學術論文意譯的技巧

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+Jg9X7wWSsMn29IDjp8uksesCihY6wT3mvw+BFq9mP3uahTahH2agi6UKRb9ZlZwHYtHlY9l/KkTs/AB5gGcXthLeiPu4IsOFa5LvNcxdLnQNbYilQEg0L6/vzBT7CXlxwgEVU2uhbT4ooc538wAiyIAGuC9098hM+veJRQl6IMaTEP/tupN1bF3it/97+kN66VdL89FtcBJjAfrMIYK4+5Fl/Zwz6/xgV7guxD1MG1AFaG/cCGDGu+kMZK9QdUDxrhzFtIw7/gvkTPIlie3HJE80DQZA8HVV5wg6xmWoA4A6ofjDb/JCi+tdOhJK65Y4nXoKci2qgvHoAMJvhgeJK0LmCh0CZA89KhgMcW1FndqofDhl4GGoeMW9YUGYaI5pDV4bDiC2FrNBc64nEVc0yIlAhEVUybeK0QTGlh7CmrDTPtx9AI23Sec8G6wY4zAzGFKyc8Zm8rOFqJ4HbMEEjzgBLzxBV3gLlid8VsuFc8UITVw8LnJFPDQ+T1pZ/jBQuAb31MnQaxNSbHsD8ivA5zVCUsHQQ5Cs9k9UIxeG4XbkJ2U0RWHApXQmMXB0cSM6QoioCfYMvj0UBMUcg04SHKSlzRGRy0fKvxjOKYHr9o5aRzAJz1O/4orye5S+kfZVPsw7oW1fTBjdcM1nxN9/cBsxllDnXMq+6wn3jj3yTH0RepZZy5KWY+EsTfUGIy7D6N5cdceOPuVmET3B61a1do96xTt2ePvPdw7NxpLw6+S7nhrAGxU+zC49B7N3zPUftj/zMbE4HDhekGJt6t2+OOkcE3dwXa3uSTw6rKyTtOx+qDvAYaY5z27cXOkTmmRY8N0aXDD4sxzrnEYcsK45RWYn/ooNAg1pDsVISavj2u2qLacFy1axr6+p6YPBzT2Oeqe7Qpbt7CltAth96q+duh+dLJa6WWmyTX43cFATg0PR20Pe04WI2JoIdPTHUDmsQpEK2+ZLaHl0iPTcJZGtf8CugGHCpFHFky20PhJ9GYJgbIiIjMT8JEjKzPmBrjpoJR/j7GkDValPD+GikMWZOxFxGFJgV4IgojrOC5qHybOOSxHXQr8IZ0D1sO0xZSaCbiE04lKwNoNgktxR+Jj8SV0TitypQAhLGIOqLQ/iQewfXgGuQoV8v+ZPiZ7Zgu7gKjiVnxUbxVAnHF49E4k9+nQO3Xg7LiVfiHuJv0MlE5JTA0lHKJ1JKD12whdm5EjocFXV0NyzK70BdTfREmTRGNT6KKJ4o39Ic/Vv5JlPYNsi+muGXdFdFHg7oNQ+YTPhFWmPXDAzobHYzdurq/6d7A4Ovcoqb73XGyZcOQX6L2tkCoCd/HzQzqNtpGhXnQXKGw2jxoMLJOH2ZdGAXOAD+d5mj6xBjn2fiBfyZQMDE1jmtIjypdDY/c+8uX/kWyb/wu3rs3Hhh950mt4wjWwfPelrBrbLQvGuT54eqoMZVZVTmNAuFgr7PlQGzkTXX0WP+e3x6+Ruq/QXIdvv+MadAC1ldshx4ZYyneN0aTfQ1PhwaO6PERZ+OqI3dKHd/PfOO7ksO87oJ/mEhqRhSF884ljzRoHktYjQU0LK3vT2AaEk5+0K9wbIYAIzIbKsE/xcTEV2aHb5NfP8RIhFViJN+P/RbW/G4ceEUhAItrfLQnhEeh6W1rbnBhlTqHdN9Q/ZzP2Lff37ThFz17F/Uf2ae3HjpZ+Ff2w3uSCQiCN64Ss8qL5OA1P8HKsjRFkJLo58G3/IrXiZ+oxGPJv+FrwmyLn/Mt15Z8Q17zk7M/54f8Kvn3Zwff8mwYXAlDSXxNXgM/TMSB4rOIDOkpEmCV4QDj6oPR1KK9IoCSFfFD8ffJd0gOgQNNDNKrKoeApsuyS1W8PhHXvA+71q0xZ7gwvlA05NUUXXYkH9YF78klJX/OtHDLPD4u+OxD5HVyfIjHyj/hFpgNrpw3EOC0GAFjFBcRf4pv8TehydEw7qI9qQZ6XvENn8IXcMYdTk7guIgsouGB0W0/kYdOcp1k3TCvyQdNwpmRnF6CDZJtmAaPxjmpClWO88EvH9ZMJEyDcJB7PaKxpaLY3xl9rtK8bTaIG9XXdWLRN1tuNzqeqSM6TezMs05cOKpGRJMZPBsVf5BrDsTDfc7Gg87jb/Kk/TZfy+bftCz5fmjoOG/F5fEsWh/9ed/eOy9KGYw1ECB99e7fSB1Lv2rb/n+a7prSUSQNFGV1L/5BaKCLaUkuM9FLK7FEeY1bE4/Z/J52VXfDObaLKGy8RvP3eg2YFt4OOpNPY4nFdfvJ4eeW2J8t9rxSNvpcge3g/JGD82zPzne/XGJ/fq79+XnnD37ywWPO6IuV4ROP6h1P2RufdvbXs1JZVTE95lN0vFu/qO54MNjc3dD22wOnyqPHbtZ7lju3XqUfmRE68GP98E9HVn9JPV7TfIOk9z7mPvVQrHOX3vtkvHtPtOsJpW+v3v1qtOO5aMdBve9F3faaPvqKPvqqPvKaOvCCNnBQGzigDT+jjezXbfv0kaf10YO6/RXd8Ypuf0O33K02L44dL1ebqvWmCrWhVDlVHTtWpdRXx06Uas0LYsdq9Oalemu13lKlN1WKr40VeludfmiW/ObtoVeuG33q6oFdvxrefbPnueLAq1WB91aFWh+Lj55gSXObuNgYFCeOphCY4fyUo+pw3LJFO1Gi1y+NHFkefKPC/0ZB6FBx6FCJ/83C0Nsl3tfmjrxYFB9uFBZK9coiLOLBX6hDQ+jh0MJKyOnXfAo5CdVpf3qh7/WiwFtFgTfPjsLAWyXBt6tDL5UHXq6MHF/pO7xs5OlSx/OltgOFw8/MH9o/n6/O50udLxRe6iEmH/F5j97x3Ozh11aHmnYFzfsd7a/E/SM8QZF751SP4yUR1sk0d32/FmxyYKQ9bD3EcmN7s+fpJZn4h/qrP5Pir62Kh09oPq+uNunhYT3cq4d7EoMXw2psSPc36oFGLIaL2E3GfzwPF/fhTEMyu6mQRVJOGyAR1mNVsXziv2jUFx/eOL/7d5J9/T9EfD1ncw3CRihRzENA3AIEX0JX1rI30Plq+MD31fqVWvM8+cUf+fd9VZOPsYGJRLhTy6bvBZ/+SfI8+H0Dez38c6lrjtR1k2S9Jd88N/vwbMnXuEWPNob2/qq7NCvctwLrofdtUrvWR6z7ZcU83HTY0XlSR4dAFP5jAIX/WK9BlElkO627OGY5q/yvr37p59Kh70kNv5De+4HU/Eup87qMoz+WTv6ndOKn0omfSSd/Jr6K8VPxwwtH4g/OjlM/k479Qjp5i9QySzo+S2qu/kzfxp+PbJ0V7HsSe8f9kxoIqqOyyvwyqZbhGdLQrdLwTGn4nmm2Gsm+fMrQAqmjShqZf9mRVZndd2Q13ylZ5ko9hVJ3odRVKFmLJcuZ0V0mDdZJ7pXSyGLJuVrSt0vqI1L0QUnZLOlbpfAGybtaciyVbIuk0UXSUJ3krJH0dUb3Aim8XFLWS8rdknaPpG2Q4msleb2kbzT18EEzpaFZ0uBMaXSe5CiQ+mdI/XdJ+mqDvt6g3yP5qiTz9dKJn0ttv5Hafys1XSO13C71Vn0i8MhvfAcLY+3bdNWGe4q37CeUxVrQ+2znL2KVUnC+1HSddOw3UtNvpfbrpdZrxOj4nZjMN34peY49wrrB6gflYBBrrdkvKnSFPRrBAW8rTGrQc+jN70sNV0stXMBvz43ma6VT10md10p9t0rexZf1Fecd4Tn+TDryI+n4TyTzbwwtvz7zHHlkYx/lBd8mfpV8+ufGT8T9dtwhNdwuHZmV0bryG7a98z1vPKAFujH+HgInXBViCs2RuPhzHVw562LDL4V3/SR4cp1wUJ3tYcXpth3W+1/rerG6Z90PWkunWZd+rXfFF3pW/F3vmi+6H/6668Gv9q76onXF3/at+ObI0q+0r/zXiPUFwhESz39Ii7ezdSJhCBLVEHyTc76eogz4e14Jtjyis9l6t4a7t4TfqQse2OZ4fXXE/KRn/8ymAjF1nq43x7j0STy+IkIe3ePVI7iKwsxFh30LpM67pKFCsUj8ZZLvtZqg0+wdPuX3dAQf+j8td0rd10mt86WWcqm1SLLUSJ0FUuedUtONUsuNUt9s6eQc6WTNZGuZZCmc6H8w07bjMteKKZF9Vzj2SfLdUuCeidGHL/NUSrZlkn21ZK42qm27AwPtepwYUw7HCBsjcWW8ze8+wGuIqrLDq5JjF2e31vJQ//y83iKpu07qXiBpu6doO6e2lUvW2nRrTVp3tdFSZeCFtTZNfFtj7BZfzw6j+NXYUZvWW5XeX5jWO8/QP0/qvkNqZEv8Qmqo+bLnsRn66JuYUrfiiRO36s7hiKup6HP1N2b2FWa8Mz+3ZbahYbbUUJDbUWRsL8xpq0hnyrqK0zoKje3zDOb5BrP4KrXOMXXMz22ba2qcYWyaaewoSG+YJQ0tztEf+7j26OXKox+LPDjRtjyrqySTv2m6y9QyM+34rdKRG6S+ZRn641Oi23OUHZP0fRP1PdnqrgnBh7Pc95rsGzLiW684VSDVz81smZ9mLsxqK8o2F5oa5xib5xkH1koj642eLZO8D+Z3LcwwV+R0lk/oqcztq8jsLjI03SW9e6106HeSufhTtkev04MNcZFCDeAVq3p08PE7OmZJnUVSQ2GGtXRiZ0m6tdrUVZ1hqczorsrqKEhruEsKt+wRYQ7RtUidxqinXHQYInUTj8bgp5KbUOLR9v7rpL5SqbMirbMszcIoz2C0laQ3FEhdNab+qkmhbf97dNM3zQWZfQszOisl/wPZ+q5pvnuyumskS5Wxty5DPMpqY1e1gac5zidrKU2zFBn6iqTeOVLHjeIsOX775ZYN18dP3SfDfCORQM5KJ0MsMg74iWBSwnKArrOkG9VT+52dTylYzPe2q6PdnsZnvEeeZ091LvvfR38tdc9Nb75VarhFar5d6i6QnAuNTbdLDbdKXbdJHXdKRzDEG3+KOxVT4uSVL5XPE446KcYQaVpKDZhpimXJDey09r+1cfTQsoGXVgSb1saP3WhdmddaKPXOlzrmSf0VGc3XSfW/kxqx3Wzj26XWyontxVLE0zI2ACQB7CB7JAdEQBELOHSKStG4d6S3znSSs3Cu8URRRn3BFPMMqXee1DpLapsjtfP0K3O7yjO7Zqb1zc5ovcNgmZNrKU1vK5O6yyd3lk0ZnCE1lUkna/PNHH5Ls2MHpinPm/RHs8LrpOHlWf08snKTeWbu8VtzMM2W30mv3pGmd+/FFJOAoxQFMsgrsFt/AtOAH4UDRzVIACeUti2t8zPb52eaSwzmckl/fJr62BVNhVJHaXZHRWZnpYmvbaXp7WXpvOgoz7hwiB+eNzorMi3lp0dXRWZXZWZ3VUb3XYauW6WmRV9QnS18aEiU8UjY+5orvt57h9RTw+KW2qpMltIMS5mB150MJq6KZW2yMPjVmdFens7rzooMhqWSy05rrzRENk3Sn5mkstt3T5W3Tw4+kj+6KsdcYuqtm9Zezm5JayszDiw3xR5L15+dqh/8rL4nV91l1J6YGN9+ZXDjx1tLJN89U9rLjG2l2ZbqjA7eucrUViF14ZWsywpsucJandVRmmUulfjLtjI2ZEZnaXpnGUPsya6KjN7SdB6b+S6p977/rdjeoXBFaBCPuV17b/UsuMy9ZWoLlq4ks6c8rbPK2FYltVcZ+IjuguymO6Vo83N42kxJkF2vUuS7EPFKRjoeYwVQknChMuAPOntukdrK07orjd2VfE23VKR1VaR3lqfbVuSGd39spHai7Z6/H9n0LRZle2lOa5nk2JCh77tyeEVGc6GxtTidZyRGpYnHKp5XZdaln6x4rBnixhPDUpHRVZneUZTRcJvUMjvfdmg52z+qkbslaS+SoAD7RI1NCZEwS3Bs4wM6AUVU7ev1vPXo6DNVYQxdYKhnwdST8yVrdW53haGrJqezwtRcJOm7puM5thRnd5fmWBZk98w2nCjKJ/NAVtiP9RwHsCegUycCnCAj/BXVyQSP2t65u33tv71zq9RfIjXcLDlWSPoTxtFVErPROjffUmpqK5KsrPZKllxmZxWTwxRldJRIzUv+uWfj/x3Y+oOWTT8xP12h+/pxfsmPuMkkyHaqXWSa5JjcW5dtKZX6WbcV2c1lph4OgHIja9hSzqNJ6xZGnFWdZmFHJGZSWAq2SZlYbDwgzHd7SSb7Lrp5mv5Ulro7U39icnTzZO8jn3Sum9I+X2qu4CwRZ3Z3oYFMRMS83d7fEo3ZfaJIFwvI3vGLHn5QrkFRvJgGkvZ4SpgG83xT2zxDd3V6Q6E0uCTTvW5KR4UBc9BWauisSOd840Xy9ekllVxYicHaumB0lKV3lYnbPjfKMs1VBnNd3nGileq/0iODJMnkqFtx+80Vn++cyQ8N7QWmjtI8CwaohHk0iSkrSeuryWPJXvyhbaVpyTXNV47KkZW56vZ8/SljZLvk2yjpeyfrz13uvX9y/TzJUpndgPNSbWouSOtbnqYfmDp6X3obkcUTk/SnJ0W2GN33So612T2LpMimCT0LpbZSI2/L+3OzWAF+ou6crj/z1wOLspoL0zrKOWONloqsTmHC0rnIMyPdUprTVpvfVT2p8Vqpc82XNX8vDjbpLfm1m2NrJG17et/aKcdLpK5KjE6auUKYhu6aLMv8LPzzaPOzZDFBuoQwDZoLIdwLVj/OsEL/dWjpogoAONTddYPUXpbTWWpqL+YyMrtZx2XGznJD+L4J8d3T+8qyhtf/w9BD326YwUrlEzNaiqXwxsv7FmY1FuAsMG/GjvL0DlYqO6GCyeR+zz3T93+y/H0pt3n2ljGOGT0VhtaaSSfvMNbfMsXR/IDIT8pkECnvhUC1CNOgRUJxv+KkFuOVVa9H+OF6rPW1UPtOUbR/vtoySzo1O62pyNiGs12c1lKUxvZgwoeWZjYXZLaWG/GJ+oqlEzMy3Uc382/D8qVNg8imaV6gilhnkVz3HlNevS78yKS2QnGMWwulthlZA4vz9P3TglvyuqvxECf0Vudayo3dnHOJeUgaysRKyAhuSPeuNBDMnrxdOnRLZteKL7ueuk2PD2HvRrQRlTo1iUHba0PLPtZCQFrCkstrLs3mcVgrTOL8KE1nI7CYWSos7OS3vBB2Qfw2w1ySzpHTUSGZSzJ7azPlR6/En43vSPc+iJnI1g98PvzwlPZCqRHzUTaRjdk5RwQmimVPJOggz07SkIIwmW953FLJf5BpyDLPk3oX4CdnthZhETLa2SGV6a3FUjOzWcxFS11VzBd74/0ch/NdiU4s4vmmgbXbXI17luaqSq+/RuraeDVKjp3HF9uPPdxaM90yz9BSldVXjtFN78DKlmVZK/Pai4VZxdk7+5zOHmtJi5DcwK3FhuGlUwaW5NnXGeUdaerjWdquKer2yUOrpNHVGV21xuZiaWTZpM7yrIY5aUMrMvW9kxwb0rrrDNEt+fr+y2KP5vFnA4uzexcaB5ca2zGIJbjlGR1lpvbSNB6Yc22m/tJf++/J4sm1FOE3GTsqCRzS20vONw2l6eZyU2dVWvtskhoTT90hjTz+G7aACqbjzduV+6WBGqmtIqulIq+tGB8+DVPYWi6ZS43dBabmu6TIOEwDdU0vCT+VkjuYjVDvjYSsWR3FOd3l2FM8GtyutKHFucF7J9ZXi0Xse+hfbZu+2zw7nbMde9dalNZVmcuZ3F5GeJjFM8XW8zVhGjDuPNmL/MGLfoKXNNY08LqtwthN7FkxsadYarxzks9MRgmXEBiFAKRRXqNjPfvZd6BytPFtLB7lhqDt9WDTExEy4LrWtfSznTeTMJrsvedjI4uye+tyuqpy20qlyINTexYYzYXprYsyOgtNPRWSZY6xcem3ROIT+MelvAb+AOSBgNhqntCpJy01n++ukGwLpc5Cg2fN1JEVEztKJ7O75G256p7J3bUZDfOYAVwhQzsbuBKH9PSBl1h7mY41E/UdH29hZVZK/ZVS601S8zWSY/3PNHsLcQpmQe19vXfRx6Nb/1cbxw8GuiK3lZCzzNhVjuFmnZislbkdLKoSfIRMTEPSRohDhQVfkt5ZmYXLgO221uS5754+tCxN2ZOr7yHgnaztnuzekNa/TBpZZGyr5YmL8NAy19g4W9L7n9fIPhLOyB6iJyBIYED++DQkAcX5XsM8Ex6L8KIrsttLsttKmRomxZBYRqaBRXlsic4KnG1OqowLfISLt65wNS/yGrqrMvvL0lurSdrlHrtJCry81N/zWqDzuLn6Y80zpQZcd87kEsPoiuzOUmN7cSaziWmwVht4Nu/rNSSXMtahpza3sSBjeEWO/vQV+Mzueyf0LzMOr8z03zMl+vAU++rswcX59hVXtJdm9y816M9cPrQGt80wsird/0BGz6I0TonuqkndNbn4sVgES/lka2VWd0VW4ikaemqlkeXZtjVSdw1/ls8WwqPrrMzlefNcz3oN4vysNHThZ1VNbi0yWCuy35olxV5aJQ7Gg3cOL5YcC7OaZkht8zPINWD4WWfEblx/V0FWIqC4tNegaX4cdZAFpIcCAU/n9VJjAS6oOOg68aGKjb3VWbZlU92rJ7TUSc2zJNcD3xp44Fstcw3inKnI6ijLNhdn8VixBTxZbL1txWQeLk+2nZO58sLHmjS+59kL4Qaf7zUIS5FpZUdVsOtM5PMtVX8XC1sJeYRFAEEH9FNgbfVI/WOhfgv+dzwejez+qXvXTwIgCq2HxAFOCFaZ0bc4p6syrasqo6sqrx2LUy4RBhLmtFQZreVZnRWGngKpYfYkPTIwPtW2MIkeasaext1vXj/NVSj5t13henTqyIM56jMT7VvSmiul+ruM4Qfz9YOT+5eyHtKJUjsr0zhCkovtrNfQUW6yLpCCD002V6Y3l+W1lZkGazN7SjLevUlqX/pPajcVSiUc6rY/tdZ3/5cbZ4lMeXOxsb2apYubIFwGYofuyoSPkAhCz/8qvAZWOEkHEfZW5VirM5tLJHlnlv7clZGtuba7paEVBs+GXH37Ff5HJmM6nWvzewozGmdKWs+LoqgpiF7gRMCwATYbr+jhJbwGTyKgSOYazPNMHYVprSXYgjRLlYgg8LJaigiqpeFlE5xrRITDicqBSXQqYrAPHjhLZ0xDYvOISekuz2ovkCy1mUQNPYVpbfNydLtN9Wnmqr8ZrDSO3kvqURpZmqE8MrG9TMJZIBdYP1s4LGNXZ3t5BiP5E/EsE08Rx+H4bMm2Okt/ckp0q6lroeTakBPddsWxGcaRFZm9i9IolPQvzGucbxxYKulPTnVtmNBbO6UVF65GstZJjfOltmJT6L5Pd9WKKLdxbloHH1qUPlg7ZbCWLJTx1EyJfyi2Slkefjjz0MmZUCo8w4RDmPzKOWBoYylXXi6We0Xm8aLspl+le3r3Bl6rrJ9B2jkreH+eb2PuQF1uV3U6sSWJDCa5a35W4x3jMg1xUJ7Ak2Uv0L1wdNQvPJrsgYVSCx9HCpmETkV2WzGOa7owDTMk18Z/sW36Fmcj7k9XlYn9jxtIcIExFb5PhRS87+P9i7J5xDhWwnz8/idLQifxxE34g8n7TZx4HLPpPWU5xwvT+msmjVSk9ZdnEzCGXr0rAbUANQDiEyR4yKv6ia1AnQf97YOPl5/6nRR8jzqc7nhynvUuqbUqr4fDAFtZTWzI9pjQXZlPKGfmwgql9mJDXw0psKyuIqlzbprnrXsSJKKzFYfTLy7EEethgX+O9PY/+J/v/ULSH8xxPXylb3d2fH+G9qwUf0LqW5zRPD/LtipD3z+hf0VGCxnrisS0iHOI4F+EFacHt1yZ34TjzElZldYqzoZJp0pzG2oy6m+RBpd+Uxk6InKcMd1akzOyMDe+YXJjoWRZYJIfntJbndZeIjXNk5rmisQZo6OE+EjMYXtxWtJMkJUTkXsZN84pSLgnDS4z6vvyoztzO+uk0bWm2MOX4Wb21GRYlxvr5xksNca2mVJbgaT1vcbnhlUQqKDmAYz6QPeNs5Q7/oBiM6ahvVAEnFQlWDRsSJZCctHgLHTXZZrxPHls5YbOEs6980cxuaj0TkaxOBITOTmyDCI5l7AI4jXDXJ7WXjfFOnuiqzLrxOxsy+1S27NFenywpfrzgyV5Q6snDC3O0HdPG15OUkPy3X05Wyhy3+W+9RPaitNw4NuK2LFGMuSdJWkcy7hqIm9UnNZGRq04GaFN1p/JV3enRTel6Y9PHVzOIWOIPDDFdXeG5+7J3dU5FKKGlmfoT0/tqcs4OSvNtSZPf/ITkUcmO9bksBAt1WKvkokcXZ5HbFVPVWmGdPI2CY9mpCbfu4EIQqqfKTXNkTr54xJTNyE3FgoLmMzJlWXWV+Y0LzCB6bRWTW+tlHpmZbbcINn3/8D/yH+2zWFWs2L7TPqh6bZl+QNLcnsWmdoqsbzpPcXZIqBoOkiuQSWbINKQkAguRMuxGWIyUDElBuBSgF1G9Psxc1fImyYwRX11+KU4w8zzpE5cg4XG1tmZ9vv/YfDBf2mlYFElwgdLldRZhe/D8ZiGdSML01eHE0GejGeanNgLn2xXYbqliB9ibkRawYp1EKtZZF7Pnn6tLImKidby/K55fFDu28UG/53SkGsUfCkHj1ecYyCpxQkUD1nYre4XKw4t/YSADsihjoVf7LpDOlE33VqSYS03ti/M7q5OayvJaZqX1lVtHF6ePbA431HHUcGmnYZTRhHhxJJ/S4h6AUJPgsRJdp4ulJ4WWUhQqsFfAboIndrZdGdOT6V0HIeuLjO6iSd+lb7zMnXT1O7SzKZ5xo6FkrIn37Eut7OS1AD3hd8qzshO0jesZLFvRWa3DetQmDFYmdddgouUMbgoi5xOb8kER4UJ/O7JZT/Ug/CpmkmZM9uOJemDS9M99092riUTSTCbNboi17l6QnDD1EGCIyKIonRzgbGj2MB67sRAiGnPJotMRbO9CN8zPb75Mn1vjro7N76VsOKzAwuzTs6UnMvTqQ+410zqWyC1zRK5hrjlIKaBAA1wLXmcuKAU/NGK0qwzILc+zh4oK3gNrY+0FWSb5xIDC+tFftVSMYHFFNqca1ud01uQ1liD8c7ruvFjJ+/Ka75Fajp/tN0itd7KXso4cWc6fo51ttRbIrVXS5zAbJtukXQx4jyLGJX1x+zzExzRcmP99em6/QH/xn+j+Pd2RbZr2eT4/VNaC9Ktlcb+uiyy7r01WbbFE60F0uE5aUdvTrPOIeKS+kqkTpKLs3KbbsnjH7bMpphsPPk7abRO0jfn6I9I+qNpkfWSf6VhsCydAzy6elJPkWS+Uzr2WxyQT+tPSf2rTZaFec6FOERTLQsMkfumk+DArlsrDbZlmZGNVzju+/rQ+i8NbvxC3z1fdW79K/Xxb+jPfjHy+Ff8W782vOoT+P9dM6TB2ZSmc1qqJg7wUMvzWqsnWNmcpVkdlYRjwga578+ObDPpj+cFH5CGV6ZZFxj6FmR61l/eVWm01pn4t90LMJdST1Fmy0wp1nTAI2gmLnilYFf8uvNiGkI8HgItFpWB38nhqC1KiXtNlhkbtCC3pTDfUpNJnr+tfGJ3LeVkE+XS3o1/PbT1u+2z0vGJEgUaHJ8sTLzvgUz3xsssdUbLfLaB1DYzo/7mK07MlC54so23Sn2/k1pvIG2R3TCburLUWyD1lhq7hOub2UpMXi0io06yAOI8NPBksdrdZRnHbpeGd3yfI5s1i3IfRTVQ4XYdvJLIebtemX9qthRyjMSbHh6uzLRW5Q3W5eIsjCzNG15MLCmyS/11JjxW/4b8oSVpvvtMrWykQpO1RjpaIr0z/xN637v48B7iBTB0mpOsLPha4VGLATEE+o+om7KwfS/e/87tUlP5ROuM9OElOJU56tPp6t5Jrh2T5e15nlrD6zOk0PqJ+v6vnaRkfoPUcXt6/a2G1ruk5tukttuMlpnp2LueUspkhp5CokWptYJvDdF7pzSUp7VUZreWptuKpBPXSUPPLALC4rtPqi80cHfuNZmB9YQeEvmXjhpDU022pSrfuUTksJpnTm6+Ja3+Nim4OnO0WuqeJ6AQTTflHblVHELdnJe3iLNH3SxpWyX9EaO2Kcu3RhpZIJHgDK6ePopzMUt650bptdtz9Ob1CVjkWM7xn8JrOM80mDENFNixC3iVIi41F+NTGdwbskhMtpeQxMo6sfjfNctrXsuBYPvBsSPQ8Wy4+Zlw20F3+0FH177AqQ0dy//9zRtMZL+ppVFXawHXQcEi6Yiey+cLP+K9ayS96bbY5i9Tnz9SJQ0tlWL35PbV5vSJoAMnnxKmEevYWf4F3+ENrvoX/Uc29T9XNPz4fPm1e+PW5wNt+wMtu2Jtjzo7d44cezRa/4RufUK3bNF7d+iWXXr307HGneFjD8kN2wLvbQ4feyLc8Kr/6av1p6WuJRnNVdkdBRJrjrjOsSK/W1xqohRXLDkf+Ufd3a25OmLOprhjOO4yR+y9uvek7m3T3W3q0Cm/+YD1QO17ZX998i7JVpXZUmxoqc41lxh7qvDVidipDkpOElf7Pitvn9i/II+AGWtL/o/wHi/MWp7ZVJAJ+sC1flL/ksyWuzKxNZGmA5RsFNUFXwOWDIW392Eo6REPPBdBGIFP5hlhBReTUsn1PDjx6F2iWuG7Ly+yc6rn3vzehabRirye+7/Y9/B3LPPIq2WxTC2VOD6mVv7JikzPqtwWkqkVIDgmuV+p8ba9GejaF2x/9vyH+2yg82lP29POlseDzY84d93wyh1XNMwSm99C1gmjUJXWW2PqIl1dzpMiSheBRhfp3plS371f1QJmDbwzfckEVS8SjAyFNAcp/ZM1Vw+u/w7hxei2gu5io3PFFQlEhpGHbi6SBhfkcSo4Vk7CWPTUGp2rs8j1WIqzOkpyWJltlYbGOycOPVmtakIWGGIIVD0fLAiUmhKmISJMg0h/Yju0mHNk440nCFjIKM8Rnrn++GXyngnajhx93zR9e/boDOlY1d/7T9yn+1/xn3w0cGxH6NgO78nt9vod7uadgcaHe3f++p2S7LdnSo1FUsua/HpKbPNIimX21VyGn9tWRZE7z1FrPHGzdHTpt3XXsfj+v+5fPIETfmRxmmdNPnWo0QX5HbXGJtLA5BqLJCLT4cdui7Xvj55Y73i9wvzEzY7XKuSOLWHrs97Op5nteNNe37tbHe89qlt36NbH9O6dOtjfzsdBN/lPbAudeix0+KHw0c3ed7a5jj+jey0Cbju+TnYX/NkHBRTvZxpIyButtRm9C4AzGE7NJw/PKWQ4RUx+p/Hkop8Q4QJov5hfYBPE8gTLwO+2Nb2j2+v7X1v5BtgVDG0FuCmi3HRgSwmve0zdi9cYwsf+yrYi275AaqySBlca4/dP48TAoPRU46UbO1h2RVLP4n/QI9YkhtwTGVAiLDCPp37HqT01kGISsNYYhC6cK3YXCRSPHnUlAMu+BIHiLNVCsGUbKqN7pfYaU1NVXleJ1FeRz3btrRb1P7Ke4siaK9m3/WsCFQtIPkp9IckYcKikAKG2QbEa9owc0WNm17HHm+de3odrV2tqqc3FawW+QeKjuzobB7inLtO+xtRZLR29UzrOgTyf7ZRHiqurKq2tgJg/G3+hnZoFeekZBoqX4fqnqZPDPfPLggwGieviRx6OwquLJ3p2wJ8PDd8pnZqf7t6Ak/y5xgLqI1J8m1F/Ic+7Ma+lRBoszhp88O8HNv2bmbOoRsSGFCy7q3Mb5xsa5ksDhdLxcul4sXT8RinavYtnyhO8+Mmy/fg5PH2fy+Ixv6h2v3qy6msNc0w9VemUV1rJTJP7pMZcyoPG8RbBM/mOFk7a2vxYxx7mDfan6GyqRoDyahB2oJm8vC3+3rrw4QXdhVPI4/RU5RLOYJeJ10iphu65CmwPwBbHynzX6okEfSI/VZbfVpZnrjT2EY3flNa48H9rsRFYD9DbqPUKqhvQpvNNA6sx1P1Ke/FnWT8cM7a6dM/DJu2xacrjE/WdmfozebYV0qGfSsMH1/OXtgRxlhPYJ5jsiBLg4Yj1jPMRH+p0vbDz+K8mdFER2PRxZeNVHUtNzFtPMWYxrxWjj2tQxuq9LPzyzPiz37AuyMFKDpJoePAq+6qJ1tL0hmKphRQP2agiybPh03LfS0lCBMAtb0xEjj5X5+jmbwctOwTePInSPkMI8uiqlx6NusaFsTy4KrTOkpSZxNVeksr5e8mpf5BpIKAgv0CSNsO+akJ805W9i4zWKoNzQ47rwTQ7sffcyx2dexN00bGKIIJW7FJ9PkFYhJoctXW3BR39ujJse6Hq0F3ZfeSWSgzdtXmJUOIi01Ak9YP8IdMpktKGVjCUlTlt5HtIOInCjygFdxZK3jV/pwdbh3SossxRGALvKHGsMhgPk/EO9jDFgqmlxIWyQ0hW4NJDqaSsTVgOhzcSjrpjMiB/+ObgPpeG9kjdtdn14JrKDD3FE/lcjixzIcmhbMoiHAvuHd+G6BnSfZo+HBxqOPn0kpizhT5ZfuikSPdAeXQM6AF2ja4fve/YDVIz3jXrg61O7YZMLYmVKoGMIp5y3T1B3/sJ5905nVXkdwERYCWJP9NJfEa2X64/f4X3AWNfmcGzZII++C53p2rOcCJEh9x5sWmIRLmqqD/mlHXN4/E2Xy2Mi7lKqp8v9S3KlXdk6vuM+p5pkc15VMII+gY2fn1g03csmHhAk+LJpnnXX+ZaO42MwHCxIfzYJ3uWGzvhue69yyEnDmERw5/3cKENBSBx6qrdaevpbhOo+qE3Xpj9STMlmMq0Zg6SihwL0Liyc6ahpyrHXGjoLZaiR5eKSorijMjBqAzzx6mEh2lHCGNn8J6fvvQjqXFxmoW0EeC/qiy2U3sJIYnRQqqvRMLT7K3O7K0RziMZJbK/+K0dFWm9ZYa+uyTznNzoye1Q43kiQSUW1N0BkU0/z2vgo/2nth2/WiIKaJtv8KxMjz9FLXCC+mQ6UDflxcktq6Q3Zk/VHY3cE6aWzK4i+JcgzQR8ExhlUFcdAt4p9qD6zta3fnm5+a7M1oXZ7wLPrZYsdRKwxSaRkjR0M9vzgPbnttVl1xMNLcjGYhIyc75aSrKARbeB9ytJP3WXNLD6M3rPyxRtw17WqYbmA8T2uDcY8Fk98ThW2CmcoADKJjiGgpkOWBl0vEjoAniJBmPYqiByB6rsUQE4xcfLmLh4Lf1hpoGcJ+C8Zvz/Kin04ARMAwWCngUGeXN2YFFa59wM9xuVcATR0pPh9p4ZsJK5Oc6VsEKSLBxC6BGKDSvA02JZ+50W4tUqk5lqAgDHMVCZZEW3uTzLvSijozajo2gCfmkb5+08VgkmKQNoCoVfsc5IAZR8XveZRYpbhFV2qKfUalQNJ4a1YdfYpmgEiMbZ0KWhJIOT59Q9SwHkpIKtyMOG0qtrDcu1PWmDVVlNWHGqYmUTgal1VWCGqC9QdEhrmSO5H/02SgKwboXM3IgV/LwSD7DE0YNxI1EnEDshVQ1SG8MCtq/8F/O8tAFx6BHMC5hjotyLM5JWP1eyrZwU2zl9YIkBM0HEQZAlQKUCJJI+vCZ35O4s/YnLPMtBfEuy+QUBJVbdAdHaVwjMvG9AERCKBmDmdadraPBOaWSVyX2/aXRd/uDy9PCj6foTeQPLc7tqKDNnADoe3vTV3oe+1TEX108k3snC2lZmeu/JwadoKzD0rs6Ob/6EG7el8ou64ictMPax8jrOiRWn7TAHs6CMegKeMNafY//tdSdnZvC2YMkwdj0k9hOmQWzvYiqp2SSM4bnI71QLf01FwgQJBiGa5AJAGFdxvTRPp7v633puJXQ1UtIWAEQBchMosgR2UOKJYB3wGUEiglUTNSmSViBiSwxWzgyQ1PcAmkbIBClA6K0I4QiM8NiAAtNgf+/Roz+S7GvyXEun+9fnxPdm63tzlSdNgfuyPI9mBx83uh/+kh4+rGkxiFJowIVjgUDUjf4QNiEWRTcAZZCITxXeBOe8/dD2wBPXjj767RMF09rmZlNo6K5KD238ZB/5EQqrJCDnSh2Lp7SWp5Fp6qnIM88X6fyu0myq1B2V6Sy55rmSue6qsPkpwe7lKIXmT1kBaQ+QmkAZUXpDy0YdjinOUaSBkMYQGEcoM2hhBNAfCmh+aP5oKSDng7RUULPDwR8niuFPYBpE2aZMoC8imyeHN15xcpbJPD/Dtcporko7drvBsva/qBQL3wZFk3MDQmuUe0BjTygbwOZUQ3bsIv7PkzNP/VYaKMtqpe5dKyXgg6LilSh9pQMBOFptctRKTQvJcUwc4LioTbPMz+7B7wWEUyLwlHj4nUWUVL+q+3uCAgFvU5UBRUhp6aHOR807fqHZGzFIMhlapS+