0.摘要
小时候在报纸上玩过数独，那时候觉得很难，前几天在leetcode上遇到了这个题，挺有意思于是记录下来
一般一道数独题，就像他给的例子这样，9*9的格子，满足 行，列 ，宫均取1-9的数，切互不相同。
那一般正常人的思路会去一点一点的推理，至少我小时候就是这么玩的，具体来说，好比 r7c9(第7行，第9列)的空格，我会找第7行有『6,2,8』,第9列有『3,1,6,5,9』,第9宫有『2,8,5,7,9』,这些的并集就是『1,2,3,5,6,7,8,9』,哦那么空格是4。就这么一点点继续往下推理。
1.余数法
他给的函数接口是这样
void solveSudoku(vector<vector<char>>& board){}
然后我就照着我小时候的思路写了一个版本
void solveSudoku(vector<vector<char>>& board) {
if(board.size()!=||board[].size()!=)
return;
vector<vector<vector<bool>>> ex(,vector<vector<bool>>(,vector<bool>(,false)));
vector<vector<int>> count(,vector<int>(,));
queue<int> se;
for(int i=;i<;i++)
{
for(int j=;j<;j++)
if(board[i][j]=='.')
{
int block_i = i/;
int block_j = j/;
for(int c=;c<;c++)
{
if(board[i][c]!='.'&&!ex[i][j][board[i][c]-''])
{
ex[i][j][board[i][c]-''] = true;
count[i][j]++;
}
if(board[c][j]!='.'&&!ex[i][j][board[c][j]-''])
{
ex[i][j][board[c][j]-''] = true;
count[i][j]++;
}
int ii = block_i* + c/;
int jj = block_j* + c%;
if(board[ii][jj]!='.'&&!ex[i][j][board[ii][jj]-''])
{
ex[i][j][board[ii][jj]-''] = true;
count[i][j]++;
}
}
if(count[i][j]==)
se.push(i*+j);
}
}
while(!se.empty())
{
int cur = se.front();
se.pop();
int i = cur/;
int j = cur%;
int block_i = i/;
int block_j = j/;
for(int c=;c<;c++)
if(!ex[i][j][c])
{
board[i][j] = c + '';
break;
}
for(int c=;c<;c++)
{
if(board[i][c]=='.'&&!ex[i][c][board[i][j]-''])
{
ex[i][c][board[i][j]-''] = true;
count[i][c]++;
if(count[i][c]==)
se.push(i*+c);
}
if(board[c][j]=='.'&&!ex[c][j][board[i][j]-''])
{
ex[c][j][board[i][j]-''] = true;
count[c][j]++;
if(count[c][j]==)
se.push(c*+j);
}
int ii = block_i* + c/;
int jj = block_j* + c%;
if(board[ii][jj]=='.'&&!ex[ii][jj][board[i][j]-''])
{
ex[ii][jj][board[i][j]-''] = true;
count[ii][jj]++;
if(count[ii][jj]==)
se.push(ii*+jj);
}
}
}
}这里ex是9*9*9的数组，对于非空格的位置ex没有意义，对于ricj的空格（例如r7c9的空格），ex[i][j][9]是一个bool[9]的数组，分别代表跟ricj相关的20个格子（行列宫一共20格）是否包含x {x=1...9}；如果包含x，那么ex[i][j][x-1]就是true（例如ex[7][9][0,1,2,4,5,6,7,8]为true，ex[7][9][3]为false），同时为了方便建立一个count[9][9]记录true的个数，count[i][j]记录ex[i][j]中true的个数，一旦count[i][j]==8,那么这个格子就可以推理出来。
那么刚开始先对整个数组扫描一遍，分别记录一遍ex和count，找到那些count==8的，放入一个队列。然后获得队列的对首ricj，把他的值填入（例如r7c9填”4“），同时找到与r7c9相关20个格子中是空格的位置，更新他们的ex和count（例如r1c9的ex[0][8][4-1]改为true），把count==8的push到对尾，如此往复，直到队列为空。
我当时的想法时队列空了应该就能解出来了吧。。。于是submit了，结果过了两个case，还有的case报错了。。。
咦？没解完。。解了的都对了。。我把剩下的手抄了一下，发现确实解不了，不是程序问题，原来我小时候一直解不出来是有原因的，不是我眼神不好，方法有问题。遂百度了一下，原来我的方法叫做”余数法“。余数法求解不了所有的数独问题，难的需要假设来推到出矛盾。但怎么假设好呢，也百度了一下。
2.递归+回溯
网上说的最多的方法，主要还是递归+回溯 暴力求解。
int row[][] ;
int col[][] ;
int block[][] ; void solveSudoku(vector<vector<char>>& board) {
if(board.size()!=||board[].size()!=)
return;
memset(row, , sizeof(row));
memset(col, , sizeof(col));
memset(block, , sizeof(block));
//memset(ex, 0, sizeof(ex));
//memset(count, 0, sizeof(count));
//推导
//forward(board);
//假设
for(int i=;i<;i++)
for(int j=;j<;j++)
if(board[i][j]!='.')
{
row[i][board[i][j]-'']=;
col[j][board[i][j]-'']=;
block[(i/)*+j/][board[i][j]-'']=;
}
assume(board,,);
} bool assume(vector<vector<char>>& board,int i,int j)
{
if(i==)
return true;
if(board[i][j] != '.')
return assume(board,i+(j+)/,(j+)%);
else
for(int c=;c<;c++)
{
if(!row[i][c]&&!col[j][c]&&!block[i/*+j/][c])
{
board[i][j] = c+'';
row[i][c] = col[j][c] = block[i/*+j/][c] = ;
if(assume(board,i+(j+)/,(j+)%))
return true;
board[i][j] = '.';
row[i][c] = col[j][c] = block[i/*+j/][c] = ;
}
}
return false;
}
这种方法是从另外一个角度记录当前的数独数组的情况，维持3个bool类型的数组row[9][9],col[9][9],block[9][9],这里为了初始化memset方便设成了int型。row[i][j]的含义是第i行是否含有j（例如初始时r[1-1][5-1]为真，r[1-1][2-1]为假，第一行有5没有2）,col，block同理。assume是递归函数，每次遇到空格就对他从i = 1开始假设，如果他所在的行，列，宫都没有i那就设他为i，继续递归往后填写，遇到矛盾(某个空格不能取1-9之间任何数)就返回。这样做就是所谓的暴力求解，这么做肯定是没问题了，可以求解出正确结果。提交，ac了，4ms，打败了83%的人。。。
3.预处理+递归+回溯
但是我想，递归的复杂度和剩余格子的总数有指数关系，直接递归有点浪费时间，何尝不先用余数法给”预处理“一下呢，减少递归次数。。于是，两种方法一起，哦了
int row[][] ;
int col[][] ;
int block[][] ;
void solveSudoku(vector<vector<char>>& board) {
if(board.size()!=||board[].size()!=)
return;
memset(row, , sizeof(row));
memset(col, , sizeof(col));
memset(block, , sizeof(block));
//推导 derivation 对于九宫格中可能性唯一的数 直接求解 减少递归次数
//余数法
forward(board);
for(int i=;i<;i++)
for(int j=;j<;j++)
if(board[i][j]!='.')
{
row[i][board[i][j]-'']=;
col[j][board[i][j]-'']=;
block[(i/)*+j/][board[i][j]-'']=;
}
//假设 assume 对于可能性不唯一的数 递归假设求解
assume(board,,);
} bool assume(vector<vector<char>>& board,int i,int j)
{
if(i==)
return true;
if(board[i][j] != '.')
return assume(board,i+(j+)/,(j+)%);
else
for(int c=;c<;c++)
{
if(!row[i][c]&&!col[j][c]&&!block[i/*+j/][c])
{
board[i][j] = c+'';
row[i][c] = col[j][c] = block[i/*+j/][c] = ;
if(assume(board,i+(j+)/,(j+)%))
return true;
board[i][j] = '.';
row[i][c] = col[j][c] = block[i/*+j/][c] = ;
}
}
return false;
}
void forward(vector<vector<char>>& board)
{
bool ex[][][] ;
int count[][] ;
memset(ex, , sizeof(ex));
memset(count, , sizeof(count));
queue<int> se;
//求解所有可能性唯一的
//get all results with only one possible answer
for(int i=;i<;i++)
for(int j=;j<;j++)
if(board[i][j]=='.')
{
for(int c=;c<;c++)
{
if(board[i][c]!='.'&&!ex[i][j][board[i][c]-''])
{
ex[i][j][board[i][c]-''] = true;
count[i][j]++;
}
if(board[c][j]!='.'&&!ex[i][j][board[c][j]-''])
{
ex[i][j][board[c][j]-''] = true;
count[i][j]++;
}
int ii = (i/)* + c/;
int jj = (j/)* + c%;
if(board[ii][jj]!='.'&&!ex[i][j][board[ii][jj]-''])
{
ex[i][j][board[ii][jj]-''] = true;
count[i][j]++;
}
}
//答案唯一的 push到队列
if(count[i][j]==)
se.push(i*+j);
}
while(!se.empty())
{
int cur = se.front();
se.pop();
int i = cur/;
int j = cur%;
for(int c=;c<;c++)
if(!ex[i][j][c])
{
board[i][j] = c + '';
break;
}
for(int c=;c<;c++)
{
if(board[i][c]=='.'&&!ex[i][c][board[i][j]-''])
{
ex[i][c][board[i][j]-''] = true;
count[i][c]++;
if(count[i][c]==)
se.push(i*+c);
}
if(board[c][j]=='.'&&!ex[c][j][board[i][j]-''])
{
ex[c][j][board[i][j]-''] = true;
count[c][j]++;
if(count[c][j]==)
se.push(c*+j);
}
int ii = (i/)* + c/;
int jj = (j/)* + c%;
if(board[ii][jj]=='.'&&!ex[ii][jj][board[i][j]-''])
{
ex[ii][jj][board[i][j]-''] = true;
count[ii][jj]++;
if(count[ii][jj]==)
se.push(ii*+jj);
}
}
}
}
0ms，ac。
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UoQAEKUIACxUuAAaLiNZ8cDQUoQAEKUIACFKAABShAAQpQgAIUUFuAASK1yXgDBShAAQpQgAIUoAAFKEABClCAAhQoXgIMEBWv+eRoKEABClCAAhSgAAUoQAEKUIACFKCA2gIMEKlNxhsoQAEKUIACFKAABShAAQpQgAIUoEDxEmCAqHjNJ0dDAQpQgAIUoAAFKEABClCAAhSgAAXUFmCASG0y3kABClCAAhSgAAUoQAEKUIACFKAABYqXAANExWs+ORoKUIACFKAABShAAQpQgAIUoAAFKKC2AANEapPxBgpQgAIUoAAFKEABClCAAhSgAAUoULwEGCAqXvPJ0VCAAhSgAAUoQAEKUIACFKAABShAAbUFGCBSm4w3UIACFKAABShAAQpQgAIUoAAFKECB4iXAAFHxmk+OhgIUoAAFKEABClCAAhSgAAUoQAEKqC3AAJHaZLyBAhSgAAUoQAEKUIACFKAABShAAQoULwEGiIrXfHI0FKAABShAAQpQgAIUoAAFKEABClBAbQFNte9If4MsGl5uu7H9wAXc83qBFy+DEK1lDIsKlrC0aoC2Pfuhh60FdCTv1AogD8GRYZ3w821dOPx5HIub6r9jhfm5PQl+Owahx+JHKNFpFY7Ms8F/34vCaDOPNnGe+LWXE7YFZlNeogvTinXQtEN3fOnYFfVMNLIpWJins/KVIeTwMHSafgea9r/jlHMLlCjMLrJtClCAAhSgAAUoQAEKUIACFKDAexDId4BIHvMEO38ajcVuIem6KRKS4sPh/+yO8ufK8a3Y2m0uVkzrjHJa72E0bKLoCMhjEfLCA0dcxM/WXXD69TeMaWYKpqwVnSliTyhAAQpQgAIUoAAFKEABClCAAqkC+QsQyaNwfdmYlOCQBdqOHI8Rve1hZaoLqSwe4b53cNL1T/y56zZ8Dk3DuHJVseWbGtD5oN0lMKjdHU79/aFbryzeT7yrMNpUf5Lqzz6OdV3TBX/kSYgN84XnpYPY9KcLLgR6YtOY0dDavB6jauup30D6O6Iv44eOY3A2rgHmnliHziXfJT3tw/B9NzDeTQEKUIACFKAABShAAQpQgAIUyF0gXwEiWfB5rN4XJGrXR8uZa7Dw07JIW0Ak1YZxxSbo87+/YFd1NPouvAGvrWtwzXExWhjm3qGiW0IKowb9MbbB++xhYbRZAOOTaEC3pCWsu41Go9b2WDnia6x//ATrZrrik83DUFW7ANookCo+UN8CGTsroQAFKEABClCAAhSgAAUoQAEKvBXI14qfuBeX8UQmKtGxRV+HdMGhDLJaqNj5K7RUJIxE38WlF3F0/wgFpIaN8PXsr1FZMfan27DlbvRHqMAhU4ACFKAABShAAQpQgAIUoAAFirZAvgJE8rgoxCrGJfYbCotTRIqyOUrUQr9xozHim4GwLZnaVCJ8tn4JGxsbdJp/J7meDLcrNg52VF7vMOcWYlSrlici/PZOzBvVG+2a2ohydmj72XBMd3GDb5w8Q+moC9+hqainy+J7iA6/he0iUNGjta2ybvvOTpi6yQOhSWIP7JjnOO48Hl92bK681rx9Twyfuxt3wtOPTYbXB5yU19v8dA3pwxyyqCc4sWoKhn7eBW3sRJ+aOqBr35GY43oVr+Iz9knRwbyXz75NUQsiHx6G8+TB+LSNnbJfNi0/Qf+xC7DTIwgJKm5vLTwRG+8Pt7U/YXDXVrAV99k5fIqBk1fhtHc0MvdWdQLU/127Wg841lPcF4azBx5kmtPE4BvYuWgc+n/SInkcrTqhz4hZWH/mGd6kTUEUzo8W42ytWF6mqOs2pnVUzGV/bPMTk5h6iI3Tn51eh9nfD8OXnzrATozPtlVH9HAch8W7riIgw3zk5Jt5nHmft8z38gwFKEABClCAAhSgAAUoQAEKUKAoC+RriZmWRT2Ux3l4yW9i6fy/UXV6H9QyzCLWJDVFk8+HokmBCYgvTF2YjwHbTyAgrU4Zonxv4vCKmzh2ZhhWr/gGDVX6khTihnkD1uKouMmwrAVKJvgjNMgTx5xH4HHQFLS8uQSbPBMhNSqLskaBCAj3wc19CzDsUTR2rnNC5Rw2HEoMOIyfBk7H6bCUDmnrQyc+EoHPrmH/0ms4cvYHbFveD5VSllWpWz5runj4HpyBoTNPIDi1gKbYiyc2GI8v7cYisffPybFr8dugWtBX3aInwRt/TxyJZf+KEJeeGcqZSeD3OgAPTq/F5Mv3MWPHr+huka/HIuuuKs5KS6Fx60rAXW9E3LuD14nWsFQ2IUe05yZ8/80f8EiNBGoK7JgQeHv8gz/Fz/4e87Hyx46w0NJCGbvOaKfzEtfc7iACeqhp3wzlDCujsm7KIGURuLb0K4zc7p3WF219XSRGh8Lv0UXsWHgR+8/8hB3OvVFezSEWzLxlT8QrFKAABShAAQpQgAIUoAAFKECBwhTIIqqTe3e0LHtilEPyx7/DzizEwPafYuhPv8P1uAe8wxP+kyyU5F7F45YiOGRkj7GrDuC8+1VcPrMPK8e2gLEokHR/Hf73hweiVNJgQk6sxfESfbH4n0s4c/AfHD+7BzMcjJRVPnNdgE2Pq8Pp98O4ePogDp46i+2T7UT4QeToeP6Nw8/jsweRR+Kq86Lk4FDVvliw+zzcL53Hxatu2LOwDyqIO+M9VuIvj6jkOtQtn03L8V7bMWVWcnDItPUErDnkBvfLV3Dp+BbM+sxS3BULD+cfsOJ25oyg1wdnY5lHZeV4L50/ggNHzuLc1smwV0xn9EUs3/Qgi6yubDqS59NaMKteHsqYzOvHeJ1CKotwx9IJycGhsu2+x1+HLohx/Av3C4ewekIHlBPFfQ5Mw9S9PkgUW5zXGjIbi375BtbK3c6rY9CsRVj8y7dobpr8GMc/c8U8ZXBIH03HrsaRS1fFGEWdV05hy/SuMBdXYt3XYsdjNZc7FtC85ZmLBSlAAQpQgAIUoAAFKEABClCAAu9ZIF8BIojMIIfZ6zG9W9XkL5PJAnH7+GYs/WkE+rRvjvZ9R+Hn37fipGdwpqVO7zw+aR2Md1kCJ5ty0JdKoGlYAbZOS7DxR2vll8WC9q/BuSDVZW818O2C7+FgkZzGI9GxxCfffC6yoBSHFA0mLMDoFmWSv0wm0YfVZ9+ih5niWii8QxOz77LIRLp6RxH80Uf7id+iQ2X95M+4S/Rg2W40JnQoByMjfQR7hYgAhzjULZ9ly29wa8NG3BdBMI2Gk+Gy0BGNzfVEu1Jom9ZCt6lrsaSjYjfwAPy92g0hqhTxOmj/y+8YI8arrUy8kaJEzT6YMqqGsrXgp6+QQ0gsyx7l5aRGCROhJA6RzZO8bCwRfgedodjrXLOhCGb9MhBNzMVX8BQ90jWHteNcrJxiDW0k4faqNbiV69ZFMoR7/osXijZqj8W0gdYwSx6ggDJGre7fY4xyg/EgPH2tugAvlxEUyLzl0gYvU4ACFKAABShAAQpQgAIUoAAFClEgfwEi0WGJXlX0mLUDZw5vwPwJTuja1AqpXxyPeHYVRzYvwxSnT9Bh4C84LDaoLqi9bfTafI3uqeu10uC0UKHLSHQxESeS7uD4/ZSMndTrVp+ivWXGT2dpmVZGKeX12ujlYJGc3ZJaXqsUqigvyiDLqeMSTegq02Ki8eDyI0SkD8ZIjNF6wQGcPn0Ef/W3TK5f3fKp/Un/39inOPlvuDijj7bDuqC86vI3EbyzH9wTZUWJhBtHcf+NSiXGDvjSrmRyICvtkgaMLVMCZDIZ5DmNOas+qXMudclbUiAuHnwo7jRA+2+6w1J1HEKsfJdhaKPIbIq4hav+uYWtpCj1yXKcPHECp/4S41d9ssXeVXHKKJ1czKqaR0HMm5pNsjgFKEABClCAAhSgAAUoQAEKUOB9Cqi5E4tq1yTQLlMPHcUOxB0dx4rgTCRe3r2Kf93O4PiR47j5KglvHuzF9IGvkLRzGbqX1VCtQO3fq7SqBQPVfXUUtehboU1tTRz4Nx4vH4pNmlsnLyFTNqAvsldUAwaSlBNSAxjrql4U+ThZtaHaW60K6NirDtY6e8J389dod6QhuvUSwajmNmhUuwKMtFQqUbe8anvid1nkc9wPUVyoghbVSyCrbmpXtEMt3c0IiPWGp8iWaWmYLvqi2D9KN4uKRU1Z1ZVVyfycS4oOS96cWq8kSii4417iujLdJwb3N0zHpF2qcyAuJUXgoTJzSOzpFJFuI+psOiDVMYSJcvmZ4khCdLAvXng/x7P7N3H53Akc8czmxtxOF8C85dYEr1OAAhSgAAUoQAEKUIACFKAABQpT4B0DRCpd1zBExYbtlD99R0/GQ7HR8/h5xxAYfQm/rbyOdrPskLxzUX6HrAXT0inLuDJVoY2SZRVBoRDEhMeqZInkHPrI+WqmhtKd0EYVx2VYgYWYu/w0fIJu4dAaxY+iiCnqtO+OL4cOQZeahikZO+qWz9y2LFqMT3FayxilMkW9UsprGsNcscos9g1C3igCK+kCRNp6UI1bZW6loM8kIOiRb/JywzI1xNIvkccTFY5g5afwkvDC/Vzy0rBsm41FhMoX6rIuKkPEg8NwWbMdR9weIDhDqpAeFCSRWd+Yy9l3n7dcGuBlClCAAhSgAAUoQAEKUIACFKBAoQqoHyBKDMDxZUvFF8H00GTkVAyonnHpVtpopIao2XsaFnvfwuCtAQi/dhEv4u1QO5vi6RVkidlliyQhLiG7BUJyJCavIYJ2Ce3/NBsmw4xploKN0yLs7Se+IHblLE67XYL71Wu4/TIEnqc2Ysapw7i8WGwe7VAqOUikbnmVx0OipZO8XC0pAdlTJCJeSagBbQ3V8Jfq7+/h+ZOFwMPNW9mQcd36MBNPnUSqBcWH1yCvhSkHN+HzTGvC1O2X2IPIXexNNXonfBW3lqyD1m2awrp+LVhVq4bqVqa4Jza+nuCez/Vz7zhv6o6G5SlAAQpQgAIUoAAFKEABClCAAu9TIIt1Pbk0L5Eh9MZpnD93CEfvhOSyn4suytUpk1yhPK972yQiwi8im07I4H8/MOtNlGVhePJEcZ8EZpVMM+4plE1tBXlaol0KNVr1wciffoXL3tM4vX0hhjdV5Ky8xpHFG/FQ5cNZ6pZP7auGgQVKKcJ6sld4lM1my7I3PniqXIZmgvIm6scAC9JFUVf80/1wvaP4fyXRtntN5RfixI7aqKD49ByC4RWSzabRYt+g6MgIRERGIzG3uE6iLw7+lhwcMuu+GIcPb8LSad9i4Gft0aye2G8q0zJCRdvqH/mdN/Vb4h0UoAAFKEABClCAAhSgAAUoQIH3J6B+gEjDFHUblVb28P76bfBUrnfK5hCfB79/zkt5UbNyfZRV2Yg4MSYh8+bVCb749/LrbCoUnz0/dgLeWexXnOhzEnsfKW6rDPs6qUu6sq2mQC4k+uzBD/37oI/TPLhHpo9gaMDIqj2GTRqESoqWgp4gUPRZ3fJZdrKEFeyrKK68wPGzPll8JU6G4Mv7odxup0wT1C/17vs+ZdmPPJ6URXjgr2lr8FxRvlo/DKin/JYZoFMJ9rUUD8RrnD36OHnZXPKVtP+Nvr1EbCDeDu16LsZt5XK0HI54P9xS7mlkKeajJcqobnote4OgyOyyz3KoV1wqkHnLuQlepQAFKEABClCAAhSgAAUoQAEKFKqA+gEi6KLmF/2g/Ci6/1b8MGsPHkVl8eIti4bX4UWYfVyx64sR2js2hYmyNSl0DJN3SY5wP4p76QMr8jg8P7AUG5JXI2UN89IFM9d6IDxdk7JwD6yZtgKPFbU3+BKdK6pGB7Ku6l3PahgYI8bLG96eR7DFLTD5U/ZplSbi9c1r4mPz4ihVRbnvjrrls+yfhgXa9W2oXK72fO0cbL4blS6LS47YZ/swf8l1ZV8se/aEVdqmzVnW9t+cVGT+hHjh2j/L8X2/Edj4VATPJNUxfKYjqqYuMRRfebN1dFDuC/TK9Wf8eSU0Qzaa/M0dbFzwt8gvErs5tf8MtZRpRxmPDF+Yk2hDX1l3KJ75x2QMPMpj8GzffDjfT75fnuNozEzjAAAgAElEQVSn6VRbKaB5y1wtz1CAAhSgAAUoQAEKUIACFKAABYqMQL7WH2lVccSiGZ4YMus0gk7Og+Pp1bBqWBNVK1WChZHYKDjIH0+vu+H2q+Qojlm3aRhvb5yyUbMUJRt3Rn3pPdwJ2oOxX8dieP/WqKwdjIcX92LT0Sco3agqEm8+ywJJFxVKx+Khywj09eiMT5pVh0HofVw6dhJ3w0RxaS2MnNId5d5T0ozEyAaO7Y1w5VgELk3vBcczvdC+YRWU1ozAixsio+nUI8SJUddx/ALVRaBGoqVe+SwAxCkNlO36I0bvdcRyz9tYMeRLXOvRGc1qlsSbp9dwcv8FeCv2H6roiJ8dqyIPWz5l3YwaZ+9M7wS76TncoF0Hg5b+hhG100d5JDBq+j2mOPyLqWdeYvu3vXG7y+fo1LAspCEP4bZ3D64qEsmMO+B/IxogJe9InNCEtuKpjXuC/dv+gV6D8qhuY40Kulbo3NIIBw9H4PhPP6D0d45oWUUf0b6P4HFsK1xFVpqOAiNejkcH9uJ8mU6wrZ2y/DGHrisuqTvPuVTHyxSgAAUoQAEKUIACFKAABShAgSInkK8AkdhABhW6/wJX821YtmgVTjwPwpMbip+LGQeoXQkOwydj8qCmKJ0uV0mz4ueYO/0evp11FD5PDmPFnMMp92mhZv9fMa/1OQwdlVWAqCI+XzQKIUt/wqabR+EqftKOknYYsWAehtR4jykzUmM0//EPTJBNh/MJbzw7s0P8pCPQroz23/yEKQMqJ39HTN3y2T0uOlZwWi4+lTbjRyx3ewX3Axvhnq6ssfUQzJk3Co0NCmFD6rR+aMO4Yh0069AD/Qd0Qz2TLKJ2GmXQae4GJC6agl/2P4bnkfXi5+1ATKwdMVnsI9Qu/cOjWwXt7Urg+Jk3uL5+Nq6jOiYe2IL+5QxhN3EJvg6cjDXXPOA6X/ykVqVdHT2m/Y4hen/CcepFRJx3xoRHodi65zvxrbk8HAU1b3loikUoQAEKUIACFKAABShAAQpQgAKFISCRi+PdGhabSosvlbnffAL/4HDESAxRplx5lKtQGTVqWMIkh9VeiSEPcPnybTz1CYfcuDzqNG8DG8sSKZlGOfRKLF/zuXEel+6+QGhiCZhXt0bLZrVQWruwAiIyRHnfwLV73vANCEKcrhnKV6yGek3qoXyWn6JXt3x2FokIf+aBS9cewCckFprGFqjRxB621Uuh0Ciy62qO52WI9r+Ly//exvOgWGibWaJqzUZoIjJ8dLKa0vhA3Dx5Etd9YqBdujpadW6FyvopBcVyMr9bl3HtwXMERGqgZOUGaNGiAcop5kHsQ/T0/CGcexIDY6sW+KS1FQzUWmRZUPOWIwYvUoACFKAABShAAQpQgAIUoAAF3rtAAQSI3nuf2SAFKEABClCAAhSgAAUoQAEKUIACFKBAAQqolT9RgO2yKgpQgAIUoAAFKEABClCAAhSgAAUoQIEiIsAAURGZCHaDAhSgAAUoQAEKUIACFKAABShAAQoUlgADRIUlz3YpQAEKUIACFKAABShAAQpQgAIUoEAREWCAqIhMBLtBAQpQgAIUoAAFKEABClCAAhSgAAUKS4ABosKSZ7sUoAAFKEABClCAAhSgAAUoQAEKUKCICDBAVEQmgt2gAAUoQAEKUIACFKAABShAAQpQgAKFJcAAUWHJs10KUIACFKAABShAAQpQgAIUoAAFKFBEBBggKiITwW5QgAIUoAAFKEABClCAAhSgAAUoQIHCEmCAqLDk2S4FKEABClCAAhSgAAUoQAEKUIACFCgiAgwQFZGJYDcoQAEKUIACFKAABShAAQpQgAIUoEBhCTBAVFjybJcCFKAABShAAQpQgAIUoAAFKEABChQRAQaIishEsBsUoAAFKEABClCAAhSgAAUoQAEKUKCwBBggKix5tksBClCAAhSgAAUoQAEKUIACFKAABYqIAANERWQi2A0KUIACFKAABShAAQpQgAIUoAAFKFBYAgwQFZY826UABShAAQpQgAIUoAAFKEABClCAAkVEgAGiIjIR7AYFKEABClCAAhSgAAUoQAEKUIACFCgsAQaICkue7VKAAhSgAAUoQAEKUIACFKAABShAgSIiwABREZkIdoMCFKAABShAAQpQgAIUoAAFKEABChSWAANEhSXPdilAAQpQgAIUoAAFKEABClCAAhSgQBERYICoiEwEu0EBClCAAhSgAAUoQAEKUIACFKAABQpLgAGiwpJnuxSgAAUoQAEKUIACFKAABShAAQpQoIgIMEBURCaC3aAABShAAQpQgAIUoAAFKEABClCAAoUlwABRYcmzXQpQgAIUoAAFKEABClCAAhSgAAUoUEQEGCAqIhPBblCAAhSgAAUoQAEKUIACFKAABShAgcISYICosOTZLgUoQAEKUIACFKAABShAAQpQgAIUKCICDBAVkYlgNyhAAQpQgAIUoAAFKEABClCAAhSgQGEJMEBUWPJslwIUoAAFKEABClCAAhSgAAUoQAEKFBEBBoiKyESwGxSgAAUoQAEKUIACFKAABShAAQpQoLAENNVt2NvbW91bWJ4CFKAABShAAQpQgAIUoAAFKEABClCgEAUqVaqUY+tqB4gUteVWaY4t8iIFKEABClCAAhSgAAUoQAEKUIACFKDAexPIS7IPl5i9t+lgQxSgAAUoQAEKUIACFKAABShAAQpQoGgKMEBUNOeFvaIABShAAQpQgAIUoAAFKEABClCAAu9NgAGi90bNhihAAQpQgAIUoAAFKEABClCAAhSgQNEUYICoaM4Le0UBClCAAhSgAAUoQAEKUIACFKAABd6bAANE742aDVGAAhSgAAUoQAEKUIACFKAABShAgaIpwABR0ZwX9ooCFKAABShAAQpQgAIUoAAFKEABCrw3AQaI3hs1G6IABShAAQpQgAIUoAAFKEABClCAAkVTgAGiojkv7BUFKEABClCAAhSgAAUoQAEKUIACFHhvAgwQvTdqNkQBClCAAhSgAAUoQAEKUIACFKAABYqmAANERXNe2CsKUIACFKAABShAAQpQgAIUoAAFKPDeBBggem/UbIgCFKAABShAAQpQgAIUoAAFKEABChRNAc2i2S32igIUoAAFKEABClCAAhSgAAUo8OEKyGSyD7fz7Pl7FZBKi0buTtHoxXulZ2MUoAAFKEABClCAAhSgAAUoQAEKUIAC6QUYIOLzQAEKUIACFKAABShAAQpQgAIUoAAFPnIBBog+8geAw6cABShAAQpQgAIUoAAFKEABClCAAgwQ8RmgAAUoQAEKUIACFKAABShAAQpQgAIfuQADRB/5A8DhU4ACFKAABShAAQpQgAIUoAAFKEABBoj4DFCAAhSgAAUoQAEKUIACFKAABShAgY9cgAGij/wB4PApQAEKUIACFKAABShAAQpQgAIUoAADRHwGKEABClCAAhSgAAUoQAEKUIACFKDARy7AANFH/gBw+BSgAAUoQAEKUIACFKAABShAAQpQgAEiPgMUoAAFKEABClCAAhSgAAUoQAEKvJNAnO8KfPFjW7T58RP8eD8C8neq7UO/ORa3dnVHm9nzcTv+wxkLA0QfzlyxpxSgAAUoQAEKUIACFKAABShAgSIoEIfnN88iUNmzOHhcvYvIDyZClADv04NEYGsMTr75YDr9nzwDH0yASB79DAeXjINjF3vYtuiKgf/bhFsRshxRYm79gl4jDiAwi2Jq1ScLwfXt63DgaWyO7eXpYkHWlacGP+5CMTdno1O7KXCP/rgdOHoKUIACFKAABShAAQpQgAL/mUCCN07eEeEh7RpoaADEPj2Mu7Efd7DlP7P+Dyv+MAJEIqhy/pfhmLn9Ip5p10R980g8OOWMr8fugk9idjrx8L10ByVaNIKp6ijVrS8xDNd3rMN+ESB650e8IOvKbug8TwEKUIACFKAABShAAQpQgAJFXkAe74cL636EU3cHNLOzQ4uOvTB8liuuBaW86MpCxbuoS96TFdQtX0BCcYEncTYcMKzzFQY3KA3EX8Nh76h3f38uoP6xmrwJaOatWOGWSvQ5iJXHIoCKQ7Fx22jUxBPsWPgnLr4JQXCcHBU0JZk7mBQEjyuJsJ5sDtVB5qu+zC3wDAUoQAEKUIACFKAABShAAQpQIH8C8ijccB6FCftN0Xf8HEyoZYJE/5s4uGY5xnz3BhvXf42a0lB47HSBe8ne6F5NF1m8+WZsO1HN8vnrucpdqcvLSsDBugHqaNnB+NJhXLt2D1E1m8EwQ6eTEPLEFc4njuG6jw/iDKqirlUnDOz8OZoYpr6556WMHLFBZ7Du0Hacf/4IAbH6MLdsgc/af4O+NUpDS/RQ/uYkxsydC8mXm/BN1CY4nz2DR2+0YV7eGh3ajcVXdcyhJXuFvcu/xG/+iiG9xJy5DvjN+nfs/qIhdEV4K7c2lBDyaDy5ugJ/XryIO4FRMCzbAPZ2A9Asi5VMBcL9H1aimlvzHzaV36plCL9zVoSEgDKtHFBFV/wfXSt8OWMZnBeNQsMSWf8RkUfcxbnQmmhZWUelYTXrS3gOl/59sfplPG791AEd59xCjKhRFnEbW38ahK4tbGDTvDMGz9wJz8iUJ0DxgOydg6+6toCNjQ1a9xgN53OBSMymLlWZeL/TWDqqFxzsxL3dh2L2vieIVqYuyRBxdydmDu6G1rai3WYd0HfSetxMXWqXXbspDeSrz6qdE79He0zHJ70W4/CO2RjWoxVs7T+B44+uuJu25O8d+vkuYxA+kZ7bMXVgZ9jb2qOr0wzse1YAWV9ZGPAUBShAAQpQgAIUoAAFKECBdxKIuYft/wSg7g8LMaF3SzSqUw827Qfi52U/oPbzndh2X/Hm+QEcYnnZCeXyMmt0rqAPPYt2aCJew2MeH4ZnhmVmiQjwmIHB69bhXHQFtG01GL2rl8Azj1WY8NdfuKfczDkvZeSIfuGCMctmY+dLbTSwccLgli1QJvgEVq8fjnl3gsWb4dvD+/wkjD3kAR2rHujb1BYmwRexdfNX+MndD4mSkmjZayEmNjIVN1RE/36LMLd1NWiL4FCe2pC/wb1j32L43oPwiK8MB/teaFkqAqcPTMD8e5EfwORl7OIHECBKRPDzoORevz6AKX3awNamFbqP+BUnfeKzTVmLfnQWPpYOqK6nOidq1qdVGUO2umJYBfHgzTmGo1MbQC/+GTaNGol1ES0x/o9NcFnkhIo3lmDk1KPK/Y7iH63FhHluMP18Jlatdcb3zQKx5ecFuBRTKXNdKt2TR1zBgq/+h2P6vTBr7UYsdioP97kjMPdfsQt8zG38Pn4R3C2+xPSVLlg1dyAq3foT01weQfFnKdt2I0R0Kb99VtybxZHwcidmb4pEmwm/i370h8WNpRjxwz74JYnC79DPdxlDkv8BTBq+BFfN+mLOquWY0jkB2+YfR2i2yxCzGBhPUYACFKAABShAAQpQgAIUeB8CcjnkMjneBEcgIV17GuYdMf3P+ehXwQ/rB/TDGkWywrRO+GSuIllB8Y/xuzDrq+5o29QOdvad0G/yhuSkAZGQoFo+ym87HFsOwt8BqSETGV7tcUKrgbvgr3h3U/wD/b65GNqtFezEErc2n32LPxTJDcr+JOLFpr7i/Cici8oeJC7wBM6J5WU6VT9BHV2RwCH2IepQRUSIFMvMXr5Ju1EecwOr/7mACLNBWPndfEzs/BVGfv4b/urVEJrB+7H5mViSlocySPLB/r2b8diwB5ZOcMbUbkNF/6fCeeIy9DYJw+l/tuBBOtAIfxm6D10P537j8G3P2Vg5YS4+MYiG+5F1uBGrBbOK1mhoVkL00xhWVrZoZG4AaR7bSAo9ht/PeUFedgj++n4ppnYfjYkDV2LDly2RYVKz5ytSV1RXXxWpziV3Ro74N8nfhQs8sQuSRi1hp3kdVzy2YcoEI2zf8jWstFW7LVLczj9EqTZjYZwpwUj9+qRSTWiIeiQaGtCQShD7YAd2+Nrh5xUj4KBsoA5qmPqj9/DduBLcGe2CnyFEpxY++aw9bEpJ0aRGBVRo+QoWIs9Nta6MPZcj9PJGHI3vgCUzBsHeSNRdfzJm+kzFHp9QJDQyQJOvpqFH1x5oaKKI7VlBen4LJvhFKP8Ay3No9136rKqr+F0OE3SdNRNONoo/SNaoLcbf6+ttOOHbA06l89/PhHyPoQMaHhdrdQ16YvXcobBWdKtJbZj43MDQg1mNgOcoQAEKUIACFKAABShAAQoUooB+XXz5qQVGr3LEpxe6om+P9mjRrAlqlTVEFWsbZcdqbN6KOMevcG3EfqzqZArNmFv4deJiuFuPwc/fNoZxuAd2LvoT09c3w+5xtTBYpbwk4H6OA4x/tA4/zHdD9ZEzsKJxCbw8ugwLpy9Cw38Wo7WRVOwp1B39B+ijYqZ37tRqFcvLzomvl2nBrkn9lOVkBqjTqC40Hnjg6rX7eFPDForXs1j/Y7gSq4HGn/VCDe3UF3UNmDcci58Sb0JuJFar5KFMQvgVHA8ALDv1RG3tRMSnJgRo1cRnzStjzxEP3AxPRO3UZBHLwRhYzRip2TEahs0xtF0NHDtwFadexcG2skYmo6Q8tZGAMt4n8VCMrm3nPqipkzomTZjXG4o+Jy9g0wf2saQPIEAkgiqayVNp2GkZXH9pBeOEJ1jdrx9WP9uPw16DMbamytOa4I8rN7XRrE+ptIcg/YyrXV+Gx0VEbJ944nXUffzYpaUycKQ8ZHGISygPH/Eg6tUbhM8tv8VPXT7F9nYOaNmyrVjj2AQV9UTh5FhXpgcw+UQ8Au4+h8xyJKobpFQsMYTNeGck//UAdO6pj7vXDsH1nifu3ryCC9fDIG+XfHf27coR+C59zqK3Ei0rtKyh+GOefOhXb4O6Oodx2zceEkurfPZTAlm2drmNIRpl7gVAu9YYiCzFlEMPVVvUhO6hLAbAUxSgAAUoQAEKUIACFKAABQpTQPGuN2kzttjswZ5Dp7Fv8WGsEZkv2uXt0Gv4eIzqWg36UpGkoEhWUPxXJCvI5eIf4wdPRfeu3VOSBqpB6rYVP/gnJw1oq5TPbRuc1H+g79SjXUpyQ3lUsA9EWcUmPuJtuqTNIIxPfRnNykpkLSmXl6EGbIyj4B+SnDEkM24ES3jA69ERsczMFra6MkQFvUQUyqCuCIClX8ok0amG9vbVRB0yvHbPrYwINHk9xmtROvL4UHQ5nlWnSiMoVqRHpQSIzCxrQJlfkXZooGTFeiiNR3gZFiOSH8Rn11SOhPC8tBGLsNch4s7ysClbIuP+UJpl0aCsyKJ6plpz0f79AwgQacCwjKFQDES5xlbJEUltC9SzFEGhF9EIfqPIi8t4yEJviuVc9TG2nPKpVjnUr0+1BplIBdQw7wfnNUNQNX1sSqIBfWNtEdBqgu+3nERvj/M463YR51eMxorf2mDWloXopljamMMhTxRLuhQZS1mUkQWfw7zhP+KkTjN07eyAbmM/R7e/v8XPKel+UqPs2p2HJu/S57JZ9Ua1gxJIJDIkiXby30/hUzb/Y7gr/sJUPSQSae4buanexN8pQAEKUIACFKAABShAAQq8DwGpEWq0H4Ipih9ZNPxun8LfG12wZdYwBOjuwcJWGTsh0bfCJyJp4N61w3D19MS9W+7KpAGkJA2o22W9egPRp+J3YplWd2x3SElucLBOTm7IQ2Vxr5K/XgYRbFmxYgBWqN4TdwVHxDIzW7H3i0ymCFdJ3iZZqJYVv+eljFyepNxqpvonizCuin4WtWijlJmIBeS41UhyxEgmlvhltalK3trQRrQk8ztoaofEG3IWfSvapzLE0YpmVzVRum5dsaAJeH7uOgJFPEgWfgsnPRWpOGVRy1w1102OKE+R4mbVFlUVG1pnOtStT7UCKYyqVINBiCd8xIZWpUuXFj+loOfjiplz9sInKQmB5/7C4g0PYGTdFU7jf8Hav9eij4Ybdl0LzbBZlmrNEGl5ZepYiMDXZTxJ3pVaFInClRk90XueBwKubsLBN93wm8tSTB7SHS3qlhUphnEpdebUbgQMC7jPcpHFdeHR2/Wk0U/dRGTYHPVEqDks3/0MQkC2drmNQRsW9S2Q+OA8Hqd1KwbPLj5ATFZ/4jPj8wwFKEABClCAAhSgAAUoQIH3JhD3aAOmTt2Ah3EpTUr1Ua5Rd3z361+YWi8G/x68D9UVSop/jJ8/qC/GrToDP7066DpmIeb1KJ3py93ZD0KGhNiEtKCIIslg3Obj2L18DNqYh+HSim/Rp+f/cCggcyJG5jrF8rJbZ5XZPJVaTMJMx5mYlfYzA5PsK4orb3Dl+gPxv1IYmJqLL4MF4UFQdIagjDzOE5t3zsSy6/7QybWML8TLLRR5FzFJZVCrUj3UT/upjQpyf3i9DoNGyiokRZ+DXjxGeIZUKhnCfO+JnpRAORO9LFcdaeapDS2YliklwkC+8Hj1JmOgKTEQd17FZiYr4mc+gACRyAyr64j+NYC4yzPR69Pe+Kz7WBwQmVyGHUeis4VqdksMnpz1Qrm2NZFVLFExH+rVJ25QBP4kSYjwfoFXEfHi/v7oZXEbC8YtxJ5Ld3H3kitmTdmC5+WsUUFbCh3JE/yzYgYW7xCfufO8hhM7tsEtpCRqVNSHMsklXV0ZYxdSlLJ3Qlv5Ycyc7Qq3255w37MEvxx5g3oOVjAWf1j0wm/jzL8P4ef/BJc2zcLsE5GIFql6r8UG9zm1W+Jd+pzFQyxBKA5Mn4XN52/A48xGzPjfToQ16I8uIrNLN9/9NIReDnY5j0ET5do7wubNPkz5eT1Oe9yAm+sc/LxPPCipT7ksFNc2LcVvu58g9e/gLIbGUxSgAAUoQAEKUIACFKAABf5zAalmAh6IfXb331cJLojtS6JEbEHHWF9lZYkc4de24FBUVyxd+yt+GPxpctJAdFyWWTBvB5CI2ISUCIn46tazG34pO58okgxWY8lGRXJDFzh9Pxdrdq9Gb40L+Pt6bskNonbl8jIRHpI2xIDWXeBQvy3apv04oGubAWgg3sWiHh7BA/ECpleuIxppxePKyQN4mpD6JpyEoHvrseWGG55r6MEwD2V0SjZDm1KAz4XVOB78djdqWdRlrNjyC3696JVhf2j5i01w9YpIS9aQRV3BxlNibyadJmhfNmNWiVgQozy08tSGFMaVO6KWCH+dProXj+LTjcnTBbtTvrX1nz9IBdiAxkxxqFNfeHg4TEwU+Tzv8dAwRYM2DaH92hd+zx7DT14OjbpPxJL/dUC5tM2tUvoT74Vdf11Bbad+aCA21cryUKc+RQVSbbGr+nX8s241jsS1Qu/WdWDXoRGkt/Zi07r12Hb6OUp2+R8WjmuB0poS6FZogpq4gwMb12LTjn9wydsADmN/wfi2ZaGlWpd9mQzRXoleJbRoXQF+xzZg9erNOHgXsB8zD5M7VYRB2fqoFOWGDX+sgMveS3hVrg9+HGyK27s24pxuR3zxaXuxXXY27WqWhnV++6ySGZfgfwbbzphj3NfGOOrsDJdDD6FpNwKL5n0pvhongZZ5/vtpWDEHu1zGIDWojjbNTeB1dCvWrf8bV0KrY/Dktnh1KRTN+3VAeelrnJw/DWt8m2JQ9yoQK0J5UIACFKAABShAAQpQgAIU+E8E5KnRhmxq1zCyhMkTV/y58iR8NHQgiwlH4NPrOLJmPlbdKo9hM4bC1iQSHrt3w7NMa7S2MoV+9HXs3vcAOjXEO5deGO7uW4Zftj9EuEEDdO1UEyYaYenKl4ShdiQuuW7H5bhqaFRJgufHlmPp4ceINLRHv551oaf4OvW83fAxqoQy0kDcFgGrnefD0XjAALQSW42EioDUql1PYG5dByVVcjPi/HdgqZsnkmp8i/F2laH4gFn6Q6JdCjrP/8a5wFeQWX2GNmUric2pb+OwxxHsv/sYwREvcef2Ziw7cR2R5oMwp6s9zPTK5VqmtJYJrMxCce7qSRy9ehGPwl7hpfdpbNy3ChejKqLfgAloZypWGiU8w+Hz5xGoI8P9q0dxOzQIL54dxfr9a0Q5XTTpNguDqxqLIJxYhfTyIPY88cUbkcUl1SiDyqUsUCMPbUh1LVE16V8cvnsah0SCR0D4c3hcd8GvJ29A30wXb+IroWvLVjBXzWtReSYkOSxVy+bxUft0XmI5EvHQqrUAx9vbG5UqVVK7M7yh+AhEe0xH94nxmH9oAeyyS9MqPsPlSChAAQpQgAIUoAAFKEABCqgtkLyfTs63yWO8cXrDauw6fxP3n75GjE5JWNZzQN8R36BP45LQEBk/t1Z+j8kbb0G3rwt2jCuDy85TsXD3LQRrl0Pjbl9jtM1t/DL9EHS/3oJ1g8zgmb78xBp4fXgRpi47iAdR4jPurQZgXIcbmLbFHpvWfwELBOPftXOxZMe/eBklg555A3Qa+j+M71kd+hLFZ+4d8fnyUlh8eiXaZNjLOQ4PDw3EiAvRaDtkO2bWNMxixx05Ijyn4ovNl6DVeBl29m0sVvkkIMBzA5xPn8RN31eI0RXbxtT5HKO69EQDg9QtkvNSRoY3foew4vAeXH7hhSCZEcpVbIUvPhmOzyoLN8Euf3MSY+bORYiDM34scQCrLv0Lz5BElLZojA7tx2FYXQuxyUvykRB0ALPW/Q63MBnKNf8T63vUEcvhcm9DebdcbG1yfSWWX7iA269CkFSiBtq3+w595b9hxCkrOE/5EQ1Ud8ZReSyk0mySW3J+fNS6mpdYDgNEapGysEKAASI+BxSgAAUoQAEKUIACFKAABXIWyEuAKOcaePVdBNICRO3WY1PHKmnBoHep87+6t6gEiP77MNV/Jch6KUABClCAAhSgAAUoQAEKUIACFKAABQpE4AP4zH2BjJOVFKCAvvVsnDpTgBWyKgpQgAIUoAAFKEABClCAAhSgAAUKVYAZRIXKz8YpQAEKUIACFKAABShAAQpQgAIUoEDhCzCDqPDngD2gACNV1HUAACAASURBVAUoQAEKUIACFKAABShAAQpQoAAFJCU64M/5HQqwxuJfFTOIiv8cc4QUoAAFKEABClCAAhSgAAUoQAEKUCBHAQaIcuThRQpQgAIUoAAFKEABClCAAhSgAAUoUPwFGCAq/nPMEVKAAhSgAAUoQAEKUIACFKAABShAgRwFGCDKkYcXKUABClCAAhSgAAUoQAEKUIACFKBA8RdggKj4zzFHSAEKUIACFKAABShAAQpQgAIUoAAFchRggChHHl6kAAUoQAEKUIACFKAABShAAQpQgALFX4ABouI/xxwhBShAAQpQgAIUoAAFKEABClCAAhTIUYABohx5eJECFKAABShAAQpQgAIUoAAFKEABChR/Ac3iP0SOkAIUoAAFKEABClCAAhSgAAUo8H4FpFLmY7xfcbb2rgJ8Yt9VkPdTgAIUoAAFKEABClCAAhSgAAUoQIEPXIABog98Atl9ClCAAhSgAAUoQAEKUIACFKAABSjwrgIMEL2rIO+nAAUoQAEKUIACFKAABShAAQpQgAIfuMCHESCSh+DIUBvY2Kj8NG2H3mOccco3vmCmQRaC69vX4cDTWGV9soDdGGDfH9v8kgqmfjVrSXh1HismDECXFrZo9akTJq9yQ2CCmpWwOAUoQAEKUIACFKAABShAgfckII/3g9va/2FgtzawE+9vzdt/hqEzXXEtKDFPPYj2mI4O7afhenSeiudaKMM7ncr7HlR/z7W2zAVibs5Gp3ZT4K5mf+Mfr8BnNvYYf+lN5koL4Uz8I2d0V7xv992EF3mbKpVexsFzadfM7+yf/o6HeXxdjzo/CraiD/YT/kXRUCmEiSjkJj+gTaol0LKeiD/G1oOuAk2egCh/D+xcshL/m2SE3RuHoLLWO2omhuH6jnW4YtoH3auJVvRroJtjN9QykLxjxfm4Pe4h1o6ZABcvwLBqPVi8vovTa8fDW7oNm0dUh3Y+quQtFKAABShAAQpQgAIUoAAF/jMBeRRu/PYNxu83Rd8Jc/FDbRMk+N3EwdXOGP1tFDZvHIGaype593ikf6dTed+TqP7+vrolnO7uPQhf0V7V99Xmf95OHAKeBIlWDGBe3hipgQbtSmbQKYTX6f98uMW0gQ8oQARomlRFnXr1oJ86GfUboYbsMj6ZdgoeIU6obF6wCVFSowZwHNOgUKY++s5W7BLBIdT5AZtd+qGsvyucei3Fo53bcG/QdDTWK5RusVEKUIACFKAABShAAQpQgAJZC8TcxbZ/AlB30jr80LMMlG9nderDuq4+nvdcAVfPQZhl/X5fZDK80+UxkyXrwRXAWVksXnt5wv2flVi0M1BUWIz+2T8hCE8DZJDaTMe2Ve1gVABcrOL9CxRsROW9918C7RJ60JRoQUeEupL8tqG//QDsFg9m8iHDq78HooXjTvgrVolFnMLXrQfB9eIuzBraA61s7NCu/3Ts9YoDEp7DpX9frH4Zj1s/dUDHObfwJnAfnFqkLDFT3jsQm0+5YMIX7WDXtD36/bgTnk+OYt5XXWEvUuEc+s/GEb+3+XiyiNvY+tMgdG0hUvWad8bgmaJ8ZGrfEvFi4xciBe8bnItShUtA4G1PRIjTFZrboIwYm6a5DZpZiBNh9+AZnDnnL/6JIkVRtNN7KbYv+wafimVp9l2HYPaBh3iwdzoGdGoGG7sOGDj7IF4o/2JMROCFvzCxb0dl321adsaAH11wOY+pn6o95u8UoAAFKEABClCAAhSgwEcuIJdDJpPjTXAE0u+MoWHeCTNWLkC/Ktq5v7MpCePhe2w+hnZrKd6jOuLLH1zgEZryHqXme5ks9Z3O+2nG973p+/GnyvtfjGg553c4GSI9t2PqwM6wt7VHV6cZ2PcsFvK0ac/pHU/UHXoSk74cgRlbbuS+hCrhGdb0Vmyx0hmL7on3VfF2ePa75solXN2dHwqhJPjvdFT+7jD9GqJFL2K9j2LRyN5ob6d4v/sUw+f+jftRqe+fuV1P/+zKEX1/NRxtRT0tRmGHVx4iawmv8fg1YGJZOs9hr3j/M1g2qida2zZDpwEzcOB5XDpLRX8S4HdmOcZ93h7NFe+szdvj83HLce6VeB+WBeKf4XbK8fd39RUaiiMSlya1EufsMOJQEGRqv/Pm0F4KT1LQJawY2xtt7cT8D/8Vh/+ZgJaiD+1meEDx/CgOWYQn/p4zHL3aN4WNbRt8OnAS1lwKzPBnIqVokfvPhxUgkiciMSkJSYqfxFiEvbiEzWuuQ7fVl7AtmcehiKVbGxZfgqWYzK2uC9FddhQLF55DiGZlDNnqimEVtNFgzjEcndoAmWLbsQ+wZnUAOsxYj60LuiD+xCI4feUK/UHLsG3TdNgHH8Cy9Z5Q7mAU/wybRo3EuoiWGP/HJrgsckLFG0swcupRBCr/jEphWKc7HAd2RMVMgeMkRPiHKx8WA3PD5PQ8jRIoXUJxJgJ+ETnsifTCFUv2+EJDV474wLs4MHsABv5yCqH6hpDIwvDgwFzMO/ka8S93YdKENTj3LBGWts3R0OwNHp5YgbGTduZzzamyuzwoQAEKUIACFKAABShAgY9VQL8e+nW3wPOV/dB1yAys3nsengHRkEkNUdXaFrVLauRJRhZ+Gr+4hMJhsjP++mUAKtxdgZHjtr19T1HnvSy1RS2V972ZPTBK9f0vl3e4JP8DmDR8Ca6a9cWcVcsxpXMCts0/jtC0f7/P6R1PvAEaNcPkFc5wdl6IobmtLdMqh+bNSoveB+HmvWAkxfng+tPksJv/jUeIENlIXldfiOvaqN9ObEESeASTB07DzmuhMLNrhxaWEbi5TwTZJuyFr+hfUi7X009MUogbFn6/Go/kFdHvt4X4QgT2cjuSwrzhIyIksfeWY3A7Ebhp3gmOUzbjRlhqgEqlhug7WD5iErZe9UF0yYowDj6Cpc63MxRKeL4NkyZtwMXnCTCv1wT1y8Tg+cUNmPzzYQSiNJr3bqTMUnt8/CqCFc3EPMWpG6IT0kbo2dQUMjXfeXNsT1F/vAgyjh4Ll0svEGVkAQPfXZg+63zy+39qz8WzuWqYE+bvv4mXGlawrmeC4Adn8NdYR8x2CxNBq6J9fEBLzOSIOTMW7ZqqgJbtjeV/dEQZ8WTkEDZ5e5OIaFcYMgWD7c3Fw1QNTk51sWvtc4SJPzSmUk1oiPWREg0NaEglKtFLRRX6aDbuO3StZyiCmZ/j08o7sKvpZIxsV1Psi1QJ/RyW47RXcHJk8MEO7PC1w88rRsDBWLHosg5qmPqj9/DduBLcGd3NpChp64QJtlk9IDIkxCb/LaOtqwXlkk2RJaWn/HOZiLiEtzHqTHfr2GPevt/QqcQ9LOr5FXYGacB6+h6s7FEST5d/DscNfnhx/zWiTNzxWPF0VvoCP8wejibGvti9wBluUWEIFQFqyw/oychkwBMUoAAFKEABClCAAhSgwPsXkBjCdvJWuNr8jd2HTmPfokNYLWIa2uXt0HvERIzuWg06eeiVHKb4bPZMDGqs2FzEGrXFe1Qv8R51xPsLfGOmqCDv72XpQxtSlfc9icrvsTm+w3VAw+Nis22Dnlg9dyisFf9436Q2THxuYOjB1EHl9I4nymiVRh07RdAnEnEbUu/J7r+6qNLGGiV2Hof35WeIahOIm2JVmkRkMcifX4V3ZC3cfKRITaiPDnV04XPIBZdEbMRy2O/4o19FaIoMpPXDvoGrx0aR5dQF3S7mdL07vk7tRtxz7JqyCoeCS6DVdJG9Y2uUvFQwu26mnE94/QgigQjRDx8LFxvU9nLH/ZO/49sIY+z9owfMM8QGZQhxW4ld/uKG6qOxfcNQWEmew3X4F1jqmfquK7KYAgJRslETdOrwP8zsVxVS743o3+cPeD2/i4D4HqjXrA9sND3gfv+oCET1gEPQebiHifiQdU/YmUoRd1mdd97c29O/uRZbnok+lxsIl+3j0EDzJXaO7otFN9+u8In0cME2xVYxZfthzfaJaGyQgBfbRuDzX+/iyJqTGG3/OSzyFifNRfy/ufwBhQFUN6lOQrS/O7Ys+AvTl3fBvqmN8/SXDbTMUK+GScpDLoWWXkoAJoeYSxq9ZklUNkv5K02qBV1NLZSqmppCl64uEReMeOKJ11H38WOXlsqgk/KQxYngTnn4hIsHyCynKKwUmoo1c+KIj0lQBqokYlPuWGXAWBO6Wjns8mXeAFZGijiqYXLGUZAZ6tYoCRHygoGZgbJOuQgM6Vp9ghYmbjjj7YKRXVxgVLUp2rTrIiLzXVFHmanEgwIUoAAFKEABClCAAhSggJoCUiPU6PAVfhI/kEXD79ZJ8UGhddg84ysE6O7D/Nq51yfRroGWVmk7z0K/ehvU1fkH91+JFyJFgCjP72W5t/W2RG7vcNEocy8A2rXGoHra+5IeqrYQyQKH1Gkn72X1a7RDXc3jcH90DU8eBsAbZdD1CzMc2XQft5/eh4efqKtqWzQyTYC3x0tlxS/WDUWXdenbCMTDwAg0yPG6WEJWJuUe3wPYqdg9W2ToVLEqhbx+B0rTogsmzbaGZsVmaFPfFJKwc5jy2UScdt+Mo75dMThDBkI8fK4/USZWVO/RGVWUr9iW6NCrtggQeaZ0RALjZuMxx/AY9h5aiwlOd3HL0y95GZdYyqg4pCa26GWjBffLt3HkTihqe1+Av3jTt+5pJ5I/xHuvWu+8ubUXj4Ab95VLAy06dEEtfcU7eUV0+rIBFt/0SOlzAl7ffazsY5l23VBX+bErbVRo1wM1RYDovrcHfONEgOjto51yX9H5zwcUIMpqk+q6MPc7ji/2/Avf+MZZ7ACvyMRJDrC8JReBHe0cAiy5zU2mWzOdUNYgEw+thnk/OK8ZgqrpY0ESDegb5xQcUtytAeNyim29QhH5KlLkDJlDOykKgZGKa8YoZ5zDtEmkKhFeCcSpTIdGGbGW9WB9uB/ai0P/Z+8+wKMq1j+O/zaNkEBIAgRCb8bQpIQmRapBBFSQjggi0kWuvYLXekEF6QooyhX+CBcVVAQUpSslKoiAQZDeeyjp+z8bSAjJJrsbUjab73meaHbOe87MfObE59nXmTnf/6jVWzfp633Gz+er9dbC8Qo3ZjhxIIAAAggggAACCCCAAAL2CsREztG/P5H6G8mg2y1f+t18VKbefRpVp4kqDuyocV/v0pV0CSJr39nS1mj5TuMuD2OVR8qR7mtYuoK0N7H52dZ3uB2p679+N5PxZevWa7beNFOxWmobIm3e+Yt+WnNFVwqFqmWL27Rt7qdav2aN9ht5klJNG6u0h1n/JCRNK1D1IeM0rGbqV8V5KCC0kE5met6It7yA7PoRUMlf5/Yf1WfvLtWDM3uobCZfP5Ov8QgyZvrcm6ofRW9TWFnpx8gLOmqZIJHyXrPrMdeTPKm/qxpflVMdZl3c/Jb6DF+iU4WrqU2XrvrXgxc1//W52p8c5Raghl0byuuXjfrt2w1ae8KY3uMepi7G8rKkb7MOfee1XV9i3LU1SyaP5O/KJrl5eKQa/xuzTkypHwrjQ9JHyx5dqbvohL/n8yyAu4oae/SYrp7VlRTpeGOmzfUP5sva99sxYwOv3D7c5Fe5qoqc3anDpgCVKFHC+Cmuwofn69XXv9Rhm2vhPBVUO9R4QaB0ZP3POmakVmMPrdeG40ZBQC3VDLzVOWlxOvTtBI194yNtK/+QXv3gC63+cZb6lTHuHxWh9YeSdlHiQAABBBBAAAEEEEAAAQTsFnDziNPulQv11c7LN2/XYaykuGRsY+FdrLDxv8ItR+bf2cyxkVr/95WUeq/8bexlFFNGdcrY+h/tdjfVSqCt73BeCq4drPjda7XHMo0k6biqfRt266o9q1Gs1GizyK246resYIT9rcVLjOlCFRsrtEodVfOO17ZFa3TBeFdYWIsKxhyVQgquWdqIM+tSQjmFNW1qbKJdSdE7Nmr9L7uMXWwL2zifKi0Q/LAmf/C2jB1KlLh9hqZtvL5vTsJlnTp6REeOnlF0uixHtHa821XNmrTUI/MPJs0MSjj1q9YdsPQwSNVKpJ2H5KWy9asmPQuRS77XAcse3LGH9eNXu1ORGHssrViTtGwt5PGJ+s+T/dW+fFTSi5xuHMasn7CuamwkIy+vnabZfxr5oXrG8rKk/Ykd/c5rqz5PlQgtm9Tmo6tWaZ9l0OOPafXibamSPl4qWbNq0l7GJ378TjsvWx4MY7bUqiVK6ln5uipnzxrLVD3M7V/tyAXmdpMcqc8kdw93mS+f1WUj6eJepJxKm/Zq8byf1LBvVUX9/JEm/RYtN8vfij2HJa1nMjaIPnBQJy76JM1ezOrhU7O3ugQ/pP88MU4afZ9CtE1zX/tM+9vMlLEPtnEk6tyWzzRng48eGN7t5llGxlmf2n3VtfwKzY2cpB4dF6nouaM6b+QdQ3r1UvV0u2c72kpjuVmRk1q3/AdFLV+n1Y1qKyhmnzZbpigWvkPNyqfOODt6b+IRQAABBBBAAAEEEECgIAp4VnxAg1vO08tDH9GlIX3VKjRYPjHH9cfyjzT7n+oa9npN43vIZZvf2UzGSoqvx76mik/3VPWE7Zo/fpGiGr2qcMtUlpTkTBaE03zfK2X5XpXq+1+pTL/DeahQW+OtYdNf0/OvlNXzD9WV5+5Feu+rs8beQsltyfw7nuMt9lSZJneq5IyDSYmSEnVrqniRQDU0ckarI40Cr9pqe1tSJ1S5c2/dMfc9bZ89SI/81VylTv6idX9dlFeD59S7qLdKZ3reSKgkzyAq7C9f//oaNLi2vhn3h1a+N1cPNRqlkFNfaESXSdqnML23+kO1vLZ7yfUuFVKVu5ur+IL5+mNCV7VbUEGFTxzUaeM7evEHHlf7mzcgMq5xU2CLgeoYuFVLI6eo5/3fqorbAe299jan6/f0kF8Zf+P384p89zE9sqqETv+2Q4a2kQdLMFbsXAsz+dVXlzsLa93qM7pkpG8aGMvLrr2/ytHvvLbqM8m/8UO622+rlh+YrYce+FFV3Q8abb6x/5ClRUXrD1TPiqv1yYH5GvTgbworE6U/th82vv0X0z1D7lbpW53rcV0np/6Vz2cQSYVKV1VA7Fb9b4PxGju/xnrypfvl+/1L6tttiN7dcrueee5OFbV3zp9HKTVuW0sXZg7U4JmRsiQys3wY0/+Gzpqux8pt0wdPDdCA5/5P59u+pulP1FHSckXjEYna+bXmf/a9Dlmb4uRdQ0OnvaMBLaur+OWjuhIYolbDP9SMR6rZ/drAjNtubJ7W4hV98HJ34z8uCfpn81qt33ZaftXv0ROT/627WV6WMR1nEEAAAQQQQAABBBBAwLqAe5DavzFX/+lfTSdWfKhXRw3X4y9P0fILjfX09Pf1cDVj+oQd39m8KvbT6w95aflbwzX4pf/TCSPJ8dG4e9NsdGy9CZmWpv2+l/azje9w7qU7afysZ9To5EK9PHi43ljmoT5vPapqKRObbHzHy7Rx1k8WqtRS9ZL2PPJQ1Qbl5eUeqOq1Aq8Fh7ZV9aR9bowcVYWemjTnOXWqVUyH163U+sNF1aDHGH3y3oMqbySwbJ2/uXZ3len4hLpa9tM+8pne/fqIMecrs8MknztGasb4AWpWqZhijh7UxWJVddejEzTn2UZK2iI3zWEq0kjPzX5d3eqVUeHT+3WycHMNHGCsp0s5PI33KY3RKGMGlU/ice074qEWT41WI4v1+a1amzTtyDiMjdHrdrnT2LbcONzr64FGASn7DTv2ndd2fW7Fmuml2WN1f60geZ3ZrzP+9+jpp++8eS/kwjU0fI7xprXOd6icMRMuYvtZ+Ye00pDJ8zXmrutL31L66Hy/mMzG4UizDhw4oIoVKzpyCbEIIIAAAggggAACCCCAAAIIIJCfBaLW6cm+X6vHgvFq4kQbLcfunamePWfqWMM3tWxa+6QNqnPiiDv5mzZsP2O8/ryWmtYvbUzcSNSJLwao41s7VbKH8ba4Z2va9+KsnGicHfe0J5eTz5eY2aFACAIIIIAAAggggAACCCCAAAIIZFnAfGW3Frz+nvY1eEahzrIjSeJZbfp0qj5atFSHjJ1/7urRSP45lByywJkvbNLU52cbm2SXUZtHH1KTInu15APLW9fK6u57Kjt1csjegSdBZK8UcQgggAACCCCAAAIIIIAAAggUQAFzfLyKtRqjme3q52gSxiHaxCjtXvG1fr1USrW7jNCTzZKXlzl0F7uDvW57VNPe99B70/+nnz8arx9NPgq6rY0GDxmtAXc40ZQqu3uUPpAlZulNKEEAAQQQQAABBBBAAAEEEEAAAQRcRsCeJWY5OAHLZRzpCAIIIIAAAggggAACCCCAAAIIIODSAiSIXHp46RwCCCCAAAIIIIAAAggggAACCCBgW4AEkW0jIhBAAAEEEEAAAQQQQAABBBBAAAGXFiBB5NLDS+cQQAABBBBAAAEEEEAAAQQQQAAB2wIkiGwbEYEAAggggAACCCCAAAIIIIAAAgi4tAAJIpceXjqHAAIIIIAAAggggAACCCCAAAII2BYgQWTbiAgEEEAAAQQQQAABBBBAAAEEEEDApQVIELn08NI5BBBAAAEEEEAAAQQQQAABBBBAwLYACSLbRkQggAACCCCAAAIIIIAAAggggAACLi1Agsilh5fOIYAAAggggAACCCCAAAIIIIAAArYFSBDZNiICAQQQQAABBBBAAAEEEEAAAQQQcGkBEkQuPbx0DgEEEEAAAQQQQAABBBBAAAEEELAtQILIthERCCCAAAIIIIAAAggggAACCCCAgEsLkCBy6eGlcwgggAACCCCAAAIIIIAAAggggIBtARJEto2IQAABBBBAAAEEEEAAAQQQQAABBFxagASRSw8vnUMAAQQQQAABBBBAAAEEEEAAAQRsC5Agsm1EBAIIIIAAAggggAACCCCAAAIIIODSAiSIXHp46RwCCCCAAAIIIIAAAggggAACCCBgW4AEkW0jIhBAAAEEEEAAAQQQQAABBBBAAAGXFiBB5NLDS+cQQAABBBBAAAEEEEAAAQQQQAAB2wIkiGwbEYEAAggggAACCCCAAAIIIIAAAgi4tAAJIpceXjqHAAIIIIAAAggggAACCCCAAAII2BbwsB2SPuLAgQPpCylBAAEEEEAAAQQQQAABBBBAAAEEEMiXAiazceTLltNoBBBAAAEEEEAAAQQQQAABBBBAAIFsEWCJWbYwchMEEEAAAQQQQAABBBBAAAEEEEAg/wqQIMq/Y0fLEUAAAQQQQAABBBBAAAEEEEAAgWwRIEGULYzcBAEEEEAAAQQQQAABBBBAAAEEEMi/AiSI8u/Y0XIEEEAAAQQQQAABBBBAAAEEEEAgWwRIEGULIzdBAAEEEEAAAQQQQAABBBBAAAEE8q8ACaL8O3a0HAEEEEAAAQQQQAABBBBAAAEEEMgWARJE2cLITRBAAAEEEEAAAQQQQAABBBBAAIH8K0CCKP+OHS1HAAEEEEAAAQQQQAABBBBAAAEEskWABFG2MHITBBBAAAEEEEAAAQQQQAABBBBAIP8KkCDKv2NHyxFAAAEEEEAAAQQQQAABBBBAAIFsESBBlC2M3AQBBBBAAAEEEEAAAQQQQAABBBDIvwIkiPLv2NFyBBBAAAEEEEAAAQQQQAABBBBAIFsESBBlCyM3QQABBBBAAAEEEEAAAQQQQAABBPKvAAmi/Dt2tBwBBBBAAAEEEEAAAQQQQAABBBDIFgESRNnCyE0QQAABBBBAAAEEEEAAAQQQQACB/CtAgij/jh0tRwABBBBAAAEEEEAAAQQQQAABBLJFgARRtjByEwQQQAABBBBAAAEEEEAAAQQQQCD/CpAgyr9jR8sRQAABBBBAAAEEEEAAAQQQQACBbBEgQZQtjNwEAQQQQAABBBBAAAEEEEAAAQQQyL8CHo42/dy5c45eQjwCCCCAAAIIIIAAAggggAACCCCAQB4KBAQEZFq7yWwcmUZwEgEEEEAAAQQQQAABBBBAAAEEEEDApQVYYubSw0vnEEAAAQQQQAABBBBAAAEEEEAAAdsCJIhsGxGBAAIIIIAAAggggAACCCCAAAIIuLQACSKXHl46hwACCCCAAAIIIIAAAggggAACCNgWIEFk24gIBBBAAAEEEEAAAQQQQAABBBBAwKUFSBC59PDSOQQQQAABBBBAAAEEEEAAAQQQQMC2AAki20ZEIIAAAggggAACCCCAAAIIIIAAAi4tQILIpYeXziGAAAIIIIAAAggggAACCCCAAAK2BUgQ2TYiAgEEEEAAAQQQQAABBBBAAAEEEHBpARJELj28dA4BBBBAAAEEEEAAAQQQQAABBBCwLUCCyLYREQgggAACCCCAAAIIIIAAAggggIBLC5AgcunhpXMIIIAAAggggAACCCCAAAIIIICAbQESRLaNiEAAAQQQQAABBBBAAAEEEEAAAQRcWoAEkUsPL51DAAEEEEAAAQQQQAABBBBAAAEEbAuQILJtRAQCCCCAAAIIIIAAAggggAACCCDg0gIkiFx6eOkcAggggAACCCCAAAIIIIAAAgggYFuABJFtIyIQQAABBBBAAAEEEEAAAQQQQAABlxYgQeTSw0vnEEAAAQQQQAABBBBAAAEEEEAAAdsCJIhsGxGBAAIIIIAAAggggAACCCCAAAIIuLQACSKXHl46hwACCCCAAAIIIIAAAggggAACCNgWIEFk24gIBBBAAAEEEEAAAQQQQAABBBBAwKUFSBC59PDSOQQQQAABBBBAAAEEEEAAAQQQQMC2AAki20ZEIIAAAggggAACCCCAAAIIIIAAAi4tQILIpYeXziGAAAIIIIAAAggggAACCCCAAAK2BUgQ2TYiAgEEEEAAAQQQQAABBBBAAAEEEHBpARJELj28dA4BBBBAAAEEEEAAAQQQQAABBBCwLUCCyLYREQgggAACCCCAAAIIIIAAAggggIBLC5AgcunhpXMIIIAAAggggAACCCCAAAIIZ0++uwAAIABJREFUIICAbQESRLaNiEAAAQQQQAABBBBAAAEEEEAAAQRcWoAEkUsPL51DAAEEEEAAAQQQQAABBBBAAAEEbAuQILJtRAQCCCCAAAIIIIAAAggggAACCCDg0gIkiFx6eOkcAggggAACCCCAAAIIIIAAAgggYFuABJFtIyIQQAABBBBAAAEEEEAAAQQQQAABlxYgQeTSw0vnEEAAAQQQQAABBBBAAAEEEEAAAdsCt5wgitk1UR0bNFCDBs315NqLMtuu04UjrurXsW3UoM2r+u1q5t1MvHJQa2a/qEe7ddBdjQy/O9uqc6+hen3+Zp2MzYJi/EHN7WHcp98XOpmYed02z8bu08wuxr0GfqezWWiKzfvfQsCltcPUMOl5S//TqNW96v3EZK3Ye1m3SuBQE53GK1Gnlj5s1aZB8w566JnJWp7WJqfbnub+5nPLNdAYu66z/1GcQ8gOBOd0nxxoCqEIIIAAAggggAACCCCAQH4RuMUEUYz2Lf9BJ5J6G63NX/2uKCdLKGQ8EHH6Z3ZX48v0QC0/l7uNTry4We/26aqnPlipw3511aHvI3q4awtVM+3UkgnD1emRmdp5JbfaZMXBZJKXTyH5FvaQKWPAPD0T2LyHHnrooZSfPj27qF0NT+3bMFcv9Rmh+QdyKv2QP7yCW/e8YdM1XE0qu2vfT3P1cs9+emfj2RsJtCyNtRWDjJ6GLN0/o5tZK7fSlhyv01o7KEMAAQQQQAABBBBAAAEE8reAxy01P2aflv9gpId8qqu+9y79umWptkW1UAs/Z00r3FJvs+niOO1fOE4LD3up/tPzNbVXJXkl39n8jHZ8OEgDZs/SuG876+PuZeSeTbU6dBvPyhowb4MGOHRR7gZXum+oRrfxS1NprPbNGaAe03bovwv+UvfnaqlQbjTL6by8FdpthEY39knV+0Rd2jlfzwx5X4teGq/Gi99Sq0AjP5zTbU9z/1xJe+Z0n3LjmaIOBBBAAAEEEEAAAQQQQCCXBW5pBlHMvuWy5If8Wg7RoPAg6eomLd0eVcCXmdkYQfMl7dl4QPII00MdUyWHLJeZfFXrocfVylva9eNfumzjVpxOK+Clivc8oBCj+EzkUV3NlWxE2jY462c3FanRR/9+rpG8on7QrBXHlOCsTaVdCCCAAAIIIIAAAggggAACuS5wCwkiY3nZd5blZb5q3LGuarVtIn9d1aalf1hZZpagM5s/1gsDu6qNsd9Osw69NOzVudp8Oj5Vh+2JMSv64EpN/Fc/3dfK2IOm4V3qNHCMPvn5ZMp+Jsl7nDz63X79Pu9lPXR3Y2MZmRHX70lNXXNMsZYaE45pYZ871f2Dg8aH7Xr57oZqM/ZXo/WWw3Yd18KuKPLLNzWsR7iaNmiq9r2G682FO3TR1uY3Jnd5+xjzguIP6PeDV9In03wb6MX/W6SFrzQwZJOPBJ391VgeNKibwps2UKPW9+mh0RO18mB0+uuTL4ndo+n3GbHD1+pSyn2MX5L3Zxn8k6Iycojbr48fTLsHkR1tSDypL/o1UJNRa3Vi1yK9+rCxtMnYb6Zhqwc0fOL3OpyEf+1IvLBdi98eqq7tmibtmdMsvKeenP6TjqSKSd1se383mdxkeag9fX3kYZnIZo+D5eb2tN1er5R7/aS/10zWiPubG328U+17P6HJa04q9sJv+uS5h9XpLsveU+F66NUvtOemJYV2PoP2oqTEualkix5qaExZ+2tFhM5anlUrY53p2GRkkHBE83s1UNMnN+j4H5/qX52bqGGPuTp41dqzZNSbcE4Rc8fosQfbqnGDFrq3zyhN/P6QYpKTevaMW0ZtsdIno0Lbf0P2PAMOm3MBAggggAACCCCAAAIIIJA/BLKeIDKWl323ypg+VLiROtX0lU9IezUyMhpXflmiHZdST92I17Fvn1OP4dO16nwFtXv4MfVsUkR/fzNZwwdN1vYrFih7Ysy68scMY1PnFzXvD0/VvW+QBvVupVKHlmnq4301dtXpmzYm/ue/j2vwxC3ybvSg+j7YRIGH1+qTp3rq6S+PKN6tuFq9OFkvdihu1F1BD781We/2v81YjmRnHebL2j71EfV980ttuVJF7Xp2V8vyF7Ri/DCN/emijZEvolr3N5OPjmpu/07qP+Zjrdx2WJeSp3OYvBRYvrIqlSl6fXlZgk4uf0E9BhsbDB/0V5Nuj6rf3VUV/cs8vWj8PnOnlSSTjRaknM7QIe0NHGtD/OEv9PTwD3WoWheN+Ndg3VfpnDbPe0H/mvP3tQRd3EEtMMrfXhwp/6Y9NXjYQHWsFqW1Hz+jkR/sNHazyuoRq4OrlirSWLQX1rG6Yez4kWnb7fa6Vm/8jika/u+fFdB+iEY/2lbF9m3Q3Kf666Gej2vR1Tv04NDh6lYzTru/eUsvfLbvmo29z6DjXUu6wuRbRWHljF8Ob9PRGCs3sTU2NgwSTnynV0ZM0SZVMzYTryCfDP4Lc+SLpzVy8map+j3q9eCdKnFso+a90FMj5v9z3cFK29IW2WjLjfBsfn7TtoPPCCCAAAIIIIAAAggggIALCGR5D6KYfd/Jkh8q1KKTahexTNWornvqF9LKdb9oyR+X1LRp0SQec9QWTX13tS5UelRz5w5VDR9L7CB1v2OYurz5P82JeEwT6u6wGTPxznP635sf668SD2r6gufUyO/aN8/Bg+7Tu32HauE7H6tPs2dV8/qgXIxMULdpn+vZxv5JM0oSBq/Ra72f0reTp2truzfUpFZD1fvFMkfHXyEN71T9AKNdxpvA7Kmj+plv9M6ne2WuNlhzP37sep/iNfC759XrldW6sanQ9cbc9C83FW/7mj58drxen75MO5dN14vGj9xLKPTO5mrRpoPuC6+vYO/r+zhd2aYP3/tR5wM7acKCMbrLsm+MkUR4rMd/9VjvyZr97nLdP6urSlurylaZkYwKsuYQd+bmK+1tQ/LWUwc3q8gr/9OU+8vI8oCZ76+r+PuH69u1v+vsY9WMZMBqfbU9XqX7TdOMJ6pf2yeo3z0KfmyQ5m792ZhFVENVUzZmst6J/Us/0PvbbwQlxkbp1L4tWrX1hCp3f0evtiueNO4OHzbaXtoer+RKL8arw5zPNLq2j7HZd4Ja++/V/e/t0bHb39DiifcoyGiguVM1nen0pH76JVKXHquqwIRDdj2DtYxliFk63IsoqIhx5aHTumRltlu8rbGJM8bGmkHCtcRowl8rdHnwJ1o5qJaKWAbAmM1j7Ug446ueM+bpqYZ+1/4+H1ut1/o8rW9nTNGGju+ptT3ZvTx6fktn6cGypkAZAggggAACCCCAAAIIIOA8Aln8qmNZXrbKWF5mbLTcuY6KWhIDJmNmTIc7jFkvV/TL0h0py5qu/rVMG6Pc1WBQD4UmJYcsh7tKhz+t154ZpfbG1kVX7IiJO7Fey/6WKnXrrlre8YqNjU36ifeuoW7dq0intyjiZKola7Uf0yMNryWHkmos3lxDBoVKUb9o5V7rc1QS7Koj1liqsly7jAVg7R7vpeopffJQ6bZD1dMyO8PW4VZE1Xu8pvmrVmvJx+P17KMPqEWIm/5e/5VmvTZEne99QvN2X5sZFPPPD9p4TqrSb6CaJiWHLIdJ3lUf0FDjW7R5x3Jtu5Czm+043Aa/dnr07mvJoaTW+lTQHcHGLwmxSjCa6laoaNLsnjO/r1XE4SvXZn55VdEjn/6on+Y+ajM5ZLnn2fUL9dlnn6X8zF+4RN9vPapEz9vUsOFtKpbV3b1ttN1St91H+Y6673ZLcshyuCugqjGjxvibadijeVJyyHKYCpdTSAnjl4Q4JRo29j2DqZ7za7dx4J/Jf4OWpyj9cctj49NGI/tcTw6lv31KiVudx9Q/7FpyyFLoXqLFtb/P6Ah9v+faYs9MLnfoVHY/vw5VTjACCCCAAAIIIIAAAgggkE8EsjaDKGavvrPsTm3MGmocFKWjR65tp5wQFKaK2qJ9P39tLDO7U02KGG9OOnRAUSqlO6oWvWlGh8k3ROE9LdsJJ+rkl7ZijP2vf/1LJ43oi9N76S5jwk36o6ROWdZpXZu4pKDaoQq4Kf3lruK16qikdmv/sasy170emOpGsSfsqSNa5/ZbZthUUKNqRW7+ku1VRvVuM6Z2RKRvndUSYzZH2TvaqIflZ5gx2eLsbq36dLzenLdRE1/4RA0/H67g0/t1xuhUgxolkmbjpBzGhtaV65WVfjypf84Zr3RP+0IvqxVmrTDW3jb4X79/iWoKumkGkJuMN4+nHG5B7fX8qB+MJUazNOqBWSpWpaEaN2mqVne3V6taQfKylrlI0/T643/UzNRvMUuM1pm/1+jD517SgueeV4WvPlaPslnIEtlou0OCRiLMO3UTjP2njDl3KlI49YPpJvdU/bXvGbSsR8zan64SonTSsilVkZLytcJzy2NTuqbK2TG7qVSdUPln8Pd59MTVm5aLOmRuJTi7n18rVVCEAAIIIIAAAggggAACCOR7gSx9y4zZa7y9zJKtMebRvD+gi95Py3B5o5b+eUlNjNdsJyZYvswaGwdnMlfJnhizOTFpQ+bbR0zWs/VvbN+cUrWx3KREJU/J2r4qKUFGosL43WxM1bA258a+Ogrp8o2JPGl7biRCMs9uJJ5arYnjvlbCPc/pqXZBN73G3jMwVPeMfk9eBzrp2fU/av3Rx9TdbK2l16t1S+qNEq0sFUrXsJSCBMU7FG+pwsE2uHnclPRI1xaTj0Ifnqpv79mln1f/pLUbftHPCyZp5fxJKtPxP5o9pp2CrCQv0t0ndYGbt4qHhGvkE9/qm6d/0ardl9S9bDGrs2SuXZaBg622Z9qIWz9p3zNoPOdZPMyX9xmztoyLQ+5QGUsSL+2rzG51bDy85Zn5n0AmLb/29+lm7C6e8S0yGLdM7prtz29mdXEOAQQQQAABBBBAAAEEEMinAllIEMVo7/IfkmbzVO79igbX8U31Zc6sS79/oDcWHNDPS3fqcuNGKlImWN7aqJ0HjCVTxsYyyV/8zFf+0Jz/zNOphiM02I6Yx++oIsuW0lfjS6tGnSq68RXZeDvRbyu0+pC/SlU3MjfXE0SndkTqfGL1lKU8lplK53ZvN9pdRPVLFba6P41nCXvq8FTxyiWNfuzV1r8vqUuQ343+xx7Xtr8zXx5jco/RX2vWaEdMew1qE66UVWPJD5CpkPz9CxmfPOTt4SavkpUVaMzKitx1RvFhxpu5kuOMjbL3/3bE+FRNlQIzGUYjuXNTeifmlP45a1xWyv4n9pbbkKaq+Av/KPJwnAIqh6pVjxrGzwiZow/rx/GP6bmlk7Wo710aEWJjEyKrzTepUFAlY1epjYo6c9kY8WI3EnDZ4GC1ymwutO8ZzCTbmml7EnVq3UJtMd4UF9qhwbVnL02CyL6xyTh9k2n1qU6e+CNSFxJDVDKlK4k6u2ub8fcZoGZlLMvyru+DlQ3jlt3Pr719JA4BBBBAAAEEEEAAAQQQyE8Cjn/TNJaXLbdMH3KvrwEPd9bd7dqpXcrP3brv4QGqZ9w1auNS/WmsPPMJ7aCwQrHaOMt4lXd0cqoiQad+mqmPl/2kfR4+8rMjxju4udoY+/scnDdVyw7deBd64tkNev/ZMXpr/l7Fpvreat4+S59GXExZqpJ4dqNmzfzTaFAjtQ8pfNMYJU+Q8bSrDjf51+2gGsYuSyunLtTulNeTG29KWv2B/u9g5sNv8m+oni0KK/bn8Xr7i7/SbBRsvN7878WatsLY8LdCKzUyptEUqtxWTY2lW3vnztGmc8lTfyxxX2r6KgO4RnvVvXmtzrUGGMuZPIxZOIlH/kj1tqo4HV7+kdZmkMPKaKJQltuQAUW08XavQf376PllJ1ImsJi8S+v22wKMK2J0KTaTGUsZ3DO52GRMVbM8BtEXY66NfRYcbFSRcjojL3uvtxZn3zNo7UpbZcZyz13zNXbcZsX6hWtweOmbZq8lX+3o2GTVIPG3mfrv71Gp/j43aNaHO6VizdS+qrFGLQvjllFbsvv5tSXNeQQQQAABBBBAAAEEEEAgPwpkMvXEeneSl5cVatZLTUukzy+5lWim7g089dvmjfp652U1athCo0fU19YJ09Wv7w490CZURc/9qq+/2qroKoP0r1bF5V7YdoybV4D6Pv+gVoxcrNd7P6w19zZTqN95RXyzRBHnKqrfO11UPnVvfC/q8+G9tLfj3apV5LR+XbFC288VVqNnR6qJnyWFYJK7l+WCffp63iKpWRuF169qVx2m4Hv19CP/08A5H6h/79/VqXWofE5u1bKVO+VT0UdRxqbSGR5ugWr5zIu6e8cr+v4/fbXp/+qo/u2VFVw0QecO/6mfN+3TZVVS35f7qIplEo1XXQ1+sqVWj1mqJ3oeVsd766nklUj9tGSdDphCNPC5Tgq2LMdKu2+xZ2ljs+YSmvnFJ3ri6Vj1aRGs6MjvtWDFBdUMlLanNNCKQ+00rfexsw12Ll3zqd1LHUus1ZdG/x/9rZOaVCmimAOb9M2ySLndPkRdbrPMoMraYfL2kyX9d2b/KcWpsjztdrC3Pju87L2VtTgv+55Ba5feKIvW7v9N0/s/X5tnl3jVmDW2a7sidh4zXiFfQT3GPaPmN2/QlXKpfWMTn/5vp07mLUp71qfcJc0f3Ft/39dONQqfVMTylfrjQjG1em2o6iWtIHXe5zdtX/iMAAIIIIAAAggggAACCLiCgIMJouTlZb5q27OB/K2tNHELUOMed8p781pt+HqXrjRsoMp9pmpR2Vl6d/ZyLZ+zVleLllHNjk/pP090V2jSZB4vO2LcVKzJc5o373ZNev9zrV/2qdbG+6lcrfv17H9G6MG61zaMTp57Uq7nJI0N+EJTFizRp0fiVTKkuR5+4VkNbVPm+vI0461jrXqq1aLxWv3pOzpytbpa1y9hVx3Ga7lUe/jH+r9y72vCvDVa/tkvivcPVfunZ6pv4nj1m535o+ER3EFv/q+Smn4yR4t/2KyfV24zZtIYS8vKVFDNLk+qn7GvU5Oyha8vXTPe+HbveP2v5H/17oyl2rjgI50vVFq3Ne6jN58arvCK3qmW+KWu10d1R0/WS4n/0azl8zX5Z3cFVm+vwdNfV8kp96dKEFlxqJ12A++stsG6g5tfQz35wdsqNnm2vl77f5q9wizvwEq6o8cLGjq4i0Kynh+SZ4nqqmK8Im3/9x9o6ZD66lXeXgfrbU1fao9X+qvsL7HvObd1v2M/fa7PUgd5BSqk5cPqN+xRta/ma3WJpSXcvrGxYlDHyDrafZhUY8QH6r1ntj5Z8ZU+PWpWcPU2Gvz6MxrYtOT1mU32jpuVtuTw82t3NwlEAAEEEEAAAQQQQAABBPKRgMlsHPmovTabaj633HjF+ss6O+hzLRxa1Ug9cSCAAAIIIIAAAggggAACCCCAAAIIZCaQfo1YZtGcQwABBBBAAAEEEEAAAQQQQAABBBBwOQESRC43pHQIAQQQQAABBBBAAAEEEEAAAQQQcEyABJFjXkQjgAACCCCAAAIIIIAAAggggAACLifgcnsQudwI0SEEEEAAAQQQQAABBBBAAAEEEEAghwWYQZTDwNweAQQQQAABBBBAAAEEEEAAAQQQcHYBEkTOPkK0DwEEEEAAAQQQQAABBBBAAAEEEMhhARJEOQzM7RFAAAEEEEAAAQQQQAABBBBAAAFnFyBB5OwjRPsQQAABBBBAAAEEEEAAAQQQQACBHBYgQZTDwNweAQQQQAABBBBAAAEEEEAAAQQQcHYBEkTOPkK0DwEEEEAAAQQQQAABBBBAAAEEEMhhARJEOQzM7RFAAAEEEEAAAQQQQAABBBBAAAFnFyBB5OwjRPsQQAABBBBAAAEEEEAAAQQQQACBHBYgQZTDwNweAQQQQAABBBBAAAEEEEAAAQQQcHYBEkTOPkK0DwEEEEAAAQQQQAABBBBAAAEEEMhhARJEOQzM7RFAAAEEEEAAAQQQQAABBBBAAAFnFyBB5OwjRPsQQAABBBBAAAEEEEAAAQQQQACBHBYgQZTDwNweAQQQQAABBBBAAAEEEEAAAQQQcHYBEkTOPkK0DwEEEEAAAQQQQAABBBBAAAEEEMhhAY8cvn+u3r579+65Wh+VIYAAAggggAACCCCAAAIIIIAAAnkpsGjRomypnhlE2cLITRBAAAEEEEAAAQQQQAABBBBAAIH8K+BSM4iShyG7smf5d1idr+WRkZEKCQlxvoYV4BYxJs43+IyJ842JpUWMi/ONC2PCmDifgHO2iL8V5xsXxoQxcT4B52wRfyv2jUt2r6JiBpF97kQhgAACCCCAAAIIIIAAAggggAACLitAgshlh5aOIYAAAggggAACCCCAAAIIIIAAAvYJ5MkSM/P59Rr/7jH1Gdtd5T2Nhsbu0eyRL2rFueRGB6jd61M0JLTQjV6YY3R49WxN/r8tOpkYqDrdRmlo+0oqbLKvo0QhgAACCCCAAAIIIIAAAggggAACCFgXyN0EUcI5/b5knpasWqcdbg+qz/U2ma+e0BHvu/XmZ4MVkionlLrJCceWa/L887rnjdlq6blVk1+crKU1xqlnBUuGiQMBBBBAAAEEEEAAAQQQQAABBBBAIKsCubvEzOSlgCoN1LZ9mAJTpabiz+zTqajdmv3UID0y+Gm988UOXUhM3aVEnd/5i86G3KsmpTzkHlhPHepEK2LbGSVktedchwACCCCAAAIIIIAAAggggAACCCCQJJC7M4jcfFWxbhOVCfxHX6y5MQIJsWaVrtFcXQd31m3x2/TJ2Pc1q9wEPdXIT9dWkCXo4rHL8g0O0LX5Qh7yL1tEl49eVLxKyz3NYFp2POdwLoGDBw86V4NojRgT53sIGBPnGxNLixgX5xsXxoQxcT4B52wRfyvONy6MCWPifALO2aK0fyuVKlVyzobmYqv279+f47XlboIog+54h/bXy6HJJ+vpvg6BemXDQcU0qiXv1NfctN+QWWbzTdOMUiJ5nXoG0HlczLjk8QBYqZ4xsYKSx0WMSR4PQAbVMy4ZwORhMWOSh/gZVM2YZACTx8WMSx4PgJXqGRMrKHlcxJjk8QBkUD3jcjNMbnjk7hIzqwOfoAuRm/Xb0RiZk86b5OHlITcPt+uzhyxl7vIL9tXV4xeMGUOWI14Xjl2RT7BfLk+BSqqcAwEEEEAAAQQQQAABBBBAAAEEEHApASdIEJmUcPQ7TZu2TH9fSpD5yt/64fuLqt2yorwSrujU0ZO6nOAm/xpN5L/ne/12LlHmqJ1atc1TYXVKplte5lKjQ2cQQAABBBBAAAEEEEAAAQQQQACBXBBwgiVmbgpsMUrDj0zWpFFLdMUjUNU7jtSw2r7ShdWa+Ow3aj5hnO4t00Gjex7SpFdG6r+JPgq5/1/qUpE3mOXCM0IVCCCAAAIIIIAAAggggAACCNySwNWrV7V+/XqdOHEi0/u4ubnJ19dXZcqUUWJiov788095eXllek1GJ8PCwjI6RbkVgTxJEHlW6K0J76VqjXuA6vcda/ykaaF/K731Wavrhe4q03qkxrW20guKEEAAAQQQQAABBBBAAAEEEEDAaQU2btyo4sWL6+677860jTExMdqzZ09SMmnKlCkqUaKERo0aJUeTPREREZnWw8n0AnmSIErfDEoQQAABBBBAAAEEEEAAAQQQQMBVBY4ePaqWLVsmzQrK7PD09FS5cuU0bNgwXbx4UXPmzNGhQ4eSLrnUaUxml6acK/LNa3bFEXSzgBPsQcSQIIAAAggggAACCCCAAAIIIICAKwskJCTIsnzMkiAymy1vJbf+Y0kK9e3bV8ePH9f8+fMVFBTkyixO1TdmEDnVcNAYBBBAAAEEEEAAAQQQQAABBFxPIHnmkMlkUmxsrCwzhSy/Jx+WhNGlS5eSkkO7du1Sz549VbNmzeyFSDinXxdM1pw1B3Up0V91uj+uIeEVdOX7sXr7n1CV27dRO07EyD+sl54Y3Fo+a161Ut5W5QvdaHf2NjBv78YMorz1p3YEEEAAAQQQQAABBBBAAAEEXF7AkgCyHJMnT1b//v2TkkHJZWmTQ5bzDRo0yGYTsy5sma3Zu8P07NTZmvnW/Yr+YpbWn7EseYvRwY1HVP/pSZo16zW1v7BQ078/rvgMyhOyuWXOcjsSRM4yErQDAQQQQAABBBBAAAEEEEAAARcVSJ5BtHPnTm3dulWDBw9WVFRUUm9Tzxy644479Morr+jChQvZLBGtAxv2qHh4C5XzMskzqIWemvSyWgVa0iLuCmzQUY1KesjkWUbNO4fozKY9upRR+bVcVza3L+9vR4Io78eAFiCAAAIIIIAAAggggAACCCDg0gLJew41btw4qZ+WJNGQIUN0+PDhlGVlVapU0ccff5y0T1F8fHy2e5iNyUJu7slpkETFXTynqDgr2R7L0jcj+PqkpxvtuF6e+Tbb2d7sXLshCaJco6YiBBBAAAEEEEAAAQQQQAABBAqmQPLm1AMGDNDIkSOTECxJovDw8KQ9h4oWLaqJEyeqSJEiSedsve3MccVCqtC4ok6sWKcjsWbFnd6kD1+dps0XLemeBJ3dukxbTiXIHHdMG77eLf8GISpisl5e1DW3IBKbVDv+VHEFAggggAACCCCAAAIIIIAAAgg4IJC831BcXJwef/zxpP2Hpk2bJstnyzFhwgTdfvvtSn7bWXK8A1XYCHVTQNMhGrB3kt4aulCXzMYm1T1Gq3UJd2MpmacCq/po47gR+uR0vPzr9dQTHYLlucZ6uasmUly1XzYeDE4jgAACCCCAAAIIIIAAAggggEBuCly9elXu7u5JSSFLkqhQoULatm2b2rVrp+bNm6csK7O85Sz7ZxAZPfUooSaPvG78pO51opEgkgpX6qAnHx5hpIqSj0SdsVqem2K5WxcJotz1pjYEEEAAAQQQQAABBBBAAAEECpxAqVKltHnzZjVq1CgpSWTZY+ixxx5LcbDMHLIcln/sP3kuAAAgAElEQVRb4sqXL1/gjPK6wySI8noEqB8BBBBAAAEEEEAAAQQQQAABFxewzBJas2aN5syZk2lP3dyMpWABAXrggQfSxRX55rV0Zbde4Ga82exNTUx3o4zK0wW6TAEJIpcZSjqCAAIIIIAAAggggAACCCCAgHMK+Pn5qXPnznY1LjIyUr6+vjfFRkRE2HUtQVkXIEGUdTuuRAABBBBAAAEEEEAAAQQQQACBHBYICwvL4Rq4vUWA19zzHCCAAAIIIIAAAggggAACCCCAAAIFXIAEUQF/AOg+AggggAACCCCAAAIIIIAAAgggQIKIZwABBBBAAAEEEEAAAQQQQAABBBAo4AIkiAr4A0D3EUAAAQQQQAABBBBAAAEEEEAAARJEPAMIIIAAAggggAACCCCAAAIIIIBAARcgQVTAHwC6jwACCCCAAAIIIIAAAggggAACCJAg4hlAAAEEEEAAAQQQQAABBBBAAAEECrgACaIC/gDQfQQQQAABBBBAAAEEEEAAAQQQQIAEEc8AAggggAACCCCAAAIIIIAAAgggUMAFPAp4/+k+AggggAACCCCAAAIIIIAAAgjksEBUVJQWL16sAwcOZFqTu7u7AgICFBQUJH9//6TYiIiITK/J6GRYWFhGpyi3IkCCyAoKRQgggAACCCCAAAIIIIAAAgggkH0CS5cuVdmyZdWvX79MbxodHa1NmzZpyZIl6t+/f0qso8merCaVMm2ci58kQeTiA0z3EEAAAQQQQAABBBBAAAEEEMhrgYMHD6pbt242m+Ht7a1mzZppwoQJ6WKXPFUmXZm1gvvfO2qtWDG7p+upz+vq7TFNVdRkNaRAF5IgKtDDT+cRQAABBBBAAAEEEEAAAQQQyHmBxMREeXjYl4KwxFnis/vwqtxLLw73li/JIau0bFJtlYVCBBBAAAEEEEAAAQQQQAABBBDILgGTybGsjKPx9rQz9p8Femv677ocd1xrZ76ooQMHasDDj+hf7/+gozFn9MPLg/XO1ssyW24Wu1efPj5a8/fHSdbijWJXO0gQudqI0h8EEEAAAQQQQAABBBBAAAEEnEzAzc2x9IOj8Y50N+7IOq09d5de/uAjfTJ7rJocWqY1x4uoXutS2rsmUleMm8UeWqeIQnfprnKeshp/1PUyRI6NkCPixCKAAAIIIIAAAggggAACCCCAAAKGgKMzghyNdwTZs2I3PTMoRGcjVunL+Yv084loxSaYFFCvtUrtXaO9V2J0aN3v8m3dVMHGqjjPStbik+YZudRBgsilhpPOIIAAAggggAACCCCAAAIIIOB8Ao4mfByNt7/HZl3aPksv/HuBfjtXRKFtuqlDZa+ky93866pV0N9as3uP1m0vpjaNg+RuLDjLKN7+OvNHJAmi/DFOtBIBBBBAAAEEEEAAAQQQQACBfCvg6JIxR+Pth0nQ2V27ldiot/rc20S3+RzTzhOxSrDsie0WoLqtSurPef/V1sC2alDckjKJzzje/krzRSQJonwxTDQSAQQQQAABBBBAAAEEEEAAgfwr4OiMIEfj7ZfxUJm7Oqni1jc0YvTzen3uP6pQ01MbP12mQ3Em+ddppVJHDiu4XV35J+2r7angDOPtrzU/RNr3jrn80BPaiAACCCCAAAIIIIAAAggggAACTilgeXV9QkKC3N3d7WpfTiSIzPFxSjC2DvIo08Z4c1mbVO3oqweTPwW01L8XtLypjZ7BmcTb1Zv8EcQMovwxTrQSAQQQQAABBBBAAAEEEEAAgXwrULZsWW3YsCEpSZTZYTm/Zs0aVa5cObMwh8/FHfxSb3+wX1XurKTCDl9dMC5gBlHBGGd6iQACCCCAAAIIIIAAAggggECeCYSHhyclfmbMmJFpGyx7DwUEBKhLly7p4u5/72i6MnsLPCt00b+npr+nvdcXhDgSRAVhlOkjAggggAACCCCAAAIIIIAAAnko4Ofnp86dO9vVgsjISPn6+t4UGxERYde1BGVdwCUTRIl7jyl+0+4kFa8+rbOuw5UIIIAAAggggAACCCCAAAIIIJCnAmFhYXlaf0Gp3CX3IErcd0yx839K+uFAAAEEEEAAAQQQQAABBBBAAAEEEMhcwCUTRJl3mbMIIIAAAggggAACCCCAAAIIIIAAAqkF8mSJmfn8eo1/95j6jO2u8p5Gc8xX9Pc30zT96106H+ehkg1764lBrVXGK1VTY/do9sgXteJcclmA2r0+RUNCCzGiCCCAAAIIIIAAAggggAACCCCAAAK3IJC7CaKEc/p9yTwtWbVOO9weVJ/rDY+JnKf3lhXRo+NmKcz3uFb+Z6wmrwjVm52D5X49xnz1hI543603PxusEHJCtzDkXIoAAggggAACCCCAAAIIIIAAAgjcLJC7CSKTlwKqNFBbjys6mrI9kFlXz8eryt33qk6Au0wqrXoNi+uLyLOK040EUfyZfToVtVuznxqkU7H+qnHPAA1+oJaKsUiOZxoBBBBAAAEEEEAAAQQQQAABBBC4JYHcTa+4+api3SZqXLe8fFNqNsm/8TA907WiLKvN4k9v1qJvzun2puWVeqJQQqxZpWs014A3Z2jm273k/+P7mrX1osy31H0uRgABBBBAAAEEEEAAAQQQQAABBBDI3RlEmXknXNTu7z7Wh1/uU4Xuz2tYAz9jNtGNwzu0v14OTf5cT/d1CNQrGw4qplEteae574kTJ1TsellkZGRmtXIulwQOHjyYSzVRjb0CjIm9UrkXx5jknrUjNTEujmjlTixjkjvOjtTCmDiilXuxjEvuWdtbE2Nir1TuxTEmuWftSE1px6VSpUqOXO6Ssfv378/xfjlHgijuuFZNeFWLEttr5LsjVSsgbbMSdCEyQvuK1FHdMoWMxJFJHl4ecvNwuymJlKxVqlQpRV//EBISkuOIVGCfAGNhn1NuRjEmualtX12MiX1OuR3FuOS2uO36GBPbRrkdwZjktrh99TEu9jnlZhRjkpva9tXFmNjnlNtRjMvN4rnhkbtLzKw+UWZdiPhEi+N7681nu9ycHEq4olNHT+pygkkJR7/TtGnL9PelBOOlZ3/rh+8vqnbLikr9ojOrt6cQAQQQQAABBBBAAAEEEEAAAQQQQCBTgbRTdTINzpmTsTr2+x6d+j1CQ3tNTaki8O43NKnHMU189hs1nzBO97YYpeFHJmvSqCW64hGo6h1HalhtX6sziHKmndwVAQQQQAABBBBAAAEEEEAAAQSyIhAVFaXFixfrwIEDmV7u7u6ugIAABQUFyd/fPyk2IiIi02syOhkWFpbRKcqtCORJgsizQm9NeC+5NYUUOvQjLRpqpXW6XW991ur6iQDV7zvW+LEWRxkCCCCAAAIIIIAAAggggAACCDirwNKlS1W2bFn169cv0yZGR0dr06ZNWrJkifr3758S62iyJ6tJpUwb5+In8yRBlNOmZjPvNstpY+6PAAIIIIAAAggggAACCCCAgL0Clo2nu3XrZjPc29tbzZo104QJE9LFNnpqfroyawWb3+tjrdjhMvPVffpp7SXVa3eHfPZM11Of19XbY5qqaOo3ajl8V+e9wAn2IHJeHFqGAAIIIIAAAggggAACCCCAAAK3LpCYmCgPD/vmqFjiLPF5fZiv/qOfVmzT+bxvSq5Q2Dc6udIUKkEAAQQQQAABBBBAAAEEEEAAAVcUMJkcm3bjaLxts0SdWTlWb/8TqnL7NmrHiRj5h/XSE4Pv1Jnpz+irem9qTKsAuV3doanGTKVqLw+X5i7SX4euasrUCnoyXEqM2qXF45fq18jDuhzcQU8+30c1C1/Q7wun6OOfDuiqfFSh9SMa2aOeMePoA73wZWndWWiLNuw8oEulO2j0c31Uy4mnHzGDyPZTRAQCCCCAAAIIIIAAAggggAACCNyCgJubY+kHR+Pta1qMDm48ovpPT9KsWa+p/YWFmm68Ib1q29t1avU2XUg06/Lu77WzVLgalS6n8MHddXv5dnp8ZEuVdJdiju5Tyb6vadLMSerrvlpL/7qiqN8+1oytt2nkpFmaOWmUbv/1Q33yW5TMStTFP7fKp8dYTfxwonq7/aSvdl2xr5l5FOXYCOVRI6kWAQQQQAABBBBAAAEEEEAAAQTyr4CjM4IcjbdPxl2BDTqqUUkPmTzLqHnnEJ3ZtEeJ1doq9PRqbTt3UX9+/7cqtK+vACvZkkKV2+jOMl4yuRdRmdLeiouL1pHNf8u/bRtVLWySqXAVtW4XoD1bjijGaJBXxVZqkhTvp7LB3oqPSzQSR857sMTMeceGliGAAAIIIIAAAggggAACCCDgEgKOJnwcjc8SkmXZmzlRiYWrqm2NM/p83Vp5HKymTrWLyrIgLm0yx82rsDzTrJRL+5KspJV0xkwky+Hm5SMvx1bWZakb2XWRlZxYdt2a+yCAAAIIIIAAAggggAACCCCAAAJGssQplpgl6OzWZdpyKkHmuGPa8PVu+TcIMd5KVlhVWtfS8fmf6XCt9gr1TZXVSUxIzvdYGUYvlWtYTedW/ai9V80yX92rVSvPqmqjcvKyEu3sRcwgcvYRon0IIIAAAggggAACCCCAAAII5HMBR2cEORpvH4+nAqv6aOO4EfrkdLz86/XUEx2CZUmMeFRqphp+21SsXTUVvn4zt8LBquQ2TxPer6DnO1qrwU1FwwZq6J7Jmvz4o7pibFJdsfUQjapfVG57rMU7dxkJIuceH1qHAAIIIIAAAggggAACCCCAQL4XsLy6PiEhQe7uxm7Pdhw5kyCSClcy3j728Ah5pmlDzOEI7fNrpacqppr7U7iGHp3wsR5Nim2jqTWTLyqk0GFTNCbpo6/q9x1r/KS5YehwTR2bOn6qXk0T4mwfWWLmbCNCexBAAAEEEEAAAQQQQAABBBBwMYGyZctqw4YNSUmizA7L+TVr1qhy5cqZhWXjObPOrX9bI9/6QzV6h6tc2sxRNtbk7LdiBpGzjxDtQwABBBBAAAEEEEAAAQQQQCCfC4SHhyclfmbMmJFpTyx7FQUEBKhLly7p4ja/1yddmf0Fbioe/qYmprvApIDmL2hW83QnClwBCaICN+R0GAEEEEAAAQQQQAABBBBAAIHcFfDz81Pnzp3tqjQyMlK+vr43xUZERNh1LUFZFyBBlHU7rkQAAQQQQAABBBBAAAEEEEAAgRwWCAsLy+EauL1FgD2IeA4QQAABBBBAAAEEEEAAAQQQQACBAi5AgqiAPwB0HwEEEEAAAQQQQAABBBBAAAEEECBBxDOAAAIIIIAAAggggAACCCCAAAIIFHABEkQF/AGg+wgggAACCCCAAAIIIIAAAggggAAJIp4BBBBAAAEEEEAAAQQQQAABBBBAoIALkCAq4A8A3UcAAQQQQAABBBBAAAEEEEAAAQRIEPEMIIAAAggggAACCCCAAAIIIIAAAgVcwCUTRCaTqYAPK91HAAEEEEAAAQQQQAABBBBAAAEE7BdwyQSR/d0nEgEEEEAAAQQQQAABBBBAAAEEEEDAAwIEEEAAAQQQQAABBBBAAAEEEEAgJwWioqK0ePFiHThwINNq3N3dFRAQoKCgIPn7+yfFRkREZHpNRifDwsIyOkW5FQESRFZQKEIAAQQQQAABBBBAAAEEEEAAgewTWLp0qcqWLat+/fpletPo6Ght2rRJS5YsUf/+/VNiHU32ZDWplGnjXPwkCSIXH2C6hwACCCCAAAIIIIAAAggggEBeCxw8eFDdunWz2Qxvb281a9ZMEyZMSBfbo0ePdGXWChYuXGitmDIbAuxBZAOI0wgggAACCCCAAAIIIIAAAgggcGsCiYmJ8vCwb46KJc4Sz5G7AvaNTu62idoQQAABBBBAAAEEEEAAAQQQQMCFBBx927ij8XZRJZzTrwsma86ag7qU6K863R/XkPBKKsyL0JP4mEFk11NEEAIIIIAAAggggAACCCCAAAIIZFXAzc2x9IOj8bbbZdaFLbM1e3eYnp06WzPful/RX8zS+jPMVEq2YwaR7aeICAQQQAABBBBAAAEEEEAAAQQQuAUBR2cEORpvu2nROrBhj4qHD1Y5L5NMQS301KSGkpdjiSvb9eTfCCTy79jRcgQQQAABBBBAAAEEEEAAAQTyhYCjCR9H4+1BMBuThdzck9MgiYq7eE5RcWZ7Li0QMSSICsQw00kEEEAAAQQQQAABBBBAAAEE8k7A0SVjjsbb7lkhVWhcUSdWrNORWLPiTm/Sh69O0+aLLDFLtmOJme2niAgEEEAAAQQQQAABBBBAAAEEELgFAUdnBDkab7tpbgpoOkQD9k7SW0MX6pLZ2KS6x2i1LuFu+9ICEkGCqIAMNN1EAAEEEEAAAQQQQAABBBBAIK8ELK+uT0hIkLu7fQmZ7E8QGT33KKEmj7xu/OSVgnPXyxIz5x4fWocAAggggAACCCCAAAIIIIBAvhcoW7asNmzYkJQkyuywnF+zZo0qV66cWRjnckCAGUQ5gMotEUAAAQQQQAABBBBAAAEEEEDghkB4eHhS4mfGjBmZslj2HgoICFCXLl3SxS1cuDBdGQXZJ0CCKPssuRMCCCCAAAIIIIAAAggggAACCFgR8PPzU+fOna2cSV8UGRkpX1/fm05ERESkD6QkWwVIEGUrJzdDAAEEEEAAAQQQQAABBBBAAIHsFAgLC8vO23GvDATYgygDGIoRQAABBBBAAAEEEEAAAQQQQACBgiJAgqigjDT9RAABBBBAAAEEEEAAAQQQQAABBDIQyJMEkfn8eo17eZEOxV1vlTlGh3+apmcHD9CAQU9q4vL9umpO02J7YjLoJMUIIIAAAggggAACCCCAAAIIIIAAAhkL5G6CKOGcfv9iql57aYq2XriRAUo4tlyT55/XPa/P1kfje0hfTdbSlOzRtcbbE5NxNzmDAAIIIIAAAggggAACCCCAAAIIIJCRQO4miExeCqjSQG3bhykwZXvsRJ3f+YvOhtyrJqU85B5YTx3qRCti2xklpLTanpiMukg5AggggAACCCCAAAIIIIAAAggggEBmArmbIHLzVcW6TdS4bnn5ptScoIvHLss3OECeSS31kH/ZIrp89KLiU1puT0xm3eQcAggggAACCCCAAAIIIIAAAggggEBGAs7zmntT6iaaZTYnpm+zPTHGVSdOnFCx61dHRkamvw8luS5w8ODBXK+TCjMXYEwy98mLs4xJXqjbrpNxsW2U2xGMSW6L266PMbFtlBcRjEteqGdeJ2OSuU9enGVM8kLddp1px6VSpUq2L3LxiP379+d4D50gQeQuv2BfXd12IWnGkKfxzwvHrsgn2M+YS5R82BNzw6pUqVKKvv4xJCQkxxGpwD4BxsI+p9yMYkxyU9u+uhgT+5xyO4pxyW1x2/UxJraNcjuCMcltcfvqY1zsc8rNKMYkN7Xtq4sxsc8pt6MYl5vFc8Mjd5eYWX2i3ORfo4n893yv384lyhy1U6u2eSqsTkm5J1zRqaMndTkhkxir96QQAQQQQAABBBBAAAEEEEAAAQQQQMBeASeYQSS5l+mg0T0PadIrI/XfRB+F3P8vdanoKfOFDZr47DdqPmGc7s0gxt6OEocAAggggAACCCCAAAIIIIAAAnkjEBUVpcWLF+vAgQOZNsDd3V0BAQEKCgqSv79/UmxERESm12R0MiwsLKNTlFsRyJMEkWeF3prwXurWeKlM65Ea1zpNC/1b6a3PWl0vdLceY6VTFCGAAAIIIIAAAggggAACCCCAgPMILF26VGXLllW/fv0ybVR0dLQ2bdqkJUuWqH///imxjiZ7sppUyrRxLn4yTxJELm5K9xBAAAEEEEAAAQQQQAABBBBAIJWAZePpbt262TTx9vZWs2bNNGHChHSxpd++6c1V6c4nFxx/wZzhOUdOxOyerqc+r6u3xzRVUfuqduT2ThfrBHsQOZ0JDUIAAQQQQAABBBBAAAEEEEAAgWwUSExMlIeHfXNULHGWeI7cFbBvdHK3TdSGAAIIIIAAAggggAACCCCAAAIuJGAyOTYFx9F4u6gSzunXBZM1Z81BXUr0V53uj2tIeCUVitquzyd+oJX/XFaCZzm1Hfas+tUvlnTLxKhdWjx+qX6NPKzLwR305PN9VLPQCa2dM1nzfzmu6HizAur31TMj2qn43g/04pelFRa7XJsqPqnnyvxXE/4JVbl9G7XjRIz8w3rpicFtVb6QYxZ29S0bgnIkQZQYc0b/bI/Qrzv36/jpc7psLqzAkqVVsXp94+1kVVSiEBOXsmHsuAUCCCCAAAIIIIAAAggggAAC+ULAzc2xPICj8bYRzLqwZbZm7w7TC1PHqPT5dXrvlVlaH/aq6m6Zr3XFh2rqmNrSrvmavHq3ouo1lrdx05ij+1Ry9GuaFByln157Xkv/6qKQ4uu09txdevmD9iqnA/r8hclac7SluipRF/5Yq5jRL+n1emUU91OMDm48ovvenaQn/E/qh3Gvavr3NfVGp2C5225wrkdkY4IoQed3rtSCz+Zr0cpdig++XaFVy6pkgJ+BGqW/j+zU2sXT9NJRD1UP764+D/VSeA1/p0TJ9VGgQgQQQAABBBBAAAEEEEAAAQRcWMDRGUGOxtumi9aBDXtUPHywynmZZApqoacmNZS8PBRfpbaKLZmjSbNbqEmj1ho9rIwKG5N8YoybFqrcRneW8ZLJrYjKlPZWXJxZnpW66ZlB/+iviFXa8tdv+vlEtOolXNv3yKvyvbqvQXn5uSXqjJHxCGzQUY1KesikMmreOUSL/rdHlzoGq5gTTiLKngRR3GF98/ZLmnuwotre97jmvBimCkWs58MSLh3Urz9+p68nDNbnFfvrzec7qqyn7aEkAgEEEEAAAQQQQAABBBBAAAEE8qeAowkfR+PtUTEb2xq5uSfPZEpU3MVzii4WrMDb++qtye3115ZftGnlu1qwqLleHttVpYybunkVludNyZxEXdo+S2Nmn1ade9qoUZtu8tk7ScevN8DNy0dG/sn6YVlmZzTCWXdXyp4EkbxUods7+qx6UIYQ5kSzkXEzyb1IBTW8b4gadn5EJ3ftto5GKQIIIIAAAggggAACCCCAAAIIuIyAo0vGHI23DVVIFRpX1IkV63SkQQeVurhJH776rar/+2Xd9u1zmuI+Um8/1Em3hfho30ubdDJWSQmi9EeCzhq5jMRGj6vPvZVlOr1Ry0/EqpjVrI8Ru3WZtpwKVXNjidmGr3fLv0Fvp30jWvYkiDyDdEeN9GyWkrijK/XeC+P1xZ/n5VPtXo1++0XdX9lbJpOXgmrcYf0iShFAAAEEEEAAAQQQQAABBBBAwGUEHJ0R5Gi8bSg3BTQdogHGbJ+3hi7UJbOxSXWP0WpdorA8OvVS9XfGa+gj8TJ7lFCDnk+otk9Gd/RU8F2dVHHcGxqxtaSCK9TWHTU99d2ny3RXj7TXeCqwqo82jhuhT07Hy79eTz3RIVjZk4hJW9etf87ZdiUc09evjtMfTSfqh9lVdWzBKA17cb7qzB2oyiwru/XR4w4IIIAAAggggAACCCCAAAII5AMBy6vrExIS5O5ufTuatF3I/gSRUYOR/GnyyOvGT5raSjTVkHHGT9pGhA7X1LHJhYUUOmyKxiR9bKN/vd8mVXRfPXj901Rjn+vUR+FKxpvPHh6h/JACcWwb8Zv7eeNT/Ent+Ous4tKejzupnUeKqWnrUPl5+qpq6zYqdWKbjhlTtTgQQAABBBBAAAEEEEAAAQQQQKBgCJQtW1YbNmxIShJldljOr1mzRpUrV84sjHM5IJA9M4gSz2vz5Gc1xtxC/Yc9oo61A69NmSpUWeF3xuil9+eoWp+qOvrFXJ0Je0a3F86BnnBLBBBAAAEEEEAAAQQQQAABBBBwSoHw8PCkxM+MGTMybZ9l76GAgAB16dIlXdzxF669KSzdCacscDPemPamJjpl26w3KnsSRF4hGjhtoTr9+pVmT35EnxZqqQFGoujemgFq+MyHennubH3x2R/yrTlaMx5tq+LZM2/Jeo8oRQABBBBAAAEEEEAAAQQQQAABpxLw8/NT586d7WpTZGSkfH19b4qNiIiw61qCsi6QPQmipPqNTafr99CLMx/QyQgjUTRxgD4t3EqPGImiDo+9qpaPZb2RXIkAAggggAACCCCAAAIIIIAAAgVTICwsrGB2PJd7nb1zeRKu6NSR0/Ks3U0vzlqk6Q8H6/d3H1bPUZP07c7zynylYS73nOoQQAABBBBAAAEEEEAAAQQQQAABBJIEsmkGkVlXdv9Xz46aoR1uRRV3sajavzpNL4b30ssNuur4lsWaaSSK5hRpp4HD+6t9aDHZt285o4QAAggggAACCCCAAAIIIIAAAgggkNMC2TODKP6Q/vfap4obslArly/XiunNFDHuXf180dhAyuSl0o16a8xHizSlbwltmTJJWy/ndLe4PwIIIIAAAggggAACCCCAAAIIIICAvQLZM4Mo/oKOXvRVSGiQsRORmzwr1lJw4u86HW0kiPxM19piKqTgxn00trG9TSMOAQQQQAABBBBAAAEEEEAAAQQQQCA3BLInQeR9m7o+4KfBL70sr67VdWnNPO1r+Jyal8ieCUq5AUEdCCCAAAIIIIAAAggggAACCCCAQEEVyKYMjrdCBs7Ux6PqyHz8nAI7jtdnb4arZDbdvaAODv1GAAEEEEAAAQQQQAABBBBAAAEEckMge2YQyaxEk4+qtHlIo9o40GxzoswmN11fhObAhYQigAACCCCAAAIIIIAAAggggAACCGSXQPbM8bnym94Z9oymffenTsca+w7ZOMyxp7Rj2VQ9Pewd/XbFRjCnEUAAAQQQQAABBBBAAAEEEEAAAQRyVCB7ZhD51NdTb7lp8ZTx6vOf0yrTqJka1aml0KrlVMK/iLzN0Yq6cFpH9u7SH79v1oYtx1Sy7SA9+VZX1fHJ0f5xcwQQQAABBBBAAAEEEEAAAQQQQAABGwLZkyAyKvEIrKueYz9Vt9F7tGHF91rz8xeaMr1AYzYAACAASURBVPcfHT97WXHGu82KFC+tSjXrqVGTRzV5TDNV83O30TROI4AAAggggAACCCCAAAIIIICAKwhERUVp8eLFOnDgQKbdcXd3V0BAgIKCguTv758UGxERkek1GZ0MCwvL6BTlVgSyLUGUfG/3Yrfprh6Wn+FWqqMIAQQQQAABBBBAAAEEEEAAAQQKmsDSpUtVtmxZ9evXL9OuR0dHa9OmTVqy5P/buw/AKKp9j+O/3U0PhCT0XkREROmKgA1URMVrAUVFsV0EHhZEUBHLFWyooIgVFLw2EBUFrx07KiIqNnqASA+QAulb3symkLKb7IbdsAnfeTfvkplzzpz5/HdmNv975sz7GjFiRHFZf5M9VU0qVdi5Wr4x4AmiWu7F4SGAAAIIIIAAAggggAACCCCAgJ8CycnJGjJkSKW1oqKi1LdvX02fPr1c2YwtV5Zb52lFXOvXPa1W7ppnNX5BVz18bx/VDYG3ZWX//rjGLT5d0yf3lKfZd1zZSfrymwPqduYJSqiGh7ACM0m1R3pWIoAAAggggAACCCCAAAIIIIAAApLT6VRYmG9jVMxyZvlALxFth2nSmK6KDYHkkHlsUR2u0b03HqdoLwfqyt6kLz9ZpbTAU3jco2/R8ViVlQgggAACCCCAAAIIIIAAAggggEDlAhaLf1kZf8tX3gMpb9N8PWSOIJrUTr/Onak3ftypHLtLCd2v1ITR3fT3lLv064UzdHvPWFnyNuqV8U8rfPxjuqL5Xn1Ttvz/nakm2b9rwYzn9emmTDnCW2jA6Im6qns9WR2p+mX+TM39OlkHnPHqMvQm3Xh2G1nXPq9Ji5qoR97HWt76dk3puURT3+urO09copmbOqpF0vf6c1eu4nsM0y3XddJfLy7U2n+y9fSsVrrr5tPUMMijiBhB5MuniDIIIIAAAggggAACCCCAAAIIIFBlAavVv/SDv+X96Vj+tm/1Teqpmvz8S5o35z71/udDfb2zjrqd0Vgbv16nLKOxvH++1crIU3Vqi3B5LL89V3uXvaFv64/SrLnz9Ny4Ttr+4xrtd7mUvmKO5qzpoYmz5ujFh/6lnHdn67u95jAgp9L/+Ea5g+7WlCuPMl7nVbTkKvn7bep++1OaPfsBDUx/S89+adGAkUN1TMszddPY4CeHzJ74FyF/xCmLAAIIIIAAAggggAACCCCAAAIIGAL+jgjyt7w/yOGth2jCDR20b+VSLXpjoX7YlaM8h0UJ3c5Q441fa2NWrv759jfFntFHTY3nrsLbeCov1Wl3vOr9OVdPzVmk5fln6NbRJ6meJUdblq1X/bNPUYsIi8IbnaLxT03W6YkF6ZeItufqgp4tFRdeckSVTYk9z9OJDcNkCW+mfoM7aO/y9Trg8ueoDr1sEB4xcyk76UPNemqBfkrOUJ6zzBHF9tQ9c+5RT08zMB368dACAggggAACCCCAAAIIIIAAAgiEmIC/CR9/y/t+uC4d+H22Hntpj7qc018n9h+imI1PaafRgDW+q05v9K6+XrNedX+vp/6TGsmmgvL3zilb3qLoY67UQzMHau2KH7X808c1f2E/Tb5vkFzGYCGrrWg8jlP5GanKqRelWHMfETEy8kYVL+bjeEYj1TT1UHFfAp8gyt+iBXfdp3et5+vfw3uoSXSZh+TCGqrVwXFUFaOwFQEEEEAAAQQQQAABBBBAAAEEaryAv4+M+VvedyCH9q1eI+eJN+mKc9vKsud7fbwrT/XMbIw1QV1Pb6gFr7+qsMTz9K/6ZpIn30v5PG2cd7ueto3Vw8PP19EdYpR093LtzrtQR53UWrs++Vbbeg5S44zleuH+/+nY/zygM7x20ujTzx9qRUpH9YvfrWVL1ii+5+XGm9ZWG0+lOVR23I3XZg5xQ+ATRHnb9duulrpq9iRddzSZoEOMD9URQAABBBBAAAEEEEAAAQQQqPEC/o4I8re870Bhanbq+Wo9bar+7+eGatrqeJ1wXLg+euVDnXXvhWrR5XQ1fv5FRQztqnj3SJ9wNTXLP1q2/OfqP2aYjn1ymkZda5crrIF6XnaLjo+xKrLPjbrGGJX00Ki3jMfEjEmqL71VZzQwBs/s9dbLcCUeFaPvH/0/zdtjV3y3y3TLoKaKcKapjfV1TX+yle4f3z/ok1QHPkEU0UTHN3NoR3q+MRArQpWNnPLGw3oEEEAAAQQQQAABBBBAAAEEEKgdAuar6x0Oh2w2317FFYwEkcueL4cxC05Ys/4a92T/ErBX6pKi3xJO03/mn1YKPbyp9/I3PtpHN5YNkZEs6n3tFOOnzIaOYzTrvoProjqP07Odndr76fuKbjNIt139f0Y6quTSSddPf1nXl20/SL8HfpLqsFa65J7h2v/kBE1/+xv98vcGJSUlHfzZvFNZ1f0gXZDwaBYBBBBAAAEEEEAAAQQQQAABBCoXaN68uZYtW+ZOElW0mNu//vprtW3btqJifm/LT16kh5/frHYnt1G037WPjAqBH0GUuUx3XDVNK02/R37Sm+Uce+iJr17QaXXKbWAFAggggAACCCCAAAIIIIAAAgjUQoGzzz7bnfh57rnnKjw6c+6hhIQEXXTRReXKxbV+vdw6X1eEt7pI/5lVvk1f6wevnNV449mDmhG8HfjccuATRHVO1XPLlxuPl3lbLMZs3t62sR4BBBBAAAEEEEAAAQQQQAABBGqbQFxcnAYPHuzTYa1bt06xseY7vw4uK1e6h6GwBFEg8AkiY9Yha/EzhU7lZexVmrOuGsRHKfDPswVRhqYRQAABBBBAAAEEEEAAAQQQQOCwC/To0eOw9+FI6EAQEkQGm/Ems69fflxPvfqNknMLGSNa6tSrx2nCdaeqKS83OxI+WxwjAggggAACCCCAAAIIIIAAAgjUEIHAJ4hcWfrj+bG6fWGcht42U1N7tFGCJV2bVy7R89PHa2z+PL069jjF8HqzGvIRoZsIIIAAAggggAACCCCAAAIIIFDbBQKfIMpeo4WL03TGg3M14dR6ha+5b6amrY/RsYm7dNHUBVpzwwPqzrThtf2zxfEhgAACCCCAAAIIIIAAAggggEANEQh8gsiVo4y8KLVoEFWYHCqSsCq6UVNF521VTqnX3Odry/zxuv2dHaXJOozV7KmnKb5opFHees0ZO0mfpBYVS9CZU57WjR0jawg13UQAAQQQQAABBBBAAAEEEEAAAQRCUyDwCaKotjrtmDTNmPmWzpl2hTrHFbyyzJHxl96auVhpx4xT26iSGOFqPWymFg4rXOdK10+zHtaKPt1Ur8RjaK7sXdoWdZYefG2kOpATCs1PE71CAAEEEEAAAQQQQAABBBBAAIEaKRD4BJGtqQb/Z7JWjrpX1/R/SU3btVA9pWtr0g4daDZQDzw/WE29vubepf2/vaFFukR3do8rNQLJvjdJKfvXaM74G5SSF69O51yjkRd2Vj1ejVYjP3h0GgEEEEAAAQQQQAABBBBAAAEEQkcg8Aki49jCmp2rqe+erKt//EGrkrYb6aE4NWvbRb1PPkaJFe0xb5M+mL9Tp4zrXmr0kMnlyHOpSad+unjkYB1tX6V59z2p2S2ma/yJpRNJZtldu3YZSamCZd26daGjfQT3JDk5+Qg++tA8dGISenEhJqEXE7NHxCX04kJMiEnoCYRmjzhXQi8uxISYhJ5AaPao7LnSpk2b0OxoNfZq8+bNQd9bReka33fuylPGvgzZo+KVEOPUfuPfRj5HiR1P1BkdDzbjTNujPZYIxSXGKaLcW8xcSv1pgZa3ukQPNS4/xCiq4whNLm6rmy4YlKh7liUr98TOKvXEmrG7xo0bK6dwtx06dPD9OCgZVAFiEVTeKjVOTKrEFtRKxCSovFVunLhUmS5oFYlJ0Gir3DAxqTJdUCsSl6DyVqlxYlIltqBWIiZB5a1y48SlNF11eAQmQZT5gyYMHK9Nl8zV4pv2uf+90uvHoIee+OoFnVanTAHHHi3/eIeOu+oYxZRLHjmUvm6lkup0UddmkcajZxaFRYTJGmYtMxG2152yAQEEEEAAAQQQQAABBBBAAAEEEEDAi0BgEkSxffTE55/LGVlXUVEuzfjyC9mNEUQeF0uYYmLLb3HsXaGv9x2rEW1KzEDtyFLKrgOKadxAju0f6ZnPtumOuy5Qe+tGff5Zho4f3loR5ZtiDQIIIIAAAggggAACCCCAAAIIhJDA/v379c4772jLli0V9spmsykhIUGNGjVSfHy8u+zKld6HoFTUWI8ePSrazLYyAoFJEFnCVacwcMrdoA9eWamuN1ymDmWe/XKmrtBrb6XojOvOVcvwkj1x6cC6FUoxJrFuXiLj49r/k2ZM/ED9pj+qc0+5WWO2zdRTN7+vrLBEHXveWI0+PpYRRHykEUAAAQQQQAABBBBAAAEEEAhxgcWLF6t58+a66qqrKuxpTk6Oli9frvfff18jRowoLutvsqeqSaUKO1fLNwYmQSSnMtZ9o2/XZMiRs0ZvvPqdNjSMVufo0nq5/3ysuQsi1H542QSRRfX63ac5/UqXt8SfrodeO71wZYK6X3mf8VPLI8LhIYAAAggggAACCCCAAAIIIFDLBMyJp4cMGVLpUUVFRalv376aPn16ubLjGjUut87Tihm7d3la7eM6p/Z+eq8eSrlBj1zZRqXGtvjYQk0tFqAEkUOpv7yreW9uMSanztQ2R5oyXntJy8u8gt4SVk+9rr5FncokjmoqHv1GAAEEEEAAAQQQQAABBBBAAIHKBZxOp8LCfEtBmOXM8izVK+BbdCrtU7haD5uphcOMglm/6KFxH+rMJybrxLITUVfaDgUQQAABBBBAAAEEEEAAAQQQQKC2CVgs5d5GVeEh+lu+wsYKN+aueV6TFjVRj7yPtbz17Xqg+/u6+5MBemxCV0UrW79Pn6CP+z+q6+VS7tbP9OSEX7U2JVdxXYbq5lGnKO35CXqv24O69/QEWbP/1Kzxb6j9/VN0TqPyb2L3pT+hVqbMGJ8AdC+muya9QHIoAJI0gQACCCCAAAIIIIAAAggggECtELBa/Us/+FveNySn0v/4RrmD7taUK4+q4KVX+dq9OkMn3/G0Zs+eokEH3tasj1J11IBjlPLVKqU7Xcpc85n+bny2TmxQO5JDpl+ARhCVCEXWb3ri1if0W7aX8ER31fgnx6trjJftrEYAAQQQQAABBBBAAAEEEEAAgVol4O+IIH/L+4oV0fZcXdCzpeKMfJW3tIWZKmlw4rnqZSR/LGqmfhd01MKFG2QfOEAd97ytVandFPPZBrUaeL0S/Mt7+drNw1Iu8Ami8EbqNfA8Nc8vOh6XnDnp2vr3V/pwRR1dMvFMtSrxJvvDctTsFAEEEEAAAQQQQAABBBBAAAEEqk3A34SPv+V9PRBrRIwiPD7t5lRedr7xcJmHxXg8zmK1yRpzlAZ02qsF336jsOT2Ov/4urXqzepBSBA106mXmJMRlV2u15Xv3KzRH2/TZQO7lN3I7wgggAACCCCAAAIIIIAAAgggUEsF/H1kzN/yVWGz2CJkT/lH+/K7qEn6Si1dlynXeWZLdu356UP9fFkH9YnbpW8Xr1WD3leqjiVa7c7orJ33vSb1v08dYz1mmqrSlZCoE/gEkdfDClfjXr0V9cyn2pB9rhrFei3IBgQQQAABBBBAAAEEEEAAAQQQqEUC/o4I8rd8Vagi2w3S2XWmaeK/P1Hzo7qpV/cmSnY3FKlWJybquwfHam6qQ4m9rtCtZzaWOduQrU1fdYpbpXpntjcmtq5dS+ATRK58HUg/oPwy47JcuSn6+Y33tDnyZMUFfq9eo5J958sK63G0woee4rUMGxBAAAEEEEAAAQQQQAABBBBAIHgC5qvrHQ6HbDbfJnUORoIosuMYzbrv4DFaotvr4ntf1MXlDnuKHj/bXHltuS25W1cqKe50jW8dUW5bTV8R+FRN5vcaf+Z4rfQo00RnPzBcHatxDiLHn5tlbZzgsTesRAABBBBAAAEEEEAAAQQQQACB4As0b95cy5YtU9++fStMEplJpO+++05t27YNfqf82oNLqd89ookv7VXPMZPVItyvyjWicOATRLEn67FPPlZe2ZmdLDbFxMcrxla7ntGrEVGmkwgggAACCCCAAAIIIIAAAggcRoGzzz5bX3/9tZ577rkKe2HOPZSQkKCLLrqoXLkZu3eVW1d9KyxK6HeXZvervj1W954CnyCyRCiufgPjOOzK2J6kjZt36EB4A7Voc5QSSQ5Vd3zZHwIIIIAAAggggAACCCCAAAKHXSAuLk6DBw/2qR/r1q1TbGzpiYtXrvT8nJJPDVLIJ4HAJ4iM3Tr3Ldfzd9+jl1fsM36zyGq8KM6pMLUacLOm3nu5OtWymb59kqYQAggggAACCCCAAAIIIIAAAgj4LdCjRw+/61DBfwGr/1UqqeFM0Sf/uV0vJ3XV+Bc/1LIVK/TTiu/14exb1P636brpwW+V6qykDTYjgAACCCCAAAIIIIAAAggggAACCFSbQOATRNnr9ekv4Ro45T5d3r2RIs0ph4zHzhp1u1z3/udsub57X2tyqu342BECCCCAAAIIIIAAAggggAACCCCAQCUCgX/EzBarxNgw5VvLT0ZttYUpLCpSUeU3VdJNNiOAAAIIIIAAAggggAACCCCAAAIIBEsg8COIojrp+pt76repUzT/l53KNh8nc2Zr5y/zNWXKCnW68VodFx2sw6FdBBBAAAEEEEAAAQQQQAABBBBAAAF/BQIzgijze912wW36PrNw9