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Behavior in Our Bones: How Human Behavior Influences Skeletal Morphology

為了解決Record Store的問題，作者 這樣論述：

Exploring behaviour through bones has always been a fascinating topic to those that study human remains. Human bodies record and store vast amounts of information about the way we move, where we live, and our experiences of health and socioeconomic circumstances. We see it every day, and experien

ce it, but when it comes to past populations, understanding behaviour is largely mediated by our ability to read it in bones. Behaviour in Our Bones: How Human Behaviour Influences Skeletal Morphology examines how human physical and cultural actions and interactions can be read through careful analy

ses of skeletal human remains.This book synthesises the latest research on reconstructing behaviour in the past. Each chapter is dedicated to a specific region of the human body, guiding the reader from head to toe and highlighting how evidence found on the skull, shoulder, thorax, spine, pelvis, an

d the upper and lower limbs has been used to infer patterns of activity and other behaviour. Chapter authors expertly summarise and critically discuss a range of methodological, theoretical, and interpretive approaches used to read skeletal remains and interpret a wide variety of behaviours, includi

ng tool use, locomotion, reproduction, health, pathology, and beyond.

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以輕便型手腕感測套之智慧型羽球揮拍動作辨識與評分系統

為了解決Record Store的問題，作者劉敏傑 這樣論述：

近年來，許多ICT業者開始積極於物聯網產業尋找新藍海，其中智慧穿戴式裝置因具有解放雙手及隨時量測之優勢，並隨著Apple公司與各大廠爭相投入，整體產業邁入成長期。但他們大部將此應用於生理健康方面，較少針對特定的運動項目開發各式的專業應用裝置。為此，本論文希望設計輕便型穿戴裝置，針對羽毛球運動訓練過程，可即時傳回感測器的資料，搭配AI深度學習的技術，開發一套輔助訓練系統。本論文所建構的軟硬體系統均採用開放式架構來建置，增加了整個系統的開發彈性、相容性、以及可擴充性。使用輕便型手腕感測套，而不用嵌入在球拍內，增加方便性。所收集的感測器資料取自於校隊球員揮拍訓練，並針對揮拍擊球過程進行分析。首先，

我們建構斷拍演算法，以使能有效擷取球員每一次揮拍過程的數據；接著進行AI深度學習，以CNN(Convolutional Neural Networks)與LSTM(Long short-term memory)演算法，判別拍種預測，分別可得到高達96.74%以及97.83%的準確率。我們更進一步建構球員的等級預測模型，分別得到70.27%以及80.63%的結果。另外我們也初步建構單一揮拍評分模型，以供球員及教練評估該次揮拍的狀況。期許本研究提出之系統架構與方法只是一個開始，未來可讓更多穿戴裝置應用於其他專業運動訓練領域。

Head Cleaner

為了解決Record Store的問題，作者 這樣論述：

HEAD CLEANER is a hugely original blend of thriller, science fiction, and horror that takes our love of nostalgia to task for its morbid obsessions with dead media and dead-end jobs: Clerks meets Black Mirror (with a little Groundhog Day and Russian Doll thrown in for good measure): The last Bloc

kbuster video store in the United States is hanging on by a thread. And after a crazy night attempting to track down a lost VCR rental to collect the record-setting and internet-famous late fee, three employees, idealistic Eva, cinephile Jerry, and their tyrannical manager Randy, discover that this

machine may actually have the power to change the endings of popular films, which, depending on the historical basis of the film, might also be changing the real world around them. Or could this just be an elaborate, increasingly deadly prank? When they begin receiving videotapes and voicemails seem

ingly depicting their deaths, Eva, Jerry, and Randy scramble to keep the VCR from falling into the wrong hands. And as one action-packed evening begins to seemingly repeat itself (or does it?), scores are settled and unwanted confessions begin to fly, until a Final Girl finally unravels a grand psyc

hological experiment orchestrated at the highest levels of a crumbling social media empire.

消費性電子產品綠色擴展設計之競爭力評估模型研究

為了解決Record Store的問題，作者易思亮 這樣論述：

在物聯網技術快速發展的趨勢下，電子產品的生產速度和廢棄速度也因此在不斷加快。產品生產需要消耗大量的能源及相關資源，而廢棄的產品則對環境造成嚴重的污染，因此全球環境正面臨嚴重的綠色發展壓力。已有的綠色設計相關研究，主要對新產品的生產以及廢舊產品回收過程進行了探討。但因廢舊的產品存量巨大且回收效率低下，僅僅依賴回收還不足以解決大量廢棄產品對環境的破壞。本研究所建立的綠色擴展設計競爭力模型，即是一種針對舊產品設計開發相容擴展配件，進而升級新功能，延長產品生命週期的有效評估方法，達到避免廢棄現有產品，造成環境污染，也方便消費者簡單快速的升級改造家用設施。研究中首先通過搜集三款綠色擴展設計代表樣品在亞

馬遜平台的用戶評價，以及整理已有參考文獻，歸納總結了九項綠色擴展設計競爭要素。並利用本研究中已有的三款綠色擴展設計代表樣品為基礎，依據設計原則，另外設計六款虛擬綠色擴展設計樣品，組成九款綠色擴展設計樣品，通過受測者對代表樣品針對九項綠色擴展設計競爭要素的問卷評量，經由主成分分析法，歸納三項綠色擴展設計競爭力。並通過專家組的層級分析法評量，本研究對三項擴展競爭力的權重進行分析，並以諾貝爾經濟學獎得主羅伯特·盧卡斯（Robert E. Lucas, Jr.）提出的的生產函數模型，建立綠色擴展設計競爭力模型，並對綠色擴展設計競爭力的臨界值進行了分析和計算，得到了各項競爭力的臨界值。最後，本研究應用綠

色擴展設計競爭力模型進行設計評估，成功的開發了兩款綠色擴展設計的產品，不僅獲得了紅點設計獎，還取得了不錯的銷售業績，證明了該模型在面向消費者的產業應用上，具有較高的應用價值。