Journals in Mathematics

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Nonlinear Analysis

ISSN: 0362-546X
5 Year impact factor: 1.6
Impact factor: 1.4

An International Mathematical Journal Nonlinear Analysis aims at publishing high-quality research papers broadly related to the analysis of partial differential equations and their applications. Submissions are encouraged in the areas of expertise of the editorial board. Benefits to authors We 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 Center

Nonlinear Analysis: Hybrid Systems

ISSN: 1751-570X
5 Year impact factor: 4.3
Impact factor: 4.2

A journal of IFAC, the International Federation of Automatic Control Nonlinear Analysis: Hybrid Systems (NAHS) welcomes all original research papers on mathematical concepts, tools, and techniques from control theory, computer science, and applied mathematics for the modelling, analysis and design of hybrid dynamical systems, i.e., systems involving the interplay between discrete and continuous dynamic behaviors. Hybrid systems are ubiquitous in many branches of engineering and science as they can model broad ranges of dynamical systems. In particular, in cyber-physical systems, which integrate sensing, computation, control and communication (cyber) parts into physical objects and infrastructures, hybrid systems play an instrumental role. The description of the behavior of the cyber parts often calls for discrete models (automata, finite-state machines, switching logic, etc.), while the physical (thermal, mechanical, electrical, physical, biological) parts are well captured by continuous modelling formalisms (e.g., differential equations), thereby naturally resulting in hybrid system models such as (stochastic) hybrid automata, timed automata, switched systems, impulsive systems, jump-flow models, piecewise affine systems, non-smooth systems, etc. Also, many physical phenomena can often be well described by non-smooth or hybrid models, including mechanical systems with impacts and friction (walking robots), switched power converters and biological systems (e.g., firing neuron models). Hybrid systems can exhibit very rich dynamics and their analysis calls for new and strong theorical foundations to guarantee their stability, safety, functionality, and performance. The development of systematic methods for efficient and reliable design of hybrid systems is therefore a key challenge on the crossroads of control theory and computer science. It is currently of high interest to control engineers, computer scientists and mathematicians in research institutions as well as in many industrial sectors. Contributions to Nonlinear Analysis: Hybrid Systems are invited in all areas pertaining to hybrid dynamical systems including: Modeling, modeling languages and specification; Analysis, computability and complexity; Stochastic hybrid systems; Impulsive systems; Formal verification and abstraction; Optimization and controller synthesis; Control over communication networks including self- and event-triggered control; Network Science and multi-agent systems; Fault diagnosis and fault-tolerant control; Simulation, implementation and tools; Safety, security, privacy, and resilience for cyber-physical systems; Planning and integrated control in dynamical systems. Contributions on applications of hybrid dynamical systems methods are also encouraged. Fields of interest include: process and manufacturing industries, automotive and mobility systems, avionics, communication networks and networked control systems, energy and power systems, transportation networks, cyber-physical and embedded systems, (synthetic) biology and biomedical applications, life sciences, safety-critical systems, mobile and autonomous robotics, and other related areas.

Nonlinear Analysis: Real World Applications

ISSN: 1468-1218
5 Year impact factor: 2.2
Impact factor: 2

Nonlinear Analysis: Real World Applications welcomes all research articles of the highest quality with special emphasis on applying techniques of nonlinear analysis to model and to treat nonlinear phenomena with which nature confronts us. Coverage of applications includes any branch of science and technology such as solid and fluid mechanics, material science, mathematical biology and chemistry, control theory, and inverse problems. The aim of Nonlinear Analysis: Real World Applications is to publish articles which are predominantly devoted to employing methods and techniques from analysis, including partial differential equations, functional analysis, dynamical systems and evolution equations, calculus of variations, and bifurcations theory. Benefits to authors We 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. Two papers per year rule All the authors and co-authors cannot submit more than two papers to this journal (including coauthored papers) within a period of twelve (12) months. If you or one of your co-authors have already submitted two papers within a period of 12 months or less, your third submission (if any) will be returned to you. Rejection due to poor English Some papers with good mathematics have been rejected from this journal due to the poor level of English within the paper. It is the responsibility of the author to ensure that the English language used is correct before submitting their paper. For authors whose first language is not English, we highly recommend that you have it checked by a native English speaker or make use of an English editing service. Elsevier also offers this (at a cost) via our Webshop (English Language Editing ). Please see our Guide for Authors for more information on article submission. If you require any further information or help, please visit our Support Center

