Modeling In-Network Processing and Aggregation in Sensor Networks

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Gene regulatory networkFrom Wikipedia, the free encyclopedia(Redirected from Genetic regulatory network) A gene regulatory network or genetic regulatory network (GRN) is a collection of DNA segments in a cell which interact with each other (indirectly through their RNA and protein expression products) and with other substances in the cell, thereby governing the rates at which genes in the network are transcribed into mRNA. In general, each mRNA molecule goes on to make a specific protein (or set of proteins). In some cases this protein will be structural, and will accumulate at the cell-wall or within the cell to give it particular structural properties. In other cases the protein will be an enzyme; a micromachine that catalyses a certain reaction, such as the breakdown of a food source or toxin. Some proteins though serve only to activate other genes, and these are the transcription factors that are the main players in regulatory networks or cascades. By binding to the promoter region at the start of other genes they turn them on, initiating the production of another protein, and so on. Some transcription factors are inhibitory. In single-celled organisms regulatory networks respond to the external environment, optimising the cell at a given time for survival in this environment. Thus a yeast cell, finding itself in a sugar solution, will turn on genes to make enzymes that process the sugar to alcohol.[1] This process, which we associate with wine-making, is how the yeast cell makes its living, gaining energy to multiply, which under normal circumstances would enhance its survival prospects.Structure of a Gene Regulatory Network.Control process of a Gene Regulatory Network.In multicellular animals the same principle has been put in the service of gene cascades that control body-shape. [2] Each time a cell divides, two cells result which, although they contain the same genome in full, can differ in which genes are turned on and making proteins. Sometimes a 'self-sustaining feedback loop' ensures that a cell maintains its identity and passes it on. Less understood is the mechanism of epigenetics by which chromatin modification may provide cellular memory by blocking or allowing transcription. A major feature of multicellular animals is the use of morphogen gradients, which in effect provide a positioning system that tells a cell where in the body it is, and hence what sort of cell to become. A gene that is turned on in one cell may make a product that leaves the cell and diffuses through adjacent cells, entering them and turning on genes only when it is present above a certain threshold level. These cells are thus induced into a new fate, and may even generate other morphogens that signal back to the original cell. Over longer distances morphogens may use the active process of signal transduction. Such signalling controls embryogenesis, the building of a body plan from scratch through a series of sequential steps. They also control maintain adult bodies through feedback processes, and the loss of such feedback because of a mutation can be responsible for the cell proliferation that is seen in cancer. In parallel with this process of building structure, the gene cascade turns on genes that make structural proteins that give each cell the physical properties it needs. It has been suggested that, because biological molecular interactions are intrinsically stochastic, gene networks are the result of cellular processes and not their cause. (i.e. Cellular Darwinism) However, recent experimental evidence has favored the attractor view of cell fates.Contents■ 1 Overview ■ 2 Modelling ■ 2.1 Coupled ODEs ■ 2.2 Boolean network ■ 2.3 Continuous networks ■ 2.4 Stochastic gene networks ■ ■ ■ ■ ■ ■ 3 Prediction 4 Network connectivity 5 See also 6 Notes 7 References 8 External linksOverviewAt one level, biological cells can be thought of as "partially-mixed bags" of biological chemicals – in the discussion of gene regulatory networks, these chemicals are mostly the mRNAs and proteins that arise from gene expression. These mRNA and proteins interact with each other with various degrees of specificity. Some diffuse around the cell. Others are bound to cell membranes, interacting with molecules in the environment. Still others pass through cell membranes and mediate long range signals to other cells in a multi-cellular organism. These molecules and their interactions comprise a gene regulatory network. A typical gene regulatory network looks something like this: The nodes of this network are proteins, their corresponding mRNAs, and protein/protein complexes. Nodes that are depicted as lying along vertical lines are associated with the cell/environment interfaces, while the others are free-floating and diffusible. Implied are genes, the DNA sequences which are transcribed into the mRNAs that translate into proteins. Edges between nodes represent individual molecular reactions, the protein/protein and protein/mRNA interactions through which the products of one gene affect those of another, though the lack of experimentally obtained information often implies that some reactions are not modeled at such a fine level of detail. These interactions can be inductive (the arrowheads), with an increase in the concentration of one leading to an increase in the other, or inhibitory (the filled circles), with an increase in one leading to a decrease in the other. A series of edges indicates a chain of such dependences, with cycles corresponding to feedback loops. The network structure is an abstraction of the system's chemical dynamics, describing the manifold ways in which one substance affects all the others to which it is connected. In practice, such GRNs are inferred from the biological literature on a given system and represent a distillation of the collective knowledge about a set of related biochemical reactions. Genes can be viewed as nodes in the network, with input being proteins such as transcription factors, and outputs being the level of gene expression. The node itself can also be viewed as a function which can be obtained by combining basic functions upon the inputs (in the Boolean network described below these are Boolean functions, typically AND, OR, and NOT). These functions have been interpreted as performing a kind of information processing within the cell, which determines cellular behavior. The basic drivers within cells are concentrations of some proteins, which determine both spatial (location within the cell or tissue) and temporal (cell cycle or developmental stage) coordinates of the cell, as a kind of "cellular memory". The gene networks are only beginning to be understood, and it is a next step for biology to attempt to deduce the functions for each gene "node", to help understand the behavior of the system in increasing levels of complexity, from gene to signaling pathway, cell or tissue level (see systems biology). Mathematical models of GRNs have been developed to capture the behavior of the system being modeled, and in some cases generate predictions corresponding with experimental observations. In some other cases, models have proven to make accurate novel predictions, which can be tested experimentally, thus suggesting new approaches to explore in an experiment that sometimes wouldn't be considered in the design of the protocol ofan experimental laboratory. The most common modeling technique involves the use of coupled ordinary differential equations (ODEs). Several other promising modeling techniques have been used, including Boolean networks, Petri nets, Bayesian networks, graphical Gaussian models, Stochastic, and Process Calculi. Conversely, techniques have been proposed for generating models of GRNs that best explain a set of time series observations.ModellingCoupled ODEsIt is common to model such a network with a set of coupled ordinary differential equations (ODEs) or stochastic ODEs, describing the reaction kinetics of the constituent parts. Suppose that our regulatory network has N nodes, and let represent the concentrations of the N corresponding substances at time t. Then the temporal evolution of the system can be described approximately bywhere the functions fj express the dependence of Sj on the concentrations of other substances present in the cell. The functions fj are ultimately derived from basic principles of chemical kinetics or simple expressions derived from these e.g. Michaelis-Menten enzymatic kinetics. Hence, the functional forms of the fj are usually chosen as low-order polynomials or Hill functions that serve as an ansatz for the real molecular dynamics. Such models are then studied using the mathematics of nonlinear dynamics. System-specific information, like reaction rate constants and sensitivities, are encoded as constant parameters.