y6n7A6nHh/5mR4v1aMYNXrnFf0+6B71jPG3q5RHAAEEEEAAAQQQQAABBBBAAAEEEAiGQGASRMaooVHTZmqYw4cu2uLVPtKHchRBAAEEEEAAAQQQQAABBBBAAAEEEKgWgcAkiKwxatKmnRpExSshxqn9+zKU5/LSf2PC6pjAP9jmZWesRgABBBBAAAEEEEAAAQQQQAABBBCoTCAwCaLMHzRh4HhtumSuFt+0z/3vlV733ENPfPWCTqvjtQAbEEAAAQQQQAABBBBAAAEEEEAAAQSqUSAwCaLYPnri88/ljKyrqCiXZnz5hexeRxCFKSa2Go+QXSGAAAIIIIAAAggggAACCCCAAAIIVCgQmASRJVx14uMLdpS3WUvf/FSuM0fognZMNlShPhsRQAABBBBAAAEEEEAAAQQQQACBEBAI/GxAriytf2+25n2xTfkhcIB0AQEEEEAAAQQQQAABBBBAAAEEEECgYoHAjCAquY/IDrr+8QlKuf8u3WO9Thf2bKcm8ZEqzkRZotSweSNFBz41VfGRshUBBBBAAAEEEEAAAQQQQAABBBBAwKNA4BNEB5Zp4tXTCiapfvZufV5ut0xSXY6EFQgggAACCCCAAAIIIIAAAggggMBhFAh8gqjOKZr1/ffyNke1ZFFYxGE8YnaNAAIIIIAAAggggAACCCCAAAIIIFBKIPAPeuUmadGLi7TZGaGIiNI/YZmrNH/uZ9rG5ER8DBFAAAEEEEAAAQQQQAABBBBAAIGQEQjQCCKnMtZ9o2/XZMiRs0ZvvPqdNjSMVufo0seZ+8/HmrsgQu2Hn6uW4SFjQEcQQAABBBBAAAEEEEAAAQQQQACBI1ogQAkih1J/eVfz3tyiPFemtjnSlPHaS1peZnySJayeel19izqVSRzVxgjYv/5dyrPL2q6prEc1rY2HyDEhgAACCCCAAAIIIIAAAggggEAtEQhQgihcrYfN1MJhhkrWL3po3Ic684nJOrFOLVGqwmHkzloiV3auIq8fSIKoCn5UQQABBBBAAAEEEEAAAQQQQACB6hMIUIKoRIdjumvSC90lR66y8iMUE25RXsrv+vr7zQo76mT17dxQzFFdfQFmTwgggAACCCCAAAIIIIAAAggggEBlAoFPEMmpPV8/qtETl6jJE5/qqc6/6oFh4/RxTqysuS6dOHm+nrywmYKw48qOle0IIIAAAggggAACCCCAAAIIIIAAAh4EAv8Ws7wNmv/Ee8r71z26pWu0Un94TV/HXKn/Ll2q9ya1128vvaUNeR56wioEEEAAAQQQQAABBBBAAAEEEEAAgcMiEPgEUf4ebUhvrAFDBqh9nSytXbpeCQMGqX1UmBoc11UJaeuVEsoJoswcKS2z4Mf8NwsCCCCAAAIIIIAAAggggAACCCBQywUC/6RXeIJax6Vq1R8pyk1YrUW/hqnnFa2MeYcc2v7Hb9oT2Up1bKGrmnXHS3Ju2SW5CvoY/eA1snVpF7odpmcIIIAAAggggAACCCCAAAIIIIDAIQoEPkEUcbSGXttFix76l/o+bPSu/Y168zib1j07XMNfXq/W103UsUfAa+4PMS5URwABBBBAAAEEEEAAAQQQQAABBKpNIPAJImP66RYXP6nFJ/yqVdttatezq1pGOrSj8yW6c3p3DezXTlHVdnjsCAEEEEAAAQQQQAABBBBAAAEEEECgMoHAJIhcecrYlyF7VLwSYpzab/47vq2Oizd2n7VPe7Kk8E6n61Tj1+zUDIUnxinCUlnXauZ2x8r1cqzdKjmcNfMA6DUCCCCAAAIIIIAAAggggAACCBxxAoFJEGX+oAkDx2vTJXO1+KZ97n+v9ErZQ0989YJOq+O1QI3eYF+5QfmLf5DLYlEtzYHV6PjQeQQQQAABBBBAAAEEEEAAAQQQKC8QmARRbB898fnnckbWVVSUSzO+/EL2wkmey+3SEqaY2DJr89ZrzthJ+iS1aH2CzpzytG7sGHmwoCtXW7+ao5lvrtBuZ6K6DLlZowa2UXSIZmFCtFvlwsEKBBBAAAEEEEAAAQQQQAABBBBAIDAJIku46sSbz5OZi0PKS9fOrVu1fU+uoho2V4sWLdQsMVpWL96u7F3aFnWWHnxtpDqUyAmVLO7Y8bFmvpGmc6bO0WnhP2vmpJla3OlRXdYq3EurrEYAAQQQQAABBBBAAAEEEEAAAQQQ8EUgMAki957s2vfLu3r+mdl6d1XxUKDiPiR2u0T/HnOjLu6WqLJvubfvTVLK/jWaM/4GpeTFq9M512jkhZ1Vrzij5FTa3z9qX4dL1btxmFG/mwZ1+a9eXrVXQ1o1KdeeLwdOGQQQQAABBBBAAAEEEEAAAQQQQACBAoEAJYicylg+TVf/37vKP/EK3TVrsPoc01wN60UoL223tq79Xov/+6Ie/fcPSnruNU3oFVdqNJEjz6Umnfrp4pGDdbR9lebd96Rmt5iu8SfGFc7j41DGjkzFNk1QwXihMMU3r6PM7cZk2CJBxIcZAQQQQAABBBBAAAEEEEAAAQQQOBSBwCSI7Nu0eMYiZfS9V/OfuEDNSrQalthMHU4eott79la7W4fp4Rkf6Mr/XqEWJcpEdRyhyR2LDqObLhiUqHuWJSv3xM6KKnl0pSb2ccnl8vymsF27dqleiXoZGRlKXbfOJ6eGubmFSaiC4v/884/you0+1TUL1UtLkznFktlVcxqmlJQUHfBx3z7vpAYWTE5OroG9rt1dJiahF19iEnoxMXtEXEIvLsSEmISeQGj2iHMl9OJCTIhJ6AmEZo84Vw5PXMIGDx586Ht2pOqv5Ztk6/S8brxottf2HKk7lPT3ZA274E0lFD9n5pI9M12ZtjjVizKfKXMpL2Wt1mb+pEu+qFs40shYt2et1mSs0vr5ccYjZU4d2PK3kiN/0yX/iyp+W9jKlQXvTrvxn11ybNpZ3A/rpnqy/vy2136V3OD4c7Nc2bkF2R1jg23/MlniYnyqaxZyJu+Wc6fxiJ3xFjMjgyXbrqWyLEn0uX5tLZiZaYwAiy07O7lvR7tkyRLfClLKb4EOHTr4XYcKwRUgJsH1rWrrxKWqcsGrR0yCZ1vVlolJVeWCW4+4BNe3Kq0Tk6qoBbcOMQmub1VbJy5Vlat6PW/zRvvXojGSx+GyKtxW8bu7LLYII7njkLPMG85cObu1ZfNuZZqvPnNkac8eu+omxsjqcigvJ9do26LwOvEKz0xRRr5Rxr5fezIsqhcXyavk/YsUpRFAAAEEEEAAAQQQQAABBBBAAIFyAoF5xKxcs/6sMJI/iW3VOmeTNv21Uw5LhOo0aqPWccYQo/y9Slq9S4mdjlWjqEZq2yxZm9b+qa0um2KbtFPTmIoTUv70grIIIIAAAggggAACCCCAAAIIIIDAkSoQuASRK19pW5O0McJ70saVm6Y8V0R5a0u46jXvYPyU2RReXx271S9caVFU/TY6tujX8q2wBgEEEEAAAQQQQAABBBBAAAEEEECgCgKBSRAZCZ46cbFKz9mvjJyKemEzytVRuPccUkWV2YYAAggggAACCCCAAAIIIIAAAgggEASBwMxBZEww3e6EburWzfjpfoque2WVfp19lfp2L1zX63xN+fJ3Lbqlv3qc0E7m02OHc4npfpee+vfR8jCWqerdsjXSeY8+onMbBoa06h2hJgIIIIAAAggggAACCCCAAAIIIOCfQOCzGa4DWvXqK0rufb0GNTcHKBmTSfe6WoP1vl76PtV4/xgLAggggAACCCCAAAIIIIAAAggggEAoCQTmEbMyR+Tc+61mLxqsB689WV88vkMXXNVGPzz9pLbbjYIRTXXGjXdoRO+Gsu5frfdmzNCitcYr0Pv8R1PavqyJr29Rft2+euCB1poz8Q0l5x9s3BLVVuePm6hhXeorwrFNX8x8QC8sT1V0r0n6T88t+iT/Ou2dOUWWSx/WyPaRCs/YpO9efVJz1+apzIvTFN7sTN06bbxOaBOp9a/dp4cXJyvPlqCe196oUf2aKDy9oO68vSpuf88Jfdzt/9r7Fo0+q3Vx+y8tSzHezcaCAAIIIIAAAggggAACCCCAAAII1EyBwI8gcjvYte2jF/RN8xEac8Mo9fvnFb21JttI0liV2HeULq/3viZcPVxjZu3UgDGD1TLcN7zItgPUY99sjbniMl09dbU6X3C86rrnM4pQ675ttWrGPXq3zjANi3lPd1wzXCMf/EZNr7pObcnGVgAAIABJREFUXWPLth8uY75rffzAaF1z63uKO+t0NTcmRqrbdYSXuj60b2SgXK6yaaiy++V3BBBAAAEEEEAAAQQQQAABBBBAIPQEgjKCyH2YOeu08KU1emFSMy0Ys1xp7txJhFr2aqotHy1XisNYsXaplltHqkPdBfrRB5uc1XP1WEYHdR5wsY4/qacaRCQZKSdzcSl91WJ9uS5TbcZ0UetujXT3o6cb622Kse1T/diykx7ZtefXb7TugEt251btdTaUzRKh5icZdY23sN396EVGkyXrlm2/rVFmWOn2s3KUkZUlZw4P0fkQSooggAACCCCAAAIIIIAAAggggEAICQQvQWQkbTKTftfW3Zlas6/oASyLLMaIn4MDbVzGvwvWlTSxhEV5eNOZVQl9btXUi/P1xXtL9fk7Yeo4vKiWU9np2e75jSzWXP318mTd8/p645cIJTSJV57d2H+dknswEkPZeeXmQyqqe9/SPUbhgrr58ZFGmql8+/d/v7+4/ZwUo31XntKSk+Uo8UhcCMWZriCAAAIIIIAAAggggAACCCCAAAJeBYL0iJm3/eXqnxU71WZgT9W3WRR7dH+dZPlD6zOMRJHdqdjmjRRlsalhz9PVJqpsG2FqeHx7pf/vv3r3uzXKbNxBDSOshSOIisrmafvKLWoxuL9aGttiOwzVPXcMUCOf0mC+1K2ojEM7f/xRu5iMqGzg+B0BBBBAAAEEEEAAAQQQQAABBEJcwKfUSeCOwam9372gBSdM1OPz/i1r5jotnjFdW8xRN2uX6OfhEzVnzi6tXv6L1qaVfVQrT8mffab8SU/qucHbtOmPtdqYeLEuP+5bzS3uoPEo2E+zNXfLS5oy7wxFZq/VomnTjImuw4x3qVW2FNa1XW7UvViRWYV1LYk6OIVRYZkudxllLi/RvtG2rb76Xn+dcn9dpg/+qWxfbEcAAQQQQAABBBBAAAEEEEAAAQRCR8By/vnn15qZlVeuXOmW7d6qvRybdhYrWxvUk7VdE5/UHX9ulis715zWyL3YOraUM2mH5HDK0qy+rE0TK2zHmbxbzp2pxuNnBc/S2Vo1lKVJxXUqbLCWbMzMNN5UF1tutnCfjm7JkiU+laOQfwLr1q1Thw4d/KtE6aAKEJOg8la5ceJSZbqgVSQmQaOtcsPEpMp0Qa1IXILKW6XGiUmV2IJaiZgElbfKjRMX3+iGDh3qLrhw4ULfKlRSqpofMaukN6G62UgOuYwfOWtNLi1UpekXAggggAACCCCAAAIIIIAAAggcBgEPCSKbml/yuB48I96Hx7Iq7rE1/gSdc0YrRVZcrGBrRAuddetTmvvaq5r71DgNaBZRWCtcTc+4SdPnztfCV6dpxAl1y/cr9kTd/+4SmSNNzJ/3ZwxWE/PFZZa66jRsiua8MV+vzrxFZzQNd7cZ1vg0/d+05/TctP/TaY0LnrKLaHup7rj8KGNqat8XS8xxuvaxFzXtynZ+1fN9D/6WrHrs6vR5QE+NaOvzcfhb3t8joTwCCCCAAAIIIIAAAggggAACCFSfgIcEUeB2bovvokH9W8l4c3wli01Nzr5JQ6xv6rYRI3TLvBxdNLa/GhlJnrDmF2iC8Ub5N24drmse+ku9/n2BWhXkeYrbtMW3VJ3VT2nEBYM1ePBg/WvcEu00JouOOGqobuq3QdOuH67bFsXpihtPVoIlWscNG6iMlydq4twMDRx2nKIt9dTj/Gb64ZNNyqukpyU3WxM6qXP+W3r4jSS/6vmxi2ormvXrTD343lb5+hI2f8tX24GwIwQQQAABBBBAAAEEEEAAAQQQ8FsgbGTfhnppWYpKv3zLqrgu1+iR4b3UOnK3vn3hYb3w7W7ZbQnqefVEjT6rtcIzNum7V59013VGtdX54yZqWJf6inBs0xczH9ALK2N0zjVnqV3nXI371xZN+66Vrr97lE5tEaWcDe/r0amv6+/Moke2wtXo2Aba8c0fSjUf5Vr9nZIaDFH7mE+1oWtvhS97VqvS7crPeEN33RulXHvp4wyv31b1m7XUlLknKDb9NyUt+kSLN9nU4ITOyvt2hpKy7XL+9LG2XN5fraNWyGUmrAqmCHL/d0Trs3Tqvg/05L6yE2MX7ieiqc64YYKuveRYhcXt1nszZmjRhjrqP+JcHX2sU7cO3agH3yqRXIpopv5j7tDVvRopPHq9Xpr7jjb2e0Bj622Qs/epOipql756/mHN/j5VTS552MP6PaXiYWvQV6PK2CV3uV9T2r6sia9vUX7dvnrggdaaM3GBUc9z7CK636mHzkzTzkY91KVxqj5/8zc1PmuAujTN1OcP3a03o8fq7o6v6M7/NdN1Zfa1NrpPuf1v6VxQfsKbOep74x0a0buhrPtXF9iszVR09zv0wMnJ2tKsn048OkarXrhdM5fulcPDsRz8HPj9+aUCAggggAACCCCAAAIIIIAAAggEQMDa9Krr1LXc3MGRat4hRS/ddJWuuec7tb32enWva1HdriM0LOY93XHNcI188BsV1Y1sO0A99s3WmCsu09VTV6vzBcerrpEo+njeZ0r68xXNeH+HGp11qY76bpKGX3qtnljfRn1al3zwLF+71+xTs35dlGizKeH409WlUbzioyMU3yJO1vrn695nXtLcWXfrgjY22UtNBWRReES2Nq94Rw+OulYT33LoovEXq3V4mOo2jtGBHRkFyZb8NO2yxyshMkd/vfmp6l37mB6/vp4+XbBNHc9roeUfJ8mYmtrDYlVi31G6vN77mnD1cI2ZtVMDxgxWS+suffnKh1r/16uavrDkyCOrGpw2WkMsb+iWq0fojg8b6srhnRRtvAvtmJ5Z+u+tV2vEvd+r/XXXqEusmanytr6oK2FqXqFd2S57jp1kJOFap+stI/lz06sWnXthuN6+a6RGPZeqXme3LXwM0NO+6lSwfy827hFekWrRI0HfPjJW1976nuoOHKAWEZ7a9+kBxLIHye8IIIAAAggggAACCCCAAAIIIBBAAWszW7Tqx5oT9pRccrXp0y+0McupnM1f6KNdrdWtqZEoOKmLWne7Qnc/+oQevu08FdXNWT1Xj32QrY4DLtaVl/RUg4gIYxxLycWh1LXrFD5wjEZfdrJsHz+ul/7OKVHAoV2fztTbjks0bc5zmjzAqS37cmU3hjVZw6LUqOlWzb19pEY/8ae6jb5Gx0WVbNul/T8/o/88+4225+Rp94r3tExd1TnBKke+OS7qYDbJPXDI+H/2XV9p1sRRGjVhlpaFn6rT0z/Sr3XP1e2PP60n771cJ8SV7H2EWvZqqi0fLVeKw6UDa5dqubWLOhgJM89LtNqe3Egblv6udEeednw2Vbc885eyjfTTpk+XaoNhmrvFME1pp+5NzRmPPK0v+QxdZXZle+EpdmZ7Du37+zdty3XowM5d2rHmFyXnOJS1a5dyjWRawdF42teBCmJXkY1DaX9+qzX7XbKnJivFHqFwi6f2S34Oyh4LvyOAAAIIIIAAAggggAACCCCAQHUIWO+891l9m1L6ATNzxwfTKi7j5V0O8y3vslhz9dfLkzVu3DiNu+12FdR1KaHPrZo27iy1yF6jz99Zqn/KTeRjJHF+eUa3jp+tFZktde7dT+vuMxqoZFrKYknTD89N0A3XjtTtz6yU3Z6iXVl2Ze1L0+ZvlmlLtkM5ycv1c3YLtY0vWdMYcdT5JHVOLFzncirP6KzdadeBlGxFN65bkKwKr6cGYelKzSkx/MgSp67ntdZPn+5V9+E9tHrGRE35vImGntNCBVNXmxKWojfWF8bDJZfxjJqZaPK2WI38krPoaTVrpOrVi3YfaylTo11bYR7K2/qC9n2wM5Jo4SX64yl25t5dxZ0y/m0ku8ovnvZVX1keYlfQdWOnxn9cxU2VtHEqPzuvxDGbe/PUfunPQfk+sQYBBBBAAAEEEEAAAQQQQAABBIItYL3njgFqdDAbUri/SLU96zS1ibIosnV/DWqwXj/vyNT2lVvUYnB/tYywKrbDUBXUDVPD49sr/X//1bvfrVFm4w5qaGwvHoNjtRn/jtJxNz1vzElzQD8teUVzPjAeJ2ufUCJBZFFsl5v1xITTjL5EqNnpg9Vi/VdKyrFr92+rFdX7JDU1ZrqObNFLPaK2KCndSGiF1VXjpgnuCbCjjrpUt1zbW43CjX4d018n5i3XH2n52vfX34rs09d4tMmq+C4D1Hr7D9pcYsBKWIsB6p/5kX7YazRnzVOmkdDI2Z9lPB0VVmIEVK7+WbFTbQb2VH2b0c+j++skyx9an+EpwWLy5WjLynR17H+c4qyGzSm36P6rjzbeDmaanu42jWh5hgY12qxfdpiZNE/rS04V7dnOancqtnkjRVlsatjTbLfoo+Ipdr5OPR2lTuXi1FQnlFuXUJhAy9VWv2w8H4utRCyD/YGnfQQQQAABBBBAAAEEEEAAAQQQKC8Q9uNzi5VcLn9wQOv/bKJRs/6r5rad+uK5h/RHplOOn2Zrbpe7NGXe5YrMXqtF06YZdfMU+dlnyp/0pJ4bvE2b/lirjYkX6/LjvtWz2zZpe+N/684hWzR10WLtnzxDr15jkX33D5rzUMl5e8yRJXP19mn3auZr1ylt7Yea+fjPMp5Okta8rhdXT9S9cy5TdO5mffrMY/o723y72TmaPLW5nh/9pP76aLrmj5mgx145Sdrziz6c+T9tNyaydm1coFnL79Cdz12qiAa79dojy5RalNex1FWX89vq57cXK8cYcfTDu9s0edocXbF8keZO2lLirWRO7f3uBS047nY9Pq+DwuL3avGM6dpimJV9MK+A13hc7rNntPiWiXr61URF1EnSyy/9oey+edqZ3EKjDdNm4SnGxN8P6RfjAJsYe/K0/mCocrTeg11a7hL9PHyi5szZpdXLf9HatKIhS55i5yqcY6j8B6D0Gk/7Wq2/IhZrYNnYtTVrFtqcMNGw+besmeuKbaI97spT+5vkbHzRwVjyxJlHOVYigAACCCCAAAIIIIAAAgggEEwBy/nnn+9tKEww9xuUtleuXOlut3ur9nJs2lm8D2uDerK2a+LTPh1/bpYr25iuulDF1rGlnOu3GY9kOWVt3sD4qV9hO87k3XLuTC2Y7Mh49srWqqEsTRqq+SWPasy+qZr8ZVqJx65sXtZXuIsauTEzM1OxseVmQ/fpWJYsWeJTOQr5J7Bu3Tp16NDBv0qUDqoAMQkqb5UbJy5VpgtaRWISNNoqN0xMqkwX1IrEJai8VWqcmFSJLaiViElQeavcOHHxjW7o0KHuggsXLvStQiWlSs8lXUlhNiOAAAIIIIAAAggggAACCCCAAAII1D6BcrMP1b5DDIUjcmjbO7fr7nJd8ba+XEFWIIAAAggggAACCCCAAAIIIIAAAkETYARR0GhpGAEEEEAAAQQQQAABBBBAAAEEEKgZArUuQdTWGS3nrrSaoU8vEUAAAQQQQAABBBBAAAEEEEAAgRAQqHUJojiX8W6xLF6FFQKfLbqAAAIIIIAAAggggAACCCCAAAI1RKDWJYhqiDvdRAABBBBAAAEEEEAAAQQQQAABBEJGIKymv0J88ODBPmH6epzZNz0rx+adxa+5j37wGuVMfVOu7FxFXHGG+6eiJffFj5S/+Ifi19xHXj9Q4Rf1rajKEbGN1xQeEWHmIBFAAAEEEEAAAQQQQAABBGqoQI0fQWQmfipL/rx7w6QaGh66jQACCCCAAAIIIIAAAggggAACCARfoMYniIqIvCWJFvYaFnxF9oAAAggggAACCCCAAAIIIIAAAgjUYIFakyAyY9C3b+lHuUgO1eBPJl1HAAEEEEAAAQQQQAABBBBAAIFqE6hVCSJTrSgpFKjkUN4HP8mVb/cYEMeqJJlzDuW+/InH7axEAAEEEEAAAQQQQAABBBBAAAEEaoJArUsQmeiBSg6ZbTl++FuyOz3G0pm0wz0htf2jnz1uZyUCCCCAAAIIIIAAAggggAACCCBQEwRqZYKouuFdLld175L9IYAAAggggAACCCCAAAIIIIAAAgETIEEUAEqLxRKAVmgCAQQQQAABBBBAAAEEEEAAAQQQODwCJIgOjzt7RQABBBBAAAEEEEAAAQQQQAABBEJGICxkelJDOuLclSpl5kjh0NWQkNFNBBBAAAEEEEAAAQQQQAABBBCoRIAsRyVAZTfnGW8tsy9fI1vnNgo76Ziym/kdAQQQQAABBBBAAAEEEEAAAQQQqHECJIgKQ2a+kcx