Operations Research Letters

ISSN: 0167-6377
5 Year impact factor: 1.2
Impact factor: 1.1

Operations Research Letters (ORL) promises the rapid review of short articles on all aspects of operations research. ORL welcomes pure methodological papers and applied papers with firm methodological grounding. Click the button below for the full description of the areas the journal covers. Area Editors Approximation & Heuristics Area Editor: Leah Epstein Associate Editors: M. Feldman, J. Hurink, N. Olver, J. Sgall, J. Verschae, K. Elbassioni, M. Chrobak The area covers all issues relevant to the development of efficient approximate solutions to computationally difficult problems. Examples are heuristic approaches like local search, worst case analysis or competitive analysis of approximation algorithms, complexity theoretic results, and computational investigations of heuristic approaches. Computational Social Science Area Editor: Vianney Perchet Associate Editors: P. Mertikopoulos, A. Drutsa, R. Smorodinsky This area publishes papers focusing on data-driven procedures, either from a theoretical or an applied perspective, in operation research, games, economics and other social science. The scope includes: sample/computational complexity of mechanisms, learning in games/OR/social science, empirical solutions with AI algorithms (such as, but not limited to, deep learning techniques) of complex problems, etc. Continuous Optimization Area Editor: Hector Ramirez Associate Editors: D. Jiang, M.F. Anjos, G. Eichfelder, F. Schoen, D. Orban, L.M. Briceno, D. Dadush Papers in all fields of continuous optimization that are relevant to operations research are welcome. These areas include, but are not restricted to, linear programming, nonlinear programming (constrained or unconstrained, convex or nonconvex, smooth or nonsmooth, finite or infinite-dimensional), complementarity problems, variational inequalities, bilevel programming, and mathematical programs with equilibrium constraints. Financial Engineering Area Editor: Ning Cai Associate Editors: D.Mitchell, X. He Financial engineering utilizes methodologies of optimization, simulation, decision analysis and stochastic control to analyse the effectiveness and efficiency of financial markets. This area is interested in papers that innovate in terms of methods or that develop new models which guide financial practices. Examples include but are not limited to Fintech, financial networks, market microstructure, derivative pricing and hedging, credit and systemic risk, energy markets, portfolio selection. Game Theory Area Editor: Tristan Tomala Associate Editors: S. Beal, D.W.K. Yeung, G. Zaccour, V. Ihele This area publishes papers which use game theory to analyze operations research models or make theoretical contributions to the theory of games. The scope includes (but is not limited to): cooperative and non-cooperative games, dynamic games, mechanism and market design, algorithmic game theory, games on networks, games of incomplete information. Graphs & Networks Area Editor: Gianpaolo Oriolo Associate Editors: F. Bonomo, Z. Friggstad, L. Sanita, Y. Faenza The area seeks papers that apply, in original and insightful ways, discrete mathematics to advance the theory and practice of operations research, as well as those reporting theoretical or algorithmic advances for the area. Of particular, but not exclusive, interest are papers devoted to novel applications, telecommunications and transportation networks, graphs and web models and algorithms. Inventory and Supply Chain Optimization Area Editor: Sean Zhou Associate Editors: G.J van Houtum, X. Gong, H. Abouee Mehrizi, J. Yang, A. Burnetas, Q. Li The area welcomes innovative papers focused on inventory control and supply management. Examples of topics include, but are not limited to, optimal sourcing, inventory and assortment selection, pricing and inventory optimization, capacity planning, multi-item/echelon systems, algorithms and bounds, near-optimal or