As the first comprehensive title on network biology, this book covers a wide range of subjects including scientific fundamentals (graphs, networks, etc) of network biology, construction and analysis of biological networks, methods for identifying crucial nodes in biological networks, link prediction, flow analysis, network dynamics, evolution, simulation and control, ecological networks, social networks, molecular and cellular networks, network pharmacology and network toxicology, big data analytics, and more.Across 12 parts and 26 chapters, with Matlab codes provided for most models and algorithms, this self-contained title provides an in-depth and complete insight on network biology. It is a valuable read for high-level undergraduates and postgraduates in the areas of biology, ecology, environmental sciences, medical science, computational science, applied mathematics, and social science.

Graphs, networks and agent-based modeling are the most thriving and attracting sciences used in ecology and environmental sciences. As such, this book is the first comprehensive treatment of the subject in the areas of ecology and environmental sciences.

From this integrated and self-contained book, researchers, university teachers and students will be provided with an in-depth and complete insight on knowledge, methodology and recent advances of graphs, networks and agent-based-modeling in ecology and environmental sciences.

Java codes and a standalone software package will be presented in the book for easy use for those not familiar with mathematical details.

This invaluable book is the first of its kind on 'selforganizology', the science of self-organization. It covers a wide range of topics, such as the theory, principle and methodology of selforganizology, agent-based modelling, intelligence basis, ant colony optimization, fish/particle swarm optimization, cellular automata, spatial diffusion models, evolutionary algorithms, self-adaptation and control systems, self-organizing neural networks, catastrophe theory and methods, and self-organization of biological communities, etc.Readers will have an in-depth and comprehensive understanding of selforganizology, with detailed background information provided for those who wish to delve deeper into the subject and explore research literature.This book is a valuable reference for research scientists, university teachers, graduate students and high-level undergraduates in the areas of computational science, artificial intelligence, applied mathematics, engineering science, social science and life sciences.

As the first comprehensive title on network biology, this book covers a wide range of subjects including scientific fundamentals (graphs, networks, etc) of network biology, construction and analysis of biological networks, methods for identifying crucial nodes in biological networks, link prediction, flow analysis, network dynamics, evolution, simulation and control, ecological networks, social networks, molecular and cellular networks, network pharmacology and network toxicology, big data analytics, and more.Across 12 parts and 26 chapters, with Matlab codes provided for most models and algorithms, this self-contained title provides an in-depth and complete insight on network biology. It is a valuable read for high-level undergraduates and postgraduates in the areas of biology, ecology, environmental sciences, medical science, computational science, applied mathematics, and social science.

This book constitutes the refereed proceedings of the 9th International Forum on Digital TV and Wireless Multimedia Communication, IFTC 2012, Shanghai, China, November. The 69 revised full papers presented were carefully reviewed and selected from numerous submissions. The papers are organized in topical sections on image processing and pattern recognition; image and video analysis; image quality assessment; text image and speech processing; content retrieval and security; source coding; multimedia communication; new advances in broadband multimedia; human computer interface; 3D video.

Ecological modelling is a fast growing science. More and more innovative methodologies and theories on ecological modelling are emerging around the world. This book presents models, methods and theories on ecological modelling. Topics discussed include artificial neural networks; individual-based modelling; ecological nich models; landscape and GIS modelling; population dynamics; nutritional ecology; remote sensing and decision support systems. This book reflects recent achievements of scientists in the study of ecological modelling.

Grasslands, being one of most important ecosystems on earth, provide not only forage, livestock and fur, but also biodiversity and other ecosystem service functionalities. How to prevent grasslands from overgrazing, disertification, and other types of over-exploitation is a pressing issue. In recent years, grasslands have been dramatically changing as the changes of global climate and aggravation of human activities. Protection and reasonable use of grasslands are thus attracting more attention world-wide. This book provides researchers with diverse aspects of the latest advances in grasslands research. This book also covers a varied range of topics, for example, global biodiversity review, biotic interactions in the Rhizosphere, evaluation of soil quality, insect assemblages, orchard grass, soil microbial communities, grassland biodiversity management and soil nematodes.

Due to the complexity and non-linearity of most ecological problems, artificial neural networks (ANNs) have attracted attention from ecologists and environmental scientists in recent years. As these networks are increasingly being used in ecology for modeling, simulation, function approximation, prediction, classification and data mining, this unique and self-contained book will be the first comprehensive treatment of this subject, by providing readers with overall and in-depth knowledge on algorithms, programs, and applications of ANNs in ecology. Moreover, a new area of ecology, i.e., computational ecology, is proposed and its scopes and objectives are defined and discussed.Computational Ecology consists of two parts: the first describes the methods and algorithms of ANNs, interpretability and mathematical generalization of neural networks, Matlab neural network toolkit, etc., while the second provides case studies of applications of ANNs in ecology, Matlab codes, and comparisons of ANNs with conventional methods. This publication will be a valuable reference for research scientists, university teachers, graduate students and high-level undergraduates in the areas of ecology, environmental sciences, and computational science.

Self-organisation is a universal mechanism in nature. In the past thirty years, numerous phenomena, theories and methods on self-organisation have been founded around the world. This book presents recent achievements in theories and methods of self-organisation and includes such theories and methods of self-organisation as ant algorithms, particle swarm algorithms, artificial neural networks, motion and migration algorithms, self-adaptive Kalman Filters, finite state approximations, etc., Chapters are contributed by more than 20 scientists from China, Italy, Spain, Japan, Russia, Serbia, India, and Turkey, in the areas of mathematics, computational science, artificial intelligence, aeronautics and astronautics, automation and control, and life sciences. It will provide researchers with various aspects of the latest advances in self-organisation. It is a valuable reference for scientists, university teachers and graduate students in mathematics, natural science, engineering science, and social science.

Biological network analysis is a fast moving science. Many core scientific issues; for example, ecological structure, coevolution, coextinction and biodiversity conservation in ecology, cancer development and metabolic regulation in health science, etc., are expected to be addressed by network analysis. Network analysis is becoming the core methodology to treat complex biological systems. With the quick development of this science, more and more papers on biological networks are published. This book includes such theories and methods of network biology as methodology of social network analyses, construction of statistic networks, phylogenetic networks, multi-stable and oscillatory biological networks, creation of real networks with expected degree distribution, forest ecosystem model, etc. Chapters are contributed by 15 scientists from the USA, Canada, New Zealand, China, Sweden, and Spain, in the areas of computational science and life sciences. It will provide researchers with various aspects of the latest advances in network biology. It is a valuable reference for scientists, university teachers and graduate students in biology, health science, ecology, social science, applied mathematics and computational science.