iOGhdE6NzDXoh1NIRmKKGniQLq4ToIkmuH1uq75RGAJ+RS+OSSnKsVUaiHgyESFCTZyqQvFu/SVDP9Xu0UOI0tQ+xDGF8yBDYgeIjFYP8QdhOCwOYQu/e3XqbNIQ9rZgAHqyLMlsJyc5sQATtpZl4B6TTMASd5WDJQG1T+AQdlNlRNqGlOPfITDzMib2laSdvlsIN+3k3RXYEcEy0mE8jDXmeCl4C3gerHeEJLF0sFh11F0qOu6fqBz6mP5cd2ZTvvWfiyOr0w9RQ5ue0l05sv90wuvWrQ1v/lYBC8CzKwRGlu+5O156Y1rcwrb5AaqsDnH8Z+/D16yV9aIjTi97CY54sG4YOQj6P5gwpTpoAeCOjMc0j7Few4d3bcgfnS11FGa3C58Lh5+Tk4RrbqByR6Sw04vpph6pxgIUGDxprMiIlI3CAsNxQLfos2xtKJg3cwYUJTyEJDcKmiKoTWI9SybViuvDAy9MpbwFAEJNWJjzKzrLMtgWm3lukY/Ov4mES8cVVB1QcITN1Lg0pcg0sEdvhJ07+WGpfYGqZl+ldnS3vNSmPZWn7PxZYfjl0O/3pzOjStL4Dc9Ds4BQIEIOSkleEVhAUWtQGMQ2qjjAPegJ4EISXze9VfKW3/DOxp/6zf9FlHTMFQql/salnAcEa2Ofs3pK8ltopzQLab+guzesoyGYfAUjrqCJ/ZwTR0zxPaln4mYD5gJBmUVUedBSQh7iNgeFVXx89CbtEdyIAobgRkBIUMqEVhNWDNh4NwxClEBMLBhH+0LQwEaWG8tV4k45/pGnIMsNWKjCEKfA8PUHele66T/I+mNtUldV/14SesqkkqJ+9zejcXyGqTkIDjrWLoKNPMGr0mBt7oWEbUEYaYRmBjMfW9u1feuqXEnCRUwumWYtM/YUGwJ4AhC0Ls1uKMiyFua3FuR2VE9vKc8HhdJelCboEMFJRTz6NjOYFoWZv4RfUYDsBhVDWUUEEAk99q2HvKnmoiao5ipzEY16OUyHZJNSckoNvSVbBjXPFkRXjehKBXOsibXeauUbqKpnSU57bUjWB0x4PVhgj4c0aMe3eLd/R/R6kh5BFQXHEqduCyJbpmktgdBWKZWTE0SzB7BACBJses1wvDZZOOFlmGCjOpmprBTMKMIz4dll+6N7L+utycZU5VEU0TvakTBqonNBeRJo2u6U4vbsqr5HSCdn41hdZGSOIMskKJF4ydgmjpiEepxBoAJ6OIHNCPOX2awMOwU0e0e/Odt837dCNxp7FBv3gNP25af77p56aldVXlUFd3fdglv7md0c3fEcgRErSbKsy9d0Aw3PtGyT3vVl9C3OOzclvrZzQX5J78jrpnVXfiEXa2chiLQqHxRFEqwpaIc9UkW1oeWjBgDYa0V0uhRJZELJ819y05hW5jZWZI0Xk+TAQIFkFeLS91nSyKK9ntuR6rwA7Ak6adRlEYiQuUN5hlWyVbln/nfpr+UtoC6dBxKLUz4MoxpE0sQ6Vp4ydS6kQE7NkdyQB6YlzRbgV5CznABKR7O8kNZSCWjTgI/6C6Q9lA4SVHAjEBZbc1rK79VpOJql7ZlbXBphBOcruHP0FgLOs85zYlgnak9lMVPPiT6vDrcLLSLwbinmxyEBI8zkEJQz1R/wSVpwabz2279uGl/9PXmTrtRHH4beW/vvxmzPaZ0jtdSJ4HLpdeg20EqXNiixcJwE/Bz9Cdpb6dDmJ/Oz+ChAN0snyvwo3vSgONjTUoZYLiY+uIwere969V1etUcha4hqIhMURC9xc4Svnk/hKlsZH6ORWXSz2gMaCBN4yXjLVH2saWmant5Yale3T4o/n6y9cHtyUqe6a6nzYOEyJhVij4rNywxP6oA1hEo8WJUHCQ6akrAdaZMHJZVUlZBFlGzmnhNSHMrL79kM/J/SSWqpMZP57yo09RI9VabhYlNN7Be4Q6K542AlATgZHDWXkBIMriTYRBgKeZXfBV/RgJ021Ie7g3+Gmcva4gdKI2FdRKWPJw0IehgsQQ2g6MlATdMUJRBV0/vAaIjI+s67W1+n78qzLpdZ5hE6G5up04HeAL7kSBuXS1nmSb/u/R2VEO2U7ORTR84N1EYgPvkPA4tQ95H5GomhYIViC6LDfs++3llsoOkit5Tk9AqosiAyYBkZXpaGvjvdM3lfy/fGKIV9SPIczkiYYVmTd50v9ayeEm55QNKEBgxganxdWRzWFk9znE1kN8q7kVpHwJKsaiHJJGrfUGYQGsjsPtp+5BPSX5FyXG3loOlxV28I8+5ocfe809blvOO7795b5AjTpuDtH3QnuY4q2Iz+6eaq+bcLw3blNUIPnSqGd1/o7hIKYl5x+PORUxXEp1M8cNhwyVeM89ccVFChDBBTiydqOtFO+nSedrMPo5I9wKjKH5Xkc6eRcuqqMrfOMjuo0re0BTAPN0HkWaDrKWtytopSlxQcOd5V/qm0GWZKcJHTqbOmKqAEGmrVwsr5/UvihaV0Fmc34sAm7IIzCGWgZ/DQIXUfW/iSZrgsLeZtgCFVnEJgC74ASlLhOf+9bzYVT20pNh283jT6cpT+Vr+3K1J/O61uSdWyGgeNde2ySZ9WU4zdLoxu+qXffq4fb47IgaKp0b0fBMOpFVod6B4lJD1G/kBzpYVP7SEmLv4noDvPxWX8NT7Sl5l9GDlT33PMtYBfg+nB4cdOwDu3lhi7KQGRJynMGqoRD2lLz+XjHK5yaONpBEhy8EQetdwDdPCG9I0QTZZ+APA8HhCceS3w9PZBKFWkPpMhQJdPIgni0M4rVH6JI8QflGrLaC01Nhca+pYbRuyXBLLg7I7ppeteiDM/aiUAVWhdQcBIKMH51JKaOkK9ik+jywPCL83peuw/RSsTLOE7ZkElBpFDL850LP3WK6npFLnRvSzXlLqmFUyUBuesmeQNwCDggRWN8yEoS7FltJaS1hGkYA4vOaCkwnCr7jh7p4VBlljTdw6fgnsBRh1XCXkI2CrU4NFQuUDyCVsPKxvVCeJaAABdOwOMal+k7pnUvTRMMEfDOi+CScdQnXQbBXGxCPGPrdznnPELQFQEqoazj632loeJLuu2Y3vOC3LjceXwDR7nQsLK9TP6vc24WUFGymLxhdzkMMZFIG10+wbFqAjjF0w4z9yV8b2Eg+qoz+XnowWnDq7PaK6W+6oxGklir/tF3eGWodWfk5Mbge+vdb9/f8+4O1VMvcq+6LaIx4aj4kMfhJAPkokdHj3MiDW3I6KnNjG260nlfPtsSMF939aS2eWmDS0360xP1F79uv+9fwWswpfx2cAX8MZPn3vTww1PdK022dVcOVUunbpYCLY8LBSoSKnGEZDAHBN4h1XG4af0PxcoNDRFAIUtJCI4ELolF74afN8+R+sqlplqpu+xKLoO0MUCYzsocfCLIRRiOgaXT9aG3OG053wR2BuU7XXGFhZ0dOFBqhWpN+bMMKHqCvZqgG4kBlrQs3VU3Rd9jUndOshZkNJeR8E9GoKdz2PxxD6iQWVJD6XTdcQLT4GFPgQhkMQhdzISyq6AbgWzwWzf98vgdwDoy3GsN6nOT9adywRH1LAQGkt1SYgSM1FWZ1zA/p3GWQd/2meGtX+s9cKc+2hwU0kxiDRNSEcEB6OQAxBnhnTmFPLhspAPRVWbSbN2jdZ/sr8BIyY6nikC1sgDEHQlOCsGjiFVZ2B1VOX3kdHgQtZ+Uu14WuwO9O5akLOS9Ev8JIZwzJTaxtCD1s6TPDhGHiW6vIsHFYkbC2IVgLBT+D1W55Eb+MNPQNh/6l8FaZ1C2Xu7ZkHWCPFOxsXFefmdFvhviwz1X4+0M4gqx0dD+QgMe06lGPUefsj2/Mdj6gqv7HeRAo0FnQlTV3b3156T0OmuNnQV5Q/NJQ040g7qBO1xNSAlu2gBhpqkyE1M6VAUhN72tdhK+fRdH7vmmgYKWtezKyJuF/ldqXY9d7Xj8B+7Hr/Zv+afI7v9wbvtZ8GBh/NDG0NuPu9/Z5nhvk/PceMT57kPeYw/Y390ceHub3vU6iposo75Hf0PG3rIQQtsV8kOX+R+a6FyVk4D3cdSLrQsG3r/lexTjkRtAJDoWHlXk0aj91aC