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6th International Conference on computer and Technology Applications, ICCTA 2020

作者： Karmenova, Markhaba Nugumanova, Aliya Tlebaldinova, Aizhan Beldeubaev, Alisher Popova, Galina Sedchenko, Anatoliy School of Information Technology and Intelligent Systems D. Serikbayev East Kazakhstan State Technical University Ust-Kamenogorsk Kazakstan Kazakhstan Department of Computer Modeling and Information Technologies S. Amanzholov East Kazakhstan State University Ust-Kamenogorsk Kazakstan Kazakhstan Department of Computer Engineering Cukurova University Adana Turkey Head of the Modeling and Development Department BIPEK AUTO Kazakhstan JSC Ust-Kamenogorsk Kazakstan Kazakhstan

ISBN: (纸本)9781450377492

Seismic assessment of an urban environment is a time-consuming and complicated task that also requires considerable financial resources. In this paper, we suggest an approach to the seismic evaluation of urban buildings based on data mining methods. Regarding this topic, researches have been conducted on 163 typical objects with 19 different features including various types of buildings. The k-means method was used to create clusters of similar objects, it also allowed us to determine dependencies between data. More accurate clustering models were obtained using a hierarchical algorithm. The quality of the proposed methods was evaluated to ensure the reliability of the obtained results. © 2020 ACM.

关键词： Seismology

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Modelling of conflict interaction of virtual communities in social networking services on an example of anti-vaccination movement

Modelling of conflict interaction of virtual communities in ...

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2019 International Workshop on Conflict Management in Global Information Networks, CMiGIN 2019

作者： Hryshchuk, Ruslan Molodetska, Kateryna Tymonin, Yuriy Head of the Information Protection and Cybersecurity Department of Korolyov Zhytomyr Military Institute Zhytomyr Ukraine Head of Educational and Scientific Center of IT Zhytomyr National Agroecological University Zhytomyr Ukraine Department of Computer Technologies and Systems Modeling Zhytomyr National Agroecological University Ukraine

In present-day circumstances, social networking services have turned into an effective tool for influencing social and political state processes. As a result of a systematic carrying out of information operations in social networking services, various social movements, which are supported by virtual communities of actors, are appearing. An example of such movements is the negation of effectiveness, safety and legitimacy of vaccination, in particular, mass vaccination. The dissemination and support of the mentioned narrative can threaten not only the informational but also the national security of the state itself. In order to increase the effectiveness of the research on the information confrontation between vaccine proponents and opponents in social networking services, a model of conflict interaction among virtual communities has been proposed. The model includes three layers: the dynamics of the number of two conflicting virtual communities, the growth of the resources of the information space of social networking services connected with the changes in the number of actors in the community, and the dynamics of the consumption of the resources for information confrontation. The suggested approach allows to take into consideration several aspects of the conflicting interaction of virtual communities and also provides a connection between the components of the model using the boundary meaning of the first layer of the model. The simulation results can be used for developing practical recommendations for counteracting the state's information security threats existing in social networking services. Copyright © 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0) CMiGIN-2019: International Workshop on Conflict Management in Global Information Networks.

关键词： Vaccines

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Virtual testbed as a case for big data 8

Virtual testbed as a case for big data

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Selected Papers of the 8th International Conference "Distributed Computing and Grid-Technologies in Science and Education", GRID 2018

作者： Degtyarev, A.B. Bogdanov, A.V. Korkhov, V.V. Gankevich, I.G. Pylnev, Yu.V. Eibozhenko, A.V. Department of Computer Modeling and Multiprocessing Systems St. Petersburg State University 7/9 Universitetskaya Nab. St. Petersburg199034 Russia Engineering Company NEOTECH MARINE 23 Zastavskaya Str. St. Petersburg196084 Russia