asymptotic optimal solutions, and incentive design. Mixed Integer Optimization Area Editor: Marc Pfetsch Associate Editors: L. Liberti, G. Zambelli, J.P. Vielma, R. Fukasawa All submissions advancing the theory and practice of mixed integer (linear or nonlinear) programming like novel techniques and algorithmic approaches in convex relaxations, branch and cut, polyhedral combinatorics and theory driven heuristics are welcome. Case studies may be considered if they contribute to the general methodology. Operations Management Area Editor: Mahesh Nagarajan Associate Editors: T. Huh, C. Shi, L. Chu, N. Golrezaei, R. Roet-Green, D. Saban, Y. Ding The OM department aims to publish short, focused high quality research in the area of operations management, broadly the field of operations research applied to management problems. We welcome papers that use a wide variety of methodologies, both descriptive as well as prescriptive in nature including optimization, applied probability, simulation, and game theory. Scheduling Area Editor: Marc Uetz Associate Editors: E. Pesch, B. Moseley, R. Van Stee We seek original and significant contributions to the analysis and solution of sequencing and scheduling problems. This includes structural and algorithmic results, in particular optimization, approximation and online algorithms, as well as game theoretic modeling. Alll results are welcome as long as the relevance of a problem and significance of the contribution is made compellingly clear. Stochastic Models and Data Science Area Editor: Henry Lam Associate Editors: H. Bastani, J. Dong, K. Murthy, I. Ryzhov, Y. Zhou The area seeks papers broadly on the interplay between operations research and machine learning and statistics where stochastic variability and uncertainty play a crucial role. The area values both papers that develop or utilize stochastic analysis and computation in data science problems, including but not limited to reinforcement learning, stochastic iterative algorithms for model estimation or training, probabilistic analysis of statistical and machine learning tools, sampling and Monte Carlo methods, and also papers that integrate learning or statistical techniques into stochastic modeling to enhance prediction or decision-making for a wide variety of systems. Stochastic Networks and Queues Area Editor: Jamol Pender Associate Editors: H. Honnappa, W. Wang, Y. Zhao, E. Ozkan The area seeks papers that contribute to the modeling, analysis or innovative application of stochastic networks or queues. Work submitted should propose original models and develop novel analytical or computational methods more than incremental extensions. Examples of relevant application areas include but are not limited to supply chain management, manufacturing, financial engineering, healthcare, revenue management, service operations, telecommunications, sharing economy, online markets and public sector operations research. Application-oriented papers should demonstrate direct practical impact and have a strong methodological component as well. Stochastic Optimization and Machine Learning Area Editor: Angelos Georghiou Associate Editors:, M. Claus, E. Feinberg, P. Vayanos, G. Yi Ban, M. Bodur The Stochastic Optimization and Machine Learning area of Operations Research Letters solicits original articles that generate novel insights into problems that arise in optimization under uncertainty and in machine learning. The focus is broad and encompasses, among others, stochastic (dynamic) programming, (distributionally) robust optimization, data-driven optimization as well as the interface of machine learning with traditional areas of operations research. Successful submissions in this area are expected to make a clear and meaningful academic contribution, which may be through the study of new problems, models, solution techniques, performance analysis and convincing and reproducible numerical evaluations. Advisory Board: Jan Karel Lenstra, Nimrod Megiddo, Peter Glynn