5Unc2u/csfO7XknmzsJJCRfqFa61AVisyWkpNGLj2Kkwe0YQwDZ67p/s2TAPfxjv31WYni3P8CghAV0keCR1AhL3Ls4aX5bYVmayzjczw6N3TjxdNOT4759hdpuN3Tmq+NXug4irbss/b13zFvurLfQs/c/KOzx+Z8ZnDM77w5pyvn6z66tDtwsc5MVsKbMrWnpzWt0SCWtpaPBk3ZGBpenirUX/tm7YN3ybEhS+E5+xeP0F/4lNwzE/Nk9oK05qKr7BBiypK8/W/RLAWUHwsRqGeDf2Jpxwx9+683dt9sv+tzX47tYnRYNQlYkf/awjYoO8g8D9QHgqmADbHJloqJ3E+91an97Il4ESQr/HjlrNvPRzFURl/ASFGYuuwdd13Bm+Sumpxr7KSaJezTET8jsbCjNhDE/SDkva0YEM1FeOMnGcaBKGLKmZhRh8r50CNYDxSIRW4BtFaBuvAEC/wwCk5v3cvWDgnFO+NhvjBKeq+CYGHcztgCQlaJA8rt6U4s6Vq8pFbjPrWyYGHgc9Kh2+6sm3ll2PvLNMR40uIALrVuFtH4zQUo1MKMmsoO6mD8Nx8CRG2iPX4odu+0rPgB10t+yF6YxqA+VFMSZgGMCCwS7LNlbCwhf5QW9VloX136q8t9O76oX/bv/sf/alj60/su3869OSPHc/cGH17YfitdYFXHoi9vsX97iOu9zadHe73HnEevn/w8Cq951ndh/q2PiiYKeQyP6R27h9mGiDe21ZMJL86uirPWpMAugHv4/FTJIc5v+gH+AU4PKLAJUf88Z6hkbdp4JHQGeT/tVE9TtpJ+JBc99Fdh2+R/GtyXHdTmMjqLoHYM6lFZGvAO5OnwUfIIQk3+vTNJyum9wBqwteqzOuszcGlH0umwkwI2DWAs2Ih1kJoR+WiC6jPnLTGOVLDXKkeYZzZ0rEZ0imUMM4fLbisgJ0RULhVeuNqyflMMQa394GfHv2N1Fgs2Tdk9tQaBEIRoGeJAWp90nEgfvFs+2447iKk80Tcbr/NKxSOxSoAviFuFX+fc4oXXZsHFqZ3C1yD1FYyuUtwrrJYuLwbOQuSqUBfAYbwtkOL8vFHksV/ii/NczP6FuZ3LTIOrklvq2Zf5TgXT1F3Une8yr0+l20Gl6SHam4hHM0MyhwATPtJuMyQBmZJ9goh30IVCckZ89zp4CbkbVn6rivcawWni7CCYM2z/mPytr/uXiL5Hv/64OpvA7Oxr5zeWws+xzCwmGCNLCnI9/TmkrQB4pqiNNe7GwWCI6GngHSwLzza2Xg04iZkFOJlWtTKkeAUOgtkFN3mdT9snyVF7ruyfyGewkQKeNAWCao7i0xAV/rrco7ClCu8Su5/RmioaSShSJZQkcXfE7AS36knLEVTyNq2kbYE5ZHQLDjrO3QUk5ExyTsl+eWvOvdOdt0LNwSw0wUMVz7OyDLoAQi75H+R5hIN0JAaTbgMhBUMEVOQjeF5x4dGN13z7nWS/lieQph8cNrgMgE6hscl3P7S3IFyk8iVAFFDzGLPJ4dqM5gTS0k2js/xX1OwT5OPrtXDg6ozSNqBE55jXMV2xqKo7kZ11CdoJKP7ut5RH73F9tqs+ltPew1nTIPRWpTeXpXTDB+3DJ0YE3mHhsLJDXPI60mtLOY5EIVEabl9pvgW2ksjY5YElK7tDql9zEAHpPEOY+Oc9Nf/U3pvFvCqXp8orF6agfr7Yo0/yDRkm+dQYRY5OaE/AZ2xEl/O1FJlsBUbzKV59VXfoIAjR52UJZFLj/o7QRyOvlgXCXTG3B0xx9GQ/5THXa+5m/XBA30bf2y+jdMpO3ofUa7JvMTUCbSxIM8qEDI4k4ZDd6WZF35ejx47vPjbQHpB4DSWmhoEWF1oJYwlt6B7YSnPayjK7SsCHCE1LqBgARFrAuXuHhJ7pDDwNuebesgAlVDmPDcIWMDDd1dk2MsFBnZwOxRpffTBnzT8llSoyX2fiXJ6WwnpTwk4tmPFVOhJwjTMkzw7vkfIKgjDrhbdvy/qeVH1mFV/qyd6NBB9KR5r1bxWxfVadMffIjMhwmMUWUQadZKAD0A0QOVBQH2zhhcBkYSLnWlfCl9I4IKAcmE18PDtd6fre6do+ye0LYTWmRPY+MnQ7kzvQznKvrz+5RDhcyDz9C416k99Ut/7sa7F0sCiNN9SrHYu9cK2ojxrqcm7xtheNUF+NAf5CTA8ngdyIFPix6KFA7SmqzL7yGxp8MGv29f/CPMkkEUINADaLQUMLlybhkoYHKbBCgnel/+JO3RfczAqeAfo4svIcL2+UOs66PEOaP3v+A8v1UZPem3Pyx5z8MRDzddlHl+UOVidCbOrp+JK6xJwBzDK0gbLsodKDFQonr/J4HlqJl6l8BcUD/0mNBnVaaEKDT7CtnUWiuHdtVRtIMjxdAQfMQlg4d/CpHItzwrtlPSWzeHXbg/vzMI3wZdMZBmS6QbhQWCCQXNYZ1IQ+VSk4/kED9XqjpCWw3FIdJRLxBRK2E86zNvT1PvsfOfuz+kPodBh6q3Nq58Ng5alntkJfrxMspbmtVdmUIPTn/pE1wKTuSgTEge0ka4iY+e8DOfSaf3M+dIvud4u9Nks/hgMNw4Gf0iLhGNo+ePoC1VY3WOx1E6A7To2oAAw2leazWJrqqVSntFTRPkPZ83UMNdg5UggdwvFuSS7tQTS2tT2krzWAgNMX2pnyD2BPR87uM428EHkO+8gTfYV3X5K6CGLxjkfskjxh5mGtnlClIEr6KKALDi8EPsFNdVKLQqZoJIJrof/xbb1J95nr5Nfn9O64p/6qq5qn5fXVvMlS8UXeyo+11H52Y6yz/aUfa6vYLqtPH24Ng2tOxcM60rJuiRzYIGBzd9ent+xQAJIe+j2Se4Tm5nSUyU5ZsqiVQKnbGZZs77PiAidlhICcYC9KDcizgW5q60a6puptzSztzytl1QFwW0lOxMQBHEdRfVzg0O7hci/OnuYIBz9tU0/FrKcm78P0769JmtwIXsjg0CG8NhandNbB5YZYrKBsNm2/m/0jo167zbfru/7Nl0V3/Hx6EOflrd8I7r9a/rOv40++rXww19zr7oKrUozfGcB2RBCQF2lEyhQ9ZalNy40Ns/JcW8w6XvzugnC7/t6aOt3SEma8S2LJ7BLcRzcd+cKeZjnPt290GAuTtN2fEJ/KkfbPl3deXnvIsm7YRIccH1XDhQJUoaejYKGKBjKeOBlrF2je12u/uQE7/a06K686KOECWn6vhzXBkP/4vTghk9g3Fursk7BN3no7533fY8ST0sBuRUqpix6MAiCxtJeCU0mB1gOyMWhJZ9wbf7O6OPXaG/NH9n9q+baT3QVmwZrLquv/JJl3qfa7kgbrvpCd/kn+6o+N1I81V5iOAExF5mJ2nS0A+yrc1qIwhZmNRNNVGRxbLav/7dgdFDodyPDTwUexARtgYQcmx7uP3Ky7G8aZhs7qwXhXehEwAejdgOFqRQ0ajY+YHRjbmiHFAz2qI4j+u6sPlytsnzA10k0FIZVWGHEFGoym8lWAOvaVQwgwGZusHv6E4gPDBwZcZoMUbEmiRtCEF74L93b9N1X2e6XmheSBcjvKsqxlmZTEWsQtLpMiH8n5hig5A6vNzbj+pVMtJTn4NsCrLCUGpii3tlSy21oyXzVvWWG3niQaCvhHMeHA6OeUCILprmGt96AYh1WGyB5Ig9Nij2zu9TE4dFOHI1zJPhaaQn5FgPiEcTOFHq7yrIJQHqIOisMnJp9okxLVtsAFJ0FeXZYBOkZE2zqxtAXfDlka0jIpo+3w+UfWaHIJjXQtyinA1+Lc68Y7xS1KaGtQrSP44cLJ0QZZ0tv3yqoB413Ia2RPgoeYYbgGlnnZFpmZFpnmyyzDZ2zJPfSPHJFjuWT+uvy62eRnzcwWf4VkwcWS64S6fh1Uv/+ooSquI5IhOXOjPbabKtI2AAHSLKwErTfMwMyD6NNfKXml2mB/sDrsoSixJmvvLhgoEnTWZ1uqcuH4gK1sfOR/+Djhrb+sAXNP1DYxVJ9meRfl2+thXtuIEvaXYOuAZRTQ2OB6dBdeYdnZB4n+Qw34U6JUlzjzQJfyPro