[3] By solving for the fixed point of the system:for all j, one obtains (possibly several) concentration profiles of proteins and mRNAs that are theoretically sustainable (though not necessarily stable). Steady states of kinetic equations thus correspond to potential cell types, and oscillatory solutions to the above equation to naturally cyclic cell types. Mathematical stability of these attractors can usually be characterized by the sign of higher derivatives at critical points, and then correspond to biochemical stability of the concentration profile. Critical points and bifurcations in the equations correspond to critical cell states in which small state or parameter perturbations could switch the system between one of several stable differentiation fates. Trajectories correspond to the unfolding of biological pathways and transients of the equations to short-term biological events. For a more mathematical discussion, see the articles on nonlinearity, dynamical systems, bifurcation theory, and chaos theory.Boolean networkThe following example illustrates how a Boolean network can model a GRN together with its gene products (the outputs) and the substances from the environment that affect it (the inputs). Stuart Kauffman was amongst the first biologists to use the metaphor of Boolean networks to model genetic regulatory networks.[4][5] 1. Each gene, each input, and each output is represented by a node in a directed graph in which there is an arrow from one node to another if and only if there is a causal link between the two nodes. 2. Each node in the graph can be in one of two states: on or off. 3. For a gene, "on" corresponds to the gene being expressed; for inputs and outputs, "on" corresponds to the substance being present. 4. Time is viewed as proceeding in discrete steps. At each step, the new state of a node is a Boolean function of the prior states of the nodes with arrows pointing towards it. The validity of the model can be tested by comparing simulation results with time series observations.Continuous networksContinuous network models of GRNs are an extension of the boolean networks described above. Nodes still represent genes and connections between them regulatory influences on gene expression. Genes in biological systems display a continuous range of activity levels and it has been argued that using a continuous representation captures several properties of gene regulatory networks not present in the Boolean model.[6] Formally most of these approaches are similar to an artificial neural network, as inputs to a node are summed up and the result serves as input to a sigmoid function, e.g.,[7] but proteins do often control gene expression in a synergistic, i.e. non-linear, way.[8] However there is now a continuous network model[9] that allows grouping of inputs to a node thus realizing another level of regulation. This model is formally closer to a higher order recurrent neural network. The same model has also been used to mimic the evolution of cellular differentiation [10] and even multicellular morphogenesis.[11]Stochastic gene networksRecent experimental results[12] [13] have demonstrated that gene expression is a stochastic process. Thus, many authors are now using the stochastic formalism, after the work by.[14] Works on single gene expression[15] and small synthetic genetic networks,[16][17] such as the genetic toggle switch of Tim Gardner and Jim Collins, provided additional experimental data on the phenotypic variability and the stochastic nature of gene expression. The first versions of stochastic models of gene expression involved only instantaneous reactions and were driven by the Gillespie algorithm.[18] Since some processes, such as gene transcription, involve many reactions and could not be correctly modeled as an instantaneous reaction in a single step, it was proposed to model these reactions as single step multiple delayed reactions in order to account for the time it takes for the entire process to be complete.[19] From here, a set of reactions were proposed[20] that allow generating GRNs. These are then simulated using a modified version of the Gillespie algorithm, that can simulate multiple time delayed reactions (chemical reactions where each of the products is provided a time delay that determines when will it be released in the system as a "finished product"). For example, basic transcription of a gene can be represented by the following single-step reaction (RNAP is the RNA polymerase, RBS is the RNA ribosome binding site, and Pro i is the promoter region of gene i):A recent work proposed a simulator (SGNSim, Stochastic Gene Networks Simulator),[21] that can model GRNs where transcription and translation are modeled as multiple time delayed events and its dynamics is driven by a stochastic simulation algorithm (SSA) able to deal with multiple time delayed events. The time delays can be drawn from several distributions and the reaction rates from complex functions or from physical parameters. SGNSim can generate ensembles of GRNs within a set of user-defined parameters, such as topology. It can also be used to model specific GRNs and systems of chemical reactions. Genetic perturbations such as gene deletions, gene over-expression, insertions, frame shift mutations can also be modeled as well. The GRN is created from a graph with the desired topology, imposing in-degree and out-degree distributions. Gene promoter activities are affected by other genes expression products that act as inputs, in the form of monomers or combined into multimers and set as direct or indirect. Next, each direct input is assigned to an operator site and different transcription factors can be allowed, or not, to compete for the same operator site, while indirect inputs are given a target. Finally, a function is assigned to each gene, defining the gene's response to a combination of transcription factors (promoter state). The transfer functions (that is, how genes respond to a combination of inputs) can be assigned to each combination of promoter states as desired. In other recent work, multiscale models of gene regulatory networks have been developed that focus on synthetic biology applications. Simulations have been used that model all biomolecular interactions in transcription, translation, regulation, and induction of gene regulatory networks, guiding the design of synthetic systems.[22]PredictionOther work has focused on predicting the gene expression levels in a gene regulatory network. The approaches used to model gene regulatory networks have been constrained to be interpretable and, as a result, are generally simplified versions of the network. For example, Boolean networks have been used due to their simplicity and ability to handle noisy data but lose data information by having a binary representation of the genes. Also, artificial neural networks omit using a hidden layer so that they can be interpreted, losing the ability to model higher order correlations in the data. Using a model that is not constrained to be interpretable, a more accurate model can be produced. Being able to predict gene expressions more accurately provides a way to explore how drugs affect a system of genes as well as for finding which genes are interrelated in a process. This has been encouraged by the DREAM competition[23] which promotes a competition for the best prediction algorithms.[24] Some other recent work has used artificial neural networks with a hidden layer.[25]Network connectivityEmpirical data indicate that biological gene networks are sparsely connected, and that the average number of upstream-regulators per gene is less than two.[26] Theoretical results show that selection for robust gene networks will favor minimally complex, more sparsely connected, networks.[26] These results suggest that a sparse, minimally connected, genetic architecture may be a fundamental design constraint shaping the evolution of gene network complexity.See also■ ■ ■ ■ ■ ■ ■ Body plan Cis-regulatory module Genenetwork (database) Morphogen Operon Synexpression Systems biologyNotes1. ^ "Transcriptional Regulatory Networks in Saccharomyces cerevisiae" (/young/regulator_network/) . Young Lab. /young/regulator_network/. 2. ^ Davidson E, Levin M (April 2005). "Gene regulatory networks" (/cgi/content/full/102/14/4935) . Proc. Natl. Acad. Sci. U.S.A. 102 (14): 4935. doi:10.1073/pnas.0502024102 (/10.1073%2Fpnas.0502024102) . PMC 556010 (/articlerender.fcgi?tool=pmcentrez&artid=556010) . PMID 15809445 (/pubmed/15809445) . /cgi/content/full/102/14/4935. 3. ^ Chu D, Zabet NR, Mitavskiy B (April 2009). "Models of transcription factor binding: sensitivity of activation functions to model assumptions" (/retrieve/pii/S0022-5193(08)00631-0) . J. Theor. Biol. 257 (3): 419–29. doi:10.1016/j.jtbi.2008.11.026 (/10.1016%2Fj.jtbi.2008.11.026) . PMID 19121637 (/pubmed/19121637) . /retrieve/pii/S0022-5193(08)00631-0. 4. ^ Kauffman, Stuart (1993). The Origins of Order. ISBN 0195058119. 