Chao Wang

Tian Zhao

Chuanbo Guo

Chunsheng Liu

Paraskevi Manolaki

Understanding how biological diversity affects ecosystem functioning is a key question in modern ecology. This is of particular importance in the general context of recent global changes caused by human activities, such as water abstraction, flow regulation, shifts in agricultural practices, contamination, climate changes, and biological invasions. These changes are affecting biodiversity, ecosystem functioning, and their complex interactions simultaneously. However, our ability to understand how genes, individuals, populations, communities, and ecosystems may respond to such changes is limited, despite ecologists frequently being requested to provide policymakers and managers with predictions. Indeed, the species that composed biodiversity can establish complex trophic links between them in the ecosystems. This structure of food webs is under the permanent constraint of community dynamics, which connects species, populations, communities, and ecosystems. Therefore, the response of food webs to perturbations can help ecologists to better understand the relationship between biological diversity and ecosystem functioning.

Effects of Non-Random Sources of Alteration on Biodiversity and Ecosystem Functioning

Shuvendu K. Lahiri

This book constitutes the refereed proceedings of the 16th International Symposium on Automated Technology for Verification and Analysis, ATVA 2018, held in Los Angeles, CA, USA in October 2018. The 27 full papers presented together with 5 short papers and 3 invited talks were carefully reviewed and selected from 82 submissions. The symposium is dedicated to the promotion of research on theoretical and practical aspects of automated analysis, verification and synthesis by providing a forum for interaction between the regional and the international research communities and industry in the field.

16th International Symposium, ATVA 2018, Los Angeles, CA, USA, October 7-10, 2018, Proceedings

Automated Technology for Verification and Analysis

Hongjian Sun

Bashar I. Ahmad

From Internet of Things to Smart Cities: Enabling Technologies explores the information and communication technologies (ICT) needed to enable real-time responses to current environmental, technological, societal, and economic challenges. ICT technologies can be utilized to help with reducing carbon emissions, improving resource utilization efficiency, promoting active engagement of citizens, and more. This book aims to introduce the latest ICT technologies and to promote international collaborations across the scientific community, and eventually, the general public. It consists of three tightly coupled parts. The first part explores the involvement of enabling technologies from basic machine-to-machine communications to Internet of Things technologies. The second part of the book focuses on state of the art data analytics and security techniques, and the last part of the book discusses the design of human-machine interfaces, including smart home and cities. Features Provides an extended literature review of relevant technologies, in addition to detailed comparison diagrams, making new readers be easier to grasp fundamental and wide knowledge Contains the most recent research results in the field of communications, signal processing and computing sciences for facilitating smart homes, buildings, and cities Includes future research directions in Internet of Things, smart homes, smart buildings, smart grid, and smart cities Presents real examples of applying these enabling technologies to smart homes, transportation systems and cities With contributions from leading experts, the book follows an easy structure that not only presents timely research topics in-depth, but also integrates them into real world applications to help readers to better understand them.

Enabling Technologies

From Internet of Things to Smart Cities

The open access two-volume set LNCS 12224 and 12225 constitutes the refereed proceedings of the 32st International Conference on Computer Aided Verification, CAV 2020, held in Los Angeles, CA, USA, in July 2020.* The 43 full papers presented together with 18 tool papers and 4 case studies, were carefully reviewed and selected from 240 submissions. The papers were organized in the following topical sections: Part I: AI verification; blockchain and Security; Concurrency; hardware verification and decision procedures; and hybrid and dynamic systems. Part II: model checking; software verification; stochastic systems; and synthesis. *The conference was held virtually due to the COVID-19 pandemic.

32nd International Conference, CAV 2020, Los Angeles, CA, USA, July 21-24, 2020, Proceedings, Part I

Computer Aided Verification

Hai Jin

Xuanhua Shi

Hong An

Mahmut Kandemir

This book constitutes the proceedings of the 14th IFIP WG 10.3 International Conference on Network and Parallel Computing, NPC 2017, held in Hefei, China, in October 2017.The 9 full papers and 10 short papers presented in this book were carefully reviewed and selected from 88 submissions. The papers cover traditional areas of network and parallel computing including parallel applications, distributed algorithms, software environments, and distributed tools.​

14th IFIP WG 10.3 International Conference, NPC 2017, Hefei, China, October 20-21, 2017, Proceedings

Network and Parallel Computing

Ravi P. Agarwal

Donal O' Regan

Rathinasamy Sakthivel

This monograph establishes a theory of classification and translation closedness of time scales, a topic that was first studied by S. Hilger in 1988 to unify continuous and discrete analysis. The authors develop a theory of translation function on time scales that contains (piecewise) almost periodic functions, (piecewise) almost automorphic functions and their related generalization functions (e.g., pseudo almost periodic functions, weighted pseudo almost automorphic functions, and more). Against the background of dynamic equations, these function theories on time scales are applied to study the dynamical behavior of solutions for various types of dynamic equations on hybrid domains, including evolution equations, discontinuous equations and impulsive integro-differential equations. The theory presented allows many useful applications, such as in the Nicholson`s blowfiles model; the Lasota-Wazewska model; the Keynesian-Cross model; in those realistic dynamical models with a more complex hibrid domain, considered under different types of translation closedness of time scales; and in dynamic equations on mathematical models which cover neural networks. This book provides readers with the theoretical background necessary for accurate mathematical modeling in physics, chemical technology, population dynamics, biotechnology and economics, neural networks, and social sciences.

With Applications in Translation Functions and Dynamic Equations

Theory of Translation Closedness for Time Scales

Jianxun Ding

Wei Tao

Shahed Behzadi

Applications of Nanobiotechnology in Pharmacology

Jun Chen

Emerging Micro- and Nanotechnologies for Medical and Pharmacological Applications

High-Performance Computing for Big Data: Methodologies and Applications explores emerging high-performance architectures for data-intensive applications, novel efficient analytical strategies to boost data processing, and cutting-edge applications in diverse fields, such as machine learning, life science, neural networks, and neuromorphic engineering. The book is organized into two main sections. The first section covers Big Data architectures, including cloud computing systems, and heterogeneous accelerators. It also covers emerging 3D IC design principles for memory architectures and devices. The second section of the book illustrates emerging and practical applications of Big Data across several domains, including bioinformatics, deep learning, and neuromorphic engineering. Features Covers a wide range of Big Data architectures, including distributed systems like Hadoop/Spark Includes accelerator-based approaches for big data applications such as GPU-based acceleration techniques, and hardware acceleration such as FPGA/CGRA/ASICs Presents emerging memory architectures and devices such as NVM, STT- RAM, 3D IC design principles Describes advanced algorithms for different big data application domains Illustrates novel analytics techniques for Big Data applications, scheduling, mapping, and partitioning methodologies Featuring contributions from leading experts, this book presents state-of-the-art research on the methodologies and applications of high-performance computing for big data applications. About the Editor Dr. Chao Wang is an Associate Professor in the School of Computer Science at the University of Science and Technology of China. He is the Associate Editor of ACM Transactions on Design Automations for Electronics Systems (TODAES), Applied Soft Computing, Microprocessors and Microsystems, IET Computers & Digital Techniques, and International Journal of Electronics. Dr. Chao Wang was the recipient of Youth Innovation Promotion Association, CAS, ACM China Rising Star Honorable Mention (2016), and best IP nomination of DATE 2015. He is now on the CCF Technical Committee on Computer Architecture, CCF Task Force on Formal Methods. He is a Senior Member of IEEE, Senior Member of CCF, and a Senior Member of ACM.

Methodologies and Applications

High Performance Computing for Big Data

Chao Wang

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