ADO.Net中DataSet的应用

2022-11-13 11:46:49

一、知识点描述

1、DataSet是ADO.NET的中心概念。可以把DataSet当成内存中的数据库，DataSet是不依赖于数据库的独立数据集合。也就是说，即使断开数据链路，或者关闭数据库，DataSet依然是可用的。

2、创建和刷新 DataSet 并依次更新原始数据的步骤：

通过 DataAdapter 使用数据源中的数据生成和填充 DataSet 中的每个 DataTable。
通过添加、更新或删除 DataRow 对象更改单个 DataTable 对象中的数据。
调用 DataAdapter 的 Update 方法，并将第二个 DataSet 作为参数传递。

3、DataSet所有数据都加载在内存上执行的,可以提高数据访问速度,提高硬盘数据的安全性，极大的改善了程序运行的速度和稳定性。

4、每一个DataSet对象是由若干个 DataTable对象组成。

5、DataSet使用方法：

把数据库中的数据通过DataAdapter对象填充DataSet。
通过DataAdapter对象操作DataSet实现更新数据库。

6、DataSet对象的三大特性

独立性。DataSet独立于各种数据源。
离线（断开）和连接。
DataSet对象是一个可以用XML形式表示的数据视图，是一种数据关系视图

二，思维导图

二，代码演示

private void btn_Load_Click(object sender, EventArgs e)

        {

            SqlConnection sqlConnection = new SqlConnection();                                              //声明并实例化SQL连接；

            sqlConnection.ConnectionString =

                "Server=(local);Database=master;Integrated Security=sspi";                             //在字符串变量中，描述连接字符串所需的服务器地址、数据库名称、集成安全性（即是否使用Windows验证）；

            SqlCommand sqlCommand = new SqlCommand();                                                       //声明并实例化SQL命令；

            sqlCommand.Connection = sqlConnection;                                                          //将SQL命令的连接属性指向SQL连接；

            sqlCommand.CommandText =                                                                        //指定SQL命令的命令文本；

                "SELECT * FROM tb_department;"                                                              //该命令分别查询所有院系、专业、班级，查询结果将返回多张表；

                + "SELECT * FROM tb_doctor;"

                ;

            SqlDataAdapter sqlDataAdapter = new SqlDataAdapter();                                           //声明并实例化SQL数据适配器，同时借助构造函数，将其SelectCommand属性设为先前创建的SQL命令；

            sqlDataAdapter.SelectCommand = sqlCommand;                                                      //将SQL数据适配器的查询命令属性指向SQL命令；

            DataSet dataSet = new DataSet();                                                                //声明并实例化数据集，用于保存查得的多张表；

            sqlConnection.Open();                                                                           //打开SQL连接；

            sqlDataAdapter.Fill(dataSet);                                                                   //SQL数据适配器读取数据，并填充数据集；

            sqlConnection.Close();                                                                          //关闭SQL连接；

            DataTable departmentTable = dataSet.Tables[0];                                                  //声明院系数据表，对应数据集的表集合中的第1张数据表；

            DataTable doctorTable = dataSet.Tables[1];                                                       //声明专业数据表，对应数据集的表集合中的第2张数据表；

                                                                  //声明班级数据表，对应数据集的表集合中的第3张数据表；

            DataRelation[] dataRelations =                                                                  //声明数据关系数组；

            {

                new DataRelation                                                                            //实例化数据关系，实现院系表、专业表之间的层次关系；

                    ("department_doctor"                                                                     //数据关系名称；

                    , departmentTable.Columns["Dep_id"]                                                         //父表的被参照列为院系表的编号列；

                    , doctorTable.Columns["Dep_id"]                                                    //子表的参照列为专业表的院系编号列；

                    , false)                                                                                //不创建约束（父列上的唯一约束、子列上的外键约束）；

                //, new DataRelation                                                                          //实例化数据关系，实现专业表、班级表之间的层次关系；

                //    ("Major_Class"                                                                          //数据关系名称；

                //    , doctorTable.Columns["No"]                                                              //父表的被参照列为专业表的编号列；

                //                                                          //子表的参照列为班级表的专业编号列；

                //    , false)                                                                                //不创建约束（父列上的唯一约束、子列上的外键约束）；

            };

            dataSet.Relations.AddRange(dataRelations);                                                      //将数据关系数组批量加入数据集的关系集合中；

            this.trv_Department.Nodes.Clear();                                                           //树形视图的节点集合清空；

           foreach (DataRow departmentRow in departmentTable.Rows)

           {

               TreeNode departmentNode = new TreeNode();                                                   //声明并实例化院系节点，该节点对应当前某个院系；

                departmentNode.Text = departmentRow["Dep_name"].ToString();                                     //院系节点的文本设为当前院系的名称；

                this.trv_Department.Nodes.Add(departmentNode);

               foreach (DataRow doctorRow in departmentRow.GetChildRows("department_doctor"))                //借助先前定义的数据关系，遍历当前院系所在数据行的子行，即下属所有专业；

                {

                    TreeNode doctorNode = new TreeNode();                                                    //声明并实例化专业节点，该节点对应当前某个专业；

                    doctorNode.Text = doctorRow["D_Name"].ToString();                                           //专业节点的文本设为当前专业的名称；

                    departmentNode.Nodes.Add(doctorNode);                                                    //专业节点加入当前院系节点的节点集合，成为第1级节点之一；

                    //foreach (DataRow classRow in doctorRow.GetChildRows("Major_Class"))                      //借助先前定义的数据关系，遍历当前专业所在数据行的子行，即下属所有班级；

                    //{

                    //    TreeNode classNode = new TreeNode();                                                //声明并实例化班级节点，该节点对应当前某个班级；

                    //    classNode.Text = classRow["Name"].ToString();                                       //班级节点的文本设为当前班级的名称；

                    //    classNode.Tag = classRow["No"];                                                     //班级节点的标签设为当前班级的编号；

                    //    majorNode.Nodes.Add(classNode);                                                     //班级节点加入当前专业节点的节点集合，成为第2级节点之一；

                    //}

                }

            }

        }

 1  private void trv_Department_AfterSelect(object sender, TreeViewEventArgs e)

 2         {

 3             if (this.trv_Department.SelectedNode.Level == 1)                                             //若树形视图的选中节点的级别为3，即选中班级节点；

 4             {

 5                 string  Depname = (string)this.trv_Department.SelectedNode.Text    ;                                 //将树形视图的选中节点的标签转为整型，即可获得事先保存的班级编号；

 6                 SqlConnection sqlConnection = new SqlConnection();                                          //声明并实例化SQL连接；

 7                 sqlConnection.ConnectionString =

 8                     "Server=(local);Database=master;Integrated Security=sspi";                         //在字符串变量中，描述连接字符串所需的服务器地址、数据库名称、集成安全性（即是否使用Windows验证）；

 9                 SqlCommand sqlCommand = new SqlCommand();                                                   //声明并实例化SQL命令；

10                 sqlCommand.Connection = sqlConnection;                                                      //将SQL命令的连接属性指向SQL连接；

11                 sqlCommand.CommandText = "SELECT D_id,D_Name,D_sex,D_Title,Dep_name,Dep_id FROM tb_doctor WHERE D_Name=@D_Name;";          //指定SQL命令的命令文本；该命令查询当前选中班级的所有学生名单，以用作数据网格视图数据源；

12                 sqlCommand.Parameters.AddWithValue("@D_Name", Depname);                                    //向SQL命令的参数集合添加参数的名称、值；

13                 SqlDataAdapter sqlDataAdapter = new SqlDataAdapter();                                       //声明并实例化SQL数据适配器，同时借助构造函数，将其SelectCommand属性设为先前创建的SQL命令；

14                 sqlDataAdapter.SelectCommand = sqlCommand;                                                  //将SQL数据适配器的查询命令属性指向SQL命令；

15                 DataTable doctorTable = new DataTable();                                                   //声明并实例化数据表，用于保存当前选中班级的所有学生名单，以用作数据网格视图的数据源；

16                 sqlConnection.Open();                                                                       //打开SQL连接；

17                 sqlDataAdapter.Fill(doctorTable);                                                          //SQL数据适配器读取数据，并填充班级数据表；

18                 sqlConnection.Close();                                                                      //关闭SQL连接；

19                 this.dgv_Doctor.DataSource = doctorTable;                                                 //设置数据网格视图的数据源；

20                 this.dgv_Doctor.Columns["D_id"].HeaderText = "医生编号";                                         //将数据网格视图的指定列的表头文本设为中文；

21                 this.dgv_Doctor.Columns["D_Name"].HeaderText = "医生姓名";

22                 this.dgv_Doctor.Columns["D_sex"].HeaderText = "性别";

23                 this.dgv_Doctor.Columns["D_Title"].HeaderText = "职称";

24                 this.dgv_Doctor.Columns["Dep_name"].HeaderText = "所属科室";

25                 this.dgv_Doctor.Columns["Dep_id"].HeaderText = "科室编号";

26                 this.dgv_Doctor.Columns[this.dgv_Doctor.Columns.Count - 1].AutoSizeMode =                 //数据网格视图的最后一列的自动调整列宽模式设为填充（至数据网格视图右侧边缘）；

