Feature article的問題，透過圖書和論文來找解法和答案更準確安心。 我們找到附近那裡買和營業時間的推薦產品

Begged, Borrowed, & Stolen: True Tales of Thievery from America’’s Past

為了解決Feature article的問題，作者Bridgeford-Smith, Jan 這樣論述：

Jan Bridgeford-Smith’s ​ passion is history with a twist. She looks for off-the-grid stories from America’s past--that is, stories about famous or obscure people and places where an unfamiliar detail or unexpected anecdote is tucked into the tale. In 2008, her first feature-length magazine article a

ppeared in the regional publication, Life in the Finger Lakes. Since then, her words and photos have been published in Plank Road Magazine, History Channel Magazine, History Magazine, American History Magazine, and America’s Civil War. Recently, she secured an assignment from Smithsonian Air&Space.

She lives in Freeville, NY.

Feature article進入發燒排行的影片

植基於5G多型態網路環境下使用區塊鏈技術進行身份驗證之研究

為了解決Feature article的問題，作者高一陳 這樣論述：

5G的巨量通訊和低延遲通訊兩個特性，對於企業加速數位轉型時的應用非常重要，尤其是現在已經進入工業4.0時代，網路通訊品質格外重要，結合5G通訊特性及Wi-Fi 6優點的多型態網路，儼然已成為網路新時代的架構，惟本國目前的5G架構因為成本建置考量，尚屬於NSA架構，或許未來硬體更成熟，成本較低時，或許也會採用SA。使用區塊鏈3.0的技術主要是它針對物聯網有提供相當完整及方便的函數庫，而且區塊鏈3.0的特性是不用挖礦，沒有礦工角色，而且越多人使用，驗證速度越快，與區塊鏈1.0或2.0技術不一樣。將傳統的紙本證件，使用區塊鏈3.0技術，將它轉成電子化資料，只要儲存認證完成的交易代碼，就能夠透過此代

碼找到相關原始資料，傳統書面證書或者紙本資料，轉為具有區塊鏈技術架構的數位證書，已經是未來的趨勢。利用IOTA技術提供5G驗證與Wi-Fi 6驗證結合，透過Python 跟C# .Net電腦語言，實作出應用區塊鏈3.0技術來驗證物聯網設備在多型態網路的環境下，可以達到驗證效果，這是本研究的主軸，跳脫傳統的驗證方式，且更具安全性的驗證。

After She’’d Gone

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為了解決Feature article的問題，作者Dahl, Alex 這樣論述：

Unsettling, gripping and darkly glamorous, After She’d Gone is a timely psychological thriller about the danger of beauty, the lure of Liv loves her son, Adrian. That’s why she keeps a low profile in Sandefjord, Norway: just another tired single mother, trying to make ends meet. She has never told h

er son about the secrets she carries or the life she lived before he was born. She will do anything to keep him safe.Anastasia’s life is transformed when she moves from Russia to Milan and starts modelling. Suddenly, she’s rich. She’s desired. But then she begins to see the dark side of her new life

: the high-pressure catwalk shows; the glamorous, drink-fueled after-parties; the sun-baked Italian palazzos owned by powerful men. She will do anything to escape.Selma is a feature journalist in Oslo. She’s horrified to uncover an unsavory and dangerous underworld when she writes an article looking

into the modeling industry. Then, a woman goes missing in Sandefjord... Alex Dahl is a half-American, half-Norwegian author. Born in Oslo, she studied Russian and German linguistics with international studies, then went on to complete an MA in creative writing at Bath Spa University and an MSc in

business management at Bath University. A committed Francophile, Alex loves to travel, and has so far lived in Moscow, Paris, Stuttgart, Sandefjord, Switzerland, Bath and London. She is the author of three other thrillers: Playdate, The Heart Keeper, and The Boy at the Door, which was shortlisted f

or the CWA Debut Dagger. Follow Alex on Twitter (@alexdahlauthor), Instagram (@authoralex) and Facebook (alexdahlauthor).

以監督式機器學習探討電子病歷中非結構化資料對早期預測中風後功能復原後果之價值

為了解決Feature article的問題，作者宋昇峯 這樣論述：

中風是導致成人殘障的重要原因，中風功能復原後果的精準預測，能協助病人及家屬及早準備後續照顧事宜，衛生政策制定者也能依此預測結果適切規劃人力與資源，以投入中風病人的急性後期與中長期照護。目前的中風功能復原後果預測模型皆是以結構化資料建立，甚至最新使用數據驅動方式發展的機器學習預測模型依然是以結構化資料為主。相對的，照顧病人所製作的大量敘述式病歷文字紀錄，即非結構化資料，反而甚少被使用。因此，本研究的目的，即是使用監督式機器學習來探討非結構化臨床文字紀錄於急性缺血性中風後之初期預測功能復原後果之應用價值。在6176位2007年10月至2019年12月間因急性缺血性中風住院之病人中，共3847位病

人符合本研究之收案/排除條件。我們使用自然語言處理，萃取出住院初期之醫師紀錄及放射報告中之臨床文字紀錄，並且實驗了不同文字模型與機器學習演算法之組合，來建構中風功能復原後果的預測模型。實驗發現使用醫師紀錄時，操作特徵曲線下面積為0.782至0.805，而使用放射報告時，曲線下面積為0.718至0.730。使用醫師紀錄時，最好的組合為詞頻-倒文件頻加上羅吉斯迴歸，而使用放射報告時，最好之組合為基于轉換器的雙向編碼器表示技術加上支持向量機。這些基於純文字的機器學習預測模型並無法勝過傳統的風險模型，這些傳統模型的曲線下面積為0.811至0.841。然而，不管是以曲線下面積、重分類淨改善指標、或整合式

區辨改善指標來評估，臨床文字紀錄中的資訊的確可以增強傳統風險模型的預測效能。本研究之結論為，電子病歷中的非結構化文字經過自然語言處理後，不僅可以成為另類預測中風功能復原後果的工具，更可以增強傳統風險模型的預測效能。透過演算法來自動擷取並整合分析結構化與非結構化資料，將能提供醫師更好的決策支援。