Create_materials.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "81aac0cf-03ab-4be4-9d5c-acdc6186e2fc",
   "metadata": {},
   "source": [
    "*Copyright: Fanny Lehmann (fanny.lehmann@centralesupelec.fr)* <br>\n",
    "*June, 2023*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "986be716",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "import os, shutil\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import simulation.randomfields as rf\n",
    "from simulation.utils import *\n",
    "from globalvars import *\n",
    "\n",
    "PATH_SEM3D = '/home/lehmannf/SEM2021/' # path to your personal installation of SEM3D"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "192868b1",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "run_name = 'example'\n",
    "path_folder = f'./simulation/{run_name}/' # folder to write all the simulation files"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b961c20a-4099-438a-8818-d79167560320",
   "metadata": {},
   "source": [
    "This notebook details all the steps needed to write the input files to run a simulation in a cubic domain with several horizontal layers, and random fields in each layer. \n",
    "\n",
    "Notebook summary:\n",
    "1. [Write the mesh files](#section1)\n",
    "2. [Create the mesh](#section2)\n",
    "3. [Add random fluctuations to the material](#Section3)\n",
    "4. [Assemble random materials into a numpy array](#Section4)\n",
    "5. [Run the simulation](#Section5)\n",
    "\n",
    "**This notebook assumes that you have already installed SEM3D and its RandomField module from [github](https://github.com/sem3d/SEM)**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70bd7e4e",
   "metadata": {},
   "source": [
    "<a id='section1'></a>\n",
    "# 1. Write the mesh files"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebd1b725-c372-45b8-bf9d-3f329128e38e",
   "metadata": {},
   "source": [
    "**After running this section, you should have the following files:**\n",
    "- `mater.in`\n",
    "- `mat.dat`\n",
    "- `mesh.input`\n",
    "- `input.spec`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39fdfe99-034a-4420-8299-5ae93ddda207",
   "metadata": {},
   "source": [
    "Choose between 1 and 6 layers (does not include the constant bottom layer)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8919e3b4",
   "metadata": {
    "collapsed": false,
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n"
     ]
    }
   ],
   "source": [
    "nb_layers = np.random.randint(1, high=7) # does not count the constant bottom layer\n",
    "print(nb_layers)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b7937fb-7e25-483b-ab16-a0f4fa748830",
   "metadata": {},
   "source": [
    "Choose the thickness of each layer by assigning one layer index to each of the 26 vertical points (`temp`). Then, count the number of points inside each layer to determine their respective thickness. There may be empty layers but that is not a problem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f4be3732",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "temp = np.random.randint(nb_layers, size=26)\n",
    "thickness_steps = np.unique(temp, return_counts=True)[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "440ec190-71c6-44f3-950c-b548370f07ac",
   "metadata": {},
   "source": [
    "Assign a random shear-wave velocity (S-wave velocity `Vs`) value to each layer. All layers are solid (`S`). The other material properties are : \n",
    "- compressional wave velocity (P-wave velocity, `Vp`)\n",
    "- density (`Rho`)\n",
    "- attenuation factors for P- and S-waves (`Qk` and `Qmu`)\n",
    "They are all computed from the S-wave velocity. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6e6ed584",
   "metadata": {
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     "outputs_hidden": false
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    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>thickness</th>\n",
       "      <th>type</th>\n",
       "      <th>Vs</th>\n",
       "      <th>Vp</th>\n",
       "      <th>Rho</th>\n",
       "      <th>Qk</th>\n",
       "      <th>Qmu</th>\n",
       "      <th>steps</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2400</td>\n",
       "      <td>S</td>\n",
       "      <td>1944.084579</td>\n",
       "      <td>3304.943784</td>\n",
       "      <td>2284</td>\n",
       "      <td>388</td>\n",
       "      <td>194</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2700</td>\n",
       "      <td>S</td>\n",
       "      <td>2304.859615</td>\n",
       "      <td>3918.261345</td>\n",
       "      <td>2381</td>\n",
       "      <td>460</td>\n",
       "      <td>230</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2700</td>\n",
       "      <td>S</td>\n",
       "      <td>2350.243321</td>\n",
       "      <td>3995.413645</td>\n",
       "      <td>2392</td>\n",
       "      <td>470</td>\n",
       "      <td>235</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1800</td>\n",
       "      <td>S</td>\n",
       "      <td>4500.000000</td>\n",
       "      <td>7650.000000</td>\n",
       "      <td>3170</td>\n",
       "      <td>900</td>\n",
       "      <td>450</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   thickness type           Vs           Vp   Rho   Qk  Qmu  steps\n",
       "0       2400    S  1944.084579  3304.943784  2284  388  194      8\n",
       "1       2700    S  2304.859615  3918.261345  2381  460  230      9\n",
       "2       2700    S  2350.243321  3995.413645  2392  470  235      9\n",
       "3       1800    S  4500.000000  7650.000000  3170  900  450      6"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "layers = pd.DataFrame({'thickness':np.append(thickness_steps*DX, DEPTH_BOTTOM),\n",
    "                    'type':['S']*(thickness_steps.shape[0]+1),\n",
    "                    'Vs':np.append(1785 + (3214 - 1785) * np.random.rand(nb_layers), VS_MAX),\n",
    "                    })\n",
    "layers.loc[:,'Vp'] = layers.loc[:,'Vs']*1.7\n",
    "layers.loc[:,'Rho'] = ComputeDsfromCp(layers.Vp)\n",
    "layers.loc[:,'Qk'] = ComputeQpfromCpCs(layers.Vp, layers.Vs)\n",
    "layers.loc[:,'Qmu'] = ComputeQsfromCs(layers.Vs)\n",
    "layers.loc[:,'steps'] = np.append(thickness_steps, 6) # number of mesh layers\n",
    "layers = layers.astype({'thickness':'int','Qk':'int','Qmu':'int','Rho':'int','steps':'int'})\n",
    "\n",
    "layers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9c5eb48-73db-46a0-bbb4-4f8ae3fc0aeb",
   "metadata": {},
   "source": [
    "Write the file describing the mesh. The spatial bounds (`XMIN`, `XMAX`, `YMIN`, `YMAX`, `ZMAX`) are given in meters and defined in `globalvars.py`. \n",
    "\n",
    "To simulate a semi-infinite domain, we put absorbing layers on all domain faces excepting the surface one. This behaviour is described by the booleans `PML_sides`, `PML_top`, and `PML_bottom` saying if absorbing boundary is present on the corresponding face or not (PML stands for Perfectly Matched Layer). The number of absorbing layers is given by `PML_layers`. Usually, one PML layer is enough. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "abf9a3f3-89ad-45dd-bbfa-ed4f6a22c2ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "PML_sides = True\n",
    "PML_top = False\n",
    "PML_bottom = True\n",
    "PML_layers = 1\n",
    "    \n",
    "domain = [XMIN, XMAX, DX, YMIN, YMAX, DY, ZMAX]\n",
    "comments=['xmin','xmax','xstep','ymin','ymax','ystep','zmax']\n",
    "\n",
    "# write the physical bounds\n",
    "with open(path_folder+'mat.dat', 'w') as f:\n",
    "    for line, comment in zip(domain,comments):\n",
    "        f.write(f\"{line}\\t# {comment}\\n\")\n",
    "    f.write(f\"{layers.shape[0]}\\t# nb. of layers\\n\")\n",
    "\n",
    "# write the layers\n",
    "with open(path_folder+'mat.dat', 'a') as f:\n",
    "    for index,row in layers.iterrows():\n",
    "        f.write(f\"{int(row.loc['thickness'])} {int(row.loc['steps'])}\\t# layer {index} (thickness, number of vertical elements)\\n\")\n",
    "\n",
    "# write the PMLs\n",
    "with open(path_folder+'mat.dat', 'a') as f:\n",
    "    f.write(f\"{PML_layers}\\t# Number of PML layers (0=no PML)\\n\")\n",
    "    f.write('2 ')\n",
    "    f.write('1 ' if PML_top else '0 ')\n",
    "    f.write('1 ' if PML_bottom else '0 ')\n",
    "    f.write('1 1 1 1 ' if PML_sides else '0 0 0 0 ')\n",
    "    f.write('\\t# PMLs on up down north south east west\\n')\n",
    "    f.write('1\\t# 8 or 27 control points for elements (1:8, 2:27)\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3774eec-3e8e-4b25-b38d-3db9436ac496",
   "metadata": {},
   "source": [
    "Describe the material properties"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c5afd75d-478a-4a0b-a162-e222206ccd3d",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(path_folder+'mater.in', 'w') as f:\n",
    "    f.write(f\"{layers.shape[0]}\\t# number of layers\\n\")\n",
    "\n",
    "    for index,row in layers.iterrows():\n",
    "        f.write(f\"{row.type}\\t{row.Vp}\\t{row.Vs}\\t{row.Rho}\\t{row.Qk}\\t{row.Qmu}\\n\")\n",
    "    f.write('# Material type, Vp, Vs, Rho, Qk, Qmu')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6cc8067e-89d9-4707-8ad7-742b5196c317",
   "metadata": {},
   "source": [
    "Write the general configuration file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "6fa3a7d3-d16c-4893-ac69-18bc32116a1e",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(path_folder+'input.spec', 'w') as f:\n",
    "    f.write('# -*- mode: perl -*-\\n')\n",
    "    f.write(f'run_name = \"{run_name}\";\\n\\n')\n",
    "    f.write('mesh_file = \"mesh4spec\"; # input mesh file\\n')\n",
    "    f.write('mat_file = \"material.input\";\\n')\n",
    "    f.write('dim=3;\\n')\n",
    "    f.write(f'ngll={NGLL};\\n\\n') # number of GLL quadrature points\n",
    "\n",
    "# damping\n",
    "if np.nanmax(layers[['Qk','Qmu']])==0:\n",
    "    Nsolids=0\n",
    "else:\n",
    "    Nsolids=3\n",
    "\n",
    "with open(path_folder+'input.spec', 'a') as f:\n",
    "    f.write('amortissement {\\n')\n",
    "    f.write(f'\\tnsolids = {Nsolids};')\n",
    "    f.write('\\t# number of solids for attenuation (0 if no attenuation)\\n')\n",
    "    f.write('\\tatn_band = 10  0.05;\\t# attenuation period band\\n')\n",
    "    f.write('\\tatn_period = 0.2;\\t# model period\\n')\n",
    "    f.write('};\\n\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39bb06e7-9509-4f24-92e1-400642ad2914",
   "metadata": {},
   "source": [
    "<a id='section2'></a>\n",
    "# 2. Create the mesh"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6b20ad6-1361-4fb2-a052-4b9ffcef369f",
   "metadata": {},
   "source": [
    "In `SEM3D` code, there is a single function to create the mesh and partition it over processors. The file `mesh.input` gives the number of processors to partition the mesh. This must be equal to the number of processors used to run the simulation later. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "486c3437-36cf-43bc-a7fe-0fe3e8abddd0",
   "metadata": {},
   "outputs": [],
   "source": [
    "Nprocessors = 1\n",
    "with open(path_folder+'/mesh.input', 'w') as f:\n",
    "    f.write(f'{Nprocessors}\\t# number of processors\\n')\n",
    "    f.write('1\\t# type of mesh: 1 = on the fly\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c78bd0cc-7f3a-44d5-8abb-41cda7af52ad",
   "metadata": {},
   "source": [
    "The command line to create the mesh in `SEM_BUILD/MESH/mesher < mesh.input`. It can be executed from the terminal, in a bash file on a computing cluster, or directly in the notebook for small computation. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "eef02c5b",
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-------------------------------------------------\n",
      "-------------------------------------------------\n",
      "-----                                       -----\n",
      "----- Construction of input files for SEM3D -----\n",
      "-----                                       -----\n",
      "-------------------------------------------------\n",
      "-------------------------------------------------\n",
      "\n",
      "    DEBUG MODE    \n",
      "\n",
      "\n",
      "   --> How many procs for the run ?\n",
      "             1 processor(s)\n",
      " \n",
      "\n",
      "  --> Which Initial Mesh?\n",
      "      1- On the fly\n",
      "      2- Abaqus from Cubit\n",
      "      3- Ideas (.unv) files\n",
      "      4- HDF5 Hex8 files\n",
      "      5- Earth Chunk\n",
      "      6- Full earth\n",
      "            Your choice is 1 \n",
      " \n",
      "\n",
      "Configuration SEM\n",
      "Schema en acceleration: 0\n",
      "Schema en vitesse: 0\n",
      "Duree simulation: 0.000000\n",
      "Newmark: alpha=0.000000 beta=0.000000 gamma=0.000000\n",
      "Fichier maillage: 'mesh4spec'\n",
      "Modele : 0\n",
      "Anisotropy : 0\n",
      "Fichier materiau: 'material.input'\n",
      "Sauv. Traces : 0\n",
      "Sauv. Snap   : 0\n",
      "Fichier stations: '(null)'\n",
      "Snap interval : 0.000000\n",
      "Snap selection : (nil)\n",
      "out variables : (0,0,0,1,1,1,1,0,0,0,0,0,0,0)\n",
      "Nonlinear analysis : 0\n",
      "Mirror : use 0\n",
      "Reading Materials\n",
      "mat= 0 : t=S vp=3304.943784 vs=1944.084579 rho=2284.000000 Qp=388.000000 Qmu=194.000000\n",
      "mat= 1 : t=S vp=3918.261345 vs=2304.859615 rho=2381.000000 Qp=460.000000 Qmu=230.000000\n",
      "mat= 2 : t=S vp=3995.413645 vs=2350.243321 rho=2392.000000 Qp=470.000000 Qmu=235.000000\n",
      "mat= 3 : t=S vp=7650.000000 vs=4500.000000 rho=3170.000000 Qp=900.000000 Qmu=450.000000\n",
      "\n",
      "Reading PML descriptions:\n",
      "Warning no shell flag\n",
      "Warning no mirror flag\n",
      "Creating grid mesh 34 x 34 x 33 with linear elements\n",
      "mat=4 layer = 0, idx=36 pmlnum=-1\n",
      "xp=0.000000 xw=-300.000000 ; yp=0.000000 yw=-300.000000 ; zp=0.000000 zw=0.000000\n",
      "mat=5 layer = 0, idx=4 pmlnum=-1\n",
      "xp=0.000000 xw=0.000000 ; yp=0.000000 yw=-300.000000 ; zp=0.000000 zw=0.000000\n",
      "mat=6 layer = 0, idx=20 pmlnum=-1\n",
      "xp=9600.000000 xw=300.000000 ; yp=0.000000 yw=-300.000000 ; zp=0.000000 zw=0.000000\n",
      "mat=7 layer = 0, idx=32 pmlnum=-1\n",
      "xp=0.000000 xw=-300.000000 ; yp=0.000000 yw=0.000000 ; zp=0.000000 zw=0.000000\n",
      "mat=8 layer = 0, idx=16 pmlnum=-1\n",
      "xp=9600.000000 xw=300.000000 ; yp=0.000000 yw=0.000000 ; zp=0.000000 zw=0.000000\n",
      "mat=9 layer = 0, idx=40 pmlnum=-1\n",
      "xp=0.000000 xw=-300.000000 ; yp=9600.000000 yw=300.000000 ; zp=0.000000 zw=0.000000\n",
      "mat=10 layer = 0, idx=8 pmlnum=-1\n",
      "xp=0.000000 xw=0.000000 ; yp=9600.000000 yw=300.000000 ; zp=0.000000 zw=0.000000\n",
      "mat=11 layer = 0, idx=24 pmlnum=-1\n",
      "xp=9600.000000 xw=300.000000 ; yp=9600.000000 yw=300.000000 ; zp=0.000000 zw=0.000000\n",
      "mat=12 layer = 1, idx=36 pmlnum=-1\n",
      "xp=0.000000 xw=-300.000000 ; yp=0.000000 yw=-300.000000 ; zp=0.000000 zw=0.000000\n",
      "mat=13 layer = 1, idx=4 pmlnum=-1\n",
      "xp=0.000000 xw=0.000000 ; yp=0.000000 yw=-300.000000 ; zp=0.000000 zw=0.000000\n",
      "mat=14 layer = 1, idx=20 pmlnum=-1\n",
      "xp=9600.000000 xw=300.000000 ; yp=0.000000 yw=-300.000000 ; zp=0.000000 zw=0.000000\n",
      "mat=15 layer = 1, idx=32 pmlnum=-1\n",
      "xp=0.000000 xw=-300.000000 ; yp=0.000000 yw=0.000000 ; zp=0.000000 zw=0.000000\n",
      "mat=16 layer = 1, idx=16 pmlnum=-1\n",
      "xp=9600.000000 xw=300.000000 ; yp=0.000000 yw=0.000000 ; zp=0.000000 zw=0.000000\n",
      "mat=17 layer = 1, idx=40 pmlnum=-1\n",
      "xp=0.000000 xw=-300.000000 ; yp=9600.000000 yw=300.000000 ; zp=0.000000 zw=0.000000\n",
      "mat=18 layer = 1, idx=8 pmlnum=-1\n",
      "xp=0.000000 xw=0.000000 ; yp=9600.000000 yw=300.000000 ; zp=0.000000 zw=0.000000\n",
      "mat=19 layer = 1, idx=24 pmlnum=-1\n",
      "xp=9600.000000 xw=300.000000 ; yp=9600.000000 yw=300.000000 ; zp=0.000000 zw=0.000000\n",
      "mat=20 layer = 2, idx=36 pmlnum=-1\n",
      "xp=0.000000 xw=-300.000000 ; yp=0.000000 yw=-300.000000 ; zp=0.000000 zw=0.000000\n",
      "mat=21 layer = 2, idx=4 pmlnum=-1\n",
      "xp=0.000000 xw=0.000000 ; yp=0.000000 yw=-300.000000 ; zp=0.000000 zw=0.000000\n",
      "mat=22 layer = 2, idx=20 pmlnum=-1\n",
      "xp=9600.000000 xw=300.000000 ; yp=0.000000 yw=-300.000000 ; zp=0.000000 zw=0.000000\n",
      "mat=23 layer = 2, idx=32 pmlnum=-1\n",
      "xp=0.000000 xw=-300.000000 ; yp=0.000000 yw=0.000000 ; zp=0.000000 zw=0.000000\n",
      "mat=24 layer = 2, idx=16 pmlnum=-1\n",
      "xp=9600.000000 xw=300.000000 ; yp=0.000000 yw=0.000000 ; zp=0.000000 zw=0.000000\n",
      "mat=25 layer = 2, idx=40 pmlnum=-1\n",
      "xp=0.000000 xw=-300.000000 ; yp=9600.000000 yw=300.000000 ; zp=0.000000 zw=0.000000\n",
      "mat=26 layer = 2, idx=8 pmlnum=-1\n",
      "xp=0.000000 xw=0.000000 ; yp=9600.000000 yw=300.000000 ; zp=0.000000 zw=0.000000\n",
      "mat=27 layer = 2, idx=24 pmlnum=-1\n",
      "xp=9600.000000 xw=300.000000 ; yp=9600.000000 yw=300.000000 ; zp=0.000000 zw=0.000000\n",
      "mat=28 layer = 3, idx=36 pmlnum=-1\n",
      "xp=0.000000 xw=-300.000000 ; yp=0.000000 yw=-300.000000 ; zp=0.000000 zw=0.000000\n",
      "mat=29 layer = 3, idx=4 pmlnum=-1\n",
      "xp=0.000000 xw=0.000000 ; yp=0.000000 yw=-300.000000 ; zp=0.000000 zw=0.000000\n",
      "mat=30 layer = 3, idx=20 pmlnum=-1\n",
      "xp=9600.000000 xw=300.000000 ; yp=0.000000 yw=-300.000000 ; zp=0.000000 zw=0.000000\n",
      "mat=31 layer = 3, idx=32 pmlnum=-1\n",
      "xp=0.000000 xw=-300.000000 ; yp=0.000000 yw=0.000000 ; zp=0.000000 zw=0.000000\n",
      "mat=32 layer = 3, idx=16 pmlnum=-1\n",
      "xp=9600.000000 xw=300.000000 ; yp=0.000000 yw=0.000000 ; zp=0.000000 zw=0.000000\n",
      "mat=33 layer = 3, idx=40 pmlnum=-1\n",
      "xp=0.000000 xw=-300.000000 ; yp=9600.000000 yw=300.000000 ; zp=0.000000 zw=0.000000\n",
      "mat=34 layer = 3, idx=8 pmlnum=-1\n",
      "xp=0.000000 xw=0.000000 ; yp=9600.000000 yw=300.000000 ; zp=0.000000 zw=0.000000\n",
      "mat=35 layer = 3, idx=24 pmlnum=-1\n",
      "xp=9600.000000 xw=300.000000 ; yp=9600.000000 yw=300.000000 ; zp=0.000000 zw=0.000000\n",
      "mat=36 layer = 3, idx=37 pmlnum=-1\n",
      "xp=0.000000 xw=-300.000000 ; yp=0.000000 yw=-300.000000 ; zp=-9600.000000 zw=-300.000000\n",
      "mat=37 layer = 3, idx=5 pmlnum=-1\n",
      "xp=0.000000 xw=0.000000 ; yp=0.000000 yw=-300.000000 ; zp=-9600.000000 zw=-300.000000\n",
      "mat=38 layer = 3, idx=21 pmlnum=-1\n",
      "xp=9600.000000 xw=300.000000 ; yp=0.000000 yw=-300.000000 ; zp=-9600.000000 zw=-300.000000\n",
      "mat=39 layer = 3, idx=33 pmlnum=-1\n",
      "xp=0.000000 xw=-300.000000 ; yp=0.000000 yw=0.000000 ; zp=-9600.000000 zw=-300.000000\n",
      "mat=40 layer = 3, idx=1 pmlnum=-1\n",
      "xp=0.000000 xw=0.000000 ; yp=0.000000 yw=0.000000 ; zp=-9600.000000 zw=-300.000000\n",
      "mat=41 layer = 3, idx=17 pmlnum=-1\n",
      "xp=9600.000000 xw=300.000000 ; yp=0.000000 yw=0.000000 ; zp=-9600.000000 zw=-300.000000\n",
      "mat=42 layer = 3, idx=41 pmlnum=-1\n",
      "xp=0.000000 xw=-300.000000 ; yp=9600.000000 yw=300.000000 ; zp=-9600.000000 zw=-300.000000\n",
      "mat=43 layer = 3, idx=9 pmlnum=-1\n",
      "xp=0.000000 xw=0.000000 ; yp=9600.000000 yw=300.000000 ; zp=-9600.000000 zw=-300.000000\n",
      "mat=44 layer = 3, idx=25 pmlnum=-1\n",
      "xp=9600.000000 xw=300.000000 ; yp=9600.000000 yw=300.000000 ; zp=-9600.000000 zw=-300.000000\n",
      "Writing MaterialsFound 5644 free faces\n",
      "\n",
      "Created 122964 facets\n",
      "0000 : number of elements = 38148\n",
      "0000 : number of elements = (tot=38148/fpml=0/spml=5380/fl=0/sol=32768)\n",
      "0000 : number of faces    = (tot=122964/fpml=0/spml=21588/fl=0/sol=101376)\n",
      "0000 : number of edges    = (tot=131649/fpml=0/spml=27105/fl=0/sol=104544)\n",
      "0000 : number of vertices = (tot=46835/fpml=0/spml=10898/fl=0/sol=35937)\n",
      "0000 : number of mirror points = 0\n",
      "processor with empty mirror: 0\n"
     ]
    }
   ],
   "source": [
    "current_dir = os.getcwd()\n",
    "os.chdir(path_folder)\n",
    "\n",
    "output = os.system(PATH_SEM3D + 'SEM_BUILD/MESH/mesher < mesh.input')\n",
    "if output!=0:\n",
    "    os.chdir(current_dir)\n",
    "    raise Exception('The mesher could not run \"'+run_name+'\"')\n",
    "\n",
    "os.chdir(current_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50a9790e-adb2-4f6a-891a-2d72d0c1c46c",
   "metadata": {},
   "source": [
    "Move all mesher files (starting by `mesh4spec`) into a folder called `sem` (do not change the name of this folder). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "d4019eeb-486c-4c19-9c59-06709ca79b3c",
   "metadata": {},
   "outputs": [],
   "source": [
    "current_dir = os.getcwd()\n",
    "os.chdir(path_folder)\n",
    "\n",
    "if not 'sem' in os.listdir():\n",
    "    os.mkdir('sem')\n",
    "    \n",
    "output = os.system('mv mesh4spec* sem')\n",
    "    \n",
    "os.chdir(current_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99d525f8",
   "metadata": {},
   "source": [
    "<a id='section3'></a>\n",
    "# 3. Add random fluctuations to the material"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb368b42",
   "metadata": {},
   "source": [
    "Randomly choose the correlation lengths along x, y, and z directions. Choose the coefficient of variation according to a normal distribution and ensure a positive value. Parameters are different in each layer. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "b080456f-4656-4ddb-86d2-6cf21015212e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chosen correlation length for x: [4500 3000 3000], y: [6000 4500 6000], z: [4500 1500 4500]\n",
      "Chosen coefficient of variation: [0.26436759 0.10200999 0.012179  ]\n"
     ]
    }
   ],
   "source": [
    "Nrandom_layers = layers.shape[0]-1 # all layers except the bottom one have fluctuations\n",
    "random_layers = np.arange(Nrandom_layers) # indices of random layers\n",
    "corr_length_x = np.random.choice([1500, 3000, 4500, 6000], size = Nrandom_layers)\n",
    "corr_length_y = np.random.choice([1500, 3000, 4500, 6000], size = Nrandom_layers)\n",
    "corr_length_z = np.random.choice([1500, 3000, 4500, 6000], size = Nrandom_layers)\n",
    "\n",
    "print(f'Chosen correlation length for x: {corr_length_x}, y: {corr_length_y}, z: {corr_length_z}')\n",
    "coef_var = np.abs(np.random.randn(Nrandom_layers)*0.1+0.2) # N(0.2, 0.1)\n",
    "print(f'Chosen coefficient of variation: {coef_var}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6fb31581-6b29-4843-8189-370ca91e7e02",
   "metadata": {},
   "source": [
    "The random generator is different for each layer. It is initialized at the beginning and incremented for each layer. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "856fb189-ec77-44e8-8f90-a65edd619f55",
   "metadata": {},
   "outputs": [],
   "source": [
    "seed = np.random.randint(0, 1e5) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d92d779-aca5-458e-a73c-c11421963de6",
   "metadata": {},
   "source": [
    "Assign random properties to Vs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "cbb76e41-86c5-473c-b0c0-beedf372377b",
   "metadata": {},
   "outputs": [],
   "source": [
    "correlation_mode = 3 # 1= Gaussian, 2=Exponential, 3=Von Karman\n",
    "marginal = 2 # 1 = Gaussian, 2 = LogNormal\n",
    "\n",
    "with open(path_folder+'random.spec', 'w') as f:\n",
    "    f.write('# Number of random materials\\n')\n",
    "    f.write(f'{Nrandom_layers}\\n')\n",
    "\n",
    "    for l, cv, cx, cy, cz in zip(random_layers, coef_var, corr_length_x, corr_length_y, corr_length_z):\n",
    "        f.write('# Material number\\n')\n",
    "        f.write(f'{l}\\n')\n",
    "        f.write('# Vp_Vs_Rho_flag: Calculate Lambda, Kappa, Mu and Density average from Vp Vs Density (0 = false, 1 = true) \\n')\n",
    "        f.write('# If previous option = 1, give Vp, Vs and Density on the same line \\n')\n",
    "        f.write(f\"0\\t{layers.loc[l,'Vp']}\\t{layers.loc[l,'Vs']}\\t{layers.loc[l,'Rho']}\\n\")\n",
    "        f.write('# Number of properties\\n')\n",
    "        f.write(f\"1 \\n\")\n",
    "        f.write('# Property_Name\\tavg\\tcorrMod\\tcorrL_x\\tcorrL_y\\tcorrL_z\\tmargiF\\tCV\\tseedStart\\n')\n",
    "        \n",
    "        f.write(f'\"Vs\"\\t{layers.loc[l,\"Vs\"]}\\t{correlation_mode}\\t')\n",
    "        f.write(f'{cx}\\t{cy}\\t{cz}\\t{marginal}\\t{cv}\\t{seed}\\n')\n",
    "        \n",
    "        seed += 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b9b9934",
   "metadata": {},
   "source": [
    "The random field computation is very light, it can be run on a local computer. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "c299512c",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " IPT_Temp%rang =            0\n",
      "\n",
      " ****************************************************************************\n",
      " ****************************************************************************\n",
      " ****************************************************************************\n",
      " ************************                             ***********************\n",
      " ************************  RANDOM FIELD LIBRARY TEST  ***********************\n",
      " ************************                             ***********************\n",
      " ****************************************************************************\n",
      " ****************************************************************************\n",
      " ****************************************************************************\n",
      "\n",
      " -> MPI_communications started\n",
      "          running on: \n",
      "/home/lehmannf/HEMEW3D/simulation/example\n",
      "          nb_procs    =            1\n",
      "          outputStyle =            1\n",
      " -> Reading inputs\n",
      "      -> Reading Main Input\n",
      "       ./random.spec exist?  T\n",
      "      SEM files lecture\n",
      " nMat =            3\n",
      " size(prop) =            3\n",
      " buffer = 0      -96.000000      -96.000000    -2424.000000     9696.000000     9696.000000       24.000000      0\n",
      "mkdir: création du répertoire './mat'\n",
      "mkdir: création du répertoire './mat/input'\n",
      " absPath = /home/lehmannf/HEMEW3D/simulation/example\n",
      " mesh_path = ./mat/input/Mat_0_Vs_mesh                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       \n",
      " gen_path = ./mat/input/Mat_0_Vs_gen                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        \n",
      " mesh_path = ./mat/input/Mat_1_Vs_mesh                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       \n",
      " gen_path = ./mat/input/Mat_1_Vs_gen                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        \n",
      " mesh_path = ./mat/input/Mat_2_Vs_mesh                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       \n",
      " gen_path = ./mat/input/Mat_2_Vs_gen                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        \n",
      " AFTER 5\n",
      "      END of SEM main_input generation\n",
      " -> Initialize Folders\n",
      " -> Setting folder path\n",
      "       IPT_Temp%timeFolder =            0\n",
      " IFDEF MAKELOG NOT DEFINED\n",
      "      SEM generation\n",
      "       ./random.spec exist?  T\n",
      "       path = ./RF_main_input\n",
      "       Native lecture\n",
      "      -> Reading Mesh Input\n",
      "    Mesh automatic\n",
      "      -> Reading Generation Input\n",
      " IPT%nFields will be decided automatically\n",
      " nBlocks (ideal) =            1           1           1\n",
      " vol_surf_factor =   0.16666666666666666     \n",
      " IPT%nFields =            1           1           1\n",
      "      -> Validating Input (IPT_Temp)\n",
      " INPUT------------------------------------------------------------\n",
      "   IPT_Temp\n",
      "  \n",
      "  log_ID =           -1\n",
      "  rang =            0\n",
      "  init =  F\n",
      "  comm =            0\n",
      "  \n",
      "  MESH -----------------\n",
      "  nDim_mesh =            3\n",
      "  meshMod =            1\n",
      "  xMaxGlob_in =    9696.0000000000000        9696.0000000000000        24.000000000000000     \n",
      "  xMinGlob_in =   -96.000000000000000       -96.000000000000000       -2424.0000000000000     \n",
      "  xMaxGlob =    9696.0000000000000        9696.0000000000000        24.000000000000000     \n",
      "  xMinGlob =   -96.000000000000000       -96.000000000000000       -2424.0000000000000     \n",
      "  pointsPerCorrL =            5           5           5\n",
      "  unv =  F\n",
      "  \n",
      "  GENERATION -----------------\n",
      "  nDim_gen =            3\n",
      "  fieldAvg =    1944.0845786743957     \n",
      "  fieldVar =    264147.63265932858     \n",
      "  overlap_in =    5.0000000000000000        5.0000000000000000        5.0000000000000000     \n",
      "  corrL_in =    4500.0000000000000        6000.0000000000000        4500.0000000000000     \n",
      "  overlap =   -1.0000000000000000       -1.0000000000000000       -1.0000000000000000     \n",
      "  corrL =   -1.0000000000000000       -1.0000000000000000       -1.0000000000000000     \n",
      "  corrMod =            3\n",
      "  margiFirst =            2\n",
      "  method =            4\n",
      "  Nmc =            1\n",
      "  seedStart =        55808\n",
      "  nFields =            1           1           1\n",
      "  \n",
      "  \n",
      "  PROCESS -----------------\n",
      "  xStep      =    6.9385700715291136E-310   6.9385700715291136E-310   1.1659949241853418E-321\n",
      "  stepProc   =    6.9385700715275326E-310   6.9385700715275326E-310   1.0375378562666177E-322\n",
      "  procExtent =    0.0000000000000000        0.0000000000000000        1.5810100666919889E-322\n",
      "  procExtent =    0.0000000000000000        0.0000000000000000        1.5810100666919889E-322\n",
      "  \n",
      "  \n",
      " -> Initializing Input (IPT)\n",
      "mkdir: création du répertoire '/home/lehmannf/HEMEW3D/simulation/example/./mat/xmf'\n",
      "mkdir: création du répertoire '/home/lehmannf/HEMEW3D/simulation/example/./mat/h5'\n",
      " -> Processing INPUTS---------------------------\n",
      " INPUT------------------------------------------------------------\n",
      "   IPT\n",
      "  \n",
      "  log_ID =           -1\n",
      "  rang =            0\n",
      "  init =  F\n",
      "  comm =            0\n",
      "  \n",
      "  MESH -----------------\n",
      "  nDim_mesh =            3\n",
      "  meshMod =            1\n",
      "  xMaxGlob_in =    9696.0000000000000        9696.0000000000000        24.000000000000000     \n",
      "  xMinGlob_in =   -96.000000000000000       -96.000000000000000       -2424.0000000000000     \n",
      "  xMaxGlob =    9900.0000000000000        10800.000000000000        900.00000000000000     \n",
      "  xMinGlob =   -900.00000000000000       -1200.0000000000000       -2700.0000000000000     \n",
      "  pointsPerCorrL =            5           5           5\n",
      "  unv =  F\n",
      "  \n",
      "  GENERATION -----------------\n",
      "  nDim_gen =            3\n",
      "  fieldAvg =    1944.0845786743957     \n",
      "  fieldVar =    264147.63265932858     \n",
      "  overlap_in =    5.0000000000000000        5.0000000000000000        5.0000000000000000     \n",
      "  corrL_in =    4500.0000000000000        6000.0000000000000        4500.0000000000000     \n",
      "  overlap =    0.0000000000000000        0.0000000000000000        0.0000000000000000     \n",
      "  corrL =    4500.0000000000000        6000.0000000000000        4500.0000000000000     \n",
      "  corrMod =            3\n",
      "  margiFirst =            2\n",
      "  method =            4\n",
      "  Nmc =            1\n",
      "  seedStart =        55808\n",
      "  nFields =            1           1           1\n",
      "  \n",
      "  \n",
      "  PROCESS -----------------\n",
      "  xStep      =    900.00000000000000        1200.0000000000000        900.00000000000000     \n",
      "  stepProc   =    10800.000000000000        12000.000000000000        3600.0000000000000     \n",
      "  procExtent =    10800.000000000000        12000.000000000000        3600.0000000000000     \n",
      "  procExtent =    10800.000000000000        12000.000000000000        3600.0000000000000     \n",
      "   \n",
      "  Inside 'build_random_field'\n",
      "   \n",
      " -> Inside 'build_random_field---------------------------rang =            0\n",
      " nSamplesInProc =            1\n",
      " xNStep_Proc    =           13          11           5\n",
      " xNTotal_Proc   =                   715\n",
      " sum_SamplesInProc =            1\n",
      " IPT%nTotalFields  =            1\n",
      " Check =  T\n",
      " Max Coord =    9900.0000000000000        10800.000000000000        900.00000000000000     \n",
      "  \n",
      " -> PREPARING INTERNAL LOCALIZATION----------------------------------------\n",
      "  \n",
      " -> SAMPLING----------------------------------------\n",
      " -> Gen_Group            0  making Field            1\n",
      " -> SINGLE REALIZATION proc            0\n",
      " -> VALID proc            0\n",
      " -> Calculating Sample proc            0\n",
      " -> FFT            0\n",
      " FFT\n",
      " In rang            0  L=                   13 M=                   11 N=                    5 local_LastDim=                    5\n",
      " WARNING, local_LastDim =                     5 problems have been observed when a FFTW slab is this thin\n",
      " correlation length   1.0000000000000000        1.0000000000000000        1.0000000000000000     \n",
      " size(RDF%kPoints,2) =          715\n",
      " RDF%comm =            5\n",
      " Calculating FFT In rang            0\n",
      " shape(data_real_3D) =           13          11           5\n",
      " AFTER Calculating FFT In rang            0\n",
      " -> AFTER FFT            0\n",
      " -> Gathering Sample proc            0\n",
      " -> AFTER SAMPLING proc            0\n",
      "  \n",
      " -> EXTERNAL LOCALIZATION----------------------------------------\n",
      " NO\n",
      " -> TRANFORMING AND WRITING OUTPUT--------------------\n",
      "     IPT%outputFolder = /home/lehmannf/HEMEW3D/simulation/example/./mat\n",
      "     IPT%outputName   = Mat_0_Vs\n",
      "     BBoxPath = /home/lehmannf/HEMEW3D/simulation/example/./mat/h5/Mat_0_Vs.h5\n",
      " BBoxPath = /home/lehmannf/HEMEW3D/simulation/example/./mat/h5/Mat_0_Vs.h5\n",
      " fileExist =  T\n",
      " -> WRITING ATTRIBUTES--------------------\n",
      " PATH: /home/lehmannf/HEMEW3D/simulation/example\n",
      " OUTPUT HDF5 ON: /home/lehmannf/HEMEW3D/simulation/example/./mat/h5/Mat_0_Vs.h5\n",
      "     file_mb_size:    9.6206665039062500E-003\n",
      " OUTPUT XMF  ON: /home/lehmannf/HEMEW3D/simulation/example/./mat/xmf/Mat_0_Vs.xmf\n",
      " \n",
      " \n",
      " AVERAGE TIMES (WALL)------------------------ \n",
      " Reading Inputs        =    7.1377277999999933E-002\n",
      " Pre Organization      =    1.0938214003544999E-002\n",
      " Generation            =    1.3432943997031543E-002\n",
      " Localization Ext      =    3.8851001590956002E-005\n",
      " Writing Normalization =    4.3742998968809843E-005\n",
      " Transforming          =    1.0431900227558799E-004\n",
      " Writing Files         =    8.3764064998831600E-002\n",
      " UNV Interpolation     =    3.6266440001782030E-003\n",
      " \n",
      " \n",
      " ---------------------------------------------------------------------\n",
      " -----------------END RANDOM FIELD LIBRARY TEST-----------------------\n",
      " ---------------------------------------------------------------------\n",
      " \n",
      "      -> Reading Mesh Input\n",
      "    Mesh automatic\n",
      "      -> Reading Generation Input\n",
      " IPT%nFields will be decided automatically\n",
      " nBlocks (ideal) =            1           1           1\n",
      " vol_surf_factor =   0.16666666666666666     \n",
      " IPT%nFields =            1           1           1\n",
      "      -> Validating Input (IPT_Temp)\n",
      " INPUT------------------------------------------------------------\n",
      "   IPT_Temp\n",
      "  \n",
      "  log_ID =           -1\n",
      "  rang =            0\n",
      "  init =  F\n",
      "  comm =            0\n",
      "  \n",
      "  MESH -----------------\n",
      "  nDim_mesh =            3\n",
      "  meshMod =            1\n",
      "  xMaxGlob_in =    9696.0000000000000        9696.0000000000000       -2373.0000000000000     \n",
      "  xMinGlob_in =   -96.000000000000000       -96.000000000000000       -5127.0000000000000     \n",
      "  xMaxGlob =    9696.0000000000000        9696.0000000000000       -2373.0000000000000     \n",
      "  xMinGlob =   -96.000000000000000       -96.000000000000000       -5127.0000000000000     \n",
      "  pointsPerCorrL =            5           5           5\n",
      "  unv =  F\n",
      "  \n",
      "  GENERATION -----------------\n",
      "  nDim_gen =            3\n",
      "  fieldAvg =    2304.8596147537869     \n",
      "  fieldVar =    55280.809072593045     \n",
      "  overlap_in =    5.0000000000000000        5.0000000000000000        5.0000000000000000     \n",
      "  corrL_in =    3000.0000000000000        4500.0000000000000        1500.0000000000000     \n",
      "  overlap =   -1.0000000000000000       -1.0000000000000000       -1.0000000000000000     \n",
      "  corrL =   -1.0000000000000000       -1.0000000000000000       -1.0000000000000000     \n",
      "  corrMod =            3\n",
      "  margiFirst =            2\n",
      "  method =            4\n",
      "  Nmc =            1\n",
      "  seedStart =        55809\n",
      "  nFields =            1           1           1\n",
      "  \n",
      "  \n",
      "  PROCESS -----------------\n",
      "  xStep      =    6.9385700715291136E-310   6.9385700715291136E-310   1.1659949241853418E-321\n",
      "  stepProc   =    6.9385700715275326E-310   6.9385700715275326E-310   1.0375378562666177E-322\n",
      "  procExtent =    0.0000000000000000        0.0000000000000000        1.5810100666919889E-322\n",
      "  procExtent =    0.0000000000000000        0.0000000000000000        1.5810100666919889E-322\n",
      "  \n",
      "  \n",
      " -> Initializing Input (IPT)\n",
      " -> Processing INPUTS---------------------------\n",
      " INPUT------------------------------------------------------------\n",
      "   IPT\n",
      "  \n",
      "  log_ID =           -1\n",
      "  rang =            0\n",
      "  init =  F\n",
      "  comm =            0\n",
      "  \n",
      "  MESH -----------------\n",
      "  nDim_mesh =            3\n",
      "  meshMod =            1\n",
      "  xMaxGlob_in =    9696.0000000000000        9696.0000000000000       -2373.0000000000000     \n",
      "  xMinGlob_in =   -96.000000000000000       -96.000000000000000       -5127.0000000000000     \n",
      "  xMaxGlob =    10200.000000000000        9900.0000000000000       -2100.0000000000000     \n",
      "  xMinGlob =   -600.00000000000000       -900.00000000000000       -5400.0000000000000     \n",
      "  pointsPerCorrL =            5           5           5\n",
      "  unv =  F\n",
      "  \n",
      "  GENERATION -----------------\n",
      "  nDim_gen =            3\n",
      "  fieldAvg =    2304.8596147537869     \n",
      "  fieldVar =    55280.809072593045     \n",
      "  overlap_in =    5.0000000000000000        5.0000000000000000        5.0000000000000000     \n",
      "  corrL_in =    3000.0000000000000        4500.0000000000000        1500.0000000000000     \n",
      "  overlap =    0.0000000000000000        0.0000000000000000        0.0000000000000000     \n",
      "  corrL =    3000.0000000000000        4500.0000000000000        1500.0000000000000     \n",
      "  corrMod =            3\n",
      "  margiFirst =            2\n",
      "  method =            4\n",
      "  Nmc =            1\n",
      "  seedStart =        55809\n",
      "  nFields =            1           1           1\n",
      "  \n",
      "  \n",
      "  PROCESS -----------------\n",
      "  xStep      =    600.00000000000000        900.00000000000000        300.00000000000000     \n",
      "  stepProc   =    10800.000000000000        10800.000000000000        3300.0000000000000     \n",
      "  procExtent =    10800.000000000000        10800.000000000000        3300.0000000000000     \n",
      "  procExtent =    10800.000000000000        10800.000000000000        3300.0000000000000     \n",
      "   \n",
      "  Inside 'build_random_field'\n",
      "   \n",
      " -> Inside 'build_random_field---------------------------rang =            0\n",
      " nSamplesInProc =            1\n",
      " xNStep_Proc    =           19          13          12\n",
      " xNTotal_Proc   =                  2964\n",
      " sum_SamplesInProc =            1\n",
      " IPT%nTotalFields  =            1\n",
      " Check =  T\n",
      " Max Coord =    10200.000000000000        9900.0000000000000       -2100.0000000000000     \n",
      "  \n",
      " -> PREPARING INTERNAL LOCALIZATION----------------------------------------\n",
      "  \n",
      " -> SAMPLING----------------------------------------\n",
      " -> Gen_Group            0  making Field            1\n",
      " -> SINGLE REALIZATION proc            0\n",
      " -> VALID proc            0\n",
      " -> Calculating Sample proc            0\n",
      " -> FFT            0\n",
      " FFT\n",
      " In rang            0  L=                   19 M=                   13 N=                   12 local_LastDim=                   12\n",
      " WARNING, local_LastDim =                    12 problems have been observed when a FFTW slab is this thin\n",
      " correlation length   1.0000000000000000        1.0000000000000000        1.0000000000000000     \n",
      " size(RDF%kPoints,2) =         2964\n",
      " RDF%comm =            5\n",
      " Calculating FFT In rang            0\n",
      " shape(data_real_3D) =           19          13          12\n",
      " AFTER Calculating FFT In rang            0\n",
      " -> AFTER FFT            0\n",
      " -> Gathering Sample proc            0\n",
      " -> AFTER SAMPLING proc            0\n",
      "  \n",
      " -> EXTERNAL LOCALIZATION----------------------------------------\n",
      " NO\n",
      " -> TRANFORMING AND WRITING OUTPUT--------------------\n",
      "     IPT%outputFolder = /home/lehmannf/HEMEW3D/simulation/example/./mat\n",
      "     IPT%outputName   = Mat_1_Vs\n",
      "     BBoxPath = /home/lehmannf/HEMEW3D/simulation/example/./mat/h5/Mat_1_Vs.h5\n",
      " BBoxPath = /home/lehmannf/HEMEW3D/simulation/example/./mat/h5/Mat_1_Vs.h5\n",
      " fileExist =  T\n",
      " -> WRITING ATTRIBUTES--------------------\n",
      " PATH: /home/lehmannf/HEMEW3D/simulation/example\n",
      " OUTPUT HDF5 ON: /home/lehmannf/HEMEW3D/simulation/example/./mat/h5/Mat_1_Vs.h5\n",
      "     file_mb_size:    2.6779174804687500E-002\n",
      " OUTPUT XMF  ON: /home/lehmannf/HEMEW3D/simulation/example/./mat/xmf/Mat_1_Vs.xmf\n",
      " \n",
      " \n",
      " AVERAGE TIMES (WALL)------------------------ \n",
      " Reading Inputs        =   0.19115746800162015     \n",
      " Pre Organization      =    2.0276795999961905E-002\n",
      " Generation            =    8.1449640019854996E-003\n",
      " Localization Ext      =    2.2697997337672859E-005\n",
      " Writing Normalization =    5.3469000704353675E-005\n",
      " Transforming          =    1.4284099961514585E-004\n",
      " Writing Files         =    3.3090830002038274E-003\n",
      " UNV Interpolation     =    1.5753770021547098E-003\n",
      " \n",
      " \n",
      " ---------------------------------------------------------------------\n",
      " -----------------END RANDOM FIELD LIBRARY TEST-----------------------\n",
      " ---------------------------------------------------------------------\n",
      " \n",
      "      -> Reading Mesh Input\n",
      "    Mesh automatic\n",
      "      -> Reading Generation Input\n",
      " IPT%nFields will be decided automatically\n",
      " nBlocks (ideal) =            1           1           1\n",
      " vol_surf_factor =   0.16666666666666666     \n",
      " IPT%nFields =            1           1           1\n",
      "      -> Validating Input (IPT_Temp)\n",
      " INPUT------------------------------------------------------------\n",
      "   IPT_Temp\n",
      "  \n",
      "  log_ID =           -1\n",
      "  rang =            0\n",
      "  init =  F\n",
      "  comm =            0\n",
      "  \n",
      "  MESH -----------------\n",
      "  nDim_mesh =            3\n",
      "  meshMod =            1\n",
      "  xMaxGlob_in =    9696.0000000000000        9696.0000000000000       -5073.0000000000000     \n",
      "  xMinGlob_in =   -96.000000000000000       -96.000000000000000       -7827.0000000000000     \n",
      "  xMaxGlob =    9696.0000000000000        9696.0000000000000       -5073.0000000000000     \n",
      "  xMinGlob =   -96.000000000000000       -96.000000000000000       -7827.0000000000000     \n",
      "  pointsPerCorrL =            5           5           5\n",
      "  unv =  F\n",
      "  \n",
      "  GENERATION -----------------\n",
      "  nDim_gen =            3\n",
      "  fieldAvg =    2350.2433208782195     \n",
      "  fieldVar =    819.31141073613469     \n",
      "  overlap_in =    5.0000000000000000        5.0000000000000000        5.0000000000000000     \n",
      "  corrL_in =    3000.0000000000000        6000.0000000000000        4500.0000000000000     \n",
      "  overlap =   -1.0000000000000000       -1.0000000000000000       -1.0000000000000000     \n",
      "  corrL =   -1.0000000000000000       -1.0000000000000000       -1.0000000000000000     \n",
      "  corrMod =            3\n",
      "  margiFirst =            2\n",
      "  method =            4\n",
      "  Nmc =            1\n",
      "  seedStart =        55810\n",
      "  nFields =            1           1           1\n",
      "  \n",
      "  \n",
      "  PROCESS -----------------\n",
      "  xStep      =    6.9385700715291136E-310   6.9385700715291136E-310   1.1659949241853418E-321\n",
      "  stepProc   =    6.9385700715275326E-310   6.9385700715275326E-310   1.0375378562666177E-322\n",
      "  procExtent =    0.0000000000000000        0.0000000000000000        1.5810100666919889E-322\n",
      "  procExtent =    0.0000000000000000        0.0000000000000000        1.5810100666919889E-322\n",
      "  \n",
      "  \n",
      " -> Initializing Input (IPT)\n",
      " -> Processing INPUTS---------------------------\n",
      " INPUT------------------------------------------------------------\n",
      "   IPT\n",
      "  \n",
      "  log_ID =           -1\n",
      "  rang =            0\n",
      "  init =  F\n",
      "  comm =            0\n",
      "  \n",
      "  MESH -----------------\n",
      "  nDim_mesh =            3\n",
      "  meshMod =            1\n",
      "  xMaxGlob_in =    9696.0000000000000        9696.0000000000000       -5073.0000000000000     \n",
      "  xMinGlob_in =   -96.000000000000000       -96.000000000000000       -7827.0000000000000     \n",
      "  xMaxGlob =    10200.000000000000        10800.000000000000       -4500.0000000000000     \n",
      "  xMinGlob =   -600.00000000000000       -1200.0000000000000       -8100.0000000000000     \n",
      "  pointsPerCorrL =            5           5           5\n",
      "  unv =  F\n",
      "  \n",
      "  GENERATION -----------------\n",
      "  nDim_gen =            3\n",
      "  fieldAvg =    2350.2433208782195     \n",
      "  fieldVar =    819.31141073613469     \n",
      "  overlap_in =    5.0000000000000000        5.0000000000000000        5.0000000000000000     \n",
      "  corrL_in =    3000.0000000000000        6000.0000000000000        4500.0000000000000     \n",
      "  overlap =    0.0000000000000000        0.0000000000000000        0.0000000000000000     \n",
      "  corrL =    3000.0000000000000        6000.0000000000000        4500.0000000000000     \n",
      "  corrMod =            3\n",
      "  margiFirst =            2\n",
      "  method =            4\n",
      "  Nmc =            1\n",
      "  seedStart =        55810\n",
      "  nFields =            1           1           1\n",
      "  \n",
      "  \n",
      "  PROCESS -----------------\n",
      "  xStep      =    600.00000000000000        1200.0000000000000        900.00000000000000     \n",
      "  stepProc   =    10800.000000000000        12000.000000000000        3600.0000000000000     \n",
      "  procExtent =    10800.000000000000        12000.000000000000        3600.0000000000000     \n",
      "  procExtent =    10800.000000000000        12000.000000000000        3600.0000000000000     \n",
      "   \n",
      "  Inside 'build_random_field'\n",
      "   \n",
      " -> Inside 'build_random_field---------------------------rang =            0\n",
      " nSamplesInProc =            1\n",
      " xNStep_Proc    =           19          11           5\n",
      " xNTotal_Proc   =                  1045\n",
      " sum_SamplesInProc =            1\n",
      " IPT%nTotalFields  =            1\n",
      " Check =  T\n",
      " Max Coord =    10200.000000000000        10800.000000000000       -4500.0000000000000     \n",
      "  \n",
      " -> PREPARING INTERNAL LOCALIZATION----------------------------------------\n",
      "  \n",
      " -> SAMPLING----------------------------------------\n",
      " -> Gen_Group            0  making Field            1\n",
      " -> SINGLE REALIZATION proc            0\n",
      " -> VALID proc            0\n",
      " -> Calculating Sample proc            0\n",
      " -> FFT            0\n",
      " FFT\n",
      " In rang            0  L=                   19 M=                   11 N=                    5 local_LastDim=                    5\n",
      " WARNING, local_LastDim =                     5 problems have been observed when a FFTW slab is this thin\n",
      " correlation length   1.0000000000000000        1.0000000000000000        1.0000000000000000     \n",
      " size(RDF%kPoints,2) =         1045\n",
      " RDF%comm =            5\n",
      " Calculating FFT In rang            0\n",
      " shape(data_real_3D) =           19          11           5\n",
      " AFTER Calculating FFT In rang            0\n",
      " -> AFTER FFT            0\n",
      " -> Gathering Sample proc            0\n",
      " -> AFTER SAMPLING proc            0\n",
      "  \n",
      " -> EXTERNAL LOCALIZATION----------------------------------------\n",
      " NO\n",
      " -> TRANFORMING AND WRITING OUTPUT--------------------\n",
      "     IPT%outputFolder = /home/lehmannf/HEMEW3D/simulation/example/./mat\n",
      "     IPT%outputName   = Mat_2_Vs\n",
      "     BBoxPath = /home/lehmannf/HEMEW3D/simulation/example/./mat/h5/Mat_2_Vs.h5\n",
      " BBoxPath = /home/lehmannf/HEMEW3D/simulation/example/./mat/h5/Mat_2_Vs.h5\n",
      " fileExist =  T\n",
      " -> WRITING ATTRIBUTES--------------------\n",
      " PATH: /home/lehmannf/HEMEW3D/simulation/example\n",
      " OUTPUT HDF5 ON: /home/lehmannf/HEMEW3D/simulation/example/./mat/h5/Mat_2_Vs.h5\n",
      "     file_mb_size:    1.2138366699218750E-002\n",
      " OUTPUT XMF  ON: /home/lehmannf/HEMEW3D/simulation/example/./mat/xmf/Mat_2_Vs.xmf\n",
      " \n",
      " \n",
      " AVERAGE TIMES (WALL)------------------------ \n",
      " Reading Inputs        =   0.22902196500217542     \n",
      " Pre Organization      =    7.4585600013961084E-003\n",
      " Generation            =    2.7797219991043676E-003\n",
      " Localization Ext      =    1.1001000530086458E-005\n",
      " Writing Normalization =    1.6089998098323122E-005\n",
      " Transforming          =    4.0917999285738915E-005\n",
      " Writing Files         =    2.0993590005673468E-003\n",
      " UNV Interpolation     =    8.0200999946100637E-004\n",
      " \n",
      " \n",
      " ---------------------------------------------------------------------\n",
      " -----------------END RANDOM FIELD LIBRARY TEST-----------------------\n",
      " ---------------------------------------------------------------------\n",
      " \n"
     ]
    }
   ],
   "source": [
    "current_dir = os.getcwd()\n",
    "os.chdir(path_folder)\n",
    "\n",
    "# remove the homogeneous materials created by the mesher\n",
    "if 'mat' in os.listdir():\n",
    "    shutil.rmtree('mat')\n",
    "\n",
    "output = os.system(PATH_SEM3D + 'BUILD_RF/randomField.exe')\n",
    "if output!=0:\n",
    "    os.chdir(current_dir)\n",
    "    raise Exception('RandomField could not run \"'+run_name+'\"')\n",
    "    \n",
    "os.chdir(current_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b7ac88a",
   "metadata": {},
   "source": [
    "<a id='section4'></a>\n",
    "# 4. Assemble random materials into a numpy array"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6fdb462e-04c8-4070-9410-2c34900639bf",
   "metadata": {
    "tags": []
   },
   "source": [
    "Read the material values from the `h5` files and store them into a dataframe `df`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "244e0a66",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "df = read_random_field('Vs', 0, min_field = DX*FMAX,\n",
    "                          Xinterp=DX, Yinterp=DY, Zinterp=DZ,\n",
    "                          interpolation=True, path_case=path_folder)\n",
    "\n",
    "for material in range(1, layers.shape[0]-1):\n",
    "    temp = read_random_field('Vs', material, min_field = DX*FMAX,\n",
    "                          Xinterp=DX, Yinterp=DY, Zinterp=DZ,\n",
    "                          interpolation=True, path_case=path_folder)\n",
    "    df = pd.concat([df, temp])\n",
    "    \n",
    "\n",
    "# Since RandomField extends the domain, we need to restrict to the inner domain.\n",
    "df = df.loc[(df.x>=XMIN)&(df.x<=XMAX)&(df.y>=YMIN)&(df.y<=YMAX)&(df.z>=-DEPTH_TOTAL+DEPTH_BOTTOM)&(df.z<=ZMAX)]\n",
    "\n",
    "# create the constant bottom layer\n",
    "Xtemp = np.unique(df.x)\n",
    "Ytemp = np.unique(df.y)\n",
    "Ztemp = np.linspace(-DEPTH_TOTAL, -DEPTH_TOTAL+DEPTH_BOTTOM, int(DEPTH_BOTTOM/DZ)+1)\n",
    "temp = array2dataframe(layers.iloc[-1].loc['Vs']*np.ones((Xtemp.shape[0],Ytemp.shape[0],Ztemp.shape[0])),\n",
    "                        Xtemp, Ytemp, Ztemp, 'Vs', order='xyz')\n",
    "df = pd.concat([df, temp])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9f6d1c3-eacb-4b42-8d55-2f5ada24b3fd",
   "metadata": {
    "tags": []
   },
   "source": [
    "At the interface between two layers, there are values from both random fields. In this case, the mean value is kept."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "7964a6ec",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "temp = df.groupby(['x','y','z']).mean()\n",
    "df_unique = pd.DataFrame({'x':temp.index.get_level_values('x'),\n",
    "                          'y':temp.index.get_level_values('y'),\n",
    "                          'z':temp.index.get_level_values('z'),\n",
    "                          'Vs':temp.Vs.values})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81b8c18e",
   "metadata": {},
   "source": [
    "Convert the dataframe back to a 3D matrix. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "17b678ee",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "mat = dataframe2array(df_unique, 'Vs', order='xyz')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24aa69aa-1014-433e-82b0-a49822895ea1",
   "metadata": {},
   "source": [
    "Restrict to the acceptable physical values `VS_MIN` and `VS_MAX`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "b394786a-9c3b-4cff-8317-83f7dd4aee74",
   "metadata": {},
   "outputs": [],
   "source": [
    "mat = np.where(mat<VS_MIN, VS_MIN, np.where(mat>VS_MAX, VS_MAX, mat))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "827fcca3-dc09-44e8-804f-5e8346b9dc38",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Vgkd1IaPHwaHg6cE4lwHEde8GhwCCZDYOPmAjTnvECXjKEx6Oww87ZNKYe4o+shEzGkY2DAaDwXB7gpELw50esXdFWZatalBXXnklvvvd7+Lkk0/u3f+cD30Or/7d9ybT9zRUQby6rCWIHtylTSxyRH8FazUmFAUAAg14MiK6ZFBR8oT43+w579Ara+ocnxiYiX8jojZmkzCV0z0WIXg4juxFemx0ZjbkpDL/xvDcI9kIu3bjsLtswk899oF40hMehrvc5cDROe0NusgGAGzbtg2HHHJIklIZ2TAYDAbDbRFGLgx3aoz1rrjqqqtw8cUX45RTTmntG0LAL//Ca/GNr34H4DDZXwGS1fv6RLJfusjGBIIQh5FeF4U0stMJMdBZ5anqoD0ug2IN3Hnm5Id05X9gamn8HhkUx+CZ1BcCJ54JTJM2JSlURhZCiCQvNgmUcycizYR0V5vqOGHQWpXhCMHDEcMVwOGHbcJjH/NAPOHxp+KAA/YbH2svwMxYW1vDpz/9aTz4wQ+Gcy79WGbDYDAYDLc1rH9rW4PhdoIpvSuICF38+/vfuxrP/bnXYNu2Hep7cIkgpBX/BtlgEFzhELokUJT9kjECjhVpJ2Q2Yu8KhoMjqozV+mwM5DmOTwReITBN81cglY3VjAIzoFWemLlq3pd6ZGg374HqVPnjxKzZhyh3yqVePSfckYVwrv6xFkKp504gBgJ7ODdiVC8DnGc4VH6RWL0qBOCKK3fg7e88D+/8y4/CFYT9Nzqc8cSH4YynPhL77bfP0FVcGjnhnc/n6Z703sN7j927d3caxJctm2wwGAwGw3rAyIXhToe8d4WYg/tXfLvIxd/91cfwltd/IGU7qo3TTsijYWZI0zfXQyz6UGTVoHLiEoPw7CjSRE+O67pIUhYkE4AAAPMiTZBDGMxCeGLwvN5EL9+eKJaTFQLjHYCV6U33aqVmKc24MpwrV4skgkOAY4AwnDkBhGyw1+1lokDwyUTPYJCbVYQpEouR7IZzBcCMsCtg67YF3vX2j+I97zwXRUH4kbvdBY9/woOx+adOwcaNq6PnP3p9snswkoboo4kSqijti883PRtGNgwGg8FwS8BkUYY7FUIIKMuyVwbVxLXXXouvfe1rOPXUU7G2VuJ/PeNP8c1vXNqZzeiC2I4duNC/QlfFqAZiJmS8fm09S0I0ZRewQ0sG1fZXSCDPNF02Fff1Lkh37vx8GIBzyhty44USmwkeCAAIPsBpBqKe2eg5c82u9I2fuofHbE4AnIdmLMZOlkFrHgjdRCRoQ79iRjj22MOw+fST8JifOgUbNixPNnbv3o1Pf/rTeMQjHjFKEHK/Rmzo12cQNxgMBoNhvWHkwnCnQN67Ig+4xnDdddfhwgsvxOGHHoNf+p+vhV8sppmvAYjYX7wJDYVS9UuTbMSyrRMWmCMJEJlTw6vBbaKRqkFlq969Y2un77BaiDQryqxG/BV+zr3Vo6pGecI2CA4InLJH4yfMyTORGvdl/TDyzAY0C0FheY8FtOxtKg9MVGvoB0AyUWt+emO/wOBdu0DEWFmd4cfv9cP46TMfjoefdn/MZuMJ5F27duEzn/kMHvnIR44fq3Va/WQjyqiMbBgMBoNhvWDfJoY7PKJpe21tbSliAUjgeM6HvornPuNP4H0pxmrngMKBC6n4xJTxhXhMEDDrIBaAZiYgAbCOhYLEJF10j9c6J0DnQno+klmgopC5zYBQaNaBGJ6AsOIANQKPIThC2FCAo48iSIaBQwB7r/KsansPhl9h2X7AX0HRWB0I5BkUAMcAx4aDPSfOnmV7Ri1LIOPJjyOnxvoALj2oZCBEgdXI9SwD3JpkRZwr4NwMhSvECM4M9iXYlwi+BC9KuDUvGZQpkq/FArS2QOFmcDRHuUb42hcvxx/9zl/hMQ/6VTz2lF/Brz//T/DJ8z5fqw5VG0Pv2z1BlFDNZrNEJogIZVlix44duOmmm7B161Zs27YNu3btSj4kg8FgMKwPXvWqV4GI8KIXvSg99vCHPzzFI/Hnec97Xm2/Sy+9FKeffjr22WcfHHrooXjJS17Sqkz58Y9/HCeeeCJWV1dx7LHH4t3vfvctcEbDMM+F4Q6Nrt4VU7Ft2y788jPegCuuuBa1qLfLfI3MW5Ea401IbySfhqvkQ9FmwUDMSFSmcH1+oHoURc+HkpRAEKIT/Rp64F6PtOPURK+aXkYY1F/BxJIoKZD5MUbOl1EvBZv7K7yvyuCqLKslmxoZn0CVvyIdMF63jAxQdpHLABc0k9IcT4lLmn7wQjZCyPqTuHZmI469KOFCaI0t8ygABha7gS//5/fwpc+9G754F1Y2znHiA47Fk578cDzwQfeCi80J1wl9no1IwAG0KlFZZsNgMBj2DJ///Ofxtre9Dfe5z31azz33uc/FK17xivT3PvtUBUG89zj99NNx+OGH4zOf+QyuvPJK/OIv/iLm8zle+cpXAgAuueQSnH766Xje856H973vfTjvvPPwnOc8B0cccQQ2b958859cD0wWZbhDYqh3xRR84XMX4SW/+lYsFosljgnJRFCMZifKp8ZkUMnMjZRrnHImDMleNGVQnK9Kcz2o9nMMZh/a47OW1eUUsItBumhPVKs7IUxs6hcCKFWOynwNfSfPLOM3shvV0w1fCQBXcu/2neOveRBXZCFwUO8H6cvNVRZldynbTukWDs0saWUtmWsAEWPDxhnuc8Ld8OPH/RCe9eynj89zL5FLqCKIqEY2YjUqg8FgMPRj27ZtOPHEE/HmN78Zf/AHf4ATTjgBr3/96wFI5iL/u4kPf/jDeNzjHocrrrgChx12GADgrW99K1760pfiBz/4AVZWVvDSl74UZ599Nr761a+m/Z7+9KfjhhtuwEc+8pGb+/R6YUtRhjsc4ips9FcsSyx+73feixe94E1LEgsCFS6TO5FkI5xT6RTVVT9qwJ7krxAVEXhO4MKBC4dQkEiXeiRUAYwwo04ZFDlX/RQFAjF8AfgNrrcfRRMejDCv+leQKzJ5UqEkIgBepFTwAJVCFqb6K0g9JKT/Yw3kQwgIWV+RtL1nGb8nmE/yMXJwTCjWkKRWIXiZZ99aS2DQ7hLUyHA4cip3KqRxH5zM0ZcAyzwDD0uMAsTbAleZ5mWuBYAZdu0E/vMz38F7//J8PPIhv4rTTnsRXvji1+JrX//O+HXcA3SZvwFgsVhg586d2LZtG2688cYko4pV1wwGg8FQx1lnnYXTTz8dp512Wufz73vf+3CXu9wFxx9/PH77t38bO3bsSM+df/75uPe9752IBQBs3rwZW7duxde+9rW0TXPszZs34/zzz78ZzmY6TBZluEMhhIC1tbU9ylZcd91NeObPvwbXXnODPBClRzFu6gygJGBlx7Xu0/pU9QsBYCUYTrVPgZP9og8MNWy7piSokmRJZdqgzefU51FMr+7EBYFn0smbQAjBJ9EUcz3LIDIrn6pH9c29dmwfQJnhPNBwFqJdljaTeul/paGfEgwfbSwTjfClh/Nxew3mlbwE5vhSgeDEF+49XMmTxnfOiX+jFBIStL9IoJgxyTIbAIIDeN7TTTwDkQPBwROwYMKXL7wSz3/hm0AE7LfvCh7y4HvgKU96JI499qjxC7AkItmIyIsjrK2tJYlV0yBumQ2DwXBrY9euXUnuuR7o8r+trq5idbVdBfD9738/vvjFL+Lzn/9851g/+7M/i7ve9a448sgj8ZWvfAUvfelLcdFFF+Gf/umfAABbtmypEQsA6e8tW7YMbrN161bs3LkTGzdu3LMT3UsYuTDcIRBlUHuarfjYR7+EV/zee1tGKQCZLyIbL8ntKQWOo3MkSK4wegecko1U4antr2A3Ibuh/gR2UvKWtVISZ/KdPn9FmLWrO+X+AUIlowoAMCOwm0ZcoEZtisbneB46tzQJDeSB6WVpkwxJ/RUyP06KNOqqbsUMLAIcD/grauftQR5wAdoNY4TAMIMXXv0bspGjorZ9CF4yGqLFAs/mg+eZhoY2JCzyTJT8u217wIfPuRAfPue/QAQcsGkVD33wvfCYx5yMe9/zRyeNvwymko2mZ8PIhsFguCWxa9cuHHPX/bDlaj++8UTst99+2LZtW+2xl7/85fj93//92mOXXXYZfvVXfxXnnnsuNmzY0DnWL/3SL6Xf733ve+OII47Aox71KFx88cW4+93vvm5zvjVg5MJwu0ezd8UyxCKEgN/6jXfi/E9/tVbWdAxMpL0cuCIaGIg7AaDQLfIIFqi8Chz/1VCW+olBe3xtokdZII+6v4LzoJpi9+zxa0XOyfyViESTdf585wn7oMSiy8xcHZOVbDit7MSjjUCAWJbWZUSkRgw0gKfog2ACLeSFmlqW1i2qMrYEp/ImNYgnQpQOqD0vhj0WkbgxIPdPLHvr1SBPQkiomS1aoeFO5+QQycYNWz0+cO6X8U/nfglFQTj4gI047WH3xZmPfxiOPOIu4+e+JHKyEeVRMYOYdw83smEwGG5JrK2tYcvVHpdccFds2n/vXQBbbwo45n7fw2WXXYZNmzalx7uyFhdccAGuvvpqnHjiiekx7z0++clP4o1vfCN2795dW6QBgJNOOgkA8O1vfxt3v/vdcfjhh+Nzn/tcbZurrroKAHD44Yenf+Nj+TabNm261bIWgJELw+0Ye9q7IuKKK67Fzz39VVjbvWuUHKRj6iq89H7QPZRjyPOsS/wZNEsxxVtRlZhNB8i6crfnKNujN/CsPRb7HRTqCclM2EPwYPAMlQwqT+DUiIaOF2VKU4J4QJrQhSzoj1mX5CeJhEmfTtWgqHf+eRYCZUDhWckVwAh1YtBAkkE1Mii1ErgQKRoBYM9wC7kG/UKxbHwCeCbm7WY39RBKMISkMEmvDd64kkoOTxnbzwBQAac+n2tvXOD9H/wc/u6DnwU54IcO3gePeeSJeNLjTsWhdzlodMxlEOdoZMNgMNxWsGl/ty7kIo23aVONXHThUY96FC688MLaY8985jNx3HHH4aUvfWmLWADAl7/8ZQDAEUccAQA4+eST8Yd/+Ie4+uqrceihhwIAzj33XGzatAn3vOc90zb/+q//Whvn3HPPxcknn7xH57ZesGpRhtslmBllWSYZ07LE4gP/9B943Z/+Y8p2yKDZL51kg4BCdPQTWEiWrcAk8pKIRZcMqjm3IGVgqZggm8qGiH0vyBWN0rSRK1XhsWwfJlePitkE0kpZsXqWjNsVxUt1pzEZVDREk/yRDN7jrwEDZQAFhmvIoBIx0IEj2WDvUfhK1jQGXpQgzdAEaHUrEpKZjpmdOxODZ+MeiwhP0h09ZjWYJPvRtX+JkEoIj847eAAM+BI/fOSBOP20B+IJpz8YBx04/IW5t8jJRgghvWeNbBgMhvXG1q1bccABB+Dqi9Yvc3HoT3wPN9544yi56EJeHeriiy/G3/zN3+CnfuqncMghh+ArX/kKXvziF+Ooo47CJz7xCQCS6TjhhBNw5JFH4jWveQ22bNmCX/iFX8BznvOcWina448/HmeddRae9axn4WMf+xhe+MIX4uyzz75VS9Fa5sJwu0PeuyKv1z8FZVnirOe9CV/72nfaFW4o+yWukgPSRZucZgiW8FfMNPCPZWmB1Aujdrj4bEG1Yw/NjYm0l4YiSNZgSJYVCpYMR5QRZfIpQCVU2pkbLL0uhqQ49QMwqBSDNWnWhSiTZVH0QuRGb/WYjGQ4nPa7QAjZtVTyQgxCV9lbBi2kMV+TWKQxs104SNM9x83MRs8VZRZikfsrUJnuo0E8prUYrM0SpxGLRARnLmVhJEvkAV+KLC/eLsUMfoYlvDDR20Og2SquvHon/uKvP4Z3vO88OEf44cM34fRHn4QnPPbB2H//fcfHWwJ5ZqMoitRjg5mxe/fuWp+NaA6fzWZLe6gMBoMhIoAROusqLj/OemFlZQUf/ehH8frXvx7bt2/H0UcfjTPOOAMve9nL0jZFUeBDH/oQnv/85+Pkk0/Gvvvui2c84xm1vhjHHHMMzj77bLz4xS/GG97wBhx11FF4xzvecasSC8AyF4bbEfa2d8XF37oCv/xLb8DOnTuXOCYqGRSQDMi92wMqa8JAYFoNHmsIgdwUhVL10Va3LXT3rlCy4cEV0ZkA5iD+jexYzD29KwCpjsTTvAxBe1e4wKlykozZPzf2DAJXFaSaY2oWIhGYANBCK3FNuqgMrJVyjNi/AiGRhlYWQrcHt5vjdQ4PSAd2mZB6ShxcrEbWmGM0b4cR4szMCCyGeZ47+ILBzsEVxTAp0jK+Q2OH4OGI4Yiw/z4F/ufPbcbjNp+CjRvb2uL1RE42Qgj47//+b9ztbnfDfvvth/l8njIbRjYMBsMYYuZiy0U/sm6Zi8N/4tI9zlzcmWCZC8PtArF3xZ6YtgHgTW/6F/zNX58HLGHaBhPIIa0Qd29TPZeyDxOqO8nwkbTIAWLGICZHOpVR8cHGk80V69RBuqDkExkDA2AKou9v9sYgJQYEQKsnEbnK/zA1uxH0h5xIiXxIcXDqyh0PCPU/QGVLPadQ62tRejhfrZcEDp3ypDSlsoRLMqjq+Vq2g/W6MMn2sSztwJyqXRlcKNHT40tWQ0zcTbLBRAhz+bfpx2iCiOBoBp7J1At2CAsPKkPKbLAjuJl+zDMrIZKyw0NwTrqHBx9w49aA17/lbPzZW8/GrCDc/W4/hCc87hQ8+pEPxOrqyvAFWBK5vLEoCtxwww2JbOzatStt0+zDYWTDYDD0ISC0rJB7Oo5hGixzYbjNY296V+zatYZnPfO1+N73ttT8BQBqcqUcBEr69ineCgBVmdmJEq3AaFePaowpv+flVSfMJxsikHhDiFwmd5LnmqvWDGj/jSWIQgm9plqmNe7XNUf1P4x1545SIgaDvIzlGvKtvvlw6Vv+CkZm5Nb0RnSVyPahUzbVN34kLmK1ripbdZGfEPuNTOnQLSky9VdAzrcYPu8A1v4k3ePHpoOAkAyiAkwB1GEkbO0bGDSQjWL2IDBmM8JxP3YEHv3I++Pxjz0Fs9n6rld94hOfwP3udz/st99+etx6ZgMwsmEwGLoRMxeXfeOH1y1zcfRxl1vmYgIsc2G4zWJve1d8+cvfwYtf9Bas7e5poEPpP9kxIUGem8a5UyAYZVA1YgA041ZGnuHoGTSXO2nFHxAnctCX2YgI3GHCpqrSE0FlVJTxrUK9GFNURJ7hvJCAVFZWzdlRmpRIjZgF1I8xvmJO6q+gkpPPIJINkJP9m/cAM7DwIG4bt1OPDSCNF9jDlYBLsjQMn3ccP+R9Q7IsBKShH3FFNti56cQCkAyWVoSS10ercJF4NkCuRjYC8Wg39diNHAAC640TGBxKRNM5SGVU+Xwm9BqR7uFA6YELv34VLrzwn/G6P/9nzOcOx9/jKDzppx+KUx9ywlJ+qC7E93113HpmIycau3fvxq5du+C0K72RDYPBYLh1YOTCcJvE3sqg/uQ1/xf//M+fTqub045JQAEwJhILoF3daSALEYCKhEz1V6Rxqx1iJ3BWtqEJCiUu06o7pd4VFNLYzJJZqBGD2oRY/RXtwDM3hydSEIIMn2dexs7Zh1oTuloQr1IiyshGPIZrmlD6zpszP4ZuL1WeosKtUYkq+jEYvf6KtE88+7hZYDAk/dJJilARCybXDqKzMTkEUfQRaUf1ae+H5OlxdbIBSEYQHMCh1HuKQVSoV2ViNoeViBTyVbIogS9+5XJ88SvvA9H7sDp3OOE+d8OTn3AqTnrAvZYmG3lFqS40q8RFsuG9h/e+t/StkQ2D4c6D26Kh+44OIxeG2xxitmJPZFBbt+7AGWf+AbbftD09NkXZRBBiUXMG51s0jNw1f0UfsqcqGVQ+QP/kOM965Ns0f2eRcAWOJKHdtK5zfEArXzUzHCqn0cwGmKtysqU2upsQIBJQLzOblb2VQFYN7MlwIbKpISlOHsQTI20v5xPaYzbPWWVNeU2t2pgQosFK2LgMcJ4n9a6QOXDy0OTZksBCDIQU6fPqr4iND8f9FZqB0HvU+QD2ElzLcV1VljjNpyLKXddUAv2cbEj1tYAAhBKAVOLqJASxv0mH30bOXb5adi+Az37he/js59+DsGs3Nq7O8cAH/jjOPPPhOPHE4wbPOSp2lyEkkWzEffrIRlNGtWwpa4PBcPtBAEthk3UYxzANRi4Mtxk0e1csSyw+9R9fxcte9i4sFmUVM3HbWpGPyCA4R+3eFbXD1gM2dTBPzj70yqDSk0iph5iVQNVDbxgxZeE00AxcdRpnlRI1qxGxBMGD3Z6zhXgOAa7U+YFFjz8QxNfK0qayt/UGdOIHqDIS5OWCTAoko38jq+4U+0yI+oeS4Tzton6MsepODkKE2Ed/hax4xWxJnz9D/C3dAWpX8z0EktK0HkIK5GL1ziugLYOqXiI1xgf5iwngwmmjx+lmfjH/S4dwQMgGc0DQAB1gOGj/iSAVuiYTzQVQuBWUpcOnP/0tfPrT3wQRY+PGGR70oJ/AGWc8HMcff2z9nDNPxZ6ij2yUZYnFYpGeN7JhMBgM6wcjF4bbBEIIuP7663HFFVfg2GOPXerLPYSA33nZe/GpT/1XWwbVI1diDcTIkQSGY4jJDJf+U89udExVsgPol0E1H6OKK9TGH7sMlAXxXVWjJIoHB9YyuRN7VwDghUcRu2erVCfErIZOsTJyk6z2B67JpDqnHHs35N2zCeAg60uu6KnwlDIc7dX4PPAnkJSoZQhx6fBjdJ9w9G9Eo3c9y9EsUUtwKtGaFgTLPaGvgY4UylIzEEhN8pCRt4AALoZ7WNQqZumKvxS4Cq3XqH3K2lixeT1dlWojCNkIWm1KHwA1j91EYNCin2ju3Al87GP/jY997GsgAjasOtzjxw7H/3rh03DMjx6p89h7I2Y6jyXIRuyzEWVUBoPh9gmTRd3yMHJhuFURzZiLxQI7d+7ElVdeiR//8R+fvP9VV12PZz7zT3HDjduAiW988T6QSoCqfYZCw8pfMbRB9Xsg9FeD6ttft2vFf9w4sw5S0ocYVDIAyvwkQjqywK11TGlC1+WvcI3V8+B1FTvoj4NmTQYmFv0bKhfKZUTIxkz8qnBiJGeueyIGQB4is1KpWO6tADqa6wUGFqWcSw8RaRIYIRhCVhl+OIhHRTbza1pr6MdA8GUloSpIOnovVUrYZddTCUoZUr0Bpuq+iBm7KWTTuUKyYkpmg49WdskKCYEoksQrysrGXi+5Fg5ceuzeWeK/Pv99/PIv/gmIGKsbCF/a/H2c+bTTcNdjjhyd47KYSjaa3cONbBgMBkM/jFwYbjU0Tduz2WwpA/Z73nse3vb2D6UGcpNieJZggaMLOn8u+53yx4ayD40d4vaUIuO8IlGfjAitqlI9w+s5ZA9OkWbFMqdoyJNU8iRZDT1FYQZwC63uNCGodUTAovJLsNfypySTpGazuFR9qU1c0pjIA24GFoxYl6ne1K7zhNUPUJdBxQ7aicBkFZ7Ii5Qrt1EPoSpBm5MiyepovSft26GBNlU9Tcb8FbVzY4Jb+HTt2KmcqiFLi8ZwqajV56+okzcuHOAoleodQwge5Cpi5Irs66MQUhTXB8kDzqNGmofAixKFb2RPGNi9Ezj7Axfg7A9+AUTAQQdvxCP+x0/i8U96OH7krodPmPVy6CMbi8Wi1j3cyIbBcPuBZ4Zfh64L6zHGnQVGLgy3CmK2wnufvBXOuUnkoixL/OqL/wJf+q9vSZDnKPNWDGUi1Eib+ys6sgTZP9OIRb5rx7hJdRXlSekJam07On7PAwwtk0r1pzmEWonTJmIgSjGLo6vNNU3WUALC6/YdsheCjpmTDYb4NzCNuEQjODPDaeM9ZhZiQNXLHYkDYnO6AX9EhDzPgJfGfqwZlwBodajuaxYbKXWNX2++p9WUMr0bFTSa1WHmqjQwUJsFewb7spJMOQJmTozhbolmibO6L6VZmrjVB0XZ51CxgEQ2WMgaF6HqHB9ih/VGFkaJpvOh38gfH2fg+mvX8E9/9Wn8419+ArM54eAf2g+nnf5APPFpj8JdDj1o9NyXxRjZsMyGwXDbR9Cf9RjHMA1GLgy3KGLllrIsW9WgnHM1mVIXvvu9q/GcX3odtu/YWX8iBfVVCoEJWqZJw0RXyyN0I4/NqBGpN5+vnRf6TdiNxyjOLQ8yR1Z4B2VRugFL6+wUNLKew9SyorSIfgk5kVoTNlSBVgriy2z7nrmnPhiAVpviFMF2Vo3KpxQkWyH8sbtMayQwAVXZ27EeDfk580KW16W1HiUuxaRGbiWtTpkjDxCLjrMHwAikFaeYwWUmeSICaffuiABpctdHEuolauN9p+Q6qPW8p+wtAHgwuNBjR+lU5o+JJLNGNgp9f068pm5Ns0bkJMsCgB2D2VfFBgJLCeGFNOubOjYWHuQDnJuBPXDtlp3423d8DO9/58fgCuCHjtiEzT99Mn76KY/AgQetf5OrLrIRF0oWi0XaJicbs9nMzOEGg+FOBSMXhlsMY70rxjIX7/3r8/AX7/jXtP8gcrIRV+WXQV+2ouGt4BgTTazuVMur9GRN8k1qDw+MX5eTRMmO7M3BV893BTnMoLV2GdjuCk9CDlwZkzoTsw81IpITg6xqFAlD05C8yoaMHIKIwF6ISPRBBK66clOHvIu9FykUgC5/Rd7vIpKN6OiPXb/rOYXGKUNIHlMu8WqTokQ2lGDREv0rQoHU74J0/KoXSEOWRgSPMNp4L15PEGkHcCCaNTyECIDrnT2qScl9hA5yJ0F59XXDTipyAQQuPTyXQAAK1/OVpP1GELg1dqxuxR64+vvb8Z43nYP3vvkclLyGo+56KE4/4yF43JMehv037Tt8UfcA0fxdTbMiGzGz4ZxrGcSNbBgMtxw81qcU7XqMcWeBkQvDLYIpvSsiuZDKQdXzu3ev4alPfSWuvvZ6eWDqFzPrtnmXudpzHftoQNYbNebJjHybHmLQnA4AWWmegsx8PFU6FThk3gnZMQbx+Zjpec+Z6Xb4IEQuVf8hrrJA8Tp0mpmZa36MrjETD4RUeIphuBAMbo+ZQ1fZHbKytLUxVcaU5G7qr8ga9Y1BhVI1k3Sg6OmQLYgzKZgeBzSc4aiF6KwEeOEBhKqiVCOzoZsizAnBUUt+087qBATPwNyJx2IiQpOI5P6OSDLlDyFvJYtnYui9k8+TgULLG1MxkzFdgI+vVRA/jHOF3HNrZSdp6RybCjAxCtqALd/fhre//my84w1no5gRjrrbIXjckx+KzY9/MPbdd+Pk6zEVRjYMhtsevKhf12UcwzQYuTDcrMh7VzDzYO+KXGoQt/nqV7+LF77gzdi9e3dUyAO6Ilw1mWuPJ7Ig1cmkB0cmO9FbIXPU8YYIRnbclEtYMoZIm6sNIvk6OsYJHPsCdAfx+ZgcglRTCkD0guiGvXNJZWnTin4VxAJIRu50vgwhLtQOkLsPwPpysQbJTkufAlDjtcszMMxAyCQ4PUj7MANBV+adXlJmOO6fW+WvqG+TPB5xO6rM4RIETyQueu1J/SQRxEDwvvKrKNFgRwirBZjGjeEykAOvaAbHUVWaWEaVM2saw2m4y3stm8QMKlnem4TKYzJE3Ra+VUWq2T2cnfg1POl9SqyVv4qeQfP5a1Ypyi1j344SuPTb1+ONr/4A3vSaD2A2Jxz5IwfhoY88ET/3zNOxumFlcOw9QU424nXvIhtNz4aRDYPBcHuGkQvDzYYQAsqy7JVBNRGfi9mN17/ug/jH//uJmlQqxcAMwMdgm6NiRYNYXfUe+n5uPdfUIHVtUwXNnQF+l8ypljEZWYWfCq7GjrYNplB5Ikb3Z7iFypvUm8EaxLNme2qZDF1RLzokKTkoC+KpDFKeFACjhFQx6s+OpJ4UjefrDeio8oCw+iB4uhcgejjE+K7EiLKsDupkY8i43YSDE4Kh1yGWvZXz7zaXByXRvWQ7L3sbAE8MrMz09ZeSs+kkuk6ZAL8CcO6vGOqDwlCjdzsj0n0ABq15Cf6dSxk5MXHrezKWDgaJpGoRQAPm7XS+5IRMlSUcSwfyQEHJGyeDeN6LQ7If4x6jSDb8Avjexdfje9/5KN73zvMwmxOOPe5I/PQZp+IRj34gVlbm49dgCcTXoItsrK2tpe7hRjYMhvWFGbpveRi5MKw7cikAjwRQOWJAc9NNO/Dc5/wZrrzymkoW0wNZ1Vb5C+tK6JSmeLpfCsy6iEF9gbcuUZryXU8dv3IVVEzJFgwMWSNCsmoegLiy26ciir0HMolJl5SGo5k5AK7UoHwJ022ujSdo5/AgBEgkQy6VqGV0N8XrPH9yYA6JAMgqNafkS9d9Fv0YrueFqx+XdUy9ppNvpbp/I89syL2Z9e1QYijytfHXPsqgeGUmpWCh0iztXQG9prmEyRODV0h6XgyMnfqgEBDm1S3lwbWO5q0xmOF26+vWlGZ1EBgwV4Z752rJsu6TFrO9C7FPCQHZ6xRcQOBQSecYIDc9CGeWKmqRaABCNi66cAte85W/xR///t9ituJw3L2PxhOf8nCc+sj7rXsFqCGy8a1vfQshBBxzzDFGNgyGvUQAwS8rG+gZxzANRi4M64qmaXuZTtvOOXzhc5fhlf/n5bL/1EABJBVtmr0rYqTUNUzyY/QMSvXfkwxqHVCTOiEjG/HJqeddmz6BUGifBUEgzoInTqbqpqSnNb+O7tkSFEsPg0qa1NgxsHa3bhOFOoEReQ4CpSCbY+g9dO6cE5FqLBlfSW0cSb0A2t1uGjFCThJiliWOSYlw1ao1ZQyvr/EeUG+UV5nDKylbX/aFAYQNBULRzibUmxkS2MtMwxwIK+PG7QhPATx3bSLipLdFOmMlG1wGzEolaxPuVSICFkoCCgcfQsXpQ2gTGDVv00CmzJGrkY2YfQn6erN2WO/aP3Co9etoz1feM+UacOEXLsOFF7wXHu/Cxo0ruPdP3g1POPPhOPkh97lZyUb0nsUKert376712Yh+jdlsNpoRNhgMhlsaRi4M64au3hXL4A9e/j782zkXopYyiMF231gkxIK7shUdRCN6FmiIWDR3a8qg8qxG17GWRDMbUctsDEheup7Jg9QCcdUY2q06C4CH5qvVneLKdf465qvw0QPiyIF9W0c/jGq7JHfi/PVpSH4YwEiGo05g1JcjzStUdpVoQ+c5B12XyklCixQhIzBAi2wMIfkrmuce5xcJjB6TCfAbZ7rCPiGQJQe/Cul5QZTKySY1XjOjAMC7IBWn+vwVmewIDuBFkNyYgxLZnsxGPIYPmvmq3sO1oNw5+OCra+wD3G7fWwCg8xjIfCu6TwgeqeViyPqeEAaJRffYhIJWsLYb+ML5l+ALn70YRMDqhgI/+YBj8aSnPAInPuAe60o2vPeYz+dpzKIoUo8NZsauXbsAIMmo5vN5ymwY2TAY6gisVenXYRzDNBi5MOw1hnpXTME119yIZ/78H+OG67ciRkK11X1W6Ut8giQ4CA5CLFqRfgdi9qGQPxJpqJ5un1f+ZCOT0dqwawp7+P2eOEw8d527eCtoGd+5BNRlrv+vPAugtjFZVo1llbkvoK35IHSV2XEM5agyPw/5K3ICgfrKPoBaBkaIlKsbiYfAnAha6rNBkRSJhIq58kGQzn1KhiMG/knmRFW2g4Fec7gQl+62fEn2g0i0WO7t1SLuLAaTIbM9AeUG6QLeImaa1cnJRiAgzKQPxaQMBzOw5lHErupOCH26zvoeqMnrFh4ujJOv6Jvg0ktX70J8H8zQbEk1Zmta8fOiWZ62RooKhFDKlq4ABw+EEq7Z0K82rr5XGkREti8Alu7h53/iWzj/kxeJ4RwLPODke+BnnvlTuM99f3zwnMcQP0dz5FlgIxsGw3R4rI8saj3GuLPAyIVhrzDWu2IM//aRC/DKV/w1yrIc3TatljPDr0CrRY0Ti7RF0YjKs1VdXRivZyeapGJoYl3T2MvsRo1g6b+xqVzaYKS6k4sBX9osC/7iar0OSZ4lw4HxIFsOoPKVRCmy7tkQD0jVL0KOGzjUMxM9iGRDgnjJPkggpc/3BMR9xvB8zHg5qmxGJG8uveyDpx1Xyhs1pAhI2Q8ZUwhMyohMlbvNCDwvQE57iHMAeyS/Ssom6HjeMcKGDllTPrcs+8cOCKtVtm+0whMz3JoHonG7Y8x4vpKBIslihXhVJpzzosxK2VavU05gWOcicjeedB+lubpZOl83myOEEiGuCgR5Azhyeo3lNZaKXONkE+zUGD/H5z51MT73H38GImCf/Vdwyqn3wpOf/ij8+HF3mzTPiBBCraRt37GnkI0onzKyYTAYbikYuTDsMWKVkz3JVoQQ8IJf/nNc+JWL656DEbBz4JWqKO3o9oCSkJ7l/vyxQmIWdoiRYsvG0X+QqZPpOfYYqPZPlXlpOmNjidZILAarO2ngr9WdXJDApCIG/dWIuCwrGVQ2gZaMKCMb0BX6qTKioOmavMt39FbUOodnx5psPAcQjcDxGHHMwNWYyM6OOaao+oPOenUrVumUsM/AYTBYZYhxG7O6V6DVC8T7RBj9zMGvTn/veWKEVQKK+uuQV3giZsSukJSM25QM+H2QOTjQwiPWFqj8OtwtS0uVyLL7rTlmk8CAAZI3K0fD98C8GGiXp82b9TlUZIPjduMZlzj/KOnLzeFgYMdWj3P/5cs490NfAhGw/wGreMgj74PHPP7BuNe9jx0ctitzMYY+shFCSGTDOdcyiBvZMNzRYZmLWx5GLgxLI8qgYjWoZb+cvn/pD/Dsn38NdmzfVUmaYvw4IFOguRhbB6P5bFU/MEBFIyofAAPgQueg86lUWYxY3baVUdhT5Pv3zC95EBqPU2OjvEkelbLFpJeEWYNBVs16RQySD4BDrdM0l75mxB2CBO2VRCmkSlQEUFWNqHmCyXvQcRI1sgENOEkkY6p5EsIwcM5dGY6qelYWxCKSjVi1aKJWPxIRruYrxvXMW8IVGWEAYdW1ZDhdiOWD/ZzAM2lgR6TXANDGe2i9l8oiIMylxGsTnRWegvQpiWNNqfAknd4zIpLt0CpR6wFa+EHjdusQ+X1BlTQr5jZitqQihBPL02ZkQxIjXohDJBzMdUKi5yCkqZ/EV+WZgZuuX+Dsf/gcPvT3n0UxczjwoI142KNPxJN/5jT88FGH1vaLvrW9QR/Z8N7De49du3YZ2TAYDDcLjFwYlsKyvSua+Pv3fRxvfsMHqmpS8YkUi0Xdiz5Oqr9eKRBiY4MhRELgAC4o2jWyld+e8wLEj5GfS20nyhqvoSIbUzIbE5CqUXF1OG7HZ70gkio+TiUe0UPAOlhzBRiAdtvuru4E5D4IldME7WkQ5UkTJhZSMOhqY8YzS+bw7BoQplc6EmKRrUpToSvJ0dDdKKeqAdZY6dtEYBgACZHiJJhBr/QKqAzvXeZ2iqZiHSkgAOSAlUKIxYSLygD8qkv+CmrcJ1zGZoaEQAwuHPwc4PlEfwUgjfFi0F+QVI2KhK2jRG2sLDZk6K8dmxmu1F4dxNLzQ6933xXglFUYkGZRRt4AxAzMFDCzkjsC0az2OLOvOr1HshG7sk/ts6LzL9wcCMAN167hA3/zGXzwbz8N54CDfmg/nPZT98cTn/LIPcpcjCGSjbxZaU42Yp+NKKOK/y5T8c9guC0iMEmp+nUYxzANxMtoUgx3Wuxp74qItbUSZz3r9bjoG9+bJIOKK96+IPBqMTmCZwBhRlX2IX8iRh1J9hIlE5AGYFNPhzv+5bhqundko6WcGmNFkGOjDEDoNmFLlaKcbDjAh6rb9pR5eQ/ymoFQwU/MQERC2Dx2XPufVOkozlMOoPuj6kLdca9N9W+EWPqUIY39BohB4wTS6nXr3LQsbyQJDk4yIkv07ACA4CD+CnIpWxLvS7mF6yZ2Tyzvh4mBZ2BGWHXgQl8PR8CMOq+nnhiwFkYldayNLQkALaQpIw1li/J9S4+ibN97uYm/aeTuM253jg/N5LnsRtJzI00FNuc5Vp62Nj6HRFClGptka2om8voOcm9PuO9i0QFf7sahRx6I05/8EDz+zEfg4EMOGJ3X3iKXUeWf8U3PhpENw+0FW7duxQEHHIBPfPWHsd/+e0/Wt90U8LDjL8eNN96ITZs2rcMM77gwcmEYBTOjLMtkul72y+Wb3/g+znr265Pud9IxSUytoS+31nH4AIC7iEVrcGigzQgzkuZe3UO294v/dm2cPx94j7IaTaVU7VDUeBKQgKlkIIx3PY7bw7Po6jXDMRb8iwxqmIhUZAOpuhMwMYhHFaznc6nqT1H6l4jk3phILNJYwYtcJ/NWyOP9hCxW2JrU6C52kWfJR9T0913bQ0lw0d2LQYaqE5hQOPBK0Slr6oIHg1ep1qE7ybVShi8jG8ygNW2eN7lDd4ALAPKvEUaNGNSwyEoWjyCSQmlmOFw1Kjs08tK0redDyP6I2ZL+7TuPkYhVvKahJkvMyQYHlU5N/byMsj6WrFFgDyLAFcDhRx2Exz7xFDzujIdj/037ThtvLzBENmKfjSijMhhuizBycevByIVhEHnvijylPhVve8O/4K/f9RFU6YL+legIBiFsmIHdtFuTAVmZLdzkaD6Q7AMXoywdiaXqZ0uesey7JI4RtT4hrvoP7zK2TQ0hAL5aoB2fE2tZ2rrGvSZNQhbgM4NL0Z67Cf4KAGnlPsVsWuHI9ZjD6yVjR6afXijS4Fh9JUP7quG2L5tQmbSr+FiI1LLBZpXVAQauqf4d5tP8FXF7ub+r9w6DVVKEzvMvKQArbjTDUREtAnklhq57zMaOYvTuICKySl+VXksZvYVkv6ZnsjSIj74UNU7H47ekWUDLuD16jDhgDOiH+nbUvDr95xCCdA4nKDHyIj3szWxk46fPiV7JnZCNYgYcdvSBeMBDjseznvck7LvvxpEz3Xv0kY1m93AjG4bbCiK5+NhXj143cvHI4y8zcjEBRi4Mndjb3hU7duzCs5/+Glx+2VX9MqgOssGFQ1gpupvidc0TgF+pyzzGAvjgIBF532dNjItU6rS03Glse81s5NssRSw0CMnnJb0jYtnXjl28+DEmrRjHAKpkANOM24AQCwCdRKSqGIVENoKuGE8ONlEPvOLxquZzDQLTWAUeP0AlX4lEkwCA+ku8ipRn/LqmvhiOwLMZQBOIESIR0SC9EbTFlf3cyM2A+CuKJXwrpWQfiKTxXmzQyASwc22yERhurb/kb2v8EOAWUZIWeqVJXefWn9XhRraEhRVNlGYBQOz5UiPamYSuRjaUWICXuF+DlnnWbBbpPFkzOzXCGVhJ/1RSBPgZAcRwDihmDnf90R/CTz/5oXj0Y0/G6oaVSePsDZpkA0DLHG5kw3BrIpKL8y78Eey7DuRi+00Bj7r3pUYuJsDIhaGFZu+KZWVQF3zum3jJC96Exdpi8j6