27                     DataGridViewAutoSizeColumnMode.Fill;

28             }

29         }

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AcOLDcw/vpp59WaguCBatly5buBuK59957XbL5GWecYT179qy0TGlpqS1atMg9OOCDDz6wyy+/3EaMGGHr1q2Lr48b0qmnnuq+x2GHHVaTQyDqAIoatWnTpqF3IyPheqB4VPD6EumH3wHhlaoxrjmTm5tr+fn5OifXw/HgmaVzo+HIy8tzL87JrKysht4dUQP4/fyYUIiGQO6WJsp5551nt956a4W2q666ylU+O/300yOXWbVqlf3617+2n/3sZ+714IMP2iuvvGJ9+vSxP/3pT5XWJ4QQQgghhBD1gTyiteTSSy+1adOmVWjDw/nZZ5/ZRx99VKH9iCOOsOOOO65C27/+9S97/fXX7d///ncFLyY5gnfccYedc845NnHiRNtll10qLIfX89lnn7ULLrgg7nklHPjII4+07t2727hx4+zmm2926+jfv7/zrhK2e9111zkL2COPPGI//PBDvFw34vW5556z888/384+++w6Oz6iaYEXHOv2+PHja9xv+vTp9te//tVV4Nt4440jl3/yySdtypQpSbfxxz/+0X7xi1+kvvPNgGeeecbdD7hOq+Krr76yc8891x3nwYMHx9uxbnN/OPjgg22fffapcj3XX3+98zr+3//9X632vaG54YYbXJhm2BD3wgsv2EsvvWQ33nhjpWXKysrcsZo1a1bkOvfff3/3ucdXlQy2NSa45/N9o9h0003d56l6rnju/PKXv7QDDjjAZs+e7b4z5+Vmm21Wl7ucVubMmePOj0mTJtkWW2xR4TOee1xTe++9t/3hD3+o0JYqwWU9GHsfe+wxd+zx4Hrmzp3rjumVV16ZNI3n+++/d/tcVb+mAmOaUaNGuftTEJ4HK1eudPe0ZKTaD7iXPvTQQ+66xUgfBoP+TjvtZIccckjk8oyROPaMmRKx++67O0O+EKJmZIwQJZdh/oIFtnzZMhdylwhy8rp06Wr9+vW19u3bV7leRCfhUjyAPPPmzXOictddd423PfzwwzZz5sxKy/OA50FDnmYQxCTLn3nmmS5fNAzhtIQmsWxwGz169HCDTz4jrJMQX38T5SHITdPvNzfZZOsTjR8MCp9//nnK/QcNGmQnnXRS/P0777zjBlq+jXX16tWryvXQzxtOyDG57bbbbOnSpe495//zzz/v1sN5HLVdBmFff/11hTYP1YNZ38KFC1P+Xg3Fu+++675rqiCUTjvttPgx5ljddddd8TauP3LMU4HB2H/+859KuT2IK35Xoh5SAcNB1CCtqfHll19GztHLMeZ3SgSGRPKxd9hhhwrtTz31lM2YMaPSNhpzCOKQIUPcfZ/cf9Ir2FeMG6RlYMzEQBQFfTGcspyHa5doGaCaN2KeqJqmxu23324LYs9+4HjsvPPOzmiLIZe6Ch4qlfPMpVich2sRY+3Pf/7zKreDIZdriec19y/GHMA1j2GZsQUiauutt3b7sHbtWnf9IoaSQegxvx1GawwjLNuUwDASNNZz7Kk9wdgEoci9D0MYzwPqVlTFJpts4taH4ckvmwieIW+99Vb8t5g6daq7N3pwAHDef/zxx/G2E088MW7YIzUAQwKG/aCxz/Piiy9W6/krhKhMxghRvAQMuItig+ZkBWBiY7jYzWuRbdRho5SEKPDwwpPo4caG5TLYFrzReXjIcYPEOhgFN9j99tvP5s+f7wZA4SJELBdc9oknnnDWaoQo3g0KDuEN9VZUX4SImyvr44HJMh6WxaO6LCbWRdPg6aefdrm+DG44x1977TVn6U/kiQwPohmIMVCNEoRRsA3OIUQTxawA4Th58mTncWdAD7/61a9cHjMvvE14T8PbYB+D14gHgwgCuynAsWeg6w085HRj8Nl2220j+yOUgoYwBsiE1JOLnYoBIBnfffedu1ckup+EQZzwW2IU4F4TvBd4unbtanvssUet9qshQIzz3Ri0pgJz6J188skV2rxRbsWKFW7ATOG4xg5ChXMK8f2Xv/zFPet4z3MGzyZim3PVTw+FMYPjdNBBB9nixYvdM9K38XzafvvtK6yf4+BFnYfj0pinm7rvvvucMRaPMPUWLrroIicO8TAiRhGJcMUVV0R6e3v37l3JexqFF1tc34gq7pVERjE+uPjii91nXGs8X7neogr8IPg59l44eYYOHerulQgr6jyEiTKkNBYYZ3BeYTDju7366qu24447uilDOD4YfMKGeM8XX3wRacDnmkTUYwyNqqMwevRod5w4pxkPeQMV2+bZ4o10/rdn/zAM8PzEqRAWnUypFnUf5FoJGvSFENUnY4QoN/ZFsZv4rrvtFmnZ8vDAeuzRRy0/SShGsm1w40PAMuDHuhdVNMj3w2rKg5EBeiJP5J///GdnxWZATxgPVmpusDz4sZSyvzzYeFgG4aZ/yimn2DXXXGOHHnqoa0OAEkrMQALLNp7cYBjuUUcd5cKxVIWwaUHeL78zvzkPWLzov/3tb1NenvPAn3+cIwyGwucjVmzOOQZZnIeEhCM2GWQgQIGH9dixYyutH4/fG2+8kXS7QTinEbdNhc0339wef/xx9z8h8Ey7ExUGmghEE9c0x5gBFsc4fFwYtPprnOudQRP3EL8sggvPwz333OMGWhid6Md6gst62Ab3AQbfRGVAOCSf8wnvUDJvYkPD9+T7e88d+8xx4b5LaPdvfvMbd78NHlM8XP6cTQWOD/dQjAxNBY4L3xchyjEJFvTB8OEH4kTGeAFDmD2vYBtwrnEuIXZ4ZgW3wbHHmIWwaMzwjP39739fqR0BdPfddyddFgPNyy+/XKFt+fLl7trzqS0eBCvHCU8bcK/kfurhekMIE710/PHHV9oWBgRSeDDcRQlVvHO8wvtCmCuCuzHC9+SF2AtGaSAW8YzyHCFiJgoKMGLk9JE1QRCXGFuC8NzgnETwYmDlXsA6+Eu0GGBciUp9IGw76C0NwnMu6lkV/G2FEDUjY4RoOsA6htWZfAEGL+QW+AdSuN+BBx7ochzwYlRlySS/gZskD1K8LyyLoOQvVmsEZ/jhFAUPNyzADMwYKL/99tv2aEx0e7Aw4kllGyJzwFvvz0EGrQxesVIHIXTvkksuqdDGYArx889//tO9x2ASVSkaYektz0EIzYo69xngcY1kCgyeEEwMrDhWiKbwceE9YWWAd4VjznK8GLh6gUCYGGGEHD/6ITo49oSQBSGEkPsHuWuJcv4YoJPL25ghsoOoEX//wyCAAdB/X38+YvTwx5RB6DHHHNMwO5wm8Mh5cYiQrM335V7As4FnTTCaiHMomJLSXCGnMRzNgYGY40reZ5BUqpyzPqIRkqXBYLxLVdzjKeX8boogQAmdxQhH6HEUePn/8Y9/pLQ+om/C5yTHmTZyRYF7Q9Rzh3ER0ThRTJgwoUJ+rwfDbZTxVQiROhKidQgPFzwaWDJ90SAGmOGHVYcOHZwQZABIojw3RSxrCE1ueL4wkW9jfQw29913X2fJZ1kEJDl5eAKwiAZDeQgNRqhSUIZQKsQFnhq8tHi1+AyBzHIIjFtuucWFBbLPbEM0PRAgeLR8qOf9999fqQ8FGYLFheiHZxzjhCdREaKwxZp+GF3wjHo4lxKFWIVD99iXqgpvEG7cVEAQcq3iOUFQfvPNN5X64C2hMJAHiz1CzwtM34Y1P9gPglN28NtwryGMjMEVBi8/ECWED+84+4KXhGIz4ek++L3x8iA0GeAhUsLXPWINLxh5WI0Zwh+5D/rpByhuFTwnERDcG8lv9IWGouZZ5p7qvdoeRC7neFOE69UXJgo/fxJBP/Kdee5gDCI6Bu8+IfjcK/AoEW3hQx0598iLbArFizDUsK8ef32Ro4khIwjfL1hwiAgnvMTXXnttvA3vHucVUSAe8hWJTiH8lmuSewL9ODeD2+A+SSh0MjjuPONJvUgG1yn53Y1ZiOLR5D6HcGc/EeL+eUB6RriIUxjGPxjogve5KAgRRxQG00+4l3KvZIzD/Zf9IHUhWV7uNttsE/+f1CaWSTa9iY/GEELUjIwTosVFhfbpl4ts+szKeZB9erSzHbbq6h7CQG4CYo1CPljteCCHQ9w83377rRv8M0hkoEOoHGG33AjDN0/yQwhL8dCXwRQPFLblB4W+zffxsCyfkb8VVXiJz/B2+iqS5FkQsudDgcjL4LtR2GK77bZzDwKELt+PsGWfI4rXhQcw/aLCiETjgN+a0DtEoBeC4SJEQI4UEFLHVD2cOwzcg5VpH3jgAWeUSGSQ4NxlMIqA4bzGao9VGPDCRBWLAQbGnGtslzDzVCCHCLiOqqri25BgcSfcjkGVrzzrixDx3ofnM6jxIHq4Lhk8BY819xGuuWQGIa5TwoHxZDNoxrDF+jBoEY3h2+gXtZ6+ffu6fCc+I7+J3DbOBX4fHw3B4I9zCUNZU4LfgOOMdx5jh48a4RV1LBi08hsRukehHsJvERTAfZZj0hTBw4QRwkc3hHMOo8CI4XPrOF58d44lzyRfIId2zj0G8/4caszFmzw8w4gU8CkxPuKC57l/FiOYyO2OEtZcJ0RwcK7wTEWsc80El/UeToTrbrvt5sRLuB9wfSNSOY5huPcSycA9D+8cy+Hh575JER2fg+/hd8ZY0piFKM8dvgf3fXLqOcbesMlYC8N4OD/bQ+42zxeeb+Ta8uwiCgdRSwGhIBxPjP8XXnihG8ewLL8bx5vZA3w4PsfTP1uiIB2B8zxRmG4UPvKN3yjVvHQhRDkZJ0SLiopt+Yo1Nu+H5ZU+yyortsKh7eNClBsgDyzeL6ui2i59KSSAp8jnM/CQYyBU1YTb5B+QRE8RCXImUoFBFl4XPDDBbSAGsND5NvrRRtgLOaB4URisMUhlQIqVkRBhQtu8F4ebvP8+DDJSCTcSDQfGEn6nYL4M1nt+w3AODWAhJtT78MMPdwOm6sB2eMgT6uSLFXkYwOIN4LzBq8+5xuCA6wav3LHHHuu264s7eIMJgocBVSKSFRdrDGCtJxSfa98PFBF35CZhCAgfJ+BYcR1yTOoCBtiIBtZXVX4tobo+VJr9Y/CECMOD6u8BiLFE3u3GDoNyBD2/B+dqoirERK9wjuJ94oWQ4F4XFCLcw1MRcY0Nnlt4vbleSf9A2HgwjmIoAa5H/7zDa8f1Srg2sAz3CK4/nk/B84HjlSivrzGCKCdKgMiRYOoJ3i9/j8RjSj+