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Science and Information Conference (SAI)

作者： Chun-Che Huang Tzu-Liang Bill Tseng Ming-Xuan Zhou Department of Management Information National Chi Nan University Taiwan Industrial Manufacturing and Systems Engineering Graduate Program Director The University of Texas at El Paso U.S.A.

With concerns about climate change growing it could be that green energy will begin to play a major role. Green Energy requires to resolve the optimization problem of electronic distribution, control, and storage with decision rule support. Due to the characteristics of green energy data nature - time dependency and variance, and big data, a novel approach to induct rules is required without re-computing rule sets from the very beginning, when new objects are updated to information system. The proposed approach updates rule sets by partly modifying original rule sets, hence a lot of time are saved, and it is especially useful when extracting rules from big data sets. The rules comparison helps decision maker to explore the marketing and qualified decision for renew energy distribution.

关键词： Data mining Big data Green products Distributed databases Renewable energy sources Heuristic algorithms

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A framework for simulation-based task analysis - The development of a universal task analysis simulation model

A framework for simulation-based task analysis - The develop...

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IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support (CogSIMA)

作者： Anastasia Angelopoulou Konstantinos Mykoniatis Waldemar Karwowski Modeling & Simulation Graduate Program University of Central Florida Orlando FL USA Dept. of Industrial Engineering and Management Systems University of Central Florida Orlando FL USA

Over the last decades there has been a growing interest in modeling human performance and analyzing human activity and human operator behavior to improve system design. A variety of tools and approaches which are based on task analysis methods and tools have been proposed. As technology advances and tasks become more demanding, human work changes increasing the need to create new methodologies and tools for task analysis to face the new challenges. The main aim of this research is to develop a new framework and model for simulation of human tasks performed by individuals with varying levels of skills, considering operator workload and human errors. In order to achieve this goal and fill the current gaps, a review of existing task analysis methods and tools is conducted and a model named UTASiMo is proposed. In addition, the Unified Modeling Language is used to describe the conceptual model and illustrate the different constructs and concepts included in the model. Finally, we present a case study and a preliminary simulation model designed in AnyLogic TM .

关键词： Unified modeling language Analytical models Training Data models Conferences Decision making Human factors

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Design of robust PSS in multimachine power systems using backtracking search algorithm

Design of robust PSS in multimachine power systems using bac...

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International Conference on Intelligent systems Applications to Power systems (ISAP)

作者： Md Shafiullah M. A. Abido L. S. Coelho Department of Electrical Engineering King Fahd University of Petroleum & Minerals Dhahran KSA Industrial and Systems Engineering Graduate Program Pontifical Catholic University of Parana Department of Electrical Engineering Federal University of Parana Curitiba PR Brazil

This paper proposes a new approach for the simultaneous stabilization of multi-machine power system networks by tuning the parameters of power system stabilizers (PSS) using backtracking search algorithm (BSA). To enhance system damping, damping ratio based objective function is considered and widely used conventional lead-lag type PSS structure is used. The ability to lead the optimal design of the PSS regardless of the initial guess proves the robustness of the algorithm. The performances of the approach are investigated for two different multi-machine networks subjected to three phase fault and the results obtained from simulation verify the effectiveness of the proposed technique. The simulation results are also compared with a well-established swarm intelligence paradigm in this field called particle swarm optimization (PSO), which also gives confidence on the proposed technique.

关键词： Power system stability Sociology Statistics Damping Frequency control Time-frequency analysis Linear programming

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Elephant Herding Optimization

Elephant Herding Optimization

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International Symposium on Computational and Business Intelligence (ISCBI)

作者： Gai-Ge Wang Suash Deb Leandro dos S. Coelho School of Computer Science and Technology Jiangsu Normal University Xuzhou China Dept. of Computer Science & Engineering Cambridge Institute of Technology Ranchi INDIA Industrial and Systems Engineering Graduate Program Pontifical Catholic University of Parana Curitiba Brazil

In this paper, a new kind of swarm-based metaheuristic search method, called Elephant Herding Optimization (EHO), is proposed for solving optimization tasks. The EHO method is inspired by the herding behavior of elephant group. In nature, the elephants belonging to different clans live together under the leadership of a matriarch, and the male elephants will leave their family group when they grow up. These two behaviors can be modelled into two following operators: clan updating operator and separating operator. In EHO, the elephants in each clan are updated by its current position and matriarch through clan updating operator. It is followed by the implementation of the separating operator which can enhance the population diversity at the later search phase. To demonstrate its effectiveness, EHO is benchmarked by fifteen test cases comparing with BBO, DE and GA. The results show that EHO can find the better values on most benchmark problems than those three metaheuristic algorithms.

