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Complex Adaptive Systems Modeling is a peer-reviewed open access journal published under the brand SpringerOpen.

Complex Adaptive Systems Modeling (CASM) is a highly multidisciplinary modeling and simulation journal that serves as a unique forum for original, high-quality peer-reviewed papers with a specific interest and scope limited to agent-based and complex network-based modeling paradigms for Complex Adaptive Systems (CAS). The highly multidisciplinary scope of CASM spans any domain of CAS. Possible areas of interest range from the Life Sciences (E.g. Biological Networks and agent-based models), Ecology (E.g. Agent-based/Individual-based models), Social Sciences (Agent-based simulation, Social Network Analysis), Scientometrics (E.g. Citation Networks) to large-scale Complex Adaptive COmmunicatiOn Networks and environmentS (CACOONS) such as Wireless Sensor Networks (WSN), Body Sensor Networks, Peer-to-Peer (P2P) networks, pervasive mobile networks, service oriented architecture, smart grid and the Internet of Things.In general, submitted papers should have the following key elements:-           A clear focus on a specific area of CAS E.g. ecology, social sciences, large scale communication networks, biological sciences etc.) -           Either focus on an agent-based simulation model or else a complex network model based on data from CAS (e.g. Citation networks, Gene regulatory Networks, Social networks, Ecological Networks etc.).

Complex Analysis and Operator Theory (CAOT) is devoted to the publication of current research developments in the closely related fields of complex analysis and operator theory as well as in applications to system theory, harmonic analysis, probability, statistics, learning theory, and other related fields. Articles using the theory of reproducing kernel spaces are in particular welcomed. CAOT is published in four regular and four sectional issues per year, the latter organised in two sections of two issues each. One section concentrates on Higher Dimensional Geometric Function Theory and Hypercomplex Analysis; the other focusses on Infinite-dimensional Analysis and Non-commutative Theory.   Bibliographic Data
Complex Anal. Oper. Theory
First published in 2007
1 volume per year, 6 issues per volume
approx. 1200 pages per volume
Format: 15.5 x 23.5 cm
ISSN 1661-8254 (print)
ISSN 1661-8262 (electronic)

AMS Mathematical Citation Quotient (MCQ): 0.56 (2011)

computational biology, bioinformatics, computational chemistry, computation in engineering, computational fluid dynamics, molecular dynamics

Computational Biology and Chemistry publishes original research papers and review articles in all areas of computational life sciences. High quality research contributions in the areas of nucleic acid and protein sequence research, molecular evolution, molecular genetics (functional genomics and proteomics), theory and practice of either biology-specific or chemical-biology-specific modeling, and structural biology of nucleic acids and proteins are particularly welcome. Exceptionally high quality research work in bioinformatics, systems biology, cybernetics, ecology, environmental sciences, computational pharmacology, metabolism, biomedical engineering, epidemiology, and statistical genetics will also be considered.Review articles will generally be commissioned by the editors and should not be submitted to the journal without explicit invitation. However prospective authors are welcome to send a brief (one to three pages) synopsis which will be evaluated by the editors.Benefits to authorsWe also provide many author benefits, such as free PDFs, a liberal copyright policy, special discounts on Elsevier publications and much more. Please click here for more information on our author services.Please see our Guide for Authors for information on article submission. If you require any further information or help, please visit our support pages: http://support.elsevier.com

computational complexity presents outstanding research in computational complexity. Its subject is at the interface between mathematics and theoretical computer science, with a clear mathematical profile and strictly mathematical format. The central topics are: Models of computation, complexity bounds (with particular emphasis on lower bounds), complexity classes, trade-off results

for sequential and parallel computationfor 'general' (Boolean) and 'structured' computation (e.g. decision trees, arithmetic circuits)for deterministic, probabilistic, and nondeterministic computationworst case and average caseSpecific areas of concentration include:

Structure of complexity classes (reductions, relativization questions, degrees, derandomization)Algebraic complexity (bilinear complexity, computations for polynomials, groups, algebras, and representations)Cryptography, interactive proofs, pseudorandom generation

Complexity issues in:
learning theorynumber theorylogic (complexity of logical theories

Computational Economics, the official journal of the Society for Computational Economics, presents new research at the interface of computer science and economic and management science. Articles span the fields of symbolic information processing, numerical procedures, computational aspects of mathematical programming, hardware developments, operational research, artificial intelligence, user interfaces, database interfaces, and software research.Computational Economics also publishes state-of-the-art reports from invited authors, brief software reports, and critical reviews. Lastly, periodic special issues are devoted to in-depth studies of current topics of interest to the readership.

Officially cited as: Comput Econ

Computational Geometry is a forum for research in theoretical and applied aspects of computational geometry. The journal publishes fundamental research in all areas of the subject, as well as disseminating information on the applications, techniques, and use of computational geometry. Computational Geometry publishes articles on the design and analysis of geometric algorithms. All aspects of computational geometry are covered, including the numerical, graph theoretical and combinatorial aspects. Also welcomed are computational geometry solutions to fundamental problems arising in computer graphics, pattern recognition, robotics, image processing, CAD-CAM, VLSI design and geographical information systems.Computational Geometry features a special section containing open problems and concise reports on implementations of computational geometry tools.Benefits to authorsWe also provide many author benefits, such as free PDFs, a liberal copyright policy, special discounts on Elsevier publications and much more. Please click here for more information on our author services.Please see our Guide for Authors for information on article submission. If you require any further information or help, please visit our support pages: http://support.elsevier.com

Computational Geosciences publishes high quality papers on mathematical modeling, simulation, numerical analysis, and other computational aspects of the geosciences. In particular the journal is focused on advanced numerical methods for the simulation of subsurface flow and transport, and associated aspects such as discretization, gridding, upscaling, optimization, data assimilation, uncertainty assessment, and high performance parallel and grid computing. Papers treating similar topics but with applications to other fields in the geosciences, such as geomechanics, geophysics, oceanography, or meteorology, will also be considered. The journal provides a platform for interaction and multidisciplinary collaboration among diverse scientific groups, from both academia and industry, which share an interest in developing mathematical models and efficient algorithms for solving them, such as mathematicians, engineers, chemists, physicists, and geoscientists.

