data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAKAAAAB4CAYAAAB1ovlvAAAAAXNSR0IArs4c6QAAIABJREFUeF7tXQdUFEYT/ui9HV0QKyiiqKioWFCxY9dYY+81McaS6J+YWGJsUWOL3dg7VlCKKGIHlCKCIr333uF/s+cdd94BB2LNzZMH3m2d/XZmdnZ2V6Y0rrwcdUQyGgDKgPLcOipQTDEBYQHIy69dBeUQ7aoMZECf02/6B0UAhcIVy8vJo3nD5lBTUZeoY/Gp8YiKj5QorbhEgu0UbB+/nZWUbGbcAMa6xlXW+zIiGBk5mayrkpI4vlWVl/jUqkkriYqXqUsAQh7cQSyWqO4aJYpJikZMbjRMTU1rlK+uEmekZyA7NQedLe0qLbKgqAB+4T4wbWQKGZmaDHHdtLK8vBwx4bFo28gGyorKQoVGJ0QhIjMc9c3q17gyBQUFuDwOwIvYlCrzUo9H2FmjHkcTsTGxaKjVCCYGVY+XWAAmpSchPTcNHA6nxo193wzJyckw0TWBlrq2UFEP3njDxMSEfXbp0iUUFhZi7NixEld3/fp1ODo64uLFixgxYgRyc3OhoqKCFy9eoGXLlhKVQwNckFiIZg2ai03/OOQhjBoase8ePnyIpk2bQk9Pj/3/0aNH6NixIz+fs7MzBg8ejMuXL7N28donWDCvrRI1TiBRQkQCbJt1EsrmGeSBeqb1aloUS6+gqIRm+4MgY2pZbf4FrTQw2yCNpYuNjUUX825QVCC1Ip5EABidGIXUshTo6upWW9mHSpCUlAQTFVMYvVUn/q+eQ6u+JquuqKiIgSg7OxtlZWXsR0lJCR4eHnBwcEB6ejpkZWWhra0NFxcX9OzZE3l5eaw/kyZNwpw5cxAeHo42bdpg2bJluHr1Kq5du4bmzZujRYsWrBw7OztMmzZNbPeUipVhpCFezbn43YClJXeQXF1dER0djfv378PGxgalpaVYuHAhtm7dCk1NTdb+JUuWYN26dTAyMoKcnBxu3bqFzp07s/aqqalh9+7dOHv2LNavX49Tp06xMiSh4OBg9G87kJ/06YunkNeWhbwCqSjgwIED6Nq1KwIDA9G+fXvGJ2o31Um8LCgoYHxNS0uDra0tDIyM0fpsPGT0zPhlft9GC2kFZfj3ZbZQk2a2UMcc3UT+Zwq5imjWUPyEpUQiAPQO9UL9Blwx7e/vzwacGuPn54eBAwciKCgIOTk5TAJNmDBBEn7UKk1KfCpsGrdjNuV9f28YN+VKFiJ3d3eoq6tDQ0ODMYwGhgaQpE1cXBxTf02aNGEM1tHRQWZmJpPm9evXh7e3N+rVq4eMjAx06NABz549Y30ksrKy4udp3Lix2HbrKHCgraAj9rsbPtf4AIyPj2cgox9qA4GKJC1JRkNDQxQXF8PCwgIEFkVFRdYHGniSyiUlJSBpm5KSwn536tSJgYM+l4SozIHtBvGT3vW5AxV9ZTYx8/Pz8fr1a9y+fRv9+vWDl5cXm3ykeRo0aMAmJP2fB9Bhw4ahtBxodzFZCICzrDRx9GU2CulLAZppqYapGtH8T5QLVGDVtHINIwLA+2H32AARrVixgjXSyckJa9euxcaNG0ENCggIANkFHxKAIS9D0K/tANaO+8+9YdTUUBLef/A0OoocEAjF0fWnV/kA/OANqaICAqBj+8H8FFk5WXD3d2WSlogkLQGdpB5pGx8fH8yYMYNNPt5kVFZWZpO6YcOGUFFVg+2VdCEAtjNQwvPkQpS8s64jAE5QCmP1ZGVloUOjjuBoVW7KiQWgsTFXxZC0oJlHYpjUwZgxY5jt9OTJEzRr1ozN4A9FgrPY+9k9PgBJ6j05uArlia8AlOO1emsUyKnWoBlkKhPXKl8kKJfmomnOc1amkYkpDMasZxKLiADIURRvnlx9fJmpcaKM0CdIcN2P8rJSpCoZI165YQ3aSEmrbqdceQka5r6ASmkOZGXl0GbhHuSXybE6yK4dbDtUqL67Pp4okC9gGqOmVFJahidKTVGiRG6Oqqm1WiE0k14yiZ6XlodB3YZUmUEEgPde3QUPgKTm5OW5dsOHpuysTJSWVUwnAuCgDtzG33vmBcPGBuxvsuUSV1lDQb8hUgZsZgsTUrlaWlpMXVVHCXGxUFGr3p1CBrSe84+oVxSDwmV+TB2y+pX0wFESD8Arj5z4EjB0zxzYKcciqPVCNLDj9oNsP0lWx9mZmWR5VElkAmWkpaAg0BU2rw4iu+0kZLWdyPIQ74Z0HCaSPyDUH+SpUFNXq45N7/V9YUEhcjJz0KGVbbXliADQK/QOH4DBx35Bw8S7SFUwQJZBa3D0uCCoKyotKUFiVBhaZD+FrJwcDBZfQQa4zCEm8maxl99dPgDJzkv+nzVSJ5zlr4pzMzMQtMAehiqy1TbNsE1XqMzficTERAYGUkU00cQRgbDN1fEoWOpbAUBlPQZCcXT54aUKAO6eBY51T5g4TOEn9fx5IhqlBFbbRnkZwOpMOLO3yT4ku83MrGIBQP8ne5HUJ9nn9R5uhYFlRyEADu00vNp6PocEIgC8G+LJB2DoltGwMNZC7pCt4OjqMSM29qU/IvweinHpCneHY9oQLbr1ZQsAEseUlyQAj+gzYiRJreRnbrAN/Au5gzYiXb8NAwapER4TvXzvwqCxPkurr6+Pu+snoO3CvXyXis89T7Q4uwQKspL53hR3+DJbh2wcMuzJnq2Mko8uQOMFR/kLAj0Vfegp64tNfun+BaaCqZ2v986BbN9lrL20+CHA3JveGZ00JHOS6h4MQURUFFvNp6am8t05VDHxkRZbZLORPT7bPA8KKmrIbP0tn3fD7UZ+Dviqtg0iAPQM9uAD8PXWMdAcugpmVh1YQTRQXpM7oK3qO1sFYqohP6z+kdd48+YNWwWSe0TQtUMqhFaGr169YuWaBh6BWc/xSNdrzZeAwzqPYH/TKk6/EVfq0IC6bJgFu3mbcebMGWaX+nrfgdW5HyUGoPy2p8y+JQCSi6QqMyP6+HK0nL+XSRtWv4oBCITi6KL3eb4EDPtnLtD7RwZIXv/vz+wiMQB1DrxEVHQ06+OoUaOEfLJkbtAqmwBInorv2yqwSZ7RmuuVIO0xosuoagf/c0ggAsDbL9z5AAz7ayy0Ju5kqmDkyJEoLCjAkyk2aKleYaGodxkKeY4hMq7uE+mP7qFQhEdE8AeABoJHJCXInUPuHfrtIB8MjlU3pAkAkDeL7/h4Qr8hF4AGBgZw/mMmOs/dhLt378La2hrhL/zR8vxSiQEo99cTNmA0IQhYBMTKKObECmEAqhowEIqj815n+YuQN/vmodxhCVvEkduFjP+Hs7tJDEDt/cEMgLTzE/VWEvLqJKkYEhKChIQEPH36FN/bKKC0pAKApD1GdRv9OeCr2jaIANAjyI0BkAASvn08AyBJry5durAVsM/UdkIAVLHsiLKCPBSGB4hUxjkUgrCwN0zVkp1FM5dHpGZ5ACRfWR+lUGg3t0OqrjVfjYzs+g1LfuepJ3QbcJfyNJgEwE5zNvLLiot8A+Od30JOAg2s0qor8iZtYn5AagNJX7IDK6PYkz8xAJLNyCaAmiEMVMW7hM7dPcOXgBEHFqC052Iht0zcjkUwjXla7aBQAp2DIQxkPBuQ+s0jmsj37t1jkpskOQNgaSnSW43nS8Bvuo+RqB7Q3JPMvy1ZeTVMJQJAt4BbfAkYsWMCNCbsYDYHuT8U5OXhO72DEADl9U1QXlKM0nSuihIknYMvERERydQHgZjK4BHZhKR+yUYkKcQAaNkFqRzuJjapEd4s9nxyWwSAHWf/yS+LpMzLO84oyax6r5IyGLeyhbKBKauXiPxdVbkm4k6tRKsFe5m0YRNAzYiBUBydvXOaD7jIgwtRbP+dEADzEqKQGFQ9AJW09WDWsRfzo5H7hyaKoP1M/CSekQomDUIALCstQ1qrcXzejbaXfJuyhpip0+QiAHT1v8kHYOTf30J9/Ha+A7O8rAyyPjfAyRMF27utklFURp7DDGa80ywmRgpKGlJ7L1++ZHYgDa44APJm8e0nHuDU56pvcqaSBLSdtYFfZUxYKHT2TIe2YvUiUFZZDcWrbzLpQW2gxUFV7pv406sYAMnmYvWrG8NQvWJXRrDfp2+f5AMu+vB3KOy2UAiAT78fgM6qkkXyaO97geiYGGa+0CJEVbXC10mmA+1cEAB9fX35AExtyQUdTd6xPbnSUBLyC/RFYlECcjJzYWthi4iYSKSkp6B1c2u8jn4NZT0lxEckYOwALsDrkkQAePOZMx+A0bsmQXXsX3wAkgR843wKOYHeb525lTdFQ1ML5gs2MUlDEoYWHQREHhEASDXTDCbp2Ff5FXQsuyBZh7ttQ0wc04PbYY/H7nwAknlwY/0MBkAqk2wgDXmgjdNPQjag0ZJ/kHnzKPID74s0smQDV30RAHkr8cp6IgJADWMGQnF0yuMEH3AxR75Hfpf5bNFEEozcR34LHdBJU7LtNPU9AYiLj+cDUHDyEgBpYhMAaStxYWtZ5o5JtaoA4Lhekm+TPn/xHKWaxWhr0xZOly7DrktnprUC/APY2Fm2sMTl01cw3IG7KKxLEgEgbajzHNExuydDefQWFtVAthLtiDyf3RnWAosQaoyKpS3ygx+LtEuTZnF0NOsMbWwLMpEAQNKHAEjfMQC26IpkbSs+AHmzmACoY8qNjuEBsMPMP9i+KgUgPLztiraXfxYCoO645Ug9VaGmBRtXtP4umxg0kCSZafAqo4Qz/xOSgMYa9SoNRjjhdoy/CIk9uhi5necy8BHfyM59/l0fiQGottsf8QkJ/KgdQTMhJiaGr5KpH9YZXkIApEXIhN5cp7QkRBrgltdN6DXWZfvjgkS8OX3sDOxb28PYsHbRNJW2QUlMMIKz73W+xIvbOxWKozYxw59WYrRJHTjHTsgGpMI1e45B1u0zIvVo7A1ETGwsH4CCTOT54UjSkS+uTZY3A2CSFncriz4f7/At+9vjkTu0TLg+RNqnJgnYfsZ65iMjCUZ+QJsrK4UAqNKyC/KZpBalgrWezPYjCUgLq6qiTBLP/QLrBf+wScjq1zSBsab4gTju+m9FMMKxJcjuOJuBj4gWXAGL+wkBULlZe5RmpaE4/o1II1V3PUdCYiJfAgr6KiMjI0E/BBzaq7XJvo+ysnIkt+CufIl33/aZJAn2+GlcPJ3RwaE92yXhqXviC/1cuXQVAzs6QllJOMawRhWISSyjIgaAtKHOk4Dx/0yD/Ig/sX//fnzzzTdo27YtXszrKgLAyqSN+t5AxigKSerevbuQw5dcO9RRmmEkDRtF3YBOiy5IFAAgbxa7P3TjA5C23m6smw6b6ev4XcrJzECLhKeQxA9dpm+GKB0L5hgnokGsygZMOv+rMAC1TBgIxdGxW0f5AEw4/iOybGcJ2YBKb3xgXJIu0bilthvOTBMyW8jUEAdAKogiWNrlPERZeRmSLSsAOLHvZInq4SW6fu8a+g/rx2ITKVaRJidFxpCfcfLkyYjwi4Jd+8qDcWtU2dvEYgF47ckVPgAT9k2H7LA/2JYXG7DycrxZ2g8tlLnhS9WRGtkxcXF8CSjobyMAknHNY2LjaGfoWHVFoiY3nk5wFrs9dIVWPa4EZABcPx1tp66trvo6+T75wmoRAJpoiY/yPepymK+CE08sRUb7GUIATH/ihiSvy4yPVZGaujosf9zFpCZJapLygkTaiCZ2ZQAkFTy5/1SJ+09Szt3PDf0H92NmEdVJRMKBFwLmdtED/e37S1ymJAnFApAiOnhhO0kHZgJD1vEBSYWaK5egJOBOteUXcuojybAFW+WShCFbSHARQgAk2+9dACZocIMXCYCT+nH3UV0f3IKmMdeFQ45ZAmCbKWuqbUNdJEi5+BtaL9zHbFlWv3Z9mGiLB+AR50N8wCWfWo40G25QKw0qaY+Q5YMltgGVdvgiKTmZTV6aqIJSmuw+WoAQUYyhedwt9n1ic+7uB/FuygDxAbXieFJQWIDb/h5wHDKQqdwAf38mODrY2rJFFNnJzmddMKhXRYhXXfBWLABpQ52ngpMPzkLZoDX8/5PKfLJ6CkzTKRSqalJR14TJtrts4GhGCc4syklBkTzXBqmRJjEu4Fh1RbwAAHmz2PX+LWgYcwMGaF/1+joC4O/VNaFOvhcBoE59BkJxdPjGQT4AU06vQEqbKWz1S9qDfoeuGCIxABW3+yA5JYUPwKoWSgbB50QAOHXgdIn6T8C9eOsCejjaM7Bt3bwFe3btYuVxdHVx4vQpdrTA7ZYb6qs1gEWTugvBEwtApwcX+YBLOTQbpY6/s/1cMnapIa+/7yViA2rYj0L2nfMiHVbe6YfExCS+ChZkIs0qAidJRipb8cF+EQDyZvGt+zehbsiNkqGoEAJg68m/ScTg902Udul3WC/cxxZhbAJwzFBfpyIyRbD8g9f28wGYdvZnJFlPYs721q1bsz6+/nmYxABU2PYUKampOH/+PNsGrSoa2vDleXayL8GCG4BAEnD6oJkSdT0rOwuP3zxE3/59Gejmz52LWy43WV6aOAcPH0Y3++5MHV8/dwOO3SsirSWqoKpEcmIWIbShzpOAaUfmonjAahY5QgsFc3NzhC12kHgRovS3L+Li4lkAK511qIqJDIAtuyFOjTvDiInTHGewv296u4gA0HrS6vfuv7gCDh06hMOHD7MzI3///TfSndag9aL9fJvLjNOAgVAcHbi6jw/A9HMrkdDyW2YT0kDSAEb+Pg4dlCsc0Yqm5ijLy0ZJGneXRZAUd/ggOblCAvIilSkNua4WLVrEku/YsQMDVLmr6HgLrp+OeDdj8CyJ+PMm6g0y5NLY2RCipMREHNi/H56edzB//nwMGTaUH8N49dw1OHatQwCKOxNy4d45PgDTj85DYb9f2JYQSR7ym4Uv6SMCQLX2fZH79FalTCQVS0azIBPJgUqHg4goNLx15j3oWnVDrAAAebP45j0XqBlydwJ4ErDVxF+ZC4VWiHV9eo9WgQMGDGBunoijP4oA0Ey3gdjB3Xd5b0VE9PlViGsxnv2f+k48MCjPRd6Dq0L2HGkAQfuO7U+bWSK3cQehXZp3AyYIhEQUzWMUckEIgLQImTV0jkQAJPvP/bkbBg8dxCYJ+RjJ7UaHp5YuXcokP52PobG7efEWHO0/MABpQ50nATOPLUB+n1X8/9NOSNH+xTBKCa22c7QVJ/vnXSYByQH97kqOHLM0i2kfl2ax6tPDDIAxqub8WTxzyGz2t4uXM1QNuAEDpMqurZ0G00GLmMFM+8uhoaFCRytJvVcW4/f8+XOmEnl08+ZNdu7lXaK9VzI9FO7vQ+tF+xAREcGSNNBriAa64sPr/3Haw5eAWRd/QazlONbvoUOHMpcGSVUe8UApjpG06Hj3VCLZzGQCCRJNEFosNI3jqsy4ptwgVJKAs4fNrXaMeAku33KCZcfm7JgFBTdQ/TQuNMEJ4LTwO3roKAZ2GgQ9jvhgXIkreyehyE4IbajzAJh1fCFyHX7mRlqkp7PPeedca1uhYD4CIXWU9juL3P6CbsvuiFHhMpmYyJvFzl43oKovDMBc836sTeSWoYHhuQ4oLwGSJAmtwGnTnkwHmt0kGQhUffr0YQNMETo0s6l+QSlEUTtku9GARP67TAiADfUaMRCKo72XdvMBmH3pV0Q3G8P2u6ksAiGVxyOyf8kLQAClQaZVLU0a0gq0YqY8NDFoMpEvjgJPBQ+BUZtJizRq1AjGry6KAHDO8HnVDhH1mbbhwpJfwdrGmvGCThPShgHxj0wmaiNFAlE7Xj1/jUE9BkFNtfojDdVW/jaBCADPeJ7iAzD7xCJk91zB1BytPsPCwoQOIhHTxB3qJjHOAxavIRRwwMLH3564o8+pk5SOpItZxFUhABJYeEx0vnuDHSskIgn4asNgPGsyiQGJ1Dcd7qYVHM/d4+bmxiYKuX1or5jUKc1sykt1kSQiVwapc5KAdNKPAEz5aWAJwHTgiqROqyfroTLfiW8uEAAb6jcSy9/dF3byVXCO02pEmn/DQMWbHCRJeCqXeET8pHbSAfVVq1axXRNqF/H6zp07zPnPOzxPbaF0lJ8kH0+SkydB/clBGJvUR2xT7jkQ4t28kQuqxAD19ei5I4yv48Zz99wJcCRReeetSe3T+PCEDk34LX9uxbxx82FkID4gQ1Lg8dKJAJA21HkSMPfU98i0X8aY8uDBAzYjCUA8JtIhbt4RTYpMpr1ZUonEQOoE2Y4UMEoLGJIE5Fht164d36iltDxJU+T6F/St7RGl3IQvAeeOmM/+JjeMnBZ354LskcJNXeHTcAKiyzhsMGiQeLcmUBpiGkkxkn7EVPqbpB3PZiRAkPSl76g8Yiz9EPgoBo8kFf02lUlDu+ebkDTbk0lOIlqENDXmmgnv0q7zf/MlYN6V36GuqY0QE+7RUt5ZZV4eQalL35FKJqlM/CWXFUkd4g+1k4i3kKG+EM/JjCAQkmustc8fkLWwR0xj7kk40h7zRy2sEgsut50RkvgSixcvrhFmKApn9crf8MeyDVCX4HBXdYWLAPCk+3E+APNOL0ZqmymQ09RnA0i2AUkRHtFsIQbQwBLQHj9+zEKr6AoKGmQyYEk9kiokQFIEB0U0ExEjeZKGJIHu031o2GscopS4B8KJibxZnJGdAY/nbtDS1uIaxJu7QVdDBZ7qDojVqP66iOqYIO57k+xg9Mhxh6pMMRJn3eYDUCZHFr06OYgt8rTbKegbcW2k/Ktr0EslAo8UWsNft1dtmlBtHj25fFhHnkUT+TRENx7CB2ByQgrG9q48dCo8Ohw7jm3Dho0bqgzGrawBJHh8vHzxw8wlEp3yq6ojIgD89+YR/gVA+Wd+gLG2Km7rD4dpfTNmI/Cko7hCSeLR7Ozfvz9/5opLR1KLwEfBDSQZkRiCIUn/IqH7SkQqNmKdev3qNWYNq1jJnXc7C019Tbbf2744AOpKH+e4KE2cJ6q23LjFmARMHFj5HmtoRCh83jxhKsu4IALNVfOqBVFdJQjSaI+kzDw2yds17gCLhuIdxmTP7z65E9YdrNm1JbUh4gldF9K7XV/Ytqn+6GWNAHjB4xy09Lmh88UXV8BeKwVhJRyk6bSAGqdubycoKy1BalQousq+hBzKEeewDhHg1lGSXYr+dlz1xaMnAU+gpqkKJeWaH66uDaN5eWjQoiKj0Ltzn2qLCY8JR1xKLIyM68ZGqrZCgQQJ8Qkw0TNBQ1PxNiol9fD2wLMIX3z33XdVngasrl66HsXfJwALJyziB3ZUl0fc9yISMCElAW5PXWFYzwD1VMqg8lrUv1ebiqrNo26A5Po92FmN1IRUDO8xEirKlZ/VqLY8aQIRDpSWlcLJ8xLUdFTZPT/vQ7R6P37sOJobWaJLh661Lkrs9Wy0QgoIDUBkHNf3VVNilz3WhmQAi4bNKlUftSlSmqeCAy9fv0RUbgSz2yk87n2IMEJXypVkl2BID9FbGCQtWywAa3rDpyQ3aCopKKFlk1ZQkK/8ELikjZamqx0HLricA8eMw24B40U81a4kbq5z585BQU4Bvax713pFLARAuuHz6avHMG0s2S2kSeHBCPe9W20fmrazh25D7hVgOnIcNDUR78aotiBpglpzgCTWzUcuyCrMxOjRo9/LbuM1gg7N0x5/pF8UutjWXA3LKL8TjHAv4C4MzCS//yVw3SD0rydBcKqiKmK/vcwcvVmZWTBUMkZ9w5pfFVtr7kszoqi4iMX8ZeVmstsk6oJIBZPL7Yn7U/TrXvNgVZFwLNqG410v5unpyfx7vAPRtKNADSfU0y2jRG/+HIzephXRvYq9FwNyCii6WXFonNfRyEnO/LMX6dEZ6GbzfjZIXTDwv1RGXn4e7oVyD2PVFQBpf5tsyYduj9DHrm+N2SkCQMFDNRRCRSqT9gXJI08AJHFLoKQtI9rmit02SgiA8jYjIVfPCoXXRINFX429zA/Hyk3MQ/d29jVusDRD7TmQkpaC4OQgJCYlsjuy6+LavdOnTzN17nrFHX06Vu+ierf1IgC87O0EA2PuxTu0x0jOV3Iu29vb49ixY+ySHDqgRFEs9Hny7nHoLWAuypq0goyyJkrDRE+jvRh5nn/Jo2yOHDpaC1+iXXvWSnNKwoHU9FQEJQWwnQ/ajaIghvelCxcuMDC7Ormid6c6kICvo17hWaQf29iXhAyyQ2CuInxgRly+AnVj+BRwHczxkfEY20/yU/uStEOapnoO5Bfkw+vlHdh1sWN79n371hwwgrWQPU9xkxQf4HbZHX0617w8sSH5EbHkyY+rc09+SXEJIsIj0LeraOxd9eyTpnhfDhTTIiTAA