Complex modeling of the behavior of marine objects under the influence of real external excitation is the most important problem At present, the accuracy of direct simulation of phenomena with knovvn physics is comparable to the accuracy of the results obtained during the model experiment in towing tanks. Particularly relevant is the creation of such marine virtual testbed for full-featured simulators and when testing the knowledge base of onboard intelligent systems. Such integrated environment is a complex information object that combines the features of both the enterprise system and the high-performance modeling tool. Integrated environment based on these basic principles is designed to solve in real time the following problems: 1. Collection and analysis of information on the current state of dynamic object (DO) and the environment, remote monitoring of the state of objects. 2. Evaluation and coordination of joint actions of DOs, proceeding from current conditions, with the aim of optimally common problem solving. 3. Centralized support for decision-making by operators of DO control in non-standard situations, organization of information support for the interaction of decisionmakers in the conduct of ongoing operations. 4. computer modeling of possible scenarios of situation development with the aim of selecting the optimal control strategy. 5. Centralized control of technical means. 6. Cataloging and accumulation of information in dynamic databases. Modern architecture of computer systems (especially GP GPU) allows direct full-featured simulation of a marine object in real time. Efficient mapping to a hybrid architecture allows even the ability to render ahead of time under various scenarios. The report discusses general concept of high-performance virtual testbed development and the experience of creating on their basis full-featured simulators. © 2018 AlexandrB. Degtyarev, Alexander V. Bogdanov, Vladimir V. Korkhov, Ivan G. Gankevich, Yury V. Pylnev, Anatoly

关键词： Decision making

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Beyond Low Earth Orbit: Biomonitoring, Artificial Intelligence, and Precision Space Health

arXiv

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arXiv 2021年

作者： Scott, Ryan T. Antonsen, Erik L. Sanders, Lauren M. Hastings, Jaden J.A. Park, Seung-Min Mackintosh, Graham Reynolds, Robert J. Hoarfrost, Adrienne L. Sawyer, Aenor Greene, Casey S. Glicksberg, Benjamin S. Theriot, Corey A. Berrios, Daniel C. Miller, Jack Babdor, Joel Barker, Richard Baranzini, Sergio E. Beheshti, Afshin Chalk, Stuart Delgado-Aparicio, Guillermo M. Haendel, Melissa Hamid, Arif A. Heller, Philip Jamieson, Daniel Jarvis, Katelyn J. Kalantari, John Khezeli, Kia Komarova, Svetlana V. Komorowski, Matthieu Kothiyal, Prachi Mahabal, Ashish Manor, Uri Martin, Hector Garcia Mason, Christopher E. Matar, Mona Mias, George I. Myers, Jerry G. Nelson, Charlotte Oribello, Jonathan Parsons-Wingerter, Patricia Prabhu, R.K. Qutub, Amina Ann Rask, Jon Saravia-Butler, Amanda Saria, Suchi Singh, Nitin Kumar Soboczenski, Frank Snyder, Michael Soman, Karthik van Valen, David Venkateswaran, Kasthuri Warren, Liz Worthey, Liz Yang, Jason H. Zitnik, Marinka Costes, Sylvain V. KBR Space Biosciences Division NASA Ames Research Center Moffett FieldCA94035 United States Department of Emergency Medicine Center for Space Medicine Baylor College of Medicine HoustonTX77030 United States Blue Marble Space Institute of Science Space Biosciences Division NASA Ames Research Center Moffett FieldCA94035 United States Department of Physiology and Biophysics Weill Cornell Medicine New YorkNY10065 United States Department of Urology Department of Radiology Stanford University School of Medicine StanfordCA94305 United States Bay Area Environmental Research Institute NASA Ames Research Center Moffett FieldCA94035 United States Mortality Research & Consulting Inc. HoustonTX77058 United States Space Biosciences Division NASA Ames Research Center Moffett FieldCA94035 United States UC Space Health Department of Orthopaedic Surgery University of California San Francisco San FranciscoCA94143 United States Center for Health AI Department of Biochemistry and Molecular Genetics University of Colorado School of Medicine Anschutz Medical Campus AuroraCO80045 United States Hasso Plattner Institute for Digital Health at Mount Sinai Department of Genetics and Genomic Sciences Icahn School of Medicine at Mount Sinai New YorkNY10065 United States Department of Preventive Medicine and Community Health UTMB GalvestonTX77551 United States Human Health and Performance Directorate NASA Johnson Space Center HoustonTX77058 United States Department of Microbiology and Immunology Department of Otolaryngology Head and Neck Surgery University of California San Francisco San FranciscoCA94143 United States The Gilroy AstroBiology Research Group The University of Wisconsin Madison MadisonWI53706 United States Weill Institute for Neurosciences Department of Neurology University of California San Francisco San FranciscoCA94158 United States Department of Chemistry University of North Florida JacksonvilleFL32224 United States Data Scienc

