This book introduces recent results on output synchronization of complex dynamical networks with single and multiple weights. It discusses novel research ideas and a number of definitions in complex dynamical networks, such as H-Infinity output synchronization, adaptive coupling weights, multiple weights, the relationship between output strict passivity and output synchronization. Furthermore, it methodically edits the research results previously published in various flagship journals and presents them in a unified form. The book is of interest to university researchers and graduate students in engineering and mathematics who wish to study output synchronization of complex dynamical networks.

This book introduces selected recent findings on the analysis and control of dynamical behaviors for coupled reaction-diffusion neural networks. It presents novel research ideas and essential definitions concerning coupled reaction-diffusion neural networks, such as passivity, adaptive coupling, spatial diffusion coupling, and the relationship between synchronization and output strict passivity. Further, it gathers research results previously published in many flagship journals, presenting them in a unified form. As such, the book will be of interest to all university researchers and graduate students in Engineering and Mathematics who wish to study the dynamical behaviors of coupled reaction-diffusion neural networks.

This book intends to introduce some recent results on passivity of complex dynamical networks with single weight and multiple weights. The book collects novel research ideas and some definitions in complex dynamical networks, such as passivity, output strict passivity, input strict passivity, finite-time passivity, and multiple weights. Furthermore, the research results previously published in many flagship journals are methodically edited and presented in a unified form. The book is likely to be of interest to university researchers and graduate students in Engineering and Mathematics who wish to study the passivity of complex dynamical networks.

Anisotropic Particle Assemblies: Synthesis, Assembly, Modeling, and Applications covers the synthesis, assembly, modeling, and applications of various types of anisotropic particles. Topics such as chemical synthesis and scalable fabrication of colloidal molecules, molecular mimetic self-assembly, directed assembly under external fields, theoretical and numerical multi-scale modeling, anisotropic materials with novel interfacial properties, and the applications of these topics in renewable energy, intelligent micro-machines, and biomedical fields are discussed in depth. Contributors to this book are internationally known experts who have been actively studying each of these subfields for many years. This book is an invaluable reference for researchers and chemical engineers who are working at the intersection of physics, chemistry, chemical engineering, and materials science and engineering. It educates students, trains the next generation of researchers, and stimulates continuous development in this rapidly emerging area for new materials and innovative technologies.

Methane Emissions from Unique Wetlands in China: Case Studies, Meta Analyses and Modelling is a landmark volume in the development of studies about methane emission from wetlands. Although there are books about methane emissions from rice paddies, natural wetlands and reservoirs, this book is the first one that provides information about methane emission from wetlands in China. Moreover, the book picks up very unique wetlands, alpine wetlands on the eastern edge of the Qinghai-Tibetan Plateau, and Three Gorges Reservoir (the world's largest hydroelectric reservoir) as cases to study methane emissions. It reviews and meta-analyses methane emissions from rice paddies, natural wetlands and lakes in China during the past twenty years. Furthermore, this book acts as bridge to connect microbial ecology and modelling: it both describes methane-producing bacteria dynamics and methane emission modelling.

This book intends to introduce some recent results on passivity of complex dynamical networks with single weight and multiple weights. The book collects novel research ideas and some definitions in complex dynamical networks, such as passivity, output strict passivity, input strict passivity, finite-time passivity, and multiple weights. Furthermore, the research results previously published in many flagship journals are methodically edited and presented in a unified form. The book is likely to be of interest to university researchers and graduate students in Engineering and Mathematics who wish to study the passivity of complex dynamical networks.

This book introduces recent results on output synchronization of complex dynamical networks with single and multiple weights. It discusses novel research ideas and a number of definitions in complex dynamical networks, such as H-Infinity output synchronization, adaptive coupling weights, multiple weights, the relationship between output strict passivity and output synchronization. Furthermore, it methodically edits the research results previously published in various flagship journals and presents them in a unified form. The book is of interest to university researchers and graduate students in engineering and mathematics who wish to study output synchronization of complex dynamical networks.

This book introduces selected recent findings on the analysis and control of dynamical behaviors for coupled reaction-diffusion neural networks. It presents novel research ideas and essential definitions concerning coupled reaction-diffusion neural networks, such as passivity, adaptive coupling, spatial diffusion coupling, and the relationship between synchronization and output strict passivity. Further, it gathers research results previously published in many flagship journals, presenting them in a unified form. As such, the book will be of interest to all university researchers and graduate students in Engineering and Mathematics who wish to study the dynamical behaviors of coupled reaction-diffusion neural networks.