BAF5dAc+wVPYhrLi2thrQoBLIY22RomBcNpXGgARQudyJs6CzY/ve5zo2BTSzsQx5YRbvQ8dx6jKaGARptgJj4VyFUPpERCofQL17dPt86iv3w+eQ/qP7ZfdX34ptJC5DK8ZKsGR4VhlU/5j1OUlZU6BORHIJVQo2idTULhmLyeSLGJgVQHYta+ffzOpQ5R+a8v4LHMBzBy7EyI2eqlG1cy6H/RWp8Z7OBwwUfrrcDcyg3fVsWTsDAeR3jsiM+nuZNBFLFkd5k7xHY9njrinJm3qqvwKRDIWAWGVh8OxHsmUheKQLwHKMIkxfvJH7wnXeFxwzG3PCsT9+OB7/5FPxqEc/ECsr80lj7w0ss2G4rcHIxa0HIxeGGvamdwUA/NHL34eP/Mv5CLm2eQTsCGHjPFVHGotPhSSQyKCGPi8iGSDAO2jhmIkhdk4sqP54a/y43URiIftyIgKRDCSEnnFCSE3uJh0iba9Bc0/Z19qcSi+BYM+FZdQJTNTXTw82039qmYycwDSD7SnEIj+HVEI3yyjUvCXNgGwJXXzK6uixplZ3Yodef0UicIlsSCWoZao7SYYDyV9Rk3uRPF4zckeJXGhnRHqRqpFV46Z9O64dl6HTuN3arvlZweO9K4D49gsaZHdJsyqyEfusYEmvTgz+4z1Zm2tX9/BIOpfIcMg9pVmzIJXOHLrvlYDl7ovAHuUsYMOGFdzjnkfhCU96CB72sBMxm938hSKbWQ0ALbIRq1EZDDcHIrn4twvvum7k4tH3/p6RiwkwcmEAgCSD2tPeFddddxN+5sl/gO03bdeOtzwh7ALCrABvmE2XQRHgVa8+dZE8zCD+itqBq4xEa5gwzFlaiBWIKun96MpmROfKavYcA6AyLF32NsqgkElX8sZzoEy+FFd7F15e+0lBPBArG8mfWlUJsXt0e5fAQft4j0mBuCIZSo2m+gACd0uzugiMI0pduievGgdp1Beva72UbkdVKQA8c9InZKq/goRwg/QaF8VgBsITS7+LgYCzIjAacwcCu+nG7b6mjDVzfE42ygBXLtO/AipTovr7g2PzvTbHT36dCedQkRAhCM1sSfc+kXROlGYFNcMvI8ND+xi1zIZ+ljo4aSA6W4JwauaLstKzQAARY2XF4fj73BVPfNLD8OAH3+cWySbkZCNmNpxzndWoDIb1QCQXH/7KMetGLh57n0uMXEyA9bkwtGRQyxKLT3zsv/Dy33m3VJNy1eooQyU/upJXDw4IvGGuMqURYqFP+xkQVuJa/3igLUQE3UQkxTDas0HJgevKVgwhVFWFUjzL2QJ9g2xwRrr6DpE/LsGV/p7ru/rADPahyj5kg6UmcTqPoDpw9izN0jA1OxD/kxtgqyxDlFPJ4xrYYLo0q8o6RIIVgFCVvK0atGVjxYClh7zkj6f5BYbT8ZndqIQqaugpk9XVOnJT5q2Il2jmWtv1jg8tTUtVCV4CIZS+8pkQizFYsxFhArGQuSlR84xCDdcSb3vNrLiU2atPikGL/gxHLYhmBuK2qiYi8CgpjE0aW54ZhQ++eu1CAPRVWyoDAZ0PEagoUgYiSehk9HTnhOBBPdmDiJqUjTm11GAtz9xs6Nc46YoIN45RM347mUsAS78RCkCQamRDn9EB3CIisr2MvbYGXPD5S3HB598NImC+4nDEXQ/CWc97Mu5/v3vcLGQjVpqKyMubr62t1chGXo3KyIbBcPuDZS7u5IjZij2RQYUQ8Nu/8U585j++WtemdyEG7F5WJ8PGmcigJmYf/Gomg+LsiezuzYcKTmQik0sphSyQ1wzE4PTUKzFFupG28UuWpw0BVA5vz8i4GbNcX55Y3SkSkRBLyFY9FmI43jx4IkdZRqR/blXDujhX1+EtaGLIGF49XxFYUgfyMj4AOY+KHDXN4UBGNuL2U1ekoT04XAyCI1lyQoy6JETQQgMT/BXJW1I4Iedj3oqIst+EHY3cki3RMSFZs6nG7ZjhoCBBf02aBVTerWysGGAv0zQxZdpkAAxWeAIyUjqQ2YneEka1YDBRQtlnxq5nNjKyESVQmOj7AFJTUCKHEMrsM1BWMnKyESiAZ8VSkjo/g35WMogYGzfM8MD7H4sznvRwnHCfH5s0zt6iL7PxxS9+Eddeey3OPPPMW2QehjsOYubi7K/8KPbdf6Ra2wRsv8nj9Pt8xzIXE2CZizspmr0rliUWW668Ds/6+T/G1q3bMbyUrtChw8oMYTX+UXuqe54ElBsaMqjmvxrDiFRCTdsFTdc2eS0/G1eki/p4yLMHiAeZRhJiII5s/HS1hsrT+mn+CoJcI4QAV8rBJhmMmQH1V7jUNbrKCEmDOk6+eAeH1FuikRHpRZQQIY7NSYaURE/NYHOEWNSe46rlnMRxlTZeDtoVRMucXIM65scjkHZjJiBUcqqp/oqA2JCtOkbMlkh1qez8HWlZ5GllaeW0pCkeiOE8Ad6LpwIsGYhZ4/yVdA6VUCVXr4jFa0I65Zw4kY0hczjW6seon79mKKJfI47lpmcfOPiqfG/MjhLXxowBfDqmkruxzzZyDskvQVowjrh6/1M3galls5pjNu5t1vJuzJRkjmNJUgYQtGR26nLvOrqHZyWFMZveZNGDOz0cO3cBH//UN/HxT30DRMC++8xxyoOOw5lPfjju8RN3mzT2ssgzG/E1DSHgox/9KC655BIjF4Y9hgfBT1/WGxzHMA1GLu6EiKnoaLpethrUO9/+EbzrnR8G+6pL7zgIYVWb4jXIAQPJt5CP5wsgrLrx7ENccSWg3JANoFmI3myBSqE6ZVC1OVJagGTPiO03JqmnOqRRCY7SJonJsEqUJpIXACAfQD76CaiSOyHmH1rpB1lhBnqJiMuDqVj9RhlXQBjsnA10l2itS5NkHCncI9dX7sOJwabKugAhBnFUyZZEaZZIaGIGglVeM4UkCJmK0i/U/AB9fTtk6xjMdkmIGufPAWAh9exZNXnovabQ8dtlZqtR2Us2CpFsaPaJMDE7wFJBimoBNSEstESrkmPKyAaXoWqMNzD3VC0qXgDNQDAk2wbqX+AIwev9UT+HdgAvki85aZcdbMJ5px4TrhoyroPEsr+RbIAQy+pOl2aREhUCCkrG7yTN0rnWPr5m+vr1kUIiEM3S9uz0h306L0KdkESIV6ffwyHHlOe272Cc87Gv4UOf+SpcQdi07yoe/qB74KmPfzjufrcfnnj+0xHvg6IosGPHDuy3337rfgyDwXDzwcjFnQhx5fTyyy/Hddddh+OOO24pUlGWJV7wv96Er174HQmiYnAco+O+IJ4IfqOatvMN8t+Lakk/MFCuUspWTJmhd0DYgDYRoSy+j0SClXD0EYtOcOrknRrvoQoMOs99mRKzupFboP86tqbEmdSqWiFvBfDISsmqJt5Nfd2z6jfVvZJVZGLNa8Qgj+Nz4xmUGKCHjAxIVsPXDdAdPoC+bELLWwHWbEW+LQ8GwdWc6te19jxyAqN+BidbTr62juQ8QSgABG1UGUkKRX165rfASIajVipYJT5g1sxGKX0rih4SoMSiy8Sca/AJkO3kQiRONOW6MpQANCVSJFkAzt5YlRFZzdgTyBERIeSEUslACuBrr2p23iFmvXrIdqqKhfiBB7DTzEHVC6bv7IOayPPPW8nAZNNQaZasMbCWLe7OijTBgDRa1OuU7nTtHu7h0wIGAeCiWM4c7gC/oYBTUrl1Z8A/n3ch/t95/wUi4MD9V/GoB98HT33cw3D0Dx86acyp2LZtG/bff/91HdNw54JnB8977yPy5iKYDCMXdxLkpu21tTXcdNNNSxGLiy++Er/83Ndj546d1YON1f20CgekMo08cwgrNK3bdsw+bJRSnLE2LWcypOaMGYBfAXhG3TIoV/+dY0UnXx9s8EowtyVKeVBAUfSDKqBbtuKUF1mTXMruwK81p3KA0ClyCRGVldQl6Kp8X+dsoJF9qF2rdgAfOIACpfnkpudeMFKWpUkUiKWMZvJAkNPqTqzqsmn3blUrQM4j1DIQSDmPXEIUEDMWPSu6HeePdA9UTdKGpshOx8mC2Vq2CJWRmwEER8BsWtYFgJr6q5X9OCb7ANY+FQwNYOOQHd3b+0AglVpFdq7SpLj63uGtYQ5g108Sao+TkopCV89DSJ8tQ1dAMgzVdW1lNjjUyUbMbk4uTcsxfZHkWdEjEIkBQkUIYvM9eU+MmO71/AMxuKiqrsnh+gnMUIZDsjDVY5FswCnx9iVANNg93DtG2OhaUiuZrzRuvH6bxz98+AL83w9/HnCA2xjwtMc+BE99zKk4/C4HD573GHbs2IEjjjhir8YwGAy3LIxc3AkQZVDe+1SNY5k+FH/5znPwrnd+REskDiAPuB2h3FCAC9T9CgPwBcFvoCr7kHYQ4iAJAk6ZBwbgY7ZiSszFEJIQ46GomkAWFDTnGbKMRQ8aIZT8E8cekkXpgalkuDE/fB6olX6pfhdgBi18tnKbBbCNFfgYbIclGsQRSP0jWQDPWQDfJaGKr6UetQsuBVNVACcLxyr4GjGHV7Kmapu6t6LKQFQmbwmUJ/coUGKRV3fimC1KZLsyxzOPZx/SXMnFkB2OGbwQsy1DvBU0c+3z11X4PrkOZd3GCQRe+HTfB7DInXgkA8EMeN84RnVfcZwD6fmTS5nOZUzGrJ8DvRWeGj4I1nkPHSPPFoRo4qasJKy+cfveq5Hk59e2lYHJfSAEUJi+zBCgZuz8tSMAcA1pVkU2lmm0CBJPDqtZmoOQjcBlek8SXCIbpQsIG6eZw8k5MDksNjDYzfDuj3wW7/nw+XAOOPyAffG4h94PZ25+KA45cDkj7Pbt27HPPvsstY/BkCOAELrf1UuPY5gGIxd3YMTeFWVZ1qpBTSUXu3at4elP/yP84Krr2ivnQ8clgt+nQMxCVgF8dwaCAZQbRpripZ1kz9IBYaUaoJZE6ZwUxPCcy6DyjXX8xAW8kAq3hPch9bvonDeSNCsdmhlukad7+hG3hw9qWs12o3yFvg72Hq7UALrjTGoBPCrDdU5BBi8At3tLtAJ4zgJ4MIhdui6TqlqhIiJOO2Xkc02VmGR5GE0/xhDi8zmxIFD9nHrOP+jxmseoKFpFYGJvDThK12KyhEiJVI0UeFl1JhJpFZyMzVGiN3UlPlZJI0IBQlj4NCYDNW+FXijNWPQHs00Pl8zPAWAxZQ94K+S8Q5L31MbN/o5yJEBLBkRyN9Wvo4F6bg4HMm8FUKtEJVmOaY0WI9lIfWWKimjLOLV3V3VsYrAres+hJs2CAyMAhVSfqO7fgdMGEIo6ARMJVXW8EAKgZINB4JUCeT+bIXhiWewp9P4nISgBwBVbd+Nt//Ip/MW/fBIFAUcevD9Of+iJ+JnHPgL77btxcNzt27eb58KwVwhw8MtpCXrGMVnUVBi5uINiqHeFc26UXHzlK5fghb/2FuzevSZ+CKAq0dizsscAsDKDnwOd8kaKq7fV6iM7oFyNJTonnBekdwXn1aDSgmLWsyIfrkQyYU9KQahcgkjOI2YgejMw8YRGJEoA0pzZh+SvmASVNeXzqJ7TFXhOf8KBpNt2CJhUPQrITOTRoyFhcQzga033dE59Tety1J7jqgmdNHabkIFA21/RlCa1qjvFYHzCPRXJT07AEoFBTmCyDASh7m8YAMFJpSDdp5YtQY85PEqIhoy8ca4M9UBk0qzYIK/vurIE+s0MRwxgU4aoFP2grMaTNtLD4OuVDgEtr6vzRbx2Pd4KQKuFTfBXyD1T6NsuvlmzDATXCVntvGP5245rW+vv4DKZFZN2cke6PoPnPlC5rGnkFttSlU2YAjH2y+dmvBNyzwa4nn0T6RRGiZ3LpE5hzkqMAoIv00hE7UyGd0GzzgNZI82GeADfu+YmvPrP/gLf/tbFePVv/8rguRq5MBhufzBycQfEWO8K51wiHV34k9f9Ez7wwf+oCEiM0oqsapJKBKJkiEEI+8wQ3EiEnWUIQkHwqzGcxWgGggkoV9CWQTWyGpFscJwf1xMWg/ANGVQ2oZqESoN80mxM35zbJ8HSu8K3plwjGvlY7KUazxB5qa2whwCslSKl0QB5sL8EcyPAjnOoS2jqEqJ42Sf6AFhWpONY8d9Y3UmWt5UWafAajzdlfKe6uVScNlYMGpFm5Z6PJrq8FZzfSemmHSJGADvNrcRSog0i1pSmyUbTpFNAfC/K0SLpCD6kUseSgci6fEcD80iGIy8lC88ScmZyH4D2qG9H01sRswXyvp5eElua72VEhIr6c9WHlbySKicCMC2I1yBdJw1kGV8Ckq8s/Q1UUrzB6+rqt5CDyB6z92H3HSkIEC9KX2ZHhqqX6OWZS+R2ymn7VSh5aWY2vFxbH8vqMsJKAR4hFrX5l2u46bJvIpRrOOzgcYnUjh07sO+++04a22Doghm6b3kYubgDIe9dwcy9vSv6ZFFbt+7ALz7rT3HVVdehd0k9RZvyBciFxNZ+gwR3U+IChlSD4mYviip2qZENglaDWmls3weSnZ1HYhVqAait+temGk3bUz47IkHS8Unj4lECwwy3xv3HyAMO3R6LABfGJQ9pl9KDSp8qSFUBPGfBd0NCxPWgvw9VdafUXSKRjvz59qS6sw/xmLngJ/drOFB6LUclRA1/Rf04XKtIFWkTR76xjDTLZb4UNYfHYzczEJFYAP0VpJrnL/p/UuKTlc7t2T+w75Qo1TqHAwilSKjk5iIQTZP4CBHJGggmb4kG8F7vBQIAB3IiFxsybreugdO+HfH37LOJudv/EkYqSLUqMWmWlNhJditVEOs7b7m2cq2ya5lLszJvBQDtHr4EKUQkYDETo/dNqHrMJB9I2qebWLTPn5Sgs/ZEQXr/p4WBjnkyVKLq0HmMpvG7LAIwdyoJVUKXepG05+XXduGm738L7BcAgE2bhqtAMbNlLgx7jQCHYLKoWxRGLu4gCCGgLMtOGVQTXbKoj3/iQvze//deLBaLycdkALxSwM+zx+IXYo98IJB8eXWasKmxA0fiAg0MMLh6nxAN0oT6mDG2qhY0ZbwgRGRa+K77B2iGI0Wo6anENPLTidWgJnw2kU6O8kZmlGUuel5XXpSZvyIfj2q/5xKi+BjVJtsDrldSaq7qpxX4jEhEE7abeHUdXMoQOFCt5C2D2+VpJ/grejMQejMkTflAsB1UJ5cfo/KryH/rcidU/oqJq8WsN0eqNKRkKEnTKF53nVOqQjQ+vtOO2dE3wGrAr2UgmtdgKXN4zBY4rfBUvQeGXnoGNPitCFItqG16K+J/lsrsREmakiMHlWbp9IL6IOL7i2UfwjBBahEC59JpR0N/H2WPxKJr/CoTI/+kzE7hIB0+YxPGYQSEek+U5EuJmY3KIC+3gUO5kVSON0LmofJULSqQ3l8UyVFmvtfXdm3nTdhx5SXgUKZxbrjuGnznO9/BQQcdhAMOOKAzo2SlaA2G2x+MXNzOEXtXLBYLCR4mVA3JZVEhBPzuK96H8z7+JSSTaayIMnRcIvgNhazM5hvqMmaSEGU65TCL2YeJq/AuEgshIrEJLQOgTNWVRouSo7Fv3jwi8vr17ypiVBuzNSk99iDJqYgTs/SWWIq8BIZb8/VjxKBFVyBB9TnwWilB0oSjOLhaH4fR8qxyIioJ4s4gvhXAc0hBS1UVaixgqZhX0xyeAvgkd4oBp1yEqcbwptQqnj8DHd4SDRwplr8dN4fLFQpIFZI0UAWoV5rGGuRPqe6Uzp+ip2Die0k7g1clWuveCvZK4qjybgDcmRXpHB8AF1VgSqAqKGZ9H7iqvGo671ZDwOb5E2pyJ1fZi2NflKE7K7AatwfM4XCA1waLaVHAUzJej50456VmG9mSmpFbT148MXue2QkhlmiO4+bvET3vJrFojpndiwRZkfUbXfp84bhN9ylL+W+VvXXNN/0OuffWtl+PXTdeXSMWBOCoI4/Ezp07ccUVV6AsSxxwwAE46KCDUJYljj76aKysrNwimYs3velN+OM//mNs2bIF973vffHnf/7neOADH3izHtNwy8EzwU95P08YxzANRi5ux2iatqd22o6yqKt+cAOe8dzX4oYbtyFFqnEJOy3stlfhw6xA2KBykK7D5Y+pT0NWxPQwPVmNHNKdG3Uiki9oOiSvAzMAzyimEIs0gJCEmtFbiZE+jZQ6iUOGitRM+ohhhtOmdbWXZSh7UXq4sls6FVdV4xgMIW9uTcpIunRiQ0vF7T4O9cA8y0CkKk8EULesqfsY1S8xwxGzC5FC9PkOpkqzYpAm89SAPu7fM0ScQ/MY9epW9QxEWpWdcF8x8gxHO1vSVd0q6H03dRXekcsyHBVJBPReaI5TC367T6DeObzKaiBeV8pkdD3nLQ3c6nKdWuM9Qo1sBAdZiV+iNG1QN3mtElWQkskMZB4IUtLUTSy64FxRSaecQyjUkwKWTGie2cgmxbFLd1cGokEmo0cEhUvzHqPcXZmdpjQpL9GbPpOXIC+eGGGD00ybHEtK1GYfNuqFCRA/BnqIRRd23Xg1dl13Zfv7ggg/9qPH4F73uheYGTt27MD111+P66+/Hv/7f/9v/Od//ifue9/74qCDDsJ3vvMd3Pve90ZR9Pfj2FP83d/9HX7t134Nb33rW3HSSSfh9a9/PTZv3oyLLroIhx66vg0BDbcO/DpVi/KDX96GHMS5aNRwu0Gzd8UyDfF2796N177hffjQv30dvhzpXZGBGeCVAmE2/ZbxBCEiuQwqKbI4q1CUFuPVTEjTu9CVWjI26JgaG8Ux2yeCSqI0iYhIRgR+icZ4PqCY0OQuqjyIY/fsAU9Gc99Sy8zGzBAqSVRnBiJqrnuyD12Ty8tQcgqG+gP4TLsyGDblEqqIXPIzPK3u8SsC01HdaZnzhvZ80ICuPldqX1fErFJGRkbPQYL2ODdqyr269mFhkENEJJKN5ENW6dvkvh1JBlUPinU0MNWlaYzl+iwwpBwqd2Qvm30rqnMS38CUYDnJnbTCndMYfmxmLXN47TlOF5QCa9EH0kpsS2R2UG8imHtLujIQUzI7rWNQ/o4SVDK7NrxjhNVxkschgEm9dVSRmD5vhZwSY8c1l2Ft67VVBifblohw7t++DT9616Pb8/IeX/jCF3DOOefgHe94R6ra9bCHPQyPeMQj8MhHPhLHH3/85OpaQzjppJPwgAc8AG984xsByHvg6KOPxq/8yq/gt37rt/Z6fMOth61bt+KAAw7Au790X+yz/94T0x03efzPn/wv3Hjjjdi0abl+LXc27P0703CLIpq219bW9ohYlKXHi37znfh/H/7qcsTCEcr9CpQbpMpTcLUYvr09gHIG+I1q3M6n6OIPSWbDSVbDz4C1fTRYmXJnMuDWgCJKjrIxU91/R2meDAhBSJ2wpxyjklqR04yJ/tt37lQGFAstZzsyPOkx3G4P8oxJVJ8ZWNSJBVD1gIjhbeyvENjXViKnE4vYSE/+J/vJlZTGW6F2E1SN96ZmIHRllxyYJHD1sUlcz37xnLrGd+l/hCKbKzQ4dEMvWn4MYon740qxXlentKJ2XbXjcZQ1TZUpgaKHQsZNnaNDkGaVWUAr5z1OLAAhEUSu+lwo5CeMXFc5tu/s5RC7PJN6TkIIOs+AoMHvZGIxqyRB+Q+IwOwlbxavKeSemkos5AI4bRIHoHDwhXyu+ALwFFL/jtqcBoiFnH81X8wKWWDZx8HvU6DcQCgLxpBaQjJa7e7ktWtQFEIMiBGIZftlMztKLNrXVavGaQWpeP6lY/gJxAKAXEvNblRGes1wBKkeFSuQAfI9tX3Ld7B247Xo+1BjZmza1C13KooCJ510Ep7//OfjhhtuwKWXXoqPfvSjeMhDHoJzzjkHp5xyCt7+9rdPujZDWFtbwwUXXIDTTjstPeacw2mnnYbzzz9/r8c33DYQ2K3bj2EaTBZ1O8JQ74op+O6lV+Pnn/unWKytDa/sNxBWCvhVSnKMGJlHmVOSJHCVfShjtmLsvaiyEO9IZFDI4tUsOG/NkwG3GMg+5Md1JOblUM2vc8wmQlVxqrktoU4wCADKACqh1Z0mogwoFqEmFeN8ks15MoPWtNv2QKCcAny9kDGfwSz9K5J/oGuiaZ82Scib7gF5GVWuSYHG0PQZVLIczQCkIIiTrj6Wsp2afYiI55F7S+Tx9jVgvYn7/BUtbwmg2YpGv4yhYJMaGRDk11X+m0zMUZo1gVik8eP5aUW3ODBzJiFiVIZj9W0B0zIcjpxUhJq7ygMQ/SncXeZViAV6e3fUvBVUkQq4AuCKUI5KiBwDLvOp5HMp1GtGJOWpgxI4AoqpQXxGJIW4AWEWEJt9w8tnRvRYMVTOhfEMRHw+sDTHk2II1YJDX2YnEouu7FGre3gkGCsOYR4J+DACicei6ZVrjx1Sx+8d116BxfYbuwdM5wMcfMABg8fevn07nHPYb7/9cP/73x/3v//98ZKXvASLxQJlWQ7uOwXXXHMNvPc47LDDao8fdthh+MY3vrHX4xtuGzBZ1C0Po2G3E4QQsHv3bpRaUnJZYvFX7/84fvbZfyzVoChb2afGyn4GJkK570yIQp8JIGYhCgLPCOUKYW0/QphRdyO95jEgvSvCSjZWGlMCbSYtbxnnWQLF2vLZB+erObMT3XcgIKqf69tzkk71HYKyHwSpUCVVXaoOx0MrxbTmxZPR2CgeM8kj4lg+ADtLkWVMynAAeW+JuPYuxmMNNHWVOE52mewD0O2diJWj+tbJA1hWivsaxIGy1fJC7tGZk4DLUWv1ufvU2/4Kl50/KVnh7BpEojBZOkVyH0XPQsyWMIJmdnztJpDO05yyAEOgmNlReSADKVtUyZQ65hQD8S6jbbqmSgri+2BGwEweG7+uKmuaV/4Kp12l43nFADNodsODEeaQEqoTyAsDag5nzUQW+hkgma1owM/nGjhIED8WwBeFGqQdeNUhbJB/yxmjdO3MRo4QiUXTHE5OOpkXDtCFmMVGwtpGwmIfhzCblh2IWRSoKV4+46sshNyjcg8Erq6BZNomfh8QIawSwkzJAaPKPgSPpkrau25i0T20zPGmK76JxbZr+09SUWiGbQjbt2/Hvvvu2zq3+XyOjRuHO3sbDLclvOpVrwIR4UUvelF6bNeuXTjrrLNwyCGHYL/99sMZZ5yBq666qrbfpZdeitNPPx377LMPDj30ULzkJS9pEeuPf/zjOPHEE7G6uopjjz0W7373u2+BMxqGZS5u42Dm1BRvqHdFH3atLfDcF7wR37r4+60vDgAaxdbHi6ul5b5FN6nomicBYZ5JmrLKTshiIWrsUyZzYM/A2XcPq6GaoIbQRhDeCV/vd9GaBFXzZPl2HyUV7WNoNajsUrGrwtpUhSqOqTKoavW4H5G4wAe4RUe3vr40zED2AejPQDhUfoUxdFV3qq/qaxnVNKaQGnZT3RV6jILSKilFCRHkshBzrHqZRhztu4F6BiLWtWIEUIgeixETsy7aNjMcXWQLgGRfomyqL9tWO29ohSBC3mmaIO/P5K2I50KEqjTtNHJE0H4G5LRiUmUOJx/q5Vn1WGGu5zGlChGJWRhzNaEPdMbOzzsUaMmUmsfLS2kzQbMVU+8pgF1oEVxmRqnafgQGeRIjN1CVs51AEoQIcCIiwWnOjCGfR6mMdWNO8b3Rd22zErVR6iQdtKHm9RHfBGKVp+raNs8nySdJpGSYR0/F+E0byjXcdLk0xxuDLAyNv17btm3rJBfrhbvc5S4oiqIV0F111VU4/PDDb5ZjGm55BKxPpaf+ZZ1hfP7zn8fb3vY23Oc+96k9/uIXvxhnn302/uEf/gEHHHAAXvCCF+DJT34yPv3pTwMQ79Hpp5+Oww8/HJ/5zGdw5ZVX4hd/8Rcxn8/xyle+EgBwySWX4PTTT8fznvc8vO9978N5552H5zznOTjiiCOwefPmvTndvYJlLm7DiKbt2HtiWWLx9Ysuw+Yn/h6++e3LuolFB+QLqMBiXzedWEBKxta8EnkGYlb9zpopKB1QbkAs2z7pIC4at2PGJMtqxOxDmjEzaKHEAlOPoRWnUI09mn1YdPevoMYf7MR7EsBAGRtkTZtTnuEgKPGJxCRjMDEDsWz2QaZYN0GPZSDyykt9x6CUKxBSgMIBs5kEXBPurUDcKaOJq+TOyYpwmEnWzBdKOjEt+8DxPAjJT5A8EJAgKzQ8ELmca0rzPYL4SWJVKLme1epz1z0gQWM8z64MBFXeCi15GwoAswKhmJbZCeCKWChcrtWfzxDmhDAn8Sw4wK+4ySVUGYB3ATyn1JMiGZljpqixUl6TTk2REDmXyluHghFmDF8wPPozEIEZwUlPii4JERWF+jYKhBVgscJY7EMoN+p9NnrmmXQqC+DJZZmNFZGNlqvi1/BOSRj1E4vWMQC9RyjzVoSUNWtmtxhVoYzB0r/xus4dkJqWMhACELy8ZqnSVQW/tgtbv39RL7FgVLskAjvhoylmLm4urKys4H73ux/OO++89FgIAeeddx5OPvnkm+24hjsPtm3bhp/7uZ/D29/+dhx00EHp8RtvvBHvfOc78drXvhaPfOQjcb/73Q/vete78JnPfAaf/exnAQD/9m//hq9//ev467/+a5xwwgl47GMfi//zf/4P3vSmN2FtTd5rb33rW3HMMcfgT//0T3GPe9wDL3jBC3DmmWfida973a1yvhFGLm6DiNmKaNreExnUr7/sXfif/+t12Llr9/TjOkK5TyHeh66V8I5v1kBIjZcG76ZILgrA7wuE/QCeTwjgAaAEikWHDKpPQgWW7tndsVsnqGSRWjUyFnHM9BPnygy34NYKZO/4zCmLQkTguRpDV1x/QBgqo3dfLF5JqGKQzyn7wBPWWXKbb559qAfaXCMbsrKPXlLROgYxeBaDTFJiUFSyHJW81E5dswZjHoAYaIuBtwCr4dbPHfysktO1z1vn1ZF9iNciZnJiXxDW7FOqHjRC2OMxEhlALkyrSvRGuVPQMsFc9EvGOo8xEwIDJ8FxKCRIFxMz1+6tOqEau7aukhBtmIFnSpCJeyhndoyZvOZd/SVyEzMgpNhTgF8hsAbKU847FFU1pUQKCgeeO3gXKrKhRmfPHlzwdHM4OTWHS/bFrxLKDcBiA7CYM7xrX4Mxc7gMWySywaszhI0OvFEM06XjQXJYSafqr18ynUcSRxWJ8whSPrYY/w6R7BTABWcZqMrQLt9DdbKxtuNGbP3+Ranrduc5d2SZp3yfbd++Hfvss8/NlrkAgF/7tV/D29/+drznPe/Bf//3f+P5z38+tm/fjmc+85k32zENtyxih+71+FkWZ511Fk4//fRa0QAAuOCCC7BYLGqPH3fccfiRH/mRVEzg/PPPx73vfe+aJ2jz5s3YunUrvva1r6VtmmNv3rz5Vi9IYLKo2xhiNaioqZvauyJi2/Zd+IXnvw6XX/GD9GmelysHuoNhnjmp7NQXMDV2YkgVFlmZnDa3QJLhqMmgmhKqvDkej5i2m3CQmvRRoqTzyk+pNQyzHKOHJLQeI4ADgwLH9g/jggFmrZWPmnE7/VYQQlFJXuAZtChRrLX9GGOI5VZjJqKra3bE8r0l6hiTHzGQdPBdJCE+JtJvqWYDQFZ4ORq5hxGD5fr7hPQCiyQlqTpCgAuo3eOTzeEkBNApgQusjcxYbrRmg7yhxnhpyMa155mSdA24u5qj5UhEpMMDkHZ0SBmSpKDj6RKigACeueQrSXONZEv+kGZ7+nyYt2VNfSDnUFJQDwfVejYgMEhN3k0JUZhxLTtQP38Csn4IwbFIfFwBhADy0rdi6ApIZiB6gyjNNaEQGVmIl8AzisCAXzL7AJUzRUO8Y5RBmjaKHBIoMrYxJp2qroEQDE8MXtGMS5KSMdCRFYuyKTjGEMHLy8+ubb0O23/wve6TIwyu7ky5B2+JBnpPe9rT8IMf/AC/93u/hy1btuCEE07ARz7ykZbJ23D7hWcHvw6VnuIYW7durT2+urqK1dXV1vbvf//78cUvfhGf//znW89t2bIFKysrOPDAA2uPH3bYYdiyZUvapqvYQHxuaJutW7di586dt5o3ycjFbQh574qYrVgG/3nBN/FrL3tnklElUP3fXJJDRPCrYsCeVgdVdi3zBndVGfz6v9n2PItfXB0DusbvsTa9epaY+gOs6iAN03Ztiaw+l5R+CIzZQo81Nr4eA6zfvRBSFVwkBUgVs/JDsvoxxo5Rm24Q0y+vSIM4lNr/om9a2e/NhnC5B6LqA4FOsjGErt4SXQQmzkGCM5qUfUhjOaRVe+ZU20kyOB1NzBgx49G/0u9qgbYTAuP0Xve+uteGzl2XXfPMTt1bkjfI04P1VEXqHB/RyxBXc+X33Fch1bgyf0APsegCaWYndlRO1aRiNq0j0JZrG+VDXQG8q7+v1EiOmWS6phq3fcHgwtUkRAkOFdnQ18iDgLlmQEaPEElClCkVgCvEAxE7ZgfpWZFfgxBlaSPHSDIvAHABJaQKlWdWAuN6Fy2a0qk0ZiwfHM+/EB9IyiIEBxemvWc9McIKdZ5H7q0AA+ycFtUYJhY5dly3BbtuuKp/dSVPSXc8P6XaU/Rc3Nx4wQtegBe84AU3+3EMdwwcfXS9N8vLX/5y/P7v/37tscsuuwy/+qu/inPPPRcbNmy4BWd324CRi9sAogyqLEuEEJaWQAHAy1/9fpxz3hdqRsdexIwGRPubdLUT4J1onDtJQiNDAhJiIFpfTBfheaCIpyHKiTopQOO7ihlubYkMB0mmwvnIWrJyj337RONwIxCl/JeC6jxrzbeM3oPQjt6IREIcw+CVQr6/meW5MqR51L+7p2UgUmdr3bspi2piKEPRGWiDgXkxObNTZTiqgFw8DYoC8JxlIJglDaaSo8mr8JEkaJaBSTwwxKLFJx9qwSDHi0zDBvQaSaNq36hNH2qQF1CvvJTD1WQvMYCH9D8gl17/sbMXf0VGRPIMiwvw8d6P5ZpZiAhPqOaTTzCvnJW6cUOJUbOrNAA/41F/Rf5cXIVHvLYMDL0yDCAUbZlSMwMRsh4wgeT1dBOyZvEY0d+Qj8szoPTVPUueUWhzoKXM4UTgooA0XSRwwXVi5KmTHJYu9N5XrbkSEFbkXk/d2QElu651g0lzvO9j902xh0XPXdhHLPTx1fl87PSxY8eOmz1zYbjjI4AQJn5PjI0DCHHIm+h1ZS0uuOACXH311TjxxBPTY957fPKTn8Qb3/hGnHPOOVhbW8MNN9xQy17kxQQOP/xwfO5zn6uNG4sP5Nt0FSTYtGnTrVpRzcjFrYy97V1x6fevwXNf9EZcf8PW8Y3z485EQ7xMtsLPRbrR+x5tLCsHAsIqUj+MKVFmlCjVsiD5Ps0qVF5N2FOJBYsxPAWRBAk+UH0XInCdRIR2RaI+SDAhcgaCA890yrqa2bdKzj6gKLtlUBWBIZVQSZBCzJLVYE4ru8OnXs8+tElBW0I1pfJS7RgE8LxI5CCQzg/11fcqgK+qAw1lOPIMBEPq9EeDNcWOyYPz4vo4qAJh/T/YUVXVJxK5qVmteO4U/RrpKKn/RZqvziE4SAA4McMBciqdknEreVf0/rSvQYA0Y+st+5sHjy6uqOsDsds1hq9BgJZPzQLWnJREYhTBDIQlzOGABsszAjn9QHBIJW+ThIirTiuJWLhxWWkswRsoADPSljj6Xin7MxCJWPRIV6mozi0UUhyAnBiNqCGT7IN8bObHoNrr1czCcCDJPsz6iUWOQCJji1noWmd2vb7pguo227ZcgsWOrehdkGJG6irYQyzAwMrKOLm4uQ3dhjsH1lsWtWnTptEO3Y961KNw4YUX1h575jOfieOOOw4vfelLcfTRR2M+n+O8887DGWecAQC46KKLcOmll6ZiAieffDL+8A//EFdffTUOPfRQAMC5556LTZs24Z73vGfa5l//9V9rxzn33HNv9YIERi5uRYQQsLa2tsfZinP+/ct4+aveJ9VsJoKJpL67w1LEolyl4ZKxje1DAbDKoNKKc1a5Kb3PsyhzUvbBVdsTAxQoBZwR/dkHMYb3ZR/SVKLci6uV3MFxa8cQeVaLRJBKz+I4ukoeSUfhp/srYjaAiSRDkOaq8qmOQGjMX9GVgWhmOAZPG/qaF0UtgHeoglcinQdnJf2cAxwGMwO1Y1CoavpHnbp2n45ZDWqU+uRYiWfkGDEAZ2W3XFB176pfo5ccDmQ4mlI1z0Fet8JNyjzE8f2M6oFsHghSyLxVWbqoR9bUeQxlqGn8okgZiFhOOfeBsB4XE/wViVAxA3OSEsIeYG1qh4Jq55PmBMDPQre3pENCxSyVs3jmhHBO9e24IJmXhoSIC+jCD9UyEHGFfyo5IopFGxhUyOdvCBXppLKdgZBytsPHqGUgCogRW4swUFA/R889G5x4V/p6WNSOS0AoS2z/wfew2L0DvcQicG2fTuh85hMyF9u2bbPMheF2if333x/HH3987bF9990XhxxySHr82c9+Nn7t134NBx98MDZt2oRf+ZVfwcknn4wHPehBAIBHP/rRuOc974lf+IVfwGte8xps2bIFL3vZy3DWWWelbMnznvc8vPGNb8Rv/uZv4lnPehY+9rGP4e///u9x9tln37In3ICRi1sBe9u7IoSAF7/sL/HZL3yjWhHsWyHKH3KEsLHQQGxaJBucrmzVlvY7jpcdNqwoeXD921PGhzioDKqZpegDA263jhEJjzaEAlB1uE6dmGVbV04bPmUf4niqgZZsSX8WgwMPyqDqQYsDOy2VGxiBCC6uQA6gV76lJlYugvZ/AMAB5AlSOqvKVIyhSURiBqPpq0hzArRp4ri/IpINdiyVnSDnHBhwuuLZGbxDy8xSO1hurugGl3c8J5H7TLjdGZkELJdlaZAe4ntNCaE0EudUAWsKGACvzqprpRWiCDoW2qvgnliC5aEgM8tAyOsBDdYlA+O0bFZvvBelOo3zSBkIyraD3ocEdBmD+yBZFKpJ2eJ5c5n5S5wDOSHPfj4unUrXwDkpQ7siZIV9VQkNPsBx0boGcq142ICem8OjLIlIspO+2w/URMpwRG9Jy3Rez0BACfJS5nCtXkVOr28kMPGjMTAIckzvAMy5k9B1IXiPrVd8C2GxSx6or5w0vhuo+/3W+OzasLoyetzt27fXyncaDHuC9evQvfdj5Hjd614H5xzOOOMM7N69G5s3b8ab3/zm9HxRFPjQhz6E5z//+Tj55JOx77774hnPeAZe8YpXpG2OOeYYnH322Xjxi1+MN7zhDTjqqKPwjne841btcQEYubjFsbcyqO9fcS2ecdbrcdO2gdUjoPVN5wuSL92B7r61eUICFDHlZsPFgDvXS8SgjlCVsR07JY0kqJQsPjrG6xyjzCpINd/nzXREXHItUZnDJ0yNO4zZ6Tmn2vI4Zc8pW+CyLMcYKJNJoXDgopJPITCc58p7oRNnndDQMarmbwBYuxzzDAAjRGI0EAjl/Sty5GbmXEIVCMC8mJQZ0NNAKDiVxUxBJiF5CiK5o7xKToHOwLsLjpyWW5Wg3zcrRvWYw5McrOcYeRWmVMaW1F8QpkizAL+q1yov89m4BpFsBGaVQU33PjCyak3xRnBAGaS6VWrkhuoaBJUWTSFIVXan0rfk1be6xpBrG3pJQotshAAOUOmb3NNT3rhepVOIcqRMloTCSaGMGAh7XeEvOJVInoTE7kkkVFHuBOr1QAxJp9I1yDMQ2syHg/qXvFaA6zOHA63KVl3jRrLBBYR8AUDwowQxlAtsvfybCAvtYdHaNCPvKavWMcnGwxsnmFx37tyJo446anQ7g2EIgQlhPZro7eUYH//4x2t/b9iwAW9605vwpje9qXefu971ri3ZUxMPf/jD8aUvfWmv5rbeMHJxCyJmK/ZUBvX+D/wH3vC2f07EZAqYCGF1ekM82QfwIyVm08yDbh9lUFOIBSDZB5UotfbJ+U8KlFFVgwImHoMrGVR2Lpwdoyv7MKSzb5MNSBAsUmpwOUGnH9Q43KUgUImIL7IMig+gUr7A3cSXMZZKjZ4CQM2gjqvgvREQjxm7q7nK/3wB8LwAHOmquwSaYobtqUA0k5XprgwHNbIFUs/fATOXrtkUb0kAI69SVfNrEMGjfg1YSdxk7wOQZH2x8Ro7TubwLmmWdwCvFJPkQ5G4hg2UjuOhrxf6ZS4eDJ53VwfKDdXsWCoaAala0rQ3FFQ6lVeDouy5kGU2dBsiMAXJzE0kSIF0IcTJnRgWmTmcWFb8sykzAF9I0Jz7HJqgLFPgZyyfVwy9BuqtGHh/RQlXzRyeZyAK9XlEAhIkE1eE6ZWtEEIiralqlJa9TXPzjIJdCtiD9u4YXTQBwDMnHzzqM0NRVJWjdKOaiX5tF7Ze8W3pYZFWVPJBJcuSFnPygzUm1Jzf/vuNeyluqWpRBoNhfWHk4hZAs3fFssRiba3Ez/6v1+F7l21JmtZJX1ZOGjNNzVYAUQZFU2MNqTQyh3grRFcijwMVOWmOpZ22O59rPqZjkpqwe/dpDuF5UAbV8nZFs/WE4DWNEdSUmc0plo+N1yJKchJRiORlhCSkgF+/7HlFV5kDV9mSzklxb/ahtipPEmRGKUYKhqdq1OeEMHNZr4pq9V18FZmJW1M9QT0DbsK9L4GQBFhEBLhCKkal4L07UzCefahWycWALavdsTFeJH3DGYj2MXJzOIBaf43gABRFB93qG188Ts0O2nHlOV6gaOIGxIsyJp3K5yo7yYlykZnDs54VzdmOZTjqTd10xZ5ljo6zm37g9ffECPMBczgALqvPM+mdo/6YAWJROwaC+sFUU1loliiotyJAZJRcBewcJnpLYnnaIOZwQErT6i8TzOHozu40KlyV3sv1dqj3BuqZl2Q3kIhF79hx7gDWdm7Dzmsu72+OF4nF0JE1K931ku/YubNnthVuiT4Xhjs+wjrJovakid6dFUYubmbE3hUxKFi2Kd5F374cz/2NN2PnTtW6qvmuZt5E+6Pdz500uJtALKIqSQyB8ZHxOQYSf0VXtkLkF/EPVJIej3o1qAmTqzXSiwSmb7FVjaLODx+i9hxDvv3iPLMvzb4AnjWL0CQJ6W+CdA2Pfg3PiSBNOnUNzlun6cTbwPFYMbMRoAH9tOyDTFEuKEGq8ESvAkMCjD75kF91g/6Kpolb9N3qr2hW4uo6dXRnOFzuK3B1E3f0wkzpqRGPwZBV4rwBXq3pHnPNxB1L007JcDhylUSpUMKpF5cCg7gyRufwxAgbnBixO8aty2eEHIYVB7iZBq8T7i+9XkB2Lrk5PGuQRyxz5eBFy79EdifNWf8XMxBR7ZbIAMn97F13V+/WeFGaBQDzKCEEuPRSjGFG6CMwHl4bf3b4dloZCP3MZpLmeBP7SwgR6SAJBeBjeVoAKBkFnHxEd5SzHQQpySWRVKZjxmC+abzvIRadQzuH3Vuvw/YfXIpWqiJf9cg+IzvXSvqIhW78E8ceMzoXIxcGw+0TRi5uJjBzjVjsiQzqze8+B+/5u492966IQ8WKOenAQNhQiHxgomkbADhmH6oTqH9h5MEHtDLQDP3SqSbZ0NXAse1qTy06Mhx9pAIQr0I5vvKcI1ZarAXQJKuZgAZBzexDD7EYPgbJSvxM5d5BSIHrIhu5Eb1jPMp/IQK7QuVcSjSYxuVDLMG1DCNb1khB4arKTgxQEJcFVkUGNdlfMYeWQpUAk6kyMFeZglyaFTNnwxmO6CfguELunLznIAHxkIE5EgugfYxa0z2g8mtAKxzp22rs7ON5xOwDZfdvvbqV3q9M4hlYLVqB78BFQFglbcYm1zZJvjwD3KH/V3nNoP4/L0/LDPb6GDNC8JXEqm+xWnsf9JnD09CagQgEhJmb3BQQEAkY5qwkLL5CQuJRBr3OSGSDieCdF6nRhAxHmkcIWpwi6yDvWczhXRmI0JZO1cat+UCAMkh3eQQHlCG1HBok341j1CRUkOvPauQODD3fKsofu3d3XncVdt2wRfbpXAhiMbPr58/Q90y6zfLFIcUhBx04MhPpc2GyKMPeIrBDWIdStOsxxp0FRi5uBuytaXvHjt34xRf+GS79/lU1s+TocQkIqwRZxpu2j3eQu6BjevG7gIA0jwDNVqjRe9pBMpIQkZGGmNXII8xWv4vRY3DNj8E6Thy6hey55vOtgCFmi3JJ05Trq/Fji+wQxFBZFJWJ22cm7qF5t46REREiYFakzs6hTz7EmJThiGSDCPAzl7IPInWSO6NvjgzArziw6+ktoZNmqnopBIqB+DQBkSQXZP88+wBIX4SYgaCswzdiec+JK/DRHC4FcFwtWyIZiPZcPUljvD4Tb6u6FUcj8qySZ418XjBJx/vcxNtVmtVHH0iIXaMx+dxTViztQ5XcCbrYTnVCFqsTTTWHMwCIxx1FyYD3cnkd9Zan9Qjgefcqf16imCAEhj0Dc8mCcVxzGbnBxIeTlYHN55FnIBgiU+QC7D0cphOk9NmuixnsnHgr4geYR814L/Mal2dFssHVAwCo6nYufyUSW/FIbY639VrZijvSDrGiW9/KR7y4AxmLiANGPBfMjO3bt2P//fcf3M5gGIMHwU8OJobHMUyDkYt1RsxWfPKTn8R97nOfWufFKfjIv38JL/+Tv4Uvl7iNSWvgF8OrSDkYWq6yGN4uW3BFIMlWyEobtLY/+r+oGSKD6ppStk8K1pUXIUqaJkWYnLIirc2p8X0Wv8szcjDlEDm5giOEIjunUJGaZgCfVqUHxpVfZHU1OM0+aHNA9Pkq0jG6MxyVD0JWyUORZQrKSCqGXrj6aUjVsCr7ICuiMeuh43IW0JEQiyn+ihhghkKavQWgJh9y3G1gjsQiP98cdRO3rOgnMhTQuercef7JX5EZiHRHdiHaFtJ9KN3Jp3kf0nnMHQIJQYrVp+INysxwVH+Tpk7VIybeNAeVCsbXJpaiTt3Qu+blWUsju9YGlN9fzMlbIWPRFOVNOvcwqxOkOB/2Sgrie9g5Ceody2fQ1OyOc+CZEErHDrwmphrW+4LnResNxN7LvT4UwDcyEEHlYyGwVCQbMN4DSIF+rzkcABdckY1YnpamlaetEwu0jsWZNC6+l3b84FIstt+Y712HGtSFmORnFt8QnA4+5RbYf/9xuZPJogyG2yeMXKwTYu+Ksiyl3J/+vQz+4M//ER/8yPnywR+LgcSVcvQEAU6a4skHfz+x4MbvUQY1BRL8IZWljciD9DS+eiTBEI/BlAPEjaKkqTnZvnkyV6VsR4bO/4ibR3/BWACfXodsv/S0I/gYf8VVTI9J8ob8GBURIa08RfqUTjL6KvJ9JhwjZQqiwXomv3sGwKE3eE+ns+LUl9CQuOR/K9kgAN6RVHeaGGEyRDoV1F9REa6MFESiFqAZiHqn6zEQkUqxGCBXEa6UgUBnBoLV6DNUmpayeyrMWD02IolsdiPvO3dukLBmd+uYDQCA4AhhpZICjYIZtCaktdYLgytpVvUeoBTYuzDt+pKkdERmxrIizhmRAaRnRfN+8Kj8FV1nUqscBiD4ACYHRwQuhWCM8WMmVD0sGtmd+H7lXarvIV08cdq3ZKI5HByJAknArZXThBRAX+QOc3g2lz4ksqH3KZyQohC1iug23ncRi86xo6Q2BGzbcrGWms0/3FBd38Cy6OPQcc3jZ1G1wNCJxoftgQcOdzgGjFwY1gcmi7rlYeRiHdAlgyqKYjK5uGHrdvzMr7weV//gerSW1OPKHdfJBgEIBYk5scuT0UAczhOqbEWXn6F5btCytCNf5OmpUP3QwLjNg9QyHB2LYi2E4WpQPYeRISmu4CYelLIltfHUuD00jdYXe0G6kgkJKmIVqt5JdTflq4JsufbsCpVYsZamxWBWpHmMmGkQqVMkiVVjOIldKgmVJyRpz5TqTkSu6osiIabco4wUYreDd8DP0XuMpnyIXdAaAU7ueZU7jd9esZhCpVHvq+wUS97KnKZnH0LBlTwLBCoqUsCRxGRkY6hsbBPOueoYTrwfACdZUs0jkSMw3ELvv5HGg8xBFyiihHE6eYkZtmYmRF7/oIUdKHWb9kA690mHgJBiySI58RTtznwVBCW01cE9WL7dRrM7RTqGC9X7nX2QG3fWc211Jw6h3Tm86YEIIWvACLm3ptbL14UFgDplb8xZVlJlXySTmDa899h6+UXSHK8pgyIgGbcZoKJ7TE7/yS9VxiSyRag0NAEHjsidoizKPBeGvYXH+kiallsuvnPDyMVeoq93RVEU3UbsBv79M1/Fb7/6r7BYlMMbNsiGdxCSwCPSGUXMPnRlAWoBdPxO0JW8ZcrSxtXlrL9W+pfjATqW2WKgPO1E5Msubj8W/GeHqW3XJgXZ1yEDSPKhiYjzoOxcCiGA6eC+UX1IS9lOmX8MIGSSTjJP0MzGSGna1Pm5Y4uaxAXSB0MkKJS9aOOn7tW8nCRZcS2coL6ILAiCgycGlg0wgaq6kxNviRAuaAfmehaGq0hrkCDVJVQSyALaCyIrz9s1Qgz6uwhS3t06ZQoAeCIx2S6T3YnyoUZWQ0hBqN4LsTuzD3C+W9bUhbgPBSEAecUoOVbPB4eu2vdlOeoEjYTwOM0+kNfrgN5rkTJCTelUloHgwMCakmTSz7kNNNkYL9yvQUABNYdLZkPM4QAp2eCgmZqJncMBCeQlu1E3hwOumyTrfd1b+jc/NkcyK8UcEKV1Aw3yQrmGm2JzvLjK0tw0DZNYaP21ikUO+nxS6tFoDs4MbNy42jmviB07doCZzXNhMNwOYeRiD5H3rmDmlml7LHMRQsBv/MF78cn//Fr6Ypt0XJKmeMhWwpoZjSY80GvaRv5wNhaTBNytqLx3Yuj2PeS7c3YMWb6tJE0TiUWtw7QGg/GrK18hy4ebcoja9g7ACqWVdzHDDuyfrcoN+ktmpAZupLKhy0qnWtuTVKFiQAKKFBDrqiq6cgZ90EyYlhyV1das2VpH+VQPlk7KAxmOXOIiDRcZmKk5PITWinfr1CHEp5lNqFV2cg4+9VWABsrTsi4RQUuHxeyD3F/dlZ0I4q/gYmKASQSG+Go4C9RYV4clk9LTeLDWcbs5bkUeSINeUpIvjewmnDgzaCG9SaoKRPVzyqVZnK7DtHOP5yHXUr0lUFIQvFRnA4PJ1crIClHg8exDNCwDAIlHodgtH0gBkGzavBq3Na+eDtoiHZLMBgFS3aoMlfypLyDvOv8+I3YBsJeu7AC0G3cBLtXzMiV7xtAFFz2HjKzVG+TptSSCX9uFm674VtUcr2tMLydHrucE88+kzjHkiyR9PmePkweOOOwug6e1fft2ADBZlGGvYbKoWx5GLvYAIQSUZTlYDWqIXGy55gY85azXYvuOHfKAfvqOxu8FSSYhJyNU/StflPqneiw5ZjiWAM+oKkubBezJwJ0fF5Avtqn9GxBXmpebU25KrY3V+COSjRTE8xLZByDKhqtxSa47RxO3fulGIhcXxyeHsJkMinOywf0SqlilqjavDOkxDVwCSTlaciRlKyeQGAli5eLFgKa26lxUvooo8wkgYMVN9j7EQDkoESFQ1a8C0CConmNhDcCnVh+SFfyg5EUN15rtGpJQMWTVtzPAVKIh85FgjR3JqjCqe2D03AuhapSTIv2XdaVZSLIElWEJ6RTifqHyV5BmTzV2l9iyeR0TsRi+xjUfSCmeDED8FUwkkpmB7APrG6ZZOayivlRlIIi0h85y2R0m6XMhwbDsV0BIAZecPh94JuZwFjY2mSCRc9poT+SF7IME4KSv3Ky7RO+Yx6JlDi8DqCBwkPuNhrw7Siz6Kox1Nchb27kNu66/ot0cL+6ulcXYOZXgdR2XVfpK6f1be47RSZajT24GwsEHHdA9tmL79u0oigKrq8MZDoNhDJ4d/DoQg/UY484CIxdLIO9dwfph3pdy7iMX//hvn8Or3vyP1XMOiMwgBhat1XcihBVKq5yD0J18gerVnbhCHgi6ctgaTn7n+uHzoGp6cI3JlXoSsmB+DGmVzKXLmAL4WtajY1qg7udyogFCamInpTqBIkwLMPsIUhw3pBVtpCpUtS7gU8BacSquehazOikIoWVeDsSTujvHwDBKT1jr5weO8q6B4J26/RUpeNfrEOVDVbZiCfMyNPhHzD4UFSlwQRZigVofjPjCTS7PSg7BiR6fiMS4K5dBpWntQJCh78eBz4vatSd9TQrS7I7PsjT9q8gUyWme3cmkWWKErzw20mySpXLYlEvMDCxys3eVgYhN3ABIUKom7opYjDcPjfdCUMNWUQZwqeOQZrs6zOGyiBJasrFq3IycMcC7ZVuexc88TFohYG70l2h2Dl/4NDcmaO+VoJnAiffwmhZu0FK8ec8KTp/jrlr4GSAWncNvux47r7ms9nrVEIkFUSKlnQg9VcFyKV37yaoAhyPsu88+g3ONfosp1bEMBsNtC0YuJqJp2h7rtN0kF2Xp8Usv+wv819e/U9MyJ6QovRqT9T9hRWVQI7wiooskyDnEDeor5AyIBKrHsNc1zSgRGd6oObGBfbqgwfsymYGcWNT20YA4fV9qtiU+txTZ0X1ABDhNwsQvfu230RyPA3c3zOsZmx20LKusdvMUgpXMtfV7s+4piJWTtHwmhCQsZV6ea+O2uPJMMajPfBWZhEr6PkzzVyT5kLTb1jG5Ilwo+gkidzfGk3Hr3gPpg6E5khBSH5ah68uAeFKoyj7UgneHms9KsocsZXyXkQ+5kLI1hCrAlAyHBJ5RRqUn3kks2tcgeydpTwY4LZ/LsZxwv3ae1kJVdaoxbr6XlJHV19IRaDbt3SVEpE4Sqs8bQtjt4eI97JRsELTU7PQAPvknQCgmmMPl9Cf0l3BFvgMSm/UsVagK13+DMYBFJBaN7E6rQZ4HIesY3vRB9GDndVuw+4Ytci4t74Qer2QhQ31SKEA+YxoLBPWFMS1X20Be2W/KS7Vt2zaTRBnWBQxCGPx0nz6OYRqMXExAzFZ47yc3xMvJxcXf24Jn/OabsGPHzuWO6yDG3bzSCIa/nwZJQvwuyp4PgWWFjJaQDw0EYpz+kx1zT7IVusqfH6dFGJq7aHDQR0Zqq8lOftJzSnzG5hgzHJ2Dk2Q1/BwpU0JlAEqgWDL7kIzfcSVUfRVxxZIaEiqRTlUSij6kTtkAMJN7DEC9NG3PKUqFI9e5Cl1bHSZUVagKErkW0ejrp6cmmQFqjwmn48b3QYCQmIYZdyoYal4mJz4QRq+Eqk4s+s+i1kvAibREsjuh0sQPnDsXalJvBpi6X07kktRJug9O9legDNpMLyMoFGVvmTQr3guJWEy7xlK1KEhvCRDCwqfzDsSgIvaWqC+khJjh6Ang8w7fLgjZwMwheCVk83FzOHNQ6VR2fZvm8JxsOP1MdZhOEL2Sw9g9nJCyPukzmFCRjR5i0YXoA0mV8gjy+Z0XD+HGfciMnddcVjXHi/s1ocQCvR4LJClU7eHk6dB7tIPsSBnp2iOD5wlYd27D+sFkUbc8jFwMoNm7YplO2845hBDwyrd9EP/w4U+nVaxJ3/8E8IrU42+J+uPiUEqRp4eTV2IMcR9PkKoq+kBgSKagLxDWYw6dR/NxHpAi9SJwp4cjP9f8sd5sxRhcFfCyy69rvapTOiYtcQwNTFCIRMQzpCncSGnavgwHNf4Is9wUGz0Z01fHvUpB6vp/VyuZSTELgqrr9PTeEg5hxtp1G0Aux4Ecq3mOgRlcDAfwtV4NxCq5cWJm99NL01J27p0lb+N9rtmgaHKfAtZjVHKYLLsTLwCjRgQZQCi6jcWd1wAAhwDHkQ7EMqoMykzI9YkJsejLcLR6l0QJHUNuLaZR+Z8kmLxkVzq8OwWTyodipsIBBUlDT+qeVxcCB3Ah18oBYA8p2UrxWlLNxK1XqEUsmqgRLkDIVvJrBMl4DGQgOFTEYmhcKaWspDiu9k/5blEpW/Mub5IJDlrlKjB2Xns51m7KiIWeWTUXli+domdZJiMPvTOMZZFb+8p/qKEQnnKq27dvxz777DNdUmYwGG4zMHLRg67eFct8yHnP+K0/+yC+f/V1AFidhdXndK+0wwG8WlRG3Cayla90LCA1N5pEXgBpQtU0ehOAWYqv0+p7kkCNEIsW9iRbMYGMNFeUuUAK2KaUpuVsdbO1nT6XN8aLhuqer95u5CZ33ZEdSSd1vcBUZkRCJQVTpVP5PiACz4HYKbmvKRyQ9VfoK2/Z6GwdwJVMJB5uZG4MwM+FJFQSJarG5QBwyColFaKzn00PLhkVIXSOAFdoHwwl8Z7hgqsTxFjmaCSQywlM4KCGWynpC+72VdTmlTwGzQAzC0pj8B5vhyXIi9yTWgksJyPxsyHrXRKPS8xAyaPZk9p81UtDRODCpWxJVZGsnd2pSNVQAN/IFEDuWSYWQp5VjOpCgBKLZnYH1XuamibuuZtc2QtQIuj02mWfFalilL4/MCvSMRDaWafea+D03tQeFukDNyKX28V9vNzXY/Q5Nt/jELB9y8XwazvrY8sJyjWOzVdde5P69kOlZtF9T8XnOmSi3o9XSDRZlGG9EJgQpvaWGRnHMA1GLjoQQsDa2trS2YqIL3/ju/jl/++9WFvrqMaRLYWnz/K4WFxAm+KNl1LSWE+MoiprAhqkAO0PdQYQVjqe6DoA6eq7xIIp+B1F43tyEkJ3M7nBXQgSiMTDZjEW65it7MOQljhD2ocAqMcgXVvfnVlJGMr+xMGJwCskfo2gRsdyuqKzs4JUrJZE9fKTjqW5FjvW1d6p5mXSTshavSjKZlgCdYf2CrmnkDov9w+bZx/0HnYSvFIInSVva+eOGMA3qg81moyVMQDX+ZInuI6MSR9Cklvpm6EreFeZSLznppqXAc1uRpLVGJcI6OxREYkF9wexNYIY/Roxe4L0VD+YwT7KoKj7Gjiu3WOxagKhoyJV72FUoqTXj5i0shPk3iOWPhhaiUqub79xu3kN0ueAk4whBQCl0M8hCRUH3yIvadymiXtNJXyOEKJ0aiIDp7yHRVNqlMmcmKE9XMaJRdrHe2z9fk9zvDj5mJ5L1aryLyVUixd9R808Xp0pix5iAWYUE75HrIGewXD7hZGLDFEGFatB7QmxePU7/h/+/l//Y7yBXjYsFyJzYdIyklPmCi0d2vz+iyt1cbE5BkCswfgc4198+UH0n/gdGGLvi7RC3hhuD4kFgHQuPEIyYraCXf1LrxWHuSzo0y/YuEg3BVLxpXEAknFzEkeRxOSZnonHSKvATslGJAy+X0KVZ1K60DQvew6SfYBL2Y14On0IYCUWVaCcG8Pl3pIXSquSqhl2ucZ40iANlT49N0WryThfIWfW3hITAtjU2ZokiAWreTkAVAKFHytN232cZnYnlsuV6H141T0/d0a1Op4GRHVtmTMGG03TUao2kSRH6RQRiQwwa45HXVkYZukRwsMkIZf5SLCOatzQlhU2EVIVpXZ2p5oLISykGz2DZBFlZXrmJe9Dkq/rAEDYHdLLJYsU8p4JCEAPseiEcwCJB8bBSWYjI85dGQj24z0s0vE5ehXk2nIuW+yZYyjXcNP3B5rjZRUJa/68/HM7VISkdbXjF0vcpuvzPs/c1h5nuJKxsjLvnHsOIxeG9YKHg1+qKH3/OIZpMHKhmNK7YghXX3cjfuElb8TV192AvBzfGNgRfGqKR9XnPvev5HtdTe/75qbGH1I2FClQnpQh4Oo0Or9clMBUFahklQpLBO8pbd58OPM55mQlEYUZJeP2EOLztepZcZE19KyqxcM5DB8gvwYx6NOMhZv68nd5S2JgMuuQUMWKUUuQF7leriIJrPIWqJUgMJr+BylNOx7A50F2KGJzOAareRk0Upp21r3K39KP600WSNZQ3cTUdLXS3SBcTvwNgTX74xmFd0oM5QIvld3Rm1iq5khUHe/drgaBwvcr03r3sHWCGEvsgiBa/T5fRXYQDl6JRS4fahDPrA8G5ffw0t6H7PMyyxoBkL4YqILQKdKpiGoe8l5xu7SMLLH4KlJlp/qVCDycQauZw1nIBs9IJHAhvsmG77N4f+X3cL1rNlcSKnmbpcWNqc3xKKCqztQ4T+m5QdW2zklzvMuHmuOx6GgLDBi3s4xU1yA5sWhCF1q0mnB9HwbcQhbPimL8/Ldv326yKMO6wGRRtzzu9ORimd4VffjUBd/Ar77qXQgdfS16RyLJVoQZVUv1+Q5EqZqRZB/0Q3tGSzXFYwDckEGFjqxGbZ6hvdjVi2xljGcyVgDGpUH56lnHkGmzbCG4JKTmflPmFjMcrexDfLxAJXXW+QJYuulgIgkEYF4lYhKBaWV3pnlL0nyJEOaQgCx+SZfdxKw2LbAETB2Ny+R3JAmV3A8s597hF+gDo0kSSIMpKXfJJJkWlwXDU6RT+Xyl9GhI/UUClBQERtHwVeTzkqpT3e/nWpBdSDAMaHlPr7KNkesrvC/21XDVuOkgqErI6g5ETsrsYokV+BCzKLp9UXT6KvSQiNIphPFrXPUQkKhXMjFarrijalZtXgi9XpG8NwHPCT5fqQiuKqM6AbGqVxxAzpHgFpx6SzBJyV+ek5Lj5TJoPEOqbsUBlTk8BvSNJoHSSJFH5H/VPqT3guNIwH3qlt23wiGLNf09LGqlbwGsbd+Kndd1NMeLCNJkkYeyXpmPq/oga8+txhzir1Em1VFRCqiIBQDM5uOhh5ELg+H2izs1uWBmlGWJsiwBYGliEULAS1//fpz3mS9XUo488uUqqED+MBHCalYNagwE7c6NFMxHg/XQbL0Gu82NKPuFizRVYQV5b78paGQWqm+PtOhercDF+UYT54ThI3cJGalKh9TUe9dYMWAYze7E18vp9SKo1Amj17eXJGRBKxf1axD9GstIp2oVpDQYCSvViiV8qMlQGNAyqOMkQVac9XoWEmDJqcm9OWRe9ugnCXnwTkUVhPOcwEs0xmNoVqRLZ19kK+QMLflLldxoqrQl3r/MImNxhZINeYy8kA5q7MKafRiUD+VBtv6PtMKT9PDo8FXkx+FueVbLV8HVCjkFGvRktI4Rsv4VWbDLxIkUSNWs1L5NK3VNI6GRIMZVfhQV2aAgK+ldJIZ7pFP5uFRNFmERENjBzUhLbFdkuffcIVmsvE9GjRQA9U7cBCG5S5jD5SDRJyMfMpFsVB8EDLjqAy5m0aZ+H+3eeh12XHOZ9sBogCCfuZ61NDT1f+/kxCF+mADV9l3+jfglp69ja9VIPTn5XqsrK6PntH37dhxyyCGj2xkMYwhwCOsgaVqPMe4suNOSi5itiKRg2S6gV1+3FU/7jdfj+htv6v+gpurfFPQB8KsALdEUr0USqPGZHwPX+BggpR0nrMCnD/y4YKX7JOnQgHE5SsIHv/50A9ZqJMyVtGcKGEjdyVvHcd1ZGCZoUDH9GCyWBPnbZaRQVxBb14F7um13IV4DAPFEYjZqsDrUlApSBPDMqTE8oJIy0HRpC3JtehVYxG7ZcSoUtR1QH8My2nRyCCtaDYhQ146H1M6uBg+VZ7l+E3Z+/OAY3kEMqj5I9+kwQmPiKn8jGG+OGxsOivxNXsCp1xfIsw+RrJFeg6DvPUbqRk7ZPo25dF6DGAxHxu3UdK9jD1a3Cr63f0WzfGqqxhUlRWG8NC2ATvlQGtfFcfVhfc18CCiWzKCl5o7Q5nhrVfaBiYF5vb9GF7HoQpMgpgxiqY0jC/3Q7LzAQk66Xsfm36wVlCh+WDBrrD98hXdcvwW7r4vN8Xo2Yh6uwhVN+X1VnwbBQNlVXLoa15X1MTZsWB0ZU8jFXe9619HtDIYxeCb4dZA0rccYdxbc6cjF3vSuiPh/H78Ar3jzP6SMx6TjUpRBQb5wCKlKVG/wjgkkQb88Y58HZqTSrJPPqkcGFQPuFGhHCRVX208+Rko3UCurgR6DcvSWDPkrKPuFCyEbaXGhS/LVNa0+IpIRo9xb4soliEU8SE7U4oVzFIvsCInIDdzZat/YcRKpdC5lHmQhUbIPQ43xAvUbpGsdnZUQxI7TsfZx133TOn0C/Gqji3IetKohFojXyMnK9mz66rC8jlxlLFwBXwTtPg3xrLCrk8S4ooxhg3TdvBxSIO/BQhDRLc2KCGPZB0SCrCvZ+vmwTCZVfC7ZmNluSZoVyXIU/zf3GQM5sJMPACIHT5mvwmsWpnEd2qb1rmGz4D1lEB18YKBkFEyD15chfVuambpal28Gwi7pv8GQz4mwKvfxlEvM8VwKqr0vCGiZuGv37ESCKGPJCkwimCTN8VJVLnBNCsXM2HHNZdi99TrUpLWNiTMg9/cQsRgiJfomJ3Rsph6OFrGIKz6B4To+Kxdraz0HrGCGboPh9os7FbnY294VIQSc8Zt/hou/d7mk8zEtwGSnMqgmm4hZ8Bhkc/W0rMRh+aZ4mb+iJknqmmuMQwmdmQRq/MFZrw6q4qDxa9B3/CzITmQjSFDNBVKZ2UnXGBAZVH69XPa92SGhCsD0DEcerBUkMiqGmGCHZE4x8zG0+KfXIcQANzDcYmSf9mGk2lhHcBlNu6RjEzvN1kw31sqYTgL4WJo29mnQF6/LvOxdQFihydkHdkA5g2jctXKR4+HgvVqBRm/2gaOEKr4nSoYrCcVSvSW8EJF8Rd/l1a0iOcqrW8V+FFPkQ67S8scu15FsoF9+VfW86JEPNSoWcdDXnWlyk0u5X+oeg1q2t+M6cGCpjrxM7xK96Umb67FjlFnWCF6kbxmHhp9jNPsg85UP3EABNHNSzCCoL4ggGa+uxXcoAe5pwNdt4q4GmnT+usow1BwPQCpRy8zYce0VWMu7brcmzkhv/L4V15jpJHR/4GRPd+5bctbLJj8fXdhoEgv9QD70wAO755PBPBeG9YIZum953GnIxd72rvjuFT/Az/7vN2Lbjh0aaFP60u9beQdku6oaVA8ay9MlIRGPqYF1Z4YjW3kHYvCOWvYBWCKITTtIsMaFjpOP27X9xPOI1zWsZjtMyD5INaieg2RELidHMYky+S6IgWN2DeTaErKFxXoPDAaoXOIYQDJEckENw7k2NGtPK+s03n2kmrnWqdSJCCBhX1MIos8al8VtXRa01iVUagqeMTBfzljrVzgjCRJcel15Jw/R/Wf6fA8GZizm2JHx4zy4lMZ4IV4LlTr1mpdZgzoeDy7z/g+kdU6n+ksC+xrZqxHFpq9C/5caJy4j66xewFRKVybPnRK1asV+RJ4Vry9iE0XVQmoVqiFzeCIWVA/Gm0SOi4psBPXI9DWM7IKnIE0kXXWWBH19MxM3O5L3Felr2uX56boGOl8OAeSULOZlyeNx8wn3EIvO8Z0Dh4BtV34b5e6O5ngRrHxiaMi+alDxuYybtNBV6S6fZyxH3DXmwuOHDh33UuzYscMyF4Z1AbND4CU+IwfGMUzDHZ5crEfvivd86FN4/fvOThmPhBS4ViVk5YtKvih4HmVQA8QinyuybEUj+9C3+s/AtKZ4cWcNWLXi5rTKUPniVteYhJosK67WxxXRqVc7ELRqS7VPzXAex83GDA7L+StIiVj8jNBrMFiXP2Yf+gal6l8mCfThGa5EWtkfnd6QOZzQkFDJiiAzksxjmTtaZCqkpyYXIF3fRvZBnh33V+RSESYgbGCRaYFBPKExHkViUV+BplipB5B7LIjuP/VtIQLxxMZ4DM2EtIPW4EJ3FSrGctKWWJpWUg214J2YO6+DvKR+MIvUqjzE4hXQi1JVjRo6fX1z9gbvQF2ixqzx53T/DkM8KkIQ9Z5I1xfZ9aV0r1fEYjz7EOcrxQSqN3Ect9mVvTavov8+rhUggBBQJgavyDlQ9LSM3GjpGkdJVrOkcNDrqydOSvAnabMABO+x9fKLENZ29c8nJxbxArfm2FiMyIlGJAH9J9lPLJjTQkjr8cCgUt5LBx1ywMBZCixzYTDcfnGHJhd7K4NaK0v8/MvejIsu+X6me+1B/FyGrDpLqn642VltrlCS0PzeSyvkaQETWkp/z5rixS/QbNxqAmhV9diT7AOAVJZWpd3DHa0hJIFn7eM0A21Wz0pADJKXO/3QvF4an6Wqi5FIxPlOmHsTIoMioEC96WBfZ+8+YtE5OCS4ctX1TeOP7Nqlf2+WTk1SJ+jYjhAcTS54wASUG2Klqmp12KukCJBVzdxk7ClIEDcx+8AAyiIoUWZQDOY9ekvTyvUP8nqOSVsKoAweUjJVu06rlGxsfqEhUersKxGPqT1GACEKywTwrDe+fJ4VSZIk92y7yhcHr8H79OxDJAnytwaNA/4SCeC56ivSMS6ArPRv9ZEUX7cp8H3FBAq5vgBS/5LUnX7GvbKmTjiHMFeyC2r4KjLvQx6fe73GQyTc1d9vHFceYh+TrGpX8xqHcg1bv/9NhDL6FRp3ZExA6Wda56uUjtczwZSxkFWR1ncBD3weqiTMKSGtj1kRCwA44ID9eyYQd2Fs374d++8/vJ3BMAUehI46ans0jmEa7rDkImYr9lQG9fEv/jd+43V/jbXd48azHNIUD4hLibXV/J7VRe8wShIo+yUU9dX3aWVTq387g3dC6v2Qti+rbSZJp5rnF1fOBrIaDDkXdiPzzw9DAOv1irF1JEd9ZC5mRXoPQtW/TBWJc3HsSdkHdMugEpHLJFRBg8tSeyoMTK3jMLJtHDcOGasecV3aItP3YIwHsC4LLuVeYLGZBq32g342511A2NBNElxuRI1drSFkj53D1OK0DJHcVF29s+cKrkrTBrnPXHByT5QBwPTO1lLOVaUtpF3OgZTtc81sCatxG+3Aun4dMgkVNHjTG07Kzg6/PuLJGA7e69kSpJXxZbMPoPa4Ph83VFInBuCLadmHON/AHlxoH5NGNa6io8qXzKvf+9C8DpHE8FxH8uO9MWUzls+X7P6qeXmARDayAmqjxKKJ2Fcp7pse16pvDL3OziGs7cLW2Byve7Dqg6F1/Sltw+mDo+MDPScWXc+hTSw4kqBILPrG9nWismnTOGmwzIVhvSD2qr0nBn2tXwxt3OHIRbN3xZ4Qi9f+7Yfxlx/6d/nALJCCVmDgi4kIYUUaOLVK9zWCwHy1medYuileM8PR8lTsYfYhPcdVcBm/KkazD1PedCnIlj8DS8ZiGeNycOj2V0QSE+eu82U0SNNEJBmU09gPqFbtushGwLQKUnGDQmOBGckHVtBu2SMkputSNQOg3AicuvtOIBbpGJxXkKJ0jFo1I65Wb6XbdmXcHkPse+BXg8reJGiNpKDPwB2P09e/oyahKlQGk1WrcVNKp6qJPK+i1FmSNZKNwHChWMq4DUCkkzG41ICcOaAq0VuvDAQIeZmUfcirW3FA9NZ4SBM1NMhnbV6Q7EMXSWhfB3ndAkGue5jWPV3ePo3sQy5Ry7unBy0nHEiLCSzZHG+OlEkRcpT1RdG+HfmMPQLCBJ9QJXtCJXMCSznZLl9Fc245segZO2Jt+1bsvObyacSiewP5f61/RReBGCAW+hnV+RxIjN1xAs2sRRlaUdn+I5kLwDwXBsPtGXcoctHsXbFsU7xtO3bhKS/7c3z/qh9UBIGQAkFE3sD1FXJ2QFilKjAYgwbYuUSnt6JTBu+qFfu+cZuN2xADq2kzq4hF4zCpLG2+8l59p9amMAVBsw9p13hte7IPDEjmYYwkZMF7iLIk6h+3ha6eFs2sRjYpCgAtRnpRNMFcy97E1y1okYBICppjDsYP+XQpZh9Y/EBJLxF0jG75UQz6+rT/dTlVJDGAnzuE+XQSL9KpdnUnACJt8b7SuHshGwGhtZo8fBBo+Vmd95wakpkOEhOJRY90qnYd8nuCuSK2Wfahb559vSX6JGrpPemW+zyL1elapuiQ+Sq8ynBY7pdlmsMRETzFXiRU88MgMBwXLZLIgPg6ul77/Dpk72MfF3mYdFyMknAPliZ6efahw1/iUZHaSJKWyj6kayxEnAi15ng5Ca8SCMs1x9v5A2mO1z0BlqDfIb4pOy/MoLk7ZiVGiEVXToIAJRZdu6oUqmO5d4yDhxAsc2FYN4R1MnSvxxh3FtwhyAUz14jFnmQrPnPht/CCP3kX1hY9q0NAWnkHkGQ+DIhefCqxgPoLGiSh2Sm7Rl4g5RaXXX0Hqn1CHHeIxASkb5CuxEDtl6K6BpSNOwZZGUUl6crHjdkHZERDsw+YWJY3nQqhLoNqZjW6AhSPdoWTJrou3EwX5hhwOWnoAjMok5t1jk+oG7hjZZZlMjy1pm35iiWrGRiVNj+ucpP2jF6iMZ5f0dKpgYCoo5clfK2tX9/FuyDVwIqB0rRFdaMHxyjnLNIWz6nJ2nD2AaBFEGlTX2larUJFSjzhgWIBjPW8qB1GO6Ona6jBZL2qE5C6AETpUwex6IJzGvSz9oX1cpMlaUtRD5brl6BfBlYLnp3cK6HQ8XqqkrXHBwL5WvahKUlqkjmgSlMulX0QEb+ci+sgiQ1/iYcfLUCQz4GB1CNF/lZvzhhJjMRioDleRTaQqrolydPId9TO67Zg9/VbUpPHzonkxKJzknGRrGO5Jq0i5Ssm7fG7K9Rydv27DttNLBhAmBF2l7tw6aWX4uCDD8a+++7buhbbt28HAPNcGAy3U9zuaVg0ba+tre1xNajf+Yv/i+e9+h3DxKJ5XALCqkPYKF9+nMtn+vaB1mQfqe7ETshGKCRbUa5W5GMSdOEMjcOwk4CbZ7qqH+cbV8t7iMUQKGZL5h3jNs8Llb9ifGAZ168AYR/J8vSN2zqGQ7e/IgbuMbsT5wtIh9np/FCuU5RBKSlCofNckde55VENnDwZU1ffZd4S+PGMEApK4/Zdi5hB65MOkXMgcqCikCZ6+gOC1KwfK14AwBOjXGUhCeQ0Syi/OziQVznEIoBLL+qIjFhMPX2/wlLS1hEwly7ffjWgXA3wha+6qWc70UID+JHqVlQ4KcequRyeyf0ZdGV98L1cBpVbNbMP2fVVvwYjILA0QyNeoq8IA6nJH7n69WUAZQAtPFB6sGclLwxGSK/JFIQC4JlmueYEPw/yUwTJTHRNy02oIObkGqBwCHNC2AiEDSIf9a49buv0mcEU9HpW55KPy3MSSd5M5lsWHmGJ7EMkL7H5YvqZOc2wBLkfouE67acStSnHcU7uX82kk5OVDg4h/TTPe/vVl2HXdVuy5xpXK66UDBKL7l1rSPs2xwdiifXWsCpZ65WBMiu57yAWKw68zxxH/ciRuP7663HBBRfgP/7jP/DVr34VV1xxBa6++moAIokCsMeyqO9+97t49rOfjWOOOQYbN27E3e9+d7z85S/HWqN531e+8hU89KEPxYYNG3D00UfjNa95TWusf/iHf8Bxxx2HDRs24N73vjf+9V//tXU9fu/3fg9HHHEENm7ciNNOOw3f+ta39mjehpsHAbRuP4ZpuF1nLmK2wnu/R6Tiuq3b8OiXvAY7t+3sDMb7wA7gVVfJoPJoMVsdzwN1JizfFM+hZvTOvRqdc80+y7vOg5p/FNV3VJQD9e07NM/aA13Zh5jh6agG1QdZTUS9zOxI9iGSl0mUmRq/RpIRX7chkuUHnqfq39TZmyErwst29e46hmYF6j6bqipZvCenZh9IdTdJSqX7iwc4asfrsyjduC69+VxwLCvPpdb9n7nBi8EElCuh6rjdMS4XQOm1ChUDtACKNZKV7IkXmtc0+0AaAAL1hnCAVLeKJWRjUD8inUr75r0eGGCu3sDM/a9T5cnokw7V+zQEr5kaUt9XTD8MfCZKFrF+jbskVJXUCQA7ab5I07MPASzfNOn6dkmoXJLeyLVpv/Z91wFEmdzKVcec4C/hVDa3g4g3zOwiKySVqvVnjZrHkJuzvu1Yc7zFjdekvduDMlK97t60iq4WRV1d6zkMZzt6Mhbp482ntaj2vqWv76u/hxWHsGEG5wj3vMdP4PDDfgghBGzduhXXXXcdrrzySjz3uc/Ftm3b8JM/+ZM44ogjsHXrVmzcuLHnJPvxjW98AyEEvO1tb8Oxxx6Lr371q3juc5+L7du340/+5E8AAFu3bsWjH/1onHbaaXjrW9+KCy+8EM961rNw4IEH4pd+6ZcAAJ/5zGfwMz/zM/ijP/ojPO5xj8Pf/M3f4IlPfCK++MUv4vjjjwcAvOY1r8Gf/dmf4T3veQ+OOeYY/O7v/i42b96Mr3/969iwYcPSczesPzwT/DoYutdjjDsLiEdrrN72EHtXlGW5xzKoD3/2K/itv/jbZPyWges/nVnoGYB5RixGJ6skQdUpUwPrMEUGFb/wUQXawJIBbN/jQwH0EseQL/AsWzEybtpnqgxKzzseZ6nboMtfEcfMxqb8Gge0a7iPzK/mr8jGpD4D98D914sADQrGNf9pl5p0qmPqjXucycHPGDxbbmU4rNQ19inAJg0CC6cSH9nHEyNoVmQy1gIKL7IeIogMh4cb42HNy/MTKjRFVutKwHlZMp6WfRIpFxidn1F1MievRQjT5tU1ToskEsQE3ZBQCREf9j60jgH9/ItvuJHStAAm9UgBkLqMs65KyGfEtGssxKKbJHAuoQqVvwRAZRCf6hXS+5bItbINXf1h5N/p/grmgJsu/zb87h1JYpg9ieSpYFSkoW9sauxbW6GCkq76NlIhjSuSx1yXu2ako75v9rxKoZrP+1UHXpXVJecI//Wf/4yVlZXWtG+44Qacd955+NCHPoQPf/jD2LVrF44//nicdtppeNSjHoVTTz11j30Yf/zHf4y3vOUt+M53vgMAeMtb3oLf+Z3fwZYtW9Jcfuu3fgsf/OAH8Y1vfAMA8LSnPQ3bt2/Hhz70oTTOgx70IJxwwgl461vfCmbGkUceiV//9V/Hb/zGbwAAbrzxRhx22GF497vfjac//el7NFfD+mDr1q044IAD8LMf+1ms7Ne+35bF2rY1/M0j/wY33ngjNm3atA4zvOPidieLYmbs2LEDu3fv3iMZVAgBz3/du/GSt7yvTiyAtOoOXTHnAkl2EWVQPJtu3GaIrAcrkAxEHJMqDtO1T1jBNH+FZh9AqBX/GI19+w6ejzuTAD+odCiXiSxDLMJMzjmtsg2MC6j0qavfx8BcQwGE1fq4gxIqRpJB9WYf4r0QpWS5qX/i1JJ0Kv8yjpmdGaS62ArBzyupUzPjNeUY1UIkgYoiyZwCGIG75SdD0qk01UyKA3Ji3CUCBQaX2sVvYG1CSEIQ6VRNh64yHzgUKOAWDLc7gHYHsA+a4ZsuHaJdofJLqNQJcweeschlZgGefL3q11oQ78aEAD5mCVyp2QeHJJeR9fee97IPWtu/P4BNUienukqvDfwCg8O0my2EoLFnfo0rCZULABYqoVqIhEoyFtNJokcmT0uSJCGbfs7wBbckVFOJBSCkgJ2TaztXGVXPuDmGiEUcN0qoMC