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Blood in the Machine: The Origins of the Rebellion Against Big Tech

為了解決remove的問題，作者Merchant, Brian 這樣論述：

The true story of what happened the first time machines came for human jobs, when an underground network of 19th century rebels, the Luddites, took up arms against the industrialists that were automating their work--and how it explains the power, threat, and toll of big tech today. The most press

ing story in modern tech begins not in Silicon Valley, Seattle, or even Shenzhen. It begins two hundred years ago in rural England, when working men and women rose up en masse rather than starve at the hands of the factory owners who were using machines to erase and degrade their livelihoods. They

organized guerilla raids, smashed those machines, and embarked on full-scale assaults against the wealthy machine owners. They won the support of Lord Byron, inspired Mary Shelley, and enraged the Prince Regent and his bloodthirsty government. Before it was over, much blood would be spilled--of rich

and poor, of the invisible and of the powerful. This all-but-forgotten and deeply misunderstood class struggle nearly brought 19th century England to its knees. We live now in the second machine age, when similar fears that big tech is dominating our lives and machines replacing human labor run hi

gh. We worry that technology imperils millions of jobs, robots are ousting workers from factories, and artificial intelligence will soon remove drivers from cars. How will this all reshape our economy and the way we live? And what can we do about it? The answers lie in the story of our first machin

e age, when mechanization first came to British factories at the beginning of the industrial revolution. Intertwined with a lucid examination of our current age, the story of the Luddites, the working-class insurgency that took up arms against automation (at a time when it was punishable by death to

break a machine), Blood in the Machine reaches through time and space to tell a story about how technology changed our world--and how it’s already changing our future.

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錨定含吡啶與吡唑雙配位基於氧化鋅奈米粒子的合成、催化與水中的應用

為了解決remove的問題，作者廖建勳 這樣論述：

本篇論文選擇以吡唑、吡啶以及含有羧酸根官能基的含氮雜環碳烯為主要結構，藉由中性分子化合物 (NHC-COOH) (5) 錨定在氧化鋅奈米粒子，成功合成出氧化鋅奈米粒子載體 (ZnO-NHC NPs) (9)。而且有機分子修飾在氧化鋅奈米粒子上，能使得氧化鋅奈米粒子載體 (ZnO-NHC NPs) (9) 均勻分散在高極性的溶劑中，因此可以利用核磁共振光譜儀、紅外線光譜儀進行定性與定量分析，並用穿透式電子顯微鏡量測粒徑大小。 除此之外，也把氧化鋅奈米粒子載體 (ZnO-NHC NPs) (9) 與鈀金屬螯合鍵結成鈀金屬氧化鋅奈米粒子載體 (Pd-NHC ZnO NPs) (1

0)。並且應用於 Sonogashira 偶聯反應，探討分子式觸媒 (Pd-NHC) (6) 與載體式觸媒 (Pd-NHC ZnO NPs) (10) 的催化活性。研究結果顯示載體式觸媒 (Pd-NHC ZnO NPs) (10) 的催化效果與分子式觸媒 (Pd-NHC) (6) 相當，這結果可證明不會因為載體化的製程，而減少中心金屬的催化活性，而且載體式觸媒 (Pd-NHC ZnO NPs) (10) 可以藉由簡單的離心、傾析後，即使經過十次回收再利用，仍然保持著很高的催化活性。 工業廢水是近年來熱門討論的議題，廢水中所含有的重金屬離子往往會造成嚴重的環境汙染。而這些有毒的金屬汙染物

不只汙染了大自然，更是影響了人類的健康。因此，如何從廢水中除去重金屬離子是非常重要的技術。在本篇研究中，利用氧化鋅奈米粒子載體 (ZnO-NHC NPs) (9) 當作吸附劑，把廢水中常見的鋅、鉛、鎘等金屬，以及硬水溶液中的鈣、鎂金屬成功吸附。接著利用氫氧化鈉當作脫附劑，成功的把金屬離子脫附下來，並且進行再次吸附，也達到很好的效果。除了吸附與脫附的定性分析，本論文也進行吸附的定量分析實驗，發現與文獻其他相近系統效果相當，尤其在低濃度金屬離子的吸附更是優於許多文獻數值。

Charles Evans’’ Watercolour Rescue: Top Tips for Correcting Your Mistakes and Preventing Them in the First Place

為了解決remove的問題，作者Evans, Charles 這樣論述：

Let Charles Evans help you rescue your watercolor paintings from common problems and mistakes with proven solutions.Watercolor is a difficult medium to master, and even experienced artists are prone to making mistakes. Over the years he has spent working as a professional artist and demonstrator,

Charles has built up a huge bank of expert advice, tricks, tips and techniques for fixing common problems, correcting mistakes, and learning how to avoid them in the first place. These problems include: how to remove ’cauliflowers’, the importance of placing your water container correctly to avoid

spillageswhat to do if one color is bleeding into anotherhow to make distant hills look - well - distant, and why you keep mixing muddy color.Each ’problem’ will begin with a question asked by one of the author’s students, such as How do I stop my trees looking like lollipops?, followed by Charles’

solution, and a short demonstration of how to prevent the problem occurring next time you paint.

基於PPG信號和卷積神經網路測量血壓

為了解決remove的問題，作者黎文雄 這樣論述：

光體積變化描記圖法 (PPG) 是一種非侵入性和低成本的技術，現在被廣泛應用於許多血壓量測的研究中。儘管 PPG 信號的品質對血壓演算法的準確度有很大影響，但有關PPG信號的品質檢查並未得到重點關注。在實際量測時，除PPG信號外，反相PPG信號、雜訊、運動信號都會被採集，這些錯誤的信號如果無法去除就會導致錯誤的預測結果。因此，為了解決這個問題，本文提出了一種用於檢查PPG信號品質的新型卷積神經網路 (CNN) 模型，此使用新型CNN的品質檢查模型已被成功訓練和驗證，具有高精度和高性能。此外，本文還設計了另一個卷積神經網路模型來計算血壓，該模型可以自動檢測 PPG 信號中的重要特徵。最後，品質

檢查模型和 CNN 模型都成功嵌入到 Matlab 界面中，用於測量和收集更多數據，以便將來校準模型。