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Annotation This book reviews, coalesces, and expands what we know about how older adults successfully experience the aging process, and how they feel about and live with chronic illnesses.
As research progresses, it enables multi-robot systems to be used in more and more complex and dynamic scenarios. Hence, the question arises how different modelling and reasoning paradigms can be utilised to describe the intended behaviour of a team and execute it in a robust and adaptive manner. Hendrik Skubch presents a solution, ALICA (A Language for Interactive Cooperative Agents) which combines modelling techniques drawn from different paradigms in an integrative fashion. Hierarchies of finite state machines are used to structure the behaviour of the team such that temporal and causal relationships can be expressed. Utility functions weigh different options against each other and assign agents to different tasks. Finally, non-linear constraint satisfaction and optimisation problems are integrated, allowing for complex cooperative behaviour to be specified in a concise, theoretically well-founded manner.
This book is designed to train graduate students across disciplines within the fields of public health and medicine, with the goal of guiding them in the transition to independent researchers. It focuses on theories, principles, techniques, and methods essential for data processing and quantitative analysis to address medical, health, and behavioral challenges. Students will learn to access to existing data and process their own data, quantify the distribution of a medical or health problem to inform decision making; to identify influential factors of a disease/behavioral problem; and to support health promotion and disease prevention. Concepts, principles, methods and skills are demonstrated with SAS programs, figures and tables generated from real, publicly available data. In addition to various methods for introductory analysis, the following are featured, including 4-dimensional measurement of distribution and geographic mapping, multiple linear and logistic regression, Poisson regression, Cox regression, missing data imputing, and statistical power analysis.
From the long-stemmed pipe to snuff, the water pipe, hand-rolled cigarettes, and finally, manufactured cigarettes, the history of tobacco in China is the fascinating story of a commodity that became a hallmark of modern mass consumerism. Carol Benedict follows the spread of Chinese tobacco use from the sixteenth century, when it was introduced to China from the New World, through the development of commercialized tobacco cultivation, and to the present day. Along the way, she analyzes the factors that have shaped China’s highly gendered tobacco cultures, and shows how they have evolved within a broad, comparative world-historical framework. Drawing from a wealth of historical sources—gazetteers, literati jottings (biji), Chinese materia medica, Qing poetry, modern short stories, late Qing and early Republican newspapers, travel memoirs, social surveys, advertisements, and more—Golden-Silk Smoke not only uncovers the long and dynamic history of tobacco in China but also sheds new light on global histories of fashion and consumption.
This book compiles and presents new developments in statistical causal inference. The accompanying data and computer programs are publicly available so readers may replicate the model development and data analysis presented in each chapter. In this way, methodology is taught so that readers may implement it directly. The book brings together experts engaged in causal inference research to present and discuss recent issues in causal inference methodological development. This is also a timely look at causal inference applied to scenarios that range from clinical trials to mediation and public health research more broadly. In an academic setting, this book will serve as a reference and guide to a course in causal inference at the graduate level (Master's or Doctorate). It is particularly relevant for students pursuing degrees in statistics, biostatistics, and computational biology. Researchers and data analysts in public health and biomedical research will also find this book to be an important reference.
This book constitutes the refereed proceedings of the 7th International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction, SBP 2014, held in Washington, DC, USA, in April 2014. The 51 full papers presented were carefully reviewed and selected from 101 submissions. The SBP conference provides a forum for researchers and practitioners from academia, industry, and government agencies to exchange ideas on current challenges in social computing, behavioral-cultural modeling and prediction, and on state-of-the-art methods and best practices being adopted to tackle these challenges. The topical areas addressed by the papers are social and behavioral sciences, health sciences, military science, and information science.
Planning is the branch of Artificial Intelligence (AI) that seeks to automate reasoning about plans, most importantly the reasoning that goes into formulating a plan to achieve a given goal in a given situation. AI planning is model-based: a planning system takes as input a description (or model) of the initial situation, the actions available to change it, and the goal condition to output a plan composed of those actions that will accomplish the goal when executed from the initial situation. The Planning Domain Definition Language (PDDL) is a formal knowledge representation language designed to express planning models. Developed by the planning research community as a means of facilitating ...