/kNSsYYUUXPDTrMQYxVFeZpJqEyjmVTYn8LWa9liIW3mPrZd+A37UN6DNvx/uhL2ORkfY6GsSiBzViISfQGL9NLNKQ6rFwHVNjQiIWcT5dxAIADjzwQJxxxhl4znOeg4MOOghXXnklfud3fgfbtm3DC1/4Qhx00EH48pe/PHgefbjxxhtx8MEHp7/PP/98nHrqqbW5bN68GRdddBGuv/76tM1pp51WG2fz5s04//zzAQCXXHIJtmzZUtvmgAMOwEknnZS2Mdz6iIbu9fgxTMPtShYVe1d89rOfxY/92I/hsMMOW2r/y66+Fk/9/T/H1m07MPqt3ZC3hDkAbWc9aTWNOmRQ8TO+yNP/SAFlaMigxg9S/RDqx0qBanM4RtUBe8q3aj7nmCGYUHkpUEYq+sbVa5syD/rYVK7IyMzh+T5xbFTXIc8+dPai6ENcIIz75FIyrqQDrfH6siJd4ys5ZK+r183XtQ/ZNrXTj0EmQQ3GnGKS5BKn6dIpj1gRyOmxSFZxy8bquKtePE9BSPXUwBKVRKfYzcCCq5XyeY/MiQG3q1+i1GUwpkBwC7kYk6sCLXLjNnTn6unUeDC+HkxZJ/AlSFLMZJEDUSGlZzV6Y2IQdTRu4+HsE1CRIyBmivTeZAbHzFFfwIr6698ctyah4pD6YETTuZv4Zcw6O3n/N8eN5nuSADsreSuypiWqaDlSuZX2nMkzRwyA2tmSkPle+lCV1NXz0ZuB9cOCwbVeL7XxyzXcVGuO10BOCvqKDQzWWObswxX9n/t9n1exIlXfIaJ5uzpa9TsBYbV+3lNeqliG9tBDD8VTn/pUPPWpTwUgXoof/uEfHh+ggW9/+9v48z//8ySJAoAtW7bgmGOOqW0XY4otW7bgoIMOwpYtW1pxxmGHHYYtW7ak7fL9urYx3PoIoPXpc7GcnuBOjdsFuch7VzAzZrNZ6ro9FW/7l3/HG//pIwh+GdcuxEA7B9Incgwsh0qmqlm47z6kxh+hkC/vRA4SKRi4kRnd5CGHqzYFoBVb0P/l0neceIx4oKxSVAqwufreiob0yYcg1M4/Bu5D5WNT4NkXFOWPRVmWVgVKhGHK5LqM3jnxjLdFvE4h+5kyvu4cMxw5MUrjZvrn2qpiH7HJp5r3PPAMpzewyLZ9Z8CawyN0yqBi74y4qxhTNbCck2Y5pgeWft6W6BAAWqgpnCTAxkqRCGmxOwC8RGM8D8k+wIELZD0PhNm3GvkxV8RiSmM8gnglfEbAMN4YTy5Au4pU3pckXuO4eXyUlpCOeUhDOXIV2SAAYaFVs0gWBeKYTGpun5p9IDGwhyJknb210pFnFB2laeP5JIN0R+Qp/Uv0Ps18ILHKETHVAto+NH0crdK0IUDpVyWhUonPnjXH07Epe/0S+ZD7xq/twk2t5ng19li94EMeifQB3f5w5yiBqn14UPUrMGjOjlLQjkSG+ILK7pCLCfAbCvCsqD3vJ3wHb9++vdPM/da3vhWvfvWrB/f97//+bxx33HHp78svvxyPecxj8JSnPAXPfe5zR49tMBj2HrcLchFN24B8IC9DLtbKEj//6rfhwou/V30Id6z2NvH/s/fv0bocZZ04/nmq+n3fffY5OScBcsNcCJeFBrkMEeNhFIEBohNdMrBcOiiE4TZhoUvQBRGNiAj8HJSBuGBgXKI4LjJ4YXTWEG4hDMzwJXIzkYtMRjAQhuQkwXByztln7/12Vz2/P56q6uruqu5+936TuMP7nLXPu/fb3VXV1d3Vz+f5PBcmAFOh0RvLaqxgxwuu220hlx6IwtSJL/DW/JDrtgU0MtmgevuxrXfTEMAY036sCDvjlu03gia7ScZXEKLaAXCWXXeMByNjOyEISPDvVB2mNRkY7gdGFZJBjZ22/Wek8IsS1NwlKcz9TEo03gBsK4xKF9ropjKO4JHAlGBZjBVWbwF313SoGnJjmN7XX8sEqFKUDiYH7DIB3M3CeCnFMvJlZ4A3HftgRRFGUStw+ZMHMHfZnXKF8Ro1DxjKKgEWZgHFklnO2wVii3U8KowXnU9jbMa6LFL9VzQca3x8gg0GBlZKagek4jrgQEImViZmsJRlwFr3XJIAi0Uqe+t8/ZLK2vpZ8ylkWYDFIso7ANeHrDQ2jqtgd48jsp4DYDZSJ2RsEgKCsCSOkrLWZJmHxrh6alg0+2bMTx7D1rdv6xTH88tUcKuqcVSqwxpYJLCFf65bI4k2uliL3AllYswYEKbNNIFFGDsBZl/RSTnNyGOkWE6cOJGMrfiVX/kVPP/5z+899qEPfWj4/dZbb8VTnvIUPPGJT8Qf/MEfNPY766yzcPvttze+83+fddZZvfvE2/13Z599dmOfxz3ucb3jXMm9J4zlZHriJbTx3SJ7AlwAQFwQT2s9Clx85Zbb8HP/v/+Eza0t1wgaliOvsLWtwiG+AgN9xPp+ESm8rs2h29AozrtBtTR09tS0EUvdeKt4sjl0VvzMMWMlTv8asxp97EO22nZ7nOT35xAHU7c7MEqnyDRAQnyILzjox+xYB1Uudv5gCfZtWAh97RI/F+15NbwYwwHU+0fKRi8AYqkanKutECvvzBa+0B5rEoV1rM84ID75Pt2mbx+QNKkmsma7mgqGdlIYTxRgIhdLYGx9z2l0QQwD2DZQZhHF0gE+IkC77FYJd5mGWAaV3Jnn9pyHwngA2ACKXdjyAr75vo/42lhjJXEAESxY2AcCmGih+AoikhfxxMUxMMT9jZyyq9NMl1x/2+uiFPdvtcQDCZiDALrhp1kYshZ4aWR1cileYzc1AIPAonMyxrnAqhqMe+bBrxcpt8Kxz8v8+Hdw0hXHS9auk9YiYJHhZmJgsYh4IJZNeMCh5kn3ULdumfTCYwHY9UnHhcvvPZlMBoeXq859+umn4/TTTx88HhDG4ilPeQouuugi/PEf/3Hneh0+fBi//uu/jrIsw5iuvfZaPPKRj8Rpp50W9rnuuuvw8pe/PBx37bXX4vDhwwCACy64AGeddRauu+66ACaOHTuGT3/603jpS186apwruefF8pLcolbZokbLngAXSinESa2KougGY7fkqr+6Fn/w/o82Uko2JNJ+gu7n3XI0QMgc1xJ/XMOSHivYQPBlrS1pPC4bVFss5E3jFcshy3hKoU1JvDH2rR35HFnvBhXv709YRXPhFHcGQtD82D7CnFH9w+77JvsQIzKMi69oaMLuz8IxDylWo334UHxF8+K7eIVFq3qjGxzuNzndwrqMO0GhdZmKxhZtI1ISAK4hClXFIBcsy0DtgtMagM24TtXtygT4w2wpAciqINiS64KSfZPBAOamURiv45pV2aAIS/pTDTX3rlMjFUvL0NsRk0AUpSFFdI9Fd1qoeTGs5AUFpzTQXCeH8DEKbUalPn8OxdRS1zJ2odIgsGONJF5lAfaBADNpxTFEH1xFrj0k1acNGJjweJAICCsAuOBw6lTLbmehYtTK/VD9Euh6FHHqX7ABWsxddwIgwAJdd7v6vqtBjFxyCn3F8U452bzrCLa+c6SbEap9Lo1nPQUs8vcbe1RCQNJG1mcAi1yhGt35T4OQESreheEMc+tFFlgMPudOTpw4seMaF4AAiyc/+ck4//zz8Xu/93u48847wzbPNjznOc/Bb/3Wb+GFL3whrrjiCnzpS1/CVVddhbe85S1h31/6pV/Cj/7oj+LNb34zLr30Urz3ve/F5z73ucCCEBFe/vKX4/Wvfz0e8YhHhFS0D37wg/HMZz5zx+NfyUr2uuwJcNGWPubi5PYcP/3bb8PNtx7B2Cy7jG7wtVck+pTz1HEN8QdFbjjslDdaBFhYACbxQmzHKERj5b6XR0qCJav1XSxJayXy598+Vjv2wRuuwhj7R8nEErgdv5jiQxrsgwMbFYYrObdOhuIK3W68flvMxITuexT+vo68f3PQ92ILa2ZsfZW//XcehAsDQeLiMhJYAKKQx7ESXrmU+4nBlQ3uGUzkgmPH++SHUy0gVb2hoC2Bt2r2wWp0A7gZAhLsAPsQK9gWUBtc90lWWA1/QikpLYoKqAO3fbtNhV9cycSWrEqGqhYIKHaB8BLHQa05jgO4W9fNXUs18lqCFHgq14ksEFdPr4Pvm4cY8CCT1I6JMWxBBUnz7NybBkCitVKbJMc+QIlfvi8REtxAaXwSAsDNZwtYxdcOVsCRPxcPLMZmEIOjMRrX3rMbgk+Cm6GMh3Hyzv+H+bF/AvoyDnJGB6ewOSj23al22a88sGivK9E6nwMWOUMJx8aK5pCCkc2sFZ1noQEs4Ap1DsjGxsaOU84Cwi589atfxVe/+lWcc845zfG4Z+zQoUP4yEc+gpe97GW46KKL8KAHPQivec1rQo0LAHjiE5+Iq6++GldeeSV+7dd+DY94xCPw13/916HGBQC86lWvwsbGBl7ykpfg6NGj+OEf/mF86EMfWtW4+Gcky8r0tMoWNV72RJ0LX9PCy0033YSqqvCoRz2qsd/JrTl+6JffiGq+BR4Zk2EJ+ZoKHbOM/ErxceO1StjYDSpiNXybyf6tdLYQSBiRzSl5zNj9gTob1iLnT9x0A+PokxEV8asb9a5Qo/vxCr+FZO0JqK6nCcdwDNaviLdbgMpF2Qce524V6x1mwT4Aia/wBdUiNxwgDTS88i1xDyNBAtuQeccS1+5IQ0XbfG2FbG0NyWBEBFc9XEH1FJNLStmMr/Dt+pvAK0IhSxKzpKY1CyjvDNC2bQT7B3eZnsBbKgWpjunHsrizqUqeN3hAMKCcGTB4mgdivtp08OPXChXZ3mM6bQCwZBrAMtQD8e22QQyjrjuxYIA0vAtVuIz9LEkNLAYMF549YLjYgxrs9R+ILrDIjBsEWMs4+U//D+Xxu+obxTUT3OR8v4nnnd2g/JHUOjbs4zfGa6xvz/2uUuuhBw5x36ZVodvYDvBgCOiFkhgLTpiK/Ni97N+/Dzf8f3+FPvnVX/1VAMDb3/723v1WspI+8XUufuojL8Bk/+7rXJQbc/z3Z/zRqs7FCNmTzEVRFNje3u58f9fRu4DJFFoXAIS2tuUcXJWNBR1w74aBrE6N70leFoGpaICNgReYtwjGbEXEagDo+uUzhK1YBPp5BZ3Dn8lTaUhfisHcId6q78c7QHUzuFttOx6Uf7kGbx7J8e9TRi4CLFQZXQ0/rsBq1GOmxjEju/A7GQdgFJq1E/qA4iIVut39RRUWzjrFlU+BKkeptj+6d8OBu2+JonSeC7APEh0OAol7zzyKfVAkgdaR5mPAQMGDrjNx2lRV2qAISfVlRCAmM7DSJOtXdFyojNTUIEBc1LAI+wCpy9HKIhXcZdwNxyyqFrkAgFRMRp+QkYJ9yk2stXWqM6tUpzAeABfHogYZnvgoCwvSBDISuFy7v6Xng5EO9o8ZHoJT3Lmei7BtgQxX4nrU7Iet1JQQRVw0W3/XhEJ3YyvUK+XiWMit8e758GtEO9YmGESGa1gEtzq22LjtazDzzc57qLMc9hMa2WcnttO0AUcMyvysdLgFbq41cV/M3FvwkzXBrukIrbbG1TpoUoyLuRgbW7GSlazkn5/sSXDRdotiZnzta1/DzTffDEWA9b6xSoN0AZ9yyJoKXG7DGiOF6gaU4liYpPBSWJ2jRTSb1ckdN6radDwWg1BLou+F0pCF058iGvMChygEQFS/fNA0qUWfjCjN7lBH/hhiybqFyDKWmd8gBhK0m5qweEwx0DACLEaLO6Yxz1HboWaHHytDMgeNrXkR9RMDi1EgkRmouLe2QkfB9hZh6+ZZe2TbY42FS+fZUd6jwHAjQeQebFhN4BkWC6iNQVIIqGWgAWJQgw0GaG4bMRl9QqTk2swluxMz4Ok+BrLxJbAOWGSyOzXmmBz7YBi6lJoHpNSoh5orC22admAVqYZknM8/yBWRU1IYrugHFo0+4DN11eNWLv0uCK40AjnGhMIx2YxQ7bnw291zEICGT8nqmaPU2Py9OVS/RKHp6sQEkoTLo543Nq17JpealhkEn0p3fHE8awyOf+v/CrDIDgLhIc9h5ty29vakLcozFslNjp0w6Tw4Hlj4d1GnWU1gDywSY0q9LyvTHy8JSMxFnPVpJSvZjVgsJ1vUqs7FeNkT4KK9kMcB3VtbW/i7v/s7zOdzXHzxxaAPfK5hHYqtaVprYDJFOTHQxLDzNKvRlhAnkLK6x7+zU6Z9vn8CaLKY5T24QRGGsw25Y2hR9sH3025r4JAcy9Mej7fki7sMxgELJxaczqAVQAFHbItT4lx8RXcwCfFvYB9foaKpdYAg2YxX+PtvlQZIZOc65Y8ZpfCwAzzR9aTm5q4Ydq5TXUt2Tiw7v3cHCnwQMIgEQIAalnGZIxfAOmB59+yDjNVCMUNtAwzTjKnIpcmp0oXxUuwDXM58nylpdBpoy6DSgwTVHIqLiwBqBZsUSWG80in5I+9nVTJ0BWdZpwboAtCNUWB2bm0A9ZxMzPAwE0zh/jYsGYhGsA/J2iIJ9oFd8K4ll0XPz8cIYVun5u0A2zIGirUL1aKuU6SUZIgCQJoark7y0CXYMkZdWySXiCD+nlFnR2IO4EeYuPQab6sSx751E2yZKY7nx+H7i75qv15yy84Q8CCgH1j4tbTdiQd3OdABwBYEO+1mlGsAi8TgD54yHKidyxa1kpWsZG/IngAXbfHMxR133IEvfvGLOOOMM3DRRRehKKJcsDkhgtIFoACli/CSsFUJLueNWI2QoWisckwOXKzV+wdWA7UynBRG3g0qUliD8u5eCsrUu4ySyG1q7DHBDWrkAQRRypSvglsBrDjHyQNw8+ZZkVQ/sabtLjH7GhFM8qIcM0BuKvztt3gjLa1T8MkfM9x6LQbQpVf4Ed0PCJ102jNROtuMtLeREWYEEMCgBoriAS6gNuEGVbMPFAK4w5xoksrOC/nk21a7AFV1uyCxfGLiFFoGqPLZnYZnm0iBDUNXcu2tlSDgwGroDIjpVNxut9sCMdaC5oA2ArxCcE4fkGMGbVuXVSdW3iOGx6cJ9mCDGcr5uY+N/WAAZkqBfSCnrXOkuMNnUPJAkTywGFdbg+Dc2iYk50MALEvBvVZl6sbYbF8K5BaIMdb5+BOYCFzQSDQOwAOLwFi3Ym08U8KuN1dBvQ9YNE/EgVdujtsDIXZmfea6Crcpt3CsUxyv264//57No+wl2QbGAItcm1UTWMTgx0wIPNP1INvS88489dRDuREHydW5WMlKdiKrVLT3vuxJcEFE2NjYwN/93d/hUY96FB784AdH20Y0EFbtZu0Mns4AtlIBtpzD0hyLBD10ApaBBtZpuPggAhu5bFApiXZSFs0Vv08i9mORx8MSFmIeGG7/ls5BlgJbEqotu3aD61QMovrEv9tLAWNxrIZnH5KzaVCzD72mvvqTnXtSDDwGX/ZVD0iIlF3PlsABhEWDw9vuVpp0w2dccE1TgbaJDDrJYUaKKlzqXDairVjFvQHcAVgklNfY4i5Az4JLd92ggIIwOiFHaaCjwG0fXyLKqlfcHROjldxv1XDF7ebJSGE8ZVxhPCipKQHU6WMjtyF/DG0buaY9/cRzQcxQW1ZctACwqiSTUWaO4fo3U0qChAaIsXXaVEuAXVNAm63pkVRlbwDiQlXFIAaSWpZQ11YZyz6QEpBgGUop2G0HuvxPJtaGW8Ci2y41U9MaF8dD0QPdJw5YyKXuTliT9ZHxlFsb2LqrWxyv3e5At9Jmbrtfx/uQic29vhLAItqP+xgLcsBi2k13GO6B9nWKx0DjmYsVuFjJsmQFLu59WSAFy30n8YtzY2MD//f//l9UVYUnPvGJDWABZJTKBfohpaGKCejADPrQQehTDkLt2wfofO5YBsPqBLCoB1X/eMWb5KUPA1F6xw6SnfJqmmt4eAkn3wj1z0KzQ87d2lvuItYjMzRhHgbuKmJxY1IVAd4rbTyGkwDcOdUv19bcskt5a5U4+DBEOe4UuesTN8/aK++eVVECZkIgd+sYNR9mH9oHkVcCqG63dzqYpY8EGBEXHwVFSoAXW8BasDUhwHi0VZwZYAufMlVBQYGgDUFtW4k92Dbi3uIUNQsLqyQl6Ri/dCLlApfdXG9bqC0LtWmAuXEpWFuzwQzMTScjVLNdOU/xwFdQpUWxyZjMCVRh8F72/aht7vSj/BzDzbGxgDFgY8CVAW1aKDN+nmEZaqsS9yFIHiRtFVC5uZ1XwvZE82CIUU1J3HJGBBYrUlKPZqrEJayyUkG9Mr03nASIp+NYyAVax/cblRZqztCG6mxtQ8KoMxGpGij6OVaWoOcW2t1zcr+hw1iM6UdFDARpBV9Bndl2ayLZCFiMlPmJoxK8vXWi/3zR0+4II0bWNuKNWbl0su6/ZL0ceGCRH1Y1VXlgAff+GTDcHDp0SmaHWjY2NlZuUSvZ8/KOd7wDj3nMY3Dw4EEcPHgQhw8fxgc/+MGw/clPfnJ4v/qfyy+/vNHGLbfcgksvvRTr6+s444wz8MpXvrJT5+3jH/84Hv/4x2M2m+HhD3843v3ud98bp9cre4q5uPXWW/HlL38ZZ5xxBsqyTC4+dkmZdYl0bX3XGmo6EwqcLXi7jtUImaAWhGlUeYsl3KLLrRdxYoVmp7i2TrGzZ/yFRSiiNNZSGdqg+lc/1nh6w4vEg5qFGA4OxyiQWMZdZ6w47R7FACpqpP9Mjjv+BIAKIEhGpFEuYR7ApW6luH2uX9beDWownW0sljvxFd6o6n9vj5cNixI+oh/v0sJshRWIfL4YkcKcutXaBfka7TbZB66sYzXESswTdw2HbjinVMZB6LFCHrtQMUHYBw0XK5EHFql+lEHw/dcg2LJ2obKEUNE6iGWo+XB2p8ZcVBZFKR0yGJaj88nNRVkJM5K4CHEAd2AfQOLrPtOjg4oZgNG+0GGTfdCQ6wdEhgmtJNX0gjVM5B5zrmBKufiSughjss4IA+TjZgaKMPpDBRiJIySTBRfs3L76J4HKbg2LZlxFnU0NVgwgcle3FsKMbH7ndmzddRtCwcXMOOKmBvTw3uNta7+wNmeMSAzUhoxUn8zB0NXexbNk4Rr69txnDlQEtjfa9oABtyhmxsbGBk45ZRiErGQlY+S+Yi7OOecc/M7v/A4e8YhHgJnxJ3/yJ/ipn/op3HDDDaGUwotf/GK87nWvC8esr6+H340xuPTSS3HWWWfhU5/6FG677TY873nPw2QywRvf+EYAwM0334xLL70Ul19+Od7znvfguuuuw4te9CKcffbZuOSSS3Z9zjuVPQEuqqrCF7/4Rdxxxx147GMfi/379+P2229P7uur2AbJmm8GpL0PEUhrEDTYZaCybMCqBMoSGFlXQ9KYttKlxr+HfmOwQS4b0oLMg0uZ2jmnPusS0AuUGsONwAYvUBQwxFe0xuExFhk5XwYAX+cCAsgWTc0bUtN68OM35dgc3kFqWgsJ2vVK1BgAUzmL+EigyACojNLZjrwR6viK1mQ7RUrAB+T+dtv996NTppJyShmjMAQbAQKrCTyhTsXeYK3uUd47IGbOAVhaYsluJQPND46dMtrK7hS7UFHkNuSvn9S8GFBYYyltiP0QddTNZcjoJJ8Nn/15BVXZCETkxbdrJiypZp3rir+NcwHcDMkIZTVlC9C140BsZYXh8JmtggGkR9w8A+jEPgRgXNYgJgTKO/+90UDJesDnZpi1c6FiB4gYmHh3MnfMSFe4cG1CBWqfpcw2tPm4MB4gc3by2wPF8WLFfwAEDW1LvtK4Xo9z7WeL44EbwKKznQAzU5KJLDG+UIPFD6TRaXf/MczFyi1qJcuU+wpc/ORP/mTj7ze84Q14xzvegb/5m78J4GJ9fT1UjW/LRz7yEfz93/89PvrRj+LMM8/E4x73OPz2b/82rrjiCrz2ta/FdDrFO9/5TlxwwQV485vfDAD4vu/7Pnzyk5/EW97ylvsUXOwJtyhmhrUW//Jf/kucccYZ0FrD2gSNDWB9MnMVaaOfHto/32l+U3AH0AVobQ3qlFOgDh0CHTgAmk7Tq7u3hg/FVlDzh6paqRwYVqevUAkajXctPG0eu0uFvhe4IzzTACUWe1VBAp8rCFuSehHBgYURLIcofgS1LT9jXLOCxK5TjQbrMXtXJ9ZO4fHZnUY0H5osAT2PjonaDq5q8ZBdvYMUA5UVf0xkVQyZPXva6IuvCG5DzqUFzgUKcL7WpEY/MgJGajcg5dxZNCsUJVBsWhQbBmrTSBE5yyBTZ2oa1YdhKM+mgKBZXJ1UaUHzpmtWECPbFfpdh/xcKCjoCpjMGdpYoDLObYi7bdcnH4GEbh/ipka1C5W1gKlAWxV0xaOABeBAwoxgC127ITlHNQ0lsQ9+zEbmggGYKYN7gEViwOCZCxCHBHBLu1aSXaTuuQAs+q9ncNmDgqoIxTZDzyHV16ueOfbdWBtlnqoldqEqrIbestBbFmrTgk52CyrmO4CwnK0UwN71S8ASuQxaUifFGIMTt92M+d3f7gcWTvnv3IY1mZiN6Yqf8z5gkWMsyK33WWDR4/7FBJh9WhiLxOWxMbBonVbuam7NuzWq2rJyi1rJ/U2MMXjve9+LjY0NHD58OHz/nve8Bw960IPw/d///Xj1q1+NkydPhm3XX389Hv3oR+PMM88M311yySU4duwYvvzlL4d9nva0pzX6uuSSS3D99dffw2fUL3uCuZhOp3jsYx8b/pasUHKx2i/NaaGwgaYiHd5ZXmH3342wxvXtI4ZNCtZTUgoo6gxUXJbg+RyozOJWdwtxnfIvjKbRuR5DYsxUIf+iaZ8fEi+BEdq1sAqZ9r0VzQdwO2VbrM3j2g+SeilGb65gSKB6WyM1bZ/Ex3i9QLu6XF4ZyB3LEl/RW7+iNTa2LOBlwcBtNU+PJUxDW/GgWuEfaxEmSL0EQJSpwWJiENAP9FfPbhTFswBveUu+uN2MquxdmWT9jk7Qsq+Bwf6MsIA/PgOlc+lxaVPD7VHZcJ+FNKwuIJi2XRXtMewDyRpAc6f8RxePkQ/+ZgBmjcAqH8cRf8+WwbDiG+8tACPWOwnW5Uahw0bAMlC7qYUvaOEK6jWTQK5dYbvIYxdC577oyzzVlnBfVEYSSZQMJlO71hXo3m9h3ezPOhczTwyLk7d+DaaaI6tK9yj9jb5bh8R/Dx47ZDzL9N+bMcoNwuzTWfBgFZJzlRsKQ94F5z3ke3oGK+/1zc3NFXOxkqWJ4OvdMxf+3j527Fjj+9lshtlsljzmi1/8Ig4fPoytrS0cOHAAf/VXf4ULL7wQAPCc5zwH559/Ph784AfjC1/4Aq644grcdNNN+G//7b8BAI4cOdIAFgDC30eOHOnd59ixY9jc3MS+fft2dc47lT0BLgBZ1Nlp1doFVxtjMJk0q33OJhMAzYJFDYs9A7QdKcexSZ+axwxigdQOUeYW0ho8m0FvitsFz7fBVTVooRPrWc8rrg00vHK5aLG2esiusbotzsxLGPkIXSIc5tPmKoDZKbFDIGPMSxkRKHBKJRlALUJdOqWikZrWK5JcK++N6tvWsRULgUWx0saaA6O/DTaMoofhaJ+ldYAibB8JLCwbtBmOOB5DgIat54JcXYERWafqk3G1NZx7ErGweLYRU0HNyt7MnZiMnDSyWxmxcIMgQdaE/roPzMJ+cFrBjyta17EPdfxTLqVtsp9t426x5jEEd/2ofsYUJPahWlONdWVILMG5NUlwuHVZmIRFY5Dupiv2gdsYWRiPAMm8ZTw4MmClQBrDQDHhoqQioCgucBFQdNbx0ZW9fRYrC5fSmJrtlnDgFlJzxa0bqPcc7sIYHPvW/4UtN/PALV7DRr1Quof741NdxIacsH+7gYxxJjAWiTHJu5Fg3H2XbFbJPu2K4mEZpeYXDAhDPAUe+IBTu51GcuKEBMOvYi5WsixZtlvUueee2/j+N3/zN/Ha1742ecwjH/lI3Hjjjbj77rvxl3/5l7jsssvwiU98AhdeeCFe8pKXhP0e/ehH4+yzz8a/+lf/Cl/72tfwsIc9bNfjvS9lz4CLWIiE5m9HzAPA+nSSOKJ1PFzgbRSeEccALHQPDrIbBGiC0rrBathS6mo0YjUYoJJGKdWNPny61PZQBjXznk0x0HD7Lhq0HY51xxAce8MUsRqJ+AtOv/R6z8EzDSRW8RgoZYdr3dwNtR3fExVqkDB2iFXaDcorHTGuCX9UDliM78Y1wi4GAk32IaGcMtw+AyChsY0g97CS2iLM3FC+k8LORSvhBhWUeQa4tODKKaveak1YKBtQO46DUAOCoLjHdR+YpbJ3puJ2dy7cPVz6qsX1jcaIGIy2YbwyUFU6cLs9F3L7MoyPHwAj3NQDY6wUd7I7NRR3oBPAXRXIZoTKyrwJEhrsg3teKK4z4hT+MUARcPswgvscOwTQyz4AoZ9UEUagCY6UBXjTBpZTrqQF9xV5hBTHO/6tm2CrnuJ44TwyGwbWjxhYJLf3rG3BaNFzPNk6+QSH/xCYIztrBv7HbVsloK19Cuw6TPVpC4AnMv8PPHQoMyoR7xayYi5W8s9VvvnNb+LgwYPh7xxrAYjnzcMf/nAAwEUXXYTPfvazuOqqq/Cf//N/7ux78cUXAwC++tWv4mEPexjOOussfOYzn2ns4+ONfZzGWWed1YlBvv3223Hw4MH7jLUA9ii4AMQ1yiSCqPfvy1/klAT9sU4SIi8E905v7LRbadfVmM0AZrGubm0DW8YFOI4UV3QtRav7X7JgY4FzIqARMAgPMgbaabBDiTYBhPoX/qXG5Ip0LTLnNrI4RoAA7Zdn/EI2dQHCsaJKF+ztQWhAps2ug7Bkg+rL0NI+jsHQcykOGDaOUHq9ohiUvTjDDsGl23Q9MILrE9P4gm1ySjbqxwGMmNWAKO1hxA7kjHFpCSDFO6Czd0cykatMZi4CsOivrSFgQ6pOgyX97Zj5Dd14lx6nRTUUMGZYR777x4SgwGYYWLTFakgArWcf2NYYA9FcBhcdwGh2GaGG59mLKRgoyAW1G4BUqJSdngB27mPd6xkHyXuwGBhFC0AtBhTJsMNTrcD+0obsclZB7gvfbMULFcdTVgGog7cBatbYUA7MuOfQzLdw/Nae4nie7iR07sPYEBL+zq0LfQYmP5+ZdbIXWDBLJrRGvzU4ZgXYqQfenW4lWxvVz1K8jeFux9ZxtgB4Wu996qkH0ScbGxuYzWbB/XklK9mtLJu58Klld9SGtdjeTscd3XjjjQCAs88+GwBw+PBhvOENb8Add9yBM844AwBw7bXX4uDBg8G16vDhw/jABz7QaOfaa69txHXcF7Jnnt7YLQqoq3S35dD62nBbiJTklihAFseKwFssPvjtAOQdKumdcTj/XVIKMBpKy5s4VS28IWLEk5/Eyyn9UkG96C8Yxu+Zi/DBqLM5+c6i+QnfLwgSGsXv2LczYKJjSD791Pb2d+7Fx07Zz9wCyX70FrouZ637oeGixs4NasH4imKLQz81KGppCm0Gwv3ZX7AtQoP+IjpNgLmbojM9vCjjUWg3usjkXbOcPZ9ZlLcFCrYJEHE+/BFIacRUAOK+41xwfGG0se5JRMq5W0n1bOsCxX3b7WxAYWylqd2gku22QYwBjIG2zqo7YnAMwE4YrFXjmrTZB+uZGBZfYp5Sb0aoVD/VhENlb//kSbY9p2wSSyG/cEKe5RkGioDbxxUh9MUBPfsQX7/k4Eya6QrtuiEpCwEDIMmepSHudUMsj42f5+Z+7WKMMLLjfPMEThz9f4PAIlvrIZWXNSEeiKX7QMcVqbM9233eFQpoAYtEsxwBi7ivACxUbHFx2wiwk2ipJOABA8zFiRMnsH///tFugCtZyT9XefWrX40f//Efx3nnnYfjx4/j6quvxsc//nF8+MMfxte+9jVcffXV+Nf/+l/jgQ98IL7whS/gFa94BZ70pCfhMY95DADgGc94Bi688EI897nPxZve9CYcOXIEV155JV72spcFtuTyyy/H2972NrzqVa/CC17wAnzsYx/Dn//5n+Oaa665L09974CLthRFkXSL2twum5ahXYj34dauGwvnwhMKxXmz3O76AQDSKigmdbVwsSbaOFaDIUHBnH9JdKS9XxSnkARM8WE95xaABlCDDaCen7EKZdxPdIy0H4MNrsfsQcVYhBBZ1IgAFLXe7hWNpLXPAsXWyLn2Y7MShB2/fAeHaCyK7eYYGsd4pcWzD/57tVjZSM9WeJaAQKGIWDiNlkJn2YjyNkKh9EqwKL8KrEX1CBlteuz3bE2yf/mu6aZhjbjgBE1OA4MKJQAfxxEryA1Q5lP0BsOtU7pjADNGmEEVu8J4rrJ3FFLI6AaBMwA7BaxSg2xSY/tEAKNyLGiw2mfmggFUUw8sWkHy7QBuU7vW6RLOGDIaKYLKulZIOIqRD+AGiSvU2DgWQDLKudSxXLJjTFwNjJQLlUVINTvYtgNe2yfuwolv3yKZ7uLDfO5XDywU6r9HSbQ6xAag3K6x4aHdiuCxjLcY98TjEYxmdx+1DBfu0xao1+JU34nblf1xrTZPPdjv7rTKFLWSZct9lYr2jjvuwPOe9zzcdtttOHToEB7zmMfgwx/+MJ7+9Kfjm9/8Jj760Y/irW99KzY2NnDuuefi2c9+Nq688spwvNYa73//+/HSl74Uhw8fxv79+3HZZZc16mJccMEFuOaaa/CKV7wCV111Fc455xz84R/+4X2ahhbYw+Aix1w86ryz8fmvflP+aK/AlPl9pChQSG/ri8CxghTR820uydgSsxrk6mqwsaCNEowS4JE+PT2WLHFZiTutf4aARXLM/tNVHffz0xe83WA5htp3YIJd6iR2wG6Ueh2/WNv3AYlVLsyHAxuqWjxwmyrnCtUCuKGJxPWg0kKXw/2E7U4f4cglpt1mWwST1MCi0W4bTDirOOB1pwUCt4GgnLdZDWaLUGAsUtQpqrkxth8Fl9GKGYrqwHB3Qq60fGtGHLAA51meNvtgrSjBCmJcCJ7mfSCGGVRKCtsYQDTBRNOFih37AKWkvsYIYQBm6hVKDxUd8AIBZJ3iV4MNJgcsRgaIEymXjKCOMSHrHOF6XKjYMJRxrmCJ7ckAbktQhoUpLjiZpajbT+SmhhYIZXJxPNITK0HoxHCJAwabBwCcvPt2bB09Ag+SO8c5UBCARXKg7jO7nWXdoUQsA3MwtKSO9sDCXZLuVkZPjQvngpeJyQKcW5OPNYq3eyOP6h7kgQUXzTat5UF3Jw8uVszFSpYl9xW4eNe73pXddu655+ITn/jEYBvnn39+x+2pLU9+8pNxww03LDS2e1r2DLhoLzRa6yRzccr6WnLtb/xB0Wef9Ch7ITh5G4CRyuCsAS7QyULFtJiC2unLpz9kgp5pYLqGDquRGmwfsEB3G8c1JFT3PBYeN0dAw7UZLH8xgBlrCPWT2J7fkNYpATTci7+3C0p8uvasdrEZQ234lLFDhQ5jsGEs1ByjK26H45QoidT63qeGZUbDPUa2iXYxin1Qqj5G1XEVMgcO9ObG14jJaErb3SiAmFAQZOyNAIDz7EOIqfDN+bmwPDpw250MlKmLtmmQc/tyINQrsg1XEck8Jbdnj5saIiWYAC6UKO4sTJGibkanWAwYduqMD61t9VxQbZQAYBRgZ87dbOwcVNbFi6gGkNdAyEIlNpcoC5Wx0JZ7z797Qgxt3LyUrjK7AwTCPnTBYjulbUoajEmJENNWtw+gUElgwMzYuOtb2D7xTzUo7nTl3HV7gcXAyyYCFslzANz2zDm64Owk8RGxHclNWgpddtYfv/5lAucDVsoxFhM497S67zGvXGDFXKxkJfcH2TPgoi25gO5D693o+LCgeR2U0VyJl2AgUUTCaswR3HesK9Dm+95tP95KSAACqxHX1ajmsPNmtfDRXXJTwSWLuiYUIdSq2DHQAAIjYAHRTha4+xouUT1ggMGN67tQWlq4YzxboeS8TVG3R6lK6czQ26jT4o7qh1FEx4zGupqS+/p7wk9GyBTlv19AoZRTtdExkRLsEGhgHxrVnbsxGX2ioqByxDFV7EBAFyq6Cs3cASqxtLNXcWnrdJ3ENdjoGadYxJ3rTLRbI6MTC+gidrioApRdLHDbkKu4TRSOI+i6YrjH09H5VtTNCJUTb8mvyIbK3lxx7fbVl6K3tNAmDxJiQKfhlP1KGD8JtObeOa77MXn2wRKskfSxIJIK3IWW64NxYBns6znUrlB1+4DdNm6Y5FgTAXonvv0NzDfvri9Cp12EzGk9aBuuw7yxKgcs/LEmM41hjcv33TZ2MDgAEePOtZ1ExIMoTsTEhD2p/UX9p51AsrEljvNp5PvkxIkTq0xRK1mqMBN4CczFMtr4bpE9Cy6yAd37+1NvBX3UVXtup6BtKo3D40gV5GvEalSiEAFoWtw75kaAzRg/4GZnoVAaAZjug5qsgasKxIApt4Ecq9FuMrFLDMrgGAggmrMdgA0GgAlqkOT6blgFqX0MD2enoubv7MCB8S/YOKNUTmwELNptu0MD2ICzGJYMvYVR90porurGV3iJm6H29y1LYLZ9Bwi821gMBpgtlMq/4JnZZezKuQ3FjIhjNIL1PnVj5/pJMByRIsNsA1AkZwVnOz6VqRdrTDNWwiLU6BAApcT6Gvdd+WDv/nNpuFBVzorvZkVuv/5xGsWSEWogaJmZQw0Mq0gyQo2tbA7AaAvWPlaCGo+ZLaPMSICkj2W4GhYLzDUz1DY7cKVAFep4DYKwD21l1WWeStW9iCVOV6wqANsuaQCxAzGUd6FKAIts+wCokjThG9/5f5iXd/eABgQWMS0evQ0Ajz5gwXmDhbgSIulCx5A1jCyh62SF4LKEqG5IjBW8O2sjcUPr1FJgicGwU3TmpLGmjQCbJ0+exPr6+uB+K1nJWLGgpRTRW0Yb3y2yZ8FFLqD70P61XiORl/CCjSzHO1WYh4Qcq0GmBiKxe5DvfxndEhGoKAR0TArASi0CruawZQnYFiAbaW1vjC2yhrE/lxHzZgkBWCTb9+kVI7AhlubhtmPxwCIGjew3cOzmFDVauqJoQ43H18wCTASz5l7mFYtC1nPzqdL2xnE0XvLxOSl3zMh5sA03qEihJN1gNQi1YmcXcJ2StgiWOSjngUEDnDLXzHAUxmZ9TEaP21BUxA9K3A5JK1jLLpXmgOrPDOut25GS34ypIHGhqgkTkLPsLqJUc2VD4HZ8BVNpaYFauWOVBhbduZCjrWZxNWEAxoaieEqlXagYgCksrMpnkWpkRvLKfkUhONhNxuAcqG0jrF6UCjnO/GZd+ligLsBGlWe6xs+1j5khUsJuVexAjBTnbLhQOcMCMC54W7qwuPvOr8KUW+7tmFL8o/dF9mXjGYuW1SNs8ndF6uAaWCT3cKAkh2t8Rqj0ZoIpOFmQ0AOLlItXI9FVekpgJz1Ayvc+AlxsbGysmIuVrGSPy54BF6mYixRzcdqB/T0LfiQdtsEpb1HWI2JvpU+9KHYmvglfEZYZtesURFnIvggX7J5AdTYhvQ8qitUw21tA2QVnvRIx/EFZZbiASfmSFTpF8RjuHIeqcjdM9ZLNhr1LUmKXzPC6YISanzbof1JPguZAYRZBL5CMXd6XWflzJDjjtShaVZShhSUoNlVMLyXhFo6KVQWdxJ9K5iU/FF/RdmXyhfTkXhk/Dza4TnmFsumq1CjiF9BiPiYj24+r8UBKiVuYsjBO2yHLUKzQYIF8nMEIkNCoreEzDkVuZX3uV2CWgm2twG0501ZgOKxjecSKzwvMtYARFpAQuXz5T2ualb1JSWVvM5HA7bHpaQmeuXOZvlzsSq8LFTP0VrruRSwxO6AMQ2049zpiWM2uVkXPfDADIf4lDRbBVIMYJmgja5GwG61xJ8SYEkfv/AfYap5/fTDqVLN99wVS/bVRCdBpxDMWueY925EbnKUssBCw2QTb8TYmdNyZguSG676zw7Vr5fAR79CVW9RKli33VUD3d7PsGXDRFq11shDJqQfWZG33KQJ3IP4oFWIoyBVTQr3qL+keI0KH+ubWq22xZKN9fYlFj5QCSEGtiQJhqzm4LEMq0KTESm273WgfMgiJrKxyLx3PVow8jUCvx3PtrcuZsXD8xVA//t3qAeaEUBUOLAQXquzg0q5TXtx4WQFmQm5OGPqkAzIjgEU4H5WOrwisRmsexJK9GPsAUu4YcYWK6yfIENLtMBhDWaRi9iG4aLGLs3DjH7pUIYtUnCI1VvgVUNmarA6gbkH2QYK9m8oqN9LSMigOsnZWfrncI4LkoWp3lDDB/klPBIb7oQEwE8jzmlnPOnEgxoJnEeAbw3g5t6ZGdfMWkLGVY2LIN0nQJQ8Ci4ZYx3J4dyCWOBUuK0hMhYD0RsHEDLDIzkUUa0AWwnaEjFEA+6J70XxW5Rbu/vbXYHM1LPw0Wch6ltwYgYodpaJ1jASQvl5ROln2/YRNHOLEcsDCThBY9PY2ZIBF4ywy95D16/uIU1YjMoCdPHlyFdC9kqXKKubi3pc9Cy5yAd0POGW/VJsFHMBwG3YANOJCaxRS0MoXncJ6vQ0t0Ge0s1c9eIwWtqAopeXFrjWU3hfqalhrYOdzcFW/ZP00jh0CQYIF7Sw6yGvEAwDAgiW1b7vDjJUejJCtpR182ysGoIpqVyMHCHxK2sA+xJmcXEX0hTJ/WWErMFEoHQtDVhQ5ncks1QcskPmuoaqO9cdnlvScqIOwg5uMAwTe2t5Ab077GR0gHsdxEEDQsLZGcGS7LlQ+89XY7FYAXMwSgwv3EmDr/M57slsZhvJsYWunZFpaEvCsRtZJ8GKJG7EScbthnsMFF6XeEEYHbtf9ADylurK3aQECVQO+MAcVQ1f5tLFeGiCmsihKf9cxGN3YlbZwZYS9a/UTrhCLy6I1FpjLomMJCJW9x9xvtlvPoQGQLWDnBu5GhFXAdrWJ40e/DrZpJjd+3D1W6+40wG6Hg3LbHSORsyYAeVcnDyx6GAsBANQZfHisE0p/Z8jc2kao1+rUgYnBVNVwCvWNjY1QoXglK1nJ3pQ9Ay7GukVNi0Jeqj7lYFjoHJOxqJLeWuzJfVcX1gMstxbZ8bpArzSAhjsRq5ylD/5lsxzU4RU/rTRUMYG4TzHs9hbYVIAdF5zBcIXAMrEVTR8fNECEJU6/rDqDrX+V7DG+SRbQMBS8XQHKu0G1d4vGwwow/sVeAapERrPIDNPXvGi1zYpgCpK2rVQw1pUAGQacy8z4W1XAiJyzhD3EMRVpEGDZiBtUL/MQjYKEFWElluE4i0HfOK1N9xMrzEzcyhQlvw4VkouFjYGyEQMQrmErMJwjBTnEZIybaUVK4jic60ojpoLyEKYNLNrSKRDIFpZIrPdgDCquTgzZDhhpsxqIgqylM6DoyQiVlMoKY+HuUoKk6BUcSrDg2hXLj7k00OW4TFrexYwriwI+PN7FmBQEFDo9F5ZHFccLLmwMbJ84io1j3wQX6Qc7Xq7y0++ehSyCddtz1gLPWHBiefGAIZeK1rtRJY71MwcPLBKjlkvYM19+CWgDC6BbUDDeSK3D3C8H1voTrgCrmIuVLF9WblH3vuwZcNGWXEA34NZK/z72XzIQgk39grkTsNHuCwRtGLDiqw3VSkG7iJY40A8AsGaw8sGzEGTj91nSjR9qGWiAZuvwCg5bA1s2WY1YGEC1DwsFYDMzjHeBks5HHcrgdBwH+UvN8AUPAwhjAQlkRw7O69WuiBpmDmy4drLB2+xAxZiaF5pgfawGnDtHiSyr0ejGn280ZzEgIPh4hRpok3JuUAPAot1PIzWtC94OMQTs7j2q1Tq5BsMAJozZKTjWx3+AJdNXCxB0B+fiKzJZd7qB4TakRFaESMsemgQW5sIHblP9TJKbHymyB2fHlwXAKgnCXqgIYcgIJYo72xgQUGO+vBhl5ZgelqN9b6C00JVnR01/Wlovc+OK4zX7aaalJQmy9vq0Y0YWmgMjGaEavBPDVeAu5R4kABMNViRuU1jM2HJi4w6cPHFE7okUuKI63qkX1yU3+jFHL6LkWgFhRFPtRoxpcup64i9iAJB6LsJQVHdcjW2RxKeQBBbRjo1NEUJb37eWOaiWFbhYybJl5RZ178ueBRc55gLwi2naEkVAwxjYUIR3fN/4FzYBVoqioURIlchFpITuFsw4xc6nn2VVsxqSJYThS6cumtY20yG8ywgpDYpYDTZz2HIOWAurALOGhVgbVizHtKxjziNGuk+OOuM6FR/glEnvbcKWXRD2AheAkWQeZOwuMy+7ea8cmGCWbFCL1LwAgquRr/juA8NV6YLAW0qEZyuGrmDT+u/MoKQcOLWDCh/DMQtoBgZn61+wux/d9oXYhxBE7dr2gMCzZs5CSz5TVAioHs9yBGXdPSahMCO7wHAk5tMDGHSV6rrdVnwCLKAUWNXF8YYeNQYkI1QLkLXT0tZxIABIyTEDwKJ9PjS3IMOI3bRsVbfLQJ1RyMcQlMaB3uF+yMc+ODBS29EZgK/snZgQn3YXaTYldqECA3arksxh7jL6OhjJCu2hC8bx49/C5uZdqIv5JMStH7vL49FjLXGxIVlgwT3riFvnc8BC0sn2DDxnAPL3aQ9w4NSx3Nil+71bevatrcDFSlby3SB7BlyMrdDt9h5uD5AFz2eHchbgiDEfBQTyMEaUIO3qQ1iXUjXuY+RQh8fg50YLpGDL4vbTo6CP77ypFcWsBus1qMkMhgywxqAeVqMtpmDwFOm5iIGG/y/8zULzj9VZFVzqXAIKFysDl/s+KO2JeUgVy4slGmuI1XB1MlA4t7m+wPBUW+3vCLAzkuB2D2I82BgZp+uFgboAn2MgQi0JiMLVVtA9sABRNpg4DNcdK5l0JMYCDBhfSXvAIcYHoqdAQsOFim1g7cjFCSxScRtVJtUsubHKYALbRexTneaBRfdcIBmhyNUh4JYLFWrAHh+TAhaduWi5ULGPNTLCLNIQ+8AMcgHV7bnuuFBVolkz2+ASuBCA2TYga9281Q+MhQU7kN9IpQtIDZKxc80MZZuGFG0Jdl7BK/WsyLlRAZ5xO3r31zHfPhZZmFrNwinnFYMyxeCC9LwA5FmL96V6A1ouk4lO8qlo5b2FTLmakB48NWCKflKSu238Zw5Y9L1ruP5+f6LIbVtW4GIlyxZeklvUirkYL3sGXLQlF9AN9Gc0bIp/1SMoFHHQdgACi2hxGQluOaEPbhXwGxkgOmYXVQOLwAI0XjKLBaPmhyJAQ2kFnjBo4lgNZnBZsxrNnhl2Bkkx2zeEWPEiBiZWKv4yJMrT9r0hI6ngXHbQeKn6iuPynneZnKyziieYgkFx7AUUwc7QAASBfRg4z6z4e0QBXDTT3eoq7xrhRWIlqJH9B0DLOo5G6li5J8enMQWkFgUrlsrxkfIc0t3CKe3cUmJ7iva1RfZjwEghPKsliBuIrl9yEgRY9GUcUpELFbv72MW7gzgfYN/oBg7ARoCsExjuWB6/P0FFdS/G3XUejMgjKPPNll2BQL+GkWQAirIuqe3htLF+zG4ioObsfPpl3pMxFY3BMdSWAdgmQYJqWRSslQDr+hEdByzEK6+7lsUxFWwYbEp4UHPs5K1SHC/XrDQwTgYuVdK472KKlK+63VL+fXG8TgSP/4M5FDNtSwBF3mUpa/UaGHTquBiwtDuNm43Xbb9ORbsfPGUYNGxsbKyyRa1kJXtc9iy46HOLMiODj3PijK9icdyGuGY4ZbRhXd9VHxQV8HNuJA5kxPvsVtpFj6x30bDsFKblAA0whWwxBABqDcploGKXgcqaee06NdbYDAZmznfAv/QV11ZHDzTaVBMD5NmbXF+REsEK4iHBDKUAKj1zMTBQRgAjDYkAgSkIzvgt/uqlt4zvUKK2qwmJksUAlRbatOIeWrUoepv1xfRI/PfBDOPul1xBPC+WrfPvTjAPrVoRxqeO9alIeTFffOWzSxEayngzMBw1K2VcFegWW9Arlusq3UoFVx0GhBlMzIdFf+C2lwZjwAwUrh3rqkj2uPS4U3PAosnatEGMr1PhFUaf6Wwx9sHXC6nb1nBZqMhVOQfVbk7MUFtV454ZEknT684dGZanNa4csGiLP95ag6N3fw0Vl3mLPxBCcPp087DDgkKOcfOMaGdp6klFyywxJTDpsTG88SHRcdzRSINOfGjvcTFjEfeTWd9OO3SwZwBynhsbGzjllFN691vJShYRRq0y7LadlYyTPQMu2hY9z1yE+IN4X2dxa33ZbXPEG4IgSoryQIDgcrGn29yJiPuFs+I62kVedMsFGgDqNJDaW9LQBBi8A7CRtHTVig9pDaUnmO+fQRPDzksJyhwAgUwWWLNBma7bjv72QMOBQbbkXJp0P7BIDdm3Ucj1tW5FUpUoZuDWdWABIaNS0zo9yyqSfPMuqDhU9R4/zP62fbYlwxJfYgl6pDLJAKySrDztoGHjLOJBaXcsiAcwrIat4V4kqNwXIqDaHckyYBVU6g507kmqx+reHrMl2V8Ryb3EteLdOw8uI1Q8igYgUByCty3X5ObCgduwUeC23FvMFmwEODA4Sh3rFW9JZDAGIIRrVDG0YbdWijUfcG1kJoMrCfbOATIVxWr42hrEgJp7MILFDAiIgVF9nSwilocB5XPqLmAUMbbEXUe/hsrO3S3QnbsALChS3tsKvv9vqNuk9t8EFt0BCCPha1W0e46BRXLsGmIQSG2MgUV7/BxtSxwKIBn03WEsMtvasrm5ld/o5OTJk1hfXx/cbyUrGSs2ZaTYYTsrGSd7Bly0RWsxPxljUBTN09g3neJ4exFrLaI7vUWIEeIoRBlDUEhHWYfG9gMEZYjBLaCxHGGKUkN6t4dgeRqP0UeNRxGItMQjaA3MZvJSrAx4vg1uxc+wNsCUx7koeEu+BeAsurYwgKHAXvQuLP6l3n4Ru99DMUBAgmBdWlpVLrhcxVY+hY6bk3IF4EbHarQkVq5Z1VZ8ZutqduTjHsQa7oFFwpUlqo8gDIEDhmHXBeIRyFu7W641SqztJlKmpPo2h8DtRQrjKcPCclAdWO02Oaapdf2Ywca4Sz8y7oH88ymam2U7KrjcwgIJMNJ0XaNG9W0mWrjuBVfWBWFHYB+oAYHX1eHZB0iq2Wq88k5EQGWhSitpwEMWBfmo3c2a7cUlBHMSu1BRyPbHrm0pxphikbxUZht33f2PsLbsZSM8e9xroB8DmHLAwhfoTDbO7l5P6fASH8YZJkCeWwApYOHHE3+OGC9RVGcvdavlgEW7rfbvDHzfQx+SGUgtK+ZiJSvZ+7KnwIXPlATU4KKqqg64mE40sJlphKOPsXEOufFAMkNxKT8gqcDLvsCe32mhFlPfUj1u59ceV1ldCqtBzXa6L+EluU8B8L7gBEgA6qQQxc5amPkcrLdlBOP1SERlgxvuU+ziNJgRwEbjPOJ0sn0vYI/BfFC+FsARWI0hQJBRDuL2rQJs4cAQAyiF1RivSgKN4nOxFV/VcQ8wgLYqjIkBGB2lmh0QIhJFV/mAXwY5RU/mIQ1iArDIABgAnfoXxlooQ1CaAqAfHCEzqJTrXlebji4iyTi8qwn7wGCzWOapwNqE+ApybEbt1gM02wtzMDIVsCLl1irxlyHDgDVufgYK15XGuYOlXNUScSBW5o1C6lPOtx3PQ1k1wEgDEMDPB7n7T+YqsBVjg/F9bR/fKslnPN/escrff9vzEzh64htgm3afbbAVmWGEdZDQTcbR2AH1jRlffM9IJNcHhyYcsKjbqY1KUmGcQQnw0AssUuNqbxs65wFgkT041Z+r43FowC2qqipsb2+vArpXslRZpaK992VPgYtYlFJQSiXjLtam40+rbw1eRMg1pktIGlq4ehcFXIwBS3Dl4HgGlHiCKHS+krSLE6it4su5+eMMVFLcwb/wmkPpfNnXZsIyB8C5ghFIK6gJgfe7XKxVBbs9B7JZwRxo8OllU6ceWxsd2AhAY+7iXkZnG4LEV3A9N1ZFwdtGtjeCt2O2Yoy48bIFaCqshnWWdlVxL0ipU7kOxD0ooLKRAsyUBQTJflAHbkvbESh1rIa06+eKBDxGx4wRAglTxJC0rg4QAMjXv3DAYojlIFLhspOrpwFFzsjM+cDwMAeoU/QOZF1qgA0HzBYBMFbV/cRjkrobwShcZ4oCgNJKVqzRcQ/UqGEhlb0pgKds6tiygqpsbz81syCggl3GACaun6W+ybbcuy4259uCyWJz+xhOnLi9F1jA4W8euvNzjIVfFtsKNUefpnap7TTLkIKeyaZdpXHnwtZhNAgwBTrZrDo79xlMkv26w/uCtxNsRxawRMCCCDg0ENB94sQJAFgxFytZqlimpdQBWxXRGy97FlwA+XS067PZ6Db8rRIW1db3OxVC033KTJwirOtCfrsGAs6C7hU5AJItxlL0zlvGw1C3knqvLFN8fnwUBBRT6GlUV2N73ojVkADsEcpJaNx9MokLzpoDGtZZa9usRiwMUMoNKpoYH1AZ3JwqSGD4IvkF3LGhH6+bK8B4RYKRidVYwBKslNSPIHJKtVPafZG4DIixLuI0BxLacQ9srcvMRTIxY8FWZWuru7/ELYDUrn+BCtDGZSgajZRYUq4yohS9DEtO5eQuiPHAwlvh+yQUwosaIAhLx2hmqOoMDa5uA6XZlFipBgBbyXz4wG14N6cRc0BzA4oC1RuAIKTjbcWBlHGq2XHi+Qfl5sVyHcvDUb/1SfUDi7YQKZzYvBMnNo7IeSXHgGCc6Ws3XLM+YIHUdncyVc1IdAz6zEDZAyyMA9BAZ71lCGsaA4vOmrwIAIg3D13K3FzkxDZZmUOH+sHFxsYGAKyyRa1kJXtc9hS4iN2igHw62gP7pou3Hf3eBhoppnfRtn0Ar1Tyhrg2OcWBlwU0AKll4IKRffpIcm/JHfWQOCj1LkUFuZuyL9wdSmA1ACrWALsmyt/2HDQ3nViNXjHwk90cpwvQl/oJ3mJIzsWH8sAiJb5tdqyVFuKnUWgvd2wbWKTadp88pbpauBGgMXbSGe5cKRH3AKe0e73UZ2YCapemBQLE2cWWkCKwL94HATFZF6qyP3DbS2McFUOR1ImwbPurevvxhcxTTSW8HVPRYB5YVODFArcFjNTHeJDIQbn2PvUeQNXAYjxgVCApjufaZvfcN1iNdlvMUNtGPrMFAmPQTbDGQJXC9vi6Jsm2W2Lh3aLqftpFHkOmKI6aHXtfM+PYyduwuXUX+jReG8pepNsNRw4BC2ofEEnVVKrrXTmsBZmzcPFRma0E2Cn1z/Uia687lwAsctajXJt9bAbcOy86dr61hePHj+PAgQPJ+d/Y2MC+ffuC2/NKVrIMca/2pbSzknGyiDv3PzvJpaMdw1z0rb+E7nuFo5/dCgFQlqAqApUEVZJTfD0Fnu9l9IvWW4sVgZVUfpaMrfU/SSW5eyHUsQc0hwANA3HZslEPu+3Mx2oUCmo6gz6wH8Whg9CnHADNZkBO6WUAJqC4npOAMEETwE4ZdsawhXfjWGDwsY+1By8FYGeA2QdUa47JUlGrPp3s+F5q0QQ7JdgpwxQMQ/k7qAYWeZBASsk2pcBawSqGLRBSpI6ZCa9Qs6KG+1RoW2uwljoVRotSyQzwfBywqDtisaBXLh+Ia9solrZdZqeOEbcSq/uY50mRgAnR1ZVzh7JuzP2zIW466ToeRN7VSYlizwLs2FpYAnz9ilFiXdVtZ40nUq5VggYBlQGMBSoDNia8bdW2kTiTBQKc1NyGuSNGSHnLxkjq28Sc2MBY9LiquZkI8+FYMOuKMlri7NrIzDh6/BZsbv0T4N3y2vvAAd0exiKs8349aIwv+qVP2S4lr0RqYzAyJLeyMJIJYOHH5IHF2NXI6/w5FobhwvdUd1vYISd9TIgzODU3ETRZ/O3f/i0++clP4ktf+hJuvfVWbG3VyVdOnDiB/fv3j2dgM7K9vY3HPe5xICLceOONjW1f+MIX8CM/8iNYW1vDueeeize96U2d4//iL/4C3/u934u1tTU8+tGPxgc+8IHm6THjNa95Dc4++2zs27cPT3va0/AP//APuxrzSu458TEXy/hZyTjZ8+AiWaWbF/FDGSHRosytn/DlkGT28YY/yRTUpLn7QMZC4sevANYObJAUPcMy+4HoA8qdDzm3IJRcu8QMXZqRQyHnzgOtQJMCen0NxSmnQB88BWp9HeSD/Bl1HYyx6wJDfO6ZBGysAWY/o1q3MFMrmZhSA3Xnl3tPNwDMFDBrAjbMzGUdW0RSYIQUoBV4ApgJy4/LAOVBhXdrGV+0jqIAeRXuS+us7inY5a3u3OM+JcNVNdgolKvBQBLHgrSC2OyIQaXLhNWOe3DnSErVIEYxDKxY3nss9SmxbMJ8eLChIqDhwUbjmKh44RgJrIVyfI6xgLVgIz+5CeHKglymppxSpsir7STgrTQoNipQZYVaG2OSYwbmzRoWFKAAOYAk4Kges0uBOwAsOl0540d3vtEAGzIsi7uO/SO253dnz4NJkm1IrE0/sKDMA8z+v7bVyYt1WeSSyIbF6JADFizAIpXW2gODHGOxiP4f78sxAzPE0OTQWu64hDsoM+MHn3ARfuRHfgSPfvSjsb6+jttuuw3XX389LrnkErz4xS/Gddddh4MH+4O+x8irXvUqPPjBD+58f+zYMTzjGc/A+eefj89//vP43d/9Xbz2ta/FH/zBH4R9PvWpT+Hf/tt/ixe+8IW44YYb8MxnPhPPfOYz8aUvfSns86Y3vQm///u/j3e+85349Kc/jf379+OSSy5pAKWVrOS7WfacW1QsObeoR553Nv6/v795uL2FBxAd4xbbmPVdRH/t7ybyf7knRHwCAABcSX/td8dyzkP6Cf7wxxlmHcAEIUtLrqDV4p0JECBoUKEBF6th5wZcmlF1NQCIwt4GIzG41KK4A5AsLpWAQmJavKq36w9wYMO7gRgBZ0kmgzPfR9KOTTDWiDJoSVxZMG6czN3MTrUblQMrjgXxCiQDo+swBHFWdzCFmI1OVe92ulFm0Hw4cBvw64ZoSBIgTg5wiVI9pkBgzuoef0eIXagI6FFkk/2gmUUqfAKt1LEMQImrmTEh1exYIWOhXHYngqSOZZcUgYFk9W2uhB0aCvuPAVtYUwgCwNiBpoE58cAi2X5rvitb4u7jt6AsN/LtkWRh6wvobACLzHYZQP5gVab18Do4uwdYGM6kohW2ZSfAwkuWkCBpexBYpLbZzGWMmJvU2A4dPAilFE499VSceuqpAICyLHHZZZfhox/9KN71rnfhyJEjeNKTnoSnP/3pePrTn44nPOEJnYyQffLBD34QH/nIR/C+970PH/zgBxvb3vOe92A+n+OP/uiPMJ1O8ahHPQo33ngj/uN//I94yUteAgC46qqr8GM/9mN45StfCQD47d/+bVx77bV429vehne+851gZrz1rW/FlVdeiZ/6qZ8CAPyX//JfcOaZZ+Kv//qv8bM/+7Ojx7qSe0dW2aLufblfMhcH1tdGHb8r/zm38BIB9f3GjX+xqIQC3x0QRhVk84rRUhgHXyp6iJ0Z0cyQKBB0SdBbBH0S0JsAbUHoc+9GtSxxrIaaTByrcQD60ClQ+yNWoy3GAQugX/tW7mcC8AywayxuVBPOsxptYXTT4Lp2eSJshpkB1RQwcdD+ALBI9UOAKNGKHHNlYZWFJRNYjbbYnsxTXoKbEymQ0tKWCzK1xoy6gbiyUHMJCm5Wm45YDa1gSMZsyEomozk7S/14d57gNuQqlgsIU7Ul3Lk5Na27AhbGWt3Fuk7OEMEBJHEPmyrkWn962qYLlQasBW0bFHMIwzHSqZgjF7IwZtSshgaBrXHsQyUMRFlCjQAWzXNyKbNd/IpyR8scW1g2IcapeaJdI1JOjCnxnbu/1gssLMnzM5j9i/uBBQH5B8/WwKK7mw/OzrxsI2CR6pc1YNZUR5NP2D2SDWSBhWu74yY1gs1ouHy2D866hAHaZXhsy2QywXOe8xz80R/9Ed7whjfg0Y9+NC677DJ8+ctfxk/8xE/gQQ96EJ71rGfh+PHjuTMNcvvtt+PFL34x/vRP/zRZiO/666/Hk570JEyndVzmJZdcgptuugnf+c53wj5Pe9rTGsddcskluP766wEAN998M44cOdLY59ChQ7j44ovDPiv55yWWaWk/Kxkn4wRuHgABAABJREFUe4q5aEuOuTg0ElwAXdp4Z1K/RONGYzVFbsolcRsaAKh+OUeLeZy7flBSb4gEO9P6Nd02D59bncPKKRAcpe5l8eunCcEAErPRx2osMo2NuhoamExk7qytq4WXViybi7RrJHYGkLGyC952PkiSKSplVWYHpnrHjBrsKZd20jpGI8dqtIUBsGcE6riHdjYneKLGO1+zATC+WJucrkEdfEwuSNkp1FaajTM/AaLoqmqYeQAia7qRFKusGMbHDbgbJTcfXFmXYrV7fdPB2xzNxwIZuOCs1GiyDl7a9S98HAcTL5SeFszCPITMXiRZ4iCpY+XatSzdzOCqTjXbJ/V2AioDqgQwW6RZjba0GRgvcWC4Z0zk5mOJl3LPDXqK2Xmpqro4XnblIXkmVQ8sCoxFJk14L7DwYL/l6lSPh11NmTTGJuuYtEzTVgN21gUWYcyZcxoF6j2wiNf4NlubaNcDi07z1q137e/jdkY8RxsbG3jgAx+IF73oRXjRi14EYwxuuOEGfPKTnxysfcHMeP7zn4/LL78cP/ADP4Cvf/3rnX2OHDmCCy64oPHdmWeeGbaddtppOHLkSPgu3ufIkSNhv/i41D4rWcl3u+wpcNF+yecCuk89sG98mxixWC8qQVeJwIb3D3b/51+fI0cRW/g8mAnBlDTQx0ih7q/xe2uZGJ6IQupelIWkiZ1aKYIXWfWXIsopOVpBFxpcTUGswGUFns+HM1AxJG1tKxOKnEj9HSsPNjjUwCBDi6Wm9f2JzgUu5CeAtNKNJXFMDSzyV6pdtA5gGXhweRpR78H5SLTBSFAuHdDwblZsGaryxdQXCNyuJL4iFAh0AzO2TmfqXajC/Vot5jakXEYrYolNEQaHwyPcN5dDheHa9S/ANoBsSY/MwwoYM6g0gOW6PWoq7QwJsBYF3oGqyi4WZ+LACBnr5lPat9agXuCic3WfdgGgpOCC5D0QIokD8+BO1rAuSNuen8DR47eArX9Om6qun05Gf6A6+4aje6ktvcDCtmraNLY7YJFhJIgZqDh5uRmAKQjckxVqCFj0YCEx2rTtHX0vQN9mzqBhGbocti+NwegbGxsNxkFrjb/8y7/Ef/gP/wGveMUrssd95StfwUc+8hEcP34cr371q4c7Wsl3layyRd37sqfARVuKoki6RZ12YD2kdxwj7TVv6fcPiUtQpX1FYESMwwJMQ0/7QGR9Y6dXNuoKLAcKJJXYoMmNUI5G9UESH7DlUqAygwsLXuM6WnJZyIYIVGhQRaC1KTCNWI3tEly1YjUYsu8YNOqBhlNEUNSAQ+pqjDiNVp74ZuMATxg8dde5YiijJMUqukxBr7D/rxn3ALiUphEQaALNcZW9QxwIM3QlVlsAdVxH31iZhVmymXoP7TgQiKlYOeZokZuF2TbiOBqAwIWZe6t0SNHLfg7Go1+fVtvDAstyfsHQkWIIfD2KAVAWgyvNAJUusNqdgWqxV4lJEPepRA2LRkwFs3Mbc8XxlJJg9LEFAglSoC8aS6OaOXOYb0d+4eTWUZw4caswZak2Ic8YIpYktQ9kl+Q8cPxLLsaiB1gQox9YWAnezo3NTAg87XbcWHJ6gED8fDZ2o+gn13CblvA3ZC7GwjL0PNVk1wQ1hgHc2NjoMBS/8iu/guc///m9xz30oQ/Fxz72MVx//fWYtbJF/sAP/AB+7ud+Dn/yJ3+Cs846C7fffntju//7rLPOCp+pfeLt/ruzzz67sc/jHve45Pi+/vWvdxgTAPjRH/1RfPzjH+89t5WsZC/KngYXWmvM5/PO94cO7NsVwoyXQI4+81yDKy41RolJMg7R78tiHDScqVu+stZXw3UK5O56aPYFSKxCxWDt3255a+CgtK4dEYGMBjacxVcx7MwVc4NTJu4hVgN2JkpUZcCb2859Cgsp7VRGYMSN0xfcAxAUlYbFMXJB6Jf6GqMQq7VXXNlKvYehNti7QbUmsck8eMcYhyFZlMOFAreZobabQdiCNyzgAAG3+l0kcFvGTA5YWGmSgKDq9AVvs2SE8mlc0223KmSzc6WDYzswDlynKqm3WQ02XpsT9ZoYEoi9yHPFDMxdliv3TzJ9ybhd3W/pJqxJcgzxcHG8uk3Ic6Mc7HL3indX61jJ5eTl/ulj1RqMDLCx+W2cPHlHAljIusaAJBPgaFz5wfcCCyYAuerX1hVITYljJIaARVuH932aaR5Y9Imv1J5lLAjytk81RK3PxoEQ989UqxYBWDTxSHq0Y946KXBx+umn4/TTTx889vd///fx+te/Pvx966234pJLLsGf/dmf4eKLLwYAHD58GL/+67+OsiwxmUwAANdeey0e+chH4rTTTgv7XHfddXj5y18e2rr22mtx+PBhAMAFF1yAs846C9ddd10AE8eOHcOnP/1pvPSlL02O7dxzz8Vtt90W/vYxG0960pMGz2sluxdhLnav9ayYi/Gyp8BFyi0qxVyc6qt7do0nO+wYIfC2vXAulKUl8wX52AJ2BZ64Tkm4LNemmtUgUbp66POddkOuYra8zKTGQYNLXwar4SLo9ZYCVdKPLSwwYzj38yVnoHKxGoUGuACtUR2r0WY12uKrfqfe+rEFUcl1DxbCConq2wPCQGElSJapkLErC7CRsmSWQaw7AMbCiDI2OuNSZKlnBox1SmIECFJSMXTVLVgnbbcyLjk3JzaArrr79M9DF8DUp1sH24cibS6bmbfALxL3QI5rl6ryDmxQRml34gPle+s9xHEgIBcrIWdgYUfFPcAyUJpOtEEy7oF9/JaV4ngjgZyMyN27qg7aFtF1at6wlimXJW4YWDT6YMbxjVuxuXVXCLBvj8IDC3A/YxHI4j7GIrOdmMGWoJtEZvQ8CbDIpaKVGheZGAsCqjUFO+kaA2qTUEYCs5jZlAMW8d8ZYJGKDfPPToq58c9XCjyVZrjg6cbGxo6rc5933nmNvz1IedjDHoZzzjkHAPCc5zwHv/Vbv4UXvvCFuOKKK/ClL30JV111Fd7ylreE437pl34JP/qjP4o3v/nNuPTSS/He974Xn/vc50K6WiLCy1/+crz+9a/HIx7xCFxwwQX4jd/4DTz4wQ/GM5/5zOTYtNaB8dja2sIzn/lMHD58GK997Wt3dK4rWUxW2aLufdlT4KItuYDuUw+sQYNgajqglujeWMgC7ZV0/wsjVlWGGxqDeJ1i7FzenfXa1qlRff+LjDvTBwBX5K4eW/1i2E3j7t3MBBjUTiTKZY8Bdjf+To8EXWmg8qwGYGcWPBkz4Z2h9++inO+5UtBag3kG75vOZStWo4qCvcecqxipRQEhSMYodw+ooQJ7DBTWW/7jR1rVmcw0ABi3yEJiW4CFFElROKzDRdS0djul2VulKfZjL424Qo3NuKQUuLIojG9QlHhJUZq2hgOQOIQeliMGSMLGeJTtt6txt6ar8QFGNu7Bgwg4hsDHcoDGZ54C0Ih7ACSbk42Dt8Nc16NmY0Kq2SHx7ARZBpWenXJVzj1Y6KsLUVALDHXbln1d20roRg/0htgRZsbdx7+B7fmxXrOhLeR56wUWhABuktv9pqSWzpJOOzelzALMkCEHLOdrXBBQrilgkmcshoBFdrMHFqnj+lCLAxYdVsKtQymXztxYPaCbrU8xJBsbG3jAAx4wuN9O5dChQ/jIRz6Cl73sZbjooovwoAc9CK95zWtCGloAeOITn4irr74aV155JX7t134Nj3jEI/DXf/3X+P7v//6wz6te9SpsbGzgJS95CY4ePYof/uEfxoc+9CGsrQ0nk3nBC16A48eP49prr01mz1rJSu4PsqfBRS6gWwpo1WwDEC2QO1Bsk4dQrYQzewq+iWIStq/xnTo9iBUF/QpGrF9eSe9VHmiEqxZFPxBAEAqtgerzzr0xO9KdqWB/tSTKs2FYVwWb/braXl8HTXWZ0/EZqLY0sCUKjCksaExdjUX7i3zF2xmo+OS2mN3HTZpIFRWdouan9T4WVuax4S5lGQUzrCUM+YaxFAJxTQfTPRgGNET3BOs+0HWfqm8iQqS0M0LF4UXiEbg0ckyIw6jb9hmXBEihjuWopHaDWiDORIC6Gyep4K5U3/cJlzIX6D4EyhoAguU48seDXaG2noH6gGrbVcADIACFuIfw0FhAmXydiGRXJqpwHrXtCxl6F6owDqIALJABFl0hKfBIcr8oSEyPRZ1Bq32ezBbfufsfB2tYMJEDFpl9ED3/PUDJu3V1NwqwUIk1gtjNeY6RgGSEQgbsWQKqfQoodg4sUsszQwpzUg5Y9LXpUmS3lqFeYIHWvo2uSNZ674bUJym3qJ3KQx7yEFmTW/KYxzwG//t//+/eY3/6p38aP/3TP53dTkR43eteh9e97nULjen1r389PvzhD+Mzn/kMTjnllIWOXcnOZQCDL9TOSsbJngYXuYBukeaSGy96jfXG/9Hn+zs0EDHV13uy77n1ethNwDPB+dQDZioWelXKQt9gNRZts/V3HA/ilbj6fBbW9xNdOreCbceUOJcg6zOYOFC4jIeYQChKDXJ9sWM1UCC88BYDFMgPLIrVUGsFaMYuVqMCl2U+AxVDLJq9J+wGqeDqaMC5Tolvt7ELVCCwDGVElXMI2YmBd2nzynY8RrQCnXtHS8pZcl18ijvesyiNIn+xuMBt3QNGmkq7MCZUupgVf/OMiXswknI2DiSP+xSAVFcJ9+cUg5FRwg7IuWMCq+HS3Fpw182JGSgNaER2p+DmxAw2gHKUp1eqAhDM6c2myrIccd8ebJADBSgKd8yIuYYAEQGD9f5xDEsDOAKwtsJ37v46TLWZbde6ZzgwhLm+CZKRqmf7ELBIFYXzxfGywIJdDQuTjkJgAqr9Ojm2wfUvrPnpTVYjbUjpW8gZcl8aoP34+1dcKg6sXjMSbUbr7HQkuLi/Kt3ve9/78LrXvQ4f/OAH8bCHPey+Hs53lazcou592VPgYmwqWtm3px332VjAneKfsvYvbEQPOhvVFla4GIRF28qJblm0qzooWBSaXfYSv/UVQiabIDtgFrpdCJuhrdS7YAirYQsEANCnWy0yBHK+9WpLu2Ol6J11QG0xH7mhzhCs3sJqTINPu92eS6yGV1LLBXtlQmGl1oOBglUMKCvMBVM+XSQAZQzIEjg5qbq+b0liNRgkbJlPRToWGDND+YJ1sfKOWmmXU4ncnOCrdI9X3AmQYG9n3feVtGsXqnRsArsCf/0FAltWdJeBK6iJY2qiMOCjynMZkTRIigKSV9LY+bkvmDbW2LruhXv66+oUjqHxQMRjmKpydRbGXVcFKTgILfeKhU2yGrFYAAgMx0D7bl6MKXHXd76G3sKDcrsHxiKnjNse4FADj8xWbhbHa2716aV7gEXFULY7MoYo3OV+lRxbrP8nz6vHHhaARS542zecPFDeIe3DvIEgu7Z4AwIlvo4MOGuzYbeokydPJovf7XX50pe+hOc973m44oor8KhHPSrUw5hOp/eoG9hKVnJfyZ4CF23JxVwYY1r0xIAkihQtTSKg4bMacVSZeUfqbHuACuCpYzS8JXwOaFNnjF8G2AgtNF4k7N7QPVrt6C6k1oUygN5mcAHYqfwEJWAnfSRefARXLbx0QbYK4Akk5mU3faXEsxrwsRproqxuGrAtwSMCHf15TIwo7cYq95WzHiv5S9zaHNBwuhkxoEzlFMExCqsAAmWMu08d0PBA2deZSIlh6NLHCHSlHbzNVpRvVXFHCe8VZtC2aaSnbbsiMTulneWcPLhZKM4EiFL7uixELhuSV+ARwFc8PFcrI8fSRBLiNriOe/AxFe06Eql5QCUsTBuMxMHbgC9ax1IDwym+qQD7nFhisFZRZisKnzWrIZqmABEAuudeSUhVbeM7R31xvLTibl2TMbBIWdP7GIl6e4aR6AH+zDVjkRJy1yRVz8Yr3NUB3Z8pKzPmvvcGA7AT1GApnpghxsLUGbA6Z5UBFhxt684hOmvo+r7h+lP3V+bic5/7HE6ePInXv/71jYxWq1S095J4ALyMdlYySvY0uEhlizpx4gRuvPHGHbeZYi3qzyWliS3qhdnXoghuMUOViEa0L/EM7gXpskMhsqAtjdUAagtueHh7zGoLdUEgA6hNAJti5TYTwM5QV5fdyXgTokgBlYAyIGY1ULMnajGmJD8WFwBLCmpaABOx5rG1sOW2VAtPAWMrwEJiK3KjiNgFckqSqynBrMA9lYobwhyABbeqXxMcqxEApQ+wJqBygduL5AW2BF0JGGGfxiwGMXHn8fi2rMtylT6jdhpTa93+8IBgxH3q0/mieU6dtqNCe+4wqYe3QLAmG6me3Yx78K5CvuZIK3g7AItxc67gXLuiuhfSDNdt5KzhigGlssHoKhozQ4AIKQliZ5sYe0K2t0/g7kZxvB4ZEWPBOe87GXDG8u8y9VVADn/GrlBdRVzmN5UxiiFrV3VAA0QhhiHe3ndOjc4SO4b1Ktlx/jhUaGTAanaKvN0ofm+1u1OQmJxI1vcNBzufOHFix9mi/jnL85///MFaHSu5B2VJblFYuUWNlj0FLnJuUcwSwHjrrbfiy1/+Ms477zwk2Ojd9Q2BFrH37FKUdJd+xGeHkjoRtSko2ceYc6PoUzuFnAG2aKS6HaMuM6VfIu3Dg3JOEO3Kv5yXkYKWCcUcwFwUAC7Egml9vaQlMg0xq8HMsJrBM3dePnZ2WX05BZSUgio0sE8sy2wqcaEyFZSVzEmerRglFtClU1dIuXvKlySj6P5qSQJYNKVWImV/ARoCYppuN4NimmCkocADdYB1wAFSIFCXvHC9B3Ht4AAM2Fqn4GUUXxecTzyc3SlmHtiyi3uAKMlKDSrVPnC7L+6B3NrDvkYFS5XzbHajzDxwWTkrdH0T+7ZTbk5eSe8DFo0u4Mbjqm5Ta+yB5Wyl6t3aPopjx74FX8OirWh7hZWyRSWjZdEBh6Q13W1PL6suXexQDYu+7XObDPwGAFsQzP40qhlcXjMKvj/OTNJ9NlyWUhPSAhYC6uvj+pibRYAFAGxtbydaa7Z78uTJ+yVzsZKVfLfJngIXbSkKGf58PsdXv/pVHDlyBI997GNxxhlnYE0VODkvu4pge81bQEmk6H/A2yqT9qvFxet8Pt6AAVTcSBW7FNcmpy8G4/h2HWXiQdouuwgVal1tNjBx84W+DFbDZW+hbcdqFI7V8P0soIf39kUEbUnYEzBUKTEhIdvVbvrqTINTxRRASkNNJlKrYBsgtQ0zr9KsRrsVY6Cq9sBaCq4Ud4B3ofLZjFQlyvY49ykArFFUJorJcRW9waBMETW4+hhSy6Mn7qFhDbeg0qKoOHruEm13xscSxN4CIxS59jBbZ7Gm8OkVq0Xqa7Cx7ryjjEvWz6dMczt4W4DFcME6354HRhIkzOHa+XS3cdudeWgAi3TbYdzezYkEnI6ZBWEE0jUsUi5a7CZlc+suHD9xG5BZRwOw6Ln1h4GD+xwBLJJ6uIuFyQGbOM4opcObCcGu54FF7i2SIhyotc3mgEXceOJ7MQjkj0sf5oF6YhuJEYsz+XovfORDM53VsrGxcb+MuVjJfSs+F8cy2lnJONlz4MJXHwaEuQCAz372s9Ba44lPfCL2Ob/O6aTA5rwMFhixqMUNyUcfFT04lgY9EIONXbQZj3USp6GFuDaF+MwBIDA0gGjojYJ9ffT5gkIAAnXulU8GMOH6Jb8sVqMEUNashpm6wPAlCkHiGIo5CYPCDFuI+1Rw1VoAaFBOowiipKbGxIKLdexbM7AMkCmxvWW6sRoMaFOBzBhwUIMNMpKjP/TZvK3zwoAuXRG2FusgUsdqyPcSEk1lFOw9RphBVQ1GauU3YjUSLj2SEWoYlDeyFrHMh5yei39Q/cyDB0tAN+4hdhcCA9YYhNgEhmSEWij+w7iMPd0UsLELVVzEL041O1YIyt3TDtpxBL7QLRDYByxSoiAuU8c2bsPm5rddp2nF2+q6qOTQ45IFFt7IlOyDpdhlIhOS7MB1RrfUDtYBi9ShBNgJwexLx1j0vX9yS3E4htwaN8RYpLaVApSS89kGFm7iQwrmzHF9wAIEPODQqeltkdxfYy5Wct/KKlvUvS97DlzEcscddwAADh48iO///u9vFKSZTZqn5l/uiBfG0e/0MRCEQ52FYCWL+x7bTmrVJjTiNFCwKOguO9Ru4ExjRKm3GCCgJvDlOxeCUxJMbY20BdeK+RLARmA1nN5tUQeG3yOshiHoTQ8sGcb34yd2SX0xFCpfGERPsHZKJUmXrIEp5yi3K0yMARtC2p0p1ShDV8ZliNGN74WBcBfFX5tYDEMbRl2sJCU6OkyyRKm5V04cqh263syg0gVuR5PZ9PF3IMDfplCSEYoWADAA2IhLU2zBZ7YSAA2nWCfSxnoQM8a+H+IeDNdjd8zJEPNgrXXuU91+UsyD5Do1LiMUIIvF8PPFQAAWcZvttn3wNpEC9OJVt48evwXb82NuaUkr3igom+o17OOlh7HoW1uyoEIGGmpcdBkJl6ls3hxfYCIIqGYKdp/O0hLZOP3ceKLtvcDCZNp2wEInOsjalVjYSA8s2vv4sfQBCwA4eKA/lmI+n6MsyxW4WMlK7geyJ8GFtRY33XQTvvWtb0FrjYc85CGdSpdrs/ypBVrZLZTtdbZNcIxjwih8xFbQuv3aGtprQRzqzB9aMOCyQ3EFoKIoyHFJ6JqiDwPnh+S37bAPN6EEeamruXzN5OpdFFwr5UtgNRQTaBuhrob1Gah8yvUlxWp47/JiDkkNXLq+ZpCYFyBfwG/oHIg7MUSVp2VUAZpNMOUKSiug2kZVGbDNOYY7YUYxF19/7iwDsb3f8clMjoVgKLbOL32BE7KEopRaGtK2CwoPuDXhMtJOaZuRhmLtWAQPNEYxDxCWQ27v5n7NWgyt6thO2VKL3KcRGOkAglbwtvKxGlGq2bHpaRWUMFsuSDyuURH6zVjxWVOvO1g7eJt1PUfsaoP0FTNktrjr7pt7i+MBDEv5GhJA04iTGy770/RuaNH63IgdSB0fF8/r9O2OnafHxwRUawp2LfOMtNmBxHll3z2UcYUKxqCe230AWOQ6jQFYbJBiyFhSMRahPSennNIPLjY25H64PwZ0r+Q+Fp+7ehntrGSU7DlwsbW1hb/9278FM+OJT3wiPvOZzyTT0a6vDefUri2dtXg22f+/qziHWDkHgbW4A3l6eeftU/PXCYCJKIFsAZQCNADqnF+yqRGAhixqv3qCVAmPLdrDPj79XTCgK/nxwe1mxvVLdBeshp8DAoU+ADkHM3FZVryCsURcpg1Bn3QKGwG8xrAzlsxTFlgWrcFQmE0IFWtguo7JpHRzaGHKOayxNZIGBFhsG6fYDy0BbvKZoSrrfLQ9NcOjrgsZA121WY6a1ZAicwI2ZJSirKoduE/5GhbKp56Fz/jUwzzY8f2EQGfHcvh69ki1nRgfely12sHblm0jDmB0sDyzuF/ZOj9Yp+0QvI2wj1WQtMkj3bSEWajdfer/ffv+2asD/a01+Ke7vwZTbeXbJcAqctmzUkJhnaa6487YelPRRsAiSRZ7V6jkeXPLlbAr5T4FnnWBBQPZmAVEX+dIB1BUcDQlWdcuV7Ojj7GgxLCYs20yXCB5MeI9A+DUAUbixIkTAFbgYiUruT/IngMXN954Iw4ePIjv/d7vhdY6W6X7lPXhnNopoeg3rzoAXRCyY4kDqhm1y5GXMfp5bh9y7Wtns91SdQXvBJ09WlpYBoyQUYXhdEYVZaDa5UQRAGqAAKCaAnYaxWrkKuou0o8lFI7VsFbcp3jWrBa+03OJLaQEkrnZdC5UZGHXGLAM0g5smN0BjcYtRJPQuVYTFGxke1WimlfQ2yUWKk/OrnYFM2r3KQcmY1ajATRFVFVCGUD8bHIiN26YbmOD4sbkq2gPjDUwFs1YhA47YG34K/iQ0ziXptCVMaFgXSNIOQIxQgzU7EBd5G7cnId7porrafRkt4rmgSsD4nyQeHNOXDE8kqxWDIxa7JiQrWGRah9gVKbEsWPfGgYWxHLP5PbxQ8xhOL89W3W7BmzdTVEq2gxo8cAiZU5h2jmw6Fs/PVhCAlgEu0GODbECLPok7ULlgEUGkJgpQrbD9rZUm4dOOdA7ho2NDezfv7/jhbCSlexWVgHd977sOXDxhCc8obH45Kp0l0NuISPEvyCts9JzWGg52rqAxDemNwiraJsRa7rvYreZmzzYCEN2fYTNS0BMBGE04H/Cl9HvkeykS7LAZAvAVm0tC0AjAIF8q2P6U5BYDVTO2q1q96lwjZYVq8EKehMgY0FGYW3fHCgYpiBozdg0unZ78mhuuNXM1wrsLdHTCWamgtoHgA2qcg47VHDSAQthOdoKU32hZZgudayV4FjNLqVtL7BotWgqFy/j07sC8C5UBIATwcTWjq40HRRu9gpsiJYBQP0uVFFGqJTi3nAXYg4uSGxrEDP65veF2CJg5D8bKWk99+CJmMoAPcAiKURgTe4YH79CsPF5RuOWrEBq9PqkoFBV27jr7v6q21JknqFsgK5JGQMsssaHXku8XCfVtVWFPWJgkRp/ua5hJ+l6MqHSNcY90UC9b1jrukOSvizS65Otq4x3gBDyzI+fJ89RtsVMkAUWBDS8R/zxswFvAg8udv3eW8lK2tID6hduZyWjZM+Bi8lk0gATOebi3PMfiM9+45uSR99Z79tLlrzrRyxkXikIijo1UsQCI5XmvhuTABQAKVGcYSFAwDMO8Rt1Jzd4Asyw8YGKLv3mMoCGb9uNkcOXyxECQmYoQM7HtFmNRMX1RYbgs0KpAGgkfsLM3L0kjva9gxxzX5Gb8u3N+qWrigr71yqUhYXSwLxaoq8WAKW8+bNAoScgNmLVMXNUZStWw9gRQduN1h0Y8SyW6t6quVNhhq4qcb1rgJGa1UAMNNixQwYS5LxAxqU6u5O3sotYWMBScD+hOMB6AFh0T1P+2VDl3DM8Eavh224Pz4GlXMnDZPC2BahiAFF19BG3jfU1LCK3Kd+2RrvyNgSsqsUMK9vzEzjqiuPlgIEv4aJybj3IW8Tj7Z6xSC6R3hVKdY9DBCySijhLVXOdKaBkCagOFIDOzI3DVNl5y6zpQ8AiqzS571SmyrgfS3vZl+esBhapZs0EyfUvBVbCNVPAuWeflRmJyCoN7UpWcv+RPQcu2pJjLh508AB4RqhmkJeKkeBhMm0L0hiLJ3X/bDECrOEAB7tdum3m/Hs7jRNLdqiC6/ajnOQ8lpvrM5E5MCPjprq2FRIviB3otoG48ON37YRz6MMyC/SnGiDAsRozrl2bluE+hShWY86AA4D98SDDkEbDwrZe4fOqAE4UrgXG+r5tbFUF1qdzKGWxVU1Q2bTLxeLiWA0CSE0wLepYDbuxBa5y5tCMGIY2NgNGogvv58VPDzN0OZbliGI1KsmeJDNlkqxGW9haqMGCdZ55qNPGEvvLvED8h42DsGuGp8FqQLTdOG2ssiONHn7cTOIK5W5GbgCCOt6hM0Qt3/eBpXob1UkW3MI36KIFYHP7KI6d+FY2wUAABI3Cnun9ZIlK91MDwpTSyyEddk7I1ulu4/7kDwEWKgcsFFDuL+qCqGhNt2O8s9mbwrl1NwVXqNRxfUwIoydmpJ+xaGfPiudEqoDnQG+zTY42EBEecNqh5HFefHXuFXOxkmXLKhXtvS/3C3CRYi4OxTEXRFL7wJ+tlWJoqhzLOOTeDAjKpS1YXi6WxOpYAV6rDorCGA0wvG2b7WMqL0kwxA+7sGDrXvS7WYxjZoHCkMNYaovx7oQQgStGwyXLP6+LnkX72hEQ1bsQ4GRcTY2Qs3+XawMBTVaD5L7iNnOi0KvMSGP9gyEQNjfXoFFhe1uU/32TEpUyUFpg8clyOHFBU/IXM47VmEw0qBB3KGvngxmoyBgo41F2SmIF1t3kRqztytV8YBrpPsUMZauoiriLTwiF2Tx4bQZYszUOWIxnHsQwUT+UDJfidiBtbB/LkcoSJTeSkYxVPjnCmLSx1jRYjiSrIUNCo/J2IYAgx440+gAaNSzq5aLrohXPy4nNb2Nj40jWFco/o8I+IQ2AQl9I7tC4m0cAi05CMrCr+p2Bc849TdtQYqg5fgLKA0XaiOH69cC0s9Gt3TkAIKxSZslqAwtqfV/lmQcPAjrvPj/exDFADSxiMNNoIzXQyPiyvraWOpMgGxsbOHCgPy5jJSvZsSxBj1nJeNlz4KJt1SiKIslcHFrflzfcK4KduYrOzMCcIzYjt0KOFAWwIqGOmQAjvt1kWV5UQ20NPQAEYEKYTCqXdYhgSgXr/QoWdG/qAPGWts7eYuvfrEsA7rVFsPUZgY3ddkMQpUUZBK3AFAzTqOK9+5Mh7gIaO3HKou6ftPFrnRw/ryaYBxxtsX9fif00B2tgbhW8l/wyRB4zARtKTTAtKmeNNzBV6Z45uae1K+w2PraCAGNRlJ7lkJnwlvAA0FLCDGV8pqt2f3ExPIYv4scMCZ7nCBSMEA51JeprSECT1YCMNVjvG6lmR6aNZRLg5oLEbWBMHFjIMAPWmCwLU89IVMTPgw2lPNron2u4K6NdNqDB4G0PlICTm9/Gxuadwsxk2g1s74AMAgtCUpMWRgRBYe4yA1y7zCY1fAZKm03dygoo9+teYJGr6J3KVOjPxQMLUok1whlmkmuke4epBGPRWGI7AAtZZscDKJ6gBunN4cpn7haige2RrNyiVrKS+4/sOXDRlpxb1Kn7x2WLEkZDVnRy9QmUoQAERqufqZcIAShIih0xwU4YVnFwDUKih0XUXSKANENpA8CALWAqBWuUo+9G8TIDnUAKRfsYE3JZbiNvid1KDDbCiw7pl9iO+3AgoIhZDefa5AEWB6vxDvuAAzSVFP4CE8yEJTOUImht6kBt1984Se2osLE5C39NixIVNBT7QhsEg1niuJ1KIXq/nqDQUxQsCr49uQVm1Ruo2xl5Vbn5aUbKB9Ull4EqAhbDNTYIgAYxQ1tTV4iHcRb4/rSx7GIlku6NDYVaKmOz08LIRrrmmFvJp8+NWIUYEFjHxoS0sW7cUlAviq8Y0xUYUHUgdq7thhLZAyxSQkw4euIWbG/fHYBXcwxyYla5ZwXpu5vdf5TShqNjiBIGErcH+WuS3OpcZTmzdLMFKm4AiwY5oIFqvUicn3PcshSABTeOjQxZOUzigEVScse6vuKYkZadqPVLNLYMAAuMRdG9/g22mdrn6KRxDsP3z4q5WMk9JSu3qHtf7hfgYmurm9rwtAPraXNVW4L1Tl4YrEkMOJZkoa4wzm1nBONAROI+48ZFFcCmVfNilMKZHgkpoJhK2iZmoDyu4auvUfw2Hm6qIS5+Vt4V7DLdOmUtAI2RWMbpiNndwvfeHcDrmol9dvqYBxCwCWBTzq/SDJ5SXb9jWazGnIC5V0g0ZusVjCaQAkzOFWMHMq8mKGBgeAqGRcHb0LQt44BFRTMsLd0VnPZDwKQgoJA6GswWpprDmFa2Ay/M0FXprMU5cNBkCQQxS3Mhm/5YABjACKMRq8GADwxnpx3FzINl5560SME6yyC2jnloFsPrTRtrTC/LEbtVEaROhLKUCpXvFc9YxEpip+2IMQEIVMSgb1iYLe46JsXxcqMjyKVPWddDO50j0tsb2bfi7GFAqMmT9WaNgEWXAWAxMmXGZjXBrGuAuvUdiOWWjZePFLDICevMmD2o4K57Frk1MhUMHzMWXcDhGovWoTZAsBFj0ZYU7vPHtZkVa4eNDytwsZJ7TAaeu4XaWcko2XMJpce6RZ26vra7nMQuFalZdylJC7Eo7eoebWnIPAF4DbBrsohbJYWzxkR+D2IZAqhg8FT6sVM/fle7I2R/2enJONswA9o4ZcGno00Agt10Ql6X9QyD/+G6DsmuumBgUgKTE4zpMcbkBENtMWDY1SFZQh9wLlrHNfRRBfqOgt6QwnQFDCb5/JcLh9QQFAz2wfIMlmdgVijsNjRKWFJQmO/qXJqdMYTVmIDUDMV0P2Zr65it7cdkMgX5OApmFPNqRM2LVvPWQrGrns1iUSZr6+uSuzYBWAiD0d2uQVBQrFzMhwWqCiiNuDEuYKHygdsUMQ8krYMgoIuthXWuT/6nBhbjwZIsDRbeVcvCwhfFy4klX3W7vx8/bgKBtHLDtGBmWGvAPc+CFMf7am/VbUmyx0CmxgQQKcMDj1wura8AC3ZgNLGV5blOV92GAxY2Oz5btIBFvCPLuaVBSex6m+7bJhLDUdjYMycjgEXuuNR4HLZ0rG6CefJtZoAFWteGATHaDMgKXKzk/ibveMc78JjHPAYHDx7EwYMHcfjwYXzwgx8M27e2tvCyl70MD3zgA3HgwAE8+9nPxu23395o45ZbbsGll16K9fV1nHHGGXjlK1/ZiTP++Mc/jsc//vGYzWZ4+MMfjne/+933xun1yp4DF23JuUWddmAdVjOYvDqdXtxGvdYJkllpKnEadgoYXVv0F1JwU7v69mcAz5zJJ6RZ2oWWHpujCMAENdjQNdAYHP+ISfJdECNU8yZvwL6HgIbvCzzyPMZ1AWWAySZjdkzAhj4p2WJgGZkkl8l2erczoOfA5DigvkPA3QqzqkTBBhOqQGOc0UeKxUzAhp3AKKAsChBtQWEbGptoFD/ZtSiAJgBNoIp1TNf2YbZ2AGtmBoUpmEeSpcxQpnSKYuyDJxqY3GtW3FdisAEA1jpgMTaNrwJZgjIWysoPrAGbSgKzewCMZR8rkbPuKvePoOHcx4wFKuvAwnhg4etlxGljYyATAw35HTBkwWp8kcC4OJ4Eb9ftA1ZcwFpAydgS/3T3P6DsKY4nNSzEGJHTM4eARdieuawSnJ3POkUMUf4zwIIcsECieQZgJgSzL0Mt+Ireqb45DyyCoSR3q3pgEZ9Ha1sOsKTOQw5zz0oq7a+jN2yBcJ5tNqOdXrxxym5bfAwTUMwmmRHW4utcrGQlyxda4s94Oeecc/A7v/M7+PznP4/Pfe5zeOpTn4qf+qmfwpe//GUAwCte8Qr8j//xP/AXf/EX+MQnPoFbb70Vz3rWs8LxxhhceumlmM/n+NSnPoU/+ZM/wbvf/W685jWvCfvcfPPNuPTSS/GUpzwFN954I17+8pfjRS96ET784Q/vZKKWJnveLSpX5+LA2howIxinnJOBFNrillVy0ehhf38pF//mXlgeaUhF4UyDY/RSf2hcXC8UjXA7kOtvp6DAgxkAbABsJZpKWdAGJKbdg8WNo+9HDHm0eD3Tpxb24jQHuay7oGV8NwwUcwBzB10MC9NUIILm3X6Usy8PtB7+pwrCagBgRZjMLHjGLonQ7s8j9EgKRBUqJZlbNG+DbAmyUtWZoWCxaAaqPpFYjaKwgJ5A3JF8BioL5gSwYYayY1LTtmI1KltbxZW72cbQPswgU7l7UwVlTY60vjagi9XQ0WHW3YYL2GiCVd23XmdaShWs8+Njy4MsRww66noaqpm6umc6mEhqWGTdYKKAeUjwtjFzHD9xG4yZ59kI5085VHW7T0luWMWT2zlbdVt24N7aDRQxFu0ligGYKcHOBoBF8tg6jXHH0u9jW/qAhUGyLocHK32EQA5YBKNPojsQwAlg0Wg0CezQYZMCaFJxqoW8bGxs4IwzzhjcbyUr2Svykz/5k42/3/CGN+Ad73gH/uZv/gbnnHMO3vWud+Hqq6/GU5/6VADAH//xH+P7vu/78Dd/8zf4oR/6IXzkIx/B3//93+OjH/0ozjzzTDzucY/Db//2b+OKK67Aa1/7WkynU7zzne/EBRdcgDe/+c0AgO/7vu/DJz/5SbzlLW/BJZdccq+fs5c9x1y0X3w55gKI1jm3YNqZFEKrZgwTsRrDMrCCFyQwjblhSZcjo2N3olh7JTr8eBMYg3uMqqM7VNGLL345cPRDY+31aYlZjQA4uNnFbiX04VyzyCBYs2lZ7lMQ94PpCcbaUcb0KKPYdG46Ehlb7zvk1wGHDxPjIgvQpoI6SlDfIehtxoQraFhMlEkeAwzdC2kxNEOl11BOpphPJkBhQLQtVmbawqh0PqNFAZiAaAKt92M224e12QFMp/ugVAF5UJ070yLZpxw4IGvqZ94aYTSMYzZyk2ONAxb+AeuOmVh+lFUgawBjJB20AZQdD/yslWtXB4N7TkN+Y888sA1Zl9gKsPAOS6OmA3JvWWfoiFkNZg5tN/fvBxYpKctN3HX3P2Jencifsy+O16f4uwUhZdWPP7ND88aj9KawLqcPd2xHxhWKCahmBDPLFJtgm60nAafI99bvGAIWia/9shEHjMfb893VwCLVLpO4fbXjV0KbLVYi3t5mk2JgAch7ekhWblEruceEl/gD4NixY42f7e3twSEYY/De974XGxsbOHz4MD7/+c+jLEs87WlPC/t87/d+L8477zxcf/31AIDrr78ej370o3HmmWeGfS655BIcO3YssB/XX399ow2/j2/jvpL7LXMBOCDSVircAslT5wxSoVEcabepaENQNkdH+j5HKLghvWV6Y/i0RoG0vBhjnahh5Rqh4Mq4WmedeHsMBUiOFW/di/XqCMM02I/d9BE3TKX7UstbdVesRlSgQ1lAbRKKTbm2ZsLQ64yKCKZSjX2zzRH3ZqAgAFNmlFuFZNYhYDoxdZFAQiiq58sj7FhIo6R9cj9pBm8B2m4DVkPBgvmeYTUUFZhOCwAWvGnAqoKxJs1qtMUDC+YWGFGNfeDT3EqHAFC7VI2uQA6pYxOZftl9x8RQPrajU0zBF5xjqJ6+qMU8sHPD4wWeClEShbGJrcVtdiVmTIjUwgTu5vZRHNu4FcwmOzpWwgioHnzqa/fkVt1BYAFnUMimoq2BRYqRIAvApEvzCbBQ4Em38cAA2NzYOKSiTfUblO8MsPBgKfk4c56BQbbJOkthDlhAJzZ6iQxP8fVmIG+ijL6fFMOqxsmTJ1fgYiX3jCzLiunaOPfccxtf/+Zv/iZe+9rXJg/54he/iMOHD2NrawsHDhzAX/3VX+HCCy/EjTfeiOl0ilNPPbWx/5lnnokjR44AAI4cOdIAFn6739a3z7Fjx7C5uYl9+8ZlTl227Hlw0cdcjHpbaok/AOBcbFrZm3YoFP/mLPUh4NnvsFstGiQWRzHPAXCuAa7dZJ70lgy6fPu2XAxFw8C76/G3muEm+Oh7WS7avnJz7y1wVtdv6IUUq8yOBKAoCbibUADQtgJmgJmoAGhMwhK/KB4gBniuw3GsLKazCgYKxvrEnsu6MIxK7xOlg61zodp2bh4Mw8sEGhqAhtYM8D4UWhChZYPKlC7bTGummEGVuDN1a140TgS1ZsTA3LjK3u7CS+W64SFKwYwGGAlrBRNApi4fwVHKV5e2dyH3Kfa+/8JZWF91281Bqi0PLIZqeTRS6UbAoi6Gh5rBSMzLic1vY2Pz9mz64aCsmv6ka95lqO/+D8aZqO3wzPg1tefYHGNRg4M0sLAAqjUFFHlgQVWPlt/jgjUWWOQky9BE25oAIH6ntY+jhYBFp78MqGtj6Nl0FXOxkvuPfPOb38TBgwfD37NZPu37Ix/5SNx44424++678Zd/+Ze47LLL8IlPfOLeGOZ9KvcLcJFlLhZRsgh1HEKw1nOw2C/cXqJ5BYLehmTOUA7UxIr6Is133sjSgIxfNlrLjRdLr1/BiM79y9or6Uyo09A2KIndCbm2U4rHUlgNBrS7ZRg+Sws7n+HdnwABKJhhNrR4ywHgqYXex7CKQIpR+noXIzKD9fVDVoE3xUatpwY2riZPOaV7B+dICsazGvCxGnOwUsJugGCXCjZEGVFUYFpMAO8yZEtUxoJt5YDFAg8OM5RnOVBnsYIRVoOpZjW67IM3YPQo7izXIVQLj1xQQl3jofuL2bEKTQDRjqeIwYa4VDGYaKEigVBNINGu3dFOp8sAjp08gs3tuwL46Qwfsq4pk7+tPefTG7jtt2WUVw8s8oyEzHVOEW+v7Q0hwMyoF1ioKmKu4kOZJS1sIuV3MJ4qpO+D6JwaY40+k5IBDvF4c9uRARbeCNNreMkBC+pum5dlrpUgK7eoldxjwjTCkjqyHSBkfxoj0+kUD3/4wwEAF110ET772c/iqquuws/8zM9gPp/j6NGjDfbi9ttvx1lnnQUAOOuss/CZz3ym0Z7PJhXv084wdfvtt+PgwYP3GWsB3A9iLnwqWk74VKe+G9cJJK6zkMxQXLh709kNd8WukbMcWoIuCXqboOYASoi5bKSLe9reFp8ACXQsGFwwWEUpaCM3sNF6ZvddCcXOl9rVA4kDBXfLQHqLZRwkyImf3QpBCmnrCtCbkBS0lZ+j5fRCANRcQd1N0N8B6ChhVpbQlQUbguozv2LcJaqnqQBYg1lDshpXIDYgrgH4MubNx2pUaxNUxRSsLUjNoWj7HorVKECYQKt1zCZrmGEdU73fxWqMEAcsYNvuUzVCJgbIGJDP5uTjNWBcDM0izIOo/gFYkHWQwIjCnlqbWGCDjKjPfaqZJSqO8LLDgVgiLWDR2dxIpyvxIsdP3IrNzXHAot8jcwBYoMtYIPo+ZKTLHE8+iUdyO4OMzTIeTEC5T4OLLihnwBXJzK2+7Biq9LhywCKApcyYgfz3bcYi3hIYltx4CC54u9lPDiDE/WW3EUC69SUDZxw6LXMGbjfmFbhYyT0mUQbwXf/sVqy12N7exkUXXYTJZILrrrsubLvppptwyy234PDhwwCAw4cP44tf/CLuuOOOsM+1116LgwcP4sILLwz7xG34fXwb95XsSeaCiAJw8IFi1tpO0Fhf7vdmgyO2+7cdR6AlWIzGaejBetn+ngmuyLZYJTUBk+gt1edgOzRu/6nloPBweOqd4x13JqEd9yNKC8K8LerP7ZtrHBO34affvZBHFTkcIQRxn9LzelrMhBGyp6Yv38J9kAFwQmFiGRNboSoUsM/CKCmuV9nmfTy2S+r8rsP1AADFFSx5rcFguMr12I65ZjWYoTCXDFRMILaw3OdzsRMpoNQc4CmmSsMrUsJqmK67DjNUYDmGAEKUgcopqRL/rOoI2iH2wfpaNXHcR4vViBirUEDPPZyLuE9Zqt2nXCPNeAofeRGPWVHWqp8UZnzn+DdQVr6GRfdIS5ACcLl0rGgq0dnt6N/Oll2K3PR2clmhcudBGTcor1CbfTrpy+XbTdWTCG33PKhy66UgAIYL66G5lHcHlzi+B6ww5NZk3Z1HC7lVcu5OADqPUPg+vGeaG8gCpx0atvKuwMVK7m/y6le/Gj/+4z+O8847D8ePH8fVV1+Nj3/84/jwhz+MQ4cO4YUvfCF++Zd/GQ94wANw8OBB/OIv/iIOHz6MH/qhHwIAPOMZz8CFF16I5z73uXjTm96EI0eO4Morr8TLXvay4Ip1+eWX421vexte9apX4QUveAE+9rGP4c///M9xzTXX3JenvjfBRSyFCxSrqqoDLtZnM5zY3u43QC+q81Bk7YuUXJ/rXl7oPY12tOZ28wSyCihFZQoUgQtGjkzUo8aa/a6oWRg7YaiqPp/dpnCNwUYHaOyq5bgD9wIso9P0MSYtMDMw5fkuuFlh22rnYqAYE21Qmt0/PgqA2iZgG3L3FMBsvYTRSoAGj5+0nFXFnzs7vz8uLNgSFFdAuGuXtBQQwWIWlAziCopLWK2g7RwKFsbOsPgVyYkLZgeg1QTaFSMMsRplCWV9zYtFzsO5QXFd2VtYDJ/m1d1kKfcpwkB/qqWIWoxKLR13A4A7wMKtH27EPlZDHjwXFq5UDZxGuABaNvinY/+Iymz1P0eaxKIfnUL7GRw6H6AGFmHdiLc7BT7jUSTxE027T7Sds/EVgFyu3hoWxgWm57ZzVscfBSxyx+bWsjxjIcavIWABnR4PgGaCkJ5tDWknW4uABQE49eApmQNrWcVcrOQekwEAv1A7C8gdd9yB5z3vebjttttw6NAhPOYxj8GHP/xhPP3pTwcAvOUtb4FSCs9+9rOxvb2NSy65BP/pP/2ncLzWGu9///vx0pe+FIcPH8b+/ftx2WWX4XWve13Y54ILLsA111yDV7ziFbjqqqtwzjnn4A//8A/v0zS0wP0AXCglgZOpoO5poQFxB++ukq2X0I5UndicRATrMpNwSKu4QzU9VvIZgKHAakAxUDitPZMlJQxPSerJXiVCO9evqfQrLk6xn+4SWQ0vLk5jV3Pf7gNoWiwdYwJyVsclnIc2BEUGdq5gSGMyESaACLBMsIsqsI3Bu9uoAnCMUEBqXEzWLOasoWYWSnGH1ei0NbD4Edy8FBoWOljnlRXLvjSxCILtF6YChgrYNQueF9CQWA1YUX2ZNZiHgz3HiyxpEqtRAGYK0gbGljDGZoOQm+KV/fb1bLMacl9x0ACNZDxboAI5wuMpc06wAhrarEbzEDBZMKE3+5SMuKkpEuCycHn2V0l8QGKNMLYUYGF7algQYJmhyrxRJQYOfbfnEGORyrzk9+hL+cpgwOR7DoxFtoaFBxbpwXWeFl+/wrWdu0RDFbs5fVnq4+shRm3sHFikgswbs5ZjMzJZnGOXrAeceih1CnU7zi3qlFOGQchKVrKwLDnmYqy8613v6t2+traGt7/97Xj729+e3ef888/HBz7wgd52nvzkJ+OGG25YaGz3tOxJcBG7RQH5dLRrqfR31Pr0BbJaXy8+KPfpFm4fFM6NKry7g84EEv+DuQvWnjFIW5CWdnOVaxfoADyRHw9eqJQXqzegLwMIBItd/OWSJDTFtTKjKriMQOhPQ6tY5rdHNDi4d9i5c8kDQIVFoY04oigLW409qfw9QQzQJmGfNtgyE1hFmK5VMCAoxSjtEtyNnAXeKqfgs/NJh4WFhkK1VFbDYM0nhhJWAyXIzCVLG6JA66WIhlIG4AkKNUGhK4elDKqykixOHRlA7PXJwM89OYqOoZrs0dClYevSB8f9tVgNMmCmaH1SAiwwDCwaXRGiwO1WcHiUJUo5f5jKzvFPx2+GtWX2NEINC69E5hgF9AMH6ll8PSORr+rtGImMFi4xFtwYS9w2K8BOVS+w0InAbxm3W3cTxzJkKcm5F/m1qX1k6CeH77nenBpvzIK097EE0ALAInQkGDQ9lBQgcWtv/PWhU/rdnba3t2GMWYGLlazkfiJ7Ely0JZeOdt90RPaaFpXLfYv3IhJeDhSU0WYWywH3qSFh5QpsiQsVSICG0rZZGG8349cAa/HOnx8Aii1Al8gWYtpJFwCcYtbdMAhmRgwgYD5fgJkA67J1+eKAi51HV8shAKgUuPJEk4YyFlobkAKMJXBGERzbN0Hm3Z4UNx1LwGRiwAUF0FQtwzJDBC40WDGUrVBxIWl1bcxqNM9lBGGSFKYCBkUoQKlpG6gAbUoQWbDVYF7iEsWFeywLTCeSx4vBMLZCVfmMCouyTz6LQeQ+5ak69iHdTuJf2eWMHgIIzi2LXF+S/Y2aNEDfZR8A1E03KnGDKstNHN+6A9bmM/xYJYaTvhoWfg6yrAfyoMNvj12dAH8ufjs3k1O0j3cxFrm2WZEAi4TCDTCoZOjE4QFYMEAZd6chYAH0XLYcsAAytWw43HI5PGLVALDwWaxb2wDkYyxSgMSPodXWoQHQsLEh8Twrt6iV3BNC3L/WLNLOSsbJ/QZcpJiLA7MFUmPGCzoL1R9b1Gi3ihsB0AT2sRNRoeWlGO6ZwJWGqSTAlZR1zVO/zjS2cw3YNYJdg7NuA3obdXVady47ZTcax0SN+F+XxZqA0QqeZ6dccDaDTHNo/aPwfRTKwlRapl6bEAxMBFSmts4PtSc7JQANAzzXIR6ElYXdDxBJqh7uTVw/UkgGbGJWw1oXw+Bh87KCwoXVoEkF0ASECkrPhUUJqZXzucQXlzpWo1AT6MnUzVsfq9EWf8O05yBmNer92PnpEcbEZaQlAIFQV6M2XlCwKtTDWLRg5Pb8GO4+eSsYOT8gx1g4YBF03U4GJKd8Z7r2SnbueEkRnGYFQvsuhiLLKuTYDhJgwZM0Y0HMQJUAFt4Y0dM2MA5YpLZJHwjTTq3thO734e8+YKGRLTiSK0zfGE/q+wywyIGc7fk82b+XEydOgIiwvr7eu99KVrIj8ffmMtpZySi5X4ALn462LfvXdqiMUPRS80qzbb5Qdsw6eIq5qJWCUGCPo33GtJMVAlstOk0JebHEjrk9lrFRQgQugMrfPYahSkDNkaX7d9ZP88UZG2oX6iNnBYTEUcDI9Ki5Bc98vQMMukiN7ZqNdmBGlKLJpJL4DGJYMwZcDAMasgoTWJSs3XxVteJEwFLcjYjAWsNowLtgaGtgoZz7VEZT2YEwHKvh+lK0BbJz8eMmC1su033KT1WRYDVysRqLsBzeDcnWfy/0koqOi8WzGmLiD7EaTAyCrh/5kbfxie1v48TWHWC22Qxv7K/HDqtuy3Z03Gbax+eK25M/vx5GAj5jVEZptpqkhkXGFSoJLMK4ubMexdtNkWkWzXPm1jYAnRiLeL3rnavW9vg1YjVAipJj7YQVuZMaBBapAfUACwB4+EPOy5yBiM8UlQOTK1nJSvaW7Elw0V6Acm5RpTWNRbWXhh7YFmI0GS5Xfqyr72JBdAt1MGIywNrWrTLy5r9RzTvzpVNQQ46odqr/AWWnV7/VBKsBnrDU7LAARbnuO4eOQQYJUx3F21Ab9JfFaihm0Ek3RwpQMwurlBQ8dAHbuzFcBLu1U4ylDgVDa5+ZCDCJgO2xfTZj94taUWADIskMBWIJ5B4z2N7ton3aicRlVFyAjIHimtVYbgaqfSFwlFBCaQMyFchVa7snWY1ClZCIEINyPgfDYEfuU+30tP770Ftq0o37fjiFbgD2gcqo44PEpuHaSKwnd2/dhpPbd2XdjBgsdQ1L+OS22f36A7fFratXWc7VoPDH92zvTUULwBYETDL3P4uRJCf99TMAWyC7VvcFWQMYfzu1JlbW9+5sM+SdlXPbSjIWPS/LRg8pYOE+U+dIBJw6EHOxsbGB9fX1FbhYyT0j91FA93ez7LkieinJBXRf+D1nSwE8jRCQ3GbHxt8qkYarydcqk3zhFJex2iVv5nWMiSt+pxn+X+MlMgIQpJuX0lhkVMhClZyYHUjIzqQBuAxUPBHrWTz/uxZn4SMljA9FLmY7bj96qQkTAKl8vQHgOKC2LSaoYKvhoneLdKkLC2M1jNEwllBoA60sJkW66nyfpFLRyiXRABeusF7hAnGNsBsY4/4zQojARQEzKWAmGpUuIKmvfD/LE8YEVmswTQRUEKD0NpSaQ+kthDSybu/dywSEAgozTCfrWJsewGy6D0VRjKyI7bNGpJRar+X5BzqmMEdUBY+kaVl2zznLj2IFhjAwlg3YSrE9ZsZ3Tt7SDyyIwfsYKPsgQ8Q4ZPfpV86Hir71AYeYscgr/2lg4Y9VZf5Yh9WyBvscsIjZh9w2KHSYor7jGsfmduoDFhr9t1Su0xTGHQAW/sB//Id/wA033IBvfOMbOHHiRKfA7TLS0F5zzTW4+OKLsW/fPpx22ml45jOf2dh+yy234NJLL8X6+jrOOOMMvPKVr+zoDR//+Mfx+Mc/HrPZDA9/+MPx7ne/u9PP29/+djzkIQ/B2toaLr744k4F5ZWsZCV7lLloS465OLS2Jr8QfD2xpiIdFNIxwdUJ+7hnHdyfVrmXYxSH0Gl3hK7jC2D5cUPLS5kZUfG7MQ0NbY6spX7ftqt1iz1YSHzzKpp7x2gMvTzHtV9XQYatr5AHOd7IMFa9zBnNCABKAkpAkwWTwmRawpICKYYxCrxDnE6NXxQqp1saq6C1pIfdNhpUeAeInUu4lIphlXOfMtZV795ZvYukIYcI0IDRdayGshaKGBTci5YXq2F5LRpQCVKlY2tYrtsSpXafcqyGLiXugQ2qKhWrsYj7lH9gPDPio2yHuTlv2uhjOcmxJvL8WVhrcWzzCLbKY/l2FQNrDNpy61Ii8Nd/0R9PMILRGFF1Oyc0BCwmBNYpzkX6VVX+2KEFpA9YyHrUc+2G0r9mxkTtL6JfuWc8vfUsE983AWtmYy+wkG1P+pF/CQ3GXXfdhZtvvhlFUeABD3gAvvKVr+CJT3wiTpw4gf379++YuXjf+96HF7/4xXjjG9+Ipz71qaiqCl/60pfCdmMMLr30Upx11ln41Kc+hdtuuw3Pe97zMJlM8MY3vhEAcPPNN+PSSy/F5Zdfjve85z247rrr8KIXvQhnn312qBnwZ3/2Z/jlX/5lvPOd78TFF1+Mt771rbjkkktw00034YwzztjR2FdyL8iyLJtLsY5+dwhx24SwB8QY07A4fOlLX8JsNsMjHvGIxn5Xf/bz+K0PfCjfUEAF9RTkc7UPAxBbcB04xw5k+Iq1DmhUMx6EdLaw4OnAZVFW6l34uICUkzQDtDWcn1afzJx3pNcUJwA9pKhZhp4PAwaOctK3SQB/LAOjdLLJiby1lCHTorcNFEswPdl8o7N5CVP1dyqFy+p9GICaWAnWd4M3VmOCCsYMKNDMmCqDsurfj6cWVVFATxjMBFLcCAr3Uu23AhoGxKT2c9c61LuwEkMzeBEsYzIfTldLhqFLpyBahrZG+mGLOPicqgpq4BoAAJVlrwuLnJMFmRJkrLBNZGHNBGlgM7Zied9+QqExGGU1B7MZZQOIhRzr0q2VEb8dVWfLELBoi2WD72zcjLLaluPbCq43bMwY2ATIrTPtAHEmp9S3A7pJ3J/8MxGK21Fz38A4tFYgPyaWwiHNdLXU3E+qbjf7txBmkwGYCYG0uDQ2+3aApgVKPIwPjIXvp9UvE2Cn1NgQAIUfbiIjnT+2AyxaRhefFAytYztpeW0N3LiQOUsyLDvI8RDujR4KZahJRYSvfPSvMXXZG621OHr0KO6880783M/9HL761a/iggsuABHhj/7oj/BDP/RDmEzG176pqgoPechD8Fu/9Vt44QtfmNzngx/8IH7iJ34Ct956K84880wAwDvf+U5cccUVuPPOOzGdTnHFFVfgmmuuaYCSn/3Zn8XRo0fxoQ+JHnHxxRfjCU94At72treFczn33HPxi7/4i/jVX/3V0WNeyb0jx44dw6FDh3Dum38bat/a8AEDYje38M1f+Q3cfffdOHhwuOr8d7Pcr92iDg3dTN7CHaUl9Q5Ou0ZcBHEJ2geYfYBZkwrPAWn0aR2jOieQZtDMAlMLFAYhKnxZcDF6AVq1xLnxbZO88Gr3suUZGHwXisWqqbYt9JajTmDdG7rd04ieW6Z6AsClAm8ReJNgt4AJVTBGodBR2fNccyPPg5hg5wq2JJhthQlVIBgofz5jG+vrRAG2KGAnGqZQ4j61bNcmIkArmMlE3KcmE4kxIp/Ca4lMAzlGiQtYO4W1CqQrkJqLG5WOMtgspVsNYTUmmOgZZpP9mE3XMZ3OoGhMnIt1LEhqX/8w+iXbwqemHcNsxGJshbtOfA1ltSXsTqo3hgCLrRpYJHfq6dor5EOMBjJNeMaiL16BXPxbZ2iQW8kDi+6xLnajh+3IKc4eHJgWsGieVxdw+W1WdRX9DnZKsBkpENjYnmAs/HE7ARZBdgEsvEyjtPBKKTzgAQ/AIx/5SHzuc5/DF77wBTzpSU/CxsYGnvWsZ+FBD3oQ/s2/+Td45zvfiZtvvnmw7b/927/Ft771LSil8C/+xb/A2WefjR//8R9vgITrr78ej370owOwAIBLLrkEx44dw5e//OWwz9Oe9rRG25dccgmuv/56AMB8PsfnP//5xj5KKTztaU8L+6xkJSsRuV+Ai5xb1Gnr6+PXU4L4qCphALj1b5TkdnNWQLsG2InQ6F5ZXwYYIAKoAGjKAjSmDmiEgM4lqOsK4IJCDIuNgECDOt+pON0pBhrA8NSMxGHy0raA3gL0FkNtWrFow0Ar+eFRim1sl0ycAhPsloJmg2quUVDdvlY7jG+IxhUcZ0oNqhS4UpIeFhWYCWpZIEABpK2AjULD6AKgEjXQ2EncSWveSJ43W0xgphOYaQGrSdghssCSYzXAU7CZge3UMRgWSs1Bau7meDmxNABcKlKNOlZjDWvT9TpWo7P05qqC50SyjXm3QEUGBAuq05IlpTLbuOvE11CZEuGBbd32k0mFyaSE2mRQJpsZO2CRjbHw2nkuRsIZWfIxGhwYiyywMD2sJQF2ojLAAknGor1TFlioLmMRC6EHWAj+zHVZN4BE/0kXKkkanAMWQAZYjLXiZI7ry/bVFjvw/jnvvPPwsIc9DD/4gz+I22+/HR/72MfwhCc8Af/1v/5XPOUpT+nEZ7TlH//xHwEAr33ta3HllVfi/e9/P0477TQ8+clPxl133QUAOHLkSANYAAh/HzlypHefY8eOYXNzE9/+9rdhjEnu49tYyT9T4SX+rGSU7MmYi/bCXRRFKMITywP2r+/sXoip+7BC84L2wYz4Rgp3rzKE1nYvYVm0XQXyvs4yJ0Ze+5w6HqYksKFAmAiv3mo4ry8394l/j2NYfAzqyMkenMNo6v2+3N08XhKVp4idrly6LZrBWww9NVL92uQfDSIeBCIBBDiXJwZAykqVaCgQMapqXErSvncrAaKxWKCYGxhbQOsKVhEoZIba4V0b/EHg2CsfQwGpdeFcm3Yab9Ltj+S5sNopFAqKqxbSXFYKWgVrYmZTXJqUcn6MTLB2gTo5gxJnoCpQzMRlynIFU5m+WnBpIeMuj8x9zHYoMgiOPf76EDAvN3D35jdhbB7kzmYVCm2xcXKC1H0jVnV2MRA9wMO7KiV2EWDCoXZQ+/FkQKpyU+Z4SJ2dLONAgJ1RsrZD2xWqPe3ekJRr2yqAJ/2MRW6bdZXps8e1Om58H8f8t7erbp+9wGKspIDFDppTanh98KlolVK46KKLcNFFF+HYsWP4X//rf/Ue/5WvfAXWysvz13/91/HsZz8bAPDHf/zHOOecc/AXf/EX+Pf//t8vOOKV3O9klS3qXpc9CS7akg3ons0arMOOUsZS61cG2kzGrlPROtcgZ6yTGBCvrAeePCFjLU+FG7Nv09TnMvZVkX2mYqBhCca44lo9L6KdgLTUe250GwPzRABgCBNTwhxTsIowXZvDaA2K0tDG+y+sCwKAVTAWQX1R2qAyCoU2vVW8xxJPBAnFgdVSHoIArQ1YezYOWKTehYR4pzuyWouiRAxr2cVqKBCaMRQ7FqcsWaqDwmFZ3MDuEQuSBmBhbQHASgE/NQ/KtDUFlgdsCL5auIKCxL2Li5Mxrq5GH4uSjctA4nuGIoPN+Qkc37pzAFiUIGJsnJzAhTokGnf/tdi0sNkBh+xD4iwquaKksk7ZLCvAbEG2ueI2DBAE2FmmhgX6GYtGrEVCcsCiyTikgQVHwCJlNGEkhuw3qsQB/s9McTz4PnNtDsmSgAUA6JHgop0t6ld+5Vfw/Oc/v/e4hz70objtttsAABdeeGH4fjab4aEPfShuueUWAMBZZ53Vyep0++23h23+038X73Pw4EHs27cPWmtorZP7+DZWspKViNxvwEUq5uL0gweBdYK1DKoAjhkCxC/HMUtmzVWH/SOgwa0X7o4kAAmC3lRgxfJTRCxGVA9jRxo6AVB1/ntUDBYNOmtpXKh9JRllpP3aShjvsgzx7dgCIQOVP6ndgD0BAQx7EiAYCaqcAMVMgcmXQdvdWQQ9wWgU2qAyGkRW6l0wdlbFO9UPA6i03PsAoCxUUaLiCTRXMNT/+I/SQxQBmiUzlGcbjImes8UATVaIpJ4KFMAMpRkwzmkewGIuRUNSsxrCOM1BugSxgbUFSJVgOz7gtE9EoZRYCgJQ6AJFIawGc4WqMq0MVIu6TxE2tu/Csc07kOdeGWtrcpNsbU3Dd6n9ZNFr9k2RWu4ZDU74KwVWoA9YWCugNjlM5yaVZDMgMUPTNLBgyDrUCYaO+u6z4VgN8LR93i1gQdRV/uGmK8NYyP0V/dE6n9B26zi4dttB3/G2fw7AAsCoDFAbGxs4dOhQ47vTTz8dp59++uCxF110EWazGW666Sb88A//MACgLEt8/etfx/nnnw8AOHz4MN7whjfgjjvuCFmdrr32Whw8eDCAksOHD+MDH/hAo+1rr70Whw8fBiBxIxdddBGuu+66kObWWovrrrsOv/ALvzA4zpXcd0KMHhfMxdpZyTjZk+Ai5RaVYi4KbzEhgCMDKAxqf92wcPYvgMlsURHQICZ57/uaV1Tv0zrEJ0cZEFdsyleQRgQ0YoCxGyEAE4ArBpQCx4Xv2mNUmTnobZ9CwHbIQR8ZNZcBNNgV8PMVo8kCyvuYsLsuRGDYwbGnthIDmAOYOyVWE7CmoAsDUkBlhrNx9Y7ff7JCfAsXRQW2onUYOFprB+OPt5FVwFxhsmFgtUYxKWEL8d1n6qbTHcvQBCu3ZxuKwp+UywxVYanhXc5dhpUzBwvdB3IF5wgWzMtb2ti5R8lcWLBVAjgEicKaKZZ3fjWrQVRgOhF3LcsWxhhYW3WY0z45sX0bNub/FEwgqbtkfb2CMcD2dh4wiSsgksfLdiuubD7dbWs792mo3khj089orfjnn2FWTvnvARY5GQQWhYvfSGwDUAOL1LFu/UsxLYADFonjPH5MKTMBeKRABw0Di97nOjMRu7I5jQAXJ0+exPd8z/fsqP2DBw/i8ssvx2/+5m/i3HPPxfnnn4/f/d3fBQD89E//NADgGc94Bi688EI897nPxZve9CYcOXIEV155JV72spdhNpMCnJdffjne9ra34VWvehVe8IIX4GMf+xj+/M//HNdcc03o65d/+Zdx2WWX4Qd+4Afwgz/4g3jrW9+KjY0N/Lt/9+92NPaV3EuyLLZ7BS5Gy54EF23JuUUBiUWRIG5ChdNJGKBtHg0ysuIsSP4lFr8gckCjr622GkAgydoyd0CDWF74M8fx79YTxc+LGzwzQi0yGvtg9mq3FNyngp3TNl+euwIbQeEUFbNmTYS1Ukr665OhUyQIcNHWoNrUsAQUkwrQqpGGdqGTyXRaVfWjSUqyCJG7uXbLBAjQALBdQG+7a1JYYFpJ6k8lsRq7Xkcd22C0uEuBIUCDKap5sCRWg7TMCzMYGhJ0To7YGAtlx+yjQFSB7TSwGkrN4eNomBXYTkYDs+G9fKwGY1IUAE16WI2oVWYc2/4mtspjsJn7XimLYmIw3yZUo2OMEoxEWB9qTVkRw0Z5aCkAk7S2TCmLC1ENSiwnL49X4NECFr41DyxSPffFV/g2zARAkQ4KDwdngIUp8uABaG7zcSfhvaFbOwPB22xhYOF3oIE7PMOQ7NYINAZc+DoXO5Xf/d3fRVEUeO5zn4vNzU1cfPHF+NjHPobTTjsNgOgI73//+/HSl74Uhw8fxv79+3HZZZfhda97XWjjggsuwDXXXINXvOIVuOqqq3DOOefgD//wD0ONCwD4mZ/5Gdx55514zWtegyNHjuBxj3scPvShD3WCvFeyku92uV+Ai1wqWsAtbDmndccw8AQgQ2DLgVbY9YIKpzjHLuIeAOxSnyKQsBrHxJrGimHXDHiflYJ7ccbKHQ8+GCG9YRiaRGmhsNMu2gfSQeGtXYbbYiR9KAJrQrBkQVphYgwwBxQYttpFILJDRMQAz11ALQBoluJ6UDDV0Jt8ke4UlGEwFJgslK6EQyKGXYKFngBQpQDnPsUK0IUBGwWlDCw8Ss7IGC1aCWgxqqYQKcRq1AX8dlso0Ct67GI1SBunmAq4EUV5uaxGYJ9c9ikoBlsNUnOwLbAMVoOIBO3HrMbUODDVjNVgtji6+XXMq41awW+J1hbr63OcODHNXD53jxNawKLZHrl5zT1L3eQHcRv9STKC8p+tHO7WqIlOKvhybNq2M3TLjgEW7Zod8XY7qYeU7DdT6brhQpXCYbllZSRjEf/OqQ2tfZYhY9o5efLkrsDFZDLB7/3e7+H3fu/3svucf/75Hbentjz5yU/GDTfc0LvPL/zCL6zcoFZyv5IXvehF+Pmf/3k8+clPXlqbexJctBf0hZiL3E4EkHZ7MyROY1HTbTCVJZsHjCvuZBBSujZ32JmQJeiTBXASYGLw1ILXrVijRxhtU8WaOicwkVodAKQwoEnHr+zsBNAEGkCjkvegtW3oOikCTYAtPQWmkMJvlUFRWeeqxcMF71pdJr8zBLspQdDF1Er9BkUgAqxtBoUvKkFBYhUytDIApSt4p+1yF+3H/QiroaGtgWUtQeFFBV+0wO5w2WgqNAR2rIZ3adO2ckUhval9Ca5G5ICRV94YcgOzioDpEmM1bJ2Bii1JtXAHBD2rsTThKANVFKtxYvPb2K42EhnN5O+iMFhfL3Hy5MQlEehGOAtThkQbYQ85J0tQ1HXU4jDffbwAXPxFxveHOOvOxASXGjttqiewO6208g/0B28PMhYKyX4tQd6qGZbFeWl2UvQG4ie3DHF+qUsW5IsHm5leID3OZcsYNz6fLWolK7knJBh7l9DO/VHuvPNO/NiP/RhOP/10/OzP/ix+/ud/Ho997GN31eb9ps5FVVWD+bCzQq0F0AONgsRc70CH6ECLeDx3RYGgLEGXhGKLoLcBmiPUdtstF01MUNsa+jsT6DunmHxHQ2+Qr4WWfjuN6Ssy//FEanaYfYCZAVaJm9ZSSux51sTHarSK6+249fgcFaGcFthcn2Jz/wRbMw21ZlGShp4M16IYsq4TxC2EKw2eK5htJaliXa0LKa63qDTPvAasGqgIXCmo0kJZA2KznHoXTqEho6G2C9CWFlcqU9Z9DPma9ZxDEFfvwhQTmBnBFtpZYaWwHlHiXBaoRt0dhmMTfIq2zsM3JCP7Jgu2vq5GAYJxBfykiN8y62rIQ1OAUGBtekpCqZdzm0xqYFFlKsMHNqJdMJJqbdUDi/RMcDbzmbBHwoV5xrFTyM+luh0GFj2aeBJY1GtUH+SpCgwDC6JuxW4gm/o1AIu+GItc0HeGfWiOJzPYtDdZt7F7UE4bUcl4BS5WspL7Tv77f//vuO222/Abv/Eb+OxnP4vHP/7xeNSjHoU3vvGN+PrXv76jNvcsuIjZi8IFkNqEc3HFFmMK4WWt716L02I3IqZgaV2KLs0EbQjFNkFvAWrbMQO+St1u2obEaRSbGtOjBaZHNfQJ8rXQdq/buBdiABprQDVrFtjbtXirnAMaEnwsP5Hj/gjJK7Z2WmBzbYrq9AIbh9aBgwSaMfTEglR3kjIJWjvDbvxuXIXtuYLZ1pioCposjPWpYnYnBKBghtpWUFsKtK2bIGAJiixBUt3SfAK9pUFbBVTJsFZDwcBFuwy0MKYjAusCtpjAFoW4UpEvDOnOZSk3FyA3Vri56vYph8THS4tfhbVrDmhMpeChKiGKegmicsH2+iQ3bskKtbExbQCLWNX2cT157NYPLIisq7XDmUa8oSbdAZEVYJExM0pxvAhYNJpxq07uVvdgOb8ZZkJAovCe77tRU4IbH7ATdN6oweOzB1j0usq2wEGDjOhjLNzymNl0r5pg9+1b693OzDh58iROOeWUe2lEK/muE1/nYhk/91M57bTT8JKXvAQf//jH8Y1vfAPPf/7z8ad/+qd4+MMfvqP29qRbVFu0lpXZGBN+9zJb09gsS9FHIo23myl9hP9Q2LvtQ9BqaociYACi+DtvF59tJLS9m/aZUGxrYFtseLZg2DX3MrYYhppDfSthNcwE8nKrAFUiBITv+rEkNF7s8pKsGxe9KKe0jGlfgScKm5M1YD/EfWq7xGS7AipAw6Isdxfo7Idh5s6lxdU4iCVZv6DPhyPVhwUwL0KeKV2YkNK4YrUU5ULwnUJhKhguJNWtlqBwSrpPjQFlrYG5QH3biNUwzpprnbvRMupPuOffx2MwgBgsEcKDOPb69+3XiNVgVyk8aIPsMlAt90U2m1XY2JjA2vhBr9c9zyhYqzKuTnITdoEFh+MBEpcq1bS41/uxy4LWFWFMGGAXHN85nGAnBOjU9XZ7Z4K3a7W6fX9ZgCUhg+kpjscAKFmUT5q0BfLupQOMRWqbRyWpO6ABLDKDzR7nj70XZf++fYP77DageyUrWclypCxLfO5zn8OnP/1pfP3rX99xsoL7BbhQSoGIUFUVptNmVd1CqcgyxGHRZq5fBJ4oX8qa23657KRRcnq0JWgLqSJNLEWcMv68i3dB0BVBnwC4YokLKIBQlLhtEaNF4JfbPwIasAI0lBR1htot2PAYo2BX3QpQ2sj71l2DkLkp+q6/ydZOilDum6LcNwWYoUqLyXyOsipQTAxM2ZeGdpwK2gjKduiXqE6pWgdsD7eXY+cIaNS7KCoLrBlYpUJmqN3eVHK/qhonEaB1JUW+yCumy7hxhdXgaQm2Sp5jksxQBA80dtJP6u6O7x/HmvjiAiOehtF4kLgRq0Fq22WgcrEaVu8qCF0AmARW217FHmDuplz129lZ7lLblbJgENh6BZ3Txyc0YgLgA+7bNTTic7ATNRJYtFdzTiEdt4k6wIKiPNxhSLmK3G7dzLpCFcjfBH3AIkH9NsBBH7DouzXbNrV7QU45MAwaVm5RK7lHZVmuFEtjzP/5yf/8n/8TV199Nd73vvfBWotnPetZeP/734+nPvWpO2pvz4ILIgoxFkSUDOq+9dZbQe1A7whohPdNJRt66zhQ+G/E4Nwntz53yTpocUEXJy/vTeNfMrtwcCNFUJWr7jx3QEYL0AgZSHbzInJzbrXTPecQZqaqAd5Om6foFwNd25qZMS0qUaaYnfvR2MZS2wh2qrE92YdCVzg5n2BmSqj5zoLCo2E2+pD7UMbMPu0pK6f0Wew4w5XvAoC2DD6poQCwIqhJJYqbZ36hd3evAnJvVrrmBJlFP1cGUnVkCUuPTz8MYTUkk5m7A9rP3q5FRYpv5DIV9OhdsCdtJdLOouFXUKpy4KBwAGdo7uoGfbYmpbjX1amr2LcZDXbpZbv3n2XlgF2aEYtdrVI6vrUCpHPAAophJgVIJ45n919P/Ic3HbXBN7sV3+YYC2JJzawpGYDNnujqAxYZhNlmLEITEWORvXUHgEUWW9zLjIWX0w72uzsx8wpcrOSelRW46JXv+Z7vwV133YUf+7Efwx/8wR/gJ3/yJ0P9l53KngUXbYnT0Vpr8X/+z//BbbfdhgNr+3Bs43j6IL/YasBOrRSsi+I6l5IFyX8u6+YGavepCvAcuPWu476/3QKZCtCVvICtdtY5192uX1KKwBNhNkI9ikqMl7HONq4p7rpYEwAizFE4K59YLjUZuRS7zNz0/2fv36Muy6r6bvwz19r7PJfqqr5XddvdNi2igKBEROyflxBF+udoiLxeoj99ReJtoI0RiKIRowxMJMMxFM1AxBEvaCIjwUSjAQS5SSSi5EWUixENUdEXi3tfqup5nnP2WvP3x1prX9c+5zznnK6uKs8cNFV1zt5rrb3PvszvnN/5nQBY4SipT6liZo6dKtCnjCwJZuZZklSlDI5d5fEim5eh9SBHFhP7XfjU78LIqLN47DkgZJbU46tAK6vpU7BY6nZszMGHgkorC6SK+FRD4Zdwyo8xeaJiRXpWo0C1wXkAKPD1zRfHloou9zcfze+rPc19/AzuhwZY1BmNXP1R9GaH1LTwoAgZCwj9QTzDX05BXMg+5swoviyClHEuG+JjNkXanyf52wgsMjxpRUPAoGQILLQHLIC+5LWKolYWA4vMd2SwjMYp2lSo7DW+CoVqdLCLY7MRmfhkBwcHqOoWXGxtaw+RvfCFL+Rrv/ZrueaaazY25hUDLlLm4vDwkHe9612oKnfeeSenPvQXfGgMXLRNiE3kYuinJyAjTQx2tQX2svSd9+RaUWIBDUCACAZqpaWNOIYhY2Kc4iZx3CRBu4GMTKeLN0BqeleFaHc47eMTZMFF9wDACNZ6Zi50TrTGY9WhPuyfGoitfBipKHwSxhenCJ5iplg83sm4es6yU+ARX0StS1ARjJ2hYiLQiHSdNTMOttXvQsRBqXgbZFv9ug1UWlQ4jVkeD2BcTaupva91TaIHVwq4lHWIjfXqa2pzErRNUxhTAw2tb5YN62Z0wIsbABufkZGVTD+Y5PiPybUmOpVoq5dLZ/+Q/c1mFBKlKwKLnAXKl2JkOLYqYBU/KVqeuNbhfhM7ghsZKlPVMETbmejm74qCEXwxBxxYiYCm9SGE+0Baz6vcvG1g0TowbW/T3zcDENpvm/B7ZPZdEVhsIka0rH3qLTfP/f78+fMAW3CxtQfNkv7LJsa5Eu3bv/3bNz7mZQsucr0u7r33Xt797ndz+vRpHvWoR2Gt5arJfKWK/OB0gYYCs5TW31B1RnuIfkBvTbAhLfpUCBMriUKyOjYSavmRFLSNLzY45k3X8vOG3wk6IQCZaajRkKjOJaLjEc4F1rxoBSeRPmXCuDvG4b0wrQxmqdfunO9F0EIwE+WAMhSFzyrKmQ/HrIFDv44NaUeCGIf3FilD2/N5XbznUi5a2xhVdGpjUbgg1kPpQ0R3jX4Xg2PxFk0tWZwHdbXiUHAcN+Wct4u1oyKUSnTKj/GbLDp5tfMfamcWZjWW+UFGzTb7RgA1qw6Gakz1RRP/KU3X96xyk6ZnnekMUfvYqQO3REpTf/d6qhFggUYV49xDSVEjPWBRH0QNitSbCEr7c9d3xXBe8aiYCCyGJz6ww3rAIj4TAh1VUZN6pPTWDCHTcVxnfgmAsCqwyO13sf2j09ddN/f7c+fOYa1ld3eFd/XWtraMbWlRF90uW3DRNlVlNpvxgQ98gM/6rM/i1ltvrb87nE3jRow/3ef5kvHdpzuKM4TO2JX2KDxrgo0+1Vlbf99AVkNcExDuBGzXABv1/m2gQeh5YYPCZiwsXC+UrlZiRDtEXq1RjNeYcZhXUL3M+MEROKIAA6VxMAvjJ0DjVgQCjW8lzHZKZjuAKqZSyukUMxNEUzH1epYc9FIq3EGBGsVMZviU1ejd5qs8HwUQZ5qO80YoyhkOg182Qr/AiZb4f6KhQL/OnrQKczajDAXtxnrBKXfhM/Gby2qIBtAyyGr0sidL/yDLXOsFk3LIlZX6/zXTNbu3bcpYjMzXBhYwntHor7cOzkQp28aaB52IR41Bd/L3dspY4EPheT9j0qzdZD4P1623bWDRzKGisZN83l1Xq2hWplbHG9klelhmr/rrBSrR2UdozK6ne2bZ/TaVGDyOXb2g5iJ15zbmslXG39rWttazyx5czGYz3vOe93B0dMTDHvawDrAAeMyZW3jvR86Gf4xRkWRJgJCAxiSl3aOv4NrdvNeEGnHnGry0AcB6fnrj8CdhlgQONoHG49q0hGpPwYGZBoWozQCN4JA5BGcAVQrjmE4FKz7WOazpeFplRsPZN06ZqENd6EKcegMs06sxu4mE4tGjMkToxCmoZzL1gQ6mazrPMbEkXuAwulci2HIWnCIJReKLmgAusjAHcGQpQroh+M9Ga+bNvMzJceYJznk6nz4UNyMx6r1Jcke6uSNQiog8OKX9x+Qac2ovexJBTSjc38zjONdMVFWYTmUALLpF1g3wyBVf101EW8Cit0VNx2oARuvbROnz+fMn4lFrQvF2podNPd6o4lUsXhfFD/YV1BApfglqaX0viGitbDY8qogP5wILYZi+zT8opP31SPF2OtZ5wGKsQ84Y4KiBxVqZsuOZANcsABdbGdqtPei2zVxcdLuswcUDDzzAu971Lvb397nhhhsoy3KwzTW7e3SftNr5o/lYj+/8SnCmKePLwCWHsYkTrmN1S4f6pcrGGCJCfBdWNPSmflZjFUtrteD3wO9FStM0gA2pUdmaaRMRKgrKnRnVrMCopzAO9REI9JSblgEEXQZG4GQfYqAImaqJrdCZ4LyJ1IzxH2OuHGRakxWYCAf7E/CKmTp2In1K1KP+eLfnIIKcPpyamj6F9YgVZBIyApugNhkU74q6LYSKxxSpYHuD9ClMTZ8y6kB95zJ6MLIaqvSyDZuMrjbqUwFYJBpVeoasOlf3ShDxOJcc/rExF2U0WkpRuWrken/T+rANYpKUbWZkBTEhY+HKRIXqHUOiUS0ANTkT8ThjIsDuDAoaZbhNAEwivgu+pUV3yszbAIvhd+mP4Z4pzZsPRM19VsVn9bwrI1d61QEWF9NEFqpFnT9/fgsutvag2rbm4uLbZQsuzp49yx//8R/zsIc9jE//9E/nfe9730CKFuDUQE4rDzSU5RzCURNCnUbrvahdRsexrL2Uel9p1goxerwoqyEMazoym5iI7DWm6Adp/nVeSgK6A25H66ibXgBTaeumz0ywVIQtvEm9GKatfSZlhbrQDMxVyy1+dKpYdH5ECQUY70KRu3excR8DGVo97rVkBL9bcLAbdhYH5dEUOxXU62h/guOYAOKEoqrwhxa1YHameGtjv4v16xsCaDUwaz6wpsKrRDldt3ZWQ4iJrBZ9CjxiUsF2Ltuwhg2yDQK46NBuCtBAwzGEGKmYkz2Zu+D6b0kGtihCw8HZbH7Uf2w8iUg1J4XcNM2b00NjzhyKQCE426+xiPvjg4yxMMi61Y392nO3nhshGyKjMrYhK5HPxIT6DDCWQAPr774IWPgArjMHPAAW0qaGkc/JhczmuGBULmPRSqhffGAR7dqrT839PoGLfh3l1ra2tcvXLltwsbu7y+Me9zhuvPFGIBR0VxnJu5C5GDOp/8iqJK4DNCAWhRNeYi2gsc7Q7X0lKWxCow614ktkwMBKIIBeEHVR9mQRGJDgq1Unw1tSHJgjxR4J4iU23F4+8zNwGOJBTCnC1a1QlI5qaihMAJ9j9KllT5tYZSZNsYk4mFgHNX3K4L0s56dnvQhBC5gWO6FTuPMUR45yGuYIUdwNOOgOuGCwMQLrS4dO/Cg9ZJU5Api0sWmiQ5FIbQp9OzaZ1Uh0P6CmT4VsyibpU4ZwE9hAP+oUa68o0ZYF0U2htiawQawNEeZmNpr+Pz7+2wTglQmJe5+c81ysW2r5WK+CmCFVKfRekblZg7SGXPE1EtXIivx1EK719GTyve9ilqHfia4FLLwxeGMwg8KGCKZ7DfLq519ShTIGQzdoJeKbmpO5wGLsu7GrZE7WOwZ/VgEWGxVGO6apKtecWg5cbG1rD5rFBqAbGWdrS9llCy6uvfbaDpgoioKjo6PBdtfs7R+DYioNGkh/bChy3wYapGVr/qVQ77MMlSft3iva3hR7I0SiqWsoNEUA05rz8viLzYRB1ILbF9w+kRqkFEcSOnlXi+tX/KItBCqxTErHzFlEPWUR1KGM+FqGFhbQEUbHD0DgiAKK4HwV4mKmwcefcc6Pscz5MoLfg4O9HVDFTD2TaYVUEpvTrVnYTviN7VRCE0U0dGsvBSldLArfQBdvIVyYPkmbmhoEiOSjw8eeI/5Zg6+I6CW2hhdRfBaYrfTjt7IaqSi8/d1yx7PcrZ6KoxLoGO+todDpL9HM0V2PMT6Ci/nAoCneHtKtdI78cRtYZL83irMGLc2AbhAkcvuF38Oxc8BR4/97G4DFYF+C4pmYkUwMAVhQ79sCHxKQgbE6fr3OAxZuTjZjkFWX5u8jndHrvaS7Vw0sxtDIRbTrrlkMLrYytFt7UC2C842Ms7Wl7LIFFzkp2hwt6rq9/RWuh9bYyeNUus2Y1slqtIev/6/18dIh9Mw/E81orGh7Df9NkFZGIyAjbTfWXR7F5c2A3xWmu2EcWzl2LyhyZLAKVTUMwR0LEAhomz6FoSyq4AwIVNUGGt8JVJOYMkEQ72N9QHCWVm58l86tCH7HcrgTHGRxSnE0o5iGDuFG1y/MEULfFKsOP7UggkyqwFk3oQh2FWpTu0dyfRn6onVornYyjbiNNAk0sd+FqgGNfRlMkqBNzuumwrrtc5JSoSm9OA7Olr+E2/vnemuE773P95foNpoL4MMYjQCjtyZtAb4RylB6mGWLt3VY49HZxiiuNKgtqLmYnQGWy4aI8dm5nZEWOBju6yVkGfsn38fC75xilIk0KS/DTIiQakoWAYucJc+n935JL4carDTbtLMS2RIY4uWx7jN5TbPGLFSB2hZ0b21rV55dtuCib+0O3W27qpywlGM9+l3ri/b7xOQ3OZb1siKa3jEaXxhesy/2Yw2f3p/9oFhm/uONLT0g05JjTOdpHZ9NgIlyvoxOdAU7h47iQDE+zOG8XT1NGXebRRlaNDgPhqA8JcKgKPzYEwiojT01orSt1SrIaEqoozh2bUbP1Aqz/QmzfcBXmKlhclQhLmU1NtNTQ45StkHQQtEydPEmS6lZdR6LEJSzfCeroVHu9vjzdE5vkk5NoMUriEdw8RiWBWZLpZsaBah2A7/aNlmrQZ25CJSoinnH0XHOZejYp4yEx2QPtd4/ft9Xp0r7jxaPi+IKCzZFPXIPpyxfMDj4ce35jK/iRUaUnULWwZEUo/rrVrzRLCgBH3qq9ilYhOdGXFRmzdGywCIFrjL7DIBFZj8J90Y2c5NLZqZNLybgWOL9tc1cbO3Btm1B98W3KwZcjGUubj55iuKC4I2iZSuas7JPGneMDnX8cC0HsTV4PU7ym0O3Z7JSrlmfZd7YfdvgjVJnNabgE9CYR586pmkBh1dZuArwUBwqu4cV/tBgq/nKTUuZgBQh+u8iwJvYCq9mjZ4arRNQS+mWMZOkGAnFzRLpNHOzAcv8Vkbwu5bD3cBdkwomR7Oo0rWZKL0AUkndxdsbQUtBbKJPbaYoHKQGRp6YbajP5fKX7txfTEJaL4wVxg+1CVG2dwN0s8ZGumrTTv2tbyIyWhgbBPEWdc32rQLtwbekjMwY1a/u2m1GKE2iuEkCFoMVRipUVGPLrB8TAXMmzSuiOGMi3alrISMRr8+RTIyTcK/3zavEHhjtLEwYIwEL70M2o713nc2YBxCywLyJCOX2C19J5tKMz5sxYPEQ2DJ3T+pzsbWtbe3KscsWXORoUbnMxandXQwGnDZ0HgO+0IamLPk42cI1tIiunejdXJHypQcPdRpFk9GQpK4EzUtplSn6GQsFX4Ru2AOKVtuWeUFJC2i06FOoNsBuXTNQ7Qvn9gU5gp3zhknlYCYYVZzLOD9LzFsfXtx22uroXNhA2REBty59CkAEbwsQF6PyDhOpLVn51myL3vnjawlH5ST82ynF1FEeedzMYnFrKx0JYDwYV+GrAkWw1ofMhiRXdHNNAiGdAh+K0fHRGd7AY0wIVBzf/OYi3WDFsc/XaIS41VW7TvUtLtRe3oaTTqfttS9SjGqnH1ujRod3EbAIBdbD/T0GPzGdrELqNSEJtNSqaN0b1sQialXb+yo+uSOwqDMSrW1EPB4TO8sPvW4RxclIlqbOyI5nLHxs6NfdL1aDjWQzhDFZYG1tM/LtCLAItXaZbPem8PEKtowC1Llz57j++usvwmq29vfWEitkE+NsbSm7bMFF34qiyGYujOk++AUB3ypeFcXHQtx8h9XOzqMXVxNjar3YOqG/3AthwVztvwu95n1hzc2reD0goxZcmcamVqLqL2Wh9c5Pok/hJCprangBWgHT18YfLGuxGaD0HBYF7AJemcw8ZubqRoTLNtfLR1rDHxW2/v0NLnYKJlJ25ijdLDOxANiOO2alqqOjQY1n6YHyZoVqr6DaA1tVVNOSclqF31p9zBKsdg11WHbO1B3h1Si2CJ3CgaZPwhoW48WICRSmQO1p0ne6EeecOFa3hkLE0VDAVk0XZrZKAEnDPOEEysrAqX/ViSjGeIxRZrORa7UGFvk1199nncVW3cVY1zcDOrEMlJ0EJF7cc5vrEbMp/ZFj7Yw37YyEdPZFYiald/0FR30RsEgPWRnsK0rIsgz2i3UdmWxGQCPao1clANJkM4ZF3/MzFgFYhHPR/y6t6lK18+fPc/vttz/Uy9jalWwbokVtwcXydsWAizFaFBCjvvmrQlSwM0GdBiWkgtqZX+l5nEAKrf0TH7eVCZdsOjwzVv5dHpr3FVAVAQyYSltgoLfTcdIycewEYtDQZVt8AGILO22nd+TIJoJgfHgJqg8vytI6PIFPPTuuMyWh9rLuyGuE6Y6FHQL1aKbsVI7qvGFiK2ZufHxdBqJJbBqXIulK7dyGjMOatKC4AEdRn8tQE2BCwXMuq3HsOQQ/sRxNIn3KQTGdYWc+OHp+Dl/+GIchXmBqQxxcCM0ITXOPjtPAjpdH7Drgvs44hOjwptyqWB8T1xboUw34W4/a1F5h40HrgPfYyODOtfpZ1zSXszb//EvHtAywyFOl0jhjwZPgnLudsa7bQlcVIs4ZnWmR2EVbYn1IZ3+NILZPdYq/kQRVKBWTd9ZVQn2GdPcM8/vwnTE1WEhm8HXflr4tymYkZbRu2Ckea8xmCNDtCdKCi5lz3ACLwTfk76WLBziqsXdyy7a0qK1t7cqzyxZc9NOtYwXdsGyMUTAVkUuugYWRajFT5mDJsbILaAMNA+q1QdNr0qd0Am4SxhYPMo1Fz9rOyi+YI3dwcd0+9iF0NqzXuPByyq09+MOLz5RFQ2GlCq4y4ZxjmBQVjljnsHSh8Fg6SfAT4WBi2FXHwaxgx1aok9AxYOBEL0BGI7Ml5zacDgexQFg30RRKQDFBuclbFG0KnelLxK4wX5TSnRWT0PfOK3Yaemp4tVip8As6hY+Ia7YPIfS5UB/pU4B4xLZB2aYeRaZ2gBUQP0XwrRqAMRCwbEgquoE+B2iCQ+tb1KplbE7sg+wjekSCtllhcCr7XatzczSgYpmMxZyoeDYIomAk1FhIjKq3hjASpXAzTnoq5A91Fnn6kAhg8vdZaoDXZAgaulBNwYrr6x+SmFgUnlGMkpiJkMxzQgkiCvNoUupTF/DB4RB6vzT/rtcqEWhmshIe5jTyS5Guhy7culOWC7fZ9rnY2oNu6XbYxDhbW8ouW3DRt5S5UNUB8DiujycINviINDUadDtdr+o3JqAS1Uq1RXGq3x2rOqVCyL7sRcazj70pDIgukXVYZvyy6UsmFaHgPN64kkJyC6YIWKfraKScQVWFl1GFgvVYcbXvUo1w3pc6pOiIHFHG961SqMc6V6/DqY3Rw9XPUU2l8WBccKB9VHXJOdBLU6fqCHxwbGvXoQYaSTY0HeyKZgS3W+B2C4yrqNwOZTWFKjXvm+egL2cC4aKsWi6QrYKTuOGHtxSg3sQOzw5p9aLYXHftFqBRgxiH1sB1fWngrrXqNdoStAKpXsP7BArmzatzQU0jJSsjN1jcf+RaU5UusAi71BayEIqts32Z/VNasmeJJhXuq/zcXWDRm7c+Pga3iiL4kSaSCVigmWyGeLwLClsDYNHLZrTPWV30ncmaJmDhozJWqk8JX8bnRpbK2wYWme80N9uDk80oJ8uBi61a1NYeVNuCi4tulzW4EJG6kLooClQ1Cy4ql9FRX3YOJNZoUNdcesvaHbFrJzxmHiC+6CsQ31zBa9VSGPC7sWeZgMwifaqOih1z7G4uP9CnYp0GGsbWVop/3olZNLMgFKLM1EYHyFOaKkiUimeWosMrgzyhEksVgYZ4xYpDZ4aiqKiq9W8Nqx4/K2p/39gg3yqS2uut7nTWibB4HgKYDBF6jF+QDVg+pK5WmNqdQDPziq0qbBUcQ9HkoK/+xK2PI8n+uiBFW3dzlg2BAAFNylDxejKmyQCo2xwAaOR/Q/qwXRieP5Y13ljazpJ4vFY4P1sSWKS6jqGjmYq3x+hG4Q6XrCqUiMcZg5a2u39MfiRgoT5mXzLrAwbAQlIEJoGaoNc9mNsjC4BFqrHoPqNEYvH2GLCovfn6UMIyCcdjlEEWRVUijbKXlZAmm9GlQKVNkgRzqx5E0mlp+m2ME8nnAYvM5+0J2otcJlo0x5bNXGzBxda2dmXZZQ0u2majtGFVVUwmk853pbVMF3E/c1n/3DYebC3FGehTdZ0GNFnoRc/jMRpSrHdQH9+jSSFq0/SpXp3GMhHv0VMUX8h+EoKnvgw8fuOosxqrvKAS2Sbsaai8QQk69QlogC5dsD0+UXCiK2sodhwHVaBP4QKw9GtG62s/prL1OTTWgQ1rFxskaeetb6k5MJjKh7EEbCdrsr5EbIhEl/EaUqRyFDOHw4YMygL61DImIghBNSj8+rqRfhfdSQBMcNyUGImOdRrJcd5E8bkoWoNjAK2lblOfk81Gig1GDDbb4yGtqd81ewgMQn1Fvnt10+MiOffdJ4IYH2ogymxaoAYWqUt7f4Qwf95p9rFpXldOtgsOHIJk6ktS4Xe+P0YQlvCShAKGjn7kZw3GNbFbqXo7yGYkQOK1n82QWhDCq8H2aFIirZqO3lqNeHwHWPSyIKPRrnbGQrufj9qQhtUKB9SzzrOd3rt4MIMqFy5c2IKLrT2otu1zcfHtigEXqQtorqh7KXDBcphAOg/zLn3KSwQaqUHzOv5JKEcIWQ2lrqUI4ifrO1daNupQdebBtehZ6wAZA2qkUZ9ynolX8KHWYbYsGMjcyGlVlU9Nw0J5a9GiB1WZ8Zd+JghghSNbQkmM1nuMi+pTbWrConHmfeUsOJi4Gc5YZDKLijcjGYdlwG97fAWqosa6xia0x0Yc5yB1WzAtC4xxVH6CrWaYKi51U/QppJOdSQXhmnh/6wKmto+qJkaQU7YhOqTrgtfWZNq6boP6FHhv4983NE+G6+Sc4JyZr87WAg5jxdehqLuf0Yj7G49agyuK2nnu7496ZESCVdK1mQl0BGAZAc+cPhXZrIN41BGyGXOAxXjGYngta/rOQ6oZabP6Uj1JrpAcwk+kA9BB3SQwKFUNsyAYjxcbMx/NsygBG8k9n0Yz1W2AMPZwGQEqSz6Q9vd2F26zzVxsbWtXnl3W4KJNixKR0aLuMtuwqWXL8vEkuZaZlxuCVdAZ6EyDg52yGnHflTGBUNdShLUqMo19KYSgPLXG2Eh4P7qdML5MqWspOmOvgtoFtDAcpf097PiKyllK46jUjDrryzjxImBLx9QV9bmZmAqvghVt6FNLL7d3kJE3nrpsiwMxSjn10blY7xYSAesEDmJWwwimnOGtCQ3DMMGhWhpYDGs42mAmJJFcR1FpffUpYuZn0lLp8hTOBWVV1WNKquZhvkDd70KiilJQ6gq1Dev1u0geWLghwjmPYNsEadha4Wxpm79tAhNJ4raZZ73raihF63HOYEzKlgzXaYzHa8u5711ETdftEWAhihYGZ+fIMisLgEV61vTpStHhtsNnQmjYNw4sAsiJ4/d97lhXsghY5Bx2VQn01dbx9GlS4gOFM9MxZC6wSBma9m8Q1KvAm+Zdlmpm6loQhrUg6eUmAxA+L/Ow6Lvl9/FZ2lvXtuBia1u78uyyBhd9G5Oj3SkWHGbrebhcsnfBcBpCWNJSn9IcfWqlwcN/uhsKqxXFTBUzlZjVWHP1AroTz0OiZcW+F2vUOTdrt3BkLTveM6sshXXNvISi6mbzJTxq6f1dhGmkGFVAaWeggVJlE+Vi5fUHZSUmnsMTFnGW8qjCzIKjsW60XgjnmSMbMw6CLz0UYd1GqrWBQHKdfUvhykirKJz1Mw5J6nZKI3VrqgrUhrl0fYqWxOh2AyhMC2hoNtq84iTNHD6iY3yI7AtzsxrLYvGaPpX8sH7vjoT+l7R2Q09jglO/s+PwXvAZXy8573nKUJPRCH0m+gQfQq+gMjaFzB0fHvWCydUWaPozHyCp1zbiOCtkwIE2+2rq+N3/NVJR+Dxg0VKa6nyn4VrISdG2aFL1cbXHjPfXcMoGWPTBXVK+Epu7plKV2/A6TIDOiuK19/lotGvRVXsMMGI91906nxblvd/Sorb24NuyAeRlxtnaUnbFgYtc5mKvXOIwpfXHuheQ9P8ZGsjV9CkDTFrTrCmR7/fA72mddTDTVr3DskAjd8xCt2jbN+5O8wIcpuANmvrTLlo6zjWdlwVHWVR4jVK03ix1brI/V5x+FvtF7Ox6dCZYdfVx5OhTy9T+h8MOQGNaxILFJOF65ALq88tlXuaZAHZmYAZqHCo21FGYnAzt6tYGGoirswIbo08VUBUlYitmvqSoHKYKaZSmKHx962YuQnbGu+jwLbyQljiPtepRVIdSakCzTg1F7vrt9+5IUnUB1Cxqepica8X7wM9MIGM4TximDyxquo20o+LDATyCs0APWKTsSVAYM81zo2UiPgLyLrCou3a3gEXzeesYE34cnIa4b4uy1J67lne1OsjC1JmK0caeCi4Wb/e+q/fNZIcSQDNoUHVrWfi3D/dz/90hHjXgjcGk7pSd/caAha+zI11AlwcWzbkN10vm6AZzdD8fAovyzCEPu+2mkf2CXbhwAVXdgoutPai2rbm4+HZZg4tcr4tc5uLEgqKy4cCtv+vw72PUqOWGFqwHjqi7v3rD5uhTO+B2FKcECs+RIl42InObJFzrNMMgkhjG7kfKlh0eLFWUJ3WAUY8pFWs8FeP0qaVSKgacNbWMLl7ZsRXeBepCNae5Xm6tg8OrJVwJ0fqp4i9AOfNrd8BOc6JAZetXf6ijkPrn0FYX8ZVNbeMuiCcUOkcwM1JMfazpjFBNCqiLwpWycuAlnKdFDeKWtlBIbcQBpqYcjYKAFc9b3eNEicAsfS7r/Q4da1FvQse0jgJV+Lw5b16VukcELee6d58klaLsfaXErtYw6KpdDxAyFpjhb9Y42vknZXK2A7oc/hYJvLVVn5p1BmDhJWRDcnPj+435GrCEgEtN+TI2JkUt+KDmh4lZke536hsg1KyZuv5EGc5pUvF2DiC0gEV/XQ0Y6M8nHWCR1tB8m4CfZPZrivi7VLC0/dgFnQEWNx0ihXLtyWtG9gl2/vx5gC242NrWrjC7rMFF38ZoUYezfHO9vmVfKe0PDUHFiUA9qL9a0WGvZW49gT4lWgMNEXL9sY4zOFrEsY40ULUqQr8LiO+LNaLeAvWLLXKorDi8Ssg4rDs0hGL5A4saQ7nj0FJREwodXYbrPH/MjicQirYpImAyFFSol5gNWBTpXjCjCLojVDsVUwqkUiYHFfaQQF3zrO1EC3TrKIxicCiRPrXWxRPNxHqMeLgm9VSA4PitCwRE0FKYlqG+QbzFTkOPCKNuI0XhoXufaUCA1w4IgOSYbwAJtIBZAt4Sf/BNKGl1pupke5omfqoa/OpBVrELIhKwMAbcSOpvEbBwpcXYXEYkAIYAMIZWO9tqWpmR9rHFPEd/bo0Ot6TO2sMKI0k0oUHmQTvAYrgwbTnsI453JeRyMIn2FXp29C38JjnlrXQecsXbnqDY5TMNBFONhREGWRAgU88RAEdYt6F/1tJ4WXWwDsjozFJv0d2hARYAJ/fng4bz589TliU7Oztzt9va1ta2bdbhotrmxN0vARsr6P6MM6cX77ysM5wyC0nNSQLQ0PC2PM5yh0OrYJ1QHAn2EMwhoXAg0mzWG5wgczsBX8ZeHfWLeM27TgARHBZVgz2CHeeweEpxWTrFMla/vrzAQYHcXyD3FphzwsQ5CvUUsRh2rbUbqMqCasfi93yQ0jUVIg5DBpgeE5RpIRydLLlwY8n5GwsOrzboxIH1OFk/Ui+ACdWeiFZ4ZxFmCInilCvyPOYEAl4KvLF4sahIcGjFZZ2S4x9ElAPeLTi62nJwasJs30DpQt8Os1jtbdl5oEApokxsanpnyPdcWNVStNmgvkAkXE9i3GCe9Yltpj4W51Per+9eNrd501dhPq1q9Do3GprjjXSwDrvmiZEBTMxTrNLA8smMnRIxCVgM59ba6e6bV4MuBBYmf8gKWnUrJaT+08dEimQ4E+HZOibpm7IZ/UlFfAD2pp+VSEAgD7ol9VUZ7BW+zQKcBFQGz4RA1WqPZqQFmEeeIebqWQ0sYDlwsb+/P2AhbG1rV4K9+MUv5glPeAInT57k9OnTPP3pT+f9739/Z5snPelJQYK99d+znvWszjYf/OAHufvuu9nf3+f06dN83/d938DX/d3f/V0+93M/l52dHT790z+dV7ziFQ/24c21yzpz0X8gjWUubrjqxBKDNcnfBZsN/xEzxjGA380gr8xCEkwVaicURWNGo2amrFmnUQekI7aQSiEp4czzO3I0ht7YVhV3FJrHeVHKwoUO5xGMuSU59rnIpyAwszCL3bRtcJ2t9RijzMZ470uDR8WXBb6W0VWKKvQmMGhYu+rqHqERqn1LtR/GsVPP5JzHTsGoxmZu69GnFDqR8gAC2uSI9eVbVUxzvavHiKsjvBvrqRE7haOKqZRyOkMqCepTbMApT8pQahBTod7GxnoxOp0tCj8OMGtWqL1md51Mw9pKCY1ZU2RXqMRGbguAxbyu3KqE3iz95nj1vhE0+dirpL+/T/UM+ftfRPFGQn1I7zEu+Fh5MiJFW5N9hvQdEY8XGZeiTXSg7GNDEZfqEFp7acpYgNRAsp0Z8nMybxnKGom2FSTNh8XbGldDve2AlpUpek+Zh1z2oa0y1c+5ZClX2vTnSMpUnXGtx55ofjhBuOaqqzPH39i5c+c4cWKJ9/PWtraORZrlRsY5hr31rW/lnnvu4QlPeAJVVfGDP/iDPOUpT+FP//RPO9f9t3/7t/OiF72o/vf+/n79d+ccd999NzfddBO///u/z9/93d/xjGc8g7Is+bEf+zEA/vIv/5K7776bZz3rWfzqr/4qb3rTm/i