Human space exploration beyond low Earth orbit will involve missions of significant distance and duration. To effectively mitigate myriad space health hazards, paradigm shifts in data and space health systems are necessary to enable Earth-independence, rather than Earth-reliance. Promising developments in the fields of artificial intelligence and machine learning for biology and health can address these needs. We propose an appropriately autonomous and intelligent Precision Space Health system that will monitor, aggregate, and assess biomedical statuses;analyze and predict personalized adverse health outcomes;adapt and respond to newly accumulated data;and provide preventive, actionable, and timely insights to individual deep space crew members and iterative decision support to their crew medical officer. Here we present a summary of recommendations from a workshop organized by the National Aeronautics and Space Administration, on future applications of artificial intelligence in space biology and health. In the next decade, biomonitoring technology, biomarker science, spacecraft hardware, intelligent software, and streamlined data management must mature and be woven together into a Precision Space Health system to enable humanity to thrive in deep space. © 2021, CC BY.

关键词： Earth (planet)

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Beyond Low Earth Orbit: Biological Research, Artificial Intelligence, and Self-Driving Labs

arXiv

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arXiv 2021年

作者： Sanders, Lauren M. Yang, Jason H. Scott, Ryan T. Qutub, Amina Ann Martin, Hector Garcia Berrios, Daniel C. Hastings, Jaden J.A. Rask, Jon Mackintosh, Graham Hoarfrost, Adrienne L. Chalk, Stuart Kalantari, John Khezeli, Kia Antonsen, Erik L. Babdor, Joel Barker, Richard Baranzini, Sergio E. Beheshti, Afshin Delgado-Aparicio, Guillermo M. Glicksberg, Benjamin S. Greene, Casey S. Haendel, Melissa Hamid, Arif A. Heller, Philip Jamieson, Daniel Jarvis, Katelyn J. Komarova, Svetlana V. Komorowski, Matthieu Kothiyal, Prachi Mahabal, Ashish Manor, Uri Mason, Christopher E. Matar, Mona Mias, George I. Miller, Jack Myers, Jerry G. Nelson, Charlotte Oribello, Jonathan Park, Seung-Min Parsons-Wingerter, Patricia Prabhu, R.K. Reynolds, Robert J. Saravia-Butler, Amanda Saria, Suchi Sawyer, Aenor Singh, Nitin Kumar Soboczenski, Frank Snyder, Michael Soman, Karthik Theriot, Corey A. van Valen, David Venkateswaran, Kasthuri Warren, Liz Worthey, Liz Zitnik, Marinka Costes, Sylvain V. Blue Marble Space Institute of Science Space Biosciences Division NASA Ames Research Center Moffett Field CA94035 United States Center for Emerging and Re-Emerging Pathogens Department of Microbiology Biochemistry and Molecular Genetics Rutgers New Jersey Medical School NewarkNJ07103 United States KBR Space Biosciences Division NASA Ames Research Center Moffett Field CA94035 United States AI MATRIX Consortium Department of Biomedical Engineering University of Texas San Antonio and UT Health Sciences San AntonioTX78249 United States Biological Systems and Engineering Division Lawrence Berkeley National Lab. BerkeleyCA94608 United States DOE Agile BioFoundry EmeryvilleCA94608 United States Joint BioEnergy Institute EmeryvilleCA94608 United States Department of Physiology and Biophysics Weill Cornell Medicine New YorkNY10065 United States Office of the Center Director NASA Ames Research Center Moffett Field CA94035 United States Bay Area Environmental Research Institute NASA Ames Research Center Moffett Field CA94035 United States Space Biosciences Division NASA Ames Research Center Moffett Field CA94035 United States Department of Chemistry University of North Florida JacksonvilleFL32224 United States Center for Individualized Medicine Department of Surgery Department of Quantitative Health Sciences Mayo Clinic RochesterMN55905 United States Department of Emergency Medicine Center for Space Medicine Baylor College of Medicine HoustonTX77030 United States Department of Microbiology and Immunology Department of Otolaryngology Head and Neck Surgery University of California San Francisco San FranciscoCA94143 United States The Gilroy AstroBiology Research Group The University of Wisconsin - Madison MadisonWI53706 United States Weill Institute for Neurosciences Department of Neurology University of California San Francisco San FranciscoCA94158 United States Data Science Analytics Georgia Institute of Technology Lima15024