30H9MWNGzcwaND7BZZS8AZdC9KtWzfU+sScuHPB79tRaf7PkwMksVweOsNxxEDmvyMJKHhbWU1bTeqXyqBFTYRvJOw6dKlpEYCsmDMhNS9FmuNL4UDAS388e+0HRRklaOprwHGoY62aTvGh+3fsh6lZfeTn5mNE75G13leu26e6atUdaaZPwQFnzxto19OmVjsiThcuo6tlN3B03v8lLSkAP8XofwZ1UmDCyWsnMH7auBpJL7oxKzMyC3bta6FyxfRbCsDPAAyfqgmZWZm4du8qxkwcLXRrRWXtCQoMQvSLGPTtVncLSSkAP9Xofyb1Hr9wDOUaZejRowc7OiqO6EASPe110+Um1i/eUKcBJVIAfiZA+FTNOH35FMbNHssObdFhJqsWVuxAloKiAtLT0uHtfR8qqsps9+vB/QcYajdcCsBPNVhfY70EwPFzuOdHThw/ge4teiA2IQbFJSXQVNfAQ9+HmLVkJjsmcfbMWSkAv0YQfMo+vQvAsb3GCx00OnnpBCbM5X4mBeCnHKmvtG4pAL/Sgf0SulVSWoKzLqfx7QzuVciBAYHIi8tHe2vuPdF0GOsM+5574fmLoBfIjMpCxzZ1F0giXYR8CUj5QG2kd4Td/G6hmSX3ypCiwiJEh8RgSC/uAffcvFzceHwNVq24F8fTQaTY0Dj+93XRLCkA64KLX3C52zzYAAAUkklEQVQZ4VHhoCgoOtcjVy7H9nQFTyNGxkTiVeQrlJeXoby0HN06dq/T04pSAH7B4Pkami4F4Ncwil9wH6QA/IIH72touhSAX8MofsF9kALwCx68r6HpUgB+DaP4BfdBCsAvePC+hqbXGIDRcdE4cP4UnALCEJKQzF6d0lZVQR+L+pjQ0x4DHISvVPsamCTtw4fjQI0AePO2Cybt+AcpTfoAyhWvn7PmlZdBJvwRJtWXxd41W6sNcMwpKcPqF2lwTshDSuHHfTNeVU4WnXSV8YOFFjro1O0LkB9uqL7OkiUG4EOfhxjw5058t2QJzA21cNkvGhd8uK8HdTM3wOweFlCSl8XqI9fRTy4Rm1ZU/pIRga+jRwz6NVbFD220oa8i91G5m19SBqc3uVjslYJrdvXQRU8Kwo86AAKVSQzA/ovmwaD3aByb3YPd/keq9/TjcKTnFWFWd3PIy3EvEfeLSkPnqSvhtWwG2lm3E9uv/wWlIUm2BPt7SX4R0odg0O3YfMxyTUJIP/GRwB+iTmmZwhyQCICP/R7DbuNhnNq0HAqRPnC9dQt79v0jVNKxf/9FUEAgNmzaiE6/XUa3jGf4c9mvYvnd2jUKF4cYw1z7098VqLQ7DLnDmkj01rAUPHXPAYkAuHXvFiwNVUTotikoTIrAy+Bg9HRwwPF/jyA3KxWduvVmFxglJSTAcfBgTDt0H0FXT+D+7p0Qd1uq2qUwZM9pDHlJXpiWoM/03MK1a9fYex88okeyKYiSLlSqipR3hyF7WGPIydTyVlcJ2idNUjkHJALgorUrsSu/GdJ3joW2qiJ73MV19zzMbFcAQ0053AgswDMZO/y08TB7a2PFeV9s3bgBqacOQ02V+8qlIPEAOHb0N+xNDrp5n25ep4dRhg8fju3bt7Pbm+zs7Nit+506dcLdu9yLMH/55Rf2e/ny5ewtjSNHjrDb+//44w/21gfdwE9XzNFlSnQbwMqVK+Hk5MTKpjc46D2OTZs28ZsjBeCnnR4SAfC7jeuwM6s+CvZOQFjoS5xd1QvLHZQhKDTiM0txungSfvp1PZac8cFfWzYj5+xhKCuJGvg8AE6ZNBG9e/dmoKDgRwIJvSxEdxN26dKFfyvXxIkTQbey03Vxe/bsQWxsLDZu3IizZ8+yd0l4APTy8mL31dEVtPSKE53molug6E5DKo8HYJKMPJIC8AsA4JodG7A6sR7C/hyO4Md34XtgGuZ1V4aaYkXjPV8DfrJd8dOWExi84zYeXz6OuGMHxD5kwgPg1s2b2GM2dBsX7xGbhQsXMqlG0ouARHeP0LsbBCgCDj0hSg/knDp1ir1FQhKOXhGi57nooRx6bYhUMX1Pp7tIwtItTlQePetFtzlJAfhpQSdYu0QS8MrNKxhx0Rf//jwJg5pz8MMIa1joy2Jel3IoygHOL2WQnV+MZIv5mLlwKRotvwj7XH+cX7tObE+7e8ZiXXcO/C8eYKp32rRpzIb82ER3u9IiJE+6CGGsdxjVC6/ehOKvNdsx0pH7+HVdELsY6bYL5i6fjSYNmsL9vAe/WIkASCfoG02bg57jZuPSgh44emg/MlxXQl5BFSoK5JIpQWCeGVb/44wzz5IwZ/t5/GWjjoXTxb9Xtjk0Ax4ZeXAZWq8u+lfrMk6GZGOPbxbu9DCpdRlfU0bVRsrMnKGIaPfzt2FpbomDJ/djzuR5CA59ATPTBtDV0a2yy/QCe0R0BCyaWGDXoZ2YMWEmQsJeotfInqC3iTXUNZAeklkzAFLq1ds34M9IRQRtn4km+uo4fvwYgq9tglJhIooaDsL3K/9kqrTTOmfEX9+HZ4ePQEuT++LSu0SSZ8C9OJQpACOaqKGFjiJ6mKow3+K51znkZfyg45pfUo7A1CKcCsmBe3cTmKt/enfQB+2whIXbD+8O78f3WOrWVq0xf+pCzF46Eyd2ncSiVQvRvnU7HNi0F/oGpsxeFyS6ayYxMQZTF8+AX4Afdv6xCxPmjcf+LQew6/Au+AX4suT2ne0ZuHlUrQS8/8QbF91u4npwJEKTM7Dy19+wdjj34WVaONA1vvSQIdlV7sHx6P39VrRI98X8kd9g9JDR4GhXfoPS0ehsLPJNxo7uemipq4gZHsnQlpWFieqHfQdOVU4GTdQVMLOhJjgKH3cXRkIsfJJknt630X9cP9BpOSJdjh5S01Kwfe0OJs30FNLR1bIerDuPRu/+UxES/AhFhfmwsLSFt+dZPLt/BneCYpFRrospY6bip3Ur2LOuCUkJrDxakN6+4IkObSvel6sUgG+i3mDpts24EluMskYdAXkl4OkZNBowHWF/DBdaAfO4Ne4fL5zetw0w7w6UFMIg0Q+/DOqB2d/OFPsuxaO0Aox7mohRTdTw74ts7LbRxwiTunsM+ZOM4hdcKdlqe4/uwbI1S0FX+vKIdrRC37yCqRYwsG09yMkrQl5RDcX5GSyJrLwKZFCG4uJCXPONRUxGKRqZNUbgywB+Gaoqqvh73U5MGj1ZaBEoFoABwQFw/N9qxDbpDyi8daPE+AOlRUCD9sjYORZaZPy9QxY/OeHV/RuAqi5QrwX328RQjNXJwKG1m6GkKPzIoOO9eLgn5WGQsRr2tdMHh1Y0UvqkHCAQ+r94zlTug6cP2CKRry5lgA4WprC30BRrJnmH5eHBi0ghA4o0I520275mO1pbtRHxiogFoM3kb/HcpDcgKwCIh8eBDmNgUU8XL9cNFSsBx+y9i7N3ngNBN1laPqVF4mcLOaz5/if+Rw9SCzDsfjz22hhguImos/qTjoK0chSXFMPFwxm7Du/EI59HyM7lPpzd2UIPXSz0kFNQAo23QqiguBSyMjIIjM6Ae2AiS0ePWXe06YS5U+bB0cGx0jsIxQJQbuYqwJB7WBkZcZB/fgklZu2BRh3x9/gOWODQXOwQOflGY/guT+DxSchoGaPcwh6Q4QYpqAY7I2TXZtQz+rQrXym2asYBkoj0xIOPvw/uP/ZCeogTZMtLcN0vFqM6mrHCfMLToKIghybGWlBp5IjunXvBxroddDm6kH07/pXVKh6AK49ygVOQjbEyQQhJSIWf2UBYmejgyS8DWWXiqKy8HJ3XueCx/wv0THZHWLk2opq8DVBNfoN/+zXFhBHcax6k9OVxICk5Djt+G4LSknzQWJPUIyKQAjKQlZPHzB9PoVHjt+aXBF0UC0CFcXNQ1qQLkBIBx/zHuFFujnKztjg3tztGta86dOnEw3B8u/8eOMFXMbIJB/sV7MD0dZQfrk3pjQG93u+dWgn6VHdJSHhXmEB1V+4XWtKr14HYt3E0P4hEtQSQKQdy3y4HysqBb2btQvt2PSTuoVgAHjx9GAuuPEJRA1vI3f4bpb0Xo0czI3gs7SvW9hOsraikDK1+vYrQV6+h+PgEihwWAwkhGMPJxok/t4ndmpO4tR8zIU1u2mos/JiVfr51lZQU48jepQgLdGWNVCspx+TXZZAvB443kkWaMlcaGje2xaxFu6Es4WPjlbph1mxbh9XPMoDsJMCyN2790Bt9rIwl4tBujxDMP/EY8L8GGJhjrY02ls/9oVp7QKLCP1Yikn6kWT6sT/xj9ea963ny6CYuHl7CZ4hBAReAROcbyCJcoyLAo983/0MPh7ES1VkpAH/dugZrXZ+yhUe3ju1wZzlt4ktUJhKy8mG65AJKn12FikwJMi6egbzch3UuS9YyaaracuDcifXw9TrBNJiJuSUaW7VBkyevIVtQhDfdrBAeGoTIYH+Ul5WhWVtHTJkt+mKquLrFAtDrkRd6bz6IkqwUoO0wrBzWnr/7IWkHWqy6gmCPC4CSOvY4tsWsCTMkzSpN9xly4Lb7Gdw6twbNbbvArGV7yClp8s0pWoSUFmUjPjQAAffc0d5+MkaOXSpRL8QCcP+xfdjh7g2dzHB4t5iBTaPb4cf+3DviJKXeW1zh7nQW8tnx0JMvRcyVG1+O/SdpJ/9D6bKzM3Fg52y069MTapxGaG/XRwiAz556ITsxBI9v3cSEadtgaCjwjGoVfBILQEI0/azbtharUxvi9+Ft8L/B1jVid+d1znjofA7Hx/eGhromBvV5v8fxalS5NHGdcyA9PRmnj62GQUM96BiZwqiBNYxNGzGzLCk+GrFhz5CVGo/kyDSMHPMTdHQke3G1ymAE7lkQBcwd2BG7J3asUacaL7+EcI8zCNq6Gs2bindc16hAaeJPwgESRN73XeB+eSsKsuJQLiMDNR196Jo0gAZHH7KyMshKS0FqbBRyUxPZ+XAFNUP0GbYEXbsOrFbrVQnAG+7XMeTYbbSx6wmfXx3ZcUxJKL+oFFoLTsMw5BoiTp2sthGSlClN8/E5QOC7c+cKnI6thIpCzdwBeUXA8Enr0aPHEOakroyqBCAFolpNm4b45sPg84sjbBpI9jjdtecxGPzXTcxSCsWe1X9+fM5Ja6wTDgQEPMKeLdOhpVwz8PEqzy6Sw3c/nYC5eavaAZBy7T++H3NdgzFp7CgcmWYnUccm7PPC6ZMn4f7dOHTv1F2iPNJEnxcHcnOzsfrn4ZDNj2NqtjZURlsj6mb4Ze15qKmKD7OrNiCVxPDk5QtwJq8eXu5ZhCYGVZ/dyMovBmfGPoxTDMORdVuk6rc2I/eJ89CYnzm5BXdd9kJLTTiErqZNy8gpRE/H+Rg97nuxqrhaAFKFefm5GLlwOr6ZNgUzRvavtA2FxaW47OKKM2euYe/Pv1V7fqCmnZGm/zgcSElJwMofekNTuRTystxoptpSaVk5sgplsWbzLejri0ZCSQTA2IRYHDl9CCk+W6Bp5QiTVg6waW8LQwMDFBTk40XAczx49AT5YW7orBEG78y2GDfxV3Rq10lsJHRtOyPN93E4cOrYn7h1dSeMOBR4+v6UmJaF/sO+x+jxtJUnTFWvgt2u4dHNTVAvCEIvC0W0MlHAgzeFeKT8PcybNENObg6UlJRhYmgMOrr5s8VpqCjIIL+4HFeeF+BVli4MrCdjwawV798LaQkfhQOFhQVM+uVlxkFLXUVsnUoqqjC1sIJuPVPIyMoiPSEW0SEvkJ+TJTZ9enY+VDUNsf4vD5EghSoBuHzFaKilueLHPhqQl+Maokcf5KLDFDe0seIeTOLRtn1/YZDsZphxKvZ8D9zLwbOspvhn/0OpLfhR4PP+lYSE+GHDSkdoaqhDVUn02IWBWSO06dEPiiqqQpWVFBUh0NsDMaEvRBqRX1SCzKxsrFh3Hc2atRX6vkoAxsbH4uDqNujVTA5xGaUY1kYVf3goYOX2lyLBBTfcrqP4/lQMaKkC36gi0ApISUEGjzVWYe6U+e/Pma+5BIrvJU/HZxB7eNVpHy78+ws4OtpQeifwWLdefdgOGA45efGBJXR+xM/jBuLDQoVGq7ikDMmpaRg9dT0ch0yXHICkYv/6ZzOeBz3HLPMHyMovQ1L9ZZg340cRONA9LD8vc8SoBgFoZqSAkaf0YWfTHmPGLETL5i2/Zvi8d99kNIDyD38cWqJ27ty6AE/unYM+RwcK8hWR7wpKSug+aiJU1Ku2C4sLC3D3/DHk53DPkBCVlJYhKTUdHe3HYt532yQHIC8lXdfw74m/0bFjHzj2GVypOi0qKsTJC8cR+cYXfftNQOf2kvkNJeKMNNEH5wBJsD9+HY3QIC/ocXSgKABAc5tOaNZBsvGMCHzG1PG7AGzcvBN+Xe9UcwB+8J5LK/gsOEAXDdACJCEmGBwtbSi9PSYrKycH+9GToaapLVE7SQp6nDoE+k1UVFKKlLR0GJo0w4btnkKeEYncMBLVKk30xXOAAPjL0v6IiwyAhoYm1JS5ixB1HQ7svxE+UF5dZx9eO4+UWO4d4rkFxcjOyoShWUus3+oqBWB1zPuvfk8qeM3KYYh69RjySirgaHBXuqYWlmjTs2bPb7x4cAdv/H1Y/tSsPJQW5aNe43ZYs/Ga0I6IVAL+V9FWSb+3b5oN/0eXUQo5GOtxVa65TUc068C94FNSehPgixf3PdniPjE1k50ltu40HN/9uEdqA0rKxP9iurOntuDmhU0oKQP0ONpsIULgIxDWhOh8SMBdNzAXTFo65GUBx9E/Y8ToRVIA1oSR/7W0EREh2LpmLJRUNKCnb4yM+EA0bWuL5rZda8QKUr+khtUNLJGRlojC/Gz8+Os5mJmZVw/AwKwiXIvLRTE78S6l/xoH8nJzIK+ggJTkWLzwc4G2ngEMGzSpERtoAZKWEIcWbfvDyMAE/fUVYGssermliA343bMUXIzPwdQWmlB8v0CIGjVYmvjr5UBRGXD4RRZGGKthexvhsyJCALyVmIdVIWm4P8oEim/3fr9etkh79jE5UFRaDrvzsVjbjIO+hhX7yEIAXBOcBhl14BdbDnsSgW4/pRvo6TCy4M3yH7Ph0ro+fw6oqamx91ny8/Nx8OBB9jwGj+jdmClTprD//v44jW05/s+y4mhHpQCkdzkUFRUZAOk+YCkAP38gfOwWEi7oQaH69euz5zEOHz4MBweHt7dlcVtDl57TY0MjRoyoGQBdXV1ZoUpKSuxyQQKilKQc4HGApxUJgCTl6JmNNWvWYPz48Xwm0c4KScOIiAg4OjrWHICGhoYg8aqsrCxyK7p0KKQc4IGQLqlXVVUVAuDt27dhZGTEXqqqFQDp5SF6w42QLQWgFGyVcYBAqKKiwn54EtDb2xtJSUlM7ZJdWCsAkg1oYmLCnmCQAlAKwKo4QPgQBCA9mdajRw+2iC0oKKgdAO/cucOMSyqc7ECpDSgFYWUc4AHw999/x4QJwlcwEwjpccn+/fvXzAaMjo5mxiWtdHiuGOkQSDkgjgOEEfqh10tzc3P5q2A6XxwWFsYWIPRaqURumFylcmzsUvV7YNJhkHKgMg4QAOkBcR6RaiZ3HtEy71SoFcpU7gfMKy2H2fUIbOyqixlWdXMmVDpUUg4QBw4EZWHZvVREOzaEisAum8hecHBWEX4KTIVzQp6Uc1IO1BkHBhip4o+WurDUFHhkmkJTS+M+YsgLRXhX7NLUWeekBX2GHJADZNSAcvFn1fkN/j+MYcfMmwTwTAAAAABJRU5ErkJggg==

