6.4_AllForestBaselineWiden30.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "37537991-efbb-47fd-89a6-67f0dc4d05c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import qmc\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from datetime import datetime, timedelta\n",
    "import shutil\n",
    "import os\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from math import sqrt\n",
    "import subprocess\n",
    "import warnings\n",
    "import matplotlib.pyplot as plt\n",
    "import geopandas as gpd\n",
    "from matplotlib.colors import ListedColormap\n",
    "import contextily as ctx\n",
    "import matplotlib.colors as mcolors\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "839986be",
   "metadata": {},
   "source": [
    "### In this Notebook we make the all forest landcover inputs for heatsource scenario testing:\n",
    "- Find grasst nodes by filtering shade values form a model run without topographic shade\n",
    "- Make all nodes landcover equal to the forested buffer scenario\n",
    "- if nodes were previously grassy, they are widened by %20 in the new forested model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#reading in data with relative paths so that it can be used by others who clone repository from github\n",
    "#os.chdir(os.getcwd())\n",
    "script_dir = os.getcwd()\n",
    "\n",
    "\n",
    "base_source_folder = os.path.abspath(os.path.join(script_dir, 'scenarios', 'scenario_inputs', 'baseline_vegetation', 'July_baseline'))\n",
    "\n",
    "#reading in a model run without topographic shading, for use in determining whether a reach is forested or not\n",
    "notopo_source_folder = os.path.abspath(os.path.join(script_dir, 'scenarios','scenario_inputs', 'notopo_models','july_notopo_extended'))\n",
    "\n",
    "#lc data scenario with buffer, to be used for adding grass to previously forested areas\n",
    "buffer_source_folder = os.path.abspath(os.path.join(script_dir, 'scenarios', 'scenario_inputs', 'vegetation_buffers', 'july_allforest_extended'))\n",
    "\n",
    "#Temperature Observations folder\n",
    "observed_temp_path = os.path.abspath(os.path.join(script_dir, 'observations', 'TemperatureDataObs'))\n",
    "#Temperature observations\n",
    "obs = pd.read_csv(os.path.join(observed_temp_path, 'HourlySummer23Master_riverkm.csv'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b2326640-d03f-4a06-ad29-d0511b3592ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "#getting date times for modeled outputs\n",
    "control_file = pd.read_csv(os.path.join(base_source_folder, 'HeatSource_Control.csv'))\n",
    "cc_start = control_file.loc[7]['VALUE']\n",
    "cc_start = pd.to_datetime(cc_start)\n",
    "cc_end = control_file.loc[8]['VALUE']\n",
    "cc_end = pd.to_datetime(cc_end)\n",
    "\n",
    "#reading observed hourly temps for model range (for joining datetimes)\n",
    "obs[\"datetime\"] = pd.to_datetime(obs[\"datetime\"])\n",
    "obs_clip = obs[(obs[\"datetime\"]>='07-01-2023') & (obs[\"datetime\"]<'07-06-2023')]# + pd.DateOffset(1))]\n",
    "#obs_clip = obs[(obs[\"datetime\"]>=cc_start) & (obs[\"datetime\"]<cc_end + pd.DateOffset(1))]\n",
    "\n",
    "# Make observed temps compatible with heatsource outputs\n",
    "obs_clip = obs_clip.dropna(axis=1)\n",
    "obs_clip = obs_clip.set_index('datetime')\n",
    "#obs_clip.columns = [float(col) for col in obs_clip.columns[2:]]\n",
    "obs_clip.columns = [float(col) for col in obs_clip.columns]\n",
    "#obs_clip.drop(19.2, axis=1, inplace=True)\n",
    "obs_clip_mean = obs_clip.resample('D').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "16f9171b",
   "metadata": {},
   "outputs": [],
   "source": [
    "notopo_shade_df = pd.read_csv(os.path.join(notopo_source_folder, 'outputs\\Shade.csv'), skiprows=6,\n",
    "         usecols=range(1, 187))\n",
    "notopo_shade_df.columns = [float(col) for col in notopo_shade_df.columns]\n",
    "notopo_shade_df = notopo_shade_df.mean()\n",
    "notopo_shade_df = pd.DataFrame(notopo_shade_df)\n",
    "notopo_shade_df = notopo_shade_df.reset_index()\n",
    "#notopo_shade_df.index = obs_clip_mean.index\n",
    "notopo_shade_df.rename(columns={'index': 'NODE_ID', 0 : 'July Shade'}, inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8695f635-c82b-4af8-a7ea-08ae911ed103",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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    },
    {
     "data": {
      "text/plain": [
       "CompletedProcess(args=['hs', 'run', '-t'], returncode=0)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Scenario Type: All Grass Baseline\n",
    "landcover = pd.read_csv(os.path.join(base_source_folder, 'inputs\\WFK23_lcdata.csv'))\n",
    "morphology = pd.read_csv(os.path.join(base_source_folder, 'inputs\\WFK23_morphology.csv'))\n",
    "landcover_notopo = pd.read_csv(os.path.join(notopo_source_folder, 'inputs\\WFK23_lcdata.csv'))\n",
    "landcover_buffer = pd.read_csv(os.path.join(buffer_source_folder, 'inputs\\WFK23_lcdata.csv'))\n",
    "changed_nodes = {}\n",
    "\n",
    "#making a new folder for allgrass scenario\n",
    "original_folder_path = base_source_folder\n",
    "new_folder_path = os.path.abspath(os.path.join(script_dir, 'scenarios', 'scenario_inputs', 'sensitivity_analysis','july_allforest_widen30'))\n",
    "shutil.copytree(original_folder_path, new_folder_path)\n",
    "\n",
    "#adjust control file so it write output to correct folder\n",
    "control_file = pd.read_csv(os.path.join(new_folder_path, 'HeatSource_Control.csv'))\n",
    "control_file.loc[2, 'VALUE'] = str(os.path.join(new_folder_path, \"inputs\\\\\"))\n",
    "control_file.loc[3, 'VALUE'] = str(os.path.join(new_folder_path, \"outputs\\\\\"))\n",
    "control_file.to_csv(os.path.join(new_folder_path, 'HeatSource_Control.csv'), index = False)\n",
    "\n",
    "landcover_km = landcover.copy()# Create a copy of landcover DataFrame\n",
    "morphology_km = morphology.copy()# Create a copy of morphology DataFrame\n",
    "reach_shade = notopo_shade_df#[(notopo_shade_df['NODE_ID'] <= km_start) & (notopo_shade_df['NODE_ID']>=km_end)]\n",
    "##print(km_start)\n",
    "    #changed_nodes[km_start] = []\n",
    "\n",
    "#loop to remove shade at each node, narrows channel only where its currently grassy (where shade >.2 in baseline scenario)\n",
    "\n",
    "for index, row in reach_shade.iterrows():\n",
    "    #assigns bankbuffer shade to each node\n",
    "    landcover_km.loc[index, landcover_km.columns[8:48]] = landcover_buffer.loc[index, landcover_buffer.columns[8:48]]\n",
    "    landcover_km.to_csv(os.path.join(new_folder_path, 'inputs\\WFK23_lcdata.csv'), index = False)\n",
    "\n",
    "    # only widens currently grassy nodes\n",
    "    if reach_shade.loc[index]['July Shade'] < .2:\n",
    "        print('widen @: ', index)\n",
    "\n",
    "\n",
    "        #if you want to only increase shade at previouslt grassy nodes, uncomment the below line\n",
    "        # landcover_km.loc[index, landcover_km.columns[8:48]] = landcover_bankbuffer.loc[index, landcover_km.columns[8:48]]\n",
    "        print(morphology_km.loc[index,morphology_km.columns[5]])\n",
    "        #narrow currently forested reaches by 20% when changed to grass\n",
    "        morphology_km.loc[index,morphology_km.columns[5]] = morphology_km.loc[index,morphology_km.columns[5]]*1.3\n",
    "        print(morphology_km.loc[index,morphology_km.columns[5]])\n",
    "        morphology_km.to_csv(os.path.join(new_folder_path, 'inputs\\WFK23_morphology.csv'), index = False)            #morphology_km.to_csv(os.path.join(new_folder_path, 'inputs\\WFK23_morphology.csv'), index = False)\n",
    "            # changed_nodes[km_start].append(index)\n",
    "\n",
    "os.chdir(new_folder_path)\n",
    "subprocess.run(['hs', 'run', '-t'], stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "83527b13-9d09-444b-90ce-135af266f4b3",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Length mismatch: Expected axis has 744 elements, new values have 120 elements",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[6], line 4\u001b[0m\n\u001b[0;32m      1\u001b[0m base_tw_df \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mread_csv(os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(base_source_folder, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124moutputs\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mTemp_H2O.csv\u001b[39m\u001b[38;5;124m'\u001b[39m), skiprows\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m6\u001b[39m,\n\u001b[0;32m      2\u001b[0m          usecols\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mrange\u001b[39m(\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m187\u001b[39m))\n\u001b[0;32m      3\u001b[0m base_tw_df\u001b[38;5;241m.\u001b[39mcolumns \u001b[38;5;241m=\u001b[39m [\u001b[38;5;28mfloat\u001b[39m(col) \u001b[38;5;28;01mfor\u001b[39;00m col \u001b[38;5;129;01min\u001b[39;00m base_tw_df\u001b[38;5;241m.\u001b[39mcolumns]\n\u001b[1;32m----> 4\u001b[0m \u001b[43mbase_tw_df\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mindex\u001b[49m \u001b[38;5;241m=\u001b[39m obs_clip\u001b[38;5;241m.\u001b[39mindex\n\u001b[0;32m      5\u001b[0m \u001b[38;5;66;03m#mean july\u001b[39;00m\n\u001b[0;32m      6\u001b[0m mean_daily_base \u001b[38;5;241m=\u001b[39m base_tw_df\u001b[38;5;241m.\u001b[39mresample(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mD\u001b[39m\u001b[38;5;124m'\u001b[39m)\u001b[38;5;241m.\u001b[39mmean()\n",
      "File \u001b[1;32mc:\\Users\\RIPHAWK\\anaconda3\\envs\\heatsource9\\lib\\site-packages\\pandas\\core\\generic.py:6002\u001b[0m, in \u001b[0;36mNDFrame.__setattr__\u001b[1;34m(self, name, value)\u001b[0m\n\u001b[0;32m   6000\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m   6001\u001b[0m     \u001b[38;5;28mobject\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__getattribute__\u001b[39m(\u001b[38;5;28mself\u001b[39m, name)\n\u001b[1;32m-> 6002\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mobject\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__setattr__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mvalue\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   6003\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m:\n\u001b[0;32m   6004\u001b[0m     \u001b[38;5;28;01mpass\u001b[39;00m\n",
      "File \u001b[1;32mc:\\Users\\RIPHAWK\\anaconda3\\envs\\heatsource9\\lib\\site-packages\\pandas\\_libs\\properties.pyx:69\u001b[0m, in \u001b[0;36mpandas._libs.properties.AxisProperty.__set__\u001b[1;34m()\u001b[0m\n",
      "File \u001b[1;32mc:\\Users\\RIPHAWK\\anaconda3\\envs\\heatsource9\\lib\\site-packages\\pandas\\core\\generic.py:730\u001b[0m, in \u001b[0;36mNDFrame._set_axis\u001b[1;34m(self, axis, labels)\u001b[0m\n\u001b[0;32m    725\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    726\u001b[0m \u001b[38;5;124;03mThis is called from the cython code when we set the `index` attribute\u001b[39;00m\n\u001b[0;32m    727\u001b[0m \u001b[38;5;124;03mdirectly, e.g. `series.index = [1, 2, 3]`.\u001b[39;00m\n\u001b[0;32m    728\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    729\u001b[0m labels \u001b[38;5;241m=\u001b[39m ensure_index(labels)\n\u001b[1;32m--> 730\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_mgr\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mset_axis\u001b[49m\u001b[43m(\u001b[49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlabels\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    731\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_clear_item_cache()\n",
      "File \u001b[1;32mc:\\Users\\RIPHAWK\\anaconda3\\envs\\heatsource9\\lib\\site-packages\\pandas\\core\\internals\\managers.py:225\u001b[0m, in \u001b[0;36mBaseBlockManager.set_axis\u001b[1;34m(self, axis, new_labels)\u001b[0m\n\u001b[0;32m    223\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mset_axis\u001b[39m(\u001b[38;5;28mself\u001b[39m, axis: AxisInt, new_labels: Index) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m    224\u001b[0m     \u001b[38;5;66;03m# Caller is responsible for ensuring we have an Index object.\u001b[39;00m\n\u001b[1;32m--> 225\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_validate_set_axis\u001b[49m\u001b[43m(\u001b[49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnew_labels\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    226\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxes[axis] \u001b[38;5;241m=\u001b[39m new_labels\n",
      "File \u001b[1;32mc:\\Users\\RIPHAWK\\anaconda3\\envs\\heatsource9\\lib\\site-packages\\pandas\\core\\internals\\base.py:70\u001b[0m, in \u001b[0;36mDataManager._validate_set_axis\u001b[1;34m(self, axis, new_labels)\u001b[0m\n\u001b[0;32m     67\u001b[0m     \u001b[38;5;28;01mpass\u001b[39;00m\n\u001b[0;32m     69\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m new_len \u001b[38;5;241m!=\u001b[39m old_len:\n\u001b[1;32m---> 70\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m     71\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mLength mismatch: Expected axis has \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mold_len\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m elements, new \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m     72\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvalues have \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnew_len\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m elements\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m     73\u001b[0m     )\n",
      "\u001b[1;31mValueError\u001b[0m: Length mismatch: Expected axis has 744 elements, new values have 120 elements"
     ]
    }
   ],
   "source": [
    "base_tw_df = pd.read_csv(os.path.join(base_source_folder, 'outputs', 'Temp_H2O.csv'), skiprows=6,\n",
    "         usecols=range(1, 187))\n",
    "base_tw_df.columns = [float(col) for col in base_tw_df.columns]\n",
    "base_tw_df.index = obs_clip.index\n",
    "#mean july\n",
    "mean_daily_base = base_tw_df.resample('D').mean()\n",
    "mean_july_base = base_tw_df.mean()\n",
    "mean_base = pd.DataFrame(mean_july_base)\n",
    "mean_base = mean_base.reset_index()\n",
    "mean_base.rename(columns={'index': 'NODE_ID', 0 : 'Mean'}, inplace=True)\n",
    "mean_base['distance downstream'] = 18.3 - mean_base['NODE_ID']\n",
    "\n",
    "\n",
    "allshade_tw_df = pd.read_csv(os.path.join(new_folder_path, 'outputs', 'Temp_H2O.csv'), skiprows=6,\n",
    "         usecols=range(1, 187))\n",
    "allshade_tw_df.columns = [float(col) for col in allshade_tw_df.columns]\n",
    "allshade_tw_df.index = obs_clip.index\n",
    "#mean july\n",
    "mean_allshade = allshade_tw_df.resample('D').mean()\n",
    "mean_allshade = mean_allshade.mean()\n",
    "mean_allshade\n",
    "\n",
    "mean_allshade = pd.DataFrame(mean_allshade)\n",
    "mean_allshade = mean_allshade.reset_index()\n",
    "mean_allshade.rename(columns={'index': 'NODE_ID', 0 : 'Mean'}, inplace=True)\n",