5. ^ Kauffman SA (1969). "Metabolic stability and epigenesis in randomly constructed genetic nets" (http://lis.epfl.ch/~markus/References/Kauffman69.pdf) . Journal of Theoretical Biology (22): 437–467. http://lis.epfl.ch/~markus/References/Kauffman69.pdf. 6. ^ Vohradsky J (September 2001). "Neural model of the genetic network" (/cgi/pmidlookup? view=long&pmid=11395518) . J. Biol. Chem. 276 (39): 36168–73. doi:10.1074/jbc.M104391200 (/10.1074%2Fjbc.M104391200) . PMID 11395518 (/pubmed/11395518) . /cgi/pmidlookup? view=long&pmid=11395518. 7. ^ Geard N, Wiles J (2005). "A gene network model for developing cell lineages" (/doi/abs/10.1162/1064546054407202?url_ver=Z39.882003&rfr_id=ori:rid:&rfr_dat=cr_pub%) . Artif. Life 11 (3): 249–67.8. 9.10.11.12.13.14.15.16. 17. 18. 19.20.21.doi:10.1162/1064546054407202 (/10.1162%2F1064546054407202) . PMID 16053570 (/pubmed/16053570) . /doi/abs/10.1162/1064546054407202?url_ver=Z39.882003&rfr_id=ori:rid:&rfr_dat=cr_pub%. ^ Schilstra MJ, Bolouri H (2 January 2002). "Modelling the Regulation of Gene Expression in Genetic Regulatory Networks" (/bio/maria/NetBuilder/Theory/NetBuilderModelling.htm) . Biocomputation group, University of Hertfordshire. /bio/maria/NetBuilder/Theory/NetBuilderModelling.htm. ^ Knabe JF, Nehaniv CL, Schilstra MJ, Quick T (2006). "Evolving Biological Clocks using Genetic Regulatory Networks" (/viewdoc/download?doi=10.1.1.72.5016&rep=rep1&type=pdf) . Proceedings of the Artificial Life X Conference (Alife 10). MIT Press. pp. 15–21. /viewdoc/download? doi=10.1.1.72.5016&rep=rep1&type=pdf. ^ Knabe JF, Nehaniv CL, Schilstra MJ (2006). "Evolutionary Robustness of Differentiation in Genetic Regulatory Networks" (/viewdoc/download?doi=10.1.1.71.8768&rep=rep1&type=pdf) . Proceedings of the 7th German Workshop on Artificial Life 2006 (GWAL-7). Berlin: Akademische Verlagsgesellschaft Aka. pp. 75–84. /viewdoc/download?doi=10.1.1.71.8768&rep=rep1&type=pdf. ^ Knabe JF, Schilstra MJ, Nehaniv CL (2008). "Evolution and Morphogenesis of Differentiated Multicellular Organisms: Autonomously Generated Diffusion Gradients for Positional Information" (http://panmental.de/papers/FlagPottsGRNALife11.pdf) . Artificial Life XI: Proceedings of the Eleventh International Conference on the Simulation and Synthesis of Living Systems. MIT Press. http://panmental.de/papers/FlagPottsGRNALife11.pdf. ^ Elowitz MB, Levine AJ, Siggia ED, Swain PS (August 2002). "Stochastic gene expression in a single cell" (/cgi/pmidlookup?view=long&pmid=12183631) . Science 297 (5584): 1183–6. doi:10.1126/science.1070919 (/10.1126%2Fscience.1070919) . PMID 12183631 (/pubmed/12183631) . /cgi/pmidlookup? view=long&pmid=12183631. ^ Blake WJ, KAErn M, Cantor CR, Collins JJ (April 2003). "Noise in eukaryotic gene expression" (/abl/pdf/blake2003.pdf? file=/nature/journal/v422/n6932/full/nature01546_fs.html&content_filetype=PDF) . Nature 422 (6932): 633–7. doi:10.1038/nature01546 (/10.1038%2Fnature01546) . PMID 12687005 (/pubmed/12687005) . /abl/pdf/blake2003.pdf? file=/nature/journal/v422/n6932/full/nature01546_fs.html&content_filetype=PDF. ^ Arkin A, Ross J, McAdams HH (August 1998). "Stochastic kinetic analysis of developmental pathway bifurcation in phage lambda-infected Escherichia coli cells" (/articlerender.fcgi? tool=pmcentrez&artid=1460268) . Genetics 149 (4): 1633–48. PMC 1460268 (/articlerender.fcgi?tool=pmcentrez&artid=1460268) . PMID 9691025 (/pubmed/9691025) . /articlerender.fcgi? tool=pmcentrez&artid=1460268. ^ Raser JM, O'Shea EK (September 2005). "Noise in gene expression: origins, consequences, and control" (/cgi/pmidlookup?view=long&pmid=16179466) . Science 309 (5743): 2010–3. doi:10.1126/science.1105891 (/10.1126%2Fscience.1105891) . PMC 1360161 (/articlerender.fcgi?tool=pmcentrez&artid=1360161) . PMID 16179466 (/pubmed/16179466) . /cgi/pmidlookup? view=long&pmid=16179466. ^ Elowitz MB, Leibler S (January 2000). "A synthetic oscillatory network of transcriptional regulators". Nature 403 (6767): 335–8. doi:10.1038/35002125 (/10.1038%2F35002125) . PMID 10659856 (/pubmed/10659856) . ^ Gardner TS, Cantor CR, Collins JJ (January 2000). "Construction of a genetic toggle switch in Escherichia coli". Nature 403 (6767): 339–42. doi:10.1038/35002131 (/10.1038%2F35002131) . PMID 10659857 (/pubmed/10659857) . ^ Gillespie DT (1976). "A general method for numerically simulating the stochastic time evolution of coupled chemical reactions". J. Comput. Phys. 22: 403–34. doi:10.1016/0021-9991(76)90041-3 (/10.1016% 2F0021-9991%2876%2990041-3) . ^ Roussel MR, Zhu R (November 2006). "Validation of an algorithm for delay stochastic simulation of transcription and translation in prokaryotic gene expression" (/1478-3967/3/274) . Phys Biol 3 (4): 274–84. doi:10.1088/1478-3975/3/4/005 (/10.1088%2F1478-3975%2F3%2F4%2F005) . PMID 17200603 (/pubmed/17200603) . /1478-3967/3/274. ^ Ribeiro A, Zhu R, Kauffman SA (November 2006). "A general modeling strategy for gene regulatory networks with stochastic dynamics". J. Comput. Biol. 13 (9): 1630–9. doi:10.1089/cmb.2006.13.1630 (/10.1089%2Fcmb.2006.13.1630) . PMID 17147485 (/pubmed/17147485) . ^ Ribeiro AS, Lloyd-Price J (March 2007). "SGN Sim, a stochastic genetic networks simulator" (/cgi/pmidlookup?view=long&pmid=17267430) . Bioinformatics 23 (6): 777–9. doi:10.1093/bioinformatics/btm004 (/10.1093%2Fbioinformatics%2Fbtm004) . PMID 17267430 (/pubmed/17267430) . /cgi/pmidlookup?view=long&pmid=17267430.22. ^ Kaznessis YN (2007). "Models for synthetic biology" (/1752-0509/1/47) . BMC Syst Biol 1: 47. doi:10.1186/1752-0509-1-47 (/10.1186%2F1752-0509-1-47) . PMC 2194732 (/articlerender.fcgi?tool=pmcentrez&artid=2194732) . PMID 17986347 (/pubmed/17986347) . /1752-0509/1/47. 23. ^ "The DREAM Project" (/dream/index.php/The_DREAM_Project) . Columbia University Center for Multiscale Analysis Genomic and Cellular Networks (MAGNet). /dream/index.php/The_DREAM_Project. 24. ^ Gustafsson M, Hörnquist M (2010). "Gene Expression Prediction by Soft Integration and the Elastic Net—Best Performance of the DREAM3 Gene Expression Challenge" (/article/info%3Adoi% 2F10.1371%2Fjournal.pone.0009134) . PLoS ONE 5 (2). /article/info%3Adoi%2F10.1371% 2Fjournal.pone.0009134. 25. ^ Smith MR, Clement M, Martinez T, Snell Q (2010). "Time Series Gene Expression Prediction using Neural Networks with Hidden Layers" (/viewdoc/download? doi=10.1.1.173.4686&rep=rep1&type=pdf) . Proceedings of the 7th Biotechnology and Bioinformatics Symposium (BIOT 2010). pp. 67-69. /viewdoc/download?doi=10.1.1.173.4686&rep=rep1&type=pdf. 26. ^ a b Leclerc, RD (August 2008). "[/msb/journal/v4/n1/full/msb200852.html Survival of the sparsest: robust gene networks are parsimonious (/articlerender.fcgi? tool=pmcentrez&artid=2538912) "]. Mol Syst Biol. 4 (213): 213. doi:10.1038/msb.2008.52 (/10.1038%2Fmsb.2008.52) . PMC 2538912 (/articlerender.fcgi? tool=pmcentrez&artid=2538912) . PMID 18682703 (/pubmed/18682703) . /articlerender.fcgi?tool=pmcentrez&artid=2538912.References■ Bolouri, Hamid; Bower, James M. (2001). Computational modeling of genetic and biochemical networks. Cambridge, Mass: MIT Press. ISBN 0-262-02481-0. ■ Kauffman SA (1969). "Metabolic stability and epigenesis in randomly constructed genetic nets". J. Theoret. Biol. 22: 434–67.External links■ Gene Regulatory Networks (/science/generegulatorynetwork.shtml) — Short introduction ■ Open source web service for GRN analysis (/) ■ BIB: Yeast Biological Interaction Browser (/bio) ■ Graphical Gaussian models for genome data (/notes/ggm.html) — Inference of gene association networks with GGMs ■ A bibliography on learning causal networks of gene interactions (http://www.molgen.mpg.de/~markowet/docs/network-bib.pdf) - regularly updated, contains hundreds of links to papers from bioinformatics, statistics, machine learning. ■ http://mips.gsf.de/proj/biorel/ BIOREL is a web-based resource for quantitative estimation of the gene network bias in relation to available database information about gene activity/function/properties/associations/interactio. ■ Evolving Biological Clocks using Genetic Regulatory Networks (http://panmental.de/GRNclocks) Information page with model source code and Java applet. ■ Engineered Gene Networks (/abl) ■ Tutorial: Genetic Algorithms and their Application to the Artificial Evolution of Genetic Regulatory Networks (http://panmental.de/ICSBtut/) ■ BEN: a web-based resource for exploring the connections between genes, diseases, and other biomedical entities (/) Retrieved from "/wiki/Gene_regulatory_network" Categories: Gene expression | Networks | Systems biology ■ This page was last modified on 19 March 2011 at 16:15. ■ Text is available under the Creative Commons Attribution-ShareAlike License; additional terms may apply. See Terms of Use for details. Wikipedia® is a registered trademark of the Wikimedia Foundation, Inc., a non-profit organization.。

