Transformer - CALCE.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reference:\n",
    "\n",
    "D. Chen, W. Hong and X. Zhou, \"Transformer Network for Remaining Useful Life Prediction of Lithium-Ion Batteries,\" in IEEE Access, vol. 10, pp. 19621-19628, 2022."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import random\n",
    "import math\n",
    "import time\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.cm as cm\n",
    "import pandas as pd\n",
    "import glob\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torchvision\n",
    "%matplotlib inline\n",
    "\n",
    "from math import sqrt\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def drop_outlier(array,count,bins):\n",
    "    index = []\n",
    "    range_ = np.arange(1,count,bins)\n",
    "    for i in range_[:-1]:\n",
    "        array_lim = array[i:i+bins]\n",
    "        sigma = np.std(array_lim)\n",
    "        mean = np.mean(array_lim)\n",
    "        th_max,th_min = mean + sigma*2, mean - sigma*2\n",
    "        idx = np.where((array_lim < th_max) & (array_lim > th_min))\n",
    "        idx = idx[0] + i\n",
    "        index.extend(list(idx))\n",
    "    return np.array(index)\n",
    "\n",
    "\n",
    "def build_sequences(text, window_size):\n",
    "    #text:list of capacity\n",
    "    x, y = [],[]\n",
    "    for i in range(len(text) - window_size):\n",
    "        sequence = text[i:i+window_size]\n",
    "        target = text[i+1:i+1+window_size]\n",
    "\n",
    "        x.append(sequence)\n",
    "        y.append(target)\n",
    "\n",
    "    return np.array(x), np.array(y)\n",
    "\n",
    "\n",
    "# leave-one-out evaluation: one battery is sampled randomly; the remainder are used for training.\n",
    "def get_train_test(data_dict, name, window_size=8):\n",
    "    data_sequence=data_dict[name]['capacity']\n",
    "    train_data, test_data = data_sequence[:window_size+1], data_sequence[window_size+1:]\n",
    "    train_x, train_y = build_sequences(text=train_data, window_size=window_size)\n",
    "    for k, v in data_dict.items():\n",
    "        if k != name:\n",
    "            data_x, data_y = build_sequences(text=v['capacity'], window_size=window_size)\n",
    "            train_x, train_y = np.r_[train_x, data_x], np.r_[train_y, data_y]\n",
    "            \n",
    "    return train_x, train_y, list(train_data), list(test_data)\n",
    "\n",
    "\n",
    "def relative_error(y_test, y_predict, threshold):\n",
    "    true_re, pred_re = len(y_test), 0\n",
    "    for i in range(len(y_test)-1):\n",
    "        if y_test[i] <= threshold >= y_test[i+1]:\n",
    "            true_re = i - 1\n",
    "            break\n",
    "    for i in range(len(y_predict)-1):\n",
    "        if y_predict[i] <= threshold:\n",
    "            pred_re = i - 1\n",
    "            break\n",
    "    return abs(true_re - pred_re)/true_re if abs(true_re - pred_re)/true_re<=1 else 1\n",
    "\n",
    "\n",
    "def evaluation(y_test, y_predict):\n",
    "    mse = mean_squared_error(y_test, y_predict)\n",
    "    rmse = sqrt(mean_squared_error(y_test, y_predict))\n",
    "    return rmse\n",
    "    \n",
    "    \n",
    "def setup_seed(seed):\n",
    "    np.random.seed(seed)  # Numpy module.\n",
    "    random.seed(seed)  # Python random module.\n",
    "    os.environ['PYTHONHASHSEED'] = str(seed)\n",
    "    torch.manual_seed(seed) \n",
    "    if torch.cuda.is_available():\n",
    "        torch.cuda.manual_seed(seed)\n",
    "        torch.cuda.manual_seed_all(seed)\n",
    "        torch.backends.cudnn.benchmark = False\n",
    "        torch.backends.cudnn.deterministic = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Load Dataset CS2_35 ...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Program Files (x86)\\Anaconda3\\lib\\site-packages\\pandas\\util\\_decorators.py:188: FutureWarning: The `sheetname` keyword is deprecated, use `sheet_name` instead\n",
      "  return func(*args, **kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
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      "Load datasets/CALCE/CS2_38\\CS2_38_9_7_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_38\\CS2_38_8_17_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_38\\CS2_38_8_18_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_38\\CS2_38_8_19_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_38\\CS2_38_8_30_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_38\\CS2_38_9_7_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_38\\CS2_38_9_14_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_38\\CS2_38_9_21_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_38\\CS2_38_9_28_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_38\\CS2_38_10_04_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_38\\CS2_38_10_05_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_38\\CS2_38_10_14_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_38\\CS2_38_10_21_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_38\\CS2_38_10_28_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_38\\CS2_38_11_01_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_38\\CS2_38_11_08_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_38\\CS2_38_11_15_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_38\\CS2_38_11_22_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_38\\CS2_38_11_24_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_38\\CS2_38_12_06_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_38\\CS2_38_12_13_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_38\\CS2_38_12_20_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_38\\CS2_38_12_23_10.xlsx ...\n",
      "Load datasets/CALCE/CS2_38\\CS2_38_1_10_11.xlsx ...\n",
      "Load datasets/CALCE/CS2_38\\CS2_38_1_18_11.xlsx ...\n",
      "Load datasets/CALCE/CS2_38\\CS2_38_1_24_11.xlsx ...\n",
      "Load datasets/CALCE/CS2_38\\CS2_38_1_28_11.xlsx ...\n",
      "Load datasets/CALCE/CS2_38\\CS2_38_2_4_11.xlsx ...\n",
      "Load datasets/CALCE/CS2_38\\CS2_38_2_10_11.xlsx ...\n"
     ]
    }
   ],
   "source": [
    "Battery_list = ['CS2_35', 'CS2_36', 'CS2_37', 'CS2_38']\n",
    "\n",
    "dir_path = 'datasets/CALCE/'\n",
    "Battery = {}\n",
    "for name in Battery_list:\n",
    "    print('Load Dataset ' + name + ' ...')\n",
    "    path = glob.glob(dir_path + name + '/*.xlsx')\n",
    "    dates = []\n",
    "    for p in path:\n",
    "        df = pd.read_excel(p, sheetname=1)\n",
    "        print('Load ' + str(p) + ' ...')\n",
    "        dates.append(df['Date_Time'][0])\n",
    "    idx = np.argsort(dates)\n",
    "    path_sorted = np.array(path)[idx]\n",
    "    \n",
    "    count = 0\n",
    "    discharge_capacities = []\n",
    "    health_indicator = []\n",
    "    internal_resistance = []\n",
    "    CCCT = []\n",
    "    CVCT = []\n",
    "    for p in path_sorted:\n",
    "        df = pd.read_excel(p,sheetname=1)\n",
    "        print('Load ' + str(p) + ' ...')\n",
    "        cycles = list(set(df['Cycle_Index']))\n",
    "        for c in cycles:\n",
    "            df_lim = df[df['Cycle_Index'] == c]\n",
    "            #Charging\n",
    "            df_c = df_lim[(df_lim['Step_Index'] == 2)|(df_lim['Step_Index'] == 4)]\n",
    "            c_v = df_c['Voltage(V)']\n",
    "            c_c = df_c['Current(A)']\n",
    "            c_t = df_c['Test_Time(s)']\n",
    "            #CC or CV\n",
    "            df_cc = df_lim[df_lim['Step_Index'] == 2]\n",
    "            df_cv = df_lim[df_lim['Step_Index'] == 4]\n",
    "            CCCT.append(np.max(df_cc['Test_Time(s)'])-np.min(df_cc['Test_Time(s)']))\n",
    "            CVCT.append(np.max(df_cv['Test_Time(s)'])-np.min(df_cv['Test_Time(s)']))\n",
    "\n",
    "            #Discharging\n",
    "            df_d = df_lim[df_lim['Step_Index'] == 7]\n",
    "            d_v = df_d['Voltage(V)']\n",
    "            d_c = df_d['Current(A)']\n",
    "            d_t = df_d['Test_Time(s)']\n",
    "            d_im = df_d['Internal_Resistance(Ohm)']\n",
    "\n",
    "            if(len(list(d_c)) != 0):\n",
    "                time_diff = np.diff(list(d_t))\n",
    "                d_c = np.array(list(d_c))[1:]\n",
    "                discharge_capacity = time_diff*d_c/3600 # Q = A*h\n",
    "                discharge_capacity = [np.sum(discharge_capacity[:n]) for n in range(discharge_capacity.shape[0])]\n",
    "                discharge_capacities.append(-1*discharge_capacity[-1])\n",
    "\n",
    "                dec = np.abs(np.array(d_v) - 3.8)[1:]\n",
    "                start = np.array(discharge_capacity)[np.argmin(dec)]\n",
    "                dec = np.abs(np.array(d_v) - 3.4)[1:]\n",
    "                end = np.array(discharge_capacity)[np.argmin(dec)]\n",
    "                health_indicator.append(-1 * (end - start))\n",
    "\n",
    "                internal_resistance.append(np.mean(np.array(d_im)))\n",
    "                count += 1\n",
    "\n",
    "    discharge_capacities = np.array(discharge_capacities)\n",
    "    health_indicator = np.array(health_indicator)\n",
    "    internal_resistance = np.array(internal_resistance)\n",
    "    CCCT = np.array(CCCT)\n",
    "    CVCT = np.array(CVCT)\n",
    "    \n",
    "    idx = drop_outlier(discharge_capacities, count, 40)\n",
    "    df_result = pd.DataFrame({'cycle':np.linspace(1,idx.shape[0],idx.shape[0]),\n",
    "                              'capacity':discharge_capacities[idx],\n",
    "                              'SoH':health_indicator[idx],\n",
    "                              'resistance':internal_resistance[idx],\n",
    "                              'CCCT':CCCT[idx],\n",
    "                              'CVCT':CVCT[idx]})\n",
    "    Battery[name] = df_result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### If the original data set cannot be read successfully, you can simply load the data I have extracted: CALCE.npy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "Battery_list = ['CS2_35', 'CS2_36', 'CS2_37', 'CS2_38']\n",
    "Battery = np.load('datasets/CALCE/CALCE.npy', allow_pickle=True)\n",
    "Battery = Battery.item()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1ec19851c50>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Txo0bEx8fT0JCAp9++ikmkwkHBwc++OADkpKS+OWXX5g7dy6JiYlV5jFu3Dj2799PXFwcAwYMKBc433333cTFxREXF8frr79+WWWvDQmohRBCCCGukV69YNEi/X1xsb78n//oy/n5+vKXX+rL2dn68rff6suZmfryd9/py2lpl5//+fPnqVevHgC5ubn07t2boKAgAgICWLVqFQDjx4/n2LFjmM1mxo0bx/jx49m2bRtms5mPPvqI0tJSxo0bR5cuXQgMDOTjjz8G9J7ekJAQwsPDCQgIYNKkSeV6jSdOnMisWbMqLdfSpUsJDg5m4MCBtnUhISH4+/uTmppKs2bNbOvbtm2Lk5MTTZs2JSgoCAA3Nzd8fX1JSUmpsu7u7u6293l5eTd0bLtMmyeEEEIIcQsrKCjAbDZTWFhIamoqmzZtAvS5lVesWIG7uzuZmZl0796dQYMG8e6775KQkEBcXBygB8rTp09nzZo1ACxcuBAPDw927drFhQsX6NGjB6GhoQDExMSQkJBA69atSU5OJiwsjMjISCwWC8uWLSMmJqbSMiYkJNCpU6dKt40cOZLQ0FCWL19O7969GT58OHfccUe5fZKTk4mNjaVbt27VtsXEiRNZsmQJHh4ebN682bY+OjqaDh064O3tzfTp0/Hz86tFy9aeBNRCCCGEENdIVNTF9yZT+eW6dcsve3iUX27QoPxykya1y7NsyAfogeOwYcNISEhA0zRee+01tm7disFgICUlhfT09BrT27BhA/v372f58uUAZGdnc+TIERwdHenatattrmYfHx+8vLyIjY0lPT2djh074uXlVbtC2zGbzRw/fpwNGzawceNGunTpQnR0NL6+voDe0z5kyBBmzJhRrhe6MlOmTGHKlClMmzaNOXPmMHnyZIKCgjhx4gSurq6sW7eOhx9+mCNHjlx2OasjAbUQQgghxJ9EcHAwmZmZZGRksG7dOjIyMtizZw8mkwkfH59azbWsaRqzZ8+mT58+5dZHRUXh4uJSbt2oUaNYtGgRaWlpjBw5sso0/fz82LJlS5XbXV1dCQsLIywsDIPBwLp16/D19aW4uJghQ4YQERFBWFhYjWUvEx4eTv/+/Zk8eXK5ILxfv36MGTOGzMxMGjRoUOv0aiJjqIUQQggh/iQOHjxIaWkpXl5eZGdn06hRI0wmE5s3b+bEiROAPh45JyfHdkzF5T59+jB//nyKi4sBfXaOvLy8SvMbPHgw69evZ9euXZcE4PbCw8PZsWMHa9euta1bv3498fHxbN++nXPnzgFQVFREYmIirVq1QtM0nnzySXx9fXnxxRdrrLt9r/Pq1atp164dAGlpaWiaBuhDViwWyxX1pFdHeqiFEEIIIW5hZWOoQe9dXrx4MUajkYiICAYOHEjnzp0xm822ANPLy4sePXrg7+9P3759mTp1Kg4ODnTo0IERI0YQGRlJcnIyQUFBaJpGw4YNWblyZaV5Ozo6EhISgqenJ0ajscoyOjs7s2bNGsaOHcvYsWMxmUwEBgYyc+ZMYmNjGT16NJqmYbFY6N+/P0OGDGH79u18/vnnBAQE2Oo3depU+vXrV2ke48eP59ChQxgMBlq1asWCBQsAWL58OfPnz8fBwQFnZ2eWLVt2zW9YVGUR+62ic+fO2u7du294vpqmyZOQhBBCCFFOUlKSbazv/yKLxUJQUBBff/31JTcS3moqO5dKqT2apnWu6VgZ8lFLs2Nm0/VfXTlw5sDNLooQQgghxE2XmJhImzZt6N279y0fTF8tGfJRS1mOzdjn6M8rMYtZO+D9m10cIYQQQoibqn379hw/frzcuvj4eIYOHVpunZOTEzt37rxm+T777LNs37693LrIyEieeOKJa5bH5ZKAuhais7OZcr4BRS0fZ52mEXkwgZnt/G92sYQQQggh/lACAgJsU/hdL3Pnzr2u6V8JGfJRC0vS0iiyaKAMoAzMSs3g5SOHb3axhBBCCCHEH4AE1LVmvXnTemPiRymnic7OvonlEUIIIYQQfwQSUNfCsCZNMCoFmqa/rEF1VFbWTS6ZEEIIIYS42SSgroVgDw+2dezIXSlpKE0DTW84L5PpZhdNCCGEEELcZBJQ11KwhwdrS5xZ8OGHGC0llGgWxh49KsM+hBBCCHFTGY1GzGYzHTp0ICgoiB07dlS7f1ZWFvPmzbMtJycns3Tp0utdTA4fPky/fv1o06YNvr6+PProo6Snp5Ofn09ERAQBAQH4+/vTs2dPcnNzOXnyJCEhIfj6+uLn58fMmTOrTX/SpEkEBgZiNpsJDQ3l9OnTtm1RUVGYzWb8/Py49957r3nd5MEul2na1KlM6taNUqMRIzDMo5SjcZNxcXShrVdbQm8Ppd8dlT/BRwghhBB/Ln+EB7u4urqSm5sLwA8//MDUqVPZsmVLlfsnJyczYMAAEhISAD3YnD59OmvWrKl1nqWlpdU+GbGiwsJCAgIC+PDDDxk4cCAAmzdvpmHDhnz33XdkZGTw4YcfAnDo0