a/4)n^2<=an^2+bn+c<=(7a/4)n^2 ,求解出n

a/4)n^2<=an^2+bn+c<=(7a/4)n^2 ,求解出n,出于《算法导论》函数的增长p27。不知道书上n0=2*max(|b|/a,√(|c|/a))是如何得出max里面的式子的,求解答

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a/4)n^2<=an^2+bn+c<=(7a/4)n^2 ,求解出n

a/4)n^2<=an^2+bn+c<=(7a/4)n^2 ,求解出n,出于《算法导论》函数的增长p27。不知道书上n0=2*max(|b|/a,√(|c|/a))是如何得出max里面的式子的,求解答

定序列A={A1,A2,...,An}, 要求改变序列A中的某些元素,形成一个严格单调的序列B

Problem Description 给定序列A={A1,A2,...,An}, 要求改变序列A中的某些元素,形成一个严格单调的序列B(严格单调的定义为:Bi<Bi+1,1≤i<N)。 我们定义从序列A到序列B变换的代价为cost(A,B)=max(|Ai−Bi|)(1≤i≤N)。 请求出满足条件的最小代价。 注意,每个元素在变换前后都是整数。 Input 第一行为测试的组数T(1≤T≤10). 对于每一组: 第一行为序列A的长度N(1≤N≤105),第二行包含N个数,A1,A2,...,An. 序列A中的每个元素的值是正整数且不超过106。 Output 对于每一个测试样例,输出两行: 第一行输出:"Case #i:"。i代表第 i 组测试数据。 第二行输出一个正整数,代表满足条件的最小代价。 Sample Input 2 2 1 10 3 2 5 4 Sample Output Case #1: 0 Case #2: 1

Istanbul-tools安装错误:github.com/ethereum/go-ethereum/crypto/bn256/cloudflare.gfpMul:重定位目标runtime.support_bmi2未定义

<div class="post-text" itemprop="text"> <p>I am trying to install Istanbul-tools to run an IBFT ethereum network as shown in this tutorial here <a href="https://medium.com/getamis/istanbul-bft-ibft-c2758b7fe6ff" rel="nofollow noreferrer">https://medium.com/getamis/istanbul-bft-ibft-c2758b7fe6ff</a></p> <p>I am installing istanbul-tools via their makefile using</p> <pre><code>go build -v -o ./build/bin/istanbul ./cmd/istanbul </code></pre> <p>After fixing some initial issues, as the code base hasn't been updated in a year, I then received the following error:</p> <pre><code>github.com/ethereum/go-ethereum/crypto/bn256/cloudflare.gfpMul: relocation target runtime.support_bmi2 not defined </code></pre> <p>I also cannot find the Cloudflare file in any location in the go-ethereum folder. Can someone point me in the right direction? Cheers!</p> </div>

求解递推关系式,求得bn,cn的每个值

实现递推关系 #include <iostream> using namespace std; void fun(int C[1],int B[1],int k1,int k2,int k3,int k4) {for(int i=1;i<21;i++) C[i+1]=C[i]+k1*C[i]-k2*B[i]*C[i]; B[i+1]=B[i]-k3*B[i]+k4*B[i]*C[i];//求解这个f cout<<C[i+1]<<B[i+1]<<endl; } void main() { int C[1],B[1],k1,k2,k3,k4; cout<<"输入参数:" ; cin>>C[1]; cin>>B[1]; cin>>k1; cin>>k2; cin>>k3; cin>>k4; cout<<fun(C[1],B[1],k1,k2,k3,k4)<<endl; } 报错 :\c++\die'dai.cpp(19) : error C2664: 'fun' : cannot convert parameter 1 from 'int' to 'int []' Conversion from integral type to pointer type requires reinterpret_cast, C-style cast or function-style cast Error executing cl.exe. die'dai.exe - 1 error(s), 0 warning(s)

如何使用golang.org/x/crypto/bn256计算e(g1 ^ x,g1 ^ x)?

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Crazy Bobo

Problem Description Bobo has a tree,whose vertices are conveniently labeled by 1,2,...,n.Each node has a weight wi. All the weights are distrinct. A set with m nodes v1,v2,...,vm is a Bobo Set if: - The subgraph of his tree induced by this set is connected. - After we sort these nodes in set by their weights in ascending order,we get u1,u2,...,um,(that is,wui<wui+1 for i from 1 to m-1).For any node x in the path from ui to ui+1(excluding ui and ui+1),should satisfy wx<wui. Your task is to find the maximum size of Bobo Set in a given tree. Input The input consists of several tests. For each tests: The first line contains a integer n (1≤n≤500000). Then following a line contains n integers w1,w2,...,wn (1≤wi≤109,all the wi is distrinct).Each of the following n-1 lines contain 2 integers ai and bi,denoting an edge between vertices ai and bi (1≤ai,bi≤n). The sum of n is not bigger than 800000. Output For each test output one line contains a integer,denoting the maximum size of Bobo Set. Sample Input 7 3 30 350 100 200 300 400 1 2 2 3 3 4 4 5 5 6 6 7 Sample Output 5

Inversion

Description The inversion number of an integer sequence a1, a2, . . . , an is the number of pairs (ai, aj) that satisfy i < j and ai > aj . Given n and the inversion number m, your task is to find the smallest permutation of the set { 1, 2, . . . , n }, whose inversion number is exactly m. A permutation a1, a2, . . . , an is smaller than b1, b2, . . . , bn if and only if there exists an integer k such that aj = bj for 1 <= j < k but ak < bk. Input The input consists of several test cases. Each line of the input contains two integers n and m. Both of the integers at the last line of the input is −1, which should not be processed. You may assume that 1 <= n <= 50000 and 0 <= m <= n(n − 1)/2. Output For each test case, print a line containing the smallest permutation as described above, separates the numbers by single spaces. Sample Input 5 9 7 3 -1 -1 Sample Output 4 5 3 2 1 1 2 3 4 7 6 5

限制头文件#include<stdio.h> 一个整型数组中(数组长度不超过20)除了两个数字之外,其他的数字都出现了两次。请写程序找出这2个只出现一次的数字。

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jsoup抓取dopostback的网页错误 ,是不是BODY出什么问题

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ele2.parent().parent().select("td").last().text(); if (ele2.text().replace("更多信息", "").length() >= 2) { System.out.println("项目名称:"+newclass+":"+inittext); System.out.println("链接:"+newclass+":"+newsurl); System.out.println("招标时间:"+newclass+":"+newstime); } result.add(ele.child(newclass).text()); newclass++; } return result; } static String getListData(int pageNo) throws Throwable { int table_number=0; URL url = new URL("http://ggzy.jiangxi.gov.cn/jxzbw/jyxx/002004/002004001/MoreInfo.aspx?CategoryNum=002004001"); HttpURLConnection conn = (HttpURLConnection) url.openConnection(); conn.setRequestMethod("POST"); conn.setRequestProperty("User-Agent", "Mozilla/5.0 (Windows NT 6.3; WOW64; Trident/7.0; rv:11.0) like Gecko"); conn.setDoInput(true); conn.setDoOutput(true); conn.connect(); String body = "__CSRFTOKEN=%2FwEFJDZiZTk0MWFhLWQyOTItNDM5My1hYTZhLTliZTQxZjJmNTQ3Zg%3D%3D&__EVENTTARGET=MoreInfoList1%24Pager&__VIEWSTATE=R8GA6T%2FIFGHg0Gb6XTFK3FRWaT%2BnnifWmbfVtNOsNxO8aGNCF9BvV0HlGq0NCTtFJ9Z%2FOB0SJrX5ocfMmneceh63ACvs4tmktSDuFT%2BQ%2Fj2UdQTyQH9sF1LqsGpBpaBMRI5ihQzcPnaoZSOI5y4wAcCT2eSyOIAJLzDfEahy8vHcO4rY5X1OgnRqUbqhAGYgciiDdJzOwhBLhOtH6KpEt6GD0PCWIYpVFGwufdKPRpBoyvMvVLxns62e4opbzTrVK2mOGWLS%2FF9b3YqhnYe2eTEllzjFQdzl7hJb%2FjB%2F46YVl9ABjjLZ3ZCw%2B28bT805n3aL8nlrByAuHCArCYh%2F2rD7hA52KBZ0WDP6AgnR%2Bw7TDhCchpWEvukASBok8AyUK%2B7Zx6JztNgDXcsn%2BzpcoMqr40%2BlnDwG1aoJIQ4y%2F%2BTLjieFXqu0cIcteyE6fCByvda3M7Drq%2Bzttil8VbGD1QQOPAeNnNSa3MBdqRsgh6BzfhD3LlqtCVPc3p5bukA4JaUCBdw0U9jaULfcXz8V0zBElOQUmOvTm5nMc%2FDp2clYlEhHNZ3w1%2BJwWgFwhSX%2BFcugoXQBMmsAexF9ibA%2FcMQ87Rl1aP4e%2Fcj%2B%2FHzBF%2Bpkh03mr%2BlsSkFN%2BDSEleVQ6hD0i7SToPdSAATi5bvVl96tXl7y8cI%2B7dHeqZiGV7cxcue1xuAzVbTfm3IrqlEtWS4ol8%2FAXmF7CUIRLKFayFsLgVDBfq%2FuHl0lxfv5L6yZyXvLoXDa41Mzwa812VHnwyYwNVRrdalfcbketek1F5nHFkeeaRNn9JEFIFsIyBKUx12p9P1E%2B%2B3MBkdiK3o%2BRwywLHwNJuWS%2Fi3U362Unj2nuQxyFndOBF%2FeHr%2Fby8jnLaZNWeLJjy578rRlYB1ggnM4qmuNwQqKBP0%2Biqa7mRKZlMsr8y7bq%2FKnO4%2BJcUyzNh2WeQnQlMT%2BRsWfgOIf3U6%2FxNgNNkwcJxggQao6AO1Jptj8JHJQXa9nu4Tu1dgR885Mexnueg5eMppcBP3fnIGE65%2Fd6ZBhww0f6rL%2B2IdLV9XEUgVvT8sED7eaNlLJymFQ8EkSZXfl0TTuZFezfXouhqaEvR5HMGlE5USdIfphpRH3fg4KXma6WtfxWtAHpqVZKmA9ii6JzLluqbvifl7Q4lGGNbXDi3FtdVUegXncyFoQ1tQoXigUVhOgq0wbe5xim918qTLP6nNC%2BFDiyg09JmFj98wgpAhmLA2bvlfci1jaFWMy5hEYz%2BP5OAbERPOaVkH8PLmXcdrJ9RCPf8rxLLQE7ReKs5NSTrwdVHe3MSTn8cartsPfiD8LejzyTduW5gHO6uCj0SFAOz9CsBspqlEyVT5AXACrDswjg169gPOtQ%2Fq5kipvk5Gn18Np3uMMFXgKYnNDfiJzUO2%2FEA36pEzcHj1DP%2BTiW5N7tcWln3ewRJ23Y6gXFD%2BlAX6OdMWKOEZdbtmJ%2B1a%2BvFhFXGGQverFGOPaGQp1hhWe9XB5wvHngNka6mkw9%2BuWcM%2BMZn1KxdM6wE%2BzvMYVjEL8K12JX6b38xqQRF3NOFR%2BzC4lvKzwaMyxGmH7g8S9wqt218PbVFEIEtNZH1oCxgI63%2BlQn5G%2FnfqIbk1FE0eu0HeQGvfMCxC9%2FS4%2FSEzYowgsOiHcNlZjAm%2FRPVpQqTvfuXGVHnEgjsKxnQxMB1EaHRXUa0rAZrJ7juFLurRin8QH6945EaXIJ8S1Hx84dotDj5FngVEYK6NqyvKlUixbs4H4Kz92vI49uyJXxLtEGOJ2ZCuVIzcE%2BBNDEFBMvd5qVbiIUqxw72qGUUHPZKYm10KxR10FtS1YGtRTEHLTlr5G0dAPa3sel9kYAjQyXTNFrkcWULeErpdyErVB3jSbAwPmujnu8hfDHCV%2BrzYnVy5L9Mmxk3pSNPuH1Vu9v8UY8CCD4TWc55U1iQr9Rh%2B2UricncYaG7AyWbPs9RcF2b6gRzNTPZNilWTvZZhIpo3hMbACfryhaJC%2F32FF0M7XgmsfgXe1Ir2hY0JjiIItKbnMgO64hJRJUUnM2ViHKQzorMq7GBL%2F47p%2F%2FV60VPNxovQ0KFLdjDlVwXsZspCEZyCiD0H3e6wNDrgKQnWJumoRmii%2F%2FJsO4MV5XZyb6CpWDTW1Kam%2FgveKxPC7VfajOxnYz63kA1Xn46IPsAqZENBgMR7gPOfbKAYuM9YXyxAyYAl9g6Y2ceXK8HqpWqjAXMCmk%2FOL8Ny03HLvg1lTth6Y2dYxEpRw05h9%2B0MmAvrEUZdzCVvlw3BTqlis3NBFXvXvDAQf5%2BH2A8allFp2SBAUNVeakknNQDiqdvewMMbG5Tubw9fp7UzbJbR2wihx4OWReiztXdJyRasdoUUwI0LXMWQgKLyaIc3UT9uzISnbinlcfNwUrjDlKl3BIeuduSnIV4mXE5S1K0OREcXIuLL5Vn2h9TSoCxquOWmsC42kF8mqWPXL2zLE7wTUJcwDD4ru4Qmozz6jJppayfGeId%2BeZN6owQF1k1im37PSftJhv3IP37vasnuAsRF3BQt8tMU5ue1IXmZzgufvxafd8FWD08L0RnKrhbEKGBmZ0dmEWTczG3a0078owGsAcw4M6E3kWlxAMm68EfQNtkRLZ5On%2FyHQfhQsrl0TF3cW9eAVv1ii3lZmgmVTafb%2FJLX%2BRz5BjpzBwiKHLbnrrm0edokZ%2Bqc9CRvhMSiM6lBH%2BIcPRRB8bE711N5Z%2B0nR174rTn2KvUmLXFIpqY0jG4PLDcompSsTFzHQ5KA7qlM%2BxLOOegYMN%2F0imSXqPLOyj7Uyrd9%2F93xtq58NCfGPd9rSO4Kb10nPMEyhwDOLy0mmYOiV71Z5Nl5CDaWkXRtl8U7gqgGjiFpvcvrEl80gQayaKyMFFSC4y4oYDOkMhrs7tuow%2B6yo%2BWw4yj392%2FBciTvdS2N7WA2U5oDZlFYbKBGE3zpKbAIkSlr6AKO7BMbQzoHXY4Gvpu3tmrrPGT3HlvkYe1URZJIjas%2FcJsFV%2Bxo4EXffAcE48vYf1Df9QDvOp42hhhu6oqcSPo23S6u36hER2rXhSBvAwPGAEgl5A8z6J%2Bi2%2FM485Kgn%2FSYbf3JhX8Uk%2FskKK9XVkDDy76MVBEHSGq9hcHHUK%2Bc1UCTEdCx4XGfG0x%2FKnqXKZVacC%2Flj0ovGzglJ7dZoE05AGVCJG9ySkznToPYeB2rTja2PVCIk%2BimzAjfEmT6TmHSnXu%2F1%2BP%2FjU7jjWjHj7gBpp%2FBAh8fyWczEpDoWJjfEmDf25hbM%2Bsc%2BPl4DsDmlBz6gR3L03waT5GuCpr1jNFUJTpHknF0t9kduDKZ5PsB%2FDUNeHtZXzbQWZYmifYYKlL45nRI%2FtJ0LoGUOEiAK38wrEDmtXBujhhQDDXBe7%2FcvZ8fbxVUNwvu49TWNH69sHsHkMkvWqyO%2FfQtC8MP5xGsRabOBgh6IcPIS50oQ9jXeNmnKjItDfK3lfEdeuLT8MnTyjFg02zOku8UPqBQXzXxfshH219tdbppzB1kqj0EsQm61iVD4%2BfkEMli0Dlf2Lgt1ifkA761c6nvrFOWlXS79W%2FjOBNQzgCfjkxmvKlRmYgJ7ssMvf%2FazLJJzbAyhiihN%2FlZ9yxqnOJZsDc5xdu7V2hqz9KUzXKp4%2Fi%2BIWuCY7ypDLPYTLtpFOiDyA79UaBwZsN7b8uYAIkv5OQbKY%2BFrcyKfZBgxTVm4PS9nAP7Oha1FKZ3P3D2D1WZBCbcrZhSlJwptgx5kFAi7vLQh%2B3waKKdbI0eDgr2Fa5pc5LUPPeaBYB6oxKYjAiX78wm2XY6tspPvPa61Wr88rRmqnS5v39jVcjPyWnAa%2FRSxLLVjYwcSVciMJ07rIZkNSMdCMFcHy1Yj1bf3EiBteG6HT1FEpLHH18yA6KFlUBwPgcMwQRyQUGJfXvXF%2BBPjDS6pYaKIGxemX0OYj4nbGvw3%2FoKfll%2Bmj7v8JFKBOlHxGNC57aJKsmJiYvi5EZLfP7TamucSZDqZDlvO5YdfQ9kmAbdbn2aVP80hdycVtoNoezU58UG26cQ08pRXq5usaSG9c%2FMrH5DA06NVHwtnn7V5GVMYvG51S5vg81UpOUOZ7GtVWqQPmSREIC5zLnz%2BWhfcKpJD3%2BBqE%2BbvGExa7n5DUZQOZXIkpairuJAiAYNnzkrEOX98OVUjCAtwrF%2B22oAqzoYmu6Z5T1fevCGqpEPk9c5xhYKJMgfAzCtE5BMAgVaCfX9RqCpaUx4d%2BiSoqRE32%2B32rgBMefkkE%2B4CtTcQPdjKZEV21s4G%2BAeWThGT4BuxEaQ394v0FY7bROLY8hcTMSiAGvZhhCJQSLXbUbf%2FKTfn1FmkQaIrIXglb3fofqqNKonGG6rJlCrJhIxAOKDziyblQEzxerKwMka87VrJYB3WjB%2BycQkN5gcyMWOWWZEK0vc%2B7amwF5jXHX1KPezbjJV9JqsrofqkGRBUX9bwIjPE3ele8vyJ3BbYEJPLW7Cx%2Fg%2BMtKyvsbpa2gS5d9Psx6Tm1h2%2FfgjI09%2FxBhMDWRW%2BLZMHpwrcY2MaLLNmIR0dkl%2BvXoDvvrKoAABx8RPyWUZKa4UE5mgvJmeAi2dYso2i1yRnrCM4inirsuDvdPAjc6kNeKe%2F0JGT2TuQbcpPtSrdtu%2FyuGsI6%2FIyeOtq8mGyoPaNUSrCiQVrt7Uw4aJcRRr8qLpkJNACL87mcbT65gjkujII45IYiBE%2B9vykoLbtaI36K6suSoXiSgw6%2BQisRYgCkU%2BQWGaeeVomL%2Bv3REKxSBJN47BHceC869XdhI4aqm0hPRi1tA9UVd4UeftEE8vv4OLNRvq%2B8%2FzZndsUCUnit9IAFV450VclUsQPyFDxZbwGFnNarIF45fZu1rrQwUxA9rQvpnrQkSiN4CCA05gqbRajvMBw%2Fu2OhfXZsL2tFySJKesS%2BwUeRNSqDwysLZRUQ8W08VC4gTKWSYZbTuN0egLlJr7HnVeMFpFyhz8guxnwZbQi6vjOGW62PLK1bgwcEoZGMcPHoGZ43qCiNJ00xz7Gggjux%2B%2BRvwrolKnr1sYg3%2BwCPAx6M9vg766KzOc%2BOckrFF%2FW6sJK63DWXhwFuzbpsQZWE9WaRgoNaKoNMQM6InBOYZuDlw78a2yUCWuFDfdj1kWTUHTYtFc3svCOscM5oPPdERLoATn93o%2FHMex4oTj0ilUowq9LmWB%2FxGUCSjsCg2FJdS1F65fgIQJrTdMSpHxGjU%2BKyFIHa7vGG1KXwom%2BN7k%2Fl8eA4CN1F0jIBbU%2FBQkFPkBrkZ1UT9jJvXHAT4JdR8z9hr6PsWXsqv0v6Uofowi6q9vfpgAna4gxBbq19BYFbK7HD%2FvfdAOviQX9V9eOIUWY6drovP6lWFCnINiuTNeJ9s6QoKsvV17KsdFHhQNrSIHxDZ7GydlPXakgyXZXKgmqVYGkMDtDfoY5dsCwbtoIgbv5Docg7ocxWmfR9Md8%2BeFZgnMWZAFxW5sX7roM6pve5Ytc1VQLahIoDgHjSAktsQ9rJwj87I3%2F5WT6nrk7pYcrudA%2FzCiU0Y6URm4SJ7kZ3iWLYKAXzL1j4GB1K0tuIrC4xmB78SgBzD0wTZ%2BCoiAwypXpHkeZ2X7UpZgJO%2FSDjL4Lc9sRi1L5KZOcr071h8OyEww8U5UmzD5zELm0lG%2BrBUAKkudP45QnxWg%2Fn0YJYexZh8E0%2BHMbThaoCy5o1vi9etqE2s9k7OeBihts8sFz3M7GAGs%2BQ7gBNJnrREpQwRK%2F8Wj9qnUuP5m4sfPbHB6hQWJDQxhDaqOo70eWocSUsLDURgi7AhpNgasaGIOe2haehDVR0CZvadbVVyZxdHl9sAh97Hcs%2Fo0s77vurK%2F0zmMiKEzlPqO7gi5FVHW0exZMFTG52ZFA7EluyAZJDljEBMEwJbSbAmw09ePtmEucqLnU%2Bh1Aq3vvAXBpQMwkLCaQ0xR52OljWVGC%2Fo35LTfprXfUOKW1H4KB75rGlaJXLR9l90irw3pA2C2rl1Lh9qjsyjcK9sb188Nx%2Fwqj74%2FHl7KC2Qti8TXj0YD0hllXQftwZAXEqefSdNOcnoWNsdtGh8ipphVChQXRUSh6iuLF8yaLQDyyNsHdunUpmHdxGKxfO70Q%2FJwS3BYE1NY3nJKiEe64C53Nhkulavm8JU2MApzc4WaHTwsA9xtgqvJHb%2F1DukzFyRyJpj3WvNdNyY%2BSI2UYKp4JPr0okpoPKstcQN%2Bjnf2xndS4zm6Va1nrR9%2B9RJNAMbEF5H0TxLBxo5jQDg1f7VYhZzGPCnU%2B8eVYGI0j95KLthUWfTJgPGiquTfhlefVOW7rrDK7sr9rynj6OLhNeKvC%2F5VDCIF8JlI0FslDAEoWDAq9JEUmb%2BtaSjoR2A1CEbVcD7RGx4w96eJb3JZ269eq7qPNIcdBsdybPNJhPclIprNnrEnn5AnXBVZ1SnOFHLhKMeK61KvkmZOa6hy%2FLzEvC2s87%2BeLji5aR1ibtG3DXQUP%2FZFdQ1zdNZjjeKgfeIC4Uc9R%2B3Sim1wB%2FA%2Fgz3QGrOQnLfhWLNESHWEkwVESH7fMkj3Uf9msFnYD6tgMieBPHNNvaFqW46CTZ2nJ6HdcPQZqycNhec%2BpxK4GRxMtXkolKAcJxoITr4up0Vgig9qkeDMHFG2fPiwrd3j%2B2x7N4p4xd6IxoDiMni5q0iu3o6KuHVwIdxQImGsPPQwebyvzbsG%2FHqr1hZy6WdkbL%2BmOeClujFHKdhvDLDykfpoTL6jHcjqKUnncIGixbjSkNRLugpadA5JTdeX8uMry3NcB0o0rdhNBrbEiq8Im4eaTsPbQUH8A8IG3ByZmzn3yBmSMx4vrZsp2mFRBIW4FSvsX1bVhDX2AsPkxQh6FiqyO4LwNTDf%2BM6vhfaOdXcI2%2FTqWuirFyMOiF7NOXYH1jwrxJNeHFyGv0D143HcVAsfYFbQuo7JhTQEfzH7sJtqp5ji7wk%2BGbodgr184lcMKBO9i4hvNzuDgeeZ4z187zol30b3qGcxJVgseTVs9R4r8GAEnQTu43VfEvxD1X4%2BACXwgfBp%2BbMsFgo1nV%2FMVnoaXnBawJz0Azmw3u463%2FA1oePsVONDNxwJMTIgyjt35L2unuczFJXfhl6u0JwdyX0NzRklj0fCEGA2pLGH%2BO%2FooEP8hpjq0yusZupAHC8Y99RfqcRCu3duTgdGvQULfZYgQT%2FdzJJSjqFrrMtugp1P3zQXkBioENxaDytQ97LdI90qVJvfSRQRNjeJy79z6GNB%2FUKmBtdRstFEW%2Fm9SEv2FaNNi0a32DyEWv2FexTbY32SKxWLJIIXflG89qNGiZscet4H%2FgQnew4hd2TZJA1J1ISbc3z1NFXNBoqZOncWkur5lQtRRbokZgeM15WPNRK9m6tAaz5gdiVHLzQwQKcu42hfeP0zbJ5jLI52LWrksNecpzrLXtun0MJl0fmSj%2B0my1ppCej8pmoi%2BvnTTaLQ%2BhMO9gamCa4pn1MGbmOGVUMAbk3e0i%2F%2BoFpSF%2FzdrxPEu7ut9SE01QE6q%2BD2LRypVWPgBN8wBOtnxqP7o7okmssRJ6M8jsv3GxmSPMGKsAZ1fsAG3dzlCEW8ukF0Vu%2B1EZLF8r%2BJFwrDiPyfxTydr4%2F3V5mm91qfQ9EyuTb8sr9RT1Unnp%2BDx2LvoiCiRJK87oD9RO40KsKQWwNlPKfOI33Bs6S6U43SftVz6cAMSG3vIWgriacCqhioATPpt3NvbaFBXdZFZaTVeP3R1TPjoNHoqXY7OKTz7oIIvpxVvOuUXdfaC96%2BvPBV2dRzLHEZaoXrXnCMcc%2FOuEsBVn1hhyvGZy5Arhl6spK2Jd4h8h6EpCVlf4Df1JfDvAcbW%2BY4vrpA7J7z53K8Bf639OT421XZDkSwLJrLbwUdT9aOs50wDPM5a5tR15q4uxgDbKfoFSjgLQqCEQHuzkBuHvmlF6IkmN%2Bic0gVvL5iZRjK3D4lAW3X6VvHwiu1rYlfWOhRKsQCNiFFEVmgKVekcnaxnjEHylAqt%2FMm%2F28XjxKcN5xuGzJCrNK%2BmnT%2BJxGLuKnj3NlVh2tXm0o9cwiIvdK5hViOONrWuSCWifxHpsv2pSLFlG56I7o9P4k6C4i9%2BGgL%2BHgFlA3X%2BXOgg417IOPIIAebZT7EvLnez35bb0hcl8Kh0h1RUOvnKdEX%2FOyHDLtv06mS6EfLC22ZlUzY3I1yOnkBQSFiH%2BHPE6ZFQFGiYi%2FueWdTItMvh8YwOI2XI5eEjwOrh8ZbPl1E1rrV%2FwgG%2FqcjMph1NSZGlT2c%2Fk3GjlalurLa3sYoxLtAzjDNNE079FfdjRvi%2BVBocq%2FJq58CIfs5S4msNSbhWT5blI7YMA3w%2FmtZZQXZX4uQzK60cGEPsRY0fhSQm%2Frz3q9dV7CO%2FJSbqUkjmtvW6a65MWOcDfbA0ou5i4357p7vNF21NlYQldXYAbSVWXL5H%2FFngmpjfQD7agnctcICbQWLF030FiZnMVsMwYzATGn090oj3u0RXAS0KPpqxyHpxchYFXcRAS0Y94GVQhv7plpOLOj6DqrlwFAoz1%2B3jPcPVrhAyF42CtpXpXMKhlHo4Uv2jWpR9OjaDLFZlTlc%2BeEbM7%2BWPF7hZQewTPoUpfMkzGo61Awc97stEsU0nIjqFBUTEj%2B%2BQVvoLm10po75Vi9LOPKZbsNVHoFJRJ4ySb3GDF9IiLxsjBwOVNyv7UWHDvtCKI02tsqCbM9V169jJNQxRlysg1O6qO%2Ftkg5Ci0L%2FFxqwssQL%2BmTkHPTEysb10nQjynAuvpkGsv0RCwSpX8seS04p8IvIEdhfncsqSMkmuDhN%2FXdu9os5RF2dPsCh1bJPzCJEtVsVQV7l209jeUMCfIWW1RObkWN5ZWURKrv1T0pk816dvl207xpyOSTzcJw7abDJJqq9nu9aT%2BxO2Ew7rnEHGHDoHbILjo2gCVbMZ1nxeMdFsr6kMW9c5Ilj2tiISyd4fKon47Oz5Qz48KJwsHN374kwxB2j0dCMOWMyckT4Dfq5PCt%2FovjV2Xwnz9e9r%2Bo2yB0V0U%2B4hSUWQD%2BsYT9G%2Fv%2Fh%2BTQ5LAC3ylUp8N%2FQ5B9ZuQt0MtaQCFzAGhAw7jCEJo6M8Stn2v8fICR%2FdwwWKYjXa6vHYdCiAWY6b4lN6CAuEdbPr%2ByGPSoSkIg%2FbzPE8dYqqLocyf2689E3LeyTJnpVeZAxBZ3UXiGLBgetDM11w4nZoYaeYEdObtzYHa2gotPoZuGrucLUhKH3AYo1MZfWeY%2B2M8ny%2BZMhk4Pj23j3r4D0DsGB8fpLTE2qxAr%2FaL03Q8CLofiRlgqkdPSf6YVm6PRS3DwyepHOMsv9FoNMwSVEbcLlci3E3SrDCKyiSCn%2FMLkiqpw3GTeDFY38ViypHMnmA%2BFVjRbfy%2BmVicHIa5c%2B5%2B0wJOOI0ihy%2FO%2FHYPUQ5ejGUQOSwdlERolwrkXSo8XE74XOrH79IOVtD8hLp7a1DNw4HqxqmqJG4elQNbjhKxerl1cce%2F5R01EbPK%2B0NBr%2FkZ65K%2By70aSTwsFy%2F1QixRefwWfH0D6nOCvE6kKau4bE2Si1OL%2FK3IJUcyImEYzjVDhhFepWfgSggxtPL01kJ0PrtjAiVjjQloF1edXPRV%2BJ2lZN%2Bzw9TctjdZOQqsH3RxhxtTk3KqFaVNHWSoCvSsiJYEcZpSNYRsw06k7dmh8cNvYhr0GMupoklYLS63nYF3VYnFVIB0p6FF9koP4yefjIOA9sdSkLllL5BHl%2Fj16%2BvVpzgv%2Beb3zuPpEMKGGJmrKIpuJXvNNJJJHXJpvD2T3U2Df1IXm1T4Q%2Febj4GOOsUHwmbQQMeEfNEjenCnEaRarDqKmzqtjSYJ4Dka8oiW7PrXXC9ZOpcl8exdLXNxXnIorOpFPvh9AuLZjxAz5oPH3PqUGRV%2Fx8ov5mC5ffR68Fg0ZL8UFOdk0PQNJxgznSteDD40d34wIvVBQhY7euKxfmMOjXQALCRmQ%2F1fBv7JMD8ItbTprjtYJdrOb%2FdZ%2BDWXtpkvTl6fFR6vH9kA0r2napqxgYr4XlKp5B%2FhbzQChYm1oB0YQJL8c61h91b2bspSzGBjc75JLiG1uIP5nD4MWLNpTLFuVh6g%2BoaETjG2phvRhyCwxoBEtUHWC1PtHlv1PKyYdfVARag510x1wtEgnKd1AJQfP3w%2FfO0oQlz5ImsHg%2FHcDnvlIP6zgdmKftURwNYbWQHWFIxScAez%2FqM%2Bg9l5qgV4QWGwl1viWg7iHhLPzo40eurcjroqw3kharlIWlfl4im3duI7KeCqkFdio%2B0yPgcbnAQEbSbsGbxfwSzaEtscnJEqbxHFzq7CL5JlNNCBEapnGT2Juu6wJp2WJhvuY12%2BHOzrzfAFBcsqk1CEct5UyJN2sa9zATOgxLtjKWqujYnAaKWeNXBqZn3baLZuq6jC1f6zT2taC8CIHHSEPb8uRD2LEZ5gd%2BiLewl%2BGzKo%2BibvZAgSCQH3ZTwW39IgklVDC2FYdo5192QQBjLhT2QpJsQgbHSrV3qlxbSqaiSNrbcO9k00qSqtShK%2FHl768l4tjSOoyD%2FZqI9aIkuOhpENJEoe9ueaQUrhTN1EF140KOvth%2B8%2BcXGyNK83ayWjI23dKSe2mcGfRXS4SGUKo3wVXxJbOGAQXGmeUJnxQyvCQqo2afnL609%2Fnr3l0XCOSHOzDlEA%2Frz187z7WfblEqCCgUQluVa3%2FcftNx09RlJ3hlra6T%2FUS81g4Qh%2F%2BN9mH%2FHRmJNRmN9KvmIgECCMhn0WUEPR1IfE%2BUA2s9rABW3RxeudiKmWkCx3EWg5cK3wxWVJ00xLu%2FHF13z3fuvnGuCufE9FWbmU9%2FclCFhws%2BdR50pIjCFr9dhLuPAZvXwZhScYqxD9WI4kt2OqPygvv6kVLSxF4clj5dR6sUUcb3RKOWzbsHYX%2BodwsG%2F265wpvroV246F7Au2L%2F0iqEvwxdCu%2Bm7poZkxeB0SQB6DIn6pfn8impx4%2FnAwL97Ln1ZY0YmgJR3AJXTd%2Fq2UXfw0XIjsoJO%2BwvRreGC8P03sLC%2BnjVB%2Bnmypjysejrb3XSrryZftfmYhQJXUeT8wn7SVUk%2FNq9d248rQRJrr2370y1eDcAcpl7uHbJPltHwjS1PL2pTUUSTo0nhIi4KW1bRHqZ5sTCfv3TVPMN68WgjAu0g4P2Pho1KQzBcAxmchFy992wlZuFLXvIiMoIoAaO4OYWxEBVIbI7ra1Bwow8xpIDu03ePqBUnu4VH3tTL5kZK12e4i6iYWxAxCvOTEBJtOqjcaIZLqGygTb6ZBOJY1R6G3tFSHxBkPqNkes62DYWjwEkPomNl2azNMoyqhCb9fn9Oj5kv4LJBA7Y5NPgDsnl0Sdm6tXsYu5C8Tum%2BT5lQV6%2F5iq0y1eXPZ4GbJkwGwUxNl208RBLc5Q4%2Fn3Yk246uBoeHKLJeriav9WTqODCvSsmu4rJzCBFu8Bo1BWox60XcijvXcOEZveedfIm4OK0%2F70R%2F5IojwDz3V82HpBdsHHLbDQzdY3ME%2B3nR4jIo1A0h95YlnNYEgimBiQ8kxL7261d3tcfxSD3V8dOs9IFXRTSCPNOyJQ94ocyV6cygERuHkKa57SudKIZj%2F3G2AQmE3X34waF8YD2NhnCBwhqmJI12AF%2BLRodmzvRCxWhxrnMRNuheg%2Bq5x3MOYH4CJuSMHKlUElUV7rr8SjaqjG4cnRBpkltVt7NTlOx%2FIToLtbMevaO2Yv4TfwxjQSpmK03QQQzu7JuP%2F%2F1keKqeK%2FLPg9vGXSHTeA7oeJq429nvS20EGdJXUXmL7sSlNOGe3Sf%2BzQBPIrVg0XKV02SNoB%2Ftt5vUBLtNLSUlLbtc7m1kQs9vBrcGWw%2BF2b7oTOk7F6uh2Ddxsxu9fgePBqk8TtWXzaHYCNK9rmS8cMFB3CkYpoTOYxgzJN4EuDDr79BUtBbi%2BlhXMh8YJxcq8SazgW%2FlEyc%2F7REEieNk%2BTx4We9X6DGPaiHVDhneqUqccUVlizzG%2BTeKfHtFD0w7X7BVjgZuhOMTqZHrKsaCtm3Vo1Pz29vvNX3xIv6AXJREpcoBzHkveqXYPm8tk6FfPT0yMsGKPw35poG5xgAfnklA6hT9oaRyrFtETPIfdK6SNo7aPPO9oBEHdyZUQX4TDbSOWjW%2B50uuAi443FouLG1bSrR98y0U74p1Gp9M9dXgCiS8ER5rrq5s6PDihsp7F8QwvkhN0A13Nu0WVwDaWKT2ddjcGJOTC6kBnZI%2BnFkiCM7aO83YOfogXFYUjWsCYoySCEiZtUDqbydq7c7T7aSO8sGvaK5I%2FHujPI9zxD7AM%2Bufi1ObtMQOMy1jHjDiD%2FM3B6%2BOMo2z%2F4dWADO6LeCAau4TIqo8eU6QGq9Bfn9kUYWI4qWxysZo4IMfyGPV34VsAIbArIflS6Me7jgcjDVCfuNGfztiOzVsXpH1B0%2BXOLV4aGKE4VGmUGGdjRpAfuluzMqr2o6u%2B6OVEVjo2nZvyqE6dj%2BsGQAuCzOrCjixbf%2BaZuXhYJQ4M7a4x2arMwKPq6rm%2BLeAbl9gu0eSHm%2BUfTOcY0F3cvtr9iVscdqNMZV7UtQ3dRv8EtRRbmfvluxSpEvaDmUK7%2F5xu9kyXJ3KuahVYzacZxSbjPu66Uo3fSl7SVJA7GJUAoVzTwOfRYVSSMOlZeqTN%2BAxXoVosJ2iedgJ5aFPmWkOwfHERscFKWnDVdVYxJWBDVm6QOG1qhFWsKiqn8w%2BmLwaFfuAz4mcSBvAQJ6BIpb6ma9mB%2FlzONvEMBS2zQNNguSpXmR94uDGFfBjPNl%2BWoIoRVAxQha2F8sshaz8xqU4KGzNN6nIPZuOPNTC0KCCoXLjSpqBGy42787VviEsrE1Ytx0xgv%2FybxChwxawacs5EX2B2qjzgC2blIvjXpcdCvpz4jUUtFHDTvAxEgpOHEDTEk3j0ixSv2qPCFNT%2Fm6AxlqJRy1R6xTNGXc0D3PtUbRw09T6XQifw%2BaDQ8nqdV%2Fct2Mnga7pwOcHYn%2Bw%2Beilv5i3WaFUPcO40KXxtHkF%2BfVwIdQ9M4%2BQvyGAZndYKwVHpSTv0n5vV9Hlg7TROLbREiXV2Y7ZQzzcozFMH8Hp0wWtcmPjfBNaOrKWd8583Mp9slsgX62oKKv3KJ4HFQYDUwEFVHhm8MM9PRHXuxe5fNge8Grs7p2v5Ny5N0KPuzWYjvHDiCSMkyDTnu%2F1CluYxZspWBhmWSgf%2BWQJlen5rrwrM9cLzMW%2B9xHsPDiYWq402vJcwTmwhBdMmEIt6hsoeVqq5tft5yLUmvRDB5uPeRrFvq07vZHHbpsoODeFuf7uLhoTXJ2jl8exD%2FMGLka3%2BnqBeFb1Gt7c7O%2FXYlAxHQf33J1nfhny57yKJ7Dws7Si0o6hWRgEXXj4VrarJK6uoy5rEcb8lixQfc4eg1kp3VM47D8pFGQvQosm3Fj2HbowXtot2m%2Fb%2Fw57FM4wTVhzYXUR9IoYVseLQmpuuSsu340uV3vSvZPvTSLcw1HVF%2FhnJfH5br26L6Vz5OUlPF5PBXBn3nK4H7vEUyv8kfkmVIQdOksLHEz0tJkkzbeQ7%2FGup7VwjurhMgI6LowUQad4XoX%2FCGyG4xH995bkmihy2QXpp0lxZKYAUR4AkkV0WPKdhFS4O%2BIs2Opzxj9KX85J%2BFmLOtWDtUyE5QBQI9XolsP7MIMZzBh%2FmfpmAwq07N0rUy%2Fmb%2BL%2Fi2bkS3AN0TfATcprrGlFbnlizVqQDE%2FcECVXxcys%2BsJBMbnuxqpmC6K43ZY%2BWFGYJOcr5XmjMXv51%2FdSke%2F5NTNqDdAQtRcDGCyOw0IQ%2FeJYF8LxU2MSLbjBdso%2Brj3oNdKHgaQaaU4yohSE1aUCuPX2rWhNtKjx7gjZRtXOUK9RUga3H%2FqWnqcEEiHUQFHXerqdqsb7xthkLqpenME4xBqatQjp67IoQ2HTREEO%2Fm3ZMdu5hMt%2FfQhY50ifD8PA8ex1%2BIFKn3pJya1rmc4TUVRpmLAkuTsatZihSXPWzcWwQllw9ZyM1eea8HDGUlV7JdAhuwOp%2BFS13GNKE4JXFu5JWjHTAoakiqh3PYdef5ZbA%2FYthLo0iaqlGokoG%2FoXIMkKqhOWTK8GKWrtfuzpiGVdJHgbtYZpKC8RAGXqRVTTeUNpeuq1%2FQDCnEXnSylVn4qAVlvO4cmxLHYHTlbMOLeurZDkc6X49SUZnbdHgWWsAgK3X1UqiWXrntj0U6EevkyujJNHlA9lgcAK1FWywrct%2FhRzNQN6G4zxLayxd0xCzuHFg4IRBK8OSaMIsu6fMemjeCMooJbKlJK7984iOOj%2BKk8Hg4hd03ws5A84dmGP7DFPTRFChHtt9C%2FOynKrQaZPP2s75Hf33PC3hbkp3lpv3lvu%2FDhhM70gKKtEhgB%2FUfHB3o2iCZJ2vfGRG%2Boa9hH4rYgSNhKy%2FdjCLWguPWsuE39pJkO61JpueK4%2BytKDiMzi2XDylhZgozEnoft3k%2BQPJKo7Vi5tYABMaViNdJUHnmjKjItfasoyJoDg%2FKXduuPLCA1aTYcveSkE2QqeqVA9Tx1JD2PDanU7UjTSNGLRJKi%2F0nZ7bLs0t3rKnQFossCaGc747BDkBdT0ImFgi%2BCjnI4ASSAetR%2FndYAfVaXOD2eKsE%2F0T6RT57jhJ8sxFvG86us2RKb0oSdU5KcbQovb1R8bDb12lLgriXuSz7THaWXTl56riFgUmzJ9vvmOYqibWWs%2Bb6yNkJO9pIdgSW526gBDd%2BnB9lqEtMph%2FaRcwMBLdIwzpXHhG6Y0b0DzQAlbY0mh7r91OyzduaFxCVrl4y62rOVY4iCUr7g7kl%2FsWJDsQYEF%2FI4%2B%2FfOZ%2FvIBVi32XWk%2BmZqjZHlFhCu3y9AB7WHS%2B%2Bd%2F0ndAIlsK9F6d8U8M6DPelReGH2aiotAU0gzKCNVSM39ItnxqOsJhTVaprCi3rIFBsQ1R5nFWKkzUAIKg74PN5D190UriSvNu6aPFMfmqnZvonyZ6R6z%2FasRGe80jZY1JTRKvSHWOQrxr4iqrkNR8j8GmCC3X9WMEgM0%2FoFGI1fyhWYFGhmL5Q0EE3c9eTNtIvigBdKCg7Zg9q57nY%2FrpUvO9ZAhAFM1EwiBw%2Fl999lv9ES9EYXgaUr90BK5QJLtYK7mc2%2BX77BnoJBNXrP4STds4DoVrSo1fX%2FIHkDbmqRNUPVcAWvGsN%2B%2BGvvqBWxaFVIrF2AkEg6EIfB%2Br%2FMA0VGA7A%2B1FgJV%2FYlNM%2B4FOGLhbfk5f2IA9wv0ZOmPdGWQzLacRNqrWCAgmZG7%2Bx0QEVEbVtVVGgONYsN6SMzHFwGAXD71oxfL9QWKLiyirNE3wculFdqjQvcT7QqrburXIflRuZLk1pZl8zNXlfAaxC6jAfvKEi69y%2BFWzlhOuJ7J5TOlADRQi3RGjSqZ2pG36U1sEPEvj9muxXXw9w6OO6hdigBqixD%2FPDxBqkBADGfbtz0ceaO2Nnk5zXYG3pbabjm1Dlugc%2F%2BtvWv4dDL97jkI7MWGVyVGYEqiNUwFZ2NuI%2B5PiM3p3q8W%2BqkhQX8NKokBxpK10qP4f7TBicf6USEmj7q5WHy62eSXpxlGFUgykWUitn8m%2B3vBz3x4lQoJgpMnvgclPuIjng3ifxbYKYa2BCjesKrz7a3yD02kKvTxuo3XU2DA1sWkji9m5bFPrAYChr66gkbVQfCd7o9fgThrr%2FDJzQrZj6w2WYeR4BcLlj36WcX70d3YjzbPE4dSHKXW1t6XzpTONGsyq5RzCWn5GHI9Z%2FTk8kyrAwR877kGxkpvfuc27I2I%2BMi2KvuOC9D5XZgvU4U9hpJTHGJXOpaAPNkSSTJsxWhECQdZkgc6KZPkKA%2BYrRBZiNxXSnQ6yrD%2BG9N4YiIqQC01ANIvqfuFiAaKz1fpwhQMz2fZ3P2xzNS%2FRQnPQFF1eowLi0duQn8CLKUwOVkUVXOiNbS2KtDWGZBp66tX96uN7ZbchW8iWe1ZSYoOcojVEEjBfEOxbzDPy67PsbCafRvp0j3QSu7lRNxl%2Fk5ZLeMPsBhNCIIee39a4DaG2Ac5RAClgRbx9JcpQz3%2FhboFFxeij%2FCmHPJN0wwzp%2F9avDFSQrt8%2B3%2FnDsRu4Gmvd4BefWMYtoeP%2FhZ%2FotE%2FpYMaSEfcTIvDHCcb2kgQfrUKATwyGhj%2BWV7DmiJY1hQPVIg4Sx6L153xNvnIpbaP7hv%2B4mkyHf2YGfRntLDulrTcY5qEm5I6IgqJm8ABfH5a9Qwo0%2B1nJrR62no18tHIHw15IX6EnPuhxED2NNztDcGCvB087B5Pj0PNvOfstNVlawYryRih0cMhLEOoqC8gEbn0w%2F5cVnqr%2BkXx1viJTfOF%2B1%2BHcEn%2FD%2Bq6Rt8EZz5Alwhaw225lQE%2Fy0m9xpCMtRcAxo7WgyzVTD6nL72fcg7l7mr6aRBCkPM0dld3B3mlr2CZOlVgdAk916%2FMS0ULEUk1sQV%2BRsbrtSvFKVKZx0F5J83KAxDMXsHRp2ZXR3brRdYbyAO3iiSbIK0bhhRGb688yPczmGxOZ2rCzzh5NAPQHw%2Bihwj%2BBEBP6xJFr0tMLmHjlxvrkS6%2FuTMnN5CxJwoPmvMGeb5z%2F2RNSEjCal5VbokGZpXVPn6bCED9jauiGdUtZBRgm6rUQ5ZAqtTJ0278N5Im3dLF48TP%2B8sa60AlUJGAYuUl%2Ba0JQKBM2AMsgltAOHYh%2FosXsZlH2BPft7xetDDrc17wLvHE%2B7Ob0TS0rZIRUgiYyvPRFDWA5jrVR5lNsVlwWDuNh7hu4tkCdry3YJhI5dH5qmizceD8x6U82RKyyJ3WgOEPu9nMksXT5R%2FV5cBKHMaGmRbFd0Bqg1CvYCNQ6ucvIctHB%2BRxJpR5Cn24SM54Ga6ug24jL9D%2BoTTqKqNSqw06yLhWhyT8oWi16NyrKVqR3JO1iVwldDFYtsar4Gf7I2b5i%2BSCRW0K57SGg1CSc7viRHa%2FOC7Mlf4cBABs%2B2GzPGVs3DRbPqLHYqiywjf9kQ3E3dVhG2HmzCzFjNJhw09Ujmf2JIspGEbESCLwHzlH0lisp4BZxEVUN1%2BCPLTTfAiZd5nOKc5pD1TLlahgJh9Q%2Fgbjr%2ByuXA0PgnG6qYTD3T7ybA9DSjHK0mzl5af6iq5CE3PyiQQwqGUUbNEndOjf31Di0RX3%2FEXV2AjyXUOJbDoJLFbMmdJEDFPLZ3hE3tatXfZjzMRdYIzVxnsXWBXxIMXaOKiex8aEG4qnVOeLD7xkKyASzySIHEkBmtz7sxkhzdZvkDOTw1VpDXoL7Dz1E3Iovkekq6I7pnCfAudOHJgS%2BEp0QGZ1935Lu2NTcpiMLUdA2i9H6neAC4y2T4CjQ5s3YNMteiQrxF7VQneIvWV2eVXvc4rBhveJjm%2Fy2eFPo66w2MkJzi64d4ImYaOhJApXfBxyAY8RUCeyHjL1kX4MlJUOHWvduZctiU6rv%2Fh%2FeKzvptAZgjltERRc9idlACV4ZdxpZtUz6GumiuZ3CvulhQ5G9rEqTbfCZ50zmRW9iAwD69UtVYv4qAa8LXvLV1%2BpILR4hTwenBZG21zfvcvlwVTNR%2Bj2I5PTni%2F8xfV6dnlA8PR8YxnPYOa40g99GgDrERiUnzsuICmQdeoHFdqNAp8Zb2ggpiWXDPmHA49NCqzYybWN1Juop9XyGIFwf2ep7CBnpB8LHiSvpEVFRYgzrFo5650LWWV8O1CLy4aePlwlGiv92JCYuI97zGqvrnPGrjyhb8YVkkP19x0n3ylJ5babc%2FJEQ0MQZ4hkUfWGIeCbOrgfcTD%2BupwOlurRF%2FxuIKrOTeK1pjcNJA%2FqXIVsLxvRwBefqR8MaXWrMBeajxAU0hEEkK%2FN3uLkobLZipb2%2Bfvi83S5IG%2B5JemMw78HyoWyz%2F5UsknI1eCbBjdvn%2FUPs%2Bl553rOaOemC5nRRdHLDu%2BUhzn6t2CNkX1jg3FT4OeObWa0dfZgVWlErigp2uVT0QSp2fnGZjg%2Fbfqdugv6jF8HQxIbYNSSjEA9%2B6yxw%2F7ADg0oL3PeYzS7BoomDgjXsR0YQd03ts%2F3TemF1hF7j6V0DAxGqOP696tOio5F6v%2BqmsYDtqx4IUnpLK11MoknxMDp%2BSlrxP8g44yD6EokCG4SJMi3gxPTdSdCcBQvtvStGevoQx30L%2FybODtR5ztNpAB%2Bh4c17ELrg7M6GovGRZNhboG45%2BMGFriZ%2BomayA5vwgIpAzjkB8hBAy2rCNjNg6oBsOr0RDf05zxk6FUAOZqNGBT0qRFn9og%2F0LyVo6fz8pU80dWdv8nQdtXephrGo%2ByhE3vk5r3YQ1ECFrhxfZcItS0UyO7%2BkG2HnJc0bqVGIXP6cFRvxUexx%2Fu3sJuWUl6WsoHjbp6%2FRGkpaYliJE%2B5yB1U9Sd0gjrguX8fr%2B4RuhDJEO4G37cqoOXn3JYDvtJCANOwCZS%2B4PCY8FlaZIA%2BG5nKHW3x%2B5gq0bKrw%2BM4aceOPbIj5x7I0m5lMefsnc69yg62FDsNe%2FpdFIJU5Xm1XqoN28VWuwrMv9u%2B1dwMVsR5AmjHoeTTvkidhF1wL%2Bz6f4mSTPyEmDxZqD2w2v%2BIXQnh8EnZ3PSTkrl8jRsJH0a4CDaGYBl7oAjFfJjIsPkuM1AjHJhxPIBZIuTGbBRfFzqLzPE4Xpt0nfvM0Q34wMrQRR6Q5hvjHRkgFVLZaf0G63FRLx7NVXoQ91VXuKTJ7lQYFZFef81%2F44NVI1y2hO2RbxQNtbMw530tHv%2BPdY8HgLqLTcs1l8a539ISZw0ncsEOaZzmr7BX%2BSkJTvvCa3%2BhOBU4E5ExYxVxuSy6gfIYaVeI92ZtcA6UPVEiLqcRyXIjWyiih5125qz%2By1QtK2zYTmMk17SjyrloqOD%2FOuEDKe2w7CIg25W3qYYh9LfBH1RDZOttSLv%2FyN2graLzytC1yQkzJnoSJrqW%2B0KRU9ArmDFhupB25fPCX2xn%2FU4vPKkxTycQ57me3FJdDmdM3aGwf15tRWA3QaCXYp3pJtd8aCNMQiFgUnJIJYmTAuGcSAfWPM0EZCZBoBoe7EJ9Aeu1ek38rVumTdKfjV5ir6i8Vs%2B9aBVetkc6%2BkvmXgcLoW11d5QQ88dxxAWJaBy2WK8zwMNu8XxXbn3bAbI4jDtBl1OmM93QbCgbUzk4PWRcBdlOb7W%2FfdDPJWOQuUc81nhT1pZmZEmzW6cjGi5d7iIU92T751j4DMApqBogwYe3b6Ro0dLA9WB8k%2Bo%2BlYQVbzyeoumNRTiwdlNKP0zxbAo01t6ZwcBuOm1uiljwOLF3MEu%2BltfeTeJ4sDM%2F91fq0gUiZoZhcfftS6XvOrB%2FGEy7J7CXgAsHtddKaNwnKVvpZxWvJ9AMIQ6zB%2BYYI0HPz95ad%2BuWRdaaE%2FKOHkp4Jj%2FXpkPxpJjLm5F3PphAZcDrLJ3nY2PFO4ycw2S3LCotfruqWhg3HagMvH7aMhR%2BCYzlVJp2RrkJ2xfyu4YjUaPdD5Q5lBg2vInrpZHJDUm4nN%2BwvMqnvogQHk%2BX37SDnqLmM6u3IcxE7qL8fcl085gXHX6e1X1lvp3zXDgKvYYLlqC8S5LUSO6sNOxV%2B9S%2BdB3JlUDdcgfd8%2F56XZ2ekMYTvcXp6XCtqXnbUpVGpVuqWDYqc6mymvmJchXtk7UBtbfCmgLP3utLWs36zx5RgWqwVZmUJQCRFnEP8mR5ZWkMulG4dUfhgZ41XTyGWjmdqtnzAuSagjTayogsqq3LRt0lvLlITaQK7P79YH6zAHbd1LmfLMawJ8A0t9klKNB2aLWay0ku%2FPXRn1usguZ2nTH53iL%2FYjXNK2D0sY81IRvWOUy38%2BkFyGpdKgPbi3RHSHFXm6MC8pY19aVpXmK4vrldIMtxXZKjv94AEQ2tCT1vlz0fR61tlPsiVuiLTuytDk99xQvR2RnlwnicPbtcFHziaQWl%2FAmjeim9PCCNMCj%2FT1%2FJneFbznFuz4TpuUabUfQICuwuMmRroCGlxhmH%2BWVgiRwpkMUf4oiSIeXuhM4Ac99zB1JskBV3%2FQT6sqKolNeiLgenatg25mFe%2BhQdyXpYtMWu1%2FysQugywSI8ua%2FfkPehhb5LjAU%2FSvCMSmIf2xU434k80Lxk56yUE24p%2BtcBmwkfjGJJYmLGdXG5U%2F3o5NhdUPZO0Nmuqz%2BOUaJ6GmvuV4SujxojObrh5kLm297qQl9MMFA7NFo2o2q08Kp7Qed%2BYa8WILqM9uoIuuTk3FMYfdT3eJaKPM0CWTEmpNPBP1J2r%2BnIy9W6MXw6MnysqTK0qIZq8hf9EkxY7gVPNDWvfVRWSQ2X0BLT6c%2BWrgw3HOJzvzmSglOWzXvzi0L64aZk0hYbxAXRnl4jnmWF34USfrpURI5yLW3lzUMtPP7x0%2FmFY0JCSG2e%2BzQDTiWxvWhbj%2FgXBL7yVW8kWEx90YQw0hWk06R5CCXFzaNxBN9No6PI7Pu5Etq%2B%2FcOg2iISxLZqF%2B7P%2FWn1yMcdjyTovtsTsZOtUm7zoWHQPfmA6via2blvWm3pV8B%2BC4WLcrmSaoG36XsCOCfCBR69Ne8S048YzZV6CuI0qjP3TYY7Yxrj6Z8snls0U9n9Lz2idOhR7FCQ1Fl2ODPIBzsAzKLhaVZ9k%2FUSczQnz%2BU%2Fcv5NV%2B3p5TscsQ4LwfRV0F3C23iGRnyDHqZdPHqwnWHBhrOfTdv2sngqorHVPexQbCVCFWZtCpw80X7vK7%2F0y82Qxk0CZQyQxyRPf6eD9ieZj%2B4lNZCzlX%2BLbc8f45ZaCb4W8SpVGzZRfMKBm15du5qzT2NzY4TbSsNtydmpQo0FprOJOFcGAovXh7P9BKS2Bto4JjqG5BekZVN8wKUKe6ujNqNec%2BZf6Jde6kF0Vv3c8QKEu9jvcUhM0WyqVoyq2JfNqd0U%2F%2FHlBazFSjNS1GdAuz%2BfcFZQNJnwS5%2Fet95rm7ejIe9u8pmA60f8sQvS6ADFr9RbWLUuzq5GAdWK%2BxRIax%2FbvFaWrAo9lt1pFn5iIHp9WC01xe9euTjHK1SJbPGIjR5zqsm%2BuwX0i6itvSqEQAJL1vlB7USMX0dK9uoNJpUQy12YX8sxZWUmT7BHNkPmv1NCDE2k27y%2BaVBLb8Y%2BUU%2BuacYvPeMNHDja3a9GtvVaauuynEuMvCHaKlTgwbCKTUagPqva6vR27t%2F2y1%2FNp7wtfDZFpNCQb82NAwXjfvCTTp0dVDmEjNzFtoHZXVlmI8llzk3UxsqnnSrQ%2FtG9aW7lLz8iiZRh1rPBdVU4VnaL4dV%2F0zhIfyUiq1s1D%2FPKPqs%2BAfunKqeYUDkJlp%2BJ8W0S85QG%2F58V%2BsfhMiIpxqv1xQLIWXl7QjpqmORm9TpeVTT6zEhmi5QCNiF75diYyiLHNuKWWkytZe6iNXnM2kja%2BXWA9mU8Pqr1SG3GQka6D7mPtsNS57JAUO8xHt%2BFREJT4%2FwBIeSuaRHJK%2BU%2FMYOt2luGn48SOZO5XpKmlzNgpVhKD79OCFLAg%2BXdGFheLFHWmhfHbGVldwPQ5WMysxUibs0bSq3Q824%2BWOqGZrE0AXyYHoUI1a8RNqJ6QsGufiZoy9ydVhVCEVhtCDQvM%2F503GUdjrkXTVr9VW6yt4aTZZ74fQvuPK5cR1z5Q%2FGZEGsVzpJrABbCMO2rSqqwMuI3i7RcB1VOkUFqBA6%2FeWGXgxNFqu4HdA33y9k9ypONazxHqf8k40B%2F5zhHdXygRfIcjIw%2FJx1rlSDKjn9K99s%2B1sua13R%2BcwVyK6cpJZtxsmKPK58POuF9Z5OP8D6oi7YdmIVBlREJtbzKiqbsG%2BrZ6z9s1iT4op3l7HOV70AiVglQaUs1qBCwiMA6L3UrfU6e2BwhjfuaETglU7VARFFjrXO53fRtroAtcUQxhVv6Xf2zPEwaq76h3q8dOixYMTwRHAMYWEbjn8F0a2dUqJhyo9yIdec8Sxgw8FoF%2F%2BQ6seFptzK5dRAq6tsu%2BsW81JR469n6H1IupT3qhXfORiDjafamCPMBMwpYDImst40OrAuN4uP72of8z5gi1KO08hiwH%2BS3WRVwCr7PJ8cEz7gaFTzinuMmbE4LETI4IZ1yDGfOiYlX7wPLwM2gtSKju65VbsYlZLauGg45rDkJyp53h8SfOsXuR8f8qnJtpcsWxKeNpW3WJrmCKFEs7xj0nMHzKDwc4EViiXFEnoFpUqwQsxWkENTOLMU5GtYGqqTtTGnBSvKvtUZX1soq8LTtEHiHtC%2BvL8j325FG2%2B2EysTGCmSV8oZfD3vKuKExRYrHBzKxF9xoJinP3DakFaZAHJ%2Bvq5XX5Tqf31vpQ3B%2FzJzF3opVPxqeUjbXbEfac3Cf9%2FRXE8QpWAtCduTZHihEHDXFLX2KvGfd2UotKpgNPf2abw56CoQgyU5vsd71Mrz%2BqvS9owXMJh0zk5eENU1ZGOA2eD9dSKFag2HnlAtyiBvb3btpaOAsQF%2FqRDYfyurUGyh24%2FgulNFL60FFb%2Fdl6ykbObFdT7Iu%2B3kUzWaakcZ%2FiBgp6nvBz7O7JQmWlnqzbW8zy3%2B%2BqvFM7AYWnouN%2BC0rSCYlxoYBU8tJ9GCI1Sl1OZ%2FSXJssbhbDzkiX3wPSQeVNv8MchF9xgoL8Re9MX2Jwr3zr4Tv2fXBcTvUv9Wyw7tJroU5ehHNUhRv%2BgVwkQwvmpDsH%2BRDCLbIW3mP243Pk6mxA4t4sy6nBVEoZuiHRPbuDhRw1KUMIGkx71PPxLCsT4kH9nLs6bnn45t6QXwWGGdonKkFDngoQotIcUEtrR3WPvqK0kcu%2FnwMXCk8B2wqsZwlQOHh6Y%2BHAzxz4cA4Vzyh7oKfYQrOaLfYF9M4KtHWHUJJ6VnANUOgppeBp44QrOli8YZForFaerHV3N8TenW88puztLRYNykHUl77IJlnXFSiJShx%2FOG3nDCjCWk4OHvbzSVsz9Eavb88y89i6rnxuXCyV%2B8KHMGX1QQ1xVfvJ8XB4Reg1hIC4OV5dQZmImP7XSymS1e3fHEbYgiiax4tfIVVGEfZOUVc6C2ynev5oI8qos%2BHwRU%2FfMVV6pCOIXHVjAhz6WtbxCFQZD%2B2zP4mB8bp8focHWLJd2BSlggENyw0Wed%2BWF9A1O1SPdbfq%2Fr2xD8XV72eW5S9%2BMPhWtH8Ixs6Oy6s2e%2BPiKuLpQNoF3GVwSBPXJB1hFL2EF7LNlujPH5lnKrYArVXNcFDKSO5wwZ1vi4fWqEyDZeTzKPJKBq4b8MKK2vFle2hR0HY9u%2FYhFtBH5uZNaw8vFmFtRwISvXVLZ9oWwuog8l0P0ieGYzIeO3KkURraNlU9SSwnwNfNbpxlaTrqqegFx%2FfDo%2BoltJxqZrCBUVtGH0VEZTNhb6etzUgW0YUqotw2oMjxH3NRLKEoMgCxr1gT0FYSzklRhg3V4C%2Bb7CZfwHHg7o5ll%2F8bm%2F0EEfnm0JjwqxWFDW1R7WOAOLqItE2RUbYf9Fl8T%2Frib0pz4SNEGPabmhJHTNtSKuIcd2T969n%2F9LA6kZm2AIPiWM8fJa4LXpTcB0q5n1we2di9NoMnnSQ%2FhITBBFNVRs7kO3YYos4BWqbGxaRAvA78oIUuvg0uFd%2FiogYSG80lkSP5C6IxNvyRiCkPVSDd56Gt5xPMyQLbcsAKWyGAvY85CAkQ6j4zXTKSO8U2BXGOHWjoS8yK2rL8wu6w%2BdHxmA41Wd%2FuNU8%2BWxwvDtDQ%2FgBE8B%2BpeWqYSkfFR5AlpRGJJduJlkg0bEHpqGvSHJOkzG4Fwjtl%2BiV2stn4ThxIszFQ6ywCZKEd8MHeGMXszh9%2F%2FaFspkP3dCavzfT3ynJ4lS1B1ZqeRJOpgD4nrPPa4MEW26TMvAtBW58SM%2Fp%2BixMiOhNqSHIixspkDVDeGlWJQxM46gTX18kJhRZvc663cgH4y54zjfJj%2BhI4Lg2rNicyX1sl%2F85GePqT5LWX6FFGRr3KIIPofk95eSEEYPClmZOe2Dj9%2Br3XbFypSHNGMy2nb5e8thI%2FJdEzQTZzN6fYSvDcycyoUFfWknem3gzIrh364z2wpw5V%2FxJ5WZVLIeR1xeCWIdT77uiwokJk7jAOY8P40G76fpHP%2Bs71j7pjWor9ndBkPf%2FnRYIoiL4Hwix%2FSz5L7mZX%2B%2FSx3G7liIphmPVfXNsntQXO7HB6UviSUc%2FEK9D5%2FbWuQ6TJJKDX2LfZjPT5wc78hwlDLyQC%2BgtIvbD7ijNpklcYKh1dvzvlzQWohWu%2Fry4czXH4pq7md1F%2FiXyBzRKR%2F3Df4%2FhgdugIdXQ%2Bzh%2FMoVCk%2F26iOTyTN0nkBAxlqwydgl1EeKBRDBd7KB%2FNqyKNriZr3vOcBG2NyY7sRE%2By%2Fmc70xfFGtXOfeo0DyTqvcrPvW5vhBcRKjHPpCimXbgHbFYOr6%2F%2F5c%2FuAYLtOvAVFWRf9f8nhcG0kGkpFYIdI27YSXfU%2B8299ouwgTsypuBa7Zh537QeidY1x7S68lE5XCQugaZplA5pnwNJmz3pfMu2X7WKJTrhIhutnxwibF1KGQcbk0eWxpBRTXC5q4EKPdStvbhQDJr3eGoRDCwqfbx9XgeFOk73X3QcMUOVSqlpUATnmldusUzh%2B9mLvobjkjxn3cUWHWsRbc5MwBMnnAAIY6jgqoyQY5tx5HAp22zJ8MjS27NYGB22VcgN8SHOJdJPxM1kT2vhjLCUI9jOlhu4%2Bw8zLoQVGcLwzYb39YbWZcDaBUqWe%2FYJRPts9x8BQANEiSm96zOqbu%2FRYUzAyK0QGmVVAEEMrRuBKZ3kmZ49zBmIIHlMVCMh8vUKOlvAeX9D%2BlJ7nictgxo%2BoFJOxvdqrTsbBdOWSNXNuMr5lweaXkWTdE2GL40p0Mk%2BDjSA0RLXy1natbzWhSkWtREgAs8L%2BXniB3jzx9OQmOblyW1QU2EXivsq5uuRqYmmrd8MWq3oHMTk3%2F5geMAcwJwJZKdVQTqZ1Y4%2FyIfRDCTpsy%2FoPEwknfNlyGUHyscGa%2FFpB2tcgfCkdQS9UEh6DEpVP%2FuxJ3a30OLZQO82NBj8Ae%2F6JQxS7psR57V0L1YsiDvbq4JGuWq7GDExF7TtoTqfLCmdDDDQM%2BHow%2BV2AtKch5LOrQTkEQg6CwR5mEa7QXtWBQx4iwSJhOaS8lBUKXJIzBIZaaswYHSsWTp3BmuEhwoBzRyUrthHd6ECuhrl36kmyqrBK%2FMyUUtIwZ3Nbpd%2FT646h0bIKMTrCbXhOI0Yu%2FZYMBAGme1%2BozHQmum%2F%2B%2BStBcn1Ve24O%2FMxzAfxz1cVJ5G9OPaiF4j4zWdL43KW%2FqSxefHFhPgnDuCoUx8V5lxFpIgz%2FeIUXfX9j2iM8%2BjSMOKl4%2FU1ZqmUBqcofDq0jQckYHVi%2Bj5OANSSpUhlvfv%2BnTssLauF87osRvai8vPrLsWprUgEpDtaTTwkk7SnHfRKqhU5bkS%2Fl3yawH3nE15uK02zHFIK0lDULwp%2FlJkM4h2ZMy7RXIUGsNBlUeD88rP2c%2BAvL%2By1zc3LngkvBxY5QWQhuj9Wt9RZgAwmNIlYlqYM53iGBH50FLdTeywuTXoAGpM7ItSkYGKmHxChv3i4bf31%2B0Tq03HIniCq3tWBjIazq9zCeqy7qPYxPGl2FNP2i6vqUuUefirH6RTxeZ4u7I5qYAySHqjog5n3gW38BhUFz0PX0BelWhYU9QUp3om4TnUkLGfO%2F%2BaiXtjZ%2B4isHdzjfBK9vwArmsq0%2F4nvXpIvJ95kwVK5X1ec%2FmQc0xtxiRcfPfYxfdeVvwqGljgQ%2Fm9ZcxIR1A42GQwH%2Fujg2Os0kffd4Vornnh5vXQDIuEnP%2FaXkbQwy3yF2chxUKbb9UFHPCbD%2FvnjHnCwTo%2Fg5nuQ0EFpgxS3%2FR3yEnC%2Bygaj8zHQRzEGXswh7SWvxsHA2ELPBn31l0y3OxnUn%2FW%2BKy43uSH%2F88HttnsX2PMCLp2wcLwtyRQNYJbitKpAOl2N7GrKIJsEtxCN8Fdw4yBezWUgq%2BaWGc6Oa0mybKGWqzXqjrJ4yuD8Vw6qBR2acI78wy84TjopdqZ%2BLiQWwlFu%2B8E%2FNiJqiB00%2BuW6D05dP2MKGC7%2B8swyMzvX53LL3rDPOO9jvD3Zu1zWb7sBPJ56gRbdbVnwoWzWYJPUspmIi5RlbpLuCzwLJ51pZNF%2FW8WKtAZ%2BVCxfPP%2BVfinhYXtHWORq4EQ%2B%2BSXqfJ%2Bbl9MRKfCg5RXLcJ1HGAHxkHCVBxQF%2FIfhNpZOWky49J8ybHsx%2Fip7HM0V64Y1OVQGEg4Fh%2BoBnJkQls0EJVZDCGFG7NsZwcLbr%2BH1HyPZcVeTbK%2Bs%2B4LnHn9aZGTfqxZt%2Brsc1ds6ALz8VIqjaZ%2BwttQPJeA%2FzbbEvYzu6nnMzOZg6G6qHx%2FmrTZG2FDZ5D6nWhjfed7HO42%2Ftq6MsLwf78CMWMwg1cPUVubKERLOmSZHBijcOI9ql7aZFXlVxby%2B4ZZIK0CxP3um9eQwYTW3nKboqxyv858ggPbxB8d4IdlUdN6gfrf%2BmvBqhzp8uI2qMxnet6ILAbYA%2F2%2BBk57S4j5PcAb5borQGj%2FVgHPrrVUICu9P%2FKGZwnvc1mublCCtQm9g8gmVz9ABYf%2B3N6ZNQXzFiYq6tQP0%2Bz9ALMWhPuKhgVdfVaVH5LJLFJuj8BMuT0IffQ8Qnq8U6CMlXzd21XgJBRX59WxM7X2vHqFjnnYoler3Yj2UTj1YW8uHD27ruqggGcUQDrPV0jV0yHTlwzJUZjp%2F7%2Bj4RRUfXOlaNOlHM0ELqRangRStc4zI6ISjSLdluDjFVdZ6cDnkDQd4FxnfgJju1xsZ3CIx7GrtB3o26JeU8JrFnPUU4ZbUGrOubNsa8jVigX68vl7TUdLLqESfw%2BZR1uHKwXtXDFO%2BtYjSVtW8vZzkGn7kn32CSMVnEb%2Bk%2Bf1Wf2eH3WzJj1ywzZ8rkoJ%2FsY6771F0Oj3YaFFB1M5x%2FtMmeUecKrZytrrjnPY0wrreslbuBX43u7LcANulKB6gRRyiclZoJHn0Z9kygjBodCUorx8g2mNVNxGn1U0FyTrqAJsQjMAPn%2BicazkqgWBXVs5KhI1%2F0gY29jQ%2FcWlMkO7QkhijYx%2Fw1fNbkhKYKrouowiVk7ulnXiVZAl%2FRPFoCE65rZICTIx7FIxiHU7V2mUey69xdaoH2Tu69XDSTAT%2FNZDuUVXixXMrcQ%2FNG%2BaIK4acnTw6gjmtAd412kZk%2BrvHdLWzmOPMtZ0lrvf3wd7gugLzgo0yQAfW3AZFmLtdhneqmvPchqhxJ27ch%2Fyt1%2B4UBUIkZjZcl01JDx7Pun9RWCDk3X1hfEg230se2LoQJLjq0C8%2BquPghvTxLa9muWrQLwI8u6gTPr9VxJDxfjfLR%2FWndVpp9T2HpaWE5%2FNJdaqphmfR8%2B3FB1HvTlMX6Vy3UhOZHMQR9DFZ8S7mwxnjsX4nuUpzFi4xkeamVPuXeLexytvdlzFiseqiDtWI20E4wJp8BVhp%2FOY5nFgWXxaUWned8hKDznls3ov0eEdO%2FJRTAiFq3bFVXGNoWOKrrQq8yXpOOmGXzPN5VTfUCQl3OYzF5eqj2iH9ftKnFBB5%2B%2F%2BsJKHoRBpglLA0egSBqJLPTsx5b7UYov%2FIrIDMeK3ombhreKoAFgmSEswjv23ZyZyk7h0U8wQ%2BbqFqx%2Bsz8psBB%2F%2BZm7LMpCetDLGZRGem%2FnRVKFDlTk%2BLNMIC2nW1QIGc2DkrWeHmNBCTX27YVU3jepDn96%2Bf%2BHxHZj3sX0Mv2qGlZ2pQ%2FO0SBHf6wgD9TvhTdcNgZ4EACewDKyTbmk5myV1VHb%2BlvR7J321hLjZeDtOPibicX5UpofyAZzFqBN3CHp%2Bf4ADDwK9AEdAvMNmVQljqo6ert9pAp5LwgFE%2B%2BvGAQYeH6dRi8nGRMcmqVONC5kLFWZzjnj%2FEU0mfCGdLE7acBdis4AY9tr%2BqU8vjdVw%2FsmBDIqg2PCBfbTc%2FINBoegRgwxhgjhsM202IEB94bMMhafDhjMTqyDVXb%2F7heyREfRlCxLkCQybyV97o5fgJbQ1ltGbOvG54PnEu8W3ESg4oAA3fDLssa4swkCwqo%2FPy6aW1tOe2jm5Iemvi%2F8OCkSKxiesPQI%2BlpC9TDcMb1s7Qmk5BIUQZ30X42xS1F6p%2B75ZKuWgQjoDuNlAdzk%2F2pxutgCDPkHtsdjX%2FgBF1r2N%2Bn4sn61VUOHXVyAreq%2BmYGGkeJHnGxVsuKXIT%2BazUTUfTprp1DS1l2wT1VKfj7AsMo3xkd2yHyD8KJrFEmxhQ9tVf7I%2FiGrdmUnEFBwwefTII2%2FyrCn3fU2KqmQmGCmODI4aZbW4%2FRo8%2FDmpMdcoOFv4lf%2BWF%2F390NWOZFvKucPB5hXk3jTRPcwgmuinx47cggaKLS3LdMNuxjUz11S8GG3NH95ZFDZyHYyAGzxFSj%2Bme8DvQg7Dq%2BSvBLDyHS1Y1yKjL%2B0T4lzJ4jUp7pZHXQ%2BlUC8PdCo2sAnW2XEMakVTQmquiTU6WFPsP3EDXu82sptRQkZxUwPJLzIPIkYGozCTsBcSMG9fWjZplGeupehl3KFqKOFgs7MRxKXpwkWVcbv0goZqqJ6e%2BwJPWFGogSFrHmn%2B0Z1qAfy5tgn0du5TrwzykWH1fbCMs9DZyIprF4TLGwhu0WMsmwadasC0MPQtfqcC96E%2BTpk1%2Bd7ckKZQ3JQRs6ZmQwYluvUUFw%2FyyU1cCBoDi2CJOKlOrjcZnlYSHz5w5MFoB8GBpvSUdqnSsn2SHHEM%2BLYEvyBoNN3ikG6qxM%2FEM7CDKpNsaW6rS3sG7PwEhKYdinB16AxWGJFn9Cu3aOwkESgo4AQ1iaLqU2gHq5kXtoOMcG97vd2nuIAODxDnD6myqsVsXFb7vjfhb9rGNu2%2FeFapCT92fZSRNN6CzUVKHAv4xcU06s1UGHQm0Cx8EL%2FoonHaolxY5RgOFvuau1vcy9hT5VAcX9KxOd3Do2LnUT5CQqinptHpfxVyzumohUoS85AQ63hPs828YM1vfDRJNoaRI1%2FB%2BDbkRY4%2FqGO0%2BbDhtdWBWvL1AOfeO6xCyKJhGIYRr9AoWt7rBR4FOzqO9J4PgchLnJQNDJ8%2BknGpAMylzBf0Q9Ddkp6ov6NWbOFMLczI02E38bPZdxqmTv8WUBzAvJAFBH5RVlrbAZM%2B0PI3RzgobITkdwcgvXXUuuHA1FtJDlS%2BYqpGdPWyWwMhELdyVh4Vv6kLxJwyh%2F6%2BNc2y3wOYVpw1DE2lVFEFde%2BtOSPQ5PsikYE4XfIdtEB6fsvtaDHoy9EY3JnSSx1i7JpBcb6quY95ntOgfkW4YHsiZFz2hRLw07ac%2FClaGlUbJ4t6qgRPq3IkoHQE9afu2Fm8s5989DDRT6h7kB0%2F4d6BXnZfUtL9AQ%2BUqnaThvXcby2CKwJQ867SjyudidZ5Eo4see2uahiBFfzwCbUF0tQV92O6laBTOU%2Bf9SDOerPCFCqCkmGu5QgPP8A8Vvi1ARyGvP993B5QC28nJbOZMX2QIxZ%2Fyls5rqmf7LOpm4FOArpjb4qROfen4w2A68CNUmJ17pxiCDhF%2BcSxS%2FF6HbYyYGMoUOPXpSy%2F8KYW8ZVePkfblZpn5ibsJhYv0Xd6%2BNYNZgtnb02GZ%2BjOkWlWuAQBBf73inOVcheH5vbyJjtkI%2FqQLy8PhIBaPPC1bL7Ocpo3OdOyk812pjs9Z5j5vAAsyHK%2BjB9qH1FoXkrxeUgLF%2BBkLI5B0GavmerOHkXq5SJboURNouJUf9pcznl30KueXk%2FjHpQ8pE0UUzyWSIH1DDFA%2BPVjV8%2Fjn3vygSOF56bNDCua02YxPzOHiHTcXRbLtg%2BPE8jFUnInaKmBYMoQTaRTo2%2FsXQ%2B0LJXXWkcVBvXQSHhMKmFrSObQbLu05Q0QY8gPsQpaX6fs3gBZhosN%2FjKBZaNOEKKX%2Bww9aZQX6Fn5OoQtOuMo91F9IBeLCz%2FLZ1hrvMYZGREVV3fkF0TrYbjAu2pbRwvjPQ0aTK9FLS0dmQ%2BrqniJvo2zCHPHZMycIlBFyzJvqygwWUREXsQoPjp1aMB0Hq3%2BsQDaoxz2RiRtxXAJ2jp3DghWRonJ0KEJtDRXXPCI03Y1c%2BJTUWqJpCnibC%2Bxt8qKFFazOV0HT0Z1cBqDJQLVerbZTSLwkowct3ztH3Uc7Kub3powTo8COnUpOQxQiTEkqOfU4myTRwK9kJzDm3Zivr0fWOBfDprVpbeD0V13TG5h5h6Dc%2BNHm%2BX1yNJX6%2FuAtix%2BbiNAOqE8G1K9JY4gJSuyXw7EFDReKKynY55VsasSRR1SjjQn6lDVtJ5ORdRnYtIp4qYfRFFPohZQaXcGFXUY3QZfyu%2BOEgzTb4p1xFBJRHYYDdXEi2vr5fs18XVpH3UlfIKfP6RBrP15iUInuZYdW%2FQEVBsBr30YBGs%2FYUBnXr0eb8zVBKnL4G4tY48seWGgyCvDx22DAX6KIKSgjj27GVjUcU7zUkjDp%2FobOKdwA9jqdrECuWrhd%2FibT3%2Bh2wgXavBlTqmpj7nPOXUSPaYPNUrH321SAuy9eIxSAWVyBFm3jVTed3Zal96HkBeUh399DL97qM9rJUgAoMxPZS65S62kv0VllwBsulsmr3%2FPsiZhlXkvrPlfG69Yw85h1tcLBjmYCtHyqC4Du528ehgXWiURUuGyt4y82JAdr6yLIjjs6qOUot%2BONhoY3VKrbZ1Xw96435AfYligff%2BwbkdjYXEr%2BsSHHcRObkY8Wv4frrXNTAAMws7IcBrbI4JpE9Ks52CCIwidq3uN0X%2FnZkUAmVo%2B20LuLUh6Pua0XwByOBUJxyYj0b1vmIyr1yt6RJVeH0i3zaIluAWZPLSirQaKUly1xzgc4S%2BHjKAr%2FwP%2F%2BfZHlyAJ7eCdfJ%2Brq5CmNUMgJsJyD6ir4gTWEIaOUgi%2B%2BfQ0GfIrPHVFE9ytc388skjB3Q79mqUB1OIwn6HvF4ngRZejAMrHWL2ap36M%2FmtQ91sdTXzHH0fYy7WtBROLOFj98Wcn6SVL%2F5Mj5ktxVL9IRMA9Bn5mxu2KVccKWPM%2F%2BeAcybc9S4Ata9wI%2FVc%2FNEwpTzbedJY3UCGZ%2F5ilaawUsuPa3ndvtRdt%2BJYeavAXnHRSbgldgCRBkZYvRLtVYPJaqE7f5cMsy%2Bj3SA0rDgHpKvtF%2Bf%2F8kYxeRqUW2vHBEiCjSf%2BwlBMyaXZapfMeBjrzzgWUs6%2FjaDIFPuLrmGTvz3PuNrThdm9v7K71jqVv7Rc4792oJvt2BXfuyy35D2cIOwfjJe1eWjlqK%2FA2HD6io%2F52fuNX2WPv6308o%2BXkof%2BoJhGi6UmXxyKoPPWYr%2BtS0CNrbXl%2By03ZGF%2F9iaKJ3MbpZu%2FqKs2zLvB7OGkn0NNCCWIzpZ5q6%2FwWFfx%2BmAGi7IozXrGuVMc4SfW%2FhbGHiU42erfVB%2BnyvUr2BOvkiVWzBlKvi7jUuzPXkQxNV8l6Ws0uwkJMJWbeJmNPj24t%2FYNrsimwXWe%2BpPoOfLe4CvKU2ecpW5iwROBhvYLLw4OPYIl%2FXTf4AczeQuCseipWdi1h4wW8F6pfJKDUIvZHSRLnh%2BuY%2FoqFtxx89rtJMxZmbJMbFaPUuukeStsWV3rm0iIv3WQZb9LlrhjUMvDmDPRa0e6lJez09SpqTEeFJmu4zlrTPTKvkRqKs8AeOdEVXcOF%2FXUJ%2BbQwuBX0r%2FnJsXT4cNycbXGpwA7ieqd9n7el7Ica2ikZjxmw1dXt8pf7wdU0tYp90zCSbAj82olnD7c1ZIkyAp049G5E%2BnTESJAUB2uyEkDo16smeLbkk%2Bd2%2FX5NXnrTVZ48msjwEe2F6jRTEofF5YQmnJDTUTAMerAzk9uZ4cg%2FOiILYoMfNU8UKJtD2aNWsLCWDhtwfKdhYfjLXV2mp1GABCSlElRNKj84inDBpYhXj%2FVnScl3fgo6l%2FnqrMkbc%2FF33R5QjwvKYu176JZM8QOj3QVUEDbDzDI6xHpK2L63C%2BDU%2B5peCoZCahlarlid6NI0MTvPX7ADrSWLqVKTBvvCXl7e%2BXMhIdSq%2BtP31qbusHpiQwkG06jTogDKWSLCgamxHFmMCGmx82cIFUOQZH3X3JbRWf8jWcZ%2FqyXx9AYqqTqa%2FNML2WXIyTBx3qVVQtk5MCnrXxog8%2BN0IQKXhmrI%2BwsIY0Rm2UEjNSwdhXlo6%2BFbxP%2BHcARVVovaArIB%2FhbHQWL39zzDyxwQOrvaJjB5s%2BP6gKL4obduXZYcEaNw9qCBXaR2zywCL%2F13YuxWLZOWRLmRcKc02nVjfqGUlyfBOLhuuKluuqenR8HXoaFs7ANqHf9dfNBmBo%2BYvkbsoT9Ec0gh%2FJ63kFTVStzoO%2BDcmuBrN%2Fe%2F7eIrEAnDABm1w5divkaQAaWgTmOWL6gOKaBVJfqjV9Rx%2BqTO2DDpXYA8afRPnil8c9nLjmxQExCmEkUlpLiEJD1bkTMsxmfJwnX%2Fv4wMUA88BNWKt8oeiQZ%2BIoUgmznqjPpgq9qit2N81IJhsgOjGEFHhKVnAwmwgvv8V9P1NaStOsqpI7ZHOnnYSxTQruknxbeu%2BwQSsnMfHSNgAjL8ViTgQWme1TnJeKcnKTVYJewRbyMSU3jbGiKTk6bbs%2BAfS5YtPIwW2ADw71DA3bJclcWKT4QzyHB2xHXwEmXcvNBpSePOY%2FPOBSUiYC%2BEC2uKb5l9oqZo%2FQEFRgQLYf4g5mjSqVQ%2B5UBPLaDfe1htlGC6IGsFexhogmRMScQc3fCNnx%2BYKddeWj23HFpbT2rh5OalM58HILT26pg%2FTH2UTSY5jTWVEZeTxTUdBRC1I9BY3oIlVwbo1%2BW61lYqEcsdMU8ta%2BjIX90tPGP4ZKj4YQHTLP%2BtKOVwpR5GpViuAQBiEdz%2BZHQ9enHQ0Oi%2ByCU4ad1nb64mdG2Q2tueU2zJpuwuNTOWgcaz0Byz0PMqXxb4IDkyu5PJJ4SN4YlPGEHbiellCPLWp%2BjMwuNv0ZzFh49ADS0NTTkp4uTHemvgjS%2FhWYvhx0UgxQGrBxoX79hkAdrZNLDgJ%2FZ8ilb%2Far%2BqwGunY57zCuI8%2BNoi0EgI8SGdNi4IX8WQU7l5bkaPdTaDJizYHSLGNg%2BMII91XQRhK3oSCPiYZy24uEMBBUO%2FZmFnfZNZwJoUzncilEIFIJ4qkdDfOKOfxYAPvhFkvrKKRs2Y%2BiZHtyw%2BOmD6BQsiP8P6pSYWQmKNJGicvfhxOsL7ODC1iU6R6oT%2Fw0tiZ%2F2H8g2HZYG%2BU%2Fy6Zl6ipHK29LWz1Dc07T1S8v5KN%2FB9qpZnXrbrTVaiamgKnmEIOjvGbNmrkowHvmgXmHxl5xfDUh2y4JTPRnNLp86fj%2FgDKIKNmycyrj3286%2BYPbFqaMYWZbYWDgTONQ6p0zpO92jBUjIl0ar%2F4Z3qOPnobtDjHbzfqFdkuz75frKYnjwfK8slE%2B8LnOgPa9jBi6mVDMV5cmtdxUi3AQA7VfkHpWW0hAfwIVKzYxm4D38%2Bi%2FHpNLSCx89pdP7nXz37AAnahCedci6WehK8E%2BNpy6ac%2FVyG3WFRCLHBsLm2l1bgIdzW05RTX1SCpSh2EUxFqhOpT3f5p8MOU55Zz2udjjT51973cAusK3aaAyc11EG7iA7NE3%2FQC%2FTnXD%2FzUMT2kwITbLO0WPk7JyGRkVL2E2pCzjF6cf%2BwylWXl8StKX0meeIRGxm2SwTyWDKvwHtKIj0kwdE7m0tnkW18ha3a20bEkazYnsT%2FnZgYz7egM7Yb1KoVDOgLQj2bVzGuuSbP0u9hHDJ42yVYvpKxavnujfjEbd2p2wBecxNp7o6qNbT%2Fa1m3kbBAsPCPLenScyx8oFa3J%2BzWgVX%2FKeJEP282ebgDyYkeceetRxJP4BxmjjGGJNz9LFWLN4yRg2YJarH7hpttp%2BzVvUT1z2oF17Yq8Wk5L0gAXYdmcXeqd60OZ%2FoHgz%2FnVpJAt5Bs9ArejroT%2F19KY3vLtx484W7F7MMPLIK6la9ZngGTegM%2Bg1Tb5c0g9nuzapxMDvhNrxKHG1qfN7Zde0tcdGJzwKb%2BfdNHtB3ADIX6kaI%2FfALRg19A7NHejTEimVzt%2BEUXwRokD%2Bah%2BeZBS24ois%2FbRQFXRAeevxeJckcpckoaBcK89Rv24TF938ohmdfjzUrxNCqNai5ouaZeoc4NBY1F3By8JFLQXD9s7tQSIrqqQmZd4o1dXUK3niyjdgxr4mcPq4YGzq7v%2FEeuNpZ%2F%2FMe%2Fr30BVRiVjRvLFOsa8HeT06LYvktbWZQlKfd8sD4%2FvkqYzbf2S3z4oU4GZnnI%2F0zedHnUwA1VOMzWdrqV0Sf2pJ99pOCgk60TxKuSaFxZTYrIDFs7O1%2FMNZxWTRdMVE25yMqoxMulRAY2GqifCargGcEl3cfLcQwJKk63uQLjuLAgQNoeSmRtvqGq4WPFwlR5E3o9DE1SVnKSEPvNzDw2OmZjvtbkw146sUTBVHGxBBrRztgTGMbSVkz1GLISBwSQZ1foZ%2BgwYvvPs2lpfQzD2MdUQvQKOnBLouMnofka8WiXvncKi4uCtTz9iq%2BfSp3YTilb8QTKlaUt68zxkP3aOT4QOaPcZ7JsyKLGaliT405SzYzmetArlOJ3Vj47rmanVnLddhII2CrrqID3vchQUhm5TjoFZ7GuYjnRll%2F4E4vsFgYzSnpAPEcnobNC5nHHVgYmLZO0m7MzXtwFU7LYYYO2sBrurTqkr87z1OY5yiZvOyVoc%2BYmm5pSkMP88D0xg6hsUUfbZLdV0%2BSrVwVt4%2B%2B7wGPiAlxeGb8F49UHRpxSi3Ze%2FeAGDLJLAJcY8qgWjUhJHfpAYQYXVnIfQ0oMzTsKnooH%2BES1ruiTQE29g0kpvsy%2BO7L1k1wGNbJP6ve8r0hKRsAvXcqQEdAA8JuXFAO3z46GuGhV5m4U%2FQ4jWpRzeJfeUQUmfX%2BYSjkIUv6pSe6GtHCEUq1gWpKOiCN9kxdVvhnkNeSBqGSfjjYIDUEiaMUviOCy9SdzdZDzMWT2CFqYB4wp%2FpB%2F8l91Inru0ljdK7%2FA62BjFW3skNkBi8Z45Pas5nQdycFoaPT%2FB1HF%2BXbTSl%2BEG5rsIa%2B%2Bqsn1y8LsLhf2%2BWZki47all2C%2BoSlzy6CKV3Sdz1hgRVlR%2BAgkXFYgo0wj1QuPxDoVPgWjTyfwJ%2Fbk2jIx%2BuchJtVZ9FPclaYT78st7sma%2FPM32OqSjvJyJNAAEfvpy1tqsw4Dt2kGaSQX%2Byng809vsZTIPJHLCvwDM5jGooDlOPZekv05xmsfvD8fsEoDC0zSD762GV032%2F1tvlnwer42DZEGLJTcjPleD3PLT%2F4SxLw4AJClxN5j5F73goomrJWTAQSTraDOhx4ijJMDJGujDYQp20SWONxDli7QdA7RDLyajNihXAzxcfK1MmMka8bS6F55C3gKfmlLApmNEv%2BbAoTWAPJ3pNtEWPFHK5EGt29kFV60JocNMK%2F6n1AybcrKBKXShsMtNi4znGwnQ3V1VA0r5ppFMQvyGGRBmwbjebd%2B6Mut32%2BkfQojHJkZoI6GHSWURV9vh1gB%2FS7jEKos8mM2ql7GliHOeqL2xVdwTXeb9j3Ge37rLDd1VpGgQOpky0v4nf4CYAfumRKIfOvePnpangZaycd5Ee4LXc50VrIRRTH%2F3RRgPV%2BdcvlgNMRAp7M7xUPO07O5jhzfuzobqcE2vVaIxnW4E%2FKkxw4f10w9N0YifaiINs9%2FMLwYmlULS%2FlOoWgGLuCe6CUDBXE%2FLnopiILPEHxyR6cujrom6%2BzmR23T8ZKlNlpWGfCBB2K06uLQuxMpf5CCRNPYQtNecQiZOiaOCB7GKtrBlNMZLAeooFnQ3HTJYaFyhFuHQutuUzyMqk7Rk6pKTHTGEJRK7fhKwqImAQcDSYHy5to8s4vJvZZ5H41uMcZeOKK1pZoinVooNT543JQ26QNarwCND9wBZlaEhj240vXt0xtgubfV9Ed8XjH3mIJgcnvtImJHG5D6mgHHxtw8Sw1wWbYy9RZv%2BJVusZTz0yyDs4Wm3wpinRvvwcXOZg%2BHBf3jdr%2FMbFlBmESbdhAc91ZLUXJjl%2B4L5KZX%2B8agilc655uwzQYjWiCtmsBnw66mu6%2Fn3s5d4MXlXf0sGAGloyc60jYedAqjB8sJx%2FDluEV%2BA1Rty%2B88hy4q8YUSmOLzyu49gG7kBaCxb0h2XHUJDKiMk08mPnKE0JaigzcA%2BjaBsy4qrBQP3t%2FMosH4NQqWTaWugIsB3oqXSh2WAhed4q%2FVp1abrf3IEL1EI8XNr%2Bczb1N%2BMWNuaA59VKVG7wvWep03UITogCpySugS7iL%2FGlvU%2B4EsDiyrxybZGYmD7VXWyUufxvy3H%2BSJVo1N18vHxsb0bb5nTtUKYWRtffqE7cld5XQuQ9YTEYkqk8R%2F%2FJVJqQ2jNlGIxwTSyZBnJDdTZyJKKyndVx0C2mNrV5el78LkFlrpK4hZweTexJ4hKnK0hKmcPXLnQWwwaZzFi0qh8Xarp%2BcB8HwVEgxLNhOmeiLm5KyK2KZ2N4cIAU4GLKajhh07%2B6C34L42wvGb4tcHETivIuIIL1AddblxNkvkdzgO0tWlsy70MH0ylkypYFQWPbDa2Djfka6eTEXt5QeVLrxbWbOxLTL%2F4Fh%2BHZmnuanHzeGj1lb1P%2F2KWkDVN3XANA298JLf1lXQcCZWbOZwqQzmHyeCjfuRUF882fXjnyHDcMymzGnguX3Amqwv8ED3hviuFmk63FBvgnGJCUh2qwDmN%2F8mlJCpNnHA5KCFVjFDIwjjp1BKK4WtzgaC4RleqIfMycMMxgu8J2jMVkEcOv2GqavO85%2B99oMCAJRPKFmj3eacD%2BJGVpoegDzYNZsBDYmSZMrsMUw%2FFWnV2qQgoIIftymEwaONVXA128w7MSYq06bJoE2rR49PnoM%2FlPlxy7qSmwvVTV8qOfZolg8JkNthc8j2RajwmU2E0090GVEo1xGGNHM89EI9uOnzV7AhqsW8bzkPh1rFT0PoU5VJG1qgke2YQvfhGIU0N42dUwrBcWCfESbYagOUY6CjEs6dtYxMT6bBXegXiyEvuaYlO8QJit6DzeUKsBKKPVuMs8BzzOMIQ0aZu%2Fg42Rgo36noQmsnxXP5mnIqYmY2Eqd0Dkm7odvmq7feR8vZjR13yf0jJx9rDiEr16e0OC3%2BJ9HoEeUCelN%2BplV8A0%2Fl%2FY3xwWunMLzYjGBI8Z5p9afyqLdB7PeRcNzJDWZueG1W2SL1XkHp4dDIDtGfXXPk1vscGyQszS%2FtdMeIwkDlA%2Fse9OYOCIPvcC2LCJ6Aq1GDh%2FpeUXZWIcvd02NipNhAhDOnei5U3xKnqW4QtbezyeYv2aYs6xOE6i5uLD3hbCgFGelWFrjGocCJiOMcJy%2FNX8fCWTFy5MinnXwDzoV6WS9LuCGUP5MXnGWHxCwLc81AmuZFwOkln9CUZQtjvTfgbAIDDTxWvgfOiGy20pKIrGrwYMQT%2BND60uiVHxWqqTBOfv%2B1Z6WwZQDfWEDSnmv20YhETe9wvUl28R5lP%2FDTQsKKhK0lFVJ8L0jto6qlRspJV17DJ02WZ6WPKlICsn6xqXOlYKHwWjdfpi4TJhWcapPAlbakMiuMTmAEaCdwjnOD4KXEJApVbUrPmMXEf0okqg%2Ba%2Fwwsqc2tc13i%2BKS2%2Fj2Lui7hJbUnVQApiKzHqPfigowIr8YqrHnSTZ6SUPtDq6%2F%2Bf%2FJTQwjcgg64TiU0vigsNg1X8VUTkx5fWThuufKF8HpvxVE6VUbrWKuko7JcPaqZiZP4gblPagiHzPh5CLGE8vCZuKVKqo%2Fm%2Bssoaj7H8pPureCYSrz1EBmnHBv%2BlBkqtxKPwu0O0cnDD9iGT%2Fq41mTOQCjqRQm%2BgIFej766zRA3Zhq7TbsK9fWFFZx23jE2E3LyrZ89kVRnxiDLwdEbhs44%2F%2Fg%2B%2B78mOryf5Kf%2BLEOO%2BX%2F4u2YubePQRVVHmSEfxduhuEe0%2Fq%2FtXeaobyaYy%2BHrT97UholRUnmDPa39QteIgWUU5oFfZ4FP4DvK9nZ47DkJsCSgu9%2FLmkyfGFijsqTYr7PhFT%2F1SP%2Fv1XYI0Iu4qJlKPX5z7CeWpvYl6hsFXy1TbnkbJpWok%2B9gb29%2BnPAlb4tM9kTaNZa8o4FzLpVB9xZw%2F7CuP13Pk3rPhqtM5EwB2pT5sq9Ur95CkdiuKYzvUoMawO07Y5weZPnkTaVR0TrOAUjkgEQKQKvaGhaCfQB9lvq4Oo78KjA0SRpN3uzwr4RyjWqfiTMUdh1KK4vSMHQCAUjPISnL2xDeD2rNOVEENhDGaGLC69MnZaLaXjnp%2BI3kLzffKv9RVfmJ%2FjQzywv6o40OWdL2NOqesQ5ZbfIm1gfMwYiLOfUEs8lou1c9uVLmO%2Bqts4xh1Ckntcf%2Fsznf9S0km5hZUBsWGSroVldAvEoX%2BuRC%2BtY3H2bixGny%2FzDX2b4cAjmvXKSDH4ea81kN4Ka2YUyaQBcpWdTDeg8%2B57kUG2BQQy6yRabE4QY6aERCYT05dMJtJZazuM9hgAoIFF5ZmIswnz%2Bvku0P5OBjCTNb%2BVyBkTnlRgOISOuL0%2BjHp2h3w9GZViMIqM33br2zSZuWqYvDU6XCFz76TTdp1NsM9Orj1cVTDY8NPgqNQA2klXQLg4UvfqsAYGGSmj3EFRmIi3eZed5TqmjlxgPA4dovko0Hc1X0lrp0ge%2F5EdhYGSl5WzYA5UNmzPLeT6zLc1Qn5h61yCKE2FOsRqTfdF8Q%2BoevwTFH1Y6JiywnAWK4eVcrNXvXH2rQgSYxKQjlTZd3PTR%2FRNQKAVC3N8S%2BYVGRXE0NgOFT6XeEl3XhGtTjwdOCyllLNbRckKusY5n6UQBcpICHkHd6NtiE1w519vW40iAMmBF6KaI%2BTjuAHPRKFWT3brZ7JCh%2BF6I33k%2Bs0Mgy6rvKZbRe4UugqXBwDPIi2tgVTyx5HgoNigZKMNMJgM585CMM0wmzM6DsC39v05vg8lhkcpPbHpi0lvzWt7GFCNrgvxqX92z3GfTkmOqP8OzgPDrNuPRSNocUEFO9sIcSGbzCnYURFQ0KoMF44QHGnu4Ju0wMtINYuh49wuH%2Fqmcs9eSgUMtyHJcL0fD404ROUoMhV0oX%2FqWI1L5QHPiQAuDffnKPJ%2BREnIS7iP2DJIJqkOktrwbvai5zOMBXeMtUzXDd4eF7V59M2e825Bxxf6VxebWC1H3unY4Qhkhud8iL83xcxTBbkCETihe8OdsTGsSRm2yIle0Om9hwRSqwkF%2BQRDpjQN1QVf%2FmPmsZ03b9ogd7T2B98gk2lVhrJccXXGTNvEm2NkFiTm%2B6BNxzwg%2FXkT91mlb5hgrb4LyhSyM1wJGff6bv2lt45Agf4Im%2FDPsfbCi5pMxBCTM0BwpKDv29y%2FEea1WyO%2FoSyCt7Qr%2BCXjqwMZML3KPC%2Fhg5EZvo0Hv%2Br7FaXzM0bh1CIVqnKbsNq5aA5cImHRAiCOs%2BFGv7HUhnfNq4w004AEAvMWUrE7E0QGm%2F5bU5Fz0SFcDAfvuyuWOfwLq6SnYiry9Krm%2Fh2Ao7w5U9a68IDyJNC%2BjYp7sIwXwWE3DGsZTvdKc7kGVWcdBm1Y8WyuDSuxHSrwRd%2Fegb5KpqtyV0PKpUwKbIAejA5D6%2Bnn31yiTaJYyUl3dCgekOly1pq0wr%2BdjJ8B1HIJVhP41eMCnS0oJwP250XNxZqPQjlLDFaiItWu%2Bg1L5KBt11Ios2uzMmPiGTNQpBl992qmzrtR%2B6B%2Bu%2Fkq0OH1b%2FbwWTtjmDnnktl%2F81WcuS3gk5o3F9dWPSeywMPTXoTdjZ12gs1eXziOHxBO2aehu2a0w5hKjaGkkEK1ZDGnwYCrVWISt%2FfJpLA6tp02JENGdsFt3oq4Opc6u1uCzxnffCFEfOR9kO9e0nTeFETiib%2FhbPSWFO9jxh3L8n4fFpVHvr5Oe%2B%2B84b9ullpAOpopD0YdTQjPzek2LJi4uqUk2DhHQ4ghwIXXeHFT4GRSTNvZD4iCStVVn2u4bZZQlaREvVmDp6TbQxqxwDWgQJWP6%2BXrXSNs2prhI7WFeS%2FYOmCZixE%2Fnfdd5t%2FCOSLKwz2%2FQaOCaFQDAuj5a3FgfUrJQ35rcUmiGv%2BbnEvCnmYtDTYLV37DLilb%2BNZvsovx99zOwCDRop0aj717w41VMUU3Q%2FxJZBz3WNpkMsgHKKb7KdG9nc%2F6%2FMh7uHkdb2oOwpa5JqReCKKjPp3P9ue9vZtE4m3C%2FdOdfKgTRpZM%2FFGn7RQilxxE7JAa9Y1Mg3Zn19ionZrNsBHwAqu8AfogWScgprNlX6dvFQ8GQDuV3%2Bv3Mb9fYTe9lIsZL%2BMK%2BbWHj0kjWBxgs1ih13Fk9ZF0OWK6zH8NhOkmlKojf5R%2FaDk2uQRO38xoPt9%2BabDJ17x01eaiLNkTOAzYEu8N5DgB8WpyFBJEGHOLElBVbNVFZ7Zg4FxZOyJC7DaqL%2BevHCDY8IjGuWwKIc9DRYXHwEqn%2BGv9VqtQ4UG9zjpKp%2FdGF51I6hkof1CY%2BipZ511cpwToqLJDW5ia0Xkijwqy1i2sidbuQXt9yu%2Fd9spZHq61qrOoOPFAGBDWm%2FycplMjZivfz4jdlw%2B1P9ja0tYu7FlMjYIuyGTHFhaZuhGPfgO7Eh6Br5herDE5SFCeCeDFcCKNS6ZLxjpYm95YDVsE5zRG0xZMJIVHRqN5tUz7VYY%2Bl0E8DB4fvFJiFToAEqw3XU6GDQGxsadGdVZ5ceTsRMCxX8v0H0wlp1DcilSXx5c8zCburssEO0J0phgWUsdup6OkJBnRVPxRtZNAAiLXKN%2BPh9b7HYQf3J2%2FojIHfM5hwpSD35IHlnk5wJMYsTT4U9yyoQncfceKgAu1q6iW2sQqXLDThENfBdfhp8TNGVc4JBgpV3HeYUphw3vV%2FFM23dxw%2F3SfAOvpd5HLHckinic36Pfyef1TGBKzY6eHppNJYjZoeE3%2BM7yqafrBNg6m18DkaYgQKoiNuxcqZPvu01C49kKDY84i4wxOW9ka7dz2JDyjvtl6L8B1xgN4h9atXAvBqghAy9IdYgN9vaGG1zBIMJRsIIHODaqJroEmO5xRnL2SvEnD5ayf3jIRieXxqfb8c%2F2UXdaXLbSgz3zHeUDGf1eHeb2H%2FBNHhSCtvVttYTeUXHX96teJKKVUCPlR3LQJKdAL9l2mxh6QkqMNtyvZe0XvbCOmer%2FIeCqaCMmcFYcFXO6tUsefCAlyBAlJtxBmSBagxkpSTCfv2iFG0t6pe9jtY68lK8by%2FxS6dHXgMw4UfTCD6IA9Oj8VKMaH63pswYPH2XligfWu90J8lZ1hVNeeqIDfESAT7FKg6dZriIWh45ebPwv%2BdDCD9GMeV4DbdzRBqz8Alr8ctFr1e1%2BN41oodWkgO9iBraQqmNil2NwKJWWiV2RgDcr111B4x5inx1GZcAlQb23oc1DorBr34yTAkLz9VWiiBjvC8uMfR3WOJL71y4e2SenKsHvVCm1fxnyFD%2BUDk%2BOWUkwSaxpeVquxAafMFM42Bk7ba5g1sTXluiuknwoen2x2xdom7vXUDCEZSlwLcZcb%2FtB2sTftK2f086