The book reviews theories of nucleosynthesis in big-bang cosmology. It introduces the standard model of cosmology, astronuclear reactions, numerical techniques for nucleosynethsis, and describes in detail the theories that go beyond the standard models, enabling readers to grasp the physics of big-bang nucleosynthesis on the basis of cosmology, general relativity and nuclear physics. In addition, the authors provide insights into the theoretical constrains required by observations. As a consequence, readers find out that big-bang nucleosynthesis still has windows opened to another cosmology. Although the book focuses on highly advanced topics, it is concisely written and mathematical derivations are explained step-by-step, making it accessible to graduate readers. Thus it is a short monograph appealing to a variety of readers interested in nucleosynthesis of big-bang cosmology.
In July 2008, European and Japanese specialists in neurosurgery, neurology, interventional neuroradiology and neurointensive care joined together to discuss the latest developments in the management of cerebrovascular disorders at the 4th European Japanese Joint Conference on Stroke Surgery, held in Helsinki, Finland. This collection of papers from the meeting deal with aneurysm surgery and management of subarachnoid hemorrhage and stroke, arterial dissection, intracranial arteriovenous malformations and fistulas, and microneurosurgical bypass and revascularization techniques.
The intention behind this book is to illustrate the deep relation among human behavior, data-centric science, and social design. In fact, these three issues have been independently developing in different fields, although they are, of course, deeply interrelated to one another. Specifically, fundamental understanding of human behavior should be employed for investigating our human society and designing social systems. Insights and both quantitative and qualitative understandings of collective human behavior are quite useful when social systems are designed. Fundamental principles of human behavior, theoretical models of human behavior, and information cascades are addressed as aspects of human behavior. Data-driven investigation of human nature, social behavior, and societal systems are developed as aspects of data-centric science. As design aspects, how to design social systems from heterogeneous memberships is explained. There is also discussion of these three aspects-human behavior, data-centric science, and social design-independently and with regard to the relationships among them.
This book focuses on all-pairwise multiple comparisons of means in multi-sample models, introducing closed testing procedures based on maximum absolute values of some two-sample t-test statistics and on F-test statistics in homoscedastic multi-sample models. It shows that (1) the multi-step procedures are more powerful than single-step procedures and the Ryan/Einot-Gabriel/Welsh tests, and (2) the confidence regions induced by the multi-step procedures are equivalent to simultaneous confidence intervals. Next, it describes the multi-step test procedure in heteroscedastic multi-sample models, which is superior to the single-step Games-Howell procedure. In the context of simple ordered restrictions of means, the authors also discuss closed testing procedures based on maximum values of two-sample one-sided t-test statistics and based on Bartholomew's statistics. Furthermore, the book presents distribution-free procedures and describes simulation studies performed under the null hypothesis and some alternative hypotheses. Although single-step multiple comparison procedures are generally used, the closed testing procedures described are more powerful than the single-step procedures. In order to execute the multiple comparison procedures, the upper 100a percentiles of the complicated distributions are required. Classical integral formulas such as Simpson's rule and the Gaussian rule have been used for the calculation of the integral transform that appears in statistical calculations. However, these formulas are not effective for the complicated distribution. As such, the authors introduce the sinc method, which is optimal in terms of accuracy and computational cost.
Applied data-centric social sciences aim to develop both methodology and practical applications of various fields of social sciences and businesses with rich data. Specifically, in the social sciences, a vast amount of data on human activities may be useful for understanding collective human nature. In this book, the author introduces several mathematical techniques for handling a huge volume of data and analysing collective human behaviour. The book is constructed from data-oriented investigation, with mathematical methods and expressions used for dealing with data for several specific problems. The fundamental philosophy underlying the book is that both mathematical and physical concepts are determined by the purposes of data analysis. This philosophy is shown throughout exemplar studies of several fields in socio-economic systems. From a data-centric point of view, the author proposes a concept that may change people's minds and cause them to start thinking from the basis of data. Several goals underlie the chapters of the book. The first is to describe mathematical and statistical methods for data analysis, and toward that end the author delineates methods with actual data in each chapter. The second is to find a cyber-physical link between data and data-generating mechanisms, as data are always provided by some kind of data-generating process in the real world. The third goal is to provide an impetus for the concepts and methodology set forth in this book to be applied to socio-economic systems.