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PHYSICS460Wiley Interdisciplinary Reviews-Energy and Environment Y461IEEE PHOTONICS TECHNOLOGY LETTERS462IEEE Transactions on Human-Machine Systems463Fuzzy Optimization and Decision Making464Energies465IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS466INTERNATIONAL JOURNAL OF REFRACTORY METALS & HARD MATERIALS 467EARTHQUAKE ENGINEERING & STRUCTURAL DYNAMICS468OPTICAL MATERIALS469IEEE TRANSACTIONS ON SOFTWARE ENGINEERING470COMPUTER-AIDED DESIGN471FINITE ELEMENTS IN ANALYSIS AND DESIGN472ENGINEERING IN LIFE SCIENCES473Journal of Infrared Millimeter and Terahertz Waves474Journal of the Association for Information Science and Technology475INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE476AUTONOMOUS ROBOTS477INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN ENGINEERING 478Frontiers in Neurorobotics479ADVANCED ENGINEERING INFORMATICS480JOURNAL OF NANOPARTICLE RESEARCH481BIOTECHNOLOGY PROGRESS482JOURNAL OF INTELLIGENT MATERIAL SYSTEMS AND STRUCTURES483IEEE TRANSACTIONS ON COMPUTERS484GEOTECHNIQUE485COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE486JOURNAL OF THE SCIENCE OF FOOD AND AGRICULTURE487ELECTRIC POWER SYSTEMS RESEARCH488CONTROL ENGINEERING PRACTICE489International Journal of Applied Glass Science490Ad Hoc Networks491PLASMA CHEMISTRY AND PLASMA PROCESSING492Archives of Civil and Mechanical Engineering493JOURNAL OF CHEMICAL AND ENGINEERING DATA494FOOD REVIEWS INTERNATIONAL Y495COMPUTER VISION AND IMAGE UNDERSTANDING496NUMERICAL HEAT TRANSFER PART A-APPLICATIONS497ARTIFICIAL INTELLIGENCE IN MEDICINE498IEEE SENSORS JOURNAL499JOURNAL OF MICROSCOPY500Pervasive and Mobile Computing501TRANSPORTATION RESEARCH PART D-TRANSPORT AND ENVIRONMENT 502Journal of Manufacturing Processes503IEEE MICROWAVE MAGAZINE504JOURNAL OF NUCLEAR MATERIALS505IET Renewable Power Generation506Islets507Leukos508MECHATRONICS509International Journal for Numerical Methods in Biomedical Engineering510MATHEMATICS AND MECHANICS OF SOLIDS511Science of Advanced Materials512ADVANCES IN ENGINEERING SOFTWARE513INFORMATION SYSTEMS514IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT515IMAGE AND VISION COMPUTING516IEEE Intelligent Transportation Systems Magazine517INTERNATIONAL DAIRY JOURNAL518IEEE TRANSACTIONS ON NANOTECHNOLOGY519ENGINEERING STRUCTURES520AMB Express521INTERNATIONAL JOURNAL OF ROCK MECHANICS AND MINING SCIENCES 522JOURNAL OF PHYSICS AND CHEMISTRY OF SOLIDS523POLYMER COMPOSITES Y524STEEL AND COMPOSITE STRUCTURES525Foundations and Trends in Information Retrieval526THEORETICAL AND APPLIED FRACTURE MECHANICS527IEEE SIGNAL PROCESSING LETTERS528INTERNATIONAL JOURNAL OF ADHESION AND ADHESIVES529MECHANISM AND MACHINE THEORY530Materials Express531MODELLING AND SIMULATION IN MATERIALS SCIENCE AND ENGINEERING 532Journal of Building Performance Simulation533EUROPEAN JOURNAL OF LIPID SCIENCE AND TECHNOLOGY534IEEE MULTIMEDIA535ADVANCED ENGINEERING MATERIALS536Food Bioscience537IEEE Transactions on Dependable and Secure Computing538ENERGY JOURNAL539IEEE Antennas and Wireless Propagation Letters540COMPUTERS AND ELECTRONICS IN AGRICULTURE541MOBILE NETWORKS & APPLICATIONS542Journal of Grid Computing543Electronic Materials Letters544Bulletin of Earthquake Engineering545JOURNAL OF MICROELECTROMECHANICAL SYSTEMS546IEEE ELECTRICAL INSULATION MAGAZINE547MICRON548INTERNATIONAL JOURNAL OF GENERAL SYSTEMS549Advances in Biochemical Engineering-Biotechnology550RAPID PROTOTYPING JOURNAL551DESIGN STUDIES552NEURAL COMPUTATION553COMPUTATIONAL LINGUISTICS554BIOPROCESS AND BIOSYSTEMS ENGINEERING555JOURNAL OF GEOTECHNICAL AND GEOENVIRONMENTAL ENGINEERING 556JOURNAL OF FLUIDS AND STRUCTURES557INTERNATIONAL JOURNAL OF CONTROL558FATIGUE & FRACTURE OF ENGINEERING MATERIALS & STRUCTURES559JOURNAL OF SENSORY STUDIES560Food Additives and Contaminants Part A-Chemistry Analysis Control Exposure & Risk A 561Journal of Biomedical Semantics562BELL LABS TECHNICAL JOURNAL Y563ADSORPTION-JOURNAL OF THE INTERNATIONAL ADSORPTION SOCIETY 564JOURNAL OF NON-CRYSTALLINE SOLIDS565Flexible Services and Manufacturing Journal566JOURNAL OF CELLULAR PLASTICS567Journal of Display Technology568MINERALS ENGINEERING569COMPUTERS AND GEOTECHNICS570MACROMOLECULAR THEORY AND SIMULATIONS571Journal of the Association for Information Systems572Energy Strategy Reviews573Automated Software Engineering574PRECISION ENGINEERING-JOURNAL OF THE INTERNATIONAL SOCIETIES FOR 575Progress in Natural Science-Materials International576COMPUTERS IN INDUSTRY577IEEE Transactions on Systems Man Cybernetics-Systems578IEEE Journal on Emerging and Selected Topics in Circuits and Systems579Analytical Methods580INTERNATIONAL JOURNAL OF HUMAN-COMPUTER STUDIES581JOURNAL OF VIBRATION AND CONTROL582APPLIED ERGONOMICS583Journal of Real-Time Image Processing584MEASUREMENT585IEEE JOURNAL OF QUANTUM ELECTRONICS586Electronic Commerce Research and Applications587NEURAL COMPUTING & APPLICATIONS588Fuel Cells589GOLD BULLETIN590CANADIAN JOURNAL OF REMOTE SENSING591International Journal of Simulation Modelling592COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING 593ECS Electrochemistry Letters Y594COMPUTERS & SECURITY595JOURNAL OF POLYMERS AND THE ENVIRONMENT596MICROSCOPY AND MICROANALYSIS597KNOWLEDGE AND INFORMATION SYSTEMS598POLYMERS FOR ADVANCED TECHNOLOGIES599JOURNAL OF WIND ENGINEERING AND INDUSTRIAL AERODYNAMICS600Journal of Bionic Engineering601INTERNATIONAL JOURNAL FOR NUMERICAL AND ANALYTICAL METHODS I 602Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discover Y603Crystals604MACHINE LEARNING605Physical Mesomechanics606MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING607JOURNAL OF COMPUTING IN CIVIL ENGINEERING608TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY609JOURNAL OF VACUUM SCIENCE & TECHNOLOGY A610SCIENCE AND TECHNOLOGY OF WELDING AND JOINING611JOM612Personal and Ubiquitous Computing613IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS614THIN SOLID FILMS615TRIBOLOGY LETTERS616JOURNAL OF MATERIALS SCIENCE-MATERIALS IN ELECTRONICS617SOFT COMPUTING618COMBUSTION THEORY AND MODELLING619METALLURGICAL AND MATERIALS TRANSACTIONS A-PHYSICAL METALLUR 620DRYING TECHNOLOGY621Carbon Letters622Food Security623MATERIALS AND MANUFACTURING PROCESSES624RESEARCH IN ENGINEERING DESIGN625POLYMER SCIENCE SERIES C Y626INFORMATION AND SOFTWARE TECHNOLOGY627SIGNAL PROCESSING-IMAGE COMMUNICATION628FLAVOUR AND FRAGRANCE JOURNAL629JOURNAL OF MICROMECHANICS AND MICROENGINEERING630TRANSPORT IN POROUS MEDIA631METALS AND MATERIALS INTERNATIONAL632JOURNAL OF BIOMATERIALS SCIENCE-POLYMER EDITION633GRANULAR MATTER634Journal of Nanomaterials635MEDICAL ENGINEERING & PHYSICS636JOURNAL OF MECHANICAL DESIGN637INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 638JOURNAL OF BIOACTIVE AND COMPATIBLE POLYMERS639JOURNAL OF STRUCTURAL ENGINEERING640International Journal of Mechanics and Materials in Design641International Journal of Engine Research642JOURNAL OF SYSTEMS AND SOFTWARE643Computer Networks644INTERNATIONAL JOURNAL OF HEAT AND FLUID FLOW645APPLIED SPECTROSCOPY646JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH647ACM TRANSACTIONS ON SOFTWARE ENGINEERING AND METHODOLOGY 648IEEE MICROWAVE AND WIRELESS COMPONENTS LETTERS649JOURNAL OF ENERGY ENGINEERING650Journal of Photonics for Energy651International Journal of Social Robotics652JOURNAL OF FOOD SCIENCE653ACM Transactions on Sensor Networks654FLOW TURBULENCE AND COMBUSTION655Biomedical Signal Processing and Control656HOLZFORSCHUNG657IET Generation Transmission & Distribution658Lighting Research & Technology659PATTERN RECOGNITION LETTERS660IEEE JOURNAL OF OCEANIC ENGINEERING661CALPHAD-COMPUTER COUPLING OF PHASE DIAGRAMS AND THERMOCHEM 662APPLIED BIOCHEMISTRY AND BIOTECHNOLOGY663PROPELLANTS EXPLOSIVES PYROTECHNICS664JOURNAL OF HYDRAULIC ENGINEERING665ACM TRANSACTIONS ON PROGRAMMING LANGUAGES AND SYSTEMS666EXPERIMENTS IN FLUIDS667WOOD SCIENCE AND TECHNOLOGY668Cluster Computing-The Journal of Networks Software Tools and Applications669Recent Patents on Nanotechnology670MARINE STRUCTURES671INFORMATION PROCESSING & MANAGEMENT672JOURNAL OF THE ACM673JOURNAL OF CONSTRUCTIONAL STEEL RESEARCH674INFORMATION SYSTEMS FRONTIERS675BIOLOGICAL CYBERNETICS676JOURNAL OF HEAT TRANSFER-TRANSACTIONS OF THE ASME677PHYSICA STATUS SOLIDI A-APPLICATIONS AND MATERIALS SCIENCE678DIGITAL SIGNAL PROCESSING679STARCH-STARKE680APPLIED OPTICS681INTERNATIONAL JOURNAL OF REMOTE SENSING682JOURNAL OF MATHEMATICAL IMAGING AND VISION683IEEE-ACM Transactions on Computational Biology and Bioinformatics684IEEE Power & Energy Magazine685ECS Journal of Solid State Science and Technology686JOURNAL OF SURFACTANTS AND DETERGENTS687COMPUTER SPEECH AND LANGUAGE688ENGINEERING ANALYSIS WITH BOUNDARY ELEMENTS689COLD REGIONS SCIENCE AND TECHNOLOGY690WORLD JOURNAL OF MICROBIOLOGY & BIOTECHNOLOGY691JOURNAL OF WOOD CHEMISTRY AND TECHNOLOGY692PHILOSOPHICAL MAGAZINE693BIOTECHNOLOGY LETTERS694SPE JOURNAL695Progress in Electromagnetics Research-PIER696VACUUM697JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING698Food Biophysics699JOURNAL OF CULTURAL 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中国计算机学会推荐国际学术会议和期刊目录（2019年）中国计算机学会中国计算机学会推荐国际学术期刊（计算机体系结构/并行与分布计算/存储系统）一、A类序号刊物简称刊物全称出版社网址1TOCS