    "mean_allshade['distance downstream'] = 18.3 - mean_allshade['NODE_ID']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c69c938e-6258-4adf-b978-cf5e195cd0e5",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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bl2Ss2lZrbenr8ePHqVq1KnZ2dtSqVUtXUq5dpy1zftcPP/zA3r179X7iDkQGmtdScHAwefLkwdXVNcm4krNy5UoWLVrE8OHDKVmypG65tguEvb19vPs4ODjE6yIR15EjRxg/fjwdO3akYcOG8da7u7uzd+9etm7dyoQJE8iVK1eCrdSJadWqVbznaO/evYwcOVJvO39/fw4cOEDHjh31Kghev36Nj48Pt27d0nV9sLe315XIx8bG8vr1a5ydnSldujTnz59PNiY3NzcAdu/eTVhYWILb7NixA4Bhw4bpLR8+fDgA27dvN/g5eFffvn31usLs3buX4OBgvvnmm3gDmWm7NFy8eJFbt27RpUsXXr9+rXt+QkNDadSoEUeOHEmwyiIh5cqVo2LFirqKoZUrV9KqVSuyZcsWb9t9+/YRFRXFkCFD9Lol9O3bF1dXV93zcPbsWfz8/Ojfv7/e2BI9e/bUPd9aa9eupUyZMnh5eel9Vmhff+9+Vgghsg4ZSE0IIdLIy8uL6tWrs2LFCnr37g1okr2aNWtSokQJg/dja2tLhw4dWLlyJe+//z6PHj1KtCzy1q1bKIqil6C8uy+thw8f8sMPP7Bly5Z4fZDfTWwdHBzInTu33rIcOXIYZRA3bULj4uKiW3b8+HHGjh3LiRMn4iUJgYGB8b7UJuXDDz8kX758rFixgkaNGqFWq1m1ahWtWrXSO2ZK3bp1i8DAQDw8PBJc7+fnp/d33MQa3iZChQoVSnD5u8+tlZUVxYsX11tWqlQpQDOafVK008sdP36cTp06cfz4cT788EMAsmfPTtmyZXXLjh8/TvXq1RMcpK5ChQo0btw4yWOVKFGC7t27M2rUKDp37szatWsTHXsgKUePHqV37974+Pjw448/6q3TXpRJqD9sRESE3kWbuG7evEmbNm0oX748f/75Z4Lb2NnZ6R7jxx9/TKNGjahTpw4eHh58/PHHycZdsGDBBJ+jx48f6/19+/ZtFEXh+++/5/vvv09wX35+fhQoUAC1Ws3MmTOZO3cu9+7d0xtLwZC+8sWKFWPYsGFMnz6dFStWUK9ePVq2bEm3bt10r7cHDx5gZWUV77Mpb968ZM+enQcPHiR7nKSOH9edO3cAkpzu8NatWwD06NEj0W0CAwPJkSOHQTF06dKFadOmMXToUP755x++/fbbBLfTPs7SpUvrLbezs6N48eK69drf737W2traxnuf3rp1ixs3bsT7DNV697NCCJF1SNIthBBoWl2UBAaqMnQAse7du/PVV1/x+PFjIiMjOXnyJHPmzElxHF26dGH+/PmMGzeO9957j7Jlyya4nVqtRqVSsXPnzgQTHW2rcmxsLB9++CH+/v6MGjUKLy8vnJycePLkCT179ozXgpSapMlQV69eBdB92b9z5w6NGjXCy8uL6dOnU6hQIezs7NixYwe//fabwa1bWtbW1nTp0oWFCxcyd+5cjh8/ztOnT9M8CrlarcbDwyPB/uJAvC/YiT2HiS1P6HWXWjlz5sTLy4tjx44REhLC5cuX9fqC165dm2PHjvH48WMePnxI165d03S8r7/+mtevX/PLL7/Qt29fFi1alKJB2S5dukTLli0pX74869at0xssDSBfvnwAPHv2LN5Fi2fPnvH+++/H2+ejR49o0qQJbm5u7Nixw+ALLrVr19ZdtDEk6TaU9nU8YsQIfHx8EtxG+56YPHky33//Pb169WLixIm4u7tjZWXFkCFDDH4/TJs2jZ49e7J582b27NnD4MGDmTJlCidPntQb3Cwtg+cl9rmY2EWQpGgf16+//pro9GcpmV+7c+fOjB49mr59+5IzZ06aNGmS4phSS61WU6FCBaZPn57g+ndfw0KIrEOSbiGEQNOam1CZr6GtPp06dWLYsGGsWrWK8PBwbG1tUzVwTt26dSlcuDCHDh1KcmotT09PFEWhWLFiulbQhFy5coX//vuPv/76S2/gtL1796Y4trSIjY1l5cqVZMuWTVcCvXXrViIjI9myZYte63BaSjC7d+/OtGnT2Lp1Kzt37iR37tyJJjqG8vT0ZN++fdSpUydVSUVKqdVq7t69q3de//vvPyD5ubJB8xpavHgxe/bsITY2ltq1a+vW1a5dm1WrVulGU09oELWU+vnnn/H39+fPP/8kR44cTJs2zaD73blzh6ZNm+Lh4cGOHTsSTKy0SdjZs2f1EuynT5/y+PFj+vXrp7f969evadKkCZGRkezfv1+XtBsqIiIixd0akqNtDbW1tU22emDdunV88MEHLFq0SG/5mzdv9AaDSy5hrlChAhUqVGDMmDH8888/1KlTh/nz5zNp0iSKFCmCWq3m1q1belPFvXjxgjdv3lCkSBHdshw5csQbyTsqKopnz54leXwtT09PQHPBLbGqH+02rq6uyT4/hihcuDB16tTh0KFDfPHFF/Eu5GhpH+e///6r12IdFRXFvXv3dLFot7t165ZeN4Xo6Gju3bunNz2Zp6cnly5dolGjRmm6qCGEyHykT7cQQqD5snTz5k1evnypW3bp0iWDp6zJlSsXH330EcuXL9dNUZOakZxVKhWzZs1i7NixfPrpp4lu17ZtW6ytrRk/fny8llJFUXj9+jXwtnU17jaKojBz5swUx5ZasbGxDB48mBs3bjB48GBd/9+EYgsMDMTX1zfVx6pYsSIVK1bkzz//ZP369XTq1CnRL92G6tixI7GxsUycODHeupiYmHSZYipulYSiKMyZMwdbW1saNWqU7H3r1q1LbGwsU6dOpWTJknot8bVr1yYkJIS5c+diZWWll5CnxR9//EH79u2ZPn263nzYiXn+/DlNmjTBysqK3bt3J1qOW65cOby8vFiwYIFe6+q8efNQqVR6c4mHhobSrFkznjx5wo4dOxLtehEaGppgf+f169cTEBCQYB/3tPDw8KBBgwb88ccfCSarcT9zrK2t472f165dG2+6O+381+++9oKCgoiJidFbVqFCBaysrHQl+s2aNQNgxowZettpW2ebN2+uW+bp6cmRI0f0tnv3XCSlSZMmuLi4MGXKFCIiIvTWaR9n1apV8fT0ZOrUqQn2qY/7/Bhq0qRJjB07lkGDBiW6TePGjbGzs2PWrFl6z/miRYsIDAzUPQ/VqlUjd+7czJ8/n6ioKN12S5Ysiff8d+zYkSdPnrBw4cJ4xwsPDyc0NDTFj0UIkTlIS7cQQgC9evVi+vTp+Pj40Lt3b/z8/Jg/fz7lypUjKCjIoH10795dlwQklKAZqlWrVrRq1SrJbTw9PZk0aRKjR4/m/v37tG7dGhcXF+7du8fGjRvp168fI0aMwMvLC09PT0aMGMGTJ09wdXXVJRfpITAwkOXLlwMQFhbG7du32bBhA3fu3KFTp056z0uTJk2ws7OjRYsWfP7554SEhLBw4UI8PDwMbklLSPfu3RkxYgRAmkvLAby9vfn888+ZMmUKFy9epEmTJtja2nLr1i3Wrl3LzJkz9ZK/tHJwcGDXrl306NGDGjVqsHPnTrZv3863336baHIal7b1+sSJE3pzKYOmb3iuXLk4ceIEFSpUIHv27EaJ2crKihUrVhAYGMj333+Pu7s7X375ZaLbN23alLt37/L1119z7NgxvWnO8uTJo+uHDpqy45YtW9KkSRM6derE1atXmTNnDn369NFrqe3atSunT5+mV69e3LhxQ29ubmdnZ1q3bg1oWiwbN27MJ598gpeXF1ZWVpw9e5bly5dTtGhRvvrqK6M8J3H9/vvv1K1blwoVKtC3b1+KFy/OixcvOHHiBI8fP9bNw/3xxx8zYcIEPvvsM2rXrs2VK1dYsWJFvL7Dnp6eZM+enfnz5+Pi4oKTkxM1atTg0qVLDBw4kA4dOlCqVCliYmJYtmwZ1tbWusEE33vvPXr06MGCBQt48+YN3t7enD59mr/++ovWrVvzwQcf6I7Tp08f+vfvT7t27fjwww+5dOkSu3fvNviCoqurK7/99ht9+vShevXqdOnShRw5cnDp0iXCwsL466+/sLKy4s8//+Sjjz6iXLlyfPbZZxQoUIAnT55w8OBBXF1d2bp1a4qeb29vb7y9vZPcJnfu3IwePZrx48fTtGlTWrZsyb///svcuXOpXr267rPD1taWSZMm8fnnn9OwYUM++eQT7t27h6+vb7zz8umnn7JmzRr69+/PwYMHqVOnDrGxsdy8eZM1a9awe/duo1/UEUJYiIwfMF0IIUxv8eLFCqCcP39et2z58uVK8eLFFTs7O6VSpUrK7t27DZoyTCsyMlLJkSOH4ubmFm96nMTEnTIsKYlNx7V+/Xqlbt26ipOTk+Lk5KR4eXkpAwYMUP7991/dNtevX1caN26sODs7K7ly5VL69u2rXLp0Kd6UQ+9OqaM1duxYxZB/F97e3npTJjk7OyslS5ZUunXrpuzZsyfB+2zZskWpWLGi4uDgoBQtWlT5+eefdefm3SmqkpsyTOvZs2eKtbW1UqpUqWRjTsi7U4ZpLViwQKlatari6OiouLi4KBUqVFC+/vpr5enTp7ptihQpojRv3jzefQFlwIABesviTnWkpT0Hd+7cUZo0aaJky5ZNyZMnjzJ27Nh4U0klJX/+/AqgLFiwIN66li1bKoDyxRdfxFuX1tdjSEiIUrNmTcXKyireFGtxxX2dvPuT0NR9GzduVCpVqqTY29srBQsWVMaMGaNERUXpbaOdri2hn7jv4ZcvXyr9+vVTvLy8FCcnJ8XOzk4pWbKkMmTIkGSnZIsb/7vnU+vd6Qi17ty5o3Tv3l3JmzevYmtrqxQoUED5+OOPlXXr1um2iYiIUIYPH67ky5dPcXR0VOrUqaOcOHEiwSkNN2/erJQtW1axsbHRvRfu3r2r9OrVS/H09FQcHBwUd3d35YMPPlD27dund9/o6Ghl/PjxSrFixRRbW1ulUKFCyujRo/WmNFMURYmNjVVGjRql5MqVS8mWLZvi4+Oj3L59O9Epw959zFpbtmxRateurTg6Oiqurq7K+++/r6xatUpvmwsXLiht27ZVcubMqdjb2ytFihRROnbsmOB0b3El9D5KSGKfb3PmzFG8vLwUW1tbJU+ePMoXX3yhBAQExNtu7ty5SrFixRR7e3ulWrVqypEjRxI8L1FRUcrPP/+slCtXTrG3t1dy5MihVK1aVRk/frwSGBio206mDBMia1EpihFHcBFCCAsxa9YsvvrqK27fvq3rU5hWMTEx5M+fnxYtWsTrkykyzqtXr8iXLx8//PBDoqNFm6uePXuybt26FE1dJYQQQgjzJn26hRBZ0pkzZ3ByctIbNCitNm3axMuXL/UGLBMZb8mSJcTGxibZJ14IIYQQIqNIn24hRJayfv16Dh06xIoVK+jTp0+aB9kCOHXqFJcvX2bixIlUrlw52b6EIn0cOHCA69ev8+OPP9K6dWuDRvoWQgghhEhvknQLIbKUESNGEBwcTO/evfntt9+Mss958+axfPlyKlWqxJIlS4yyT5FyEyZM0E2PNHv2bFOHI4QQQggBgPTpFkIIIYQQQggh0on06RZCCCGEEEIIIdKJJN1CCCGEEEIIIUQ6kT7dqaRWq3n69CkuLi6oVCpThyOEEEIIIYQQIgMpikJwcDD58+fHyirx9mxJulPp6dOnFCpUyNRhCCGEEEIIIYQwoUePHlGwYMFE10vSnUouLi6A5gl2dXU1cTQJUxSFwMBA3NzcpDXegsh5s1xy7iyXnDvLJefOMsl5s1xy7iyXnDvjCwoKolChQrrcMDGSdKeS9oXq6upq1km3oii4urrKG8uCyHmzXHLuLJecO8sl584yyXmzXHLuLJecu/ST3PMpA6kJIYQQQgghhBDpRJJuIYQQQgghhBAinUjSLYQQQgghhBBCpBPp053OYmNjiY6ONsmxFUUhKiqKiIgI6bdhQczlvNna2mJtbW2y4wshhBBCCJEZSNKdThRF4fnz57x588akcajVal6/fm3SGETKmct5y549O3nz5pWLNkIIIYQQQqSSJN3pRJtwe3h4kC1bNpMkLYqiEBsbi7W1tSRNFsQczpuiKISFheHn5wdAvnz5TBKHEEIIIYQQlk6S7nQQGxurS7hz5sxpsjjMIXkTKWcu583R0REAPz8/PDw8pNRcCCGEEEKIVJCB1NKBtg93tmzZTByJEGmjfQ2balwCIYQQQgghLJ0k3elIWpeFpZPXsBBCCCGEEGkjSbcQQgghhBBCCJFOJOkWqaZSqdi0aZOpw7BYDRo0YMiQIQZvv2TJErJnz55u8QghhBBCCCGMT5JuodOzZ09at26dYcdbsmQJKpVK9+Ps7EzVqlXZsGFDhsWQkPv376NSqbC2tubJkyd66549e4aNjQ0qlYr79++bJkAhhBBCCCGExZCkW5iUq6srz54949mzZ1y4cAEfHx86duzIv//+a+rQKFCgAEuXLtVb9tdff1GgQAETRSSEEEIIIYSwNJJ0iwQVLVqUGTNm6C2rVKkS48aNS3D7hg0bMnDgQL1lL1++xM7Ojv379yd6HJVKRd68ecmbNy8lS5Zk0qRJWFlZcfnyZd02y5Yto1q1ari4uJA3b166dOmimz8aICAggK5du5I7d24cHR0pWbIkvr6+uvWPHj2iY8eOZM+eHXd3d1q1amVQK3WPHj309gPg6+tLjx494m17+PBh3n//fezt7cmXLx/ffPMNMTExuvWhoaF0794dZ2dn8uXLx7Rp0+LtIzIykhEjRlCwYEHc3NyoWbMmhw4dSjZOIYQQQgghhPmSpDsDKIpCaGioSX4URcmQx9inTx9WrlxJZGSkbtny5cspUKAADRs2NGgfsbGx/PXXXwBUqVJFtzw6OpqJEydy6dIlNm3axP379+nZs6du/ffff8/169fZuXMnN27cYN68eeTKlUt3Xx8fH1xcXDh69CjHjx/H2dmZpk2bEhUVlWQ8LVu2JCAggGPHjgFw7NgxAgICaNGihd52T548oVmzZlSvXp1Lly4xb948Fi1axKRJk3TbjBw5ksOHD7N582b27NnDoUOHOH/+vN5+Bg4cyIkTJ1i1ahXnz5+nffv2NG3alFu3bhn0/AkhhBBCCCHMj42pA8gKwsLCcHZ2Nsmx37x5g6ura7ofp23btgwcOJDNmzfTsWNHQNNnu2fPnklOOxUYGKh7bsLDw7G1tWXBggV4enrqtunVq5fudvHixZk1axbVq1cnJCQEZ2dnHj58SOXKlalWrRqgaaXXWr16NWq1mj///FMXh6+vL9mzZ+fQoUM0adIk0dhsbW3p1q0bixcvpm7duixevJhu3bpha2urt93cuXMpVKgQc+bMQaVS4eXlxdOnTxk1ahQ//PADYWFhLFq0iOXLl9OoUSNAU6ZesGBB3T4ePnyIr68vDx8+JF++fMTGxjJixAh2796Nr68vkydPTvL5F0IIIYQQQpgnSbqFUTg4OPDpp5+yePFiOnbsyPnz57l69SpbtmxJ8n4uLi66Ft+wsDD27dtH//79yZkzp65F+dy5c4wbN45Lly4REBCAWq0GNIlq2bJl+eKLL2jXrh3nz5+nSZMmtG7dmtq1awNw6dIlbt++jYuLi95xIyIiuHPnTrKPq1evXtSuXZvJkyezdu1aTpw4oVc2DnDjxg1q1aqld3GhTp06hISE8PjxYwICAoiKiqJGjRq69e7u7pQuXVr395UrV4iNjaVUqVJ6+46MjCRnzpzJximEEEIIIYQwT5J0Z4Bs2bIREhKS4cdVFAV7e/tU3dfKyipeaXp0dHSS9+nTpw+VKlXi8ePH+Pr60rBhQ4oUKZLscUqUKKH7u2LFiuzZs4eff/6ZFi1aEBoaio+PDz4+PqxYsYLcuXPz8OFDfHx8dOXhH330EQ8ePGDHjh3s3buXRo0aMWDAAKZOnUpISAhVq1ZlxYoV8Y6dO3fuZJ+HChUq4OXlRefOnSlTpgzly5fn4sWLyd4vpUJCQrC2tubcuXNYWVkRGxuLtbW1blR3IYQQQgghhGWSpDsDqFQqnJycMvy4iqIQGxubqvvmzp2bZ8+e6f4OCgri3r17Sd6nQoUKVKtWjYULF7Jy5UrmzJmTqmNbW1sTHh4OwM2bN3n9+jU//fQThQoVAuDs2bMJxtujRw969OhBvXr1GDlyJFOnTqVKlSqsXr0aDw+PVJfZ9+rViy+//JJ58+YluL5MmTKsX78eRVF0rd3Hjx/HxcWFggUL4u7ujq2tLadOnaJw4cKAZvC3//77D29vbwAqV65MbGwsfn5+1K1bVy/pFkIIIYQQQlguGUhNJKhhw4YsW7aMo0ePcuXKFXr06IG1tXWy9+vTpw8//fQTiqLQpk2bZLdXFIXnz5/z/Plz7t27x4IFC9i9ezetWrUCoHDhwtjZ2TF79mzu3r3Lli1bmDhxot4+fvjhBzZv3szt27e5du0a27Zto0yZMgB07dqVXLly0apVK44ePcq9e/c4dOgQgwcP5vHjxwY9F3379uXly5f06dMnwfVffvkljx49YtCgQdy8eZPNmzczduxYhg0bhpWVFc7OzvTu3ZuRI0dy4MABrl69Ss+ePbGyevv2K1WqFF27dqV79+5s2LCBe/fucfr0aaZMmcL27dsNilMIIYQQQghhfqSlW+io1WpsbDQvidGjR3Pv3j0+/vhj3NzcmDhxYrIt3QCdO3dmyJAhdO7cGQcHh2S3DwoKIl++fADY29tTpEgRJkyYwKhRowBNC/aSJUv49ttvmTVrFlWqVGHq1Km0bNlStw87OztGjx7N/fv3cXR0pF69evz999+AprT/yJEjjBo1irZt2xIcHEyBAgVo1KiRwS3fNjY2utHQE1KgQAF27NjByJEjee+993B3d6d3796MGTNGt82vv/5KSEgILVq0wMXFheHDhxMYGKi3H19fXyZNmsSIESN48uQJuXLlombNmnz88ccGxSmEEEIIIYQwPyolo+aUymSCgoJwc3MjMDAwXvIWERHBvXv3KFasmEGJZ3rRlpcbWqbctGlTSpQokeqycID79+/j6enJmTNn9Kb9EoZL6XlLT+byWrYUiqIQGBiIm5ubyc+dSBk5d5ZLzp1lkvNmueTcWS45d8aXVE4Yl5SXCwICAti2bRuHDh2icePGqdpHdHQ0z58/Z8yYMdSsWVMSbiGEEEIIIYRAyssFmoHCzpw5w/Dhw3V9qVPq+PHjfPDBB5QqVYp169YZOUIhhBBCCCGEsEySdAs2btyY5n00aNAg3hRjQgghREbbtm0boaGhfPLJJ6YORQghhAAk6RZCCCFEJhEQEEDbtm1Rq9U0a9YMFxcXU4ckhBBCSJ9uIYQQQmQOhw4dIjo6mtjYWMLDw00djhBCCAFI0i2EEEKITGLfvn262zExMSaMRAghhHhLkm4hhBBCmNzMmTMpUaIE9+/fT/U+9u7dq7stSbcQQghzIUm3EEIIIUwqJCSE77//njt37rBly5ZU7ePBgwfcunVL97ck3UIIIcyFJN1CCCGEMKmVK1cSHBwMoJc4p8T+/fv1/o6NjU1zXEIIIYQxSNItUk2lUrFp06YMPc79+/dRqVRcvHgx3Y9raRo0aMCQIUMM3n7JkiVkz5493eIRQghDKIrC/PnzdX/fvn07Vft5N+mWlm4hhBDmQpJuodOzZ09at26d4ccNDw/H3d2dXLlyERkZaZR93r59m169elG4cGHs7e0pUKAAjRo1YsWKFRn+RUx7ocDa2ponT57orXv27Bk2NjaoVKo09WMUQghLdebMGS5cuKD7OzUt3Wq1Wm8QNZCkWwghhPmQpFuY3Pr16ylXrhxeXl5GaTk/ffo0VapU4caNG/z+++9cvXqVQ4cO0adPH+bNm8e1a9cSvW90dHSaj5+YAgUKsHTpUr1lf/31FwUKFEi3YwohhLmbN28eAA0bNgQ0FypT+ll87do1Xr58iZOTE7ly5QIk6RZCCGE+JOkWCSpatCgzZszQW1apUiXGjRuX4PYNGzZk4MCBestevnyJnZ1dvJK/dy1atIhu3brRrVs3Fi1alJawURSFnj17UqpUKY4fP06LFi0oWbIkJUuWpHPnzhw7doyKFSsCb1ugV69ejbe3Nw4ODqxYsYLXr1/TuXNnChQoQLZs2ahQoQKrVq3SO866deuoUKECjo6O5MyZk8aNGxMaGppkbD169MDX11dvma+vLz169Ii37ZEjR6hRowb29vbky5ePb775Ru8LZGhoKN27d8fZ2Zl8+fIxbdq0ePuIjIxkxIgRFChQACcnJ2rUqMGhQ4cMfSqFECLdBQQE8PfffwMwYcIEHB0diY2NTXHlz+HDhwHw9vYmW7ZsgPTpFkIIYT4k6c4AigKhoab5UZSMeYx9+vRh5cqVeuXhy5cvp0CBArrWi4TcuXOHEydO0LFjRzp27MjRo0d58OBBquO4ePEiN27cYMSIEVhZJfzyVqlUen9/8803fPXVV9y4cQMfHx8iIiKoWrUq27dv5+rVq/Tr149PP/2U06dPA5qS8M6dO9OrVy9u3LjBoUOHaNu2LUoyT3bLli0JCAjg2LFjABw7doyAgABatGiht92TJ09o0aIF1apV49KlS8ybN49FixYxadIk3TYjR47k8OHDbN68mT179nDo0CHOnz+vt5+BAwdy4sQJ/v77by5fvkyHDh1o2rRpqgcpEkIIY1u6dCkRERFUrFiR2rVrU6JECSDl/bq1FxQbN26MjY0NIC3dQgghzIck3RkgLAycnTP+x8VFRVhYxjzGtm3bArB582bdsiVLltCzZ894SW5cixcv5qOPPiJHjhy4u7vj4+MTrzU4Jf777z8ASpcurVvm5+eHs7Oz7mfu3Ll69xkyZAht27alWLFi5MuXjwIFCjBixAgqVapE8eLFGTRoEE2bNmXNmjWAJumOiYmhbdu2FC1alAoVKvDll1/i7OycZGy2trZ069aNxYsX6x57t27dsLW11dtu7ty5FCpUiDlz5uDl5UXr1q0ZP34806ZNQ61WExISwqJFi5g6dSqNGjWiQoUK/PXXX3pfMB8+fIivry9r166lXr16eHp6MmLECOrWrZum51cIIYwl7gBq/fv3R6VS6ZLulFwcjIyM5MSJE4Am6ba2tgYk6RZCCGE+JOkWRuHg4MCnn36qSyjPnz/P1atX6dmzZ6L3iY2N5a+//qJbt266Zd26dWPJkiWo1WqjxZYzZ04uXrzIxYsXyZ49O1FRUXrrq1WrFi+uiRMnUqFCBdzd3XF2dmb37t08fPgQgPfee0+X7Hbo0IGFCxcSEBBgUCy9evVi7dq1PH/+nLVr19KrV69429y8eZMaNWroXayoU6cOISEhPH78mDt37hAVFUWNGjV0693d3fUuNFy5coXY2FhKlSqld8Hh8OHD3Llzx6BYhRAiPR0+fJibN2/i7Oys+z9QsmRJIGUt3SdOnCAsLIw8efJQvnx5aekWQghhdmxMHUBWkC0bhIRk/HEVRcHePnX3tbKyilcundzANn369KFSpUo8fvwYX19fGjZsSJEiRRLdfvfu3Tx58oRPPvlEb3lsbCz79+/nww8/THHc2i9s//77L5UrVwbA2tpa13qi/TIWl5OTk97fv/76KzNnzmTGjBlUqFABJycnhgwZokvWra2t2bt3L//88w979uxh9uzZfPfdd5w6dYpixYolGV+FChXw8vKic+fOlClThvLly6fL9GchISFYW1tz7tw5XauPVnIt8kIIkRG0rdxdu3bFxcUFIFUt3dpRyxs3boxKpdJ9zkufbiGEOVIUJckqUJE5SUt3BlCpwMnJND+pfU/nzp2bZ8+e6f4OCgri3r17Sd6nQoUKVKtWjYULF7Jy5coEW3HjWrRoEZ06ddK1Qmt/OnXqlOoB1SpXroyXlxdTp05NdWv58ePHadWqFd26deO9996jePHiurJ1LZVKRZ06dRg/fjwXLlzAzs6OjRs3GrT/Xr16cejQoUSfHy8vL06dOqV30eP48eO4uLhQsGBBPD09sbW15dSpU7r1AQEBejFWrlyZ2NhY/Pz8KFGihN5P3rx5U/J0CCGE0b148YINGzYA8Pnnn+uWp6alWztYZ6NGjQCkpVsIYbZu3bqFh4cHX3/9talDERlMkm6RoIYNG7Js2TKOHj3KlStX6NGjR7wW04T06dOHn376CUVRaNOmTaLbvXz5kq1bt9KjRw/Kly+v99O9e3c2bdqEv79/iuNWqVT4+vry77//UqdOHbZs2cKtW7e4fv068+fP5+XLl8k+jpIlS+pasm/cuMHnn3/OixcvdOtPnTrF5MmTOXv2LA8fPmTDhg28fPmSMmXKGBRj3759efnyJX369Elw/ZdffsmjR48YNGgQN2/eZPPmzYwdO5Zhw4ZhZWWFs7MzvXv3ZuTIkRw4cEBXxh934LhSpUrRtWtXunfvzoYNG7h37x6nT59mypQpbN++3aA4hRAivfzxxx9ER0dTs2ZNXVUSvG3pvnfvnkHThr1584YzZ84AmpZuQPp0CyHM1h9//MGrV6/YuXOnqUMRGUySbqGjVqt1LQSjR4/G29ubjz/+mObNm9O6dWs8PT2T3Ufnzp2xsbGhc+fOODg4JLrd0qVLcXJy0rVMxNWoUSMcHR1Zvnx5qh5HzZo1OXfuHKVLl2bAgAGULVuW2rVrs2rVKn777Te++OKLJO8/ZswYqlSpgo+PDw0aNCBv3ry0bt1at97V1ZUjR47QrFkzSpUqxZgxY5g2bRofffSRQfHZ2NiQK1euBEvdQTOf99atWzlz5gzvvfce/fv3p3fv3owZM0a3za+//kq9evVo0aIFjRs3pm7dulStWlVvP76+vnTv3p3hw4dTunRpWrduzZkzZyhcuLBBcQohRHqIiorSzc09ePBgvXX58+fXTRtmyEwW+/fvR61WU6pUKQoWLAhIS7cQwjyp1WpWr14NQGBgoImjERlNpSQ3z5FIUFBQEG5ubgQGBuLq6qq3LiIignv37lGsWLEkE8/0pigKsbGxWFtbG9R3pGnTppQoUYI5c+ak+pj379/H09OTM2fOUKVKlVTvJytL6XlLT+byWrYUiqIQGBiIm5ubyc+dSBk5dxln5cqVdO3alXz58nH//n3s7Oz01leoUIGrV6+yY8eOZC9ment7c+TIEQYOHMisWbNQqVTUq1ePY8eOsX79et3MGsL8yHvOcsm5S50jR47g7e0NgIuLC0FBQRkeg5w740sqJ4xLWroFAQEBbNu2jUOHDunK81IqOjqa58+fM2bMGGrWrCkJtxBCiATNmjUL0HSleTfhBsP7dR8+fJgjR45gZ2enV8EkLd1CCHO0atUq3e3g4GAZ7DGLkdHLBb169eLMmTMMHz6cVq1apWofx48f54MPPqBUqVKsW7fOyBEKIYTIDE6dOsWpU6ews7OjX79+CW5j6AjmEyZMADT/w/Lnz69bLn26hRDmJjo6mrVr1+otCw4OJnv27KYJSGQ4SbqFwaNuJ6VBgwbxphgTQggh4po9ezagGf/Dw8MjwW0Maek+fvw4Bw4cwNbWlm+++UZvnbR0CyHMzb59+3j9+jUeHh68efOGqKgoAgMDJenOQqS8XAghhBDp7tmzZ6xZswaAQYMGJbqdIS3dEydOBKBnz57xBoeUebqFEOZGW1resWNHcuTIAchgalmNJN3pSFp+haWT17AQwli004TVqVMn3mwLcWlbuu/fv5/gtGGnTp1i9+7dWFtbM3r06HjrpaVbCGFOwsPDdVWlnTt3xs3NDdBMeSiyDkm604GtrS0AYWFhJo5EiLTRvoa1r2khhEiNyMhI5s+fD8SfJuxd+fPnx8HBgZiYmASnDdO2cnfv3p1ixYrFWy99uoUQ5mT79u2EhIRQpEgRatWqpUu6paU7a5E+3enA2tqa7Nmz4+fnB0C2bNlMMiy/OU09JQxnDudNURTCwsLw8/Mje/bsui+xQgiRGmvXruXFixcUKFCANm3aJLmtlZUVJUqU4OrVq9y+fVtXbg5w7tw5tm/fjpWVFd9++22C95eWbiGEOdGWlnfq1AmVSiVJdxYlSXc6yZs3L4Au8TYVtVqNlZUUNFgaczlv2bNn172WhRAiNRRF4ddffwU004QZUjlTsmRJrl69yq1bt2jatKluubaVu0uXLnrJeFzSp1sIYS4CAwPZvn07oCktByTpzqIk6U4nKpWKfPny4eHhkWCftIygKArBwcG4uLhIS7cFMZfzZmtrKy3cQog027FjB5cvX8bZ2VlvPu2kaBPquCOYX7p0ic2bN6NSqfjuu+8Sva+UlwshzMXGjRuJjIykTJkyVKxYEUA3Yrn06c5aTJp0T5kyhQ0bNnDz5k0cHR2pXbs2P//8M6VLlwbA39+fsWPHsmfPHh4+fEju3Llp3bo1EydO1F0lSoiiKIwdO5aFCxfy5s0b6tSpw7x583SDs2j3PWjQILZu3YqVlRXt2rVj5syZODs7G/UxWltbmyxxURSFyMhIHBwcJOm2IHLehBCZyZQpUwD44osvdKP2Jkf7/zruCOaTJk0C4JNPPsHLyyvR+0p5uRDCXKxYsQLQVOdov9NJS3fWZNL61cOHDzNgwABOnjzJ3r17iY6OpkmTJoSGhgLw9OlTnj59ytSpU7l69SpLlixh165d9O7dO8n9/vLLL8yaNYv58+dz6tQpnJyc8PHxISIiQrdN165duXbtGnv37mXbtm0cOXKEfv36pevjFUIIIbKSo0ePcvz4cezt7Rk6dKjB93u3pfvq1ausW7cOgDFjxiR5X0m6hRDm4MGDB+zfvx/Q5B1aknRnTSZt6d61a5fe30uWLMHDw4Nz585Rv359ypcvz/r163XrPT09+fHHH+nWrRsxMTG6f6xxKYrCjBkzGDNmDK1atQJg6dKl5MmTh02bNtGpUydu3LjBrl27OHPmDNWqVQNg9uzZNGvWjKlTp5I/f/50fNRCCCFE1qBt5e7Zsyf58uUz+H7alu579+4RExPDjz/+CED79u0pV65ckveVPt1CCHOwZMkSFEWhUaNGejMtSNKdNZlVn27ti8/d3T3JbVxdXRNMuEHzD/r58+c0btxYt8zNzY0aNWpw4sQJOnXqxIkTJ8iePbsu4QZo3LgxVlZWnDp1KsGRVSMjI4mMjNT9HRQUBGiSfHOdy1gbm7nGJxIm581yybmzXHLujO/ixYvs3LkTKysrRowYkaLnNl++fDg4OBAREcGuXbtYvXo1AN999128/bx77rSDUEZHR8v5NGPynrNccu6Sp1ar8fX1BeCzzz7Te67iztOd0c+hnDvjM/S5NJukW61WM2TIEOrUqUP58uUT3ObVq1dMnDgxyTLw58+fA5AnTx695Xny5NGte/78OR4eHnrrbWxscHd3123zrilTpjB+/Ph4ywMDA832hasoCiEhIQDSN9iCyHmzXHLuLJecO+PTjjTeunVrcuXKleJWnWLFinHjxg2+/PJLFEWhefPmFC1aNN5+3j13arUagNDQUGlJMmPynrNccu6Sd+jQIR48eICbmxsNGzbU+yzSzuDg7++f4Z9Rcu6MT9sQmxyzSboHDBjA1atXOXbsWILrg4KCaN68OWXLlmXcuHEZGxwwevRohg0bphdPoUKFcHNzw9XVNcPjMYT2YoCbm5u8sSyInDfLJefOcsm5M65bt26xefNmAL7//vskBz9NTOnSpblx4waPHj0CYPz48Qnu591zly1bNkAzkGlqjisyhrznLJecu+StWbMG0Ayg9u7Uq9quNiEhIRn+GSXnzvgMfR7NIukeOHCgbjCzggULxlsfHBxM06ZNcXFxYePGjUnO8al9Yb948UKv/9iLFy+oVKmSbpt358+OiYnB398/0TmJ7e3tsbe3j7dcpVKZ9YtWG585xyjik/NmueTcWS45d8bz66+/olaradasme5/b0rFnXGkefPmVK1aNdFt45477XcEtVot59LMyXvOcsm5S1xAQAAbNmwAoE+fPvGeI+2UYYGBgSZ5/uTcGZehz6NJRy9XFIWBAweyceNGDhw4oDfIgFZQUBBNmjTBzs6OLVu24ODgkOQ+ixUrRt68eXWjBWr3cerUKWrVqgVArVq1ePPmDefOndNtc+DAAdRqNTVq1DDSoxNCCCGynh07duj6Mo4ePTrV+9GOYA6a1nJDyTzdQghTWrlyJZGRkbz33ntUrlw53nqZpztrMmnSPWDAAJYvX87KlStxcXHh+fPnPH/+nPDwcOBtwh0aGsqiRYsICgrSbRN3VFIvLy82btwIaK42DBkyhEmTJrFlyxauXLlC9+7dyZ8/P61btwagTJkyNG3alL59+3L69GmOHz/OwIED6dSpk4xcLoQQQqTS2bNn6dChA7GxsfTo0YO6deumel8NGjTA1taWjh07puiCuEwZJoQwpUWLFgHQq1evBFtBtSXlYWFhREdHZ2hswnRMWl4+b948QPOPNS5fX1969uzJ+fPnOXXqFKB/xRs0o5QXLVoUgH///VdvIIKvv/6a0NBQ+vXrx5s3b6hbty67du3SayVfsWIFAwcOpFGjRlhZWdGuXTtmzZqVDo9SCCGEyPzu3r1L8+bNCQsL48MPP2TBggVp2l+pUqV4/fo1jo6OKbqfTBkmhDCVCxcucOHCBezs7PTm5o4r7lhQQUFB5MyZM6PCEyZk0qQ7uVG/GzRoYNDI4O9uo1KpmDBhAhMmTEj0Pu7u7qxcudKwQIUQQgiRqFevXtG0aVP8/PyoVKkS69atw87OLs37dXFxSfF9pKVbCGEqixcvBjSzNiSWTNva2pItWzbCwsIIDAyUpDuLMGl5uRBCCCEsW3h4OC1atODWrVsULlyY7du3m3RWD+nTLYQwhYiICFasWAFA7969k9w27lzdImuQpFsIIYQQqTZ37lxOnjxJ9uzZ2bVrl8nHRpGWbiGEKezevZuAgAAKFixIo0aNktw27gjmImuQpFsIIYQQqbZu3ToAJk6cSJkyZUwcjfTpFkKYhvazsEOHDrqKm8RoW7ol6c46JOkWQgghRKo8ffqUkydPAtCmTRsTR6MhLd1CiIwWGRnJli1bAGjfvn2y20vSnfVI0i2EEEKIVNm8eTMANWrUoECBAiaORkP6dAuRec2ePZvffvvN1GHEs2fPHoKCgihQoAA1a9ZMdnvp0531mHT0ciGEEEJYro0bNwLm08oN0tItRGa1cuVKBg8eDMCnn35Krly5TBzRW9rS8nbt2mFllXybpvTpznqkpVsIIYQQKRYQEMDBgwcB80y6pU+3EJnHw4cP+fLLL3V/+/n5mTAafZGRkbqqnw4dOhh0Hykvz3ok6RZCCCFEim3fvp2YmBjKli1LqVKlTB2OjrR0C5G5xMbG0r17d70E9fXr1yaMSN++ffsIDAwkX7581K5d26D7SNKd9UjSLYQQQogUM8fScpA+3UJkNtOnT+fw4cM4OTlRpEgRwLyS7pSWloP06c6KJOkWQgghRIqEh4eza9cuwPySbmnpFiLzuHDhAt999x0AM2fOpFy5coD5JN1RUVFs2rQJMLy0HKRPd1YkSbcQQgghUmTPnj2EhYVRuHBhqlSpYupw9EifbiEyh/DwcLp27Up0dDStW7emV69e5MyZEzCfpPvAgQO8efOGPHnyUKdOHYPvJ+XlWY8k3UIIIYRIkbil5SqVysTR6JOWbiEyhxkzZnDjxg3y5s3LwoULUalUZpd0r127FtCUlmu7thhCku6sR5JuIYQQQhgsJiaGrVu3AuZXWg7Sp1uIzCAyMpJZs2YB8PPPP+umBzOnpDs6OjpVpeUgSXdWJEm3