From reviews of the first edition: "well organized . . . Recommended as an introductory text for undergraduates" -- AAAS Science Books and Films "well written and illustrated" -- Bulletin of the American Meteorological Society

This volume comprises select papers presented during the Indian Geotechnical Conference 2018, discussing issues and challenges relating to the characterization of geomaterials, modelling approaches, and geotechnical engineering education. With a combination of field studies, laboratory experiments and modelling approaches, the chapters in this volume address some of the most widely investigated geotechnical engineering topics. This volume will be of interest to researchers and practitioners alike.

Explains how natural selection, combined with methods in statistical physics, can predict and explain the assembly of ecological communities.

These volumes contain the contributions to the Second European Conference on Unsaturated Soils, E-UNSAT 2012, held in Napoli, Italy, in June 2012. The event is the second of a series of European conferences, and follows the first successful one, organised in Durham, UK, in 2008. The conference series is supported by Technical Committee 106 of the International Society of Soil Mechanics and Geotechnical Engineering on Unsaturated Soils. The published contributions were selected after a careful peer-review process. A collection of more than one hundred papers is included, addressing the three thematic areas experimental, including advances in testing techniques and soil behaviour, modelling, covering theoretical and constitutive issues together with numerical and physical modelling, and engineering, focusing on approaches, case histories and geo-environmental themes. The areas of application of the papers embrace most of the geotechnical problems related to unsaturated soils. Increasing interest in geo-environmental problems, including chemical coupling, marks new perspectives in unsaturated soil mechanics. This book will provide a valuable up-to-date reference across the subject for both researchers and practitioners.

This book goes beyond the truism that 'correlation does not imply causation' and explores the logical and methodological relationships between correlation and causation. It presents a series of statistical methods that can test, and potentially discover, cause-effect relationships between variables in situations in which it is not possible to conduct randomised or experimentally controlled experiments. Many of these methods are quite new and most are generally unknown to biologists. In addition to describing how to conduct these statistical tests, the book also puts the methods into historical context and explains when they can and cannot justifiably be used to test or discover causal claims. Written in a conversational style that minimises technical jargon, the book is aimed at practising biologists and advanced students, and assumes only a very basic knowledge of introductory statistics.

Practical Guide for Biomedical Signals Analysis Using Machine Learning Techniques: A MATLAB Based Approach presents how machine learning and biomedical signal processing methods can be used in biomedical signal analysis. Different machine learning applications in biomedical signal analysis, including those for electrocardiogram, electroencephalogram and electromyogram are described in a practical and comprehensive way, helping readers with limited knowledge. Sections cover biomedical signals and machine learning techniques, biomedical signals, such as electroencephalogram (EEG), electromyogram (EMG) and electrocardiogram (ECG), different signal-processing techniques, signal de-noising, feature extraction and dimension reduction techniques, such as PCA, ICA, KPCA, MSPCA, entropy measures, and other statistical measures, and more. This book is a valuable source for bioinformaticians, medical doctors and other members of the biomedical field who need a cogent resource on the most recent and promising machine learning techniques for biomedical signals analysis. - Provides comprehensive knowledge in the application of machine learning tools in biomedical signal analysis for medical diagnostics, brain computer interface and man/machine interaction - Explains how to apply machine learning techniques to EEG, ECG and EMG signals - Gives basic knowledge on predictive modeling in biomedical time series and advanced knowledge in machine learning for biomedical time series