This book focuses on the competitive situation and policy outlook of China's provincial economy in the 13th five-year period. It begins with a general evaluation report on the country's provincial comprehensive Economic Competitiveness, followed by analyses at the international, national and regional levels, industrial and enterprise levels. On the basis of domestic and international research findings, it further enriches our understanding of provincial competitiveness, analyzes the domestic and international situation, explores new changes, new norms, new situations and new challenges concerning China's provincial economy in the past few years, reveals the characteristics and relative differences of different types, defines their internal competitive strengths and weaknesses, and provides valuable theoretical content to guide decision-making.

Optimization techniques have been widely adopted to implement various data mining algorithms. In addition to well-known Support Vector Machines (SVMs) (which are based on quadratic programming), different versions of Multiple Criteria Programming (MCP) have been extensively used in data separations. Since optimization based data mining methods differ from statistics, decision tree induction, and neural networks, their theoretical inspiration has attracted many researchers who are interested in algorithm development of data mining. Optimization based Data Mining: Theory and Applications, mainly focuses on MCP and SVM especially their recent theoretical progress and real-life applications in various fields. These include finance, web services, bio-informatics and petroleum engineering, which has triggered the interest of practitioners who look for new methods to improve the results of data mining for knowledge discovery. Most of the material in this book is directly from the research and application activities that the authors' research group has conducted over the last ten years. Aimed at practitioners and graduates who have a fundamental knowledge in data mining, it demonstrates the basic concepts and foundations on how to use optimization techniques to deal with data mining problems.

Optimization techniques have been widely adopted to implement various data mining algorithms. In addition to well-known Support Vector Machines (SVMs) (which are based on quadratic programming), different versions of Multiple Criteria Programming (MCP) have been extensively used in data separations. Since optimization based data mining methods differ from statistics, decision tree induction, and neural networks, their theoretical inspiration has attracted many researchers who are interested in algorithm development of data mining.

"Optimization based Data Mining: Theory and Applications," mainly focuses on MCP and SVM especially their recent theoretical progress and real-life applications in various fields. These include finance, web services, bio-informatics and petroleum engineering, which has triggered the interest of practitioners who look for new methods to improve the results of data mining for knowledge discovery.

Most of the material in this book is directly from the research and application activities that the authors' research group has conducted over the last ten years. Aimed at practitioners and graduates who have a fundamental knowledge in data mining, it demonstrates the basic concepts and foundations on how to use optimization techniques to deal with data mining problems.

MCDM 2009, the 20th International Conference on Multiple-Criteria Decision M- ing, emerged as a global forum dedicated to the sharing of original research results and practical development experiences among researchers and application developers from different multiple-criteria decision making-related areas such as multiple-criteria decision aiding, multiple criteria classification, ranking, and sorting, multiple obj- tive continuous and combinatorial optimization, multiple objective metaheuristics, multiple-criteria decision making and preference modeling, and fuzzy multiple-criteria decision making. The theme for MCDM 2009 was "New State of MCDM in the 21st Century." The conference seeks solutions to challenging problems facing the development of multiple-criteria decision making, and shapes future directions of research by prom- ing high-quality, novel and daring research findings. With the MCDM conference, these new challenges and tools can easily be shared with the multiple-criteria decision making community. The workshop program included nine workshops which focused on different topics in new research challenges and initiatives of MCDM. We received more than 350 submissions for all the workshops, out of which 121 were accepted. This includes 72 regular papers and 49 short papers. We would like to thank all workshop organizers and the Program Committee for the excellent work in maintaining the conference's standing for high-quality papers.

This book focuses on discussing the issues of rating scheme design and risk aggregation of risk matrix, which is a popular risk assessment tool in many fields. Although risk matrix is usually treated as qualitative tool, this book conducts the analysis from the quantitative perspective. The discussed content belongs to the scope of risk management, and to be more specific, it is related to quick risk assessment. This book is suitable for the researchers and practitioners related to qualitative or quick risk assessment and highly helps readers understanding how to design more convincing risk assessment tools and do more accurate risk assessment in a uncertain context.

This book focuses on discussing the issues of rating scheme design and risk aggregation of risk matrix, which is a popular risk assessment tool in many fields. Although risk matrix is usually treated as qualitative tool, this book conducts the analysis from the quantitative perspective. The discussed content belongs to the scope of risk management, and to be more specific, it is related to quick risk assessment. This book is suitable for the researchers and practitioners related to qualitative or quick risk assessment and highly helps readers understanding how to design more convincing risk assessment tools and do more accurate risk assessment in a uncertain context.

This book proposes a bank risk aggregation framework based on financial statements. Specifically, bank risk aggregation is of great importance to maintain stable operation of banking industry and prevent financial crisis. A major obstacle to bank risk management is the problem of data shortage, which makes many quantitative risk aggregation approaches typically fail. Recently, to overcome the problem of inaccurate total risk results caused by the shortage of risk data, some researchers have proposed a series of financial statements-based bank risk aggregation approaches. However, the existing studies have drawbacks of low frequency and time lag of financial statements data and usually ignore off-balance sheet business risk in bank risk aggregation. Thus, by reviewing the research progress in bank risk aggregation based on financial statements and improving the drawbacks of existing methods, this book proposes a bank risk aggregation framework based on financial statements. It makes full use of information recorded in financial statements, including income statement, on- and off-balance sheet assets, and textual risk disclosures, which solves the problem of data shortage in bank risk aggregation to some extent and improves the reliability and rationality of bank risk aggregation results. This book not only improves the theoretical studies of bank risk aggregation, but also provides an important support for the capital allocation of the banking industry in practice. Thus, this book has theoretical and practical importance for bank managers and researchers of bank risk management.

This book proposes a bank risk aggregation framework based on financial statements. Specifically, bank risk aggregation is of great importance to maintain stable operation of banking industry and prevent financial crisis. A major obstacle to bank risk management is the problem of data shortage, which makes many quantitative risk aggregation approaches typically fail. Recently, to overcome the problem of inaccurate total risk results caused by the shortage of risk data, some researchers have proposed a series of financial statements-based bank risk aggregation approaches. However, the existing studies have drawbacks of low frequency and time lag of financial statements data and usually ignore off-balance sheet business risk in bank risk aggregation. Thus, by reviewing the research progress in bank risk aggregation based on financial statements and improving the drawbacks of existing methods, this book proposes a bank risk aggregation framework based on financial statements. It makes full use of information recorded in financial statements, including income statement, on- and off-balance sheet assets, and textual risk disclosures, which solves the problem of data shortage in bank risk aggregation to some extent and improves the reliability and rationality of bank risk aggregation results. This book not only improves the theoretical studies of bank risk aggregation, but also provides an important support for the capital allocation of the banking industry in practice. Thus, this book has theoretical and practical importance for bank managers and researchers of bank risk management.

Global Change and Future Earth is derived from the work of several programs of the International Union of Geodesy and Geophysics (IUGG). It demonstrates how multi- and inter-disciplinary research outputs from the geoscience community can be applied to tackle the physical and societal impacts of climate change and contribute to the Future Earth programme of the International Council for Science. The volume brings together an international team of eminent researchers to provide authoritative reviews on the wide-ranging ramifications of climate change spanning eight key themes: planetary issues; geodetic issues; the Earth's fluid environment; regions of the Earth; urban environments; food security; and risk, safety and security; and climate change and global change. Covering the challenges faced by urban and rural areas, and in both developed and developing counties, this volume provides an important resource for a global audience of graduate students and researchers from a broad range of disciplines, as well as policy advisors and practitioners.

Climate change mechanisms, impacts, risks, mitigation, adaption, and governance are widely recognized as the biggest, most interconnected problem facing humanity. Big Data Mining for Climate Change addresses one of the fundamental issues facing scientists of climate or the environment: how to manage the vast amount of information available and analyse it. The resulting integrated and interdisciplinary big data mining approaches are emerging, partially with the help of the United Nation's big data climate challenge, some of which are recommended widely as new approaches for climate change research. Big Data Mining for Climate Change delivers a rich understanding of climate-related big data techniques and highlights how to navigate huge amount of climate data and resources available using big data applications. It guides future directions and will boom big-data-driven researches on modeling, diagnosing and predicting climate change and mitigating related impacts. This book mainly focuses on climate network models, deep learning techniques for climate dynamics, automated feature extraction of climate variability, and sparsification of big climate data. It also includes a revelatory exploration of big-data-driven low-carbon economy and management. Its content provides cutting-edge knowledge for scientists and advanced students studying climate change from various disciplines, including atmospheric, oceanic and environmental sciences; geography, ecology, energy, economics, management, engineering, and public policy.