你不知道的學術寫作中引號的正確用法細節

如何正確使用引號可能會讓很多作者感到困惑,尤其是對於那些英語不是母語的作者來說更是如此。使用引號是用來說明此文本逐字引用自另一文本來源,用以強調重要的單詞或短語,或在首次出現專業術語時使用引號。文本引用有“切入式引用”和按文字區塊分隔開的塊引用方式,還有引用內嵌入引用的方式,而且根據主題、風格指南、甚至國家的不同,標點符號樣式也會有所不同。接下來將通過詳細說明幫助您在學術寫作中正確使用標點符號。

字體

引號的樣式-字體應該是怎樣的呢?這一點在科學寫作中非常重要,因為根據樣式可以將引號(quotation mark)和角分符號(prime)區別開來。角分符號通常在遺傳學和其他物理科學學科中使用。根據《芝加哥格式手冊》 (CMOS) 的說明,引號通常被稱為“捲曲”(curly)引號或“智慧”(smart)引號,常用在大多發表的文本中,且與文本的字體相匹配。我們不應混淆智慧引號與直引號  (“)。直引號是電腦的默認引號格式。此外,單引號通常被用作角分符號來表示度量符號(如英尺和弧分)和數學符號(如  x’y’);但這並不是正確的用法 – 使用角分符號 (ʹ) 才是正確的用法。您可以通過電腦的文字處理程式將引號輕鬆更改為正確的格式。

文本中的引號使用

無論遵循哪種風格指南或身處大西洋的哪一邊,關於文本中引號使用的具體規則都達成了一致要求。例如,如果您在寫作中要直接引用別人的話,那重要的則是要確保讓讀者明白,這些話不是您原創的。如果故事中有人物角色對話,那麼引號可以將角色的話語與作者的話語區分開來。

引號也用於強調新出現的詞或短語,這在科學寫作和技術寫作中尤為有用,如句子: One of the several branches of zoology, “ichthyology,” concentrates on the study of fish. (“魚類學” 是動物學的幾大分支之一,主要專注於魚類的研究。)請注意,為了與美式英語的風格保持一致 ,句子中不僅將新詞用雙引號標注,而且逗號也放在了在引號內。

參考文獻列表中的引號使用

不同的風格指南對參考文獻清單中引號的使用有不同的規定。人文學科的論文寫作遵循美國現代語言協會 (MLA) 規定的引號格式。社會和行為科學學科的作者通常遵循美國心理學會 (APA) 的風格指南,而生物科學和工程領域的作者則參考科學編輯理事會 (CSE) 的風格指南。例如 MLA 和 CMOS 在書中文章標題處使用引號,而 APA 則不使用引號。書名不加引號,但期刊(包括報紙)則加引號。請注意以下示例中的差異:

MLA: Bagchi, Alaknanda. “Conflicting Nationalisms: The Voice of the Subaltern in Mahasweta Devi’s Bashai Tudu.” Tulsa Studies in Women’s Literature, vol. 15, no. 1, 1996, pp. 41–50.