2b/s2br75Zu666671jnlFu6zBRd/GMhcndxZobS97wczzAVpAo1ZWygWDVjAhKEExjXxZQ9O8LzE6VvVPpPnPRIpN7VOZFRBS2jQFP1XwsyIUJaOBjztxOGcpxFHNARoqUr8081OFQnljHG5mqQhAJvlOSps+tcKTRULWYVYU9cOpqCoqTCgMH4kGLj++oDueg51QFC6VMjlfUR74hj61gWJn1banEpVnjI8O2wYa39FVn5KkXpDm3wB9ypfCURmoE+IUczBjct5j1AelrtHztOS1m7IzLRAgxIxJvL+O3+9ibG7TTRaqr6PBm1C50kEmUjk6spGKNAYs+nKwXQuNGAU/D1hEqlMYbfi9tr5vfRP+F4EFmcZ8Qc7VxPt6GJevC8CbEbvzGpBiGBNJUrQdYNHaRvAwi4CM4b51xqK1FtJ6tAXUWuZDqpsO3SxeX0kVSm1OGatZ1fCT5hnZjWuNN9Wrfy+Gv1dOzjpsI6OZGFM47JlDpGhv76nOf5KqqihGFBuTDO02c7G1B9MeqoLu173udZ1/v+IVr+D06dO8853v5Eu+5Evqz/f397npprz4we/8zu/wp3/6p7zxjW/kzJkzPO5xj+NHf/RH+f7v/35e+MIXMplMePnLX84dd9zBT/zETwDwqEc9ire97W285CUvecjAxRVFixrLXFy9qJFP6/mrrf9WtRRBS92G6w9TNnmNwQPQEOxMKA4Eex7MARujTwkBUxgHMpMwrsaF64KsxTJrdxY9KJjMFH9omWhDn8qdmGXeOY1fILiqwM8K3NSilVDisOKyylDHXHygT00K2PHMdiyuiPQpRuhTSw8cTAvh6OqSczdNeOCmkgvXWvxuoE/5Me77sc1g8ag3eF9gSOtf8eLJ/D6BopP+k3BuNHZW3oCpFfy+5fCGXS5cv8fhqQm6E4qXNkefCtkZrzb8523dXbu5kTc0lQk0oVT7IbEPSb9oeznrPmBEAmgoy3kZizawGH5vjAau0U7me2ni6eMKSx71msllUDvhDbDo7ZscbmGE7yQdYBGOprWvBZ/pc9SpM8hmLCKwIPd1eBbm+x0liuxQbjZRqLKUptR5O3VX74Oc/ofx3+0i7MFcrd+mu3rpAIuwKFPvlzKQg2MemSsAi6MOHaoUx2P2PsHebMrv/d7v8c53vpO/+qu/4oEHHuiA3wsXLqyVufjzP/9zvvIrv5IbbriBU6dO8UVf9EW85S1v6WyzKUrJz/zMz/Cwhz2M3d1dnvjEJ/KOd7xj5XVv7fK2+++/v/Pf0dHRUvvdd999AFx33XWdz3/1V3+VG264gcc85jH8i3/xL7hw4UL93dvf/nYe+9jHcubMmfqzu+66i/vvv5/3ve999TZPfvKTO2PeddddvP3tb1/p+DZhl3XmIkeLmk6ng+2u3tvLJMtHxmz/Y4OZhwZjaAM0WlmDVc2ogQps7KfhraKT6AMdJwg6wiQKheBSv1pAMNZjxTNbo2hYCCpW7jCM4UQpS1fTvpIG/TL1GrktQrG84LxBCYpT4qP8KevJuEqMNPrCEpt2I14pXKDUiKbGdOv8sEJ1wlKdiPSpmWdy3mGngngf5S5Xd9brBFO7+7VUdOlTm4gmmtAUDJDKISZJ6W6op4YR3F7JwV5JTZ86nGKPFFGPr4btw45t8TR4LQLA8JZ8UXjbVk0n2lZWQ2tA0xRoDyk6/Xmb2opYxKsGaxwivYwJQVSp33U7NWcDMMajxlBZm1Vm8j6uKducrwEWkKtpCYXM4Vk1D1jkCu7r1GjeyRePGskXRNe1A7ku2OHzBCyGxxMoW1YyUFzrqo/Mfn48m4EEoYr2WlNz2JiVSKpS7eNvU6T6c4UhzKBTet0MsbXGJvOTzxKP1XkoMgosPnP3Xj5j/xxPfOIXs3/VGT7xiU/w8Y9/nL/+67/GWsuv/uqv8vmf//lUVbUWuHjqU5/KIx7xCN785jezt7fHT/3UT/HUpz6VD3zgA9x0000bo5T8p//0n3je857Hy1/+cp74xCfyUz/1U9x11128//3v5/TpJWo6t/bQ2oZpUbfddlvn4x/5kR/hhS984dxdvfc85znP4Qu/8At5zGMeU3/+Dd/wDdx+++18yqd8Cu9+97v5/u//ft7//vfz67/+6wCcPXu2AyyA+t9nz56du83999/PwcEBe3t7xz7Ude2yBhd9K4qCg4ODwefX7u+tdl21n9vxTaI0zuVqQ0ag4eJAKSrXTssfZ+zWWgSJ3Z7Dq0ct+CI48QvHlt5gma9FhR3vmc4MTgyT0uEjBbrSY1KE+gFQFfw0UWsUsUl9ylKY+ZmHRacrMcBd0VBejK+CRC/hJdmsfZlUSe9qkhBNn9mGPmWrigqL1flN45a6LkXQCRxESWWplPIg0ad0I/Sp8PMXJOxbqx1Zx0a6eANGFO+LqJYFYgLYaGo11u0UnuhTu3AScEpx4YjJAxXG+QjKxs7TMZ4QdVE4JM3UbhfvdbmK9UQZlSnfZDSEkePRAAraNKQWYEhmjMf5+ADqONkxiNACFtmMhvgILobXtsbvQ+1Evu4AQ3gD5bKhoi1g0TjAad/UiyJHWfIqUNABFvXjNQILHx327r4xM1v14UrL0Xexd8QY2BlTXKof8r5zWYSsTQ9YxCMcp4Rq73y0jh3BjGUz2upUg31HshL1eoegCKsDYFEQgMUj9u4HDCdO3sDu7h633HILt9xyC957PvrRj2Kt5SUveQkf/OAHufbaa/lX/+pf8RVf8RX8g3/wDzAZQJizj33sY/zFX/wFv/ALv8Bnf/ZnA/Bv/s2/4WUvexnvfe97uemmmzZGKfnJn/xJvv3bv51/+k//KQAvf/nLec1rXsMv/uIv8gM/8ANLrXdrD51tmhb1N3/zN5w6dar+fBm1s3vuuYf3vve9vO1tb+t8/h3f8R313x/72Mdy880382Vf9mV84AMf4OEPf/j6i36I7O8FLaruc7HOxdWus23ecauPKdHpVcF4wbim0d4mWBdCGLM4MtiDQKEyR+PjLz+d1ut20wI9LHAHFluFF0uBi30E1ls7zqDTgknlAn2KKtCnTHWs1dar7pKK8bbAlZaqsBEgOQS3HHVn3vQ1fcri9x1H+xa348HOUZ9aZv3tvxfC9GTJ+dMl506XXLgm0qfMZqhHwQ0yWA3UEJ1ZjA/qUyarPnX8gxBAvIWqgMriXQBi0rSpXu8gAKzgr7IcnjnBhZuv4uD6ffwJoFDE5ml4C23ATBGS1G1Qhor0KaBpfrfAjrWMFuXMm6hq5jEmHY/WGYI2TakvTWuMjwCPAXAQiSCpmA8sVCNlKmdeUNeNZqRhghoS+CJPSfIqHWBBZ+kNsIDcqVOwOaUlrdWOfJYKpTHokHG7U88OZ9rLaC0t0Z1yYFIH577eTzyVIdcDrxfwGH6TJGU74xE0iEezD5rPqozWZcQXhc9RvArH5KaDDrAAePju/RFYBNvd7apFGWM4c+YML33pS3n3u9/N937v93L77bfzrne9iy/90i/l5ptv5hnPeAb/+T//58yxd+3666/nMz/zM/mVX/kVzp8/T1VV/NzP/RynT5/m8Y9/PLAZSsl0OuWd73xnZxtjDE9+8pMfUtrJ1h46O3XqVOe/ReDi2c9+Nq9+9at5y1vewq233jp32yc+8YkA/O///b8BuOmmm/jwhz/c2Sb9O9VpjG1z6tSphyRrAVcguMgVdH/K1Vc3LwNt/dezpeKMKVDX/q897homCEYj0Kjo1lGsW0uhgq0M5YGhuCCYQ8IcNa+HpU5AXnVE0MqihwX+0GKO6NVSZBe03Lrjpu6oQI8M7rCg9AHEFOIy0cexdY9PoNbgCosvLGoJsqG1Iz3CWV647riNATexTPctRycMR7sGigoi0FhbuckIbt9y4foJ528qOLzB4E44KHx0oHMXzvIR9fqycAUSgYY436rVqI413tgcRgV1BeJNvBwdqRZEWL2Ool6ZCH6v5PD6q7hw81VcOH2S6hqLToBi+eZ9c4809V7QkuZBQVNDsal6kNZq1AdQ470NvP3Y5GIewDcmOtk+L73qNWTiKlN0gUW8ZyU2yVQ1+axDk/7Kz20kAoucwx0zPtkao9gErl3b0aH8xNqNzE/pNaibdYFF81IQNKpcZWbVAJT6uR1oshKSo4zVjRRHgIWVwK/KHGf+BLaUrXoWnoNjrV1TnsWmRbfG8wOQAomySKSP9uYqHMWZI6ToPlsm4nnE7n3NB0sWaT/2sY/lv/yX/8LHPvYxfu3Xfo1bbrmFN73pTQv3ExHe+MY38q53vYuTJ0+yu7vLT/7kT/K6172Oa6+9FtgMpeRjH/sYzrnsNmmMrV3iphv87zjTqvLsZz+b3/iN3+DNb34zd9xxx8J9/viP/xiAm2++GYA777yT97znPXzkIx+pt3nDG97AqVOnePSjH11v079n3vCGN3DnnXceb8EbtMsaXCzbofva/f1sLeDggjmuA588rz7QCG/AtcCGEDMaPmQ0xLPRrIZ1huIwAo0Dwczi+GsOLgiFKv6ggAsGf2CZ+EzPixUKwxPQ8LMCPbL4Q4vMFOcNVhzFXOdtGbpTeOm7osAVlqooAnUnOtG1hv+xV96Mr6Uw3bNMTxiO9i2zHUWNj5mT8QtwOeAr6EQ5vGbC+TMl528smZ4CnXiw7QLh1X/jOuMwsyGrUYVGfvPA2PEOIt0+oQ+Fj0XhwrqF8+0JBC0N01P7HNx0FRduOsnRdRP8rrSyGutaQ5NpMg029rtoZxo2Z6oWYyXbtiad+pSxcJrPGhij+MLgyyFFra4Z8DBsUgcQOoPj8vDbe0GN4GwDLDrYRaIsbyYborGWaUDNEVPv60TAyGCbOgI/kKKV6FzTZCU6ICFS3nQ0pFLTnQaR/Rpg9b5RAfFUhakL1KW/X2a28JuOA4uw1AxNqpYtHEoqj/X2SIBjUfF2e7dSHJ+xey9l+7db4vJOfS4AyrLkS77kS1BVXv7ylw+0/9v//dmf/Rmqyj333MPp06f5vd/7Pd7xjnfw9Kc/nac97Wn83d/93eLJt/b3xx4icHHPPffwH/7Df+CVr3wlJ0+e5OzZs5w9e7am73/gAx/gR3/0R2vBg9/6rd/iGc94Bl/yJV9SU/2e8pSn8OhHP5pv+qZv4k/+5E94/etfzw/90A9xzz331BmTZz3rWfyf//N/eP7zn8+f/dmf8bKXvYxXvepVPPe5z13jpK1nV1TNxRgtan8yIbos4ztH+dh1HMf6T0d4a/YvxhUHF8JYok0MqqNYmX8fLT22dQIzEB+qzH2pYMEaj+vVUhzn3kodyP1h0/OiKB1awMyZ2MBpzaJkZymtY+YCz720VZSf7cqsHtuNi+fT2aKW+xXvMFrhMAhu6Wj32PhqQUvl6CoDXiimjuLIgxeMaqfL9ipuqFphdrJkdhLwij3yTC44XGWx3o84iMc6BFCw3uMrCwLWOtRIq5H3+lK6wUFvaDChA3lbrnP97I+eKDja2wVVZOqZHBxgDkNRuFYbmCNa3SlcAQl1GqF2wG1Edhjy10pVGSBmHMaknY3iSsEUgsvgXJ0Tia+d1UowmaJlYxRnZI4qVAQWkhSQ2t8Ni5CbIw1ZAzcybpCxBbG52ozY5bqdlajXHY/HG4ZnVJvzkbFAQcoVYcckdI8Opu39srVfqUYif3woeerSWCG2Mg4eYj+RdlF/86Uf1FgATCKwaNOhwjyLQfr58+e58cYbO5/983/+z3nmM585d79P+7RP481vfjOvfvWr+eQnP1nz31/2spfxhje8gV/+5V/mB37gB7jpppsGqk7HpZRYa7HWZrcZkw/d2tYAfvZnfxaAJz3pSZ3Pf+mXfolnPvOZTCYT3vjGN/JTP/VTnD9/nttuu42v/uqv5od+6Ifqba21vPrVr+Y7v/M7ufPOOzlx4gTf/M3f3OmLcccdd/Ca17yG5z73ufz0T/80t956Kz//8z//kMnQwhUGLsb6XMASvreE90ry06V54q9u/Tx6Pyi2wtjpNSgKOPA+AIHah8u9gwxQ6fw0tSQQI9hp2E5FKAqPlq4eJ/CoVzNRQacFTKGUiioWayuhOdNsULx6jLHj/1cuNREzWKnwCCLgRikDy0+g1sY6/BlOLNZXkUqxjvpU3MdAtWupdgEFM1MmRxVUCWhsQlXJcrBnEa2QmWVyMMMemdAnYrTh1zGOQoHK1jBejUeKKijyKGxGfSr2ojDBGRcc0nJiNtFTQ3csRzuRK155ioMjioMKcYpzZrmztCCWERab1hqc56Yo3GcKuZe1YXhNREPnbsk7lBCBRWFQY7GZDFqg/eQ7a4uE6H5y0vtCpsZ4vDVIOXx+JEpRAhZ9MybVM+QLmJX5MrYeUDGhlqedJYnqTd6bwWO6DSzGr9gxYBELzbNfKjVjbrBfG1g0L4da+SmbzYjPnnq+JkPRNBXsn7f0aeZcxx44uWZ8aqE4fdDpYwHjwALAmMXX8Llz5/i0T/u0zmc33njjAHDkLMl19gvAjTGBHkigi/zrf/2v+chHPlKrOuUoJa997Ws7Y7QpJZPJhMc//vG86U1v4ulPfzoQlH/e9KY38exnP3vhOrf20NtD1edi2HOoa7fddhtvfetbF45z++23D67Rvj3pSU/iXe9617HW92DaZQ0ulu3QXW+74IdGgNhvTKPznpwmSd+vvNjMZxsAMCZmNYhStHVGo+ZBHGe0LuIRFXRmWw3wNIAP6zGizNaKtAYNJ+dtoGlgKE0VVVxip+0VGyulvVLk3yuICdSIRIPIcYmXNhPoF87a2pcLQMPE7tFm6YxM1uEW8BPhcBLGFwf2QCkqh0QN/fWa96Xxo/qUU8oDR3HgSM28N6E+Jd7Exo/p+iFQjmRRF+8ln+ASwETN/FCPkSgHLN3Mz+Kh8o3DKAzVyT2qk4Aq9nBKcb7CTl3IaqzZUT0dB2poatlNkAVOLuMxG+u1X2hB+hQmE7hwmCnm1QAsqsJAph8END0wjB12zg7Rf+1G/0XrDECiWTlrsqBF0/F17vW0bxOVt8bjc9mUDLAIQM3H56Gt50mWMgr5HhBkgEUvm5EFOeH/g8M/OBQQjytNVKuan5VoS8MmINDvhh2OoZux6ErKQrhm2ictHa+nXZhSdxsXsoIQpnCUNx3SfyR44DN37+XTM8AChk5/zg4ODjrdiI9jd955J9deey3f/M3fzA//8A+zt7fHv/t3/66WloUupeTHf/zHOXv2bJZS8tKXvpTnP//5fMu3fAtvfvObedWrXsVrXvOaeq7nPe95fPM3fzOf93mfx+d//ufXkeakHrW1rW2ta5c1uOjbvMzFUtanMBV10Az11BK060jRZq3/vjnO2J1onNQZDY2IKPW/CzZ/4VYUN+LXBQUnocBRVRYnMJkEipAxEQwcY+G+NU/aq4rRWgcUxuERZt7GF+n4i2qRKyoQlHwKH5xm9VhcdASO36W6M1/0C1yK0ikY5xDvw7mhmju+IMEZHANSQujEvqPMItiwM491Lqha6voSrmqF6VUF06uIDrQyOajwU0vhqzWi6PUhIAp2VuGnBSpBH98X8RplExkHQEzHOTJS4VyIxDfOVt7GKC7dOQTdtxzt7QT61MxTHhxhD8LvjYOGanaMEFdmU+0Ao6b7psSTOV9SV+NyMzUKPQtdt4t8g7oWsAiZza4lKVrv8llBYzy+NLisk6kN9WZw7WtQoYrO8+i+Npex0IaSJ8Nz1K1PyNCdvPQAQnpmNjUKObqTRknf7G0cgcUwQqWtP1vgQhqAOJZhQMPVnINH+Wtd66Pow6r0TTdgEUcpHMVNgQrVvpZKcTxi55N8+t4DmQNOB7IYXJw7d46rrrpq4XY5u+GGG3jd617HC17wAr70S7+U2WzGZ33WZ/Gbv/mbfM7nfA6wOUrJ133d1/HRj36UH/7hH+bs2bM87nGP43Wve92gyHtrl6gNE7qrj7O1peyyBxciUj+IrbWoKt77QdTEOd/OGh9zEuqMBg6cgdRvbCNAI/e+WXvI4LVFNkOgvhjtHb/09llu8pTNcUcNGCjLKkYRwS3hqM2jyAjUGQ2z6/CVYNXVEvH9nhe65G8gRJ9CTKt23WB8iBILilvqApkzmYTmegBSebwWkZ4VnBLXv+USel32IhJwk2b94pRiFnp26IgM5fHWL7g94WBvgqhDZpNAnzoI9KxAa1mzVkOBmcXOwpF7q5iywkts1rhixqqZIPzhKbB2hroCcCEbsEJWIz+HoBPLdLIPVwNeKQ6OKC/MkJmizm4kDR+s4eFrup/bmQ2Edv2MohlgMXSkxQh+ZyxD2AUWgxWZ5ODmI/mguNJkm9h1siG5ugQNvRp855i6+3ojWKODfdM22nNs01yo4tXWz7BkEqlpaPdwu/SicWnYtHV/TkwbWKRJc2Cle/xDZz+YmdMgr/kdxou3e4eNweOQARBIDfISsGjbJDbJmwssALMEuDh//jwnT55cuN2Yfd7nfR6vf/3r526zKUrJs5/97C0N6nK1Lbi46HbZg4u22ZjWr6qKSeptEe3kJy2H4nB74IvoZK4SLI1sDp3QvOgT0ID1L76+P9DOamSjYizlmwqKOBOzGgSgYcPgVjxOV60ZCHv5WWqAB+CxJnDKRIZgIFiKuy1cOFURNYKiMz2xVVR38ThfLO2bD6gvyQm1TdZBjccs4PAv+xMHUoLgtURRnIZMRpAsTa98U/+ExzYBLYRZEc+Bg2LqMd613JcN0aeujkDmwFFecJioYLYJ+pR1ISsmhEbPWmhUt9pQF2+g3fk60EAioJTgbC5vI7+UEaoTu1QnUlH4jPLcjOLIbbwoHIaZjaQEFmoIhhkL7f1LrFBNCoxh2LU77jsAFnE7Yzyiof7ESpeuk/Z3pcAAWERHPIEWoH/jJrCRy1hIDNd7SRmL7sJFFB+DB/150zSagEVr7rq+QGNQJmNze074cB56VQqo0AMWvf2Ss6/tFeWLsKELLPoWxhtGz1KDyu76pR4v/MRDgKhGKW4+pJ/8aYBFngrVNmvLhdtcuHBhrQ7dW9va1i5NuyLBRa7uwooJdJJYP6AWql3wO9R1CsLyUXCgpsTopAlAJ6CBasxqrB6FTXvWL//+2o41dPeVKl4CBYDwsramovKW0lRrF1aX4plVsbBalLJoIvftsY2MRR4b64MBLYQjkoKToagqPBaDW98RFRDrcRJrHbzHaIx206YfLXfiuyy7FGUuA4jRVP/h8NaE9WdoHMdZe6hp8EHhSqnVrfrrXxX/qhVmVxXMavqUZ+eCgwMTOpH71QFqOgTjQ8ZHq5C5stYHsCEp+LQpRaVGtUmoAsdeAv1o7hxL0qfYMUx3TjAlHE9x4ZDiQigKH3YKXzf1aepnhNODbG1C7UiLBiA0ST0supxMY1LX7WHGQkkZC8XXDeV62c+oOKVmWNTsI21omFGJzq5oWI2hqT9JW2lIzmgNLHpHJ4oj1HgMpWjDPF2nPMwrkSalPqnXddc2ptLUBhZDmBNSxn7CYK15Zz+tM0ldZ5z9tN+gMWIDSIx06aZpVUO6k9RZD58p3pbCMTk9BiwWUKFa5vs/Ys9UlfPnz69Mi9ra1pa1h6qg+++zXfbgok2LEpHRom5rLcxCPYYQekdMzgPnQ5Sm2gE3CS+whe/6MQAi1GogUqUXpLaUp47pRMS3lvTexTWTZgOB0JBtMagzWByzqqC07cLq1R32MLZQzYoYWDcBaIiJTbiW2H/elwLVpMC6UNcQmq25+uth0fYxngxCaK6XdlOwfhbO1XIrXDB8QAPiobzgcNZSlBXeBCfX9WVZjrPwev22t/4ANEJEek0+nwT1qQt7lvKggqqkPKwopjGjsab6VH0kzgTFVgCjmGKGFxOcr3V98mhKgWWGdwUhqt/Q5Pr1LKu8W7QwzE7tMzsFeMUcTplcmGKOfKjl8hs6EKCw+f4V4TmiYIWqbDfH6wILCAAhV6vhfaAr6UiDPjGhLsgWmm7D5jvxsRg8f6x1Yz5Jzm5rXaJBEnsesIjZwKjX3drXx64NXbBTZzEjsJC0tJbDnmzg6MfgUUcVqr6dFIxS1cXbbUsF2v3sAg3IYUh3CvKwMidjkQBQ++rU5hg7gaXwmSqh70fPUvF2H1gY9FjAAqAs5nctBrbgYmsXx7a0qItulz246NtYUfeksJwf2Ue8UB5AcaBUewTqVArC9p+/y/gBaZv4gK5fFDGsVGc0fB85LGftF2F9rbdfbmuoLAlQuW5hNZFyoUh0GlcbG6CqmoixsQ19CqDK0FOOM1s74hz0iCqSrKKjWN0XjScmFW2H7JYPvSIyc7dXtOzwgRpUYlAwhqKo8DY4TKkWYWVrr19DJN14BfGBm38Mdaux8bUQpleVTCHUHxx5ysOoPkWoQVj7ELzA1IaViketkhSAN0GfSmSRmjoIGFtFIJOXYT22GcHv73C4H4vCjyqK8zPKowq8j7Xb687TvVqM8Rx5C4VQFZmu2yqB6iQEqpP1A2GHEDX3mXMQMwARWITC8N78SZ2p5/jXe0dgMSZFq4DY4T2Q4vI1sKD7vDDi8UJs/NcfWwYAIa1cYnFXrq4hZEBC1ncocNUAi/7DJmUX8mpTcS0jVKiwpmEwQDsr73+j9bMvBzn6NSmgmNJTnjnE2G7/oYk4PmNR8XbGrjp5w8JttuBia1u7Mu2KAxdjmYvdyeJDFQLIKA9CQtztQLUHWsT3/VCGPGsDR7ANNtL7ddYCGu1tjmnS+ksz76JCjeXHdr6RckzRNWtCp+1qXfqUemYxqyHi66xGyJisS39plIMUg/EO5y1WKpzMk0FdamiwUCX6lPN1VmAV+lS7FiQ1HmRahstFNLzsnYSuzsIC+tQS6SAhckyiUKV4jLiaf53WPyrPusiMUO1Zqj0LqpgZIbtxaDGyGfoU0KkhMtaFGqLIxW+OYbVgU5ojgaJwjbrgsJkl6FPLTCyClpbq6iLUFDmPPZqGonAXG/itAMrav1qSgi1OCIdHPWBRbxNAsvP5rIcRxVeKNX3aDVH2VZnt2PGxA/eS3AnxyekfAxZCrFUaBk2clxqAZ9dMkwnofhepX71+FBIBQoj7zKEtaQboqIAdBxbhepBaGri1JzBOk9J4DH3qUgv+Dj7v0K7a2ZjYjDBfvB2AxRgV6uG7xwMWALu78wu1nXMcHBxsay629qDblhZ18e2yBxf9XhdFUWTBxd7OZPDZ3HERiiMojsKj35cBaLiScNbmAYx5flP6rgCZkndCju13xehh2rn98kvRQm22W7S00e/UYI2rC7QntsLFYsZ2LcXS91872qgGNzO1HzIpgsxt5Q2bkORSLJPDCicFhXX4wqVT1XUSl56q5k+ghaHC1Oe5qBJ9aomi9QVziQpUNtSWaIEajy1DY7pAB+nTdo53ntK519ZlYkyI1ns1YNanT/kJHBUFk0mFViXFrMKk2iS/ZtF5XJ0422QcBGwR1KdC07v1r59wnixWA31KBUwxi47fkD61LKjpRKStwe3v4vZDUbiZOsqDQ8yRx4nBulDvsDCzEb1XYxRfGqqipDRV1oH3XursVb17e30RWEiMrvcmCl23J3nFqbp7tGtUirrOvI5mhFLGwulIQbQQ+u7ksgAoPhudb9ZkRYNKUst8BEAdYBF/yNQfR52J56u9iceJ4MeABTTH2KdsZXqYhNOcmueNUaGGVhfit/fpAAsylDdFCt+hQqVrdx1gAbB74uq5358/H7gE66hFbW1rS9mWFnXR7bIHF32z1g5oUarKA+fHSFGLTRDsDOws/NtZxe2B221lNNbxXbrP+votvLRLlNmo/ii9SMRjxZO02HOKLEvdNynyDUyjUpPT0ABPNbx4nVvSWcxMmNgFVVSfsniwvpa5Vclzlpc1UaCymCruZx2mcNFfGzoco+Pk5o3nppoElZRCHeI1RkjXpR/F7sfewFETlezQp5a+CPPb1dF6H7JJdlYFCWYrwQmUNelThLFmSUVGFTvzFDMXO52v05m6OYYkdWugkWyV5jfLZhyOcf+GOQRmKashGDuLRcyBkLeonqgzWG5bEfxOUXcKl8pTecUeHVEcOkiZjX6GT0MwRIziJ4aqVuwZThKi6HO6dktQuWrECHr7GtBdYlaiazWw6NRnSGd/r4K1Sj8W5FUwZkwWOsjsOhP66/QD+klxPNdjob0ma0ayGb05wy3dAhb1mUihHB8y0sUws5It3o7BnkS3mlu8PZrNSEEL0/p8WF8BaUlhrqaepZnJlJ7JmQOw3f0Mfi1gAXDixHVzv0/gYkuL2trWrjy74sHFbDbj3e9+Nzfu7/DR+w82M4cT7DngHKEYfDfSp9ZjewTr7Z96PUnmu84+85wZCTSBmsbkw8ujMI7Uvy0vF5sZaiTLMtNmbMEjErp4i+ZrKdr7zjOD4l3RKur1GJvkXDUWcq9OfUkR7zgZxnl04sHELtKj9StLzChdmdukPpWch+M1v+vOl0AYswIzi9H6SDmq6VNjtJ0lanKSm6WVDeIEANZjiioW1q7fvA8R3MTiJoE+Jc5iK4dUJtC0jtmYcewYvAb6WnCrPGJi0f8G+l3Uq3M2XqOxO7QKYj1i/Po9NZIZwe3t4vagbuJ3eIjMEoUq5mhEcaVtrr3uSsO/Ij3HWAbqUpr6LFRzmuMZYTax2d57zocsh3SARXLItQ5u5AZPalU5YJGkaJ2Y7DNPJDbQGyn6ViVbjG5NcL5z6nU+9i9sd+yWBFbFgwlS2f3i7URBGgRCtFFqyhVvj+0X5m1qIdqgpCmAH4ajXKRqzquxENsFOBblEbv3rgUsAPZPXDv3+wsXLrCzs0NZLpas3drW1rJt5uKi22UPLubRos6dO8cf/dEfceLECe647Rb+9Oy9m5/fC+UFKC+AiuImQeK2ftaPBXpbGYD5E1BnMWqQobAWVUiCBKGPN5yopzAVrlpGinbB3RU9ujS+EMYOUp/aBTFL3Khd+kGM3HsT6S+KtVXQ9jc++0Je1mKAj+JI0SOLGsVMKnzZypgs0RRq3gRd9SmN8q3drtIrD6+AFtiZw2MDfcpWeBN7OQxu9eNdPwGImRqIqQnUIzVJxWZ9+pQWUBVFcLB8ok8pEjlbq/XUaK6gsDoDvnnXBAqYwTtLrmj22IcB4E2gT1Wh87WNClfhd+hHIFZ8W9VN/CJfvfIU0yP8A8JsYvDZjttpV211xh5SndRTN6zsfRXoShFYZKVsxeM9PWCRHocaAd1YbUegQhmrg0acXkEsneLt7jEFRSVrhvsmsEMmi2IkHK/XmM3o05acgJcBiDLi0QgsAtDorqV+YOeyEuqz2bMALJpO2p39tPfCqINOSYEqZTvam0Rq1Vxg0cVppTgetftJPm1NYAFwdHhu7vfnzp1jf39/8A7f2tY2bcu6W8uMs7Xl7LIHF31LBd0f/vCHefe7383tt9/OIx7xCN761/c+6HOLdus0tAC3E5r21WHUlQZu/qzfWe3Xz3JB9NEvFMNMDdY6pr6gNK6OsM18t/g564/0rG6+JeHFNsPUQ5RFcKhVQ1O5ZdadZY1ArEcoKNThncWY6EwTG22tAza8IIcl5jCAGC08uuOi1n6kCq08OCFqHx2MwnuEdkZjvYLzNghLGa8O0NCoLLDiozKMD0zD+s3MhdqMRF1j/ToKjFDtlLADoSjcU1SRPpVUi9b8CYCahiUmFeS3FMDWahAoNWBlakIWSCJ9yrYzYht6XRWGqtiDXUEPLfi+Yl4kzaTO3ZKbXVuKTsN1KRJobWV+/SbSrKyMU/T6wCJF7Y34WP9k6HOdjPF4NRlgkTIIGmRVBfrVCLVS1SBjoZiYhvUj9RCiYHVIW/IKFFBlZH9T8Xjq69MugE+N7nIZRSFlMOzg+JvfRVrbN8eaq7Nqire750MVxOaLtzcJLABu/pRHzf3+3Llz22LurW3tCrUrDlwYY/joRz/KX/7lX/LYxz6Wm266CYBT+7sXdR2CIBWR2x+a9vkJ+BLa77KVXAsBYpkqSugqjZLUilaVzEyO6IwUzjJBijZWIqjKwqZ3aaR2Cr8ZHGYUqXU1Bo+VMPZYV96lqesKuAIT6VPWVEGqFFpF24vXniuWFBVkZiE2YHQG9ESI/on4WqJ2JRMYNrhzUUY3AiVsdl1LDh8GrYoWU9uFa6WWGB5zohfPmFgo6os6I4D4oN4UJ/cU6/nQIviJZVrTp0Aqh3WKIdGn8td8rl9DdgoTKFLhnlJQj5GKdLHqsalNmRqHWO+TaGZqorqV+OiIrv84zmf/laoyA2DRWVvKKjjBRqWl/uKdTUXLeWfWexBMRJ6NmSQ1S35ukzpr56RY01g2V9OQgMX4uKpQMKylcl4Q7TWli5EMa4IksK8MNlGt0pjGU4ng5wELTdSl7ne+n31oHUn4zcaKt9PztDGfqGu5fRLtahAJUiiVyUUAFgB7+1fP/T7J0G4zF1t70G1Li7rodtmDi/aDaTab8bGPfYzZbMadd97ZKRS72OCibalpnzkADkIkvCppCsHXydnFAL1PaQ2FianwPjgJ63TbRqCqm3Wk6FrIaJg+xalnofHV/LGNjZKzCkZdcHYj9Wu0XiBj/eLZEDEuqNkJ4jBS4TVIoa5TKyAIhQN7n8Nh0cJgdyvcjkQ+uJArJj2e2dq5S/06nBZY3LHOS379gBqM9zFD4gM1SFapAxkfn1SnIQHoeTVrZ0zCBIE+pWXM/HhDUTnsLF47K9OnunMgNpxrDXDMSAVq8N7G7MYGFK68QcThKVBs3VOjpg6tOod2HcoQNMgDC6XlFLuxGgtlZgPAy00VaGx1rob2JCKBXxW6bg9H9z6tKa8YhQSobWT4MAlaCXMASwQ7OkeKtnswMXPlBZ+pNzHGh/5HxfAaTn08cgGSrhTucL8E+fvWDdBI7/N8ZjZRq3QgVz1evO2Bx2wYWIBw4qr5Bd0XLlxgf39/g3NubWtbu1TssgcXyR544AHe9a53ISLccMMNAwWKU/t7G5trXWa2qFBOIxaI7zhftgZtxICOB5RjGHmKDYWGBLlY9YbKGQbZhP7u8yaMlIXSVMxcgVelNC4ou/SkaBeOFa32gQS8bUAM6rEau0lvwolTGzjk3qFSBGdXUi3C6lX4KTvFuRIbi/vtjsftOYIy7fq1FKlfhyWAom5GY03qVxxf+zUI0tCz1rrOCYOqK2KJUOiPUt9AG/h9MUI1Kagm1PSpskOfWjMqGoMXnjIAYp3FBmqNxNG6YKZeobP1PS8SqWb1Bsc5joYCFdYnFFaZDXuLhq7bMWORm0GM4suYscjcz6pEpa+hmQgsvJpaWnawTtEBGBdp9nW1qEIPMImOUBPjfT0Cdhop2mHPDq9JxKB9POFitcbjrFBZqcdojzmmvtfOZtRUpXq/lNHp75fJ/NZHEnttkKFrEa/5Glgk0NKtsWgfdimOz5h8gk/bnV8fcVwTkYUF3efOndsqRW3toti2z8XFtysCXJw9e5b3vOc9POxhD6MsSz7xiU8MtpkUm3D0GhtQZ1YcJ0QwwRy1wEaiTtWclvmjWxkWMaZdpliwYIxDlNCZmTDRsOZh8Z3TBEWlbnTn1ISi7ShFWy0jRTt2SAKICU6FgvEuFPSSHN4hGFDyzk12yuTs0jjToVYgUQxWd0bFC/bAYmMTRr+jULoYXfV4ydxuy04XF9ymTwkV7d9sblO3gXV/67SMJG3qCdmk4LIkbveaTnSkHoVjUYKqWMw4bEC5KdGnjhJ9ylvEOYqZx6ifS59a+ocQULENfQrFSKKZbYbaFK7TVt+OuuTft6RF51HBFJEAPRuHd3h8qfjaj4ADMYqbCK7I0/JCGXEui6NBJjbNn0kuSCykNmZYYeC8YGwkYw4oM0FtyhlT057aVotUjBxv6Mpta+CVzIrHOekVfcf9jMdZqIoRsELM1GayEqaVzWhDpAQscgpV4SiHQGVe5/BURN7OWKTfrA0s2jYRxyN3P7lxYJFsGSnabc3F1i6KbWlRF90ue3DxiU98gve85z189md/NmfOnOFv//Zvs030br1hfhTl2FZ7Y50/6q9WyW6EKDswbSglrgQttfX+Ho467BebGTtRnCIzxahSEKVoWV6KdiwSXMvcxgh15UK9hpIDMWmsRYsO/9VOuSaHtzUGNnIuFpzxzFeJPtWuFQgZDR/VZ9ajT9nK4X3suGhM7EkRIpjH7xKei/u21+djVsNiWI/6FdafHJ/oSMfxG5C3PhConfTEOJcAUHXlO6g3vgWdwOF+AV4ppo5y6kIWa1P0KSS6/hroZrGvRuisvQx9av5NEGZI/P4EakzMAuWzJyYWHuUoOs02inrB1rUA3RWJUaodaTKKnYLimDlwQUWpfwReJUhIt4q3O89H0ZoilS3ARuL90T0TjRRtngoV6FkyAiwiENF2VF/qOTVmM3LHgtWgCtUzG9WtckpTdfF26zdICZhODcrgGEJmr78O7dXTdc+nH338iY0N8nrLb4DFJqlQ7fV69q+6fu42qeZia1vb2pVnlz24uO666/iiL/oi9vYC7akoikETPYDrTp7AiOCX7m61pLUf6BmgsdawCsUMmAoq4Esf6FMmvbyWd06l9w8vwpT4NvXKxFbMKkthHG4O0FiqoFstE1MxdUFWtDQuCCa2e20sve6W+yHgpaFPiXoE16IHrJF1ANAgIeoo8SJ1p+q8lOuSYxKjqB6YllE1KChzaRmdwA3SpwwOF+lTDRBYT32qPX7zr2b8VQUEamtTj1BEXfyoaSa2EfrUbhFkolUxU2Uyc0gVKCg+Uw+w+jGAkSmoadGndKQofNl5W96jCKFMmTp7EuaR+jodUHTaNB4TlJDUm+xl4Qld1WtgQTvqHrTANNKo+oSl1KfCYzqHVt8LLWBRr0ta+6oMIuzheICBFG2zcxjXZMGS90Q545wUbYz4+9SropWVMJ7KCn4ALILSVAIWaSX1fhE89M9N/MF6wKJ3DHUfjGYdCajM67ytYjCdom+FUilPX3xgUc8zmV/nuM1cbO2i2jbrcFHtsgcXIlIDC2ikaPt2zYmLUDiWARobG1rBTg02ZTVKRSZBGrXyS8hyDt50rc9tqNMoJ46ZL5jEvhSGoRTt0oeVfAA1VK5xTKypAjf4mOAoN37oGE0MEHuMT3Ki60XWQwZJwDUUpKZO4zidsEfGjxK6UhHcARtVj2DO2pen7UBDnwrB3qD4lUxjLcU6l2hnjaVinYu9NjZAn6KJxKesSeq0vTH61I5wuBMyXuIsxnnKQ4/R5PyuWesT6Std6kq8Pms5YGl6TqwzEbF2RRXnD3pdseNm8U8jUQkpbdObW4xSlYIWud8wwLwOjap1WZoYvc9RfQIG0tHMZ+hxEXrJ5GoaHDKqChUyFvn6oFBPAlnqVjr3I1QoXxKKtwc9N9Lv1spKtI9Dw3z9n7WhruapXrlz06hM9dYoSe2Kpo9FfVOHGoudmy6gplfPgl4UYLHM82oLLrZ2sWxbc3Hx7bIHF33rd+hOVmy45uKhNAGYGZiFfxeFR/YczkhQdsnSPZagTpmQj59SRPpUoDaJb6RonS4uDM9NF2NzeF809EcJmYe50XXJcBX6ZgDRIAkbt7W+qgtNj1f4LNl/qW/qNCSqNhnj4rGs7kwLoTjYSWvtnS7ea7Z9l5bylhKpND5mOdanTwFQKrNJETIClce6eO5Ve9StVa1x0oM60SyoNpmkmrMmfaoAv6NcOFEgTimOHOWRC5K3m6BPkdZeNMcQMz/eW4J06waeTyJYk19r4venKH12d6NMJxZjh/UVqWv3oD4jblh3nY71AL29ozpTBhyoBEdemuJ+7QELFfJStIkqNAIsBMVXoSZtkM1QwYw11ovAorJmABFS74p+tq7p8A2piLp9sA2IzBxDD1gk+lSiXRmGxdsaQcRog7xIhWqvPmQsNl+8nTMzch227fz589x8880P+lq2trWtXXy77MHFvA7d2W03TYta19allgPWK+5cEV5CFnZ2Hc4KYpRZrAxfpuR5EDmTfp2Gh9hXoDBu6TqN3JrDn8F5Tg57N7qeHOLlfq+aPhUHd7Zo1u0rxGtNG1oPDAAa6hqcKwC3OSnXtPYW0DAanFAnJp7/NbM98djFVIE+pUFiVfAbADKCLy2+TAXVUFQVuOBs5SLqI8uciydVC4x3qIsAryVHuupvm+ZTK8z2C2b7gCp2qkwOHaaKSjzLiBWkg5g7X7pOKlC72cxMfymizCpBfZ8aRO20JmARWlFnHGovo6pQyXHOqxulGTST8Qs9JNrAojOvCcCiElNnRTr7xht+DFioS9/2jydkM4Z0vlCI7kpw1tSftSlUAUQMlaZcT1K2PWs6P0PhjfjM6j93NWVyQoYpgYzO+tsZi7SbCnbiKc7kqVCP2v0kd1wEYAHD93LOLly4sK252NrFsTqiuYFxtraUXfbgAsKDTKMTOkaLgvpRvhlbExRAJ4sNxjeRvfbLZoklp0hX6qeh52PDNKNMJg4/gZkzMdI77hzN9eOFuguuLRzTyjApqvDyYyhFm+saO8+6TqGvaUJr1yMI+AQ0Jg7voHBV7F69urpPo1BlR6VcBwpe8xY58lEqZhcJvSKsi9QaPV4vkMHwEo7BSxkPyIRi+dY1sNbdEguqZzaMr04xXilnsaDab4Y+hbetfiY+9IrYUM8ORHA7wkGLPlVc8EwOPcYvoE8t27wvRq1rGpjSAxrr1cskx3Z3ohwejvSpMMp0xw4yA9BVQ8rRAZ2PhdYjPRdEJTTmM+NX0+CZJE3GopIc5TPUfQyelbSyGTWw6B+Pok5jH+xhNoNSW8CivZ+vAZTp6VtJvPvbx5HqJ+rGhfTfP03X7Rx9KvTpCBmZthJvABYaNSy6QIXCUZw5aoBF3K8BFg82FaptW1rU1i4d29KiLr5dEeCibUVR4L3He4/p8U19P9y0juXCq8cGGy0RVd903CYVFgYu0sKBx9SixAscFphD2DMVM2uQHVfL3FY9usegK++Imditdxq7bVcYythkzKDMnKyZIGpe30Gq1JGY5WvVUgj4wjKLAk6hAVuFRApPyqKsNHb8UyPI8oCqq5WnQn3IGkDJgDem7jcgzoesgKa1b6AOoVblUkSDak8o415POQsAK+iu45Cg3GRnnvLIgQvKZetG60NGyYTmLgSf09gZlVpEHOvKDNf0qauUgxNlUJ86dJSHDklgaWP0qTbQcIQovQk0QlnULby5gtuObf7IQ2dsNwYsYrbA+6iG1LPg4A+zIWHuKIU71j8jFkSHTEnXvBcotAMsmpd6+CW9xuLt/hEpc/puhIyFxHPZX4+zoHaYDRj0oxg4+wms961fY1I/JeqajX7xdqJd+QzVKwELlRAYac8TaiwOujUWCiUPBbBYLnOx7XOxta1duXbFgQsbFU6ccwNwMSks0yqf1VjJeoGjY2czOvKOrV2TAomGbaz4OgLpVnRgjICZmVCrgaKFMtl1eBOiazO1q6n+xEXPWnKxliBFayLNaS0akgmOcwIrRttF22tEdQWwUNm47kjhcVU7Mjky9iLeTtykUMVXNrhDJtCnQvfn9elTWhhmhamvu6KqECd4Mcja9KkgE2vMDK9FcOR8G8isSp+K+xjB7VjcDgHIVDA5qpDKgMvr/h97FgWqgkIqKimDzG1rzetmTTBCtV9QRfqUmfpAn5pF+tS66lNpnW3p1Jjh6GY28r9FG1gAaC/kJqJ4EfxERjMWouDG6jMkZAdspk9F3d+hGqmFSD0e7LCWQERxRvG9ZzcCoqHwebQvBxrrfHrZDKSuv8jtKUbxVpGinw2It5f2MjPaPQ6ImfPWruEazq0yFW/nsyNJxjcHVHJUKNCaCtW/5Q36kAALAOczHRt7dv78eU6ePHkRVrO1v/e2pUVddLsiwEWfFgUBXJRl2d1umboDVoxv9oHGOmO1hixMpBwpCD72jgjfHafmoaNagyCVwDkTKASxTmOqgrE+0gXGVz73mASctRhxOLWIDxSnsP4VnOp0sD2aUAiaVrUzsTZ9ykA1KRAbpHiNdy31qTUpSADe1v00VLSm74TmW+vVUSBh7QBohZ2BmYWaB3QdRzo6ngLOlPVFZHyM1hOcsWUzXvkpBC3hKN6rMvMUB0pRuQ2svz6KppgaAI+RRepcx5lA8DuWw51Ya+LAXqjYOYjqU24OfWoFG2Y2UpaTARWnXmLr/k8F1K6ULHsrgYP5wCJJ0XZ/fWM83gnezQEWSC3CQG9dTiT7VnI+9NToAov231LfjUwxuidQ8Torihkd02QsBnQn0SGwID6SBgXa0tmPHMjRlH3IA4tmnO4ROB8ryjrAImzXrrFozzcRx6P2PsEdOxenxqJv1kwWbnPhwgX29y+CiuPW/t7blhZ18e2KABdtE5FRxSiTScG3TVt/rhV3jO+H4RjrjawYKt/8qzSBh18tUSg7Fg1u12mUAMZTTBy+EMRApcNiy6XvLwkvbZd2UsW2Mg+u1WRrzEbnElCJWv8eBBebyK3Ltw+OmrcWHwvC8Z5Ck/rU8mPno6vUUrSQElceY12YV8Yc0eWuGzFQ7VrYDWs3M08xrcAFdZzjnZfe2U+9HOpieUW8jzQNt8S5WXzlqDXoXsURZaRPKcXMhfWvUSNTHwKQ1KfqOhmpqLCEnimbok8JF07sRPpUxeTQR/UpX1Pn0nrWeV/VmY14I8xmh6NyrxCLpI2EpnAjGQuvMlp83wYWYdpmDGs8mn3uNaDHJyBEdzsjHm/AZwrKUy8Hr33AovU4Oo9+pYqMAIQALHLnIWQKrIDr/UDNs7Q5C3VhfAR21iiuhVWS4MQwYyGdZnzNUbXXD/0Kba/0irebd8tDDSwAJjvze1yo6jZzsbWtXcF2xYELGC/qLgvLwXQ2ul/7taatzzZnQiBnp0jjenmNVERtxCF4RMJLMfSm6EfNlpurQKkOi3p5ZenQiSNl+ft1GsdYLojgKFonWTHqkSiNurJTLXQc5xBrzMi5LoHtBpsIYA0VDQXJ+iooFVHNpWYtchoTfaeoFK1CvYexDgoXwVMTtV/JARXwE8N0EgYQB+V0hk5tdGQWnZAFJ0sEtRYxLtBzVLEbAXjRTCiodq2C6snRDJxppETXuENr11ALjFQ4ic0H23SijdCnyhZ9Stk5nGGmgeazrILWkpMNsrWdb40Palg2AYvu+Uv9JSyKy0TdJfYAyZ3xVPDsnal7VvT3H+tTYYzHW5qu3K1lpZoEi2bqK1Lxdht0dDMI6vPZDK+EPhYZYNGmO/WzKy1I2poxgItcxih9H4YaZjOCRHC36aHGbGldz2H644YeR+3i7fRr7kRg8bCHEFgAFMXOwm22Bd1bu2i2pUVddLsiwcVYl+5JtjFU3vosp42BDG29QNPrskY1q81iBCoNzq9TsLErsEiIsnlNxcSLj6RdHC4KOrUwjR8UIatReUtZVAOFqKUtOQ4myp+qiRSkBtLVmverzZAFGgEILKhHCDIsZM9TBFjOFFhxOC16617PGRVAnIUk2mQcpqjwRgZOySqDawHTooBdKI8cxhHocQCZDNXxF98Fj8lRFwjOaq7PwdLjh4zAURHoFmbqsEeKraLmj19z/fEYPEWTeYwd4MNXCSytl9XwO8LBTnC8pFKKg6pu3hdqKtY8hswNY4znwswiVqhsk7FoB1PqhnKaOlV3Bw19KjJ3jsbC73Zjvt4GIoqOAAsktJpwrch8utYTVUgRJM7RHjc1wBtmM6TbCbxPERNFS4VeXUeW7qS9/Wow0D2GQYNATV9p/Ryuz3G0uni785uH9Tdys9J9JqPIxLNzplu8HZ4QekkAC4BysrdwmwsXLmwzF1vb2hVqVwS46CtTjGUuJuVqh5t1JzaW2ggvbVsXaQYpQq2LuhdP0Jc8dS2qhOBiT4qk+L5ovPz3AlAFNZ4d45hRsLNbMUuyq7LKua3fwIFqk9bsfQAEehyneny7BDRMFTIwYl0zbxYMLAknM+sWWgXnazjTAogPwC7ICnt86REbyKNe1uxJYaAyBZRh7aH5XayNWVd9Ki6r7airBsqRxJj4OjUsAFhwuzZS7jTQv2Zh/ZtQnwpYqKXgRhAT8LHHiS4sal/822gh+H043J0ECtjUUU4d4gShS59a1VIdw86e56DqjRcFJUQU76mpQ12/OTitIakxPCavgngdacwXHWtMaODXM+cFX2qvi3RyrgNodIkK1Y/cEwqW+3KyoQ5FuwX10tzPYpTKgkmUx86+fbpTa4i2pGxPCk8zxdvaPv7MMzxRoayhS5+KACQBi84aUEynxqI9nvKZu5+8JIAFwM7O/IxEVVUcHh5uMxdbuzi2zVxcdLsiwEXfxmouprNqKUxwLJetH8EafLncaE4bJ9XgMMbFz5dwUuY4sYqt6zQUT2GqWgIxm3lYpmJJwSj4C6GfBgaKosIXYR1+CRnCURNinYaJTqli8EEpZk2qTXBSbOhbBqh4rKnwkvjgRb3dsZ8hcd1a06eUEGpdX8o1RObBTqPzJ2BthZbR2THrZx18aQJNRKmb3zm3ftPB+gDEYgj0KcXX6k0bK6ie2Ej/CgXV5Sw073Opk+JaGYfwh5eCwlWoFrViU7LhOTrmFWQEt1vgdlOncyin06g+paEj+aKsW39I4/FWQvTbai3T21+nDoqdg0lUjNIqdNEeFInHeoYcsEhUoHaNRed7o2Ftpu9AhzVBC1hA67kdgIV3oUno4GhUBpmK9pxVIWC6Gdpm3KRA1xqvPs72b938fbx4OwpBZNaSMjJeQ4+gwfrpAwsZAIv2Q+pSoUK1bWd3vsTsuXNhrdvMxdYuhm0Lui++XZHgYqxL922nr+Oj958Hhq/+dsxqaVekG0xrvQGXHSE/m8fWFABFEfFBSnYMECCx6HG+41EYpYr7OzWLgcaSR4AHpgV2Suwv4PDGYKzr1A0MbBnKvwhqkuQmTRO52hE4Dn2qTS8AUVN3XFYhAA01ddRxvbRDolWY2Bl8hkZnyce46/EHjX9ToCqQCJKwDlMG9SlnFhfJL5omNL8rKGYV3hUt+d9V19430yleNbQb360LZCTSv4K6lXEOiU0HU0+GtelT0Os14Rt52E2BpRKOyoY+ZSrFOCiq2IRQGemrEa5eYzzehM7ukvrmDI5BYuZiOEqqv9BqTs+IkZsuUID6wKKVPRClMoItFLKSvVrfK+29TaxXq3tn9GohBCBT95AaBSZgERbR7J/AgxnUV+QkZbXeOTVAtCZkfupzw5jiXjcj07emeHu4ftOvsbiEgYUI3HTzw5nNZhhjEJGBLPz58+E9vO1zsbWtXZl2RYCLZWlR15zYHz7vtfPHGovo/yO9uXQ0kpZeNvN1/QUkKCs5NbVClDGemWt+PoOyqIOH9GB3H2g4b5i5ECFdtZN3cHwtBS44itZjitS4r+94LXvWtT6/rmhleHyF+PDdMvSxLnu5/XkYT9P5jAXyaQ9lDYe0pgiVcZ0gWnH8Ky4PRAXqOg0Iij0yqfA2OJVZutqy4CP6R135X1dT1gLYWL9xnI91GoGCFK4b2UCPlKBwQA1ME03QtKlrG3kEmobGSFCfUm8xMr/gv95hgakV0EDBm05CZkMqT1mFJoRtCpUSHGlnpO4VkRPJSxH3MdAh6AJgEWBhf/0SC7BHMxaiVFZiIXV35+ZZODxfaU2dpnw1GIh1Hd4ElaY+3YkguzvACPSyEoMlpYhRC9ijrWzGuKTsvOJtN6ixaICFFe1kqwWFHUdx+qgjGKXIJQksjBE+53P/vzz1q/413vvOe9gYU/934cIFdnd3a+n4rW3tQbUtLeqi2xUBLvo2VtB93amMpnbmhZP+uk4AmOh6UXeDjW+uXiRsXgQwmUVrRkPKMDjXZB5CO4D1Vlt1lKeIDnZ4qfejbMveX8HxNbXjq0axRYXGxn1+0BBqfJzBnNKWRAWZ+SBzOyd6nGtNlTPT2j8SNFrqU8dxqHPeFWjLWUcdcBwp1/HfWQDjFT0sMSgYQ1FGuhpJ5nN5kJQ75yq2JS3ssVQ4TFTO2sDjRCxiqlAzEYHMehmB1lEM1h/Uraiv8fXvegCvBYU6vA8qVNqi76wOllprE0FLy7SMfTU82GqGqcBPBSdDulF7BSIEFSWjg0CBEY93Da2nP0CjpDQ8XybWERjJ1EIgsd5BsmgnOeXhedgH0Ip6zUjRtjIIY6pbolDmssNksr2tzGYEHdJ/aqTH+Bxg4dWMFG/LoMeFr/uSxMFb2ZNAhXKUpw8H50y5dIq3kxkjfM4/+HK+4Z/+AgDee1QV5xyqivceH1M89913H3t7e0t18t7a1tY10dBgcxPjbG05uyLBxVjm4ur9BQoW6TmXyWas9AgUiU5Mk4tvKzlVy3bxzaTroQEEwTxWXJ2qz6XdlzEjsTlf/YILjft8dMZWdSAFEC8wLeoPxDooPSI+Ao2xNS+4oSVGa5NSkSqFDjn9WZCylDXM6EDGqvBY7EKHesHvKwRnOtY11BQh2hSh1V6+ggQP6qjEHhEa9xUOX4ai57WbDgogqTbG4Sl6ax/+nsc69/Hc+NaOtvOb9jsXrLL+Rt3Kzhzqg1RrACFjdSzL/x4hI1aEPjIAuNA8cVP0KcIxqIXKTmAHKns46NsQmsK3HXHJHok1HvVE2dchOAh0pWbP9v1kjK+d7vT3tnkFV0iH8lPv265B6O1rxeO8QB/sQChCbx1Pe8xw3IorBJt5fmaLsCOo6RRv96JPuf1QaoWtBjw0gGYecFLiO2IAmxQTMxbGdIWyJuJ45M7HedjOeS4VM0b4rMc+iW/4lle0PgvnImUnUiZDVfnFX/xFHnjgAZxzFMUV6YZsbWt/r+2KuKuXpUWd2l+svR0GbP19U7SpaK7X9KryBmtClmCs4/YywZ2lOnkvQ8EYUAq6jfuSY70oUj1GQYKGhmSd4p1FxVBYh6/7O3Qdu2O519FprFqSqNaH6LfDxlXNH3H+t7GGonBUWoSxdQwM5KlMufnSuWpnTKSO2ofC/oRVVzFRgVmBnYEn1vHYYTH7SmPHddVrj/QjWkQ9xa4O7uLpc216lqR8XZIrXYc+Fafo17EUVZRQXR8IhENo6GsKGJPUpxxJ/2hdk2yjUOVwJgNHvG1GfEAAlRkplG6OopksZGITmBhmFtKaNIo95JxyjQ0N85kArekM/ZFjRqF3PDUkMOASUBweDYH62b1maqWpGgC0712NrUFydKdwZecAe7tBXtOFu3UCYkih81ELWPSTuzviePTeJ7j9EgIWIvDIz/pCnvEdvzp3u1R/8RM/8RP89m//Nr//+7+/BRZbuzi2pUVddLsi7+yiKDg6Ohp8ftWCrqFZywCN7gdzHMeF3pShtFWsnRhXclrKuW5t1AUEntI4Zl5Gwcvya5boZOUi1X3HerGyTZom1WmY2rHzwbEzwXVcuVdackqjXGzdHVxrQkXWqVnm+ZECmM70a0D6dRqLFz82n3aoWSGqnFTE1ql1MMRu3a0u4daEfhoBaKwpcystZ18hdLmIjrRsgD4lgFG8seH39IrRKKO7ckagRYmBTh1LovN5s3wfinnXUH1mfYGN8rYiFW0pkrUVuqIZ8VTORArUOLAQVbSa05U7LCrzpUbw2wCLuj9fdPIrG4ulR5zygQdNk0Xx3gyoSamuI2fexyymbYQN2nmNlNkVkV4gJYKGTr1DOuRW8bZ0azoS3Sn3HOl33u6EHWoqVI8mBUEVai6wuISoUAK33f5I/umzXrVwW1Xl3/7bf8tP/dRP8YY3vIHHP/7xF2GFW9saW7Woh8CuSHAxlrmwmW6sx7LO7vFl2b7aehG2nA77wOrdDVXkDafC7cQDr5bp4jt60RtmPaDhIv1g5lZzIlMrun6UPREXojL+4iVnJHSDY2cgqjiJeHQioXO1aFNcvIqJolLUjpKox2ToU6uN3asB8Q7xoZ5iE/UIgsF4h/cWFY8xcd3rZh0I61XX0HeMqcAK3ptQu7Em0CA6yoLDx27Ym6B+hUEFtRLpWYH+kjJVovmi2zkLzX6a6HwGQi8N6/GFiRmNBQXby86sraJ2XPP7knp2LHuOmgdBipSXhacolIOjvLqUeGrVtMF30ak2JlMbpgI6rIXQHrBooY3W2uJ4GWARirNbTflaohjtxnq59XqkprY1n6ZjaV0PPWCRswQA0jkIHzab18XbmRFS07x+jUVnXLpZEEGhVIrTh73i7UCFutSAhQg8/BGfy3d8z6sXbquqvPzlL+fHf/zHed3rXrcFFlvb2hVuVwS46NOixgq6bz99XWg+1M9OrzQpMdXWmrtOe2tEyoth7pgTPvPdqLUVjxFl5vP0iWWc+bB/GNengvBEuzlGh+PcUSlF63NHoPMsUvxZTBeyajBHFY4CNYQCZRupKsduJCedv6o0/TRSca/zZrlC83k+t4BaG2R5ncNhsIyDmGXpQpL+0wZ8JTCgid6k60nRCoAvwIemcQEcuY0AsJpgEmVcPUFAIJ3KsUzS8hNIzCaVaSKs8wTtIl0w/uJfIJx/rellCkjhoPAbp0+pb5NmQtM+g2s1ZcsfR3rktKlKZc316przgvgxCesGWOSuJyMe7yVci73vvUoPWPT3DcDCGAaAxflwJbTXlO4P069r6AMEAS10MKev6U6mvXXrOPO1FGmbXJZkSHdqBZVGireVdsaiXcgdriuz4yjPHA7WLyifdYkBC2OEh33aY5cGFr/4i7/Ii170Il772tfyxCc+8SKscGtba9mWFnXR7YoAF30by1xce/IE+WzDhqxVuG2Mw8V6CrRVa7GChf4UFqfEgvDGychFxkat58W2C8KNOKw4Kh+Ui9ajZbRrMmLX6qxzurwXHCLIwFERkgMCptW4bxlwFZylscFjcS8x6yD93gureeyhHqEFYqCngGSOcQl2t2zTa9JPK+qDwwvze4wss3bC2hOXPJC9NleQLFD3aohuI6Jug/SpcB+qK+vxGzCz4rXY+gkEkKppyuiNYstAn8Kv2W+ktSLVAhNrBLRFM0sOar8buTU+dnlO2T8GazHi0YpxcJCc8fq7Bk2nHhhFTzY1bechBgCGJ6BWm8pkM+ri7R5wEqGW0c4VTAsKdY3FIL0SthgUUof/N5JqRXrUK+YUb9OlO7Wto5jVm7P5t7T+P2RnQ4O8o9gfpLGJOB61+/FLqsYC4LZPfRTf+dzXLdxOVfn3//7f84IXvIDf+q3f4gu/8Asvwuq2trWubWlRF9+uSHAxlrm4/uSJYZS4k8/f3BqcWgrjcD6oAWULrJedsrWR62n2JyUntwx1ao55tTGKGKLU6zuRadFNj4gmCtse93hdh5OJ0okgq/FQVHX0OleTsNS5lkhnizSVEJOswMiK0fWeN0pDJws+UeyJEK+PdcGAUUVnQXkKE+hTZKPqiz3fIWAT2lS49FtqLbe8+jUY3dZAa3IVTmyoA2Fd6lfbkTOJORWAUouetW5jPQGsFzgKhevFrEJL8NaE+pAN0Kcas62oegvwSWjm1gYWzeq09S+PziI46P3GgYYVft3uV11god6A6aaAjfggEDDRVqAlrrMdqc9mMzxeh30eALyP9Ku2yEMMFIgEyShXU6G6NKP8PR+exE0WCNrUK8EjEjuj986P02FmpX0MIQmaGhf254zntVkgaAMs+qdlIo7P2ru0gIUI3Hjjp/Bd//x3Fm6rqvzH//gf+b7v+z7+63/9rzzpSU968Be4ta1t7ZKwKxJcjGUurtqdzN+xDhX2v9DeBsczxXYUl0LhduDkLpKMnUeZ6YwrUSFqAx23NxOtzp+rfv8I1CzRbGwxfcp4RadFDTSsDQXhbad6FewYZg40oSb6HRWzxM0vrF50uUg4H1Yd3lmQGFmXXGR9WdN6avEGfMiMqCg2qkMpii7ZY2SeNSDJBbBnuupQq1oASVI3NVRCwbnWwHG9lEAd79aicfkqhxgXgcx6QCBQ10AObSjpF8EUHj/xc8HvqrO1Vbq8OxpE1AWtL35B0Vko6u+H4RKdR6TL9my+j+AgE8gQ8WikQmX640Up6z7FKjj5SRVK1aCSASwxm9HetQYOQgtY9LIZ9Tr6ACoxIWXwWUO9Cp2322mEeU3+6mPIfNfuAN6mgamCKR3FmellAyw+5ZaH88++/62Djts5+/Vf/3W+53u+h1/7tV/jy77syy7CCre2tRHb0qIuul0R4GJZKdoghccKF4gAvgs+Vr7IpNUZG7r1FIasR7rEmq0oVXTGXFSh8j2gsSKxZxCtDrQVF52wdRy9kNXxWiA4kLxzepxT3Xaq0wdFEZ3qSH9Y/UwAsRuzrSq8mOCwm00VVvci66ZNzVpvbFEJUqtEupT1SOyLEpz19ZxpxbYcsV5B8ibqNNodqGPluYhvqVutN35Bk/ExJhRtb+LcQwQaM4OJWTZvlWJnhjcmZADWpE+1rSjKwWdKaLopeJgJOcHYdqG0kWHE3wfl30CjyuyrBmbWgAja4x6KKJihhGsCFkRg0bdE77K5/hAawFFHFarV+C5lJbJ0p/i37hqpOZNjdKdm/xzdKZ/9ajfq6zfV04lSnr58gMWZm25fGlj81m/9Ft/5nd/JK1/5Sr7iK77iIqxwa1vb2qVkVwS4AKK0YHh6j9GiANyidtijZlpebizgE40UmtUdM2sUF+spVi2wztksAzSC6tR69JUkQeu1CPKZpK7VXQd12bOcXvaK7ZzfRFlpRyOPa3UwcxaUflRdYHvYGJ2U1Z3qkC0JICaxMVL3cWiKzVdZeR179U1kXTr9RcYj64twaMgKgKjHaxGuZKmCAyg0v8VaZomNeGM0OICkQJ9a/fdM60ctxjlUpZORWRfg1eP7BiipaGh8F8dfFwkIYJ3ABRtHclBqoE9tAOjlfv2jaQBNUuWvx1T87do1Gm02n3jUQa5toYiiFmbGZOlOIooXwUjXuYZWMfeAwpXoV4r3lqLXWM+ID/3sTReYJRd/Ed0p26siFpLnnuXtGpaxztv9Z7Wmc9MpIm+OTSaenTMHg94YoXj70gMWN56+lef+4P9YCli89rWv5Vu/9Vv5lV/5Ff7xP/7HF2GFW9vafNvWXFx8u2LARdustXjv8d7nH4bdUNYKZuqXlkcjHUkQ8b2u2cez9r5Wmki+W7aT94gloGFipDoJ+I/psy9rSfUnUZykk3lYZ82mW7gpIMGdWDMKLiGjEX2VEKEeFj8vt/YupSKBmJQZsDbUC7jIv171PDezFHWRfK5BXfP3Zc+7NP+vRZ0xqbMOIjXQXddS/Ypo6MYeHPWw1nWuv5CRMVDRALxOv45jFMtnNmwyPrb5YOYRu7mMj6iiR7YWKRDr0TLQp1YpyNfevwzKjniMgYOqGGybagOc5gMDIoFGZaVbaBz2EdQqrgcsailaiQC+R1mClhTtwJlvhKy9t4PrL2VJJJMJ0dTMbq7SlMSATnfMLN1JAyDp1rA0x5GoUCZTuzIsBm8oYDLx2NNHg0jApZixALjhhpt53gt+fylg8cY3vpFnPvOZ/PzP/zxf/dVffRFWt7WtLWFbWtRFtysSXKSun865wQOxNIZZ+82SK6ewvpb6HJ1DPJVaaDesU6E0DR3JLRhjnjltRfLreorwDl+qGV7GRMB19nW1eg6sG7G2rYhedHzFY/BrRpODpK3GItb5jfuOMyp1hDrQwV0NCJZzeMefMqEpYBFJYyGqXhdWsz7fvvs7+SgVm6dsHMfCno0MKuJBfHT21u9JERR4mkJ5xNVZjZD9WpPepICzmNSvwzrUBSnd8PX641v1EDMBxjqwWtcnrJvxEQWpTKM+ZRVTzkJ025vQ0XvRb1DrbAdgQSXYnfx14X3Ivvp+DUVdn+HRmQSIKdq55I3xOAzOyCBjUReXS57yZWLhuZFhwbRXwZJZU5xTBZyEPj3L1EMsS3fKqTstLN6ONRbSg13N/SKdzxSQHY+9MRVvN99fisBCgKtO7vPP/+UfYu3iZ/hb3/pWvvEbv5GXvexlfP3Xf/2Dv8CtbW1rl6xdMeCiTYuyNrzknXOUZTnYLmuqx/Sbcs6l1PUNoWPtEvUUS5gVxbmkEOMpbL6T9/GXbFsFhrFQWS0ii6RoF8F3S/DaQ2F4t2naepdcv3Ef9bjLAAJT79m2EEG2aGLSmeSwj695Ef2oPzYugZjNNcCLi6VxJyGAjdDbYd1ahxBYjtF/TX0vVlXNGo6P2mGNSUtMYB0LCTobOmC7IgCZOTUsxw1IpfGbLt4+0KdMGD9bEX3M8a0TcJZQsuBRMYF6Fxv4NV3gu0eSnFsSDSpzsRoTCqWzHepjPYu2aVQdYKEhe1Bqprg7XoU9YJH+mhStcotKxduD3z9mEIjAov8YrQECGbAyh+7U7pthM9SrfPG20C/eHmR6YvF2P0NuStcCFs2OOxFYfOolBCwArr3uRr7vR/7nUsDif/yP/8HXfd3X8ZKXvIRv+qZvGn/Pbm1rD5FtKU0X1zaljXhJmYhgjMnWXYw/9CRyYxI5WGma4mW3XmiFCWpQMx9kaa0J0rGdSNcxLviQaDc4V+C8YeYKSlNFffjFP+X8nn6mLqxWDdx+wWMZFsYvZ6m+I9BiVG3ozkxF6KdcMSRa5G3sXIfGfTY6ihqpU66OVI/ttWguE53PNHazZrf0mnNrF8CoAVeAs2hVYHxwqMP5WP/pZ8THXEaBzFn3sWcS8KbAG4sTS1IRC1ml9WVchVhj4gMAXOU6mTu+WtQVaGXxVYHR9vjrnXch1N/IzCJHBqYWZoro5tYPMbMxs5gDQS4Y5MhjfNWaB7yjCyxqa44xFVGb0TiLdIFFy4zxeKtUNt/tGyKukuE3qfN27o5Oa7KZSZ1KUKLKAAvbAgh9AbRaGjZXK5JAUKYhX5PNGK7Fa/ivPWY6U00zvu6AbSpU//Wzay5NYHHNtdfzfT/yPymKBQqLwB/+4R/yNV/zNbz4xS/mW7/1W7fAYmuXngU5us38dwx78YtfzBOe8AROnjzJ6dOnefrTn8773//+zjaHh4fcc889XH/99Vx11VV89Vd/NR/+8Ic723zwgx/k7rvvZn9/n9OnT/N93/d9A//2d3/3d/ncz/1cdnZ2+PRP/3Re8YpXrHSqNmVXJLiAQI3KKUbZJXijIWgfgYZ4kFBXcWxrQeXKFzhvqbzFiNZAY5EU7ejQ8b8qAg0hSNxa8ZRmzAlefGOk10IABAZXA422o7TcyyO3VRtoBO36BsisYwaN1CmLR3prbq/p+I5kAzRsjAo7HHapNY/NVvtfvsDg6lqKtOa1zkcc3EuBF4sLjP4lwddy46uxOBPGx4SeEbnzvdLSRXH12rvAUdZcezrv6gpIQMM71Af61LrXYQAyYL3HHAGHgqlcDWZWX3/3ShLAOIM5NJgDgUODqaZUR4cZYJH2aBzuvAhFcMXF58V+A7AQnLH11s3o2kjYZpxLr5IBFt3ahUA/ymQzDHgzBBaJajRX3ckM78CQselmM7S3n8+upd6q83koIm8DC2gXb5sdz+T0If1Xj8FfksDi6quv5flLAot3vvOdfNVXfRUvetGL+K7v+q4tsNja1lr21re+lXvuuYc/+IM/4A1veAOz2YynPOUpnD/f3PPPfe5z+W//7b/xa7/2a7z1rW/lQx/6EF/1VV9Vf++c4+6772Y6nfL7v//7/PIv/zKveMUr+OEf/uF6m7/8y7/k7rvv5h/9o3/EH//xH/Oc5zyHb/u2b+P1r3/9RT3etl1RtKi2jcnRTgrLhaNlBtQmkwFUapt6CuOp3OKH6NgWnXoKfEjJp+9y9RQL/GEhStG2JG5TJ284XuF2zvn2rcJtiT0pkhM2f9zF9Kmmk7dr1VN0+ffHhwPtPh2tZm80L/1VLdHFJF0LGmVRN0D50l4PkLHzcWwTSDUrYa0uNvpaVCS/xDUTvWlHUf9Q1le1JOjazeMya5eomGVZvVC+vXz1BTZKLLeVynLdr5c3rccn1lFApE8VWquK9TtHr3oM4oFpwR4FD2SW4jVF1sfu2UCyUhdpb71leRWwhOLtntWABRPmGKyvJr71ZtSGPpQDCCYUbxuT6XYdAcJYPQQEaqrNNfob6VWRnmkN6JDWfqG5YOjo3csJaSpBb3/WAAtz49Gg6HtHHI/Zv7SAhQhcddUpnv/C/0lZ7i7c/k/+5E/4yq/8Sn7wB3+Qf/bP/tkWWGztkrWHSi3qda/rdrF/xStewenTp3nnO9/Jl3zJl3DffffxC7/wC7zyla/kS7/0SwH4pV/6JR71qEfxB3/wB3zBF3wBv/M7v8Of/umf8sY3vpEzZ87wuMc9jh/90R/l+7//+3nhC1/IZDLh5S9/OXfccQc/8RM/AcCjHvUo3va2t/GSl7yEu+66a/0DX8Gu6MxFjhblZcnIYeYqmvki0JxckAgVPIVZj/ZQGh+kaL3FeWllH5q1L/PM7m/j1Nb/JQqLj30l5tv8SyIoRCW+d0NfyY17vCyBbWUI+pmH9V5aaVwSMFiR4tQ2MaDW4AqLKyyVsUHSdQPR+2Dd80FLinYtE1CxgcPvLd73KULrjY2AswW+sDhbhF4U8fpYFwiktXspAqAxpnfO16c3CSY011OL9/2s3drLD/SpqSCHIEeCODa2/mDDMY5mhqMji2qmF4OE+7QNLPr3mxjFFxlgERXykAAs6rRNe18JxeQ5ipF6aWUsWguiW7zdX4+NYGYUIEhUvxqhUOUDKBL/bI8ZZ5FW1/LBfBolinvHhtTAQnrZnEsRWADs7u7w/Bf+TyaT/YXbvu997+NpT3saz3ve8/je7/3eLbDY2qVtusH/gPvvv7/z39HRMhFruO+++wC47rrrgJD5m81mPPnJT663eeQjH8mnfuqn8va3vx2At7/97Tz2sY/lzJkz9TZ33XUX999/P+973/vqbdpjpG3SGA+FXbHgYixzccPjDilvvoDZq5DSkXsZG5SJme94igqooXIFhShWYj1Fq05jKVeh80w2gT6lhpkP9RRGPNVSUrTzZrM17UbnUIbCKMusOq2ncYAXjTu+6iwBo1PzoMoS4y6xbolRVulSnFajq7TmC6kjXFEEoFEUiKmA1MF7PapNuE3D79etAck5pMs7qDVFSIsO0DAJfGnraTp3nJFtBHwEGt5avNGYLdkgkDEFzloqE4q2E3DcCL0JaYBGrBfCG4ysB0zr8RN9amZgZjHerXE9BlPtrktiZqAsHWOdpaUDLHp0J6N4K0gmgeNDhKXjyHdITy2qUP8KMSmUmJGiTRmLBsw0e1vjY2BneM2F3kCE+rPewaT9vJpBIKZpIJgr3p4DLMRngZPgkR3XAIvWQi5VYHHVVSf5wX/1J+zunly47Z/92Z/x1Kc+le/6ru/iBS94wRZYbO3vnd12221cffXV9X8vfvGLF+7jvec5z3kOX/iFX8hjHvMYAM6ePctkMuGaa67pbHvmzBnOnj1bb9MGFun79N28be6//34ODg5WOsZ17YqhRfVtFFyc3uXjk48C4M5bpmd3qT66g1YGnYYo2Z6psMC0Gj89oQg3NKRLVCYl9JKwMTtSLSNFO8d3S0pQJnavlkjVqDLKU8s83iWuO0cZMoBL9JOFllNd6o7bjrSv3dhMUj1FX4q2Tbk55gtOGopTWLAjSbp6lSWGG9kgfuxsARZEHeIVqRtJrK6yJHGpY4pZIf682ou+9sWiVGzKzKmXIFfcPl8rTiBGQ/2HAqpYrUL0u0fNWukIYo1JOAgQDWXtTi2WRepnC4cOw8bGkd7b2NPF1RuorilDC0EZqq0qVrQbSS43fjs4kAqod3dCkf+st63XeE25do1Gc1+LKM6EZnX9jiHG+KbR5+AHi/UXLfpQGwwkKdpc74xAv4oytq29w37jzeysSUGY7nqUplg8T3cal6L1Wo/QP8Bu5+3eeWHiKW4cFm8XXJrA4sSJEzz/he9gd/fUwm3/4i/+gqc+9ak885nP5IUvfOEWWGztsrCoqr6RcQD+5m/+hlOnmvtlZ2dn4b733HMP733ve3nb2962/kIuA7tiwEX/ITdGizpZ7tV/tyccew8/Dw8/jz80VB+eUH1kjwvnS05O+q/ioRlRXC+ArRq6bSfnTPAYE1yEVftTWKON5KxCEXtThL4V60mCJqcuxDWbegqt49p5W9xTIagVpUh7HhCsvt5wflt1JetQpwRCFiYN7HtrXkPSNUbv06LF+yDpmnGqV7F+nQbx92v6Uqy4bghjVT4Au9QHZBOdsCNNpFOnoTPwhsTdX8sEVALxzZQVlbMUvnXOV66jaJaPtoqaNcoL67p1Gs34ooaEBoLPqxgTMo/UnZ8z5yl63AFYtCBDT+Ukyb6OqUKJUSojYOMcrU7XxjTF0jriXPZrSdKzIknRJkHj/poqIwxkn9C6mHqseDv1AOoHR3z8zXPCGbWaVJT17n4Xm/z160TibzxW62F2PPaGwwFP1aA8Zv9jfOrOhcF+D6Xt7+/z/T/yP9nbu3rhtn/5l3/JU5/6VP7JP/knvPjFL16qqd7WtnYl2qlTpzrgYpE9+9nP5tWvfjX//b//d2699db685tuuonpdMq9997byV58+MMf5qabbqq3ecc73tEZL6lJtbfpK0x9+MMf5tSpU+zt7fFQ2BX7dBjLXFy9c1V2e7Prmdx+yP4TPslNX/whDnaEcneGnaMSNS9o02TlTVSJMj01p/T9Etabp4q1FEl5qjCOqRs2czquJUnX0BiqrSy0pvJUi+IUHN/NUFeSOlQTlU5jr0G5kXlStC0a0pLso85mkuo0In3KhB4gbqlamGXMxuyLmauYdVwTwGiQzmVm0VmBdY0Eal556BiDCzhT4gqLN7YGjhtRh4qUtaosqErLrCgQWwV6lmxI5hYTZHQ1UssifWpj42sE894GAOZs6HaeOUfe6wBY1ANFS+DAyDA8EBSfesCiZalA2mXoRZCi/8MHY3D+QyYlB2eSxK0Ux62HiDUWzKE7zSn6zn8XgIXJ1lIMgVMaz+w6zA1Hg/12jOOz9z/Ow3YvLWBRlpbnv/Ad7J24ZuG2SQrzaU97Gj/5kz+5BRZbu7xswzUXS0+ryrOf/Wx+4zd+gze/+c3ccccdne8f//jHU5Ylb3rTm+rP3v/+9/PBD36QO++8E4A777yT97znPXzkIx+pt3nDG97AqVOnePSjH11v0x4jbZPGeCjsislc9C2Xuaiqium9ix/wtlBu+vwPc9/RLu6jJdXf7eEPLFoZKtdEg3OFfF1LWYBAY2qrOZWmwqmJFKf5Nk9aOSlPCamxXsvR6EWul6PMpFR/PkMQPrMkGcjjWj7SvokOyg11KuRHHtzGfT5LDTvWgsEaKgxGZzgtse3oerYT87LkNxhTzDpuRqMfX45BdNQVNYUH4xHrYuR4vUwaJjSM83VWw23ut4x+b2WaTJJ1DvEuqk9tkD7FNJwjM7x31hu9lTlJTDs8EjMnzo0Ai3bWQcE7iXUIwwyDL2Ggmxr3VcIvEnaTzp5GQLSrilR/G9ck/eMgZjMMOGOH9Kuo7jQEsNIAiwxASEXfRobPz5oOlq1BSRmL4THkOm+n8eyew9wwRSQC2jjnjnE8du/j3HaJUaF2d3d57r94EydOXLdw2w996EPcfffdfPmXfzkvfelLt8Bia5edPVRqUffccw+vfOUr+c3f/E1OnjxZ10hcffXV7O3tcfXVV/Ot3/qtPO95z+O6667j1KlTfPd3fzd33nknX/AFXwDAU57yFB796EfzTd/0Tfz4j/84Z8+e5Yd+6Ie45557ajrWs571LF760pfy/Oc/n2/5lm/hzW9+M6961at4zWtes/5Br2hXDLhYJEV7/vx5/uiP/og9U/Z3HZhDKBSkUIqbpxQ3T1EH1SdKig/tUp0v0ZksJSPZp04lazprR+qUEDtuD+kOfS5wzsJL29QdjzuSmnUkfhkTiLUkyboOUXCWNlNPESL2Yfx1ncjx+o/NStGmdbk4U+rkvYZcrEiM3jdOb2iOJoiOdWI+nvUBknpTd8Ret9O2VY+v4vUVO2GnY2qfkyERZonBW3S19m9J62jWWbwrwvqscWglWBeLtvFr0ZvqVbXqscBFsBFUltalTzXzmHDLqo42vVQN4EBUcc7UILEzllGqSaxb6Jn3glilS1kL5z/1uPBeKIxmfuR05/WlaCOwsBr6WLTGhIa2JGYImEJmoQ8swr7tXh4hQ9GtschLc0sGWHTrT1QFY7STjBLxsFthbpjVmZP0XriUgcXzf/j3OXn1TQu3PXv2LHfffTdf9EVfxMtf/vItsNja1o5hP/uzPwvAk570pM7nv/RLv8Qzn/lMAF7ykpdgjOGrv/qrOTo64q677uJlL3tZva21lle/+tV853d+J3feeScnTpzgm7/5m3nRi15Ub3PHHXfwmte8huc+97n89E//NLfeeis///M//5DJ0MIVBC76VhRFLQ/2sY99jD/5kz/hlltu4TP1Q/zO/fOdkiCr2FNdsVDeOIMbZ0w8VPcWVB/ap7i/wDiYTvOgRRZ6VAbwODVBlz0WiorEzrRqlgIXbac5ORyqps7kmdifog8csmuem5VIcq5NwbbGDM26QCOd8WGkfYnszpzv2utSEp0k1Wms+rIM+zYgZoPZkl6dhvEOfCMlvFZhNQEgGa3wLhQmmxjBzgOk5eFAHVFvFSVb09RprAWQ4uWdzquoC0XyxkfQsmbGRMCXFl9aUEW8oagqcAlo5DJJx12+BZ9c7QDuvNr6z7WAEjHAkhmiqgwzFxxtn4BFWlT8ecUos4nBmNRFuz2uopq/D5Pz7v1I7QYBGGTVnVSgaAOL7rip/sbQr4cIAMFlAUK/V0VvrSO9Kny9vPbnXWABdG4HIx52PcWN0w5HVmIO9dIEFjt87w/93lLA4qMf/ShPe9rTePzjH88v/MIvYLNd2be2tcvAVuiuPTrOsTZfvP3u7i4/8zM/w8/8zM+MbnP77bfz2te+du44T3rSk3jXu951rPU9mHbFggtrLVVV8Vd/9Vf8xV/8BY9+9KO55ZZbuPUT/4ur7/9/ePN9t/IXh1dz4AvO+W4n0kqFnTlOlRgor6vYu+6TzNTiHygo/m7C9BO7SAWzaUH7xbTI2tkNl1RnNChPGeNwXlr0qtFRyEVy21SN8IEuLLBehvKUHLomIm4G1JtlsgRj2KsdaUd97JfgR+hCjHw2NFOvLVGcmtnXc9rHsiXr06e8tbXylPcyqrK0wtCgFk1ggFiYLMcdO0chAVGDRsW00I/SI8YxTvs6xtoF0BCxD+6ti5mYTTQdFNTCzMaAQQQa4gI9J6nEzbN59099T/oCIw5fWYxNxdop8r0aUOpL0RrxuCrWNPTV6zTexQZmEwMRBPb3V8BYBjkRnwrYFwKL9lHH70RRmwEWGsEDY7SlVjYjAxCCStW40pRiB2AlNRfsr7GuExmrsdgLcrP9/awoj979xCUHLHZ2dnjeD76Vq6+9ZeG2H//4x3na057Gox71KH75l3+ZorhiXYWt/T2wh4oW9ffZrpgnRp8WJSLcd9993HfffTzhCU+oK/GL8no++8Qn+ewTn0AV/s/RKd50362898J1HPiC+9wO1ZIv9pDWB3uqglMVBRdw5y3l2Qmzj+7ip4JbQo52LLvhW528FU9hXIiwad7xW6zgRCfD0HawNxH9bY9r6syGm5shWOZeDc5GETMkSXq1v95lnfg2mGifw7YUbU5ec7Cqud82v4/H0M4MrJEtEcCGLhQpJTWs01h9aDBoHVkHkQoVE7Ne88DA/F8xUXCCBGnIUBlJIGb96y5lBZq1+xporFVsnswI1SQADWMdZgZl5cCHl80mZGjVtehTrWwSAuqXuR6jtSJlyaHenziOnO251PE3bgGLvtVqSphazrX9nfeC9ya7tC6w6N6XIkplwFoGl04CLPOAxbxsxljx9lhn8gQsQjaj+10IbPSOTqKY957D3Dgd7LcjjsfuX3oZi7IQnvsv3sy113/qwm3vvfdevvIrv5I77riDV77ylZTlYirx1ra2ta217YoBF207Ojrir//6r6mqii/+4i9md3e3/s7Y66jj7gIP372fh+/+KQB/e3SC373/Ft7+wBmqpZyS4WvVnnDw8AOKhx/gDgT3kR38h3fRqcFNbdbZWSa7URhtSdn2sw9F/fniFXezEsMCa7cR+k1q2td0BV+9qLV9lvvrbTIEktl6aOPgq+V8iW/VUsyL4C9HMRuTzw2frVqnEf5r12lYX1G5tuO1ej8NALyl0PAbWuPQULHLJuo0Uj+NJuvgojO3Xl1MA5JMfb6TYMAmpJCxsXHfDoFiUymTmQMniPo6Q3js+pL2+lvZpPCc8qg3IesjOpdCldLwJlKZiMCkT3UCxYngJzICLDReRSZt3pyClD2QIZgPDS/bwKJ7LkSUygJmmB8NoCIHLKQDLIZrDcDHMiwm93XEchxYEGF/K6cUb69h8bbzQrEfgIUk1MylXWMxmZQ85wfeyPU33rFw2/vvv5+nP/3pnDlzhle96lVMJpOF+2xta5e8pYjZJsbZ2lJ2xYGL+++/PxRu7+1hjOkACwBTXD+676075/m/b/xz/u8b/5zX33sbr7/3Ns65kvvdZCXtfbun7Nx+nur2Q/xM8B+Z4M7u4g8tOjXH6nvR5e4JwyzB6g3UWiuOo6fGevNqCJafq+tA+zpD0ETyF+0/dke3o7FRex83st7lzaQ+Dpqr/2gcu2UcyP42/eL4kNlpq4qtaBIa9xlbhW7SPqggNZmv1UBMxDDgLSl4LeIQ62unVJdqOjg+fjvrgE89C9b/HYXWb0lyfDdIhRPBl8JhaWKdhqWczTAzE0H1YvC5aP0BGUhwkCNgMjXQA3rFyVpniDT0yoDBRSqiYVk7zSzt/SF2pW71m0hD1E6+N/XfW3vHYukeTSr2yGgDizBzO8sS5rTCQADDx0ttLJuBBiqULNurAhmlQqWg03jxdhtYNFnnHbl0gcX3PP/13HjmEQu3PXfuHF/1VV/FqVOn+PVf//WlGoNtbWuXg21pURffrhhwISKcPXuW97znPTz84Q/n1KlTvO997xtsZ4prlxrvrmv+hruu+Rs+Ue3wP+6/ibc9cDP3uR3ur0qqYzgk9Uu5VMwtRxS3HKEO3Mcm2A/tUp0vcNXi8ebVBTUOmMMwi/Sh1Sk44X3ZLrAey5SsYqY1bnAMNuFIJknbfCfv444bT3Z0IIbOaRKlXHqkEUvnuELr4vj1znHyl1KdRvAEXY8+tX6dBlUMBolHjSAm1MWsPbaYUGyuQX2qq3i2ZtZBWlS4gGQaKhx5es/yY4c6jamdwC7Y3Yri0FAc+dCh3a9Pn4qH0AF6GF9nfRRwXvGu5963qDtGPN4I1cQEHz9DSwoAMt/jQjUAi6FpdLTz1ERFcUUXhKZgSAIWJCGNPv2KHLCQDrDoWxrT1DUsrXm1GSN3DKPF23ue8sbDDugKT1h/SVKhJmXBd3/fazh98yMXbnv+/Hm+5mu+hrIs+c3f/M2HrPHW1ra2tSvDrhhwcXR0xP/6X/+Lz/mcz+H06dPcd9992SZ6xuxynNzWdcURT7vur3nadX/NOVfyhw+c5i3338LHq13OuTKrsd614YtWLBRnphRnpkw8+E8W+A/tMXugRCphNlvtZwnFyoEfO1ZPsZoD1c2UtJWi5nHxF0f2DSKbAgRdylc/s3Mc5ak5E2Sc00YadZ3sgDLMRq3mVPc54oC1uAQ0In3Ke4PVZaSEx39BgRAd1wrnQtNBY2aomNjFe30FpCTXmmpA6utZ1xxZoAa6CliPUQd+M71RsMLsRMHsBOAVe1QxOfJIRaBP+fUfvQFomDq6rn6GeNfPG4TvNMjfqhWqMtVYZGhJvYxFMq+CeB2pYWlF++uMQHdu3wMWxNnHsgu0vsvVQ3ifeljkirdbYCVbvJ2+a68xfZfPjpj90MeiTyGzKI+5BDMWRWG553tfzU2f8piF2x4cHPD1X//1eO/57d/+bU6cOHERVri1rV1Ee4jUov4+2xUDLnZ2dviH//Af1jrcYx26g612gVxlZ3zZNf8vX3bN/8uht7z3/LX89r2386HZCc67ksMVdOvFwM71R1TXVxQK7r6S8kM7TO+doDNw1fLFdMM43LCewm2gnkLreoqi5QSvK+06BxAsVWC93Lipi/fcQuJ88LVrksBcO2u0KjgaTpYruk+SvyvTp1p1GsY4Zho7bUd+vF9Dpahmq0en2UNUnjKt5nrrmbaABho6vDeNzdZtrqdUFPXhWz+raUXrqDcBYAS3V3KwB6hiZsrkcIaZEguil7ngFh+CNWXwunsWHGcC4KmBBbQvdImOvLXQf2zOk6Kte1zU2QEZ7luMZDO8xHrpTjoDmA8QUj1IHwgo2gIiYzUW7X4W7T0D/ay7g2LFI3WDvO5xhOLtj3Hr5NLqvG2t8H/9/36WU9d8Gqo6EDtp29HREd/4jd/IuXPn+J3f+R1Onjx5EVe6ta1dHNvSoi6+XTHgAgKgSLUJRVHgnBt5uA5fWMe1XeP4vJMf4/NOfoxKhfeev4433Hcbf3V0kgu+5LxfHhRo/YKH4poZXDPDKvjzBe5vS6Yf30VnNpKRx18U82su+vUUqe5h6Jgdp3ZjvFh5OWdyTPbWD4BRUl1as5g4RdQ1rDebeVj68Nsrz/fpWGati85Tu3FftxYm14V9SRNABJeaSqqGbtXJs9TlMzG50yV0gUbKUDUUnjUVlkxwLlONgN1kF2/AJRlaDX1GTKywXqZx5vzxQxH14STw2cU5ysMKe6ihb4dbD8j0ddWN8Rz4Ai2gKvqqUG1gkTIL3f2t8TgPxmRqIVQwZrzJp4jirGB7tQtxpaQ7pf9pF1h0LRWqW4b1WqrpmZPJFo9IyqanXX0OWua8UJxodd6mOTs7UvHY/Y9fcsCiLC1f9Q0/iy1v4Q//8A/Z2dnhhhtu4MYbb+Taa6/tNMGbTqc84xnP4KMf/ShveMMbuPrqqx/ClW9ta1u7kuyKAhdtSw1/nHMZje7watqUFaI87qqP87irPo5X+PPDa3jjfbfyZwfXcs6VXMhylOebCNirKuwjKyYcoOeF2dkdZh/Zg5ngZpbhy3exazmsp1it+VuO8jTs5G1ISj3rOJNhrtRHYgQQrDBo3c+4RXECWTtTEkZrZzR0AcVpuckMCWKt37hvcKWI4IoiPBFUEacY5/BucSf2RVddqtOw3uO9RaWRihXZBBgQXCzAB+rfcRNZL2L9SqJPGfXdPiNrdtnWAo6u2oGrAK8UhxWTQ484EK+opp4sS47XlqI1ii8M9irl8Gi4zpDR8KPnyLRpkGkAAK/ZSURBVMQaCzDZ/hleZSGwyD0oREKjvlxrvWa8bl1DmDNSrjTWZvTHzNKdZD6wiFQoM6CIeWSv23k72aUKLIrC8u3P/k/c8en/HyC8+z7xiU/wsY99jPe9733ce++9/NzP/Rx33XUXT3va0/iX//Jf8sEPfpA3velNXHfddQ/x6re2tQfRUixjE+NsbSm7osCFiNQv1wQuqqrKgIs1Pcc5ZgQeuXcvj9y7F1X42+kJ3nDvbfzJhRu44Avuc5OV5jd7FZOHK5OHH+KPDO5syezsHjq1+JlEJ2TxuMNths3fmnqK9TpuN/Sr9bpX92KpHYWoJkMAK/+uNf8+9o8YyJfm17vMbAEQbKavyPC5lvvtUlZOllxhxkTQQnC2qacofAXeILp+0baoAdesu93Fe73md+GPen1KqEEwrv5uDIwurg8KG4nVBshor8/Iul22jVDtl1T7hEzSkWNy6HBqsDOw4mJTwsVzGKP4UpiWln2ZZbdRIrVIeh/S6g0x1uma6NAPsgc9YAGdkzsPWIQs5pxsRtyvb+1eFX0ibE4RKoUlhg33glnj0V1HeeMR3eLtcMdeqsDi2+75DzWwgPAOvPHGG7nxxht55CMfyQc/+EEe85jH8Cu/8iu84AUvYHd3l+/+7u/mb//2b7n++uvn0qe2trWtbe04dkWBi7YZYzDG5OsulvIk1jcRuG3nPN9y5s8AODvd53fvv5l3nDuzlsSt2fGY248obz9CK6H6cMnsQ7u4gwmmqkajqcndnHfowTHr11OsW5yb714dHLJhoWd+5fPHDWtX1pailaRQ0xTGj52HZS6hYXZn2KejAQSLemYs89uFcRbVwix9CwhoIcwoY1GcoXAVuFj/sUaTunadRlpP6o0SVKLWo8AhoVuy90F5CmmUlTZTp9HtM2J8qF8J/1yfPuV2Cw52ixAwMYJUBZODWa1ANaRQeVQVYxQ3EWZFAlL9tUTHOkrEdr8JjjyqHSCWrpUELLyaVmaj2coDavP3dAAW4431xprZ+XhO5wGL3BUtRKL1oJO3dFWhWntaEzIW9oYZ/ZRFIcqj9y49YCEC3/Kdv8zDP+MfztlGuP322/mxH/sxvuu7vguA7/iO7+Btb3sbX/zFX8zVV1/NU5/6VL72a7+WL/uyL7tYS9/a1i6KbWsuLr5dseAC5hV1b6Br7wp20+QCX3/DB/j6Gz7QSNzefzOfcDs8UJV4OX7UVgqlvGVKecsUZp7ZJ3eo/nYXf6GEGbhj1H7UYxJetsOIe+OsrnOPjdVTjAOY5bpvmwiK5gGCddfbSNGynuMLdLMwi+V+lzvnZmTNzd6rU8mC01iZEkrAGwoXIutGHRwzEzMYHkCDFK2jCBLFoguzDvMsHXUaOzWnUzQWnIfz7TZCnypq+d82fWrVtTdjxzEK4ejkhKOTgFfKw4rywGNqClW4O6uJUBVj82nMEJmsspNXCbS4/no10KTCNrlzFSlGReYkasxKzGmstxggLGqCl/tOArjsAaBQpjPMZhgznwr12fsf55ZLDFhYK/xfX/ejPOKRX7pwW+893/M938Pb3/523vKWt3DbbbfxnOc8h6OjI37v936PV7/61bz1rW/dgoutXXnmdRi1WHWcrS1lVxS46Kd1i6KgqqrMdsVDrijWlri9b2Z5xwNneMv9t/LxapcHXEklKyhPWaU8PaM8PUN9UJ6q/maH6v4JUoFzxwcayfoRd91QPYWJVJjQZ6ALYLog8DjM83Fg1O/ge1zT1i3TrnkI/16nSLnbyXv9Ph3tqGx7/5Qt2QD1zQiVCRkupxZxSc6V+Bt2z8dxEoapTgNN+/gOGFinsDrFwTVmNBQQ75HSgWymn4aYNn3KY6k2J3MLYITZfsmsRaEq7j+i2hWq6TxgEbtc52os5sjChoPS0OMiByyM4kxsrNcvjF7QWK8NENrP5UShKjJ0p6w6VbzA5tVYhKPLdN5WKPcrpAYWzT20Yyo+e+/SAxZFYfimb/s5Hv3Yuxdu673ne7/3e3nLW95SA4tkOzs7PPnJT+bJT37yg7ncrW1ta3+P7IoCF30by1yoHwKOh9KuMhVfft2H+PLrPsSht/w/D1zPG++9jbOzE5xzBVNZIftgoLh2RnHtLDS+eqCg+tsJ0/8/e+cdJ0V5//H3zOzu9cJxDQQEQcSCAoKIBRsRlHJH0Kix18Rgb1hiSdNEE2uimGgsMZqfcIgFBVGKomiwAyooIEi53rm2M/P8/pid2dk+d3vAQebzep0eu88888ze3t7zme/38/k0p6C06Wiqh67fqrXrKTRCE7eDG5tYTlCxIEIIQdB5ytxkd5UPhhIj3SJFxmPRNmLOXhcF3dhABuZNhhCEb7yjEQ3hsH0qPhEzqyWaRTC6x2VJQnhMgbwAHZRAQrgkhFVR6tLUAMhWircAJKEDeqCFreuVKfMoRReINkM8rXg0hGLoNISUbNUBkGQ024Y+WNFIMnfFOofRQqWngtoiQ0e0QSaxiN6yFC9TwtRCRCMWEgJk0OTohEUKtFjF0j3ErTyYGouwYD2zbhheJTMF6tFb9UTgGuUAsbGfy6hYSLaKhfn72JOJxc8v+qtjYnHbbbexYMECli1bxsCBA3f9Al246Ekw/3B0xzwuHGGfJxfRKheyJw9d3b4HVpQYqbLGcTmVHJdTiSokvmzOY3F9f3aoGdT5fbTh6/SckgRKtopyiEqq3oTa6kPd7sNfnQpqpPNU5yQp8Zynun53OXRDpwdcX7Sod8SDSHw+2WqTglBilKzQPLquxHmbUOxX3O48JQX+HzyqJwT3he9GJVBAUzwBnQYomlElURIELzpZNxiEBRSj918yKxrJZ61IAKqCFEggR9aQvIH5owiYu7J4zaxKChBCNz3LAlWaJIiMMB2TgpAlnXa/FJdYSFbFwngF7YMM8hZ4H4cdqwsJSQZNjqOxCLQmRbOxjXaQ6SAmbOJtexXCJA/R5hSBCkkkzPwL431hb+qUZR1SNTz57SGp5KZ4uycSC0WROeuChxg+sjThWF3XueeeeygrK2PZsmUMHjx41y/QhYseBolu0lwkP8X/DPYpchGtLSpa5UKS03fXkpwhxjvWIwmOzKrhyKwa/JpgQ0cvFtX2Y11bL1o0hRZSunQ6JUNDObCVlANb0dtk1B1e/JVpiA4Z3a8kQc5DN9jGZlhHIrbDUJSbmlHnlTGE6gI9IPaNzHno/LrtxCioedi1VrTJbazDSVe0qpETchhtTGQrmTFncsF9xsZTk73IaKjCYwifhfHzMzaEyZEBIYz2I2NbrIFsI19Jtk9JugLtgRtfMiDpSLKWvIVu4ARG+1Qgd0U3fp5ma0/XbG5tG+eA+NrjEbR3RBIAQTTdQrB9ydRYRF26aUUblVgEE7tDV2Qea1QzohKBQKEjtnjb/vkSdk4i5zTF23qIeFuyrk9OV5HyYou3exqxkCT42XkPMOLIMxOOFUJw33338a9//YulS5cydOjQ3bBCFy5cuNjHyEU4YmoulMzdvhY9cAdXCEOEFwIHu2KZgMXtfvUIAZvbs1hc15evWgrYqXloFl20uE3V8Q1qxzeoHd0voVb48NelIBo86H4ZIWJZdyZuUzK88L2IEPei8A12Z9csW3cgBdiIhrMWk9hrlkIIAUJHkkSU9XYOxqY/uv7DXtnoGiGQI6pGupAjgoajIdG5zFYyWQRCF2MQuk5DCgifA4uQdQ0pRPicrPuUAlYXjVHHSERwnc4t6YFNvx5on1LsFrqd12mE8GoJhCIHc1eEQJEMnYaQ7Hf5E/xwA5WLEFcnSY96mNBjz2kSCw0ZSdKxv6lkSUdIIHkjtQ3BXInor4VVzZBFlDBx4xUJX41pbxtawcB6E5vPSbIe9saOlX0RcIUKEItoydtHpFfTt4cRC0WWOGHCpYw66pyEY4UQ/PnPf+bJJ59kyZIlHHLIIbthhS5c9FAIYX02Jj2PC0fYp8lFLM2FrOTs9rXIZsQroGrC6m32hBONWLANkyQYmNrE5X3WAeso70jn3bo+fN+Ry49tmTTpPoTU+U2a7BX4+rWT0rcFXSj4K32oO1LRWzwIVULX7W8XJ+sOXrO1wRahG+xkWqcA6+6ucfPV0FPEa+tx3vIlmx09YRtre9tN59cey4rWWUZJ/PVa5EroYaQr+WqJ2YcfWjky79538WcoGQF1gYlB15F1DV0kLzYP3JvGSKT3JCC4XZkbhBa00JVlFWThiNg5OoEUCAa08QrZCu+LVfEx7VzjuTqZpxAQlQQYVrYCrPeT/XfGJBaaLId4nVnnB6I5O0EkQYh4LjoHMn43IkhMtDmlkKOM9UbuBwQSUro/QCxCr6/HEgtFpuTMexh3/GUJxwohePTRR3nkkUdYvHgxhx9++G5YoQsXPReuFe3ux/8oudh9aaSaLlDksJK7Ym59QFV1ZFlC0wSyHNnaZSHOm7rY18K5RRvQdEGDlsp7DcWsaOpDg+qjSU9B7wLRkBTw9enA16cDoYNa60XdloLW7AM/hrgz0RwxHhQ2omE0sxjd/5Bcz7lRTTETjaOH1Tn7bAhvrQgVmkuShiEnTryTjF/hsbdlaXSXuFpGhK5ZiufC1fnZwytHesA5zHiNu040UGR0xbCiVYUHRVNBmMF9SQbUseuslSUA3YP5wxSSZli9oiXR3mSf3Pi/btNqSLpmXIMIrSZJgSpFdFenwFSBDXksK1pZil4FlCUdIYMmxbKiJWq7U/yUbJNYSFGdpgJlHMJPGK2aYVUyAivQReStBEkSSGkdSHmqTbxtrLknE4spP73dMbGYPXs2999/PwsXLuTII4/cDSt04cKFi1DsU+TCsRXt7tRcJNi1eDwyqirweCQ0jUCvsvGHWraREkebHwF53nZK8zdTmr+ZZs3LyoYClgQsbps1r7Ex6CQkGbz5frz5hvOU1uhFrfbir0g1LG7V6G5WCdcsgZlhHUzyju481VmEVwhMh6jkQvsM8mNtyKTuW69sIxSR4mqPtaqubIJFGDlC0hBCRpJMx6+uQxceZMmPjjdhcJ/jtZt37uXA+0qXjSTsQE6C3pnyQIwXLcKZTHSPtbIESEIB2ajAIIyfJTEqJl36mUoglIA2SgCBTA1Vl0HoEcRCCvk+uCEPfxVlU0cRlXyKuMTCqMRKMSoFRFQegpcSzL8Ify3iOT9FrWYQSizCz6jIOiJNQ+mthrwmSAbN75nEQuK0aTdx3Im/SjhWCMHTTz/Nb3/7W958803Gjh27G1bowsVeAPMeRXfM48IR9ilyEQ5FUejoiPRl7LqpaecRqxARCuPPomLtaYxKhqYJq7OoK/dsMxV/mMVtPqtbevPFznx2dtXiVgJPjh9vZispB7Sg7/SgbvXir0sFvxRicetMrG2/ux+pIRCYxpPJ3LW2i9SDRCN2hcDYHidOzA46T0U6ZSX3qxWNaFg2mglfi9jvb2GKh9ECLTSa9UzX1yxFrLnbgvsAZBvREAJZMxyMZF23qlXJQKAYyd26Ya1sVTW6qeqgm3kX2IkGgQa+JCGBaXUrS+1RHaFEgIWEp1LbIUsCXQJJFjYthnm8BLKISiyM9io74bO/P81zRp7PaI8MaCWiXZbVshX+fhe2/0erZoQSC/NzQ7HE2x0RH8oeBIel1fQ4YiFJ8JPTr+GECdclHCuE4F//+he//vWvee211zj22GN3/QJduNhLIAkRcBhMfh4XzrDPk4tolQtv6oG07aJzChGaCKvIUqASEXtDKEWpJthF37oe8KjXRXRBuHnuOOsyLG4rODqzHGSZL5vzeLu+P1vas9ipe0Isbh3dTZUMFxYlU0UZppJCK3qrgn+7F7UqDeGX0FRnVZLo55ND7qAa9yq1pDfusmSvECROxe7EzFGtaLsjxyDEijawgwpv9+osAlsuS1cChoBY7yZytMuC+yQJ3aOAx0gGR9PxaGZ7UHeQgWBFQIhoSe+JEZX+2YmGOYhg+5TxUHIkyTZxyL/9WlCnEQ2GIN34bZAI1VHIsm781kXJsdBtm/lo6zAJQjCcz35sZLCeKdIOr67YKWpssbjxqodWLAI3bQLEgoDGwr6UFEljREbPrFgcdfQMTpk0K+FYIQT/+c9/uPnmm5k/fz4nnnjirl+gCxcuXMTBPkUunFrRykruLl2DuffXdYGmGy0DmiZikoJEN6JNTYaiSMacNoP3kDmdkGoRanGrC1jflhticdvmIB1cEpH1HzlNI2WwRsrgNvR2GX+VB/+OdGiXDeepmJunxAuX0awgtljC3M63mETPeRDWbImOjn2+0GwKe1tM19OfjfMZd8Gtdi+pezIvjI1ZMLFallSEkAKvr8MSVEwEg/u0QNZFdwX34VFQPQpmcJ9HsyVhC09n3xC2uY3/hRsRCJTAezGy7auz85sVE6N9yhDKWySpS6+LCCsTBNorFYGqRf78DD2O8XsczQBCkfVgNSPs529WG2MlYRsVC9n6d0grWIxgPbPCEuoKZf7bPmfYfOZawskKgRsIGSr0Coi3bcSlJxOLE0+5nEkl9zgaX1ZWxrXXXsucOXM45ZRTdu3iXLjYG6EDsZ21OzePC0fYp8hFOGILunvR9Q525zA1E5pm/GE0/x9efZAcbN5E4I+xXYehC2HNqQtDG9HpNUqRFrefNvVmedN+NKtew+I2StVFJOj3klN0Uvq1k7pfG0KT8Vf68O9IRW/zIPxSF+4w2647jjDX+SyRCN3Qmc5TsUXQTt494VoKM3k82cqDAZv+A80SVydTLbHaSYSdHBkrj71mZ79H5l1jzdYmFEo0uk68zOA+VfFiD+7TVAVFaIENZbJkwAOyhiYUJBFsr4Mkqw6B1ib7z01BDbQMCTRJsdHn+DBD9Kx2JQzb12jQAloHIUfOK8tGm5QmyyhhCdmmNiPaJ6ge1Yo2kizEEpNHv8bQioU9z0IO2LcY5C8MkjAqFr1Ck7eh5xILWZY47sQLHBOL1157jSuvvJKXXnqJ0047bdcuzoULFy4cYp8mF7EE3bJ397lFQSSZEEKEiLcVxUnrVJQNQGBDBca2SdNEwtapeJzAtLjt52lgRsEP7OhIZ0ldH1btLKJZ84ZY3DqnZQLJI/D1bcfXtx2hgb/Gi7otDa3Fi+6HwM4twSyxHrcLt3UHGz5nK5cDuQh2QmAgOUIgLI0GmAnhupC65OgViqBdrBSolnSb81QU+1xzc9flTXXgfajZ2oSMeUX3Bvd5Vfy6gqJqSJpmSFZEclUeIYGQ7IJqLVh1kARap4hz2PvR/rpI5loN+1dF6LajYlcBJcmIE4yVyB0cJ6JWLGQ5aDdrrcnUjEjmGkwSEXkt0extwfy8MxcUqqOI5TRljbDNad6gMd4jUsCNKtRpSpF1RLqKkueP+MxT0HsssTh2/DlM+el9jsa/+eabXHrppTz//PNMmzZtF6/OhYu9F67mYvdjnyIX4RvwWJULqZsqF0IEfOWj3PkLPWHkOsPF27Icv3XKqTBcDmwI7FUSu8WtiNKOEGu9fQIWt+eygVp/SojFbbPsJfKVjTKVIMTURVLAV+jHV+hH6OBv8OLflobUKEAFTeu8yDy4bLu4N7qTU1d+4qEbOc2aVzhK8o53RltSQGCD2h2EwH58vDayrsGebhDQUggleecpKSh8Ntcb6j6VpCDcZ5IYgawKFDWQhE3ndBoRP00JkMISHySzfSpJfYk1v/E+MwP2JKEFk7zt75XAhhuISyykgAZCUUALK/PrQkJWRFRXuZD8DIggFomtaOWQ8eZnsEk6FFmErUfErWYgSVG1GYpiaCzkvPYIgqogGN4DXaFkCcYeM4NpZ/7Z0fjFixdz0UUX8fTTTzNjxoxdvDoXLvZymP2+3TGPC0fYp8hFOGJrLnrRHc1zuo5FCrqaUQFGlcEkFmbAXjgpcEYubHfubCTFyNLAGRHCbIAJhd3itknz8mlTHosb9qfKn0qz5kOVOq+nkGTw9fKTmtOKJnnQmjyo21Lw16eAGuo81XlEd57ShZLc3jpkc20SmO7PpuguomGsMrKNTAQsgJPLvDBeCzOoTsaPIQpOlhwZhC7y52cIjjs3dwSzR/dK6F7jF0LSwKOq6LqCgtot7lACD5Jk6EvkkCyKJFuzAucQkhJM8gaUQLiekYMRaC+Twg4yvwu0NCFHJrDIsvFcNGJhKpH0KGJqswUrertTrGA9k1jEFmhLknGnMLzd0SQU0RK7FUVDStegl9/QutnW0pNboQ4f+RN+es6jjsYvW7aMc889l8cff5yzzjprF6/OhQsXLjqPfY5cSJJk9R3HcouS5e65bDsp0Gy33MIzKpwQA/NvoD2xW9VEQPxoEA1dF3HJQazzeDzBP966LtBFIA1Xik42Eq03S/FzYm4F47PL6cDDJ035LK7vR7k/I8Li1ll9yHCe8mSreLJVUtmJtlPBv92HvyYN/BK6Jidx18Dez67aNASd3aiGz+qEEHSNyYTMi4qQTEKQnLjabCMzWr2kbtENmFoKHW/omrtDSwGEOnHJVqq52RYT//WI86aRJIQH/B4vkk9D1T14/CqSagT3GZveLorvrdck2N7UrToNCLZQSQGRv9qMGu3tYeowzHA8u846ADN5W1Iiw+503SAi4STAOJWp+YqmColXzSCSWIjgN1ZuhixC7gOZeo+Y2ox0DcnUWNiuI0VSGZHR8+xmZVli5OjTOfvCfzgav2LFCs466yweeeQRzj///LittC5cuAhAhJtdJDGPC0dI3iezB8NsixJR3xDdYftoP5eMokgB/QRWToUe7sHYCXgUCUWWAi5RWKLwWHPKshTjWsPGSWHr1EPndDoPBC1uf7P/p/xt8Ptc3/dLRqRVkCe3koLfIEcJEPWnk6GRemArWUfXkjGmjpQDmvCkt6N4/YTbZXYGsmRs6gQyeuDOshRo7+kse4kUs3oQQjGC5QLz6pa7TdehY8wbFJkH2mKSeR0CFMB4LRSrDSn4WiQHQ6+ioOMJdP8br0UyPzv73MYdcbnbXg8kEIqEP9VLR6ZCe4YH3SdAMRy5kl63BEJW0GUFXQq6Z5n2vElDAtnjibrlVnU5QCxCW6XMd6Us6egyUXU/sqQHxkX7UyFFWtFa3wbbnSL3v6bTVLSQxSCxiLYWYyWRv086oGT4g8QCjJ5MejaxOHzEBMfE4uOPP+bMM8/kT3/6E5dccknSxELTNO68804GDRpEWloagwcP5ne/+13IZ78Qgrvuuos+ffqQlpbGhAkT+O6770Lmqa2t5dxzzyU7O5vc3FwuvfRSmpubQ8Z89dVXHH/88aSmptK/f3/uv//+pNbuwkVnIInu+3LhDPtc5cIOj8e4PE3TrO+D2HV3fEIzKgKtU4HNu+KgLSnWnMEqiUEIAm3HoUneIn7lQZYlqwISnqVhD+2DxBWMcP4RYXHbmsPb9f1Z15pLi+ahxZal0RnIqTpp/XYiBrShd0j4K32o5WnobQq6Kgc23g4Rdmc3essQEb3anUWwvcYU+praj663xsgR67ULzTt3JzzyMzIyp0PHtKJNDlbmhaSFVTWS1FLY5k729YiALKGm2nQafh2P38ilUAjYx3YVAZ2GACRJDWuf6npLmRAiotXJmC+2BsMkFkFhtxTynDlHpNDabJOKUUGQ7G1LUsRzUasZgf/HIxa6kFFkPYRHKkpQvB0+oQeNkRk19OlhxALg0OEncu6lzzka++mnn/LTn/6U3/72t1x55ZXdUrH405/+xBNPPMFzzz3HoYceyieffMLFF19MTk4O11xzDQD3338/jz76KM899xyDBg3izjvvZOLEiXz99dekpqYCcO6557Jjxw4WL16M3+/n4osv5oorruDFF18EoLGxkVNPPZUJEyYwe/ZsVq9ezSWXXEJubi5XXHFF0tfhwoWLnod9jlyEt0VB8uQikZNTPNiF2vbUbaM/2NY65Wgdxv/jkYKuVu1CLG4DRCiZ0D5ZgmHpDQzx1aMoEj+0Z/FO3X581dKbFs0TZnHr/LWVfYKUfu2k9GtHqBL+Ki/+HWnorR6jdSrBDeZ4Sd/24DfhyHnKwXoxNtBmWJ2EBl3OphBh/4oUmneP81RoTocc2FwmKwg3X4toWpjuCe4Lfz26S2wuofsUOnxGa5OmKng0DUkjjiDc2S9iSPtUAF3WaYR8AATblVIUgRrFfUEXkpFhEfJ+Md6liqwb0SFCNqoXYS1UumGZFfFW0HUpShJ4ULxttTuFaTMkzGRwKZyLYP5SR9N7KIqOnOHHk+dHCyMliiQ4PL26xxELWYYhQ4/igiv+7Wj8l19+SUlJCbfffjvXXHNNt7VCffjhh5SUlDB58mQABg4cyEsvvcR///tfwPi79/DDD/PrX/+akpISAJ5//nmKioqYP38+Z599Nt988w0LFy5k1apVjB49GoDHHnuM008/nT//+c/07duXf//733R0dPDPf/4Tn8/HoYceyhdffMGDDz7okgsXuwduW9Ruxz7dFiXLMpIkRdVdOFRIB4YaQmtdF6ha199cZtuUIkshLU6a0zmj3XmUg3MaayXhnIl+P4y2KOP/sox17RFzOli2EVwFg1KbuLzPtzw2+AN+N3AVU3M30tfTRBZtMX34E87tEfj6dJAxqoHMcTVkHFaLL68FxedHlpNrNZHRw1qG7O03gfM7mCf8ygQKQphfUmA+LalsCgNKYONvCJW7q8XJrJYEW5yCr0PyLU5yyNzGmvVuqZZgW68QSve1fEkgvBL+VA8d6QodqR7wqiAF7GiTaYEL7K11yYMuK2iS0QonJLP2EPr+i4DRa4SdWMR+kxo1fiHHaIUS0TfzlhVtlA+RgDIjbrCeHqWaYeo2oq3VHB91LbJBLKReka97iqQxKqOa/ik9i1hIEgwZOobLr57vaPzatWuZOnUqN954IzfddFO3aiyOOeYY3n33XdavXw8YJGbFihVWXsamTZsoLy9nwoQJ1jE5OTmMHTuWlStXArBy5Upyc3MtYgEwYcIEZFnm448/tsaMHz8eny9YuZ44cSLr1q2jrq6u267HhYtYkPTu+3LhDPtc5SIcsRyjnGwN7dawptBahhBHp65+1oe2MjmrFCTat9iTvIUtYC8iZ8PB+swxkiThse31Qq69i/vhvr4WzivawHkBi9uVTYUsa+hLg5pCk+7r0p13SQZvbgeePM3QkjR6DeephpQQi9t4lYvYiOE85YgQxDufTFDqYr+Ln1y7kBK4m25sM8OF5slZ0YbmimhBXYmUrPNU8O69FFItSa6VTAq0ucUW3icX3CcU8CteSAF0gcevgWb+/JJ8TSQQksfgi5LJG/SQgL3wao8QesBqNugYFf4ZJUvCoChRWjTNqoIeJQTPak3S5UD1wj5CBFreIouHuohdzZACv5G6HjivbUhQvB32GgqQJB0l0yQWoUiR1B7ZCiVJMPjAUVx+9auOxn/77bdMmTKFX/3qV9x+++3dLt6+9dZbaWxsZNiwYZY+8Q9/+APnnnsuAOXl5QAUFRWFHFdUVGQ9V15eTmFhYcjzHo+HvLy8kDGDBg2KmMN8rlevXt16XS5cuNjz2OfIhdOsC0TiO5h+NXruhCesLckkBXHtaBOsWdOFVX2wb+BDWqccTG0nBYqdFKjCsqNN5m9Ud197nredSTlbmJz3I02alw8bClne2JcaNZHFbSQC3WaG81SOH0+OnxTRjL7Tg3+bD39dKromo6sxbpM6gt15KrEVrdP72JHZFMnbuppXaKZt6xC4uy4F2oSSt6I1rk+FgDB5VwT3JaeliCL+FWFzSxpCyEYQWzKvhyyhphhzy0IFVULRNEyW1y3uUJKMRtA5TRZBrYYmeQyrWT3MitZOAiRDgxGNWJib+cgqgbBE1LoezYpWD7SjSobiMQrpiFbNsIiF9VxQEBXUe0QxTgCUrA6kXvb3hAGDWFTTx9cacb49CVmW2H/QcH5x7RuOxn/33XdMmTKFiy66iHvuuWeXuEK9/PLL/Pvf/+bFF1+0WpWuu+46+vbty4UXXtjt53PhYo/BbYva7djnyEU4YqV0I6WDaIp7rHBQArMTAM1mPh/L5jX2RMFv7Rt4e/XByd+XWEM8ntBKScIqiQPoIrhWTdOtnUCnrz2ALMXPxLxtTMzbRpuu8ElTb96p70+5P50WyUN7QhFtlFYNCZRMFeUglVRa0Fpk/BWp+KtSEKqM5pfpqnPYrgyri2bramR0hCnSO7XeINEIJnjH37Q7+Sg1fuxSnNeiO7UUekBL0T3VEoESuEgtkNPQTWJzCXSvgu4FI09D4NFU0KVAdkM3hOtBINsCEKC2NqLrUVqhrAqGnViEvo8sJyZJEC6otqoZUYhFUGgdbMs03zRm/oUURbytC8mocoScK6xCIuQIbYYi64hMFblX5A0jL1qPJBYA/QcczK9uWOho7KZNm5gyZQpnnXUW9913nxWM2t24+eabufXWWzn77LMBGD58OJs3b+a+++7jwgsvpLi4GICKigr69OljHVdRUcGIESMAKC4uprKyMmReVVWpra21ji8uLqaioiJkjPlvc4wLF7sUZhm/O+Zx4Qj7tOYCYlcuZDk14jFz023pKhzt4YKDFIWodrTJ/G1QFMnSVdj1FE6tYqPBcLAy5lRVPeqcTi7dPkZRZMs2tzuu3bC4reSe/T/lr4NXcEufzxiRWkFvpYVUOro2KaCka2QMbCJnbC3Zo2vJOKARb0ZbwOLW/j7p/AY+mjYh+r3Xrs0roSEJo1qSrObBuDpTUxJbV5Lsmu12v91hy2vqNIxSQPfZ58rm3JadcDda80oSwiPjT/HiT1XoSPGARwXJ1Jd0w18sCTwp3qhvW01PXLEwWsdikAcRWbEwbnSYjlGRWRWmjiKaFW04IbEfHJ4Cboei6MhZfny9In//FQSjMip7HLGQJCgq6sevbnzb0fgtW7Zw+umnM23aNP7yl7/sMmIB0NLSEjG/oijouvEzGDRoEMXFxbz77rvW842NjXz88ceMGzcOgHHjxlFfX8+nn35qjVmyZAm6rjN27FhrzHvvvYffH3TzWrx4MQcddJDbEuXCxT6Kfa5yEV4+jlm5kFMJ30NJkvGHUMZoI/L5gratnUW4o5OuBx2dujIfGJUCs3UqXmhfZ2AP2Aufs6uIeu0AUteseD2S4OD0eg7L/NKwuG3LZWHNfnzX3ivM4rZzc8spOqkD2kgd0Ibul/BX+egoT0Nt9SBUJak9X9B5KiCwDSgHjH911XlKBDIvgi09csB5Kpl5zdnDdSU6ZnBfcrBrKcwAQxCBZptk5pfiaCmSqxwZRMNmzSup6CLweiRRPTJKaaAqRmVK1lVDKKhhaxHqvg2lhEDTpQhiYdrLmsQiWkq21ZokRQ/Wg3Bthvm+NEJ54lnKRrO3ja73MKAoOnKmHyk3lsaimj6+tiivwJ6DJEGfvgdw7a3vOSIJ27dv5/TTT2fixIk89thju5RYAEydOpU//OEPDBgwgEMPPZTPP/+cBx98kEsuuSSwfonrrruO3//+9xx44IGWFW3fvn0pLS0F4OCDD2bSpElcfvnlzJ49G7/fz1VXXcXZZ59N3759Afj5z3/Ob37zGy699FJmzZrFmjVreOSRR3jooYd26fW5cGFCEgKpG1qaumOO/xXsc+QiHLEqF5KcEfc4j8cQR8uKZBEDOcqO27jbH3+jYW8/slvH0tm2JHt7gGInBcE5k6uShCZ52y1uu0peQtrGuuHaZQmGpdUzrF89QmBZ3K5u6U2bLNOgpTpgRpEfELJXkNK3nZS+7QgNOmp9+LelobZ4DUG47o0yjxNIgTNaXesBnQZdaL2Rwr4LWtwSIBqi03NGQ+d0JU432va2LDs5Mn8ayWoSQrQUAW2J0UqWuH0q0Z+MoN2sHqgYJS82B+NQTfYYn8RCoGg6kvl5laROQ5KMDbzPp6N2hM1j/DAC758om3mzmqEHWpPsS44ltMa4ARIegifZjjPGRGl3knS0qHoPg1hImR1IuZGf4z1VYyFJUFQ8wDGxKC8v5/TTT2f8+PE88cQTu5xYgGEZe+edd/KrX/2KyspK+vbtyy9+8Qvuuusua8wtt9zCzp07ueKKK6ivr+e4445j4cKFVsYFwL///W+uuuoqTjnlFGRZZsaMGTz66KPW8zk5Obz99tvMnDmTI488kvz8fO666y7XhtaFi30Ykkimv6YHQtO0kErFl19+SWZmJoMHDwYMMiCEoHbTBaitn8SfyyayNmHehZct8bWOR4n/h8DuOhU+l8lNZCmxIDrWPCFzCmFoReKQgmjXFW+Mtc4A7GQh4Tyxrj2wTtOqtjuufWtbKu817seq5kKaNS9Nui/Mwx9A4BEauhx/0ybpGkJWEDqoDT46tqbgbw51njLu7jvh57E2t3rA8tasEMS/PkUkPp9559icNxYhkPFbuo54kERoRkSolsJwWpLQEA6E9xLR8yYs0iXptjvoCTZXQsORVkYYJMP+9oq2hvDrjIXwazAE4ZFzO31vKBghepHrFqDpeHQdVfIEajxa3DmFv5WqrwyLUJNYIEG6z09LR/jP2vISC12PZGinhB5sTTLIRPB7E3ZHKjCrEVHmlIM5GSFzBlqtTCJjfIX93CUdb3ZoxcKo4Ek9mlgUFO7Hjb/+2BFJqKys5PTTT2fEiBE8//zzUTKZXLhw0RU0NjaSk5PDSUfehscT2QrfWahqG0s/vY+Ghgays7O7YYX7Lva5T7FobVFm5cKwZ9WMaoOck3iyKLQrPGxOkaUuh+yFz6Xr0a1jOwOhd1OVJMY6reoDyVVJ5EB7iH2dpptVV9dZ7G3h54Ub+HmhYXG7vKGYD5v60KD6aNK96J1wnjJ3TZIM3l4deHt1IEQTWpOXjm0pdDSkoKsKTtrxY6suOlshcHYuGX0XCs0jXZyEdYVdmzcg40UI2TAuQAtrcUrGeSoYYmgg+txdVcYEBeHBuXUhO7ZTjnlOSQKPgooCwhBZ63hQdHuSd/jP0jSTCBKLwGQhUxutRxCNwGm6hCzHaE2yaTMUWQ9zodJj5FsE5ozR7mTXe8hy+E0MHZGRqBWqZxELgKys3ow85g+sWbOGgoIC8vPz8XqjE/mamhqmTZvGIYccwnPPPecSCxcudgWMkn73zOPCEfb5TzKzLcoQa+vouo4sy7T5B9LRKvAoErZsnxAkeh+F50qYjk7hRMNJbSh8Ux0ro6IzCCcv5pxd7HACQjUT3UaIRKiblWnFa3ejcuZaFJwjz9vO9PzNTM/fTJPm5YOGQt5r7EutmkKL8Hbpc0aSwJPtx5PtJ51mtGaJ9vI0/DVpCL+EpgZC7CKvMOHc4faryRCCiF72KPPqwpO021LwDr5ht2q0OElhz3UOBjkSu1BLoVj5DPa5kw8xDJ0b0X0ECRtR0AJaDYRAFhqSZdJmXEMksQD7+8/Y5IuI1iXjOSO7IpYrVFzRN5HvOzBJR/zjwisgEBRvKzn+QOuZ7TlJ77HEIjs7i9t+9xktLa1UVVWxefNm1q5dS25uLvn5+QghrMyHuro6SkpKGDRoEC+++GJMAuLChQsXexv2eXLh8XhobW1F0zSLWEiSRJ/BN6BpV1G++SVqKp8h1VuNx6OT4gvpcXAMUwwORssPdqLRhb2QfaNubraTqhR0lhR0gRDZszSSsbgNCSxUdcPZSiZxhSjGU1mKn0l525iUt41WTebT5nzebehHhT+dnZqXDinar0HiF8CTrqIMaYEhLWhtMv5yH+2V6Qi/jO5XOiVWDj9baIUgoB+wIsq6/kYIbnJVEDKSpFmb0+TE1bJtA6l348Y6upYi2WpJ+NyS0JEkLSGJcVrhiE+QkgjusxYiGTa0MgbR0HU0f3tUYmH+ypjEAoxMD/uFmKncigRa2AXGE1qHujuF/jzkQDXDIM5R5kTEJRZSTqyKRVWPE28D5OXlc/Pdn+DxeMnO9pKdnc3gwYNpa2ujqqqKHTt2MG3aNHJzcznxxBNZu3YtRUVFvPzyyyHp1S5cuOheuILu3Y99nlzIskxzczMtLS2kp6eHbE4VxUfx/udR0zySyvoaBhT/SHPdv0j1luNR9C7f4ZdthEII4WxjHAceW5tTd1UKdgkpiFJ9cESI4pzSdLNSVXOTZLZ+ROpJnKw8TdE5OrOC43Iq8esSq1t68XZdf37syKJFV2jD+R95+0ZTSdVRBraROrANvUOio9JHR3k6WoeC8MsJd6TxNq3Cat1RMTUO5lFdJQQGTZGjCsKNcybpPBXmtCSQDD1FclzAEoSbVm92O96kxM+IiLBBM+8ieeIVK7hP6lIafQQkCV1RkFMjrWgl7G1JEI3UWJUHKzsk9LnowXpGVQISVzOksGA9q0IS8X4QKIrYK4lFbq+8ALGI/PxITU2lf//+9O/fn2+++YaysjKeffZZ1q5dS3Z2Nr/4xS+YNm0ap556KpmZmXtg9S5c7OMQOGshcTKPC0fY58iFuYE3hdu9e/emurqalStXkpWVRVFREYWFhaSnp9Pe3s6XX34JwNFHHxu4e3QBuq5Tvf0Ntn3/J3Kz6vEqkJLS9fWEt04ZAu6w1ikHc0WSAuOufjIJ2RBKCqzWqST3PJ4Q3UeCdTrreYpoGwt3s5Kd6l8C5/PKglGZtYzKrLUsbhfV7sf37bnslLy0Jgztiw7ZJ0jt105qv3Z0P/hrfHTsSENr9SL8QfvUKEuKC2OjK9vGBp2njDmSSa62tfTYKg/dYkVrc1oK1ZV0vcUp8rXQui24T4IQomG6WukC9G5ISg7RaQidbgvui4F2v/k6RVZlrRyLaBoLWQ+QoehWtIZ8O3icaVkbnlVhJxchydthL6UuJLxZHTGJxaiMKop7ILHIycnllhjEIhw+n4+5c+eSnZ1NVVUVq1ev5rXXXuOOO+7gnHPO4ZRTTmH+/PluJcOFCxd7NfY5tyghBO3t7UHhNsZm1u/3U1VVRWVlJTU1NaSlpdHR0UFubi6HH344ihL9j7qu69SWv0P1j4+T4tmIR9FI8QU3sE5cjKKNsYgGxsZY1URIMrfTeYLPGdZLcqCqEc861pHrVKBKEo8UdOXazXWabWN23Uo8qKoIIUHha7UnmCeyzU10PiFgc3sGi+v6saa1Ny2ah2bhC54gAFlX0eXEm1hZN9yphB6wuN2eitrsC7G4jeWkFDKPUBExN82dc56SHThPhc8be9PurFUrwmkp0OJkbKoNtQXCmdNSotfLJBrCYW6E7MAtKsTVKgGJceoWZThAmeeNMbfDT2jRvpOarz6y/m1s9gXpPi3CLcq0frVak0Iely2HJ1NobeRaBNZsWdGGtmCZj4e3UJluU3bSYZ7HOq+iQ6YfJUy8LSHwSVqPJRbZ2TnM+s2n+HzpCce2trbys5/9jPb2dt566y2ysrJCnl+/fj0rV67kwgsv3FXLdeHifwqmW9TJR8zCo3TxDrENqtbOki//5LpFOcA+Ry6qqqoAoxQtSVJUK8AdO3awdu1a0tPTaW1tJS0tjcLCQoqKisjMzIx757u24n2qNj+GT/4Wj6Ihyzo+X9esaE2EJGPHObfTzbwd0dqHdicpiE+Igq5TiQiRquohgX+xYCcasdq8OrvuHR3pLKkr5tMWw+K2OWBxK2kqQnFOLuwQAtRGr2Fx22g4TwktGXJhn9zcpMpINueo8LmcbH7DN/HRheZdIxeh0KyfmSnr7vpcNggNGZGwWuKEXECQDJi/ZbG0JU7JRbzrsLeTGXUCY+ZYsJMLKZAj4ZEFPq9GS3sMK9qwikUwOA9r82+3jQ22O0Xaxsayoo2wr8WoimimFa2iI2f7UbIjxdseNMZkVvZIYpGa6uXX964lJSVxK1N7ezvnnHMO9fX1LFq0iJycnN2wQhcu/rdhkYvh3UguVrvkwgn2ubaoxx57jIceeohTTz2V0tJSJk6caPWx6rrOH//4R4YMGcIJJ5xAUVERqqpSXV1NZWUlq1atwufzWa1T2dnZEZv9vKLjySs6HoCG6lVsXHs7OekVeDxGRaMrYXOSJFmZEeEVDTscsUAp1NFJ0wSaHmyK6GoYXrTQPlmmywnmxpzGcaY+I15on9NuFDuhMKyHk9eo9PG1cG7RRs5lIzX+FN5vKOLDpj60Kh5qVDlKlkZiSBJ4c/x4c/wI0YzWrNC+PRV/XSqoEprqIXwTHM2NJ+rcIWJiObBJNYlGd1rRdqe42m4Xaw8aTNaKVkRdc9e1FKENRkJ4zG26rZUscuPdFdjb5wwCY1j/Bh8LJyVmpTaYFyJFSRO3Kg9RWqF0YVjRarF0FFIgWM+WXREyZ9RgvQTJ29l+pGyV8PdQiqwyKr1nViwyMjO59TefOCIWHR0dXHDBBVRVVfHOO++4xMKFCxf7PHZ9DOhuxj333MOSJUsYMmQIv/vd7xg4cCBnn302L7zwAueccw5PPPEE++23H0VFRYDhJlVcXMzhhx/OCSecwNChQ2lra+Ozzz5jxYoVrFu3jvr6eqIVeHLyxzDyhMUcMOYreg36Dw1tx9DY7KW1TbLC9hwjMNyotkjW5tpsTwKHW7ew0yqKhCJLFuEwSYHW2fWFz6lI6LqxaTcJTKev2bZmWZastZrrtGdqdGXjKknGnObcqma2enVtmQC9ve2U5m/h/kEf87t+H3FRwbcc4K0nV27FIyIThJ2tE5R0P2lDW8geW0vGqHrS+jfiSWtH8fpJ1qBbFx6EkNGFBxkNCc3mPJVgbXGeE3hsG1yBFJhbomuvgwk5QIIECjoKMqqhe3ASKhKxxnCC5rHmFbY1J0MGAg1Chk5DKCAUS7htfHWHwbpBJoJfkvWzNF8Xo2InbEGEBjmzswDLFUqO/F01iEg0EmDTUeiRonzzOVmKMicx5hQgKzpydkeAWIQiRVI5sqcSi4wMZt31X1JTE9+59Pv9XHzxxWzZsoVFixbRq1ev3bBCFy5c2GG6RXXHV2fw3nvvMXXqVPr27YskScyfPz/k+YsuuijgNBr8mjRpUsiY2tpazj33XLKzs8nNzeXSSy+lubk5ZMxXX33F8ccfb5lH3H///V16nboT+1zlQpZlxowZw5gxY7jvvvv46quv+Ne//sUNN9xAeno6Rx99NBs3buTggw+mV69eYe5RCoWFhRQWFqLrOjU1NVRWVvLFF18gy7L1XPhxAFm5w8k68ikAdjZ+x/YNjyCrH+D1dCDLidtwYl+PrdfZgetUvLe+bNu4S1JoC1XE+hwu135ctNC+rtCNWPkcTqok8c4XIjLXhU2f0jUxfKbs59Re2zi11zbadIVPmnpbFrfNmhd/VIvbGLCdXknTUA5oJfWAVvQOGX+FF39FOppfRrQ70xDEQvBuePc5T5nb62hC8+RFylLUnI5uq5bYztIdIYbBmXVb8rZuaycLfz26un7ZoiwiML+qaZFBdlKQYIU6RoUSHkMDYfwuhN8j0EXgiBB3J3NOm44iLFjPaH2K0RIpJLzZ7UjZQSJqjkwNiLeLeiCxSE9P5+a7PyYtIzfhWFVVueKKK1i/fj1Lly4lPz9/1y/QhQsXPQY7d+7kiCOO4JJLLuGnP/1p1DGTJk3imWeesf6dEuYedO6557Jjxw4WL15s3ay44oorePHFFwGj9evUU09lwoQJzJ49m9WrV3PJJZeQm5vLFVdcsesuLgH2OXJhhyzLpKWl8dprr/GTn/yEO+64gzfffJMnnniCq6++mhNOOIGSkhKmTp1Kfn5+yAZTlmUKCgooKChA13Xq6uqoqKhg9erVCCEsjUavXr0idB0Z2Qdy4Mi/AtDa/CPffXkT6d7v8Hk78Hr0mMLkRNBFsOXJrBJ0xY41/Dg9WvtQF1hBNFKgyIk1DvH29fYKjiQFW6iSyueAEPG8qunWnNE0Kk6QKmscl1NpWdx+tTOPxfX92NqRSbPko72LRR3Zp5PSv52U/u3o7QJ/TSrqjjT0dsPiVu+qoxUBK1rrES2q85SzZYe/XpEb325znrI+soJWtCGC8C7CTijsJKarRCP0dQsmsYe8HgKEI1KbiEQZ8yue6O8FIUIzLsJhJm8jRSEdso6mRw+6C3eFCicWQf1M2HGyjpTVEUIszMN7MrFIS0vjlrs+IiMjL+FYTdP41a9+xRdffMGyZcsoLCzcDSt04cJFVIjQCm5S83QCp512GqeddlrcMSkpKRQXF0d97ptvvmHhwoWsWrWK0aNHA0br/+mnn86f//xn+vbty7///W86Ojr45z//ic/n49BDD+WLL77gwQcf3KPkYp9ri7Lj3Xff5eijj+aMM86grKyM0aNHc9ddd/H555+zdu1aTj75ZJ577jmGDBnC5MmTefLJJ9mxY0dEC5Qsy/Tu3ZtDDjmE8ePHc/jhhyPLMmvXrmX58uWsXbuWqqoqdD2y/SEtsz+HH/t/DDnqM4qGvUuLNIP6pnRa2yRUtWutU8aaIlunnGrzw0fJtvYhWQ5s4OVIcXiieULmDLQiaRrWXLouUKPM6WTd5rV6orQ5hayzC7zNo8jWnGabl6kniYdY+0KvLDgyq4Zb+3/Jowd8wM19PuPo9O3kKy2k0975BQYg+yClbzsZR9aTObaG1IMb8OW2oPj8yHLnWoYir0yxWpGMlhs16XahwKrRrfYpPakWp2hzmy1OUje2ZQHWmnU81prp9tfDsKQNvh5J/vGL8nukahJtfiWSWJi6LjN52yQ/UQiCFOYKBcHqRki7kxQ8DmG2QoUSIyXQCuXN9UesVUL0WGLh8yncfOcHZGQlrj7ous61117LRx99xDvvvEOfPn12wwpduHAREya56I6vboZ58+Gggw7iyiuvpKamxnpu5cqV5ObmWsQCYMKECciyzMcff2yNGT9+fIh99cSJE1m3bh11dXXdvl6n2KcrF3PmzOGhhx7ioosuCnlckiQOPPBAbrvtNm699VZ++OEHysrKmDt3LjfffDNjx46lpKSEkpIS+vXrF1LRkCSJvLw88vLyOOigg2hoaKCiooJvv/0WVVXJz8+nqKiI3r17R9jbpqQVMfiw3wG/o6O9jm0bHsff+CopvhZkSRDDDTchQlungkF7sVp94u2/JUnCo2BZ43ZHwJ6hfQisjyihfZ0Qa9svKbT6YFQ0kg0sNK/R1KTEE5k7WbgswbDUeg7drwEh4If2LN6p6xuwuFVoFimWC1fC6ezvQwV8hR34CjsQOqh1XtTtKajNKeh+meT218E77ZHZFLE+MhJfgFFhiFUdSE5oHipuDrZldW+1xKg8GASmq4LwUEgAQrGC+8zXINlKDBjVCglI9ai0qeFuUQRD8GIF6wksC1l7u5XZ7hQ1WA/jOC1QUTOb18CusYh8c5oBeT2RWKSmpnLTnSvIyol+d9EOXde56aabWLp0KUuXLqV///67YYUuXLjYnWhsbAz5d0pKSkQ7kxNMmjSJn/70pwwaNIgNGzZw++23c9ppp7Fy5UoURaG8vDyi6unxeMjLy6O8vByA8vJyBg0aFDLG1BSXl5fvMZ3XPk0uZs+enXCMJEkMGjSIm266iRtvvJGtW7cyb9485s2bx+23386oUaMoLS2lpKSEgQMHRhCN3NxccnNzGTp0KE1NTVRUVLB+/Xra29spKCigsLCQ/Px8PJ7Ql9qX0otBh9wB3IG/o4nvv7qL1vaPSPE24fXoeL1dbJ3SQ92SzA25fd2OyHdgTLSAPV0ENvZO5olyGeFzypKUlOsUBImGphl2mFJgV9Pl0D5CXbei6Uk6uxeWJBiU2sTlfdYBsL0jnSV1ffispZBm2UejKiV2ngpnWBjdLN7efry9DecpvUnGvy0Nf31KwHkqcmPpdPHR24WiOU91Xv8Q3Xkq+ma3c5BtzT3dq6WQA9a+AeoZUiXpWkK4ZP1XhKV4S4HEdCHkwPfO55cC+oo0X/Q3uyaMN3I4ORIE252iOUYFqxmRv0ZmsF7IcZIAIaEoWsAVSrPOYyJFUjmyxxKLFG789XJycvsmHKvrOrfddhtvvvkmS5cuZeDAgbt+gS5cuEiMbm6LCr9pcPfdd3PPPfd0erqzzz7b+n748OEcfvjhDB48mGXLlnHKKacktdQ9jX2aXHQWkiTRv39/rr32Wq655hrKy8t55ZVXmDdvHnfffTeHHXaYRTQOPPDACKKRnZ1NdnY2Q4YMobm5mcrKSjZu3MjatWvp3bs3hYWFFBQU4PWGbva8viwOHv0QAKrawo5Nz9FU829SffV4FEFXw1oN9wHjezvR6KpbkklajOqDkbptCqNjVgpE/E2nogRteBMljjv9bPCE2eYGLTo7oacIO1cyIvNY6Otr4byiDZzHBqrbfbzX2IeVzX1oUH00BrI0oi8svn7Fk+lHOVgnlZ1oOxX82334a9LAL6GpCoRoLTqHICGQQ4PqkkyuDs3v0Lth026gu7UUoVBsr6NdWxLpUhVvhdEfMax5RWDlRvJ1/LUL631u2lkHKED4ezmObax5TKzEbqPdSYmoZsSa06Iw2R1I2ZFtoymy4QrVE4lFSoqPG25fTm6vxNUHXde5++67KSsrY9myZQwePHg3rNCFCxeOoJOc94d9HuDHH38MybnoStUiGg444ADy8/P5/vvvOeWUUyguLqaysjJkjKqq1NbWWjqN4uJiKioqQsaY/46l5dgdcMlFDEiSRJ8+ffjVr35l9cG9+uqrzJ07l3vvvZehQ4dSUlJCaWkpBx98cATRyMrKIisri8GDB7Nz504qKirYsmULX3/9NXl5eRQVFVFQUBDSJwfg8aTT/8Ar4cAr0bQOyje/SE3Vs6R5q5EknbQkWpOCSwzqM2K2TiU4jccjW8F2xh19YxMTsYF3sFxziD0kT9V0q/rQVZE1hLZymfa2yeZzdFpk7gC9vO38tGAzPy3YTJPm5YOGQpY39qVWTaVZ86KazlOdLBAoGRrKga2kHtiK3ibj35GCvzIVTZURHYmTvKPdoTYRssEVutVmk5zzVLA6YCAoNO8OQhCLaHSPliI4hxA6kqQlWHPi8EHb9h0hZEyqLEv+QAXJRp5EOLEInyNoRWsIvMMqYBhVv2jEwmyT0qIYCJiVDkUSgYpIEEICT05bBLEQ9Hxicf1tS+nVe0DCsUII7r33Xl544QWWLl3K0KFDd8MKXbhwsadg3kjubmzdupWamhpLpzVu3Djq6+v59NNPOfLIIwFYsmQJuq4zduxYa8wdd9yB3++3blwvXryYgw46aI9aX+9zCd27GkII6uvree211ygrK2Px4sXsv//+TJs2jenTpzN8+PCoqeAmWlpaqKyspKKigqamJnr16mVZ3MZjv7qusnnd32hvfINUbzkeWSfa8M6mT+vm7VHJEHabcJKIHW2MXVzdmfC6eDoJs0pgzhePFDi5flOobRVVoqzRyTzR1hzROuZwLrN6E45WXeGTxnzeaehHjZpKvZaKP8GdfEnXEHL8MXob+KtSUStS0dsUdFWy2nLsMBOp40JA6EZZM8iW9bTZf+80BTtWurWwWpyMaomDVHCHyduS0G0Gt7HJkdPkbRkNLXDe0IpDsJVMEs6SzWMxSkvLEJi/o7mZhm9WhYxN8xlC8dYOr0UsjGBFPYREGFa0UtTzgIgIwTOPtztGRcyp6JDdgRJFYyGhMy6zokcSC49H4obbl1FQdGDCsUIIHnjgAf7617+yZMkSDj/88N2wQhcuXDiBmdA9YegN3ZbQ/c76Bx0ndDc3N/P9998DMHLkSB588EFOOukkS7f7m9/8hhkzZlBcXMyGDRu45ZZbaGpqYvXq1dZ+8LTTTqOiooLZs2dbVrSjR4+2rGgbGho46KCDOPXUU5k1axZr1qzhkksu4aGHHtqjblEuuUgSjY2NvPHGG5SVlbFw4UKKi4stojFq1Ki4RKO1tZXKykoqKytpaGggJyfHSgdPTU2NeZyu61Rvf4PabU+Q6tmKRwkSjc6Si5B5g30YQOJqQaJz2cXQieZzUkmwiIsU/F/4MV25fsNpixCi4WQeiE+KzDYv8+l4InMn5+vQ4KudvVlc358t/ixaNIU2InvmnJALdN3qjxOqhL/Kg39HOnqrB6FK6LqxgXZECISxWYy+IdcDjkSSEe7moH3K0QZeaICEJIm41QGn5EIOGadh+hxBaFtWZ8hFOCkzKg7BvAsRszUpHM7KVVpLA7VffxLyWJpPRQjoUGWLWEBo5SJIECLJhUEYiFinLNlULUK2jTW+VxQdOacdT7Yaod1IkTQOT6+iX0prwmva3fD5vMy8cQF9+x2WcKwQgkcffZQHHniAxYsXW3cWXbhw0TNgkYsDr+8+cvHdQ47JxbJlyzjppJMiHr/wwgt54oknKC0t5fPPP6e+vp6+ffty6qmn8rvf/c4SZIMRonfVVVfx+uuvI8syM2bM4NFHHyUzM9Ma89VXXzFz5kxWrVpFfn4+V199NbNmzUr6epOBSy66Ec3Nzbz11lvMmzePBQsW0KtXL6ZNm0ZpaSlHHXVUhHuUHe3t7RbRqKurIzs728rSSEtLi3mcruvUlr9D9Y+Pk+rZgCSppKVKSW9khQh0cMepFMS62x66PoM0hGzgIeI4J3OZDlb2ubtCCuKN0YVABPbddnF8outLNEaYgpcYbV5OyYw5ThfwbWsvFtXux/ftubRoCi0YH56OyIXQo975Fzr4a3yo21PRdnrRVRmhJUMu7AgSjXiEwDm5sG36JXtIXXDT3jVyEbZmAuTIWrmDSkgCUma8bfXAWFNZEWu8M3KhtzZSs3ZVyGNBLYTRCBZ83CAX9sqDJOkh6d6mTa0sgaaH/2yjVTOMOU1iIWUZ5saabe0pssro9CoKe2DFwuv1cM0tb1Lc1xmxeOKJJ/jDH/7AokWLOOqoo3bDCl24cNEZ7Gly8b8Ml1zsIrS2trJo0SLmzZvH66+/Tnp6OlOnTqW0tJRjjjkmwj3Kjo6ODoto1NbWkpmZaVU0MjIy4p634sc3qN/xHD55HR5Fw5ciQtqdoPN3900xOEQKm53oFnQRugZND7ZidYYUhJOLkHMEiIYTUuDoXKrA40lsxeu04hKu/TBhEo3Okgs7hIBN7Vksrt3PsLgVXpo1b/wqQRTXqWhDtEYF/49pqI0poIKmRXGeckguwjfcsdqFukIuws9jVsxiC5dDEZtcRJw40D6lx12jsxYw+zVoyDb6Ejq3Q3LR0kCNrXIhS4b3lk8RtKmhazVJhelGBYSQi5DkbUkPqT7EaqGSJd3QWOQaxALMpHLj2J5OLK666Q369kvc1iSE4Omnn+bOO+/kzTff5Nhjj90NK3ThwkVnYZGLwdd1H7nY8LBLLhzAJRe7AW1tbbz77rvMmzePV199FVmWLaIxfvz4CPcoO/x+P1VVVVRUVFBbW0t6erpV0cjIyIhboaiv/i8VPzyKl9V4FJUUn3C8kY17d9+Wo+EkUyLeBry7SYGuG61YQo+t+egMuQg/LnzOZPQU5nrN60+kJ3F6vu1tabxb35dPW4po1jw0x3SecgBdR8iGc5G+04O61Ye/zrS49WC82M420/HGmETDqEQ5IARxyEXouGC1BGJXB5ySCzvxiZfT0XlyEXmezmaAaC311H79qXF8IJE73Wds8ls6wolQbB2FmdhttTjZyIVsE+2HC8KRBN68NqTMYMuUeWxPJxa/uv4V+u2fuK1JCMHzzz/PrFmzeP311znhhBN2wwpduHDRFVjk4oBru49cbHzEJRcO4JKL3Qy/38/y5cuZO3cu8+fPx+/3M2XKFEpKSjjppJPiirpVVaW6upqKigqqq6tJTU21KhpZWVlxN/lN9avZseERPOITZNpJS5OSFkZbFQ2JiOpIZ+cyU7E1nZjOS10lReHVB0ctWFHIRejzuvX6xQssdLpuu+OUiWjHxKveRDtfjT+F5fXFfNjch8Zwi1sH1QuTXESco0VB3eFFrU5F9yvoHTKJnY+cVgf0gJYijrjaIbkwSYOZSyFL0asDXSEXdpg5HWZblrH27nk9RKBSYtYKYgXsaTvrqP3mswBpMM6Q7lMRQKuNXARbpaLpK6QI0mESBDvpiCrezulAyQoVbysYQvmxmZU9kljIMvzq+nnsf8DRCccKIXjppZe4/vrrmT9//l7vQ+/Cxb4Ol1zsOSQfX+uiU/B6vUyYMIHZs2ezdetWysrKyMrK4pprrmHQoEFceumlvP7667S2RoodPR4PxcXFHHHEEZx44okMGTKE1tZWPvnkEz744APWr19PfX090fhiVu5whh75FANGfEKF/zdsKj+ChmYvrW1SSJtOZ2BogiWLWOi6sJyYOgvjhrWERzFIjxJIB7fC65KAx2ObM7CJVxPNmWDP7fHIViaHuUZN6/r1A9YaFUVCkrHmS7jWOOgdsLj986CP+PMBK7ko/xsO8NWRr7SEiHI7CyVdI2VwGxlj60kfU0vqAU14MtpRvP6QjIquQEZHCAVdKAghkFGRwgLrOgtjK60ghCcwtwclMK9ZJUgGAg8CxRJxGwQm0ZoT/1wNjiAQkoIuKQEHKmHNbeo2wNj82olFtNPYrWgjzhXQZsSyorVXM0JMchUdObcdb5YacZwi6RyZ0TOJhcejcMEVTzkiFgBlZWVcd911zJkzp1uJxbZt2zjvvPPo3bs3aWlpDB8+nE8+Cba3CSG466676NOnD2lpaUyYMIHvvvsuZI7a2lrOPfdcsrOzyc3N5dJLL6W5uTlkzFdffcXxxx9Pamoq/fv35/777++2a3DhomdDYAXpJfPl4DPbhQG3ctFDoGkaH330EWVlZbzyyitUV1czceJESktLmThxYlythaZp1NbWUlFRQVVVFYqiWK1Tubm51l31trY2vvjiC7xeL4cffjher5fW5i1s2/AItC/F5+3A69XxdIcwOrDJNoLmEgujE92Rt1c2Eo1zuu54oX1OrHijrdvuaCWBFTSYqFKSaN0mKYLuaZ1q0RQ+bc7nnfp+VPjT2al56ZCiaAhiVC7CYYrIhV/CX+lF3ZGG3qYg/DK66C4r2mDlwanTUqKKhPnhJ2Fa0cZvQ3LqFhW05g06T4VXSyRURIK5THIRdT3CXLeRENXeUE/Dd5+HjE0PuEW1+j2BrArj+sIrD7GqGeaJYlrRKjpKbjtSpo6MCFRVDPTkViiPR+GymS8weKiztqZXX32Vyy67jJdeeolp06Z12zrq6uoYOXIkJ510EldeeSUFBQV89913DB482Ari+9Of/sR9993Hc889x6BBg7jzzjtZvXo1X3/9teUqeNppp7Fjxw6efPJJy65yzJgxll1lY2MjQ4cOZcKECdx2222sXr2aSy65hIcffniP2lW6cLErEaxcXINH7obKhd7OOxsfdSsXDuCSix4IXdf55JNPKCsrY968eWzfvp0JEyZQWlrKaaedFvdNres6tbW1liBckiSrbWrjxo3k5eVxyCGHRLXIbWstZ9v3j6G3LkKWmklPleO2Bjl1izJ1CvHah7qLFHRVA6FqOrIkWeu051TEQ7z2qRA9iYh0yHI6T8jatSBpiyUydyoON7Uwfl3iy515vF3Xnx/DLW47SS7sEBr4q71o21PRWrxGOrjmgBA42sAHnZbiOk85zbmwEZ/wFqcQl6VOkws7gjkdkrURj782g1w4yMMQ4G+spGH96pCH030qug4dWrgVbZBcxHOFMrUbUa1oZSxiAYSQi55OLC7+5TMMPXiCo/ELFizgoosu4vnnn2fGjBndupZbb72VDz74gPfffz/q80II+vbty4033shNN90EGL72RUVFPPvss5x99tl88803HHLIIaxatYrRo0cDsHDhQk4//XS2bt1K3759eeKJJ7jjjjsoLy+3gltvvfVW5s+fz7ffftut1+TCRU+BRS4GXd195GLTYy65cAC3LaoHQpZljjrqKP70pz+xbt06VqxYwfDhw3nggQcYOHAgZ555Ji+88AJ1dXURLVCyLJOfn88hhxzC+PHjGT58OO3t7XzzzTf4/X4kSaK2thZdj2yJSU0rZvDwP3DgUZ+w32Ef0KacS31jBi1tRotSBBzQUmOzLgXyHiR0IYLWrJ1FYL9stiSZ+gmzLcmwe3UwT5RTe5TgnEbbiLM2r3iSBft8gNU6lUybl8AgFMHWMT30+rsArywYnVXD7QO+4G8HvM+sfl9ydPp28pUW0pQkWpEU8BX5SRvZRMa4WjIOryUlfydKih9F8Xd5XgPGhtdoRfJYrVMyKsmWru0tTkZlwGxv6nobmQEJ3ZrbJCh6t6wZiag3DNpVmTZVQbcRCzss1yghRyzBICDBipkdupBCiIUdqT2aWMhcdMXTjonF4sWLufjii3n66ae7nVgAvPbaa4wePZozzzyTwsJCRo4cyT/+8Q/r+U2bNlFeXs6ECcH15uTkMHbsWFauXAnAypUryc3NtYgFwIQJE5BlmY8//tgaM378eItYAEycOJF169ZRV1fX7dflwkWPgi6678uFIzi5BediD0KWZUaOHMnIkSP53e9+x9dff83cuXP529/+xlVXXcWJJ55ISUkJU6ZMIT8/P+QuvizLbN++nZqaGg455BDS09OprKzkm2++QVVVCgoKKCwspHfv3hEZHKlpvRl0yB3AHfg7Gti+8WmaGuaQ4m3C69Hxeo3+7ES7+fBfRVmSrENMQbiZvt0VeJTghslqnQq4OjmxyY0GuzWuRQZsbU52OPmoCa8w6EJEdZ7qCuytWwZpEQHnLWc2weGQJTgkvY5D0usQAn5oy2BR3QDD4lbz0Cx8XfphSTJ4czrw9NIMi9smD9pWH/76VMPiVrU7pnV+fnOzblAOW1BdN2gp7GcxCIcWqGrEqzokPq8caMHSkUPWHEuwnXixoRt9WTIyWzyyjqqHr1WyKhZWtoX5K02wmmGsI2zegMYiFrE4socSC1mC8y6ZzUGHnupo/LJlyzj33HN5/PHHOeuss3bJmjZu3MgTTzzBDTfcwO23386qVau45ppr8Pl8XHjhhZSXlwOEhGqZ/zafKy8vp7CwMOR5j8dDXl5eyJhBgwZFzGE+16tXr11yfS5cuPjfhEsu9iJIksShhx7KoYceyl133cV3333H3LlzeeaZZ7juuus47rjjKCkpYdq0aRQUFHDbbbcxd+5cPvzwQ4qLiwHo1asXQ4cOpbGxkcrKStavX09HRwf5+fkUFRWRn58fQTS8vhz2H3YDcAOq2sKOjc/SVPsiEtVkpAt83q5t4CTJSK82SYHd4jYqEuzknZICR/cepOCcJixxufmcLHXphrMsSSHdMKablaN2pjjns9vj2v8fi2glWrokwQBfM7/s+w0A2zoyeLe2D5+2FNKseWnSfVGD+BJBksCTreI5RMUnWtBbFNRtPvw1qYgOELqS1I18O9EIVh7iOE85hmy1RYlAxSF2IGDnfifsa8YmBA9u8B3AVrmyW9HqAtQwHhBwbE5czQh/TtFRerWjZKjoYcd6JL3HEgtFkZky/VYOPWKKo/Hvv/8+Z511Fo888gjnn39+QrvtrkLXdUaPHs29994LwMiRI1mzZg2zZ8/mwgsv3CXndOHifw5Cj7j50uV5XDiCSy72UkiSxNChQ7n99tu57bbb2LRpE2VlZbz88svceOONHHjggVRXV/Pkk09G3PWSJImcnBxycnIYMmQIzc3NVFRUsGHDBtasWUN+fj6FhYXk5+dHZHB4POn0H/or4FdoWgflP/ybmqrnSPNVoyiCFB+hcNQ6JZAk2bohbm9FSqb6YMJOCmJJVsMRbYx9LVpgTkVxrnGIBY9HsoThpnDbqeYjFmKSInulpJMb+P18O7mg+Hsu4Huq/amWxW2T5qGJlC55OUkSKBkaytBWUmhFa5HwV6SgVaYiOmR0v5IUITBytW1WtDZnKGe2uLEgW5vr0GqJ81yK2FBsPxrdEG5LxqsbT19ikotQnYROeLecKd6O/rqK0GpGcFqLWEgZkX9ge7LGQlFkzr7gQUaM/pmj8R999BE/+9nP+NOf/sQll1yyy4gFQJ8+fTjkkENCHjv44IMpKysDsG4KVVRU0KdPH2tMRUUFI0aMsMZUVlaGzKGqKrW1tdbxxcXFVFRUhIwx/22OceFin4Xl9tQN87hwBFdzsQ9AkiQOOOAAbr75Zt566y2OPfZYmpqaGD58OGeddRYnn3wyjzzyCD/88ENEX74kSWRlZTFkyBCOOeYYxo4dS2ZmJj/88APLly/n888/Z9u2bfj9kT3yiuJjv8EXM+zoZfQf8QVKzp3UNPRhZ6tMe3vXr0eWJetLBDQaRpuPkxcj+nyKIlmCakkiaYtbJTCnrhNiG9vlOQM/F9OK1xNDT9GVfY79+s1KkVkl6iryvW3MKPiBvwxayQP7r+SC/G85wFtPrtyKIiItSZ1CSRek7b/TsLgdW0fqkAY8mW2Gxa3U9XkDs1uaB5MQBHUaySFUS6HjTEvh5L0iG1a0KDH0JbbZLCvaUAG2vTXMdIyKZkWr6Qb7DK9YCPZuYnHGz+9zTCw++eQTfvrTn/Lb3/6WK6+8cpcSC4Bjjz2WdevWhTy2fv169t9/fwAGDRpEcXEx7777rvV8Y2MjH3/8MePGjQNg3Lhx1NfX8+mnn1pjlixZgq7rjB071hrz3nvvhXyOL168mIMOOshtiXLhwkW3w3WL2oewdetWJk+eTN++fXn55ZfJzMykvLycV155hbKyMt577z2GDx9OaWkpJSUlDBkyJO4fz507d1quU01NTfTq1csK7bMLA8Oh6zrV216ndvts0DaRmS6TkhLPZlUP0U7EghXaR+yKRmfSt3XdyASIFdrnBI5C+xytKb71relmZeo0kg1AhKCbVXc4T5njWnWFVU35vFPfnwp/Os2aF3/A4jaao1Q0RHWeUiX8VR7U7WnorR50VULoiQuvztyiAsQtYEcbzw3KuZOVHJgzOeepRG5RViVGQGtNFc2b1hBuRatqEh2aYmVcCCJD8OJb0ep480OJhYKOhpG8PSa9kgJfEncTdhEUReanZ/2Wo469xNH4L774gsmTJ3P77bdz00037XJiAbBq1SqOOeYYfvOb3/Czn/2M//73v1x++eX8/e9/59xzzwUMK9o//vGPIVa0X331VYQVbUVFBbNnz7asaEePHm1Z0TY0NHDQQQdx6qmnMmvWLNasWcMll1zCQw895FrRuthnYblF7ffL7nOL2jbbdYtyAJdc7COoqanhiCOO4LTTTuPxxx+PaGcSQlBdXc38+fMpKytjyZIlDBs2jJKSEkpKSjj44IPj/jFtbW2lsrKSiooKGhsbyc3NpbCwkMLCQusPXDTouk5t+TtUb/0rKcoPeBSNFJ/otH2sMVeoSDmaRqOr6dtGhSTUhtaJKNppPkUi8uI0VyO8MhJNT+HkNTDnsq8p2lqd/myi5ZR06DJfNufxdn1/fuzIolUotIrYpNS6pgQkROig1ir4t2WgN3sMQbjmjTq2K1a08bQUnSUXodAitu6J1ubUilYInY7qrTT9EBqulu5T6dAkdF2Kb0UbINlaWNVCknWknHY82WGibnS8st5jiYUkwfSf3cO48c42zmvWrOH000/n+uuv5/bbb98txMLEG2+8wW233cZ3333HoEGDuOGGG7j88sut54UQ3H333fz973+nvr6e4447jscff5yhQ4daY2pra7nqqqt4/fXXkWWZGTNm8Oijj5KZmWmN+eqrr5g5cyarVq0iPz+fq6++mlmzZu2263ThYnfDIhd9f9F95GL7ky65cIC9mlz87W9/44EHHqC8vJwjjjiCxx57jKOOOmpPL2uP4Z133uGUU05J+IdRCEFdXR2vvfYaZWVlLF68mEGDBjFt2jSmT5/OYYcdFtXW0kRbW5tV0aivryc7O9uqaKSlpcU9d23F+1RtfhSfvA6PouFLEZDgTryJeJt9XbdpKhJcf6LAPrCLzOOTgk7lc9g2713J54g2TjM8RkND+7o4lx3mWs0lJvuaakJiQ2smb9QO5Pv2XFo0hRaif9g7qXBIugaykd2gNXjxb01BbUiJIBpdIRd2hBINGRk9CXJhh5F3IWNUTGLN6ZRcAPhrfqRh4/qQxzyKjqqZNlBRQvBs1QxF0kPIhekKJUdxhfKgcXRmRY8kFooicfSxP6P0rIccjf/mm284/fTT+cUvfsFvfvOb3UosXLhwsevgkos9h72WXPzf//0fF1xwAbNnz2bs2LE8/PDDzJkzh3Xr1kXY8rmIj4aGBt544w3mzZvHwoUL6dOnj0U0Ro4cGZdodHR0WBWNuro6srKyrIpGvFRxgPrq/1Lxw6Po7Z+RlQFeb/LtPk7E4I6rBIEKQDyRdVeTzO2hfU5IjJPzhYT2dUMqunk+QaDSJGI7Tzmdy8z92NiezeLa/aJa3HaGXNghBGjNHtRtKfhrU0CV0FUForgeRcznKD1cgwDBiO88pZEoHA9AklR04cGoX2hRqyWdIRcd1Vto3BSsXMiSMI4VUkgLlPUcInY1Q9GRe7UjZ2iEt0qlyCqj0qso7oEaC1mWGH/KlUyaeluE6100fPfdd0yaNIkLL7yQe++9N+5nnQsXLvYuWOSizy/wyImr5omg6h28s8MlF06w15KLsWPHMmbMGP76178CRvtN//79ufrqq7n11lv38Or2XjQ3N/Pmm28yb9483nzzTfLy8pg6dSrTp09nzJgxcf9gd3R0UFVVRWVlJTU1NWRkZFBYWEhRUVFIeT4amupXs2PDIyj6J3g9flJ8kVWKrmzkTbJhb5/qTAtSPD2Fk+RtJ+s2My9MJEuw7Bka8fQUnSEE9n+bsBMNJwnjsdZut7htUj2048WfoDoQjVxEnK9FQa324d+WBn4Jza8Qa5PujFzY26JCW5xCj3VGLmTJj4atnSvw0iq2aomGB6TEeTIAHVWbafzh+8DcASvaFJ22DjmCXJjGvCJaNUPRkXu1IWeYY4LnTpVVRmdUUuDteRULWZY48uif03f/Etrb2+ndu7flehdNI7Zx40ZOO+00zjjjDP7yl7+4xMKFi30MFrkovqL7yEX5311y4QB7Jbno6OggPT2duXPnUlpaaj1+4YUXUl9fz6uvvrrnFrcPoaWlhbfffpuysjLeeOMN0tPTmTZtGqWlpYwbNw6PJ/YG0O/3U11dTWVlJdXV1aSlpYUQjbhC8sbv2LbhYRT1Q7yeDnxeY1PqV3W8CUhB3Dv7xm3a+FkaDucCo/qgyIZjVHibU2fmgWCVxNq829qcOj1X2BghBJoeKVxPhhCYz0GwdSpZfQpAVUcKHzT14f3GPjSqPhp1HyJKloYTcgGAriNkBb1dRt3hRa1IQ7TL6H45hBB0nlyEXBlyYBNutk85qTREkItwCAAVJDMpPP7c7ZU/0LR5g0UshCST4VNp7ZBDHKBiibdlSSAkgZJnEAsghFz0ZGKhKDInT/wlp07+NUIIy4yiqqqKpqYmcnNzWb16NePGjWPYsGFs3ryZSZMmMWXKFB577DGXWLhwsQ/CJRd7Dnsludi+fTv77bcfH374oWXHB3DLLbewfPlyPv744z24un0TbW1tvPPOO8ybN49XX30Vj8fD1KlTKS0t5fjjj48QkNuhqio1NTVUVFRQXV2Nz+ezNBrZ2dnxheTNW9i24RG01ndIS+nA65Xi9/Y7bJ2y39mPdX5Hd/bDSQGRrUNO1xS+OTfbnIxJsc7TlTYsO8w2L+geQhCy1kB7f3c4TzWqXj5oKGJ5U19q1VSaNC+azXmqM+TCDuGX8Fd40Xakobcp6H4ZhLPci8SCbkEw+i6+PkOWVKMykQhmW5TACtcLni245vbyTTRv3WC4qgUIWUaKSktbMHfDIBYE2vzCXj9ZR+nVimIrMhrXIPdoYgFw8qmXc1rJb6I+Z2rErrjiCj788EMGDBgAwIgRI5gzZ46j9ikXLlzsfbDIReFl3UcuKp9yyYUDuCF6LhwhNTWVKVOmMGXKFPx+P8uWLWPu3LlceumlqKrK1KlTKSkp4cQTTyQlJVQ45fF4KCoqoqioCE3TqKmpobKyks8++wyPx2NVNHJyciI2+mmZAxhyxF8AaGstZ9v3j6E3LSLF24bXoye88x4NppWreSrT4jaCaDih3WYrS1jrkP2uflf1odFC+xwneceBSZg0TcQVmXcGuo71szCzOeK1ZDlBtsfPab23clrvrREWt20otHUxpkfyCnz9OqBfB0IDf5UXf0UaotFj6DQcWNzGmR0JPZBJISdI8e701EEyIcDI0tACoXdYeSihlZ5gS5VR0QAzXM8OWdGRerWjRJFJyWg9lljIMhxy6HExiQUYn10DBgxg4cKFfPPNN8ycOZPq6moWL17MAQccYDnmjR8/Pu5NEhcuXLhw4Qx7JbnIz89HUZSoiaNu2uiuh9fr5Sc/+Qk/+clP+Nvf/saKFSuYM2cOV111FTt37uT000+ntLSUU045JcI9SlEUS/Ct6zq1tbVUVFTwxRdfIEmSRTRyc3MjWhVS04oZPPwPwB/oaK9j24bH8Te9Soq3BV+AaHSlDidJYUQDsyXJycGRD4W7OUm2/3c5cVwObtzNILxYm3enL4EgVKCuarp1OZ0mGjE0I0IIS6eSjAlPmqwxPqeC8TkVdOgya5pzWNSwP1vas9ipK7TTtbtSkgK+Yj/ewnZ0IaPVetG2p6A1+UAFPYbFbXzY87U91iPhzlPO36pRXjgJjLqCMbmudyDQoraQQXhqd+icsqIj57XjSdfQw87llXRGZlT1UGIhMe74syn92V8cja+srOT8889n5MiRrFixAk3TWLp0KfPnz+e8886jra2Nzz77jEGDBu3ilbtw4WK3wk3o3u3YK9uiwBB0H3XUUTz22GOAIegeMGAAV111lSvo3kPQNI2VK1dSVlbGK6+8Qm1tLRMnTqS0tJRTTz01rnuUruvU1dVZFrdCCIuE5OXlxe2J9nc0sH3j07Q3vEyKtwGvR8Lr7b7WKSSQk9FTBMY4aR1KhPAMi2h6CiftXBC/7cusvATF6/HncyoOh9AskVjjnL6mmpBY15rLwtp+bGjPYafmodVONKK0RYVDCEDoIRtzIUBv9KBv9eFvSEVSQVO9jnIuJFSEgzE6MlJArB3beQrHblGt29bTvP3HkMd8Hg2/XyamFa2io+S1I6UbdMdOLnpyK5QsS4w9ZgY/PedRR+NramqYPHkyBx10EC+++GJEhULXdT755BNGjx7t6i9cuNhHYLVF5V/SfW1R1f9026IcYK8lF//3f//HhRdeyJNPPslRRx3Fww8/zMsvv8y3335LUVHRnl7e/zx0XWfVqlUW0di+fTs/+clPKC0tZdKkSXF/MYUQ1NfXU1FRQWVlJZqmUVBQQFFREXl5eXF7pFW1hR0bn6Gl7iVSvfV4PAJf2M3nLrlOBcTg0Hk9RbQx0dKxhRAJqwWJAvJUTaB0pxWtLUeju6xorWNUHVmWIlqyuvqamha3b9fux9eteQGi4UVLFFQXhVyEPy9aFLRyL/6qdCQ/CZynEpMLCFjRJnSewjG5aNm6np07guRClgSypKNpUsTxsqSDjEUsgBBy0dOJxeijpnHm+U84Gl9XV8fUqVPp378/c+bMieoc5cKFi30PLrnYc9hryQXAX//6VytEb8SIETz66KOMHTt2Ty/LRRh0XefLL79k7ty5zJs3j02bNjFhwgRKSkqYPHlyVK2FCSEEDQ0NVpaG3++noKDAspiMRzQ0rYPyzS/SVPUsqd5qPIpOik9y5JIUb7NsajTAmfNUos2yHphPlkDTY9/RdzKXfYy9dSpam5Mjt6gwMhOr+tIVchH6XCAePQ6BcTqXiW0d6XzaVMC7Df1o1jw06T6IQiCEABxka4AhJNfavWg7fKiVqeCX0f1KSOWh8+Qi5Mow6wyWTsOhFe3OLd/SUrENsFnR+nRa26OsRdLx5geJBQTJRU8mFgCjxpzOORc95WhsQ0MD06ZNo6CggFdeeSVCD+bChYt9Fxa5yLu4+8hF7TMuuXCAvZpcuNj7IIRg7dq1FtH49ttvOemkkygpKWHKlCn07t07LtFoamqyKhptbW3k5+dTVFREfn5+TGvc8vJy1qz5kn4FP0DrHGS2kp4qk5ISvwKQaLMc0jpFnNA+h21KpmOUPUsjfAPtZFMdjTREqxIka0Vrr75A97RO2eeF2ESjs85T1f5UltUXs7K5D42ql0Y9xapUdJZc2MeJDgm13IdWnopoV9BVKeA81VVyYYfAzMyQJD2hKHzn5m9oqdweoq9I96kR5MLUWMjpoaJuCUGKrDEmo5L8HkgsJAn2H3QoM29c7Gh8U1MT06dPJyMjg9deey1C/+XChYt9Gya5OKXXhd1GLt6te84lFw7gkgsXewxCCNavX09ZWRnz5s3jyy+/5Pjjj6ekpISpU6dSVFQUl2g0NzdbFY3W1lby8vIoKiqioKDA6qnesmUL33//PYcffjj5+fmAUUmp3vY6tdtnk+rZiqLopIbd0OyqI5NJOOzrdrqpjtbyZHdzMqsRXSEXIefRdEvEnkhP0ZlNPMTXUzh+HWznjGbH29l1hY9rVL2saChieeN+1GkpNGo+dAG61HlyYYdQJdQqL1qdD702xRCEx3GeSkwurJNitjWFisKDFrMATT+spaN6R4hwO5xcyIqO3LsdT5oayMwIwidpjM2s6JHEQpYlDh0+nguueMnR+J07dzJjxgxkWWbBggVx9V4uXLjYN+GSiz0Hl1y46BEQQrBx40ZLo7Fq1SrGjRtn2UT27ds3fvBeIDSroqKC5uZm8vLyAKMtYtSoUeTk5EQ9Ttd1asvfoXrrX0lRfsCjaKT4DCtVJyneuhAxxd66LqwwvO4J7QvqKeK5OTmpSJjnsyB1PbTPXFuI81SU6ktXyEXI46b+A2etU07W36IZFrfvN+3H5rZMmjUvfikOIXBQ4VB0FV3yoNZ4Ubemou/0ggpamPOUJGkBgpAAMTQXQaJhZFY0bvqa9prtIWPt5MIkFnKabgm6TaRKKmMye2bFQpYlhh1yDBdfOcfR+NbWVs4880w6Ojp46623yMrK2sUrdOHCRU+ERS5yL8AjdQO5EB28W/+8Sy4cwCUXLnochBD8+OOPzJs3j3nz5vHhhx8yevRoSkpKKC0tZcCAAQmJxpo1a2hqagKgV69elvNUop7r2or3qdr8KFr7GrIyJXwpsZ2iIHr4XbQxJpKtEphjYomhoXPkIkS03sXQvkTnNLUf3ZWMbq7V+j5J5ykI5Iig8EVzHovq+rPVn0WzFmlx65RcCCVIUIQArd6LttWH2pgCfoNoJEsu7NA72mja+j0dNaH23Ca5sBMLAAU9oO7o2cRCkmDosKO47Kr5jsa3tbVxzjnn0NDQwKJFi2LeVHDhwsW+D4tc5JzffeSi4V8uuXAAl1y46NEQQrBjxw5eeeUVysrKeP/99zn88MMpLS2lpKSEwYMHh2xYm5qaePfddykqKmLkyJEAVkWjoaGBnJwcK0sjNTU17rnrq/9LxQ+P4mU1HkUlxRc9Rbuz4mNzYxy+2e6qS5Km6ZjtMZ0JrnOip5C703nK1joVL7QvkSNWtLXH0ql0tX1KExLftubyZk0/NrTnWha3XSEXdggBerMHdasP/04ftMhoqoe4gm0ngm6h07hhLe11lSEPp/hU/H4ZJT9ILAAUSUcTMqmSylGZlfTugcQCYMiBI/nFdQscje3o6OC8885jx44dvPPOO/Tq1WsXr86FCxc9GS652HNwyYWLvQZCCKqrqy2isXTpUoYNG2YRjby8PKZOnUrv3r157bXXIrzs29vbrRyNuro6srKyKCoqorCwkPT09LjnbqpfzfYND+PVP8XjUUnxGZUDR4QgzmbZng7uZBOf6HxmlcBEd1RKTDIQj7h0xYErnBSZa+0KuYi35mTbp4SADe3ZfNhQyMfNxexUPTQTuwIWj1zYIekaapsPbbsPf3Uqwi9Ft7h1SC4aNqyho67KekhWdFJ6t6EKKYRYgBG/55P0HkssJAmK+wzghjs+cjTe7/dz0UUXsXHjRpYsWULv3r138QpduHDR02GRi6xzu49cNP3bJRcO4JILF3slhBDU1dXx6quvUlZWxuLFi8nKymLAgAE8+OCDCcOwOjo6qKqqoqKigtraWjIzM62KRiLx587G79i24WFE23ukp6p4vVL8Db+DzbLdeSpe+1CnrGhtGRVd1VOEj4kW2gddIxfh5zHhJB3caUXCrBLFq5R0dr7t/kzeqevLpzsLo1rcyroGcSySTYRXQfR2Gf8OL2plGqJdRvfLtuyNRORCo+H7NXTUVxtrUHQ8vdvIyFRpUSPX4kHjmKyKHkss+g8Yxsyb3nEUaKeqKpdddhlr165l6dKlFBYW7oZVunDhoqfDIheZP+8+ctH8oksuHMAlFy72enzzzTf85Cc/4cADDyQ3N5dFixbRt29fSkpKmD59OiNGjIif8O33U1VVRWVlJTU1NaSlpVkVjczMzLgb3dbmLWzb8DC0L8Pn7cDr1SM20F2pbtgrGp1tnVJVPUKMHi2foqttWCHnCrhZdUfeR8RaIcQhqitz2cfZCUx4RaMza7MfVxWwuP2wqQ+Nqo9G3YeEgC7Y2toh/BL+Ci96sxe1xQutMsIvoYvoFRFJqNR/t4aOhhqLWMhpgjRFiyAXqZLKqIxKinw9k1js128IV9+yzBGx0DSNK6+8kk8++YRly5ZRXFy8G1bpwoWLvQEuudhzcMmFi70aK1euZMqUKcycOZPf/OY3SJJEU1MTb775JvPmzePNN9+kd+/eTJs2jdLSUsaMGZMg4VulurqaiooKqqurSU1NtSoaWVlZcTfQba3lbPv+MfTWRaR42/B6dDye5PMk7ERDEF9gblxDJLmwozN6is44PEH81qnOiKsVW4sUYaSoU3PFcp7SREjquFOdSjyNTUPA4nZdWy++ac2jWfOgxXWe0hEJN9ACSQiEkPDX+FC3p6JFcZ8yyYXaXGURCyCCXPRkjYUkQZ8+g7j2tvcdEQtd17n66qtZsWIFS5cupV+/frthlS5cuNhbYJKLk9PP7jZysaTlPy65cACXXLjYa7F+/XpGjRrF/fffz69+9auoY1paWli0aBFlZWW88cYbZGZmWkRj3LhxCRK+Naqrq6msrKSqqgqv12sRjXip4gAdbTVs2/gEatNreD1N+HwS3rjZE842y/Z08JihfQnIhf2ckgxCj73B7qp9bNTQviTyPiCQZK47D+2Ltq5oMFvS9IAsIW6augMBv6oKOiQPq5oLeKe+H5X+9KgWt50hF3YhjRCgNXjxb00x3KdU0FSZhu++hJRtFrGAUHLRk4kFGBqL62//0DGxuPHGG3n77bdZtmwZ+++//25YoQsXLvYmuORizyHxp7iLbsV9993HmDFjyMrKorCwkNLSUtatWxcypq2tjZkzZ9K7d28yMzOZMWMGFRWhFpNbtmxh8uTJpKenU1hYyM0334yqqiFjli1bxqhRo0hJSWHIkCE8++yzu/rydisOPPBA3nvvvZjEAiA9PZ3p06fzwgsvUF5ezuzZs2ltbeWcc87hwAMP5Nprr2Xp0qX4/f6IYxVFoaioiOHDh3PCCScwbNgwVFXl888/5/333+fbb7+ltraWaPzcl9qbQYf8mgPH/pf9hn+E33MJdY1ZtLRJ+P1d5/Nm6J2xcRfougixZe0sZMnQi8iy8X9VM+YLycDoAjwe2ZrTnM/I6OietQLWtSe7VlmWrAqOJBmEJNl50xWNE3LK+d3+n/DYASu4rs+XHJFaSW+5hRQi32txEUZiJQk8uX7SDmsmc1wNaSMbkLMb8Ba3hhALO3oysZAkyOtd2Clicdttt/HWW2/xzjvvuMTChQsX8SFE9325cAS3crGbMWnSJM4++2zGjBmDqqrcfvvtrFmzhq+//toSEl955ZUsWLCAZ599lpycHK666ipkWeaDDz4AjDvqI0aMoLi4mAceeIAdO3ZwwQUXcPnll3PvvfcCsGnTJg477DB++ctfctlll/Huu+9y3XXXsWDBAiZOnLjHrr+nwO/3s3TpUubOncurr76KpmlMmTKF0tJSTjzxRHy+2Hc5dF2nrq6OiooKKisrkSSJgoICioqK6NWrV9wNkqq2sGPjM7TUvUSqtx6PR+DzGs5JiuJkYxX9zrm9ogHdc1ffHtrXbVa0gVYkiO3k1BUtiKn9CM+8SKZ9Kpp43WnlImbmR8DidmFNP3ZomVR1pNJKvDtqIpGUGwBJ9VPx3ve0VzWFPJ6p+GnXZI7O7Lni7V55hdx053/xehPfWdR1nbvvvpuXXnqJZcuWMXTo0N2wShcuXOyNsCoXKT/rvspF+8tu5cIBXHKxh1FVVUVhYSHLly9n/PjxNDQ0UFBQwIsvvsgZZ5wBwLfffsvBBx/MypUrOfroo3nrrbeYMmUK27dvp6ioCIDZs2cza9Ysqqqq8Pl8zJo1iwULFrBmzRrrXGeffTb19fUsXLhwj1xrT4Wqqrz//vvMmTOHV199lZaWFk4//XRKSkqYMGFC3DwMXdepr6+3sjSEEBQUFFBYWEjv3r3jEg1N66D8h3/TXPUskqggPV0mxZe4hSfR5jaWGDzy/A7E4QHSYLd37aqeIiJLIoabVbJWtPaWrO5K8jbHONKpOAwxVFXBZjWHRbX78XVrb1q0aBa3DsmF30/5e9/RUd1sPZYqqRyeXkOKpPVYYpGXV8jNd3+C4sC6VwjBH/7wB55++mmWLl3KIYccshtW6cKFi70VLrnYc3DbovYwGhoaAMjLywPg008/xe/3M2HCBGvMsGHDGDBgACtXrgQMEfPw4cMtYgEwceJEGhsbWbt2rTXGPoc5xpzDRRAej4eTTjqJxx9/nC1btvDaa69RUFDAzTffzMCBA7nooouYP38+O3fujDhWlmXy8vIYNmwY48ePZ8SIEXg8Hr799luWL1/O6tWrqaysRNO0iGMVxcd+gy/moKOXM2TsGpScu6hp7MPOVpn2GHtBJ7cCdN3YUJvEQtcFerQDHexazSEhrVOqbrUNJXNvQgnMZ4m3NYGmGxWIZGBvyYJA+raWXPsYGK+BCPzf3j7W1ddAkmBwaiO/6vsNfx28gt/uv4rJ2Rvp42kmS2oDoSOjJ54IDJJmW0eqpHJURgX9Ult6JLEAyM3N56a7/uuYWDzwwAP84x//4J133nGJhQsXLpxDCENcmPSXey/eKVxysQeh6zrXXXcdxx57LIcddhgA5eXl+Hw+cnNzQ8YWFRVRXl5ujbETC/N587l4YxobG2ltbd0Vl7NPQFEUjj/+eB5++GE2bdrE22+/zf77789dd93FwIEDOffcc5kzZw5NTU0Rx0qSRG5uLgcddBDHHXcco0aNIjU1lfXr17N8+XK++uorysvLI7QxALLsoc/Asxk29h0GjPgSX+97qWkcwM4WmbYk94ayLFkOU3aNhpM9fLTP0nA9hVFNSayniPe5LNuIhhDdo3swz2mSGHPeZIiG+Zp5bGRL00UogekiOeqXspMLi7/jkQM+4I8DP+bMvO+ZkP0j+/sayZFakUU8omGI3SFILPK8HV1byG5Abq88br77v3g8ie8mCiF45JFHePTRR1m0aBHDhw/fJWv64x//iCRJXHfdddZjPV3/9vzzz9O7d2/aw+5GlJaWcv7553fLOVy42NshdNFtXy6cwSUXexAzZ85kzZo1/Oc//9nTS3ERBbIsc/TRR/PAAw+wfv163n//fQ4++GDuu+8+Bg4cyFlnncWLL75IfX19xN1rSZLIycnhwAMP5Nhjj2XMmDGkp6ezceNGli9fzhdffMGOHTuiCsllWaawfwnDxr7FgJFfklr4IDVNg2loMlppkqkWmGLwoENSktUHRbYE0d2xeQes6oCx1iDRUJMkGooiWV/WWpMUxAN4FDmEwMhSKInrCgq8bZxZsInLitdx/8CPePCAlVxS8DWDfXXkyq14ROjmVQAI0eOJhSRBTk4ut9z9CV5v7HZDE0IIHn/8cR544AEWLlzIqFGjdsm6Vq1axZNPPsnhhx8e8vj111/P66+/zpw5c1i+fDnbt2/npz/9qfW8pmlMnjyZjo4OPvzwQ5577jmeffZZ7rrrLmvMpk2bmDx5MieddBJffPEF1113HZdddhmLFi1Ket1nnnkmmqbx2muvWY9VVlayYMECLrnkkqTnd+HChYuuwCUXewhXXXUVb7zxRoQ/e3FxMR0dHdTX14eMr6iosAKiiouLI+6emf9ONCY7O5u0tLTuvpx9HrIsM2rUKO69916++eYb/vvf/zJq1CgeeeQRBg0axIwZM3juueeoqamJSjSysrIYMmQIxxxzDGPHjiU7O5sffviB5cuX8/nnn7Nt2zY6OiI3hLIsU9B3IsOOeo0hR60lvc9sapsPpXmnQls70dudHMAgF5LVPmU6TyVHNKJs3pMkGpLNIcqjGJkhJjFyNkGCtQZ6sGQ52D6VaD3xYBEM2Wih6i4Ck6mo/CRvO/cOXMUjB3zIlcVrOCSlml5yKz7hB0nCgxZJLHrYjba0tFRuucc5sXjqqaf4/e9/z4IFCzjqqKN2yZqam5s599xz+cc//kGvXr2sxxsaGnj66ad58MEHOfnkkznyyCN55pln+PDDD/noo48AePvtt/n666954YUXGDFiBKeddhq/+93v+Nvf/mb9Ps+ePZtBgwbxl7/8hYMPPpirrrqKM844g4ceeijptaelpfHzn/+cZ555xnrshRdeYMCAAZx44olJz+/CxT6BbmmJCny5cASXXOxmCCG46qqreOWVV1iyZAmDBg0Kef7II4/E6/Xy7rvvWo+tW7eOLVu2MG7cOADGjRtn9fKbWLx4MdnZ2VYv8rhx40LmMMeYc7joOiRJYvjw4fzmN7/hq6++4ssvv+T444/nqaee4oADDmDq1Kk89dRTlsA7HJmZmRxwwAGMGzeOY445htzcXLZu3cp7773Hp59+ytatWyPaHEzkFZ3AsKPmMGj0V2T1e4b6llE07fTQ2paczaudaJh33YXoxCY+DHaiAaZgOrk1Ang8nbSidXA6y4pWDlrROiEaiRBLU5LsvKmyxnE5ldy9/2f8dfAKru/7JQdINRyfsT2iYtGTuEVWVha3//4rfL70hGOFEDz//PPceeedvPbaaxxzzDG7bF0zZ85k8uTJERq1vUX/dvnll/P222+zbds2AJ599lkuuuiihETYhYv/FbhtUbsfiZV0LroVM2fO5MUXX+TVV18lKyvL0kjk5OSQlpZGTk4Ol156KTfccAN5eXlkZ2dz9dVXM27cOI4++mgATj31VA455BDOP/987r//fsrLy/n1r3/NzJkzSUkx3GZ++ctf8te//pVbbrmFSy65hCVLlvDyyy+zYMGCPXbt+yIkSWLYsGHccccd3H777WzcuJG5c+fy4osvcsMNN3DMMcdQUlLCtGnT6Nu3b8Qf/PT0dAYNGsSgQYNobW2lsrKSHTt28O2335KTk0NRURGFhYVRHaty848iN/8FABrrvmLHxkfw6p8iy+2kpjhLnI4Gu7uSLBticInEd+0TzaeqwmpzCk/c7izCXauihfYls1bjHMKyuO3MvNH+/NjnNQmR+Vp09TUA8EiCUVm1HDSwmpc+a6a2pctT7VJkZmZyyz2rSEnJTDhWCMGLL77ILbfcwquvvsoJJ5ywy9b1n//8h88++4xVq1ZFPLe79G/JVpJHjhzJEUccwfPPP8+pp57K2rVr3c95Fy5c7FG45GI344knngCIKFk/88wzXHTRRQA89NBDyLLMjBkzaG9vZ+LEiTz++OPWWEVReOONN7jyyisZN24cGRkZXHjhhfz2t7+1xgwaNIgFCxZw/fXX88gjj9CvXz+eeuopN+NiF0KSJAYPHsysWbO45ZZb2LJlC/PmzWPevHnMmjWLMWPGUFJSQklJCQMGDIjYqKalpbH//vuz//7709bWRlVVFRUVFaxfv57s7GwrHTzaZiS71+FkH/k0AE3137Bj019RWj/E6/Hj8+pd3sDabVedWtzGQ7RNdjyLW6ewJ5Ib5MWwuZWSqM1KkoTHFuBuEAIj+8JJ0ngsyLK9miNZpCvZ10CPUrHvCeYmGRkZ3HLXf0lNdWbdOHfuXK6//nrmzp3LySefvMvW9eOPP3LttdeyePHiuHbTewMuu+wyHn74YbZt28aECRPo37//nl6SCxc9Bqpo75aWJrWzAaj/w3BzLly42MUQQrB9+3ZeeeUVysrKWLFiBUcccQSlpaWUlJRwwAEHxN2sd3R0UFlZSWVlJbW1tWRmZloVDTN4MRrq6ur4/NO3yUtfQqZnFT5vB16vbmgXNB2Pg9C+WJkTVmifZCRnO8rMiJP/YA+sc0JenN7xN9q7jO/jZV44mc+eMRKvouEkUDDWOWOFASbCzhadf81porEp9A+omcuxp5Cens4t9/yXjIw8R+Pnz5/PFVdcwUsvvcTUqVN36drmz5/P9OnTUZQgg9Q0LdAiKLNo0SImTJhAXV1dSPVi//3357rrruP666/nrrvu4rXXXuOLL76wnt+0aRMHHHAAn332GSNHjmT8+PGMGjWKhx9+2BrzzDPPcN1111lW5MmioaGBvn37oqoqzz//PGeddVa3zOvCxd6MtrY2Bg0aZFURuwPFxcVs2rRpr78hsavhkgsXLnYjhBBUVlYyf/58ysrKWLZsGQcffLBFNA466KC4G2u/329VNGpqasjIyLAqGhkZGdaxVVVVrF69moMOOoj99tsPgLbWcrZ9/yh669sgmsnKkBMHxzkItDM/Qkwhcyw4DZczcjlCk7Ej1tWF5G271iGcaHSWXNihajqyFGzJcpJo7uScZquX4eoVnxzt3Knz/JxGmppDP85NorInkJKicNtvPicjK9/R+DfeeIOLL76Yf/3rXyGOTLsKTU1NbN68OeSxiy++mGHDhjFr1iz69+9PQUEBL730EjNmzAAM/duwYcMiAk137NhBYWEhAH//+9+5+eabqaysJCUlhVmzZvHmm2+yevVq6zw///nPqa2t7dZA0wsuuIAFCxawfft2qz3WhYv/dbS1tUU1S+kqfD6fSywcwCUXLlzsIQghqK2t5dVXX6WsrIx3332XwYMHM23aNKZPn84hhxwSN+FbVVWqqqqorKykurqa1NRUioqKUBSFDRs2cNhhh1nuYeFo2bmV8k3/RG15gxRvCz6PHnXj74Rc2EmDVdEgciPslFyEn9NsnbLfze8KubDDXtEw0VVyEX4+STYq8InSwTurt7BrVcLnbt6p89z/NbKzpWeQi7S0VG6+80OycqK//8Lx9ttvc9555/H000/v0bvuJ554IiNGjLCqDFdeeSVvvvkmzz77rKV/A/jwww8Bo9IxYsQI+vbta+nfzj//fC677DLuvfdewKhkHHbYYcycOdPSv11zzTUsWLCgW9tUTznlFA499FAeffTRbpvThQsXLroCl1y4cNFDUF9fz+uvv868efNYtGgR++23H6WlpZSWlnLEEUfEJRqaplFdXc0PP/xAY2MjPp+PPn36UFRURHZ2dvxqSEcD2zf8g/bGuaT4mvEqOl6v8018LNIQTjQcE4I4hMa8mw/d1z5lZX4I4orMnZAL+zmtSolkcILwY5MRc4dXYdraBf98sZGW1j1PLlJTU7jprg/JyenjaPzSpUs566yzeOKJJzjvvPP2qMtROLloa2vjxhtv5KWXXgrRv9lJ++bNm7nyyitZtmyZpX/74x//iMcTlDQuW7aM66+/nq+//pp+/fpx5513Whq7ZFFXV8eyZcs444wz+PrrrznooIO6ZV4XLly46CpccuHCRQ9EU1MTCxYsYN68ebz11lvk5+czbdo0SktLGTNmTFSisW7dOrZv387w4cPRdZ3KykqqqqrweDwUFhZSWFhIbm5u3M2bqrawY+MztNS9RKq3HkXRSfF1XUthh/lRk5AQOKiWmJa2IvCfWFWCzlY4dCGsqkO47qGz5CJ8vaZGxSQwyTpF2eeub9R54eVG2sKq/6ZYfnchJSWFm+58j9xezgTF77//PmeccQYPP/wwl1xyiWuf2gUMHDiQuro67rzzTm666aY9vRwXLly4cMmFCxc9HS0tLSxcuJCysjIWLFhAVlYW06ZNo6SkhHHjxiFJErfccgsffPABCxcuJCcnxzpW13Vqa2upqKigqqoKSZIsotGrV68E1ZAOflz/BO0N80j11eBRBD5f5LiutDuZ5CAqIXBALqK1TpmwE41k26fswu1EbU6J5jJhEo3O6DMSoXmnzlP/bqAbW4s7jZQUHzfcsYy83gMdjV+5ciXTp0/nT3/6E7/85S9dYuHChQsX+whccuHCxV6EtrY2Fi9eTFlZGa+99ho+n48hQ4bw9ddfM2fOnLghibquU1dXZzlPCSEoKCigqKiIvLy8uERD11UqtsyloeIp0rwVgYqG8VxXtRTBuUOJhiNyEWcDb29Hkh3a5jptnyIgro7n5NQZQmPXZ3TWIcqO5p06/3ihAf8eckr0eCTOu3wOBw4djS8aAw3DJ598wrRp0/jtb3/L1Vdf7RILFy5cuNiH4JILFy72UrS2tjJ9+nQ+/PBDioqKqK+vZ/LkyUyfPp0TTjgh7iZPCEF9fT2VlZVUVFSgaRoFBQUUFhbSu3fvEHvOcOi6TvW216nd/gSpnm0gVDIyum5rGzp3MBU83oaz01a0gaGxzu9kPvv644X2JVMt6WrmRVOzxj/+1YiqhT6+O9qifD4vM879J6qeRWNjI7m5uVZ1LJqryhdffMHkyZO54447uPHGG11i4cKFCxf7GFxy4cLFXoiOjg7OP/98Vq9ezeLFiykqKuK9995jzpw5vPrqq7S2tjJ58mRKS0s5+eST41rnCSFobGykoqKCyspKOjo6yM/Pp6ioiPz8/IREY9vG52iuKSPVswWPopEawwXTmThctwLx4oX2dcmKVjeC9aKlgzsiFzHG2IMAJck4RSIS5eScqiYwo0h0PX5CePNOndnPNURkWuxqcuHzebn2lkUU9hkGYIU/VlZWUldXR1ZWFu+99x4/+clPGDFiBGvWrOG0007jhhtu4Pbbb3eJhQsXLlzsg3DJhQsXexlaW1s544wz2LFjB4sWLaKgoCDkeU3T+OCDDygrK+OVV16hoaGBSZMmUVpayk9+8hPS09Njzi2EoKmpyapotLW1kZ+fT2FhIQUFBSEOONFQW7Gcys2PkSKvx+PR8PkEstQZ5yk9JG3bvi470XBSBYl3TrOiYd98d5VchI8xtRTdbUWrarqxGY+i/2hq1nni2YaIY3YlufB6PVxzy5sU9z0s6vMdHR1s27aNX/7yl3z88ccUFRWhaRqTJ0/mySefjNuG58KFCxcu9l64n+4udiv++Mc/IkkS1113nfVYW1sbM2fOpHfv3mRmZjJjxgwqKipCjtuyZQuTJ08mPT2dwsJCbr75ZlRVDRmzbNkyRo0aRUpKCkOGDOHZZ5/dDVe0+6EoCocffjhLly6NIBbm8+PHj+eRRx7hhx9+YOHChfTv359f//rXDBw4kPPOO485c+bQ1NQUcawkSWRnZzNkyBCOOeYYxo4dS2ZmJj/88APLli3j888/Z/v27fhjNPfnFZ3AsKPmMmj0V2T1e4b6lpE07fTQ1gaaFvUQRzBSkyUkSUIIU2Td9V2zLEsoijGnLJviaoGqJbcTVxTJEmkLYVRLNE1YupJk4FFklMC6hTDIiTl3rH36rqoLeL0errrpjZjEAoywqUGDBrFo0SKWLFlCQUEBeXl5vPzyywwaNIhrr72W5cuXoyXzxnDhwoULFz0ObuXCxW7DqlWr+NnPfkZ2djYnnXRSSFDVggULePbZZ8nJyeGqq65ClmU++OADIBhUVVxczAMPPMCOHTu44IILuPzyyyOCqn75y19y2WWX8e6773Ldddd1e1DV3gxd1/n888+ZO3cu8+bNY8uWLUyYMIGSkhJOP/10cnJy4rap7Ny506poNDc3k5eXZ/XWJxLxNtR+yeav7yIzZTMejx+fN4Y7U4zKRTjMu/7xWqfs4xJB1YQlpo6le0gmp2NXWtFqmqCuQeOfL0aSRbOK0p2QZYmrb3qNfvsf6Wj8xo0bmTRpEj/72c/485//jN/vZ8mSJbzyyivMnz8fIQSrVq1i4MCB3btQFy5cuHCxR+CSCxe7Bc3NzYwaNYrHH3+c3//+91ZQVUNDAwUFBbz44oucccYZAHz77bccfPDBrFy5kqOPPpq33nqLKVOmsH37doqKigCYPXs2s2bNoqqqCp/Px6xZs1iwYAFr1qyxznn22WdTX1/PwoUL98g192QIIVizZg1z5szhlVdeYf369Zx88smUlJQwefJk8vLy4hKN1tZWS6NhiniLioooKCiIq+8A2Nmwjm0bH0FRV+L1+PF5dWuT3VlyYYcuApoKOm9FG8vxyrSi1YXhPOXIijZBy5ap/ehOK9rGJo3ZzzUmPU8ieBSZcy56hMNHzXA0fvPmzUyaNImpU6fy6KOPRrRCaZrGypUrOeaYY9w2KRcuXLjYR+B+mrvYLZg5cyaTJ09mwoQJIY9/+umn+P3+kMeHDRvGgAEDWLlyJWD44Q8fPtwiFgATJ06ksbGRtWvXWmPC5544caI1h4tQSJLE8OHD+e1vf8tXX33FF198wbHHHsvf//53Bg8ezLRp03j66acty9pwpKWlMXDgQI466iiOO+44CgsLKS8vZ8WKFaxatYrNmzfT2toa9dwZOQcxdOTjDB7zOQUHvkmzNomGphRa2yRUNYlWJ8lsczIrGrHbhSJfkOgPewKtU55AK5IeaHNK5p6M2dqkC5BkrNYmLcmWrF0Nj0fhimv+45hYbNu2jcmTJzNp0qSoxAKMFr7jjjvOJRYuXLhwsQ8hvjrThYtuwH/+8x8+++wzVq1aFfFceXk5Pp+P3NzckMeLioooLy+3xtiJhfm8+Vy8MY2NjbS2tpKWltZdl7PPQZIkDj74YH79619zxx13sGHDBubOncsLL7zADTfcwLhx4ygtLWXatGn06dMnoqKRmprKgAEDGDBgAO3t7VaOxnfffUdWVhaFhYUUFRVFFZKnZQ5gyBEPAtDWWs53X84ipWM1qb4OvB7dUX5GrGsCM0NDWKLmmJUHh/t683hV061wvQgXJ6ccQQTms5lxddWKNvZ6u6ctyuNRuGzmCwwacpyj8eXl5UyePJkTTjiBxx9/3CUPLly4cPE/BJdcuNil+PHHH7n22mtZvHhxwnYZF3sekiQxZMgQbr31VmbNmsXmzZuZN28e8+bN45ZbbuGoo46ipKSEkpIS+vfvH0E0UlJS6N+/P/3796ejo8OyJd2wYQMZGRkUFRVRWFhIZmZmxLlT04oZfvRzAHS01bB1w99Qm94gxduCL0miYV+mmaURsnYHUwvLx9ZoDzJhVBxE4FyQjLuq/RpN5ykALUFwX7RCSne5vHo8Chf/8hkGDz3B0fjKykomT57MmDFj+Mc//hHXytiFCxcuXOx7cG8nudil+PTTT6msrGTUqFF4PB48Hg/Lly/n0UcfxePxUFRUREdHB/X19SHHVVRUUFxcDEBxcXGEe5T570RjsrOz3apFFyFJEgMHDuSGG27g/fffZ/PmzZxzzjm89dZbDB8+nBNPPJEHH3yQDRs2RG0T8vl87LfffowcOZITTjiBgQMH0tjYyMcff8yHH37I999/T1NTU/RjU3tzwKF3MfSo/9L30A/o8FxCXWMWLW0Sfr9IynnKdJ0Cg2jouqGr6CoURbK+THG5I4eoBOdUFClAjIy2LFXVY7Zl7aqGKlmWuODyfzD04AmJBwPV1dVMnTqVww47jGeffTahdbELFy5cuNj34Aq6XexSNDU1sXnz5pDHLr74YoYNG8asWbPo378/BQUFvPTSS8yYYfRyr1u3jmHDhkUIunfs2EFhYSEAf//737n55puprKwkJSWFWbNm8eabb7J69WrrPD//+c+pra11Bd3dDCEEFRUVzJ8/n7KyMpYvX84hhxxCSUkJpaWlDB06NK4YXFVVqqurqayspKqqipSUFKuikZ2dneDYnWzf8Az15U+Rk6Xi8Qh83thrNWxaneVXmGQg1vjOis0thyiiZ14k4xalacJynpIkI+fiyedDBd1mFaWrbVGyDD+/+G8cMWq6o/F1dXVMmTKF/fffn5dffjmhg5gLFy5cuNg34ZILF7sdJ554ouUWBYYV7Ztvvsmzzz5LdnY2V199NQAffvghELSi7du3L/fffz/l5eWcf/75XHbZZRFWtDNnzuSSSy5hyZIlXHPNNa4V7S6GEILa2lrmz5/PvHnzeOeddzjwwAOZNm0a06dP5+CDD47bb69pGjU1NVRUVFBdXY3H47E0GomscTWtjfIf/k1z9fOkemvwKILw/WxnyEX4cRBqcZuUk1U0K1qHQYBOzldRrfLCnOaQx5MhF4oi8/NOuEI1NDQwbdo0CgsLmTdvHikpMWLaXbhw4cLFPg+XXLjY7QgnF21tbdx444289NJLtLe3M3HiRB5//HGr5QkMS8srr7ySZcuWkZGRwYUXXsgf//jHkLaLZcuWcf311/P111/Tr18/7rzzTi666KLdfHX/uxBC0NDQwGuvvca8efNYtGgR/fv3p6SkhOnTp3P44YfHJRq6rlNTU2MJwhVFsXI0evXqFZdo6LpKxZY5NJQ/RZqvEkXR8XmFI6vXRBUEM0sDCSttPJn5dF2gC1C60Yq2rl7jHy9EVi668umuKDI/O//PjBpztqPxTU1NlJaWkpmZyeuvv+5qq1y4cOHifxwuuXDhwsUuQWNjIwsWLGDevHm89dZbFBQUWK1To0ePTkg06urqrCwNwKpo9OrVK+GxVdteZfuGB8jNrMfjkUiNcyPdaXuSbn5Uxgnt69R8AWG5rgcqDCK+aDseaus1nnoh+ZwLRZE54+f3Mfro8x2N37lzJzNmzECWZRYsWEBGRkbSa3DhwoULF3s3XHLhwoWLXY6dO3eycOFCysrKWLBgATk5OUybNo2SkhKOPvrouI5CQgjq6uqsioamaVZFo3fv3lGJxrZt21i3bh2HH344omMVNVufIFXZhKLoEUSjq8nbsdLBk0ny7qoVbW2dxlP/jiQXnaleKIrM9J/9lrHHXeJofGtrK2eeeSYdHR289dZbZGVlOV6vCxcuXLjYd+GSCxcuXOxWtLa2snjxYsrKynj99ddJSUlh6tSpTJ8+nWOPPTauw5DZemVWNFRVJT8/n8LCQvLz81EUha1bt7J+/XpGjhxJr169Qo6vKV9G1ZbHSJG/w+PR8PkEuoYjm9t4pMFONAQO26cSaC5UTaA4tKKNRi7kwFqcfMLLEpSceTfHnPCLxIMxWhnPOeccGhoaWLRoETk5OY6Oc+HChQsX+z5ccuHChYs9ho6ODpYsWcLcuXN59dVXkSSJyZMnM336dMaPHx/XcUgIQWNjI5WVlVRUVNDe3k5GRgY7d+5kxIgR9O7dO+6566s+pmLzo6htn5GVIZHiixOyR2IyYF+XE41GZwXdqqoH0scjg/tqajWefjGMXMiAMNqt4kFRZE6ccBmTpt3jaB0dHR2cd955lJeXs3jx4ggC58KFCxcu/rfhkgsXLlz0CKiqyvLly5k7dy7z58+nvb2dyZMnU1paykknnRRXKCyE4LvvvmPLli2kpKTQ0dFB7969KSwspKCgAK83jl8t0Fj3FTs2PoxHfIZX8ePzRlYpnJIBu0OVRTTobita3SqTSBLUNeg8HV65CLRXxfuEVxSJSVNu5MRTb3B0Xr/fz4UXXsimTZtYsmRJQgLnwoULFy7+9+CSCxcuXPQ4aJrGihUrKCsr45VXXqGxsZHTTjuN0tJSJkyYQHp6esj4F198keLiYo488khycnJobm62NBrNzc3k5eVRVFREQUFBwvyFnQ3r2LbxERR1JV6PH59PR5ElVFU4ap+KZX8bTjSSIRd2aJpgyzaVOa+FWtHKcnwbWkWW+MnpV3PKabc6Oo+qqlx22WV8/fXXLFmyxMqcceHChQsXLuxwyYULFy56NHRd56OPPrKIRmVlJRMnTqSkpIRJkybxhz/8gWeeeYZ3332XQw89NOL4lpYWq3WqqamJXr16WYLwRHkMLU0/sH3DI4j2paT62vF4pYSOTk6yNUynKKPFKXmCUVWj8sxLTY7HK4rEyROv5NTJv3Y0XtM0rrzySj799FOWLl0aYhPtwoULFy5c2OGSCxcuXOw10HWdzz77jLlz5zJv3jwqKioQQnDTTTdx+eWXJ0z4bm1ttSoaDQ0N5OTkWOngifIZ2lrL2fb9o4jWt/F52/B69ahEwwm5CK+C6LpwnKMRDZ0lFyedehmnl/zW0VhN07jmmmtYsWIFS5cupV+/fl1aowsXLly4+N+ASy5cuHCx10EIwZ133skTTzzBmWeeyYoVK/juu+84+eSTKSkpYcqUKQmD99rb262KRn19PdnZ2VaWRlpaWtzzd7TVsHXD31Cb3yDF24LPq1tkoSvkwg4zHbwzFY2qapVn/pOYXMgyHHLYeC78xX8czavrOjfccAOLFy9m2bJl7L///o7X5MKFCxcu/jcRO4nKhQsXLnoghBDcdtttPP3006xYsYLZs2ezevVqPv/8c8aNG8eTTz7JAQccQElJCf/85z+pqqoi2j2UlJQU+vfvz+jRoxk/fjx9+/altraWDz74gI8++ohNmzaxc+fOqGvwpfbmgEPvYujY/9L3sA/o8FxCXWMWjU16XJ2DiXgFClmWQgThui7QNBH1GqzXJPEpkWWJccef0yliceutt7Jw4ULeeeedbiMW9913H2PGjCErK4vCwkJKS0tZt25dyJi2tjZmzpxJ7969yczMZMaMGVRUVISM2bJlC5MnTyY9PZ3CwkJuvvlmVFUNGbNs2TJGjRpFSkoKQ4YM4dlnn+2Wa3DhwoULF7HhVi5cuHCxV+H3v/89TzzxBEuWLOGggw6KeF4Iwffff09ZWRnz5s3j888/55hjjqG0tJRp06ZRXFwct6Lh9/upqqqioqKC2tpaa/NaVFRERkZGgmpIHTs2PUd7wxxSvQ14PAJfFKMqVROO0rjtom9V05Ewvg+3oq2sUnn2/2JXLmRZ4qhxP2XGzx9LeE4wiMVdd93F//3f/7F06VKGDh3q6DgnmDRpEmeffTZjxoxBVVVuv/121qxZw9dff20lfF955ZUsWLCAZ599lpycHK666ipkWeaDDz4AjFatESNGUFxczAMPPMCOHTu44IILuPzyy7n33nsB2LRpE4cddhi//OUvueyyy3j33Xe57rrrWLBgARMnTuy263HhwoULF6FwyYULF7sQ27ZtY9asWbz11lu0tLQwZMgQnnnmGUaPHg0YG+G7776bf/zjH9TX13PsscfyxBNPcOCBB1pz1NbWcvXVV/P6668jyzIzZszgkUceITMz0xrz1VdfMXPmTFatWkVBQQFXX301t9xyy26/3t2BdevWoSgKQ4YMSThWCMHmzZstovHxxx8zduxYSkpKKCkpoV+/fnHJgqqqVFVVUVlZSXV1NampqRbRyMrKinusprVR/sO/aa5+nlRvDR5FYBpVdYVchD6uQ4BoSBJU1Wg8F4NcyLLEkUdN5Wfnz054PjBes9///vc888wzLFmyhEMOOcTRcV1FVVUVhYWFLF++nPHjx9PQ0EBBQQEvvvgiZ5xxBgDffvstBx98MCtXruToo4/mrbfeYsqUKWzfvp2ioiIAZs+ezaxZs6iqqsLn8zFr1iwWLFjAmjVrrHOdffbZ1NfXs3Dhwl16TS5cuHDxvwy3LcqFi12Euro6jj32WLxeL2+99RZff/01f/nLX0JCx+6//34effRRZs+ezccff0xGRgYTJ06kra3NGnPuueeydu1aFi9ezBtvvMF7773HFVdcYT3f2NjIqaeeyv7778+nn37KAw88wD333MPf//733Xq9uwsHHXSQI2IBxt39gQMHcuONN7JixQp++OEHzjrrLBYsWMBhhx3GSSedxEMPPcTGjRujth15PB769OnDEUccwYknnsiQIUNoa2vjk08+YcWKFaxfv576+vqoxypKKvsNvpSDxi6n/4gvkHN+TU1DMS2tMu3tyd3TURQZRZFQFCNUr7Zei3H9MGLUpE4Ri/vvv5+nnnqKxYsX73JiAdDQ0ABAXl4eAJ9++il+v58JEyZYY4YNG8aAAQNYuXIlACtXrmT48OEWsQCYOHEijY2NrF271hpjn8McY87hwoULFy52DTx7egEuXOyr+NOf/kT//v155plnrMcGDRpkfS+E4OGHH+bXv/41JSUlADz//PMUFRUxf/58zj77bL755hsWLlzIqlWrrGrHY489xumnn86f//xn+vbty7///W86Ojr45z//ic/n49BDD+WLL77gwQcfDCEh/+uQJIl+/fpxzTXXcPXVV1NRUcErr7xCWVkZ99xzD4cddphV0Rg6dGhEVUJRFIqKiigqKkLTNGpra6moqODzzz9HURSropGbmxtxrCz/f3v3HxTlde9x/M2iFEPCL5Fd6HgjTStq9IoBRdSgTVBKogGLVaMmDDr0hgs4htRKf2hsWtpIfmiMv2qTTJ00Nib8UAoJCWISmmjBISOIErVJTGOvuysibjAqyO79w+FptqghZhFrPq8Z/mCf8zxzzuo4fs45z/n2I2zI/YQNuR+n08knhzbQfLqMAf3/ibe3E98rn4h7Rd7eXgT5e3f73OQFI0ffzf3pz/foOV1/H5999ll27tzJqFGjrr5TPeR0OlmyZAkTJ05k5MiRAFitVnx8fAgMDHRrazabsVqtRpsvBouu613XrtTG4XBw9uzZL31pX0REro5WLkR6SWlpKTExMfzoRz8iNDSUMWPG8Ic//MG4/vHHH2O1Wt1mVwMCAoiNjXWboQ0MDDSCBUBCQgImk4mamhqjTXx8vFtxuMTERA4dOsSpU6d6e5j/kby8vLBYLGRmZlJZWcnx48fJzs6mtraW8ePHExsbS35+PgcPHrzMqoQ3gwYNYuTIkUyePJkRI0bgdDqpr6+nurqapqYmTp48ifMSb3ebTCYihmczfHwF/zWmHt/QJzn52W2c+dzEufNXNx7nv3XRZPJi2O0TeSDjxR7d73K5WL9+PU8++SQVFRXccccdV9eRrygrK4vGxkZefrlnL5mLiMj1T+FCpJd89NFHxvsTb7zxBpmZmSxevJgtW7YA/5phvdTs6hdnX/+9EnK/fv0IDg7+SrO4cnleXl6EhISwaNEiysvLsVqtLF26lMbGRu68807uuOMOVq5cSX19/WXDQkhICCNGjCA+Pt6Y8W9sbKS6upoDBw7Q3Nx82XsHhScxbFwpQ6L3c5NlPSc/G0bb596cOw8dF3q2feqLAchk8iJyeBzpma/2+N7nnnuO/Px8ysvLGTduXI/u+7qys7MpKyvrVjvDYrHQ3t5Oa2urW3ubzWYU77NYLN1Oj+r6/cva+Pv7a9VCRKQXaVuUSC9xOp3ExMQYp9eMGTOGxsZGNm3aRFpaWh/3Ti7Fy8uLoKAg0tLSSEtLw+FwUFZWRnFxMVOnTiU0NJTk5GRSUlKIjo7GZHKfnzGZTAQHBxMcHMywYcNobW3FbrfT1NTEhQsXGDRoEKGhoQwcOBBv7+5bmQZapjDQMgWA1hM1HD24HH8/G/29L+Djc/n6GV25xcsLvjs0hoX/W9ij8bpcLrZs2cLy5cspKytjwoQJPf+yrpLL5SInJ4eSkhLefvttt62CANHR0fTv35+qqipSU1OBiy/x/+Mf/yAuLg6AuLg48vPzsdvtRviurKzE39/feE8kLi6O1157ze3ZlZWVxjNERKR3KFyI9JKwsLBuL8QOHz6coqIi4F8zrDabjbCwMKONzWYjKirKaGO3292eceHCBVpaWr7SLK5cHX9/f+bNm8e8efM4c+YMr7/+OkVFRcyYMYOgoCDuu+8+kpOTiY2N7RYWuoJKUFAQQ4cOxeFwYLPZOHz4MO3t7YSEhGA2mxk4cCD9+nX/pzhwUCxRk98EwHGqgeMfraGf6336e3fg09/9FCmn6+KKxXduiyIjZ0ePxuZyudi6dSvLli1jx44dxMfHf41vqueysrLYunUrO3bs4JZbbjFW1wICAhgwYAABAQEsWrSI3NxcgoOD8ff3Jycnh7i4OMaPHw/AtGnTGDFiBA888AAFBQVYrVZ++ctfkpWVxbe+dfEFloceeoh169bx05/+lIULF7Jr1y5eeeUVysvLr8k4RUS+qbQtSqSXTJw4sVtxsMOHDxvFyCIiIrBYLFRVVRnXHQ4HNTU1bjO0ra2t1NXVGW127dqF0+kkNjbWaFNdXU1HR4fRprKyksjISLeTqeTr8fPzY9asWfz5z3/GZrOxdu1aTp8+zezZs4mMjOThhx+murq6WyE3uBg0AgICGDp0KBMnTmTs2LHcdNNNfPjhh7zzzjvU19dz/Phxtz/DL/IP+m8io1/gtph9BH+nCEf7ZBxtPpw950Wn04XTCbcOGcn/LOnZf5xdLheFhYU8/PDDvPrqq9x1111f67v5KjZu3Mjp06eZMmUKYWFhxs+2bduMNqtXr2b69OmkpqYSHx+PxWKhuLjYuO7t7U1ZWRne3t7ExcWxYMECHnzwQR577DGjTUREBOXl5VRWVjJ69GieeuopnnvuOdW4EBHpZapzIdJL9u7dy4QJE/jVr37F7Nmzqa2tJSMjg82bNzN//nzg4olSjz/+OFu2bCEiIoLly5fT0NDAwYMH8fX1BSApKQmbzcamTZvo6OggPT2dmJgYtm7dClw8yjMyMpJp06axbNkyGhsbWbhwIatXr9ZpUddAe3s7O3fupKioiNLSUry8vJg+fTozZ84kPj6e/v0vUUXvC9ra2rDZbNjtds6cOcPAgQMJDQ0lNDT0S+/9/LOjHDuymn/+30dMvmdHt21al7N9+3YyMjJ4+eWXmTFjRo/HKiIi8mUULkR6UVlZGT/72c84cuQIERER5ObmkpGRYVzvKqK3efNmWltbmTRpEhs2bHCriNzS0kJ2drZbEb21a9detoheSEgIOTk5LFu27JqOVS5W937nnXcoLCxk+/btdHR0cO+995KSksL3v/99Y8vO5Zw5cwa73Y7NZqOtrY2goCDMZjOhoaFup4F9HWVlZaSnp/Piiy/ywx/+0CPPFBER6aJwITesEydOMGrUKBYvXszPf/5zAHbv3s2UKVN4/fXXufvuu/u4h3Ij6+zs5N1336WwsJCSkhLa2tpISkoiJSWFhISELz2x6OzZs0bQcDgcBAYGGisaXataX9Ubb7zBggULeOGFF5gzZ85VPUNERORKFC7khvbaa6+RkpLC7t27iYyMJCoqiuTkZJ5++um+7pp8g3R2dvK3v/2NoqIiSkpKaG5uJjExkeTkZBITE91WoS7l3Llz2O127HY7ra2t+Pv7GysaPT1W9a233mLOnDls3LiRBQsWdCv0JyIi4gkKF3LDy8rKYufOncTExLB//3727t37pdtTRHqL0+mkrq7OWNE4duwYU6dOJTk5maSkJAICAq54//nz5zlx4gQ2m41Tp05xyy23GCsafn5+l7znr3/9K7NmzeKZZ54hPT1dwUJERHqNwoXc8M6ePcvIkSP59NNPqaurM4qcifQ1p9NJQ0ODETQ+/PBD7rrrLpKTk7n33nsJCgq6YhBob2/nxIkT2O12Tp48iZ+fH76+vjidTqKjo4GLFdxnzpzJqlWreOihhxQsRESkVylcyA2vsbGRsWPH0tHRQUlJiU7HkeuSy+WiqamJwsJCiouLOXjwIJMnTyYlJYXp06cTEhJyxWDQ0dFBc3MzpaWl5OXlYTabmTRpEuXl5Tz22GMsXrxYwUJERHqdwoXc0Nrb2xk3bhxRUVFERkayZs0a9u/fb1T1FbkeuVwu/v73vxtBY9++fUycOJGUlBTuu+8+zGbzFYNCa2srv//973nppZf49NNPGTx4MKmpqcyaNYuYmBiFDBER6TUKF3JDW7p0KYWFhdTX13PzzTczefJkAgICKCsr6+uuifSIy+Xi6NGjFBUVUVxcTG1tLePHjyc5OZnk5GS+/e1vdwsLjY2NJCUl8cgjj7BkyRLefPNNow5HYGAgs2fPpqCgQCFDREQ8TuFCblhvv/02U6dO5a233mLSpEkAHD16lNGjR/P444+TmZnZxz0U+WpcLhfHjh2juLiY4uJi3nvvPaKjo42gMWTIED744AOSkpLIzMxk5cqVbgHi/PnzVFVVsX//ftVBERGRXqFwISLyH8jlcmG1WikpKaGoqIjq6mq+973vcezYMbKysvjtb3+rlQkREbnmFC5ERP7DuVwumpubWb9+Pbt376aiogKTydTX3RIRkW8ghQsREREREfEITW2JiMd1dnayfPlyIiIiGDBgALfddhu//vWv+eJchsvlYsWKFYSFhTFgwAASEhI4cuSI23NaWlqYP38+/v7+BAYGsmjRItra2tzaNDQ0cOedd+Lr68vgwYMpKCi4JmMUERGR7hQuRMTjVq1axcaNG1m3bh1NTU2sWrWKgoICnn32WaNNQUEBa9euZdOmTdTU1ODn50diYiLnzp0z2syfP58DBw5QWVlJWVkZ1dXV/PjHPzauOxwOpk2bxq233kpdXR1PPPEEK1euZPPmzdd0vCIiInKRtkWJiMdNnz4ds9nM888/b3yWmprKgAED+NOf/oTL5SI8PJxHHnmEn/zkJwCcPn0as9nMH//4R+bOnUtTUxMjRoxg7969xMTEAFBRUcE999zDsWPHCA8PZ+PGjfziF7/AarXi4+MDQF5eHtu3b+eDDz649gMXERH5htPKhYh43IQJE6iqquLw4cMA1NfX8+6775KUlATAxx9/jNVqJSEhwbgnICCA2NhY9uzZA8CePXsIDAw0ggVAQkICJpOJmpoao018fLwRLAASExM5dOgQp06d6vVxioiIiLt+fd0BEbnx5OXl4XA4GDZsGN7e3nR2dpKfn8/8+fMBsFqtAJjNZrf7zGazcc1qtXarpN6vXz+Cg4Pd2kRERHR7Rte1oKAgzw9ORERELkvhQkQ87pVXXuGll15i69at3H777ezbt48lS5YQHh5OWlpaX3dPREREeonChYh43NKlS8nLy2Pu3LkAjBo1ik8++YTf/e53pKWlYbFYALDZbISFhRn32Ww2oqKiALBYLNjtdrfnXrhwgZaWFuN+i8WCzWZza9P1e1cbERERuXb0zoWIeNznn3/erYibt7c3TqcTgIiICCwWC1VVVcZ1h8NBTU0NcXFxAMTFxdHa2kpdXZ3RZteuXTidTmJjY4021dXVdHR0GG0qKyuJjIzUligREZE+oHAhIh43Y8YM8vPzKS8v5+jRo5SUlPD0008zc+ZMALy8vFiyZAm/+c1vKC0tZf/+/Tz44IOEh4eTkpICwPDhw/nBD35ARkYGtbW1vPfee2RnZzN37lzCw8MBmDdvHj4+PixatIgDBw6wbds2nnnmGXJzc/tq6CIiIt9oOopWRDzus88+Y/ny5ZSUlGC32wkPD+f+++9nxYoVxslOLpeLRx99lM2bN9Pa2sqkSZPYsGEDQ4cONZ7T0tJCdnY2f/nLXzCZTKSmprJ27Vpuvvlmo01DQwNZWVns3buXkJAQcnJyWLZs2TUfs4iIiChciIiIiIiIh2hblIiIiIiIeITChYiIiIiIeITChYiIiIiIeITChYiIXNb69esZMmQIvr6+xMbGUltb29ddEhGR65jChYiIXNK2bdvIzc3l0Ucf5f3332f06NEkJiZ2K24oIiLSRadFiYjIJcXGxjJ27FjWrVsHgNPpZPDgweTk5JCXl9fHvRMRkeuRVi5ERKSb9vZ26urqSEhIMD4zmUwkJCSwZ8+ePuyZiIhczxQuRESkm+bmZjo7OzGbzW6fm81mrFZrH/VKRESudwoXIiIiIiLiEQoXIiLSTUhICN7e3thsNrfPbTYbFoulj3olIiLXO4ULERHpxsfHh+joaKqqqozPnE4nVVVVxMXF9WHPRETketavrzsgIiLXp9zcXNLS0oiJiWHcuHGsWbOGM2fOkJ6e3tddExGR65TChYiIXNKcOXM4ceIEK1aswGq1EhUVRUVFRbeXvEVERLqozoWIiIiIiHiE3rkQERERERGPULgQERERERGPULgQERERERGPULgQERERERGPULgQERERERGPULgQERERERGPULgQERERERGPULgQERERERGPULgQERERERGPULgQERERERGPULgQERERERGPULgQERERERGPULgQERERERGPULgQERERERGPULgQERERERGPULgQERERERGPULgQERERERGPULgQERERERGPULgQERERERGP+H8pJgthQKpIwQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot3Dcube(mat,\n",
    "           np.linspace(XMIN, XMAX, int((XMAX-XMIN)/DX)+1),\n",
    "           np.linspace(YMIN, YMAX, int((YMAX-YMIN)/DY)+1),\n",
    "           np.linspace(-DEPTH_TOTAL, ZMAX, int((ZMAX+DEPTH_TOTAL)/DZ)+1),\n",
    "           vmin = VS_MIN, vmax=VS_MAX)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4d3017a",
   "metadata": {},
   "source": [
    "<a id='section5'></a>\n",
    "# 5. Run the simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "0dc66458",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from simulation.simulation_files import *"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb4bc31f",
   "metadata": {},
   "source": [
    "Define the source as a double-couple moment tensor. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "d0499cb2",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "strike = 48\n",
    "dip = 45\n",
    "rake = 88\n",
    "M0 = 2.47e16\n",
    "Mxx, Myy, Mzz, Mxy, Mxz, Myz = compute_moment_tensor(strike,dip,rake,M0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4376230-68b7-461d-bdcd-f7e7b9578cde",
   "metadata": {},
   "source": [
    "Define the source position at the center of the domain and in the constant bottom layer. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "e90efa62-2365-4514-8806-7714f566e150",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Source is located at (4800.0, 4800.0, -8400)\n"
     ]
    }
   ],
   "source": [
    "source_x = XMIN + (XMAX-XMIN)/2\n",
    "source_y = YMIN + (YMAX - YMIN)/2\n",
    "source_z = ZMAX - 28*DZ\n",
    "print(f'Source is located at ({source_x}, {source_y}, {source_z})')\n",
    "\n",
    "source=pd.Series({'type':'moment',\n",
    "                 'x':source_x,\n",
    "                 'y':source_y,\n",
    "                 'z':source_y,\n",
    "                 'moment_xx':Mxx,\n",
    "                 'moment_yy':Myy,\n",
    "                 'moment_zz':Mzz,\n",
    "                 'moment_xy':Mxy,\n",
    "                 'moment_xz':Mxz,\n",
    "                 'moment_yz':Myz,\n",
    "                 'func' : 'file',\n",
    "                  'filename' : f'{path_folder}source_time.txt',\n",
    "                 })"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dce53308-1957-40d8-b6b8-a060ad268e3c",
   "metadata": {},
   "source": [
    "Define the source time function as a ramp function parametrized by the magnitude `Mw`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "c8a8f8e9-dfd8-4e39-96b0-96a2fa825001",
   "metadata": {},
   "outputs": [],
   "source": [
    "t_final = 20\n",
    "dt = 0.001 # sampling time step for the source\n",
    "t0 = 0.5 # initial time of the source\n",
    "Mw = 4.9 # magntiude\n",
    "tau = 10**(0.5*(Mw-6.69)) # formula depending on Mw \n",
    "\n",
    "tvec = np.arange(0, t_final + dt,dt) \n",
    "source_tf = pd.Series(np.where(tvec>=t0, spice_bench(tvec-t0, 1/tau), 0), index=tvec,dtype=float)\n",
    "\n",
    "# write the source time file\n",
    "with open(path_folder + 'source_time.txt', 'w') as f:\n",
    "    for t,s in zip(source_tf.index, source_tf.values):\n",
    "        f.write(f'{t} {s}\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "af200747-462a-4d02-b08a-a759120181b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2)=plt.subplots(1, 2, figsize=(8,4))\n",
    "ax1.plot(source_tf)\n",
    "ax1.set_xlabel('Time [s]')\n",
    "ax1.set_ylabel('Force')\n",
    "ax1.set_xlim([0,4])\n",
    "\n",
    "ax2.loglog(np.fft.rfftfreq(source_tf.shape[0],d=dt),np.abs(np.fft.rfft(source_tf)))\n",
    "ax2.set_xlabel('Frequency [Hz]')\n",
    "ax2.set_ylabel('Amplitude')\n",
    "ax2.grid(True)\n",
    "fig.suptitle('Source time function')\n",
    "fig.subplots_adjust(left=0.1,right=0.95,wspace=0.3, bottom=0.15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ddf0b2c1-b2af-43b3-9f2c-3e49e4cd001e",
   "metadata": {},
   "source": [
    "All simulation inputs are included in the `input.spec` file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "c11cf18b-104b-4878-b771-6302143604a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(path_folder+'input.spec', 'a') as f:\n",
    "    f.write('# duration of the run\\n')\n",
    "    f.write(f'sim_time = {t_final};\\n\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "001a901c-aa41-44d3-9d68-9d200e417047",
   "metadata": {},
   "source": [
    "Write the source parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "6d3779bc-37b6-418d-b661-2292e55bc49a",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(path_folder+'input.spec', 'a') as f:\n",
    "    f.write('# Introduce a source\\n')       \n",
    "    f.write('source {\\n')\n",
    "    f.write('\\t# coordinates of the source (x,yz) or (lat,long,R)\\n')\n",
    "    f.write(f'\\tcoords = {source.x} {source.y} {source.z};\\n')\n",
    "    f.write('\\t# type (1: impulse, 2: moment tensor, 3: fluidpulse)\\n')\n",
    "    f.write(f'\\ttype = {source.type};\\n')\n",
    "\n",
    "    if source.func=='file':\n",
    "        f.write(f'\\ttime_file = \"{source.filename}.txt\";\\n')\n",
    "\n",
    "    if 'dir_x' in source.index:\n",
    "        f.write('\\t#direction (only for impulse)\\n')\n",
    "        f.write(f'\\tdir = {source.dir_x} {source.dir_y} {source.dir_z};\\n')\n",
    "\n",
    "    if 'moment_xx' in source.index:\n",
    "        f.write('\\t# moment (xx yy zz xy xz yz)\\n')\n",
    "        f.write(f'\\tmoment = {source.moment_xx} {source.moment_yy} '\\\n",
    "                f'{source.moment_zz} {source.moment_xy} {source.moment_xz} {source.moment_yz};\\n')\n",
    "        \n",
    "    for key in ['func','freq','tau','gamma','ts']:\n",
    "        if key in source.index:\n",
    "            f.write('\\t{} = {};\\n'.format(key,source.loc[key]))\n",
    "\n",
    "    f.write('};\\n\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "83ad870f-e455-4843-be46-366a53b3b3cb",
   "metadata": {},
   "source": [
    "Define a regular grid of sensors at the surface."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "66bca9be-f5dd-49f2-988f-923a99e30275",
   "metadata": {},
   "outputs": [],
   "source": [
    "sensors = pd.DataFrame(columns=['x','y','z'])\n",
    "\n",
    "for x in np.linspace(XMIN + DX/2, XMAX-DX/2, 16):\n",
    "    for y in np.linspace(YMIN + DY/2, YMAX-DY/2, 16):\n",
    "        sensors = pd.concat([sensors, pd.DataFrame(np.array([[x, y , ZMAX-1]]),\n",
    "                                                        columns=['x','y','z'])])\n",
    "        \n",
    "sensors.to_csv(path_folder+'sensors.txt', index=None, header=None, sep=' ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "36f0fe87-4e78-49e8-90b3-165de585fec3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "9bd48902-ec0c-41b8-8e4d-ecbe13f8e3e2",
   "metadata": {},
   "source": [
    "Write the sensors description in the `input.spec` file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "da86f9ff-8a4d-43da-914a-a6d1559df176",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(path_folder+'input.spec', 'a') as f:\n",
    "    f.writelines(['# Sensors description\\n',\n",
    "                  'save_traces = true;\\n',\n",
    "                  'traces_format = hdf5;\\n\\n'])\n",
    "    \n",
    "with open(path_folder+'input.spec', 'a') as f:\n",
    "    f.write('capteurs \"S\" {\\n')\n",
    "    f.write('\\ttype = points;\\n')\n",
    "    f.write(f'\\tfile = \"sensors.txt\";\\n')\n",
    "    f.write(f'\\tperiod = 5;\\n') # solutions are synthetized every period internal time step\n",
    "    f.write('};\\n\\n') "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19e17fc6-5c76-462c-8eb0-610ffbe5cbc7",
   "metadata": {},
   "source": [
    "Supplementary parameters for the simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "5479801d-83a2-49dd-bd33-a2bd1d68c381",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(path_folder+'input.spec', 'a') as f:\n",
    "    f.write('snapshots {\\n')\n",
    "    f.write('\\tsave_snap = false;\\n')\n",
    "    f.write('\\tsnap_interval = 0.1;\\n')\n",
    "    f.write('\\tselect all;\\n')\n",
    "    f.write('};\\n\\n')\n",
    "    \n",
    "with open(path_folder+'input.spec', 'a') as f:\n",
    "    f.write('# Fichier protection reprise\\n')\n",
    "    f.write('prorep=false;\\n')\n",
    "    f.write('prorep_iter=10000;\\n') # without protection (otherwise the files are too large to be written)\n",
    "    f.write('restart_iter=0;\\n\\n')    \n",
    "\n",
    "with open(path_folder+'input.spec', 'a') as f:\n",
    "    f.write('time_scheme {\\n')\n",
    "    f.write('\\taccel_scheme = false;\\t# Acceleration scheme for Newmark\\n')\n",
    "    f.write('\\tveloc_scheme = true;\\t# Velocity scheme for Newmark\\n')\n",
    "    f.write('\\talpha = 0.5;\\t# alpha (Newmark parameter)\\n')\n",
    "    f.write('\\tbeta =  0.5;\\t# beta (Newmark parameter)\\n')\n",
    "    f.write('\\tgamma = 1; \\t# gamma (Newmark parameter)\\n')\n",
    "    f.write('\\tcourant=0.2;\\n')\n",
    "    f.write('};\\n\\n')\n",
    "\n",
    "\n",
    "with open(path_folder+'input.spec', 'a') as f:\n",
    "    f.write('out_variables {\\n')\n",
    "    f.write('\\tenP  = 0;   # P-wave energy (scalar field)\\n')\n",
    "    f.write('\\tenS  = 0;   # S-wave energy (scalar field)\\n')\n",
    "    f.write('\\tevol = 0;   # volumetric strain (scalar field)\\n')\n",
    "    f.write('\\tpre  = 0;   # pressure (scalar field)\\n')\n",
    "    f.write('\\tdis  = 1;   # displacement (vector field)\\n')\n",
    "    f.write('\\tvel  = 1;   # velocity (vector field)\\n')\n",
    "    f.write('\\tacc  = 1;   # acceleration (vector field)\\n')\n",
    "    f.write('\\tedev = 0;   # deviatoric strain (tensor field)\\n')\n",
    "    f.write('\\tsdev = 0;   # deviatoric stress (tensor field)\\n')\n",
    "    f.write('\\tedevpl = 0; # deviatoric strain (tensor field)\\n')\n",
    "    f.write('\\tgradla = 0; # gradient Lambda\\n')\n",
    "    f.write('\\tgradmu = 0; # gradient Mu\\n')\n",
    "    f.write('};\\n\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8bcac8c3-7aed-43cc-98b7-45c2daa825b2",
   "metadata": {},
   "source": [
    "To launch the simulation, run `ccc_mprun PATH_SEM_BUILD/SEM3D/sem3d.exe` by adapting the path and the number of processors."
   ]
  }
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