转眼之间，又到了年终故事会时间。 回顾这一年，我通过学院学习了很多在线课程，特别是热门技术中上新了很多吸引力极强的内容, 对于我所从事的日常工作，是非常有帮助的。通过论坛，了解社区动态，很多新的活动、新的信息发布，都是在这里了解的，比如FR11内测活动。通过认证，我通过了认证考试。通过招聘，我了解了数据分析岗位的具体要求，需要掌握哪些技能，才能把BI工作做好......对于2021年，我最想总结的是任务板块。 一、缘起 刚开始，我只知道有社区，但是并不知道有任务。更可怕的是我只知道有F豆，而不知道有F币的存在。 一次偶然的机会，社区开展了一次线上活动，关于FineBI的入门学习。对于这个BI，我一直处于摸索阶段，很踊跃的参与了网上直播。老师讲的很仔细，我听得很入迷。通过老师介绍，课后可以领取相应的学习任务， 我第一眼看到300F豆的奖励，便疯狂的参与了任务，想想大转盘，最多就是100豆，这个可容易多了。对于一个初学者，说容易，不是说实现出来很简单，而是有图有真相，学院资源可以参考：https://bbs.fanruan.com/course-168.html学习。还不会，可以参考帮助文档https://help.fanruan.com/finebi/doc-view-1000.html。遇到不会的操作，可以在微信群互相交流，完全可以自助实现任务中的要求。 说容易，其实也不容易，初次接触FineBI，完成这个入门任务，也花了很多时间。作品提交PDF如下： 最终，获取F豆奖励，很是开心。请忍住别笑，我认为这是很大一笔收入，因为大转盘转了好多天都没有100豆。 二、深入了解 通过这次任务，不仅学习了FineBI的入门操作，还了解了社区里面可以做任务，获取奖励，而F豆是最基础的。从此我经常浏览任务板块。首先，要了解任务性质： 分类中包括文档、功能体验、二开、模板、学习、其他类别，每次根据自己的熟悉程度选择。然后，明确任务的难度，非常容易、容易、有点难、困难，遇到我不熟悉，而且难度极大的，我是不敢领取的。为了通过认证，2021年，我对于认证相关的任务一个都不会放过。特别值得一提的是认证考试的任务： 题目虽然总共5道题，但是我整整花了一周时间，才交卷。要想通过FCRP认证，需要更多的付出。 当前，正在开展测试用例任务。因为我现在知道社区的任务不仅仅能提高自己的操作能力，更能获得相应的F币。但是，番薯是个庞大的集体，您看昨天还有几十个账户，今天一开电脑就提示： 想赚F币还是很难的。不仅要对FR测试版本的操作相对熟练，而且要付出大量时间。没有哪项任务能随随便便成功。 这不，在这一年里，因为对手机端社区APP了解的不深入，发生了一些小插曲。 由于任务级别为容易，所以很快通过手机端领取了任务，等我提交的时候，发现无法通过APP提交任务。但是电脑不在身边，以至于扣掉了信用分。对于我这个有强迫症的人来说，可以接受扣分的处罚，但是不能接受信用分里面有超时扣分的记录。不知居委会能否帮处理一下。建议修改APP中的任务领取逻辑，如果能通过APP领取，就能够通过APP提交；或者不能通过APP领取。很不愿意看到信用分是个负数，莫名的凄凉...... 三、终章 现在，我对任务是又爱又恨，爱的是通过任务，可以针对性的检验自己的学习成果，而不是看了忘了，学了没效果；恨的是有的任务难度大，时间不够的话就没法如期提交。但是，不论怎样，我都会认真领取任务，认真完成任务。毕竟提交了还有F币在等着你呀。。 最后，提2点小小的建议： 关于认证相关的任务，能否分专题发放任务。比如针对FCRP认证，按照考点分类发放任务，且多多益善； 目前任务分类很全面了。2021年结束了，有没有一个看板，可以看到自己这一年完成任务的总体情况。通过年度任务看板，了解自己掌握软件的程度，特别是没有完成，甚至是没有领取的，后面要针对性的加强。 2022年会有哪些任务呢？我很期待！

1.本次测评体验功能点 功能点名称 【基础】B/S端修改布局&新自适应效果 体验成果 当没有处于新自适应模式下： 决策报表预览方式只有2种，工具栏显示“转换至新版”，预览后，也无法修改布局。   当单击“转换之新版”，模板处于新自适应模式，     工具栏显示“转换至旧版”，预览方式新增了“开发者调试”，单击预览： 通过鼠标拖动，调整组件布局。 保存后，从设计器打开模板，发现布局已调整。 调整浏览器窗口大小： 2.产品体验心得 优点： 1.该功能点有相应的帮助文档。 2.新自适应模式切换后，有保存旧模板。 3.之前必须对模板进行修改，通过预览才能验证修改是否合理。现在是通过预览结果，来修改模板布局。真的是所见即所得。 4.调整浏览器窗口大小，自适应效果存在。 通过操作，提出2点建议： 1.模板设计过程中，body的布局方式必须是绝对布局。   如果是自适应布局，很多功能点无效，可能只能调整组件的大小。 2.一旦切换成自适应布局，组件位置又发生了变化。需要继续采用开发者调试调整。   3.结尾 以上第2点，是否可以通过开发者调试一次之后保存，就能保证自适应布局下，也是所见即所得。

1.本次测评体验功能点 功能点名称 【基础】报表新前端和控件样式 体验成果 1.经典样式：     预览   2.FR11：     预览   2.产品体验心得 优点： 1.该功能点有相应的帮助文档。 2.通过操作，发现控件更加的简介、美观，功能更加全面，例如日期控件：   3.结尾 希望有帮助文档，讲解控件的后台结构。通过学习，可以自己定制开发个性化控件。一方面是二开的控件，另一方面，能否通过现有的控件，通过少量的开发，实现新的功能。比如：选择日期的时候，起始时间只能选择星期一，终止时间只能选择星期五。

1.本次测评体验功能点 功能点名称 【基础】自定义模板主题&组件复用 体验成果 1.自定义主题   预览：   2.组件复用     预览效果： 没有设置跟随主题   设置跟随主题   2.产品体验心得 优点： 1.该功能点有相应的帮助文档。 2.对于模板主题，操作很简便。有两种途径：服务器-模板主题管理。新增、删除操作直观。 3.跟随主题样式，设置模板背景、样式，更加的高效。 通过操作，提出1点建议： 自定义主题后，选择模板，我希望对设计的内容添加主题，结果除了内容外，模板的其余行、列都应用了主题。 3.结尾 对于普通报表，如果设置了样式跟随主题，组件的样式和单元格样式，是否重复了？

一、选手简介1、选手介绍帆软社区用户名：chenliang。职业简介：目前就职于物流行业，主要从事信息系统开发、运维工作。2、参赛初衷论坛里面有很多精彩的数据分析案例，涵盖很多行业。自己希望通过这次比赛，进一步掌握fineBI的使用，主要是掌握常用的图表使用方法和步骤。同时，进一步理解商业智能需要解决的问题，如何通过信息化工具，完整的、专业的得到我们想要的产出。也希望和更多FineBI大神交流学习，进行数据可视化分析，让数据成为生产力。     二、作品介绍1、业务背景/需求痛点近段时间，正是高考结束，等待分数查询的时间。一旦分数放榜，广大考生就要进行志愿填报。填报志愿需要了解哪些内容呢？国家分数线、学校分数线，除了分数，考生也应该多参考志愿学校的招生数据。这些数据包括学校的整体情况、本年度招生计划。从录取方面来看，有必要对学校的情况以及招生计划做信息公开。从填报方面来看，有必要了解志愿学校的整体情况，包括招生、学习、就业各方面。本作品通过浙江大学招生计划近几年数据分析，给广大考生做一个招生概况分析，为即将填报志愿的过程提供数据支撑。该选题是浙江大学的招生计划数据分析，只要替换数据源，可以通过简单的设置，进行复用。如果有其他的数据指标，也可以在此基础上进一步扩展仪表板。2、数据来源   八爪鱼采集器提供参考数据；   为了更好展示浙大的整体情况，参考2021高考志愿填报服务平台网站https://gkcx.eol.cn/school/114/provinceline数据。3、分析思路   根据志愿填报服务平台的介绍，对学校的软件、硬件情况进行整体的说明；   根据参考数据内容，重点分析招生计划人数。主要从招生区域和招生专业两个维度进行数据分析。4、数据处理（4张Excel表+1自助数据集）   4.1 全国招生计划表（八爪鱼提供的参考数据）：                  4.2 学校概况           4.3.学校关键词           4.4 毕业生就业去向。其中经度、维度通过网站查询获得。           4.5 根据浙江省招生计划表，进行数据清洗，得到自助数据集。主要包括：选字段，例如组、自定义字段、当前时间、选科要求等字段，不参与数据分析，对这些字段进行过滤。               新增列，表中的专业字段，不仅文字内容很长，而且不利于分类统计，我们对专业名称字段按照括号的位置进行截取。       if(find("（",专业名称),left(专业名称,find("（",专业名称)-1),专业名称)       新增列，表中的时间字段只有“年”，确实我们可以将这个字段通过转换，指标转换为维度，但是转换成维度之后，数据自动进行了分组，         设计人员无法识别分组对应的是哪一年。因此进行简单的字符转换。               过滤，对于科目这一列，发现有3行数据是“科类”，且对应招生计划为空，因此需要将这行数据过滤掉。        5、可视化报告   第一部分是对学校概况的分析。主要包括以下几个部分：   学习指数、生活指数、就业指数、综合评分的仪表盘，通过这些指数卡数据，可以大致了解学校的整体概况。        教学点，例如博士点、硕士点、国家重点学科、实验室。        学校概况，采用词云图，进行简要说明。       就业生就业去向，采用流向地图。从图中可以看出毕业生主要流向浙江、北京、上海、广东、江苏。        第二部分是对招生人数进行统计，数据源包括近4年数据，均采用柱形图，统计全国各省招生人数。因为进行了降序排列，很直观的看到，本省考生招生人数最多，几乎占总数一半。              然后，利用指标卡，统计近4年来招生总人数，除了2019年，都是逐年递增。这个增长趋势和上面的全国各省招生计划增长趋势大体是一致的。       按照专业划分，首先按照文科、理科、综合大类划分，       然后对专业细分，采用漏斗图，因为专业类别较多，只统计招生计划前10的专业大类。除了综合性学科实验班，一些高新技术专业增加了计划数，为这方面的考生提供了更多的机会。           最终结果呈现的页面布局。                                    三、参赛总结数据分析，首先要明确需求是什么，要有针对性的分析。有了目标之后，才能针对性的找到原始数据，才能找准数据维度和数据指标。也只有围绕需求，才能产出需要的结果。    克服的困难：一方面是数据的清洗。看似简单，实际上需要对分析的数据有全面的认识和专业的了解，本次数据的选取可能还有不全面不科学的地方，也请各位大神批评指正。    另一方面是图表的选择。不能为了做图表而选择图表，应该是围绕我们的分析内容，选择最合适的图表作为基础，紧紧围绕需求，不能生拉硬拽。同时，遇到不熟悉的图表，要多参考帮助文档，更多练习。    感谢帆软提供这次比赛机会，可以让自己通过实操，不仅仅学会分析工具的使用，同时提高数据分析的思维能力。最后，借此机会，祝愿所有老师桃李满天下！所有考生都能金榜题名！浙江大学招生计划.pdf (2.2 M)

我中意悟帆新品笔记本，想要免费拿，请你来助力！ 请回帖评论“顶你”，助我免费拿周边！ 编辑于 2021-4-14 09:09

选手介绍企业名称：*****有限责任公司选手简介：陈亮、信息系统管理员，从事公司信息化建设方面的工作。 业务背景由于公司会议室数量不多，同一天之类，可能出现多个部门需要协调会议室的情况。为了日常工作更好的交流，IT部门主动设计会议签到表单，由IT部门统一调度会议室的分配；通过会议签到表单，对参会人员的到会情况进行实时统计，实现会议签到的无纸化管理。 待会议签到表单运行稳定后，进一步扩展到会议室申请，会议通知下发，会议日志等功能的全流程管理。 使用对象 会议签到的发起部门，如IT部门。 参会人员，可能是某一个部门，也可能是多个部门的人员。 设计思路 针对会议签到场景，设计必须的字段，包括会议名称、开会时间，参会人员信息等。 为了交互性更好，对会议名称通过下拉框选择，本来需要默认会议名称和时间，但是一天内可能有多个会议，因此设计成可编辑。参会人员单位和姓名同理。 签到时间需要采用操作时的系统时间，因此设计成不可编辑。 布局排版、颜色搭配，尽可能美观。 作品价值 使用表单前，会议室申请需要人工处理，经常多个部门重复申请。 会议签到表单发布后，签到工作实现无纸化，参会人员只需要通过手机扫码哦，填报少量信息，就可以实现签到。 根据表单权限，管理员可实时查询、发布签到情况。 便于会议室管理全流程的功能扩展，IT人员只需要考虑表单怎么设计，数据的管理交给简道云。 作品截图： 1.PC端： 142956 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UIECBAgAABAgQIECBAgEAv/MgBVx699dhj6+Pqq78Xb3/Xsb3RWzmsyutTzjq791L4vK+8nlKYNU6QVQ610vbS088qgq0bnvlMnPuJc4pgK43YOvr4M+INBx4Wr3nd/pFeJr+QRm2l0Vp77fWG+PjHPxVr1jxYhJHVEVvlYCsFXD6zJyDcmj1rVyJAgAABAgQIECBAgAABAoVAHt2Tpiam0Vs54Hr44XXxqb/4XLzj8OOLgKscXO1Ye2w8csfxcfYH+796mI5POtAqh1lp5Fb5+zjbaSritQ98IlKw9aG/uqA4N43aOuaEM+OgQw8fOx3x9d1RW/N0OmIKtV635xti6dIz48Ybby5Ga42OPhkp2Nq69dneVETB1tz+wxZuza2/qxMgQIAAAQIECBAgQIDAIhUYNj3xgQceitPPOK8IuPL0whRiferPT49n1hxXLJ/8s9MmHmqlwKo8/TB/HyfIqoZeKcy68Yk/L5ZixNZxZ3TCrePOiIPf8u549WteH3vtc/C8nY64z+sPjrSkqYcp0Erv1brggouKUCuFjmnqaB6tJdhq1z9a4Va7+kNrCBAgQIAAAQIECBAgQGCRCOTRW+Xpifn9W3evWh0nvu/0gYArBVTnf2hZMXrrt+uPjeu+fXIsPeWsweCqHGLl7SmGWTncSu/XuuzaC+OO5/+sGLV1+gVnx9HHnVEsxxx/Zhz61vfEq/bYN171qn3iVa9aEq9+dWfZY48lscce+0RnvSRe85p9Wrmk6YYpyHrb295djND69Kc/F9dcc10x/XDduseLUCuN1kovja+GWuWXx5uKOHf/cIVbc2fvygQIECBAgAABAgQIECCwyAWaAq70gvm6gOvEpWcVwVYKuDY9cFx8+UunxPHvL01P3M0wK4dax554Vnz6y+fGLes+Erc//2dFwHXc+04fCLbS9MnjTzw1/uVfbot77lkdq1evjXvvXRNr1z4Ya9f+Kh588NdFSPTggw9Hm5c0Misv5UCrPFIrBVsvvPBisaRAUrDVnn+8wq329IWWECBAgAABAgQIECBAgMAiFMgBV/n9W+URXOUpiuXphR+76PS4744T44Unj43H1p4Yl112SvGy96LMJKcc9gOtzgvj//vly4pQ6+7nPhwr7vlQnPPxD/ZCrVQ2jdhKwVZq28/uuLMIs371q4cjLY888lg8+miaxpdGPT0eadRTmtLXxiW1rbykMCsHWtWRWinYEmq18x+ocKud/aJVBAgQIECAAAECBAgQILBIBHK4ldZ1AVd6B1d6yXz6FcUjjlran4bYHaH1V585tRdybd14QvzkpvfHZZd9IJad33knVjm4qm6n6YVp37JzTo8UaP3gJ+dECrTScv09H4qPf25ZL9TKZVMbUltSm1Lbfv3rR2PduvWxYcPGGB1NyxPx1FNb4sknNxVLConmw5JGZuXl+ee3914WPyzUSv3l0w4B4VY7+kErCBAgQIAAAQIECBAgQGARC+SAK79/K/+CYh7BlabMXX319+Lt7ziq9x6uNHqqPErrwx89Pa65ZmkximvbUydFWtate3/87JcfiOtWnhr/+L3T4htXnxZX/e/T4h+/e1p8/8Yz4yd3nhX3jC6LB589p1jufHRZXHXNOXHOR8aGWnm0VmpDakt+yXoKtZ54YlM89dRTRai1deu2eP7552Pr1q1FQJSDojav83TDvE79kJfq9EOhVvv+oQq32tcnWkSAAAECBAgQIECAAAECi1AgB1xpnUZwpeWZZ7ZFCrg2btwUjz22PtJ7uD560SfjrW9/bxx+5MlFuFUNudJIrPMuOL0YvZXCrn+9/QOx5ldL49EnT4nRZ04tlrSdgqx//tlZcfWPlhWjts49r/8+rfzC+FRXqj9dK10zXTu1IbUltSm1LY3KSsHVtm3Pxosvvhg7duwolhwOzad1CrLyUu6PvL0IH8t5ccvCrXnRTRpJgAABAgQIECBAgAABAotBIIcoaZ1CofI0xRQmpfdWpRFT1113Y/G+q/FCrvI0xBxYTWSdRoSVQ630bq10zTxaK7UlBW9phFkKttKIp5deeqm3lO9jvm8vhuduvt+jcGu+96D2EyBAgAABAgQIECBAgMCCEiiHQdWAqzyKK4dc551/cbz5T95VTFc88thTeqO5ihFdJ57Ze2dWCrqGBVt5hFYKtVId6WXxqc5Udw61yqO1crCVwrfUxnKbU2eUv7d9e0E9PIv0ZoRbi7Tj3TYBAgQIECBAgAABAgQItFsgh0I54CqP4qqGXOkXCy/58hWxdOmyIpRKI7pSQJWmE6awKi0p7CoveX8qk8qmc1KglepIdaU6U4A2LNSqBlvt1tS6hSwg3FrIveveCBAgQIAAAQIECBAgQGBeC+SAK62bQq40XTGFUCmMunvV6rjmmhVFQPXhj3yiCKve+96TipfRv+Wth0da0kvh074UZKUyKcxK56Rzc6CV6szv1SqP1KqGWqltPgTmUkC4NZf6rk2AAAECBAgQIECAAAECBCYgkEOuFHANC7nyaK4USpXDrhRWjbekYCyflwOtVF9dqJWun9szgaYrQmDGBVocbj0Tt577uthvv/1i5PLVE4NYdcm4Zf/zh+fHET/eOrH6cqlVlxTtuPi+vGPset2XXxv77HdhfOPpscfsIUCAAAECBAgQIECAAAECuyuQA6W0rgu50svdUxiVlhRM5SWFVXkpB1h5Xy6Xw6wcaKX60iitPFKrHGqlNvgQaItAa8OtFBalYCstEw2NUnCVyg+EUKsuGTg/lamtr1Ku30GdkG3kw9fFk/2dg1ubr4tP7L335EOzwVrG/ZZM9t7vy/Fv45ZUgAABAgQIECBAgAABAgQWqkA55GoKusphVw69hq1T2bw0BVpCrYX6VM3v+2pluJWDrU5I1QmXagOpGvsxI6i6wVM/8KoPq1LoVRscdUdt5aCtuk6jwHJ7q8fy985IsdXxP1/bGYmW9w9b17YjorjOsGM1FHYRIECAAAECBAgQIECAwAIXGC/oykHVRNd5RFi13gXO6PbmuUDrwq0cFPXDqCQ8mYCrGl51QqWBqYhjAq9OcDR2dFbn3MZpkTV1TeSZGBPCjTmpex+lKZnpHOHWGCg7CBAgQIAAAQIECBAgsOgFqmFU+XsOrIaty2Wr24seFsC8EGhRuNUJkprCm/rgq8Z51SVx6lfu604jHBsSpTNSXf3QaniZ3oixIe/zKurpTVlcHf/wur8bf9pgNxCrXr8u0OuXMXKrpqftIkCAAAECBAgQIECAAIEhAtWgarzvQ6qxm0DrBVoRbuV3ZZWDnKFy3WmCY0dZDT2jE2T1AqiacjWjr3KbcuC07akVxbTC/giwmjBu89Px0HUX1r/Tq3TZsSOwaurqjlYrm4w9r1SpTQIECBAgQIAAAQIECBAgQIDAIhSY23CrGyr1RkdNuAM6I63SO6vK4c/Q04e+LL5zRt1L5l+4+8vF6K+BOtPorQ9fF5vi/iLoSteue1dX2je0XTVBWnGNbmjXD8/GjiYTbg30hi8ECBAgQIAAAQIECBAgQIAAgZi7cKsa5nRDn2EvWS/v74/a6gRAKRz72n9/fcOvK3ZGRvWDo3LPd0OkppFd5eJ55NhU3oXVvcf64Kt/L994Ol1QuFVmt02AAAECBAgQIECAAAECBAgQqBOYu3CrrjXTsW/IKK3Bd2OVLtQNnOqDr1K5btg0bLRYqj8dy9MYy2fm7VymHNTVbXfCL+FWdrMmQIAAAQIECBAgQIAAAQIECAwTWDThVnRHXFXDp4lM9cvv30pBVFMIlsOrppfiD+uIvL+Y0lhMfdwat577uoHpjRNpa67HmgABAgQIECBAgAABAgQIECCwGAQWT7iVp/mVpx82ThPs/DphHlmVQrFyyJX3V9cp/Mrlxn2X2JBRZp0Hz8itxfAP0D0SIECAAAECBAgQIECAAAECuycwp+FWHulUDYgm+31gNFZTYDTwnq9OeDRslFVu20Ddk7TOIVf9O7Yi0miy4QGYcGuS3IoTIECAAAECBAgQIECAAAECi1BgTsOtYd45FJpSsNQYGPVHY33owg8V78hqmmY4rH3F/iHTHNOxFIyNbXsnrJpIcNf5RUbTEhv9HSRAgAABAgQIECBAgAABAgQIRMzhryUO4x8yVTAFXhMKoirh1kPX/SieHLhWP2Tq/+riQIExX+rDqs4vMI4ZlTWk/WMqTTsqbR0sY+TWoIdvBAgQIECAAAECBAgQIECAAIGxAi0budUNjMrvxeq2OY/mGjfg6gVG24oXslenHebphmkE1fApgWWobshU06ZUV1391X3l2ga2e20d2Nv9ItyqU7GPAAECBAgQIECAAAECBAgQIFAWaFG41QlzGoOh7lTAMaOlynfULZPCq8G6OsFZ2t85v/993MCsOxprzFTDajg1rFy5feXt6vnlYzXbdWFaTTG7CBAgQIAAAQIECBAgQIAAAQKLRqAl4VYn2BozkqobFtW9p2pYIJVHeJWnHOZ9qZ5qQJVHco25duURSOXKdRaHu+3LbaktU6ln4Gsl3MptGQzl+mek48OO9UvZIkCAAAECBAgQIECAAAECBAgsHoEWhFudYKsuwOqPsqp0yMCvHpaPdafy5SmEQ0dxlc/pvPuqfP1qAFaUXnXJ2HCrXM1kRm0NCe1qr9u7RuXeevttECBAgAABAgQIECBAgAABAgQWr0ALwq2p4Y//gvkho8EaLpfqTKOz7tyyNSL60xbLwddkt3sjrUpB25gRYMPaVDonX7c5ABtWkf0ECBAgQIAAAQIECBAgQIAAgYUpMG/DrYXZHe6KAAECBAgQIECAAAECBAgQIEBgMgLCrcloKUuAAAECBAgQIECAAAECBAgQINAqAeFWq7pDYwgQIECAAAECBAgQIECAAAECBCYjINyajJayBAgQIECAAAECBAgQIECAAAECrRIQbrWqOzSGAAECBAgQIECAAAECBAgQIEBgMgLCrcloKUuAAAECBAgQIECAAAECBAgQINAqAeFWq7pDYwgQIECAAAECBAgQIECAAAECBCYjINyajJayBAgQIECAAAECBAgQIECAAAECrRIQbrWqOzSGAAECBAgQIECAAAECBAgQIEBgMgLCrcloKUuAAAECBAgQIECAAAECBAgQINAqAeFWq7pDYwgQIECAAAECBAgQIECAAAECBCYjINyajJayBAgQIECAAAECBAgQIECAAAECrRIQbrWqOzSGAAECBAgQIECAAAECBAgQIEBgMgLCrcloKUuAAAECBAgQIECAAAECBAgQINAqgZENGzaEhYFnwDPgGfAMeAY8A54Bz4BnwDPgGfAMeAY8A54Bz8B8fAaM3GpV1qgxBAgQIECAAAECBAgQIECAAAECkxEQbk1GS1kCBAgQIECAAAECBAgQIECAAIFWCQi3WtUdGkOAAAECBAgQIECAAAECBAgQIDAZAeHWZLSUJUCAAAECBAgQIECAAAECBAgQaJWAcKtV3aExBAgQIECAAAECBAgQIECAAAECkxEQbk1GS1kCBAgQIECAAAECBAgQIECAAIFWCQi3WtUdGkOAAAECBAgQIECAAAECBAgQIDAZAeHWZLSUJUCAAAECBAgQIECAAAECBAgQaJWAcKtV3aExBAgQIECAAAECBAgQIECAAAECkxEQbk1GS1kCBAgQIECAAAECBAgQIECAAIFWCQi3WtUdGkOAAAECBAgQIECAAAECBAgQIDAZAeHWZLSUJUCAAAECBAgQIECAAAECBAgQaJWAcKtV3aExBAgQIECAAAECBAgQIECAAAECkxEQbk1GS1kCBAgQIECAAAECBAgQIECAAIFWCQi3WtUdGkOAAAECBAgQIECAAAECBAgQIDAZAeHWZLSUJUCAAAECBAgQIECAAAECBAgQaJWAcKtV3aExBAgQIECAAAECBAgQIECAAAECkxEQbk1GS1kCBAgQIECAAAECBAgQIECAAIFWCQi3WtUdGkOAAAECBAgQIECAAAECBAgQIDAZAeHWZLSUJUCAAAECBAgQIECAAAc0yu8AACAASURBVAECBAgQaJWAcKtV3aExBAgQIECAAAECBAgQIECAAAECkxEQbk1GS1kCBAgQIECAAAECBAgQIECAAIFWCQi3WtUdGkOAAAECBAgQIECAAAECBAgQIDAZAeHWZLSUJUCAAAECBAgQIECAAAECBAgQaJWAcKtV3aExBAgQIECAAAECBAgQIECAAAECkxEQbk1GS1kCBAgQIECAAAECBAgQIECAAIFWCUxvuLX8gtj2xi/FxvFucdPK+OgeB8Uf7HlIfPLe4YVfuvaiGDl/5dj67vpK/P5eH4tvbBl+bufI2rj6j/cf9zqDtVTOuesrxfmHrtg2WMw3AgQIECBAgAABAgQIECBAgACBOReY9nBry7nLY2vjbW2NW5cdUARGKdyqDa+K8/shUyrz9OYbikAshWFpO4VW+dyHv3hATdjVPz9dZ9iSQ6sUpOUyud7UjLy/KYRrvF0HCRAgQIAAAQIECBAgQIAAAQIEZkxgesKtNGLrgD+KreecV6zTdv0Irn6wNXLpmkihVHPA1Sn/heu2xvZVl3fKXrqmwEihUxq99c0tzxZh2e/tdXnckpm6o63KIVU+lNY5sBo4pyjQuV7/vOaALAdj5bptEyBAgAABAgQIECBAgAABAgQIzJ7A9IRb3fbuumSP2HbAOfH45rTjmdix+ZX+nZSmIvbDo37YNTZo6p/a2eoETf1z8/HB/Tm4yiOtUoCWgrT8aQ7UBsOtal3lOqrTInPZ8rVyeWsCBAgQIECAAAECBAgQIECAAIGZEZi+cGv5BZGmJP52+YWlgKvT6Bz81I/S6gdc6XgeDZVDqPK+XE8Ororam96J1T2W6igv+Rpl0vL1ctmzz/1YLxirvXapgny+cKuEYpMAAQIECBAgQIAAAQIECBAgMMMC0xRu3R+/2f8PY/SuzkitNILryUvvKZqeQ580MuvKSw4dCJlyiJTWI+d/vPeS+V5AVA2uuqO/esejE4xVR1ENmvWnFo4d9TVYMnL9vZfYd+pPbV/xo48Xbf/idZ13f9UFZJXafCVAgAABAgQIECBAgAABAgQIEJhhgWkKt2aoldVwK6L3nq5i9Fb3eG1oNWTUVjlQK0K1mimLvf057Lp0Te89XX9x7wMDL7OfoTtXLQECBAgQIECAAAECBAgQIECAwAQEZj7cmmDIVA6detMOa8KtiPyOrf5Ir175UviV6xs6wioHYzncKrUzhWVrf/hfe6PMUv3laYlpNFrzaLEJyCtCgAABAgQIECBAgAABAgQIECCw2wIzH26Vm9gNkMovjy+HRuWixXYpcCqfk6c69kZYjTmxH0ZNNNxKdY6cf2V884MHRQq3nojOCK183YF2dts1tO6a9thFgAABAgQIECBAgAABAgQIECAw/QKzFm7lQKo64mn7qst779oqj8BKt5oDpRRi5WO5njwyq3ZKYuncoQFUN6Dqvb/rrq/Ep+7dFrcuSyHXytiyeWvc9KU39V5wn9tStKM0XTF3SW7X0OvlgtYECBAgQIAAAQIECBAgQIAAAQLTJjA74VY3DMqB1LB1NfiKu74SeeRUno6Yzu3tK43syuFXlslh1LBr5f29cKs4sfMC+brALNdXvU6+Xg63BuvLR60JECBAgAABAgQIECBAgAABAgRmQmB2wq2JtHzTylh27hXxnXu21pTuvGdrTPjVLVkXLOUwauhIqurIraKumnCrFKD1QrWaFtpFgAABAgQIECBAgAABAgQIECAw+wLtCbdm/95dkQABAgQIECBAgAABAgQIECBAYJ4LCLfmeQdqPgECBAgQIECAAAECBAgQIEBgMQsItxZz77t3AgQIECBAgAABAgQIECBAgMA8FxBuzfMO1HwCBAgQIECAAAECBAgQIECAwGIWEG4t5t537wQIECBAgAABAgQIECBAgACBeS4g3JrnHaj5BAgQIECAAAECBAgQIECAAIHFLCDcWsy9794JECBAgAABAgQIECBAgAABAvNcQLg1zztQ8wkQIECAAAECBAgQIECAAAECi1lAuLWYe9+9EyBAgAABAgQIECBAgAABAgTmucCMh1s7duyI0dHRWL9+vYWBZ8Az4BkoPQPpb2P6G+lDgAABAgQIECBAgAABAlMXmPFwK/3nbefOnVNvoTMJECCwQAXS38b0N9KHAAECBAgQIECAAAECBKYuMOPhVhqx5UOAAAEC9QL+Rta72EuAAAECBAgQIECAAIGJCgi3JiqlHAECBGZAQLg1A6iqJECAAAECBAgQIEBgUQm0O9zavj3i+usjLr444uijI9729oi3HmZZDAapr1Ofp75Pz0B6FnwILEAB4dYC7FS3RIAAAQIECBAgQIDArAq0N9z61rc6YdZ7jhRmLYYwq+ke0zOQwq70TPgQWGACwq0F1qFuhwABAgQIECBAgACBWRdoX7i1YUPEaadHCLWEetXAKz0T6dlIz4gPgQUiINxaIB3pNggQIECAAAECBAgQmDOBdoVbKbRIU9GqoYbvTMrPQHpGBFxz9kfDhadXQLg1vZ5qI0CAAAECBAgQIEBg8Qm0K9xKo3LKIYZtHsOegfSs+BBYAALCrQXQiW6BAAECBAgQIECAAIE5FWhPuJXep2QqojBrWJhV3Z+eFe/gmtM/Hi4+PQLCrelxVAsBAgQIECBAgAABAotXoB3hVvolvKn+EuJhN8YrMdoJhQ67KuKF7nY1DEnfi7IvRFx1+MRCpKvujohK+dFXBp+W+77VqevmxyNeWN3Zvm/YL/s1tC23t7iHiMj1pv2p7vInX6d8P0Vby4VK2zdP4KX8Q9tcqidtVts1evNwy9SmYcdze8v1ZYOJrtMz41cUKx3k63wTEG7Ntx7TXgIECBAgQIAAAQIE2ibQjnDr+uunPmprMuFWNZga6I1SiDUs6EmhUqojh0WpXN7OIdSwMKfczrrwJoc9A21KX16IuO/xfkhUDYzGq7fc3nzdtC8HZHlfupdy0FQXFKaQrVymuP+bohL39ctU25qvVdxr1zu1pVxnLjORdRq9lZ4dHwKzKLBr9D/i6488M84Vd8VvHro9fvDES+OUixBujUukAAECBAgQIECAAAECBBoF2hFuXXzx8NE/dSFHCkdyOFMOd6qBTDnYSdujG/qjvMr1FscqI5BGd/RHeJWvkesc1oZyveXtch3l/eXtaniUr5X2p9CsuL9SCJePl+uobpfL5NBudEPfL5fP4VYuU31sUgBVbt8Y65JXbm9duJWO5ZF26dpFPZURYblNE1mnZ8eHwCwKvPLILTFyw7VxwAObhlx1V2xcvTJGbrw2zli3c0iZ/m7hVt/CFgECBAgQIECAAAECBKYi0I5wa7K/kFgOecqhUTlwKe/PwU0RpqRpjNEZcVWMIOpuV4OUXl1pqmNNoDQs/KrWk7+X25P31a1TW9NosHKbc1hUDpfSueXgqq6uYWVSPTkczOeVr5f29e7/sH7wWL5+uY7i3vLjNxpx8/pOGFcOt4r60kC07tTNfN28TtevTgHNx5rW6dnxITDLAq/8+qYivBobcPWDrWWP7JhQq4RbE2JSiAABAgQIECBAgAABAkMF2hFuTeV9WzmMKYdG5UAmhS91UwRzoFUmKU8tzCFM+Xhve7QbKHWn4uX6y20YFsSMV6YId3oXGtxII63StVKYVR71NO3h1r1jpxj2WtKdHpmnEKZr56Cq1BfFvmq4VZiXpiH26ixvpPeRdYPHfI1hluX96dnxITAHAmMDrskHW6nZwq056DyXJECAAAECBAgQIEBgQQnM33ArBSZFuDLkhfI5+KmGVTmQKQckRWjU7dccWJWPl7dzvWlfKdTpvdS+XLa8PV64VS5b3S4Hdb37nuGRW9U2pO955FZqQ57aWA6uiv03d8olx/y9rq7p2ifcWlB/kObbzfQDro29qYgTHbGV71W4lSWsCRAgQIAAAQIECBAgMDWBdoRbk52WmIKRIrRKo4H+uf8erd7IrVLgVRei5JCm7lhRd3cE0YBpaeRRHul12I1jA7bGOvOvOlbqz/WVQ7Z87fyuq3LolqdEloO2YdetK5PuvxryDQR1+eJ53b337HbzvYV7UUc67+Y0kq07sivdSypXDreqAWOutnbdvdaw+6nuNy2xVtHO2RPoBFzfL6YpTjbYSq0Ubs1eX7kSAQIECBAgQIAAAQILU6Ad4dZkXyifA44ikElT6XJolN6PNdofUZXLTShcKYUqdaOs6gKlNDKpOnosX7O6rquzWqYaRPXCpNIUy16od/j0v3MrBVPVdpavl9uT2t0L9rrv5CqfVw23qvfZcyu9z6taZqLfvVB+Yf5lmmd39fJjt8dX16X3xk3+I9yavJkzCBAgQIAAAQIECBAgUBZoR7h1/fUR7zmy/+LyiQYbqVw5VCmCmG7QNayOcliTy1TPK9eZy5TDrTQlL3/qAqF8TnldV2f5eNpuDLfSLz12P3kUV7V8tb66OtO+FD5VR27l+yvamS+U15WRW6mOariVR36V21A3LbHqkIOw8nkT3U7PTHp2fAjMYwHh1jzuPE0nQIAAAQIECBAgQKAVAu0It7Zvj5jKS+W7IcuYkVvDwpEUtqRPngaYy1UDl6aApy5Qqp6f6y2vJ1Im1V391E1LzPXWtSUfy+u6MtVwqzySqqmd6bz8svdyuFWtL1+7Gm7VBYupbArG8ui7fO5E1umZSc+OD4F5LCDcmsedp+kECBAgQIAAAQIECLRCoB3hVqL41rcmNnqrCEgmYZdGKKVf70uf8milHHTlqnJok0KVuoAnj2yqC4vqyudwphxY5RFX+Vh1Xa07h0lpXXdutXyuL5XvfcYZyZbOydfJ55fXA96DUzcLz2Rbds315euXXcsW+Xha5wCvWk+5HdXtNGorPTM+BOa5gHBrnneg5hMgQIAAAQIECBAgMOcC7Qm3EsVpp09tamI1+PB94TumZ8WHwAIQEG4tgE50CwQIECBAgAABAgQIzKlAu8KtDRsipvLLicKshR9mlfs4PSPpWfEhsAAEhFsLoBPdAgECBAgQIECAAAECcyrQrnArUaTQIo3KmeoL5sshiO2FFXqlZyI9G4KtOf2j4eLTKyDcml5PtREgQIAAAQIECBAgsPgE2hdu5T5I71NKLwwXci2sgGoqgWN6BtKz4B1b+V+H9QISEG4toM50KwQIECBAgAABAgQIzIlAe8OtxJF+Ce/66yMuvrgzXXGqv6g4lUDFOXMbqqW+TtMPU9+nZ8CvIs7JHwgXnXkB4dbMG7sCAQIECBAgQIAAAQILW6Dd4dbCtnd3BAgQCOGWh4AAAQIECBAgQIAAAQK7JyDc2j0/ZxMgQGC3BIRbu8XnZAIECBAgQIAAAQIECMSMh1ujo6Oxc+dO1AQIECBQEUh/G9PfSB8CBAgQIECAAAECBAgQmLrAjIdbO3bsKP7zlkYnWBh4BjwDnoH+M5CCrfQ30ocAAQIECBAgQIAAAQIEpi4w4+HW1JvmTAIECBAgQIAAAQIECBAgQIAAAQLNAsKtZh9HCRAgQIAAAQIECBAgQIAAAQIEWiwg3Gpx52gaAQIECBAgQIAAAQIECBAgQIBAs4Bwq9nHUQIECBAgQIAAAQIECBAgQIAAgRYLCLda3DmaRoAAAQIECBAgQIAAAQIECBAg0Cwg3Gr2cZQAAQIECBAgQIAAAQIECBAgQKDFAsKtFneOphEgQIAAAQIECBAgQIAAAQIECDQLCLeafRwlQIAAAQIECBAgQIAAAQIECBBosYBwq8Wdo2kECBAgQIAAAQIECBAgQIAAAQLNAsKtZh9HCRAgQIAAAQIECBAgQIAAAQIEWiwg3Gpx52gaAQIECBAgQIAAAQIECBAgQIBAs4Bwq9nHUQIECBAgQIAAAQIECBAgQIAAgRYLCLda3DmaRoAAAQIECBAgQIAAAQIECBAg0Cwg3Gr2cZQAAQIECBAgQIAAAQIECBAgQKDFAsKtFneOphEgQIAAAQIECBAgQIAAAQIECDQLCLeafRwlQIAAAQIECBAgQIAAAQIECBBosYBwq8Wdo2kECBAgQIAAAQIECBAgQIAAAQLNAsKtZh9HCRAgQIAAAQIECBAgQIAAAQIEWiwg3Gpx52gaAQIECBAgQIAAAQIECBAgQIBAs4Bwq9nHUQIECBAgQIAAAQIECBAgQIAAgRYLCLda3DmaRoAAAQIECBAgQIAAAQIECBAg0Cwg3Gr2cZQAAQIECBAgQIAAAQIECBAgQKDFAsKtFneOphEgQIAAAQIECBAgQIAAAQIECDQLCLeafRwlQIAAAQIECBAgQIAAAQIECBBosYBwq8Wdo2kECBAgQIAAAQIECBAgQIAAAQLNAsKtZh9HCRAgQIAAAQIECBAgQIAAAQIEWiwg3Gpx52gaAQIECBAgQIAAAQIECBAgQIBAs4Bwq9nHUQIECBAgQIAAAQIECBAgQIAAgRYLTG+4tfyC2PbGL8XG8W5408r46B4HxR/seUh88t7hhV+69qIYOX/l2Pru+kr8/l4fi29sGX7uuEfu+kpx/ZFL1wwW7bbt0BXbImJtXP3H+xflUlurS1PbByv1jQABAgQIECBAgAABAgQIECBAYCYEpj3c2nLu8tja2NKtceuyA3pBUW14VZzfD5ZSmac331AEYilQStspdMrnPvzFA/phVyk4K4dRY4KoIeFWCtTSeb+31+Vxy5D7SNdLZcbUOaS83QQIECBAgAABAgQIECBAgAABAjMjMD3hVhqxdcAfxdZzzivWabt+BFc/2EojpnJIlEOqsbfYKf+F67bG9lWXF4FSHmmVQqg0euubW54twrJeGNUNt8rlUhB1xSVX9AK1cuiVttP1y/Wndo2c//H49j1jW5T25HYLt+p97CVAgAABAgQIECBAgAABAgQIzJbA9IRb3dbuumSP2HbAOfH45rTjmdix+ZX+fZRGVPXDrH7Y1Qun+mdUtjojufrn5sOV/UPCrTFBVHXkVvV7rr5mXRdu5RFfOVSrOc0uAgQIECBAgAABAgQIECBAgACBaRaYvnBr+QWRpiT+dvmFpYCr09oc/ORRUoPv5OoHXOl4511X/dFR5X25noGgqhtK5fNiSLj1qXu3xa3LLuq/p6saZpW/l4K4dP285ACuLtzK+4Rb0/yEqo4AAQIECBAgQIAAAQIECBAg0CAwTeHW/fGb/f8wRu/qjNRKI7ievLQzpy+HPikYuvKSQ3tBUQ6M8jpNA8wvme8FROMEVxGdYGzg5fJDgqk8LbE38qscZiWg8vdKQJYOp/toCrcajB0iQIAAAQIECBAgQIAAAQIECBCYIYFpCrdmqHXVcKv6vqscSJV/UbESTJVHe+WgrRjllc/Nv5ZY/j4kIBNuzVA/q5YAAQIECBAgQIAAAQIECBAgMEWBmQ+3uqFRHqE1kXVv2mH33N6Uw+Im8zu2+iO9euXT8YZwKx9LIdXdd1858IJ6I7em+AQ5jQABAgQIECBAgAABAgQIECAwhwIzH26Vb64bVuURUOlQeWRVuWixXQrGyufkEVgpKOtNYcwnN4Vb3ZFfb1mxNn577WXjhlvVIC63IV9/IFTL17cmQIAAAQIECBAgQIAAAQIECBCYNYFZC7dyIDTwfqyI2L7q8t67tqphUQ6+UsiUj+V6cvDUe4dWJhsn3MrFBkZqpZ3dIK0Iyyp19M7pbuQ25Dal3Xnf4Ciz6pm+EyBAgAABAgQIECBAgAABAgQITKfA7IRb3bAoB1LD1tXgKwVOebRURGc6Yjq3t680sqsXNA25Vu94Vy8HZ70wqhxujSOcgqxqW3O4NWYk2Th1OUyAAAECBAgQIECAAAECBAgQIDB1gdkJtybSvk0rY9m5V8R37tlaU7oTbFUDpVxwIFiqjLrKIVYRbpXCsIGQLFU0TriVr5GDuV4olhthTYAAAQIECBAgQIAAAQIECBAgMOsC7Qm3Zv3WXZAAAQIECBAgQIAAAQIECBAgQGC+Cwi35nsPaj8BAgQIECBAgAABAgQIECBAYBELCLcWcee7dQIECBAgQIAAAQIECBAgQIDAfBcQbs33HtR+AgQIECBAgAABAgQIECBAgMAiFhBuLeLOd+sECBAgQIAAAQIECBAgQIAAgfkuINya7z2o/QQIECBAgAABAgQIECBAgACBRSwg3FrEne/WCRAgQIAAAQIECBAgQIAAAQLzXUC4Nd97UPsJECBAgAABAgQIECBAgAABAotYQLi1iDvfrRMgQIAAAQIECBAgQIAAAQIE5rvAjIdbO3bsiNHR0Vi/fr2FgWfAM+AZKD0D6W9j+hvpQ4AAAQIECBAgQIAAAQJTF5jxcCv9523nzp1Tb6EzCRAgsEAF0t/G9DfShwABAgQIECBAgAABAgSmLjDj4VYaseVDgAABAvUC/kbWu9hLgAABAgQIECBAgACBiQoItyYqpRwBAgRmQEC4NQOoqiRAgAABAgQIECBAYFEJCLcWVXe7WQIE2iYg3Gpbj2gPAQIECBAgQIAAAQLzTUC4Nd96THsJEFhQAsKtBdWdboYAAQIECBAgQIAAgTkQEG7NAbpLEiBAIAsIt7KENQECBAgQIECAAAECBKYmINyampuzCBAgEPGfO2LbS7v6Ei9ui9/8Z//rRLaEWxNRUoYAAQIECBAgQIAAAQLDBYRbw20cIUCAQKPAs3cvj5GfP9Atszl+dPN3Y2T1lsZzqgeFW1UR3wkQIECAAAECBAgQIDA5AeHW5LyUJkBgkQu8/PgdceotP4yRm38UI//0/Rj5p2s72zdfGyM3fj9G/rn7/Sf/FH/7+Avjagm3xiVSgAABAgQIECBAgAABAo0C7Qm3Nnw7lo6MxEheTvx2rK82vVJm2S8HC6z9TOn8kZGoHu+X/nlcPvLZuL6/Y9yt31z9nn7b/q/PDT/3js8W5YZfu/5SRf3f2zzm4MTvqX/qeG0dOD5ydFzyRP/csVvJquRa08bOOZti5VHvGaeusbXbQ2B+CTwcn7/xuzFy171x00Or49rbl8fIv/57sX3TQ3fGh/7lezHys/8ovt9w549i5MZ/ieU7m+9QuNXs4ygBAgQIECBAgAABAgTGE2hJuFUNRtL3kRgpB1zdYKsXGhUhUimYSd//+mf9+x0SMvWCnaaAql9LsVWcUypfBE7ltvXKd9vdGKz1Cnc3SuFRNTia4D2Vaxy3ranO0r1Uy5frSttrPlMKAat9kAt3rUfGDcryCdYE5qvA2viLlT+Ky57ptP+3a2+Mkbsf697Mb+POO66Nsx55qfP9qTvi/7nhul7ZYXcs3BomYz8BAgQIECBAgAABAgQmJtCScKumsUWQclRvJFARKJXDq+gGSdVAqFRV9ZzieypfCXhKp9RsdsKnXqiWSlTalk8qgqITjitGoA2UzwWq6w3fjpNH/ip+HE91wryGe8mnVu8p7++sx2trnVln34TaW2Ne3HPqlyEmg+3zjcB8F0jh1g/i/77jp/GZX9zWuHz29uti5MYVwq353uXaT4AAAQIECBAgQIBA6wXmSbhVF8pEFMFK7QiqjvvQIGgy4VZtaFPTnm65S5/4ZTGNb2JhUX4+aurLhyrrofeUyo3b1prwKyLKdRbbQ00b2ll77UrjfSUw7wVSuPW9GPmXH3Xfs9Wwvim9g0u4Ne+73A0QIECAAAECBAgQINB6gdaGW4MhS30o0xhuFWHLkPduTSbcqi1bDXnK3+vb2vwklM9vKNl0T+m0cdtaf50Jh1u19XfbK9xq6DiHFo7A4LTE4r6eui8+cdsP41OPV+7StMQKiK8ECBAgQIAAAQIECBCYGYFWhltFaDXw3qr6wGh4uNUpP/DOrrJfU0hTLpe2a8sOhkQTCeKq1Q5+H6xv8Fj+Ns49pWITaGthVnrnVnFOemH8wJTPfM08Gqz7QvmhI7pyuf400lINNgksIIGacOvlnfHEqh/GmY9WblO4VQHxlQABAgQIECBAgAABAjMj0LJwqxvyDARb6cYnEW6lgCeFNU1BTF0I1B0V1fu1xhwA1ZUtv3tqzPGxbS3Cr8ZfHBwn3JrIPSWmMW1JO8fWPdCeE78dd3/76OHhVvm5y0Z17wYzcqssZXvBCtSEWxHx7N3LhVsLts/dGAECBAgQIECAAAECbRdoUbjVHZmUQ6UBuU5AU32PVTEKqTTiKI/4GqkLX8r11YZA5QKl7e5L368v7cqB0dm/3Nx5EXw5uCpv197LQEXdL2MDqFxqwveUTmhoa9Uu15/WRdg1nlk+YZidcCsLWS9oAeHWgu5eN0eAAAECBAgQIECAwLwUaEm41Q13GkZbFQFMKcjKAVMvyOqObmoKcXo9NCyg6RUob4wdiVX/4vZ8Tk35fGjoeki4NZl7Kuquufa4oVM658jer1IObWI+kNpU10/jXidXYE1gPgukcGt5fHLjy/2beODG3svlB9679fhtMXLDdfGNbf2idVvr16+v220fAQIECBAgQIAAAQIECExQoB3h1kSCkWqZSkA1NvxqEKic21CyOFSMnioFOs3XqgmYxrtAzdTBdErzdeorHa+tL1792VKQNTZUK66Z73XDt+PAz/2sdKHOvfUCxdKR5sCvXNA2gfks8Nu4/47vx8iNE1x+/kC8OM7tCrfGAXKYAAECBAgQIECAAAEC4wi0KNzqvrS8PK0vbZeny3VHMhXvxapM+StCmeq56XulXOExyXArnTNQ/8AIsqrwNIdbE72nUjOa29oNtLr1Vke6DYRbEdGbFpnbUe6P0jWFW2UM2wtbYGfsfH5bPDfusmNCDMKtCTEpRIAAAQIECBAgQIAAgaEC7Qi3hjbPAQIECCxsAeHWwu5fd0eAAAECBAgQIECAwMwLCLdm3tgVCBAgMFRAuDWUxgECBAgQIECAAAECBAhMSEC4NSEmhQgQIDAzAsKtmXFVKwECBAgQIECAAAECi0dAuLV4+tqdEiDQQgHhVgs7RZMIECBAgAABAgQIEJhXAsKtedVdGkuAwEITEG4tuuryYwAAIABJREFUtB51PwQIECBAgAABAgQIzLaAcGu2xV2PAAECJQHhVgnDJgECBAgQIECAAAECBKYgINyaAppTCBAgMF0Cwq3pklQPAQIECBAgQIAAAQKLVWDGw63R0dHYuXPnYvV13wQIEBgqkP42pr+RPgQIECBAgAABAgQIECAwdYEZD7d27NhR/OctjU6wMPAMeAY8A/1nIAVb6W+kDwECBAgQIECAAAECBAhMXWDGw62pN82ZBAgQIECAAAECBAgQIECAAAECBJoFhFvNPo4SIECAAAECBAgQIECAAAECBAi0WEC41eLO0TQCBAgQIECAAAECBAgQIECAAIFmAeFWs4+jBAgQIECAAAECBAgQIECAAAECLRYQbrW4czSNAAECBAgQIECAAAECBAgQIECgWUC41ezjKAECBAgQIECAAAECBAgQIECAQIsFhFst7hxNI0CAAAECBAgQIECAAAECBAgQaBYQbjX7OEqAAAECBAgQIECAAAECBAgQINBiAeFWiztH0wgQIECAAAECBAgQIECAAAECBJoFhFvNPo4SIECAAAECBAgQIECAAAECBAi0WEC41eLO0TQCBAgQIECAAAECBAgQIECAAIFmAeFWs4+jBAgQIECAAAECBAgQIECAAAECLRYQbrW4czSNAAECBAgQIECAAAECBAgQIECgWUC41ezjKAECBAgQIECAAAECBAgQIECAQIsFhFst7hxNI0CAAAECBAgQIECAAAECBAgQaBYQbjX7OEqAAAECBAgQIECAAAECBAgQINBiAeFWiztH0wgQIECAAAECBAgQIECAAAECBJoFhFvNPo4SIECAAAECBAgQIECAAAECBAi0WEC41eLO0TQCBAgQIECAAAECBAgQIECAAIFmAeFWs4+jBAgQIECAAAECBAgQIECAAAECLRYQbrW4czSNAAECBAgQIECAAAECBAgQIECgWUC41ezjKAECBAgQIECAAAECBAgQIECAQIsFhFst7hxNI0CAAAECBAgQIECAAAECBAgQaBYQbjX7OEqAAAECBAgQIECAAAECBAgQINBiAeFWiztH0wgQIECAAAECBAgQIECAAAECBJoFhFvNPo4SIECAAAECBAgQIECAAAECBAi0WEC41eLO0TQCBAgQIECAAAECBAgQIECAAIFmAeFWs4+jBAgQIECAAAECBAgQIECAAAECLRaY3nBr+QWx7Y1fio3j3fCmlfHRPQ6KP9jzkPjkvcMLv3TtRTFy/sqx9d31lfj9vT4W39hSf246r77urXHrsgPi9/a6PG6pP3Vwb7edtW0YLOkbAQIECBAgQIAAAQIECBAgQIDAHAhMe7i15dzlsbXxRjoBUwqf0jI8OFobV//x/r0yT2++oQjEUhiWttOxfO7DXzxgIOwaDLc69XTKbhsMt8YJyaIUwuX2lteHrtjWeKcOEiBAgAABAgQIECBAgAABAgQIzKzA9IRbacTWAX8UW885r1in7foRXP1ga+TSNZFCqeaAq1P+C9dtje2rLu+UvXRNIZICrDR665tbnh0MrCJivHCrHFA1juIycmtmnz61EyBAgAABAgQIECBAgAABAgR2U2B6wq1uI3ZdskdsO+CceHxz2vFM7Nj8Sr95pVFQecRVRD/sagyZilrKI7D61UYM7s/BVjnAqm7na6VwLW+Xa+xtTyLcytdNoZ0PAQIECBAgQIAAAQIECBAgQIDA7AhMX7i1/IJIUxJ/u/zCUsDVuYkc/NSP0uoHXOl4nuqXR3WV9+V6Bt7TdddXihFd+bx0xcFy5fBrcFrimHCrFMBVA7Hq9/L10jVze4Vbs/PgugoBAgQIECBAgAABAgQIECBAIAlMU7h1f/xm/z+M0bs6I7XSCK4nL72nEM6hTxohdeUlhxZBVDUoSt9Hzv947yXzvYCoGlzlkVS90VGdYKz6cvndDbf6I8vGPiS57mq4NbakPQQIECBAgAABAgQIECBAgAABAjMtME3h1gw1sxpulUZIFaO3userYVQOoDojvJpHbg2cm8Ozul9o7N5irlu4NUN9rloCBAgQIECAAAECBAgQIECAwCQEZj7c6gZQdaO1hu3rTTusCbf679jqj/Tqle/eeA6ghoVb5esOhFS7MS1xEuaKEiBAgAABAgQIECBAgAABAgQITJPAzIdb5YZ2w6ryS9wHg6hy4YgoBWPlc/JUx2I6Y2+KYv/cXGc6/tYVN8bVf7x/dEZoDb5zq3+GLQIECBAgQIAAAQIECBAgQIAAgfkoMGvhVg6kqu/H2r7q8t67toaNwEohVT6W68mjrwamFXZ7IIdbKRD7STxYCrcqXbRpZXxkj4vjG1s6+9N55XNyoJbrK48EGxjxVZouWd1fuaKvBAgQIECAAAECBAgQIECAAAEC0ygwO+HWBKf7VYOvNHIrB0x5OmIKtXr7SiO7yuFX74X0BVTnpfN1IVgeGVaUL7Ux1TUQaJXexZXCuNSGaltz6DZ47WnsKVURIECAAAECBAgQIECAAAECBAiMEZidcGvMZWt2bFoZy869Ir5zz9aag52XwlcDpVxw3GCpFILlEV95nYOygTArVVwKtDZGZTpjtz6jtHIPWBMgQIAAAQIECBAgQIAAAQIE5kagPeHW3Ny/qxIgQIAAAQIECBAgQIAAAQIECMxjAeHWPO48TSdAgAABAgQIECBAgAABAgQILHYB4dZifwLcPwECBAgQIECAAAECBAgQIEBgHgsIt+Zx52k6AQIECBAgQIAAAQIECBAgQGCxCwi3FvsT4P4JECBAgAABAgQIECBAgAABAvNYQLg1jztP0wkQIECAAAECBAgQIECAAAECi11AuLXYnwD3T4AAAQIECBAgQIAAAQIECBCYxwLCrXnceZpOgAABAgQIECBAgAABAgQIEFjsAsKtxf4EuH8CBAgQIECAAAECBAgQIECAwDwWmPFwa8eOHTE6Ohrr16+3MPAMeAY8A6VnIP1tTH8jfQgQIECAAAECBAgQIEBg6gIzHm6l/7zt3Llz6i10JgECBBaoQPrbmP5G+hAgQIAAAQIECBAgQIDA1AVmPNxKI7Z8CBAgQKBewN/Iehd7CRAgQIAAAQIECBAgMFEB4dZEpZQjQIDADAgIt2YAVZUECBAgQIAAAQIECCwqgXaHW//5XMToNRGrzon49z+NuO3AiNveuAiWAzv3m+473X9y8CFAYEEKCLcWZLe6KQIECBAgQIAAAQIEZlGgveHWo1/thFn/9tZFEGY1BHbF/R8YkTx8CBBYcALCrQXXpW6IAAECBAgQIECAAIFZFmhfuPXiuohfHB+x2EOt6gi15JFcko8PAQILRkC4tWC60o0QIECAAAECBAgQIDBHAu0Kt1JwU0w/bBjJVA19Ftv35CPgmqN/Li5LYPoFhFvTb6pGAgQIECBAgAABAgQWl0C7wq00MmmxhVVTud/k5EOAwIIQEG4tiG50EwQIECBAgAABAgQIzKFAe8Kt9E4pUxEnFu4lJ+/gmsN/Ni5NYPoEhFvTZ6kmAgQIECBAgAABAgQWp0A7wq30a4DT+kuIK+KV390dcev+8buH74robu98cn3EtisjbvtKvPy79cXxupFiLz+7IyJ2RDx88JiwqajvxZWV/V+JXb+rLz+2/lQ2uu3YnemXB/oVxcX5b9ZdLzAB4dYC61C3Q4AAAQIECBAgQIDArAu0I9wavWZKo7aKsKpHVg6r+uFWCpd6AdeT6yK2/bgxiCqCrRSG3bYiXh4ScBVlnnxzt8zj/RCtF3r1rz/Yxl5j6zeKOicYeKXRW8nNhwCBWRXYNfof8fVHnhnnmrviNw/dHj944qVxykUIt8YlUoAAAQIECBAgQIAAAQKNAu0It1adUxkJNX7AU4RG3RFZ5dFRu16s3O+L6ys7Sl+LUVz9axXn9gKqTij2Sm3AlcOrywaCspdf7IzeKo/u6o8W61+n3N683Q/Mmsvl8sU6ufkQIDCrAq88ckuM3HBtHPDApiHX3RUbV6+MkRuvjTPW7RxSpr9buNW3sEWAAAECBAgQIECAAIGpCLQj3Jr0LyQ2TwOsC75S4PRKmjqYlsoIqeJY0quEXZ0gqTuNsCZIS8frQqli30BdK2JXY+9MdEpjJfhKbj4ECMy6wCu/vqkIr8YGXP1ga9kjaXrz+B/h1vhGShAgQIAAAQIECBAgQKBJoB3h1iTft5WnGb5SHqVVCpN6o7eKQCqNrorOe7eKaYlXRnmEVhFEdachDg2gXlwZnemFL8UrL3c5u9cr9pdGe6X3eQ17/1anjv70yV0PXx/x8CGTHrXWH711YFPfOkaAwAwKjA24Jh9speYJt2awk1RNgAABAgQIECBAgMCiEJiX4VYnJIreCKzOyKs8+mlFxIvrOy+UT6O1UjeWg6judhFqDYRSlVFRtw3/Xly/F6YNhlk5eEsvs++HUN0RXpXr9UaMVUaSlc9r3hZuLYp/pW6ytQL9gGtjbyriREds5ZsSbmUJawIECBAgQIAAAQIECExNoB3h1iSnJRbhUmWaYDEaa9uV3WmCP+79WmIOh4pzKuFSOtYLmCbi173mYLj1xs6orqLu7hTGSlg1oWtUzsntblybljiRXlOGwIwKdAKu7xfTFCcbbKWGCbdmtHtUToAAAQIECBAgQIDAIhBoR7g1yRfK142OyuFWPLuq+BXDV8pTEod1ZE3YNfZ9WWNHcFXDrRRA9aZC1tRZDaiq19j18BURD18+MNKrek7tdy+UH9az9hOYVYGXH7s9vrpu+5SuKdyaEpuTCBAgQIAAAQIECBAg0BNoR7g1ek3Ev711EuFOd4RUd2pgZ2RUnpaYwqj8a4adqYH5Vww7AdGKeDkej+q0wXSsOrqrCKxqRlR1wq0fl96t1W1PYu1NV+yHYr3gq8deszGBUGwg4Epeyc2HAIF5LSDcmtfdp/EECBAgQIAAAQIECLRAoB3h1n8+FzHJl8qnAGvgBfADIVQ53FoR8eQVnZfKZ/CaAKpuNFjn5fARUQmeemHVk2/uT2vs1tk51n9p/EAg1X2PV3XkVl2Z8fcdGJHcfAgQmNcCwq153X0aT4AAAQIECBAgQIBACwTaEW4liEe/OsnRW/2RUWODoG64lX4dMX9K7+gqwqW0vxdyVYKyfE50RoMVgVWvbL5ud7RWqd5+O7r1vbh2MFTr1Ttko7aufL3SOo3aSl4+BAjMewHh1rzvQjdAgAABAgQIECBAgMAcC7Qn3EoQvzh+ElMTS2FPwy8b9gOnBVQ+OfkQILAgBIRbC6Ib3QQBAgQIECBAgAABAnMo0K5w68V1EZP85cQFGV41hXXJJzn5ECCwIASEWwuiG90EAQIECBAgQIAAAQJzKNCucCtBpOAmjUya1AvmF9CorGHBVvJILoKtOfzn4tIEpl9AuDX9pmokQIAAAQIECBAgQGBxCbQv3Mr+6Z1S6SXziz3kKu7/QO/Yys+FNYEFJiDcWmAd6nYIECBAgAABAgQIEJh1gfaGW4ki/Rrg6DURq87pTlc8cJG8k+vAzv2m+07371cRZ/0fhgsSmC0B4dZsSbsOAQIECBAgQIAAAQILVaDd4dZCVXdfBAgQ6AoItzwKBAgQIECAAAECBAgQ2D0B4dbu+TmbAAECuyUg3NotPicTIECAAAECBAgQIEAgZjzcGh0djZ07d6ImQIAAgYpA+tuY/kb6ECBAgAABAgQIECBAgMDUBWY83NqxY0fxn7c0OsHCwDPgGfAM9J+BFGylv5E+BAgQIECAAAECBAgQIDB1gRkPt6beNGcSIECAAAECBAgQIECAAAECBAgQaBYQbjX7OEqAAAECBAgQIECAAAECBAgQINBiAeFWiztH0wgQIECAAAECBAgQIECAAAECBJoFhFvNPo4SIECAAAECBAgQIECAAAECBAi0WEC41eLO0TQCBAgQIECAAAECBAgQIECAAIFmAeFWs4+jBAgQIECAAAECBAgQIECAAAECLRYQbrW4czSNAAECBAgQIECAAAECBAgQIECgWUC41ezjKAECBAgQIECAAAECBAgQIECAQIsFhFst7hxNI0CAAAECBAgQIECAAAECBAgQaBYQbjX7OEqAAAECBAgQIECAAAECBAgQINBiAeFWiztH0wgQIECAAAECBAgQIECAAAECBJoFhFvNPo4SIECAAAECBAgQIECAAAECBAi0WEC41eLO0TQCBAgQIECAAAECBAgQIECAAIFmAeFWs4+jBAgQIECAAAECBAgQIECAAAECLRYQbrW4czSNAAECBAgQIECAAAECBAgQIECgWUC41ezjKAECBAgQIECAAAECBAgQIECAQIsFhFst7hxNI0CAAAECBAgQIECAAAECBAgQaBYQbjX7OEqAAAECBAgQIECAAAECBAgQINBiAeFWiztH0wgQIECAAAECBAgQIECAAAECBJoFhFvNPo4SIECAAAECBAgQIECAAAECBAi0WEC41eLO0TQCBAgQIECAAAECBAgQIECAAIFmAeFWs4+jBAgQIECAAAECBAgQIECAAAECLRYQbrW4czSNAAECBAgQIECAAAECBAgQIECgWUC41ezjKAECBAgQIECAAAECBAgQIECAQIsFhFst7hxNI0CAAAECBAgQIECAAAECBAgQaBYQbjX7OEqAAAECBAgQIECAAAECBAgQINBiAeFWiztH0wgQIECAAAECBAgQIECAAAECBJoFhFvNPo4SIECAAAECBAgQIECAAAECBAi0WGB6w63lF8S2N34pNjbd8F1fiT/Y85BiGTl/Zdz0pTfF7+/1sfjGlqaTusc2rYyP7nFQce4n751A+V6RtXH1H+8fv7fX5XFLb1/dRqdcbt+wdW17u22rXuPhLx7Qu99c36ErtpUu3rnmyKVrImJr3LpsbPl0Xu01S7XYJECAAAECBAgQIECAAAECBAgsRoFpD7e2nLs8ttZJlkOtIsiJiE0r45sfvHgg/BkeWg0GPykYawzRBtowuXCrGlD1q+q0oT5oqj+Wwq1c/qVrLyrudSDc2nR/fPODTYFdfb39NtkiQIAAAQIECBAgQIAAAQIECCxegekJt9KIrQP+KLaec16xTtvlEVw51Mkjl4atUwj0lhVra3qjH2ylEU55NNSEA67uqKrxy48Xgo0NmpruLbc13dc3tzxbjMrqBWelNm1fdXkRenVGb1Vvf+w1qyV8J0CAAAECBAgQIECAAAECBAgsVoHpCbe6ersu2SO2HXBOPL457Xgmdmx+pcZ1vACpckppKmI/nOqHXb2wqHLawNfuqLHxy3baNix8y/vzSKx8jRRw9evutC1/zyO3vnvPjcWUyt6orVK41TwCrT7cyqFafSCWW2ZNgAABAgQIECBAgAABAgQIEFjYAtMXbi2/INKUxN8uv7AUcEXkECYHQxNZ5wCofG4/2Mod0g+4Up35nHy0vM71VEOpcpmpb/fbUQRN3SBt5NIbYsvmKEaZpet+8NxDSwFYZ0pmen/Y2PuqtqQ+3OqNXstTPKun+U6AAAECBAgQIECAAAECBAgQWAQC0xRu3R+/2f8PY/SuzkitNILryUvvGeQrjcAafK9WZ7RUNXjK4U0aAXXlJYcOvJerHJCNnP/x3kvmh41iynVNNAQr1z9sO1+rXHe1bG57urc8cqsXZu3myK1BXN8IECBAgAABAgQIECBAgAABAotTYJrCrWa8PHKqHy5VRjuVfiWwaQRW81WGHe2EZyPnX1m8uL0XLtUUz+0cDN8qBXMoNcERUyn8ysFdrr+4x1zPuC/Grx+5VWmVrwQIECBAgAABAgQIECBAgACBRSkw4+HWsJFNtQFSDnxScFT6dcXqiKhh3+vqzNcvjnXrrCuXej+HT/3jNcFSuY3ppO73ujalesrhVi5bjPrK9Qi3FuU/PDdNgAABAgQIECBAgAABAgQITI/AjIdbw5qZg6QcCjWO2Kp5IXw+vx9E1Vyp9/6rNd2DNWFV6bQxdXYDqPxy+KJoDqXyyK3q91SoFKIJt0rANgkQIECAAAECBAgQIECAAAEC0ywwS+HW2F8hbAyzSjeZR17lqX350PZVl/fetVUbcOVgqzoyqi6w6lY6GG6NbfPAu7MmGW5decnHiveG9e4jh2LV9uUb7K3rA7nsMlHHXnU2CBAgQIAAAQIECBAgQIAAAQILSGCWwq1hYp3gJo/e6gU/uXg3AMrHh62r5+XgZ+j7tUr1jg3G+qHWQL3dsCy1YWB/qa5q+8ZMS8z3ldYTDrc67RkYPRad6Y7pevnF9uWqbRMgQIAAAQIECBAgQIAAAQIEFovALIRbgwFWOQAaCIkK8a2xZfMk6TetjGXnXhHfuWdrcWIOtsaGVmPrzWVTQJRHbRWB0TijqfJ5Ay+GzyO50mWGTUssN6Ep3CoFadlrIvdTrt42AQIECBAgQIAAAQIECBAgQGAxCMxCuLUYGN0jAQIECBAgQIAAAQIECBAgQIDAXAgIt+ZC3TUJECBAgAABAgQIECBAgAABAgSmRUC4NS2MKiFAgAABAgQIECBAgAABAgQIEJgLAeHWXKi7JgECBAgQIECAAAECBAgQIECAwLQICLemhVElBAgQIECAAAECBAgQIECAAAECcyEg3JoLddckQIAAAQIECBAgQIAAAQIECBCYFgHh1rQwqoQAAQIECBAgQIAAAQIECBAgQGAuBIRbc6HumgQIECBAgAABAgQIECBAgAABAtMiINyaFkaVECBAgAABAgQIECBAgAABAgQIzIXAjIdbO3bsiNHR0Vi/fr2FgWfAM+AZKD0D6W9j+hvpQ4AAAQIECBAgQIAAAQJTF5jxcCv9523nzp1Tb6EzCRAgsEAF0t/G9DfShwABAgQIECBAgAABAgSmLjDj4VYaseVDgAABAvUC/kbWu9hLgAABAgQIECBAgACBiQoItyYqpRwBAgRmQEC4NQOoqiRAgAABAgQIECBAYFEJtDvc2r494vrrIy6+OOLooyPe9vaItx628Jd0n+l+032n+08OPgQILEgB4daC7FY3RYAAAQIECBAgQIDALAq0N9z61rc6YdZ7jlz4YVZTYJfuP4VdycOHAIEFJyDcWnBd6oYIECBAgAABAgQIEJhlgfaFWxs2RJx2esRiD7WqgVfySC7Jx4cAgQUjINxaMF3pRggQIECAAAECBAgQmCOBdoVbKbhJ0/GqwY7vfZPkI+Cao38uLktg+gWEW9NvqkYCBAgQIECAAAECBBaXQLvCrTQySZA1vkFy8iFAYEEICLcWRDe6CQIECBAgQIAAAQIE5lCgPeFWeqeUqYjjB1sp/EtO3sE1h/9sXJrA9AkIt6bPUk0ECBAgQIAAAQIECCxOgXaEW+nXAKf6S4iH3RivxGgnFDrsqogXutt1I8CKsi9EXHX4xEKkq+6OiEr50VcGn5T7vtWp6+bHI15Y3dm+b9ivGza0ra69TfuSl19RHOwL3wjMQwHh1jzsNE0mQIAAAQIECBAgQKBVAu0It66/fuqjtiYTblWDqYGuKIVYw8KpFF6lOm7u/oJjKpe3UxCVAq7Rm+uDs3I7c2hV7IuIHJDl/RNZp9Fbyc2HAIFZFdg1+h/x9UeeGeeau+I3D90eP3jipXHKRQi3xiVSgAABAgQIECBAgAABAo0C7Qi3Lr64PhAaFvKkEVV5lFQ5NKqO3CoHUWl7dEN/lFe57uJYJZQa3dEf4VW+Rq5zWBvK9Za3y3UU7Uz9MtoJy6YSbqW6k5sPAQKzKvDKI7fEyA3XxgEPbBpy3V2xcfXKGLnx2jhj3c4hZfq7hVt9C1sECBAgQIAAAQIECBCYikA7wq3J/kJiEQ51R1qNCY3yFMXSdMU0wioHSEX56Iy4KqYddrfLQVTa7gVlaapjaVRXDreGhV/VevL3cjvzvrRO9eW2lfdPZDu5+RAgMOsCr/z6piK8Ghtw9YOtZY/smFC7hFsTYlKIAAECBAgQIECAAAECQwXaEW5N5X1bObAqh0a9QKphimAOtMok5amFvVFV5QJ5uzvS6uabonjzVp6CWG7DsFBqWJndCbeSmw8BAnMiMDbgmnywlRou3JqT7nNRAgQIECBAgAABAgQWkMD8Dbd60wJLI7TK4VYeYVUNq/J0xnIIlcrmTw6sysfL27netK8uYCuXLW8Lt7KwNYEFI9APuDb2piJOdMRWRhBuZQlrAgQIECBAgAABAgQITE2gHeHWZKclptCoCK3SdMF/7r9HqxdulQKvcsCUt9OL35umAhZBVBW0OzWxHG4ddmP33V/jXK9o75Ayqb6mtuQ2161NS6x2ku8EZl2gE3B9v5imONlgKzVWuDXrXeaCBAgQIECAAAECBAgsMIF2hFuTfaF8DnqKkVP3jg238oiqXK46equ2E0vv1aobZZXfsVUOt+pGj+VrVtd1daYyuxNueaF8bU/aSWC2BV5+7Pb46rrtU7qscGtKbE4iQIAAAQIECBAgQIBAT6Ad4db110e858jJ/WJiDo/KoVFv5NZhw+vqjfg6vF+mel65znydcrg1uqEHGOl9XXXl83l5PazMVMOt5JXcfAgQmNcCwq153X0aT4AAAQIECBAgQIBACwTaEW5t3x4xlZfKp+CoHBpVQ6ocLOV1fpl8+QXy1Tp636u9UzMtMddbbkPeV10PKzPVcCt5JTcfAgTmtYBwa153n8YTIECAAAECBAgQINACgXaEWwniW9+a2OitIsCahFx6gfzN6zsnlF8mn4OuXFX5vVd1QVR55NZ44Vg52ErhVf6M97L68nlN22nUVvLyIUBg3gsIt+Z9F7oBAgQIECBAgAABAgTmWKA94VaCOO30/lTBpnBnsR9LTj4ECCwIAeHWguhGN0GAAAECBAgQIECAwBwKtCvc2rAhYiq/nLiYwq7kk5x8CBBYEALCrQXRjW6CAAECBAgQIECAAIE5FGhXuJUgUnCTRiZN9QXzCzXoSh7JRbA1h/9cXJrA9AsIt6bfVI0ECBAgQIAAAQIe7BWaAAAgAElEQVQECCwugfaFW9k/vVMqvTR9sYdc6f6Tg3ds5SfDmsCCEhBuLajudDMECBAgQIAAAQIECMyBQHvDrYSRfg3w+usjLr64M11xqr+oON9Gc6X7TNMP032n+/eriHPwT8MlCcyOgHBrdpxdhQABAgQIECBAgACBhSvQ7nBr4bq7MwIECBQCwi0PAgECBAgQIECAAAECBHZPQLi1e37OJkCAwG4JCLd2i8/JBAgQIECAAAECBAgQiBkPt0ZHR2Pnzp2oCRAgQKAikP42pr+RPgQIECBAgAABAgQIECAwdYEZD7d27NhR/OctjU6wMPAMeAY8A/1nIAVb6W+kDwECBAgQIECAAAECBAhMXWDGw62pN82ZBAgQIECAAAECBAgQIECAAAECBJoFhFvNPo4SIECAAAECBAgQIECAAAECBAi0WEC41eLO0TQCBAgQIECAAAECBAgQIECAAIFmAeFWs4+jBAgQIECAAAECBAgQIECAAAECLRYQbrW4czSNAAECBAgQIECAAAECBAgQIECgWUC41ezjKAECBAgQIECAAAECBAgQIECAQIsFhFst7hxNI0CAAAECBAgQIECAAAECBAgQaBYQbjX7OEqAAAECBAgQIECAAAECBAgQINBiAeFWiztH0wgQIECAAAECBAgQIECAAAECBJoFhFvNPo4SIECAAAECBAgQIECAAAECBAi0WEC41eLO0TQCBAgQIECAAAECBAgQIECAAIFmAeFWs4+jBAgQIECAAAECBAgQIECAAAECLRYQbrW4czSNAAECBAgQIECAAAECBAgQIECgWUC41ezjKAECBAgQIECAAAECBAgQIECAQIsFhFst7hxNI0CAAAECBAgQIECAAAECBAgQaBYQbjX7OEqAAAECBAgQIECAAAECBAgQINBiAeFWiztH0wgQIECAAAECBAgQIECAAAECBJoFhFvNPo4SIECAAAECBAgQIECAAAECBAi0WEC41eLO0TQCBAgQIECAAAECBAgQIECAAIFmAeFWs4+jBAgQIECAAAECBAgQIECAAAECLRYQbrW4czSNAAECBAgQIECAAAECBAgQIECgWUC41ezjKAECBAgQIECAAAECBAgQIECAQIsFhFst7hxNI0CAAAECBAgQIECAAAECBAgQaBYQbjX7OEqAAAECBAgQIECAAAECBAgQINBiAeFWiztH0wgQIECAAAECBAgQIECAAAECBJoFhFvNPo4SIECAAAECBAgQIECAAAECBAi0WEC41eLO0TQCBAgQIECAAAECBAgQIECAAIFmAeFWs4+jBAgQIECAAAECBAgQIECAAAECLRaY3nBr+QWx7Y1fio1NN3zXV+IP9jykWEbOXxk3felN8ft7fSy+saXppO6xTSvjo3scVJz7yXsnUL5XZG1c/cf7x+/tdXnc0ttXt9Epl9s3bD3h9tZdwj4CBAgQIECAAAECBAgQIECAAIFpE5j2cGvLuctja13zyqHWpWs6JTatjG9+8OJe2JXCpOGh1da4ddkBvbIpGGsM0QbaMLlwa3gI1mmDcGsA1xcCBAgQIECAAAECBAgQIECAwJwJTE+4lUZsHfBHsfWc84p12i6P4Hrp2ot6odSw0VBpfwqN3rJibQ1GP9gauXRNPPzFTsg14YCrO+Jr/PLjhWDCrZrOsYsAAQIECBAgQIAAAQIECBAgMGcC0xNudZu/65I9YtsB58Tjm9OOZ2LH5ldqbmy8AKlySmkqYj+c6oddw0dZlerpjhobv+zUpyXmAC+Fbz4ECBAgQIAAAQIECBAgQIAAAQKzIzB94dbyCyJNSfzt8gtLAVdEDn2aRmxVjx26Yltx9+Vz///27u5ZkrO+D/jxn+ALXzhvVAXf4ATFV8jGgfKtE7uSXCSYlG+ITGEFOyko7HKSqrhscpGCkrEtqEoqVHzhKMYV1mU5QhVvQIAACaGVVkLs8iYksauXlbQrIa2Q2F2t2vXrmV/PMz1v58ycPqd75nOqlpnpl6ef/vT3HO1+6ZkzKbYSZlJwxf65T64tH3OcLt9O2NxNptwq6T0nQIAAAQIECBAgQIAAAQIECHQqcEjl1iPVq297U3X+1OhOrbiD6+mPnZ6eeHEH1vTnao3ulmoXT1kWxd1Wn7jlnQvf1rj3/t9uPmR+0V1TOdZ+S7B22Tbv9aJjTZ+0VwQIECBAgAABAgQIECBAgAABAl0KHFK5tXyKeefUpFya3HU1Kokmr5fdgbX8KIvWjsqzvfd/ovrk+95ezd4BNtkv5zldvk3W18/y87vcodWC8ZIAAQIECBAgQIAAAQIECBAgcPQCnZdb5V1T5R1Qcwuksjgqfrtiud+y5/PGzOPX68Zjztsu6GfLrVHpNnVXWTnHo79ejkiAAAECBAgQIECAAAECBAgQIFAIdF5uFceaeppFUpZVS+/YmvOB8Ln/oqKqPth4v8lbCOeUVcWsZsYcF1lTH0Sv3CrEPCVAgAABAgQIECBAgAABAgQIHK/AEZVbs7+FcGmZVZjknVdTd09VVfXyA7c2n7U1t+DKYuv9d1RPFuNV8wqr8frpcmt2zjGHPzt9Z33cSWE22jnnud/zKqfkOQECBAgQIECAAAECBAgQIECAwHoCR1RuLZrc5LO24g6udoGVRVTe3bXosb1fFk0LP19rXHDFeLPF2KTUmhp3XJbNnWdVVc0xfRbXoottOQECBAgQIECAAAECBAgQIEDg0AWOoNyaLrDKgmqqPKpP7VL13IUDnuMzd1Tvvfnj1f8+faneMUum2dJqdtzcNu7Cyru2Yn4LS7HxELmfu7RmTS0hQIAAAQIECBAgQIAAAQIECBylwBGUW0d5Oo5FgAABAgQIECBAgAABAgQIECCwSwLKrV262s6VAAECBAgQIECAAAECBAgQILBlAsqtLbugTocAAQIECBAgQIAAAQIECBAgsEsCyq1dutrOlQABAgQIECBAgAABAgQIECCwZQLKrS27oE6HAAECBAgQIECAAAECBAgQILBLAsqtXbrazpUAAQIECBAgQIAAAQIECBAgsGUCyq0tu6BOhwABAgQIECBAgAABAgQIECCwSwLKrV262s6VAAECBAgQIECAAAECBAgQILBlAsqtLbugTocAAQIECBAgQIAAAQIECBAgsEsCnZdbly9frs6fP1898cQT/jCQARmQgSID8bMxfkb6IkCAAAECBAgQIECAAIH1BTovt+Ifb1euXFl/hvYkQIDAlgrEz8b4GemLAAECBAgQIECAAAECBNYX6Lzciju2fBEgQIDAfAE/I+e7WEqAAAECBAgQIECAAIH9Cii39itlOwIECHQgoNzqANWQBAgQIECAAAECBAjslIBya6cut5MlQKBvAsqtvl0R8yFAgAABAgQIECBAYGgCyq2hXTHzJUBgqwSUW1t1OZ0MAQIECBAgQIAAAQLHIKDcOgZ0hyRAgEAKKLdSwiMBAgQIECBAgAABAgTWE1BuredmLwIECByKgHLrUBgNQoAAAQIECBAgQIDADgsot3b44jt1AgSOX0C5dfzXwAwIECBAgAABAgQIEBi2gHJr2NfP7AkQGLiAcmvgF9D0CRAgQIAAAQIECBA4doFelFtnfnev2tsr/vzYh6u/qqrq1T/95Wrvv9zTQrq3unXv9+r1sWJm33Kc9vN33VY90RrNSwIECByngHLrOPUdmwABAgQIECBAgACBbRDoRbk1BXnutupf7/3+/HLrK79Xl2DvvW9qDy8IECAwWAHl1mAvnYkTIECAAAECBAgQINATgcGUW/UdWuM7unpiZxoECBDYWEC5tTGhAQgQIECAAAECBAgQ2HGBfpRb4zuymrcmtt6WWBdbM29P3PEr5/QJENgKAeXWVlxGJ0GAAAECBAgQIECAwDEK9KLcmv/ZWos+c+sYtRyaAAEChyyg3DpkUMMRIECAAAECBAgQILBzAv0ptz51YYJ/7rbqV9sfBt9+nXdy7Wfbet9/Ud3y1OQQnhEgQKAPAsqtPlwFcyBAgAABAgQIECBAYMgCvSi3Zn7j4fi3GjZ3dJ27rfpn//J/+U2HQ06auRMgMFdAuTWXxUICBAgQIECAAAECBAjsW6AX5dbUbOPzt8Z3ZTXlVvVMdcc/36v8lsQpKS8IENgCAeXWFlxEp0CAAAECBAgQIECAwLEKDKTcqqoqSq/xHV3HKubgBAgQOEQB5dYhYhqKAAECBAgQIECAAIGdFOhduVXfrTX+/K3mzq1zt1U/9+F7Kr81cScz6qQJbLWAcmurL6+TI0CAAAECBAgQIEDgCAR6V27VBVZZbsWHwf/Yh6u/qjFGb0/Mty0egY9DECBAoFMB5VanvAYnQIAAAQIECBAgQGAHBHpRbtV3aOVvQ2wVWbOfszUpuKb2y/1XPeZvWdyBi+sUCRDov4Byq//XyAwJECBAgAABAgQIEOi3QC/KrX4TmR0BAgS6E1BudWdrZAIECBAgQIAAAQIEdkNAubUb19lZEiDQUwHlVk8vjGkRIECAAAECBAgQIDAYAeXWYC6ViRIgsI0Cyq1tvKrOiQABAgQIECBAgACBoxRQbh2ltmMRIECgJaDcaoF4SYAAAQIECBAgQIAAgQMKKLcOCGZzAgQIHKaAcuswNY1FgAABAgQIECBAgMAuCii3dvGqO2cCBHojoNzqzaUwEQIECBAgQIAAAQIEBirQebl1/vz56sqVKwPlMW0CBAh0JxA/G+NnpC8CBAgQIECAAAECBAgQWF+g83Lr8uXL9T/e4u4EfxjIgAzIwCQDUWzFz0hfBAgQIECAAAECBAgQILC+QOfl1vpTsycBAgQIECBAgAABAgQIECBAgACB5QLKreU+1hIgQIAAAQIECBAgQIAAAQIECPRYQLnV44tjagQIECBAgAABAgQIECBAgAABAssFlFvLfawlQIAAAQIECBAgQIAAAQIECBDosYByq8cXx9QIECBAgAABAgQIECBAgAABAgSWCyi3lvtYS4AAAQIECBAgQIAAAQIECBAg0GMB5VaPL46pESBAgAABAgQIECBAgAABAgQILBdQbi33sZYAAQIECBAgQIAAAQIECBAgQKDHAsqtHl8cUyNAgAABAgQIECBAgAABAgQIEFguoNxa7mMtAQIECBAgQIAAAQIECBAgQIBAjwWUWz2+OKZGgAABAgQIECBAgAABAgQIECCwXEC5tdzHWgIECBAgQIAAAQIECBAgQIAAgR4LKLd6fHFMjQABAgQIECBAgAABAgQIECBAYLmAcmu5j7UECBAgQIAAAQIECBAgQIAAAQI9FlBu9fjimBoBAgQIECBAgAABAgQIECBAgMByAeXWch9rCRAgQIAAAQIECBAgQIAAAQIEeiyg3OrxxTE1AgQIECBAgAABAgQIECBAgACB5QLKreU+1hIgQIAAAQIECBAgQIAAAQIECPRYQLnV44tjagQIECBAgAABAgQIECBAgAABAssFlFvLfawlQIAAAQIECBAgQIAAAQIECBDosYByq8cXx9QIECBAgAABAgQIECBAgAABAgSWCyi3lvtYS4AAAQIECBAgQIAAAQIECBAg0GMB5VaPL46pESBAgAABAgQIECBAgAABAgQILBdQbi33sZYAAQIECBAgQIAAAQIECBAgQKDHAsqtHl8cUyNAgAABAgQIECBAgAABAgQIEFguoNxa7mMtAQIECBAgQIAAAQIECBAgQIBAjwWUWz2+OKZGgAABAgQIECBAgAABAgQIECCwXEC5tdzHWgIECBAgQIAAAQIECBAgQIAAgR4LHG65deI3qhd+9qPVkzMnfKm66703Vj/51ndU//Gh6ZXf/cjy5bFP+8+P33Br9dlmmNHY08ualYufnPrjety9j31jeptn7qg+8Ja3V++8/YWqqs5Uf/rmt80cP+fTPpfpgbwiQIAAAQIECBAgQIAAAQIECBDoWuDQy63nbj5RXWrPelwkZSmUjx/5y8/URVK+zsdJ4bS8FPuJG36r+uRzL9bFWVNujcupHCsfZ4qoBeXWjz79wbrMasZrn0tVVYsKuTmbWkSAAAECBAgQIECAAAECBAgQINChwOGUW3HH1o1/v7r06/+2foznzR1c47IpCqsohfbef0dzZ1e8jhLpnmfvrEuucl2ccxZNo7JrVHSN7qgaFUzLyq0syHKMj9/y8YV3YMVxX37g1uZOrtE8f7u67fR8eeXWfBdLCRAgQIAAAQIECBAgQIAAAQJHLXA45dZ41q/f8pbqhRt/vfr+hVhwsbp84Y2ZQqvetLi7qi6rnrlYnfmLD1W/8gd3VF9+djzYeJsosP7Hc7Fs9BbBfB0F00HKrZV3bi24k2s8m6mHeeVWlmhZqk3t4AUBAgQIECBAgAABAgQIECBAgEAnAodXbp34jSrekvjaid8sCq45c27dyZVlVXvLLIvybYXlY9xp9dcf/fkDlVv/6aEXqrve+8FxUVZVVbvMKl8X5Vt53Hyr4rxyK5cpt9pX0msCBAgQIECAAAECBAgQIECAQHcCh1RuPVK9+rY3VedPvVHPNO7gevpjs+/py8KqLICyFJq5s2rROZ/64+rdf/CJ+m2My+7cKkupeJ5vS2ze+liWWXGs8nVRwOU0Yp7Lyq3cziMBAgQIECBAgAABAgQIECBAgMDRCRxSubViwuPiKEupdvEUJdX7bn5nfSfW6C2I0+NFsdS+wyuXLfpA+SzQslCL8iyLtPqtkGWZFYcrX69x59b0jL0iQIAAAQIECBAgQIAAAQIECBA4CoHOy63yjqfmhMbl0ezdWqMPja+LqXHZ1C7C4nWUU+uUW9X4uHEH1oMPfqL5APl6XnPKrSzIYn15HlmSzc6/OUNPCBAgQIAAAQIECBAgQIAAAQIEjkCg83IrzyHuoFr4lsCFZddo7yyyyru6ctlB7tyK0WK/X7j9TPXap/9oZbnVLta8LTGvpkcCBAgQIECAAAECBAgQIECAQD8EjqjcGt2RFWXR6G6n0et2WdSUXy2bLLLKcmuyyfRYeXdW3nVVvi1xsk/rbYixYsWdW+W+MZ/JuYzW5LL6LY/lxp4TIECAAAECBAgQIECAAAECBAh0JnBE5VZVtUunKIOizHr+wmfqD4dfVGzFma9TbrXvumq/hTBLr6aMKsutFdzz5pPlVpZqK4awmgABAgQIECBAgAABAgQIECBA4BAEui+3Fnw4e7t8ytf5wfFZFuXy8rEukFrjNqXSeHm+zhKrLrdan+OVd47VjivKrfZ8mlLsEC6CIQgQIECAAAECBAgQIECAAAECBNYT6L7cWm9e9iJAgAABAgQIECBAgAABAgQIECCwUkC5tZLIBgQIECBAgAABAgQIECBAgAABAn0VUG719cqYFwECBAgQIECAAAECBAgQIECAwEoB5dZKIhsQIECAAAECBAgQIECAAAECBAj0VUC51dcrY14ECBAgQIAAAQIECBAgQIAAAQIrBZRbK4lsQIAAAQIECBAgQIAAAQIECBAg0FcB5VZfr4x5ESBAgAABAgQIECBAgAABAgQIrBRQbq0ksgEBAgQIECBAgAABAgQIECBAgEBfBZRbfb0y5kWAAAECBAgQIECAAAECBAgQILBSoPNy6/r169WVK1f9YSADMiADMiADMiADMiADMiADMiADMiADMnDoGei83FJsKfZkQAZkQAZkQAZkQAZkQAZkQAZkQAZkQAa6yoByS2N66I1pV2E1rh+EMiADMiADMiADMiADMiADMiADMiAD7Qwot5Rbyi0ZkAEZkAEZkAEZkAEZkAEZkAEZkAEZGGwGlFvCO9jwtptar7X3MiADMiADMiADMiADMiADMiADMrB7GVBuKbeUWzIgAzIgAzIgAzIgAzIgAzIgAzIgAzIw2Awot4R3sOHVxu9eG++au+YyIAMyIAMyIAMyIAMyIAMyIAPtDCi3lFvKLRmQARmQARmQARmQARmQARmQARmQARkYbAaUW8I72PC2m1qvtfcyIAMyIAMyIAMyIAMyIAMyIAMysHsZ6EW5VZ27vfr3b3l79ZNvfUf9Z+/m26vHrl6bKl3a2/zO/dPrz/zXG5v9Y5z2+gz31SsPV//zpz5W/b9r0/vn+nmPL33qA83YP37DHy3ct7r3D+vtFh27Pfaqc8rx0mW/4165cqH665s+UP23p2bPcdUx23MsXWevy4Xq5E0T99n1i76hRvu1ty+d85zfeeLZqRy05+f1ImPLZUMGZEAGZEAGZEAGZEAGZEAGZGA3MtCDcqtdxMwWH1nIZLkTpc9P3PChpryJ13u3PNSUIFkK5fYZ5ixPlhVUuW0+xj7l9lH2tEuZ0baToqd93ByrfFx5Tudur/7dT39w6hzLcy7HKp/nuc/bdtUxy3Hi+fS5jq9L4Vyvb17Prm+Pl69zjm3HsC6vY27vcTd+GLnOrrMMyIAMyIAMyIAMyIAMyIAMyMA6GehBuTV74aKEKYud6RIlth8VKcvu6mnvE69j+yhWyrJqGVrc5fUnb75x6i6w9txy/7qYufm36jvQ9lNutefXPqfZ9aOyadk5Zzm0aI6zYy52nDdGaTd3/bnbq9/86T9ceGdbWKXpr73vQzMlYc4/TT3Ofm8wYSIDMiADMiADMiADMiADMiADMiAD0xkYQLk1v4AZlUmzb1/MCzxb5IxOvCxocttFj/MKnHYJFfvmdv/9qa/PlGHzx151Ts/Wb/drF1mrzjmPlfOZflviqmNem7pTa55TFlNR3q1aH3Opr0HrLaZ5XeadS67L8/A4/c3Kg4cMyIAMyIAMyIAMyIAMyIAMyIAMzGagl+VWWYqUhUp5AeeVI7k+yp34DK95d1DNK2Vyv/bj/G3bJdHk9aK5tsddtF15TvOKnnJ9e8zy9bxya9/HHJdR845VjrGfY5TXMeZXes4bP7ZvF3rleXk++w3MhIkMyIAMyIAMyIAMyIAMyIAMyMCuZ6B35VaUHuUHwpeFSnmx5pUjsT63b3+eU+5bFiy5bNHj/G0nZVbsVxY4eex5pVp5jEXblecUxy4/NysLu0XnVY6/n+Ipty+Pmcvicd7y6XmPHMrPyAqL8tqV4+X802Z2/NF4+UHy8ViOXY7luR/cMiADMiADMiADMiADMiADMiADMiADmYEelVuTciMLkJjkdKEyuXCz5cjozqC6FGm9FS5PNh7nFVZZvGSxkp/JNW/b8m2J7fXz5pqFT44ddybN2y7m1j6neJ37xZzuu+/Wmc+pKs8tnx9NuTW5NjnHXzjxcP12yvL6jeY0XQjOO9ecez6mkYJrkvm08chEBmRABmRABmRABmRABmRABmRABiYZ6EW5lUVGlkrTF2hUjLQLk7oIan5T36gYipJl1dva2oXU9LEmMLE8SqLZD0gfzec/3D/6XKwsdtqP888lx9/fObXn1j7n9vp8Pa/cylJulWMzRvwGylZJWF+nn/pA8xscc9t8XLQ+zNs+5etF1+wg1yrn4DEz5lEWZEAGZEAGZEAGZEAGZEAGZEAGdiMDPSi3RkVPu0gpA1i/9a8osrKoyVIky5N2cVOOkc8PUphk6VaOO784GoVl3vZ53PbjqnNqb5/nXM5ldpvRPBbN8SDHnDfGKrtYv+w6lvNt36VWrsvnq46X23ncjR9WrrPrLAMyIAMyIAMyIAMyIAMyIAMyMC8Dx15uzStR2hNtb9MuPWZLm8UXu71v+1jt1+0SZtmxDlJurTqnmGdZZC07bnvO7bFzfXt526I+RnG31vQxRyVkFopRtv3fj/xl9djVa1WM3x47lrXHy3nEY9s1xnvy/OS6peXkeJN15Tiec5EBGZABGZABGZABGZABGZABGZCB3c5AL8qt+M2G5dvU8nlZbEQRk8vbb/mLEiXXlY/t7SLs7UJnP98A5fjLPgMqC5mylFo2/rJziv3K45YWy8aMdfOKptxn2TFny6hRoZWm7TnEcfLazbOeHW/yzTZbbk2fbxyzfbw8B48TRxYsZEAGZEAGZEAGZEAGZEAGZEAGdj0Dx15u7foFcP5+CMmADMiADMiADMiADMiADMiADMiADMjA+hlQbl1ZH0/w2MmADMiADMiADMiADMiADMiADMiADMjA8WZAuaXcqj8zyzfi8X4j8ucvAzIgAzIgAzIgAzIgAzIgAzIgA+tlQLml3FJuyYAMyIAMyIAMyIAMyIAMyIAMyIAMyMBgM6DcEt7BhlejvV6jzY2bDMiADMiADMiADMiADMiADMjANmVAuaXcUm7JgAzIgAzIgAzIgAzIgAzIgAzIgAzIwGAzoNwS3sGGd5taZufi/zWRARmQARmQARmQARmQARmQARmQgfUyoNxSbim3ZEAGZEAGZEAGZEAGZEAGZEAGZEAGZGCwGVBuCe9gw6vRXq/R5sZNBmRABmRABmRABmRABmRABmRgmzLQebl1/fp15YkCTQZkQAZkQAZkQAZkQAZkQAZkQAZkQAZkoJMMdF5uVb4IECBAgAABAgQIECBAgAABAgQIdCSg3OoI1rAECBAgQIAAAQIECBAgQIAAAQLdCyi3ujd2BAIECBAgQIAAAQIECBAgQIAAgY4ElFsdwRqWAAECBAgQIECAAAECBAgQIECgewHlVvfGjkCAAAECBAgQIECAAAECBAgQINCRgHKrI1jDEiBAgAABAgQIECBAgAABAgQIdC+g3Ore2BEIECBAgAABAgQIECBAgAABAgQ6ElBudQRrWAIECBAgQIAAAQIECBAgQIAAge4FlFvdGzsCAQIECBAgQIAAAQIECBAgQIBARwLKrY5gDUuAAAECBAgQIECAAAECBAgQINC9gHKre2NHIECAAAECBAgQIECAAAECBAgQ6EhAudURrGEJECBAgAABAgQIECBAgAABAgS6F1BudW/sCAQIECBAgAABAgQIECBAgAABAh0JKLc6gjUsAQIECBAgQIAAAQIECBAgQIBA9wLKre6NHYEAAQIECBAgQIAAAQIECBAgQKAjAeVWR7CGJUdS1YAAAA25SURBVECAAAECBAgQIECAAAECBAgQ6F5AudW9sSMQIECAAAECBAgQIECAAAECBAh0JKDc6gjWsAQIECBAgAABAgQIECBAgAABAt0LKLe6N3YEAgQIECBAgAABAgQIECBAgACBjgSUWx3BGpYAAQIECBAgQIAAAQIECBAgQKB7AeVW98aOQIAAAQIECBAgQIAAAQIECBAg0JGAcqsjWMMSIECAAAECBAgQIECAAAECBAh0L7D32c9+tso/ebh8HY/51V72xhtvNPt97nOfqzfbZNn169eb8e666656vE2Wvf766814n//85+vxNll27dq1ZrwvfOEL9XibLLt69Woz3he/+MV6vE2WXblypRnv7rvvrsfbZNmPfvSjZrwvfelL9XibLHvttdea8b785S/X422y7NVXX23G+8pXvlKPt8myH/7wh81499xzTz3eJsteeeWVZrx77723Hm+TZZcvX27G++pXv1qPt8myl19+uRnvvvvuq8fbZNlLL73UjPe1r32tHm+TZT/4wQ+a8e6///56vE2Wvfjii814p06dqsfbZNkLL7zQjPfAAw/U422y7NKlS814Dz74YD3eJssuXrzYjHf69Ol6vE2WPf/88814Dz30UD3eJsuee+65ZryHH364Hm+TZc8++2wz3te//vV6vMNeduHCheYYjzzySH2MTZY988wzzXjf+MY36vE2Wfb000834505c6Yeb5NlTz31VDPe2bNn6/E2Wfbkk082433zm9+sx9tk2fnz55vxvvWtb9XjbbLs3LlzzXjf/va36/E2Wfb973+/Ge873/lOPd4my5544olmvO9+97v1eJsse/zxx5vxHn300Xq8TZY99thjzXjf+9736vE2WRZj5N87Y5z42mRZnGOOF+cZX5ssi2uQ48V1iK9NlkVGcrzISXxtsiwynONFjuNrk2XxPZbjxfdZfG2yLH4G5HjxcyC+NlkWP6NyvPg5FV+bLIufoTle/ByNr02Wxc/4HC9+zsfXJsviv0E5Xvx3KL4Oe1n8tzSPEf89ja9NlsV/63O8+O99fG2yLP4ukuPF30fia5Nl8XelHC/+vhRfmyyLv8vlePH3ufjaZFn8XTPHi79vxtcmy+Lvwjle/H04vjZZFn9Xz/Hi7+vxtcmy+LdEjhf/noivTZbFv3VyvPj3Tnxtsiz+LZbjxb/H4muTZfFvxRwv/r0YX5ssi3/L5njx79n42mRZ/Fs7x4t/b8fXJsuiC8jxog+Ir02WRVeR47lzq+b0PwQIECBAgAABAgQIECBAgAABAkMUUG4N8aqZMwECBAgQIECAAAECBAgQIECAQC2g3BIEAgQIECBAgAABAgQIECBAgACBwQootwZ76UycAAECBAgQIECAAAECBAgQIEBAuSUDBAgQIECAAAECBAgQIECAAAECgxVQbg320pk4AQIECBAgQIAAAQIECBAgQICAcksGCBAgQIAAAQIECBAgQIAAAQIEBiug3BrspTNxAgQIECBAgAABAgQIECBAgAAB5ZYMECBAgAABAgQIECBAgAABAgQIDFZAuTXYS2fiBAgQIECAAAECBAgQIECAAAECyi0ZIECAAAECBAgQIECAAAECBAgQGKyAcmuwl87ECRAgQIAAAQIECBAgQIAAAQIElFsyQIAAAQIECBDoscBHn3y82jt5otq788/92SaDkyequLa+CBAgQIAAgc0FlFubGxqBAAECBAgQINCZgGJri0u9kyc6y42BCRAgQIDALgkot3bpajtXAgQIECBAYHAC7tja4nLrzj8fXB5NmAABAgQI9FFAudXHq2JOBAgQIECAAIGxgHJLueWbgQABAgQIEFguoNxa7mMtAQIECBAgQOBYBZRbyq1jDaCDEyBAgACBAQgotwZwkUyRAAECBAgQ2F0B5ZZya3fT78wJECBAgMD+BJRb+3OyFQECBAgQIEDgWASUW8qtYwmegxIgQIAAgQEJKLcGdLFMlQABAgQIENg9AeWWcmv3Uu+MCRAgQIDAwQSUWwfzsjUBAgQIECBA4EgFlFvKrSMNnIMRIECAAIEBCii3BnjRTJkAAQIECBDYHQHllnJrd9LuTAkQIECAwHoCyq313OxFgAABAgQIEDgSAeWWcutIguYgBAgQIEBgwALKrQFfPFMnQIAAAQIEtl9AuaXc2v6UO0MCBAgQILCZgHJrMz97EyBAgAABAgQ6FRhyufXmF69VL198sBqdw0PV/3l9EdVL1Sfv+LPxdrNlVoxTXb1Q3fSZTy3cZqhOi0QsJ0CAAAECBPYvoNzav5UtCRAgQIAAAQJHLjDU0ibm3S63Pv36ter/n20XVI9Vp6rLS8utvTtHxdikKJstwIbqdOSBckACBAgQILCFAsqtLbyoTokAAQIECBDYHoFBljbfer662LoEZ8+d3sedW1F0HfRr+V1fffc76NnangABAgQIEJgVUG7NmlhCgAABAgQIEOiNQN/LmWXz2/jOradfKd7WOLpb6z//sKrOnmvf/TXcO7l6EzQTIUCAAAECAxZQbg344pk6AQIECBAgsP0Cy8qjvq+LcuulixeqU9XV6uTZ/dy5NSmp6s/Zql6q95s+z9HdXdvyFsXtT7AzJECAAAEC3Qsot7o3dgQCBAgQIECAwNoC08XOpPzp/fLmrYkHf9tg3J0VHyD/F3Pu0qpLr8sXRm9xvPzo4D9gfu1g2JEAAQIECBBoBJRbDYUnBAgQIECAAIH+CfS+xLpzTuFWF1svV7dfujp+W+F+PkvrSv1h83WxlaXVeJzJb1KMca6OP5R+9CHzQ3+LYv8SZ0YECBAgQGB4Asqt4V0zMyZAgAABAgR2SGCQ5da48Jr+zK2yBHuomv+bE8ttRs9Hd2qN7tCaKr7mlWoDXLZDUXaqBAgQIECgMwHlVme0BiZAgAABAgQIbC6wPeVW3HV1uRrdhTW662pKJ+/WmlNQ1aVWbHz1QnXTZ7bnw+Tj2voiQIAAAQIENhdQbm1uaAQCBAgQIECAQGcCW1NuPf1KVVWjtx7u3bn/O7f26v2S9+Cf39V3vzwzjwQIECBAgMD6Asqt9e3sSYAAAQIECBDoXKDv5cyy+U3eljj6zK0z5x6tTlVV9dLFR1e+LbG5W6sqC6387K4syWbfxrhsPn1c13mAHIAAAQIECOyAgHJrBy6yUyRAgAABAgSGK9DHQmblnJrflFhVZ8+drn+z4csXH2x+s+GkuCqvy5Xq5Nnv1eVXLF3+QfHl2xrL8mt4ZVcp4DkBAgQIECCwnoByaz03exEgQIAAAQIEjkRgZZE05zOq7DOckutIQuQgBAgQIEBgywWUW1t+gZ0eAQIECBAgMGwBRdVwiqp1rtWw02n2BAgQIECgHwLKrX5cB7MgQIAAAQIECMwVWKcwsc9wCrG5F91CAgQIECBA4EACyq0DcdmYAAECBAgQIHC0Aoqq4RRV61yro02ToxEgQIAAge0UUG5t53V1VgQIECBAgMCWCKxTmNhnOIXYlsTUaRAgQIAAgWMVUG4dK7+DEyBAgAABAgSWCyiqhlNUrXOtll99awkQIECAAIH9CCi39qNkGwIECBAgQIDAMQmsU5jYZziF2DHFymEJECBAgMBWCSi3tupyOhkCBAgQIEBg2wQUVcMpqta5VtuWV+dDgAABAgSOQ0C5dRzqjkmAAAECBAgQ2KfAOoWJfYZTiO0zBjYjQIAAAQIElggot5bgWEWAAAECBAgQOG4BRdVwiqp1rtVx58vxCRAgQIDANggot7bhKjoHAgQIECBAYGsF9k6eqNYpTewzgFLs5Imtza0TI0CAAAECRymg3DpKbcciQIAAAQIECBxQ4KNPPl4puAZQVN15wDmePFHFtfVFgAABAgQIbC6w97f/4c9Vf+8f/ePq77z17VU8z8d8/ndv+Pl6WSzPP7Eu/pTr4nmMk8viMZ/nfvGYx8r1+brcJp7n8eN5bhvLYvvcJ5bffffd/jCQARmQgQ4ykD9r42fvP/mVf1P903ffVP3iu95TP4/X+fwX/9V76ufxmMtjXS5v71uvK8ap98kxYr93vac+VrNfrhuP/0vv/rXR8cbbxXFybrlvLovlMU7534787037vzPl69wm/xsU63J9/rftTT/zjqn/HqVXro/t43k85roYrxwrxy/Xxz7xJ5fFcXI+sSzHz2X5mPvl+nyd+5Tzj3XlvHJdObccJ8ePx1xfrovneYzYJl6X63P/PJ9cn69zfT7mXMqxYllsn/vEGDmX3D7W5fO/9Q9+tjYrt8s55br28XJeOcai9XE9ctvcJsfOx1wec2ofL7cpx4jn5bnlunjM+cTzGDfGK7ePZfk6t8/HXFc+5nFyWbxub5/jxTZx/PJ1PI8/uX/OL7eL5blfbpPHzP1yeY6VY+S+Oad4zHU5fvv7IZbn+LF/7hOP8SeOkc9z/HjMceN5bFO+LrfLdbk+Xsd63/+j65y+6RKP+bx0jGuQ1rlPuT6fp3O5bSwrr2HsH8vyT2wb63Pf/J4rt8s55br28WJ9bp/r4jHHzPW+/yfXt/QKp3iddqVbee1zfXqWY+S62D6Wl9c0x/f9PzFOr3gsPcvXaVlejzTP65Lr4jGfxzbl+PE815fr4nkeozxue988Vmyf+7S3yWPk8tw2jhv7l2PkXHK+sS6f5/d47J/bxfMYN9flMXKf8li5Lh7b633/9//7/28AiklicXrV8xIAAAAASUVORK5CYII=2.手机端： 142957 3.数据查询 142958 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 编辑于 2021-4-9 14:23

一、数据背景挑战赛系列直播-蛋壳公寓事件可视化分析解读，练习数据资料下载。主要包括蛋壳公寓整体经营数据，各区域租房数据和公司发展数据。二、数据模型142424142425 142426 三、数据分析1.总体经营情况分析1424272.原因分析2.1营收与亏损数据1424282.2房间数占比情况1424292.3未租房与房间数对比1424302.4已租与未租占比142431小结：数据显示蛋壳公寓营收在增加，而且大部分房源已租，但是亏损额非常大，占营收的比重也很大，特别是2020年受到很大影响。北京、深圳、上海，目前租房情况较其他地区要好，需要考虑其他地区业务增长，加大全局性考虑。 3.全国分析完之后，针对上海，分析房间租房情况。3.1租房价格1424323.2未租房面积1424333.3户型占比1424343.4未租区域前10 1424353.5未租小区前10142436小结：上海蛋壳公寓租房价格大多在2000左右，房屋面积在12平方米左右，户型为3室一卫、4室一卫。九亭房间数最多，就小区而言，九城湖滨国际房间数最多。这也是受到房屋面积、位置等因素的影响。四、数据报告142437

一.选手介绍： 企业简介：物流行业 二.作品介绍： 业务背景 公司对于培训工作一直很重视，部门领导要求做一个问卷调查，从多个层面了解公司培训情况。问卷调查按照系统设置的时间点，进行问卷回收，回收之后，需要对问卷进行统计分析。一种方法是用Excel列出每个统计问题的情况，一种方法是通过网上选型，自己设计问卷调查仪表板，了解公司人员对培训的反馈。选用简道云作为问卷调查、统计平台，进行仪表板的开发，通过图表组件汇总培训工作反馈情况。 数据来源 使用的数据是来自问卷调查回收的数据。 为了能公开数据，进行了相应处理，仪表板中数据并不代表公司的实际问卷调查结果。 三.设计思路： 数据分析思路 前期，公司通过调查问卷，对调查人员进行公司培训需求的信息收集，主要从公司培训组织、培训课程、培训讲师、培训总体评价4个方面，对公司培训情况进行问卷调查。网上调查结束后，需要对调查结果进行数据统计。 因为所有题目都是客观题，而且选项都一样，对每一个评价指标都分为5类：非常满意、满意、一般、不满意、非常不满意。所以，针对问卷调查的份数进行了抽样取数，选取80份问卷。 使用文字说明，也可以说明问卷结果。但是采用图表展示，结果就会更清晰，直观。而且通过柱形图、饼图，很容易看出各指标的评价情况，特别是调查人数的总体情况，在图表上一目了然。和文字最大的区别是，针对每一个指标，可以交互操作，根据自己的需要，筛选出需要的数据。 数据分析结论 通过仪表板设计，分析得出：公司培训的时间安排、时长、培训方式、培训课件、培训内容、讲师语言、讲师掌握课程的程度、培训的总体情况等方面做的比较好，其他方面则需要改进。特别是培训内容要和实际工作结合起来，理论联系实际，通过培训，要更好的指导我们的工作。 图表类型选用技巧 1.对所有调查问卷进行数据统计，采用柱形图，便于比较，可以直观看到数据的大小； 2.采用饼图，可以直观看到每个指标的数值，以及每个指标占比 布局排版、颜色搭配技巧 根据指标的分类，对每个指标进行配色。 http://bbs.fanruan.com/source/plugin/votes/function/editor/attached/image/20210105/20210105093647_26329.png 四.心路历程 参与本次活动的初衷是学习简道云平台。之前就使用过简道云做公司的培训需求问卷调查，线上提交问卷，自动统计问卷结果。通过参加本次活动，进一步了解简道云平台的使用，如何应用到工作中去。 通过实操，关于仪表板模块有以下建议： 1.由于是免费版，每个数据组件都需要设置指标分类颜色，且尽可能统一标准。后来，在工作人员帮助下，开通了试用版，可以一键设置仪表板整体样式。希望免费版开放一部分功能。 2.针对本人采用的问卷调查，由于指标分类完全一致，希望筛选组件能实现如下功能，可以根据指标值筛选。目前是可以通过每道问卷题目筛选，无法通过题目答案来批量筛选。 3.图表组件中，柱形图的网格线，设置是否可见。 4.本人设计的仪表板都是图表组件，目前可以通过鼠标拖动缩放，建议设置面板，手动输入组件的大小。 五.简道云仪表盘的价值 1.图形化、定制化统计数据，界面交互迅速； 2.组件丰富，直观、清晰的反应数据； 3.可以单独设置仪表板样式，也可以通过仪表板样式整体设置； 4.应用场景丰富，实现定制化需求。线上发布，通过公开链接，即可访问。 设计过程中，遇到账号登录、绑定，仪表板样式无法使用等问题，帆软老师都一一帮我解答并解决。在这里一并表示感谢。也希望通过本活动，和大家一起深入学习简道云平台，更好的运用到工作中。 141256 141257 141258 141259 141260 141261 编辑于 2021-1-20 11:08

