This book develops a framework that shows how uncertainty in Artificial Intelligence (AI) expands and generalizes traditional AI. It explores the uncertainties of knowledge and intelligence. The authors focus on the importance of natural language – the carrier of knowledge and intelligence, and introduce efficient physical methods for data mining amd control. In this new edition, we have more in-depth description of the models and methods, of which the mathematical properties are proved strictly which make these theories and methods more complete. The authors also highlight their latest research results.

This book investigates first language (L1) and second language (L2) use in Chinese university classrooms, focusing on the experiences of four Chinese EFL teachers who were teaching non-English major students at four different proficiency levels. It examines these four teachers' actual use of L1 and L2, including the distribution of their L1 and L2 use; the circumstances, functions and grammatical patterns of their language use; and their language use across different frames of classroom discourse. It also explores their attitudes and beliefs regarding this issue in depth, as well as their own perceptions of and reasons for their language use and possible influencing factors. Through its detailed analysis of the teachers' language use, as well as their respective beliefs and decision-making techniques, this book contributes to L2 teachers' professional development and L2 teaching in general, especially with regard to establishing a pedagogically principled approach to L1 and L2 use.

General Airgap Field Modulation Theory for Electrical Machines Introducing a new theory for electrical machines Air-gap magnetic field modulation phenomena have been widely observed in electrical machines. This book serves as the first English-language overview of these phenomena, as well as developing systematically for the first time a general theory by which to understand and research them. This theory not only serves to unify analysis of disparate electrical machines, from conventional DC machines, induction machines, and synchronous machines to unconventional flux-switching permanent magnet machines, Vernier machines, doubly-fed brushless machines etc., but also paves the way towards the creation of new electrical machine topologies. General Airgap Field Modulation Theory for Electrical Machines includes both overviews of key concepts in electrical machine engineering and in-depth specialized analysis of the novel theory itself. It works through the applications of the developed theory before proceeding to both qualitative analysis of the theory’s operating principles and quantitative analysis of its parameters. Readers will also find: The collective experience of four award-winning authors with long records of international scholarship on this subject Three separate chapters covering the principal applications of the theory, with detailed examples Discussion of potential innovations made possible by this theory General Airgap Field Modulation Theory for Electrical Machines is an essential introduction to this theory for postgraduates, researchers, and electrical engineers.

The Art and Science of Helping: Developing Fundamental Skills in a Multicultural Age introduces the fundamentals of practicing helping-skills to undergraduates, graduates, and those preparing for entry-level helping professions. The text emphasizes best practices of the art of helping while rooting these practices in empirical, scientific findings. Readers will learn skills and techniques that prepare them for counseling and other helping professions while also developing multicultural competence and self-awareness. Chapters teach helpers who are training to navigate the different phases of helping, including connecting with clients, helping clients discover new understanding, and empowering clients to take action. The Art and Science of Helping aligns with a typical semester and includes ready-to-use classroom activities to develop helping skills and microskills. Each chapter includes multicultural considerations as well as reflections and exercises designed to enhance self-awareness--both critical competencies for burgeoning helping professionals.

Machine learning represents a paradigm shift in tomographic imaging, and image reconstruction is a new frontier of machine learning. This book will meet the needs of those who want to catch the wave of smart imaging. The book targets graduate students and researchers in the imaging community. Open network software, working datasets, and multimedia will be included. The first of its kind in the emerging field of deep reconstruction and deep imaging, Machine Learning for Tomographic Imaging presents the most essential elements, latest progresses and an in-depth perspective on this important topic.