Knight tour MILP.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyomo.environ import *\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import random \n",
    "import operator\n",
    "from pyomo import environ as pe\n",
    "import os\n",
    "\n",
    "os.environ['NEOS_EMAIL'] = 'XXXXXX@gmail.com' \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "n=64\n",
    "Xdic={}\n",
    "itr=0\n",
    "for i in range(1,int(sqrt(n))+1):\n",
    "    for j in range(1,int(sqrt(n))+1):\n",
    "        itr+=1\n",
    "        Xdic[itr,'x']=i\n",
    "        Xdic[itr,'y']=j\n",
    "    \n",
    "AllTour={}\n",
    "for i in range(1,n+1):\n",
    "    for j in range(1,n+1):\n",
    "        if i!=j:\n",
    "            AllTour[i,j]=sqrt((Xdic[i,'x']-Xdic[j,'x'])**2+(Xdic[i,'y']-Xdic[j,'y'])**2)\n",
    "            \n",
    "            \n",
    "allowed={}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{(-1, -2): 1, (-2, -1): 1, (-1, 2): 1, (2, -1): 1, (1, -2): 1, (-2, 1): 1, (1, 2): 1, (2, 1): 1}\n"
     ]
    }
   ],
   "source": [
    "test={}\n",
    "for cc in [-1,+1]:\n",
    "    for rr in [-2,+2]:\n",
    "        test[cc,rr]=1\n",
    "        test[rr,cc]=1\n",
    "print(test)                    \n",
    "for i in range(1,n+1):\n",
    "    for j in range(1,n+1):\n",
    "        if i!=j:\n",
    "            #print(i,j, (Xdic[i,'x']-Xdic[j,'x'],Xdic[i,'y']-Xdic[j,'y']))\n",
    "            if (Xdic[i,'x']-Xdic[j,'x'],Xdic[i,'y']-Xdic[j,'y']) in test:\n",
    "                allowed[i,j]=1\n",
    "                allowed[j,i]=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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    "allowed"
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  {
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       " [23, 8, 14, 24],\n",
       " [23, 8, 24, 14],\n",
       " [23, 8, 15, 32],\n",
       " [23, 8, 32, 15],\n",
       " [23, 8, 16, 22],\n",
       " [23, 8, 22, 16],\n",
       " [9, 19, 1, 18],\n",
       " [9, 19, 18, 1],\n",
       " [9, 19, 2, 17],\n",
       " [9, 19, 17, 2],\n",
       " [9, 19, 3, 18],\n",
       " [9, 19, 18, 3],\n",
       " [9, 19, 10, 25],\n",
       " [9, 19, 25, 10],\n",
       " [9, 19, 10, 27],\n",
       " [9, 19, 27, 10],\n",
       " [9, 19, 11, 17],\n",
       " [9, 19, 17, 11],\n",
       " [9, 19, 11, 26],\n",
       " [9, 19, 26, 11],\n",
       " [9, 19, 12, 18],\n",
       " [9, 19, 18, 12],\n",
       " [19, 9, 1, 18],\n",
       " [19, 9, 18, 1],\n",
       " [19, 9, 2, 17],\n",
       " [19, 9, 17, 2],\n",
       " [19, 9, 3, 18],\n",
       " [19, 9, 18, 3],\n",
       " [19, 9, 10, 25],\n",
       " [19, 9, 25, 10],\n",
       " [19, 9, 10, 27],\n",
       " [19, 9, 27, 10],\n",
       " [19, 9, 11, 17],\n",
       " [19, 9, 17, 11],\n",
       " [19, 9, 11, 26],\n",
       " [19, 9, 26, 11],\n",
       " [19, 9, 12, 18],\n",
       " [19, 9, 18, 12],\n",
       " [9, 26, 2, 17],\n",
       " [9, 26, 17, 2],\n",
       " [9, 26, 10, 25],\n",
       " [9, 26, 25, 10],\n",
       " [9, 26, 11, 17],\n",
       " [9, 26, 17, 11],\n",
       " [9, 26, 17, 27],\n",
       " [9, 26, 27, 17],\n",
       " [9, 26, 18, 33],\n",
       " [9, 26, 33, 18],\n",
       " [9, 26, 19, 25],\n",
       " [9, 26, 25, 19],\n",
       " [26, 9, 2, 17],\n",
       " [26, 9, 17, 2],\n",
       " [26, 9, 10, 25],\n",
       " [26, 9, 25, 10],\n",
       " [26, 9, 11, 17],\n",
       " [26, 9, 17, 11],\n",
       " [26, 9, 17, 27],\n",
       " [26, 9, 27, 17],\n",
       " [26, 9, 18, 33],\n",
       " [26, 9, 33, 18],\n",
       " [26, 9, 19, 25],\n",
       " [26, 9, 25, 19],\n",
       " [10, 20, 2, 19],\n",
       " [10, 20, 19, 2],\n",
       " [10, 20, 3, 18],\n",
       " [10, 20, 18, 3],\n",
       " [10, 20, 4, 19],\n",
       " [10, 20, 19, 4],\n",
       " [10, 20, 11, 17],\n",
       " [10, 20, 17, 11],\n",
       " [10, 20, 11, 26],\n",
       " [10, 20, 26, 11],\n",
       " [10, 20, 11, 28],\n",
       " [10, 20, 28, 11],\n",
       " [10, 20, 12, 18],\n",
       " [10, 20, 18, 12],\n",
       " [10, 20, 12, 27],\n",
       " [10, 20, 27, 12],\n",
       " [10, 20, 13, 19],\n",
       " [10, 20, 19, 13],\n",
       " [20, 10, 2, 19],\n",
       " [20, 10, 19, 2],\n",
       " [20, 10, 3, 18],\n",
       " [20, 10, 18, 3],\n",
       " [20, 10, 4, 19],\n",
       " [20, 10, 19, 4],\n",
       " [20, 10, 11, 17],\n",
       " [20, 10, 17, 11],\n",
       " [20, 10, 11, 26],\n",
       " [20, 10, 26, 11],\n",
       " [20, 10, 11, 28],\n",
       " [20, 10, 28, 11],\n",
       " [20, 10, 12, 18],\n",
       " [20, 10, 18, 12],\n",
       " [20, 10, 12, 27],\n",
       " [20, 10, 27, 12],\n",
       " [20, 10, 13, 19],\n",
       " [20, 10, 19, 13],\n",
       " [10, 25, 1, 18],\n",
       " [10, 25, 18, 1],\n",
       " [10, 25, 9, 19],\n",
       " [10, 25, 19, 9],\n",
       " [10, 25, 9, 26],\n",
       " [10, 25, 26, 9],\n",
       " [10, 25, 11, 17],\n",
       " [10, 25, 17, 11],\n",
       " [10, 25, 17, 27],\n",
       " [10, 25, 27, 17],\n",
       " [10, 25, 17, 34],\n",
       " [10, 25, 34, 17],\n",
       " [25, 10, 1, 18],\n",
       " [25, 10, 18, 1],\n",
       " [25, 10, 9, 19],\n",
       " [25, 10, 19, 9],\n",
       " [25, 10, 9, 26],\n",
       " [25, 10, 26, 9],\n",
       " [25, 10, 11, 17],\n",
       " [25, 10, 17, 11],\n",
       " [25, 10, 17, 27],\n",
       " [25, 10, 27, 17],\n",
       " [25, 10, 17, 34],\n",
       " [25, 10, 34, 17],\n",
       " [10, 27, 3, 18],\n",
       " [10, 27, 18, 3],\n",
       " [10, 27, 9, 19],\n",
       " [10, 27, 19, 9],\n",
       " [10, 27, 11, 17],\n",
       " [10, 27, 17, 11],\n",
       " [10, 27, 11, 26],\n",
       " [10, 27, 26, 11],\n",
       " [10, 27, 12, 18],\n",
       " [10, 27, 18, 12],\n",
       " [10, 27, 18, 28],\n",
       " [10, 27, 28, 18],\n",
       " [10, 27, 19, 25],\n",
       " [10, 27, 25, 19],\n",
       " [10, 27, 19, 34],\n",
       " [10, 27, 34, 19],\n",
       " [10, 27, 20, 26],\n",
       " [10, 27, 26, 20],\n",
       " [27, 10, 3, 18],\n",
       " [27, 10, 18, 3],\n",
       " [27, 10, 9, 19],\n",
       " [27, 10, 19, 9],\n",
       " [27, 10, 11, 17],\n",
       " [27, 10, 17, 11],\n",
       " [27, 10, 11, 26],\n",
       " [27, 10, 26, 11],\n",
       " [27, 10, 12, 18],\n",
       " [27, 10, 18, 12],\n",
       " [27, 10, 18, 28],\n",
       " [27, 10, 28, 18],\n",
       " [27, 10, 19, 25],\n",
       " [27, 10, 25, 19],\n",
       " [27, 10, 19, 34],\n",
       " [27, 10, 34, 19],\n",
       " [27, 10, 20, 26],\n",
       " [27, 10, 26, 20],\n",
       " [11, 17, 1, 18],\n",
       " [11, 17, 18, 1],\n",
       " [11, 17, 2, 19],\n",
       " [11, 17, 19, 2],\n",
       " [11, 17, 3, 18],\n",
       " [11, 17, 18, 3],\n",
       " [11, 17, 9, 19],\n",
       " [11, 17, 19, 9],\n",
       " [11, 17, 9, 26],\n",
       " [11, 17, 26, 9],\n",
       " [11, 17, 10, 20],\n",
       " [11, 17, 20, 10],\n",
       " [11, 17, 10, 25],\n",
       " [11, 17, 25, 10],\n",
       " [11, 17, 10, 27],\n",
       " [11, 17, 27, 10],\n",
       " [17, 11, 1, 18],\n",
       " [17, 11, 18, 1],\n",
       " [17, 11, 2, 19],\n",
       " [17, 11, 19, 2],\n",
       " [17, 11, 3, 18],\n",
       " [17, 11, 18, 3],\n",
       " [17, 11, 9, 19],\n",
       " [17, 11, 19, 9],\n",
       " [17, 11, 9, 26],\n",
       " [17, 11, 26, 9],\n",
       " [17, 11, 10, 20],\n",
       " [17, 11, 20, 10],\n",
       " [17, 11, 10, 25],\n",
       " [17, 11, 25, 10],\n",
       " [17, 11, 10, 27],\n",
       " [17, 11, 27, 10],\n",
       " [11, 21, 3, 20],\n",
       " [11, 21, 20, 3],\n",
       " [11, 21, 4, 19],\n",
       " [11, 21, 19, 4],\n",
       " [11, 21, 5, 20],\n",
       " [11, 21, 20, 5],\n",
       " [11, 21, 12, 18],\n",
       " [11, 21, 18, 12],\n",
       " [11, 21, 12, 27],\n",
       " [11, 21, 27, 12],\n",
       " [11, 21, 12, 29],\n",
       " [11, 21, 29, 12],\n",
       " [11, 21, 13, 19],\n",
       " [11, 21, 19, 13],\n",
       " [11, 21, 13, 28],\n",
       " [11, 21, 28, 13],\n",
       " [11, 21, 14, 20],\n",
       " [11, 21, 20, 14],\n",
       " [21, 11, 3, 20],\n",
       " [21, 11, 20, 3],\n",
       " [21, 11, 4, 19],\n",
       " [21, 11, 19, 4],\n",
       " [21, 11, 5, 20],\n",
       " [21, 11, 20, 5],\n",
       " [21, 11, 12, 18],\n",
       " [21, 11, 18, 12],\n",
       " [21, 11, 12, 27],\n",
       " [21, 11, 27, 12],\n",
       " [21, 11, 12, 29],\n",
       " [21, 11, 29, 12],\n",
       " [21, 11, 13, 19],\n",
       " [21, 11, 19, 13],\n",
       " [21, 11, 13, 28],\n",
       " [21, 11, 28, 13],\n",
       " [21, 11, 14, 20],\n",
       " [21, 11, 20, 14],\n",
       " [11, 26, 2, 19],\n",
       " [11, 26, 19, 2],\n",
       " [11, 26, 9, 19],\n",
       " [11, 26, 19, 9],\n",
       " [11, 26, 10, 20],\n",
       " [11, 26, 20, 10],\n",
       " [11, 26, 10, 27],\n",
       " [11, 26, 27, 10],\n",
       " [11, 26, 12, 18],\n",
       " [11, 26, 18, 12],\n",
       " [11, 26, 17, 27],\n",
       " [11, 26, 27, 17],\n",
       " [11, 26, 18, 28],\n",
       " [11, 26, 28, 18],\n",
       " [11, 26, 18, 35],\n",
       " [11, 26, 35, 18],\n",
       " [11, 26, 19, 25],\n",
       " [11, 26, 25, 19],\n",
       " [26, 11, 2, 19],\n",
       " [26, 11, 19, 2],\n",
       " [26, 11, 9, 19],\n",
       " [26, 11, 19, 9],\n",
       " [26, 11, 10, 20],\n",
       " [26, 11, 20, 10],\n",
       " [26, 11, 10, 27],\n",
       " [26, 11, 27, 10],\n",
       " [26, 11, 12, 18],\n",
       " [26, 11, 18, 12],\n",
       " [26, 11, 17, 27],\n",
       " [26, 11, 27, 17],\n",
       " [26, 11, 18, 28],\n",
       " [26, 11, 28, 18],\n",
       " [26, 11, 18, 35],\n",
       " [26, 11, 35, 18],\n",
       " [26, 11, 19, 25],\n",
       " [26, 11, 25, 19],\n",
       " [11, 28, 4, 19],\n",
       " [11, 28, 19, 4],\n",
       " [11, 28, 10, 20],\n",
       " [11, 28, 20, 10],\n",
       " [11, 28, 12, 18],\n",
       " [11, 28, 18, 12],\n",
       " [11, 28, 12, 27],\n",
       " [11, 28, 27, 12],\n",
       " [11, 28, 13, 19],\n",
       " [11, 28, 19, 13],\n",
       " [11, 28, 19, 29],\n",
       " [11, 28, 29, 19],\n",
       " [11, 28, 20, 26],\n",
       " [11, 28, 26, 20],\n",
       " [11, 28, 20, 35],\n",
       " [11, 28, 35, 20],\n",
       " [11, 28, 21, 27],\n",
       " [11, 28, 27, 21],\n",
       " [28, 11, 4, 19],\n",
       " [28, 11, 19, 4],\n",
       " [28, 11, 10, 20],\n",
       " [28, 11, 20, 10],\n",
       " [28, 11, 12, 18],\n",
       " [28, 11, 18, 12],\n",
       " [28, 11, 12, 27],\n",
       " [28, 11, 27, 12],\n",
       " [28, 11, 13, 19],\n",
       " [28, 11, 19, 13],\n",
       " [28, 11, 19, 29],\n",
       " [28, 11, 29, 19],\n",
       " [28, 11, 20, 26],\n",
       " [28, 11, 26, 20],\n",
       " [28, 11, 20, 35],\n",
       " [28, 11, 35, 20],\n",
       " [28, 11, 21, 27],\n",
       " [28, 11, 27, 21],\n",
       " [12, 18, 2, 19],\n",
       " [12, 18, 19, 2],\n",
       " [12, 18, 3, 20],\n",
       " [12, 18, 20, 3],\n",
       " [12, 18, 4, 19],\n",
       " [12, 18, 19, 4],\n",
       " [12, 18, 9, 19],\n",
       " [12, 18, 19, 9],\n",
       " [12, 18, 10, 20],\n",
       " [12, 18, 20, 10],\n",
       " [12, 18, 10, 27],\n",
       " [12, 18, 27, 10],\n",
       " [12, 18, 11, 21],\n",
       " [12, 18, 21, 11],\n",
       " [12, 18, 11, 26],\n",
       " [12, 18, 26, 11],\n",
       " [12, 18, 11, 28],\n",
       " [12, 18, 28, 11],\n",
       " [18, 12, 2, 19],\n",
       " [18, 12, 19, 2],\n",
       " [18, 12, 3, 20],\n",
       " [18, 12, 20, 3],\n",
       " [18, 12, 4, 19],\n",
       " [18, 12, 19, 4],\n",
       " [18, 12, 9, 19],\n",
       " [18, 12, 19, 9],\n",
       " [18, 12, 10, 20],\n",
       " [18, 12, 20, 10],\n",
       " [18, 12, 10, 27],\n",
       " [18, 12, 27, 10],\n",
       " [18, 12, 11, 21],\n",
       " [18, 12, 21, 11],\n",
       " [18, 12, 11, 26],\n",
       " [18, 12, 26, 11],\n",
       " [18, 12, 11, 28],\n",
       " [18, 12, 28, 11],\n",
       " ...]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def cross(i,j,a,b):    \n",
    "    m1 = (Xdic[i,'y']-Xdic[j,'y'])/(Xdic[i,'x']-Xdic[j,'x'])\n",
    "    m2 = (Xdic[a,'y']-Xdic[b,'y'])/(Xdic[a,'x']-Xdic[b,'x'])\n",
    "    x1,y1 = Xdic[i,'x'], Xdic[i,'y']\n",
    "    x2,y2 = Xdic[a,'x'], Xdic[a,'y']\n",
    "\n",
    "    a1,b1,c1=-m1,1, x1*m1-y1\n",
    "    a2,b2,c2=-m2,1, x2*m2-y2\n",
    "\n",
    "    if m1!=m2:        \n",
    "        x0,y0 = (b1*c2-b2*c1)/(a1*b2-a2*b1) , (c1*a2-c2*a1)/(a1*b2-a2*b1)\n",
    "        cond1 = min(Xdic[a,'x'],Xdic[b,'x']) <=x0<= max(Xdic[a,'x'],Xdic[b,'x'])\n",
    "        cond2 = min(Xdic[a,'y'],Xdic[b,'y']) <=y0<= max(Xdic[a,'y'],Xdic[b,'y'])\n",
    "        cond3 = min(Xdic[i,'x'],Xdic[j,'x']) <=x0<= max(Xdic[i,'x'],Xdic[j,'x'])\n",
    "        cond4 = min(Xdic[i,'y'],Xdic[j,'y']) <=y0<= max(Xdic[i,'y'],Xdic[j,'y'])\n",
    "\n",
    "        return (m1!=m2) and cond1 and cond2 and cond3 and cond4\n",
    "    else:\n",
    "        return False\n",
    "\n",
    "check = []\n",
    "for (i,j) in allowed:\n",
    "    for (a,b) in allowed:\n",
    "        x = {i,j}\n",
    "        y= {a,b}\n",
    "        result = x.intersection(y)\n",