很久很久以前，我还是一个程序员。 http://bbs.fanruan.com/source/plugin/votes/function/editor/attached/image/20201215/20201215181314_32353.gif 那个时候也没认识帆软，也不知道帆软报表平台，当时用其他软件开发系统主程序。项目最后，采用软件自带的报表工具进行报表的开发。虽然用户提出的报表需求数量不多，但是开发过程很漫长。报表取数很容易，但是报表的格式修改很复杂，行高、列宽等调整需要花费大量手工操作，最后预览的界面也不是很友好。那会还觉得，需求比较少，可以凑合用，但如果报表量很大，且用户非常关注报表的质量，那就需要另辟蹊径了。 http://bbs.fanruan.com/source/plugin/votes/function/editor/attached/image/20201215/20201215181501_39705.jpg 1、需求多，没人怎么办？ 怕什么来什么。就在不久前，公司开发了一个新项目，主要是针对客户关系管理的。一开始，我们进行网上产品选型，因为我每天都在使用帆软报表平台进行报表的开发，且将帆软平台和其他公司的产品进行了综合对比，就跟领导反馈可以采用帆软平台搭建我们的项目。后来，由于各种原因，公司决定自主开发客户管理系统。 项目初期，并没有什么报表需求，主要的业务数据，都在系统界面中通过表格形式展示，实现增、删、改、查的基本功能，完全能达到用户要求。可是项目试运行后，用户都慢慢参与进来了，管理部门和业务部门，陆陆续续提出很多报表需求。这一下子把我们IT人员给难住了。IT组不仅要修改系统bug，还要进行报表开发。由于系统是自主开发，公司领导对项目很重视，对于整个项目节点有明确的时间要求。我们的工作重心和精力都在系统上，但是业务需求积累的越来越多。没人，时间紧，怎么办？ http://bbs.fanruan.com/source/plugin/votes/function/editor/attached/image/20201215/20201215181535_28751.jpg 当时自己的第一反应，自己来，采用开发工具自带的报表功能：连接数据源，设置数据集属性，排列字段，选择布局，选择样式，生成报表数据等等。但一想整个流程走下来，要不断的修改，最终效果也一般般。于是，果断放弃这个方案，兜兜转转想到了帆软。报表发布之后，我们的客户关系管理系统，只需要写一行代码，在报表模块，添加报表路径即可。就算后期需求变动了，只需要修改报表模板，不需要对系统做任何修改。大大减少我的手工工作量，0行代码实现报表集成开发，省时省力。对于需求方和用户的要求，可以快速给出响应。 通过客户关系管理系统的开发，我也认识到，很多时候，这个需求文档也是有多个版本，的确有必要对需求进行综合管理，实现可记录，可追溯，可推动项目进展。在开发过程中，IT人员和需求人员可能需要协同工作，实现需求和开发的平衡，这也需要数字化系统的支撑。自己也通过这个项目锻炼了需求的理解能力，面对业务部门的需求，不再那么惧怕。就算需求多，也得根据轻重缓急一个一个来，可不能像一开始那样着急忙慌，丢了西瓜捡了芝麻。资源不够，就要向领导多汇报工作进展，特别是遇到需求反复的情况，要全盘考虑能否如期实现，不能达到要求就尽快向领导反映自己的难处，保持信息的畅通。不然，又难又累，只能被铺天盖地的需求压得喘不过气。 http://bbs.fanruan.com/source/plugin/votes/function/editor/attached/image/20201215/20201215181700_27913.gif 2、大胆尝试，学以致用 在这个项目中，根据这个项目开展的进度，IT组负责人也对项目管控有所要求，让我们了解项目管理方面的知识和技术。就在这个时候，通过帆软网络课程——如何提升数据投产比。如图1，数据团队和业务团队的交互。 http://bbs.fanruan.com/source/plugin/votes/function/editor/attached/image/20201215/20201215181909_33145.jpg 图1：网课截图 通过这次课程，我了解了项目管理的整个流程是可以实现线上化、流程化、规范化，了解了提高需求管理的意识和方法。看完网课后，我决定立刻尝试。立刻就申请了需求管理平台的使用。需求管理界面，如图2。 http://bbs.fanruan.com/source/plugin/votes/function/editor/attached/image/20201215/20201215181927_56377.jpg 图2：需求管理界面 目前处于测试阶段，后续，我想可以借助该平台，从项目全流程管理的角度，对我们的需求进行可视化管理，重点是需求的内容、需求的变更，需求完成的时效，需求完成的质量。这项工作，不仅仅是对工作的一个记录，而是通过这个平台，让需求人员知晓需求的整体情况；让开发人员知道需求完成的好不好；让项目负责人知道需求进展；让所有人了解需求的提出、需求的协同跟进，实现业务与IT的协同，最终得到高效的产出。 我相信不论是一个大的项目，还是一个小的需求，都可以通过该平台实现，让业务和IT人员实现实时的互动。我的角色也需要从研发转为数据运营，如果仅仅停留在写代码的层面，无法考虑需求的初衷，无法考虑用户的感受，无法将业务流转变成有价值的数据流，是没有意义的。也只有站在业务角度思考，会更清楚的看到哪些数据是可以直接使用的，哪些数据是需要清洗的，坚持数据思维，最终生成让用户满意的报表。 http://bbs.fanruan.com/source/plugin/votes/function/editor/attached/image/20201215/20201215182126_27400.gif 3、项目测试，平稳落地 按照项目的整体部署，客户管理系统开发完之后，进行了集中的用户测试。开发人员可能大部分注重功能测试，而没有进行过多的业务测试。我坚持业务测试，通过测试，IT研发人员明白了系统中的漏洞，后续如何优化系统；需求人员结合客户管理相关制度规范，也明白了哪些业务可以通过系统实现，哪些业务可以在系统中扩展。可见，系统测试是很重要的一个环节，也是对整个项目上线把关的一个环节，测试的越充分，系统就运行得越稳定、可靠，保证项目有质量的交付。 如果测试过程，只有研发人员开展，测试结果功能上都正常，那么上线之后，肯定一堆问题。通过全面测试，测试人员可以针对系统，提出很多宝贵的意见和系统优化建议。所以我一直坚持业务测试。 其中，帆软的测试任务给了我一些灵感。番薯参加了FineReport测试用例任务，就是测试工作的一部分。通过这个活动，不仅可以学习报表开发，了解FineReport的新版本功能，还能赚取F币，一举两得。我通过参加这个活动，也学到很多开发方面的思路，比如测试如何开展，测试用例如何设计，测试反馈如何提交，这些内容，都是我可以运用到自主开发过程中的宝贵经验。如图3，测试用例账号领取。 http://bbs.fanruan.com/source/plugin/votes/function/editor/attached/image/20201215/20201215182148_89915.jpg 图3：测试账号领取 通过自己的努力，我希望在今后的软件开发过程中，研究出一套测试平台，对软件上线前的测试工作，以及上线后运维阶段的工作，进行一个全面的管理，保证项目如期交付，保证交付的项目，经得起用户的检验，经得起时间的检验。 盼望着，盼望着，盼望着今后的我，在数据化运营方面可以想的越来越全面，成为一个周到的数据追梦人，也希望各位的小梦想都能成真！努力向前！ http://bbs.fanruan.com/source/plugin/votes/function/editor/attached/image/20201215/20201215182337_85517.gif

1.选手简介1.1.选手介绍帆软社区用户名：chenliang职业简介：现就职于物流公司，日常负责信息化系统的开发和运维工作。帆软的可视化平台，一直是我工作的一部分。 1.2.参赛初衷 FineBI工具是进行数据分析的大势所趋，借此机会，学习大数据分析的平台方案，希望通过实践，提升分析工具的操作技能。 希望得到官方的指导。 和更多FineBI大神交流学习，进行数据可视化作品以及分析思维的激烈碰撞。 2.场景介绍2.1.业务背景介绍&数据来源 日常工作中，主要是业务系统的运营，很少使用财务系统，但是业务流最后要进入财务系统，必须对财务数据有个全面的了解。 目前开发的报表，取数是业务系统，但是最终要和财务数据进行比对。所以对财务数据进行分析，可以全面掌握财务数据包括哪些经营指标，本年度如何分析，同比、环比如何分析？进而反推在业务系统中，如何取数，才能和财务保持一致。 数据来源：挑战赛中，官方提供的财务详细数据。包括2019年和2020年上半年的数据。 2.2.分析思路 围绕分析主题，拆解了财务经营中的收入、毛利、利润率等数据，结合销售额，按照区域、产品、月份、销售员等维度，分析2020年上半年，经营数据的整体情况，了解销售额、毛利的增减情况。 从销售额、毛利、毛利率，得到整体经营状况； 从生产厂家，产品，了解市场，了解供给侧和需求侧对销售额、利润的影响； 从销售员的角度，判断销售员对销售额的影响； 最终比较得出2020年下半年该从哪些方面努力。 2.3.数据整理（1）Excel数据源，如果表头是多行138400需要手工整理成一行；138402（2）财务分析过程中，需要使用毛利、毛利率进行统计分析。毛利可以在公式中，计算：毛利=销售额-成本额毛利率=sum_agg（毛利）/sum_agg（销售额）千万不能直接拖动指标值，然后求和。结果是错误的。 2.4.完成分析报告 对于全年的对比分析，采用条形图，很直观看出增减情况；条形图设置坐标轴、指标值、颜色、标签； 对于销售额、毛利同比分析，采用折线图，且只选择2020年数据，设置过滤条件，将2019年数据过滤掉，同时数值很大的情况，需要将数值格式由默认的个改成万、百万； 对于产品对比分析、销售员销售额分析，选择折线图，重点是设置颜色，保证按照年份，生成2年的折线图； 对于区域分析和生产厂家的销售额分析，采用饼图，很直观的反应占比。根据颜色和角度设置环形饼图效果。 通过图表的分析，得出2019年-2020年销售额、毛利、毛利率总体财务数据，然后从产品、区域、生产厂家、销售员多角度分析下降的原因，进而明确下半年需要从哪些方面努力。 附上最终作品，见导出的PDF文件。 2.5.总结 本人工作离不开帆软平台，有工作需要，也有自学。让我感受最深的是，在学习过程中，不论遇到什么问题，都能从多个渠道，例如论坛问答、官方文档、线上讲堂、线下课堂中，找到解决方案。 本次任务，也得到BI老师和伙伴们的指导。讨论群中的信息非常及时、全面的解答了我的疑问，一并表示感谢。 140015 140333

这段时间一直开发报表，主要是业务部门的报表。在开发过程中，如果加上一些js功能，报表就会生成不一样的效果。 今天是教师节，做一个模板，请各位高手指教！ 135924 135925135923 编辑于 2020-9-11 10:59

很喜欢鲁迅先生说的一句话：“其实地上本没有路，走的人多了，也便成了路。”数据人追梦的过程不也是这样吗。 曾经，Excel是我又爱又恨的数据处理工具 2016年之前，我一直在使用Excel进行数据的整理和分析，感觉Excel功能非常强大，强大到可以批量生成SQL语句，生成的语句可以直接在数据库中 运行，达到我的预期效果。 但是，Excel是原始数据，不是我最终想要的，可能数据需要清洗，又或者需要逻辑运算，又或者需要对表格的格式进行编辑，才是我们想要的结果。对！很多软件，都会提供报表模块，生成的数据一般是以清单的方式，列出我们需要的数据，这些数据是不能满足我们实际情况的。于是，我又钻进Excel VBA宏代码的圈子，学习表格怎么生成，单元格怎么取值。最终，通过后台程序，生成我们要的报表。还有谁记得这个界面： 129726 回想Excel处理数据的阶段，我是一行代码一行代码的写，一个单元格一个单元格的测试，验证没问题，再将程序发布出去。如果当时就有反馈，我要当机立断，赶紧修改；如果是后期提出新的需求，就需要回到曾经的代码行，修改完，打断点验证，没问题了重新发布……个中复杂程序以及其中痛苦，只有自己理解。 这个阶段，大部分精力在编写程序，测试程序，以及程序的维护上。需要花很大精力学习Excel VBA的语言，需要花费大量人力维护后期的调整，确实节省了大量的手工工作量，但是，工作压力就都转移到IT人员的身上。 更难受的是，程序都是一个一个孤立的，使用的友好性大大降低，操作不友好，直接导致使用率不高。我花费了大量精力开发的数据报表，最后没有人使用，没有让这项工作推动我们的生产，推动我们的数据革命，这真的让我失望又挫败。 初遇Finereport，强大的数据处理能力让我追悔莫及 2016年之后，由于工作需要，我认识了帆软，认识了Finereport强大的数据处理能力。太后悔了！曾经那个连Vlookup都不会的自己，为何没有早点接触帆软的产品。 这个报表平台不仅仅可以让我少走弯路，更重要的是，它颠覆了我的认知，原来报表可以这么做，而且这么做确实可行、可信。 近两年，我一直在从事FineReport报表开发的工作，通过这样一个平台，我们的开发报表，居然不用写一行代码。 另外，通过软件的使用，虽然不用编程，但是可以高效的学习数据库的理论以及具体操作,包括数据查询、新增、删除、修改，特别是夺标的链接查询，实实在在让我学到了很多实操层面的知识，并运用到报表开发过程中。 和Excel阶段对比，FineReport不仅仅是做了一张报表，数据生成就玩了，而是这张报表做完了只是开始，要花更大气力，把报表发布出去，思考如何才能让用户接受。 试问，你点个按钮，等上几分钟，出来一张Excel，还是打开一个网页，瞬间看到我们想要的数据，并且根据需要选择导出格式？实现这个就不是几行代码能解决的。FineReport，不仅仅有大量的图表，更动人的是可以下载插件，实现定制化需求。 感谢FineReport，让我从痛苦的EXCEL阶段，自然过渡到数据、格式自动生成的阶段，而且这个自动生成的是所见即所得的，不用打任何断点，就能快速生成报表，看到我们想要的数据。 学习学习学习，强大的工具也需要相应的能力进行匹配 FineReport报表平台，不仅仅是解决了报表开发的问题，更好的减少了IT人员的工作量。 对我而且，不论是学习，还是开发，还是后期运维，IT工作变得不那么乏味了，不再是反复的更新代码，而是更多的学习新知识，学习业务知识，支撑高效的生成报表，高效的生成数据，高效的展示数据，进而高效的处理生产中的问题。我想这应该就是指的数据生产力吧！ 为了把这个平台用好，只要工作允许，我都会参加城市课堂线下培训，并且肯定是带着问题去，带着答案回。 令我印象最深刻的一次是，我第一次参加城市课堂的情景，那时候我还是个小白，去培训之前，我一直不会使用复选框控件，就算是看了帮助文档，还是没学会。直到我参加城市课堂，老师细致的讲解了，在变量里面设置分隔符，问题立马解决。 129745129729 当然，要想把这个平台使用好，也不是那么容易的，需要多操作，多总结。刚接触的时候，对FineReport不是很了解，一边学，一边摸索。 现在，我在使用软件的过程中，遇到的问题基本上可以在线得到解决。遇到不会的操作，先去论坛问答区找找答案，确实很紧急的就会联系在线技术支持，每次都是当下解决，而且有跟踪反馈，切实为用户解决问题。 现在，我电脑一开机就会看到下图，FineReport已经成为我工作中的必需品。 129730 最后，感谢帆软平台！希望能在这里认识更多的数据人，了解更多的大数据技术，学到更多的分析问题、解决问题的能力！

盼望着，盼望着，小夜灯终于到了。。 125862