CF8fHw4e/YsqampBAUFkZOTQ6dOnVi5ciXt27evNI/z58/j7u4OwKxZs0hMTGTBggVkZWVx1113sX79elq2bMmZM2do1KjRJcdfzYNdZNq8y9SrSRMci4spUBpGg5EOThrR+RnkFuWy9cRWZu6cScLoBPwa+d3sogohhBDiBuu1qNcl6x71e5QxXcaQX5xPv/9e2uk2wjyCEeYRZOZn8pev/lJuW9SIqMvK//z589SrVw+A3NxcHnroIc6dO0dxcTHvvPMODz30EOPHj+fYsWOYzWYeeOABtm3bRlJSEmazmeHDh/P8888zfvx4oqKiuHDhAs8++yxPP/00UVFRTJ48maZNmxIXF8eQIUNo0KABkZGRAEycOJHGjRvz/PPPX1KupUuXEhwcbAumAUJCQgBYuHAhrVq1sq1v27YtAE2bNqVp06YAuLm54evrS0pKSpUBdVkwDZCXl2d7wvXSpUsJCwujZcuWAJUG01dLAurL1K29Pz89+QRPDe1MsXcuI7p/RmT7JAAy8jLw/tCblJwU/Br5ca7gHK6OrpiMMtZaCCGEENdHQUEBZrOZwsJCUlNT2bRpEwB16tRhxYoVuLu7k5mZSffu3Rk0aBDvvvsuCQkJtvmiK/ZQL1y4EA8PD3bt2sWFCxfo0aMHoaGhAMTExJCQkEDr1q1JTk4mLCyMyMhILBYLy5YtIyYmptIyJiQk0KlTp0q3jRw5ktDQUJYvX07v3r0ZPnz4JU9eTE5OJjY2lm7dulXbFhMnTmTJkiV4eHiwefNmQB9qUlxcTK9evcjJySEyMpJhw4bVsnVrRwLqy2Ro347gxEQ+2NeFCONW9qXv46CpDd9kZDCkYUMWDljIA7c9AMDLG15macJSgpoGMfW+qdzrc2VjdpKzkvnXnn+RX5xPZ+/ORARGXMsqCSGEEOIaqa5Hua6pbrXbG9RtcNk90gDOzs624Dg6Opphw4aRkJCApmm89tprbN26FYPBQEpKCunp6TWmt2HDBvbv38/y5csByM7O5siRIzg6OtK1a1dat24NgI+PD15eXsTGxpKenk7Hjh3x8vK67PKbzWaOHz/Ohg0b2LhxI126dCE6Oto2/CI3N5chQ4YwY8aMcr3QlZkyZQpTpkxh2rRpzJkzh8mTJ1NSUsKePXv46aefKCgoIDg4mO7du3PnnXdedlmrIgH15XJ3J/r119nTowf/1/xx5mUW82WO/pCXDefO8fGdfWxfMTzi9wgedTz4ZO8nfBL7yRUH1C9veJlvkr6xLf/V/68YDbUftySEEEKI/w3BwcFkZmaSkZHBunXryMjIYM+ePZhMJnx8fCgsLKwxDU3TmD17Nn369Cm3PioqChcXl3LrRo0axaJFi0hLS2PkyJFVpunn51ftuG5XV1fCwsIICwvDYDCwbt06fH19KS4uZsiQIURERBAWFlZj2cuEh4fTv39/Jk+eTPPmzWnQoAEuLi64uLhwzz33sG/fvmsaUMssH5cpOjub3r17M9HRkRfOOPBlTvnA9oWjR+m4axftY2L4uLA5j3R/g+AWwSRmJF5xno1dGgPQN+hlaBHOV6cOXFUdhBBCCPHndPDgQUpLS/Hy8iI7O5tGjRphMpnYvHkzJ06cAPTxyDk5ObZjKi736dOH+fPnU1xcDOhDJvLy8irNb/Dgwaxfv55du3ZdEoDbCw8PZ8eOHaxdu9a2bv369cTHx7N9+3bOnTsHQFFREYmJibRq1QpN03jyySfx9fXlxRdfrLHuR44csb1fvXo17dq1A+Chhx5i27ZtlJSUkJ+fz86dO6/5jaTSQ32ZorKyKCixVPlRJN9iIc560SXl57MyMxO3ps+Tl7WP7VlZ9PD0vOw83ZzccPDswE/u/cHVwrDjv+PjkU2wh8fVVEUIIYQQfwJlY6hB711evHgxRqORiIgIBg4cSOfOnTGbzbYA08vLix49euDv70/fvn2ZOnUqDg4OdOjQgREjRhAZGUlycjJBQUFomkbDhg1ZuXJlpXk7OjoSEhKCp6dntbN+ODs7s2bNGsaOHcvYsWMxmUwEBgYyc+ZMYmNjGT16NJqmYbFY6N+/P0OGDGH79u18/vnnBAQE2Oo3depU+vWrfDa18ePHc+jQIQwGA61atWLBggUA+Pr68uCDDxIYGIjBYGDUqFH4+/tfcXtXRqbNu0zR2dn03BuLBesTE7UKP6thUootZvNlB8LPrXuOhTkuFDV+UF+haTzj7c18612wQgghhLg5/gjT5t1MFouFoKAgvv7660tuJLzVXM20eTLk4zIFe3jwcssW+kLZhxGL5eKy/auCYk0jKiuL3KJcdqXsqnWeg7v9H61aXpxmpqbAXQghhBDiektMTKRNmzb07t37lg+mr5YM+bgC791+OxcKYXb6b1jQcDAYeLFFC5LO5xF3MhMTBRx3d6lwlIYRjYLMaNwW/Q2A9JfTaeRS/VyI0dnZ9E04SJFdgG4AfJ3l1AkhhBDi5mnfvj3Hjx8vty4+Pp6hQ4eWW+fk5MTOnTuvWb7PPvss27dvL7cuMjKSJ5544prlcbkkKrtCRVkOKM0CRiMa4OngwOqOgdBR3/7twWxeXhyLd6vDnG3fjnP5h8g4OJu3s+NtaSRmJNLIpRFbT2wlKSOJpzo9ZZshpExUVhbFpaVguPhlggWNF44ep4tHAxlHLYQQQog/jICAANsUftfL3Llzr2v6V0KGfFyhof6emDQwWjQcDQZ6VbjZ0E95MHSnkR8+X06i2cz+ux8mrJl+M0DXZl0BiE+PJ784n3sX3csza5/hQMals3f0MpkwFRdbh5Ggv1BYUKzNOH3J/kIIIYQQ4saSHuorFOzhwaYunYnKyqKXp+clPcVt28LkTXcDG5g9G1JS4Kt3v2LHyR1kGhsxdPMHrE47Rpf0/bZjEs4k4N+o/F2nwUYjUS++yKcP9mGzeTgnmp2jVOnDP+bvmMY7bZZc97oKIYQQQoiqSQ/1VQj28GBCq1bVD7soLaVk07vsKVlLdHY2CUYf/nIkhfPej7DJrR8rTusPhXmh+wt0alrJIzkLCghOTKR02xb++clUDJYSQIEycrb5CPy3byQ6O/v6VFAIIYQQQtRIeqivs+jcXCY+05Eik4lecXGUahqlAMqApkx4NL6XXyN/pZVHK7IKs2y91Jt+3YSj0ZGehQ0AaONXl+juQygpm+PROtb6QJGRe+Ni2WLuKOOphRBCCCFuAumhvs6isrIoMpkoNRgoKgumrRyUgRBPT3w8fVBKse7IOrp90o3G0xvTe0lv/rnjn1BQAICXtwthf+2DVmywjqNGD6qVsk3HJ4QQQoj/PUajEbPZTIcOHQgKCmLHjh3V7p+VlcW8efNsy8nJySxduvR6F5PDhw/Tr18/2rRpg6+vL48++ijp6enk5+cTERFBQEAA/v7+9OzZk9zcXE6ePElISAi+vr74+fkxc+bMatOfNGkSgYGBmM1mQkNDOX1av9fsn//8J2azGbPZjL+/P0ajkbNnz17TuklAfZ318vTEaLFcvKkQUICDUsy54w6CPTxYePo0ffbtI8bSiPzifM7knQFgX9o+SvP1py66pYwh2N2dWemN6GSpczEDTQOtlNtUDkIIIYT43+Ps7ExcXBzRD699AAAgAElEQVT79u1j2rRpTJgwodr9r0VAXVpaWvNOdgoLC+nfvz+jR4/m6NGjJCUlMXr0aDIyMpg5cyaNGzcmPj6ehIQEPv30U0wmEw4ODnzwwQckJSXxyy+/MHfuXBITE6vMY9y4cezfv5+4uDgGDBjAW2+9ZVsfFxdHXFwc06ZN495776V+/fqXVf6aSEB9nQV7ePDE2bPWJykCmoZmsfBYUhK/Fxfz6rFjPH34MBvOnWNWlgmXlo8B8HSnpzmRfYKhS/8CwLqNbpCby3MjA9k9bgyveHvrJ08BmoUnVo0gMz/zZlVTCCGEEAC9etX8mj69/P6LFunvMzMv3fcynT9/nnr16gGQm5tL7969CQoKIiAggFWrVgH6I7qPHTuG2Wxm3LhxjB8/nm3btmE2m/noo48oLS1l3LhxdOnShcDAQD7++GMAoqKiCAkJITw8nICAACZNmlSu13jixInMmjWr0nItXbqU4OBgBg68+KC6kJAQ/P39SU1NpVmzZrb1bdu2xcnJiaZNmxIUFASAm5sbvr6+pKSkVFl3d3d32/u8vLxLpiIG+OKLL/jb3/5WYzteLhlDfQMMb96cT/LzKbUO0QD4b9u2GH799ZJ9u5tf4d0+L3OhbhtyGg8kffdi5ob9woTXLoCbG0yaBG+8wZ5lGah7ABRGgyN/7fkeDeo2IOV8Co1dG+NgkFMrhBBC/C8oKCjAbDZTWFhIamoqmzZtAqBOnTqsWLECd3d3MjMz6d69O4MGDeLdd98lISHBNl90VFQU06dPZ82aNQAsXLgQDw8Pdu3axYULF+jRowehoaEAxMTEkJCQQOvWrUlOTiYsLIzIyEgsFgvLli0jJiam0jImJCTQqVMlky8AI0eOJDQ0lOXLl9O7d2+GDx9+yZMXk5OTiY2NpVu3btW2xcSJE1myZAkeHh5s3ry53Lb8/HzWr1/PnDlzamjRyydR1w0QfNddDPz8c1Y2b66vUAo0DQt6BzNg7cFWPNq4CcUuLjywbx9FFhcc/f7B0wPe4E5v64X12GPwxhs03ngSx17eXLA+9vycqTHv7V/D+BUDiRkVQ5dmXW50NYUQQggRFXXl+zdocPnHc3HIB0B0dDTDhg0jISEBTdN47bXX2Lp1KwaDgZSUFNLT02tMb8OGDezfv5/ly5cDkJ2dzZEjR3B0dKRr1660bt0aAB8fH7y8vIiNjSU9PZ2OHTvi5eV12eU3m80cP36cDRs2sHHjRrp06UJ0dDS+vr6A3tM+ZMgQZsyYUa4XujJTpkxhypQpTJs2jTlz5jB58mTbtu+++44ePXpc8+EeIAH1DfN4vgdrS0oodjBx8a5CXZPSUuqkp2P29ibAxYUlaWkUWixoQJHFwlc/FnEmx8CzkcBttwHwnz1PMsdlJy/kZFIKrMzMZDWu0HEu/TbP59F6iwB4teertPRoeSOrKoQQQoibJDg4mMzMTDIyMli3bh0ZGRns2bMHk8mEj48PhYWFNaahaRqzZ8+mT58+5dZHRUXh4uJSbt2oUaNYtGgRaWlpjBw5sso0/fz82LJlS5XbXV1dCQsLIywsDIPBwLp16/D19aW4uJghQ4YQERFBWFhYjWUvEx4eTv/+/csF1MuWLbsuwz1AxlDfMPc/eA9bxo7lmdWr6FPsgLG0VB9PrWmkGQwkN23KKk2jV1wcn6amoml60O1gsfDiB4+zc2WqnpDJBIDKz+fkhwsotYvNLQDuvmQ2G8rHp1OYt3se7k7unC04y9GzR29shYUQQghxwx08eJDS0lK8vLzIzs6mUaNGmEwmNm/ezIkTJwB9PHJOzsXJDCou9+nTh/nz51NcXAzos3Pk5eVVmt/gwYNZv349u3btuiQAtxceHs6OHTtYu3atbd369euJj49n+/btnDt3DoCioiISExNp1aoVmqbx5JNP4uvry4svvlhj3Y8cOWJ7v3r1atq1a2dbzs7OZsuWLTz00EM1pnMlpIf6BvFo5Um3ZV8RvDOaaW2asSE52TaeGtCDa+sUeGXDPxTwhKsrwa++SvAjDS7u+69/ob37HoPjNvOB9hcs5cbcK1DQM+g1PhzwOp51PJm0aRJTtk1h5oMzySrMwmQ00dS1KcPNw2td/gW7F3D/bffTpn6bq2wJIYQQQlxLZWOoQe9dXrx4MUajkYiICAYOHEjnzp0xm822ANPLy4sePXrg7+9P3759mTp1Kg4ODnTo0IERI0YQGRlJcnIyQUFBaJpGw4YNWblyZaV5Ozo6EhISgqenJ8ayZ2VUwtnZmTVr1jB27FjGjh2LyWQiMDCQmTNnEhsby+jRo9E0DYvFQv/+/RkyZAjbt2/n888/JyAgwFa/qVOn0q9fv0rzGD9+PIcOHcJgMNCqVSsWLFhg27ZixQpCQ0Mv6WG/VlRZT+itonPnztru3btvdjGuSnR2NnfH7KW07OOMdUx12XsD+qAQA4rZt9/B6BbelyaybRvcfz8LN2xgTIX5rQFC69WjrsHA6aIiGuXsZs2WZ+jbpi9n8s6QX5xPUmYSQU2D+Jv/33gp+CXbnbAnsk7w12/+Ssr5FF7p8Qr/6PoPDmYexHeuL71b92bjsI3XqVWEEEKIW09SUpJtrO//IovFQlBQEF9//fUlNxLeaio7l0qpPZqmda7pWOmhvgmCPTz4qpEfPy6axoE77qCwcxc8t+/gR3MgYB26oUGppvHsscNM2XmYOx2bMuXeJhefhhgcDLm5DM4yUVpwmshfj+i921YbrF+d6O6E7l+xuySTBtomjDkHAdibupe9qXv5195/seHxDRw9e5SP93zML6d+AeCbpG94pvMzfJP4DQAtPFrUqn6appGWm0bCmQSSs5I5f+E8nbw70cun19U0mxBCCCH+QBITExkwYACDBw++5YPpqyUB9U0S1qERYV5e8OyzdCGG4tnOFfbQwKDQNI0UL0ghlZC4NDabzXpQ7eDATz/B3+4/Q/8Xv8MysG31GTo2IMOxAZl176DNb3OABO70upMXu7/IM2ufYXnicv4b/19i02JpWLchY7qMYfKWyTi+7YhmvYly4t0TbcldKLlAqVZKXVPdctnkXMih7Zy2pOamllv/UvBLdG3WleWJy+nQuAMdmnS4pIgllhI6LezED4//QBPXJrVuSyGEEELceO3bt+f48ePl1sXHxzN06NBy65ycnNi5c+c1y/fZZ59l+/bt5dZFRkbyxBNPXLM8LpcE1DfT//0fK9qM48EDTjQJLCGqtNA2fhoo/x4osj5ivKyX+r77IGbYXE5vWcuXD31omxnkUso2P5+mGUn2Hg6FJThaTvB056eZsm0Kv6T8woGMAzza/U3cWv2FNOXA5EEBfL5xGkfz9/B6wJJy46cHfzmYjPwMdv19V7mc3JzcGGEeQVPXpvg38uf2+rfj4eSBq6MrRaVFjFw1kgk9J1QaUEefjGZ/+n7i0+MloBZCCCFuQQEBAbYp/K6XuXPnXtf0r4QE1DfZ4L86MRiAYH47dox/njxpHT9dNvTj4thqR6Xo5elpO1Yp8Hl/DD5OL7B0zymS6uZwwMODpWfOVBJYX3z0ebGDJ9z5EoeLMmgfE0OJ7+uk5u5iysMrmZjpTFFaBgBOqgEfDfqQ3WdzWGVx5PfY9QxyN1DfuT4HMg7gWediWZKzkvnl1C881PYhpvaeWmldnQ3OtG/Ynj2pe2zrdp/ejY+nD0ZlxN1Jn1syJUd/ClKppZQtJ7bQxbsLbk5uV9C6QgghhBDXnwTUfyAPn7md7xc0oP59WUwNc2XXiKdZ2u8+nIqKqOvblze6242hLtO4MUePaHj37c/DxTvg229xa9+ej1NTbYG5l8lERnGx3VNkAE2jyLEhSfn5UOd2ztRpQ/EFN4q5OG3OBU3jw731OdrEAmjsK3JgbswHkKZPefNb9m/M3zXfNs765R9f5tQLp2hmakZVgpsHszRhKTkXcvjx+I8M+WoIXs5eNKjbgEO/HwLgdM5pAF776TXe3/E+Mx+cyfPdnq+x/RLOJLA0filFpUUUlxZTbCkmqzCLWX1n0aBugxqPF0IIIYS4EjIP9R9IQAD8rYMHH3ZuxV0NvLir52i2TZvO/70WRZ/EtpcG01b1vRSvtfma0voNYPJkhjVpQh2DASPgZDAw3rshpooH2T0GHRSlQExOTvmebQ2OFhbogbh1X/fmgxje4eJ0e2PWjQFgX/o+vN28aeZedTAN8ETHJ8gtyuWLhC9YvG8xAHnFeRz6/RAvBb9EvTr1bAH1j8d/xK+hH891fc52/JJ9Sxi1epRt+btD37H+6Ho0TSM1J5X3t7/P/N3zWbxvMV8nfs2237YRl3Z9v3q6XCeyTvDjsR8pLKl5cn0hhBBC/PFJQP0H4uoKEyZAUJC+3GV8bxxPHic0fyUvvFD1cfXrw/f7vDkzcjzs20fjfTn81KEDb7duzU8dOlD3900Uxz7LnReSaEIe+tiPWkyXqMD9tgv6e+vQkwecvFi3eirGNkugSX99N6WIS4sjoFEA0dnZDI6Pp31MDB137cInOprW0dF03LWL9jExvHfOhZbNHyQ2NZbY1FjCA8L5qM9HADzf7Xm83bw5V3iOEksJBzIO0LdNX9uUfinnU4hPj+ffcf+moLgAgE9iP+GfO/4JwH2t76NoUhF5r+WRNT6LjHEZnHzhJF7OXizYvYDKHDhzgPBvwjl29ljN7XGNLE9cTuh/Qmk3px3fHfruhuUrhBBCiOtDhnz8SZhMUHh3KEyHpn+9h9seGUjwoUMwciQH2jjA+UR+3zeJHi17cFrVY3dJPZo2DsbT3YeGJhPR2dkUV5LueU2f4VoVncP591zSinuQMfQw0Bx4CaPBgUmbJhF/Jp5GbUbSIza28lD9gh6YJ+XnY2oznrD2d/DxkZ8526AvP2YcwtXRlRbuLdj79F4cjY6sSFpBUWkRqbmp3L/kfp7r+hwTN03kQMYBQB/eYW5iZtOvmxgaOBSlFEZV+YTya4+sZdLmSQwNHEpdU11m/DKD3KJcJt07idWHVvNFwhfUNbrjtnUePXpaONNiIaYSL6LmPMaYMdCjx6Vp/nT8J/ak7uHU+VMcO3cMB4MDnnU8Wfyw3us+b9c8ikqLGNt97CXH/pLyC95u3mTkZ7Dq0CoGth1Yw9kVQgghqmY0GgkICEDTNIxGI3PmzOGuu+6qcv+srCyWLl3KmDH6t8zJycns2LGD8PDw61rOw4cPM3bsWA4fPozJZCIgIIDZs2fj5ubG3//+d/bv34+maXh6erJ+/XrOnTvHsGHDSEtLw2Aw8NRTTxEZGVll+pMmTWLVqlUYDAYaNWrEokWL8Pb2Jjs7m8cff5zffvuNkpISXn755Ws+I4gE1H8irQf6w3PP4fzVVzBrFgDnHn4CozKy+FvwLsgiZnAGP/sVwq+LaVeyj033bwJg9KFDLEhNrTJtzbEe+U08iTactK7RH0ajNbib0V3+RuIFWGUy16bfm2JN49uzOdTpNJcfLRZKHRtCnW9RSuFodASgqLSIfnf0IyIggseWP8bDXz4MQLdm3YhJiSH0P6E0cmlEblEud7e8u9r8/Br6ARDxbQRBTYN4I+oNAPrf2Z+dKfo0Pp/u+5gmG58nuc2/WRk/HaMy0mTLYzz2VwurDn6H+/m7eGdCQ95/Hzp1gm+TvmXe7nk4GZ1o37A9Fs3C746/2/Kcv3s+CWcSeLLjk5fcULkvbR/BzYPxcvZi4d6FtKnfhvE9x9ei5YQQQvwZRGdnE5WVRS9PzyqHc14OZ2dn28waP/zwAxMmTGDLli1V7p+VlcW8efPKBdRLly69rIC6tLS02icjVlRYWEj//v358MMPGThQ70javHkzGRkZfPbZZzRu3Jj4+HgADh06hMlkwsHBgQ8++ICgoCBycnLo1KkTDzzwAO3bt680j3HjxvH2228DMGvWLN566y0WLFjA3Llzad++Pd999x0ZGRm0bduWiIgIHB0da13+msiQjz8TpWDWLAq27WapiuAODhPTPIx6J9IZth/uP1LKa+9HM2/iDhxLwGjQfxGis7MBLh1nbT8yRCkwGLBolnK7OJ79haau3hzPfJzSWj5002QdwnHBYqEUMBideG3AV+X2ecz/MdaGr6XvHX1JHptM1PAoxnQew/TQ6Sx6eBHtG7anqWtTAAIbB1ab34A7BzDSPJLVh1bbgmmAz/d9zr70fZib6I8zHTL1X6w7OwsXkwvPdH6G48nF3NHtCA9/+TAbT6whLT+FNw48wu7Tu3nvgffInZBLwcQC9j69l7hn4vh55M+2tKfcNwXA1qNexqJZOJF9gtvq3cabvd6kYd2GvLf9PUosJWw8vpFn1jzDrfb0UiGEELUXnZ1N7337mPTrr/Tet8/2P/haOX/+PPXq1QMgNzeX3r17ExQUREBAAKtWrQL0R3QfO3YMs9nMuHHjGD9+PNu2bcNsNvPRRx9RWlrKuHHj6NKlC4GBgXz88ccAREVFERISQnh4OAEBAUyaNImZM2fa8p44cSKzrB16FS1dupTg4GBbMA0QEhKCv78/qampNGt28R6stm3b4uTkRNOmTQmyjoN1c3PD19eXlJSUKuvu7u5ue5+Xl2cbMqqUIicnB03TyM3NpX79+jg4XNs+Zemh/hNyvqM520f/h4iG0KHJKZp0mQDAvM5QNyCIEf/eS+gxKG5rJDo7m15xcRRpGgbgnrp1aX/sGB23buXz/gPY7upysddZ0wDFHXXqkJqdRm5WEv8K/oBfzmcT1/TS3u076tShSNNQgKeDAxc0jbZ16/JKixbE5+VhsfaIW4BWLvWqrE995/rc63Mv9/rcC0DPlj0Z1mEYr/74KtGnornT685q28NkNPHpQ5/ydOen6fZJN+5pdQ+9W/emkUsjkncm80bPKcSlxTF3zwwAvo/4nvta3wfAvjOxAExNHEmXkV1Y++suJpSMxdXRtdo8/Rv5AxCfHk/35t2J/D6SDcc38Po9r1NUWkRrz9Y0dWvKvP7zGLpiKIkZiRz5/Qgf7/mYV3u8Sut6rQH9YTdfHfgKH08f7mpR9dd3Qgghbg1RWVkUWTuUiiyWcs+XuFIFBQWYzWYKCwtJTU1l0yb92+c6deqwYsUK3N3dyczMpHv37gwaNIh3332XhIQEW692VFQU06dPZ82aNQAsXLgQDw8Pdu3axYULF+jRowehoaEAxMTEkJCQQOvWrUlOTiYsLIzIyEgsFgvLli0jJiam0jImJCTQqVOnSreNHDmS0NBQli9fTu/evRk+fPglT15MTk4mNjaWbt26VdsWEydOZMmSJXh4eLB582YA/vGPfzBo0CC8vb3Jycnhyy+/xGC4tn3KElD/SdnmPC/w4qOQmbywORJXdy/2PNSNv3yVQO/jRWwv1FiydStFbvqQBAuwPTeXd995h+5Hj/KUnx/RQ4fyZnIyG86e1XupNY0jhYXg6AGNujLszHHc4l2gEbZp+drXrcsnbauelQRgSVqa7b0BiM3JYfQhfdq8YU0qmR6wEm/2epPh5uGYjJf0rVeqa7OuaG9c7P09fu44t9e7HdPJUFTacrQmsbRv2J6eLXtyMvsk9y66l/zifEwGE83dm7PrtP4QG0NGILSsPi8fTx9cHV2ZvGUyI8wjWHVoFSeyTzBz50xin4619a4PvHMgZ185y5rDa2wzpsSkxNgC6nEbxvFFwhdM7T21VgF1YUkhGXkZZORn0L5he+o41CHlfAoHMg7gYnKhR8tKBoQLIYS4YXp5euJoMFBkseBoMJR7vsSVsh/yER0dzbBhw0hISEDTNF577TW2bt2KwWAgJSWF9PT0GtPbsGED+/fvZ/ny5QBkZ2dz5MgRHB0d6dq1K61b6/+jfHx88PLyIjY2lvT0dDp27IiXl9dll99sNnP8+HE2bNjAxo0b6dKlC9HR0fj6+gJ6T/uQIUOYMWNGuV7oykyZMoUpU6Ywbdo05syZw+TJk/nhhx8wm81s2rSJY8eO8cADD3D33XfXmNblkID6z87Zmb8sfh7LHeMZFvQErTz/SYTz43gPeJ9HEgvYfGATDBpkmxbPAmwOCmKy5y/0KXaj3Q54M+8Imzw8KHFwKPfkRpQRDY3zjfLKZRlc14N9X3hwyEljxBMKCgsp+L8p1HngblSfUKKzs/nMLqA2Ap+mplJsHerw7zS7R6xXVzWT/qCYGqWlgdEIDRuWW31bvdtIfDaRgwccyTsezbOjLTRrpD8CXilFcItgCooL6NasGz6ePvx3/1LWLejJijw3giv/kG1jUAa+efQb9qbuJbcolxPZJwB9ekG/hn62DwFODk4AxJ+Jtx0bkxLDY/6PoWkaSxOW0sW7C0+YL948kXMhh2+TvqVb8260a9AOTdMI/zacn47/REZ+xsX9Juhzir+99W0+3vMxCsVvL/xGc/fmNbeZEEKI6yLYw4OfOnS4pmOoy6UfHExmZiYZGRmsW7eOjIwM9uzZg8lkwsfHh8LCmqds1TSN2bNn06dPn3Lro6KicHFxKbdu1KhRLFq0iLS0NEaOHFllmn5+ftWO63Z1dSUsLIywsDAMBgPr1q3D19eX4uJihgwZQkREBGFhYTWWvUx4eDj9+/dn8uTJ/Pvf/2b8+PEopWjTpg2tW7fm4MGDdO3atdbp1UTGUP/ZlZTgHr+Co5OeIS/8EVxcwCnkLt64PZI+K9MZtmEDplJ9Jg80DUeLBY8uT7H3iBsvv6Sx6ttSgufPZ+7MmSiL5eKTG63727qlFWABRxRhmQU89HoH3MfrPa44OmKZMYvx7ybyVNIhRh06ZAueARo7OlJisdjmxi57xPo1UVoKnTvDF19UutnR6EhgIEx9y8kWTAM0d2/Of8P+y7ePfcurPV/lMf/HWB2+iti543jjDbvqVyP09lDG9xzP3tS9AIw0j6SwpJAp26aU2+/o2aO8vVW/ieKuFnfZbpRMzU3lTN4ZerfuzeHfD5OWm8ap86f4LPYzRqwawfdHvgdgf/p+liUso2uzrrwd8jYLByxk9V9X42LS/+iN7DiSJQ8vQUMjKjnqsppPCCHEtRfs4cGEVq2ueTANcPDgQUpLS/Hy8iI7O5tGjRphMpnYvHkzJ07onTtubm7k5Fx8kFvF5T59+jB//nyKi/X5vw4fPkxeXvnOszKDBw9m/fr17Nq165IA3F54eDg7duxg7dq1tnXr168nPj6e7du3c+7cOQCKiopITEykVatWaJrGk08+ia+vLy+++GKNdT9y5Ijt/erVq2nXrh0ALVu25KeffgIgPT2dQ4cOcdttt9WY3uWQHuo/u/x8PPqHMTkU1nqs5bmuz3Hvy4qdz8/noV8PEAxEzZvH59OmwWef0SenLl3/7w2e6JGEU8/OlO4NhL2/0LxrPe77ATb3fUkfU20pRTMYbUG1EXDZ0pSpIU3ot+BptMJf6TflH3oZDAbe/iSZj1rtpzhNf2iLPnxEf3uqqMg2nASwfQW28PBhvjl3jiEtWvCUt/fl1VvTICuL6JISlsyYAc7ODMvOrvSPV3Gxnn1t7k8ICLj4/v4Zp9nbOJUWt1soURpOSnGupAQFtKxTh/Z16zKsSRPG/TgOgGc6P8NncZ+xZN8S3uz1pi0db7eLdQtuHszWE1vRNI2ff9NvcmxTvw3t5up/FPrf0Z+1R/Q/RokZiYB+U+b+Z/bTwqNFucfBl+narCudvTsTuT6SqOQoHg98vNo6FpcW82bUm8ScjuGTgZ/QyrNVzQ0jhBDipikbQw167/LixYsxGo1EREQwcOBAOnfujNlstgWYXl5e9OjRA39/f/r27cvUqVNxcHCgQ4cOjBgxgsjISJKTkwkKCkLTNBo2bMjKlSsrzdvR0ZGQkBA8PT2rnfXD2dmZNWvWMHbsWMaOHYvJZCIwMJCZM2cSGxvL6NGj0TQNi8VC//79GTJkCNu3b+fzzz8nICDAVr+pU6fSr1+/SvMYP348hw4dwmAw0KpVKxYs0J9BMWnSJEaMGGGbWvC9996jQYNr+wRldavNKNC5c2dt9+7dN7sYt468PHB15ecWMPIhuP/B0eQV5/FNzBI+3R3MYz9GE28KwnHXdtqanfky4B2eOjGRlcsKadivM/7os1S88URrYnq35fUBy4jKysI86RUWtvFj5d13g9ID6rcbNGDCRx/BZ59R8sw/cJg/21aMaSdOMPHXX8tPq2fB+hRG9ABY0zBqGvN8fSE7m6fLhoUoxcd33klA3bpE/fwzverVI9hucujokyd5//hxYgsKUAUFeJ4/zzlHRy44OZFev74tTyelLhlKsvD0aT74OYXDS7wpXd0MQ+55omNiiLrjDttXcdGHDhFlMtHLy4stu44x69RZLjSvy1ljETU9IMdJ0/iqXUvOZ/7C44GPs3DPQh647QHbGOkyn+z9BP9G/nRr1s12V/I7W99h/dH1/Dj0R0Z9N4rvj3zPucJzdGraibqmuuxP30/XZl15K+QtujfvXuOl8MDnD+BkdGJN+Jpq95u/az5j1o0hqGkQSx5egl8jP9u2pIwkluxbQmFJIU4OTjSs25CGLg3pc3sfGrs2rrEMQgjxZ5OUlGQb6/u/yGKxEBQUxNdff33JjYS3msrOpVJqj6ZpnWs6Vnqo/+ys3a49T8KjB+B8PwcclAN5TvDzkMY8ulFxe3sn6rjrNync97fGfNkRut1Th59/SKB56ww8/5+9M4+Lqtz/+Puc2ViEAQFlEcQ9FxTNJXIJzVJLU7N9MSuvZVrZbV+sX9Zt8XZLK+tqmWVleculzTUTTUNNBXfBDVABQZZhh5k55/fHMzOsbqVl8bxfL2TmLM95ZgDnM9/5PJ/v95+xznshpr276dhlP3Hd+kBwBAELF7L8sj44zBbMwBXjx8PmzbzFFIwtX+bBGtOIDwjApChUae7YPQVVFZrag6qC00me3U5CejpYLB7P9tysLHYVFVHl7Y25tJQ1rmpz4mefMaBZMxwmkzjeYoFTLPDwrKZWFPD3ZyA5nQoAACAASURBVE5mJvelpEAI8OgBPsxWiLnzTq58/HGqjhzBrKrMaNuWKenpVBmNKBkZOAw6tDSAUuV5HKejStfZYzfxdNc74IsvmLD/OLzYqt5x43uMr7ftuQHP8dyA5wD4/PrPsTvt7Dixgy7NurD60Gpmb5vNybKTZx2zt/z25RhV8fuwPWs7Kw6uwNvozV2xd9HUuykAs7bM4s1NbxLpH8m2CdvqjXG44DBvbnoTi8FChaMCuyY+Dtxw9wYpqCUSiaSRsXfvXoYPH87o0aP/8mL69yIF9d8dl6Be2gE+6g43KwYMqoGhB2DwnsMon32Gj8MBJ3MACO7cnKFDxakiIScEHnmEufnXkR3bloBXLoMrriB/+WKKEubywrz3SXjqRcZs3szlmzcTZ95GaYcefHtL7WnEWa0kxMYyf9ky9mZnU9GvH/FNmzLj2DGqdB0UBRUwm0wEmUz4FBWJRYSufeEWC1t1Hc1goEJRmJ+dTZzDwfzt23EMH157sWRD6DqqrhNvtZJ4/fUkXHcdSzt3FiLede6izEySunalwmxGRwjwuVlZVBiN6O54nTNdp841zbpOfECACPH//nvik5OJe/55sUjyHDEZTPQMF2+SR3QYcc4dFo2qEbvTjkNz8MWuL3gj8Q0AuoV2Y1CrQRSUFzB5ubDpdAzuiN1pJ688j9Amoew/uZ+C8gKGtRtG5XOVroenU1RZxMmyk4T7hfPKz69gtViZ1HvSOT82iUQikfz16NSpE4cPH661bdeuXdx55521tlksFjZv3nzerjtp0iQ2btxYa9vDDz983rsfngsXTFArivIRMBzI0XW9SwP7FWAmcA1QBozTdX37hZpPo8UlBHeEQpa/EFUGxUB8GozcuBNGK6xz9KXo4wP4TviSe27syYZD0KJOEESbpm246ypYuDGMiJ07WbB4GvvbteCju+6nqqCAn1u3Jubyy/lxVQ/qLAD2EGe1go8PV3bsSFVpKdsLSxmWXMqK3k1wuPKqOzgcTExJQQ8OxuBw0P7YMUKaNSPf3x/N5bPWFYW52dl0z8tj7rBhZyVyVU3jrWVr2KnG8vAzz1ClKKjuxZguQg4eZO4116C7rqMqCknFxZ77ta7jaXgD4UYzfgYj9jKFtJMOAgOhc4gXTY1GQk0mlp44wVuZmTjvuQeL3c6aPXvYFRzMopwcxvj4MKG9yNE+352z6nIw/yBxc+M4WXYSENF+aYVprE9fT1yLOG5ZVP0u6P6e93PT1zexdP9SZg6dyac7PyU1L5Wcx3I8ySSKomD1smL1EnP94cAPeBm9pKCWSCSSRkxMTIwnwu9CMcuTDXzxcCEr1B8D7wLzT7F/GNDO9dUHeN/1XXI+cYnAp3b683MvL5o3aY653IzTrQ1vu42AVqPoenQDqRtyuTkAGvLpL9m3hI1RkHUygoiUrUy+Yya9pt1GlckkwukVhYS4OOJOIabdJISFUVVcLDokqhrlSiqa3h0dcOqQ7BatqopTVUmJimKfqkJRUS1B69B13ilXsNeI8vNGRSky0aa5kSLNQVERFOz14q5BPtw383W6LVjKQO0SKkeKEGmnwUBsdjbJzcNQ0PgyOBinweBZINnObGZ/ebmoJrs83u5rBSsmzDYvHusYxqOXhNPyMnjnHZjzP/jXv+CASXTCqnQ4xBsBAIOBSmB6ZiZL8/NB11lVUAAVFcS0bMmVO3aIXFJgTffu4vk6jwI7yhrF/Zfez7asbWzI2MDn13/O8AXDeXHdi7y47kUA7uh6B/NHzUdRFPwt/mzI2MDDKx4G4N1h73rEdEOE+4WzO2e35/5Xe77iROkJJvee/LvnLpFIJBczuq571r9I/pr83jWFF0xQ67q+XlGU6NMcMhKYr4tHsElRlABFUcJ0Xa/fck/yu7EUFPFji2lwuRBHX7+/BNgH7dvTNW0ZBV8up08fOFUDone2vEOfo9AqOQ28vFjx8LVs9UnGjAYYMAPxTZuecR7x0dGYt2+nStcxG43cOHggG4uKqNA0UfStUwXW6lojXL/wqlNh9y9NYEClZ9eVzuYceKIDsz6A/v3h+HFIMUHfS8By3XVU/LKdHiSxRY/0XMfs5YWqgKaoOF2xfbjm0d7Hh9TKSjS3kHZd26IofBvbxSNy234j7DEWC7i6szI1JZtyTROfENQ832Ag1dcXKio82xZlZpJksXiegyqHg/kZGXxSUOAJ/l/TrZvwjNtsJOzYQfzEicR9+WXt2JEzYDaYeWmQiOfTdA1VUVl711rWpa9jbtJcdp7YyYj2IzwvCuNixzEudhxHbUepcFTQLuj0/riwJmGsPrTacz/xWCJvbXqLSb0myRcaiUTyt8XLy4u8vDyCgoLk/3V/UXRdJy8vDy8vr988xp/poY4Ajta4f8y1TQrqC0SRswx3T6AbYm6GRf8H776LUlZG05GDKCoS+07VOOiFdRCUcRJatGDPdXGwehEtj83i7vZPEv/OO8Q9/fQZ5xDXrBlrIiNJeOUV4jt3JuiGZ1gWbmNhQbZo7gL1gzP06o0qcN3PPzOpRz+WtXPwti4q2wYUnukZStz+6tMiIsRXos3G/I4d2fvRe+SmpGBwOnGqKqqmgepdvazQ9R+h4rpOSlUVrby9OVRRgeZ0euwzdac3YoSI3lu7Vti+P91u48OorBpzV8CVs60BeysqaoVYh+TmMtfLy/UwdYyaBqWlVGoaGlDpcDB/82bmt2zJ3OxsHJqG+a23WLtyJXExMczJzGRRbi5jAgOZEHWGFo4uVEU8lm6h3egW2o0BLQfwQsIL9AjrUe/YSGvkWY0Z7heOrdJGaVUpvmZfT0v4dFs60QHRZzWGRCKR/NVo0aIFx44dIzc398wHSy5avLy8aFHX73oO/JmCuqG3cQ3W2xVFmQBMABHOLfltPPHTM8QNCkdHx7jnS+4A2LUL+vUj+5OVhI0bQp8+sGlT/XO9jF7VNhGDwZMW4VuRztPNmkF6uojoOwviOncmLi2NfUnlDHz2OOsPRrDhBiuTp4RS3i+bb47YyLKUVf+GKIjfDPc7/9hYxltNZPgWous6Kgae8GrXoC0i0WYjPjlZLHwEiI4WQ7oWOG7xr/8noANOYF9ZWfVGtboHksPVeKbm9XbuhEGD4NprYVlgIdxbY+7uQT0X0Ksflq6zIDy8Ro8chWGbNtGi3eVoYUbQdTRFYa7RiCMry1PFrzSZmK8o7MrM5L7UVGEfyc+HjRuZcOutp33+G6LcuxVq55e5/YiNeysyzz33G2hpbUmHoA44NAe/Hv+VNUdEiP62zG1EB0Sz5fgWvk/9nn5R/bi6zdXnPL5EIpFcjJhMJk8rbknj5c8U1MeAmqWvFkBmQwfquj4HmAMih/rCT+3vxfFpjxPx/L9xqHCs6Bil9lK0fFcp99FH4ZJLCLXb2bHjID4+DY/hbfLG19sPKAajkUGtBgHgZ/aDNm2gzmrbM7JiBd7/28LLhBEeDt26wYbZVvJesbJou/AfV2hatdB0NYLRFFjq5w8O14JCVQV0/CLsDV4mobCwVldG9zi62sACw9NR42M8g6IQXyear3Nn+OADuOEGWHoggPvKFKo018AK1YLc/cZAB931uITrqbrrZGhBAcdzilDDmqIpCorTiUNR608zN5dF7oqI2z5SWCjeeZ4DiTYbA5KScLjub3F1yzpXUX19x+u5rMVlWL2sLD+4nK/3fg2IiL4hbYfQ76N+2DU742LHSUEtkUgkkr8Vf2br8W+BsYrgMsAm/dMXhtJhgwFwqNUpH86aP/n9+8FopGtXaNu24TF0XSff7mpLajAQ0zyGXuG9aGJu8tsm5edH9L1Xcve9Kt7e8Nln8OST0KMHdNStvO/Tja5pYShOpTqsuuZnGjVENlBP4Lpx51/XombVWK9x+wwogFFReLdd/Wq4lxeMHy8isMf1svJTt1iu8g4SOlyvM4jre12rCbqOUYHswEAywzVMrihBVdcxak4U18JIRdMwO52M/e47xlhqLxKM3bmTxMJCJqakMDElhUSbrd7jmJOZSZ9t2+j+669EJyZyzc6dHjHtZtFv+OjSYrTQKrAVX+35ihcSXqBVQCu6Nu/K9uztJGUlYdfsfHvLt8wbOe+cx5ZIJBKJ5GLmQsbmfQHEA8GKohwDXgBMALqu/xdYhojMO4iIzfvzwgP/5jTZLNIInSoYVJFD7aj7VuoMfbdfuOIFUpQlnmPzyvJIzk7mqjZXnbd5jhkD+/aJlJEPP7Sy/34rn24KZYN3NvOys3HoOmjUfjMAxOyNJC6+4RQMT/51djabbDZ2lpVV62gFDMCVeZH4hTrI9S6lQte5NywMEM1kvFSVTj4+dPfzI89uP+vEjY8ft7JtcQzvJmUy+cABdEWv9/bVUzh3vzlAvHdY2r+/2O50orrSTpyuWEG3oA5XVR546CEqjx+nXUUFh3x90YD/jBnDf5KTcQcCzsvOZm1sLLtKS5mblUVmZaVo9X4qXAsoY5s0YWJKCgBjQ0M9jznRZmN6RgaZVVXcGxZWr4qdaLMxLasY2k3BnxxmXP4PArwCPNXqXhG9alxKZ+zSsRzIO8Av9/7i8XWfDqfmJL88n9yyXAorCql0VBIfHf+bFwJlZsKaNVAnMlUikUgkknPiQqZ8nNbI6Ur3kIG1fwDhk8ViQXeF2qgaq/3Qbs4gqJt6N60Wsl268F3qd9g1O7OuOb9ZkEOHgp+fEDh33QWKYuV2rIwNDeXt9YX88K8Aps4rZV7xMRSgb3YLhoae3poQZ7XWEoTPH07jx/wCoaaBVYuNvNypDc8+W/u83+IjdjN5Mlx2GdwbFU53qy8JhYXs2mhirZbLiYgCYfeAeisJNKhlL9F03ZMUogOoKpqqkgbQvr041sfHI4Sd7og/1xhVus70jAyW5uVVK/jTiU9dZ0BKCjMUxeM7d4tyoLY1pKgIpk9nwqhREB9f7Vc3toawVuxVwBLYnVirlU3HNtEqoBVFlUWM+GIEr175KoNbD6ZnWE8+2/kZ6YXpWIwWHJqDwwWHiY+OB8DutGMymAB4fcPrPPPTM2h6rf6a5D+RT6B34Fn+ZGrz00/wxhvi903XYdYs8bOTSCQSieRckJ0SGwFVc97HPGEigb5BhDUJo6CigH9dCvcXt6fTxlRx0Bk69y3dv5QAFYojQvBbuBDDjk8BcGh1zQK/j549xVdd4qxWLhtuZUERXFJm5fHev03sxlmtTGsdzc82m4ikM6rc3jOA/pf+zonXoVs38eW+ZpzVykvz4cBBX/LusmHXNI+gPyU1ha/HFuK+X2NbDetLzXg/dB0VyHQvFm1ISNdpWGPRdTqlpvLzJZd4tlW5FmEC9a0h4eFM+OEHiI+v7VdXFBzgWbx5Q6cbGNF+BP4Wf5Kyklh7ZK0Q1K7OjyO/HMmunF0A+Fv8KXyyEEVRuPGrG7Frdn647Qf6tOjDM/2eIcQ3hBCfEAK9A7EYLFQ6K/k25Vv6RPQ55/bngYFw443gdMKxY/Dgg9C1KwwYcE7DSCQSiaSRIwV1I8Dc53IA3hs5B7pcj1NzsurQKnx25QEuQX2GCvXX+77mbkV0HARhHQGYtWUWbw1964LNvSaKArff/vvHibNaWdu9W3XTlAHnvythQ0ydClOxkmjrxsqcQppZTNh0O4UOB8klJcQ2acLMY8eoPF24/Nlm47iaDITk5pIRGCh+vnUq1BFmM0ZFQQGivLyEtWXPHpYHBHiCVQDMrkWYS0+erHeZMevXk9iuHdN37WJTUVHtMBNgXlYWn544gUVRqNR1OvgUcEnrMbyy4RW+2vsVbw0RvztuMQ1QVFlEhi2DSGsk69PXM/qS0QDER8d7Ktc12Zq5lZFfjuSbW77hug7Xnfq5q8Po0SJW8d13IStLPC1Ll0JVFbz3HkyceG6d5iUSiUTSeJGCuhFQtXsnu5+4E2u3KNogxPDits/CtzVKwWcQ1Lqu41TBNysPxo3D+Pg1AKzPWH8BZ37hqGkD+TOuvXmelUmPQFkZeHtX7xsVHExCYaFHZI8JCQFcfu4SCy/HRHLfa6WUXpGFf1ON0iodbx/YX1aGVuc6OpDtbnup60RnZREQFYXZ27ue/7mwEBbtt/FA0yyq+vfHHa8ysqiIJ+d9xNLnn2e6O6gciLbbeXrmTGKOHGHAlCk48vI816nJgfLyWvf3lZZiDJ8Aubs5XJDCJcGiEh7WJIyskiz8Lf4UVRYxZsEdxEXHUlBRwICWpy8XhzYJBSC7JBuA40XH+SjpI8b3GE+YX9gpzxsyBA4dErdvvllkif/yC/z3vzBpEgwfDjKlUyKRSCRngxTUjQB12jQOGQ5wk8+nLLl5CU7Nyb73n+a5mgedQVBHWiN5uw/cbuyBV0gIBuVMfgXJ6RgwQDSEqSmm4dRCX10WzmuvQck70OmwlcGtw8lLhhkz4JP9Nobs2SE6M9bM64ZaCSLtKytZ2a8fAHv3wsiJMHeuWAS6ejWMX1oI/6CWvSQsYS0cOMAbhYW1srjbN23KhD59eLVvXxw1tnssJ6cp7TpQefyaz5kS3oxwv3DynsjDx+TDl7u/pKV9CIOeepdtA15hW+4GACqOXIre7dRDNvNtBlQL6vHfjWfFwRX8stHAzeHPMG6cOO7DD6F5c2E9X7MGrr9epLJUVMAdd4hul4oCt9wCI0dCWA0tXlwsflZn+DORSCQSSSNFvjw0AtS8Am48CRE2sFXYOF58nKmRB7j+is50WrdHHHQGD3WQdxBHowPxen8bAL1sGQC0CpBh9r+FHj3g22/P/vjbbhOtzUND4WsRmEFyshDDCe9ZmRDajfTLMlh6Mq9mpLXAVTWOvbT6EwmHA44eFYvybrpJtGr/t28ATwFOjyAW5yVceqkrK7t6vFhg9KhRJGVk1KtKn8knYVJVRoe2ItzPyvIMGy99W4xzu4HNH40D4Mjcf2HxmsZbu55m5uaZTLqlA75zhehtCLPBTJB3EFnFInUzOTsZgIz1g1jVFI+gnj5d+PPj40UFesQIEXcIMKFGeHdgoPh6+mlRse7USVStk5OrffESiUQikdRECupGgOryvl5yUtg93OJo6f3xdGrWCb76Cl577bRjlNnLCE8voHjJ//AbfRNR1iia+TYjxCfkgs9fIoI8aka75eZCUBDcdx8MGwYhIVaW/EO0If/4SC4tDoYweDC8kpFORmUlAO8UFjDKZqO7xUrXrrB9e/V4oaHw2DVW/DPb88C+fWiKgsHhYG9UFMsHDvR4qlVd59Z163gzPl4sUPT19QjqUIOBphYLw4OCSC0rI6W83OOdtigKBQ4HvgYDD5eUwKRJXPfgw3xXUgodwdhR5fktbdl4opj27WFsYChdA/rzWB9f2s02YbNBzM02hk8t5LrIAPr4WWsWzAltEorZYGbz7lyyS7L5z9Vv8sjzl6EoUOWsYvy343n7u3FcETUIiwWuGlrFT3kL6WnpQedmnRt8zlUV1q8XX88+K55vN3l5kJEB3buflx+vRCKRSP7iSEHdCHDGdGFH7m42RMF41YiiKPQ8DkM3rINJLwmzaO/epx3j1i630vnVufi9fzPcvJiCee+TU5qDflZtBiXnkx07RLxg//7wv//B8uVi++rV0L9FOFVJ4Tz4ILydBblhdl5IO4ITqNI0Vp4o5MpYK7NnN5y9PCE8nJgVK5i/axdzRoxkfWx33JVqVdN4v3Vr8lq3xnHkSPVJror0qMWLGba7Ncn9H2TJ87XHTbSJ7pdVlZU8iII+bhz28lKRza2AU3fyUnEqNIGfsuDDY9m0e+dy9iwZgaLAB7/Y2NtuB3tPaszMV+k8txu/fGjFZBJZ0iMjx/P8VRNZsCwd5VgcW1e15dvAb1h+cDltAtuwYNcCXhr4El5eIsWjw9BfSI58kXRbOk9c/gT/uvJftea7PWs7Jy+bzc1flmOz6fzjppdpEdDSs3/cOJEKkpQk7j/2GISEiOZEUF3NlosaJRKJpHHwZ3ZKlPxReHuT6wuVJjze50gb9PhxNyQmCrNoYuJph7AYLbwZB5rZBDYb27KE9eOOrqf4HF5ywejaVQjpadNqb581C/r0gS5dRAU1NBTy1wTgrFRRNFCcKn28Arj/ftEt/lTE3X03UQ8+6MrERqhCRUFTFPJciR8GqN1pEqB7d1baLmPbtvpjJhQWUqVpQtijYDe43ssreJrVKAY8VhWn4qR7+2XscTmSTkYUopg1NKDCobHVUcjKlWJRZ2wsHP1qChajhduHtueVm8Zx1fBCRi0cxexts1l2cBlWLytR1igUBVq0gIiqeJbfvpzmvs15Z8s7ODVnrflOWzeNecnzSMxax4qsz1i693v27wdXsZ9HH4V33qk+Pi1NWGgAcnLguuto8HmQSCQSyd8TWaFuBKhp6QzJgYHGdjRv0pz88vzqTonTp4uvUaNgyZJTjrH8wHKOWaG0fTR+BgNGVfzqnO8casmZURRRna7Lhx+KpjgWi3BhFBXBNdFW0td144BvIa2KAhjY18qwN898gfigIIwZGdW50zqgKXz/uYmnn7GyPrY7j6UeYlOZiMqzKApjR44kbmzDySnxAQGYVZVKTUOr8zbe4HTy6MKFzLjjDtFMRtcxOxxMHtyc4kgbE1Oyya6qQlUUnLqOrsHQy02kp4sUjmXLYEupjdG7MkgqKUFpEoOPXYPusyBrGVXOAjqHdOa6L68jwCuAefM+xe60ExnUjtcHv84dS+5gd85uuoUKg7Su6yRlJ3FXt7v47/D/suLgCj54uRtTPgWTCX79Vfiwa+L2tYN43nv1Ej8HiUQikTQOpKBuBCg5OQD8dNVn0LI3nUM6U9rmf8BPcOWVIvJg+vTTjpGQnkDP4+C3+wC07uipdH+y45MGs4ElfzzuhDwQCx7vuQdmz4avp1kBKytXCsG9bx+0bXv6seKsVtZ37860AxlsPlmCzasSXYXEngdJyPElvpmVH9r1YPDDNtrdUMjDVwSw/D9WivuJtIyGxnurTVseOODKPXf5rtvm5TF440ZGtWzJqC5dmH/yJNmbNhG6cye7Jk9mclIS9rqDGWFtlwMM133p8mQpN1Slc1SphLwax+g6+HUEv46Y1i/g/cXL2BYRxcc9/Olru47D6d8x97q5DGo1CKCWoFYUhUMPHaK4shiDauDa9tfiNx66tRKJIO3anfp5S7TZSDAV8thHAcT8SbGMEolEIvnjkYK6EbE3P4VO9CbIJ4h/9n8C+EmYQR9/HFq3PuP5I1JcN4xGT4V6d87uCzZfyW8nPBymTBFZy25atRKV7ejosxsjzmrlBUcMcZ+mo/7jCLoCBrNGYnkh74yxMnAg+GVYGVlqpb0Dbv5YCM6XXxbRcxMnwjffiKi6uDhY8IMdrS0u37SI1zsYFMTh4cP5xGRiDTD2yBGubN6cqmuugcOHRSt1N3Vaqv+o7mBdL020b6wb1Vfj9vr+t7IyG54ZdQNVRiOa4qSj0cLVba6mhX8LVt6xkqtaX8XxouNsyNjAyEtG4mX08rQz/zn9ZyoiynnxxQbeKdTA4xPXNMyqyppu3f60rHOJRCKR/LFIQd2IuPWbsbzTsSV2p52FyyYyB2DlSlGl/uknuOqqU57bM6wnTvUXcUdRPJ0SJRcnvXqJr5q0by9+zOdCz56w/NUArk9TPUIx9EQAixfD4sWiGUp2tkjASE4WFfAnnxSxfIoCDzwAgwfD8eOwfmYA6kyqLR8u0asZDJRrGretXEmzwkIqO3RAMxhAq9uqBk9l22y3k1lUVF2WP83qP0VVWX7/ZKry83EqCgYM3Nn/DVr4twDg6jZCKC/et5iHVjwEwEfXfcTd3e8G4JUNr5BXluc57lTU9ImX2zUe+7yQjQ+cX0GdaLMxP1vkbY8NDZWCXSKRSC4SpKBuDEydCi+9hNMVIfbr8V9JtblSGj77THzddttpBXW4XzhFNbyvrQNFRbtPRJ8LOXPJn4zRCEOjrKyxVrdqb1NpZf58GDgQtmyBvn3hhx/gGtE8kzddHu1t22DmTDhyBObMgVu7WbkxqjszSg6xwdWm3LOmUddJCwsjrWY3FXcuXo2W6YrTSUtdJ+DkSTJ8fevtb2ow4G80YlIUDlZUiMegKIwJCWFdQQGapmF0Layck5nJ3MxMwr28eCIykr5RfcG/EzS7msXOllxisxFntRLaJJRfbDYuW7eQEmMQmsmfEJMJdJ1ch4MOPj4Ma9qULTVarysKdAg11RPAQIOCeE5mJnOzsqjSNAocDhQgwGisdTu7qopse7UBZl52Nm+3a0dScXG98SQSiUTyxyIFdWOgUycAHKpI+dARbcRrUadFdF1ySnMw1SgC+pn9AIjwizifM5VcpNTt4OiO3MvKgshIkS5Sk+xs0Q2yQ4fqSvmCBQBWRtNDeI0LC/nsxAn2lpXVrjDXaSJT086hGwykQXUbQ9f+6NJSnr70Uk879USbjYHJyVTpuqfHje4ZUmfpyZNMP3pUnF9czA95ecxs2xql6wx01cj3JQqrk5NZGxuLwdqVoia3sFkzgx2wl7Gvxvz2lZWy1JX17tmswsdNU/k4yf1wdOZmZaFpeP723IL48+xs1tdo6+7BHSlS97Z7k67zQGoq7nySD7KyeK99eyaEh3ue3/iAACmyJRKJ5A9ACurGgOtzfqeKx6rhqCuozxCY2z2sOztqnFNUKQRAueP0Qlzy96ZXL9HgpC6hoXDokGjAGRJSv5miW6AHmUzcl5pae2cDXmgTLqt0Q8cg8rPdYhqE/cKhi5R0h66zKDcXJ6CrKk5NY3Fubq1x7LrO0pP5oBpr+bQTCgsJj7wGMo43/DdSo7V73f3VcxX/2PXaLSwrnU4eSEnB+RvDqg1ATVOME5h84AAAUw4elF5uiUQi+QOROdSNgQ8+AKor1AD23/CT94jwrl3JKxeRCh2DO56PGUr+hoSGCjENp36/NiE8nNnt29Pbz48BViujgoKo6843qSrvdujAfWFhJXyi8QAAIABJREFUmBoYw6SqxNeJLXHH9KkIYbuzpER0ZNc00DSauBc7uircJmBMSAgG12JJdB0VCMrKYtimrZgU9y+/ftoKuud+Q8doGop7QrqOQdNowCV+VoSaTLR2902vgVMXlfAKd+a3ppFQWAiIqv2r6ekk2my/6ZpzMjMZsmMHczIzf+OsJRKJ5O+LrFA3Bl56CaZOpWtEd4J8ggjwCmBbBJyMbkZwWs5ZDbEubR3eblE0aRJGpQAQHe4kkt9D3epyos3G9IwMUsrL6eDjwxORkZ4K69jQUOZnZ7O3tNTjX665302c1cqMtm2ZmJqKBtXeY0XBaTCQXFICuk6o00no4cNUtm3LG4cP46whhJ3AfWVltHM6cTqdLk+3BpX5KF7BQjN7xLTuEcqKqqCjuErUNYS1qnruBTjNhJxI40CNxw3QzsvLY1PxeKh1nQCTSdwuLeWoqpJtt1c/phqCXtN1trg81e6rbyku5o69e/kiJwcd8FJVZrRtS1Jxsed5dLeGb8i7XeBwUOxwkO8Uf+urCsTffoyvr7SVSCQSiQspqBsDrjLhN7d/D8HhWAwWJvWahN+nP1Qfc4aPnY8XH6eVu0jndFLkFJaPpfuXym6JkvNKnNXKkpiYU+47W/GWZ7fXrwDX+T3PNpnIbt9e3Kkhuj0VZlXlQGSkZ7uqKWhlR9AtTUExoOiaOFRRUTQnbfMzORgcUS2y3WPVuq5OodFOYQ0xrWjw1oqfePj1F0FRKC2Fj0d/wwP7JqMcOwY2G/j78+qHH/JsQxGXdSviNQT20tzcWtev0LRa3ut6nMG7DTA3K4tdpaXSViKRSCQupOWjMRAcLJI+mjcHoFVgK97t9jSWQ2lnPcT80fO5vsuN4s7TT2NSxYfveq3e0xLJxUN8QMDpKwZ1vdrur7qG7xrbDZrG6zHD8FKNqOiYVAPgQEXHy+HgsU8WYnA6UF2Rf4rm8k3XHNN9Xfe4gK7o5J04we4dQuauXQtRGz7HWeoK9naNF3/jjZgMdUwxdcdr6HHVeUy/1WriJtxiqW4lX8NWIpFIJI0VWaFuDCxbBqtWwbRp1dtckWJnS7BPMFc+Ngs258DAgcQ0j+Gz0Z8xvP3w8zxZieT84O726LaPhJhMJBYVuRYH1qCm/9m9SdPQVbWWOFWBd2fMYML119M/IICEgADiY7uhqyoJtkL63TeBAStXc8DrCG9cE8vbg19m1UaVTXmbOdmpqRjB7cWuYwcxKgovJU9j0OOvwwg/+t32ACa/X1H6xsOzz9Z6TAmxsczPzmaTzcbOsrLq+MG6lfC6j9NFkNHISYejwX1nQ6yvL/lVVeJ5cm1bkZ9PRkWFjO6TSCSNFkU/xX+6Fys9e/bUt27d+mdP469Fly6wZ48Q0RZL9fYWLaCwEEpL4YYb4Kuv/rw5SiR/AO44uUKHg+SSEsa47FBzs7KoOnkSs8nEvX5+xIwbx/RbbyVp0CCOVlWhA2ZFYe2DDxK3a1f1gFVVYDKRlJVE1oAeXHMQpo/vyJMt9vHjnT9S6awk3C+c7l/czt3xM7mseSeSiotZuHcDTWwlNNH96XD0KAN/Xsh/hlZy5Pso1IS1lPuY8C6z81bryfz4TD7Zzv3899r/0iuidreeDQU21hcVEjx3LsuNRlIGDiQkNZWmoaF85+1dO0Gk7hsH1/1oi6VB33Td21FeXiTabPVbwdfAoiisjY2VoloikfxtUBRlm67rPc90nKxQNwb27BHf7fbagjolBZYsEaHC99135nEOHIDVq0UTmICACzNXieQCcioP9oQ6iwP58kuWWK28qutMPXIEDRG/lzBvHnE2G+zaBSaT+AK8jF7kzHmTZRn5DB40Aj7qw+BPBwOQPiWdige3YzFW/+1dYU/i10PJBKwpJ3WtH14P3cJLvVui+pSSuz2DQrWAzJB8Flyxjb1ZuyhzlvDTkZ/oFdELp1PEEWoa3HuZlZUrrUQ/8wwTTp4ULSvHjuXVJUv4pq4Nw13BbiBy8OmWLc/43L2ans76MySEuKMGpaCWSCSNDemhbgxMngyBgeDuLAeQmwujRgmRvXJl/c4cDfHrrzBpEkyYcOHmKpFcDMTEQFSUJ37PAJjd8XyDBsHDD4u+6i46hnRk3MBHuOaul+gWcSkWg4XOIZ0B+HD7h9gqbSQeTaTSUYndaeeWLrcwquk7PP/Oh3y2+y1Gj5rK2G5jYeJELrUepH1hHjfc25roYRGUPleMj9GH6e+dQFHglVfENXNzRSfL1asBLy/xiZO3Nxw8SHyrVpjqRvnVvO26b3I6ib/7bli/HtLTT/uUxAcE1Is0rIsKBJkaCjeUSCSSvzeyQt0YMBrB6axdmXI44Mcfxe0RI0QSSPfupx9n1Cjx3Z2KIJH8zYmzWlnTrds5xcMZVAPtgtrRrmk79uTu4aX1L2ExWHhu7XPcFnMbC3YtYP+k/fTo0YGPP4arr67O6wbRqv3AAXhya08S0n7izVklBJqbowflcOml0KMHZJdk4231ZudOK2lpcPKkWHssJmCgl6+V6fZYtgVlU1yeR+iRI3T39SVp3Tr2xseTm59Ph5AQnnA6ievdG664At54Ax59VIyh6/DFF9CunejeY7MR99JLvBcby8SICLGoUVFQgUiDgcqyMnIsFpzAxNRU1hcW4udaPFnXV+2ORUwqKfHYSip1HYuiYFZV7g0Lq/+JgUQikVzkSA91Y6B9e/EKrWnVojonx5P6AcAzz8C//nX6cZxOIc5ffBGef/7CzVci+YvzxOon6B3RmxcSXiA2NJahbYYydulYz/6KZytqWUAa4vvU7xnxxQi8ln/M/Y8UcFnnMG7ucjPr0tYzaN4QFvTdw5U9WhMSAjNnwkMPVZ974oRorDNrliikHz8Od/Xcw1LncJpsWYveqTN2u445aQt07gxNmsD998N//lM9SJMm4tOoN98U6yyCg6GigsROnZh/9dVkBwbyQ79+2GumjLip4dOu6atOtNkYkJTEmZZEzna1UJdIJJI/G+mhllTjakdc6wXP6PrRX3ONSAEZNOjM47i92Dln1wxGImmsTL9qOgA3dLoBALvTjsVo4UTJCdo2bXtGMQ0wvP1wjj1yjOPXBdIr1sfz57s+fR2aWsGs10O59hv4+GOIixP7/vEPcfv22+Hnn6FVK7FdUaD5oM5kTNlNp2hf1t34LpXzv6RXs0toqigQFgZZWbUnsGNHddnb11eI6rw84kpKiCst5dUvv+Qb9+ANUaeFe5zVKlrCn8Xztyg3VwpqiUTyl0IK6sbAiy/CCy/U3ubOsu3dG157Ddq0OfM4+/eL7ydOnN/5SSR/c0wGEzd1vumcz4vwjyCiO1Q4KsgtzSXSGkl2STaBXoE8Oms1B0tactddsYD4AGrPHoiKEmuP+/WrHic8HD7/HMCX7Gzo8c7dZDx+N4EhNQ5wtxSPixOpP277hxtVFd4Ulz8lPiwMk91OldkMioKrlU1tv7aiYFYU4l2LmAvPMq4vxGRiyI4djAkJkcJaIpH8JZCLEhsDzz9fP5PWXaFesEC80rqrzxKJ5KLj5fUvEzUjipu/vpn3tr5HmF8Ydy65k0+SPwHAt/M6mrzUlFH/ns7UqaceJylJFKO//hrMZhg+XKw1JjxcLExUFNi0ydNI5nTEtWpFwj//yf2HDzOqqAiz04kCqLrO1du20dvPj1HBwcLusW0bT86bx/SjRz3nNzUYiLZYiPX1paOPD7G+vvT28+PqwEA+z8lhVUEB96WmMsct9CUSieQiRgrqxoq7Qp2aCq+/Dt9//+fORyKRnJLbYm5jcOvBrDm8BoBmvs0IbRJKdmk2AK1GfUw5Baw8tPK048TGivfXN90kgkHS06GkBLRHHmVjjwfhwQdFhXrEiDNPauhQ4iZP5v2bbqL3/v04cFWoNY21PXqwraSElfn5sGkTiQ8+yBtRUbXe2Pf09+dIXBxJvXqxt3dvknr1YkbbtvxYUFDrMotyc8/lqZJIJJI/BSmoGyt12xe7fdYSieSio1NIJ1bfuZpXr3wVgAFRA4SgLhGCukmrfQCM6zbutOMoinCANWkirCG7d8PAgXCoaS/6bZvJsqFvc/izX+CSS848KVWFiRPBz4/4e+4R8YK6jqqqOFVVtCV3Okk4coSE2Fghtmv4rcfUjDZxkVBYSN1l8g0dJ5FIJBcbUlA3Vkwm8fnv+++L+6daWCSRSC4aeob3pHtod67rcB3NmzQnIS0Bp+bkUMEhJvWaxJ3d7jznMfftEw6PrVth1SqxnKKk5NzGiAsOZk2PHrzUujX/bNkSg6KgahqKpvFpUBArBg7EqOugaSjAE5GRDXqjC11dGUG8OLmPS7TZmJiSwsSUFBLP0FxGIpFI/gzkosTGTGysKFGdLe7GMGdTvZJIJOed7mHd2X7fdgBimsXw9d6v0XSNzH9mUlJVQmpeKk29mxLsE3yGkQQ//gh33CEsIG+/LXzVsbG/7f21O2v6yh07cOo6qCoOYF9wMPtcaSGKrmNSFEYFB8P06fDRR0LFR0UxJzOzlsf6Mbud1w8dIjEoiPgdO6hy2UXmZWfL9uYSieSiQ1aoGzs+PuJ706ZnPtZ9TN++F24+EonkrJg6YCpZj2ZhVI2YDCaKq4rp8G4HFu1d5Dnm+9TvSTyaeMoxBg4UqZlPPy3ux8TAuHHivXNBgQj++OEH+PLLs5tTQmEhVZqGBniWNdZod66rKk5dJyE/H558EtLSPPtqeaV1neSkJBgyhISdO7HX8F67Y/gkEonkYkIK6sbOqFGiccNrr535WLfv+iyjryQSyYVDURRCm4SiuARppH8kfmY/dueIT50W7l7IiC9G8HzCqZswGQyi82JYWPW27GzYvl00SoyIgGefhVdfPbs51WzVXu/jT1dnRbOqEu8Wzx98AJGRQH2vdGxBAa+uWkVhaGitF6qaMXyJNhuvpqdLG4hEIvnTkZaPxo6qwiOPnN2xaWni+yefiLwtiURy0aAoCp1COvH5rs/Jr8inmU8zADS9dgTewt0L2XJ8C/vz9hPXIo7nBjwHwBUfX4Gu6+zZrRJY3pNlU95g1iwYMwYCA8W5lZUi0j4yEiZNqj+Huq3al548yb+PHvUsNFSAGfPnE/eJiPsjIEC0cYyIEJ7qtDQWpacT+9NPzLjlFqoMBjh+HAUwKAojgoN5IjKSOKuVOZmZTD5wAKeuY1FV1nTr5unGeC6t4iUSieR8ICvUkrNnwADRnviJJ/7smUgkkgZ4uM/DhPmFsWDXArZlbQMgvzzfs3//yf3csugW3tv6HseLjuNr8vXsMxvMGFUjhqA0jjSfQdt2Gg88AM2bi7S7Hj3grbfEOuY+fWpft7xc9IiaN0+I6onWllzdwsreX2vXbDQgb+BAkXvt5QU331zd7vztt5nQty8rx4+nqFUrIaZd6K4qfG9fX+KMRhJtNiYdOIBd19GASk0jobCQRJuNK3fsYOqRI1y5Y4esXEskkj8MWaGWnD2hodWpIBKJ5KLj1phbGdp2KCVVJdyy6BYAckpzPPu3Z4kFjZvHb6Zr8661zl1952oAZmyawSMrH6GwopCm3k2x28WixaQkeO45yMurf12zGYKCqpdk+PjAhAnQJjSAVYpClSZq1GZVIX74cOEnOXoU/u//qru0Ll0KnTvDL7+INujuVuiuLoxmVSX+zjuhSxcS+vZFa9HC4782uGwgbg+3E6hyiWxZpZZIJH8EUlBLJBLJ34hA70ACvQPJKxPKN6c0B03XUBWVcns5La0t6Rjc8ZTnD2kzhPmj5mMxWNB0jSq9gsREH267Da6+WjRZXbwYkpNh2jQ4eBBatIDly6vHMJvdhWcr3W2xvLErG3R4rGsocX5+YqXj0KEwerQ4Qddh507R8tzfn7G6zrzsbCp1HQXo6OPD8KAg5g8ZwvSwMNIUBXQdxeXLvjYoCIAgkwlVUdB1XQjwGl5raQORSCQXEkWv25L6Iqdnz5761q1b/+xp/OWRLzASyd+TgvIC3vv1PRbuWchVra/inu730DGkI6pybg6/e7+5l4+SPwKg5HEHO3cYuPlm0WkxJQUWLRJi+pJLRNTe//53lgNv2iS6MXbvDp9+CidOwKBBUFoKZWXgWpw4JzOTB1JTcdY8t+7rlaJgVBQ0l/BWFQWnrqMqCrPatWNCePgpvdYSiURyNiiKsk3X9Z5nOk5WqBshbp9hlaZhli8wEsnfiipnFc+tfY53h73LpN4NrBw8A5WOSrZmbmXFoRXV2xQb0dFNuf56kQhy113w73+LfVOnQvv2px8zN1ckh/TvD3u1HgT0vIW2HzwJM2cKZX7ypMjq8632dOfZ7Wh1B2ogINtRQ2Q7XbcVXSfPbvd4rd3HVEobiEQiuUBIQd0IkT5DieTvS5BPEAoKOaU5nCg5wVd7v+LadtfSKrAVg+cPZljbYTx6+aOnPD+vPI9+8/p57htVI029m4I3zJhR//g7z6I547p1cOONwoe9dJmZ15O/oLQTmLt3F9F5X30Fv/4KTz0lzNiICD6TongaugCiQu2yewAef3XNurU7ESSjooL52dloNc53e60TbTbmZ4u27WNDQxv8/y/RZmN6RgYp5eVYFAWzqnKvK19wUW4uY0JCGuz2KJFIGidSUDdC3Fmx7gq122cokUj++hhVIzo609ZPw8/ix+OrHye0SSjRAdGsS19Hr/Bepz2/bpdFg2I4xZGwL3cfHYI7cLLsJEv2LeGmzjcR6B1Y77j4eNi4EaZMAZNJFKTNZuDSS8UBt94qNjz+uOecOKuVhNhYj/D1Nxr5T3q6qEJrGoqqolOdAOIW281NJnLtdv6blYWKENG4bCDvtmsn5pOcXK/zIsD0jAySSkqo1DSy7fZ6j2NLcbHn9qqCAgApqiUSCSAFdaOkblbs761OSz+2RHJxcWWrK0lIS6BnuLD95ZTmUFhRiENz0My32WnPNRvMeBm9qHBUAFDprGR9+noGtBwAQGlVKSdKT1BcWUzs7FheGfQKP6X9xI+Hf8ShORq0mQQHi6+nnoKVK8FqhQ8/hMzDXXkeQNP49/gUHq/T3CXOaq31f8qolBQSPv+cjGbN+GD4cJxqDV+4SzjXFMLu6vSEsDBPJfrV9PR6nRfnZ2fzYVYW59qyalFurhTUEokEkIK60VL3heq3Iv3YEsnFx+o7V6PpGjq6x/6RWya6E55JUIOoUh8rOua5vztnt0dQv7juRd745Q1PzJ7JYOJk2Uk6hXTivp73nXbcq68WPmqAJUvAz88LfvwRBg/GtmEXKFGnPT9u2DDiTCYSNY1P7HYqVRWtjgWkFq7EjygvL8//S3WtJGbXOb+l/2vd7o4SiaTxIhu7SH4XDfmxJRLJn4uiKBhUA0bVSJBPkBDUpUJQh/ieWQQObTMUgAAvYQdzZ1nbnXb+/cu/0dE9DWN6hPUgtzSX3hG9Maqnr9GoavW6wx9+gC+/RHSMAV7eM9pzXFIStGzZQHKIosBVVxHXty9rHn2Ul48fZ3bLloz6+WdUTaufAqLroOusOHCA0bt2ccX27dy6dy/hZjOxvr509PGho48Py/PzOR0tzGbqLoccYLUS43owczIzGbJjB3MyM087jkQi+fsiK9SS34X0Y0skFzfNfJsRZY3CqBrpH9WfSP/IM57zSNwj/HjkR9IK04BqQX0w/6DnGPe+zOJMckpzWLxvMXEt4phw6YRzm6C7r7ndzssvi+aJ5eWQkQGTJ4umMm7mzYOBAyG6pS9xKSnEvfEGOBxMeOEFFvcdw+pekeKE79Yxe3AfdFVBB9YbDPU70lRWNjidUJMJL1XFmO9F3xY+3Bcd6ml1XjPGb4Pr07kHIyKYfvQoIH3VEkljRlaoJb8Ltx/7pVatpN1DIrkIearvU9zZ9U76tOjD+rvX0zHk1E1d3HQK6cT47uM9991Cek/uHs82t6B+es3TPBr3KEWVRXy287PfNsnZs9kw5WumToWCArj8cvj5Z1cF24Wuw7PPwnvvgY7Cmq6PoBcXQ1kZNu/m+Jt78L6vL+/v2kVU+i482R+K0mDc3qkYFRzMjs5xHBzenRZfdfD8n5ZXZ5GihvhUbnFubq3ti+rcl0gkjQNZoZb8bs6XH1sikZx/7ux2Frl2DfDsgGc5UniEuUlzPVaOPTnVgrp/y/5sz96OQ3Pw6uBXOVRwiJ0ndv62SU6YQJwTiqZBkyZiU79+tQ9RFJg9WzSSOXYMBif9m48++jd3j9OxPvAAg90Hvvoq8cnJmOx2qszmmiO4vtcJ2WtAa/v7C/F+1VXV29yfxlVqGhqiGmVWVa4PCfFUqEH6qiWSxooU1BKJRCJpEKfuJNI/kmW3L2Nf7j525uykmW8znu3/LD3CetAmsA3LDy7nUP4hQnxCPAsfAfLK8jhefJyYZjEoZ1EhNhjAz6/6vs0Gu3eLhoqvvAJbt8KK6l4zHD8O3t6IMnZyMrz2GuTnw/jxxIWFkfDPfzL/6qtJviSe0BM72OXbB3NMCGU+ZfgeOED3/cV8XjWEyKE2jlnKADApCmNDQykuFkl+rVtXX69mOlKQycTyvDySSkr4X04Osb6+VOo6ISYTScXFJNps7CotrZdXLRORJJK/L7L1uEQikUjq8X8J/8eL614kOiCaQw8dovXM1rTwb8FtMbfxQK8HWHVoFUM+G+I5/vaY2/l81+eUP1uOl9GLLu91YU/uHlbfuZrBrQef5koN8+23MHKk6FS+ZQvs2QPTpsHEiXD77XD99a4Db71ViOr8fNG2/NFHRX90d6k7NVW0cvT2hpwcaNIEx5Br2LvqGP8Zu5N582BzXi5rd+4iM+VSqpKtxMXBPfeIJJJRo+rPbU5mJvelpp5y7gao1TJ9dvv2xPj6ykQkieQviGw9LpFIJJLfjHshYt/IvqiKSrotnYGtBvJArwc4ajvK25vfBmDF7SuwellJL0znq71fkVaYRpvANuzJ3UOgVyDdmnf7TdcfMkRYPHr3hj59xLbNm4Xdo5adefZsEXAdGAhffy06Lfr6is6L5eXQrp1QxUuXoq9aDYcOYfQ2E2NJ5d2ZTlTVQNzLLxP39tt8839JbAmJJToaunaF0aPF2sVazhHO7JN21rm/KDeXPLtddqiVSP7GSEEtkUgkknoYFAOBXoF8dr1YaNgrvBff7P8G5wgnU1ZO4YcDP+Bt9GZIW1Gl7hHWg9JOpRhVI/ty9wEwY+iMs4rpawiLBSbUCQzp00eI6lr4+0M3l2ivWU7uWaOg9NFHsHQpX9/8FTc6voBhw1AqKzmSkM5X21ozLTERBRgZlcTIu0XXxA8+gMTE+kl8IHzS7kSPs2FMSAiHysvRqfZeuxOR3C3Ok0pKUIAAo5EChwMFiPLyopOPzynbo0skkosHmfIhkUgkknoYVANOvbrWmmHLoKCigJ0ndtI5pDNArdxps8GMUTXy8vqX2Za1DQBN11i4e6HnmE+SP+H5tc9jd9Zv631BCQykMqwlraJ1ju/K533LFACyVu3k9dfBkWcDoHjNFrKzYdUq8Ck7yYM+c7GkpdQbbkJ4OLPbt6ejjw/RFgujgoN5IjKyofWNhJpMvH/8ONOPHkVDpIM8GBFBnNXKk4cOcXlSEkvz8kivrCStspLk0lLP7fU2G//NyuKKpCQmpqSQaLNdsKdIIpH8PqSglkgkEkk9DIqBosoirl1wLQBzr5tL55DOdAzp6BHUxVXFtc45XnScqWunsjVzK8PbD+fn9J+57/vq7onjvhnHS+tf8gjuPxLLmBH0fHkUEV0COegvmslc/cGNlBwrxHTkAABHliTz2GMwekgpQQNj0F55jaqsvAbHmxAezt5PP+XI5MksueceAr79loZWJGXb7SSXltballxSwpzMzFrpIKfDDszOyuLKHTukqJZILlKkoJZIJBJJPTo3E6L5eNFxAK5tfy27H9iNl9HLs68uTcxiIWBok1C+u/U7WgW2wlZpo8pZRaWjupGKu2vjH8o774iuMcDrc4PRvvsBpaQEc2Ya6DqV/sG0b3Kchx6COU8dIYxsnk0bz0YtrvY4hw7B4cPw/fcwaxZUVYHTSfyHH561h3JMSMg551XryG60EsnFjPRQSyQSiaQe93S/h8X7FpNVklVvX/ug9hhVI49f/nit7VYvKy38W7A1UyQxhfgI/3RuaS4R/hGkPZxG9Mxoz4LHPwujERh+jbjTpQvs24clIACaN6e3Ar2LT8BrcMWES+hUsBG0y0XfdICHHoJly8TtNm1g5kwICSGua1fW5+QwvWNHUsrLsShKvcp0tMVCX6uVuVlZZNbp1NjUYMDfaKzloQ4wGqnUdQ6Ul+NwmbmDTCYSbTbmZ2eTXVVFqNksPdYSyUWAFNQSiUQiaRCn7sSgGOptNxvMfHPLN3QK6VRvX+eQzizat4jXNrxGh6AOAOSWCUEd2iSUd4a9w+WRl9c6Z/rG6VQ6Kpl6xVQAFuxawKRlk9B0zXNM6uRUmjdpzr/W/4uZm2ey8o6VJGUnsWT/Et4a8hZtm7Y99wf4xBPwv//BgQNgMsG6ddjffJudWgyXAtf4JMANMyAtDQICRF7fsmWi//lTT8GAAeDlBU4n+PoSd999LOnXD4KDYcsW5syfzytVVaS7xHNaZSVpOfXfTNzerBmfdar9XCa6Wpu7G8mASA+Z7Irrq+lCn5edzdrY2PMuqmVutkRy9kjLh0QikUjq8Wbim6w4uAJVafhl4pp21xAdEF1v+/NXPM+UPlO4qfNNnoSP3NJcNmZsZPgX/8/encdVXaUPHP8cVgERZFPAPXEBFTI1cYuy0sxcW+dXtli2aVlNjpPtme3TYtuYraZji62a5pKoJWqaoIILbiQqgiiogGz3/P443MuOF2RTn/e87uve7/d7vl/OdZrp4fic5xnO0I5Dy7U//yLuC5btXWY7XrhrIQ7Kgbsi7rK93JzdAIhoGUFadhq/7fuNIM8gFu5ayJLdS6iRzp3h9ttNMA3pE9sdAAAgAElEQVTg7IxKiGfNLycAODLsTk5+vdgEyGvWwKJFptTIa6/B1VebYBpMV5pXX4Urr4QjR0w3moQEJqxdS2d39zNOIy2//CbN6IwM8koE01b5lA6mAfK0rvVUEGtA/9S+fZK7LYQdZIVaCCFEOVl5Jl1hQJsBZxhZWr/W/Wwr0C08WrD53s2E+ITwbcK3LN+7nN3HdpNfmE9X/64kZSTx2trX0OhSXRavD72eQW0HcV+v+8o9/9pO19LCowXxafFM7jsZhap5Csn48WUm3w+nxB1Menwq+h0XWl7dneef78FTNwBbt5oxv/4KFa3WPvCAeYGptffMMxAfz9ixY89YYq+iduVlW51bFYX+pYJqF6VsZfhqizWgl7rZQthHAmohhBDlODqYVI8Xr3ixxs/wcPGgs29nViWt4o8DfwDw6K+P4uHiwQuXv8CGgxt478/3CPUPLbVRcUzXMZU9EoBQ/1Di0+JZuGshGl3rOdmOr78CwOzZikEOv8P0aFi7Ftq0qTiYLmnLFpg3z6xqx8fb2o5b25ADfHz4sK1j4vjAQNuYkqytzq250sfy80krKMBVKVubcx8nJ1sONcD9O3eWy6uuKG3DmoOdUFSir6L612iNBhTgWBSwW+8DKszblhQRcSGTgFoIIUQ51tzpkrWoayI1K5Vr5l4DgI+bDz1a9OCr+K9s58DkXSekJVBgKSArL4vkE8l08u2Es6Nzhc8M8w/jg40fMOor08jlSNaRs5pjOQ4OYLEwfjycHPE+/Pw/c3706DPfu2QJvPIKDB8Of/wBWjMhKKhU0FxRAF2Zz48cKbdKDbAd09J8QlAQMZmZRMXGkleiC82nKSm8ExLC5N27S7U7B8qNBUxLyCL7y2yY1MDWrCwmJSba7iubt21NEZHW6uJCJTnUQgghyrGuUI/9euxZPadl05bEjI9h7V1rib03lo+u+4h149fx662/2sb0aNGDFh4tyDydyYp9K+j2QTfijsRV+syH+z7Mdzd9B0CQZxCXBl96VnMsJy0Njhzh8GF4oPBdhjsvYcsri02b8zN59FGzyfGqq+D4cUhJMcHqvHkwfTrs3m33NCrLo7aylt6Lzsggv0yAnKc1C9LSyqVtVDT2TAqKnlXyvrJ52xWliAhxIZEVaiGEEOVYK3hYc6lrytXJlb6t+pY6d2mrS9Fa8+TAJ3lxzYv8e8C/eXLQkwDEp8YD0NWva7lnWXX06cjxHJOXPGv4LK7tdO1ZzbEcHx8A/u8KyM31YVnGEOzYW2g4OUHHjqYcH0B8PPj6mgohAO3amet2qCyP2srf2ZnRW7ey+dSpctdclCKiaVNWlMjf9nV2pruHB06U39hYFQWctlhwwFQasT6/ZN62da7WFerazukWorGTgFoIIUQ5w0KGcUngJXi6etbJ85VS5BTk0KJpCxwdHNlxdAcvrH6B9cnrae/dHg8Xj0rv1VrzeszrAAR4BKC1RinT+DuvMI+3171NR5+OjO5qR4pGFaZNM+/u7qZ/i5NTcTnqMworan4zfTq88Qb8+COMHAkly+b9/DMcOAD33w+qfONyax51dEYGvs7ObD55knWZmWzJzsYCzK2gBJ8CRvr5cY2PD5MSE20BcCHwUGIi74SEmD8rrVFAC2dnmjg42HKocy0WUvPzsRQ9S2Papa8uUeXDEXgnJKRUSkfJnG8hLkSS8iGEEKJCldWhri2vX/06hx49BMDJ3JNsOLgBpRS3h99e5X1KKbTW9Anuw8r9K3Gd7sqxnGMAbEvdxpTlUxjzddUbG+0xeDDMmgW33ALNmsHBg9W4OSDApH3s3Alt28J114Gra3FAnZlpVq1PnQJLZUkdJlD9d9u2TAgK4oPOnbmxRQvKh96l9fH0JD0/v9I0kMKi8w7AQ61asS8yks29e7M/MpKHWrWyPb+qxJD0Ckr9gcn5/kjapIsLkKxQCyGEKOfTzZ8SmxKLm5Nbnf4c68py7+DeJE5KtPu+r2/4GoBfEn8h35JPfGo8A9sOpGdgT54c+CQzfp9BoaXQlgteE0eOmL4v48bBpEkmHrabUrB0aelzAQHmoQCenhAbC25upo61nc6UBlIyFcNZqVKbD12UYqy/P2syMytNzSiZugHFKR5WDlBpSoeU2hMXMgmohRBClFNgKQCqX4e6voX5m9SKbanbGNBmAEopAjwCsGgLx3KO2ZrL1MSiReb9uedM6vNZCwgoXqF2cIAOHUwqSPPmpuuiHcqmgSxOT2dnTg7+zs6Eurtzsacnr/79Nztzcgh1d7eV2ENrknJzeSkpid6enoS6u1dY+q7s8x9KTCS3KD2krasrEZ6eXOPjY9t0aC3NZy3v51SUTiJ51OJCIwG1EEKIcpwczL8eHuz9YAPPpGptvNrg6eLJA788wJI9prNjQloCYEr2+Xv4s/PoTh745QEW3LgA7yb2B3lXXWXSnP38TK+WwkKTR11jLVrA4sVmdbpdO3joIVNi7+KLTUCdmwsjRsDvv8NLL5nrJW3bBn/8QWReHpF5eZCXx4QhQ6BPH8CUrhu0eTMFZX7sLkqvNO/PzSXmxAlb/eqyIr28bIF2dw8PXv37b35ITzet03NzWZSejqUoaH6rY8dS5fScgXsCAysM1oU4n0lALYQQohxrqoR1pbqxUkoxb+w8vtv+HXdE3MHQL4dyWbvLGNNlDF5NTED3aeyn/LbvN/IK86r17NatzSsz03QZf+klUxWvxp55xlT/yMuDw4ehaVNzHG8qm7B7d3GayO+/lw+ob70V4sqUE/T2hp49AZNyUdF/WxVVEs8vKnt3pqA30suL7DI53tbc7DyLpVw5vQJMYxgJpsWFRgJqIYQQ5Vg3I971012sumNVA8+masM7DWd4p+HsObaHnIIcbgi9gbsuvst2/UjWEVo2bUmAR0CNnt+smQmke/c+y4n26WNbTbbZts3kluTmmi6M//43zJ5duhoIQHa2GfvYY2aMi4t5ORc3v4ny9sYJygXVjpQPqp2r0a58rL9/qfbp1ucVAvtycmzVQMAEFZLqIS5EdRpQK6WGAm9j/vc3W2v9cpnrbYDPAe+iMVO11r/U5ZyEEEKcWYhvCADuzvYWYG5YOfk5PPHbE4DJq844nYFC4dXEi22p20g5lcKcuDncFn4bYOprx6bE0r9Nf8CU4vtx54+2aiEAIT4hDGw7EKXgiruXMfCiq2p/4t26QUEBfPIJRETAjBmwaxckJBSP2bYN/vc/k3MyYICpa2315Zfwr3/Brl1Eenmx+uKLbTnU1rzqcS1bsjUri7eSk8kpLCTC05MprVvbvYpcsn16RNOmvHHggO1a4unTpcZaMF0VZYVaXGjqLKBWSjkC7wFXAcnAn0qpn7TWJf5fgieBr7XWHyilQoFfgHZ1NSchhBD26RPch8CmgbTybNXQU7FLgaWABQkLcHJwoqt/V/xe9eORvo/w0pUv2ZrFvLPhHVtA/cHGD3h82eMcffwovu6+bE3dyuivStetviPiDga2Hciu9F0M+XII+x5Ipa2/X+1OvFcv8/7AA+Z97Vrw9zfdGsF0WnzoIVi50iRwX1qmK2SrVnD11ab83jffELl7N99Pn17ux0R6eVWr5XlZ1vbpLyUlVZhCYlUITExMpLuHhwTV4oJSlyvUfYDdWuu9AEqp+cBIoGRArYFmRZ+9gEN1OB8hhBDVUKjPruxcffJ09STt8TSUUjRzbUZnv85sP7qdpIwkcgpyAEjLSrOND/YMNuey0/B19yU1K5UgzyC+HP0lF/lcBICHs2kuE5sSi0Yz4h8pxC2r5YA6JAT274eNG+H666FfP3jySUhPNyvXLVvCggWmnXlgoHmVFBVlXgDffWcSvutQZWklJRXamZ8txPmkLgPqYOBAieNkoMyv1jwLLFVKTQI8gCvrcD5CCCHs9PPOn0nNSiXuSNyZBzcSzd2a2z6H+Yfx56E/ad+8PcemHGPq8qnM2TLHdt2aypKalUoXvy5c2eFKDj5acecWa+710LHlOxPWirZtTeAMpqRIy5amrEh6uim117x5+dzrkrSGPXvgv/8tfk4dqSytpJmTE28mJ1OoNa5SMk9cgOoyoK6omVPZxku3AJ9prd9QSkUCc5RS3bTWpbYUK6UmABMA2rRpUyeTFUIIUcxBmUa6/Vr1a+CZ1EyYfxhfx3/Nw4sf5u1r3qZD8w7kFOTw9Mqn6R3Um1FfjQJKr1pXxt/d1LLuOeDMY2vM1RXmzYPwcLPKPHGiqVV98cUmz/rLLyu/9+qrYflyCA0trhhShyK9vPi+e/dy50f5+RGdkWELpl9KSiLK21tWqsUFoS4D6mSgdYnjVpRP6RgPDAXQWscopZoAfkCpZQCt9SxgFkCvXr2q6oYqhBCiFlhTPa4Pvb6BZ1IzY7qOITopmnyLaZFtXWV+YfULpcalZpl/3Qz5cghDLhrCo5Hl6+JZm8PsPZJKYddqNTasnltuKf4cGWneR4+G4OCq72vf3rw7OJh25nfcYYpo1zNr/eqYzEwGx8XZujGuCA+XoFqc9xzq8Nl/AiFKqfZKKRfgZuCnMmP+BgYDKKW6Ak2AOlwCEEIIYQ9r2bzGXoe6MmEBYawYt4L3r30fgJu63cSf9/wJwLUh1wLQ0acjHZp3IDs/m2V7lnEq71SFz/J186Wf2508cW9nDh2CrVthzhw4VB+7fp55Bu6+u+oxYaZbJM2awfz5sGZN3c+rChW1IBfifFdnAbXWugCYCPwKbMdU84hXSj2vlBpRNOwx4B6lVBzwP+AOrbWsQAshRAOzrlBPWT6lgWdSO9yd3UlMTwSwbVLcfO9megf3Zu2BtWi0rY15WY4Ojsy+7hPeefhqmjaFed9mMW4c3HhjvU2/at26mfdTp8wmx61bi6+dPm1yrOuRb4na2BUdC3E+qtM61EU1pX8pc+7pEp8TgP51OQchhBDV18arTan3c11Ofg7zts0D4NDJQ7Rq1oqmLk0ZNncYi3cvBqB7i/J5wVZdumgC22fi5uTGRx5teeyZeHp3bVEvcz+jrl3Ne7t2ptnLt9+ayiAHD8LDD8Pff5uWj7XpyBGYMgVmzjQr40ViMjOZlJhoK61XCDxUURm9tWvhH/+A2FjT7VGIc5x0ShRCCFFOR5+OKBSdfDo19FRqhZuzG3dffDcP9n6Qo9lH6epngtCJfSYysvNI/D386eRb+Xf9x3f/IGZfLF12fUR6cDqD/+8vrgm5pr6mX7WgIPjlF1Ojet8+E1D/8AM8/bQ5rov85c8/hy++MEF1WPHKfnRGRqlW5AB5FZXRe/llSEqCZcvghhtqf35C1DMJqIUQQpRTaClEF/3nfDGyy8hy54aFDLPr3g7eHZifNZ+0v1IhGFZsjSei6TX4+ppF4QZ3TVFw7+MDQ4eatI+QEHjzTdiyxWxYtKaG1IaJE83Gxy5dSp2O8vbGWSnySgTVLhW1OQ8Lg59/NvW1hTgP1OWmRCGEEOeoDQc3ALBi34oGnknjEBZgVmGzrxsL+W78FBNPUFDpdOWaKrAUsC11G9n52QAcPHGQ5XuXs2jXIn7a+ROpWanM2zqP9cnr7Xtg9+4QFwdPPQXJyaZSyD33wI4dplkMwKZNpnZ1Tbm7g8UCK1aYro5FAXSklxfRERHcFxjIKF9f7gsMZGVERPkqHzNmmFSRgxXX/hbiXCMr1EIIIcqxbkrsE1RFQ5ELSN9WfXFQDnT164q/W0vST8Xz/vvg6pPGnLglDO04FD93P5SqqAVD1Z767Sle/uNlNty9gd7BvVm8ezH3/HyP7fp1na7j510/A3D4scO0bHqG5i39+8Nrr8H06TBgAFxxBcyebXKtH3wQ3n3XrFynp8Nikz/O11+b9uczZsCECcXPio01rc/z8qBpU8jJMYG0i4vp7phtfgmgZUv4/Xe46CJb+bwqKQWdO8Pu3dX94xKiUZIVaiGEEOW4OJo8hqh2UQ07kUaiQ/MOZD2RRex9sQxs149gH1/uvx/25a1j3A/jGDF/BD0+7FGjZ8ckxxDmH2ZreT6803DW3LmGZy97FoDkE8m2sSU/V2rkSLNqnJYGQ4bAe++ZwPmSS0zOclaWOf/gg8X3tGxpyvPdc0/pZ82fDzExZjU5Oxvc3Exg7eQEnUrknD/+uFm1ttf8+fDnn6YyiRDnAVmhFkIIUY61U6I1DUFAE6cmADx/+fNobbIp1u8znQkvDb6Ud9a/Q05+Dm7ObnY/U2vNliNbuDHsRnzcfABo2bQlLZu2JMQnhGdXPcsdEXfwfvD7RH4caWtEc0Z+fsWfXVxMXvWKFfD66yYgTk4u3TBm0CDzKmvjRoiIgKVLy1/73/9MpY7gYHi0uCFOTGamrWNipSvVSUnmPSvLvu8jRCMnK9RCCCHKsTY5ee/P9xp4Jo3TnmN7aP3ixby57g1aNWuFb3Y/NJqd6TsBWJCwgMiPI8/4nEd/fZTjp4/To0X51e0AjwD2P7yfiX0m0sm3Ex9d9xHdAs5iY2GPEj/D1xf++AMOH4bcXJNT/fvvcOutsH+/CaS1NkF4r14VP89a3cPLy4zfudPWJfGpffsYHBtLTGolvwD861+mM6QE1OI8IQG1EEKIcqyrpd0DKq/NfCFzcXLmIr82RLbqx7SB0/jkZRNcxqeaFes5W+awLnldqXu01lwy6xKeXmlrx8DAtgO5vN3ljOoyimPHTHqzda+gUoo2Xm24ZNYlfB3/NXf3vLtcXfAFCQtYtGuRfZMeMgSGD4cXXjCB9IABptLG1q0maP7yS5g7F8LDoXdvyMiA8eNh3LiKnxcWBvfea9I3rr0W/vOf0l0SCwqI/uWXiu8Fs2ly/nz75i5EIycpH0IIIcpp3qQ5AOEtwxt4Jo1TG6827H7hR9vxwC/yCP/aieVb4rmlG8SnxTO6y2iSTyTj5eqFp6snSinyCvPYnLLZdt+YrmMY03UMYPb/TZoEL70EU6ea6z/t/InYlFhO5p4kNiWWJk5N6OJnStUlpCXwxG9P0N67Pdd2uvbMkw4IMAE0mI2F7u4QH2/yocGsTv/3v3DihDk+dsxsZqyMoyN8+KH5/NVXEBRElLc3Lg4O5FksuABRw4dXfO+LL8LOnaaWtRDnAVmhFkIIUU6+JR+A0wWnG3gmjdtNN8E//wlhXVwY5DKZz17qSfzfB9l7fC9uzm60frM1P+z4AYDle5ezLXUbC3ctZN/xfazYu4LVSavJK8wDTLW73r1NzxSrVUmrAFNab9T8UcxYM8N2bciXQ9iVvsv+vOqSHBwgNBQ2bzabDt3doV8/8Pc316+5xmxQ/O03+54XFQUhIUR6ebEiPJwX2rdnRa9e4OzMS0lJxGRmlh4fFwdz5sCnn9Z7a3Qh6oIE1EIIIco5kWtWKWNTYht4Jo3b8eNw+eUmNnyu/2v8MON65u36LxZt4cr2VwLYAt6SFTo6vNOBK+dcyWWfXcaRU0cAs+D7+usm7cOqf+v+gFkRb9G0BYdOHjI/N+e47XlHso7UbPLh4bBmjVmFDg83QbY1zzoiwlT0yM+371lamz+IlSuJ9PLi3xs3wqxZDF63zuRTx8WZoHrZMhPE5+aa++66y/6fIUQjJikfQgghygn1D2XRPxZJ2bwzmDnTFMwYNw6WLDEV6zqm3kT/dn0Y2nEo9y68l7TsNIByK8mzr5tNiG8Irb1aAyYDYuPG0inLY0PHsuHuDfQK6sWSPUtYuW8lANtStwEQ3iKc+LR4tNbVr4E9fToMHAiFhWZ1GkwKx++/w9ixsH27KbVnj5wcWLUK5s0zwfn11xM9eTJ5PXuafGqLxbQfv/pqM37IEOjY0QTYThKKiHOf/FMshBCiQva25b6Qde5sOns//LBJ11i/Htq0CSOsk9mkGOARQFqWCait71Z3XXxXqSB40yZ47DGTdTFqFHh6mvO9g3sDEOYfxpdbviTzdCZbU02LxsHtBxN3JI6M0xk0d2tevcm3bAm33176XMeO5gXQpxpNfdzdTQOZuDhb+8gob28cCwqwODriqBS+Tk7cP3kyAOPS0ojMzoZ27ao3ZyEaKUn5EEIIIc7CL7/AwoUmi6FvX/j+++Jr/h7+ZBeYWt5bU7fSxqsNS29dysxrZpZbUb7pJvjmG7NCHRdX/ueE+YfRxqsNiccSOXjiIEGeQUwbNI0DjxzAq0lxvef07HRmrJmBru/c5B49TDD9wQfmuE8f8x21RmvNxMREPhwxgg9HjODy228nxsnJdGZMS6v6uUKcAySgFkIIIc7CzJmwa5dZWf75ZxgxovjawDYDmXypWZUd03UMA9sM5KqLrmJin4nlnuPoaNKQFy40+wXLurTVpdwYeiM9A3sydcBU+rfuj4+bD62atbI14gFYl7yOab9NY1f6rlr/rlUaPNi0KP/qK+jYkei2bSlwdEQrRQGQD6bluFLkOToSHRgI06bBgQP1O08h6oCq999gz1KvXr30xo0bG3oaQgghRK1avdoU3Hj8cbM/sCoWbUGhKNSFvL72dSJaRjC041AOZB4gJjmGm769idV3rGZg24H1M3mr3FzTotzdnZicHAavX0+eszOOjo7oggLyi76Ya0EBK598ksgNG8wXH1jP8xTCTkqpTVrrSrobFZMcaiGEEKKW7Ntnsh5KrlLb69df4ZVXTGzp62vysytjXZF2Uk68ue5NhocMZ2jHoYz+arStEoh1M2S9cnU1LyDS1ZUVX3zBF927w+jRXJyfz+b162HbNsYtXUpkTo65J1va24tzn6R8CCGEELXk889NXeqCgvLX3n/fFMDYZgp08N57Zt+f9S+Kp083PVWGDTO51PbqHtCdT2I/YXHiYlKzUm3tyVOzUon6LIq31r11lt/qLHTowOcREXyUnMzkY8cY164dH7z1FpEJCdCpkxkzdix07QrPPttw8xTiLElALYQQQtSSm2+GKVNMJbhx4+CRR4qvXXIJtGlTXETD0xOCgkxa8ebN0K2b2Yz43HPFpfMsljP/zGcuewaAXxJ/IS07jVB/k4B95NQRViWt4pFfH6nq9joVfe215Lm4UOjoaErn+ZiW9nz0EcyaZT5nZcGOHaV3cwpxjpGAWgghhKglXbrA3Xebz82bg1dx8Q0uvdRsWmzSxByPGwc/mCaKBARAQoLp/P3wwybwvu46s3h7JgPbDqRvq76sP7ie0wWnCfYMZuXtKxnddXTtfrkaiOraFRcnJxwBFwcHotq3Nx0YO3Ys7spoZW15LsQ5SHKohRBCiDrw9ttmhXnvXpMTnZBQ3ICwrOBgGDMGYks0prziCvt7njw16CkOZB7gz0N/EuARQFS7KLanbQdg3ph5tfBtaibSy4u3Onbk48OHCXJ1BaWIee01ol97jajkZCKBmNBQovv1I2rrViIbbKZCnB2p8iGEEELUkWPHTDB9553w6afwxx/FTQkrUlhoyudZ5eebxjEtW5qgu8qflXOMr7Z9xZUdriQtO40tR7bQwqMFvYN706pZq9r5QtUUk5lJVGwseUWxhjPgoBQFhYW4aM1bc+Yw+dZbyXNwwCUvjxV9+xLp7d0gcxWiIvZW+ZCUDyGEEKKOuLmZDIe77oKffjK9T6pSMpgGyMyEXr1Maecz8XHz4f7e9xPiG8Knmz/lqZVPEeARwMj5I4lPja/5lzgL0RkZ5JdYuMsH8rSm0MGBPEdHFvTrR56Dgzl2ciJamryIc5SkfAghhBB1xM2teO9dTfj5mTzrXmdcHystLCCM2ZtnM3vzbP46/BfHco7VfBJnIcrbG2elbCvUDoACCouu+wcEQNE5Jycnonx9q/0zYjIzic7IIMrbm8iSSetC1CNZoRZCCCHqUHa2qS+dmFiz+0eONKkj06aZaiDLl5uGhFUJ8w8D4LPYzwA4XXC6Zj/8LEV6eREdEcEoX18cAU1xMF0IzPXyolApNGCB8kv0ZxCTmcnguDie2rePwXFxxGRm1ub0hbCbBNRCCCFEHfrmG3j6adNSvKZ274ZFi0zjmBEjYMGCqsf3Ce5D76DetPduD0BOQU7Nf/hZivTyok+zZoAJqEspakUOUKA10cnJ1Xp2dEYGeRYLhWDK8mVknP2EhagBCaiFEEKIOnT77aYj9yNnUQ569Gj46y/T9OXll01Zvap4NfFiwz0bWPgPE8Xn5DdcQA0m9cPFwaHKoMMlP5+ow4dr9FxbWT7Z0CgaiORQCyGEEOcABwc4dcpscnzmGZP+MXFi1fd4N/FmUNtBNHdrXj+TrESklxcrwsOJzsjA19mZxenp7MzJwd/ZGR8nJ1o6OjLOwYHI9u2r/dy3OnZkQVoaY/39JYdaNJgqA2qlVCvgZmAgEATkANuARcBirbUdPZyEEEIIcbbWroX+/U3qR2wsnLYjLTrIM4hVd6yq+8nZIdLLi0gvL2IyM5mUmEie1mwHXJViZUREjYLhmMxMJu/eTZ7FwprMTLp7eEhQLRpEpQG1UupTIBhYCLwCpAJNgE7AUGCaUmqq1np1fUxUCCGEuJD17g1Tp0LPnrB0aUPPpubKltLL05rojAwiP/8cWrUyHW6q8ayyOdQSUIuGUNUK9Rta620VnN8GfKeUcgHOkMUlhBBCiNrg7AwvvVS9ewosBXR7vxuT+kziwT4P1s3EqqlsKT0XpUzu84cfQkhItQJqaw51nsUiOdSiQVW6P6CSYLrk9Tyt9e7an5IQQgghqrJzJwwaBBs2VD3OUTmSeCyRvcf38uOOH/lxx49YGjhb01pK777AQEb5+nJny5Zszcpi9GOPcemIEcw6dKhaz1oRHs49gYEMad6cL1JSpHSeaBBn3JSolOoPPAu0LRqvAK217lC3UxNCCCFERZo1g61boVOnqscppWji1IQle5awZM8SEtISWHvXWiJbR9bPRCthTcsYHBdHrsWC5fBh6GDCig27dgEwISjI7ud9kpJiW/H+NCWlxjnZQtSUPWXzPgb+AwwAegO9it6FEEII0QACA2HTJvD2No1jquLm5EZU272SxNEAACAASURBVCjeuPoNAI6fPl4PMzwza/6zbb28RE3qBdVoQV5ZTrYQ9cmesnmZWuvFdT4TIYQQQtitQwd49ll47TU4edKU1auIm7MbOQU5tGrWCoDs/DNE4PXEmv+cWzKoLgqM/Z2dbeNiMjP5IiWFhKwsknJzUYC3kxMuDg6MDwwsl5OtgPisLC7dtIkgV1emtG4tq9WizlVV5aNn0ceVSqnXgO+AXOt1rfVfdTw3IYQQQlQhKgpcXU3jmNmzIT4eZswAH5/iMUMuGsLHmz9m2d5lAGTlZdXaz4/JzCQ6I4Mob+9qB61la1N/kJxMbJaZ29wjRxi0YgXdb7uNqLg4W7Bsk2vCkQ0nT/LfTp2YGRLC/bt2YcG0MJ+bmmrGnTzJovR0VkkKiKhjVVb5KHPcq8RnDVxR+9MRQgghhL2ioswLTKMXX19brGkze8RsDp48yP6M/bxy5Sv0DDTrZenpkJRkyvDVRExmJoPj4mwVNlaEh9coqLbesyAtDbKyTNqH1izIySE9OblUOkdFFqSlEeXtXb6teZF8a1k+CahFHao0oNZaX17ZNaVUi7qZjhBCCCFq4pNPKr92IvcEwZ7BTOk/xXbu7bfhhRdMgxhX1+r/vNquAT3W35+lx47Z0j7Grl5N98GDS6VzVHZfdw+PSsc5W8vyCVGH7G49rpTyAsYC/wC6Ypq+CCGEEKKROHYMUlIgNLT43A3f3MDaA2sZ03UMe47toalLU1o0bcHXX5vrRfsAq622a0BPCAqChQv5uKCAPEdH3ho7ls4ZGUxu184WvB8vKLDlUFs/Lz52jO4eHkRHRPDq33+zMycHV6Vs1yM8PW0/42xSVISoyplaj7sBIzBBdE/AExgFSHdEIYQQopF58EHTRTE9vficNWe6mWszun/QnYl9JvLqVa/ywgtw4gS4uNTsZ5XMga6tALX7mDH8tXUrBUXH25WCAwdwAFwdHFjxv/8RedllzBo0iHuLyuvtz81lUXo674aE8MuxY+VWqffn5rI4PZ13QkJsbcprmqIiRGUqLZunlJoL7AKuBt4F2gHHtdbRWjdwVXghhBBClPPkk/Dmm6XPtfduD8DANgNxd3a3BdjXXQfduoF1/15NRHp58e+2bWstMI3OyjLBdIkSemA2GuZZLESfOAG33FKurF6+1ixIS6s03zqv6HrZFBUhaktVdai7AceB7cAOrXUhVJrzL4QQQogGFhYG48aVORcQBsBVHa7Cw8WDrHwTUMfHQ9++8Ouv9T3LykV5e+NUWGjLo7ZSgAsQFRsLn3/OWH//UtedlWKsvz/OleSvaCAhK4uSq4G+JUrzCXG2qmo9Hg7cCDQDliul1gCeSqmW9TU5IYQQQlTPRx9Bu3ZQUJQ3kZdsAuqEtAQ8nIsD6lGjwMsLrryygSZagUgvL1bPmcOo1FRaHj9uC6w1MOnoUSITEmDQICYEBfHfTp3o4+nJKD8/VkVEMCEoyNbS/L7AQP7bqRODmjWzPTs5L8+2KlgIPJSYKG3KRa2pModaa70DeBp4WinVC7gF2KCUStZa96uPCQohhBDCft7eMHAgWCxw5Ag8dX8o3G8qfZRM+Xj2WQgKMl0XG5PIzz/ne2DId9+RUuJ8rKVofXnVKmjXjglBQeXak5cswwdVd1zMk3J6ohbZXeVDa70R2KiU+icwqO6mJIQQQoiauv56uPxys9kwNxduGeHPP2/RdOoETg5ONHFqAsD48bBjB6xYAYMHN/CkKzC2b1+WFm08BBhr/TxrFtx+u33P8Pdn6fGKW627SDk9UYuUriSBXyn1JPC+1vpYJdevANy11gvrcH7l9OrVS2/cuLE+f6QQQghxXtHarF5PmgTr1sGBAw09owpYLMzato0FhYWMbdGCCXfeaXZQbtpUeZ/1Csw6dIiPDx8mz2IhV2vT1lxrTmvN+MDAcqvcQpSklNqkte51pnFVrVBvBX5WSp0G/gLSgCZACBABLAdm1MJchRBCCFFHtDaVP1q3hj5XJXH89HG6No8gMBBuuw2WLGnoGVZi4UImjBzJhLFj4dtvTTDdqlW1gmmgXGrIrEOHbCX3Npw8aRsjxNmoalPij1rr/sB9QDzgCJwAvgT6aK0f0VpXnpwkhBBCiAb36acwdy6cOgXTV0/nmrnXoBS8/z48/LCpDGLdwNioDCrKLv3+e8jPh7Q0WLkSpkyp+r4zKJtXXVWetRD2OuOveVrrRK31Z1rrl7TWb2mtf9Va59TH5IQQQghxdu66y2RJ3HEH7N4SQNqpozg7a+6/Hy65BP7zH2jWDPLyGnqmZXh7w5w5ZndlYiKsXQtDh5oc6rNQtuRe2WMhaqJ6f28ihBBCiHOSUrB1nT+FFHA4I4NduyAnRxMctp9Jj5wmJweOH4fTpxt6piWEmZJ//PEHtGkDnTpBVlbNnqU1FBbaSu51dXennasrHxw8SLuYGNrHxDB62zYppSdqRAJqIYQQ4gLxxvMBACxbm0rnzjD92x+4eV17ErrdQG4u+PjAJ5808CRL6tLFvE+YAI8+CikpJj+lJsvp114LTsVbx7ZnZ7M/N5fYrCyScnPZn5vLD0ePcllsrATVotrOGFArpXzqYyJCCCGEqFtBXia9IaBdGh9+CLn+6wFIykiiaVN46y0YMKAhZ1iGmxu88IL5/OabxcvnNVmlXrrU9rGqvOn8ovrUQlSHPSvU65VS3yilhilVSU9PIYQQQjR6ES0j+Pr6r+nVrjP33gs7j8cD8M417+DubjYpdugAO3c28ERLuv9+8/7ss8XRfnZ29Z8zaBD07w9UnTetgIxGuUtTNGb2BNSdgFnAbcBupdQMpVSnup2WEEIIIWqbv4c/N4TdwLrkdQAEuAcwqc8kotpF2cZMmgRRURXf3yB8faFFC0hKMr3SoWYr1EeOmNfp06Val0d4eNDW1RUfR0fAtDl/9cABZh06VHvfQZz3Km3sUuFgpS7HlM3zAOKAqVrrmDqaW4WksYsQQghRc9vTtjNy/kh+vfVX2jdvz55je0hIS+C6ztcBptHL4cMwapTZyNgoxMWZGtQbNsCwYbBmTfVzU6xfZtMm6Nmz3OUhcXGluipe3bw5v4aHn82sxXnA3sYu9uRQ+yqlHlZKbQT+CUwC/IDHgHlnPVMhhBBC1Jvjp4+TeCyRbanbAJi/bT4j5o8gr9Bs9OvbF0aPbkTBNEB4uFmp7trVHCck1Ow5Hh6mWkgFpJyeOBtVdUq0igHmAKO01sklzm9USn1YN9MSQgghRF0I9Q8FYMH2Bfxz2T9txydyT+Dn7ofWsG2b2Q/YsWNDzrQCbdqApyccPFj9e11d4YEHwM+vwsvWbokfHz5MEwcHFqens/nkSca1bEmkNdVEiErYE1A/qbX+uuQJpdQNWutvtNav1NG8hBBCCFEHvJt4E+wZzPK9yzl48iD9W5uNetaAGqBfP4ia+jZtInbx3rXvNeR0S3NwMC3ImzSBmTOhaVO4884z35eba17r18OIEfDNNybALqO7hwexp06RVyId9tOUFFZGREhQLapkz6bEqRWc+3dtT0QIIYQQ9SMsIIyDJ80q70XNLwJMQA0m1ePJJ2FhwWTe3/h+g82xUk2amPcvv4TvvrPvnpMnzfvq1fDzz2YJvgLRGRnkl9lblidl9IQdKg2olVLXKKVmAsFKqXdKvD4DpJ6MEEIIcY66MfRGmjdpDkBHH5PXYQ2oAaZMsb9gQYNZtgx++sm+sW5u8NFHZmUaKs3BjvL2xrlM8riLUkR5e5/NTMUFoKoV6kPARuA0sKnE6ydgSN1PTQghhBB1YXzP8UzsMxGAKztcyW/jfqNHix626xZtwT23Xbn7atr1u040a1bxzslTp2DHDtNqHMzq9I4dcNNNMHIkODubQLyCKmeRXl5ER0QwyteXtq6utHN1pau7O5N37y5XRi8mM5OXkpKkq6IAqsih1lrHAXFKqblaa1mRFkIIIc4jd/e8m89iP8PX3ZfL219e6pqjgyNPe+3D2bn43JYtJh79/XcIDq7nyVZk/354+mnTjeaSS4rPjxwJv/0GCxbAmDGwcSNccQWsXGkKbF90EXz7remc6OEBrVtD27alHv3LsWPFedS5uQBsKEobmRAURExmJoPj4sizWHBxcGBFeLjkWF/gqkr5sG5E3KyU2lL2VU/zE0IIIUQdaN2sNdF3RAPw886fWbF3he3aZZ9dRvMrZ/Hoo8Xjs7NNDPvXX/U7z0o5OsKcOWajoZXWJoAG+PNP8969O/z4I3TrZo6/+sq8b9wIY8fC88+XemxFedRW1pbl0RkZ5FksFAJ5FovkWIsqq3w8XPQ+vD4mIoQQQoj6o5SiQ/MOAEz7bRptvNowuMNgThecZnXSak4XnGbGsvcZeeJX3p7RgvBwSE6GoupyDa9VK5P2ER9ffC4/36R2fPSRCah794Zp00yXGqsePcyKdHy8Ccg3b4bMTFsXRmsedV4FQbV/0ZJ9lLc3jkph0RpHybEWVLFCrbU+XGLMEa11ktY6CUjFtLoXQgghxHkgLCCMRYmL2HRoE2lZZhXWzcmNpNw4fl56Aq1Nc8GXXjpzw5f41Hjmbplb95NWCsLC4Jdf4NFHTS6KiwvMmgU33mhSPDZuhIIKslbDwuB//zOr3FOnwsSJsHUrUJxHfV9ODqM8PAgpUV5vbmqqLZfa+scgAZEA+8rmfQNYShwXFp0TQgghxHlgZOeRALwe8zqpWakAtPNuB8B3C7NQymRHpKTAa69V/az52+Zz1093oStJm6hVo0bB8ePwzjvwyCNwyy0msB42DNzdITAQIiPL3zd8uFndvuQSs1o9dy68+KLtcqSzMx8MG8b3d9xBe3f3UrcuSEsjOiODAq3RQIGU1RPYF1A7aa3zrAdFn13qbkpCCCGEqE83d7uZYSHDSEhLIC3brFBbA+rs/GwApk+Hdu3MKrXFUsmDgGauzcgrzONU3qk6njUwZQpkZJjmLhs3wrp1cPQo3H67qe5x6FDFOyjvv9+keXh7m8TwYcNKp44U5Uqza1eFLcmjvL1xcXDAEXBxcJCUD2FXp8Q0pdQIrfVPAEqpkcDRup2WEEIIIerT7eG3c/jkYVwdXbk0+FJCfEIAyMorrpX33HPwyiumYWFlNh42mwLTstPwdPWs0znbhIWZ93XroEWLmt2/dKnJwXZ2Nt0YAbQu15J888mTdPfwYEV4ONEZGUR5e0uFD2FXQH0fMFcp9S4mVegAMK5OZyWEEEKIenVj2I22z+vuXsfOozuJahdVKij28DDv110HN99s9gXOnGkKZzg6mmvfxJus0NSsVNumxzoXFgbt29e8UHa3biaY7tABPv8cTp8udblkS/LVmZm2duT/LlNuT1y4zpjyobXeo7XuC4QCoVrrflrr3XU/NSGEEELUp89iP+Oyzy5Da01nv86svH0lfVv1LTcuMdEs4n7yidkTWHLF2t3Z5BxbNzfWi0GD4IknTEBcE8OHw0MPmVSPRYtMZ0XAWoi7bCk9aUcuyrJnhRql1LVAGNBEFW3v1Vo/X+VNQgghhDinnMo7xeqk1Yz+ajQ/3PxDpeN27DDvhw7BhAmlK3/4uPmQlZ+Fn7tfHc+2BFdXuPvumt/fvDm8/Tbs22eW4S+/3BTczjb542VL6Uk7clHWGVeolVIfAjcBkzApHzcAdv0dh1JqqFJqp1Jqt1JqaiVjblRKJSil4pVS86oxdyGEEELUojB/k4uckJbAidwThMwMYfZfsysce+SIqUi3Zo3JkrBq6tKUsV3HEtk60jbu99/LZVE0Tj/9BM88YzothoebDY1paeVakge6uPDqgQPMOnRI2o8LwL4V6n5a6x5KqS1a6+eUUm8A353pJqWUI/AecBWQDPyplPpJa51QYkwI8G+gv9b6uFIqoGZfQwghhBBnKyzABNTjwsfh5uTGnmN7OJB5oNy4v/6Ca681r99/hwceKL6WnZ+Nh4sHuQW5uDq5smgRjB8PSUmmd8qcOaYDuLVxYaOTlweDB5sW5p06QUICLFwIwML0dKxVrffn5vLD0aM4AK7SfvyCZ0/ZvJyi92ylVBCQD7S3474+wG6t9d6iUnvzgZFlxtwDvKe1Pg6gtU61b9pCCCGEqG0BHgGkPZ7GEwOfwNnRmQ7NO7D96PZy4yIiTOnmRx4xOdTXX198bc2da9iWuo1r510LwJAhsGwZ+PtDYSFMmmSOGy1XV/NbwgcfmLJ6Q4YAJo+6ghYxWJD248K+FeqFSilv4DXgL0ADH9lxXzCmIohVMnBpmTGdAJRSfwCOwLNa6yV2PFsIIYQQdaBk7nNYQBjxafHlxjg4wBVXFB/v3w9xcSZLorVXa8JbhLN492Lee890/77ySjPOzc3kXbdsWcdf4mw4OED//ubzE0/YTkd5e+MEFQbVGsgoKCAmM1NK6V2g7Kny8YLWOkNrvQCTO91Fa/20Hc+uqBtn2bZJTkAIEAXcAswuCt5LP0ipCUqpjUqpjWlp9bhrWAghhLiAhfmHkZCWQIGlojCy2B13wOuvg0VbmLFmBifzTpJyKoWHpqazaFHpsYGBZ25f3qikpUFmJpFeXqy++GJG+frS1d2dUX5+/F+AyVS1AK8eOMBlsbE8tW8fg+PiJK/6AmPPpsQmSqlHlVLfAfOAu5RSTex4djLQusRxK+BQBWN+1Frna633ATsxAXYpWutZWuteWute/mU6FgkhhBCibozqMoqrOlyFk0PVf6E9apRJ5cjJz2Hab9NIz04HYNGGeCZPLj02Jgb++c+quy02GklJEBAA8+cDEOnlxffdu5PQpw/fd+tGWn5+qeH5WlOIpIBciOzJof4CUzJvJvAu0BWYY8d9fwIhSqn2SikX4GbgpzJjfgAuB1BK+WFSQPbaN3UhhBBC1KU+wX1YettSfv/7d0LfCyU+tXz6B8DkydCnD5wq6qrYJ7gPAPtOxdO8eemxW7fC++9DSkqdTr12tG5tyujFV/y9y7Yld1ZK2pFfoOzJoe6stQ4vcbxSKRV3ppu01gVKqYnAr5j86E+01vFKqeeBjUWtzH8FrlZKJQCFwONa6/Tqfw0hhBBC1BVPF0+2H93O5pTNtkogAAWWAgothbg6ufLMM/Dl4gy4Fjr7dmZcyGNsXd6DYxeBj0/xs+64w5SMrqp9eaPh4AChoZUG1Na25G8lJ5NTWIi3kxO5WtPZ3b0+ZykaAXv+cd6slLK1SVJKXQr8Yc/Dtda/aK07aa0v0lq/WHTu6aJgGm08qrUO1Vp311rPr8mXEEIIIUTd6ezXGYDbvr+NI6eOACa9I/g/wVw629QbCA2FrCG3AdDMtRkDsl7ngyf6W3uj2Li4nCPBtFW3bqYutZ+fKapdge3Z2ezPzSU2K4vt2dn8cPQol8fGSh71BcSef6QvBdYqpfYrpfYDMcBlSqmtSqktdTo7IYQQQjS4Jk5NmNJvCgCbDm8C4ODJg6RmpXJZ28sAuOkmeGXsA9zd6i2evXUo143KZcO+eIoWcUt57TV46616m/7ZmToV7r0X0tPhzz/LXV5QSbEEaU9+YbEnoB6KqTt9WdGrPTAMGA5cV3dTE0IIIURj8a8B/wJgx1HTdzw1y7SOuCbkGtuY2yNu5x8dHqZtkAfv/fk+fT7vRnpO+YBz1SqzOfGc0KkTvPyy+VxB6kfZPGorJ5A86guIPWXzkrTWSZgGL9r6KnFeCCGEEOc5HzcfDj56kEf6PgJAWpYJlP3dSweUl19uGgse3Gxyrd/d8C4AixMXM2vTLPYe38vPP8NXXxXfs/f4Xlug3ih5e5s25CV7rBeZEBTEfzt1oo+nJxEeHjhg6gY7nFO1AcXZsqds3gilVCKwD1gF7AcW1/G8hBBCCNHIBHkGoYoCResKdYBHQIVjLQcvBuD51c+z7/g+hs0bxr0L72Xm+pnl6lBf9M5FdH2va91NvDb07WtaPYJZYp8xw3ZpQlAQ6y+5hBsDAlCYlccCSfm4oNiT8vEC0BfYpbVuDwzGzk2JQgghhDh//LbvN8b/OB6LtnBrj1vZ89AegjwrSJIGpj/hz8wrPwHgm4RvAPjuxu94c+ibHDwIN94I0dFmbJ/gPgy5aEh9fIWamzsXli41n6OjYfp0yM0tNSTK2xsXBwcpnXcBsiegzi8qZeeglHLQWq8EIup4XkIIIYRoZPYc28MnsZ+QlJGEm7MbHZp3wNHBscKxrVrB1V36E9kqkr8O/wVAZOtIAJo2hb/+Mk0IAU7knsCrSSNv1e3qCm3bms+PPw4nT5pzJUR6ebEiPJx7AgMZ0rw5X6SkSKWPC4Q9AXWGUqopsBqYq5R6m4pb2QshhBDiPGatQT3m6zF0frczH236qMrxnXw7sXb8WkZ3GU3zJs1JOZXCgE8GsC8nlt274YYbzLgdR3fwdfzXdT392uPuDo6Opj1kBS0fP0lJ4Yf0dD48fFjK510g7AmoRwLZwCPAEmAPUt1DCCGEuOD0DOzJ9aHX4+XqRWDTQDJO25cjfEPYDUzpPwU3Jzf+OPAHsSmxFY7LL8yv8Hyj9Mcf8O67sGdPqdPRGRnka207lvJ5F4ZKOyUqpToCLbTW1nxpC/C5UmoQ4A1IR0MhhBDiAtLEqQnf3PBNte9zUA5MHTCVAksBLo4uxKfGM28ezJwJv/8OlwRewqbDmziZdxIfN58zP7AxcHY27/HxEBJiOx3l7Y2zUuQVBdUuSkku9QWgqhXqt4CTFZzPLromhBBCCGE3Jwcnuvh1YVXSKpydNV5ekJkJE/tMBCDz9DmUGhEaat7XrSt1OtLLi+iICEb5+tLW1ZWL3Nx47+BBhsTFMevQoQaYqKgPVQXU7bTW5Tohaq03Au3qbEZCCCGEOG/1aNGDPw/9yaHW77BkCTTxzGZb6jbAbE48ZzRtanZevvIKbNxY7vLC9HSScnNJyM5mbmoqS48f595duySoPk9VFVA3qeKaW21PRAghhBDnv5cHv8yc0XO4t9e97N0L017fwxsxbwDYalyfM941TWtYu7bU6eiMjEqrN1TWqlyc26oKqP9USt1T9qRSajywqe6mJIQQQojzVXCzYG7tcStNnJqwdy+89aFJ8/j11l/p0aJHA8+umkaMAB+fci3Jo7y9K92ktis7W1apz0NVBdSTgTuVUtFKqTeKXquAu4GH62d6QgghhDjfbDy0kSFfDiGaZ7ntjc8AWLnYi+XLG3Ze1aYUhIXBrFmQkgLffw+5uUR6ebH64osZ1KxZuUBrf26upH6chyoNqLXWR7TW/YDnMO3G9wPPaa0jtdYp9TM9IYQQQpxvjmYfZfne5bz4x3PMif8YgJcPXs3bqz+hRMW5c0NUlHm//noYMwaeeQYwmxOH+vpSWRKLpH6cX85Yh1prvVJrPbPo9Vt9TEoIIYQQ56+hHYdyetppPhv5me1ccy9n/Huu5VxLo+a55yA5GRYuNMcpxWuO1lbkFQVbY/3962d+ol5UWodaCCGEEKKuODs623Km/6/7/3Ho5CHij24jKws8PBp4ctWhFAQHm88XX1wqoLa2Io/OyMDX2ZnF6elsPnUKBSw+dozuHh5EejXyluvCLvZ0ShRCCCGEqHVhAWFsuW8Ln4z8hFD/MDYcXM/wpz5r6GlV39q10L+/Caajo+GKK0yBbSBy0SL+/cUXTAgK4hpfX5Jyc9mfm8sPR49ymbQlP29IQC2EEEKIBuHi6EL3Ft1xcXTh7p7jATjV9qty4+bPh4cequ/ZVUNAAAQGwsiR0K8frFxZ3PDl//4Pnn4aKJ83nS9tyc8bkvIhhBBCiAYX0TKCI/88Ajk+/PgjDBoEzZtDQQFs2AArVoDFAg6NcSmwY0f49lvz+ehR8Pc3pfSGDCkeozVj/f1Zevy47ZSztCU/b0hALYQQQohGIcAjgFUbYdQoWLLExKOxsfDmm/Dzz400mC7Lz8+sWO/YUfp8ZiYTgoIA+PjwYfIsFo4XFPCPhAS8nZxwcXBgfGCgbUxJsw4d4q3kZHIKC4nw9GRK69aSe93ISEAthBBCiEYhKSOJOcdfZs6y+xnYz2xYDAiA6dPN+4IFpjJdo68EEhEBDz5YfDx+PBQFwNaA+d5du4qv5+YCsOHkyVJjwATTJcfuz81lUXo6qyIiJKhuRM6F3/WEEEIIcQHQaD6O+5As7xi++QaCgsyq9LRp8NdfptTzgQMNPUs7zJ4NbdrAokXmOCSk1G8BVdWgLnutorGSe934SEAthBBCiEahjVcbPJw9+G1bPH/8AUOHQsuW5tro0bBpk9n71+i1bm0i/+HDzfF//mN+IyhSVQ3qI3l5pSp/RDRtWm6MA+Dr7Fxr0xVnT1I+hBBCCNEoOCgHQv1DWbJ3EbmJbcle8SgODmZlt0UL85o92+wBtDYobLS6dIGvvoL1601AnZAAPXsCxSkd1rzoAq1JzssDIC4ri8tiY1kVEQHAzIMHUYAGfBwdySgsBGDy7t1Sx7oRkRVqIYQQQjQao7qM4oTjXvqPW86p/JPlrj/wALzySgNMrLpcXODGG+H11015kltvLXV5QlAQCX36sC8yktAynWysKR3RGRnkWSxowBHo1awZCrAAeRaLpH00IrJCLYQQQohG44mBT/DEwCcqvZ6Tc45U+7CyYwdlVeX0XBwcyLNYcHFwYKy/P2syM23HUnKv8ZCAWgghhBCNUoGlACeH0qGKo2MDTeZsfPyx6aA4Z06Fl8uW08vVmql79xLq7s5bHTuSnp9PlLe3Lb1jQVoaY/39Jd2jETmXfscTQgghxAWi90e9cX7BGb9X/fB71Y9/LfsXAMeOmeyJJUsaeILVkZQE8+bB6dOVDpkQFMT4wEBis7LYnp3N6sxMPjx8mIcSE23BdExmJpN372bF8eNM3r1b2pY3IhJQCyGEEKLRmdp/Kg/2fpCbu92Mv4c/C7YvAKBpU1i7FlJSGniC1REWZvKod+6sclhFJfLySpTIs+ZUFyI51I2NpHwIIYQQotEZGzqWyXlGuwAAIABJREFUsaFjAfh++/fsSt+F1hoXF8XevQ08ueoKCzPv8fEQHl7psLK51AAuJfKpo7y9S+VUSw514yEBtRBCCCEatdFdRwOwfO9yLg2+FE9XzwaeUTV16gROTrB0KfTvD23bVjisotbkHo6ObM3KItLLi0gvL1aEh/PFObU8f2GQlA8hhBBCnBOumnMVz696np9+gn79TMWPc4KLC3TvDp9/DgMGVDl0QlAQb3XsyLasLJJyc0nIzubeXbuYdeiQbcznR47w0eHDDI6LkzzqRkICaiGEEEKcM/7+//buPD6q6vzj+OdkISskLIksArIvAUENKK4gLrihFkXrhhaXWtefpbV1a6u1Yq3aWq0rWrXuYhWtCoogKCiETQRklX0LJARIIAnJ+f1xZrIxSYgzk8nMfN+vV17Jvffce88wDj6cPPd5dq8nNhYSEtwDimHjo4/g1lth40aoo/U4uFzpAzX2efOrlUfdNCmgFhERkbBwYqcT2V64nXPOgWnToEOHUM+oAdq3h3POcT8vWVLn0KHp6Qfl5HrblXvzqGMAY4xakDcRCqhFREQkLGQkZ5Bb6FZqd+2Cm26Cr74K8aQa4sgj4Re/gObN4fe/hyee8DlsSFoaM446igtat2Zw8+Y827NnRX71kLQ0/t69O7HGUG6tyuc1EQqoRUREJCxkpmSyvXA74Eo6v/02rF7tjs3bPJ+n5jwVwtkdgrZtXZOXkhIYPx5uuw0KC30OHZKWxn/79+fbY46pCKa9dpaWUm6tWpA3IQqoRUREJCzcecKdfDHmC8DFprm5cMkl8OGHkH3pJ9z8yc0UlTTxJxXz890TlV4//NDgS3jTPmJB5fOaCAXUIiIiEha6tOxCv8x+1fYdOAAjRwLD7wFg3Y66H/gLudTU6t/ryaf2xZv20TM5mS6JiSyuZZVbGo8CahEREQkLG3dv5OaPb2b0O6Mr9qWmwty5lWN2l20PwcwaID4erHUr1TExMGYMbNnSoEvMLijgphUrWFZU5LOsnjQ+BdQiIiISFtIT03lt8Wt8tuYzrLUV+7OzK8fk7W/iK9RecXEwbpz7+ZtvGnRqXWX1JDQUUIuIiEhYSG2Wyv1D72fX/l1s2et7VXfllia+Ql3Vvfe67w1M+/BVVi9n9266zJ7NKQsWcOPy5ar80cgUUIuIiEjYyMrMAuC9Ze9V7JuzaQ7NYhJh+Xl0LRpd26lNT2oqzJrl6v81gLes3sktWlTsyysrY21xMTMKCnhmyxaGLVyooLoRKaAWERGRsDHgsAEkxCZUBNQbd2/k0ncv5YJeP+Pp665l+ClJIZ5hAw0ZAi1bNvy0tDRGtG5d6/ESa1VOrxEpoBYREZGw0Tq5NRvv2MirF74KwKq8Vfy460fGHjOGNoeV8MJ3/wzxDBto8WK4/34oLW3wqb5SP7yaGaNyeo2otvdBREREpElqk9ym4uesDJcCMmv9t0zOWc6a8unccuwtoZpawy1cCH/8I/z859CjR4NO9aZ+/HX9ehbs3YsB0uPiaBYTw9h27RiSlhaUKcvBTNWnZMNBdna2zcnJCfU0REREpIkwfzLuh83HQPt55N+ZT3pimKzOFhW5MnopKX5fanZBAcMXLaKkvJxmMTFMHTBAQbWfjDHzrLXZ9Y1TyoeIiIiEtQeGPcCpXU7lD+eNBWBp7tKg3s9al/p8//0BuFhyckCCaXDl9ErKyylDLckbmwJqERERCWv3nHwPU6+aypgTRgCwZHvDuw82hDHQvz8cfniALvjoo/Dww35fRi3JQ0c51CIiIhIRdq7pTDNSWV+wIaj32bsX0tOhd+8AXXDGDFi1Cu6806/LDElLY+qAAfx1/Xo2l5Tw1KZN3L5qFSXl5eQfOEBKbCy3HX4417dvH6CJi5cCahEREYkIc76NofQv2/nV2uCWzisqgiefhG7dXOqHMX5eMCsLPv4YSkqgWTO/LrW4sJD3d+4EYM6ePQcdv2HFCgAF1QGmlA8RERGJCGPGQFFBEu3aBfc+mZnwv/+5BeX58wNwwawsOHAAVq70+1KH0oJcbcoDTwG1iIiIRISUFJi7bSbnv3k++fvyg3qvLl3gsstcs0O/ZbnSfw1tQe7LqIyMesesKCriuc2b/b6XVFJALSIiIhHjjff2MGn5JP42628V++ZsmsOa/DUBu0d+Pvz9725FvFevAFywd2+IiYH//Me1IgdYuhSeeQa2bWvQpa5v357fduxYEeAZoEdiIm3j4yvGrC0u5oYVKxRUB5ACahEREYkY3316DAB/+eov7D+wH4BjXziWnv/sGbB77NkD//63y9A4cCAAF0xMhEGD4MMPYfRot+/rr+HGG39S9Y/0uDi8ad0xwDXt2nGrj5IkSv0IHAXUIiIiEjFmfnoYL1/wMgDLdyxnZ5F7QK/MlgXsHp06wa5d8MYbMHRogC46c6brmLhpE+TmwhVXwFFHudbkDeSrfJ6vNuWHkh4ih0ZVPkRERCRiGANHtzsagCW5SwIaSNd0ySWwb1+ALhYfD6ee6qL0zExX9ePII2Hy5AZfyls+b/quXQxNT6/olli1TXlKbGyAJi6ggFpEREQiyLRp8NobvZjymy8Y3PFo0hLTWH3ramJN4ALIrVvhT3+CsWMhu96m1A1w0knwyScud7p3b/ew4ssvQ14etGp16NfZto0ht9zCkOefhyqtx4ekpXFW69YVZfVUQi9wlPIhIiIiEWP1avjfh/EMTB/G7uLdvLPkHTKSM+ic3jlg99izByZOhA0boLAQyssDdmlXPuThh933n1r94+GH4Z134IUXDjpUM29aedSBoYBaREREIsa118KWLZCUtpdOf+/E6HdH8/ri13ls9mMBu0ePHrB9u0t1Tk11K9ZB0a+f+97QgNpby89HY5eaedPKow4MBdQiIiIScVKbVRaIXpO/hnFTxmGtDeg9jjsOxo93RTqComNHaN4cPvusYec1b+6++wior2/fnmd79mRw8+Zc0KYN/VNSqh2fXVDAQ+vWMbug4KfOOiopoBYREZGIsXOny22eNg2Mp3hcm+Q2WCyFpYUBucf69e4eZWWuW2JD0psbxBiXS/3ee+4hxUOV5Gm97iOgBuifksLiwkI+3LGD4YsWVQTPswsKGL5oEff++GO1/VI/BdQiIiISMVJT3XN9q1bBpjs2sebWNbRIaAHA7uLdAbnH7t0wZYpL+9i716V+BM0rr7ia1GeffejnXHWV+17Livz0XbsoKS+nDCgpL2f6rl117pf6qcqHiIiIRIyEBKhsANgOgLREV+miYH8B7Zv7X9GiXz/3QCK4Cnfnnw/PP+/3ZX3r3bvh57Ro4R5K7NTJ52Fvner95eWUA5/m5VXUqm4WE0NJeXlF/Wo5NFqhFhERkYjz2mtulRoI+Ap1VY88AldeGfDLVjdxIpxySuX2yy/Dffe52n3eyL6q6dPd/sGDfV5uSFoat3TogAUsMKOggFMWLgRg6oABPNClC1MHDKioXy310wq1iIiIRJxrrnFfzz4Lw44YxtZfb6V1cuuAXHvlShfL3nknjBkTkEvWLS8P1q2r3H79dfegorWus8z48dXHf/QRPPoo9OpVa6rIwr17q22XWsv0Xbv4fefOCqR/AgXUIiIiEnFmz4Z2LuODpPgk4mLiWLFzBZ3TOpPSLKXuk+uxe7e7vjfF+I47oKAAJkzwc9K1ue46Vw/Qy9s98cgj4fvvDx4/fjzExcE557gHE1NTDxoyKiODKfn5FdvxxijFww8KqEVERCTiHHNM9e0v133J6a+ezudXfs7wrsP9vvbq1ZXbyclQWurXJetnzMH7srLgm28O3h8XB7/6FVx0Ua01/bzdEf++cSP7ysrolJjIK1u3sriwkJ2lpdValjeG2QUFB7VKDycKqEVERCTizJ/vgt6LL3bbWRmu6+AZ/zmDoruKSIhLCNi9/vzngF2qYbKy4M03oU8fV1qvTx+3/9//hm3bXE5KHfqnpLB63z5KrGVtcTEzCgpgyxZigISYmEbLo/aW6/M+DBmO+dt6KFFEREQizssvV8+SaJvalkHtB1Fuy1lXsK72Ew/B4sUwejQsW+bnJP11+eVw9dXwww/Vm79MmuSeynzrrTrrV0/ftYtSH6X1ymncsnmRUK4vqAG1MWaEMWa5MWaVMeZ3dYy7yBhjjTHZwZyPiIiIRIff/Q48hSsAMMbwwLAHAMgt9K9w9N69LnW5qMht5+ZC375uYbhRdekCL77oOsssXVq5f98+19xl/Hj45z9rPX1oejrxPlJJYqBRy+Z5y/XFNvJ9AyloKR/GmFjgKeB0YCMw1xgzyVq7tMa45sCtwLfBmouIiIhEF+8DiVV1TOvICR1PIDYm1q9rDxlSGb8WlhRSllhI376ZZGT4ddmfxhhYsADat3dPSnbtCvv3u9zpHj1gxoxaTx2Slsb0gQN5ZetWlhYWknvgABnx8WAt+61lcWFho6ReDElL4+/duzNhyxbaJwQuFacxBTOHejCwylq7BsAY8yZwPrC0xrgHgL8C44I4FxEREYkiO3a4tOIzz4TOnd2+vhl9+eoXXwX0Pie+dCILty7Evuu7K2Gj8DZwOf10GDTIrVCnp7sc69dec2VJWrTweao3YB66cCEl1lI1i2WOp3W59wHGYJldUMAtK1dSYi3s2cMnO3cybeDAsMqjDmbKRwegarXxjZ59FYwxRwEdrbUfBXEeIiIiEmV27IAbbqhzgfYnmzvXdUdcuRIWb1tcsf/BB+HttwN/v0P25puuBvW+fW6FOss9iFktHcSH2nKpASYGta+67/uXeGpih5NgBtQ+6rtQ8adljIkBHgd+Xe+FjLneGJNjjMnJbYQ3VkRERMJb9+6wZo17bq+qE148gfum3efXtfftc31WSkrgwj4X0qeNq67x2mswbZpfl/bPuedCSopL+UhKqgyolyyp87TacqnB1asOtpr3bxaGNbGNreVfJH5f2JghwB+ttWd6tn8PYK19yLOdBqwGvK162gJ5wEhrbU5t183OzrY5ObUeFhEREanw9tvuAcL773fb3Z/ozur81ez4zQ5aJ7fm++3f8+ScJym35cTFxPHbE37LEelHHPL1z3vjPFbnrea7G7+D8jji4uCdd+C77+CBB4Lzmmq1fTvcey8895xrE/nCCy7AjomBrVuhefNaT51dUMArW7cC0CIujoV79zIqIyPo6R6+7n9V27ZNJt3DGDPPWltv0Yxg5lDPBXoYY7oAm4BLgcu8B621BUAb77YxZjowrq5gWkRERKQhZs1yleO8AfUJnU5gdf5q3v/hfcYePZZ/zf0Xz89/nlZJrTgi/Qg27d7UoID6ix+/oKi0iDmb5nB8x+MB92zgpEkhCKiTkyEnBzp2hJNPdoH0ySfDlCnwySeu1l8thqSlhTSIDfX9/RW0lA9r7QHgZmAysAx421q7xBhzvzFmZLDuKyIiIuL197/DihWwaJHLqR5/3IskxiWyJNelQSzJXcJxhx9H7m9ymXvdXE7odEK915w5E846C9auhZnXzARge+H2iuOPPQarVgXl5dQtNRXmzYP16119aoAPPnCBdT1pH+KfoNahttZ+bK3taa3tZq190LPvPmvtJB9jh2p1WkRERIJh5054913YuCGWPm36sGLnCgBaJ7XmxI4nAlBaVkrB/oJ6r1VSAnl5UF4OmSmZwMG1rffsgZEj3XOCIZWYCI8+6sqdNMDsggIeWreO2QX1/3kESijuGShqPS4iIiIRb+hQV/nDGJiSNYVWSa0AeO+S9yrGnPDiCbRObs0nl39S57WGD4dvPd0zfj35cQC2FW6rNiY11aUtFxYG7jX8ZLffDtbCiBEuwbuOXGoITSvwcG8/rtbjIiIiEvFiYlwwDdAmuQ0rd67k6GePZuqaqRVjMlIyqqVuHIpPVrng27vi7WUMzJkDY8f6N++AmTbNfS1eXO/QULQCD/f24wqoRUREJCo89RTcdpv7uU1yGxZsXcBpr57GnmLXwCQzJfOQ2pJPmQKnngqbN0NxWTEAI3s18cfDhgyBDRvg+OPrHeptBW5w9Y53HTgQ9Ol57+kNTOfs2RNWqR8KqEVERCQqrF1b+Wxe6+TWFftTm6UCkJGcwbbCbawvWF/ndcrLobTU/bz/wH7GHjWWi/pedNC4jz6CXr2gSbTQSEqC1q3hv/+t/Fq71ufQIWlp3NKhAxYoB/66YQPPbd4c1Ol5248boAx4f8cOhi1cGDZBtXKoRUREJCo88kj17cNbHE7xgWKMJxeke6vulJSVMGPdDLq17Maa/DW0SmpFXEwc3Vt1Z2XeSs7odgYjRrh0ZIDiA8VYa1mxcwWd0jqRGJdYcf0WLVxX8KSkxnqF9ThwAH72s8rt445zNf58WLh3b7Xtibm5Qa9JvbO0lPIq296OieGQS62AWkRERKLGihUut/mKK2DFzdXznq89+lpO6nQSbVPbctfUu3hryVv0y+xHuS1nUPtBPDf/Ofb8fg8xpvIX/OmJ6czbMo9eT/ZizrVzGNRhUMWxk06Cl15yDyg2CfHxsHCh+/mxx+CVV1yy9/jxcOed1YaOyshgSn5+te1g83ZMLPE0HQynjolK+RAREZGo8d57cOWVUFAASfFJJMVXLh/HmBj6ZPRhwdYFPDPvGfL35zNz/UyyMrLIysyiqLSItbvW8sEHcOKJrmrIqltX8ey5zwKQW1Q9t8MY9/XAA7B6daO+TN9iYmDAAPd10kmV++fNO2jo9e3b82zPnvRJTuaIhAQ+ycvjuc2buXH5cm5cvjwoqRhD0tKYPnAgF7RuTeeEBLolJbG4SZRJqZ9WqEVERCRqXHMNXHxx3ZXjHvrqoWrbWZlZZGVkAXD1+1fz24wZJCZWVg3x1qL2VSFk92647z7o0we6dQvMawgI72SGDHH92Wt65RVYt45lnsB7bXEx7+/YUXH4pa1bmTZwYFDSMT7auRPvY5A3rHC/RWisFug/lVaoRUREJGocdpiLJWPqiICuOvIqjm53NMOOGMaQw4cwovsIBrYdSGJcIjPXz+TYYbl8/jmkphUz8o2RfLX+K+Dg5i7gHkrcuxcuOviZxdAaMgTOPx+efdb38XHjmJiQ4OpX++DNbw606bt2UbOmyMQm8VRn3RRQi4iISNTYtQsmTKg7BePKAVcy7/p5fDHmC2aNnUXP1j1Jik/iy6u/5NqjrqWkrASAfQf28eGKD8ktyiUpLsnnCnVMDKSkBOvV+CExEd5/3/1BDB0KRUXVj0+YwKgZM2o9PVj5zUPT0w9Kn2iM/G1/KaAWERGRqLF1K1x7rXswsaZaFmMrDO4wmOdHPs/UD9sweDDsyHc1qBPjEnnq7Kd8ls4rL4e77oKpUw861DTk5cGXX8I2T6fH8nLYtw/OOovrd+3i2UmTGNy8OQNTUuickMARCQkMTElhQGpqUPKbh6SlMeOoo7igdWv6JCczMCWFCVu2BL1sn7+UQy0iIiJRo1s3WL/elWTeuhU2boTsbBdHfvCBG3PhhbWfX1pWyl0bBtDt8EmUWlciLyE2gWuOusbneGPgb39zC8LDhwf61QRA27bu+8iRrovijz9C9+4uhzori+tfeQXGjeOGlSsrzyl2/5CYs8c1xAl0fvOQtDR+26kTJy9YUJH+Eax7BYpWqEVERCRqxMdDx47uYcFbb4XzznP7jYE//tF1567LqrxVbCpeztg/fENcggssE+ISOFB+gOznsvnbrL9VG28MlJS4YH34cHj5ZSgshC++gEcfddshdeqprlj299+7ILpVK1dG76ijICsL8vKYWMfqcLDym8Mtl1oBtYiIiESNvXvhX/+Cdu1g/3544w3X9fChh1yAfffddZ/fvVV3msU2Y8l213KxW8tutExsSVxMHDv37WTeloNL0AFceimkp8PVV7tujTfeCOPGwZ/+FOAX2FCJie5fEuAm1LKlq0ndr58LqIFRu3fXevr+8vKglNALt1xqBdQiIiISNfbsgZtucqWY77nHPY+3dq0LpJs1c1VA6hIfG09GTC+e+HgKa3ZuZNWtqzin5zkAZGVkVQTaVd19t6v28dJLsHw5HHOMa0s+cyYsWxb419hgnsC5tv3XL1jAsz17Mrh5cy5o04bLMzPxVAxkRkFBUFqEV82lHty8Oc/27Nlk0z1AOdQiIiISRTIzXe50ixaQkAAzZrh86oIC91zehAmuxF1d5ZX7NT+RyaVP8/ay1zmz57CK/VkZWXy25jMOlB8gLqYyxHrxRSgrc2nKLVq4FOQePdxXk9Cnj/teo1sihx0GXbvC2Wdzffv2FQHtQ+vWVRsWrBbhQ9LS+G///gG9ZrBohVpERESiRmwsZGRAbq5L/xg2zMWRqamugty118KiRXVf4+Obn2TbuG08PuLRavuzMrMoKSthVd6qavu3bHGpJTfeCJMnuyyL++93xTT+/GeYPj3AL7Khmjd3eS933FF9vzEut/rwwyEnBw64rGZvi3CvcGoRHiwKqEVERCRqlJTAgw9C587w/PNw773wv/+5leqTToKVK+GEE+q+RoyJITMlkxYJLartP6bdMYzOGu3znF/+Es44w90TXOvyZs1c7nYd5Z4bT1xcZevHqpKSXL3qQYNglfuHQtUW4X2SkzmrdetGnmzTY2x9RRebmOzsbJuTkxPqaYiIiEgYKix0q9G9e7uO261awccfu3bkh7rI+s9/wtNPw9Klvo9v3L2RK967gifOeoKluUt59u01XNf7Li67DHbscAvCCQlu7L59LmZt0jZtgm+/dWVKPGkdswsKGLpwISWeODLBmKC1Ig8lY8w8a212feO0Qi0iIiJRIzkZ8vNh4ULo3x86dIDrrqsMpqdMqVxFrs1hh7lza1NaVsqX677kv8v+y88n/pzpsXfz9SyLtdCmTWUwDWEQTIP7Q/rZz1wCeHk54MralVZZlA1WK/JwoYBaREREooYxboV6wwZXi7qmt9+GP/yh7muMHg1vvVX78S4tu9C1ZVeW5C5hRPcRALz47kZ+85uDx86ZA9dc4x6KbNLmz3cl9T77DFAedU0KqEVERCSq/N//uQob77xz8LFHHnFl9PzVL7Mfi7Yt4pbBtwBwyc1LGDxsG3tL9lYbt20bfPqp69jYpHXs6KJ+T55LzTzqPsnJ3L5qFc9t3szsggIuXLyYY+fNa/ItwwNFZfNEREQkqjz5pHso8ZRTDj7WsqUra7dsWWU1uZrGj4f//McVwKjNgMMGMGn5JNbtciXmNiR8yiU5Z9Huh3Zs/nVlkHnuua4KSJOXkeG+llSvs/1xXl5FHjW4FuExQHmVbWi6LcMDRSvUIiIiElUKC13Biu7dfR9/+GHX06SoyPfxzp3h2GPrvscdQ+7gtZ+9xlk9zmLIjDxYfQYAmSmZlJWXVYwzxpXvmzwZtm//Ka+mEWVlVQuoa+ZRe5XX2G7KLcMDRQG1iIiIRJWYGNexsLaO2qNGweuv+64iB/Dzn7sGMHVJT0znsv6XcUT6EXz5aUu++NZFyxNHTyQ2Jrba2DVrYMQImDatoa+kkWVluZQPTxBdM4+6NsFqT96UKKAWERGRqHLDDdCvn6vo4UtWFlx6qasUFwiTV38CF1wDQKukVuwp3lPteO/eMH7ixyxM/yMPfPkAm3ZvCsyNAy0ry/0r5J574MEHGTJhAtP79OGX7drRo2rpkhqC1Z68KVFALSIiIlHl1Vehbdu60zZWr3YdFFeuPPjYPffUXTavpufeXg/A6KzRdPlHF+7/8v5qx+PjLX9bPYbx3/yJ+6bfx9M5Tx/6xRvTiSe6mn9/+Yv7Q8jJYci+fTzdqxddkpPrPDXSy+rpoUQRERGJKiUlrgV5XdkKRUWu3nRp6cHH+vSBPXsO3l+b/OVZ0BMu6zuGWRtmkVtUPae4qLSI3m16M2bAGB7/5nG+317H046h1L+/S/j25k3HxLg/SGBURgZT8vNrPTXSy+opoBYREZGosn+/W3nu3t11LfSlf3+YNMn3scsvd1+H6qWHs+gxAZ79aA6Z6ZkHBdQpzVKYec1MwKWEpCU04W6DcVVCx8JCV/eva9eKKh4TtmwhMSaGvsnJHNW8OQs8//K4qm3biOuiWJVaj4uIiEhUueAC+OAD16PktNPqHnveea76RlkZ3HILjBnT8PsVFUHKI245/MxuZ5K3L485183hsomXMWfTHAB+uPkH4mIqg9XLJl7GH4f+kZ6tezb8ho1l1Cj47jv44Qe3Wn0IDyiGG7UeFxEREfFh+nRo1QoGDKh/bHKyC8BPOqmyZfjtt8PAgYd+vyeegAvL3+DzKz8nMyWT7YXb2Vuylze+f4P0xHSOO/w4YkxlSLZt7zbe+P4NPl/zecNeWGP79a+ha1e3ar1+fahnE1JK+RAREZGo0pBn43y1GM/OdoH2oVq2DPLWXcoJv4c9JXvon9mfpbmu4+A9J9/DBb0vqDY+MyWTFgktmm4utdfxx7suNxkZ8Pzz7kHFxMRQzyokFFCLiIhIVMnPhxUrXBW41NS6x1oLb77pHkR87z2YNQs+b+DC8b33ulbn774LV1zhgueXFrwEQFZG1kHjjTFkZWQxd/Nclu9YTq82vRp2w8aydq1LSB85Ei65BC666KCl+9kFBbyydStLCwvZby1j27WLyK6JSvkQERGRqDJqFBx3nEv9rU95OVx2Gdx8s9s+++yG369NG9fufNAgsNayvmA9I7qP4Murv6Rry64+zzmq7VHkbM7h8vcuZ9f+Jlpu7qmn3Cq1918ly5dXOzy7oIChCxfyzJYtzNi9mzl79nDDihU8t3mzj4uFN61Qi4iISFRZswbS0tyqcX1iY12c2Late+Zuzx648EKYMwc2HWL/lRdegA0b4KabYPaGbzj+xeOZdOkkzut1Xq3n/GX4XxjZaySDOgwiPbGJlpvzVvw45xz3vUbrydpak0/MzY24VWoF1CIiIhJV1q5t2PieVQptpKTA++837Px162BcMYfMAAAVIklEQVT2bJcd0TejLwBjJ43lo8s+YnCHwT7PSUtM48zuZzbsRo0trkYYWVRUbdPbmrykRlC9raSEO1evZvquXRUl9sK9rJ5SPkRERCSqbN8OM2ceFP8dkpgYePllt0J9qK6+GubNc2X60hLTSEtII7col6lrptZ53o6iHQx+fjBvfe/jycimoGZAXVhYbXNIWhrTBw48qDX5osJC/rphA3P27GFGQQHPbNkS9q3JFVCLiIhIVDn3XDj55ENP2ajpqqtcPvSh6tTJFcHwlum7ZuA1dGjegdO7nV7neSnxKczdPJfV+at/2kSDrZ6AGlxQHQ2tyRVQi4iISFQpLXXP0XXo0Dj3e+MN+OorF1gDPD7icTbesZHs9nX3C0mKT6J5s+bkFubWOS5kqgbUzZv7DKi9RmVk1HmpcG9NrhxqERERiSoLFjTu/bZsqcyhbmiZ5oyUDLYXbQ/OxPxVNaBOSakzoK7amrykvJz8AwcwQHpcHMXW0qshhb2bIK1Qi4iISFTZsMHVki4paZz7nXaaq3s9d27Dz81MyWTb3m2Bn1Qg1Ayo60lKv759e8a2a8fCwkLWFReztriYxYWFLCsq4v0dO8I6j1or1CIiIhJVTj/dlcLLzXU1ooOtb1/3IGPVaiGHamjnoewt2Rv4SQVC1YB6ypT6u+TgSuZVVVblZ28edThW+1BALSIiIlGlXTu3St1YcdsHH8Dkye5hxoZ66LSHAj+hQBk+HNLTYd8+6Oq7QU1NozIymJKfX7EdS2VQHc551AqoRUREJKpMm9a499uxw6V7FBdDlepxhyx/Xz4z1s3AGANAh+YdOKb9MQGe5U/Qu7fry754sevLvmcPjBlT5yneXOqJubmMysigf0oKr2zdChDWtagVUIuIiEhUWbXKpXyMGOE6IQZbnz6uRN/y5XDkkQ07d2fRTo585kg276ls1x0XE8eO3+wgLTHEwWdurkvzuOkmuPRS18GmnoAaXFBdtVNiuAbRVSmgFhERkagybBhs3OgeSmyMgHrwYFc6z1s2ryFaJ7dm5jUz2bXf1Wj+ev3X3P3F3awvWE//xP4BnmkDTZ4MV14JM2a4F1izLnUUMdZHj/WmLDs72+bk5IR6GiIiIhKmRo2CTz5xVd48WRRB9e9/w0svwZdf+n+tsvIyYkxMRfpHSG3e7DrclJa69pMRyBgzz1pbd8FwtEItIiIiUWbixMa9X16eSzP+qTnUVcXGNMKS+qFq396tTG/ZAh9+6FaqH3kk1LMKCdWhFhERkaiyeDFMmtR490tPh/x82BagctK3fHwLD3/1cGAu5o/Nm92LOuMM+PZbeOwxCLPMh0DRCrWIiIhElVNOcQFuY8V+w4e7Ihj1dN8+ZF+u+5LurboH5mL+mD8fRo+GF1+EFi2gvNw1d0lJCfXMGp1WqEVERCSqXHhh48Z8s2bBww+7eDMQmsU2o6Sskdo81sX7EOIvfuECaoAw7XToLwXUIiIiElUmTIC9jdh8sFkz10QmUCviTS6ghsouObt3h2YuIaaAWkRERCSIRo1yFeYOoTP3IWmSAbV3hTpKA2rlUIuIiIiEkc7pnSk+UBzqafgOqKM05UMBtYiIiEgYefmCl0M9BUcpHxWU8iEiIiIiDVe1zWSUp3wooBYREREJI3+Z+ReueO+KUE9DKR9VKOVDREREJIws37mcrzd8HeppVA+oW7Z0NagTE0M3nxDSCrWIiIhIGGkW00SqfHTp4r4feywYA0lJcN99sGdPaOcVAlqhFhEREQkjTaZsXmoq/Pe/lfUAly6F55+HE0+EM88M7dwamVaoRURERMJIs9hmTaNsXmEhHDgA3bq57Y4d4ZlnIDs7tPMKAQXUIiIiImGkc3pn+mX2C/U0IC8PLr7YrUoDNG8OF1wQuJaQYUQBtYiIiEgYuf2425k1dlaopwFt27rv339fua+szK1Uv/VWaOYUIsqhFhEREZGGi4+H6dMhPb1ynzGwfz/MmgWXXBKyqTU2rVCLiIiIhJFXF73KMc8dw77SfaGdSHExLFoE5eWV+2JiXA71kiWhm1cIKKAWERERCSM7inYwf8t8istC/GBicTHcdhv84x/V9/frB1OnwvLloZlXCCigFhEREQkjCXEJAKEvnedt7PLyy9X3Dx3qvt91V6NOJ5QUUIuIiIiEkWaxzYAmFFDXNGYMnHNO9YcVI5wCahEREZEw0uQDaoCjj4ZVq9wDilFAAbWIiIhIGGnfvD0Ad00NcUpFjCeMjI09+FhWlntY8YorGndOIaKAWkRERCSMnNb1NGJMDG8taQK1nh95BObPP3j/GWe4pi+33db4cwqBoNahNsaMAP4BxAIvWGvH1zh+B3AtcADIBX5hrV0XzDmJiIiIhLvWSa05r+d5oZ4GjBvne3/LlvD22407lxAK2gq1MSYWeAo4C+gL/NwY07fGsAVAtrX2SOBd4K/Bmo+IiIhIJJi0fBK5Rbl8sPwD3lnyDntL9oZ6Sr7t2wevvw5z5oR6JkEXzJSPwcAqa+0aa20J8CZwftUB1tpp1toiz+Y3wOFBnI+IiIhI2IuPiQdg576djH53NFe/f3VoJ1Sb0lJX8SMnJ9QzCbpgpnx0ADZU2d4IHFvH+LHAJ74OGGOuB64H6NSpU6DmJyIiIhJ2zupxFot+uYiy8jLGfTaOnM1NNGBt0QJmz4b27UM9k6ALZkBtfOyzPgcacwWQDZzi67i19jngOYDs7Gyf1xARERGJFkcediQAw44Yxhc/fsHekr2kNksN8ax8yM4O9QwaRTBTPjYCHatsHw5srjnIGHMacDcw0lob4h6aIiIiIuEjKyOLWBPL6rzVoZ5KVAvmCvVcoIcxpguwCbgUuKzqAGPMUcCzwAhr7fYgzkVEREQk4pzd42z23rWXxLjEUE8lqgVthdpaewC4GZgMLAPettYuMcbcb4wZ6Rn2CJAKvGOMWWiMmRSs+YiIiIhEmoS4BMptOTPWzWBn0c5QTydqBbUOtbX2Y+DjGvvuq/LzacG8v4iIiEikW5a7jFP+fQrvXPwOF/W9KNTTiUrqlCgiIiISxvpk9MFguPr9q8l8JJPMRzJ5JueZWsevL1hPuS1vxBlGvqCuUIuIiIhIcCXHJ/Pk2U/y/fbvAUhtlsrIXiMPGpezOYcXF7zI0zlP8+gZj3LHkDsae6oRSwG1iIiISJj71aBf1Ttm6pqpPJ3zNAB92vQJ9pSiilI+RERERCLImvw1jJsyjg9++ICSshKW71jOlNVTmL5uesWYs18/m09WfsLS3KWhm2gEMdaGV5+U7OxsmxMFLSxFREREforP13zO6a+eDsBz5z7HDzt+4LFvHvM5Nikuibw781R2rxbGmHnW2nq70yjlQ0RERCSCDO8ynLnXzWXQ84PILcrllmNvYVTfUQB0a9kNgI27NzJ59WTu/uJulu9YzoC2A0I55bCngFpEREQkghhjOKbdMcSaWO7+4m46NO/AmIFjqo05LPUwkuKTeP+H99l3YF+IZho5FFCLiIiIRBhjDJ3SOvHjrh9ZtmOZzzF9M/oy57o55O/L58r/XgnAuT3O5ZJ+lzTmVCOCHkoUERERiUArblkBQEp8Sp3jisuKmbVhFrM2zGJdwbrGmFrE0Qq1iIiISAQqLCkEIKVZ3QF129S2rL51dWNMKWJphVpEREQkAt34vxsB1/hFgksBtYiIiEgEmrl+JgCtk1qHeCaRTykfIiIiIhGod5vedGzRkYuzLg71VCKeVqhFREREIlByfDJFpUWhnkZUUEAtIiIiEoGW5i5l0bZFai/eCBRQi4iIiESg+Jh4APYf2B/imUQ+BdQiIiIiEeiek+8B6q9DLf5TQC0iIiISgbx1qFU2L/gUUIuIiIhEoAkLJgAQHxsf4plEPgXUIiIiIhHoX+f8i+uPvp7MlMxQTyXiqQ61iIiISAQ6ut3RPHves6GeRlTQCrWIiIiIiB8UUIuIiIiI+EEBtYiIiIiIHxRQi4iIiIj4QQG1iIiIiIgfFFCLiIiIiPhBAbWIiIiIiB8UUIuIiIiI+EEBtYiIiIiIHxRQi4iIiIj4QQG1iIiIiIgfFFCLiIiIiPhBAbWIiIiIiB8UUIuIiIiI+EEBtYiIiIiIHxRQi4iIiIj4QQG1iIiIiIgfFFCLiIiIiPjBWGtDPYcGMcbkAutCcOs2wI4Q3Fcal97n6KH3OjrofY4Oep+jR2O/152ttRn1DQq7gDpUjDE51trsUM9Dgkvvc/TQex0d9D5HB73P0aOpvtdK+RARERER8YMCahERERERPyigPnTPhXoC0ij0PkcPvdfRQe9zdND7HD2a5HutHGoRERERET9ohVpERERExA8KqOthjBlhjFlujFlljPldqOcj/jHGdDTGTDPGLDPGLDHG3ObZ38oY85kxZqXne0vPfmOMecLz/n9njDk6tK9AGsIYE2uMWWCM+ciz3cUY863nfX7LGNPMsz/Bs73Kc/yIUM5bDp0xJt0Y864x5gfP53qIPs+RyRjzf56/t783xrxhjEnUZzr8GWNeNMZsN8Z8X2Vfgz/DxpgxnvErjTFjGvt1KKCugzEmFngKOAvoC/zcGNM3tLMSPx0Afm2t7QMcB9zkeU9/B0y11vYApnq2wb33PTxf1wNPN/6UxQ+3AcuqbD8MPO55n/OBsZ79Y4F8a2134HHPOAkP/wA+tdb2Bgbg3m99niOMMaYDcCuQba3tB8QCl6LPdCT4NzCixr4GfYaNMa2APwDHAoOBP3iD8MaigLpug4FV1to11toS4E3g/BDPSfxgrd1irZ3v+XkP7n++HXDv68ueYS8DF3h+Ph94xTrfAOnGmHaNPG35CYwxhwPnAC94tg1wKvCuZ0jN99n7/r8LDPeMlybMGNMCOBmYAGCtLbHW7kKf50gVByQZY+KAZGAL+kyHPWvtDCCvxu6GfobPBD6z1uZZa/OBzzg4SA8qBdR16wBsqLK90bNPIoDnV4BHAd8Ch1lrt4ALuoFMzzD9NxC+/g78Fij3bLcGdllrD3i2q76XFe+z53iBZ7w0bV2BXOAlT2rPC8aYFPR5jjjW2k3A34D1uEC6AJiHPtORqqGf4ZB/thVQ183Xv2ZVFiUCGGNSgYnA7dba3XUN9bFP/w00ccaYc4Ht1tp5VXf7GGoP4Zg0XXHA0cDT1tqjgEIqfzXsi97nMOX59f35QBegPZCC+/V/TfpMR7ba3teQv98KqOu2EehYZftwYHOI5iIBYoyJxwXTr1lr3/Ps3ub91a/n+3bPfv03EJ5OAEYaY9biUrVOxa1Yp3t+XQzV38uK99lzPI2DfwUpTc9GYKO19lvP9ru4AFuf58hzGvCjtTbXWlsKvAccjz7Tkaqhn+GQf7YVUNdtLtDD8xRxM9wDEJNCPCfxgyeHbgKwzFr7WJVDkwDvU8FjgA+q7L/K82TxcUCB99dQ0nRZa39vrT3cWnsE7nP7hbX2cmAacJFnWM332fv+X+QZr9WsJs5auxXYYIzp5dk1HFiKPs+RaD1wnDEm2fP3uPe91mc6MjX0MzwZOMMY09Lz24wzPPsajRq71MMYczZuZSsWeNFa+2CIpyR+MMacCMwEFlOZW3sXLo/6baAT7i/ui621eZ6/uJ/EPdxQBFxjrc1p9InLT2aMGQqMs9aea4zpiluxbgUsAK6w1hYbYxKBV3E59XnApdbaNaGasxw6Y8xA3IOnzYA1wDW4xSJ9niOMMeZPwCW4ak0LgGtxebL6TIcxY8wbwFCgDbANV63jfRr4GTbG/AL3/3OAB621LzXq61BALSIiIiLy0ynlQ0RERETEDwqoRURERET8oIBaRERERMQPCqhFRERERPyggFpERERExA8KqEVE/GCMKTPGLDTGLDHGLDLG3GGMifEcyzbGPPETrjndGJMd+Nk2HmPMH40x40I9DxGRxhBX/xAREanDPmvtQABjTCbwOq4r2x889VEbtc6xMSbWWlvWmPcUEYl2WqEWEQkQa+124HrgZk8nr6HGmI8AjDGneFayFxpjFhhjmnv2/9YYs9izuj2+yuUuNsbMMcasMMac5Bl7hDFmpjFmvufreM/+ocaYacaY13FNizDG3GuM+cEY85kx5g3varExppsx5lNjzDzPtXrXfB3GmFRjzEueeX1njBlljBlrjHm8ypjrjDGPeX6+yjNukTHmVR/X83lPY8zFxpjvPefNCMR7ICISClqhFhEJIGvtGk/KR2aNQ+OAm6y1XxtjUoH9xpizgAuAY621RcaYVlXGx1lrB3u6tf4BOA3YDpxurd1vjOkBvAF4U0MGA/2stT960kVG4brExQHzgXmecc8Bv7TWrjTGHAv8Czi1xlzvxbX07Q/gaeVbAnxnjPmttbYU15HwBmNMFnA3cIK1dkeN1+BV2z3vA8601m4yxqTX/6crItI0KaAWEQk842Pf18BjxpjXgPestRuNMacBL1lriwCstXlVxr/n+T4POMLzczzwpKfddhnQs8r4OdbaHz0/nwh8YK3dB2CM+dDzPRU4HnjHdfAFIMHHXE8DLvVuWGvzPed/AZxrjFkGxFtrFxtjbgHetdbu8PEa6rvn18C/jTFvV3m9IiJhRwG1iEgAGWO64oLd7UAf735r7XhjzP+As4FvPMG0AWwtlyr2fC+j8u/q/wO2AQNwKXv7q4wvrDqNWq4ZA+zy5nzX9TJqmdcLwF3AD8BL9Yyt957W2l96VqzPARYaYwZaa3fWMzcRkSZHOdQiIgFijMkAngGetNbaGse6WWsXW2sfxj2o2BuYAvzCGJPsGeMrXaKqNGCLtbYcuBKIrWXcV8B5xphEzwrxOQDW2t3Aj8aYiz33M8aYAT7OnwLcXGXuLT3nfwt0BC7DpZsATAVGG2Na+3oNdd3T82fyrbX2PmCH59oiImFHAbWIiH+SvGXzgM9xweiffIy73fsAHrAP+MRa+ykwCcgxxizE5VnX5V/AGGPMN7h0j0Jfg6y1cz3XXYRLpcgBCjyHLwfGeuaxBDjfxyX+DLSsMt9hVY69DXztTQOx1i4BHgS+9Ix9zMf1arvnI54HH78HZnjmKyISdkyNRRQREYkAxphUa+1ez+r3DOB6a+38AFz3I+Bxa+1UvycpIhIhtEItIhKZnvOses8HJvobTBtj0o0xK3B1txVMi4hUoRVqERERERE/aIVaRERERMQPCqhFRERERPyggFpERERExA8KqEVERERE/KCAWkRERETEDwqoRURERET88P+8QFpQB0HB7QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Rated_Capacity = 1.1\n",
    "fig, ax = plt.subplots(1, figsize=(12, 8))\n",
    "color_list = ['b:', 'g--', 'r-.', 'c.']\n",
    "for name,color in zip(Battery_list, color_list):\n",
    "    df_result = Battery[name]\n",
    "    ax.plot(df_result['cycle'], df_result['capacity'], color, label='Battery_'+name)\n",
    "ax.set(xlabel='Discharge cycles', ylabel='Capacity (Ah)', title='Capacity degradation at ambient temperature of 1°C')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Autoencoder(nn.Module):\n",