    "        if len(result) ==0 and cross(i,j,a,b):\n",
    "            print(i,j,a,b)\n",
    "            check.append([i,j,a,b])\n",
    "            \n",
    "check            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5248 2624 336\n"
     ]
    }
   ],
   "source": [
    "new_ckeck =[] \n",
    "for [i,j,a,b] in check:\n",
    "    if [a,b, i,j] not in new_ckeck:\n",
    "        new_ckeck.append([i,j,a,b])\n",
    "print(len(check), len(new_ckeck), len(allowed))    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"fig = plt.figure(figsize=(9,9))\\nfor i in instance.i:\\n    plt.scatter(value(instance.Xloc[i]),value(instance.Yloc[i]),label=str(i),s=20,c='k')\\n    #plt.text(value(instance.Xloc[i]),0.02+value(instance.Yloc[i]), str(i), fontsize=8)\\nselectedTour={}\\nw=0.1\\nfor i in instance.i:\\n    for j in instance.j:\\n        if i!=j and (i,j) in allowed:\\n            if value(instance.U[i,j])>0.1:\\n                w+=0.1\\n                plt.plot([value(instance.Xloc[i]),value(instance.Xloc[j])],[value(instance.Yloc[i]),value(instance.Yloc[j])], c='k', lw=w )\\n                selectedTour[i,j]= value(instance.D[i,j])\""
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#MAD=100\n",
    "model = AbstractModel()\n",
    "model.N =Param(mutable=True, default=n) \n",
    "model.i = RangeSet(n)\n",
    "model.j = Set(initialize=model.i)\n",
    "\n",
    "model.U = Var(model.i,model.j, initialize=0, within=Binary)\n",
    "model.flow = Var(model.i,model.j,bounds=(0,1), within=NonNegativeReals)\n",
    "model.G = Var(model.i, bounds=(0,64), within=NonNegativeReals)\n",
    "model.demand = Var(model.i, within=Binary)\n",
    "\n",
    "\n",
    "model.OF = Var(within=NonNegativeReals, initialize=5)\n",
    "def initvalx(model,i):\n",
    "    return Xdic[i,'x']\n",
    "\n",
    "def initvaly(model,i):\n",
    "    return Xdic[i,'y']\n",
    "\n",
    "model.Xloc=Param(model.i,  within=NonNegativeReals, initialize=initvalx,mutable=True)\n",
    "model.Yloc=Param(model.i,  within=NonNegativeReals, initialize=initvaly, mutable=True)\n",
    "model.start=Param(model.i, initialize=0, mutable=True)\n",
    "\n",
    "def rule_w(model,i):\n",
    "    return 1+0.05*random.random()\n",
    "model.w=Param(model.i, initialize=rule_w, mutable=True)\n",
    "\n",
    "def Rule_D(model,i,j):\n",
    "    #return sqrt((model.Xloc[i]-model.Xloc[j])**2+(model.Yloc[i]-model.Yloc[j])**2)\n",
    "    return random.random()\n",
    "\n",
    "model.D=Param(model.i,model.j,  within=NonNegativeReals,initialize=Rule_D, mutable=True)\n",
    "\n",
    "\n",
    "\n",
    "def rule_C1(model,i):\n",
    "    return model.G[i]-0.001*(model.demand[i])==sum(model.flow[i,j]-model.flow[j,i] for j in model.j if (i,j) in allowed)\n",
    "model.C1   = Constraint(model.i,rule=rule_C1)\n",
    "\n",
    "def rule_C1A(model,i):\n",
    "    return model.G[i]<= model.start[i]\n",
    "model.C1A   = Constraint(model.i,rule=rule_C1A)\n",
    "\n",
    "\n",
    "def rule_C2(model,i,j):\n",
    "    if i!=j and (i,j) in allowed:\n",
    "        return model.flow[i,j]<=model.U[i,j]\n",
    "    else:\n",
    "        return Constraint.Skip\n",
    "model.C2   = Constraint(model.i,model.j,rule=rule_C2)\n",
    "\n",
    "def rule_C3(model,i):\n",
    "    return sum(model.U[i,j] for j in model.j if (i,j) in allowed)==model.demand[i]\n",
    "model.C3   = Constraint(model.i,rule=rule_C3)\n",
    "\n",
    "def rule_C4(model,j):\n",
    "    return sum(model.U[i,j] for i in model.i if (i,j) in allowed)==model.demand[j]\n",
    "model.C4   = Constraint(model.j,rule=rule_C4)\n",
    "\n",
    "def rule_C5(model,i,j):\n",
    "    if (i,j) in allowed and i<j:\n",
    "        return model.U[i,j] + model.U[j,i]<=1\n",
    "    else: \n",
    "        return Constraint.Skip \n",
    "model.C5   = Constraint(model.i,model.j,rule=rule_C5)\n",
    "\n",
    "def rule_C6(model,i,j,a,b):\n",
    "    if [i,j,a,b] in new_ckeck:\n",
    "        return model.U[i,j] + model.U[a,b]<=1\n",
    "    else:\n",
    "        return Constraint.Skip \n",
    "model.C6   = Constraint(model.i,model.i,model.i,model.i,rule=rule_C6)\n",
    "\n",
    "\n",
    "def rule_OF(model):\n",
    "    return sum(model.U[r,c]*model.D[r,c] for (r,c) in allowed) - 100*sum(model.demand[i]*model.w[i] for i in model.i) \n",
    "\n",
    "model.obj1 = Objective(rule=rule_OF, sense=minimize)\n",
    "\n",
    "from pyomo import environ as pe\n",
    "\n",
    "\n",
    "solver_manager = pe.SolverManagerFactory('neos')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING: Loading a SolverResults object with a warning status into\n",
      "    model.name=\"unknown\";\n",
      "      - termination condition: optimal\n",
      "      - message from solver: CPLEX 20.1.0.0\\x3a optimal (non-)integer solution\n",
      "        within mipgap or absmipgap; objective -3278.0618396146137; 38262292\n",
      "        MIP simplex iterations; 292458 branch-and-bound nodes; absmipgap =\n",
      "        0.285235, relmipgap = 8.70134e-05; 94 integer variables rounded\n",
      "        (maxerr = 4.9e-08).; Assigning integrality = 2e-08 might help.;\n",
      "        Currently integrality = 1e-05.\n",
      "OF=  -3278.0618376905068\n",
      "G=  0.03200000008820001\n"
     ]
    }
   ],
   "source": [
    "model.N=n\n",
    "instance = model.create_instance()\n",
    "instance.start[1]= 1\n",
    "s=1\n",
    "f=1\n",
    "results = solver_manager.solve(instance, solver = \"cplex\")\n",
    "print('OF= ',value(instance.obj1))\n",
    "print('G= ',value(instance.G[f]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Solver Status: warning\n",
      "1 18 1\n",
      "2 33 1\n",
      "3 27 1\n",
      "4 21 1\n",
      "5 36 1\n",
      "6 53 1\n",
      "7 43 1\n",
      "8 28 1\n",
      "9 34 1\n",
      "10 49 1\n",
      "11 59 1\n",
      "12 42 1\n",
      "13 52 1\n",
      "14 62 1\n",
      "15 45 1\n",
      "16 39 1\n",
      "17 54 1\n",
      "18 64 1\n",
      "19 47 1\n",
      "20 32 1\n",
      "21 38 1\n",
      "22 23 1\n",
      "23 29 1\n",
      "24 14 1\n",
      "25 24 1\n",
      "26 7 1\n",
      "27 13 1\n",
      "28 3 1\n",
      "29 20 1\n",
      "30 26 1\n",
      "31 11 1\n",
      "32 1 1\n",
      "-3278.0618376905068\n"
     ]
    },
    {
     "data": {
      "image/png": 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BgwcxZMgQ7Nixg1zsAW5sS0ZGBjZt2kTKdJSiaKhTQeGMCxcuoE2bNnB0dMT79+9JfZs2bXD58mXs27cPtWrVkmibJ06cwOfPnwEAVapUKdE0MWVBIBCwYiu8vb2Rm5srseOrqKhg9OjRiI+Px7x588iME0C0qNnQoUPh4+NDkpJRKCXl9u3bGDVqFJYsWYKUlBRSb2xszJltCQwMxMePHwEANWrUwJAhQyR6fEWhWEufKwr6+vrkn0KWkZRnzRfPnj3DrFmzEBoayqo3NDTEihUrMGLEiBIPRRan7xiGwZ49e0i5f//+xMGQFvLSdyNHjsScOXPw6dMnvHjxAocOHWLloJAEenp6WLJkCSZMmABHR0ecPn0aAJCVlYWdO3fi4MGDGD16NHr06FHiKX1cIS/9J21kxXa+e/cO27ZtY63RAYhmNo0dOxYDBw6EmZmZxNtlGIY1W8rFxeWn6buVHoZhOH1t2bKFoSgHX79+ZWbNmsVoaGgwAMhLW1ubWbBgAfP9+3dO2z99+jRpU0dHh/n06ROn7ck7f/31F/m9OnXqxHl7V65cYdq2bcv63wDANG/enDl37hzn7VPkF2pbZIuFCxdGMT+55lOnglJqcnJyGD8/P6Zy5cqFLhgjRoxgXr16JRUd/fv3J+1OmTJFKm3KM2/evGHU1NTIbxYVFcV5m7m5uUxAQABTrVq1Qv8rVlZWzJMnTzjXQJEfqG2RTahTQeGMM2fOME2bNi10wrdv3565fv261HQ8fvyY1f7jx4+l1rY8Y29vT34zBwcHqbX7/ft3ZuHChYy2tjar3zQ0NJiZM2cyX758kZoWimxCbYvsQp0KisSJj49nBg0aVOiEr1GjBhMYGMgIhUKp6nF2diYa+vXrJ9W25ZmbN2+S301dXZ15+/atVNt//fo14+DgUOj/qFKlSszmzZuZ7Oxsqeqh8A+1LbIPdSooEuPz58/MtGnTGHV1ddYJr6OjwyxevJj58eOH1DV9+vSJ0dHRIVpOnz4tdQ3yjJmZGfnt5s2bx4uGGzduMB06dCh0IWnSpAntTyWB2hb5gToVlFKTnZ3N+Pr6MhUrVixk+EePHs28efOGN22rVq0iWho3biz1Oxl5Jzg4mPx+FStWZNLS0njRIRQKmaCgIKZmzZqF/scGDBhAh50VFGpb5A/qVFBKRWRkJNOoUaNCJ7yZmRlz69YtXrVlZ2czNWrUIJr8/f151SOPZGdnM7Vq1SK/4datW3nVk5aWxixdupTR1dVl/b+pqakxHh4eSh95r0hQ2yKfUKeC8kfExsYy/fr1K3TC16pVi9m/f79MeO379+9n3WWnp6fzLUku+ffff8nv2KhRI5no27dv3zJjxowp9P9XoUIFxsfHh8ZbyDHUtsg31KmglIiUlBTG1dWVNd0QAKOnp8csW7ZMpk4u8efw8+fP51uO3PL582fWyMDJkyf5lkSIiopiOnXqVOgC1LBhQ+bEiRN8y6OUAGpbFAPqVFCKRVZWFuPt7c2UK1eOdcILBALG0dGReffuHd8SWVy/fp01c0HW9MkbLi4u5Pfs27cv33JYCIVCJjg4mKldu3Yh58LS0pKJiYnhWyLlF1DbolhQp4LyW44dO8Y0aNCgkMG2sLBg7ty5w7e8Ihk+fDjROWrUKL7lyD3x8fGMQCAgv2lsbCzfkgqRnp7OLF++nNHT02P9n6qqqjIuLi5McnIy3xIpBaC2RfGgTgXlpzx8+JDp3bt3oRO+bt26TGhoqEw82yyK169fM6qqqkSvrBoneWPAgAHkN508eTLfcn7Ku3fvmPHjx7OcIABMuXLlmPXr1zNZWVl8S1R6qG1RXKhTQSnEx48fmSlTprBOHgCMvr4+s2rVKiYjI4Nvib/E09OTdcdDkQxnz54lv6uOjg6TkpLCt6RfcvfuXaZLly6FLlwmJibMkSNHZPbCpchQ26L4UKeCQsjMzGTWrl3LlClThnXCq6ioME5OTsz79+/5lvhbvn//zno2Gx4ezrckhUEoFLJSI69YsYJvSb9FKBQy4eHhTL169Qo5F7169WIePnzIt0SlgNoW5YE6FRRGKBQyhw4dYoyMjAoZ3u7duzP37t3jW2Kx8fX1ZQ2l5uTk8C1JodixYwf5fatVqyY3jxIyMjKY1atXMwYGBoUuapMnT2Y+fPjAt0SFhNoW5YM6FUrOvXv3mO7duxc64evXr88cPHhQroaIc3NzGRMTE/Id1q1bx7ckhSM9PZ21KmRQUBDfkkpEUlISM2nSJEZFRYX1/16mTBlmzZo1TGZmJt8SFQZqW5QT6lQoKUlJSczEiROLNK5r166VS+N67Ngx8j0MDAyYb9++8S1JIVm4cCH5ndu1a8e3nD/i/v37TI8ePYq84EVERMjVBU/WoLZFuaFOhZKRkZHBrFq1qshh4ClTpjAfP37kW+If07NnT/J9PDw8+JajsLx//57R0NAgv/XVq1f5lvRHCIVC5vDhw0UOzXfr1k2uhuZlAWpbKAxDnQqlQSgUMmFhYUzdunWLDFh78OAB3xJLxcOHD1lG7NmzZ3xLUmjEU2QPGzaMbzmlIjMzk/Hy8mLKli1b6GI4YcIEuQgi5BNqWyjiUKdCCbhz5w5jYWFR5NS6o0ePKsRQ7/jx48n3GjJkCN9yFJ7o6Gjye6uqqjIvX77kW1Kp+fjxI+Ps7FzkdMeVK1fK/HRHPqC2hVKQXzkVaqDINe/fv8e8efOwfft2kZeYh56eHhwcHNCvXz+oqKggMjKSR5Vs9PX1YWZmVqLPfPz4EXv27CFld3d3CavilytXriA1NZVvGYVo1qwZ7t27h9zcXEyfPh3u7u4l7jtZomLFivDx8cGUKVMwbdo0cl6kpqbC09MTW7ZswapVqzBkyBAIBIJiH1dW+68gJTn3fmZbypUrh4ULF2Ly5MlQV1fnSqrUOHLkCHbt2kXKHTp0wIkTJ3hUVDR/Yjf5gDoVckpGRga8vLywbNkyfP/+ndSrqqpi6NChmDBhAsqUKcOjwp/z8ePHEn/Gz88PmZmZAIBWrVrB3Nxc0rJ4JTU1FZUqVeJbRiFGjx6NadOmAQAiIyNhb2/PsyLJ0LBhQ5w4cQLHjx/HtGnTEBcXBwB49uwZbGxs0LlzZ3h5eaFly5bFOp6s9l9BinPu/cq2ODs7Y8GCBShfvjyXMqVKcHAwsrOzAQCmpqbo0qVLiRxKafEndpMPVPgWQCkZDMMgODgYpqammDNnDuukb9OmDfbt24cZM2bIrEPxJ2RlZWHjxo2k7O7uLpMnvSJibm6O6tWrAxBdOE+fPs2zIsliaWmJ+/fvY8OGDawL5cWLF9G6dWs4Ojri3bt3PCqUHr+yLX379sWDBw+wfv16hXIosrKycOTIEVK2s7OjtqWUUKdCjrh9+zY6d+6MYcOG4cWLF6Q+/65r8eLFqFOnDn8COeLAgQPEsP/vf//DsGHDeFakPKioqGD48OGkfPDgQQiFQh4VSR51dXVMnToVCQkJcHNzg5qaaACXYRhs374dxsbGWLZsGdLT03lWyh2/sy1Hjx6FqakpfwI54sCBA/j06RMA0aOxnj178qxI/qFOhRyQmJiIMWPGoHXr1rh8+TKpr1ChAjZu3Ih79+6hd+/ePCrkDoZh4OXlRcrOzs7Q0NDgUZHyMXDgQOjp6QEA3r59i2PHjvGsiBvKly+PdevW4eHDh+jfvz+p//79O+bOnQtTU1Ps37+fFV8g71Db8p9tGTp0qELEiPANdSpkmPT0dCxduhRGRkasQCI1NTVMmzYNCQkJmDJlCrmzUkQuX76MO3fuAAC0tLQwceJEnhUpHzo6Ohg8eDApr1u3jjct0sDExASHDx9GZGQkGjVqROpfvnyJ4cOHo1OnTrh16xaPCksPtS1s26KpqQlra2ueFSkG1KmQQRiGQVBQEExMTDB//nykpaWRbQMHDsSjR4+wZs0alCtXjkeV0kH8AjZy5EhUrFiRPzFKjK2tLVRURObizJkzePDgAc+KuKdXr16Ijo7Gpk2bWP93V65cQdu2bTF69Gi8ffuWR4Ulh9qW/xC3LX379kXZsmV506JIUKdCxrhx4wbMzMxgb2+P169fk/omTZrg9OnTOHjwIIyNjXlUKD2eP3+OiIgIUnZzc+NPjJLzv//9D127diVlRR+tyEdNTQ2TJk1CQkICpk+fzhoe3717N4yNjbF48WJkZGTwqLJ4PHz4ENOnT6e2BYVti3jcEKV0UKdCRnj9+jUcHBzQvn17XLt2jdRXqlQJW7Zswd27d9G9e3ceFUqfDRs2kKDAXr16sYaiKdJHfDrp3r178eHDBx7VSJeyZcvi33//xaNHjzBo0CBSn5aWhgULFmDChAk4ceKETMZbvH//HvPnz8eYMWMQGxtL6qltEdmWli1bol69ejwrUhyoU8EzP378wMKFC2FiYoK9e/eSenV1dcycORMJCQlwcnKCqqoqjyqlz7dv3+Dv70/KipbsSh5p2rQpuZPNzMzE5s2beVYkfYyMjBAREYEzZ86gadOmpD45ORnz5s3DuHHjZObRUHp6OrZs2QJra2scP36c1FPbwrYt4vFClNJDnQqeEAqF2LNnD0xMTLBo0SLWdLUhQ4YgNjYWq1atUqh8EyVhx44dJEOhiYmJwkagyxMCgYBlgH19fUlCMmWjW7duuHPnDvz8/FC5cmVS/+DBA4wdOxbz5s3D+/fvedEmFApx9OhRWFtbY+vWraw+MjMzo7algG1p1aoVz4oUC+pU8MDVq1fRvn17jBo1ihXo1bx5c5w7dw6hoaFKPRyXm5sLb29vUnZ3dydBghR+6dSpEwwNDQEASUlJ2L9/P8+K+ENVVRUTJkxAQkJCoemIJ06cgLW1NbZs2SLV/Bb37t3DmDFjsGDBAtbjKWNjY2zevBnz5s2jtoXaFk6hv6YUefnyJezs7GBmZsaaklalShX4+/sjKioKXbp04U+gjHD48GE8e/YMgGidgZEjR/KsiJKPmpoapk6dSspeXl4yGUcgTQwMDDBu3DgEBwejW7dupD4zMxNbt27FkCFDcOTIEU6Thr179w5z5syBo6MjYmJiSH2FChUwb9487NmzB61bt+asfXmB2hbuoU6FFPj+/TvmzZuHBg0aYN++faReU1MTf/31FxISEuDo6Kh0zzZ/hvjMgokTJ0JXV5c/MZRCODk5QVtbGwAQHR2Nixcv8qxINqhevTpWrVqFLVu2wMTEhNR//PgRCxcuxJgxYxAdHS3RNtPS0uDr6wsbGxucPHmS1GtoaGDs2LEICwvD4MGDqW3Jg9oW7qFOBYcIhULs3LkTRkZG+Oeff1jTzoYNG4a4uDgsW7YM+vr6PKqULe7evYsLFy4A+G8BI4psUaFCBYwaNYqUxbMSUkQL3u3evRt///03KlSoQOpjYmIwfvx4/PXXX0hMTCxVG0KhEIcPH4aVlRW2b9/Oipvo2bMnQkJC4OzsTC+aYlDbIh2oU8ERFy9eRJs2bTB27FhWwFarVq1w8eJF7N+/H7Vr1+ZPoIwificxdOhQspgVRbYQzxly6NAhPH36lEc1soeqqioGDhyIsLAwjB07lpVa/tSpU7CxscHGjRvx48ePEh/7zp07GDVqFBYtWoSUlBRSb2pqiq1bt2L58uUk7oXyH9S2SAfqVPwJT58CU6YABgaAioro75QpwNOneP78OYYOHQoLCwuSAhYQJQ/auXMnbt68iU6dOvEoXnZ5//49goKCSNnDw0Pyjfyi7yjFx9TUFH369AEgytK4YcMG6TQsZ/2nq6sLZ2dnhISEsBarysrKwo4dOzBkyBAcPHgQubm5AACNN29QY8UKNLewQMs2bdDcwgI1VqyAxps3ePv2LTw9PeHk5ESWagdEC2EtXLgQu3btQosWLaT+HeUBqdgWCgDgt4ndDQ09agN4/otddiUmeo2RlCCZ5/hxwMYGyM4WvQAgNRXftm7Fsq1b4QUgKyeH7K6lpYWZM2di1qxZZFEmStH4+voiO+837dChA9q2bSvZBn7Sd/D3B3btAkJCAEtLybapwLi7u+PEiRMAgG3btmHRokXcTlOU4/4zNDTE8uXLYWtri7Vr15JgypSUFCxZsgQHDhzA/F69MHzrVghycqCSZ0NUf/yARng49oSHYx2A7DznAxDFZI0cORKjRo2Cjo4OD99KfuDctlAIxRmpyARwo8Drkdj2dxzokk2ePhUZtbQ0YtRyAfgDMMrJwcqcHJZDYWdnh8ePH2Px4sXUofgNGRkZ2LRpEylL/E6iiL4jZGeL6m1sZPaOVxbp1asXWQ77+/fv2L59O3eNKUj/NW/eHDt37sTChQtRqVIlUv/48WM4bNgA24wMvMizIfm2xSQ3F6tzc1kORe/evREaGopJkyZRh+I3cG5bKCx+61QkJnq9S0z0ai/+AhCatzkbgC+nCmWJNWtYBm0LgDoAJgAQT1jctnJlXL16FYGBgahZs6aURcone/fuRXJyMgCgRo0asLKykmwDBfruEAA3AA/F98nOBmjQYbERCASsTKfe3t5kGF/iiPUfA2AmgL8L7iMn/aeiooL+/fsjLCwMEyZMgKamJtkWCsAUgB2A5ihsW1qUL4/t27fjn3/+QdWqVaUpW27h3LZQWJQ4psLQ0EMHQH7YbGBiotfrX+2vUAQEANnZiAdQEcAkAOJfvhqAAADX0tPRoUMHPhTKJQzDsIKoXFxcJL/kcl7fAcA+AFYAvAH0BHA/f5/sbGDPHsm2q+A4ODigfPnyAIAXL17g4MGD3DSU138MgK4A/