cbv4czxXwcPKhdIxSsYQUdcYn5ditiBUJytc61WBAszz9W9fUahDD%2BN5ftdc5HGjpcEzTLmEGpaD4GQ%2Fi1xj%2BSjqCzvoTL%2Fvvbe%2F%2BJc1j6FLO%2FdgGnDcxQ%2FauxRn7waTydRcwVJQTWQqPdkR6EaziR%2FhUcjBbrW30s66AVLTr67hhU1E5iH0gw3j903iIKJPTLML4RE9IPjj7PgOTsCV9AH9Xjxh9OUMVvBGGzLfOQBbm9tiz8JB3p%2F64olqtmB78McHs3ztBC81qhvMzW1WN6eaFUUkfoIKjWrx%2Fjhy5KIYodjZ787HJnP%2Bo8WshBfhm8vc3d2T43WVNBgE7mVTwowIp3xFk%2BcRR7jod4OeaIzjz%2B1icYCs0cS4tGFpwFVllztFeLdDE%2Bnu8Kz8W%2FwkdStlSqaoiLQCsjCrVd1j%2FT%2Fsyxuc7tU7eudmimAHV0Vo3hWuEtULduMsQ7KT92Dk30TYQLSKp%2FR6IFLRDTATPm0s5X2YKORvEXVxH3fkG8eQff9xYdbY2nbhzj59QqFoxe8LojyN2WcrYZEV3Z%2BWJQJsvAeBIaSWYqACJhIVGtdN%2B9JVby%2FrEDhpSCV2A%2Ba4YA1OEGVJBClTsfREBeHpx9HZ7h3zHc6yULzb7SWXasCtN3NRrZDCszpUgXA64Ah9hCvc7bAjpMjznSc664JODRSaMscRfJ6%2FVtrXPWP0hmk8xDlIveswQ6b6yRwOUnMOiQw5CWoumdbUf8FM3f9ZaRucNJdzcvc0nqiwuCVWdzDux2R0e1XYm%2FXDOK5GGEHDLIkMqOuJAb4jBV9VCClcI3TUjZOiswBhFDJa%2BGktnrrknseWGZvyWEkOBeKL0sR5fwvuEFlLnVGkbvG86Z2xsWFYI55f3OQme%2B5b1xD%2BwrVfpJIfvy9TpTXb0HjVYRugRubYO8D5djHX8q%2Ba5p%2By0rcQIAyG%2BE1JNV9nnJLXCLb4SgnuLOYAfpaHn3l6ebkty02B5hBAHCbbzRzY2S1O0vk28IDSJdlYcaY1TzSzckVPJhxAXsy0f2xWwMIkCI1GvjITJ5s3hpe3dyIq2LOM0svtu%2BNECt9WLlkEE2nrDTk5rSmIMEf1hDWFAeOm5C7Yagm2ydVWKCzChgoIzznHElILd9oNuasnp5BU5ta9Ncjzov%2Bk9S8KIIKF6EJuxbf9SUA9WIGJ45i4Hur2UTukDJdsYY3sBWasMyaoyerEmJAt6xGW%2FgqQGx%2BiBb%2FSBrL5IVJx5p02EPjlFg8fYny3N84vWXcQtSNKGv4U9FKih3ar1f4%2FXutRsj2PIWlXCnMcsEdzbaHCZxdvi612XTbVvf8saxM%2Ba6FuqoIT4GhNSq8vBc4oD2Ekr8Y8LmPFWx0trEFtqJq6Bh%2F3YQpH8uUZRcaw1cddJhLIbCqp3q0gox8hUGGJ0ccFhyVT95HuOG%2FZy7ZkT17V4hfL4pL84X5DsoLd%2ByQC7zi9Vq2rb65bPlLboX0b0IlO6xkibY6KggXQALrxYia2lN0jG2DMZkNXppmT31UXgNQoAhKtH8z%2BHCRMLQY1BKkC%2B2QuwUMZQmzWkmMoAJpNGcJX3yz4hN1btp0vExzS9xyiQ6fmA7l23u8mHokWVBltVBJxLkq4nY7ESr1uiu2rTyaK0Gvb9HkXqiKV3ak15UlJyrtSfduUJbsKHlXLj2W%2B2g5YzV9PHR6Wcsk%2B%2Ft4TZHBZWMwKsZWRInevE6oBLbx%2BUUYyO%2BWclyf6bXOqQKTPOxrvoWV7Xybz%2F%2FioP9mWNnZLZg%2BewJDbgZUGHPoSnkRFb1wgdDGCGqlh5dbjxazL4htXJ32bGyYRkw13Q%2BA6wuBFpFNa5RchJ8GcElcbN0DGWi9ZMFuvQVHg7suRl57%2BUwJ07tvjPBP97iQ9UhaKqDJx21%2B8OaJR4rHI6BHcZVgUl9nD8G66DhORZe2BsmJ2tR34vQAXVhg2exAToRyxTwRK5myb5m%2Bwr3eZE6YhR77Lp91vVQhPcBm7vodA7h6RLhBrxx%2BjC%2Biritc08tFAux59LJ5SgKSrb%2BnYuY2Hbtm6YN%2B2gEOO1bfQY4ifG%2BbbVcSowQGvKZpo5rnHgBpxs3dZaFGZSazLs70QDK201outxa9q9JrddeZUKJnZWvr%2FOm%2BinR7d8alL1r5%2Bu1ZhSNfza3eFOm4Wz%2F8CGwNR93r3tEAWJI895qlbtM8iBE6qSxIekgVlr9C%2BoxuB5zYbP4roljDgJXWCwXTHq%2FwEFY5Mfu%2FpGDD%2FWV8sT0jl0BB71AyivelD28pbK9gTa%2B%2BxvavSqo6Tn4FO5GETL1M7ZwRrGVkvim17IM5amlibro%2BS7NdBcUKBCI2Ary2sSAqmEeHRVfS018%2BHXqusmVjqTxsEFAcwzkK9%2Bn0cfWhFSq5olKmdU3kGSDZenVSzoOzrAGGPLIz1qvPW3Fn8oOx0CgnpXR6FR%2FNgplrRZ4yXGLz3nwap%2FCASpxkVOLqiHZYq91AHb%2FAh1fCB8CXs9YUdBSfDglM7dyvmqmdS0abOqOeKKBbHIXe0%2BVtiHROApdGy4vWBwM%2B%2B664K3ThnueXnkDni4uBXyh5KybSCVi35xpc4WWJwVYKVsnrzc32dEblIt6QdZCFxixc%2Fy1fsBj95b9ohMs9IQUGrfqhW%2BSWipO5ozGdUQof%2BI%2BrtjwHYpR%2B2l44H%2F4blOa6SFy57wEpvKwtjdAAwYR6i1I8LLw9orXk6pzSnfK87wFn%2BbwQ0bSuXu8x4swLuQNR3bLAtJyMi5ollHazMFkkvzCNGiCTUGc67w2G0CEYot1TL%2BB3%2FXGUP%2Bj%2FGJ0ZPhyYDu%2FEX0g%2FjK9yW9GC1K7oGEuJl4iTgAJ7vpHgy0X4M%2FzNyPZrnQ07T4iPVCG%2BytGg257JXzaZeppI7GvtCyECxFHdrp7PKgpwwqDf2WPcYcvB0zF5pFU49w4%2BNLg%2FfDO1%2B%2BUOo7Ou6fR2C4X2pWcCac36O0rZB%2BqnlbwVwpaETwm1QLhSZtbQNZQsjeaoqDup%2FfgW4poBuTrj63Q0M7m4TVk1TCCkhJlGw8W90Ly18vVUKd%2Bw74ph%2B%2FQoJkGzCHHKkc2cwyxsz1SnMhYdICknzN4RaoS0Yjv1VUa%2BWmabcnpxyOT8n1XASf3pW4rGhDiKaqpO2sXIG%2BXSoeGH0JIPJQYI7573gJMwPa3ScElk0p1%2BWdR4egJk7t3LDGqUPsbVlHuHLMls6NDd6OoFagv%2BtNc7QiyozsN3Eep4bq0inTEdWvU8YXBd0WyolsqrZuYN58AFtCLrKdSINrOIW1iiJk%2BXOnJJFMge0VU6yWCiHZejKRTEY%2F7TGaQh5QJZImmKGe5r6LhCStoMtvzH4GN7daMp2NdDSaxVcxlWuu%2Bah9aLByHgkVILsqeRaRBHj1e5kpag%2FnJ3%2B9L4cmpVyGB7h%2BhtpRhV1x3MdXpMI6W4ZyE2Yx97%2FMY7A%2FOwR80BXtZJB%2BUvQ3KmROYejTWjJy1bKVy50Z1o7s09I4%2B7evpby7nEZ2ed7KOImGEpUms2vxinS5dBEBzyRZPqAmehJR0T6VgoN8gFDgU3shfjmkJrzFgZ%2FPhSbbD2MsNOZ8xfh70GOJ3RvVjcWKjnkvt8r1EHbD5r6wSuahgFsIoGq8Yc6iUzcC9Q45NzdqXZ7%2BRlHiy4liaDvdah4EZ6zqhKI9WQd3jPV%2BhMw6AxwIkfBClVuk7GuVxcAYVSZaLEPTCVNxunR40LzZL5c9%2FXINxxSjwtkn7EhUKud60p3NtILzk5hUeSeQE6QYMsqmX5t%2FcbedsnOoqQ9S5o9scXhiHuC07aPpXqeWGOojCcBbN6WAYZ19cMS3E%2F%2FN3boApmO0EAWs3t7Wwok7t3s%2Ff2RaW0HzFKk68HWyyxzJQk8h2FIMx98HCrU%2FafEb37SL7ZkYiYPivB6hNnKECB%2B%2BfFfEJUetiR12ItYNnbmk3cAaeX3PIpzZ2jHfadDiIKbkuseUrJx058Fr%2BzjTh95Z%2FMewG0%2B1OxphPncFN1FwL5lSKwMpYHocMQAKa21a%2BvcYYQV1mapEor4d%2B0L2LfcOb8EZoSJyYvXD74kKK1VVNJo0yjoBNEmSxXl3Hn2kj8ZUrm%2BaZJKgvGnr4vNebXX1et3N0ytVLiKIhgfxu17bmZ3OnPyWu1Lj%2FJn4PRmca2pkgby9jZFFo%2FC9ChnxcTMOO2WkGtui42jyJMlJXjabWxEPs6gyjPGU%2BcmOH0iDf5%2Bskw%2F5H%2BooXKeaYp8ZHx%2B%2Byp%2FHY1qJ5qrzfMYMBqiWFn45EiH0xpeeGyplRF29HA7vxwRdBXgFwVt6ktAOGRnUY1Xwzp%2BKH8hKFT8weWlmwXUluqg3yF6ctrMkbU67K529FUGD9WORNOGfY7WFnO%2Fq7UpEDsm1OTA4blPoggxykwTLaM%2BfSQDugR%2B5YQKcFNZLue%2Fc0D2HcOl%2BMnlbJv5s7o6uaz59sRb6eALpYiSe895BWifR98oRwDGSxYNVUEjt0qYldBtyQehB7F%2B1upge2T1REfjNIhlHTJkHLOodqWxIoXnG%2Fi1PuI39ppvi1tuu%2BRxhF84fgt1t95iTG3DXkk2SVklxEIALugu2nQl%2FlvhzHQRjo9tHctEg%2BFro2Lu3CQ6ieui8P6ZU8OOoND6YD%2FGsl%2BA4A6vp89VINmDeVoBgwoGcDbekd%2BEH4h67azkl810eqthw9zDLZ2QkTeZaVDNZt9UjLcGCgQsZ4ANjDZCQ0KkgjAUyPT7HeXIuF3a4L%2BkdNHRYfgWpMAJLDL3NGLCnHArIgW79kDe6DEmOAojoS%2F5XyVcVh%2F9%2FHmXTsYaxUSrHY1u0ovAfUgDhJkHaP1vwtxG4PPOXhNhZwHIE4XKg5Dw3gsldAL2OmPIkGA7wWn9Qd8FZOanozbdPR50OuqrmQipx6dQ8Qjl5%2FQXXRKNH4EZmC34du%2BP8ve%2Bb1n%2B0%2BHmXqE3LtAEUkVBjntjGjstF%2FzVINmO9vBM5hLhUhP0k1Du6%2FolIFvnwKvQbzK67uX8xdJs20iD0Cpjy4CsRwUie8IIZnKzO3Nx%2FxaSM5Lh%2FkBIKByHVjANLYR0r9ENlJLCabM3VyToH2CUGyFxtKy5mGFG4TFPmfGbSybAOaKOm2xRm15ouOqEIgaETRDeTXGvThS%2F92vCimOCIUym6SLs8SXNXxhnsw1vz6IEjB3HFPdbefX04Gv4ywRMH%2FFJyY1Sg6bu6oVAX4TbiP27gR2a1xH25je%2BRW15c4Ug7AVdyG3sOc0xjEImRXDl3ztVoTqQ0GidtWkgYNuwMR06pFO%2BBBR2%2FOVqX7wY5yHYJgDn1YSTfqNF%2Bag2UjWm8CBYhrTbLHI%2FVQtNKPtbktKcVYfLMI2uHL0MbjXcTekC4KAPGkHhQEbENY%2BELTemWkbQkqe4jZWrJ5bMoaOEb3c%2FprG6OBv1uGKb9yRhdES8SWGbrG%2BxkBDqDEO63ovrZ2KlKxQb7CGiDmVuiuVqhs8A%2FvR%2B%2Bar78xa8uRkHxjZY7J80LSy6kpKY3QwOe1%2BAV%2Fm5k0j8KwiJqlUbtKC7uxwMAOPgTovjKrh958w7DVy1wn5SyAP8rZcLIh2bJ2zVX8Ctv%2BmMKjXqO%2Bsh8Sr%2BdbWtDgF2pV4bqVPIRWWgC9Um2VtJTzUz5oluNFyQ0zpqaPKs4CgDA0KxbXMt3weywR2r2ePrV6%2F4bDj90z%2B%2BO08KvNJA6HWD5GQ7d6PS3VAUXJziWf3ijnD68hmc3G%2BG9hheQlZA2BOcJCJa4Xp5XDI7Rbmk1LvX1hKAt%2BwTAdKpIPLjINpKekxuIRdQmK0837SOxtS3iz1bBKsvPSulPxGtMyjC98iB8YtHnP4vQTJwCd7VRZkO7Y7fffmbkJrG4V4s%2FnX2mWaKvgrzXSSNcDWYW%2BVJ55LLqmHp1hwQePaf9v1ZF1FNMujLJqwQ%2Ber0u9V3E0hoim8%2F3b6tmfPHcD6D%2BFtKmigWvROpnaFSZZaZ4dREvwzS5oZ4wIJaCH%2FdQQG4eO37RKhODpOCVhzLFJvSQscFSRYsXEvlhI%2BIuQhmIfCORXZz%2BMJbqfSSYRRWgYZ9Fe6OWSkQRYBrAqMlu1d7k07qk8Qk2hJ5k7Y0i3irWjWnmUJ1g4gaWz9GQXmtcjoCzdTLGnOAMS7XYfUCiAo6Bmg%2FTrSmc0WPOq84MCm7%2BYwYGuWTrh7y0yYnLRZjOfFsP8d9gKPY55Gvk2ywrwsARCH4TnsL6hH0nZlX1NbHFyhrpN2o%2B8Bl4jqRmoc%2BZphrbljXhwIQXeZI5Xu3YjhGT3cEfn%2FocF12MhH%2FaJOgQq7G7lbyci5xfHB7cu6YCgipJ1%2FKzamKTmPNUBF9iG3XhbB9WpBYCqXRBwMcPxJ0KTqSKH5sPOhSM4aIO9pEQGcf6npVXGrB3rbxrri54h6MQrz6A%2Bv6SXnXqSjRmiHLdwI7Qbxawe2MAi4f9RQFVEyhrMNpwpdUqVRlzoJJduXKtzhgvhLksv63aVv9l7Ed6ECxiN50aZE5tODPhzeiJ8gcjj9NrfW5j0xV7fjWwY4UnQ6vyh3%2B5v9CV1gRfP%2B9shR%2BQLpsxlc61xg4nge%2Fe085ZD%2BiM%2BVuoQniZaF3fjE847qLLkqVqgZu0S8kwzMLYRDMJhPzNmCUjIJyx0gXRuBlmhkUKBFwo0ZVS9rno7XColJlxSOhKq%2BOE2lMn9TXKHcWq6jtCIuLv999CNJBXzCr%2B4Y2c%2FiuBjWDpvQMAS6btbA9jCA4dkR5YVCmB45OK5kdv1k0jus%2FgE1tsPlOu6LjcvlpIWvJgBppWjsUbGmkozM06uiWIHjKX327RFbnd7f41ljLU6FwVCSvb7LV5qPd5TOa%2FQwFzNZYgc0n37dJwKMkb1T%2F1senx90Ja9S5MM4xXnga1cPqxKUBw0ol%2Baz7ulDZzGVcWx1poyLwVDOmcwGLAnecMijG8rsm%2BLKD1t1WEeUpfxUsXbqzUslqJOobtA2sfje00u8e%2Batm5AHtem1MaP4XjD5yazxzkgR4%2BW%2Bo0Tos3N31%2F5AIPZr0jXx4Y23xTUmW2C7e0jb5%2BruP4EEkrlYNQAKcCpwXJAosjyHL3RwUXBe12z89uvJT9cSSu2fVPLbwcdjYy8l8aVFuGXjc2Y%2FSJd%2BpZD9xew1OmCjv00M%2F839OymuuLcMjWOmq439pyrPxuk1s68kCATYh7YEB1I02dl7cchD1KkiXeyBWJMZHqwQ4E5rnIYF2NdZ%2BVtlBnjWRvkqWJ3%2BoayPn9wDu7d0RRhpt4WlXMh%2F1nS97%2BEpDFZKVfRaMGgZ4UTLQLxSPqmnkS0TB4IUN5EmGkqkO9fJeNaaa6%2BoP7Zs0%2BXvdiYw5vdl3T%2FZGNBuNleJcb5%2F5sJZ1uKqkeFGMMCQbuPHEB1fCmG%2Bg6C%2BpqK0qDilCn%2BHi%2FxfzFyXdaHd5Ysz8W2JRDqsQy3KKM58rrQS3nOz1TCVp3CPL1Ua4tgPUap7%2BtX3pwr2cCDreduEJe6P9dCaib%2FqkLTc8v12AVq1VQOYB7xgJgkTzaf9Qtfgx8gw5lANz7XKu7c87IDwfS8%2BL2w8UDUTWWzLRCuIhCwa4EaoUSiiMXE7X8Srf3aDF4p6xEn1A3OhpuK%2BMPE1TqpOoKOy45E%2BvItKTTb4Xe%2F6YDiRYNMWhIlS7tkNQ911Bvj%2BpH8DnxPr3SbPBPYU1qqPUdozc9%2F15zAfoIeK7RMINGGIS1E8%2FgARk%2FdfGbDHN2RPdmakz6J8zTELM%2BalzMCAx6lgOfrEp2Q8FFteLN8smm4EPjr%2FdRXp9rasrCEpl0dbqEoBp48bJ3b8dLWhflOHkXkYWftOeKsKRNMXP2tZrCF5M7XAm51CCAnS%2FBaRcS8OavHKy7bVfviEWVTixukKzC2WXzCG5mi1ZNjSLNjxWweSZsC%2BdlwITinzicpSr56WLoU18QuZj3IAo%2FCfVORhyU40biw8jcNgyMJthStgOj3fTN5bRgeE7OCQDOYuZX9jCUvueQKROuStQ6Fsa7KRL9j72SUir7RKQbKgS36HQdiawb2lNyz5elkvqqYKpbgndUz3s7fh%2Ffu9FW6g%2FBcPoBZuCrwMCsUERXqijmK8g9RKdEoy%2FZt617qjvfDdP417FGq5tsH5RFmIEw2QGMU5y3siX5ur5otPbJHZPnZe5X%2FDjQWG5JtgtOBezWCP1Fs9azqsMvypo6o1LYgI0N9OMczhs1sgteylgEvnesckCTHlpuJ7rox4cs2tmt%2FbiK4mcHFqLw%2F9CC3IisX7Epcpwxa7FBPxk1h2SvFtcxGEE7woajzmMZqVSBS5sJKtdPJz81ac7skmoEPjF%2FBw3wsuAD2YZOrGJEZpxdXCVGdCfy3pXLsWgYC3E7m5SG%2FDDxaSl9Gokx%2FDFXC%2BnsbVQLWq5SGNBMH4CgJjWn37cA9EfGaKTVz2ObmitM6yYVTH%2BfMviMDDeuZoj1ED%2BLWi2ZvX5rJ1gmp%2FLTiYkW0ganzGgUdAnq4Z9qxZ0LTmSoyViCvpA%2F8PqU%2F6Qt2ZbPN%2Bh%2Blc%2F5TA9L3Pj6KbiTkYCqjHb9OuD6es%2FnU06kyav7bs%2B%2BfD7TIAwjFRPgMLfEw%2F99%2BPmLs%2BUPluxyniAd2lMxyhsrTpJdk%2F53kosAfqRBOnK0LWB%2B0ME6ysnIb2l0q5Gl5tocRptJJ9hO7Q5upFy9fTqkrh%2FpyIqxaAfue1treBIHHrcXsBi6LINCAQ%2B8c%2F4aKotYjtqB%2FmeCvNuqPvRfCZ0CxFsnVBmXF5dIh5%2FcYUAANw1HYXjIftpvR2esvj1UjfWUShhtTUyblzm9kD%2BqjCGH9XZLQ91zawHoY%2Bpy5cvrONmsv98tZm5Kp5UMUVfan9onuypL0OLlJnESswiYNd2qLdvMvrfw6VFGC1NNS3NlrGGFWT%2F%2Fw6bhxLxMIiw%2Bcc2qW3Lk2ITTaguwVRybQn8V7WC%2FnbdUJ%2BFnLE5M%2BWSR1h13IWvxSKM3sXAFtZxz9JEMkyyPLYdVHL90D%2B3i%2FRxT0i5Y%2BKghFOJOM2UhSP5%2FiTFpUpTfZYNlq93dWGdkNqO2KlcdFjhw4vyOKwDSD0xWlheVJ4zGCfcpiEV1P%2BNdJxB1DzWsj94Afu3oS4I%2BQItCJTviOKfy2sH5PSRIgGgOnCITylU5v%2Fl5xTkwNDc6OEoq5Ri6HuC4ircbiW2qQY%2B28U455Z4pbUa6khlCGWD1sdOEiG8jspnfQWlkguspWZgEWiQ4h2vwa5ZlI9rHxKhnepxY608faO9grvRK9IEFxMvxJfd6%2Brn4RAObdUGhOEu5%2BnhuTcDAxibx7%2FDkNXUxNhtpOomVxZVp%2By4pM2ZGllB8ly1stqygQmtYKA69Z1MtK7DASsCIvs3tugEAnfg0GPYgDOkHaNPomubWxmFSUfKjrByX7t%2FT0AGHxuoxfbMQTyEJVWmt%2BUw4aG4krfiDRPQnKElYS3Kb5YLEuDEILkWkCV%2FkrxLYkj9MuPaLWwOc2RU4INpnhDL9PjbviK%2BqR145%2Bo93HElQHJ6BlkEj63AUicJChy9MKNJjeFpv%2FlxeNYqlMbMQa0Q8lEcqG%2BYtvfQW8s4L8b5NFLGSXJPO5L94OHnfehtC5FrpfE32wvvnEob7OPW9EGPq3DpCYe0uU6WfOeZmdNAhuElWnAaYCMuJjowj%2Bjp8Clpu5R94pf1HYhhwikQvJg%3D%3D&&__EVENTARGUMENT=" + pageNo; conn.getOutputStream().write(body.getBytes()); byte[] buff = new byte[62768]; int count; ByteArrayOutputStream out = new ByteArrayOutputStream(62768); InputStream in = conn.getInputStream(); while((count = in.read(buff)) != -1) { out.write(buff, 0, count); } conn.disconnect(); return out.toString("UTF-8"); } 是不是body有问题: Exception in thread "main" java.io.FileNotFoundException: http://ggzy.jiangxi.gov.cn/jxzbw/EpointSecurityError.aspx?ErrorType=CSRF&ErrorMsg=Cookie%ef%bc%9aNo+CSRF+cookie+supplied.URL%ef%bc%9a%2fjxzbw%2fjyxx%2f002004%2f002004001%2fMoreInfo.aspx%3fCategoryNum%3d002004001 at sun.net.www.protocol.http.HttpURLConnection.getInputStream0(HttpURLConnection.java:1835) at sun.net.www.protocol.http.HttpURLConnection.getInputStream(HttpURLConnection.java:1440) at zfcg.ztb.JxztbActivity.getListData(JxztbActivity.java:127) at zfcg.ztb.JxztbActivity.ListData(JxztbActivity.java:68) at zfcg.ztb.JxztbActivity.main(JxztbActivity.java:30)