Lesson study is a professional development process that teachers engage in to systematically examine their practice, with the goal of becoming more effective. Originating in Japan, lesson study has gained significant momentum in the mathematics education community in recent years.
As a process for professional development, lesson study became highly visible when it was proposed as a means of supporting the common practice of promoting better teaching by disseminating documents like standards, benchmarks and nationally validated curricula.
While the body of knowledge about lesson study is growing, it remains somewhat elusive and composed of discrete research endeavors. As a new research area there is no coherent knowledge base yet. This book will contribute to the field bringing the work of researchers and practitioners together to create a resource for extant work.
This book describes several aspects of Lesson Study, amongst others: it gives an historical overview of the concept, it addresses issues related to learning and teaching mathematics, it looks at the role of the teacher in the process. The last two sections of the book look at how lesson Study can be used with preservice mathematics teachers and at university mathematics methods teaching.
This book presents selected peer-reviewed contributions from the 2020 International Conference on "Physics and Mechanics of New Materials and Their Applications", PHENMA 2020 (26-29 March 2021, Kitakyushu, Japan), focusing on processing techniques, physics, mechanics, and applications of advanced materials. The book describes a broad spectrum of promising nanostructures, crystal structures, materials, and composites with unique properties. It presents nanotechnological design approaches, environmental-friendly processing techniques, and physicochemical as well as mechanical studies of advanced materials. The selected contributions describe recent progress in computational materials science methods and algorithms (in particular, finite-element and finite-difference modelling) applied to various technological, mechanical, and physical problems. The presented results are important for ongoing efforts concerning the theory, modelling, and testing of advanced materials. Other results are devoted to promising devices with higher accuracy, increased longevity, and greater potential to work effectively under critical temperatures, high pressure, and in aggressive environments.
This book focuses on multiple comparisons of proportions in multi-sample models with Bernoulli responses. First, the author explains the one-sample and two-sample methods that form the basis of multiple comparisons. Then, regularity conditions are stated in detail. Simultaneous inference for all proportions based on exact confidence limits and based on asymptotic theory is discussed. Closed testing procedures based on some one-sample statistics are introduced. For all-pairwise multiple comparisons of proportions, the author uses arcsine square root transformation of sample means. Closed testing procedures based on maximum absolute values of some two-sample test statistics and based on chi-square test statistics are introduced. It is shown that the multi-step procedures are more powerful than single-step procedures and the Ryan-Einot-Gabriel-Welsch (REGW)-type tests. Furthermore, the author discusses multiple comparisons with a control. Under simple ordered restrictions of proportions, the author also discusses closed testing procedures based on maximum values of two-sample test statistics and based on Bartholomew's statistics. Last, serial gatekeeping procedures based on the above-mentioned closed testing procedures are proposed although Bonferroni inequalities are used in serial gatekeeping procedures of many.
The authors of this book assert that Grid Square statistics, a method of aggregating data within a geographically defined Grid, may be an effective solution to approach geospatial data for big data integration. Grid Square statistics is a technique that allows us to collect and analyze data based on Grids and makes it easier to understand patterns and trends. Sustainability, a key concern for the future of our society, often involves balancing multiple independent objectives. These objectives and key performance indicators must be shared to solve multi-dimensional optimization problems with some constraints related to sustainability issues. However, many of these sustainability issues are challenges that are part of global systems, which are modeled as a complex system consisting of many components. Social, economic, and environmental aspects in our socio-economic systems require solutions designed for the sustainability of our society.
These conditions imply that Big Data must contribute to evaluating our current situation, actions, and changes to continuously change our behavior and society based on a Plan-Do-Check-Action (PDCA) cycle. The PDCA cycle, also known as the Deming cycle, is a four-step management method used to improve processes and products continuously. We must understand how to construct a platform by using Big Data to do so. Thus, extracting meaningful information from Big Data is crucially important in the advanced information society.
This book provides case studies based on Grid Square statistics. It shows their applications to socio-economic and environmental problems such as tourism, where we analyze visitor patterns to optimize resource allocation, SDGs indicator, where we monitor the sustainability of our socio-economic systems, and disaster management, where we face natural disaster to enhance preparedness. Finally, the book proposes a World Grid Square Statistics Reference Architecture for constructing data applications across organizations and domains and reports on design concepts, system architecture, and the implication of a data platform for World Grid Square data and statistics.