Space biology research aims to understand fundamental effects of spaceflight on organisms, develop foundational knowledge to support deep space exploration, and ultimately bioengineer spacecraft and habitats to stabilize the ecosystem of plants, crops, microbes, animals, and humans for sustained multi-planetary life. To advance these aims, the field leverages experiments, platforms, data, and model organisms from both spaceborne and ground-analog studies. As research is extended beyond low Earth orbit, experiments and platforms must be maximally autonomous, light, agile, and intelligent to expedite knowledge discovery. Here we present a summary of recommendations from a workshop organized by the National Aeronautics and Space Administration on artificial intelligence, machine learning, and modeling applications which offer key solutions toward these space biology challenges. In the next decade, the synthesis of artificial intelligence into the field of space biology will deepen the biological understanding of spaceflight effects, facilitate predictive modeling and analytics, support maximally autonomous and reproducible experiments, and efficiently manage spaceborne data and metadata, all with the goal to enable life to thrive in deep space. © 2021, CC BY.

关键词： Predictive analytics

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Author Correction: The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data

引用

Scientific data 2021年第1期8卷 72页

作者： Gilberto Pastorello Carlo Trotta Eleonora Canfora Housen Chu Danielle Christianson You-Wei Cheah Cristina Poindexter Jiquan Chen Abdelrahman Elbashandy Marty Humphrey Peter Isaac Diego Polidori Markus Reichstein Alessio Ribeca Catharine van Ingen Nicolas Vuichard Leiming Zhang Brian Amiro Christof Ammann M Altaf Arain Jonas Ardö Timothy Arkebauer Stefan K Arndt Nicola Arriga Marc Aubinet Mika Aurela Dennis Baldocchi Alan Barr Eric Beamesderfer Luca Belelli Marchesini Onil Bergeron Jason Beringer Christian Bernhofer Daniel Berveiller Dave Billesbach Thomas Andrew Black Peter D Blanken Gil Bohrer Julia Boike Paul V Bolstad Damien Bonal Jean-Marc Bonnefond David R Bowling Rosvel Bracho Jason Brodeur Christian Brümmer Nina Buchmann Benoit Burban Sean P Burns Pauline Buysse Peter Cale Mauro Cavagna Pierre Cellier Shiping Chen Isaac Chini Torben R Christensen James Cleverly Alessio Collalti Claudia Consalvo Bruce D Cook David Cook Carole Coursolle Edoardo Cremonese Peter S Curtis Ettore D'Andrea Humberto da Rocha Xiaoqin Dai Kenneth J Davis Bruno De Cinti Agnes de Grandcourt Anne De Ligne Raimundo C De Oliveira Nicolas Delpierre Ankur R Desai Carlos Marcelo Di Bella Paul di Tommasi Han Dolman Francisco Domingo Gang Dong Sabina Dore Pierpaolo Duce Eric Dufrêne Allison Dunn Jiří Dušek Derek Eamus Uwe Eichelmann Hatim Abdalla M ElKhidir Werner Eugster Cacilia M Ewenz Brent Ewers Daniela Famulari Silvano Fares Iris Feigenwinter Andrew Feitz Rasmus Fensholt Gianluca Filippa Marc Fischer John Frank Marta Galvagno Mana Gharun Damiano Gianelle Bert Gielen Beniamino Gioli Anatoly Gitelson Ignacio Goded Mathias Goeckede Allen H Goldstein Christopher M Gough Michael L Goulden Alexander Graf Anne Griebel Carsten Gruening Thomas Grünwald Albin Hammerle Shijie Han Xingguo Han Birger Ulf Hansen Chad Hanson Juha Hatakka Yongtao He Markus Hehn Bernard Heinesch Nina Hinko-Najera Lukas Hörtnagl Lindsay Hutley Andreas Ibrom Hiroki Ikawa Marcin Jackowicz-Korczynski Dalibor Janouš Wilma Jans Rachhpal Jassal Shicheng Jiang Tomomichi Kato Myr Computational Research Division Lawrence Berkeley National Laboratory Berkeley CA 94720 USA. gzpastorello@lbl.gov. DIBAF University of Tuscia Viterbo 01100 Italy. Euro-Mediterranean Centre on Climate Change Foundation (CMCC) Lecce 73100 Italy. Climate & Ecosystem Sciences Division Lawrence Berkeley National Laboratory Berkeley CA 94720 USA. Computational Research Division Lawrence Berkeley National Laboratory Berkeley CA 94720 USA. Department of Civil Engineering California State University Sacramento CA 95819 USA. Department of Geography Environment and Spatial Sciences Michigan State University East Lansing MI 48823 USA. Department of Computer Science University of Virginia Charlottesville VA 22904 USA. TERN Ecosystem Processes Menzies Creek VIC3159 Australia. Max Planck Institute for Biogeochemistry Jena 07701 Germany. Laboratoire des Sciences du Climat et de l'Environnement (LSCE) CEA CNRS UVSQ UPSACLAY Gif sur Yvette 91190 France. Key Laboratory of Ecosystem Network Observation and Modeling Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing 100101 China. Department of Soil Science University of Manitoba Winnipeg MB R3T2N2 Canada. Department of Agroecology and Environment Agroscope Research Institute Zürich 8046 Switzerland. School of Geography and Earth Sciences McMaster University L8S4K1 Hamilton ON Canada. Department of Physical Geography and Ecosystem Science Lund University Lund 22362 Sweden. Department of Agronomy and Horticulture University of Nebraska-Lincoln Lincoln NE 68583 USA. School of Ecosystem and Forest Sciences The University of Melbourne Richmond VIC3121 Australia. Department of Biology Research Group PLECO University of Antwerp Antwerp 2610 Belgium. Joint Research Centre European Commission Ispra 21027 Italy. TERRA Teaching and Research Center University of Liege Gembloux B-5030 Belgium. Finnish Meteorological Institute Helsinki 00560 Finland. ES