This leading international journal promotes and stimulates research in the field of artificial intelligence (AI). Covering a wide range of issues - from the tools and languages of AI to its philosophical implications - Computational Intelligence provides a vigorous forum for the publication of both experimental and theoretical research, as well as surveys and impact studies. The journal is designed to meet the needs of a wide range of AI workers in academic and industrial research.

Computational Intelligence and Neuroscience is a forum for the interdisciplinary field of neural computing, neural engineering and artificial intelligence, where neuroscientists, cognitive scientists, engineers, psychologists, physicists, computer scientists, and artificial intelligence investigators among others can publish their work in one periodical that bridges the gap between neuroscience, artificial intelligence and engineering.The journal provides research and review papers at an interdisciplinary level, with the field of intelligent systems for computational neuroscience as its focus. This field includes areas like artificial intelligence, models and computational theories of human cognition, perception and motivation; brain models, artificial neural nets and neural computing. All items relevant to building theoretical and practical systems are within its scope, including contributions in the area of applicable neural networks theory, supervised and unsupervised learning methods, algorithms, architectures, performance measures, applied statistics, software simulations, hardware implementations, benchmarks, system engineering and integration and innovative applications.The journal spans the disciplines of computer science, mathematics, physics, psychology, cognitive science, medicine and neurobiology amongst others. Work on computational intelligence and neuroscience refers to work on theoretical and computational aspects of the development and functioning of the nervous system, which can be at the level of networks of neurons or at the cellular or the sub-cellular level.Topics of the journal include but are not limited to computational, theoretical, experimental, clinical and applied aspects of the following:Neural modeling and neural-computationNeural signal processingBrain-computer interfacingNeuron-electronicsNeurofeedback, neural rehabilitationNeuroinformaticsBrain waves, neuroimaging (fMRI, EEG, MEG, PET, NIR)Neural circuits: artificial and biologicalNeural control and neural system analysisLearning theory (supervised/unsupervised/reinforcement learning)Knowledge based neural networks, probabilistic, spatial, and temporal knowledge representation and reasoningLearning ClassifiersFusion of neural network- fuzzy systems- evolutionary algorithmsBiologically inspired Intelligent agents (architectures, environments, adaptation/ learning and knowledge management)Bayesian networks and probabilistic reasoningSwarm intelligence, Ant colony optimization, Multi-agent systemsComputational aspects of perceptual systems; Perception of different (visual, auditory and tactile) modalities; Perception and selective attentionLong-term, Short-term, and Working memoryMulti-level (neural, psychological, computational) analysis of cognitive phenomenaIntegrated theories of natural and artificial cognitive systemsInformation-theoretic, control-theoretic, and decision-theoretic approaches to neuroscienceMulti-disciplinary computational approaches to the study of creativity, learning, knowledge and inference, emotion and motivation, awareness and consciousness, perception and action, decision making and action, etc.Cognitive systems from artificial life, dynamical systems, complex systems perspectivesNeurobiologically inspired evolutionary systemsFeatured contributions will fall into original research papers or review articles. Articles are expected to be high quality contributions representing new and significant research, developments or applications of practical use and value. Decisions will be made based on originality, technical soundness, clarity of exposition, scientific contribution and multidisciplinary impact of the article.

Computational Linguistics is the premiere publication devoted exclusively to the design and analysis of natural language processing systems. From this unique open access quarterly, university and industry linguists, computational linguists, artificial intelligence (AI) investigators, cognitive scientists, speech specialists, and philosophers get information about computational aspects of research on language, linguistics, and the psychology of language processing and performance.

Computational Management Science is an international journal focusing on all computational aspects of management science. These include theoretical and empirical analysis of computational models: computational statistics: analysis and applications of constrained, unconstrained, robust, stochastic and combinatorial optimisation algorithms: dynamic models, such as dynamic programming and decision trees: new search tools and algorithms for global optimisation, modelling, learning and forecasting: models and tools of knowledge acquisition. The emphasis on computational paradigms is an intended feature of CMS, distinguishing it from more classical operations research journals.Officially cited as: Comput Manag Sci

The goal of Computational Materials Science is to report on results that provide new or unique insights into, or significantly expand our understanding of, the properties of materials or phenomena associated with their design, synthesis, processing, characterization, and utilization.Papers that report on modern materials modeling are of interest, including quantum chemical methods, density functional theory, semi-empirical and classical approaches, statistical mechanics, atomic-scale simulations, mesoscale modeling, phase-field techniques, and finite element methods. Not all topics that potentially fall under the category of computational materials science are appropriate for the journal. For example, submissions that focus on the design of components for structural applications, describe electrical behavior in a device, or characterize thermal or mass transport without extensive accompanying input and associated discussion from computational materials science methods of interest are best suited for other specialized journals.Reports of advances in technical methodologies, and the application of computational materials science to guide, interpret, inspire, or otherwise enhance related experimental materials research are of significant interest as long as the computational methods or results are a primary focus of the manuscript. Contributions on all types of materials systems will be considered in the form of articles, perspectives, and reviews.Benefits to authorsWe also provide many author benefits, such as free PDFs, a liberal copyright policy, special discounts on Elsevier publications and much more. Please click here for more information on our author services.Please see our Guide for Authors for information on article submission. If you require any further information or help, please visit our support pages: http://support.elsevier.com