ACM Transactions on Computer Systems ACM http://dblp.uni-trier.de/db/journals/tocs/ 2TOS ACM Transactions on Storage ACM http://dblp.uni-trier.de/db/journals/tos/3TCAD IEEE Transactions on Computer-Aided Design ofIntegrated Circuits And System IEEE http://dblp.uni-trier.de/db/journals/tcad/4TC IEEE Transactions on Computers IEEE http://dblp.uni-trier.de/db/journals/tc/index.html 5TPDS IEEE Transactions on Parallel and Distributed Systems IEEE http://dblp.uni-trier.de/db/journals/tpds/二、B类序号刊物简称刊物全称出版社网址1TACO ACM Transactions on Architecture and Code Optimization ACM http://dblp.uni-trier.de/db/journals/taco/2TAAS ACM Transactions on Autonomous and Adaptive Systems ACM http://dblp.uni-trier.de/db/journals/taas/3TODAES ACM Transactions on Design Automation of Electronic Systems ACM http://dblp.uni-trier.de/db/journals/todaes/4TECS ACM Transactions on Embedded Computing Systems ACM http://dblp.uni-trier.de/db/journals/tecs/5TRETS ACM Transactions on Reconfigurable Technology and Systems ACM http://dblp.uni-trier.de/db/journals/trets/6TVLSI IEEE Transactions on Very Large Scale Integration(VLSI)Systems IEEE http://dblp.uni-trier.de/db/journals/tvlsi/7JPDC Journal of Parallel and Distributed Computing Elsevier http://dblp.uni-trier.de/db/journals/jpdc/8JSA Journal of Systems Architecture:Embedded Software Design Elsevier http://dblp.uni-trier.de/db/journals/jsa/9PARCO Parallel Computing Elsevier http://dblp.uni-trier.de/db/conf/parco/10Performance Evaluation:An International Journal Elsevier /performance-evaluation/三、C类序号刊物简称刊物全称出版社网址1JETC ACM Journal on Emerging Technologies inComputing SystemsACM http://dblp.uni-trier.de/db/journals/jetc/2Concurrency and Computation:Practice andExperienceWiley http://dblp.uni-trier.de/db/journals/concurrency/3DC Distributed Computing Springer http://dblp.uni-trier.de/db/journals/dc/4FGCS Future Generation Computer Systems Elsevier http://dblp.uni-trier.de/db/journals/fgcs/5TCC IEEE Transactions on Cloud Computing IEEE https://dblp.uni-trier.de/db/journals/tcc/6Integration Integration,the VLSI Journal Elsevier http://dblp.uni-trier.de/db/journals/integration/7JETTA Journal of Electronic Testing-Theory andApplicationsSpringer /journal/108368JGC Journal of Grid computing Springer https://dblp.uni-trier.de/db/journals/grid/9MICPRO Microprocessors and Microsystems:EmbeddedHardware DesignElsevier http://dblp.uni-trier.de/db/journals/mam/10RTS Real-Time Systems Springer http://dblp.uni-trier.de/db/journals/rts/ 11TJSC The Journal of Supercomputing Springer http://dblp.uni-trier.de/db/journals/tjs/中国计算机学会推荐国际学术会议计算机体系结构/并行与分布计算/存储系统一、A类序号会议简称会议全称出版社网址1PPoPP ACM SIGPLAN Symposium on Principles&Practice of Parallel ProgrammingACM http://dblp.uni-trier.de/db/conf/ppopp/2FAST Conference on File and Storage Technologies USENIX http://dblp.uni-trier.de/db/conf/fast/ 3DAC Design Automation Conference ACM https://dblp.uni-trier.de/db/conf/dac/ 4HPCA High Performance Computer Architecture IEEE http://dblp.uni-trier.de/db/conf/hpca/5MICRO IEEE/ACM International Symposium onMicroarchitectureIEEE/ACM https://dblp.uni-trier.de/db/conf/micro/6SC International Conference for High PerformanceComputing,Networking,Storage,and AnalysisIEEE http://dblp.uni-trier.de/db/conf/sc/7ASPLOSInternational Conference on Architectural Supportfor Programming Languages and Operating SystemsACM http://dblp.uni-trier.de/db/conf/asplos/8ISCA International Symposium on Computer Architecture ACM/IEEE http://dblp.uni-trier.de/db/conf/isca/9USENIX ATC USENIX Annul Technical Conference USENIX http://dblp.uni-trier.de/db/conf/usenix/index.html二、B类序号会议简称会议全称出版社网址1SoCC ACM Symposium on Cloud Computing ACM http://dblp.uni-trier.de/db/conf/cloud/ 2SPAA ACM Symposium on Parallelism in Algorithms and Architectures ACM http://dblp.uni-trier.de/db/conf/spaa/ 3PODC ACM Symposium on Principles of Distributed Computing ACM http://dblp.uni-trier.de/db/conf/podc/4FPGA ACM/SIGDA International Symposium on Field-ProgrammableGate ArraysACM http://dblp.uni-trier.de/db/conf/fpga/5CGO Code Generation and Optimization IEEE/ACM http://dblp.uni-trier.de/db/conf/cgo/ 6DATE Design,Automation&Test in Europe IEEE/ACM http://dblp.uni-trier.de/db/conf/date/ 7EuroSys European Conference on Computer Systems ACM http://dblp.uni-trier.de/db/conf/eurosys/ 8HOT CHIPS Hot Chips:A Symposium on High Performance Chips IEEE /9CLUSTER IEEE International Conference on Cluster Computing IEEE https://dblp.uni-trier.de/db/conf/cluster/ 10ICCD International Conference on Computer Design IEEE http://dblp.uni-trier.de/db/conf/iccd/ 11ICCAD International Conference on Computer-Aided Design IEEE/ACM http://dblp.uni-trier.de/db/conf/iccad/ 12ICDCS International Conference on Distributed Computing Systems IEEE http://dblp.uni-trier.de/db/conf/icdcs/13CODES+ISSS International Conference on Hardware/Software Co-design andSystem SynthesisACM/IEEE http://dblp.uni-trier.de/db/conf/codes/index.html14HiPEAC International Conference on High Performance and EmbeddedArchitectures and CompilersACM http://dblp.uni-trier.de/db/conf/hipeac/15SIGMETRICS International Conference on Measurement and Modeling ofComputer SystemsACM http://dblp.uni-trier.de/db/conf/sigmetrics/16PACT International Conference on Parallel Architectures andCompilation TechniquesIEEE/ACM http://dblp.uni-trier.de/db/conf/IEEEpact/17ICPP International Conference on Parallel Processing IEEE http://dblp.uni-trier.de/db/conf/icpp/ 18ICS International Conference on Supercomputing ACM http://dblp.uni-trier.de/db/conf/ics/ 19VEE International Conference on Virtual Execution Environments ACM http://dblp.uni-trier.de/db/conf/vee/ 20IPDPS International Parallel&Distributed Processing Symposium IEEE http://dblp.uni-trier.de/db/conf/ipps/21Performance International Symposium on Computer Performance Modeling,Measurements and EvaluationACM http://dblp.uni-trier.de/db/conf/performance/22HPDC International Symposium on High Performance DistributedComputingIEEE http://dblp.uni-trier.de/db/conf/hpdc/23ITC International Test Conference IEEE http://dblp.uni-trier.de/db/conf/itc/ 24LISA Large Installation System Administration Conference USENIX http://dblp.uni-trier.de/db/conf/lisa/ 25MSST Mass Storage Systems and Technologies IEEE http://dblp.uni-trier.de/db/conf/mss/26RTAS Real-Time and Embedded Technology and ApplicationsSymposiumIEEE http://dblp.uni-trier.de/db/conf/rtas/三、C类序号会议简称会议全称出版社网址1CF ACM International Conference on Computing Frontiers ACM http://dblp.uni-trier.de/db/conf/cf 2SYSTOR ACM International Systems and Storage Conference ACM http://dblp.uni-trier.de/db/conf/systor/index.html 3NOCS ACM/IEEE International Symposium on Networks-on-Chip ACM/IEEE http://dblp.uni-trier.de/db/conf/nocs 4ASAP Application-Specific Systems,Architectures,and Processors IEEE http://dblp.uni-trier.de/db/conf/asap 5ASP-DAC Asia and South Pacific Design Automation Conference ACM/IEEE http://dblp.uni-trier.de/db/conf/aspdac 6Euro-Par European Conference on Parallel and Distributed Computing Springer http://dblp.uni-trier.de/db/conf/europar/ 7ETS European Test Symposium IEEE http://dblp.uni-trier.de/db/conf/ets/ 8FPL Field Programmable Logic and Applications IEEE http://dblp.uni-trier.de/db/conf/fpl/ 9FCCM Field-Programmable Custom Computing Machines IEEE http://dblp.uni-trier.de/db/conf/fccm/10GLSVLSI Great Lakes Symposium on VLSI ACM/IEEE http://dblp.uni-trier.de/db/conf/glvlsi/11ATS IEEE Asian Test Symposium IEEE http://dblp.uni-trier.de/db/conf/ats/12HPCC IEEE International Conference on High PerformanceComputing and CommunicationsIEEE http://dblp.uni-trier.de/db/conf/hpcc/13HiPC IEEE International Conference on High PerformanceComputing,Data and AnalyticsIEEE/ACM http://dblp.uni-trier.de/db/conf/hipc/index.html14MASCOTS IEEE International Symposium on Modeling,Analysis,andSimulation of Computer and Telecommunication SystemsIEEE http://dblp.uni-trier.de/db/conf/mascots/15ISPA IEEE International Symposium on Parallel and DistributedProcessing with ApplicationsIEEE http://dblp.uni-trier.de/db/conf/ispa/16CCGRID IEEE/ACM International Symposium on Cluster,Cloud andGrid ComputingACM/IEEE http://dblp.uni-trier.de/db/conf/ccgrid/17NPC IFIP International Conference on Network and ParallelComputingSpringer http://dblp.uni-trier.de/db/conf/npc/18ICA3PP International Conference on Algorithms and Architectures forParallel ProcessingIEEE http://dblp.uni-trier.de/db/conf/ica3pp/19CASES International Conference on Compilers,Architectures,andSynthesis for Embedded SystemsACM http://dblp.uni-trier.de/db/conf/cases/index.html20FPT International Conference on Field-Programmable Technology IEEE http://dblp.uni-trier.de/db/conf/fpt/ 21ICPADS International Conference on Parallel and Distributed Systems IEEE http://dblp.uni-trier.de/db/conf/icpads/ 22ISCAS International Symposium on Circuits and Systems IEEE http://dblp.uni-trier.de/db/conf/iscas/23ISLPED International Symposium on Low Power Electronics andDesignACM/IEEE