EEIIIQx25MgR/P39yZUrF3Xr1jV1OPFIS7cQlm/lypU8f/6cAgUK0LlzZ91yc0q6Dx48iL+/Px4eHtSrVy9F99X26ZaB1LIOSbqFEEIIYbANGzYA0LJlyxSVU2YU6dMthGVTFIWpU6cCMGTIEGxtbXXrzCnpXrJkCQBt27ZN8WehtqU7MjKSyMhIY4cmzJAk3UIIIYQwSGxsrC7pNsfScpCWbiEs3a5du7h+/TouLi707dtXb525JN137txh9erVAHz++ecpvr+Li4vutpSYZw2SdAshhBDCIPv37+fZs2e4u7vz4YcfmjqcBEmfbiEsm7aVu2/fvroWYS1zSbp/+eUX1Go1H330EZUqVUrx/a2trXWJtyTdWYMk3UIIIYQwyF9//QVAp06dsLe3N3E0CZOWbiEs1/nz5zlw4ADW1tZ89dVX8dbHTboVRcno8AB4+vSprrR89OjRqd6P9oKC9OvOGiTpFkIIIUSygoKCdFOF9ejRw8TRJE76dAthuaZNmwbAJ598QuHCheOt1ybdMTExBAcHZ2hsWr/99htRUVHUrVs3xQOoxaUdTE1aurMGSbqFEEIIkax169YRHh6Ol5cX1atXN3U4iZLyciEs08OHD3X9pIcPH57gNtmyZcPBwQEwTYm5v78/8+bNA9LWyg0ybVhWI0m3EEIIIZKlLS3v3r07KpXKxNEkTsrLhbBMs2bNIjY2loYNG1KlSpVEtzNlv+45c+YQGhrKe++9x0cffZSmfUnSnbVI0i2EEEKIJN27d48jR46gUqn49NNPTR1OkiTpFsLy3L17l99//x1IvJVby1RJd0hICDNnzgQ0rdxpvfgofbqzFkm6hRBCCJGkZcuWAdCoUSMKFixo4miSJn26hbAsiqIwaNAgIiIi+OCDD5JtQTZV0r1w4UL8/f0pUaIE7du3T/P+pE931mJj6gCEEEIIYb4URWHp0qWAprTc3EmfbiHMR1hYGOfPn+f06dP4+/szYsQIXbKptXHjRnbs2IGtrS1z585NtgXZFEl3TEyMbpC3r7/+Wvc5kxZSXp61SNIthBBCiEQdP36cO3fu4OzsTNu2bU0dTrKkvFwI03rz5g1Tpkxhz549XLlyRa/qZPv27ezZs4fcuXMDmpJt7dRgI0eOxMvLK9n9myLp3rVrF0+ePCF37txGu/goSXfWIuXlQgghhEiUtpW7ffv2ODk5mTia5EnSLYTpbNq0ibJly/LLL79w8eJFYmNjyZs3L61atcLDw4OLFy/i7e3NkydPABg3bhyPHz+mWLFifPfddwYdwxRJ9+LFiwHo1q0b9vb2Rtmn9OnOWiTpFkIIIUSCwsPDdVP4WEJpObxNugHUarUJIxEi63j+/DkdOnSgTZs2PHv2jFKlSrFq1SoePXrE06dP2bRpE0eOHKFgwYLcuHGDevXqsWXLFmbMmAFoRgXPli2bQcfK6KT75cuXbN26FYBevXoZbb/SpztrkfJyIYQQQiRo//79BAUFUbhwYby9vU0djkHi9rWMiYnBzs7OhNEIkfkdPHiQdu3aERAQgLW1NV9//TU//PCDbj5trdKlS3P06FEaN27MnTt3aNWqFQBt27alWbNmBh8vo5PuFStWEBMTQ7Vq1ShfvrzR9ivl5VmLtHQLIYQQIkFnzpwB4IMPPsDKyjK+MsRt6ZYScyHSl5+fH506dSIgIIAqVapw5swZJk+eHC/h1ipatChHjx6lXLlyADg5Oelauw2VkUm3oij4+voC8Nlnnxl135J0Zy3S0i2EEEKIBJ0/fx6AKlWqmDgSw0nSLUTGUBSFvn374ufnR7ly5Th+/HiiyXZc+fLl49ChQ0yePJmPPvqIQoUKpei42qTb398/VXGnxIULF7h8+TL29vZ07tzZqPuWPt1ZiyTdQgghhEiQNumuWrWqiSMxXNykW+bqFiL9LFq0iC1btmBnZ8eKFSsMSri1cuXKxfTp01N13Ixs6dYOoNamTRty5Mhh1H3HbelWFCXZqdKEZbOMWjEhhBBCZKjnz5/z9OlTVCoV7733nqnDMdi7fbqFEMZ39+5dhg4dCsCkSZMy9DNCm3QHBQURHR2dbseJiIhg5cqVgPFLy+HtQGoxMTGEh4cbff/CvEjSLYQQQoh4tK3cpUuXxtnZ2cTRGE6lUun6n0vSLYTxxcTE0L9/f0JDQ/H29mbYsGEZevzs2bPrWoXTs8R8y5YtBAQEULBgQRo1amT0/Ts7O+s+q6Rfd+YnSbcQQggh4rHE0nItbYm5lJcLYXxTpkzhzJkzuLq68tdff+lVl2QEa2trXal3epaYawdQ69mzZ7o8RpVKhaurKyD9urMCSbqFEEIIEc+5c+cAyxpETUubdEtLtxDGdeDAASZMmABo5tYuUqSISeJI737djx8/Zs+ePYAm6U4vMoJ51iFJtxBCCCHiscSRy7W0rVKSdAthPPfv36djx47ExsbSqVMnunbtarJY0jPpjo6Opn///qjVaurXr4+np6fRj6Gl7dctSXfmJ0m3EEIIIfS8evWKhw8fAlC5cmUTR5Ny0tIthHGFhYXRpk0bXr9+TdWqVZk+fbpJR9tOr6RbURQ+//xztm/fjqOjI7/88otR9/8uaenOOiTpFkIIIYSeCxcuAFCiRAndl0JLIn26hTAeRVHo168fFy9eJHfu3Kxfvx5HR0eTxuTu7g4YP+n+7rvv8PX1xdramtWrV1OjRg2j7v9dMld31iFJtxBCCCH0aPtzW+IgaiAt3UIY04wZM1ixYgXW1tasWbOGwoULmzqkdGnpnjVrFlOmTAHgjz/+oEWLFkbbd2KkpTvrsDF1AEIIIYQwL5bcnxukT7cQaRUZGcnevXtZv349y5YtA2D69Ok0aNAARVFMHJ3xk+41a9YwZMgQAH788Ud69+5tlP0mR/p0Zx2SdAshhBBCj6Un3dLSLUTKBAUFcf36da5du8aBAwfYtm0bQUFBuvW9evVi0KBBJoxQnzGT7sjISAYMGICiKAwcOJDRo0eneZ+GkpburEOSbiGEEMKE1qxZQ4ECBahTp46pQwE0fQvv3LkDWH7SLX26hUhcUFAQgwcP5sCBAzx69Cje+vz589OuXTvatWtH/fr1TTpw2rtSknQvXbqUBQsWsGzZMooVKxZv/caNG3n16hUFCxbkt99+y9DHKUl31iFJtxBCCGEi+/fv55NPPiFnzpz4+flhZWX6oVa0g6gVLVpUN1iRpZGWbiGS9/333/PXX3/p/s6XLx/lypWjcuXKtGnThho1apjFZ1JCDE26FUXhhx9+4MGDB4wdO5alS5fG22bBggWApjVf+9mRUWQgtaxDkm4hhBDCRCZPngxovjjevXuXEiVKmDgiyy8tB+nTLURybty4we+//w7AX3/9xccff2xRF9kMTbqvXr3KgwcPAFi1ahWTJk3SGwju1q1bHDx4EJVKlWH9uOOSPt1Zh3levhJCCCEyuZMnT3LgwAHd39pk19QsfeRykJZukfWo1WquXbtm8CBnI0aMIDY2lpYtW9K9e3eLSrhBP+lO6jFv3bpVdzsmJobp06frrf/zzz8BaNq0qUlGZZfy8qxDkm4hhBDCBLRT02jLN80l6c4MLd3Sp1tkNSNHjqR8+fKMGzcu2W137drFjh07sLW1ZerUqekfXDrQJt0xMTEEBwcnup026W7Xrh0ACxcu1LWOR0VFsWTJEgD69euXjtEmLrGk+/79+6jValOEJNKJJN1CCCFEBrty5QpbtmxBpVIxdOhQwDyS7uDgYP777z8gcyTd0tItsoJr164xc+ZMQDPd1dmzZxPdNjo6mmHDhgEwePBgSpYsmSExGlu2bNlwcHAAEi8x9/Pz49SpUwDMnDmTypUrExYWxpw5cwDYsmULfn5+5MuXj+bNm2dM4O94t0+3oiiMHDmSYsWKMXbsWJPEJNKHJN1CCCFEBvvpp58AaN++PZ07dwY0Sbep57+9ePEiiqJQsGBBPDw8TBpLWkifbpFVKIrC0KFDiY2Nxd7entjYWHr27ElkZGSC28+fP58bN26QK1cuxowZk8HRGldy/bq3b9+OoihUqVKFAgUKMGrUKABmz55NaGiobgC1zz77DFtb24wJ+h3apDsoKAhFUfjuu+901Qc3b940SUwifUjSLYQQQmSgO3fu8PfffwMwevRoypcvj42NDa9fv05w2p6MlBlKy0FaukXWsW3bNvbu3YudnR1HjhzBw8ODa9euMX78+Hjb+vv761pPJ06cqBvEy1Ill3RrS8tbtGgBaErMixcvzuvXr/n+++/Zu3cvAH369MmAaBOmPQdqtZqvv/5a1+0IICIiwkRRifQgSbcQQgiRgX755RfUajUfffQRlStXxt7envLlywOmLzHXDqKWWZJu6dMtMrOoqChdqfiwYcN4//33mT9/PgA///wzp0+f1m179epV2rdvT0BAAOXLlzdpomksSSXdERER7NmzB3ibdNvY2DBixAgAfvvtNwA+/PDDBOfuziiOjo66zyttC7e3tzcgSXdmI0m3EEIIkUGePHmiG7jn22+/1S3XJrmmTrqPHj0KwPvvv2/SONJKystFVjBr1ixu375N3rx5dZ8nbdq0oXPnzqjVanr27Ml///1Hz549qVixIgcPHsTGxoY5c+Zk+HzU6SGppPvQoUOEhoaSP39+vYuIPXv21Os6Y6oB1LRUKpWuxBw0XY8GDRoESNKd2UjSLYQQQmSQuXPnEhUVRb169ahbt65uuTkk3Xfv3uX+/fvY2NhQr149k8VhDFJeLjI7Pz8/Jk6cCMDkyZNxcXHRrZs9ezZ58uThxo0blC5dmr/++gtFUWjXrh1XrlzRtaRauqSSbm1p+ccff4xKpdItd3R0ZPDgwQDkzp2bli1bZkCkSdO2tE+YMIFRo0bpBogLDw83ZVjCyCz/MpcQQghhIbZt2wZA//799ZabQ9K9b98+AGrVqoWzs7PJ4jAGSbpFZjdmzBiCgoKoWrUqPXr00FuXM2dO5s+fT5s2bQBo2LAhU6ZMsfgKlncllnQrihKvP3dcQ4YMwc/PDx8fH+zs7NI/0GSsXbuW+/fv06BBAwBd0i0t3ZmLJN1CCCFEBnj69CmXL19GpVLRpEkTvXUVK1bEysqKZ8+e8ezZM/Lly5fh8WmT7saNG2f4sY1N+nSLzOzKlSv8+eefAMyYMQMrq/iFq61bt2bXrl04OjpSr149vdbezCKxpPvy5cs8evQIR0dHGjVqFO9+Tk5OuinWzEHRokUpWrSo7m9JujMnKS8XQgghMoB2UJ9q1aqRK1cuvXVOTk54eXkBcOHChQyPTa1Wc+DAASBzJN3Sp1tkZqNGjUJRFNq3b6/XTeVdPj4+1K9fP1Mm3JB40q1t5W7cuDGOjo4ZHldaSdKdOUnSLYQQQmSAXbt2AZovwgkxZYn5pUuXeP36Nc7OzlSvXj3Dj29sUl4uLImiKAZvu3//fnbu3Imtra3e9FJZUXJJd0Kl5ZZAku7MSZJuIYQQIp3Fxsbq5oRt2rRpgtuYMunWlpY3aNAAW1vbDD++sUnSLSzF2bNn8fDw4Kuvvkp2W7VazciRIwH44osvKFGiRHqHZ9YSSrrv37+vmyrt448/NklcaaVtnZekO3ORpFsIIYRIZ2fPnsXf3x83Nzdq1KiR4DbmkHRnhtJykD7dwjK8efOGjh078urVK+bOncvz58+T3H7lypVcuHABV1dXvv/++wyK0nwllHSPHTsW0HyWmWJsDGOQlu7MSZJuIYQQIp3t3r0bgEaNGiU6P26lSpUAePDgQYJT4KSXyMhI3fzcmSXplj7dwtwpikLfvn25d+8eoHmtLl68ONHtIyIi+O677wAYPXp0vHEhsiJt0h0cHExUVBRXrlxh2bJlgGYaNUulTbpjY2PlMywTkaRbCCGESGfapDux0nIANzc3XbloRg6mduLECcLDw8mbNy9ly5bNsOOmJykvF+Zu/vz5rFu3DltbWwYNGgTAggULEq3OmD17Ng8fPqRgwYIGlaJnBdmzZ9cNEhcQEMDo0aNRFIUOHTpY9NgU2qQbpLU7M5GkWwghhEhHAQEBnDx5Ekh8EDUtU5SYa0vLGzVqlGlGOZakW5izixcvMnToUAB+/vlnfv75Z3LkyMGDBw90F+jiev36NT/++CMAkyZNssgRudODtbU1OXLkAGDTpk1s374da2tr3XNlqezt7XW3JenOPCTpFkIIIdLR/v37UavVeHl5Ubhw4SS3NWXSnVlKy0H6dAvzFRwcTMeOHYmMjOTjjz9myJAhODo60qNHDwD++OOPePcZNmwYgYGBVKxYkW7dumV0yGZNW2L+zTffANCvXz9KlixpypDSzNraWjegpSTdmYck3UIIIUQ60k4VllRpuVZGJ91v3rzhzJkzgKalO7OQPt3CXA0bNoxbt25RqFAhlixZoqsu+fzzzwHYtm0bjx490m2/fv16li5dipWVFfPmzdO9toWGNul+8+YN2bJl44cffjBxRMYhg6llPpJ0CyGEEOlEURRduWhypeUAlStXBuDWrVsEBQWla2wAhw4dQq1WU6pUKQoVKpTux8soUl4uzNGVK1dYtGgRAMuXL9cljABeXl54e3ujVqt12zx79kyXjH/zzTfUrl0744M2c3Gfw2HDhpE3b14TRmM8knRnPpJ0CyGEEOnk+vXrPH78GHt7e+rXr5/s9rly5dIlvxkxmNr+/fuBzFVaDpJ0C/M0atQo3UBfCX0e9O/fH4CFCxcSHR1Nnz59eP36NZUrV9ZNhSX0aZPunDlz6uYwzwwk6c58JOkWQggh0om2ldvb25ts2bIZdJ9y5coBcPv27XSLSysz9ucG6dMtzM/+/fvZuXMnNjY2iU5n1aZNG3Lnzs3Tp0/55JNP2LFjB/b29ixbtgw7O7sMjtgy1K1bF9AMSOfq6mriaIxHku7MR5JuIYQQIp1o+3MbUlqu5eHhAZDuc3Xv3r2bmzdvYmVlRYMGDdL1WBlN+nQLc6JWq/n6668B+OKLL3RTA77L3t6ezz77DICNGzcCMGXKFN2FOBFf3759CQwMpHfv3qYOxagk6c58JOkWQggh0kFkZCRHjhwBUpZ058qVC4BXr16lS1wAhw8fpk2bNgB069ZNN+1OZiHl5cKcrF69mvPnz+Pi4sL333+f5Lb9+vXT3f7ggw9kTm4DZKYWbi1JujMfSbqFEEKIdHD16lUiIyPJkSMHZcuWNfh+6Z10nzhxgubNmxMeHk7z5s1ZuHBhuhzHlKS8XJiLyMhIvv32W0DTpzt37txJbu/p6cmXX35J2bJlWbJkCVZW8lU9K9Im3eHh4SaOxLQy02e4jakDEEIIITKjs2fPAlC1alXdtECG0A4MlB5J97lz52jatCmhoaE0btyYdevWZcq+otLSLczF3LlzuX//Pvnz52fo0KEG3ef3339P56iEuZOWbo3OnTvj7u7O5MmTcXd3N3U4aSJJtxBCCJEOzp07B2iS7pRIr5bu69ev06RJE4KCgqhXrx6bNm3SfbHLbKRPtzAHAQEBTJw4EYAJEyYYPJiiEJJ0w86dO1m7di3W1tZ8+eWXFp90m7RmZcqUKVSvXh0XFxc8PDxo3bo1//77r942CxYsoEGDBri6uqJSqXjz5k2y+y1atCgqlSrez4ABA3TbNGjQIN567VQNQgghRFppk+5q1aql6H7plXQPHz4cf39/atSowfbt23FycjLq/s2JtHQLc/DTTz8REBBA2bJl6dGjh6nDERYkqyfd4eHhDBw4EICvvvqKihUrmjiitDNp0n348GEGDBjAyZMn2bt3L9HR0TRp0oTQ0FDdNmFhYTRt2lTXH8YQZ86c4dmzZ7qfvXv3AtChQwe97fr27au33S+//GKcByaEECJLi4yM5MqVK0DqW7qNOXr5tWvX2LVrF1ZWVqxcuRIXFxej7dscSZ9uYWoPHz5k5syZgGY6K+1rUghDZPWk+6effuLu3bvkz5+fcePGmTocozDpJ4B2KhWtJUuW4OHhwblz56hfvz4AQ4YMAeDQoUMG7/fdQSp++uknPD098fb21lueLVs28ubNm/LAhRBCiCRcuXKF6OhocuTIQdGiRVN0X23SHRAQQExMjFG+rM+YMQOA1q1bU7x48TTvz9xJS7cwtR9++IHIyEi8vb1p3ry5qcMRFiYrJ923bt3ip59+AjT/uzLLRWKzuuwWGBgIYNSa/aioKJYvX86wYcPiDWSzYsUKli9fTt68eWnRogXff/99ov1tIiMjiYyM1P0dFBQEgKIoKIpitHiNSRubucYnEibnzXLJubNcxj532kHUtKXlKdmvdvouRVHw9/dPdrTj5Pj5+bFs2TIAhg4dmulenwmdO+2IzzExMZnu8WYWmfnz8tKlSyxduhTQtHJDyj4DzF1mPnfmIu7o5cZ8ns393CmKwsCBA4mKisLHx4d27dqZbaxahsZnNkm3Wq1myJAh1KlTh/Llyxttv5s2beLNmzf07NlTb3mXLl0oUqQI+fPn5/Lly4waNYp///2XDRs2JLifKVOmMH78+HjLAwMDzfbFoCgKISEhACkaOVeYlpw3yyXnznIZ+9ydOHECgHLlyukuKKeEm5sbgYGB3Lt3L82ji8+YMYPIyEiqVq2a6njMWULnLioqCtC0EmW2x5tZZObPyxEjRqAoCm3atKF06dKZ7jWYmc+dudA+r0FBQUZ9/Zj7udu0aRN79uzB3t6eyZMn6xo5zZmhMZpN0j1gwACuXr3KsWPHjLrfRYsW8dFHH5E/f3695f369dPdrlChAvny5aNRo0bcuXMHT0/PePsZPXo0w4YN0/0dFBREoUKFcHNzw9XV1agxG4v2YoCbm5tZvrFEwuS8WS45d5bL2OdO25+7Tp06uLm5pfj+uXPnJjAwkMjIyFTdXysiIoJFixYBmkQge/bsqd6XuUro3Gn/L6tUqjQ9fyL9ZNbPy3379rF//35sbW355ZdfMuXrL7OeO3Oifd2o1WqjvobM+dwFBwfz3XffAZo57StXrmziiAxj6PNoFkn3wIED2bZtG0eOHKFgwYJG2++DBw/Yt29foq3XcdWoUQOA27dvJ5h029vbY29vH2+5duRzcxV3dHZhOeS8WS45d5bLWOcuIiKCq1evApry8tTsL1euXNy+fZvXr1+nKZ6VK1fy8uVLChUqRPv27TPt6/Ldcxe3T3dmfcyZQWb7vFSr1YwaNQqAL774ghIlSpg4ovST2c6duXF0dAQ03VuN/Ryb47kLDw+nffv2PH36FE9PT0aPHm1W8SXFIpJuRVEYNGgQGzdu5NChQxQrVsyo+/f19cXDw8OgASwuXrwIQL58+YwagxBCiKxFO4iau7s7RYoUSdU+jDGCuaIoTJ8+HYDBgwdnqdGTZSA1YQqrV6/mwoULuLq6MmbMGFOHIyxYVhpILSIigtatW7N3716cnJxYtmyZ7vFnJib9DzxgwABWrlzJ5s2bcXFx4fnz54Cm5EF7hef58+c8f/6c27dvA5ovMy4uLhQuXFg34FqjRo1o06aNbj430Fxt9PX1pUePHvG+aNy5c4eVK1fSrFkzcubMyeXLlxk6dCj169fPFPPACSGEMJ2483On9kq9Mebq3rNnD9evX8fZ2Zk+ffqkej+WSJJuYQp//fUXoBmwMK0DIIqsLask3REREbRp04Y9e/bg5OTEzp07qVWrlqnDShcmnad73rx5BAYG0qBBA/Lly6f7Wb16tW6b+fPnU7lyZfr27QtA/fr1qVy5Mlu2bNFtc+fOnXhfTPbt28fDhw/p1atXvOPa2dmxb98+mjRpgpeXF8OHD6ddu3Zs3bo1nR6pEEKIrEKbdKd0fu64jJF0a1u5e/funSn7cidF5ukWGS04OJiDBw8C0KlTJxNHI8yVosCiRfD/HkiJygpJd2RkJG3btmXXrl1ky5aN7du3U69ePVOHlW5MXl6enHHjxiU7Kfr9+/fjLWvSpEmi+y9UqBCHDx82JEQhhBAiRbTThZky6X748CF79uxBpVIxePDgVMdhqaytrQFp6RYZZ9++fURFReHp6Unp0qVNHY4wU6tXQ58+0KAB/P8aTYKyQtLdvXt3du7ciaOjI9u3b8fb29vUIaUrk7Z0CyGEEJnJu4OopVbOnDmB1Cfda9asATTVYcWLF091HJZKystFRtu2bRsALVq0sJgBoETG0xbzPn6c9HaZPenetm0ba9aswcbGhm3bttGgQQNTh5TuJOkWQgghjOTy5cvExMSQM2dOChcunOr9pLWlW9tN65NPPkl1DJZMkm6RkdRqNdu3bwfg448/NnE0wlwFB8POnZrbyU29nZmT7oiICIYMGQJoxj9o2LChaQPKIJJ0CyGEEEZijEHUIG2jl9++fZuzZ89ibW1Nu3btUh2DJZM+3SIjnT17lhcvXuDq6pqp+6SKtNm2DSIjNbffvNH0705MZk66p02bxp07d8iXLx/ff/+9qcPJMJJ0CyGEEEZijEHUIG0t3drS8oYNG+Lh4ZGmOCyV9OkWGUlbWu7j44OdnZ2JoxHmau3at7ejoyE8PPFtM2vS/fDhQ3788UcApk6diouLi4kjyjiSdAshhBBGYoxB1OBt0v3mzRuio6NTdN+///4byLql5SDl5SJjaWe/kdJykZiQkLel5Vpv3iS+vTbpDk8qM7dAw4cPJzw8nHr16tG5c2dTh5OhJOkWQgghjCAiIoJr164BaRtEDSBHjhy68nR/f3+D73fjxg2uXLmCra0tbdq0SVMMlkySbpFRHj9+zMWLF1GpVHz00UemDkeYqW3bICICPD0hRw7NsqT6dWfGlu59+/axbt06rKysmDNnTpYbcFCSbiGEEMIItIOo5cqVi0KFCqVpX9bW1uT4/zezlJSYawdQa9KkCe7u7mmKwZJJn26RUbQDqNWsWZPcuXObOBphrtat0/zu0OFt0m1IS3dmSbqjoqIYNGgQAAMGDKBixYomjijjSdIthBBCGMHhw4cBTWm5Ma7gp7Rft6IoutLyTp06pfn4lkz6dIuMoi0tb9GihYkjEeYqNBR27NDc7tABsmfX3DY06VaSGnHNQvz000/cvHmT3LlzM2HCBFOHYxKSdAshhBBpFBkZycyZMwGMVtad0qT78uXL/Pvvv9jb29OyZUujxGCppLxcZISwsDD2798PSH9ukbjt2zWDphUvDpUrpyzpVhQlxeN6mJuLFy8yceJEAGbMmEF27ROQxUjSLYQQQqTR0qVLefLkCfnz56dnz55G2WdKpw3TlpY3a9YMV1dXo8RgqSTpFhnhwIEDREREULhwYcqXL2/qcISZ0o5a3qEDqFTg5qb5O6mk29HRUXfbkkvMo6Ki6NGjBzExMbRt2zbLDZ4WlyTdQgghRBrExMTw008/ATBixAjs7e2Nst+UtHRLabk+6dMtMkLc0vKsNiiUMExoqKalGzRJN7xt6U5qILW4/0csOemeOHEily9fJleuXMybNy9Lv08k6RZCCCHS4O+//+bu3bvkypWLfv36GW2/KUm6z549y71798iWLRvNmzc3WgyWSvp0i/QWGxurm59bSstFYnbs0JSWFysGVapolhlSXq5SqXSJt6Um3WfPnmXKlCkAzJ07Fw8PDxNHZFqSdAshhBCppFardV8qhg4dipOTk9H2nZKke8uWLYDmy78xY7BU2pZutVqNWq02cTQiM1qzZg1Pnz7F3d2dBg0amDocYabWr9f8bt9eU1oOhiXdYNkjmEdERNCjRw9iY2P55JNP6KBt5s/CJOkWQgghUmnTpk1cv34dNzc3BgwYYNR958yZEzAs6T5w4AAAPj4+Ro3BUmmTbpASc2F8arWayZMnAzBkyBBdciREXNHRsHOn5nbbtm+XG9KnGyw36X716hXdu3fn+vXr5MmTh99//93UIZkFSbqFEEKIVFAUhR9//BGAgQMH4qb9JmUkhrZ0BwcHc/r0aQAaNmxo1BgslSTdIj1t2bKFq1ev4urqqpt7WIh3/fMPBAVBrlxQvfrb5Yb06QbLS7rVajWLFy+mdOnSrF27FpVKxYIFC3QXkLM6SbqFEEKIVNi9ezfnz58nW7ZsDBkyxOj7N3T08mPHjhETE0OxYsUoWrSo0eOwRNo+3SD9uoVxKYrCpEmTAM3Ftqw6/ZFInnYAtaZNIc5HUqYsL7927Rre3t707t0bf39/KlSowLFjx7L89JVx2SS/iRBCCCHiUqvVjBs3DoD+/fvrEmRjMrSlW1taLq3cb8Vt6ZakWxjTnj17OHfuXLpdbBOZx44dmt/vjm2Z2ZLuBw8eUKNGDUJDQ3FycmL8+PEMHjwYW1tbU4dmViTpFkIIIVJo3rx5nDp1CmdnZ0aMGJEux9Am3UFBQURFRWFnZ5fgdpJ0xydJt0gPiqIwceJEAD7//HNy585t4oiEuXrwAK5dAysreHeojczWp3vatGmEhoZStWpVNmzYQOHChU0dklmS8nIhhBAiBR48eMA333wDwM8//0y+fPnS5TjZs2fHykrzbzqxEnN/f38uXLgAwAcffJAucVgiKysr3Xyw0qdbGMuRI0c4fvw4dnZ26XaxTWQO2tLy2rUhRw79dZmppfvVq1f8+eefgOb/oSTciZOkWwghhDCQoij079+fkJAQ6tatS//+/dPtWFZWVri7uwOJl5gfPnwYRVEoU6ZMuiX/lkrm6hbGph04sVevXuTPn9/E0QhzllhpObxNuiMiIDIy8X1YQtI9e/ZswsPDqVq1qlRbJUOSbiGEEMJAy5cvZ9euXdjb2/Pnn3/qWqLTS3L9urWl5Y0aNUrXOCyRtsRckm5hDKdPn2bv3r1YW1szatQoU4cjzFh4OPz/o5lmzeKvd3V9O2d3UiOYm3vSHRISwpw5cwAYNWqUrrpIJEySbiGEEMIAL1680A2cNG7cOEqXLp3ux0xuBHPpz504bdIt5eXCGObNmwdAly5dZJYAkaRDhzSJd8GCUKFC/PVWVprEG5IuMdcm3eHh4UaP0Rj+/PNP/P39KVGiBG3jTkQuEiRJtxBCCGGAwYMH4+/vT+XKlRk+fHiGHDOplu7nz59z/fp1VCoV3t7eGRKPJZGWbmEswcHBrFmzBiBdu5SIzEHbn7t587ct2u8yZDA1c27pjoqKYvr06QCMHDlSb5pGkTBJuoUQQohk7NixgzVr1mBtbc2iRYsybCqUpJLugwcPAlC5cmVd32/xlvTpFsayZs0awsLCKF26NLVq1TJ1OMKMKcrbpDuh0nItbb9uSy0vX7VqFY8ePSJv3rx0797d1OFYBEm6hRBCiCRER0frWraHDBlC5cqVM+zYSSXd+/fvB6S0PDHS0i2MZfHixYBmADXptyqScvMm3L8P9vaQ1FAbhoxgbq5Jt1qt5pdffgE0/xO1cYqkSdIthBBCJGHhwoXcvHmTXLlyMWbMmAw9dlJJt/TnTpr06RbGcPPmTf755x+sra359NNPTR2OMHPaVu4GDcDJKfHtLDnp3rZtG9evX8fV1VW6W6SAJN1CCCFEIgIDAxk7diwA48ePJ7v2m1IGyZkzJxA/6b537x737t3DxsaGunXrZmhMlkLKy4Ux+Pr6AtCsWTOZlk8kSztVWFKl5WDZfbpnzJgBaMY3cNM+EJEsSbqFEEKIREyePJlXr17h5eVFv379Mvz4iY1eru3P/f777+Pi4pLhcVkCKS8XaRUTE8PSpUsBTWm5EEkJDISjRzW3E5qfOy5DWrodHR0B80q6r1+/zsGDB7GysmLAgAGmDseiSNIthBBCJODevXu6K/pTp07VJXEZKbHyciktT54k3SKtdu3axfPnz8mdOzfNk8uiRJa3dSvExICXF3h6Jr2tpQ6k9vvvvwPQqlUrChcubOJoLIsk3UIIIUQCvvnmG6KiomjcuDHNkqsVTCcJJd2RkZHs2rULkKQ7KdKnW6SVdgC1Tz/9NMNmLBCWa906ze8OHZLf1hL7dAcFBekqP6SVO+Uk6RZCCCHe8c8//7BmzRpUKhXTpk0z2YjF2qQ7JCRE98Vr3bp1vH79mkKFClG/fn2TxGUJpE+3SAs/Pz+2bt0KwGeffWbiaIS5CwqC/18LNSjptsQ+3cuWLSMkJAQvLy+54JsKknQLIYQQ7/jhhx8A6N27NxUrVjRZHG5ubrrkUduve968eQD07dtXt07EJ+XlIi2WL19OTEwM77//PuXLlzd1OMLMbdsGkZFQqhQY8nKxtJZuRVF0peUDBgyQqfNSIeM7qAkhhBBm7M6dO+zfvx+VSpXhU4S9S6VSkTNnTvz8/Hj16hX+/v4cP34cGxsb+vTpY9LYzJ0k3SK1AgMDmT17NiADqAnDrF2r+d2hAxiSj1pa0n3w4EFu3LiBs7Mz3bt3N3U4FklauoUQQog4tP04mzRpQpEiRUwcjf4I5tpW7tatW8v0RcmQPt2Zzz///EOuXLn4888/0+0YiqLQs2dP7t+/T+HChenSpUu6HUtkDsHBsHOn5rYhpeVgeQOpaVu5u3fvjqurq4mjsUySdAshhBD/FxMTo5uX11xakrVJ971791i2bBkAX3zxhSlDsgjSpzvzmTJlCq9fv9ZdGEsP06dPZ9OmTdjZ2bFu3TqZkk8kS1taXrIkGNobyZJauh89esSmTZsA+PLLL00aiyWTpFsIIYT4v507d/Ls2TNy585Ny5YtTR0O8DbpnjVrFiEhIZQqVYoPPvjAxFGZPykvz1yePn3Kjh07ADh//jyRkZFGP8bRo0cZNWoUADNmzKB69epGP4bIfFJaWg5vB1ILCdFMM5YQc0m6//jjD9RqNR988AHlypUzaSyWTJJuIYQQ4v+0Zas9evTAzs7OxNFoaJPuy5cvA9C/f38ZxMYAknRnLkuXLkWtVgOaafMuXbpk1P0/f/6cTz75hNjYWLp06UL//v2Nun+ROYWEvC0tb9/e8Ptpk25IvMTcHJLu8PBwFi5cCMg0YWklSbcQQgiBpiVt+/btgGbUcnOhTbpB8yWsR48eJozGckif7sxDURRdSbk2ETl16pTR9h8ZGUmXLl149uwZZcuWZcGCBXJhSxhk+3aIiABPT6hUyfD72dqCk5Pmtjkn3fPnz8fPz4+iRYvSqlUrk8WRGUjSLYQQQgCrVq1CrVZTt25dvLy8TB2OTtyku1OnTri7u5swGsshfbozj+PHj3Pr1i2cnJwYPHgwACdPnjTKvm/cuEHNmjU5ePAgzs7OrF+/HidtNiTMzqlTcOSIqaN4KzWl5VrJ9es2ddIdFhbGzz//DMCYMWN0FzJF6kjSLYQQIstTq9UsX74cMJ8B1LRy5sypuy0DqBlOysszj0WLFgHwySef0LhxYyDtSbeiKPzxxx9UrVqVixcvkitXLjZs2GBWF9yEvuvXoW5d8PGBoCBTRwOhofD/YQYMHrU8Lm2JuSFJt6IoKT9AGs2fP58XL15QrFgxmSbMCCTpFkIIkeUdOnSI+/fv4+rqSvuUdMzLAGXLlgWgZs2aMrBTCkjSnTkEBwezZs0aQDNntvY9cPfuXV6+fJmqfT558oQ2bdrQv39/wsPD+fDDD7l8+TIffvih0eIWxqUoMHCgZtCxiAh49szUEWlKy8PDoVgxqFw55fc3tKUbSJeBA5Pybiu3ra1thh4/M5KkWwghRJanbUnr3Lmz2ZWWVqtWjePHj7Nt2zbpZ5oC0qc7c1izZg1hYWGULl2a2rVrkz17dsqUKQOkrF93aGgoy5cvx8fHh8KFC7N582ZsbW2ZNm0au3btknnvzdzq1XDw4Nu/U3m9xahWr9b8Tk1pOaQs6c7oEvN58+bh5+dH8eLF+fTTTzP02JmVJN1CCCGytICAADZs2ACYX2m5Vu3atfXKzEXypE935qAdQK1Xr166i041a9YEDCsxj46OZtCgQeTJk4dPP/2UPXv26MZuOHXqFMOGDcPKSr4Om7OgIBg2TH/Zq1emiUXrzRvN/NwAXbqkbh/apDuxgdTizqCRkUl3aGiotHKnA/mUEUIIkaVt3bqVyMhIypQpQ5UqVUwdjjASKS+3fDdu3OCff/7B2tpar7WtRo0agGEt3aNHj2bOnDmEhobi6enJuHHjuH37NkePHqVyamqCRYYbP15TTl6iBPy/S7/JW7rXr4eoKChXDipWTN0+kuvTrVKpTDKY2rx583j58iXFixenW7duGXbczE6GoRNCCJGlbdq0CYAWLVpI+XYmIkm35fP19QWgWbNmeuXf2pbuU6dOERsbq6tqeNeGDRuYNm0aAMuWLaNr167yHrcwV6/CzJma27Nnvy3pNnVL94oVmt9du6autBySLy8HTYl5REREhiXdoaGh/PLLLwB8//330sptRNLSLYQQIssKCwtj165dADRv3tzE0Qhjkj7dlm/1/zOszz77TG95uXLlcHJyIjg4mJs3byZ439u3b+vuN3z4cLp16yYJt4VRFBgwAGJjoW1baNoUcufWrDNlS/eTJ3DokOZ2akvLwbCk29HREci4lu65c+fy8uVLPD09pZXbyCTpFkIIkWXt3buX8PBwihQpQoUKFUwdjjAi6dNt2R49esTDhw+xtraON6q4jY0N1apVAxIuMQ8PD6dDhw4EBQVRp04dpkyZkiExC+Nau1YzJ3e2bPDbb5pluXJpfpuypXvVKs0Fgbp1oUiR1O8nuT7dkLFzdYeEhOi1csu83MYlSbcQQogsa+PGjQC0atVKWsEyGSkvt2zHjx8H4L333sPZ2Tne+qQGUxs8eDAXL14kd+7crF69WkpkLZCiwE8/aW5//TUULqy5rW3pNmXSHbe0PC0MLS+HjEm6586dy6tXryhRogRd0/rgRDySdAshhMiSYmJi2Lp1KwBt2rQxcTTC2CTptmz//PMPAHXq1ElwvXYwtXeT7sWLF/Pnn3+iUqlYtWoVBQoUSN9ARbo4cAAuXNC0cg8c+Ha5tqXbVOXl16/DxYtgY6OZKiwtkhtIDTIu6Q4JCeHXX38FpJU7vUjSLYQQIks6evQo/v7+5MyZM9Ev9sJySZ9uy6Zt6U4u6b527RrBwcEAHDlyhP79+wMwfvx4GjVqlAGRivQwdarmd69eEHe2RFO3dGtbuT/6SD+u1DCnlu7ff/+dV69eUbJkSbqkpaO6SJQk3UIIIbIk7ajlLVu2lKv6mZD06bZcISEhXLp0CdDMUZ+Q/PnzU7hwYdRqNWfPnuX27du0adOG6OhoOnTowHfffZeRIQsjunIFdu0CKysYOlR/nSlbuhUFVq7U3DZG9bW5JN3BwcHSyp0BUvSs3rhxg7///pujR4/y4MEDwsLCyJ07N5UrV8bHx4d27dphb2+fXrEKIYQQRqEoii7pltLyzEnKyy3X6dOniY2NpVChQhQqVCjR7WrUqMHDhw/ZtWsXW7Zswd/fn+rVq7NkyRKsrKRdyVL9f5Y32rWD4sX112mT7tBQCA+H/w/unSH++Qfu3wdnZ2jRIu370ybdQUGgVmsuMrwrI5Lu33//ndevX1OyZEk6d+6cbsfJ6gz6RDp//jyNGzemcuXKHDt2jBo1ajBkyBAmTpxIt27dUBSF7777jvz58/Pzzz8TGRmZ3nELIYQQqXb+/HkePnyIk5MTjRs3NnU4Ih1I0m25kuvPraUdTO2XX37h5s2bFCxYkM2bN5MtW7Z0j1GkjydP3rYmjxgRf72bm6Y/NWR8ibm2tLxtW01f87TS9ulWFPh/D4l40jvpjtvK/cMPP0grdzoy6Jlt164dI0eOZN26dWTXXpZJwIkTJ5g5cybTpk3j22+/NVaMQgghhFFpW7mbNm2Ko6MjiqKYNiBhdNKn23Jp+3MnVlqupU26AZycnNi6dSv58uVL19hE+po1C6KjoX59eP/9+OtVKk1r9/PnmqQ7iUIIo4qKgjVrNLeNNbC3gwPY20NkpKbEXJuE62+Tvkn3nDlz8Pf3p1SpUnTq1CldjiE0DEq6//vvP4OmW6hVqxa1atUiOjo6zYEJIYQQ6UU7VVjr1q1NG4hIN9Kn2zKp1WpOnDgBJN/SXblyZVxcXAgJCWHFihVUqlQpAyIU6SUoCObP19xOqJVbK3duTdKdkf26N22C168hf35o2NB4+82eHV680CTdCc35nZ5J98OHD3Vz2Esrd/oz6NlN6fyGMh+iEEIIc3Xr1i2uXbuGjY0NzZs3N3U4Ip1Iebllun79OoGBgTg5OVGxYsUkt3V0dOTQoUNEREQk2youzN+iRZrE28sLkvpo1vbrzsjy8j/+0Pzu3fttebsxaJPuwMCE16dX0q0oCr179yY4OJjatWtLK3cGSPEoE4MHD2bWrFnxls+ZM4chQ4YYIyYhhBAi3axduxaABg0akCNHDhNHI9KLlJdbJm1peY0aNQxqeatSpYok3JlAYCD88ovm9vDhCQ8qppXR04bduqWZN1ylgj59jLvv5ObqTq+ke8GCBezbtw9HR0d8fX11lUEi/aQ46V6/fn2C5T61a9dm3bp1RglKCCGESA9Pnjzhp59+AqCrsTrmCbMk5eWWydBB1ETm8t13mpLxkiXh00+T3jajpw3780/N748+gsKFjbvv5KYN0ybd4eHhRjvmw4cPGTlyJACTJ0+mVKlSRtu3SFyKk+7Xr1/jlkBPf1dXV16ZaqZ6IYQQwgCDBw8mODiYmjVr0r17d1OHI9KRlJdbJkMHUROZx+nTMHeu5vb8+ZrBxZKSkS3dUVHg66u53a+f8fdvaNJtrJZutVrNoEGDCAkJoV69egwePNgo+xXJS3HSXaJECXbt2hVv+c6dOyn+7mR6QgghhJnYvHkzGzZswMbGhgULFsg8vpmcJN2W58WLF9y5cweVSkWtWrVMHY7IADEx8PnnmmmzunUzbJCyjGzp3rRJc5z8+ZPuZ55aGZ10z58/nyNHjuDo6MjixYvl/2AGSvFQAMOGDWPgwIG8fPmShv9/Z+zfv59p06YxY8YMY8cnhBBCpFlQUBADBgwAYOTIkVSoUMHEEYn0Jn26LY+2tLx8+fIJVlWKzGf2bLh4EXLkgGnTDLtPRrZ0L1ig+W3sAdS0tEl3Rgyk9ujRI0aNGgXATz/9RIkSJdK8T2G4FL98evXqRWRkJD/++CMTJ04EoGjRosybN09K9YQQQpilMWPG8OTJEzw9Pfn+++9NHY7IANKn2/JIaXnW8ugRaD+Of/kFPDwMu19GtXTfvg3792sGUOvdO32OkZEDqc2fP5/Q0FDef/993UVokXFSdc3miy++4IsvvuDly5c4Ojri7Oxs7LiEEEIIozh9+jRz5swBNF86HB0dTRyRyAhSXm55ZBC1rGXQIAgNhTp1oFcvw++XUVOGLVyo+d20acJzaBtDRpWXx8TE4Pv/zulffvmllJWbQJoKJXJr6zuEEEIIM6RWq/n8889RFIVu3brRuHFjU4ck0llMDMTGStJtaSIiIjh37hwgSXdmFxkJw4bB5s2aku0//kh6irB3adOP169BrU7ZfQ0VdwC1zz83/v61kku6tReJ05p0b9++nWfPnpE7d24++uijNO1LpI5BL9OmTZty8uTJZLcLDg7m559/5vfff09zYEIIIURabdiwgYsXL+Lm5sb06dNNHY5IZ7GxUKkSlC0LKpXt/5dJn25LcPbsWaKiosiTJw/FihUzdTgindy7B3Xrvh2t/NdfoVy5lO0jZ07N79jYxJPVtFq5UlO+ni9f+gygppVRLd0L/99s36NHD+zs7NK0L5E6BrV0d+jQgXbt2uHm5kaLFi2oVq0a+fPnx8HBgYCAAK5fv86xY8fYsWMHzZs359dff03vuIUQQogkKYrCpEmTAPjqq6+kOisLOHMGrl3T3A4L03xZlZZuy3Ds2DFA08qtUqlMHI1ID1u3QvfumgTT3R2WL9fMfZ1S9vbg6gpBQZoSc3d348b55ImmJR5g8OD0GUBNS9unO+5AakeOwJAh8MMP4OSU9qT78ePH7Ny5E4A+ffqkej8ibQx6GfXu3Ztu3bqxdu1aVq9ezYIFCwj8/6tDpVJRtmxZfHx8OHPmDGXKlEnXgIUQQghDbN26lUuXLuHs7MxXX31l6nBEMo4c0bQuTZsGTk6p28eOHW9va1u6Jem2DEePHgWgXr16Jo5EpIf58+GLLzS3a9SANWugcOHU7y9XLk3S/fIllCplnBhBM3VZ794QEADVqsHw4cbbd0Leben+919o1Urz96pVMHBg2pPuxYsXo1ar8fb2plSpUrocTmQsg6/d2Nvb061bN7p16wZAYGAg4eHh5MyZE1tb23QLUAghhEgpRVF0M2wMHDgQd2M3hQijio6Grl3h8WPNF93UNsbETbpBkm5LoVardSOXS9Kd+ajV8P+iI/r3h5kzIa0Vzrlzw927xh9M7Y8/YPducHCApUshvVOcuEn3y5eaUnZtAh4envby8tjYWBYtWgRA37590xasSJNUF0y4ubnJHIpCCCHM0p49ezh79iyOjo4MHTrU1OGIZKxfr0m4QdPSkxovXsD/x+ECpE+3Jbl69SqBgYE4Ozvz3nvvmTocYWSnT2tKtp2d4bff0p5wQ/pMG3b79tuW7Z9+gowo3tUm3bGx8PHHcOeOZooyRYGIiLQn3Xv37uXhw4fkyJGDdu3aGSlqkRoyXrwQQohMJW4rd//+/fEwdPJXYRKKovkirpXapHvXrnf3K6OXWwptf+5atWrpRp0Xmcf69ZrfH3+saUE2Bu0QHcZq6Y6N1fQ3DwuDhg0105llhGzZ3vYZP31a01d9/HjN38Zo6dYOoPbpp5/q9iVMw6RJ95QpU6hevTouLi54eHjQunVr/n3nv+2CBQto0KABrq6uqFQq3hgwTOG4ceNQqVR6P15eXnrbREREMGDAAHLmzImzszPt2rXjxYsXxnx4QgghTODQoUMcP34ce3t7Ro4caepwRDJOnNB82dT677/U7UeSbssl/bkzL0WBdes0t43Z0Grslu5ff9V8Frm6aqYKy6hprFWqt4OpWVtrnqsqVTR/p7Wl+8WLF2zZsgWQ0nJzYNKk+/DhwwwYMICTJ0+yd+9eoqOjadKkCaGhobptwsLCaNq0Kd9++22K9l2uXDmePXum+9FeRdUaOnQoW7duZe3atRw+fJinT5/Stm1bozwuIYQQpqMdsbxPnz7ky5fPxNGI5GhbuT/8UPP7zh3NXNspEROj6YepT5JuS6Aoii7prlu3romjEcZ24QLcvw+OjqkbqTwx2qTbGC3d4eGacnKAWbPSNsBbapQoofk9d67mc/D/U3PrtXRHRUWhVqtTtN8lS5YQExNDzZo1KV++vDFDFqlg0hqeXe9cll6yZAkeHh6cO3eO+vXrAzBkyBBA03KREjY2NuTNmzfBdYGBgSxatIiVK1fSsGFDAHx9fSlTpgwnT56kZs2aKXsgQgghzMLx48c5cOAAtra2fP3116YORyTj/n3YsEFze+pUqFlT80Xz3j0oWdLw/Zw9a82bNypy5NCMfK7pHy59ui3BgwcPePLkCTY2NtSoUcPU4Qgj05aWf/RR6mclSIi2vNwYLd1bt2qm7CpcGD79NO37S6n16+HRI83nH7wtwY/b0g0QGRmJozYjT0ZkZCRz5swBpJXbXKQ66T579iw3btwAoEyZMlSrVi3NwWiHsDfGKLO3bt3SzSVeq1YtpkyZQuH/X7o6d+4c0dHRNG7cWLe9l5cXhQsX5sSJEwkm3ZGRkURGRur+DgoKAjRXaBVFSXO86UEbm7nGJxIm581yybkzLUVRdOXkPXr0oFChQgafCzl3pjFrFqjVKho3VqhQQTP1z6VLKv79V9G1/iRHURT27tV8nfHxUTh7FkCFWq0p5ouJiZHzaoa077cjR44AULVqVRwdHeVcWQBDPy/flparaNdOwZinNmdOzX5fvUr7fpct0+yra1dFN4hZRsqfX/OjPa4mz1YRHq5gb2+v2y48PNzgftm+vr48fvyYAgUK0KVLF925kv91xmfoc5nipPvx48d07tyZ48ePk/3/Q+69efOG2rVr8/fff1OwYMGU7hLQTBcxZMgQ6tSpk+YSiBo1arBkyRJKly7Ns2fPGD9+PPXq1ePq1au4uLjw/Plz7OzsdPFr5cmTh+fPnye4zylTpjBeO7JBHIGBgWb7wlUUhZCQEEAzn7qwDHLeLJecO9PauHEjJ06cwMnJiWHDhqVoLlI5dxkvOBj+/FPTmbFv31ACA2MoViwbly7ZcflyBHXrRiazBw1FUdizR9OE5u0dxvnzDoA1oaFRAERHR8u8tGZI+547cOAAANWrV5fzZCEM/by8ft2K//5zxc5OoW7dQIx5eh0drQEX/PzUBAYGJ7ntkSM2rF9vy9ixEbi7639nf/VKxa5drgC0bh1MYGDKSrjTQ3S0FeBKeLhCaGgoVlZWqNVqXrx4gZUBnc2jo6OZMmUKAIMGDSIiIkLXJ1z+1xmftiE2OSlOuvv06UN0dDQ3btygdOnSAPz777989tln9OnTJ17JuKEGDBjA1atX4/W9To2P4nQaqVixIjVq1KBIkSKsWbOG3r17p2qfo0ePZtiwYbq/g4KCKFSoEG5ubri6uqY55vSgvRjg5uYmbywLIufNcsm5M52IiAgmTJgAwNdffx1v8MzkyLnLeEuWQHCwitKlFdq1c8LKCsqWhU2b4MEDB9zcDGvRefpU4epVzRfRNm2yMXeuZrmjo+Z/c2xsrExxaoa077kzZ84A0KhRIzlPFsLQz0vtOAs+PlCokHHPbdGimt+vX1sl+7oZNQpu3lRhb2/HH3/or1u6FGJiVFSrplC9uotRY0wt7YQbEREqsmfPjoODA2FhYdjZ2Rn0HvH19eXhw4fkyZOHQYMG6ZWky/864zP0eUxx0n348GH++ecfXcINULp0aWbPnp3qUScHDhzItm3bOHLkSKpbypOSPXt2SpUqxe3btwHImzcvUVFRvHnzRq+1+8WLF4n2A7e3t9cr8dDSjo5uruKO4C4sh5w3yyXnzjRmzZrF/fv3KVCgACNGjEjV8y/nLuPExsLs2ZrbQ4aosLbW3NZeK7l1S4Whp0H7xb5aNYU8eVS8nXHK5v/HipVzaqb8/f11XRXr1asn58mCGPJ5qR2voV07w9/PhtImpiEhKiIjE5+K7Pp1uHlTc3vxYhXDh0OcFIblyzW/P/3U+DGmljZHjojQBKRNuiMjI5N9j8TExOhauUeMGEG2bNnibSP/64zL0OcxxaOXFypUiOjo6HjLY2NjyZ8/f4r2pSgKAwcOZOPGjRw4cIBixYqlNByDhISEcOfOHd0otlWrVsXW1pb9+/frtvn33395+PAhtWrVSpcYhBBCpA8/Pz9+/PFHQNMVKKEvGcK8zJoFd++Cu7tmblytUqU0v1MybdjOnZrf2iI3W834aajVmkxeRi83XydPngSgbNmy5NR00hWZxL//wtWrmjmoW7Y0/v7d3N7Ob53UCOba6cpAc7Hvu+/0YzxzRjNVV6dOxo8xtbRJt6JAVFTKpg37+++/uX37Nrly5aJ///7pGaZIoRQn3b/++iuDBg3irGakEkAzqNpXX33F1KlTU7SvAQMGsHz5clauXKnra/38+XPCw8N12zx//pyLFy/qWqmvXLnCxYsX8ff3123TqFEj3Qh9oLmyc/jwYe7fv88///xDmzZtsLa2pnPnzoCmpKJ3794MGzaMgwcPcu7cOT777DNq1aolI5cLIYSF+eGHHwgODqZq1ap07drV1OGIZNy7B2PGaG7//DPEvUaiHbH8yRP4f7fDJEVHw969mtvvJt2KIkm3udMm3TJVWOajHbW8USPIkcP4+1epDJs2TBvHsGGa+6xfD6dPa5ZpBlCDpk3ftpybg7it9imZqzs2NlZ3AXrYsGE4OzunW4wi5VJcXt6zZ0/CwsKoUaMGNjZv58C0sbGhV69e9OrVS7dt3MQ4IfPmzQOgQYMGest9fX3p2bMnAPPnz9cbwEw7lVjcbe7cucOrOO847WBvr1+/Jnfu3NStW5eTJ0+SWzu/APDbb79hZWVFu3btiIyMxMfHh7najmBCCCEswtWrV1m4cCEA06dPN2iQGWE6igL9+kFYGHzwAbw7zIq7u+aL9KtXcOsWVK6c9P5OnIDAQBXu7mqqV9eU+Glbv6Sl2/ydOHECINXdE4X50ia77dql3zFy5YLnzxOfNuzWLbh8WdOS/e238Po1/PUXfPMN7NsXt7Q8/WJMDTs7dKOox52rO7mke/369dy8eZMcOXIwYMCAjAhVpECKk+4ZM2YY7eCGjPo9btw4xo0bl+Q29+/f1/v777//Tna/Dg4O/P777/z+++/JbiuEEMI8jR49GrVaTdu2bXUXZYX5WrpU82XXwQEWLCDBPpSlS2uS7v/+Sz7p1hbYffhhDNbWmibud8vLY2NjURRF+i+amdDQUC5dugRIS3dmcuIEjBsH58+DlRW0bp1+x9K2pSXW0q1N/Bs21EwxNn48rFoFBw/C99/Dgwfg6po+5e9poVJpPiPDww1v6Var1UycOBGAr776ymwHec7KUpx09+jRIz3iEEIIIVLk1atX7NixA4DJkyebOBqRnBcvYOhQze3x40l0Hu5SpeD48eT7de/eDVu3go2NwrBhEYAm29a2dGvLy0HzhdTa2jqBvQhTOXXqFDExMRQsWJAiRYqYOhyRRidPapJt7cCGNjaa93mcIlOj05aXJ9bS/W5re5EiMHAgTJ8O2n8Z7du/7UNtThwdNUl3eDi60ceTSrpPnDjB1atXcXZ2ZvDgwRkVpkiBFCfdWn5+fvj5+aFW689nV7FixTQHJYQQQiRn27ZtqNVqKlWqpDejhjBPX30FAQGa1us4M3DGox1M7d9/E98mOhqGDNHcHjgQSpV6+13kbUv3264GMTExknSbmaNHjwKaVm6pQrBsM2e+fT9aW0PPnppy7uLF0/e4SfXpfvAAzp7VtBrHbW3/9lv480/QTq1sbqXlWtp+3Ya2dG/atAmAVq1akSM9OtGLNEtx0n3u3Dl69OjBjRs34pWHq1QqYmNjjRacEEIIkRjtl4zW6Vm/KIxi+3ZYvVrzhfzPP4kzrVd8hoxg/vvvmmmAcueGH37QX/dueTloku6Epv0UpnPs2DFASsst3a1bmnmwQZPAjhuX/sm2VlLl5dpW7vr1IU+et8tz5oSvv9YM5li4sGa9OXo7bVjySbeiKGzcuBGQ/4fmLMVJd69evShVqhSLFi0iT548cnVSCCFEhgsLC2PPnj2AfMmwBP/vasjQoVClStLbaosW/vtPM5DQu18zXr7UfLEH+PFHyJ4dAgPfrtcm9LGxb1u6pUHAvERFRfHPP/8A4O3tbeJoRGopCvTvD5GR0KSJZpCyjEwLkiovT2ogtxEjNLF/8IGm37k50rZ0GzKQ2rVr17hz5w729vY0bdo0o0IUKZTipPvu3busX7+eEol1xhJCCCHS2Z49ewgPD6do0aLSrcnMnTkDp05pRuQdOTL57T09NV/cAwPBz0+/lQo0LVSBgZoy9TgTpuhoW7rjJt0ygrl5OXPmDOHh4eTMmZOyZcuaOhyRSsuWwYEDmlbZefMyNuGGxFu6nzyB/1/ToW3b+Pezt387baG5Skl5ubbq68MPP5RpwsxYiq/vNGrUSDfapBBCCGEKcUvLpeLKvM2erfn9ySeGzYXr4KAZ8Ajil5hfuAD/nyGOWbM05ervejtlmCTd5urw4cMA1KlTR96/FurVKxXDh2tujx2bcSXlcSXW0v3/Smtq1YICBTI2JmPRlpcb0tItpeWWIcUt3X/++Sc9evTg6tWrlC9fHlvtJeX/a2lu4+4LIYTIVGJiYti6dSsgXzLMnZ+fpi83wKBBht+vVCm4f1+TdGuncFYUzQBsigKdOkFiXYG1X0tiYlRYW1sTGxsrSbeZOXToEKBJuoVlGjPGkdevVVSsmPTAiOkpsZZubWl5+/YZG48xGdrS/fDhQ86fP4+VlZXkYGYuxUn3iRMnOH78ODt37oy3TgZSE0IIkd6OHTuGv78/OXPmlC/tZm7BAoiKgho1oHp1w+9XujTs2aM/gvnhw3DokKZM/X/snXWcFPUbx997fcBxdHcrId0lIUgpIghYKIhIKNiYGIgNIlg/QiwQFKWUlC6lu4/uuuM6dn9/fG9u946Ljdl+3q/XvWZuY+a5m435zOeJjz/O+blm0U2G6JZzE88hJSWFjRs3AiK6vZWVK+HXX0MwGEz8738GsvhvLsOye7nRqOqzjxyBdevU7dmllnsL1jrdCxYsANR7qbgz57O5iSlTVMO7nj1dX76gNzanl48aNYpHHnmECxcuYDQaM/3Il5ogCILgbLTU8p49exKUWxtswa2kpKg6T7DN5YbsO5i/845aPvUUlC+f83O1l0RKChmvD3G6PYdt27YRHx9P0aJFueOOO9wdjmAjaWkwfLhaHzECmjZ1Xyya6E5LU30ekpJgwAAlwLt0gUqV3Bebo1jrdPvyFI8TJ1TTu/vug/QJg16NzaL72rVrjBkzhpJZO5sIgiB4MVlHIPorJpNJl/9FjDYEVWdMJpNPn2T4En/8AefPq0Zoffva9tysonvdOuVyBwebxxPlhOa6iej2TLTU8rZt2xLgqa2jhRz55x84dsxAoUJG3n/fvbGEhkJEhFq/cgVefx127FBjwaZPd29sjmKN033t2rWM/gi++H34/PPqQkqnTuYyI2/G5k+7Bx54gNWrVzsjFkEQBLfw888/U6hQoYwRVP7K/PnzqVixIh07dnRINH/44YcULlyY4cOH634xY/fu3Zw6dYrw8HA6d+6s67YFfdEaqA0dqlLCbUEbG3bsmHKx3n1X/T54cO4uN5id7tRUs+iWTDzPQRMJMirMO/nhB7V84IEUChZ0byxgruv+6Sf47DO1PnOm9zZQ07DG6V6yZAlpaWnUq1ePKu7oZOdEli2DBQvU5/kXX3h/ajnYUdNdo0YNxo4dy4YNG6hbt+5tjdSeffZZ3YITBEFwBTNnziQmJoa1a9dyzz33uDscl3Pt2jVGjRrF7NmzAThz5gw9e/bk77//Jl++fDZta+LEiYwdOxaAr7/+mvr16zN06FDdYtVc7i5dutgcm+A6du2CDRvUCdOwYbY/v3x55WIlJcHPP8OqVcrBfvXVvJ9r6XQHprc3F6fbM0hJSWHDhg2AiG5v5NYtmD9frQ8YkAzYeDXNCRQrptKQ33tP/T5ypKr/9XYsne7ChcPS1xMyPcZXs76Sk+G559T6qFHgK1MF7epeXqBAAdauXZtxtVLDYDCI6BYEwatISUlh8+bNAMTFxbk5Gtfz559/MmzYMC5dukRAQADDhg3jp59+Yt26dTzwwAMsWLCA0NBQq7b19ddf83x6G9vWrVuzYcMGRo0aRYMGDWhiSxetXJDRKN6B5nL36QNlytj+/IAAqF4d9u2DMWPUbYMGmUeJ5YZlIzVJL/csduzYQVxcHIULF6Zu3brcunXL3SEJNjB/PsTHQ40aJho18ozsEa2uG6BuXfjkE/fFoid5Od3x8fEsXboU8L3vwy+/VE00S5RQ4+h8BZvTy6OionL8OXHihDNiFARBcBo7duwgPj4e8D/RPWHCBHr37s2lS5e488472bJlC1OnTs1wuJctW8aAAQOsEiwzZsxgeHp3nVdffZV169Zx3333kZyczIMPPsjVrDNd7ODEiRPs2bOHgIAAevTo4fD2BOewb59K9QTbG6hZotV1X7+uHPP0BIo8kUZqnotWz92uXTup5/ZCZs1Sy0cf9Zx0Xy29PDwc5swxi1VvJ6+a7hUrVpCQkEDFihWpX7++GyJ0DhcumJtmTpgAkZHujUdP7P7ES05O5vDhw/JFJgiCV7PeoiWmJr79gVWrVvH6668D8NJLL7F9+/YMN7ply5YsXLiQ0NBQ/vjjDwYNGpTrZ/2sWbMYMmQIAKNHj+aDDz7AYDAwa9YsqlWrxunTpxk4cKDDdbXaaJS2bdtStGhRh7YlOIfERBg4UKUHdu8OLVvavy2trhvgscegcmXrnpddIzWp6fYMpJ7bezl1CrSWTo884t5YLOnSBQoUgG++8Z00ZMjb6f4p/crm/fffj8FTroDowNixqoyhSROV3eRL2Cy64+PjGTx4MPny5aN27dqcPn0aUKPEPvzwQ90DFARBcCbrtIGe+I/Tff78eQYOHIjJZGLIkCF8/PHHGV/qGh07dmTevHkEBQXx888/06ZNG45Yzm9CfR8MHTqUQYMGYTKZGDZsGJ9//nnGCUBkZCTz588nPDycFStWMG7cOIfi9tX6NV9i7FjYu1elBU6f7pgbpjndgYHw2mvWP8+ykZrUdHsOqampGRc527dv795gBJv5+We1bN/eujIPVzFggBoX9thj7o5EX3Jzuk+ePMn89OL6wYMHuyU+Z/DPP+Zsii+/VGVGvoTNf87YsWPZvXs3a9asyXSS1qlTJ3799VddgxMEQXAmRqMxo6kP+IfTnZqayoABA7h8+TL16tVj8uTJOT62Z8+ezJs3j4IFC7Jlyxbq16/PlClTMBqN7Nmzh8aNG/O///0Pg8HAa6+9xtSpU2+74l63bl3+97//ASqd/caNG3bFfeXKlYxjdd9999m1DcG5LF8Okyap9Zkz1agwR7j3XuVcvfkmVK1q/fNkZJhnsnPnTmJjYylUqBB169Z1dziCDZhM5q7lnihufU2cQe5O95dffonRaKRz584+817avh206+mDB0OzZm4NxynY/DL9888/mTJlCq1bt850clW7dm2OHz+ua3CCIAjOZP/+/ZlEoD843W+99Rbr1q0jIiKCefPmEa5dTs+B+++/n3379tGpUycSEhIYNWoULVq0oGnTphw8eJDSpUuzcuVKxo8fn2ON5sMPP0zZsmVJS0vj8OHDdsW9ePFijEYj9evXp1KlSnZtQ3AeV6/C44+r9REjoFs3x7dZsiTs3297Ix1ppOaZWM7n1jIQBO/gv/9UY6vwcNUcUXA+lk639j2dmJjIrVu3mDZtGgBjtC6TXs7Bg9C1q0orb9fO3IjT17BZdF+5coUSJUrcdntcXJxP1RQIguD7aKmOWnduX3e6//rrLyZMmACoSRQ1tPzdPChfvjzLli1jypQphIeH8++//5KUlESPHj3Ys2cPHTp0yHMb2r6ypqhbi6SWey4mEwwZAhcvwh13uL97cHaN1KSm2/1o9dySWu59mGdz4xGzuf2BnJzuGTNmEBMTQ61atejSpYsbI9SHkyehc2d14bZRI1i40HzBwdewWXQ3btyYJUuWZPyuCe1p06bRokUL/SITBEFwMlo9d9u2bQHfdrpTUlJ44oknABg5ciT9+vWz6fkBAQGMGDGC3bt38+ijj/L111+zcOFCilnOa8mF6tWrA3D06FHbAkcdl+XLlwMiuj2RefNgwQLlMP/yi/tPmGROt+dhWc8tTdS8i+RkmD1brXtiarmvkl1Nd1xcHF988QWgmpZ6+wSAixehUyc4d05dsF261Lcv6lg9p7tDhw7Mnz+fDz74gHvvvZcDBw6QmprKF198wYEDB9i0adNtc7sFQRA8FZPJlHES2LVrV1asWOHTTvfWrVu5fPkyRYsW5dNPP7V7O9WrV+cHzfawAUec7uXLl5OYmEilSpWoV6+ezc8XnItWx/3qq+AJk2ssG6lJerlnsGvXLmJiYoiMjOSuu+5ydziCDSxZosb2lS4NHTu6Oxr/ITun+8aNG9y4cYMiRYrw6KOPujE6xzEaoXdvOH4cKlWCFSsyz1z3Ray+RLJmzRqSk5Np3bo1u3btIjU1lbp167J8+XJKlCjB5s2badSokTNjFQRB0I2oqCjOnz9PcHAwd999N+DbTveKFSsA1fRSS6d3JY443Zap5VLG5Fns3AmbNyt3OX1Mu9uRRmqexz///AOo1HKp5/YuvvtOLR99VE0SEFxDdk63xrBhw8iXL58botKPn36CLVsgIgJWroSyZd0dkfOx2um2pGrVqhndaAVBELwRLbW8cePGGSnSvux0a6K7c+fObtm/pdNtMpmsFs+pqaksWrQIkNRyT+Trr9WyTx8oVcq9sWhk10hNarrdiya6ren/IHgOUVGwbJlaf+op98bib2TndAMEBwczYsQIN0WlD3FxarwkwOuv2zadwpuxSXQfOHCAixcv5voYSf0TBMEb0FLL27ZtS/78+QFITk4mNTU140TdV4iOjubff/8FlNPtDqpUqUJAQABxcXFcvHiR0qVLW/W89evXc+PGDYoWLUqrVq2cHKVgC9HR5tm9nuJyQ+ZGaqGhUtPtbpKTkzM+b0V0exf/+59qlNi5M1Sr5u5o/IucnO7+/ftTpkwZN0WlD598AufPq7Ty555zdzSuw6Yzy44dO2IymXK832AwyNVkQRC8As3pbtOmTaY0rfj4eAr6WCePNWvWkJaWRvXq1alYsaJbYggJCaFSpUqcOHGCo0ePWi26tdTynj17+tzFEG/nhx8gPh5q14bWrd0djRlJL/cs/v33X+Lj4ylevDi1a9d2dziClSQnw/Tpav3pp90biz+S1enOly8f8fHxjB492q1xOcrZs/Dxx2r944/Nf6c/YNMZzNatWylevLizYhEEQXAJFy5c4NixYxgMBlq1akVoaCgBAQEYjUbi4uJ8TnRb1nO7k+rVq3PixAmOHDmS0TE+N0wmk4wK81BMJvjqK7U+fDh4Uqm9NFLzLCxTy6Ung/ewYAFcvqzKRnr1cnc0/oel0x0QEMjcuXOJj4+nYcOG7g3MQV57Tf1NrVvDgw+6OxrXYpPorlChQrYzugVBELwJLdWxXr16FCpUCIB8+fIRGxvrk3Xd7q7n1qhRowbLli2zupnarl27OH36NOHh4W6PXcjMmjVw6BAUKACPPOLuaDKTndMtWXjuQ+q5vZNvvlHLwYPN7ynBdWgOsNGoLiB2797dvQHpwH//wY8/qvWJEz3rYq0r8O4Bb4IgCHagie42bdpk3KbVdftaB/PTp09z5MgRAgICMrq0uwutg7m1Y8M0l7tLly5e36nV19AaqD36qOfNVbVspCZzut1LfHw8mzdvBkR0exNHjsA//yhRJA3U3IPmdINyhr0dkwnGjFHrjz0GjRu7Nx53YLXobteuHSEhIc6MRRAEwSVYNlHT0ESdrzndK1euBKBJkyYZrr670DqYW+N0m0wmZs+eDUhquadx/jz88Ydaf+YZ98aSHZaN1CS93L1s2rSJ5ORkypcvT1V/aVHsA2hjwu69F9zUBsTvsZzsmZjovjj04uefYeNGyJcPPvjA3dG4B6vTy1evXu3MOARBEFxCQkICe/bsAcjUDdtXnW5PSS0Hs9N97NgxjEYjAQE5X/ddu3YtR48epUCBAvTp08dVIQpWMG2acpFbt4a6dd0dze1IIzXPQeq5vY/ERPj+e7U+bJhbQ/FrDAaVYp6Y6P1O95UroPV/e/11/5jJnR2SXi4Igl9x4sQJTCYTkZGRmTpoa063t4ruF154gSZNmnDq1KmM24xGI6tWrQI8Q3RXrFiR4OBgkpKSOHPmTK6P/d///gfAgAEDKFCggCvCE6xgzx747DO17kljwizJrpGa1HS7B6nn9j5+/x2uXYPy5aFbN3dH499YdjD3Zp57Tr2m7roLXnrJ3dG4DxHdgiD4FceOHQOgatWqmZwXzen2xvTygwcP8vnnn7Nt2zZ69OhBTEwMAHv27OHKlSvkz5+f5s2buzlKVV9bLX3Ya2513devX+f3338H4CkpKPQYTp2Crl0hJgbatvXczrOWTrfUdLuP6Oho/vvvPwC395MQrEfr1zBkCKS/fQQ3oYlub3a6Fy+G2bMhIECNoPPnpnwiugVB8CuOHz8OcFt9oTc73Z9p1iOwb98++vfvT2pqakZquSf15NBSzHOr6/7pp59ISkrirrvuorE/dlvxQK5dgy5d4MIFqFNHjRPy1JMny0Zqkl7uPtavX4/RaKR69eqUL1/e3eEIVrBli6q7DQ5WoltwL1ozNW91umNizCUKL7wAjRq5Nx53Y7PoltpuQRC8GU10a46rhrc63RcuXODH9BkckydPJjw8nL///pvnn38+o4maJ6SWa2jN1HJyuk0mU0Zq+ZAhQ6QO1AOIj4eePeHwYShXDv7+G9zcky9XpJGaZyCp5d7HJ5+o5cMPQ5ky7o1F8H6n+5VX4Nw5qFoVxo1zdzTux2bR3bVrV6pWrcr777+fZ02eIAiCp+FrTveUKVNITk6mZcuWjBo1ip9++gmAL7/8MsPp7tSpkztDzEReTve///7Lvn37CAsL4+GHH3ZlaG7jzBm4ft3dUWRPWhoMGACbNyuhvXSpEt6eTHbp5VLT7XpEdHsXR46YpxK8+KJ7YxEU3ux0r1ljnvX+v/+pruX+js2i+9y5c4wcOZLffvuNKlWq0KVLF+bOnUtycrIz4hMEQdAVy5puS7zR6Y6NjeXr9AK8F9PPkh544AE+/PBDQLnGpUuXpnbt2m6LMSt5Od2ay923b18KFy7ssrjcxeHDUKsWtG/v7kiy57ffYOFCNb5m0SLwoJdSjmhOd1oaBAaK050bhw4d4ttvvyUlJUXX7V69epXdu3cD0N5TX9xCJj77TM1S7tHDO97n/oA3Ot0mE3z5pRo3B2rOu7R0UNgsuosVK8aYMWPYtWsXW7dupUaNGgwfPpwyZcrw7LPPZnzICoIgeBqpqakZ3b1zEt3e5HRPnz6dGzduUL16dXr16pVx+8svv8yTTz4JQM+ePT0qRVtzuqOiom470b916xZz5swB/KeB2vvvq/TtvXvV/GtP44cf1PKFF9SIMG/AstY8IED1MhDRfTsLFiygcePGDBs2LKNxoV6sWbMGgLp161KiRAldty3oz8WLMGuWWn/5ZffGIpjxNqf7wgUltp99VsV8773w6afujspzcKiRWsOGDRk7diwjR44kNjaWGTNm0KhRI9q0acP+/fv1ilEQBEEXTp8+TWpqKqGhoZTNMihSSy/3VKfbZDJl+j01NZWJEycCalxYoEWbWYPBwHfffceKFSv41MO+8cqUKUO+fPlIS0sjKioq032zZ88mLi6OWrVq0dpbFJ4DHDkCv/xi/j290bPHcOkSLFum1h97zL2x2IKI7twxmUx8/PHH9O7dO+Mi465du3Tdx59//glI13JXcuWKGvdlT9LCl19CUhK0aOE9F9f8AW