Partial Differential Equations in Applied Mathematics

ISSN: 2666-8181

Partial Differential Equations in Applied Mathematics provides a platform for the rapid circulation of original research in applied mathematics and applied sciences by utilizing partial differential equations and related techniques. Contributions on analytical and numerical approaches are both encouraged. All manuscripts should be written to be accessible to a broad scientific audience, who are interested in applied partial differential equations and their applications in physical and engineering sciences. The covered topics include, but are not limited to, inverse scattering transforms, initial and boundary value problems, the unified transform (Fokas method), Lie symmetry method, Hamiltonian theory, Darboux and Backlund transformations, Hirota bilinear method, Riemann-Hilbert problems, d-bar formalism, long-time asymptotes, etc. Papers dealing with mathematical modelling and analysis for traveling waves, solitons, lumps, rogue waves, breathers, optical solitons and non-smooth solitons are particularly welcome.

Pattern Recognition

ISSN: 0031-3203
5 Year impact factor: 8.4
Impact factor: 8

Pattern Recognition is a mature but exciting and fast developing field, which underpins developments in cognate fields such as computer vision, image processing, text and document analysis and neural networks. It is closely akin to machine learning, and also finds applications in fast emerging areas such as biometrics, bioinformatics, multimedia data analysis and most recently data science. The journal Pattern Recognition was established some 50 years ago, as the field emerged in the early years of computer science. Over the intervening years it has expanded considerably. The journal accepts papers making original contributions to the theory, methodology and application of pattern recognition in any area, provided that the context of the work is both clearly explained and grounded in the pattern recognition literature. Papers whos primary concern falls outside the pattern recognition domain and which report routine applications of it using existing or well known methods, should be directed elsewhere. The publication policy is to publish (1) new original articles that have been appropriately reviewed by competent scientific people, (2) reviews of developments in the field, and (3) pedagogical papers covering specific areas of interest in pattern recognition. Various special issues will be organized from time to time on current topics of interest to Pattern Recognition. Submitted papers should be single column, double spaced, no less than 20 and no more than 35 (40 for a review) pages long, with numbered pages. Benefits to authors We 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 Center

Patterns

ISSN: 2666-3899
5 Year impact factor: 6.5
Impact factor: 6.5

Patterns is a premium open access journal from Cell Press, publishing ground-breaking original research across the full breadth of data science. We?re all about sharing data science solutions to problems that cross domain boundaries. Accessible Good solutions deserve a big audience Patterns reaches a broad, global audience of computer scientists, researchers in data intensive domains, data stewards, and policy makers. We adhere to the FAIR Principles to make sure that the data, software, workflows, algorithms, and other research outputs we publish are findable, accessible, interoperable, and reusable. Boundaryless Good insights fuel action in all domains Patterns is the home for data scientists and researchers in data-intensive fields in both academia and industry. The journal shares data science solutions across the spectrum of disciplines, including computational, physical, life, and social sciences, and the humanities. Constructive Good decisions need good data Patterns publishes ground-breaking data science research that is both theoretical and practical. We ensure that the research reported in our articles is quality controlled through rigorous, cross-domain peer-review. Patterns brings together research from across domains in academia and industry to: - Share knowledge about how to best develop and run data science infrastructures, tools, and services -Communicate solutions and best practices for data science algorithms and methodologies -Discuss the human and environmental impact of decisions made using data science -Develop new cross-disciplinary methods for efficient data analysis, processing, archiving, and use Patterns publishes original research in data science, particularly focusing on solutions to the cross-disciplinary problems that all researchers face when dealing with data, and articles about datasets, software code, algorithms, infrastructures, etc., with permanent links to these research outputs. Patterns also promotes cross-community conversation by publishing opinion pieces and review articles. Patterns is committed to the high-quality publishing values shown by its sister journals in Cell Press. Patterns will publish top-tier original research and provide a fair, rapid, and rigorous peer-review process via a dedicated team of professional editors, supported by the expertise of our scientific advisory board. Visit the Cell Press website for more information about Patterns - http://www.cell.com/patterns/home