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High order aberration correction

High order aberration correction

引用

10th European Particle Accelerator Conference, EPAC 2006

作者： Andrianov, Serge Chechenin, Alexey Department Computer Modeling and Multiprocessing Systems St. Petersburg State University 35 University Avenue 198504 St.-Petersburg Russia Forschungszentrum Juelich Germany

ISBN: (纸本)9290832789

It is known that modern accelerators fall under nonlinear aberrations influence. The most of these aberrations have harmful character, and their effect must be maximally decreased. There are a set of approaches and codes to solving this problem. In this paper, we consider an approach for solving this problem using the matrix formalism for Lie algebraic tools. This formalism allows reducing the starting problem to linear algebraic equations for aberration coefficients, which are elements of corresponding matrices. There are discussed results evaluated using suggested approach and nonlinear programming tools. Some examples of corresponding results are given.

关键词： Nonlinear programming

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Normal form for beam physics in matrix representation

Normal form for beam physics in matrix representation

引用

10th European Particle Accelerator Conference, EPAC 2006

作者： Andrianov, Serge Chechenin, Alexey Department of Computer Modeling and Multiprocessing Systems St. Petersburg State University 35 University avenue 198504 St.-Petersburg Russia Forschungszentrum Juelich Germany

ISBN: (纸本)9290832789

The modeling of long beam evolution dynamics in nonlinear accelerator structures has raised new interest in the effective methods of nonlinear effects calculation. Moreover, it is preferably to use both analytical tools and numerical methods for evolution modeling. Usually the standard numerical methods and computer codes are based on the concept of symplectic transfer maps, whereas the analytical tool is the theory of normal forms. The method of normal forms can be realized in symbolic and numerical modes easily enough. In this paper, we discuss the normal form theory based on the matrix formalism for Lie algebraic tools. This approach allows using well known methods of matrix algebra. This permits to compute necessary matrices step-by-step up to desired order of approximation. This procedure leads to more simple structure of matrix representation for very complicated structure of this map does not allow using this map for practical computing. Therefore, it is necessary to transform this map in more appropriate form. In another words the new matrix representation for the map is particularly simple and has explicit invariants and symmetries. Some examples of corresponding results are given.

关键词： Numerical methods

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Some approaches to long time beam evolution

Some approaches to long time beam evolution

引用

2005 International Conference on Physics and Control, PhysCon 2005

作者： Andrianov, Serge N. Computer Modeling and Multiprocessing Systems Department St.-Petersburg State University 35 University avenue 198504 St.-Petersburg Russia

Some problems of long-time evolution of particle beam are considered. It Is known that most of real physical effects are very sensitive to properties of numerical Integration methods used for beam evolution Investigation. That Is why a researcher should guarantee not only accuracy of any computing but qualitative properties, which are Inherent In the system under study. In this paper some variant of geometrical Integration methods Is discussed. It allows to apply high performance computing systems more effectively. Some practical problems of particle beam evolution are considered.

关键词： Particle beams

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Some approaches to long time beam evolution

Some approaches to long time beam evolution

引用

International Conference on Physics and Control

作者： S.N. Andrianov Head of Computer Modeling and Multiprocessing Systems Department Saint Petersburg State University Saint Petersburg Russia

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