Takes a text string as input, and reports a new text string containing all but the first character of the input.

pt:_ sem o primeiro caractere ca:_ sense la primera lletra

All but first of empty word.

Takes a text string as input, divides it into words treating one or more spaces as a word separator (only spaces count; punctuation is part of the word) and reports a text string containing all but the first word, with one space between words and no spaces at the beginning or end. (Note: consider using SENTENCE➞LIST and processing the resulting list instead of doing recursion on sentences in text string form. List operations are faster.)

pt:_ sem a primeira palavra ca:_ sense la primera paraula

All but first of empty sentence.

Takes a text string as input, and reports a new text string containing all but the last letter of the input.

pt:_ sem o último caractere ca:_ sense la darrera lletra

All but first of empty word.

Takes a text string as input, divides it into words treating one or more spaces as a word separator (only spaces count; punctuation is part of the word) and reports a text string containing all but the last word, with one space between words and no spaces at the beginning or end. (Note: consider using SENTENCE->LIST and processing the resulting list instead of doing recursion on sentences in text string form. List operations are faster.)

pt:_ sem a última palavra ca:_ sense la darrera paraula

All but last of empty sentence.

pt:a primeira palavra de _ ca:primera paraula de _

Takes a text string as input, and reports the last character in the string.

pt:o último caractere de _ ca:primera lletra de _

pt:a última palavra de _ ca:darrera paraula de _

Last of empty sentence.

Takes a text string as input, and reports TRUE if the string has no characters in it of any kind, otherwise false.

pt:a palavra _ está vazia ca:paraula buida? _

Takes a text string as input, and reports TRUE if the input contains no characters other than spaces (therefore, no words when the string is considered as a sentence), otherwise FALSE.

pt:a frase _ está vazia ca:frase buida? _

Like JOIN, takes any number of words (text strings) and reports a sentence with its inputs concatenated, but inserts a blank space between the inputs. Consider using SENTENCE (Lists palette) instead.

de:füge Wörter zusammen _ ca:uneix les paraules _ es:unir las palabras _ fr:fusionne les mots _ pt:uma frase com as palavras _ firstbffirst1

1bf

Takes a sentence in list form and reports the sentence as a text string.

de:Liste $arrowRight Satz _ ca:llista $arrowRight frase _ es:lista $arrowRight frase _ fr:liste $arrowRight phrase _ pt:uma frase com as palavras em _

Takes a sentence in text string form and reports the sentence as a list of its words.

de:Satz $arrowRight Liste _ ca:frase $arrowRight llista _ es:frase $arrowRight lista _ fr:phrase $arrowRight liste _ pt:uma lista com as palavras da frase _

report a list in which each item is one letter from the input word

de:Wort $arrowRight Liste _ ca:paraula $arrowRight llista _ es:palabra $arrowRight lista _ fr:mot $arrowRight liste _ pt:uma lista com os caracteres da palavra _

join all the items of the input list into a single word, and report it

de:Liste $arrowRight Wort _ ca:llista $arrowRight paraula _ es:lista $arrowRight palabra _ fr:liste $arrowRight mot _ pt:uma palavra com os caracteres em _

SENTENCE is the main constructor for sentences, represented as lists of words. It takes zero or more inputs, each of which can be either a list or a text string. If a list, the input is assumed to be a list of words. If a text string, it is converted to a list of words using SENTENCE→LIST. Then all the lists of words are appended to form a new list of words. If the inputs are lists of lists rather than lists of words, SENTENCE, like APPEND, does only one level of flattening, reporting a list of all the items of all the input lists.

ca:frase _

pt:lança o erro _

Takes a (possibly deep) list as input, and reports a human-readable text form of the list (namely, Lisp notation).

ca:notació textual de _ (

)

Helper function for word/sentence library. Reports its first input, unless that input is empty, in which case it gives its second input as an error message.

Takes a text string as input, and reports the first character in the string.

pt:a primeira palavra de _ ca:primera lletra de _

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,_ ,_ ,_ 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