Reacting to the perception that the break, early on in the scientific revolution, between alchemy and chemistry was clean and abrupt, Moran literately and engagingly recaps what was actually a slow process. Far from being the superstitious amalgam it is now considered, alchemy was genuine science before and during the scientific revolution. The distinctive alchemical procedure--distillation--became the fundamental method of analytical chemistry, and the alchemical goal of transmuting "base metals" into gold and silver led to the understanding of compounds and elements. What alchemy very gradually but finally lost in giving way to chemistry was its spiritual or religious aspect, the linkages it discerned between purely physical and psychological properties. Drawing saliently from the most influential alchemical and scientific texts of the medieval to modern epoch (especially the turbulent and eventful seventeenth century), Moran fashions a model short history of science volume

Written specifically for use in the educational program of the production division of Seagram Distillers Corporation, this volume provides a fundamental explanation of the physical and chemical processes involved in the operation of a grain alcohol distillery.

Distillation: Fundamentals and Principles — winner of the 2015 PROSE Award in Chemistry & Physics — is a single source of authoritative information on all aspects of the theory and practice of modern distillation, suitable for advanced students and professionals working in a laboratory, industrial plants, or a managerial capacity. It addresses the most important and current research on industrial distillation, including all steps in process design (feasibility study, modeling, and experimental validation), together with operation and control aspects. This volume features an extra focus on the conceptual design of distillation. - Winner of the 2015 PROSE Award in Chemistry & Physics from the Association of American Publishers - Practical information on the newest development written by recognized experts - Coverage of a huge range of laboratory and industrial distillation approaches - Extensive references for each chapter facilitates further study

The first edition of this textbook was published in 2021. Over the past two years, we have invested in enhancing all aspects of deep learning methods to ensure the book is comprehensive and impeccable. Taking into account feedback from our readers and audience, the author has diligently updated this book. The second edition of this textbook presents control theory, transformer models, and graph neural networks (GNN) in deep learning. We have incorporated the latest algorithmic advances and large-scale deep learning models, such as GPTs, to align with the current research trends. Through the second edition, this book showcases how computational methods in deep learning serve as a dynamic driving force in this era of artificial intelligence (AI). This book is intended for research students, engineers, as well as computer scientists with interest in computational methods in deep learning. Furthermore, it is also well-suited for researchers exploring topics such as machine intelligence, robotic control, and related areas.

The multi-volume set LNAI 13713 until 13718 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2022, which took place in Grenoble, France, in September 2022. The 236 full papers presented in these proceedings were carefully reviewed and selected from a total of 1060 submissions. In addition, the proceedings include 17 Demo Track contributions. The volumes are organized in topical sections as follows: Part I: Clustering and dimensionality reduction; anomaly detection; interpretability and explainability; ranking and recommender systems; transfer and multitask learning; Part II: Networks and graphs; knowledge graphs; social network analysis; graph neural networks; natural language processing and text mining; conversational systems; Part III: Deep learning; robust and adversarial machine learning; generative models; computer vision; meta-learning, neural architecture search; Part IV: Reinforcement learning; multi-agent reinforcement learning; bandits and online learning; active and semi-supervised learning; private and federated learning; . Part V: Supervised learning; probabilistic inference; optimal transport; optimization; quantum, hardware; sustainability; Part VI: Time series; financial machine learning; applications; applications: transportation; demo track.

Zimmerman (educational history, New York U.) examines the history of Scientific Temperance Instruction, a curriculum on the evils of alcohol which was originally developed and advocated by a grassroots movement, and ultimately was mandated in all American schools for a time. He traces today's debate on drug and alcohol education to issues raised in this seminal episode. The debate over STI, claims Zimmerman, was really about the balance between expertise and populist desire in determining what should be taught to America's children. Annotation copyrighted by Book News, Inc., Portland, OR