Developed by the late metallurgy professor and master experimentalist Hubert I. Aaronson, this collection of lecture notes details the fundamental principles of phase transformations in metals and alloys upon which steel and other metals industries are based. Mechanisms of Diffusional Phase Transformations in Metals and Alloys is devoted to solid-s
The author has been studying mild cognitive impairment since 2008. The reason was that his mother was diagnosed. Furthermore, he found a cure, and his mother was healthy until 2018. By the way, she has been taking donepezil since 2016 because pharmaceuticals stopped producing her treatment medication. The author regretted not taking measures in 2016. To treat his mother's Alzheimer's disease, which has occurred since 2018, he has been inquiring all over the place. By importing the drug from Germany, he returned his mother's Alzheimer's disease to mild cognitive impairment. In 2020, his mother was diagnosed with pneumonia and then had a stroke. On January 3, 2021, she went to heaven. January 3, 2021, was when a respiratory specialist in the intensive care unit of Hunt Regional Hospital in Greenville, Texas, sent the results of the inflammasome clinical study he was studying. The results of the study were to validate a treatment for respiratory failure for COVID-19. The author's mother went face-to-face with the disease to leave one of the cures for COVID-19, and she gave everything to humans. The name of this treatment is the Soon Joe treatment.
The focus of this book is on providing students with insights into geometry that can help them understand deep learning from a unified perspective. Rather than describing deep learning as an implementation technique, as is usually the case in many existing deep learning books, here, deep learning is explained as an ultimate form of signal processing techniques that can be imagined. To support this claim, an overview of classical kernel machine learning approaches is presented, and their advantages and limitations are explained. Following a detailed explanation of the basic building blocks of deep neural networks from a biological and algorithmic point of view, the latest tools such as attention, normalization, Transformer, BERT, GPT-3, and others are described. Here, too, the focus is on the fact that in these heuristic approaches, there is an important, beautiful geometric structure behind the intuition that enables a systematic understanding. A unified geometric analysis to understand the working mechanism of deep learning from high-dimensional geometry is offered. Then, different forms of generative models like GAN, VAE, normalizing flows, optimal transport, and so on are described from a unified geometric perspective, showing that they actually come from statistical distance-minimization problems. Because this book contains up-to-date information from both a practical and theoretical point of view, it can be used as an advanced deep learning textbook in universities or as a reference source for researchers interested in acquiring the latest deep learning algorithms and their underlying principles. In addition, the book has been prepared for a codeshare course for both engineering and mathematics students, thus much of the content is interdisciplinary and will appeal to students from both disciplines.
Over the last few decades, there are increasing public awareness of adverse events involving engineering failures that not only led to monetary losses but also more importantly, human injuries and deaths. Whilst it is vital for an engineering professional or student to acquire the necessary technical knowledge and skills in their respective field, they must also understand the ethical essences that are relevant to their profession. Engineering professionals like biomedical engineers, need to appreciate the fundamentals of best practices and recognise how any derivation from such practices can have undesirable impacts on human lives. Through this book, it is hoped that readers would draw the relevance between the study of ethics and biomedical engineering. The book would be a useful source and reference for college-level and university-level students. Moreover, the contents are written so as to also provide valuable insights even for existing biomedical engineers and those enrolled in continual engineering education programs.
This book identifies fear of movement and injury as a primary issue in chronic pain management. It provides a detailed treatment manual on exposure-based techniques for the reduction of pain-related fear and disability in chronic pain.
This book provides statistical methodologies for time series data, focusing on copula-based Markov chain models for serially correlated time series. It also includes data examples from economics, engineering, finance, sport and other disciplines to illustrate the methods presented. An accessible textbook for students in the fields of economics, management, mathematics, statistics, and related fields wanting to gain insights into the statistical analysis of time series data using copulas, the book also features stand-alone chapters to appeal to researchers. As the subtitle suggests, the book highlights parametric models based on normal distribution, t-distribution, normal mixture distribution, Poisson distribution, and others. Presenting likelihood-based methods as the main statistical tools for fitting the models, the book details the development of computing techniques to find the maximum likelihood estimator. It also addresses statistical process control, as well as Bayesian and regression methods. Lastly, to help readers analyze their data, it provides computer codes (R codes) for most of the statistical methods.
Edited by the leaders in the fi eld, with chapters from highly renowned international researchers, this is the fi rst coherent overview of the latest in silicon nanomembrane research. As such, it focuses on the fundamental and applied aspects of silicon nanomembranes, ranging from synthesis and manipulation to manufacturing, device integration and system level applications, including uses in bio-integrated electronics, three-dimensional integrated photonics, solar cells, and transient electronics. The first part describes in detail the fundamental physics and materials science involved, as well as synthetic approaches and assembly and manufacturing strategies, while the second covers the wide range of device applications and system level demonstrators already achieved, with examples taken from electronics and photonics and from biomedicine and energy.
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