关键词： Optimization Benchmark testing Sociology Statistics Particle swarm optimization Electronic mail Search methods

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Search for Gravitational Waves Emitted from SN 2023ixf

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The Astrophysical Journal 2025年第2期985卷 183-183页

作者： A. G. Abac R. Abbott I. Abouelfettouh F. Acernese K. Ackley S. Adhicary N. Adhikari R. X. Adhikari V. K. Adkins D. Agarwal M. Agathos M. Aghaei Abchouyeh O. D. Aguiar I. Aguilar L. Aiello A. Ain T. Akutsu S. Albanesi R. A. Alfaidi A. Al-Jodah C. Alléné A. Allocca S. Al-Shammari P. A. Altin S. Alvarez-Lopez A. Amato L. Amez-Droz A. Amorosi C. Amra A. Ananyeva S. B. Anderson W. G. Anderson M. Andia M. Ando T. Andrade N. Andres M. Andrés-Carcasona T. Andrić J. Anglin S. Ansoldi J. M. Antelis S. Antier M. Aoumi E. Z. Appavuravther S. Appert S. K. Apple K. Arai A. Araya M. C. Araya J. S. Areeda L. Argianas N. Aritomi F. Armato N. Arnaud M. Arogeti S. M. Aronson G. Ashton Y. Aso M. Assiduo S. Assis de Souza Melo S. M. Aston P. Astone F. Attadio F. Aubin K. AultONeal G. Avallone S. Babak F. Badaracco C. Badger S. Bae S. Bagnasco E. Bagui J. G. Baier L. Baiotti R. Bajpai T. Baka M. Ball G. Ballardin S. W. Ballmer S. Banagiri B. Banerjee D. Bankar P. Baral J. C. Barayoga B. C. Barish D. Barker P. Barneo F. Barone B. Barr L. Barsotti M. Barsuglia D. Barta A. M. Bartoletti M. A. Barton I. Bartos S. Basak A. Basalaev R. Bassiri A. Basti D. E. Bates M. Bawaj P. Baxi J. C. Bayley A. C. Baylor P. A. Baynard II M. Bazzan V. M. Bedakihale F. Beirnaert M. Bejger D. Belardinelli A. S. Bell V. Benedetto W. Benoit J. D. Bentley M. Ben Yaala S. Bera M. Berbel F. Bergamin B. K. Berger S. Bernuzzi M. Beroiz D. Bersanetti A. Bertolini J. Betzwieser D. Beveridge N. Bevins R. Bhandare U. Bhardwaj R. Bhatt D. Bhattacharjee S. Bhaumik S. Bhowmick A. Bianchi I. A. Bilenko G. Billingsley A. Binetti S. Bini O. Birnholtz S. Biscoveanu A. Bisht M. Bitossi M.-A. Bizouard J. K. Blackburn L. A. Blagg C. D. Blair D. G. Blair F. Bobba N. Bode G. Boileau M. Boldrini G. N. Bolingbroke A. Bolliand L. D. Bonavena R. Bondarescu F. Bondu E. Bonilla M. S. Bonilla A. Bonino R. Bonnand P. Booker A. Borchers V. Boschi S. Bose V. Bossilkov V. Boudart A. Boudon A. Bozzi C. Bradaschia P. R. Brady M. Braglia A. Branch M. Branchesi J. Brandt I. Braun M. Breschi T. B Max Planck Institute for Gravitational Physics (Albert Einstein Institute) D-14476 Potsdam Germany LIGO Laboratory California Institute of Technology Pasadena CA 91125 USA LIGO Hanford Observatory Richland WA 99352 USA Dipartimento di Farmacia Università di Salerno I-84084 Fisciano Salerno Italy INFN Sezione di Napoli I-80126 Napoli Italy University of Warwick Coventry CV4 7AL UK The Pennsylvania State University University Park PA 16802 USA University of Wisconsin-Milwaukee Milwaukee WI 53201 USA Louisiana State University Baton Rouge LA 70803 USA Université catholique de Louvain B-1348 Louvain-la-Neuve Belgium Inter-University Centre for Astronomy and Astrophysics Pune 411007 India Queen Mary University of London London E1 4NS UK Department of Physics and Astronomy Sejong University 209 Neungdong-ro Gwangjin-gu Seoul 143-747 Republic of Korea Instituto Nacional de Pesquisas Espaciais 12227-010 São José dos Campos São Paulo Brazil Stanford University Stanford CA 94305 USA Università di Roma Tor Vergata I-00133 Roma Italy INFN Sezione di Roma Tor Vergata I-00133 Roma Italy Cardiff University Cardiff CF24 3AA UK Universiteit Antwerpen 2000 Antwerpen Belgium Gravitational Wave Science Project National Astronomical Observatory of Japan 2-21-1 Osawa Mitaka City Tokyo 181-8588 Japan Advanced Technology Center National Astronomical Observatory of Japan 2-21-1 Osawa Mitaka City Tokyo 181-8588 Japan INFN Sezione di Torino I-10125 Torino Italy Theoretisch-Physikalisches Institut Friedrich-Schiller-Universität Jena D-07743 Jena Germany Dipartimento di Fisica Università degli Studi di Torino I-10125 Torino Italy SUPA University of Glasgow Glasgow G12 8QQ UK OzGrav University of Western Australia Crawley Western Australia 6009 Australia University Savoie Mont Blanc CNRS Laboratoire d’Annecy de Physique des Particules - IN2P3 F-74000 Annecy France Università di Napoli “Federico II ” I-80126 Napoli Italy OzGrav Australian National University C

