This second edition of the International Handbook of Mathematics Teacher Education builds on and extends the topics/ideas in the first edition while maintaining the themes for each of the volumes. Collectively, the authors looked back beyond and within the last 10 years to establish the state-of-the-art and continuing and new trends in mathematics teacher and mathematics teacher educator education, and looked forward regarding possible avenues for teachers, teacher educators, researchers, and policy makers to consider to enhance and/or further investigate mathematics teacher and teacher educator learning and practice, in particular. The volume editors provide introductions to each volume that highlight the subthemes used to group related chapters, which offer meaningful lenses to see important connections within and across chapters. Readers can also use these subthemes to make connections across the four volumes, which, although presented separately, include topics that have relevance across them since they are all situated in the common focus regarding mathematics teachers. Volume 1, Knowledge, Beliefs, and Identity in Mathematics Teaching and Teaching Development, edited by Despina Potari and Olive Chapman, examines teacher knowledge, beliefs, identity, practice and relationships among them. These important aspects of mathematics teacher education continue to be the focus of extensive research and policy debate globally. Thus, as the first volume in the series, it appropriately addresses central topics/issues that provide an excellent beginning to engage in the field of mathematics education through the handbook. Contributors are: Jill Adler, Mike Askew, Maria Bartolini Bussi, Anne Bennison, Kim Beswick, Olive Chapman, Charalambos Charalambus, Helen Chick, Marta Civil, Sandra Crespo, Sean Delaney, Silvia Funghi, Merrilyn Goos, Roberta Hunter, Barbara Jaworski, Kim Koh, Esther S. Levenson, Yeping Li, Niamh O' Meara, JoengSuk Pang, Randolph Phillipp, Despina Potari, Craig Pournara, Stephen Quirke, Alessandro Ramploud, Tim Rowland, John (Zig) Siegfried, Naiqing Song, Konstantinos Stouraitis, Eva Thanheiser, Collen Vale, Hamsa Venkat, and Huirong Zhang.

Mathematical Methods in Science and Engineering: Applications in Optics and Photonics helps students build a conceptual appreciation for critical mathematical methods, as well as the physical feel and intuition for select mathematical ideas. Throughout the text, examples are provided from the field of optics and photonics to clarify key concepts. The book features 13 targeted chapters that begin with a brief introduction to the topical area and then dive directly into the subject matter. Students learn about properties of numbers, methods of mathematical reasoning, Euclidean geometry, the fundamentals of complex number theory, and techniques to deal with finite as well as infinite sums and products. Dedicated chapters speak to key concepts of multivariate calculus, the properties of analytic functions of a complex variable, Fourier transformation, methods of solving partial differential equations, the Sturm-Liouville theory, and special functions, including Euler's gamma function, Riemann's zeta function, and the Airy and Bessel functions. Elementary matrix algebra, vector calculus, and probability, random variables, and stochastic processes are addressed. Mathematical Methods in Science and Engineering is well suited for graduate-level courses in optical sciences, physics, and engineering.

Combining insights from academic research and practical examples, this book aims to better understand the link between financial markets and innovation management. First, we are back to the very definition of innovation and what it means for financial and non-financial companies. Then, we analyze if efficient innovation management by companies is recognized and valued by financial markets. Finally, we focus on innovation within the financial sector: does it really create value outside the financial sector itself. Are Financial innovations value ... or risk creators?

Reliability Modelling and Analysis in Discrete Time provides an overview of the probabilistic and statistical aspects connected with discrete reliability systems. This engaging book discusses their distributional properties and dependence structures before exploring various orderings associated between different reliability structures. Though clear explanations, multiple examples, and exhaustive coverage of the basic and advanced topics of research in this area, the work gives the reader a thorough understanding of the theory and concepts associated with discrete models and reliability structures. A comprehensive bibliography assists readers who are interested in further research and understanding. Requiring only an introductory understanding of statistics, this book offers valuable insight and coverage for students and researchers in Probability and Statistics, Electrical Engineering, and Reliability/Quality Engineering. The book also includes a comprehensive bibliography to assist readers seeking to delve deeper.

Ranked Set Sampling: 65 Years Improving the Accuracy in Data Gathering is an advanced survey technique which seeks to improve the likelihood that collected sample data presents a good representation of the population and minimizes the costs associated with obtaining them. The main focus of many agricultural, ecological and environmental studies is the development of well designed, cost-effective and efficient sampling designs, giving RSS techniques a particular place in resolving the disciplinary problems of economists in application contexts, particularly experimental economics. This book seeks to place RSS at the heart of economic study designs.