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This book adopts the methods of field investigation and case study,
systematically summarizes the development course of Nanjiang County
in Sichuan Province since China's reform and opening-up, and
comprehensively sorts out its main experiences of promoting green
development and rural governance. These experiences not only have
great enlightening and demonstration significance to the 80
counties that belong to the Qinba mountain area, but also have
important reference value to promote rural revitalization and
realize effective governance in the vast mountainous areas and
counties throughout the country. Therefore, this book is not only
suitable for ordinary readers interested in China's reform and
opening up, but also suitable for professional researchers
interested in China's green development and rural governance.
This book contains three well-written research tutorials that
inform the graduate reader about the forefront of current research
in multi-agent optimization. These tutorials cover topics that have
not yet found their way in standard books and offer the reader the
unique opportunity to be guided by major researchers in the
respective fields. Multi-agent optimization, lying at the
intersection of classical optimization, game theory, and
variational inequality theory, is at the forefront of modern
optimization and has recently undergone a dramatic development. It
seems timely to provide an overview that describes in detail
ongoing research and important trends. This book concentrates on
Distributed Optimization over Networks; Differential Variational
Inequalities; and Advanced Decomposition Algorithms for Multi-agent
Systems. This book will appeal to both mathematicians and
mathematically oriented engineers and will be the source of
inspiration for PhD students and researchers.
Road Traffic Modeling and Management: Using Statistical Monitoring
and Deep Learning provides a framework for understanding and
enhancing road traffic monitoring and management. The book examines
commonly used traffic analysis methodologies as well the emerging
methods that use deep learning methods. Other sections discuss how
to understand statistical models and machine learning algorithms
and how to apply them to traffic modeling, estimation, forecasting
and traffic congestion monitoring. Providing both a theoretical
framework along with practical technical solutions, this book is
ideal for researchers and practitioners who want to improve the
performance of intelligent transportation systems.
Statistical Process Monitoring Using Advanced Data-Driven and Deep
Learning Approaches tackles multivariate challenges in process
monitoring by merging the advantages of univariate and traditional
multivariate techniques to enhance their performance and widen
their practical applicability. The book proceeds with merging the
desirable properties of shallow learning approaches - such as a
one-class support vector machine and k-nearest neighbours and
unsupervised deep learning approaches - to develop more
sophisticated and efficient monitoring techniques. Finally, the
developed approaches are applied to monitor many processes, such as
waste-water treatment plants, detection of obstacles in driving
environments for autonomous robots and vehicles, robot swarm,
chemical processes (continuous stirred tank reactor, plug flow
rector, and distillation columns), ozone pollution, road traffic
congestion, and solar photovoltaic systems.
The Asian Summer Monsoon: Characteristics, Variability,
Teleconnections and Projection focuses on the connections between
the Indian Summer and East Asian Summer Monsoons, also including
the South China Sea Summer Monsoon. While these systems have
profound differences, their interactions have significant impacts
on the climatic regimes in the region and throughout the world. In
summer, the ASM engine pumps moisture transported across thousands
of miles from the Indian and Pacific Oceans to the monsoon regions,
producing heavy rains over south and east Asia and its adjacent
marginal seas. This book reviews the different subsystems and their
impact, providing guidance to enhance prediction models.
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