gWwBMA4AN/z95Gz/tPW1sbEiRMRGhoKO7Epn1kQ/Y+KO7z5tuVGRgYrPTjl10jFtlBY/Emg5ngAFSC6YVhd1A4CgcBJIBBECQSCqHwPUSH4LjJfNQGkilWrA1gI4DGAEQBU/iCiW5k5e/YsHj4UmVBdXV2MHz9e8o18J5ceXAKQn4boPUR3hBORd0coth/l9+jo6GDSpEmkzNnqpXn9IgRwV6x6BwBjALvytslj/1WtWhV7hUJcA1DwSb8G2LZFTYrZORUBqdgWCosSORWGhh6qAPIfSB1JTPR6VNR+DMP4MQzTmmGY1hUrViytRtkhLy5CC6JhyXwqAZgLQLfAfpTiIZ7nYMyYMShXrpzkGxHrk0UAqohtYgD4AagPYJW6utKuZ/GnTJkyhdz9Xbp0Cbdv35Z8I3n9pwqg4AOOdwDGAGgH4LKWluTblgJCHR20B3AdgHteXXkA8QAW4D/bIqTxEyVCKraFwqKkIxW2AGrnvV8pWSlygIMDkJfffzVEJz0AJAIIy99HXR2gqV+LTXx8PI4ePUrK4vkPJIpY31UEsL6IXVIBeGZmomHDhggNDVX69NPFpVq1arC1tSVlTkYrxPpvHIAuRewSBaBTWhqGDRuG589/NWFN9kixtIRQTQ0CAGsB3ARwC0AtsX2EampI6duXF33yiNRsC4VFSZ2KmXl/ryQmel2RtBiZZ/p0Yti0AUwV20T8YXV1gEYXF5v16/+7vPfv3x9GRkbcNCTWdwAwBIB46hvxkLdnz57BxsYGXbp0wd274oPtlJ8hHrC5b9++UmeMLESB/hM/wzQhGj3MJzg4GKamppgzZw5ZjVLWSXJwAJM32iMA0AZA3QL7MGpqSBoxQtrS5Bap2RYKi2I7FYaGHr0hevwMKOMoBQDUqyeaC6+jA6irYzJEzzwB0bDldU1N0XYlXgWwJHz+/Bk7d+4kZU6nehXoO3UALmKbKwkEWD9xImt49OLFi2jVqhUcHR3x7p3yzJz+E1q3bg1zc3MAQE5ODnx9JTwprED/9QeQf5ZlApijpobhFhZk98zMTCxfvhxGRkbYtm0bd7NSJERW9ep4tnIlcrW0ICwQSChUU0OulhaerVyJLJoFslhI1bZQWJRkpGJW3t9YAEc40CIfWFoC9+8DTk6oamAAO7FN63v2lNnkO7LI1q1bkZaWBgBo0qQJunbtym2DYn0HAwNMEAiQ/4T6AcOgia0tnjx5Ajc3NxIjwDAMtm/fDmNjYyxbtkyqy1jLG+KjFZs3b5b8byXWfyoGBnATCMimgBo1sPfsWVy5cgVt2rQh9UlJSRg/fjxat26N8+fPS1aPhPlmZoaYffuQbGWFXF1dMAIBcnV1kWxlhZh9+/DNzIxviXKD1G0LhVBspyIx0at7YqKXIDHRq2FiopdyP2yuVw/w8QG+foW72PB48PHjeP1aeWbYlobs7GxWamd3d3cIxC4SnCHWd+WEQoyZMoVs8vLyQvny5bFu3To8ePAA/fr1I9u+f/+OuXPnwtTUFAcOHKDxFkUwaNAg1KoligJISUlBQECA5BsR67+x376RDJ7xz5/j+PHj6NixI65fv449e/agWrVq5GPR0dHo2rUrrK2tZXqdkqzq1fHa0xPRFy7gzq1biL5wAa89PekIRQngzbZQANC1P0pN8+bN0aVLFwBAbm4uNm7cyK8gOSE8PBxv3rwBAFSqVAn29va86HB1dSXvjxw5goSEBABAgwYNcOTIEURGRqJRo0Zkn5cvX8LW1hadOnVCVFSU1PXKMmpqaqzfc926dZw6X3p6eqwpgvkBoioqKnBwcMDjx4+xYMECskw7AISFhaFhw4aYNWsWvn79ypk2Cn/Iim1RVqhTIQHEn9f5+fn90cqDyob4VK/JkydDi6epgCYmJmREgmEYeHt7s7b36tUL0dHR8PX1ZS1jnT/MPnr0aLx9+1aqmmUZR0dHkpI+JiYGp06d4rQ9FxcXqKiIzNjp06fx4MEDsk1XVxcLFy5EfHw8HBwcSH1WVhZWr14NIyMjbNmyRebjLSglQ1Zsi7JCnQoJ0K9fP9TLC878/Pkzdu/ezbMi2SY2NhbXr18HAGhoaGDy5Mm86hGPBdixYwe+fPnC2q6mpobJkyfjyZMnmDZtGtTFZiHs3r0bxsbGWLJkCXmGq8yUKVMG48aNI2XOkmHlUatWLQwZMoSUxSP+86levTr27NmD69evo3379qT+48ePmDRpElq0aIEzZ85wqpMiHa5fvy5TtkUZoU6FBFBVVS007CsUCn/xCeUmIiKCvLezs+N9DYPu3bujcePGAIAfP35g27ZtRe5XtmxZrFmzBo8ePcKgQYNIfVpaGv7++2+YmJggMDBQ6eMtXFxcyDPs48ePIzY2ltP2xJ3CgIAAfPjwocj92rVrh6tXryIoKAg1atQg9Q8ePECPHj0waNAg8viLIp+IO7GyYFuUEepUSIixY8fCwMAAgCjpSv6S0BQ279+/x+XLl0lZ/ILAF0UtjJUjttpsQYyMjBAREYHTp0+jSZMmpP7NmzcYMWIECRZUVurXr48BAwaQcsFHSpKmY8eOZMZHZmYmtmzZ8tN9BQIBhg8fjsePH2PJkiWsFT4PHTqERo0aYfr06YVGqyiyz+vXrxESEkLKsmBblBHqVEgIfX39IoPGKGyCg4PJKE6XLl3QvHlzfgXlYW9vj/yU8q9evWKNpvyM7t274+7du9iyZQtrGevr16+jQ4cOGDFihNLOBhKPM9q1axc+ffrEWVsFnUJfX9/fplrX1tbGvHnzkJCQgNGjR5P67OxsrF27FvXr14evr+8vnUuKbLFx40YSHyNLtkXZoE6FBBEPGjt16hRZyIYiIj09HeHh4aQsS3cS2trarOevXsVcQltVVRVOTk5ISEjArFmzoKGhQbYFBgbCxMQECxYsULrgXQsLCzRr1gyAqN/9/Pw4bW/o0KEwNDQEIBoN279/f7E+Z2hoiJ07d+LWrVskeRcgmhLr7OyMZs2aITIykhPNFMnx48cP1v+YLNkWZYM6FRKkdu3asLKyIuWigsaUmaNHj+Lbt28AgHr16qF///48K2IzefJkEoR59epV3Lx5s9ifLVOmDFauXInY2FhYW1uT+vT0dCxevBjGxsbYs2eP0sTaCAQC1miFj48PsvOWnucCdXV1TJ36X+L8kk5nbd26NS5evIjg4GDUrl2b1MfExKBPnz7o168f4uLiJCmZIkF2796Nz58/A5BN26JMUKdCwoh7yHv27MHHjx/5EyNDCIVCBAYGkrKrqytUVVV5VFSY//3vfxg+fDgp/4lTWLduXYSEhOD8+fNo0aIFqU9MTMSoUaPQrl07XLmiHMvmDB8+HFWqiNaDffv2Let5Nxc4OTmRnBR3797FxYsXS/R5gUAAGxsbxMbGYvny5WRqLAAcO3YMTZo0gZubG6ePciglRygUsh43y6JtUSaoUyFhzMzM0Lp1awC/DxpTJq5evYpXr14BAHR0dDB27FieFRWNuFN44MCBP85BYWFhgVu3bmHbtm2sCPSoqCiYm5vD1tYWL168KKVa2UZTUxNTCmQs5XJmTIUKFTBq1ChS/tO4Ji0tLcyePRsJCQlwdHQkM1lycnLg7e2N+vXrw9vbm9ORF0rxOXHiBOLj4wEABgYGMmtblAXqVEiYgkFjGzduRFZWFn+CZISgoCDyvk+fPtDX1+dRzc9p2bIlOnfuDEB0ESlNhlRVVVWMGzcO8fHxmDNnDjQ1Ncm2AwcOoEGDBpg7d67crKT5J0yaNInEmdy6dYvzWTHiy1sfPHgQz549++NjVa1aFf7+/rhz5w4sxBYr+/z5M9zc3NCkSRMcO3ZM6acQ84248zh+/HiZtS3KAnUqOOBPg8YUlSdPnuDGjRsARCmUBw4cyLOiXyMeC7Bly5ZSJ7XS19fHP//8g7i4OAwbNozUZ2ZmYtmyZTA2NsbJkycVMt6icuXKGCG2XHdxA2D/FFNTU/Tp0wdA0RlS/4TmzZvj3LlzCAsLQ926/y1I/vjxY/Tr1w+WlpZ4+fJlqduhlJyHDx+SrK0qKipwcXH5zScoXEOdCg7Q0NCAs7MzKXM97Cvr7Nu3j7zv0qULec4uqwwYMAB16tQBAHz69Al79uyRyHFr166N/fv349KlS+QRGSByPL28vDBy5Ejcvn1bIm3JEuIjd6GhoZxfgMXb2759OwkOLg0CgQBWVlaIiYnB6tWrSU4aAIiMjMSUKVOwcuVKmt9CyojHPVlZWbGCbCn8QJ0Kjpg4cSLJOX/37l1cunSJZ0X88PnzZxw7doyU5WFxH64zpJqbm+PGjRvYtWsXGdECRHe+EydOxKxZs8iCSIpA06ZN0a1bNwCioDofHx9O2+vVqxdMTU0BAKmpqT/NkPonaGpqYsaMGUhISMDEiRPJFHKhUIjg4GAMHjwYe/fupfEWUuDjx48sh59OI5UNBFzfQfv5+TFOTk6ctiGrTJw4kcydtrKyQlhYGKftXblyReaezwcFBZG1UIyMjLB+/XoYGBjAzMyMZ2W/5tu3b6hevTr5PY8fP06G1SXJjx8/sGrVKqxYsYIVe6OmpobBgwdj+PDh0NXVlXi7f4q+vv4f9d2RI0dIls0yZcrgzZs3rNkVksbPzw8TJ04EIBohevLkCSczAh48eAAPD49Ca4dUq1YN48ePR7t27WRq2e0/7T9ZZOnSpZg/fz4A0ZTgmzdv/tFvLYt2syhkqe8WLVp0e8GCBa2L3MgwDKevLVu2MMrKo0ePGAAMAEYgEDBPnz7lW5JUyczMZKpWrUp+g4CAAL4llQh3d3eivXfv3py29fLlS8be3p60l/+qXLky4+fnx+Tk5HDaPtfk5uYy9evXJ9/Lx8eH0/Z+/PjBlC9fnrQXGhrKWVtCoZA5dOgQY2RkVKj/unfvzty7d4+ztpUVebct8s7ChQujmJ9c86lTwTG9e/cm//ju7u58y5Equ3fvJt/9f//7H5OZmcm3pBLx7NkzRkVFhXyHR48ecd7mtWvXmHbt2hW6ODVt2pQ5c+YM5+1zyYYNG8j3MTIyYnJzczltb86cOaS9Tp06cdoWw4gudF5eXkzZsmVZfaeiosI4OTkxSUlJnGtQFuTdtsg71KngkePHj5N/fn19febr1698S5IKQqGQadGiBfnu//zzD9+S/ggrKyvyHZycnKTSZm5uLrN3716mevXqhZyLQYMGMfHx8VLRIWlSU1OZMmXKkO9y+PBhTtt78+YNo6amRtqLioritL18Pn78yDg7OzOqqqqsvjMwMGBWrVrFZGRkSEWHoqIotkWeoU4FjwiFQsbU1JScAF5eXnxLkgoXLlwg31lLS4v5+PEj35L+CD6/x48fP5jFixczOjo6rIuTuro6M336dObz589S0yIpZsyYQb5Ht27dOG9vxIgRpD0HBwfO2xPn0aNHrJHK/FfdunWZsLAwRigUSlWPoqAotkWeoU4Fz2zevJmcBHXq1JH75+PFgY87fC4QCoVMy5Yteb0revPmDTNq1KhCF6eKFSsyvr6+THZ2ttQ1/SkvX75k3cFzHW9w69YtljP29u1bTtsrimPHjjENGjQo1H8WFhbMnTt3pK5H3lEU2yLPUKeCZwoGjYWFhfEtiVOePn3KCAQCqcYicIn481tDQ0Pent/evHmT6dixY6GLU6NGjZjIyEheNP0JQ4cOJdrHjRvHeXvm5uakvblz53LeXlFkZWUx3t7eTLly5Vh9JxAImHHjxjHv3r3jRZe8oWi2RV6hToUM8Ndff5EToXPnznzL4RTxWRO9evXiW06pkaVIc6FQyOzfv5+pVatWIeeiX79+TFxcHG/aisuVK1eIZk1NTc4DGENCQkh7FSpUYNLS0jht71ekpKQwbm5urFgPAIyenh6zbNkyJj09nTdt8oCi2RZ5hToVMkDBoLHbt2/zLYkTvn79yujr65Pvefz4cb4lSYQlS5aQ79S6dWven4enpaUx//zzD6Orq8u6OKmpqTFubm5MSkoKr/p+hVAoZNq2bUs0L1q0iNP2srOzWU7Y1q1bOW2vOMTGxjL9+vUr5BjWrl2bOXDgAO//X7KIotoWeYQ6FTKCeB6CkSNH8i2HE7y8vMh3bNCgAefTBqXFhw8fGC0tLfLdLl26xLckhmEYJjExkRk3bhxrSBgAU758ecbb25vJysriW2KRBAYGEq1VqlThfEbEmjVrWI+LZOWiHRkZyTRq1KiQc2Fubs7cunWLb3kyhaLaFnmEOhUyws2bN1lBY4mJiXxLkig5OTlMnTp1yHfcvHkz35Ikyvjx48l3s7a25lsOi9u3bzOdO3cudHFq0KABc+zYMb7lFSIrK4upVq0a0blz505O2/vy5Qujp6dH2jt58iSn7ZWE7OxsxtfXl6lQoUKh/hs9ejQvwaWyhqLbFnmDOhUyhJmZGTkx5s2bx7cciRIeHs66U/7x4wffkiTKgwcPyPdTUVFhnj9/zrckFkKhkAkNDWUZ3/xXnz59ZC6obfny5URf8+bNOR89cHV1Je1ZWlpy2taf8PnzZ2b69OmMuro6q+90dHSYxYsXK9z5VBIU3bbIG9SpkCGCg4PJyVGxYkVeg8Ykjfid8l9//cW3HE7o2bMn+Y7Tpk3jW06RpKenMytXrmQ9fwbAqKqqMs7OzjIzrz8lJYXR1tYm+s6dO8dpewkJCazHRLGxsZy296fEx8czgwYNKuQY1qhRgwkMDJSZRzfSRBlsizxBnQoZQhaDxiTB7du3WcGCr1+/5lsSJxw9epR8TwMDA+bbt298S/op79+/Z5ycnFipxgEwZcuWZby8vGQitfGkSZOIrkGDBnHe3sCBA0l7kyZN4ry90nDmzBmmadOmhZyLDh06MNevX+dbntRQFtsiT1CnQsb4999/ZTJorDSMHDmSfCc7Ozu+5XBGbm4uY2JiQr7r+vXr+Zb0W6Kjo5muXbsWujgZGRkxhw4d4vX/LzY2lugRCARMQkICp+2dO3eOtKetrS3Ts2QYRhRL4Ofnx1SqVKlQ/40YMYJ59eoV3xI5R1lsizxBnQoZ4/Pnz6ypgKdOneJbUqlITExkPQe+ceMG35I4xdfXl3zXevXqyUWGVKFQyERERLBWCs1/9ejRg7l//z5v2iwtLYkWV1dXTtsSCoVMs2bNSHsrVqzgtD1J8eXLF2bWrFmMhoYGq++0tbWZBQsWMN+/f+dbIicom22RF6hTIYO4uLiQE6Vv3758yykV8+fPJ9+lY8eOfMvhnO/fv7MyIx48eJBvScUmMzOTWbNmDWthr/zA00mTJjEfPnyQuqbIyEiiQ09Pj/ny5Qun7e3cuZO0V61aNZmddlsUT58+ZaytrQs5htWqVWN2796tcNMslc22yAvUqZBBCgaNyUMmxKJIS0tjKlasSL5HcHAw35KkgqenJ/nOXbp04VtOifnw4QMzZcqUQvEWBgYGzOrVq6W6kqZQKGQaNmxINKxZs4bT9jIyMpgqVaqQ9oKCgjhtjwvOnz/PWqkz/9WmTRvmypUrfMuTCMpqW+QB6lTIKOJBY5MnT+Zbzh/h7+9PvkPNmjXlanGr0vDq1SvWwlh3797lW9If8eDBA9aMFvHHOuHh4VKLt/Dz8yNt16pVi/P/o0WLFpH22rZtK5dxTTk5Ocy2bdtYKeTzX7a2tsyLFy/4llgqlNW2yAPUqZBRzp49S04aHR0dmQ8aK4hQKGRlA1y9ejXfkqTK8OHDyXcfPXo033L+GKFQyBw5coQVgCo+CiMNhyktLY2V/CkkJITT9pKSkljxCVevXuW0PS759u0bM2fOHEZTU5PVd1paWszcuXOZ1NRUviWWGGW3LbIOdSpkFKFQyJoytnLlSr4llYhTp04R7bq6usznz5/5liRVrl+/Tr6/hoaG3K80mZWVxaxfv77IlTTHjx/P+febO3cuadPc3JzTthiGYcaOHUvaGzZsGOftcc3z588ZW1vbQo5h1apVme3bt8tVvIWy2xZZhzoVMsyOHTvIyVO9enW5Chrr27cv0T516lS+5fBC+/btyW/w999/8y1HIiQnJzMuLi6sxzv5QZTLly/nbCXNt2/fsiL9uV774t69e6QtVVVV5uXLl5y2Jy0uXbrEtG7dupBz0bJlS+bChQt8yysW1LbINr9yKgQMw4BL/Pz8GCcnJ07bKC5XrlxBamoq3zJYZGVlYfTo0fjy5QsAYPbs2ejfvz/MzMz4FfYbHj9+jAYNGgAABAIBHj9+DCMjI87ak8W+A4CLFy9i+fLlAIAyZcogLCwM3bp141mVZIiNjcWMGTNw7NgxVn3t2rWxevVqWFtbQyAQFOtYxe2/1atX4+zZswCArl27YtasWSUXXgJmz56Ne/fuAQBsbGzg7u4u8+decRAKhQgICMBff/2FxMRE1jYbGxusWrUKderUKfbxpHn+vXnzBhMmTAAgsi1bt25FtWrVivVZfX19heg/WWfRokW3FyxY0LqobWrSFsMnqampqFSpEt8yCjFs2DD4+fkBAI4cOQILCwueFf0eb29v8r5///6cOhSA7PbdoEGDsH37diQlJeHr1684cuSIwjgVpqamOHr0KE6cOIFp06YhNjYWAPDixQsMHToUnTp1gpeXF1q1avXbYxW3/8aOHUuciosXL2LWrFmc9vuYMWPg4eEBADhx4gTs7e05a0uaqKioYNSoUbC2tsaqVauwatUqZGRkAABCQkJw6NAheHh4YM6cOTAwMPjt8aR5/m3fvp28Nzc3R/PmzYv92Y8fP3KgiFISVPgWQAGsra2hrq4OAHj48CEx3rLKp0+fsHPnTlLON8rKiJqaGmxtbUk5PDwcXI/+SZs+ffrg/v378PHxQYUKFUj9pUuX0KZNG4wdO7bQ3fCfYmpqihYtWgAAcnNzceDAAYkc92eYmZmhRo0aAIDv37/j9OnTnLYnbXR1dbFo0SI8fvyY5TBlZWVh5cqVMDIygr+/P3Jzc3lU+R9fv37F4cOHSXnEiBE8qqH8CdSpkAEqVKiAPn36kHJERAR/YoqBv78/0tLSAABNmzZFly5d+BXEM4MHD4aWlhYA0V38uXPneFYkedTU1ODs7IyEhAR4eHhATU00yMkwDHbu3AljY2MsXboU6enppW7Lzs6OvA8LCyN32FygoqKC4cOHk/LBgwchFAo5a48vatasib179+LatWto164dqf/w4QMmTJiAVq1aycT/bUREBOlvIyOjYo2CUWQL6lTICOKG9PLly3j16hWPan5OdnY2NmzYQMru7u7Ffq6uqBgYGGDAgAGk7OXlxaMabilXrhzWrl2LR48eYeDAgaT+x48fmD9/Pho0aIB9+/aVarTGwsIChoaGAER3rgVjOiTNgAEDoKenBwB4+/Yt5+3xSfv27XHt2jXs3bsX1atXJ/X37t1Dt27dYGVlhSdPnvCiLScnhzUyZWdnp/S2RR6hToWMYGxsjNatRXEvQqEQGzdu5FlR0YSFheHNmzcAgMqVK7OcIWVG/G73yJEjSEhI4FEN9xgbG+PgwYM4deoUGjduTOpfvXoFOzs7mJub4+bNm390bFVVVdYjpaCgIE4fKeno6MDKyoqU161bx1lbsoBAIIC9vT0eP36MxYsXQ0dHh2yLiIhAw4YNMXPmTHz9+lWqus6ePYukpCQAQPny5dG7d2+ptk+RDNSpkCHEn3n6+fnh+/fvPKopGnGDO3nyZDLsr+zUqlUL5ubmpCweyKrI9OjRA3fv3sXmzZtZgXxXr15Fu3btMHLkSOKEloRBgwaRi93z589x48YNiWkuCltbW6iqqgIAzpw5g/v373Paniygo6OD+fPnIz4+HqNGjSL12dnZ+Pfff1G/fn1s3rwZOTk5UtETFBRE3ltbW0NTU1Mq7VIkC3UqZAhzc3MSNPblyxfs3r2bZ0Vsrl+/juvXrwMANDQ0MHnyZJ4VyRbiTuGOHTvINGFFR01NDRMnTkRCQgJmzpxJgo4BICAgAMbGxti7d2+JYiP09PQwaNAgUg4MDJSo5oJUrVoVXbt2JeX169dz2p4sUa1aNezatQs3b95kTcdMTk7G5MmT0aJFC9y5c4dTDQ8ePMCDBw8AAOrq6rCxseG0PQp3UKdChigYNLZu3TqZChoTH6Wwt7dHlSpV+BMjg7Rp0wa1a9cGIIox8Pf351eQlClTpgxWrVqFmJgY1uOE9PR0BAQEwNraGseOHSv2/7StrS15pn716lU8f/6cE935iDuFe/fuxYcPHzhtT9Zo06YNLl26hP3796NWrVqk/uHDh5g7dy48PDzw4sULTtoWdxr79OnDmmVEkS+oUyFjDBgwALq6ugCAhIQEHD9+nGdFIl69eoWQkBBSdnd350+MjCIQCDB48GBS3rBhg9SGjmWJ+vXrIywsDOfOnWPlGEhKSsLff/+NsWPHFuvxQvXq1dG5c2dSFh8e54ImTZrAxMQEAJCZmYnNmzdz2p4sIhAIMGzYMMTGxmLZsmUkgBUQTSG2tbXFmjVr8O3bN4m1+f79e5KbBACN05JzqFMhY+jo6LAClGQlaGzjxo1kLnvXrl3RrFkznhXJJl27dkXFihUBiByx8PBwnhXxR5cuXRAVFQV/f3+UK1eO1D969Ajjxo3D3Llz8f79+18eQ3z04OjRo5w+UiroFPr6+iIzM5Oz9mQZbW1t/PXXX4iPj8e4cePIiFFubi6CgoJgZWWF/fv3S8RpPnDgALEtrVu3hrGxcamPSeEP6lTIIAMHDoSKiqhrTp8+TZ418sWPHz9Ixk+AjlL8ioKxJrLiFPKFqqoqHB0dsXXrVowZMwYaGhpkW2RkJKytrbFp0yaS96QgLVu2JBeZzMxMznO4mJubk5TQSUlJ2LdvH6ftyTr/+9//sG3bNnh7e6Nly5ak/uvXr1i9ejXs7Oxw9erVPz5+eno6y/GmoxTyD3UqZJAqVapgyJAhpMx30NiuXbvIHWK9evXQv39/XvXIOlOmTCHBilevXv3jqZWKhK6uLqZOnYrg4GD06NGD1GdmZmLbtm0YMmQIDh8+XCjeIn/6Yz4HDhzg9JGSmpoapk6dSsrr1q1TuAypf0L9+vWxZcsWrFq1irUOx/Pnz+Hq6gpXV1c8e/asxMc9cuQIWVOkevXq6NSpk8Q0U/iBOhUyinjq64CAAN6CxoRCIcupcXNzI6MolKKpWrUq645L2UcrxKlWrRpWrFiBrVu3wtTUlNQnJydj0aJFGD16NO7evcv6TK9evUjg3ocPHzhPpe3k5ARtbW0AQHR0NC5cuMBpe/KCQCBAt27dEBwcDBcXFxL7BYicZzs7O6xatarYj6iEQiErTmb48OHUtigAtAdllA4dOqBNmzYARHdzW7Zs4UXH8ePHER8fD0CUOXLMmDG86JA3xB8RBQcH/1GuBkWmRYsW2LVrFxYuXEhiUADRyqgTJkzA7Nmz8fbtWwCiR0riUwwDAwM5HT0oX748Ro8eTcrUKWSjoaGB0aNHIywsDFZWVsQRyF+rxcrKCoGBgcjOzv7lca5evUoyB+vq6rKy0lLkF+pUyCgCgYB1Ydq4cSMvQWPiBnXChAnQ19eXugZ5pEWLFmS12ZycHJnNkMonKioq6N+/P8LCwuDo6MhKdnT69GkMHToUPj4++PHjB6ytrUk8RkxMDOfJqdzc3Mj7Q4cO8Za6WpapUKEC5s6di4CAAHIDBIhWNF27di1sbW1x6dKlnzqA4tNIraysWCMfFPmFOhUyzNChQ8kaCElJSdi/f790Gn76FJgyBQ91dclQs4pAgKn0TqJEiDuFW7Zs+WkworKjo6ODyZMnIzQ0lDXzKSsrCzt37sSQIUNw8eJF1rbjTk5o2aYNmltYoMaKFdCQ8EhQgwYNYGlpCUC0aJr4ejcUNsbGxvD19cWaNWtI8j5ANPvJw8MDU6dOJU6Zxps3qLFiBdQ6dSKxRioARihCLEWe3YSBAaCiIvo7ZYqoXokotlNhaOhRwdDQY52hocdzQ0OPLENDjxRDQ4+LhoYeLbgUqMyoq6tLP2js+HGgaVPA3x/rxC6CQwDU7ttXtJ1SLAYMGIC6desCAD5//ixzGVJljapVq+Kff/7Btm3b0KhRI1KfkpKCpUuX4pFYqu6w3Fy8Yhio/viBihERaDh8OAyuXJGoHnGncPv27VJfC0OeEAgEsLCwwIEDB+Du7s7Kb3Hjxg3Y29tjjZsbKtnaomJEBDaIrWZrJRCgp5ubxPtPqojZTaSmAgwj+uvvL6pXIrtZLKfC0NCjAoAbANwA1ADwDEAigBYA6nGmjsIKGrt79y4uXrzIXWNPnwI2NkBaGt5nZyNAbJM7wwBpaaLtSuZ5/ymqqqpwdXUl5fXr18tUhlRZpVmzZtixYweWLFmCypUrk/pnYsHKQgA+ee9VcnKgmpGBup6eEh2x6NmzJxo2bAgA+P79O7Zt2yaxYysq6urqcHBwQHh4OIYOHUriLYRCIYKuXIFJZiaW5OSwbIsHw3DSf1JDzG4iOxs5ANIBMACQna10drO4IxVLIXIe3gIwTUz0apCY6NUEQFkAirtOsAxQoUIF1mI/nAaNrVkjOgkATASQH8HRBkDH/H2yswEFXtpb0owbNw4GBgYAgLi4OERGRvKsSD5QUVGBpaUlQkNDRY513mJf4nhDZJDyEeTkoMrevRLTUDCuydvbWykzpP4J5cqVg6enJwIDA9GuXTtS/w3AQhRtWyTdf1JDzG4KAbQDUA7AMuQ5FoBS2c3fOhWGhh4CAMPyis8ABBoaenw3NPSIAeAEkVNG4RDxoLGDBw/iKVceb0AAkJ2NDwCOiFX/AEDC4rKzgT17uGlfAdHX14ejoyMp05kEJUNbWxtOTk6I1dDAyALbsgCYi5VVcnJQ4Zhk73EcHBzIdNaXL1/i4MGDEj2+olO/fn34+PjgoKYmTIrY3gKAIO89F/0nFfLs5mUAzQDcgchpmgcgNn8fJbKbxRmpqASgfN77TgBqAfgIwBSALwDngh8QCAROAoEgSiAQRCUnJ0tKq9JiamqKPn36AOA4aCxvqfVQiDzufGIgOvknAEgS249SPFxcXMgw8MmTJ/Ho0SOeFckfNTMysBuiZ7B1xOorFdhPRcLBsNra2pg0aRIpU6ew5AgEAgzIysIDAO5i9aoAVhXYV9L9Jw2ep6ZiGEQXx4di9YUWblcSu1kcp0JN7H0KRI9B6gO4llc3teAHGIbxYximNcMwrcXnoFP+nIJBY5Jc0IeQF1w1GSIPux3+u4tgAPgDMAKwUl29RMtYKzt16tRhrSnBd4ZUeUSoowMAaAvgHkT/mz0AXP/JfpJEPEPq5cuXERUVJfE2FB2hjg7UAXgBiAPQHsBoAGWK2E9eSE1NxV9//QVTAMFFbN8PoKF4hVjwqiJTHKfiI0QjjQAQn5jolZqY6JUL4HZeXW0uhFHY9OrVi2QgTE1N5SZozMEByDOeDSAy2I8A9BXbJRXA7MxMNGzYECEhITSFcTERdwr37NkDOoJXMlIsLSFUE93f6EP0v3kKbAMmVFNDSt++RXy6dBgaGsLW1paU6WhFyRHvPxOI7kgLWjCu+k/S5ObmYtu2bTAyMsKKFStQVPaghgAGileoqwMjCz7AU0x+61QkJnplAzifVzQ2NPTQMzT0UIFoRBwA4jnSRhGjqKCx/JX9JMb06cSpyMcUwFEAx/Pe5/P8+XMMHToUFhYWuH37Nii/xtzcHK1atQIAZGRk8JYhVV5JcnAAo6b2y30YNTUkjRjBSfvi597+/fuRmJjISTuKCt/9JynOnz+P1q1bY/z48UhKSiL1bQQCVBHbzx3/jfICENlVsaUXFJnizv6YB1HsSQUAT/NeZnnbFnGgi1IEI0eOJEFjL168kHzQWL16QEgIoKNTyLnoo66O+9ra8Jk8mWgAgEuXLqFNmzYYN24c3r17J1k9CkRRGVKzsrJ+/gEKi6zq1fFs5UrkammRO958hGpqyNXSwrOVK5FVvTon7bdq1YosdkUzpJYcvvuvtDx9+hRDhgxB165dER0dTeqrVauGgIAAzJw9G/kuRgUADvk7qKuL7GlIiMi+KgHFcioSE71uAegC4AwAXYgehV0A0C0x0SuUM3UUFtra2pg4cSIpczIMa2kJ3L8PODmxM8M5OUHtwQM4+/oiISEBHh4eUMszDgzDYMeOHTAyMsI///yD9HQ6Iagohg0bhv/9738AgHfv3uHAgQM8K5IvvpmZIWbfPiRbWSFXVxeMQIBcXV0kW1khZt8+fDMz+/1BSgHNkFo6+O6/P+Hr16+YNWsWGjZsyFqiXVtbGwsXLsTjx48xYsQIeF+6RLZN0tCAtpjdxP37IruqJAi4fibu5+fHODk5cdpGcTlx4gQqVSoYLy57fPz4kcz2KMjbt29Ru3ZtMl8+KiqKDKtLm/j4eMyYMQOHDx9m1desWRMrV66Era0tBALBTz5dMhSh7wDgn3/+wbx58wAALVu2RFRUlMR+I1lGEfovNzcX9evXx4sXLwCIHAtZsW1cowj9VxJycnKwbds2zJ8/Hx8/fmRtGzlyJJYtW4bqeaMqUVFRZO0TNTU1vHz5kiyvoKgsWrTo9oIFC1oXtY2u/SFnVKtWTWaCxoyNjXHo0CGcOnUKjRs3JvWvXr2CnZ0dzM3NSX5/ioiJEydCS0sLAHDnzh1ckefUxEpGwQypUkmbT5E6Z86cQcuWLTFp0iSWQ9GhQwfcuHEDu3fvJg4FwLbBtra2Cu9Q/A7qVMghshY01qNHD9y9exebN29mLWN99epVtGvXDqNGjaJLf+dRsWJFjBSLAvdSkix7isK4cePIuhaxsbE4deoUz4ookiI+Ph4DBw5Ejx498ODBA1Jfs2ZN7Nu3D1euXEHbtm1Zn0lMTGQt9OihJMGYv4I6FXJI69atYW4uyiWYnZ0NX19fnhWJhv0mTpyIJ0+eYObMmWRePyCaQmliYoJFixbR59BgZ0iNiIjA8+fPeVRDKQllypRhZUilTqH88/nzZ0ybNg2NGjViPcrV1dXF0qVLERcX99NHuRs3biSPosVneCkz1KmQU8RHKzZv3iwzwZFlypTBqlWrEBMTAysrK1KflpaGhQsXwsTEBHv37lXqhbUaNWqEXr16ARAttESX1ZYvXFxcyAXmxIkTiI2N/c0nKLJITk4OfH19YWRkBC8vL+IcCAQCjB07FvHx8Zg7dy5Z0LEgaWlprKnhdJRCBHUq5JTBgwejdu3aAERLQwcEBPz6A1Kmfv36CAsLw7lz59CsWTNS/+bNGzg4OKBjx464du3aL46g2Ig7hf7+/txkSKVwQr169TBo0CBSphlS5Y/IyEg0a9YMzs7OSElJIfWdOnXCrVu3sH379t/GRgQEBJDP1q5dm/U/ocxQp0JOUVVVhYuLCynLatBYly5dcPv2bfj7+6NKlf/Sw9y4cQMdO3aEvb09Xr16xaNCfujduzdMTERLLKWmpmLnzp38CqKUCHGncPfu3awLE0V2iYuLQ79+/dCnTx/ExMSQ+tq1ayM4OBgXLlwo1iMMhmFYAZouLi5QLWIlXWWEOhVyjKOjIwkai4mJwenTp3lWVDSqqqpwdHREfHw8Zs+eDQ0NDbItKCgIJiYm+Pvvv/FdSRbcAURLe4tfmNavXy/5DKkUzujcuTOaN28OAEhPT8fWrVv5FUT5JSkpKXB1dUXjxo1xTGwlVD09PaxYsQKxsbGwsbEp9vTuU6dOkcdeenp6rDgbZYc6FXJMmTJlMG7cOFKW9aAxAwMDLF++HHFxcRg6dCipz8jIwJIlS2BsbIxdu3YpTbzFyJEjUa5cOQDAs2fPcOTIkd98giIrCAQC1jN0Hx8fZGdn86iIUhTZ2dnw9vaGkZERNmzYQBx3gUCA8ePHIyEhAZ6enmSad3ERt7Xjxo1DmTIFl0ZTXqhTIee4uroS7/r48eOIi4vjWdHvqVOnDg4cOICLFy+iZcuWpP7du3cYM2YM2rZti8uXL/OoUDro6uqyMqTKulNIYWNra0se6b19+xYhISE8K6LkwzAMjh49iiZNmsDNzQ2fP38m27p06YI7d+5g69atqFq1aomPHRsbixMnTgAQOSfiuUso1KmQe+rVq4eBA/9bD0+egsbyg6J27txJ0lcDwO3bt9GpUycMGzZM4adbOjs7k2exFy5cwN27d3lWRCkumpqacHZ2JmUvLy+ZjGtSNh49eoQ+ffqgf//+ePz4MamvV68ewsPDcfbsWfLo6k8Qt7EDBw5EPSVZ06O4UKdCARB/Nr9r1y58+vSJPzElREVFBaNHj0Z8fDzmzZvHGoYMDg6Gqakp5syZo7D5LapXr856FCRPTiFFlCFVU1MTAHDr1i2lntHEN58/f4aPjw+aNm2KkydPknoDAwOsXr0ajx49wuDBg0uVFj8lJQW7d+8mZTqNtDDUqVAALCwsyLTN9PR0+Pn58ayo5Ojp6WHJkiV4/Pgx7OzsSH1mZiaWL1+O8ePHIyIiQiGDGcUNU1BQEN6/f8+jGkpJqFy5MkaILddNH2FJn+zsbAQEBMDKygpHjx4lMVkqKiqYNGkSEhISMGPGDOL8lQY/Pz+SE6h58+bo3LlzqY+paFCnQgFQpKCxmjVrIjAwsFBK3M+fP2Pp0qUYOXIkoqKieFQoedq2bYsOHToAALKysrBp0yaeFVFKgvhIYVhYGF6+fMmfGCWCYRicP38ew4YNw7p161izx3r06IHo6Ghs2rQJlStXlkh72dnZ8PHxIWV3d3elWAywpFCnQkEYPnw4K2gsNFS+V6TPT461Z88eVKtWjdTHx8dj0qRJmDlzpkKtJyLuFG7atAkZGRk8qqGUhCZNmqB79+4ARBlSxS88FG5ISEjAlClTMGPGDLx+/ZrUV6tWDYcPH8bJkyfRpEkTibYZEhJC1lmqUqUKhg8fLtHjKwpqfAuQJvr6+oWWsZVF9PX1S/wZTU1NTJkyBQsWLAAgGoaV5NLjfKCiogIHBwdYWVnB1dUVe/fuRWZmJgDg3LlzuHTpEgYPHozhw4dDV1eXZ7Ui/qTvAMDKygo1atTA69ev8fHjRwQGBrKmC8s7inzuASKn8MyZMwCArVu3YsGCBSSHjCIgK/335csX7N69G5GRkayp53p6erC3t4ednR26dOki8XYZhmE92poyZYpEHqcoJAzDcPrasmULQ5EOSUlJjKamJgOAAcBcvXqVb0kS5fXr14yDgwP5fvmvSpUqMZs3b2ZycnL4llgqVq1aRb5TkyZNGKFQyLckSjHJzc1ljI2NSf9t2LCBb0kKRUZGBrNy5UpGX1+fde6rqqoyU6dOZZKTkzlt/8qVK6RNTU1NJikpidP2ZJ2FCxdGMT+55tPHHwqEogeNVa9eHXv27MGNGzdIDAIAfPz4EZMmTUKLFi3I3aI8Mn78eDLi8uDBA5w9e5ZnRZTioqKiwlp9dv369UqTxI1LGIZBWFgYGjZsCE9PT6SmppJtffr0wf3797FhwwZUqFCBUx3itnTEiBESi9NQRKhToWCIG7bQ0FCFDBpr27Ytrly5gqCgINSoUYPUP3jwAD169MCgQYOQkJDAo8I/o1y5chgzZgwpi68tQJF9Ro0ahbJlywIAnjx5gqNHj/IrSM65e/cuunbtCmtrazx79ozUN2jQAMeOHcPx48fRsGFDznW8fPkSYWFhpCwemEspDHUqFIymTZuiW7duAERBYxs3buRZETcIBAIMHz4cjx8/xpIlS1gxFYcOHUKjRo0wbdo0ViY9eUDcKTxy5Aji4+N5VEMpCXp6epgwYQIpU6fwz3j37h0cHR3RqlUrXLhwgdSXL18eGzZswP3792FpaSk1PT4+PmTUqXv37hIPAFU4fvZcRFIvGlMhfQ4fPkye/5UpU4ZJTU3lWxLnvH37lhkzZkyheIsKFSowGzduZLKzs/mWWGz69+9P9Ds7O/Mth1ICXr58yaiqqpL+i46O5luS3JCens4sW7aM0dPTY53DampqjJubG5OSkiJ1TampqUyZMmWIlsOHD0tdgyxCYyqUjL59+8LIyAgA8PXrV+zatYtnRdxjaGiIHTt2ICoqCubm5qQ+JSUFzs7OaNasGSIjI3lUWHzEh1d37Nghd6MtykzNmjVhbW1NyjRD6u9hGAYHDhxAgwYNMGfOHFa+if79++Phw4dYt24dypcvL3VtO3fuxNevXwEARkZG6Nu3r9Q1yBvUqVBAlDlorFWrVrh48SKCg4NRu3ZtUh8TE4M+ffqgX79+Mr/oWrdu3cgQa1paGvz9/XlWRCkJ4jlH9u7di6SkJB7VyDZRUVHo1KkTbG1tWfFfjRo1QmRkJA4fPgwTExNetAmFQpZT6ObmBhUVesn8HfQXUlBGjx5NluNNSEjAsWPHeFYkPQQCAWxsbBAbG4vly5ez8gUcO3YMjRs3hpubm8yukSIQCFijFRs2bEBOTg5/giglon379mjXrh0AUYbUzZs386xI9nj79i1Gjx6NNm3a4MqVK6S+YsWK2LRpE6Kjo9GrVy8eFQJHjx7FkydPAABly5bF6NGjedUjL1CnQkEpGDSmaNNLi4OWlhZmz56NhIQEjB8/niQCy83Nhbe3N+rXrw9vb2+ZTGlub2+PSpUqAQBev37Nij6nyD7iTqGvry/NkJpHWloalixZAmNjY9bCXOrq6pg+fToSEhIwadIkqKnxn5dRPNB2woQJCpXMjFN+FmwhqRcN1OSPgkFj9+7d41sSr9y9e5fp0qVLoWBOExMT5ujRozKXbOrvv/8mGjt06MC3HEoJyMrKYqpXr076b8eOHXxL4hWhUMgEBgYyNWrUKHT+DR48mImPj+dbIovo6GhWgq2XL1/yLUmmoIGaSkrNmjUxZMgQUlb2oLHmzZvj7NmzCA8PR7169Uj948eP0a9fP1haWuLRo0c8KmQzefJkaGhoAACuXbuGGzdu8KyIUlzU1dUxdepUUl63bh0YhuFREX9cv34dHTt2hL29PWudjqZNm+LMmTMIDw8ngeWygrittLa2Rs2aNXlUI19Qp0LBKRg09uHDBx7V8I9AIMDgwYPx6NEjrF69GgYGBmRbZGQkmjVrBmdnZyQnJ/OoUkTVqlVZy8DTvAfyxYQJE6CjowMAuHfvHs6fP8+vICnz+vVrjBgxAh06dMD169dJfeXKlbF161bcuXOH5NSRJZKSkrB3715SpsmuSgZ1KhSc9u3bkyXEMzMzadBYHpqampgxYwYSEhIwceJEEtWdm5sLX19f1K9fH15eXsjKyuJVp7hBCw4OVqiVWRWd8uXLs4L7lMUp/PHjBxYsWAATExMEBgaSeg0NDXh6epIYJ1VVVR5V/pzNmzeT875du3asJQEoxeBnz0Uk9aIxFfwTFBREng9WqVKFycjI4FuSzHH//n2mR48ehZ73GhkZMQcPHuQ13kI8DsTT05M3HZSSExcXR/pOIBAwCQkJfEvijNzcXGb37t2MoaFhofPIxsaGefr0Kd8Sf0tGRgZTuXJlojsoKIhvSTIJjalQcqytrVGtWjUAoqG9ffv28axI9mjSpAlOnjyJw4cPs57vJiQkYNCgQejZsyfu37/Pizbx0Qo/Pz/8+PGDFx2UkmNiYkISJjEMA29vb54VccOVK1fQrl07jBo1ComJiaS+RYsWuHDhAoKDg1G3bl0eFRaPoKAg8oi4evXqrERmlOJBnQolgAaNFQ+BQEAy+Hl5eZHFoQDgzJkzaNGiBSZOnCj1uJT+/fuTwNLPnz+zpuJRZB9xp3D79u348uULb1okzcuXLzF8+HCYm5sjKiqK1FetWhXbt2/HrVu30LlzZx4VFh+GYViPqKZOnQp1dXX+BMkp1KlQEpycnKCtrQ0AiI6OZi3UQ2GjoaEBd3d3JCQkwNnZmTz7FQqF8PPzg5GREVavXo3MzEyp6FFVVYWrqyspK1OGVEWgR48eaNSoEQBRvMG2bdt4VlR6UlNTMXfuXJiYmGD//v2kXlNTE3PnzkV8fDzGjh0rs3ETRXH+/Hncu3cPAKCjo8PK80MpPtSpUBKUNWisNFSsWBE+Pj64f/8+evfuTeq/ffuGWbNmoWHDhggLC5PKqM/YsWPJTJXHjx/LzTomFMXKkCoUCrFjxw4YGxtj2bJlLMfa1tYWcXFxWLp0KfT19XlU+WeI28TRo0fzstaIQvCzYAtJvWigpuwQGxurNEFjXHDs2DGmQYMGhYLQLCwsmDt37nDe/rRp00ibPXv25Lw9iuRIS0tjKlasSPovODiYb0kl5sKFC0yLFi0K/f+3adOGuXz5Mt/ySkVCQgIjEAjId4qNjeVbkkxDAzUpAIAGDRrA0tISgMiZ3LBhA8+K5AtLS0vcv38fGzZsYN3FXLhwAa1atYKjoyPev3/PWfsuLi5k6uupU6dkKlEX5ddoa2tj0qRJpCxPI4XPnj2DjY0NLCwscPfuXVJvaGiI3bt34/r16zAzM+NRYenx9vYmI46WlpZo0KABz4rkF/4TrFMkxpUrV5CamvrLfczMzHD8+HEAwNatW9G5c2fo6upKQx5BX19fbo1QftCrvb09Fi9ejI0bNyInJwcMw2D79u04cOAA5syZAw8PD2hpaRX7uMXpOwDo0KEDWYBpxowZrNVopYE89x2XFKf/TE1NoaamhpycHFy5cgXr16+X+gqcJem/b9++YdmyZYXytWhra2PmzJmYNWuW1G0HF5w4cQJbt24lZXNzc5w4cYJHRUUjL+cedSoUiNTUVLII1c/o3bs3tm/fjmfPniE9PR2XL1+Gg4ODlBSK+Pjxo1Tb44Ly5ctj3bp1mDx5MmbMmIEjR44AAL5//445c+bAz88Pq1atgo2NDVnI7FcUp+8AUWxFvlNx9uxZTJ8+HeXKlSvdlykBitB3XFCc/qtUqRJ69epFVgw+ceIEzM3NpSGPUJz+y83Nxfbt2zFv3rxCM51GjBiB5cuXo0aNGlxJlDqhoaFkwbe6deuiV69exTpnpY28nHv08YeSIRAIWKmf9+/fL7dBY7KAiYkJDh8+jMjISBLhDwAvXrzAsGHD0LlzZ9ZUu9LSrFkzmJqaAhAtqx0aGiqxY1O4x97enrw/deqUzKXNP3v2LFq2bAknJyeWtnbt2uHatWsICAhQKIciJycHhw4dImV7e3uZdCjkCepUKCGWlpYoU6YMAODdu3d0eqkE6NWrF6Kjo+Hr64sKFSqQ+suXL6NNmzYYM2YMKynQnyIQCFgXpuDgYJlcup1SNA0aNEDLli0BiEYEgoODeVYkIiEhAYMHD0b37t1ZSd5q1KiBvXv34tq1a2jfvj2PCrkhIiKCOE9ly5ZFnz59eFYk/1CnQgnR0tJiZYoLCgriUY3ioKamhsmTJ+PJkyeYPn06K3HOrl27YGRkhCVLliAtLa1U7fTo0QMVK1YEAKSkpODUqVOlOh5FuoiPFIaFhZGhdz748uULZsyYgUaNGuHgwYOkXkdHB4sXL0ZcXJxC372LB8xaW1uXKA6KUjTUqVBShg4dCjU1UUhNdHQ0YmJieFakOJQtWxb//vsvHj16hEGDBpH6tLQ0/P3332jQoAGCgoL+OL+Furo6hg0bRsqBgYE0Q6oc0blzZ5I2/+vXrzh69KjUNeTk5GDTpk0wMjLCmjVrWKNdo0ePRnx8PObPn09WWVVEbt26ReKT1NTUYGNjw7MixYA6FUpKpUqV0LNnT1KmoxWSx8jICBEREThz5gyaNm1K6l+/fg17e3t07NgRN27c+KNjDxkyBJqamgCAuLg4REdHS0IyRQqoqqrC1taWlIOCgqSaIfXUqVNo3rw5pkyZguTkZFJvZmaGmzdvYufOncTpUWTERyl69uxZrEBpyu+hToUSI/5s/uTJk3ITXSxvdOvWDXfu3IGfnx/LcF2/fh3t27eHg4NDiX/7smXLkoWqALCWmKbIPgMHDiTTMV+8ePHHzmVJePHiBRYsWIBevXqxcpzUqlUL+/fvx6VLl9CmTRvOdcgCb9++xYEDB0hZ3BZSSgd1KpQYU1NTtGjRAoAoaEz8JKNIFlVVVUyYMAEJCQmYNWsWNDQ0yLa9e/diwoQJ2LJlC9LT04t9TPFn8+fPn8ebN28kqpnCHXp6ehg4cCApc+kUfv36FWvWrIGtrS1u3rzJ0rBs2TLExsZi2LBhChs3URT5+WUAoHHjxmRGFaX0UKdCyZGloDFloEyZMli5ciViY2NZwbKZmZnYunUrhgwZgqNHjxZrOLxu3bokIp9hGOoUyhm2trbkQn7t2jU8e/ZMosfPycnB/v37MWTIEAQFBSE3NxeAaAbRuHHjEB8fj7/++ossNKgspKWlYcuWLaQ8ePBg/sQoINSpUHIsLCxgaGgIQHRHk5+Yh8ItdevWRUhICM6fP09GiwBRgpsFCxZgzJgxZMXEXyE+bHvw4EF8//6dE70UyVO9enV06dKFlCUZ13TlyhUMHz4cq1evxtevX0l948aNcfv2bWzbtg3/+9//JNaePBEQEIBPnz4BAGrXrq2QU2X5hDoVSk5RQWN0JoH0sLCwwK1bt+Du7s7KbxETEwNHR0f89ddfePfu3U8/3759e9SuXRuAaFntw4cPcy2ZIkHERwqPHTuGL1++lOp4z549g6urK9zc3PDixQtSX61aNaxatQqrVq1iObHKBsMwrABNV1dXuVqeXR6gTgUFgwYNIlPHnj9/juvXr/OsSLlQVVVF7969ERYWhrFjx7LiLU6dOgVra2v4+voWmd9CRUWFdWHat28fGeamyD4tWrQg639kZmYiLCzsj47z5csXrFy5EnZ2drh69Sqp19XVhaurK4KDg9GtWzelipsoipMnTyI2NhaAaC0NR0dHnhUpHtSpoEBPT4+VT4FOL+UHXV1dODs7IyQkhDXdNysrC9u3b4eVlRUOHTpUKN6iX79+MDAwACCKar906ZJUdVP+nNJmSM3OzkZgYCCsrKwQHBxMHEoVFRVYWVkhLCwMo0aNYjmqyoyXlxd5P27cOHLeUCQHdSooANhBY1evXsXz5895VqS8GBoaYvny5fD390fDhg1JfUpKChYvXoxRo0bhzp07pF5LSwtDhgwhZTq9VL7o1asXefT18eNHnDlz5refYRgGly5dgq2tLdauXctaIbVNmzYICAjA3LlzWY/UlJ2YmBhERkYCEDlzrq6uPCtSTKhTQQEgChqzsLAgZTpawT/NmzfHzp07sXDhQlZ+i7i4ODg5OcHT0xNv374FIMqQmv9s+M6dO4iLi+NFM6XkqKurY+jQoaT8uwypT548gbOzMzw8PPDq1StSX6NGDaxZswa+vr4wNjbmVLM84u3tTd4PGjQIdevW5VGN4kKdCgpB/Nn88YMHUaNzZ7Rs0wbNLSxQY8UKaChCHoSnT4EpUwADA0BFRfR3yhRRvQyioqKC/v37IywsDBMmTCBZNAHgzJkzsLGxwYYNG6Crq4sePXqQbZFjxihe3ykw1tbW5BFFTEwM0jp1KtR/nz9/xvLly2Fvb18o34S7uzsOHDgACwsL2Y6b4On8S0lJwe7du0nZ3d2d0/aUGepUUAgtW7ZEw+rVAQDpubnYlpYGAcNA9ccPVIyIQMPhw2GQlytfLjl+HGjaFPD3B1JTAYYR/fX3F9UfP863wp+ira2NiRMnIjQ0lLWSYnZ2Nnbt2oUhQ4agjlisxb6cHLxXpL5TcMqVK4dBrVqRsndGBjn39MPDcdzGBlYDBiA0NJTE1KioqGDo0KEIDw+Hg4MDawE7mYTH88/Pz48klmvRogU6d+7MWVvKzm+dCkNDj4WGhh7MT15q0hBJkQ6ab99iZlISKfsAyA8ZU8nJgWpGBup6esrnXe/Tp4CNDZCWBhQMhMvOFtXb2MjsiEU+VatWxdKlS7Fjxw40btyY1H/69AmbT51C/vJP2QA25b2X+75TAjTevMHc27dJORzAcwARAJrk5sIzJwffxRLTtWvXDoGBgfD09ES5cuWkLbfk8Hj+ZWVlwcfHh5Td3d1lezRHzinJSEUygBsFXjShgQJRJSAAtkIhquSVEwEEF9hHkJODKnv3SlmZBFizhmXMLgBYCYBlwrKzAbHocFmmSZMm2L59O5YuXYoqVaqQevFJpz4AxPOjym3fKQFVAgLQRChE/gMsIYDOAKzA/h+tr6+PdevWwcfHB/Xr15e6zj9G7PyLBrAi7y8Ljs6/kJAQJCYmAhA55eJ5eSiSpyROxdHERK/2BV50QrwCUeH4cWjn5sJZrG48gKtiZZWcHFSQx6ybAQHEqB0G0BPAbAC9AbzI3yc7G9izhw91f4SKigr69OmD0NBQLFRXR8FFqj8DGAQgfzURue07JaDC8eNQycnBBLE68TGlcgDWA3iQmwtzc3P5u9POO/8eAegF4C8A5mDbFi7OP4ZhWNNIp0yZwopLokiekjgV1oaGHumGhh7vDA09jhoaevw0LZtAIHASCARRAoEgSnxpXYpso5KXXKmXWF06gJc/2U+uEEtffRL/PdZ5CqABgLkAUgvsJy9oaWnh75wcxAMYVWDbRQDiT9rlsu+UgPx+sQQg/kxZAMAVwJO8v5olWHBOlkhOTcVUAE0B5K/H+wNAVMEdJXz+Xb16FVFRolY0NTUxadIkiR6fUpjiOhW5AN5DdFNXFUBfANd+5lgwDOPHMExrhmFaV6xYUSJCKdwjzMuqKZ7loBqAVj/ZT67Q0yNvpwMoK7YpE8AyAMYAtmtoyGVGSqGODqoB2AXgDP47sUeD/YxSLvtOCcjvF30AI/LqKkLUn+sBlC+wn7yQlZUFLy8vGAHYCNFjnXxqABhe8ANi56kkEE/J7eDgwJqaTeGG4jgVgQAqJyZ6GSUmepkCyA891wRYI+UUOSfF0hKfVVWxXaxuB0QX23yEampI6dtXysokgIMDkBcdXxvAuiJ2eQ/AMSMDbdq0wYULF6SnTQKkWFpCqCa6x+0G4BaAswA247+RCrntOyVAvP82ANgN0R39SLF95Kn/GIbB4cOH0bhxY0ybNg1fithnG4DK4hXq6sDIkUXs+We8ePGClfbczc1NYsem/JzfOhWJiV7xiYlen8TKkQBS8oo1uRJGkT5JDg7YJhAgfwCyEYAeBfZh1NSQNGIE5I7p04lTAYjukKqIbS4v9v7u3bvo0qULrK2tJb4cNVckOTiAUftv4LwlgK4F9pHbvlMCxPtPH2xnIh956b8HDx6gZ8+eGDhwIBISEki9+Jh1UbYF6uqAh4fEdPj4+JDptz169ECTJk0kdmzKzynOlFJPQ0OPmmLlngDyc7++4EgXhQfSqlbFOrFc+O4QPdMFRHdJuVpaeLZyJbLyclnIFfXqASEhgI4OoK4OTQBTxDbXEQjwt709tLW1SV1YWBhMTU3h6emJb9++SV1ySciqXh3PVq5ErpYWuePNR+77TglQhP778OEDJk2ahObNm7NSjZctWxb//vsvdCv/Ny7hjv9sC9TVRedlSIjoPJUAqamp2Lp163/t0WRXUqM4jz8mA3hhaOjxwtDQIwZAZF79DxQ9ikyRUy5cuIC3n0SDUuU1NDBcRweMQIBcXV0kW1khZt8+fDMz41llKbC0BO7fB5ycAAMDTBIIkL/M0m2GQZ+pU/H48WOMELsbzMrKwqpVq1C/fn34+fnJdLzFNzMzxOzbh2QrK+Tq6ipW3ykB8tp/mZmZWL16NYyMjLBlyxYyOqCqqgpnZ2ckJCSgdu3aePnhAwCggpYWRujr/5dR08lJdF5aWkpM086dO8mNgLGxMSwleGzKrylO8qplAIZCNGJVFaLJAFcALElM9HrMoTaKlBFf72OwgwMeT5nyi73llHr1AB8fwMcHlQGMGDcOO3bsACAK6tq/fz8CAgLg4uICd3d3sgz8x48fMXHiRGzcuBFeXl7o1q0bj1/i52RVr47Xnp547enJtxTKHyBP/ccwDCIiIjBjxoxCjwl79+6NtWvXkgXxxAMmJ02fDu2lSznTJRQKsX79elJ2c3ODigpNHi0tfutUJCZ6+QHwk4IWCo/ExMQgOjoaAKCmpsZa4EiRcXd3J05FaGgoXr16hZo1a6Jdu3a4evUq9u/fj1mzZuH169cAgPv376N79+4YNGgQuTujUJSN6OhoeHh44Pz586z6Bg0aYO3atayRgaioKFy+fBmAaPG0KRzfrBw5cgRP8zJzli1bFqNGFZxoTeES6r5RALBHKXr27Kk0U6+aNm1KRh1yc3NZ6XwFAgGGDx+OuLg4LFmyBDpi0/kOHjyIRo0aYfr06fjy5Yu0ZVMovPD+/XuMHz8eLVu2ZDkU5cqVg7e3N+7fv1/oUYP4KIWtrS0MDQ051SjenpOTE/QkPE2V8muoU0HBhw8fcPLkSVK2t7fnUY308RCLOPfz88P3Agl4dHR0MG/ePCQkJGD06NGkPjs7G2vXrkX9+vWxadMm5OTkSE0zhSJNMjIysGLFChgZGWHbtm1kaXY1NTW4ubnhyZMncHFxKbSo2du3b7F//35S5jpgMjo6GufOnQMgiumYOnUqp+1RCkOdCgqCg4NJAGKLFi1