8bb0rPdOPIFY+95Djz83u9sLPO0mKgNsPXIoigAACCCCAAAIIIIAAAggggEA1CTAHUSG0mRzKfekTuVIPBJTevnytu13l2wPaLo0hgAACCCCAAAIIIIAAAggggAACgRIgQXRIkrze/pD4qIwAAggggAACCCCAAAIIIIAAAiEhEBrPPLmytOGDZ/TsktVKyw9Tw16X65YbzlCziBJGees1Z+wkfWLMEV2wJOjMKU/rxo6RIQFJJxBAAAEEEEAAAQQQQAABBBBAAIGaKhASCaLcda/riQ/r6PpHZ6tH7E59+sh9mvlJRz04uKlshbKu7F3aFnWWHnxtpDrUkJyQOUMRY4xq6qlBvxFAAAEEEEAAAQQQQAABBBA4cgRC4BEzl7LT7Gp31rnqkmCTJaKJuvWqr9SkfcovEQf73iSl7F+jOeNv0LUjb9dj7/6pdOeREyiOFAEEEEAAAQQQQAABBBBAAAEEEAiWQAiMILIo/qTRmlB4hPY9P2nhB6k65pqWKjlQyJHnUpNO/XTxyME62r5K8+57UrNbTNf4E+NU2bvEMjIylLpuXYWGdVL2KK7CEtLevXsVkZnp7ldWVpZyU1LcdZxOp9YVtl8vLU2xHtrZuHGjHFHhHrYcGauSk5OPjAOtQUdJTEIvWMQk9GJi9oi4hF5ciAkxCT2B0OwR50roxYWYEJPQEwjNHnGuHJ64hECCqPDAHRla89HLemFRkloNvVOje5ZO/ER1HKHJHYuQuumCQYm6Z1myck/srKhK7OLi4tSwQ4cKS+X/tUe5FZaQ6tevL2dansz3kcXExCiuYUN3HavVqg6F7efGbyw18qmoyaOOOkqKraynlXSghm8uMqrhh1Gruk9MQi+cxCT0YmL2iLiEXlyICTEJPYHQ7BHnSujFhZgQk9ATCM0eca5Uf1xCI0GUv1NLp9+vhc6BGvv4WHVOKNsth9LXrVRSnS7q2izSGDFkUVhEmKxh1kpHD/lOymxBvltREgEEEEAAAQQQQAABBBBAAAEEapNASMxBlL5ynt6xX64HJ15UOjnkyFLK9t3KdFjk2P6RnnnmQ2044JAra4M+/yxDx5/WWiVfdHbYAuNwKHfOx8p94h05124t7gYpp8MWEXaMAAIIIIAAAggggAACCCCAAAJ+CJQdquNH1UAVzdOO39Yr5beVGjVsVnGjiWdN1VOX7tCMiR+o3/RHde4pN2vMtpl66ub3lRWWqGPPG6vRx8cGcATRIRyP3aH897/nlWWHQEhVBBBAAAEEEEAAAQQQQAABBBA4fAIhkCCKVMdRL2nhKE8Ix+ih104v3JCg7lfeZ/x4Ksc6BBBAAAEEEEAAAQQQQAABBBBAAIGqCoTAI2ZV7Xro1ONRstCJBT1BAAEEEEAAAQQQQAABBBBAAAH/BUgQ+W9GDQQQQAABBBBAAAEEEEAAAQQQQKBWCZAg8jGcFqOcK3W/MUF2jo81KIYAAggggAACCCCAAAIIIIAAAgjUDIEQmIOoZkCZj5Hlf/RzzegsvUQAAQQQQAABBBBAAAEEEEAAAQT8EGAEkR9YFEUAAQQQQAABBBBAAAEEEEAAAQRqowAJIh+jaj5i5m2paJu3OqxHAAEEEEAAAQQQQAABBBBAAAEEQkWABFGoRIJ+IIAAAggggAACCCCAAAIIIIAAAodJgATRYYJntwgggAACCCCAAAIIIIAAAggggECoCDBJdRUj4diwXa7te6tYm2oIIIAAAggggAACCCCAAAIIIIBA6AgcGQkiu0NKy5TCbQXysVF+R8B8i1nR4n7lfU6enMYPCwIIIIAAAggggAACCCCAAAIIIFDTBY6IBFH+17/L/DEXS4N6ip03vqbHjf4jgAACCCCAAAIIIIAAAggggAACARM44uYg4o1jAfvs0BACCCCAAAIIIIAAAggggAACCNQSgSMuQVTyUbFaEkMOAwEEEEAAAQQQQAABBBBAAAEEEDgkgSMuQXRIWlRGAAEEEEAAAQQQQAABBBBAAAEEaqEACaJaGFQOCQEEEEAAAQQQQAABBBBAAAEEEPBHgASRP1qURQABBBBAAAEEEEAAAQQQQAABBGqhAAmiWhhUDgkBBBBAAAEEEEAAAQQQQAABBBDwR4AEkT9aVSxrkUWZIx5XzsMLqtgC1RBAAAEEEEAAAQQQQAABBBBAAIHgCYQFr2laLhJwyXh3Wk6eZHeAggACCCCAAAIIIIAAAggggAACCIScACOIQi4kdAgBBBBAAAEEEEAAAQQQQAABBBCoXgESRNXrzd4QQAABBBBAAAEEEEAAAQQQQACBkBMgQRRyIaFDCCCAAAIIIIAAAggggAACCCCAQPUKHHEJIldWjnJf/EiOleurV5q9IYAAAggggAACCCCAAAIIIIAAAiEqcOQliHLylb/4BznWbg3RkNAtBBBAAAEEEEAAAQQQQAABBBBAoHoFjrgEUfXysjcEEEAAAQQQQAABBBBAAAEEEEAg9AWOuASRxRL6QaGHCCCAAAIIIIAAAggggAACCCCAQHUKHHEJIrkKePPe+FIHzr9Xzl2p1enNvhBAAAEEEEAAAQQQQAABBBBAAIGQEwgLuR4FuUNmfqhWDyLKs8uxOtmtaGvfTIqNCrIozSOAAAIIIIAAAggggAACCCCAQE0XOPJGENX0iFXSf2fqfmXfPc/949iwvZLSbEYAAQQQQAABBBBAAAEEEEAAAQSkI24EUagH3flPihw/r3N3M/y8k6QIzyFyrEqSM2mHLIl1FXbaCRUelvkYneP7vwvaPLsHo4oq1GIjAggggAACCCCAAAIIIIAAAkeegOfsw5HnEDJH7Fzzj3Jf+sTdn3AzmeMlQWT/YpXyl/4qW+c2lSeINu4obtPWp5OsPHYWMvGmIwgggAACCCCAAAIIIIAAAgiEggAJomqMgnPdNmXf+rwskRFSfKyi7rrMvff8D3+SY+UGWRrFy9auSbke2b/5Q/av/5DFSOxE3nZxue2sQAABBBBAAAEEEEAAAQQQQAABBA5FgATRoej5UdecGNucH0jmj7FYoiOLazvXbpN9+RrZ2jaRtW3jcq06t+5xb7c0qKeDtcoV87qiVk/K7fWo2YAAAggggAACCCCAAAIIIIAAAr4KkCDyVSrA5Vwu831qpRdzncXiPZ1jbjHfUJbz6ELpQHbZ6l5/L78nr0XZgAACCCCAAAIIIIAAAggggAACR6AACaLDFHR3Ish4Jb17VFFOnrsXFSWHzO3uRI9Rx7UnXRbj/8ourn375TS2VbSY211pB2SJryNrnWj/JqzOzJHTSExVuX5FHWMbAggggAACCCCAAAIIIIAAAggcNgESRIeNvmA0kPk6ek/Jnqp0K2faQjn+3Fxh1dw7Xi5INBmlom65UGFnda+wfMmN+Z+uLJjs2mokp5wuhV9wsiJHDvK5PgURQAABBBBAAAEEEEAAAQQQQCA0BUgQGXExEx/OdVtDKkLmaCH7l6tkadnwYL/y8uX4eX3A+unpMTezcfvXv7tHKlnbNZX1qKYB2x8NIYBAzRdwrEqSa3eanP+kyGpcn6wntJW1cULNPzCOAAEEEEAAAQQQQACBI1yABFFWrnJnvh9yHwPzAbKc5/+nsJM6FidpnAdylLdoWcD66u2RttxZS+TKzlXk9QNJEAVMm4YQqB0C+Yt/lMOYNL9obrOoScNIENWO0HIUCCCAAAIIIIAAAke4AAmiEPoAOPfuV77xOvuixf3msy27jTl/Mt2rys86ZGzfsU+5L36k8FM7lzuSvA9+ks145MxqvB3N25K/8FtjHqQDsnVsobBTj3cX8zayqKgNsx9VmfjaHHGQ/9HP7mYihvSTJbFuUZP8dw0SyH/vezl3p5f6zBxq9/M//Emu5D1ybDVGpbRqpPBBPd2jU1h8F7B/84cca7bK2qK+ws890feKhSXNkUH25Wvdv/HoqN98pSoUnSPmWynD/XiM99D2WlA79+VPjGGgToX1aC9bj6MrbNL+42o5fjfuEY3qKfzCPhWWZSMCCCCAAAIIIIBA7RcIjQSRK1dbv5qjmW+u0G5noroMuVmjBrZRdMmMiC9lfIiXpySLD9WqpYgrI1PO3zYW78tMwrh27pPMH/Pfxk/Z/rv2Zih/8Q+yGX+IlF0cP/wt5/ptFf6x5/6jctNOKbNbcYLIHFlUlQRQ2f2X/d2cRNvsq7mEn9mVBFFZoBryu/3HNQVzXZX4zBxq1x0rNxSPSnEY54Ctc2sSRH6imob5S3817NpUKUHkTNpRfH6SIPITv0zxonPEHAFa3Qkiu5GEN0eAWupEVZogMpNDBfePJiSIDi3k1EYAAQQQQAABBGqFQEgkiBw7PtbMN9J0ztQ5Oi38Z82cNFOLOz2qy1qFFyP7UqYqEcl9ZEFVqh1yHZcxn1Du04F7tM3rqJ/0TOXN+dhrf4vqmY+MZN/0rCLGnO+1bGUb7D+vU/4rnxtvUsuQNdF4S9oJ7RR547mVVXNvz316sXseKGu3o2RtkiDzjxzVi1XkdWcrd8Yid5nIyZeXe5TFHJWUa0zObS5m323HtnL/u3gx5lLKuu0F9xviIm44R7Yu7Upv9/CbmcjKvve/ldYpOl6ziehHrvPvjXBGnaK+m4lBS2SE1CheEUNPccfLafTBGhcj69HNFXnbxeV6aY5QsC/9TeaoM2v9usaE4b0LJhwvPF4Zj07KapXLeOuctWE92cwRZrl2dxLG0ryBou681N2m6e74e4uiN9FQAAAQNElEQVRcxufEWj/ObZT3vpHESzFGCBl1IoaeWrzv4vgao9Z8Xcz5rPLf/s5dPHLi0FKxypv7mWT8IRtmjBbytpQyio6SpU3j4r57rWMkOszPjMsYGWcx3tRn1gkzEpLmZ9NcimJljlry9XPmbV8VrS/52TSPvaojovz9nDk2bHefy+GXn66wPp08dtEcLVT0ObPVjZHdeJuiNfLg9bbseeVLrDzuqHBl8fltJK98vSZU1F51b7PkO5Q19plS1wQzoVZ8bTLim/PoWwVvpDRG75jnXaAWj7GKjXI3bzH+O/rpMaV2VXRNt3+0Qo4fVit6yghlTZ6nomuCpW60+5piXjfN64e5mCNRs26Y4R55ZG4vez3LfuD14muCp+tIqQ4E8peS1zPzeM17QmHfG+XmynlPgvsa5tqXcbDvhcdbdM13X8+27ZErK894e6dRv4JzsfiaEGZT9Iwb3UeS9/oXchhJcXMkbslrsXmfyLnvVXeZyu49FZ2LgeQKpbZyHnlLLsO97H0klPpYUV+8XQPNe60jaaec5ujsELqelbzXlr0mlDzO3Bc+LOh7mc9zRRYlt3k6R4q2F10ji7+P+Nqoj+XMvrv+2Cx7Sppsxvclf8+rYiPj/C66jpT8nlD0vcjH7pQrVuk1oVyNqq0I1HcLT3sv+b0oeuoIT0VYF2SBst8tzO+Opf6+qhcj537ju73xFEbRvadUHeNvpZwpbxR8HzFeKOTpe4K3Q/B0HfH694a3RgKwvuTxNqobqdzem4y/BwcGoOXgNlEd10D3vafE9bvoXnuevb7+F7Y3YAcYAgkip9L+/lH7Olyq3o3DZFM3DeryX728aq+GtGpi/G4uvpTx38R8e5hzW+Aw/emBy+GSc2eqP1XKjR4qWdnbfEIu4w8bc0JZb4u7nvEf8/X1Mn+ML+RVXow/yN2jkYzFsT9LliaJPjflNL5ImnXNibFdURHuf1sa1JPLuAgWtemxsXwj6VG4T499N7Y7N+9yV3Vl5nhsouxKl691Shxv2TZ8+r1k340KFsPe7GOxoZG0MZNknhbzES93OSN+DiPBFFb0B2mJvpv1LMYb5xybjBvJ8W2NUWIFbRecUwWtmu7mlw1zcWRkuffvNL78uvakF9QpufMqHK/5hak4PmVi5TT6b450sBU+QunpOFXKaL9s4SV777FG8WfGfX6nGY9PmnU89N18dNPnz5nnXVW8tmTfjX9XefHQ9wrbyslzH1d4BZ/3cp8zo0FnyfJViVUFnSo6vy3Gl/qauFgdznLXkVLXpsLzrqqP31ZkUmmsylQuGgFqPjpsfu6Vn1/cd7OoJT3SWFfiummsc+XmGyNVUwtGjhrnftml1DXBw3WkbPmA/V72embcD4r67v7yYm4vvI6492n2vcTxlryeubcbiVCzjrel+JoQbRgVLq6ia21hUq54fZlzpFybJfpe0blYrl4tWWEmh8zrkPveUxMXL9dA815rfqdy3ztC6HpW6l5bgXdR31Xm81xBlVKbPJ0jRQWKvmsVfx/xtVEfy7n7vrnwO2Yl9zhPTRYZWczzuzC+pb4neKrkxzrzu2OF30f9aKvCoiX3U8H1rMI2vGws+b3ISxFWB1mg7HcL9+7K/H1lrnKYfyMU/s1W9m8l81ysyvcRT9cRr39vBNOhxPGGGX9PKt34qQFLtVwDzb/9Sly/i+619cIO/o+8gaCyBqKRQ2vDoYwdmYptmqCCQwtTfPM6ytyeoYNf43wp41svgvHolG97Ll2q7KNiVWmjZB1vI4hcFTws5q1OVfsS6PYCbVTV4/JWLxSOt7LPs6ftlfXbm3tl9bw5eVtflfaqWqeiet6O11u/q3t9RX2vqC9VreepzUC25an92rKuouttKB+j+3HmCu4Vns6RUPlMVNYPT32v7lhU1sfq7k917K+2HXNtO57q+AwEex+BjEkg2wr2cVdH+6Fw3ayO46wp+6jK57Oie3pVjtvT3xNVaceXOlU5Xl/arY1lgmVlMRquzph7iE2+Nr06Xk9ab9PjVxpzZ8ihnYvv1APbr9eMUR1V8L/j+VJGGjp0qIf2WYUAAggggAACCCCAAAIIIIAAAgjUToGFCwumXTnUowuBEUQ2xTWNVfbO9MIRQ3al78hSTNM4HXz+zZcyh0pBfQQQQAABBBBAAAEEEEAAAQQQQODIFAiBOYisiu/UW/Fvf6ZfU4/XyWF/a+mqcPUY2FA2R5ZSdh1QTONG3suUiFugsmZH5keBo0YAAQQQQAABBBBAAAEEEEAAgSNVIAQeMTPp87T9yxf11Dt/K8MZow7/ukmjz26tyPSvdPfYD9Rv+qM6t5HDY5koHpQ9Uj+7HDcCCCCAAAIIIIAAAggggAACCARIIEQSRAE6GppBAAEEEEAAAQQQQAABBBBAAAEEEPBbIATmIPK7z1RAAAEEEEAAAQQQQAABBBBAAAEEEAigAAmiAGLSFAIIIIAAAggggAACCCCAAAIIIFATBUgQ1cSo0WcEEEAAAQQQQAABBBBAAAEEEEAggAIkiAKISVMIIIAAAggggAACCCCAAAIIIIBATRQIgdfcB4jNlautX83RzDdXaLczUV2G3KxRA9somrecBQi4gmZcWdrwwTN6dslqpeWHqWGvy3XLDWeomdZrzthJ+iS1qG6CzpzytG48RsSqAs6Abcrz01+cQwGz99hQvrbMH6/b39lRemuHsZp9bzO9fRPnike2IK50pX2naY/v0BX3DVXLcGNH3u4j3s4Nb+u57xxS1MrHhXvMIYEGoHK5mHB/CYDqoTVROibcXw5NMwC1vX0XDvfy3crb/cPbeu4r/gfJa0y4p/iPGaAa3mLi79+MnCcBCoiXZly1ZLFve8814YaprqU78132vT+4pt84zjV/S14tObrQPoycNS+6Ro161rVin93lzN3q+vg/17vuWLzdlZ/2rev+m15wrc0p3X9iVT3xdPrpT1yqJy7Fe3GmuZbPvMM16+d0l8PPWFVzT2vf7uz7XL++87Tr/jGXuoaMXeBKLrxVeDsH/F1f+8Cq6Yi8xIV7TDX5e9qNl5hwf/GEVU3rvMSk1N65v1RTMA7uxtt1KsfL3yfcV4IfIm8xOcDfLcHH97IHbzHx929Gb+ePl92y2k+BWvKImVNpf/+ofR3OVe/GYbIldtOgLjlauWqvHF4SY6wOlIBL2Wl2tTvrXHVJsMkS0UTdetVXatI+Ze9NUsr+NZoz/gZdO/J2Pfbun0p3EqtAyVfWjt0v/xTt4RyqjDSA213a/9sbWqRLNLx7nBx+xYrr2iEHwhKhhHY9NWBgDyUWj6P1dm3ydm54W098qhwfj3HhHlNlz0BU9BgTiftLIHCr2IaXmBxsjftLFWUPoZq369QepXj8buXt/uFtPfcV/4PjLSZ7tZ+/W/znDEgNbzHx929GzpOAhKOCRmpJgsihjB2Zim2aIPMpASlM8c3rKHN7huwVHDybAiFgUfxJozXh4tZue/uen7Twg1Qd06elwvJcatKpn6558Dm9+PAwxX/xpGb/nKp0YhUI+ErbcPjlv0+pxKVS04AVyNukD+bv1ClDu6ueMWzcv1hxXTvkOFhj1bprb53UtaVii++C3u4j3s4Nb+uJT5Xj4zEu3GOq7BmIih5j4u81i3MlEKEobsNLTIq3c38JKLdvjXm7TjVTnsfvVt7OCW/rua/4FoeSpbzFpJUa83eL/5wBqeEtJv7+zch5EpBwVNBI7ZmDyDzIUs/nuuRyOSs4dDYFVMCRoTUfvawXFiWp1dA7NbpnnDH/0whN7li0l266YFCi7lm2RbkNiFVA7b00FtXRH39XQSucQ140A7napdSfFmh5q0v0UGObu2H/YsV1LZDRKNdWuXPA27nhbT3xKWcaiBXcYwKhGLA2/Ltmca4EDL7Shri/VEoUzAIerlM71xg75L4STPWK2/YQE3c4PKzn75aKKQO29ZDtuacELBZeGqolI4hsimsaq+yd6YUjhuzGKJUsxTSNM8YSsQRdIH+nlj4+UU/+0VrXPz5d485pZySHHEpf95N+3Z6rgtPYorCIMFnDIohV0ANi7sBf/wQlcA5VS2Tk2KPlH+/QcWceo5iCbymcK9UjX8levN1HvJ0b3tZz36kE2v/N3GP8NwtqDe4vQeU9lMa5vxyK3qHV9Xid4r5yaKiHWNtjTIw2Pa7397rGvb5K0QmIPd+/qmTvR6VakiCyKr5Tb8Wv/0y/pjrl2v+3lq4KV48uDVXwv8/7IUJRPwVcSl85T+/YL9eDEy9S54SilJxFju0f6ZlnPtSGAw65sjbo888ydPxpbdWYWPlpXJXi/vo3VgPiUhVov+s49q7Q1/uO1SltIgvr+hsrrmt+o/tUwdt9xNu54W098fGJ2+dC3GN8pqq2gv5eszhXqis03F+qS7rsfrxdp7ivlJWqvt+9xcTben+va9zr/Y9loOy5p/hv718NizmptX9VQrV0nrZ/+aKeeudvZThj1OFfN2n02a0VxWshgxywXK15fozuWZpRaj+JZ03VzOsb6a/5M/Xy0k3KCkvUseeN1OgLOqqOhVgFOSgFzTtS9Ytf/sQl+HExbo7fPaDxXw3UjLt7q27R9cnvWAW/p0fCHvKT39QdT9k07pFLC15zL2/ngL/rjwS94B1j6bhwjwmetO8tlztX/L5meTuHfO8DJUsLlIuJMV6b+8vh+pRUcJ0a2VZ7Pf594u2c8Lb+cB1bTd2vt5jco/9zPqUp/N1yGALrLSZV+ZuR8ySYAaxFCaJgMtE2AggggAACCCCAAAIIIIAAAgggUHsFaskjZrU3QBwZAggggAACCCCAAAIIIIAAAgggEGwBEkTBFqZ9BBBAAAEEEEAAAQQQQAABBBBAIMQFSBCFeIDoHgIIIIAAAggggAACCCCAAAIIIBBsARJEwRamfQQQQAABBBBAAAEEEEAAAQQQQCDEBUgQhXiA6B4CCCCAAAIIIIAAAggggAACCCAQbAESRMEWpn0EEEAAAQQQQAABBBBAAAEEEEAgxAVIEIV4gOgeAggggAACCCCAAAIIIIAAAgggEGwBEkTBFqZ9BBBAAAEEEEAAAQQQQAABBBBAIMQFSBCFeIDoHgIIIIAAAggggAACCCCAAAIIIBBsARJEwRamfQQQQAABBBA4JIGePXuq5M9JJ52ksWPHatu2bYfUbsnKTqdT8+fP18aNG92rd+7cqT59+mj79u0B20dlDaWkpOiGG25Qbm5uZUX92v7rr79q6tSpMo+RBQEEEEAAAQQQ8CZAgsibDOsRQAABBBBAICQEunfvrnnz5rl/5s6dqylTpmjdunWaMGGC8vPzA9JHu92uBQsWFCeIYmJidMUVV6hOnToBab+yRlwul55//nkNGTJEkZGRlRX3a/sJJ5yg5ORkrVmzxq96FEYAAQQQQACBI0uABNGRFW+OFgEEEEAAgRonEB8fr86dO7t/jj/+eJ199tm67bbb3Emiffv2BeV44uLi3KOUzP+ujmXPnj1asWKFe9RSoBebzaYLL7xQr776qsxEFAsCCCCAAAIIIOBJ4P8Bu2IMl5oDyqsAAAAASUVORK5CYII=" alt="" width="748" height="398" />
总结就是，余数法推到减少假设次数 + 递归假设求解子问题 。因为问题规模固定是9*9,因此损失的空间复杂度也能接受。
写了这个着实激发了我很大的兴趣，于是后面又写了生成题库的模块，图形界面的模块。。。