enVFgnEGkYGzjvsV55s+VYNVpPMc8U4lGoh/3Bwx2HD+OJZEIXK9RQtQvC1zPbI9jz7WJAZBl+O24Tlkfojho4GqM8Kzge2AQ4XzkHomIxDjAs4DvmygkNmgkJecbAUlfjhMee6CNMQ+GFN8PuAY47hgCgwZSjAMYnLj2MahwL+a3xGNKG4Y8RHMwqoVtcC8IVlSOItH3EEIkJuOEKA/dHbbu6V5RBC2LWII9hNgmA+txuOgBy1dVCAGw2HFT5eYYHDBUBeKRAWQwDJOHFhbZYBuDBm7GCBNypsgp4T0PSjwneLcIF/JheAgVxA1WWKp5isYNnokTTjihQs4Rg+yoYkAMtLG6+zBFHsr09UQVK8Kw4itIIyo5V8lLJryM9wyMED8U5uABzoCDsD0KUwTbGMj5Ij5sgwJGfqoDBiVNFcQOoYvcW/xxCxYS4hrzMBBj0OQLp3D86OuJKlbE8kQ/eDCK4W0JRmfgqeFaZTk+S6VYkd8uhifypvgtgjmhfh/YZ5ZPNk1CQ8F3454Z3DfOJcIaOfZ8B+5lUcWKqDAcDMEkhYFj4ds4fhjvUq0o3ZggRN8PyPn+/hjxN1hwCBAGvjAOzxRCJ4HngQ/7xJCJN8uDuL/zzjud9weDZlRhncYE1xW/P+eEF97VgfOF68CHdPpiRcHzJ2xM4zmLQYp75+WXX+6EO8Yn7tVAji1wHbJvbCNY/IhtEOmAt5Vweo4x/TAwIKZp4/ds7MceEHJeOBImy3OIdCEM7z53E3j++OuUe47/br4IEaIVoUgIru/n21h/sKgRxZswEHK+sx7ubzx3ML7+7W9/c88nvM8YIFg3nlSfO8p874ilvscAACAASURBVLzYLr8PBns8rI05MkeIpkrGCVFuLPn5iT2bBQWFNXpQNQQ80MJFKAih9aXiw/0Y0PLw4sFImFEQvAeEyAAP7UwoQtFcePDBBysVlcEKjxgJF2XAAhzMO2IQwEPZE1WsCENF2CBB6CMeTR7OVD2sbnEhCh2xDTwG4RzGpgah7uHj7AsJYfkPWuMRAxiLvDcEDw3H0BNVrAgh9Pe//z3+nuub34hw+yBUy/UTq6dSrMjnrSG+gFDhqCIe7D8CJSq3sqEhzxhjXPgcwpiGiOQ7IaSiihWxbLASLgNXriVfITVcRbgpwfnF9+A88cIS711wntAgUdcgx4PnBZ59723yIATwxBHuSo5zeJqXxgbPN4xvGGmS5fslgntccPqOqGJFiSox42FFJBHxxD0vPL8vEQnkSGLsRqR5yPXmPV427ts+BJr7g28jTLWxVywOQ5gsRkieIeEiRJxLPBOAfHkfEk6kDWMSBCP3rqBXmzoHtGHwDBda5L5HxWvuX9TfICcY73KqYGRluxjlm8L0TUI0RTJOiCIykwnNdItQ8jC5KTOIjJrqJRGIRqyrWBWx8mGx4wbOQy1omQ32g0TzvSFSveeGB56mcGk6hOdARHzgQWOQiMhksOTDt4KeNb9sMKclqlhRsHgDlmwGEliuscbjXSUXkvOPZRFfWJ4ZqPIQ923hcxvBxaAYsZAM1oP3tTHDID04HQBCn2saUcNxQcR7ww7CIBhKx7L080QVKwrnhfHbUFSGvFR+aw9eAdZNKGmiYkXsJ4MxvLIIDfoRzku/KAjrx7NWk8F7OkAkR03twD0MLxT3Ro5VVLGi4LJ4/zhvGRj7tqackuC/G+cb3wOvD2IIYZMop5P7BF4mBt2cF5w3RMqwrvD0S6wT4eW9840dvFuISUKRuSarA+cE51CQqGJFwDWI8GEb3JP5DThWFF5D0OOd8/OAImS5N2JkwigSTsvhuvTzEwOGEd4HCx1VFSraGMCjy32E5wbHhQgtjGuIxGARJ/qQ0uFDk4Ohy3jdec74MHnukRgzOWf9dRouGsR9kvsahnf64KHmXPah6TyD2BYC1kf25IWm7WN7nOPca/39Iwp+O/alqjQsIURlMkaI8kDuFnsokKgeDEUMgxDtGLtRtmpV+3AX8pT83F5UYAxPio03iqJBWNq8tY22GTNmxPvwMAt6IrCEMlAAQn1YngccDyQs3jzYuOF6CO3x84iK5gnnAUVDeJgy0MFyzwMdkYNXlMEX3iCKRIRBTAbDwasqVoSBgqIRQDEHingxZykPe85vBCreJ/rgKfRteFqDsH8+dDARhFdGVZ9tbGDc8ceL/DpENyF8TDtA/hlhkvwODITDMNivSbEifmu8B3g9yePD64lg5cU9IFGxIu4R/D4MnHxRI+4vibbHoLEpgtjy3hTO/0TFijzcczm3Md6E4VghahkMN3YwguB1I0+b6sEcB+b5ZJDu84K5R3A/4JnIcSGsm37cLygsQ18vVnm+YNhEmDb2XO1kIGr4/Xn+40ELG3eSgegkRDn4HPVFiLieOJ4YgIluwDOHgCT8E+MTBcXoi2jnekOEcj8A9sXPweor5XsQbz6PN7hNpicJFtrhf4oV4R1trPhzivPLG7j5PTBgBHMqEeb0C1+nPEcw1AfhN2FZjO/BsHyiTTifaeO34UXkDilJ/C48KzmGeKV94SQEMM8nfodg+Dn44kfJ6oPwu7MfTcEgI0RjJGOEKJawYUOH2aLFiyw/ZPUKwsOWG2aHjh0S9gnCwAWPYxTcKP1AmkFheLoKBgsIyGD+KW1+GQaJeJT8RMyAhY7iE1h42TYhtiyDZ4QBMPkjWAo9bDcqpM4/CKLgJo6lMtXCSaLhwapLoQUeioRi+yJEWNGx+vKAZnAZ9G7WBM4ZtsODm/M0aEFGaLEffIbIpJKgbwvPS8pDv6qQMgYITQU/nQoDS8S3L0KEIQkv1Lvvvuuu52B4X21AwHJf4Hdl4JaqYGTQ6qck8UWNmO4p0fIIkeYOxxBjgq8qHYbBMaGYTQG8uogmhA1GDUB4UfTF5yZiuPTPOQb5PDMofOO9UxhCgusjV88vG4RzkGJ44SiLxow3ElEfAXEYVTAoCu6bCByuca51X6wIYcN9leudPFrufcGCQ2GCxcnCIaZBfEGiINxrEa/h9sYsQsFHgyAegzA2CRZyQzDy/cIgwMPfGUGPcY/7UzD1gePP7wM8C/mcNi9cgfEQYbrkOBOR43NEacNrG4RzO5gHHIWfXUAIUTMySohuscXm7uXhhsVNjnCKmhbiSPTAAW543NgY5EVZk33uTlVtQXxxIQ8FkYK5YgwMeFUFYXuJciXYRrighWi8MNBkME24E6GdGEbIqfEgRgnLw+uDZRiPJ8aLqOqqWKUJYwou76Ed7wjGEYpAMDgl9NtP3wKc6wxcGZxhyGDwlCwCIQyDO19Eh+0lCiVvTLDPWNkZqBIOxqA+ePzIESMyAnFOhAKeNX4zn5sZxBfWiTr+vtAR9yx+ZwbDDH6Zu5Df0guJYLEivx6/bNR9Dm8DuWZRJPMENFbYZx/+CL7iefCYco9DiHgjHucyAj3Yh2MVFOj0ZVCbrMJzQ8P5Fy6oQngtBkzOCb4Dg2u+P+KU//GO4hkN5y769YFfNng+IMLCnqrGBPvMecB39L+Zb5s0aZIzACeqWu37cf/xy3JfQsQG75uEa3Kf4x7HdVnbqWxYN8c5ajoTQvy5l/g0myCIuKgQ9aYO90OeZ+TLBsHzTyjsFVdcUSkclt+JZxD3SMJ5ST0h1znYj5xcvOL+GUNOPRV8vYhNBbbDvZ9nHOdAU44YEKIhyRghGgUWMiySDOTC3sq6gJAwHtQ+30aI+oBwbs4zzmfCx6LyexE9DJKwOGPBpR/iKVG/sAcTCHXk4U74KQPbqBw6wnQZMOGVIbQWEcy++bDAqsDD76dPQPBWVa26MYBXiWODuCSXKFhYyMOAhUE8/TAaEF6PiEzUL6poEIMnlsUQxW9MRW7a+D0xiBGK6fHFiq688soKy0bd58gj5neLgmI+iaazaKxgcCEM0hN1TBnM4w0hhwyPIN55jmMwXBwjixcqpGyQD4hnm2iUpgheJcQn12NUCHJVy+LZQhg0Fbi/8X25P3qvoW8jLzscDhu1LP0QGQhEqjEj2KPqSDAPN4Yh/tZ2nznOUQYg7i9EiQQLGnm4T/qUneYEufLhgkbA8UEAEvkTJf55TvHswQjDvTI4vQ6QjkJYMwYZvN08qzDgV2f6FWoX8JtjwOIe4ivLCyGqR1bsoV2G9Y+HNflhDKKqYxVqKN7/8DNbunyFHXn4QVV3TgCDaSzgeIgSVbyrDX4uTizHDARrA4NWQk2wACZrq876GEBHDZpF3UM15jvv/aftOHJbGzZ0cGQf8i3J90PMENKKJbeqgilUBQ16f6oC6y8VLhlQBT2ZqS4b5TXhOmJ97G+4YITfv0TLhsGz4PN0KPARXl+64Ngw0KnKy0AIXXVCiLGaIwL8fKvVwS9LQRkGV8GwQnLQ/fysiZbdaaedKkwlw4Ca3w3PQKIwfAa/FJViu8Fl0wVh3QzwUikCggD1nvhEc4d68LLQj1BqjgteL3L3gvn5iPZgYSnWideLtvowXFYFg2TOy2CqRioQlshvzXfmO/jvC3h4E7Uhzr1H2LexHkLPE6WjpBO8gAiMRAKAfeb35b4T/B60YZQIG4c5z7nf8N0wyPF96YfniyiDqPmAg8ti/CHaIdiPe0OiZYPb8Pvi26o7BgtvN11wj/ThyFV5gzl3+W48f6IiXTB2MBblvh9sC09Blgr85lHbIFwdERvcBvCM4lmVbNkwGOkwzHDOhNfXlCBiBGMkxyBciV2IdJDRQpQbEoN/buDhefbqArxP5A6MHj26kkWuuiA2seAFp2GIagPyIsjPI1Qw0Q2V35k8GapJivqnvoSoqB9SFaKifqmOEG3u1FSINleqEqKi/qmOEBWNEwlR0dBkdGgu1l2fP1Qf+DwtX3I8XTBYwQpOeAphPlj+KTqB0PFQKIE2wiw9eFgawqoqhBBCCCGEyCwyWohSjIKKk/VlbSfklykcarJ+LI0IRQ9hP7TNnz/fhYIQEuLbggU2CEGigBF5JOS+sg8IUcJwySMhhIfCTYTc4QX3xZHw3lLQpiZhvqLuwTiCQUEl4RsGvE/8BlEFhUT68FWZm0KUTn3DseClc7IcrtGq5gUX9Ysv1uNrC4imB9eRxhmiIcloIdqYixWRS0GYpoebPQUTKKBA0ZG//vWvLsGetmARjjPPPNMVLWF7eESDYUu0UT2OHBgmcGceSNYBxx9/fC2/rahLyFUjv0gP94bBD251/BsWBCi/gX4Hi4suVecsR9dow6NzsunD70dYbnAaHCHSSUafeeSXIPbwGtYHeCLxQtYkh4VKbsH5QH1hIsqYk49x1llnuZt/uFgReYXAwzmcH0obYrRbt27OS0sBJf6H1q1b1+QrinriqR972te5jXeaiObO9hutsEMGF6RUYEnUDwyQKCzjCwtlOhSjIiKmuhVvmyvB4kKiYUDAYHCn2FVUtXbR+GEeadLHJERFQ5HRZx7hCNxEEYr1VTX3u+++c1UWq1s1F4HI1AJA+XnWQ+GUPfbYw7WR40kbIbo8kJMVJhJNjwUF7Wz6uk5VdxT1Qp9WedaqVZkLZRcNA0KU+ybGM/0O5c8T0jV0LMrx87zqeDQchOb6dCEJmaYJY0khGpKMjqdoKsWKmKPqjTfecIMQQmsJKX7yySddG0L6xhtvdDmfQgghhBBCCNEUyGgTFhZVvI71Vf49WKyIEueErmDhJ7EfSy4W7nBbMNcCActcg+wf3k7m1KLMNqG5vg2LJB5XQmR8exCKEHlvLOvDeokAZ75G8k4RslDdOSWFEEIIIYQQoqZktEcUz+LkyZNt1qxZ9bJ+RB7Va5kE/emnn3bTp1TVFoSquMwnOWbMGBd6u/POO7vpVk4++WTbb7/9KrRNmDDBXnvttUr7cO6559qtt97q/sdrSmEmihuxPT+ZNi8KIAkhhBBCCCFEOshoj2g6ihUdeeSRzku55557uoJAbDNZWxA8l4hRPLf0wbOJ1xTvalQbocAeRC1ClcIWTFFDPmmHDh3s5ptvdt+X4kdM6fL3v//d9b/00kvr5RgIIYQQQgghRJiMFqIUK0Kg1WexotmzZztBGqz6OGTIEPc3qs3z7bff2r333us8ncOHD3dTedB23XXXxdsI1QX2H88nwpLvMXjwYLfsFltsYXvvvbdbNyHAl1xyifOwIlw//PBDW7Bgge2zzz7xfVWxIyGS8/3337spn1KBa+70009PaOiaNm2au2ZPPfXUau/Hu+++a59++mmNlm3KYJzDmEYqQioccsghGVX5+KmnnnJT3vC9awrpIETMcG7VV0Vafj+2ccopp9R71dvbbrvNtttuOxc9lApUESVSiWvXV4KNaquKf/zjH7btttvaqFGjUur/8ccf25tvvummewsW/qE2BMfqpJNOsoEDB6a0LiGEaCpktBD1uZJBT2JdwhyfFCuqyWTs5G/yAGKaFnI/gVDicBvgGWXQcO2117oqur1797bFixfbVVddFe+HOJ04cWLC7Y0dO7ba+yhEpoHxZtKkSW4+Xww6iVi6dKkTmkcccURCIcrglsiEqsQkqQOffPJJhbYXX3zR3n77bVdJOwx55higku1fU4V7NkY2xAD3NN/2yiuvOINbuI3prDJJiP7000/ufOnYsaM7R5PNsUlKCIbScD+eMTw7eCYkEoksizGzOrCdrl27uv+pXXD11Ve7VBH2mf1Ixl577RWfaqy6PPDAA+4ZnKoQ/eKLL+zuu++20047LS46o9qC/Pe//3XHKmhQZrodIpqIWoo6B0mVISXGFzPEyIVA59wNro97CdO2kY4jISqEaG5ktBAlvBWPYH0VK+rZs6crVoRQrC5YUu+6664KbSNGjKjUFuQPf/hD/H8emqJ50aN9CyspM1ueW7EKc6c22da17YbB0U/rSmxtYbnxIzs2vuzbMcdaZpcPNPOLy2zRmuKUlhXRMLDE6ENUAlAwDMMP00hwTwFEKIPnumDq1Kl2zjnnVJqmgmrfzFMc3C7gTdlqq62apRD1EBWChwgQGeS5R7VlGtQPwLhBJXXOv2RClGm/eJ4g1onOIU2EFA+Ez6BBg5LOLY2AIvWDc49tYHBFQEUZRjxUf6cPryB4cR988MH4ezyyRPYgpj2PP/54SkKUZcNV8HnP/vnCfB7WF9wG0I/rmeMRPHaktWDURSwOGDCg0rGhDsMuu+xiRx99dNz4e9lll9lFF13kjjHHiRf3CcYbbBfB/5e//CVeXf/ggw92RhbAi4twPuaYY6r8zo0RjPAIcYzibdq0qfAZYptjDNxLg8eaoo0YJjwYLoL3sfCyHNNggUchRNMjo4WoL1aEVXbLLbes8/Xz4Pv3v//twnmYC0+I2nDFvj1scUxE/vXlpRXajxrRyS795YYB4PgnFtvj09e4/9u2zLbHj+pvQ7qXV06e9n2e/eq+BQmXnfDkYnvsizX1+TWaHYTX4j2iWBjh8PXBNtts47ygQT744AN37/rnP//phGeQ5ixCRXIQNBS4q2qAzjmLCNhpp53sP//5jwtfRRROmTLF3nvvvSqfWcOGDXPnPB5CDCUIKgRwIhBff/7zn12/E044Id5+3nnn2Zlnnhl/TyV7DCwnnnhihWVT4fjjj6/kqaVeAmksN910U4V2vL7ecOHBS0tUEWI+OPf3gQce6I7VbrvtZs8884ztuOOOFZbD8EtF+4svvtiF0XpImeH65PtwrPje/D7jx4+3TTfd1IXYI8w5LkEBjXi98MILnZDFC9vUYOyDIeTRRx+t5InGWML3hqFDh7rjwswCwD0Og5KH2hXB789xw8jnl8UgUl+OBCFEeshoIZrOYkW1hfUQmlNVW3XWRxEj0fhp2zLLbjuot+03rL099GnFysp/3qObbdQ62454eGG87cAtNrLu7VrYu/Py7O/79bDr3l4e96Ju3DnHnjm2v5329GI7ertO1jG07AGbb2TdYstOfm9ler5cE4YQ3ZdfftmF3xFqyMAZTw4DfKIX6hK8nAzGGZTNnTvXtbFd8twZ7OKxAYRwMjHQXLj++uvtkUcecf8zkCccNaqtuYO3nO+dCggfPG3e+4RXj8gbhBKfIdTmzZvnzmu8hb4f20B84fULilvOSTxWCFG8XvT3obeJwPgb9ljyfAw+I1kvwqSqdUWB6Pztb3/rDDQeUlIICw63ec9aeP+4psKGHI4V+0gqD5+H4dokxD74GeKagoRERWHo5nuxf367HDdfl4HjFzwurI/82ahtNXYQh4hyvL94Rj2cW+S/HnroofE0IUQ/Y7Arr7zS3nnnHVcLAyOAhxBlPjvqqKOcBx4Rf8YZZ8SXHTdunCu4iKFOCNE0yWghms5iRUHrajIIUeqZN8cGdWlp//58tZ0+qos99/Va69E+xwbH2i596rHItt/v3MWe/2ZDG8sG2zbp2tL+9VmgrV2srduGZV/4Zp0TL7T5fsG2f8baTl/f1q1dtm3arVW8bWqsrWugjWW77nCQWY9hdX5MM5GSUnOicru+bSp9Nrx3a1u0uthenrVhHtjf7dDJhnZvZV//VGijB7ezs59fYt/8VJ4HvXvs/Z6btrP2rbJteK/W9uPaisseN7KTDStrVWk7ojIMLsnvIlyPHE48Sxi18BRFQb877rgjYQEjvAcMeIMD5iAM9NkGoZTAwI4CJ7QRAoeXAS9pJoDg9qG3iM4ZM2ZEtjV3CE0Mh4BzDjA1F4P+YHhp8JzD88m5iyggnxiRSVgoIc08q5hazIPHn/MMccQ6w3mjLMu5x/rw7IXp06ePWy6Rh5Ztsb8e5rpG+JKP7UHkERKbCptvvnm8CB8gKjfbbLMKbVHeXooLEVabyLjLNUa47QsvvODeh4sQsY0g/A4I/IMOOsiJTsQZnmpCeIHQXApvcdwQYQjR4PGjsCDb+PLLL1P63o0FjhPV+sP3IsZDeD//+Mc/2ujRo10bwvONN95wAp9ijISFB38nPKTTp0+PL0v60e677x5fluJOLCuEaLpktBBtjMWKFn/0os1cONPe7DbUbpq23LoX9rRvlxbajhu3sd7FrW3unBVWtFkP+3H+KmvRMSfeVry+7dv8UnurzNyy3WLL3v/xKpcjuFXP1vaP91bYMYMH2VPvLDF2aauYELlxfb8HYv36dMiJt3Ut6GkPfrK+LSZ2bns3tuwglv3JikvLnAC64e3YsrF+D326ynptlFPe9saP1m3GbDuo6xbWX0K0TigsKbMp76+0PTZpV6frfX9+nvWM/W6Hb90h3kbo7+eLC5IsJRg4EsrIgJyBJgYtBvy858U9hdDGMORMUXQkUQEjcqMYfGMUC4sL7iPk2CEGfJgi++G3yyB95syZbnBGPl1zLVbkQSwE80ER8VFtzR08mryCYOzAa07IayLx54sacY7gLeS884V2woWDOB+33npr5ylFuHohikeeOawp5OOXpQAXRaLwBPqcTs7TcF5oEK4lxJ0v6IPH0BcwYhusDzGXqhD1UAyIZTk+5L4ijBIVDaLf559/7raBVzTRuUPuLIII8YRgx+iEBxBxxPfluCOouP6C8H249ll25MiRro1xB4Zw7h/sA+OF4LH32/Bhq00F0gS4DlOtLi6EyGwyWoimo1gRg6PqFCs6ZruONqnVL+za7GPN9jcjO+Lxs/rZXjERsjyv1M7/jRkZfAfvv2EZ33bK4S1iInKFnf/ST27Z/+PD8meePRl7dRvbwmzsoNgXX2JPTl/j2qL6tRhjdnWnlpazj9mS2PvPYq+Bm7ewnEP7W89uy+zFb9fa53y/2KquY6HYM/ebmAB+KrfEsn+5yq564kjbJCZm+tf4yIl08K/PV9tx23eyK/frGW/7++vL7Pmv1zbgXjV+GDgSDhksEMTgHM+QJxz2R0geoWRUdQ0uF4SwWzwkF1xwQTwPyoP3809/+lOFNrbBIP+GG25w62QbFPugAEpzL1aE54RBPCA68SZFtYloyNMjjNkXNSK8kePHORQFohWRFSzSQ2EgPHhPPvlkXAwzpRBe6SuuuMJ56lOFffCF+PCmUaSHUEy8sTUtOkUEAikoeM3wrN1zzz3OOxucNg0ogHP++ee7EGW8s1xniEQMR/T3xXZ8G9WqCclHrBKOz7KIdLx8r7/+uitkFsxTRQQTuo+I5R6A6OTa5J7x8MMPu+PINUs/pmfzxXuYbo3f6He/+12Nvr8QQjQFMlqIYvnEakcoXLjYR11Qk2JF50xdYo+Rsheaeuyej1bZpa8tS7rsfYf3qeGeVqRTmxb29LH9bWDnDacHD0byCa/ev4dd9svKlRFveGeFXfLqUuvUunzZrYaogEBj59J9etiaglLb4Za58bbrDujpwsIv+M9PiRfMcBB9XNfJChNRkRQvjof+vAhh9Pmc1QGP1EsvvVShDQ8P4Xu+WNGdd97pBsf0S7W4S1OFIiYUlvEgoKLaRGI4PzGUeq8puXiJqq0Ths65G3yOEYqKh68pFOIjj/qhhx5ywpR9DkLRG39d8p3IXySsG3GMePVeVN8GFGfCy4tIZdnDDjvMCdqoFJznn3/eGYvwcJLriPCk6I4HMUsuJOsjr5L9Q/ATwkqbzwkXQojmSEYLUTyVVLPDc1kf1KRY0R9362Y5X3e0e0LtecVltiJvw7QdF/6iu81fVWR3f7gq3kYIZ11AWhHTetw8bYW9NTevwmd/26ub/bCm2O76YFWF9rkryosSMPXH6VN/tCt2zrPRda/tRR1Cnii/V/C8atkiyxVHEolxRpmY0CMEEs9IGIoVUYE0CN5L5gauatDOsnhLKHBCYRk/LyaFThgo4zX101DgWcE7y6CY8Ely6yjw4gugNEc49uTy4T0CqqFi6ONYBedw9NRXFePGAiGt4WJFeO0ItaX6bDBHlOMTLlYU9HBimMUbefbZZ1dYH8WKEEqcV8E5NBFIhJkHt8Hvgjea3yTskcfbny4wxtx333323HPPuXOA8FZfLChMsGiQL5TkDTlcc/6zoHEnGC7LNY2IJ9yeMN0weObJcfT9CGfGYII4Bo4ZfYjOoqouBgK8qmHjOMWPWAdhv4RdCyFEcyCjhSiDOHIyeKjUV7EiQpqqU6zos0X5NntZYUwlVP5sWPdWdsIO5Q/3g7fq4Cqh0pZbVOZEY13zxeICKykts32HbdgZcgpb5mTZ3kM2PIip5DprWXmebcvYOGWXQW2t10aVJ/0WojmBhwRxiVfSg0eSHLGgECW8jrBHCq5UBeF6fkoDwmyDMOBHKJCXCr5YEW0UCIFw2GFzAqEdDH8GvFBATm6U54jcP0JEqbbZHOH3Ducj4ilHcNGeqFiRB+8cApEw2ETr47giRMMg0oLbIByVa4KwXAQv6+Lc9KQzVJwiTgg79s97fAnxRcwFxXQU5LmSj01o7MCBA1PaHuumwBKh4WeddZYTtEAb05f4Nvo98MAD7jhRrIhKxRid/D6zvxgSiHLwFXO5d/A/nln2x89fLIQQzYGMFqLpKFbE+qtTrGjJupIKHqogeKoGdy3PL2vXMttK2ph7v6agxHISTBk3amBbF375RUQBGgoM/XxAG3tlVq6tK4zeR8J0/TYBLxp+V9patciyfWKC9KMf8uPrz6Z4SqeWrp+oG3KyY8d5aDsb0LmldWid7YoWvf5drvvsvXnlBYfGBQoOUUX3s0UF9lPsXHrmq7W2S+wc2LZ3ueeje/sce2L6GndOvDc/z50D41SsqMbgJfFeS4gyaDGQJD+Mye5TgaInhPqFB+4M+BncEx7IwJRQX99Gf9rIi2uu4DXy3mDAiMh7pn/Amxf8zIM4wnPcXIVoomJFHAs8bsnmEqUPHn3EU03yaYkkwhuPsMMgwrnItcB28fIhVDlXgwWCpeQ4VQAAIABJREFUtt9++wpTeiSD/aOQUbJCR4kglJ1XVW1RUHzp2WefdedOonzuKCgohkEoOEdqVBtTO2HAYtyByKeaLjmknKfdu3d3lWFpC0Y+cAwI6/UVdxsz3IPIySV/HQMG35+iTtwrDznkEFcFlxBk4DwhNYpqxZzHHHs/BRMQtYYhyS9LBWEEfHhZIUTTJaOFaDqKFTFXZ3WKFR09oqMtatnevgi1d2mT7cTDn19c4t7felBvm7O80K55a7l7j0CJEn9n79rVvllaGBOKlXP+hnZvaRft3cOWrF3sxEdYjCJwXGGiFzcUXrkltt3vVxTZpDeXOZE6sl9/169ruxbOQ5tbVGpXv7HMNj1ExYrqitY5WfaH3bq5Csa8xu/YOS5Eb5q2wk78WWf7e6Dg0FnP/egEKJz8+GJ7+th+tsl6YwKVco/8V/m8oXjR8bAHlz37+R/t6RkqVhQFA8JgwSEGQG+99ValKSvCFW+rOyE9gzby2cIwsKO4DB5YQn3xOvk27mG0MQ0EVVCbI4SWIr6BKqQUeqE4jG+LIpiLJzaAcYSwXgQDBXq8YMWT6gs+BfsmAw/e/fff7/IlfY7u5Zdf7sQUU8L4AkEIVfIhERB4sLmOCA3G8BJlDEYIUsMBL6AvGFTf8F25jvr371/Bm1wVeJapKIxg8kWNWM/xxx9f4fqnH4LKG5mirnPAG+tBfNW0YFNDQMGqW265xf3mnBdEeXBP8+8J5/ZTuzAVFecfUWmEUPN/MDScQlBexLMsqU7k34eXFUI0XTJaiDalYkXH79DZjth2Q47KRjHRWbJJu0ptHy+sPEl3It6bl2+HPLjAnj6mvytO48WLJ6owEWK3tMzsN9t0iBcwumq/HtYtJkRVrKh+yC0sdb9Ti/UDI6bPCfLQJ6ucl9MTNCjkFZXaoQ/9kHDZhz9dbU9+uTZyWVERrmVCb6loi7fnoosuctUuwzC4ZjBW1xDez2TvFDHBuEUhFV+siEGab8sEmM+RSBO+t6g+VMkldQQhFPSaMrAP5z3jLQ3PkRnkuOOOc7nNQa8fUH2WebHx5JE76cNV2QZ5vlxHPBcTzbtLrqQPE65Jga+agJAmionrLNV0GuC7I0SpRoynnulr8IZG9UOINmcDCaHGfp5QCAtFPPY+7BhvetBQH142bMSfMmVKwmWFEE2TjBaiTalY0ZNfrqlQmCiKv/2iW/x/BOJtY3vba7Nz7aWZ6yL7F8VEyer8UudNJcw2zLVvLU9arIjlbjuol93xwUonaEDFiuoepCO/UyLyi8tir+hw7tosKzbAABVvKAN4b1Tiug5f2/RjIM/8h1EQloiHhJA8ch79XIuJYFoIBrR4PRmAIULxHLAepnkBvDDkvlGYhUEuAzS8pXgMmhPMkYpRDxiwjhkzppJA4RhwrDxUhT3ggAPSup/pgu/J9w2TqFiRB28Vgolwz3B1Zbz5iYoVhUFwEfGDd4ttIjDD4AHkPOd3YLuE2hK6yTQlnPvsnxeoYYg6qE5obFVQwIi5PIEczTBnnHGG84QyXYo/LrR99dVX7n8iIMj5DBqtMTaTX8tznmPH98MYRKEkrkNylIm68nCsOG+bM1X9bsmMCrVZVgjRNMloIZrOYkV4SNgGD1/yaZgAm1yJcFuiYkVzVhTZvJVF8WJFQXyxoiVrK1Y/JTcQATt7WeIcWEJpr3pjmW3bp7UTkS9+u0G0VlWsqG3LbPein4oVieYMg088nVWFyNHP57QRguaLCHnwPlG8xFd4raq4EOtjYEaeHWG4FH/hf4Qo2xk3bpwLfWSQTCggbQyAm+OAjXBG7yHj7+abb16pD0IiWGwnUb/mAAaJ8Hy1qYDI4pwJg1cTL3O4WBGGWnIrg57THXfc0YVNQlBoJYPfj32mGFeyOUYRdjWNUMLIk2hZXwwI+DtqVMWwI74T09TwCrb5a5RrjOWCxY54fhMKTzv9CN/3RZJ8v2AVa9qj8jyJbEiUp0uRKSIAyDMXQojmRkYL0XQWKyIvhgeTb2NQGdWWrFjR4C4t7cxdutors9bZuqLywW7vmODbsmdru//jDd7S3h1ynFAkj3DBquKk+5gXW8/t7620R4/qZy2ysyoIUUhWrAjwts4PbEPFikRzJFWvGv2S9eV6D4cwJiPoPQkvS+GXILwPtzUnEE9VTVtBIRhemQAeT151ub4ottlmG/eqqi0VogRwmGOOOaba601l2aqKFUUVE6uqwBjilOJCHiraBqvaIjzDwj6KZH18ASMhhGiOZLQQxdPAA6A+vKHgQ3MhWMAkWdtRIzrawohiRR5XDOjN5fbDqvLKg7sNbmeT9q8YWjy0WysnWMc+sKBKIer5cU2xKzYU5vU5ua666o9ri61/TGBOPri8WNHFry6NtwWXY/8oZKRiRUIIIYQQQohEZLQQpVjR5MmT661YUU0494Ul9ihT442K/rxzmxb27HH9rXR9+B9Te+QXVSxA8+68PPvFnfNcld1UOe/Fn1wYbpjjR3ay0YPa2gmPL7Znju1vW/Zs5YoVEXrr2y585ad4pVUVKxJCCCGEEEJURUYL0fouVlQTzhndzVp+3dHuNsJiywsOvTor1176dq2tzC+1fe6aV2mZotLy+R89VEZdlV+9udcSVUslx3R1TNA+MK6PXfLaUps4qostXF1sr8zOjbchfD3kmf4fxYp+nme7Nw5tL4QQQgghhGhkZLQQpVgRxYQQpPUVnltdPl2UbzOXFdqwga1cYaIvFhW4nNDZy8tDcV+dnVuh/7Durdw8kgdsvpFt3bu1mzM0zF6btrN9h21kbXOyrEvb5EWEWB/bnfL+SleEiLxUXlTP/Vm/NtarQ45NiwnPV2P71L1dC9e208Zt7fXv1tnUb9a5YkW7DWrrcleFEEIIIYQQIoqMFqIUK6LKHcWCGgs/rSuxlQu+tcH9XrETN+prO9w71/p3zLHWBaXus58PaGP/mZlr2/Vp7TyVfUtybGLX/vbyzHXWblYbs69WWe8FK2L92rq2EbF+u+a1t53WtrOdY23/mbrOBizKtW1WrHOFkXw/VzU3tr7exTl2Umy7P7tvrvWJic5dh7S3jnNa2Nfz82yzTdtb+zbtYttYbXlfrLAf8tq5Ngojleavto9j291nYEzMdiqxdipWVCs2bbvWNKNnwzGwTa7l5xe4aVZEw8D9mUJyS5curdacjs0VjgOVVZkyJGpalkyDY8HURrpGG46ffvrJjZ+YkidYTVg0HfLy8qruJEQ9ktFClGJFlF5vLN5QOHu/YdZm+XP2wcc3W4s+vWzgZwvsL3t2s+9WF9n7i/Lsuu172dipsbaN17d9nGe5sX6THijv12V5ke0X63f9yF52kO/3cZHd83SebXtgrN/95f2GFRTZ/2L9bljf78/9u9lc2mblWYt+vWxAbLt/2j3WFluW0NtbfhXrd/0C6xNr67KyyPZdmGc3jSxv6x1qGzu1yPosb2EbN/TBbIIwvGzdosR+1f2Hht6VjKdwndl7b7/W0LuR8cybvSr2+qqhd6PR8P47lef0zFQWzFkRe33T0LuR8Xz47hsNvQuiFuRkVy+VS4i6JGvGjBllWJ2Zj+yBBx5wE7Yz3Uhj5/0PP7Oly1fYkYcfVON1rF271m6++eZGVazIivIsv6DQikuZKiXLFRxirk4KCdVpW1msraRmbRRKKqqqrV17eTECFMR+0zvv/aftOHJbGzZ0cGSft956y44Yd5g9edPpNqhfjzTvoRBCCCEyjUn3vGBLi7vaPffc09C7IjKQjPaIpqNYEaG/p556qi1ZssS9ZzJ6xH5CWra1NrGXp2Ob9c2BLonaLr74Yjeh+4knnpjSsh999JFdfvnldtttt1nHTr3j/VavXm2n/d/vbfz48TZ69OhKy0LrFNtE9WmRXWY52Y3fGCSEEEKIpk22Iv1FA5LRQrQ2xYq8N3XlypXu/WabbeZE7U033RRvA3J5mPg7O7s8Z3KTTTaxdevWVeg3bNgwO+KIIyotG2TPPfe0/fbbL+k+ffHFFy6nKhFMV8M2VqxY4d7jsWS9fP8HH3zQTZzNNsj5mDZtmo0dOzb1AyKEEEIIIYQQKZLRQrQ2xYoQdbfffrttvPHGzoOICDzssMNswYIFroACLFq0yL