Energy Transformation towards Sustainability explores how researchers, businesses and policymakers can explore and usefully improve energy systems and energy consumption behavior, both to reflect the reality of climate change and related environmental degradation and to adapt to the expanding periphery of renewable energy technologies. It introduces the reader to a suite of potential policy pathways to the necessary transformation in societal energy consumption, usage and behavior. Solutions discussed include energy efficiency, energy security, the role of political leadership, green public policy, and the transition to renewable energy sources. International contributions address the range and depth of current research from a position of advocacy for 'energy stewardship' as the driver of this transformation. Case studies illustrate the range of various countries to diminish energy use. Finally, policy avenues are covered in depth.Machine Learning for Subsurface Characterization develops and applies neural networks, random forests, deep learning, unsupervised learning, Bayesian frameworks, and clustering methods for subsurface characterization. Machine learning (ML) focusses on developing computational methods/algorithms that learn to recognize patterns and quantify functional relationships by processing large data sets, also referred to as the "big data." Deep learning (DL) is a subset of machine learning that processes "big data" to construct numerous layers of abstraction to accomplish the learning task. DL methods do not require the manual step of extracting/engineering features; however, it requires us to provide large amounts of data along with high-performance computing to obtain reliable results in a timely manner. This reference helps the engineers, geophysicists, and geoscientists get familiar with data science and analytics terminology relevant to subsurface characterization and demonstrates the use of data-driven methods for outlier detection, geomechanical/electromagnetic characterization, image analysis, fluid saturation estimation, and pore-scale characterization in the subsurface.Robert Guion's best seller is now available in this new second edition. This noted book offers a comprehensive and practical view of assessment -based personnel decisions not available elsewhere in a single source. This edition more frankly evaluates the current research and practice and presents challenges that will change the basic thinking about staffing systems.This new edition suggests new directions for research and practice, includes emphasis on modern computers and technology useful in assessment, and pays more attention to prediction of individual growth and globalization challenges in the assessment process. The book will be of interest to faculty and students in Industrial Organizational psychology, human resource management and business. IO psychologists in private business and public sector organizations who have responsibilities for staffing and an interest in measurement and statistics will find this book useful.
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