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3D Image Reconstruction for CT and Pet: A Practical Guide with Python

為了解決Web video download的問題，作者 這樣論述：

This is a practical guide to tomographic image reconstruction with projection data, with strong focus on Computed Tomography (CT) and Positron Emission Tomography (PET). Classic methods such as FBP, ART, SIRT, MLEM and OSEM are presented with modern and compact notation, with the main goal of gui

ding the reader from the comprehension of the mathematical background through a fast-route to real practice and computer implementation of the algorithms. Accompanied by example data sets, real ready-to-run Python toolsets and scripts and an overview the latest research in the field, this guide will

be invaluable for graduate students and early-career researchers and scientists in medical physics and biomedical engineering who are beginners in the field of image reconstruction.A top-down guide from theory to practical implementation of PET and CT reconstruction methods, without sacrificing the

rigor of mathematical backgroundAccompanied by Python source code snippets, suggested exercises, and supplementary ready-to-run examples for readers to download from the CRC Press websiteIdeal for those willing to move their first steps on the real practice of image reconstruction, with modern scie

ntific programming language and toolsetsDaniele Panetta is a researcher at the Institute of Clinical Physiology of the Italian National Research Council (CNR-IFC) in Pisa. He earned his MSc degree in Physics in 2004 and specialisation diploma in Health Physics in 2008, both at the University of Pisa

. From 2005 to 2007, he worked at the Department of Physics E. Fermi of the University of Pisa in the field of tomographic image reconstruction for small animal imaging micro-CT instrumentation. His current research at CNR-IFC has as its goal the identification of novel PET/CT imaging biomarkers for

cardiovascular and metabolic diseases. In the field micro-CT imaging, his interests cover applications of three-dimensional morphometry of biosamples and scaffolds for regenerative medicine. He acts as reviewer for scientific journals in the field of Medical Imaging: Physics in Medicine and Biology

, Medical Physics, Physica Medica, and others. Since 2012, he is adjunct professor in Medical Physics at the University of Pisa.Niccolò Camarlinghi is a researcher at the University of Pisa. He obtained his MSc in Physics in 2007 and his PhD in Applied Physics in 2012. He has been working in the fie

ld of Medical Physics since 2008 and his main research fields are medical image analysis and image reconstruction. He is involved in the development of clinical, pre-clinical PET and hadron therapy monitoring scanners. At the time of writing this book he was a lecturer at University of Pisa, teachin

g courses of life-sciences and medical physics laboratory. He regularly acts as a referee for the following journals: Medical Physics, Physics in Medicine and Biology, Transactions on Medical Imaging, Computers in Biology and Medicine, Physica Medica, EURASIP Journal on Image and Video Processing, J

ournal of Biomedical and Health Informatics.

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在CLIL情境下發展合作學習以增進國小學生的數位說故事能力、英語口說能力及理財知識

為了解決Web video download的問題，作者范庭毓 這樣論述：

英語教育已在台灣推廣多年，近來，內容和語言整合學習 (CLIL) 受到越來越多的關注，因其為一種將英語融入現實生活環境的有效方式。隨著科技的進步，數位說故事能培養學生多種技能，例如敘述現實生活中的故事、用英語記錄故事以及與小組成員討論故事。因此本研究旨在發展 CLIL 情境下的合作學習，以提高小學生的數位說故事、英語口語能力和金融知識。參與者來自兩個班級中共43名六年級學生：實驗組（n = 26）和對照組（n = 17）。整個實驗設計為期9周，目的為調查實驗組學生在合作學習方式下發展的數位說故事結果是否與對照組的個人數位說故事結果不同。所有參與者都參加了金融知識和英語口說的前測，接著參與金融

知識課程和數位說故事的引導，以幫助他們有效地設計出能結合金融知識和英語口說的數位說故事影片。此研究問題的分析主要採用描述性分析、獨立t檢定、配對t檢定、單一 ANCOVA 和 Pearson 相關性分析。其結果表明，實驗組在整體之數位說故事表現、英語口語能力和金融知識方面優於對照組。而在金融知識方面，實驗組和對照組在後測中皆有顯著進步。並且在口語能力方面，實驗組在內容、語法、詞彙等方面較對照組有顯著提升。但是，在流利程度和熟練程度方面並無顯著差異。在 DST 性能方面，實驗組在見解、語音和圖像方面均有顯著提升。然而，在經濟性和原創性方面沒有顯著差異。此外，合作學習和英語口語能力之間以及合作學習

和數位說故事表現之間皆顯示出正相關。此研究亦進一步探討研究結果對未來英語教育的影響。

The Complete Guide to Blender Graphics: Computer Modeling & Animation

為了解決Web video download的問題，作者Blain, John M. 這樣論述：

BlenderTM　is a　free Open Source 3D Computer Modeling and Animation Suite　incorporating Character Rigging, Particles, Real World Physics Simulation, Sculpting, Video Editing with Motion Tracking and 2D Animation within the 3D Environment.Blender is　FREE　to download and use by anyone for anything.T

he Complete Guide to Blender Graphics: Computer Modeling and Animation,　Seventh Edition is a unified manual describing the operation of the program with reference to the Graphical User Interface for　Blender Version　3.0.0, including nearly 100 pages of completely new content.The book introduces the p

rogram’s Graphical User Interface and shows how to implement tools for modeling and animating characters and created scenes with the application of color, texture and special lighting effects.Key Features: The book provides instruction for New Users starting at the very beginning.Instruction is pres

ented in a series of chapters incorporating visual reference to the program’s interface.The initial chapters are designed to instruct the user in the operation of the program while introducing and demonstrating interesting features of the program.Chapters are developed in a building block fashion pr

oviding forward and reverse reference to relevant material.The book is also available in a discounted set along with Blender 2D Animation: The Complete Guide to the Grease Pencil.

合作學習策略對高齡者體智能訓練成效

為了解決Web video download的問題，作者林欣慈 這樣論述：

高齡化社會的來臨已成為世界各國所關心的議題，而如何能讓高齡者減緩老化讓高齡者健康老化是當前的第一要務。本研究以體智能板為研究工具，並經由合作學習的教學方式發展出專為認知功能發展的運動課程，希望對高齡者認知功能的延緩有所助益。本研究目的：探討合作學習的教學方法介入高齡體智能板認知訓練課程後，其認知與功能性體適能之影響。研究方法：本研究總共募集了42位65歲以上高齡者，參與每週三次為期八週的合作學習與個人學習體智能運動進行課程。並於課程參與前後以功能性體適能測試、台灣版額葉評估表與台灣數字奇偶序列測驗來了解其合作學習與個人學習教學的差異性。研究結果：經過研究發現，合作學習與個人學習組除了下肢柔軟

度外，其餘功能性性體適能項目都呈現顯著，合作學習組在下肢肌力與心肺功能測驗中顯著進步個人學習組。在認知功能測驗上，合作學習與個人學習組在八週後，都有顯著效果，合作學習多數都顯著高於個人學習組。研究建議：經由研究發現，合作學習運用在體智能板認知運動中對高齡者是有相當的益處，建議合作學習學方式可廣泛運用與推廣。