9yuhctgnr11HdGWBhMmQJLlkDBgu6OzHOwS3SnpKTw22+/0a1bNypWrMiyZcuYMmUKly5d4tixY1SsWJG+ffvqHasgCIJDaPXcVapUISAg88efpzrdqampDBkyhIIFCzJo0CC2bNmCyWTit99+49SpUxQvXpzHslFEgYGBdOrUiYiICDdEnTMGgyHHuu5p06YB/tNAbfx4MBrNv3ua6J4zR6VoN20KNWu6Oxrr0dLLAQwGJbqlpluRnJzM4MGDeeWVVzCZTNRMP7AHDx7UbR+nT5/m119/BeDRRx/VbbtCzixerKYKPPig7fXYt27BV1+p9ZdfBj/46PUavMXp3r8feveGq1ehfn3Yvh1GjJDXUlZsFt2jRo2idOnSPP3009SoUYOdO3eyefNmhgwZQv78+alUqRKffvophw4dcka8giAIdpNTPTd4ptOdnJxM//79mT59OrGxscyaNYsWLVrQoEED3nzzTQBGjhxJuPbN7CVkV9e9atUq/vvvP4KDg7O9iOBrHDsGP/+s1p94Qi09TXSnN8XH23STpdOtiW5xupVh0rVrV2bOnElAQABffvklU6dOBeDAgQO67WfixImkpqbSoUMHGfnnZOLi1Eimnj3h8mV12zffwNmz1m9j2jS4eRNq1ACLKiXBA/AWp3viRHWBtksXNXbuzjvdHZFnYrPoPnDgAF9++SXnz59n0qRJ1KlT57bHFCtWTEaLCYLgceQ0Lgw8z+lOTEykT58+/P7774SEhDB58mQef/xxwsLC2L17N8eOHSM8PJzhw4e7O1Sbyep0JyYm8swzzwAwbNgwihcv7rbYXMUHH6iTlHvvBe0QbtummtB4AgcOKLciKAj693d3NLZhmcQi6eVmFi5cyOrVqylQoABLlixh5MiR3Jl+dnzixAkSdbDTrl+/ntEM8WUpDraKuDgYO1a51daSlATLl0ODBvDtt+q2MWNUanhyMkyYYN12YmLg88/V+ksvZX7vCO7HG5zuS5cgfWgKb72lmm4K2ROU90Mys2rVqrw3GhREu3bt7ApIEATBWeQ0Lgw8y+mOi4vj/vvvZ+XKlYSFhfHnn3/SpUsXAD7//HNmzZrF77//zsCBAylWrJibo7WdrE73Rx99xNGjRyldujTvvfeeO0NzCSdOmBuUvf22qoMLCVFjw6KioEoV98YHZpf73nvB215iBoNyu1NSxOm2ZPr06YDKjunatSsApUqVIjIykujoaI4cOUK9evUc2sfXX39NXFwcd911F/fcc4/DMfsD77yj5mMbDDB9ujnzxRKTCTZsgH/+gbVr1Qg/TYiVLauaoHXsqMY03X23cq9feQUqVMh+n5cuweTJMHUqREdDqVLwyCNO+xMFO9Gcbk8W3V9/rS4CNW8OLVu6OxrPxirRvXDhQqs32EtyU/yOr7/+mq+++orFixdTsWJFd4cjCDmSW3q5u5zuqKgoPv30U27evElCQgIJCQkcP36co0ePkj9/fhYvXpxp5E6RIkUYM2YMY8aMcWmcemLpdB89epQPPvgAgEmTJhEZGenO0FyC5nJ36QLNmqnb7rpLpZf/95/7RbfRaE5997bUcg1NdGunOf5e03327FmWpXfF0yYbgOqxcOedd7J582YOHjzokOhOSEjgiy++AJTL7Q99GRzl2DGYNEmtm0wweLBaWhwizpyBoUNh6dLMzy1RAu6/Hz78ELTpiu3bK9G9erX6nNHmJGucP696ScyYYRZytWqpx2kCT/AcNKfbU9PLExLUhRuA5593byzegFWi+/7777dqYwaDwe+/2PyRTz75hKioKNatWydNUwSPxWQyceLECcCznO5XXnmFefPm3XZ7wYIFWbp0KS1atHBpPK5Ac7pPnz7NkCFDSE5OpkuXLn7RgPPkSfNonrffNt/epIlZdD/0kFtCy2DtWnWiHxmpakW9Ea2ZmjjdilmzZmE0GmnTpk3GRS+NO+64g82bNztc1z1r1iyuXLkizXRt4MUX1cWhe+5RNdVTpijhbTSq5YwZSszExKi03d69lbBu1041N8zuusY77yjRPWOGSlvXvJCtW+G++5TLDapB4tixqo5b0so9E093un/6STVPq1hRvTaF3LFKdBst26sKggUnTpzIGPuTdeauIHgSFy9eJD4+noCAACpVqnTb/e5wumNiYli0aBEAb731FqVLlyY8PJzw8HDatGlD6dKlXRaLKylatCiFChXi5s2brFu3jrCwMKZOneoXztjnn0NqKnTurMbzaDRpopae0ExNSy3v29d73S+tmZqIbnUON2PGDAAGDx582/1aXbcjHczT0tIyxhM+//zzBFt2sxOyZdUqWLAAAgNVI6o77lDid/JkeOop+O478+dB8+Ywc6ZypfOiTRuVar5qlXK1v/tOTSIYNEilAdetC198ocS7H3zkejWe7HQbjep1C/Dcc5mnRgjZI/8iwSFWrlyZse7PJzWC56PVc1eoUIGQkJDb7neH0/3nn3+SmJhIrVq1GDdunF+ITlBZUTVq1ODff/8F4I033sg2+8DXSE42z+XOmoqnNXnevl2lnluMXXcp8fHw229q3ZsTl8xOtxJ//pyFt27dOk6cOEFERAQPPvjgbfffcccdgGMdzP/44w+OHz9OkSJFshX2QmZSU2H0aLU+fLi52/OkSUoIf/GFEtyhofDee+rzwpbPhHfeUaJ75kwl3CZPVrf36KE+gzxskqSQA57sdC9bBgcPqteSvOWtwyrRPXnyZIYOHUpYWBiTtXduDjz77LO6BCZ4B5aiW5xuwZPJrZ4b3ON0z549G4ABAwb4jeDW0ER3rVq1eNHWwbJeyt9/w7VrULo0dOqU+b477oD8+VUn48OH3TdyZeFCNbe3YkXVCdlb0YxWk0md5vjzRWGtgVr//v0zPucs0ZzuI0eOkJqaSpCNltWNGzcyGiCOHDky230Imfnf/2DfPihSBMaNM99uMCj3sGRJNc3g/ffVZ4OttGqlsmlWrDAL7hdegI8+ct8FPcF2PNnp1rreP/UUFCzo3li8Bas+WSdOnMjDDz9MWFgYE7VcgmwwGAwiuv0Io9GYqZu9iG7Bk8ltXBiYne6EhASMRiMBTi5yu3LlCitWrACU6PY3hg8fzoULF/joo48I9ZMZI1rH8ocfvj0VLzAQGjaE9euVw+Uu0a3Vmz/yiHfXeZrTy9WKv4rumzdv8lt66kJODnSFChXIly8f8fHxHD9+nJo1a1q9/Z07d9KnTx+ioqIoWLAgI0eO1CVuX+bGDXjzTbX+zjtKeFtiMKhaa0d5911YuVJ9tnz9NQwZ4vg2BdfiqU737t3m15bIPuuxSnRrNbtZ1wX/ZteuXVy/fj3jdxHdgieT27gwIJM7k5CQ4HS3Zt68eaSlpdG4cePbGhv5Ay1atMiUKePrXL8O6eX7PPZY9o9p0sQsuh9/3HWxaURFqZRBUPWf3oz5ooZ/i+7Zs2eTmJhI7dq1adq0abaPCQgIoFatWuzYsYODBw9aLbqnT5/OiBEjSEpKolKlSvz+++8UL15cz/B9jrg45Qxeu6YurA0b5rx9NW8OGzdCoUL2ueWC+/FUpzu9fQMPPmhu1CfkjRdfxxbcTdYTZhHdgieTl+gO177dcE2KuWVqueD7/Pqr6lJcv75qZJQd7m6m9t13alxR586QQ0KI12Du4+XfNd2WDdRyK2HR6rqtaaaWlJTE4MGDGTJkCElJSXTv3p0dO3bQsGFDfYL2UfbvVx3Df/9dZZF8+aXzm0+1aCGC25vxRKf7xAlIP33BTyrDdMOut/vZs2dZuHAhp0+fJjk5OdN9n2tJ/oLPo4nu4OBgUlJS/NZJELyDvGq6AwICCA8PJyEhwenN1E6fPs2GDRswGAw85O75UIJL0FLLc3K5wSy6d+1STdey6ffnNJKSIL30l2eecd1+nYUmZkwmVcDqj99Pe/bsYdu2bQQHB/PII4/k+litrtuaZmrvv/8+M2bMICAggPfee49XX33V6eU43ozJBN9/DyNGKMeyTBnVzKxdO3dHJng6nuh0f/SRavbZtau5AahgHTaL7lWrVtGrVy+qVKnCoUOHqFOnDidPnsRkMslVTj8iMTGR9evXA9C2bVtWrVolTrfgsdy8eTOjFCK3Ltn58+cnISHB6U73nDlzAGjXrh1ly5Z16r4E93PkCGzZourfcktsqFIFChdWNZ/79qkab1cxfz5cuaIEgbfO5rYkq9PtT6LbZDKxaNEiRqe3x+7Vq1eead/WOt1nz57ls88+A+CHH37g4YcfdjxgHyYtTdVSf/+9+r1LFzWST7LwBWvwNKf73Dnza/n1190aildi86XJsWPH8uKLL7J3717CwsL4/fffOXPmDO3ataNv377OiFHwQDZt2kRiYiJlypShbnqupIhuwVPRUstLlixJgQIFcnycq8aGSWq5f6HNve7SBUqVyvlxBoPZOXB1ivk336jlU0/5xrxVf+1efvToUbp37859991HVFQUZcuW5Z133snzeZazuo1GY46Pe/PNN0lISKB169YMHDhQt7h9lY8+UiIlMBA++AD++ksEt2A9nuZ0f/qpysJq29a7p1u4C5tF98GDB3ksPT8uKCiIhIQEChQowLvvvstHH32ke4CCZ6J1Xe7UqRPB6Wc3IroFTyWv1HINV4wNO3jwILt27SIoKIg+ffo4bT+CZ2A0mkV3bqnlGu6o696/H9atU8Lgqadct19nYr5woFb0qOlOSUm5raTOk5gwYQJ16tTh77//Jjg4mFdffZVDhw5Ru3btPJ9btWpVgoODiY+P58yZM9k+Zvfu3cxKb2//6aef+t2YQ1vZtg3eflutT5umOpJLFr5gC57kdF+5At9+q9bF5bYPm9/++fPnz/jSKV26dIaDBHD16lX9IhM8Gq2eW0S34A3kNS5MwxVOt+Zyd+3alaJFizptP4JnsGEDnDql5pj26pX3490hurUTqZ49wVeqHZzhdPfp04fixYt75LnOwoULee2110hOTqZLly7s27ePCRMm5JrZY0lQUBA1atQAcq7rfvnllzGZTPTr149mzZrpFrsvEhenRgOmpkLfvu6ZRiB4P57kdE+cqOJo3Fg12xRsx2bR3bx5czZs2ABAt27deOGFFxg/fjxPPvkkzZs31z1AwfO4fv0627dvB6Bjx44Zottf0vcE7yOvzuUazna6TSaTpJb7GVoDtX79zCdQuaGJ7v37wclVDoASB9psbl9ooKahdyO1Y8eOsWjRImJiYti3b5+j4enK9evXefrppwF4/vnn+fvvvzMEtC1YpphnZfny5Sxfvpzg4GAmTJjgWMB+wAsvqF4OZcuq0g1JChDswVOc7ps3YepUtf766/J6thebK7c+//xzYmNjAXjnnXeIjY3l119/pXr16tK53E9YvXo1JpOJO+64gzJlyojTLXg81opuZzvdUVFRHDt2jODgYHpZY3sKXk1KCsybp9atSS0HdZJeujRcuKC6mLds6bTwADX6JSYGqlaFTp2cuy9XorfTPXfu3Iz1W7duObQtvRk9ejQXL16kVq1ajB8/3u60b62ZWlanOy0tjZdeegmAkSNHUqVKFccC9nEWLjRnj8yaBUWKuDcewXvRLtSmpqofd/XbmDJFfU/Urm1dxpaQPTYfPssP2/z58/ON1n1F8BssU8tBpaWBiG7Bc/GUmm6t43/jxo2tTvsUvJdt29SJStGi0KqV9c+76y4lug8edEx0z5oFhw+rk6RmzW53J86fVydTAE8/7Vv1pllFt6M13b/++mvGuieJ7kWLFvHjjz8SEBDAzJkzCdOsMTvIyen+4Ycf2LNnD4UKFeKNN95wKF5f5+JFGDxYrb/wAnTs6N54BO/G8u2cmAjuOG2IigLNU33tNd/6nnA1Notuk8nE9u3bOXnyJAaDgcqVK9OgQQNpqOFHZBXd4nQLnkxCQgLnzp0D3F/TrYnu1tL20y9Yt04t27a17USldGm1vHTJ/n2fOQODBqn1CROgfHl48EFo316NL/vrL9i9W90fEgJPPGH/vjwRPdPLDx06xJ49ezJ+9xTRff36dYYOHQrACy+84HCJn6XTbTKZMBgMHDt2LMPlfv311ykitm2ujB8PV6+qC2fjx7s7GsHbsRTdCQmuF923bqmLtjduqFrufv1cu39fwybRvXr1agYPHsypU6cwmUwAGcJ7xowZtG3b1ilBCp7DiRMnOHbsGIGBgbRr1w4Q0S14NlFRUQAULFgwz8Zlzna6tX4Ybdq0ccr2Bc9i7Vq1TP+otJoSJdTSEdH9229qWby4Olk7c0Y1wpk40fwYg0E54K+9BsWK2b8vT0Rzuo1Gx0W3pcsNniO6x4wZk5FW/u677zq8vRo1ahAQEMDNmze5dOkSISEhdO/enWvXrtG4cWNGjRqlQ9S+S1qa+X334YcQGureeATvJyBAXRRNTnZ9XXdammoGuG+fuhD855++MU7SnVh97f3YsWP06NGDSpUqMX/+fA4ePMiBAweYN28e5cqVo1u3bpw4ccKZsQoewLz0AsW2bdsSGRkJII3UBI/Gsp47r4wcZzrdly9f5vDhwwC0siXXWPBKUlNV53JQTrctlCyplpcv279/rZb8zTfVdv74AwYOhOrVYcAANcbs0iXYvFl1Lfc19HS6tXpuzeWNiYlxLDgdWLFiBT/88IMuaeUaYWFhGSWEu3btok+fPhw5coQKFSqwaNEiQkVF5sqmTSq9vFAh6NDB3dEIvoL21nZ1B/PXX4dFi9TFoz//9J3JFu7E6msWkyZNonnz5qxatSrT7bVq1aJ379506tSJiRMn8uWXX+oepOA5ZNd5WZxuwZOxtokaONfp1lzuOnXqSIqmH7B7t0rNi4yEevVse64muu11us+cUWLaYIA+fVQznvvvVz/+Qlan296a7n379nHgwAFCQkLo168f33zzjUc43RPTUxaGDx+u6+SYO++8k2PHjjF48GDOnz9PREQEixcvplSpUrrtw1f5/Xe17NVLuZOCoAfh4ao3iCud7h9/hI8+UuszZkDTpq7bty9jtdO9Zs0aRo8ene19BoOB0aNHs3r1ar3iEjyQgwcPsnv3boKCgnjggQcybpdGaoInc/LkSQAqVaqU52Od6XRr9dySWu4faKnlbdpAYKBtz3VUdGsprq1aQZky9m3D29ErvVxLLe/atSvlypUD3J9efurUKZYuXQrAc889p+u2tbru8+fPExAQwNy5c6lbt66u+/BFTCaYP1+t9+nj3lgE38LVTve2bTBkiFp/7TWVISXog9Wi+/Tp07l+8NapU4dTp07pEpTgmcyZMweALl26ZKqNFadb8GROnz4NQMWKFfN8rCucbhHd/oFlEzVbcbSmW0st9+emN1p6udGoTnPsEd0mkylDdD/00ENEREQA7hfd06ZNw2Qy0bFjxzybQ9qK1sEc4Msvv6Rr1666bt9X+e8/lWFSoADcc4+7oxF8CW1smCuc7sREePRRVUN+333w3nvO36c/YbXojo2NzXCBsiNfvnxO6/gruB+TyZQhuvv375/pPhHdgiejXQx0p+iOjY1l586dgHQu9weMRkhPbLC5iRqYne5r11RtuC1kTS33V/Rwunft2sXRo0cJCwujZ8+eHiG6U1NTmT59OkBG53I96d69Oy1btmT8+PEMHz5c9+37KlpqeffumTtOC4KjuNLpfvttOHRIfQfNmCHjwfTGpj50Bw4c4OLFi9ned/XqVV0CEjyTnTt3cuTIEcLCwrjvvvsy3SeN1ARPxhbR7az08s2bN5OWlkbFihUpX768rtsWPI99++D6dcifHxo0sP35RYuqkx2jEa5cMY8QswYttbx1a/9NLYfbnW57aro1l7t79+5ERERQsGBBwL2ie/HixVy4cIHixYtzvxOK9IsWLcrGjRt1364vYzKZ33f+fKFLcA6ucrq3bIFPP1Xr334L0npGf2wS3R07dswYFWaJwWDImOko+Caay92jR4+Mq/0a4nQLnkpcXBzXrl0D3Ot0Sz23f6GllrdqZXZcbSEwUI36unRJ/dgiurXU8r59bd+vL6H939PS7Esvt0wt75eep69997mze/l3330HwBNPPEGIdOvyCHbvhhMnlCN5773ujkbwNVzhdCcmwhNPqAu9Dz+sUssF/bFadGuzbgX/w2g0Zpx8ZE0tB2mkJngumssdGRmZMeIuN5zldEs9t39h73xuS0qUMItua5HUcjPm9HL7RPeaNWs4efIk+fLlo3v37gBuTy8/efJkRgO1p556yi0xCLejpZZ37apqugVBT1zhdGtp5aVKweTJztuPv2O16LbGJRJ8k82bN3P69GkiIiLo1q3bbfeL0y14KrakloNznO7k5GS2bNkCiOj2B0wmx5qoaZQsCXv32jarW1LLzWRNLzeZTBiNRgKsKFJMSEhg2LBhADz66KMZnwvuFt3Tp093WgM1wX400f3gg+6NQ/BNnO10S1q565ASeSFPtNTy+++/n3DtkpsFUtMteCq2im5nON07duwgISGBokWLUqtWLd22K3gmhw8roRwWBk2a2L8de8aGSWq5Gc3pTk01n+ZYW9f91ltvceTIEcqUKcOHH36Ycbs7RXdKSkpGA7Wnn37a5fsXsufAATh4UL3eevRwdzSCL+JMpzstDZ56SqWVP/KImjEvOA8R3UKupKamMnfuXAAGDBiQ7WPE6RY8FU10V6hQwarHO8Pp1uq5W7duLX0v/AAttbxFCwgNtX87topuSS3PTFanG6y7MLxlyxY+//xzQNVPFypUKOM+rZFaUlKSy7/vlixZwoULFyhRosRtzUwF96G53J07gxUVTIJgM850un/8UTX+LFwYvvhC/+0LmXGr6J4wYQJNmjQhIiKCEiVKcP/993P48OFMj/nuu+9o3749BQsWxGAwcPPmTV222759ewwGQ6YfLZ1MMLNmzRouX75M0aJF6dSpU7aPkZpuwVNxxOnOrmmkPUg9t3+hR2o52C66//xTLSW1XGFupGa+0JWX6E5MTOSJJ57AaDTy6KOPZtRya1g2EXW12/3VV18B0kDN09BEt1zoEpyFs5zuxERVyw0wdqyklbsCt4rutWvXMmLECLZs2cKKFStISUnhnnvuyeQyxcfH07VrV1577TVdtwuqEcmFCxcyfj7++GPd/jZfYcGCBQD06dMnw9HOijjdgqdy+vRpwPaabqPRSFJSksP7NxqNIrr9CJNJnyZqoBqpgfU13WvWqKV0T1Zkl16el+h+5513OHToEKVKlWLSpEm33R8UFERYuu3kyg7mS5YsYcWKFQQFBTllNrdgH2vWqM7lwcGSlis4D2c53d98A6dPQ9myMHKkvtsWssemkWEaqamprFmzhuPHjzNw4EAiIiI4f/48BQsWpIANrRu1Lpwa33//PSVKlGD79u20TbcJRo8eDSjHVc/tgnK1SpUqZfV2/ZFdu3YBuQsGEd2Cp2Kv0w3qgp92gm0vBw8e5Pr16+TLl48G9gxsFryKEyfg3Dl1Et6smWPbssXptmze5qjY9xW09HJLpzu3mu7//vsv48L7N998Q5EcbJ+IiAgSExNd5nQnJCQwatQoAMaMGUOVKlVcsl8hd0wmeOsttf7UU1CsmHvjEXwXZzjdMTHwwQdqfdw48z4E52Kz6D516hRdu3bl9OnTJCUl0blzZyIiIvjoo49ISkrim2++sTuY6OhogBy/7PTe7s8//8xPP/1EqVKl6NmzJ2+++Wamk25LkpKSMjlf2lVuk8mkWxqq3mix2RufyWRi7969ANSpUyfH7Wjp5ampqR77v/AmHD1ugiIlJYXz588Dqqbbmv9nYGAgISEhJCcnExsbS+HChW3aZ9Zjtzbd9mzRogVBQUFyTD0YPd53f/0FYKBpUxPh4erE3F6U023g0iVTntvZvx+uXjUQHm6iUSPH9uuNZHfs1NeSgdRUMBgMmEwmUlJScjy+b7/9NkajkQEDBtCrV68cHxcREcGVK1eIiYlxyfv5o48+IioqirJly/LGG2/41GeIN3/XrVwJ69cbCA018eqr8p4TnIfqDWIgPj7v7wJrMJlMTJkSytWrBmrWNPH44/73+tUba98HNovu5557jsaNG7N7926KFi2acXvv3r0dmhtpNBoZPXo0rVq1ok6dOnZvx9rtDhw4kIoVK1KmTBn27NnDK6+8wuHDh5k/f36225kwYQLvvPPObbdHR0d77IeOyWQiNjYWwK4GTmfPniU6OprAwEBKly6dcfEiK4npl99SUlJyfIxgPY4eN0Fx6tQpjEYjoaGhhIaGWv3aDA8PJzk5mUuXLmU0TrKWrMdOy7pp3ry5vDc8HEffd3Fx8MEHBQEDPXokEB2d7FA84eEGIJLLl+HGjWhym3S1dGkIkI+mTVNJSIhz2mgZTyW7Y5eSEgzkJzExlaCgIFJSUrh+/Xq2EzhSUlIyLpCNGDEi1/eqdmH+4sWLTn9Pnzx5MqN7+nvvvYfRaPSpzxFv/a4zmeD11wsAQQwalExERAI+dFiswluPnTdiMIQC4dy6lUJ0tOOTVS5fhqlT1bnNa6/FExcnWaqOYm25kc2ie/369WzatOm2Rh6VKlXi3Llztm4ugxEjRrBv376M+ke9yGm7lnVRdevWpXTp0nTs2JHjx49TtWrV27YzduxYnn/++YzfY2JiKF++PJGRkTafmLsK7WJAZGSkXR+KGzduBKBmzZqU0AoMs6FQocJAQVJS4omU9p0O4+hxExTXr18HlMtti2NdoEABoqOjCQgIsPn1bHnsjEZjRufynj17ynvDw3H0fTdpEly8aKBKFROjR4cTGupYvp6mDdPSDBiNkeT2Ev7vP7Xs0CHIL19n2R077WvZZArKEN358uXL9v+zefNm4uPjKVq0KM2bN891lrf2WWI0Gp3+v37zzTdJSkqiU6dOPP744z73feCt33VLl8J//6nMkrffDiEy0v8a23nrsfNGtAEKaWnBunzmvPkmxMcbaNLExCOP5EMOn+NY+x6wWXQbjcZs66LOnj2bqbOnLYwcOZLFixezbt06ypUrZ9c2HN1us/QCvGPHjmUrujW3LCta53NPxbI7u63s378fUBclcnv+uHFFgaukpjbP2KfgGI4cN0GhNVGrUKGCTf9HzclKSEiw6/+vHbft27dz8+ZNChcuTOPGjeVYegH2vu/On4dPPlHrH35owMFWAIBKKSxcGG7cgMuXDTnWjFo2b2vf3uC3J1BZj53mC6SkGAgMDATU+Ut2x3ZdekF8u3btMh6bE5azup35nl64cCGLFy8mODiYL7/8MtcLAd6Mt33XmUzmjs/DhxsoXdq98bgTbzt23opW9ZqY6Pjn+8mT8O236oLJhAkQECDHTg+sfQ/Y/Cl+zz33ZOrqaTAYiI2N5e2336Zbt242bctkMjFy5Ej++OMP/vnnHypXrmxrOLptV2sYVtqfP0GzoNVz161bN8fHJCbC3LnhQDBQ26o5qILgCmxtoqah16zu5cuXA9ChQ4c8T+QF7+attyA+Xs3mfvBB/bZrTTO1I0fU/aGh0LSpfvv2drRGaqmpmfuOZIfWqLWdFV3oLEW3s0hISOC5554D4IUXXqBWrVpO25dgG0uWqMySfPng5ZfdHY3gD+jZvfzjj9WFyPbtU+jQwfHtCbZhs9P92Wef0aVLF+68804SExMZOHAgR48epVixYsyePdumbY0YMYJffvmFBQsWEBERwcWLFwGVrqLVXV28eJGLFy9y7NgxQAnBiIgIKlSokNEYrWPHjvTu3ZuR6T3v89ru8ePH+eWXX+jWrRtFixZlz549jBkzhrZt21KvXj1b/yU+izWie+VKiIvTrvAEk5qamuNoMUFwJY6K7vh4x2qnVqxYAagLlYLvsmcPzJih1j/7DF2d5pIl4dCh3EW35nI3b44uDruvoH0NpaTkLrpTUlIySqnat2+f53ZdIbq/+OILTp48Sbly5XjjjTecth/BNiw7lo8aZR7rJwjORK/u5Rcvmr+rXnwxEbB+2pSgDzaL7nLlyrF7927mzJnDnj17iI2NZfDgwTz88MPZNijJja+//hq4/Ytu5syZDBo0CFCjOywbmGkjvywfc/z4ca5evWr1dkNCQli5ciWTJk0iLi6O8uXL06dPH/lysyAlJYWDBw8C5NrYLnPfuWBSUlJsfh0IgjOwdUa3hpZe7ojTfevWLTZv3gxA586d7d6O4Pm89JI6Ge/XTzndemLNrG4ZFZY91jrdO3bsIDY2liJFiljVxNXZovv69esZzdM++OCDjIuAgvtZsAB27oQCBeDFF90djeAvaBdTHRXdEydCUhK0aGGiZcucxycKzsOuOd1BQUE88sgjDu/cmq7f48aNY9y4cbk+5uTJkzZtt3z58hmdSoXsOXr0KMnJyeTPn59KlSpl+5iUFPUlZCZEZnULHoM708vXrFlDamoqVatW1a1sRvA8li6F5ctV/fCECfpvP6/0cst67vTr0UI6lk63Vt6RXT8a7Vygbdu2VtVNO1t0f/jhh0RHR1O3bl0GDhzolH0ItmMymecaP/uszOUWXIfmYzmSXn7zJqT7kbz6qr4ZWYL12Cy6Fy5cmO3tBoOBsLAwqlWrJieZPsC+ffsA5XLndCKybh2kN4hOJ1hEt+ARGI1Gh51uR9LLJbXcP7BMNa1SRf/t5yW6o6Lg7FklMPV22b0da9PLbannBjKmlThDdJ89e5Yvv/wSUGNKpReE57BqlarlDg+H0aPdHY3gT+jhdE+dCrduQd260L27Whdcj82i+/7778dgMNzmJmu3GQwGWrduzZ9//mnTmB7Bs7Cmnvv337PeIqJbsI64uDiuXLlCyZIlnVKOcPnyZZKSkggICLB5IoIeTvfKlSsBSS33ZS5eNI/qeukl5+wjL9GtudxNmpg73AoKa9LLU1NTM8aJWlPPDWan29q5rLbwzjvvkJiYSOvWrW1uTCs4F83lfuopKF7cvbEI/oWjTnd8vBppCcrl9tFBCF6Bzf/6FStW0KRJE1asWEF0dDTR0dGsWLGCZs2aZYznunbtGi9KwYtXk5foNhrhjz/UeoUK2q3B0r1cyJbjx49z9913U6lSJfLnz0+BAgWoXLkyNWvWJDk5Wff9aanlZcqUsbmxn6NO99mzZzl06BABAQHcfffddm1D8Hz++UctGzQwi2O9yaumWxPdUs99O9k53VnTy3fu3MmtW7coVKhQrheYLXFWevmhQ4eYkd7l6KOPPpIxTB7Eli2werW6kPPCC+6ORvA3HHW6p02Dq1dVNla/fvrFJdiOzU73c889x3fffUfLli0zbuvYsSNhYWEMHTqU/fv3M2nSJJ588kldAxVcS16ie8sW5fQULAhdusD//gfidAs5MX369Iw0TkvOnDnDhQsXbE4BzwtNdFcwXxGyGkedbu3vbNq0KYUKFbJrG4Lnk57MQKdOztuHtU63iO7bsXS6tTTtrBeFLeu5rU3ldpbofv311zEajfTq1SvT+ZXgfrR+DY8+amkyCIJrcMTpTk6GTz5R6y+9pD4XrWinJTgJm53u48ePZ9Q0WVKwYEFOnDgBQPXq1TN1Exe8i9jY2IxjmVM3Vy21vGdPMDdXFdEtZM+WLVsA1Rjx+PHjxMTEUKpUKUB169Ube5uogeNO9+rVqwGp5/ZlTCbXi+6sJ0qnT8PJkxAYCKLRbseamm7tApm1qeXgHNH977//Mn/+fAwGAx9oecyCR7BvHyxcqBpPvfKKu6MR/BHN6U5JgWx6QebKzz+rvh+lSkH6wCfBjdgsuhs1asRLL73ElStXMm67cuUKL7/8Mk2aNAFU5+vy5cvrF6XgUvbv3w9AyZIlKZ5N8ZLJZB4V9sAD5pMbEd1CdqSlpfHvv/8C0KdPH6pUqUJERARFixYF4Nq1a7rv0xHR7YjTbTQaM9wzqef2XY4ehTNnVNfy1q2dtx8tvTwx8fbGN5rL3agRpOtAwYK8RHdaWhrr168HrG+iBvo3UjOZTLz88ssAPPbYY9SuXVuX7Qr6kD69jT59oGZN98Yi+Cea6AbbUsxTU829CJ5/PvN2BPdgs+iePn06UVFRlCtXjmrVqlGtWjXKlSvHyZMnmTZtGqCcUpl57b1onctzSi3ftUs5LOHhKrVcRLeQG/v27SMuLo6IiAjuuOOOjNs9VXQ74nTv2rWLa9euERERQbNmzWx+vuAdaC53q1bObWCWP785kyhrXbfM586d7BqpWdZ079q1i5iYGCIjI7nrrrus3q7eTvfChQtZu3YtoaGhvPvuu7psU9CHEydgzhy1Pnase2MR/Bd7Rfcvv8CxY1C0KDzzjP5xCbZjc013zZo1OXDgAMuXL+fIkSMZt3Xu3DljtNT999+va5CCa8mrnltLLe/aVZ0QmkV3iDRSE25DSy1v2rRpprrJIkWKAM5JL7d3XBg45nRro8Lat29vcwM3wXtwRWq5RsmS6uT/0iWoVs18u9YiQUR39mQ3p9vy+0nLSGnTpo1No7ksRbfRaLRqtndOpKSkZLjcY8aMsasHheAcbt5UNbBpacpcaNjQ3REJ/kpQkPpJTbW+rjs1Fd57T62/9BIUKOC8+ATrsVl0AwQEBNC1a1e6du2qdzyCB5CX6NZSy/v0UcuQEO0ecbqF29FEd/PmzTPd7otOtya6JbXcd0lLM3cud8VhthTdGmfPKgcjIMC56e3eTF4jw+yp5waz6AYyMnjs5dtvv+XIkSMUL16csWKlegS3bsHkyfDpp0p4A7z+ultDEgTCw9Vr01qn29LlHjHCubEJ1mOX6I6Li2Pt2rWcPn36tnE/zz77rC6BCe4jN9G9ejUcPKhchO7d1W2SXi7kRk6i21lOtzbKEFzrdF+/fp116Tm/Xbp0sXm/gnewfTtER0OhQq5xv7LrYJ7eq4/GjSEy0vkxeCPa95LRCIGB6hdNdNtbzw0QHh5OQEAARqORW7du2S26b968ybhx4wA1nzu7BrWC6zCZ4Msv4d13QbsOfOedqnN5mzbujU0QwsKU6LbG6RaX23OxWXTv3LmTbt26ER8fT1xcHEWKFOHq1avky5ePEiVKiOj2ci5dusSVK1cwGAzceeedme5LSIChQ9X64MHqpBNEdAs5c+PGDQ4dOgRwW42zs5xuzeUuWrRohoC2BXud7gULFpCamkrt2rWpXr26zfsVvAMttbxDB9U53NlkN6tbc9plDHzOWFZ3BASEAuaa7tWrV3Pz5k0KFixI/fr1bdquwWAgIiKC6Ohoh+q6P/jgA65du8Ydd9zBU089Zfd2BH1YsgSee06tV68O48bBQw+55j0uCHmhjQ2zxun++WflchcrJi63p2FzMdKYMWPo2bMnN27cIDw8nC1btnDq1CkaNWrEp59+6owYBReiNVGrWrVqhvjQeOcd9UYuU8bc0RMyi26p6RYs0bqWV61a9bZO+M5yuh2Z0Q32O93z5s0DpKeFr+PKem643ek2mUR0W0OQhaUQEKBqoLTvp88++wxQ3cKDgmxP+HO0g/nJkyf54osvAPjkk0/sikHQl7lz1fLxx+HAARg4UAS34DlozdTycrrF5fZsbBbdu3bt4oUXXiAgIIDAwECSkpIoX748H3/8Ma+99pozYhRcSE6p5Tt3qhongK+/zpzSKE63kBNaanmLFi1uu8/ZTrc9qeVgFt22ON03btxgZboa69Wrl137FTyf+HjYuFGtu0t0R0WpGd1BQVLPnRuWTndgoDpjTU1NZf/+/SxduhSDwcDo0aPt2raWUh4TE2PX88eOHUtycjIdO3akW7dudm1D0I+UFFi0SK0PHpz5go0geALWOt0//wzHjyuXe/hw58cl2IbNojs4ODijW2eJEiUyugRHRkZy5swZfaMTXE52ojs1FYYMUQ2E+vaFrJpCRLeQEznVc4Pnim4tw8MWp3vBggWkpKRQp04datSoYdd+Bc9n/XpIToYKFTJ3EncmWUW3Vs/drJl5nJhwOzk53Z9//jkAvXv3pmrVqnZt25GxYfv27WPOnDkYDAY+++wzDAaDXTEI+rFunWqaVrw4tGzp7mgE4XascbotXe6XXxaX2xOx+XpegwYN+O+//6hevTrt2rXjrbfe4urVq/z444/UqVPHGTEKLiQ70T1xIuzYAYULq0YjWRHRLWSH0Whk69atQPai29np5Y463SkpKaSkpFg1+uu3334D4MEHH7Rrn4J3YJla7iqtlLWmW0st79DBNfv3VgIC1I/RCAaDEt3nzp3jp59+AuCFF16we9uOiO6vv/4aUKLflvnggvP44w+17NVLUsoFz8Qap3vNGuVyFykiLrenYrPT/cEHH1C6dGkAxo8fT+HChXnmmWe4cuUK3333ne4BCq5DS70Ds+g+dgzeekvd/9lnZtfFEss53SK6BY2jR49y48YNwsLCqFev3m33a0739evXMRqNuu3XkRndQKZeBtakmN+8eZPly5cD0LdvX7v2KXgHrq7nhsxOt8lkdrqlnjtvtO8mrZHat99+S3JyMs2bN6elA5amvaI7NjaWH3/8EYDhclbsERiN8Oefar13b7eGIgg5Yo3TrY3zfeAByYLyVGxyuk0mEyVKlMhwtEuUKMHSpUudEpjgerZt20Z8fDyFChWiWnru5McfqytrHTvCoEHZP89yTrc0UhM0tNTyxo0bZ+sWa0630WgkJiaGQlo7fAdITEzMaAZYzc7835CQEAIDA0lLSyMuLo7IPGYyLVy4kJSUFO68807uuOOOjHFlgm9x5Qrs2qXWO3Z03X410R0TA7t3w4ULEBoK2bRJELIQFARJSeb08svp6QKOuNxgv+j++eefuXXrFjVq1KCDpCp4BNu3w7lzKhXXle9rQbCFvJxuy4tHDzzgkpAEO7DJ6TaZTFSrVk1qt32UFStWANCxY0cCAwMxmeCvv9R9L72UczqlpJcL2bF582Yg+9RygLCwsAxXWa+67lWrVhEXF0fZsmXtLncxGAw2jQ3TupaLy+3baA5zvXrmlG9XEBlpvrD5669q2bKl2fkQciar0w1QuXJlejtoadrTvdxkMvHVV18BMGzYMKnl9hC01PJ775X3lOC55OV0//uvuiAbESGlR56MTaI7ICCA6tWr6974SPAMtO7LndJzJw8cUFeAw8KgbducnyeiW8iO3JqoaVimmOvBn+mXeu+7776Mho/2YO3YsOjoaEkt9xPWrlVLV6d1Gwxmt3vOHPfE4K1ozdQMBnOmzejRowl0sHDXnu7lmzdvZs+ePYSFhfH44487tH9BPyS1XPAG8nK6tdTyHj1UJpTgmdh8Vvrhhx/y0ksvZaRwCr5BbGxshjPZuXNnALTKgfbtzW/47BDRLWQlNjY2oylfbqJbSzHX40JeWloaCxcuBByflW2t071o0SKSk5O54447qF27tkP7FDybNWvUsn171+9bc9ZPnlRLcTKsQ/tu0hqpFSpUiCeffNLh7dqTXq41UOvfv3/G557gXg4fhoMH1etEJrcJnkxuTrfJZM7YkItHno3N3csfe+wx4uPjueuuuwgJCSE8ixrTuxOx4BrWrl1LSkoKlStXzhijoonuLl1yf66IbiEr27Ztw2g0Uq5cOcqWLZvj4/QcG7ZlyxYuX75MZGQk7dq1c2hb1jrdWmq5dC33bS5fVpk/AG3auH7/lg0s8+WDJk1cH4M3on03lSihPoNGjhxJAR3m6Ngquq9evcrcuXMBeOaZZxzev6APmsvdoYMq4xAETyU3p3vfPtX0ODRUlUkInovNonvSpElOCENwN1o9t+Zyx8Wp2ZUAXbvm/lxL0S2N1PyPW7duMXz4cPLly0enTp3o0KGDVanloO/YMC21vEePHoSYu/vZhTVO940bN1i2bBkgqeW+jvZZWK8epF8ncimWortNG8vmlUJuaOnlffsOoE+fCro1L7NVdM+cOZPk5GQaNmxIE7li4jFo7qCDiVGC4HRyc7q11/E998hsbk/HZtEttUi+SVbRvXYtJCdDxYpQs2buzxWn27+ZOXNmxuzb7777LlMjsrxEt15Ot8lk4o/0bx5HU8shb6d7/fr1DBo0iKSkJGrVqmV30zbBO9BSyx1MoLAbS9Et9dzWo303BQWF07atfnPebBHdRqORb775BlBjwqSBmmdw/jxs3arW77vPvbEIQl7k5nRLarn3YFenoePHj/PGG28wYMCAjBEcf//9d8aMZ8G7OHfuHAcOHMBgMHB3+hmdZWp5XucIZtdF5nT7I1qKdfv27alTpw4mkylDrLbNrQMf+jndBw4c4Pjx44SGhtIlr3oIK8jJ6U5MTOTFF1+kXbt2nDhxggoVKjBr1iw5kfZxtCZq7qjnhszd0kV0W4/mdOudgKV1L7emkdry5cs5ceIEkZGR9O/fX99ABLtZsEAtmzeH0qXdG4sg5EVOTndUlBplGRgIPXu6PCzBRmwW3WvXrqVu3bps3bqV+fPnExsbC8Du3bt5++23dQ9QcD6rVq0CoFGjRhnOY3rWbJ6p5SBOtz9z/vx5Nm7cCMAPP/zA3r17OX/+PD/99BPz5s3LM5VSL6dbSy3v1KlThgvlCNk53QcOHKBhw4Z89tlnmEwmnnzySfbs2UPTpk0d3p/guVy9qmrmIPcpDs5Ec7oLFoSGDd0TgzeifTfp/bVki9P922+/AfDoo49mfK4I7if9sIg7KHgFOTndmsvdti0UK+bamATbsVl0v/rqq7z//vusWLEiU92kZR2n4F1kTS0/cQKOHFEugTUlcCK6/Zf58+djMplo3rw55cuXB6B06dI8/PDDVjUX02tkmCa69UgtB7PotnS6H3vsMQ4ePEipUqVYtGgR06dPJ1K67/g8Wj13nTruO6lp10653U8/bXZvhbzxBNG9Y8cOgIwsMsH9nDoFq1er9X793BuLIFhDTk63NipMLh55BzZ/fe/du5dffvnltttLlCjB1atXdQlKcB0mkyljPrcmujWXu0UL6zp6SiM1/0Vzcezt3q3HyLAzZ86wbds2DAYDPXXKr9LSyzWne/v27Wzfvp2QkBC2b99OmTJldNmP4Pm4u54boGxZuHTJffv3VpyVXm6t6E5OTs4Yr9qgQQN9gxDsZtYsNWapQweoVMnd0QhC3mTndF+6BJs2qXVpBugd2Ox0FypUiAsXLtx2+86dO3MdDSR4Jvv27ePixYuEh4fTsmVLwLbUchCn21+5ePEi69JtQHtFtx7p5dps7pYtW1LSsuOUDaSlqZEbJpP6PavT/d133wHQp08fEdx+hjvncwuO4WynOyUlhaSkpBwfd+DAAVJSUihUqBCVRN15BEYjfP+9Wn/iCbeGIghWk53T/eef6pylSRNITzQUPBybRXf//v155ZVXuHjxIgaDAaPRyMaNG3nxxRd57LHHnBGj4ES01PK2bdsSGhpKcjKkl3jbIboDSU4Wp9tf+OOPPzCZTDRp0oSKFSvatQ09GqnpkVr+8cdQvTr88IP63dLpvnXrVkZ2z9ChQ+3eh+B9XLsGe/eqdXfVcwv242ynG3J3u3fu3AlA/fr1pdmih7B+vWo+FREBDzzg7mgEwTqyOt2pqTBxolqXiaXeg82i+4MPPqBWrVqUL1+e2NhY7rzzTtq2bUvLli154403nBGj4ESyppZv2gSxsVC8ONSvb902zKIbkpKMOkcoeCpa13JHZlRrTvfNmzftKk24du0aa9KtyPscmPuidbI9eFAtLZ3uOXPmEBsbS/Xq1WnnzhxjweVo9dx33pm5g7jgHTjL6Q4MDMy4MJdbB3NNdEtqueegudwPPQTph1AQPJ6sTvePP8Lhw1C0qOr1IXgHNtd0h4SE8L///Y8333yTffv2ERsbS4MGDahevboz4hOcSFJSEmvTZ+Fkrefu0gUCrLwkI6Lb/7h8+XLGa8fe1HKAwoULZ6zfvHmTYjZ0qjp79iy9evUiNTWVOnXq2P0ZFBcH27eb1yGz062llg8dOlTcKj/D3aPCBMdwlugG5XbHx8db5XSL6PYMYmMh/Voxgwa5NRRBsAlLpzspCcaNU7+PHaumWgjegc2ie8OGDbRu3ZoKFSpQoUIFZ8QkuIjNmzcTHx9PyZIlqVu3LmCez21tajlYzumGlBQRJf7AH3/8gdFopFGjRlSuXNnu7QQHB1OwYEFiYmK4du2a1aL7v//+47777uPChQsUK1aMadOm2R3Dli3m9FOtWbnmdG/dupVz584RHBzM448/bvc+BO/EE5qoCfbjrPRyUKL70qVLOYpuo9HIrl27ABHdnsJvv6kLq9WrQ3oLG0HwCiyd7m+/hdOnVYPN4cPdG5dgGzanl3fo0IHKlSvz2muvceDAAWfEJLiIpekKu1OnThgMBrZuhV27lMOdbnxbRWCgeV2cbv/A0a7lltg6Nmzu3Lm0bduWCxcuUKdOHf777z+aNWtm9/7XrzevZ3W6z507B8ADDzxA8eLF7d6H4H1cvw579qh1Ed3eibOdbsi5pvv48ePExsYSFhZGrVq19A9AsJmZM9Vy0CCQpCXBm9Cc7lu3YPx4tf7WW+bbBe/AZtF9/vx5XnjhBdauXUudOnWoX78+n3zyCWfPnnVGfIIT0bo+9+jRA4DXXlO3P/64bfWLBgMEBqYBkJxs0jVGwfO4evUqq9OHnOohum0ZG/btt9/y0EMPkZiYSPfu3dm4caPDXYGzE92a060hDdT8j/XrVWfYWrXAzqb4gptxttMNOYtuLbW8bt26BMlwdbdz/Ljq0WAwgPT8FbwNzemOj4fLl6FqVem+743YLLqLFSvGyJEj2bhxI8ePH6dv377MmjWLSpUq0aFDB2fEKDiBY8eOcfDgQYKCgujatSurVsE//6hU8bfftn17gYFKbMvEMN/nzz//JC0tjfr161OtWjWHt2ft2LDExMSMZo3PPfccCxYsoKCDxUzJybB5s/n3rE43QLVq1WgvRb1+h4wK836c6XRrnz15iW5JLfcMZs1Sy86doVw598YiCLaiiW6Nd9/N3E9J8A5sFt2WVK5cmVdffZUPP/yQunXrZjRWEjyfRYsWAWpUWGRkoQyXe9gwsGf6U1CQSisXp9v3WZDe6lsPlxusHxs2b948rl69Svny5fn0008JtKxrsJMdOzLPvcxa0w3w1FNPEWBtV0HBJ4iLg/QpcXTs6N5YBPtxRXp5Tt3LRXR7DkajWXSLOyh4I5Zp5HXrQv/+7otFsB+7zyQ3btzI8OHDKV26NAMHDqROnTosWbJEz9gEJ6KJ7p49e/Lnn/Dvv5A/vznF3FaCgpTYTk2VQilfZ3t6q++7775bl+1Z63R/9dVXADz99NO6pWtqI6EiI9VSc7q1rurBwcEMkja3fseUKSqFr0oVcGASneBm3JVebjKZRHR7EOvWqcZTkZFw//3ujkYQbCc83Nw/6f33rZ8uJHgWNp+5jh07ljlz5nD+/Hk6d+7MF198wX333ZcpHVPwbG7cuMG6dLXRrVtPevdWt48ebX/toia6xen2ba5cucKFCxcAMjreO4o1TveOHTvYsmULwcHBDBkyRJf9grmeu3Nnc2dbgKpVq/LJJ59QsWJFSsiAZr8iJgY+/litv/WWpPB5M+5qpHbhwgUuX75MQECAbp+Tgv1oWSt9+96episI3kBQEHz5pWrw2bOnu6MR7MVm0b1u3Tpeeukl+vXrd9t4n+vXr2ecQAuey9KlS0lLS+POO+9ky5aqHDgAhQvDiy/av01zTbc43b7M7t27ASVKtZNOR7HG6Z46dSoAffv2paROXa2MRtiwQa137ZpZdAO86MgbQvBavvhCndjUqAEPP+zuaARHcJfTrbnctWrVEkPCzSQlqc92gIED3RuLIDjCM8+4OwLBUWwW3Rs3brzttuXLlzNt2jQWLVpEgmWBpOCRaKnl3bv3zmia9sorUKiQ/dsMDpZGav6ANne2fv36um0zL9F948YNfkm3KobrOJRy3z64eVOVVbRqpW6zFN2C/xEdbeDzz9X6uHFm0SZ4J+5yuiW13HNYuhRu3IAyZaBtW3dHIwiCP2N3VcCpU6d4++23qVSpEn379iUgIIAffvhBz9gEJ5CSksJff/0FQEDAIE6ehFKlYNQox7ariW6p6fZtNKf7rrvu0m2beaWXz5w5k8TERO666y5atmyp23611PKWLc013fHxakyU4J9MnRpKdLSB2rXhoYfcHY3gKK7oXp5dIzUR3Z6Dllrev7+5JlYQBMEd2CS6k5OTmTNnDp06daJWrVrs2LGDs2fPsmHDBubMmUPfvn2dFaegExs2bCA6OprixYuzcmVVAF54ARzNgNNObkR0+zbOEN25Od1GozGjgdrw4cMxGPR7fWmiu00b5XaDEtyJibrtQvAirl2Db74JBeCdd6RRjS/g7vRyEd36c+OGKoU7cCDvx966BQsXqnUpFREEwd1YfVoxatQoypQpwxdffEHv3r05e/YsixYtwmAw6DK6R3ANC9O/gZo1G8727QZCQkCP5szmkxsR3b5KUlISBw8eBFzndK9YsYLjx48TGRnJwzqeNZlM5s7llqIbJMXcX/nkE7h1y0D9+qaM5pKCd+OO9PKbN28SFRUF6FuGIyjefhs++wzGjMn7sX/8oS6i1qwJcv1DEAR3Y3XF2tdff80rr7zCq6++qlsDJcG1mEymjHru5OTHAejTB7L0w7MLcbp9nwMHDpCamkqhQoWoUKGCbtvVnO7Y2FiSk5MJCQnJuE9roDZo0KBMs7Md5cQJuHBBvW6bNVNph6GhqulOXJw+7wnBe7h+XY0JA3G5fQl3ON1a34uKFStKY1mdiY8HrYpxzRqIjYUCBXJ+vJZaPnAg6JgkJQiCYBdWn1r8+OOP/Pvvv5QuXZqHHnqIxYsXk5aW5szYBJ05dOgQx48fJzi4MJs2VQLgqaf02baIbt/HMrVczzTvQoUKZWzP0u0+f/48ixcvBuAZndt2aqnlTZqo+ZdgLrGIj9d1V4IXsGABxMcbqFUrjR493B2NoBfucLoltdx5/PorREer9eRkWLky58deumS+X7qWC4LgCVgtugcMGMCKFSvYu3cvtWrVYsSIEZQqVQqj0cgBa4prBLejpZbXqPEWsbEGqlWD9u312bZmTqaliUXkqzijnhsgICCAwoULA5nrutetW4fJZKJhw4bUrFlT131appZraEa6pJf7H/Pnq2Xv3sniiPkQrmikJqLbdXz7rVpqk1aWLMn5sXPnQloaNG0K1ao5PTRBEIQ8sVkhVa5cmXfeeYeTJ0/y008/0adPHx555BHKlSvHs88+64wYBZ34888/AYiNHQDA0KH6pVwFB6sNpaaK6PZVnCW6IftmahvSh2i3bt1a9/1ZNlHTENHtn8TEwPLlar1nT5l56Eu4Ir08Li4uU9afiG7nsHs3bN2qjunkyeq2v/7KedqEZWq5IAiCJ2C3QjIYDHTp0oW5c+dy/vx5XnzxRdauXatnbIKObNq0iS1bthAU1IRTp0oSHAyPP67f9kNDlegWp9s3MZlMTpnRrZFdM7WNGzcC+ovu6Gg4dkytW04gE9HtnyxZolJVa9Y0UauW0d3hCDriivRyUP0oAK5evZrRbFJEt75oLnfv3tC3ryoHOn8e0r+WMnHiBGzZonoz9Ovn0jAFQRByRBeFVKRIEUaPHp3hhAmex/jx4wGoVu0jQH1xlSih3/Ylvdy3OXv2LDdu3CAwMJA777xT9+1ndbpjYmLYs2cPAK1atdJ1X0ePqmXJkpCe1Q6Ya7pFdPsXv/+ulr17S7MlX8OZTndoaChB6TvQUsx//PFH0tLSaNSoEeXKldN/p35KbCz89JNaHzYMwsKgUyf1e3Yp5lqztQ4doHRp18QoCIKQF6KQ/ICdO3fy119/YTAU4OzZtoBKLdeTkBDtpRQkDfZ8EO2CWq1atQgLC9N9+1md7i1btmA0GqlcuTJlypTRdV+a6K5RI/PtmtMtjdT8h/h4+Ptvtd6nj3tjEfTHmU63wWDI1EzNZDIxbdo0AIYMGaL/Dv2Y2bPVzO3q1eHuu9Vt3burZVbRffMmfPGFWn/ySZeFKAiCkCciuv2ACRMmANC06afExgZStar5i0svQkI0iyiYVGfYCoJbcWY9N9zudGup5Xq73ABHjqhl9eqZb5f0cv9j2TIlvCtWhIYN3R2NoDfOFN2QuYP5li1bOHDgAOHh4QwYMMA5O/RTtNRyyz40mujeuhWuXDE/9tNPlfCuXVtSywVB8CxEdPs4hw8f5vfffweKcvmyKuIeMkT/ObShodoGg0lx1hmO4DZcJbo1p1trouYM0Z2X0y2i23/QUssfeEBSy30RZ6aXQ+YO5prL3a9fPyIjI52zQz9k2zbYvl2VsA0aZL69bFmoX181UtOyVS5fhkmT1Pp770FgoIuDFQRByAUR3T7OpEmTMJmKExGxnaioMEqUgMGD9d+P1khNRLdv4swmamBOL7927Rqpqals3boVcE7ncnG6BVDN0xYtUuuSWu6buMrpPnfuHHPmzAEktVxvNJf7wQehWLHM92VNMf/wQ/X53bgx3H+/y0IUBEGwiiB7npSYmMiePXu4fPkyRmPmbq+9evXSJTDBcaKiopg7dyOwllu3KlK6NKxaBcWL678vc023iG5fIy4ujmPp7b5dkV6+e/du4uLiKFSokO5N20ymnJ1urZGa1HT7B6tWqXFhpUpBixbujkZwBs52ujXR/d133xEfH0/NmjWdkp3jr5w6ZW6KNmzY7fd37w7jx6sykZMn4auv1O3vvy+ZK4IgeB42i+6lS5fy2GOPcfXq1dvuMxgM0kTLg3jzzWkYjauBqlSooE4yq1Vzzr7MNd0hIrp9jL1792IymShZsiQlS5Z0yj4sG6lpqeUtW7YkQOc6iKtXVb0fQNWqme8Tp9u/sOxaHhCQ87xfwXtxldOt9aAYMmQIBlF7uvHmmyojpUMHyC7pqWlT5X5fvaqyVZKSoG1buOce18cqCIKQFzaf0Y4aNYq+ffty4cIFjEZjph8R3J7Dxo2X+OWXYUBVypRJYN065wluMI8Mk0Zqvoez67khs9PtiiZqFSpAeHjm+0R0+w+pqfDnn2pdUst9F1eJbrWvYB577DHn7MgP2b3bPCbso4+yd64DA6FrV7W+Y4dajh8vLrcgCJ6JzaL70qVLPP/8805zvAR9+OKLvUAFwsJOsXVrGBUrOnd/2smNpJf7Hs6u54bMNd2a0+2Meu6cUstBRLc/sX49XLsGRYtCu3bujkZwFq5KLwe47777KFGihHN25Ie8+qrKPnnoIVWjnRNaXTcoAe6Erw1BEARdsDm9/MEHH2TNmjVUzZqbKXgUv/7akfLlD9K06WXKlq3g9P2J6PZdXOl0JyUlceHCBYKDg2nSpInu+8mpiRpITbc/MW+eWt53n1mYCb6Hs51urXs5SAM1PfnnH1i6VL03338/98d26aIy7ZKT836sIAiCO7H5dGPKlCn07duX9evXU7duXYLNaguAZ599VrfgBPsxGAx8+mktoqNLu2R/Irp9E6PRyJ49ewDniu6IiAiCgoIyShMaNWpEeNb8bx0Qp1tIToZff1XrMsfXt9EuqDg7vbxChQp06tTJOTvxM4xGePlltT5sWN5lcYULq5FhiYnQqJHz4xMEQbAXm0X37NmzWb58OWFhYaxZsyZT0xCDwSCi208R0e2bHD16lLi4OEJDQ6lZs6bT9mMwGChSpAiXL18GnFPPDbk73SK6/YOlS+H6ddW1vGNHd0cjOBPte8lZ6eU9e/Zk2rRpvPvuuwTKUGhdmDdPzeUuUEA1UrOGDh2cG5MgCIIe2FzT/frrr/POO+8QHR3NyZMniYqKyvg5ceKEM2IUvABL0S2N1Bxjz549PP300xkOsztZu3YtAM2aNSPIyXm4Woo5OKee22iE9Mln4nT7MVpzpoEDJbXc13F2evmdd97J0aNHGTBggHN24GckJ8Nrr6n1l14CKZEXBMGXsFl0Jycn89BDD+k+ykfwbsTp1o+JEycyd+5cOnXqxMGDB90ayz///APA3Xff7fR9ac3UQI0L05vz51W9dmAgVKp0+/0iun2f6GhYuFCtP/KIe2MRnI+zG6kJ+jJ2LJw4ASVLwvPPuzsaQRAEfbFZOT/++OP8qhXECUI6ZtEtc7od5cCBAwBcvXqVzp07ExUV5ZY4TCYTq1evBqCDC/L3NKe7evXqTukCrNVzV6li+Xo1I43UfJ/fflOzfO+8E5zYjF/wEJztdAv6MXcufP65Wv/6a5VeLgiC4EvYnFyXlpbGxx9/zLJly6hXr95tjdQ+1z41Bb/Cck63iG77MZlMHDp0CIAyZcpw7tw5OnXqxPr16ylTpoxLYzlw4ACXL18mPDycZs2aOX1/muh2Rmo55F7PDeJ0+wNaavkjj8gsX3/A2Y3UBH04cACefFKtv/wy9O7t3ngEQRCcgc2ie+/evTRo0ACAffv2ZbrPIGcxfkvm9PJkd4bi1Zw7d45bt24RFBTExo0b6dSpE8ePH6dTp06sW7eOYsWKuSwWLbW8VatWhIaGOn1/Dz74IBs3bmTw4MFO2b4murOr5waz6E5IUPXfUkHjW5w+DWvWqPWHH3ZrKIKLsGykZjLJhRZPJCYG+vRRFzvvvhvGj3d3RIIgCM7BZtGtpZsKgiWZG6lJfq69aDXclStXpmLFiqxcuZLWrVtz8OBB2rRpw6xZs2jatKlLYtFEtytSywG6detGt27dnLZ9Lb08L6cbVIq5pDdah9EIU6bAX3+psT1JSerHYFBzc++9190RKmbPVst27aBCBffGIrgGy0S8tDRpnOdpmEwwalQ+Dh82ULYszJkjx0gQBN9FvBxBF6SRmj5oortGuh1bqVIlVq5cSenSpTl06BAtWrTg5ZdfJiEhwalxpKWlZXQud5XodjZ5Od2WY8Glrts6rl2Dnj3huedg2TJYuxa2bIGdO2HHDiW6PQGTCX78Ua1LAzX/wVLASTM1z+PLL2HhwhCCg03MmyfdygVB8G1svqZ4991355pGrrljgn8holsfsopugFq1arF3716ee+45fv75Zz755BMWLlzIjBkznNLlG2D37t3cuHGDiIgIGjVq5JR9uJLUVNUVF3IW3QEBSngnJEhdtzVs2QL9+sGZMxAWBm+/rZrUhYYqx7t/f/WYGzegcGH3xrp7N+zfr2J78EH3xiK4DkunOyVFvU4FzyAlBT78UK1//DG0aOHeeARBEJyNzaK7fpaWrykpKezatYt9+/bx+OOP6xWX4GWI6NaH7EQ3qCZjP/30E/369WPYsGEcPnyYNm3asG3btoweC3qiXTxr27at0+dzu4JTp8wn3eXK5fy4/PlFdGfl4EGVmh0Sojq8h4fDhQswYYK6mFG9OsybB3fdlfl577yjnrtihRLn7kRroNazJxQq5NZQBBdi+dElX0uexV9/wcWLBooVM/LMM1JsLwiC72Pz2fTEiROzvX3cuHHExsY6HJDgnWSu6ZY8PnvRRHfNmjWzvb9Xr160adOGXr16sWHDBhYvXuwU0e3KUWGuQKvnrlYt9wZp+fPD1asiujVSUuD++82p+Vnp2xemTYOCBW+/7957lej++2/3iu74ePj5Z7UuqeX+haSXey7TpqnlgAHJhIQ4v1GnIAiCu9GtpvuRRx5hxowZNj1nwoQJNGnShIiICEqUKMH999/P4cOHMz3mu+++o3379hQsWBCDwcDNmzet2vbUqVOpVKkSYWFhNGvWjH///TfT/YmJiYwYMYKiRYtSoEAB+vTpw6VLl2yKXzAjc7od5/r161y+fBlQs6pzonDhwvROn6mya9cu3eNISUlh3bp1gO+I7rzGhWnI2LDM/O9/6n9XtCg89ZTq+t27N3Trpu779dfsBTeYG6gtXaqarbmL996DixehfHnPaeomuAaDQcaGeSJnzyqnG+DRR2XaiSAI/oFuonvz5s2E2VgwtXbtWkaMGMGWLVtYsWIFKSkp3HPPPcRZnPHGx8fTtWtXXnvtNau3++uvv/L888/z9ttvs2PHDu666y66dOmSIWgAxowZw6JFi5g3bx5r167l/PnzPPDAAzbFL5iROd2Oo7nc5cuXp0AerbO1Mg9niO5t27YRGxtLkSJFqFevnu7bdwea051TPbdGvnxqKY3U4NYtGDdOrb/zDnz3nUrTnj8fliyBIUNyH8HUpo26iHHxoqqpdgf79sGnn6r1KVMsP6cEf0ET3eJ0ew7ff68uxLVta6J6dTdekRMEQXAhNqeXZxWmJpOJCxcusG3bNt58802btrV06dJMv3///feUKFGC7du307ZtWwBGjx4NwBptwKoVfP755zz11FM88cQTAHzzzTcsWbKEGTNm8OqrrxIdHc306dP55ZdfMpy8mTNncscdd7BlyxaaN29u098hSE23Hmii+4477sjzsZroPnHiBNHR0URGRuoWh5Za3r59ewJ8ZFi1ON2288kncOWK+p8NHWr780NDoUMHWLRIud1OqILIFaMRnn5aia3evaFXL9fuX/AMgoNVYz/5WvIMjEaYPl2tDx7s3lgEQRBcic2iO+vJfUBAADVr1uTdd9/lnnvucSiY6OhoAIoUKWL3NpKTk9m+fTtjx47NFGOnTp3YvHkzANu3byclJYVOnTplPKZWrVpUqFCBzZs3Zyu6k5KSSEpKyvg9JiYGUBcdTCaT3fE6Ey02V8Sn3AQDEExycrLH/k88GU1016pVK8/jVrhwYSpUqMDp06fZtWtXxkUqPdCaqN19990+cxyV6DZQvbqJ3P4kJboNxMbm/riccOV7LiszZsCuXfD5547Puj1/Hj77DMDAhAkmgoKw6//RtSssWmTg779NvPqqYzHZyv/+B5s2GShQwMQXX+QdvzuPneAYuR077bspOdm+97SgLytXwsmTBiIjTfTpY0o/LnJgvA35vPRe5Njpj7X/S5tPzWbOnGlzMNZgNBoZPXo0rVq1ok6dOnZv5+rVq6SlpVGyZMlMt5csWZJDhw4BcPHiRUJCQiiUpY1tyZIluXjxYrbbnTBhAu+8885tt0dHR3vsC9dkMmU0t8ttzJseJCYGAAWBYGJjYzMuoAjWs3fvXgAqVqxo1XGrU6cOp0+fZvPmzdyVtXW0nSQlJbFx40YAGjdu7BPHMSkJTp1SFwtLlowhOjrn92tISD4ghGvXEoiOtr3W0JXvOUt+/DGEZ59VufHt28fSsaNjubSvvRZOfHwoTZqk0qFDLPa+DFq1Up8LmzbB6dMxREa65rPy8mUDr7wSARh4/fUEIiKS8/wb3HXsBMfJ7dgFBRUEDNy4cYvoaElldjfffKM+Y/v2TSY5OV7ec16KfF56L3Ls9EczYvPCIT8kNjYWY5YOOQVz6qqTByNGjGDfvn1s2LDBkZCcxtixY3n++eczfo+JiaF8+fJERkba/Tc7G+1iQGRkpNPfWObkhGACAwN1TXf2F44dOwZAgwYNKFCgQJ7HrXHjxvz1118cPnxYt//32rVrSUxMpGTJkjRt2tQnPpAPHACTyUBEhInq1QvmWoesXYczGsOJjAy3eV+ufM9p/PUXjBlj/n3Pnvw40p5i/37ziK2JEwMpVMj+11a9elCzponDhw38+29Bl83IHjECoqMNNGpk4sUXwwkMzPtYuuPYCfqQ27HT6vjDwiKQryX3cuUKLF6s1keMCCEyUtWlyXvO+5DPS+9Fjp3+WPt/tFl0R0VFMXLkSNasWUNiYmLG7SaTCYPBQFpamq2bZOTIkSxevJh169ZRLrchulZQrFgxAgMDb+tEfunSJUqVKgVAqVKlSE5O5ubNm5ncbsvHZCU0NJTQ0NvHWhgMBo9+0WrxOTtGy0ZqqampHv0/8UQSEhI4efIkAHfeeadVx00bFbZz507d/t8rV64EVNdyX6nnTr+WQY0ahlzHhYFlIzVDruI8N5zxnrtxA7ZtgxYtwLLH3n//qXFcaWlq/vjZs7B1q/2xm0zw6quq7vKBB6BVK8f/hnvvhcOHYdkyA337Ory5PJk/H375RY2G+/Zbg02p9q76vBT0J6djpx3/tDT73xeCPvz0k6qtb9wY6tc3YDLJe86bkWPnvcix0xdr/482n1U/8sgj3LhxgxkzZrBq1Sr++ecf/vnnH1avXp1RC2otJpOJkSNH8scff/DPP/9QuXJlW8O5jZCQEBo1asSqVasybjMajaxatYoWLVoA0KhRI4KDgzM95vDhw5w+fTrjMYJtSCM1xzh8+DAmk4kiRYpQvHhxq56jie4DBw6QnOz42JXU1FRmzZoFQI8ePRzenqdgbRM18NxGaoMGwT33QKlS8Nhjqi7yyBHo3l11Wu/SBebNU4/dutW+EV3Jyaph2pIlEBgIEyboE7vl6DBnV+L88w8MHKjWR4+GRo2cuz/B89G+m+Rryb2YTObZ3E895d5YBEEQ3IHNTvfu3bvZvn07NWvWdHjnI0aM4JdffmHBggVERERk1FNHRkYSHq7SAS9evMjFixczUm/37t1LREQEFSpUyGi41rFjR3r37s3IkSMBeP7553n88cdp3LgxTZs2ZdKkScTFxWV0M4+MjGTw4ME8//zzFClShIIFCzJq1ChatGghncvtxCy6A0lKktkstmLZudzaK2YVKlSgcOHC3Lhxg/3792eIcHtZtGgRZ8+epVixYvTp08ehbXkS1o4LA88U3VevKiEMKq4ff1Q/BoM6kW3YUAnusDAID4ebN5WzbEUT/AyuXIE+fWD9euUQT55s3f/LGtq2VRkE587B3r0q5dwZbN2qOpQnJalu5R995Jz9CN6FzOn2DPbsgYMH1WdU//7ujkYQBMH12Ox0N2nShDNnzuiy86+//pro6Gjat29P6dKlM35+/fXXjMd88803NGjQgKfSL422bduWBg0asHDhwozHHD9+nKtXr2b8/tBDD/Hpp5/y1ltvUb9+fXbt2sXSpUszNVebOHEiPXr0oE+fPrRt25ZSpUoxf/58Xf4uf8Ry/m1ysmc2lvNkbBkXpmEwGHSd1/3VV18BMGTIkGxLKfRm0SKweKs7DW93uv/4Q6WP168PmzbBM89A4cJKcFeqpAR5RIS68NW4sXrOli3Wb3/vXmjaVAnuggVVzeXw4frFHxYGd9+t1v/+W7/tWrJvn3LU4+KgUyeYPdvxDu6Cb6BdEJY53e5FS4S8+271OSMIguBv2HxaMm3aNIYNG8a5c+eoU6cOwWaLE4B6NtgY1nT9HjduHOPGjcv1MVotrCUjR47McL6zIywsjKlTpzJ16tQ8YxDyxvJlkJQkHWJtxR7RDWpe9+rVqx0W3YcPH2blypUYDAaefvpph7ZlDTExyllNS1Op0VkGCeiKLU63uabbefHYyty5atmvn6rpbtECJk6EDRuUa2xZjdCihRLPW7ZAemJPrqxcqVzh2FioWlVdCLHxJWgVXbuqiwNLl8Irr+i77ePHoXNnVffevLm6SOGCa0aClyDp5Z6BJro7dHBvHIIgCO7CZtF95coVjh8/npGqDcpxc6SRmuD9WIpucbptx17RbdlMzRG++eYbQNVyV6pUyaFtWcN//5lPgi9dcp7ojo1VM6fBO53uK1dg9Wq1btmELDQUOna8/fFadczmzXlve/NmuO8+dYGhQwcl7osWdTzm7NDqujdsUP9b7f/sKCaTuhhx8SLUras6uVs2mhMELeNBnG73kZoK69apdS3rRRAEwd+wWXQ/+eSTNGjQgNmzZ1OyZEnpfCcAqvGShjjdtpGamsqR9Bxoe5xuUOnlRqPRro7jcXFxzJw5E4DheuYV54Jl+vO1a87bj+ZyFyumUrLzwtNEt5Za3rAhVKuW9+M10b1vH9y6pdLOs2PfPnMTtq5dYcGCzCUielO1qhoreP06nDihBLIe7NihfsLClItuzTEW/Atxut3Pjh0qu6lwYbjrLndHIwiC4B5sFt2nTp1i4cKFVLPmDFDwGwwGCAxMIy0tUJxuGzlx4gQpKSmErR3UuwAAYOlJREFUh4dToUIFm55bq1YtQkNDuXXrFlFRUVStWtXm/c+ePZvo6GiqVKnCPffcY/Pz7cHVotsalxs8T3RrqeXWjtoqXRoqVoRTp1Q2QXapnCdOqE7oN25Ay5bw22/OFdwaVaroL7q//14te/eGMmX02abgW0gjNfejpZa3a5f5Ar0gCII/YbMt1qFDB3bv3u2MWAQvJzBQOdwpKSK6beHQoUMA1KxZ02anOjg4mDp16gB5N1O7du0azz33HLNnz84oAzGZTBl9DZ555hmXzOY2mVwnurUmatZ24tZEtyfUdOeUWp4XuaWYX7yoBPeFC1Cnjmqapleqd15UqaKWUVH6bC8pSc3jBjVSTRCyQxqpuR/tc0zquQVB8Gdsdrp79uzJmDFj2Lt3L3Xr1r2tkVqvXr10C07wLjTRnZTk5kC8DHvruTXq16/P9u3b2blzZ66jvt577z0mT57M5MmTGT9+POPGjaNMmTLs2rWLsLCwTH0anMmJE2oMloYrnG5rRbfWSM0TnO7589W87UaNVHq2tbRoobrCZ+1gnpqqUsqPH4fKlWH5ctemY1eurJYnTuizvcWLlXNetmz29e2CAJJe7m6Sk1VzRxDRLQiCf2Oz6B42bBgA77777m33SSM1/8bsdLs5EC/DUdGtNVPLzelOSUnhl3RbMDQ0lP3799O3b1/Cw8MB6N+/P0Wd1UUrC1nFoCucbm9ML583Ty1tcbnB7HRv2aKyCrS2G7NmqdrKIkVgxQqViu5KNKdbL9GtpZY/9pikrAo5I43U3MvWrZCQACVKwJ13ujsaQRAE92FzLqnRaMzxRwS3fxMUpNLKpabbNvRwuiF30b18+XKuXLlC8eLFOXv2LG+//TYFCxYkISEBcF0DNTCLbi2T3ZOcbk8R3Zcv25daDmqed0iIyiY4flzdlpgI2uTF11+3zTnXCz1F98WL5pnfjz/u+PYE30WcbveifY7dfbf5AqAgCII/4vwCTsFvCAoSp9tWjEZjRk23vaK7Xr16GAwGzp07x5UrV7J9zA8//ADAwIEDKVasGOPGjSMqKorx48czZcoUmjRpYt8fYAea6G7ZUi2dJbqvXzdv29q+j5roTk52rzOmpZY3bmwWq9YSGqpS0sH8v/76azh7FsqVAxdeX8mEZU230cEBBz//rLq6t2gBNWs6Hpvgu0gjNfci87kFQRAUVqWXT548maFDhxIWFsbkyZNzfeyzzz6rS2CC96E53XJyYz3bt28nJiaGAgUKUN3aHOgsREREUK1aNY4ePcquXbvo3Llzpvtv3rzJggULAHjssccybi9SpAivvfaa/cGnc+wY9OgBDz8Mb76Z+2MTEkAz5Lt3V3ObnSW6NZe7bFnrm4VpNd2gmqkVLKh/XNZgb2q5RvPmqpHa5s3QqxeMH69uHzdOjddyB+XLqzTwxETlVNvbbdxkMqeWSwM1IS+kkZr7SEgwN3SU+dyCIPg7VonuiRMn8vDDDxMWFsbEiRNzfJzBYBDR7ceIo2A7ixYtAqBLly6EODC3qX79+hw9epSdO3feJrp/++03kpKSqF27dkb9t568+iocPgxffglvvJF7CuGOHerkt1QpNXsanCe6ba3nBiVIDQYl7OLi3CO6T52CNWvUur2iu0ULmDhROd2ff67+xzVrujcVOzgYKlRQTveJE/aL7h071JzxsDDo10/fGAXfQ76X3MemTSprqFw567ONBEEQfBWrRHeUxYyXKL3mvQg+R3CwcrpTU6Vwy1oWLlwION71v0GDBsybN4/t27ffdp+WWv7YY49h0Lmobts2+P13tX7linK9cxO5Wrpzixag9W1ztui2tp4blODOnx9iY91X1/3iiyr9ukMHc8dvW9Gaqe3ebf4/vPeeWYC4iypVzKK7dWv7tmE5m7tQIb0iE3wVcbrdh5ZaLvXcgiAIdtR0JyYm5njfhQsXHApG8G4kvdw2Tp8+ze7duwkICKBbt24ObatleoH0b7/9xuLFizNuP3HiBOvXr8dgMPDwww87tI/seOONzL9v2pT74zXR3by5WXRfvaqcZb3R0sttzdp3ZzO1Vavgt99Uk7lckorypFw55SSnpakLCI0aQS7T5FyGo83UZDa3YCvSSM19yHxuQRAEMzaL7oYNG2bbJfn333+nXr16esQkeClmR0H681mDllresmVLihUr5tC22rZty5AhQzAajfTv35+dO3cC8NNPPwHQqVMnypYt61jAWVi7FpYtU+6pJug2bsz9OdmJ7uRk5whce5xuMIvu+Hh948mLlBTQqnOeeQYc+Tg1GFQ2gcYHH5i7xbsTR0X3H3/IbG7BNiS93D3cugX//qvWpZ5bEATBDtHdvn17mjdvzkcffQRAXFwcgwYN4tFHH9WlKZPgvZhFt+SRWYMmunv27OnwtgwGA1999RWdOnUiLi6OHj16cObMGX788UcgcwM1PTCZQHu7P/UUPPqoWs9NdJ89q34CA5XzWqCA+TWjd4q5yWS/0601U3O10z11Khw4oC5GvPuu49tr104t774bspT5uw1HRfeXX6rlkCEym1uwDkkvdw/r16tMmypVoGJFd0cjCILgfmyu8Pvqq6/o3r07Q4YMYfHixVy4cIECBQrw77//UqdOHWfEKHgJIrqt59atW6xOz71ztJ5bIzg4mN9++42WLVty4MABWrZsydmzZ8mfPz+9e/fWZR8af/2lUsnDw1XHcs1NOnBAOZFFitz+nK1b1bJePbObXLSo6mR97Zq+J2YXL6q06oAA20duuSO9/NIlePtttf7BB9n//2zl6afVhY1evTynntIR0b1tm3rNBQfDsGH6xiX4LuJ0u54zZ8yfZ5JaLgiCoLAr4fDee+/lgQceYOPGjZw+fZqPPvpIBLeA1nxbRHfeLF++nOTkZKpVq0ZNHQcNR0ZGsmTJEkqWLMnZs2cB6NOnD/mtnZllBUYjvP66Wh81CkqXhuLFzWnc2oiYrFimlms4q5ma5nJXqqTmVtuCO0T32LEQE6M6ug8erM82Q0LgiSfM/2NPQBPdFy7Ynr6vudz9+qnu94JgDeJ0u5bly9Xn2LZtqtHhyJHujkgQBMEzsFl0Hz9+nBYtWrB48WKWLVvGyy+/TK9evXj55ZdJkUvJfk1wsBLbaWkeUDzq4Vh2Lde7o3ilSpVYuHAh4eHhADyu85youXNVV+zISHjlFfPtrVqpZU4p5q4U3faMC9NwdU33v//CzJlqfcoU306bLlxYvW4ATp60/nmXL8OcOWpdplIKtiCN1FyD0QjvvANdu6rmmA0bwvbtcNdd7o5MEATBM7BZHdWvX5/KlSuze/duOnfuzPvvv8/q1auZP38+TZs2dUaMgpegOd0iunMnLS2NJUuWAPrUc2dH06ZNWbduHb/88gsddM7v+9//1PKFFzKnQWuiO7sO5ikpyvmAzKJb6x/nLKfb1iZq4Pqabu3CxaOPZm5+5osYDPalmH/3nWq416wZyNeMYAuSXu58jEbVTHPcONVP46mn1MVXW0t7BEEQfBmb1dFXX33FnDlzKGQxILVly5bs3LmThg0b6hmb4GWEhIjTbQ2bN2/m2rVrFC5cmFaaUnUCjRs3ZsCAAbpu02RSLjdA9+6Z70ufWsa//95+grtnDyQmKqfT0n32ZKfbFaJ79WpYs0ZdsHr/fefvzxOwVXSnpMBXX6l1cbkFW5H08txZs0b1f4iNtX8bv/wCf/6pSnm+/15dJAsL0ylAQRAEH8FmdfSo1qY4CxEREUyfPt3hgATvRUS3dWip5ffeey/B2hmhl3D5shLIBgPccUfm+2rWVM53QgKkTyzLYOlStWzWLHNTL2eLbnucbleJbpPJ3GxoyBCoUMG5+/MUKldWS2tF9++/qxrwUqXgwQedF5fgm4jTnTPR0dC3rxLJ339v3zZiY83ZOm+/DTpXMwmCIPgMNncv1zhw4ACnT58mOTk54zaDweC0dFnB8wkJUWI7Lc2Hi1J1QBsVplfXcleyf79aVqumOpdbEhCg3O7Fi1VqoZYGHBMDEyeq9f79Mz/HGaI7LQ2OH1frnux0//OPGqsTGqoaqfkLtjrdkyer5TPPmEtYBMFaxOnOmQ8+UPXXABs22Nf07KOP4Px5dTFtzBh94xMEQfAlbBbdJ06coHfv3uzduxeDwYDJZALIaAaVlpamb4SC1xAaql4DJlMgJpNJ9wZhvsDRo0c5dOgQQUFBdO3a1d3h2My+fWqZ07CCVq3Mols7AfviCyWqa9aEhx/O/HhniO4zZyApSZ1s2zOGzBWN1Cxd7qFDoVw55+3L09BEd1RU3o/97z/VDT84WKXACoKtSCO17ImKgkmTzL9n14sjL06ehE8+Ueuffiop5YIgCLlhcx7wc889R+XKlbl8+TL58uVj//79rFu3jsaNG7NmzRonhCh4C5rohmBSxVbIlrlz5wLQtm1bIrU2zl6EJrpr187+fq2ue+NGJSxv3IDPPlO3vfOOOdVTwxmiW2uiVrWqfZ3AXdFIbcUK9T8KC4NXX3XefjwRS6c7/ZptjkyZopb9+0PJks6NS/BNJL08e8aOVc0JW7dWn5NnzqgfW3jpJXWB8+67oXdv58QpCILgK9gsujdv3sy7775LsWLFCAgIICAggNatWzNhwgSelS43fo2WXg7BMj4uG5KSkpiSriIGDRrk3mDsJC+nu0kT5SxdvKiclM8+U3WDdeuq2sGsOEN0O1LPDc5PL7d0uYcNgzJlnLMfT6ViRVXXHx+vegTkxPXr8Ouval1m/Qr2Iunlt7N5s3pvGQzqwlb9+ur2nMY9ZsfatfDbb6qsaNKkzL06BEEQhNuxWXSnpaUREREBQLFixTh//jwAFStW5PDhw/pGJ3gVYWEiunPj119/5eLFi5QpU4aHHnrI3eHYjMmUt+gOD1fzWQEWLFCp5QDvvqtOzrLiTKfbU0X3smVqZnl4eOY55/5CSAiUL6/Wc6vr/vFH5aLVr68u5giCPYjTnRmTCZ5/Xq0/8YSao60N0bBWdKelwXPPqfWnn4Z69fSPUxAEwdewWXTXqVOH3ekzg5o1a8bHH3/Mxo0beffdd6kiQxn9mtBQLZdXRHdWTCYTn3/+OQCjRo0ixAs7Qp05A7duKecotwZl2gnc66+rzraNGsF992X/WE10R0frd1LsyLgwcH5N97hxajl8uOrI7Y/k1UzNZFIdlUHVvIuLJtiLON2ZmTtXXfTLlw/ee0/dZqvonjlTjY4sVEhdUBUEQRDyxmbR/cYbb2A0GgF49913iYqKok2bNvz1119M1trMCn6JNjJMRPftrF69mt27d5MvXz6GDh3q7nDsQnO5a9bMvYu0VtedkKCW772Xs2gqXNi8fv264zGmpZnniNvrdDuzpvvECdi6VblvL7+s//a9hbxE96ZNcOCAOhYDB7ouLsH3kEZqZpKTzT0kXnnFXNqiie7du9WF1dyIjzeXx7z1FhQr5pxYBUEQfA2bu5d36dIlY71atWocOnSI69evU7hwYelW7eeYR05LI7WsaC73E088QZEiRdwcjX1o48JySi3X0E7gQAnw3Jq0BwUpt+TmTZVi7mizrL/+UuNrihRRM8HtwZnp5atWqWXz5lCihP7b9xbyEt3ffquWAwaAF/YbFDwIPdPLf/8dtm+H8eO9M/ti8WLVcbxkSXjhBfPtZcuqXgunTqmLgp065byNSZPUZ2ylSipbRxAEQbAOm53u7ChSpIgIbsFCdIeI023BoUOHWLJkCQaDgee0QjgvJK/O5RqlSplr/N5/P++TUz3ruqdOVcsnn7x9jri1OFN0r1yplrmd1PoDuYnu69dVCiyo1HJBcAS90ssvXlQjDydMgG3bHI/LHfzwg1oOGmT+nNOwJsX86lU1lxvUZ3toqO4hCoIg+CxWO91PPvmkVY+bMWOG3cEI3o055VjSyy2ZlD4MtVevXlS3t9DYA8iriZolixcrN8Qat7loUTh+3HHRfeyYalJmMMAzz9i/HWeJbqMR/vlHrYvoVsvsRPdPP6kGanfdJQ3UBMfRy+n+7DP1ugR9SmFczdWrsGSJWn/00dvvb9UKfvkld9E9fjzExECDBioLRRAEQbAeq0X3999/T8WKFWnQoAGmvIarCn6JZXq5iG7F1atXmTVrFgDPay1jvZC0NFVjC9aJ7vLlzR2q80KrCXRUdH/9tVree69Z1NmDsxqp7dmjTnwLFICmTfXdtrehHZ9z5yAxUc0rB9VATUstf/pp70zhFTwLPZzua9fMny+Qd92zJzJnjvofNGqUfbaS5nRv2aI+7wMDM98fFWXOJProo+ynUQiCIAg5Y7XofuaZZ5g9ezZRUVE88cQTPPLII15bmyo4BxHdt/PNN9+QmJhIo0aNaNOmjbvDsZsTJ5Q4Cg+HypX13bYe6eXx8aAl2ThaZ6g1UktNVY2H9Go0r6WWt2tn+V7xT4oVUxcfYmNVHWnNmup2aaAm6I0ejdQmT86c+RIT41hM7iD92i+PPZb9/XXqQESEuqCwd695drfGm2+q/2GnTtC5s1NDFQRB8EmsvlY5depULly4wMsvv8yiRYsoX748/fr1Y9myZeJ8C4A0UsvK0aNH+eSTTwDlcntz3wMttfzOO293QBxFD9E9Z45qxla5cu6N26zBstZRzxRzrYlax476bdNbMRiyTzHXxoT17y8N1AR9cDS9PCZGiW5QDRrB+5zuAwdUHXpQUM5p4YGBqsEjqItfluzcCT//rNa1mm5BEATBNmxKEAoNDWXAgAGsWLGCAwcOULt2bYYPH06lSpWIjY11VoyClyBOt5m4uDgeeOABYmJiaN26Nf369XN3SA5hSz23rTgquk0mc9rjsGGOXxQICTGfqOslupOSYN06te7v9dwaWsZE//6qg31IiLnRkzRQE/TC0fTyr75SF/Rq1YLevdVt3uZ0//ijWnbrBsWL5/y47JqpJSTAyJFqfeBAaNjQOTEKgiD4OnZX5QQEBGAwGDCZTKSlpekZk+CliOhWmEwmhg4dyr59+yhVqhRz584lKMjm6XwehbXjwuzBUdG9dSvs2KE66VrZ7zFP9K7r3rJFbatECef8D70R7QQ/Jgaio81OZOfOUvMu6IcjTnd8PKRPe2TsWHP2hTc53Wlpqjkh5JxarpFVdCclwQMPKOe7QAHVsVwQBEGwD5tEd1JSErNnz6Zz587UqFGDvXv3MmXKFE6fPk2BAgWcFaPgJYjoVkydOpVffvmFwMBA5s6dS+nSpd0dksNYOy7MHhwV3V99pZb9+5ubsjmKVtetl9NtmVruxVUGuvLCC7Brl0pdPXwYzpxRrwGtA70g6IEjTve0aXDlippJPWAAFCyobvcm0b1mDZw9q7JJevTI/bHNmqkGaadOqcZpffvC0qXq83DJEv37eQiCIPgTVttvw4cPZ86cOZQvX54nn3yS2bNnU0yvM1zBJ8g8p9vL8u90YtOmTYwZMwaATz75xKubp2kkJytRBJ7ndF+5Ar/+qtYdbaBmid5jw2Q+9+0EBKixYILgTOxtpHbzJqS35OCVV9R2IiLU796UXq41UOvfP++52hER6j25c6f6rDpxQk0WWLQI2rZ1fqyCIAi+jNWi+5tvvqFChQpUqVKFtWvXsnbt2mwfN3/+fN2CE7wLyznd/thILSoqir59+5Kamkq/fv0YPXq0u0PShSNHlEtUsCCUK6f/9h0R3ePGqYsCTZrom5Ksp+iOiYF//1Xr0kRNEFyLrenlN27ApEnwxReq7KF0aRg0SN3nbU53bCz8/rtaf/xx657TqpUS3SdOqO/0P/+EDh2cFqIgCILfYLXofuyxx7y6+7LgfPw5vXzr1q306tWLy5cvc8cddzBt2jSfeb9YNlFzxp9kKbpNJuv3sWsXfPONWv/4Y31j0rOme+1aVVdZrRpUrOj49gRBsB5r08tjY9XnyBdfmJ3s2rVVR31tjrzmdHuL6P7pJ/UZVr26Sh23hjZtYMoU9X/7/Xfo0sW5MQqCIPgLVovu77//3olhCL6Av4ru+fPn8/DDD5OYmEj9+vVZvHgxEdrZmQ/gzM7lYBbdqanqZNeaUVEmE4waBUYjPPQQtG+vb0x6Ot2SWi4I7kNzutPScr+oN3y4uct3nTrw9tuqiViARecbb0ov//ln9RkJqsGktRczH3hA/e3t2+v/uSoIguDP2N29XBCy4grRff36dZKSkpyybVsxmUx89tlnPPjggyQmJtKtWzfWrVtH2bJl3R2arjhbdOfLZ3aSrE0x/+UX2LBBPffTT50TE+gruiW1XBBcj/l7KWe3OyUF/vhDrU+bBrt3w4MPZhbc4D3p5V98AY88ov7egQPh+eetf25QkCrbEcEtCIKgLyK6Bd2wFN3OqOk+cuQIVapUoaOHqJdPP/2UF198EZPJxPDhw1mwYIFPOdwa2rgwZ3Qu17ClrvvWLXjpJbX+xhvOqTPXy+m+cAEOHFAu0913Ox6XIAi2YSm6c7oWvHWrSi8vVgyeeOJ2sa3h6U63yQSvvw5aO5Fnn1XuvbnfiiAIguAuRHQLuuFMp9tkMvHcc88RHR3N7t27dd22vUybNg2AcePGMWXKFK+fxZ0d8fFw/Lhad+Z8aW0QgjWi+733lJitVs02B8cW9BLdf/6plg0amC8sCILgOiw/lnP6WlqxQi07dsxZcIPn13Q/9xx88IFaHz9eNYTL7e8RBEEQXIfvqQTBbWQeGaav6F60aBFLly4FID4+HpPJ5NZGZVevXuXIkSMAjBw50meapmXl0CHlnhQrBiVKOG8/1jrdhw7BxIlqfdKkvEfg2IsejdTOnIGxY9X6gAGOxyQIgu1Yk16uie7OnXPflpZeHh+vasQDAx2PTy8OH4Yvv1RZNd98A0OHujsiQRAEwRK5BirohrOc7sTExIzZ1wBGo5Hk5GTdtm8PW7ZsAaBmzZoU9WEL88ABtbzzTufuxxrRbTLBiBHqxLlHD+je3XnxOOp0G41qzFB0tBpl5iPT4wTB67B0erP7WoqONo/0y0t0W1YPeZrb/cMPannvvSK4BUEQPBER3YJuWM7p1lN0f/rpp5w4cYJSpUpl3JaQkKDb9u1h8+bNALRo0cKtcTibgwfV8o47nLsfa0T3tGnwzz8QHq5cbmfiaCO1yZNVrPnyqZpKH6w8EASvwGDIfWzY6tXKta5RAypUyH1boaHmbXmS6E5LM4tubaa4IAiC4FmI6BZ0wxmN1E6fPs0H6UVqn3/+OQHptkW8HgOUHUAT3S1btnRrHM7GU0T32bPwwgtq/f33oWpV58bjiNN98GBARlr5Z5+pk3lBENyH9t2U3bVgLbXc2pF+ntjBfPVq9RlZqBD07OnuaARBEITsENEt6IZZdAeQlKSP6H7xxRdJSEigbdu29O/fn3zpFqQ7ne7U1FS2bt0KiNOtF7mJbpMJnn5aneQ2a6aaBTkbe2u6k5Ph6afzkZRk4N57VdyCILgXLdMkN9GdV2q5hid2MP/+e7UcMMA8flEQBEHwLER0C7ph2bAmKcno8PbWrFnDvHnzCAgIYPLkyRgMhgzR7U6ne+/evcTHx1OwYEHudHaxsxtJSYFjx9S6O0X3zz/DX3+p8oUZM1zTvMhWp/vyZZg3Dx56CPbuDaJoURPTp6vUVkEQ3EtO6eWnTsHRo+ozxdqRfp7mdMfEwPz5av3xx90biyAIgpAzUmko6Ial6E5MTHN4e1OnTgVg6NCh3HXXXQCEh4cD7nW6tdTy5s2bZ6S7+yLHjqmT1AIFnDML25KcRPelS2Zn+623nN/QTSNrTfetW/Daa2oEWIECUKSI+ilYEHbvNs8yB6Wyv/kGSpd2TayCIOROTk635nI3bQqRkdZty9Oc7t9+g4QEqFVL/R2CIAiCZyKiW9ANPZ3ulJQUli9fDsATTzyRcbsnON2bNm0C/Ce1vFYt5zu2OYnukSPh+nWoXx9eftm5MVhi6XQvXw5PPQWnT+f+nLp1oX17E/fcE0f37vmdH6QgCFaRk9Nta2o5eN6sbi21/PHHJbNGEATBkxHRLeiGZdqvoxO9Nm3aRExMDMWKFaNx48YZt3uS0+0votvZqeWQvehevFi5OEFBMHNm5os6zkYT3QcPQpcuar1yZfj8c9Ws6No1dTHgxg3V1K1dOzXL3GSC6Gh9+hkIgqAP2TVSMxph1Sq1bovo9qT08uPHYf16NRbt0UfdHY0gCIKQGyK6Bd0wGCAgIBWjMchhp/vvv/8GoGvXrplSuN3tdF+6dIkTJ04A0KxZM7fE4CrcIbpjYyEpSZ0QP/usum3MGOV0uxJNdKelqdf1qFHwwQfm2wVB8B6ySy/ftUtdPIuIUA0arcWT0su1MWGdOkHZsu6NRRAEQcgdEd2CrgQFGUlOhuRkk0Pb+euvvwC49957M93ubqdbc7lr165NoUKF3BKDq3Cl6C5USLk1RqM6Ef72W4iKUrXkb73l/P1npWpVKFNGnWBPmwatW7s+BkEQ9CG79HIttbx9e9uyaDwlvdxolNncgiAI3oSIbkFXAgKUw+2I6D579ix79+7FYDDQRcvtTcfdTre/pJYbjXDokFp3hegOCFCNya5ehX//hY8+UrdPnKgal7ma/Pnh5EnlkEmdpCB4N9k53fbUc4PnpJevXq0+owoWhPvvd28sgiAIQt74butlwS0EBjouurXU8mbNmlFUyztOR3O6RXQ7lzNn1Izq4GDl+roC7VCPGKFSzDt3hj59XLPv7AgOFsEtCL5AVqc7Ph42bFDrtopuT0gvT0w0l98MHAjpX4uCIAiCByOiW9CVoCAlurOOZrEFTXR369bttvs0p9sd6eUpKSn8999/ALRs2dLl+3clWmp59epml8jZaKL7/Hl1kvzllyJ6BUFwnKyN1P74Q13Yq1gRata0bVue4HS/9RYcOAAlS8J777kvDkEQBMF6RHQLuuKo052cnMyK9Ly/rPXc4N708l27dpGYmEjhwoWpUaOGy/fvSlxZz61hmdTw0ku2nwwLgiBkR9b08u++U8snn7T9wp67ne6NG+HTT9X6d9+pqQmCIAiC5yOiW9CVoCAltu11ujdu3EhsbCwlSpSgYcOGt93vzkZqWmp58+bNM3VU90XcKborVIDXXnPdfgVB8G0s08sPHoR169SIy8GDbd+WOxupxcWpedwmk2qe1quX62MQBEEQ7MO3lYPgcjTRbe+cbq1redZRYRrudLo10e3rqeXgHtH90ENqfzNnymguQRD0w9Lp1lzuHj3sG7PlzvTyV15Rs7nLl4dJk1y/f0EQBMF+pHu5oCuOOt251XODe53uTZs2Ab7fRA3cI7q7dlU/giAIeqI53bduwaxZan3oUPu25a708pUrYepUtT5jBkRGunb/giAIgmOI0y3oiia6U1Nt74B1+vRp9u/fT0BAAJ1zaCnrLqf79OnTnD59moCAAJo2berSfbuaK1fUrGyDQeqqBUHwfjTRPWcO3LihSliyTKO0Gnekly9ZAg8+qNaHD4dOnVy3b0EQBEEfRHQLuhIcbL/TrbncLVq0oEiRItk+xl1O948//ghAq1atiNDOunwUzeWuWBHSr3EIgiB4LVp6+fr1ajlkiKrptgctvTw2FoxGx2PLDaMR3n8fevaE6Gho1Qo+/ti5+xQEQRCcg4huQVe0kxt7nG6tnju7ruUa7nC6jUYj06dPB2DIkCEu26+7cEdquSAIgrPQnG6wv4GahuU119hY+7eTFzEx0KcPvPmmapz2zDPwzz/S70IQBMFbkZpuQVcsu8TaQlJSEqtWrQJyrucG9zjdq1evJioqioIFC/KgluPnw4joFgTBlwiyONPp2RPKlLF/W2FhSrinpakUc8351pNz56BzZ/VZHBICX3+txpsJgiAI3os43YKuaOnlqam2vbTWr19PXFwcpUqVon79+jk+zh1O97Rp0wB4+OGHM/bvy4joFgTBl7B0uu1toKZhMJiFtjOaqV2/rurNDx5U3dXXrxfBLQiC4AuI0y3oSnCwSiu3Nb1cq+e+9957MRhyfq6rRfe1a9eYP38+AE899ZRL9uluRHQLguBLaKK7YkW45x7HtxcRoRqy6d1MLT5eOfH79ys3fuNGFbMgCILg/YjTLehKSIha2up0W1PPDa5PL//xxx9JTk6mYcOGNGjQwCX7dCexsXDmjFoX0S0Igi9Qo4Zajh5tfwM1S5wxqzslBfr1g02boFAhWLZMBLcgCIIvIU63oCua052WZr3ojoqK4tChQwQGBuY4KkzDlU63yWTKSC33hwZqAIcOqWWJEpBDA3lBEASv4tlnVcp27dr6bE/vWd1Go+qovmQJhIfD4sVQp44+2xYEQRA8AxHdgq6EhqplWpr16eVaannLli0pVKhQro91pdO9detW9u/fT3h4OAMHDnT6/jwBSS0XBMHXCA7WV8TqPav71Vfhhx+UCz9vnhoNJgiCIPgWbk0vnzBhAk2aNCEiIoISJUpw//33c/jw4UyPSUxMZMSIERQtWpQCBQrQp08fLl26lOt2DQZDtj+ffPJJxmMqVap02/0ffvihU/5Of8LsdFufw6eJ7ty6lmtYOt0mk8mOCK1Hc7n79etHZGSkU/flKezfr5YiugVBELJHz/Tyzz8H7dRk+nTo3t3xbQqCIAieh1tF99q1axkxYgRbtmxhxYoVpKSkcM899xAXF5fxmDFjxrBo0SLmzZvH2rVrOX/+PA888ECu271w4UKmnxkzZmAwGOjTp0+mx7377ruZHjdq1Cin/J3+REiIbenliYmJGaPC8qrnBrPTDWrMmLO4desWc+bMAfwntdxkggUL1Hrz5u6NRRAEwVPRK73855/hhRfU+kcfweOPO7Y9QRAEwXNxa3r50qVLM/3+/fffU6JECbZv307btm2Jjo5m+vTp/PLLL3To0AGAmTNncscdd7Blyxaa56AMSpUqlen3BQsWcPfdd1OlSpVMt0dERNz2WMExQkNtE93r1q0jISGBMmXKUK9evTwfbym64+PjCQsLsy/QPPj555+Ji4ujZs2atPKTXL/du1VNd2go3H+/u6MRBEHwTPRIL1++HAYNUuujR8NLLzkalSAIguDJeFT38ujoaACKpHdw2r59OykpKXTq1CnjMbVq1aJChQps3rzZqm1eunSJJUuWMHjw4Nvu+/DDDylatCgNGjTgk08+ITU1VYe/wr8JCVEvKaPRuvRyy67luY0K0wgODiYoSF0rclZd94ULF3j99dcBGDZsmFVx+QK//KKWPXqAn2TTC4Ig2Iyjc7q3bYMHHoDUVOjfHz77TM3/FgRBEHwXj2mkZjQaGT16NK1ataJOeseTixcvEhIScltzrZIlS3Lx4kWrtjtr1iwiIiJuS0l/9tlnadiwIUWKFGHTpk2MHTuWCxcu8Pnnn2e7naSkpEzpzDHp37Ymk8nptcX2osXmyvg0p9toDMBoNOYpWC3nc1sbZ758+YiJiSEuLk73v81kMjF48GCuX79Ow4YNeeaZZ1x+fN1x3IxGmD0bwMDAgSY89CXt8bjj2An6IMfOe3H1sStQAMDArVu2f1YePQrdukFcnIGOHU3MnKkEtz++7OQ9573IsfNe5Njpj7X/S48R3SNGjGDfvn1s2LBB1+3OmDGDhx9++LY05Oeffz5jvV69eoSEhPD0008zYcIEQrUW3BZMmDCBd95557bbo6OjPfaFazKZiI2NBXCZW2v+X4Rw48YNAnMZihoVFcWRI0cICgqiSZMmGZkOeREeHk5MTAyXLl2iRIkSOkRt5vvvv+fvv/8mNDSUKVOmkJCQ4LKZ4BruOG4bNwZy9mwEBQuaaNkyGisPhZAFdxw7QR/k2Hkvrj52wcEhQD6uX08hOtr68ZWXLhno0qUAV64EUq9eKjNmxJKYCImJzovVk5H3nPcix857kWOnPzFWpj15hOgeOXIkixcvZt26dZQrVy7j9lKlSpGcnMzNmzczud2XLl2yqhZ7/fr1HD58mF9//TXPxzZr1ozU1FROnjxJzZo1b7t/7NixmYR6TEwM5cuXJzIykoJarpmHoQngyMhIl72xChbUsgGCyZcvX64119oFltatW1O+fHmr96F1MA8KCtK1q/jx48d54403ABg/fnyOPQOcjTuOm9ZArU8fKFlScsvtxR3HTtAHOXbei6uPnXatNyEh2OrvoJgYlUp+6pSBKlVMLFsW6PeftfKe817k2Hkvcuz0x9r/o1tFt8lkYtSoUfzxxx+sWbOGypUrZ7q/UaNGBAcHs2rVqozO44cPH+b06dO0aNEiz+1Pnz6dRo0acdddd+X52F27dhEQEJCjcxoaGpqtA66NG/NULEeiuYLwcM3ZDiY1NTXX/VqmltsSnya6ExISdPu70tLSGDRoEHFxcbRr144xY8a49bi68rglJ8Nvv6n1hx82SG2hg7j6PSfohxw778WVx848Msy6z8ukJFXDvWuXEuzLlhmQHq4Kec95L3LsvBc5dvriFaJ7xIgR/PLLLyxYsICIiIiMOu3IyEjCw8OJjIxk8ODBPP/88xQpUoSCBQsyatQoWrRokcmFrFWrFhMmTKB3794Zt8XExDBv3jw+++yz2/a7efNmtm7dyt13301ERASbN29mzJgxPPLIIxQuXNj5f7gPExam9eYLJiUlJcfHJSQksHr1asC6+dyWaB3M9Uz7/uyzz9i4cSMFChTg+++/JyDAo3oMOpVly+DGDShdGtq3d3c0giAIno0tc7qNRnjsMfjnH1UL/tdfUK2ac+MTBEEQPA+3iu6vv/4agPZZzvRnzpzJoPRZGhMnTiQgIIA+ffqQlJREly5d+OqrrzI9/vDhw7fVA8+ZMweTycSAAQNu229oaChz5sxh3LhxJCUlUblyZcaMGZMpfVywD617eV6ie82aNSQmJlKuXDlq165t0z40pzs+3vpautxISEhg3LhxAHzxxRdUqlRJl+16Cz//rJb9+0MuJfiCIAgCts3pnjwZ5s6F4GCYPx8aNXJubIIgCIJn4vb08rwICwtj6tSpTJ061abtDB06lKFDh2b7+IYNG7JlyxbrAxWsJiRES7EIznUE2+LFiwHlctua3qK30/3ff/+RkJBA6dKleeKJJ3TZprdw6xYsXKjWBw50byyCIAjegC1zuv/8Uy3Hj4fOnZ0WkiAIguDh+E8OreASgoMz1nJ0uk0mEwvTld59991n8z70drq1hm6tWrXyu/qWBQsgIQGqVxcHRhAEwRos08tz8w6SkmDrVrXes6fz4xIEQRA8FxHdgq5YI7p37NjB2bNnyZ8/Px06dLB5H3o73Rs3bgRUF3V/45df1HLgQKSBmiAIghVoTrfJBHFxOT9u2zY1Dqx4cchmKIogCILgR4joFnTFLLpDchTdC9LnU3Xp0iXXkWLZsWwZJCZWB/Rxuo1GI5s2bQKU0+1PxMXB8uVqPZvWB4IgCEI25MsHWq/N3FLM169XyzZt5KKmIAiCvyOiW9CVkBBtLWenWxPdtqaWf/QRdO0Kq1aNAPRxug8ePMjNmzfJly+fVaPlfInt2yEtDcqWFRdGEATBWgwG65qpWYpuQRAEwb8R0S3oimV6eXaN1E6ePMmePXsICAige/fuVm/3q6/g1VfVelxcJKCP062lljdr1oxgc/B+wb//qmWzZu6NQxAEwdvIq5laWhqktwuhbVvXxCQIgiB4LiK6BV3Jq6Zba6DWunVrihYtatU2f/wRRoww/56aqux0PUW3v6WWg7nBT9Om7o1DEATB28hrVvf/27vz+Kiq+//j78mekM0AIQQBQREihiWAEVBAQAkIBjdcUEGR9muhbtVa/amIVqmtSwsPS2mL0Lrg0qq4gbIlLIYdRCFGQJYiCQhIIEJCSM7vj+udTEIm60wmQ17Px4PH3Jm5c+ZkjjfjO+dzz/3qK2sWPCpKamJFVACAShC64VHVhe7alpa/95708yXbVf5qXqEeKS93Xbm8qbFnugndAFA71ZWX26Xl/fpJgYEN0ycAQONF6IZHVRW6f/zxR2VmZkqqWejOzJRuvlkqLbUC99/+5vpsRL1nuvPy8vTdd9/J4XCob9++9WrL3+TlSXv3Wucm9u7t694AgH+prrx8+XLrltJyAIBE6IaHVRW6P/30U5WUlKhr1646//zzq2ynpES65x6puFi64QbpH/+wFmkrmzEIr/dMt11anpycrJiYmHq15W/sWe6LLir7n0cAQM3Y5eWVzXQbwyJqAIDyCN3wKNdLhlVcSK02peWvvSZlZ0txcdI//1kWtiMi7D3C6z3T3ZTP56a0HADqrqqZ7h07pAMHrD8U9+nTsP0CADROhG54lLuZ7qKiIi1cuFCSdM0111TZRmGhNGWKtf3oo5LrJHR4uL0V4bGZ7qYculm5HABqr6rQbZeWp6ZKYWEN1ycAQONF6IZHubtOd0ZGho4fP67WrVurTzV/+v/b36zzjdu0Kb9queQauus3033ixAlt3LhRUtML3aWlzHQDQH1UVV5OaTkAoCJCNzzK3Uy3XVo+atQoBQS4/8/u2DHp2Wet7aeecg3ZFtfy8vrMdK9du1anT59WYmKi2rdvX+d2/NH27VJ+vvXZXnyxr3sDAP6nqpluQjcAoCJCNzzKNXS7ntO9YMECSdWXlr/0knTokHThhWWXCnPlWl5en5luu7T8sssuk8PhqHM7/sie5U5JcR0vAEBNubtO9/ffS999JwUEWJcLAwBAInTDwyqb6T506JB2794tyQq57hw8KL34orX97LNSUNCZ+3iqvLwpn8+9Zo11S2k5ANSNu+t027PcPXqUBXMAAAjd8KjKQveGDRskSZ06dary0lzPPScVFEi9eknXX1/5Pp4oLy8tLdUXX3whqWmGbhZRA4D6cVdeTmk5AKAyhG54VFnoDlBRkVVebofuXr16uX1dQYG1gJokTZsmuav4di0vLywsVGlpaa37uHXrVuXn56tZs2bq3r17rV/vz4qKpM2brW1mugGgbtwtpGavXD5gQMP2BwDQuBG64VGu5wgXFlqBuCahe/lyKxB26CBdeaX79l3Ly633KKx1H+1Z7tTUVAVVVsN+Ftu8WSoullq0kM47z9e9AQD/VNlMd16e9PXX1nYVZ1IBAJogQjc8yjV0FxVZoXv9+vWSqg7dixZZt0OHVt2+a3m5pDqd123/EeCSJjjV61pa3sTWjwMAj6ksdH/yiXXbu7cUH9/wfQIANF6EbnhU2XW6rdB96NAh7d27V5KUkpLi9nWLF1u3Vc1yS2Uz3QEB1v/x1OW8bvv63FX9EeBsxSJqAFB/ruXlxljbH31k3Y4a5Zs+AQAaL0I3PCowUJKsGe6iotIaLaKWm2uV5Dkc0uDBVbdvh+7gYOv/eGo7011cXKyvvvpKUtV/BDhb2TPdhG4AqDt7pru0VDp5UiosLKvYInQDACpqWie0okEEBJSqtDSgXOiualZ5yRLrNiVFat686rbt8vLAwEhJtZ/p3rZtm06dOqWYmBh16NChVq/1d0eOSNu3W9uEbgCou2bNrD8UG2OVmG/YIJ04IZ17rnW5MAAAXDHTDY8LDCyRpBqHbnt2oLrScqlsptsO3bWd6bZLy1NSUuRoYic1r1tn3V5wgRQX59u+AIA/CwiQIq2vIR07Vr60vIl9tQAAaoDQDY8LCLDKy0+dMs5F1Hr37l3pvsbUfBE1yfWc7rrNdNuhu2fPnrV63dnAvlSYm6EAANSCXWJeMXQDAFARoRseZ890HzhwxLmImruQm51tndMdFib1719923Z5ucNhbdRnprup+e476/bCC33bDwA4G9iLqS1fLn3/vVVyfsUVvu0TAKBxInTD4wIDrZnuLVu+kVT1Imr2LPeAAVbwro49012X0F1SUqLNP0/3NuXQ3bGjb/sBAGcDe6b7zTet2yuvrNn3GACg6SF0w+Ps0L1//0FJVZ/PbV8qrCal5VJZ6Lav012b8vJvv/1WJ06cUEREhC5sgtO9u3ZZt01s/TgA8Ao7dP98FhWl5QAAtwjd8LigoNKft4IluQ/dxcVSRoa1XZNF1KSy8nJjrNBdm5nuTZs2SZJ69OihQOvaZk3G6dPSnj3WNjPdAFB/dnm5ZC2edvXVvusLAKBxI3TD4wIDzc9bVuh2t4ja6tVSQYHUsqXUrVvN2rZnuo0JlVS7me6mfD73vn1W8A4JkRITfd0bAPB/9ky3ZF2GsVUr3/UFANC4EbrhcUFB5UO3u0XU7NLyIUOsy6/UhB26S0utE+dqM9PdlEO3fT53hw41/6wBAO65hm5KywEAVeF/v+FxruXlNVlEraal5VJZeXlJSYikms90G2Oa9OXCWEQNADzLtbyc0A0AqAqhGx4XFGRvBbs9nzs/X1q71tqu6SJqUtlMtx26azrTvWvXLuXn5yskJEQXXXRRzd/wLMEiagDgWfZMd7t2UnKyb/sCAGjcCN3wONfycnfncy9dKpWUWNeMbteu5m3bobu42Cpdr+lMtz3LnZycrJCQkJq/4VmCmW4A8Kz+/a3be+6xFlIDAMCdoOp3AWonONgO3SGVznQbI730krU9cmTt2rbLy0tLAyUF1nim2165vCmezy0RugHA0wYOtBYDtb+XAABwh9ANj3MtL6/s/OnPPpNWrpTCwqTf/KZ2bZddp1uSwmscupvyImoSoRsAvKFZM1/3AADgDygvh8eFWlfzUnz8uWcsomaM9Pjj1vakSbW/fFVYmOu9iBqVlxtjtGHDBklNM3QfPy4dOmRtc043AAAA0LAI3fC4hIQWkqT09BvOeO7996UNG6TISOmRR2rftsPhGrxrNtO9f/9+/fDDDwoMDFRyE1ztxl5ErXnz8qvtAgAAAPA+Qjc8Ljzcqi/v2rVHucdLSqQnn7S2779fatmybu2XnT8XXqOZbru0PCkpSeHl69ObBErLAQAAAN8hdMPjgq2FxVVcXP7xt96Stm6VYmNrfy63q7LcHFGjmW4WUbNuCd0AAABAwyN0w+MqC93FxdKUKdb2b39rBe+6KgvdtZvpbqqh2y4vJ3QDAAAADY/QDY+rLHTPnSvt3CnFx0v33lu/9l3Ly2sy071+/XpJ/hO6N22Snn46TPv2eaY9e6abRdQAAACAhkfohsfZofvUKevWGOn5563txx6r/yVWXMvLq5vp3rdvn77//nsFBgY2+tBtjPTnP0uXXiq9/HKYRo6Ufvqp/u1SXg4AAAD4DqEbHldxpnvjRmuWOyJCmjix/u27lpdXN9O9Zs0aSVK3bt3UrBFfUPXwYSk9XXrgAam42KHgYKMtWxyaMMEK43VVWkp5OQAAAOBLhG54XEiIdWuH7nfftW6vvtq1NLzuXMvLT506pZKSErf7rl69WpJ06aWX1v+NvWTVKqlHD+mjj6zPbsYMow8+KFBQkNHbb0svvlj3tnNzpaIiKTBQatvWY10GAAAAUEOEbnic60y3MdJ//mPdv/FGz7TvWl4uqcoS86ysLEmNN3QfOCClpUn79kkXXiitWSNNmiT161eil1+29nnkEWnRImu7uNhaBf6SS6xLrn3zTdXt27Pc7dtLQUHe+zkAAAAAVI7QDY9zDd2bN1ul5eHh0ogRnmnftbxcktsS81OnTmnDhg2SGm/o/uMfpYICKSVFWr/emvG2/epX0p13WiXiN98sPfOMdP750i23SOvWSYcOSf/9b9Xts4gaAAAA4FuEbnica+i2Z7lHjKj/Amo2u7w8KChKkvuZ7i1btqiwsFDnnHOOOnXq5Jk396DcXOmvf7W2n3tOiooq/7zDYT3fp4905Ij05JPS//5nrQA/aJC1z88Ls7vFImoAAACAbxG64XGuods+n/uGGzzXvj3THRQUI8n9TLfr+dwOh8NzHfCQP/xBKiyU+vaVrrqq8n3CwqT33pOSkqTu3aXZs6U9e6Snn7aeJ3QDAAAAjRtnecLj7NC9fr20fbsVHEeO9Fz7dugODIyU5H6muzEvovb999KsWdb2009bs9runHuutG1b+cd69pQCAqxzwfPypISEyl/LyuUAAACAbzHTDY+zQ3d2tnU7fLgUGem59u3ycjt012Smu7GZNs1aVfzyy6UhQ2r/+shIa/Zbqnq2m3O6AQAAAN8idMPj7NBt82RpuVQ20x0Q4H6m+4cfftDOnTslSZdccolnO1BPe/dK//iHtV3dLHdVeve2bt2F7pMnpf37rW1mugEAAADfIHTD4+zrdEtSaKhnS8sl19BtTXlXNtO9Zs0aSVJSUpJiY2M924F6eu456dQpazE0e0G0uqgudO/ebd1GR0txcXV/HwAAAAB1R+iGx7nOdA8bZoU+T7LLyx0O99fptkvL+/bt69k3r6fsbGsxNEmaOrV+bbmGbmPOfN71fO5GuI4cAAAA0CQQuuFxrqH7xhs9335NrtOdlZUlqfGcz22M9Oqr0iWXSKdPS0OHSgMG1K/N7t2lwEDpwAFrYbaKWLkcAAAA8D1CNzzODt0hIdKoUZ5v3w7dxlQeuktKSrR27VpJjSN0//CDdN110oQJUkGBtXja3Ln1bzc8XLr4Ymu7shJzFlEDAAAAfI/QDY/r1csqAb/7bikmxvPt2+XlxoRJOrO8fNu2bSooKFBkZKQuuugiz3egFhYssILxBx9Yf4x4/nlp2TKpTRvPtF/Ved3MdAMAAAC+x3W64XFt20rHjnnvPGJ7pru0NFTSmTPd9vncl1xyiQIDA73TiWqUlkq//7301FNWaXnXrtLrr0s9enj2ffr0sc4RX7fuzOe+/da6JXQDAAAAvkPohld4M+vaobukxArdFWe6fX197mPHpDvukObPt+7/3/9JL78shYV5/r0qLqZm/6Fj9Wpr0bbgYKlnT8+/LwAAAICaobwcfscuLy8psa5N5m6m2xeh+5tvrMXS5s+3zmmfPVuaOdM7gVuyStdDQqQjR8ouESZJL75o3Y4dK7Vq5Z33BgAAAFA9Qjf8jj3Tffq0tWKb60z30aNHtW3bNklSampqg/Vp927pN7+xZp5zcqxztleskO66y7vvGxoqdetmbdvnde/cKb33nrX9m9949/0BAAAAVI3QDb9jh+7iYit0u85026uWd+jQQfHx8V7vS1aWdVm088+XXnpJ+uknaeBAacMGa8a7IVRcTO3ll61zyocPL1vdHAAAAIBvcE43/I5dXm4JKzfTvXLlSklS//79vd6P+++X/vKXsvtDh0oPPCClpUkBDfjnLNfQffiwdT1wSXrooYbrAwAAAIDKEbrhd+yZ7p/vlZvptkP35Zdf7tU+fPKJFbgdDunOO60Anpzs1bd0yw7dGzZIr7winTxpLZ52xRW+6Q8AAACAMoRu+J2gIOvf6dOSFOGc6S4uLnYuonbZZZd57f0PH7auQS5ZM9v2omW+0rWrtVBbfr51HXBJevhh712yDQAAAEDNcU43/FJZiXnZTPfGjRt18uRJxcXFqUuXLl5770mTpLw8KSlJevZZr71NjQUFlV0W7MQJqV076YYbfNsnAAAAABZCN/xSWYl5Wei2S8svu+wyBXjppOq337b+BQZK//639y4FVlt2iblklboHB/usKwAAAABcELrhl8pCd1l5+YoVKyR5r7Q8N1f61a+s7f/3/8oHXV+z+xITU1b6DgAAAMD3OKcbfqliebkxptxMt6cZI02cKB05YpVyP/64x9+iXm64QVq6VBo1SoqK8nVvAAAAANgI3fBLruXlJ0+eVE5Ojg4fPqywsDD16tXL4++3ebO1YnlIiFVW3tjKtyMipLlzfd0LAAAAABVRXg6/5FpefuLECWdpeWpqqkJCQjz+fkuXWrdXXildfLHHmwcAAABwliJ0wy+5znSfPn1ay5Ytk+S963NnZFi3gwZ5pXkAAAAAZymfhu5p06apT58+ioqKUnx8vEaPHq2cnJxy+xQWFmrSpElq3ry5IiMjdf311+vAgQNVtjt+/Hg5HI5y/9LS0srtc+TIEY0dO1bR0dGKjY3VhAkTVFBQ4PGfEd7hek63JC1evFiSd87nLimRli+3tgndAAAAAGrDp6E7MzNTkyZN0urVq7Vo0SIVFxfrqquu0k8//eTc54EHHtBHH32kd999V5mZmdq/f7+uu+66attOS0tTbm6u89+8efPKPT927Fht3bpVixYt0scff6zly5frF7/4hcd/RnhH2Ux3M0nSDz/8oICAAPXt29fj77Vpk3TsmBQdXXY9bAAAAACoCZ8upLZw4cJy9+fOnav4+Hht2LBBAwYMUH5+vmbPnq0333xTgwcPliTNmTNHSUlJWr16tS699FK3bYeGhiohIaHS57Kzs7Vw4UKtW7dOvX++1tKMGTM0YsQIvfDCC0pMTPTQTwhvsUN3cHC0iout7e7duys6Otrj72WXlg8YYF2fGwAAAABqqlGtXp6fny9JiouLkyRt2LBBxcXFGjp0qHOfLl26qF27dsrKyqoydGdkZCg+Pl7nnHOOBg8erN///vdq3ry5JCkrK0uxsbHOwC1JQ4cOVUBAgNasWaNrr732jPaKiopUVFTkvH/s2DFJkjFGxph6/NTeY/etsfavPqzQ7VBQUJQzdPfv398rP6t1urhDgwYZNcRHeTaP29mOsfNfjJ3/Yuz8E+Pmvxg7/8XYeV5NP8tGE7pLS0t1//33q3///rr45+Wh8/LyFBISotjY2HL7tmrVSnl5eW7bSktL03XXXacOHTpo586deuyxxzR8+HBlZWUpMDBQeXl5io+PL/eaoKAgxcXFuW132rRpmjp16hmP5+fnN9r/cI0xzvPUHQ6Hj3vjWQEBYZLCFBgY6XwsJSXF+YcbTzl9WlqxIkaS1Lt3gfLzSzzafmXO5nE72zF2/oux81+MnX9i3PwXY+e/GDvPsydiq9NoQvekSZP09ddfa+XKlfVu6+abb3ZuJycnq1u3bjr//POVkZGhIUOG1KnNRx99VA8++KDz/rFjx9S2bVvFxMR4paTZE+w/BsTExJx1B5b9d5igoCjnY1dddZViYmI8+j5r10rHjzsUG2vUv39kg5SXn83jdrZj7PwXY+e/GDv/xLj5L8bOfzF2nlfTz7FRhO7Jkyc7FzM799xznY8nJCTo1KlTOnr0aLnZ7gMHDrg9X7syHTt2VIsWLbRjxw4NGTJECQkJOnjwYLl9Tp8+rSNHjrhtNzQ0VKGhoWc8bq+O3li5ruB+NmlmrZ+mgABro2PHjmrTpo3H3ycz07odMMChoAY8Ws7WcWsKGDv/xdj5L8bOPzFu/oux81+MnWfV9HP06erlxhhNnjxZ77//vpYuXaoOHTqUe75Xr14KDg7WkiVLnI/l5ORo7969tVqlet++fTp8+LBat24tSerbt6+OHj2qDRs2OPdZunSpSktLlZqaWs+fCg3BXkjN4bDKy71xqTCpbBG1K67wSvMAAAAAznI+Dd2TJk3S66+/rjfffFNRUVHKy8tTXl6eTp48KckqfZgwYYIefPBBLVu2TBs2bNCdd96pvn37lltErUuXLnr//fclSQUFBXr44Ye1evVq7d69W0uWLFF6erouuOACDRs2TJKUlJSktLQ0TZw4UWvXrtWqVas0efJk3Xzzzaxc7ifs0B0UZIXugQMHevw9ioulFSusba7PDQAAAKAufFpePnPmTEnSoAqJZs6cORo/frwk6eWXX1ZAQICuv/56FRUVadiwYfrrX/9abv+cnBznAlqBgYHasmWL/vWvf+no0aNKTEzUVVddpWeeeaZcefgbb7yhyZMna8iQIc72p0+f7r0fFh4VEWHdduzYVffe+6xuu+02j7/Hxo1SQYF0zjlSt24ebx4AAABAE+DT0F2TVb/DwsL0yiuv6JVXXqlRO+Hh4frss8+qbTcuLk5vvvlmzTqKRsee6Q4IiNJjjz3mlfewLhUmDRwoBfi0JgQAAACAvyJKwC/ZofvnMxG8gvO5AQAAANQXoRt+yS4v91boLi6W7KvXcT43AAAAgLoidMMv2TPdJ054p/3166WffpKaN5cuvtg77wEAAADg7Efohl/yZnn5iRPSjBnWNudzAwAAAKgPny6kBtSVt8rLly+XJkyQduyw7t96q2fbBwAAANC0MIcHv+Tpme6CAunXv7ZmtnfskNq0kT7+WLr+es+0DwAAAKBpYqYbfskO3adPW4ueBQfXva2CAik1Vdq2zbo/caL0pz9JMTH17ycAAACApo3QDb9kl5dL1mx3fUL3735nBe6EBOm116ShQ+vfPwAAAACQKC+HnwoNlRwOa7s+JeZLl0qvvGJtE7gBAAAAeBqhG37J4ZDCwqztul427Ngx6a67rO3/+z8CNwAAAADPI3TDb9V3BfOHH5b27JE6dLDO4QYAAAAATyN0w2/VZwXzzz6T/v53a3vOHCky0nP9AgAAAAAboRt+yw7dtS0vz8+X7r7b2r73XusyYQAAAADgDYRu+K26lpdPny7t2yd16iRNm+b5fgEAAACAjdANv1WX8nJjrFXKJemJJ8pfegwAAAAAPI3QDb9Vl/LytWul7dutsH3ttd7pFwAAAADYCN3wW3UpL7dnua+9lsXTAAAAAHgfoRt+q7bl5cXF0ltvWdu33+6dPgEAAACAK0I3/FZty8sXLpQOH5ZatZKGDPFevwAAAADARuiG36ptebldWn7rrVJQkHf6BAAAAACuCN3wW7UpL8/Plz780NqmtBwAAABAQyF0w2/Vprz8P/+Rioqkiy6SevTwarcAAAAAwInQDb9Vm5luu7T89tslh8N7fQIAAAAAV4Ru+K2antO9d6+UmWlt33qrd/sEAAAAAK4I3fBbNS0vf+MN63bQIKldO692CQAAAADKIXTDb9WkvHz7dmn6dGubBdQAAAAANDRCN/xWdeXl2dnSgAFSXp6UlCSNGdNwfQMAAAAAidANP1ZVefmWLdLAgVbgTk6WMjKkyMgG7R4AAAAAELrhv9yVl2/cKF1xhfTDD1JKirRsmRQf3/D9AwAAAIAgX3cAqKvKysv37JEGD5by86XUVGnhQik21ifdAwAAAABCN/xXZeXlc+ZYgTslRfr8cyk62jd9AwAAAACJ8nL4sYrl5cZI77xjbd9/P4EbAAAAgO8RuuG3KpaXb91qrVgeEiJdc43v+gUAAAAANkI3/JY9011YKJWWls1yp6VJMTG+6xcAAAAA2Ajd8Ft26Jas4G2H7ptu8k1/AAAAAKAiQjf8lmvoXrNGysmRQkOlUaN81ycAAAAAcEXoht8KCpKCg63tuXOt2xEjpKgon3UJAAAAAMohdMOv2bPd//2vdTtmjO/6AgAAAAAVEbrh1+wVzH/6yQrgI0f6tj8AAAAA4IrQDb/mel731VdLkZG+6wsAAAAAVETohl9zDd2UlgMAAABobAjd8Gt2eXlEhLWIGgAAAAA0JoRu+DV7pnvkSKlZM9/2BQAAAAAqInTDr118seRwSHff7eueAAAAAMCZCN3way++KH37rXTllb7uCQAAAACcidANvxYeLl1wga97AQAAAACVI3QDAAAAAOAlhG4AAAAAALyE0A0AAAAAgJcQugEAAAAA8BJCNwAAAAAAXkLoBgAAAADASwjdAAAAAAB4CaEbAAAAAAAvIXQDAAAAAOAlhG4AAAAAALyE0A0AAAAAgJcQugEAAAAA8BJCNwAAAAAAXkLoBgAAAADASwjdAAAAAAB4CaEbAAAAAAAvIXQDAAAAAOAlhG4AAAAAALyE0A0AAAAAgJcQugEAAAAA8BJCNwAAAAAAXkLoBgAAAADASwjdAAAAAAB4CaEbAAAAAAAvIXQDAAAAAOAlQb7ugL8yxkiSjh075uOeuGeM0bFjx+RwOORwOHzdHdQQ4+a/GDv/xdj5L8bOPzFu/oux81+MnefZWdDOhu4Quuvo+PHjkqS2bdv6uCcAAAAAAF85fvy4YmJi3D7vMNXFclSqtLRU+/fvV1RUVKP9S9GxY8fUtm1b/e9//1N0dLSvu4MaYtz8F2Pnvxg7/8XY+SfGzX8xdv6LsfM8Y4yOHz+uxMREBQS4P3Obme46CggI0LnnnuvrbtRIdHQ0B5YfYtz8F2Pnvxg7/8XY+SfGzX8xdv6LsfOsqma4bSykBgAAAACAlxC6AQAAAADwEkL3WSw0NFRTpkxRaGior7uCWmDc/Bdj578YO//F2Pknxs1/MXb+i7HzHRZSAwAAAADAS5jpBgAAAADASwjdAAAAAAB4CaEbAAAAAAAvIXQDAAAAAOAlhG4/98orr+i8885TWFiYUlNTtXbt2ir3f/fdd9WlSxeFhYUpOTlZn376aQP1FLZp06apT58+ioqKUnx8vEaPHq2cnJwqXzN37lw5HI5y/8LCwhqox5Ckp5566owx6NKlS5Wv4XhrHM4777wzxs7hcGjSpEmV7s/x5jvLly/XqFGjlJiYKIfDoQ8++KDc88YYPfnkk2rdurXCw8M1dOhQbd++vdp2a/tdidqrauyKi4v1yCOPKDk5Wc2aNVNiYqLuuOMO7d+/v8o26/J7F7VT3TE3fvz4M8YgLS2t2nY55ryvurGr7HvP4XDoT3/6k9s2Oea8h9Dtx95++209+OCDmjJlijZu3Kju3btr2LBhOnjwYKX7f/HFF7rllls0YcIEbdq0SaNHj9bo0aP19ddfN3DPm7bMzExNmjRJq1ev1qJFi1RcXKyrrrpKP/30U5Wvi46OVm5urvPfnj17GqjHsHXt2rXcGKxcudLtvhxvjce6devKjduiRYskSTfeeKPb13C8+cZPP/2k7t2765VXXqn0+T/+8Y+aPn26/va3v2nNmjVq1qyZhg0bpsLCQrdt1va7EnVT1didOHFCGzdu1BNPPKGNGzfqvffeU05Ojq655ppq263N713UXnXHnCSlpaWVG4N58+ZV2SbHXMOobuxcxyw3N1evvvqqHA6Hrr/++irb5ZjzEgO/dckll5hJkyY575eUlJjExEQzbdq0SvcfM2aMufrqq8s9lpqaan75y196tZ+o2sGDB40kk5mZ6XafOXPmmJiYmIbrFM4wZcoU07179xrvz/HWeN13333m/PPPN6WlpZU+z/HWOEgy77//vvN+aWmpSUhIMH/605+cjx09etSEhoaaefPmuW2ntt+VqL+KY1eZtWvXGklmz549bvep7e9d1E9l4zZu3DiTnp5eq3Y45hpeTY659PR0M3jw4Cr34ZjzHma6/dSpU6e0YcMGDR061PlYQECAhg4dqqysrEpfk5WVVW5/SRo2bJjb/dEw8vPzJUlxcXFV7ldQUKD27durbdu2Sk9P19atWxuie3Cxfft2JSYmqmPHjho7dqz27t3rdl+Ot8bp1KlTev3113XXXXfJ4XC43Y/jrfHZtWuX8vLyyh1XMTExSk1NdXtc1eW7Eg0jPz9fDodDsbGxVe5Xm9+78I6MjAzFx8erc+fOuueee3T48GG3+3LMNU4HDhzQJ598ogkTJlS7L8ecdxC6/dShQ4dUUlKiVq1alXu8VatWysvLq/Q1eXl5tdof3ldaWqr7779f/fv318UXX+x2v86dO+vVV1/V/Pnz9frrr6u0tFT9+vXTvn37GrC3TVtqaqrmzp2rhQsXaubMmdq1a5cuv/xyHT9+vNL9Od4apw8++EBHjx7V+PHj3e7D8dY42cdObY6runxXwvsKCwv1yCOP6JZbblF0dLTb/Wr7exeel5aWpn//+99asmSJnn/+eWVmZmr48OEqKSmpdH+OucbpX//6l6KionTddddVuR/HnPcE+boDQFM2adIkff3119WeL9O3b1/17dvXeb9fv35KSkrSrFmz9Mwzz3i7m5A0fPhw53a3bt2Umpqq9u3b65133qnRX47ROMyePVvDhw9XYmKi23043gDvKS4u1pgxY2SM0cyZM6vcl9+7vnfzzTc7t5OTk9WtWzedf/75ysjI0JAhQ3zYM9TGq6++qrFjx1a7KCjHnPcw0+2nWrRoocDAQB04cKDc4wcOHFBCQkKlr0lISKjV/vCuyZMn6+OPP9ayZct07rnn1uq1wcHB6tmzp3bs2OGl3qE6sbGxuvDCC92OAcdb47Nnzx4tXrxYd999d61ex/HWONjHTm2Oq7p8V8J77MC9Z88eLVq0qMpZ7spU93sX3texY0e1aNHC7RhwzDU+K1asUE5OTq2/+ySOOU8idPupkJAQ9erVS0uWLHE+VlpaqiVLlpSboXHVt2/fcvtL0qJFi9zuD+8wxmjy5Ml6//33tXTpUnXo0KHWbZSUlOirr75S69atvdBD1ERBQYF27tzpdgw43hqfOXPmKD4+XldffXWtXsfx1jh06NBBCQkJ5Y6rY8eOac2aNW6Pq7p8V8I77MC9fft2LV68WM2bN691G9X93oX37du3T4cPH3Y7Bhxzjc/s2bPVq1cvde/evdav5ZjzIF+v5Ia6e+utt0xoaKiZO3eu2bZtm/nFL35hYmNjTV5enjHGmNtvv9387ne/c+6/atUqExQUZF544QWTnZ1tpkyZYoKDg81XX33lqx+hSbrnnntMTEyMycjIMLm5uc5/J06ccO5TceymTp1qPvvsM7Nz506zYcMGc/PNN5uwsDCzdetWX/wITdJvfvMbk5GRYXbt2mVWrVplhg4dalq0aGEOHjxojOF4a+xKSkpMu3btzCOPPHLGcxxvjcfx48fNpk2bzKZNm4wk89JLL5lNmzY5V7j+wx/+YGJjY838+fPNli1bTHp6uunQoYM5efKks43BgwebGTNmOO9X910Jz6hq7E6dOmWuueYac+6555rNmzeX++4rKipytlFx7Kr7vYv6q2rcjh8/bh566CGTlZVldu3aZRYvXmxSUlJMp06dTGFhobMNjjnfqO73pTHG5Ofnm4iICDNz5sxK2+CYaziEbj83Y8YM065dOxMSEmIuueQSs3r1audzAwcONOPGjSu3/zvvvGMuvPBCExISYrp27Wo++eSTBu4xJFX6b86cOc59Ko7d/fff7xznVq1amREjRpiNGzc2fOebsJtuusm0bt3ahISEmDZt2pibbrrJ7Nixw/k8x1vj9tlnnxlJJicn54znON4aj2XLllX6+9Een9LSUvPEE0+YVq1amdDQUDNkyJAzxrR9+/ZmypQp5R6r6rsSnlHV2O3atcvtd9+yZcucbVQcu+p+76L+qhq3EydOmKuuusq0bNnSBAcHm/bt25uJEyeeEZ455nyjut+Xxhgza9YsEx4ebo4ePVppGxxzDcdhjDFenUoHAAAAAKCJ4pxuAAAAAAC8hNANAAAAAICXELoBAAAAAPASQjcAAAAAAF5C6AYAAAAAwEsI3QAAAAAAeAmhGwAAAAAALyF0AwBQRw6HQx988IGvuwEAABoxQjcAAC7Gjx8vh8Mhh8Oh4OBgtWrVSldeeaVeffVVlZaWlts3NzdXw4cPr1G7/hTQn3rqKednEBQUpBYtWmjAgAH685//rKKiIl93z63du3fL4XBo8+bNvu4KAABOhG4AACpIS0tTbm6udu/erQULFuiKK67Qfffdp5EjR+r06dPO/RISEhQaGurDnnpP165dlZubq71792rZsmW68cYbNW3aNPXr10/Hjx/3dffq5dSpU77uAgCgCSF0AwBQQWhoqBISEtSmTRulpKToscce0/z587VgwQLNnTvXuZ/r7PWpU6c0efJktW7dWmFhYWrfvr2mTZsmSTrvvPMkSddee60cDofz/s6dO5Wenq5WrVopMjJSffr00eLFi8v15bzzztNzzz2nu+66S1FRUWrXrp3+/ve/l9tn3759uuWWWxQXF6dmzZqpd+/eWrNmjfP5+fPnKyUlRWFhYerYsaOmTp1a7o8HlQkKClJCQoISExOVnJysX//618rMzNTXX3+t559/3rnfjz/+qDvuuEPnnHOOIiIiNHz4cG3fvl2SZIxRy5Yt9Z///Me5f48ePdS6dWvn/ZUrVyo0NFQnTpxwfqb//Oc/de211yoiIkKdOnXShx9+WO79xo4dq5YtWyo8PFydOnXSnDlzJEkdOnSQJPXs2VMOh0ODBg2SZFUvjB49Ws8++6wSExPVuXNnSdL//vc/jRkzRrGxsYqLi1N6erp2797tfK9169bpyiuvVIsWLRQTE6OBAwdq48aN5T4nh8OhWbNmaeTIkYqIiFBSUpKysrK0Y8cODRo0SM2aNVO/fv20c+fOKj9vAMDZi9ANAEANDB48WN27d9d7771X6fPTp0/Xhx9+qHfeeUc5OTl64403nOF63bp1kqQ5c+YoNzfXeb+goEAjRozQkiVLtGnTJqWlpWnUqFHau3dvubZffPFF9e7dW5s2bdKvfvUr3XPPPcrJyXG2MXDgQH3//ff68MMP9eWXX+q3v/2tsxR+xYoVuuOOO3Tfffdp27ZtmjVrlubOnatnn3221p9Bly5dNHz48HKfwfjx47V+/Xp9+OGHysrKkjFGI0aMUHFxsRwOhwYMGKCMjAxJVmDOzs7WyZMn9c0330iSMjMz1adPH0VERDjbnDp1qsaMGaMtW7ZoxIgRGjt2rI4cOSJJeuKJJ7Rt2zYtWLBA2dnZmjlzplq0aCFJWrt2rSRp8eLFys3NLdfPJUuWKCcnR4sWLdLHH3+s4uJiDRs2TFFRUVqxYoVWrVqlyMhIpaWlOWfCjx8/rnHjxmnlypVavXq1OnXqpBEjRpwx0//MM8/ojjvu0ObNm9WlSxfdeuut+uUvf6lHH31U69evlzFGkydPrvXnDQA4SxgAAOA0btw4k56eXulzN910k0lKSnLel2Tef/99Y4wxv/71r83gwYNNaWlppa913bcqXbt2NTNmzHDeb9++vbntttuc90tLS018fLyZOXOmMcaYWbNmmaioKHP48OFK2xsyZIh57rnnyj322muvmdatW7vtw5QpU0z37t0rfe6RRx4x4eHhxhhjvv32WyPJrFq1yvn8oUOHTHh4uHnnnXeMMcZMnz7ddO3a1RhjzAcffGBSU1NNenq6s/9Dhw41jz32mPP1kszjjz/uvF9QUGAkmQULFhhjjBk1apS58847K+3brl27jCSzadOmco+PGzfOtGrVyhQVFZX7DDp37lxuvIqKikx4eLj57LPPKm2/pKTEREVFmY8++shtf7OysowkM3v2bOdj8+bNM2FhYZW2CQA4+zHTDQBADRlj5HA4Kn1u/Pjx2rx5szp37qx7771Xn3/+ebXtFRQU6KGHHlJSUpJiY2MVGRmp7OzsM2a6u3Xr5tx2OBxKSEjQwYMHJUmbN29Wz549FRcXV+l7fPnll3r66acVGRnp/Ddx4kTl5uY6S7prw/UzyM7OVlBQkFJTU53PN2/eXJ07d1Z2drYkaeDAgdq2bZt++OEHZWZmatCgQRo0aJAyMjJUXFysL774wlkGXtnP26xZM0VHRzt/3nvuuUdvvfWWevTood/+9rf64osvatTv5ORkhYSElPtcduzYoaioKOfnEhcXp8LCQmcp+IEDBzRx4kR16tRJMTExio6OVkFBQZXj06pVK+f7uT5WWFioY8eO1aivAICzS5CvOwAAgL/Izs52njdcUUpKinbt2qUFCxZo8eLFGjNmjIYOHVrufOaKHnroIS1atEgvvPCCLrjgAoWHh+uGG244Y6Gv4ODgcvcdDoezfDw8PLzKPhcUFGjq1Km67rrrznguLCysytdWpqrPoDLJycmKi4tTZmamMjMz9eyzzyohIUHPP/+81q1bp+LiYvXr16/ca6r6eYcPH649e/bo008/1aJFizRkyBBNmjRJL7zwQpX9aNasWbn7BQUF6tWrl954440z9m3ZsqUkady4cTp8+LD+8pe/qH379goNDVXfvn2rHB/7DxKVPVZx9XsAQNNA6AYAoAaWLl2qr776Sg888IDbfaKjo3XTTTfppptu0g033KC0tDQdOXJEcXFxCg4OVklJSbn9V61apfHjx+vaa6+VZAVB14W8aqJbt2765z//6XyfilJSUpSTk6MLLrigVu1W5ptvvtHChQv16KOPSpKSkpJ0+vRprVmzxhmcDx8+rJycHF100UWSrMB5+eWXa/78+dq6dasuu+wyRUREqKioSLNmzVLv3r3PCMTVadmypcaNG6dx48bp8ssv18MPP6wXXnjBOZNd8XOuTEpKit5++23Fx8crOjq60n1WrVqlv/71rxoxYoQka+G1Q4cO1aqvAABQXg4AQAVFRUXKy8vT999/r40bN+q5555Tenq6Ro4cqTvuuKPS17z00kuaN2+evvnmG3377bd69913lZCQoNjYWEnWKuRLlixRXl6efvzxR0lSp06d9N5772nz5s368ssvdeutt9Z6NvSWW25RQkKCRo8erVWrVum7777Tf//7X2VlZUmSnnzySf373//W1KlTtXXrVmVnZ+utt97S448/XmW7p0+fVl5envbv36+vvvpKM2bM0MCBA9WjRw89/PDDzv6np6dr4sSJWrlypb788kvddtttatOmjdLT051tDRo0SPPmzVOPHj0UGRmpgIAADRgwQG+88YYGDhxYq5/3ySef1Pz587Vjxw5t3bpVH3/8sZKSkiRJ8fHxCg8P18KFC3XgwAHl5+e7bWfs2LFq0aKF0tPTtWLFCu3atUsZGRm69957tW/fPufP99prryk7O1tr1qzR2LFjq60sAACgIkI3AAAVLFy4UK1bt9Z5552ntLQ0LVu2TNOnT9f8+fMVGBhY6WuioqL0xz/+Ub1791afPn20e/duffrppwoIsL5qX3zxRS1atEht27ZVz549JVlB/ZxzzlG/fv00atQoDRs2TCkpKbXqa0hIiD7//HPFx8drxIgRSk5O1h/+8AdnP4cNG6aPP/5Yn3/+ufr06aNLL71UL7/8stq3b19lu1u3blXr1q3Vrl07DRo0SO+8844effRRrVixQpGRkc795syZo169emnkyJHq27evjDH69NNPy5VXDxw4UCUlJeXO3R40aNAZj9X053300UfVrVs3DRgwQIGBgXrrrbckWZc5mz59umbNmqXExMRywb+iiIgILV++XO3atdN1112npKQkTZgwQYWFhc6Z79mzZ+vHH39USkqKbr/9dt17772Kj4+vVX8BAHAYY4yvOwEAAAAAwNmImW4AAAAAALyE0A0AAAAAgJcQugEAAAAA8BJCNwAAAAAAXkLoBgAAAADASwjdAAAAAAB4CaEbAAAAAAAvIXQDAAAAAOAlhG4AAAAAALyE0A0AAAAAgJcQugEAAAAA8BJCNwAAAAAAXvL/AUhmKujvORa0AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 7))\n",
    "plt.plot(mean_base.index/10, mean_base['Mean'], label='July Base Model', color = 'black')\n",
    "plt.plot(mean_allshade.index/10, mean_allshade['Mean'], label='July All Shade Model', color = 'blue')\n",
    "\n",
    "#plt.plot(max_dailymean_scenario.index/10,max_dailymean_scenario['Max Daily Mean July Temp'],  label = 'Shade Scenario Model')\n",
    "\n",
    "\n",
    "plt.ylabel('Maximum Average Daily Temp (c)')\n",
    "plt.xlabel('Distance Downstream')\n",
    "#plt.figure(facecolor='black')\n",
    "\n",
    "    # Adjust layout\n",
    "plt.tight_layout()\n",
    "plt.grid(alpha = .2)\n",
    "#plt.ylim(21, 25)\n",
    "#plt.vlines(trib_dict_downstream, ymin = 20, ymax = 25, label = 'Tribs',color = 'Orange', linestyles = 'dashed')\n",
    "plt.legend()\n",
    "plt.title('July Mean Daily Temp WFK 2023 Heatsource Model')\n",
    "plt.savefig('JulyDailyMean_withobstribQ.png')\n",
    "plt.show()\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.19"
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