+2330ZTUxOmTZuG++67DwDwzDPPoLu7G+fPn0dHRwf++7//GxaLBT/60Y+wdu1aJYv41FNPjfvnRURERFOLJhNn0u6sMRvMuGPRHajKqVKOcewAJdpUHTHwXQDfU92/G8AzAO4VQtgAbJBS7oJvD9xzAHLh62T5FwCPqJ73fQAFAE4DcAH4VaLGCzzy5iORT4qTH332R1Gd9+CDD+Lhhx+Gw+GATqfD888/D51Oh2XLlinnTJ8+Hffddx927tyJBx54AH/961+Rl5eHf/qnf1LOWbNmjXLb7Xbj7rvvRnd3N7Zt24b09NATIJ544gn8/Oc/R2VlJQDg+9//PqZNm4aBgQEYjUZ0dHTg5MmTWLJkiWZNRERERPEUWE4ZSAiBfEs+6rvrAQA9Az0jziGKpykZxEkpNwPYHOIxq+r24wAeD3OdQQD3D/1JOY8//jg2bdoEr9eL3bt344YbbkBNTQ3S09Px4IMPYt++fbDb7XC73bj44osBAOfOncOMGTNCXvPMmTM4fPgwdu3aFTaAA4D6+nrcfvvt0OmGE8YmkwmNjY344he/iIaGBtx5553o7OzEnXfeicceewxmszk+b56IiIhoSKQgDgBy03OV210DzMRRYk3JckqKL51Oh8suuwyzZs3C9u3b8fd///eYM2cOTp48id7eXjz66KPKBt6KigqcOXMm5LVmz56N5557Dhs3bsShQ4eU40KIEedWVFTg1VdfRXd3t/JnYGAAM2bMgNFoxL/8y7/gyJEj2LNnD958802ldDLYtYiIiIhGK5ogLjstW7nd7eiO+TVcHhf2N+1HQ8+UaKkwoUgp4ZXeZC8jrqZkJm4yirbEMVk+/PBDHD16FAsWLMBLL72ErKwsWK1WHDt2DE8++STKysoAADfccAMefPBB/PSnP8U3vvENeL1eHDhwQFNSedttt8HlcuGzn/0stm/fjgULFmDatGno6upCV1cXcnN9v8natGkTvvvd7+LZZ59FTU0N2tvbsWvXLtxyyy145513UFBQgPnz58NqtcJgMCgZu2nTpoUNJImIiIhi0evsVW5Hk4nrHog9iHvr9FvYVbcLBp0Bmy7ehJLMktgXSvBKLw63HEZddx06HZ3osnehe6AbAgLXzr4WqyunxsxiZuIoJH+HR6vVirvvvhs//OEPsWHDBvzsZz/DCy+8gMzMTNx///244447lOdkZmbib3/7G9544w2UlJSgpqYGW7duHXHtL3zhC/jpT3+Kq6++GseOHcPcuXNx1113YebMmcjJycHZs2fxrW99C7fccguuvfZaZGVlYdWqVdi9ezcAoKWlBbfddhuys7Mxb948rF69WulE+a1vfQuvvPIKcnNzcf/9KVkJS0RERHHUOzAcxKkzbmo5aTnK7WgzcVJK1HfXo8vRhfou3346t9eN9+vfH8NqU5OUElJK7G/ajz8e+iP2nN+Dk+0n0W5vh9vrhsvrwpun3pwyc/wE51gkjhCiGsDZs2fPorq6WvNYU1MTSktLk7AqSgT+PImIiKauR99+FE63EwDwyPpHYDFZRpzj9rqx+a3NkFJCCIHNn9kMgy580dtH5z/ClmNboBd6eKRHOW7QGfCP6/4RVpM1zLPJr3egF7/d91t4pReZ5kzUddWFPPeORXdgccni8VtcFOrq6lBTUwMANUNjyiJiJo6IiIhogrIN2nCg+QBsg7ZkLyVlOd1OJYAz6oxINwZvzGbQGZTxA1JKTfYulC3HtgCAJoADfAHh3oa9Y1l2SnntxGto629Dh71DE8BdPfNqPLDmAVxec7lybH/z/iSsMP4YxBERERFNQFJK/H7/7/HSoZfwX7v/CxdsF5K9pJSkHheQlZYVtoFaTrqqpDLCvrhI1XB7zu+Bx+sJew75HGo5FPT44uLFKMkswcrylcqxUx2nNI1qJisGcUREREQTUFNvk9KpsH+wH7/5+DdotbUmeVWpR93UJMucFfZc9b64SAO/IwUSfc4+HLlwJIoVpjZ/ljSQEELZv5ibnovq3GoAvsYn/nl+kxmDOCIiIqIJ6PCFw5r7/kCuvb89SStKTepMXKimJn7qTFykgd/Nfc0RX/uDcx9o7p/tPIvf7/89Pm3+NOJzU8W57nNBj+ek5UCv0yv3izKKlNtTIRPHEQNEREREE4yUEocuaOepSilhG7ThqY+fwtdWfA0FGQVJXGHqUO9ti5SJy02LfsxAqPLYBdMWoLatFm6vG+d6zuHNk2/iYMtBzCuchyOtR9Az0INTHacwO3920AYrqeZs19mgx/PS8zT3rebhJjH9g/0JXdN4YCYuidgZdGrgz5GIiOLtWNsxpRzPbDDjyxd9GSa9CYAvi/Cbj38TVeMMGjtNOWVa+CBOPUPO5gzfjKbF1hL0eGlmKRZNW6Tc33l2J7ocXdh9breS3XN73Wjo5VBwIHQQp57bBwAZxgzl9jtn3sErR1/BO2fembQlygziksRsNqOrqwtut5tBwCQmpYTNZoPRaEz2UoiIaIrYfW43nj/wvHJ/fuF8zMifgXuW3QOjzvf/m15nL7YeHzmHleIvlnJK9UiASB1FW/qCB3HZadlYU7km4rqaepsinjPV9Q/2K/tGAwUGcepMHADsbdiL7ae2o62/LWHrSySWUyZJXl4e+vr60N7eDq/Xm+zl0BgYjUbk5eVFPpGIiCiCTnsnttVuU37Bm2HKwJUzrgQA1OTV4M6ld+KZT54BABxpPYLatlrMKZyTtPWmgh6nKogzRwjizNEFcW6vO2TwkJ2WjbLsMpRnl4cMUAAGcQBw5MIReGXw79GB5ZQZpoyg56mb0UwmDOKSRAiBrKwsZGWFT8sTERFR6njz1JvKzLCSzBJ86aIvaUr0ZhfMxvKy5djXuA8AsP30dgZxceZwOdBp74TZYIZJb9LuiYtQTqnJxDltyuDvQBf6LoQMPvz77tZWr8ULB14I+VpNfQziDrYcVG7PLZyL423Hlft5Fm0Q55/hFyg7PXxgPlExiCMiIiKaAJp6mzTzrjbO26gJ4Pw+O+uzShDnDwZ0gjtk4qGxpxFPfvTkiOHbAKATOk2QFoxRb0SaIQ0D7gF4pAcOlyNo85FwAZi/ZHNB0QJcN+c6tPW34WDLwRGt9LscXegf7A+ZYZpqpJSo66rDBdsF9Az0oHugG3XddQB8yZH1Nes1QdyIPXFBPiejzqjZKzeZMIgjIiIimgDU7eQXFC1AVU5V0POsJisyTBnoH+yHR3rQO9CraW1Po/dJ8ydBAzjAFxSEG/TtZzVZMeAeAOArqQwaxKlKIXPTczUz5Yx6375HIQQurboUADA9dzr+eOiPI67T2NuI2QWzI65pKnjj5BvYVbcr6GMz8magLLsMOWk56B7oRr4lHxaj9nNPM6RBL/San292WnZUP9OJiL+2ISIiIkoyh8uhycKtrV4b9nz1fp9IQ6Upel324c8y05yJTHMmzAYzrCYrrppxVVTXUO+LCzWPTJ2Ju7jiYuV2miEt6PmLihfhyhlXYmX5SiwqHu5cGaoz41R0tPVo0ON6ocfa6rXQCR3uWnoXLq+5HHctvWtEcCaEGJGNm8y//GAmjoiIiCjJ9jfvh8vrAuDbC1eeXR72/Nz0XJzvOQ8A6HR0ogY1CV9jKlAHxPcsuwelWaUxX0MzZiBIcxOP16PpTLm8dDmcbidOdpzENbOuCXpNIQQ+M+MzAHz7wPwB/wfnPsCaijUR9+pNBerP8orpVyDPkoectBwUWYuUMtfSrNKwP7MMU4ZmZERgyeVkwiCOiIiIKImklNh7fq9yf1X5qoglXuoMQqSh0hQdKaUmiBvtF3x1tidYENfa3wq31628hsVkwVUzr8JVM6PL9C2cthA7M3eipa8FLo8Lb5x8A7cvun1Ua50snG6nsifQoDPgMzM+M6oySKvZCqiSo5FGRkxkLKckIiIiSqK67jq09vsGDpv0JiwpWRLxOZpySjvLKePBNmhTsqHpxnSkG9NHdR11F8RgA78bexuV26WZsWf6dEKHG+bcoNz/tPlTnOs+F/N1JhN1MJxpzhz1PjarUduYZrKOFwAYxBEREREl1d6G4Szc0pKlMBvMEZ+jzhJ1OjoTsq5Uo/4cx/LlXrMnbnDknjh1wFWSVTKq16jJq8GCaQuU+3+t/asyW3AqUgdxkTqEhhM48Hsy74ljEEdERESUJLZBG45cOKLcX1m+MqrnqYMMNjaJD/XnGDhjLBaaTFxAOaVXenG8dbgN/vS86aN+nQ2zN8Cg8+2MauhpwP7m/aO+1kSnbhAzliBuRGMTZuKIiIiIKFb7m/Yr+6MqsiuibqSRk56jlJT1DfYp16DR0wRx6aMP4tRBRmB3yrOdZ9Hv6gfgG+pdmV056tfJTc/FZdWXKfffOPHGiFlyU4W6LDXY7MRopRu0JbL+weqTEYM4IiIioiSQUuKjho+U+9Fm4QBfcwf/F1ApJbodbG4yVp324XLKsXQtVJfsBe6JO3zhsHJ7wbQFY55Rtq56nfL3wDZow46zO8Z0vYlKXZYaWBIZi8Bfduh1+lFfK9kYxBERERElwenO00rgkGZI08z/ioY60JiMJZVSSrx58k08tfepCdGYIx6dKQFtyV6/qx9e6QXgK6U80jpcOqve0zZaZoMZ18weHkuwu343OuwdY77uRKPJxJlGn4mbWzhXuT0zf+aY1pRsDOKIiIiIkkCdhVtWugwmvSmm56tL/trt7XFb13jZ17gPO8/uxNmus/jbqb8lezlxK6c06AywGC0AfIFq/6CvfLK+q165bTVZUZVTNYbVDltSvEQpy3R73dh2YltcrjuRaBqbjCETl5OegzsW34HVlatx07yb4rG0pGEQR0RERDTOegd6caz1mHJ/VfmqmK9RZC1SbrfaWuOyrvHS5+zDtpPDwUZ7f3KDUK/0osfZo9wf6/ww9b44fwBy6MIh5diCaQugE/H5Gi6EwPVzr1fuH209irquurhce6KIV2MTAFhcvBgb524cU/OaiYBBHBEREdE429e0Tymzq86t1gRk0SrKUAVx/ZMriHut9jU4XA7lft9gn/J5JIPNaVNa9GeYMmDUG8d0PXXzDf+11V1IF02LrXQ2kvLsciwtWarcP952PMzZk0/gnDhiEEdEREQ0rlweFz5u+Fi5f3H5xaO6zjTrNOX2BduFSTMnrLatFgdbDmqOSSlHdHIcD063E8daj6HF1qIci0eQoGluMmhDfXe9EohkmDJQlRufUkq1hdMWKrfru+rjfv1EcbqdaOhpCNlhVUqp2RM31kzcVGFI9gKIiIiIUoXD5cCznzyL7gFfN8kMYwbmT5s/qmtlp2XDbDDD6XbC4XLANmib8FkKp9uJV4+/GvSxnoGeMZcxxuqlQ99XwjwAACAASURBVC+NyFrFo+18YDllU2+Tcn9BUfxKKdUqciqU2429jXB5XGPOKEajvrseTb1NuKj0oqgG1asNegbx649+jRZbCxYXL8Ydi+8YcY7dZYdHegD4GgCNx3uaDJiJIyIiIhon75x5B+d6hjsxXj79cmVgc6yEEJiWoc3GxVu8s3tvn35baSBiMVpQmTM8J61noCfU08bkYMtBvHnyzRGZPq/0Bi07jEsmLmBWnHq0gDpjFk9WkxWFGYUAAI/0oKG3YcQ5Hq8Hx9uOx62baVNvE36z9zfYenwr/lr715ifv6tul5IFPdhyEM19zSPO0TQ1YRZOwSCOiIiIaJyoG05cPfNqXFp16Ziup95L93rt66jvjl8Z3Z8O/wmP7XgM+5v2x+V6Tb1N2H1ut3L/2tnXoiSzRLnf6+yNy+uoXbBdwEuHXsLOszvx5sk3NY+FKt+MSyZOVU55tPWo8t4sRgtq8mrGfP1Q1EFxsJLKrce34vf7f4//2v1fmj2JoyGlxOsnXleyZI29jTE9v8vRhV1nd2mO7Tm/Z8R56p/TRM80jycGcURERETjxF9GCfjGCoyVOohrsbXgv/f+d1w6E7b3t+OTpk9gd9nx+onX49J0RN3MZXredFxUepGmfDIRmbgPz32oZBM/afpE81iobFS8yynVrzO/aH5CSin91GMLDl84rPlM2/vbsbdxLwBfGWNDz8hMXSxq22txpvOMcj/WoPDjxo/h8ro0xz5t+hT2QbvmmDq4z0ob+89mqmAQR0RERDQOnG6nMidMJ3RxySqom5sAvuzIq8dfHXPQ1enoVG73D/aP+MLf3NeMZz95Fj/Z+RP8x3v/gcaeyFmYvoHhjMrK8pUQQmiCuERk4vyft5/LMxw0hAoa4/FzCXWNeAz4Dqc6t1q53dzXjF988AsliNxVt0tTHts3OPpGMh6vB6/Xvq45Fhh8RRJsrITL68K+pn2aY+qfUzwC7KmCQRwRERHROFB/Gc1Oy45LRqYiu0IZLO3X0teCj85/FOIZ0ekd0AZUx9qOae6/Vvsaattr0evsRbu9HX8++ueI++fsruEv+f41Z5sTm4lTd50EtEPRxysT52fSmzAjb8aYrx1OviUfK8tXKvcdLgeOth5Fz0DPiLJYdcfHWO1t2DtiwLzL69IEyZGos9IXlV6k3N5zfo/mlxDqv4vMxA1jEEdEREQ0DtRBQ156fAYNmw1m3L/qfty28DZcUnmJcnz76e0jslCxUA++BoDjrdoGIIFNVFr6WnCk9QjCcbiHy+3SDekAtBmrwMBxrOyDdnTYOzTH2mxtyu1EZuIyTBkQQmiOlWWVQa/Tj/nakdw8/2ZcNfMq5X5jbyN21e1S9q75xTrSoa6rDm+ceANNvU14+/TbQc9RB+qRqP99uLzmciWw73J0obatVnlMnaFVB/2pjiMGiIiIKCW197fjaOtR6IQO6cZ0WIwWWEwWlGaWJqSNeWAmLl4KMgpQkFGAhdMW4ljbMXQ5uuBwObD91HbcNP+mUV0zMMBp7W9Fe387CjIK4PK4ggaIb516C3ML54bstjngGlBupxt9QVxgOaVXeseUoZRS4vCFw8hJy8GAe2DE4+qh6F0DIzNxQghNU5LR0gkdMowZms6KpVmlY75utGblz8L2U9sBAGc6z2g+e79YyildHhee3f8snG4n3q17Vzmem54LIQQ67b7yW7vLHtXf7UHPoPJ3SC/0yLPkYUXZCuXaH5z7APOK5gFI3L83kx2DOCIiIko5XunFM/ufUb58qllNVnzrkm/BYrIEeeboqTMPuem5cb02ABj1Rlw/53o89+lzAIC9jXuxsnwlrCYrrGZrTMFRsP1pte21KMgo0GRwjDojhBAY9Ayitb8Vb558E9fNuS7oNdVZGn8QZ9QbkWHMQL+rH17phc1pG1PJ3KvHX8We83ugF3rMKZwz4nF1ENfjGJmJ0wt93BqPWM1WTRBXnlUel+tGozizGHqhh0d6tD8vvVEpeWzqbcI7Z95BTW6NZi9dMD0DPXC6nSOOf3bmZ7GnYY/y71G0zU26HcOllFlpWdAJHVZVrMKuet++vdOdp9Fqa0WRtUjb2IR74hQspyQiIqKU0zPQEzSAA3xzqU50nIj7a6r3AOWk58T9+gAwt3AuZhXMAuDLSv3vD/83fvLuT/DEnidianYSrLTxWKtvX5w6M1KSWaIp3Xu//v2gs9c8Xg8GPYMAfNmuNEOa8pg6aAuWHYtWfXe90qLeIz042np0xDn+ckopZdDXcnvdo379QIH74sYzE2fQGVCcWTzi+OXVlyu3O+wd2H5qO5755BlNsBlMsACuPLsci4oXafZkRltOGay0ODc9F/MLhwfff3j+Q03WVyd0ccmSThUM4oiIiCjlqAOqTHMmlpUsQ55leJ/aWPaThXxNVfYhNy3+mTjAFyDdMOcG6IV271VjbyOaepuivk6w/WL13fWwD9o1++Wy0rJwSeUlmFs4Vzn2p8N/GvH8wP1w6v1i6jEJpztOR71GNY/Xgy1Ht0Q8r93ejm5HN+wue0xNOEa7JrV8S35CXy9QebY281eYUYiLKy4ecd6gZ1AJ0EMJLE016Ay4fs71EEIoWVUg+g6VoUokV1euVm5/0vSJZu5hpjkzoeMZJht+EkRERJRy1F8iq3Orcdui27CsZHhuWywNGsKxDdpQ21aLQc+gJvOTqEwc4NsjF2yIeKhujIGcbqfypd2gMyjBgFd6caLjhCZLl52WDSEEbl1wq/Jl3O6y48WDL2qCGPWerDTjcBYOgCYAPHIhfHOUUHaf2z2i2Yrf8rLlytq80ouf7vopfv3Rr4OeG88gIbAkNbDRSaIFBnGX11yOdGN60D2LzX3NYa+lDsKtJiu+seYbymDxsWbi1KXFNbk1ytgMl8eF3+37nfIYm5poMYgjIiKilKPOiuWk+QKq0WQUwnG4HPjVh7/Cs/ufxR8P/lHZm6QTuoQ3aFg/fb1SVumnzj6Gow4+Ms2ZmFc4T7l/rPWY5jr+92ExWfC5RZ9TgqBz3eew/fR25TzNfjjD8OcMAHMK5iiBRYutJaqZc2rdjm68dfot5X5g8HLl9Csxu2C25lhge3y/WxfeGtNrh6PuFqpuoT9eKrMrldu56blYXLwYQoig3Tfru+pHHFNTZ+JmF8xGQUaBcn80QZz675A6iBNC4Lo51wUNNDleQItBHBEREaWcYOVcGcYM5Vg8MnE7z+5Uvqyq94llmbMSXhZmNphx70X3apqMRJuJC/xs/F0CAeBkx0nNIHB1o4nq3Gp8ZsZnlPvvnn0XJ9p9ewvVDS/UwbJ/rbPyhwPOX+75JZ795NmIc+f8th7fqpRGFluL8eWLvoySzBIAwLWzr0VOeg42zt2IW+bfglkFs0Z89pdVXYY7l9yJOxbdgcXFi6N6zWgsK12GpSVLsWDaAmyYvSFu141WQUYBbph7A+YWzsUXFn9BGW8QbIZdi60l7C8ugnUW9VMHcaNpbOL/JYrfzPyZ+OqKr8Ko03aIZWdKLXanJCIiopSjaTISJBMX7ZfRULocXfjg3AdBH6vIqRjTtWOh/uIb7TDtwCCuKKMIuem56HJ0wel24mT7yaDXB3wle2e7zuJUxykAwMuHX8YDqx/QZHICgwAAWDBtgWageG17LZr7miM2AznWekzzvBvn34g0Yxo2XbwJA+4BJWDR6/RYUb4CK8pXwOFy4HjbceV5l1ZdmpAsj9lgxu2Lbo/7dWOxpnIN1lSu0RwLNQfvXM85TWmrmrqcUt2UBgjIYEf5yw/1LwKClRZX5lRiSckSfNz4sXKMnSm1mIkjIiKilBMsE6fOKPS7RjY28UovTnecxpajW/DYjsfwo3d+hHPd54Jef/up7SE7HarL7BJN3UAl2kxc4HBlIYSmpFItMIgTQuD2RbcrgUL/YD+e3f8sGnuHSyQDyykBYFHxIszMnxlyHcF4pRd/rf2rcn9F2QpU5VQB8O3lC5ZxAnxBx7LSZbhzyZ24c8mdKVemF+pzqeuqC/kcdRAeuKdRPYojmiDueNtxpRtmuNLiwCYsGaaMoOelKmbiiIiIKKVIKTUBjT8Tp/4yqs7E2QftePvM2zjUcmhEK/aPGj5SGjz4NfU24dPmT4O+dp4lb8T5iaTOcnQ5uiCljNhgQ924xB/gzC+aj93ndmvOC7W/ymqy4o5Fd+A3+34DKSWa+5o1jTOCZeIMOgO+vPzLePnQy9jfvB8AYHMGb3vv9rqVTpv+n2O6MR3XzLom7Psin1DzD8/3nA/5HE05pSF0OWWkvaS2QRv+fOTPyv3FxYtDlhaXZpWiOLMYLX0tAKCUyJIPgzgiIiJKKQPuAWVmmVFvVIKKUF9GXz3+Kg62HAx6rcDslpQS205sU+7PK5yHfEs+3qt/DwBww5wb4vMmomQxWmDSmzDoGcSgZxAOlyPiEHP1eAV/9qMypxLpxnRNcJthzAj5BbwmrwY3zbsJrx57FR6pbbUfLIjzUweFfYN9Qc/5/f7fK+WafjPzZ8Z9OPtUFTguwK+xtxFe6Q36M9Vk4gLKKdX/3nQPdKPL0RVymP3+pv3K368scxaun3N92LV+YfEX8OapN1GeVR507l0qYzklERERpZTA/XD+zJRJb1Lmq7m8LqVZhj8TAPiyTEtKlij3A4O4kx0ncbrTN+tMJ3S4ZvY1+Oysz+L6udfjrqV3YU7hnMS8qRCEEJrGEdF0qFSXkqr3lAV2WPQPaQ5lZflK3DB3ZNAaLohTD3MONoB60DM4IoADoJRRUmRzCob/DpZklijljC6PK+SYBnXwHm5PnNvrxs92/Uyzl02tvX+4K+hl1ZdFDLwLMgpw55I7sa5mXdjzUhGDOCIiIkopoQYNjxhcPLS/R73P5x9W/wNumX+Lcr/X2avMQ/NKryYLt6JsBQozCqHX6XFJ5SWYXzQ//m8mCoEllZGos5DqLMvVM6/GyvKVyv2q3MiBU2C7fyD4nji/TJMqE+ccmYkLVWLJIC56s/JnYXXlaszIm4HPLfqc5mfU0NMQ9Dnh9sQZdAaY9CbNsX2N+7DjzA78n2P/RxMAqv/+FVgKQKPHckoiIiJKKer25oFNFTJMGUoGqH+wH1nmLE0QZzFZlKYZtkEbpJTodfYiNz0Xhy8cVjIZJr0JV864chzeTWTq0rZYM3HqZhJGvRE3z78Zi6YtQoe9Q5ORDKXIWgSd0MErvcoxdWAYSJOJCxKwqUs9/Ux6E0vtYiCEwMa5G5X7FdkVypD18z3nNYG6X7hySgBKebLfue5zStMfm9OGO5feCQCagfehSi4pOszEERERUUpRZ+ICZ1QFjhkY9AwqAYhJb1KGEGsCo6GgUN3db3XF6pCt3Meb+j1GysRJKbWDuYOUPs7In4FVFatgNpgjvrZBZ0BhRqHmWGAmR03dOTFYOWWwrqE6oUv43L2prDwrciZOM+cvSCbVqDeOOOZ3pPUIGnt8++16HMGz4BQ7/o0nIiKilKKeURWYDVAP/O539WsyP+oMkqZEcSi7oA6QgpURJos6iIvUtt/hcihDts0GsxK0jkVgV8Gw5ZTmCOWUQQK7S6suHcPqqDSrVNkX2trfCqfbqXncK71Kpk0IETR4X162POxrbD+9HX3OPqXJTYYpI6pfAlBoDOKIiIgopWgam6SHz8RpMhCqx9Tz1/yZuE77cHCYZwnf9GM8aeZ4RWgBrykdDVP2GIvAUsdwjU3SDGlK4DjoGRwRUASWWJZllY2YJ0axMRvMKLT4sqVSSrT1t2keV48XSDOkBR1RsWH2hrCDzU+0n8CB5gPKfZZSjh2DOCIiIkopXfbhjFlgh0XNmAGXPeT+MHUpWPdAN6SUmuAwUufG8RT4nsIJ9X7HIrCcMrAJhpoQQvO6gXvg1Pc3zN6Ar6/+OodAx0Ghdfhn1Nrfqnks0n44wFc2u7RkadgA/e3Tbyu3A8uYKXYM4oiIiChlON1OJVDRC/2IfWuBWatQ+8MC98T1Onvh9roB+EoyJ1KpWLigKFCozpRjUZpZqrkfadh4uFlx6nJKBm/xU5RRpNxuswVk4lRBXKS/1+GCOJfXpdyeSL/kmKwYxBEREVHKCCylDGyIEVhOGaq8MHBPnGafnWVilYoFZuL8e96C0WTijPEJkrLSsnDdnOtQklmCLyz5QsTzNc1NAsongw0ip7FTB3GBmbhITU3UggX+gXsigZFlzBQ7BnFERESUMtTNR4LtywlsbKL+AqsOGjTNQgZ60WHvUO5PtCyDUW9UShi90qvJrATSZOIiDGKOxaVVl+KBNQ9g4bSFEc9VZ+ICG5mogzh1sEdjM9ZySr9gmbjr5lwHvdBrjrGccuwYxBEREVHKiBTEBWbi1EGD+jGzwaxkHdxeN+q76sNeN9miLalMRGOTWIUbM6C+zyAufgosBUqZa5ejCy7PcOmjwz38i4xw4yGA4Jm60sxSrChfoTk2Ef8dmWwYxBEREVHKUAdxwbIB6sAlsDtlYFCTb8lXbp/qOKXcnkidKf2ibW4yETJd6tdVjxnwSq+m3DOemcJUZ9QblQxyYIdKdYfQSOWUgZk4ndDBbDBjfc16GHW+WXJGnZFBXBwwiCMiIqKU4R8HAAQve1SXizncoffEAdogTj1/baKVUwIBDVvCBHGa95ukICkrLUu5rd7DqN7Pl25Mj8sMOxqmaW6iCuLUv8iIlIkL/HfEYrRACIGstCzctug2VOZUYuO8jWGHg1N0+LefiIiIUoa6AUmw5grqL6lOtzNsEFdgKQj6GhMxy6DZ6xeunDIB3SljpQ6O2/vblduapiZxarpCwwqthTjWdgyAdl+cppwyxj1x6jLehdMWRrUnkqLDTBwRERGlBCllxD1xRp1RacLg9rrRM9CjPBYuE6d+vnqG3EQRdTllAubExUqdyewe6FZGN2hKPc3cDxdvocYMqId9Rwrsg2XiKDEYxBEREVFK6LB3KJ320o3pQfd8CSE02Th14DAiE5cxMhNXnl0+YmzBRKAOyNTZtkATobGJUT8cCEsplRJYzohLrFBjBtSBfbg5cACDuPE08f4rQ0RERJQAdd11yu2qnKqQQ6eDlYz5GzSoBcvEVeVWjW2RCaLpTunSllO6PC7Ud9ejz9mn7H8SQkT8wp5I6lLVdruvpJKdKRNL/UuJDnuHkgHVzImL8Hci8HE2n0kc7okjIiKilFDXWafcrs6tDnlesCAu3Zg+IugzG8ywmqya4KIyu3LM60wETTmlKhMnpcTTnzyNuq467fkGS1IzivmWfJzuPA0Aygy+cJ1CaezMBjNy03PR5eiCV3rRYe/ANOu0mD53ZuLGD4M4IiKiScgrvegd6EWPswc9jh70u/pRk1uD4sziZC9twlJn4qpzqkOeFyzbEKqRRlZaljaIy5mgQZwqI6LOxNkGbSMCOAC4qOyi8VhWSOospz+IU+/NitRgg0anMKNQ2TfaamsdEcRFysQFdq9k2WviMIgjIiKaRKSU+NORP+FA8wF4pVfzmFFvxMPrHo7YBjwVdTu6lS+nRr0RpVmlIc8NLJsEQn95VX/BDXdesqmDUHUmrtXWqjnPoDNgw+wNuLji4nFbWzBBgzg3g7hEK8oowon2EwB8YwaklJrulLFm4vhzShzuiSMiIppEmvuasb9p/4gADvDtbVI3JJgMOu2d2HZiG053nE7Ya3Q7uvHnI39W7lflVEGv04c8P9hA41DdEJeVLlNuzyqYNYZVJlaoOXHqeWAz8mbgoXUPYXXl6pD7BcdLsCBOPXTaZDCN+5pSQaG1ULnd2t8Kh8uhzOYzG8wRS2wDH5+ITX6mCn6yREREk0ifs0+5bdQZUZZVpm1aEWYG2EQjpcTzB57Hrrpd+N0nv0NjT+OYrud0O7GvcR8u2C4ox7zSi2f3P6vsrwJ8wUo4wbIHmebMoOeuqViDiuwKTLNOw03zbhrlyhPPP3QZ8M398v8SQB30z8ifMWHK3/Isecp6uwe64fK44PQMB3HM8CRG4JiBsXYr9Y/roPhjEEdERDSJqLMRcwrn4Ourv47Z+bOVY/2ufni8nmQsLWbNfc1o7msG4Avo/nL0L0EzjNHaenwr/nzkz3jyoyeVfWqHWw4rQZ0QAhdXXIw1lWvCXidYOWqmKXgQZzFZsOniTfjmJd+ckEO+/XRCp2QYpZTKl3P1MG31F/hkM+gMyEnzDWP3z/djOWXiqf8OtNvbNfsnow3iFhcvBuD7Gc0pnBPfBZKCQRwREdEkEiwboc6ebD+1HY++/ShePvTyuK8tVp82f6q539zXjPfr3x/VtbzSi0+aPgHgC3Rr22ohpcSOszuUc66YfgVunHcjjHpj2GvFkombTNT79fxNQtSZuMKMwhHPSabAkko2Nkm8NGMassxZAHzD7ht7h7Pj0e733Dh3IzbO3Yivrvhq0P2lFB8M4oiIiCYRdTbC/wVJHcT1Ofvg9rqxv3m/Zr/TROOVXhxsOTji+Fun3kKnvTPm67X0tWjudw9042jrUSULZ9KbsKYifAbOL9iX1akQxKkDH4fLgQHXgFKea9AZkGfJS9bSglIHce32dmbixkmRdTgbp+5cGm0QZzFZsLpyddjmQTR2DOKIiIgmkWBfZEMN1O0Z6BmXNY3GqY5TSgCRYcpQRiO4vC5sObZFaaYQrbNdZzX3m3ub8c6Zd5T7qytXRz14OFiAMBWGS6u/hDvcDk2Qn2/Jn3BNKAIzcepSYmZ4Ekedka3vqlduc+bbxDKx/m0lIiKisIJl4kIFGBM5iHuv7j3l9pLiJbhl/i1KI4tTHadGlFqG4vF6cKz1GD5u+FhzvLa9VtlvZ9QZcWnVpVGvLViAMNUycQOuAU0pZUFGQTKWFFaBZXhNrbZWuLwuAL79fSY9u1MminpfnHoG4kQdn5GqGMQRERFNIk5XkD1xIQZRdw90j8uaYtXY06h0i9QJHS6pugTl2eWacsfXal/TfIEM5Y2Tb+C5T58bMVpB3SBlVcWqmDJpgSMGdEI3Ybo2joVmT5x7YMI2NfFTZ+L8ATngC7KTPQJhKlOPGVBjJm5iYRBHREQ0iQQrpwwVYHTZu8ZlTbHafW63cnvhtIVKV8erZl6ldCS0u+z4pPGTsNexD9rx0fmPwp5j0BlwWdVlMa0vMOOQYcqYcKWGo6EOTu0uuybwnYhBXG56rvK5D3oGlePcD5dY0zKmBT3OTNzEMvn/i0RERJRC1N0pgzU2URuPTJzD5cDxtuNwuBya4y19Lfjw3Ido7Gkcsb9NvX/tkspLlNtmgxmXVA3f73KED0L3Ne1TSuxCWV62HFlpWRHfh1pgkDAVSikBbZmo0+3U7ImbiOWUep0eOek5I45zP1xiWUyWoP9NYSZuYjEkewFEREQUvWCZOJPeBIPOALfXrTk30UGcx+vBUx8/hZa+FpRnl2PTqk0QQqDP2YenPn5KCeyKMoqwpGQJlpYsRboxXdmrpxO6ER3s1PPY+l396B3ohU6nG1EO6fK4sOf8HuV+TW4NKnMq4ZVe7KrbpVx/bfXamN9X4H6rqVBKCWgzKX3OPnQ6fF1AhRATbryAX4GlYES3UmbiEq8oowhnB7XNgpiJm1gYxBEREU0i6g59/i+zQghkmDJGNDLpGeiBV3oTVgp46MIhpbV/Q08DTnacxOyC2djXuE+TmWvtb8XfTv0N209vR176cBv7fEs+9Dq95prqDpK1bbU41noMAPDAmgcwzeor83J73Xjx4ItKps5itOBLF30JRr0RzX3NShC3omzFqAZwB+63MurCz5WbLNTllI29wxnSnLSciLPzkkW9L87PrGcmLtGKrEUjOr4yEzexTMlySiHEA0KIfUKIQSHE01E+Z7MQQgohrg04/kMhRLsQolsI8SshxMT8rxwREaWEYN0pgeBfsLzSm7AOlVJKvHv2Xc2xj85/BK/04uPG4U6R6gBSSokOe4dyP9g+LPX7cHvd8EovvNKLV468AsD3nl46+BKOtx1XzrtixhVKEFKSWYIvXfQlbJy7EdfPvX6M79LHoJsav/NOMw5nsCbykG+1YEGc+n1QYkzPmz7iGDNxE8uUDOIANAH4AYDfRHOyEGI2gNsANAcc/xqAzwNYAWAmgKUAvhvXlRIREcUgWCYOCLMvzpGYksra9lplkLbf8fbj+ODcB5oM2T9f/s+4fdHtKMksGXGNYPuwQr2Pht4GeKUX/3Pof3Ck9Yhy/PKay0cM8Z5dMBurK1fHLfiaqFmqWAV23fSbiE1N/IIGcSynTLgFRQswI2+G5liovz+UHFMyiJNS/llK+QqAjogn+zwB4P8DMBhw/MsAHpdS1kkp2wE8CuAr8VspERFR9Nxet7LvTS/0miAl1G/JuwYS06Fy59mdym1/+aGUEq/VvqYcX162HBaTBUtLluKqmVeNuEaRdWTwEOp9eKUXfznyFxxsOagcu7TqUlw98+qEt5vPTstO6PXHS6jPNlRL+YlAPSvOj41NEk8IgVsW3KLsDy2wFIwofabkmpJBXCyEEPcA6JBSvhHk4YUADqjufwqgXAgx4r/mQogcIUS1+g+A8kSsmYiIUlNgKaU6eFHPRVNLRCaurqsO57rPAfAFk59b+LkR++4yjBmazpPVOdUjgq1Cy8jgwaQ3hdyD9knT8MiBiysuxobZGxIWwF035zoAvsBH/T4ms1DBz0Qup8xJz4FeaIMH7okbH7npufjaiq9hXfU63Ln0zmQvhwJMjSLvURJC5AHYDCBU6yorAPVmAv//CTMDjgPAtwF8L57rIyIiUhtwBd8PB/g6RQaTiA6V6r1wS0uXYnHJYggh8NKhl+CVXqQZ0nDv8ns1rf3TjGkothZrhjaHamtvMVnC7uVbVroMG+duTGgG7pLKS1CTW4OctJwpsxco1PuYyOWUOqFDbnou2u3Dg8lZTjl+yrLLUJZdluxlUBApHcQB+DcAv5RSNoZ43AZAPVzGn4HrC3LuzwE8HXCsHMCusSyQ+6NOOgAAIABJREFUiIjIL9R+OADIMgefhdbnDPa/rNFr7mtGbXstAF/J1brqdQCARcWLYDVbcbz1OJaXLQ9aKpmTlqMJ4kJlhizG8EHcpVWXJryEUggxYvzBZGfQGWDUGTWz9awm64QPUgsyCrRBHBubEKV8OeVVAB4SQrQIIVoAVAB4XgjxyNDjhwEsUZ2/FECDlHLE/1mklN1De+eUPwAaErx+IiJKIcFmxPmtn75e2SOnLv/rdfbGdQ3v1b2n3J5fNF+TTavJrcGGORuCBnAAsKpilXJ7XuG8kK8Rbi6bTuiC7pOi6AQGbBO5lNIvsLkJM3FEUzQTJ4QwwPfe9AD0Qog0AB4ppSvg1JVD5/jtBfAQgFeH7j8N4B+FEK8B6Afw/wP4bQKXTkREFJLTEzoTl52Wjf+19n/B7rLDYrRg97ndAIC+gfhl4jrtnZrGIv4sXLRm5c/C+unr0dTbhKtnXR3yvHCZoXxL/pTpFpkMaYY0TWAfKuCeSAKDODY2IZqiQRx8YwDU+9PuBvAMgHuFEDYAG6SUu6SUbeonCSE8ALqklLahQ08BqAawD4ARwAsAfpjgtRMREQUVakacX6Y5E5nmTHilF0IISCnR7+qH2+seVbv9Vlsrdp7diel507G8bDneq39PaaAyI28GyrNj698lhMDVM0MHb37hhgpPhqBjIgssRWQmjmhympJBnJRyM3wNS4I9Zg3zvOqA+xLAI0N/iIiIkipSEOenEzpkmjKVjEufsw+56bkxvZaUEi8ceAGt/a040HIAeZY8fNI43B1yXU1sWbhYhCunLLYWJ+x1U0HgrC8GcUSTU6rviSMiIpo0wjU2CZRpzlRuj2Zf3NHWo2jtbwXgC+h+9/HvlIYYZVllIwYBx1O4TNw067SEvW4qCCxVncidKf1y0nI0Yyc4dJqIQRwREdGkEUsQp+5WGWuHSimlZpg3AHjk8AiDdTXrEtodMsMYOhPHIG5s1OWUZoNZE+xPVEIIrKtZB53QYWX5SnanJMIULackIiKaisJ1pwykntEWayZuz/k9aOwNPn0n35KP+UXzY7perMI1Nsmz5CX0tac69d+bwozChI9qiJcrZ1yJtdVr2dSGRsXj8aCpqQktLS0oLi5GRUVFspc0ZgziiIiIJolo98QB2nLKWDpUnuk8g7/W/jXk45dVXQadSGwhT+CeuHxLProcXVg/fX3CX3uqU5eqTob9cGoM4ChWbrcb9fX1OH36NJxOXyVDT08PgzgiIiIaP12OLuV2uOYfwOj2xHXaO/HCgReUDpRlWWWozKnEB+c+UM5ZVrosliWPSmAm7p5l9yA7LZtf4uNgftF8vH36bbg8LqwoW5Hs5RAlhNvtRl1dHc6cOaMEb9nZ2ejp6YHD4YDb7YbBMLnDoMm9eiIiohTh8rjQ3Nus3C/NLA17fqx74pxuJ5779DnYXXYAgNVkxV1L74Lb68bHDR/D5XVh49yN4xJIBTY2yTRnMoCLk9z0XDy07iF4pTds2SrRZOV2u/Huu++iv78fAJCbm4vZs2ejsLAQ7777Lnp7e2Gz2ZCTk5PklY4NgzgiIqJJoKWvRWkukm/Jh8UUuoMjoA3iImXipJR4+fDLuGC7AAAw6Ay4a+ldyE7LBgA8eNmDsA3aUJJZMpa3EDWT3oSyrDI09jaiLKsMJr1pXF43VXBYdmJIKdHd3Y2srCzo9fpkLydlnTx5Ev39/bBarViwYAEKC4f3flqtVvT29qKvr49BHBERESXe+d7zyu2K7Mj7OdRBXM9AD1weV8hs1p7ze3C09ahy/6b5N6Eyp3L4WmlZmkYpiSaEwD0X3YOT7ScxM3/mpGm+QanL4/HgwIEDaGxsRE1NDRYuXJjsJaWckydPoqurC21tbQCAZcuWjQjUrFbfuGibzTbu64s37g4mIiKaBBp6GpTb5dnlEc9PN6Yrs7UGPYN48qMn0d7fHvTcY23HlNuXVF6Ci0ovGuNqx85qsmJZ6bJJ0QKfUpvT6cSHH36IxkZfR9fu7u4kryj1DAwM4Pjx47hw4QK8Xi8qKiqCZtoyM33/Penri23sykTEII6IiGgSUAdx0WTihBBYUT7cuKK5rxm/3PNLHGw+OOLcTkenclv9HCIKr6+vD++99x46OzthNvvKVG02G6SUSV5ZavFn33Jzc7F48eKQmVBm4oiIiGjc2Aft6LB3APDtVyvOLI7qedfPuR43zL0BBp1v94TT7cQfD/0RW45uUTpQeqUX3Y7hzEFuem6cV0809bjdbpw/fx7vv/8+7HY7cnJysG7dOhgMBrhcLgwODiZ7iSmltbUVAFBeXo6qqqqQnSetViuEELDb7fB4POO5xLjjnjgiIqIJrsfZo9zOt+QrQVkkQgisqVyDqpwqvHDwBXTafRm3jxo+QlVuFZaWLEXPQI8S0FlNVjYRIQrDZrPhxIkTaGlpUYKAkpISLFu2DHq9HlarFd3d3bDZbEpmjhJLSqlk4oqKisKeq9PpYLFY0N/fjz179iAnJwfz588fj2XGHTNxREREE9ygZ/i3+mZ97F8MS7NK8cDqBzAzf6ZyzJ/ZU2fh8tLzxrBKoqnv6NGjaGxshMfjQV5eHpYsWYLly5cr3Sj95Xr+9vaxamhoQEtLS9hyzMmeQYq3rq4uuFwuZGRkwGIJ37UXAPLyfP+d6+jomNT7F5mJIyIimuCcbqdy22QYXabMbDBjdsFsnOo4BQDKPDj1frhcC0spiULxjxAAgLVr1wZtnDGWPVddXV3Yv38/AMBisaCmpgYVFRUwGoe7yra1tWHPnj2YM2cOZs2aNZq3MeW0t/saNkXKwvktWrQIZWVlkFJqPtvJhkEcERHRBDfWTJyfeoi2w+UAEBDEcT8cUUhOpxNOpxNGoxHZ2dlBz8nIyAAQfRDndDpx6NAh5OXlQafzFcj592wdOXIEtbW1qKysxNy5c6HX61FfXw8pJU6cOIGysrKoMk9TXVdXF4DhDFsker0ehYWFiVzSuGA5JRER0QSnycSNYc+aOojzZ+LY1IQoOj09vr2p2dnZIWcXxlJO2d/fj3fffRfNzc04cuQIent7AQDz5s3DypUrUVBQALfbjTNnzmDPnj0YGBhQGnh4vV4cPXo03OVTgjo7OtmHd8eKmTgiIqIJTp2JG205JeCbHednHxwqp7QPZ+K4J44oNHUQF0pGRgaEEOjv74fX61Wya4EaGhpw6NAhuN1u5Vhzc7Ny/YKCAhQXF6O7uxt79+5FR0cH3n33XXg8HlitVjgcDjQ3N6O1tTXqMsKpyG63Y3BwEOb/y96dB7ed3neefz+4AR7gfYuSSEnULXWL6kPd6u74jrNxnE2y3qSSbK7x7NasU5naZDZVnmTsmUxNNplydrJTZTuxM3ZqYmeSTJzEY8eOz77Ul9SSunUfvO8LIAECxPnsH9Dvxx9IgARFkACp76tK1bj58FALH36/z/dxu/F6ves/YReRSpwQQghR5rainTLnnjipxAmRVyEhzm634/F40FoTjUZzPsbY+5ZMJmltbTVDmHEsgXEgNWSqS8888wxer5dYLFOR7+zspKenB4B33nknKwg+aoxWytra2rzV0d1KQpwQQghR5oox2ARWh7h4Kk44ntm7Y1M2/J78b06FeNQVEuIAsyK0tLS06j6tNe+++y4A3d3dnDlzJmt/ltvtXnU0gc/n48knn8TpdGKz2WhtbaWrqwu/3080GuX27dub+rx2ikgkwre//W2+//3vc+3aNaamppidzUzZfdRaKUHaKYUQQoiyl9VOuYk9cV6nF6UUWmtiyVhWK6Xf48em5He7QuQSi8WIRqM4HA5zeEk+RojLVYkbHBxkfn4er9fLoUOHUEpRW7tcAa+urs75mlVVVTz33HMkEglzmMmpU6d4+eWX6e/vp729fdcHmZmZGeLxOPF4nP7+fvr7+837rF/DR4X831oIIYQoc1ntlI6Hb6dUSuF1LO8bmQhPmJdrPLv7DaAQhUqn06vOabNWfNZr2/N4PMDqEBeLxbh16xYAx44dw+HI1FL8fr+5d87aSrmSz+fLqgL6/X66urrQWnP16lXS6XQhn96OZVQ229raOHTokPm9cLvduz7A5iKVOCGEEKLMFWs6JWRaKo39cOML4+btEuKEyAS4F198kXQ6zdmzZ83KmHEWWUNDw7qvka8Sd+vWLRKJBI2NjbS0tJi322w2ampqmJuby1uJy6enp4fx8XEWFha4f//+rj47zvh6NjQ0sHfvXnp6eojH4yilzED8KJFKnBBCCFHmijXYBLL3xY2HlkNctWdjbx6F2I3m5uYIh8NEIhFeffVVJiYy1WojxBVyvliuPXGBQIChoSFsNhvHjx9fVc07fPgwnZ2dtLa2bmi9drudkydPAnDnzp2CjjbYqYwQZ1Q6AVwu144+sHszJMQJIYQQZa5Ye+Ig+5gBa4iTSpzYDWZnZ3n11VcfetjH5OQkkAkKyWSSt956i2vXrrG4uLjmId9WK9spVw4zMc6Ss6qvr+fUqVMPVVFqbGyko6ODdDrNO++8s6oVdLcwQvGjdpRAPhLihBBCiDJnbafczJ44yH3MACCTKcWOlkgkuHr1KhcuXGBubo67d+8SCoXWfV4wGOR73/ueWXEzDtN+/PHHOXLkCEopc4BGQ0NDQWPsV1biRkdHzWEmBw4ceKjPbz3Hjh3D5XIxMzPD8PDwlnyMUrIe2SAhLkNCnBBCCFHmilmJ87l8OW+XECd2qvHxcX7wgx+Y7Yp+vx+tNXfu3AEyEyHfeOONnOepTUxMEIlEGB8fJxwOEw6HcTqd1NXVceDAAc6ePWtWxwrZDweZFj+bzUY8HieVSpmtmF1dXVu2d8vlcnHs2DEAbty4YZ4pt1skk0mSySQOh+OR3P+Wi4Q4IYQQoswVc7CJtZ3SSkKc2ImGh4e5ePEisViMuro6nnvuOc6ePYvNZmN8fJxQKMTt27eZmppifHx81fPD4cw5iYuLi2YVrrm52ay4NTc3c/78eY4ePcqePXsKWpNSKqulcmFhAdj6s8za29tpamoikUhw7dq1Lf1Y282oano8nkfuUO98JMQJIYQQZa5YRwxAdjulwWV34XF4cjxaiPJmBLODBw9y7tw5qqqq8Hq9dHR0oLXOqkoZ+92sjBAXiUTMsLXyzLHKykq6u7ux2+0Fr8to+YtEIoRCIZRSG548uVFKKU6cOIHdbmdsbMysAO4G0kq5moQ4IYQQooxprbNCnNO+uUlsuSpxNZ71z74SYivcv3+fCxcumEErlUrR399vhqv1GPveOjo6sn6G29vbgeU9bgDT09NZZ6lprc1pjrFYjPn5eWDts9oKZYSNqakp0uk0FRUV29IG6PP56OrqAshZedypck2mfNRJiBNCCCHK2MoAZ1Ob+6e7wlmx6ja/V1opxfbSWnPr1i1u3LjB7OwsAwMDaK3NaZA3b95c9zUSiQSRSAS73U5FRfbPdX19PW73ctVaKUUymSQQCJi3RaPRrFBnVOJyTY/cKCNsGNW/ra7CWRln0E1NTe2aSZVSiVtNQpwQQghRxoo51ARyV+L8bglxYvtorbl58yZ37941bxsaGqKvr4/p6WkAc1rkWozQVVVVtaqSrJQyz1xTSpn72UZGRsxgk6va53Q6cbk2//fMCIKRSGYCbCFHExSL3+/H5XIRiUR2zblxcrzAahLihBBCiDJWzOMFACpcOSpxMtREbBNjn9r9+/dRStHb20tlZSVLS0vcuHHDfJxSat0qktFKma/KZbRUNjQ00NnZCWTC4sWLF0kkEjlDXGVlZVFai9va2rIqettZiVNKmYeSW9tJdzJpp1xNQpwQQghRxopdiat2V9NU0ZR1W0NFYaPThdis69ev09fXh81mo7e3l9bWVvbu3Wvef+zYMbxeL1prs4qVj7USl0tdXR3PPvssjz32GLW1tfT29uJ0OpmYmOCll14yq30+3/Kwn2LshwOw2+2cOnXKvL6dlTiApqbM3/GVIU5rzeDgYEFn6BVqdnaW/v7+LW3dNPZMSohbJgctCCGEEGUslire8QKQ+S39x5/4OJdGL3F75jZ+j58jjUc2/bpCrCcajdLf34/NZuPs2bNm0Ojs7GR2dpbGxkb27dvH5OQk0WiUxcXFVXvdrIwQt1aVyzppsrW1Fb/fz6VLlwgGg2ZIbGxsZHBwECjOfjhDXV0djz/+OOl0Omt/3nYwzrQLBoNorc3q4uDgIO+++y6NjY089dRTm/oYoVCImzdvZu37q6+v39zC84jHM7/MKkar624hIU4IIYQoY/Fk8Y4XMHidXp7d9yzP7nu2KK8nRCGMfU3V1dVmgANwOBycPXvWvF5RUcHMzMya+7mmp6cLCnEr+Xw+nnnmGW7cuEF/fz+QGQSyFSEOlls6t5vb7cbpdJJIJIjH47jdbtLpNPfu3QMyASyVSnHr1i3a29s3dIZdLBbj9u3bDA0NZVXfjO9vsWmtJcTlICFOCCGEKFOReISb08tT+opRiRPLkskkU1NTVFVVFW0v1KMoHo9z8+ZNOjs7V52xZmU9sHktRvUtX4ibmJjgrbfeAjJVtI2+sbfZbBw/ftw8GLuurs68r9ghrlSUUlRWVhIIBAiHw7jdboaHh829ZUtLSwwODtLX10dfXx8f/OAHC/o6jo+Pc+XKFZLJJEop9u3bRzweZ2xszAxaxZZIJNBa43Q6sdlkJ5hBQpwQQghRhpLpJJ9/8/PMRJYP7HXbt7cla7e7d++eOSHR4/HQ2NhIQ0MDLS0t23KmVyGi0Sh3795l7969276vqlCDg4MMDQ0xPj7Os88+mzcIGfua1mstXC/EGe17e/fu5fjx4w+77KxqYGNjI4lEImt/3E5nDXG1tbVmFc4YGmM9R+6dd96ht7d3zdcbGxvj0qVLQOZrd+zYMSorK7l9+zbAQ4W4oaEhwuEw9fX1NDU15fxFilThcpM4K4QQQhTZzOIMX7n6Fb53/3sPvdn//uz9rAAH4HLIm5himpubAzLtfEtLSwwPD3P58mWzylMO+vv7GRwc5LXXXss646ycGMMzEokEb731FolEIufjjErcZkOcMVWytbW1aJWZp556ivPnz++qaqwRpkOhEKOjo0QiESorK83wav15Gh8fX3fYiTEIpquriyeeeMJ8fSNcbTTExeNxrl69yv3793nzzTfN9tZcj7N+HJEhIU4IIYQosq/d+BrXJ6/z/fvf5+7s3fWfkMP1qeurbpNKXPForc09VS+88ALPP/88hw8fBpYHZpSD2dlZIBOQXn/9dTN4lotEIkEgEEApRVVVFeFwmLfffjvrlxepVAqtdcETBn0+H0opIpFI1mHcBiPc7ZbWx61iDXFGxfngwYPm7cb3yBhGMjMzk+NVlhlhqrGxMSvsOp3OrPsLZQyWsdvtANy9e5dkMpn340qIyyYhTgghhCiygcCAefna5LUNPz+t09ycurnqdtkTVzzRaJREIoHb7cbj8VBdXc2BAwdQSpl7cEotmUwyPz9vHlydTCZ54403zGBXDmZmZtBaU1dXxxNPPIHL5WJqaso88y2ZTPLDH/6QV155peB2SrvdnveYgUQiQSwWw263y7j5dRjHJRhDYioqKmhvb8+a+Olyuejo6DAft5Z83z8jXOWrwOZjfG+bmpqoq6sjHo/nrMYV+nPzqJEQJ4QQQmyhtF5dSVhP/1w/kcTqM7IS6Y29SRL5WScbGlUFpRQulyuralRKgUAArTV+v58zZ87Q0dFhBrnp6elSLw9YbqVsamrC5/PR29uLUoq+vj6Gh4fNNr5gMGi26xUSvqxVJCujlbKiomJXtT5uBZ/Pl9VuevDgQXPgiaG6uto8jmB2dnbNX17kq4g9bDulEeK8Xq9ZBb9///6qMCiVuNwkxAkhhBBb6GEqOrlaKQF8zt0zdKHU8o2nN94oljLEpVIpJicnzQEedXV1KKU4ffo0nZ2dpFIp3njjDW7fvl3SiqHW2gxxjY2NQKY178SJE0BmWIbRxgfLb9oLqagY35eVra3SSlk4pZRZdfP5fGbFzVqJq66uxufz4fP5SCQSzM/P53wt6y828lXiHjbE+Xw+6uvraWhoIJFIcP/+/azHSYjLTUKcEEIIsYU2WonTWnN9cjnEPb//+cxvz12VnGo9VezlPbKMN6srJz4aVaJShLhoNMrQ0BAvvvhi1qAHY8+SUoqTJ09y4MABAO7cucPo6Oi2r9MQCARYWlrC5/NlheG9e/eyf/9+0um0OdLeaiMhbq1KnFifcf7boUOHzMql2+02p68aX2ejGpevpTKZTKK1xuFwrBom87AhzvjZMCaCGtW4/v7+rL9/EuJyK4/5uUIIIcQuldKpDT1+MDhIOP7gjaqrgvcdeJ8Z5GRPXPHkq8QZAWM7QlwikWBmZsb8YwQUyLxhjcfj2O32rHPMlFIcOXIEt9vN9evXmZiYMCss280YUd/a2rqqtfHYsWOEQiFmZmbMzwUyn1chEyWN/VxSiduco0eP0tHRYf4iADI/Q36/n7m5OTPk1dfXMzQ0lHcC6lr70ux2OzabjVQqRSqVMgeVrMdaiQOora2lubmZyclJ7t+/z9GjRwEJcflIiBNCCCGKKJnOnq6WSG1sH5u1Cne06Sg2ZcPtkA39xZRMJolEIthstlVhYLvaKe/cucOdO3ey2iEdDgcNDQ00NzfT0dFhDjXJ9ea1paWF69evMz09TTqd3vZDkK3njLW2tq66XynF2bNnmZqaQinFxYsXgcKHU1RWVmKz2YhEIiSTSbNyZARdCXGFcblcZpXN6rHHHiMajZph2Tik3diHuTKUrxXilFI4nU5isRiJRKKgEGcdWuP1es3be3p6mJycZGBggK6uLjwejxniZLBJNglxQgghRBHFktlv/pcSSwU/V2vNjakb5vVjTceKti6xzHjzmGs4xnZU4mZnZ7l9+zZKKXMvUGNjIzU1NVnrMd5Y5+Lz+aisrCQcDhMIBKirq2NxcRGPx7MtB5UHg0Gi0Sher9es5qzkcDhoa2vLmjBZ6ERJI2AvLCwQDoepqalBa21W4qSdcnO8Xm9WePL5fGYQW1payroP1q+GuVwuYrEY8Xi8oO9xLBYjnU5ntXZCpr25tbWV8fFx7t69y4kTJ6QSl4eEOCGEEKKIVoa4aHL1nqB8RhdGCS4FAfA6vXTVdRV1bSLDCAJGG5fVVu+JS6VSXLlyBchMC+zp6Xno12pqaiIcDnPjxg1isRjRaBS3282JEydyVseKyTivrqmpad0pkV6vF7vdTiqV2lA1pbq6moWFBRYWFqipqclq1zPOJhPFoZSipqaG6elpgsFg3hCX7/u30X1xuapwhp6eHsbHxxkZGeHIkSPm30UJcdlksIkQQghRREvJ7MpbrqMC8rG2Uh5uPIzdVtjeErExK/fiWG11O+XU1BSRSISqqioOHjy4qddqamoClqtiDoeDWCzGxYsXuXTp0oYHTawUDAa5du1azq9FMJj5ZUO+KpyVcRA4bKwlbuW+OKnIbC3je2l8b63WC1IPG+Jy/R2sqqqitraWZDLJ6OgoqVQKm81W8F67R4VU4oQQQogiiqVWVOIS0Zx7TFbSWmcdLXC8+fiWrE9kt1OuVOx2ylQqRTAYNI8JmJiYAKCjo2PT+9jq6+tpb29Ha83+/fupra1lcHCQGzduMDY2xuzs7Kaqcrdv32ZqaopAIMC5c+ey3kQb0z0LCXGQ2cMWDAY3dED3ymMGJMRtrUJC3HqVuKWlJWKx2Lphfa1qOGT+fgQCAfr6+szXl3MBs0klTgghhCiile2UaZ0mnlr/t9MT4QlmI7MAuOwuuuu6t2R9Yu03kLlCXDwe55VXXuHixYvMzMwUfDab1pqLFy9y4cIFBgcHs85Va2lp2eyngc1m4/HHH+fMmTNmSNy3bx8vvPAC9fX1ZlUu38TB9RjhKRgMcvnyZfPzTiQSLC4uYrfbzWrZerq7u9mzZw9tbW0Ff3zjtUOhEFprCXFbzBriVv6MF7InDuD69et897vfZWkp/15grbV5NIZ18qpVW1sbNpvNHGQjQ01WkxAnhBBCFNHKEAeFtVTenVk+FPlw42Gcdtnzs1XWauVyu90opUgkEuYb2enpaQKBAOPj47z22mu8+OKLDAwMkEwmVz3famhoyAxtd+7cYXp6mng8TkVFxZYO5vD5fDz99NPs27cPYNXhyYWIx+MsLS2Z+8/Gx8e5desWsFypqa6uLrg6Ul1dzenTpzdUifN4PDidTuLxuDk0AyTEbRWPx4PX6yWZTGYddwGFV+IA0un0qqMhrKanp1lcXMTn85ktwblezxr4C/1lwaNEQpwQQghRRLlCXDSx/nCT+di8ebnDX5pzvx4F1tHmuUKcMdJfa22+cTUOJfb7/Xg8HkKhEO+++y4/+MEP8ga5xcVFrl/PtMe63W5isRiXL18GMlW4rW4NU0px8OBBbDYbExMTZvWxUNZz9M6cOYNSinv37jE4OLih/XCboZTKaqmUELf18rVUrve1XzloJpFI8O677/LDH/6QRCL7mBXjEPu9e/eu+ffg1KlTnD9/nvPnz3Pq1KmNfSKPAAlxQgghRBGtHGwChYW4SHy5Wudz5t4nIjYvGs3sUfR4PHkHJawcbmKEuI6ODt773vdy5swZ3G43S0tLhEKhVc/XWnP58mVSqRTt7e2cOXMGyLwRttlstLe3b8WntorH46GjowOtNQMDAxt6rjXENTY2cvLkSQDeffddBgcHga0PccbHh0xLpYS4rZcvxK1XiVt5rEU8Hmd8fJxQKMTIyIh5ezqdZnp6GqUUnZ2da67FZrNRU1NDTU3Ntp+DuBPIV0QIIYQoopwhbsUxA1prUulU1m2L8eVKSYVLzsDaKoWcM7ZyX5wR4nw+Hzabjba2NvMA5Vwh7t69ewQCAbxeLydOnKC+vp4nn3yS3t5e3v/+9+P3+4v6Oa2loyNT1d3ovjhriAPo7Oyyn77zAAAgAElEQVTkwIEDaK2JRqPY7Xbq6+uLu9gcrBMqJcRtvVwhTmtNIpHIe/A8QGNjIw0NDVlHdBjfL2M/KGRambXWeL1e+T5ukkynFEIIIYpo5XRKyK7ERRNRPvfG5wjFQ3zsxMe4MHSBpeQS04vT5mMqXZXbstZH0VqtlAbjzaXRBpbrTCsjXKzcOxQMBrl9+zYAp0+fNtvM8u392WqVlZmfJWM4SKFtnCtDHMDhw4eprq7GbrdTW1u7LcMmrO2URvCWN/9bxzhwfmFhgXQ6jc1mIxaLobU294vm4nA4ePrpp+nv7+fatWuEw2EzuIVCIYLBILW1tXJYexFJiBNCCCGKKJZYe7DJpdFLzERmAPjzy3+e8zUepUpcLBbj+vXrdHR0bEvQmZycBNYelGAELyPEGZU4a4izhiOD1porV66gtaarq8us1pWSy+XC5XKZw0EKGSyitTY/L+vXSSm1ba2gBmtYNlr2JMRtHYfDQWVlJaFQyDxkfa2DuVcyvjcrK9SDg4NZIW6tX6KIwkg7pRBCCFFEuSpxS4nlFsuh+aF1X+NR2hM3PDzM6Ogob775pjl2fKuEw2Gmpqaw2Wxmm2Eu1hCXSCRIJpM4HI6s4Q1GiLNW4ubm5giFQni9Xg4fPrxFn8XGKKVyBs61LC4ukk6n8Xq9qwZWbDeHw4HP5yOdTpstfhLitpbRUmm04BZSvTYY35uVYW1sbIxEIrHmGY1iYyTECSGEEEW03hEDydTaY+lddtcjdbzAzEymKmkMAxkayg65o6Oj5gHZm6G15v79+2it6ejoWLMV0BrirFU4aytZRUUFSimi0SipVMpcK0B7e3veoSmlkK/1M59ye6NttFSm02lAQtxWW7kvzrondD3G98ZopaytraWxsZFUKsXIyIi0UxaRhDghhBCiiNabTmm0UubzKLVSplIp5ubmgMxh0Fprrl69Sl9fHwDJZJLLly/z9ttvF3zAdi6RSISXXnrJDIhdXV1rPj5fiLOy2WxUVFSgtSYcDpNOpxkfHwfY9pbD9Wy0EleuIc4gIW5rrQxxD9NOaXC73eYUyqGhoQ1V9cTaZE+cEEIIUURrVeKS6SSB6NpTAh+lEBcIBEilUlRXV3P06FG8Xi/Xrl3j+vXrpNNpGhsbM5M8Uymi0ehDv/G7du0aCwsLeDweenp61j04uJAQB5kKVzgcJhwOm9P4qqqqyu5g4lytn2spt31L1q+nw+GQcfNbrLq6GpvNRjgczmqB3Eglznq9paUFt9uddQB4ufxs7WTyt0AIIYQoolyVOOO2ucgcaZ1e8/kVzkcnxBmtlMYAkP3793Pq1CmUUty6dYv5+eUD0CORCAsLC1lvBFeKxWK8+OKL5jlmALOzs0xOTuJwOHjuuefWPZsKlkNcMplcswphrXBNTU0B0NrauuUHeW/Uw7ZTlssbbWslTqpwW89ms5nHYMzPz2/o58Fut2e1Ervdbmw2G3v27DFv83g8q86VExsnIU4IIYQoopyDTR6EuNnI7LrPf5QqcbOzma+HdYpjZ2cn1dXVaK3NSZKQae165ZVXePHFF3n99deZnZ1d1WI5OTnJwsIC/f39QGZfzo0bN4BMu2ahI/ELrcQZb3QDgYA5BGI7zk7bKONNs/XsrrWUW4irqKgwg4GEuO1hHW6y1t+BXKx/z4zL1l+elMvP1U4nIU4IIYQokrROk0glVt1utFhaz4J7qvMpPvnCJznRciLrsY/KGXHWMfbGG0aDUTmanl7+eo2NjZkDRKanp7lw4QIXLlxgZGSE0dFRlpaWzCpdKBQiHo8zMTFBMBjE7Xavuw/OyqgSJBIJlpYyATzXG1gjsM3NzTE/P49SatXnUg6UUubX1FrdzEVrXXYhzrp+CXHbw/g5npiYQGuNx+MpeFiP9XtkhLiKigrzlzXlstdyp5NaphBCCFEk1v1wTrvTDHRLySW01llDTRp8DfhcPmo82W/6fa7yeOO81Yzx/Q6HY9Ubc6NN0QhtsBw+9uzZg8/no6+vj7m5OXMwysoK2OzsLDdv3gSgp6dnQ+1b1kqcUe3Ldb6ay+XC7/eba/P7/WXbJlZTU0MgECAYDNLY2Jj3cfF4nGQyidPpLPnxAlZVVVUEg0EJcdtk5XCTQqtwkDvEARw6dIhwOExra2uRVvloK8v/0yilDgJBrfW0UsoH/BaQAv5Qa726T0UIIYQoA9b9cD6nj4iOkEgnSOs08VQ8qxLX4Mv8Vrrakz1571Fpp7SOGl+5h2ytwSD19fXs2bOHrq4uBgcHmZ2dZWpqirm5uaxKwY0bN4hEIlRWVmbtxymENcQlk5kjIfK1YjY0NJghrq6ubkMfZzsZb8rzVeISiQSvvvpq1lCTctrbV1tby/DwcNlUB3e7iooKKioqHmrIjTXEWS/X19fz/ve/v3iLfMSVazvlVwAjpv8e8DPATwOfKdmKhBBCiHXcnr5tXvY5fbgdy2/8Y8kYs4vLe+IaKjIhzu/2Z73GozLYZK3zotYKccaQC4fDQXd3N0888QQ1NTVorc3ABcv7ug4fPrzhaYY2mw273Y7WmnQ6jcPhyFths+7nK+cQZ+zfMyorK926dYtQKGSexVZuYWnPnj2cPXuW7u7uUi/lkaCUore317xe6H5SWA5uTqezrM5L3G3KNcR1A9ceXP4p4CPAB4CPlmxFQgghxBoi8Qjfvf9d8/rJlpN4ncstSHPRORYTmeDitDnNNsqV7ZSPYiVuJZ/PZ77583q95mWbzZYz4FmDlBFWIFO9aWlpeaj1WVsJc7VSGurq6syQWFtb+1AfaztUVlbicDiIRqPEYtlNTcFgkMHBwazKW7m1LdpsNlpaWsq2XXU3qq6u5qmnnqKhoaGgqa4G42dnI8FPbFy5hjgFaKVUF6C11n1a6ymgep3nCSGEENtOa803bn/DPNS71lvL051P43Esv/kfXRg1L9dX1JtvmB/Vdsq1DpRWSpn74ioqKsyqUFVVVc6qmnWPV319vdk6eOTIkYduCSw0xDkcDh577DFOnTq1oX1D200plbcaNzQ0hNaa/fv3m48pxymbYvs1Njby9NNPb+jsQwlx26Ncf51xFfgk0An8E4BSqh3IfziMEEIIUSJvDL/BlfEr5vUfPfSjOO3OrHbKkfkR87KxHw5WT6P0OcurjW0ztNZ5Q9R6e22qqqqYn5/H5/PhcDgIhUJZ54VZ1dbWYrfbSaVSVFVV0d3dzdLS0qYmRVpD3HpvRtva2h7642ynmpoaZmdnCQaDNDc3m7cbxyO0trZy+PBhZmZmaGpqKtUyxQ5n/DKj3Fpyd5tyrcT9OvAh4ADw7x7c9j7gOyVbkRBCCJHDYHCQb9z+hnn9TPsZjjUfA8iqxA3PD5uXjf1wkKmQfLjnw1S4Knhv93uzgt9O1tfXxze+8Q3efPNNpqenV53ptlY7JWCGiIaGhnWrQ0arnc1mo76+Ho/Hs+lR/9a2vbUqcTuJUU0xqqCQOdA8FAqZlTq73U5zc3NZDTURO0tjYyO9vb0cOXKk1EvZ1cqyEqe1fgd4dsVtXwa+XJoVCSGEEKuFYiH+8upfktaZYRDt1e38+OEfN++3hrhANGBetlbiAJ7Z+wznOs/tqjfOk5OT5oHdk5OTVFZWsm/fPjo7O0mn08Tjcex2e94qV1tbGw0NDbhcLtLpNLW1tVl731Y6deoUx44dK1oLV6HtlDuJ8bWxHvgdDAbRWlNTUyNDKERRKKXkGIFtUJYhDuDB0QI9QFYTrtb6pdKsSAghxKNuMjzJlbErnGg5QUtVC//tnf/GQizT6e9z+vjZUz+L07785t862MSqsWL1OV27KcDBcqWtq6uLsbExwuEw165dY3x83PwNfa7jBQxKKTN02O32Nc82Mx5TzBCykXbKncLYq2QdbGK0UpbzUBYhxGplGeKUUh8B/pzVg0w0IL8mEkIIse0i8QhfvPhFFuOLXBm/wunW0/QH+oFM4PiZEz9DrTf7jXC+1siVlbjdJpVKEY1GUUpx9OhRjhw5wsTEBNeuXWN2dpaLFy8C2ZMky81urMQZIc5aiZMQJ8TOVK574v6QzPlwVVprm+WPBDghhBAl8Z1732ExnqkuLcQWeGlguTHkvV3v5VDDoVXPsbZTGipdlXicuyMU5LPywGibzUZbWxvHjx8HYGlpCbfbzeHDh0u5zDXt5kqcEeK01hLihNihyjXEtWqt/6PWerHUCxFCCCFG50d5a/StnPdVu6t5oeuFnPflCnHWoSa7lRHijGMCDK2trXR0dOByuTh79mxZV7h2YyXObrdjs9lIpVIkk0kikQjxeBy3213WxyMIIVYry3ZK4BWl1MkHA06EEEKIknqx/8VV0xUNNd6avPu6coa4Xd5KCfknTyqlOH36NFrrnOe9lRMjxDkcjl1zwLSxzzAajRKPx80qXE1N/p9hIUR5Ktf/K70C/J1S6vPAuPUOrfWfl2ZJQgghHkUzizPcmL6R9/4aT/5R9o96JS7fQd47ITAYIW63tFIaXC7XqhAnrZRC7DzlGuL+2YP//u8rbtdkBp4IIYQQ2+LC0IW8VTgAvyf/cI5cg01yTabcbdY7A24nqKysxGaz7bqAY90XFwwGAQlxQuxEZdfLoJSyAf8TcEhrvX/Fn64CX+P/VEpdUkrFlVJfWuNxJx48LvDgz3eVUsdWPOb3lFIzSqmgUuqzSilnvtcTQgixu4TjYd4efdu8fqr11KrHrBXiHoV2yoWFBd566y1CoZB5224IcR6Phw984AOcPn261EspKiPERaNR5ufnUUpt+mB0IcT2K7sQR6ba9haQ2sRrjAH/DvjiOo8bAX4KqAMagH8A/tq4Uyn1a8D/CvQCB4DTwL/exLqEEELsIG8Ov0kinQCgrbqN062r39CvFeJWnhNnU7ZVxxDsdNevX2diYoIbNzItp7FYjKWlJWw2244fluF0OndE6+dGGCFuenoarTVVVVW7Zs+fEI+SsgtxOtOzch9o3sRr/K3W+u+A2XUeF9BaDzz4mIpMcOxWy//H/mXgMw8eMwP8W+BXHnZdQgghdo5EKsHrQ6+b18/vPU+1e+XxpeB3F95OWeetw27bPaflBINBZmZmAJiamiIUCnHlyhUAGhoadl0A2g2MPX7T09OAtFIKsVOV669e/gj4qlLqU8AAkDbu0FoPFfuDKaWCQCWZUPtpvbz54Thw1fLQK0CHUsqvtZ5f8Ro1wMp+hI5ir1UIIcT2uDx2mcVEpi2wxlPD8ZbjxJKxVY/ze/OHOJuy4bK7iKcy53LttqEm9+/fBzITHJPJJK+99hqxWAyXy8XJkydLvDqRi1GJSyaTgIQ4IXaqcg1xX3jw3++Taa+ETKVMA0X/FabWukYpVQH8b8Cg5a5KwBrWgg/+W7XidoDfAP5NsdcmhBBi+6V1mlcGXzGvn9t7Dpuy5dzjVuFce9+Xx+ExQ9xuGmqyuLjI+Pg4Sil6e3t5/fXXicVi2O12HnvssR3fSrlbGSHOICFOiJ2pXEPc/u3+gFrrRaXU54BppdQRrfUUEAasvTPGr1tDq14A/l/gSytu6wBeLvZahRBCbK2bUzeZjWQ68r1OL73tvQA52wPXaxn0ODwsxBaA3TXUpK+vD601e/bsobGxkSeeeIJ4PE5LS0vWQdmivFhDnNPp3NHDZ4R4lJVliNNaD67/qC1hA3xAOzAFXANOARce3H8aGFnZSgmgtQ6yXKkD1v+HXQghRHmyVuHOdpzNeVRAoao91UwtTgHQVNm06bWVg1gsxvDwMADd3d0ANDc/9FZ2sY2sIa62tlbeqwixQ5VliFNK/WK++wo57Fsp5SDzudkBu1LKA6S01okVj/sgMEEmrFUAvwcEgJsPHvIl4LeUUt8EFoHfAf5so5+PEEII0FrviDeMg8FBhoKZ7dd2Zedc57lNvd4L+18gFAuxt2Yve/x7irHEkhsYGCCVStHc3ExVVVWplyM2YGWIE0LsTGUZ4oBPr7jeRGatoxR22Pe/Jnt/2s8DXwZ+SSkVBn5Ua/0yUAv8MZnKWxR4E/iQ1nrpwfO+AOwDLgFO4Ktkgp4QQogNuDx2mX+8848cazrGTxz9iVIvZ02vDrxqXj7Veooqd3ZI6azpNEPevtp9677e/rr9/Pq5Xy/qGkspmUwyMDAALFfhxM7hcrlQSqG1lhAnxA5WliFOa521J+5BZe0/AHcLfP6ngE/lua/Scvkvgb9c43U08MkHf4QQQjwErTV/c+1vAHhz5E2e2ftMWU1p1FpzYegC46FxDjce5sb0DfO+8/vOr3r8R49+lM+98TmUUvzEkfIOpFtheHiYeDxObW0tdXV1pV6O2CClFFVVVUSjUTnkW4gdrCxD3Epa66RS6nfJtDn+SanXI4QQonBz0bms66FYqCxCXCgWYmR+hNeGXuP+XGZU/uWxy+b9PQ09OfewNVc289vP/zaw+hy43U5rTV9fH5Cpwu2E9lix2lNPPUUqlZIBNELsYDsixD3gJ9P+KIQQYgcYDg7zT/f+ifml7FlQoXiuAb/bK5aM8dk3PrtqbQaHzcGHDn0o7/MftfBmGBsbIxKJUFFRQUtLS6mXIx6SceC3EGLnKssQ96DqZlUBfBT4VgmWI4QQ4iF86+63GAgMrLo9HA9v/2JWuDF1I2+AA/jAwQ/smkmSD2t8fJxwOMyBAwfMPVTG4d5ShRNCiNIqyxAH/MiK6yHgL4A/KsFahBBCbJDWOmeAAwjHyiPEWZ3fd57uum6+f//7dPg7eLrz6RKtrPRSqRTXr19ncDBz2o/X66Wjo4O5uTnm5+dxu910dHSUeJVCCPFoK8sQp7VeGeKEEELsIMbh1rmEYrnbKcPxMMPBYbrqura0XTGWjHF3ZnlO1r985l+ae/QONhzcso+7EywuLnLp0iXm55erlAMDA3R0dDA5OQnAnj17sNvtpVqiEEIIModblx2l1Ot5bn8l1+1CCCHKy1R4Ku99ufbEpXWaL771Rf7rlf/KV65+ZSuXxt3ZuyTSmWNDmyuby2LISjmYmJjg5ZdfZn5+Hp/Px7lz53A6nQQCAebn55mdnQWgoUG+XkIIUWplGeKAY3luP7KtqxBCCPFQphbzh7hc7ZSDgUHzOfdm7xFNRLdsbdZWymPN+f65ebQsLS1x6dIlEokELS0tPPfcc9TX15ttk3fv3mV+fh6llJwtJoQQZaCs2imVUr/44KJdKfULgHXXdA8wu/2rEkIIsVEzizPm5cfaHqPT38nf3/x7IHc75co9aqMLoxyoP1D0dSXTSW5N3zKvH206WvSPUWpaa4LBINXV1QW3PYbDYdLpNLW1tfT29ppDS/bt28fAwADj4+MA1NXV4XCU1VsHIYR4JJXb/4k//eC/buDfWm5PAxPAJ7Z9RUIIIfJ6a+Qtrk9d5/l9z7O/br95+/TitHn5RPMJuuu7zRC3mFgkrdPYVKYZRGvNzembWa87PD+8JSGub66PWDIGQK23lpbK3Tcmf2JigosXL9LV1cWxY4VVGpeWlgDw+XxZUycrKytpb29nZGQEgPr6+uIvWAghxIaVVTul1nq/1no/8G3j8oM/3VrrZ7TW3y71GoUQQmQEo0H+/ubfc3fmLl9++8uMh8bN+6ztlI0VjThsDnxOH5AJbYvxRfP+yfAkgWgg67VH5ke2ZM1ZrZRNx3bVmPxoNEo6nWZ6OhOgx8fH0VoX9NxYLBNsc50fdvDgQfPrJCFOCCHKQ7lV4gDQWn8YQGX+1WjRWo+v8xQhhBDbbGh+yAwJiXSCz7/5eard1XidXjOkOW1Oar2ZPVRV7ioiiQiQaamsclfRH+jnv1/776tee2R+BK11UUNWWqezQtzR5t3TSjk6Osrly5fZu3cvc3NzQCbUhcNhqqqq1n3+WiGusrKSo0ePMj8/LyFOCCHKRFmGOKWUF/hPwC8CKaBCKfUTwHGt9b8v6eKEEEIAq6tliVSC2Uj21uWGigYziFW5q5gMZ8bUzy/N8+7ku7w88HLOalE4HmZ+aZ4ab03R1jsYHDTDZaWrkk5/Z9Feu5QWFxd555130FozMjJCMpk075ueni4oxBntlB6PJ+f9XV1dxVmsEEKIoiirdkqL/wjsBZ4HEg9uexv42ZKtSAghRJZCWh5PtJwwL1e6Ks3Lf33tr3mp/yUzwHmdXj524mN013Uvv/5CcVsqb0xmT6XcDa2UqVSKS5cumcHN+K/xuU1N5Z8SarVWJU4IIUT5KctKHPAR4JTWek4plQbQWg8rpdpLvC4hhBBkWhPHFsbM6//quX+FTdlYSi4RTUSJJqJ4nV72+PeYj6lyL1eEjOEiAN113fzU8Z/C7/EzsjDC/bn7QPaEy83SWme3Uu6SqZQ3b940z3Vrb2/n7t3MIeYdHR2MjIwwOztLKpVad0qlhDghhNhZyjXEOYEF6w0PWiy37uAgIYQQBZsMT5oHZvs9fvweP5Ad1FayVuIAHDYHHzj4Ac51njMrR8b+OYDgUrBo6x1bGDNfz+v0sr92/zrPKH/j4+P09/djs9k4c+YMLpfLDHEtLS0Eg0FCoRChUIiamrXbUo12SglxQgixM5RrO+VbwD9fcdsvAq+XYC1CCCFWsLZSdvg7CnqONTi1VrXyL576Fzyz95mstsYaz3LYKGaIuzG9XIU73HAYu62w89PKVSQS4erVqwAcOXKEmpoafD4fzc3NuN1u6uvrqa6uBmBhYWGtlyKdTpNIJFBK4XK5tnztQgghNq9cK3G/BbyklPpfyAw1+RbQC5wr7bKEEEIAWccJdFQXFuLa/e38Wu+vsZhY5HDjYRy21f8EWQeZBKPFC3F9c33m5cNNh4v2uqVy69YtEokEzc3N7N+/HI7Pnj2L1hqbzUZ1dTWjo6PrhjhrK+Vu2CcohBCPgrIMcVrrW0qpI2Sqb9fJHPT9z7TWw6VdmRBCCIBoYrm73WilLIT1QPBcsipx0WBRjhmIp+KMzo+a1/fV7tvU65WDYDATcA8fPpz19VFKmddzVeLS6TT9/f14PB7a2zPbzNebTCmEEKL8lF2IU0o5gUGgS2v9R6VejxBCiNWWkkvmZbejePuoPA4PboebWDJGIp1gMbG4ai/dRg0Hh0npFABNFU2bfr1SS6fTRCKZ8/YqKiryPs7vz4TrhYUFtNbE43EuXbrE7OwsdrudtrY2lFIy1EQIIXagstsTp7VOkDlWQHo6hBCiTG1ViFNKZVXj5qPzm37N/kC/eXm9SuBOEI1G0Vrj9XrXnDrpdrtxu90kEgkmJiZ4+eWXmZ3NnOOXSqXMCpyEOCGE2HnKLsQ98BngDx9U5YQQQpQZ6xEBHkdx2/CKPdzEGuJ2Qyvl4mLmwPK1qnAGo6Xy4sWLRKNRamtrzcO/jdeRyZRCCLHzlGuI+w0y0ylDSqkBpVSf8afUCxNCCJFdiSt6iPMWL8Sl0qmsSZq74WiBcDgMbCzEAezdu5dz586Zxw0YIc6oxMmeOCGE2DnKbk/cA58q9QKEEELkZ63Eue3FreCsHG6yGeF4mGQ6CWTOqVvrHLudwghflZXr7+1rb29ndnaWzs5O9u7dCyyHP2NfnREKvV7vVixXCCHEFijLEKe1/nKp1yCEECI3rTWxlKWd0lm+7ZShWMi8vBsCHGysndLv93P+/Pms23w+n/k6Wmvm5zP7Dtc7EFwIIUT5KNd2SiGEEGUqnoqjtQbAaXdiU8X9p8TaThmIBjb1WrsxxG2knTIX43mLi4uEQiGSySQ+n0/2xAkhxA4iIU4IIcSGbOV+OIB6X7151tlEeGJTLZW7LcQZUyWVUmZFbaOs7ZSBQCYk19bWFm2NQgghtp6EOCGEEBuylfvhACpcFXTXdQOZ1s23x95+6NcKxXdXiDNaIH0+Hzbbw/0T7nQ6cblcJJNJJicnAWmlFEKInUZCnBBCiA3JqsQVeT+c4Uz7GfPy22Nvm+2bG5VViXPt/BBnVM6Mg7wfllHFM0KcVOKEEGJnKdsQp5SyK6XOKaU+9uC6RyklDftCCFFiW91OCXC06Sg+ZyZoBKIB7s/df6jX2W3tlMVqf7Tup7PZbFlHEQghhCh/ZRnilFL7gXeAbwN/9uDmDwN/WrJFCSGEALJDnNuxNb9bc9gcnG49bV6/OHrxoV4nHA+blyvd64/kL3dzc3MA1NXVbep19uzZQ0VFBQ6Hgz179mC324uxPCGEENukLI8YAP4/4O+B3wFmHtz2A+AzJVuREEIIAOLJuHl5K/bEGc60n+HC0AUAbk7dJBKP4HNtbJjHbmqnjMViLC4uYrfbN105a2xs5D3veU+RViaEEGK7lWUlDngS+Dda6xSgAbTWAUCa9oUQosS2o50SoKWqhQ5/BwDJdJLL45c39Hyt9a5qpzRaKWtqah56qIkQQojdoVz/FVgEsn7dqpRqBGZLsxwhhBCGrTzoe6Xe9l7z8tujGxtwEklESOs0kAmbTruz6OvbTkaI22wrpRBCiJ2vXEPcPwL/SSnlAVBK2YDfA75e0lUJIYRgKbE9lTiAky0nzfA1EZ5gdGG04OfupiocQCiU+Xw2O5lSCCHEzleuIe63gb3AHOAH5oHHgN8t5aKEEEKsGGyyhXviIDM45XjzcfP6pdFLBT93t4W4WCxTAfV4tjY4CyGEKH9lGeK01vNa6x8BngV+Fvgx4Cmt9XxpVyaEECLrsO8tmk5pZW2pvDpxNevjr8V60Hela+dPpjRCnNstp+0IIcSjrixDnFLqBQCt9dta67/SWr+k9YONDUIIIUrKuiduO0Lc3pq9NPgaMh87GePOzJ2CnrebKnFaawlxQgghTGUZ4oCvK6XuKqV+WynVUurFCCFEsS0sLXBx9CILSwulXsqGbdd0SoNSipOtJ83rt2duF/S8+7PLB4TXeXf2MJBkMkk6ncbhcMiZbkIIIco2xLUC/w/wEWBIKfUPSqmPPBhwIoQQO5rWmi+9/SW+dv1r/PFrf5wVNnaC7RxsYuhp6DEv35m5s5EbRVsAACAASURBVO6UylAsRF+gD8iEwKNNR7d0fVttaSnzNZcqnBBCCCjTEKe1Dmutv6C1PgecBm4DfwIMl3ZlQgixeeF4mMnwJADRRJQvvf0l3hp5i7RO83c3/o4/eOkPeHfi3RKvMr/trsQBtFe3U+GqAGAxvrjulMprk9fMoLe3Zi/Vns0djl1qMtRECCGEVVmGuBUGgJvAINBU2qUIIcTmzS9lz2gywtufvvmnvDXyFvNL8/zDzX8gmU6WaIXQP9efFYSstnuwCWSqadZq3K3pW2s+/p2Jd8zLJ1tOrvHInUH2wwkhhLAq2xCnlHpaKfUFYAL4v4GvAZ2lXZUQQmxeIBrIefvQ/JB5OZKI8LXrX+Obt7+57fvm+gP9fOHiF/jq1a/y6uCrWfel0ikS6QSQCVYuu2vb1nWo4ZB5eSg4lPdxWmtG5kfM68eaj23puraDhDghhBBWZRnilFI3ge8CbuDHtdY9Wuvf11qPl3hpQgixadZK3ONtj+fdr3Vl/AqvDr7KX737V9u1NICsVs5XBl8hlU6Z16PJqHnZ4/CglNq2ddV6a83L1pbOlWLJGOkHA41ddpccLyCEEGLXKcsQB/wx0Ka1/gWt9YulXowQQhRTcCloXm6qbOLnTv0cz+1/Lm8g6g/0Mx7avt9hWQethGIhrk9dN69bq4JVru0d22/df7dWiCvFnr1cpqeniUQiRXktCXFCCCGsyjLEaa0/Kwd7CyF2q2B0OcT5PX6UUnzw4Af5zWd/k0++8Ek6/B2rnvPm8JtZ1/sD/Xz57S9zcfRiUdc2vzTPTGQm67Y3ht8wLy/ElkPcdg8Lse6/W+vAb2u10Ov0buma8gmHw7z++utcvXq1KK8nIU4IIYRV2YQ4pdQ3LJd/oJT6fq4/pVyjEEIUg7USV+OpWb7srcHn8vFjPT9GrbcWu1o+D+zK+JWs4PI/bv0P7szc4WvXv8ZcZK5oa7s3e2/VbQOBAbMSaK3E+T3+on3cQlj3360V4qyVuO0avLLS4uIigFTihBBCbImyCXHAK5bLL67xRwghdjTrnjjrPi9DZ00nv3n+N/n0+z5NU0VmKG88FefdycxetbROMxGaMB9/c/pmzo8TS8YYDg6b+8PySes0/YF+FuOL9M31mbc7bU7z8utDr2fWHlteeylCnNFymkgnsvbqWVkDXqnaKY1z3eLxeFFfT0KcEEIIAEepF2DQWv8Hy+VPlXApQgixZWLJGJFEpjrjsDnWHLqhlKK3o5dv3v4mAFfGrtDb3ks4Fs563GBwkGf2PrPq43z+zc8zGZ7kZMtJPnbyYzk/xsziDH/17l8xujBKlbsqq4r1/oPvNz/21fGrfPDgB7MCaLV7e9splVJ4HB6iiUy7ZCwZw+fyrXqccT+Urp3SCF3JZJJ0Oo3N9vC/M9Vam2FQQpwQQggor0qcSSk1luf2/DOlhRBiB8gKQZ7qdac7nmw5aT6mP9BPIBpYdc5c31zfqmrbP937J/NA8RtTN1ad96a15uLoRf7z6//ZPDg7FAuRSGWOD/A6vTzd+TStVa1ApvL19tjbWe2U2x3iYEVLZSp3S2U5DDYxQhxsvho3NTWF1hqXy7WpMCiEEGL3KNd/DfKNPNveUWhCCFFk+fbD5VPlruJA/QHz+tXxq1ktjZCpPP3Od37HHEDSP9dvtj8CJNNJwvFw1uO/+s5X+dr1r5mhbaWuui5sysZTnU+Zt70+/HrW+re7nRKyQ1m+fXG7KcQNDw/z5puZoTbNzc2bXpcQQojdoWzaKQGUUr/74KLTctlwCBjc5iUJIURRWatohYQ4gNOtp7k7cxfIDDg523E25+O+fuvrdNd187c3/jbnx3XYHAwEBvj6ra+vWoc1nAEcqMsEx5MtJ/nWnW8RTURXHVJeihDntlsmVOarxCVKH+KMQSTw8CFOa82dO3cAOHjwID09PUVZmxBCiJ2vrEIc8CMP/uuwXAZIAxPAr2z7ioQQooiskyRrvIWFuCONR3DZXcRTcaYXp7k2eS3n47TW/JdL/2VVIAP43JufW9VSCfBExxN86NCH+Mwrn8mq1nXVdQGZ9sXe9l5eHng563lOu7MkAcntXA5x1rBmtVsqcZOTk0QiEXw+Hz09Pdt6sLoQQojyVlYhTmv9IwBKqc9qrf+PUq9HCCGKbTYya16u99UX9By3w82xpmNcHr8MwFBweXtwT0MPgWiAqcUpYHW7pnF9ZYDzOX385LGf5GjTUQBaqlqyjhewru2Jjid4ZfCVrNfwu/0lCRWFVOKs58R5nNsf4tLpdFEqcQMDAwDs379fApwQQogsZbknTgKcEGK3ml6cNi83+hoLft5jbY/lvP35ruf5+BMfzzpTDuBQw6Gs/WxWJ1pO8ImnP2EGOIDn9z9vXn668+ms0FDnq6OnIbuVrxStlJB97tv4wjj3Z++vCqilPmLAGuAAEonc+w7XEg6HmZ6exm63s2fPnmItTQghxC5RVpU4K6XUrwLvA5oA892E1vo9JVuUEEJYDAQG+Jtrf0NjRSM/f/rnsdvsaz4+rdPMRZfbKRsqGgr+WPvr9lPtrmYhtpB1u9/tx+v0cqjhkHlenNvh5qNHP8pgcPU24vd0v4f3dr931e1ddV389PGfZi46xzOdz6y6/8k9T3Jr+pZ5vRSTKSE7lL008BIvDbzEh3s+nHXEQqnbKa2tlPBwlTijCtfR0YHT6Vz7wUIIIR45ZVmJU0r9W+D3gUngaeAd4ARwtZTrEkIIq3+8848EogHuzNzhwtCFdR8fjAZJppMAVLoqs6pK67EpG6dbT2fdppSi2pMJU8/sfQabsqGU4iNHPoLf4885OKWlsiXvx3is7THe2/3enC2IB+sPZrVYGh93u+X6mhln2Rm28py4ZDJJf3//mtW1zYa4ZDLJ8PAwAPv27dvwGoUQQux+ZRnigF8APqS1/g1g6cF//2egrbTLEkKIZSPzI+bly2OX1318VitlReGtlIbTbdkhrtpdjU1l/je+v24/v37u1/nE058ww16tt3bVa7RU5Q9xa1FKZbVc7q/d/1Cvs1nWPXH5bGUl7saNG1y7do2bN2/mfYzRTun1ZgLkRkPcyMgIyWSS+vp6qqtLE5aFEEKUt3Jtp2zQWl8yriillNb6ZaXU35VyUUIIkY9xsPZaZiIz5uWNtFIamiubaa1qZTw0DmRaKa1WBsNKV+Wq16jz1m344xrOtJ+hyl2Fw+YoXYhbp3qptd6yPXFLS0tmhWxsbIzjx4/nPHzbqMRVVVURjUY3FOK01mYrpVThhBBC5FOulbgJpVTrg8uDwDmllByQI4QoKysDRSQeWfPx1smUDb6NhzjIHnBS51s7kK2caOhxeDY95fBQwyG66rpKNi1xvRCXTCfNllWHzYHDVpzfVabTae7cuUM6nQYyw0qmpqbM+7XWLC4uMjIywsTEBIBZRcsV4u7evculS5fM1zPMzs4SCoXweDy0tDxc1VQIIcTuV66VuK+SOSfuK8CfAN8DksAXS7koIYSwWvkGfDA4yJGmI3kfP7O4uUocQG97L3dm7hCIBjjXeW5Dzy32/rBSyNdOmUglcNqdWa2Ubod702FzYWGB4eFhRkdHzTbJ9vZ2RkdHuXfvHsFgkPn5eYLBYFZYs9vtNDU1ce/evVUhTmvN3bt3SaVS7Nmzh6amJvM+owq3d+/enFU+IYQQAso0xGmtf9dy+bNKqatANfDt0q1KCCGWJVIJEuns4Rb9gf41Q5x1T9zDVuLcDje/fOaXC358T0MPt2duA/B42+MP9THLSb5z3yKJCH67vyj74RKJBCMjIwwPDzM/P2/eXllZyYEDB2hsbGRsbIxAIEAgEDDvd7vd1NTU0NjYSFtbGy6XC6UUyWSSdDpthrKFhQVSqRQAExMTZojTWptVvM7OzodauxBCiEdDWYa4lbTW6499E0KIbWQNC4Y3ht+gu66bnsbV3d+JVMI8HsCmbDmHjmyFH+35UeZj81S5qzi/7/y2fMyt5LK7ct6+GF/E7/E/9GTK+fl5JicnsdvtWdUzp9NJe3s7e/bswe9fPuD88ccfJxAIYLfbqa6upra2Fo9ndbuqy+UiFosRj8fxeDKhMhhcPpB9amoKrTVKKRKJBFprnE6n+VghhBAil7IJcUqpPyvkcVrrX9nqtQghxHqsYcGQTCf5iyt/wc+d/jkONx7Oui+4tPzG3e/xr3umXLE0VjTyiac/sS0fazvkq65FEpn9iA9TiYvFYrzxxhtZh3TX1dWxf/9+mpubsdtXf6/a2tpoa1t/YPJ6IS4ajbKwsIDf7zeDo8uVO6gKIYQQhrIJcVgO9BZCiHIXTS6HuEpXJU67k0A0QEqn+MqVr/BLZ36Jrrou8zFzkeVDvjczIfJRl29PnDFUZiMhbnJykoGBAeLxOLFYDL/fT1VVFQ0NDXR0dBRleIsRyKz74owWzOrqahYWFpicnJQQJ4QQYkPKJsRprQvf5CGEECW2lFgOC63VrXz0yEf5wsUvmEHuu/e+y8ef+Lj5mEB0ee/UdrVS7kb5plOGE2Eg+/uyVohLJpNcuXLFDE4Oh4OzZ8+aZ7sVi9udWa9R5Usmk4TDYWw2G4cOHeLixYuMjY1x8OBBCXFCCCEKJqOvhBDiIVgrcV6HlxpvDb/a+6tm9WZofijryIG56HIlTkLcw8vXhmp8rW9M3zBvq3SvPifPYOx7q6mp4dChQzz55JNFD3CwOsQFg0G01lRXV9Pc3IzL5SIUChEKhUgkMoNynE5n0dchhBBidymbSpyVUqof0Lnu01p35bpdCCG2U64BGrXeWvZU72FofgitNbdnbpvnugWjy/ugpJ2y+CKJCH1zfdyduQtkzsg71XIq52OXlpbo6+sD4NixY9TVbd33I1eIA6ipqcFms9Ha2srg4CCjo6NmBU4qcUIIIdZTrpW4TwGftvz5Apk9c39SwjUJIYTJGuKsbXvWyZS3pm+Zl2ejywd9SyWu+MLxMN++u3wKzWOtj9FU2ZTzsXfu3CGVStHS0rKlAQ5WhzhjP1xtbeZnoKOjA4DR0VFppxRCCFGwsqzEaa2/vPI2pdQ3gX8P/P72r0gIIbLlG2V/pOkI37n3HQDuzt4lmU5iV/bsPXE+CXHFdnfmLvHUg/1tNgfvO/C+nI8Lh8MMDQ2hlOLIkfxn+hWLNcRprbMqcZAJcy6Xi2g0ap5JJyFOCCHEesq1EpfLVWDnH3IkhNgVrFMQrSGuqaLJrLTFkjEGAgNEE1FiyUwlxmV3UeGs2N7F7jI/eewnsSkbTtvy3jEjwAE8tecp/B5/zufevHkTrTWdnZ1UVubfM1cs1hC3tLTE0tISTqeTiorMz4BSyrwsIU4IIUShyrISt5JSygv8c2Cq1GsRQghYUYlzLIc4pRQ9jT28PvQ6kGmptN5f660tyuj63WhsbIyrV69SVVVFV1dX3nPYett7OdF8gqXkEn/w0h9k3ed2uHl+//M5nzc3N8fExAR2u51Dhw4Vff25WEOctQpn/Rnw+XwEAgFppxRCCFGwsqzEKaXSSqmU8QcIk9kn93+VdmVCCJGRNZ3SmT3V8EjjcpverelbWfvhZKhJfuPj4ySTSQKBAFevXkXrnPOtgExY8zl9q24/v+88Ptfq27XW3Lx5E4Du7m7z4O2tZg1xK/fDGVZOxZTplEIIIdZTrpW4H1lxPQTc0VqHS7EYIYRYaa3zyPbV7sPtcBNLxghEA1ybvGbeV+eTEJdPKBQyLyeTSebn5829Y7k47U5cdpfZSlnpquRc57mcj52ZmWFubg6Xy0V3d3dxF74Gm82G0+kkkUgwNZVpJln5Ofl82aFTKnFCCCHWU5aVOK31iyv+vC0BTghRTtaqxDlsDg78/+zdeYwk6Xke+OeLjLyvyrqr6+5r+mJPzz1kzwzJIUVRognLkqAVsTYsCTS8gAStZF0WJNuCzPX+sytogQUtSLRMETqWsiULkk2aw0vicIbT0z09fU6f1V1VXWdWZuV9RUbEt39ER2RmVdbZdWY9P6DRmRGRWZE1Mz391Pt979tx1Hl+c/6m8/hQpPkSwf2sXC7jzTffxKVLl1Aul9d+QROmaSKfz0MIgf7+fgDW8se11M+C+/jhj684DHxsbAwAcPjwYajqzv780q7G2SF1aYhbWoljiCMiorXs1UochBCvAngeQLj+uJTyd3fnjoiIalbaE2c72XWyIbzZBiID23pfO01KicuXLyOdTiOdTiOZTOLs2bPo6+tDLpfDu+++C5fLhZ6eHjz11FNQlOY/OywUCpBSIhAIoKurC9PT00gmkzh8ePXRoC8Pvoyv3/06RmOjeH7g+abXpNNpLCwsQFVVjIyMPOlH3jCv14t83vo5ZCAQcEKdrb4Sp6rqit8jIiIi254McUKI/xPAvwJwA0Cx7pQEwBBHRLvKMA1nCZ8Qomn156nOpyCEaNjXFXAH0BHo2LH73AljY2NIJpPw+XyIRCKIx+O4dOkSBgYGkMlkUCxaf4Tncjm43W4cPXq06fvYVapwOIyODut7tLi4CCnlqo1gzg+fx/P9z8Pj8qx43fT0NABgaGhoV/ab1Ye2pfvhgMZKHKtwRES0Hnv1x33/AsBLUsrnpJSv1v16bbdvjIioYSml6m8aHgKeAIaiQw3HBqIDLdeZcn5+HgBw5swZvPjii/jQhz4El8uFqakp5HI5hEIhnDt3DoA1ZNsOdUvVhzi/3w+fzwdN0zA3N7dqgxPAanKy2vfV/prbPdh7JfUhrtkeP5fL5VzDpiZERLQeezXEFWBV4TZFCPELQoj3hBCaEOLLq1z3GSHE94UQaSHEnBDij4UQbUuu+YIQIvH4mv8ohOD/YYkOuKJWCyIr7cECgBNdJxqeD0YHt+2edoOU0plt1t7eDiEERkZG8NGPfhTt7e1wu9145plnMDg4iP7+fhiGgXv37jV9r/oQJ4RAZ2cnAODSpUv4xje+gffeew+PHj1CtVrd8H3aIW7p3rOdslYlDqgtqWQljoiI1mOvhrj/C8C/FZv/kfUMgH8P4D+tcV0UwBcAHAJwAkA3gN+3TwohPg/gp2HtzTsK4ByA397kPRFRi5jLzzmPV1seuTTEDURbaz9cPp+HYRjL9nkFg0GcP38en/rUp5zKk90RMpFIrPhegBXiAODEiRM4fPgwQqEQqtUqZmZmcOXKFXz729/G2NjYmtU5m5TSCXFLu0DuFPt7I4RAJBJpeo0dMBniiIhoPfbknjgAfwPgWwB+WQixUH9CSrn6Lnfrmr8GACHE8wBW/FuTlPLP654WhRB/COD/rjv2swB+T0o5/vj9fhfAHwL4d+v7GETUimayM87j/kj/itd1BbvQE+rBfH4eHpen5Spx9vDqaDTa9Hx9g45IJAJVVVEsFlEqlZzQIqXE2NiY05kyFLK6Tfr9fpw+fRqnT59GoVBAPB7H7OwskskkPvjgA7S3t69Y1aqnaRp0XYeqqru2VNGeSReNRuFyuZpew0ocERFtxF4NcV8FMAWrKtZ8A8X2eA1AfTu5MwCu1j2/AmBACBGVUmbqX/h4GebSzQ6t9WN3IgLQGOJWGxkghMBPn/1pXJi6gJNdJ5eNItjv7KWUq81yswkh0N7ejng8jsXFRfT39yOfz+PKlSvOEOxjx441DTnBYBCjo6MYHR3FhQsXEI/HUalU1nWPpZK1fzEQCOzafsSuri4cPXoUPT09K17T09ODmZkZdHd37+CdERHRfrVXQ9xZAJ1Sys0NHNoEIcTrAD4P4Hzd4RCA+rCWfvx7eMlxAPglsEJH1PKklJjOTjvPV6vEAUB3qBufPfHZ7b6tXbFWJW6pjo4OxONxJJNJVCoV3L59G4ZhwOfz4emnn15XgLGXJq53b5y9lDIYDK7r+u0ghMDJkydXvaa9vR2f+MQnduiOiIhov9urIe4mgHZYe9u2nRDiJVjVv5+SUtZX4vIA6jcw2H9TyTV5m98H8OUlxwYAvLlFt0lEe0CymERZt36+FHAH0OZbuwrViqSUyGazANZXiQPgjA6YmJhwjg0MDODMmTPrXupoX6dp2rqu3+2mJkRERNthr4a4PwXw10KI3wMwV39CSvm9rfxCQohnAPwdgH8hpXxjyekbAJ4G8Pbj5+cATC1dSvn4vtKoVers997KWyWiHSKlhG7qcLuWB4uZXONSyoP633mxWIRhGPD7/esOYPaeMMMw4PV6cfbsWfT29m7o69pfa6OVuN1qakJERLQd9mqI+38e//7/LTkuATTfFV5HCKHC+mwuAC4hhA+AIaWsLrnuDID/CeAXpZR/0+Stvgzg14QQX4M19uDfAPjjDXwOItpHKnoFf371z/Fw8SEkJD489GH86FM/2nDNepuatDp7T5rdtGM9FEXBmTNnkM1mcfz48U018WCIIyIi2qMjBqSUygq/1gxwj/02gBKAfw3gnz5+/EcAIITICyFefXzdrwDoAvClx8fzQoh83ft8CcB/AfAegDEA12GNJCCiFnR55jLuJ+/DkAZMaeLtybdRrjZuzd3IfrhWVi5b35eNhDgAGBoawpkzZzbdhZEhjoiIaI+GuCclpfwdKaVY8utnHp8LSSnffPz4Zx+Hw1D9r7r3kVLK35JSdkopo1LK/21pNY+IWkd9lQ2wllWmyqmG5+vtTNnq7Epc/Xy4nWCHv/XsiVtYWGCIIyKilrQnl1MKIf7tSueklL+7k/dCRAfHfH5+2bF0KY2+cB+AxqYmQXfwwDY1ATZfiXtS663ELS4u4sKFC5BSYmhoaMX5bERERPvRngxxAD6+5PkhAKMAvg+AIY6ItpwpTcTz8WXH0+Vav6KGKlz04DY1AWohbqcrcesNcfPz85BSYmBgAGfPnt2JWyMiItoxezLESSmXhjgIIX4Jje3+iYi2zGJxEVVzeTBIlVIoaAUEPcGGzpStvh/OMAyYprli58nNNDbZCvUhTkoJKSUUZfnOAPv+Ojo6DnTYJiKi1rQnQ9wK/l8Ak2Aljoi2wVx+runxtybewlsTb+GlwZcaKnWHwq29H+4HP/gBCoUCPvaxjzWttu2FStyVK1cQj8fx+uuvLwubu7Vnj4iIaCfsp8YmowD4f2Mi2hb1++GG2oaWnb/w6MKB6UxZqVSQSqWgaRrGx8dXvAbY+UqcoihQVRVSSszOzkLTNOTz+WXXMcQREVEr25OVOCHE0llsQQCfAPCXu3A7RHQAzOdqIe5E1wlMpieXXaMZVkfEoCeIqC+6Y/e201KpWkfO8fFxHD16tKExiGma0DQNQohNjwp4Em63G7quwzAMAM07VTLEERFRK9urlTix5Nc8gH8F4Bd286aIqHXVL6c82n4UqrLyz7gORVq7qUk6XWvmomkapqamGs7XL6Xcje/D0qWTS5ucSCmdELcbIZOIiGi77clKnJTyZ3f7Hojo4KjoFSyWFgEAilDQHepG1BdFsphsen0rL6UEapW4/v5+TE9PY2xsDENDQ05g262llLalwWxpJc5ueuJ2uzlagIiIWtKeqsQJIU4LIX5zhXP/WghxYqfviYha30JhAVJKAEBHoANulxsCK1eYWjnESSmdStypU6cQCARQKBQwP19bbrpbTU1sa1XiuJSSiIha3Z4KcQB+DUBihXNxAL++g/dCRAdE/VLKnlAPAKBULa14fSuGuHw+D9M0kcvloOs6AoEAfD4fRkdHAQAPHjxwrt3tShxDHBERHXR7LcS9AuC/rHDurwB8dAfvhYgOiPqmJr2hXgDA60deb3pt0BNExNtaIysfPHiA7373u3j33XcxPW114IzFYgCAoaEhuN1uJJNJp0LHShwREdHu2mshrltKmW52QkqZAdC1w/dDRAdA/XiBnrBViXu2/1l8ZOgj+MjQR9Ad7HbO90f6W6qpyfz8PD744AMAwMLCAu7fvw8ATgVOVVUMDVkjF8bGxgAA2WwWABAMBnf6dgEsD3FL98QxxBERUavbayGuIIQYbHbi8fGV1zcREW1Ss+WUHpcHnznxGXzmxGcalk+20lLKbDaLy5cvQ0qJrq7az8gGBgacShxgBTohBGZnZ1EoFJBMWg1fOjo6dvyegeWNTViJIyKig2avhbjvAfjfVzj3CwD+fuduhYgOglwlh4JWAGAFt3Z/+7Jrnut/DopQ4Ha5ca7v3E7f4ppyuRzeeustJ1ytR7lcxrvvvgtd19Hf34+XXnoJhw4dQiAQwMmTJxuu9fv96O/vh5QSV69eha7rCAaD8Pv9W/1R1sWuxNmdJ1cKcRwvQERErWqvjRj4PwC8I4RoB/CnAKYB9AP4XwH8LwA+vIv3RkQtqGEpZain6VLJ0fZR/MZHfwNuxQ2vuveqOw8fPsTi4iLu3r2LD3947T8mDcPAxYsXUSqVEIvF8PTTT0MIgeeee27F1xw+fBhTU1NOUOzs7Nyy+9+oSCQCRVHQ29uL6elpLqckIqIDZ0+FOCnlNSHEjwL4AwA/A0DCGvZ9F8BnpJTXd/H2iKgFLQ1xKwl5QjtxOxsmpUQ8HgcAJJNJlMvlVbtGSilx5coVpNNpBAIBvPDCC+uapRaNRtHZ2YlEwmogvJshLhQK4Yd/+IehKAqmp6eduXB2ALdDHUMcERG1qj0V4gBASvn3AE4IIY4C6AYQl1Le3927IqJWNZer2w8XXjnEbbWFhQVcu3YNp06dQl9f34Zfb89zE0KgVCo5x2ZnZ9HX14d4PI54PI5CoYBnn30W4XAYAHD37l3MzMxAVVW8+OKLGwo6R44ccULcbu2Hs6mq6vyu6zoMw3COsRJHREStbs+FONvj4MbwRkTbqr4SZ48X2AnT09MoFou4fPkyXn75ZScUVSoVJJNJ9PX1rdoFMx6P491333Wee71eVCoV3Lp1Czdu3Gi4dmpqCidPnkQikcDdu3edpZN2sFuvrq4uDA8Pw+1275mA5Ha7oes6NE2DqqpIJpMolUpQVXXX5tgRERFtt73W2ISIaMeY0kQ8H3eeb/rCBAAAIABJREFUr7accqvlcjnrHkwTFy9eRD6fBwBcuXIF7733HiYnJ1d9vT2zzXby5Em43W4YhgGXy4Wenh4MDw8DAFKpFEzTxPXr1or048ePo7u7e9l7rkUIgbNnzy5rfLKb7CYn9pJKe1zCkSNHoCj8XxwREbWmPVuJIyLabovFRVRNq7NhyBNC0LMzc8+klE6Is/eZXbhwAc8++6yzv218fBxDQ0MrVuPsJZSAFWR6e3sRi8VQLpcRi8XgcrlQqVQwMTGBTCaDsbEx5PN5hEIhHD16dPs/5A6xO1BWq1VMT08jnU7D5/Ph8OHDu3xnRERE24c/piSiA6t+PlxveOeWUhaLRRiGAZ/PhxdeeAFtbW0oFot4++23nWuy2SxSqdSq7wEAL7zwAl5//XW43W6EQiF0dnY6jUq8Xi8CgQB0XXeGeJ8+fbqlKlR2Ja5UKuHWrVsArKqkvT+OiIioFbXO/8mJiDZovZ0pVyOlhJRyQ6+xq3CRSMRpMBIIBGCaJgA4g7fHx8cBwDlezw5x4XB41XlobW1tAABd1xEIBBqGercC+7Pfu3cP5XIZ0WgU/f2tM5CdiIioGYY4Ijqw5nN1TU02UYnTNA3f/OY3ce3atQ29zg5xdmMRr9frdIrs6urC2bNnnfb59+7dwxtvvIH333/fCYumaaJcLkMIsebA7Vgs5jweGBhYtVnKfmRX4goFa2D76dOnW+4zEhERLcUQR0QHVqKYcB73BDdeictms6hUKpienm5aLVvtdQAaukOGw2F88pOfxEsvvYRAIIAjR44AAG7fvo1qtYqpqSk8evQIAFAulyGlhM/nW3NppF2JA4DBwcF13+N+YYc4AOjr69v10QdEREQ7gSGOiA6sYrXoPA55Nz7Mu1wuAwAMw1jWLXI1SytxNkVRnCrSsWPHEAgEANSC2M2bN1EoFJyllPb51bS1taGrqwujo6Prun6/sUOcoih7qmsmERHRduLObyI6EKSU+Or1r+JR+hF+7NSP4VjnMZSrZee8T7Vmit29exeVSgVnzpxZc1mePVQaAJLJJNrb29e8j2KxiHw+DyHEqnPaXC4XPvzhD2NxcRGHDh3C+++/j5mZGbz//vtORW2tpZSAFW5efvnlNa/br9rb26GqKo4ePYpgcGe6ixIREe02VuKI6EAYWxzD9bnrSJfT+PLlL0M3dWe8gCIUeFweaJqGu3fvYnx83KmWrWZpiJNSIplMYm5ubsXX3L9/H1JK9Pf3O10kVxIIBDAwMABFUXD27Fn4fD6kUincvXvXOX/QhcNhfPrTn8axY8d2+1aIiIh2DEMcER0I9Z0oAaCsN1bhhBBIJBJO85BEIoG11Ie4xcVFvPnmm3j77bdx8eJFzM/PL7u+WCxicnISQogNhw63241nnnnGuvfHyzgZ4ixsZEJERAcNQxwRHQimbGw8Ut+Z0qt6AQALCwvOsY2GOMMwkMlknOravXv3GkYPSClx7do1pwoXCm18D15nZ2fDEGuGOCIiooOJIY6IDoRcpXF55GRm0nnsU32QUjaEOHt55GrsitixY8fQ3d2Nc+fO4ZOf/CQ8Hg9SqRQWFxedax88eICFhQV4PJ4nasBx4sQJRKNRqKq66p46IiIial1sbEJEB8KyEJeuhTi/2498Po9SqQSv1wtVVVEoFJBOpxvmrC1lV+JGR0fh9Xqd46Ojo7hz5w7u3buHjo4OSCmdfWznzp2Dz+fb9OdwuVw4f/48DMNYdcg3ERERtS5W4ojoQFga4ibSE85jv+p3qnBdXV3o7OwEYFXjVmKaJjRNgxBiWZgaGRmBqqpYWFhAOp1GNpuFrusIBALo6dn4PLqlXC4XAxwREdEBxhBHRAdCtpJteF7Ra/vZvKq3IcRFo1EAQD6fX/H97Cqc1+td1ljD4/FgeHgYgNWN0l5WuZ4RBERERERrYYgjopYnpVxWiavndXmdqltnZ6fTMMQeqt1MfYhr5vDhw1AUBXNzc5iamgLAEEdERERbgyGOiFqeZmjQDG3F89VyFYZhIBKJwOfzOSGuVCqt+Jq1QpzP58Pg4CCklEin0wAY4oiIiGhrMMQRUctbrQoHAOW81WWyq6sLAJzGI6VSacUOlXZnytWalBw9etRZaul2uzc1VoCIiIhoKYY4Imp5S/fDLVXMWssm7RDncrng81ljB+ywttRalTjAmuPW398PwKrCcSg1ERERbQWOGCCilrdaiDMMA1pRgyvoalju6Pf7US6XUSwW4ff7l71uPSEOsOa66breMKSbiIiI6EmwEkdELa9+OaXb5W44Vy6X4Vbc6OjogMvlco6vtS8uk8kAQNOAV8/v9+OFF15AR0fHpu6diIiIaCmGOCJqedlyrRI3FB1qOFculeERHmcppW21DpW5XA6pVAqqqjoz5YiIiIh2CkMcEbW8nFarxA1EBxrOlcoluIV7WYizK2zNQtzk5CQAoL+/H6rKVelERES0s/i3DyJqeflKbWj3YHTQeVzVrNECYX94WedIuxL36NEjZDIZPP3002hra4NhGM7ct6GhxqoeERER0U5gJY6IWl6xWqumtfnb4HdbVTZNs2bH9XT2LOscWb/XLZvNOtW3ubk5aJqGaDSKtra27b51IiIiomUY4oio5eU1qxJXLBZx58YdyKo1+003dABAKLh8ftvShiX5vPUeExMTAFiFIyIiot3D5ZRE1NKklChVrQ6T2WwWaSWN+GIcIiRgGAaA5gO7XS4XnnnmGZRKJdy+fRuZTAb5fB7JZBIul8uZ/0ZERES00xjiiKillaolmNIEAMiqhEu44BZupAtpqC7rj8BmIQ4ABgasJigTExMolUq4desWAODQoUNwu91NX0NERES03bickohamr0fztANeIQHQgi4hRu6rjvLKdea9RaNRgFY++EALqUkIiKi3cUQR0Qtzd4PV9Wr8CpexGIx9Ph7YJom9KoOl3CtWImz2SEOAMLhMGKx2LbeMxEREdFqGOKIqKUVtAIAoFq1Qlw4HMYz3c+g3d0Ol3Dhldgr8Hq9q75HfYgbGhpa1smSiIiIaCdxTxwRtTR7OWW1WkVUiSIUCkHTNPxQxw/BlCaCgeCaoSwajUIIASGEs0+OiIiIaLcwxBFRS2uoxLm8CIVCKJfLAABFKGsupQSsxifPPPMM3G43PB7Ptt4vERER0VoY4oiopdkhTq/q8LqtEFcqlZzz6wlxADhSgIiIiPYM7okjopZWrBYhTQnd0BFQA/D7/QgEAs75tTpTEhEREe01DHFE1NLyWh5VvQoAaAu2QQiBYDDonF9vJY6IiIhor2CII9omN2/exLVr13b7Ng68glZAtWqFuPZwOwCr+mY3M2GIIyIiov2GIY5oG5imiQcPHmBiYgKGYez27RxoxWrRCXEdkQ4AgBDCWVLJEEdERET7DUMc0TaoVCrOY13Xd/FODjYpJYpaLcR1tXU5544cOYKenh4O7iYiIqJ9h90pibbB0hC31jBp2h6aoaFqVqFXdbiEC7FILbANDw9jeHh4F++OiIiIaHNYiSPaBvYcMoCVuN1U0AqQUqKqV+FVrPECRERERPsdQxzRNuByyr2hWC1C13VIKRHyhqCqXHxARERE+x9DHNE2YIjbGwpaAXrV+v5H/JFdvhsiIiKircEQR7QN6pdT2k01aOcVqoXajLhA2y7fDREREdHWYIgj2gasxO0N9TPiYiF2oSQiIqLWwBBHtA0OUogrlUp4++238ejRo92+lWXqxwswxBEREVGrYIgj2gYHKcTduXMHyWQS9+/f3+1bWaa+EmcP+iYiIiLa7xjiiLaYlPLAjBjI5/OYmppyHpdKpV2+o0aZUgamaUJRFEQD0d2+HSIiIqItwRBHtMV0XYdpmg3PW9Xdu3chpXSeJxKJXbyb5VL5FADA7XYj5OWMOCIiImoNDHFEW6y+Cge0bojLZrOYmZmBoig4cuQIAGBhYWGX76pRupgGYIW4oCe4y3dDREREtDUY4oi2WP1+OKB1Q5xdhRseHsbg4CAAqxJXX5nbbdliFsDjEOdmiCMiIqLWwBBHtEmmaeL27dvIZDINx+0Q5/f7AbRmiEun05idnYXL5cLRo0cRCoXg8/lQqVSQy+V2+/YAALqpo1ApAAA8bg/8bv8u3xERERHR1mCII9qkqakp3Lt3DxcvXoRhGM5xezllMGhVfloxxN25cwcAMDIyAp/PByEEurq6AOydfXH14wUigQiEELt8R0RERERbgyGOaJPs/V+lUgkPHjxwjtuVuVjMmkvWaiFucXER8Xgcqqri6NGjMKWJu4m78Ea8AGrfl1QqhTfeeAPT09O7cp+5cg66rkMIgbZA267cAxEREdF2YIgj2gQpZUPF6d69e04Fzg5xnZ2dAOBUg1qFHVhHR0fh8XjwNx/8Df7k8p/gq2NfhWZqSCaTME0T9+/fR6VSaQi4O2khY4VJVVXZ1ISIiIhaCkMc0SZks1lomga/34++vj4YhoFbt25B13UUCgUoiuJU4gzD2FPNPp6ElNKptA0PD2MyPYn3pt8DAFRlFWVPGYZhYG5uDvPz8wCs/XNLO3buhETGCtlutxsBT2DHvz4RERHRdmGII9oEO8h0dXXh1KlTUBQFU1NTGB8fh5QS4XAYLpcLqqpCStmwZ24/S6fT0HXdaWTy9TtfbzivBK0/Um7cuNEQXOfm5nb0PgFgMbcI4PGMOA9nxBEREVHrYIgj2gR7KWVnZycCgYAzJ+327dsAgGg0imuz1zBRnoApzZbZF1f/uW/M38BkZrLhvBJUIIRwOnT29fUB2J0QVz/om8spiYiIqJUwxBFtkGEYWFy0qjz2vrejR4/C5/M51acFuYCvXv8qLmQuYKo81TIhzq5AtrW34Rv3vrHsfEmU8Nprr2FoaAhDQ0P40Ic+BCEEEonEju8NTBdqg74Dbi6nJCIiotbBEEe0QalUCoZhIBqNwuu1OjKqqoqTJ08613x75tsAAEVR8E7mnZYIcbquI5VKQQiB+6X7SJWsSpeqqM41iUICkUgETz/9NJ5++ml4vV50dHRASunskdsJUkpkSlaDGVVVOeibiIiIWkpLhjghxC8IId4TQmhCiC+vcl2fEOJvhRCzQggphBhpcs0XhBAJIURaCPEfhRDubbx12gfsapRdhbP19/ejt7cXsVgMwmXNJBNCQEK2RIiLx+MwTRO+sA/fn/y+c/wTRz7hzGBLl9PQTeuzzuZm8Xe3/w5aQAPQfEnlrfgt/PXNv8Z8fusC3sTEBMbGxlDWy1BdKhRF4XJKIiIiaiktGeIAzAD49wD+0xrXmQD+J4Afb3ZSCPF5AD8N4HkARwGcA/DbW3ebtB/Z+8Ls4dY2IQReeOEFvPLKK06oURQFAqIlQtzU1BQAYFJMoqxb3SY7Ah34yPBH0Oaz5rBJKZEqpSClxF9c/Qu8M/kOvjn3TZSNMhYWFhoavJSqJXzl0lfwxrU38Ec/+CMY5pM3f9E0DdeuXcOtW7dQNstQ3VaVkN0piYiIqJW0ZIiTUv61lPJvACTXuG5eSvlFABdXuORnAfyelHJcSpkA8LsAfm5r75b2E03TkMlkoCgK2tvb17xeEQoUoez7EKdpGuLxOKYr07hfvO8c/5HjPwJVUdER6HCOJYtJzOZmkSxa//lJITEjZqDresNsvYXCAhKLCeTzedyZuIM33n/jie/TbqgCAJqpQVWtEMfulERERNRK1LUvOdDOALha9/wKgAEhRFRKmam/UAjRBqBtyesHtvn+aAfouo7Lly87oU1Kic7OTrhcrjVfKxQBAbHvB37Pzs5iobKA9yvvozNiLSM93H4YJ7pOALAqcveTVrhLFBKYzk43vP6R+QjDYhhzc3NQVRW3bt2C7JLO/DgpJb7xwTfww8/8MBRl8z9b0jTNeb+KWXEamvjd/k2/JxEREdFewxC3uhCA+rCWfvx7eMlxAPglAP9uJ26KdpY9uHp+ft6p7Bw+fHjF601pOo8VxarE2eFiv1pcXMT90n34Aj4AVmj7qQ/9lLNstL4SN5mZdKpwNsWjYDw/Dv+cH7lcDqlUCnfn7kJKCa/Xa1Xpygncnb+LE30nNn2f9vfZE/AgHAlb8+xUX0PzFSIiIqL9riWXU26hPIBI3fPo499zTa79fQCjS369uq13RztierpWVdJ1He3t7cv2w9UraAXnscfjQdWsIplcdWXvnlepVFA0ik718bMnPouwN+yc74/0O49vzt/EXK6xiYnb7cZ4dRyVSgWplNXVMlW2fvf5fPB4PACAO7N3nug+7RDnj/qtBjOKYFMTIiIiajkMcau7AeDpuufnAEwtXUoJAFLK9OO9c84vAFM7dJ+0TTRNw8LCAoQQ6OjogBACJ0+edCpQzeS1vPPY5/MBAogn4/t6X5ymaSibZSfERXyRhvPDbcM43XN62euG2obgVa0xDJpbw5w2B0MaSOtp5HTrZyF+v98JcROJiSe6T3tPnC5q32uOFyAiIqJW05JrjIQQKqzP5gLgEkL4ABhSymUbkx6fszc3eR8/r0hravOXAfyaEOJrAAoA/g2AP96Bj0B7xNzcHKSU6OrqwosvvghN06xgtopcpVaoVRQFHo8HZaOMxcVFdHd3b/ctb4tKpYKyWUZUsYrRSxuFCCHwE6d/AslCEnN5qwrndrnxySOfxO2F23h78m0EAgHcWbyDm/mbkGGJxeoiXC4XvF6v07VyOtO4l26j7EqcodQ6XbIzJREREbWalgxxsMYA1O9P+6cA/gTAzwgh8gB+REr55uNzpbrrbj/+fRTAOIAvARgB8B4AN4C/APCFbbtr2nNmZmYAAIcOHYKiKGsGOKBxOSVgVeM006ro7acQZxgGEokEuru7UaqUUDWrUFzWHj+7YUg9r+rF51/4PK7NXUPYG8ZobBR+tx8xfww/ePQDeL1ezCvzEEKgO9wNt9vtNDGxK3HxfBy6oUN1be6PJjvEVVH7eQ2XUxIREVGrackQJ6X