http://dblp.uni-trier.de/db/conf/islped/24ISPD International Symposium on Physical Design ACM http://dblp.uni-trier.de/db/conf/ispd/ 25HotI Symposium on High-Performance Interconnects IEEE http://dblp.uni-trier.de/db/conf/hoti/ 26VTS VLSI Test Symposium IEEE http://dblp.uni-trier.de/db/conf/vts/中国计算机学会推荐国际学术期刊（计算机网络）一、A类序号刊物简称刊物全称出版社网址1JSAC IEEE Journal of Selected Areas in Communications IEEE http://dblp.uni-trier.de/db/journals/jsac/ 2TMC IEEE Transactions on Mobile Computing IEEE http://dblp.uni-trier.de/db/journals/tmc/ 3TON IEEE/ACM Transactions on Networking IEEE,ACM http://dblp.uni-trier.de/db/journals/ton/二、B类序号刊物简称刊物全称出版社网址1TOIT ACM Transactions on Internet Technology ACM http://dblp.uni-trier.de/db/journals/toit/2TOMCCAP ACM Transactions on Multimedia Computing,Communications and ApplicationsACM http://dblp.uni-trier.de/db/journals/tomccap/3TOSN ACM Transactions on Sensor Networks ACM http://dblp.uni-trier.de/db/journals/tosn/ 4CN Computer Networks Elsevier http://dblp.uni-trier.de/db/journals/cn/ 5TCOM IEEE Transactions on Communications IEEE http://dblp.uni-trier.de/db/journals/tcom/ 6TWC IEEE Transactions on Wireless Communications IEEE http://dblp.uni-trier.de/db/journals/twc/三、C类序号刊物简称刊物全称出版社网址1Ad Hoc Networks Elsevier http://dblp.uni-trier.de/db/journals/adhoc/ 2CC Computer Communications Elsevier http://dblp.uni-trier.de/db/journals/comcom/3TNSM IEEE Transactions on Network and ServiceManagementIEEE http://dblp.uni-trier.de/db/journals/tnsm/4IET Communications IET http://dblp.uni-trier.de/db/journals/iet-com/ 5JNCA Journal of Network and Computer Applications Elsevier http://dblp.uni-trier.de/db/journals/jnca/ 6MONET Mobile Networks&Applications Springer http://dblp.uni-trier.de/db/journals/monet/ 7Networks Wiley http://dblp.uni-trier.de/db/journals/networks/ 8PPNA Peer-to-Peer Networking and Applications Springer 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大数据专业词汇英语Key Terminology in Big Data Analytics.In the realm of big data analytics, a comprehensive understanding of key terminology is paramount toeffectively navigate and harness the vast sea of data.Here's a glossary of essential terms that will empower youto engage confidently in big data discussions and endeavors:Data Analytics: The systematic examination and interpretation of data to extract meaningful insights and patterns.Hadoop: An open-source software framework thatfacilitates distributed data processing, enabling the efficient handling of vast datasets across clusters of computers.Cloud Computing: A model for delivering computing services, including servers, storage, databases, networking,software, analytics, and intelligence, over the internet ("the cloud") to offer flexible and scalable access to computing resources.Data Lake: A centralized repository for storing vast volumes of raw, unstructured data in its native format, enabling flexible exploration and analysis.Data Warehouse: A structured repository of data, typically consisting of historical data, organized and optimized for querying and reporting purposes.Data Mining: The process of extracting hidden patterns and insights from large datasets through automated or semi-automated techniques.Machine Learning: A subset of artificial intelligence that enables computers to learn from data without explicit programming by identifying patterns and making predictions.Artificial Intelligence (AI): The simulation of human intelligence processes by machines, encompassing learning,reasoning, and problem-solving capabilities.NoSQL: A non-relational database management system designed to handle large volumes of unstructured or semi-structured data, offering flexibility and scalability.Hadoop Distributed File System (HDFS): A distributed file system that enables the storage of large data files across multiple commodity servers, providing fault tolerance and high availability.MapReduce: A programming model for processing and generating large datasets that is used in conjunction with Hadoop, where data is processed in parallel and aggregated to produce the final result.Business Intelligence (BI): A set of techniques and technologies used to transform raw data into meaningful and actionable information for business decision-making.Apache Spark: A fast and versatile open-source distributed computing engine that supports a wide range ofbig data processing tasks, including real-time stream processing.Extract, Transform, Load (ETL): The process of extracting data from disparate sources, transforming itinto a consistent format, and loading it into a target system for analysis.Data Governance: The policies, processes, and practices that ensure the reliability, integrity, and security of data throughout its lifecycle.Data Visualization: The graphical representation of data to facilitate the identification of patterns, trends, and insights.Data Scientist: A professional who possesses expertise in data analysis, machine learning, and statistical modeling, responsible for extracting insights and building predictive models from large datasets.Big Data: A term used to describe extremely large andcomplex datasets that traditional data processing softwareis inadequate to handle.Data Quality: The degree to which data conforms to predefined standards of completeness, accuracy, consistency, timeliness, and validity.Data Security: The measures and practices implementedto protect data from unauthorized access, use, disclosure, disruption, modification, or destruction.Open Data: Data that is made freely available to the public without any copyright, patent, or other restrictions, promoting transparency and innovation.Data Privacy: The regulations and ethicalconsiderations governing the collection, storage, use, and disclosure of personal data to protect individuals' privacy rights.Data Curation: The selection, acquisition, preservation, and documentation of data to ensure its availability,usability, and authenticity over time.Data Lakehouse: A unified data management platform that combines the scalability and flexibility of a data lakewith the structure and governance of a data warehouse, enabling both operational and analytical workloads.Modern Data Stack: A collection of cloud-based toolsand technologies that facilitate the collection, storage, transformation, and analysis of big data in a scalable and cost-effective manner.Data Fabric: An architectural approach that enables the integration and interoperability of data across diverse systems and environments to provide a unified andconsistent data experience.By understanding these key terms, you'll be well-equipped to navigate the ever-evolving world of big data analytics and leverage its transformative potential todrive informed decisions and achieve organizational success.。

建模算法（Modeling algorithm）1. Monte Carlo algorithm. The algorithm is also called random simulation algorithm, which is the algorithm to solve the problem through computer simulation. At the same time, it can verify the correctness of the model by simulation. It is almost the method that must be used in the game.2. data processing algorithms, such as data fitting, parameter estimation, interpolation, etc.. Games usually encounter a lot of data that needs to be processed, and the key to data processing is that these algorithms usually use MATLAB as a tool.3. linear programming, integer programming, multiple planning, two programming and other planning algorithms. Most of the problems in the modeling contest belong to optimization problems. In many cases, these problems can be described by mathematical programming algorithms, and usually solved by Lindo and Lingo software.4. graph theory algorithm. This algorithm can be divided into many kinds, including the shortest path, network flow, two points diagram and other algorithms, involving the graph theory problems can be solved by these methods, need to seriously prepare.5. computer algorithms, such as dynamic programming, backtracking search, divide and conquer algorithm, branch and bound algorithm. These algorithms are commonly used in the algorithm design, competition will be used in many occasions.6. non classical algorithms of optimization theory: simulated annealing algorithm, neural network algorithm, and genetic algorithm (three). These problems are used to solve some difficult optimization problems, which are very helpful for some problems, but the implementation of the algorithm is difficult and needs careful use.7. mesh algorithm and exhaustive method. Both are the most violent search algorithms have advantages, applications in many competitions, when focus on the model itself and despise the algorithm, you can use this violence program, it is best to use some advanced language as programming tool.8. some discretization methods for continuous data. Many problems are actual, data can be continuous, and the computer can only deal with discrete data, so the discrete difference, instead of differential summation instead of integral thought is very