Matlab的simulink如何使用写好的C语言?

我按照教程,将C语言程序包装成了.mexw文件,但接下来不知道如何使用,在simulink中创建S-funtion组件之后,不会进一步设置。下面是C语言程序(多元线性回归,读取.csv文件中的表格,输入x,y。在所输入的最近区间求z=(x,y)的回归)求大佬教如何使用 ``` #include<stdio.h> #include"math.h" #include <stdlib.h> #include<string.h> void FreeData(double **dat, double *d, int count) { int i, j; free(d); for(i = 0;i < count; i ++) free(dat[i]); free(dat); } //解线性方程。data[count*[count+1])矩阵数组;count:方程元数; //Answer[count]:求解数组。返回0,求解成功。-1无解或无穷解; int LinearEquations(double *data,int count,double *Answer) { int j ,m ,n; double tmp, **dat, *d=data; dat = (double**)malloc(count * sizeof(double*)); for (m=0;m<count;m++,d+=(count +1)) { dat[m] = (double*)malloc((count+1) * sizeof(double)); memcpy(dat[m],d, (count+1) * sizeof(double)); } d = (double*)malloc((count + 1) * sizeof(double)); for(m = 0; m < count - 1;m ++) { //如果主对角线元素为0,行交换; for(n = m + 1;n < count && dat[m][m] == 0.0;n ++) { if( dat[n][m] != 0.0) { memcpy(d, dat[m], (count + 1)*sizeof(double)); memcpy(dat[m], dat[n], (count + 1) * sizeof(double)); memcpy(dat[n], d, (count + 1) * sizeof(double)); } } //行交换后,主对角线元素仍然为0,无解,返回-1; if ( dat[m][m] == 0.0) { FreeData(dat, d, count); return -1; } //消元 for(n = m + 1; n < count; n++) { tmp= dat[n][m] / dat[m][m]; for(j=m; j <= count; j++) dat[n][j] -= tmp * dat[m][j]; } } for(j=0; j<count; j++) d[j] = 0.0; //求得count - 1 的元 Answer[count - 1]= dat[count - 1][count] / dat[count - 1][count - 1]; //逐行代入求各元 for (m = count - 2;m >= 0; m --) { for(j=count-1; j>m ; j--) d[m] += Answer[j] * dat[m][j]; Answer[m] = (dat[m][count]-d[m]) / dat[m][m]; } FreeData(dat, d, count); return 0; } //求多元 回归方程:Y=B0+B1X1+B2X2+......+BnXn //data[rows*cols]二维数组:X1i,X2i......Xni,Yi(i=0 to rows-1) //rows:数据行数;cols数据列表;Answer[cols]:返回回归系数数组(B0,B1......Bn) //SquarePoor[4]:返回方差分析指标:回归平方和,剩余平方和,回归平方差,剩余平方差 //返回值:0求解成功,-1错误; int MultipleRegression(double *data, int rows, int cols, double *Answer, double *SquarePoor) { int m, n, i, count = cols - 1; double *dat, *p, a, b; if(data == 0 || Answer == 0 || rows<2 || cols<2) return -1; dat = (double*)malloc(cols * (cols + 1) * sizeof(double)); dat[0] = (double)rows; for(n=0;n<count;n++) //n=0 to cols-2 { a = b = 0.0; for(p = data + n, m = 0; m < rows; m ++, p += cols) { a += *p; b += (*p * *p); } dat[n + 1] = a; //dat[0,n+1]=Sum(Xn) dat[(n + 1) * (cols + 1)] = a; //dat[n+1,0]=Sum(Xn) dat[(n + 1) * (cols + 1) + n + 1] = b; //dat[n+1,n+1]=Sum(Xn*Xn) for(i = n + 1; i < count; i++) //i=n+1 to cols-2 { for(a = 0.0, p = data, m = 0; m < rows; m ++, p += cols) a += (p[n] * p[i]); dat[(n+1) * (cols + 1) + i + 1] = a; //dat[n+1,i+1]=Sum(Xn*Xi) dat[(i+1) * (cols + 1) + n + 1] = a; //dat[i+1,n+1]=Sum(Xn*Xi) } } for(b = 0.0, m = 0, p = data + n; m < rows; m++, p += cols) b += *p; dat[cols]= b; //dat[0,cols]=Sum(Y) for(n = 0;n < count; n++) { for(a = 0.0,p = data, m = 0; m < rows; m ++,p += cols) a += (p[n] * p[count]); dat[(n+1) * (cols + 1) + cols] = a; //dat[n+1,cols]=Sum(Xn*Y) } n=LinearEquations(dat, cols, Answer); //计算方程式 //方差分析 if(n == 0 && SquarePoor) { b = b / rows; //b=Y的平均值 SquarePoor[0] = SquarePoor[1] = 0.0; p = data; for(m = 0; m < rows; m ++, p ++) { for( i=1, a = Answer[0]; i < cols;i ++,p ++) a += (*p * Answer[i]); //a=Ym的估计值 SquarePoor[0] += ((a - b) * (a - b)); //U(回归平方和) SquarePoor[1] += ((*p - a)*(*p - a)); //Q(剩余平方和)(*p=Ym) } SquarePoor[2] = SquarePoor[0] / count; //回归方差 if(rows - cols > 0.0) SquarePoor[3] = SquarePoor[1] / (rows - cols);//剩余方差 else SquarePoor[3] = 0.0; } free(dat); return n; } //输出回归方程,并输出误差估计 void Display(double *dat, double *Answer, double *SquarePoor, int rows, int cols) { double v, *p; int i, j; char ch='X'; printf("回归方程式: Z= %.5lf", Answer[0]); for(i=1; i<cols;i++) printf("+%.5lf*%c",Answer[i], ch+i-1); printf(" \n"); printf("回归显著性检验:"); printf("回归平方和: %12.4lf \n 回归方差:%12.4lf\n", SquarePoor[0], SquarePoor[2]); printf("剩余平方和:%12.4lf \n 剩余方差:%12.4lf\n", SquarePoor[1], SquarePoor[3]); printf("离差平方和:%12.4lf \n 标准误差:%12.4lf\n", SquarePoor[1], SquarePoor[3]); printf("离差平方和:%12.4lf \n 标准误差:%12.4lf\n", SquarePoor[0] + SquarePoor[1], sqrt(SquarePoor[3])); printf("F 检 验 : %12.4lf \n 相关系数: %12.4lf\n" ,SquarePoor[2] / SquarePoor[3], sqrt(SquarePoor[0] / (SquarePoor[0] + SquarePoor[1]))); printf("剩余分析: \n"); printf(" 观察值 估计值 剩余值 剩余平方 \n"); for(i = 0, p = dat; i < rows; i ++, p ++) { v= Answer[0]; for(j = 1; j < cols; j ++, p ++) v += *p * Answer[j]; printf("%12.2lf%12.2lf%12.2lf%12.2lf\n", *p, v, *p - v, (*p - v) * (*p - v)); } system("pause"); } //主程序 int main() { double data[4][3];//定义矩阵,4列3行,4列为临近的四个点,3行为X,Y,Z; FILE *fp = fopen("C://BK.csv", "r");//打开文件(对应的文件名和路径) if (fp == NULL) //如果文件打开失败则结束 { printf("file open error\n"); return -1; } //定义Y的数组,Y[0]为Y的值,Y[1-1000]为该Y对应的Z值 double A[1000]; double B[1000]; double C[1000]; double D[1000]; double E[1000]; double F[1000]; double G[1000]; double H[1000]; double I[1000]; double J[1000]; double K[1000]; //运用循环语句,将文件中的数字矩阵存入到数组 for (int i = 0;i<255; i++) { fscanf(fp, "%lf,%lf,%lf,%lf,%lf,%lf,%lf,%lf,%lf,%lf,%lf", &A[i], &B[i], &C[i], &D[i], &E[i], &F[i], &G[i], &H[i], &I[i], &J[i], &K[i]); } double x,y,z; scanf("%lf%lf",&x,&y); //输入已知的x,y double X1,X2,Y1,Y2,Z1,Z2,Z3,Z4; int j; for(j=0;j<255;j++) { if(A[j]<x && x<A[j+1]) //判断x处于表格的哪个X值区间 { //判断y处于表格的哪个Y值区间,并将锁定位置最近的四个数据记为(X1,Y1,Z1)(X2,Y1,Z2)(X1,Y2,Z3)(X2,Y2,Z4) if(B[0]<y && y<C[0]) { X1=A[j]; X2=A[j+1]; Y1=B[0]; Y2=C[0]; Z1=B[j]; Z2=B[j+1]; Z3=C[j]; Z4=C[j+1]; } else if(C[0]<y && y<D[0]) { X1=A[j]; X2=A[j+1]; Y1=C[0]; Y2=D[0]; Z1=C[j]; Z2=C[j+1]; Z3=D[j]; Z4=D[j+1]; } else if(D[0]<y && y<E[0]) { X1=A[j]; X2=A[j+1]; Y1=D[0]; Y2=E[0]; Z1=D[j]; Z2=D[j+1]; Z3=E[j]; Z4=E[j+1]; } else if(E[0]<y && y<F[0]) { X1=A[j]; X2=A[j+1]; Y1=E[0]; Y2=F[0]; Z1=E[j]; Z2=E[j+1]; Z3=F[j]; Z4=F[j+1]; } else if(F[0]<y && y<G[0]) { X1=A[j]; X2=A[j+1]; Y1=F[0]; Y2=G[0]; Z1=F[j]; Z2=F[j+1]; Z3=G[j]; Z4=G[j+1]; } else if(G[0]<y && y<H[0]) { X1=A[j]; X2=A[j+1]; Y1=G[0]; Y2=H[0]; Z1=G[j]; Z2=G[j+1]; Z3=H[j]; Z4=H[j+1]; } else if(H[0]<y && y<I[0]) { X1=A[j]; X2=A[j+1]; Y1=H[0]; Y2=I[0]; Z1=H[j]; Z2=H[j+1]; Z3=I[j]; Z4=I[j+1]; } else if(I[0]<y && y<J[0]) { X1=A[j]; X2=A[j+1]; Y1=I[0]; Y2=J[0]; Z1=I[j]; Z2=I[j+1]; Z3=J[j]; Z4=J[j+1]; } else if(J[0]<y && y<K[0]) { X1=A[j]; X2=A[j+1]; Y1=J[0]; Y2=K[0]; Z1=J[j]; Z2=J[j+1]; Z3=K[j]; Z4=K[j+1]; } } } fclose(fp); //结束文件读取 system("pause"); //关闭文件 //将(X1,Y1,Z1)(X2,Y1,Z2)(X1,Y2,Z3)(X2,Y2,Z4),输入到data矩阵 data[0][0]=X1; data[0][1]=Y1; data[0][2]=Z1; data[1][0]=X2; data[1][1]=Y1; data[1][2]=Z2; data[2][0]=X1; data[2][1]=Y2; data[2][2]=Z3; data[3][0]=X2; data[3][1]=Y2; data[3][2]=Z4; //若符合矩阵格式,则进行矩阵的多元线性回归方程运算,求得Answer[0](常数),Answer[1](x的k值),Answer[2](y的k值); double Answer[5],SquarePoor[4]; if(MultipleRegression((double*)data,4,3,Answer,SquarePoor)==0) Display((double*)data, Answer, SquarePoor, 4, 3); z=Answer[0]+x*Answer[1]+y*Answer[2]; //将x,y代入到求出的回归方程 printf("Z=%.5lf",z); //输出z的值 return 0;//结束 } ```