    "    def __init__(self, input_size=16, hidden_dim=8, noise_level=0.001):\n",
    "        super(Autoencoder, self).__init__()\n",
    "        self.input_size, self.hidden_dim, self.noise_level = input_size, hidden_dim, noise_level\n",
    "        self.fc1 = nn.Linear(self.input_size, self.hidden_dim)\n",
    "        self.fc2 = nn.Linear(self.hidden_dim, self.input_size)\n",
    "        \n",
    "    def encoder(self, x):\n",
    "        x = self.fc1(x)\n",
    "        h1 = F.relu(x)\n",
    "        return h1\n",
    "    \n",
    "    def mask(self, x):\n",
    "        corrupted_x = x + self.noise_level * torch.randn_like(x)\n",
    "        return corrupted_x\n",
    "    \n",
    "    def decoder(self, x):\n",
    "        h2 = self.fc2(x)\n",
    "        return h2\n",
    "    \n",
    "    def forward(self, x):\n",
    "        out = self.mask(x)\n",
    "        encode = self.encoder(out)\n",
    "        decode = self.decoder(encode)\n",
    "        return encode, decode\n",
    "    \n",
    "    \n",
    "class PositionalEncoding(nn.Module):\n",
    "    def __init__(self, d_model, dropout=0.0, max_len=16):\n",
    "        super(PositionalEncoding, self).__init__()\n",
    "\n",
    "        pe = torch.zeros(max_len, d_model)\n",
    "        position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)\n",
    "\n",
    "        div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model))\n",
    "\n",
    "        pe[:, 0::2] = torch.sin(position * div_term)\n",
    "        pe[:, 1::2] = torch.cos(position * div_term)\n",
    "\n",
    "        pe = pe.unsqueeze(0).transpose(0, 1)\n",
    "\n",
    "        self.register_buffer('pe', pe)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = x + self.pe[:x.size(1), :].squeeze(1)\n",
    "        return x    \n",
    "    \n",
    "    \n",
    "class Net(nn.Module):\n",
    "    def __init__(self, feature_size=16, hidden_dim=32, num_layers=1, nhead=8, dropout=0.0, noise_level=0.01):\n",
    "        super(Net, self).__init__()\n",
    "        self.auto_hidden = int(feature_size/2)\n",
    "        input_size = self.auto_hidden \n",
    "        self.pos = PositionalEncoding(d_model=input_size)\n",
    "        encoder_layers = nn.TransformerEncoderLayer(d_model=input_size, nhead=nhead, dim_feedforward=hidden_dim, dropout=dropout)\n",
    "        self.cell = nn.TransformerEncoder(encoder_layers, num_layers=num_layers)\n",
    "        self.linear1 = nn.Linear(input_size, 1)\n",
    "        self.linear2 = nn.Linear(int(input_size/2), 1)\n",
    "        self.autoencoder = Autoencoder(input_size=feature_size, hidden_dim=self.auto_hidden, noise_level=noise_level)\n",
    " \n",
    "    def forward(self, x): \n",
    "        batch_size, feature_num, feature_size  = x.shape \n",
    "        encode, decode = self.autoencoder(x.reshape(batch_size, -1))# batch_size*seq_len\n",
    "        out = encode.reshape(batch_size, -1, self.auto_hidden)\n",
    "        out = self.pos(out)\n",
    "        out = out.reshape(1, batch_size, -1) # (1, batch_size, feature_size)\n",
    "        out = self.cell(out)                 # shape (1, batch_size, feature_size)\n",
    "        out = out.reshape(batch_size, -1)    # (batch_size, hidden_dim)\n",
    "        out = self.linear1(out)              # (batch_size, 1)\n",
    "        \n",
    "        return out, decode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train(lr=0.01, feature_size=8, hidden_dim=32, num_layers=1, nhead=8, weight_decay=0.0, EPOCH=1000, seed=0, \n",
    "         alpha=0.0, noise_level=0.0, dropout=0.0, metric='re', is_load_weights=True):\n",
    "    score_list, result_list = [], []\n",
    "    \n",
    "    for i in range(4):\n",
    "        name = Battery_list[i]\n",
    "        window_size = feature_size\n",
    "        train_x, train_y, train_data, test_data = get_train_test(Battery, name, window_size)\n",
    "        train_size = len(train_x)\n",
    "        print('sample size: {}'.format(train_size))\n",
    "\n",
    "        setup_seed(seed)\n",
    "        model = Net(feature_size=feature_size, hidden_dim=hidden_dim, num_layers=num_layers, nhead=nhead, dropout=dropout,\n",
    "                    noise_level=noise_level)\n",
    "        model = model.to(device)\n",
    "        optimizer = torch.optim.Adam(model.parameters(), lr=lr, weight_decay=weight_decay)\n",
    "        criterion = nn.MSELoss()\n",
    "\n",
    "        '''\n",
    "        # save ramdom data for repetition\n",
    "        if torch.__version__.split('+')[0] >= '1.6.0':\n",
    "            torch.save(model.state_dict(), 'model_CALCE'+str(seed)+'.pth')\n",
    "        else:\n",
    "            torch.save(model.state_dict(), 'model_CALCE.pth', _use_new_zipfile_serialization=False)\n",
    "         '''\n",
    "\n",
    "        # load the random data generated by my device\n",
    "        if is_load_weights: \n",
    "            if torch.__version__.split('+')[0] >= '1.6.0':\n",
    "                model.load_state_dict(torch.load('initial_weights/model_CALCE.pth')) \n",
    "            else:\n",
    "                model.load_state_dict(torch.load('initial_weights/model_CALCE_1.5.0.pth'))\n",
    "                \n",
    "        test_x = train_data.copy()\n",
    "        loss_list, y_ = [0], []\n",
    "        rmse, re = 1, 1\n",
    "        score_, score = [1], [1]\n",
    "        for epoch in range(EPOCH):\n",
    "            X = np.reshape(train_x/Rated_Capacity,(-1, 1, feature_size)).astype(np.float32)#(batch_size, seq_len, input_size)\n",
    "            y = np.reshape(train_y[:,-1]/Rated_Capacity,(-1,1)).astype(np.float32)# shape 为 (batch_size, 1)\n",
    "\n",
    "            X, y = torch.from_numpy(X).to(device), torch.from_numpy(y).to(device)\n",
    "            output, decode = model(X)\n",
    "            output = output.reshape(-1, 1)\n",
    "            loss = criterion(output, y) + alpha * criterion(decode, X.reshape(-1, feature_size))\n",
    "            optimizer.zero_grad()              # clear gradients for this training step\n",
    "            loss.backward()                    # backpropagation, compute gradients\n",
    "            optimizer.step()                   # apply gradients\n",
    "\n",
    "            if (epoch + 1)%10 == 0:\n",
    "                test_x = train_data.copy()\n",
    "                point_list = []\n",
    "                while (len(test_x) - len(train_data)) < len(test_data):\n",
    "                    x = np.reshape(np.array(test_x[-feature_size:])/Rated_Capacity,(-1, 1, feature_size)).astype(np.float32)\n",
    "                    x = torch.from_numpy(x).to(device) # (batch_size,feature_size=1,input_size)\n",
    "                    pred, _ = model(x)                 # pred shape (batch_size=1, feature_size=1)\n",
    "                    next_point = pred.data.cpu().numpy()[0,0] * Rated_Capacity\n",
    "                    test_x.append(next_point)     # The test values are added to the original sequence to continue to predict the next point\n",
    "                    point_list.append(next_point) # Saves the predicted value of the last point in the output sequence\n",
    "                y_.append(point_list)             # Save all the predicted values\n",
    "                loss_list.append(loss)\n",
    "                rmse = evaluation(y_test=test_data, y_predict=y_[-1])\n",
    "                re = relative_error(y_test=test_data, y_predict=y_[-1], threshold=Rated_Capacity*0.7)\n",
    "                #print('epoch:{:<2d} | loss:{:<6.4f} | RMSE:{:<6.4f} | RE:{:<6.4f}'.format(epoch, loss, rmse, re))\n",
    "            if metric == 're':\n",
    "                score = [re]\n",
    "            elif metric == 'rmse':\n",
    "                score = [rmse]\n",
    "            else:\n",
    "                score = [re, rmse]\n",
    "            if (loss < 0.001) and (score_[0] < score[0]):\n",
    "                break\n",
    "            score_ = score.copy()\n",
    "            \n",
    "        score_list.append(score_)\n",
    "        result_list.append(y_[-1])\n",
    "    return score_list, result_list"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### optimal parameters of my device : Intel(R) Core(TM) i7-7700HQ CPU @ 2.80GHz, Win10\n",
    "\n",
    "Rated_Capacity = 1.1\n",
    "window_size = 64\n",
    "feature_size = window_size\n",
    "dropout = 0.0\n",
    "EPOCH = 500\n",
    "nhead = 16\n",
    "weight_decay = 0.0\n",
    "noise_level = 0.0\n",
    "alpha = 0.01\n",
    "lr = 0.0005    # learning rate\n",
    "hidden_dim = 32\n",
    "num_layers = 1\n",
    "is_load_weights = False\n",
    "metric = 're'\n",
    "re mean: 0.0536\n",
    "\n",
    "Rated_Capacity = 1.1\n",
    "window_size = 64\n",
    "feature_size = window_size\n",
    "dropout = 0.0\n",
    "EPOCH = 500\n",
    "nhead = 16\n",
    "weight_decay = 0.0\n",
    "noise_level = 0.0\n",
    "alpha = 0.01\n",
    "lr = 0.0005    # learning rate\n",
    "hidden_dim = 32\n",
    "num_layers = 1\n",
    "is_load_weights = False\n",
    "metric = 'rmse'\n",
    "rmse mean: 0.0690"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "seed:0\n",
      "sample size: 2713\n",
      "sample size: 2659\n",
      "sample size: 2623\n",
      "sample size: 2599\n",
      "[[0.        ]\n",
      " [0.02590674]\n",
      " [0.00307692]\n",
      " [0.00578871]]\n",
      "------------------------------------------------------------------\n",
      "re mean: 0.0087\n"
     ]
    }
   ],
   "source": [
    "Rated_Capacity = 1.1\n",
    "window_size = 64\n",
    "feature_size = window_size\n",
    "dropout = 0.0\n",
    "EPOCH = 500\n",
    "nhead = 16\n",
    "weight_decay = 0.0\n",
    "noise_level = 0.0\n",
    "alpha = 0.01\n",
    "lr = 0.0005    # learning rate\n",
    "hidden_dim = 32\n",
    "num_layers = 1\n",
    "is_load_weights = True\n",
    "metric = 're'\n",
    "\n",
    "seed = 0\n",
    "SCORE = []\n",
    "print('seed:{}'.format(seed))\n",
    "score_list, _ = train(lr=lr, feature_size=feature_size, hidden_dim=hidden_dim, num_layers=num_layers, nhead=nhead, \n",
    "                      weight_decay=weight_decay, EPOCH=EPOCH, seed=seed, dropout=dropout, alpha=alpha, \n",
    "                      noise_level=noise_level, metric=metric, is_load_weights=is_load_weights)\n",
    "print(np.array(score_list))\n",
    "print(metric + ': {:<6.4f}'.format(np.mean(np.array(score_list))))\n",
    "print('------------------------------------------------------------------')\n",
    "for s in score_list:\n",
    "    SCORE.append(s)\n",
    "\n",
    "print(metric + ' mean: {:<6.4f}'.format(np.mean(np.array(SCORE))))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### use grid-search to determ parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Rated_Capacity = 1.1\n",
    "window_size = 64\n",
    "feature_size = window_size\n",
    "dropout = 0.0\n",
    "EPOCH = 500\n",
    "nhead = 16\n",
    "is_load_weights = False\n",
    "weight_decay = 0.0\n",
    "noise_level = 0.0\n",
    "num_layers = 1\n",
    "metric = 're'\n",
    "\n",
    "states = {}\n",
    "for lr in [1e-4, 5e-4, 1e-3, 5e-3, 1e-2]:\n",
    "    for hidden_dim in [16, 32, 64]:\n",
    "        for alpha in [1e-4, 1e-3, 1e-2]:\n",
    "            show_str = 'lr={}, num_layers={}, hidden_dim={}'.format(lr, num_layers, hidden_dim)\n",
    "            print(show_str)\n",
    "            SCORE = []\n",
    "            for seed in range(5):\n",
    "                print('seed:{}'.format(seed))\n",
    "                score_list, _ = train(lr=lr, feature_size=feature_size, hidden_dim=hidden_dim, num_layers=num_layers, nhead=nhead, \n",
    "                                      weight_decay=weight_decay, EPOCH=EPOCH, seed=seed, dropout=dropout, alpha=alpha, \n",
    "                                      noise_level=noise_level, metric=metric, is_load_weights=is_load_weights)\n",
    "                print(np.array(score_list))\n",
    "                print(metric + ': {:<6.4f}'.format(np.mean(np.array(score_list))))\n",
    "                print('------------------------------------------------------------------')\n",
    "                for s in score_list:\n",
    "                    SCORE.append(s)\n",
    "\n",
    "            print(metric + ' mean: {:<6.4f}'.format(np.mean(np.array(SCORE))))\n",
    "            states[show_str] = np.mean(np.array(SCORE))\n",
    "            print('===================================================================')\n",
    "            \n",
    "min_key = min(states, key = states.get)\n",
    "print('optimal parameters: {}, result: {}'.format(min_key, states[min_key]))"
   ]
  }
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