gamrKsyLp0rdvX7KOwtevX7Fr164i9zM0NMTOnTtx69YtmJubk/qUlBRMmTIFzZs3ZzlnFIq8wzAMgoODYWpqir/++ovlcPfr1w8PHjzAunXrUL58+SI/7+vrS5ztTp06oVWrgqn0JIt4LIWNjQ1q1KjBaXuUwlCnQsnJyMhgJYixs7PjUQ0/qKiosBLjrF+/nkSwF0Xr1q1x8eJFHDhwALVq1SL1jx49Qu/evdG/f3/ExcVxqplC4Zrbt2+jc+fOGDZsGF68eEHqGzVqhMjISBw5cgQNGjT46efT0tKwefNmUuZ6lCIpKQmBgf/lA6bTSPmBOhVKzrFjx/D161cAojtxCwsLnhXxw5gxY1CmTBkAQEJCAo79ZuEtgUCAoUOHIi4uDsuXL2c9tz169CiaNGkCNzc3fPr06RdHoVBkj8TERIwZMwatW7cmAZYAUKFCBfj6+iI6Ohq9evX6xRFEBAQEkP//2rVrY9CgQZxpBoBNmzYhKysLANC+fXu0b9+e0/YoRUOdCiWGYRhWgKatrS1UVVV5VMQfenp6mDDhvzUixYO9foWWlhZmz56NhIQEODo6ktUjc3Jy4O3tjfr162PDhg3IFlt2nUKRRdLT07F06VIYGxuzHgGqq6tj2rRpePLkCSZPngw1td9nImAYhnUOubq6cmpbMjIysGnTJlKmoxT8QZ0KJeb69et4/vw5AEBXV5fzOwlZx8XFhRi+M2fO4P79+8X+bNWqVeHv74/bt2+zRns+f/4MV1dXNG3aFMeOHSMBbhSKrJB/c2FiYoL58+fjx48fZNugQYPw6NEjrFmzBmXLli32MU+ePInY2FgAgL6+PhwdHSUtm0VQUBA+5CXXql69OoYMGcJpe5SfQ50KJUZ8lGLgwIFKP/WqZs2aLGMkHvRVXFq0aIFz584hLCwMdevWJfVxcXHo168fLC0t8ejRI4nopVBKy40bN2BmZgZ7e3uSiwUAmjRpgtOnTyMiIuKPcrGIj1KMGzcOBmLp/yVNwVGRomahUKQHdSqUlOfPn+Pq1asARPEBtra2PCuSDcSHTffu3UvufkqCQCCAlZUVYmJisGrVKujr65NtkZGRaNasGZydnZGcnCwJyRRKiXnz5g1GjhyJ9u3b49q1a6S+UqVK2LJlC+7evYvu3bv/0bFjY2Nx4sQJAKJzwdXVVSKaf8b58+fJqKKOjg7rMSZF+lCnQkkRH6WwsLBAdRleqEiadOjQAW3btgUgWtNAPHq9pGhqamLmzJl48uQJJk6cSFIF5+bmwtfXF/Xr14eXlxcJLqNQuCYjIwMLFy6EsbExAgICSL2GhgZmzZqFhIQEODk5lSr+QXyEb9CgQawROy7w8vIi78eMGYNy5cpx2h7l11CnQgn58uULjh49SsrKOI30ZwgEAtZoha+vLzIzM0t1zMqVK2Pz5s2F7v6+fv2KadOmoXHjxrh+/TqNt6BwhlAoxLFjxzB+/HgsWrQI6enpZJu1tTViY2OxcuVKMgPqT0lJScHu3btJmeuAyYSEBBw5coSUuR4Vofwe6lQoIeHh4eRCaWJigpYtW/KsSLawsbFBtWrVAIjmvu/bt08ix23atClOnTqFgwcPsp5TJyQkYNGiRWRFRwpFkty7dw9jxozB33//jZSUFFLfokULnD9/HiEhIRIbTfDz8yMOS4sWLdC5c2eJHPdneHt7E2e8b9++MDEx4bQ9yu+hToWSkZOTgwMHDpCynZ0dmQZJEaGurs5K77tu3TqJjSIIBAIMHDgQDx8+xNq1a1l3hjdv3sSIESPwzz//0PwWlFLz7t07zJkzB46OjoiJiSH1VatWxbZt23Dr1i2J5qXJzs5mrZ3j7u7OqW358uULWQwQYKfbp/BHcZY+p8gJ+vr6+Pjx4y/3OXfuHNmnXLlyaNGixW8/I2nEAxdlFScnJyxevBjp6emIjo7GhQsX0KVLF4kdX0NDAx4eHhg5ciQWLlyIzZs3Izc3F0KhEOHh4YiMjISdnR0GDhwIDQ0NibVbWuSh7/igOOeetEhPT8eBAwcQFhbGitdRV1fH8OHDsXHjRk76MTg4GImJiQBEjgvXwd/+/v5k+mvjxo3/OLBUlvruV8jNuccwDKevLVu2MBTZQCgUMq1bt2YAMACYxYsX8y1Jppk0aRL5rQYNGsRpWw8fPmR69epF2st/1a1blwkLC2OEQiGn7VPkn9zcXGbHjh3M//73v0L/R8OGDWOeP3/OWdvSti3Z2dlMzZo1SXv+/v6ctkdhs3DhwijmJ9d86lQoEZcvXyYnoaamJpOUlMS3JJkmNjaW/F4CgYB58uQJp+0JhULm6NGjjImJSaGLgoWFBXPnzh1O26fILxcuXGBatmxZ6P+mdevWzKVLlzhvX9q2JTg4mLRXsWJFJi0tjdP2KGx+5VTQmAolQjxBjIODAypXrsyfGDmgQYMGsLS0BCByvr29vTltTyAQoG/fvnjw4AG8vb1ZU+MuXLiAVq1aYfz48Xj//j2nOijyw/PnzzF06FBYWFjgzp07pN7Q0BC7du3CjRs3WCvqcoW0bYv4NNJJkyZBW1ub0/YoJeBn3oakXnSkQjZ48eIFo6KiQrz7+/fv8y1JLoiMjCS/mZ6eHvPlyxeptZ2SksK4uroyampqrLtPPT09ZtmyZUx6errUtFBki69fvzKenp6MhoYG639DS0uL+fvvv5nv379LTYu0bcuNGzdIW+rq6kxiYiKn7VEKQ0cqKNiwYQNZzrtHjx5o0qQJz4rkg549e6Jhw4YAgO/fv2Pbtm1Sa7t8+fJYv349Hjx4gH79+pH679+/Y86cOTA1NUVwcDDNb6FE5Obmwt/fH0ZGRli5ciUrENPe3h6PHz/GokWLoKurKzVN0rYt4qMiw4cPx//+9z9O26OUkJ95G5J60ZEK/vn27RtTpkwZ4t0fOXKEb0lyhZ+fH/ntatWqxWRnZ/Oi48SJE0zDhg0LPTc3Nzdnbt26xYsmivQ4e/Ys06xZs0L9365dO+bq1au8aJK2bXn9+jVr5O727ductkcpGjpSoeTs3LkTX79+BQAYGxuTOAFK8XBwcECFChUAAC9fvsShQ4d40dG7d2/cu3cPvr6+RA8AXL58GW3atMGYMWPIlD6K4vDkyRNYWVmhW7duuHfvHqmvXr069u7di6tXr6JDhw68aJO2bfH19UVOTg4AoHPnzjRxnwxCnQoFRygUsnLxu7m5kTUoKMVDW1sbkyZNImXxIDFpo6amhsmTJ+PJkyeYNm0a1NT+SzWza9cuGBkZYcmSJUhLS+NNI0UyfP36FTNnzkTDhg0RERFB6nV0dLBo0SI8fvwY9vb2vJ3P0rYtaWlp2LJlCynTZFcyys+GMCT1oo8/+OXQoUNkqLBs2bJMamoq35Lkkrdv37KGXWXlccPjx4+ZgQMHFhoSr1GjBhMYGEjzW8gh2dnZzKZNm5iKFSsW6tdRo0Yxb9684VsiwzDSty2bN28m7dWpU4fJycnhtD3Kz6GPP5QY8btqJycn6Onp8ahGfjE0NGRlCBQPFuMTY2NjHDx4EKdPn2YFyL1+/Rr29vYwMzPDjRs3eFRIKQmnTp1CixYtMHnyZCQnJ5P6jh074ubNm9i1axdZl4ZvpGlbhEIh65xzdXUt1UqqFA75mbchqRcdqeCP6Oho4tmrqqoyr1694luSXBMVFUV+TzU1Nebt27d8S2KRk5PDbNmyhalUqVKhO9wRI0bQ/pdh4uLimP79+xfqt1q1ajH79++XuREnaduW48ePk/b09fWZr1+/ctoe5dfQkQolRdyzt7GxQY0aNfgTowC0atWKJBLKycmBr68vz4rYqKqqwsnJCQkJCZg1axZrzZC9e/fCxMQECxcuJOslUPjn8+fP8PDwQOPGjVlLeOvq6uKff/5BbGwshg0bJnOL/knbtoi35+joCAMDA07bo5SCn3kbknrRkQp+eP/+PSsxzrVr1/iWpBCEhoaS37RChQoynR74yZMnzJAhQwrd/VarVo3ZvXs3k5uby7dEpSUrK4vZsGEDU758eVbfCAQCZty4cTKd0EnatuXRo0ekLRUVFebZs2ectkf5PXSkQgnZvHkzSYzTrl07tG/fnmdFisGgQYNQu3ZtAEBKSgoCAgL4FfQL6tWrh9DQUJw7dw7Nmzcn9W/fvsWoUaPQvn17XL16lT+BSsqJEyfQrFkzuLi4sJa479y5M6KiorBt2zaZTugkbdsiPsNk0KBBqFOnDqftUUoHdSoUkIyMDNbQPJ16JTlUVVXh6upKyuvWrZP5jJZdunQhF6sqVaqQ+lu3bsHMzAzDhw/Hy5cveVSoHMTGxqJv376wtLREbGwsqa9Tpw5CQkJw/vx5mc+7IG3bkpycjN27d5Oyu7s7p+1RSg91KhSQffv24cOHDwBECXKGDBnCsyLFYty4cSTSPSYmBqdOneJZ0e9RVVXFuHHjkJCQgL/++guamppk2/79+9GgQQPMmzcP379/51GlYpKSkgIXFxc0adIEx48fJ/X6+vpYuXIlYmJiYG1tLXNxE0Uhbdvi5+eHjIwMAEDLli3RqVMnTtujlB7qVCgYDMOwpnq5uLhAXV2dR0WKR5kyZTBu3DhSlpXppcVBX18fy5YtQ1xcHIYNG0bqMzIy8M8//8DIyAg7duwgazlQ/pysrCysW7cO9evXh4+PD3JzcwGIVqOdMGECCajV0tLiWWnxkLZtycrKwsaNG0nZ3d1dLhwvpednwRaSetFATely9uxZEtSko6PDpKSk8C1JIXny5AkjEAjIbx0TE8O3pD/i0qVLTOvWrQsFc7Zs2ZK5cOEC3/LkEqFQyBw+fJgxNjYu9Lt27dqViY6O5lviHyFt2xIQEEDaq1q1KpOZmclpe5TiQwM1lQjxO4nRo0ejfPnyPKpRXOrVq4eBAweSsre3N49q/hxzc3PcuHEDu3btYgUH3rlzBxYWFhg6dCieP3/Oo0L54uHDh+jduzcGDBiA+Ph4Ul+vXj1ERETgzJkzaNasGY8K/xxp2haGYVgjgM7Ozqwp0hQZ5mfehqRedKRCesTHx7PunuPi4viWpNCcP3+e/Nba2tpMcnIy35JKRWpqKjN//nxGS0uLdXetoaHBeHp60oRDv+DDhw/MpEmTGBUVFdZvV6ZMGebff/9lMjIy+JZYKqRtWy5dukTa0tLSYj58+MBpe5SSQUcqlIQNGzaQmQh9+/aFiYkJz4oUm86dO5Opmunp6di6dSu/gkqJnp4eFi9eTBaqyicrKwsrV66EkZER/P39SWwARfTbrFmzBvXr18fmzZtJLIqKigomT56MhIQETJ8+nRUYK49I27aIj1I4ODigUqVKnLZHkRxqv99Fcbhy5QpSU1P5lvFb9PX1YWZmVqLPfPnyBdu3bydlRZtGKqt9161bN0RHRwMA/v33X3To0AEWFhb8iiolNWvWxN69ezF16lR4eHiQtUM+fPiACRMmwMfHB15eXujatWuxjymr/VeQ4p57DMPg4MGDmDlzJp48ecLa1rNnT6xduxaNGzfmSqZU+fLlC/z9/UnZ3NwcJ06c4Ky99+/fIzw8nJRbtWpV7Pb+xHZSJItSORWpqaly4fF+/PixxJ/Ztm0bSb/cuHFjdO/eXdKyeEVW+87Gxga7du1CSkoKUlJScPz4cbl3KvLp0KEDrl69iqCgIMyePRtv3rwBANy7dw/dunXD4MGDsXr1atSvX/+3x5LV/itIcc69e/fuwcPDA+fOnWPVGxsbY+3atejbt69CzVLYtm0b0tPTAYhiQ3r27Mnp9wsICCAjPu3atUObNm2K/dk/sZ0UyUIffygAOTk5rEBBOvVKemhoaMDGxoaUw8PDZT4ZVklQUVHBiBEj8PjxYyxatAg6OjpkW0REBBo2bIgZM2bgy5cv/ImUEklJSXByckKLFi1YDkXZsmWxbt06PHz4EP369VOoc6+gbbGzs+P0+/348QMRERGs9ijyBXUqFICIiAi8evUKAFCxYkXW83AK91hbW5PI9Pj4eFy7do1nRZJHR0cHf//9Nx4/foyRI0eS+uzsbKxZswZGRkbYvHkzcnJyeFTJDRkZGSSmZOvWrcRpVFVVhYuLC548eQI3NzeFzAcjblvKli2LPn36cNre4cOHyYhrrVq10LFjR07bo0ge6lQoAOJBTZMmTYK2tjZ/YpSQ8uXLs4ytPCXDKinVq1fH7t27cePGDZbBT05OxuTJk9G8eXO5yDBaHBiGQUhICBo2bIjZs2ezYkIsLS3x4MEDeHt7o0KFCjyq5Bbx/2Vra2tOE3Xl5uZi3759pGxnZwcVFXqJkjdoj8k5t27dwpUrVwAA6urqmDJlCs+KlBPx0aHQ0FCFX0ujbdu2uHz5Mvbt24eaNWuS+kePHqFXr14YMGAAHj9+zKPC0vGzPB2mpqY4fvw4jh07BlNTUx4Vco+4bVFTU8PQoUM5be/SpUskbsfAwAD9+vXjtD0KN1CnQs4Rv5MYPny4TK9uqMjUr18fbdu2BQAIhUL4+PjwrIh7BAIBbG1tERcXh6VLl0JXV5dsO3LkCBo3bgwPDw98/vyZR5Ul49OnTxg3bhxat26NS5cukfry5cvDx8cH9+7d4/wRgKwgblssLCxQsWJFTtsLCgoi762srOiIq5xCnQo55u3btzhw4AAp0xX8+EU8qGzr1q1KsziXtrY25s6di4SEBIwdO5YE8uXk5JC1Lw4dOiTT8RYZGRnYtm0bHB0dsWPHDhI3oaamBg8PDzx58gTOzs4KGTdRFAVty+DBgzlt7/Hjx7h9+zYAUawK16MiFO6gToUcs3HjRmKoO3fuLPPLJis6ZmZmqFatGgDg69ev2LlzJ7+CpMz//vc/bN++HVFRUazVJD99+oRNmzbBzs4OV69e5VFhYRiGwcmTJ2FjY4NNmzaRFTEBYMCAAXj06BHWrl2LcuXK8ahS+hS0LcWZNlwaxEcpunfvjqpVq3LaHoU7qFMhp6SlpWHLli2kTEcp+EdFRQWDBg0i5fXr1yvlap8tW7bEhQsXEBISgjp16pD658+fw9XVFa6urnj27BmPCkU8fPgQjo6OmDNnDt6/f0/qGzdujJMnT+LQoUMwNjbmUSE/SNu2JCcnIzIykpTp7DX5hjoVcsqePXvw6dMnAECdOnVYi1tR+KNHjx4oW7YsAODJkyc4duwYv4J4QiAQwNraGjExMVixYgXr+fjVq1dhZ2eHVatW8ZLfIikpCX///TfGjBmD+/fvk/qyZcvCxcUFd+/eRc+ePaWuS1aQtm0JDQ1FdnY2AKBJkyYKk4lUWaFOhRwiFApZQVSurq5QVVXlTxCFoK2tjQkTJpCy+MqOyoiWlhY8PT2xbds2WFlZkXiL3NxcHDhwAFZWVggMDCQXFS7JyMiAn58frK2tWc6empoaRo4ciYiICPTt2xdqakqVaJiFtG1LZmYmQkJCSJmOUsg/1KmQQ06ePIm4uDgAolz348aN41kRRZypU6cSQ3z27FnW3bCyUq5cOcydOxcBAQFo3bo1qU9NTcXatWtha2uLS5cucZKNVCgU4vjx47C2toafnx8rbqJr164IDg6Gm5sb9PT0JN62vCFt2xIZGUlmB1WpUqVE68lQZBPqVMgh4ncSjo6OMDAw4E8MpRA1a9aEtbU1KStyMqySYmJigk2bNuHff/9F9erVSf2rV6/g4eEBZ2fnQgt0lYb79+9j3LhxmD9/PpKSkki9sbExNm/ejNWrV6NGjRoSa0/ekaZtYRgGgYGBpGxra6vUo0SKAnUq5IyYmBgS1CQQCODi4sKzIkpRiAe37d27Fx8+fOBPjIwhEAjQpUsXHDhwAO7u7qz8Fjdv3oS9vT2WL19eqvwW79+/x9y5czFu3Dg8fPiQ1JcvXx7z5s3Dnj17WCMmFOnblqioKOJAamlpcT5tlSIdqFMhZ6xfv568Hzx4MOrWrcujGsrPaN++PUmGlZWVhU2bNvGsSPbQ0NCAg4MDIiIiYGNjQ1IyC4VChIaGYvDgwdizZw+ysrKKfcy0tDRs3rwZ1tbWrBkF6urqGDNmDMLCwjB48GAag1QE0rYt4qMUAwYMoCOuCgJ1KuSIlJQU7N69m5TpNNKSo/HmDWqsWIHmFhZo2aYNmltYoMaKFdDISw8sKQQCATw8PEjZ19cXmZmZEm2jSJ4+BaZMAQwMABUV0d8pU0T1Mkq5cuUwe/ZsBAYGEkcMEK1YuX79egwbNgznz58HwzA/7T+1V69w5MgRDBkyBP7+/qzfunv37ggJCcHUqVNp3MRPkJZtye8/3U6dWBlLR3bpwkl7FOlT4gdYhoYewQDy13oOTUz0svnV/hTJsWXLFhJk1rJlS1aCIcrvMbhyBXU9PSHIyYFKXmIf1R8/UDEiAhWOHMGzlSvxzcxMYu1ZW1ujevXqePPmDT58+IB9+/Zh9OjREjt+IY4fB2xsgOxs0QsAUlMBf39g1y4gJASwtOSu/VJSv359bNy4EZcvX4aXlxdZHfPNmzeYMWMG2hsZYePLl6goFLL6Ly48HNNCQ3G7QJBngwYNMG3aNJoUrhhIw7aIn39LxLKr9hUI0Hf6dImffxR+KNFIhaGhx1j851BQpEhWVhY2btxIyu7u7mR6HuX3aLx5g7qenlDNyCAXpFwADACVnByoZmSgrqenREcs1NXVMXXqVFL28vLiZHYDANFIhI0NkJYGZGeT7wZA5GCkpYm2y/CIBSAa4enUqRP279+P6dOnQ19fn2y7npCA1llZmJiTgyQAzwEMA2CRm8tyKCpWrIgFCxZg9+7d1KEoBtKwLeLn39ecHOwQ2zaNYTg5/yj8UGynwtDQox4AbwDXANCelzLBwcFITEwEAFStWhW2trY8K5IvqgQEQJDnTOQAmANAH8B8sX0EOTmosnevRNudMGECSfx07949XLhwQaLHJ6xZQ0YnvgNoBKACAFZS7OxsQE7yZqirq8POzg7h4eEYNmwYVPMucgwAfwA1ARgDCBb7jBYAD1NThIWFYcCAAXTZ7GIiDdsifv6NAvAjr74JgG5577k4/yjSp1hnnaGhhxqAvQCEAEZAdJNHkRIMw7Cmejk7O0NDQ4M/QXJIhePHoZKTgwcAGgJYDiAdwFqInAxANGJRQcIZMMuXL8965MFZMqyAAOJU9AXwGMBnAF0BeAHIAkTb9+zhpn2OKFu2LGbNmoW7WloQf3CThf/6DQCGA4gDsPrVK+jo6EhVozwjLduSf/5lADguVt8BQP6YCBfnH0X6FNeVXwCgHYApiYlez3+3s0AgcBIIBFECgSAqOTm5VAIporTGUVFRAABNTU1MnDiRZ0XyR8qPH5gMoDmABLH6dABBYmWVtDSJt+3m5kbeHz58WKJ5GAhiK6KKhyJmAZgGoDGAwwCY1FTJty0FGmdk4BiAYwAqidXXBXAFoj6sBW76T5GRlm3J75fV+O+OVABg5U/2o8gvv3UqDA09WgP4C0BAYqJXscamGIbxYximNcMwrStWrFhajUqP+N3tyJEjUalSpV/sTREnKysLe/bsgRGAzRANtRXkkNh7IQd3uQ0aNIBlXoAkwzDYsGGDxNuA2KyGIwDqFNicAGAggJ4qKnKZ4TO/XywBPAVQD0BTiEZkOhaxH6V4SMu25PfLCbG6rgDK/mQ/ivxSnJGKxgBUAdgYGnp8NzT0+A7RI00AGJxXV4YzhUrOixcvEB4eTsrid72Un8MwDM6fP49hw4Zh/fr1+Ca2rQeAMLFyGIAXAIRqakjp25cTPeLTS7dv346vX79KtgEHB0BdHYDopF4gtkk85O5Mbi5atGiBiRMnylVCrhRLSwjzsi3qA3gC4B7Y09e47D9FRJq2JcXSEtdVVUmMjzpEz9PFof2nGJQkkkkLgG7eK99OqRYoUyTMhg0byPLZPXv2pCv4FYP4+HhMnjwZM2bMwBuxaHJjgQCHAZwEYAWRcwGIRi98ADBqakgaMYITTT169EDDhg0BAN+/f8e2bdsk28D06cSpAEQxBpXz3jMAeuK/k10oFMLPzw9GRkZYvXq1dPJnlJIkBwcwv0nhzGX/KSLStC1JDg5YLzZDxw5A1QL70P5TDH7rVCQmeu1MTPQSiL8AvMzbHJpX94VTlUpKamoq/P39SZkmu/o1KSkpWLp0KUaMGEGeEwOihZGmT5+OsH//haWWFrk4uYt91h/A/UWLkCW2HoUkEQgErP7z9vZGTk7Ozz9QUurVE+Wh0NEB1NWhCWCK2OYvKiqI3rgRvXr1InXfvn3DrFmz0LBhQ4SHh3M33VUCZFWvjmcrVyJXS4uMWOQjVFNDrpYWnq1cyVn/KRrSti2v1dURIlYWb432n2JB51zJMDt27MC3b6KBexMTE/Tp04dnRbJJZmYmdu7ciSFDhiAiIoJcHFVVVTFs2DCEh4fDzs4OGRYWiNm3D8lWVsjV1UUfiEYvAOArgMCPHznV6eDggAoVKgAAXr58iYMHD0q2AUtL4P59wMkJMDDAZIEA+XH8t4RCpDZvjhMnTuDo0aMwMTEhH3v27BmGDBmCrl274u7du5LVJEG+mZmx+o8RCJCrq4tkKyvE7NtHEyeVAGnbluDgYOTkjYp0rFQJTWn/KSx/5FQkJnrVzhuhoImwOCI3Nxfe3t6k7ObmRufdF4BhGJw+fRpDhw6Fj48Pfvz4QbZ17NgRQUFBmDVrFsqWLUvqs6pXx2tPT0RfuIDoqChYzZpFtu3bt48MB3OBtrY2Jk2aRMqcrF5arx7g4wN8/YrKQiFGjB3Lak8gEKBv37548OAB1q9fj3LlypHtFy5cQKtWrTB+/Hi8f/9e8tokgHj/3bl1C9EXLuC1pye9wy0B0rYtGRkZCAv7L4ppCO0/hYZepWSUo0eP4mle9sNy5cph1KhRPCuSLWJjY+Hk5ITZs2eTxD0AUKdOHaxfvx7e3t7FWhCpf//+JGvjmzdvWOsRcMGUKVOgnhf7cPnyZdZjGi4QH9YODQ3Fy5eiJ5fq6upwdXXFkydP4OrqShbYYhgG27Ztg5GREZYvX05SN1MUB2nbliNHjpBRkWrVqtHlBRQc6lTIKOJTvZycnFjLQyszycnJWLRoEUaNGsUaqi9TpgxmzZqFoKAgmJVgGFVbWxtWVlakHBQU9Iu9S4+hoSErYyEnoxViNG3aFN26iXIWCoVC+Pj4sLaXL18e69evx4MHD9BXLPL++/fvmDNnDkxNTREcHCzT8RaUkiFN2yIUCrFv3z5SHj58OF0hVsGhToUM8vTpU5w/fx6AKC7A2dmZX0EyQGZmJrZt2wYrKyscPnyYFTchns5Z7TczBIpi2LBhxNBFRUUhPj5eotoLIj56sH//frx9+1Zq7W3duhXfxRJl5WNqaoqjR4/ixIkTZJYKIJp2OGzYMHTu3Bm3b9/mVCeFe6Kjo6VqW65du4YXL14AAHR1dTFgwABO26PwD3UqZJCIiAjyfujQoahRowZ/YniGYRjs27cPTk5O2LRpE9LT08k28YWnDAwM/riNqlWrkrt5gPvRilatWpEh4JycHPj6+nLaXr9+/VC/fn0AwNevX7Fz586f7tu7d2/cu3cPGzduJEGlgOhRTevWrTFmzBjW4yaKfCE+MiYN2yJ+Lg0aNIguPa8EUKdCxkhOTiZ3EoByTyO9efMmzM3NYWdnx0rUVK9ePfj4+MDLywu1a9eWSFv29vbk/YkTJ5CSkiKR4/4M8X7dsmUL0jhMT6yiosJKbLR+/fpfBqSqqalhypQpSEhIwLRp01ijP7t27YKRkRGWLl3KcvAoss/79+9ZF3mubcvTp09x/fp1AKL/QboIonJAnQoZIzQ0lOQvaN++Pdq1a8ezIunz5s0bjBo1Cu3atcPVq/+ts1m2bFnMnj0be/fuRfv27SXaZpMmTdCkSRMAQHZ2NkJCQn7zidIxaNAg4hClpKQgICCA0/bGjBmDMmVEiW+fPHmCo0eP/vYz5cqVw5o1a/Do0SMMHDiQ1KelpWH+/PkwMTFBUFAQjbeQEzZv3oysrCwA0rEt4rEUFhYWqFatGqftUWQD6lTIEJmZmayLmXhqZ2UgLS0NixYtgrGxMfaIraaprq4Oa2trhIeHw8bG5o/iJoqDnZ0deR8aGspppklVVVW4urqS8rp16zi9OOvp6WHChAms9oqLsbExDh48iNOnTxPHCwBev34Ne3t7mJmZ4caNG5KUS5EwGRkZrMdsXNuWL1++4JjYiqPiI4EUxYY6FTJEZGQkPn/+DACoUaMGhgwZwrMi6SAUCrF3716YmJhg4cKFrGH1wYMHIyYmBuPHjydTP7miW7duqFKlCgDg06dPiIyM5LQ9R0dH8p1iY2Nx8uRJTttzcXEhAalnz57FvXv3SvT57t274+7du9iyZQtr4alr166hffv2cHBwwOvXryWqmSIZgoKC8DEvuZs0bIu4U25qaormzZtz2h5FdqBOhYzAMAwCAwNJ2cXFhbM7clni2rVr6NixIxwcHFjrdDRr1gxnz55FeHg4CTLkGjU1NQwbNoyUuR7aNzAwwLhx40iZ6+mlNWvWZF1M1q9fX+JjqKqqwsnJCQkJCZg5cybJuQGA5RiKJyKj8AvDMKxppFzbluzsbAQHB5OynZ0dBAK6PJSyQJ0KGSEqKgpPnjwBAGhqamL8+PE8K+KWV69ewd7eHh07dmQNnVeuXBlbt27F7du30bVrV6nrsrKygpaWFgAgISGB82mULi4uxOCeOHECsbGxnLYnHpy3d+/eP16ptEyZMli1ahViY2NZjkp6ejoWLVoEExMT7Nmzh9MMpZTice7cOTx48AAAoKOjw7ltOX36NJKTkwEAFStWRM+ePTltjyJbUKdCRhAfpejZsycrfbIi8f37d1aQXz4aGhqYPXs2EhISMH78/9s777Amli4O/0JXQEWxxYYKqNi99t4VO4K9NxQUNdbrp9d6r6JeRUVEECuoKCjYe+9dsFHsJfaC9JLM90dgbhYslGw2CfM+Tx4yk2XP2Uxm9uyZM2dGC5Ygp1ChQpy19Nu3Z96gWbVUrlwZPXv2pOXceA9yQpMmTdCwYUMAQEpKCry9vfN0vsqVK2PPnj04c+YMx8X95s0bDB06FI0bN+YE2zLUj7IHbPjw4byOLZk9rn369OF4sxi6DzMqNICXL1/i4sWLtKx8k9EV5HI5tm7dCltbW/z999+c9M9OTk549OgRlixZkqd8E6pCeenbxYsX8fLlS17lKXsPtm3bxuty1sy7pa5bt04lAamtW7fGzZs34efnR+NSAODGjRto1qwZ3N3d8fbt2zzLYeSM6OhoHDx4kJaVlxbzQVhYGPW2GRsbw9HRkVd5DM2DGRUaQGBgIJ27b968Ocrq2OY6Fy9eRMOGDTF8+HDOjaVevXo4d+4cgoKCsrVPh7qwsrKiqb4zkm/xScuWLVG3bl0AiukDX19fXuU5OTnR5X0fPnxQWbIvfX19jBo1CtHR0Zg1axaMjY3pZ+fOnYOTkxPWrVvHa04OBpc1a9bQsaVr166wtbXlVZ6yZ69Lly6czfwY+QNmVAhMbGwsDhw4QMu6tPTq2bNn6Nu3L1q0aMGJTShVqhQ2b96MGzduoGXLlgJq+HOU2+HAgQOIjY3lTVZm78HatWuRmprKmzxDQ0NMmDCBllW9nNXc3ByLFy9GREQEJ/A1OTkZmzZtQu/evXHgwAEWb8Ez3759w+bNm2mZ72Wkb968wblz52i5f//+vMpjaCa6v7xACXNzc7qsSlMIDg6mSyitrKxgZWXF+9JJvomNjcWSJUuwcuVKjmvdxMQEU6dOxZ9//pnjdL3qbruKFSvCysoKz58/R2JiIrZv354tV25u265fv36YMWMG3r9/D6lUiuDgYE7eDFXj7OyMhQsXIjExEWFhYTh37hxat26tUhlWVlbYtWsXJkyYgDFjxiAyMhLAf5vCbd++Hc7OzpzcF0Kj7X1PGT8/P7oKp0aNGpxU9DklO/1vy5Yt1FCsW7cuChUqpPbxVpfaT2shhPD68vHxIYwfk5qaSsqXL08AEADEz89PaJXyRFpaGvHz8yMlS5ak15Tx6t+/P3n+/LnQKuYIPz8/qn/58uVJamoqr/IWLlxI5TVo0IDI5XJe5Y0bN47K69GjB6+yZDIZ2bJlCyldunSW34aTkxN5+vQpr/LzG+oeW2JiYoi5uTmVd/jwYV7lMYRl/vz5N8lP7vnMqBCQ3bt3005oaWlJEhMThVYp15w5c4bUqVMnyw2jQYMG5NKlS0KrlysSEhKIpaUlvZagoCBe5b1//54YGxtTeXx/b48ePaKyRCIRiY6O5lUeIYTExsaSv/76i5iYmHB+J0ZGRmTmzJkkJiaGdx3yA+oeW1avXk3lValShchkMl7lMYTlV0YFi6kQEOWlXi4uLjQ/gjbx5MkT9O7dG23atMHdu3dpfZkyZeDv74+rV6+iadOmwimYBwoUKAAXFxdaVk4gxAclSpTA4MGD1SavatWqsLe3B6B4uFizZg2v8gBFuvCFCxciMjKSM72TkpKCpUuXwsbGBn5+fpDJZLzrosuoc2yRyWScpdCTJ0+Gnh67teRbfmZtqOrFPBU/5tq1a9SyNzQ0JG/fvhVapRzx7ds3Mn36dGJkZMR54ixQoACZN28eiYuLE1pFlSCVSomhoSG9vmvXrvEqLzw8nMrS09Pjfcro2LFjVJ6pqSn5+vUrr/Iyc/nyZdKoUaMsHq7atWuT06dPq1UXXUHdY0toaCiVZ2FhoTN9n/FzmKdCA1F+khgwYABKlSolnDI5QCaTwcfHBzY2Nli+fDnd9RAABg8ejKioKMyfPx+mpqYCaqk6SpcuzYli5zs5Vc2aNdGuXTsAitwenp6evMrr0KED7OzsAADx8fHYtGkTr/Iy06RJE1y+fBkBAQGcXSzDwsLQtm1bODg40EyzjOyh7rFF2aM2duxYnen7jFzyM2tDVS/mqcjKq1eviIGBAbXub9++LbRK2eLkyZOkZs2aWZ4qGzduTK5evSq0erxx69Yteq0GBgbk9evXvMo7ePAglVe4cGHy/ft3XuX5+vpSeRUqVOA9IPVnxMXFkfnz55OCBQtyfl+GhoZk2rRp5Nu3b4LopU2oe2y5ffs2laWvr09evXrFqzyGZsA8FRqGl5cX0tLSAACtWrWiiY80lejoaPTs2RPt27enewgAit0Od+7cicuXL6NRo0YCasgv9erVo/k00tLS4OXlxas8e3t7mqQoJiYGW7du5VXe4MGDUaxYMQDAixcvsG/fPl7l/QxTU1PMmzcPkZGRGDJkCK1PTU3Fv//+C2tra6xfv572HUZW1D22KHvu+vTpo3OJ+xi54GfWhqpezFPBJT4+nlhYWFDrPiQkRGiVfsqXL1+IRCLhxBQAIAULFiSLFi0iCQkJQquoNkJCQuj1Fy1alMTHx/Mqz8vLi8qztrbmPZp+9uzZVF6zZs14lZVdrl27Rpo2bZrFM1ajRg1y/PhxodXTONQ9trx9+5YTU8V3vBFDc2BLSjUIb29v2gkrVapE0tLShFYpC6mpqcTLy4sUK1Ysy4A+bNgw8ubNG6FVVDtpaWmkYsWK9HtYv349r/JiY2NJkSJFqLz9+/fzKu/Nmzcc4/H69eu8yssucrmcBAYGcnIuZLy6d+9OIiMjhVZRY1D32DJ37lwqr0mTJrzKYmgWbPpDQ5DL5Rx34cSJEwXbjfNnHDt2DLVr18b48eM5G1s1b94cN27cwJYtWyAWiwXUUBj09fUxceJEWl61ahWvaabNzMwwZswYWuZ7ealYLOZspMZ3QGp2EYlE6NevHyIiIvD3339zggAPHDiA6tWrQyKR4OvXrwJqKTzqHluSkpI4O9zynQKcoUX8zNpQ1Yt5Kv7j8OHD1LIvVKgQ7wF4OeHRo0ekS5cuWZ4GraysSFBQEO/ZHbWBzFkDjxw5wqu8Fy9eEH19fSrv7t27vMq7efOmWgNSc4NUKiUjRowgIpGI8zstWrQo8fT0JCkpKUKrKAjqHls2btxI5ZUrV06w4F6GMDBPhYagvNRr1KhRGpGn/suXL5g0aRJq1KiBw4cP03ozMzMsWbIEjx49gpOTE0QikYBaagaFChXCqFGjaFm5PfmgfPnynP1G+PYe/PHHH2jevDkARUDqunXreJWXG0qXLo1Nmzbhxo0baNGiBa3/8uUL3NzcULt2bRw9elRADYVBnWMLIYQjz83NDQYG+WobKcav+Jm1oaoX81QouH//PiepkdB7HaSkpJDVq1dzAruQnq551KhRWpeMS108ffqU6Onp0e/rwYMHvMq7cuUKJ5X1u3fveJW3Z88etQak5gW5XE6Cg4M5sS4ZL3t7e/Lw4UOhVVQL6h5bTp48yUmY9uXLF17lMTQP5qnQAJSfMnv16oWKFSsKogchBIcOHULNmjUxadIkzlx0q1atcPv2bfj5+WlNMi51U7FiRfTs2ZOW+fYeNG7cmC7XTUlJwfr163mV17NnT1hZWQFQPP0HBATwKi8viEQiODo64uHDh3B3d+c8nR85cgQ1a9aEm5sbJzZIF1H32KLspRg+fDgsLCx4lcfQMn5mbajqxTwVhHz8+JGzgdL58+cF0eP+/fukY8eOWZ7qKlWqRPbu3cviJrLJuXPn6HdnYmJCPn78yKu8nTt3UnklSpTgfXOolStXUnnVqlXTmt/Fu3fvyJgxY7LEWxQpUoSsWrVKJ+Mt1D22REZGcr7bqKgoXuUxNBPmqRAYHx8fJCUlAeDOW6uLT58+Yfz48ahduzaOHz9O6wsVKoTly5fj4cOHcHBwYHET2aRFixaoV68eAEUUvK+vL6/yHB0daVKhDx8+IDAwkFd5I0eOhJmZGQDg0aNHnN+MJlOyZEn4+vri9u3baNOmDa3/9u0bJk+ejJo1a+LgwYOKtfQ6grrHFuVN57p16wYbGxte5TG0kJ9ZG6p65XdPRXJyMildujS17P39/dUqe8WKFaRw4cKcpws9PT0yduxY8v79e7Xpomts27aNfp9isZgkJyfzKs/d3Z3Kq127Nu/eg0mTJlF5nTt35lUWH8jlchIaGkoqV66cxTPXoUMHcu/ePaFVzDPqHlu+fPnCSaF+6tQpXuUxNBeW/EpA/P39aScsXbo07zcfQhQD6r59+4i1tXWWAbVdu3YkPDycdx10neTkZFKqVCn6vQYEBPAq7/Pnz5wBne8dPB8/fsyZRtDWoMekpCTy77//kkKFCmUxrF1cXMiHDx+EVjHXqHtsWbZsGZVXs2ZNrZkWY6geNv0hEIQQTtKi8ePHw8jIiFeZ4eHh6NChA3r27MnZ3dHGxgb79+/HiRMnULNmTV51yA8YGRlh/PjxtLxq1Spe3epFixbFsGHDOPL4pHLlyujRowcta0oyrJxibGyMqVOn4vHjx3BxcYGenmLIk8vl8Pb2ho2NDVasWMHZbVcbUPfYkpaWxtkxd/LkyWy6lPFjfmZtqOqVnz0VFy5cUFtA3/v374mzszNnuSPSg9Q8PDzU4iHJb3z48IEYGxvT7/rChQu8youIiOAs/Y2OjuZV3tmzZ6m8AgUKkE+fPvEqTx3cu3ePtG/fPosHz9ramoSGhmrN07c6xxZCCNm1axeVV7x4cd6DhRmaza88FSxjCY8oP0kMGTIElpaWKpeRnJyMNWvW4O+//8b3799pvZ6eHrp27YpBgwahcOHCOH36tMpl5xZzc3M0a9ZMaDXyTPHixTFkyBD4+fkBUHgPchsod+nSJcTGxv72uAYNGuDGjRsghGDq1KlwcXHJlbzsQAhBpUqV8PTpUyQmJmLatGkYPXq0VrddjRo1cPz4cRw6dAhTp05FVFQUAODx48fo1asX2rRpAw8PD9SuXTtH581u+6mKv//+m75v3bo1bt68ma3/y23fU/aMubi4wMTEJMfn0FTU3Xa5RVvGTWZU8MSzZ88QGhpKy5MmTVLp+QkhCAkJwfTp0/H06VPOZ02aNIFEIkGlSpVUKlNVfPz4UWgVVMakSZOoURESEoLnz5/TPA85ITY2FsWLF//tccOGDcONGzcAACdOnIBEIuE1e+LQoUMxf/58AMChQ4c4GT61FZFIhG7duqFjx47w9vbG/Pnz8e3bNwDAmTNnULduXYwePRqLFi1CyZIls3XO7LafKnjz5g2uXLlCyyNGjMi27Nz0vWvXrlF5RkZGvBqyQqDOtssL2jJuspgKnvD09KQbTnXs2BHVq1dX2bnv3LmDNm3awNHRkWNQlCtXDqtXr4anp6fGGhS6Ro0aNdChQwcAinl65XlnPmjUqBFt28TEROzbt49XeR07dkSxYsUAKAa1Cxcu8CpPnRgZGWHSpEl4/PgxJkyYQDfgIoRgw4YNsLGxwdKlS+mSTU1h165ddGxp3LgxKleuzKs8ZS/FgAEDWGI8xi9hRgUPfP/+nT69AoqgJlXw7t07jBo1Cn/88QfOnTtH6y0sLLBmzRqsW7dOK9xjuoZy+/r5+fHqShWJRBg4cCAt79q1C2lpabzJMzIygpOTEy2HhIToVJ4HAChWrBg8PT0RHh4Oe3t7Wh8bG4s///wTdnZ22LNnj0Zcd1xcHMeQHDBgAK/yXr16haCgIFpW1VjG0F2YUcEDW7ZsoTeWKlWqoFOnTnk6X1JSEpYsWQIbGxts2rSJDm4GBgb0SYtt6iMcnTt3RpUqVQAoDMrNmzfzLq9IkSIAgLdv3+Ls2bO8ynN0dKQrC6Kjo3H58mVe5QmFnZ0dDh8+jMOHD6NatWq0/tmzZ3ByckLr1q1x+/ZtATUEDh48iPj4eABAhQoV0KRJE17leXl5QSaTAVDEbtSpU4dXeQzthxkVKkYmk3GW302ePJkuY8sphBAEBQWhatWq+N///oe4uDj6WdeuXXHv3j2sWrUKRYsWzbPejNyjp6fHiZlZs2YNHYj5wMTEhBPbsHPnTt5kAYrlrJ07d6ZlvpezCo29vT3CwsKwdu1aTt86f/486tevj5EjR+Lt27dq10smk3HaesCAAbkeW7JDfHw8J1ss81IwsgMzKlTMwYMHaZyDhYUFhgwZkqvz3Lx5Ey1btkTfvn3x4sULWl+9enUcO3YMBw8eRNWqVVWiMyPvDB06lG6s9OTJExw6dIhXeX369KGeqbCwMDx48IBXecpTLnv37uX8JnURQ0NDjB8/Ho8fP8bkyZPpd00IwebNm2FjY4PFixcjMTFRbTpduHABb968AaBIsd+1a1de5W3bto1uOFi5cmV069aNV3kM3YAZFSpGeRnp2LFjYWpqmqP/f/PmDYYNG4YGDRrg4sWLtL5YsWJYt24d7t69i44dO6pMX4ZqMDU1hbOzMy0r/w74wNLSkvM74NtbYW1tjYYNGwJQT0CqpmBhYQEPDw/cv38f3bt3p/Xx8fGYPXs2qlatil27dqkl3mLHjh30fe/evVGgQAHeZMnlco7HdeLEiTSQlcH4FcyoUCF37tyhAZT6+vqcjIu/IyEhAYsWLYKtrS22bdtG6w0NDTkZAVnchOYyfvx4OvCePXsWd+/e5VWesvfgxIkT+PDhg9rk8R2QqmlUqVIF+/fvx/Hjx1GjRg1a//LlS/Tv3x/Tpk3j1VsUERFB4zn09fXRp08f3mQBwNGjRxEZGQlA4RUZMWIEr/IYugMzKlSI8lxznz596M6Sv4IQgp07d6Jq1aqYO3cuEhIS6Gc9e/bEgwcP8O+//9LAPIbmUq5cOc5gz3fsQdWqVeluqTKZjBOlzwdNmzZFmTJlAAAxMTHYsmULr/I0kQ4dOuDOnTvw9vbmJLN7+PAhhg0bhrlz5/Ji3Cl7otq1a5ft/Bm5Rfm3O3r0aF5zoTB0C2ZUqIh3795xOr5EIvnt/1y7dg1NmzbFwIED8erVK1pfq1YtnDp1CqGhoWxrYS1DOZht586dePfuHa/ylJcU7t27l9ecCnp6eujZsyctr169muZLyE8YGBhg3LhxiI6OxrRp02BoaEg/O3z4MHr37o0NGzaorC0+ffqEY8eO0bKyx4gP7t+/jxMnTgBQtLmbmxuv8hi6BTMqVIS3tzdSU1MBKDJaZsw//4hXr15h8ODBaNy4Ma5evUrrixcvDl9fX9y+fRtt27blXWeG6mnUqBEaN24MAEhJScH69et5ldeyZUuO9+Dw4cO8ymvfvj31mqkjIFWTKVKkCJYvX46HDx9ylnYmJSXBx8cHjo6OOHLkSJ4Nrz179tBcJLVq1eJMv/CBciyFg4NDrjLEMvIvzKhQAUlJSfD29qbln3kp4uPjMW/ePFSpUgXbt2+n9UZGRpgxYwaio6MxZswYFhCl5Si3/7p163j1Hujr66Nfv360vHPnTl6DBgsUKIAxY8bQMt8BqdqAtbU15s6di/Xr18PW1pbWv3//Hn/99RdGjhyJe/fu5ercycnJCA4OpmW+vRQfP36Ev78/LbNlpIycwowKFbBjxw6al718+fJwcHDgfC6Xy+Hv7w9bW1ssXLiQswzN0dERjx49wtKlS1G4cGG16s3gh969e6NcuXIAFIM03yszevToQVcZPXv2jOP94gPllNZnzpxBWFgYr/K0hfr168Pf3x9z5szh5Le4f/8+RowYgTlz5uR4Ouzo0aN0WWepUqXQunVrVaqcBR8fHyQnJwNQXA/L0MvIKcyoyCOEEE5QU+bMlpcvX0bjxo0xdOhQSKVSWl+3bl2cPXsWwcHBbJ8OHcPAwIAzD+3h4cGr98DMzAw9evSgZeWlh3xQvnx5TvItXU+GlRP09fXRq1cv7N27F8OHD+fEWxw9ehSOjo5Yv349JyD7Z2QEcWfQr18/Xld/paSkwMvLi5YnT54MkUjEmzyGbsKMijxy+vRp6to0NTXFqFGjAAAvXrxA//790axZM7qrJKB42ti4cSNu3LiBVq1aCaIzg39Gjx6NggULAgDu3buHM2fO8CqvX79+9AZw5cqVLDvXqhplt/iOHTvw/v17XuVpG2ZmZpgwYQKCgoLQrl07Wp+cnAw/Pz84Ojri4MGDv4y3uHHjBh4/fgxAMe2kHCTLB7t27aKelNKlS/O+bJWhmzCjIjc8eQK4ugKFCmFV+/a0eriDAwwNDTFnzhxUqVIFu3btop8ZGxvjf//7H6KiojBy5EiNjpswev0a5dzdUadVK9Rr0AB1WrVCOXd3GL1+LbRqWoOFhQWGDx9Oy3zHHpQtW5ZjpJ4cOJDXtmvSpAkaNWoEQPGEqxxTxPiPsmXLYunSpfD19eVkwP348SPmz5+P4cOHc/KZKPe9w66utL5nmzYoVKgQb3oSQji/0QkTJtD9XhjZg42bCphRkVOOHAFq1QL8/BAVG4uDSh+VDQyETfny+Oeff+i8JKB4ioyIiMA///yj8eu9C126BLv+/WEZGgr9+HiICIF+fDwsQ0Nh178/Cl26JLSKWoPyfiAHDx5EdHQ0r/LG1KxJ3/unpeELz22n7K3w9vbWuC3CNYl69eph27ZtmDdvHt1KHlDktxg9ejRmzZqF7/v30773JD6eM7bMO3mS17538eJF3LlzB4Bibxnl7LCM38PGzf/IllEhFktGi8WSm2Kx5ItYLEkRiyVSsVhySCyWtOBbQY3iyRPAyQlISABSU7FG6aPCAGalpeFdelAVoAh0unDhAgIDA7ViWZbR69eoNHMm9JOSoJdpO229tDToJyWh0syZ+c7yzi22trac/RnWrFnzi6PzhtHr1+jr64u66eVEABlbQfHVdo6OjjTB24cPHxAYGKiyc+sienp66N69O0JCQjBy5EgYGxvTz06cOAH7hQvxV1IS4tPSOGNLNwBVU1J47XvKXoqhQ4dyEnsxfg0bN7lk11PRDEA5AC8BRAAoDqALgONiscSKH9U0kBUrgPRcFF8BbFT6KEbpvdjUFFu3bsW1a9fQvHlzdWqYJ0oGBECU3ikuAPD5wTGitDSUVFoOy/g1ystLN2/ejG/fvvEip2RAAPRkMigvZv4XQJxSWdVtZ2hoiAkTJtAy3wGpukLBggXh6uqK4OBgzv4tyQCWALAGsEHp+Iw25avvPX36FKGhobSs7GFj/B7lcRMALkPRfnz2PU0mu0aFi1TqUVIq9agjlXrUAjAuvd4EwB/8qKaBBAQAqamIA9AbQGZnrwmAuQCi9PQwdOhQXrcl5oNiR45QS3sYFI1sCWCv0jF6aWkoxnOCJV2ibdu2NFlRfHw8/Pz8eJGT0XZ9ofCaAcAXADOVjuGj7caMGUMDUsPDw3H27FmVnl+XKV26NBYvXoyNGzeivtJY8QFASvr7SgDapL/nq+95enpSY7BTp06ws7NTuQxdRnncvAGgHQBnAI0BZKz3y0/jZrbuelKpR5JYLGkpFkuuisWSewAyorKSANzMfLxIJHIWiUQ3RSLRzU+fPqlQXYGJU9ieyQDOZ/poIIBIAAsAmMbHq1cvFaGXvsztEIBn6XWfAfSF4mnpa6bjGL9HJBJxYg88PT1pdkRVktEmxuBa+VsAyH9wnKooWrQohg0bRsssGVbOqV27Nq7K5fAHUCbTZ3UBKC/qVHX7ff/+HRs3/udzZcmuco5eQgKkAEYAaIT/HjYfAPiW6bj8QE4epYtC8Z3VAGAIhUHdUSr1eJH5QEKILyGkPiGkvk7NzZmZAQAMAGQEkxgAOAtgO4DymY7TNuTpT5xHMtXLAKyCwi27FkAyj1su6yKDBg2ic9QvX75ESEiIymVktB2gaKMMEgCc+8lxqkLdAak6iakpBgO4DqBBepUegMx+LVW336ZNm+hus1WrVuVMxzB+T1JSEhYZGMAWCgNeefLvDwBipTIffU8TybZRIZV6hKYfXxqKcasEgB1isaT8r/5Ppxg8GDA0RGEAJwH4AzgDgJNtwtAQGDJECO3yzGd7e8Tq60N55q+60vsvANwA1NbXx+XLl9WrnBZjYmICFxcXWuYjWdRne3vI0xMjVQNgr/RZRjojuYEBPnfponLZVapUQZf08xJC4OnpqXIZuk5G+4mhMCzuAVgHoIjSMapuP5lMxgkenjx5stZN2QoFIQTHjh2Dk5MT5qWmIrNvWgRgD/5rP776niaSo1+QVOpBpFKPdwBmp1eVxX/xFbrP1KkKowEKD8VgAFnCMA0NgWzsUKqJvB88GNtEIuqyqwwgDIrOUVHpuKjYWEycOBETJ07kPcmSruDq6kqzK16+fBnXr19X6fnfDx4MopRtcZnSZyEAngMgBgZ4P2iQSuVmoOw237RpE28BqbpK5varAWBspmNU3X4HDhzAs2eKic6iRYtiiJY+DKmb+/fvY9SoUZg9ezYn7bqF0jFOACoolfnse5rGb40KsVhSUCyWjBGLJco+7+5K701Vr5aGUrkyEBwMFCxIjQuKoaGiPjhYcZwWkiQWY4XSngWTAOhDEZR6X18fSwwMYKq0DO7y5csYMGAAli1bxm4iv6FUqVKcbcpV7a1IKVsWT5cuhczEBHIDA9QAkJGWTQ5gtb4+ni5dipT0JaCqpn379qheXeHXio+P58zTM35P5vZTRm5gAJmJicrbTzn+ZezYsTTglvFjMjaIGz58OMLDw2m9hYUFZvTowfFWTE7/y1fbaTLZ8VQYQbHk/atYLHkgFkseAwhI/ywVQP5YJ5OBvT0QHg44OwOFCgF6eoq/zs6Kenv7359DQ7l8+TKeffgAAChkaIihBQuCiESQmZoirndvdA0Oxt79++Hg4EDdpDKZDLt374aDgwN27NhBt39nZEX5aT4oKAivVbxu/XuzZngYGIhPDg6QmZrSgQ0A/IyN8bZOHZXKUyZzQOqaNWt4CUjVZTK3X0bf++TggIeBgfiuws29bt++jfPnFeHmBgYGcFXK3sngkpSUBF9fX/Tu3RtHjvwXcWZgYIAhQ4YgJCQEMaVL0xU7DfT00Bjgre00newYFUlQGBGvAFhB4dV5C8VKwxZSqYdq/bjaQOXKwNq1QEwMIJMp/q5dq7UeigyUN6Lq0a8fHp8/j9s3buDuuXN4NXMmUsqWRbFixTB79mwEBASgfv369PjY2FisXLkS/fr1w4ULF1i+gh9Qt25dmko7LS2Ns3mTqkgpWxavZs7E3XPnUOL6dZQvrwh5iktIwIEDB1QuT5nMAanKuQ8Y2UO5/TL3PVWi7Cnr06cPTWLG+A+5XI7Dhw/D0dERvr6+nCzJbdq0QXBwMCZNmgQjIyPO9vQ9Fy7EnZs3eWs7Tee3RoVU6pEklXoMkUo9bKRSD1Op1MNQKvUQS6UejlKpxzV1KMngn8ePH9N5fj09PfTt2/eXx9va2sLb2xv//vsv3eYbUNxMJBIJxo8fTzdDYvyH8tO8j49PtnarzC16enqcKZfAwEDIZDLe5BUoUADjxv0XYsV2L9VM3r59y8l+ypaRZiU8PBwjRozA3LlzOZvl2draYv369Vi+fDk1xI4fP47Pnz8DAEqUKIH2SvtB5UdYqC8DADhbLLdp0wZisfgXRysQiURo3bo1du/ejcmTJ8NMaSnt9evXMXDgQCxZsgRflVKX53e6d+9Ot7r/+vUrtm3bxqu8bt260f1mXr9+jYsXL/IqTzkg9dKlS5wdehmagbe3N52mbNq0KRo2bCiwRprDu3fvMHv2bIwcORIPHjyg9cWKFcOcOXPg7+/P8dASQjge3r59+/K6Pb02wIwKBr5+/cqZK1R+us0OhoaGGDx4MEJCQtCnTx8abyGXy7Fnzx706tUL/v7+SElJ+c2ZdB99fX1MnDiRllevXv3L7a/zSoECBeDg4EDLygMgH5QuXRr9+/enZeat0CySk5M5O8pKtHSlmqpJSEiAt7c3HB0dcezYMVpvZGSEESNGYO/evejVq1eW3aVv3bqFqKgoAIqdqJX7Wn6FGRUM7Nmzh97w7ezsULt27Vydx8LCAjNnzsSOHTvottiAYjXA6tWr0bdvX5w9ezbfx1uMHDmSbmMdERHBGcT4oG/fvnQwvHXrFiIjI3mVp+xO3717N968ecOrPEb2OXv2LDKyHFeoUAG9evUSViGBkcvlOHHiBHr37o2NGzdy4ibat2+PoKAgjB8/HqamP17kqOzh7datGwoXLvzD4/ITzKjI56SkpCAoKIiWBw4cCJFI9Iv/+D3W1tZYu3YtVq1aRQMFAYX7fdq0aZg1axbCwsLyJEObMTc3x6hRo2iZ76f5UqVKoW3btrSsPBDyQb169dCyZUsA/AWkMnIOIYSTzdXNzS1fu+ovXLiAhg0bYuXKlVDeTqJatWrYsGED3N3dUaZM5sTp//Hq1Su6ggbIuYdXV2FGRT7nxIkTNMioePHiKgsyEolEaN68OXbv3o2pU6fSJ3MACAsLQ926dTFmzBhOEFR+ws3NjU4THT9+HC9eZMl2r1IGKSXeOXbsGPjek0edAamM7HH9+nX6OzM1NeUYtvmJZ8+eoW/fvmjZsiVu3bpF6y0tLTF//nxs3boVdevW/e15du3aRb2uTZs2hZWVFV8qaxXMqMjHqCPIyMDAAAMGDMDevXvRr18/6oYnhMDPzw82NjZwd3dHUlLmPV91m4oVK3Jcz3wvv6xRowZq1qwJAEhNTcWePXt4ldejRw9UrKjIw/rlyxf4+/vzKo/xe5T7+ogRI1CkSBHhlBGA79+/Y9asWahWrRrHO2tkZIRRo0Zh79696NatW7ZSlcfFxWH//v20PHDgQF501kaYUZGPuXPnDp1f5zvIqEiRIpg+fToCAwPRoEEDWh8bG0s7enBwcL6Kt1B+mj99+jTvWUmV3bPBwcGc+WNVkzkgddWqVbwGpDJ+zfPnz3Hp0iUACi+ictvoOjKZDBs3boStrS3c3d05v/sBAwZgw4YNcHFxyVFG0dDQUOp9q1SpEieGLL/DjIp8jPLceteuXdXy5FKxYkUsXLgQR44cQbVq1Wj98+fP0adPH7Rq1YrjktRlmjdvjj/+UGxUnpKSwrv3oG3btihZsiQAxYofvgNER44cSZezRkRE4Pjx47zKY/ycXbt20ffdunWDjY2NgNqoj7Nnz6J+/foYPXo0Z6q1YcOGuHTpEnbs2IESJUrk6JxpaWmc73PAgAF5jkPTJZhRkU95/fo1zp49S8vqDjLq3LkzwsPDsXbtWhQrVozWX7hwAQ0aNMCIESMglUrVqpO6yZzaOigoiNc05wYGBpykZjt27ODVM1SoUCG1BqQyfkxMTAwnm2p+WEb65MkT9O7dG23atMHdu3dpfZkyZRAQEIArV66gadOmuTr3uXPn8PbtWwBA4cKFYa/FWzPwATMq8imZg4wy5r/ViYGBAcaPH4/o6GhIJBIaz0EIwZYtW2Bra4t//vkHiYmJatdNXfTt2xelS5cGAHz69AknTpzgVZ6DgwNMTEwAKLKo3rx5k1d5bm5u9Cnu2LFjePjwIa/yGFkJDQ2lMUsVK1ZE69athVWIR2JiYjBjxgxUq1aNs9KlQIECmD9/PiIjIzFo0KA8bfGu7OF1dHSk/YmhgBkV+ZDMQUZCL4WysLDAypUr8eDBA3Tv/t8GuPHx8ZgzZw6qVq2KwMBAnYy3MDIywvjx42lZHd4D5e+Y72RYlSpVQs+ePWl59erVvMpjcElLS8Pu3btpuVevXjrpqk9LS4OPjw9sbGywfPlyjsdvyJAhiIqKwrx5836abyK7PHz4kHo+9PX10adPnzydTxdhRkU+ZP/+/YiPV2zUW7FiRTRu3FhgjRTY2tpi//79OHHiBGrUqEHrX758iQEDBqB58+Z0fxJdYuzYsTAyMgKgiD3gO4dHv3796PsLFy7g5cuXvMpTdrdv27aNLmFm8M/p06dpLEHRokV10ktx8uRJ1KtXD+PGjcPHjx9pfZMmTXDt2jVs27ZNZRumKXspOnbsiOLFi6vkvLoEMyryGTKZTOODjNq3b487d+5g/fr1dNdLQLE1e6NGjTB06FCVbxsuJJaWlpzkVNu3b+dVnpWVFZo3b07LyptL8UGLFi3ouv+kpCT4+PjwKo/xH5ld9RnGqy4QFRWFHj16oEOHDrh37x6tL1++PAIDA3Hp0iWV7mvy8eNHTrAxW0b6Y/JvOjUdxNzcnGOp/4hLly7RtMnm5uZo0KDBb/9H1WSsCPgVBgYGGDt2LPr3749//vkHq1atoi5Nf39/BAcHY+bMmZg+fXqOloJpKgMHDsTRo0cBKALB7t27h1KlSvEmr0uXLnRzsf3796NPnz6cDeF+RnbaLjMikQgSiQRDhw4FAHh5eWHatGk6dYPLTt9TN48ePaI3WwMDA7Rp0yZX7adpfP36FYsWLYKnpyfS0tJovampKWbNmoUpU6agQIEC2T5fdttuy5YtdJff6tWrw9LSUq1trjVtRwjh9eXj40MYmkPLli0JAAKA/O9//xNanWwTHR1NHBwcqO4Zr7JlyxJ/f38ik8mEVjHPdOjQgV6XRCLhVZZcLic1atSg8pYvX86rvOTkZFKqVCkqLyAggFd5DEL69etHv+/hw4cLrU6eSU1NJWvXriXFihXLMg4MHz6cvHnzhjfZ8fHxHLl79uzhTZY2MH/+/JvkJ/d8ZlTkI27dukU7hYGBAXn9+rXQKuWY06dPk9q1a2cZVBo2bEguX74stHp54vDhw/R6zM3NSUxMDK/y/Pz8qLzy5cuT1NRUXuUtWrSIyqtfvz6Ry+W8ysvPvHjxgujr69Pv++7du0KrlCeOHj1K7OzssvT7Fi1akJs3b/Iu38fHh8q0srIiaWlpvMvUZH5lVLCYinyEcp6Avn37/nKzHE2lTZs2uHXrFvz8/GgiJ0Cxr0HTpk0xcOBA3gMP+aJTp06oUqUKAEWm0S1btvAqb+DAgTRm5eXLl5wleHwwduxYGBsbAwBu3ryJy5cv8yovP+Pl5UVd9W3atMn1zsNC8+jRI3Tp0gWdO3fmLEe2srJCUFAQzp07RxPI8QUhhDN2urm5ZdkCnaHEz6wNVb2Yp0IzkEqlxNDQkFrbN27cEFqlPBMTE0P+/PNPYmRkxHl6MTExIXPmzCGxsbFCq5hjvL296XVUqlSJ9yeiv/76i8pr2rQpr7IIIWTUqFFUnqOjI+/y8iOxsbGkSJEi9Hvet2+f0CrlmE+fPhE3NzeOtwUAMTMzI+7u7iQxMVFtuhw7dowj/9u3b2qTramw6Q8GmTNnDu0YzZo1E1odlfL06VPSp0+fLK7R0qVLky1btmhVvEVcXByxsLCg1xAaGsqrvMzG5rVr13iVd+/ePSpLT0+PPHv2jFd5+REvLy/6HVeuXFmrfv8pKSlk1apVnD4AgIhEIjJ69Gjy9u1btevUuXNnqsfEiRPVLl8TYUZFPichIYFYWlrSjhEcHCy0Srxw/vx5Uq9evSzGxR9//EEuXLggtHrZ5s8//6S6t2rVind5Q4cOpfIGDBjAu7z27dtTeVOmTOFdXn5CJpMRW1tb+v2uWbNGaJWyhVwuJwcPHiRVqlTJ0n9bt25N7ty5I4heDx8+5Bg2jx8/FkQPTYMZFfmcDRs20I5RoUIF3gPyhEQmk5EtW7aQ0qVLZxmc+vTpQ54+fSq0ir/l1atXHLfv7du3eZV3+/ZtTgDvq1eveJV36NAhKq9QoULk+/fvvMrLTxw8eJB+t4ULF9aKKcB79+5xVj4pe1lCQkIEDegdO3Ys1adnz56C6aFpsEDNfAzJFGQ0ceJEuseGLqKnp4dhw4YhKioKc+bM4eTlDwoKQrVq1TBr1izExsYKqOWvKVu2LCf9L9+prevWrYtWrVoBUKQ79vLy4lVe586dYWtrCwD4/v077wGp+Qnlvj569Ohs5R4Rio8fP8LV1RW1a9fm7HlTqFAhLF++HA8ePBA0rfjnz5+xbds2Ws4PG7GphJ9ZG6p6MU+FsBw/fjxfBxm9ePGCDBgwIMtTUMmSJYmfn5/GLg27du0a1dXIyIj3ueSQkBAqz8LCgsTHx/MqL/O8v6a2gzYRHh6uFfEqycnJZMWKFaRw4cKcPqmnp0fGjRtH3r9/L7SKhBBClixZQnWrU6cOWwKtBJv+yMd06dKFdgw3Nzeh1RGMS5cukYYNG2YxLurUqUPOnDkjtHo/pEmTJlTPuXPn8iorLS2NVKpUicrz9vbmVV5cXJzWr1DQNJRX1jg5OQmtThbkcjkJDQ0l1tbWWfph+/btSXh4uNAqUlJSUkiZMmWoflu2bBFaJY2CGRX5lEePHnGCjKKjo4VWSVBkMhnx9/fnDBYZLwcHB40Lwtq9ezfVr3jx4rwvo1u1ahWVV7VqVd5XDcyYMYMTjMfIPR8+fCDGxsb0+7x48aLQKnEICwsjbdu2zdLvbG1tyYEDBzTOC7Bjxw6OVzMpKUlolTQKZlTkU1xcXGjH6NGjh9DqaAxxcXFk/vz5pECBApwBzsjIiEyfPl1jpohSU1NJuXLlqH4bN27kVV5MTAwxNzen8g4fPsyrvJcvX3ICUoWK8NcFFi5cSL/HBg0aaMxN+t27d2TMmDFET0+P09eKFClCPDw8SHJystAqZkEul5MGDRpQXRcsWCC0ShoHMyryIZ8/fyYFCxakHUNTXfxC8urVKzJ48OAsT0/Fixcn69ev14hVMsuWLaN61axZk/ebhUQiofI6duzIqyxCdG9/CiFISkri7Kuyfft2oVUiSUlJZOnSpRwjFQDR19cn48ePJx8/fhRaxZ9y6dIlqq+xsbHGxHhoEsyoyIe4u7vTjlG7dm2NeXLRRK5du8aJX1C+iZ88eVJQ3b58+UJMTU2pTnzr8/TpU85T5f3793mVd/XqVbUGpOoiW7dupd+hWCwmKSkpgukil8tJcHAwqVixYpb+1LlzZ/LgwQPBdMsuTk5OVOeRI0cKrY5GwpaU5jNSU1Oxdu1aWp48ebJgy7K0gYYNG+LSpUvYuXMnypUrR+vv3buH9u3bo0ePHoiKihJENwsLCwwfPpyWlZcM8kHFihXRs2dPWuZ7OWujRo3QuHFjAEBKSgrWr1/PqzxdgxDukvEJEybA0NBQEF1u376N1q1bw8nJCc+ePaP1VatWxeHDh3HkyBHY2dkJolt2efHiBfbu3UvLkydPFk4ZbeVn1oaqXsxToX527txJLe0SJUqwIKMckJCQQBYtWsTxDgCKpFASiYR8+fJF7TpFRUVxdImMjORV3vnz56ksExMT3l3VgYGBag1I1SXOnj1Lv7sCBQqQT58+qV0HqVRKRowYQUQiEed3WrRoUeLp6Smo5ySnTJs2jerfrl07odXRWNj0Rz6jUaNGtGPMnz9faHW0kjdv3pDhw4dnceEWK1aMrF27Vu3xFt26daM6jB8/nldZcrmck+7877//5lVe5oDUTZs28SpPl+jVqxf93saOHatW2QkJCeSff/75oQE+adIk8vnzZ7Xqk1diY2M5uTMOHDggtEoaCzMq8hFXrlzhzFGzIKO8cfPmTdK8efMsxoWdnR05evSo2vQ4efIklV2wYEHePSb+/v5UXunSpXmP0l+6dKlaA1J1gSdPnnC8Aw8fPlSLXLlcTgIDA0mFChWy9Itu3bqRiIgIteihajw9Pel12NjYaNVGbOqGGRX5iL59+9KOMWLECKHV0QnkcjkJCgoiVlZWWQZRe3t7tQzmcrmc1KxZk8pdtmwZr/KSk5M5+6f4+/vzKu/Lly+c1UqnTp3iVZ4uMGnSJE4QpDq4fv06adasWZZ+UL16dXLs2DG16MAHMpmMk5Rr7dq1Qquk0TCjIp/w4sULzrr/sLAwoVXSKRITE8mSJUuImZlZlmVybm5uvM9nb9y4kcosV64c71Mwf//9N5VXr1493r0Hrq6uVF737t15laXtZM4pwrfX7PXr15zdbDNelpaWxNvbWyOWX+eF/fv302sqUqSIVmzEJiTMqMgnTJ8+nXaMtm3bCq2OzvL27VsyevToLIFpFhYWZPXq1bwFpiUmJpLixYtTebt27eJFTgYfPnwgJiYmVB7f28dHRERQWSKRiERFRfEqT5tZuXIl/a6qVavGm8EXHx9PFixYwPEiASCGhoZk6tSp5OvXr7zIVTfK2T6nT58utDoaz6+MCt3drvIHXLp0SaN3p8zA3NwczZo1y9H/xMXFYcOGDbSsa0uhNK3tHB0dUadOHfj6+iI8PBwA8PXrV0yaNAnr1q3DihUr0KVLF5Uu5TUxMYGLiwsWLlwIQLG8tG/fvio7f2aKFy+OwYMHw8/PDwDg4eGB5s2b5+pc2W2/hg0b4vr16yCEYOrUqXB1dc2VvNySm76nbmQyGdasWUPLfCwZl8vl2LlzJ/7880+8fv2a81mTJk0watQolClTBlevXlWp3LySm/YLCwvD6dOnAQD6+vqYMGECH6rlG/KVUREbG4vixYsLrcZv+fjxY47/Z+vWrfj27RsAwNraGl27dlWxVsKiiW1XvHhxNGrUCOfOncOqVavo4BsZGYlu3bqhY8eOWLlyJapXr64ymS4uLnB3d0dKSgquXLmCa9euoVGjRio7f2YmT55MjYrQ0FA8e/YMFStWzPF5stt+w4YNw/Xr1wEAJ0+exJQpU2Bubp5jebklN31P3ezfvx/Pnz8HABQtWhSDBw9W6fmvXr2KyZMn49q1a5x6GxsbTJkyBQ0aNFCpPFWSm/ZTzsXi6OiI8uXLq1KlfAdLfqUDyOVyTseYNGkS9PRY06oDkUiE1q1bY/fu3Zg0aRIKFixIPzt+/Dhq1aoFV1dXld2sSpUqhQEDBtAy38mwqlevjg4dOgBQ/M48PT15ldewYUNUrlwZAJCYmIjQ0FBe5WkjHh4e9P24ceM4v7m88OrVKwwaNAhNmjThGBQlSpTAxIkTERAQoNEGRW54//49tm/fTsu65uEVAnbn0QGOHDmC6OhoAEDhwoU5GRgZ6sHIyAhDhgzBxo0bMXbsWGrUyeVyeHt7w8bGBitXrkRKSkqeZSkPfEFBQVnc06pGIpHQ935+fvj+/TtvskQiEcdo2rVrF9LS0niTp23cunULFy5cAAAYGBioZHooLi4Oc+fORZUqVbBjxw5ab2RkhJkzZyI6Ohr29vbQ19fPsyxNY/369bRPNmrUCE2aNBFYI+2HGRU6gPKTy5gxY2BmZiagNvmbIkWKYP369bh79y7atWtH62NiYjB16lRUr14d+/btU0RJ55I6deqgdevWABTz68op2fmgU6dOqFKlCgDFNMaWLVt4lWdvb48iRYoAAN69e4ezZ8/yKk+bUPZM9evXD2XKlMn1ueRyObZt24YqVapg0aJFSExMpJ85Ojri0aNHcHd3R6FChfKissaSnJyMdevW0TLzUqgGZlRoOffu3cOpU6cAAHp6eizISEOoWbMmTpw4gf3798PGxobWP378GL169UL79u1pgGduUB4AfX19ER8fnxd1f4menh4mTZpEy6tXr4ZMJuNNnrGxMZycnGhZ+ek5PyOVSrFr1y5azstN8NKlS2jUqBGGDRsGqVRK6+vWrYuzZ88iODgYlSpVyou6Gs/OnTvx4cMHAEDZsmXh6OgosEa6ATMqtBzlJxdHR0dUqFBBOGUYHEQiEbp374779+/Dw8ODPn0DwOnTp1G3bl04Ozvj/fv3OT53t27daOzB169fsW3bNlWp/UOGDh0KCwsLAMDTp09x8OBBXuU5OTnBwEARRx4eHo779+/zKk8bWLduHVJTUwEAzZo1Q/369XN8jufPn6Nfv35o3rw5bt68SetLlSqFTZs24caNG2jVqpXKdNZUCNGcjdh0DWZUaDEfPnxgQUZagJGRESZPnozo6GiMHz+ezk3L5XJs2LABNjY2WLZsGZKTk7N9Tn19fUycOJGWV69eDblcrnLdMzA1NYWzszMtK0+58YGlpSU6depEyzt37uRVnqaTmJjI2cFVOc4lO8TGxmL27NmoWrUqdu/eTeuNjY0xe/ZsREVFYcSIEToZN/Ejzp49i7CwMABAwYIFMWbMGIE10h2YUaHF+Pj40BtRw4YNWZCRhmNpaYm1a9ciPDycc8OMjY3FzJkzYWdnhz179mQ73mLEiBF0vjsyMhLHjh3jRe8MJkyYQG86586dw507d3iVpxywefLkyVx5dHSF7du34/PnzwCAChUqcLan/xUymQybNm2Cra0tFi9ezDFc+/Xrh4iICPz9999qXbarCSh7KYYNG4aiRYsKp4yOwYwKLSU5ORleXl60zEcCHAY/2NnZ4ejRozh8+DCqVq1K658+fQonJye0bt0at2/f/u15zM3NMXr0aFrm23tQtmxZ9OnTh5aVlzHzQdWqVVGvXj0AiptjUFAQr/I0lcyu+okTJ9KpoV9x7tw5NGjQAKNGjcK7d+9ofYMGDXDx4kUEBgbCysqKB401m8ePH+PAgQO0rOzxY+QdZlRoKbt27aJPbmXKlOEEtjG0A3t7e4SHh8PT05PzpHT+/HnUr18fo0aNwtu3b395Djc3N7p89cSJE3jw4AGvOitPse3cuZNzs+KDgQMH0vd79+5FUlISr/I0kZMnT9J2NTMzw6hRo355/NOnT+Ho6IjWrVtzvElisRjbtm3D1atXNT5rKJ+sWbOGegPt7e05hj0j7zCjQgshhHCeSlmQkfZiaGiICRMmIDo6GpMmTaJPoIQQjttaebmfMlZWVnBwcKBlvpNhKa/lT0lJgbe3N6/yWrRoQZdNfv/+nfcAUU1Eua+PGDEChQsX/uFx379/x8yZM1GtWjXs3buX1hcoUABz585FVFQUhgwZkq8T43379g2bNm2i5ZzGpjB+T/79dWkx58+fx927dwEoBgzlADqGdlK0aFGsWrUK9+/fR7du3Wh9XFwcZs+ejWrVqmHXrl0/jLdQ9h74+/vznmpaWZ63tzev3gN9fX3079+flgMDA3kNSNU0IiIicOTIEQCK1UQ/ctXLZDL4+vrC2toay5Yt4yRYGzRoECIjI7FgwQKYmpqqTW9NZePGjXT5dfXq1dG+fXuBNdI9mFGhhSg/ubAgI92iSpUqOHDgAI4dO8bZM+TFixfo378/WrRogRs3bnD+p1mzZvjjjz8AKGJtfH19edWxd+/eKFeuHADFXgt855Ho3r07vSE+f/5c4zax4hPljcO6d+8Oa2trzuenT59GvXr1MHbsWI4x2ahRI1y5cgUBAQG0rfI7aWlpnDTzLA6NH5hRoWU8efIE+/fvp2XlpEQM3aFjx464e/cu1q1bh2LFitH6S5cuoWHDhhg2bBjevHkDQPEEq+zG9fLyUkk68J9hYGAANzc3Wl61alWeMoT+DjMzM85qh/ySDOvLly/YunUrLSu3cXR0NHr16oV27dpxkqiVK1cO27dvx5UrV9C4cWO16qvphIaG4sWLFwAUK7EGDRoksEa6CTMqtAxPT08WZJRPMDAwgIuLCx4/foypU6dy4ma2bdsGW1tbLFy4EAkJCejTpw9Kly4NAHj79i0nFwEfjB49mnoP7t27R7eO5ot+/frRWICrV6/iyZMnvMrTBDZs2ICEhAQAQO3atdGqVSt8+/aNk+49g4IFC2LhwoWIiIjAwIED2RP4D1CONxo3bhwKFCggnDI6DDMqtIiYmBhs3LiRllmyq5xj9Po1yrm7o06rVqjXoAHqtGqFcu7uMOJ5U668UKRIEfz777948OAB54k9ISEB8+bNQ5UqVRAcHIzx48fTzzyGDQMRiYBChQBXV0DFN2ELCwvOxnV8B4iWKVOGk+nxxKBBWtN+OeLJE8DVFanm5vD8809aPbF/f6xfvx7W1tZYuXIlzawJKLKdRkVF4a+//lLZjqW6xo0bN3Dp0iUAiuBoFxcXgTXSXX5rVIjFkqliseS0WCx5IxZLksViyWuxWBIkFktqqkNBxn9s2rQJcXFxABS5DjK2pGZkj0KXLsGuf39YhoZCPz4eIkKgHx8Py9BQ2PXvj0Lpg46mYmNjg9DQUJw6dQq1atWi9a9fv8agQYMQunUrjNLrbsvluAgAsbGAnx9QqxaQHvCnKpSDBg8ePIioqCiVnj8zo2v+N+QEpKXhs5a13285ckTRTn5+2BMXhzfp1UUA/DtrFlxdXWkCLEARS3P9+nVs3bo1TxuL5Qcyb8QmFouFU0bHyY6nwg1AGwByAM8BlAHgBOCKWCyx4k0zBgeZTMYJ2mJBRjnD6PVrVJo5E/pJSdBLSwMBkBEFoJeWBv2kJFSaOVMrnnjbtm2L27dvw9fXF8WLF6f1N6OjoRxJQcN5U1OBhATAyUmlHgtbW1vOShXl36eqMXr9Gn19fFAvvZwEwCf9vba13w958kTRPgkJIKmpUE5j9g3AI6VyhQoVsGvXLly4cAENGjRQr55ayJs3bzjTgczDyy/ZMSr8AFSUSj3KSaUeVQBMTa83BeDw839jqJJ9+/bh+fPnAIBixYph8ODBwiqkZZQMCIAoLY2WRwEoAUA564EoLQ0llfZS0WT09fUxZswYREdHY8aMGTD6Qe6BEACcbbhSUwEVZ91UHqA3b96Mr1+/qvT8GZQMCICeTAblrALLAShn79Cm9svCihWK9gFwHMD1HxxiBmBx48Z49OgR+vbtyx4qsomXlxfS0vt+ixYt6EopBj/81qiQSj3+lko9nitVnVN6n/0dkBh5ggUZ5Y1iR45ALy0NLwB0B7AZwCcAPQH8DcXNSS8tDcUOHxZQy5xTuHBhLF26FI8KFMCPNm5uCiAuo5CaCvj7q1R+27ZtUTN9WiIhIQF+fn4qPX8GGe3XF4qbKwDEAKgK4Ex6WRvbjxIQAKSm4g0Uv09lRABGAogCMOvhQ9b3c0BCQgJ8fHxomSW74p/cBGpmrCX7DOCHyfhFIpGzSCS6KRKJbn769CnXyjEU3Lp1CxcuXACgWBHg6uoqsEbaR0J8POZAcRNS9k7IAfyVXh8IQJSeGEfbqJSQgGAAZwFUVqpPAWicBQAgLg6qRCQScbwVnp6e9KlQleilr4LQB6B8S30JoC0ULtPHSsdpHentUgCATKm6HoBbADYCKK10HCN7BAQE4MuXLwAU2Wd79OghsEa6T7aNCrFYYiQWS7YBGAbgO4BeUqnHD1P3EUJ8CSH1CSH1LS0tVaRq/oUFGeUeuVyO/fv3w1Ykwj9QzMVnoJzs+CWAAQCa6+nh/n3OpIF2YKZ4fm8Fxfy7LYCiAG4gk1FhZpb5P/PMwIEDaWzHq1evOCmiVYU8fVWDPoCbAEqCe12hAOwATDMwoMHMWkV6uxQFcBSABYBGUFxr3R8cx/g9P9qILb9s7S4k2TIqxGKJJYBTAIYAeAugtVTqcZFPxRgKpFIpAgMDaZm577LP7du3MXToUCxcuBDvlJIz/QHgPBTTH94AlM3eK3I5hg8fjrlz52rXVtuDBwPpeSwMAUQC+AiAs0TL0BAYMkTlok1MTDhL9PhYXvrZ3h7y9H1RygN4B+AJFANSBqkAVqamolevXggODubFY8IbSu3XAYrf5lUopj4oPLWfrnL8+HE8eqQIcTU3N//tRmwM1ZCdJaXVAFwD0BzAXQANpVKPO7/8J4bKWLduHR0cmzdvzoKMssGbN28wc+ZMODs7IyIigtaXBrAFiiC4FgAMAIwDEA1gGhQ34wwOHz6M3r17w9fXVzt2xpw6ld6UMsjSuQ0NAZ6MUhcXFxgZKXwHV65cwbVr11R6/veDB4Nk2u67LIBtUAxOTZXqv337Bnd3dwwaNEh7Unpnar8fDsw8tp8uorydwciRI1GoUCEBtck/ZMdTEQKgUvp7AwDBYrHkavprNH+qMRITE7F+/XpaZl6KXxMXFwdPT084OTnh1KlTtN7Y2BijR4/G4WXLMNjEBMh0cypkYAB3ExMcnjMHrVu3pvUZ+2g4Ojri8OHDmr2RVeXKQHAwULBgFuMChoaK+uBgxXE8UKpUKQwYMICWVe2tSClbFk+XLoXMxIR6LDKob2CAc8bG8Bg2DKVKlaL1T548wYQJEyCRSOjKKY1F4PbTNR4+fIhjx44B+PlGbAx+yI5RYaL0vgYUU30Zr7J8KMVQEBAQQJPdWFlZcbIpMv5DJpMhNDQUvXv3xtatWznZBjt16oQ9e/Zg3LhxkLVti4eBgfjk4ACZqSmISASZqSk+OTjgYWAgLHr1wr///ov169fD1taWnuP9+/eYO3cuRowYwdlnQeOwtwfCwwFnZ0UmTT09xV9nZ0W9vT2v4pUDNoOCgvBaxTkjvjdr9tP2e7RrF1q4uSE4OBguLi6cFRIXLlxAv379sGLFCnz//l2lOqkUgdtPl1DOmdKzZ09UqlTpF0czVImIz42AAMDX15doytbcR48e5SQL0lQ+fvyITp06oUaNGnj48CEAYOXKlfnaU/Gztrt58yZWrlyZJZtjjRo1MGXKFE7myZwgk8lw4MABrFu3jkaPZ9CpUye4ublxnooz+PjxIzp37pwrmbpAmzZtcPbsWQDAzJkz4e7uDkD9fe/Tp0/w8vLCwYMHOZudFS5cGM7OznB0dIRBJo8HwNrvZ2jT2NmgQQOUK1cOiYmKLCbnzp1Dy5YtBdZMt1iwYMGtefPm1f/RZ2zvDw3lxIkT1KAwMzPDyJEjBdZIs3j16hWmTZuGcePGcQyKEiVKYNGiRdi0aVOuDQpAkVyqV69e2Lt3L4YPH87ZzOvYsWNwdHSEt7c33fCJoUDZW+Hr64t4gZboWlpaYt68efD390fduv+tn4iJicHy5csxYMAAXL58WRDdGPzi6+tLDYq6deuiRYsWAmuUv2BGhYaiPCc9cuRIFC5c+OcH5yPi4uKwevVq9OnThz4RA4oVCM7OztizZw/s7e3pjpZ5xczMDBMmTEBQUBDatWtH65OTk7Fx40b07t0bBw8e1Ox4CzXSrVs36mr++vUrtm3bJqg+VatWha+vL5YuXcrZH+PZs2eYOHEiJk6ciGfPngmoIUOVpKamYu3atbTMtjNQP8yo0EBevnyJI+mbP7EgIwUymQzBwcHo1asX/P39OcsFu3Tpgj179sDZ2Zm3bINly5bF0qVL4evry9lu/tOnT5g/fz6GDRuGu3fv8iJbm9DX18ekSZNoefXq1YIbXCKRCO3atcPu3bsxYcIEumU7AFy+fBn9+/fHsmXL8O3bN+GUZKiEixcvQiqVAlAED/fr109gjfIfzKjQQPbt20ff9+jRA5XzecT3iRMnMGHCBLi7u3MG/lq1amHLli1YuHAhSpYsqRZd6tWrh23btmHevHkoVqwYrX/06BFGjx6NxYsX5/sn3xEjRtDle5GRkTQKX2iMjY0xfPhw7N27Fw4ODvQJViaTYffu3XBwcEBoaCgn0JehPRBCEBISQsuurq4wNjYWUKP8CTMqNIyYmBjOcsj8HJwZGRmJ7t27o2PHjpwlgaVKlcLixYuxceNG1KhRQ+166enpoXv37ggJCcHIkSM5A9eFCxdQrVo1/O9//0NsbKzaddMEzM3NMXr0f6vNPVS8iVleKVasGGbPno2AgADUr/9frFlsbCx8fHxQs2bNLAGeDM0nLCwM0dHRABQG5Lhx4wTWKH/CjAoNIyQkBMnJin3a6tSpky+jlr9+/QqJRIIaNWrg4MH/duooUKAAXFxcEBwcjI4dOwo+V1qwYEG4urpSfTJITk7GkiVLYGNjg40bN0Imk/3iLLrJhAkTaFzLiRMn8OLFC4E1ykqVKlXg7e2Nf//9F2XL/rc6PsOY7dSpk3ambM+n7Ny5k74fPHiwVqxW0UWYUaFBpKWlYffu3bSc34KMMoKsrK2tsWrVKho3IRKJ0KFDB+zduxejRo2CiYnJb86kXkqXLo3FixfDz88vS36L0aNHo379+pyg0vxAxYoV0atXL1oODQ0VTJdfIRKJ0Lp1a+zevRuTJ09GwfQ9RgCFMVS7dm24urri48cfbnPE0BCkUinOnDlDy8pxPQz1wowKDeLkyZP48OEDAKBkyZLo37+/wBqpj6NHj6J27dpwc3Pj5IVo0aIFbty4gSlTpmj8k0edOnXg4eEBf39/zkqDu3fvok2bNujduzeePHkioIbqRXnq7tSpU/j69auA2vwaIyMjDB48GBs3boSLiwv1ssjlcnh7e8PGxgYrVqxASkqKwJoyfsTu3btpQHD79u1Rs2bN3/wHgy+YUaEhEEKwY8cOWs4vQUYPHz6Evb097O3t6eY/gCKDaHBwMM6dO6dV+53o6elh8ODBiIyMxLx58zirUUJCQmBnZ4cZM2YgJiZGQC3VQ7NmzWjbpaamYs+ePQJr9HuKFCmCdevWISwsDO3bt6f1MTExmDZtGqpXr47Q0FAWb6FBxMfHcwI0lXOlMNQPMyo0hPDwcJrsytDQUOeDjD5//gw3NzfUqlULR48epfXm5uZwd3fHo0eP4OjoqLXTP6amppg/fz4iIyMxaNAgWp+SkoLly5fDxsYGPj4+Oh1vIRKJON6KoKAgrVlZUaNGDRw/fhwHDhzgTGk9fvwYDg4OaNeuHcLCwgTUkJHBgQMHaJK1MmXKwJ6lMxcUZlRoCMpBRm3atEGJEiUE1IY/UlJSsGrVKlhbW2Pt2rX0pioSiTBmzBhER0dj5syZGhc3kVvKlSuHgIAAXL16FY0bN6b1Hz9+xLhx41C3bl3Oah9do0+fPihdujQAhSF54sQJgTXKPiKRCN26dcO9e/ewatUqFClShH525swZ1K1bF87Oznj//r1wSuZz5HI5AgMDablXr14qS3zHyB3s29cA3r59i9OnT9OycoCbrkAIwcGDB1GzZk1IJBJOvonWrVvj9u3b8PX1VVu+CXXTqFEjXL58GTt37kS5cuVo/b1799C+fXv07NmTLofTJYyMjDB+/Hha3rFjh9ZNHRgZGWHSpEl4/PgxJkyYAH19fQCK3/SGDRtgY2ODpUuXIikpSWBN8x8XLlygG9eZm5tzst4yhIEZFRqAcpBRw4YNUbFiRYE1Ui33799Hp06d0L17d84+HZUrV0ZISAhOnz6NOnXqCKegmhCJROjfvz8iIiKwaNEizkqD/fv3o3r16pgyZYpGBzTmhrFjx8LIyAgAEBERobWZR4sVKwZPT0+Eh4dzXOyxsbH4888/YWdnhz179mid0aTNKHt4HRwceMuoy8g+zKgQmISEBE6Q0YABAwTURrV8/PgRLi4uqF27NsftXahQIfz777948OABevXqpbVxE7mlYMGCmDNnDqKjozFs2DBan5qaCg8PD9jY2GDdunWcVOTajKWlJdq2bUvLygHJ2oidnR0OHz6Mw4cPo1q1arT+2bNncHJyop43Br9ERkbi5s2bABTp4fv27SuwRgwAyLr3rw5jbm6ucevN9+/fj7i4OACKICNbW1uYm5sLrFXeSElJgaenJxYuXIjv37/Tej09PTg7O2PBggU5jhnRxLb7ETlpO7FYjC1btmDChAmQSCS4ePEiAEXswfjx4+Hl5YWVK1eiU6dOfKmrNgYNGkQDcs+ePYvw8HAaa6FJ5KT97O3t0b59e/j4+GDevHl0KfT58+dRv359DB8+HP/8849GXmdO0cT+t2XLFvq+WbNm0NfX1/qxUycghPD68vHxIYwfI5PJiLW1NQFAAJC1a9cKrVKekMvlJCQkhHNNGa/27duT8PBwoVXUWORyOdm9ezepUKFClu+uS5cu5NGjR0KrmGc6duxIr0kikQitjkr58uULmTx5MjEwMOC0nampKfnnn39IQkKC0CrqFO/evSNGRkb0e75y5YrQKuUr5s+ff5P85J7PjAoBOXDgAO0URYoUIbGxsUKrlGvu3r1L2rRpk+WGaGtrSw4cOEDkcrnQKmoFiYmJZMmSJcTMzIzzPerr6xM3Nzfy6dMnoVXMNUeOHKHXY25uTmJiYoRWSeVERESQbt26ZekH5cuXJ4GBgawfqIh58+bR77Zx48ZCq5PvYEaFhtK2bVvaMaZPny60Orni3bt3ZMyYMUQkEnEG0SJFihAPDw+SnJwstIpaydu3b8moUaOyfK8WFhZk9erVJCUlRWgVc4xMJiNVq1al17Jq1SqhVeKN48ePkxo1amQxLpo2bUquX78utHpaTWJiIilRogT9TgMDA4VWKd/BjAoNJCwsjPMU+uLFC6FVyhGJiYnE3d2dmJubZ3minjBhglY/UWsSt2/fJq1atcpyc6pSpQo5ePCg1j35ent702uoWLEiSUtLE1ol3khNTSXe3t7E0tIyS/sNGTKEvH79WmgVtZJNmzbR77Fs2bJaaWBrO8yo0EBGjBhBO0bfvn2FVifbyOVyEhQURCpWrJhloOzcuTN58OCB0CrqHHK5nOzdu5dUqlQpy3fesWNHcv/+faFVzDbx8fHEwsKC6h8SEiK0Srzz9etXMm3aNGJoaMhpu4IFC5IFCxaQ+Ph4oVXUGuRyOalVqxb9DpcuXSq0SvkSZlRoGO/fv+cEGV2+fFlolbLFrVu3SIsWLbLc2KpVq0YOHz4stHo6T1JSElm2bFkW75Cenh5xdXUlHz9+FFrFbPHnn39S3Vu1aiW0OmojOjqa9OrVK0v/KVu2LAkICCAymUxoFTWe06dPc4yyL1++CK1SvoQZFRrG/Pnzacdo1KiR0Or8FqlUSkaMGJFlfr9o0aLE09OTuR/VzPv378nYsWOJnp4epz0KFy5MVqxYofFxLK9eveKskrh9+7bQKqmV06dPk9q1a2cxLho1asRWMfyG7t270+/L1dVVaHXyLcyo0CCSkpI4QUY7d+4UWqWfkpCQQP7++29iamrKGfwMDAzI5MmT2VOCwISFhZF27dpluTlZW1uTffv2aXS8xYABA6i+Q4cOFVodtZOWlkY2bNjAGQsyXgMHDiQvX74UWkWNIyoqivNgExERIbRK+RZmVGgQW7Zs0fggI7lcTgIDA0n58uWzDHjdunVjnVmDkMvlZN++fcTGxiZLW7Vr146EhYUJreIPuXbtGtXT0NCQSKVSoVUShJiYGDJz5kzOdCgAUqBAAfLXX39p9TJzVTNhwgT6/XTp0kVodfI1zKjQEORyOcft6e7uLrRKWbh27Rpp2rRplhtU9erVyfHjx4VWj/ETkpOTycqVK0nhwoWzxFs4OzuT9+/fC61iFpR/Z3/99ZfQ6gjKkydPiJOTU5Z+JxaLyZYtW/J9vMXXr185HtMTJ04IrVK+hhkVGsKZM2c4QUafP38WWiXK69evyZAhQ7IMapaWlsTb25ukpqYKrSIjG3z8+JG4uroSfX19Tjuam5uTZcuWkaSkJKFVpOzevZvzO0tMTBRaJcE5d+4cqVu3bpZ+WL9+fXLhwgWh1ROMf//9l34XNWrU0OipvfwAMyo0hB49etCO4eLiIrQ6hBDFEr8FCxaQggULcgYxQ0NDMnXqVPL161ehVWTkgvv373PSYme8KlWqRPbs2aMRg3Jqaipnis3Pz09olTQCmUxGNm/eTEqVKpWl/fr27UuePXsmtIpqhf1ONA9mVGgA0dHRnCAjofdykMlkJCAggJQtWzbLwNWrVy8SHR0tqH6MvCOXy8mhQ4dIlSpVsrRxq1atNGLVxfLly9kT6E+IjY0ls2fPJsbGxpy2MzY2JrNmzSLfv38XWkW1EBQUxPFosX1UhIcZFRqAm5sb7Rj29vaC6nLlyhXSqFGjLDeaWrVqkVOnTgmqG0P1pKSkkDVr1nCSTgEgIpGIjBw5krx9+1Yw3dhc+e95/vw56devX5b+WrJkSbJx40adzkpKCDf2Zs6cOUKrwyDMqBCczAOnUAGPL1684Czly3iVKFGCbNiwQecHp/zO58+fycSJE7PspGlmZkYWL14sWEyDclR/165dBdFBG7h48SJp0KBBlv5bt25dcvbsWaHV4wW2SkgzYUaFwKxYsYKzikLdLt7Y2Fjy119/ERMTE85gZGRkRGbOnKmTu0Uyfs6jR49I165ds9ycKlSoQHbt2qX23yfLP5B9ZDIZ8ff3J2XKlMnSfr179yaPHz8WWkWVovwQNGTIEKHVYaTDjAoBSU1NJRUqVKAdY8OGDWqTLZPJyJYtW0jp0qWzDEBOTk7kyZMnatOFoXkcPXqU2NnZZfltNGvWjNy4cUOturBMiTkjLi6OzJs3jxQoUCDLg8KMGTN04kEhc+bVW7duCa0SIx1mVAhIcHCwIEFGFy5cIPXr1/+hq/TcuXNq0YGh+aSmppJ169aRYsWKZfmtDB06VG07aZ46dYrKZXs6ZJ+XL1+SQYMGZWm74sWLEx8fH62e0pw1axa9npYtWwqtDkMJZlQISLNmzdQaZPTs2TPSt2/fLINMqVKlyObNm/N9Eh3Gj/n69SuZMmVKlniLggULkoULF/K+kybbfTJvXL16lTRu3Fhngq/j4+NJ0aJF6XXkh91stQlmVAjE9evXOUFGb9684U3W9+/fyaxZs364/Gz27Nn5ZvkZI29ERkZy8qlkvMqVK0d27NjBa7zFpk2bqDxNTWGvycjlcrJjxw5Srly5LO3Xs2dPEhUVJbSK2Wb9+vVU94oVK2q1x0UXYUaFQCi7JQcPHsyLjLS0NOLn50dKliyZZSDp168fef78OS9yGbrNyZMnSc2aNbP8pho3bszbTpqJiYmcDbYCAwN5kaPrxMfHk0WLFv0wod2UKVM0PqGdTCYjVatWpXp7eHgIrRIjE78yKkSEEPCJr68vcXZ25lWGJvLmzRtYWVkhLS0NAHDz5k388ccfKpVx7tw5TJ48GXfv3uXU29rawtnZGdWrV1epPFVhbm6OZs2aCa2GRnHp0iXExsYKrQYHmUyGY8eOYdu2bYiJieF8NmjQICxZsgTlypVTqcz58+djwYIFAIDGjRvjypUrKj0/X2hi+3369Albt27FyZMnOfXFihXDokWLMGbMGBgYGAik3c85evQo7O3tASjGitevX6NQoUK8ydPEtvsRmjRuLliw4Na8efPq/+gzzftF6Qjr1q2jBkWLFi1UalA8efIEM2bMwN69ezn1xYoVw8SJE2Fvbw89PT2VyVM1Hz9+FFoFjSM2NhbFixcXWo0sDBs2DI6Ojti0aRN27tyJ1NRUAMD27duxd+9eTJ8+HTNmzICpqalK5Lm4uGDJkiVISUnB1atXcfXqVTRu3Fgl5+YTTWy/4sWLw93dHQ8fPsTKlSvpw8fnz5/h6uoKLy8vrFy5Eh07dhRW0UysWrWKvh81ahSvBgWgmW33I7Rl3NTcO48Wk5CQgPXr19OyRCJRyXljYmIwY8YM2NnZcQyKAgUKYN68efDz80PXrl012qBgaB9mZmaYOHEigoKCOE9KiYmJWLhwIWxtbeHv7w+5XJ5nWSVLlsTAgQNpWfkGw8gddnZ22LBhA9zd3VGiRAla/+DBA3Tq1AndunVDRESEgBr+x8OHD3Hs2DEAgJ6eHiZOnCiwRoycwu4+PBAQEIAvX74AAKysrNCjR488nU8mk8HX1xc2NjZYvnw5UlJS6GeDBg1CZGQk5s+fDxMTkzzJYTB+RdmyZTFnzhycOXMGderUofVSqRRDhw5F48aNcfny5TzLmTRpEn0fHByMV69e5fmc+R2RSIT27dtjw4YNWLx4MczMzOhnhw4dQs2aNTFp0iQ6bgnF6tWr6fuePXuiYsWKAmrDyA3MqFAxhBDO09XEiROhr6+f6/OdOnUK9erVw9ixYznur8aNG+Pq1asICAhQ+bw2g/ErWrdujZs3b2Ljxo0oWbIkrb9x4waaNWuG/v3748WLF7k+f506ddC6dWsACoN67dq1eVWZkY6RkRFmzZqF6OhojBo1CiKRCACQlpaGNWvWwNraGp6ennSaS518+vQJ27Zto+XJkyerXQdG3mFGhYo5fvw4Hj16BEARWDNq1KhcnSc6Oho9e/ZE+/btER4eTuvLlSuHHTt24PLly2jUqJFKdGYwcoq+vj5GjhyJ6OhozJo1C8bGxvSzXbt2oUqVKpg9e3auA+CUpwx9fX0RHx+fZ50Z/1GqVCn4+fnh1q1baNWqFa3/+vUrJk6ciFq1auHw4cPgO5BfGV9fXyQlJQEA6tWrhxYtWqhNNkN1MKNCxSh7KUaOHJnjIKNv375h6tSpqF69Ovbv30/rCxYsiIULFyIiIgIDBgygTxgMhpCYm5tj8eLFiIiIQN++fWl9cnIyFi9eDFtbW2zevDnH8RZdu3ZF5cqVASj6hPITLEN11K1bF2fOnMGePXtQqVIlWh8REYGuXbvC3t4eDx484F2PlJQUeHl50fLkyZPZGKelMKNChTx69AhHjx4FoJjDzEmQUVpaGtatWwdra2usXLmS434cNmwYoqKi8Ndff6FgwYIq15vByCtWVlbYtWsXLly4gPr1/1tp9u7dO4wcORINGjTA+fPns30+fX19TmzFqlWrVBIIysiKSCRC79698fDhQyxbtgzm5ub0s2PHjqF27doYP348Pn36xJsOQUFBkEqlABRelH79+vEmi8EvzKhQIZmDjJQt/19x/Phx2nE/f/5M65s1a4YbN25gy5YtKFOmjMr1ZTBUTfPmzXHt2jVs3boVpUuXpvW3b99Gq1at4OTkhKdPn2brXMOHD6eevqioKGqwM/jB2NgY06dPx+PHjzF27Fi6ikwmk9EHHg8PD06guCrIHIc2fvx4GBkZqVQGQ30wo0JFfP78meOizc4y0oiICHTr1g2dOnXCw4cPaX2FChV++NTHYGgDenp6GDp0KPWuKa9K2rNnD6pVq4Y///wT379//+V5zM3NMWbMGFr28PDgTWfGf5QoUQLr16/HnTt30K5dO1ofExODKVOmoEaNGjhw4IDK4i0uXbqEmzdvAgBMTEwwduxYlZyXIQzMqFARvr6+SExMBKCYp/xVkNGXL18wadIk1KxZE4cOHaL1ZmZmnPlpNqfI0GbMzMywcOFCREZGcnJPpKSkYOnSpbCxscGGDRsgk8l+eo4JEybQJ+aTJ0/i/v37vOvNUFCrVi2cOHEC+/btg7W1Na2Pjo5Gjx490KFDB04QeW5R9lIMHjxYKxJRMX4OMypUQGpqKmfZ28+CjFJTU+Hp6Qlra2usWbOGZtwUiUQYOXIkoqKiMGvWLJZvgqFTlC9fHtu3b8+yYunDhw9wdnZGvXr1cObMmR/+r5WVFRwcHGhZeYqRwT8ikQg9evTAgwcPsHLlShQuXJh+durUKdStWxfjxo3Dhw8fcnX+58+fIyQkhJaV42gY2gkzKlTA74KMCCE4fPgwatWqhYkTJ+Lr16/0s1atWuHWrVvYuHEjZw6awdA1mjRpgsuXLyMgIABly5al9eHh4Wjbti0cHBzw+PHjLP+nPJXo7++vNemKdQkjIyNIJBI8fvwYrq6uNPeOXC6Hj48PTcyXnJyco/N6enrSANwOHTqgRo0aKtedoV6YUZFHCCGcuV5XV1fOmv0HDx7A3t4eXbt25aTCrVixIvbs2YMzZ86gbt26atWZwRAKPT09mgV2wYIFnNVMoaGhsLOzw7Rp0/Dt2zda37RpUxpblJycDB8fH3WrzUjH0tISXl5eCAsL4+wZ8v37d7qFQEhISLbiLWJjY+Hn50fLLNmVbsCMijxy+fJlGmRkbGyMcePGAVBkhxs/fjxq165Nc9kDiuCzZcuW4dGjR+jduzeLm2DkSwoWLIi5c+ciMjISQ4YMofWpqalYsWIFbGxssH79eqSlpUEkEnG8FV5eXjl+ImaolurVq+Po0aM4dOgQqlSpQuufPn2K3r17o02bNrhz584vz7F582YarFulShV07tyZV50Z6oEZFXkkc5BR4cKF4eHhAWtra6xbt44Goenp6cHZ2RnR0dGYPn06x5vBYORXypYti23btuHatWto2rQprf/06RNcXFxQp04dnDhxAk5OThCLxQAUuS92794tlMqMdEQiEbp06YJ79+5hzZo1sLCwoJ+dO3cOf/zxB0aPHo13795l+V+ZTIY1a9bQ8qRJk9hGiDoCa8UckpICjGz1BAfKu+KZmRn2BgfTz+6fFaN69RqYMmUKYmJiaH3btm1x584d+Pj4cPZKYDAYCho2bIiLFy8iMDAQ5cuXp/UPHjxAx44d4ejoyMnY6TF8OIhIBBQqBLi6Ak+eCKE2A4ChoSHc3Nzw+PFjzl5HhBBs3LgRNjY2WLJkiSIF95MngKsrDpqZ4Ul6m1kYG2No8+ZCXgJDhWTLqBCLJS3FYslBsVjyXiyWkPTXfJ510zhSUoD5jY7A83wtdHrlB6/4eGTk+LMAcO3JIjx+HE2Pt7a2xr59+3Dy5EnUqlVLEJ0ZDG1BJBKhX79+iIiIwN9//w1TU1P62cGDB+G5Zg0ytua7I5fjAgDExgJ+fkCtWsCRI0KozUinaNGiWL16Ne7du4cuXbrQ+ri4OPzvf/9DNSsrBNnZgWzYgFXpe3wAgHNqKkwbN2btpyNk11NRD0BnAMLuiysw4zo8wey7TjBFApKRig1Kn31Vem+kb46VK1fiwYMH6NGjB4ubYDByQIECBTB79mxER0djxIgRtP/I5HIoZ7RYmfEmNRVISACcnJjHQgOoVq0aDh06hKNHj8LOzo7WP3//Hn1TUlAvLQ1n0+v0AYyXy1n76RDZNSr8ARQC0IBHXTQeh2crYIhUJAMYAiBzPkA9AGOhh02l+kAikbBUswxGHihdujQ2bdqEmzdvosUPllvvA7BJuSI1FWBZNzWGTp06ISwsDF5eXiimlHvnrtIx3QCUyyiw9tMJsmVUSKUen6VSjwS+ldF0un0LgBFSEQPgQKbPOgIIA7AecgyMDc76zwwGI1fUq1cP52JjEQygYqbPFikXUlMBf3/1Kcb4LQYGBnB1dUW0gQGmAHT6KoNmygXWfjoBL4GaIpHIWSQS3RSJRDf53NlO3Yji4gAARgAyIiT0AAQDOAqgRqbjGAyGahDFx8MRwEMAI5Tqe2U+kPU9jcQiPh4roHgYs0yvKwRgWuYDWftpPbwYFYQQX0JIfUJIfUtLy9//g5ZAzMwAAEUAXAOwFEAoAEcAoh8cx2AwVER6nzIBsBGAF4AtALI4y1nf00zS28UewEcAvgC8wR03lY9jaC9sSWkOOFhkMFJgCEDhrZgBoHumY1JgiINFhmT+VwaDkRcGDwYMFX1PBMAVwLDMxxgaAkNY39NIlNoPAMYAGJj5GNZ+OgEzKnJASMWpSIXhL49JhSFCrH6/7TmDwcgBU6dybko/xNAQkLC+p5Gw9ss3ZDdPRW+xWPIYiljEDCaKxZLHYrFkOz+qaR7rT1TGP3WCEY+C1GORQQoMEY+C+KdOMNafqCyQhgyGjlK5MhAcDBQsmPXmZGioqA8OVhzH0DxY++UbsuupKASgMoBKSnUW6XVlVK2UpmJkBMy/Zg+3luE4Vt4ZpFAhQE8PpFAhHCvvDLeW4Zh/zR5sJSmDwQP29kB4OODsrMikqaen+OvsrKi3txdaQ8avYO2XLzDIzkFSqccWKOKi8j1GRsCmc5UBrE1/KeZ4uyNrfAWDwVAxlSsDa9cqXgztg7WfzsNiKhgMBoPBYKgEZlQwGAwGg8FQCcyoYDAYDAaDoRKYUcFgMBgMBkMlMKOCwWAwGAyGSmBGBYPBYDAYDJXAjAoGg8FgMBgqgRkVDAaDwWAwVAIzKhgMBoPBYKgEZlQwGAwGg8FQCcyoYDAYDAaDoRKYUcFgMBgMBkMlMKOCwWAwGAyGSmBGBYPBYDAYDJWQra3PGdqBubk5Pn78KLQav8Xc3FxoFTQO1nbaDWs/7YW1nWoREUJ4FbBgwYKPAF7wKkRA4uLiLM3MzD4JrQdf6PL16fK1Aez6tB12fdqLLl9bOhXmzZtX/Ecf8G5U6DoikegmIaS+0HrwhS5fny5fG8CuT9th16e96PK1/Q4WU8FgMBgMBkMlMKOCwWAwGAyGSmBGRd7xFVoBntHl69PlawPY9Wk77Pq0F12+tl/CYioYDAaDwWCoBOapYDAYDAaDoRKYUcFgMBgMBkMlsORXuUAslgwAsEOpqqZU6nFfKH1UhVgsOQuglVKVDMBnANcBzJFKPcKE0EvViMUSYwCuAPoCsANgBOA1gPMA/pVKPR4JqF6uydR+cgAJAN5B0X7eUqnHRYFUUwk/+H0qM0Iq9diiPm1UTz64PiMAYwD0A1ADgBmAjwCeATgGYLVU6vFdOA1zj1LbvZBKPayU6lsDOJNe1Po2zA7MU5E7hv+mrO2kALgG4B4ASwDdABwXiyUFBNVKBYjFEgsAVwCsBNAYij4QDcV1jgTQSTjtVEYKgBsAvgKoDGAggPNiseR/gmqlOjJ+n8ovzU+JmH107vrEYkkRAJcBrAXQAkABAFEAkgDUB7AQQHmh9GOoDuapyCFisaQsgPbpxZtQdIjBYrHkT6nUI004zVTKW6nUozEAiMWSv6Do8CWgeKq/JaRiKmAtgLrp7/8F8D+p1CMVAMRiSRvohqGt3H7VAAQCqAXgH7FYclkq9TgrpHIqgF6fjqKL17cWwB/p770AzJBKPRIAQCyWmADoAuCDQLoxVAgzKnLOMChuPF8BDAbwCEBJKDrFfgH1Ujnp0wQV04vJAF4KqE6eEYslhaGY8gCAMCgGNrr8SSr1OPPDf9RipFKPR2KxZDiA2+lV4wCcFUwhRr4jvd/1Sy+GA5golXrIMz6XSj2SAOwVQjeG6mFGRc4Znv53l1TqESkWSy5C4c4bAd0xKiqIxRLltcYEgLNU6qHVLlgAtvjvN39B2aDQZaRSjztisSQWgDmAmkLrowIy/z4BwEIq9fgmhDI8oGvXp9zvzmcYFGKxZD2AsUrHrZBKPaapWzkV86O2y1fogqtXbYjFkhYArNOLW9P/+qf/7SoWS364wYoWn1c22wAAAllJREFUkjGnewtAIgARgFViscT6l/+l+YiU3ue3jp9x7bpw3T+KOdCVqUdA967vZ/3uKRTXpktkbjutDPrOC8xTkTOGK70/KhZLAEA/vWwIYBCAVepViRcyz8k/BGABYBSAWUIqlkcioRicDQA0F4slovzgrRCLJfWhiLQHgAdC6qIidDHmQBldu74I/NfvmmX0O6nUYxmAZTr2ZM9pu0yrP/IFzFORTcRiiSmAPkpVhdNfZkp1I9SqlHpQfsowFEwLFSCVesQA2J1erAtgsVgsoYa1WCxpKRZL2gqiHE+IxZLqADYrVa0XShdG/iR9mWhgerEegCXpy0sZOgjzVGQfJyjmpAHgD6nUIyPwDWKxZDAU0yC1xGJJPeXPtJTSYrHkKhS/D7v0OjmAA8KppDImAKgGhVHxJ4DxYrHkOQAxgGIAJABOC6adashov9IAykFhGBIoco3owlNTxvUp4yeVevgJog0jO7hB0e/+ADATin73BAoPKEOHYJ6K7JPhhXjxA6PhABRzacrHaTNGABpBMQCkQZHXoZ9U6nFOUK1UgFTq8RVAUwBToUgKBSgCyWKhiJM5LpBqqsQIQEMojKRnAHYBaCWVeiwWVCvVkfH7VH6VFVQjxi9JDzJtBoXRfhUKI7caFAbvOQDTAXgIpR9DdbANxRgMBoPBYKgE5qlgMBgMBoOhEphRwWAwGAwGQyUwo4LBYDAYDIZKYEYFg8FgMBgMlcCMCgaDwWAwGCqBGRUMBoPBYDBUAjMqGAwGg8FgqARmVDAYDAaDwVAJzKhgMBgMBoOhEv4P5ZaKB7+Hn9wAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 648x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#instance.U[4,5].fix(1)\n",
    "x=['A','B','C','D','E','F','G','H']\n",
    "y=['1','2','3','4','5','6','7','8']\n",
    "#f, ax = plt.subplots(1, 1)\n",
    "\n",
    "\n",
    "fig=plt.figure(figsize=(9,9))\n",
    "plt.rc('axes',edgecolor='grey')\n",
    "#instance = model.create_instance()\n",
    "\n",
    "\n",
    "for i in range(1,int(sqrt(n))+1):\n",
    "    for j in range(1,int(sqrt(n))+1):\n",
    "        if (i+j) % 2==1:\n",
    "            plt.scatter( i,j,s=1700,marker='s',color='black',alpha=0.2)\n",
    "        else:\n",
    "            plt.scatter( i,j,s=1000,marker='s',color='w',alpha=1)   \n",
    "\n",
    "plt.xticks(range(1,9),x,fontsize=14,c='midnightblue',fontweight='bold')\n",
    "plt.yticks(range(1,9),fontsize=14,c='midnightblue',fontweight='bold')\n",
    "#plt.axis('off')\n",
    "if (results.solver.status == SolverStatus.ok) and (results.solver.termination_condition == TerminationCondition.optimal):\n",
    "    print('feasible')\n",
    "elif (results.solver.termination_condition == TerminationCondition.infeasible):\n",
    "    print('infeasible')\n",
    "else:\n",
    "    print ('Solver Status:',  results.solver.status)\n",
    "#plt.show()\n",
    "plt.xlim(0,int(sqrt(n))+1)\n",
    "s = 1\n",
    "f = 1 \n",
    "w=0.1\n",
    "itr=0\n",
    "plt.scatter([value(instance.Xloc[f])],[value(instance.Yloc[f])], s=100,c='b', marker='X')\n",
    "contin = True\n",
    "while contin:    \n",
    "    for j in instance.j:\n",
    "        if f!=j and (f,j) in allowed:\n",
    "            if value(instance.U[f,j])>0.1:\n",
    "                    itr+=1\n",
    "                    w+=0.04\n",
    "                    plt.plot([value(instance.Xloc[f]),value(instance.Xloc[j])],[value(instance.Yloc[f]),value(instance.Yloc[j])], c='k', lw=3 )\n",
    "                    plt.scatter([value(instance.Xloc[j])],[value(instance.Yloc[j])], s=100,c='r')\n",
    "                    #plt.scatter([value(instance.Xloc[f])],[value(instance.Yloc[f])], s=100,c='r')\n",
    "                    plt.savefig('Knight Tour' + str(itr)+ '.png', format='png', dpi=200)\n",
    "                    f=j\n",
    "                    print(itr,f,s)\n",
    "                    if f ==s:\n",
    "                        contin = False\n",
    "                        break \n",
    "\n",
    "print(value(instance.obj1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}