important.9. numerical analysis algorithm. If you use high-level language programming in the game, those commonly used algorithms in numerical analysis, such as equations solving, matrix calculation, function integration algorithm, you need to write additional library functions to call.10. image processing algorithm. Cup title has about a class of problems with graphics, graphics and even if the problem has nothing to do, we also will need pictures to illustrate the problem, these figures show how and how to deal with is the need to solve the problem, usually use MATLAB for processing.The following will be combined with competition issues over the years, the ten types of algorithms are described in detail.The following will be combined with competition issues over the years, the ten types of algorithms are described in detail.A detailed description of the 20 algorithms2.1 Monte Carlo algorithmMost modeling problems can not be separated from computer simulation, random simulation is one of the most common algorithms.One example is the 97 year A title, each part has its own calibration value, also have their own tolerance level, while the optimal combination scheme will face is a very complicated formula and 108 kinds of tolerance selection, it is impossible to obtain analytical solutions, then how to find the best solution? Stochastic simulation is a method to search the optimal solution in the feasible interval of each parts in accordance with the normal distribution of random selection of a calibration value and selects a tolerance value as a solution, and then through the Monte Carlo simulation algorithm of a large number of programs, from selecting a best. Another example is the last of the lottery second Q, needs to design a better solution, the first scheme depends on many complicated factors, the same can not describe for a model that can only rely on random simulation.2.2 data fitting, parameter estimation, interpolation andother algorithmsData fitting is used in many questions, many problems associated with graphics processing and fitting relationship, is an example of 98 years in the United States A title game, 3D interpolation of biological tissue sections, the 94 title in A altitude of the mountain cut paths through mountains, interpolation calculation,There is also a lot of noise, may be the "SARS" problem also need to use the data fitting algorithm, observe the trend of the data processing. Such problems in MATLAB have many ready-made functions can be called, familiar with MATLAB, these methods can be used with ease and ease.2.3 programming class problem algorithmThe competition there are many problems and mathematical programming, can be said that many of the models can be reduced to a set of inequality constraints, as some function as the objective function of the problem, meet this kind of problem solving is the key, for example, 98 years of B problem, with a lot of different type can describe clearly, to more convenient solution with Lindo and Lingo software, so the listing plan, so also need to be familiar with the two software.2.4 graph theory problem98 years, B 00 years B, 95 years of packing locks problems reflect the importance of the problem of graph theory, there are many algorithms for this problem include: Dijkstra, Floyd,Prim, Bellman-Ford, maximum flow, two points, etc.. Each algorithm should be implemented once, otherwise it will be written late in the game.2.5 problems in the design of computer algorithmsComputer algorithm design includes many contents: dynamic programming, backtracking search, divide and conquer algorithm, branch and bound. For example, 92 year B problem using branch and bound method, 97 year B problem is a typical dynamic programming problem, in addition, 98 year B problem reflects the divide and conquer algorithm. This problem is similar to the problem in the ACM programming contest. It is recommended to look at the book "computer algorithm design and analysis" (Electronic Industry Press) and other computer related books.Three non classical algorithms of 2.6 optimization theoryThe optimization theory has developed rapidly in the past ten years. The three algorithms, simulated annealing, neural network and genetic algorithm, are developing very fast. In recent years, the competition is more and more complex, not what good model for many problems we can learn, so these three kinds of algorithm many times can come in handy, for example: 97 years of simulated annealing algorithm A problem, neural network classification algorithm B problem for 00 years, 01 years as B problem this problem can also be the use of neural network, and the competition for 89 years A questions and BP algorithms, was just 86 years of proposed BP algorithm, 89 years passed, that tournament title may be abstract reflect today'scutting-edge technology. 03 years B gamma knife is a researchtopic, the current algorithm is the best genetic algorithm.2.7 mesh algorithm and exhaustive algorithmJust like the exhaustion method, the mesh method is only the exhaustive problem of the continuous problem. For example, the optimization problem in the case of N variables, then the space for picking these variables, for example, in the [a; b] interval, take M +1 points, that is, a; a+ (B-A) /M; a+2 (B-A) /M;...... B, then this cycle requires (M + 1) N times operation, so the amount of calculation is great. For example, 97 year A problem, 99 year B problem can be searched by grid method, this method is best in the operation speed is fasterIn the computer, but also to use high-level language to do, it is best not to use MATLAB as a grid, otherwise it will be long. Exhaustive method is familiar to everyone, do not say.2.8 some discretization methods for continuous dataMost of the programming of physics problems are related to this method. The physics problem reflects that we live in a continuous world. The computer can only deal with the discrete quantity, so it is necessary to discretize the continuous quantity. This method is widely used and is related to many algorithms above. In fact, the grid algorithm, the Monte Carlo algorithm and the simulated annealing use this idea.2.9 numerical analysis algorithmThis algorithm is specially designed for advanced languages.If you use MATLAB, Mathematica, you don't need to prepare, because there are many functions in numerical analysis, such as general mathematical software.2.10 image processing algorithmIn the 01 year A question, you need to read the BMP image and the 98 year A question of the American tournament. You need to know the 3D interpolation calculation,In the 03 year, the B question requires higher, not only the programming calculation, but also the processing, and the digital model paper also has many pictures to display, therefore the image processing is the key. It is important to learn MATLAB well, especially the part of image processing.。

Video sensor networks (VSNs) has become the recent research focus due to the rich information it provides to address various data-hungry applications. However, VSNimplementations face stringent constraints of limited communication bandwidth, processing capability, and power supply. In-network processing has been proposed as efficient means toaddress these problems. The key component of in-network processing, task mapping and scheduling problem, is investigated in this paper. Although task mapping and scheduling in wired networks of processors has been extensively studied, their application to VSNs remains largely unexplored. Existing algorithms cannot be directly implemented in VSNs due to limited resource availability and shared wireless communication medium. In this work, an application-independent task mapping and scheduling solution in multi-hop VSNs is presented that provides real-time guarantees to process video feeds. The processed data is smaller in volume which further releases the burden on the end-to-end communication. Using a novel multi-hop channel model and a communication scheduling algorithm, computation tasks and associated communication events are scheduled simultaneously with a dynamic critical-path scheduling algorithm. Dynamic voltage scaling (DVS) mechanism isimplemented to further optimize energy consumption. According to the simulation results, the proposed solution outperforms existing mechanisms in terms of guaranteeing application deadlines with minimum energy consumption.Article Outline1. Introduction2. Preliminaries2.1. Network assumptions2.2. Application and energy consumption model 2.3. Problem statement3. The proposed scheduling solution3.1. Hyper-DAG extension and multi-hop channel modeling 3.2. Communication scheduling algorithms 3.3. Scheduling with DCTMS algorithmPurchase$ 31.503.3.1. DCOS procedure 3.3.2. OSTA procedure 3.4. The DVS algorithm 4. Simulation results 4.1. Simulation parameters4.2. Simulation with a real-life application example 4.3.Simulation with randomly generated DAG 4.3.1. Effect of the