Fast Image Match

Given two images A and B, use image B to cover image A. Where would we put B on A, so that the overlapping part of A and B has the most likelihood? To simplify the problem, we assume that A and B only contain numbers between 0 and 255. The difference between A and B is defined as the square sum of the differences of corresponding elements in the overlapped parts of A and B. For example, we have A (3 * 3): a1 a2 a3 B (2 * 2): b1 b2 a4 a5 a6 b4 b5 a7 a8 a9 When B is placed on position a5, the difference of them is ((b1-a5)^2 + (b2-a6)^2 + (b4-a8)^2 + (b5-a9)^2). Now we hope to have the position of the top left corner of B that gives the minimum difference. (B must completely reside on A) It is clear that a simple solution will appear with very low efficiency when A and B have too many elements. But we can use 1-dimensional repeat convolution, which can be computed by Fast Fourier Transform (FFT), to improve the performance. A program with explanation of FFT is given below: /** * Given two sequences {a1, a2, a3.. an} and {b1, b2, b3... bn}, * their repeat convolution means: * r1 = a1*b1 + a2*b2 + a3*b3 + ... + an*bn * r2 = a1*bn + a2*b1 + a3*b2 + ... + an*bn-1 * r3 = a1*bn-1 + a2*bn + a3*b1 + ... + an*bn-2 * ... * rn = a1*b2 + a2*b3 + a3*b4 + ... + an-1*bn + an*b1 * Notice n >= 2 and n must be power of 2. */ #include <vector> #include <complex> #include <cmath> #define for if (0); else for using namespace std; const int MaxFastBits = 16; int **gFFTBitTable = 0; int NumberOfBitsNeeded(int PowerOfTwo) { for (int i = 0;; ++i) { if (PowerOfTwo & (1 << i)) { return i; } } } int ReverseBits(int index, int NumBits) { int ret = 0; for (int i = 0; i < NumBits; ++i, index >>= 1) { ret = (ret << 1) | (index & 1); } return ret; } void InitFFT() { gFFTBitTable = new int *[MaxFastBits]; for (int i = 1, length = 2; i <= MaxFastBits; ++i, length <<= 1) { gFFTBitTable[i - 1] = new int[length]; for (int j = 0; j < length; ++j) { gFFTBitTable[i - 1][j] = ReverseBits(j, i); } } } inline int FastReverseBits(int i, int NumBits) { return NumBits <= MaxFastBits ? gFFTBitTable[NumBits - 1][i] : ReverseBits(i, NumBits); } void FFT(bool InverseTransform, vector<complex<double> >& In, vector<complex<double> >& Out) { if (!gFFTBitTable) { InitFFT(); } // simultaneous data copy and bit-reversal ordering into outputs int NumSamples = In.size(); int NumBits = NumberOfBitsNeeded(NumSamples); for (int i = 0; i < NumSamples; ++i) { Out[FastReverseBits(i, NumBits)] = In[i]; } // the FFT process double angle_numerator = acos(-1.) * (InverseTransform ? -2 : 2); for (int BlockEnd = 1, BlockSize = 2; BlockSize <= NumSamples; BlockSize <<= 1) { double delta_angle = angle_numerator / BlockSize; double sin1 = sin(-delta_angle); double cos1 = cos(-delta_angle); double sin2 = sin(-delta_angle * 2); double cos2 = cos(-delta_angle * 2); for (int i = 0; i < NumSamples; i += BlockSize) { complex<double> a1(cos1, sin1), a2(cos2, sin2); for (int j = i, n = 0; n < BlockEnd; ++j, ++n) { complex<double> a0(2 * cos1 * a1.real() - a2.real(), 2 * cos1 * a1.imag() - a2.imag()); a2 = a1; a1 = a0; complex<double> a = a0 * Out[j + BlockEnd]; Out[j + BlockEnd] = Out[j] - a; Out[j] += a; } } BlockEnd = BlockSize; } // normalize if inverse transform if (InverseTransform) { for (int i = 0; i < NumSamples; ++i) { Out[i] /= NumSamples; } } } vector<double> convolution(vector<double> a, vector<double> b) { int n = a.size(); vector<complex<double> > s(n), d1(n), d2(n), y(n); for (int i = 0; i < n; ++i) { s[i] = complex<double>(a[i], 0); } FFT(false, s, d1); s[0] = complex<double>(b[0], 0); for (int i = 1; i < n; ++i) { s[i] = complex<double>(b[n - i], 0); } FFT(false, s, d2); for (int i = 0; i < n; ++i) { y[i] = d1[i] * d2[i]; } FFT(true, y, s); vector<double> ret(n); for (int i = 0; i < n; ++i) { ret[i] = s[i].real(); } return ret; } int main() { double a[4] = {1, 2, 3, 4}, b[4] = {1, 2, 3, 4}; vector<double> r = convolution(vector<double>(a, a + 4), vector<double>(b, b + 4)); // r[0] = 30 (1*1 + 2*2 + 3*3 + 4*4) // r[1] = 24 (1*4 + 2*1 + 3*2 + 4*3) // r[2] = 22 (1*3 + 2*4 + 3*1 + 4*2) // r[3] = 24 (1*2 + 2*3 + 3*4 + 4*1) return 0; } Input The first line contains n (1 <= n <= 10), the number of test cases. For each test case, the first line contains four integers m, n, p and q, where A is a matrix of m * n, B is a matrix of p * q (2 <= m, n, p, q <= 500, m >= p, n >= q). The following m lines are the elements of A and p lines are the elements of B. Output For each case, print the position that gives the minimum difference (the top left corner of A is (1, 1)). You can assume that each test case has a unique solution. Sample Input 2 2 2 2 2 1 2 3 4 2 3 1 4 3 3 2 2 0 5 5 0 5 5 0 0 0 5 5 5 5 Sample Output 1 1 1 2

等差数列的问题,采用C 语言如何才能进行求解呢??

Problem Description A sequence b1,b2,⋯,bn are called (d1,d2)-arithmetic sequence if and only if there exist i(1≤i≤n) such that for every j(1≤j<i),bj+1=bj+d1 and for every j(i≤j<n),bj+1=bj+d2. Teacher Mai has a sequence a1,a2,⋯,an. He wants to know how many intervals [l,r](1≤l≤r≤n) there are that al,al+1,⋯,ar are (d1,d2)-arithmetic sequence. Input There are multiple test cases. For each test case, the first line contains three numbers n,d1,d2(1≤n≤105,|d1|,|d2|≤1000), the next line contains n integers a1,a2,⋯,an(|ai|≤109). Output For each test case, print the answer. Sample Input 5 2 -2 0 2 0 -2 0 5 2 3 2 3 3 3 3 Sample Output 12 5

求解任意两个点的距离的问题,用C语言程序的设计的方式来做怎么做呢

Problem Description PM Room defines a sequence A = {A1, A2,..., AN}, each of which is either 0 or 1. In order to beat him, programmer Moor has to construct another sequence B = {B1, B2,... , BN} of the same length, which satisfies that: Input The input consists of multiple test cases. The number of test cases T(T<=100) occurs in the first line of input. For each test case: The first line contains a single integer N (1<=N<=100000), which denotes the length of A and B. The second line consists of N integers, where the ith denotes Ai. Output Output the minimal f (A, B) when B is optimal and round it to 6 decimals. Sample Input 4 9 1 1 1 1 1 0 0 1 1 9 1 1 0 0 1 1 1 1 1 4 0 0 1 1 4 0 1 1 1 Sample Output 1.428571 1.000000 0.000000 0.000000

用字母a-z表示每个函数图像上的的所有点,怎么利用C语言的程序编写的代码的方式来输出的

Problem Description 在数学中,我们经常会遇到,关于函数的问题,在画一些函数的图像的时候,最长用的方法就是“描点法”。 “描点法” 的具体步骤如下: > 计算出函数在某些特定点的值 > 在坐标系中标记出这些点 > 用平滑的曲线连接这些点 但是,在实际的操作中,我们会发现,前两部的计算量还是相当大的,所以,我们想编写一个程序,能够在一个坐标系中直接的画出各点。 为了简化这个问题,给出如下y 关于x 的函数表达式 y=a1x^b1+a2x^b2+a3x^b3+...+anx^bn 表达式不超过5项,并且 其中每项的系数 -10 < a <10 , x的指数 0 <= b < 5 表达式中,所有的字符串都是以 ”y=” 开始的 ,在之后的字符串中只含有x ,+ , - , 0~9 这些字符,不含有空格。无非法表达式输入。 特别的: 当x 的指数为1时,省略指数, 例如: y = 2x^1 应表示为 y = 2x 当x 的指数为0时,省略指数和x, 例如: y = 3x^3+2x^0 应表示为 y = 3x^3+2 当x 的系数为负时, 例如: y = 3x^2 + (-1) x 应表示为 y = 3x^2 – x , y = 2x + (-2) 应表示为 y = 2x - 2 在如下坐标系中画出,x属于[-30,30] 所对应y属于[-30,30]的图像。 Input 多组数据输入,每组数据的第一行给出一个n (1<=n <= 26) ,接下来的n行,每行有一个函数的表达式。 Output 对于每组输入数据,在第一行输出,”Case:#” ,# 代表当前的组号。 画出该函数的图像 x取值[-30,30]时, y在 [-30,30]内的点 。对于给出的n个表达式,依次用字母a-z表示每个函数图像上的的所有点。两个图像的交点 或者 图像与坐标轴的交点 用 ‘.’ 表示。输出格式如下所示。各组之间无空行。 Sample Input 2 y=-x-1 y=x^4+1-x^3 Sample Output Case:1 y^ | a | a | a | a | a b | a | a | a | a | a | a | a | a | a | a | a | a | a | a | a | a | b a | a | a | a | a | a b| a | a .b -----------------------------.+------------------------------> . x |a | a | a | a | a | a | a | a | a | a | a | a | a | a | a | a | a | a | a | a | a | a | a | a | a | a | a | a | a

数列的翻转表的一个算法的问题的求解,用C语言的程序编写怎么实现的啊

Problem Description Let { A1,A2,...,An } be a permutation of the set{ 1,2,..., n}. If i < j and Ai > Aj then the pair (Ai,Aj) is called an "inversion" of the permutation. For example, the permutation {3, 1, 4, 2} has three inversions: (3,1), (3,2) and (4,2). The inversion table B1,B2,...,Bn of the permutation { A1,A2,...,An } is obtained by letting Bj be the number of elements to the left of j that are greater than j. (In other words, Bj is the number of inversions whose second component is j.) For example, the permutation: { 5,9,1,8,2,6,4,7,3 } has the inversion table 2 3 6 4 0 2 2 1 0 since there are 2 numbers, 5 and 9, to the left of 1; 3 numbers, 5, 9 and 8, to the left of 2; etc. Perhaps the most important fact about inversions is Marshall Hall's observation that an inversion table uniquely determines the corresponding permutation. So your task is to convert a permutation to its inversion table, or vise versa, to convert from an inversion table to the corresponding permutation. Input The input consists of several test cases. Each test case contains two lines. The first line contains a single integer N ( 1 <= N <= 50) which indicates the number of elements in the permutation/invertion table. The second line begins with a single charactor either 'P', meaning that the next N integers form a permutation, or 'I', meaning that the next N integers form an inversion table. Output For each case of the input output a line of intergers, seperated by a single space (no space at the end of the line). If the input is a permutation, your output will be the corresponding inversion table; if the input is an inversion table, your output will be the corresponding permutation. Sample Input 9 P 5 9 1 8 2 6 4 7 3 9 I 2 3 6 4 0 2 2 1 0 0 Sample Output 2 3 6 4 0 2 2 1 0 5 9 1 8 2 6 4 7 3

在区间之内寻找整数的算法问题,怎么利用C语言的程序的设计的办法实现本问题

Problem Description people in USSS love math very much, and there is a famous math problem . give you two integers n,a,you are required to find 2 integers b,c such that an+bn=cn. Input one line contains one integer T;(1≤T≤1000000) next T lines contains two integers n,a;(0≤n≤1000,000,000,3≤a≤40000) Output print two integers b,c if b,c exits;(1≤b,c≤1000,000,000); else print two integers -1 -1 instead. Sample Input 1 2 3 Sample Output 4 5

A poor officer 进行求解

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