Pervasive and Mobile Computing

ISSN: 1574-1192
5 Year impact factor: 3.4
Impact factor: 4.3

Special Issue Proposal Note PMCJ exclusively reviews Special Issue proposal forms submitted here through the designated submission system. Proposals submitted via any other means will not be considered for review. For more information on how to prepare and submit a SI proposal please check https://www.elsevier.com/physical-sciences-and-engineering/computer-science/journals/how-to-prepare-a-special-issue-proposal. Aims and Scope As envisioned by Mark Weiser as early as 1991, pervasive computing systems and services have truly become integral to our daily lives. Tremendous advancements in a multitude of technologies ranging from personalized and embedded smart devices (e.g., smartphones, sensors, wearables, IoT) to ubiquitous connectivity through wireless mobile communications and cognitive networking infrastructures, to advanced computing techniques (including mobile edge/fog/cloud, data analytics and machine learning) and user-friendly middleware services and platforms have significantly contributed to the unprecedented advances in pervasive and mobile computing. Such cutting-edge pervasive technologies and paradigms have led to the convergence of cyber-physical-human systems with applications to smart environments (e.g., smart homes and cities, smart grid, smart transportation, smart health, smart agriculture) with the goal to improve human experience and quality of life without explicit awareness of the underlying communications and computing technologies. Additionally, the huge amount of (real-time) data collected via pervasive devices coupled with advanced data analytic, machine learning and AI (Artificial Intelligence) techniques for reliable prediction and decision-making are making breakthrough research in pervasive computing and applications, such as self-driving cars, predictive maintenance in the industry 4.0 environments, mobile recommendation systems, etc. The Pervasive and Mobile Computing Journal (PMC) is a high-impact, peer-reviewed technical journal that publishes high-quality scientific articles spanning theory and practice, and covering all aspects of pervasive and mobile computing and systems. Topics include, but not limited to: Pervasive Computing and Communications Architectures and Protocols Pervasive, Mobile and Wearable Computing Systems and Services Cyber-Physical Systems and Cyber-Physical-Human Systems Smart Systems and Applications (smart homes, smart cities, smart manufacturing, smart transportation, smart grid, smart health, smart agriculture, etc.) Human-centric Intelligent Systems Cognitive Computing Trustworthy AI in Pervasive Systems Machine Learning and Deep Learning in Pervasive and Mobile Computing Federated, Distributed and Embedded learning, Learning at-the-edge in Pervasive Systems Learning on Streaming Data and Continual Learning in Pervasive and Mobile Systems Big Data and Data Analytics in Pervasive Computing Systems Internet of Things and Social Internet of Things Internet of People and Internet of Vehicles Edge, Fog, Mobile Cloud and Opportunistic Computing in Pervasive and Mobile Systems Enabling Pervasive Communication Technologies (e.g., wireless LANs, cellular, hybrid, ad hoc and cognitive networks) Wireless Sensors Networks and RFID Technologies Urban Sensing and Mobile Crowdsensing Participatory and Social Sensing Machine-to-Machine and Device-to-Device Communications Positioning, Localization and Tracking Technologies Activity Recognition and Tracking Context-aware Computing Location-based Services and Applications Pervasive Service Creation, Composition, Discovery, Management, and Delivery Human User Interfaces and Interaction Models Trust, Reliability, Security, and Privacy in Pervasive and Mobile Computing Systems Performance Evaluation of Pervasive and Mobile Computing Systems

Physica A: Statistical Mechanics and its Applications

ISSN: 0378-4371
5 Year impact factor: 2.9
Impact factor: 3.3

Recognized by the European Physical Society Physica A: Statistical Mechanics and its Applications publishes research in the field of statistical mechanics and its applications. Statistical mechanics sets out to explain the behaviour of macroscopic systems, or the large scale, by studying the statistical properties of the microscopic or nanoscopic constituents. Applications of the concepts and techniques of statistical mechanics include: applications to physical and physiochemical systems such as solids, liquids and gases, interfaces, glasses, colloids, complex fluids, polymers, complex networks, applications to economic and social systems (e.g. socio-economic networks, financial time series, agent based models, systemic risk, market dynamics, computational social science, science of science, evolutionary game theory, cultural and political complexity), and traffic and transportation (e.g. vehicular traffic, pedestrian and evacuation dynamics, network traffic, swarms and other forms of collective transport in biology, models of intracellular transport, self-driven particles), as well as biological systems (biological signalling and noise, biological fluctuations, cellular systems and biophysics); and other interdisciplinary applications such as artificial intelligence (e.g. deep learning, genetic algorithms or links between theory of information and thermodynamics/statistical physics.). Physica A does not publish research on mathematics (e.g. statistics) or mathematical methods (e.g. solving differential equations) unless an original application to a statistical physics problem is included. Also research on fluid mechanics intended for an engineering readership as well as ordinary economic/econometrical studies falls outside the scope of Physica A. Specific subfields covered by the journal are statistical mechanics applications to: Soft matter Biological systems and systems biology Chemical systems Econophysics and sociophysics Traffic and transportation Phase transitions Complex systems Deep learning, genetic algorithms and other methods of AI Benefits to authors We 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 Center

Physica D: Nonlinear Phenomena

ISSN: 0167-2789
5 Year impact factor: 3.1
Impact factor: 4

Physica D (Nonlinear Phenomena) publishes research and review articles reporting on theoretical and experimental work, techniques, and ideas that advance the understanding of nonlinear phenomena. The scope of the journal encompasses mathematical methods for nonlinear systems including: wave motion, pattern formation and collective phenomena in physical, chemical and biological systems; hydrodynamics and turbulence; integrable and Hamiltonian systems; and data-driven dynamical systems. The journal encourages submissions in established and emerging application domains, for example applications of nonlinear science to artificial intelligence, robotics, control theory, complex networks, and social and economic dynamics.