APA: MacLean, E. L., Krupenye, C., & Hare, B. (2014). Dogs (Canis familiaris) account for body orientation but not visual barriers when responding to pointing gestures. Journal of Comparative Psychology, 128, 285–297.

CMOS: Joshua I. Weinstein, “The Market in Plato’s Republic,” Classical Philology 104 (2009): 440.

CSE: Powell JM, Wattiaux MA, Broderick GA. Evaluation of milk urea nitrogen as a management tool to reduce ammonia emissions from dairy farms. J Dairy Sci. 2011;94(9):4690–4694

單引號、雙引號和標點符號

單引號和雙引號的使用規則不同。在美式英語中,先使用雙引號表示引文,然後使用單引號表示引文中的引文,而英式英語中的使用規則恰好相反。請參閱以下示例:

American: “I know,” he said, “that I heard him say ‘help me’ as he fell.”

British: ‘I know’, he said, ‘that I heard him say “help me” as he fell.’

在美式英語中,句號和逗號放在引號內,其他標點符號則放在引號外。這種用法符合邏輯嗎?答案是不符合,但此用法早已起源於手工排版時代,因為當時印刷工人不想讓小小的標點符號外掛在整個文字區塊的結尾處。在英式英語中,所有的標點符號都放在引號外,除非此標點符號是引文的一部分。在科學寫作中,CSE  建議標點符號的使用應遵循英式英語風格指南,因為這是“國際公認”的用法;即便如此,您仍需要經常查閱作者指南。

文字區塊和題詞

具體的引文格式取決於段落的長度。按 MLA指南規定,超過四行的段落應使用文字區塊格式,不加引號。按APA 指南規定,如果段落超過40個單詞,則使用文字區塊格式。其他風格指南規定可能有所不同,請務必查看確認。

題詞通常是指寫在建築物、墓碑或其他物體上的銘文。儘管題詞可直接引用,但不用加引號,而是使用特殊的字體和格式來突出顯示,提請注意。

寫作技巧

寫作中不要過度使用引號-不論您遵循哪種風格指南,重要的是要正確且謹慎地使用引號。在強調某些詞語時,不用每次都使用引號。請務必檢查,確認自己使用的引號樣式符合學科規範。

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揭開研究總體和樣本的奧秘:瞭解其在統計推斷中的作用

研究總體和樣本是所有科學研究的基石,能夠賦能解鎖隱藏在資料中的奧秘。瞭解研究總體和樣本的動態變化對研究人員而言至關重要,因為其能保證研究結果的有效性、可靠性和普適性。本文揭示了研究總體和樣本帶來的深遠作用,及其兩者的差異與重要作用,重塑了我們對複雜現象的理解。最後,本研究有助於研究人員做出明智的結論,並促使他們在各自的領域取得有意義的進展。

總體的定義

研究總體又稱為目標總體,是指具備特定特徵、且讓研究人員感興趣的整個群體或一些個體、物件或事件的集合。研究物件的定義是基於研究目標和調查研究中的具體參數或屬性來確定的。例如,在關於新藥物藥效的研究中,研究總體涵蓋了所有可能從藥物中獲益或受到該藥物影響的個人。

傾向於從總體中收集資料的情況

在某些情況中,研究必須要從總體中收集資料,以獲取對整個群體的全面瞭解。以下幾種情況更傾向於從總體中收集資料:

  1. 總體規模較小或易接觸 在某些特定的組織或界定明確、規模可控的群體內開展研究時,從總體中收集資料是可行的。
  2. 進行普查或完全枚舉:在政府調查或官方統計等情況中,有必要對總體進行普查或完全枚舉,以此確保描述準確並能消除抽樣誤差。
  3. 研究獨特或關鍵特徵如果研究側重於探究某種罕見且對研究有重要影響的具體特徵或特點,如罕見疾病、瀕危物種或特定遺傳標記等,則可能需要從總體中收集資料。
  4. 法律或監管要求某些法律或監管框架可能會要求從總體中收集資料。例如,政府機構可能需要獲取關於收入水準、人口特徵或醫療保健利用情況的綜合性資料,以制定政策或分配資源。
  5. 精確度或準確度有要求:某些情況有高水準的精度或準確度要求,對此研究人員可能會選擇從總體層面來收集資料,以便獲取更加可靠的總體參數估計值。

樣本的定義

樣本是從研究總體中精心抽取的、具有代表性特徵的子集。樣本是規模較小、易管理的群體,透過研究樣本可得出具有普遍適用性的推斷。樣本必須要準確反映出總體的多樣性。相比於研究整個總體,研究樣本使得資料收集更加高效且更節省成本,而且基於樣本研究結果可推斷出關於更大總體的結論。

抽樣的定義及其重要性

抽樣是指從感興趣的較大群體或總體中抽取樣本的過程,以便收集資料並做出推斷。抽樣的目的在於獲取可以代表總體的樣本,即該樣本可以準確反映出總體中的關鍵屬性、變化和比例。透過研究樣本,研究人員有信心針對更大的總體得出結論或做出預測。收集樣本資料比收集總體資料更具優勢,且因受限於實際約束條件也有其必要性。以下是選擇從樣本中收集資料的個別原因:

  1. 成本和資源效率:從整個總體中收集資料往往成本高且耗時。抽樣降低了成本與時間消耗,特別適用於龐大或分散的總體。
  2. 時間限制:相比於研究整個總體,使用樣本開展研究可以更快地收集與分析資料。透過重點研究較小的群體,樣本研究可節省時間,促進研究人員更高效地獲取結果,這對於時間緊迫的計畫或迅速決策而言極為重要。
  3. 資料收集的易管理性:樣本研究使收集的資料更為詳細、資料收集更為便利、資料分析更加可靠,由此有助於全面瞭解相關研究主題。
  4. 統計推斷:從精心抽取且具有代表性的樣本中收集資料有利於進行有效的統計推斷。透過運用適當的統計技術,研究人員可以將樣本研究結果推廣到更大的總體之中。
  5. 倫理考量:在某些情況中,從整個總體中收集資料可能會帶來一些倫理挑戰,比如侵犯隱私、或給參與者帶來負擔等。而抽樣可以減輕資料收集的負擔,從而有助於保護個人隱私和福祉。它能為研究人員提供有價值的資訊,同時確保遵循倫理標準。

示例:

調查研究:評估新藥在控制症狀以及改善患者(患有特定疾病)生活品質方面的有效性。

總體: 確診患有某種特定疾病的患者

抽樣框:本研究的抽樣框將涉及對醫療記錄或資料庫的訪問,其中包含確診患有某種特定疾病的患者資訊。抽樣框中符合納入標準的患者將被抽選為方便樣本。

樣本:從當地診所抽取 100 名符合研究納入標準的患者作為方便樣本

樣本含有更大總體中確診患有該疾病的患者。

總的來說,相較於研究整個總體,抽樣使資料收集更具成本效益、統計分析更為便利、研究更加實際可行。但抽樣也面臨諸多挑戰,如抽樣偏差、無應答偏差和潛在的抽樣誤差等。

為最大限度減小偏差並提高研究結果的有效性,研究人員應選用合適的抽樣技術,明確定義研究物件的總體,建立一個全面的抽樣框,並監測抽樣過程,發現可能出現的偏差。將研究結果與已知的人口特徵進行對比可驗證結果的有效性,並且有助於評估結果的可推廣性。正確理解和應用抽樣技術可以確保研究結果準確可靠、且能代表更大的總體。慎重選取總體和樣本能夠促使研究人員得出有意義的結論,並幫助他們為各自的研究領域做出貢獻。

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