766iv78MMPbcCAAfFlSfBn/lK8lyz7+eef27hx4+yHH36wp59+2jp27Ghbb711vP8777xjOTk5FYQobWwrCO9LSkrs3//+d4X2Lbfc0q2PkGu2wfbZr969e9tLL70U36dvv/3WidLhw4dX+3gIIYQQQgghRKpkjBBFdOYXFFhRYaH73/PLX/7S/UWQevAUtm7dOqUqcH/4wx/syy+/dB7E9u3b2znnnGM9evRw/7/44ot27LHHxvviiaRCmc+dPPvss50gfPvtt61Dhw52yy23OPG6xRZb2GWXXRZf7tBDD6203euvv95ee+0169atW7xt8eLFTkxOnz69QtsZZ5zhhCheX7y4QHjw66+/Hu931llnOWF977332jXXXFPl9xZCCCGEEEKImpIxQhSv5yeffGrffTfb1q5Zk7AfnseBgwbZdiNGuFDV6oDQPPDAA+3KK690f8PcfffdTrAi+OqC448/3v72t7/F3yN6hwwZUqmtOus75phjXOEqIYQQQgghhKgvMkaIErI67/u5tvlmmzmPZSIKY4L1vffec3mc1RWinTp1silTprh8S4oT9e3bN/7ZhRde6DyxQZFYFb7Q0d5772377rtvpc/btGnjtunB0xrV5iEM+JRTTrEff/zRzfuFcGb6Gs+RRx5pJ510koSoEEIIIYQQol7JGCGKCCSvs3Pnzi43E3LzCu3BJ9+2vUZtZUMG9XJthM4WFhQ44VpdEH2jRo1yIpSQWcSsZ/vtt7euXbvatttu6/IxU8EXDWK/mcScokYIxTC++NGIESPcft95552R/di/PfbYwwnc//73vzZ79uwKeafkj95xxx12yCGHVPu7CyGEEEIIIUSqZDf0DqQbBCkZorzyCgrt0ef/Z/MWLo23BfNHq0tBTMA+++yzzqOIqHvjjTfibQhc35Yq5Kn+6le/cgWEKCr02GOPRfZj3XhiyS1F6FI46cknn6wghIH1MCULYhmvKd5TQnlPO+00l9uKkE1VJAshhBBCCCFETckYj6gHmblmXUHsVWirVudaVnZLW7G6wBYuWWOtW+VY21Yb+uKJxCtJISGmVaHYT6tWrRKuG+FHoZ+FCxfGl/dt5J7CbrvtZldddVXSfVy+fLlbB6G9vrjQFVdcYW+++Walvmxj3rx51q9fP7d/hPAiOI866ihXrTcKCh09/vjj1q5dO7v00kvdPKoI2IMOOsi9fNVfCh0xvQ39gmHGQgghhBBCCFEbMk6Iwitvz7FHp37tvJ9t2g6wfz0/xx57cZ5tu0VPO/2YEfF+7777rsulZGoVCgwh0rbaaquE6+3SpYs999xzbpoUePXVV23ChAmuzYcDI0gRj8mgqBF5nIjFqmAfTz75ZOd1JXS3Ovz85z+3u+66yxVWuvzyy933C3LeeefZBRdc4OYaxcMqhBBCCCGEEHVBxgnRwoJ8G7F5N+vTfRtbm5tvk6a8aAfusYNtvVl/67BRK8uLiUQfnrvddtvZ5ptv7jyMFPKJKnJEESK8nptttpllZWU576kHT6JvCxYQ8kL0oosucsuGBSTzkZLLSa7mP/7xD7vtttvcPlGNNwz7eN9999mmm24aL0z0s5/9zAlH5gkN4osV7bXXXk40e08qYb0PPfSQyzMFvMBMI4MQHT16tMttFUIIIYQQQoi6IvOEaGFhTFBmW8f2HWzFqhZWUrzOenZtaUMHlgtI8ju9EA0KsMGDB0eujyJEhLDWBEQkhY0AQXrjjTe6tt1339023nhjF6JLGC0ht7SRwxmGfSTct6o28MWKfMEihKgvsLR06VIbNGiQ64dQZm7U4cOHO9EqhBBCCCGEEHVJxgnR4uISKyoqdv9nZ5n9fMQm1qVj23hbcXFxSgWLKPRDIaHjjjvOFixYYN98803K++CLEDFnJ7mgX3/9dbzg0HXXXedEJOIWjyaf9erVq4JHtab4YkVR/PrXv47/T44oXlYhhBBCCCGEqA8yRogSIkuhITyia9eujbf/7pAd3V/fRlgqeZxZ2ckLCnfs2NFuueUW9z+hsEEIa2V9vmhR1LK+CBHggfzqq69cHineTzyhVNe99dZbXYgthYoQh0wJQyhtVbA+KvQGv5cQQgghhBBCNBYyRoi2aNHCNh4wwGbP/s4+//zzxP1iIrR//42tfbt2Nd7WH//4R1egCO9q+/btq+zPXKEUHHrhhRds6NChdsMNN7gKua+88opbftKkSS5E9/TTT7cnnniiyvVRwMjPBYqndeTIkTX+LkIIIYQQQghR12SMEMXLyRQlw2JCr6SkJGE/PKdt2ra1joGiQ9Xlz3/+s5100knuf7ywTI+SDPJMH3jgAZeHyn4efvjhts8++8QLH/npWA444ID4MhQ6apdALJNn+tRTT8XfDxw4sFKf3/72tzZmzJjI5fHYPvzww64AkhBCCCGEEELUNRkjRLOzs61zp07u5SkoKLDXX3/dttlmG+vTp0+dbYsiP9WB4kK77rpr/D3CMSwew23JtsH6mHIlGVHb8FDAiKldhBBCCCGEEKI+SJ4I2cyhKNGaNWtcXmhNeeuttxKG+pIrSo5nsnlDqU6LGK4L3n777aRhx2EoiESoLxV7gyDQmZd0/vz5dbJfQgghhBBCCBEkYzyiURA2i+eQAkE15V//+pcrItS9e3fr27dvhc8+++wzmzBhgn344Yc2YMCAyOUJoUVAMqUK+EJHYfBysp1kkFvKfKZ4eKMgJBlx6YX3J598Ytdcc43ttNNObo5RjgfbYPsTJ060q6++2k0jI4QQQgghhBB1SUZ7RPFUTp482WbNmlXjdVBICAF3xhln1Mk+nXPOObbDDjtUelFBt7YwdyjTxvh1kpf66quvurDk8847L14FWAghhBBCCCHqk4z2iFIE6Mgjj7QePXpUe9mVK1faKaecYkuWLLG5c+c6kXfwwQc7YYugw8vJNCz0O+KII9y8o8BUL1dddZX7/8ILL3ThwVdeeWV8vX/605/sxBNPdP+zTrZx2mmn2dFHH11h+yxLWHCQ6dOnO+8rVXODUJiI4kkUP2Ku0vz8fFeMaN68efGCSOeee67zzvIXUSqEEEIIIYQQ9UXGCFHCUufNm29Ll/7kciCD+Dk3gaJGXbt2Wz+nZ+KpV/CC7rXXXi6kFb755ht7+umnnchDELIeRCfeVvp17tzZeR/J4SQnkzBa1rH77rs7kYqwPPPMM+NFiFjf1KlT7eyzz3YCd5NNNqmwfXJLqfC7//77x9uWLl3qRHWw7f7777c5c+a4/ylCtPPOO7v///e//zkh6mG77JfyQoUQQgghhBD1TcYIUeb0/Orrr6w0JkiZUzQReCgX//ijdezYIakQZeqU8ePHu/8RhYg8xKefN5Sqs1TCRZzSjxxRBDCeUkKC77jjDjfXJ8Lvo48+coWD/LQyrA/Rird18803d57bKNgGc5Z6EJfkiIbbPGz/pZdecoITQUw+6j//+c/451tuuaUddthhtmzZsmSHUgghhBBCCCFqRcYIUUTe4pjwGj16tJuv07WVltqiJSuta6eNrF3bVq4tLy/PHnnkEefZTBUq47788st29913W6f108Mg5vC0sl08kqzPCzyEMPvw/PPPuzxN8jXxfsLChQvd+ljm+uuvd2KV/swhSkGkRN/Nh9niJWW7/fr1q9SP74Ynls8JG0YQMx+ph/lLKVaUSPgKIYQQQgghRF2Q0cWKVq/Js9POv9s++Hx2rdc1bdo022effZxnExClp59+uq1YscKJSMTmbbfd5j7r0qWLPffccy6fk/zPIBQrQrSSZ0o4L/1eeOGFpMWKyEOlCNF+++3nPLW///3vI/shkpmWhe3SB48q//sX3txEywohhBBCCCFEXZFxQpTQ29L1LzyiefmFVlxcEm/jVR3++te/OtE5cuRIJx4RchQRGjdunPM2Iv74/JlnnokXHMJrieij4i77Q/EiChNR1IhwXooV4ZX0/f72t7+5sF8KCSX6ToTb5uTkuG0ceOCBdtBBB8VFscevjylbyB1l+hb6syze1KOOOsouueSSmh1YIYQQQgghhEiRjAnN9SAzP/vqR/vkyx8tv6DQWrbubm99+KN9v7DE+vXuYLuO7B3vS8EgQli33357e/311938nEx1Asy1SZgrhYbIy0TkMScpYa+zZ8+2gQMHuv7kjuJ5JEf0nXfecZ+z7I033ujmIGV+UEJkEZpsh8+jvJ/Mdcp8n2HYx7vuustOPfVUtz22Q6EltpUovJgc1O+++86GDRtmu+22mwsBRoT65X0IMWKVHNOhQ4e6+VCFEEIIIYQQoi7IOCFaUlxsa9bm2ZJlq62wsMSyrIWtXlvg3rdrm23FRUXOwwgU98FbyPs1a9ZYUewzT2lpqSsmdOyxx7rPCc3Fi3nyySe7fE8q51IgiXVQsIjcSwoEBZelqi5QNAiPJB7PBx980D744APXxjoJt8WL+etf/9oOPfTQSt+H9VO5l5BePw0NYpm+vnBSsC/FihDJ2267rfXq1ctND3P++efbK6+84vYZEMq8CCtmP9h3IYQQQgghhKgrMk6IIsa2GdYj9upsK1atszfefdv23HG47fazYe7zvLzcuBDFQ+ihmmyQjh072k033eT+f/HFFytth/Bc8i4Rsngc8YziXSVXlGXxiAJtTLtCQSLCeJnCBRCMhM3SD+GYCPbx9ttvr9TGfKZhfLEi5iql8BFeXjytbOOMM86IL0PxI/JOJ06cWOl7CyGEEEIIIURtybgcUbyahKy6V0GhE52FhYG2/IK4EK0NJ5xwgt18882uMBHVcaMKEwH5o4jTvffeu1JOZ13jixUxr2kY8lR90SKmjunbt2+97osQQgghhBAic8k4jygi0+vMjdq1tgv+7wDr26uz1YH2jEPBH8Jiyd/0BYIIvUWcEjKLt5GQ2B9//NHlaFLICI+pD62tL/y+RBGcsqWwsNDlrAohhBBCCCFEfZAxQhRh1T0m9BbHxJ8vxuOZN3dp/H+EaqfOna1169Y13tbw4cPdX4RoEB9iS4jsL37xC5fbSQEiKuUSwnvttde6vEyYO3eua7v88stdKC9QXGj8+PFVbp/t3nHHHe5/8lIppiSEEEIIIYQQjYWMEaJMbYKQ+3Hx4oTVZAHBSj88mKmC8GzVqlWldooGHXLIIZW8kL6oURBfwIh8UUAIH3zwwU6M8oKgxxTx6iv4hiEPliJDQEGkYK6rZ6uttnIe0ijYNsWRqKArhBBCCCGEEHVNRgnRzTfbzL08iD+KBSE6a+MBHTt2bGQ7lWnDhYQSwT74Akap4IsaRYHwfOihh5Iuj9DkFQUFjChqJIQQQgghhBD1QUYnAjJ/J5ViZ82a1dC7IoQQQgghhBAZQ0YLUUJkjzrqKBs0aFCt1/Xee+85zygFiDzvv/9+pbYo6HfQQQdV6rdq1Sr7zW9+Y2+++Wat908IIYQQQgghGgsZE5obRXFxsX399dcuLDdRNdlUIcR32rRpLj8zWRusXbvWhb76wkRsf7/99nPC+N5777VevXrZ/vvv76aaQeAiRoUQQgghhBCiuZDRQpQKuRQCQpCme7sUJeI1ffp0V3ToiiuucJ8xtcucOXOcOI0qMiSEEEIIIYQQTZ2MCc1F/DFtCuGueCJ58X6vvfZyxXl8G0IQcVpSUlJv+0JhIjyiDz74oB1++OEVPqMIEcL04YcfrrftCyGEEEIIIURDkjEeUcJcP/n0U5s9e7atXbMmYT+q6w4cNMi2224769G9exr3cAMnnHCCHXfccfFpW4QQQgghhBCiOZExQhQP5/fff29bbrGF9ezZM2G/wsJCe/fd/1kuIrCehChe1wkTJrjiRPPmzXNe2j322CP++dFHH20nnXSShKgQQgghhBCiWZIxQtSF5ubmWqdOnaxv376uLTevwO5/4i37xS7Dbeig3q6NcN3CwoJ6Dc0l/3Offfax1atX2yuvvOJyQoNzev7www82ZcoUO+SQQ+ptH4QQQgghhBCiociYHFEPgrSMv7FXXkGRPf7C+zZ/4bJ4G5/XN1THxeO5ww47uPzUVq1a2YABA5yX9Oyzz7YRI0bEK+oKIYQQQgghRHMjYzyiHmTm6jUFtnptga1ck2tZ2S1t+coCW7BotbVunWPt22zoS2gsuaUdO3Z0U7FQZAhvZl1x44032hNPPOGmjpk0aZKbtgURypyivKiqC3hImWaGfhtvvHGdbV8IIYQQQgghGoKM84jCy+/MsT/+/VW79OZp1rbtAPvX8+Xv7/z3pxX6vfvuu/bYY49Zbm6u3X777TZz5sx62Z9Ro0a57Zx88sn23//+t9Ln55xzjvOenn766fWyfSGEEEIIIYRIJxnnES0oyLeRW3a3Ab1H2Jq1+XbV5BfswD1/ZttusbFt1K6V5eWui4fnjhw50rbaaisXSnvMMcdY9wTFi+6//35788037emnn3ZezaqgWNH48eNdnmjXrl1twYIF1rt3b7eee+65x6699lrXD2/s4sWL7YILLrDRo0dbly5d6u5ACCGEEEIIIUQDkXFCtLCg0Hp0z7KN+m1kK1dnW0lxrvXokmODY+8hP78gLkSDwm/gwIEJ10nl24ULF9rOO++cdNsUJvruu++cqN13331t9913t2XLljkh2rJlS9txxx1dJd1hw4a5/oQGE5K75ZZbOiEqhBBCCCGEEM2BjBOiVMMtLi52/2dnmY0aOcS6dmoXb+NvdQoWvfHGG1ZaWmp77rlnlX3fe+89e/vtt5039MQTT4zs86tf/Sr+Pzmid955Z8r7IoQQQgghhBBNgYwRollZWa46LfOErlmzJt5+zK9Hur++jXBYvJP/kKvPAAAaVklEQVTZ2cnTZxG0c+fOdaG02223nU2cOLFSH4oLDR482HlMyTP1xYeEEEIIIYQQIpPJGCHaokUL23jAQJs9e7Z9/vnnSfv133iAtWvXLun6qKJ74IEH2iWXXOIq3EZBEaJHH33U5YJS+RaRu9dee9XqewghhBBCCCFEUydjhGhOy5Y2YtttbbPNhlnJ+jDcKPCctmnT1jp02Cjp+jp37mz33nuvbbrpps7TGgWitk+fPq4IUUFBgWujOFEQ8kUTCdlOnTrZI488YptssknSfRFCCCGEEEKIpkTGCNHsmMDs1Kmje3kQh0yXsm1MoCIYqwPhuzvttFOt+w0YMCDpshQwEkIIIYQQQojmREbOI+qhKBGVaYuTeEjTBaL4ySeftPnz51druUWLFrk5SPke4fU99dRTLj9VCCGEEEIIIRoTGeMRjYKQWqZF6dixY9WdQyBeKVZE3meqMA9pjx49Ij+jmNGtt95qRx99tB1wwAEJ+1Ekac6cOfHtfvrpp3bLLbe4qWNWrFhhrVu3dsuuXbvWzjrrLLvmmmuSel2FEEIIIYQQIt1ktBBF/E2ePNnGjh1rw4cPr9ayiD6KFVXHg3nuuefahRdeGPkZOafPPvusHXfccfb9/7d350F2VeXegFefPpA5JGQCTAhhDENIGMJ3w2AoEVAGLSSKCIqFYMFFwZJJrA+K0qKIgiAOiCBQFoiAgHILsC4oSJhkEFBGL8P9AlwgCSJJSLo76eHrd1mnbxK6QwJknZDzPLDr9Nl77X3W6T+68qu11rtmz+6z3VtvvZW3eKmNdMa2MbE/af/+/dMRRxyR9yA9++yzV+u7AAAAlNTQQXTAgAG5WFCMVK6uCI7LFiEKERKPPfbYdOKJJ6a99trrHfeMGzduuXZz5sx5R5unn346/fnPf05//OMf33Et1ouee+65+XNbW1vT1VdfnUNrfI9w5plnphtvvDGdcsop6Ywzzljt7wQAAFBCwwTRmNI6e/ZLad4b81Jbd4hbVoS5mtg/dMSIkd2hcWwaPLjvyrm9FRKaN29enhq77bbb9hpEa2Ja7cMPP5y3ctlxxx2Xu/aZz3ym13ti1PPJJ5/Mnzt16tR87p577lmu7/G5+++/f3rllVf6/GwAAIB6a5ggurS9PT377LOpq6szVat9f+0oYDR37rxcXXdlQfT9iGm0hxxySJ5KG8E11nmuzPTp0/M2MdH/GIG97bbb8hrQuO/VV19NV111VU/bSZMm5Wf/4x//WCN9BwAAeL8aJoh2dnSk119/LRcnmjBhQj7X0dGZ/mfOm2nE8CFp0IB++VxLS0veu7N1hVHTvsTo48KFC/PPsW40Rjvj3DPPPLNcu7Fjx6YhQ4bkn+P1wgsvzD+fc845ebptbwWFaoWJrr322jxSGmtD4zOiqFEE0DfeeCOvc437a6Jd7FU6aNCg1fwNAQAAlNEwQbQ3C95uSf/+f69Ip3714DT937Z9T8+I9ZhRZCjEaGoE2FNPPTU1Nzcv1+7Xv/51+tSnPtXrM3bfffe81cqKYlRzzz33XO5crahRfNZ5552X7r333nTzzTf3XP/hD3+Yvv71r6crrrjiPX0fAACANa3hgmgEuM7uI3R0dqa2tvbU3tHRc672uqrOOuusdMIJJ6QHH3wwj25GIaExY8b0XK8VJlrZXqWPPPJI+sQnPvGO8zG6umJBo6amplycKAoTRbGi+fPnp8MOOyxddtllaaONNsrFl/oKvAAAAGuDxgui3cfjT89Jjz75empdsjSt129UmvXwnPTfr7SnsRsPSXvtunFP21iTGaOSu+66a7rzzjvT5MmT0yabbLLc87bbbrv8Gms2awWMlp1mWytgtDIRXPsqUhQhc8WCRiGm/sY+phMnTsxbuPzgBz/Ie5BGH6M6b22N6JVXXplHTbfZZpt03HHHrdLvCAAAYE1quCDa0d6eFi1uSW++9XZasrQ9NaVK9/u2/H7o4ObUvnRpHjUNMSIZxYHifawdjTWba8L666+fRowY0ef1Bx54IAfdKVOm9BQr2myzzfL7CLEx4hr7jkZl3b/97W/5nmhfOyKULliwYI30HQAAYHU1XBCNILfDlqO6j2HprQWL0l333Zum77pD2nPq1vl6S8viniAaFWhr+hqxDFE06M0330xbb711HhVdXS+++GKaOXNmn9dffvnlvO4zgmcUJ7r44ovTaaedlosSzZo1Kxc/uuCCC9I3vvGNnrWhEZpj2m7cd+ihh652nwAAANaUhguiMcpZq4jb2rokh84ly5yrjYCujssvvzzdd999eUTy3abh9mbatGnLFRxa0eGHH97zc61YUQTe2It0Wd///vd7Rm0jGO+xxx6r3RcAAIA1reGC6LIhc/CgfumsEw9OG4/a4H09M8JtFCOK/UHfiyhWtN9++/V5PdaDbr/99vnnKFbU1+fEFN+aaBNtAQAA1jYNE0QrlUoaNXp0ev311/NU2mX9v4X/W5k2quYOGzb8XUc2Y+3l+eefn6viPvTQQ+m1117LU2NXFFNpY+/PmDIb02ijuNCKRYOi2u2MGTP6/CxbsQAAAOuShgmi1Wo1r+GM7VBaW1r6bBf7f44bt2kaMnToSp/X2dmZCwBFyNxqq63yET/35pBDDsmvcT0C7LJiHerRRx+dTjrppD4/K0Y2V6zWW7u3rzWpEaRjXev48eNX+j0AAABKa6gguk13EI2jJsJkVJQd2h06V3dtZ9wTBYLer1XZ8/PEE0/s9fzBBx+cj94MHjw4b+kCAACwtqnUuwP1FNNmL7nkkvTcc8/VuysAAAANo6GD6IABA9JRRx2VJkyY8L6e88tf/jKdcsop7/n+Bx98MB100EF5/eqyYvuVWDt69913v6/+AQAArE0aZmpub6LS7VNPPZUmT56cBg0a9J6fE/t8Pv300z3vYzuXJ554ote2UazoyCOPzNNma2tKIxDHFN3oQxQmGjNmTDrwwANzNd6oqBvThwEAANYVDR1EYyuXlpaWnr03PyiLFi3qtXBRhNO///3v6YgjjsijndEmzkXwPPfcc3ObKGY0e/bsvGZ1ypQpH2i/AAAA1gYNE0RroXPJkiW5SFHN3nvvnV/ffPPNnnOxH2fswxkFjvoSo6kvvvhiHrWcO3duDpAxuhq+/OUv91pg6Jxzzkn33ntvGjJkSE+ho9q5mrjv0ksvTb/5zW8EUQAAYJ3UMEE0AuNjjz+eXnj++bRw4cI+20X43GzChLTzTjunUaNG9tkuRjNjXWdMy41QGuF21113zdeuu+66VaqG25fYziXCbGwPAwAAsK5pmCAa029fmj077bDDDmn06NF9tosR0/vvvz8tXryo+13fQXTYsGHpmmuuSSeffHIudnTsscemt956K33lK19516m+0e6YY47JxYleeeWVHDj33HPPnutf+tKX0le/+tXV/o4AAAAfBg0TRGtTc2P/z4033jifW9zSlq78zd1p370mpa0n/Otcbfruu4XJ9dZbL4+AjhgxIo0bNy7tsccead68eT37kd5+++3p1ltvXe6ehx9+OG2wwQa5TYymRgC944470gsvvJA+97nP9bR77bXX0s9+9rP02c9+9oP8FQAAAKwVGm77lgikXfHafbS0LU2/+89H0iuvvdlzLq5/EFpbW/PI57JHnAtRJTem3u644449a1E32mijPJoaa0R32mmnvOYUAABgXdQwI6I1ETPnL2xN8xe0db8uTk2V9dMb/2xNL/3P/O5QWE2DBzT1tI0wGKOjMQ33jTfeyKOpERxDrVhRjHDGVN9o+/zzz+dputFun332ecc60RULE/34xz9ON910Uxo8eHC68MILc/XcCKG1++IzQ6xDjUJI0W78+PFr+DcEAACwZjXciGi4497/Tief+8f0nR/fnwYM3DRde+u/3v/iuseXa/fAAw+kG2+8MS1evDj9/Oc/T88991zPtVqxotjv8/jjj8/rSmN67S9+8YueSryratq0aemGG27I60zvuuuud1w/9dRT8zTgr33ta+/p+wIAAKxNGm5EtK21NU3dYWSasMnOaeGi1nTuz25Nhx24W5q87bg0aOD6qWXxop7puVOnTk2TJk3KU2mPOuqovB60plas6LLLLsuhdOzYsamtrS1v/dLc3LzSPtSKFe23335p5MiR6aWXXsqjqvG8CLLnnXdebheVfqOg0dlnn52mT5+ehg8fvuZ+MQAAAIU0XBCNqbaj+jelQZsMSm8taEod7YvTiGHNaXz3+xDrOGtBNMJmzaabbrrcc2rFiubMmZMeeeSRPE03pt6uSlisFSvaa6+98vTbCKK158WepNttt11ut2jRojzdd+LEibkYEgAAwLqg4YJoVMON9Z2h0pTSnrtulTbcYFDPufb2jtUqWDRw4MDU1NSUR0NjbWilUsnTayNcruixxx7Lr7ViRb054IADen6OkBpTggEAANYlDRNEIyzGSGQExtg2peYLB+2UX2vnYjpsjE5GoFyZCK6x7cpVV12VCwx97GMfS6effnqufhvnYquWFcX2LtEWAACgkTVMEI11m+M2HZ/D41//+te+21WraXx3u4EDB630ebViRTNnzkyf/vSnc3C9/vrr81rO733ve3kf0BXF+SiABAAA0MgaJojGKOeUyZPTxG22ztNz+5JHTvv3T4MHrTyIxvrRa6+9Nm/XEvuAhig4dN111+VzMfq6oqOPPjrNmDFjuXNRBOmQQw7p9TNia5io2rvZZpu9y7cDAAD48GiYIBoBc+jQIfn4IESw3WWXXd713LKisu6qnFvV5wEAAHwYNeQ+ogAAANSPIAoAAEBRgigAAABFCaIAAAAUJYgCAABQlCAKAABAUYIoAAAARQmiAAAAFCWIAgAAUJQgCgAAQFGCKAAAAEUJogAAABQliAIAAFCUIAoAAEBRgigAAABFCaIAAAAUJYgCAABQlCAKAABAUYIoAAAARQmiAAAAFCWIAgAAUJQgCgAAQFGCKAAAAEUJogAAABQliAIAAFCUIAoAAEBRgigAAABFCaIAAAAUJYgCAABQlCAKAABAUYIoAAAARQmiAAAAFCWIAgAAUJQgCgAAQFGCKAAAAEUJogAAABQliAIAAFCUIAoAAEBRgigAAABFCaIAAAAUJYgCAABQlCAKAABAUYIoAAAARQmiAAAAFCWIAgAAUJQgCgAAQFGCKAAAAEUJogAAABQliAIAAFCUIAoAAEBRgigAAABFCaIAAAAUJYgCAABQlCAKAABAUYIoAAAARQmiAAAAFCWIAgAAUJQgCgAAQFGCKAAAAEUJogAAABQliAIAAFCUIAoAAEBRgigAAABFCaIAAAAUJYgCAABQlCAKAABAUYIoAAAARQmiAAAAFCWIAgAAUJQgCgAAQFGCKAAAAEUJogAAABQliAIAAFCUIAoAAEBRgigAAABFCaIAAAAUJYgCAABQlCAKAABAUYIoAAAARQmiAAAAFCWIAgAAUJQgCgAAQFGCKAAAAEUJogAAABQliAIAAFCUIAoAAEBRgigAAABFCaIAAAAUJYgCAABQlCAKAABAUYIoAAAARQmiAAAAFCWIAgAAUJQgCgAAQFGCKAAAAEUJogAAABQliAIAAFCUIAoAAEBRgigAAABFCaIAAAAUJYgCAABQlCAKAABAUYIoAAAARQmiAAAAFCWIAgAAUFS13h14P+bPX5geefRv9e4GvKv29vbU1ta2Cu060n/c9XgaMWxwgV4BAI3sqedfTaPGb1jvbtCgPrRBdNCggam5uZIe++uT9e4KrJIhQwan/v37reT6kDRp8pR037Pzu9/NL9cxAKAxVTZIm2++eb17QYP60AbR7bfdKh+wrpgyZUr6wx/+UO9uAADAGmeNKAAAAEUJogAAABQliAIAAFCUIAoAAEBRgigAAABFCaIAAAAUJYgCAABQlCAKAABAUYIoAAAARQmiAAAAFCWIAgAAUJQgCgAAQFGCKAAAAEUJogAAABQliAIAAFCUIAoAAEBRgigAAABFCaIAAAAUJYgCAABQlCAKAABAUYIoAAAARQmiAAAAFCWIAgAAUJQgCgAAQFGCKAAAAEUJogAAABQliAIAAFCUIAoAAEBRlSUdXSmOpZ1dqft/AAAAWKOqJ557eerq6EgdHe2p5bX/qnd/AAAAWMdVX2+ppq7OptTZ0ZWqHU317g8AAADruGpT83qpqyulpkpXamqyZBTqqbOzM7344otpyZIl9e5KQ/jIRz6SNthgg3p3AwCg4VQr3UE0AmhnR3cIrQiiUE9dXV1p3rx5qVqtpv79+9e7O+us+D3/85//TMOHDxdEAQDqoFqprpe6Ov4VQI2Iwtph5MiRacMNN6x3N9ZZMfL89ttv17sbAAANq1qprJ86m9pTXh0qiAIAALCGVZsqzampM9aHdsaQaL37AwAAwDquUmmupuZKNcWrqblAeOGFF9IZZ5yR5s6d23MuiijVzv3qV79KN9988zvumzVrVvrmN7+Z282ZM6dklwEA+BCp5gJFXZXU1FlJXUZEoaFFkIwQOnv27HTDDTfkdaqxXnX77bfP13/3u9+lY445JhdSina///3v0yc/+cme+9dbb700dOjQ1NT9tySC6j777JO22GKLen0dAADWUtU8Ctr9j8b4h6MYCo3t0UcfTffcc09auHBham9vT3/6059y6Iy/D9ttt11Pu0MPPTTdcsst6f77718uiE6bNi0fbW1t6bTTTkv9+vVL66+/fho3blw9vg4AAGupSmxj8L9HvbsD1EvsXXr88cena665Jp155pk5RJ5//vn5/eGHH56vx9+JeI2gue+++6azzjqr12fFvRdddFF67LHH0pVXXln4mwAAsLarxkhHV21E1NRcaEgtLS3phBNOSC+//HJ+v3jx4hw2jzvuuDyiWWuz4rlJkyalCy64oG79BgDgw6laSRFEK7lQkQFRaEyxtvPzn/98WrBgQbr77rvTU089lc4777zl2kSxossuuywddthhafTo0fncqFGjen1ejJpeeOGFaeLEiWnp0qXppz/9aQ66AAAQKjEKWomR0IrRUGhUMeX27bffzmtDY9Szo6Mj/1w7Nt1007T33nunarWa9t9//xxE49z06dN7fV6sL7399tvTmDFj0sCBA/NaUwAAqKl2x9AUR8qlioRRaEQRPCMsRqGiGB0dNmxY+s53vpMLFPVWrOjGG29MI0aMSLvttlsdew0AwIdVNcW03BxFK6mj3r0B6iLC5o9+9KN00kknpeHDh+dtV4499thcrGjzzTdPzc3NuaLuqujs7MxTcyPQxn1xxEhqjLTG2lJr0QEAqNa7A8DaJfYKveOOO5YrTBTbtey8886rdH+sL/3Wt76Vvv3tb6cpU6bk0daYyhvPuPjii/OUXgAAGttyQdROotDYjjzyyPTRj340vfDCC7kwURQwihC51VZbpU022SRP173iiivStttum3bfffdenxHVdaOwUbQfOnRoPjdy5Mj0/PPP55FSAACoWBcK1EydOjWPXEYRoloBo/nz56fW1tYcJg888MD0wAMPpI022ihPub3tttuWuz9GQ//yl7+kL37xi3mdaU0ULfrCF76Q7rzzzhxyAQBobNWmWpGirvjfBi7QiGJd59NPP51HM8Ozzz7bU8BowIABqVKp5FHQJ554Im255ZY5nF5//fX5ngMOOCDfE6Ogs2bNSq+++mr67ne/u9zzx48fn04//fR02mmn5am+cYwbN6749wQAYO1QrXRnz86uf9XOBRpTjHieeuqpOUyGCKa1AkZbbLFF3o7lwQcfzHuB3nTTTXmt529/+9uedaOxnvQnP/lJ3ld0xRBa069fv3TRRRflz3n55ZdzMI1ACgBA46k0VyqpuamSKnEIo9CQInRecskl6ZZbbsnHzJkzl7seBYyuvvrqvG3L2LFj0ymnnJKr6oYIsVHUaJdddknHHHPMu37WySefnANrBFEAABpTdfPmF1N7938d3cerlbfr3R+gDmLqbUyfrYktXCKMRqGiMHny5BweY3Q0xBrRGTNmpGnTpuWtWQ4//PA0ceLE3ObdxL0f//jH81RfAAAaU9PVV1/dtXTp0hRHFBK59NJL87Q8oLxYl/nQQw/lsLbhhhvWuzvrrPgb98wzz6QJEyakjTfeuN7dAQBoONU99tgjb6kQa7yi4iUAAACsSdWokhlBNNZ5RUESAAAAWJMq9e4AAAAAjaVa7w4A7zR37ty8VydrRldXV54JAgBAfQiisBZpamrKxXOEpDVv0KBB+QAAoDxBFNYisY3KZpttVu9uAADAGmWNKAAAAEUJogAAABQliAIAAFCUIAoAAEBRgigAAABFCaIAAAAUJYgCAABQlCAKAABAUYIoAAAARQmiAAAAFCWIAgAAUJQgCgAAQFGCKAAAAEUJogAAABQliAIAAFCUIAoAAEBRgigAAABFCaIAAAAUJYgCAABQlCAKAABAUYIoAAAARQmiAAAAFCWIAgAAUJQgCgAAQFGCKAAAAEUJogAAABQliAIAAFCUIAoAAEBRgigAAABFCaIAAAAUJYgCAABQlCAKAABAUYIoAAAARQmiAAAAFCWIAgAAUJQgCgAAQFGCKAAAAEUJogAAABQliAIAAFCUIAoAAEBR1fa2trR0aUfq7Kp3VwAAAGgE1TtnXpH+MXqLNO7gf6t3XwAAAGgA1bfn/CMtqI5JLZ317goAAACNoLrlPv8njR48Kg3rl9J/1bs3AAAArPOqOx66b1qyZElqbW2td18AAABoANX2tiVp6dL21NkV1YpULAIAAGDNqt418/L0xpgt0riDdkjtbW/Uuz8AAACs46oLe4oVdaSurqX17g8AAADruEq9OwAAAEBjqaz0LQAAAHzAJE8AAACKEkQBAAAoShAFAACgqOqWH9stjRoyOg3r1y892zyw3v0BAABgHVfdccZ+acmSJam1tTVV1hta7/4AAACwjjM1FwAAgKIEUQAAAIoSRAEAAChKEAUAAKAoQRQAAICiBFEAAACK+v85+Nf8JPHxdAAAAABJRU5ErkJggg==" alt="" />

码农公寓

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