8HQC/s8K50JLnK66Le1yN+63Hv+iAqVQqSCQSUBQFfX19635dfSUOAPw+PypaxRkSvl/cuXMHY2NjOHPmDIp6EYpQIIS1x2yl5aR+tx8vDb7UcKw90I5T3adwc/4menp6nON2II75YzBMA7PKLKp6FdOpaQx3Dm/qnpuGOC6nJCIiohbDPXFEK5idnXWWUrrd7nW/rn5PHAB4vB4YioFcLue01N8P5uasZZHJZBJlswzFZf1xsZmZa+eHz694riPQgd5wr1ONezD3YBN3a7FDnIZaN1BW4oiIiKjVMMQRraB+KeVGLA1xQgj4Q9acsv1SjSsWiygUrGWhmUwGFaPiNDUJeTce4oaiQxiMDjY951bc6A31wuu1GqCMJ8Y3d9Ooq8RJLqckIiKi1sUQR9REuWw1IlEUBb29vRt6bb6SX3bMF7aWDj5JiNN1HVNTUzsyOLz+PovFolWJUzZfiRNC4FPHPgVVUeFWGqua7YH2hkrcdHrzzU3sEFc2axXPZvv3iIiIiPazltwTR/SkZmZmIKVEb2+vM+B7vZZW4gDAHbCCSyKRgJRy1REFS1WrVRSLRVy9ehWZTAYjIyP40Ic+tKF72qh4PN7wvGyW4VKsSlzYE272kjUdbj+MX3nlVyCEwEx2Bl95/ytwK268NPgSqkbVCXGz2dlNvb+u6zAMA4YwkKnUpoCwEkdERESthiGOWlomk0Emk8Hg4OCGgtN6l1IWtAK8qheqUvtPaWljEwCQLgm/349SqYRsNotoNLrsmmaSySR+8IMfwOqxY5mdncWZM2c29Hk2wjRNJBKJhmNlswxFfVyJ28RySps9X+6prqfwG6/9BlRFRcATgG7q8Lg9UBQFmUoGmXwG0dD6vkc2TdOwoC3grdxb6PXUqqcMcURERNRquJySWtrFixdx9epVjI+Pr/s1xWIRqVQKqqquOhLgzfE38R/+/j/gSxe/BM2wlvFV9AqK1eKya0vVErq6ugAsX1Kp6zoymWXz4wEADx48gJRWAOzv70cgEEClUkEymVz359moVCoFXdcRCoWcDpIVs7YnbqtCUcQXcWa4qYqK7mC3U427P3d/w++naRomy5OQQjYc53JKIiIiajUMcdSypJQolawxgHfu3MGFCxfwne98Z9lSwaXsKlxPT8+KSykregXfGfsOAOBR5hEuT18GAKTL6abXF6tFdHZ2AsCyKtf169fxve99D+l042vL5TLm5625aq+++iqeffZZ9Pf3A7CqcdvFDpnd3d3w+62GLE+6J249esI9TogbXxjf8Os1TUPeyDth0+Zxebbi9oiIiIj2DIY4aln17fyr1Sri8TgKhQIuXLiAq1evrtggxA5ZqzU0uTF/w6m+AcDbk29DSolUKeUcqw87E+kJiKCAEALJZBKGYTjncjlr+WU2m234Go8ePXL25dmdG+15dfb4g+1gh7iuri4nxJXMkrMnbttCXKgW4h4tPtrw6+2mJnbYBIDuYPe2LTslIiIi2i0McdSy7Bb5gNUdMRaL4amnnoKiKJicnMQ//MM/LKvKSSmRSllBLBaLrfje78+83/A8WUzi9sJtJIu1ZY4D0YGGa7548Ysoe8owTbNhOWSlUgHQGDqllJicnAQADA0NOccjkQi8Xi8qlYpTZdxKmqYhk8lAURR0dHQ0LKd05sQ9wZ641fSGah0qZzIzG369pmmomlUnbKqKis+c+MyW3iMRERHRXsAQRy0rn7e6RA4NDeFTn/oUzp8/j+PHj+O1115DW1sbSqUSLly40LBfLp/PQ9d1+P1+pwq1VKqUwsPUw2XHvz/xfaRLtSWRhyKNTVGklEi4rCqfXe2TUjYNcYlEAsViEYFAwNlLB1hhNBKxmoMsrdw9CdM0AVhVOCklOjo64HK54Pf7YUgDmqnB5XJBEcq27THrDffC7XZDURQkSokND0bXNA2a1Jyw+fMv/zyOdhzdjlslIiIi2lUMcdRyDMNAuVx2KnHBYBAej8dZVhcOh/HKK6/gxIkTAIAPPvgAxaLVjGRxcREA0N7evuL7T2drc8x6w71QhPWf0XhqHLcWbjnnuoPdON1zuuG1aWmFPHvJoqZpzrLI+tBiV+GaddUMh60W//YyzCchpcTt27fxta99DePj4w1LKQHA7/ejYlohU1GsAGd/3q0W8UbgU33wuD3QTA1TC1Mben25XLYqcY/3xPlU33bcJhEREdGuY4ijlnP16lV861vfwtzcHAArxC0lhMCxY8dw6NAhGIaBa9eurXspZaZc6yQ53DaMs71nnef1e+La/e343NnP4Rc/8ovOsayRhS50ZLNZlMvlhuBmP65UKpibm4MQAoODg8u+/lZW4q5fv4579+5BSomJiYmGEHdt7hr+7uHfYaI8ASEEFEXZtqWUgPXPpCfUA4/XWlL5ML682rmaUqmEqqw6zWh8boY4IiIiak0McdRSKpWKM6jbrq6FQisHjzNnzsDtdmNhYQGJRGJdIS5broWnqC+K88Pnm14X88ecYDLUZu1rE0JA81sNOBKJhLOUEqiFuKmpKZim2dAdst5WVeKklJiasqpdLpfLCZY+nw+KV8Ff3fgrPMw+xPX8dScYbVdTE1tvuLYvbiI5saHX5oo5GNKAy+WCqqhwK+7tuEUiIiKiXccQRy1laddGIQQCgZX3cHm9Xhw5cgQAcOXKFeTzeaiq6lS7mslUapW4qC+KQ5FDONJ+pOEan+qD310LYKOxUedxXrX26i0sLDRU4iqVCkzTbNrQpF44HIYQAvl83tnLthmapsEwDLjd7oZOnF1dXbiXvAfd1KEoCnp6epzllWFPeNNfbz3qm5tMp6fXuLpGSolswQrXqqrCq3rZlZKIiIhaFkMctRS7smQHAb/fv2xu2FKjo6PweDxOoDp58mRDm/ql6pdTRr1RAFhWjbOrcM7XqAtxKWlV+5aGOMCaUZfP5+Hz+dDT09P067tcLgQCAUgpneYtm2FXKoPBoDO6ALBCXP3ePq/XC7fbqmpt53JKwJoV53a7oQgF8ULcGRuwFk3TUDGsgeRCCPjV5k1piIiIiFoBQxy1jEKhgFQqBVVVce7cOQBAW1vbmq9TVRXHjx8HADz11FMYGRlZ9fr65ZRhr1WZOt55HF3BWhfJdn9jY5ShtiGnIUhKSwFuq/K2dPD33bt3ATRvaFJvK/bF2Y1f/H4/urq6oKoqFEVBrCOGe4l7TV+z3cspe4JWcHV73MgZOSRTyTVeYSmVSk4HTYD74YiIiKi1McRRy7CrcL29vejp6cFrr72Gs2fPrvEqy+joKD796U87YW4lpjSRrTTuiQOsZZuvjrzqHF86I86rejEQqR2rBqxB4/a8ODuw2cGqWUOTh4sP8edX/hw35286IS6dTi+7br3sOXPBYBCqquLll1/Gyy+/jKn8VMMg83rbXYnzuX2I+WPweDwwpYmJ+Pr2xS1rasLOlERERNTC1N2+AaKtIKXE9LS1h2pgwApL0Wh0Q+9hLxlcTUErwJTWPrSAOwC3q/aaZw89C8M0UKwW8dLgS8teO9o+ismMtd8tr+YRQ615SiQSQSaTce7b7qh5Z+EOvvPgOzjdfRrvPHoHmXIGdxJ38HOnfw4AGoaGr5dpmjBN01lOae8ZtJu5vHXrrRVfu92VOADoCfVgyjOFHHKYSEzgZby85mtKpRKqZhWqy/ojrX4/IhEREVGrYYijlpBOp1EoFODz+dDZ2bltX6d+P1zE19j8RAiBFwdfXPG1I7ER/MPDfwAALBqLDSGura3NCXH2/rSKXsF/vfFfUawWMZWpzUzTTR3jpXEoioJsNgtN05w9gOvx3nvvIZlMwuezqlX1HTCllLi9cHvF1wY9y8c1bLWeUI/zeR6lHq3rNaVSCZrU4FI5I46IiIhaH5dTUkuwl1L29/dva1fCZk1N1mu4bdjZF7dQWoA36HXO2WMDgFqIuzxzGcVqsel7XZm94lTONlKNK5fLmJubQ7VadUZoF02JAAAgAElEQVQU1M/Rm8vPNXzGpXaiEtcb6oXqViGEQDwfR7VaXfV6U5q4OncV94r3nOWUbGxCRERErYwhjvY90zQxMzMDoLaUcrs02w+3Xkv3xVX81ow4VVUbQlwoFIIpTbw1sfKyxnghjqrfCjdLm6OsZn5+vuG5EKKhEnc7XqvCLQ1sQogdqcR1BjshhIDH40HBKDgVypXcmLuBb09/G1k96zQ28areVV9DREREtJ8xxNG+F49brejD4XBDGNoO9Z0ply6nXI+R9hHncc5lVcL8fj86Ojpw7tw5fPzjHwcA3Ji/gVQptep7JWFV4DZSiZubm2t47vP5GsYp3E7UQtzzA883XBtwB5xK4nayO356PB6UzNKaIe6N+29A13UAcCpxAffKswGJiIiI9juGONr36huabPeA56WDvjeqfl5cUk/ixIkTmAvO4YsXvgg9pCMUCkFKie+Pf3/N96oIay5aLpdDpVJZ8/pqtYpEIgEhBNrbrREI9YPQs+Wss/dOEQqeO/Rcw+u3e9C3LeQJwSVc8Hg80ExtzTEDAgKGYUAIURsxwD1xRERE1MIY4mhfKxQKmJubgxAC/f392/71FouLzuON7okDGvfFzRfmoUd1XE9dx0x2Bn957S8BAA9TDzGdtYKpW3FDVZr3H0qVU04YW081Lh6PwzRNtLe34/DhwwAa5+jdSdxxHo/ERhDzxxpeX9+JczsJIRD2hp2q2mJhcdXrQ25r2Wd9RZFz4oiIiKiVMcTRvlUul/HOO+/ANE309vY27O3aDrqpYzY36zzvCfds+D28qhf9EStsSinxrbFvOefs/XZvjr/pHHvm0DMrVpVSpRQ6OjoArG9fnL2Usre3F319fXj11Vcb5uLVd6U80XViWVXTHq2wEyLeiBPKcpXcqtdWdWtvoEtxOcdYiSMiIqJWxhBH+9bdu3dRLBYRi8Vw7ty5bf96M9kZ6Ka196oj0LHpTo31Syrnco171GayM7ibuAvAqkidHz7fsDfteOdxpzJXrBYRarPuYa1KnGEYiMfjAKwQB1hVOLvaVaqWloW4pXYyxIV9YSdE5rTVQ1y+nAcAKK7aH2ecE0dEREStjCGO9i07uJw5c8YJI9uhalRxafoSvvfwe86xoejQpt9vtH10xXNfu/M15/GprlPoDHbitZHXMNQ2hM5AJ37k+I80LHM0PSZUVUU+n0e5XF7xfROJBHRdRzQabdgHF8/H8dVrX8UXvvsF51h3sBsdgY5l77GT1a2IN+KEuLyWX/XaomaNYWhYTslKHBEREbUwDvumfalSqSCfz8PlciESaewSKaVEqpRCophAu78dncEnG/793QffdYZ024baNh/i7H1xzSpbD1MPncevjLwCwFqC+S9f/JfO8Zg/hoXCAgAgXU6jvb0d8XgcyWQS/f39kFIuWwpZv5TSNp+fxxff+aJTXbSd6jnlPP4np/4J/tsH/w0A8Onjn97U592M+uWU+er6Qpzd1ARgiCMiIqLWxhBH+1IqZbXfj8ViDRUYwzTwn9/7zw1hqDfUi5/80E+iL9y3qa91cerismODbYObei/ACmWHIoecTpDNDEWHVgyK9ZW4VCmF3o5exONxJBIJaJqG27dv45VXXnHGLUgpnflw9SHubuJuQ4CL+WMYjY3i1eFXnWPP9j+LoCcIv9uPgej2zuCrF/HVVeJWCXGGaaBctSqQ9f8e7FQTFiIiIqLdwOWUtC8tLlodC2Oxxg6KY4tjDQEOAObyc/j6na9v6utU9AqK1eKy4z2hjTc1qXc4dnjV8yOxkRXP1S91XCwtorPTqjQmk0ncuHEDuq5jYmKids3iIiqVCoLBYMMcvWSxto/umUPP4Fdf/VX8xJmfaOjsqAgFJ7tPrno/2yHqjUIIASEEinoRptl8P15JL8E0rHMuxQVFKHj9yOs7eatEREREO46VONqX7BBnt9i3TWemm14/tjiGv7z+lzBMA//oxD9yBkqvZSY7s+zY6Z7TTzz0eiQ2gu+Nf2/F86t1vmzz1cYCpEopRKNRqKqKQqHgHHe7a5Wo+qWUQgjcWbiDxdJiQ1OVMz1nNvU5tov9z0cIgZJZgmEYDZU2W7lahmEaAKxw+1uf+K0VRzIQERERtQr+bYf2HdM0kclkIIRYVombytaWKP7kmZ/EpelLGE+NAwCuzl4FAGiGhn/+7D9f19d6lHnkPA57w3iu/zm8PPjyE34CK8QJISClbHp+tUpfe6AWXBeLixBCoKOjw1kyCQCapgGwllLWh7jpzDS+8v5Xlr1nZ+DJ9g1utYjP2ueoCAUls4RqtdoQTG3Faq1KF/AGGOCIiIjoQOByStp3SqUSTNOE3+9v+Iu9lLIhdPVH+nG29+yy199N3G1aYWumPhR+4sgn8ENHf2jdVbzV1M+LW0oRCrqCXSu+tt1fC3HpchqGaTjz4mx2iMvn8ygWi/B6vYjFYvhg4YNl7yeEQJu/bdnx3eRxeeB3+yEUAVOayJWbjxkoVUtOiAt6gzt5i0RERES7hiGO9p1SqQQAy4Z7p8tpFDRrSaFX9aIr2IXTPaebvsffP/z7dX2t+uWZK4WuzXpx4MWmxzsDnatWlLyq12luops6ZrIzzr44W32IA6y9g0IIpypZr83XticrWPVjBlLFVNNrynoZhmEtpwz5Nje3j4iIiGi/YYijfWelEDedrQWugcgAhBAIeUJ4uu/pZe9xc/4m4vn4ql8nV8khXU4DANyK+4mbmSz1XP9z+NVXfxW//fHfbji+npEII20jzuOJ9AQikQieeuopHD5sNUypVCoAGr9XVaPatCNms5lwe4Hf7Xf2wdljBJaqX07JEEdEREQHBUMc7Tsrhbj6gNIfrVXN/vHJf4zPPf05/Morv4ITXSec40tnvy1VHwr7wn1wKa5Vrt6cmD8Gv7vxc6xnxtlwbNh5PJ4ahxACx48fx5EjRwDUKnHFohV+/H4/pjJTy2bCAXs3xHldXqcSV9JKTa8pVAowTdMJ7EREREQHAUMc7TvrCXEDkdpMM6/qxZmeM2gPtOOjox91jl+bu4bF4uKKX6ehsreDM9J6w71rXjPcVgtxE+kJp0GKx+MBYIU4KWXD92rp6AXbXg1xHtXjVOJWCnH5srVc1KW44Pf4m15DRERE1GoY4mjbVatVzM7OrtiJcaPsYOLz1SpWpjTXFbqG2oZwpP2I85rV2vw3NEmJbu1+uKV+7NSPWd02/TE83//8mtd3BbsQcAcAWEsKFwoLAKyB16qqQkqJarXqfK8CgUDT/XBAY6OUvaS+EmcP9F7KDnGKS4FfZYgjIiKig4EhjrbdrVu3cOnSJczOzm7J+zWrxC0UFqAZ1hLCsDeMiDey4us/dvhjzuPL05eRKWeWXSOlbGhqUl/Z2w4vDLyAX3/11/HL538ZXtW75vVCiGXVOJvXa71e0zTne+X2ujGZnmz6XnutM6XN4/I48/hWDHGVxyFOUZYtSyUiIiJqVQxxtK2klM78smw2uyXvVy5bf6GvD3FLl1LaFZxmRmOjGIoOAQAMaeD7499fdk2qlEKx+ng/mdu/I0sOI77Ihvbd1e+Lm0jVQpy9pLJYLELTNCiKgoXyAqpmFYA1Z+5Hn/pRuIQLp7tPoze09vLN3eBVvRDK6pW4QsXqRupyuRjiiIiI6MDYe33FqaXk83kndNlVoSdRrVah6zpUVYWq1v71rV9KudbSRyEEPnb4Y87Q64tTF/HKyCuI+qLONfXz4foj/auGwt1S36FyPD3uPLZDXCZjVRj9fn/DUsqRthGcHz6PlwZf2pOjBWwel2fN5ZR2iFMUZV0NYYiIiIhaAStxtK0SiYTzeCtCXH0Vrj5Y1VfiBqODa77P8c7jOBQ5BAComlV88/43G85v53y4rdIX6YNbsYadp0opZMtWpdNeTplOW+MRAoFAQ1OT0fZRANjTAQ6wKnHOckq9eYjLlawh4KzEERER0UHCEEeb9vDhQ1y7dm3VhiX1Ic5ud/8kmu2HqxpVzOZq++3WE7qEEPj0sU87z6/MXsFMdsZ5Xl+JW08o3A2qomKwrXZvdjVuaSXO6/M27IcbjY3u3E0+AY/L4yynrOiVZedN08Rizuou6vf7nUYvRERERK2OIY42paJX8MXvfxF/8N4f4Pbk7abXSCkbQly5XH7iDpXNQtxcbg6mtAY+dwQ61l2ROdJxxJkbJ6V05sYt7XS5VytxwPJRA0AtxNnfqzzyTtOXNl8bYv7YDt/l5njVWnfKZiFufH4clWoFqktFJBCBx+XZ6VskIiIi2hUMcbQpb429hdnyLLJ6Fn/2/p81vSadTkPXdQSDQfh8voa5ZZtVKFh7oBqamjxB1eyTRz/pPL4Zv4nF4iLi+TiqhtUEJOKNIOJbudPlbhuJjTiP7X1v9nJK20J1oen1e53HVZsT1yzEXZu4BgDwB/wYbhvek/sWiYiIiLYDQxxtyo2ZG87jqcWpphU2uwrX1dXlhK4nDXHJZBIA0NZWa4tfvx/O3ue2Xn3hPhztOArAqsb9YPIHDaFwJ4d8b8ZgdNAJL/P5eYwlx/B+4n2UDWsPmRACSSPpXL9fllICjXPimoW42zNWBdjv92OobWhH742IiIhoNzHE0abIai206YaOeDK+7JqFBasC1NnZuSUhTtM0ZLNZKIqC9vbagOqNNjVZ6vzweefxpelLuJu46zzfy0spAWvJ4aGwFVyllPjj9/4Y35v6Ht7NvgsAOH3mNGaLtf2C+6oSp9YqcfZyUFupVMJkZhKKUODz+RqWlRIRERG1OoY42pRMwWqaYbf5f//u+w3ndV1HKpWChMSb8TfxZ+N/hrHi2BOFuEQiASkl2tvb4XJZ89RK1RISRavipwgFfeG+Db/vsY5j6A1bs9I0Q8PN+ZvOub1eiQOwLMB4PB5kXBmcOHEC3navU8UKe8M7Mu9uq6xWiRufHkdWz8Ln98GluNYcK0FERETUShjiaFMW81ZXwPb2dggh8MGjDzA7O4tcLofFxUWMj4/DNE3cN+7j6vxV6NBxI3/jiTpU2sszOzs7nWP1DUh6w71wu9wbfl8hBF4//HrTc3u9Egc0Dv22dXV1YfTIaMNogZHYyL7aN+ZxeZwRA0srcTcmreW8AX8AhyKH2NSEiIiIDpS9PSiK9qRyuYy8loeiKPD7/Whra0OqnMKlS5carotrcYwZY2gLtEFVVZTNstMSfqPqO13Wh7j6pZQDkc1XzU51n0JvuBdzuTnnWH+kf1/MHltpKWGuknM6VgL7az8cYC2ntEcMaIaG27dvo7+/H36/H/fj9yGEgM/vw1CU++GIiIjoYGEljjZsMb2Isll2WtlHIhHoPh1erxehUAixWAzd3d0oBAsIh8MAassu64dob8T8/DwKhQK8Xm9DU5NHmUfO4ydZUieEwGdPfBYelweqouJc3zl87unPbfr9dlLYG256PFfJIVGojXh4kpC7G1RFdSqrhmng9t3buHnzJhYWFrBQWYDX44XL5WJTEyIiIjpwWImjDUtkrWBgBzMAUKMqPv76xxuWM16/eB2ulLV3ze12Q1EUzOfmUS6X4fP5nOtM03QaWDQjpcStW7cAAMeOHXOWBEopGypNI20jT/S5RmIj+M2P/SYUoUBV9td/Gi8MvICLUxcbjuUqOSwWa5XP9kD70pfteR7VAyEEpJSoyioWFxfhUl1IVpOItFmjH9jUhIiIiA4aVuJow9LFNAA4zUUAa0D2g8UHDdctlhqXTnq9XmT1LFKplHNsenoa3/jGN/DBBx+s+PUmJyeRz+cRDAYxPFz7C3u8EEepajVKCXqCW9K0w67E7TcfHf0ojrQfaTg2k5tB1bTm3fnd/n2xNHQpn+pzxlfopg7DMHBz8iYMacDv9yPmj+3pOX5ERERE24EhjjbMDnFLq2d3Enecx1Wjikw503De6/UiraedWW8PHjzA5cuXoes6ZmdnIaXE+Ph4Q/MTXddx5471vidOnGj4mvZwa8Cqwu2nph1bLeaP4eee/zm8fqTWoGUiVatStvv3XxUOQEPDEl3qAICkloTb7Ybb7eZSSiIiIjqQGOJow1Ilq5LmUlzoDnY7x28v3HaqJvVVOLdiLbG0K3HJZBI3b97EzZtWK38hBIrFIh48eIDr1683VOUePHiASqWCtrY29PVZ4wNylRz+9P0/xd/e+lvnumYdGg+isKe2P65+qel+XEoJWGMGbJ6AFegWqgvO3EE2NSEiIqKDiCGONixXyQEAFJeCM71n4FOt/W2ZcgZzeau7Y/1erOHYMALuALweL3TomE3N4sGDB1AUBc8++6wzuHtsbAwAkE5blb5KpeIcO3XqlFNpe+fRO7i1cKvhnp50P1yrqG9yYkrTebxvK3GqB6FQCC6XCydOW5XYZDWJgD8AAKzEERER0YHEEEcbli1nAVjLKdt8bTjWecw59/U7X0dFryBZTDrHOgId6A51QyjCGkStZ6CqKl566SX09/c73SYrFWugc6lUgqZpuHPnDnRdR29vLzo6avvd6t8bsJbc9UU2PuS7Fa3UqXI/V+I6OjrQ39+Poixi6PgQ3EE3vD4vvKrXGdJOREREdJDsvw4OtOuylVqIi3gjONd3DtfnrgMAxhbH8IcX/xAd/lro6gh0QAiB8dS4NY7AreP8+fOIRKyGFPUjA2wzMzOYnJyEEAInTpxoOJev5Buef/zwx52h0AfdiiFuv1biHu+JE0Lgv9/+7wCAWCwGABiMDvKfOxERER1I/BsQbVhes0KUy+VCxBfBU51P4RNHPuGcn8vN4Wb8pvO8I9DhtIEPBALw9ficAAc0D3G3b1v76wYHB51ZczZ7OScA/PzLP4/XRl/bmg/WAkKeUNMGL/s2xKmeFc9xKSUREREdVAxxtCGmaaKoFyGEgKIoCHqCEELg9SOv48dP/3jTyki7v71hltejzCMYpuE89/v9zuDwzs5OAEC1arXGHxkZWfZ+hWrBecz28o1cigsBd6DhmKqo+/b7VN/YZCnOhyMiIqKDiiGONqSiVVA1q06r//rA8Fz/c/hnz/yzhrbwQgjE/DFEfVHE/NYyuKpRxWxutuGaI0eOoLOzE8eO1fbXRSKRhoqd/Vp7NpwiFATdwa3/kPtcfYdKAGjzte3bZYerVeIGo4M7eCdEREREe8f+/Jsd7ZpcMQcJCUVR4FN9y8LB8c7j+Pzzn0fIEwIAHO04CrfLGjFQXzm5NH0J7z56F98Z+w7+x+3/ASNq4PkXn0fCSKBiWg1OBgcHly0NtJdyAisvHTzowr7GEPfMoWd26U6eXP0PBJbyqitX6YiIiIhaGRubEEzTxKVLlxAMBnH69OlVr7UHfbsUFwKewP/P3n3Hx3Xdd97//GaAQW9EIdFIkAQJdpEUKVE1lmzFkqvsuEWxFTm2bMd2vE52N5sn9m5kx9nkSXmy2eRxLMdF67jENVIs27IlS6IoiV0kJTaRIIlGgOgYDNrMYHD2jzscohMUCYEDft+v17w4c8+955577gVf85vTJt2nPK+cP7zlD6nvqaeqoCqxfXH+Yg62HARgb9PeMcfsatzFwuyFtIRa6Bvs4+6CuykvL5+Q9+hJTbLTsmd0fdeaFYUrONlxEoA7l9+Z1GMG/eafdPvtVcl7TSIiIiKXS0Gc0NraSmtrK36/f8x6bJPpHYjPTOn3TRh7NVp6ajo1xTVjti1bsGzK/UfcSKKLZfaCbFZdv4q0tIktLeNb4mSimxffzMLsheSl51GcVTzXxbksMRcb8/ntq95Ob7hXQZyIiIhc0xTECY2NjQDEYjGi0WhikpHJ9A5eWF5guiBuMsVZxbyl5i0cbj1Meko62YFs/D7/hFY5gP7h/klyUBA3E2ZGdWH1XBfjili3cB1PnHiCETfCXdV3sW3xtrkukoiIiMicUxB3jQuHw7S1tSU+Dw0NTRvEhYa86f39/omzIM7ELUtu4ZYltyQ+O+d4tf3VxNpz5wWHgpOff9TyAlOtiSbzR156Hn9w0x/QPdg9ZlF5ERERkWuZJja5xp09exbnXOLz0NDQtPufD+JeS0vcZMxs0taVqYK4MS1xGhN3TSjJLqGmuCZpZ9gUERERudL0rega5pxLdKU8P/5scHBw2mP6huILfU8zscmlurHiRkpzSsds6xnqmfz8YXWnFBEREZFrm4K4a1hvby+9vb0EAgEqK701ty7WEtcf8caqXWxik0uRnprOp2/6NB/d+tELZRvqnXTfMd0pA+pOKSIiIiLXHo2Ju4adb4UrLy8nM9MLyIaGhujs7CQ9PZ2MjAwOHz5Mb28vkUiEcDhMe087cOW6U46Wl5aXeD/lmLiIxsSJiIiIyLVNQdw1amRkhLNnzwLeotrhsLfAdkdHB42NjeTl5bFmzRrq6+vHHBcZiZCWlkYgELjiQVxuei5mhnOOUCREbCSG33dhnTDnnGanFBEREZFrnoK4a1RrayuRSIScnBxyc3MJhbwWroGBAQBCoVDifUlJCWvXriUtLY3Te07T1u/NZnmlxsSdl+JLISs1i75IH845esO9FGQUJNJ7hnqIxqIApPpTSUuZuI6ciIiIiMh8pzFx16impibAa4UzM9LT08ekx2IxgkGvS2Nubi7Z2dmkpqYyEB1I7HOlW+IA8jPyE+/Hd6k82XEy8b6qoGraRclFREREROYrBXHXoHA4TGtrK2ZGRUUFAKmpqfj9/jH7dXV1AZCRkQF43RkHoxdmr5yNIG70uLjxk5u82vFq4v3KopVX/NwiIiIiIslAQdw16PzacCUlJYmlBSZrjevt9YKo80FcJBYh5mKA150x1Z96xcuWm56beD96mYHhkWFOdZ1KfF5ZqCBORERERK5NGhN3DTo/K+X5ZQU6+jt46tRTNIWaWGErEosq9w/3c7z/OLFzMa5LvW7MbJCz0QoHkJc+aobK8IXulHXddYnxcIWZhRRlFc3K+UVERERErnYK4q4xwWAwsTbcwoULOd5+nB+88gPCw2E6BzoJ+AOsLVzLwMAAh/sOc2bwDEMtQ+xt3UuK78LjMltB3IKMBYn37X3ecgbOOZ45/Uxiu7pSioiIiMi1TN0przHnJzQpKytje912vn3w24SHveUF8vLy8Bf6qampASA4HMTn8+HzeY/J8MhwIp+sQNaslK8styzxvjnUjHOO/c37qeuuA8BnPrZWbJ2Vc4uIiIiIJIN5GcSZ2afNbL+ZRczskYvs+14zO21m/Wb2KzMrH5UWMLOHzazHzNrN7IuzXvhZFIvFaGpqYnhkmD19e3iq9imcc4n0lJQUwmlhsrK8AG1oZIgUv9f6Nn5NtpKsklkpY356fqKVbzA6SFOwiSdOPJFIv7XqVhZmL5yVc4uIiIiIJIP52p2yGfhz4M1AxlQ7mdlq4BvAu4AXgL8Gvgv8RnyX/wFsAKqBbOApMzvjnPvm7BV99jQ2NtI10MX+of2khS6ssba0YCmNwUaGR4bpHOhkb9teWsIthEfCBAIBAP7o1j8iOBTkZOdJorEoNy2+aVbKaGaU5ZZR21kLwPde/l5iRsyCjALuWHbHrJxXRERERCRZzMsgzjn3EwAz2wJUTLPrB4FfOOeeiu//eaDNzJY7504BHwYedM51AB1m9nfA7wFJFcSFh8McbTvKo7sfpT5UT0HRhQW0ty3exltWvoWv7fsaDT0NAGyv305DTwPOOVJSUgj4A6SlpFGSXUJJ9uy0wI1WlnMhiBu9Vtw7Vr+DgD8w6+cXEREREbmqOefm7Qv4EvDINOmPAZ8bt+1V4J1AAeCA8lFpNwHdU+SVD1SNe90az2PS18MPP+zOe/jhh6fcz7tNF2zevHnK/R588MHEfvv27Zs2z3/9+b8m9r37fXdPuV/5yvIx53+9r2njPRvdvx36txld0759+xJ5Pvjgg1Put3nz5jm9pku5T7omXZOuSdeka9I16Zp0Tbqm+X1N8VeVm2GcMy9b4i5BNhAct60HyImnMS79fNpkPgv82RUt3SxbXbI68X78mLfR/OafMu31kOJL4S01b5nTMoiIiIiIXC3MjZrYYr4xsy8BFc65B6ZIfwzY7Zz7n6O2HQf+G/Ac0IXXEtccT9uG1/2yYJK88vFa40arAHacOXOGqqqqy7+gy7Dv7D6Cg0HWlKxhUc4izGxMen+kn7/d8bdEYhEABgYGCPWGKCouYkPpBu7beN/rVlbnHH/x7F8kxsLdu+ZezUgpIiIiIvNSXV0dS5cuBVjqnKubyTHXekvcYeC68x/MLBdYChx2znWbWXM8vTm+y8b4MRM453rwWuoSxgdKc2lL+ZZp07MCWdx33X38qvZXNPc2k5mZSWamN0tkdtrUrXSzwcy4Y9kdPHnySdYuXHvRsouIiIiIXEvmZRBnZil41+YH/GaWDsScc9Fxu34b2G1mdwI78Wa03OW8SU0AHgE+b2Z7gSzgj4C/fB0uYU6sKFpBSXYJf/3cX4/ZPl1Xy9lyy5JbuHnxzVdVICwiIiIicjWYl+vEAZ8HBoE/wZuBchD4FwAz6zOz2wCcc8eAjwBfAzqB1cDofoNfwGt5OwXsB77vknR5gZnKScuZMAZuLoI4uLpaMkVERERErhbzsiXOOfcQ8NAUadnjPv8Q+OEU+0aAj8df1wSf+cjLyKNroCux7fXuTikiIiIiIlObry1xchkK0sfO2zJXLXEiIiIiIjKRgjiZID9j7CSbCuJERERERK4eCuJkgvz0cUGculOKiIiIiFw1FMTJBGkpaWM+B/yBOSqJiIiIiIiMpyBOJlD3SRERERGRq5eCOJlgdcnqRCC3bfG2OS6NiIiIiIiMNi+XGJDLE/AH+PRNn6a1r5VlC5bNdXFERERERGQUBXEyqZy0HHLScua6GCIiIiIiMo66U4qIiIiIiCQRBXEiIiIiIiJJREGciIiIiIhIElEQJyIiIiIikkQUxImIiIiIiCQRBXEiIiIiIiJJREGciIiIiIhIElEQJyIiIiIikkQUxOqa6kwAACAASURBVImIiIiIiCQRBXEiIiIiIiJJREGciIiIiIhIElEQJyIiIiIikkQUxImIiIiIiCQRBXEiIiIiIiJJREGciIiIiIhIElEQJyIiIiIikkQUxImIiIiIiCQRBXEiIiIiIiJJREGciIiIiIhIEkmZ6wLMc36ApqamuS6HiIiIiIhchUbFCv6ZHmPOudkpjWBmtwI75rocIiIiIiJy1bvNOff8THZUEDeLzCwN2Aq0ALFZPNUZYOkM9qvACypvA9Q8OLuSta5n+ixdDZK1jpPNa63nZHqW5oKe35m7nGdJ9Tz7kqWO58P/SclS18nuYvU8G8+SHygF9jrnwjM5QN0pZ1H8Jswomr4cZoZzrm4m+8U1zWR/ee2Sta5n+ixdDZK1jpPNa63nZHqW5oKe35m7nGdJ9Tz7kqWO58P/SclS18nuYvU8i8/SqUvZWRObiIiIiIiIJBEFcfPDF+a6ADJv6FmSK0XPklwpepbkStBzJFfKVfEsKYibB5xzD811GWR+0LMkV4qeJblS9CzJlaDnSK6Uq+VZUhB3benB+/WgZ64Lcg1QXc8+1fHrQ/U8O1Svrw/V8+xTHb9+VNevj6SoZ81OKSIiIiIikkTUEiciIiIiIpJEFMSJiIiIiIgkEQVxIiIiIiIiSURBnIiIiIiISBJRECciIiIiIpJEFMSJiIiIiIgkEQVxIiIiIiIiSURBnIiIiIiISBJRECciIiIiIpJEFMSJiIiIiIgkEQVxIiIiIiIiSURBnIiIiIiISBJRECciIiIiIpJEFMSJiIiIiIgkEQVxIiIiIiIiSURBnIiIiIiISBJRECciIiIiIpJEFMSJiIiIiIgkEQVxIiIiIiIiSURBnIiIiIiISBJRECciIiIiIpJEFMSJiIiIiIgkEQVxIiIiIiIiSURBnIiIiIiISBJRECciIiIiIpJEFMSJiIiIiIgkEQVxIiIiIiIiSURBnIiIiIiISBJRECciIiIiIpJEFMSJiIiIiIgkEQVxIiIiIiIiSURBnIiIiIiISBJRECciIiIiIpJEFMSJiIiIiIgkEQVxIiIiIiIiSURBnIiIiIiISBJRECciIiIiIpJEFMSJiIiIiIgkEQVxIiIiIiIiSURBnIiIiIiISBJRECciIiIiIpJEFMSJiIiIiIgkEQVxIiIiIiIiSURBnIiIiIiISBJRECciIiIiIpJEFMSJiIiIiIgkEQVxIiIiIiIiSURBnIiIiIiISBJRECciIiIiIpJEFMSJiIiIiIgkEQVxIiIiIiIiSURBnIiIiIiISBJRECciIiIiIpJEFMSJiIiIiIgkEQVxIiIiIiIiSURBnIiIiIiISBJRECciIiIiIpJEFMSJiIiIiIgkEQVxIiIiIiIiSURBnIiIiIiISBJRECciIiIiIpJEFMSJiIiIiIgkEQVxIiIiIiIiSURBnIiIiIiISBJRECciIiIiIpJEFMSJiIiIiIgkEQVxIiIiIiIiSURBnIiIiIiISBJRECciIiIiIpJEFMSJiIiIiIgkEQVxIiIiIiIiSURBnIiIiIiISBJRECciIiIiIpJEFMSJiIiIiIgkEQVxIiIiIiIiSURBnIiIiIiISBJRECciIiIiIpJEFMSJiMwCM3vEzB65zDz+1Mx+cYWKJBdhZm8wM3eZeSw2sz4zWxz//ICZ1Y1K/4qZfeUyi3pVMrM6M3vgCuc5pv5mi5k9a2YPzfZ5pjl/lZk5M6uaqzJcjWURkakpiBORpGZmG8zsB2Z2Lv7l+bSZfcvM1s112S7FZF8inXP/0zl3zxwVaUqz8WU9GU0WYDjnGpxz2c65hsmOcc59wjn3iVF5XJV1aWYPmdmzc12Oi3m9gjwRkauNgjgRSVpm9gZgN3AWuBHIAbYALwDvnLuSJSczC7yO5/KZmf/1Op+IXNzr+X+AiFweBXEiksweBn7gnPtD51y983Q55x52zv0FTN6tcXyrV7zr0GfMbI+Z9ZvZrni3uM+YWYOZdZnZX43af0K3u4u1CJjZn5tZbby1sD7+2RdP+wpwG/Cn8fRz8e2J1hAz+6SZHR+XZ058/zvjn/PN7J/j+Xea2c/NbNk0ZXog3hL0WTNrABri21eZ2eNm1mpmZ83sy2aWFU/7BbAY+Er83Hsmq9P4tkQr06guWh8xs8PAALA6vs/nzOwXZhYys5Nm9s5ReVxnZtvNrMfMus1sv5nVTHItfjNrNrPfHrf9C2b23KjPD5rZMTPrNbMDZvb2aernDWa2M37/O83sp2a2NJ52G/AV4Hz3yT4zu/diXdFGP4+T1aWZ3R2/1sxRx/ima7GLPyfbzex/mllbvLz/Nf4MPxWv15fMbO2oY94b3xaM3+fvmFlRPO13gD8Fbht1bZviabeY2TPx+ugys1+NK075VPcyfvxbzGx3/F6eNLPPjEt/s5m9Ej/n08CSae7PpPcgnnarmb0Yr8taM/sTu/iPBgvM7NFRZf+dcee7Mf6cd9qFv+GUUenOvL/TF+NlednMbh6Xx4fN7FC83lvM7EvjynBr/LhQPJ9Vo459xMy+a2b/Er+uFjP7oHm9EXbHj9luZuWjjvmUmR2Jp501s/9/3LP1iJl9L55nB/CdSeq5zMz2mdnDo69XROaYc04vvfTSK+lewArAAW+6yH6PAI+M2/Ys8NCozw7YA1QCmcDTwAngS0AA2AREgN+I7/8G77/PMXk+ANRNdV7gg0AFYMBWoAN4cKoyxbc9BDwbf58PDAK3jEr/KHAqnqcBzwD/CiwA0oC/Ao4CqVPUzQPAMPBlICt+7UVAO/CZeB5FwJPAv4w6rg54YLo6Hb8fUBWv5+fi9ZASr9u6+GsT3g+L/xUIAtnx414A/kd8/xRgI7Bwiuv5S+DJUZ99QD1wf/zz+4BuvIA5BXgXEAa2THZfgVuAbUBqvE4fBV6Y6p6Pu86qGT4XY+oyfh9Pjdt2T7zcGVNc90NAFPhE/LruAUaAXwNr4uX/HvDMqGPuBtYD/vj92Al8Z7Jnb9S2dcAQ8HEgI37/7hp3LdPdyzvi13FnPH0d0Aj8Tjx9afx+fCR+HduAtvF1PN3fXXzbErwfCT4Rv/YNeD9Q/NE0+TwbP+at8XO/NV6WG+PpNUAIeG88fQlwEPjcuP9HXgKWx/f5R+DUqPSPA63x6/cDecCt456bXwILgXTgJ8Cvxz07Q8A74sd/AugHfsqF/7u2A98cdcy7gWq852oVcBL4i3F5RoH742XOHFWWqvi9bAD+y6X+H62XXnrN7kstcSKSrEri/569Qvn9vXOu0Tk3APwIKAf+zDkXcc4dAA7jddV8TZxz33bONTnPXrxfvN90Ccf3AD/G+4J73keAbzjnHN6XrZuAjzuvNTIMfA6vpefGabIewfty2x+/9vuB4865/+2cCzvnOoDPA/fPoCVjJr4Qr4dh51wkvu2rzrkDzrkR4J+BXLwvzeAFz4uBJfFjDjrnWqfI+xvAnaNawe7C+6L8o/jnj+AFozvief073hfgj06WmXPuBefcLudc1DnXBXwBuGl0S8aVFr+XDwMfG7X5Y8C3nHOD0xx62jn3lfh1/QLvR4KnnHNHnXNRvCAu8fw6555wzr3inIs555qAv+biz+PvA084r6V7MP638eS4faa7l38I/JNz7mnn3Ihz7jDwT8CH4+n3AQedc1+PX8cu4JsXKdNk7gMOx+sj6px7OX59H7vIcT91zv0sfu6f4QXtvxdP+xTwqHPuh/H0erwfDT48Lo+/dc6dcs4N493HZWZWGE/7DPCX8euPOeeCzrnnxx3/Bedcq3NuCO95vmFc+nbn3H8452LAt/CCru+O+r/rx4y9zz9xztXG/985jveDzfj7vMs59634dQ2M2v5O4AngM865v71I3YnI60xBnIgkq7b4v+XT7jVzLaPeDwDt8S9Ko7flvNbMzez3zexgvBtZD96v8iUXO26crwHvM7NsM1uD16J3/kvuCryWkeZ4V6seoBPvF/vKafI8F//CeN4K4MbzecTz+RXeL/OLLrG8kzkzybbm82+cc33xt+fr+oH4uZ82s0Yz+3uLd+0czzl3EtjBhS/WHwG+N+qLaSVwetxhtXhB4gRmttG8LqnNZtaL18phQPE013clfAPYbGZrzWwR8Da8gGA6LeM+DzDxmc4+/8HM7oh3DWyNX9u/cvHnsQp49SL7THcvVwD/edyz9XmgNJ5ewcTnY7Ln5WIu6T5Pc64zXPjbWQG8d1zZ/4WJfxPNo96Pv/4qLqH+4sdnj0tP3NNRz/X4+5z4f8rM3mNe9/AOMwsCf8HE+zxVHf8J3t/TYxcps4jMAQVxIpKU4l/YTwC/c5FdQ3hdBUcru8zThwDGBRNT5hkfF/O/8H6JL3bO5eN9KbdRu43M4Lzb8b6wvR+vheAJ59z5L33n8LpbFjnn8ke9Mpxz35smz/HnPYfXjW50HnnOuXTn3NkpjoFx9RwfOzNZUDCT60xw3ljHB51zS/C64/0m8MfTHPJ14AEzK8ZrSfj6qLRGvC57oy0nPhZwEj/A6466xjmXC/xGfPv5+3ZJ1zKFCXnEWz9/hNdy9Ht4LSVHr8C5gMTkFT/Fa2laFr+2D12sXHhdJVdexqnPAV8a92zlOOfOj9Vrwgt0Rhv/ebzJynmp93mqc1XFywRe2b81ruy5zrnxQdZ06ri8+rskZlYBfB/4W6DcOZeH1zpv43ad6jl+B149ftvMUmetoCLymiiIE5Fk9nHg/Wb2N+ZN4mDmTe7xETP70/g++4A3mtlKM0s1s88y8QvepTqBF7R83LxJJzYyfVetPCCGN9YsFp+QYXzweY6LfMGLd7X7Bt51fwivZe6854FjwJfNrATAzArM7LcusfvfN4EtZvYJM8uM12mlxSeMGFXW8ZOL7APuNbNSM8vAG4932V/8zJt8pcLMDOjFG8MXm+aQH+HV9zeBY865faPSvgE8aN7kHH7zJt14R3z7ZPLi5+w1s4XAF8elnwOKzazgki9sbB4TJmrB64r4IeBBLt4Kd6kCeGOuepxz/eZNfvMnk5RriZmljSvTPeZNDpNuZgEzm3GXYOAfgP9kZneaWUr8tc7Mbo+nfw/YZN7kHylmdgNeS+x0JrsH3wPWm9nH4n/z6/AC/69NmsMFbzeze+LPxj14YybPt3R/Ga8V/Lfi1+03s2ozu3vml88/AP+Pmf1G/Pg8M7v1Eo6/VDl43/M6nHNhM9uA1y10ptrxfjgpBx6N/12LyFVCQZyIJC3n3LN448CW4AURIeAA3sQVj8Z3+w7wQ2AX3i/0+XiTZVzOeUPA7+J9IerFGxvz1WkO+SVei9ALQBdei9z4WeD+DlgX76rVxNT+D7AZr4vh46PKFMMbAzYE7DazEHAI74vojBewdt76ZjcDb8abYKMnXv71o3b7IvCeeNfQF+Pb/h5voodX469arsx4xTvwJp3pw7uencDfTFP+QeC7eBNTfH1c2vfxZl38Ot4EG18A3u+c2zNFdh/Bm5AmBDyFN9HEaE8DPwNq4/ftHZd0ZZ7J6hLn3At4rUC5XBjTd0XEuzl+HPiimfXhPYvjn8fv493Dlvi1bYyPYbsLL7hsib/+6yWc91G8v5s/x+sO3YYXWBXF00/jPa//Ge+5+yu8wHE6E+6Bc64Ob+KWD+ONDXwM7+/z7y+S19fx6qUHb1KSB51zO+Nl24v3N/FxvOe6E+++TDl75njOua/idR/9p/g5jsfznBXOuWPx830/3mX2b/HG0V1KHr14dRkDfmlmeVe8oCLympj3w66IiIhcTczsMbzZDf9orssiIiJXF633ISIicpUxs614LSCr57osIiJy9VEQJyIichUxs51467v9t3gXQxERkTHUnVJERERERCSJqCVuFsVn9dqKN/h7utnURERERETk2uTHWzNzr3MuPJMDFMTNrq14C2WKiIiIiIhM5za8JYMuSkHc7GoB2LFjBxUVFXNdFhERERERuco0NTVx2223QTx2mAkFcbMrBlBRUUFVVdUcF0VERERERK5iMx5+NS8X+zazT5vZfjOLmNkjMzzmITNzZnb3uO1fMrOO+CKi/2xmqbNSaBERERERkRmYl0Ec0Az8OfD1mexsZiuB9zCuCdPMPgp8ANgCVAMbgc9f0ZKKiIiIiIhcgnkZxDnnfuKcexTonOEhXwH+MxAZt/3DwP/nnKtzznUAXwR+78qVVERERERE5NJc82PizOx+oNM590szG5+8Djg06vNBoMLM8pxzwXH55AP5447XbCYiIiIiInJFXdNBnJktAB7Cm85zMtnA6GCtJ/5vzrjtAJ8F/uxKlk9ERERERGS8azqIA/4a+LJz7uwU6X1A7qjPefF/Q5Ps+7+AR8Ztq0DrxImIiIjMC786+StOdZ1i+YLl3Fh5I3npeRc/SGQWzMsxcZfgTcAfm9k5MzsHVALfNbPPxdMPA9eN2n8j0DS+KyWAc64nPnYu8QKaZrn8IiIiIvI6aO5tZvuZ7TQFm9h+Zjv/uPMfaetrm+tiyTVqXgZxZpZiZumAH/CbWfoUSwNsBTbgBWcb8Wa1/BTwD/H0R4A/NLMlZlYE/HfgG7NdfhERERG5utR21o75PBgd5EeHf0RsZMZLe8kcisVidHd3E4mMn8cwOc3X7pSfZ+z4tA8C/wd4wMz6gHucczucc+2jDzKzGNDtnOuLb/oaUAXsB1KB7wFfmuWyi4iIiMhV5lTXqQnbzvae5e+e/ztWFq2kpqiGZQuWkZaSNgelk/Gi0ShdXV10dXXR2dlJMBhkZGSEBQsWcMstt8x18S7bvAzinHMP4U1YMlla9jTHVY377IDPxV8iIiIicg0aHhmmvqc+8Xnb4m3satgFQHAoyN6mvext2kuKL4UbK2/kLTVvmauiXvPC4TAHDhygo6MD76u8x8wwM7q6uhgYGCAzM3MOS3n55mUQJyIiIiJypTQFm4jGogAUZBTw1pq3kp6Szu7G3QxGB4lGo4RCIQYHBunp7WFj6UbKcsvmuNTXllOnTtHd3U0wGGRgYACfz0dBQQGFhYUsWLCABQsWcOjQIZqbmzl37hzLli2b6yJfFgVxIiIiIiLTON11OvF++YLl+MzHm5a/ievyrmPX0V280vkKwcEgw7FhQr0hjrUfu+QgrqO/gydOPEFxVjF3Lr+TVP9k0znIZDo7Ozl69CgAI26EFl8LBeUF9FovJwdOEu4N46v3sSZrDYCCOBERERGR+e5k50nAG2dFD7z44ov09vYSjUYxjI25G1ldsZpHTzxKJBrhYNNB3rj8jRfNtynYxL+9/G8UZBTgnONM9xmOtR+jIdjABzd+kIzUjNm+tKTnnGP3y7v5ZccvycnNYXXpahp6G2hsa5ywb0N3A0uiS7Au4+DBg2RnZ1NdXT0Hpb58CuJERERErkLOOZ45/Qy7GndRllvG9WXXs7pkNSk+fX17PQ1GB2kMegFBV1cXw8PDdPo6AUhPT2fJkiUsWbIE/PBk45OE+kPUd9TTOdBJYWbhlPk653js2GN0DXTRPdhNLBajtbUV5xwdHR00tzXz4S0fprKkEjMDoK+vj10v72JV1Soqyipm/+KTQEtLC7+q/xUhFyI3K5dX+17FfDbpviOMsH9oP2/wvYHGxkYKCwsVxImIiIjIleGc4+nTT/P0qacBONlxkpMdJ8lMzWRj6Ua2VmylJLtkjkt5bajtrMU5RzQaJTOWSXYgm+uvv57c3FzS0tISARbA6kWr2XNqD4ODgxxtO8ptVbdNme+hxkMcrjtMX18fGekZpKeney19wPDwMCf7T/LFx79IRVYFI5kjbFm+hUPHD/Fq16sUnizkD279A6qXJ2cAciX9/NDP6Yh2sGDBgjHBW6o/lXeveTdpKd49euzoY/QM9ZBXkEe9v56AP0AgO0AoHCInLWcOr+C1URAnIiIicpUYcSO8UP8CO+p20B/pn5A+EB3gxYYX2dm4kxsrb+Q3q39TU9pfYc45IrEIPvPh9/k50XECgP7+firSKigtLaWkZPIAeuvSrew9vZehoSFOtp+cNIgbGhripZde4kcnf0QoEgJgYHCAcCQMwJqKNbQPtjM4NEg4EqY2VAshqGurY8SN4PP56Ix08tN9P+WN/W9k3bp1+HzzcunnCSKRCGZGSkoKwyPDHG48zI6zO/D5fGRnjZ2AfnXxajaUbkh8vqXqFn52/Gf4/X6CBCEG7efaycjJ4Palt7/el3LZFMSJiIiIXAUGIgN899B3OdN9Zsz2JflLWF64nJfOvkTPUA/gBRq7GnYRCoe477r75qK481JHfwdf3/d1esO9E9L6+/pZlLeIysrKKY+vWVhDamoqkUiEk20nvaDLLgRYvb297Nmzh4ZgAx3DHeTk5OBGHH39fcRiMXw+H+/Z8h5S/Cl8++C3CQ+HCQ+F6Qn2MDQ0BEBhYSHOOY51H6OqropQKMSWLVtIS5vfwfyBugN87ZmvMRAbwPkcKSkpjLgRYi5GTnbOhC6UGxZtGPN5Sd6SSfNdU7Jm1so8mxTEiYiIiMyxUDjEN/d/k9a+1sS2rEAW6xau467qu8hIzeCOZXdwqvMUz9c/T21nLQBHWo8QHAqSl543V0WfV545/QxnO84yMjJCdnY2fr8f8FrhfM5HZX4lCxYsmPL4gowCFmQt4FzkHH2DfbT0tlCeVw5AW1sb+/fvJxqNcmrkFOXl5fh8PoaHh+kf6Mc5R2ZGJssKl5HiS+FjWz/G9w59jx5fD2npaQwODnr7xNc3S01N5YXgC2yKbWJwxyBbt24lL2/+PgePvvIoweGg9yEGw7HhRFpZYRkfu+ljfHXPVxkaHiIrkMWKohVjjl+Us4hUf2piqQiAoswiirKKXpfyX2kK4kRERETmUPdgN9/Y/w26BroAb1HiO5bdwRuWvgG/z5/Yz2c+VhStoLqwmm/u/yanuk4BcLj1MLcsuWVOyn61GRgYoLW1lfz8fAoKCi7p2GgsyksNL9HV5d2H3mAvmZmZZGdn09/bz3XZ17FyxcoxY+DGMzOqi6s5132OcDhMXU8d5Xnl1NfX88orr+CcI5Yfww06fObDZz4+e9tn+Ztf/A1twTZur749MXHNopxFfPaWzzI8MkxsJMZjxx6jP9JPWW4ZO+p2EAgEiC2Isb1jO9XhaoaeH2LTxk2Ul5e/9gq8SoXCIVqCLQAUFRWRnpbOyMgIFjOKsou4/8b7WZi9kA9u+iCHWg6xuWzzhAmA/D4/lXmVY5aLSNZWOFAQJyIiIjJnOvo7+Mb+bxAc8loYfObjPevew3Wl1015jJmxYdGGC0HcuWs7iOvv76elpYWWlhZ6erzupj6fjxtuuIHi4mL6+/vp7e1l0aJFEwKw7u5u9uzZw/r16+n0ddLa7rWEFmYW8qbcNwFe11UKIDs7e9qulOetLl3N8yee94K47jqqUqp4+eWXAShaXMRTnU8lyrGlfAulOaV84R1foKGjgdXlq8fkZWak+lNJ9afy/g3vT5QnLSWNZ049A34oKinidNdpmtqb6NnTw/WrrmfVqlXTBpvJpq67LjFmsLqkmk/e/Mkx3VTPW1qwlKUFS6fMZ3wQt7pk9ZT7Xu0UxImIiIjMgZZQC9/c/83EBCYpvhQ+sOEDM/piuaZkDY8de4wRN0JDsIGewR7yM/Jnu8hXVGtfK0+ceIK2vjbevvrtrCpedcl5NDU1ceDAgcTnlJQUsrOz6enpYe/evdx888289NJL9Pf3s2nTJioqxk7LX1dXRyQSob6+ngNDB4hEI6SmpvLWrW/ltsrbqK+vp76+nkgkMuPAaFXpKnw+H7FYjNr2WhaHFgNQtaKKX3X8iqFhb2xbdiCbO5ffCUBOeg5rK9bO6JrPt9SuKl7FT478hObeZgoLCwmFQjzZ+STNh5oJBoNs2bKFlJT58VX/1XOvEovF8Pv9rCxeOWkANxOVeZXTfk4m8+POioiIiCSRuu46vn3w2wxGBwFvOvQPbvwg1YUzmzI+M5DJ8sLlnOzwFqHe37x/RotLXw3Cw2GePf0sz9Q+Q0+wh2g0ios5Vt2ximAwyJkzZ6isrKSwcOo11sBrkaqt9cYGlpaWUlFRQXFxMT6fj4MHD9LU1MTOnTsZHvbGTh0/fpzS0tLEODfnHM3nmjkXPkekI8LLQa+1LC83jw2lG8jIyGDVqlWsWLGCoaEhsrKyZnR9Jdkl5Gbm0tPXQ3tPO/Up9eSn5fNCzwuJLrOpvlTu33T/ZU1tX5pTyidu+ATP1T3HM6eeIScnh9TUVI50HCFQHyA3N5c1a2a/u6BzjsHoIJmBzNd0fHNvM3U9dawrWUdueu6k+7x67lUA0tLSWLpg6pa2i1lRtILy3HKaQ828reZtSd1aqSBORERE5HVyLnSOnxz5CWd7zya2paekc//m+1mSP/nseVPZXLY5EcTtbtzN7VXeeKor9cV0MDpIe387lXmVVyzPI61HePTwozS2NdLf703mAXC8/jg7D+yk82wnzjlCoRC33Tb1GmsAoVCIUChEIBBg8+bNY6bZX79+PV1dXQwMDFA3WEc//VSPVHPgwAGqq6vJy8uju7ubZ9ufpSXckjjOzChbUMbC7IWJbX6/f8YB3Pk8li1Yxkt9L9Eb7KUjp4NzgXN093Yn0t+34X2JCU8uh9/n545ld7CmZA0/PvxjznKWkpISDp07RGVtJVVVVYmJUMDrvru7cTeV+ZUTZm98LWo7a/n3I/9OMBzknavfydaKrTM+dsSN8MsTv+SFhhdwzvHs6Wf53U2/O6FeBiIDnA16fy/paekszl/8msub4kvh92/8fcLDYdJT019zPleDebmohJl92sz2m1nEzB6ZZr/18f2646+nzGztqPSHzCxqZn2jXitfl4sQERGReecXJ34xJoDLSs3iI1s+cskBHMC6hevIT/e6UPZH+nno1w/xjzv/kfqe+ssuZyQW4Z93/zMP73mY7xz8DiNuZEx6eDjM3qa9PFn7JLsbdxMeFMP6SwAAIABJREFUDl80zwPNB/jy9i9z/Mxx+vr6AMjNyfWm5I9G2HliJ+AFOcFgkHB4+jzPnvXqsbS0dMI6aSkpKWzYsIHmcDOHwofoSO9gT+8eWlpa2LFjB8899xx7X9lLS7hlTICalpbGypLpJy+ZiU3LNiWmwN/Xu4+W2IVA8e4Vd1/xCTUWZi/kwa0PsiBzAYFAgEBmgH9v/XcefvZhhke8lshjbcf4p53/xM8O/Yxv7f0W7f3tr/l8kViE/zj6Hzy882HqW+vp7u7mhboXLimPnQ07eb7++UQg3x/p52v7vpb4YeK8M91nEs/C4gWLL3tdRDNL+gAO5m9LXDPw58CbgYxp9msCfguoxwtoPwX8EBj9l/Vj59wHZqmcIiIiMoeisShmht/8s961aiAyMGZSherCat6+6u2veYpzn/nYtngbT5x4IrGtta+Vf9n7L5e9EPipzlN0DnQCcKz9GL8+9Wvuqr4rkf7o0Ud5+dzLic/bT23nvk33UZFXMSGv8/Y37CcYDGJmFOcV897r3wup3tTx7e3t9Af6ue222zh27Bjt7e10dHRMOtPi8PAw9fX1NDQ0AIzZxzlHa18r2WnZFBYV0l3YTUm4BPMZUX+UgtIC+tu9iU5ODnjBQk5ODr293rpwmRmZLFuw7DXV2WgrS1ZSWlpKd5fX+pae7gUNN1TcMGuT0KT6U3lbzdv41oFvkZ+fz8DAAC81v8T2U9sxn/HUyadobWslHA4zNDTE8dbjtA22UVhYyKJFiy6a/466HRxsPsjI0AhnOs7Q3ddNLBZLpDemNRKJRQj4AzMq74HmAxO2RWIRvnXgW/zWut9iY+lGAE50nCAa9ZYFWL0oeSciudLmZRDnnPsJgJltAab838Q51w10x/c1IAYsNzNz538WEBERkXnpkRce4eljT2M+IzUllUBqgPRAOusr1nP/1vvHTO9/JRzvOJ5o0arIq+DD13/4oscMDQ1x8uRJiouLWbhw4YRAc0v5FnY37qZ7sDux7fxC4MfajlFTVEN6ajrVC6pZXrh8xmU90XFizOdnTz9LeW45a0rWEB4Oc7TtaCJtYGCA+vp6Xq1/lVtrbuXt6ycPTBu7GgHIyMjgv7z5v1CeV05HfwepqamUlZUR88XIzM6kuLiY9vZ22traJg3iamtrOXnSC8Dy8/MT67Z19HfwH8f+g1Ndp0j1pVKaU0ooGkosAp2amkpXRhdvedNbaG9rZ9/OfWRlZZGXl0coFMI5R0ZmBssXzLyeplKaW0p6ajqFRRfG9S3OX8zbVs3uOKya4hretPxNPHXqKQKBAOFwmMePPE5GRgZt7W2JFq1IJMJ393yXob4hFqYt5L5N97F+7fopyxYKh3js5cdo72gfE7ilpKTgMx+RaIRINEJbX9u0gfx5XQNdtIRaCIVCxKIx7ltzHzu6dhCKhBhxI/zwlR/SOdDJDRU3cKz1GCMjI/j9ftYsSt4lAa60eRnEXSoz6wGy8VrjvjAugLvHzLqAFuCfnXP/NEUe+cD4aaEu/hSLiIjI666jv4PnTj7nBVUxiMViDIWH6KWXHaEdXL/4ejaUXv6YobPBs9QH69mwaANHWy8EPmtLZjYT4ZEjR2hubqauro68vDxqamooKSlJfNnOSM3g92/8fdr620jzp/Fk7ZOJACw4FGRP0x4Adtbv5D/d8p8oyLj42mnOuQlBHMCPDv+IT9zwCToHOhNd9Jxz9PZ4rVjhSJinDz/N3rq9vGH1G7hr5V0UZBTQ29vLsBumPeR130sLpLEwxxtzVpRVRGFmIZ0DnURHovzwlR9ya9mtOOdob2/HOTchsGht9ZYBWLt2LYsXLybmYuw4vYNnzzybKFd0JEpDsGHCNexu3M3epr3kpecRy4lRlO0Fm4ULChlxI6SkpJAVmPn4t6n4zEdRVhHNvc2JbXevvPuK/zAwmTuW38HtS2/nv//0v9PQ1sDAwAB9fX0MDQ1RklFCcMTrqtrX14dzjqahJp44/AQLixeycOHCSfNs62ujN9RLLBYjkBogKzuLvOw83rXuXew8sZNdp3YxPDxMc2/zjIK4Y+3HGB4epquri7K0Mrrqu9iUs4ljmcdoH/Cek6dPPc3Tp55maMibzTMzkMmSgkvvdjxfKYgDnHP5ZpYF/C5e18rzfgB8FWgFbgR+bGZB59y/TpLNZ4E/m/XCioiIyGV7qf4lItEIfr+f0kWlDA8PEx2O0tXVRXQ4SkNPw5RBXDQWpWeoh/SU9ClnFxweGebXtb9mR/0OnHP87PjPxqTPZEzU+fXPzIy0tDSCwSB79uwhPz+f6upqFi5ciM/nIyuQxdKAN2Pf/Zvu52DLQX7+6s8ZiA5cKPNIlKNtR2fUla+9v52eIW+9tbSUNDJTM+ke7CY8HOY7B78zZuKJmowa1uWt49XhV+lwHfT29tIb6uXn+3/O7rrdbC7djDvrsBQjEokAsCh30ZiFmLct3paonyNtRzjSdoSh0BC3czvBYJD8/Au/kQ8ODtLb20tKSgpVVVU0h5r58eEf09bfNuX1lOeW0x/pT1zTiBsZ03IJkJXtBW63V91+0fqZqXUL1yWCuOrC6tc07vG18vv8vGvDu/iHp/6B/n5vCYu1eWv55N2f5K+e/SsaWxtxzuHz+cjLy+NIzxFeOvISd5fcPWlrXFt/G8NRL0AuLCpkU+Um7ll5DwUZBZxtP8sudhGNRmkJtUw4dryugS72n91PeMhrFawprCEjLYPB0CDrc9dTm1c7JgA/35VyacHS17y0wHykIC7OOddvZl8B2s1stXOuzTl3dNQuL5rZPwDvASYL4v4X8Mi4bRXAjlkpsIiIiLxme057LVSZmZm857r3sLlsM0daj/D3T/w9kWiEM+1nID78pmewh+1ntnOu7xzdg92EwqFEPh/a9KEJ65u19rXyw1d+OOUX2tKc0jHdDUdGRhgcHKS/v5+BgQG6urro6urCzHDOUVlZyfr166mvr6e2tpaenh727dtHWloaN954I3l5eYm8zIxNZZuoLqzmydon2X92fyLtcOvMFgU/2XlhYonqBdXcsfwOHt79MNGRKB0DHXQMdAAQG47huhw5KTl86pZPEUmL8Pjhx9l7ai/hcJjWtlaeaH+C8SNUKgvHrs11U+VNNPY0jhljN+gfpC3SRnNz85ggrq2tjZZwC50jnRQ1F/GLE78YM6lKeW45b1z+Rp4+/TQtvS1sqdjCPSvvubAmXX9bYl2+8+5dcy+nu04TjUW5rWr6GTEvxQ0VN1DbWctwbJh719x7xfKdqeuXXM+mwk3U99azNnctH3jjB8jLzaNmUQ2NrV7X1qysrMSYwO1nt7O5bfOkrXGtoVaiw9740beteRu/sew3EmlLi70fEIajw+xp2kNpTinXlV436XjMtr42vrLnK4SHwwyFhzCMbSu2sbRiKS+++CIDvQOs8q1i/cr17G/ez7m+c4ngsaaoZjaqKWkpiBvLB2QC5cBkP+lMOU7OOdcD9IzelsxrT4iIiMxX/ZF+atu89cWysrKoKarBZz4q8ypJDXgzJTZ2Nya68j1+/HGOtR+bNK/nzjw3Jojb2bCTX574JdGR6KT7Z6RmcO+ae2lubqahoYH+/n4GBwcnBDrnmRnLly/H7/ezbNkylixZQkNDA/X19YRCIQ4fPszNN9884TtHTloO7177bt684s385fa/xDlHY7CRUDh00bXJznSdSbxfUbSC0pxS3rX2XfzglR+M2S8UDFGQVcCiRYsoLi7GzPjELZ/gzavfzA/2/oAjTUcYGRnBZ77EWECfz0dlwdggzsx499p34zMfB1sOApCZlUl7qJ3m5mZWr16duL6W1hZ2BXeRU5DDo0cfTeQR8Af4zRW/yY2VN+IzHyuLVhIdiSYm2ajIq+CjWz8KeJNnBIeCdA92kx3Ipiy37JKmxp+pjNQMPrLlI1c835ny+Xy85/r3cObMGa677rpEsL9q0SqeO/YckUiErMwszIy83DyaupvYeWQn9y6cGHCe7T6Lc46UlJREV9jzqoqrMDOGY8O4Ecdjxx5L/ODRM9jDm6rfxNqFXvfh5+qeSwTdQ0NDrMteR3lJOZmZmdx88828+OKL9Pb0UmAFPHjDgzx6/FF+3fZr0n3pbCi//O7N88m8DOLMLAXv2vyA38zSgZhzLjpuvzcD54DDQBbwJbyJTo7F098JPIcXnG0FPgN87nW6DBEREZkFB+oPMBwbJiUlheXFyxNBTV56HjkZOfT399M31Ef3YDcFGQWc6T4z5vjRQUlDsCERGL187mUeP/54Yr8UXwpvXvlmijKLePz44+Sk5XDv6ns5d+YcdXV1if3MjIyMDLKyssjMzCQnJ4fCwkL6+voIBALk5FwIuvx+P0uXLqWyspKnn36arq4ujh8/TiwWo7e3l8zMTMrKyigqKrrQ1bJgKae7TuOc40jrEbYt3jZt/YxeAuH8mlzXlV5HW38bz55+FvC+gOcN5xFIDbB+/dgJMZYuWMof/+Yfc6TlCD87/DPaw+2cO3cOgEAgwKKciTMhpvpTee/691JTXMP3X/6+1300FGRwcJCuri4KCwtxznGy5SSRkQgZGWMnH//Qpg+NmVXSzKacJTHgD1CcVUxxVvG09TAfLFmyhCVLxnbj3LBoA0vLltI90M1vX//b1HbWcnDkID09PTzT8AxvDL1xzDMH0BL0WpUDqYEJ9ZaTlkNuWi7BoSDDw8OkBlLZ3bg7kf7dQ9/lpsU3cVf1XRxvPw54rbjbsrexJHtJIrgcHch1d3dzYP8B3r/t/aScTcEiRnH+/L9fl2JeBnHA5xk7Pu2DwP8BHjCzPuAe59wOoAD433gtb4PAHuBu59xQ/LgPAN8A0vCWI/h/nXOPvC5XICIiIrNif73XxTA9PZ1VJRda0cyMyvxKznWcIxqN0hzyxjMNDXtfCzJSM/jkjZ8kPyOfr+/7OnXddTjnON5+nK0VW9nTuCeR16KcRbxv/fsoyigiFArx2Zs/i8/no76+nrq6Onw+H6tXr6akpISMjAz8/okTXozuJjleSkoKK1as4PDhw9TW1ia2d3Z20tjYSCAQoLS0lMrKStaUrEksbXCq69S0QVwoHKI37E1UMjQwxKGdh1hatZTly5dzV/VdrClew/GO4xx99SgLcxZSXV2dmD5/NDNjXdk61pau5at7vkpnZyfRaJRAaoCS7JIpz7+0YGni/WDqIDEXo7m5mcLCQgYHB2kdbMXv94+pr+tKr7siywJcKzIDmfzJnX/C0PAQOWk5LC1YytG2o2RmZdLZ18muY7u464YLy0kMRgfpGfA6m6WnpU86Oc7yguW81OKNM/X5fPhTxj7POxt2cqztGIPRQQACLkBZWhmFhYVjfgA4H8g9//zzdHR0cPLkSdKG0/Cl+MYsWi7zNIhzzj0EPDRFWvao9/8G/Ns0+fz2lS6biIiIzJ3wcJhjrV7XyPT0dNYvXD8mvaqoir21e4lEIolJKYaHh2lpaWFR2iJOvHyCgoICygJlnB45jc/n40jbEWqKaqjrqQO8AOaBzQ/gwo7nnnuOvr4+Fi9ezOrVqzl+3GuJ2Lhx46TT51+KJUuWEAqFiEaj5OXlkZOTQzAY5OzZs/T19VFfX099fT3lNRfOM7qVbTKj0339PsL+MMePH6epqYl169ZRXlxOYVohg8cGIQUqKyunyc2ri9uX3s7hxsN0d3eTlZXFgowFU+6fk5aTmK0yLSONrnAXmS2ZrFu3jv7+fjqi3pIE560qXsVba956saqScVL9qaT6vXpckLmATWWbeGHoBfr6+theu507r78zESi39bclJhdZmLNw0slF7lxyJ75+H/s69mFmlJWVkZKSMmZ20fMTy8RiMXIGcrB0o6ho4lIUmZmZbNy4kd27d/Pqq68CXrfn8Qu6X+vmZRAnIiIiMpnjbccZGPRmbVxcuHjCemZVRVX4fD5isRgN3d4MeQMDA4yMjJDry6W9vd1bmDrWT3NXM2VlZZzuPM3upt2JcW1LC5bSda6LI0eOJNbUamhooKmpiZGREQoLCykrK7vsa/H5fGzYMHac0MKFC1mxYgWhUCjR6nf2xFlixPAH/ASHgtOOizsfxIWHwuSSS3p6OikpKfT19bFr1y7Kysrw+Xw451i0aNGkrXDjrSpexU3VN/FKyyvctfKui84ZUFVQRedAJ4FAgL7hPsLhMB0dHYRCITqjnaRmesHHp7Z9irLcy69HgW2V29jbtJe0tDTq++s5UX+C1cu8mX3a+9oTQVxZ/uT1XZBbwPLM5ZyLnKNpqIlIJEJ3dzfhcJj33vBe9rTsIea8v4WOjg5qMmtYsGABVVVVk+ZXUlJCTU0NtbW1OOcu+weP+UhBnIiIiFwz9tbtZcSNEEgNsLF844T0stwyUlNTCYfDNHU1kZqSmlggedOKTWyu2Ex3dzfNzc3k9eQxODiIL8uXGCvmRhzpvem83OLNtLh48WJKS0t56aWXiEajZGVlTRhDdqWZGbm5uaxfvx6fz8fp06exQYP4ELHm3mZqiief6e9s0Avi+gf6WZK6hMrKSlauXMnp06c5ceIEzc0X1j0bP9ZquvLct/E+ouujidaf6VQVVCVm1exJ6QEHzc3NDMQGGIwNUpBSQMA/+dg6eW3+L3t3Hhz3dR34/nt73wA0urFvBEiA4E6CJEhRomRRlmw5TuxY8djWRFasJC4n8xyPk6mpeu8lE2uSTE1SSRxneXGiOLJkO7JlWxrFViTHji1Rm0VxFXcQJPYdaHSj9/2+PxpoAgRAghvQpM6nCoXfcn+/vj9AFPr0vfecqqIqmkqbCIfD+BI+fnLyJ/kgbjQ8mg/i6jwL14CbmfrrNrkZYIBUKkUsFsNmsOGacPHr23+d75/+PuOhcewZO1WOKnbu3HnZ0bW1a9eydu3aG/ykt4+CDOKUUi1AQGs9rpRyAP8dyAB/rrVOXP5qIYQQQtwqwuEwR48ezdc+uzRhxY00Hh7nYPdBAGx2G5sqN81r47F7sJgtJBIJ/FE/TJIP4jY3bqa6vJrq6mrsdjuNvkbORs/idOZqjOmsZnR0lO3u7ZgsJjZv3kxdXe5N7wMPPEA2m50zFXA5tLa20tPTgz1tJ5qJYjQaGQwOzgniuia7ODJ4BJPRRMdEB1prIpEIHo8nP/LW3NxMbW0tXV25BCnFxcWUl19doomlBHAA68rWYTKYSGfThFWYqdQUw8PDTBhypQ3MZjP1JfVSM+wGu6PhDrp8Xfj9fo6PH2cyMInH7aFnPLf202w2U1Oy8Eic2+3mzjvvxNJj4eSRk/mMqx6Th1AoxGTvJL+z53d458w7jKfGKS8vx2qdX4JALF1BBnHAM8BvAOPkMkZ+AEgD1cD/tYL9EkIIIVZcj7+H02OnSaQTJDNJkpkkGZ1hS9UWttdsX+nuXZUzZ87g8/sIBAL09fVRX19Pc3MzDocDrTXnzp3DaDTS3Nx8za+htaajp4OvHfwakWgEo9HI+tr1CybYMBqMlDnLCIVDpNNpYskYmUwGq8lKnffiKERVVRWrbKs4NnEsv+4nEAiwyrQKT5GHXbt24XK5Lt73kmQcy8VkMlFWVkZppBRfzIfL5WJgaiB/PpaK8c2j3ySZSeaPJRNJjNpIdUk1xcXF+eN2u52NGzfe9D47LA5ay1s5NXoKs9nMWHKMklQJvaFeIBfE1ZUsPCIkrt2Gig24HW4CzgDBUJBXTrzCQ3sfoteX+7lbrdbLjn56vV6a47l/pzMffNQU59bGDQ4O4vF4sCfsmA3mq/4AQMxXqEHcGnJp/wF+BdgHhIGjSBAnhBDiPSwYD/L1w18nnU3PO3fed55GdyNuu/uWGKXw+/38uPPHnIudo85dR4tqIdubpa+vj+bmZurr6zl37hyQm5Y4kyhhsWAoEAvw4rsvsr5mPTsadgAQS8T4ux//He+OvAvkpvZVVlTyK1t+ZdF+VRZV0j3aTSadIRG/+GZ09tQvu91OhaeCumAd8XgcgzIQCoW4v/J+duzYMSeAW2lVVVWUDpUSi8RwuVwMBgfzgeeRoSNzAjjIlQ9Y51xHRcXiWSRvth01Ozg1egqAMTXGGr2GUCaEUgqj0XjZDJfi2hiUgV11u5gITRAMBXm9+3X2te0jEMklJClyFF02KQ1ArXfu2rVVFavYsmoLR44c4dSpU/l/QwslNBFXp1CDOAVopdRqQGutuwCUUsWXv0wIIYS4vfVN9S0YwEFuxOmZd59hPDLOpspNfHzTx2/q2qvr9Z23v8OZyJncaE8RnE6dplpVU5+qp7OzM58UBGBkZISOjg6SySSlpaV4PB7KysooKSnJBSRGxd+99nec7TvLq52v8qVf+hJWo5W/ePkvGJoawmAwUFJSgsPhYF/zvssmxKguqQZyWSnjiVx5geby+SOBVVVVrBtfx9vRt8lkMtzpvpMNazdctjTASqiqqqLYVEwqkUJnNeFkOJ/cZHZZhDJHGXc33Y3/gp/4VHxF32i3lLXgsrgIJ8Noi2Y0OkooHcJkMqGUoswhQcDNsLNuJ690vYLNZmMiPsGPT/yYeDz3b2BV2aor/v/EarWy2rmarkgXFoOFTTWbqK2txe/3093dTTabxeFw5Kcgi2tXqEHcu+SKajcAPwZQStUCwZXslBBCCLHSfFFffrulrIVt1ds4P3Geo8NHARgO5YryHhs+xp6GPQU57Syrs/zw9A95a/CtfBIOyE2Tm2CCnlAPTTRB18Vrzp49m5+i5fP58Pl8dHZ2EkqHOB05zYRhIl+AOx6P8+3D36ZnpIdgNIjRaKSyopLV5avZVr2N3fW7L9u/mQx86Uw6f8/1tevntZsZKdxn2EeGDMWWYtasWXPdP58bzWq1UuYto9hXTCQaweVyMRYZYzwyzkQ0t87MarLyn9f9ZyxGC2+Fcr8Xr9e7Yn02KANtNW283vM6JpOJUTVKOBPGas+to/I6Vq5vtzOXxcXmys1MhaaIx+O8fOZlstksJpOJBk/DFa9XSnFv3b2UjZThMXsoL81Nm9ywYQOBQAC/3y9TKW+QQg3ivgD8PZAEfm362P3AT1asR0IIIUQBmIhM5LfXla1jW/U2nGZnPoib7dTYqZsexE1EJuiY6GBDxYY5RYC7J7s5N3GOYlsxHrsHr8NLqb0Uo8HIyx0vs//8frTWWK1WWitasRqtnBrLTZ8z2owcGjuEURmpt9WjUPkAbtOmTTgcDiYmJjg9dJqfDfyMRCYxpx4VwInuE2itMZlM1FTV8NDmh9hZt3NJz1TvqUcpRSaTIZPJYFAGNtbOXwtms9lobGykqysXbdbX1y974pKlamhooLirmKHwEC6Xi9HwKL3+3vz5Kqp45+cXR+XcbveKP8tMEAfgUz4yOoPVasVpcWI337wEOO91exr2cGToCCaTiXQ6N+pvs9qoLqpe0vWlRaU0TTWhlMpPKzYYDLS3t9Pb20tDw5WDQXFlBRnEaa2PA3svOfY08PTK9EgIIYQoDDMjJ0C+xtligdrp0dN8oPkDN21K5YmREzx38jlS2RQ/vfBTHm17lMbSRg4NHuKF0y/k66bNMCgDDe4Gevw9+Slamys386vbfhWL0ULHeAc/PPtDtNYYDAY6Ih2ciJ2ALOxz76PEWkJVTRVnfGc4GDlIn+6jsrYSv99PMBhEa43FYiGVSuUDuOaGZn5tx69R7758UerZyl3lGA1G0pncG9gKZwVO28LTv9asWUNvby/ZbJbVq1df40/y5quursZr89Lt7yaVTHHed57zvvMABINBHEYHyqzyv7NCWLNU6aqktriWweAgTpcTs8WM2WyWUbibrLaklgZ3A9lsloA/QDQWxelyUuVaWkmHmcDN6XTOWb9qtVqlZMANVJBBHMB0aYFWYE41Sq31ayvTIyGEEGLlzZ5OOfNm1m62U+YomxPgQS7gG4+M3/AkEFprXul6hZ9e+Gn+WCKd4J8P/TN2k51IKkIsFmNqagqz2YzVasVqsWK2mOnx9wAQj8XxmD08suMRLMZcAbPW8lYcZgf/8M4/YLfbCUQCuFwuspksx0LHaHQ08pdv/SWxVGxOf4qLi3ElXTgMDu7dfC8vdL5AJBZh++rtPLLjkUULWy/GarLmRiGmgziva/GgwWazsWfPHjKZTEGv8zEajaytXcsh/yHCkTDnJnIJY7TW2BN2SopLaGtrI5PJ0N/fT3390oPem2l7zfZ8AXKLJfffiayHu/n2rd7HN6a+gbfMixcvSqkl/3/E7XYDUFpaeoWW4noUZBCnlPoI8A3g0kQmGlj+/LxCCCFEAYilYkSSEQBMBhNumzt/zm13zwviIDel8kYGcclMkudPPc+JkRPzzmV1lkgq17+pqSnsGTseg4dwKIw/7SdBgqKiIpxOJ8lUklZPKx7P3Gx3VUVVGJQhn7CkuLiYTCbDRGACndaY1cUpfgZlYEPFBj7Q8gGivihjY2Ns3bCV1qZWpuJTtHhbMBqu7W1Dc2kzJ0dyibLb69ov2/ZWebPaUtsCJyGZvJiNMhaL0WxpxuVyUVNTg1KqoKa7banawsvnXp6TzGdmBFrcPK3lrTza9igvdrzIZHSSbVXbsJqWVtetvLycPXv2FFyCn9tNQQZxwJ+Tqw/3Va11ZKU7I4QQQhSCS0fhZk+TbCptyk+Pm+287zz7Vu+7ptdLZ9Oc952n0lVJqb2UYDzIt459Kz8yAtDsbeaB5gf4t45/oy/QB+SKXlcaKmkvbWfrxq0EAgEmJyd5ceBFJqYmCAaDWAwWdtTvmJO2H3IFoSucFYzokXwCBLPZTFXVxalcHoeH9tp22mra8qNsXoc3P3pUXVS95PU7i7m7/m4m/BO4TW621W+7rnsVimpPNWZlJp26GBClIilqi2ppamoqyEyms2vGzZCRuOXRWt5KS1kLgVhgznrXpSiE6bi3u0IN4qq11n9xrRcrpT4PPAZsBp7RWn9mkXabgaeAmUnsh4H/qrU+NauqZemBAAAgAElEQVTNnwC/Re5n9W3gC1rr1LX2TQghhLhWc9bDXfJGdlfdLg4NHiKcCPPLG3+Z7534HgB9gT4S6cSSP0Wf4Y/5+Zdj/8JwaBibycZH13+Ul869RCgRyrfZXb+bD7d+GKPByOd2fY7R8Ci+qA9j1Mi5E+coKyubs06scbCRP//JnxNJRbir/i7atrYt+No1xTWMhEcWPFfpquR39vzOTQ846krreF/p+wBumxEFp9NJibmEieQEOqtJpVPUqBqsZit1dYWXxXTG7JpxICNxy8mgDHgcl68NJ1ZGoQZxbyiltkwnOLkWQ8AfAx8ELpe+aIBcMfFewECukPj3gA0ASqnfBD4F7CRXbPyHwB8AX7rGfgkhhBDXbKH1cDMcFge/t/f3SGfTWIwWXu95nZHQCFmdpcffQ2t565JeI5QI8VbfWxzoP0AincsIGU/HefbEs/k2BmXgF9f9Yj5V/0xmyEpXJZWuSo4dOwYwL5V4c20zf/rxP8UX9LG6avW8UbgZNcU1HBk6suC5u1bdtSwjRna7Pf99Zi3WrU4pRYWzgonkBKl0inAozE7HTurr6zGZCvUtYa6URomthKn4FHaz/YoFp4V4LyjUf7FvAC8opf4RGJ59Qmv9jStdrLV+HkAptRNY9KMlrbUf8E+3VUAGWKOUUjqXnukx4Mta657pNn8EPIEEcUIIIVZA/1R/ftvrnJ9sw6AM+SQhazxrGAnlRrMuTF64YhDnj/l5ved1jgweIZVdfMKJ3Wzn4S0Ps8abq4fW29tLR0cH69ato6GhgUwmw/j4OAAVFfPX4pW6Sil1XX5q1mKFuIusRWyp2nLZa28Ur9dLeXk5lZWVy/J6y6WmpIbT/tMk4gncGTcOo4PGxsaV7tZlGZSBR7Y9wqHBQ2yu3IzZWJhlHIRYToUaxH12+vtvXXJck0t4ckMppQKAi9xo3P/UF3MibyJXeHzGMaBOKVWitZ665B5uwM1chTs3QQghxC2lx99D50Rnfr+h5PLJJ9Z41vBm75sAHB85TomthNay1nlr6QBOjZ7i2ePPktGZOcfLneXUFdfla9CVO8t5ZNsj+elsQ0NDnDiRq8d2/PhxotEoIyMjxONxHA4HRUVXlxVyxqXr2R7e+jCdE53sqtu1bG/gjUYjd9xxx7K81nLaVrONo4NH0VHN5qLNVFRU5FPCF7Ka4ho+UvyRle6GEAWj4II4pZQB+EXg3HKtPdNau5VSTnKFxXtnnXIBs4O1wPT3okuOA3wRGaETQghxE2it+fdz/57f31q99YoZJxtLGzEqIxmdIZQI8VLHS7zU8RIeh4dt1du4t+nefObG/d375wRwtcW1vK/pfWyo2ADA2vK1TMWnaK9tx2a2AeDz+Th69Chaa0pLS/H7/XR25oJMl8vF9u3br3nao8Vo4YMtH+TN3je5a9VdbKrcxKbKTdd0LzFXWUkZ93vvz+83NTWtYG+EENeq4II4cqNtB8kFUMv3olpHlFL/AIwrpdZrrcfIrYObXeZgZmVzaN4N4CvkkqTMVge8fqP7KoQQ4r3FF/XRN5XL/GgymHig+YErXmM1Wbm76W5e7Xp1zvHJ6CQ/u/AzPHYPbTVt+KK+fLZJozLySNsjtHhb5gRgl05hDIVCHDx4kGw2S2NjI5s2baKzs5N4PI7b7aa2tnZOkd9rcU/TPdzTdM913UPMN3vUzel0zlu3KIS4NRRcEKe11kqpC0All6yHWwYGwAHUAmPASWAr8Nb0+W3AwKVTKQG01gEujtQBFGSqXiGEELee2QlNVrlXLTnd9wPND7CrbhfnJs7RMd7BhckLJDO5GmG9gV7aato4OXoy376lrIW1ZWsve89YLMaBAwdIpVJUV1ezadMmlFKsXXv560RhmB3ENTY2ynsVIW5RC6eFWnl/BXxbKXWvUqpRKdUw87WUi5VSJqWUjVxhcKNSyqaUmjeJXin1QaXUVqWUUSlVDHyZXKKTM9NNngJ+Vym1SilVBvwP4Mkb8HxCCCHEkgXiFz8jdNsvXX59eSW2Etrr2nmk7RF+dduv5o8Ph3Kfk84u2n2lKYupVIoDBw4Qi8XweDy0tbVJEHCLMZlMeDwe7HZ7vq6eEOLWU3AjcdO+Nv39Z+SmVwKo6e2lzM+4tAzAI8DTwGeUUmHgQ1rr14FS4G/IjbzFgHeAB7XW8Vn9aCRXP85Mrk7cn1zbIwkhhBDXJhCbFcTZri6Im212wpDR0CgTkYl8MGcymFhfvh7ITZfs6+sjmUyyadMmzGYz2WyWgwcPEgqFKCoqor29/bqnTIqVceedd5LNZuX3J8QtrFCDuOtaZau1fhx4fJFzrlnb3wG+c5n7aOD3p7+EEEKIFeGP+/PbS51KuRCnxZmvt5XKpvLZKwEa3Y2Mj4zT19fH5ORk/ngqlaK9vZ2uri58Ph82m43du3ffNrXT3ouUUhLACXGLK8ggTmvde+VWQgghxHvDVOziUuzrGYmD3GjcVDx3v3cG3gEgm80S6YtwbDhXpNtkMlFTU8PIyAijo6OcPn2awcFc8pOtW7fmC2ELIYRYGQUZxCmlHl3s3FKKfQshhBC3k9kjcVdaExcIBDhw4ACtra0LFnGuKqri7PjZOccSiQRlhjLsdjtr166lpqYmH8gdOHCArq6u3Gu73ZLNUAghCkBBBnHA/7xkv4JcXwe5CcW+hRBCiEKVyqQIJXKVbQzKQImtZF6b4eFhzp07R2NjIyMjIySTSc6cOUN1dTVWq3VO25qimnnXFxmKcOKkurqahoaLOcTKy8vZvn07R44cQWtNS0uLJDIRQogCUJBBnNZ6zpo4pZQJ+N9A58r0SAghhFgZM1MfAYqtxRjUxcTS6XSakydP0t/fD8CJEyfILefOnevs7GTTprkZJ2cnN5lRaa6EFBQXF887NzMqF4lEqKysvCHPJIQQ4voUaomBObTWaeAPgf93pfsihBBCLMVMMHU9jg0f4+8P/H1+f/ZUysnJSfbv309/fz9GoxGPx5N/zbKyMpRS9PT0kEgk5tzT4/CwrXpbft9itFBrqAUWDuIAKioqaGpqklE4IYQoEAU5EreIEnIlAYQQQoiCdt53nmePP0uFq4LHdjyGyXD1f26Pjxzneye+N+dYqS33Z7Cvr4/jx4+jtaakpIS2tjbsdjuvvvoq8XicTZs2cebMGUZHRxkcHGT16tVz7vOfNv8nPtDyAfwxP26LmzdeeQMUFBUVXftDCyGEWDYFGcQppf7wkkNO4JeBH61Ad4QQQoir8t0T3yWaitLj7+HQwCHuaLjjqq4fnBrk+ZPPzzteYi9Ba01nZydaa9asWcO6deswGHITa+6++26SySRFRUXU1dXNCeLi8Th9fX309/dTUlLCzp07KbGV4Pf70VpTXFycv48QQojCVpBBHLDvkv0Q8C/AX61AX4QQQoirEklG8ttd/q6rCuKC8SDfOvYtUtnUvHNeh5dIJEI0GsVisbB+/fo5UxytVms+kUllZSUmk4lAIMA777zD2NhYfrplNBolGo3icDgIBoPA4lMphRBCFJ6CDOK01pcGcUIIIcQtIZ1Nz9lPZpJLvjaVSfEv7/4LwUQusLKb7Xyw5YPs795Pia2EzZWb6e/NJTEpLy+/7Bo1o9FIVVUVAwMDjI6OopSiurqaeDyO3+9nYmKChoYGpqZyiVMkiBNCiFtHQQZxSqm3tdbzPrZUSr2htd67En0SQgghliIQC8zZn4hMLOk6rTXPn3qegakBIFdO4FNbPkWzt5mdtTvzAdv4+DiQSzZyJS0tLSSTSUpLS2loaMBms9HT04Pf72d8fJza2lqGh4cB8Hq9S35GIYQQK6sggzhg4yLH1y9rL4QQQoir5Iv65uz7Y34S6QRWk3WRK3JOjJ7g+Mjx/P6HWz9Ms7cZIB/AZTIZfL7c/ZdSdNvlcrF79+45x2aum5iYYHBwkGQyidvtxu2+fBFxIYQQhaOggjil1KPTm0al1KeB2fNEWgHf/KuEEEKIwjEZm5x3bDQ8SoP7YhHt4yPHCcaD7K7fzXnfeeLpOBd8F/Lnd9TuWHAd3fDwMJlMBrfbPa+I91I5HA4cDgfRaJSzZ88C0NjYeE33EkIIsTIKKogD/uf0dyvwR7OOZ4ER4HeWvUdCCCHEVbh0JA7mBnEd4x08e/xZAF4+9/KC99hRu2PeMa01XV1dwPUFXUopKioq8jXkLBYLNTU113w/IYQQy6+ggjitdROAUuolrfUvXOt9lFKfBx4DNgPPaK0/s0i7DwP/D7AJiAMvAb+ntQ5Mn38c+H1gdqXU7Vrrc9faNyGEELe3hYK44dBwfvvt/rcve71BGaguqp53fHJykqmpKaxW63UHXevWraO4uJh0Oo3X68VoNF7X/YQQQiyvgiwIMxPAqZz5f8mubAj4Y+Cfr9CuBPgToAZYB1QAX7mkzXNaa9esLwnghBBCLGoyuvB0SsiVD+j0dV72+kpXJRajZd7xvr4+IDcKd71Bl9lsZtWqVaxZs0bWwgkhxC2oIIM4pZRdKfUEEAPOTx/7qFLq95dyvdb6ea31C1xhDZ3W+hmt9Y+01tHp0bcngLuus/tCCCHeo7I6iz/mn3d8JkPlkaEj+Vpti6krqVvw+EwpgKVkpRRCCHF7K8ggDvgLYBXwPmCm2ukR4OGb/Lr3AKcuOfYhpdSkUurU9DTNBSml3EqpxtlfwMJ/iYUQQtx2QokQz518jozOAOA0OzEZcqsWwskwsVSMw0OH8+1/ad0v8bGNH6O9rn3OfeqK5//pyGQyhMNhlFIUFRXdxKcQQghxKyioNXGzfATYqrWeVEplAbTW/Uqp2pv1gkqp+4DfZO5I3HfJjc6NAruB55RSU1rrby5wiy8CX7pZ/RNCCFGYQokQr3W/xsGBg6SyqfzxRk8jE5GJ/FTKQ4OH8lMtbSYbO2p3YDaaqS2u5eDAwfx1tSXz/9SFw2G01rhcLlm/JoQQomCDODMQnH1AKWUnN73yhlNK7QaeBT6htc6PxGmtT89q9pZS6q+BjwMLBXFfAZ665Fgd8PqN7a0QQoiV0j3Zzemx02yu2ozX4WV/937e6X9nTvAG0Fbdxi+u+0WeP/V8Pojb370/f35r9VbMRjMAVa4qKl2VjIZHKbWXUumqnPe6wWDuT2JxcfHNejQhhBC3kEIN4g4CnwP+v1nHHgUun9LrGiil2oAfAp/VWv/4Cs0XXcgwvaYucMm9r7+DQgghVlwyk+TFsy9yeDA3HfLY8DHsZvu8TJS1xbXc33w/a8vWMj4+TpHp4tTHWOri55A7ai6WEFBK8em2T3Nq9BTrytdhUPNXOkgQJ4QQYrZCDeL+O/CaUuoTgFMp9SNgJ3DnUi5WSpnIPZuRXOFwG5DRWqcuabcJ+BHwhelEKJfe56PAa+SCs3bgC+RKDgghhHiPCCVCfOvYtxiYGsgfi6aiRFPR/H5NcQ3vX/N+WstaUUoRDAZ5++238Zl981afVxVVUVM8t0RAqb2UvY17F+9DKAQg6+GEEEIABRrEaa3PKqXWkxt9O0Wu0Pdntdb9S7zFHzB3fdojwNPAZ5RSYeBDWuvXgf8GlANfU0p9bdbru6Y3PwU8Sa74+ADwZ1rrp675wYQQQtxSxsJjfOPoNxbMODljY+VGHt7y8JzZF5OTubVvxoQR7HPb76jdcdUzNWQkTgghxGwFF8QppcxAL7Baa/1X13IPrfXjwOOLnHPN2n6MXFHwxe5zs7NhCiGEKFAXfBd45t1niKfjQG7a4xrPGs77zs9pt6li07ygbKYcgAMHmUwmn4zEZDCxrWrbkvswNTXFqVOnSCQSmEwm7Hb7lS8SQghx2yu4EgPTUx5TgCwoE0IIsSKODB3hqSNP5QM4i9HCp7d9mk9t+dS8ts3e5nnHZoI4i8GChYuFu9dXrMdhcSypD/F4nAMHDuDz+TAajTQ3N8taayGEEEABjsRN+zLw50qp3710HZsQQohbT2+gl1e7XmV9+Xp21e9a6e7MobVmODRMNBWlpqiGN3rfmJNJsshaxKNtj85bxzbj0qAsm83m17ABuM1ufNlcApSdtTuX1KdMJsPBgwdJJBJ4vV7a29sxm81X+2hCCCFuU4UaxH2RXHr+31RKjQDZmRNa69Ur1ishhBBLFk1GOTp8lDJHGc+dfI5IKkKnr5OWshZK7aUr2jetNT/u/DFnx8+SyCSYik8t2K7KVcWj2x+lxFaSv+7O6jt5a/gtAB5ofmDeNaFQiGw2/2eLHaU76FbdNJQ0sMazZkl9O378OIFAAIfDwc6dOyWAE0IIMUehBnGPr3QHhBBCXJ/nTj3H2fGzc45pren2dy8YxGmtCSaCFFuLb/q0wVe6XuG1ntcu26alrIWHtzyM1WTN9+/o0aMk+5NUWitZ1bCKu1bdNe+6mamUJpOJdDqNPWPnM7s+s+S+dXV1MTAwgMlkor29HYvFcuWLhBBCvKcUZBCntX56pfsghBDi2k1EJuYFcDN6/D1sr9k+51gqk+LpI0/T7e9mR+0OHtr40E3r25mxM/z0wk/nHFNKYcBARmewmWzct+Y+9jTsIZPO4PP5mJqaYnx8nLGxMUwGE02pJrY4txAMBEkkEsRiMeLxOLFYDL8/l8myqqqKgYEBIpHIvD6cPHmSqakpdu3aNWeUbWxsjDNnzgCwbds2yUYphBBiQQUZxAkhhLg1HBs+xtnxszR7m9latRWTwYRSikODhxa9psffM2c/k83wgzM/oNvfDcDhwcO0VbfR5Gm64f0dDY/y3RPfze9XOCv4+KaP47F7SEQTTCYmSYfTBMeDvHL+FaLR6JzrDQYDtbW19Pf3c/z48UVfx2Qy0dTUlA/itNb50cVIJEJ3d+5ZOzs72bBhA5Ab6Tt27Bhaa1pbW6murr7Rjy+EEOI2IUGcEEKIa9IX6ON7J74HwImRE/yfU/8HyKXRz+hMvl1NcQ1eh5cTIycA8EV99AZ6GQ4Oc953ni5/F4l0Ys69f9T5I35r12/d0GmV0WSUbx37FslMEgC3zc1Dqx/CnDDz88M/X3DEzGAwUFxcTElJCSUlJZSXl2O32zEajQQCAQwGA1arFbvdjs1mw2az4XQ6KS4uxmAwYLfbicViRKNRnE4nAL29vfn7d3V10dDQgMvlIh6Pk0gksFqttLS03LDnFkIIcfuRIE4IIcRVm0kMspB0Np3fdtvc/Pbu38agDIQSofwo3BPvPHHZ+w9MDXBu4hyt5a03pL9ZneXZE88yGc0V4TZoA2viazh26Fi+jc1mw2AwUFRURFVVFW63G5fLhcEwvxrP5s2bl/S6TqeTWCxGOBzG6XSSyWTo6+sDwOPxMDk5yfHjx9mzZw/hcBgAl8slpQSEEEJclgRxQgghrtp53/n89McZBmUgqy9mZTQbzDy49kEMKhcENZY2zptKOcNtc7O1eiuxVIx3Bt4B4OToyRsWxO3v3p8v0p1Op1mXXYdZm7Hb7ZjNZsrLy2ltbc0X5b5RiouLmZiYIBgMUllZydDQEKlUCrfbTXt7O6+++io+n4/u7u58sDgzYieEEEIspmCDOKWUEdgN1Gutn1VK2QCttU5c4VIhhBA32fGRi+vBdtXt4qMbPgrkRrwy2QyZbAaT0YTJcPHPzJaqLbze/ToZncFitLDas5pmbzMt3ha8Di9KKQamBvJBXMd4B1mdzQeB12oiMsGrXa8CkEqlqIhXUGYpw+12s3v37pua/bGoqAggXzeup6cHgMbGRiwWC1u2bOHgwYOcPXuW2tpaQII4IYQQV1aQQZxSqgl4EWgADMCzwC8Avww8uoJdE0IIAUxEJ/LbGys35rcNyoDBaMBsnF/XrNJVyRfv+iLhZJia4po5AR6QG5E63U14MozBZgAH9E/1s8q96rr6+mLHi6SzaVLJFKlAipbiFjwez7zMkDfDTHbJYDBIIBAgEAhgNpupqckVDq+qqqK0tBS/38/Q0BAgQZwQQogru76PN2+evwX+FXADyeljrwD3rFiPhBBC5Pmivvy21+ElkUiQTCYvc0WOx+Ghwd0wL4ADOH/+PIFAAHfazcTEBJlMhjNjZ66rn1PxKTonOkmlUoyOjdLmaKOyopLdu3cvSwHtoqIilFKEw2G6uroAaGhomDNt0+v1ArlpniBBnBBCiCsryJE4ctMoP6a1ziilNIDW2q+Uml8dVgghxLJKpBNEkrlMjkZlZLBrkJ7uHgDKy8upq6ujsrKSRCLBuXPn0FpTXFzM6tWrF0wSArlpjuPj4yilWF+5ngs9FwiFQrwz8A4bKjbQ4G64pr72+HvIZDKMjY1RZixjXd06du7cuWg/bjSj0YjT6SQcDjM4OAjAqlVzRxY9Hs+cfQnihBBCXEmhjsRFAMfsA0qpcsC3cPO5lFKfV0odVkollVJPXabdh5VSbyilAkqpEaXUk0op9yVt/kQpNTHd5qtKqZv/0a0QQhSw2aNw6XCa7q6LCU7GxsY4cuQIP/nJT3jttdcYGBhgcHCQM2fO8Pbbby86Wjc6OorWGq/Xy/u3vx+H0UEoFCIQDPDE20/QH+i/pr72+HsITgVJp9M0eZvYsWPHsgVwM2bWxQFUVFTMC9I8Hk8+G+VM+QIhhBDicgo1iHsZ+OvpZCYopQzAnwA/XOL1Q8AfA/98hXYl0/etAdYBFcBXZk4qpX4T+BSwE2gGtgF/sOSnEEKI29DMejid1WQjuWyUe/bs4YEHHmDz5s2UlpaSTqdJp9PU1NSwbds2bDYbPp9vwQLZWuv8erDq6mrKveV8dPVHMWNmbHyMnv4evvTcl/jey9/jyJEjDA0NobVeUl+7/d3E43EA7tp414oESDPr4iCX0ORSZrM5H+jJKJwQQoilKNTplP838AIwCViBKeAM8MBSLtZaPw+glNoJ1F2m3TOzdqNKqSeAv5x17DHgy1rrnun7/RHwBPClpT6IEELcbmZG4iLRCGWGMrxeb35dV2NjI42NjUQiERKJBKWlpSil8Hq9vPLKKwwPDxMKhSgqKiISiTA4OMjQ0FA+e2NVVRUA97bfi9Pl5Pud3yccD5PKpHh56GX2xfYxODjI3XffjdvtXriD08LJMCPBEZKpJCaDiQ11G27iT2VxM0Gc3W6noqJiwTZer5dgMChBnBBCiCUpyCBOaz0F7FNKbSc3AjYCvKH1rAJEN8c9wKlZ+5uAd2ftHwPqlFIl033Mm56Geek7ikUDSCGESKQTWIyWW66w80wQFw6HabQ20tAwf72a0+mcE5A4HA7q6+vp7e3l+PHjZLNZAoFA/rzFYmHt2rXYbLb89ffuvJe1a9fy5OEniSajpFNpDkwdYK9hL6FQ6IpBXJevi0QiV5WmrqQOq9l63c9+LSoqKmhubqaiomLR33VTUxORSGTeejkhhBBiIQUZxCml7tVav6q1PgIcWabXvA/4TeCuWYdd5EYBZ8y84yi65DjAF5EROiHEEj138jmODB3B6/CyqXITmyo3UV1UTW+gl6HQENuqtuGwOK58oxXgi/rIpDMkEgncxW6qq6uXdN2aNWvo6+tjcnISAJPJRFVVFbW1tZSVlS24Vq2muIbHtj/Gk4efJK7iaLPm54GfsyWy5bKvNRmd5MWOF/NTKddWrL3Kp7xxDAYD69evv2wbp9PJ7t27l6lHQgghbnUFGcQBP1RKjZBb0/aU1nrkZr6YUmo3uVp0n9Bazx6JCwPFs/ZLpr+HFrjNV4CnLjlWB7x+g7ophLhN+GN+jgzlPp/yRX3s797P/u79FFuLCSaCALze/Tq763eTzqbZVbeLYlvx5W55Q0WSEZ49/iyRVISPrP/IvDptvqgvn6Ckzlu35HVmTqeTjRs34vf7qaqqorKycknX1pbU8kjbIzx56ElMJhO+lI+f9/+cDesWnx75/ZPfJxgNEovFsBgs3N1y95L6KIQQQtwKCjWxSTXwZ8BHgD6l1A+UUh+ZTnByQyml2sglTPms1vrHl5w+CWydtb8NGLh0KiWA1jqgte6Z/QUM3Oj+CiFufWfHzy54fCaAm9n+yfmf8ErXK/zDO//ARGRiTttoMsrhwcP4Y/4b2jetNf96+l+5MHmBkdAIXz/8dTonOvPnI8kIkWSEZDKJURmp8dZc1f2bmprYvn07NTU1V5VkpKm0ifub78dkyn32eG7y3KJt46k4Z4fPMjg0SCad4b6K+1hVKdMUhRBC3D4KMojTWoe11l/TWt9JLnDqIJdQZEk5ppVSpunMlkbAqJSyLVQaQCm1CfgR8AWt9QsL3Oop4HeVUquUUmXA/wCevKaHEkKIabODuJ21O9lavRWL0bJo+6n4FP908J/mBHLfO/k9nj/1PH/787+dk/J/tuHQMAf6DxBKLDR5ICers3T7u3mz900GpgY4MXKCU2OniMVihMO5hCLfPPpNTo6eBODC5AUAkqkkbpMbd8nl16XdSG3VbfkgbjI2STAYzGe1nG0iOkEsFkNrTVVxFR+996PLXlZACCGEuJkKdTrlbD3kMlP2AtuXeM0fMHd92iPA08BnlFJh4ENa69eB/waUA19TSn1tprHW2jW9+TWgETgMmIFvkytJIIQQ1ySRTtDj78nv71u9D7fdTSqTojfQi9PiZDw8zg/O/oBYKpZvF06GeerIU3xu1+cAODdxLn+/L7/xZe5bcx8bKjZQXVRNNBnlJ+d/wsHBg2itOTZ0jM/t/lz+XqFEiHMT5+iY6OCC7wLxdHxOH7PZLBPjE2ityWQylJSU8J3j3+FjGz5Gb6AXgGQySZOtiZKSEpZLkbUIm8WGUopYKsYbB94gE89QVFQ0pxbbeGScVDIFQEtty5xzQgghxO2gYIM4pdQe4DeATwDDwNeBX17KtVrrx4HHFznnmrX9GLkyAovdRwO/P/0lhBDXrdPXSTqbBqCqqAq3PTeSZTaaafY2A1BdVM3mqs0opej2d/P04adJZVP4Y36eOvIU22vmf571sws/442eN7in6R7e6n2LaCqaP3IRAb4AACAASURBVNc31ccPzvyAvkAfiUyCyejkZfsYDoexGWzcU3oPbwbeJGKK4HQ6ef7U85gNZrLZLOl0mlp77bKmxFdK4bF76DP1kUqlGA+N4zF7CIfD84K4ZCq3Zq/WU7ts/RNCCCGWS0HOL1FKnQH+g1yNuF/SWrdqrf9Uaz28wl0TQoi8RDrBm71v0jHeseRrjg9fLHa9rnzdou1mUtE3lTbx8NaHMUwvCR4JjfDyuZcXvCaZSfIf5/9jTgA340D/AYZDwwsGcCW2kjnTOUOhEO3F7ayrX8d9nvvITGXyWR5T2RTJZBKzwUyjt3HZyyN4HJ78lMr9/v28MvkKfb6+OW2GA8Nks1mMRiPVJUvLnCmEEELcSgoyiAP+BqjRWn9aa71/pTsjhBAL+Y/z/8FLHS/xjaPfWFIgF06GOTN+Jr+/rXrbkl6ntbyVhzY+lN/PTRLI2VG7g5aylnnXlNpLaatpW/B+RmVktWc1D659kM/f8Xk+Vv0x6kJ1+Hw+pqamaLQ0srp0NTt27GBjy0buLb0XpiCVyk1RTCaTVFoqKXWXLqn/N5LHfjGIS2aTjCXHeL1vbhLgoUBunZzZbKbCtXBxbSGEEOJWVpDTKbXWX13pPgghxJUcHjqc3/7G0W/w+Psfx2ycl0Mp793hd8nqLAANJQ2UO8uX/FptNW1EU1Fe6ngpf6zEVsLHNnwMgFMDp/jX8/9KKpNib+Ne7mm6h0Q6wdGho3Pu88ktn6S1rBWryYrP5+PEkROEQiEqjBXcYb2DaCZKXVEdra2tKKVYv3490WgUAwbeCb5Dxp0hEo6wzrmO0tIVCOJmjcTN6Ji8GEBndZbR4CgAFrOFMkfZsvZPCCGEWA4FE8Qppf5Na/3h6e1XAL1QO631fcvaMSGEWEBWZ0mkE3OOPXnoSXY37GZjxcYFg7mZ2nCQG0G7WnetuotIMsL+7twEhc2V0+vmurvpPtnNL6z6BbZs2oLRkEvdbzFacFlchJNhAKpcVWyp2kIikeDYyWP09+cS/jqdTpqbm+ns7CQej7Nt2zZqa3NryZRStLW1EY/H2a12MxgfpNZRy1r3Wmpqrq68wI0weyTOYDCQzWZJpVKEk2FcFheBWIB4Ijf1s8RRgs1sW/Y+CiGEEDdbwQRxwBuztvezSBAnhBCFYKG1ZX1TffSd6OMl80t8cssnWeNdkz83HhlnJDQCgNlgZnXRao4ePUpNTQ2VlZVLft0Hmh+gylWFP+6n2dZMMBikoyM3EjXQO0CFtwKz2Uw4HCYSidCQbeBE5gRmk5mPrP8IfX19nD59mlQqhcFgoLm5mebmZoxGI3V1daTTaSyWueUOjEYj7e3tvPnmmxgjRjDB2rVrVyRtv9fhxW6343K5cDgcjI+Pk8lkGJwapLW8ldHwaL4QeY17+YNMIYQQYjkUTBCntf7fs7YfX8GuCCHEFY1HxufsK6Xya9UiqQjfPv5t/svu/4LH4QHI11kDWFu2lu7z3QwNDTEwMEBFRQWbNm3C6XTS1dVFX18fW7duXXC6olKKLdVbGBgY4MihiyN7ZrOZVCrFkSNH5rQv0kXcW34vWzduZax7jL6+XBKQ8vJyNm/ePCe7pMFgmBfAzbBarezevZs333wTs9lMfX391fy4bhi33Y1SCq/XC+QCzHQ6TfdEN0opvvba10imkhiUgZpSCeKEEELcngomiJtNKTWktZ7311cp1ae1bliJPgkhxGxjkbH89h0Nd/C+xvdxeOgwP+/7OZFkhFgqxrMnnuWz7Z/FZDBxavRUvv368vUMv5tLtmsymRgbG+PVV1+lpqaGgYEBAA4dOsQ999yD1Wpd8PXHxy8GkUop9uzZQ0dHB8FgEKfTicvlwmq10tHRgQ5oBs8NMjw8jNFoZMuWLdTW1l51Zkmn08l9992HUmrFimebDHP/bJlMJtLpNG92v8kLR18gHAmjlKKsrIw7Gu5YkT4KIYQQN1tBBnHAYpVZpWKrEKIgjIVzQZzf78dUYaLYVsy+1fto9jTzTwf/iYzOMDA1wMvnXmZP/R6GQ9NBm8FEqS5lIDNAaWkp7e3tnDlzhv7+/nwAZ7VaicfjvP3222zYsIHBwUE8Hg/19fX5ET+fzwfA9u3bcblclJSUsGvXrnn9nJiYwOfzMTw8jMlkYs+ePbjd7mt+7kuTiqyEtpq2fMKWmf70jfSRyWQwGAw01jTymd2fobZEasQJIYS4Pa38X+NZlFJ/OL1pnrU9Yy3Qu8xdEkKIBY1FxkjEEwSDQcZ6xkhuSGKxWKh31/PBtR/MZ5F8u+9tBqcG89e1eFvwjeYCsJqaGqxWK9u2bWPVqlV0dHRQVFREc3Mzb775JsFgkLfffhuA/v5+xsfH2bJlC6lUilgshsVioaam5rIjao2NjfmAb/PmzdcVwBWKD7d+mNriWqqLqvmrn/4VYcJkMhmMRiO71+7m0+2fpsgqn/kJIYS4fRVUEAfsm/5umrUNkAVGgF9f9h4JId4ztNYcOHCARCLB3r17MRqNi7YbD4/nE2g4lZOenh5aWloIBoNsKN5Ab0Uvp8ZyUyj7p/rz1zbZmxi5kEtwUl19sRB1aWkpd9xxcfrf3r17OXz4MD6fj5qaGkZHRxkaGiIQCOQToXg8nitOiayqqqKurg673U5dXd01/FQKj91sZ0/DHgDWVazjTf+bmE1mPrHrEzy44cFlL0AuhBBCLLeCCuK01vsAlFJf1Vr/9kr3Rwjx3jI0NJRfa9bb28vq1asXbhccIpVNkUwlsRlsWA1Wzp8/z4ULF0in0wDs2LCDodAQ/pg/f53b4CZwIUA2m2XNmjXY7fZF+2KxWLjjjjvIZDKYTCYikQiHDx9mamqK7u5ugHxyj8sxGAy0tS1c9Pt28Ogdj9LkbmJ97XpWeVetdHeEEEKIZbEyK9OvQAI4IcRyy2aznD17Nr9//vx5MpnMgm1n6rQlk0nKLeVYrVYymQzpdDofmHWe6eT91e/PJ+Lw+/14p7xks1lWrVrF+vXrr9gnpVR+zZfT6WTv3r1zAsuyMilk7bQ4eXDLgxLACSGEeE8pqJG42ZRSvwHcD1QA+bkxSyn2rZT6PPAYsBl4Rmv9mUXaVQP/CLQDVUCT1rpn1vnHgd8HZlf03a61Pnd1TyOEKHR9fX1EIhF8Bh+T6UmiE1FKzpawe+PuOe2GQ8P5aZKpVIr1nvXs3buXcDhMcXExNpuNzs5Ozp49y1DHEA+2PMjB0YOUTpZSaatk48aNrFq16pqm/BkMBjZu3EhFRQWxWIzi4uIb8uxCCCGEuLUU5EicUuqPgD8FRoE9wHFyAdm7S7zFEPDHwD9foV0W+BHw0GXaPKe1ds36kgBOiNtMOp3m3Llz9Cf6OZ4+Tr/upyPSwTMnniGVSc1p+8qFV4BcAFdrraW2pBaHw0FFRQU2mw2A5uZmVq9eTTabJXAhQLupnVZnK/X19TQ2Nl73mq3y8nIaGqTaihBCCPFeVZBBHPBp4EGt9ReB+PT3h4AlVW7VWj+vtX4B8F2h3ajW+u+Bg9fbYSHErau7u5t4PE5PpgeHw4HNmgvG/BE/533nmYhMkNVZhoJD+VG4ZDLJBueGBUfDlFJs2LCBuro60uk0ExMTKKUWXWMnhBBCCHE1CnU6ZZnW+vDMjlJKaa1fV0q9sAJ9+ZBSahIYBr6qtf67hRoppdzApbm7b49UcELcxlKpFBcuXGA8NU7WkQXAaDLmi0h//Z2vY7aY2VK1hUz24hq5els9penSRac0KqXYunUrqVSK0dFRKisrcblcy/JMQgghhLi9FWoQN6KUqtZaD5OrDXenUmpiBfrxXeAJctM6dwPPKaWmtNbfXKDtF4EvLWfnhBDX7/z586RSKYYNw/npkJWuSnxWH+F0mHgijtli5vjI8fw16XSa6nQ1qFxpgMUYDAZ27NjB0NAQFRUVN/1ZhBBCCPHeUKjTKb/NxTpxTwA/BQ4D31rOTmitT2uth7TWGa31W8BfAx9fpPlXgKZLvu5enp4KIa5FPB6nu7ubcDpM0BzMH//klk/ms0yGQ2HGx8ZJpXJr47TWOKIOilQR1dXVlJeXX/Y1jEYj9fX1WK3Wm/cgQgghhHhPKciROK31H87a/qpS6l2gGPj3letVrjuLntA6AARmH5OCs0IUlvHxcQYHB/F4PPkC2plMhjHLGGazGYCWshYqXZXsatjFv038G8lUkmQqiTIoysrK8Pv93Gm5E6fTydatW+XfuRBCCCGWXUEGcZeaHgVbMqWUidyzGQGjUsoGZLTWqQXa2qbbAVin9xNaa62U+ijwGrngrB34ArmSA0KIW9DJkycJh8P09/czNjaGyWQilU3Rn+7Has6NlN3ZcCcA97fez1vn3iKbyRKJR4hGo4RCIUpSJXiKPOzYsSMf+AkhhBBCLKeCCeKUUk8upZ3W+teX0OwPmLs+7RHgaeAzSqkw8CGt9evT52Kz2s1U+m0CeoBPAU8CVmAA+DOt9VNL6acQorCEw2HC4XA+Ycn4+Dg2m42uWBc4c20qnBW0eFsAKHeV8zef+hsyOsPL+1/m+QvPE5+Ks6d0D5s3b6akpGQFn0YIIYQQ72UFE8Qxq6D39dJaPw48vsg51yX7i76u1vrhG9UnIcTKGh0dBaCqqoqpqSlCoRDBUJDOWCfeUi8Ad666c870SKUUJmWiraUNHdJoNA31DdTX16/IMwghhBBCQAEFcVrrx1a6D0KI29dMEFdZWYnZbCYUCjGUGCJlSKGUwmF2sK1624LXVldX43K5MJvNbN68WdbBCSGEEGJFFUwQJ8TtRutcHhx5w7/ykskkk5OTpEmTtqXxer10d3dzPno+nzWyva4ds3HhNW5Go5F9+3IJc+X3KYQQQoiVVpBBnFKqm0UyQWqtVy9zd4S4alpr3nrrLbLZLHv37pU3/itsbGyMRCbB/uh+Dh06xD2r7gHAn/ZTVFQEwM7anZe9h/wOhRBCCFEoCjKIY/56tlrgs8A/Ln9XhLh6iUSCyclJAFKpFBaLZYV79N42MjLCYGIQpn8Nr/W+xvvc7yM1msJms2ExWii1L160WwghhBCikBRkEKe1fvrSY0qpl4D/Bfzp8vdIiKsTDF4sHB2PxyWIW0HZbJbx8XHGk+PYPfb88QnXBNXV1ZhMJsqcZTLSJoQQQohbhmGlO3AV3gXuXulOCLEUlwZxYuX4fD7S6TRBFcRkuvi51anxU/k6b2WOspXqnhBCCCHEVbslgjillB34r8DYSvdFiKV4LwVxWmvGxsZIJpMr3ZUFjYyMEM1ESZvTi7Ypc0oQJ4QQQohbR0FOp1RKZZmf2CQE/NoKdEeIq/ZeCeK01rz77rv09/fjdrsLLomL1prR0VHGkmM4HI5F25U7ypexV0IIIYQQ16cggzhg3yX7IeCc1jq8Ep0R4mpkMhnC4Yv/qd6uQdzsAA4gEAgwOjpKVVXVCvfsolAoRCwWw6/9+XWJtcW1DAYH57STkTghhBBC3EoKcjql1nr/JV9HJIAThSiZTObrwc0IhUJzjt2OQdzsAM5oNFJfXw9AR0fHvJ/HShoZGQEgYUnkj71/zfvnjRZ6Hd5l7ZcQQgghxPUo1JE4lFJ3AzuBotnHtdZ/tDI9EmKuiYkJDhw4QGlpKTt37syP9ExNTQHgdDqJRCK3XRCnteb48eP5AG737t2Ulpb+/+zdeXxdV33v/c/vHM2DNViWNVmWR9nxJNsZnDkEAiRMpQ1tCpSES8NwGS7leW7LDdwSCqR9egstlxYKhBAupDckDZQ2NAlNgpsEJ8F27HjUYMmK5nmejqRz1vPHPjrWaMuyZenI3/frpZe1915777W3tvw6X62116KtrY2enh4aGxvJy8vDOUdbWxuZmZn4/f4FqWtzczOjbpQh3xAJJGBmFGUUkZ6QTudgZ6RcfEz8gtRPREREZC4WZUucmf0l8CzwQeC2cV9vWch6iYxxznHixAlCoRDt7e3s27ePwcFBABoaGgDIy8sDll5LXFlZGTU1NZEAt3z5cnw+Hxs2bIhsH7s/r7zyCocPH16Qeg4NDdHV1UVvqJf4BC+kZSVlER8Tz9aVWyPlkuOSF6R+IiIiInO1KEMc3sTe1zjndjvnbhz3ddNCV0wEvG563d3dJCQkkJqaSm9vLy+99BJNTU20tbXh9/tZu3YtZsbw8DChUGihq3xRDA4OUllZCcDVV1/N8uVnuiGuWrWKpKQk+vr62L9/P1VVVYAXaltbWy95XZubmwEIJYUi3Sfzl+UDcFPRTaQnpOMzH+8sfuclr5uIiIjIhVisIa4fODbXnc3sU2Z20MyGzezhs5TLNbN/NbNGM3NmVjRNma+aWZuZdZnZd8wsdq71kqXBOUdZWRkAGzZs4Prrr2f58uUMDQ2xf/9+AHJzc4mLiyM+Ph7nHIFA4GyHjBoVFRWEQiHy8/PJypo4GIjP52PTpk3AmQCVnp4OwNGjRy95kB2rw3DCmakP8pZ5raNJcUl87obP8Wc3/xnbc7df0nqJiIiIXKjFGuL+Bvhzm/tY5Q3AV4AfnKNcCHga+N3pNprZHwN34b2btx4oAb44xzrJElFfX09vby9JSUkUFhYSGxvLNddcQ25ubqTM2EAfCQkJwNLoUtnb20tNTQ1mxsaNG6ctk5+fz/XXX8/GjRvZvHkz119/PSkpKfT390da8Ca72AOh1NfX09DQQFtbGwD91h/ZNhbiAPw+PylxKRf13CIiIiKXwmId2ORf8N6J+xMzm9APyzm39lw7O+d+BmBmVwIFZynXDHzbzGa6Dx8GvuGcqw4f7y+A7wFfmsU1yBISCnld8pxzlJeXA14rnM/n/R3E7/eze/duKioqGBkZiXQzjI/33sWK9hAXCoU4dOgQzjlWr15NSsrM4SczM5PMzMzI8rZt23j55ZepqKggPz+fpKQkQqEQPp+PiooKysrKyMzMpLi4eEL3zLkIBAKRegKkpqXSMdQBgJmRl5p3tt1FREREosJiDXE/BeqAvwMGFrAeW4HXxy0fBgrMLM051z2+oJmlA+mT9p8xQEr06O3tZd++fSQmJpKQkEB/fz/JycmR1rYx07VQLZWWuNOnT9Pd3U1SUhJXXHHFee2blZVFQUEBdXV1HDt2jMzMTMrLy1m3bh2VlZU452hvb+fAgQPcdtttkWA8F319fRNa9nzLfIRavG6cyxOXaxRKERERWRIWa4jbDmQ55xb6k28KMD6sdYX/TZ20HuCzqIVuyRmbD214eJjh4WG6u7uJjY1l165dU+Yam05qqjdDRl1dHUVFRbPaZzEaG5hk06ZNxMSc/38bV1xxBU1NTTQ3N0feVRtr0czNzaW/v5+enh6ampoio3rOxdgk66mpqWRnZ9OR2AEt3rb8tPw5H1dERERkMVms78QdBzLPWWr+9QHLxi2nhf/tnabs3wFrJn3dOK+1k3lXVVVFZ2cnCQkJFBYWkpKSwp49eyIDdpzLqlWriI+Pp6urKxJeooVzju7ubpxzkXCUlpZ2jr2mFx8fHxn0ZPxxzIzNmzdTWFgIQG1t7QXVeayeBQUFXHHFFTT3n7nnYyNTioiIiES7xdoS9xPgZ2b2DaBp/Abn3AuXsB7HgB3AvvByCVA3uStluF5dnGmpA4jaVhfx9PX1RUah3LFjB9nZ2ed9jJiYGDZs2MCxY8coLS1l5cqVUfNcnDhxgqqqKrZv387g4CA+n4/k5LnPqVZUVERHRwdmRklJCdXV1SQmJpKcnEx+fj4nTpygtbWVwcFBEhMT53SOsRA3Vs+G3obINr0PJyIiIkvFYg1x3wz/++ik9Q7wn2vn8EAlMeGyfjNLAILOuZFpyiaMO2Z8eDngvBdrHgb+u5n9O960B/8TeOj8L0eizVg3ymAwSEFBwZwC3JjVq1dTVVVFb28v9fX1FBTM7VXJsRaxlJSUeQ+C/f39nD59GiDyb3Jy8ozn7Q30khyXjM9mbtw3M3bv3h1ZXrv2zBhFcXFx5OTk0NDQQF1dXWTi8PM1FuJSUlIYCY7Q0tcS2TZ+ZEoRERGRaLYou1M653wzfJ0zwIV9ERgEPg98MPz99wHMrM/MxndzHMTrNglQGl5eHV5+EHgcOAhUAkeBr17ApcklNjo6Sn9//7kLTnL69Gk6OjqIj49ny5YtF1QHn88XGfCkrKzsvOZLGx0d5dSpUxw9epRf//rX7N27l+PHj19QfWajtLQ0MkBIb6/Xe3i6ESkHRwZ59Mij/NV//hXf2vctAqNznw9vbKCY2traOU07EAqFGBwcxMxITk6mqbeJkPPudVZSlgY1ERERkSVjsbbEXRDn3P3A/TNsS5m0PGOTRrg17gvhL4lCBw4coLW1lW3btpGcnMzw8DArVqwgLi5uxn36+/spLS0FYPv27WctezblbeWEXIjirGIKCgqorKyMzLVWVFQUKdfd3U1LSwvr16+f0NLlnOPw4cM0NjZOOG51dTV5eXkThvG/mLq6umhoaMDn82FmBINBYGqIe6PrDR478hhdQ14v4pb+Fo63HGdX3q45nXfFihWR0T87OjrOe7qB/v5+nHMkJyfj8/mo76mPbFMrnIiIiCwlizLEmdmfz7TNOfcXl7IuEr2Gh4cjEz4fPXo0st7n87FixQry8vLIycmZMtpiaWkpwWCQ/Px8cnJy5nTu0tZSfnzoxwDctf0utuVso7i4mAMHDlBeXk5BQUHkvMePH6e9vZ2UlJQJE4afPn2axsZGYmJiKC4uJjk5mY6ODk6dOsWRI0e4+eab56Vb5ViAXbNmDV1dXbS3twNnQlzIhdhbtZfnq56f0mJ2uOFwJMT1D/dzrPkY6zLXkZWcNe25WvpaON15mi0rt5ASl8KqVauoqKigtrb2vEPc+K6UMPF9OA1qIiIiIkvJogxxwJsmLefhjfb4EqAQJ7PS2tqKc474+HiGh4dJSEggOTmZ9vb2yFD3fr+f4uJi1q5di5kxODhIY2NjZNTEufrZ8Z9Fvn+q/Cm25WwjJyeH9PR0urq6qK6uZv369ZERIAE6OzsjIa6zs5MTJ04AUFJSElmflZVFfX09vb29dHZ2XpTWuKGhIVpbW8nJyaGrq4vW1lZiY2NZv349lZWVkRCXnJzM4cbDvFT9Eo29Z1oHE2MTGRodwjlHVWcV3UPdJMQk8L3ffo+2gTbi/HG8f8f72ZA18T23oZEhHjzwIP3D/fy66td8aOeHIiGuoaGBrVu3ntd0BpND3PiWOIU4ERERWUoWZYhzzk0OcZjZZ5k43L/ItEpLS2lsbIx0g1y3bh2rVq0iNjYWMyMQCNDY2EhDQwPt7e2cOHGCnp6eyIiJzjny8/PnPEIieK1QY7qHvJBmZmzatIlXXnmFU6dOsXr1aoaHhxkdHQW8bozgtSAePHgQ5xxr166d0Drn9/vJy8ujsrKShoaGCw5xFRUVlJWV4ZwjOzubQMB7p239+vXExcWRkZERKXu4/TDPVj07Yf+ijCJ+f9vv88SxJ6js8Cbufvzo47QPtNMT6PGuJzjMjw/9mDu33sn23O2RfU+2nozcp95ALw8eeJBPX/tpli9fTnt7Ow0NDZGpB2ZjLAynpqZOGdQkNzV3pt1EREREos6iDHEz+HugBrXEyVk0NTVRUVExYV12dvaE99ri4+MpKiqiqKiIxsZGDh8+TF1dHYODg5EgsGbNmjnXoW+4b8KymRFyIXzmIysri6ysLNra2qisrJww79rYnGyHDh1icHCQjIyMaVsDx4e4LVu2zLlLZTAYpLy8HOccfr+flhYv9CQkJESuPzMzk5iYGJKSkjjRfmLC/m9e92ZuWXsLPvNRkldCZUclAKc7T089lwvy2LHH6B/pZ2h0iKNNR2numzhvXmA0wEtvvETJqhLa29upra2ddYhzztHR0RGpc3Nfc2RQk+VJy0mITZj9jRERERFZ5Bbl6JQzWANoeDmZUSAQ4MiRIwCR0JaUlDTtqIpjcnNz2bNnDzExMbS3tzM6Okp+fv6EFqjzVds1ccJq5xwdA17AGGuNA28i8bHgBN5IlIcPH6alpYW4uDh2796Nzzf1VzQtLY3k5GQCgUCkq+NcdHR0EAqFSE9PnzAC58aNG/H7vYFg4+LiuOmmm9h15S4ae850ofzs9Z/l1nW3RqYUKMktYWfuzinnKMktITs5O3Ifnix9kmdPPTslwI15rf410rPSiYmJoaOjI9JF8lwGBwcJBALExcWRlJSkrpQiIiKypC3KljgzmzwXWzLwZuCxBaiORIGxed0CgQBZWVmUlJRw5MgR8vLyztlSlZGRwZ49e6iqqiI/P5+VK1deUF1qumqmrGvua44M7pGRkUFOTg5NTU3U1Hhl/X4/wWCQuro6AHbu3Dljd04zIy8vj4qKCqqrq8nKmn7QkJkMDQ1NCIDLly+nsLCQ9vZ2gsHglNav5ORk3uh6g6DzRqnMSspiRfKKCWV85uPObXdy9aqrOd58HJ/5yE7JZkfuDoZGhvjxoR9T0z31vgDkpObgnKO5r5nh4DBHWryfW01NDbW1tbN6N3F8K9xwcHhCkNbIlCIiIrLULNaWOJv01Qx8DvjUQlZKFq+amhqam5uJjY2lpKSExMRErrnmmsjcY+eSkZHB7t27ycnJmXX3xJALUdZaRm+gd2Jdpgkrk1ueNm3aNOE8eXlngsbq1avPObl4UVERPp+PxsbGyDxusxEMBvnNb37Diy++GAmQy5cvx8zYtWsXV1111bTXPz4UrUqf+Z4Wphdye/HtvG3j29iZtxOf+UiKS+Ke3fewMWvjtPtsW7mNawuvjSy/XPMyBau8CdFra2tnNa9eZ2cnAINxg/zlf/4lhxoPRbYVLJvb5OoiIiIii9WibIlzzn14oesg0aO/vz8ykuO2bdsuaECS2QqMBnj4tYep6aoh1hfLXTvuYtOKTQRGA9R1100pf7D+ILvzd5OW4L0Dl5qaSn5+PnV1dcTExFBQUEBtbS0JCQmzanlKSEhgeo+EPQAAIABJREFU9erVnD59mvLycnbv3j2reldUVDAwMOBdQyCAmc1qKP/xwbQwbfaDjYyJj4nngyUf5GjzUXz4yErO4hcnf0FibCLXFl6Lz3z8quJXDIwM0DnYSeNwIykpKfT19dHa2nrO1tGxlrjqoWpGgiMTtmlQExEREVlqFlVLnJltMbP/McO2z5vZpktdJ1ncBgcHee211yLvsuXnz//7T845Hj3yaKTb5EhohEcOP8LB+oOUtZUxGhqdsk/XUBffeOkbnO44M+hHcXExcXFxrFy5kuXLl7Njxw727NlDbGzsrOqxbt26SGvcbN4d6+vro7LSG3xkLOimpaWdcxh/59ysW+LOxu/zU5Jbwvbc7eQty+MT13yCe3bdQ3xMPLH+WK4quCpS9uXalyOtqPX19TMdEvBaF3t7ezEzBtzAhG15y/I0qImIiIgsOYsqxAH/HWibYVsL8KeXsC6yyDU1NfHrX/+arq4uEhMT2bZt2yU5b0V7BeVt5RPWhVyInx3/GU+XPx1Zd9Oam4jxnQlIo6FR/uXEv0RCXlJSEm95y1vYuXMnZkZhYSGpqamzrkdiYiKrVq3COTdlRM7JnHMcPXqUUCjE6tWrufLKK0lOTqaoqGjG8s9XPs83XvoGe0/vjUwXEOePY2XKhb0zOJM9q/ZEBkqp7qxmJN5rUevv7z/bbvT39+OcIykpifbBMwO9bF6xmTu33jkvdRURERFZSIstxN0APD7DtieAmy9hXWQRc85x4sQJgsEgubm5XHfddbNuwbpQFW1nAtO2nG3kpOZElsfmhANvZMbbN95OUmxSZF3bQBsvnn4xsuz3++c8RQB487mZGfX19ZEwEwwGp5RraGigra2NuLg4Nm3aRHp6OrfeeuuM7wweqD/Ac5XP0T7QzrOnzswNtyptVSRoXWzLEpaxdeXWyPKhVu+9tuHh4bPuNxbyYhJjGBjxWuLi/HF8oOQD8xY4RURERBbSYgtx2c65ruk2OOe6gRXTbZPLT1NTE/39/SQlJbF7926SkpLOvdNFcqr9VOT7XXm7uPfKe1mTMXFeuaykLLKTs9lTuIcvvOkLvHPTOyPb9p7eS/vA7KYGGBge4ED9Adr62zjZcpLv7/8+r9S8EtmelJQ0oTWutLSUp556KjJxOMDIyAjHjx8HYPPmzRPmzJtOc18zvyz95bTbCtLmd5CQ6wqvi3x/su0kQ0FvJE3n3Iz7jHUlDfgDkXXLk5ZfUDgWERERWcwW28Am/Wa2yjlXO3mDma0CBhegTrIIVVVVAbB27dpL+mG9Z6iHln5vbrcYXwxFGUXE+eO4e9fdPH7scY43e2GpJK9kQr2uWXUNhxoOUd9Tz2holH8r/Tfu3nn3Weve2t/Kj177EZ2DnRPWV3dWMxwc5qY1NwFea1xtbS11dXWRsNPU1ER6ejoAZWVlBAIBMjMzzzlaZ2A0wKOvP8pIaGTa7YXp5z+oyflYlb6KwrRCarprCBHidOA0m/2bCQaDM76719nTyYHuA/SO9pKY7L3rN3kKBBEREZGlZLG1xL0A/LcZtn0K2Dubg5jZp8zsoJkNm9nD5yj7PjOrMrN+M/uVmeWP2/Zw+Bh947404fgCGxwcpKOjg5iYmFlPIXCxnOo40wpXmF5InN9r1Yr1x3LX9rt475b3ckfxHdxUdNOE/Xzm4z2b3xMJbRVtFRxv8QJfW38bR5uOThhV8Y2uN/jeb783JcCNeabiGcpaywBvHrf8/PwJrVWDg97fO/r7+6mursbM2LZt2zkD75OlT0ZC6nRWpc3//R4/3UD9iDeoSSAQmKk4rzW+RuVgJT7/mf/OFOJERERkKVtsIe5rwH81s4fM7FYzKw7/+wPgk8BXZ3mcBuArwA/OVsjMNgMPAR8FsoAy4J8mFfuGcy5l3NfMnyblkhg/sfO5Rla82MaCE8C6zHUTtvnMx5X5V3L96uvx+/xT9s1Py+fqgqsjy78s/SVt/W1859Xv8OiRR/llmdeF8WTLSX544IeR97tmsr9uf+T7DRs24POd+XUe62LY1taGc47c3FyWLVt21uMdbjzMaw2vRZbfvO7NE7anJ6STHJd81mNcDJuyN+E37/71hfoiXSpnUtpZCkBM7JlnYWxidREREZGlaFF1p3TOHTGzO4B/BO4BHN5k3+XAO5xzR2d5nJ8BmNmVwNle4vkg8JRz7tlw+S8CLWa2zjlXOecLkQmcc4yOjl60gUfGJnbOyMg4a7nR0Ch+u7CBQ8DrYvhK7SsERgMcaz4WWT/T5NVnc9v62zjWfIz+4X56Aj18+9VvExj1Asr+uv3kpOTwZNmTkVa15Lhk4vxx07bIVbRXEBgNEB8TT0pKCjfeeCPOOV544QX6+vpwzkUC73RzwTnneKX2FUpbS2nsbaR/+MwokCW5Jbxp7Zt4rvK5yLpL1W01zh/HqvRVVHdW4/f7aRlumTHEDQ8PMzI6gs/nw+8/E5zVEiciIiJL2aIKcQDOub3AJjNbD2QDLc65U2ffa862Ar8dd+5uM6sOrx8LcR81s48C1cBfOecem+5AZpYOpE9aPb+jQESJ8vJyysvLue6662Y1sfS5jIW4zMzMKdtCLsQTx57gePNxRkIjxPpiyU/L5x3F7yBvWR7OufMOI/968l853Hh4wrqNWRvJW5Z33nVPjE3kjuI7ePyoNwjrWIAb82+l/xb5PjMpk7t33s3AyAAPHXiIkdAI79v2Pl48/SJNfU2MhkYpaytje852gEhLW1xcHMPDwwwNDU1otRwzdg+OtxznydInp9QxKymLd29+N2bGVQVXRVr8bllzy3lf71yty1xHdWc1Pp+PluGWGUeo7OntoS/YR2zcxD8QLE+68OdMREREZLFadCFuTDi4zVd4G5MCdE9a1wWMTdb1v4H/J1zmrcBjZtbknHthmmN9FvjSfFU0Wg0ODnLqlPdjbGxsvOAQFwwG6e7uxswiA3eM91rDaxMC10hohOrOav7hlX8gMTaRWF8sdxTfwbac2c0p19zXzOtNr09YZ2a8bcPb5nwNO3J2cKDuAKc7T89YJm9ZHnfvupuUuBQAPnPdZxgODpOTmkP7QDtNfU0AnGg5EQlxY1JTU2lvb6etrY2BgQFiYmIi88/1Dffxw4M/pGOgg2Bo6lQEGYkZ/OGOPyQ+xnv189a1t9I91E1ybDIleSVzvubztSZzDVR6UzA0DzbP2BLX0NFA0AVJiDkzoff65esj7yqKiIiILEWLNsRdIn3A5BeF0oBeAOfca+PW/7uZ/QT4PbwBWCb7O+DhSesKgBenFl0aqqur6e3tZcuWLRPexxrv1KlThEIh4My7bBeiq6sL5xzLli2b8j7caGiUvVV7Z9x3cGSQQQZ59MijtA20ccuaW87ZKvdc5XNThre/ZtU1E+aGO19mxrs3v5u/f/nvCbqpQQrgdzb/TiTAgdcqN2ZL9haer3wegNKWUrqHuklLSItsT0lJob29nZqaGm/fzMzIdT5d9jRNvU1Tzvfh3R+mKKNowuTk4M3ddveuu+d4pXO3Km0Vsb5Y/H4/3cFu2nrb2MjE7qvOOY5Ved1b4+LiyE3N5a0b3jplugcRERGRpWaxDWxyqR0DdowtmNkyYE14/XRmnKzKOdflnKse/wXUXczKLibDw8McP36c6urqyHD/kw0MDFBTU4OZYWb09PQwMjL90PWzNRYEp3sf7mD9wci7Y4mxifz5rX/Oh3Z+KDJIxnjPnnqWJ449wWhodMZzNfY2RqYMAPjQzg/xx1f9Me8ofscFXQNAdko2b17/5mm3ZSVlkZ+WP+02gJUpK8lNzQW8lsZfVfxqwvaUFC/8Tb5X1Z3VHGo8NOV4BWkFrF++fkqAW0gxvhjy0/Ij77k19zVPKVNXV0ddZx1+v5/UlFRWZ6xmY9ZGYv2XZtJ3ERERkYWyJEOcmcWYWQLgB/xmlmBm032y+wlwe3gEzES8ES1fGRvUxMzuNLMUM/OZ2VvxBkL5xaW6jsWsoaEh0sJWXl7OwMDUkRQrKioIhULk5+eTnp6Ocy7yPttcBINBqqurAVi5cuWEbSPBkQmtcDevuZn4mHiKVxTz/pL3k7csjy3ZW8hOzo6UOdR4iB8e/CEDw9OPAjnW2gVe61fximLWZKzBZxfn1+bmNTfzqWs/xZ/d9GcUpp2Zf+1N69501v3MjDuK74gsH248TE1XTWR5LMSNlV25ciUhF5rwvt14k7tjLhYZiRmRUT67BrsmbBsZGeHkyZP0jPaQkZGB+WzCz1ZERERkKVuSIQ74It7E4J/HC16DwPcBwnO93QjgnDsJfAR4EGgHNgPvH3ec/wbU470n97+Ae51zzyPU1XmNjImJiQSDQfbt20d9fT0NDQ3U1tZSVVVFbW0tZsbGjRsjA2tcSJfKN954g6GhIdLS0sjOnviBfX/9fnoCPQCkxKVwzaprIts2rdjEJ/d8kveXvJ9PXvtJdufvjmyr7qzm269+e0IIAqjvrudEy4nI8rmC1VzlpuayLGEZb9v4NlamrGRX3q5Zhaq1mWvZsnJLZPmXZb+MdPtMT08nNjaWlJQU9uzZQ1paGq/WvhrpRhnriyUjMSPy/baVs3s/8FLLSMyIzP02eXTO8vJyAoEAI7EjJCd70x4oxImIiMjlYvH0n7qInHP3A/fPsC1l0vLjwOMzlL3xYtdtKejv76ejo4PyoXLWFK4hvSudgZ4BXnvttSllCwsLSU5OJjMzk8rKStrb2+d0zp6eHioqKgAoLi6e8C7bcHCY/6z6z8jyzWtvnnFgixhfDO+94r2sSF7BMxXPeK2Dg518f//3ec/m93BlwZUAE4bW37pya6T74nwpyijiM9d95rz2efuGt1PWWsZoaJS67joONR5iV94u4uLiuO222/D5fJgZvYFenj31bGS/W9bewq68Xbxc+zIblm9gWcLZ549bKGkJaZHulO397Tz11FOsX7+enJwcTp8+zWBoEEs98xysSNG0AiIiInJ5WJIhTuZXW1sbdYE6KkYr6GzuZEfODkpyS+js7MTv90e+YmNjWbfOmxB7Wfoy/H4/HR0d9Pb2kpqainOO1tZW6uvryc/Pn9K6Nqazs5NXX32VkZERsrOzp5R7tfZV+oa9ya3TEtK4Kv+qs9bfzLix6EaykrL452P/zNDoECEX4ucnfk77YDubV2ymrK0sUnbypNeLRWZSJjcU3RDpRvpM+TM09jRyuvM0t62/jeIVxQD8quJXDI0OAd7Q+zcU3UCML+aCRti8FNIT0iMD5gwEBxgdHaW8vJzm5macczTGN0a6W+Yty5swEIyIiIjIUqYQJ+etr6+PmqEaYhO81wxfb3qdt9zwFjZunDr5dUVbBc9UPENTXxNr49eyYmAFp06dYsWKFVRWVtLT43WBbGtrY8+ePRw8eJC1a9dSWOi9I9be3s5vf/tbRkdHycnJYffu3RNa4QKjAV48fWYA0FvW3DLrgS02Z2/mU9d+ikcOP0JjbyMAL5x+gRdOnxl8dHvOdrJTFm83vZuKbuK1+tfoCfTQN9zHvpp9gDff3MasjdR21/Jaw5kW0ndueueiGsDkbNITzkwhMRD03lsMhUJ0dnbSRx/NNGN4z8JiD6QiIiIiF9NSfSdO5lF/fz9dI13Exp4JSy9WT51J4WTLSR5+7WEaextxznFq+BSBUIC6ujoOHTpET08PCQkJxMfHMzQ0xCuvvEJvby/l5eU452hububVV19ldHSUgoICrrzyyilTGeyr2Uf/SD/gvUO1K3/XeV1LRmIG9151L5tWbJqyzcy4de2t53W8Sy0+Jp63bZwaYDoHO+ka6uL5qjOvcG5esZmNWVOD9mI1Nm1CQkICA8EBNmzcEAnwDQkNmM/7fmPWRtYvX79g9RQRERG51BTi5Lw1dzfTF+ybEOIO1h+koadhQrkXqidOp2d+ozvJm1s9NTWVHTt28OY3v5n1670P4ENDXpe/wcFBysrK2L9/P8FgkNWrV1NSUjKhBW5geIDmvuYJrWa3rL1lTq1M8THxfKDkA+wp3DNh/c1rbiYrOeu8j3ep7cjZwaq0VVPWn2g5QWV7ZWT57RvffimrdcFi/bEkxyWTtTyLlTkryV2dy/bt24nJjqHDeQPkmFnUXZeIiIjIhYqOflWyaIRCIaq7qjGzCZNtB12Qnx75KR8o+QDZKdl0D3VPGfERoC2ujd+96ndJT0uPhLKCggJOnjxJKBQiPj6eQCAQGcRk7dq1XHHFFRMCXFVHFT967UcT5njLSclhV975tcKN5zMf79r0Lnbm7iQwGmBl6sqoecfKzLhz6508cfyJCff8mfJnCDlvGohVaauiIpBOlpGYQf9wP/4YPy9Vv8R1q6+joq4isv3K/CtZmbLyLEcQERERWXoU4gSA5uZm4uLipp1Ee7yBgQGaA834/X7MjF15uzjWfIzh4DBtA218c983yVuWR1p8WmSfNRlraOlroX+kn55ADzWDNWSknzlPXFwcmzZtoq2tjeLiYl580euamZWVNSXAAeyv2z9lku47iu+4KPO3FaQVXPAxFkJWchYfu/pjnO44zYMHHgS8YD1m/HQE0SQtIY26bm86i301+yLv/AHE+eMW7aAzIiIiIvNJ3SmFxsZGfvvb37J///7IXGMz6e/vp224LdKV8qqCq3j35ndPKNPQ08DJ1pOR5ZK8Eq5adWbEyGcqnmFgeIC+4T46Bztp62+jsKiQa665hmVpy8jPzyc5OZmdO3dOCXAAtd21E5avKriKdcvXnfd1L0UFaQX4zT9l/Zbs6Axx4wc3meymoptIjU+9hLURERERWRzUEneZGxwc5PXXXwcgEAgwMDAQmTx5Oh3dHfQF+1gWswyf+chblkdheiFpCWm8UvtKZN6yMX7zsyV7C4axv24//cP9dA9187W9X5tw3IzEDNZkrOFo01E2Zm3krlvumjKICUDPUM+EiZ//5Po/YXnS8gu9DUtGrD+W/LT8Cd0qC9IKyEzKXMBazd3Y4CaTLYtfxnWrr7vEtRERERFZHBTiLmPOOQ4dOsTIyEhkXVdX11lDXE2HFw5iYmNYkbwiMpDI2sy1rM1cy+DIIMeaj3Go4RCNvY3cvOZmEmMTAXjLurfwi5O/mPa4nYOdkXB2vOU4b3S/wZqMNVPP330mnBRlFEXle17z7eqCq6npqsHMWJ2+mndueudCV2nOViRPnMA7PiaekeAI7978buJj4heoViIiIiILSyHuMlZRUUF7ezsJCQnk5eVRVVVFZ2cngUCA1NRUMjMzef311+nr6yMYDBIMBintLAUgNjaWnNScKcdMjE3kqoKruKpg6oTbVxZcSVVnFcebj+P3+YnzxRHrj6U30Dvh/S2AU+2npg9x41qYCtMLL/QWLEk783ZSlFFErD82agZnmcn65espyS2hbaCN92x+D8uTluOcIyE2YaGrJiIiIrJgFOIuU+3t7ZSXl2Nm7Ny5E+ccVVVV1NTUEAwGSUhIYNu2bdTX10/Yr2O4gxh/DHFxceSkTA1xZ+MzH3dtvwvn3IR33cpay3jk8CMTgtyp9lPctv62Kceo7TrzPtzq9NXndf7LSUbi2QeoiRY+8/G+be9b6GqIiIiILCoKcZehkZERDh06hHOO9evXk5WVxfDwMADBoBekhoaGIgFu9erVrFmzhpiYGGoO1TDa573zNl1L3GxMHqykeEUxn7nuMzT1NfHokUdxzlHfU8/A8ABJcUmRcs19zdT11EWWo3UkSRERERGRC7EkR6c0s0+Z2UEzGzazh89R9n1mVmVm/Wb2KzPLH7ctzsy+a2ZdZtZqZn8x75WfZ845Xn/9dQYHB0lPT6e4uJihkSEquyoZjBmcULa+oZ7mQDPdsd30ul6C/iBtg22R7bmpuRetXlnJWWxduZVVy1ZF6nmq41Rk+0hwhMeOPhaZ96wwvTDquwqKiIiIiMzFUm2JawC+ArwNSJypkJltBh4C3gv8Bvhr4J+Am8NF/hzYDqwHUoBnzey0c+6H81f1+VVTU0NjYyMhX4iY/Bgeef0RTrWfYjQ0SkdXBztjd7I2fS2Dg4Oc7DvJ0b6jVL5Ria92Yt5PjkuelxC1Pmt9ZPCSY03H2J6znTe63uBnx35G24AXIGN8Mbz3ivde9HOLiIiIiESDJRninHM/AzCzK4Gz9bn7IPCUc+7ZcPkvAi1mts45Vwl8GLjXOdcGtJnZ14H/AkRViHPO0T7QTlVjFfte20fDUAPBtCAJpyYODpGWnkZZoIy09DQa2huoHqomNjZ22qH+c1Jypp3D7UJtyd7C85XPA3Ci9QRPHHuCQ42HJsxf97aNbyM7Jfuin1tEREREJCo455bsF/BV4OGzbP8F8IVJ68qA9wAZgAPyx227Fuic4VjpQNGkrxvCx5j267vf/a4b893vfnfGct6P6Yxdu3bNWO7ee++NlDtw4MBZj/nhv/+wu++Z+9x9z9znSm4vmbFczvoc983ffNN9+bkvu/ueue+sx5yPayq5vcTd98x97svPfdn96Jc/OusxDxw4EDnmvffeO2O5Xbt2TTj/Yv456Zp0TbomXZOuSdeka9I16ZqW9jWFv4rcLHPOkmyJOw8pQPekdV1Aangbk7aPbZvOZ4EvXdTazbMVySt464a3snXlVl5/6HUOc3jacrH+WD5z3WdwzjE0OsQDPHCJawobsjbwO5t/h8oTlZf83CIiIiIii4m5cd3Ulhoz+ypQ4Jy7Z4btvwBedc49MG5dKfBnwAtAB15LXEN42x687pdTxm83s3S81rjxCoAXT58+TVFR0YVf0AV45PAjNLY2snrlanKW5bB5xeYJE2UPB4f5zivfoaW/BYDAUID+gX4y0jO4tuha3rXpXZe0vj8+9GNKW0tJiEngjuI72JW3a166b4qIiIiILKTq6mrWrFkDsMY5Vz2bfS73lrhjwI6xBTNbBqwBjjnnOs2sIby9IVykJLzPFM65LryWuojFFDo+UPKBs26P88fxiT2foK2/jYcOPgRAfEI8AIVpl35S7bu238UbXW+Ql5o3YZoBEREREZHL3VKdYiDGzBIAP+A3swQzi52m6E+A283sVjNLxBvR8hXnDWoC8DDwRTPLMrPVwOfwRrNckuL8ceQty6M4q3jC+lVpqy55XWL9saxfvl4BTkRERERkkiUZ4oAvAoPA5/FGoBwEvg9gZn1mdiOAc+4k8BHgQaAd2Ay8f9xxvozX8lYJHAR+6qJ4eoHZWpu5dsJyRuKU3qMiIiIiIrJAlmR3Sufc/cD9M2xLmbT8OPD4DGWHgY+Fvy4bW1duZe/pvXQMdHDzmpsXVbdQEREREZHL3ZIMcXJh4mPi+fS1n6ZzsJPsZM3HJiIiIiKymCjEybTi/HGsTFm50NUQEREREZFJluo7cSIiIiIiIkuSQpyIiIiIiEgUUYgTERERERGJIgpxIiIiIiIiUUQhTkREREREJIpodMr55Qeoq6tb6HqIiIiIiMgiNC4r+Ge7jznn5qc2gpndALy40PUQEREREZFF70bn3EuzKagQN4/MLB64CmgEgvN4qtPAmlmUK8ALlTcCah6cX9F6r2f7LC0G0XqPo81c73M0PUsLQc/v7F3Is6T7PP+i5R4vhf+TouVeR7tz3ef5eJb8QC6w3zkXmM0O6k45j8I/hFml6QthZjjnqmdTLqxuNuVl7qL1Xs/2WVoMovUeR5u53udoepYWgp7f2buQZ0n3ef5Fyz1eCv8nRcu9jnbnus/z+CxVnk9hDWwiIiIiIiISRRTiloYvL3QFZMnQsyQXi54luVj0LMnFoOdILpZF8SwpxC0Bzrn7F7oOsjToWZKLRc+SXCx6luRi0HMkF8tieZYU4i4vXXh/Peha6IpcBnSv55/u8aWh+zw/dF8vDd3n+ad7fOnoXl8aUXGfNTqliIiIiIhIFFFLnIiIiIiISBRRiBMREREREYkiCnEiIiIiIiJRRCFOREREREQkiijEiYiIiIiIRBGFOBERERERkSiiECciIiIiIhJFFOJERERERESiiEKciIiIiIhIFFGIExERERERiSIKcSIiIiIiIlFEIU5ERERERCSKKMSJiIiIiIhEEYU4ERERERGRKKIQJyIiIiIiEkUU4kRERERERKKIQpyIiIiIiEgUUYgTERERERGJIgpxIiIiIiIiUUQhTkREREREJIooxImIiIiIiEQRhTgREREREZEoohAnIiIiIiISRRTiREREREREoohCnIiIiIiISBRRiBMREREREYkiCnEiIiIiIiJRRCFOREREREQkiijEiYiIiIiIRBGFOBERERERkSiiECciIiIiIhJFFOJERERERESiiEKciIiIiIhIFFGIExERERERiSIKcSIiIiIiIlFEIU5ERERERCSKKMSJiIiIiIhEEYU4ERERERGRKKIQJyIiIiIiEkUU4kRERERERKKIQpyIiIiIiEgUUYgTERERERGJIgpxIiIiIiIiUUQhTkREREREJIooxImIiIiIiEQRhTgREREREZEoohAnIiIiIiISRRTiREREREREoohCnIiIiIiISBRRiBMREREREYkiCnEiIiIiIiJRRCFOREREREQkiijEiYiIiIiIRBGFOBERERERkSiiECciIiIiIhJFFOJERERERESiiEKciIiIiIhIFFGIExERERERiSIKcSIiIiIiIlFEIU5ERERERCSKKMSJiIiIiIhEEYU4ERERERGRKKIQJyIiIiIiEkUU4kRERERERKKIQpyIiIiIiEgUUYgTERERERGJIgpxIiIiIiIiUUQhTkREREREJIooxImIiIiIiEQRhTgREREREZEoohAnIiIiIiISRRTiREREREREoohCnIiIiIiISBRRiBMREREREYkiCnEiIiIiIiJRRCFOREREREQkiijEiYiIiIiIRBGFOBGRy5iZPWxmD1/gMe4zs6cuUpVkDszsHjOrXgT1+ICZHT9HmXmpq5n1mdmNF/u4F8Jc9EAMAAAgAElEQVTMbjEzt9D1EJGlRyFOROQSMLPtZvaYmTWFP2xWmdn/MbOtC12382Fme83s/vHrnHMPOOduX6AqzcjMqs3snoWux+XEOfeIc27L2PLF+CPBeZw7xTn34qU4l4jIQlOIExGZZ2Z2C/AqUA9cA6QCVwK/Ad6zcDWLTmYWdwnP5TMz/6U6XzQzs9iFroOIyOVCIU5EZP59F3jMOfcnzrk3nKfDOfdd59zXYPoWi8mtXmbmzOwzZvZbM+s3s1fMrDC8rsbMOszsr8aVn9KV61xd2czsK2Z2Ktxa+EZ42Rfe9o/AjcB94e1N4fX3m9ne8Pf/1cxKJx0zNVz+1vByupl9J3z8djP7dzNbe5Y63RNuVfusmdUANeH1m8zsSTNrNrN6M/u2mSWHtz0FFAL/GD73b6e7p+F1kRY7MysK3+ePmNkxYADYHC7zBTN7ysx6zazCzN4z7hg7zOw/zazLzDrN7KCZFZ/lmt5jZofMrNvMTpjZR8ZtG6vDB83sSPh8+8xs00zHm+b4iWb29XH3+FdmdsW47bFm9r/CLcOtZvbX4frfP67M98PPVV/4ej81zX37kpn9h5n1Ah8b/3yZ2X3AB4APhI/RZ2bLx+3/8XD9us3sp2aWOunYf25mz4Wf9WNmttPM/iBcl24z+6GNC47he3bLuOXrzezX4evvMLNfneV+/b6ZHTezHjNrM7Nnx21LMrO/NO/3Yuxn/3vhbVvN7PnwPl3h56vkHD+bD5nZ6+FrOG5md52tvIjIdBTiRETmkZltADYCP75Ih/wg8HvACryA8SyQDawH3gx8zsxuvoDjlwG34LUW3gl8AvgIgHPu48CLwAPhrms50+z/T8BqM7t+3Lo/AJqBX5uZAT8HUoCdQB5wBHjSzt6SU4B3HzcDa80sK1yXX+GFtR3ABuDvwnW9HS/sfTxc16vP8z7cDbw9XM/y8Lp7gfuANOB7wP8xs5Twtm8DzwFZeD+bjwBd0x3YzPYAjwFfBjKBjwPfMLPfnVT0j4DbwsdrAv7hPOr/deBNwE1APvAa8B/jgtKfAr8L3Bze3gtcN+kYrwC7gWXAp4Gvm9ltk8p8DPhiuMxD4zc45x4AHgEeCf8MUpxz7eHN+XjP7Ca8n+mVwGcnHfvu8HnTgcPAE3j3owTYDrwLeP90F29eN+XngEfxnp0c4H/NUDYJ+AnwaefcsnD5B8YV+QHevbzDOZcK3ApUjNv+tfA++UAp8POZnuXwHwv+AvgvQAbe/fuumd0wXXkRkZkoxImIzK/s8L/1F+l4f+ucq3XODQD/jPfB8UvOuWHn3CHgGN4H4jlxzv3EOVcXbi3cj/ch/C3nsX8X3oftj4xb/RHgIeecwwtu1wIfC7dGBoAv4AWxa85y6BDwOedcf/jaPwSUOuf+t3Mu4JxrwwsTH7KL0/3xy+H7MOqcGw6v+55z7pBzLgR8By+4jLW2DYevYXV4n8POueYZjv1h4BfOuX9xzgWdcy8A3wc+Ok0dmp1zQ3gBaVZB1LyW0w8DXwy3/A7h3WM/8I5wsXuAv3bOlYWv72tAy/jjOOd+4Jxrdc6FnHNPA08z9Vn4gXPu1fDzMjCb+oWNAJ93zg065xrwgv3k63vQOXfCOTeC98eBNcD/DD8DbwAvMPOz/gng6XBr92D49+M/zlGfzWaW5Zwbcs49D2BmK4C78P4YUA4Q/v07Ev7+mHPuufA+/cD/AIrwAup0Pgd8xTl3MHxfXwpf2z1nqZuIyBQKcSIi82vsg3H+RTpe47jvB4BW51xw0rpU5sjMPmFmh8NdArvwWgqyz7XfJA8Cv29mKeEufFcBPwxv2wDEAQ3h7mddQDtewFh1lmM2hcPImA3ANWPHCB/nV4DDa3W5UKenWdcw9o1zri/87di9vid87ufNrNbM/tbCXTunsQqomrTuFF4InPZ8QB9eq+BsZAEJ488Rfkaqx52jILw8tj0E1I4tm+d/mtnJcLe/LuB2pj4L092n2Whxzo2OW+5j6nM7+VnHOTd53UzPehFeq/I5hcPn2/ECalm4C+tY19Gi8L/THivc9fXx8M+8hzP3Y6bfmQ3ANyc9t3+E1yItIjJrMQtdARGRpcw5V2Fm5XjvBj17lqK9TA0fF/rBrhfAzJLDrQRnPaaZXYfXHfE2YJ9zbtTMvonXVXFMaBbn/U+8D+B/gNdV7ulwawt43QIHgaxJH+LPZfJ5m4C9zrm3nsc+4N2TSLgysxim/8A9m+uMCLcM3Rs+5nrgF0AP8KVpitfitSqNt47wu34XQRswFD5HabhOfmD1uHPUcSagjLXejQ/Rfwh8CngrcNQ5FzKzXwA26Vznuk8hFuYPxtV43W9nJTyq5Yvh7r43A0+bN1XCsXCRjcDr0+z6Pbz7vcs512pmGUAHU+/TmCbgC865f5pt3UREpqOWOBGR+fcx4A/MG0iiMNzKkW7e4Bn3hcscAN5sZhvNG3Tis0z9oH++yvFCy8fMG2WxhKld9sZLA4JAKxA0b86tD0wq08Q5PhyHu00+hHfdf4TXMjfmJeAk8G0zywYwswwz+73wu0mz9UPgSvMGx0gK39NVZvY7k+o6eXCRA8DvmFmumSUCfwVc8KiK5g3oURAOAT3AKN69nM7D4Tq8y8z84feh7mXifZqzcKvaw8BXws9bAt57WA74ZbjYj4D/N/y8xeF1AxwfZtPC19DmXZ69Fy/cn68mYP1F6uJ6Pr4D3G5m95pZgpnFmdm03YLNLMfM3mdm6eFntwvvXgWdc63A/8V7XjeEyxeY2fbw7mlAP9BlZmnAX5+jXn8HfMnMrgz/Tsab2VVmtvvCL1lELicKcSIi88w5txfvPbDVeCGiFziEN9Ljv4SLPQI8jjeYRC3eYA6/ucDz9uINDvFJvGDxl3gtBzN5Bm8Qh9/gtSZ8Jlyv8b4ObA13Bas7y7F+BOzC+zD85Lg6BfHCwBDwqnmjGr4OvDdcdrbXVoM3EMfbgEq8D97PANvGFfsL4M5w19B94XV/izdIRln46xQX533FNwG/xesW+DrwMjMMpOGcexmvpesrQCdeePtT59w/X4R6jPl/8AZ+eQmvW+Y1wFvDzwTA/wf8a7hMPV4Y2Y/3cwEvBL4AnMALYrfjtS6er+/hdZUdG70xcy4Xc76cc8fwnrM/wmsVbgT++wzFDW9wmSoz68N71/S+8LuK4AXs3wDPhLf/mjPvvP03vO7CXXi/22drbcc590285/K7eL9j9XjPyUxdb0VEpmXeH51ERETkchVuKasH/sQ5938Xuj4iInJ2aokTERG5zJhZmpm9I9x1N4Uz3UqfWuCqiYjILCjEiYiIXH58wP14I4PW4XW3vD08RYSIiCxy6k4pIiIiIiISRdQSJyIiIiIiEkU0T9w8MrN4vFGrGpl5qGkREREREbl8+YFcYL9zLjCbHRTi5tdVeMM3i4iIiIiInM2NeFPDnJNC3PxqBHjxxRcpKChY6LqIiIiIiMgiU1dXx4033gjh7DAbCnHzKwhQUFBAUVHRAldFREREREQWsVm/fqWBTURERERERKKIQpyIiIiIiEgUUYg7D2b2NTN70cz+2cySFro+IiIiIiJy+dE7cbNkZtuAjc65G83sk8BHgG/N9XjOOXp7exkYGCAUCl20esqlFxsbS2ZmJn6/f6GrIiIiIiKXAYW42bsBeDr8/b8Df8kFhLiOjg7MjKysLPx+P2Z2Meool5hzjr6+Pjo6OlixYsVCV0dERETm0YH6A5zuOM3q9NVsXbmVpDh1zJKFccm6U5rZp8zsoJkNm9nD5yi718yGzKwv/FV5KephZulm9piZ9ZpZvZn913GbM4Du8PddQOaF1CMQCJCRkUFMTIwCXBQzM1JSUhgZGVnoqoiIiMg8aulr4efHf87hxsP84uQv+Oa+b9LW37bQ1ZLL1KV8J64B+Arwg1mW/6xzLiX8tW6mQma2c5p1W8wsfg71+Hu81sk84B3Al83sTeFtnUBa+Ps0oGN2lzEzhbelQT9HERGRpe9Ey4kJy33Dffzk8E9o6WvBObdAtZLL1SXrTumc+xmAmV0JXJSZr82sAHjazP7YOfdv4XU7gWeA9wK/mW09zCwZeB+w0znXCxw2s4eA/wL8OnysL+CFv9unO7aIiIiILE1lrWVT1rX2t/LNfd8kJS6FNZlrWJOxhq0rt5Icl7wANZTLyWIenfKrZtZuZvvM7NbpCjjn6oB3Az80s7eHBx95Gvi0c+58Q9ZGwJxz4//MchjYGj7XEaDKzF4EbgMemu4gZna/mTkzc8Dp86zDonX//fdz1113nbPcxz/+cb70pS8BsHfvXnJycua7aiIiIiLzqm+4j9qeWsDrgfPOTe+csv1o01H+9eS/8g+v/AOB0cBCVFMuI4t1YJM/A04Aw8BdwL+ZWYlzrmJyQefcq2b2e8DPgFHgT51zP53DOVOAnknruoDUcef6H+c6iHPufuB+ADMrYgkFudn4x3/8xwU9//33309paSmPPvrogtZDRERElo7ytvJIl8lVaau4tvBaMhMz2V+3n+quagZHBiNlu4e6Odl6kpLckvM6x0hwhGPNx1ietJzC9MKLWv/LTWA0QMdgByPBEUaCIwwHh0mISWB1xmp8tpjbsGZvUYY459yr4xZ/ZGZ/CLwT+NsZdqkDhoAkYK6DoPQByyatSwN653g8mQejo6PExMzfYzvfxxcRETlfA8MDJMYm6h3sBVTaUhr5fvOKzQAUryimeEUxIReiqbeJfTX7ONRwCIBDDYdmFeJ6A708U/4MGUkZDI4M8nLNywDcsPoG3rrhrfh9mr5oNkZDo7xU/RLOOYoyi/jRwR8xEpo66FxBWgEf2vmhJdHdNVqi6Ixvi5rZauA54KvAHwI/N7Nr5nCOcsCZ2eZx60qAY3M4VtQ7cuQIV199Nampqbz97W+nre3M6Et33XUXOTk5pKWlccstt3Dy5MnItnvuuYfPf/7zU473N3/zN7z73e+esO6+++7j7rvvPms97rnnHj760Y/yrne9i+TkZJ588kkaGhq48847yc7OpqioiK9//esAPP300zzwwAM88cQTpKSkUFxcDEBRURFPP/105JgPP/wwe/bsiSybGd/61rfYuHEjubm5kW6g3/rWt8jNzWXFihU88MAD53H3RERELtxIcIR/ev2f+Nrer/HXL/w1vyz9JTVdNRpE4xILjAYobyuPLG/O3jxhu8985C3L4y3r3hIJ2pUdlfQMTe7gNZFzjp8e+SmHGg/xfOXzkQAH8NIbL/GDAz+YcgznHI29jQwHhy/0spaUJ0uf5D9O/QfPVj7Lg/sfnDbAAdR11/3/7L13dFvXma/9bBQWEAALSIK9d6pRVLUkO5adcbfs2Ikdx3GcOC4zcRwn4zuTuZnlJDdl1kzyeTJz780ksW8mztixPXHsOFbkxCUuqlYXJTax906AJAg2APv7A+IRQZASJJGq+1lLS8A5++yzUUie33nf9/fy3L7nLov377yFHIQQhhPn0wN6IUQE4JVSTs0aFwOsBT7Enx55D3A18PU55kzEL+B+IqX8jxPbHsKffnn9iTq2UNcxKoR4FfieEOKLQDZ+U5N7FuQNOA1vvvnm+TgNt91222nHTE1NsWXLFh5++GF27NjBjh07uP3227n1Vn/+94033sizzz6L0Wjkqaee4vOf/zz79+8/5Zz3338/Tz/9NP39/cTHxyOl5MUXX+SXv5yztDCAl156iT/+8Y+88cYbjI2NcfXVV3PLLbfw4osv0tXVxfXXX09eXh5btmzhf/7P/3lW6ZSvv/46u3btIioqio8//pj+/n7a2tpobm7m2LFjrF+/ni1btlBaWnpG8yoUCoVCcaZMeiep6athZ8tO2ofaARieGGZX6y52te4iOiKa5cnL2Zi58bKIKFzsHO8/rokCu9lOQtTcfWFjImPIic2hYbABKSVHuo+wKWvTvPNW9VbR5Ji/6qbF2cIzO58hMSqRkYkRcuJycE26qB+oJ9mSzKNrHsWoN57bi7sMONJ1hH3t+4K2CyFItaYSpg9Dr9NTP1CPlJLe0V5ePfYqU94pViSvoCihiHDDfKb2Fy/nM2/sH4Fvz3h+P/A88KAQ4i1gu5Tyh4ARf1StCPACNcAdUsoagnEC35RSvjq9QUr5ByHEA0DHma4D+ArwLNCFvz7uO1LK98/wdV7y7N69m9HRUb75zW+i0+nYvHkzt912m3bn78EHH9TGfuc73yEhIYHR0VGioub/Q5KUlMS1117Lyy+/zOOPP86HH36IlJJrr7123mOmue2227j66qsBOHbsGF1dXXz3u99FCEFWVhaPPvooL7/8Mlu2bDnr1/zNb36T+Ph47blOp+P73/8+YWFhlJeXs3z5cg4dOqREnEKhUCgWlerearbWbMU57px3zND4EB81fcTHbR9zz9J7KEwoPI8rvPxpGGhgwD2ATqdDJ3Qc7Dio7VtiX3LKY5cnL6dh0F/ZU9NXM6+I8/g8/KnuT3PuS7Ik0ePqQUrJlHeKjmH/Je3hrsPamK6RLrY3b2dz7pzef5ct41PjuCZdTPn8tW49rh7erJ47EJJvy+cLK09mfL3X8B5/afgLAJU9lYBfoH8i5xN8Mu+Ti772heZ8thj4DicMP+bYd9OMx33A6hDnnARenWP73D8Vp1+HE3+bgfNOKBGy80VnZyepqanodCezbTMzM2lubsbr9fIP//APvPrqq/T392tj+vv7TyniwC/+fvSjH/H444/zwgsv8LnPfS7gHPORnp6uPW5paaG3t5fY2Fhtm9frZfXqkL4yIZ0DIC4ujrCwMO15VFQULpfrnM6hUCgUCsV8+KSPt+veZnvz9qB9n8j5BFkxWRzrOUZVbxXuKTfgT/P77bHf8vdX/72KyCwQFV0VvHJ0fn+8Uvupb+YWxBdoj1udrUx4JuaM8uxp3cOge+6Ww3cU38GUb4qXK15mdHJ03nN92PSh1tbgSuDjto/ZWrMVn/SFNH5V6qqA50XxRZqIm8nypOULsr7zjXJwUASRkpJCR0cHPp9PE1mtra0AvPjii7zxxhu89957ZGVlMTAwQEJCQkj5+bfffjuPPfYYR44c4dVXX2XXrl0hrWdmIXd6ejrp6ek0Nc2dfjBX0bfZbMbtdmvPu7q6QjpOoVAoFIrzwYRngpcrXg6ou4oyRrE6fTXFCcWkRfvb2ubH53N78e1U91Xzh+o/MDo5ytjUGNV91SxLWnahln9Zsb0lWERPk2RJwm62n/J4S7iFZEsyXSNd+KSPxsHGoBo616SLvzSeFBNFCUXU9PkTzqIjokmLTkMIwePrHmdr7VYG3YOMTIzgmgy8mezxeXhu33MsS1rGjQU3Eh0RfaYv95JBSslfGv4yr4CzmWw8UPYAvzzwS4bGh7CZbBQlFAWMSbYmE2mMDHASTbYkk2hOXNS1LxZKxCmCWL9+PZGRkfzLv/wLf/u3f8vOnTt58803ueWWW3C5XISHh2Oz2XC73XzrW98Ked7w8HDuvfdeHnjgAfLy8igpKTnjta1Zs4bY2Fh++MMf8vWvf52wsDBqa2sZGRlh7dq12O123nrrrQABWlZWxm9+8xtuvfVWGhsbee6550hImDufXaFQKBSK80n/aD8vHn6R3tFebVtRQhF3ld6FKcwUNF6v07PEvoReVy/vNbwHwIGOA0rELQCdw510DncCYNQZWZa8DJ/Phw8f4fpwrsq8KqR58m35dI34bxjXDdQFiLgJzwQvHXlJ6yMXb4rns8s/y5vVb9Iw2MAthbdoN5atEVbuW34f4Bcxh7sOMzo5SkZMBr86+CttjoruCqr7qrkm6xo2Zm28LKOyXSNdmog16AzYTDaMeiNGnZEkSxLXZF+DJdzCw6sfpqavhuKE4iBnT53QkRObQ2VvpbatLKXsvL6OheRScadUnEeMRiNvvPEGr776KrGxsfzTP/2T5iL5wAMPkJWVRWpqKqWlpVx1VWi/0KZ58MEHqaio4IEHHjirten1erZu3crRo0fJzs4mPj6eL37xizgcDgA+/elPYzAYsNlsWv3a9773Pbq6uoiLi+ORRx45rSOmQqFQKBTng4aBBn6+9+cBAu6a7Gu4f8X9cwq4maxMWRnghOgYcyzqWheDzuFOXjj0As/seCYgCnmh2N9x0qSt1F7Kp0o/xd1L7+YzSz/DlpIt8xqazCbPlqc9rh+o1x5Peid54fALNDuatW03F96MQWfgztI7eWrTU0FRu2mEEJSllLExayMZMRl8df1XA+rzprxTvNvwLv+269+0eq/LiZnvY0liCU9c9QR/vfav+fLqL3Nr0a1Ywv1tnWMjY1mfsZ6YyJg558mMzQx4vtS+dPEWvcgIZVO7eEw3+25qaiIrKytgX2dnJykpKRdgVReWnp4eMjIyaG9vv6yiYVfq56lQKBSKM2fAPcDBzoN81PSRlh42fSF/Jg2i//PAf2oXt+sy1nFb0cVTX38q+kf7ebfhXY52H9W2xZvieXLDk+e9vGF0cpTj/cfJiMngPz7+Dy3V7survkx23NnVmnl8Hr7//veZ8vodLf92499iCbfwwuEXAsTIzYU3syFzwzmtv2mwia21W+ke6Q7Yfs/Se1iWfH6is1JKxj3jRBojz+p4x5iDtqE28m35887x3L7nNCfPu5fcfdYRNMeYg5/s/Aken4fihGLuL7v/rOZZaJqbm8nOzgbIllI2h3KMSqdUnDeklDzzzDPccccdl5WAUygUCoUiVPZ37Of3Vb8PqCW3hFu4f8X9Wu1bqFyVcZUmCva07sFsNJNjyyEzJvM0R4bG8f7jNAw0sDZ9LXGmuHOeb8IzwZ/r/sy+9n1BtU397n56R3tPW3N2pkwbxnQOd3JL0S0B80speeHwC7Q6WwOOiY2MJSs266zPadAZyI7N1qKLNf011PXXBQi4G/JvOGcBB5Adl81X1n2F/e37eaf+Hc305rXK17BGWMmMyQwQxk2DTexu201mTOY5n19KybGeY7x1/C2GJ4bZUryF1WmhG81JKXm77m12tuzEK71ER0Rz3/L7gn4OJjwTtDhbtOczI51nSmxkLF9Y+QVana2sSlt1+gMuYpSIU5wXRkdHsdvtpKWlsW3btoB9ZrN5zmNefvllrTedQqFQKBSXOlJKPmj8IEDApVpTuX/F/VgjrGc8X0F8AenR6bQNtQHwbsO70ODffnPhzSGn/83F0PgQLx5+EY/Pw6GuQzy65lFsJpu2v2GggW3HtzE8Pow5zMw1OdewPGn5KSNpHzZ9yMdtHwdsMxlNmvCo7KlccBG3u3W35vj5SsUrfHX9V7U1dru6gwQcQGFC4TlHBPNseZqI+2PNHwP2XZ97PVdnX31O889EJ3SsSV/DEvsSfvrxT3GMOZjyTfHsvmexm+18adWXiDBE8F79e2xv2Y6UksqeSrJiskiNTg35PFJK3FNuTEYTA+4B3qx5M0CY7mzZeUYibkfLDj5q/kh7PjQ+xLP7nuW24tsCnCXrB+o10Z9sSdZSJ8+WnLgccuJyzmmOiwEl4hTnhVNZ9CvrfoVCoVBcCCq6K9jbtheJRC/06HV6DDoDuXG5rMtYt+Dnax1qDahdu63oNspTy8/aiEIIwebczTx/8PmA7cf7j1M/UM+69HWsTV/LyMQICeYEzGFz3zSdi2M9x/D4PIA/5fD5g8/zyJpHMIeZkVLyRvUbDLgHAHBPufnt0d9ysOMgV2dfTW5c7pwiaGYtWFZsFjfk34Bz3MkrFa9o5zzXvmdNg0183P4xsRGxlKeW807dO9q+HleP3/TiRN3Zka4jc85RFF805/YzId+WP+f2a3Ou5drc0/fIPRtMYSbuXnI3z+1/TrtR0OPq4XfHfsfIxIhmtjJNRXcFvaO9pEenEx8VP9eUGlJKfn3o16esXewb7WNsaiyktEopJXvb9wZt9/g8vF75Oh1DHdxSdAsGnYGq3iptv+qJeBIl4hQKhUKhUFxxjEyM8OrRV/FKb9C+qt4qEqISyLXlnvN5hseH6RrpIicuh8OdJ5s1r05bvSBCMd+Wz/V511PTV8OUd4re0V6klPikj12tu9jV6m/nExsZy1fXf3XOnmVzcaznWMDzAfcAvz74ax5a9RCDY4OagJtJw2ADDYMNJFuS2ZS1iSX2JZpDoE/6AkTEvcvuxRJuwe6xY9QZmfL5GzdX9lSethfbXPS6evlz3Z81q34gIMozzdaarVT3VRMdER3QPHsmZ1sLN5OEqASiwqIC+rytTFnJdbnXnfPcpyIrNosHVz7IW8ff0urk5hNeO1p2ABCmD+Ov1/71Ka32B9wDc84jhCBMH6Y5ZXYMd4SU7tg21Kb1yQs3hPPomkd5peIVelw9AOxt30vDYANL7EsCPqfSxDP/blyuKBGnUCgUCoXiiuNoz9E5Bdw09YP1c4o4KSVjU2MMTQxhMprm7c3l8XnY0byDDxo/YMo3FbR/efLCNBgWQvijOzn+6E7ncCfbardpJhDTOMYcHO05GtQAeS5GJka0FM3piJqUko7hDl6qeIlkc7I2NiM6gyRLEvs69mnRn66RLv776H/zdt3bXJV5FcuSljE2NcakdxIAc5hZS4kLN4RTnFhMRXcFAL858hvCDeGUpZRxa+Gtp01rdE26eK/+PfZ37A+pCbRz3MmBjgPz7s+KzcKgO/fLYyEE+bb8AAFyY8GN58W4Jc+Wx1fXf5VXKl7R3lfw1+pdm3Mt7zW8F/BeTXon+e2x3/Lomkfnfe2dI51B2zKiM7it+Db2d+zX0mTbnG0hibiDnQe1x0vtS7Gb7Ty65lFer3pdM7wZcA/wYdOH2rjYyFiSLclBc12pKBGnUCgUCoXiimNmKt012deQE5dDi7OFvzT4mzC3Odu0/X2jfbxT/w59rj6c405NjADct/y+oMhRs6OZN6reCGgdMJPYyFiyYrIW8NWcJMWawkOrHqKqt4o/1f1Ji3YAHO48HJKIq+qt0nGGCFcAACAASURBVARZVkwWy5KW8Ub1GwDU9ddR11+njd2QtYEl9iVszNrIzpadHOw4qIlW57iTbbXb2FYbWAufag2sw7q58GY6hju06N6EZ4I9rXtYnrScjJiMoPW1D7VzpOsIhQmFvFH9RsBrFEKQZ8ujfqAeKSVRYVHcUXIHXSNd2mc7m4yYDDqHO/FK74JGytamr6WiuwKJ5FOlnyIqLGrB5g6FmwtvpnGwEdekiyRzEp9e+mmSLEnU9tcG1QJ2DnfyQeMHXJ93/ZxzTUfIAIx6I3cvuZvSxFKEEKS70jURV9tfS4m9ZN7axknvJC8deSkgqrcixe/IGm4I556l95BmTeOd+ne0dN5pps+n8KNEnEKhUCgUiiuK/tF+2ofaAdALPRszN2IKM5EQlaBd6HcMd+CTPnRCx9aarQEGDjP5sOlDTcT5pI8/VP+Bfe375j23UW9kS/GWRb0YFUJQai+lJLGEofEhfrzjx0gpaXI04RxzzttDa5qZr7XUXsqa9DWMTI4EiSCjzqjVftlMNm4vvp3rcq/j47aP2dO6h9GpUeZitpmGJdzCF8u/yK8O/Ip+d3/AOmaLOK/Py38d+i9cky4tVXSa3Lhcbiy4kRRrir9x90gnJQklmMJMlCSWsCxpGQPuAYbGhxgaH2J4fJiosCg2525m0juJT/rmjayeDRkxGTxx1RNMeadIsZ7/NkSWcAuPr3+cXlcvmbGZWpQtz5Y3p6HLR00fsSxp2ZxplTNbGNxRckdAj7r06HTtcdtQG/++69+5ufBm3FNunGNONmZt1CJoHzR+ECDgUqwpATc0hBBszNpIeWo5x3qOsa12m3bTZGnSpdvTbTFQIk6hUCgUCsUVxZHuk1G4gvgCrbF2dEQ01nArwxPDTHon6XH1kBiVSIujJeD4MH0YU74pLcWwb7SPhKgEdjTvCBBwYfowrs+7nnhTPG8dfwtLuIXbim47Ze3RQiKEICYyhjxbnhY9O9J9hGuyr5n3GCllgJ17bpw/pXRzzmZGJ0cD3CVL7aVBNXbTomhT1iYOdR5id+vuoIhkiiVY0MRGxvK1DV9jd+tuLXJXN1AXZHTSMdyBazLYEO3O0jspTynXxHGKNSVIOCVEJczr2BlqreCZci4OoQuBJdwS5Oa4PGk5HzV9hMfnYUPmBtqcbbQOteKVXv5Q/QceWvVQ0E2GmSJudkqjzWQj0hip9dgDAqKvR7uPcm3OtWzK3hSQXrosaRk3F9485w2NSGMkq9NWkxmTye7W3aRGp55xC47LHSXiFOeNX/3qV/zsZz9jz549F3opCoVCobhCkVIGpFLOrk1Lj0mnsqcS8KftSSm19MDoiGi+uv6rRBgieOnIS1T2+scd7jrM9bnXB7jtFSUUcXvx7Vpk50K66i1PWq6JuPqB+lOKuAH3gGbGEWmM1ESIEILbi29nRfIKzfJ9Y+bGeecx6o2sSV/DmvQ1/HL/L2kYbND2zReV0gkdK5JX8Nbxt5BS0j7UzvjUOBHGCG1M42Bj0HHLkpaFlCaq8BMfFc/frPsbBt2DFCYU0uPq4ad7fopP+mhyNNEw2BBQ1zY2NYZz3An46+riTYFOlkIIcuNyg8xwpvFKL+82vMuBzgMMjQ8BEGWM4q4ld522/jDRnMiWki3n8nIvW3QXegGKi5NPfOITREREYDabsVqtrF69mh07diza+T744AOSkpIWZK5PfOIT/OxnP1uQuRQKhUJxeTGz9ircEE5RQqCd/MzUsFZna0DaWUZMBpHGSIQQAeLvSNcRGgcbtfYBkcZI7l1274Km5p0LM3titQ21ndIApHXo5OtNj04PipJkxGSwOXcz1+ddHyCuTsUN+Tdoj6ejnfMRFRalRep80kejI1C0zTZsWZG8gtuLbw9pHYqT2M12ihOL0QkdyZbkgP5uHzUFunp2u05G4RKiEjTH0ZncUngLV2eduvfdzPYaS5OXLoiBzJWMEnGKefnJT36Cy+XC6XTypS99iU996lMBDUoVCoVCobjUONR1SHtcmlga1KNtZspWx3CHVjsHgQKvIL6ACINfxDjGHLxy9BVt3/Lk5Wfd+20xiI6I1gTllHcqIDVuNrNF60KQGp3KluIt5MTlcEfJHaetB5zpClrbV6s99vg8AameT216ik8v/XRIfckUp2Zj5kZ0wi8LGgYbAr73M78vSZa5b7hbI6zcUHDDnHVrm3M3Bwm2suSyhVj2FY0ScYrTotPp+NznPkdfXx99fX3s37+f9evXExMTQ3JyMk888QRTUyftk6urq7nhhhuw2WwkJibyD//wD3PO++1vf5vy8nJaWlq46aab6O3txWw2YzabaWxsxOfz8c///M/k5eVhs9m466676OvrA2B8fJzPf/7z2Gw2YmJiWLVqFV1dXXzrW99i+/btPPnkk5jNZr785S+fl/dIoVAoFBc/IxMjAamUK5JXBI2ZWa/VN9pHs7NZez5T4Bn1Rs1VDwjoB1aeUr5QS14wZgqymUII/L3s9rXv41DnoYCavsyYzAU7/5r0NTy06iEK4gtOO7Yw/mTqaUV3RUAPsimv/3ojNjKW2MjYBVvflU6cKY5lScu05zOjcTP7+53O4j/NGli3Fm+K57rc63h8/ePad7AwvjDIoVRx5qg45kXCt97+1nk71w/+6gdnNN7j8fD888+Tl5dHfHw8HR0dPPPMM6xevZrW1lZuvPFGCgoKePzxxxkZGeH666/niSee4Pe//72/9uDIkYD5pJQ88cQTVFRU8P7772O1Wnnrrbe499576e4+ebfn3/7t33j11Vf5y1/+gt1u5+tf/zqPPPIIr7/+Os8//zxOp5O2tjbCw8OpqKjAZDLxgx/8gJ07d3Lvvffy2GOPLcj7pVAoFBcblT2VHOs5xqR3kinvFFPeKXz4WJa0jA2ZGy708s6Y4fFhzOFmLRKwGPSN9vHSkZc084XYyNg5mzqHG8KJjYzFMebAJ31aCphe6IMMOdanr2dPa2Cdd25c7gVxIjwdmTGZWv+tVmcr6zPWA/7o1rP7nw2w6Qd/fdqFutDOjMkkMSqR3tFeJr2THOo8xLqMdQEGM9mx596QWxHI1dlXa8Yjlb2V9Lp6STQn0jHUoY053Xd79ncmPcYfvU6ISuCR1Y/gmnRhDjOrVgELgBJxZ4AQ4gfA1UAP8ICU0n2Bl7SofOMb3+Cb3/wmY2Nj6HQ6fvOb36DT6SgrOxkCz8nJ4ZFHHuHDDz/k8ccf549//CNxcXH8/d//vTZm/fr12mOPx8P999+P0+nkT3/6E5GR86dA/OxnP+MnP/kJGRn+Ozff/e53sdvtjI+PYzQaGRgYoK6ujuXLlwesSaFQKC5nBt2DvFzx8px1Te1D7eTZ8pjyTpEQlbBojnsLhdfn5bXK1zjcdZjoiGiuy72OspSysxZz3SPd/KXhL2TGZnJVxlUIIRiZGOG1ytcCbM3Bb5M+33mSzEkB9TvgTyObnSIZHxWv9SSbOe/FSEb0yUjczJTJw12HgwQc+KMlF+r7I4RgTfoattZsBWBv+17Wpq+l13XS5XJmaqtiYbCb7RQlFFHTVwPAR80fcXvx7fSM+nvECSHmdBadyWyRNzNyJ4QIcspUnD1KxIWIEGIpUCCl3CSE+ArwEPC/L/CyFpVnnnmGxx57DJ/Px65du7j11lvJzs4mMjKSb3zjGxw4cAC3243H42Ht2rUAtLa2kpubO++cjY2NHDt2jO3bt59SwAG0tLTw6U9/Gp3u5B/ZsLAwOjo6+PznP097ezv33Xcfg4OD3Hffffzwhz8kPPzivmBRKBSKc6V9qP2UxhT/vuvfAUiMSuQr679y0ZoHTHgm+O3R31LdVw3A0PgQr1W+xq7WXdyQfwP5tvwzulvvnnTzq4O/YmRihMreSs1B73eVvwtIdQS4KuOqAPe92dgtdm1d08w0B5nJtTnX0jTYhA8fdxTfQZwpLuQ1n0+SrcmE6cOY9E7iHHcyPD6MJdzCjuZA07KVKSvJicu54D25Vqas5O26t7VWD83O5oBWBQnmC2vdf7lyTfY1mog70nWEAluB5oeQYDr9jaFwQzg2k00zD5puUaFYeC7O3+wXJxuBP514vA34JxZQxJ1piuP5RKfTsXHjRvLz83n33XfZtm0bK1as4OWXX8ZisfDjH/+YrVv9d8vS09NpbAy2/52moKCAp556ittuu4133nmHpUv9fyTm+kOdnp7OL37xC665Zm4r5Keffpqnn36a1tZWbrnlFnJycvjKV76iQvQKheKyZvquOPit1ctTyznef5ydLTsDxvWO9nK8/zgliSXne4mnZcA9wAuHXgjqHwb+aNrzB58nz5bHPUvv0Xq4zcfetr28Xf92QI8qgFePvYp7KjBhJs+WxxL7ktPa0dvN9qBt002tZ5MVm8UTVz2Bx+eZ1/ThYkAndCRZkrQoXNdIF50jnfSN+mvNww3h/N2mvwvZcXKxCTeEsyJ5hda2YU/rnoDvS2LU+em1d6WREZNBVmwWzY5mfNIXYNgTanrtnSV38nbd2+TH51/UPxOXOiHlKwgh8oUQCScem4QQ3xZC/KMQIuSwhxDicSHEASHEpBDiVyEeEy+E6BdCLFhjsVOtQwgRI4T4byHEiBCiQwjxNzN2xwJDJx47gYvzVtsisWfPHqqqqigtLcXlcmG1WjGbzVRXV/Pzn/9cG3frrbfS19fHj370I8bHx3G73ezevTtgrrvvvpt//dd/5a/+6q+orPT32LHb7TgcDhyOk+krjz32GP/4j/9IU5PfTri/v5/XX38dgPfff5+jR4/i9Xoxm80YDAYtYme3208pJBUKheJSps/Vpz0uiC8gz5bH8qTlc46t6K5Y1LVMeafYWrOVH330I/5c92ctQjg+Nc6Lh1/k2+9+mx9v/zHP7XuO3x37He83vE/HUAe/PfrbgAvyDZkb2Jy7OSBdsX6gnp2tO9nbtpcDHQcC3JGllDQ5mnil4hXeqH4jSMABAQLOEm7hi+Vf5IvlX2R12urT3uxLMgdeeBr1RjJj5zf5iI+KvyQuVmemtnW7utnevF17vjp19UUj4KZZk75Ge3ys55hmahIVFkVUWNSFWtZlz3x9BFOjQxNx2XHZPLr20aBG7YqFJdRI3G/wpw/2Ad8H/grwAMnAV0KcoxP4HnADEKoX7I+AKiBsvgFCiDIp5aFZ20qBeinlxBmu4//gf09SgFzgHSFEtZTyfcABTDd8iQaCE8gvM5588kmeeuopAJKSkvj+97/PTTfdRFRUFA8//DA//vGPWblyJffcc4/WQ85isfDOO+/w5JNP8sMf/pCwsDC+/OUvB9TFAXz2s5/F6/XyyU9+kvfee4/i4mI+97nPkZeXh9fr5dChQ3zta19DSsmNN95IV1cX8fHx3HXXXdx55510d3fz2GOP0dHRQVRUFJ/61Kc0J8qvfe1rfOELX+DZZ5/lM5/5TIDIVCgUikuduaIRM1PlZlLTW8OEZ2JRapv6R/t5qeIlzX78o6aP6BjqIDsum4quCm2djjEHjjGH1t/r3YZ3tTn0Qs+Wki2Up/rdHNekrWFb7TZNfH7Q+IE2Vid0pEenc7DzIBXdFUE1a/NRnFDMnaV3ntFFf3xUYDPjZHPyRZuWeibMFKeHOw9rn5FO6Lgq86oLtax5SbYkkxmTGeSmqaJwi0u+LZ9kS3KAKyWEHolTnB9EKH2/hBCDQLyU0ieEaAGuBVzAISnlGX2iQojvA2lSygdPM+4a4AfA/wMelVKum2NMGnAA+LKU8s0T28qAPwN3Sil3zj5mvnUIIaLwC7MyKWXViW3/DKRIKT8vhFgGfEtKeY8Q4q8Bg5TylOmUQogsoKmpqYmsrKyAfZ2dnaSkXHzuVYqzQ32eCoXifODxefjue9/VIl5Pb35aE2g/3/vzAMOKae5achcrU1Yu6Doquit4vfL1INF4pqxOWx1kBOIcc/Kj7T+ac7xBZ8Dj8wRtL7WXsj7df7MwyZLEC4dfoH+0n825m1mTtuas0uxnukZvyNzAzYU3n/EcFxutzlZ+vjf4xmZZchl3L737Aqzo9FR0VQSk9AGsTV+rGnwvMsf7j/PrQ7/WIuA6oePpzU9fVP0PLyeam5vJzs4GyJZSNodyTKi3lQQghRA5gJRSNgIIIaxns9DTnkyIMPxRsfuBeW0HpZTtQojbgT8KIe4HOvDXrX31VAJuHgrwi9qqGdsO4486IqWsEEI0CiG2449Ifn6etX8H+PYZnluhUCgUitMy6B7UBFxsZGxAhC3JnDSniKvurT4nETcyMUJUWBQ6oWPKO8Vbx9/i47aPtf0GnYHChEIqeyoDjjPoDGwp2UJ6dDqOMQeD7kH+XPfnAOG3MXNj0PmiI6Ixh5lxTbqC9s0UcJHGSJbal7IiZQUZ0RkBQu3h1Q8jpTynGulP5n2Sd+rfIcIQMec6L0XsZjtCCGbfwN+QdfG2pSixlxBVGxVgTpMQpUxNFpuC+AIeWvUQb9e9TdtQGxszNyoBd5ERqog7AnwLyADeBhBCpALDi7SubwLvSimPnIiszYuU8mMhxF3Aa/hTPP9OSvnKqY6ZBzPBr8cJaF6oUsq5u1YHruc7wHfgZCTuLNaiUCgUCkUQAe58sy5k12esZ3/HfnzSx5q0NZohRP1APR6f54zTAd2Tbl6vep2q3irsZjtbSrawtWYrncOd2pg4UxyfXfZZUqwpdAx1cLjrMP3uftKj0ylLKdOaMU+v1RJu4TdHfgPA0qSlQWmL4De6yojJoKq3Kmgf+MXbnSV3UphQeMrXdK4mV9dkX0N2XDaxEbFYIxblnvV5J9wQTlxknOYcCJAfn3/aBs4XEoPOwOq01QGptSqd8vyQHZvNo2sePavfH4rFJ9RP5Angp8Ak8IUT264H3lnoBQkh8oAHgRVncFg7MA6YgIazPLULmP1bOhoYOcv5FAqFQqFYUGb2yZot4hLNiTy+/nFGJkbIjculbqAOx5iDSe8kLY4Wcm2hWX2PTIywt30vH7d9rEU/elw9/GLvLwLGldpL+VTJpzQzjNTo1NMaH5TaS/nCyi/QPdLN2vS1845Li06bV8RdlXEVpfbSkF7LuSCEIDNmfjOTS5UkS1KAiNuUuekCriY0Vqeu5sOmD7UIYqJZibjzyaUu4KSUjI2NMTIywujoKDabjejo6NMfeJET0qcipazAb7E/c9vzwPOLsKaNQBJw/MRdtEggUgjRDWTONisRQmQC7+E3XGkCXhdC3Cql/Jgz4zj+lNFiKeV0c5gVwLGzfykKhUKhUCwMUkoaB0867851IWs32zV7/MKEQva0+s2da/trTyviOoY62NW6i6PdR/FK77zj9ELPTYU3sS593VlFuwriCyiILzjlmJmNqWcSbghnfcb6OfcpQiPZnKylviZbkuftf3cxERMZwzXZ1/BR00eUp5arhtGKUyKlpK2tjYGBAVwuFyMjI3i9J3+nRUREcN111wX0Ib4UCVlaCyFMQCEz0gsBpJQfhXi84cT59IBeCBEBeKWUU7OGvsLJfmwA9wAPALfMIeAS8Qu4n0gp/+PEtoeAN4UQ158Qn6GuY1QI8SrwPSHEF4Fs4Esnzr8onGu+vuLiIBRzIIVCoThXKnsrNYdHIQRZMVmnHF8Yf1LE7e/Yz9jUGLm2XHLjcoMugve37+f1qteD5oiOiCYuMk47b2xkLPcuu5e06LQFeEXzk2INNIq6s+ROavtr2ZC5gUhjqAbXirlYnrycna07mfRMclPBTZfMdcgn8z7J5pzN6HX6C72UILxeL/39/XR3d+NwOMjPzyc1VTk5nk+klDQ3N9Pf34/b7WZ4OLBCKiIiArPZzOjoKGNjY3R0dJCenn6BVrswhCTiTpiH/JrgdEOJXwyFwj8SaPhxP/5I3oNCiLeA7VLKH0opxwCt4YsQYgiYklJ2zzGnE/imlPJVbUFS/kEI8QB+k5MzWgf+dgnPAl346+O+c6K9wIITHh6Ow+HAarWi1+svmV+iikCklLhcLoxGVeyrUCgWD6/Py7babdrztelr56wnm0l2bDZGvZEp7xQTngkOdh7kYOdBbd99y+/DFGZCSslHzYH3YzNiMrS0RYGgtr8Wx5iDFckrzouICjeEc2PBjexq2cVVmVexKm0Vq9JO3aBbERpxpjj+x6b/AbAorScWk4tBwEkpcTgcuN1uxsbGcDgc9Pf3B0R6qqurSU5OvuQjPZcSzc3NHDt2MnkuMjKSvLw8rFYrFotFu05ra2vj8OHDNDQ0EBERgdFoJCYm5kIt+5wItcVALX5x8x9SytHTjVf4OVWLASklIyMjuN1ufD7fBVidYqEwGo3ExcWh11/4Py4KheLypGOog59+/FMATEYT39j4jZDE1P6O/bxV+xbjnvGgfTcV3MTGrI0BtvPhhnAeKn8o5Ka+CsWVxuHDh2lrawvaHhMTg91up6OjA5fLRXl5eUith6SUuN1uwsPDkVJy8OBBvF4vZrNZ+2e1WomI8Neeut1u6urqsNvtJCVd/A3mzwcOh4OdO3cipaSoqAiz2UxCQgIGQ3Csyufz8d577zE+7v+daLPZuOqqC98jcTFbDCRLKX98tgtTBCOEwGq1YrVeHo5XCoVCoVg8BscGtceZMZkhR8NWpa5iZcpK2ofaaRhsoKq3SnOXbB3ytyM43HVYG7/EvkQJOIViHhwOB21tbej1eux2OyaTSRMM0yIrLCyMo0eP0tzcfEoR19/fT2dnJ729vYyNjWGxWLDb7fT2+s2LBgYGAsbbbDZSUlJobGxkdHSU1tZW7HY7paWlREWF3sj+csPn83HkyBGklOTk5JCfn3/K8TqdjuXLl9PU1ISUEovl0q2vDFXE7RBCLJurxkyhUCgUCsXi4hhzaI+nbftDRSd0ZMRkkBGTQWF8If93z/8FoHO4E4/PQ0X3yT/tZSmn7OqjUFyxSCmpqvI7pubk5FBUVDTnuLS0NKqrqzVTDbPZHLDf7XZz7Ngxenp6tG1CCEZGRhgZ8RuiL126VCvXcLlcOBwOBgYGNGFnNpsZHx+np6eH3t5esrOzKSgouOxLO8bHx9m3bx9SSsLDw4mIiMDj8TAyMoLJZJr3M5lNYmIiiYmXvsNpyCIO+L0Q4uf468U0pJS/XvBVKRQKhUKh0JgZiYszxZ31PInmRAw6Ax6fB8eYg6PdRxmb8pehx0bGntYsRaG4FDgX4zifz0dFRQWTk5MkJiZit9uJjIykpaWFwcFBwsLCyM2d3+nVYDCQlJREe3s73d3d5OXlAX7zk4aGBurq6vD5fBgMBnJycrDb7Xg8Hnbv3g1AfHx8UAmOx+Ohq6uL9vZ2pJSUl5cjpaS2tpa2tjYaGxtpb2+noKCAzMzMy7YWr62tDafTOee+JUuWXHFlLaGKuIdP/P/YrO0Sv+GJQqFQKBSKRWJmJC4u8uxFnEFnIMmSRPtQOwDv1r+r7StNLFUmW4pLksnJSRwOB4ODgzgcDpxOJzExMaxYsQKTyYTX62VsbCwoKgYwMTFBbW0t2dnZmM1mjh07ptW89fT0cPToUaxWKy6XC/CLhdNFvGaLuJGREfbu3Yvb7Qb80bqSkhLCw08ay2RlZdHa2jpnNMlgMJCenh7kprh8+XKysrKorKxkYGCAY8eO0dzcTElJCYmJiZfdz3N3t9/jcMmSJURGRjIxMcHY2Bgmkwm73X6BV3f+Oa2IE0LogFuB43O0A1AoFAqFQrHIzGzOfKbplLNJtaZqIs45fvKudom95JzmVSjOBo/Hw+TkJCaTSdvm8/kQQoQkQtrb2zl8+HBQu5+BgQE+/PBDVq1aRWVlJSMjI5SVlZGWFtgeo6amhtbWVsbHx0lJSaGlpQW9Xk9hYSEOh4O+vj7Nrj4jIyOk1gEJCQno9XqcTifj4+McPXoUt9uNxWJh6dKl2Gy2oGOWLFlCcXHxnGYcpyI6Opr169fT09NDVVUVLpeLvXv3kpCQQFlZWYBQvJQZGxvD6XRiMBjIyMi44qJucxHKN0UC+4Dg2xcKhUKhUCgWFZ/0MTQ+pD1fCBE3G3OYed4G2wrFQuPz+ejv79eiVV6vl+LiYqxWKy0tLfT09JCbm0txcfEp5/F4PFRVVSGlJDY2FpvNRlxcHGazmaqqKrq7u9mzZ482vqKiAr1eT2JiInq9nsnJSTo6/B2pent7tZq00tJSMjMztbUODAzgdruDBOB8GAwG4uPj6enpoaKigoGBAYxGIxs2bJg3iieEOGMBN/PYpKQkEhMTaW5u5vjx4/T19XHgwAHWrVt3WaRXTkfhpgWyIgQRJ6WUQogGwM6sejiFQqFQKBSLi3PMiU/6W9FYw60Y9edmXjBXo+7ixOLLLvVKcfExXcfV0tLC5ORkwL7q6uqA583NzeTn559S2LS0tDAxMUFMTAwbNmwI+A6vWrWKAwcO0NXVpYmq7u5u9u/fj06nIy4uDoPBoPV3m7b6j4iICEhb1Ol0JCQknPFrTU9Pp6enRzMwycvLW3TjEZ1OR05ODikpKWzfvp2BgQFqamooKTkZZZ+YmKCpqYmkpKRz6o8mpWR8fJyhoSGtTq2goGDRBOP0+6jaKpwkVMn/r8BLQojvAM2A1thMStm68MtSKBQKheLSxjHmICosijB92FnPsad1D2/WvKk9P9coHEBiVCJp0WlaSqUQgvKU8nOe91JkYmKCtrY2DAaD5nY3/e9iEbXTLoUmk+mijUBMTk7S0NBATEwMSUlJ8753w8PD1NXVAWCxWEhNTSU1NRWn08nhw4cJCwsjMzOTnp4eHA4HHR0dWkRs9jwVFRWaeCgsLAw6pxCClStX0tTUhM1mw2q10tDQQHd3N0NDQ/T392tj09PTtTq4rKysBREiycnJrFq1ipqaGnQ63XQPsPNCREQE5eXl7Nq1i6amJnJzcwkPD6e/v5+DBw8yMTFBR0cHa9asYf/+/URHR1NYWHjaVgUej4f29nZ6e3txOp1MTEwE7I+OUCmx0gAAIABJREFUjiY5OfmM1joxMcH4+DgRERHzpn5ON1gHzkpQX66EKuKeO/H/X/CnVwKIE48vzt8oCoVCoVBcIA50HOC1yteICoviyauexBRmOv1Bs9jduputNVsDtp2Lqck0QggeXv0wjYONDI0PYTfbSY9JP/2BlyEVFRVamtZMUlJSKC+/OIRtXV0dtbW1GI1GkpOTSUtLIy4u7qISmQcPHqSvrw8Ak8lEdnY2GRkZWhRtbGwMIYQmnNLS0lixYoX2Gkwmk5biKIQgMjISh8NBa2vrnCKupqYGh8OBEIK0tLR5L+x1Ol2Ak2R+fj75+flMTk7S399PX18fRqORwsJC+vr68Hg8ZGQsXFpxcnIyycnJ5+SWebbExcVht9vp7u6mqakJgPr6eq120O12a2YrLpeLnp4eNm7ceMq+abW1tTQ2NmrPjUYjMTExeDwezVDmTEScx+Phgw8+0KKyWVlZlJSUBN2sGBkZwePxYDKZLpsav4UgVBF3/m4fKBQKhUJxifNa5WsAjE6OsqdtD5tzN5/R8ZU9lfyx9o9B28+lvcBMDDoDBfEFCzLXpYrT6aS7uxu9Xk9aWhrj4+NMTEwwNDREV1cXHo/nrGuUzpWJiQmGh4eRUnL8+HEApqamaG1tpbW1lcjISK0GKiEh4YIKutraWvr6+ggLC8NgMOB2u6msrKS2tpaMjAwiIyOpqqoiPDxci/TM5Zw4871OTk7m2LFjOJ1OHA4HsbEnI9But5ve3l50Oh2bN28mMjK0xvczCQsLIyUlJaAZ96ZNm/D5fIsiEi7U55OTk0N3d7cW/RRCUFBQgBCC2tpa3G43er0em81Gb28vBw8eZNOmTfNGIqeFelFREampqURGRiKEoLu7m3379s1r/z8fXV1dTE5OYjQa8Xg8NDc3MzAwQFlZGdHR0dq46SjczO+BIkQRJ6VsWeyFKBQKhUJxOdI53HlG41ucLfz26G+D3PYA7OYrz0Z7saitrQUgOzs7wEBj+/btmnhYzNQtl8tFb28v4+PjmoCc/n9qKtAMPCsri6ysLDo6Oujo6MDtdtPU1ERTUxMFBQUUFhYu2jpPRUtLC3V1df6U3PJybDYbPT09NDY2MjAwEBC1mX6d4O+Fdir0ej1ZWVnU1dVRV1fHmjVrtH2tra1IKUlJSTkrATcfERERCzbXxUJcXBxWq5Xh4WEiIiIoKysjPj6e8fFxjh8/jpSSzMxMCgsL+eijjxgeHub48eNztjmYmprC5XJpdXczo2XTtXVDQ0NnFHXs7PT/biwuLiYmJoaDBw8yMjLCjh07KCoqIicnByGEEnHzEJKIE0I8MN8+1exboVAoFIqTTHgC60TGPGMhH9s32sd/Hfovpnz+i3ibycZdS+5ie9N2rBFWihNP7danCI1pAWUwGIIaN9tsNpxOJwMDA4si4nw+H5WVlbS0tMwp1MEflbJYLIyNjREREaFZzxcVFWnW9729vdTX11NXV4fH42Fqagq32014eDg2mw2bzYbZbD6nKJCUkrGxMS3iMpPBwUGOHj0KwNKlSzVhlpSURFJSEk6nk6amJvr6+rDZbNoFu8ViCSnalZ2dTWNjIz09PQwPD2O1WpFS0trqt2KY3RBbEcy0uO7s7CQzM1N73yMiIsjJyaGvr4+8vDwMBgMrVqxg586dNDQ0kJGREdDyAfzRsGkX0NnpjtN1pOPj44yOjs7Zj282ExMT9PX1IYQgOTmZsLAwrr76aqqqqmhubqaqqorBwUHKy8uViJuHUPMEvjvreeKJYztQzb4VCoVCodDoH+0PeN7j6gnp7vTo5CjPH3yesSm/6IsKi+LBlQ8SZ4ojsyy4Lkhx9rS3+01dpi8eZ2Kz2WhoaGBg4GRvPiklDQ0NAJjNZsxmMyaT6bQGGFJK2tvb6evrIzc3F6vVyrFjx2hpadHquSwWi2bqMP2/0Wic9/sihCAuLo64uDj0ej01NTUBES84GeEICwsjMTExqLF0qBw9epSWlhasVivZ2dmkpqai1+uRUlJZWYmUktzc3Dnr1mJiYigrK9PeB6fTidvtPm0Ubprw8HAyMzNpbGykrq6O8vJyRkdHmZiYIDw8XF3Qh4jZbKagIDh1eqZjJfijdmlpabS3t1NdXR1UEzo4OKiNm4uYmBi6u7uprKzEYrFQXDy/4+30z5OUksTERO1nUK/Xs3TpUhITEzl8+LDWImI6Ami1Ws/49V/OhJpOGVATJ4QwAP8E1C3GohQKhUKhuFTpdweKuLGpMZzjzgBnSSklXunFoDNoAm9v+14cY/47zmH6ML5Q9oUFq4FTnGRaWAEBVvLTTJuGOJ1OvF4ver2ejo6OIAt8IQQ2m401a9YERSaklHR3d1NbW6v1Huvq6iI6OhqHw4Fer2f9+vXnLETy8vIQQjA5OUlUVBQmkwm3283AwAADAwOMj4/T3t7OwMAAa9euPaVpxWxGR0e1qNfw8DBHjhyhurpaM/5wOp2Eh4fPKRBmI4SgtLSUmpqaOQXffOTm5tLc3ExXVxcul0uruYqNjb1ojF0uJ4qKiujq6qKzs5OsrKyApuTTIm6+7+y0iOvt7aW3t5eEhAQcDgeDg4OsWLEiIF21srKSpqYmhBBzunba7XbWr1/P7t27tZspsbGxl0W/u4XkrCp2pZQeIcTTQDXwi4VdkkKhUCgUly6zRRxA10iXJuJcky5+sfcXuCZdXJd7HQc6DjDuGUdw8qL0poKbSI0Obsp9ueJ0OjGZTEFRscWgr69PSxGcK6pgNBqxWq0MDQ3hcDiIj4+nq8vfJnfaRMTlcjE2NkZ/fz+dnZ0BYnBwcJDKykpNcJhMJuLi4mhvb9ccFZcvX74gkSQhBHl5eUHbMzMztdYER44cweFwcOTIETZu3Bjy3HV1dUgpNffHpqYmnE4n9fX12piioqKQzV+m0yzPhOmebS0tLdTX12vnOpf+Zor5iYyMJDc3l+PHj1NVVcXGjRsRQuDz+bTv83yRuNnf58HBQRoaGvB6vezcuZN169YRFRWFy+WiqakJnU5HeXk5iYmJc85ntVrZtGkTnZ2dSCnPuHXBlcC52C5FAyqWrVAoFArFDGanU4JfxJUk+tOXdrbsZMDtv7u8rXbbnHPk2YIvzC9XGhsbqaysRKfTkZiYSGpqKna7XYtu9ff3a82Zz4WRkREGBwepqqoC/FG4+aI5MTExDA0NMTw8TExMjObKNzOi0NbWxuHDh2lsbCQtLQ0hBD09Pezbtw8ppRalysjIQKfTkZeXp0XMzoeJhhACi8XCunXrePfdd3E4HEFOj/MxHcGbdjOMiorS+rm1tbUhpcRsNs8ZyVxo8vLyaG1tpb29XTMyUSJu8cjNzaW1tRWn00l7ezvp6en09fXh9XqxWq3z3mix2WysXLkSl8vF8ePHaW1t1Rqpu91uTchNp/qmpqaeVtSbTKY5b1Io/IRqbPL0rE1RwB3AnxZ8RQqFQqFQXMJMC7SZdA37Izken4cDHQdOeXxUWNSCNPW+FBgeHtbSFKdTELu7uzEYDGRnZ5OVlcWePXvQ6XR88pOfZGJiAo/Hg8VimbPx9bTxRXx8vGZnPzk5ydGjR7WLR/BfQJ7q4nA67XBkZES7gI2NjQ0QX6mpqVRXVzM8PMzg4CA6nY4DBw4gpSQrK0szI5k95/nGYDCQmZlJfX09TU1NIYm4jo4OLfox/T4KIYiNjT3vtWgmk4nU1FTa29txu92AEnGLicFgoLi4mEOHDlFTU0NycrL2szOzJcNshBCkpqYyOjrK8ePHNSfS1NRUJicn6evrY9euXVpK5Pm4AXC5E2ok7tpZz0eAF4F/XdjlKBQKhUJx6SKlnDOdsme0B4Cq3ipGJ0dPOUdGdMYVU+9TUVGBz+cjMzOTgoICOjs76ejowOl0UldXx8jIiL9+0OuloaGBhoYGfD4fQghMJhNWq5WoqChNcAwNDXH06FGioqK45pprGBgY4MiRI4yPj2MwGEhISCAhIYGMjFO/xzNF3HQ0YXY6l06nIzMzU0s983q9eL1eMjIyWLJkyUX1GWZlZdHQ0EBnZydFRUVBzoPTTLtlTtcMpqZeHCm9eXl5mrA0m80YjcYLvaTLmtTUVC19tra2lu7ubm376TCZTBiNRq1NRmJiIikpKRw8eFBLS55OMVacG6Eam8wWcQqFQqFQKGYw5Z1iZ8tOrcWAQWfAK71IKXGMOZjyTrGvfZ82PtWail6np9XZGjBPRkzGeV33hWJkZASHw4HRaKSkpASDwUBOTg45OTnU1NRQV1enXTwCWsPisLAwpqamGB0dZXT0pCBubW3VIl+jo6Ps2LGD4eFhwF/HU1ZWNq94mc1METd9Drs9uEdfbm4u7e3tWr1QVFTURSfgwF/rNB3NqqmpYeXKlQH7pZSaeYtOp8PtdmM0Gud8zRcCi8VCUlISXV1dKgp3Hpg2otm5c6fmfBoTExPSz890xLa3txfwp1lO179VVFTQ2tpKZmbmRfczcikSajrlHinlujm275BShl4le4kjhPgBcDXQAzwgpXRf4CUpFAqF4gIz4Zlgf8d+tjdvZ2RiRNueHp2Oc9yJY8zfX6m2v5bGQf8FkRCCz634HNER0dQP1POfB/7z5HExl0eakZSS0dFRIiIi5jS/mHY+TElJCdqfnZ2tRd3CwsLweDxaBG7Tpk1ERETgcrkYHh5mbGyM3t5eBgcHmZqaIjw8nImJCYaHh9HpdAFNg0MlPDxcmwf8Img6rXAm0/21du/ejZSSpUuXzpnmeTFQWFioRTpzcnICxFB9fT01NTUB41NSUi4qN8DS0lL0er2qkTpPxMXFsXTpUo4fP87ExITmShoKMTEx9Pb2EhkZqdUxCiFYtmwZ+fn5C9qk/Uom1HTK0nm2XzFdR4UQS4ECKeUmIcRXgIeA/32Bl6VQKBSK80j7UDs1fTWU2kuJi4xjV+sudrXswj0VeE8vxZrCHSV3sK12m9Y24M91f9b2F8UXER0RDUBWbBYmown3lJswfRip1osjhe1cqaqq0u7iR0ZGYrFYMJvNJCcnExMTQ0dHB8CcF4fh4eGkpaXR2tpKamoqExMTmgvkdDTAarVqfaMyMjL44IMPmJycpLi4mMnJSQYGBigqKjrr3lJms1kTcfHx8fOKQJvNRnl5OV6vd1Gagy8UJpNJE8fNzc2sWLEC8JtOTEc5ly1bxtTUlNbX7mIiMjJS6zunOD9kZWWRnp7O6OjoGdV0JiYmUldXF5SCPJ0GrVgYTinihBAPnHioF0J8Hpj5G6wQCK7evnzZyEkjl234++QpEadQKBRXAJPeSd6qfYu97XsB2Ne+D3OYmW5Xd8A4S7iFq7OvZl36OnRCR0JUArX9tQAMuge1cWvS12iPDToD9yy7hz2te1iZupIw/eLb7C82Ho9Hi7TpdDrGxsa0iFljYyMRERFMTExgsViIjo6ec46SkhIsFgvp6el4PB7MZjM5OTlzjg0PD2fdunU4HA7NKfJcRYjFYtF6VJ1OnF0q9ucpKSk0NDTgcDi0bdP1fKmpqVoPNxXtUkyj1+vP+EZIbGws11133Vk1mFeEzukicd898X848L9mbPcB3cBXQz2REOJx4IvAUuA3UsoHTzH2/wM+g7+NgQP4hZTyB6Ge62zXIYSIwd/37iZgGPiBlPKnJ3bHAsdPPHYCqiJToVAorgCGx4d54fALdAx3aNtcky5cky7teWxkLFdnXU1ZShlG/UnThQRz8MV/bGQs+bb8gG15trzLqq1AV1cXHo+H2NhYNmzYwOjoKC6Xi8HBQZqamhgfH8dkMrF06dJ5I1xGo1ETbUajkcLCwlOeMzo6el5BeDbMvHCNj49fsHkvJFarFZ1Oh8vlYmpqiqGhIbq6utDr9RQXXzHJVYrzgEqZXHxOKeKklNkAQohtUsqbz/FcncD3gBuA032yzwJPSylHhRCpwNtCiDop5X/PHiiEKJNSHpq1rRSol1JOnOE6/g/+9yQFyAXeEUJUSynfxy8mp/86RAODKBQKheKypn2onRcOvxBQ6zab4oRiPrv8s+h1wbVQCVHBIm5V6qrLqqjf6XTS0tJCVlYW0dHRms0/oLlAms1mzGYzSUlJZGVl4XK5tMbZFyvTgjA6OvqyiSjodDqio6O1nnHTPfNUnZJCcekRqjvlzQDC/9s2SUrZdaYnklK+dmKOVUDaacbWzNrkA4JuUQoh0oA/CSG+LKV888S2MuDPwJ3AzlDXIYSIAj4NlEkpR4DDQohfAl8C3j8x17eA/4c/Uhc0t0KhUCguHyq6K3jt2GtM+fxW2TqhI9eWS11/XcC4ZcnL5hRwAAmmQBGnEzrKU8sXZ8EXgPb2do4cOYLP56Ojo4OMjAwGBgYYHh5Gr9fP2VfKZDJdEnUxMf8/e3ce3+Z1Hvj+d7ASBLiDICmKixZqpWRrtyxbjhwnzt7F8SRtkzjb3NxO27m9ndve9tPe1l2n/cxyO5Pepm2atJmknSZxnUzi2K7r2HG0RDu1cRH3fQEJAiBAgFjP/QPiK1IkRUgiRZF6vp+PPsb74rzv+wC2JT085zxPYSF79+696z11D6rCwkL8fj+NjY2Ew2Fyc3MXXKYqhHhwZVV2SCnlUEr9DRAF2m+c+yml1G8vV2BKqd9USoWBfsAFfOPWMVrrfuAjwN8ppd53o/jI68CvaK3vNMnaAiitddOMc5eA+hvPugJ0KqWOA+8BvrpA3C8qpbRSSgNddxiDEEKsWdM9qB50WmvebH+Tb175ppHAOawOPr330/zc7p+bM/7WpZEz5dpycdpuVjXc4dlBnn1lmj4vJa01zc3NNDQ0kE6nyc/PJ5VK0dXVxcTEBDk5Oezdu3feqpSrSWVl5Yo16V4u0826w+HMcuAdO3Y8sBU1hRALy/Z31/8M1ABPkZnlArgI/PGNX0tOa/2nSqk/Ax4FfprMcsb5xp1RSj0HvAwkgd/QWn/zLh7pIrMPbqYAYPzurbX+rSzifhF4EUApVYskckIIwene07ze9jr1nnqeq3/ugVlGF0/FSaaSNI82MxmfxOPycGHgAk3emz/Pc+e6+eSeT+J2ZvZFmZSJtE4DmWprDuvtl6FV5lfSOpbZUn2o6tAyfZL7J5lMcvHiRUZGRlBKUV9fT01NDUNDQ0xOTuJyufB4PJIYPKBmthZwu92Ul5evYDRCiLuVbRL3EeARrfW4UioNoLXuu7FfbdnozI9tG5RSz5IpsvJrCwztB6aAXKDjLh8XBm5dM1EALLwRQgghxIJ6Aj283vo6NrONdl87AA1DDRyuPkxlwcqW0Y8lY/zDpX+gY/z2f2RsLtnMx3d/fFai9sGtH+T7Ld8H4MPbPrzos9635X1YzVZqCmvYWLx6lq1prUmn07OSsUgkwtmzZwmFQthsNvbt22cU/Zhv6aR48EwvZ52amnogG5MLIbKTbRJn5ZZZKqWUg8zyyvvBQqbQyBxKqRrgh8AfkZn1+o5S6kNa6zN3+IxWQCultmutm2+cexS4dpcxCyHEQ0trzUvXXppVVn9ad6B73iTOG/bSMtrC7vLdFDoK57y/lLH987V/XjSBO1x9mA9s/QAmNXvnwcGqgyTSCUzKxIH1BxZ9XpmrjJ9/5OfvKeb7JR6PMzo6itfrxev1kk6n2bt3L2VlZYRCIU6dOkU8HicvL48DBw7M2wBbPNiUUhw+fJhUKrXmlooK8TDJNok7B3wB+P9mnPsUcDrbBymlLDeeZybTdy4HSGmtE7eMswKfBr5NJnE8APwSmb5st97TQyaB+3Ot9ZdunPsc8H2l1DM39rFlG8ekUuol4A+VUp8BNpApavKxbD+jEEI8bFLpFPFUnBxLzqyf6Lf52uZN4AA6xzs5UnNk1rmR8Ah/deaviKfinBs4x78//O9nlepfSm92vEmjt3HWOY/TQ441h6GJIcryynhf3fvYULxh3utNysSTtU8uS2xLRWt9RzMsqVSK8+fPMzo6Omfv4rlz59i1axe9vb3E43E8Hg/79u1b9fvdHmarobCMEOL2sv0d+NeBHyul/g3gVEq9DuwHHr+DZ/0O8Hszjj8BfA34tFLqNeC41vpPAA18FPgzwEamJcB/Z/7G2gHgN7XWL02f0Fp/70aT8oF5xt82DjLJ4peBITIJ5Is32gsIIYS4RTKd5O8v/D1d/i4q8yvZVb4Lq8mK2WSmYbBhwet6Aj2kdRqTMjExNUGbr423O98mnooDmabYp/tOL0uidHnoMj/q/JFx/Hj143xw2weN4ztNfh4UWmvC4TBOpxOfz8fZs2epq6tjy5YtWV0/NjaG1+tFKYXb7cbj8eDxeOjv76e9vZ0rVzI/E3U4HJLACSHEA0BlWy1MKVVCZvatjkyj77/TWvctY2yr3nRhk66uLmpra1c2GCGEWGIne07y6vVXFx33iUc/gdlk5p+v/bPRIPvRikcZnBjEO+md95ocSw7/4Yn/QK5t6WYM+gJ9/O35vyWZTgJQ567jU3s+NWe55Grj8/loaWlhfHyckpISpqammJycBOCxxx6jtHRur7pbNTU10dHRwebNm+c0fe7r6+Pq1aukUqms7yeEECJ73d3dbNiwAWCD1ro7m2sW/VHajeWNPcBGrfX/e08RCiGEWBOiiShvdy6+UGGreyvbPZmkYEPxBq4OXwXg0tCl2143lZyiYahhzrLLuxWcCvKNS98wEjiP08PHd318VSdwgUCAlpYWRkdHjXM+nw8As9lMKpWioaGBp59+etGZs+nrSkpK5rxXVVVFSUkJ8Xh8VmVDIYQQK2fRJE5rnVBKJYDVt75ECCHEsjjTd4ZoIlPbymq28tSGp4gmoiTTSZLpJOl0GofVwdENR41rNhVvMpK4aRaThZrCGjaVbGKnZydd/i6+2/RdAJq8TUuSxGmt+U7Td4xZwFxrLp/Y8wlyrDn3fO+V0tHRQVNTpg2CxWJh06ZNuN1uzpw5QzKZ5MCBA7S0tBAIBOjp6WHTpkxtMK01gUCAnJwcHI5Mxc1kMkkwGEQpRXFx8bzPWy0NuoUQ4mGR7aL2/wr8J6XU/3lrIRIhhBAPn/5gv/H6Q1s/xP71+xe95tGKR+kY7yAYDVJTVMPmks3UFNbMKmCSY83hfzX/L7TW9AR6CMfDuGyue4r16vBV2sbagExlvp975OcoyZ0747RapFIp2toyn2fTpk1s3rwZm80GwFNPPcXU1BTFxcWk02nOnj1LZ2cnubm5jIyMMDIyQjweJycnh3e/+92YTCbGx8fRWlNYWCh73YQQYpXI9nfrXwXWA59XSg0D6ek3tNarp+mNEEKIJeGL+IzX6/Kz6w9mNVv5+O6P33aMy+aiuqCankAPWmuuj15nX+W+u44zrdO82npz396hqkOrqlfbfIaHh0kkEhQWFrJjx45Z782cMfN4POTn5zMxMcH58+eNMUoppqam8Hq9lJeX33YppRBCiAdTtknci8sZhBBCiNUjrdOMR2+2D1jqWa3tnu30BHoAuDh4kT3r9tz13rXBiUFCsRAATpuT925+75LFuVL6+jI1xaqqqm47TinF9u3bOX/+PHl5eZSXl1NeXs7IyAjNzc309/dTUlJCT0/mu/Z4PMseuxBCiKWRVRKntf7acgcihBBidQhEA0aBEJfNhd1iX9L7by/dzuutrwPQ7e/m21e/zfO7nr+rRK7L32W8riupW/JY77fJyUnGxsYwmUxUVs5tmH4rj8fDBz7wgVnnLBYLLS0tjIyM0NjYSCKRwO12y0ycEEKsIqu3LJcQQogVMXMp5XLsLXM73bN6xF0ZvsK3r36btE7f5qr5dY3fTOI2FM3fvHs1aWpqQmtNZWUlVuvdNUN3OByUlJSQTqeNWb0dO3asyv54QgjxsJIdzEIIIe7IcidxAM/WPUsineB072kgk8iZlInn6p/LekYurdN0B7qN49qi2mWI9P7xer0MDw9jsVjYtm3bPd1r27ZttLS0kEqlKC8vp6CgYImiFEIIcT9IEieEEOKO3I8kTinFh7Z+CK01Z/rOAJnecgrFc/XPZTVrNBwaJpaMAZBnz1vVFSkh0wwWoK6ujpyce2uPUFRUxOHDh5cgKiGEECtBkjghhFgBjSONnB84T2V+JfVl9ZS5ylBKobVmMjF5z2X1l9NYZMx47Xa6l+05Sik+vO3DaK05238WgIahBsryymYtt5zPzOQPMkspV/NyQa014+OZYjLZ7IUTQgixtmWdxCmlzMAhoEpr/U2lVA6gtdaxZYtOCCHWoFgyxrevfZtEKkHrWCtvd75NqbOULe4tNI40EpgKcKjqEM9seoZwPIzHdX+rBmqtuTx8mcn4JAfWH8Bmts16/37MxE1TSvGR7R8hrdOcH8iUyX+j7Q1qCmuoLqxe8LpTvaeM8ZAplrKahcNhEokEDofDaNIthBDi4ZVVEqeU2gC8AlSTKYbyTeADwE8Dn1q26IQQYg1q97WTSCVmnRudHGV0ctQ4PtN3xphJeqTiEZ7b+Rxmk3nWNYlUAovJsuQzTKf7TvNKyysAXBu5xqf2fAqHNZM4pNIp/FG/MbbYUbykz57PdCLnDXvpDfaS1mne6XqHT+755Lzj0zrNWx1vGce7y3ezq3zXsse5nKZn4YqLl//7FkII8eDLtjrlF4H/BRQC8Rvn3gaOLkdQQgixlrWMthiv3blurObbVxm8PHSZ/3n5f5JKp4xzP+76Mb//1u/zPxr+h1Hufz7Te8JuR2tNNBElrdP4Ij7+pe1fjPd6A7185fxXCMfDAPRP9BtVIoscRfetZL/ZZObD2z9sHI+ERxYcG4gGmEpOAeC0OrPeQ7eUYrEYb775JteuXVuS+0kSJ4QQYqZsl1MeAn5Ga51SSmkArbVfKVW0fKEJIcTao7Xm+th14/i5+ucozyundayV66PXcdlcDIYGafe1z7quebSZlxtf5qP1HyWlU7zV+RZaa1rHWvlByw94svZJCh2FRuXGnkAPr7S8wuDEIEcg0u5mAAAgAElEQVRrj/Lslmdn3S+eitM13sX1seu0jrXij/qpyKtAo+fMEg6Fhvjy2S/z2f2fnRXXpuJNS/313Faps9R4HZwKkkqn5sxOArNmNEtdpVhM93/798jICNFolIGBAXbu3HlPSWQ6ncbnyyxhlSROCCEEZJ/ETQK5QHD6hFKqFPAteIUQQog5+oJ9TMYnAXDanFQVVKGUor6snvqyegAjOXPanFwbucbx7uNApjqj0+Zki3vLrETrbP9Zzvafpbaolo/Wf5S3Ot7i4uBF4/2TPSepL6unO9BNPBWnJ9BD13jXnBm8odCQ8dqkTDxR8wTHe46jtWYsMsaXz315VkK0uWTz0n9Bt2E1W8m35zMRmyCt0wSmAvPuyZtZeGVm4nc/TSdd8Xicqampu97H1t3dTXNzM8lkEqvVSl5e3lKGKYQQYpXKNol7DfhvSqn/HUApZQL+CPj+cgUmhBCrQXAqSI4lJ+tlhY0jjcbrre6t887QKKXYWroVgMr8SqaSU5zrPwdkErLm0eZ5793t7+a/nPgvaK1nnU/pFH955i+zim/ak7VP8t6691JZUMm3rnyLlJ69F04pdd9n4gCKc4uZiE0Amb2FgWiAjcUbZ32PY5Mrm8RprY0kDiAYDN5VEtfW1kZLS2bpbV5eHps3b17VFTaFEEIsnWyTuN8EvguMA3YyM3LNwHuWKS4hhHjgXRi4wMuNL5Nnz+MLB79AkeP2K8xT6RSXhi4Zx9Mzb7czXdQjEo/Q6M0kgOOR8QXHz0zgCnIKCE4FFxzrcXrY4t7CFvcWro1cM8r4l7nKeHrT00aMtj02/vHSP5JI35z9W5+/nlxb7qLxL7UiRxHd/m4Avtf8PQDev+X9PFH7hDFmVguE3KVtgZBOpwkEAhQWFmIyzb+tPBKJEI1GjeNgMEh5eXnWz9Ba09LSQnt7O0opdu/eTXX1wpU4hRBCPHyySuK01kHgmFJqL7AZGAZOaH1jd7sQQjyEppcshmIh/unKP/GLh37xtuPbfG1GgZA8ex517rqsnmNSJp7f9TyRixG6/F3GeavJym8f+22sZiuvXn+Vkz0ngUzZ/w9t+xC51ly+dOZLs+5V5CjiaO1R6tx1s5LO6sJqlFL4o34+uPWDs5ZNbnFv4YV9L/D1hq8bhVKyjX2plTjmLp98rfW12UncjJm4pWyBEI/HOX/+PD6fj7KyMg4cODBrZiydThOLxRgYGADAYrGQTCYJBucm0vF4nGQySW7u7ERYa821a9fo7u5GKcWePXukL5wQQog5sm0x8C6t9Y+01heBi4teIIQQD4HpGSGA/mA/J3tOsr9y/4JLKy8O3Pztc0/FHqMISTasZiuf3PNJvnzuy8betY3FG43Klu/f8n6qC6tJppPUl9VjMVnmLKsE+LUnfm3e51rNVj6y/SMLPn9D0QY+t+9zfK/le5iVmcNVh7OOfSkV5c4/25nWaUzKRCwZM5ZbmpWZ4tw7LwQSj8cJBoNMTk4SDoeNf0ajUeM7HRkZ4cSJE9hsNqampojFYsRisyuBVlVV0dXVxcTExOxY02lOnTrF5OQkhw8fnlWsZDqBM5lM7N+/n7KysjuOXwghxNqX7XLK7yulhoGvAH+vtR5expiEEOKBNz2jNtOr11/lhx0/ZM+6Pbxn03vIseYY700lpmZVpdxbufeOn2m32Hlh7wu8dO0lglNB3lN3c0X7dHGUmZRSHFx/0Fgm+YGtH7ijxPFWlQWVi842LreF+tJ5w17K88rnzMLd7vNqrQmHM/8e29vbGRsbw2QyzUrWZlJKUVJSwoYNG2hoaCAQCMx53263k5OTg9PpZMuWLfT19RGNRonFYtjtmeS+r6+PUCgEwMWLFzl69Cg2m41EImHMwB06dAi3e2mXggohhFg7sk3iKoCPA58F/kAp9Trwt8ArsqRSCPEwmlnGfqZYMsbp3tMMh4b53P7PGUnE9bHrRjXIiryKuy64kWfP4zP7PpP1+Kc3PU08Fcdlc3G4emVmz5bSQjNrg6FBylxlXBm+Ypy73XccDoe5cOHCnFkyAJPJRFFRES6XC5fLhdPpxOVykZuba+yDKywsJBgMYjKZsNls5OTkYLfb5xQeyc/PZ3x8nGAwiMfjIZVK0draCoDdbicajXLx4kUOHjxoxFJQUCAJnBBCiNvKdk9cmEzS9rdKqR3AZ4C/AVLAQ7VYXyn1x2SanI8An9JaR1Y4JCHEChgN30zitri3sKl4E+f6zxlFNbr93bzV8RbPbH4GmF2VcmfZzvsWZ549j+d3PX/fnrfcnFbnvOc7fZ00jjTOaqReWTD/H0/9/f1cvXqVZDKJzWbDZDJRXFxMXV0dJpMJh8OB2Ty3/9xMDocjq4qThYWFjI+P4/f78Xg8dHV1MTU1RUFBAfv37+fEiROMjo5y9epV8vPzAYx/CiGEEAu5mw6o3WQqU/YAd74eaBVTSu0Ctmitn1RK/RLwOeCLKxyWEGIFeCe9xuuawhqeqH2CIzVHeKvzLd7qeAuAH3X9iA1FG1hfsJ7WsVZjfDZVKR9G8Xic/v5+rFYrHo/HWH44k1IKm9lGPBWfdb5hqGHWcXVBNY9VPTbrXDKZ5Nq1a/T19QFQWVnJ7t27sViWrxl4cXExnZ2d+P1+EokE7e2ZZunbtm0jNzeXgwcPcurUKXp7e429cZLECSGEWEzWmyOUUoeVUn9LpjLl/w18B3jYah4/Abx+4/WrwJEVjEUIsYJmLqf0uDxAJsE4tvGY0T9Na823rn6L8wPnjfL8HqdnxRpQL6fpfV/3orGxkcbGRi5dusSPf/xjEonEvON+ZsfPYDPbKM+bv2z/EzVP8PkDn59VYCYSiXDixAn6+vowm8088sgj7NmzZ1kTOICiokwhFr/fT0dHB4lEgpKSEkpLM/8NFBYWUlFRAcD4eKZ1hCRxQgghFpNtdcpmMgnby8CHtdbv3M3DlFK/TGYp5i7gH7XWn55njB34S+AZoBjoBP4frfX37uaZdxqDUqqQzFLR9wMTwB9rrae75BYB0z9OD9yITwjxEPKGb87EeZwe4/V0O4Av/uSLTMYnCcfDvHr9VeP9+vK1NQsXi8VobGw0yuoXFBTg8XgoLS0lmUzS0dGB1hqXy8XWrVvJycmZ9z7xeJzBwUGUUjidTsLhMK2trezcOXfp6e6K3dSX12NSJv70nT8lFMsUCcmx5PBc/XPs8OyYNT4cDnPq1ClisRgul4t9+/bdt0QpJyeH3NxcIpGIMQu3ffv2WXvnysrK6O/vN44liRNCCLGYbH8E+d/JJDwLd43NziDwh8CzwEKbCSxAH/AU0Htj7LeVUnu11q3zXaCU2qO1brjl3E6gXWt964+GF4vhL27EsA7YBPyrUqpZa/024AcKbowrINP8XAjxkIkmokYZe4vJMqfYRp49j+frn+fvL/79rPN2i33FSvMvl4aGBkZHRzGZTCilCAaDBINB2traZo0bHx9naGiIvXv34vF45tynr6+PdDqNx+Nh27ZtHD9+nK6uLsLhsLH/zOFwkJ+fT15enlEw5qkNT/Ha9deoLKjk+frn5y180traSiwWw+12s3//fqxW6/J8GQsoKioiEomgtaa8vNyYnZvm8XgwmUyk02lyc3Pve3xCCCFWn2wLm3xp8VFZ3edlAKXUfmD9AmMmgRdnnHpNKdUKHODmLJhBKbUeeF0p9Xmt9fdvnNsD/AvwM8DJbGNQSjmB54E9WusQcEkp9VUyVTnfvnGv3ybTauH9t95bCPFwmLn/yu10z1vGvs5dx1MbnuKdrpsLF56seZJcW+6csauV3+9ndHQUi8XCU089hd1uZ3x8HK/Xi9frJZVKsXHjRgoKCujo6GBkZIRLly7x9NNPY7FYSKfTjI+PMzo6Sm9vLwC1tbUUFBSwceNGOjo68Hq9c55rt9s5evQoOTk5HK4+zIH1B2Y1J58pnU4b99i9e/eKJEhFRUUMDAyglGLbtm1z3rdYLLjdbrxeLwUFBfPcQQghhJhtwSROKfUDrfUHb7x+G5jbNAfQWj+9TLFNx1EKbAca53tfa92vlPoI8AOl1CeAATL71n5Fa32nSdYWQGmtm2acuwS898azriilOpVSx4FR4JN3eH8hxANOa00inUBrPW/T7mQ6yfGu48bx/sr9C97rmc3P0Bfso3O8k+LcYh6veXxZYl4p06XyN2zYQG5uJjktLS2ltLR0zjLI4uJiTp48id/v5+LFi2it8fl8pFIpY0xhYaExS7djxw7Wr19PJBIhGo0SjUaJRCL4fD5isRjj4+OsW7cOYMEEDmBsbIxEIkF+fj5O5/yVLZdbWVkZ169fp6qqiry8vHnHVFVV4fV6552lFEIIIW51u5m4EzNev8MCSdxyUkpZgG8A39RaX1ponNb6jFLqOTJ79pLAb2itv3kXj3SR2Qc3UwAw/tTVWv/WIjG/CPzeXTxbCLHCQrEQf3fh7xgJjxiNsj+y/SOzxlwcuGgspXTZXLdN4kzKxKf3fpoufxcVeRXzJoWrVSQSwev1YrFY2Lhx46LjlVLU19dz4sQJRkZGjPP5+flG4ldSUjJrr1h+fv6c/WEtLS20tbUZzbIXMzw8DEB5+fxFUO6H3Nxcnn322duOWbduHSUlJdhstvsUlRBCiNVswSROa/0fZ7x+8b5EM4NSygR8/cbh/5bFJf3AFJALdNzlY8PArTvKC4Ds/raA8V29CKCUqgW67jIWIcR99qOuHzESziQYWmvO9J3hydonKXJk9jCl0il+3P1jY/yTtU9iNd9+eZ7ZZGZzyeblC3qFTFdSdLvdWScehYWF7Nq1i/HxcdxuN6WlpQsWOlmIy+UCWDSJSyQStLW1GQVXVjKJA+Y0AZ/PfC0VhBBCiPlkW51yUGu9bp7zvVrrJW8zoDJ/2n2FTHGR92ut44uMrwF+CPwRmaTpO0qpD2mtz9zho1sBrZTarrVuvnHuUeDaHd5HCLHKxJIxGgYb5px/q+MtnDYne9btoT/Yjz/qByDXmsuB9Qfud5j3TTQaJZlMLrj8z+fzARi9zbJVU1NDTU3NXcc1HU84HCYejxOPx43Ebqauri46OjI/z/N4PFLxUQghxJqSbXXK+f8UX/j8vG4sj7QAZsCslMoBUlrrW5sBfYnMPrj3aK0ji9zTQyaB+/PpAixKqc8B31dKPaO1vpJtDFrrSaXUS8AfKqU+A2wgU9TkY3fyOYUQq0/DYAOx5Nw+ZxcHLwJweejyrFm3IzVH1tTyyJmSySTHjx8nHo9TX19PbW3tnDHTM3F3msTdK5fLhVKKcDjMmTNnmJiY4NixY8aevFvj27VrFzU1NVnNhAkhhBCrxW2TOKXU7954aZ3xetoWoOcOn/c7zN4v9gnga8CnlVKvAceBfwC+AMSAoRl/8P6J1vpP5rlnAPhNrfVL0ye01t9TSn2KTJGTrGO4cfxLwJeBITL741680V5ACLFGaa053XfaOD5ae3TWsknA2AcH4LA6OFy9tloFzNTZ2Wk07r569SrxeJy6ujojEYrFYoTDYcxm832vpmg2m3E4HEQiEQKBAADBYHBWEqe1xu/PzJiWl5dLAieEEGLNWWwm7tiMccdmnE8Dw2RmqbI2c7/YPO+9f8Zh1n/i3lhq+dI851+/0xhuvB8g02ZACPGQ6BzvZHRyFMj0cnvXxndxefgywan5W2Merj68pmbhphttl5SUYLVajWWItbW19PT0cP36deLxODt37kQpZcxyFRUVYTLNba+w3PLy8ohEbi7SCIVCVFRUGMfhcJhkMonD4bjjPXdCCCHEanDbJE5rfQxAKfUlrfUv3p+QhBDi/po5C7dn3R7sFjv7KvfxVsdbc8bazLYHsmG31vqOZ5zi8TidnZ10dXWRTCZRSmG1Wkkmk3g8Hnbt2oXb7ebixYt0dXWRSCR45JFHGBoaAu7/UsppLpdrVoXLW4ucTM/QFRYW3te4hBBCiPsl22bfksAJIdYkf9RP82izcfxY1WNAZkllcCqI1ppoImqMeazqsQeuYffQ0BCXL1+mrq6OTZs2LTo+FovR2dlJd3c3yWQSgIKCAoLBIPF4HLfbzaOPPgpARUUFhw4d4ty5c/T39xMOhwkEAphMJtavX7+sn2sh08VNlFJoreckcdNLKYuKiu57bEIIIcT9kG1hk+liIc8AHmYsd1zuZt9CCLGczvadRetMG8zNJZspdZYCYDVb+dmdPwtAJB7hu83fxW62c2zTsQXvdScCgQBNTU14PB5qa2uxWDK/HWuticViiy4DTKVSdHV14XA4uHLlCslkkqamJtLpNIDRIDuVSrF7925cLhfJZJK2tja6urqMJtsej4ctW7ZQVFSE3+8nEomwbt26WbN6brebw4cPc+bMGWOWq66ubsWaZ3s8HgoKCqisrKSpqYnJyUnS6TQmk4lUKmVUzpQkTgghxFqVbYuBPwB+kUzRkZ8C/gb4BTKNuIUQYlVKpBKcHzhvHE/Pwt0q15bLzz/y80v67KamJnw+Hz6fj/b2djZs2EBNTQ0NDQ2MjY2xfft2Nm9euL9cd3c3zc03ZxAdDgfRaJSWlpY5YxsbG9m2bRsXLlxgcnISgLKyMrZs2TJryWFRUdGCiU9hYSGPP/44586dw2az3Ta25Wa32zl69CiQ+R4ikQiTk5Mopbhw4QLhcBibzXbfi64IIYQQ90u2M3GfBN6ntb6glPqU1vpXlVL/DPzyMsYmhBDL6srwFSKJTIGMIkcRW0u33vE9xsfHyc3NvaMCGqFQCJ/Ph8VioaCgAJ/PR2trK62trcaY5uZm0uk0mzZtYmxsjLy8vFkVGKf3hFksFmw2G0888QS9vb1MTEzgcDiMoh6XLl3C6/Xi9/tJJBLk5eXx6KOP3tV+sby8PI4dy8xEPigVH10uF5FIhPb2doaHh0kmkzidTvbt24fZbF7p8IQQQohlkW0S59ZaX5g+UEoprfVxpdR3lykuIYRYcsGpIGOTY2wo3oBJmYwecACHqg5hUndWaTEUCnHq1CkcDgfvete7sk4aenoy3VkqKyvZvXs3Pp+PtrY2RkdHsdvt1NTU0NrayvXr12lvbyeVSmE2m9m8eTObNm0ilUoxPj6OUopnnnkGi8WCUoq6urq5nzkYpK2tjUQigcfj4cCBA/dUUfJBSd6m5eXl4fV66e/vB25+p9PLU4UQQoi1KNs/5YaVUhVa6yEyveEeV0qNLWNcQgixpCbjk3zxJ18kmohypOYI79/yfoZCQ8b7j5Q/gtaas2fPEovFePzxxxdNBMbGxtBaE4lE6OzspLKykmAwiMViwe12z5vwDA0N0dvbC2A00S4pKaGkpIRwOIzVasVut1NUVMSlS5eIxWLGUsnr16/T399PaWkpWmvcbjdWq3XOM2bauHEjAwMD5OTksG/fvhVpCbCc8vPzgUz/uJ07d1JdXf3AJZpCCCHEUss2ifufZPrE/SOZ/XA/BJLAV5YpLiGEWFLN3maiiSgAJ3tOcnD9QWLJTENrm9lGnj2Pzs5OvF4vAK2trezYseO295wu8gHQ0tIyaz9adXU1u3fvNhKKeDxOS0uLMQtXW1trJCDTXC6X8drj8XDs2DHC4TCFhYWMj49z9epVQqHQrH1ti7HZbDz9dKb+1FpMbtatW0c8Hqe0tNSoWimEEEKsddm2GPjdGa+/pJS6DOQD/7JcgQkhxFLqC/bNOn67823jdUluCZFIhOvXrwOZZKezs5OqqqrbJgYzm177/X7sdjv5+fmMj4/T29tLLBZj7969mEwmTp48STgcRinF9u3b2bhx46IxW61Wo9BISUkJR48epauri9bWVrTWlJeXZ/XZ12LyNs1kMmX1XQohhBBryV1tGtBan1rqQIQQYjn1BHpmHV8aumS8Lskt4cqVK6RSKSorK7FYLPT09NDV1cXu3bvnvV8sFiMSiWCxWHj88cdJpVLG0ka/38/Zs2cZGRnhJz/5CR6Ph3A4jNPp5MCBA3c9Y2Qymdi0aRNVVVWkUikcDsdd3UcIIYQQq9uCSZxS6qvZ3EBr/dmlC0cIIZZeOB5mdHJ0wfd1RDM2NobNZmPnzp1MTU3R09OD1+tFaz3vTNZ0Q+nCwkJMJtOsvWZFRUUcOXLE6Ks2vexy586dS7Lkz2az3fM9hBBCCLF63W6Hu8rylxBCPNB6A70LvpdKpQgM3UyyppdE5uTkEI1GCYVC8163WENpl8vFkSNHjH1vJSUleDyee/kYQgghhBDAbWbitNafuZ+BCCHEcunx31xK6bQ5mYxPGsfj4+NsydmCx+OhsrISyOwh83g89Pb2Mjw8jMVimdWjbXh4mK6uLgBKS0sXfG5OTg5HjhxhYGCA8vLyNb03TQghhBD3z9qqNS2EEPOYLmqSTqc56DlIOp0GIDaV2ddWaC+cVUkSMGbNrl+/zg9/+EOjqqTP5+PChQtordmyZQslJSW3fbbFYqGmpga73b4cH00IIYQQD6GsCpsopboAPd97WmspCyaEeGBprRkODwPgG/Phm/IxNj6GPc+O1pnf1rZu2DqnSEhpaSkWi4VkMglAW1sbBQUFnD17lnQ6TU1NDVu2bLm/H0YIIYQQguyrU754y3El8G+Bv17SaIQQYon5o35iyRipVIpULEVuQS555jx8IZ/RzHu+JZEWi4XDhw8Tj8dpbGwkHA5z8uRJ0uk069atY9euXbI8UgghhBArIts+cV+79ZxS6lXgj4E/XeqghBBiqUzPwk1OTlJgKaCiooKD5oO8MvAKqVSKzbmbKS4unvfawsJCAKLRKFeuXCGdTuN2u9mzZ48kcEIIIYRYMXfVJ+6Gy8CTSxWIEEIsh+HQzSSu1lJLZWUlDoeD0fAoE8kJDlYeNGbkFrJ+/Xp6enqwWCwcOHBgVjsBIYQQQoj77a6SOKWUA/gC4F3acIRYO7xeL+l0mvLy8pUO5aE2HBomkUgQj8dx57nxeDzYbDZqO2sBWO9Zv+g9zGYzR48eXeZIhRBCCCGyk21hkzRzC5uEgBeWPCIh1oBkMsm5c+cAeN/73ofZbF7hiB5eg6FBJiczLQW2Vm7FbDZTXFyM3W4nFostWl1SCCGEEOJBk+1M3LFbjkNAq9Y6vMTxCLEmBINBo4x9NBrF5XKtcEQPp1gyhj/qZ3JyEoWifmM9kOkDt2vXLvx+/237vAkhhBBCPIiyLWzyznIHIsRaEggEjNeRSESSuBUyFBoiFouRTCZxO9x43B7jvYqKCioqKlYwOiGEEEKIu5P1njil1JPAfiBv5nmt9R8sdVBCrHZ+v994HY1GVzCS5ReLxejs7KS8vJyioqKVDmeW4dCwsZRyg2eDVJQUQgghxJqQ7Z64/wj8GnANiMx4SwOSxAlxi4cliQuHw5w5c4ZIJEJfXx/Hjh3DarWudFiGoYkhI4nbtn7bCkcjhBBCCLE0sp2J+7fAIa31peUMRoi1YGpqiqmpKeN4rSZxY2NjnD9/nkQigVKKWCzG9evXqa+vX+nQDG3DbaTTaWw2G5vKNq10OEIIIYQQSyLbZkeTZGbhHmpKqT9WSh1XSr2klMpd6XjEypqcnOTkyZNcv34drW8Wb52ehZvuPbYWk7i+vj5Onz5NIpGgvLycRw88ikbT3d1NMBg0xsXj8RWLMa3TdHu7AXA6nZTnSasHIYQQQqwN2SZx/xn4XfUQbyhRSu0CtmitnwTeBj63wiGJFaS15urVq4yPj9Pa2spPfvITYrEYAAMDAwBG0YxIJLLgfVajrq4uLl26hNaaqtoqenJ6+NLlL3E2dZZEOsHVq1fRWtPe3s4bb7xBU1PTisQ5EhwhFAmhlKKsqAyXTYrLCCGEEGJtyDaJ+y7wMWBCKdU589cyxvageQJ4/cbrV4EjKxiLWGHDw8OMjo5itVrJycnB5/Pxzjvv0NfXx/DwcKaARgkMTA0QjUZnzdStZpFIhObmZgCqt1RzInyChqEGANI5aUbTo/j9fs6cOUNzczNaazo6OvB6vfc91mvd19BaY7fbWV+0eENvIYQQQojVItsk7ptAP/AbwO/f8isrSqlfVkpdUErFlVJ/v1Rj79Tt7q2UKlRKfUspFVJKDSil/t2Mt4uA6XViAaB4KeMSq0cymaSxsRGA7du3c/ToUUpKSojFYsYMVbIgybebv83p0Gl6p3rXzJLKa9eukUqloAheGXyFodCQ8Z4yKZJFSZRSjI6OorXGlm8jrdNcunTJmKmcKa3TBKKBOeeXQkt/C5BZSlnhklYCQgghhFg7si1sshtwa62nFh25sEHgD4FnAcdSjVVK7dFaN9xybifQrrWe+7fG29/7L8h8J+uATcC/KqWatdZvA36g4Ma4AmB8kc8g1qi2tjai0SiFhYVUV1ejlOLw4cO0tLTQ3t4OwMXIRVBgtpi5NHGJaDRKbu7q3kbZ09ND31AfHbEO/BY/JnPmZ0AmZSKtM43NvQkvH3viY4QCIf6171/pjnaTmkqxX+/n8uXLHDhwwCjzn9Zp/vLUX9I61Mrudbt54dALWM33VtlSa825c+cA6BvvQylFbm4uFXmSxAkhhBBi7ch2Jq6Re5x50lq/rLX+LuBbqrFKqfXA60qpD884t4fMnrX9d3JvpZQTeB74Ha116EYlzq8Cn70x5CTw3huv33/jWDwE0uk0LS0t9Pb2MjIyQkdHB0opdu3aZSQkSim2b9/OkSNHOHz4MCmVAjLFTaLp6KqfiQsEArx98W1eGXuFQeugkcC5bC4+f+DzlLnKAEikE3RPdjNmHaM72p252AXHg8fpHeqlt7eXUChEY2MjTQNNNHQ0MO4f50eNP+LPXvkzIxm8W+FwmJGREUZGRvAn/TgcDkwmkxQ1EUIIIcSaku1M3DeAl5VS/xUYnvmG1vrHSx5VlrTW/UqpjwA/UEp9Ahggs2/tV7TWd5pkbQGU1npmFYZL3EjctNZXbuwDPA6MAp+c7yZKqReB37vDZ4sHWFNTE11dXbPObdy4kbnF9bEAACAASURBVMLCwjlji4uLiaduVmS0WCyYlInR0VHWr1+9+7I6OztpDjWT48zB6XQCUF1Yzc8/8vPk2fPYWbaTkfAIAC83vjzrWrPFjMpTvON/B/NVMzaLjcHwIEOJIWKxGGazGZ3WtHhbONF2gqNbjt51nKFQCIBYOkYsHcOT58FisuB2uu/6nkIIIYQQD5psk7j/duOf/3TLeQ2Yly6cO6e1PqOUeg54GUgCv6G1/uZd3MoFTNxyLgDkzXjWb2URz4vAiwBKqVqga+HR4kE3ODhIV1cXJpMJm83G1NQUdXV1bN26dcFrZu4TczqdTExM0N7bTm1tLUVFRfcj7CWhtSYajeJwOAgEAvgSPoqKM/EfrT3KM5ufwWzK/O+/v3I/p3tPE0nMrsTpsrmYTEzidDrxRX38YOQHpEmT0Amj2EtJSQlTU1NMTEzwVtNb95TETUxk/hcuqCygylEFQJmrDJPKdtGBEEIIIcSDL6u/2WitTQv8WtEEboZ+YAqwAR13eY8wkH/LuQIgdA9xiVVscnKSy5cvA7Bjxw6OHTvGsWPH2LZtm7GMcj79wX7jtdVqJT8/n1AyxLVr11ZVlcrm5mZ++MMfMjAwwFhojJiOYbVasZltvKfuPUYCB1CQU8AXDn6BHZ4dxnfjznXzwt4X+OkdPw1AUXERaUsaZVWsW7cOl8tFYWEhDoeDvLw8lFJ0jHXQP94/bzzZmE7ioqaby1dlKaUQQggh1ppsZ+IeWEqpGuCHwB+RmfX6jlLqQ1rrM3d4q1ZAK6W2a62bb5x7FGly/lBKpVKcP3+eZDJJRUUFtbW1KKVwuRbvNTY4MTjruCC/gNhUjEAgQG9vLzU1NXcVUzQaxefzUVZWhtV6bwVAFhMMBunszHQQaWtrwxf3YbVYUUpRmV8578yW2+nmFx79BeKpOCZlwmLK/PayLn8daPhO03eM3nmQmYED2OreisVkwe/3E4lEeOPKG3z2XZ+dc/9shEIhkukk4VTYOCdFTYQQQgix1mSVxCmlfneh97TWf5DlPSw3nmcGzEqpHCCltU7c7VillIdMAvfnWusv3Tj3OeD7SqlntNZX7uDek0qpl4A/VEp9BthApqjJx7L5fOLBND4+jt/vZ8OGDZhM2S+pu3btGhMTEzidTh555JF5Z968YS/Hu49TW1TLvsp9xvmZM3GQKb2fX54Po9DS0kJFRQU2my2rOEKhEFevXmVqaopIJILWGrfbzWOPPXbb2cB7Md3IfHrWMBwOM54Yx2rLJI7rC26/t89mnvvZ9q/fT1qn+X7L9wFmFTDZXbEbh8XBxd6LRCIRznSf4RPJT2CzZPcdTUsmk/QGejkeOE55Trnx/chMnBBCCCHWmmxn4o7dcryOTJJzAsgqiQN+h9kFPz4BfA34tFLqNeC41vpPFht7yz0DwG9qrV+aPqG1/p5S6lNkipzcURzALwFfBobI7I978UZ7AbEKaa1paGggEokwNDSEzWYjHo9TWlpKWVkZBQUF8yZCAwMD9Pb2YjKZ2L9//7yzXsGpIF85/xXC8TAXBy8yEZvg2MZjTExNMBYZmzO+aaKJx1yPEQ/HaWlpYffu3cZ7HR0ddHd38/jjj+Nw3Ox6MTU1xZkzZ4zKliaTCbPZzNjYGO3t7dTV1S3F1zRHb28vfr+fnJwctNbEYrFMEpeb+R4q8yvv6r4Hqw5S567DpEzkWHL4UeePcFgdPFL+CBpNaUEp4/5xJuOTnGg6wdO7n76j+4dCoUxFTAuz/r2WuySJE0IIIcTaklUSp7W+NYlDKfWrzN1Ddrt7vMiNgh/zvPf+bMfeMi4OvDTP+dfvMo4AmTYDYg0IBoNEIplCG36/3zjv9/tpbW0lJyeHsrIyNm3aZFRcnG4nAFBfX09+/tz/xFPpFF9v+Drh+M0le2+2v4lJmXBanca5PHseodjNLZUnIifYGNuI6lVUV1cb1S17e3uNRHPjxo1AZlbp7NmzRKNRiouLeeSRR8jJyWF8fJwzZ87Q2tpKdXU1drt9Sb6rVCqF2WwmFovR3JxZTbxz506Gh4fp7+9nPDlOkS1T1GSxmbjbKXLcLOzy7JZnjdcKxb7KfQz6BvH5fLzd8jbHdh27o9nGiYkJQqkQNuvNGTyP04PDulhbSiGEEEKI1eVe9sT9BdBL9jNxQtwXXq8Xr9dLOp1Zsrdu3TpsNhsul4vc3Fy8Xi8jIyNEo1F6enoYGBhg165drF+/nv7+fiKRCC6Xi+rq6nnv3zrWOqsC5bQ32t7AZbu5Z+5w9WHe6XqHWDLTc95itdCT6KE8Xc7Vq1d54oknSCaThMOZZHA60dRac+HCBYLBIE6nkwMHDhjLLz0eD2VlZYyMjNDf38+mTZvu6bvy+Xw0Nzfj9/vZtm0bk5OTJBIJSktLGTeNczJwknQ0TSKdwGa14bQ6KcyZ21phKeyr3MfbHW8TCAToDnbTPdzNhooNWV8fDAYJp8LYnZnEtsxVxk/t+KlliVUIIYQQYiXdSxK3AViaaQAhlkggEODcuXNGAgdQW1trFNEAKCsro76+PlP6v72dwcFBGhoaGB8fZ2Qk0+usrq5uwVmgLv/NrhEH1x/EF/HRMZ4pijpzdm6nZydFjiLe7ngb76Q3czIH+qP9mAIment7Zy2fHB8fN/ajeb1ebDYbhw4dmrN/rrq6mpGREXp7e9m4ceNd741Lp9OcO3eORCKz1fT69etorTGZTBRWF/KNq98gkUgwMDGA2WzGbDGzrmDdsu3FK3IUsdm9mUAwQDAY5I3Lb/CFii9kff3w6DDRVJR8ez5KKf7dY//OKK4ihBBCCLGWZFvY5Ku3nHIC7wa+teQRCXGX4vE458+fJ51Oo5RCa43dbqe4uHjOWKUUBQUF7N27F7fbzdWrV+np6QGgsLCQysqF9311+7uN19tKt1FbVMvXG74+K7krchRRkluC2+lmd/luftz1Y/6l7V9QJsWgbZB1yXW0tLTMagA+NTVFY2MjPT09mEwmDhw4gNPpJK3TmJSJUCxEy2gLm4s3Y7fbCYfD+P3+eT9fNvx+P4lEAqfTSVlZmVGNsnZjLa91vobWGovFgtvtxmzOtBOocC1vpcd9lfu47r3OxMQEFwcvEp4M43IuXhE0Go3infCiTAq73U5hTqEkcEIIIYRYs7L9W86tP3ofAX4N+IelDUeIuzO9BDEajVJUVMSuXbu4cuUK69evv+3MkVKKmpoacnJy6OjooKKigurq6gWviSVjDIYGjWurC6uxW+x8cs8n+drFr9ETyCSC20u3z7rH4erD/KT3J0zEJsAKQwxRFa8yEiez2UwqlaKrK5MI7tmzh6KiIt5oe4OTPSfZ6t7KeHScodAQDquDpz1PE+uL0d7ezsGDB+/ou5qYmGBycpJAIABkZia3b99OKBQilUrRo3pmFWeZ3i8IUJG/vEncDs8O8nLycOY6CU+GOXHtBO879L5Fr/P5fEwmJ8mx5wBQ7Li7xFYIIYQQYjXItrDJZ5Y7ECHuRUtLC2NjY9jtdvbt24fD4eDJJ5/M+vqysjLKysoWHdcb6DVK75e5yoyiGXaLnRf2vsCb7W8STUZ5etPsyopWs5V3b34332n8DgCD5kE8SQ92U2ZFclVVFd3d3QBs3LiRiooKXr3+Kqd6TwHQ6G007hVNRDkVPEWdqY6RkRF8Pt+s5aK3MzU1xalTp0gkElgsmf/9PR4PJpOJxx57jP5gP3919q8WvH5d3rqsnnO3LCYLe9btITAZIDwZ5mTHSZ7Z94wR63z8UT9fPPNFBgIDFBVlCqcU50oSJ4QQQoi167aNs5RSO5VSv7XAe7+plNq2PGEJkb2hoSHa29tRShkJ3HLpDnQbr2sKZzfttlvsfHDbB/lo/UfnrYi4d91eylyZRFGbNGPOzGxXZ7yT0+HTBJNBCgoK2Lp1K6+1vmYkcPPxx/yE8jOVL5uamozE8na01ly+fNnYA5dMJjGbzUYCmEwnebnxZeNe+fbZlTltZhsludkli/fi0YpHsdls2O12hqJD9PX13Xb8+YHzjExk9jLm5GRm4u5HnEIIIYQQK2Wx7se/DsxtepXhBX5jacMRInvJZJKmpiYaGhoA2LFjR9YzUndDa02zt9k4ri2qvaPrTcrEe+veaxz3pfqYdE7SQQfdoW4C7gCHDx/mXzv+lZM9Jxe9X9AcJCcnh0AgwODg4KLj+/r68Hq9WK1WPB4PAG63G5PJhNaaN9reYCScSYasZisv7H1h1vVFjqJlK2oyU0VeBXaLnby8PKbSU7T3t992/IW+CySTSUwmk1EERpZTCiGEEGItW2w55RPAry7w3j8Dv7204QiRvStXrjAwkOnpXlNTw4YNmXL0WmsmYhNMxidx2pxGtcK75Y/6eb31dYZDw8ZeMZvZxubizXd8r63urdQW1WaKoyi4mrxKXl4eAEPRIV7veJ3TvaeN8Ts9O7FZbDQMZhLV8rxyvGEvaZ1maHKId298N21NbbS0tFBRUYHJNP/PZSKRCI2NmSWZu3btMgqZdKe6+aszf0X/RP+s2bz31r2X8ryVaZI9vdcwHMlU+uwL3n4mzpTOfOaZTdllOaUQQggh1rLFkjjPjQbYc2itg0qp0mWISYhFhUIhBgcHMZlMHDlyxGicHU/F+er5r876i39BTgHP7XyOTSWb0FrfcUL33abv0u6bPRt0qOoQubbcO45bKcWzdc/y12f/et73b03gPrb7Y0zGJ+kP9hOJR/jZHT/LK9dfMfbmRR1R8vLyCIVCdHd343Q66e7u5tFHHzUagWutuXTpEslkkoqKCtaty7QJiOXHeOfKO3NiqCms4XDVYQCO1BwxZgXfteFdd/x571Z1QTUtI5mm64OTC88yaq3xhjLtG2YlcTITJ4QQQog1bLEkblIpVaW1nvOjcKVUFRBdnrCEuL3W1la01tTU1BgJHMDFgYtzZm6CU0G+euGrOKwOLCYLH972YXaW7czqOUOhoTkJnMVk4UjNkbuOvbqwmp1lO2kcaVxwTJ27jn+z+99gNpnJz8nnV4/cnBDf4t5Cb6AXgHZfO0e3H+Xs2bO0trYa+936+vrYvDkzU9jd3Y3P58Nut7NtxzZeuvYSvoiPeCo+65k2s41yVznP73reSHTfteFdRBIRnFYn9eX1d/2Z71R1YTVmsxmlFMORYdLp9LyzjKFYiMmpSeBmErcufx12i7SwFEIIIcTatVgS92Pg/wD+r3ne+2XgR0sdkBCLicViDA0NYTKZjEQFIK3Tty0GEk1kfubwT1f+iefrn2d3xe5Fn3WqZ+79jm44Sp497y4iv+m9m99Ls7eZtE7P+/67N757wT5nW91bebP9TQBax1r5yPaPUFJSgs/nM8ZMTmYSm6mpKZqbM/v4du/ezbnhc1waujTnnp/f/3k2FG+Ycz7XlstH6z96Zx9uCVQVVKGUwmwyE0wGCYaDFOUXzRnni/qMxNXlcPGBrR+gvuz+JZtCCCGEECthsSTuj4HTSqli4BvAAFAJ/ALwMeDw8oYnxFw+nw+tNW63e1Ylyuuj1/FFMomMw+rg15/8dUbCI3z53JdnJUtpneZb175FUifZu27vgs8JxUJcHrpsHH9m32cozClcksqHbqebw9WH5y1gUuQoYn3B+nmuyqjIq6DIUYQ/6ieSiHB+4Dw7d+zkxIkTxr62iYkJAEZGRkilUng8HgpKCjjRdGLe591pkZblZrfYKXOVMWQZIplK0u/vnz+Ji9xM4naW77ynGVIhhBBCiNXittUptdZXgA8AjwNvAk03/nkE+KDW+uqyRyhWvXQ6zfj4eFZl8LMxPeN0ayXKEz03E5QDlQewW+xUF1bzszt/ljx7HusL1hvL7LTW/P/s3Xl4XOV5///3o9m0y5b3Be9sLhgDAQIJYSlJIDRL25BmaxOytFmaNm1Kmi+hTdIsNClN82tIGrIQkhCSsCRAgBhwAsSsxoCNN7zIErYly5KsXRpptuf3x8w5nhmNpJE00iz+vK5rLltnzpw5MxqdOfe57+d+fr3z1zx/+PlRn+fZQ88StVEgPkZrzZw1zK2am7MOjVedchXvW/8+PvnaT6YsP2PBGeNOUP6GFW9wf36i8Qkqqiu46KKLeN3r4kFMf38/1lo6OuKNWBYsWMCmpk0MRYZGbC99YvJCUV9Rj6fMA0BHX+Ymua3drUSjUcrKylhYl59GLCIiIiIzbdzJvq21jwOnGWPWAPOBNmvt2D2/RZJs3bqV5uZmzj33XBYvnvpk0U4QV19/vHlFc09zvOMj8Vb+r132Wve+sxefzdmLzwagP9TPj1/4Ma19rVhruXfXvYSioREZnFA0xOZDm92fL1p+0ZT3O50xhrXz1wJwxZor2Lh/I1X+qqyySecsOYfHGx+nZ6iHgdAAmw9v5uIV8cnNy8vLGRoaYnBw0H2vymvLefqlzKWmp80rzOkeawI1eLzxIK5roCvjOke6jwDx8XBzq+bO2L6JiIiI5NN488S5rLX7rbVPK4ATRzQadUvZRtPZ2elOA3D06NEpP2coFKKvrw+Px8Ps2cfL65KzcOsWrqOuvC7j46v91Xz43A+zpHaJu+yhPQ/xwCsPpJRcbm3ZymB4EIiXG2bbCGWyLl15KZ+44BP8w0X/kNV4O2+ZN6Vb5KamTQxHhgHcKQtaWloYHh4mEAiw5egWwtH472phzUJev/z1QHzsWaaxcIWgNlDrZuK6BjMHcS3d8c6VPp+PORWa4FtERERODFkHcSLJgqEg1999PZ/6+afY17ov4zrWWnduMoCOjo4pl1Q6maVZs2a53Qp7hnrYcXSHu854maxKfyUfOvdDLKtb5i575uAz3LPjHmI2hrU2ZazahcsupMxM75+KMYYldUuo9ldn/ZhzlpzjBqtONg6gtrYWgMbGRgACdQGebz5eNvqmNW/iylOu5LNv+Cx/e/7fTvtrm6zkTFxPsGfE/Z19nTQdbQKgoqIiJ2MVRURERIpBYZ69ScF7ct+TNPc20x/u57uPfTdjcNbS0kJ3dzd+v5+YJ8bQ0JDbNXGynKze3LnHS+eeOfiMm0VbOXsli2vHL9ks95Vz7WuuTcmwbT2ylXt23MMr7a+4k3oHvAFes+Q1U9rn6TIiG9e4iUf3P8oDhx/g6PBRhofjmbm9w3uJxCJAfGzfKXNPwRhDXXldwQZwEJ/fz8nEdQdHTld551N3Eo6GqaioYPWC1ZOat09ERESkGBXuGZwUtGf3H5+Uur2/nSeef4JoNIq1lkgkwvDwMLt37+Zo6CjPRJ/hoe6H2Na3zW20MRmdnZ0cOXIEj8fDsmXxLNpwZDilOcnrV7w+6+35PX7es+49nL/0fHfZ1iNbuX3r7e7PToOUQnXOknOYVR6fJ28gPMDjBx6nI9TBC30vAOCt8dIUbHLXf9PJbyrIJiaZJGfi+ob7Uu472HyQ55qfo8yUUV9fzyUrL8nHLoqIiIjkxbiNTUTShcNhWrriY5Hq6+vp6uriiX1P0N/Wn5KRax1u5dmBZ1lYs5Dy8nL2du7l8NHDrFixYsLPGYlE2LEjXjK5evVqysvLAdjSvMXtuDi3ci6nzj11Qts1xvC2098G4JYjOtIbpBQib5mXS1ddyr277nWX+fw+fLU+1p61lq29WyH+q2LNnDUFO/4tk/QxcS+++CInnXQSs2fP5p5n7yEcC1M/u56FtQunfcyiiIiISCFRJk4mrKm5ib5wH+Xl5dTU1LBw4UKao80EI8H4BM0eD36/n6ZIE7Pr481HysvLidkYLx580S3zAxgcHKShoYG+vr7Rno6hoSGeeuopenp6KC8vZ/Xq1QCEo2E2NW1y13vd8tdNKsvkBHIXnHRByvLXL389sytGzk1WaM5efPaI/aypqSFSEeGV9lfcZZevvnymd21Kyr3lBHzxLGjERmg61MRLL73ESy+/xPbu7QQCAWpqa7hk1SUFXRYqIiIikmvKxMmE7W+LNygtD8SzYX6/n3kL5xFbGOPqM6/GGMNwZJinH38aEg0fvV4vlRWVNA420tDQwLx582hqauLo0aNYazl48CCvfe1r2b59O8uXL2fBggVAfNLqzZs3EwwGqaqq4oILLiAUC/HArgdSyihrAjXuNAKTYYzhrae9lbXz1zIcGWZx7eKiCOAgno17y6lv4RfbfpHSYXPz4c1uh81qf3VKI5diYIyhtqLW/TkYC+Ib9vHE3icI2zCL6uOTnp+18Kw87qWIiIjIzFMQJxPWeCze9dDr8zK/aj5tA20AvNz6Mh7j4cJlF9IX6nObaVT5qwiGg9TNquPIkSNs27ON6oZ4F8aysjI8Hg/9/f08++yz9Pf309/fz/z58+no6GDLli1EIhHq6+s577zz8Pv9PLr/0RGTdF+y8hJ8Ht+UXpcxhjVz1kxpG/mydv5aPvuGz/Jy68s8tOchgJQs3MlzTy6asXDJagO1+P1+QqEQR3xHsGHLnsE91NXW4fP7uHjFxW7JpYiIiMiJQkGcTNihnkMA+H1+Llt9GbvbdvNy68sAvHTkJV468hLl3nJ3/XMXn0trfyt7O/ZSWVnJ3sG9XDT/IlasWMGyZcs4ePAgr7zyCv39/QAMDAywe/duDhw4gLWWxYsXs379ejye+Mn6oe5DKftTG6gt2A6SM6kmUDNqEHrK3FNmeG9yoyZQw9w5cwlHwnT5u3h16FU85R5q62qpCdRwzuJz8r2LIiIiIjNOQZxMSCwWo22gDWMMXp+XpbVLWVO/hv5QPwc6D7jrOc1GAE6ZdworZq9gb8de5s6ZSzAaZO35a/F6vLQF24jVxoiYCF7rxRPwEBmK0NDQAMCqVatYu3atm0Wy1tLS1+Jue3X9aq469aopZ+FKxbyqefg9fkLRkLvMGMOa+uLMMNYGauONWvzx368zBx7Exyzq9y4iIiInIgVxgrWWffv2UVlZydKlS8dct727neHYMF6vF7/Hz+yK2Rhj+PBrPszhnsM8c/AZtrduJ2qjAFT5qlg+azkGw5LaJTT3NuMp8/Dd576bst1oLMosM4uucBfhrjCXzL6ExYsWpwRwAN1D3QTDQQAqfBVce+61RVkmOF3KTBmLaxfT1NXkLls+a3nRzqE22uTnlb5Kzlt63gzvjYiIiEhhUBAn7Nu3jz179uDxeFi8eDFlZaN3+jvUcbyUcm7V3JQAamndUq458xquPOVKthzewuHew1y47EK3c+Clqy7l51t/nnG7nnIPffThxUt/RT+21rJ+/foRAVpL7/Es3OKaxQrgMjhn8Tk0dTVhjGF1/WquPvXqfO/SpDlz4KW7bPVlBT1/n4iIiMh0UhB3gmtra2Pv3r0ARKNRent7mTUr84kzwKFj8SDO6/Myr2pexnVqAjVctvqyEctPn3c6F5x0ATuP7sRT5iHgCeD3+mnta3WboADMmjWL2ctn4/ONLJVLLqVcXLs4uxd5gjl3ybksn7WcgDdATaAm37szJWvmrKHKX8VAaIB3rH0Hy2ctZzA8yIrZK/K9ayIiIiJ5oyDuBBYMBnnppZew1uLz+QiHw3R1dVFXV+dmuIaGhggGg0QiEaLRKAc7DgLxTNxoQdxonPnYnMm1HVuPbOWu7XelLHu161XIMC91ciZuUc2iCT3/iWRu1dx870JOVPor+czrP8NQZIi68rp8746IiIhIQVAQd4KKxWJs2bKFUCjE/PnzWbBgAdu3b6e1tZWGhgbmzJnDqaeeymOPPUYsdnzuseauZgB8fl/OAoX1i9ZT5atib8denj74NAAHew5irU0pl7TWppZTKhN3Qgh4AyqdFBEREUmiIO4EtXPnTrq7u6moqODss89maCjeTbKjowOAlpYWamtricVilJeXU11djcfjwQ5ZZlXMwu/3M7cyd9mek+eezJo5a9jWuo2B0ADBcJC2gTYWVC9w19l6ZCv9ofg0BAFvgDmVc3L2/CIiIiIixWL0DhZFzBjz98aYF4wxIWPMbeOse40x5oAxZsAY84gxZknSfX5jzC3GmG5jTLsx5j+mfednQHNzM01NTZSVlfGa17yG/kg/L3e+THO42V0nFouxr2EfjcFGBmYNUL+mnhVrV1A+q5y6unhZW65L9owxrJi1wv351a5X3f8Hw0E27N3g/nzB0gvchikiIiIiIieSUs3EtQBfBt4MVIy2kjHmdOBW4M+Bp4BvAHcAlyRW+XdgHbAGqAY2GmMarbU/nr5dn159fX1s27aNqI0SWBzgNw2/Yf+x/QC0DbYR9UdZXrkcay1bOrbwysArHG47zPNdz6dsZ3bFbPwef873b/ns5exs2wnAno49nH/S+bT1t3HHtjvcLFxNoIZLV12a8+cWERERESkGJRnEWWt/DWCMeQ0w1sRn7wd+Z63dmFj/BqDNGLPaWtsAXAt81FrbAXQYY/4b+BBQlEFc32AfG57cwJ7uPXR4O6hqr0q5v6amhv2D+1m+YDkdBztoGW7B4/Fk7BI5XY0zTp17Kg/teQiIB3HPHHyGR/Y9kjJ59VtOfYvGSImIiIjIictaW7I34CvAbWPcfx/w+bRle4C3A7MBCyxJuu9CoGuUbc0CVqTdXp/YRsbbLbfcYh233HLLqOvFf03HnXPOOaOu99GPftRdb8uWLWNu89qbr7XXP3y9/fwjn7dnX3X2qOstXLPQ/mDzD+zXn/i6vf7h68fc5nS8pvVXrbfXP3y9/cKjX7A/3/DzMbe5ZcsWd5sf/ehHR13vnHPOSXn+Qv496TXpNek16TXpNek16TXpNek1lfZrStxW2CzjnJLMxE1ANdCTtqwbqEncR9r9zn2ZfBr4Qk73bppV+6v509V/yrlLzmXLD7aMup63zMtHzvsIAOFomK/xtZnaRVd9ZT3vPeu9tOxtGX9lEREREZESZuJBaWkyxnwFWGqt/eAo998HPGet/VrSsleAfwX+CHQSz8S1JO57LfHyy9kZtjWLeDYu2VJgU2NjIytWrJj6C5qC7zz9HXa8uoOVfBY8rQAAIABJREFUi1Zy0uyTOGfxOZw892S3OUjPUA//8+T/EI6FAejv76evt4/58+ezful63r3u3TO2r5FYhG/88RsMhAYAOG3eabzzjHdS4Rt1eKOIiIiISFFqampi5cqVACuttU3ZPOZEz8TtAM5yfjDG1BKfYnqHtbbLGNOSuN9J/6xPPGYEa2038UydK3mOs3z7m3P/horXVuAty/wrryuv4wPnfIAXW17kxZYXqa6upro6noxcWL1wJncVb5mXd697N5uaNrFmzhouWnZRQb2XIiIiIiL5VJJBnDHGS/y1eQCPMaYciFprw2mr3g48Z4y5HHiGeEfLZ228qQnAbcANxpjngSrgn4EbZ+Al5FxNYLQq0ONW1q9kZf1KDvccpm2gzV2+qGbRdO5aRqvqV7GqftWMP6+IiIiISKEr1Ym2bgCCwOeId6AMAj8AMMb0G2MuBrDW7gY+DPwQOAacDrw3aTtfIp55awBeAH5li3h6gWytmL0i5ed8BHEiIiIiIpJZSWbirLVfBL44yn3VaT/fBdw1yroh4O8StxNGetCWTRZPRERERERmRqlm4mQKzlx4JlW++Bxyr1nyGo1HExEREREpICWZiZOpqfBV8LELPsaRviOcMveUfO+OiIiIiIgkURAnGdVX1lNfWZ/v3RARERERkTQqpxQRERERESkiCuJERERERESKiII4ERERERGRIqIgTkREREREpIgoiBMRERERESki6k45vTwAhw8fzvd+iIiIiIhIAUqKFTzZPsZYa6dnbwRjzOuBTfneDxERERERKXgXW2ufzGZFBXHTyBgTAM4DjgDRaXyqRmBlFustJR5UXgwoPTi9ivW9zvazVAiK9T0uNpN9n4vps5QP+vxmbyqfJb3P069Y3uNSOCYVy3td7MZ7n6fjs+QBFgHPW2uHs3mAyimnUeKXkFU0PRXGGKy1Tdmsl3A4m/Vl8or1vc72s1QIivU9LjaTfZ+L6bOUD/r8Zm8qnyW9z9OvWN7jUjgmFct7XezGe5+n8bPUMJGV1dhERERERESkiCiIKw1fyvcOSMnQZ0lyRZ8lyRV9liQX9DmSXCmIz5KCuBJgrf1ivvdBSoM+S5Ir+ixJruizJLmgz5HkSqF8lhTEnVi6iV896M73jpwA9F5PP73HM0Pv8/TQ+zoz9D5PP73HM0fv9cwoivdZ3SlFRERERESKiDJxIiIiIiIiRURBnIiIiIiISBFRECciIiIiIlJEFMSJiIiIiIgUEQVxIiIiIiIiRURBnIiIiIiISBFRECciIiIiIlJEFMSJiIiIiIgUEQVxIiIiIiIiRURBnIiIiIiISBFRECciIiIiIlJEFMSJiIiIiIgUEQVxIiIiIiIiRURBnIiIiIiISBFRECciIiIiIlJEFMSJiIiIiIgUEQVxIiIiIiIiRURBnIiIiIiISBFRECciIiIiIlJEFMSJiIiIiIgUEQVxIiIiIiIiRURBnIiIiIiISBFRECciIiIiIlJEFMSJiIiIiIgUEQVxIiIiIiIiRURBnIiIiIiISBFRECciIiIiIlJEFMSJiIiIiIgUEQVxIiIiIiIiRURBnIiIiIiISBFRECciIiIiIlJEFMSJiIiIiIgUEQVxIiIiIiIiRURBnIiIiIiISBFRECciIiIiIlJEFMSJiIiIiIgUEQVxIiIiIiIiRURBnIiIiIiISBFRECciIiIiIlJEFMSJiIiIiIgUEQVxIiIiIiIiRURBnIiIiIiISBFRECciIiIiIlJEFMSJiIiIiIgUEQVxIiIiIiIiRURBnIiIiIiISBFRECciIiIiIlJEFMSJiIiIiIgUEQVxIiIiIiIiRURBnIiIiIiISBFRECciIiIiIlJEFMSJiIiIiIgUEQVxIiIiIiIiRURBnIiIiIiISBFRECciIiIiIlJEFMSJiIiIiIgUEQVxIiIiIiIiRURBnIiIiIiISBFRECciIiIiIlJEFMSJiIiIiIgUEQVxIiIiIiIiRURBnIiIiIiISBFRECciIiIiIlJEFMSJiIiIiIgUEQVxIiIiIiIiRURBnIiIiIiISBFRECciIiIiIlJEFMSJiIiIiIgUEQVxIiIiIiIiRURBnIiIiIiISBFRECciIiIiIlJEFMSJiIiIiIgUEQVxIiIiIiIiRURBnIiIiIiISBFRECciIiIiIlJEFMSJiIiIiIgUEQVxIiIiIiIiRURBnIiIiIiISBFRECciIpLEGPO4MSZkjOk3xvQaY3YaYz46gcdbY8yl07iLIiJyglMQJyIiMtLXrLXVwCzgS8Atxpg3zNSTG2O8xhgzU88nIiLFRUGciIjIKKy1MWvtnUAncD6AMeaCRLbumDHmVWPMl40x3sR9OxMP/V0ik3dXYnmTMeaDydtOztgZYy5N/PxuY8x+YBCoSiz7hDHm6cT2XjbGXJS0jcuMMVuMMT2J/XnKGDN7mt8WERHJMwVxIiIio0hkxN4LzAH2GGNOBTYC3wEWAG8A3gr8K4C19k8SD73KWlttrb1mgk/5TuLBYi0wkFj2EeCviWcFnwB+lrT+7Yl9mQUsAv4FCE3wOUVEpMgoiBMRERnpc8aYbmCIeNB0vbX2t8AngXuttXdZayPW2leBG4Frc/S8/2qt7bTWDllrbWLZTdbaBmttBLgFWGWMmZO4LwSsBhZba0PW2mestQOZNiwiIqVDQZyIiMhI/2mtnQXMBn4MXJEomTwZuMYY0+3cgB8AC3P0vI0ZlrUk/b8/8W9N4t+3AauAF4wx+4wxXzDGeHK0LyIiUqC8+d4BERGRQmWt7TPGfBLYTTwL1wr81Fr7t2M9LMOyPqDK+cEYs3iU54tNcP+2A+9NbHM98DBwkHjgKSIiJUqZOBERkTFYa4eB/wBuAG4D3mWM+UtjjN8Y4zHGrDHGXJn0kFbg1LTNbAHea4ypM8bUAf851f1KPP+1xph5iUU9QDRxExGREqYgTkREZHw/I96h8grgzcDfAc3AMeBuYHnSuv8P+LwxpssY88vEshuINyo5TDyg+02O9uudwE5jzADxpie3EW92IiIiJcwcHzctIiIiIiIihU6ZOBERERERkSKiIE5ERERERKSIKIgTEREREREpIgriREREREREikjJzhNnjJkFfB+4CugFvmqt/e4o6/498W5itcBDwEettb1J9/8l8CVgJdAB/JO19tdZ7EMAOA84glo+i4iIiIjISB5gEfB8YlqbcZVsEAfcTPz1LQZWA48aY3Zbax9LXskY80bgC8AbgQPE2zN/G/hA4v7LgW8B7wGeBuYANVnuw3nApqm+EBERERERKXkXA09ms2JJTjFgjKkiPp/P2dbaXYllXwcWW2v/Om3dnwPN1trPJn4+HXgJqLfWDhpjngR+Yq39wST2YzWwf9OmTSxdunRqL0pERERERErO4cOHufjiiwHWWGsbsnlMqWbiTiEeoO5KWrYVeFOGdc8gXkIJgLV2tzEG4GRjzA7gfOC3xpi9QDXwMPBpa21P8kYS5Zuz0ra9CGDp0qWsWLFiSi9IRERERERKWtbDr0q1sUk18XFwybrJXAZZDfSkLetJrLsA8AHvBi4H1gJziZdXpvs00Jh2UymliIiIiIjkVKlm4vqJNylJVgf0ZblubWLdwcTPN1trDwMYY74CPJBhO98iPp4u2VIUyImIiIiISA6VahC3F7DGmNOttbsTy9YDOzKsuwM4C7gDwBhzGmCAfYkxcYeAcQcOWmu7iWf7XImyTBERERERkZwpyXJKa+0AcDfwZWNMjTFmHfAh4NYMq98GXGuMWWeMqQG+AvzKWutk4X4I/L0xZmHi/uuB+6f9RYiIiIiIiGRQkkFcwieJZ9COABuAL1prHzPGLDPG9BtjlgFYax8FvpxY5wgQAz6VtJ2vEW/1uQtoIN718p9m7FWIiIiIiIgkKckpBgqFMWYF0NjY2KjulCJ59s8P/zNvOfktXLHqinzvioiIiIirqamJlStXAqy01jZl85hSHRMnIpLi5s03AyiIExERkaJXyuWUIiIARGIRwrEwkVgk37siIiIiMmUK4kSk5AXDQQDC0XCe90RERERk6hTEiUjJC0biQZwycSIiIlIKFMSJSMkbDMdnDIlYBXEiIiJS/BTEiUjJUzmliIiIlBIFcSJS8lROKSIiIqVEQZyIlDy3nFJBnIiIiJQABXEiUvLccsqYyilFRESk+CmIE5GSp3JKERERKSUK4kSk5DmZOAVxIiIiUgoUxIlIyXPGxKk7pYiIiJQCBXEiUvJUTikiIiKlREGciJQ8lVOKiIhIKVEQJyIlT1MMiIiISClRECciJc8pp9QUAyIiIlIKFMSJSMlTOaWIiIiUEgVxIlLyVE4pIiIipURBnIiUPLecUlMMiIiISAlQECciJU9TDIiIiEgpURAnIiVP5ZQiIiJSShTEiUjJcxqbqDuliIiIlAIFcSJS8lROKSIiIqVEQZyIlDyVU4qIiEgpURAnIiXPLadUd0oREREpAQriRKTkqZxSRERESomCOBEpeSqnFBERkVKiIE5ESp5TTmmxxGwsz3sjIiIiMjUK4kSkpFlrCUaCeMu8gMbFiYiISPFTECciJW0oMgRAbaAWUEmliIiIFD8FcSJS0pymJjX+GkBBnIiIiBS/kg3ijDGzjDF3GmP6jDHNxphPjLHu3yfW6TPG/MoYU5thnbnGmA5jzLPTu+cikkvOeDgnExeOqZxSREREilvJBnHAzYAXWAxcDXzJGHNZ+krGmDcCX0isswTwAd/OsL3/AnZN296KyLRwOlOqnFJERERKRUkGccaYKuAa4AZrbZ+1ditwK/ChDKt/EPixtXartbYX+DzwV8aYyqTtXQKcDPx42ndeRHLKLacMqJxSRERESkNJBnHAKYCx1iZnzrYCZ2RY9wxgm/ODtXZ34r8nAxhj/MSzep8E7GhPmCjfXJF8A5ZO5UWIyNSNKKdUd0oREREpct5878A0qQZ605Z1AzWjrNuTtqwnad3PARuttduMMWeP8ZyfJl6WKSIFxC2n9KucUkREREpDqQZx/UB6c5I6oC/LdWuBPmPMGuLlluuzeM5vAbelLVsKbMrisSIyTVROKSIiIqWmVIO4vYA1xpyeVB65HtiRYd0dwFnAHQDGmNMAA+wD3gUsBPYaYwAqgApjTCuw3Fo77GzEWttNPNvnSjxGRPJI3SlFRESk1JTkmDhr7QBwN/BlY0yNMWYd8aYmt2ZY/TbgWmPMOmNMDfAV4FfW2kHgV8Aq4gHgeuDfge3A+uQATkQKl7pTioiISKkpySAuwWlEcgTYAHzRWvuYMWaZMabfGLMMwFr7KPDlxDpHgBjwqcR9QWttq3MjPlYunPi/iBQBTfYtIiIipaZUyymd8sZrMiw/SLyZSfKyb5N5brj0x97GyHFvIlLA1J1SRERESk0pZ+JERNxySjU2ERERkVKhIE5ESlowEsRgqPbHE/AK4kRERKTYKYgTkZIWDAep8FXgK/MBCuJERESk+CmIE5GSNhgepMJbgbcsPgRYUwyIiIhIsVMQJyIlLRiJZ+KcIE6ZOBERESl2CuJEpKQFI0EqvBX4PCqnFBERkdKgIE5EStpgeJBKX+XxckpNMSAiIiJFTkGciJQ0p7GJyilFRESkVCiIE5GS5pZTqjuliIiIlAgFcSJS0kaUU6o7pYiIiBQ5BXEiUtJUTikiIiKlRkGciJQ0dacUERGRUqMgTkRKmrpTioiISKlRECciJS0YjmfiVE4pIiIipUJBnIiUtGAkPiZO3SlFRESkVCiIE5GSFY6GicQiVPoq8ZR54svUnVJERESKnII4ESlZwUgQgApvBWWmjDJTpkyciIiIFD0FcSJSsoLhRBDnqwDAV+ZTECciIiJFT0GciJQsJxNX6asEwFvmVRAnIiIiRU9BnIiUrMHwIBAvp4R4EKcpBkRERKTYKYgTkZKVXk6pTJyIiIiUAgVxIlKy0sspfR6NiZPiFbMxjvYfzfduiIhIAVAQJyIlK2M5paYYkAJwqOcQL7S8MKHH3L3rblb+fyvpHe6dpr0SEZFioSBOREpKQ2cDR/qOACqnlML15T9+mXfd/a4JPaalr4VgJEhnsHOa9kpERIqFgjgRKSnvuec9XPfodUDqPHGQ3RQDA6EBNT+RadcZ7KRvuG9CjwlFQ8DxixMiInLi8uZ7B0REpqq1vxWDYUH1ArqHuuka6gKOl1MmTzGQXk75m92/4alDT7G7Yze72nfR1N3Em1a/iYff//DMvgg5ofSH+t2gLFtuEBdRECcicqJTECciRe8dv3wH86vmc/977iccC4/IWIxWTnls8Bh/cedfEPAEOG3uaVy49ELKveUc7Dk48y+iCP1k609YVreMy1Zelu9dKToD4YHJB3HKxImInPBUTiklp2Owgw37N+R0m7e/fDs723bmdJuSG0f7j/Jc83PuOKFQNMRwZBjIUE6Z1p3SydTd/Jab2fqxrdzxl3dw/pLzGYoMzeRLKFr//vi/890t3833bhSsO3feyb5j+zLep0yciIhMxYwHccaYOmNMReL/xhjzAWPM+2d6P6R0ffu5b/Nnd/zZhE+QRmOt5cP3f5hbXrglJ9uT3Hqk4REAt0wyFA0xHI0HcW53Sl/myb6dx/jKfO6ygCegIC5LXcEu9z0uRE4wny/X3nftqMeN/lA/URslGotmvT1l4mS6WGv546t/xFqb710RkSzlIxP3ALAu8f9/A74O/Kcx5st52BeZpIf3P1ywJ2/7OvcRtdGcnYh3DXURioZ0Yl+gHm6Ij11zTnDD0fDxTFw4iK/Mh7csXjmeXk7pBHQ+z/EgrtxbnveT/2IQiUXoC/UxEBrI965ktO/YPqpvrGZH2468PL+1lsHw4KgXk/pD/QATmvIiPcMskitPvPoEl9x2CVtbt+Z7V0QkS/kI4k4HnMlx3ge8CbgY+Os87ItMQttAG1f+/Ep+svUn+d6VjA50HQDIWdDltKt3sjtSOGI2NiKIC0VDKWVnThYORnanVCZu8py5ygr1Ys6BrgNEYhEauxrz8vzO8WK0bqhOEDeRigFn3UJ9z6V4tQ20AbhNoUSk8OUjiPNYayPGmMVArbX2ZWttIzAnl09ijJlljLnTGNNnjGk2xnxijHX/PrFOnzHmV8aY2sTygDHmR8aYVxP3bTPGvC2X+1mMnLbYTd1N+d2RUThBXK5Kjlr7W4H8l2bJSNuPbqdjsIOAJ3A8ExcLp5RTOp0pYWR3ylEzcdHhccuKWvpauOEPN/Dt577tngCdSLqC8ZO9gXBhZuL6QvHjVL6yVs7xJ9N0FdZaN4M5oSAudrycciA0wLbWbTnYU5HjF2V0AUukeOQjiNtvjPkA8DHgDwDGmLlArs8EbibefXMxcDXwJWPMiBZqxpg3Al9IrLME8AHfTtztBQ4BlwB1wOeAO4wxp+R4X4uKc5A/1Hsoz3syUn+on/bBdiCHmbh+ZeIKlfMZXDV7FeFoGGstkVgkpezMaWoCGcopM2Tiyr3lxGxs3Pnk7tx5J1/d9FX+YcM/8N9P/3fOXlOx6B7qBgo3K+RkuvK1f07wGLEjP0fBSBBL/CLBRC4OJWeYv//C9zn/h+cX7PsvxcW5OFsMQdyNm27k/238f/neDZG8y0cQ91ngq8RLKb+WWPZnwJZcPYExpgq4BrjBWttnrd0K3Ap8KMPqHwR+bK3daq3tBT4P/JUxptJaO2Ct/aK1tslaG7PW/g7YC5yXq30tRs5B/nDv4TzvyUjJpVO5+jJSJq5wORmwpbVLCUVDblDmBNzBcFo5ZVp3ykyZuIA3AIz/+ekY7KDMlDGnYo6b9TmROGVXhTomzjkpzVsQN0YmzgkwYXLllMFwkLaBNkLREM29zVPcU5HiysT9vvH3/OaV3+R7N0TybsaDOGvtY9bapdba1dZap2f7z4E/z+HTnAIYa+2upGVbgTMyrHsG4NakWGt3J/57cvqKxph5xMf0jeg1nyjfXJF8A5ZO+hUUMOcEuRCDOKeUEnI/Jq4YvtyysbV1K3dsvyPfu5ET7QPxrOuS2iUpY+GSxw6ll1Nmk4mD8TOvxwaPUV9Rf8I2QlEmbmzO8SJTRnfKQVwk6G6juU9BnEydcyGqGL7nIrEILX0t+d4NkbzL22TfxpjZQE3a4lzNsFsN9KYt687wfM66PWnLetLXNcZ4gduBXyUye+k+Tbwss+QlZ+JiNkaZKZzpBqcjiGsdSGTiSqSc8vrfX8/m5s2898z35ntXpqxtoI1KXyWzArMIx8Ju1mOscsqUKQYyZeI82WXiOoc6mVMxJz65eCw301kUk+QgzlqLMSbPe5TKOSnNdzllpu6Tkw3ikruu9ofj29DJrORCMZVTJnfGrfJX5Xt3RPImH/PEXWiM2Q90AI2JW1Pi31zpB2rTltUBmWqeMq1bm7yuMaYM+Fnix78d5Tm/BaxMu108ob0uQJFYhJiNpSxzDvLhWNjNhBSK6czElUK2JRgO8njT40XxRZ2N9sF25lXOw+/xp2TinMYkI8op07pTOutnzMSN8/s+NniMOZVzCHgCJfHZmCinsYnFFuTnKd+ZOKecMlMmLrkEdcqZOJVTSg70hoqnnNL5m3LGq8vM2dW+i6vvuLpgKzBONPlIofwf8BDxueJWJW4rE//myl7AGmNOT1q2Hsg0YdAO4CznB2PMaYAB9iV+NsCPiDdI+XNrbcZvXGttd2LsnHsDCq/ecIJOvflUvv3ct1OWJR/kC625yYHuA+6cYDkfE1cCmbhNBzcRjAQn9VrSg/lC0D7Yzvyq+W4Ql9J5MhbOvpxyEmPijgXj5ZQBb6AkPhsT5WTioDA7VBZMJm46xsSpnFJyrJgycc5x27nAKjPn5s0389C+h1IumEv+5COIWw182lq701r7avItV09grR0A7ga+bIypMcasI97U5NYMq98GXGuMWWeMqQG+Qrxk0vnm/z/i4+D+LGnZCaOxq5EXW19MWZZ8kM/XuLg/NP6BF4+8OGL5ga4DnFwfH86Y8+6UJZBteXh/fE61TBnWsfz+wO+Z91/z8jbn1mjaBtqYVzUPn8dHzMZSfufDkeHM5ZSZphiY5Ji4ORVz8Hv8JfHZmKjkIK4Qr8rmu7HJtI6JCx8P4lROKblQTI1NlInLj0gswj277wFyN4WTTE0+griXgWUz8DyfBCxwBNgAfNFa+5gxZpkxpt8YswzAWvso8OXEOkeAGPApAGPMcuDviGfxjiQe12+MuX4G9j/vorEoFjuiXCf5hDVfQdynfvcp/uOJ/0hZFrMxGrsaWTtvLZCbL6OhyJB7sloMX27j2dCwwf3/RAKPA10H6Ax28l9P/9d07NaktQ8cz8TByDK1YDg1iBt1sm/PyCAum0zcnIpEOeUJmIlLnhS4EDtUFko5ZS7HxCVn4pz3XJk4yYWZbGzy020/5YmmJyb9eDeIUyZuRj3R9ITbETpf829KqnwEcbcDdxtj/soY84bkWy6fJFHeeI21ttpau9ha+93E8oOJZQeT1v12Yp1qa+27ElMNkMgQGmtteeI+5/a10Z63lDgnDOmBWko5ZU9+yim7gl0pJ0IQL3scjg7nNIhzSin9Hv+ET9S3H93O1tZMPXDy42DPQXa172Jpbbxp6mROHm996Vb3Pck3a208E5cYEwepJ8fD0eHxyykzZOKyaWwyFBliMDwYHxPnPTHHxBV8Jq5AyimViZNiMJOZuH977N+48ckbJ/14ZeLy486dd7r/VyauMOQjiPsOcA7wC+DxpNtjedgXGUNyEGetdZc7B/n6inoO9+UnE9c91D3iSpBToz0dQdzyuuUTPlH/+IMf5x83/OOU9yFXnFLKt53yNmBiY/yS51+7Zcstud+5SegP9TMcHWZe5Tw3CEsem+WWU/rG6E6ZeF1OEAjZNTY5NngM4ITOxHUPdeMxHqAwx8QVTCYuw5i45PdrQt0po8e7riYHccnHZ5HJmMkxcUORIbYd3Tb+iqNQEJcfzxx+hiU1SwBl4gpFPoK4GmttWYabJw/7ImNwTi4GwgPuVTo4fpBfU78mL+WU4WiYYCQ44uRsOoI4p1xj+azlEz5Rf6XjlYLKUGxo2MBJtSdx9qKzgezKKdPnXZtbOZfG7sIYF+eUdYxWTjkUGWIoMjRiTFwuJvvuDHYCuJm4iZyIl4quoS4W1SwCCjQTl+/JvrPMxE3kuJI8/2F/qB9fmY9QNETHYMcU91ay8eOXflyQ86Pmwkxm4oYjw7T2t7rH8IlSOWV+DIQH3GO+MnGFYUaDOGOMBzhmjPGPu7LkXfJYjuQvLucgv2r2qryU8vQMx6f1yxTEGQynzDkFyG0mbkXdCoYjw1lf8e4MdnIseCzjVfh8CEfDbDywkSvXXOmWC4538vj0oaepubGG5t5m9+RxdvnsEWWs+dI+GJ/eYl7V8XLK5AyH8zlJLqf0eUYZEzfBxibHgvFMXH1F/Qk7xUD3ULd7VVZj4kZKnool3VjllLe+dOuIjsDp6zpB3Jr6NYBKKmfCwZ6DfOj+D/G9Ld/L967kXDgado93M5WJA3j56MuTerwycfkRDAeZUzEn/v8sM3E/2/YzPvbAx6Zzt05oMxrEWWujwCGgcrx1Jf+STy6SB88PR4cJeAJU+6rzcoKUPMlwsgNdB1hau5Rybzl+jz83mbj+I5SZMpbWLsViM56QZbLv2D4g81X4fHj28LP0DvfGg7hEpmm8wOO5w88RioZo6Wtxg9HZFYUTxGXKxCXvW89QPIgbUU6ZqTvlBCf7TimnPEGnGOgKdrGkNhHEFWA5pTMmLl9lP2PNEzdeEPdPD/8Tu9t3j3ics25nsBOL5dS5pwJqbjITnm9+HoC9x/bmeU9yz/lbgdTj3gfu/QD/+9z/5vS5rLXu8XKqQZwuXsyswfAg9RX1QPaZuN83/p7bX759OnfrhJaPcsobgO8bY1bk4bllApJPLtIzceXecsq95Xnp2OicnKcfRA4b9K23AAAgAElEQVR0HWDV7Ph0g7nat9b+VuZVzqPKXwUcD3xa+1vH7K7lfNFnG/RNtw37N+AxHv505Z+6Qcp4JYD7O/e764WiIXxlPmr8NSlf+PnkTDQ/r3KeG4QlZ4Sc7onp5ZQxG3OnVxgrEzdmEJfIxM2pnIO/7MSbYmAoMsRwdNjNxBVaOWUkFnF/f/kupxxtnrhqfzUw8u8wFA0RtVGue/S6EY9z1nUuIpxSH6860ITf029z82YA9nXuy/Oe5F6m4RIQ/974zSu/yelzJV/wmmoQ1xnsPOGOvfk0GB6ccCYuFA0xEB5wy9slt/IRxP0CeCfQYIyJJt/ysC8yhuSTj4IK4sYop8x1EHek/wiLahaNKEG86embuOrnV41aXul80RdKOeWGhg1cdNJF1JXXHc/EjZM92t91PIgLx8L4PX6q/dUFk4kbr5zSydgmZ+Kc36Pze8mUiZtwY5MTMBPnvLeFWk6Z/Bl1Sg+/8sevzOjf41iZuIHwgHtFOz2IG44O4y3z8uC+B9l4YGPKfc66URv/ulQ55cx5vuV4Jq7UGskkn2Anf2/2Dveyq31XTp8r+bg62eYmkVjEvQhSKN2SS10kFiEcC084E+ccs1T6Oj3yEcRdlrhdnuEmBSSlnDLpSu9QZIiAN5C31upuJi4SdDMqwXCQI/1HUoO4aG4ycQurF44oQTzUeyg+V9MoZWSFlIk72n+UF4+8yJVrrgSOd2Ic73c3IhPn8RVUENc20EaVr4pKX2XGxiZOoJE8Ji49y+b8fpwui5BdY5NjwWNUeCuo8FWckGPiuoLxLOfimsVA4WXinM9oXaCOwfAgG/Zv4N8e+zeea35uxvZhvMm+Z5fPBjIEcZFhrj75albMWsFnHvkM0Vg8YIvGou7xzlFfUc/8qvkqp5xmMRvjhSMvUOWrYjA8WHJBs5OJ85Z5Uz63Q5Eh2gba3ItWueBsv8Zfw672XZO6sBKJRVhWF59uWMHBzHCCttpALd4y74QycaAmNNNlxoM4a+0To91mel9kbCnllH2ZM3FRG53xcV/J81M5XwhN3U0Auc/E9R1hUfXITJxzQHK6FKZzg7gCyMQ90vAIgBvEZdPYJBQNue9pKBoiHI1n4mr8NQVTFtE+2M68qnnA8XLI5ADTzcQllVOOCOKiYXxlPowx7jrZvD+dwU7mVMbLSk7E7pTOe+tkQfM1Ji4UDbkXdZI5n9EF1QsYDA+6J6FOCe5McMspR2lsMlYmrjZQy9ev+DovH32Z27be5i6H1IsS1f5qltQsKbmgIt2vd/+a3+37Xd6ef++xvfQO9/KO094BlF5JpVMiP7dyrntsTD7O7+4YOT5zspztn7fkPELREHuO7ZnwNiKxCHMr5wKppaAyfZwLdRW+Ciq8FVln4pzjnzKm02PGg7j0Cb6na7JvmTrn5KLCW5GSiRuODrtBHMxMN6tkTjklHL865EwvkMsgLmZjHB04mpKJc7bpnDRlCuKste6XfCE0NtnQsIH5VfNZv3A9QFaNTV7tftW96u9k4gqtnLJtoI35VfMBsi6nzJSJSy6lBPCUeVKuSGdyLHjMPQkPeAJEbdTNmJwInPd2VvksKn2VecvEff3Jr3P+D88fsdz5jDqfDydTld7SfCA0wO8P/H5a9m28KQbqyuswmIyZuIAnwDVrr+HCpRdyw2M30B/qd9ebVT7LXbfaX83imsUln4m78ckb+eaz38zb8zvj4d535vuA0mtu4gRC86vmuxcLksc+57Kk0jmuXrDkAmBy4+IisQh1gTqg8Eq5S5VzPKv0VVLhq5h4Ji6HGdPOYOeIqoQTVT7KKR/PcHsMTfZdcJwrKCtnr8w4Js7NWMxwKVnylXfn5NEJ4lbOWgnkJog7NniMSCzCoupFKeOkrLXuASlTENfa30p/qJ+AJ1AQ5ZSPNjzKm1a/iTIT/3PPprGJU0rprBeOxTNW1f5qgpFgQQQs7QPtzKuMZ+LGCuLGLKdMZOLSlXvLxx0T5wzwznaMYa5Za/nRiz/KS2bUCZKq/dVU+arydiLV3NecMQvlnIA6QZxz/EoP4v7zyf/kjT9746gZ9akYa7Lv/lA/Nf4a/B5/xkxcwBvAGMM33/xNWvtb+cZT33DXc05e4XgmrtQbmwxHhvMy/tqxv3M/BsMVq66g3FteckGccwyZVzkvcyYuQ6fUyXKOk2ctOAtfmW/CQZzTmKquPBHEFWBn3FLknGtV+irjmbg8lVM2dTex9JtL+fFLP87J9opdPsopUyb5BpYCtwN/MdP7ImNz/vhWzV7FseAx96QkuZzS+Xk0b/3FW/nMw5/J6X4ll1MmB3GVvkr3pC0XQZyT/l9YvTClxK53uNd93kwnf84J44pZK/JeTjkUGaJ9sJ21c9e6y5KDju6h7owH1/QgLjkTB+QsG9cZ7OSmp2+aVKOAjJm4cbpTpn9mnbF+6QKewLiZOLeccpIXM4YiQ9y8+eZJZ2ubupv4yG8/wq93/3pSj58K570p95bHM3GR/GTiwtFwxvfd+XwuqFoAHP+bdJrhOO7dcy8Wm3UQt6t9l9tqHuLHouseuS7j38NYY+IGQgNU+6szB3GJTBzAa5e+lnef8W5uevom93jknLzC8Uxc+2B7SZf0hqKhvAZxzvHP5/Gxpn7NtJdTdgY7aexqnNbnSOZc9JhXlRTEJWfiOnKfiav2V7N23toJNzdx/p6mKxMXiob45jPfHPPzdrj3MLe+dGtOn7fQOed/zljwfDU2+Z9n/odgJMjTh57OyfaKXT4ycSmstS3APwDfyPe+SCrnj8/JbjlXvIciQwQ8gayCuJ1tO9l6dGtO9yu5nNIN4rrjnSmdsU25COKcg86imkUpJYjJB6NMJ3/OFaq68rq8Z+KcBhSzK2a7y5KDjn999F95yx1vGfG4TEGcz+OjJlAD5C6Iu/eVe7nu0esmfFJkrY2PiUtk4twpBiZTTjlKJm68eeKcTJzbKGaCmbjbX76dT/3uU/yh8Q8TepzDea35mPLBea0BT4Aqf/4ycREb75iWfhHAySI4Qf6h3kNAaiauobOBHW07gOzH1Xzw3g/ykd9+xP35j6/+kZueuYlf7fjViHXHGxNX5avK2BzKycQ5rll7DcFI0P2bHJGJS8zVl3wxxlrL/z3/fxnHC2YSjobZ0zHxsUkzZTia30ycE8QBnDLnlGnPxH3yoU9y5c+vnNbnSOZ8/jNl4pbXLc9pJi75AtBZC8+acCbOCeJqA7VA7psqPXXwKT7zyGe4a+ddGe8fjgzz9l++nQ/f/+GcNnwpdJPNxDkXsnMxJu7Y4DF++NIPgcl3Ni01eQ/iEiywKN87IamcPz4niHOuZrvllFl08RsID+T8QJcyJi5yfEycMx4OpjcTl3yylCmIc563NlBLJBaZUjvqSCzCY42PTfoqu5ONcjrhQWrQ0TrQysGegyMet79rv9t5MH2KAchdEOdsZ6IlgX2hPkLR0JiZOOcEdrzulJkyceXe8lGDMmvjmZsR5ZRZZOLaB9q55LZL2NW+i/v33A9MfnyN8xryEUA5rzXgDeR1TJw7VURaoOQEtmNl4u7bc5/7/2yCnZa+Fp5veZ6j/UfdZc7v4O7dd49Yf7QpBqKxKMFIMGMmLmZjRGIR93gD8S5+cPxYk56Jc6Z5SB4X19DVwCce+kTKaxzLL3f8ktO/czpbWrZktX4m09k0plAycRCfm6+hs2HaxjtHYhF+t+93HOo5NC3bz6RvuI8KbwXV/uoRmbgLll7Aod5DGY/RMRvjukeum9AFgOQgbt38dbT0tdAx2JH140dk4nJcTuls79EDj2a8/4Y/3MCLR14EUs9FJuOn236a8SLe7vbdBdewJSWIy1Mm7ifbfsJgeJA3r34zO9p2FETPgXzLR2OTv0m7fRx4EFButMAkl1PC8ROh4UhqY5OxMhD9oX53YuRc6R7qxhDPuA2GB7HWxoO4WdMXxCWf/CePwcmYiUsc3JyTL2dOp9H0DPXweNPjKcustdz3yn2c8d0zuPynl3PPrnsm9RoyZuKSgo7B8CDdQ90jBgnv79zP2nnxEszkyb6dIC5X2R8niJtoUOhkVJzulM4JVvJ2simnHG1MXMA7ejll73AvURsdWU6ZRSbuiVef4I+v/pHPbfyce5Kw79jkSrPcIC4PY0Kc5w54AvExcXkal+IEb+kXOdIbmzg/J2fi7ttzn/vZyOZk7MG9DwLxUlrnwozzPmw8sNH9W3M4F5iSJ5eH478vN4iLHd/35ODY4fzNORfDnJNXg6HCV+FebEk+LjnHoGwD/Oa+ZiyWLz7+xazWT7fxwEYW3LTAzWzm2nSOiWsbaON7W7435jjf5CDu5DknE46FM178yoXNzZvpGe4hGAnO2MWR3uFeagO1lHvLCUVDxGzMDdrWzV8HpM4V62jtb+WmZ27i3lfuzfq5nM+4k4mDiTU3cU7cK3wV+Mp8Ob+I5fztbDywMeMF2Fu33upewMs2051Jc28zH7n/I9z45I0j7nvdra/j609+fdLbng7O8cztTpmHMXGNXY3UV9TzvjPfx3B0eNLfnaUkH5m4L6XdPg7sAz6Uh32RMbjllLPjmTjnSm+2Y+JiNsZgeHBCV9my0TPU456cDYYHaR9sZzA8mPNM3JG+I1T7q6n2V2csp6zx14yZiXNKD8cbF3frS7dyxU+vcL+wNzdv5tKfXMo7fvUONzCYbCCcKROXHHQMhgeJ2VjKVb9oLEpjV6M7ji59igHIXSbO+QKeaFDoXPV3yynLplBOOcFMnPO7cLtTesdvFOPYfnQ7AL/d+1uGIkP4Pf5Jj6/JayYuevxErBAycenvvduoIRHkO5zPTcdgB08efJI/P/3PgexOxu7fG8+cRmIR9+/FOSGNxCJuZtWRfPxJvmKc3BQmPROXXKbqcII451jjdKes9FVSZsrccsr0uTyBrE+0nAD0wX0Ppoz5y9b3X/g+Fjttgc10ZeLaB9q5/CeX8/EHPz5iYvVkTiUCxMspYeodKje9uiljidnD+x92/5/r787R9IX6qAnUpDTwco7JzuvNlElxjrETaQzkXgDyBli3IB4gTiaI85Z546XcObiA1DPUw2U/uYyGzgb3WHak/wg723emrBezMbqCXZw+7/T446aQifvf5/6XcGxkGXM0FqVrqIu9nYXVPGeqmbhjwWMpx7pjg8fcz/fu9t3ulEZj6Q31UuOvcT83uSipfOed7+SXO3455e3kSz4am6xMu62z1n4oMTZOCohzlbu+op7aQO3IckrP2OWUzh/9UGQopyd5PcM9LKpZ5D5H+vQCEM++TDkTN9DKour48yQHPi19LVT5qlhWt2zsIC4R8Iw3Lq4/1E/URgmGg9y18y4u+OEFvNLxCt99y3fZ+ndb3dc5GWNl4kLRkLvd5CzCod5DhGPhkZk4j2/ayiknm4lLL6fsD/XjLfPiLfMSioYwmJQT4kyZOOexycZqbOJkRNxyygk0Ntnetp2F1Qvxe/zUBmq5+uSrpx7E5SEL5rxWv8ef1zFxY2XiqnxV7ufV0T7YTszGeHDvg8RsjL9Z9zfA+GPiBsODbDyw0b1o4Jx8OL+DWeWzRpRUJp/kJF/Icd6rjEFchkxclb8KOH5BxsnEOa9tTsUc/B5/SibOCQazPdHqHuqmLlDH7PLZGTMDY+kMdrplm9NVAjZdY+I+8tuP0NDVAMDzLaMHrymZuPqTgbGDuLaBtjHLS2M2xptvfzP//fR/j7hvQ8MGt5PwTAVxyZk4iH+unQshThCXqQusE8Q5n81sJJdTzq+az4KqBRM6GU8J4nLUGXdH2w4eb3qc51ueT7nw8WhDakllz1APFsuKWSuAyX/ee4d7+d4L38Nb5uVQ76GU1+A8/6vdr05q29Nl0mPiYmH385xciv7h+z/M+3/9fgDe/5v3848b/nHcbfUN91EbqOX0eadPqrNpumgsyj277ynqbrP5KKccOQI8vvyOmd4XGZtzcuH3+Flau3TUTNxoJ6/JB6ZcjovrHup2g6tgOHh8eoFExhByl4lbWL0QYEQmblHNIuZUzskuiBsnE+eciA5Fhnj28LOUe8vZ/6n9fPy8j7vZPOe9nOj7mCkT5y3zUmbK3HLK5PXgeFOTU+eeSpkpm9YxcW5zjgmOiXPGNqWXUw6EBvB7/G5gVeGrSJ3IO20c52QamziZuOTJviG7csrtbdt53Umv46uXf5UbLr6BtfPW0tjVOKkupnkN4qLD+D1+jDFUevOfiUs/BjmZheRS2iU1S4jZGJ3BTu7dcy9LapZw2crLgPGvqG88sJGhyBB/ve6vgZFB3LvWvotHGh5JyegFI0E8xgNkzsRV+asmlYlzxsQ5y40xI+aKc96PbE+0uoe7WVi9kI+95mPct+e+CXVG/MX2X7ivYTqCOGvttGTirLU8dfAp3n/m+zlt7mnuXHCZJHexnV81n9pA7ZilXNfcdQ3nfP+cUad+aB9oJxgJ0tKfGhgdGzzG883Pc/nKy92fZ0JfqI8af01qEBfqw2M87vdqpnK4qWTinOeaaHOT5CCu0leZk+Of8/03GB50L3wsrF7IxsaNGddbUbcCmHw55Y9e/BG9w73882v/GUi9IOAcS6crqz1ZU+lO6YzbTc7mtvS1uOcah3oOZfV6nYsNfo+f0+edPm7wf8uWW7j95dtHvT/9s1iM8lFOedUoy988o3sh40oO4pbULEnJxGXTnTL5RD8XVxR/t+93PLj3QXqGetwgLjkT51wdg/gfZbYHmdG09re6Gb/0xiaLaxZTX1E/ZndKt5xynEyc8z47V5urfFXuY8tMGRXeCgbDgzR1NzH/pvk8efDJrF+Dk2FLniDYeT1OOSWkfgk7B9Y19Wvck0znSrSzX8lBVyga4q2/eCtPND2R9X45nC/gSY+JS5snzgnKnMAq+SQeRhkTl2mKgQxdAx3pmTi3Ucw4mbiB0AANnQ2cOf9M/uWif+G6113HyfUnE7VRGrsn3k48n+WUzoUcYMySpkgswmU/uWzMUrWpGCsTV+2vTmlqc9rc04D4ydEjDY/w9lPfjt/jp8JbMe7J2P177qc2UOuWX6YHce9f935C0RAP7H0AiAcIwXAw4zFgrHLK5GOuY0QQl5aJg3iAmhzEueWUWR4Du4JdzCqfxSfO+wRlpoybN9+c1eMAbtt2m5udmo4gzjlpj8QiOW1k0DbQxrHgMc6YfwYXLLmAzc2bR21ClZyJM8bEO1SOUu4WjUV5vvl5Dvce5uo7rs74now2+fzGAxuxWHdS8Xxn4moCNdT4a6j0VeasnDK5FBviY+52tu3M+nc7HeWUzvfkYHjQ/f6+cs2VPHnwyZSxks7rdM41JlNOGY6G+dZz3+KS5ZfwvnXx3/OeY8dLKp3v5KMDR/PazCfdVOaJWz5rOZB6IWAgPEBrfyvhaJiOwY6sxsz1Dve6x9R1C9axrXXsIO47z39nzHG+CuImwBjzBmPMGwCPMeZi5+fE7aNAbi7tS844JxS+Mh9La5dyuPcw1tqsx8QlH1xz0dzk+j9cz7X3XUs4Fh5RTrmoetGILoTD0eEpdYY80n+EhVUL3e3B8cYmi6oXUV+eOYibcCYuejwTl3xy7KjyVzEYHuRw72FiNjahq+RdQ13UBmrxlHlSlvs9/tRMXDA1E1fuLWdxzWL3JNNpAJIpE/f7A7/ngb0PsOngpqz3yzHZcsr2gXaq/dXueLfkQCw5E5f8mYDcTDEwIhOXZWOTXe27sFjOXHCmu8wpVZrMAO2JZuJ6hnq4Y3tuCh6S5zIba0xca38rjzc9PqELDxMx2pi43uFe9+TT4QRxv9j+CwbDg7z9tLcD8S6yY52MxWyMB/Y+wFVrrnIvHiUHcR7j4XXLXseSmiVuSWUoGsJi3WNA1mPiMpRTVngrMJgRY+KSg7jFNYszl1Nmm4kb6mZ2xWyW1i7lnWvfyQ9f+mFW2fEdbTvY0rKFT5z3CQxmwkHcL3f8ctyulsl/VxOdizFdc2+z+53gjHf6k/l/wvlLzufowFF3Kop0yUEcxEsqRyvB2nNsD8FIkA+c9QF2tO3gmruuGfEd4GTo0oO4hxseZnb5bK5aE7/WPWNj4oZTx8Q5mbgaf42b6R0riJtsOSXEM3HD0eGsS9rSyylzUQWQnIkbDA/GJ3ZfeQW9w70pWULne3Iq5ZR377qbgz0H+ZeL/oWT60/GYFLGxSVfeJnJDqXjcd7ncm95PIjL8gJROBpmeV08iEseA9of6mcgPEBjdyMWS9tA27iBfF+oz51a4qwFZ9Hc1zxmtnogPEBDV8Oo4+0UxE3M44lbOfBE0s+PAV+A/5+9Lw+TqrzSf2/ta6/V+wZ0NzuyKbggQkRUDEYjUZJI1IyaZGLUTJbJTBYdk4zPzBjHmMnEmPwMjs4YEtzQIGDcjQoqoOx0N90svdBNr9W1L/f3x63z1XfXutXdktHhPA8PUH27llv3ft95z/ue9+AHp/G9nAkTQRsPMXE9oz2IJqMQIcpGDOglr3xiPl5ZCDlQkoxOycTx/XBA9qbMd3YXRTgRxkhsJMvEKeWUvipdJo4fZgpoD/vlg5dTRlNqEEeSETqf+VT/BqODMiklhdNmzMQ1FjfCIlhUTJzXLvXn8N/tkwck58yxbKZjNTbpDWcHfQOQATG7lWPi7GNj4gyNTcL9ECCw82p2xMCeXsnUZHb5bPZYc6nEYIylLy5fJu6P+/+ILz71xQkZIszPMvPavUimk5rGLpSkUrI30aHHxJ0Kn0LAE9AEces/WI8CZwGWTVoGQJInGiVj73a+i5Ohk7hy2pUIeALs+QHpPLhsLlgEC66ZcQ1eaHkBwVhQNmYEkBdy8jU2EQQBPoePFQ+Uckogw8RxACVfOeVgdJCBwzsX34mR2AjW716f8/ce3f0obBYbvjjniyhwFuSV1A5GBvH5Jz+Ph957yPA4/vxEk1G8eezNMc0ta+lvQf0D9YwV3tebAXFlEogDoCupVIK4qaVTcXToqOY9v6t7FwDg2+d/Gw+vfhjb2rbhq89/VVZQJMDNA1hRFLG1bStWTFmBgCcAAcLpNTbRkFMS61Hlq5pwOSVd4/mam6iYuAlQIsiYuEQEHrsHSxuWApBmQbLjMmCPxg7lK6cURRH/9ta/YXpgOlY1r4Lb7kZ9Yb0mEwd8dJLKRCqBhQ8vxNMHnjb9O5FkRCooCZIrbj5MXG1BLQQIskIAfW/EphGQM4qR2AgKHNKaaua6obX2pSMvaf6crkWlYufjFKcNxImiaBFF0QLgAP0788cqimKtKIqPna73cibMBW2eNosNtQW1SItpVtEwxcTFJ46JG4wOyhKEYncxnFYnIskIOoY6ZFJKen9G7y1X8OMFgKy8aSg6hNH4KCp8FShxlyCSjKgqUpFEBC6bSybxMwomp8zYaGuBuHAinAVxeWwcg5FBmakJBRl30PlR9sQ1lTSxz80bm9itdjitTga6EqkEnj4obQRjAnFjlFP2hbKDvgEp0SUg57A62LlXLs5K1kyPiTMyNhmIDKDIVcTYTXrOXO6Ue07ugdvmRmNxI3us1F2KIlfRmBqr82XiiFnRMigYy2vTdUpASSuZoiQ1n0p9PkEJnfLc94Z6UeGrkIF4AnGnwqewqnkVu0YKnYWGhZFNhzbBKlhxedPlKHAWwGaxyZg4ArNrZq5BLBXD5pbNKkk1X8hRjRjIwcTRsUZMXE1BDUKJUNY1cwzGJlSUWFy7GOfWnosHdzyoGj3CRyKVwGMfPoZPT/00yrxl+YO4zDWRy4WPB0rRZBQ3b7oZKx9fmTcLsqtnF9JiGvv79gOQmLhiVzEqfZU4q+IsOKyOvECcCJGZoihfx2VzYXpgOr48/8v40dIf4ZHdj+Anr/+EHUNySjLaASRWsyvYhcuaLoPVYkWJu+SvL6fMMMlV/ipNJo7AT74gziJYYLPYAEj3pd1izymNo1AZm0xgT1woHpLAit2NusI6TCqahNePZUEcfU4ye8tXTvlKxyvY1bML3zrvW8zsY1pgmi6IOzr80ZibtA60Ymf3Tuzq2YVEKoHVT6zOOSMynAiztd5tc5uSN6fSKaTEFNw2N8q8ZbJCAO35fF9broHgvJxybsVc1e8rg/YkZW8jxRkmbgwhiuLs3Eedif8NQbPBBEFAbUEtALBNywyIm8ieOGIPKCkvchVJDFU8hO7RbtY4SzFRII4YP4tggd1iZ5tvwBNgFvPKBJUSXNqkxi2nzGxUtCBNFBPHsyO0OaXFNNoG2xjQoDlWiXQCDouUxPidfvbdvnb0Nfa7YwFxbNh3DiYukohgxi9nsHl6feE+GRMHZCWVRnJKq8UKu8VuiokzklPSdw+YNzY5OnwUU4qnyKStgiCguaR5XEyc0XnvGOrA2o1rmUwImJihq7FUVk5J7olayRRVVpUz1CYq9OSUvaFelHvKYREs7H6ivi0A+My0z7B/F7oKDQsjmw5vwoUNF6LYXQxBEBDwBFhRir9fz687H5W+Smw8sFE1K1KrJ85r98JpdeZk4gAJsNH353f4IUBQySmBLECna8PM+ieKIoaiQ7K+2TsX34nWgVZsbtms+3tb27biZOgkbpx7IwDkDeLonOcqYCiZuOHYME6MnMB3tn3H9GsBwMFTBwFkAdS+vn2YVT4LgiDAYXVgfuV80yCOriUtGfSunl2YUz6Hrf93L7sb1591PX706o+YjJL+TqaTbB3e2iaNFljZuBKAtMecinz0IC6RSiCajOZk4ozcKUfjo6bNmWjOLBlOkUnFh71/RSYuKu+JozxjacNSvHH0Dcai8m7PuRh8rbjvrftQ7i3H9Wddzx6bVjoNB/oOYOHDC/HQew/JGK6PiomjeY7hRBg9oz14/vDzqlm1yogkIqwoRn/zRaKtrVux+onVMsaZ1j2H1SGxuZm9J5VOsbVud89udrxRX1xaTGM0PsrUDRW+ClR4K3SZuLSYZnvSS0de0ixInQFxYwhBEE4dkowAACAASURBVCyCIPyDIAgtgiAMZx67NNMXdyb+StEz2qNKZPjZODSLiEwvnDZnTmt1WU/cOOWUZF5y07ybAEiVMLfdja7RLsRTcVT4KmTHjxfE0WJCTBw9pxaIU1YhKbEjcJCLiZPJKU8jE8eDT9qcuoJdiCajukwcICWU9F6e3P8kvHYvqv3V45JT5mLi+iP9OHjqIJth1RvqlTFx9F4ByI1N7GqZBA/QjJg4uq5PjJzA8keXs2u4P9LP+uHoWCC3nDKcCKss7wGpqj+unjiDJGbD3g3YsG8DDp46mAVxEzB0NZbMyinpPtC6x3k5ZTQZxeonVrNZeRMRWnLKUDyEUCLEQD4BeWLP7RY76zkCjHvi2gfbsbd3L1ZPXc0eK3WXypg4ul+tFis+O/2z2NyymYE8LSZOKafkrxsjJo7CaZNMpUjaDIAVsWh9ykdOGUqEkEwnZSDuszM+i9qCWjzwzgO6v7d+93qUecqwqnkVgDGAuFgWxBn1LvPFkWgyilA8BI/dg4d3Pqwrk9IKAnHU2723dy9ml2VryotqFuG9rvc0h34rR5GQDFoJQEVRxK7uXZhfOZ89JggC1s5ay14bgMyEhu6RLa1bMKtsFiuYBjyB08LEUQHNkInzVWE0Pqpap4di2UKgWbZda48zY1JBIXOntKndKbuCXfjN+7/B2o1rZTP3uoPd2NK6RfM5mZwyGZaBlaX1S9EX7mNM2WB0kBWwczH4ymgdaMULrS/gG4u+Ifv8MwIzEElGsLN7J946/tZpkVNSP2gkkR0on0vyHk7KmThAvr489P5DeP7w87LH+JacKn8VK47z35kMxBkUGOnaIxAHZK4bHSaOPtesslnoC/cx4MoHvdczIC6/uBvA5wB8HwCt3K2Qhn6fib9CDEeHMf/X83Hn1jtlj/PVR8bEDWSZOKVduzLopnPb3OOWU5J73z8t/yf811X/hUU1i+Cxe5i8s8I7sSCOMXGZnjhASp6oglrqLtUHcZm+NgIHZkcMxFIxVqXkg6qNZnriRFGUgTw9Js5hdcjYkYGo9Bl4Z0o6jh/2DUgJZTAeRCqdwlMHn8Kq5lUodZdqgrh3O981lGQxJi6HiQKdw8HoIERRlOSUXm0QJxsxoKF1l4E4E0zce13v4dWOV1nFrz/cz5wpAfNMXDgR1gSVzSXNODZ8LO9r1YycckfXDvba9P3kkqyIoojHPngMSx5ZghufuVHzGJ6Jo3vvZOik6jjGxEUH0TbQhucPPz+hJidsxAB37qlvlgdxHrsHLpsLDYUNWDFlBesrAyQ5pR74eO7wcwAgA3F8cq1MSNfMXINwIsx6TfR64oghNNMTB8hBnMPqwE8+9RN8ae6X2GNUZCO2JB85JSVv/Dpht9px2zm34aX2lzRBd3+4H5sObcL1Z13P7p+xMnFD0SHD/YE/P5FkBKFECF9Z+BVMLZ2Km5+7WbMAdKDvgOp+4kFc92g3hqJDmFU+i/18Uc0ihBIhHDil7rcjVQpFkasI5d5yFYg7NnwMg9FBzK+aL3uceinpc3YGOxlA6g31IhQP4Y1jb+DSxqxJd6mn9PSAuMzaq2ls4szKKQF1AUhLzZEryN2aj5mBmegMdppi1QjE2S12tjeG4iH806v/hIUPL0TN/TW49flbsWHfBjyx9wn2ez95/Se48okrNfcjpbEJz8QB2b64gcgA2/cLnAV5FVQpV7mo4SLZ4zfMuwHrP7MeU0unYjQ+ytbpYlfxRyanJBAXTobNgzheTqlg4hKpBCuo8GsO77Zb6atkII2/ZzuDnex8GxUYaW2h+waQJJV6zqZ0LZHqQsshmfXEaezLH5f4a4C4dQA+I4riHwDQ3dQOYNJf4b2cCQD3vHYPekZ7VHIJHsSVukvhtDplckoyvsjVE1dfWD9uEHdk8AhK3CUIeAJYN3cdLIIFHruHySx5xozeHzAOJm60G1bByjZfQEqseCaO2Bjl5hVJSHIMSm5y6cZpoTPNxBmAuD+1/AlVP6tiAG0wYk5OScfrgTg+ifE7JDnlX47/Bb2hXqyZuQZuu1sF4vb27sWi3y4ybJ422xNHQHcgMoDh2DAS6YRKTsmYOANjE0AO0JTJGQUZv4iiqDJfUTJxZkcM8DIdPppLm6X+mgF1f41RmGHitp/Yzo6hqmMuOeXR4aP40jNfwo7OHfjDvj/oylDoOqV7TwscEqAaig6xf5ttiDcTWkycchC8x+5hidfT1z2NR696VPYchU59OeVzh5/DjMAMxrwAxiDuwoYLEfAEWPKo507pc/iYjI9moIUTYVNMnMPqwN+d93dYXLuYPUZySioysREDJs41rQPKMSS3LLwFbpsbP9/+c9Xv/M+e/0EincCN825kj42ViQOM3Vn5+2o4Ooy0mEbAE8AjVz6Co0NH8Q9//gfZ8SOxEcz79Tz8dudv2WNpMc3YlBMjJ2SmJhRG5iZKOSUATRk0FRvJdZaC1gtiqztHOjG3Uurp6Qv14bWjryGeiuOypsvY7wTcp4eJo+/MiImj60u5dgxFh5hs1DSI0zDvoqKKmd5oKojw7pTPH34ed792N+wWO+69+F7s/dpeLKhawNYcAHj7xNtIpBOa6yXfEhBJRhhYaSppQoW3goE4viiar5yS9keSn1N47B7cMO8GlLpLEYwHGQiaUTbjI5dT5sPEUV4DqJm4t0+8zfZHPudiDudWO6p8EhOXFtOq76CusA4l7hLDvYmKDTwTp+VsuqV1C7a0bmHX0rTANEwrnWYI4s4wcfmFH8AJxWNWABM3AOZMmI4DfQfw4I4HAajtcnkJnSAIqCmoYUk+XfRGBhB0E9UV1pnejAYjg5pJY/tQu8qB0mP3sAraRMspe0Z7UOGrYM3HgJRY0YJX6inVlZExOaXFpJzSRE9cOBFmgMdo4zg6dBSRZASnwqcQS8YQSUZ05ZS0aFsEC9vEWgdaYbfYUVdYB4Bj4tJyJm40PoqN+zfCZXNhVfMqTZt5Yq7ISEAZqXSKfT+5euJ4Jo7MMpRySi1jE2VPHKAhp9Rh4gDpHlAOJFcxcSZHDPAyHT5Yf02efXH8Z9BieztHOlnRwUxP3EBkAGkxjZOjEqN2efPliCQjmokEL6eke49+jw++J47+bSS7fWLPE1jw6wU5becptHrilCDObXOze7WhqEHF4Ba6ChmzzMdwdBivdrwqY+EAOYgjd0oKm8WGS6ZcwpJ5Al/8GhCKh9jjdH99Y/M3sPqJ1aaZOGV47B4UuYrUckoTTJzeLMkSdwnWnbUOj3/4uKqncf0H6zG/cj5ziAPGzsQBxtc+/93SOuW1e3FB/QW4ffHt+I93/0PmINg+2I54Ki4rSp4YOYFwIoxiVzE6g50sieWZuKaSJhS5iljhQ/kelOd9aulUFRNHn195Lmm9OBU+hUgiIrF1Gcllb6gXW1u3wm1z48KGC9nv0HU2njE5ZoLWXr4nLpKMMMdKINsbrsXEkamYWRCnpTbRGl2jF8qeOBEiY7me/8Lz+N6S72FW+SyUe8vZWhCKh9h+pFUEVbpT0jotCAKWNiyVMXG0n+YrpyTgorUnAdlec1ofp5VOw/Hh45r50HB0GIt/u5gVI/KJWDLGiib8XLzxMHG8bJUvHPFMXJWvCsl0Ev3hftX3XOmrRKWv0lAlwhcbKGj9ISluX6gP1/7xWvzolR+xfdtr92LFlBV4/ejrqt7pMyBubLEHwNWKx1YD2PVXeC//p0MURdy59U547V4sbVhq2BMHSJJK3p2S/tZLXkOJEFw2F8q95aZ64oaiQ6j79zo8deAp1c+ODB7B5KLJssf4xfCjYOKUz8knViXuEgYi+P4Gek1ZT5xZOaWBO6VsxICBhIOfD0UAV4+Jo8270lfJjm0bbMPk4smsuko9O8l0UgbiRmIjePLAk7is6TI2VFmZnJN8SZmg9YZ6ceMzN8rshHNt3rRxD0QGVEk6hawnLh85pc6cOACyMQwjsREkUgkE40FNOWUud0ojJg7If1Ycf21rSSp5RiGUCBnKKXd07kD1z6qxfvd6xppfWC8llFp27ryckpI/IzllKBFiSbUesPj5Oz/HF576Anb17GLXTq4ww8SVecuYHFwrKClQFhK2tG5BMp3EldOulD1OxiZpMa0pDVsxZYXquWVMXGKU9bMRiDvYfxAdQx2mmTitqPHXqOWUeTBxWsWeWxfeilgqhg37NrDHPjz5IXZ272T9yfxnHQsTZxWshuYm/P5C1yaxGT/91E8xpXgKvvzsl9lr0x7FA0+6npZPXo54Ko7Xjr6GgCcgW0MsggXnVJ/DJMh86DFx3aPdMim4VqIJSIUCq2BFf6SffUeUgPaGerGlbQsumnSRbO0PeAKIp+JjnqVpNniGg15/ODqMZDqpllNqMHG0L5s1L9IrVALmnHaV7pSAVOS1CBaZ4VS5t5wVg97vfh8pMcU+mzL0jE0ASVJ5fOQ4jg4dxWBkkL2GEYOvFYyJs3s1f+5z+BCMBdlx0wPTEUvFNG339/Xtw47OHXjnxDumX5/icP9hdi4iyTx64rh2ACUTR6Y8gHxf4o1NeMUGfc+091Z4K2TGJ1rB5JTOrJySOZtm+uJ+/PqPEYwHmYs4IJ3XFVNWIJQIqQo0Z0Dc2OJ7ANYLgvAoAJcgCA8B+C3OzIk77fHsoWexrW0b7ll+D5qKmzSZOH7jqvHXsJuSEhcjFz+qOAfcAVNyymPDxxBKhFSV/1Q6haNDR1VMHC0kVsEqW7zpfQHjY+Ko+qh8ziJXEWwWG5vxokxAIsmI3J3S5IgBYuKUSWE+ckpKAqPJqMxJSxn8a9T4a2RySpJSAtLiyxbcDCj1O/040HcAXcEurJmxhr1HZcJIiROxtxSvdbyGRz94lFU37Ra7aTnlYGSQSWQMe+JsJkGcgbEJkDVSAKQkn7eYpiC77JxyyoQ2iCtyFaHMUzZmJg7QllRu78xuWDImTqOaft3G6xBLxdA60MoKLkvqlwCAZo8QX00XBEG3isonIHSf6DFxv9jxC2bQYbaqr8XEESNI18fvPvM7/Gb1b3Sfo9ApSbmU699zh59DwBPAubXnyh4vdZciLaYxHB3WTEgvnnwx+zdzp1T0xCmZuL5QH0bjozmZOJvFJlMH8FHtrx4bExfVZuIAYEHVAswpn4Pf7f4de+zR3Y/CbrHj83M+Lzu2wFmAYDwoYw5EUdRkaAHpunPb3JhcPBmbDm1CwwMNmg55/HdL1yYlwl6HF+s/sx4dQx1Y9/Q62Rgc3nSD1qIVkyWA/XL7yzIpJcWimkXYc3KP6hrVY+IA+fqmB+IIYPSH+9l31FDYgBJ3CXZ07cDh/sO4rPEy2e/wMwk/6PkAxf9SLGMcJyr45JiuO1pj6fotdkkjffi1g1xNaV/Opyduopg4KuR2DHWgzFMmuzfKPGXoDfVCFEUZ2FHe5/yoHSUTB8j74ng5JRUtjHq++aB9VJeJy7Qp0D46rXQaAG1zE7qnxtKmQix0madMti/klFNyMlOeiesL9WFn9062TmrJKcnYBJAKAfQ9Ty6WCgCVvkppjIVBTxxvwEPhsDows2wmPjz5IU6MnMCv3vsVBAgYjg2zPdHr8GLZpGWwCBaVpJLWxzMgLo8QRXE7gLMBDEEa9m0HcBWAT5/u9/J/OSKJCL659ZuYVTYLXzv7a7IqattAGwYjg6p+Ib6azeSUNgM5ZabiXOopZSyGUejJrbqCXUikE7pMXLm3XJXYaIG4eCqe00CDojuowcRlgAHfJzetdJoKxEWTUbjtbvPGJmZGDMRDbBEzqv6xXphEbiaOoragFsF4EIlUQgJxxXIQRwsuY+LsPogQ4bA68Omp0m2rxcRR8q8EcfQ5SHJW4aswbWxixMQRyKRZdvS+lGHW2ATIgDhOTkmbJt8TB2TcLHPJKZPackpAYuPynRWXi4nb3rmdSTVD8SwT1xfuY9JBURTxN5v+BidGTsBtc6M31Ms+49TSqQh4AppMHD8fDZAqqXpMHN1H9Pm02KFIIoIjg0cYi2XW6U6PiSN2GJCub+rp0Qrqx+Hvq0QqgT+1/AlXNF8hGwkByJNrrfu1oaiBFUL03ClVIC6cAXE5mDg9Fg6QzE0msicOkAD6TfNuwo7OHdjftx+JVAKP73kcq6etlq2DQDa54hPxX7//a9T+e62mOcpwdBiFrkI0lzRjT+8eHBs+hj/s+4PqOC05JX9fX9hwIe6/9H5sOrQJD7//cBbEReUgrshVhAVVCwBIa9DscvW0o0U1i5ASU2xgN/8e9EAcf9/qgTggY1QSOcW+o5qCGpR7y5kU7dKmS2XH82Yozxx8Bsl0Ev/94X+rnne8oSWnJAaLrl8q1HSNZiWq4UQYyXQSk4omQYCQn7GJzvWdj7EJySkBaS9R7gfl3nJmhMODOGURlGcQCdTw19fs8tkochXh9aOvy4xNCl2FECGaHnGg1xNHQW0K4UQYLpuLyVSPDqnNTahgNhbX7319+2AVrJhfNT8vEKecEwdI68uLR16ECJEZiGgZm9gtdpkkl84ZjTKq9FUyJk5PPqx3b5FD5dbWrUimk7hi6hUqJq7IVYSzq8/GS+1yN9szw77zDEEQlgiC8HcAmkRRvAOSjPIDABsBXHs638v/9fjZ2z9Dx1AHHrz8QditdllfyIrHVuCe1+5R2Srzs9hMySkzTBxJz3JVjai6pAQDNF5AqycOUEsp+ffHJ7o/ePkHOPf/nas6VhmpdAq9oV4VE0fAgE9eqC+CX3iUcspcxiZmRgyIENmCrdW/QyGTUxowcVrf66H+QxiNj6qZuLhc+kAb7iVTLmEJsMcmB3GpdAqH+w/DZXOhL9wnS5AJsNH3WumrRCKdMJQjMibOoCdO051yrCMGyHEyGWOffyQ2wr4DXk5Jx4+ViQPkJgnRZBRvHX/L8LnoOAplIpFKp/Be13tYPmm59PNE1tgkLaYZEP7lu7/EUweewr0X34uppVMlEBfuh0WwoMhVhBmBGdpMHCenBCQgrmRcyExFmexqMXEHTx2ECJGxf+Nh4nrDvaqEziiIieOTu78c/wuGokMqKSWQG8QBWcZHb04cD8pSYgoDkQGEE2H2neoxcUYgrtpXjZ7RHtkMpnzcKbVAHAB8Yc4XAACbWzbjUP8h9IZ6cfV0ZUdENrmiZCstpnH/2/cjmU7i7tfuVh0/HBtGobMQyyYtQ3NJM86uPluTaeLvK3LRVSbC31j0DTQWN+LFIy+iY7hD9rkAaW2bHpguK0RqMXHnVJ8DQG1uorVONJZICagSxPFybj4CngD6w/1szECNXwJxKTGF+sJ6xrzwxwPSdbalTbLGf/bQs7pr/1iDT47p+lIycQBUTAkVWkrdpSh0FZrviUtpOzADY+iJyzCyR4eOqu552h/6Qn1458Q7DMAri6D0OfwOv6ac0iJYsKR+CV5qfwmj8dGssYnGumEUoXgIAgTNawPIuj4TWGooagAgZ+KeOvAUgrEgK5jlM2SdYm/vXjSXNqPYVZzfiAHOtZNn4ra0bkGpuxQX1F0AQNqXWgdasXH/RpU7JSAxcVR0pFyDeuLiqbhuAU/LnRKQHCq7gl3YeGAjyr3lOLfmXMRT8az0OnONrJi8Au+ceEfGxJ6RU+YRgiDcDOA1AP8A4DlBEP4ewBYAtwP4DgD1inomPpI4Pnwc//zGP2PNzDX41ORPAZD3hZwYOYHecK+q+qjFxBnJKUfjo/A6vLpW/MqghUmZ5BFjQ9Q7BT//SRlaIG5v717s79ufU7t/KnwKKTGly8TxCfzU0qkYjg3LZGP5GpvQQhdLaffE0QbHv4aeEYhMTmnExPFyyow9OSUuShCnYuIyCeWamWvYcUomrmOoA/FUnDErPBunZOLoPBuxcbRxD0WH0DPaA7/Dr6rm8j1x9G8zPXFaibEmExfXZ+IcVochE5cW04ilYvpMXEkzuoJdGI2P4tHdj2LJI0vQOdKJaDKKQ6cOaf6OERN34NQBjMZHsaR+CWwWG6u4ksS3e7QbO7t34lvbvoUrmq/A3533dyjzlqEv3IdT4VMocZfAIlgwIzBDsz8tllSAOA0mjq7XqSUSiDs+chyANjtE5jfn1Z4Hq2A1nZzQdcEn+r2h/EAcrX18cre5ZTMcVgcumXKJ6nh+PdMyaQCAr5z9FXxxzhfZvcUXcpTGJnzQPat83CwTlxKlAhRdi7FULKfcazAyCJ/Dx64NZVT4KuB3+HFi5ARjkYgl4EMJ4ra2bkXLQAsWVi3EUweeUrFbwzGJifvuBd/FodsO4bPTP4t9fftU7IKRnJJCEAQsrF6Ind07GXOhZOKmB6aj0lcJqyAxq7ypCUWVvwp1BXWqvjgtJs5j96CuoE4mgx6JjaDAWcAGWfNR6i5Ff6Qf7UPtKHZJw6LpOr208VLV79QX1kOAgN/v/T12dO7AzLKZOBk6KZNJT0TwIwYEQWCFN3qMotpfLetZ4sF/ibtkXHPixmNsAkjXuRYTB0gAvnu0G0vrJVmkUk5J+UBNQU1WTqnYN5bWL2X7FRVFtdYNoyBwpnVtABI4iafiGI4Nw2P3oNBZCL/Dz0Bc50gnrvnDNfjd7t+NS065r28fZpXNYo7StG+HEiFD1VAkEVExceFEGNvatuGSxkvYdxFJRvCrd3+FdU+vk82J8zq88Dv86BntYd8zMXEV3gqWX2oxj4D8OuWDeku3tG7BsknLWDGK1iq6ti6ecjFSYkpWKGJFM5s2sP44xOlk4u4AsFYUxTJIYwZ+Amm0wExRFB8VRZPCYpMhCEKRIAh/EAQhKAhCpyAIf2tw7G2ZY4KCIGwQBKFgLM/zcYnvvPgdiBBx3yX3scdoQToxcgLJdBKj8VFTIM7InTKUCMFr97JFL9diZ8TEWQQL6gvrZY8zEOc1B+KooVxr6CMfWjPi+OdUMnGAvBqrHDFgVk45Gh9FSkxpMnGAHMTpGQjI5JQme+Loe6VB2nogjj7PtMA0lLpLZSwFgThiJIm9+XSzJLfU6huh8RD0/Rlt4Pw5bBlo0UzStXrijOSUoigiJaY05ZS84yRdj8F4UJ+JyyGnJEZErx+C768hVqp1oBW/3PFLzH1oribAjSajbMNSMnHUwL2oZhGT44YTYZZ8H+4/jGv/eC3KveV49KpHYREszM2tP5J135wemI7+SL/KLVKZiFX6KtEX6pOBFbpepwXkDIMWE7evbx9sFhuaS5tR5CoyZZKQSqcgZkaNKuWUWmuCXhCb/Judv8HF/3UxUukU3jr+FhZWLVQlDEA2iQjGg7pM3LzKeXj8s4+znyl74ijhUVbl+8P9sApWlYTTFIjzZ2fF8aA2V1/wUGxIl4WjqC2oxYmREzIWSRlKEPeLHb9Ala8KL3zxBRS5ilRs3HB0mL0uuQACUM0RNDI24WNh1UJ0DHWwogOBjJHYCLqCXZheOh1Wi5Wt61pMHCDdMzwTl0qnkBbTmude6VBJIE4raEh8x1AHuw/LPdI6xo8WoKgpqMHfzP8bPPrBo0iLafxs5c9gt9gNR7YYRSwZ05SqjcRG4La5GYh32VzsfucNdap8ciZOCeLG0xM3XmMTQK3MoJ7Yt4+/DQBYWL0QgIacMgM+a/w1WSZOUWyjaxOATE4JGLtF8xFOhHWllED2XPeGeuG2uSEIAuoL69msOALWRwaPoCck5Sj5MnGRRARtA22YXT4bHptHZmwC6LOKoihqulNu79yOk6GTuKzxMlnONRofRTQZZQU7uneq/JJkkvarT03+FOaUz8H8qvnMrfW9rvc038NIbITN1uSDRnUAwLIGDsRlek/pnJ9fdz5cNhebZ0fv1Wax6RawPg5xOkFcnSiKf8z8m6yuvimKorGl29jjPwDYAFQDuALAPwmCsFx5kCAIlwC4K3NMDaQevV/k+zwfl3i141Vs2LcB37vge4yuB7LSAEqsQ/GQbMQAkGVsgGzlwmVz6crISDZEN5UeXT8UHUIqnTJk4moLajUroYB5OSWBuD296v4MPqjaqOdOqWTiADmIo02KNzY5MXJCt8GfmDpaQPVAXDAeZJuWHiDm5ZRajcDss3CVJ0rIXmp/CVbBKrsuHBaHzGEKAK6ddS16vt0jM/fw2D1Ii2mWTFMidcXUKwAomLgMIKHNiTFxBmMGeDbzUP8hlakJkJV7ytwpDeSU9JxG7pQ8EzcSG9Hvicshp6TNTFdOyTlUHhmSZKYdQx3Y07sHsVRM0/Qkmoyya1GZ/Gzv3I4iVxGaS5sZwA4nwqzyefsLt6NjqANPXPME+yzlHg7EZR6bUTYDgNrcJJaKqXriRIiyUSKMiVPMzNKS+O3v24+ppVPhsDqkhDCaOznhrwkliBuLnPLZQ8/i5faXsaNzB97vfl9laEJBcp5gLKhpRMQHrQGqnji7Nijrj/RrVoV5N0u9YLPigp2ydS+XpHIoOqTJ1vNRU1AjMXGZxEirx5AHcUPRIbx45EV8ae6XUOYtw7fO+xY2HdrECkVAVk5JcXb12XBanSpJpRkmDgCTy0WSEdgtdrbfEJM9PTBd+iz+GlR4K1T3MMWimkU4MniEXcu8JEwZDYUNjGGmz64H4khO2T7UzkDc5OLJcNvcTBGjjJ9e/FMUOgtR5CrCiikrcPGUi/H0wacNxw7c+8a9KiAcjAVRcV+FpvMzP9QbkNY+2iv5z1Llq8JgdJBdWzyIK3YVj2vY93iZOEDdI03/f+uEJE2fVzlPMr1Q7J30vmsKahBPxZFMJ1Xr9IKqBWwfHrOcMhHSLeIBchBHxzUUNTAmjq79jqGOMTNxB04dgAhRxsTx64NejhZPxSFCVM2Je/bQswCAlY0rs+MpEhG2H9G5ZiAuUwig73lW+Sx8+LUPUe2vRlNJE0rcJbpM80hsRCWlBKTvmXKIZZOWMXDNQFxmrXDZXFhSvwR/bs+am+gV4T5OcTpBHHstURRTAIKiKJrrCM0zBEHwAvgcgB+IohgURXE3gEcAfFnj8BsB/E4Uxd2iKI4A+D6AuyZfOAAAIABJREFU6wRB8OT5PB+L+MHLP0BDYQO+e8F3ZY/TYk2SgdH4qGrEQKWvkhmImJFThuIheB1etthpLRAjsRFMemASHnrvIV0Qd2TwiKofDsguJFpVd/oZVRTJPACAZpO98vUAqJg/LWOThsIGOKwOTRDHG5t88akv4mt/+prm61GVnhY8vSolkAXSehsHfRc0PNgqWA3dFwHg3NpzMb9yPloGWtBQ1CD7zvl/88+jrFzRpkPf3d7evSj3lqO2oBZVviq0DqrllLQZ0wJslonT6n/g36tZd0p6zpzGJvGssclAZAB2i12VRDqtTsOePtoo9eSUxH62DLSw669jqANtg9IAcC1JZTQZZYnowVMHEfjXAHZ27wQggbhFNYtgESzwOrxsxADdR33hPvx4+Y9ZDxogbYaj8VF0jnSya3xGIAPiOHOTZDqJtJhW9cQB8llxjIkrzc3E7e/bj5llMwHAdFWfvybo3KfFNPpCffmBOFeh7P/3v3M/osmoPogzwcRRaEmqlT1xfAxEBjRBIR1vBBhpbegKdsnYq1zmJoORQVNMXGewk10bWkCTB3Hb2rbJxjPcsfgOlLhLcNerd7Hjh6NyEOe0OXFu7bl4/ZgcxPHFEUpatZJhAnEAMLNsJusxpoISgbi1s9fi1oW36n5WGvpNgNMIxBW7i2WsMUlEtaLUU8ocYMmk67ZFt2Hf3+7TPf/l3nL8zzX/g19d8SvYLDZcNe0qtA226apJUukUfvDKD2TnGZD29eHYsKY0Wgk8XTYXQokQZpbNZOcMyCpTSKkyViZOS4JM3yevKHjpyEuaLJeWOyWgBnHEzJEqobG4UXMMBpNTcuyy8vqyW+04v+58AFlli5YhklGEE2Hd8QJAdl3hQVx9QX0WxGWu/fahdpYr5Wtswobcl8+SHKU5wGX0WWjNVjJxXcEunFVxFqr8VWyvpdwDyOYptMcyJi4DaHlDOkEQVCw4H8F4ULdAMq9yHsq95ZgemM7upa5gF1w2l0zVsGLyCuzt3cv2Jr2xPx+nOJ0gzikIwo/oD6TxAj9SPDZRMRWAIIoiP2V4NwC1HZX02Af0H1EUKVtpzud5MrLLSfwfAPrDif5K8cfP/REb1mxQJZO0IDEmLhFSySltFhtLuE25U2YqzkZM3J+P/BnDsWHs6Nqh607ZPtiucqYEjJk4h9WB5ZOW476378Mr7a/I7M/39hnLKT/o+QAl7hKVZIgxcVwF12qxoqmkCYf6pSRbFEU2YoA3NukL9em6D1KSQOdHb4MDshtNTiYuEUEoHtLV4PNyQ7fdjbf/5m3cfdHd+M7535EdpwfolMGDuFQ6hS2tW3BRw0UAJJZJqyeOwgyI45kMEaJKOsO/P97YxEhOacTE8UmFrCcuLLFUynPqtOWQU+Zg4nwOH6p8VTjcf5iBuPahdnbe6Prig2fi3jj2Bvoj/Xi141WE4iHs7d2LxTWLAWSHxUcSERS5ilBfWI9LGy/F3y/5e9nzEbvZOtDKnreusA4eu0fGxFFSrZRTAtm+VlEUWaJZV1gnAx9KUBFNRtE22MbkbcVuc1V9LSZuMDKIlJjKC8S5bW44rA5cUHcBZpfPxpP7nwQAdv6UQYBqODqMRDphCOKUTFw8FUcindAFcXpMnBk5Ja2zw9FhGfAxw8TlBHH+WnQHu3Fs5JjuzD2eoXz+8PModZeyc+h3+vHd87+LF1pfYPI2LcCztGEpdnbvlMmHNYd9a8jSStwljOGaVzlPeo2oBFxsFhsrYNx57p24Z/k9up91YdVCCBAYI2AI4lzFiCQj7HznklMCYI6OgHQPKXu9lbGqeRXWzl4LAPjM9M9AgICnD2pLKvvCfUiLabzW8ZpMAk39QVr3FT/Um94TANx3yX2yYh0ZfRFLx4O4hsIGHB0+aopJ0yp8WC1WuG1u9vudI51Y8dgKrN+9XvX7enJK5T3vdXjhsXsQjAdRW1ALt92NQpd6QDfJKXl2WavYRnMzSYGilA/nCrNMXF+oLwviCuvRH+lHKB5i3137YDsrlg1EBvIaBr+vbx/sFjuaS5rhtrkhQpT1MuoxcZST0XlxWp0QIO2BlzZKrqr8oHg9Jq7SW8l64nipLsXimsXY17dP8zoyurd+tvJneOrapyAIAisMdY50ql6DgDgVaM4wcfnF2wCWc3+2K/6/bAJfywdAeWcNAVBzsdKxyox4OHNsPs9zJ6QeP/7PG3m969MQVf4qLK5VJydaTJxyxAAgVWQFCOzxXMO+vQ6vIYjb3LIZgMQkaPXERRIRdI92G4I4LWMTQRDw5LVPormkGWufXMs2sfrCeuw5ucdw4dt9cjfmVsxVJ+oa7pSAvC+CWAq3zS2rwkeTUZnshg9KRvVAHJ+w0Eajx8Sx+VAZrbueBp8WVTqHTpsTdy27C189+6uaxwHajBUFD+JIJ08Odk3FTbJB1sr+LjPGJkpzGK0knR8xwIxNjOSUBkwcS4i5eTMkp1T2wwGZnjgjOWUOJg6QrqM3j73Jrv99fftY8UGrAMAzccQu7+/bj/e730daTDNGwWP3YDgmAQ6P3YN3b3kXmz6/STWWg85pSkyxz2gRLJgemC4DcVrN4MSG94z2oHOkE1dtuAoPbH8Al0y5BB67h1Wv7Ra7qkhz6NQhpMW0jIkz0xOnxcTpjZ8wCkEQ8PjVj2P9VetxyZRLIEJEpa9SxcRT2Cw2uG1uJrczZOIUfbF0LemCuHC/IRNnBOLcNjcsgoUxhBS5mLjh2HBOEEemKe93va/ZDwdk95DB6CA2t2zGquZVsir4bYtuQ5mnDHe9ehcSqQTCibCMiQMkEJcW0zJ3VtpfSLIN6A9MJjaOQNxgdBAH+w+iqaTJcP3iw+/0o6aghsm9jUAcJfSUCOeSU1JoGcOYiUpfJc6rO08XxFHPWkpMMakbkJWWacmUgzE5w9FQ2IArmq9Q9emxOV+Z1+BB3OXNlyOeiqvmcPGxs3snDvQd0E2cyWIfkIZzA5DJsynMyimBLBtHSgc9Jq7AWSA7B1rFtlsX3op7lt3DnitfOaVydIEyeCdb2id4h0renTqUCKHEXSLr2daLW5+7lYHhvb17MS0wDXarnb0Xns3TA3G0htDvkAEOkAVx9J55Jo6ej++JG42P4mTopOY9vKhmEdJiWtYXR26XI7ERzR5lQGLeL6iX3DGpMNQX7lO9xvyq+bAIFrzbdQbE5R2iKC4TRXG5wR9tUfjYYhSAciUtBKCVJWodW5A5Np/neQDAZMWfC/N613/FUIK4UDyk6dxXW1ALl83FAI7Lqi2nFEWRVVuoGVW5QIiiKANxWkwczfzRklPSBlpXUKf5mYrdxfjGom+gN9TL5sRc2ngphmPDrEFfGal0CntO7mFJAB90syuT+KklU9E60IpUOiWzrOUTuEgygpHYiGbVjskpc/TEAbmZOJmcMqm/aRgxVXyMhYl7+sDTsFvsWNW8CoC0gZ4MnWQgTcnE0eZrVk4JqJvY+fdnWk5pwMTxhQdmbJKZE6fVSzNeJg6QHCpJPlniLsH7XVIiYxEsOZk4StL29e1j8iHGxDm8LBny2D0o95Zrfpd8EsR/xhmBGTI5pdZAaiqkrN+9HjP/cyZebHsR911yHzZ/Ubq/qY+kvrBexQzt68tIfDJMXInLpJySA/b0nsYC4gDgc7M+h6aSJuamurhmsa6LHCAl+iTPzoeJo2ucks+JZOIEQWCJcCwVY/dkLiZOryrOB7FvfeE+XRBHCda2tm3oj/TjiuYrZD/3Orz43pLv4cUjL+L5w88DUEtZz6s9DzaLTdYXRyCK9iin1akyfqG4atpVOKf6HDYfcSg6xJwp8wm/w88AN1snNEAgFSfoeh2JjaDAocPEcffUWEEcAFw9/Wrs7tnNVDN8UNHHZrFh4/6N7HEjJk6ZHD//hefx9HVPq65/KiBSz/hgZBAeuwd2qx0X1F2AQmch+1614oZnbsAdW+7QHDEAgMm+ATBZ+HjklPxjNPu00Klm4vrCfSh2FcueS6vYVumrxA8v+iErfvkcPlgEi2kZKbWX6AV/D/JMHCD1jytfh4peRq8fS8bwyK5H2PzFfX372HxE+ozkRgzkZuKU58hj9zBJPu0HhiAuw+a2DrRqngsqPNIeBgAPvfcQZv5yJloGWnQLJHzwBSnluuZz+DAjMOMMiPsYxGEAoiAIM7jH5gHQ0tHtBcDsbQRBmA5AANCSz/OIojgkimIH/weANlL4XxhKYxMtd0pASrR42YFeTxxZW3vtXgiCgCJXkWqB+ODkB+ge7caCqgXSMPDMZsmDOL3xAgBw5bQr8coNrzBTCK2YUzEHALC1LTNQNVM10jM3aRloQSQZwdyKuaqfafXEAZL7XiKdwNHho3IQp2DiAGm8gzLyklPm6InTklNqhZF7Ix/5grhQIoSnDz6NT03+FEvQqHJJAIXfmN02N7v2zBqbANobtsOSHTGQS07JD2nVSs7ovQ9Fh9RySg0mzmF1jJuJ46/j5ZOWM+fF82rPU80iBKQNyOfwyb6X/X37sb1zOyYXTWbySI/dw6RVRt83D4z5zzgjMAPHR44zAKIlp/Q7/HDb3Hil4xUsqFqAD7/2Ib51/rcYiKGNtb6wXlU53t+3H1bByj5/sbsYQ9GhnNb4E8XE8bG0YSmKXcWaowX48DuyIM7InlrZE8cPoAWy9xQP9sbKxNH7CsaCiCVj7JznYuJG46OGvTqAvF+IN7jig5LqrW1b4bV7cXnz5apjvnb211DgLMCv3vsVAPVsOq/Di4VVC2V9cbFkDHaLnRVAjBLhdXPXYcctOxi4OhU+hZb+FkwvzQ/E8axQLjklkO2rMiOnBMYP4gDgmYPPIBQPYcPeDWxtIID12RmfxUvtL7H3xZg4E3JKm8WmuSYGPAHYLDbGxA1GB1nyb7facWnTpfhTy58079u0mEZLfwsbJaJ3jdM5JxCntc/xIM4iWNh1YQjiiD1zFcoKoPFUHFvbtuLc2nNl94CZPilBEDAjMAO7enblPBbIzcQZgbhjw8dUJiYzAxKIMzI3OdR/CClRmtk6Gh9Fx1AHK5bRa5wKn2I5XU45JXdeCpwFWD5pOVv/BEGagUe5ByA53wLZdZBUN20DbZqFo4AngMbiRpm5SetAKxLpBLqCXaZAnM/hY1JPrbXi7Oqz8V7XexBFEdFk1HBP/jjEJxLEZQxTNgL4sSAIfkEQzoJkRvKIxuHrAdwkCMJZgiD4IY0+2CCKYjjP5/lYh9fhhQCBJdKRZASRZES1cX3/wu9jxy3ZxlO9njhlslLkKmI3NAWxcHcuvjP7fFanLMnTG/QNSBvHsknLDD8XVZ1eO/oabBYblk+WjEX1zE129+wGAE0mTqsnDpA7VFLCJHOnTHEgTkNSydwpTRiblHnKYLPYdHX4KjmlTnI2FiZOi7GioOdp6W9B22CbrApPyTn1dwVjQZb8eB1eU85kyoHpWu6UPBNHC7MeiAOyYFLrc9ksNvgcPgnEZTakZDqJzmCnvpzSgInT2gSVQewBANl1vap5FUbjo7IZTcl0ko2joO9YgICR2Ai2tm2VSaa99iwTZ/T6fBLEFyrIoZLMVbTklIIg4F8v+Vc8cuUjePlLL8vGVABZxqKhqEEF4vb17UNzaTP7/krcJRAh5jQM0OqJGy+I8zl8OHrnUXztHG0TIgq/08+A8ViYOCUo46WbRkxcrnlGPocPowmJiWMgzoCJI/WAWSZO+W9lUJK17qx1mgmX2+7GoppFeOOY1GmglFMCUu/Rjs4d7DqLp+Jw2pzsPOdas4AsuNrVvQuJdCJvJs40iMtc14PRQcRTcUST0ZxyylJ3qa4szEw0ljRiTvkcPHPoGfz0jZ9i7ZNrmdyZANZt59yGZDqJTYc2AQBTnmjJlI2AJx8WwYIKbwVbhwYiAzKH4iuar0DPaI+mRXx3sBuxVIyBST05pRkmTsmMeuweuGwuzWuY9gk9OeWW1i0YiAxg3VnrZNeVmWsMAC6ouwBvH387Z8EJMG9sAmTX6Wp/NayCFceGj2EgMiBbJ4iJMzI3ISOT9qF2ltsoQVx/pB/l3nJYBIu+nFJjRM5/f/a/8YvLfyE7zm13y41NlO6UGUluJBnRPReLaxfLQBz1YALqQd9aYREs7HrWuibOqT4HvaFeHB85zvwLPs7xiQRxmfg6ABFAN6Sh4neLoviKIAj1giCMCoJQDwCiKL4I4MeZY7oBpAF8I9fznL6PcXqCv/AphqPDqgTXaXPKFm69EQOU+FIlpMhVpErKNrdsxsKqhbKEdVLRJDkTN9gOt82d19wnPgqcBagvrEc0GUWVrwol7hLUFtTKmLjjw8fZgMkPej6A3WJniSsf0wPTMaloklpOmQFxh04dYomH2+6WjRgwYuKIUSDmRZmo8Qunz+GTJCEm5JRGjdQfFRNHoJsfU0C29tQXF4wH0VjSyD6Px+6RCghGPXGZc0QLrpacku+JWz11NR649AGVvT3/HFSw0OuVKXIVYTA6iFAiJJNX8tc/hdOWdad86/hbuPeNe2U/Z3LKHD1xgCQ5oQ064AkwiQnvUMkzvnSPEXAbjY/KTDk8dg9SYor9Wy9I+gzICxWUAFOiqCWnBKSep5vm36QpQyxyFUGAgFp/rWoA9f6+/bKZXfwwbaPggT2d+5OhkxAgaAJts+F3+lX9gqpjHP6x9cQltHvieGZGi6XQk19qvXcafUDXrFaR7fEPH8fmls3s/RixW4B0HdJr68kpgSyI+9tz9MepLqpexN6TlpPj0oaliKfizKWOFCF0nnOxhkCW4XunU5LRf2QgjmPiaP3SA0QE+MbDwlFcPf1qvHnsTfznu/8JILuv9Iz2oMhVhCX1S1BfWI+NByRJpSETFwuaSo6BrLsgPZcSxBU4C3DHljtU8nfaFyg05ZR2L0bjo+gN9bL3q7XP8UwcIF275d5yzXWH5vDxfWw8u/fYh4+hzFOGlY0rc8opteKC+gswHBtmYAkA/rDvD6i9vxavdbwmO9assQmQXadtFhvrz+yP9KOppIl9V2bklCRVT4tpPHfoOQDZwjYBRWLiC52Feckpz687X6WQctlcstlzenJKQH/NWVyzGF3BLlZ46Ap2sTzUTLEByN7/WmvF2dVnA5Dm0Z2RU/4vjoy88XOiKPpEUawWRfE/M48fyzx2jDv2F5ljfKIoXpsZNWD4PJ/EUN4gyhEDWuGyuZBIJ5BKp2SPazJx3AIxEBnA2yfexuVNl6OmoIYtDpOLJ8uZuKEjmFw82bA/JVfMKZcklSQZmF0+W2bR/OVNX8ZZD52F5w49hz+3/xkzy2Zqfu7rZl+H9jvaVUl/macMhc5CHO4/LEuuBUGAzWKTMUxKJi4tplmCTWFkbOJz+FDgLMgtp8xhbDImJs6EsUnHcAcAORPid/pR4a1A60Ar0mIao/FRBuxIbssnTVpB1VcC87lGDBS6CnHHuXdoXjd0ftkQcx2GschVhJOjJ5EW0zIHVM2euIyxSVpM49bnbsU/vvyPsgSEySkNmLDGkkYIEDCleApL9BqLGzVnEfKSRtqoVk9dzX5OwA+Qb2RG37cgCOy88iCoqaQJVsHK+uLotXOxQnxMLZmKaYFpbD2g80FN65SQANnEOBeI05NTBjwB3Z6piQq/0xyIy4eJIwmQ1nm1WWyag26V4XP4EIwbyynTYhp3brkTP9/+c5XRil4IgsDAm56cEpD6k5dPWs5k7FrBs8RaTNyS+iUQILC+uFgqBqc1y8TlApwAB+IyvdDKgfO5gs4jYM7YZCAywBgevUTTZrGhyFWU043STFw942qkxTTbB4it6B7tRpWvCoIgYM2MNdjWtg3D0WFZTxwvy06mk4gkI6aT4ypfFXstJYgr9ZTi4U8/jHdOvIO7X71b9nskpafQZeLiIezq3sX+bySntArSPe61ezWLeoD0vRc4C+QgLrMuD0WH8Nyh57B29lqZ0QdgTk4JSEwcAPzl+F+QSCXwzS3fxHUbr0NnsFM1qy+XnFKPCWwobGBMXKm7VMqHILBr2khOubd3LztPTx18Ci6bi6malK+npZbi3zuQG9y6bW7ZbFUliCtxl7D9Vm/NoQIk9cV1j3bjqulXoamkieVyuYKKQ1qvMbdyLmwW2xkQdyY+eaG1kOdKGggMKKVkrMJrzzJxPIh7se1FpMU0VjWvgkWwsFlSk4skEEcbjd54gXxCCeLmlM/BgVMHWBLYPtiOkdgIrvz9lXi/633cNO+mvJ5fEATJoXJADuIACSDwDJMSxCkrlvzvUtgtdrYQ+xw+TZtkCianTEQMNw2lO6Ve5MvEkRGNEmQ1lTShdbCVJY0E4miRLXQV6m4gQPY80fMq+xL592ck+wSy1yx9L0ZMHCUsMhBnIKfcdGgTq37yvRJKdy+tcNlcmFk2E3Mr5qK2oBYWwYKmkibUFtSiyFUkm5+jxcSdW3su61uZXzmfHZuPTIiSIR6oOqwONJU0qZi4fDa/f7zwH/HeLe9lzTYy5+Nw/2GkxbQmE8dbX2uFnpxyrFLKfMLv8LPiixkQp+yJo3WRAFu5pzznLDhl/6Pe+yJjEz055YG+A+iP9CMYC6rWaaMgGaURE7fx2o14Zu0zhs/DFxi0mLhidzHmVMxhIG4sTBwB3oHIACp9lTndN5Vhlomj5x2MDuYEcQBw90V346sLv6r7c7Mxt2IumkqaGKvAgzhaq9bMXIN4Ko6N+zeiP9IPv8OPWComA/W0BpqVd1b7q2U9ccoh8dfNvg5fnvdl3PvmvXi5/WX2uJKJ0xxo75CYOJJSLqlfomtsYhWsrEBXW1Crkm9T3DD3BrTf0c7WyAJnAWKpGGLJGDbu34hYKoZ1Z60DoDbtMBNTiqegwluBZw89i+WPLscD2x/A7YtuR7GrmLGJgFQ4ySWntAgWtgbw76W+sJ65U9IYjYAnwNY5Qzll3z6mdGodaMWMwAxW4OI/IwNxJt0p9YJmDNL+pJwTJwgCuz71zsW8ynlwWB3Y3rkdaTGN7mA3Gosbcfi2w1g3d53h61NQcUjrNVw2FytGnAFxZ+ITFbSh8hd1LltmOlYpqaTNgTFxTvkCsbl1M0rcJWxDnx6YDotgQW1BLUSIiKViEEVRd9B3PkHyAUo+5pTPQTwVR8tAC0RRRFewC+vOWod7lt2D/V/fjzvOvSPv15gWmCb1xCkYF7vVLjPsUMoplYYdgDopFASBLZ40PN2UnNKEsUmuimO+PXEkS1Um0jQrjs5FXWGdzCK6wlshm+WnDKq+lnvLUegs1EwC6P2ZYY8BTk5pwMTRRszLQHTdKZMx/PMb/8yKBZSMAOaMTQDgtRtfw7+t/Dc4rA58/8Lv46Z5N8EiWHDx5Ivx4pEXWXFDBuIyG9WU4ilYWLUQ51SfI3sdnrnI9fpaTBwg9cURiGM9cQaDp5Vht9rhdXjZ61Nll4wOeCbOrJxSj4k7XSCOwigJsAgWWARLTiYu4Ank7HubVjqNFT/0wufwYSQ2gngqjiKnNhNH/WgjsRHV+zGK2oJaluzpRZGrKCerw49v0HuupfVL8dbxt5BIJSQmzpYfE0dmWkD+UkrAPIizWqwocBZgMGIOxN1x7h24eMrFeb8fZQiCgJe/9DI2f0HaR2md6hntYX1Hi2sXo8Zfgwd3PAgguw/y95WZ98xHla8KfeE+JFIJFRNH8eDlD2JaYBquf+p61jfaNtiG2oJaVozUZOLs0jlvH2pHubcc9QX1unJKfn7dhjUb8PDqhzXfr9Vilb1HynFGYiN47MPHMK10GgPCY+mJEwQB59edjy2tW7C7ZzeeuOYJ/Pzyn6OusE7mgE1rZq7npfuQ35frC+txYuQEY+K+fs7XcddFd8Flc8Fj9+iuk5FEBG0Dbbig7gJWnJtVni2WaTJxeRibaIXb7pb1XdLz8XssXZ96a47T5sS8ynnY0bkD/eF+JNIJVPur81Jj0b2v9xqlnlL0R/olY5Mzw77PxCclaCHnm2fNJsS0SEUSEdz/9v34/JOfh81ik23WdEOnxTReaHkBlzVdxqpCN867Ed8+79vspgsnwhiIDCAYD46fiatQMHGZ/+85uQfDsWFEkhHMq5yHH170wzFt+IAkFyPJA5A9L0oTkrEwcUA2cfE5fAh4AprzcwANOeUEGpuYYeJOjJyA2+ZWvW5TcRO6gl0MqBU4C1DuLWfHVfmrDEEcgd2lDUuZOY3eezVbeDDDxNF5zsXEOawO9Ef68W7Xu7jrortQW1ArB3EmRgwA0uZC5/Ke5fewhG9l40ocHznORg0omTibxYa6gjo8etWjePLaJ2XPmU9yUu4tR4GzQHVOZgRmSC5hqcSY5JTK1ydQu693H6yCVda7qLRtV8YzB5/BpY9fyq4JAYJsxIDW3MiJDp65yAVm7RY7u8+VoImuhwpfRU4m7o2b3jAcUg1I4JIq83QelUwcMVzBeFDVu2wUty68FT9Z/pNxSdspSDKlBx4ubLgQoUQIu3p2qZg4swk2A3F5OlMCWWlfWkxn50nqFHuKXcWmmbiJjLrCOpR5y1Djr0FXsAuiKKI72M0KThbBgmtmXIMPT34IIKtI4e8rKmTl0xMHSIqLaDKqCeK8Di9+f83v0R/px03P3sSKsVNLp7J8wMjYpDPYiRp/jeZMN0AN4ordxabPOR334ckP8frR13H9Wdez63ksckoAuGneTVg+aTm237ydDWWvLaiVMXEEgnLdZ/Q9KOWUZGRV6inFysaV+PqirwOQ9iI9OeXBUwchQsTs8tlsfeUVD/xnzMnEaRibaIXL5pK9n5HYCOwWu2zNoOvTiJVcXLMY73W9x/Il3hHdTBBY1zvfpe5S9If7zzBxZ+KTFURB83PXcsopM4ncSGwEv9zxSzQ+2IhvbfsW5lXOw5s3vcn0/0WuIsRSMUSTUbzf9T76wn1Y1bSKPc/KxpX4l0v+RTZvzMiZMp+YWTYTX134VXxm+mcASAmpVbBiT+8e1iutKgqGAAAgAElEQVRgJBEyE7RIUq+dTE6Z2Shr/DU4Pnxc1pOgVeXVWlTovPgcPlT6KnUBDy+nnGhjEyNwRAxLSkyhzFumSvRI7kIOWX6HH9fNuo6NfKj0VjKZjlYkUglYBSu+e8F38fR12oNu+Z44ozDNxDmzLIGZnjhA+o5vmHsDFlQt0GTixrphkOX9trZtAOQgLuAJYGrpVFgtVlT4KliiRWG2Jw4AvrHoG3jwsgdVj88IzEAynUTrQOuY5JQUlDgwJu7UfjSVNMkAodK2XRkvtLyAbW3b2Dn1OrxyJs7zv4eJA6RCDjFxStDUVNKER696FGtmrsnJxJkBT3wfEa3nPBMnimIWxMWCeTFxyyYtwzfP+2bO48zEzQtuxs3zb5Yl43xcWC+NWH396OuIJRU9cSbklADGzcSJEBFJRAyZOEACEWZ64j6qqPZXozPYiWA8iEgyIlur1sxcw/5NTBx/X+UyY1EGJeDEoCvllBRzK+fivkvuw59a/oQHtz8oKWqKprC9XHdOXDyEEyMnUFNQg0JXISLJiKrQqQRx+QTdEzTi4vqzrmc/G4ucEgBWT1uNl294WcZy1fhrZEwc3fdmmTilnJJCCZpLPaW6xa4PTn4AQGLfKD+ha0D5Gh67B1OKp2B/3378dudvVc+lZWyiFS6bSybvTItp1X3DQJwBoF1csxihRIgNj88bxDn1e+KALBMXSXz83SnHdieciU9kjIeJu+GZG7C9czuWNizF79f8HksblsqO4939NrdshgABlzZdqno+HsQZzYjLJ2wWG3716V+x/zttTkwtnYq9vXtZL0G+i4QyaJGkhZOBOGu2J665tBmvdrwqySIyQIDYBL/DzypYes5d9HeFt4JZWvPfD809AaTzF01GTxsTZxEsbGaglpyNQBwBG7/Tj/svvZ/9vMovyXRS6ZSmKUUincjJsJntiVMycXqfi5d65eyJyyTe3z7/23DanFhQuQDPHXqODVImK+OxshiTiyejuaQZ29q24fbFt8skjf+64l9Zb5NW5MPELaxeiIXVC1WPk1vrgVMHxiSnVL4+AYt9vftkyQ8gnUuv3aubnJB5DiXNXrsE4mLJGIZjw6dFTsknvbmSALvVLuuJc1gd7JoTBAFfmvslAJzE0mK85hoFzxB6HV7YLXYZE9cx1IHOYCdK3CUYiY3k1RM3kbGycSVWNq7U/XmVvwrNJc1449gbY+qJA7IAY6wgDsjOSwX014kSd8lfhYmjqPZXY0/vHlYE46Xf59edz4p+pEDRklOa7YmjAhH1/WoxcRS3LboNLx55Ed/983cRT8WZIzGgz8SJENE20Ibza89nifhwbFjWAz0uEJdhaJ45+AwurL9Q5hRqtVhZb/N4JXY1/hr0hnrZtcuYuBzXLmPo7XI5JYVy7ylxl6B9qF1zpu+bx95EsasY0wPTmeeAEYj79vnfxr6+fbjluVvQWNwoU7yEE2FYBWvOPdhtc6uYQeX7or3UqHBE5kdPH5QKtvnmZ0bulABQ4io5w8SdiU9e0ObTUJi1hzebEG/v3I51Z63Dqze8qgJwgALEtW7G4trFmuYUWkzceOWUWjG7fDb29O6ZMBBHs9BIukKLMM/ETS2RgB4vqaQqI7+J6jFxNosNDquDLYI0E4simU6yMQW0UZ8uYxP+uYxAHJl9KBOdKl8V0mJa9ZkozGzctMHkzcQZyCkpeBBHMjU+FlYtxKKaRbhlwS3S/6sXQoSID3okUB9OhMedGKxsXIlXOl5BLBmTMXFV/irdxn5A0RM3xvdAifDBUwfHJafke+JiyZjkTBmYqTqu2F2MgagOiMuY51AC6rF7EE/F2fDt09ITl+N+5YNn4owGa9P3NJbzSsEnRk6rE267W8bEEQt3WdNlSKaTTC5sRk55umNW+SwcGTyCWComB3Em3+t4mTjAHIgrdhWb7on7KKLGX4Oe0R7G/PBrldVixdpZa1HuLWf76LjklD7zIE4QBDzymUfYPj+lOMvEaY7RyNwXoUQINQU17DwqJZXjAXH0nCkxxQxN+PDYPabASq4gEyAC1lQsybXf0rqix8QpVSBXT78ae3v3Ytn6ZTLmD5Du9QsbLoRFsOCWhbdgw5oNMtDKA0W3zQ2fw4ffX/N7AJCZaAFS0c2MjJkKuXwozyUVAowAbWNxI0rdpcxdlr+mzYQZJm4wOohYKnYGxJ2JT07QhZ8PE8cvxpc1XabLNNCGerj/MN7tfBeXN12ueZyMiRtsR8ATGNdgVL2YUz4HRwaPMNv28YI4n8OHGn8NG2itx8QBcnMTnokDpP4eLeBMQ7EFQWA9P0pJJe8QStWwCZVT5gD0RiCu0FWIMk8ZAzXKpIEWaT2ZaCKVyPn6Ey6n5EAcfSa/w6/5/Kunrcb2m7ezBHNB1QIAWeYxkojkJdHRipWNKxFOhPH2ibdVLqhGMZaGfWX4HD7UFdThwKkD45JT8j1xh/sPIyWmVEwcICWLyqQEkOQ5ZJ7DmLiMnHK8g77ziXzklLKeuMSobmKRqycu3/fltDnhtrllTNwbx95AsasY59acCyB7v5mRU57uKHVLUjHlsG+zTFypW+ovrSusy32wIvIGcRkmziJYxnyPjTWq/dVIi2mmAlHKqe9dcS92fWUXAwDjMTap8FVAgMDmomkVtPgIeAJ44ponMKV4ChbVLMLs8tkQIGgWcPlrsMZfw1gzpbnJRMgpHVaHTGpK4bF7JuT7ozEctIaZlSNqySn9Tj9jlZWg+bZFt+EPa/6APb17sODXC5gjaHewGy0DLVhav5T93rWzrpX9Lu96Ta9X6CpEqbtU5SaaazwChdYeNxY5pSAIzPSu1F2ad2HLTE8czSo9Y2xyJj4xMR45JQAsn6RtOAFkE+IN+zZAhIhVzas0j1PKKcfbD6cXJC158ciLKHYVjzvBBiRJJTFhfE8cbZQkueSZOEoQCKjqSe48dg9b4AnwnBw9KTuGr4DR807knLhc1wIthno9SU0lTawiqQTmlHjQIFll5CWnnEBjEwq/ww+fw6fZD6cVVb4qVHgrsLMnA+KSkXFvFssmLYPNYsO2tm15gThKei2CJed3aBTTA9NxoO9AlokbA9jge+K0nCkppgWmyYabU5wcPclApFJOeVpBnFMOlozCZrEhKWaZuJwg7iNm4pbUL2FJDjEFp1tOaSZK3JLkKZaUM3Fmk+zvXPAdbFq7Kefgdq3IC8RleuKGY8MocBZMiPFLPkGA4U8tf2IGR3y4bC5U+6vhtUvyWhkTl+eIAZvFhnJvOQ6eOgjAmImjWNqwFG23t2FS0SR8euqncfC2g2goalAdJwNxBTUMcE0kE0fX/eqpqzUBqMfumZBcgHrsydzErIGQz652pwSyOZmWlP9zsz6HHTfvQMATwCWPXYJ/efNf8NpRadC4liqKQhAE9ln5e6qxpFE11y+cCJs6Ly6rej9S3jczyiRPglwKKwJxYymwm3GnpDjDxJ2JT0xU+6shQGCMEWA+IZ4emK6qAPJBN9UzB59BhbeCMRXKUMopPwopJZB16nqv671xs3AUvMMe705JSWdDYQPsFruciUvJmTi9BaWuoI4t5DTwWsXEZZJrvhqvl/BU+aswv3I+Flap+5/44BfgXBunERMHQCb5+yiYuI9ixACF1+FFgbNAcxPVCkEQsKBqAd7veh9ABsSNMzkocBbgvNrz8gZx9L147J5xJZgzAjNw8NRBBgrG5U6ZjGBf3z7ZjEg+ppVOw/GR4yz5oSApJfAxYuKsdpmxiS6Is08AE6eQebptWRDXM9ojVecblrL3T0WT080emYlSdyliqRiGokN5D/sGgElFk8Zs568F4vT2wmJXMeKpOE6GTp52KSWQTXJfbn8ZKxtX6gIyQRBQ4i7R7okzKacEpL2D9jQzIE75Hvh9kg/+eyV3SgCqmajjAXGl7lLcvuh2/HDpDzV/7rF7JoSZITllvkyclpwSAAO9esznjLIZ2H7zdqyZuQbfe+l7+OrzX4XP4cP8qvmax1PwewPFlOIpKiYuHzklhZ4ypqmkCQN/P4Bzas4xfC5ysB1LfkZ7gN71yT9+BsSdiU9MXD3jauz6yi7UF9azC9usO+WnJn3K8DhKiMOJMK5ovkK3OkoLRTAWxNHhox8ZEze5eDI8dg9EiB8JiKNEjN/4PXYPagpq5D1xaXlPnN6Cct/K+7D5C5sBgMkpT4bkTBxtrDLwoVNh99g92PmVnayBWC94s5BcACAXiGsukYoDWpIjAnF6DpVJMffGPVZjEzNMnNfuhd/hN83EAVKf3P6+/YgkIogkzG2CuWJl40rs7N7JriFTTFwmORrv688om4FQIoS2AalKOxZWj++J29+ndqakIGDXMtAie5zMjoAsCPc5fIglY4yZPu1MXA7QZbPYZCMGThsTZ8swcRk55RtHpflwF9ZfyBLk7tFuuG1uTTOhv3ZQotU92j0mY5PxBA/iaI02MjYBpGHK+YChiQreWXntrLWGx5a4S2S9psF4EC6bK68eMJLDWQXrhH5eFRP3EcgpBUHAzy//OeZWztX8+UQxcUWuIrhtbuZ+na+xiXKtnhmYidqCWsPP7Xf68ftrfo9/v/TfEUqEcFHDRTnPEwFWGRNX3Ihjw8dkrqBme7r5c0eSWa392EyxYzxM3EUNF2HLF7cwIKgMvhh7BsSdiU9M2Cw2trgph9HqRbW/Gg6rA1dNv8rwOD4h/vTUT+seR4vJ4f7DSKaTHxkTZxEsbGYKyVHGG5R48pJIfgFz2VyoK6jTllNmNkO9BM5j98iGsRc6C1WsFbEz/Lkeb+JuVqLIv1aZt0zz58TE+R1+FSB02VwochUZM3E53sPMspmY+v/Zu/M4uao64f+fb69Jp7MnZCWrwCCYsMgiEmQdBBVXdAZQQFxQcWQUHccHFcbtER11Hv2NOCLgwiAqOvM4rjOCgiAPiKKyKAiJQEIgIQlZCFk65/dHLamuVHdXd1d31S0+79erX+m6devUuTfVVfdb3+85Z+q+Awb+Q8nEdbV38XdH/B1vOvhN/bZd6pBZh9CTevjDE3+oSTkl5IK4ROIHD/wAGFwmbrjPv/+03AyVdz1+Fx2tHUMqUysdE3fvmnt7rVtUar9pub+l8pLKipm4knLKMW1jRmV8V+Hvtb2lfcAAqL2lvffEJn1kkmoxJm6PcsqSTNxNf7mJrvYuDpl1SDEIXb15dUOOh4PdJU/P7Hxm0It9D1fh/7fackrIjX89et7RI963cnuN26s4O3BhGZ2+TB47eY8lBgabPSwEcZPHTq5p6WghwOlq72Ji58QRmdhkILXKxEUEcybM2V1OWe3EJhXWiQO4+JiLue2826p63guPvJB7334vXzntKwPuXykTt3jyYnpSDw8/9XBxW7Vj4ko/jwpB3FBL+Kd2TeWiF1zE6w543aAfG5Gb/byv12czlVO6xIAqGtc+jrWsHfAPcGb3TDa+f+OA3x6PaRtTzOactPikPvcrvFHct/Y+gF6zKdXa8/Z6HnesuoPZ3bXNxJW+KZQGHmPbx7L3xL351SO/Km6rtpyy3MzumXtm4nbumYmrVRBXzRtxteWUfZX7zOqe1f+YuAEybPtM3Yc/XbDnOKpyY9vH0tXeVSx1GSgT19naSWtLK28/7O0Dtl2qdHKTrTu2DiqL15dDZx3K5DGTufWRW4HBjYmrRSYOcmshDjXQKFwgbXhmAw+se4BX7f+qivvtM2Ufgigubl7QbxD39BPsNW6vURmTNFDmvFRbS1uvJQZGMhNXXuY5pm1MMQtw88M384K5L6C9tb243+rNq2tWiVBrpSVPHS31y8QVyyn7Wewbcl8Mvu+F7xvxvpVrbWll0eRFHDrr0AEDsiljpxSzQwAbt28cdDatMGxisKWUAymc8znj5xARvZYYKDWSQdxbD31rce3E4Zo7Ye6gyymP2vsojl1w7B7ndlzHuEF9eVE6JKY/lcbEFb4EfXD9g8VlIbbu2NrruqLP9koC4OlduS9zhzMO+1N//akhP7Y/pZm4WmRe68lMnCoqvKEOdOEM1V10FOrxj11wbL/f/JYHcaWTrNRaYXKTWl3ELJi0gLaWtl5vZJUycY9ufLQ4M1K15ZTlZnTP6HN2yvKxXMNRbYkiDCKI6+Oiob9FzKvJxFWrJVo4eObBxcxlX8c20AxXA5k3cR5Tx07lzlV31iwT19rSyomLTiy+fkaznHJ613SmjJ1SnC1wKNpb22lraeP3T/yenbt29pmJG9s+lnkT51UM4gqvn9IxcT2ph8c2PTYqpZQwuC9dSsfEbd6+uTj2rVxxiYFaZeLadk9ssn7ren7/+O+LEx0ULva392xv3ExcyYVWaSZuNMbvlQdxrdHaZ8a1kIn72wP/dsTK/wfyszf8jC+99EsD7jdl7JTishIwtExc4fNyxIK4fGVMZ1snna2de2TiqpnkaqhOP+B0zj343Jq0NW/ivGL5d7WLfS+bv4wbz75xxI6vXLFKoySQKQRuhbJ5GMTEJiXvh4UvLYcTxI2USWMmEeS+7Mt6Js4gThUVLihq+Qd41cuv4rMnf7bffQp/UIWp/0cyiFs6I1c6OpQpqCtpb21n0eRFvd4USr8xLARxO3btKE7CMKxMXB+zU9Y7E1f4Bq7c5LGTmTp2at+ZuPF9Z+Jq/e3r82c/v/h7Xx+YbS1tdHd0D/mb/8LkJr9Z/ZuqPwSrUVgkuSVaqjonna2dBDHs10JEFEsqh/PBN7ZtLL9e9Wug8syUBeUzVO5Ku3ho/UPFdb8K39AX/n9WblpZnPRnpHW1d9ESLVUFs6Vj4rbs6Gdik1pk4srG6i2ctJA/rf0TP3nwJyRSMYjrtSh4A85MCWWZuNaO4pcgo1FOOaZtDC3RUgzi+nv/e95ez+PNh7yZjx7/0RHvV1/mTZxX/NKp3/0mzGPVplXF1+PGbRsHvYRPsZxyTP/LCwxW4f+1dIzfhM4JNR0TN5oOnnkwqzat4rFNj/H0jqdpb2kfteCsWpXKKWePn01na2evyU0GW07Z0dqxu+S8wY4Zcl+GFq6TDOLUlAof7LUM4k7Z55R+L9ogd2E6tm0s23u2M71r+oimul+04EVc++pr+1yzbigOmH5AryCq8AbW1tJGW0tbcdaqwgyVlZYYqMaMcTMarpxyWtc0Zoyb0e9F6JIZS3p9SJeaOS6XiUsp7XFfNeWUg9EriOun3UljJg3rovGQWYfwh8f/wFPPPFWz9WhOWpQrR+5rOYpyEcG4jnE1+VsqBHHDyRZ1tXexatOq3MyU0/acmbJgv6n78acn/8RX7/oqZ1x/BjM+PYMH1z/IQTMPAnov9g25meBGKxMXEXR3dFeXicuPiUspVVdOWaNM3Ji2MZz+3NPZunMrF99wMe0t7cWB/qX7NeJC37BnEHf4nMM5efHJQ1q8e7AK/7/VBHGdbZ3828v+bURL/2tl0eRF9KSe4rjsTds3NUw55bj2cbRES68lEiaOmTiq5ZS1dNjs3OyLd6y6gy07tjTk31mliU1aooWFkxf2WmZg686tdLVVv07cuPZxxTYbMRMHuzOFWQ/iGv8vQXVRLKesw7coXe1dbN25teJaMrXUEi38zYH9z+Y1WJ8/5fO9auoLAULhjaKQ9Xtk4yMcNuewPRb7HkwmbsMzG3hm5zPFxwxmdspqFf7/q3kdvP/o93PuQf2Xolz3muv6nBBj1vhZPL3jaTZt37PEp5bllECvpRX6a3fymMnD+hA6ZNYh7Ni1gye3PlmzIG7+pPnsN3U/1jy9purH1GoR28IF9HCyRYUP+sWTF/f7et9/2v5s3r6Zc/7zHGaMm8Gp+5zKyYtP5viFx/Pl33y5GMQV3qs2bts4akEc5P5mBzMm7pmdz7Ar7Rp4YpNhnNu2ljbGtI0pTgbywnkvZM74OTy4/kGO2vuo4rlviZZikNKo5ZRj28cWJ2bpbO1k/qT5/PisH4/a8xfOT3tr42VQhmrh5NxEYQ+tf4hFkxexcdvGikt89KeQiat1ENfa0sp3X/vdXl+wTeycOKoTm9TSwbMOpjVauWPlHVVnskZbpUwc5N6byzNxgymnLP28adggbuxU/syfM7/Yd+P/JaguRqKcslpd7V08ufVJ5k8c2SBuJJTPdFn48C+8URS+ZSxk4orllEPIxAE8seWJYslpvcspp4ydMuAHe18zV0LvteL2COJqnInbd+q+dHd08/SOp/udZXFG94xi7fxQlAaLtcwqn7XkLH62/GdV7z+taxpTxgz/oqswuclwvr0svCYHysqfueRMWltaOXzO4SyZsaT4/1TIXhcu7krP66gGcZ3VBXHtre1s3bm1+OVOX0HTvlP3Zf7E+cPONHV3dOeCuNZOWqKF1x3wOj5z22dYNm9Z7/53jM/Nltmg5ZSQn4hj08q6fA51d3Szecdmuulu2AvRwSqM2StcoG/aNvhM3MzumXS2dhaDuVoqn11zQueEipm4RgyIynW1d3HgXgdy+6rbmTp2akP2uXBdUv4+tmjyIn7xl1+QUiIiql4ipzSz1+hBXOFaJeuZOMspVVFh8H29gjgY2fFwo6U8Ezetaxpj2sYUy1nKlxgYTCYOYNWmVcVt5eWUhVkVa9H/WgZQfSlcFFRaK67W3762trRyyKxDBjyuL730S1z+0suH/DyLJi8qzrJWy2/8Lj7mYm48+8aq97/+tdfzsRM+NuznrUU5ZeE89DWpScGEzgm85dC3cNDMg3oF2u0t7bREC7vSLtpb2nv1pWEzcT07BgziZo+fzYoLV/S5GPJg+gW7M3pvWPoG2lvaOXWfU3vvl//iqFEzcbC75Gk42cmh6u7oZtO2TWzf1X85ZZbMGT+H9pb2YhC3cdvGQU9s0tnWya3n3co7Dn/HSHSxl4ljJmZ2TBzkSirvWHkHj2x8pCG/LOlq7yqO/yy1ePJiNm/fzNqn19Kzq4dtPduq+vwqXQak0YM4yynV1OqdiQMymYkrV8jEFd4oIoK5E+YWg7ihzk55wF65C+DbV97OkXOPBPYsp6zFN3+tLa20RuuovA6KC35XmNyk1uWUACcuPLFXEFzJcGebiwgOnnUwP1/x87pOZVyrcUTzJ81nbNvYYV1UV5uJ60tEMLZtLFt2bKG9tb3Xa3M0g7gzn3dmVWvltbW0sXPXzuJaUSMdNBXaL7yXLJ25lPX/sH6PMs7CxXsjXlwWFL4tr1smbvtmxneOb9gL0cFqbWll/qT5LN+wnJ27drJ159ZBT2wCu5dPGWkTO7M7Jg5yC1Zf8dsr+OXDv+QTJ3yi3t3Zw1lLziqW2JYqzlC5/sGKyxD0pXTfwv6j8QXwUBRmv816EGcmThUNZomBWmumTFxb5D5sSi/g956w957llIVMXGt1bygLJi1g0eRFvUrqCuWUhcxPrco3Olo7RmVMSGHAfKVlBmpdTgnwj8v+kd+f//uatllJoaSyEctpBqslWlgyY8mwxsMU/hYKX0QMp432lvoFce868l2884h3Drhfe0s7O3YNnImrlfGd4/eYubTSOLzCe05DZ+LyF1rDyfwOVbUTm2TNosmLeGj9Q8XX42AzcaNp9vjZrNq0qlixAtkK4gqLv7/9+W/nH174D3XuzZ6OmHsE737Bu/fYXlwrbt2DbN2xFaju8ytLY+IKi5FnfZ24bPwlaNSNxOyU1Spm4kZ4YpPRUJ6Jg9zkJjcsvwEYeiYOcpmkb97zzeKHWnk5Za1mw+po7RiV10FhEpFK5ZQ7enbU/IO7MGPoSCt8a531AdQF3z7928M6b4Xp+Qc7oUKpwrmsZyauWoVMXOGieaQzX90d3VUFPYX3nEacNa+g3pm4B7c/2HxB3KRF/HrVr4tjSgc7Jm40LZmxhJ27dnLfmvtYOjO3JFCWgrj9p+/Pg3/3IAsnLaxqJuFGsXDS7glwCguVD2ZikyzMTnn20rOZ3jW9ob/EqoaZOFV08KyDOWD6AVWtPVNrzZSJK2SPSi/g956wN49teoydu3YOeUwcwAmLTmDjto3cuepOYHc5ZWHx2Zpm4kYhIxsRuQW/t+yZidu5a2dmZ4g7cu6RtERLsVw06/aeuHcxazoUc8fPZemMpcP6BrSvTFzh29VG0t7aXtWYuFqpdqxeIQPTyBcxxUxcPcbEtTdnJm7h5IWs27qORzc+CjR2Jm7JjCUA/P7x3RUTWQriIJfVylIAB7n319njZ/Pg+geLQVyzTWyy98S9eevz31rvbgxbdv4SNKpe/JwX8+LnvLguz114Ayh8gGdZxUzchL3pST2s3ry6WE7Z1d7FsnnLOHT2oRXbqeS4BccB8LPlP+OIuUcUyylrPdZltDJxkBsXVzETNwLllKNl0eRF/Pmdf26KzHItXHbSZcUvHIaqcLHQ1tJWfG0OdzmIkVKeiRvpoGli58SqAuTCF0eOiaustJwyq+89lRRK5X63+ncAQxoTN1r2nbovna2d/O7x3/F6Xg9kL4jLqsIyA1t3Dq+cspn+dhqRmTg1nFP3OZU3HfymzH17VUn57JRQslbcU48UyynbWtq46dybeM1zX1N129PHTWfpjKXFcXHbdm6js7Wz4gKewzFaY+IgN0NlYUzcrrSLV133Kn6+4ue5iU0y/GGwcPLCqibBeDYY2z621zIYQ20Dcl+SFLI0jVhKCbvHxG3ZPjoTm1x01EVc8bIrBtyvGMQ1cDllYQa5egdxjfjlwFAVgrjfrv4t0NjllG0tbRy414H87vHfFbcZxI2OxVMW98rEVTMcIEuLfTcLryrUcM5achb/csq/1LsbNVH4sCmf2ARyC35v79lOW0vbkAPWExaewC0P38LWHVuLC/wOZjapajxnynNYPHlxTdoayMzumcXZKddvXc/3/vg9bvrLTezYVfsxccqu4pi4knLKRg3iRjsTt//0/Tlln1MG3C8LSwwUMnH1mtikJ/WwadumproQ3W/qfrREC7c8cgvQ2OWUkCup3KOcMvwsGGmLJi1i1cFidNAAACAASURBVKZVrNu6Dmi+iU2ahUGcNIL6mtgE8pm4nh3DepM7YdEJbOvZxq2P3Mq2nm3FNV86Wjtq9g37T1//Uy476bKatDWQWd2zWPv0Wnb07GD9M+sB2LJ9S6bHxKn2iqU6JRObzOieUc8u9am9pfeYuEaZpTQLSwwcuNeBTOycOOylPoZi+rjpAPzlqb801YXouI5x/NW0v+LeNfcCjV1OCbB0xlKe2PJEsULDTNzoKCwzcM8T9wDVj4mbMnYKe0/c2yBulPiXII2gShObTOycSHdHd3GtuOGUCR4z/xjaWtr4n4f+h20924rfWI9pG0NXW2NcLA5GYfKPx7c8zvqtuSDu6R1PZ76cUrVVaWKTvboaPxM3tm0srS2t9e4SkI0lBp4z5TlseP+Gujx3YVbZjds2Nt2F6KGzDi0GcVnIxEFucpOZ3TNHZKZi7anwxcnda+4GqpudsrWllQfe+QATOifwxJYnAPzydYSZiZNGUKVMXETk1orLl1MO502uu6ObI+Ycwc+W/6xYTgnw1kPfymn7nTa8ztdB6VpxG57JXbxt2bElN7GJHwbKq7TEQKOWU7a37l4nrpECpufNeB4zu2c64U4fDtzrwOJrq9mCuNLFuht5TBxQXFqgMBGLmbjRURhCcfcTuSCu2gqCKWOn0NbSxrSuaRy74FgOm33YiPVRZuKkEVVpYhPIlVQ+8tQjTBkzZdgZphMWnsBHb/4o3R3dxecZrfLHWitk4h7b9FhxVqxCJs4PbhWUjokrBEZzJsypZ5f6VMjEbdmxpaGCuOfPfj6PvWfPmWCV09HawUEzD+L2lbc33RdIh87KzYI8pm1Mwx/blLFTmDthbnFyE0vrR8e0rmmM7xjPH9f+ERj8OqcdrR3cePaNI9E1lTATJ42g4sQmZW+AhUzcjl3DGxMHuXFxu9IubnnklrpMAFBLs7p3Z+IK5ZRbdmyhJ/VYTqmi0tkp9xq3FzeefSNnLTmrzr2qrL2lnV1pFxu3bWyoIE4De/6s5wPQ0dJcmbiDZh5EEA2fhSsondzETNzoiAgWTV5UXMu2UcbyqremC+IioiMivhQRGyJiTUT80wD7nx4RD0XEloj4aUTMKbnv0xHxQERsiog/RcR5I38EaiaVyikhF8Q9vvlxtuzYMuxvFY+ceyRd7V1s79lel0Vxa6kwOcVjmx8rllNu3LYRsLZeu5WuEwdw7IJjq1rguh4K2eV71tzT0NP5a0+HzcmVgjVbOeX4zvHsO3Xfhh8PV7B0xlLuW3sf23ZuM4gbRYXJTaC6MXEafU0XxAEfApYAzwEOA86IiHMr7RgR+wNXAm8BpgF/Av69ZJctwMuAicBZwKci4riR67qaTX/llInEig0rhp1h6mjtYNm8ZRWfJ2s6WjuYOnZqLhOXn52yEMyZiVNB6cQmje7wOYcD8Od1fzYTlzHPn53PxDVZEAfw6v1fzVF7H1XvblRlyYwl7Ny1k3vX3EsiGcSNkkWTcpObdLZ2us5pg2rG/5VzgY+klNamlFYA/wy8sY99zwJ+lFL6n5TSVuBi4MiIWAyQUvpwSumPKaVdKaU7gJ8D2XjXU0MoZI/Kv8UqrBX30PqHanKBcMLCE4D6rKdUa4W14grllE898xSAH9wqKp3YpNEduNeBxf4axGXL/tP2Z1b3LOZOmFvvrtTcx074GF975dfq3Y2qLJ2Rm9zkzsfuBPwsGC2FTJyllI2rqf4SImIyMBv4Xcnmu4CP9/GQA4HbCzdSSk9FxIr89gfL2u4EDgcqvutFxCRgUtnm5nvn16D0l4kDWLd1XU3WQDpx0YkAmS+nhNwMlas3ry6eu6e25YK4LFywa3RkKRPX3trOIbMO4ZZHbjGIy5jWllbuf+f9g57UQbW1z9R9GNM2hjtXGcSNpsIMlQZxjavZMnGFT8inSrZtAPoavdtdtm9/+/8rcD/wf/to60JgednPzQN3Wc2smImrMLFJcZ8aXIgunbm0OJtU1s3snsljmyqMicvABbtGR5YycQBHzDkCgO52g7is6e7obpi1/Z6t2lraOGD6Afxm9W+KtzXyCl8wOx6ucWXqLyEifgyc3MfdfwEOzv8+Adic/30isKmPx2zO71tqj/0j4pPAIcBxKaVdfbT1OeDqsm1zMZB7Vit82JRn4sZ1jGPymMmsf2Z9TcopW6KFH57xQ6Z2TR12W/U2qzuXiZs+bjoAu/J/clm5YNfIy1ImDuCIubkgzolNpKFZOmMp1/zhGsAgbrTMmziP1mg1E9fAMvWXkFJ68UD7RMQqYCmwKr/pIODuPna/O79v4bETgIWl+0fEpeQmN3lRSmlDP33bQC6LV9qXgbqrJtdXOSXkSirXP7O+ZsFJYSa1rJvZPZNtPdtYvn55r+1+cKugdJ24LChMbmI5pTQ0S2Ys4cq7rgT8LBgt7a3tzJs4z3LiBtZs5ZSQy4ZdHBHTImI+8G5yM1BW8g3glIg4PiLGAh8BbkspPQgQEf8InAmckFJaM/JdV7N5zpTnMHXs1F5T9RYUSiqzciE6WgprxT259cle2z1PKihdJy4L5k+czydO+ASvO+B19e6KlElLZxa/bzeIG0XHLzyeg2YeVO9uqA/N+JdwKbnlAh4EdgBfTCldVbgzIjYDp6SUbk4p3Zdf++0KYCbwS+CMkrY+DmwHHijJqn0jpXT+yB+GmsE+U/dh7fvWVryvGMRl5EJ0tMwaP6v4e1d7F0/veBrwPGm3rGXiIoL3H/3+endDyqwlM5YUfzeIGz1XnHZFvbugfjTdX0JKaTvw1vxPpfu7y25/G/h2H/taD6kRU5ihshnXIBqOwuLIAHMnzOX+J+8HsnPBrpFXyMR5MSc9O0wZO4W5E+by6MZH/buX8pqxnFLKBMspKyuUUwLMGT+n+Lsf3CrI2uyUkoavsF6cnwVSjkGcVCeFTJwXor1N6JxQnAhmzoTdQZznSQVZm51S0vAZxEm9GcRJdVLIxHW0WE5ZKiKK2bjSTJwX7CowEyc9+xTGxRnESTkGcVKdzJ0wF/BCtJLCuLheQZznSXlm4qRnn2PmH8O+U/dl/2n717srUkPw6wypTjrbOjlq76N47vTn1rsrDacwQ2VpOaXfvqpgfMd49p6wN8+Z8px6d0XSKJk1fhZ/uuBP9e6G1DC8KpLq6JY33lLvLjSkmeMqZOLMuiivvbWdh//+4Xp3Q5KkurGcUlLDOXT2ocyfOJ8Z3TOK2yynlCRJyjGIk9Rw3njwG1lx4Qq6O3Yv62g5pSRJUo5BnKSGNa59XPF3yyklSZJyDOIkNawxbWMIArCcUpIkqcAgTlLDigi62rsAM3GSJEkFBnGSGlohiHNMnCRJUo5BnKSGVszEWU4pSZIEGMRJanDjOnKTm1hOKUmSlGMQJ6mhmYmTJEnqzSBOUkNzTJwkSVJvBnGSGlphrTjLKSVJknIM4iQ1tK72LlqjlYiod1ckSZIagkGcpIY2rmOc4+EkSZJKGMRJamhdbV2Oh5MkSSrhlZGkhvay/V5GZ1tnvbshSZLUMAziJDW0U/c5lVP3ObXe3ZAkSWoYllNKkiRJUoYYxEmSJElShhjESZIkSVKGGMRJkiRJUoYYxEmSJElShhjESZIkSVKGuMTAyGoFePTRR+vdD0mSJEkNqCRWaK32MZFSGpneiIg4Gri53v2QJEmS1PCWpZR+Wc2OBnEjKCI6gcOAx4CeEXyq5cDCKvabSy6oXAaYHhxZWT3X1b6WGkFWz3HWDPU8Z+m1VA++fqs3nNeS53nkZeUcN8N7UlbOddYNdJ5H4rXUCswC7kgpbavmAZZTjqD8f0JV0fRwRAQppRXV7Jf3aDX7a+iyeq6rfS01gqye46wZ6nnO0mupHnz9Vm84ryXP88jLyjluhvekrJzrrBvoPI/ga+nBwezsxCaSJEmSlCEGcc3h0np3QE3D15JqxdeSasXXkmrB15FqpSFeSwZxTSCldEm9+6Dm4GtJteJrSbXia0m14OtItdIoryWDuGeXDeS+PdhQ7448C3iuR57neHR4nkeG53V0eJ5Hnud49HiuR0cmzrOzU0qSJElShpiJkyRJkqQMMYiTJEmSpAwxiJMkSZKkDDGIkyRJkqQMMYiTJEmSpAwxiJMkSZKkDDGIkyRJkqQMMYiTJEmSpAwxiJMkSZKkDDGIkyRJkqQMMYiTJEmSpAwxiJMkSZKkDDGIkyRJkqQMMYiTJEmSpAwxiJMkSZKkDDGIkyRJkqQMMYiTJEmSpAwxiJMkSZKkDDGIkyRJkqQMMYiTJEmSpAwxiJMkSZKkDDGIkyRJkqQMMYiTJEmSpAwxiJMkSZKkDDGIkyRJkqQMMYiTJEmSpAwxiJMkSZKkDDGIkyRJkqQMMYiTJEmSpAwxiJMkSZKkDDGIkyRJkqQMMYiTJEmSpAwxiJMkSZKkDDGIkyRJkqQMMYiTJEmSpAwxiJMkSZKkDDGIkyRJkqQMMYiTJEmSpAwxiJMkSZKkDDGIkyRJkqQMMYiTJEmSpAwxiJMkSZKkDDGIkyRJkqQMMYiTJEmSpAwxiJMkSZKkDDGIkyRJkqQMMYiTJEmSpAwxiJMkSZKkDDGIkyRJkqQMMYiTJEmSpAwxiJMkSZKkDDGIkyRJkqQMMYiTJEmSpAwxiJMkSZKkDDGIkyRJkqQMMYiTJEmSpAwxiJMkSZKkDDGIkyRJkqQMMYiTJEmSpAwxiJMkSZKkDDGIkyRJkqQMMYiTJEmSpAwxiJMkSZKkDDGIkyRJkqQMMYiTJEmSpAwxiJMkSZKkDDGIkyRJkqQMMYiTJEmSpAwxiJMkSZKkDDGIkyRJkqQMMYiTJEmSpAwxiJMkSZKkDDGIkyRJkqQMMYiTJEmSpAwxiJMkSZKkDDGIkyRJkqQMMYiTJEmSpAwxiJOkJhQRCyIiRcSC/O1zImJFyf2XR8TldereiIiIkyPi/ojYFBGXVrF/Tc9JRFwSET8f6uOzICJ+HhGXDGL/eyLizPzvvV6TkqShM4iTpAaUv1jeHhGbI2Jj/mL4zbVqP6V0fkrp/Fq1N5r6CZY+D3wxpTQ+pfThwbbbCOdksEFSH200TLCUUjogpXRNvfsBewbtkpRlBnGS1Lg+nlLqBiYBlwJfiohj6tynuoqI9n7uXgT8drT6osYxwOui1s/VMVrPJUl9MYiTpAaXUtqVUvoWsA44vLA9Il4eEb+NiKci4t6IOK/aNiPi6oi4uuT2ioj4XxHxo3w54gMR8fKyx7wvIh6OiA0RcVVEXFvaRh/PcW1EXJl/zF8i4j1l+xwdEbfm7/9zRLw/IlpL7k8R8a6I+H8R8TRwBvABYFk+S7k5Ig6NiM1AK/Cj/LbDIqI1Ij6Qb3dD/nmOGsQ52Tsiro+IJyJiVUR8JSImD3xq47KIWBMRqyPikxHRVnLnnIj494hYmW/32oiYnr/vcmAZ8IH8MazObz82In4VEesi4smI+H5ELOynD/cU/s23889DOZ6IaMsfy+r88fxvIMr2+XL+NbE5/5q5oOz+FRFxToW2J0fE0+X/HxHx9f5eU2Xtfjgi/jsiNgFvzf9/vyci7sv/TdwZESfk918GXA7MK3ndvCJ/blNZ2+VltoXX8ZcjYi1wTWGfiDg//7p+KiKui4jxA/VdkmrBIE6SGlz+YvoMYCrwp/y2I4FvkcvQTQHOBz4TEa8axlO9mVyANBH4N+BrEdGdf74zgX8ATgemAb8AXlNFm68Bbsk/5nXA/4qI1+XbnA/8FPgaMB14FfB24F1lbbwVOBsYR+6YPw7cnFLqzv/cmc9YApyS33YH8B7gLcAr8+1fA/w0IvYeqNP5QPIHwCZgMbAUmAd8dYCHHgU8DcwFjiN3vt6Tb7MT+BnwCLAvuczhTuDfIVfOCdxMPgObUpqZb3MH8PfADGAfoAf4Rj99OKDwb76d9wzxeN5H7v/vuPzxPJM/vlK3AYcCE4B3Av8cESf10yb5Y10PXEfu/wfIBXb556t2XOJbgYvzz30l8EHgTODlwGTgo8B/RsTilNLN5P5GHi553fxHlc9Dvl83AzPJvRYB5gDPAf4K2B94PnDhINqUpCEziJOkxvX+iNhA7uL568AHUkrfz993LvCfKaX/SCn1pJRuAr5MyUXxEPxbSum3KaVdwBfJXRzvl7/vnPz9/y+ltDOldDVwZxVt/ial9JX8Y27L9/GN+fvOAO5OKV2eUtqRUvo9cFmFY/jnlNIfU87WQRzPecBlKaU/5Nv//4A/krvQH8jhwHOBv0spbUoprSEXSL0sImb287g1wD+llLallO4DPsXu430J0AW8P6W0JaW0GbgIODEi5vbVYErplpTSbfljWEcucH9BRHRVcRzDOZ5zgU+llO5LKW0D/glYW9a3r6SU1uSzxT8GfgycWGWfvgi8NiIm5m+/Abg//zqpxlfyr8eUUno6fzzvTSndn+/P98gFXn9bZXv9uS2l9LX86/jp/LYd5P4vt6aUVgHfoyRTLkkjySBOkhrX/04pTSKXVbiK3MV+oTRvb+Chsv3/TC67MlSrCr/kAwyAQnnYXGBF2f7ltytZXuF2IRNW7TGUt1Gt4ZyjvYG1KaWNZY9lgMc/nA+CC0qPdx9gNrA+X965gVxmdVt/bUbEQRHxw3wJ5EZyWdAgl12s1lCOZy4l5z5/XH8p6VdExAdLyhc3AKcAe1XToZTS7cB9wFn5TW8GvlTNY/OKfYuIGeS+dPhe4dzm+3MMuYzZcFV6DT6RUtpZcnszu/9eJGlEGcRJUoNLKW0C3gEszP8LuZK88nFRi4GHR6gbjwILyrbNr+Jx5Y9ZkG8Lqj+GXQPc7stwztEjwLSyMU6L8//29/h5EVH62bqA3ce7GngopTSp7GdMSunW/D6Vju1bwL3Ac1NKE4AX5bdHhX37amMox9Pr/zx/XKUB398CFwB/A0zOf+Hwo376VckXgTfnx8YtoP8y0XKlx1nIWL+47NyOSym9rcL+BZsAImJcybbZAzyXJNWdQZwkZUBJOdvFETEBuBp4RUS8LD+hw9HkMhlXjFAXvgq8KXIThrRFxBvIjYUayKERcW7+MYfn+3hV/r5rgedFxFsioj0iDiQ3DmugY1gNzM+PMevPlcD7IuKAfPtvI1dS+O9V9PsOclmif4mI7oiYBnwG+EFKaXU/j5tObtxfR0TsB7yX3cf7XWBM5JZImAgQEXsVxgiWHNu+ZW1OBDYCG/MZp38aoO9ryAUd+5VsG8rxfBV4b0TsF7kZGS+md/ZvIrkxfWtzhxKvBAYcD1fmWnLB2+eBb5ZlCquW//u4HPhUROyfzxKOjYhjIqJwPlcD06P3ZC73kwvk3hoRLRFxEMMrSZakUWEQJ0nZ8XVyM1S+N6X0K3KZkI8A68kFPu9LKX1nhJ77GnIX/d8ld9F+HPB/yWU/+vMdciVta4HrgU+mlK4FSCmtAF5MbuzVWuA/yU2o8tkB2ryOXCngY/myuYP62O+fga/k+7mW3JirF6eUBszE5cvkXkqulHU58Ady5aZvGOCht5IrqVsJ3ETufH063+Ym4AXksoN/yJdG3kru/JT2+cD8cRUyeOeRKzncBPxPvs3++r6V3AQ1X823c9kQj+eTwH/kj2MluYllbi25/+r8ffeSC5BOIfd/WLWU0hZyr+tDGFwpZSUXkctafptcZm4F8I9AYfmBG8hN7lKYrfS0/P/J2eQy3BuBT5B7DUpSQ4uU0sB7SZJUJiJ+DVyfUvpEH/dfDZBSOmcUu6WMiYi/B96QUjq43n2RpKwwEydJqkpE/E2+RG1MRLwLWEIu6yENSb6s8wLgc/XuiyRlSVMGcRFxQeQW+dweAywaGhGnR8RDEbElIn4aEXNK7uuIiC/lyy7WRMRA4xAkqZm9lVzZ3BPA64GXp5T+3P9DpMoi4jJys13eRtmEJhFRWKh8j5+6dFaSGkxTllPmF7vdBZwMjO2rlCci9gduJ7cQ7C3k1idaklJ6Uf7+jwInAC8DusmNRfhYSumqSu1JkiRJ0khryiCuIB+Eze0niPsYsE9K6bX52xPJfcP83JTSgxGxEnhzSumH+fvfBpyRUlo2KgcgSZIkSWXaBt6lqR1ILhMHQErpqYhYQW5msHXk1or5Xcn+dwEfr9RQREwCJpVt7gAWAQ8APbXrtiRJkqQm0QrMAu7IL5kyoGd7ENcNPFW2bQO56aG787efqnBfJRcCH65p7yRJkiQ9WywDflnNjs/2IG4zMKFs20Rya/EUBk9PKPm9cF8lnyO3Zk6p+cDPb775ZubOnTvszkqSJElqLo8++ijLli0DeKzaxzzbg7i7gaWFGxExgdwirHenlNZHxKr8/avyuxyUf8weUkobyGXqiiICgLlz57JgwYJa912SJElS86h6+FWzLjHQFhFjyNWXtubXNGqvsOs3gFMi4viIGAt8BLgtpfRg/v6rgYsjYlpEzAfeDVw5CocgSZIkSRU1ZRAHXAxsBd4PnJX//csA+XVmlgGklO4DzgOuAJ4E9gfOKGnnUnKZtweBO4HrXF5AkiRJUj019RID9RYRC4Dly5cvt5xSkiRJ0h5WrFjBwoULARamlFZU85hn+5g4SZIkSQPo6elh3bp17Nixo95dyaz29namTJlCa2vrsNsyiJMkSZLUr3Xr1jFmzBimTZtWnLxP1UspsXnzZtatW8f06dOH3V6zjomTJEmSVCM7duygu7vbAG6IIoLu7u6aZTIN4iRJkiQNyABueGp5/gziJEmSJClDDOIkSZIkKUMM4iRJkiRl3vXXX8+BBx7IuHHjmD9/Pt/97nfr3aUR4+yUkiRJkjLthhtu4MILL+Taa6/lqKOO4sknn2TTpk317taIMRMnSZIkKdM+9KEP8aEPfYijjz6alpYWpk+fzqJFiyrue84553D++efzkpe8hO7ubl7wghewatUq3vve9zJlyhT22WcfbrvttuL+999/PyeeeCKTJ09mv/324+qrrx6lo+qbQZwkSZKkzOrp6eH2229n3bp17LvvvsyePZtzzz2Xp556qs/HfOtb3+KSSy7hySefZPz48bzwhS9k33335YknnuDMM8/kne98J5BbWuGlL30pxxxzDI8//jhf//rXefe7380vfvGL0Tq8iiKlVNcONLOIWAAsX758OQsWLKhvZyRJkqQhWrVqFbNnzwbgX37wh1F97ne95Hn93r9q1SrmzJnDQQcdxPe//326u7t5/etfz7Rp07jqqqv22P+cc84hIor3ffGLX+Syyy5j+fLlANx3330sXbqUZ555hltvvZVXvvKVrF69mtbWVgAuuugiNmzYwBVXXDHoYyk9jwUrVqxg4cKFAAtTSiuqacdMnCRJkqTM6urqAuCCCy5g7ty5TJo0iYsvvpj/+q//4vzzz6e7u5vu7m7OP//84mNmzJhR/H3s2LF73N6xYwfbt29n5cqVzJ07txjAASxYsICVK1eOwpH1zYlNJEmSJGXWpEmT2HvvvSsupn355Zdz+eWXD7ntOXPm8Oijj9LT01MM5FasWMGcOXOG3GYtGMRJkiRJqtpA5Y318KY3vYkvfOELnHrqqYwbN46Pf/zjnHbaacNu94gjjmDSpEl84hOf4H3vex+///3vueqqq7j++utr0Ouhs5xSkiRJUqZ94AMf4Oijj+a5z30uixcvZsqUKXz2s58ddrvt7e18//vf54YbbmCvvfbijDPO4LLLLuPYY48dfqeHoWknNomIScC/AacAG4GPpZT+tcJ+lwNnlWxqB7anlMbn7/85cCSwM3//4ymlxVX2YQFObCJJkqSMqzQhhwavVhObNHM55RfIHd9sYDHw3xFxX0rpxtKdUkrnA8VRjhFxNbCrrK0LU0pDL6aVJEmSpBppyiAuIsYBpwMHp5Q2AXdFxJXAG4EbB3jcq4GXjkpHJUmSJGmQmnVM3L7kSkXvLdl2F3DgAI97NbAGuKls+0cj4smIuDUijq/0wIiYFBELSn+AuUPrviRJkiRV1pSZOKCb3Di4UhuA8QM87mzga6n3QMF/AO4FtgN/A3w/Ig5KKT1Q9tgLgQ8PvcuSJEmSNLBmzcRtBiaUbZsIbOrrARExDzgW+Frp9pTS/0spbUopbUspfRW4mcrllp8DFpb9LBvqAUiSJElSJc2aibsfSBGxf0rpvvy2g4C7+3nM64FbUkoPDdB2xek8U0obyGX7iiotOChJkiRJw9GUmbiU0hbgO8BHImJ8RCwhN6nJlf087A3A1aUb8uPcTo6IMRHRFhFnAscAPxqhrkuSJElSv5oyiMt7B7ms2WPAj4FLUko3RsS8iNicL58EICJeQG4Skm+XtdEOfJTcZCdrgXcCr0gp/XE0DkCSJEmSyjVrOWWhvPH0CtsfJjfxSem2XwHjKuy7BjhspPooSZIkSYPVzJk4SZIkSc8CX/jCFzj00EPp6OjgnHPOKW6///77efnLX8706dOZPHkyJ510Evfee2/fDWWEQZwkSZKkTJs9ezYf/OAHOe+883pt37BhA6eddhp//OMfWbNmDUcffTQveclL6L2iWPYYxEmSJEnKtFe96lW84hWvYOrUqb22H3744Zx33nlMnTqVtrY2/v7v/54VK1awatWqPttasGABn/zkJ1m6dCnd3d2cffbZrFmzhpe97GVMmDCBF73oRTzxxBPF/X/4wx+yZMkSJk6cyJFHHsntt98+YsdZYBAnSZIk6VnhpptuYsqUKcyaNavf/b7zne/wk5/8hAceeICf/OQnnHjiiXzoQx9izZo1dHZ28qlPfQqABx54gNNPP51PfvKTPPnkk7zlLW/hlFNOYf369SN6HE07sYkkSZKk2rvzzjtH9fkOPfTQmrSzatUq3va2t/HpT3+alpb+c1kXXHABM2fOBOBFL3oRXV1dHHZYbr7DV77ylVx//fUAXHfddZx88smccsopALzxjW/kX//1X/nBD37AWWedVZN+V2ImTpIkSVJTW7t2LSeddBLnnXce5557YlLGEAAAIABJREFUbnH7AQccQHd3N93d3VxzzTXF7TNmzCj+Pnbs2D1ub968GYCVK1cyf/78Xs+1YMECVq5cOVKHApiJkyRJktTE1q9fz0knncSpp57KJZdc0uu+e+65Z1htz5kzh9/85je9tq1YsYJXvOIVw2p3IAZxkiRJkqpWq/LGWtq5cyc7d+6kp6eHnp4ennnmGVpbW9m6dSsnn3wyRx11VHEcWy299rWv5ROf+AQ/+clPOOGEE7jmmmt46KGHeMlLXlLz5yplECdJkiQp0z760Y9y6aWXFm9/4xvf4Oyzz+a4447jjjvu4J577uGrX/1q8f4f/ehHLFu2bNjPu++++/LNb36Tiy66iIcffpj99tuPH/zgB0yePHnYbfcnsr5GQiOLiAXA8uXLl7NgwYL6dkaSJEkaolWrVjF79ux6dyPzKp3HFStWsHDhQoCFKaUV1bTjxCaSJEmSlCEGcZIkSZKUIQZxkiRJkpQhBnGSJEmSlCEGcZIkSZIG5ISIw1PL89e0QVxETIqIb0XEpohYGRFv72O/cyKiJyI2l/ycONh2JEmSpGbV0tJCT09PvbuRaT09PbS01Cb8auZ14r5A7vhmA4uB/46I+1JKN1bY946U0pE1aEeSJElqOl1dXWzcuJHJkycTEfXuTuaklNi4cSNdXV01aa8pg7iIGAecDhycUtoE3BURVwJvBKoOvmrVjiRJkpRl48ePZ926dTz22GP17kpmdXZ2Mn78+Jq01ZRBHLAvuYXM7y3Zdhfw133svyQi1gLrgGuAj6WUdg6mnYiYBEwq2zx3iP2XJEmSGkZEMHXq1Hp3Q3nNGsR1AxvLtm0AKoW+NwEHAH/J/3sdsAv4yCDbuRD48NC7LEmSJEkDa9aJTTYDE8q2TQQ2le+YUnoopbQ8pbQrpfQH4J+A1wy2HeBzwMKyn2VDPgJJkiRJqqBZM3H3Ayki9k8p3ZffdhBwdxWPLZ37s+p2UkobyGXpihz0KUmSJKnWmjITl1LaAnwH+EhEjI+IJeQmI7myfN+IOCUiZuR//yvgg8D3BtuOJEmSJI2Gpgzi8t5BLqv2GPBj4JKU0o0RMS+/Fty8/H4nAL+PiC3AD4HvAh8bqJ3ROghJkiRJKtWs5ZSF8sbTK2x/mNyEJYXbFwEXDbYdSZIkSaqHZs7ESZIkSVLTMYiTJEmSpAwxiJMkSZKkDDGIkyRJkqQMMYiTJEmSpAwxiJMkSZKkDDGIkyRJkqQMMYiTJEmSpAwxiJMkSZKkDDGIkyRJkqQMMYiTJEmSpAwxiJMkSZKkDDGIkyRJkqQMMYiTJEmSpAwxiJMkSZKkDDGIkyRJkqQMadogLiImRcS3ImJTRKyMiLf3sd/ZEXFnRGzM7/eZiOgouf/qiNgeEZtLfjpH70gkSZIkabemDeKALwBtwGzgJcClEXFchf26gAuB6cDzgWXAB8r2+UxKqbvkZ9sI9luSJEmS+tRW7w6MhIgYB5wOHJxS2gTcFRFXAm8EbizdN6X0xZKbj0XE14GXDeE5JwGTyjbPHWw7kiRJktSfZs3E7QtESunekm13AQdW8dhjgHvKtr0lItZFxG8i4rV9PO5CYHnZz82D67YkSZIk9a8pM3FAN7CxbNsGYHx/D4qINwBHAweVbP4/wHuAp4C/Br4VEatTSjeVPfxzwNVl2+ZiICdJkiSphpo1iNsMTCjbNhHY1NcDIuI04NPAX6eUVhe2p5R+U7LbDyPiG8CrgV5BXEppA7lAsbTNIXVekiRJkvrSrOWU9wMpIvYv2XYQcHelnSPixcCVwGkppbsGaDvVpouSJEmSNHhNGcSllLYA3wE+EhHjI2IJuUlNrizfNyKOB64BXp1Suq3C/a+JiO6IaImIvwbOAv5zZI9AkiRJkipryiAu7x3ksmaPAT8GLkkp3RgR8/Jrvc3L7/dBcqWWPyhZB650YpN3ASvJlUp+CnhzSumG0TsMSZIkSdqtWcfEFcaonV5h+8PkJj4p3K60dlzp/stq3ztJkiRJGppmzsRJkiRJUtMxiJMkSZKkDDGIkyRJkqQMMYiTJEmSpAwxiJMkSZKkDDGIkyRJkqQMMYiTJEmSpAxpqHXiImI/4FhgLyAK21NK/1SvPkmSJElSI2mYIC4iTgeuAe4Fnpv/9wDgl4BBnCRJkiTRWOWUHwTOSykdBGzJ//t35II4SZIkSRKNFcQtIJeJg92llFcAb6xLbyRJkiSpATVSELcJ6Mr/viYiFuZvT6hflyRJkiSpsTRSEHcr8Mr87/8FfB+4AcspJUmSJKmoYSY2Ac5idxnlPwBryGXhPl23HkmSJElSg2mkTNzJKaVnAFJK21NKH08pvR84ss79kiRJkqSG0UhB3Df62P61oTQWEZMi4lsRsSkiVkbE2/vZ94L8Ppsi4rqImDCUdiRJkiRppDVSEBd7bIiYBOwaYntfIFcuOht4CXBpRBxX4TlOAj6c32cO0A58frDtSJIkSdJoqPuYuIhYDiRgbEQ8VHb3dOAHQ2hzHHA6cHBKaRNwV0RcSW65ghvLdj8HuCqldFf+sf8L+G1EvI1cYFltO5IkSZI04uoexAGXkAuWvghcWrJ9F7Ca3AyVg7UvECmle0u23QX8dYV9DwR+WLiRUrovIgD2IZeprKqdfNZwUtnmuQALFy4cwiFIkiRJ0p7qHsSllL4KEBF/TinVajmBbmBj2bYNwPg+9n2qbNtT+X1jEO1cSK4sU5IkSZJGTN2DuIKU0i/zC3z/LTA7pXRBROwDtKWU7htkc5vZc5HwieQWFK9m3wn5fVsG0c7ngKvLts0Fbl6+fDkLFiwYsNOSJEmSnl1WrFgx6Mq9hpnYJCKOB34PHA2cnd88k6GtE3c/kCJi/5JtBwF3V9j3bmBpST/+ilwG7oHBtJNS2pBSWlH6Azw6hL5LkiRJUp8aJogDPgmclVI6FdiZ3/Zr4JDBNpRS2gJ8B/hIRIyPiCXkJiO5ssLuVwPnRsSSiBgPfBS4LqX09CDbkSRJkqQR10hB3D4ppf/M/54AUkpbgTFDbO8d+XYeA34MXJJSujEi5kXE5oiYl3+O/wY+kt/nMXITqrxzoHaG2CdJkiRJGpaGGRMHrIqIxSmlBwsb8qWNQypJTCltILc8QPn2h8lNZlK67fP0XhtuwHYkSZIkqR4aKRP3FeC6/ELaLRFxJPBl4N/q2y1JkiRJahyNlIn7LLmp+79HbkbIG4DLgS/Us1OSJEmS1EgaJohLKe0it/D3JRGxV25TWlPfXkmSJElSY2mIcsqIeGtEfD4iTo+ITuBbwOqIWF42vb8kSZIkPavVPYiLiI+Sy8DNAP4P8E3gCeA04Hbgf9etc5IkSZLUYBqhnPJM4LiU0h8j4nnAXcBeKaUnI+JW4I/17Z4kSZIkNY66Z+KAqSmlPwKklP4APJ1SejJ/ez0wtp6dkyRJkqRG0ghBXLkd9e6AJEmSJDWqRiin7IyID5XcHlt2u2O0OyRJkiRJjaoRgrhfAceV3L6t7PavRrc7kiRJktS46h7EpZSOrXcfJEmSJCkrGnFMnCRJkiSpDwZxkiRJkpQhBnGSJEmSlCEGcZIkSZKUIU0ZxEXE6RHxUERsiYifRsScPvbbKyKujYhVEfFURNwaES8suX9BRKSI2Fzyc+noHYkkSZIk9dZ0QVxE7A9cCbwFmAb8Cfj3PnbvBu4ADgUmA1cA/xURk8r2m5ZS6s7/fHhkei5JkiRJA2u6IA44C/hRSul/UkpbgYuBIyNicfmOKaWHUkqfSSk9llLalVK6EkjAAaPcZ0mSJEmqSt3XiRsBBwK3F26klJ6KiBX57Q/298CIOJBcdu7+srsejIgE/Ax4b0rpiQqPnQSUZ/DmDrr3kiRJktSPZszEdQNPlW3bAIzv70ERMR74BvDxlNKa/Oa1wGHAfHIll+OAa/to4kJgednPzUPovyRJkiT1KfNBXEScWTLpyD3AZmBC2W4TgU39tDEW+D7wW6A4cUlKaXNK6dcppZ0ppceBC4DjI2JyhWY+Byws+1k2jEOTJEmSpD1kvpwypXQNcE3hdkR8DFhacnsCuYDq7kqPj4hO4D+A1cB5KaXU39MVHlahHxvIZfxK267uICRJkiSpSpnPxFXwDeCUiDg+n2H7CHBbSmmP8XAR0Q58B3gGOCultKvs/iMiYr+IaImIqcD/AX6RUlo38ochSZIkSXtquiAupXQfcB655QKeBPYHzijcHxGXR8Tl+ZtHAS8FTgI2lJRlnpm/fxHwY3KlmHcD24C/GZUDkSRJkqQKMl9OWUlK6dvAt/u47/yS339BhdLIkvuvpe+JTCRJkiRp1DVdJk6SJEmSmplBnCRJkiRliEGcJEmSJGWIQZwkSZIkZYhBnCRJkiRliEGcJEmSJGWIQZwkSZIkZYhBnCRJkiRliEGcJEmSJGWIQZwkSZIkZYhBnCRJkiRliEGcJEmSJGWIQZwkSZIkZYhBnCRJkiRliEGcJEmSJGVIUwZxEXF6RDwUEVsi4qcRMaeffVdExNaI2Jz/uWGobUmSJEnSSGu6IC4i9geuBN4CTAP+BPz7AA97ZUqpO/9z/DDbkiRJkqQR01bvDoyAs4AfpZT+ByAiLgaeiIjFKaUH69iWJEmSJA1b02XigAOB3xVupJSeAlbkt/flqxGxJiL+OyIOHkpbETEpIhaU/gBzh3EckiRJkrSHZgziuoGnyrZtAMb3sf+ZwAJgPnAD8JOImDKEti4Elpf93DzIvkuSJElSvzIfxEXEmSWTktwDbAYmlO02EdhU6fEppVtSSltTSk+nlD4BrANelL97MG19DlhY9rNsKMckSZIkSX3J/Ji4lNI1wDWF2xHxMWBpye0J5AKqu6ttsuT3u6ttK6W0gVyWjpL9q3xKSZIkSapO5jNxFXwDOCUijo+IscBHgNsqTUQSEfMi4oUR0RERYyLivcB0dpdBVt2WJEmSJI2GpgviUkr3AecBVwBPAvsDZxTuj4jLI+Ly/M3xwBeB9cBK4MXAi1NKa6tpS5IkSZJGW6SUBt5LQ5KfoXL58uXLWbBgQX07I0mSJKnhrFixgoULFwIsTCmtqOYxTZeJkyRJkqRmZhAnSZIkSRliECdJkiRJGWIQJ0mSJEkZYhAnSZIkSRliECdJkiRJGWIQJ0mSJEkZYhAnSZIkSRliECdJkiRJGWIQJ0mSJEkZYhAnSZIkSRliEPf/s3fn8VFWh/7HP2eSzEyWSUISAoQt7CKCIrLYygWX1oW2Wu1CXerSVunys/a219va9ipttdu9rbWt16XXasUuVntrvRV3UNxxBZFNSBACBAhkTybLnN8fZ2aykJDJPpN836/X85qZZ5vzPHmUfHM2ERERERGRBKIQJyIiIiIikkAU4kRERERERBKIQpyIiIiIiEgCGZIhzhjzaWPMTmNMjTHmSWPM2E72m2CMqW63WGPMN8PblxpjQu22f2Fgr0ZERERERKTFkAtxxpiZwD3A1UAesBX4Y0f7Wms/sNZmRBZgNhACHm6124HW+1hr/6efL0FERERERKRTyYNdgH5wKbDaWvs0gDHme8ABY8wUa+2OLo79PPC8tba4n8soIiIiIiLSI0OuJg44AXgn8sFaWwEUh9d3yhhjcCHuvnabco0x+40xRcaYXxljMjo5PtsYU9h6Acb1/DJERERERESONhRDXAZQ0W5dORDo4rjTgFHAQ63WbQFOBAqAM4C5wK86Of46oKjdsq47BRcREREREelKwoc4Y8wlrQYd2QRUA5ntdssCqro41eXAw9ba6sgKa+1+a+171tqQtbYIuB64qJPjbwUmtVsWd/+KREREREREOpfwfeKstQ8AD0Q+G2NuxtWeRT5n4gLVu52dwxiTCnwa+GRXXweYTspRjqvxa33eLk4nIiIiIiLSPQlfE9eBVcC5xpgzwuHsh8ArXQxq8kngCLCm9UpjzOnGmInGGQ/8BPjf/iq4iIiIiIhIV4ZciLPWbga+APwOKANmAhdHthtj7jDG3NHusMuB+621tt36ucBLQE34dSPw//qp6CIiIiIiIl0yR+cW6SvhESqLioqKKCwsHNzCiIiIiIhI3CkuLmbSpEkAk2Kd6mzI1cSJiIiIiIgMZQpxIiIiIiIiCUQhTkREREREJIEoxImIiIiIiCQQhTgREREREZEEohAnIiIiIiKSQBTiREREREREEohCnIiIiIiISAJRiBMREREREUkgCnEiIiIiIiIJRCFOREREREQkgSjEiYiIiIiIJBCFOBERERERkQSiECciIiIiIpJAFOJEREREREQSyJALccaYMcaYfxhj9hljrDGmsIv9s40xDxpjqowxJcaYr7TbvsQY864xptYY84oxZlZ/ll9ERERERORYhlyIA0LA48CFMe7/GyAZKACWASuNMacDGGNygUeAHwMjgP8FHjHGJPd1oUVERERERGIx5EKctbbUWns7sL6rfY0x6cCnge9Za6ustW8D9wBXhXe5ENhmrX3AWhsEfg6kAUv6p/QiIiIiIiLHNtxrlKYDxlr7Xqt1bwMfDb8/AXgnssFaGzLGbAyvf6b1iYwx2UB2u/NPBNizZ08fF1tERERERIaCVlkhKdZjhnuIywAq260rBwKtth85xvbWrgNu7OhLFi9e3IsiioiIiIjIMDAG2BHLjgkf4owxlwB3hj/ustZ2Z+CRaiCz3bosoCrG7a3dCtzbbp0XmAxsB5q7Ua7uKgImxbDfOGAdsBhQ9WD/StR7HeuzFA8S9R4nmp7e50R6lgaDnt/Y9eZZ0n3uf4lyj4fC/5MS5V4nuq7uc388S0m4ANdld7CIhA9x1toHgAd6ePg2wBpjZlprN4fXnQS8G37/LvDFyM7GGAPMwfWNa1+OclwtXUff0a+MMVhri2PZL2xPLPtLzyXqvY71WYoHiXqPE01P73MiPUuDQc9v7HrzLOk+979EucdD4f9JiXKvE11X97kfn6WYauAihtzAJgDGGD/gC3/0GWP8ptVPJMJaWwM8BPzQGBMwxszBDWpyT3iXvwEzjDGfM8b4gG8BtcBz/X4RIiIiIiIiHRiSIQ6owzWFBNgS/jwRwBhzgzFmdat9vwpYYB9uaoKbrLVrAKy1ZcAFwPdwtWyfAs631jYNxEV0w8rBLoAMGXqWpK/oWZK+omdJ+oKeI+krcfEsJXxzyo5Ya4+qdWu17ZZ2n8tx0wx0tv9aIK4n+LbW3jTYZZChQc+S9BU9S9JX9CxJX9BzJH0lXp6loVoTJx0rx/31oKO+e9K3dK/7n+7xwNB97h+6rwND97n/6R4PHN3rgZEQ99lYawe7DCIiIiIiIhIj1cSJiIiIiIgkEIU4ERERERGRBKIQJyIiIiIikkAU4kRERERERBKIQpyIiIiIiEgCUYgTERERERFJIApxIiIiIiIiCUQhTkREREREJIEoxImIiIiIiCQQhTgREREREZEEohAnIiIiIiKSQBTiREREREREEohCnIiIiIiISAJRiBMREREREUkgCnEiIiIiIiIJRCFOREREREQkgSjEiYiIiIiIJBCFOBERERERkQSiECciIiIiIpJAFOJEREREREQSiEKciIiIiIhIAlGIExERERERSSAKcSIiIiIiIglEIU5ERERERCSBKMSJiIiIiIgkEIU4ERERERGRBKIQJyIiIiIikkAU4kRERERERBKIQpyIiIiIiEgCUYgTERERERFJIApxIiIiIiIiCUQhTkREREREJIEoxImIiIiIiCQQhTgREREREZEEohAnIiIiIiKSQBTiREREREREEohCnIiIiIiISAJRiBMREREREUkgCnEiIiIiIiIJRCFOREREREQkgSjEiYiIiIiIJBCFOBERERERkQSiECciIiIiIpJAFOJEREREREQSiEKciIiIiIhIAlGIExERERERSSAKcSIiIiIiIglEIU5ERERERCSBKMSJiIiIiIgkEIU4ERERERGRBKIQJyIiIiIikkAU4kRERERERBKIQpyIiIiIiEgCUYgTERERERFJIApxIiIiIiIiCUQhTkREREREJIEoxImIiIiIiCQQhTgREREREZEEohAnIiIiIiKSQBTiREREREREEohCnIiIiIiISAJRiBMREREREUkgCnEiIiIiIiIJRCFOREREREQkgSjEiYiIiIiIJBCFOBERERERkQSiECciIiIiIpJAFOJEREREREQSiEKciIiIiIhIAlGIExERERERSSAKcSIiIiIiIglEIU5ERERERCSBKMSJiIiIiIgkEIU4ERERERGRBKIQJyIiIiIikkAU4kRERERERBKIQpyIiIiIiEgCUYgTEZE+Z4wpNMZYY0xh+PMVxpjiVtvvMMbcMUjFi4kx5l5jzL29PMcNxpjVrT6vNcbc1OpztTFmcW++o5PvvdIY80hfn3ewGGOKjTFXHGP7+caYNQNYJBGRQaUQJyIiRwmHjYZwyKg0xmwyxnypr85vrV1hrV3RV+eLB+0DGoC19hZr7bmdHWOtzbDWrgsfv9QYY/ugHKnAT4Dvtlu/xBizLvwzPRyPIa99+I+VtfYRIMMY88l+KZiISJxRiBMRkc7cYq3NALKBlcCdxph/GeQySdcuBXZYa9+NrAj/3P4B3AGMBEYDNw9O8frN3cA3BrsQIiIDQSFORESOyVobstY+CBwGFkTWh5uwvWWMqTDGvGeM+UKs52zfVDHcXO67xpjVxpgqY8x2Y8z57Y653hjzgTGm3Bjze2PMnzpr7miMOc8Yc8QY42+1zhhjiowxV4U/5xhj7jHG7DXGHDDGPGyMGXeMMv/QGPN+uCZrV/izJ7ztDmAxcEN4+/7w+puMMWuPcU4broGbAKwOr6sOL9caY/5sjLmr3TFnhu9RoJPTXgg80W7dT4C7rLUPWGvrrLUN1trXOitX+HvuNcb80Rhzd/ie7zPGXGqMmWOMeTVchueMMWNbHXPMexo+5wPGmN8YY8qMMfvb1V5uiryG78F/tdo29ljPB/AkcJoxZuSxrktEZChQiBMRkWMyxiQbYy4GcoGt4XWLgAdxNXQ5wArgF8aYC3vxVV8CbgCygLuAPxhjMsLfdwnw78CngTzgOeBTxzjXE0ANcFGrdWeGr+Ev4c+rgLHAHGAKUAv8wxiT1Mk5twJLgUD4u78MfAFc81BgHeHaS2vt6FgvOnz8B8C54fcZ4eU24L+Bz0XuQ9jVwAPW2qpOTncy0LoWLh1YGH7/ejg8vWyMOTOGol0IPIq7byuBO3E1eJ8CRoX3+VGr/WO5pxfhfn754fffNS39AmdFXsP34Jutjuv0+QCw1hbjfubzYrguEZGEphAnIiKd+bYxphyoB+4HbrDWPhrediXwiLX279baZmvt87jmbFf34vvusta+Za0N4cJLJjAjvO2K8PZXrbVN1tp7gTc6O5G1thm4l3DICvsC8BdrbY0xZgwuNH3DWnsoHIi+BpwIzO/knKustXussx54ADir55fbNWvtc8AHwMUA4VqmC3BhqjMjgIp2nz24ZpZfwjWlvAd41BgzuYsiPGet/Uf4fv4BSAP+aK3dba2tBR4GTgmXLdZ7+ry19q/h5+ZF4B1a1fAew7Gej4hK3B8VRESGNIU4ERHpzE+stdm4EPB74CxjTHJ423hgZ7v93wcm9OL79kbeWGurw28jTQbHAcXt9m//ub17gCXGmMnGmBHAJ4HfhbeND79Gr8FaWwEcpJNrMMZ82RjzdriZZjlwDa42qb/dgQtfAJcD71hr3zrG/odxtVURkRq7e8IhqNFaezdQBJwNbZpwVhtjbmh17L7Im3Boa7MOV9MW+RnFek/30lZ1q3Mcy7Gej4hM3PWLiAxpCnEiInJM4RqVrwKTwq8Au8OfW5uCqzXqD3uAwnbrJh7rAGvtTmAtrtbwEmC7tfbV8Obd4dfoNRhjMnFNNY+6BmPMh4BbgWuBkeFweydgWu0Wiu1SOtXZ8X8AjjfGzMWFuWPVwoGroYw0S4wEqZ1A+5Evbat9Mlott3S75E637mknenwPjTETgXSOUUMrIjJUKMSJiEiXrLVB4AfA98K/mN8LXGCM+bgxJskYcxouYPzuGKfpjfuALxpj5of76H2e2Po+/Q7XFPOLwP9EVlpr9wGP4/rx5YX7Vv0aN7DG+g7OkwU042qVmsN9uC5pt89+YHq3ruro4zHGtGkiGA5hfwxfy2jgz12c52+Ea9ha+S1wlTFmdvjndSUuFK9uf3BP9eCeduQgLsi1byYZi48CL1prD/bgWBGRhKIQJyIisbof11Tt36y1LwOfA34IHMEFjOuttQ/103c/APwCF1AOAafjhsyv7+K4/8XVzszEDbrR2qVAKbAR17QwAHw83P+rvSdwIfBF3D24Nlym1v4LOCE8kuOe2C6rhbV2Gy70vBA+x9dabb4DN2DJKmttTRen+iMwxRhzQqt1vwyf4wncz+tqYFl4MJC+1J17ehRrbR1u8JL7wvfgZ9347i/iaktFRIY8Y22v5xUVEREZcMaY14GHrbU/Huyy9DdjTB6upm6etfadGPa/ErjAWtt+GP4hyRjzCeBfrbVLB7ssIiIDQSFOREQSgjFmOfAIri/XNcDPgeOtte8PasH6WXh4/p8Dc621pw92eUREZPAld72LiIhIXLiGlsFEtgHnD4MAdxKuCedu3JxtIiIiqokTERERERFJJBrYREREREREJIGoOWU/Msb4gPm4iVFjGplLRERERESGlSRgDLA+PKVPlxTi+td8YN1gF0JEREREROLeYuCFWHZUiOtf+wDWrVvHuHHjBrssIiIiIiISZ/bs2cPixYshnB1ioRDXv5oBxo0bR2Fh4SAXRURERERE4ljM3a80sImIiIiIiEgCUYgTERERERFJIApxIiIiIiIiCUQhTkREREREJIFoYBMREZGB1twER0qhvgZGjgdf6mCXSEREEohCnIiIyEDZuwPeeBI2rIX6WrcuxQu5BVBTCWkByJ8I8z4SyjhtAAAgAElEQVQKk+eAMb37vmCd+860AOSNhST9sy8iMhTo/+YiIiL9qbkZ3nwKXn/cBar2Ghtgf7F7X3UYSnfBxudd6FqwDE46A7w+8CR1HeoqDrnjm5tgx9vwzrMtYdHjcWFx5AQYfxzMPRPSM/v0UkVEZGAYa+1gl2HIMsYUAkVFRUWaJ05EZDiyFv76ny6UtRfIcaGssiy2c2XlwckfgTGTXVPMI6WQnAIpPreUH3BBMRSKvXy+VBg3Az7+FcgdE/txIiLSZ4qLi5k0aRLAJGttcSzHqCZORESGHmuhbK97H8jpuM+ZtS7wJCX1XzneWds2wCWnwKwPw7yzoXCWW7e/GGorYMRoqC6HDc/B28+4ppCtVRyCNX/qfhlGjHLXWn7g6G3BOldjd+vV4PW7MJieBRnZ7jVvHCxc5t6LiEjcUIgTEZGhpbkJHvgRbH/DffYkwYSZ4E+HYC3UVLilrgqMB86+Ck79eN+XY38x/POOls8nLoVl10BqRtv9xkxqeZ8zGiYcBx+9HN5eA+sfa2lqGasRoyBrJIyeBMctbOlb11APB3e39Msr2d72uIZ6t9RUwIEPWta/9Qx8/MswcVbnYbip0R3rT+/fUCwiIoCaU/YrNacUkSGroR7WPex+aZ9/jqvFGUjWur5fm1+B4o2AcWXxp0N5KezcEPu5jIGLv+dCD7hBQFJ80BiEuupWSxWkBmDsNNj0AhRvgiP7XVk8HhcII/3WjAf2bHWBCCBnDHz1tp7dp4Z6d973Xna1ZuUHXM1YwVQINbtyNgbd+0lz4LgFsQ2I0lAPJe/Dw//lavliuU/5E9y9CdZBQ134td59N7j7v3AZfPhCSE3v/rWKiAxDPWlOOWRDnDEmG7gLOBeoBG621t7ewX6XA9cC04Aq4C/At621DeHtXuDXwGeBRuC/rbX/EWMZClGIE5GhxlpY9QPY9rr7nDMGLvqGq+0aCEUb4e+/hsP7+u87kpJdjV5f8KfBVT92fdniUUM9HCpxzU6tdcGztgIO7oEn73UBsbtyC+Cym9TPTkQkBuoT19ZvcNdXAEwBnjLGbLbWrmm3XxpwHfAakAP8A7gBuCm8/T+AOcBUIAN42hhTZK39fb9fgYhIPLDW9dU6sh8O74ddm1oCHLgw9T/fcaMo+tMA42pt0gIw/7y+rZFpboK//RLKD3a97/xz3IAdlWWu6aC1rnxpWa4mq7kJ7vzXltqy9t/TF1K8cPH34zfAgasdLJjS8jkzx71OOQkmHg/P/xVKi13Q6+wPv0nJbmmod5/L9sJd34LP/Js7T09EAmVSimsau2uTK2taZsuSnuVG2EzLUs2fiAwrQ7ImzhiTDhwG5lpr3wuv+ylQYK29rItjrwU+bq39SPhzCfAla+1j4c9fBi621i6OoRyFqCZORBLJmj9DbSU0N7YNbpFfzrtr7DS48ua+m8x6/ePwj9+69yleOG6R6/eVFnATZ9dVu9dADsxZ4po5HssHm+HR26G2ygXPSHBobnKDkKQGXB+21AzXVHD3FrdvYAQs/Jhr0piUDDbUMlCKDbW8jpkCI/L75toHW32tC3PG436e3lT36kt19yAUgnfWuPvZ2OCOMcY1Ux0zxQXFUYWu6WVdlQtnY6e5e9lesA4e/FnbPxZ0pfAEV/vn9fXBxYqIDBw1pwwzxswFXrXWelut+xxwvbV2bhfHPgTssdZeZ4wZgQuD46y1JeHtpwKPWWtHtDsuG8hud7pxwDqFOBFJGP95ZWz9o8DVLn323+Gh/4I92zrfb/xxsOxqGDnehbADu8I1NynuNTnFvc/Kc7/wVx12/b6qDrtAWVPpXmsrXaiMBISzr4LTPtn7a24vMlBHivfobY0NcGiPG7Wxo+3igvGffwxVR7re15ME0+ZBZi40NbiBZ+pr4fDe2Gpb2/v4l2HBed0/TkRkEKk5ZYsMXD+41sqBwLEOMsZ8HjgNiLT9iAwh1rqtTWfnuQ64sdslFRGJJ0kpHa/3p7kh8HPGuCW3AI7/kGvC9sWfwpbXoCL8S3coBFVl8NIj7vPuLXDHv/ZtOdOzYMG5fXvOCGM6D2gp3vhuGhkPJsyEr9wGj/wGtrx67H1DzbD1ta7PmZ0PJ57umlPWVLSE+pqKtqNsvvwPmH9ubAO7iIgksKEa4qqBzHbrsnADl3TIGPMJ4D+Bj1pr97c6D+FzRd53dp5bgXvbrRsHrIu51CIig23xp9yog8le13wwJxzcUjM6/8U4KRlmfejo9YEcNzBGX7f48PrhYysGfkRMiV1GNlzyPairgf073bQG+3a45pgpfteE8kgp7NvZ+TlSvHDeNTDvI8cOZcE6+M8rXA3eoRLY/iZMn9fXVyQiEleGaojbBlhjzExr7ebwupOAdzva2RhzDnAP8DFr7duR9dbaI8aYvcCJwN5jncdaW46rpWt93t5eh4jIwDrlo313rtMuhOnz4bm/wLb17pfsQA586ALXb6m5yTVbbG5yTelKtrt5zEaMhryxbt/08CAkbQazyHTBUeJfajpMmu2Wjuzb6ZbGoJu6wJfman19aZA7NrbBSnypcPJHWmp+/36b6xvXev49EZEhZkj2iQMwxjwA+IArgUnA08Bn249OaYw5A/grcKG19rkOznMzsBQ4H0gHngJ+HMvolBrYREQkrLkZqo+4YNbVYCMi3XWkFH79lZb+kulZ8PU7NWKliCSEnvSJG8r/kn4VsMA+4HHgJmvtGmPMBGNMtTFmQni/7+OaSP4zvL7aGLOp1XlW4mredgBvAH/R9AIiIt2UlOQGLlGAk/4wYhRcemN4igtcX7lt6we3TCIi/WjItkcJN2/8dAfrP6BlwBKstad3cZ4G4JrwIiIiIvFo8hw47SJ4+n73efubcOLSQS2SiEh/0Z9ERUREZGiYenLL+/ff7PtBdURE4sSQrYkTERGRYaZgiusPV1Phlp3vuAFSImEuI1vz+4nIkKAQJyIiIkODMTB1Lryz1n2+9/tHbx83Ay64FvLHD3jxRET6ippTioiIyNAx/ZTOt1nrJp//w39AxaGBK5OISB9TiBMREZGhY9ZpMGeJm1A8kONGRc3Oh8zcln0qDsGqldAQHLxyioj0gppTioiIyNCRlASf/lbH27a/CQ/80E0wv78YHvwpXPgNSAsMaBFFRHpLNXEiIiIyPEw7GZa1mjFo63r48cXwwI/gwO62+zY3DWzZRES6QTVxIiIiMnyccjYUbYSNz7es2/IqbH3NbVuwDFbfDbu3wie+CicdczpZEZFBoRAnIiIiw4cx8MmvQ26BC277drr11sL6x90S8eLfFOJEJC4pxImIiMjwkuKFMy9xy94d8MTv3Zxy7e0vHvCiiYjEQn3iREREZPgqmAJX/BA+vxJGTzp6e13NwJdJRKQLqokTERGR4c0YN+jJlJNgz1a454aWgU0qDkJq+uCWT0SkHdXEiYiIiAB4PDBhJkyc1bKuUpOCi0j8UYgTERERaS0rr+V9hUKciMQfhTgRERGR1rJGtryvODh45RAR6YT6xA2Az9/2LKkjRnW537lzx3Pdx+a0WXfr/21g9Vu7OzmirUv/ZRqXLZneZt1//Hk9r24/ENPxX182m/NOntBm3VfvXsf7+ytjOn7lZ09h0fS21/m5Xz7N4epgTMf/5ounMW1MVpt1Z//wnzEdC/DH684kN+CPfi6rqufiW5+J+fgnvr+szeft+yr42u9eiOnYnAwff/rGWW3WvbKtlBv/8npMx08dnclvv7S4zbrH3vyAX/1zY0zHL5yWzw+Wz2+z7v7ntrHq+e0xHa9nT89ea3r29OzFYmg/e9ngXeFWvgy8fPTPRM+enr3W9P89PXux6OzZ+8mqZ2M6vjXVxImIiIiIiCQQhTgREREREZEEYqy1g12GIcsYUwgUFRUVUVhYOLiFERERkdjU18LNn3XvU7zw/YfcNAQiIv2guLiYSZMmAUyy1hbHcoz6xImIiMSJ5uZmrLUkJ+uf50HlTwN/OtTXQGMDHNwDZXshNQPGTnPBTkRkEOlfCRERkQEUCoXYv38/lZWVWGtJS0sjGAxSUlJCaWkpycnJLFy4kPHjxw92UYe3rDwX4gB+/ZWW9UnJUDDVzSc3Yz5Mmj045RORYU0hTkREZABUVVWxc+dOioqKqKur63S/xsZGXnjhBebNm8f06dM73U/6Wd44KN119PrmJti9xS0v/i+ccQmcvnzgyzdclR+EtX+G99+EFB+MmQILzoPxM1zAFhkm9LSLiEjUiy++SF1dHcFgEGstPp8Pr9eLz+eLvh85ciQjR47EqI9QTMrLy3njjTc4cCC24b8j3njjDWpraznxxBN1rwfD0uVQdRhKtkOoGUZNhKZGOFTSdr9nH3BNLKfPG5xyDicfbIH7vg8N9S3rDpXAxuddn8VAjqtBzRoJmXkwchzMWQJef+fnFElQCnEiIhJ18ODBNrVEVVVVHe6XmppKIBDA5/Nx8sknk5aWNlBF7FJzczPbt2+npKSEpKQkAoEAI0eOJBAI4PF4aGxspKmpicbGRjweD6NGjeq3Pmj79+9n3bp1NDU1tVnv9/spKCggOTmZiooKPB4PY8aMIT8/n/Xr11NWVgbA5s2bqa6uZt68eaSmpvZLGaUTowvhSz9zNW+hUEs/uJpK+GAzvPg32PWeW/fwL+Da/4b0zEEr7pBXXwt//XnbANeatVBZ5pbdW1vW79wAn/m3gSmjyADS6JT9SKNTikiieeyxx6ioqOjWMV6vl/nz5zN+/PgBrzEqLS1lw4YNBINBmpubaW5uprGxkVAoFPM5/H4/48ePJyMjg4KCAjIz++YX8YMHD/Lss8+2KUtBQQFTpkyhoKAAj6fjWX6ampp48cUX2bt3b5v1I0aMYOnSpfj9qlWICzUV8NtrXW0dQOEJcMH/gxGjoZOf7ZBTV+0CbnKKW/qrOePeHbD6d1D8rvvsT4cLvwHpWfDGk7D9jZafQ3vGwLd+D5m5/VM2kT7Qk9EpFeL6kUKciCSa0tJSAHw+H8YYgsEgwWCQhoYGgsEgBw4cYN++fR0eGwkXoVCIUCgUDXcFBQX9UtaqqiqeeOIJGhsb+/S8SUlJJCUlRQOpMabN+4iO1htj8Hq9pKSktLlPaWlpLFmyhOzs7JjKEAqFeP3119mxY0eb9WPGjGHJkiVqXhkvtq6HVT9ouy4rD6afAhjAuhoiTxKMmw4zFkBaYDBK2nPBOti3M7zsgNJiN2JnXRVUl7fdNy0TRoyCnNGQPSr8fowbBKYnI3qW7oJn/wjvvdR2/af/Deb8S9t1zU2uFq78IFQecv0V9+1029RvUeKcQlycUYgTkaHGWsvGjRvZs2cPKSkpHD58+Ji1Xl6vl/POO69PmwI2NTWxZcsWNm3a1Ol3+/1+pk2bRlZWFocPH6asrIy6ujqstaSkpJCcnBwt/7EGGekLPp+Ps88+m/T09G4dZ61lx44d0SaVEbNmzWL27NkKcvHiwZ/BxnWx7ZueBVfe7PrXxYvGBnj5H7D9dUhKgYwR4PXB4f1uWoWKgy6I9kZ6Fpx2IXz4k53Pt1dxyAWx9Cw3KuhT98GG59p+tzFw2kXw0cu7/s4Nz7vmlxFf+RWMmdy76xDpJwpxcUYhTkSGuoaGBjZs2MD7779PZ/+epKSkcNxxx+H3+6mvr6ehoSG6b+vXhoYGGhoa8Hq95OTkMG3atKP6qoVCIZ5++ulonzEAj8fD4sWLycrKIikpieTk5DY1acfS3NzMnj17qKmpoaysjP379x/Vf603PB4Pp59+Ovn5+b06z5tvvsnWrS39fEaPHs2iRYvUTy4e1Fa5PnEHPoDays77bEWMLoRrfuGaHw4ma10N1+P3QHn3Bt2JSvFCsteFr8Zg12Hv7CtdkDqwG2zINcUMNbvmkJGmkp2Z9WFXo5Yf49QbTY3w88vdzydi6XI442JN3C5xRyEuzijEichw0djYSENDAx6PB4/HQ1lZGc8991yvzpmamkpOTg4pKSnRJoo1NTUUFxdH9/F4PJxyyilMmTKll1fgWGujfes6CprtX9u/t9YSDAajtXs5OTl90seuubmZNWvWcPDgwei6lJQU0tLS2oTn7OxscnJyomWoqamhrq4On89Heno66enpZGRkkJ+f32d9/6SVpkYXSCoOhfvFGRcYjpTCuoda9vvQ+XDuFwetmOzbCY/d3XVwAncd+RPcUP5jprgQlhZwtXYjRrX0/4sMLHKkFI7sD7+WQtEGdz96asZ8OPPSntWirXsYnry33fkWQGYOpPjdqJU5o+HE0wemH6O1rh9hxSHXhy9nNOSN7f/vlbinEBdnFOJEZDjbuHEj774bwy+JPTR+/Pi4GxmzP4VCITZs2MDmzZv75HwLFizos/ArMXjl/+Cfd7Z8PuMSOOVsCNa6GrzaKtfPrKbSDdxx0un9U1u34224f6WrPYtIC8DpF7tQUV3u+sFl57uAkTO6dwOWNDe5AWAO7j72fp4kF65qKlwTz7HT4OyrYNIJPf9ugOJNbhqIoo2d73PqJ+C8L8V2vuYm1+/OhtzPrTHopjbIzAN/mgtqxZvgwC4X1PbtdK+NQRfeGoMt50pKhit+BIWzeneNkvAU4uKMQpyIDHcVFRUcPHiQ6upq6uvr8fl8+P3+DgcESU5OxufzUVFRwZYtWwgGg52eNzc3l7POOqvTER6Hsv379/PKK6/0SV++rKwsxo4dy+zZs4flvRxQ1sIfb4Ytr8a2/8Tj4RNfg9wCSErqmzJUHILbv+7CB7jgtOhjrplhakbffEdHdrwN936/5fPxH4LskWA8rgbMnwGz/wVGhJsdNzf17UiXjQ3wp1tcLemxFEx197u20pUrd6ybay5vnKtR3fg8bHqxbRPN1vzprolp1ZHYyzZmMnz5VjXxHOYU4uKMQpyISM80NTVx+PBhgsFgtKlmY2NjdG63SB+74SoUClFZWRn9bIyhqamJ/fv3U1tbS2pqKqmpqaSlpZGamkowGKSmpoaamhp279591DQSs2fPZuLEiYRCoeh8etIPgnVw17dc/7lYpfhg8hxXM5bia1lyC1zQ+2ALvP+mG4QkNcP1M6uvdoOD1FW7PmepAVfbtneHWw+u9uiKH8Xex6y31v4F3l0HC85zy0Cz1jUfLT/gasMagrD1VVdrNpC8fjeC6ZFS1/wW4MLrYO6ZA1sOiSsKcXFGIU5EROJNMBhk7dq1HD7c8bxaHo+HQCAQ7V83efJkvN4eDA8vHasuhzV/cs37aspdLVRaIBy0MgEL76zt3zJ4PHDlLWrGV1MBv/6qe+2OtICrdUsNuEBdWeamNYiEMn+663uXPdL1J8wtcH3wAiPcNmPg6VXw3F9aznfNL1zTVRmWFOLijEKciIjEI2stR44c4dlnn+1ynj2/309hYWG06aXX66WhoYH6+nrq6uqor68nGAzi9XrJy8sjEEiwedDi0aaX4MW/uWH+uxswupI9EpatgOMW9O15E9WhEnj9CRe0MnNdk9NAjqvBLCuBg3vg0B5Xqzl5DsxZ6mpA2zd/tNY1w6ypdAO+dDUvXrAObvuyC4DgRi390n+66R1k2FGIa8UYkw3cBZwLVAI3W2tv72C/E4D/Ak4Bcqy1pt32e4GLgYZWq3OttZ131mg5thCFOBERiVNHjhzhiSeeiI5wmZaWRm1tbaf7RyY4P9bcgCeffDIzZszo87IOW2V74YPNUF/rmgE2Bl2TyEgTylGFMO1kGDsdmhpcP7fUDLf408GT7Gr8yva6WqNp83o28bb0vd1b4X++3TLIzGkXumkYZNjpSYjrw16jcec3uOsrAKYATxljNltr17TbrxF4ELgd+Hsn5/qFtfbb/VZSERGRQTBixAjOOussSktLGTt2LNnZ2TQ0NFBRUcHhw4fZvHlzmwFUWk+r0Jk333yTxsZGZs2apQnJ+0JugVs6Ym1sA2KMyIdx0/u2XNJ742fAeVfDo+E6hpf+DnOWaFJyicmQDHHGmHTg08Bca20V8LYx5h7gKqBNiLPWbgW2GmOmDnxJRUREBldeXh55eXnRz16vl5EjRzJy5EgmTZrEnj17qK6uZt++fdF+dCkpKfj9/uji8/koKSmJBr6NGzfS2NjISSedpCDXn3RvE9/8c9yAL0Ub3aA0/7wTvvAT/WylS0MyxAHTcU1F32u17m3goz0839XGmKuBYuAn1toH2+8Qbr6Z3W71uB5+n4iIyKDzer1MnuxqBebMmROd0D05+ehfH+bOncvzzz9PaWkpAFu2bKGxsTHatLL9dBKtp5roSHNzMzU1NQQCAQVBGbqMgU98FX7zNdesctd7bsTM3s6PJ0PeUA1xGbh+cK2VAz3pbX0b8E2gAhcCHzTG7LfWPt9uv+uAG3twfhERkYRwrFEqk5OTWbJkCS+99BJ79uwBYMeOHezYsaPD/VNTU0lKSsIYQyAQICkpiebm5uhSXl5OU1MT6enpjBo1ipqamg774hlj8Hg8FBQUqC+eJKa8sW6KgdefcJ8fvd3NWVdfA8EaF+5S/G5glYUfc5OKDzc1lfDGk7DzHTfwzOx/gSkn9u18gglmSA5sYoyZC7xqrfW2Wrcc+Hdr7dxOjpkKbG8/sEkH+90BBK21X2+3vrOauHUa2ERERIaLUCjEq6++SnFx8YB/95IlSygo6KT/mEg8O7wffnWNa1J5LGkBuOBamLloYMrVH+pqoPoIVB2G999yI7CGmsOhtda91tdARjaMnOBG/Xz/zZYpHCLSAjDrNDjpdJgwsxvfX+3mDDxU4pbzvgS+1L69xm7SwCYttgHWGDPTWrs5vO4k4N0+OHeHqddaW46r7YtS8w8RERluPB4PixYtIjs7m+LiYkKhUHQwlMhrfX09TU1Nff7dW7duVYiTxJQzGuae5WqbjqW2Cv54M5z8ERdcGuqhoS4cfmrd+9GT4MOfHNx+dZHJ1Uu2u+kUgrVuOVLq1sdSiXSk1I3g2ZnaKli/2i0zFriRPUeOg8YG2LfThT9rAeterXXnfO7P7l5FLFwGBVN6fckDbUiGOGttjTHmIeCHxpgrgUm4QU0+235f45KWD/CGP/vD56gPf/4U8DhQC5wFXAqcPwCXISIikpCMMcycOZOZMzv+67i1lqqqKsD1fYu893g8JCUlkZSURGpqKg0NDbzxxhsATJ06lYyMjOjxEdXV1bz22msA7N+/n4MHDzJy5Mh+uzaRfrPsGhg3wwWxyBQR/nRISoH9O2HdQ1B+0O375lNu6cg7a91E5PM+MmBFb6NsHzzyazdYS18aN92F17K9sPH5ljn2ALa+5pa0gAtooebYz3uoJCFD3JBsTgnR5o130zJP3I+stbcbYyYA7wHHW2s/iMzl1v74SLNKY8w6YA5gwvv92Fr75xjLUIjmiRMREelXzz//PCUlJdHPhYWFLFq0SC1iZGiprYK//NT1C+vK2Gmw4hf9X6bWmptdE8m7/61twOpIzmjw+mH8cTBmiuvb5ktrCa9ev2tiWn7AzX1YMKVt0LLWDQDz5lPw9rPdL2v2SFd7lzsWpp8CuWO6f44+pMm+44xCnIiISP87cOAAzzzzTJt1H/3oR8nNzR2kEon0k0h42foaVJe7QU584SXFB0/+vqXv2Fd+NXBzzr22Glbf3bbfmscDs5e4wBYpY2q6C26Zffjf5u6trpZy62stfQrzxkLOGNek1Hjcusj78ce5JpRxNOm9+sSJiIjIsJOfn8+CBQuizSoBDh8+rBAnQ48xbvqBzqYg2L3FNTUEuP3rsPgiOPMySErq3fdaC889CJtfdk0+R010NW/Nja727aVH2u7vSYLP3wRTTurd98Zi/Ay4+LtuwJS6Kjcgitff/987yBTiREREJOFNmTKFYDDIO++4pmbl5eVdHCEyBJ1ydkuIA1j3MKRlwWmf7N55rIUdb7uaq8lz4P/ugNcec9v2djxtSFSKz42gORABrrXUdLcMEwpxg6iuro7Kykqam7vR+VJkAPh8PnJyctSfREQSSnZ2y0w/R44cGcSSiAySSbPd9AObX2lZt/ZPrvliQ53rVxesdU0Np58CgRFHn6NsH/z9NjeKZHeMmQxX/Mj1afN4encd0iWFuEFSV1dHRUUFOTk5pKSk6JdliRvWWo4cOUJVVRWZmZmDXRwRkZiNGNHyC2lFRQXWWv37KsOLMa5p4ZEDcPu14bnX6uCvP+94/3HT3QAfMxdB/gR45f/gqfugMdjx/ll5bm62xno3amZSsluy8+HEpcOiGWO8UIgbJJWVleTk5OD1xk+nShFwQ4NnZmZy6NAhhTgRSSipqan4fD6CwSBNTU1UV1cTCAQGu1giA29EPnzmevjDjcfeb882tzyzqutzzpgPn/tu7/vXSZ9QiBskzc3NpKSkDHYxRDqUlJREKDLCk4hIAsnOzqa0tBRwg5ukpaVFuy2o5YsMK9NOho+tcE0rvX43d1xqBiR7Ydcm+OC9ltEc28ufAOdd7UacLN3lpiz4zPUKcHEkLkOcMWaitXbXYJejv+kfEolXejZFJFGNGDEiGuJeeumlNtvS0tIYO3Ysc+bMUUsYGR4WLnNLR2qr4P034b2X3aiToZDry7b4U7B0OSSnwDW/gL3vu2aXSXEZG4ateO11+L4x5jFjzCeMMfFaRmln7dq1jB49usfHr1ixghtvvLHDc82aNYunn36612UUEZGhLScnp9NttbW1bN++naeeeoqampoBLJVIHEoLwJwlsPzb8PU7Xa3dV26Dsy5zAQ7cXGoTj1eAi0PxGpBmAhuBu4APjDErjTHjB7lMw8Y555zDd77znaPWv/DCC2RkZFBdXd3r77j33ntZtGhRm3V33HEHK1eu7HD/TZs2cdZZZwFw0003sXz58l6XQUREhp5x48aRn58f/ZyUlERycjJJrZqBVVZW8swzz9DQ0DAYRRSJPzmjXY3dqImDXRKJUVzGamvt+8C/G2O+C1wAfAn4tjHmCeBOa67M+i0AACAASURBVO0/B7WAQ9wVV1zB9ddfz80334yn1RCx9913H5/61KfIyMgYxNKJiIh0LikpiTPPPPOokSlDoRA7d+7kjTfeIBQKUVNTw2uvvcaHP/xhNSEXkYQTrzVxAFhrm4C/Af8NbALOBn5vjNlmjDltUAs3hF1wwQVUVVWxZs2a6Lq6ujoefPBBLrzwQq666ipGjRrFuHHj+Na3vtXpXzJ/9rOfMWXKFAKBAMcffzz/+Mc/ANi8eTMrVqxg/fr1ZGRkkJGRQXNzM1dccQXf/va3OzxXYWEhjz/+OI8//ji33HILDz/8MBkZGcyYMYOHHnqIOXPmtNn/rrvuYsmSJX10R0REJNG0D2Yej4epU6dy6qmnRtft3r2b1atXs3v3bqy1bfZv/1lEJJ7EbYgzxkw0xvwI2A38EvgrMAEoAG4HYhgLVXrC7/fz2c9+lvvuuy+67u9//zs5OTk8/PDDlJaWsm3bNtavX89zzz3Hj3/84w7PM2XKFNatW0dFRQXf+973uPjiiyktLWXmzJnccccdzJ8/n+rqaqqrq9s0czmWc845hxtuuIGLLrqI6upqtm7dysc//nFKSkp45513ovvdf//9fP7zn+/djRARkSFnwoQJTJ06Nfq5oqKCF154gccee4yioiKCwSBr167lr3/9Kzt37hzEkoqIdC4um1OGm02eDjwJXAP807b9k9itxpgfDkrh+sv3Pz5w3/XDR7vc5YorruCss87i9ttvJyMjg/vuu49LL72Un/3sZ6xfv56srCyysrK48cYbue6666IDkrR20UUXRd9ffPHF3HLLLbz++ussW9bJKEk95PP5WL58Offffz8nnngiRUVFvPnmm/zzn2p1KyIiR5s3bx4+n48tW7ZEpx+orKzklVdewePxRKdYWb9+PYFAgEOHDrFv3z5SUlLIy8sjLy+PnJycmP8AKX2jtraWV199lcrKSqCltrX9a+S91+tl7ty55OXlDXxhRfpZXIY44E3gGmtt8TH2mTBAZRmWFi1axPjx43n44Yf5yEc+wjPPPMPKlSv50Y9+xMSJLZ1eCwsLKSkp6fAc9957L7/85S/ZtcvNFlFdXc2hQ4f6pbxXXHEF559/Pj/96U954IEH+MQnPqGJqkVEpEMej4c5c+Ywffp0tm7dyrZt22hqagJoM0dmKBQ6amTkPXv2AC4k5OTkMH78eCZPnozP5xu4CxiGgsEga9asiQa4WL300kt87GMfa9PHX2QoiNcnOrmjAGeM+UnkvbX2yICWaBi6/PLL+cMf/sCqVas49dRTOeWUU/B6vdFQBlBcXMzYsWOPOnbXrl1cffXV/Pa3v6WsrIzy8nKOO+64aB+D3nQi7+jY+fPnk5OTw9NPP82qVau47LLLenx+EREZHvx+PyeeeCLnn38+s2fP7tbccdZaysrKePvtt1m9ejVHjujXkv5SVlbGk08+2e0AB1BTU8Pu3bv7oVQigytea+KuAf6tg/VXAx2PfJHoYmjiONAuu+wyvv/977N9+3ZuvPFGkpKSWL58Od/97ndZtWoVdXV1/OAHP+DSSy896tiamhqMMYwcORKA3/3ud2zZsiW6fdSoUZSUlBAMBrv918tRo0axevVqQqFQm7+sXX755Vx//fWUl5dz9tln9/CqRURkuPF6vZxwwgkcd9xxlJSUkJ6ezt69e9m0aRMAeXl5TJo0CYBDhw5RVlbWJlDU1dXx+OOPEwgEmDJlCscdd5xGvOwDoVCITZs2sWnTpjYDzSxcuDA6jURkffvXHTt2sHXrVgBefvll0tLSor+TiAwFcRXijDGRJpKe8Lxwrf8POAMIDnyphq+xY8dy5plnsm7dOj7zmc8AcNttt/H1r3+d6dOnR0NdR3PKHX/88Xzzm99k0aJFJCcnc/nll7Nw4cLo9jPOOIMTTzyRMWPGEAqFKCsri7lcn/70p1m1ahW5ubkUFBRE/5G97LLL+M53vsO1116rfgoiItJtycnJ0S4DeXl5jB8/npSUlDZT60QGRQkGg+zZs4e33nqLxsZGAKqqqnj77bfZuXMnKSkpNDc3EwqFon/UnDhxIiNHjoy7gFdWVsaBAwfwer14PB4aGxtpbGyktraWuro6GhsbaWpqii5+v5+0tDSampqi1+73+wkEAmRmZkb7zaekpPS4TJWVlbz88sscPnw4ui45OZkFCxa06dbRmVmzZvH+++/T3NyMtZann36aOXPmMGvWrB6XSSSemHgaQtcYEwI6KpABmoEbrLU/H9hS9ZwxphAoKioqorCwsM22vXv3UlBQMAilGroaGhoYNWoUa9as4aSTThrs4iQ8PaMiIl0rLy/n5Zdfpry8PKb9c3NzmT9/PiNGjOjnkh1bKBRi9+7dbN26tVt/SO2OtLQ0AoEAqampjBgxgoKCAoqLi6mrqzuq5izy3lpLQ0MDpaWlbbbl5eVx6qmndmuu2g0bNkT/0AsuBJ5//vndajbbl6y1hEIhmpqaaG5uJjU1Ne4CvQyO4uLiSG3/pC7GBImKq5o4YBIusL0LtP5TSQg4aK2tH5RSSUK4++67mT59ugKciIgMmOzsbM4991waGhrYtGlTm64DHSkrK+OZZ57h1FNPpaCgYMB/ibfWsm3bNrZs2UJtbW2/fldtbW30O4qLi3nrrbe6fQ6Px8Ps2bOZOXNmt+/V7Nmzyc/Pj85729TUxNatW5k5cyZJSUn9eu9LS0spKiqioaGBuro6qquraWxsbBNM/X4/CxYs6HBsAZGuxFVN3FCjmriBU1hYSHNzMw899FCbZpvSc3pGRUS6r7a2lqqqKjweD0lJSXg8HoLBILt27aKoqKjN6JfJyckdBonU1FSmT5/OhAkTSE5OpqGhIVp7E1l8Ph+ZmZndDiKbNm1iw4YNbdZ5PB5Gjx6N1+slFArh9XpJSUnB7/eTnp5OSkoKycnJJCcnk5SURG1tLcFgkOTkZFJSUrDWUldXR2VlJRUVFVRWVlJVVdXmWnsiPz+fk08+ude1ljt37uTVV189an1SUhKZmZnMnTuXxsZGgsEgubm50YAXuT6Px4MxBmstzc3NRzUvhZYaxWAwyI4dO9i3b1/M5Zs3bx7Tp0/v1TUOF01NTRhjhly3mZ7UxMVNiDPGfM5a+6fw+05nabbW/mHgStU7CnGSyPSMioj0rfLyctasWUN9fd80LEpJSYmGjNahsfXnUChEXV0ddXV1NDQ0tDne5/Mxbdo0pk6dSmpqap+UKSIUClFdXU1VVRWVlZVs27aN2tpasrOzmTp1ajQYRbSe6y0yfUMgEOizsjz66KM9rnk0xuDxeKJzCvaGMYaUlJRos0pwIXrZsmXdaio63JSXl7Nhw4botFZerxe/309qaiqBQIBRo0YxZsyYXvXDHEyJHuLetdaeEH5f1Mlu1lo7eQCL1SsKcZLI9IyKiPS9qqoq3nnnHUpLS48KVQMpLy+PM844Y8BqNEKhEDU1NWRkZAxKP7DS0lLeeOMN6uvro7Wa/W3ixImMGzcuOuiLz+eLjqodDAZ59tlno30px40bx+LFi/u9TPEsUtMZDAapr68nGAxGa0DXrVvXZc1ucnIyEyZMIDc3l+Tko3uMeTwe0tPTAfc8BgIB/H5/v1xLdyV0iBuKFOIkkekZFRHpP9ba6MiOrYVCIXbu3ElRURE1NTWEQiF8Ph9JSUkkJSWRnJyMx+OhsrKyxyHQ6/VyzjnnRH+hHY6stVRXV7Nu3ToqKiqiTStbB4dIc8nW4SHyM4g0tWzdty5Si5ibm8vkyZPJzMw8ZhkOHTrEU089Ff28cOFCJk9OmLqKbovMrbhjxw6qq6vbNA+OhLdYwnXk59MXfD4fp5xyChMmTOh65340FAY2ERERERnyjDGdjpJ4/PHHc/zxxwPuF9+Oaq6stdTX1xMKhaJTGUReWy/g+tilpqZSX19PeXk5+fn5wzrAgbv/gUCAc845h7KyMrKysjr9eUTubWd9GHsqLy+PwsJCiouLAXjttddITU1lzJgxffYdg6W5uZm9e/dy6NAhqqqqqKqqorq6ulf9JFNTU1myZAnZ2dkEg0Hq6uqora2lrKyMDz74gKqqqm6fMxgMJmwTzLgJccaYe2LZz1p7VX+XRURERCQedBYajDHd7scWGepfWng8ni4nAY/0MewP8+bN48iRI1RUVGCt5ZVXXmHZsmWDNg1CXygpKeHVV18lGOz+9M4ejwe/34/X68Xn81FVVUVtbS0+n4+lS5eSnZ0NuJE9/X4/I0aMYOzYscyePZuDBw9y8OBBKisrO6ypa2pqorq6OlpjWllZSXNzM1lZWb2+5sEQNyGOthN7i4iIiIgMaV6vl9NPP50nnniCuro66uvree2115gwYUJ0lNCUlJToQB7xLBQKcejQIV544YVOa9x8Ph8FBQVMmDAh2hw10kQ10my49R8urLVUVFSQlpZ2zGBrjCE/P5/8/PyYyxtpUtvXg/oMlLgJcdbaKwe7DDL4li5dyvLly1mxYsWQ/f61a9eyfPly9u/f36PjV6xYwahRo1i5cuVR55o1axa/+tWvOOuss/qyyCIiItJPUlNTOfnkk3nxxRcB2L17N7t37z5qv55MeN6XgsEgBw8eZMyYMUcNiLNz507Wr1/fJrz5/X4mTZrEiBEjyMjIIBAIdLuG0RgTrX3ra5EmtYmqf+qGJeEtXboUv99PRkYGmZmZzJ8/nxdeeGGwizXs3HvvvSxatKjNujvuuIOVK1d2uP+mTZuiAe6mm25i+fLl/V5GERER6Z3x48d32Rfu0KFDPP7445SVlQ1Imay10WaJBw8e5NFHH2XdunWsXr2aurq66H4VFRVHBbiUlBTOPPNMTjrpJCZOnEhubm5CNxGNR3FTE2eM2WitnR1+XwR0OOxMIk0xkOhuvfVWVqxYQSgU4s477+TCCy+ktLR0UIYG7m/W2l5PSioiIiLSE8YYPvShD7FlyxZqa2tpbGyMLg0NDdTU1ERHNH3llVc499xz+62fnrWWnTt3snHjRkKhEKmpqVRVVUVHjqyqquKRRx6JzvXXehAdgLS0NBYuXNjl6JzSO/FUE/fjVu9vAlZ2ssgA83g8XHLJJdEOo+DaPf/0pz9l6tSp5ObmctFFF0W3FRcXY4zh/vvvj1ajf+1rX2vTyfSee+5h1qxZBAIBZsyYwbp166LbSkpKOP300wkEApx66qns2LEjus0Yw29/+1umT59ORkYG3/nOd9i1axeLFy8mMzOTCy64IDqZZ+X/Z+/O46uo7v+Pvz4JOySEsIVNwCAoUggVClhBpVYogtSfUpSoYAW0buBSpYKyaPFbvopav6WKqCDKVlDrikstkpa6IEgViaCQAMGFCIEEwprz+2Nubm5uEpKQdcL7+XjcR+bOnDlz7uQ+lHfOmXP272fo0KG0aNGCJk2aMGzYsOAikeD1Nk6aNIn+/fvToEEDPv/883yfe/fu3fTq1Yv77ruvwD1ZunQpPXr0yLfv6aefZsCAAcFr//a3v6Vly5a0bduWu+66q8ipoGfNmkV8fDxRUVF07dqVV199FYBNmzZx44038sknn9CoUSMaNWrE8ePHGTNmDJMmTSq0rg4dOrBy5UpWrlzJzJkzWbFiBY0aNaJLly4sX76c7t275ys/d+5czj///ELrEhERkcpTp04dunfvTt++fenfvz8DBw5k0KBBDBs2jF/+8pfBtc/279/Pp59+ypEjR8jMzOSHH34gNTWV5ORkPvvsM77++uuTnoJ/z549vPvuu3z88cdkZ2dz+PBhMjIyCkz9n7umW+gSDBEREQwZMoThw4cTFxdXtpshxao2PXHOuUUhb191zu0NL2NmFTMoVk7o2LFjLFiwgE6dOtGsWTMAnnjiCZYvX877779Py5Ytuf322xk/fjwvv/xy8Lx3332XL774gh9++IFevXoxZMgQhgwZwooVK5gyZQovvfQSffr0ITU1lWPHjgXPe/7553njjTfo0qULV199NX/4wx9YtmxZ8Phbb73F2rVrSUtLo2fPnqxZs4Znn32Wli1b8vOf/5znnnuOm2++mZycHK677jqWLVvGsWPHGDNmDBMmTGD58uXBul544QXefPNNzj777Hz/gdqxYweDBg1i3Lhx3H777QXuyaWXXsq4cePYuHEjZ599NgCLFi0iMTERgNtuu43du3ezefNmDh48yKWXXspDDz3E1KlTC9QVHx9PUlIScXFxLFmyhFGjRvHNN99w1lln8eSTT/Lkk0/y4Ycflup3NnjwYO69916Sk5NZsmQJ4I1lv+GGG9iwYUMwgC5cuJAxY8aUqm4RERGpXE2bNuUnP/kJ69evB+Drr7/m66+/LrL8nj17OOecc4Kjp44dO8bRo0c5duwY2dnZpKWlkZGRwZEjR4KLkR87doxt27YVWWdUVBTx8fHBf9uEioiIoFevXr6d6dGPqk2IC5MKFNYHuxWIreS2VIrFixdX2rWuuuqqEpW74447mDRpEtnZ2URERLBo0aJg1/2TTz7JY489Flwccfr06bRs2ZJDhw4Fz58xYwYNGzakY8eODBw4kHXr1jFkyBCefvpp7rzzzuCzXuELoV933XV069YNgGuvvZYJEybkO/773/+e6OhooqOj6dGjBwMHDuSMM84AYMiQIcH/wMXExHD55ZcHz7v33nv51a9+la+ua6+9Ntg7lfuQ7ldffcWsWbO47777uO66wufbqV+/PpdddhkvvvgiM2fOJC0tjQ8//JAVK1Zw/PhxFi9ezCeffELjxo1p3LgxU6dOZeLEiYWGuNA2jho1ipkzZ7J27VouueSSQq99surWrcuVV17JwoUL6dGjB9u2bWPdunW88cYb5XodERERKX+dO3cmJSWFvXsL9HMU8M033+QbyVSc8MnWIiIi6NKlC/Xr1yc9PZ3WrVvTvn17IiIiOPPMM4N/+M59bi53lkmpPNVpOGWoAg9dmVl1bWuNNXv2bDIyMsjOzubdd9/luuuu47PPPgMgNTWVESNGEBMTQ0xMDGeccQZ16tTJN1wxtCu9YcOGZGVlAbB9+3bi4+OLvG5R5+Vq2bJlcLt+/foF3ueWP3DgAGPHjuW0004jOjqagQMHkp6enq+udu3aFbj+okWLiI2NZdSoUUXfHCAxMZHFixfjnGPJkiVcfPHFxMbGkp6ezpEjR2jfvn2wbIcOHfLdm1Dz58+nR48ewXuZnJxcoJ3lZcyYMSxatIjjx4/z4osvcumll2rMuoiIiA9EREQwcOBAunbtSoMGDYiIiKBBgwY0bdqUtm3bcsYZZ5TLQuGtWrViyJAhJCQk0KVLF37+85/TsWPH4B/yzYxatWpRq1at4PIHCnCVr1r1xIUs+F2nkMW/OwGbSlFXDDAX+BWwH/ijc25OIeW6AY8AvYBY55yFHa8DPAGMBI4Cf3XO3V/SdtQEERERnHfeeZxxxhm89957JCQk0K5duyKfp0pJSTlhfe3atSvVX4dO1iOPPMLmzZv5+OOPiYuLY+3atfTu3TtfmcImabnvvvtYtWoVV1xxBStWrChyNqVf/OIXZGdns2bNGhYtWsQ999wDeFMA16lTh9TU1GAvX0pKCm3atClQR2pqKuPHj+f999+nX79+REZG0q1bt+BY9rJMIlPYub179yY2Npb33nuPF154gdmzZ590/SIiIlK56tSpQ48ePejRowfOuQL/r8/JyWHDhg1s3bqVY8eOBXvKateunS945S6SXbduXbKzs8nKyuLQoUO0bNmSVq1a1chJ7GqaahXiyOuBM/L3xuUASXihrKT+D+/ztQbigXfNbJNz7p9h5Y4Cy4A5wCuF1HM/0B0vRDYC3jOzbc6550rRlmKVdIhjVfnwww/58ssvg89/3XjjjUyZMoXnn3+ejh07kp6eTlJSEpdddlmxdY0dO5aJEyfSv39/evfuzfbt2zl69CidOnUq1zbnLuAYExPDjz/+yIwZM0p0Xq1atVi8eDEjRozgN7/5DX/729+oXbt2gXKRkZFceeWVTJ8+nS1btjBs2LB8+ydPnswLL7xAdnY2M2bM4Oqrry5Qx4EDBzAzmjdvDsC8efNITk4OHm/ZsiVpaWkcPnyYunXrlurzt2zZkrfeeoucnJx8M1iNHj2au+++m4yMDAYNGlSqOkVERKR6KCxoRURE0LNnT3r27FkFLZLKVK2GKDrnrgss+j01dzvwut45N9k5l1qSesysITACmOKcy3TOfQY8C/y2kGt+5Zx7BthYRHXXAQ8459Kdcyl4vXYF6qmJJk6cGJwV8eqrr+bBBx8MPlM2YcIELrvsMgYPHkx0dDQ/+9nPWLNmTYnqHTFiBFOnTuXaa68lKiqKQYMGnfTC18W1/9ChQzRr1oxzzz23wPNwJ1K7dm2WLVvG8ePHufLKK/NNvBIqMTGRd999l8suu4z69esH9//5z3+madOmdO7cmZ/+9Kecd955/OEPfyhwfteuXYPPB8bFxZGcnEyfPn2CxwcOHEiPHj1o1aoVMTExBWaHOpERI0ZQq1YtmjZtGgzfANdccw0bN25k1KhRGv4gIiIi4kN2slOQVmdm1hP4yDlXJ2TfVcDdzrlC/zRhZp2ALaHDKc2sCbAHaOucSwvs6we86ZxrEnZ+DBA+e2ZbIGnbtm0FJu/YtWsXrVu3PslPKHLyjhw5QsuWLfnnP/9JQkJCkeX0HRURERGpeCkpKXTs2BGgY6DTqFjVbTglAGZWD5gMXAS0IGRoZQkX+26E9xxcqAwgqpRNaRT4ua8E9UwECk49KFLNPP3003Tu3PmEAU5EREREqq9qGeKAh4GL8Z5T+yNeoLsZWFDC87MouERBYyCzlO3InRYxOmS7qHoeA+aH7WuL9yyfSLXQoUMHjh8/nm+tPBERERHxl+oa4oYDv3DObTazqc65x8zsfWBWCc/fDDgzO8s5lzujZQLwRWka4Zzba2a7gB7ArhPV45zLwOulC9LMPlLdFDdzqIiIiIhUf9VqYpMQjZ1zmwPbx8yslnPuv0DfkpzsnDsALAceMLMoM+uONxlJ+LIFmKceUCfwvl7gfa75wBQza2Zm7YE7CqtHRERERESkMlTXELfdzDoGtr8GhpnZAOBQKeq4GXDAt8BKYJpz7p9mdpqZZZnZaYFy7YFs8manzA68ck3H63n7BvgUWFreywuIiIiIiIiUVHUdTjkHbwjjNrwp/f+GN7nJlJJWEBjeOKKQ/dvJm7CEwAwwRY57dM4dAW4IvERERERERKpUtQxxzrk5IdvLA8MYo5xzySc4TUREREREpMarliEuXO4abSIiIiIiIqe6avNMnJn908zeL+5V1e2UU1OHDh1YuXLlSZ2blJREfHx8oXXNnDmTMWPGlEcTRUREROQUUZ164lZVdQOkoMGDB5OUlMR3331HVFRp10o/NZkZmzZt4swzzwSgf//+fPPNN4WWvffee4PbKSkpdOzYkezsbOrVq1doeRERERGRahPinHPTq7oNkl9aWhrvvfcejRs3ZtmyZVx//fXlWv/x48eJiIjQenoiIiIiIqVQbYZThjOzhmb2GzO7y8xGmFnDqm7TqWbhwoUkJCRw4403smDBAgAOHz5MkyZNWL9+fbBcZmYmDRo0CPY2vfHGG/Ts2ZOYmBj69u3LunXrgmU7dOjAQw89REJCAg0aNGDfvn3MmjWL+Ph4oqKi6Nq1K6+++mqwfE5ODpMmTaJFixa0bduW+fPnY2YkJycH23P33XfTvn17WrRowdixYzlw4ECBz1KSds+fP58uXbrQpEkTLrroIjZv3lygHoC1a9fSr18/YmJiaNWqFbfddhtHjx4FYMCAAQCcc845NGrUiAULFrBq1Sri4uIKrWvatGlceeWV+c5t1qwZjRo14p133qFp06b57t++ffto0KABW7duLbQ+EREREan5qmWIM7OzgK+Ax4HLAz+/MrOuVdqwU8yCBQtITEwkMTGRf/3rX2zdupW6dety+eWXs2jRomC5l156iR49ehAfH8/69esZPXo0c+bMYc+ePdx6660MGzaMgwcPBssvWrSIV155hf379xMdHU18fDxJSUns27ePKVOmMGrUKL7//nsAnnnmGVasWMFHH31EcnIyb7/9dr42Tpo0iY0bN/Lpp5+ydetW0tPTmTKl4EoUxbV71apV3HHHHSxcuJDvv/+eAQMGMGzYsGA4CxUZGcns2bNJT0/n3//+NytXruSpp54CYPXq1QB8+umnZGVlMXr06BLf79xz09PTycrK4uKLL+bKK69k4cKFwTLLly/nnHPO4fTTTy9xvSIiIiJSs1Sb4ZRhHgUWApOdczlmFgE8ADwGXFylLatACz/YzAurt5So7K96tmPi0O759j32+n95a/2OIs+5esAZXHN+5xLV/+GHH7Jlyxauuuoq4uLiSEhIYMGCBUyfPp3ExESuvfZa/vSnPxEREcGiRYtITEwEYO7cuYwbN45+/foBkJiYyMyZM0lKSmLQoEEA3HrrrXTo0CF4rcsvvzy4PWrUKGbOnMnatWu55JJLWLx4MRMmTKBjR2/t9xkzZrBkyRIAnHPMnTuXdevW0axZMwAmT57MpZdeyqOPPlrgM52o3S+88AJjxozhZz/7WbCev/zlL3z00Uecd955+erp2bNncPv0009n/PjxfPDBB9xyyy0lurelMWbMGIYNG8bDDz9MZGQkCxcu5Nprry3364iIiIiIf1TLnjjgHGCqcy4HIPDzAeCnVdqqU8j8+fMZOHBgcBhgYmIizz//PM45zj//fJxzrF69mh9++IHVq1czcuRIAFJTU3n88ceJiYkJvrZt28auXbuCdbdr167AtXr06BEsn5ycTHp6OgC7du3KV/60004Lbu/evZuDBw/Sp0+f4LkXXXQRGRkZhfagnajdaWlptG/fPlg2MjKSdu3akZZWcHWLr776iksuuYS4uDiio6O5//77g+0tb71796ZZs2a8/fbbbN++GIGGEgAAIABJREFUnY8//pjf/OY3FXItEREREfGH6toTdwBoAewM2dc8sF8q2KFDh1i6dClHjx4NhrgjR46wd+9ePvjgAy644AKuuuoqXnzxRbp3786FF15I8+bNAS+g3XPPPUydOrXI+kMnMklNTWX8+PG8//779OvXj8jISLp164ZzDoDWrVuzY0de7+L27duD282aNaN+/fps2LAhXwArSkRERJHtbtOmDampqcGyOTk57NixgzZt2hSo53e/+x0JCQksWbKEqKgoHn74YV5//fVir1+coiZ4GT16NAsXLqR79+4MHTqUxo0bl/laIiIiIuJf1TXErQBeMbPJwDagI15P3PIqbVUFu+b8ziUe7liYiUO7FxhieTJeeeUVnHNs3LiRunXrBvePHz+e+fPnc8EFF5CYmMjAgQNZv349t99+e7DMuHHjGD58OBdffDF9+vQhOzub1atX07dvX5o0aVLgWgcOHMDMgmFq3rx5wUlLAEaOHMns2bMZOnQozZs3Z9q0acFjERERjBs3jjvuuIM5c+bQsmVL0tLS2LBhA0OGDCn0sxXV7sTERK644gpGjRpF9+7dmTVrFtHR0fTp06dAHVlZWURHR9OoUSM2bdrEU089lS/stWzZkq1btwaXGCip5s2bExERwdatW+naNe/xz2uuuYYHHniAtWvXFjpMVEREREROLdVqOKWZ/cPMrgDuBz4CXgaSAz/XApOrsHmnjPnz5zN69Gjat29PXFxc8DVhwgSWL19OVlYWCQkJtGrVik2bNvHrX/86eG6vXr145plnmDBhArGxsXTq1Il58+YVea2uXbty55130rdvX+Li4khOTs4XnMaOHcvw4cPp3bs3Xbp04YILLgAIhstZs2Zx5pln0q9fP6Kjo7nooovYtGlTkdcrqt0XXnghs2bNYtSoUbRo0YL333+f1157jdq1axeo4+GHH2bx4sVERUVxww03BIdk5po2bRrXX389MTEx+SYlKU6DBg2YPHky559/PjExMXzwwQcAxMXF0b9/f/bv38/gwYNLXJ+IiIiI1EyWO2ytOjCzecBIIBN4FpiHN4Qy3VWnhpaQmXUAtm3bti3fRB7gPevVunXrKmiVv23atImzzz6bQ4cOUadOnapuTqW56aabqFOnDo899lilXVPfUREREZGKl5KSkjuJX0fnXEpJzqlWPXHOubFAa+CPwDBgC/AMoO6HU1R2djavv/46R48eJT09nbvuuouhQ4eeUgFu586dLFmyhPHjx1d1U0RERESkGqhWIQ7AOZfpnPuLc64HcD6wF1hhZtvM7A9V3DypZM45ZsyYQWxsLF26dKFevXrBNdlOBffddx9nnnkmt9xyS77n5ERERETk1FWthlMWxcy6Aa/gdTFGVnV7SkrDKcXP9B0VERERqXi+H04ZzswGmdlLwDogC7ipipskIiIiIiJSpardEgNm1hy4HhiH93zc34DznXP/qdKGiYiIiIiIVAPVKsSZ2TLgUmAH8FfgOefcj1XbqorjnCtygWeRquSHYdYiIiIip6pqFeKA2sClzrl3qrohFa1u3brs3buX6OhoIiMjFeak2nDOkZWVVegaeSIiIiJS9apViHPOXVbVbagssbGxZGZmkp6eTk5OTlU3RySf2rVrExsbW9XNEBEREZFCVKsQdyoxM6Kjo4mOjq7qpoiIiIiIiI9U69kpRUREREREJD+FOBERERERER9RiBMREREREfERhTgREREREREfUYgTERERERHxEYU4ERERERERH1GIExERERER8RGFOBERERERER9RiBMREREREfERhTgREREREREfUYgTERERERHxEYU4ERERERERH6mxIc7MYsxsmZllmlmamd10grK3BMpkmtlSM4sOObbKzA6ZWVbg9U3lfAIREREREZGCamyIA/4PqAW0Bi4BppvZheGFzOyXwNRAmTZAbeCJsGITnXONAq/4im22iIiIiIhI0WpkiDOzhsAIYIpzLtM59xnwLPDbQoqPAZ5zzn3mnNsPTAZGmlmDSmuwiIiIiIhICdXIEAd0Bsw592XIvs+AboWU7QZsyH3jnNsU2DwjpMyDZvajma0xs4GFXTAwfLND6AtoW5YPISIiIiIiEq5WVTeggjQC9oftywCiiii7L2zfvpCy9wBfAkeAK4HXzCzBObcl7JyJeMMyRUREREREKkxN7YnLAqLD9jUGMktYNjq3rHPuo8CQzMPOuQVAEjC0kHoeAzqGvfqf9CcQEREREREpRE3tidsMODM7K2R4ZALwRSFlvwB6AIsAzOxMwIDwnrZcrtCdzmXg9fYFmVnpWy4iIiIiInICNbInzjl3AFgOPGBmUWbWHW9Sk2cLKT4fuM7MuptZFPAgsNQ5dzDwnNsgM6tnZrXMLBEYALxVSR9FREREREQknxoZ4gJuxus1+xZYCUxzzv3TzE4LrPd2GoBz7l3ggUCZb4Ec4NZAHbXxQt1uID2w/9fOueRK/SQiIiIiIiIBNXU4Ze7wxhGF7N+ON5lJ6L4nKLg2HM653UDvimqjiIiIiIhIadXknjgREREREZEaRyFORERERETERxTiREREREREfEQhTkRERERExEcU4kRERERERHxEIU5ERERERMRHFOJERERERER8RCFORERERETERxTiREREREREfEQhTkRERERExEcU4kRERERERHxEIU5ERERERMRHFOJERERERER8RCFORERERETERxTiREREREREfEQhTkRERERExEcU4kRERERERHxEIU5ERERERMRHFOJERERERER8RCFORERERETERxTiREREREREfEQhTkRERERExEcU4kRERERERHxEIU5ERERERMRHFOJERERERER8RCFORERERETERxTiREREREREfEQhTkRERERExEcU4kRERERERHxEIU5ERERERMRHFOJERERERER8RCFORERERETERxTiREREREREfKTGhjgzizGzZWaWaWZpZnbTCcreEiiTaWZLzSz6ZOoRERERERGpaDU2xAH/B9QCWgOXANPN7MLwQmb2S2BqoEwboDbwRGnrERERERERqQw1MsSZWUNgBDDFOZfpnPsMeBb4bSHFxwDPOec+c87tByYDI82sQSnrERERERERqXC1qroBFaQzYM65L0P2fQZcXEjZbsCbuW+cc5vMDOAMvJBbonrMLAaICdvdFqBjx44n8RFEREREREQKqqkhrhGwP2xfBhBVRNl9Yfv2BcpaKeqZiDcsU0REREREpMLU1BCXBUSH7WsMZJawbHSgbEQp6nkMmB+2ry2QtG3bNjp06FBso0VERERE5NSSkpJS6pF7NTXEbQacmZ3lnNsU2JcAfFFI2S+AHsAiADM7E68HbkvgZ4nqcc5l4PXSBQWGZYqIiIiIiJSbGjmxiXPuALAceMDMosysO95kJM8WUnw+cJ2ZdTezKOBBYKlz7mAp6xEREREREalwNTLEBdwMOOBbYCUwzTn3TzM7zcyyzOw0AOfcu8ADgTLfAjnArcXVU3kfQ0REREREJE9NHU6ZO7xxRCH7t+NNZhK67wnyrw1XbD0iIiIiIiJVoSb3xImIiIiIiNQ4CnEiIiIiIiI+ohAnIiIiIiLiIzX2mbhqIhJg586dVd0OERERERGphkKyQmRJzzHnXMW0RjCz84Ckqm6HiIiIiIhUe/2dc/8qSUGFuApkZnWB3njLExyvwEttA0qyzHtbvFDZH1D3YMXy670u6XepOvDrPfabk73PfvouVQV9f0uuLN8l3eeK55d7XBP+m+SXe+13xd3nivguRQKtgE+cc4dLcoKGU1agwC+hRGm6LMwM51xKScoF7CxJeTl5fr3XJf0uVQd+vcd+c7L32U/fpaqg72/JleW7pPtc8fxyj2vCf5P8cq/9rrj7XIHfpW9KU1gTm4iIiIiIiPiIQlzNML2qGyA1hr5LUl70XZLyou+SlAd9j6S8VIvvkkJcDeCcm1bVbZCaQd8lKS/6Lkl50XdJyoO+R1Jeqst3SSHu1JKB99eDjKpuyClA97ri6R5XDt3niqH7Wjl0nyue7nHl0b2uHL64z5qdUkRERERExEfUEyciIiIiIuIjCnEiIiIiIiI+ohAnIiIiIiLiIwpxIiIiIiIiPqIQJyIiIiIi4iMKcSIiIiIiIj6iECciIiIiIuIjCnEiIiIiIiI+ohAnIiIiIiLiIwpxIiIiIiIiPqIQJyIiIiIi4iMKcSIiIiIiIj6iECciIiIiIuIjCnEiIiIiIiI+ohAnIiIiIiLiIwpxIiIiIiIiPqIQJyIiIiIi4iMKcSIiIiIiIj6iECciIiIiIuIjCnEiIiIiIiI+ohAnIiIiIiLiIwpxIiIiIiIiPqIQJyIiIiIi4iMKcSIiIiIiIj6iECciIiIiIuIjCnEiIiIiIiI+ohAnIiIiIiLiIwpxIiIiIiIiPqIQJyIiIiIi4iMKcSIiIiIiIj6iECciIiIiIuIjCnEiIiIiIiI+ohAnIiIiIiLiIwpxIiIiIiIiPqIQJyIiIiIi4iMKcSIiIiIiIj6iECciIiIiIuIjCnEiIiIiIiI+ohAnIiIiIiLiIwpxIiIiIiIiPqIQJyIiIiIi4iMKcSIiIiIiIj6iECciIiIiIuIjCnEiIiIiIiI+ohAnIiIiIiLiIwpxIiIiIiIiPqIQJyIiIiIi4iMKcSIiIiIiIj6iECciIiIiIuIjCnEiIiIiIiI+ohAnIiIiIiLiIwpxIiIiIiIiPqIQJyIiIiIi4iMKcSIiIiIiIj6iECciIiIiIuIjCnEiIiIiIiI+ohAnIiIiIiLiIwpxIiIiIiIiPqIQJyIiIiIi4iMKcSIiIiIiIj6iECciIiIiIuIjCnEiIiIiIiI+ohAnIiIiIiLiIwpxIiIiIiIiPqIQJyIiIiIi4iMKcSIiIiIiIj6iECciIiIiIuIjCnEiIiIiIiI+ohAnIiIiIiLiIwpxIiIiIiIiPqIQJyIiIiIi4iMKcSIiIiIiIj6iECciIiIiIuIjCnEiIiIiIiI+ohAnIiIiIiLiIwpxIiIiIiIiPqIQJyIixTKzDmbmzKxD4P0YM0sJOf6kmT1ZRc3LbcMFZuaqsg1Vwcz6m1lWOdSzwMxuL482VbXw72sRZR41s2mV1igRkXKkECcicgows1VmdsTMssxsv5ltNLNx5VW/c+5G59yN5VVfYcysuZk9Y2Zpgc/xrZm9ZWatKvK61YmZTTOzVaH7nHNJzrlGZay3F/AL4C9h+28wsy/N7EDgfk8uy3UqQvgfFErhj8AEM2tdzk0SEalwCnEiIqeOmYF/7McA04GnzGxAFbepNF7Aa/s5gc/RA1gMVFjvm5nVqai6w64TYWaRlXGtItwOPO+cOxLSpj8AdwNjgWigC/Bq1TSv/Dnn0oG3gAr944OISEVQiBMROcU453Kcc8uAPcDPcveb2XAzW29m+wK9L9eXtE4zm29m80Pep5jZ5EBPWaaZbTGz4WHn3G1m280sw8yeM7PFoXUU4lxggXPuu8Dn+ME593zu+5B6LzOzzYEex7dDe+rM7OZAL2RmoEfvL2bWIOxzLDazp80sHXgxZGjeWDPbFKj3PTPrGHJepJndGTi+z8w+NbNfnOB+5dZ5vZl9ARwEzjKzEWa2LlDH92b2opk1C5yTCNwL9A/0RGaZWc/wYaSBttxrZl8H7u0aMzv3BG2pBQwD3g7Z1xi4D7jNObfGOXfcObffOff5CX4/ub/3+83sH4Heuy8CbRwZ+A7sC/yua4ecc7aZvWNmP5pZqpk9bGb1wuos9LtkZv2BJ4HTQu7Jr0OadJ6Z/Tdw3hozOzOsye8Al53oM4mIVEcKcSIipxgzq2Vmo4CmwFeBfX2BZXg9dLF4vROzzez/leFS4/BCR2NgLvC8mTUKXC8RuAcYATQDPgCuKKa+1cAsM7sxEAxqFVHuMqA3cBpeD9KDIce+BYYH9v8CuBgIHyJ4BZAExAGjQ/ZfD1wEtAJSgFdDes/uAxIDdTcJXPPvZhZfzGcaDQwGGgGbgczAvljgHOB04HEA59yLwEwgyTnXKPBaX0iddwLjA/ehOfAi8I6ZtSuiDWcAUcAXIfv6AfWBrmb2jZl9Z2Z/N7PTi/k8uZ/pVrxe08+AFcAvgQSgO15gHAVgZtHAe8AnQBvgfLx7PCuszkK/S865JLzv6vaQe/JKyHnXBK7dHPiOsOGiwOdAt9DQKCLiBwpxIiKnjklmlgEcAhYC9zrnXgscuw74u3PulUCvy2rgabwwcLLmOufWO+dygL+SNyQPYEzg+EfOuWPOufnAp8XUNxJYgBcS1gDpZvZYIf8An+Sc2+ecy8ALMMHeRufcS865r50nGZiDFxpCfRjo4TvmnDsYsn+Gcy7NOXcAb/jhWSF13w783jm3OdDT+TJeELyqmM803Tm3M3CtI865lc65zwO/g514YSa8fcW5HpgVqOeoc+4vQDJeyCxMk8DPfSH7mgV+XgL8HOgEpAOvlWDY5zzn3JfOuaPAIqAjcJ9z7oBzLhUvjPcKqR/gfufcIedcCjAFGGtmFlLnib5LJzLdOfe9c+4Q8Cwh34WA/YGfsSWoS0Sk2lCIExE5dfyPcy4G7x/tzwEXhfRmtQO2hpX/Gq8362Ttyt1wzuXOnhgV+NkWrzcrVPj7fJxzWc65h5xz/fB6ZK7FC5/3hpXbFfI2K+SamNkVZvahmaWb2T68yS1ahF1qWxFNCO53zmXihZp2ZtYSL1S8HBi+mBEIywPwepdOJN+1zOxC8yah+d7M9uOF7fD2Fae0v8s9gZ+NQ/ZlBn7+0Tn3XeD3NwnoCnS2wIyYIa/+Ied+G7J9EMA5F74v93fSDkh1zh0Pa2t9vN6zXCf6Lp1I+HchfAKY6MDPPYiI+IhCnIjIKSYQQG7G6yG5ObB7R+B9qHhgewU1YyfQIWxf+5KeHOi1ehVvKF5CSc4xs7bAUuBhoI1zrjHeUEoLK5pTRBXB9gaGhTbD+xy5vZuDnXMxIa+GzrnfFdOs4LXMm0TlNeAV4HTnXDTecMCStC1UaX+XW/B6pM4O2Zc7TDN00pjgdu6MmCGvpBK0q6i2tjez0H+PxAPZwO4S1lGSe1KUbsDGQE+diIhvKMSJiJyCnHOHgRnAlMBzSfOBX5vZsMDEGOfhPYc0r4KasABvyFzvwDN61+I9A1YkM5sdKF/PvNkcLwAuxBu2WBJReP/fS3fOHTaz7uSF2JK4z8xamzcRyiN4zxN+FLiXTwL/a2Znmae+mQ0ws86lqL8OUA/IcM4dCDx/NimszHd4oafuCep5Frg7MGFIbTP7HV4P2qLCCgd6wV4FBoXs244XKCebt7RDA7zn8T7He3avvLyBF6Knm1ldM2sPPAA865wr6ayj3wHNzaxJsSULuhh4+STOExGpUgpxIiKnroV4w8h+75z7D97zWw8Ae/HC293OueUVdO0XgdnAS3jDEi/ECxIn6hGJwBsG+kOgjXPwetUeKckFnXOb8J63WhoYqvgw8Hwp2vwc8A+80HAGMDxkGOBdeBPD/A2vZy4F+ANQu2A1RbYvC7gBmGHe4t0vBl6hluINN/w2MGyzsF7IR4Bn8O5nOt6w08GBYFaUx4DRln9JhWvxehq3AKl4wxuHhQ19LBPn3H68iUf64Q3DTAJWAb8vRTXv44XB3Nk4Ly3JSWbWFPgVXgAXEfEVK/kfukRERCqOma0FVjjnHqrqtoQysw54z651DEy8USOZ2QLgM+fco1XdlspgZrOBTOfc1Kpui4hIaSnEiYhIlTCzK4G/4z1rdQPwv0BX59zXVdqwMKdKiBMREf/QcEoREakqN+ANTfwBbwKP4dUtwImIiFRH6okTERERERHxEfXEiYiIiIiI+Eit4ovIyQpMAd0bb8atcpvNS0REREREaoxIoBXwSWDZmmIpxFWs3pR8/SIRERERETl19Qf+VZKCCnEV61uApKQk2rZtW9VtERERERGRambnzp30798fAtmhJBTiKtZxgLZt29KhQ4cqboqIiIiIiFRjJX78ShObiIiIiIiI+IhCnIiIiIiIiI8oxImIiIiIiPiInokTERERETkFOefYs2cPhw+XaFZ7KaO6desSGxuLmZW5LoU4EREROTU4BznHIVL//BEByMzMxMxo1apVuQQLKZpzjr1795KZmUl0dHSZ66uxwynN7BEz22Fm+80s1cwmF1HuAjPLMbOskNf1IcfrmNlTZpZhZrvNbEblfQoREREpFz/ugv8dA7NGw7fbqro1ItXCwYMHiY6OVoCrBGZGdHQ0Bw8eLJf6amyIA54GznTORQPnAqPM7DdFlP3BOdco5PVMyLH7ge5AJ7zFu0eZ2XUV2nIREREpX397GDL3wMH98MlbVd0akWohJyeHyMjIqm7GKSMyMpKcnJxyqavGhjjnXLJz7kDIrhy8IFZa1wEPOOfSnXMpwCPAb8uhiSIiIlIZ0rZ4r1wb/pm3ffRI5bdHpBpRL1zlKc97XaMHhZvZJGAK0BBIAV4oomhTM/sOyAZeBSY757LMrAnQGtgQUvYzYGYh14oBYsJ2ty3TBxAREZHS27AKvvwPZPwAGd/Dwcz8x48f815//z/47H047//BxWOqoqUicpJWrVrFlVdeyXfffVfVTakSNbYnDsA59z9AFPBT4HlgbyHFkoEeeGFtINATeDxwrFHg576Q8hmBOsNNBLaFvZLK9glERESkVHZ9A8sfgS/XwK6vCwY48ALc1v/C+n94k5386yXY/2Plt1VEirVmzRr69+9PTEwMMTEx9OrVizfffLOqm1XlanSIA3Ce9Xi9bNMLOf6dc+5L51yOc24bcDdweeBwVuBn6BQyjYFC/o/AY0DHsFf/8vkUIiIiUiLfFTJpSWQtaNo6/77PV+dtO+f1yIlItbJ//34uueQSxo4dS3p6Ot9//z2PPvpouczuGOrYsWPlWl9lqPEhLkQtIL4E5RxgAM65vcAuvJ66XAnAFwVOci7DOZcS+gJ2lrnVIiIiUnKhPW89LoTfL4CpL8HEp+CXo/OOfRE2WGbde16YE5FqY/PmzRw9epTRo0dTq1Yt6tatS//+/TnvvPOCZZ544glatWpF8+bNmTkz74mntWvX0q9fP2JiYmjVqhW33XYbR48eDR43M5544gk6d+5Mq1atgvsef/xx4uPjadq0KRMnTuT48ePBc9544w169uxJTEwMffv2Zd26dZVwFwpXI0OcmdU2s3FmFmNmEWbWB7gZ+EchZS80s/bmaQf8D/BySJH5wBQza2Zm7YE7gGcr4WOIiIhIaR3cn7fdvC1Ex0LuZAJtzsg7Fj6hyY+7YEdyxbdPREqsc+fO1KtXj6uvvpo33niD9PT0fMfT09PZsWMHKSkprFy5kmnTprFx40bAmwly9uzZpKen8+9//5uVK1fy1FNP5Tv/5ZdfZs2aNWzfvj24b8WKFXz88cds2LCBt99+m7/+9a8ArF+/ntGjRzNnzhz27NnDrbfeyrBhw8ptyYDSqqkTmzjgCuBPQB283rQ/A08AmFkW8CvnXBLeM3AvAE2AH/ECXOiactOBZsA3wFHgr8655yrnY4iIiEiphIa4BmFDrloXMyDn03fhtLPKv00ifnHfsMq71gOvFVskOjqaNWvWMGvWLG666SZ27tzJBRdcwNy5cwGIiIjgwQcfpE6dOpxzzjn06NGD9evXc/bZZ9OzZ89gPaeffjrjx4/ngw8+4JZbbgnunzRpEs2aNct3zbvvvpumTZsCcPvtt7NgwQJuueUW5s6dy7hx4+jXrx8AiYmJzJw5k6SkJAYNGlTm21FaNbInzjl3zDk3yDkXG1j3rbNz7iHnvHESgX1Jge3Zzrk2zrkGzrl2zrnbnHOZIXUdcc7d4Jxr7Jxr5py7r6o+l4iIiBQjX4gLm4esfiOvd64oXyTBkUMV0y4ROSmdO3dm3rx5pKamsnXrVmrVqsU111wDQGxsLHXq1AmWbdiwIVlZ3pQWX331FZdccglxcXFER0dz//33F+jJa9euXYHrhe5r3749u3btAiA1NZXHH388OMFKTEwM27ZtCx6vbDUyxImIiMgpKjvkmbjwnjiA9mcX3BfVxPt55BBsXFMx7RKRMmvfvj233norn3/+ebFlf/e739GlSxe2bNnC/v37mTFjBi7sudfC1m3bsWNHcHv79u20bu1NitSuXTvuueceMjIygq+DBw9y3XXXlfFTnZyaOpxSRERETkUHQnri6heyIlD7s2Ht23nv69SDvpfCuwu89+vfg54DK7aNItVVCYY4Vqbk5GRee+01Ro4cSbt27di9ezfz5s0LDmk8kaysLKKjo2nUqBGbNm3iqaeeok2bNsWe9/DDD3PuueeSnZ3No48+yo033gjAuHHjGD58OBdffDF9+vQhOzub1atX07dvX5o0aVLmz1pa6okTERGRmuNEz8QBdOiW/33j5pAwMG/yk22fw55Tc/FgkeomKiqKtWvXcu655xIVFUVCQgKNGjViwYIFxZ778MMPs3jxYqKiorjhhhsYOXJkia552WWX0bt3b37yk59w0UUXcdNNNwHQq1cvnnnmGSZMmEBsbCydOnVi3rx5Zfp8ZaGeOBEREakZnMs/nLJhISEupnn+90eyvRkszzgHNq/19q3/B/wiseLaKSIl0qZNG5YuXVrosVatWvHdd/n/4LJq1arg9oABA/jqq6+KrDt8aGWuQYMGMWHChEKPDR48mMGDBxfT6sqhnjgRERGpGQ4dhJwcb7tufW+R78I0isnbbtHe+/nTi/L2ffYPrRknItWaQpyIiIjUDAeLeR4u12/u9n6a5fW4dflZ3myWGbshZWPFtFFEpBxoOKWIiIjUDMU9D5er40/gjnmAQZMW3r5ateHMvrDuXe992hbo2K3IKkSk5ilqiGV1pBAnIiIiNUNoiCvsebhQTVoW3BfXMW97946Cx0VEqgkNpxQREZGa4WDIpCYnGk5AKQEpAAAgAElEQVRZlBYhC/8qxIlINaYQJyIiIjVDcQt9F6d5WIjz0dAqETm1KMSJiIhIzVDSZ+KKEhUL9Rp424cOQObe8mmXiEg5U4gTERGRmqE0z8QVxqxgb5yISDWkECciIiI1w4ESLjFwIgpxIuIDCnEiIiJSM5T1mThQiBOphgYPHkzDhg3JzMwsvvApQiFOREREaobMPXnbDRufXB2hIe6H7WVrj4iUWVpaGu+99x716tVj2bJl5Vr38ePHfbU2XCiFOBEREfE/5yDjh7z3MS1Orp7Q8w7sK1ubRKTMFi5cSEJCAjfeeCMLFizg8OHDNGnShPXr1wfLZGZm0qBBA7755hsA3njjDXr27ElMTAx9+/Zl3bp1wbIdOnTgoYceIiEhgQYNGrBv3z5mzZpFfHw8UVFRdO3alVdffTVYPicnh0mTJtGiRQvatm3L/PnzMTOSk5MBOHz4MHfffTft27enRYsWjB07lgMHDlT4famxIc7MHjGzHWa238xSzWzyCcqOMLOtZnbAzN4xszYhx+qY2VNmlmFmu81sRuV8AhERESmxzL1w7Ki33SAqb5bJ0qpTP2/7SHbZ2yUiZbJgwQISExNJTEzkX//6F2lpaVx++eUsWrQoWOall16iR48exMfHs379ekaPHs2cOXPYs2cPt956K8OGDePgwYPB8osWLeKVV15h//79REdHEx8fT1JSEvv27WPKlCmMGjWK77//HoBnnnmGFStW8NFHH5GcnMzbb7+dr32TJk1i48aNfPrpp2zdupX09HSmTJlS4felVoVfoeo8DdzvnDsQCGXvmNkW51y+flgzOwt4FrgM+DcwC1gEnB8ocj/QHegENALeM7NtzrnnKulziIiIVKydm2H5IxAbB8NuhiYn2YtVlTK+z9uOaXny9dSpl7d99PDJ1yPiUws/2MwLq7eUqOyverZj4tDu+fY99vp/eWt90c+TXj3gDK45v3OJ6v/www/ZsmULV111FXFxcSQkJARD3bXXXsuf/vQnIiIiWLRoEYmJiQDMnTuXcePG0a9fPwASExOZOXMmSUlJDBo0CIBbb72VDh06BK9z+eWXB7dHjRrFzJkzWbt2LZdccgmLFy9mwoQJdOzYEYAZM2awZMkSAJxzzJ07l3Xr1tGsWTMAJk+ezKWXXsqjjz5aos94smpsT5xzLtk5F9qXmYMXxMJdDbzlnHvPOZcNTAH6mll84Ph1wAPOuXTnXArwCPDbCmy6iIjIiR3MhO2bym8x6jefhh93wZZ18ORE2Prf8qm3MpXHUErIH+IOqydOpCrNnz+fgQMHEhcXB3iB7Pnnn2fAgAE451i9ejU//PADq1evZuTIkQCkpqby+OOPExMTE3xt27aNXbt2Bett165dgev06NEjWD45OZn09HQAdu3ala/8aaedFtzevXs3Bw8epE+fPsFzL7roIjIyMjh69GiF3Reo2T1xmNkkvFDWEEgBXiikWDfg49w3zrl9ZpYCdDOzPUBrYENI+c+AmYVcKwaICdvdtgzNFxERKejIIfjz77zntTr1hKsmQ526J1/f7p2wIznv/cFMmD8FBv0Wzh3urZ3mB3tDeuKalKEnrlZt7zM7B8ePea/IGv3PJZFq6dChQyxdupSjR48GQ9yRI0fYu3cvSUlJXHXVVbz44ot0796dCy+8kObNmwNeQLvnnnuYOnVqkXVbyH/XUlNTGT9+PO+//z79+vUjMjKSbt26BSc8ad26NTt25PUsbt+eN+FRs2bNqF+/Phs2bKB9+/bl+vmLU6P/q+Sc+x8z+xOQAPwa2FtIsUZA+JPLGUBU4Bhhx3OPhZsIFP1tERERKQ87vsqbcOPr9bBwGlx9P9Stf8LTirT+HwX3OQcrn4G0LXDZBKhd56SbW2nKK8SZeffyUOD5mSOHoX6N/ueSSD7XnN+5xMMdCzNxaPcCQyxPxiuvvIJzjo0bN1K3bt4fqsaPH8/8+fOZOHEiAwcOZP369dx+++3B4+PGjWP48OFcfPHF9OnTh+zsbFavXk3fvn1p0qRJgescOHAAMwuGwHnz5gUnLQEYOXIks2fPZujQoTRv3pxp06YFj0VERDBu3DjuuOMO5syZQ8uWLUlLS2PDhg0MGTKkzPfgRGrscMpczrMeyAamF1IkCwhfTKYxkBk4Rtjx3GPhHgM6hr36n3zLRUREChE+Y2LKF17PWXZW4eVPJCcHPns/7/2lN0O7LnnvP18Nq5acXDsrW3mFOIDaoc/FHSpbXSJyUubPn8/o0aNp3749cXFxwdeECRNYvnw5nTp1olWrVmzatIlf//rXwfN69erFM888w4QJE4iNjaVTp07MmzevyOt07dqVO++8k759+xIXF0dycjJ9+vQJHh87dizDhw+nd+/edOnShQsuuAAgGCxnzZrFmWeeSb9+/YiOjuaiiy5i06ZNFXNTQphf10YoLTObAvR2zg0P2/9HoJNzbmTgfTSwG+jqnPvGzNKAsc65twLHbwQSnXPFBjQz6wBs27ZtW76HJ0VERE7af171nmEL1+p0GD2jdOuj/bADnrjJ227YGH4/3+uFe/1J+PQdb3/zdnDbnDI3u8I9Oh72fOtt3zoHWrQ7cfkTeewG7xlBgAlPQrM2Jy4v4lO7du2idevWVd0MX9m0aRNnn302hw4dok6d0o9SKOyep6Sk5E6c0jEwB0examRPnJnVNrNxZhZjZhFm1ge4GShkzAgvAL8ys4FmVh94APjQOfdN4Ph8YIqZNTOz9sAdeLNZioiIVL7Qnri4jnnb326FhdNLN9lJ5o95283bec9+1aoNQ8blPQuXvtN7Dq86y8mBfbvz3sc0L1t9oZObFPbZnYNP34UPlmnyE5EaLjs7m9dff52jR4+Snp7OXXfdxdChQ08qwJWnGhniAAdcAWwF9gMLgT8DTwCYWZaZ9Qdwzm0CrgfmAT8CZwGjQuqaDnwBfAN8CizV8gIiIlJlQkNc78Hw/ybmBa60LV6YK6n9e/K2o2LztuvUg2aBubmcg+9STrq5lSJzjzcBCXg9iqEh7GQUN0Pl56vhlT/Dewthzd/Ldi0Rqdacc8yYMYPY2Fi6dOlCvXr1eOqpp6q6WTVzYhPn3DFg0AmONwp7/zfgb0WUPQLcEHiJiIhUrayMvO2GMXD2ufB5Emz51NuX8QO0ji/83HCZISEuumn+Y63jYXdgRrbvtsJpZxZdz/HjEBlZsmtWhPJ8Hg7yL/gd/kycc7Bqad7771PKfj0RqbYaNGjAxx9/XHzBSlZTe+JERERqptCeuNzn30KHD+5L9551y+2ZOpHMInriAOJOz9t+Zz786yU4eiR/mewsWPI/8OAIeO2vXsDZ/yMsngmv/sULd5UhK2Ty6ehmZa+vdsiSDeHDKb/8T164hYITzYiIVIIa2RMnIiJSYxUW4kKDy5tzvZ9tzoAbHjnxOm/7Q56JCw9xob15h7Ph7ee88kPGefu+3eqFtdxesI/fhFp1vPD45X8CdXSCXkUOjCk/mSEhrlH4kq0nIXS5htAQ5xx8sDR/2YP7y349EZFSUk+ciIiInxwIHU4ZCHGNC+l9StsCu74puD/UiXriWp1OAZ+8BceOemvLPf37/MMYAda8Ah+9nve+spYnCO2Ja1RwHahSK6onbsu6gs8cKsSJz50qM9VXB+V5rxXiRERE/OLokbyJNiIioX7gEe/GRczGuPe7E9d3omfi6jcq+HzZsaPemnQvPZY3tLJu/fxry4WqrJkbQ0NcVDmEuKJ64lYvK1j24P7SzQgqUo3Url2brKwsBblK4JwjKyuL2rVrl0t9Gk4pIiLiF6G9Pg0b5w2VLKwnDmD3zqLrci6sJ66Q8DNghLdmXOjzdalf5m03bwejJnvPiC36Y8HzDx/0QlBZZ4ssTuhwyoblMJyydiFLDGz7Iu+zRwb++XT8mLe8waGDUL9h2a8rUsliY2PZs2cPmZmZVd2UU0Lt2rWJjY0tvmAJKMSJiIj4RVYhQymh6Mk8QifgCHcwMy+c1WtYeNDqNQi6X+Ctwfbn3+U/1u08uGyCd15kEX9Zdg7SvoaO3YpuR3ko75640HuROztlUsgk1j0uhG3/zRtOenB/+Ya47CxI/gjanw2xceVXr0iYyMhImjcv47qKUiU0nFJERMQvCpvUBKB2nfzvc6UX0RN3/BjsSM57H/48XKg6daF5W+j4E+99RAT8aiz85u68sBPT3AuChdn5VdF1l5fQcFsez8SFL/adtsV7Hg683s8BV+S/3+X9XNyyWd6Q1Xn3aDFxESmUeuJERET8wLnCJzXJ1bhZwenu03d654XOUHnsKCy4D1I25u07UYjLNfIebz26Dt0grkP+Y2YQ1xFSvih43s7NxdddFuH3pTxmpwxf7Dt0Qe9u/aFpa2gQnbevPJcZcA6+Xu9tZ+6Bde9Bv2HlV7+I1AjqiRMRkZPnnNbJqgzOwYsPeL0zucLDSmFDKo/+f/buO07K6uz/+Odi6bD0XhcRsCCiAjbsvcWoMRp7jXksifGXrrGmmGZ5NPrYNZZYoiYaQQUrVgiKIqCg7AJSpC4sC8vCcn5/nJnc98zO7M7szu6U/b5fr3ntXc7c95lhgL3mOuc61X7x77C3no4N4CC1IK5TV9jn+NoBXFS/YYmPf/lR035GNm3w89LAF2NpnYGiAW3jCpssD1X53Od4/zMcxGUyExeepwjw8VQVThGRWhTEiYhIwzgHD10NN58FbzRTKfmWas0y+GJG7LH4TFy4LH5YuLjJ8lKY9o/abTIxjyxZEFddBW8nuGe4T8/d5pctaEiwUpHh5QWg9py4cGDVc6D/2VRBXHzQvXwhLPsyc9cXkYKgIE5ERBpm6QIone23X388u30pdImChI5xQVx1krlT0eImG9bC4zfGVpqM6lDcuP4B9B0au3/qT4PtGZNiFxaP2loNT9zkA7jnboMnb4bNlendNzyUMhPBKMQGcZXrffVJ8FUpO0beq05NFMTFr70HMHNK5q4vIgVBQZyIiDRMfMZg88bs9KMliAYRYd37xO6PPTTxc8s+89mwx2+C9av9sXYdYMxBQZsReza+j/2GBWvNlewKux0AA0f4/a3VfhhnvBmToXxVsD/3Pbjrh+nNo8v08gIQG8StWRZsF/cI5hfGzIlrwkwc+PelpiZz9xCRvKcgTkREGmZt3ELSq5dmpx8twZa4IG6Pw6Bkt9hju+wHE46FUePhvJuC4wtmwpO/D4bktWoFp/0CvvP/4Kxr4ZK/QP8dGt/H1m3gvN/Asd+HU3/mg53DzgrOz3w1NstUtQneeqr2dcpX+qqM7/3LB2gPXQN/uz5xIAuZX14AYoO4bVtD1w/NHWyq4ZTxf6/AZwPLZmfuHiKS91SdUkREGiacoQBfCXHwqOz0pdBVhYYY7nkEnPTD2m1atYITQmu5DdjRB24124Ly+ADH/0+QeRs1PrP97D3IP6J23AOG7uIXya7ZBm8+6deWA/hiul+rDqBbHzjiXHjxLv9aa7bB5Pt99i4aIL3+uL9eh+LYz1lTZ+LCmiOIC2fievSHtcv99uxpMHxs5u4jInlNmTgREWmYNXGZt1VJ1iSTxguvFda+Y2rP2e3A2scOOAXGH52ZPqXCDA47O9j/+LUgY7t0QXB87KEw5kD4n9t88BkVDo7efwEevQHu/YkP8KIVKZsiE9cmhSAunXXiarbB9Enw+fT67x0O4g74TrA9973k2UgRaXEKMogzs3Zm9oCZLTKzCjP7xMy+laTtwWa23cw2hh4Xhs63NbN7zKzczFaZ2Y3N90pERHJYokycNI1wJi7Zotrxdjsgdn240RN9tqu5DRsdZJCcg9ef8NvhIG7QSP+zRz845Hv1X/O9f/k5fps3xi4mHp2T11ht2sa+d/+9fpJMXH1LKEz5G7x4t+9z+HXH274d1ofmCI45ELr39dubN8K7z9ffdxFpEQoyiMMPE10CHAR0BX4BPGFmI5O0X+mc6xx6PBA6dy0wBtgRGA+cYWbnN2HfRURyX9Um2Fgee0xBXNMJz4lrl2ImrmsvGH+M3x6xJ5z848SBSXMIz42b/TasKItdey2cfesWV7Almfn/gdt/EMwh69AZBu/U6K4C/n1KNKQynInr0Dl4PzdvTF54pGpTbPA174Pk961YG1QP7dTV9+GQM4Lz7z7vq4yKSIuXs0GcmXUys++a2U8iP1P86hGcc5XOueudc2XOue3OucnAfHwQlq7zgZucc6udc2XAX4ALGnAdEZHCEZ+FA1iz3P8CumGtX0x66QItUpwpDQniwM+R++UTcPb1PruULYNHxc6/e+FOX7ESfLAZHgYZzTwlE34d4QzYhGOTz2VriETXCmf6WrXygVxUVZLqrJ+8Ebtf19DLcOGXaDC7+8HBAutbt8Cbf0/+fBFpMXKysImZ7QxMAYqAMmAocIuZHemcm9uA6/UGdgbmJGnS08xWAJuBF4CrnXMbzaw7MAD4JNR2FvC7BPfoBsTPqB4U305EpCAkCuK218BvTwt+OQc/fO/A79RuK+lpyHDKqI4ZWAMuEyaeEixYviQ0BDKchQO//EHH4qDoSbzdDoKS0fCvO4KsVVFrmHBcZvubaF5cOBMHfkhltJ+VG6BtB3jnWVjyuc9UbyyPXccOEv/diZr7XrAdDWZbtYIjzoNHr/f7M1+F/b4NvQam82pEpMDkaibuVuBRYKBzbl98MPQIcFu6FzKz1sBjwFPOuVkJmnwO7I4P1g4F9gBuj5yLfsUWHuxeDiT6H/FKoDTuMS3d/oqI5IVkv4iGAzjwv3BK44UzcakWNsk1Q3eBAcNrH4+uJRfWvV/y65SMhj0OhfN/GxQX2e/bsfPVMqG+4ZRQe17crNf9nL8FH8HyhX54ZLQAS1Syvztlc+CDF4P9nfcNtkfsCTuM8dvbt8OUR1J/HSJSkHI1iNsLuM45tx0g8vMmIK3VSM2sFT4YBPh+ojbOuRXOubmRYZelwM+AUyKno2MjQv9K0xVI9PXgbcCwuMcB6fRXRCRvhH8RLdk19lz7Tj57AL48+oY1zdevbNtcCW88CQ9d7R/vZKgQRVUDh1PmEjPY98TaxxMFcXXNi4t+3obuAlfdD5ffCUeck5k+hsUHcW3a1s6Cxi8zEJ7nF9Mu9N3v+tVQvSX2fPUWeP72YPjxiL18YZooMzjyvGB/7vuweF5KL0NEClNODqcEKoE+QHiWfO/I8ZSYmQEP4DNsxzjnqut5SpQDDMA5t87MluEzddHfWMYCn9V6knPl+CxduA+pdldEJL+sDQVxh5wBXXv7X2J7DvC/sD50NSz81J8vm+Or7BW6Lz+GZ/4cO+dp4acwZGcY0siCG40ZTplLdjvQv0+fTfNDIXsOgKG71m6XbF5c78GxAV7b9tB3aNP0NTzfDfwadPH/r8cvM7B+dbB/zEV+AfZOXX0AeOv3gzXf1i4P5rkBTP1bcK59Rzjx8tr3GjjCB3azI4N8XnkILvpD9orViEhW5Wom7lngn2Z2lJmNNLOjIsf+kcY17sbPgzveOZd0YRUzO8TMhpo3GLgZCH91+jBwjZn1MrOhwFXAg2m+HhGRwhLOxPUcAD37++IV0YxDyejgfFmt770Kz5z34LEbExetCJfAb6jwcMq2HRp/vWwpKoLvXAXXPA1X3AWX3p644Ep8EDfuKL9m2vd+1XxBy4RjY/cTZQfjM3EbQkHc0F2gW+/g9fUcEJwL//2JH0Z59EW+2Esih53t5/+Bz8R9lWiWiIi0BLkaxF0NTMcHU59Hfv4ncrxekWDrEnzWbHlo/bdfRc5vNLPoOIU9gPfwWb73gNnAFaHL3YDPvH0FzMTPrXuocS9PRCSPbaoIijm0aZd4ba5wdqXQg7hZb8DTfwiKbBT3iP2FfdWSxt8jZk5cHmfiolq3gT6Dk1eT7BYXxI2eCEeeC72bsV7YyHE+yBywoy+2sm+C5Wbjg7hwJq5r79i24UIk0SCuuqr2MMo9D0/ep579Y8+Xzk7ttYhIwcnJ4ZTOuSrgUjO7DOgFrHYu9TrVzrlFRIZEJjnfObR9C3BLHW2r8QHhJaneX0SkoMVk4fonzowM3slnDGq2+SCmcn3s0LNCMX2SX8Q5qucAOO83sGYpPPxrf2zl4sbdY9tW/wA/1zCbSwU0l/hMXKprx2Va/x3gf271a8AVFdU+3ykUxJWv9OvFgf/sx3/eE2XipoSHUXZKPIwyXslomPGy3/6mLOWXIiKFJSeDuKhI4LYq2/0QEZGQcBDXY0DiNm3a+kqE0VLyH7/mF3geNT62YEPU1mofoBTl9H9Lsd5/ESbdG+z3K4Fzb4LO3YLCLuCDOOcaPgwwPgvXEuZAxQdtXZIML2wuiQI4iM3ErSgNtot71P5zCgdxq7+G0s9ih1EeU8cwyrA+oTmA3yyqv714zvn3q0e/zK4nKJIlOfO/pZnNds7tFtkuxRcYqcU5t0OzdkxERGLFz4dLps/QIIh7JTIKffbbMGgUdI/8ku4czJgMrz4MrYrgoj/6YXa5rroKpjwc7A8eBWddF8wJLO7hC1RUbfJFSSrWNbwEfiFUpkxXm7aw34k+yNnnhNzNPoaDuLUrgu1uvWu3DQdfSz6H50KDgEaOgz0OS+2evQf5vyvba3z2r2pT/i470ZxefwLefNL/3bz4j/UvKi+S43ImiAN+H9q+PludEBGReqxZGmzXteBw7wTB2PYamPMOTDzZL478/O3wxfTg/JtPwnd/mrm+ZtqGNX7B6nYdgzXxuvXxGbh2oYIjZv71R4PYVUsaHsRtaYFBHPjM1OHn5G4AB7FBXFhxgnmiXXr4LNDaFf7Li/LIQKNUh1FGFbX2gVw0C7dyka+AKnX7eKr/WbEW/v47uPhPuf3ZEqlHzgRxzrknQrsvOOfWxbcxs27N2CUREUkk1UxcoiAOfIn5/sPh2Vv8L1Rhc9/L3flzzvkKlMsXxh4fumtsABfVe0gQxK1cDMN3b9h9C2V5gYbI9V+ykwVxyYZFDt01NmMHsPfxiYsD1aXP0CCI+0ZBXL02VcQWnVm+EF68C076UcsYniwFKVerUyYb5L0wyXEREcmU6i3wzzvg8d/UXqjbucYHcQs/gUd+HRvARYch1myDmVMa1u+mVr6qdgAHMHDHxO3Dr78xFSpbaiYuH7TvmHgeZ7I5fInWxNt1v/TvG15jTsVNkqtcD689Dq89Vvvcx6/Bf15p/j6JZEiuBnG1vhYxs1ztq4hIYXntUZj5Knz+oc+WhYsDbyyHLZv9dvuOdWfMuvX2SxDEcy64ZqeucPZ1fm2sqP+8HHvPXLFkXuLj/YcnPt5nSLDdmCAuPCdOc59yi1nibFyyTFx4/UTwf0f6DUv/vn1Lgm0VN0luyt/8EO3pk4JjrdsE2y/dE2TLRfJMTgVGZvagmT0ItI1uh469CST5H1RERDJi+UJ471/B/sJPYc67wX58Zcq6hiJF54UlM3wsXPq/vqjD6InBUMF138CyrxrW/1RVV8FbT8PTf4TlpfW3B7+4cjwzX4Y+kXCFxfhho6la/DlMuifYVyYu93RKI4jr0S92f8CIhg3nCxdJWTo/+GJFYs18tfaxw84O/s7WbIOnbvYZO5E8k1NBHD4Dl+jhgGnAGdnrmohIgXMuds2zqJcf8EEPpD6UMiq+Alz7Tr6891EXwLk3BsU+2rSFnfYO2oUDx0xyzi/OfdslMPVRmD0tdpmAuiz5vPaxdh0Tz4cDv9RA1MZa07zrt7kSHruhZVanzCeJMnGJCpuAD9jGHRXsH/idht2zW2/oGwnktlb7uaSSmiE7wfd+BR0iSwavXw3vPp/dPok0QM4UNgFwzp0PYGbznXO/r6+9iIhk0LIvYemC2sfXr/ZZqyPOST+Ic9tj93/5hP/2O1HBil33h1mv++057/r7ZbLowJIvYPJ9tYdPrVhY/zpu1VWx64BFJQvgwP+SGF3wfMtmf4101qdaNCdYPDqqpRU2yQfxQVyHzlDcPXn7I871c+b6DIGBIxp2TzMYe2iwdMes11NfoqAladehdpayb4k/fvwP4Jk/+2Ols5u9ayKNlWuZOAAUwImIZMHAEXDZ//p5Owd9F06+Mjj37vOwemn6QdyEY4Pt3Q/2i2Anqzg4fGwQFK1dDh++BNu3J25bn5WLYepjsGiu3/9iBtz308TzX6o2+ep1dfl8euK+HFLHABGz2F/mK9LMxpV+WvtYojmGkl0dimP3Jxxb9xcCHYvhkNMbVtAkbMzBwX1KZwdLFohXU5N4mGn035gd9wyOLV8YLBkikidyKhMXZWbtgauBw4E+hAqdaLFvEZEm1GcIXPA7v55bqyKY8bIfRlizDSbdBxWhapWpBHE77A4Hn+6DvyPOrbttm7YwagJ8+pbff+keHzzufTzsdUQw/Kk+zsHjN/lS7m895TMUZZ8FxVKKWsN+34bPP4BVX/tja5cnnttUU+Ov8eaTwbFxR0HPyPp4Yw+tuy+duwe/XFeWQ8/+qb0GSJwd6Du09jHJrpptsfv7fqt57tulh//i48uP/Wf7i+mw93HNc+98ULWx9rF9Tgi2Oxb7dS5XL/V/hssX+qGWInkiJzNxwJ+B04CngH7A/wI1wIPZ7JSISItg5gMdMz/kKPpt/4KZsKIsaJdKEGcGh53pF/BOVuwh7Ihz/XyfqPKV8MqD8Kdz4V93wprl9V9j1dexa3F9/JovlgJ+OOMVd8GR5/p13KLCGcao9avh4avhjb8HAWDHYjjwuzDxJP8oKqq7L53DmbgkxU02V8KGuHObKmKHb+5/EnzrMhi2W933k+Y3fGywvc8JzbvG4cjxwfaiOZm9ds02P9zw92fCx69n9trNIT67/u0f+iHaYYNGBduJ5ryK5LBcDeJOBI53zt0GVEd+ngJMzG63RERamAHDYdzRtY93LA7Wdsukbr19kHXoGbHX31rt13T6vx/7b87rUtcvs+OOCrJh4SB0XdwCzPM+gL9eAc5HMzIAACAASURBVGWhaw3bDS67A7r3IWX1Dadcswz+dA785XxfCTT8GqKB4+BRcPQFMP5oLUyci3bdDw47yw9BPvK85r13SWjdudnT/Gf2yZsTDyN0LvXhydEiR5++BZs2wIt/jV0sG/ww5JlTYr/YySWbQ0Hc4FE+mx8/J3VwKIj7WksNSH7J1SCuq3NufmR7m5m1ds59CuyTzU6JiLRIh59dO2BLJQvXUG3bwyHfg5887L89D6+jVVXp136qS3QeHMQGPa1awb4nBvs9QkMboxk+52Dy/fDEb4OiIq1a+V/Sz/sNdElSdTCZcCYuUYXKl+71Aer27fDiXcHxcEBXouxbTitqDQef5v+eJJvv2VT6lsSuHbiizBcFeuoPfihw1MolcPeV8OfzUlsXbc67seX5t1bXXjD7hb/CP//XzzXNxfl4lRuC7fh5i1GDQ8MnlYmTPJOrQdxiM4v+r/0lcIKZHQhUZbFPIiItU8diODxuGFKPJgziotq09d+eX3o7nHVtcHzue379tGQWh4K4i/7o57917uaXNQgP1Qyv2bU2Mpxy3gex6+R16w0X3ux/SW/VgP8yi3sE24kycV9+FGyHM4xloflwGkIpybRqBYN3rn18wUw/p9Q5v+biHZf6OV8V62DG5PqvO++D2sc+fi12/cbZb/uf1VV+yHOuCWfikgVxfYYGxYLWr4aN5U3fL5EMydUg7i5g98j2X4BngDeA27PWIxGRlmyvI/3Qyqjeg5rv3mYwajzsdmBw7L1/Jm67YU0w/61NWxi4IxxzIfz8UdjvxNi24WxidA7d+y8Ex0aO84uRD0nwS3KqEmXi1q+GB37p16qLDpkMq9wQDFFrVQRDd2n4/aXwhYdUhs2YDNOehWf+FHt8fQpZs3DWuChUA+/lB/xnNn6+2bwP/N+9eBvW+nUZs7GYdriPiYoWgZ/TGi4W9M2i+q9bU+OzmXPeTW2OrkgTydUg7mHn3D8BnHP/AIYCu6a69ICZtTOzB8xskZlVmNknZpa0XJSZnWpmC82s0sxeNbOBoXNtzeweMys3s1VmdmNjX5yISN5p1QpOutIXJ+nRv/6qjE1h/28H2ysXJ24Trug4aFTsL6DxuvSE1m38duV6+MPZvool+ODpxMtTr4iZTHjB74q1PhPy8DX+PomKqdTUBH0AGDQyvbXlpOUpGR27v3No5smUR2rPIY1fezCRcNB18o+DLHTpbL/cxuqvY9vXbIv9AgT8sh53XArP3gKP3lD/PTNtUwrDKcFn46KS/bsSVbMN7rkK7v2Jn3v4f1fCupWN66dIA+VcEGdmRcBaM/vvwHLn3FLnXDqDlVsDS4CDgK7AL4AnzGxkgvvtjK96+X2gF/AF8ESoybXAGGBHYDxwhpmdn9aLEhEpBP1K4CcPwZX3pFZpMtO6h4Y/rl9VO4vlHLzzbLBf3zBEs9hrhodS7bp/+vPfEgkPp1y/ygdwdRVm2bAmdn04DaWU+gzeyWeqi1r7arLf/Vnd2dtwcJNM+O9CyWgYH1rv8ZUHExczmfOu/zvoHLz1tF/mo6rSn1u6oPmHKoaHU9ZVhCmciVtZTyau7DM/LDWqahN8PLVh/RNppJwL4pxzNfgArGN9beu4RqVz7nrnXJlzbrtzbjIwHx+ExTsLmOycm+qc2wxcA+xjZtFxQ+cDNznnVjvnyvDDOy9oaN9ERPJetiokdugcZKWqq2pnFGZPC365bNMOxh9T/zXDRVPCwutJNUY4E7epov5v+tetiM0mKoiT+pj5OaO/fsavE9e6DZxxTexw4XDBlcr1iYfxRm3fHhvoderiCw217+T31yyDSffWft66b/wwwydvhqmP1r5HeMmM5hAeTtkxyXBKSG84ZbhoUtQnb9b9foo0kZwL4iKuAe41s5JMXMzMegM7A4nqTo8GPonuOOfWA2XAaDPrDgwInwdmRZ4Tf49uZlYSfgDNOGlERKTAmUHXUGGS8Nyemm2x1fP2OzE2gErmsDN91m2vI+HQM/3PU67K3KK/rdvUzgKYwYi9Erf/en4Q6BW1Tly0QiSR8NDhjsVw9nXQrY//vH3r8iCQ27YVtm5Jfp1NG2LXRSxq7QO5g74btIlf4Dzq4at94aFEfWr2IC7F4ZTh9SJXLqo7IFs8r/axtct9plGkmdUxWSCr/h75eYrFfePrnKtnZdVYZtYaeAx4yjk3K0GTzkD8jNtyoDhyjrjz0XPxrgSuS6dvIiKSpm59YNUSv12+Evrv4Lc/mup/mQKfMdj/5NSu13MAnP6LzPczrFO32KzAt38IO+4BfzqvdttZrwXbg0dB23ZN2zcpXD0HwA/v9kFbh04w9W/BWm+V65PPtQwPe+wU+iJk7+Nh+qSgcFDUAd+Baf/w21urg+P7nODnz0azds29nlyqwymLu/ss/+aNfn299av9MOhWrWJHHWzfHruW3A5jgqVAZr3h56+KNCNzOZgCNrODkp1zzr2VxnVaAY8DPYATnHPVCdr8C/jQOfe70LHPgZ8DbwNrgYHOuWWRc/vgh192j7tONyD+a99BwLQDfvwAHbr3rbe/x+wxmCuPHxNz7LZ/f8rkj5fU+1yAsw4cwdkHxf4jcu2TM/hwQWqTbn903G4cu+eQmGOX3TeNL1ekMH4euOG0cewzMvZ1fu/WqazdWMc3fiF3XjSREf27xhw76qaXUnouwBNXHkbP4uA/pTUVVZxx22t1PCPWK78+LmZ/wfL1XH7/Oyk9t0fndvz9x4fHHPtg/jdc99R/Unr+jv268NeLD4g5Numjxdz+0uwkz4i194g+3Hh67GjhR9+az2Nvp/btoD57+uyF5d1nb78SuPViXzgEuLbfxXy4NrXv+7L+2dv6D0a42EWUj2r7g5SeC/rsZf2z15L/3av+Gz3ZBD/7G/z5fNZsb8cZbc+p/4kROfXZe+AXTFq8ndtbJ/31M8berZZx4+kT/BxXgDZteXTf63jsg9Q+O/rs6f/csEkfLebmx6Yy7dYLAYZFpm/VKyczcekEasmYT+E9gB8OeUyiAC7iM4LlDDCzLsAw4DPn3DozWxY5Hy0jNjbynPg+l+OzdOE+NPZliIhIXZbMh3c++m8AR3F36N4X1q6u+3m54sQroONWeOI32e6JSMMUd/frOc54O9s9abg+Q2FxGsM9O3T2mbj+O/hCJ1urI0MtG1nNViQNuTonLhPuxs+DO945t6mOdo8Bx5jZoWbWAbgJ+MA5F13R8mHgGjPrZWZDgavw1SxFRCTbvpwJrz8e7B/UwEW5s2XA8NjCCiL5ZKe9/c8TLoXz8viLiHQLCHUo9kMtD/lecEzz4qSZ5eRwysaKBFtlwBYgPPv2d86535nZRnx2blqk/anAH4B+wDvA+c65pZFzbYE7gNOBrcDdzrlfp9iPEqC0tLSUkpKSxr8wEZGWbtFcuP/nic916+OXP6hrbbhcVLMNbjzFz7mJGrYbXPC75M8RSdek+4K13I66ACaelLjdq48Ec9wOOwsOPi32fE0NvPR/vpLj8T8I5qUm88Rv/WLg4Nec26MZ1phctxJuudBvd+kJP3247vbOwXO3wazXa5/r0Q+uuAtu/4Gfhwvwg1tg4Aj/vLuvDJYd2P8kOFoFzCV9ZWVlDBs2DPJ9OGVjOecWAUnHMjrnOsftPwM8k6RtNXBJ5CEiItkUrk4Z79Az8y+AA9/n0QfAp6GZBDvukb3+SGHqFJqDVNdacZXhwiZda58vKoJvXZb6fXsPDoK48mZYGLtyPTzzx2C/rqImUWZw8pU+4Pv4NRg90Qd0mzfC2hXw6sNB39u0C5YmMYNDzgiGQ09/yQdyxd0T3kYkk/LwfzsREWmxwotnR7VpCyPHw+4HN3t3MuaUq3wgN/staN0WJhxX/3NE0hFeK60yvig3voLqKw/5Sq9RnTMQjKQaPGbKS/f49eqiRk1I7XlmcMQ5/gHQvV9QWTOawQRfhTL8ZdFOE2Lnxr3zHBxzYeNeg0gKcjaIixQYOR4Y5Jz7o5n1xQ//XJHlromISLYUxVWdHD0Rvvuz7C1AnimtWsHOe/uHSFMIB3HxwZRz8Nyt8MWM2OOJMnHpCl8jUfCYSetXw5x3g/0jz4OJKS43Em/CMT6ztnpp7PEhcWs3xmfjZkzy91Q2TppYTs7+NrOxwAL8ot/XRg7vAdyZtU6JiEhu2GU//7NVK//LU74HcCLNoa5M3Nz3awdwAJ3jV05qgJggrjx5u0yYPimYW7rDGDjglIb/+1DUGo5OkFGLD+IgyMZBJBv3bMPuKZKGnAzigNuA651zu+CLiQC8C+yTvS6JiEhOOPZi/0336b+CPoOz3RuR/JBsWGNNDUy+L/FzMh3ENeVwyq3V8J+Xg/19Tmj8NUeOqz0/dfBOtdtFs3FRMybD5sr07rVhLcyf6RdnF0lBrgZxuwH3RLYdgHOuAkhhdqqIiBS0rr3gqPM19FAkHcmGNZbN9sMQE2nbPvHxdNQ3F6/0M3j7H40favnpW35eH/hKtanOhauLGRxzUbBsyaCRfo24RHaa4Iu4gA8ol3+VuF0i1VVw94/g0eth8v2N6rK0HLkaxK0D+oQPmNkQQPPhRERERNLVoXMwtLCq0i9tAbFVUfc4LAi6dhiTmfvGBI8b/Py7qP+8Ag/+EqY8Ai/d2/B7OAcfvBjs73185taL7DMEzrzWZ/ZOuSp5O7PYLN2KNBYPXzATNkaGmk6f1LB+SouTq4VNngYeMrNLAcysH3A78HidzxIRERGR2lq18otUR4c0Vm7w5ffnvh+0mXAsHPAdKP0Udt0/M/dt3QbadYAtm2F7jQ8gO3SGWW/AC38N2i2Y6YOxhsxhK5sTBE1t2sFeR2Sm71Ej9/KP+oTXzIuuHZeK+Cxk5frMFJWRgparmbgbgG+Ar4BuwFJgO35BbhERERFJV7fQIKe1y33gVBWZu9Wjn1/AuvcgH8xlMoiIH8r52Tu+GmY4K1dV6ddka4hwFm7sIcmHPDa1cBC3Io0gbs3y2P1vFmWmP1LQcjKIc85tcc6dB/TCFzMZ5pw7xTm3Jbs9ExEREclTPQcE22uWwey3g/3dDmy6Sq/hIG7mq/DMn2IDuKhlX6Z/7fJVMC+UTcxEQZOG6lsSbK9cknqRkjXLYvcVxEkKcjKIC2mDz8BVZ7sjIiIiInktHMStKIXPPwz2dzuw6e7bMRTEvfNcsAxA70Ew7qjg3NIF6V97+qQgINxhdz+HLVvad4Qe/f329hpYtSS1562JW4tu1eLM9ksKUk4GcWbWy8wmAcuB6cBSM5tkZr2y3DURERGR/BQO4j6a4qsogg98+g5tuvsmGprZoz+c91tfxj8q3UxcTQ3MfCXYz2YWLqrfsGD7g38nzjiG1dTAum9ijyXLxH36Nrz3L6jWwDTJ0SAO+D/80gK7AB2AXYFtkeMiIiIikq5wEFddFWyPOahp75soiDv1J9ClBwzYMTi27Mv6g56wdSuCZQU6d4NR4xvXz0wIB3EfTYEnb667/fpVQaXQqJWLar8PX33ih6FOvh/efT4zfZW8lqtB3KHAGc65zyPz4z4HzgUOy3K/RERERPJTOIgLG31A0943vFYc+PXn+g/32116BouKb9kMq+OGFtYlPJesz5DMLSvQGKPGx84tnPtesHxAIvHz4QCqNsGSz2OPTf1bsP26irVL7gZx5UQW+Q5x+PXjRERERCRdHYv9I2zgCOjZv2nvG5+JG7wTFBX5bTPfh6h05sWFA6BkAWpzGzAcLrkl9ljF2uTtkwWt9/8c/nVnEACujatgWVPT8D5KQcjVIO5q4BEzG2lmbc1sJPAA8Kss90tEREQkf/UcGLvf1EMpIXEQFxY/pDJV4SCuR44EcQADd4ThY4P9ijpyEOHgbOCIIJvonF8M/daLYdJ9wbDRqJUqftLS5WoQ9zhwIjAP2Bz5eRLwuJnVRB/Z7KCIiIhI3glnrMxg9MSmv2enuOGUQ3eJ3S+kTFxU5+7Bdl2ZuPWrgu2JJ8Nld8KI0MLi1VXw/gu1n9eQSp5SUFpnuwNJHNKYJ5vZ5cD5wG7AE5E15xK1Oxh4HdgUOvwj59wDkfNtgTuA04CtwN3OuWsb0zcRERGRrAkHO0N39XPSmlrHuEzcwJGx++FM3PKFsLkS2nWof45bLgdxxaEgbmMdmbjK9cF25+7QZzCccz3MnwkvP5B8mYJlC4AjM9FTyVM5F8SZWWvgOOBa51xVfe2TWAbcBByFr25Zl5XOuX5Jzl0LjAF2BDoDU82s1Dn3UAP7JSIiIpI9u+4Pbz3lF6I+4JTmuWe33n5NuFVfw057Q4dOseeLu0PXXrB+NWzdAr87HYbsDBfenDyQ21odZLHMoHvfpn0N6Qpn4uoqbBI+Fy3wAjByLz8k89WH/LIC8b6e3/g+Sl7LuSDOObfNzC5yzv2sEdd4DsDMxgGDGtGd84GLnXOrgdVm9hfgAkBBnIiIiOSf3oPg/z3og7huvZvnnmZw8Z9hyTwoGZ24zYAdfRAXtXienx83aGTi9uu+Ccrwd+0Nbdpmts+NVdwj2K4rE7cplImLnztYVARHX+i3338B+u8Ay77y+98s8oFsrr1uaTa5OifuNTM7vJnu1dPMVphZqZndbmadAcysOzAA+CTUdhaQ8F8fM+tmZiXhB40LIEVEREQyr3O35gvgojp08gt7t22f+Hx4XlxU+crk18vloZQQm1VLNidua7VfTgCgVRG071S7jRkccxH88u/wg1uhWx9/fHtN3e+PFLycy8RFLAOeM7PngVJge/SEc+7GDN7nc2D3yM+hwCPA7cCF+OGTAKGvSCgH4mrz/teVwHUZ7JuIiIhIyzBkl9rH6gpSln8VbOdkEBcubJIgE7dmOVRVBvudusauLxcvOgS1e9/gfSlf6TOr0iLlahA3BpgJDIk8ohyQsSDOObcCWBHZLTWznwEv44O4jZHjXULbXYG4Gq//dRvwcNyxQcC0TPVXREREpCCV7AoHfRfeejo4liyIWzwP3n4m2O83rGn71hB1Dad895/wyoPBcFCoPZQymWgmDpSJa+FyMohzzjWqOmVjbg1YpA/rzGwZPlMXzdmPBT5L+ETnyvGZuv+yur5RERERERHPDA4/G/oPhyd/748lClKcg2dvgZptfr/vUNj94GbrZsrad4TWbfzcw+oq/2jbHua866tOxlMQJ2nK1TlxjWJmrc2sPVAEFJlZezNrk6DdIWY21LzBwM3A86EmDwPXmFkvMxsKXAU82AwvQURERKTlCc/VC6+hFrWiFNZGBlG17whn/jr5PLtsMqs9pHLJFz4ATSQ8h64uCuIkIiczcQBmdiFwONCHSHYMwDl3aApPv4bY+Wln4ee7nWdmG4FjnHPTgD2Ax4DuwBp8AHd16Hk3AL2ArwjWiVNlShEREZGmEA5SohUowyObFnwUbI8cn3tLC4QVdw8CrSWf+wzc1urEbZWJkzTlZCbOzG7EZ8W+AfYFPsUv3P1JXc+Lcs5d75yzuMd5kXOdIwEczrlbnHMDnXMdnXODnXM/dM5VhK5T7Zy7xDnX1TnXyzn36wy/VBERERGJ6tglKJu/ZXNs8Q+Arz4Otnfco/n61RDhTNyztwQLeyfKHHZSJk7Sk5NBHHA2cLRz7kqgKvLzZHzJfxEREREpRGbJA5XqKlg0N9gfnkdBXFRRazjrOj9fLizVTFzXXkFmsmJtMDdQWpxcDeJ6OedmRnfMzCLZs+ZaO05EREREsiEmiAvNiyubE1vQpEsPclpxgiDu5B/DsNHQvV/s8VTnxBW1DipfOhf7/kiLkqtB3Aoz6x/ZXgTsZ2ajstkhEREREWkGyTJxSz4PtoePbb7+NNTwsUHWrFNXOPEKGHOg3+81MLZtqpk4iH1/EhV/kRYhVwub/B04BHgCuBd4DdgGJKjJKiIiIiIFo2uoQmU4iFuzNNjuW9Js3WmwITvD5Xf6eX0DR0JRUXAufoHyVOfEgQ/iFs/z2+u+aXw/JS/lZBDnnLs2tH23mX2CX3T7lez1SkRERESaXI/+wfbc9/z6cW3awupQEBefycpVfYYkPl4riOuS+jXDmbjXHoOarbDXkX6opbQYuTqcMoZz7j3n3MvOhZe2FxEREZGCs+Oe0LHYb5evhPdf8PO/1iwL2vTMkyAumfjMWzpr3Q0KzTCqWAsv3g23XQIfTYWamsz0T3JeTgZxZtbJzK42s3+Z2evhR7b7JiIiIiJNqEMnOOSMYP/tp/0i39VVkfOdgyAvXw0OBWLpZhV3mgAnXh4UOAEf7D5/O9x5Gcx5LzN9lJyWk0Ecfu7b+cB84K24h4iIiIgUsvFHQ+9BfnvLZr/OWlSvgbELgOejzt3g1J/C6Im+YmU6zGDcUXDlvXDUBX5tvajVS+HJ38PCTzPbX8k5uTp49ihgZ+fcimx3RERERESaWVFrOPoiePR6v//NouBcjwJZNnjMgUG1yoZo2w4mnuQD3g9ehHeeCxZHX7oAdhiTmX5KTsrVTNx6YG22OyEiIiIiWTJyLxixZ+3j0QydeO06wEHfhYknB8c2b8xef6RZ5GoQ93vgN2aWq/0TERERkaZ29IXQKu7XwfjKjuJ1CM0T3FyRvX5Is8iZ4ZRmVgqEq08OAi41s5Xhds65HZq1YyIiIiKSHX2GwLijYfqk4Fi+V6ZsKuFiL5sUxBW6nAnigOuz3QERERERyTGHngGfvuXne7Vtr0xcMh06B9vKxBW8nAninHOPZLsPIiIiIpJjOnWFM38N01+C3Q7yBT2ktnCVSgVxBS9ngjgAM2sNmHNua+jYecBY4G3n3HPZ6puIiIiIZEnJrv4hybUPZ+JU2KTQ5VrhkKfw68MBYGbXAPcCE4HHzeyibHVMRERERCRnaU5ci5JrQdw44N+h/SuAi5xz44CzgP9J5SJmdrmZzTSzajN7uJ62p5rZQjOrNLNXzWxg6FxbM7vHzMrNbJWZ3Zj+SxIRERERaWJt20OrIr+9dQtsrc5uf6RJ5VoQ1905twzAzHYBugJPR879EyhJ8TrLgJuAB+pqZGY7Aw8C3wd6AV8AT4SaXAuMAXYExgNnmNn58dcREREREckqs9hsXEsaUukczHkXPn3bb7cAOTUnDqg0s2LnXAU+K/eZc64qcs5Isb/RuXNmNg6/VEEyZwGTnXNTI+2vAVaa2XDn3Ff4oZ0XO+dWA6vN7C/ABcBDDXhtIiIiIiJNp0MxbCz325sroEuP7PanuXw+HZ68Odgfc2D2+tJMci0TNw34rZmNxg+dfDl0bhSwPMP3Gw18Et1xzq0HyoDRZtYdGBA+D8yKPKcWM+tmZiXhB3UHkCIiIiIimRNeZqAlzYt78a5g+7lbs9ePZpRrQdzPgSOAT4FOwC2hc2cC72T4fp2B9XHHyoHiyDnizkfPJXIlUBr3mJaxnoqIiIiI1KVD6NfUqhY0nHLjumC7Zlv2+tGMcmo4pXOuFNjZzHo459bGnf4jkOkZmhuBLnHHugIVkXNEzm+MO5fIbcDDcccGoUBORERERJpDS6tQuewrvwi8WYuZCxeVU0FcVIIADudceRPc6jNg9+iOmXUBhuHn4q0zs2WR88siTcZGnlNLpH8xfTSzJuiyiIiIiEgCHVpQYZNFc+GBXyQO3qq3FPyi8Lk2nDIjzKy1mbUHioAiM2tvZm0SNH0MOMbMDjWzDviKlh9EipqAz6xdY2a9zGwocBW+mqWIiIiISG6JycRtyF4/msP7LyTPvq3NdBmN3FOQQRxwDbAZ+AW+AuVm4D4AM9toZgcAOOfmARcC9wNrgJ2BM0LXuQGfefsKmAk85ZxTZUoRERERyT3hwiaZnhP3ykPwx3Ph49cye92G2FQBn3+Y/PzUv8EH/4YNtQb3FYycHE7ZWM6564Hrk5zrHLf/DPBMkrbVwCWRh4iIiIhI7urQRHPiVn0N7zznt5+7DcYe6uehZcunb9VdwOSLGf4x6V4YOQ72OBx2mgBFhRP6FM4rERERERFpyWIW+85gEPfVrNj9DWuga6/MXT9dqWYDnQsCuk5d4YT/gV33b9q+NZNCHU4pIiIiItKyNFVhk/ggbkVp5q6drhVlsOzL+tu1jiuHUbkenv6jD+gKgII4EREREZFCEA7i1q/KTNn9mhoomx17bPnCxl+3oWa9Hmx365O83SFnwJX3wkGnQXEPf2z7dnjqZqhYl/x5eUJBnIiIiIhIIejayw8bBD8nbsnnjb/m0vlQtSn2WLaCuJpt8Mkbwf7RFyZu16oVjDsKevaHw8+CH9waBHxbq+GxG+CZP8PUx3xmLw8piBMRERERKQStWsHO+wT7c95r/DUXflr7WHQ4Zc02KJsD8z70Gbum9uXHsDGyLHNxD/9a9z/J7+9+CHzvV76Qyfeujp0f2KVH7Fy4ZV/54ihvPQVrlpGPVNhERERERKRQ7Lwv/OcVvz33PTj6gsZVklz9de1ja5fD338PCz+Bqkp/bJ8T4LjvN/w+qfhoarA99lAftB59gR8y2aGTP77Lvomfm6wQS/e+me1jM1EmTkRERESkUAzfHdp39NvlKxtfhKR8ZeLjc98LAjioPW8u0yo3wBfTg/2xhwbb0QCuLl2SBXH9GtevLFEQJyIiIiJSKIpaw457BvvLvmrc9davCrZLRtfRbnXj7lOf8Npwg0dBn8HpPb9b79rHOnROLQDMQQriREREREQKSTjr1Jj14mpq/JpwUcf/AHYYAzvsDkedD5fdESygvXkjVG9p+L3qM+edYHuPw9N/ftcEQVyeDqUEzYkTERERESksnboE25s2NPw6FWt9WX6Azt2g71A4/7exbYp7BEMuK9ZAzwENv19dVi8Ntkfslf7zO3X1AWc0mwd5O5QSlIkTERERESks4fXiKhsRxIWHUiZbk61Lz2A7nLXLpK3VfrFu8MVMwvdMlVnt4iZ5nIlTECciIiIiUkg6ZigTFy5qkmg4IsQGVE01Ly4cTHbp5QO5hogvbqJMnIiIiIiIK7ZUFAAAIABJREFU5IR0g7iN5TB9EqyKW04gHDwlDeJCgVFDMnHrV8PHr9cdAKaSEUxF/GvI40yc5sSJiIiIiBSSdOfEPfUHKPvMz3v78X3Qtr0/Xp7mcMoVpbCiDPqVpNbPDWvhrh/5PrZuA/ueCAeeGiyREFWeQjCZivgKlT2UiRMRERERkVyQTiZu3UofwIHPyC34KDgXHk6ZqEQ/xM4zm/02/PUK+PCl1Po5+b6gf9u2wrR/wG3fh+mTfWXMdPqRik7dYvcbExBmmYI4EREREZFC0qFzsL15Y1BhMpHwAtoA82cE2+kWNol65cH6+zh/Jnz2Tu3jlevhxbvgnquguqp2PxoTeMXPpWvdpuHXyrKCDeLMrJuZPW1mFWa21MwuTdLuPDOrMbONocfh6V5HRERERCQnFLWG9pFFrJ2DqsrkbeODuC9m+Oc4l1phk+IEQdzW6vr7OGNysD32UPjO/4vN6i1fCHPf99sxmbhGzInbeZ9gXbvRExt+nRxQyHPi7sS/vgHAcGCKmc1zzr2RoO0M59w+GbiOiIiIiEj2dewSBG+bNkDH0LIDzvmS+1WboHR27PMq18PX8/16b9FMWJt2sdm9sC49Eh+P3iORLZvhy9CwzYNP8/fbZT944a8w63V/vHQ2jD0kc5m4Lj3hjKth8TyYcGzDr5MDCjKIM7NOwKnAHs65CmCWmT0IXACkHHxl6joiIiIiIs2qUxdYu9xvb9oADPTblevh8Zv84tk77xu7+HXUF9N91iqqe9/kAVlRknCiahN06JT43Bcz/Bw4gH7DggXC27SF8UcHQVzZZz4YDFeubMycOICR4/wjzxXqcMqRgDnn5oaOzQJGJ2k/xsxWm9l8M7vOzKKfxpSvExl2WRJ+AIMa/UpERERERNKVaMHvmhpfiXLJF36u3EdTgjYDhgfbX0yHdd8E+w1ZT61ibe1jzsHCT+GZPwXHdt0/ts3AET6YAx+Efj0/CDQ7FgeVM1u4Qg3iOgPxpXjKgeIEbd8GdgX6AKcApwG/bMB1rgRK4x7TGtB3EREREZHGSVSh8uUHag+fBJ9lO/WnQVZtRVlsu/rWUzvqgtrHKhKsGTf/P/DQ1bHH4oO4otYwZJdgP5qVg8bNhyswhRrEbQS6xB3rClTEN3TOLXTOlTrntjvnZgM3At9J9zrAbcCwuMcBDX4FIiIiIiINFR/Evf8CfPBi4rYjx0GvgbDD7sGxcPBUXxA38ST40f/BqAnBsUQLf8/7IHZ/hzHQO8HAtWG7BdvTJwXbPQfW3Y8WpCDnxAHzAWdmOzvn5kWOjQU+S+G5riHXcc6V47N0/2XJxg6LiIiIiDSlcCGTz6b5ao9RO4zxQyq3bvH744/xP3eaAAtm+u1oURNIbThlr4H+8UVkP9FwymVfBtsTjoWjL0x8rXAQFzZiz/r70UIUZCbOOVcJ/AO4ycyKzWwMvhhJrUUrzOwYM+sb2d4J+DXwfLrXERERERHJGeFM3LKv/Hw0gME7wVnXwbcu88sQjDkIRuzlz4UzaWH1ZeKiwmvGxWfitm2FlYuD/cPPCea+xRs0Kna5gagdFcRFFWQQF3EZPqu2HHgZuN4594aZDYmsBTck0u4w4FMzqwQmAc8Bv63vOs31IkRERERE0tYxfkYQ0KM/nPlrHzyNPQSufhJO/UmwCHbXXtB/hwTPS7GwSTiI+/AleOga+OoTv/9NWVCgpEf/5JUrwfdnj8Njjw0cAcXdU+tHC1CowymjwxtPTXB8Mb5gSXT/J8BP0r2OiIiIiEjOig/iOnaBc27wSw/UZdSE2KGXHbukXhGyS9zC3ws/8Y89DguWEYDYSpjJ7Hk4vPlksB+erycFnYkTEREREWmZwmu7tWnrM3A9+9f/vJ3ihlSmOpQSoLhn4uMfvwZTHw32B+xY/7W6942dA7eb6gWGFWwmTkRERESkxerWG448Hxb8Bw46DYbslNrzBuzohy1WrPP7qQ6lhNSHO6YSxAGceAVMexYGjUw8zLMFUxAnIiIiIlKIJp7kH+kwg5HjYearfr9HCtm7qNZtYvdHjYdxR8O/74b1q4PrpzKcEvwcveMvSf3+LYiGU4qIiIiISGDiyX6Jgk5dYeyh6T23ZHSwfdBpfnjmFXfBfif6xboPPwc6dE7+fEmJOefqbyUNYmYlQGlpaSklJSXZ7YyIiIiISKq2b4ftNbWza/VZ9hW89RQM3wMmHNM0fSswZWVlDBs2DGCYc64sledoOKWIiIiIiMRq1SpYeiAdA4bD936V+f5IDA2nFBERERERySMK4kRERERERPKIgjgREREREZE8oiBOREREREQkjyiIExERERERySMK4kRERERERPKIgjgREREREZE8oiBOREREREQkjyiIExERERERySMK4kRERERERPJIwQZxZtbNzJ42swozW2pml9bR9vJImwoze8rMujTkOiIiIiIiIk2tYIM44E6gNTAAOA64wcwOiW9kZkcA10XaDATaAHekex0REREREZHmUJBBnJl1Ak4FrnHOVTjnZgEPAhckaH4e8JBzbpZzbgNwNXCamXVM8zoiIiIiIiJNrnW2O9BERgLmnJsbOjYLODJB29HApOiOc26emQGMwAe5KV3HzLoB3eIODwIYNmxYA16CiIiIiIhIbYUaxHUGNsQdKweKk7RdH3dsfaStpXGdK/HDMkVERERERJpMoQZxG4Eucce6AhUptu0SadsqjevcBjwcd2wQMK3+7oqIiIiIiKSmUIO4+YAzs52dc/Mix8YCnyVo+xmwO/AEgJnthM/ALYj8TOk6zrlyfJbuvyLDMiktLaWkpKSRL0lERERERApNWVlZ2tOvCrKwiXOuEvgHcJOZFZvZGHwxkgcTNH8YON/MxphZMfAb4Cnn3KY0ryMiIiIiItLkCjKIi7gMcMBy4GXgeufcG2Y2xMw2mtkQAOfcFOCmSJvlwHbgivqu03wvQ0REREREJFCowymjwxtPTXB8Mb6YSfjYHcSuDVfvdURERERERLKhkDNxIiIiIiIiBUdBnIiIiIiISB4p2OGUOaII4Ouvv852P0REREREJAeFYoWiVJ9jzrmm6Y1gZhPROnEiIiIiIlK/A5xz76TSUEFcEzKzdsB4fGXLmia8VSmQyuIS0cXHDwCUHmxa+fpep/pZygX5+h7nm4a+z/n0WcoGfX5T15jPkt7nppcv73Eh/JuUL+91vqvvfW6Kz1IR0B+Y4ZzbksoTNJyyCUX+EFKKphvDzHDOlaXSLuLrVNpLw+Xre53qZykX5Ot7nG8a+j7n02cpG/T5TV1jPkt6n5tevrzHhfBvUr681/muvve5CT9LX6XTWIVNRERERERE8oiCuMJwQ7Y7IAVDnyXJFH2WJFP0WZJM0OdIMiUnPksK4gqAc+76bPdBCoM+S5Ip+ixJpuizJJmgz5FkSq58lhTEtSzl+G8PyrPdkRZA73XT03vcPPQ+Nw29r81D73PT03vcfPReN4+8eJ9VnVJERERERCSPKBMnIiIiIiKSRxTEiYiIiIiI5BEFcSIiIiIiInlEQZyIiIiIiEgeURAnIiIiIiKSRxTEiYiIiIiI5BEFcSIiIiIiInlEQZyIiIiIiEgeURAnIiIiIiKSRxTEiYiIiIiI5BEFcSIiIiIiInlEQZyIiIiIiEgeURAnIiIiIiKSRxTEiYiIiIiI5BEFcSIiIiIiInlEQZyIiIiIiEgeURAnIiIiIiKSRxTEiYiIiIiI5BEFcSIiIiIiInlEQZyIiIiIiEgeURAnIiIiIiKSRxTEiYiIiIiI5BEFcSIiIiIiInlEQZyIiIiIiEgeURAnIiIiIiKSRxTEiYiIiIiI5BEFcSIiIiIiInlEQZyIiIiIiEgeURAnIiIiIiKSRxTEiYiIiIiI5BEFcSIiIiIiInlEQZyIiIiIiEgeURAnIiIiIiKSRxTEiYiIiIiI5BEFcSIiIiIiInlEQZyIiIiIiEgeURAnIiIiIiKSRxTEiYiIiIiI5BEFcSIiIiIiInlEQZyIiIiIiEgeURAnIiIiIiKSRxTEiYiIiIiI5BEFcSIiIiIiInlEQZyIiIiIiEgeURAnIiIiIiKSRxTEiYiIiIiI5BEFcSIiIiIiInlEQZyIiIiIiEgeURAnIiIiIiKSRxTEiYiIiIiI5BEFcSIiIiIiInlEQZyIiIiIiEgeURAnIiIiIiKSRxTEiYiIiIiI5BEFcSIiIiIiInlEQZyIiIiIiEgeURAnIiIiIiKSRxTEiYiIiIiI5BEFcSIiIiIiInlEQZyIiIiIiEgeURAnIiIiIiKSRxTEiYiIiIiI5BEFcSIiIiIiInlEQZyIiIiIiEgeURAnIiIiIiKSRxTEiYiIiIiI5BEFcSIiIiIiInlEQZyIiIiIiEgeURAnIiIiIiKSRxTEiYiIiIiI5BEFcSIiIiIiInlEQZyIiIiIiEgeURAnIiIiIiKSRxTEiYiIiIiI5BEFcSIiIiIiInlEQZyIiIiIiEgeURAnIiIFz8yuN7M3W3ofmoOZTTazXzXi+SVm5sysJHO9EhEpLK2z3QEREckvZrYxtNsWKAI2h47t4pxbnMH7vQnsB1SHDv/MOXdXpu4hmeOcOybbfRARKXQK4kREJC3Ouc7RbTO7HjjYOXdwE9/2d86565vq4mbWxjm3tamu3xKYWWugxjnnst0XEZFCp+GUIiKSMWY22MyeNbOVZrbMzB4ws+6h82+a2f+a2T/NrMLMFpjZmU3Qj7Mj164ws+eA7nHno/34h5mVA783s/5m9lKk7xvMbIaZHRp6zrNmdmNof4aZLQ7tX2Zm76bRhx5m9mDkfVoZuf6gyLndzKzKzDpE9o+LDDG8ILJvZvaNmR0Rej23mNkTkb4vMbPv1/MeOTO70sxmRvr4oZntGdfmHDP7xMzWm9kcMzs9dO7gyDVON7MvgU1Ap0hfrg+129XMXjWzNWa2yMz+bGbtQ+eHm9lrkX7PAw6N68PuZvaWmZWb2bpIf0fV9dpERAqdgjgREckIMysCXgIqgOHA7sAQ4JG4phcB9+GDmiuBB81s73ouf3nkF/jPzexmM+ucrKGZ7QfcH7l2d+AB4OIETS+I9KMHcC1+WOj9wDCgF/Av4Hkz6xVpPwWIBk09gFFAUSigOAJ4NY0+PAYMBMbg369NwAtmVuScmw2sAw4MXXtB9P7497YLMC10vfOBe4FuwP8D7jKzYcnep4hLgbMir3cyMNnMiiOv4Tzgxsj71B24BLjHzCbGXeM7wIRIfyrDJ8ysCzAVmBF5rQcBhwN/jJwvAl4ESoH+kXPx79NdwGuRPvYGLgTK63ldIiIFTUGciIhkygRgF+CHzrkK59wq4MfACWbWL9TuRefcS865bc65l4B/4gOFZH4FjAR6At/F/6L/QB3tzwf+GXePFxO0e94594pzbrtzbpNz7mvn3PPOuUrnXLVz7jeAA8ZH2k8BxptZt0gfpgGvAEdGhhIeEmlTbx/MrD9wDPBj59xq51wFcDk+OIvebypwZGT7yMj7cLiZWWR/mnOuKvR6nnHOvRl5PU/jA52YzFoCtzrn5jnntuADtu3A8ZFzVwE3OedmRq75DvAEcF7cNX7unFvrnKtKMJTyuMjPayPny4BrgIsir2Mf/J/tjyPv+9JIP8Kq8V8GDI28l7Occ9/U87pERAqagjgREcmUwcBq59yG0LEvIz+HhI6Vxj2vNPLchJxz70WChO3OuU/x2a1TokMNExiU5B7xYo6FhjeWRYb2leOzS30i/fgKWIwf7ncEPmCLZueimcTpKfYh+noXhl7nemAVwXs1BTjCzAYCfYHngLXAHqH7hy2L298IFCd43Qn75JzbDiwK9W0EcHtkGGN55P04GxhQx+uKNxhY5JyrCR37EuiAz6oNwn9mKuq43nn4YPr1yDDRW82sUz2vS0SkoCmIExGRTFkC9IoOx4sYHvkZrlZZEve8EuDrNO6zPfLTkpz/Osk9kl0n6mb8UMr9ga74IYQb4u4zBZ8Fiw6dnIIf8ngc8IZzbluKfVgS+fnf4Y6RoYe9CN6rqcBo4BzgtUiQ9SpwIjCR2kFcQ/y3T2bWCh9ARv8sVgDfd851Cz06O+eODV8g0q9klgBDI9eOGo6vZroqcq9eccNjS0LbOOcWOecuds4NxWc7jwR+lsZrFBEpOAriREQkU2YA8/DZm86RuWS3AC8551aE2p1gZseYWZGZHQOcBDyU6IJm1jfStlOkmMcuwG3AC865TUn68QhwUtw9Tkih/13xwcU6oD3wGyB+7t0U4HSgyDk31zm3GvgKP7csHFTV2Qfn3HLgZeAWM4sGMXcAc/DvI865ZcBc4OdE5tpFfv4IP+/wkxReU32uNLNRZtYWP8yxNfDvyLnbgOvMbJyZtTKzdmY23sz2SuP6L+GD4Bsizx8K3AQ8GBl6+SE+M/cXM+toZgOAX4cvYGbnmdmgyPDLDcA24P+zd9/xbVbX48c/V7IkD3mveCVxnOnsHQIhgwRS9iyjjFBayo8CX0rLKFBIgQKllEIpUAqUAGWVPRpmIJAQQvaOsxzvvWVbstb9/SHFxI6dOIltycl5v1562X70PFdHyrCO7rnnehBCiOOYJHFCCCG6hX8W6kx8M1h7gc34SvyubHfqC/iaZNThS1x+qbX+vpNhQ4E/+sexAR8CS4GrDhLHcv/4T/of41p8TUYO5Q/4ErlKYAdQzoEzhEvwlSjun7B97r+u9VgXY7jc/xib8b1ekcBZ7UoPv/CPvS+J+xoIB77splb+/8S3zq0G35/d6fvKYbXWT+Bbn/as//5i4C9Al0sZ/WPNA04ASvGtI1wK3Oq/340vuR2Cb+ZvCfDvdsPMxlem2ogvcf3eH4cQQhy3lGznIoQQorco38bdS3tyzzfRNUopDczWWi8NdCxCCCEOj8zECSGEEEIIIUQfIkmcEEIIIYQQQvQhUk4phBBCCCGEEH2IzMQJIYQQQgghRB8SEugAjmVKKQswGV9HLmmHLIQQQgghhGjPCKQAq7XWLV25QJK4njUZXztlIYQQQgghhDiYGcDyrpwoSVzPKgVYtmwZ6enpgY5FBMCSN74iJiEm0GH0im+/WMGw6cMCHcZBlZdVcfWvLg10GMc1e0UpBpM50GEcMa/LSVhSSqDDEMehyg3bMVnDAh3Gcc/VaKe6oonwGGugQxHdoLmukeGnTgp0GBQVFTFjxgzw5w5dIUlcz/IApKenM3DgwACHIgIhOSGZuOS4QIfRK2IiY0lKTA50GAfldiL/FgOs2RKCwWwJdBhHzOtsITxFPpQTvS+sogFTZHigwzjuuWzNhCobEXFRgQ5FdIMmS0OwvS/o8vIraWwihBBCCCGEEH2IJHFCCCGEEEII0YdIEieEEEIIIYQQfYisiQsgu91OQ0MDHo/sPtDTjEYjUVFRhIXJonAhhBBCCNG3SRIXIHa7nfr6euLi4jCZTCilAh3SMUtrjcvloqamBkASOSGEEEII0adJOWWANDQ0EBcXh9lslgSuhymlMJvNxMXF0dDQEOhwhBBCCNFHebSmxukNdBhCHJtJnFLqBqXUWqWUUym16CDnzVJKeZVSjfvdrtnvfrNS6lmlVJ1SqlIpdV93xejxeDCZTN01nOgCk8kkpatCCCGEOGLv5TZy1doGfv1dGTl1LYEORxzHjskkDigB7gde6MK5FVpr6363/a+5BxgDDAYmA5cppa7uriBlBq53yesthBBCiKPxfbmDGJOiwu7hvnVV2N0yKycC45hM4rTW72qt3weqj3Koq4H7tdZVWus84K/Az482vuOd3W7n7LPPJjo6mrPOOuuQ5yulyMnJAeC6667j3nvv7ekQhRBCCCHa8Hg1G6pbmBpr4p4J8ZTbPTy5tRabSxI50fuksQnEK6XKADvwIXCX1rpRKRULpAIb9zt3A/BgR4MopWKAmHaH03sg3l4xa9YsVq5cSUhICBaLhcmTJ/PEE08wbNiwwxpn4cKF5OTk8MYbb7Qee/vttykqKqKqquqwS0r/+c9/Htb5QgghhBDdYVdtC40uzcioEEbHhXJhZiRv7bXxeXETY+IsnJAUxvTkMNIiZLmM6HnH5EzcYcgBxuJL1uYA44En/PdZ/V/r9zu/DojsZKybgb3tbsu6Od5e9fjjj9PY2Eh+fj6xsbEsWLDgsK53u90dHs/Pz2fo0KGyJlAIIYQQfcba8mYARkX55kCuGxHDP6Ync+mgKOqdXp7ZXscVS0tZ4T9PiJ50XCdxWusyrfU2rbVXa70XuA24wH93o/9r1H6XRAO2ToZ7HMhsd5vR/VH3PqvVyuWXX87mzZvZuXMnc+fOJTY2lmHDhrFo0aLW8xYuXMh5553HlVdeSXR0NI8++igPPvgg77zzDlarlWHDhnHXXXdx3333tR57+umn0Vrz5z//mczMTBISEjj//PMpKyvrMJYFCxZwxx13tP68aNEihg0bRmxsLHPnzmXnzp09/XIIIYQQ4ji0rsxOariRRIvv7bNSiuxYC9cMj+GFk1N4dXYq8RYjnxc1BThScTyQcsq2NKAAtNa1SqkSfDN1Jf77xwFbOrxQ6zp8M3WtjpVGGg0NDbzyyiuMHj2aM888k8svv5zFixezYcMG5s+fT2ZmJjNnzgTg448/5vXXX2fRokW0tLTgcDgOKKc0mUxtji1atIhnn32Wzz77jIyMDG666SYuu+wyvvrqq4PGtXTpUm655RY+/fRTxo0bx8MPP8xZZ53Fli1bZJZPCCGEEN3G49Wsq2jmpOTQTs9JCQ/hhOQwlhQ34fRozMbDex+oteaJLbUMiTZzRn/roS8QndpR18LGmhaa3V7sbk2z20uEycBVQ6IJCzk25rCOySROKRWC77kZAaNSKhTwaK1d7c6bDeQCBfjWrz0MvLffKYuAu5VSq4EI4BbgoZ6I+Y8fbWVbSc/uYZadGsW9Z43s8vm33HILv//97wkLC2Pq1Kk88sgjnH/++dx1110YjUamTJnCz3/+c1555ZXWJG7y5MlceOGFQNc31f7Pf/7DzTffzNChQwF49NFHiYuLo6ioiPT0zpcV/uc//2HBggVMmTIFgLvuuounnnqKH374gZNOOqnLz1MIIYQIZiV1dpTWgQ7juLajpoX6Fi9Tk0OBzhuZTE8K4+OCRjbWOJiceOj3QV6tuX1VJVMSQ5mWFMaHBb5CsOoWD1cMjjpmJgR6w8u76tEarhgSxT1rq6h0+LaVCjUqwkMUdS1e8mwu7p+UiMnQ91/XYyMVPdDd+BqV3AFc7v/+OQD/XnD7yhzHAyuAJv/XzcCN+43zR3wzb3uAtcCbWusXe+MJBIPHHnuM2tpaSkpKeO+99ygpKSE9PR2j0dh6zsCBAykuLm79OSMj47Afp7i4mAEDBrT+HB0dTWxsbJtxu3Kd0WgkIyPjkNcJIYQQfcWrP+Qz/eGvuOjzMl7eUkOto+P15qJnfV/iK5GcnNj5TBzA+AQLFoNiRbm9S+P+UOFgbZWD9/NsrK50ADAtKZRFO+t5alst3g6Sd6dHEvr2ttS0sGhnPa/tqWdVhYNKh4ffjY7ji9MzWDw/g7fnpvOb0XGsqnTw2u6enTTpLcfkTJzWeiGwsJP7rPt9/xjw2EHGcQK/8t961OHMkAVKWloaRUVFeDye1kQuLy+PtLS01nPaf2LUlU+Q0tLSyM/Pb/25oaGB2traNuN25Tqv10thYeEhrxNCCCGCXbPTzadbyrj3g61MHhiL09bE42sreWp9FXMHWLlyVBzD4g6eUIju80NpE8PiLMSFGqk4yHkWo4GJiaGs8idkh/LfXF9CUWr38E6ejdTwEB6YlMjT2+p4N89Go0tzQWYkL+6s46eDolDA71dXctuYOGalRhz9EzsGuL2ax7fUEGkyYHN5+evmGkwGODklHON+70PP6G/lk8JG1lY5uGpoNOuqHKR5++72EMfqTJzoAVOnTiUmJoaHHnoIp9PJmjVrePHFF7n88ss7vSY5OZm8vDy8B/lH8rOf/YwnnniCXbt2YbfbufXWW5kxY8ZBSyn3XffSSy+xZs0anE4nDz74IFFRUUydOvWIn6MQQggRSFpr3lhVwKQHvuSW/25kcJKVFxZM5l8zk3nr7IFcMDSaZUVNXP5xPk+uq0RLmWWPs7u8bKiwMzUlvEvnj42zUNrspuoQs6Y5/nVbP8uKwqCgtNnN5MRQDErx6+wYrh4azefFTfxqeRkrKxwsXFvFIxurcXg0L+yox+OVP3utfQlcrs3FbWPiGBxlorrFw8SEUKymA9Oc7FgLO+udNDg93LGqgjeLWgIQdfc4JmfiRM8wmUx89NFHXH/99Tz66KMkJSXxyCOPMGvWrE6vueiii/jPf/5DfHw8qampbN269YBzrrrqKkpLS5k3bx42m40ZM2bw2muvHTKe2bNn88gjj3DZZZdRUVHBhAkT+Oijj6SpiRBCiD7J49Xc+e5m3lxTyImD47l+1mAmD4zDHGLADmTFWrhtajL/b3wCf/6hghc31zAtJYLJXUwuRNc8vLKcrwpshBgUIQaFV4PbCyd0ceZrVJwFgC21TmaldP5W+9XdDVhDFJdkRZFT72RtlYNJCb7ZVaUUVwyJJs5iZFONgzP7W7lzdSWldi8X+fen+7KkidPSj58GKFpr3smzUdzkxu7W2D1e6lq8bK5t4fLBUZzYL5xyu4d/bKtlVif/JrJjzLy9V/Nmrg23himxffc9oyRxokNLly7t8Pjw4cM77Rq5cOHCA47Fx8ezfPnyg55nMBi48847ufPOOzscd/9PGfff0gDgmmuu4ZprrunwOiGEEKKv8Hg1t761kXfXF3PD7MHcMm8ohk6aL0Sajdw1LZkl+Ta+KrBJEteNbE4P7+6qY3hcKJnRZtxe7U/gwpmQHA7Nh17rNiTKjMWg2FLT0mkysbveyXfldq4aEk2EycDpGRHk2VyMi29bIntGf2trp8qHpyRR1uxmTmo+TrDKAAAgAElEQVQ4G6odPLm1FoBT0yKOiwYohU1unt5WR0SIwmoyEGY0EBaiuDQriquHRgNwRn9foj0rpeOEe0SsL8F+L89GpMlAdpSxw/P6AknihBBCCCECyO3x8tu3NvLBhhJ+d+pQbpgz5JDXhJkMTE+N4OuCRm6dkoThOHgT3xu+LWzE7YXfTUliTAfdJV0dXNNeiEExPMbM1tqOS/W01ry4s56IEMX5mZEAzE6NYPYhZvpGxloY6U9C/jgxkYc2VvPnjTV8V2bnltFxxFj6bkLSFZtrfK/n0yf2I8Pa8QyaxWhofU07khRqJN5ipLrFw4nJYW3WzPU1siZOCCGEECJA3B4vN7+5gQ82lHDb/GFdSuD2mT3ASkWzm61VXWuiIQ5tSX4jSeEhjEo4uqYxo2It7GpwYncf2BPgzVwb31fY+dngaCI7WLfVFf3CQ3hsWhK/Gh7DD5V2rllWyvdd7IjZV22uaSHGbCA94sjnoJRSjIgxAzAtqWtbYQUrSeKEEEIIIQLA5fFy0xvr+XhTKXeePpzrZw0+rOtnZlgJUfBlnq2HIuxZXq15b2cdD60sD4omHU0uLyuKmzhlgPWoZzZHxlnwasipc7Y5/n25nedy6piVEs7FgzqfMeoKo1JcnBXFMyf2I85i5K41lTy6qZrmDhLHY8Hm2hZGxVqOunR0YmIoESGKKZLECSGEEEKIw+F0e7nxtfUs3lzG3WeM4NqTsw57jEizkRkZVj7a04Cjj71x31vfwi8/LeT+78t5a0cdO2oC3yVwVWkTTq9mdv+jS66A1rLHLfuVVO61OfnThioGR5m4bWxct61jGxRl5qnp/bgkK4pPCpu4d23VMde1tMrhprTZzWh/05ijcVZ/K2/MSTviWdBg0bejF0IIIYToY55csotpDy3h061l3HtWNr+YMeiIx7pkeCx1LR4+29szs3HdnQw4PV6e3VDFJR/ms6euhVsmJQKwwr+Zdk9wuL0UNDg7vK+8ycXZ7+SypdLOmrJmQo2KMYfY0LsrIk0GBlpNrevi6p0e7l5ThcWouH9SIqHG7n0LbjYqrh0ew6+zY1lb5WD5QUorm1zeDjcRP1Ibqx28s7fhiP6ueLyaV3fXc+XSEjZWd14WvKXW9+fXHUmcQSki+ngCB5LEBdSx9ilJsJPXWwghRKAV1Tbz1y92MrxfJC//fApXn5h5VONN6hdGVoyZ5zZVc/3nhbywqRpXN5UmLi2wMe+/e8it63iWrKHFw+7aFmrsB98PbX+/+7qEZzdWc8oAK++em8nlI+MYHmdhZQ8mcQ+tLOeSD/Oo7WDfti/zbRQ1unhvVz2ry5oZkxSGuZsSrFFxFrbWtuD0aO5bV0WVw819ExNJCuu5voLnDLCSGWniqW21fJRvo9HVdob2k8JGzvuiiPfzGo/6sVxezb9y6rhlZQVPbaujusVz2GM8uKGaF3bUU9fi4fZVlfxQ0XHyubHaQahRMTjKfLRhHzMkiQsQi8VCbW0tbrdbkoseprXG7XZTW1uLxXL0n+AIIYQQR+rLbeUAPHDuKE4emnjU4ymluGpUHGVNLkqbXDy1vooFi/M7TbwOx+d5NmocHm5dWkJzu2SgzuFh/tt7+OmHeZz5bi6f5jYccjyXV7OytIlLR8Tw4MmpxPmTmRNSI9hUYafJ1f0loXtqW/h4TwMOj+bD3QfG+G2hL3n8Is/G7lonk/p133YNo2ItNLk1/9xey/rqFm4eFddaZtlTjAbFb0bF4fZq/rallj+uqwLAozXPbKvlL5tqcGv4ofLwm6BorSlodKG1prDRxY0rynljTwPj4n3PaU9DV3p3/qjB6eGbsmbOG2jl5Vmp9LeGcPeaSpZ2kNCvrXIwNs5CSCfbbhyPZIuBAImLi8Nms1FVVYXX27fq2Psig8FAeHg4kZFHX+cuhBDi2LC3qomb31iPV4M5xIDFf7v6xMxuSbA68sX2crISIxiU2H2bNJ+ZFc38zChCDIol+Tb+9H05l32Uz/8bn8DFw2PYUdNCdnwoJmPX3wB7tWZlSTNDYi3sqWvhTyvLeOCklNZ1XD+UNuFwa34zKZGvCxq5c1kpX+TbuGFCApnRHScqhQ1O3F7IbrcX2rTUCF7cUsOa0mZm9j+616XI5uTJtVVM7BfGhcNi+Mf6KsJNBjIiTbyzs44rRsa2Ni2xOT2sL29mcKyZ3f5yvcndmcT5S//ez28kO8bM/PSubRbeHY/71ilpvJlr4185dXxX1sz/ChtZWeHg3AFW3BqWFDfh8WqWl9uZkBDapfVhy8rsLFxXxbBoM/mNLswGxR8nJjA+PpSzPy9iT4OTqYfRLGRlhQOvhrlpEcRYjDw2LZm7Vldy//pqmty6dX+8smY3RU1uzhlw/Gxs3hWSxAWIUoqoqCiioqICHYoQQghxXHpvXRGbi+uZOTQRp8eL0+1lY2EdRoOh25I4rTU1TU7irRbq7S5+yK3hmhlHV0LZkX0zFKcMiGR8Uhh/+r6cJ9ZW8tS6Stwa/m9iIleNiuvyeDtqWqhr8fDbyYmUNrp5ekMV45PCuXBYDADflzQRaTZw2YhYLhkRy0uba3hpaw0XfZDHOYOj+dXYeJIi2u7ltds/O5gV0zbJG5sUSozFyNMbqpiSEk7YEaxX8ng1b+TU8tT6Klrcmi/ybby3q54dNS3cMCGBlAgTdy0r5Y/flTEk1kJapInCBhduDb+dnMTvvi7BozXZR7m1wP5SwozEWQzUtHi5amh0r27IrZTi/IGRvJ9n4w9rqzAo+L9RsZwzIJKvS5r4uKCRd/NsPLO9jtkp4fxhQsIhx9zV4MSgoLrFQ3aMmdvHxZMY6kslksOM7LEd3kzcivJm4i1GhkX7SiStJgN/nprIwrVV/HVzDRuqHczPsFLa7CuDnZjQt7tJdjdJ4oQQQghxXPpqRwUT+sfy4tVTWo/d9Pp61ubXdsv41Y0t3Pr2Jr7eUcHjF4+jqNaO26s5NTu5W8bvTFxYCI/OTuWzvTY2VNhZVdrMVwW2w0ri9q1Rm5oaQVyokQ2Vdv6yqoLshFBGxFn4vriZqSnhGA0KI/CLsfFcMCya5zfV8NaOWhbnNnDZiFgWjI4j0uzbhHpPnS8JGBjddl2T2WjggRkp3LSkiF99XsiAKDNXj45jUEzXSg/31LbwxxVlbKlycFJ6BLdPSeLJdVV8lW/j1ilJXDI8BpdX88neCJbk2/hoz49llTEWI5OSw7liZCxNLi+mbizXU0pxcr9wSprdTOrG5LCrzEbFdSNieDanjt+NiWeiP4Z9zUFe2FEPwNelzZxR5WDCIWIsbHSRGh7CSzNTDkhIs6LM7KnvuHlMR5wezapKB/PSItps5xBqNHD/pESe3V7L50VNLClpJiHUSEKokQFWSVv2J6+GEEIIIY47FQ0OthQ3cOtpw9ocz06N4sONJdQ1O4kJ/zHZ2F1hY3NxPQXVdvJrmiiobmZEShT3nzuqw/FX7K7i5jc3UGd3MTQpkt+8ucFXOjYiifEZsT363MCXQMwfFMX8QVE8v7GaZzZUUdnsJjG8a2/9vi9pZmishQT/urX7T+rHZR/lc/vSEu6enkyl3c0JqW3LA2NDQ7h1ShKXjYjhmQ3VLNpSw9s76zg53coVI2PJrWshPdJEaMiBM23T0yK4Y2oyL26uZlu1g5hQI7+dnNRpfI1OD1azkTVlzVz/RSFWk5E/zUhhfmYkSikeOjmFBmcy0RZfAmk2Kv5+Sjpaa+pbvJQ0uihudJFqDcFoUFw79tAzUUfipsNInHvCrNQIZrX7c0oIDSE1PISSZjcXZEayoqyZJ7fW8vzJ/TAeZLawoNFNf6upwxnFrEgTK8vttHi8WA7RGKag0cXjm2tweDQn9Ttwds1kUNwwMo5rh8fy0MZqviltZn56RK/OZPYFksQJIYQQ4rizdEclAHOGt00URqT4ljlsL7VxQlY8AI0tbk5/YjlOj28Ne0p0KCajgbUF+dx4ymCSIn+cwXB5vDz+5U6eXrqHQQkRLLp6ChlxYVz/6jqyEq3cfcYIDL3cnGFmfytPb6ji26JGLhgac8jz3V7Nlko75+13bmxoCH+elcovPi3g/31eBPjWsnUkLdLMAzNSuHJkLK9sreWbwkY2VtgxGCCrk/VyABcOi+HCYTH84tMCNhykRf6WSjtXLS7g99OSeWN7Lf3CTSw6vX9roxTwJbH7Erj9KaWICTUSE2rs1tLJvmZsnIUyu5ufZkYyOtbCwnVVfFHUxPyMjtedebSmuNnFtKSOX7OsKDNe4KWd9YSHGLh8SDRerVFwQPL1wPoqyu0e/m9U7EFnKM1Gxd3j4hkVa+aE5O5bq3iskCROCCGEEMedr3IqSIkOZXi/tg2vsv1J3LbShtYkbntpA06Pl0cuHMPZY1MJNRnZVtLA6X9fxtc5FVw8uX/r9be9vYn31hdz8aQM7j07m3Cz763WK9dM7aVndqDBMWbSrCa+KehaEpdf78Th0WTHt024xiSG8fqZA/ksz4ZBQYrV1MkIPkPjQrl/RgpL8m3curQEgHkDDt1gbGxSGK9sqcHu9hLWwazd6rJmNPDgSl+nz8dmp7ZJ4MShXT0smlPTI0gMCyEh1LcubdGueuakRmDuoAFOWbMblxcyOvkzz4ryHX8j17df4XkDI3kzt4H/FTZy17iE1lLN0mY3uxtcXDcihnO68HfBaFBckCn9IzoiWwwIIYQQ4rjicHn4Zmclp4xIOmCWIDHSQoLVwraSH9dNbS/1fT9jSAKhJt/szoiUSNJiwvhiW0XrefXNLj7eVMJVJwzgzxeOaU3gAk0pxYlpEawtb8bThT3kttf4Nl0eEX/gLElWrIXrxydw3biulx/OyrCSEel7k9++qUlHxiWF4dawtarjzZ+3VDpIiQhhRLyF2RlWZnYyeyQ6lxAawlj/n69Sil8Oj6HC7uGzoo73jyts9DUXyYjoOIlLCQ9hVko4Jyb7yiP32lysqXJQ2+Llth8qeG13PVprvitvBmg9Txw5SeKEEEIIcVz5dmcldpeH00b26/D+7NSo1sQNYFtJA7HhJvpF/ZjUKKWYOyKJ5bsrsTt9mxx/vq0Ml0dz/oT0nn0CRyA7IRS7W1NgO3TziZzqFkKNigHdtLGy0aBY4F8bNryDxLC9MYm+N/gdlVRqrdlcZWdCcjivnDGAv8xOlbVS3WB8vIWsKBMfFjR2uH9xQZOv82RGJ81FDEpxz4QEfp3tW++5u8FJboOLM/tbmZkSzvM76rlnbRVflzQzKNJEWifJoOg6SeKEEEIIcVz5bGs5UaEhTBsU3+H9I1Ii2VVhw+n2rYHbVtpAdmrUAcnCvOx+OFxevt3lW1+3eHMp6bFhjEmP7tkncAT2zaptrz70JuA5NQ6GxlkwduPavXOHRPPBeZkHdKbsSLTFSFaMmQ0dbEhd1uSmyu5hVGIoBqXadDYUR04pxdn9rexpcLG97sBEv7DRRbTZQLT5wHWG+0sOMxIeovi2tBmnVzM61sLd4+P5dXYMKyvsbK9zyixcNwnKJE4pNUQplej/Plwpda9S6m6lVM9ucy+EEEKIY5rL4+XL7eXMHZGMqZMuesOSI3F5NAU1Tbg9XnLKbK1r5fY3dVAc8RFmPthQTH2zi+W7qzh99IHt14NBZrQZi1GRU91xieI+Xq3ZUdPC8LjubfqhlCLjMGb2JiSHs768Gbs/kd5nc5UvsRudKIlAdzslLYIwo+LD/ANLKgua3PTvwuyZUoqBVhMbanwfFgyO9nWzvCAzisemJTE1MZSfSPlrtwjKJA54DUjxf/8AcBFwIfBYwCISQgghRJ+3eHMp9XYXp43quJQSICvR9yZzT2UTe6uacLq9rV0r92cyGjh7XCpfbqvgkc9ycHk054xL7bHYj0aIQTEk1kJOzcGTuCKbiyaXt0tljz1p3sBI7G7NNwVtE4rNlQ4sRt9zEd0rPMTAvLQIvi5tosFfIgy+EtY8m4sBkV0rgczyJ+tmg2qT+I2OC+WhKUn06+I2F+LggjWJywK2+L+/ADgbOBU4N2ARCSGEEEHE7fHS4HBRVu9gT2UjOWUNXWpacTxrbHHz4OLtjEqLYu6IzjfcHpToa52/p7KRbf61cdmpHXfIO398Ok6Pl1d/KOD8CWmMTA2+Usp9RsSHklPdgrfdmqddtS2c/vYeLvsojwWLCwAO6EzZ2yYkh9EvIoT/5Ta0OZ5T7Sv17M5NucWPzhpgxeWFz4qaWo9VOTzYXF4GdTGJy/Sflxlp6taSXNFWsKbCCtBKqUGA1lrnAiilpMeoEEKI494HG4pbN4/e34Pnjeayqf07vihIebya99YXc0JWPGkxPVMi5/J4eWN1IR9tLKG8oYV/Xj7xoG8uI0NNJEVayK1sorrRiTnE0Do7196otCiGJFkprrNz+/zhPRJ/dxkeZ+GtHV6KbC7671fa+Pr2WuocHgbFWMiKsTBngDXgM10GpfhJZhQvb62h2u4m3r+FQEGDi2mpsmdYT8mKMjMy1sxHBY1ckBmJQSlybb6mJoOiDi+JG9KF9Y/iyAVrErcRuAvoD3wOoJRKAxoOdpEQQghxPPhhbw3h5hBunjuECEsI4WYj93+8ndV5NVw6JYNPtpQxe1gSYYdoQhBoWmsWfriVV1bmE2428ttTh7Fg+sAj/vS+0tbCuU99x2/mDeXCib4OkTVNTq5/dS0rc2tIiwnjztOHM75/7CHHGpQYwZ7KRjxezfiMmE7XzymlePyScTQ63CRHBffm0ftKJHNqHK1JnM3p4dO9DcwfFMU90zsvMQ2EM7KieHFLDZ/ttXFZdix2l5dKu/uw1taJw3d2/0ge2ljNhuoWJiSEtiZxmZFde90HR5mJNBla94YTPSNYyylvAuYDg4H7/cfmAl8ELCIhhBAiSORXNzE4ycovZgzi0in9OWdcGuP7x7CxqI71hXVc/+o6Xv4+L9BhHpTWmr9+vpNXVubzs6n9mZoZx/0fb+Pcp75jS3H9EY358Cc5FNfZ+fuSXbg9XnaU2TjnqeWsK6jjbxeP5bs75nDtyVldGisr0crOMhtbiuuZmhl30HNHpkYztZNOl8EkM9qMAnL36z74aW4DDrfmgqHBVwY6KMbC8DgL/8v1/X0o9G+P0L+LM0LiyMxMCSfKZODDfN/G3XsbnCSFGok0dS1tiDAZeHdeGjP7SfOZnhSUSZzWepPW+iSt9RytdaH/2Eta6wVduV4pdYNSaq1SyqmUWtTFaxYqpbRSan674w8opaqUUnVKqWeUUvI/hxBCiIDKq2pmYHzbkrKx6dHkVjbxv02lAHyxrTwQoXWJ1prb39nEP77ezcWTMrj/nFH8e8Fk/nHZeErrHZz9j+X8e/neLo21p7KRnzyxjPmPf8s764oY3z+Ggppm7v1wK+c//R0tLi9vXjuN88Yf3t5tgxKtNDk9eDVMyQz+BK0rQkMMpFlN7K33JUNaa97ZWc+wOAvZAW5k0pnTB0WxvbqF3LoWChp8M0L9uzgjJI6M2aiYnxHB8nI7VQ43uTZXa4lkVxmVCsourceSoEzioHVrgfFKqZP3v3Xx8hJ8M3gvdPGxhuLrflna7vgvgEuASfhmBccBd3f1OQghhBDdzeHyUFJvZ2BCRJvjY9JjAHh9la8xxdqCWipth94T7GhtKKzjly+vIaes7YqHJdvLueifK7j+1bU88mkOb60pZG1+DV6vZnVeLf9dU8SvZg7i4QtGYzD43vCdOSaVJbfMZOKAWJ5flgtAcZ2d8oYDOyq6PV4+2VzKxc9+T0WDg6gwE2MzYnjlmqlkJkTw6g8FZCVZ+fCGk7pUPtnevuYmRoNifP+YI3hlgtPAGDN7631/L7ZWOdhZ28IFQ2OC9g33/EFRGBQszm1onYmTcsqed2Z/K14Nb++1UdDoYpC85kEnKNfEKaXOBl4G2jcy0cAhC/y11u/6x5kEdOWjt38CvwWebXf8auAxrXWef7z7gH8B93ZhTCGEEKLbFdY0ozVktkviRqf5yuGanR5OGZ7EkpwKvsop5+LJPdfo5JPNpdz85gZa3F5W59XwrysmMWlALO+tL+b2dzaREhNKVaOTz7eW4/Z3Ybl0Sn8cLg+RlhD+75QhByQP0eEm5mUn8+DiHGqbnFz78hoizCH897oTAChvcPD6qgJeX1VAeUMLA+PDeWHB5DaNR/503iiW76riplOGEGo6snWBg/3jjUqLJsISlG+XjkhmtJlVJc14vL5ZuLAQxfzMyECH1amEsBCmpUawOLeBKSnhJIQZiehiWZ84cukRJk5MDuO/ub6Syq52phS9J1j/V/oLvv3hntFaNx3q5KOhlLoSqNZaf9bBp1Cj8DVZ2WcDkK6UitZatynYV0rFAO0/qju82g0hhBDiEPKqmwEYEN82iYuNMNM/LpyCmmauPXkQOWU2vtjWM0mc1ppnv83l4U9ymNA/hrvPzObG19bz02e/Jyo0hAaHm4kDYnnx6slEhZpwebwU19p5blkur/5QQIhBcdnU/oSbO34bsq9N//e51a0t/muanDz77R6eX7YXj1czc2gifzp3ALOHJx3QCGV6VgLTsxKO6jmmxoQRF2Fm5pCjGyfYZEZbcHo1O2tb+CyvgZ9kRmEN8gY4pw+K4u5lpXyV3xjwrpnHk3smJPD2XhtflzQxNsBbTogDBWsSl6K1frSnH0QpFQcsBGZ0cooV2D9Zq/N/jWx3HOBmZIZOCCFED8ur8n22mdkuiQMYlxFDbZOTCQNimTM8iXfWFeHyeDvtrHgkXB4vf3h/C2+sLuTMMSk8etFYQk1GPrl5Bh+sL2Z1Xi3zspOZP6pf6+OajAYGJkRw9xnZLN9dRX51M5ccJLkc6d+T7ZXv89m3pdmL3+3l2W9yOX10P247bfgB5aTdzWhQfPGbk4kMPbZmIDL9bd+fXl+Fw605PwgbmrQ3O8NKWIii0eVtszWC6Fkmg+LSrCguzZIdvoJRsM5HL1dKjemFx3kEeFprXdzJ/Y20Lenc9z+drYNzHwcy2906Sw6FEEKII5JX3URMuIno8AOTi9+fPpxXfzkVk9HA9Kx4mp0eNhXVdTBK17g9Xt5cXUCFf01avd3F1S+u5o3Vhfx6dhZ/v2R8a7liVKiJK04YyN8vHc9ZY1M7TBzDzEaeumwCd58xotPNswFiws2kxYTxfW41IQZFfISZp77ejcmoWHj2yB5P4PaJt1owhwTrW6UjMyjGlwR9V9zE8CBuaLK/MJOBUwb4Sj4zpDOlEEDwzsQtB95XSj1Lu2YjWuuXu/Fx5gJnK6V+5/85EXhNKfVXrfWfgC3AWGCF//5xQFH7Ukp/XHX8OFMHELSLhIUQQvRdedVNDOxgFg4gJTqMlGhfW+9p/pb33++pZuKAg7fI70hds5MbX1/Psl1VjMuI4W8Xj+Pal9ewt6qJRy4cw08nZRxR/KPSohmVdujZnxEpURTX2RmZFs3w5EjeXFPIuWNSSYoM/qQjmEWajSSEGamyezg/iBuatHdmVhQf72kgK1rK+oSA4E3ifun/el274xpfw5ODUkqF4HtuRsColAoFPFprV7tTJ9O2Ucpq4DbgI//Pi4BblVKLgSbgD8C/u/40hBBCiO6jtSa3sumQ+5aBb43ciJQoVuyp5oY5Q7r8GDVNTv69fC8vrcjD7vJw6ZQMXl9VyLzHviHcbOTln09h+uCeXyc2MjWKL7eXM2lALDOHJvLu+iJ+flJmjz/u8SAz2kKTyx7UDU3am5ISwcun9ydbNpAWAgjCJE4pZQDOBHZ2kHR11d20XZ92OfASsEAp1Qj8RGu9TGtd2e6xPUCt1rrRf+h5YCCwFjABr+NruCKEEEL0uiXbKyitd3BCVtf2LZueFc8rK/NxuDyH7NJY1djCc9/m8srKfOwuDz8Z1Y8b5wxhREoU4eYQvt1ZyTOXT2RwkvWg43SXfd02Jw+M5eShiWy459RjqktkIF0/PoG6Fk/QNzRpb1SibB4txD7B+L+hxjcjdsS/JbTWC/E1LOnovk7H1VoPbPezBu7y34QQQoiA8Xg1j3yWQ2ZCBOdP6Frz4+lZ8bywfC9XvrCK00b1Y+6IpAO6WoJv7du5T31HSZ2ds8amcsPswQxJ/nGW5g9nZnfb8+iq2cOTePzicczL7gcgCVw3GpskyZAQfV3Qrdb1J057gORAxyKEEEIEiy+2lbGzvJHfnjq0y90mZw1L4ua5Q6htdnL/x9uY+ZelnPLXpXy8qaTNeavyaiiqtfO3i8fxxCXj2yRwgWI0KM4dn3bA9gFCCCGCcyYO4G/A60qphUAe4N13h9a6IEAxCSGEEAGzrdSGUnDayH5dvsZoUNw8dyg3zx1KfnUTX+VU8Ny3uTz3bS5njkltPe+TzWWEmYycmt31sYUQQgROsCZxz/u/foWvvBJA+b/vWwXcQgghRDcorrWTHBl6xHu+DYiP4OoTMymssfPaqnw8Xo3RoPB6NZ9uLWPWsETC+tgaKSGEOF4FaxIn7aeEEEKI/RTXNZMWe/RrmUakROJwedlb1cTgJCtrC2qptLXwk9Ep3RClEEKI3hCUSZzWOj/QMQghhBDBpKTOwbiMmKMeZ98m29tLGxicZOXLbeWYjIrZwxKPemwhhBC9IyiTOKXUlZ3d182bfQshhBBBz+vVlNbbOWPM0c+WDU6yEmJQbC9t4KyxqSzdUcnkgXFEhpq6IVIhhBC9ISiTOOCP7X5OwhdrMV3Y7FsIIYQ4llTYWnB5NKkxR19OaQkxMjjJyvbSBkrq7Owot3HnxOHdEKUQQojeEpRJnNa6zZo4pVQI8BCwKzARCSGEEIFTXGcHIL0bkjiAESlRfL+nmm92VgK+rQiEEEL0HUG3T1xHtNZu4B7gzkDHIoQQQvS2fUlcdzQ2AV9zk7IGB88vyyU1OpQhSdZuGVcIIUTv6BNJnF80EBvoIIQQQojeVlzrS+K6o5wSYM7wZIb3i6So1s55E9JQSjbUFi1x+n0AACAASURBVEKIviQoyymVUve0OxQBnAt8GoBwhBBCiICotLXw0cYS9lY1Eh1mwmrpnl/bg5OsfHrzyWitJYETQog+KCiTOGB2u59twKvA3wIQixBCCHHYfr5oNWeOSeH8CelHdL3D5eGXL69hQ2EdSsGIflHdHCGSwAkhRB8VlEmc1rp9EieEEEL0GQ0OF1/lVGAyqiNK4rTW3PXeFjYU1jFneBJf5VR023o4IYQQfV9QrolTSq3s5Pjy3o5FCCGEOFwF1c0A7CizHdH1L36XxzvrirjplCE8f+Ukrj15EBdOPLIZPSGEEMeeoJyJA0Z2cnxEr0YhhBBCHIF8fxKXX9OM3ekhzGzs8rXf7a7iT4u3My87mZtPGYLBoLjzdPn1J4QQ4kdBlcQppa70f2tUSl0B7F+sPwyo7v2ohBBCiMOTX9MEgNawu6KR0enRXbquoLqZX7+2jqzECP528TgMBlmzJoQQ4kBBlcQBf/R/tQD37XfcC5QBN/Z6REIIIcRhKqhuxqDAqyGnrKHLSdy9H27B69X864pJ3daJUgghxLEnqH5DaK0zAZRSi7XWpwc6HiGEEOJI5Fc3Mzotmu1lNnaWH7guzuZw0djiJiU6DLvTg9PjJdISwpq8Ws4el8rAhIgARC2EEKKvCMrGJvsSOOWTEuh4hBBCiMNRUNNMZkIEQ5Ks5HTQ3OT3727m1L99S15VE5f863sue24le6ubsLW4GZseE4CIhRBC9CVBNRO3j1IqDHgCuBLwABFKqXOAUVrrPwU0OCGEEOIgWtweSurt9I9Px2BQfLe7qs39NU1OPttahsujOesfy7E53AB8uqUMgDEZXSu9FEIIcfwKypk44FFgADATcPmPrQMuDVhEQgghRBcU1drRGgbEhTM0OZLyhhbq7a7W+z/YUIzLo7nmpExsDjczhiQA8MLyvYSaDAxOtAYqdCGEEH1EUM7EAWcDY7XWNUopL4DWulAplRbguIQQQoiD2rdH3MCEcKyhvl+zeVVNjM3wlUm+taaI0WnR3H3GCM4am0p2ShSzH11KcZ2dSQNiCTEG6+erQgghgkWw/qYwAQ37H/CXWNoDE44QQghxaLvKbTz6+Q5CDIrMBCtZib4GJXurfFsObC2pZ1tpAxdNSkcpxbiMGMwhBk4emgjAGFkPJ4QQoguCNYlbDfyq3bErgZUBiEUIIYQ4qKLaZm5/exPzn1hGab2Dp342gbgIMxlx4RgU5PqTuLfWFGE2Gjh7bGqb62cN8yVxY2U9nBBCiC4I1nLKW4FvlVI/xdfU5FNgEjA9sGEJIYQ4nmmtya1sZJB/3VpZvYOnvt7NG6sLUCiumDaAX88eTGKkBQBLiJH02HD2VjXhdHv5YEMx80YmExNubjPu3BHJ/OXCMfxklDRkFkIIcWhBmcRprXOUUiPwzb5txbfR9y+11oVduV4pdQNwNTAaeE1rvaCT80YDi4BB/kNrgf/TWm/d75wHgOvwvVavAzdprV0IIYQ4rpQ0tHD3J7msKGhk0dWT6RcdyrlPfYfbo/np5AxumD2Y1JiwA67LTIggt7KRJdvLqW12cdHE9APOMRoUF03K6I2nIYQQ4hgQdEmcUsoE5AODtNZ/O8JhSoD7gdOAA3+j/qgIuMD/eAbg18BbQLY/ll8Al+CbBWwEPgLuBu49wriEEEL0QZtKG7nu3Z04XF7MIQaW7qgkwmLE5dF8ectMMg+yOXdmQgSr82r475pC+kWFMmNIYi9GLoQQ4lgUdGvi/LNcLkAdxRjvaq3fB6oPcV6t1jpPa639j+cBspRS+x77auAx/zlVwH3Az480LiGEEH3PV7trufKNHCJMRt66dAgnDIpn+e4qvtlZycT+sQdN4AAGJUbQ7PTw9Y5Kzp+QhtFwxL/ehBBCCCAIkzi/x4C/+GflepxSqg5wAE8CD/qTOoBRwMb9Tt0ApCulDlh5rpSKUUoN3P8GHFgzI4QQos94bX05N7y/iyEJYbzxs2wyY0M5aXACuysa2VLcwMxhh55VG5Tw475vF3ZQSimEEEIcrqArp/S7GV8C9AulVBng3XeH1npQp1cdIa11jFIqArgKX2nlPlagfr+f6/xfI9sd3xezlFkKIcQxwKs1j31bxPOrSpmdFcNfz8wi3GzE62zhxMEJrefNHHroJC7Tv83ApAGxrQ1RhBBCiKMRrEncwt5+QK11k1Lqn0ClUmqE1roC3zq4qP1O2zcDZ+tgiMfxNUnZXzqwrLtjFUKIvkZrzZLtFYyzerCaD31+IDndXu74JJfFOTVcOi6Ju04ZQMh+JZDD+0USH2FGKchOiTrISD4pUaHMy07mZ1P792TYQgghjiNBmcRprV8K0EMbgHAgDagAtgBjgRX++8cBRVrr9rNwaK3r+HGmDoAfl9YJIcTxqanFTbjZyKdbyvh/r67jthmp/HxaeKDDAsDu8vBtbj1ur2bJ7lryah2cMjiWT3Jq2F1t57cnp/OLKSkH/F9uMChunjcUo1IYurC+zWBQPHflpJ56GkIIIY5DQZnEHS2lVAi+52YEjEqpUMDTfmsApdRp+LYv2AJEAA8AtcB2/ymLgFuVUouBJuAPwL974zkIIURfV2lrYc6jS5mWFc/20gYA1pY0BkV3KI9Xc+P7u1me5/tMLjrUSHq0hSe/KyYj2sKzFwxl5qCYTq+/YtqA3gpVCCGEOMAxmcRx4DYAlwMvAQuUUo3AT7TWy4BY4O/4Zt7swCpgvtba4b/ueWAgvv3jTPj2iXugN56AEEL0dW+vLcLW4ubL7eVoDUOSrKwraUJr3WGlwq6qZp77oZQ7ZvcnLrxn+1o9vryI5Xn13DG7P9P6RzEwNpRQk4Hi+hYSI0yYQ4K175cQQghxjCZxWuuFdLKuTmtt3e/7N4A3DjKOBu7y34QQQrTjdHtZk1/DlIFxhBh/THy8Xs3rqwqYmhnHdbOyyK1sItISwm3vbCK3xkFWfNstPG0tbm54bxf5dS1Eh4Zw1yk9N9P16Y4anvuhlJ+OSWTBpH5t7kuLtvTY4wohhBDd5ZhM4oQQQvSOv36xg2e/ySUzIYITB8djNhoxhxiwOVwU1DTz21OHMntYErOHQW5lIwBrimxkxYfh1ZqtZU0sz6vnk5waiupbmJQeyRsbKrhqUj/SeyCh2lnZzJ2f5DIu1crdPZgoCiGEED0paJM4pZQRmApkaK3f9K9r01rrlgCHJoQQAihvcPDSijymZsbhcHtZvLkMp9vru3m8ZMSFcdrIH2e6MhMiiA8L4b0tVawqbOC7vAbq7G4ARiaH8+czspiUHsmpz23kXytLuO+0zG6Nt97h5ob3dxFhNvLEOYOlZFIIIUSfFZRJnFIqE/gY6I+vY+SbwOnAucCVAQxNCCGE3z++2o3bo3nkwjEMiI9oc5/WGq1p071RKcXUDCuLd9ZRVGdiZmY0J2VGc+LA6DZr4OYOieXL3bXcO28gxi50f+wKrTV3LM6ltMHJS5cMJznY9zkQQgghDiIokzjgSeADfN0gq/zHvgYeC1hEQggh2vhkSxmnj045IIEDX8LW0S4r98xJ57rp6QxOCMPQyTYscwbHsjinhk2ljYxPi+yWWD/bWcvXe+q4fVYGE7ppTCGEECJQgrWWZCpwr9baA2gArXUtvm6SQgghAqyxxU1VYwvD+h1eQhRlMTI0MbzTBA5gRmY0RgVf76nr9JzD0ej08OBX+WQnhXPFxH6HvkAIIYQIcsGaxDXh23S7lVIqEagOTDhCCCH2l1/dBPjWuXW36NAQJmVEdlsS98XOGioaXdx5ygBCuqk8UwghhAikYE3iPgGe8DczQSllwLc/20cBjUoIIQQAeVXNAAyIDz/EmUdmTlYsu6rsfJJz9J/drSmyER1qZEKa9dAnCyGEEH1AsCZxdwADgBogGqgHxgP3BDIoIYQQPnn+mbiO1sN1h5+OTWRimpVb/5fLkl21RzXWmkIbE9MjD1rCKYQQQvQlQZnEaa3rtdazgZOAS4EzgGla6/rARiaEEAJ85ZSJkRaslp7pjxVmMvLsBcMYmRzOzR/u5tvcIyutrGh0kl/n239OCCGEOFYEZRKnlJoFoLVep7X+r9b6W621N8BhCSGE8MuramZgD5VS7mO1GHnuwmEMSQzjxg928X3+4X+Ot7bIBsBkSeKEEEIcQ4IyiQM+UkrtUkrdoZSSVmJCCBFk8qqbeqyUcn9RoSG8cNEwBsSEcv17u1hXbDus61cX2Qg3GRiR3POxCiGEEL0lWJO4FODPwNlAgVLqQ6XU2f4GJ0II0efZHC7+/GkOn28tw+HytLlPax2gqLqm2emmwtbSI50pOxIbZuLfPx1OUoSJmz/cTW2zq0vX7axs5sOt1UzpHyVdKYUQQhxTgnKzb611I/A88LxSKhu4GvgX4AHSAhmbEEJ0h/fXF/PM0j0ARFpCmJedzKkj+/H5tjKW7arihasmMTotGpdHYw7p/c+vCmuaqW12MiY95oD7erozZUcSIkz87ezBXPzqNu76bC9Pnzf0oOc3ONxc9+5OwkwG7p07oJeiFEIIIXpHUCZx7eQB24F8YEJgQxFCiO6xJKeCAfHh3HfOKP63qYRPt5Tx7vpiTEZFdJiJBS+uJjUmlPyqZl5YMJkpmXG9Fluz081lz6+kpM7BXy4cw/kT0tvcv6vCV9I4sBfKKfeXnRzB1ZP68a8fSmls8WC1GDs9d3WhjZIGJ89dOJSUKEsvRimEEEL0vKAtT1RKnaCUeh4oA24H3gP6BzYqIYQ4es1ONyv2VHPK8GRmDk3kkQvHsubuebz6i6l8ectM3rpuOiEGhd3pId5q5sp//8CKPVVtxqhrdnLq375hyfbybo/vkU93UFhjZ0RKJLf8dyOvrMxvc/93u6uICg1heL/ebxaS7V/bVljnOOh5e2t8949Nkb3hhBBCHHuCMolTSm0HvgQswFla62Fa64e11qUBDk0IIY7ad7urcbq9nDIiqfWYOcTAiYMTGBAfQWZCBMtvn9Oa0GXEhnPty2vZUvxjd8avd1Sws7yR297eRFVjywGP4XB5eOyLnfzkiWVU2DpOeGqbnHywoZjfvLmB6Q8t4b9rCvkht5pFK/JYMH0gb183nbkjkvjD+1t49htf6afWmmW7qjhxcAIhxt7/FZIR45tVK6w/8Dnvb2+tnYRwE1GhfaHgRAghhDg8wfrb7e/Aa7IvnBDiWPRVTjlWSwiTB3ZeIrlvHVxipIVXrpnKBc+sYMGLq3j7uukMTIjg65xKokJDsLW4OeWv32AOMfDAuaM4bWQ/lmwvZ+FHWymssQPw2dZyLp2cwYbCOpqdHjYU1vH1jgo2FNahNcRFmIkOM/GH97eQYLXQPy6c2+YPI9Rk5JnLJ/KbNzfw0Cc5NLa4OWdcKqX1Dm6ck9grr1V7GdH+JK7uEElcjYOBcaG9EZIQQgjR64IyidNaPxPoGIQQoie0uD18sqWM2cOTutywpF90KC/9fAoX/XMFV/57Ff/91Ql8u6uSudm+cswvt1ewq9zG/72xnskD41i2q4ohSVZe/+U07nh3E19tL6epxc3Dn+QAoBSMSY/hpjlDmD08idFp0dQ2O/nJE8sorrPz+i+nEW72/XowGQ08ccl4rJYQnvxqN59uKQNgxpCEnnmBDiEqNIToUCMFdQ5cHi8Ot5fIDjYc31vjYO6Q2ABEKIQQQvS8oEnilFL/01qf4f/+a6DDHtta6zm9GpgQQhzEWU8u55QRSdw89+DdEvf5clsFdc0uLpyYfuiT9zM4ycqLV0/hsudWcu5T31HX7GL2sCTOGpvKOePSqGps4fynV7A2v5Y7Tx/O1SdmYjIamDM8iVd/KGBLSQNTMuP47byhDE6yEm9t2+wjwWrhP9dMZW9VIydkxbe5z2hQPHT+aCIsIbywfC+ZCRFkxPVeZ8r2+seEUljXwhPLi1mcU82Sa8ei1I9bCNTaXdTa3WTKTJwQQohjVNAkccDy/b7/hk6SOCGECBbVjS1sLq5ne2kDZ45JYXDSoRt9vLW2kJToUE4afPgzWeMyYnjm8olcs2g1BgUnD/mxpDHBauGjG0/C69XERphbj88dkcyL3+VRaWvhrxeNZer/Z+++4ySr6vz/v06lrs5huqenZ3pyYCJDGCTIKFkFUTGsLiIYMK7ZDX5XVjHvfr8G3J/KKorosrIGWMUAAq5IBkHyDAyTc+zpmc6Vzu+Pc293dU1Vd3Wsqp738/HoR3fXvXXr3Fu3bp3PPed8zoJp2TYNwAkzqjkhR7ISYwzXXLKMhU1VTK8ubLbH2XVlPLuni91HY+w+GmPXkRitdQNl8pOaKIgTEZGpqmiCOGvtV9P+vraARRERyctL+zsBSKQsn771Wb72ltXMG2IC7H1He7lvwwE+eM5CgqOcfPqVS5q44co17DjcTW1FeNCy2vLwMeufNq+B6miI1vqKMXeBNMZw+emFTxI8u66MO15o67/T99y+rhxBXHkBSiciIjLxiiaIS2eM2W2tnZnl8e3W2sLXIEREGAji/v6iJXzj7g2c87V7OWvhNK5eO5/zljYfs/4dz+4hZeGyk0fWlTLTuUunD7+SJxIKcMOVa2isigzqcljK5tRFB3XVWLevi1efMJAkZmtbL+GAobVW88OJiMjUVJRTDAC5+iRN/qREIiI5bNzXQXVZiL87dxEPffp8PnXhErYd6uY9P36c+186cMz6dzy3lyXNVSyaPrlzl52xYFpeXT1LhR+cTa8Ks7Spguf3dfUv29LWw10b2phbHyU0ytZOERGRYldULXHGmM96f4bT/vYtAbYhIlIkXtrfyaLmKowxzKiN8pHzF/OetfN5/bcf5OP//RS//9hammvcuKwDHX08trWNj5y3uMClLn1zvK6TL59XS8DAHze2Y63ldy+08dk/bCESCnDdpfMLXEoREZGJU2wtced6P6G0v88FXgkY4N2FK5qIyGAb9nWyOKNVrSIS4rtvP4XuWJKP3PIkiWQKgLvW7cVauHjVjEIUdUppro5wxSnNXHFKMyuaK2nvSfDJ32zi73+7iaXTK/jVVSs5Y25NoYspIiIyYYoqiLPWnmutPRe4wf/b+znfWvt2a+1f89mOMebDxpgnjDExY8xNQ6x3iTHmAWNMuzFmrzHmRmNMXcY6XzLGHPTWud4Yc2zmABE57hzuinGws4/FWbopLm6u5suXreSxLW18854NpFKWWx7bzoKmSk5onjrdGgslYAzXnD+XFc2VrGh2iWTueLGN957ewo/fupQZ1ZFhtiAiIlLaiqo7pc9a+8ExbmI38EXgVcBQ6clqgS8B9wER4GbgOuCdAMaYq4G3AWuATuA3wDXA58ZYPhEpcRsPuKQmi5qzj2974ymtPLq5je/8aRPt3XGe23WUr79l9ZRJLlIsljdXcMUpzbxifi2vWFA3/BNERESmgKIM4gCMMe8BLgCm47pSAvlN9m2tvc3bxhogZxo4a+1P0/7tNsZ8H/h62mPvAr5hrd3qbe8LwPdRECdy3PvrtsMAx3SnTHft61bw1I52/uvR7ZzQXM0bTp41WcU7boSDAa45f26hiyEiIjKpiqo7pc8Llv4V2AecCTwDrAKenuCXfgXwfNr/KzNe8ymg1RhTm/lEY0ydMWZe+g9DBJAiUrp640l++MAWTp/fQGt9Rc71yiNBvnvFKSxvqeFzr1s+6rnhRERERNIVa0vcO4BXW2ufMMZcaa39uDHmVuDDE/WCxpjzgKuBl6c9XAUcSfu/3ftdnfE4wMdRC53IlJBMWRKpFGWhYNbltzy2nf0dfXzrbScPu62FTVX8/mNrx7uIIiIichwr1iCu0Vr7hP+PMcZYa+83xvxqIl7MGHM68DPgb6y16S1xnUB6ijO/Ba4jy2auA27KeKwVuH+ciikik+Czv36Onzy8jYCBX37wLE6ZUz9oeW88yXfv3cQZCxo4c+G0ApVSREREjmdF2Z0S2GuMafH+3gacZYw5YSJeyBhzMi5hyXuttXdlLH4OWJ32/0nATmttZisc1tp2a+3W9B9g50SUWUQmxt4jvfz00e28YkkT4WCA25/afcw6P310Owc6+vj4BUsKUEIRERGR4g3ibsHNDwcukcgfgSdw2SOHZYwJGWOiQBAIGmOi2aYGMMasBO4EPmqtzdbKdxPwCWPMXGNMI/AvwI0j3RkRKQ0/eXgrSWv50utXsnZxI3ev28feI71c86tnOdITpzee5Po/b+LMBdM4Y4Fa4URERKQwirI7pbX2s2l/X2+MeRrXrfEPeW4icxqAK4AfA+80xnQCr7HW3g98CmgCfmCM+UHaa/rp5n4AzMMFkGFccPml0eyTiBS3nliSnz62nYuWNzNnWgUXLm/mnvX7eddNf2H9nqO01JYTDQc50NHHt/92+LFwIiIiIhOlKIO4TNbah0a4/rXAtTmWVaX9/S7cNAK5tmOBz3g/IjKF3fbkTtq747zn7AUAnL+sGWOeZf2eo1SVhfjxQ1uxwFkLp3G6WuFERESkgIomiDPG5NVN0Vr77okui4gcX1Ipy40PbGHlrBpOm+cSmTRWlXHWwmkc6ozxqYtO4L0/eRyA71x+SiGLKiIiIlI8QRxpE3qLiEymP790gE0HuvjmW1djzMCl6IYr12AtlIeDnNBcTXNtlJfNbyhgSUVERESKKIjzujaKiEy6Gx/YwvTqMi5ZNXPQ4xWRgUvkrR86i5Am6xYREZEiUKzZKUVEJsWLezu4/6WDXHXWPCKh3JfEqrIQ0XD2yb9FREREJlPRtMSlM8ZsAWy2ZdbaBZNcHBGZwn704Bai4QCXv2xOoYsiIiIikpeiDOI4NrPkLOC9wPcmvygiMlV19iW47cldvOmUVuorI4UujoiIiEheirI7pbX2xxk/XwEuA9YWumwiUrq2xyN89JYnSaZcQ/9L+zqIJVKce0JTgUsmIiIikr+iDOJyeBoFcSIyBs/2VnL707v54/p9AGw+0AXAgqaqoZ4mIiIiUlRKIogzxpQDHwP2F7osIlKarIUdcddl8j8f2QbAloNdBAOGOQ0VhSyaiIiIyIgU5Zg4Y0yKYxObdABXFaA4IjIF7O+DbhtkYVMl9790kM0HOtl8sJM5DRVDZqUUERERKTZFGcQB52b83wFssNZ2FqIwIlL6NnW5Od7+75tP5K3fe4RfPLGTzQe6WNBYWeCSiYiIiIxMUQZx1to/F7oMIjK1bOyEqkCSU+bUc9q8Bu5et48dbd2sXdxY6KKJiIiIjEhRBnEAxpi1wBqgOv1xa+0XClMiESlV1sLmLsPsUC/GGM5fNp0v/W49AAuV1ERERERKTFEOBDHGfBW4B7gCuDDt54JClktEStPhOHQmDDPDMQDOX9bcv0yZKUVERKTUFGtL3HuB0621TxW6ICJS+nb2uN/NoTgA8xsrWdBU6cbENWlMnIiIiJSWomyJA7qA5wpdCBGZGnZ2G4LG0hiM9z/2utUzmdNQwbTKSAFLJiIiIjJyxRrEfQ34rDHGFLogIlL6dvQYZkQhlHZF+eh5i7nnk69ElxkREREpNcUaxP0KeCtw1BizOf2n0AUTkdJiretOObt88NSTgYDR/HAiIiJSkop1TNzPgJ3AdUB3gcsiIiXsUAx6kobWcgu9hS6NiIiIyNgVaxB3ItBorVWVS0TGZGeP6y45u0JBnIiIiEwNxdqX6HmgodCFEJHSt6MbQsbSXFbokoiIiIiMj2JtibsZuM0Y8w1gb/oCa+19hSmSiJSinT2Gliho+JuIiIhMFcUaxH3L+/3fGY9bIDjJZRGREpXykpqcUm+HX1lERESkRBRlEGet1T1zERmzg33QlzLHZKYUERERKWUKlkRkzLoTKYoxTPKTmrQqiBMREZEppChb4owxn821zFr7hTye/2HgXcAq4KfW2nfmWK8F+B5wGjADmG+t3ZqxzpeAD+CO1S3AR6218bx2RGSKS1nL7Tt7uXVHDydHmzix0AXKsLPHS2oSLXRJRERERMZPUQZxwLkZ/88E5gMPAMMGccBu4IvAq4DyIdZLAXcCXwUeylxojLkaeBuwBugEfgNcA3wujzLIce6edft4uhPObS50SSbGob4k393QxfqjCcoCsCE6DWvBmEKXbMCOHsOscggWUZlERERExqoogzhrbWYQhzHm40BNns+/zXvOGqB1iPX2Ad81xuQ6Du8CvuG3zhljvgB8HwVxkoev3LGeznY4d2GhSzL+HjkY4wcbu0hZywcWV5JIWX6wCXb0JJlT4dZJWniy3bC82lKR4xPWl4RnjhhOrrPjnj0yZWFXD7xMSU1ERERkiinKIC6HbwPbya8lbrysBJ5O+/8poNUYU2utPZK+ojGmDqjLeH7OAFKmtrauGJsPdGGAvqSlbIo0BT3fHud/dvSw7miChVVB/m5JFTPKg3QlUty4sZMnDhvmVLig6fd7DH8+GGB6meW981PUR47d3p8PGu7aF+Dxw5Z3zktRPo65Z/f3QSxlaK1QECciIiJTSykFcfOByZ6utwpID9bavd/VGY8DfBy10InniW2HATcnxq7uJAuqS+mjll3SWr7xQgflwQCXzyvn1S1RQgEXnFaGAsyJHeGRtjoSFsIGHjgUYHm1ZXMXfHtTgKvnp2jJGJv2dLuhNmzZ2g237TK8fc74BVxKaiIiIiJTVVHWLI0xN2Y8VAmcD/x8kovSyeAunLXe744s614H3JTxWCtw//gXS4qdH8QBbJ8iQdyOriQ9SXjPwnLOajr2fsrpnTuZ0VLLXw4brIWl1ZYr56Y40Ac3bAnw3U0B3jUvRVcC7t4X4Jwmy74+wxtmpmiLwQMHDZfMsNRlabEbjZ3dEAlYpk/2rR8RERGRCVasNcvMvmf7gE8C/zXJ5XgOWM1A0pOTgJ2ZXSkBrLXtDLTUAWCKKcODTKontrWxurWWdbuOsKM7weQ3Io+/DR0JABbnCEijNsmbWy2vn2kJGvAa6Wgphw8vSnHDlgDfn+3YdgAAIABJREFU3xwgZSGF4ac7DAbLibWWhIX7DxoeOGR4bcv4tJz5SU0C+hiKiIjIFFOUQZy19l1jeb6XqCQEBIGgMSYKJLNNDeAt80filHn/91lrLa5l7R+MMb8HuoB/ATJbCUUG6UskeXrnEa48Yy5tB46woytZ6CKNiw1HE9RHDI1lQ2cgCWdZ3BCBDy9M8ZNtAcIBOLcpyQ1bAsytgJqwW2dVreXRNsOF0y1lYxgbZ61LO7urB86apq6UIiIiMvUU1WTfxpgVxpj/k2PZp40xS/Pc1DVAD/Bp4Arv7xu87XQaY9amrduD6zYJ8IL3/1zv/x8AvwCeADYBzwJfynuHZErr7Etw7e3P89iWtkGPP7W9nVgixZp5DcyKwI7uqRHEvdSRYHF1aNQtzJUh+ODCFFfPT7GwCj65OMXb56T6l7+i0dKTNPzl8Niazm7YEuDL6wMkrKF1qAlGREREREpUUQVxwD8AB3Ms2w/8Yz4bsdZea601GT/v9JZVWWvvT1s3cz3jTylgnc9YaxuttbXW2g9oom8B6I0ned9PHuemh7ZyxQ8e5bfP7O5fdsdzeykLBTh7cSMzy+BI3PLzbd2sO1K6p87hvhQH+lIsqQ6P2zanRwda4QDmVcKcCssDBw2pUTagpSxs7oKuJASwzK1US5yIiIhMPcUWxJ2Na/nK5lbglZNYFpGcvvunjTy06RBfeP0KVs+u5SO3PMmND2whlbLc+dxeXrmkiaqyEPO9bIy/2tnLN9Z3ciSWGnrDRaY36YKgl7zxcEtqJrYH9isaLQdjhvVHR/f89jgkrOENMy2fWZZi2jglSREREREpJsUWxE33EoQcw0sm0jTJ5RHJ6p71+zljQQNXnjmP/3zP6bxq+Qy+8Nt1vP/mJ9h7tJeLV7UAMD8K31lTx1dW19CXsty8tbvAJc/fPXt6ufqRw+zqTvJSR4KwgXmV4ziRWxarai11Yct9B0d3adrX637PiFpqx6/RUERERKSoFFsQ12WMmZ1tgfd4zySXR+QYBzr6WLfnKGsXu3sK0XCQ77z9FK48cy53r9tHJBjgvGXT+9evLwswryrE62ZFefBAjGfbR9+tMj7afoYjtLkzwU+2dJMCnmmPs6EjwYLqUP+8cBMlaODsRsumLsOuUXzaD/S58mlaAREREZnKii2Iuw/4WI5lHwbunbyiiGT34EY3bPMViwcahoMBw+dft4Ivvn4Fn37NUmqixzYDvX52Oc3RADdu6iKWZzB2oDfJt1/sZEdXgs0dCd776GFu3zmx9zK6Eim+9UInNeEAjWUBnjocY0tngiWTNNfd6Q2WSMBy34GRB4z7+6AiaKksyry7IiIiIuOj2Ko6XwYeMcY0ADcDu4BZwNuBtwJnFrBsIgDct+EADZURVsysGfS4MYZ3nDkv5/MiAcO7F1by1ec7+PWOHt4yt2LI19ncmeD/revgSNxyOJaiImSIpeC/t/VQHjRc2BIdj90ZxFrL917qoi2W4l9WVnP//hh/3NcH5J4fbryVB+G0essjbYbLkpZoHj04/7jfJUPZ32doViuciIiITHFF1RJnrX0GuBg4C7gHWOf9fjlwibX22QIWT4R4MsV9Lx3k7EWNBEbRtXBVXZiXN0W4fVcvu7ypBzYcjXP9hk5SdqB17sm2GF989ijhgOHimVHWH03wRFuc17dGWVEb4tYdPYPWHy937O7j8bY4fzu3giU1YZbXDgRukxXEAayssSStYXNXfus/cshw9z7XBXN6VBkpRUREZGorqiAOwFp7r7V2KbAEWAsssdYutdb+ucBFE+G/H9vOwc4+Xrd65qi3ccW8CqJBw42burDW8ovtPdx/IMbmThfU/XFvL19b30lLeZAvnFjD2+aWMz0aoCwAF8+McsGMKEfjlvVHEuOyT31Jy+aOBPfv7+OWbd2saQjzmpmuOWu5lx2kORqgNjJ5l4t5lRAylo2dwwfKfUk4HDekMPSlDE1qiRMREZEprti6U/az1m4ENha6HFJ62rtj/PzxHbR1xamrCHPavAZOnVs/6u31xpNc+v89QFN1GS/s7eD0+Q2cn5a4ZKRqIwH+dm45P9jUzS+29/C8F4w9fTjO9q4EP9zUzcn1YT5yQhXRoAtiPrm0io64pTocYHV9mEgAHjsUY0Xd2FIw/nZXD7ds7cFvu2qOBnj/4sr+Cb1rIwGW1oSYP8FZKTOFAzC/El7qNMDQLWteb0/qw5bDcUNzmVriREREZGor2iBOZLT+8ZfPcNe6fYQChkTKEjDwg6vWEAkG2Xe0lzeeMqs/SMnHzY9s46X9new90ktnLME1lywf0fOzOae5jPv2x/jVzl4CQFM0wJOHYxyNW5ZUh/jksiqCaa8xJy1TRzRoOKk+zF8OxbhqQQWBEZbFTdydZEFViN/s7GVxdYiLZ0WpCxtmV4QoDw3e3mdX1eTY0sRaVGW5Y2+AjjgMNcf4vl5X3r+ZneLJw4b5lZNUQBEREZECURAnU8pTO9q5a90+PnHBEj56/iLau+NceeNjvO8nT5DwMkI+uOkgX7lsFdHw8K1LnX0JvnvvJtYubuTf33Yyu9p7WDmrdszlDBjD1Ysq+D9PHeXk+jBzKoPctsNNcnbF/IpBAVw2p0+L8NihOE+2xTl1BDNaJ63l39Z1sKsnyeXzKuhIWN43KzqibUyWxVWWO4CNnYaT63O3ru3rg6CxLKh0zxERERGZ6hTEyZTytT+8yLTKCO9ZOx9jDPWVEX5w1Ro+/t9PcdbCaaQsfPOeDWw92MX33rGGpuqhB1D96IEttHXF+PuLTqC+MkJ95fgFO60VIT63qobGsgD7e5PctqOXxrIAaxqG7yJ52rQILeU93Ly1mxPrw4TzTLJy5+5etncnCRq4eUs3lSHD6vrinBW7tdwFZ3t64eQh1tvba5he5uaYExERETkeFF1iE5HRemjjQR7YeJAPnbuIqrKB+xPNNVFued8ZfOT8xXzsgsVc//ZTWL+ng9d/+wGe330k5/bau2N8/77NXLS8mdWz6yakzIuqQ9RFAiysDrGgKsgbZ5fn1T0yFDBcOb+Cfb0p7tjdm9drHepLcev2Hk6qD3PZ7HIsrkVvoifwHq2AgbowtMWGXm9fLxoHJyIiIscVBXEyJVhr+b9/eJGW2ihvP33OkOu+ZlULv/jAmVjgzdc/zG1/3Zl1vf/482Y6Ywk+ddEJE1DiwYLG8KXVtZwzgknOVtdHOLUhzP/s6OFQX2rY9f9zSxdJ4KoFFVwyM8o508t4zczxn2tuPNWFXebJXPzMlDOKezdERERExpWCOBm1nYe7eWZne6GLAcA96/fz1I52Pnb+4rzGuq2cVcuvP/xyVrXW8smfP82nfv40fYlk//L9Hb3c9NAWXr96JifMqJ7Ioo/JO+ZXkLLw063dQ6731OEYjx2Kc1lrOc3RIGVBw/sWVzKrYnKzTo5UfcRyeIiWuG3ebjdrbjgRERE5jiiIk1G79vZ1vO37j9DWNUx/twmWSlm+9ocXmd9YyZtObc37edOro9zy3jP42PmLufWvO7n8hkc52Ony1X/nfzeSSFo+fsGSiSr2uJgeDXJpa5SHD8ZYfyROVyLFY4cGvx+xpOWmzd20lAe4ZFZpNVnVh6EjAYksDY0pC7/fG6A2bFlSvHG2iIiIyLhTECejkkimeGTzIbpjSW58YEvO9XpiSa6/dxNX3fgYR7rjE1KW3zyzmxf3dfCJC5cQDo7slA4GDJ+4cAnfffspPL/7CG/4zoN8508b+elj2/mb02Yzr7H489VfOqucxrIAN23u5pvrO7nuhU62dw1MBP6rnT3s703x7gWVeSdAKRb1EbAY2rOcOk8cNuzsMVw8w1KmK5mIiIgcR1T1kVF5eucROvsSNFaV8eOHtuYM0K760WP8250v8OcNB/jZ49vHvRy98SRfv2sDS2dU89pVLaPezsWrWvj5+88klkjx//7wInMaKvjoeYvHsaQTpyxouGJeBTu6k6w76oK3dd4E4t2JFL/b1ctZjZExTwxeCA0R100ys0tlXxLu2GuYXW45uU5dKUVEROT4oiBORuXhTQcB+Pe3nURXLMHnf/M81g6uTG/c38FjW9r4h1edwOnzG/jJw9tIpsa3wn3DfZvZ3tbNZy5ZRmCMrUwnttZx/z+dy9OfvYg/fuocZtSWTtfD06aFuailjLfPc61yLxx1QfXjbXHiFl5V5AlMcvFnP8hMbvK/BwxHE4Y3zExRYo2LIiIiImOmeeJkVB7ceIjlLTWctaiRj56/mOvueYllLTVcedZcykIuWcbtT+0mYOAtp7aysKmSD9z8V/64fh8XrZgx5tf/5M+f4pmdR9h5uJuLV81g7eKmMW8ToCwU7C9/KTHG8M4Fruvn9q4kT7fHsdbyyMEYjWUBFlWV3j4B1IbBMDi5SVsM/nzAcHJdirnF39tVREREZNypJU5GrDee5Inthzlr4TQAPnLeYtYubuTLv1/Pmi/dwz/98hke2nSQ25/ezZkLpzG9JsoFy5pprS/nunteGnNrXG88yW+f3kNPLMmchgo+c8ny8ditKWNZbZijccuGjgTPtsc5ozGCyWPuuWIUCkBNGHb0GL67KcCT7Ybf7TEY4JIZ6kYpIiIixye1xMmIrd9zlFgixZp5DYBLDvKjd57Gg5sO8esnd/HbZ3bzs8d3APChcxYBEAoG+KdXL+UjtzzJz/6yg8u9udwSyRTxpKU8kn9L0bO7jhBLpvjcpcvHpVVvqlla4z7W39nQRdLCGdMiBS7R2NSH4YUOF4Ru6XKJTi5qTlFX2rslIiIiMmoK4mTE1u05CsCKmTX9j4WCAV65pIlXLmmiJ5bknvX7eHbXEV67eiDZyGtPbOE/H9nG525/jq/esZ7eeJJ40rWmXHXmXC47pZV//OXTXHvpCs5a1Jjz9f+ytQ2AU+fWT8TulbzmaIC5lUG6E5a3zClnfol2pfTVRyxbuw3nT0+xqdPQkbCc06RWOBERETl+KYiTEVu3+yjV0RCt9eVZl5dHgly6eiaXrp456HFjDF9/y2p++MAWjIFoOEh5OMi2Q938+OFt3PzodpIpy88f3zFkEPfE1sMsbKpkWlXZuO7XVGGM4asn1WKtLdlulOlW1lhSNsVFzRbTbElYiKgjuIiIiBzHFMTJiK3bc5TlLTWjChBmN1Rw7etWDHrMWsusuigPbjpEZVmI+146SCpls2abTKUsj287zKvVjXJYUyGAA1hdB6vTphGITI3dEhERERk13c+WEUmmLC/s6WB5WlfKsTLG8MmLTuDWD57Fm06ZRVtXjGd3Hcm67ob9HRzpibNmnrpSioiIiMjxaUoGccaYDxtjnjDGxIwxNw2z7luMMZuNMV3GmLuMMbPSlkWMMd8zxrQbYw4YY74w4YUvclsPddETT7JiZu2EbH/t4iaMgXtfPHDMsmTK8oXfrKM8HBy3KQVERERERErNlAzigN3AF4EfDrWSMWYZcCPwPqAReBH4adoqnwVOBBYBpwGXG2PeNREFLqQdbd386MEtPLHt8DHL7nxuD//58Fb+uH4f63Yf5bEtLqnI8pbxa4lL11AZ4cTWOv73hX2DHk+mLF+760Ue2nSIz79uRUlNxC0iIiIiMp6m5Jg4a+1tAMaYNUDrEKteAdxhrb3HW/8aYL8xZqG1dhPwLuC91tqDwEFjzNeBdwM/mtAdmAQb93dy53N7uPP5vTy3y2WbnF5dxp/+/hwqy9xp0dEb50P/9Vcyp3ULBw2LpldNWNkuPbGFL/1uPU9sa+PUuQ28sPco/3Trszy9o503n9rKW9YM9ZaKiIiIiExtUzKIG4GVwGP+P9baI8aYrcBKY0wbMBN4Om39p4CvZNuQMaYOqMt4uGiijf0dvdz2113sPNzNo5vbeGl/JwAnz6njny9eysy6cj780yf5wm/W0dEX51UrZlBfESFl4VtvO4k5DRXsOdLL7vYeZtaVEwlNXCPu5afP4fp7N/HNu19i9exavvfnzdSWh/nW207idatnTpmEHSIiIiIio3G8B3FVQGYGjXag2ltGxnJ/WTYfBz43rqUbRx29Cf71jheoLQ+zvKWGK86Yy0UrmmmpHZgm4M7n9vZP0r31YDcXLm8mYOC8pdOpjoY5eZLKWhEJcfXaBfzbnS/wwMaDvPnUVj5z8TLqKzW7s4iIiIjI8R7EdQKZg7tqgQ5vGd7yzoxl2VwH3JTxWCtw/5hLOQ7mTavk2WsvojoazrnOta9bwapZtbT3xLn+3k3EkilOmFEz5HMmypVnzmV3ew+vWjGDsxfnnjNOREREROR4c7wHcc8Bq/1/jDE1wHzgOWvtYWPMbm/5bm+Vk7znHMNa245rqetXTN3+ggEzbDDWWFXG+1+5kC0Hu7j+3k1s3N/JO86YO0klHKyyLMQX37CyIK8tIiIiIlLMpmR2SmNMyBgTBYJA0BgTNcZki2BuBl5jjDnPGFOOy2j5iJfUBFzL2jXGmEZjzFzgk7hsllPa/MZKFjZVAnDqXM3HJiIiIiJSTKZkEAdcA/QAn8ZloOwBbgAwxnQaY9YCWGvXA+8BfgAcApYBl6dt5/O4lrdNwBPAz6y1JZ+ZMh8XrZgBKIgTERERESk2U7I7pbX2WuDaHMuqMv7/BfCLHOvGgPd7P8eVD56zkJfNa2B2Q0WhiyIiIiIiImmmakucjFFNNMy5S6cXuhgiIiIiIpJBQZyIiIiIiEgJURAnIiIiIiJSQhTEiYiIiIiIlBAFcSIiIiIiIiVEQZyIiIiIiEgJURAnIiIiIiJSQqbkPHFFJAiwc+fOQpdDCmTfwX300VfoYkyK9o7D7D+wr9DFGNKhtoNs3bq10MU4rvXs30MgHCl0MUYtFY9R3pcodDHkOHRg9y7CVeWFLsZxL97Zw6H9XVT0dRS6KDIOuts7qS6CekFarBDM9znGWjsxpRGMMWcD9xe6HCIiIiIiUvTWWmsfyGdFBXETyBhTBpwG7AGSE/hSW4D5eazXigsq1wJqHpxYpXqs8z2XikGpHuNSM9rjXErnUiHo/M3fWM4lHeeJVyrHeCpck0rlWJe64Y7zRJxLQaAF+Iu1Nq8uXOpOOYG8NyGvaHosjDFYa7fms55nZz7ry+iV6rHO91wqBqV6jEvNaI9zKZ1LhaDzN39jOZd0nCdeqRzjqXBNKpVjXeqGO84TeC5tGsnKSmwiIiIiIiJSQhTETQ2fL3QBZMrQuSTjReeSjBedSzIedB7JeCmKc0lB3BRgrb220GWQqUHnkowXnUsyXnQuyXjQeSTjpVjOJQVxx5d23N2D9kIX5DigYz3xdIwnh47zxNBxnRw6zhNPx3jy6FhPjpI4zspOKSIiIiIiUkLUEiciIiIiIlJCFMSJiIiIiIiUEAVxIiIiIiIiJURBnIiIiIiISAlRECciIiIiIlJCFMSJiIiIiIiUEAVxIiIiIiIiJURBnIiIiIiISAlRECciIiIiIlJCFMSJiIiIiIiUEAVxIiIiIiIiJURBnIiIiIiISAlRECciIiIiIlJCFMSJiIiIiIiUEAVxIiIiIiIiJURBnIiIiIiISAlRECciIiIiIlJCFMSJiIiIiIiUEAVxIiIiIiIiJURBnIiIiIiISAlRECciIiIiIlJCFMSJiIiIiIiUEAVxIiIiIiIiJURBnIiIiIiISAlRECciIiIiIlJCFMSJiIiIiIiUEAVxIiIiIiIiJURBnIiIiIiISAlRECciIiIiIlJCFMSJiIiIiIiUEAVxIiIiIiIiJURBnIiIiIiISAlRECciIiIiIlJCFMSJiIiIiIiUEAVxIiIiIiIiJURBnIiIiIiISAlRECciIiIiIlJCFMSJiIiIiIiUEAVxIiIiIiIiJURBnIiIiIiISAlRECciIiIiIlJCFMSJiIiIiIiUEAVxIiIiIiIiJURBnIiIiIiISAlRECciIiIiIlJCFMSJiIiIiIiUEAVxIiIiIiIiJURBnIiIiIiISAlRECciIiIiIlJCFMSJiIiIiIiUEAVxIiIiIiIiJURBnIiIiIiISAlRECciIiIiIlJCFMSJiIiIiIiUEAVxIiIiIiIiJURBnIiIiIiISAlRECciIiIiIlJCFMSJiIiIiIiUEAVxIiIiIiIiJURBnIiIiIiISAlRECciIiIiIlJCFMSJiIiIiIiUEAVxIiIiIiIiJURBnIiIiIiISAlRECciIiIiIlJCFMSJiIiIiIiUEAVxIiIiIiIiJURBnIiIiIiISAlRECciIiIiIlJCFMSJiIiIiIiUEAVxIiIiIiIiJURBnIiIiIiISAlRECciIiIiIlJCFMSJiIiIiIiUEAVxIiIiIiIiJURBnIiIiIiISAlRECciIiIiIlJCFMSJiMhxyxiz1RjzzkKXo1gYY24yxtxU6HKIiMjQFMSJiEhRyxVoGWPuNcZcO/klmjjGmHcaY7YWuhz5morvgYhIKVAQJyIiMkrGmHChy5BNsZZLRETGh4I4EREpecaYecYYa4y5whjzjDGmwxjzkDFmado6VcaYHxpjDhljdhljPp5lO0uNMb81xuzz1vmuMaYybflWY8znjDF3G2M6gA8YYw4YY87zltcaY+LGmJ+kPecXxpgve3+fY4x52BjT5pXjN8aY+d6ytcB/AHOMMZ3ezxtGWa73D3GMrjbGrDfGHDXG3OO/fo7jOtsYc6sxZr8xZrd3/Oq9Zf8BrAX+2Svr3nzfLxERGRsFcSIiMpW8A7gQaAL2At9JW/YN4ETvZwmwEpjlLzTGNAL3A3cBc4DVwGLguozXeD9wDVAD/BD4o/eaAOcCW4ALvG0GgPO8bQLEgU8Azd62k8DNANba+4EPANuttVXez69GWa4bhzhG7/HK1wJsBW43xgQzV/Ie+x3QASz0XncO8GOvvB/wyvUVr6wzhnhNEREZRwriRERkKvm8tXaftbYXF8i8DPqDqSuBz1prd1lru3DBlEl77pXAC9baf7fW9llrD+KCoiszgpwfWmsftU43cDdwkbfsIuAGoNcYswpYA5QBDwNYax+01j5irY1ba9uAzwNnGmMqhtin0ZYrly9kHINl/nHK8DJgOfBRa22HtfaAt/6lxhgFbCIiBRQqdAFERESGEQeyjfEKe8vS7U77uxOo8v5uwgVTW/yF1toOY8zBtPUXA6cbY9rTHjOABWYAu7zHtjDY3cANXovZhcBbgEXe3+XAn621MQBjzEnAV4CT0spmvPJty7KPYylXLtmOwWy8QDPNbOCgtfZo2mMbvd9zcC2dIiJSAGqJExGRYrcFF8j081rWFgCb8tzGAaAPmJe2jSqgMW2dvcC91tq6tJ9aa23UWrsrbb1U+oattduBl4CrgWrgaVzXx4u8n7vTVv85sA5Ybq2tAV7pFyfbtsdSriHM8/9IOwY7s6y3A2g0xlSnPbbQ+719hK8pIiLjSEGciIgUux8BVxtjzjXGhLyg4su4lqg789mAtTaFG3v2eWPMTK/74tezvM4aY8wHjDEVxpntJxcZxt3Ap4F7rLUWN07u5cCZDA7iaoGjwFFjTDPwhYzt7AWa/OQh41CubP4l4xi8CDyaZb2/AOuBb3lJYRpx4wp/Z631W+H24sYXiojIJFIQJyIiRc1aewvwKeCbwEFcq9cK4AJrbftQz83wCVwr2HPeNtaT1gLltaidBbwK18LXDvwBWJXHtu/GBWh3edtq917ngLX2+bT13gNcgUsWcg9wW8Z2/heXTGSjMabdGPO6MZYrmx/hgsy9uBbO11trk5krWWsTwGuBelxr6LO47qpXpq32dWClV9ZsrXkiIjIBjLthKCIiIlOZMWYeLhibb63dWtDCiIjImKglTkREREREpIQcl0GcMabOGPNzbzLYXcaYD3mPzzbGPGKMOWyM+XrGc24Yw/gDERERERGRcXG8TjHwbdy+z8Rl2rrbGLMelxban7T1r8aYW6y1jxtjXg40WWt/VbASi4iIjIHXhdIMt56IiBS/4y6IM8ZU4oK1k621HcBTxpgbgXfj0iz/yps353FggTHmKeBrwFsLVmgRERERERHPcRfE4VIhG2vturTHnsLN5XMPcJ4x5hHgVOBLwCeBW73sYDkZY+qAuoyHI7h5jF4Cjsn8JSIiIiIix70g0AL8xVrbl88Tjscgrgo3R0+6dtwErV8FrgfuB74LdAJvAC40xlyPS2l9n7X2mizb/TjwuYkqtIiIiIiITGlrgQfyWfF4DOI6gZqMx2qBDmttG2ndJo0xv8bNTXQVLkJ+JXCXMebV1trMCWavA27KeGwucO/9999Pa2vr+O3BRHj0d/DUn6B5Dhgv341NwZ4tUFYJ8V5IpaCxBdoPuOWnX+KeV9sEZeWFK3u6ZAL2b4eqOnj7v8D6R+D+W6FuOkQrRr/NfVvBWohWQV8XvOo9sGAV3PdL9xrNc8F4Q016u+DQbpg+D9p2QdNsCATHaw+LSyoFB3ZAIgbRSpg2s9Alyi6RgP1boa7ZvT+RMqiqh0Qc9m+DhSfBRe8cWL+7A379Hfde1zdn3+bhfdDXA4tOho1PDj4HhmIt7N0K1XVw7uXwu+9BpBzqmsZhRydRrBcO7oQlp0EoAusehhnzIJCWL6ttL/R2AEGoqoXOw1BWBdNmFKrUuSUTEO9z53HbHujthup6aD0B5q+C6bOhvBq6jsLt34G+biivcuuWVcLCk2HZ6dA4a+AY/PZ77trROGv05WrbC72d7jUMEOuDd3wOKqoHr2ct/PLr0HEYprWM/vWmslTSHc9YL5x0Hpx5afb17vgh7HoJmiboe7uvGw7ughPPgbMvy+85+7bBb66HYBgaclyTZGL5dYGZi2Hp6fCnW6C6wV3bpDTFeqG2cfD3f4Hs3LmTtWvXAuzJ9znHYxC3AbDGmGXW2vXeYyfhJmXtZ4y5DNhjrX3YGHMl8Li11npj5U4EBgVx3sSu7RnbAKC1tZV58+ZNxL6Mn/3zYccT0FADobB7LJWE3ipPMIayAAAgAElEQVRoXQLnXQ63XQcdbVAVhgWrYekK2PwQVFZCZZFcxLqPQl85rDkX5s+HYB9svN9V2mvqR7fNWC/Eqlxwm0q67Zz+CrfPh1bD3uegvgrCEbd+VxBSR2H2LKADGusgOEU/ap3t0FsG0XpXiWwa5TGeaLFeiFW6c+LQbvdYfb2rtMcqYdZMSP+Mxnphep17Lxtz7FPqKKTC7nPQ9hI01uYXrCcT0FsBs+fDmefBvudhw1+godqdJ73d0L7fVSCL+bzpPAyJSjjtbHcj59AGiCTc3wHvs9J3ACrrIRh0N4VClS74KMbz5NAe6DvqArXKoLv58qHrBgel4AL3pnroDrp9SR2Fi98Lq885dpszmyF2cGz7mzgMUQtlFe4YBqtg2crsNwxmTHfvQTEe36FYC0cOuJtvocjEvcah3e69PeEUeMsHBr7rMrXOhO7dE3ccO4FkFZxyxuDrzlDmzoUD6+H5B6GhygVzMrn8usDChXDBZbD7Ke9aXWKfNxnQ1wP10/L/HE6OvIdfHXdTDFhru4BfAl80xlQbY07EJTW50V/HGFMF/DPwae+hLcA5xpgI8HJg8+SWehKUVbi7vMnEwGPWAtZ9qc5cCO/5V3dHOhiCl13snhMMuVaOYtHb7SrSy053/1fVQbgM4rHRbzOVdMeirNxVoqqnDQSt9c0uQOztGljfJl0FK1rhfls7+tcuZta6IC4UhlmL3bEpVinvmljT6M5Z/3//vQmXDV4/FHFBe659stZrfaxy54IJuFa9fCQTgHWtw8bA2W90rSxt3s237iPufOo4PKJdnHTxPncsZ8x37/+0FjhyEHa+CLs2wv4dbl8XnuRat2J93uehCM+TeMzdAAI4vAeSSXenPTOAg4FzA1xLtDHuPMgmFBnb/iYT7jwLl7njnYhBzbTcLb7hiCtTqYn3wtFDcHD3xL1GrMe1sDe0wBs/njuAA3e8J/I8jcfc99RIei4YA2e+3rX+Hsr7Rr2MJ78uUNPkbkxNnz24ziQyyY67IM7zd4DFNVneCVxrrf1T2vLPA9d5rWsA3wOmAQeAncD/TGJZJ0e0EgIhSKZVRPsruN6d0fJKeNun4X1fg3krXBewYMZzCslar6tcFJrnuceq6lz5x/KF7Ff4m2a7ytuC1QPL6qa74K6vJ21977XKqwfKNRX1drmK0Yz5Lpj1v+CKkf8e1jW5ypNfTv935t3/QMAL7HLsTyrpKvo1jVBR493MyPNGQSLuXtfvYtfUCsvPhJ5OV1Hv7XIVtrHceJgMsT5XkalvdhXiN34CTn8tzFvluhhh3TF82WtcxTOV9D6LRXSOJOKue/iR/e4aMW+lK18gCCvOzP6cYBBCIbe+f10pr8y+7lj3NxFz1xM/4E8l3XUol3BZcR3ffMX6XLnjvQOf1bHo6XSfz3R9PYCFs990bFfUTP5xnKhjmYi586i2cWTPm9YCK85214h4XnkPZDz5AVud977Vz3Dn63icsyKjUMR9dSaOF5y9ZYjln8r4/wjwqokuV0GVVbjuGYksQVxmBdcfu1PmB3G9k1PG4cR73Zdj65KBwDMSdRXIjnavkhyDmoaRbTfptaydcJoby7DsZQPLaqa5YDaWdgysd3c+EvUfGNNuFa3OdhfUvvwy2PmCe8za/MaFTbaU9x7WNQ/u8ui3NkfKjn1OpDx3q0Yi7t7naS2uQhiO5F+pSiZcWRrSxoWdcSlseNyN50wmXFBkC1wx6Ot2ZanIHEKMOy7xXtd10m/FrK6H8y8fWMdad0zCZS7w2LsFKusGWryKQfdROHrQ/V1RAxdeBT/7V1f25rm5nxeOunX886Msx3jb0BiDOP+cmrvcdWWP90LLgiHKNcEtSBMlEXOfCRNw1+raaaPfVjLpxptFKwePaevtdt9xrYuH30YoApiJuZ75n4tIuSvjSJ1+Cbz4mGuNmzFvfMsmQ/O/R2q8IK66wX2fxPtyXwNEJtDx2hInmaKV7u7yoCAuNdCNMJuy8sGtGoXW2+3KsuKswY/XNrnWws7DcGjXyFs4/Av3rCVw1Rfdb18o7Foi0ruUplKuMhKJTt3ulLFedze4tgnmLXeV2mLtKgdpLXGNGeesBQyEsgVx0dz747c++8kuQpERdKeMu/OjJu0ufE0DnHKBC5qsdS3J+W5vonQcHugSmclvIZo+L/fz/RsZxsCcZS7YbWgprs9DIu7Oh0s/CH/zD9A4Ey77GFz8vqHHN0aibv+t91nP7I7rC0WAMbToxL3WztlLveuwGTq4jESL6/jmK+Z1za2b7pK4jEXSu8GS3jKeSrleAxXVXivxMEIhN7xgvK5nqSR0HfEC/6T7TI02kVFVHZx8wcA1WCZPMuE+75Xeja2aae6z31ckN7LluKMgThx/fFv6l1Zmd8pszymmrIu9Xa7SNHfl4Mfrmt3Ft6/HXYB7O0a23ZR34S6vchWqzDuzTXNcxcG/K++PkwlHB/6fajrb3bly5qXuHAiXeYlfinRfk0lXvmiVG6von+f9rc1ZOiWUDdMSZww0tnrdKcP5V56TCdddMzMZ0CkXuNa5SNSNPU0lXUvRgR2FqZj7leFsFUW/S2jrovy2deIrXbbYuqbiOkcS3tikJae5bsHgxv8uWDX08/wA3291z3WNDIbGdnMj1ue6uc9a7D5j4TIX6OQSLnPHN5V0STxKocudtS4gKa+Gxae4v8fSPS0ZP7YFLdbjtjlnWX4ta8Hw+N6A6zri3o+uI2k3QIYIxodz8vnuWtG+b3zKJ/lJJd212x8qUd3grgUxBXFSGArixPHHxKV/Z/V3NYtmf04oPPaB++PFD9JqGo7tLlld7ypqibgr80jvmvUHADm6vjR4XfT8C7nfDS4SxbudO7LXK3bJhAsuyqth2RnuMb87YrGODfC/fMsq3PvYn9jEO3ezZcQrq/BaW7K8f34LTk2jCwpHklAi4bXElWeMy4lWwps+Ca+52gWHwbAbr9V1xP1MJmsH9jFbEOcHLxV5ZqU1xnWp9McZFkNrkd+trbwqdxCWi98S13/DJkdLXDBEf7e80ZavosZdw6obXFkr63I/JxQZGE/ZdcRNg1HsEnF3o2zaTDfeOFLmulSOented2W/VRsGxiyfcFp+20jvTjkeervctnq6BnqCDNUtdjjRCjjxFW5bxT52dipJJAAzUBeobvCuHUVQB5LjkoI4ccqirpVp0MXI62oWzhHEgVchHuYC1tfj5pubyAQofd2uYr741GOXpSc3qawdeaDhd6fM1a3UT27idwNKpbzEGF6FqphaHsZD1xFXQTr5vIEAPxx1Ff5izdSVSuK+fCu8JBvpLXE5KuFDdYf1A7GqOrdOZe3Igrhopfd5yzBtpktyUtfkPpN9PV4g1Z3vno4PvzXHnyogkx/8jnQcSLiseLrdphLuBs1Ik0uAdy3wx8SZ3Gnxg363vFEEA4mYO/Z+ApwLr4Jz3pY9Y6YvFB5IgOKf28Uu7iU1aT0BZi5yN0bG0qXS/55JTzzT1+2Ozcw8W479lvnxOE9TSff6xrhzLtbjPvt+8q3Rmn+iuxF09NDYyyj5SSXc97qf2TQccTdVMpPoiEwSBXHiBIKuQpZMua5yfT1prRRDpGL2s84Npfuo+xLrnMDWhN4utw8nvOzYZVX1rvIYirgv8XyzCPoS8YEKfTZ1zW6Qun9HNJl0rSihsKtwFUOFdbxY64K4SNR16fFFyoo7iPOThQRDrkul38JmravnZgvi/CA82/uXiLlj4Aex1Q2QyuMmRSrpKgJVQ7SmgAssIlHAju7Gw1j5XdIi0exj81JeUDLSxAyhMK6Fowg+E35ymulzRv5cPxhNeV1jc83n17+/owji0oMbcEl0Vq0d+jmhiDufU0nAlkQMR9ybeqJ1sfvMLThxbF0q/a6+GNdykkq6wKmybvjPnc/vTjkeN+B6uwcy2cZj7rs1VDa2CeDBtWxPa1FXvsni906IZty4apiRds6JTC4FcTKgosZVStr3w8GdA1+E2TL3+cqrh04t76f9N2biWuIGTS2QZZyBH8TVNcGsRe6LOd+kEda6L+DMC3e6impv0l9/TFzSa5kKTb2WOD+1deuSwQkCwl5LbrF2p0wmXMsWuFYUv7XC7+qabeLc/op6xvuXSrlzuTptgtequoGkBcOVw9rhW3/Kq918hLWNbhzPSG88jJVfzoaZbl8zg/NjMrDmye/ulyqCCo9/08UfCzcSfne7ZHJg3Fs2gSCjDlrjXsbGmQtHUK7wQLkokuM8nFivO4bTvKBmrF0q/e+ZUMT93dfjjsfc5flvIxT2xviOw/Ws/wbjaYA3/q9x1tA3R/MRCMDiNe7aoC6VE89v2S3PyNZb1+zVE4r0BqZMaQriZEBlTdodUDPQShEaoqLmz4+Uq5IS7/MmNg1M3J2qWK8LymYtyv7FWFkDZ10GL3+jm9clHHEtg/nIdeFO54/3SfQNdEOLlHlJUCZwvwuh64jbp5ddMvjxcJk3z2ARBnF+cFXmnav9QVzaeLdsNyr81rlEzE3E7Vfo/FaquuaBdStqvAm/h/ki92941A6RnAJc+S77GLzpUzBjgXveZGar9CvCMxe6ynDm58V63QhHOpbM/3wONYl6+pyLE8nvEjttFC0i/QF+Mvd4OBjY39HcyPEnU29oyf85/a/nX8OL8POYzqbcuVVePXBTZCxdKq113zeRcncs4n2uFc4YWLIm/+0E8+xF4WeaHKo8vZ0DyYrKyt36C0/OvyxDmTHf9YbpHmGyLhm5pHftzmzNnTHPnWvt+wtSLDm+KYiTAeXVA2OA0oOuoVriIuVDZyXs7XZfhOHoxOX36OsCLKx4ee51Vp4FK1/upgMoq8h/jFHSa63JzCSYqXGWdzcu7taPeNMvmCnWnTLW4wL3OcsGP+53pyzGljg/cPIn+I2UH5v4INuYpnDZwHi0jsNwxJtPzO8605TWDa+hxVW4h5sDzf+cVA5xU8AXrYCW+a67TtZAyrqKw0QEdwmvm+DMRe7z0pOR3MRP6DHS1oTQMONEe7tg/zbomoRK6WgnXIaB4HW4IK4/O+VoulP2uuOV3uI7nP4WpITXrbLIrz2xPncMZy0eaM0cS5fKVNLtc3W9OxaJ+MD8cC0jbNEcqiXO71a+d6ub/zDX++vfYJy50M1Z52d0nr8y+/ojVVbujldSLXHjLpkYeP9SKfeZstZNq5NuzjJYebY7HxRMyyRTECcDopXeHFqpgcxrMHQlpSytQpxNX5eryFRUM2FRXG+3q+zMyaO7TN10bxLnPLs++AHAcBOE1zW74+RnIfMnQi+WJA7jJZVyY8oyk3KEo67SX4xjcPyKmJ9JsX+S+rSxM9mmGAhF3D75ExH72/GnF5g+e2Dd2UtdRsnhskj658JIxpLVN7uALvPGQyLukhq07cl/W/lKepkpZ8z3snRmVGb7U+sPcW3IZrjKsZ9+vWcSKkPxmKvcD3eDJptQZOBaOVwQN5pxscmEe3/rmkc22XT/Oetd33JlVy0WsR5XvsyEVCPtUmktHN7rbrZgXYKgYNidx7Fed/3O58aJz3/fsgXB8T433ODQnoHkM7lupPR6NxhXnu269VfWue/C9EnIxyJS7mWVLuL3uFS17YU9m2HXS7DzRTdnZrau8Ma4hEMtC908tOpWKZNIQZwM8OeK65+g1g/ihuhO6U/4nWu8WyLu1imrmLgvmnjf4O44QwmFBwYi58MP9qqnDb1eXZPbzx6vC1B5lRfEFfHcaSNlvXMiW+tLpOzYLrOppKvsDDVXVawX9m2b2C8+PxD3x/BF/CAuMTCfVCBLEOe3xGWOR/Mn606frDcYhFMvBOzQgZzfghWtyr/8uW482NTAezLe/K6GNdO8yewzPi9DTc0wlJDfYpvj/fYD5omelsOfDLq6fmRBki+cdr7nyloLXoKMUYytinvBbMsIx+sdEyRP0PkxXvp6XJlnLR78+MxF7tzLt0tlMuESZx3e5x23Be4zGfeCrHnDzPuXKRTxelFkvG8dh2H/dtcy3dgKZ73BrZvr/e2fu3SFO88uvApe8Tcjv/mRS5mXFVgx3PhLxt332sqzYc4K19tm2sxjz1Vw6136AXdzbv92BdUyaXKk1JLjUrTSVW7Lq123MH/cy1BdpvzAL1clPJVyFbehJk4eC38M2kjusk6fAxv/OjAVwFD8BAHDtcTVNrmKdk+nu4CXV3vdKcdxrqGCs66ykKvrocmoTMT7oOuoS66Q3mpFxjo9na6rYsOMiSj0wDg9P4grSwvi/Ap8tuyC/UFcWspy8OZPCxx702Dxqe5L/vC+3K07/megYgRBXCjsArnMVOLpk8uPt2Tcnc/hiEuFvunJwZ+XVMpVcnNlZczFz9iaq9Ib91tHJ/gz42emHMl4s3RhvyXO6zqdy2hb4/0bH7OXjux5wXDGnJXeeNBAluksCs1a10U4UjH4hgi449u6FJ65d+BGy1D8rpfRChc4TZ8DG57wbgoE3CTiIxEKH5ttN5Vy3ZeDITj3zXDqq9z3SP9NzIzzIOG1Aja0DIyjmjHX/YyXSLn3mZwq3zFFJJlwvTcu/WB+60+bCedeDnf+0J0n9c3DP0dkjNQSJwNmLnSVhrnLB8ZTGJN9PivfUEGcte5OZqTMBYgTMci+f7xTnqmjwRu/FIZYHuPiUkl3LKqGaeULR1xFxG+1qage6JIzVfiV62zJLIIhL9hPq6ymd+nKxQ8MJrolzpiBoCviZQ5Nb4nLdo6H01qNrB3Yj4TXDS9zsu5wBE46zxuHk2WCbBjohjjS+dUaZ7nXTb8h4AcG6WP7xkMq6QJfPxCd5n1e0sfkpZKuC+pIW7H8ynGucyLRx0B2xQnkH8vmUWSmhLTulNa1uucyVLe8IcvnJTXJdfMjZ7nSgmR/bHMxJhuCgaQgTbOyn0cV1eSd2TPW64K1898Bi05xQVxljTsGofDIJ9bu7wab8XmzKZeZ9/TXuu32t+pnOca9XW4fl2aZ9ma8hMLuXCzm1tZS5F/vh2plz2bVWjfPZ0fb5M/tKcelKVTDlDGrqoM3fcJlWyIt+UC29Ou+SHSIljhvHq5w1BtXMxEtcYn8xqylq5/hdX3MUdEetP2kqxwMVVHzNc0Z2Mf0lripcpe0v3ttjq5AZeWDKxNJb+xYoi93kJEeiEwUv9WnygtK/LvX6S0kQ3WntND/ebDWBQCVNdkrnsu8ibrbDwxRlsDQXZSzqW92ZU7vmuqX3Q4xZYa1Q3dnTdfX402fkHDbrPG6ENfP8JKbpHVtS6UgOMKulDAQ/GS7FviV+lB44seV+OMaZ4xijjhIOzeGCeL87o0jvYEV63Pbrxsmi+kxr+cfX+/1cgUYxSCzhTxTKAyBPHsyxHrcTYUlp8Jb/9Gdu5XetB810/K7fme+dua12y9H+vUvEs09nKCv2x3/zPF+4y06Qd+txzO/q/pI58E0Bs79W3fOdR6emLKJpDmugjhjTIsx5nZjzB5jjDXGzMtYfrm3bIsx5ty0x+uNMU8YY6oztzkl+d3l/FaDobpM9SdDybLMr/SWlbv1JmKQvV8RyMwYNZR6b4xRPnNv+UFcPhdzP4tgKuUyOAZDuDvJ+RetqPVXYnIEIJldZpNp3RBzzWOU3ro1Ud1O+9/DqoFy+hXw/pa4bEHc/8/eu0fLktVlgt/OeOT7vB/3nvu+detx611FVcEFqqBKXiJCI8jYlIAINILC4EDbroEWVMZu1mpbaFyO3Qtn2lH6MU4PTitrRC0dxYU9Cq7VsyygQakCinrcunWf55mZEXv++O1f7Mg4EZmREZGZEXniW+usqpsnT2ZkZMTev8f3+z5bV+NNUyUZSqks6nqrN0kltbsbnlhJlwLTQYqvYZhfpfPur+5ypxyIvpZ3rpF63rCCxe4WzXJcelqrby5v0O+WDmlpdP/nsBL4XHlJTZiBelcnReMW5Oh26BpYTEjhZQERYPB8Y4U7cSN8FulS4t2cTy4c4/kfmohlQp8Fwr5TqXzRwr5LTzQqQh2UvfiGdZmkS+9Rn9MKtACdPyFIJGVU8CyjX6nJU2v2rX9WVZ3jQKLsunRP1ZpkPzNO1Jr5VAUuMthDdNTkH6D1oNacoTGKEnnGgUriQFyvPwDwQ8FfCCFMAL8K4CEA7wHwad+vPwHg41LKg6Ef6x+OFxicxNXb9PzQDVw9Vm0oaeUxKDXy5jU3RHjEj1qTOndxKtQ8NxUniVtY092oWlvPw+RRsTEJOOmJ8gYLdls5KLfrygYi7DV93SSe48kaTKdkCiN3UbhTDETTKTnhsKr02Xhma5C63NoJ9ZlD6DRxFF/DML9KwWPH56Hm7yJGnbudTaX2OEAggmd9WGWPk7UV5Z9m1+j9+XGmGo3aTQTUuRcIvSk4kV/eoOfEVZBNgp5K4oYJFkWBrw2BIZ04c3SvyG6XvoskCoZe9wp0TU+qE9fZBZ78u/004u2rJMN//jv7u6ueaFQEVZ33nWHnjm0Kgqboc0t0z58ZcR4O0FYYfZ04d/8MpO0rCPUd0w593hO3DB5HyAL1dtmJyxpcRGqOMKbBMAxaH0qKa4kJ4EAlcVLKZ6SUvwbgr0N+vQxgV0r5NQB/CuA0AAghzgFYl1J+bnJHOmVwlZkNYwclcYZBFc+wSqArNSXBrqGPopkVOMkaxUsJoEA7TvfH6VEgFscPi5M4UaGOjEennBF4leiIeS5O4vh5vS5977VmtIEzV+PtRvQcWVp4nTh13CyN77rwgrQwOqW/21JvUXDM1MRB1fX2Er1+WGLFCeWoSdzcEp1Lf2DA95JdC+/ESVef00EBxdXn6FgbbbrenR6dL3+Xav2kul9cnczbCZI4Y0BSwxTH9RPxjNOTgimmtcboHVEGd2n911UY+POO0o5n+vHGDaMfF6sqQqqEeYDoVJbY3aLv61qAQsb3PSd5fusIvhei5o1NC7HmI9nWJdhxu/V+4DXvBo6cGemj0HuHfG/e+udP4iLUIfm+u3WAd2lWqDWAvKuQFg3ciQsae8dFrVl+HyUmggOVxA3BswAghLgV1I17VHXnfhnA+4f9sRBiQQhx0v8DICMzmAmjL4nDcAW69lKEsAnPh6kkbpCfXFJwgN4YQZ0SoGq/qBDtbRCcXnwRitaC9lDjWUExS3RKlfRUB9Apec5MSgrK55Y1lTbqNSsq6R1XJ87p0XfiV9W0q6qjxBYDIUuhYejZmIU1Pd8nKsDSRvT7tRYogA671l3HN28zAgyTjiFIaYRKCMOSoj3VDTBMRF6EnR1g8yJdt7e/RHXj1DyWP4BZ2aDH9nb1dZC0ExeV1HB3jIWH4s7yjQoWbomi8cWBlywJoDqgS2/wdz3C993do785MoI5tXdcTFeFEt0wJhNMcoc4OBvW2aWE90d/jjzvzn8XuPA9rSoMRAfKfO6kJPXaKxf2P8d1gM3L9FlPBWwELBu44Z5o5sAghK3dYeufrfwxETjHHWXUHiZHnzXs+mxZ2eQBXMAeNa5glHOKJSaEMolTkFK6AN4C4DMAfhbAOwF8AMDnAMwLIb4ghPhTIcRLIl7iAwAeC/x8cewHPg6YFsnFx+nEAZTEuc7+RYs36VpLJXFjUEpzerSJjjqAvHiIjmnQrJBUgUbc1xaCKGimpcyvDdAtNiNZHBtjR3VgrKoyxXaVkbaaHVs+HD2z5brKDH5ufMGm09NFBAZ3taRL13pUUsUB0tJhlfR3KDAeJKTTnItOrFx3dG81xspRLY0P+BLglu5o+sHXdlQRQrrApfP0dw/+iKIxGkpUo9Jvk8BiQLub+j4fVbkN8HWmAnB6NO9XMem9TCvezGoScEcxzawSq2wOo1on6cR1O/R3SewP/DTAaoOu7XFDulQwqFT0uQXomuzukTnykTPAO/4ZcPfLge1rNHvpOHR8UefPU9rsUWfr0tP7r/Frl+g9b3vxaOJWw1BRVFT/mhS2/pmWel7guLhYM+q+lATVOh1XOReXHXjeuBVhFTMMtYg1uUSJjDHTSZwQ4mEhxKb6eXTY86WUj0gpXyClfAmASwDeAOBXQIndzwN4O4DfEiI04vskgFOBn/sz+iiThdeJc+NRApvzagMPGgKrBazW1FXhrIfsHSf+zJofi2tULRtU7ffsC0aoxt36YuDQaXptw6AC/Kws5MPUKedX6LvY2yFqlZSUwK0eV0l+GOXWoeutHXNGceRjlvS6wUTGrmvq5yAbCKZLLaxRsMZS5lE+cAA9fy6iO+06yTpYAF2zfoVKDjTmV2j+7nvf1HQ1KSnhsmyiSYZdg9cu0d8duYGksVuL9PzuHv3XH6wuKYXKro9SOapNAkDn0AjMEElJM3m9DnDLC4kabdrjo1PyOnUoob0AoCmxFWMwJdOwRp+J9WiGSeZxTD3vWW/tl8kfBzp7dK0vHab3Z/Ed9m47omihpgW88seA07dpE+6KiKajerPZqpgm0f9ZnB514aoN4Nxrs/9cph24TlXn239fCKHmoAPnmAtHkwCr7U6CNntQwHtV4k6cikdmZe8vkVvMdBInpfyslLKlfm4Z8c8/BeCDUsoegNsAfFlK+TgAC8A+aTop5WUp5eP+HwBPpPwI0wFXmaWMN5TdmKMKerByzuqWPBM3jiF7t0ebbZyZNT9aizQQPuh4PAnsEebtTtwM/Og/1cF/Hk12k4KVHKOCk2Nn6VrYuqJpVctHKPkwrPC5OO5McTcp82NWCUfQ040FaKQ7uNNcbdC11V7SVhqe8ugALKyrbmQgWZFu8jmsoEIlB5XLR7Q1wOYV+l13j34OnVb3RiCY6O6Rl5FdB179LiXysUiv7zj7B/prTaLGsv0AQOdhVAix39dq+yr9LKwBD72ZvqtBFNC0YAPotKqBdk2LGERh0AxgFKRL62mSeVoh9CxZY24y609nhz7fzeeAaksXEpgefer2/ucvHqZ7gwtwUXRUQ3XivI55QBhr87Lqwj0QbVOQBlYwiZOUkAeLMEF1SJvue7IAACAASURBVBb+GWQCnyX4OhxX0eMgwnXjq1KHwRPFKrujJcaLmU7iwiCEqAHgXbcqhKgFO2tCiNcBOC+l/JJ66DEADwkhblF/+9zEDnga4KF9yHhBQGNOV/D9cH3Bnt9gOStISRtXko6AEKT+NsjDjDtxSRXsgPEGo5NGmE+SH805mgFheX0hqDq/sK6oeCHUVdeh16s1MZbhfEcNqAcrqhyISXfwNb64Tp2CuWUKKqULtGPMUs2r5/gVFvl6SnK98mvadR0ccyfu3lcBL38bqfNxMLm7Se912wP0Wf1UZ+58OQ7wwtdSlw2gwgYnWGHzYutKDIhfa5C0/iD4qaa9LnnqVUzgNe9RdiQqcR5XFbvXpaB3PsV9DehCzdAkbsS5WHdIYWEYmK7baNP81rhpdns79H3deB+t9dzp7OzS4+sn+p/P593p0LFGFQr9xvB8zfF/nR75cNUawLkfzP4zARGdOOyfCQ7O/HqFowlQKQFlmWKGe9WVSAbuhidNxJm+X87FlRgzDlwSB2AHAOttf13929tlhBBNAB8BzcUx3gfg1wE8AuC9Us54ecUb2ke8YKI5R38T9ALzWwxwtTCrIL3XVZ0Op98baBSsHld0u4jNL4l9QRBh8xJFBX93gza2M3cBENTlqZhUIV9cV758IXRblu+vNsaz6XHiFKSmWbamag26xl/4OuDhnyP6pKECyuUY3mKtBUW/DDHnTpr8tJcpgPTPHJkWXf/3vEIpSKru384WfcZTt6r5P6Uo2e3Qd7OzSUWMe16lX7/WBGp16jYshwi3rBylc8ZJ5CBVxkGwq5rKeukZuv/ueSVwVIlACNVFGlfy0d2j7yaJfLgfzC4YlMSxguUoWZx0R2cW+GFVAQg1izxmgQUpiZJrN+iaWTtG10evS49bNV3QYLQWaW3odgavJezV5jr6muci4OYl+v87HkyuIDgMQZn4MHVKgDqJflVez2NsQraydl2zBEpkA8+WJmESxyJfSb+TrSvjE3YqMVNIUe4rJqSUAzkqUsotAPcGHnsEwMkxHla+4Fc4i5PENebpb8I6caJCm6Fdy3bI/uJTVAGWcvB80iAsrtOx7WwB7RCxCab7RElgx4Exxo7CpBFmdhvEiZtpHuzys8AdD6gOlgHMLRL9qe/1XLrG7Jre9FwnXRciiKhEnEVYhnXiAKKD7m7p+dDV48Pft7WobAZ2tI8YB3pJKTqGQV3NS+f16/kDyqVD8Pz2OrsUVLeVNYHrUHfuwpO6+/kD7+5PFoSgDpwwgNUjIefB31EVyTuKZhWApOth5xolhy8OWHc22uOj1/Y6wPz64FnIOLjhHupoDkq4mD46ShLnurRuJAVf2402JdrjVKfsqnm4jdP0nre/FPjGV4Bnvq2T8+B55tnLva3hHnueWArU+tDrn4V7/g+M77MFxYlcnxqsH9WaXkuE4fMYS7gvjQoukJaduOyQ1AqGwcyjJBRXLm5VKsBGAnuMEv2QLhVTOzu0Lzo9GkEYt3/jhHDgkrgSMWDaypAX8YKJWoMWu62r/Y/zHAMncVkO2TvKmBhucrojU+X2tsPn3nghT7MZB0UcigxWZxsk2d2cB978EVKgW/clO6vHge98Xc/VAdqnrdbQKpCOQ1OnWYETgWDXhSlnUsbrengzjhVtgj0InMT5LSw4mE46LA/Qe//d38BTTvV/F3Mr9Fk2L9Hvbj5Hj7PwAitb3vxCEhA5dHL/6y+uUxK9uL7/dyxucvk8deuSduKqNTqWqxfoHP3ge/dfU37PwSy9Fntdet2lkM83Ks7cpTrPQ2CO0I3njmmaTpxd1bPIfpW8cXhW7m3Ta19/D/37+FkyuP7mV+j97wtJstiCw3UHsyi4EyclAHX8jkOCPE4XuPfV402UuBPH547/G/xubF8BqmJErznjQpUNxyfzdgcCLLiVtNDDQm5JEmvpaoVnvqZKJMOVC8Q84TWwYlAh6NpFYGGftEUhcRDplCWGgTtxiBlMcLcqWDn3+1ixkltWCQ0LUggj+c24sKqDnDC4yvR4ULV4GMJEJYoK6VLwPuyaaC/2J3CA8uUTSt1QUgXfoxc2KbCvmPspl2nBifhcIEm3bF09j1ORqxiUWBgWCYwMQ2uBggA3WMkH0EhxPS2sKb82df5MX6WYZ+a2rtJx3qAC66oynuXO8gteE518nLyVaJZhn7Heps/l0aRTDP2zV9sLX7f/WgEUjUkF71mC6abrJ7N93UEwrRG6YeozJ+0AALoTV2vqRHtchaS9HbrWjikFSiGA+99Ane87v4+o9kG0FlTSHkJz9oP9FPncCaXAuLNJ1/l9r4r+2yxg2gCEPndclAwWNj0PVLWPeElcimLNKGB1ylnZZ/KAtOqiaYTc2I8QQotYlUiGHTUbfuN9wGveDbz1YxSLzBBVtezEldiPiqE3KjPmJTK3rNX4/J0W7sQxv/xqhklcawG47g6q/CZBxaBuxIUnwivVnn1Bwo4DMIN0ypAgJg4W1mhj290E3Brw7BNEu5QSqLXTVS4Hgb/DeiCgMm1dvY77eeot6nLEUSu1bOoSXLuoH/OEflIkcfMrdD/tbtKx+2c25lbUvFmPiio812arGSmnO1xx7cTNwNt+MTyxFQJYOwF852v0OklVNnnmduM64L5Xhz/H7zloZFhr7HXodcO6kOOCWe1P5geBO3Gpk7gKdbn4XEsXmddspaQkzq71d27XTwD/6F9EdxDsGt0DojKYRWH6OnFSEjvE7dF1PLecrqMdByaL0nASJ/Vx+cHn20vi3PQMjpGO09KiSyXSIwt1UY9OuTv8uYwripnArISKQaq9SWf+Dyq4iMJdt9Y88Pr369/Pr+4f7Sgwyk5ciXDwQhLXmHjjDDxvKobr9Hdu6q1+tb40kJIWyu9/Z7qA7NBpRTULMRZ2HRWspljMx6myN2lE0YniYHGdxA86u8RPdx2iXEJSZ6pap0Ak6+F8/g6DiTgLm4xCXbvtAeDEbfErtAur/Ump5KJAyk5ctU78fsj+a7PR1l2365+nixJsAM2d5WEzeYM6k6tHdWfJTJhonLmLii+v+Yno+UerCqCSvShHr0vd+yRG2kkR1Y3f3SbqKx9X12eUPYiyHOf9DJOuU38ynDW6e3R9Hzq1P2GzBqhOAhRIicrggohhqcTN14ljkZNxJ3D8/v5OIHfiwuiUfg/UtB5jo4K7rkVWQnTdcAuaaYBp3GnURXl8JC6DWUqiCT/3Pf3+VrUUq0mCS88A57+jKKkOxR1+HDqtRwtmAGUSVyIcVg2AiJ/EnbmTNuYrPvcFHtDnYLIxl41PHKsaxj22QVjeIJrc9ub+3zld6jakEUAw7BlK4tSil6QT116kRMp1ydaBZ0igfKK8QGgM6pSisl+Ew6qqIHOEJO76u4Efev/w5zHm17RRMeCjU6aorLYW6VzxfeRPToWguTWzBtxyTj9uVSmY6LFsdgqa0NIhPQOUNNE4fBr44Q9pa4Mw2DU65qzFTbodup/H4SsWhaBUPWP7CnDhexRQXHkWeOZxwGEF2BTfUWOeEn1/VX8cIjEsLHXD80b/24V1uv9aA74HrxPH6445WdEQ/9ys/79hdEo/dS6tx1gS1FvFTuKuXaTrPw80tyzURe36aOMjPOMsoemUc0tq7GBG4odJobNLNEq3Fy4ktnJEjSTkpGiQEmUSVyIctgr84tJ6ak0STOjs6q4WDwczGnMAZPrNRkpa7OJSPQdh5QgdeydAe5CSNuWkcz8MThRmYSGOohPFQcWghNnrOEhVZRRU8aw2xjPA7UYkLh6dUibvKA1De5HegwMTruSnCdANg9Qye53wDereV1ECd+iUfszy0Sl5ficpFpW4SaWSTRElCkwDzDr56HW0GuqkYNlaIMMPTkg6u9qcnQOPNN3/2+4HXvuTRDm0VBFqHElcZ4eSl+NnR//bpUPUkR6UjFWM/muAEyUp09m+xAXvL/5OHMT+7nHQAzWtx1gStBb0tVM0SEm0QcfJR2AtMygUWCMWf9nPVPD/C5rbrRjlXNwoYAEvUQG6XRDTJ5CML29Q8XMnpHBfQJRJXIlwcOA3ymzGrS+mYW5PAt0BLF/AX2/5OjBpoIb/swi+G20ynw3OYrFCVFo+ummhbzi+yOBZj6QWAGvHKdDo7OoESkD5WSn1x6yH852eppT5YfmSuDTUtUFghco9vzl3RRuNJ8XKER1oBKvFh04RTdGfaLOokNNLN98JULDYmKMO87jOG0BFJMPIpnPPkC6dg0nNKjG8zk0wiVPB/t62mtXzzVWlpXCfuFkZkavrPMvzCED7w9UosR8VN90HvPF/IF+5KHC3V/rWHabjB73nxgFmkbi+JK5S2R+c87Xq929M4zGWBI050D5TwG4cFzEMIx/0Qe7EpVknKhX6/uPGOg4n4Ex7V0lcvUUd+xLxwDRJIXShMygwxCrLYSM0BUSZxJUIB9OZRgk4F9eAU7eTj5Tr0s3k//taUwUqKcUrspgb8ePQaXXD+zZAroylnWuoGIoXPwNJHHuqJe3kLKxRgOl0dULP3STTGlHFL87xqm5fmJ+Z6auUjquj1F6kAI9tBpg+mka0AiAqGlP04iRlNivDuukNiIUArr9Lm5mPC5ZK6rMUunF6dA7mJpAA+OGpLAbWAKdH83mdXV0gcUNosmngqQJnHBx3OxQwrZ1I5rdUMYAj1w/v6vupqKatu/dJbWVGAdM5/XTosGveDkj8p/UYS4JaUwWuBfSK27mmGBF29sWGJOCOa5rZZUDbpMSBXyuAE9m142oEJQeJbVHA4nqVimbABCnbdk11rmcgJkOZxJWIglWDZ9Q9Cu58kP7m8nna1PwV5VqTqpvdlBUQ/6aeBZYO99/0gPYVSVvxNUzMTCeOfVaSYnFdz3Otn9QBmt/XKsu5Dp6dDFODZGETgTF24gI2A45DQg1pK/Tzq7QRSTfe3I2fOpqF2MK51wJv+/n0rzMIXvKRYVDH1e5Bs3jjQNQa4PTUrCKr+voKXGkot354XdiMg+NOinm4UeBf4/k+FoinEJsWhjIb9zpsESbstWY/iyCtx1gS1Fq6214kSEkCV4ZJrJc8dBK5oJI2ias1438ep6cLPfz/9RYp+JZzcfHBRQxR0Z22sI6qXc/HtZYByiSuRDhYXnnUYGLjDFG6tq8CkP0Ba61Jr5u6Eye1OmUWWFijpHV3Sz/G1de0FV/uXM3CIiwjgpi4YGXFSgU4dqP2auNCwSibXhxwIh7m18Sy4EB6emMUqg31mdS15HQpuEtj5AxoPzggXhJn+eheWQl6jFu0wa4m91mKAge4yxNUpgSUn2VgDZCu7mzzeiiEPsas1jZO4LOeidvjebibs33dICxV6OGglpPdtB3lODBM1YljOqUM78SZVn/HMK3HWBLUFZshD8Igo4CpxEfOqHM47QOC/r7TdsNrLT3DOQy8zlUqKhFRqtgbZ+jx4Mx+iXD0ujpu7XairT64CDoDKJO4EuEwVQV3VC+oSgW46yFNDfIvhNyJ66WsFvKimFkSt0oBd8e3AXI1LG3Flzf9WUji3JjG2FGo1imJsGvUifO6varaXm/H3/TiwEviQgyFuVI+TtqTEJRwcQfI6WWT/LQXlTl6iHVCGLyu1oQEIbKAOaIwQBz01D2dZIYrDbjw4Q8a+Nq0azpJMS297mTdicuSOsTzcFZ1/Amxd28K3YmrTEj50Uu+fTNxUQWYelN/p64bTuEeJ2otOrai0Sl3rgEQZAxfqQAiB/skf99p/DwBWpsF4iULTKe0bPoOK4Jir9VjdK1vBebipASuPjcewaIiw+kCUKJCfG7Cirh2rdhqrj6USVyJcPA8QBLK4nV3EnXOdfopCbUmbYxpKyD891kFOi2f/D2DF4BWmcR5SNuJA4Bb7wdWjpIhsKm8pLgTVmsiUyXPQd1Ugw3tE/rexcXCOgXmbk8J5WRAZ6wYlIhUzHgCGKyUNsyXK0+wVac0y9vGUR5xkz4HYWsArzWsyuq6tJ5xJy6r7rCfNpwUUgKXn9Vdnh7Pwx0f71wkoJI4XydOCNC80qSSuIpWER1ka1Nvaw+7uLOqWaLeKp7ht3TJ2qdaB07foX07pwWmLWahIgz4FHZjfCc9Veizarozb1VJxKox16/4zYq2l58FLj6T7hhnDb0Osb3sqvoeQ+yFAPXdzoZqeJnElQgHq5ol6VLYNQrWTbuf9lJrasn9NMi6E1epKPl736we+4ulVaf0hECKv1gMrETHxa0vAn7sF4niYNe0oAlAm3mWsvJeEhfSiQP0LN44k7i5JfpMe3sAZHZ0xpWjdI/GuT+tKiUvQqQvSkwKnkR3hveN06PXbExandLcb7jNwdjCGn2PpkWfma/ZrJQNrSpV9dOgu0dV/+eeon/v7dBacN1d6Y9vGFglGaBgTFS0GuS4YVh6Jo5tbaLmZ5lF4PSAtB5jScB7a5GC0t0tSlhO3UZJ7zST0F4HePpxYPOST0U4JUOD/TnjfCanq9YBnxqrVaV1Yf2EFhJ66lvkqed0NSW7BEFKOk+NBV1siVKJZcXqGTh/ZRJXIhyGRZvCqHRKxu0voY7coRP6McumSlNaag9vVFnS4FaOUgDlKZE52dB2uFJd9Na9lPS9ZSEmIwT9LG9Qp5bVLu26zycnAzjO4MSFDe3HNRMHKJsBG9jbUkI5q9m87p0PAvd9P4mnDINlUyBfMdIXJSYFTjyzDEp7XVrTJt0lCZuJ43Vm6RB91uac8nNTynhZzsSlPY+ctHFC3dmh83hizPNwgO6GAUpAJKKyPs735u4MZPSeU2/qmcZJmZH7Ydd0AlAU7GzS+b3r+3SwPS3FQKdH9+Tutj6HqZM4G2QZMOQ7Yap9vdWvmMz74pEb6Ji2rtAatn1NWZJkYdc0Q2C2y/yyLqIIEaFObZdJXBEhhPgBIcRfCCEuCyGeFkL8L0KIBd/v3yyEeEoI8ZgQ4kHf44tCiK8IIQoSAWWAw6eJj520ct9oAz/8IeD6gHpZYy79wsM3XpYmzYtrlLiy2ShLWacNGNiwtkgV0lAobz4rw67VK38M+MH36n9X6yrgzEhhjSX9oyiMbHydhWl8FDyvOGXYmlUS11oA7n9jvGCfRVwMczI0tCzAdKasNlkpKfCpt5NbZCSFwWtA0MJE0Bpr1YClDXXtu9l2hw0zPYW9s6Pl612X1kizCqwezeYYB8FUBQjDVKJIxuS6XH51ymGFw6pK4lh5edI2FkIoSmdBglLXoSSu1iSrCYC+33EG1SyIFno86n25Y57FrDSzCYYVJdmTtrWkrSr8xdI1NRd37Tl1fD09+1g0NdJxoqu6kytHlNKpjO6ompxgS+DqBWDz8sQPNyscqCQOwDyAjwPYAHATgDUAnwQAIYQJ4FcBPATgPQA+7fu7TwD4uJTy2kSPdppYOgS8+cMkcZslmnP9nihJwEIAWSqAsXIiK1Q6Dm3iaeXnDWM2KmaerUOGibNdA45er/9drdP5ymo4f1g3tVpXAfMYTatbC3pYXQhgPiM65SgQqttomJMXXEiDLJM411GBUozOZdaocCfOtwa4LnWdV44Ar34X8OLXa9qjEOlnTxlCCSQk7XBISd0Jfq29bUpUVo6Ml4bM4Nlsw6TroWJMrsvlUeF9FfuoPYfXku4e/XdpwgqoANCaL05Qv7tFa+L1z/PR6Rvj3Se3rhAdMSzR5fflhIqN5tMgrjIsU3DnV1XBJaC8vXqMunQszOS6JMImpZ7DLKH9WI/dqCjzUtMmg+Dv1nXUtVhc4+8DlcRJKf+dlPIPpJTbUsrLAP4NgBepXy8D2JVSfg3AnwI4DQBCiHMA1qWUn5vKQc8aGvMUUKRZeLyZuCw7cYeIzudVuLrZzKUwlQoFX2jHQWENwq5lKyvPprtR9Dmb6ZRj/EyNOUUhVsfSnNJMml2jzzlJA+K0qNaz6yxwoLSwls3rjYKgVD2gr4d6Czh+E3D0RurEMbJMkNJ4InX3aC1cOkzXztaVyc3DAfrcGRZ9DsOgOdNJwFTCR4Du4kQJCVUbdKzdPTrehYw67qOgMa9FWLLE3g7wzOPZ2n2wN9wdL9WP1Rr9Xc+s0d2jIgSzIvzgxM116d40rPQde4upzEMSa6bgLqyqvSqQxNk1GveQkuybTEtRmlWnuCiJ+7jR3aNzsnaC6M0VI7rowqIzbA4eRyAspzhQSVwIHgDwqPr/ZwFACHErqBv3qOrO/TKA9w97ISHEghDipP8HwAT4JgVDvakpBq5LVd5rF0frvmTFWfejWu+Xg3ed9BLDgKrCx1SoyjN4c80ycQ7CrtPmmdZHkOHJtUcs0OyfNs6ZuEpFy/pnIZSTFDefI9rSpKmEaVBtZJvETcPoG/DRKQMzcX7lND+zIGuxnTQdzT029b6XXmfnGgVHp27N7vgGwVTBtGURNdmwiHo6CTAVHvAVDqOSuLpO4irGdDq+jTblnOe/Q3tqVujsAjtbJPqRFbp7dD0dOqUfs+vo63pmDUexIXohXnqeobujzdrTIi6dkpOwhTVijYTNcZ28lRK82x6g8+T01BytoSm8Bx0dlcTxeawMGB9g4b6uijWyiPWmhDHrA+cXQoiHALwTqhMnpXSFEG8B8BkAe+p3HwDwOQDzQogvALABfExK+WchL/kBAB+dxLEXGrxIXfgetbBZpa2+RRSdC98D5hb7rQmC8LpCGQffh04C335UV2eykIMPC+CKCO+cj7FiVW0oOmVGm5LrEP0zSsmO6S7jVrpbWNMU4GltFrfdTz9FQrUOTwY6bfLJ1++klSkBTacM68T5kwJ/1TgrOiWQvKMpJXXeTBu44R7gm1/WVgiTmIcDtEKkWaXZoB/52cndQ5xAwidsUo2iUyp1RTb6nsZ9XmvSXrN9jTo1rcVsijbSpe+gm6HhtOsC9Vr/2tunGDiGNbmn5tzDfGr9gmYyo9lvttAZ1iljAa6FNTXvGbJP3P4Sio+WDgFf/kM1+3WMOqTdvUInIZnAdek8LK7Tfcud8agkjn1is4z1poSZTuKEEA8D+Nfqn9+WUt6iHn8+gP8I4E1SSu7EQUr5CIBH1HOOA3gDgPsBfAmUpD0J4M+FECek3BeVfxLAvw08dhTAF7P8TIXH0mG6gfa2abbh2E3ApaeB556k4H13k2ZHhiVxXJ3NEstHKKjnGZAsqqkcwG1fpcWlqIuFVMImUUFMFrBrin6S8nUuPgVAKcUNEkFotFU3bszL4PyKEpiwikVnnDZYeEa6/VTDJJBK9XEaM4FMe0JYEue7n6p1dZ9l3YlLmAzvbVMXZuM6KnDVWxQsMbVyEuCZOD5PyxPqwgF67Qb09RM5E9fQAjK1Fu0jk0Z7ic5XvUWJ3M61bPYbLgAMmqvkRCXuWhrmOcr3u+tmn8OxsFElwsKGPyObtWdRIGY6pTukKOn06BpvLegidzApMwya9QJo9vFZAzhyBrj4ZKHnuTJDd4+uKe7ssidx1PXvzSuq67Y1heJeRpjpJE5K+VkAn/U/JoS4C8DvAXiXlPIPB/z5pwB8UErZE0LcBuDLUsqOEMICsArgfOC9LgPok7gRRaIuTQpHbwB+/JdosWyrSuH/878Dz3xbm21WhlyWHOhkHXwvHaJAavsavUcWCmOGQRvHzqbyMClqEpex9HkYWH0ubRbX2aXv0LQHqzGePUcBblaKkVFoKd+aWqNYdMZpgz3CXDd9UCyVkMg4r98ocDfeX5F3HJ8XnkK1odY/I9vrxK4mS4a3rtDfvfB1dJyLh4DvfI18vSaFYBI3SfitIYbNYfPaxfvaNHDyVuCNH6Q173f+BbB5JZv9hgV5wpIFKYELT+jC55Ezw+9Vzzg9kMRxJ85xgKw1c1jYSIjw7YU7cF6xMgPGickejUPuZaer/CvnaA2oGIMZAytHge/+N1IQ/+pflnRKQMeOx8/SvzmJi7QXqtJ57qjucr2gcRlmPIkLQs27/QGA90spf3fA814H4LyU8kvqoccAPCSE+C6AKoDnxn6ws4zgJrewQhv13o6mrwwCB2RZUo4AlcQ1VCcH1JpPC5aYrzX1HGClgKOokxA2MUwKkjZTzkRIqQe+B9FMLBu46b507xUHfA1M2gC46OCgLou5OO4iTMNiwUsG/J24HmAFrk1WOMy6OGX6zmPcZLjXoUJIa0EnbStHKZk6fXu2xzcIhqUECqYgPFCpaCq8x/4Y0InjJG4a4jkAHe/RG+j/N84A3/jrbKjILJbCXmr+a8jt0bycZdPvNq/EEJ5hpePA3Bmrj6ZVrw4DCxsZEV563J0zTArss+jYcydu2Ol3evS5a009Wzlodvrs84lGuX5CJcIFH9VIAulS8trZJZrv7jadw8On6feHTpGX6rGz4X9vWrorKwTNFxYUBYwmU+GDoC7aZ4QQm/zjf4IQogngIwB+1vfw+wD8Oohq+V4p5ZBJ1RIjob1MC96O+iqGzY+NqxNXa5IIheuSCMQN96R/zfkV4C0fA+5/Ay0aWcnnTxrjEJMJQ62ZgY+g6mbIKUnKBzG/QsnppL2jig6L6VUZLLfsvzZOYZ4osLiRdyxKOCmommrV6HlZS/ez6fAoghGbSoXynlfqdfaOlwIve4sOlCYBDrZqU0jiPPsRqQsJUcG9YRDVXIjJzQsOwhmlHsqWOWnAn90wgb3dkN9J4NTttL7FeT9OioMS/twdGYfaIgsb2bVoiwEuoEiZURKnOnHD4pleVxUBKkrcyyQBuCgcuwl4y0epONiYy1Y1tAiQLvD0Y/Tz3JPEGACoC8zCVYYBvPiHgBMRSZxl91NrpyU4lgEOVCdOSvl2AG8f8pwtAPcGHnsEwMnxHdkBx9ySVj6LE2xwQJZ1Jw4g6lBrEXjozdlQKgAK1trKyNPpgpq5BcOwSnRWqDXTd17Y5LNSoQLBtNFeAl754+P1o5tFsIJYFmqlTAeexndgcDVeleQ9MadAJ47plVmbz49KU3MdYPsKBZR+MZxaA7jzFz19nAAAIABJREFUoWyPbRhYgGDQjPQ4YRgBOuWA9a/epmOdlHrmIBy/CWgukIlxWtELTnCqdZqT9CcY3KWbU/vb331lePfPY3VEdOLGkZQ4StSkOQ9cfnb/79lWgIt/WbAmDFPNeA9I4lyXOo9Mez18ijreczEp/u1l4IlvpD/WIsF1qAs3vwo8/weo67x0eLQCHVsMcCw5DYZGRjhQSVyJnGJuhQINV1IAE6sTh+yDHYA41ccjqjdpUG9TVbmoSlLj8OYLQ72tg9ykNCDp0vVkWsB8DpI4ADh5y7SPoHioNbNN4sZt7B4F9jpj2pNX/Q1QeLg6nHVxylIBS9yO5vY1CpJuPzf9Gd7VYySrzjTBScO09QzXME/JehuoWNPxiAuivURiNH//X9NTKl2HEji7rmffGKza2Zin5OMbf63tA6LARVozcC7Z8mVcnTghSBjn4lP950RKShzrLU2JbWSwR3Pnf1BRmjuEbEOzvAE8/OH+zv0gLK7T+uj0xi/QlRcwNf7IGeDulyV7DauqqdLTomtnhINGpyyRR1TrqvIlfQptA8C/L9KiVW9RQFB0OuW4BQZqDaT3ClLiAq96B9F8ShQTC2vU+e2E+DqNinGoPsaFX+UQ0N2LINWX6WRZU5ZHoalJSd0b0wbue3W2x5EETIs6OSFfuiBMSydxQuzvHvkxt0SUymZOlO6ufx4dd5i59ShwHaDapKJKcJLEVcltvUUdkXobuDbET84zTg/sJVaV7pVBKphJ4XTpHmireT3//sLHU2vqwnBWnV+rNqQTp5I4//z9KMJGR28A7Ea2Hn55B3vWpumesWASoO7rArKjFMokrkQ+sHSIbqrm/HA6nXQVXW4KMs5JUW/TBpHFfM80EIdOlAXsejoxC96QTQu45YU0j1aimGgvAvWMDL9dJYY0LTolfLMxvAY0AwJPNs/EZXyM7FcVZ+1hW4H1E+RLddBhKiEMVjYcFOzd+/3A978zP0nc8bN0LJuXhz83Ciy5b9dojw4WIZlqWW9qH7NhSWPUXmLXlJhM8sPte4+LT9NsJ0DecEJQEicCBtzSAXUT27rgkhW9Lk4nDtCzXKPi8GkqHgQ7pLMM3g/SJNqG6WNyjVl1e8wok7gS+cDiIQq8lzcA9jSKgnSL1YUDFFXDROGUpPh78CisYw6Cq3XaxBNTaiSd4qJdHyX2o2KQd2MWPkjeTNwUOnGsTsnBqZfEBYJ99iwcWycuRhK3eVnbCpR2GPs7cUEKoB/VOnDjvfk5b/MrwNpxLaOeBNz1qNYpSXOd/mIAJyg8D3jbS+j3O5vhr+f/m7BOHFPc0qLXAbYua6Vpp0uv35xXysW+NYWTguaCSiLFftGhpLDrg4tQTPNManNj2aQW29ktboF4VPD1kzbRZqq0YUxnX8gIZRJXIh84+wLg1hereYIhdLosfKMmDcumjTCLrsKk0NkFnvw7orNNShiiWqdkN2kSxwFA0a6PEuFYPUbBSS/lnAyvGdOw9+DqPsem3L3YR6dUQi5ZV4U96fYYVO69bepITNILLs/om4nDYDplHnH93UTZ29uhBH3UdZXVJ2tNojcbJr1W3+99navr76L5rqsDXJi8TlxgDomLDVkkcTub+ti7e9RBbM5RMdWwgK7vXmB6c3tZK8lmRaes1od34kRF0zyT4NRtdO7SdFyLBC+JS5lo24rqOqgwUwCUSVyJfGBhFXj1u8h0UYghnThZzE5La6FY1bLOrq5o8iD4OMRk/LDr6Ybb2ay1wJW1Ej4sHaJAupN2rifEXHhSYL8xT9hEdXWCstYcxGadxC2skXjU9ubg+0q6tD61Fou5vo4D7MPlKgp/0c7L8ZtJdOTCE0QvvHx+tL/3B8zzq7Q+++mSMkAzrbeAG+4FOjvR11pUQlxRyUxaISMpSZwHaobxygW6ro/eqOberH5aKO/JrXlt2p6VMjUnClHxjNPTRt9JsXGGztvugO7nLIHXz2rKTpxVBSCnYzuTIcokrkS+YFrDZ6KkO/5kYhxoLWo1qiKAq4R+tchxBzFeJy7lRj4O+4kSk8fCOl0Taf2upknB9vtPAaoTV9kfKLaXSRgjaz9B0wJe8TaaWzr/3ej1hzsSBZbbzhwG70cO7Tl5oUrGxeI60SB7XT3bNwr4mqjPK7/LGimXer9XXWV/4eHmc3QNXQmR8gcGKx2vHdfHmhTdDhlArx4BGgvA9lVKlM6+gJJRw+rfX/ic1NtaoTIrSjPbe0R9nl5Xdf5S3HN2DThxs6JUFojpkxQehTflOlWtAxDZeAJOEWUSVyJfMC0MpVNKWcwgnWdgUikvThC80UnoYx73ebdrFPCmpVMW8foosR8La1T9Tys77jrTpcLtS+JCgofmHPCjHwXufWX2779yBLj/jdRZj6K6cQBYYOPbzGGYAJTHXhF9HoUAzp6jZKK1MHoB0bMQaFO3qN7s37+4Q+lPetZPAIdOEaUx7P0GeY4urlPC1U2hSLtzjY7rnlfqz2xWgY3rqHvDIj/evLe6HxtzRKM0jOy+a9NGZDwjJSWcdfWeaXDdHbTnbV9N9zpFAK9TaZMvq0ZzylnNP04JZRJXIl8wbbqxoipKPJ9QxCC90aYNryg2A72u3nAnZetQbSiD3aQecSzAUsDro8R+2FXqAKRJ4jzF0ikG4aalZ+Ic7l6EULaq9fF1wu58CDhzJ3VIwmwbOLBt5ERdMQ9gURq3oEkcANz+APD2/wk4fN3odH7XgWchIAQZmfcCXSyB/iROCOD2l9L/b1/b/5oenTIkiVtYo0LeTsLOu5SUxFlV4MzdlLhJSR0+u0afY+UIJZjPfJuSRX/xYuUI7UFZUewsFc84IfFMr0PzissZqMAeuYHUfLcOQBLH6uRp70dbCelkYew+RZRJXIl8gf07ojYbqdQHrQIG6fU2BQVFSuI4gWMe+riTOFvNJCBpt9JnMVBiNpCWYuX5Uk1x9sEw4V3Tbo+KUGmr76OiUgFe+eMkPPFsCK3SVVLreZHIzwN4vZNucb2khACWD1MCM3ISp9b9uhL6CCpUug5dy0HBoNO3U0J27eL+1+TrrhqVxDWIDpkE3T0qUKwdoy7c4dNUGLnlBfT7SgV47U9SV9p1gCf/XimyKnrzfd8PvOWjGdMpK/v99QDqwkmZjZF9rQEcu4nOW1HGNZIiOIeZFKZN302aecQcoEziSuQLptoQBnXiMOWqelLUW/T5uhlIpo8brqvolAKUGLmjmZAmhZ1SoWxSVgglJoflDVoT9hJSrJgSNk0VMsPSJsaOMz1fotYC8PK3AlA+Wn6wAm1Jp9RgGmyYOXXRUGtqy4C4kAGK7fwqXcusUBlFU67WgZuer8SxAkVLb40OuR9bC5SQJDX83tkEIIE7HqJ/33AP8Op/BNx0Tj/HMIEXvx54zyeBG+/TFib8/WbpLcqJQljy3N2j/fTImWze67o7ae8M637OEjzPxpR7vFWlfaXgRasyiSuRL/CiF1a5AuBVs4soC1tv0edzCpDEOV3toQLQ/09Ctp+VwdJ0XYDiSYGXiMbiulLFS0qx4hmKjBTnksBQKodSUkA3zWH6M3cDp+/Yfz55Vq9Z7Mp0pmA6pZTh9NciodoYLLIRBrYQ4Ot1foU6aKxQ6TrRHZGzL6AuR1ARUw4IwisVKtr0OqOLdLAqpVUFztxFj5kWcPb54YWJ5hzwQ/898NafB170+vF0ZAZ5NHb36Ppa3sjmvY7eSEnw9pVsXi+v8Oi4aTtxFn03ZSeuRIkMYViDjWndAgfpTKcsgoIUq2haVd8c4oTU/arNFOeotBiYOSysURCZ1LTYo29NMQi3LE1LljK9sloaCEFKmBL9XRlvNqjYlelMwUkcZOEFEDw1vlHWVukCFR91bX5VK1TyvhDVoVzeAI5eD+xu97/nMKXj1WOUxD3xDeBqCB0zCt1dSozWToxWiNi4jjpz49gzTFsxi8KSuF36fWsxm/dqtKmr15lxSmVWdh+m8uUsOPOgTOJK5AtcHYncaApMbam3iuMzxBQYq6qlqSsTOvYksxsMKUGm5AXs1JYIR3OeNtqkiT0nTtWMDHyTwLB1Fw6S/DCnCcuGVlpR4OBomslu3mAo8+dZsF6wa5Q8jSISxMImvN822qTg6LpaCCeqqywEcNsDlMRs+bpDvEZHiZPd8Dya7zKt0brv24pKedf3xf+bcSOqE+f0aI9dWNs/T5gG190FQCha6YwiK7uPjeso4Z9fzea4poQDlcQJIW4TQnxFCHFJ/fyxEOIW3+/fLIR4SgjxmBDiQd/ji+rvip2yFwFcuYqiU3IrvYhJnGFSIFAEw2/2iGvM6YrrpLz56u3RZzcYpTrl7EEIVZ3fS3FNTLmTYhh0DK7qcE97DiPMyiXM8+ugoy+Jm2IRIAvYNdpbo1guYQhaCAhBHTano6+dQefl5K3A4iFg67J+zLOridhPlg4DD3+E7vm48KtSnr49/t+NG5bq9gT3/K6iix4+ne37HT9LpuX+8z1rcN1sZt6XN4CHPwwcOpn+taaIA5XEAXgCwBsALAFYAfCfAfwOAAghTAC/CuAhAO8B8Gnf330CwMellDM+MZoDsDplFDyj0IJWi1sLo22i04LTpc2n3vJJtE8oMUpC+2GUPnGziZWj9N0mMYFnwY5pJiecNDmqE9demt6xAP2zXgwviSvo2joOGL6K/yx04iom4I5wD7kh80fLG/R4V81N1wckcZYN3PIipRqp6NBMpxy2n9jV+AInHUWlPHQqX/Q4Fs8IziGyD96xm7J9v9YCnYNOwoJXEVBku48xoCDcLkAI8SIA9wHou0OllL8Q9zWklJcAXFKvJwA4AK5T/78MYFdK+TUhxLcAnFbPOwdgXUr5uUw+SInBMO3BbXLeVIoaaLSWdDV+VDqA6wAXn6Hh8nHPBPa6NAtRb6mu2AS9+XgA33USyLBLEtScVNewxGSwuE4B0c4W0B7x2s+FsAn7jfUAiHx04oIiFyxiMU0rhrzBMBXdTQK1gu45DLtGn2ekTpyjFaMZC6v0WEcpVA4Thrj5BcDf/CFw4QnyqovrOWrV4ouwdFXScsuL4z1/UmBmURC9PSqSro3QbYyL658HfPNvSHym6IWHIJgVVFS7jzGgEJGOEOKjAD4M4L8C8JN9JYDYSZzv9S4DaIE6kT8vpZRCiGfV724FcAzAo6o798sA/mGM11wAsBB4+Oiox3bgEdwwgmA+/9yUK9lJ0WyDukzO6PNx3Q4pT0kXWB3zpdXrUqJcbajZB0yuE8ezG24PwKgBO8/ElZW6mcLiGtEhOzsARhQCYEnqadIpWdmVjb5bwa1iwuCCTF8nrqdnkksQmE4JFD8gtutUFBulmy0dwArcN/Nr9Fq7al5tWOdrbhl4yZuAz/8b4NIz8T1H7RGSOKb/r2RgnJ0lLBsQxr7xU3SUMuXCWvbveewmWl82LxX/mg1CunQuS8q3h0IkcQDeDeClUsovZfFiUsoFIUQTwNsAfFs95goh3gLgMwD2ALwTwAcAfA7AvBDiC6CI8mNSyj8LedkPAPhoFsd3oMF0yigqgNOjDaCow6j1Nh1/rxcviZMSuHaJ1LZ4kDyp/H5cSJfOc3OBKl6u8tmaVPWrWlcV4xEG8BmlT9xsYm6FTIA7I6jVMfJAwfY6caoLMm3KF9M73cBMXL3g3aas4XXiBKnmFhl2neiUQd+2QXDd/cW7+RXq1m5fpX/HSRRufiHw9b8CvvX/qT0+hueoXRtOqXfUPup06XtqZ6T0mBVMixgt/ixOutQ5bC+NZ0+dXyH6+Xf/W/avPW3wfHNRmVhjQFFm4mwAfznqHwkhHhZCbKqfR/2/k1JuAfh1AP+bEGJNPfaIlPIFUsqXgGiXbwDwK6DE7ucBvB3Abyn6ZRCfBHAq8HP/qMd84ME0n32lKwWnR5XionoZ1dv0GXsxveK6e+Szc/FpNbMCSgDHiV6PNpr5lf5kaJJJXGXEirGHciZuJmFawNKh0QJQhkennGIQ3teJq0zXJw6g+8NPp5RSmZCXwVEfuBMnRPFVO1nYZFSz72ASV2+R6AgX2eoxChJCAGvHtc1GHJp8tQ5PjToMnT3gyb8nb7heN5/GzRWDlGn98Uy3S3v5OLuGa8e11+ssgZP6ott9ZIiiJHH/HsDrR/0jKeVnpZQt9XNLyFMqABoAwu6mTwH4oJSyB+A2AF+WUj4OwAKwrw0kpbwspXzc/wMSUikxCioGbZwDO3GV4ho0NjiJ24v3/O6eDrRY7tnpjtdrjhf/pQ09Wybl5LpbturEJUlWPbPvMombOawdS3bte+bCU7wmDBMejToPSZypOkx8LlkNdtrHlTf4k7iiU7jsmiomxAzsWdAqWLwTAnjozdT1cp3BwiZ+NObUNdeLV2SzqoPNyR2VDG1fpaJotZFPVWK72h/PsMru0YxFTfxoL9J1G7dYXBTwOjVrNNEUKAqdchHAbwsh/hzAk/5fSCl/PO6LCCFeCeBpAH8LoAng46CO29cCz3sdgPM++uZjAB4SQnwXQBXAcwk/R4k4sKoDkrguLdRFHWytt9RQ+AidOKFoT66vat7dHV/AxRXW1Q2qcrKK3aSCmGqdNvluAnNnvmxKn7jZw9Jhui72tuMHjoBPDW+K14RhUhedFSCn3dUxFG2d6Z1MmR7lvB4EMA1WVIpP0TYt+t7jdmd4/ijsc8+vAq96B/Ann40/31lvURLX6cTrxLHImeuGz2ny5+h1Nf0/jwjO9vGevnHd+N6ztUjXbrdT3FgpDBwDleuUh6IkcV0A/1H9fxqHv0UA/wrUedsB8FcAXiWl9KJFNSv3EQCv8P3d+wD8BoAagPdKGWViViITmHZ49U1KWrDnltMbPU4L9ZaqQsbsxLFUsHR0wGVXKZAdVxLX69L5XTxE0s08ozipJI5pP4nAdMpSnGHmsLBOyc/u1mibOKsuTrNKXzHoGFw1wzPtjoE3e8xdfvXfac/q5Q1MO60Y41cEHje4eBBXtt+bP4pY98/cBVx3Z/y9mPc+dztmJ87GPi/DvuNT9zWrZC6sxzuOSSM429dVoibLh8f3ns15St46u7N1T8fxJjxgKEQSJ6V8e0av8x8A/Ichz9kCcG/gsUcAnMziGErEgBXhD+M6tBhOW9ktDWrNwXRRP7jjVqlQpZE7ZNUGJXfjAs8dthaAC5aW2J7UvEy1Qe+f1Ng5jvJZieJhUani7WwOf64fHp1yikE4d3R6PaCVAyqQYfUbP0slmtTI2UzRtMHfW6UyG939WpO+6zjw7HwGFO9GKabW23QPxvUc5Q5gFH1aurqk7zjEHMkjqo3+2dPOHsU445zfaylRMk5wi4DOLnDpaWD5SPT14c03l7RvRiFm4oQQ7xBClHL9BwVR0sJOD4AkieOiomLQZubG2EiZJsILFtsS+DeFccA/JG7aWmJ7Uv5RVjV+ohsEWwyUMumzh1qTrsk4944feTCA59kqmRPxEM/KxT9vi/wJQ0wbXhI3A504gO6huPcPz8RlxcDwWCgx56vZKDvqeDm5MwzaD5dzGiLadTpWKemzOF1iuYyTTdRcoHt8ksImTk8rlibB1hVgdxu4/Gz0czy7mBwUwnKCQiRxIIuBx4UQXxdCfFoI8VohxAz1iEv0waqFLz6OMsleyiltIi5aCzGTOEWlXD6ihpS7tLHNryST34+LXoe+A6uqBRAmKWzi0X6SJKoxjWRLFBOL66Nf+yyGNG06Jc+W5iEA4aTSDdIpCyoYNS7weeL5rKKj3iKWSywmSMaqrvWWLs7FSYgtW6kUR+yV/BmqTfqOxuG5lgXsKogWKolK6bpkej5OWDbRKEcteKXB1eeAZ5+gjtqokFIzLAapUvM1Wc/BGpoTFCKJk1LeB2ANwM+BhEU+BeA5IcQXp3pgJcYDS0nyBjcaDt4WD038kDJFa5E2pmEbabejB6CFUqW06zqQHUeVzfOIUxV5fyfOmqA6W70Vn/bjB9MpE8/Ulcg1FtYVrXqEbkJ3j5KTaQbhXidO5mNGhTtxvIZwhbsUDOgHJ9+z0IUD1LyxiHf/eElcRp1jq0oJTdzzaVaVUXZE0Ua6dE/VW/Ta7ZyOWVhVon2yPxwEcPzG8b/v/OrggtfOteyKwZyEufwZR0Rnh4rHtSbFPVGxjUfxzQGbIScoTKQjpbwI4A8A/N/qv1sgL7YSswbLDpcWLrrRN6M5pzbSIQsoD0CvHCEaiuOQP0qtOVh6OQ38HnGAnp2BBKoTnAmptZJVEXnxr5SduJlEe5EC67iBQk/JkC+NUUQgDgyT7nkp86GixzRBHiryKtw5SDDzBD5PsxI0WtX+DuwgeJ2ujD67EGrmMqZSrGX3z23uOz5VeDh8HSVyeZ3n5EIoJziGQWbc48b8qtYRCKLXBS48CTz3VDbv1dmlJMwwkjFouAt38zn6TvciZvk4cZ8lxc2UKESkI4T4GICXA7gVwJcA/BGA+6WUfzvN4yoxJpgWtCqVb7ap6EbfjHpbixwMmtPp7NIGsHiIaI3SVRYFSrUrSno5DbjDx8pZrGIHMYVOnNSdtdgoZ+JmGu0l2sD3Ylps9FRV9/Dp8R/bIPg7ce3F6R4LoC0GmH7M/nWTmnstCoSggHv72rSPJBswzXwUOmWWqsTtxfiee6ZNSUFUEsfd44ceBnau5leRmBMO19GF2UlQP9uLlAT3OvvP9962KpJmxObZ3aJryjCTMWj2duj7PnsO+OZXaLYuzNCbv/NynfJQiCQORKP8BoD3Avi8lPLylI+nxDjBVU/X7cvhSPq+wEbfjHoLMGxa0KOqnE6PFt+1E3ogXLr02f30jKzBfPTlI/RfU80lCEzW7LbaUB5BzmjzbVLSseZ1Qy+RDu1F2sDjegj2mJI85SSuYmhach46cabVXxzhrkbRDa3Hgdf8xOiKqHmF570WI9B2x3BNNOcpsYjzmjwT142YkWIRK8sGGjlVpgT0Hur0aM+fX53MfG5rkd57+2p4EidENjmcN8+mBEfiWlj44fYo7tu4jorc1y5GvBdfkzPSGc8ARaFT3grgfwbwZgDfFkL8FyHELwghXjzl4yoxDrDEvH/AVbrEm2b+e5HBht89Zfjt9PYHCd2OGoA+7bMlAC3MlhqUHsfQMlNWee6QhU0gJmt2a9eQ6DN6dMoyiZtJtBaJihU3UOh26FrIA51SCCow5EEBcl8njm0YyiRuH4TIxxxjFrBsrZI6DB6dMkM593qb7oVYSZyifkYRMVxlMTBtz8VhYFpoZ5eOefX4ZN73+E3A+gngyoX+TrKUpAIpZTaF4F6XimoLK/RZk8QljkPFOdMCDp2KnotzHUw8Fsk5CpHESSm/KqX8lJTyBwCsA/hdkAH3n033yEqMBdUGdVJ6viSus0s38JHri68SVm/TItRTM3HbV4Hz3yGKGINnfo7dpJI4NWDfXhiu2pUGvS69dnuJ/s18fjFhs2Q2/B71M5ZJ3GyjWh9N9Kankri5lfEe1zAYqhgiKjlJ4kytlgno4GhWBDxKhINnnGPNxGUsbAJQMmxY8Toppt0vvrPv+BzfbGeOYVVpDWK16UlRu60q8EM/DaweA674ZPu7Hdrno6ycRsWuEjS58fmaMj4KpEs/XCzYOEP/7oXMPTMzp2TaeChEEieEOCmEeJcQ4ncAfA/A/wjgLwD89HSPrMRYUK3RjeoP4Pd2aXG44XnTO66swNVI/zxKUNWJufNrxymw4qpkc1FvCsOEUZLAUZRVnjvkir2oTNZnyxsGT5DEiUqpTjnLWFjrL/BEQUoq/jTa009OWOWwUgmf9Zg0DEN39wE9X5v3rkaJdOAZ51jqlExXzJD50pijezFOYshdwyi47nS9H+PC9NFChQCWJqiuXa0Dx89qnzqAqJTSJcE0N4ZK9jDsbNJnPH1Hsn2Xj42tV9aO0f9vhcyhus701/KcoSgzcd8E8NcA/hjAvwLwl1JG6c6WKDzsulJj9HfidmgD2rh+eseVFWqNfiNOpoX4uwvdPVr4eQC63lYG3HM6SRlXJ86qarqLaZGqXqVC1MpJgSXQ4yZxl5+l8yfdMoGbdSyuaZuBQR1XRylTLubAV9Iw4QnuZElPSwPT7u/ElRSl2QcnFHE7cVkzMI7eCNz/RmKYDINnbzNAbr4IRQeLBVpUEje3PNn3by8BkCrpNSiJMyyiWp7/TgLxMB+cHomSzK8q/94B31cUeI9nyvLqMWJbhBmHO07xx2kyRlGSuBUp5ZVpH0SJCYHpg9yZkpIWnmoDWCi4vQCgZizmgMvn6d88j8JJmXSJYz63oqtOzXnafBtzmvKYRAVqEKSk1573Uc/MaXXiLPUZY9I9OrvA1mWl/FkmcTON1qKubFcHJXGqyjypGZRBYNpXxczPUL5p6/vLLYOjA4FRimMsEpVlcm8YwJ0Pxnsu+8kNolMW4Zr1C5sY5uTp1Fz47XWASpViqVoDaC8r+qOLxKS83S26ls4+X33OBMkgd+L4vNg16hJ+67n+5/EMX17Wz5ygENGOlPKKEKIphHiTEOJD6r+lZfuswq5r+hFAyZzTIy553vnvcdFe1EabnIxxdZRFTdZO6Oc35mgDrjaU9LIJOBmrUzpdWiT980OGpWlgo6hEpgUnj3G7jRyMyiHdmRLFh2HFK2Jw8JcHA2tez0wrP3QgDpClqtKXypSzD49OGXcmTkyXsmjXwo+Vr9siJHF+WmhlCurajXmKGbp7VOx0esCxG7V42KhzcZ094JlvU8F3Z5Pighvv04q3o9Iz2erA7/N39EZ6/V5A3E6Cxm1KeChEJ04IcRbkDWcAeBzACQD/UgjxCinlV6d5bCXGALsGCF8XZm+HFobrZ2AejtFaUBUoVyUqvsW0qwagj92on3/2BTSc3Gir+ZUx1F/YI87P2efZGWFMOIkbgfYD9FNTyyRutsEiP8OuDb6f8kAT9Eyja/kpRJmWFhWQMlsBixL5hMdw6Ax/LjNEpklZjEziVEBfiCSuqvYEdLhNAAAgAElEQVRrlXRO+nw21RwiF8MB4KbnK5sBjG7O3dujDtxzT9Jr1ls0xyahOnsjHh8XC/z+v2vHaT3avqrpp64LQALVsn/jRyE6cQB+BcBvATgipTwH4CiA3wTwyaQvKIT4mBBCCiFe5XvszUKIp4QQjwkhHvQ9viiE+IoQYkZ0hnMOw6AbmBeXzg4FQf6kpuiot4l64DhaGY5Xv26HFv3D1+nnr58AXvdTtAlYQ2YFkoKrXitH+h83bcCYQieuElMKG9AbwbA5qRLFB3uuDa0gq/sjD3MzFQNARQ/v5wFm1SczLvMzq1difGCfuDiFBBY2mWYRxK6H3+d8zRahe+yJdMnpeNw25ugYHIcSN9OiTheLpY0qHsbJ/e4WJYVn7qL1zTDUOpegEydEfyeO5+L2dvqfJ+Xs2H1khEJ04gA8D8BrpaS7WUrpCiF+EcATSV5MCHEDgDcCeMr3mAngVwG8CNTp+zTInw4APgHg41LKELmcEmNBvaW50nvbtFjnQaAgK/DsVq+jFjHAM8Tp7lLCFKVixYtvWlWpIDyPuICnlmVrWuWk4PlYxXxPHs52nVJ+eNbB1MRh17/fDHjaMC0SZMn6nk0Dy/YxAZCvBLPEeMDrapzrkIVNprmechIXFN9gn8giFB4MUxVAXZrnnTQac/T+bo8K4nMrNM7BHUKnR4VjM+4er869YVL8csuL9K+sagI6pUrS/clZo03x3hPf8L2tel69TOL8KEonbgvAWuCxVfV4Evw6gA8C8HMKlgHsSim/BuBPAZwGACHEOQDrUsrPJXyvEklQb1FA3lOeJusnZqvD0mjTotnd6+/ESUmc82ojepaH/XOyhOsSX75i0ALfd6zzkzcBHqZMFgRv8lLO1nVSYj+8JG4YnXIMwgxJIQTwD94HvPpd0z4SDaaicbFsGl2CEpMFqw3HWVddd7LsizB4nbbA8RaJAizYpkFQIWfSsGyKJTo7VLA5fYd6nP3rusAzj2uhtWHgJG15g6iOG2f070wbwIj0TNehvT74XR69kZJLpoB6yV65TvlRlE7cfwLwu0KIDwN4DMApAL8I4P8Y9YWEEG8F8JyU8guiv+rwrPr9rQCOAXhUded+GcA/jPG6CwAWAg8fHfX4SijUW7RQ727TonHm7mkfUbaot9SwcUdxyVUC4nRp0VraiK6KGYaeZ8kCnV3gwvfoWOrN/epZ3/cw8PRj2bxXXHjBRlxwJ05OP/AoMV4YRrxuAv8+D0kcQAFTHkRWGDyn5/ZAwgJlcDTz4OJYnNqYdCZfvAvCsvX8q+ErXPLeV8vR/TQIPAsbZLlMCu0lPUt/wz30mOdfp9hAu9vxXovX1Ve9k+bY/HR1y9Zd0rhgemYwiVs/QYnmziZpCPB33ijIdz4hFCXa+TCAfwngcwBqAHYB/Fv1eGwIIZYAfAzA/cHfKYrmWwB8BsAegHcC+IB6z3khxBcA2AA+JqX8s5CX/wCAj45yPCUGoNoAIJSniUmGlbOEeosWv+1NAJKkxyFVUucCR84M/nurlg01y+nRgLLTA266D3jhP9i/mNZbwKnb0r/XKDBHoP0AeiYOKOmUs46KqQO7QeBrJw90yjzCtODNkULkK8EsMR6MMrfkyumbaXMHK1iw5Hu/VgA6JaCSuMr0RkLmVrSa5+HT9JhV1VZOUiI+60UlXXZ1v+WT37YkLngmLmgdwHNxuyqJ4+cVJXGfEAqRxEkpdwG8VwjxkwBWAFyQcnh0J4R4GMC/Vv/8NoC/BPBrUsrvRbzPIwAeUX97HMAbQAnfl0BJ2pMA/lwIcSLk/T8JSiz9OArgi0M/YIn9qDboht3bpoVheWPaR5Qt6m1KTpkWYpr03+4efe5hIi5Rql2j4upFes+z54AffE9+jLJHkStmuWlGpRDLWomkqFRUlzqmsEkRFOymAZ5z5eCoXs7EHQhY1XiBtnSn38Wu1rWHqn9Z5+MvimdYtU4Jk9+DdZJoLag5+8O6SGv5/OsArU49bC6OZ43DGC92guIyF5GC63R7CVhYA777dXpO2OxcicLMxAEAJOHZOAmcev5npZQt9XMLgJcB+BkhxNNCiKdBtMl/p2iaQXwKwAellD0AtwH4spTycQAWaB4v+F6XpZSP+3+QUHilBCiJqyjhj9Wj+VCYyxJ2jdTheAE1lGcT88MXhnDno1S7RoWrDEhf/tb8JHCAUruyEJP3oyip6v+nXT0uMV4YpjIsjkmnLK+HcPCaysFRKRhwMGAOMNBmsGrp1JO4hko0uv2Pc7JRlE4c+7tOKwFpKa84plIC/eqUPGPci2E9wbPGYUkcFwhGSeRcRyWUgfhDCOD+N9I6deFJXwewIIn7hJDbkrUQ4jHEiOCklKdHeNl7QV5zjL8G8DMAfi/w3q8DcF5K+SX10GMAHhJCfBdAFUDASr5E5qjWqKPiOMDpO6d9NNlDCFpYn/4W/ds0gd2OXlCHqW6xBUOcytkgcBUsjypfVoxgA9DPqRggSezcLmslsgB/z3ECUVFeD5EwTABC+1SW6pQHA5Ydz2MxDz5s1YZSQQwkcXz8RRA2AYDbX0IF28b88OeOA6fvBO5/Q38SxwJpbDEgKsTKGfadD+rEmTa8tTluXOI40d5vR68H7n458F9+T1ljVIphKzFB5Hl3+5jv/08A+EkA/yu0sMnbAPzaKC8opXzW/28hhAPgkpRy0/dYE8BHALzC99T3AfgN0Dzee6WMa15VIjHsuq64n7p1+POLiNaCr6LYpAFeN2a1qVoH7bJeCyoZXFcZeudwjiwuv575/CzUUHZeZhsVQ5vnDgIHGyW9NhwenbLnU9ArMfOwaoh378jpXxPVBnVimLHC4H0hj8XHMCxvAC97y/Te364C976q/zHuxDGqdWBvd7jAEXfEwthRpulTDo7B7GH2UXVAYnb2BcDffhHYvEx7/LSvyZwht7ublPI3+f+FEH8M8on7f32P/Z8AfgmkUpn0PU6GPLYF6tj5H3sEwL7nlhgjuAJnN4hOOYtoLui5r+Y8cPEpZVZdGS7GYFXDVbtGhesQrTOPsKoxla58vjWI2FxKzA4M1YkbWrzwXxcl9sFURTKHhQXKCveBgF2N14kDpn9NVFUxt7Pb/zgXP6d9fEWGaWk7Hmb/BM9zGOSAdZULqKMIkkkZ3YkDKAGut4HNS/Tv8jvvQ46GYAbiPhD10Y+vqMdLzCJqTVokVjZmt/LCJpxSEldeSqo4mtXhVATP8DvlXBzz0fMIO0bFGOinU1YqZRI362B1ymHgQC+PXeY8gI2fy07cwQJ34gYF2q76/aAOySTAM3HBY/W6QTndu4oAVphkv724Rt0cc0TRKeMKkgGqmCAHK+OaFilVSqhZ+bIo50dRkrjHAbw18NiPghQnS8wi5leo/X/utdM+kvGh3tILkq0sFZxevEoTb16jerL4ISUtonmtbFkxKsaAzw9MVRbLjX22wWbfQylh6top6ZThMC3diYPIbzGnRLaweG5pwNqaF7pita67RX64ylKmLNilg6VUrmstLeM/DFJS8Sfse+H547jFZdfRRexBOHJG7e1WOg2AGURRdrd/DOD/EkK8GzQTdxLAXQBeP82DKjFGCAHc/bJpH8V4UW/rQKpaJ3aY48TbOD06ZYrxTJbmz+twuGXDqxgPWrj5c9h1oHK1TOJmHUynHFa/KDtxg7GsWA7ORfpvWFBWYvZgmLTXDOqW8EzctJM406IiI1PpGF4nruwepwLHHY15UsT+ztdj7LdudDfMm7ONmcTxrGNrcfDzVo+REmn5fe9DITpxUsovADgLUpG8DOD3AdwspfyDqR5YiRJpUG8phSiDNktORuKoxLHHi9sb/twoSAeAzK9kr0fNiOkHxnOUZRI32+BO3NCRuHImbiDmloE3/Qxw7CywdGjaR1NiUjDt4VR8qTpdedgbak3VLfbBz74okRzVOgABtBfJEBxyeGHYldEFH7624haXt69SEfvo9YOft3KUuoVFsZSYIAqzu0kpHwMJmZQoMRtotDUFsNZQG2sMagGgK+fBzS0Orl0Ctq6Q8ScwmI8+TZh+2s+ALgFv6DxHWdLCZhucxA2du+BZycJsc5PH0iHgLT+XbB0pUUx4/oDBOTMJ7G5RYL91hR5rTkkS349Ge39SIEs6ZSawG7SWzq/Sd12pkJ3DoMLXoE4ciyXFSeKcHilyN+aA4zcPfm6jTaM1m5eHv+4BQyF2N+XP9ocA/gjAH0kpS5+2EsVHvUUBpl3Xni2sVDkM3MFLQqfs7gLb17TFQV5Nfpn/7sqBOZxH/Wkv0Xkp/a5mG6xCOgxMC8qTiX1eUVJODw68JC6wd+xtA88+oamWS4eAM3dN/PD2oT6nVQyZ5icl3dflfFQ6sMn2/ArFHaZNXnGDRiykG71emDYgjHgzcdtXKZG77YF4yfjtDwx/zgFEUXa3nwBwFcA/BXBeCPE3QohPCCFmfGiqxEzDqlLCwbRKUQHgkvVAnL+tGPv9c+LA6dFG3evQvxt57cRZGDqAD2g/sPlV4OGPkK9MidlFpTJ8pgdQczNloFeiRB+4ABhcV50ePVZtUGHxde/LB32tpkS//EmndMsZzixgVamQPL8CNOdUEjfEZkDKaC9Wwy+WNAS727TH3/Hg6MddwkMhOnFSys8D+DwACCE2QEqV/wTAhzC4Rl+iRL7xircB57+jBU4kaDEdhlqTqmG97ujv2etScNtVSVwtr0mcTcH60CFpno9Qm1GJ2UbF6DepjYIcMLtRosRBhWmFzy25yi/wdT9FQhKtGMXESaDa8HmiqsdcqZUQSyRHvUU2A61FojWaNnVkoyCl6sRFJHFeHBOjE+f26HUW15IdewkABUnihBBVAA8AeIX6OQrgERDFskSJ4mLtOP089rcq4JSkFDUMdTX/FcdHzQ/2oqtUVCdO0GvlEVHBRhDekHs5C3dgEOfaLwO9EiX2g6n7wXWVTd+b8/lJ4ADqxFUqgNPV885lJy4bnD1H3bjlw7TXmiawO2hdlbTsRtEfDVMViNXIxqD5fsfJrzJ2gVCUHe4yyBPutwG8G8BfSZnW5bhEiRzBUlxyIeJ14qoNqmLFlfJlSJf+Rgi1aQOo5XQmjqkZwz4j0ynLJO7gwDDj0SnLQK9EiX4YZvi66joUgOdtpphVh/2sEylL1dksYFeBm8/pfzfmgGsXo5/P8+dR4mFM1d25RjNvR6+P7tq5Tj7UTwuOoszE/T6AFQD/HYA3AXiFECKnDsUlSiSAaQFGhRK5ONUpIWjBHbWW4fQAyP4OV942bQZ34mSMTpxAmcQdJBhGPJ+4MtArUaIf3kxckE7Zy3cS55//HqSQWCI52stAb8Cc/TDWi+nziXMdoBsx7iFdNX9ZJnFpUYgkTkr5wwBWAfw4gAsAfhbAM0KIP5rqgZUokRVY1alSiV+das2PLmziOLQQW1V4yZydUwNNDjaGDkmrjcUq5aYPDAwTQIxZyVJ1sUSJfnBxzAkRNqlU1N6QIzDrxAl04kp7gewxv0LJVxT7hYvGA5M4VSA2TCoU7FwDLp/vf56r1EbzOo9fIBQiiQMAKaUEsK1+dkBU0DumelAlSmQF0wIqQhl/x0zimgu0SA71y/KBzcH5PYQA7Jw2tc1R6JQoO3EHCYYVoxPnlh5xJUoEwetq8AZyerQv5E3NtVqnYgz72jGlr+zEZY/mPCVhUYJpvNdGxQyG6sQJFcu4LrCzBVx6htQoGcwCiuOJW2IgCpHECSF+UwjxBIAvA3gNgD8DCZ2sT/XASpTICmwxYFjxK6GNtqp6jdCNc3rK3HNF++7krfLKiKL9BMEbS2nyfXBgmIOpxFKqan0Z6JUo0QfPzsYHKZXQRA4sBYKoNbXoF6DubZSduHGgMafmDzvhv+d1NSpm4E6caRGjyHW0p9/mJf087sTlSUCnoChEEgfgWQDvALAkpXyZlPKfSym/orpzI0EI8VIhhCuE2PT9vMP3+38shLgghHhUCHGb7/HrhBB/IYQo+TklsgdvrHYtfiW01qLnDuKwB+Go4fUFn6xvbpM4K56UPG/uUQPUJWYPQ6vwKtArr4kSJfrBCoJ+cLDdiCGqNWmwl5kMduLKeztzNOepGBrlFTe0E2cC198NHLuJCmiumn2D6E8MvU5cDCXuEgNRiCROSvkhKeUXpJQ7Gb3keSlly/fzGwAghDgM4GcA3Azg0wD+me9vPg3gA1IOawuUKJEAnLCMMlReb9FG1o2omoXBVXMPrUWqblaM/NJSeEh6GLijmNfPUSJ7GBZ979tXgeee3P/7MtArUSIcYeuq6wCQtC/kDZUK2Qx4nfchc1klkqM5R0lzJJ1Snfuowq8QwANvBF7yJiVK5uq/6Xb0DL+rlLHjKHGXGIjCRD1CiBsBvBTAGujrBwBIKX8hw7c5DuCbUsrzQog/BfB+9d4/AuDvpZRfzvC9SpTQMEygvTDafFu9RRuyM0IS11N0yvYSvWfcRGkaiNuJY4uBUk7+4MBUSdzeDnD1OWBupZ9O681JFmaLK1FiMjBMWvP9ew0LXs0tT++4BqHe1t2bkj4/PtTbVPjq7IX/flgnjmFYSpSspy2NqnVg6zKt1a7qzuWx81swFGKHE0L8MIDPAvgqqEv2VQC3APgLAEmSuGUhxNMggZT/DODDUspNAH8H4LTqyD0I4FEhxByADwF4aMgxLgAIEnyPJji2EgcRQgBv+CAtcnFRa1Iw2xkhiXO6VMFk7ruZUyoloGbixPDEln9fduIODnhGhkVv3B6AsCSuDPRKlOhDWBLHc9V5TeIac1ql2JvLKu/tzMEiMlHzxnETaM8eyGcl0FwAti6pJK4UNskKhaBTAvinAN4hpbwTwJb67/tBSdyo+DpI1XIDlJjdBeBTACClfA7ATwP4PIDXgpK3XwLwCQB3CyH+RAjxh0KIW0Ne9wMAHgv8fDHB8ZU4qDCM0TbRehuoWBgu06cgJVXGak36Maz82gsAenZj6MdTdMpKUZazEqlhmACEFr0JJvplEleiRDiYRu9fWF1HMTRySKcEiHXCRRvv3s7x3lVUVAw615EWA4r1YsXpxCmGj+vSd3XkDFEqpaTETlTyKaRTMBQl6jkJ6sQBmkr5GZBv3EAIIR72CZg8KqV8Wkr5VSmlK6V8DDQD9wZ+vpTy30sp75ZSvhpE3TwJ4D8B+C0Abwd1/j4T8lafBHAq8HP/yJ+0RIm4qDWVD1bcJM6lBbU5r7t4wxbjaaJigG73mJ24Uk7+4MAw6dLgYGOfX2JZrS9RIhJMR2ZwlyuPM3EAzcSx/5hH6SuTuLGgMR+teC1dWneHiaGZPvEc6dL1duIWAALY3VJFg0p+7Y0KhKJEPdcANABsAnhWCHEKwEUAQwm1UsrPQieAoU+Bb8aOoVQofwXAW0FG44aU8tuKhnl7yPtcBnA58BrDDq9EieQwVNXs6sV4z3d6ACTQXlaiKCZQH0FIZdJgv5lh8IRNypm4A4NKBYDQHYSyE1eiRHwEfRZdh+6pek7Nl6sNus+dnhYtynMBsshozutkObj/enTKIUmc4Zu1d1USt3GGhEy2rsBjz8T1xC0RiaJ04r4E4PXq/38fwO8B+BMkoFMKIR4UQpwQhGMA/jmAz4U89acAfF5K+S0AzwGoCyFuBs3KfSvBZyhRIns05jS/fBh4eH1hlYRNbrgHOHXb8L+bJuJ0GstO3MFDRc318IB8cIYjbrBRosRBhGX33zPcGRlFHXmSqNa1UAZL1pdd9vGg0QYVyMIolTG7oP6ZOEgqpi2sAgvrZDXARYOyyJYaRYl6fhS6W/ZPQL5xcwB+OcFr3QXgtwEsgpKzzwH4sP8JQogNAD8CMhSHlLL3/7N35/FxlWX/xz9XJnvSJl0p0EJbFtlRoCIoqzwCLiibssgiiCBuCAL+AKGAoqyCG8iOLMoiPCI8CIrsKpuy73QRKIWW0tK0tE0y1++P+5xkMpkkk2UyczLf9+uVVzJnm3smM+ec616u28y+BdwLrCR0qxQpvsYxoetDrlqzbHEXiaYJ4QS6036FL99g5ZVxMk5sopa4shH/r+OWuPasioyO2noFcSLdVGaNpW5vK+2WkZr60HOkfVXnjb8CgMKobYjOqa3dr6n9bYlLR/N1VtWEx9M3hTdeCv/LeJkMSskHcWZWBfwOOATA3VcRko0MiLtfAFzQxzbzgG2ylt0A3DDQ5xUpiIbRdNSa9RXExBfqpvHDUrQhUZHKMzulphgoK/H/Ok5f3S2bWvSZ0I2eSHeV1Vlj4tpC98RSPYfGQVxbO6Ti5Br6bhdEbUNnq2c2T+c3J2sqSp4Tt8TFXV/XXD/8Lz/8IHTblEEr+e6U7t5KyCLZjzzqImWirrGz1qwv7VGXmVLNQJZLRarvvC3uoWVRtXrlI77ZjLtWZXf9iYM6zRMn0l1VTed3Js5aXKqtcBAFcVXhOuce+mUpO2VhxOPlc0343TH+vKrv41RWd37G4u6Xk6ZCY1NYrsyUQ6Lkg7jIbcD+xS6ESMmJM1TmOuFmS7eFYKe2RAev55JKAT2kO455WtMLlJuOFoO4G3FPiU10oyfSTdwSF6d7T6dLe+LleEwcGUmMlJ2yMOLM1W052k3iXi/5DF2oqu5MkBJXEFTVhNY4s9Idf5kwSammbASuNLNvEOZf67irc/c+pxkQGbHqRkGqOvcJN1tb1J2yVDOQ5ZJ3S1xSTmUyJDInLDbolmDY1eVKpEeZ3590O+ClHcR1TKeTOU+cvtsFUdsYgrhVK7qvi3s45NsSF2+f2eo2dWN47mFN9D1EknLns5Ku49HUb0oEeq81y9beGlomKvM4AZeKikr6jOLSaagq0bEcUhgVqegmFHLOJRgHdwriRLqrrOpswY6zFo8eW+xS9ayyqmvrISiIK5TahnDdzXXZ7ehOmUfoELfEQdcgbsoGMHYSjF19SIpb7pISxH2XkGhkLCGj5L/cfWlxiyRSAupGhXE/K/roThmPe2hsHp5yDZVUHolN8NIdkC+FUZGKJpP1Hrr2xBlLdaMn0k18E+7pzhvtpgnFK08+6hph0dt0fLdVQVMYNXXRdTfHMIb4WpxXEFeT0fU1Y06/xmY4+PTcx5d+K/kgzsyOBs4mTPYdt8AtM7Pj3f2S4pVMpATURbVmfY0bS0fjHpKWESqf4Mx7upGXESuV6uwOVlHZ82Tf1brRE+kmsztlnIWwsYRb4iBUWKbb88+QKANTURHe6yULu6+LE0nl05sncxqC6qykOQrAh0xJZwMwsx0I0wGcC2xACOQ+Ej2+wMy2L2LxRIqvqibUcvXVWpVuA7z0L9TZct2gZ9OYuPITT/YN0c1cT4lNdLMg0k08pikeE2dW+lmL66MgLt2PDIkyMPWjO1toM/UnE3TmubemtuftZFBK/c7naOBH7n5uxrJXgTPMrAX4FvBgUUomUirqR8PCt3rfpj2aELy5xLvMZOurO2U8RkK1suWloqKzO2WuOY06sqjpRk+km8rsIK6i9BNN1DYQslNGwYV6XxROQ1NnZsnMgC2dzn/oQmVV+Fx5untLnAyZkm6JAz5OmOg7l+uBrYexLCKladSY3BNzZmqPLnzNEwtfnqEUt7L02hqn7pRlJ5XVEpc9viJObKLPhUh3qcpwg51uDz8VFaWf8r22PpoTtZ1QQaOKu4KpH0WP82/m+76nqjq77GpOuIIp9SCu2d3fybUiWl7i7f8iw6B+dFSj2su4uHQ0vUDT+OEr11Doq9bPPfSkU4tLeelIbGJhIH63ID9qiVM3W5Hu4kqQdDqqAEzAvF019Z2Bp8bEFVZtQxQwZyVM83T+19rKqs75WzUGrmBKPYjrq3yaakCkblQ4WaZ7aY1rbwsXwKRlp8ye4LUbDz+6oJeXeIoBs9BVJ1diE0OfC5FcUtENtkdBXGVl6U89U1Mfvs9xrxNV0BROXWPuburpdP6fk7i1FzQ2uYBK/VtQa2an9rJenwyRusZwQW5r7flk2d7emXUqSawiqqrpIYjrSGBR4jcgMrRSUUucVUSJfXJ0p8TUnVIkl8zulO1tychaHAdxK5eFAEMVNIXTMf/sSqAxLIvHn+d7rY3nIrQ8s1nKgJT6t+CfwE59rBcpb3WNoWa1bRXQQ5eY9qg7Zal3mclWEXWZ67EhTlkIy1JHS1xF7u6Uru6UIj3qCOKieeJqG4tdor7V1IUWw3Q0Lqui1DuSJVhtY3iP2zJb4qJeL/lea1NRYhMFcQVV0lc4d9+x2GUQKXlxrVlrLxN+t7dCZU3yTqYdF+o+WuI0Jq68xEFcRUXu7pRxpYVa4kS6i8crxVmLSz0zJYTrXJzoylL5pbmXgYnvKVau6FwWt8Rlzv/Wm/gzZhWqZC2gsqvKMLOxZnaNmb1vZkvM7N6MdQeY2dtmNtvMdspYPsbMnjSzBJzppOzUjQonzJ7GxMUTutYloLY1WzwfWG9j4tw1cLrcxIkZKlKhO2VmBtN0GlZ9CA2j87/hECkncUtcHMSNSsD8oTV1nYmuVDlTWHUNUS+GjOtu3GU932ttZUZLnLq+Fkw5vrO3As8A04ClwMcAzKwS+BXwSWBt4JfAJtE+ZwM/dvelw15akb7EJ9yeAp10Ovw0jB7ecg2FfFviVNNXXuKEN6lUCNTMwk2GpUIAl26HtTdSbb1ILnF3xPY2wGFUArIWV9d2BnH5zlUmA1NdF03dknHdTUd/V+U5cXecPKeiQkFcAZXVO2tmuxCCt0+7xzNG8kT0exywwt1fNLNZwPRon22A1dz9tmEvsEg+aupDrZd7uJFdtaLrvCzp+EKdgNrWbPHFuseGuH7WDsrIEHenTFVFXYQzWmtXfhh+rz+jaMUTKWlxS7ZHc66NTsBsTRWpcF1zZSMuuIoKqG+EDxZ0Lou7U1bnGcRVVkUJaKpUmVZA5dadchvgJeAqM3vPzJ4ysy9E6xYAmNkmwM7A81Hr3PnAd/s6sJk1m9nUzB9gciFehEgXZmFMQ237vgAAACAASURBVLodli+Fd+bCiuWd6+MuM0mb6Bs6UxT3FMXFk5gnMUCVgUtFQVx1TWcWtI4gbnlYtvr04pZRpFTFN9bt6fA7KefPusZoXlC1xBVcfVPn9RU6K0zznbg7Tmyi/1VBlVsQNwX4DPAPYBJwIvAHM1vP3dPAQcDlwA+BrwPHALcBTWZ2t5ndZ2Y79HDsY4DZWT8PFfLFiHRoaA7BWtuq0HVyZWYQF52ImyYUp2yDUVHR+5i4eBLz0eOGt1xSXHF3yqq6zqQ2HmXaW/UhNI5J3pyIIsMlHhMXt8Ql5btSPzoaC6uWuIJraArn0/ja61F2ypq6/PavjLrsptRLppBG9DfBzA4Efhs9nAv8FXjT3S+Jlt1tZg8SArtX3f1e4N5o37WAvYHtCEHfMcA84EEzW9u9213lhcDVWcsmo0BOhkNjcwje2lrDvGqZ82bFgU5TAsY9ZOsrjXQcoDYmpCZZhkac8Ka6NupaFQX6Kz8Mn4m1Ny52CUVKVyojYVRFKjlTz9Q1KmX9cKkfBVgI5FKV0T2F5d+dMm6Jq1JLXCGN6CDO3a8Hro8fm9lhwF557n4RcJy7t5nZpsAT7r7KzKqACcC7Wc+1GFicuczUD1iGS/0oqDBoXRkepzOCuPa2cDJNSm1rpnhMXDqde326LWyTxMybMnBV1aG7ZDrd2Z0ynQ5BnBl8ZKtil1CkdMWZAyH8TkoQV1PfOc5KCqu2Iepy29Y1yUl/phiIu7xLwZRbd8rbgAYz+7qZpczs08CngLszNzKzLwLvuvs/okWzgZ3NbGOgBnhvOAst0qfahnAxbl0ZdZPJaChubw/L6hI4Q4ZFk333NibOKkKGTikfZrD712G3wzrH96Tbo/Fw1TBJ4+FEehS3xEE012KerSvFVlsfyq6WuMKrbQwBczzh90Bb4qrz7H4pAzKiW+Kyufv7USKTXxNa2mYB+7n7a/E2ZtYAnELoYhn7DnAFUAscnZHZUqQ01DWGi9vK5Z0Tosba20IrXVJqWzPFWQi9h5a49rZwE5LvYGsZOeJuVR3p0ltDZtYxqyVj8mKRYqlIhRvsdDrclCel11DcEqcgrvDiCb/bVwKNoWLYyD+Iq6kLrXD1TYUsZdkrqyAOIGpd+1gv65cBM7KW3QtMLWzJRAahY8LvNFRmNbC3t4YuEEm88HUEcT2sb2+Fmobk3ITI0Iu7hq2M5oebtlmxSyRS2uIKEE8nq3KvJmqJy7dLnwxcHMS1tobHcUVqf6YY+MoPYcWywpRPgPLrTikyMtU2hO4L7l0vcO6htao2oWPGKqLulLla4txDd0p1pSxvcUvcqhXh97o91tGJSCyeay1JrdbxnKgaE1d4dQ0ZCU3oHKLRn663tfXQnMCs2AmiIE5kJKhrDBe2ioqQ9CEzLbCnk9vdsLcpBtLt4bXVjx7+cknpiFviWldG88NNK3aJREpfVTVg0JCAib5j8bCBfNPcy8DVNnRNaOIeteBqyoBSUnbdKUVGpNrGMKlmqiq0xK34MCz3dDj51iY1iOtlTJwm+haIKi9SIahvmpis7mEixZKqDmOcknT+HLMafPJLyZzzNGlq6jt790C4Bmt6h5KjIE5kJEhFc/1U14afFdFk33Fq/qS2xFlFz2Pi0m3hAqMLenmLW+LSaZi+ebFLI5IMlVWAQXOC5g81g023L3YpyoNZaPlcHM2mFWenVFfWkqIgTmSkmLpxGBdUVZPRjz1qiUvi9AKQX0vc6HHDWyYpLanKkH01lYL1NB5OJC9VNdH8oQnqTinDq6Ep9HCAjO6UChtKicbEiYwUn9wTDjwla0xcFPwkdTLsjsQmucbEtYWLSlOCapJl6KWqgGiaiYlrF7s0IslQWR0qPxTESU/iIM698xqsMXElRUGcyEhSXRtOsvEJN+5OmdRxQnFLXK7+lO3tYV1D87AXS0pITV0I9idMUcIDkXzFWR6TWsEnhVc3CrDOJGLqTlly1C4qMtJkd6eEhAdxFbm7U6bbwjpNMVDeaurg80d2dvsRkb41jQ8BXFKvDVJ4tdEcrO1toULYLOodI6VCQZzISFOV1RJnltwLdZzYJJ2rJS4K4pI6B54MnUmaVkCkX7bYJSQCStI8cTK86hpCRWpbW6hITVVGPWOkVCikFhlp4pa4eI44SG5rVW/zxLW3h0HWVeqjLyLSb5qIWXpT2xC63batDBXCFalil0iyKIgTGWkqo0lc8WhMnCV3ioE4iMs5Jq4VahIanIqIiJSyjiCuNVQIa464kqMgTmSkqazqbL2KJ+isqil2qQamIkUISLOk02EMlAbli4iIDL3axmjC7yhDpZKalBwFcSIjTXyi9XTnmLikBnFWEcVwWYFcnPZYmSlFRESGXm1DmH/TCfcSaokrOQriREaauCUuPQJa4lKV5GyJa28LQZwm+hYRERl6NXWhUjidBlxzxJWgsgvizOwkM2vJ+PnQzNJmNj5af7yZLTSz581s04z91jGzh81MIzultFVW0TFBdro9/J3U5B/Ww5i4OJ28JvoWEREZemZhyEK6LVyCk3ofMYKVXRDn7me5e2P8A5wN3O/uC81sdeAEYCPgl8BPM3b9JXCMu2syIiltqarQeJVu78wolUrobCIVFYQXk9Ua194WLjCjFcSJiIgURENTuN7iye3RM4KVXRCXycwMOBi4Jlq0FvCqu78L3AdMj7bbD3jd3Z8oSkFF+qOyqnOC7HR7svuxV0Rj4rKnGOhoiRs77EUSEREpC+PW6By+oCCu5CS0en7IbAdMBP4YPX4NmB61yO0EPG9mo4EfADv3diAzawaysyxMHtriiuQhDuLilrgk92OPs1NmD4trbw+vsU4T1YqIiBTE9M3g8b/A0kVhjJyUlLJuiQMOAW5x9xYAd38P+D5wJ7AHIXg7i9Dlcgsz+7uZ3WNmm+Q41jHA7Kyfhwr/EkSypKpCC1a6PaQGrk5w7Vk8Ji57mrh0W3iNtZpiQEREpCBWmwrj1wwVqdUK4krNiA/izOzAjCQmz2csrwf2pbMrJQDu/nt338LdP0topZtKaKm7FvgacAZweY6nuhCYlvWz3dC/IpE+ZLbEuUNVbbFLNHAd88RlRXHxmLhaTfYtIiJSEGaw4TahR49a4krOiO9O6e7XA9fnWLUnsAi4P9d+URbKnxPGzE0AUu4+18zmA5vleJ7FwOKsYwyq7CIDkqoMrVTtURCX5BNvPCYuW3tbCE5TShYrIiJSMB+ZAa88HlrkpKSM+CCuF4cAv3PPzpjQ4dvAne4+y8wqgToz24iQ/GTWcBVSpN8qq0MLVuuK8LimvrjlGYy4JS7za+oegrjGMUUrloiISFloGA37/bDYpZAcyjKIM7M1CYlKju5h/RrAfsD2AO7eZmbfAu4FVhK6VYqUprg7ZdwSV5fgcWNm0TQDGTwdErY0jC5OmURERESKrCyDOHd/i15eu7vPA7bJWnYDcEOBiyYyeKkqqMhovUp6BseKFF3GxMVz1jSoJU5ERETK04hPbCJSdiorO+eJg2S3xEFoicvs9BwnbGnSRN8iIiJSnhTEiYw0qaooLX8U+dQmeEwcQEUl3VviUBAnIiIiZUtBnMhIkznFwEhIw1+R6prYpD16XaPGFa9MIiIiIkWkIE5kpIlb4tLtIZirbyp2iQYnlTUmLg5ORzUXrUgiIiIixaQgTmSkSaXCXHHpdhizGqwxvdglGpyKVNaYuLYQnCY9YYuIiIjIACmIExmJKqtDILfzAeF3klWkQpKWd+bA8paoO2VF8hO2iIiIiAxQwu/uRCSnzXaE0eNg+ubFLsngpSpDMpPWdki/G4K6igqori12yURERESKQkGcyEi06afCz0hQEU1cHvepbG+F2sYwLk5ERESkDCmIE5HSlsqYYqCtNfyd9IybIiIiIoOgIE5ESltFdJoyC2Pj3KEh4Rk3RURERAZBiU1EpLRlTjFQWRWybo4aW9QiiYiIiBSTgjgRKW0VKSAa/1bbCOk0NE8oapFEREREiklBnIiUtopU6EpZWQU19VBhMHp8sUslIiIiUjRlF8SZ2dFm9rqZfWBmz5jZ5zLWHWBmb5vZbDPbKWP5GDN70sw0u7DIcKuITlO1o2DUGLBUmD5BREREpEyVVRBnZh8HzgX2B5qAmcDNZjbOzCqBXwE7A98Efpmx69nAj9196fCWWEQ6WuIaRsPY1UOLXMPoYpdKREREpGjKKogDpgHPu/tjHtwKrASmA+OAFe7+InBftAwz2wZYzd1vK1ahRcqaVQAWkpms+7EQyNUrO6WIiIiUr3KbYuAu4AQz2xZ4FNgHWAo8RwjmMLNNgCnA81Hr3PmElrtemVkz0Jy1ePLQFV2kTMUtcc0TYfpmsPZG0dxxIiIiIuWp3O6EWoA/AvcTWiE/BL7k7h8CmNlBwOWEgO7rwDHAbUCTmd0NVAMz3f2BHMc+Bjit0C9ApOxUVIQgbuyk8FgBnIiIiJS5EX03ZGYHAr+NHs4FLiIEZ5sCrwK7ADea2VbuPsfd7wXujfZdC9gb2A74ByFImwc8aGZru7tnPd2FwNVZyyYDDw316xIpKxWp8NM8sdglERERESkJIzqIc/frgevjx2b2K+BOd385WnSPmc0BPgXMydr9IuA4d28zs02BJ9x9lZlVAROAd7OeazGwOHOZmQ3hqxEpU/Wjw9QCjWOKXRIRERGRkjCig7gcHgVOM7N1gFnATsBGwLOZG5nZF4F33f0f0aLZwM5m9gZQA7w3fEUWKXMbfgLGrRF+RERERKTsgrjrgHWAvwNjgbeAb7v70/EGZtYAnAJ8JmO/7wBXALXA0e7ePmwlFil3FRUwaWqxSyEiIiJSMsoqiIvGsc2MfnraZhkwI2vZvcDUAhZNREREREQkL+U2T5yIiIiIiEiiKYgTERERERFJEAVxIiIiIiIiCaIgTkREREREJEEUxImIiIiIiCRIWWWnLIIUwJtvvlnscoiIiIiISAnKiBVS+e5jIeu+FIKZfQp4qNjlEBERERGRkreduz+cz4YK4grIzGoIc869DRRygvDZwLQ8tptMCCq3A9Q8WFhJfa/z/SyVgqS+x0kz0Pc5SZ+lYtDnN3+D+SzpfS68pLzHI+GclJT3Oun6ep8L8VlKAasDj7v7ynx2UHfKAor+CXlF04NhZrj7nHy2i7yZz/YycEl9r/P9LJWCpL7HSTPQ9zlJn6Vi0Oc3f4P5LOl9LrykvMcj4ZyUlPc66fp6nwv4WXq9PxsrsYmIiIiIiEiCKIgbGU4vdgFkxNBnSYaKPksyVPRZkqGgz5EMlZL4LCmIGwHcfWaxyyAjgz5LMlT0WZKhos+SDAV9jmSolMpnSUFceVlMqD1YXOyClAG914Wn93h46H0uDL2vw0Pvc+HpPR4+eq+HRyLeZ2WnFBERERERSRC1xImIiIiIiCSIgjgREREREZEEURAnIiIiIiKSIAriREREREREEkRBnIiIiIiISIIoiBMREREREUkQBXEiIiIiIiIJoiBOREREREQkQRTEiYiIiIiIJIiCOBERERERkQRRECciIiIiIpIgCuJEREREREQSREGciIiIiIhIgiiIExERERERSRAFcSIiIiIiIgmiIE5ERERERCRBFMSJiIiIiIgkiII4ERERERGRBFEQJyIiIiIikiAK4kRERERERBJEQZyIiIiIiEiCKIgTERERERFJEAVxIiIiIiIiCaIgTkREREREJEEUxImIiIiIiCSIgjgREREREZEEURAnIiIiIiKSIAriREREREREEkRBnIiIiIiISIIoiBMREREREUkQBXEiIiIiIiIJoiBOREREREQkQRTEiYiIiIiIJIiCOBERERERkQRRECciIiIiIpIgCuJEREREREQSREGciIiIiIhIgiiIExERERERSRAFcSIiIiIiIgmiIE5ERERERCRBFMSJiIiIiIgkiII4ERERERGRBFEQJyIiIiIikiAK4kRERERERBJEQZyIiIiIiEiCKIgTERERERFJEAVxIiIiIiIiCaIgTkREREREJEEUxImIiIiIiCSIgjgREREREZEEURAnIiIiIiKSIAriREREREREEkRBnIiIiIiISIIoiBMREREREUkQBXEiIiIiIiIJoiBOREREREQkQRTEiYiIiIiIJIiCOBERERERkQRRECciIiIiIpIgCuJEREREREQSREGciIiIiIhIgiiIExERERERSRAFcSIiIiIiIgmiIE5ERERERCRBFMSJiIiIiIgkiII4ERERERGRBFEQJyIiIiIikiAK4kRERERERBJEQZyIiIiIiEiCKIgTERERERFJEAVxIiIiIiIiCaIgTkREREREJEEUxImIiIiIiCSIgjgREREREZEEURAnIiIiIiKSIAriREREREREEkRBnIiIiIiISIIoiJOCM7OZZnZ/H9u4me04PCVKBjM73cwuGsT+HzWzl8yseijLJSL507lNpP/M7BIzu2SIj7mdmbVkPO7z3mQonqdYzOxEM5tvZi1mtkuxy9MbM7vfzGb2sn5HM/NhLFIiKIgb4aIvhpvZ17OWN0VfbDezqUP8fDOH6niFZGZXm9nVxS5HLma2JvBd4MyMZaeZ2QIzm2NmX8ja/k9mdljmMnd/CngW+NZwlFlkuJnZUdE57JRil2U4FermU6TQonuEVWa21MyWmNlcM7spu6LD3Y9y96PyPGZeFSXu/pC7Nw6s5D0+d7fvYiGep7/MbDLwU2B3d290978VszyZklSxFd1vHVrscvREQVx5eB7IPhkeDMwZ/qIUnplVmFlqGJ+vqgCHPRq4y90XRs/xMeAQYANgP+AqM6uI1n0VqHb3K3Mc5zLge/G2IiPMN4H3gCNGyme8QOeToj+XSIaz3H2UuzcBnwCeAO42s28X6gnL8LM+FTB3/0+xC1KKhrOHUiHvSUfERU/69CdgTTPbKmPZkcBvszc0syPM7EUz+8DM/pPZ4hM3Z5vZnmb2SrTN3Wa2erT+EmA74KSolW9+1rFPM7O3zWyRmV2c60NtZikze9PMDshafmZPNc9mNjUq1+Fm9hywHNjQzJqj55lrZu+Z2f+Z2fRon5OAA4EDo7K2mNm4XLVq2S12Uc3MaWb2VzNbChwZbXO9mf0qeq75mS2SUVn+YGYLo/ftFTPbJ9friewF3J3xeD3gUXd/z93/BbQB481sEnAG8I0ejvMAMAn4WC/PJZI4ZrYtsBlwADAZ+GzW+r6+k/F546tm9kzUMvAPM9sgY5tuPQsya2bNrNbMbjGzedH+z5nZl/v5OtzMvmdmj5rZcmDX6LhnmdnrZva+mT0YVeRgZgcCJwHbZZy7PmZmh5rZnKxjdzmfRa/nF1GZFwM/jbfp6fxsZtVm9pvo/Vsavf7v9Oc1ivTE3d9293OAs4CzzawJul53LTgjujdYGv0+K1r3fHSou6Lvws3R8lyf9Vxd8szMzrHQy2W+mZ1tZpXRivgcMTVj445j9PJd7PI8Fu5rTjKz18xscXSe2TZj/aHR9+ooC/crS8zsRjMb1dP7ZmZ1Zna+dd7f3GNmG0XrDgH+Gv3dYmYLezjGTDN7IDrXvBt99483s7XM7G/Re/1vM9s4n+fNOGZv55Oc/6/IaDO7wcI90htmlvO+xsw2MLM2M5uStfwh66EnWMZ7fIyZ/Rf4b8ax7jCzd8zsrehc1xCtuwtYC7gkKutj0fK+rgs93ZPOMbOTzeyu6L191cy+mHGMzaP/x2IL5/0nzewjuV5PTEFceWgFLifUWmNm2wOjgDszN7Jw83EOISAYSwgObrGuwR/AnsAMwod7NPBjCN0fgIcItWyN7j4pY59PAkuifbYhtCZ1CdSiY7QTWo86vrzRl/8woK/+8YcAuwGNwKvAbdHfHwPWAJ4B7jCzKnc/C7geuD4qa6O7v9fH8TMdCZwSvf64BWxvQtA0Mfr7ZDPbLlp3POE9nwY0Af8DvJDrwGZWR2hxey5j8bPA1mY2ITr5twILgIsJ7/cbuY7l7iuj92JGP16bSBJ8E3jE3e8B/hI9ztbbdzJ2EOH7OAGYD/y6H2Uw4M/AhsAY4FzgejPbsB/HgHA+OQRoAO4lnOu2BLaPynUjoaWi2d2vJ9zwPpRx7upPbfthhHPsWODUaFlv5+dDomWbuPsoQsvJI/18fSJ9+T1QT/isZduF8LndNvoMbkb43uHucYARdxvcN2O/XJ/1bNsSbrInAzsB+wLH5VPgfnwXjyPc0+xJ+D5fD9yTFYSsCaxLuPZvCGwFHNPL058flXf7aN9/A381s1Hufg2we1TGRncf38txtiUENGsQKrbPBq4iDOcYC7wM/Cqf583YpsfzSR//r68BlwLNhPfsN2Y2LbvA7v4S4V7z8HhZdM79BOFetyeTgfUJ7+90MxsfHeeeqKybEyrML4yeZ/fovTkqKuvHezl2Lpn3pK9Ey44gBP5N0Wv9nZnFXW9/Qzj/jyd8Tg4HFvf2BAriyselwL4WarmOIpzY0lnbHA5cFvXnbnP32wgnyq9nbfdDd1/i7osJJ6N8Ptiz3f1Cd29195cJH9Se9rsM2NbM1o8efx6oAm7t4zlOd/c33b0N2Jhw8jjS3RdFwczJhC/q1nmUty9XuPujHiyPlj3o7je7e7u7PwI8TedrXAWMI5ygzd3nunvOII5wMwjhJAiAu79IuFj8hdDP/cvA/oSL3o1mdnlUg3NZxgkh9gHhZCwyIkQX333pvGBfDuxmZmtnbdrbdzJ2uru/4+4rCBUyeV+o3f1Dd78mOh+2RTdPLwA79vMlne/uL7m7E77ThwBHu/tb0XF/Teg2+vl+HjeX29z9bndPZ5y7ejs/ryLchGwUVYDNd/d/D0E5RDLFFZG5rlWrgFpgYzOri67p/8zjmLk+69kWAGe4+8roOnsuIfgbSocD57j7s9F37NfAS4SgKdZKuLf60N3nESqhc56LLHQd/xpwSnQvsYJwf5MCPtfPss1y90ui88xdwELgb+7+gru3EoLrrfr5vP2538t0s7vfH/2/biIEMFv0sO3FwGHW2aPrG8D/ufubvRw/DRzr7suiz8PBwEvu/ovo/7+QUDl/sA1N98eOe1J3XxUtu9Td/+Pu6eg1jAbi1rZVhHvUtaN9nnL3d3p7AgVxZSJqqbkP+AGwB3BFjs2mALOylr1G+FBlHmtexsMWQgtTX+ZlPe5xv+j4fybUWBD9vjrjS9CT2Rl/rwdUA/OipunFhJugFOF1DtbsHMt6e43nEmp7LgcWWhjIPb2HY78f/W7KXOjul7v7lu6+A+H/9GNCgP1D4J1o+SLgxKzjjY6Wi4wUXwNWAjdFj/8MvEto0cqUz3kn+3yWd0ICM6sxs59b6Cb1QXSe2ZjQ8tcfmeeTdaPfT8bnrui4axNqkgerv+eu6whd788lnLv+z6KunSJDKL4ud+sR4+4PACcQrnXzo+5sn87jmLk+69n+G91QZ+4zFPcImfK5t3o3qoCO9XZvNZ4Q1HYc00MvpjlZx8zH21mPl2ctW07nOTHf5837fi9Lf/a7jXCPt5uZ1RB6VHQbIpRlfhR4xtYj9HDKPM/eAzhhGMpg9Xqudfc4g2n8Gg+NnvvvUXfSn8ddO3uiIK68XEyoNbnL3bO/uBBqwrKbrtch6jucp+zWvYG6GDjEzNYBdiW0JPbnuecDHwLj3b0546fO3X/fS1mXEro0ZVqjj+fqk7svd/dT3X1zwk1aO6HLQq5tPyTU5m+ca33kYuCnUXD+MeDBaPl9ZNRcRSe39QgDx0USz8yMEKzVAbMsjL19k9CCfZgNbQKDLucDC2NlMgO04wjnp92AJndvJiSSsn4+T/a5C2CjrHNXvbv/LMf2OcsaGYpzV7u7n+fuWxO6T70E/G9/jiGSh/0IAcO/cq109yujisqJwO3An82sPl7dwzHz+ayvZV2TIk0lnE8gfKeg6/cq+zuVz3MMxb1VpoXAisxjRi1Haw/imMP5vIOeKiBqJbyc0AK3N7CM0FOpN9n/q/nA/Vnn2SZ3r3X3t3rYB/q+LvT0fL2KWjePcPe1CV1WP0OovOiRgrjycjdh7Mf3e1h/JSHL2yctDMT9IqHVLlfWw57MJ/Q5Hqx7CU3pNwEPuPtr/dz/YeBFQp/qiQBmNsbM9s448c8H1s1qNn8C+KiZbRO9B/sS+n4PipntYWYbR1/25YQAs72XXW4l3BzmOtb+QKO7XxYtehX4XPQ6Pk+o4YttD7xD6LcuMhJ8hnADtBPw0YyfjxO6LO81hM/1BPAlM1s9Gqv6M0LX7lgToUVwIVBpZt+k98qXPrn7XEKQ9Ju4e6iZjTKz3S1KIkU4d60dVdLE/gOMMbN9LGRD25HQ5XRQzGxnM9vKQja3FYTa8d7OXSJ5M7NJZnYsYZzQCe6+JMc2Hzez7aPv4Co6g6v4Jnk+nV3S+msCYaxsdZRE4niiClYP4+RnE+6LKqNK5R9k7Z/ru5jtSuCE6B6gKjpPbATcMJACRy2HVwNnWkhCUkvIYeBk5ToYSkP4vIP5f2W6lFCBdiJhKFB/GxGuAraykFCm3oIpZvalPsra13VhQCwkX5kcVVR+QEhg1+u5VkFcGfHg3p76DLv7jYQT6RWELn2nA19x98f68TTnA5tETdO99U3us6yEpvEt6LuJPNf+7YSAdQXwqIUskk8TBhbHtUCXErpXLozKOzbqtvFTQkbPBYSxLX8c6OvIMI1wY7YYeAtYjc7uorlcDHw2GvvTIQpIf0LXcYpnEW4c3ycM2D0rY90RwC8GcHITKVXfJPQmeCQanxX/PAP8ge7TqQzGz4GnCIP7XyZUkLyVsf58QiXJm4Sa6MkMTdKPA6LnjTPgvkz4LsctfDdGZXk7Ond91N1nAd8mDMpfTGitzNna308TCTduiwjnxB0IY3JFBirOYL0UeIwwfn33aKxYLo3ABYQu04uJkoRkdI37f4RA7H0z+0M/y/IPQne2twg9Wm4FzstYfzDw6eh5r6V7IDBImAAAIABJREFU4oxu38Ucz3E+4b7qdkKFz8HAbu4+mFaz4whJOR4mdNHbGviMuy/tda/BG4rnHcz/q0P0/t1DCIhzDRHKZ/9tCRXmrxP+x3cDm2ZsdgawT1TWf0TL+rouDNROhO9DC+F+9Z+Ebuw9snCvLFJ6zGxPQpa2yVHTeVkxs9OBZnf/3gD3/yjhpnazPMYTioiIiCSGmV0ETHH3oeyBkRgK4qQkWciweA9wt7ufXuzyiIiIiEhpsDD9wH+AL0a9qMqOulNKyTGzbxO6TbTQtVuDiIiIiJSxqBvms4SxcGUZwIFa4kRERERERBJFLXEiIiIiIiIJUlnsAoiIyPCI0mDPIEzmqjTxUs5SwOrA4+6+stiFKVc6J4l06Pc5SUFcgTn3qb9qkVl6cNn1f/vCLYMuw5FTdxnU/r7g9UHtX3HOrYPaH+Dln+4zqP3Xb/5BfydAlqE3g5AeWkSC7Qjp0qU4dE4S6Srvc5KCOBGR8vF2/Mfs2bOLWQ4RLvjz010eH/uFzYftud9880222247yPhOSFG8DfDQQw8xefLkYpclL/vtt1/H33/4w4CnOev0+rSuj9fRubkcDeScpCBORKR8dHRXmjp1ahGLIQLNExZ0eVykz6S68BVXO8DkyZMTc06qq6vr+HtIyrwi63FC3gcpmLzPSUpsIiIiIiIikiAK4kRERERERBJEQZyIiIiIdDCz883sDTP7wMzmmtnJvWy7r5nNMrNlZnaPma05nGUVKVcaEyciIiIimS4DTnX3ZVFQdo+ZveruN2VuZGYbAlcCewKPAOcANwA7DHeBpXja29tZtGgRra2txS5KyauqqmLs2LGkUqlBH0tBnIiIiIh0cPeXshalgXVzbPpV4C53/xuAmZ0CvGtm67j74ObGkcRYtGgRtbW1jB8/HjPNJtQTd6elpYVFixYxYcKEQR9P3SlFREREpAsz+6GZtQBvAo3AdTk22wTomCvC3ZcAc6Ll2cdrNrOpmT9AMuYVkF61trbS2NioAK4PZkZjY+OQtViqJU5EREREunD3n5nZ2cBHgS8B7+fYrBFYkrVsMTAqx7bHAKcNaSGH2re2ymOjXC9NFMDlZyjfJwVxIiIiMuyO/MxGxS6C9MHdHfiPme0KnA4cm7VJCzA6a1kTsDTH4S4Ers5aNhl4aPAlTbCpTxS7BJJQCuJERERk2K05tqHYRZD8VQLr5Fj+HLB5/MDMRgPTouVduPtiQisdGdsPbSmTqHbLYpdAEkpj4kREREQEADOrMrMjojFsFWa2NfAt4N4cm18H7G5mO5tZHXAm8C8lNZFSsuOOO2JmPProo12Wf/vb38bMuPrqq4tTsEFSECciIiIiMQf2AWYBHwDXAr8AfglgZi1mth2Au78IHA5cDrwHbAgcUIQyi/Rq/fXX55prrul4vGrVKm6++WbWWSdXA3MyKIgTEREREQDcvc3dd3X3se7e6O7ru/tPo/FxRMseytj+Znef7u717v4Zd3+reKWXkvCSDexndi9dS2dv2XXbfjrwwAO55ZZbWLlyJQC33347W221FZMmTerY5qqrrmLDDTdkzJgx7LLLLsyaNatj3bHHHsuUKVMYPXo0W221FY888kjHupkzZ7L33ntzxBFH0NTUxDrrrMNdd93V7zL2l4I4EREREREZsSZOnMjWW2/N7bffDsDVV1/NoYce2rH+T3/6E2eeeSa33HILCxYs4NOf/jT77rsvUd0FW265JU899RSLFi1i33335ctf/nJHQAhwxx13sPvuu7No0SKOOeYYDjvsMNLpdEFfk4I4ERERGXaPv/Zulx+RsrT40q4/UjCHHHII11xzDfPnz+fxxx9njz326Fh3ySWXcOKJJ7LxxhtTWVnJiSeeyCuvvMIrr7wChJa8cePGUVlZyQknnMAHH3zAa6+91rH/Nttsw1577UUqleKwww5j/vz5zJs3r6CvR0GciIiIDLs/PzG3y49IWZp/ZNcfKZg99tiDxx9/nPPOO4999tmHmpqajnVz587luOOOo7m5mebmZsaOHUtbWxtvvRV6B59zzjlssMEGNDU1MWbMGJYtW8bChQs79s/sltnQEDLvtrS0FPT1aIoBEREREREZGhv40B9z2pODPkR1dTX77LMPF1xwQbdMlVOmTOHEE0/kkEMO6bbfgw8+yDnnnMN9993HxhtvjJnR1NTU0dWyWNQSJyIiIiIiI96pp57Kvffey4wZM7osP+qoo/jZz37Gc8+FKQ6XLFnCLbfcQjqdpqWlhcrKSiZMmEBbWxszZ85k2bJlxSh+F2qJExERERGREW+11VZjtdVW67Z8zz33pKWlhf3335+5c+fS1NTEjjvuyN57782uu+7KZz/7WdZff30aGxs57rjjWH311YtQ+q4UxImIiIiIyIh0//3397ju4Ycf7vj7oIMO4qCDDuq2TSqV4sorr+TKK6/sWHbcccd1/D1z5sxu+wxHV0t1pxQREREREUkQBXEiIiIiIiIJoiBOREREREQkQTQmTkSkDB1+9ePFLoKUuYULFnd5PJSfySsOndH3RiIiCaaWOBERERERGbBiz5mWFEP5PimIExERERGRAamqqqKlpUWBXB/cnZaWFqqqqobkeOpOKSIiIiIiAzJ27FgWLVrE0qVLi12UkldVVcXYsWOH5FgK4kREREREZEBSqRQTJkwodjHKjrpTioiIiIiIJIha4kRERGTYNTbWFbsIIsU36bfFLoEklII4ERERGXa1dTXFLoJI8TV/o9glkIRSd0oREREREZEEURAnIiIiIiKSIAriREREREREEkRBnIiIiIiISIIoiMtgZoeY2QNm9p6ZrYp+P2BmBxe7bCIiIiNJW2tblx8pDWZWY2ZXmNlcM1tqZk+b2R49bLujmaXNrCXj5/DhLnOirXiy649InpSdMmJmpwMHAOcDTwGLgSbgY8DJZjbd3WcWr4QiIiIjx+LFLV0ej5/QXKSSSJZK4A1gB+C/wK7AzWa2hbu/kmP7d9190nAWcESZs1XXxxt4ccohiaMgrtNRwAx3/2/W8kfN7C7gcWDmsJdKREREZIDMbBrQnuP+Jid3X0bX+527zOwVYAaQK4gTkSJQd8pO1cDSHta1ROtFRERESpaZXWlmn4r+3hd4FZhlZvsN8HgTgA2B53vYZJyZzTez2WZ2kZk19nCcZjObmvkDTB5ImUREQVymm4A7zGxXM1vdzOrNbJKZ7Qr8L/CHIpdPREREpC+7A/+O/j4W2B/4HHBSfw9kZpXAdcCN7v5Ujk1eAjYH1gB2JgxBuaiHwx0DzM76eai/ZRKRQEFcp28D9wFXAG8RWuXeAi4HHgC+U7yiiYiIiOSl3t2Xm9koYAPgj+5+N7BWfw5iZhXAtdHDb+Taxt3nu/sL7p5299nACcDePRzyQmBa1s92/SmTiHTSmLiIu7cCpwCnmFkz0Ai0uPvi4pZMREREJG8LzGxDYBPgX+6eNrMGIO+MGWZmhErtNYDd3X1Vnrs6YDlXhPupLvdU4WlEZCAUxOWQ60QjIiIikgAXAk9Ef8fj4Lan5zFtuVxMGAf3P+6+vKeNzGwnYBYhi+Vk4GfAbf0tsIj0n7pTRsys0sxONbO7zewCM5uYtf7ZYpVNREREJB/u/ivCOLWN3f3P0eLXCVm4+2RmawNHAh8F3s6Y/+2kaH2LmcXdID8G/ANYFv1+Fg0/ERkWaonrdDahb/a1hBqrp8xsV3ePg7epQ/2Et976D2666WEMOOVH+7Hxxv3qrj7o/UuhDMXY//nn/8uZP7kJ3Pnylz/FXntu02X9Qw+/wC9/eQfV1ZXU1dWw8eFjqRtVBcB7byzj3steBqC9Nc3785Zz9DXbd+z75O3/5fUnFgLwwYIVrLf1BHY4dL0ux7/wN3/lT3f+h7XXGsfVF3efE3X23IX8v5m3UFWVorWtnZk//CIfGdO5/oRz7uftBctYvqKNL+y0DofutUm3Yzz0xJtcccszpNOw09ZTuq3/y3cuZIspH+Gi+27iJ3ddRXP9KG48/MfUVFZRmarkm78/h2ffei3n+7e8ZRWnHXMXlVUpVq5o45CjZ7D5jDU71v/tjlf4/eVPMnH1UQD84PSdGDexIeexRiozqwF+A+wCjCXUVP/I3W+P1m9CGG+7WbTum+7+ULTuEOC7wHqEsbk3Aj+MuzOZWTXwS+ArQCtwsbufOnyvTkRKnbu/lvU476kB3H0uPXSJjNY3Zvx9AXDBQMooIoOjIK7Tl4Gt3P0d4JdmdjDwVzP7grs/Tj/6kudjyZJlXHftffzhxhN5953FnHDCVdzw++OHbf9SKEOx9j/zJzdx7jmHstrEZr6y37l8eufNaWqq71i/zvRJXHft96muruL6Gx7g7jse5ZP7Twdg3JQGvnzGFgC8/Mg7vPHc+12OveUea7HlHiGQvPXHT7Petl0adAE4YN+t2XuPLfnRT3L3OJmy5hh+f+WRmBn/fOx1fnP5fVx0/NYd63/8/e2orkrR1p7mc0f8kX12W5/G+s4ZMN5fsoLrbn+BS8/cleqqFACHn/Nql+c4/NqfsMsGH2fymFC+A2fsyiOznuGMO69gh/W24OTdDmW/K07JWb7a+ip+dskXSFVWMP+tDzj75Hv5+dV7dtnmM3t8hK8ctkXO/ctEj5PlEjKy/Rm4JFq/D/AnM1vH3d8H6glZ3B4jBIC3E7LKzYyOfSoh+FuXMHb3b2Y2292vGp6XJiKlzMxWA34MfBwYlbnO3acXpVAiMuQUxHUaDSyKH7j778xsMXCnmfWUaWnAnnlmDltuuS7V1ZVMnjKeZctWsGpVK9XVVcOyfymUoRj7r1rVyocfrmLK5PEAbLnVOjzz7By2+9RGHdusscbYjr+rqyupSOWukHzxwXeY8aXcLX/Ll6zig3c/ZI31m7qtmzhhNG/Oez/HXkFlZarj72XLVvKR9SZ1WR8HZitXtbP6xAbqarp+je9/7A2aR9Vw9My/AnDiEVuT7a3FC7q+lvlz+Owm2wIwpn4U7y7tuXwVFQYV4T1ZvqyVaeuO7bbN3//vVZ7855tstuUaHPCNLcM+ZaSPyXLXBuqAc909DVxvZt8F9gKucPeLM/Z728yuBb6QsexrwBHuvhBYaGbnA4cBCuJEBOAawj3NpYR5bkVkBFIQ1+lVQq3VI/ECd789apG7Dajtbecoo2Vz9vJF799Oc3P3eS8XL17G6IzWn1Gj61m8eDkTJ3a/6c9lsPuXQhmKsf/7i5cxelRdx+PRo+pZsmRZzm0XLvyA6294gB2On9pt3YdLW1n01jLW2CD3c7308Dusn6MVLl/PvfgWZ559O/PmL+FX5x4IdE0M9r0f38vjz85nv89vSCrVdWjru+8tZ+68D/jdOZ9l1huLOfUXD0PXOLCbJ//7Emd84Rs8+6Praa4bxafOO7LX7d97dxlnn3wv895YwndP2b7Luk9svzY77b4uABed+QD3/+U1dv7serkOUzayJsvdCXg2CuBiTxEyyeXSkZDAzMYQssU9nbXvWTmeM9c5SRPriox8nwDWcvcPil0QESkcJTbp9Aty3ES5+18IXS0f7mP/XJNYzr7wwltybtzc1MDSDz7seNyy9EOam+tzbluI/UuhDMO5/3XX389BB/+cX/ziDj5Y2rnP0qUf0tTUfbxWS8uHfPeYyzj9tP2pb6rutv7lR95h/W0m9pge+aWH3mHD7Tsjp+tu/CcHfeMyTj7j1rxe2yYbrsmNV3+TX593IGee+2euu/0FDjr+Tk75eZgX9aJTPs2913yFBx57g9fmdm01axpVwyc2X53qqhQbTB/HosUr+ny+Ez7zVf74n/vY9MwD2feyk/j1fj/odftxExs457I9OP/KL/Hb8/7RZV3j6BpSqQpSqQq2/591eO3FBT0cpTzkmCy3EViStdlisro9RfseDHyKkPGNaF+y9s+5L5pYV6RcvQOk+9xKRBJNQVzE3X/n7r/tYd3f3X3nPg6RaxLLacccs0/OjTfbfCpPPvkara3tzJu3iPr6mn51hRzs/qVQhuHc/6sH7si1v/s+P/nxV6mrq2bevEW0trbz5L9fZ7NNp3bZdsWKVXzrO5dy1JG7sfnm03Ie76UH32HDHXI3b70/L2RjHrNGZ0D51a9sw7WXHsFPTt2rz9e1cmVrx9+jRtVRV1vFV/fYiGvP/RxnHvMpVrW2A1BTnaK2OkVtVnfKj282iRdefw+Atxe00NjQPQjNZhgLW8KsGu8ufZ+xDaN73LZ1VXvH3/UNVdTVd33PW5au7Pj76Sfmseba3Rqoy0YPk+W2ELo6ZWoiJDHJ3HcP4DxgN3efn7EvWft32zeiiXVFytOJwK+isXEiMkKpO2UGM2sijEvZhFCzvRR4Dritr0m/e5pbzrkv5/ZNTQ0ccMAOHHTQ+Rhw0slf6VdZB7t/KZShWPuffNK+HPuDK8GdA/bfviOpyXHHX8X5536N6294gJdeepPLLruHyy67h6p129l6n6kd+y+e/yHtbWnGTQ4teO/OXsrcpxcx40trA/DCA/PZcPuer53X3fhP7rz7GWbNeZdDv3kFZ5z0JdaaMo7jTr6R83/yFf752Otcds2DHePITjruc0BoPWxrdw4/6S8AtLal2X37aUyeFGWBPPt+zjtxR6ZPaebjm63OV39wB61taU4+6hPcducjXcpw6YH/j22nb0pNZRVbrbUBR//hXK499DQO2/YL1FXVcOJtv+6x/HNfX8TlF/6LigqjvT3NEd/fhlmvvMdTj77JXgdtzq3XPcPTj79FKlXBmms1seu3Nsjr/zLS9DJZ7nPACWZWkdGl8qPAZRn77gZcCXw+ar0DwN3fN7N5hPTh8zL2fS77+TWxrpS62tq+K5gkP2aWpmsCNgMOyv7Ou3sKKS1NRxS7BJJQ5j6kSRcTy8w+BfyJMDbuKcLNTxPhBmk94Ivu/kjPR8jNuU9vcJFZenC9Sn77Qu4usf1x5NRdBrW/L3h9UPtXnJNfN87evPzT3K3K+Vq/+QdlFUGY2SWE88f/uPvSjOVVwCuEKQh+Qag4+jWwrrsvMrOdgZuBvdz9gRzH/QmwI/BFoAH4K/DTfLJTmtlUQrdKDrvqsUG8OpHSdsWhM3pdP2fOHKZNmwYwzd3nDEeZCsnMdshnu1znlGKKz0mzZ89m6tSpxS0MwLe26nOTnV7o7L1+3325K+pF+msg5yS1xHX6DfAdd78he4WZ7U9IB77psJdKRBInY7LclYQMk/Gqs9z9rKir5OXAGYR54r7k7nF23B8RKpDuzNhvrrtvHP19OjCeMHlvPE+cMlOKlLHM4MzMNnf3p7O3MbPNhrdUIlJICuI6rUOo/c7lj4QbLhGRPuUxWe6zQPe5H8K6nfo49ipCgNh7ClERKVcP0X3cLcD9hLknRWQEUGKTTs8A3+th3XeAZ4exLCIiIiID0a0Cycyq6TpmTkQSTi1xnY4AbjezYwkB2xJCTdamwApgjyKWTURERKRHZnYfIVCrNbO/Z61eG3hi+EslIoWiIC7i7s+Z2fqEhAGbEOZjaiGk+L7f3duKWDwRERGR3twf/f4kkJnAJA3MB24c7gKJSOEoiOtqKjAB+Lu7P5O5wsx+6O4/y7mXiIiI9MvCBV1n5Rk/oXznlBwK7n46gJm9mitJm5Sol7J6v26gXq+SH42Ji5jZF4D/AD8A/mlmV5hZZpB7UnFKJiIiIpKfOIAzszFmtlbmT7HLJiJDR0FcpzOAfd19S0KL3JrAn82sJlpfVnNciYiISPKY2SfM7DVgIWFeyNnAnOi3iIwQCuI6TXf3vwC4+wLgc4QJv+8ys4ailkxEREQkP5cA/wdsBkyPfqZFv0VkhNCYuE7vm9kUd38DwN3bzewA4Argr0CqqKUTERER6ds6wBbuni52QUSkcNQS1+lvwNcyF3hwGGEOudqilEpEREQkf88AGv8mMsKpJa7T0fTwfrj7UWZ21jCXR0RERKS/rgNuMbNzgbczV7j7g8UpkogMNQVxEXdfBazqZf1/h7E4IiIiIgPx6+j377OWOxoaIjJiKIgTERERGSHcXUNlRMqAvugiIiIiIiIJoiBOREREZIQwswozO8bMXjCzluj3980sr/luzazGzK4ws7lmttTMnjazPXrZfl8zm2Vmy8zsHjNbc+hejYj0REGciIiIyMhxPPB9wti4vaPf3wNOzHP/SuANYAegCfghcIOZrZ+9oZltCFwJfAMYD7wM3DDI8otIHjQmTkRERGTkOBz4vLs/Gz2+28weAG4DftbXzu6+DJiZseguM3sFmAG8krX5V4G73P1vAGZ2CvCuma3j7q8P7mWISG8UxImIiIiMHBOAF7KWvURoKes3M5sAbAg8n2P1JsBj8QN3X2Jmc6LlXYI4M2sGmrP2nzyQMomIgjgREREpgspKZbsvkBeAw4DLMpYdCrzY3wOZWSVh3rkb3f2pHJs0Akuyli0GRuXY9hjgtP6WAYBvbdX3Nr9+YkCHLrqaLXpeN5yvu6/nSur7O4IpiBMREZFh1zwm132+DIETCV0oDwdmAdOATYHd+nMQM6sAro0efqOHzVqA0VnLmoClOba9ELg6a9lk4KH+lGvEmfZksUsgCaUgTkSkDF1x6IxiF0FECsDdHzazjYD9gSnAM8B+7j4332NEmSyvANYAdnf3VT1s+hywecZ+owlB43M5yrWY0EqX+Tz5FklEsiiIExERERlBooCtzyQmvbiYMA7uf9x9eS/bXQc8amY7A/8EzgT+paQmIoWnIE5ERERkBDGz7YCtyBqb5u5n5LHv2sCRwErg7YzWsrPc/SwzayG0zj3k7i9G3TYvByYBDwMHDN0rEZGeKIgTERERGSHM7KfAsYQujZmtaA70GcRFrXg99nN098asxzcDNw+osCIyYAriREREREaOI4Cte8gmKSIjhII4ERERGXYX39112rFv7rpxkUoy4iwjR2IRKVGzt+z6WNkqJU8K4kRERGTYvf1+b/kyZBDOA041s9Pc3YtdGOnDyn8XuwSSUAriREREREaO/wX+BnzfzBZkrnD36cUpkogMNQVxIiIiIiPHjcCbhMm11dwpMkIpiBMREREZOTYDxrv7imIXREQKp6LYBRARERGRIfM8MPb/s3ffcXJVdePHP99sCpBKIPQSihSJCAIPjwIKiAVFFASM1AAiCviIFQQepIsFf2ADCwEEFIEHBKQJ0lUQVAQCUoRQE1oKaaR+f3/cu8nsZje7s8nu7Ew+79drXrtz7rn3fufuzJn93nPuubUOQlL3sidOkiSpcVwGXBMRPwQmVi7IzHtqE5KkZa1hkriI6ANsBjyVmfNqHY+k+mRbIqnOnVf+vKJVeQJNPRyLpG7SMEkcReP0EDCo1oFIqmu2JZLqVmZ6qYy0HGiYJC4zMyL+A6wOTKh1PJLq0/LalmQmkyZNYvbs2bUOpddrampiyJAhrLjiirUORZK0nGqYJK70/4DfRsQpwHhgQfOCzHyhRjFJqj/LXVsybdo0IoI111yTiKh1OL1WZjJ37lwmTZoEYCInSaqJRkviflX+vINiSBRA4DhwSdVZ7tqSmTNnsuqqq5rAdSAi6N+/P8OHD2fy5MkmcZKkmmi0JG6DWgcgqSEsd23JggULaGpqyPy0W/Tr14/58+fXOgxJ0nKqoZK4zHy+1jFIqn/La1tiL1zneazUm0TE7Zm5W/n7sZl5bq1jktS9GiqJA4iI4cB2wGoUw58AyMxf1ywoSXXHtkRSHdmu4vfTAJM4qcE1VBIXEbsA11JctzIYmEYxTfiLgP94SeoU25LeZ+edd+buu+/m/vvvZ/vtt19Yfswxx/DTn/6Uiy66iDFjxtQuQFXttNHbdVxJnfVoRFwNPAIMiIiT26qUmaf1bFjq0GbZcR2pDY12L5HvAt/LzJWBaeXP7wE/rG1YkuqMbUkvtMkmm3DJJZcsfD5nzhyuuuoqNtpooxpGJfUKBwFvAjtR/G+3SxuPnWsVnKRlr9GSuE0o/tGCRcOfzgC+XptwJNUp25Je6IADDuDqq69eeC+766+/nm233ZY11lhjYZ2LLrqIzTffnJVXXpnddtuNZ599duGyr371q6y77roMGTKEbbfdlj//+c8Ll51yyil8+tOf5ogjjmDo0KFstNFG3HzzzT334qSlkJnPZeaRmfkh4D+ZuUsbj11rHaekZaehhlMCsyle0zxgckSsAUwFVq1pVJLqjW0JcPIVD3ZpvTVXXokvfmSLNpedf+s4JkyeCVQ/nG611VZj++235/rrr2fffffl4osvZsyYMZx33nkAXHfddZx++unccMMNbLrppnz/+99n33335aGHHiIi2GabbTjxxBMZOnQo55xzDvvttx/PPvssAwYMAOAPf/gDv/3tb7ngggv42c9+xmGHHcbLL79Mnz6Ndr5TjSwzN6t1DJK6X6N9Mz0IfKT8/Q7gcuAq4OGaRSSpHtmW9FKHHHIIl1xyCRMnTuTBBx9kzz33XLjsggsu4LjjjmOLLbagb9++HHfccTz11FM89dRTQNGTt8oqq9C3b1+++c1v8tZbb/HMM88sXP+9730ve++9N01NTRx22GFMnDiRV155pcdfo7Q0onBsRDweEdPLn18Jp1SVGkqj9cR9jkU34v06xXUtQ4Cv1CyiV8Yt1eqXvfXEUq3ftJRN9h4bLP3oixemPb1U648auvVSx7A0PrHBNku9jXz20aVa/8HVlu58y4Lzv7NU6y+Hel9bIgD23HNPjj76aH7wgx+wzz77LOxFA3j++ef52te+xnHHHbewbN68ebz88stsuummfO9732Ps2LFMmDCBiGDGjBm88cYbC+tWDsscOHAgANOnT++BVyUtU98EjqIYEv4MsDHwDWAAcHYN45K0DDVUEpeZEyt+nwx8vobhSKpTtiW9V//+/dlnn3344Q9/yAMPPNBi2brrrstxxx3HIYccsth699xzD9/73ve4887hx9T3AAAgAElEQVQ72WKLLYgIhg4dSqYzw9XKdQ+Ob/H8k9uNrEkcDehwYI/MbD57eGtE3E0x426nkriIOAY4FHgX8JvMHNNOvZ0pRivMrCj+cmZe2LXQl0MTWn29rPmL2sShutNQSRxARLwPGAOsmZmfiIj3ACtl5n21jUxSPbEt6Z4p4Nu7Vq4aJ598Mvvssw/bbdcyvi984QuccMIJbLPNNowaNYqpU6dy2223sffeezN9+nT69u3LiBEjmDdvHmeeeSYzZsxY6ljUdX//z+stnpvELTMjgMdblf2b6q7pfQU4nWJY+Yod1H0tM9fooI7aM/WXLZ+bxKmTGuqauIj4DHAjxWQEHyiL+1Dc+FKSOsW2pHdbffXV2WWXXRYr32uvvTjhhBP47Gc/y5AhQxg1ahTXXXcdEcFHPvIRPvaxj7HJJpswcuRIhgwZwpprrlmD6KVu9zhwWKuyMUCnr8/IzGsy8/cUty2Q1As1Wk/cScDHM/MvEfHZsuxRYFQNY5JUf2xLepm77rqr3WX33beoc/Sggw7ioIMOWqxOU1MTY8eOZezYsQvLvva1ry38/ZRTTllsHYdaqk4dRzGE8nDgWWADimGRH+2m/a0SEROBWcD1wImZudjFpBExDBjWqnidbopJangN1RMHrJuZfyl/b/72nUPjJauSupdtiaS6VA753hz4PTAZuA7YopuGgv8beDewFrArsDVwXjt1jwWea/W4txtikpYLjfYPyfiI2CozK6cBfw/FmShJ6izbEkl1KzNfoAdmoiwngWqeCOq5iPgmcAvF5CqtnQtc3KpsHUzkpC5piJ64iLi67Kb/IXBNRBwK9I2I0cBlwDk1DVBSXbAtkaSlkkCbNzfKzCmZOb7yAbzUo9FJDaQhkjhgJYqb8D4LnErRZd8XOAs4PzN/W8PYJNUP2xJJy72I6BsRK1DcL7MpIlaIiH5t1NslItYvbzC+LkXv37U9Ha+0PGqIJC4zPwb8ALgZGAlslZkrZeaGmfmjmgYnqW4s722JE3l0nsdKDe4kiolKjgcOLH//JUBETI+Incp6WwN/AWaUPx8FvtTj0UrLoYa5Ji4zfxIRdwCXAx+PiMdaLW893a4kLWZ5bUv69OnD/Pnz6du3Yb4WutXcuXNpamqqdRhSCxHRF/g8MDYz3+7qdjLzFOCUdpYNqvj9hxTDzyX1sIboiasQFIlptPGQpM5a7tqSlVZaibfeessepg5kJnPmzGHSpEkMGTKk1uFILWTmPOA7S5PASaoPDXPKNSL+BziT4ozQqZm5oMYhSapDy2tbMnjwYCZNmsSECRNqHUqv19TUxNChQ1lxxRVrHYrUlgciYtvMfKjWgUjqPg2RxEXEjRQ34f14Zt5T63gk1afluS2JCFZZZZVahyFp6d0H/D4ifgWMBxaeiMrMX9cqKEnLVkMkccBsigkIJtc6EEl1zbZEUr07FJgLHNKqPAGTOKlBNEQSl5l71zoGSfXPtkRSvcvMDWodg6Tu1xBJnCRJqi9HfvidtQ6hoUVEAGtkphe69mYjvXRRXWMSJ0mSetzawwfWOoSGFBErAecCBwPzgYER8UlgVGaeWdPgtLgVtql1BKpTjXaLAUmSpOXZ94H1gQ9QXBsH8A/gszWLSNIyZ0+cJElS49gTeHdmToqIBQCZ+WJErF3juCQtQ/bESZIkNY5+wFuVBRGxIjCrNuFI6g4mcZIkSY3jQeDIVmUHA/fXIBZJ3cThlJIkqcc9+MxrLZ5vt/FqNYqk4XwDuCci9qOY1OQWYFvgfbUNS22a8ouWz4d9vjZxqO6YxEmSpB53w0PPt3huErdsZOa/I2Jzipt9jwMmAkdk5ou1jUxtmtiq09QkTp1kEidJktRAMvNN4Ie1jkNS9/GaOEmSpAYSEftGxM0R8VhE3FIOrZTUQOyJk6Tl0OEXP1jrEFSHLhyzXa1DUAci4qvAicAvgd8DI4GfRcS6mXlOLWOTtOyYxEmSJDWOLwEfy8wHmgsi4lrgKsAkTmoQDqeUJElqHMMobjNQ6e/AkBrEIqmbmMRJkiQ1jmso7gtX6cCyXFKDcDilJElSHYuIsRVPVwB+HhFHAs9RXBO3DXB1DUKT1E1M4iRJkupbVPw+G/hNxfMny4ekBmISJ0mSVMcy89BaxyCpZ3lNnCRJkhaKiGMi4u8RMSciLu6g7r4R8WxEzIiIP0bE2j0UprRcM4mTJElqEBGxeUT8KSKmRsT8ykcVm3kFOB24sKN9AWOBzwOrUgzb/M2S1pG0bDicUpIkqXFcCjxFMSPlzK5sIDOvAYiIbYF1llD1QODmzLy9rH8S8FpEbJSZ/+nKviV1jkmcJElS49gE2D4zq+l566pRwN+an2Tm1IgYX5a3SOIiYhjFPewqLSlBlLQEJnGSJKnHfWLb9WsdQqN6ANiYnpmRchAwtVXZFGBwG3WPBb7dbZEcvW23bbpq1cSyxXotn/+4ytfRmX399KHqtlnL/dSrjo5PNxwbk7hOiIh+wK2ZuWutY5EkqRFst/FqtQ6hUR0GjI2I24EJlQsy89fLeF/TgSGtyoYC09qoey5wcauydYB7l3FM9WXciFpHoDplEtc5fYAP1DoISZKkDnwG2BXYkpbXxCWwrJO4x4B3Nz+JiCHABmV5C5k5haKXjor6yzgcaflhEleKiDuWsLipxwKRJEnquuOBj2fmLV3dQET0pfgfsQloiogVgPmZObdV1cuAByJiV+CvFDNa3u+kJlL3M4lbZHvgO7QaelDqB+zYs+FIkiRVbT7wx6Xcxkm0vH7tQOASYExETAd2z8x7M/OJiDgc+BWwBnAfsP9S7ltSJ5jELfIw8O/MvLr1gogYAPys50OSJEmqyq+Aw4FfdnUDmXkKcEo7ywa1en4VcFVX9yWpa0ziFjkXmNTOsrnAoT0YiyRJDe3lSTNaPF97+MAaRdJwdgC+HhFfZfGJTZygrbcZ0fJzwOt+DtQ5JnGl8kxSe8sWUAwjkCRJy8DP//h4i+enjd6uRpE0nDvLh+rB6H+3fP7jbWoTh+qOSZwkSVKDyMxTax2DpO5nElcqZ2I6gWIYwjjg7Mx8rWL5o5n5rq5u/+3Z8zjqxD/w9ux5zJ+/gKMP+S/ev/3IFnVmvT2X475zG5Onvs3QwQM467jdFi57/YXp3Pzz4mzN/LnJpFdm8rXLFt31YNw9E3nwpheJCAas1Je9vjaKASst+c/7+gvTufGCRdt885WZfPPy9u+k8KUjL+LfT7zM6APex+FHthyR8ceb/8WVv72fPhEMHDSA07/7GQYNWqFFnZkz5nDGV/5I335NzH57Hgd8cRu23HathcunTJrFj0+/l7lz5zNi9YF84bgd6Ne/5cSg4x5/kdPPvBpI9ttnB/bea/s2Y73/gac45LCfcPefTmWNNVbu9Povvfwme+3zPTbbdG0ADj/0g2zy34tugfP0v1/lvO/eTlOfPjQ19eHrJ3+EtdYZttj+v3PyTbzx2jTOueAzbcY3d94C9jj2Rj618wZ8cZ9RLZY98NirfP3cvzByrWK/xx2yNay2aos6E158i28d/Ae+dd5ubLrlonstLZi/gCsueJgXnpnM/PkLGPPV/2LtkUPbjKHSNdf8hSuvvI8ATvrf0WzR+uaj3by+JEmSOs8kbpHvAjsBlwLvBx6OiI9k5qPl8pFLs/GmpuC0r+/KOmsMYfLUWXz2S1cvlsRd+YdxjNp0NT6//7bcdMdTXHjFPxixX5EgjFhvEAefWdwN/vH7XmX8Iy0v39vsvauxxfvXAOCuy//Do3dOYNuPr7vEmEasN4gxZxXbHHffqzz3SHuXBBZOOnVv/nb/M7z26tTFlu2y2xZ8ePfiVjEX/OQ2brrhn+z32fe2qLPCiv04/Wcfo6lvHya+PI0f/u9dfG/soiTuml8/wi4f25gdP7Qh1176CHfd/Awf+uSmLbZx+plX8/3vHszqqw3lM/v/kA/u+i6GDl2pRZ3M5OJL7mRUG4lEZ9bf4p3rcvGFxyx8/srsFxf+vsqqA/neT/ZlpYH9uf++Z7n4gj9zwhkfb7H+f556jenTZrd5DJv97rZn2GDt1vdHXeQD26zFGV9clGA+2Gr5dZc8ymZbrb7Yenfc8AxrrDuY/Y9+zxL3X2nq1BlcdumdXPG743jt1Sl885sX8ZvffqPH1m9EFZMh7QYMB54F/jczry+Xj6KYfGDLctkXM/PectkhwP8A76C4Ye7vgOMzc065fD/gWGAr4G+ZuXPPvTJJvV1ELKC4J9xiMtNbJkkNok+tA+hF9gM+kZk/zsx9Ke6zcltENA/Sb7NB7Kx+fZtYZ43in/YB/fvSp40bXI5/cQqjNi16VbbcfHUeePilNrf16F0TGLXzmi3Kmvot+lPOnT2fVder7sLYR+6awJatttna6mu036PTr9+i8wGzZs1lo40XTzD69Ama+hZxzpoxh/U3XrnF8ldenMpGmxc9Thu/cwSP/WNii+Vz5sxl1qw5rLvOKvTv35dtttmIRx59frH93HzrP9lxh81ZaaX+XVr/30++zP4Hncs3v3Upk6e0vOB4+KqDWGlg//I1N9HUtPhH6NJf/ZUDDmu7hxBgxqy53PvPCXz4v9tPsu97eCIHnHQ7p1/4EG/Pntdi2TOPv8HQ4Suy8oiVFlvvb3e+wJuvzuCsL9/OJf/vQebNnd/uPpo98sh4ttlmY/r378s6667KjBlvM2dO61sBdd/6Daov8CLwAWAoRXvym4jYJCL6ATcA1wIrU9za5LqIaP5ArESRpI0AtqU4uXRCxbYnUUzEdHYPvA5J9WcXipt9Nz8OopiB++haBiVp2TKJW2QIFbNTZuavgc8DN0bETstyR9/56b0cPnrxnpJNNlyFe//2AgB33/88U996e7E6M9+aw5svz2TdzRdPqP5528v8/H/+youPT2HEeoMWW96emW/N4Y2X2t5mNa675iFG73UeD/9jPBtutFqbdd58fQYnfuFGTjv2j2z/gfVbLFt/w5X55/1F4vqPv77E9Lda9mZNnjKTIUNWXPh8yOAVmTp1Zos6c+fO5+r/+yv77fu+xfbdmfVXGzGE22/9Nr+59Fi22XpDvn/OdW2+jlmz5jD2Z/fymUNaXoj/8EMvsM56wxm+SvtJ9IXX/5uDP75Ju8u32Gg4t/54Dy4/YzcGrdiPsde3vOj5+l8/xh4HbNHmupPfmMmw4Stywnm70a9/E3ff1PH9VqdMmcGQit7IwUNWYsqUmUtYY9mu34gyc0ZmnpKZ4zNzQWbeDDwFbAfsDKwIfD8zZ2fm5cDTwN7luueX91+anZkTKEYH7FCx7dsz80rglR5+WZLqQGbe3erxG4oT1QfWOjZJy45J3CJPA/9VWVAOfTqY4oz5Cm2t1CwihkXEyNaPX/7mIQ469hpO+v6fAPjZr//GoIH9+fTu71xsG5/+2DuZPWceB3/lGl59Yzqrrbp4Ivb4fa+y+ftWI9roydv6Q2tz5I/ey2bvW437r128h6k94+57lXfu0PY2q/HJvbflimu/zAc/NIpLL763zTqrjBjImRd8nO9euAe/Ouf+Fsv2PuTdPD3uDb59zM0smL+A4asWicFll9/DQWN+xI9+ciNvvTVrYf1p02YtNhTyyqv+zJ57bEf//ot6BqtZv3//fgwaWPyp9/zEdjz22AuLvYZ5c+dz2nE38Nkx2zNyw5bXqv3mogcYfcjiM6xddvNTHHTyn/jWT+/niecms8O72+/1HLRiPwaU1wJ+YqeRPPafRcNcH/7ry2yw2SoMHjqg7XWHDGDL7YshqltuvyYv/mdKu/tpNmzoQKZVHJfp02YxbNjivXzdtf7yICJGAJtTXG87Cni0nPW22cNleVveX65X7T4Xa5OAdardjqSGMJ5i+LakBuE1cYv8iOKfqD9XFmbmLeU1KCd1sP6xwLdbF7484TUuPXdvAC679l88/9IUzv7Wh9rcQP9+TZz85Z0B+N0Nj7HGiEEsaFXnsbsnsscxiyeA8+bMp2/5j/8KA/syd3bHw+iaPXrXRD7xpcW3WY3Zs+cyYEA/AAYNXoG3356zWJ25c+YvnKhkxYH9WbHVxCsDB/Xny99+PwCXX/B3ttyuSEYOPOD9HHhAUT76gP/HK69MYsSIofz9n89yzFG7t9jGU89M4MUX3+CGGx/iySdf4RvHX8qvfv7FTq8/bdosBg8ueuvuf+ApNtigZY/iggXJmSfdyI67vIMdd3lHi2UzZ8xh0pszOO34G5g9ex7jn32Ty371Vw783Hs5cPdNOHD3TfjLIxM574pH+NwZd/LqpFnMmbuATUeuzK7brr0ohhlzGFwO2bz/sVfZYO3BC5c9//Rknvjnqzz92Ou89OwUJrwwlWNO2ZFV1ygS/s22Wo1nn3yT1dcZzHP/nsTqFeu2Z8t3j+Tcc69j7tz5vP76VFZaaQD9+/frcL1ltX6jKydNugz4XWY+HBGfAFpfWDoFWKWNdQ8GdqS4/q1abbZJkhpbRLS+IHwgcARFIiepQZjElcrhk+0tuwO4o4NNnAtc3Lrw2M/t/BzAm5NnctZP7mWrd67BwV+5FoCLz/kUk6bO4sIr/sHxR+3EM+Mnceq5d9HUJ9h0o1X5xhd24IoZTy7c1uSJM5k/dwGrrlsM1Zv47DSee/hN3rv3SP567fMLJyZZcVC/TidlkyfOZN68BYxYt+Nr6M445RoeefgF5s6ZxxPjXuaIoz7I3/76DAcd+n4uveheHnygGLo3ZOiKnHzapxdb/4VnJ3PReX+jT1OwYH5y6LHb89xTb/KvB1/hUwe8i0cfeoWrLv4XfSJ417Zrss37Fr9m7MRvfZqvfuMSINl/9I4Le9K+9s1LOOd7h3DqyYtmgzxozI/4/tkHLUwuO7P+A397mp+efwsDBw5gQP9+nH7qaGDRdXH33vEU99/3LJMnzeS2mx5nw41X5b933Igpk2fy4T224FdXjAFg4itT+f5pt3Dg51pO7vK+LdfgfVsWE9Bcc+ezvPrmTHbddm1enzyLC69/guMPeQ833Ps8/3fHs6w4oImVBw/gzKO3p/ld8MmDR/HJg4sOm5+f9Vd23mMjXnn+Lf79r9fY8SMbssf+7+QX37mfO657mkGDB3DkSYsPK21t6NCB7L//BzjooHMI4IQT255Rs7vWb2QR0YdiOCQUw7MBplMM3640lGISk8p19wR+AHw4MydSvbbapHWAtrvJJTWK8bS8jj8oJlA6uCbRSOoWkblU83U0lIgYSnFdyihgMMU/VY8B12Zmx+PS2pCv/GSpDvBlbz2xNKvTtHQjJNljg107rtSBF6Y9vVTrjxq69VLHsDQqZ6fsqjWfbXuSms56cLWlG/m83erL9LLOLgl2Wcp3Y32JYnzyWGBDYPfMnFmWfwj4NbB285DKiLgf+GVmXlg+/yhF790emXl/O9v/HHBgNbNTlkMqnwM47KK/del1afl24Zhld0Puk69oOe9uT97se/z48WywwQYAG2Tm+B7bcQ+IiPVbFU3LzCVPP10jzW3Sc889x8iRI9uvePS2PRNQJ+zy+KIRLnfe2c491auJ90t/b/m8O272/dOHOq6zLI5xZ/bTqDo6fh0cm660SV4TV4qIHSnOVB1JMfRgEsUscZ8HnomIHZawuiS1dj7FdXB7NCdwpbuAt4GvRcSAiPgssAnFtbdExK7A5cCn20rgIqIpIlagGEnRJyJWiIj+retJWj5l5vOtHr0ygZO0dBxOucjPgC+Vszi1UP6TdQHQ5Zt9S1p+lGfCjwRmAxMqJg06KzPPKodK/go4jeLk0acq/tH6X4rhlTdWrPd8ZjZPSXoQcFHF7mYBd1PMeilpORURJ3dUJzNP64lYJHU/k7hFNgKuamfZ/1H8wyVJHcrM5ymuQ2lv+aNAmzcTzMxdOtj2xbRx/a2k5d6S2o5RwHCKE0eSGoBJ3CKPAF+mmEigtS8Bj/ZsOJIkSZ3T1gmg8pqz71JcHnJWD4ckqRuZxC1yBHB9RHyVImGbSjGD3Lsorl/Zs4axSZLUULbZaEStQ2hYETEIOBH4H4rrbTfLzKWfpUvL3mOrdlxHaoNJXCkzH4uITSiuKxkFDKKYCvwHwF2ZOa+G4UmS1FA+ud3IWofQcMpZcT9PMWzyP8CumflAbaPSEt3ZejJRqXNM4loaCYwA7sjMRyoXRMTxmXl2TaKSJElagoj4MMWJ58HA/2Tm72ockqRuZBJXiohPAL8BngI2i4grgCMreuBOAEziJElSb3QL8DrF/Sk3bWu2SmenlBqHSdwipwH7ZuYtETECuBS4ISI+lZmzWcJMc5IkSTV2D5DAf7ezPHF2SqlhmMQtsmFm3gKQma9HxMeBy4Cby146SZKkXikzd651DJJ6Tp9aB9CLTI6IdZufZOZ8YH9gPHAb0FSjuCRJkiRpIXviFrkdOJSKoQaZmcBhEXEB7Q9PkCRJVTr5igdbPD9t9HY1ikStRcQw4BfA7sBbwJmZ+bM26o0BLgRmVRR/KjNv74k4G8KX/t7y+Y+3qU0cqjsmcYscRTvHIzO/EBHeJFOSJC0PfkLxP9FawEbAbRHxRGbe2UbdBzPTE91SDzOJK2XmHGDOEpa/0IPhSJIk9biIGAjsC2ydmdOAhyNiLHAY0FYSJ6kGTOIkSZLUbBMgMvPxirKHgQ+3U3/LiHgDmARcTjH0cl7rSuUQzWGtitdZBvFKyyWTOEmSJDUbRHEdXKUpFDcRb+0eYAvg+fLn74AFwOlt1D0W+PayC1Navjk7pSRJkppNB4a0KhsKTGtdMTOfzcznMnNBZj5KMTncPu1s91xgg1aPnZZZ1NJyxp44SZIkNXsKyIjYPDOfKMu2Ah7rxLrZ7oLMKRQ9egtFRJeDlJZ39sRJkiQJgMycAVwNnB4RgyNiS4pJTca2rhsRu0fE6uXvmwH/C1zbk/FKyyuTOEmSJFU6mqJXbQJwC3BKZt4ZEetFxPSIWK+s90HgkYiYAdwEXAOcWZOIpeWMwyklSZK0UDn0cd82yl+gmPik+fnXga/3YGiSSvbESZIkSVIdsSdOkpZDF47ZrtYhSJKkLrInTpIkSZLqiEmcJEmSJNURh1NKkqQet+bKK9U6BKn2XvNzoK4xiZMkST3uix/ZotYhSLX3u81rHYHqlMMpJUmSJKmOmMRJkiRJUh0xiZMkSZKkOmISJ0mSJEl1xCROkiRJkuqIs1NKkqQed/6t41o8d7ZKLZc+80TL585WqU4yiZMkST1uwuSZtQ5Bqr3V/ByoaxxOKUmSJEl1xCROkiRJkuqISZwkSZIk1RGTOEmSJEmqIyZxkiRJklRHTOIkSZIkqY6YxEmSJElSHTGJkyRJkqQ6YhInSZIkSXXEJE6SJEkLRcSwiLgyIqZFxMsRcdQS6h5T1pkWEb+LiCE9Gau0vDKJkyRJUqWfAH2BtYCPA6dGxC6tK0XEh4Bvl3XWBvoBP+7BOKXllkmcJEmSAIiIgcC+wEmZOS0zHwbGAoe1UX0McFFmPpyZbwEnAp+JiJV6LGBpOdW31gFIknpMU/Mv48ePr2EYEkx5/ZUWz3vyPfnSSy81/9q0pHrLqU2AyMzHK8oeBj7cRt1RwE3NTzLziYgAeAfwr8qKETEMGNZq/fWhxd+jbW/N7lzkPWDWrEX/Orf7nq0m3peXYt3O6sxna1nsd3n+Xuno+HVwbLrUJmWmjxo9KBqzU4BhtVi/N8Tga/AY+Oi5B/BRIH348LHwsWOtP5e97QHsBLzRqmx34Jk26v4H2KNV2attHVeK74ha/719+Ojtj063SVF+sFQDETESeA7YIDPH9/T6vSEGX4PHQD0nIjYBngQ+ALxQ43AqrQPcS/HPYwen5HuUcVWnnuJqAtYEHszM3tPN0wtExNbAA5nZv6JsNHBcZm7dqu6/gO9m5m8qymYB/52ZnemJ6w9sCDwNzC/Leuv7qJnxLR3ja1vVbZLDKSVp+TGn/PlCb0q2y+FXAC8ZV8eMqzpLiOs/PR9NXXgKyIjYPDOfKMu2Ah5ro+5jwLuB3wBExGZAUCRlLWTmFGBKO/tbqLe+j5oZ39IxviWqqk1yYhNJkiQBkJkzgKuB0yNicERsSTGpydg2ql8MHBoRW0bEYOAM4HeZObPHApaWUyZxkiRJqnQ0xfU5E4BbgFMy886IWC8ipkfEegCZeRtwellnArAA+FKNYpaWKw6nlCRJ0kLl0Md92yh/ARjUquzHeG84qcfZE1dbU4BTaXuMeE+s3xti8DV4DNRzeuvfybiqY1zV6a1xqW29/e9lfEvH+JYRZ6eUJEmSpDpiT5wkSZIk1RGTOEmSJEmqIyZxkiRJklRHTOJqJCKOiYi/R8SciLi4ynUHRMSFEfF8REyLiH9FxJ5diOGciHgxIt4qt3Vitdsot7NqRLwREfdXud5dEfF2OV3x9Ijo0o1XI+LTEfFYRMwoX8fenVxveqvH/IioaoatcrrlP0TEpIh4LSIujohBHa+5cP13RMQfI2JKGfvhHdRv930TEaMi4v6ImFkej526sI1fRMRTEbEgIsZUs35EbBIR10XE6xExOSJui4h3du5IaFmJiGERcWXZNrwcEUeV5euW74/JEXFOq3V+GRGf6ua42vy893RcXf0MRcQHI2J8REyIiNEV5f0i4oGIWLcb48qyfWs+dhdXLOu2uDr6rqnV8epEXDU5XqpOROxcftdUfg8fXrH8G1H8bzEuIt5VUb5RRNwXEU3dGFuvbEcr9tUr2tOKbffKdrWT8dVve5GZPmrwAPYGPgWcD1xc5boDgVOAkRSJ+O7AdGCTKrezGTCw/H1tYBywXxdey0XAPcD9Va53F/CFpTyOuwIvAjuWx2IEsGEXtjOoPIbvr3K9m4BLgRWB4cDdwHc7uW5f4AnghPL3bShmQ/pAte8boB/wHHAcMAA4AJgErFzNe4/i3kAfBB4CxlQZw38BhwOrlK/n22VMsTR/Yx9Vv5cvA64BBgNbAZkh+qoAACAASURBVK8DuwA/A84sy58Gti3r7wD8vgfiavPz3tNxdfUzBDwOfAjYoixvKstPAL7SXXGVyxLYrJ31ui0ulvBdU8vjtaS4anm8fFT9d9wZmNjOsjXLtms14AvAHyqW3dTcTnRjbL2yHa2Ir1e0pxX77ZXtakfxlcvqtr2o6c59JMAZrd9QXdzOP4ADlmL9tYFHgROqXO8DwH3AodQmibsPOGIZHL9DgGepMuGgSMI+VvH8y8CNnVx3C2AW0Kei7CLgkmrfN2UjM7HVth4ADu/Ke688rmOqiaGN5UPKxnHtpf37+Oj0+3EgMBt4Z0XZdylONNwMfLgs+y2wH0Wy/VdgvR6Irc3Pe63iqvYzVH5W+5e/T6D453ID4M/NX+zdEVdZtqR/Mnokror9/YPiH7Fecbxax9XbjpePJf7Ndqb9JG574C/l75sCj5e/jwZ+3M1x9dp2tCKeXtWeVuy/V7ar7cVXltVte+FwygYQESOAzSl60qpd9/iImA68RNEbdVkV6/YHfkLRe5PV7rt0RkS8GRF/iYhdq1mxHErxX8DwKIYAvhIRF0XE0C7EcQjw6yw/qVU4F9g/IgaWf4d9KBrRzohWP5t/37LKGABGAY9m5oKKsofL8lp5P8WZqwk1jGF5swnFiYjHK8qa3wePAbtGxBCKXt9xwFeB/8viBr49oa3Pe2+ICzr+DD0GfDAiRgELgDeAH1GcjZ3fA/HdERETI+LaiNiworzH4mr1XdNrjlc734E1P17qlFXKv9NzEXFeLLoc4Rlgw4hYk6IHbFzZRnwd6NKlH1Xo7e1os97cnjbrNe1EB+qyvTCJq3MR0Zci8fpdZj5c7fqZeTZFt/t7gF8Dk6tY/Xjg9sz8V7X7LR1HcUZjLeDnwA0R8Y4q1l+doqt+NMWwyncCq1IkVp0WEetT9CheUs16pfsohqVOBV6jGA55fifXfRJ4GTgxIvpHxPbAXsBKXYhjUBlDpSkUf9seFxFrURyHr7dqvNW9BgFvtSprfh98h+Lzdi/FkJvplMNLIuL8iLgnIs7oxtja+7zXOq5mHX2GjqBo8y4EDqYYnvMCMDGKa0Hvjoh9uym2D1AMHdyMos24MSL69WRcbXzX9Irj1c53YM2Plzrl38C7KdqEXYGtgfMAMvNN4CvAjcCeFMnbWRQ9Yu+JiDuiuJ68O05U9uZ2tFlvb0+b9Yp2ogP1217UuitweX+wFMMpKZLw3wK3Unb3LmUsxwM/7GTdjSmGHw4pn4+hyuGUbWzzFqoYYwwMo+gBPLyibHvg9Sr3exJwdxfibaK4Hu/bFGO9h1OMof9JFdvYAvgTxdmdP1N8gf2p2vcNxZfdH1vVuQD4f11577EUwykpEulxwClL+570UfV7cmtgTquy0cA/26h7HfBe4CjgFxS9wLcBH+2hWNv8vPdUXEvzGaL4B+QhYChwJbB/+fuLwPBlGVcby5uAGcDWPRVXW981veF4tRVXbzhePtr9ex1AkVRMB8a1sfy/gSntrLsd8Ifyb/4isD7FtfBL9X9HO/uqm3a0Io6atqcV++uV7Wp78bWxvK7aC3vi6lREBMWZgbWAvTJzzjLYbF9go07W3RFYA3gqIiZSJB/vKbujB3Rx/1UNZczMKRQfnq4O5Wx2MF3rhVsZWIciaZudmZOAscBHO7uBzByXmR/MzFUzcweK3sWqZvksPQa8KyIqP9NbleU9JiJWpviCuCkzT+nJfQuAp4CMiM0ryhZ7H0TEXsCEzPwr8C7goSy+pR6ia8N5u2Kxz22N46rmM3QG8IPMnFoR51SKYekbd3Oc0H6bt8zjWsJ3TU2PV5XfgT12vNS+zLw8MweVjy3aqkLLywuAhZdO/D/gfygmL2vKzOeBB+medqGe2tFmva09bVZP7WqzumkvTOJqJCL6RsQKFFl/U0SsUNF92xnnU1wDsEdmzuzC/vtFxBFRTKPbpxzKdzRFr1Bn/A7YkOLDuBVwMsXEKFtl5uxO7H9YRHykfN19I+IAimuoOns9WbNfAcdExBoRMZhixqDrO7tyRLyPYlKXq6rcL5n5BkVv5BfK4zmUokfykSr2/66IWLE8DodSzAz5wyXUb+99cxfwNvC1KKbf/izFuP5rq9gG5bDOFSi+SPuVy5o6s345Bv9WiovRv9HZY6BlJzNnAFcDp0fE4IjYEjiM4uQCAFFcc3ICRc87FDOH7RzFNa47ULynl6nOfN57Kq6l/QxFxHuAd2TmFRVx7hoRqwPvoBhqs8ziiogtImKriGgqj9E5wCu0uga6u+Ki/e+au6jh8Wovrl5wvNRJEbFLRKwfhXWBs2njOws4hmLCsGeBN4EVo7h9zS50Q3vVW9vRin33mva0Yn+9sl3tKL66by9q0f3nI6GYHjlbPS7u5Lrrl/XfZtHQhOlUMbMkRa/brRQTT0ynOPP0Lbo4HTxVDqekOJv2IDCNYnz0/cCHurDfvhQXmU6iuCbtIsohnp1c/+fApUvxd9wSuIPiWsI3gP8D1qpi/e9U/A3uokiCu/S+oTgz9ADFbErjaOd2CR1s4642lo3pzPoUk8MkxVCEyvflTt35WfKx2N93GMVJiekUX0ZHtVp+DhUz2VIMB7mV4rqF39A9M4J1+HnvqbiW5jNEceLzbmCjirJ3U0xD/Qbw1WUdF8W1Qk+Wn6vXgN9T/DPR7XHRwXdNrY7XkuKq5fHyUfXf8asU1yDNpBhV8yNgcKs6a1HMrNivomx/igmzxgO7dFNsva4drdhXr2lPK7bfZvtVLqtZu9pRfPXeXkQZjCRJkiSpDjicUpIkSZLqiEmcJEmSJNURkzhJkiRJqiMmcZIkSZJUR0ziJEmSJKmOmMRJkiRJUh0xiZPaEBGnRMRdtY5DkiRJas0kTr1SRNwVERkRn2tVPjQippfLRi7DfZ2yLLYlqf6VbcKcsq15KyLGRcQRVayfEbFzN4YoaTlim6S2mMSpNxsHfKFV2cHA+J4PRdJy5qzMHAQMA04Ffh4R7++pnUdE34iIntqfpF7PNkktmMSpN7sOWDsitq0oOxL4eWWliDgiIp4oz079MyI+UbFs5/IM1F4R8VRZ59aIWLNcfgGwE3BCeYZrYqttfzsiJkTEpIg4PyKauu3VSup1MnNBZl4JTAL+CyAiti/PjL8ZEc9HxOkR0bdcNq5c9eayTbmqLB8fEWMqt115dryirRodEc8AM4GBZdlREfGXcnuPRMT7KraxS0Q8FBFTy3j+HBErd/NhkVQjtklqZhKn3mwu8CvgiwDlGafBwI3NFSJiP+B7wOeB4cBpwNWtEj+AvYDtgPWAIcAZAJn5BeBeyjNcmblGxTo7AFPLdd4LjAb2X7YvUVJvVp593h9YBXgyIjYFbgd+CqwOvB/4BHAcQGZuUa66e9mm7FvlLveh+MdsCDCjLPsccBDFGfi7gUsr6l9WxjIMWBP4OjCnyn1KqhO2SWpmEqfe7hfAvhExlGJo5S+BBRXLDwd+mZn3Zua8zLwWuIGigal0fGZOzcwpwOWUZ6868FxmnpuZczPzSeBPnVxPUv07PiKmAG9T/INyQmbeABwN/D4zryrbnOeB7wCHLqP9HpeZkzLz7czMsuwHmfmfzJxHMRJhw4hYpVw2B9gIWCsz52TmXzNzRlsbllTXbJPUgkmcerXMfBG4k+JMzp7Aha2qrAs826rsGYres8rtvFLxdDpFj15HXmn1vLPrSap/Z2fmMGBl4CJgt3J40jsoTixNaX5QnFxaYwnbqsZzbZS1br9gUVu0J7Ah8PeIeLocAu6wb6nx2Caphb61DkDqhPOBm4D/y8wJ0XJWyheBDVrV3wh4oYrtL+i4iqTlUWZOi4ijgScoznhPBH6dmZ9f0mptlE0DBjY/iYi12tlfVe1RZj5KOcw7IrYCbqVo/y6qZjuS6oNtkprZE6d6cCvwIeArbSwbCxwRETtERFNEfJLiLNDYKrY/Edhk6cOU1IgyczbF9bYnARcD+0XEpyOif9nubBwRH61YZSKwaavNPATsH8VtUoYCZy9tXOX+D42IEWXRVGB++ZDUoGyTBCZxqgNZ+FNmvtTGst8BJ1AMs5xMMe3uZzLzb1Xs4hxgVDkMYbF9SBLFNSiTgN2Aj1DMlPsy8CZwNbB+Rd1vASdGxOSIuKIsO4liUoCXKP55unYZxbUPMC4iZlBMMHAxxcQCkhqbbdJyLhZdoyhJkiRJ6u3siZMkSZKkOmISJ0mSJEl1xCROkiRJkuqISZwkSZIk1RGTOEmSJEmqIyZxkiRJklRHTOIkSZIkqY6YxEmSJElSHTGJkyRJkqQ6YhInSZIkSXXEJE6SJEmS6ohJnCRJkiTVEZM4SZIkSaojJnGSJEmSVEdM4iRJkiSpjpjESZIkSVIdMYmTJEmSpDpiEidJkiRJdcQkTpIkSZLqiEmcJEmSJNURkzhJkiRJqiMmcZIkSZJUR0ziJEmSJKmOmMRJkiRJUh0xiZMkSZKkOmISJ0mSJEl1xCROkiRJkuqISZwkSZIk1RGTOEmSJEmqIyZxkiRJklRHTOIkSZIkqY6YxEmSJElSHTGJkyRJkqQ6YhInSZIkSXXEJE6SJEmS6ohJnCRJkiTVEZM4SZIkSaojJnGSJEmSVEdM4iRJkiSpjpjESZIkSVIdMYmTJEmSpDpiEidJkiRJdcQkTpIkSZLqiEmcJEmSJNURkzhJkiRJqiMmcZIkSZJUR0ziJEmSJKmOmMRJkrQciogDImJcxfOLI+LiGoYkSeokkzhJUq8VEXdFxJyImB4Rb0XEuIg4osptZETs3E0h1oW2ErTMvDwzt6hRSJKkpWASJ0nq7c7KzEHAMOBU4OcR8f6eDCAi+kZE9OQ+JUlqj0mcJKkuZOaCzLwSmAT8V3N5RGxf9ti9GRHPR8TpEdG3XNY8XPDmsjfvqrJ8fESMqdx+ZY9dROxcPh8dEc8AM4GBZdlREfGXcnuPRMT7lhR3RBwUEU9HxLSIuCYizouIuyqWdxTLmhFxY0S8VvZGPhgRu1bUHVnWP7CMZ1oZ32bl8hOAA4ADypinR8QqETEmIsYvIe5hEXF+eUzfjIibImLDiuX7lT2jb0XEGxFx+5KOgyRp2TGJkyTVhbI3bH9gFeDJsmxT4Hbgp8DqwPuBTwDHAVQMF9w9Mwdl5r5V7nYfioRxCDCjLPsccBBFz+DdwKVLiPl9wK+AY4GVgQuBqoaDAk3lNjYAVgWuA66NiFVb1TsI+BAwAphIcUzIzLOAy4HLy2MwKDPfXNIOy17Ha4FBwNbAWsAjwB8iol9ErARcBnwpM4cA6wBnVfm6JEldZBInSertjo+IKcDbFAnTCZl5Q7nsaOD3mXlVZs7LzOeB7wCHLqN9H5eZkzLz7czMsuwHmfmfzJwH/BzYMCJWaWf9Q8v4bizjuxG4oZ26bcrMlzLz2syckZlzMvMMIIHtWlU9NTNfzcy3gbFU9FZ2wdbAe4Ejy9c/GzgRWA/YvqwzF9g8IlYtj88dS7E/SVIVTOIkSb3d2Zk5jKIn6yJgt+bhksA7gH0jYkrzA/glsMYy2vdzbZS9UvH79PLn4HbWX6eNbbS1zXZFxPCIGFsOu3yrfI1DgNU6iGtQNftp5R1Af+CViuP6JkWv4LqZORP4KLAb8GQ5jPOYpdifJKkKfTuuIklS7WXmtIg4GniCogfuPIphg7/OzM8vadU2yqYBA5ufRMRa7exzQdcjBuAlYGSrstbPO4rlbIqhlDuwKFGbDFQz0coCqjtxOxGYBaxa9jguJjPvBe4th15+ALglIsZl5p1V7EeS1AX2xEmS6kY5rO804KSIGAL8DNgvIj4dEf0joikiNo6Ij1asNhHYtNWmHgL2j4ihETGUIlHqDpcAe0XE7mVsu1Ncs1dNLEMpEqrJwArAGVTfyzYR2DgimjpZ/z6KZPlnEbEaQESsXB7nlSJijYjYNyKGlcNMp1Aky/OrjEuS1AUmcZKkenMpxQyV38jMB4GPAEcCL1MM+bsaWL+i/reAEyNickRcUZadRDFRyUsUSdS13RFoZt5XxvZjikTn8xSTlFTqKJb/pUjkXqeY0OXVsm41fkExFPKNcnjk8A7ink8xScrbwAMRMQ34F7AXRbIWwBeAZyNiOsUxPyEz76kyLklSF8Si67QlSVJ3i4hTgJ0zc+cahyJJqlP2xEmSJElSHTGJkyRJkqQ64nBKSZIkSaoj9sRJkiRJUh3xPnHdKCIGANsBE3DaZUmSJEmLawLWBB4sb6XTIZO47rUdcG+tg5AkSZLU6+1EcZ/ODpnEda8JAPfeey/rrLNOrWORJEmS1Mu89NJL7LTTTlDmDp1hEte95gOss846jBw5ssahSJIkSerFOn35lRObSJIkSVIdMYmTJEmSpDpiEidJkiTVwKRJkzj++OOZPHlyrUNRnTGJkyRJkmrgiiuu4PHHH+eKK66odSiqMyZxkiRJUg+bNGkSf/rTn8hMbr/9dnvjVBWTOEmSJKmHXXHFFSxYsACABQsW2BunqpjESZIkST3srrvuYt68eQDMmzePO++8s8YRqZ6YxEmSJEk9bOedd6Zv3+KWzX379mWXXXapcUSqJyZxkiRJUg8bPXo0ffoU/4r36dOH0aNH1zgi1ROTOEmSJKmHDR8+nA9+8INEBLvtthsrr7xyrUNSHelb6wAkSZKk5dHo0aN54YUX7IVT1UziJEmSpBoYPnw4Z599dq3DUB1yOKUkSZIk1RGTOEmSJEmqIyZxkiRJklRHTOIkSZIkqY6YxEmSJElSHTGJkyRJkqQ6YhInSZIkSXXEJE6SJEmS6ohJnCRJkiTVkYZN4iJiWERcGRHTIuLliDiqnXqjIuLWiHgzIrKN5RdHxJyImF7xGND9r0CSJEmSFtewSRzwE6AvsBbwceDUiNiljXpzgSuBw5awrR9m5qCKx+xlH64kSZIkdaxvrQPoDhExENgX2DozpwEPR8RYikTtzsq6mfkk8GREbNzzkUqSJElSdRq1J24TIDLz8Yqyh4FRXdze5yNiUkT8IyL2a6tCOXxzZOUDWKeL+5MkSZKkNjVkTxwwCHirVdkUYHAXtvUj4GvAVODDwJURMTEz72lV71jg213YviRJkiR1WqP2xE0HhrQqGwpMq3ZDmfmPzHwzM+dl5k3AZcCn26h6LrBBq8dO1e5PkiRJkpakUXvingIyIjbPzCfKsq2Ax5bBthebwRIgM6dQ9PYtFBHLYHeSJEmStEhD9sRl5gzgauD0iBgcEVtSTGoytnXdKKwA9C+fr1A+b16+T0QMiog+EfFh4EDguh55IZIkSZLUSkMmcaWjKXrNJgC3AKdk5p0RsV55r7f1ynrrA7OAceXzWeWj2ZeBlyl62b4PHJGZd/TEC5AkSZKk1hp1OGXz8MZ92yh/gWLik+bn44F2xz1mpte1SZIkSeo1GrknTpIkSZIajkmcJEmSJNURkzhJkiRJqiMmcZIkSZJUR0ziJEmSJKmOmMRJkiRJUh0xiZMkSZKkOmISJ0mSJEl1xCROkiRJkuqISZwkSZIk1RGTOEmSJEmqIyZxkiRJklRHTOIkSZIkqY6YxEmSJElSHTGJkyRJkqQ6YhInSZIkSXXEJE6SJEmS6ohJnCRJkiTVEZM4SZIkSaojJnGSJEmSVEdM4iRJkiSpjpjESZIkSVIdMYmTJEmSpDpiEidJkiRJdcQkTpIkSZLqiEmcJEmSJNURkzhJkiRJqiMmcZIkSZJURxo2iYuIYRFxZURMi4iXI+KoduqNiohbI+LNiMg2lvePiJ9HxJSIeD0iTuv+6CVJkiSpbQ2bxAE/AfoCawEfB06NiF3aqDcXuBI4rJ3tnAxsCWwMbAfsHxGHLvtwJUmSJKljfWsdQHeIiIHAvsDWmTkNeDgixlIkandW1s3MJ4EnI2LjdjZ3KHBEZr4BvBER55TbuajbXoAkSZIktaMhkzhgEyAy8/GKsoeBD1ezkYhYmaIn71+ttnNWG3WHAcNaFa9Tzf4kSZIkqSONmsQNAt5qVTYFGNyF7QBM7cR2jgW+XeX2JUmSJKkqjXpN3HRgSKuyocC0LmyHVttqbzvnAhu0euxU5f4k6f+3d//BdZX3ncffXyEcTAy2RRIQECjKJoHdDJDdZneTFmKvXRKWTibbDdvbH5lAugqlabdM2gFrkklCSRaXTjtuYZrZqkucbcheUrYzpbvEO7FjA91CNiHQGSp+JChESxChWLaxwWBkffePc0QuimRLsnSPztX7NXPHvs997tHnjOYene99nvMcSZKkI+rUkbjHgYyI8zLzkbLtQuDhuWwkM/dExNPABcDTR9pOZu6lGKV7VUTMNbckSZIkHVFHjsRl5gvAHcANEXFSRJxPsRjJrVP7RuEEYEX5/ITy+aStwKci4g0RcTbwiem2I0mSJEnt0JFFXOnjQAKjwDbgs5m5MyLOiogDEXFW2e9s4CDwD+Xzg+Vj0vUUI29PAA8At2emK1NKkiRJqkSnTqecnN54+TTtI/x4wRIy80lgxnmPmXkIuKp8SJIkSVKlOnkkTpIkSZI6jkWcJEmSJNWIRZwkSZIk1YhFnCRJkiTViEWcJEmSJNWIRZwkSZIk1YhFnCRJkiTViEWcJEmSJNWIRZwkSW00NjbGpk2b2LNnT9VRJEk1ZREnSVIbNZtNhoaGaDabVUeRJNVUd9UBJElaLsbGxtixYweZyfbt22k0Gqxdu7bqWFJtDA4OMjw8XHWMBTM6OgpAb29vxUkWTl9fH/39/VXH6HiOxEmS1CbNZpOJiQkAJiYmHI2TlrmDBw9y8ODBqmOohhyJkySpTXbt2sX4+DgA4+Pj7Ny5k6uvvrriVFJ9dNoIz8DAAAA33nhjxUlUN47ESZLUJuvWraO7u/j+tLu7m/Xr11ecSJJURxZxkiS1SaPRoKur+NPb1dVFo9GoOJEkqY4s4iRJapOenh42bNhARLBx40YXNZEkzYvXxEmS1EaNRoORkRFH4SRJ82YRJ0lSG/X09LB58+aqY0iSaszplJIktdHY2BibNm1iz549VUeRJNWURZwkSW3UbDYZGhryHnGSpHmziJMkqU3GxsbYsWMHmcn27dsdjZMkzYtFnCRJbdJsNpmYmABgYmLC0ThJ0ry4sIm0yMbGxrjpppu47rrrXE5cmofBwUGGh4erjrEghoaGXi3ixsfH2bZtGyMjIxWnOnZ9fX309/dXHUOSlg1H4qRF5vUvkiatWbPmiM8lSZoNR+KkRTT1+pdGo+FonDRHnTTCMzY2xhVXXEFmsmLFCrZs2eIxQZI0Z47ESYvI618kterp6Xm1aNu4caMFnCRpXizipEW0a9cuxsfHgeL6l507d1acSFLV3vjGN3LiiSfSaDSqjiJJqqmOLeIiYk1EfDUi9kfEDyPiN47Q9zfLPvsj4vaIOLnltV0R8VJEHCgfT7RnD9QJ1q1bR3d3MWu5u7ub9evXV5xIUtWOP/54+vr6HIWTJM1bxxZxwC0U1/ydDlwGXB8RP3EGHRE/B3ym7HMGcDxw85Ru12TmqvLxlsWNrU7SaDTo6io+Zl1dXX7zLkmSpGPWkQubRMTrgcuBd2bmfuChiLgV+CgwdT7bFcAXM/Oh8r2fBB6MiKsz88U2xlYH6unpYcOGDWzbts3rXyRJbddJt+joRJO/m4GBgYqTaCZL9RYqHVnEAW8DIjOHWtoeAi6Zpu87gLsmn2TmIxEB8Fbg78vmz0XE54HHgE9l5jembiQi1gBT14o+c957oI7RaDQYGRlxFE6S1HbDw8N89/FHeNMpK6uOoml08QoA+3Y/WW0QTevZ3QerjjCjTi3iVgHPT2nbC5w0Q999U9r2tfS9DhgCDgEN4G8i4sLM/O6U91xDMS1Teo2enh42b95cdQxJ0jL1plNW0vjA26uOIdVO887Hqo4wo069Ju4AcPKUttXA/ln2PXmyb2Z+MzP3Z+bLmfkl4F7g56fZzhbgnCmPi+a9B5IkSZI0jU4diXscyIg4LzMfKdsuBB6epu/DwAXAVwAi4lwggKkjbZNy2sbMvRSjfa8qp2VKkiRJ0oLpyJG4zHwBuAO4ISJOiojzKRY1uXWa7luBKyPi/Ig4CfgccHtmvljepuB9EXFCRHRHxK8AFwNfa9OuSJIkSdJrdGQRV/o4xajZKLAN+Gxm7oyIs8r7vZ0FkJlfB24o+4wCE8Bvlds4nqKo+0fgubL9g5n5aFv3RJIkSZJKnTqdcnJ64+XTtI9QLGbS2nYzP3lvODLzH4F3LVZGSZIkSZqrTh6JkyRJkqSOYxEnSZIkSTViESdJkiRJNWIRJ0mSJEk1YhEnSZIkSTViESdJkiRJNWIRJ0mSJEk1YhEnSZIkSTViESdJkiRJNWIRJ0mSJEk1YhEnSZIkSTXSXXUAaarBwUGGh4erjrFgRkdHAejt7a04ycLp6+ujv7+/6hiSJEnLkkWctMgOHjxYdQRJkiR1EIs4LTmdNsIzMDAAwI033lhxEkmSJHUCr4mTJEmSpBqxiJMkSZKkGnE6pSR1mE5bHKjTTP5uJqdaa+lx8SZJS51FnCR1mOHhYf7hsSGOW72i6iiaxuGJVwB49JnvVZxE0zm871DVERbU6OgoB/a/SPPOx6qOItXOs7tf5MVDo1XHmJZFnCR1oONWr2D1xadXHUOqnX33PF11BEk6Kos4SZKkDtXb28u+FS/T+MDbq44i1U7zzsdYfcrSvM+vC5tIkiRJUo1YxEmSJElSjVjESZIkSVKNWMRJkiRJUo24sEkH8J5QS5v3hFr6vCeUJEmqE4u4DjA8PMzDQ49x3Alrqo6iaUwcSgAeGf5RxUk0ncMv7a06giRJ0pxYxHWI405Yw4lnb6g6hlQ7L/5gR9URJEmS5qRji7iIWAP8GXAp8Dzw+cz80xn6/iYwAJwM3AX0Z+bzc92OJC0Fo6OjjO972ZsWS/MwvvdlRnO06hiSdESdvLDJLRRF6unAZcD1EbF+aqeI+Dng9XU6dAAADkxJREFUM2WfM4DjgZvnuh1JkiRJaoeOHImLiNcDlwPvzMz9wEMRcSvwUWDnlO5XAF/MzIfK934SeDAirgZiDtuRpCWht7eXffECqy8+veooUu3su+dpek/rrTqGJB1RRxZxwNuAyMyhlraHgEum6fsOiimUAGTmIxEB8FaKkcpZbaecdjl1ZZEz55V+jkZHRzn80vNe2yPNw+GX9jI6OlF1DEmSpFnr1CJuFcX1a632AifN0HfflLZ9Zd+Yw3auoZiWKUmSJEmLplOLuAMUi5S0Wg3sn2Xfk8u+XXPYzhZg65S2M4F7jx732PT29rL3YJerU0rz8OIPdtDbe2rVMSRJkmatU4u4x4GMiPMy85Gy7ULg4Wn6PgxcAHwFICLOpRiB+27576y2k5l7KUbpXlVOy5QkSZKkBdORRVxmvhARdwA3RMSVwDkUi5H84jTdtwK3RcRtwPeBzwG3Z+aLAHPYTqUOv7TXa+KWqIlDBwDoWrGq4iSaTnGzb0fiJElSfXRkEVf6ODAIjFJc1/bZzNwZEWcBQ8A/zcyRzPx6RNwAbOPH94n7raNtp437cVR9fX1VR9ARDA+/AEBfn4XC0nSqnyFJHe3Z3Qdp3vlY1TE0jT37XgZg7erXVZxE03l290FWn1J1iul1bBFXTm+8fJr2EYrFTFrbbua194Y76naWkv7+/qoj6AgGBgYAuPHGGytOouXk8L5D3ux7iTp84BUAjlt1fMVJNJ3D+w7BaVWnWDh+SbW07d43DMDqU36q2iCa1upTlu5nqGOLOElarpbqHxwVhoeLk7a+0/w9LUmnddZnyC96lza/6NV8WcRJUofxpG1p86RNknSsuqoOIEmSJEmaPYs4SZIkSaoRizhJkiRJqhGLOEmSJEmqEYs4SZIkSaoRizhJkiRJqhGLOEmSJEmqEYs4SZIkSaoRizhJkiRJqhGLOEmSJEmqke6qA0hTDQ4OMjw8XHWMBTO5LwMDAxUnWTh9fX309/dXHUOSJGlZsoiTFtnKlSurjiBJkqQOYhGnJafTRnjGxsa46aabuPbaa1m7dm3VcSRJklRzXhMnLbJms8nQ0BDNZrPqKJIkSeoAFnHSIhobG2PHjh1kJtu3b2fPnj1VR5IkSVLNWcRJi6jZbDIxMQHAxMSEo3GSJEk6ZhZx0iLatWsX4+PjAIyPj7Nz586KE0mSJKnuLOKkRbRu3Tq6u4v1g7q7u1m/fn3FiSRJklR3FnHSImo0GnR1FR+zrq4uGo1GxYkkSZJUdxZx0iLq6elhw4YNRAQbN270FgOSJEk6Zt4nTlpkjUaDkZERR+EkSZK0ICzipEXW09PD5s2bq44hSZKkDuF0SkmSJEmqEYs4SZIkSaoRizhJkiRJqhGviZMkSVItDA4OMjw8XHWMBTO5LwMDAxUnWTh9fX309/dXHaPjdVwRFxErgJuBXwReAb6QmZ8+Qv/Lgd8HTgX+D3BlZv6wfG0r8MvAoZa3nJKZLy9OeknSVJ60LX2etEnzs3LlyqojqKY6rogDPg2cD/wTYBWwPSK+n5lfnNoxIs4DbgX+HUUBdxPwFeC9Ld3+KDM3LXpqSdKy4EmbNH9+WSAVOrGIuxLoz8zngOci4g+BjwI/UcQBvwp8LTO3A0TEp4BnI+ItmflE2xJLkmbkSZskSa/VUQubRMRa4HTg71uaHwLeMcNb3tHaNzP3AU9O6f+xiBiLiO9ExH84ws9eExE/1foAzpzXjkiSJEnSDDptJG5V+e++lra9wElH6L9vSltr/z8Bfqfscwnw1Yh4JjPvmWZb1wCfmU9oSZIkSZqtWo3ERcS2iMgZHk8CB8quJ7e8bTWwf4ZNHpjS9zX9M/M7mbk7M8cz8y7gy8C/n2FbW4BzpjwumuMuSpIkSdIR1WokLjPff7Q+EfE0cAHwdNl0IfDwDN0fLvtOvvdkiuJrpv55hGx7KUbxWrMcLa4kSZIkzUmtRuJmaSvwqYh4Q0ScDXyCYgXK6XwZuDQi/k1ErARuAO6fXNQkIj4UEasioisiLqFYCOWvF38XJEmSJGl6nVjEXU8xkvYE8ABwe+vtBSLiQERcBJCZjwC/Bvw5sBs4j+K+cJN+G/ghxQjbH1CsevmNduyEJEmSJE2nVtMpZyMzDwFXlY/pXl815flfAn85Q1+vaZMkSZK0pHTiSJwkSZIkdSyLOEmSJEmqEYs4SZIkSaoRizhJkiRJqhGLOEmS2mhsbIxNmzaxZ8+eqqNIkmrKIk6SpDZqNpsMDQ3RbDarjiJJqimLOEmS2mRsbIwdO3aQmWzfvt3ROEnSvFjESZLUJs1mk4mJCQAmJiYcjZMkzYtFnCRJbbJr1y7Gx8cBGB8fZ+fOnRUnkiTVkUWcJEltsm7dOrq7uwHo7u5m/fr1FSeSJNWRRZwkSW3SaDTo6ir+9HZ1ddFoNCpOJEmqI4s4SZLapKenhw0bNhARbNy4kbVr11YdSZJUQ91VB5AkaTlpNBqMjIw4CidJmjeLOEmS2qinp4fNmzdXHUOSVGNOp5QkSZKkGrGIkyRJkqQacTrl4joO4Kmnnqo6hyRJkqQlqKVWOG6274nMXJw0IiJ+Fri36hySJEmSlryLMvNvZ9PRIm4RRcTrgHcBo8DhiuOoOmdSFPMXAQ7LSvKYIGmSxwNBMQLXC3wrM1+ezRucTrmIyl/CrKppda6ImPzvU5n5ZIVRJC0BHhMkTfJ4oBZPzKWzC5tIkiRJUo1YxEmSJElSjVjESZIkSVKNWMRJi28vcH35ryR5TJA0yeOB5sXVKSVJkiSpRhyJkyRJkqQasYiTJEmSpBqxiJPaKCIORMTbyv9vjYjNVWeSVL2IeDIi3j/Da7si4tfbnUlStSLisxHRPMLrHhuWMYs4aQ7KA+ZLEbE/Ip6PiAciYlNEvG4278/MVZn5+GLnlLQwys/316e0fSsivjWlbWdEbGpvOkntUv79z4j4V1PabynbrzjG7a+LiGeOKaSWFYs4ae6uycyTgF7gd4AGcFdERLWxJC2Cu4F3R0Q3QEScBLwZeHP5fyJiBfCvgV1VhZTUFo8DH5l8Un72LweeqCyRli2LOGmeMvOFzNwFfAB4N3BZRPx0RNwXEXsjYjQi/iQijp98T/lt3blTtxURD0fEL7Q874qIpyJifTv2RdKMvg0E8NPl858F7gPuB36mbPuXwGHgwYi4KSJ+EBHPRsSfR8TrJzcUEZdFxIPl8eH+iPjn0/3AiHhLRHw3IvqntK+IiN2t74uI1RHxYkT0LdgeS5rJbcCHWmbffIDiGPEMQBSui4jvR8RzEfFXEXHa5JvLc4CPRcSjEbEvIpoRsbI8TnwNeFN52cWBls/08RExWPZ/IiIunRrKY8PyZBEnHaPMHKE4iF9EcSL3CeANFCd47weumsVmvgR8uOX5+nJbuxYyq6S5ycxXgL8DLi6bLgbuKR+tbX8HbAb+GfAvgD6K48DnACLinRSf898AeoCbgb+JiBNbf15EnA98A/hkZg5OyXIIaPLaY8WHgAcyc3gBdlfSkT0LfJOieAO4Atja8vpHKP7mv49ixH438JUp2/gQxfnBW4B3Aldm5gvApcCz5WUXq1o+0z9PUeD1AFuAWyPiNefvHhuWJ4s4aWE8DfRk5oOZeV9mjpcHzj8D3juL9/8FcElE9JTPPwx8Ob2Ro7QU3M2PP8fvBe4tH5NtF5d9PgZ8IjOfy8wDwOcppltTvjZYHh8mMvM2ipv7XtTyc94D3AVclZlfnSHLVuCXIuK48vmHgf92jPsnafa+BHykHGF7F3Bny2u/CmzJzMcz8yDwu8B7I+LMlj7/OTN3Z+Zz5XunHZFvcV9m/lVmHgZuBU4DTp+m31Y8NiwrFnHSwjgDGIuIt0fE/4qIZyLieeD3KL6NP6LMfIZi1K0RESuBX8CDr7RU3A38THkN3NuBB4HvAOeWbe+hKOpOBL5ZTpfcC2wH1pRTqs8GfnvytfL1c3jtydhVwAPA/54pSGZ+C3gOeF9EnEUxlXOmgk/SwruTonj7XeCOzHy55bUzgB9MPsnMfcCesn1S6+IlLwCrjvLzXu1fjtgx3Xs8Niw/FnHSMYqIN1NMn7oX+ALwGPDWzDwZ+DTF9TSzsZXim7MPAo9m5mMLn1bSPPxf4HXArwPfzszD5bfiDwBXA90U18gdBC7IzDXlY3VmriynZP4/4PdbXluTmSdm5hdbfs7HgVOALxxloaTJ6de/AvzP8kRRUhuUUxfvoLh0YuuUl39I8YUNABFxMrC2bD/qphcgnseGZcQiTpqniDgxIt4L/DXFSd5dFN+OPQ8ciIjzmN31cJPuBN4GDOAonLRklN+030+xGu09LS/dQ3Eid395YjcI/FFEnAoQEWdExL8t+w4CH4uId5cLF70+Ii6NiLUt2ztAcV3MBcAtR4j0F8BlwEfxWCFV4feADeXoV6vbKEbc31rOqvkD4N7MfGoW2/wRsHbKMWGuPDYsIxZx0txtiYj9FAfcLcD/AN6fmRMU0yt+CdgP/Bfg9tlutDxRbALnAv99oUNLOiZ3A6dSjLhPurdsu7t8fi3wKHBfOZ16O3AeQGZ+G/g14I+BMeB7wH+c+kMycz/Fgkjviog/ni5IOf36XuBkYNux7pikucnMH2Xmzmle+hLwX4GvA09RHB9+eZbbfJSiCPxeOeX6nHnk8tiwjITrJkhLR0RcC7wnMz9YdRZJS1dE/ClwKDOvqTqLpKXDY8Py0V11AEmFiFgN9AP/qeoskpaucqW7BsU96yQJ8Niw3DidUloCypv6Pg38bWZ+reo8kpamiLiBYsrmLZk5VHUeSUuDx4blx+mUkiRJklQjjsRJkiRJUo1YxEmSJElSjVjESZIkSVKNWMRJkiRJUo1YxEmSJElSjVjESZIkSVKN/H9nODq8rsLsAAAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1008x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "print(\"==============Compare to DJIA===========\")\n",
        "%matplotlib inline\n",
        "# S&P 500: ^GSPC\n",
        "# Dow Jones Index: ^DJI\n",
        "# NASDAQ 100: ^NDX\n",
        "backtest_plot(df_account_value, \n",
        "             baseline_ticker = '^DJI', \n",
        "             baseline_start = df_account_value.loc[0,'date'],\n",
        "             baseline_end = df_account_value.loc[len(df_account_value)-1,'date'])"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [
        "_gDkU-j-fCmZ",
        "3Zpv4S0-fDBv"
      ],
      "name": "Stock_NeurIPS2018.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.5"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}