Meu Meaning
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Vocabulary
Although there is a long history of research on vocabulary, the vast majority of studies have appeared over the last 30 years. This new reference work will provide a comprehensive source of the most influential findings that will be both a useful starting point for developing knowledge of the field, as well as a valuable database that can be relied upon when researching vocabulary. Comprised of 4 volumes, the collection will cover 4 key areas. Volume 1 will focus on the core issues related to vocabulary knowledge. The focus of volume 2 is on incidental vocabulary learning. Volume 3 explores the deliberate instruction of vocabulary, and volume 4 looks at formulaic language.
G-Protein-Coupled Receptor Dimers
G-protein-coupled receptors (GPCRs) are believed to be the largest family of membrane proteins involved in signal transduction and cellular responses. They dimerize (form a pair of macromolecules) with a wide variety of other receptors. The proposed book will provide a comprehensive overview of GPCR dimers, starting with a historical perspective and including, basic information about the different dimers, how they synthesize, their signaling properties, and the many diverse physiological processes in which they are involved. In addition to presenting information about healthy GPCR dimer activity, the book will also include a section on their pathology and therapeutic potentials.
Uncertainty in Artificial Intelligence
Uncertainty in Artificial Intelligence contains the proceedings of the Ninth Conference on Uncertainty in Artificial Intelligence held at the Catholic University of America in Washington, DC, on July 9-11, 1993. The papers focus on methods of reasoning and decision making under uncertainty as applied to problems in artificial intelligence (AI) and cover topics ranging from knowledge acquisition and automated model construction to learning, planning, temporal reasoning, and machine vision. Comprised of 66 chapters, this book begins with a discussion on causality in Bayesian belief networks before turning to a decision theoretic account of conditional ought statements that rectifies glaring deficiencies in classical deontic logic and forms a sound basis for qualitative decision theory. Subsequent chapters explore trade-offs in constructing and evaluating temporal influence diagrams; normative engineering risk management systems; additive belief-network models; and sensitivity analysis for probability assessments in Bayesian networks. Automated model construction and learning as well as algorithms for inference and decision making are also considered. This monograph will be of interest to both students and practitioners in the fields of AI and computer science.