application deadlines 4.3.2. Effect of the cluster size 4.3.3. Effect of the number of tasks 4.3.4. Comparison with EbTA 5. Conclusion Acknowledgements References170Evaluation of service management algorithms in adistributed web search system Original Research ArticleComputer Standards & Interfaces , Volume 29, Issue 2,February 2007, Pages 152-160Ahmed Patel, Muhammad J. KhanClose preview | Related articles | Related reference work articlesAbstract | Figures/Tables | ReferencesAbstractThe World Wide Web interconnected through the internet today offers numerous specialist topic-oriented or regional search engines and systems in a largely federated heterogeneous environment. Old ones continue to exist and new ones appear in spite of the tremendous progress achieved by their generic Web-wide rival competitors, because they produce better results in their areas of specialisation. However, finding and choosing the best specialised search engines or systems for a particular information need is difficult. This is made even more complicated by the fact that these enginesPurchase $ 31.50and systems would want to carve out a niche market that generates maximum revenue for themselves. The ADSA (Adaptive Distributed Search and Advertising) Web research project has investigated the problem at some depth and had put forward a search architecture which allows many search engines to be independently owned and controlled, offering advantages over existing centralised architectures. One aspect of the architecture has been to evaluate the service management algorithms that were designed to support competing autonomous systems in a cooperative marketplace. Here we present ADSA economic model and the service management strategies that can lead to maximum revenue generation, by making informative and intelligent decisions on search price adjustments of key quantitative parameters, as well as the results of evaluation experiments and briefly discuss the need for standardised interfaces which are required if this concept is to ease development and implementation of such a marketplace in a large scale.Article Outline1. Introduction2. ADSA economic model and pricing function2.1. The pricing function3. Evaluation3.1. Tests3.2. Performance criteria4. Experimental results5. Need for standard interfaces for administrative service management6. Conclusions and future workReferencesVitae171 Geophysical wavelet library: Applications of the continuous wavelet transform to the polarization anddispersion analysis of signals Original Research ArticleComputers & Geosciences , Volume 34, Issue 12,December 2008, Pages 1732-1752M. Kulesh, M. Holschneider, M.S. DialloClose preview | Related articles | Related reference work articlesAbstract | Figures/Tables | ReferencesAbstractIn the present paper, we consider and summarize applications of the continuous wavelet transform to 2C and 3C polarization analysis and filtering, modeling the dispersed and attenuated wave propagation in thetime –frequency domain, and estimation of the phase and group velocity and the attenuation from a seismogram. Along with a mathematical overview of each of the presented methods, we show that all these algorithms are logically combined into one software package “Geophysical Wavelet Library” developed by the authors. The novelty of this package is that we incorporate the continuous wavelet transform into the library, where the kernel is the time –frequency polarization and dispersion analysis. This library has a wide range of potential applications in the field of signal analysis and may be particularly suitable in geophysical problems that we illustrate by analyzing synthetic, geomagnetic and real seismic data.Article Outline1. Introduction2. GWL structure and implementation technology3. The CWT3.1. The direct wavelet transform 3.2. Wavelets3.3. The inverse wavelet transform 3.4. Wavelet transform of a complex signalPurchase $ 19.954. Polarization properties of two-component data and polarization filtering 4.1. Polarization analysis in the time domain 4.2. Complex trace method in the wavelet domain 4.3. Two-component polarization filter4.4. Application to the analysis of geomagnetic data5. Polarization analysis and filtering of 3C data 5.1. CWT-based polarization properties of an ellipse5.2. CWT-based polarization properties of an ellipsoid 5.3. Application to the analysis and filtering of seismic signals6. Modeling of wave propagation using a diffeomorphism in wavelet space 6.1. Linear diffeomorphism of the wavelet spectrum6.2. Describing wave dispersion and attenuation with non-linear diffeomorphism6.3. The case for non-linear frequency-dependent attenuation6.4. Modeling wave propagation in the space of polarization parameters 6.5. Parametrization of dispersion and attenuation7. An estimate of phase and group velocities and attenuation 7.1. Wavelet-based frequency –velocity analysis7.2. Inversion for the dispersion and attenuation characteristics of the medium 7.3. Application to experimental data 8. Conclusion Acknowledgements Appendix A. List of symbols References172A software framework for model predictive control with GenOpt Original Research ArticleEnergy and Buildings , Volume 42, Issue 7, July 2010, Pages 1084-1092Brian Coffey, Fariborz Haghighat, Edward Morofsky, Edward KutrowskiPurchase$ 35.95Close preview | Related articles | Related reference work articlesAbstract | Figures/Tables | ReferencesAbstractThere is a growing interest in integrated control strategies for building systems with numerous responsive elements, such as solar shading devices, thermal storage and hybrid ventilation systems, both for energy efficiency and for demand response. Model predictive control is a promising way of approaching this challenge. This paper presents a flexible software framework for model predictive control using GenOpt, along with a modified genetic algorithm developed for use within it, and applies it to a case study of demand response by zone temperature ramping in an office space. Various areas for further research and development using this framework are discussed.Article Outline1. Introduction2. Background2.1. Model predictive control in buildings research2.2. Simulation, optimization and controls software3. Framework3.1. Generic problem definition3.2. Framework overview3.3. The organizational layer3.4. Virtual testing environment4. Example algorithm4.1. Basic genetic algorithm description4.2. Using the optimization instructions file5. Example application study5.1. Demand response with zone temperature ramping5.2. Case study description 5.3. Results6.Discussion6.1. Appropriate complexity level for application studies 6.2. Improving optimization results6.3. Imperfect models, initializations and predictions6.4. Other potential research areas with SimCon7. Conclusions Acknowledgements References173Design and implementation of an environmentaldecision support systemEnvironmental Modelling & Software , Volume 16, Issue 5,July 2001, Pages 453-458W. G. Booty, D. C. L. Lam, I. W. S. Wong, P. SiconolfiClose preview | Related articles | Related reference work articlesAbstract | Figures/Tables | ReferencesAbstractAn environmental decision support system is a specific version of an environmental information system that is designed to help decision makers, managers, and advisors locate relevant information and carry out optimal solutions to problems using special tools and knowledge. The RAISON (Regional Analysis by Intelligent Systems ON microcomputers) for Windows decision support system has been developed at the National Water Research Institute, Environment Canada, over the last 10 years. It integrates data, text, maps, satellite images, pictures, video and other knowledge input. A library of software functions and tools are available for selective extraction of spatial and temporal data that can be analysed using spatial algorithms, models, statistics, expert systems, neural networks, and other information technologies. ThePurchase $ 19.95system is of a modular design which allows for flexibility in modification of the system to meet the demands of a wide range of applications. System design and practical experiences learned in the development of a decision support system for toxic chemicals in the Great Lakes of North America are discussed. Article Outline1. Introduction2. RAISON for Windows decision support system3. Great Lakes toxic chemical decision support system3.1. Databases3.1.1. Parameters3.1.2. Site_Info3.1.3. SiteDesc3.2. Modelling3.3. Neural network3.4. Expert system3.5. Optimization3.6. Data visualization4. DiscussionAcknowledgementsReferences174 DSP instrument for transient monitoring Original Research Article Computer Standards & Interfaces, Volume 33, Issue 2, February 2011, Pages 182-190Tomasz Tarasiuk, Mariusz Szweda应届生-算法工程师)|公司行业： 通信/电信运营、增值服务 计算机软件公司性质： 国企公司规模： 150-500人比比你的竞争力发布日期：2011-03-10工作地点：北京 招聘人数：2工作年限：应届毕业生 语言要求： 英语 熟练 日语 熟练学 历：硕士职位描述算法工程师招聘人数：2人工作地点：北京、上海职位描述：负责交通信息预测和海量数据分析。