We present the results of a search for gravitational-wave transients associated with core-collapse supernova SN 2023ixf, which was observed in the galaxy Messier 101 via optical emission on 2023 May 19, during the LIGO–Virgo–KAGRA 15th engineering Run. We define a five-day on-source window during which an accompanying gravitational-wave signal may have occurred. No gravitational waves have been identified in data when at least two gravitational-wave observatories were operating, which covered ∼14% of this five-day window. We report the search detection efficiency for various possible gravitational-wave emission models. Considering the distance to M101 (6.7 Mpc), we derive constraints on the gravitational-wave emission mechanism of core-collapse supernovae across a broad frequency spectrum, ranging from 50 Hz to 2 kHz, where we assume the gravitational-wave emission occurred when coincident data are available in the on-source window. Considering an ellipsoid model for a rotating proto-neutron star, our search is sensitive to gravitational-wave energy 1 × 10⁻⁴ M_⊙c² and luminosity 2.6 × 10⁻⁴ M_⊙c² s⁻¹ for a source emitting at 82 Hz. These constraints are around an order of magnitude more stringent than those obtained so far with gravitational-wave data. The constraint on the ellipticity of the proto-neutron star that is formed is as low as 1.08, at frequencies above 1200 Hz, surpassing past results.

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Estimates, trends, and drivers of the global burden of type 2 diabetes attributable to PM_2·5 air pollution, 1990–2019: an analysis of data from the Global Burden of Disease Study 2019

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The Lancet Planetary Health 2022年第7期6卷 e586-e600页

作者： Burkart, Katrin Causey, Kate Cohen, Aaron J Wozniak, Sarah S Salvi, Devashri Digvijay Abbafati, Cristiana Adekanmbi, Victor Adsuar, Jose C Ahmadi, Keivan Alahdab, Fares Al-Aly, Ziyad Alipour, Vahid Alvis-Guzman, Nelson Amegah, Adeladza Kofi Andrei, Catalina Liliana Andrei, Tudorel Ansari, Fereshteh Arabloo, Jalal Aremu, Olatunde Aripov, Timur Babaee, Ebrahim Banach, MacIej Barnett, Anthony Bärnighausen, Till Winfried Bedi, Neeraj Behzadifar, Masoud Béjot, Yannick Bennett, Derrick A Bensenor, Isabela M Bernstein, Robert S Bhattacharyya, Krittika Bijani, Ali Biondi, Antonio Bohlouli, Somayeh Breitner, Susanne Brenner, Hermann Butt, Zahid A Cámera, Luis Alberto Cantu-Brito, Carlos Carvalho, Felix Cerin, Ester Chattu, Vijay Kumar Chauhan, Bal Govind Choi, Jee-Young Jasmine Chu, Dinh-Toi Dai, Xiaochen Dandona, Lalit Dandona, Rakhi Daryani, Ahmad Davletov, Kairat De Courten, Barbora Demeke, Feleke Mekonnen Denova-Gutiérrez, Edgar Dharmaratne, Samath Dhamminda Dhimal, Meghnath Diaz, Daniel Djalalinia, Shirin Duncan, Bruce B El Sayed Zaki, Maysaa Eskandarieh, Sharareh Fareed, Mohammad Farzadfar, Farshad Fattahi, Nazir Fazlzadeh, Mehdi Fernandes, Eduarda Filip, Irina Fischer, Florian Foigt, Nataliya A Freitas, Marisa Ghashghaee, Ahmad Gill, Paramjit Singh Ginawi, Ibrahim Abdelmageed Gopalani, Sameer Vali Guo, Yuming Gupta, Rajat Das Habtewold, Tesfa Dejenie Hamadeh, Randah R Hamidi, Samer Hankey, Graeme J Hasanpoor, Edris Hassen, Hamid Yimam Hay, Simon I Heibati, Behzad Hole, Michael K Hossain, Naznin Househ, Mowafa Irvani, Seyed Sina Naghibi Jaafari, Jalil Jakovljevic, Mihajlo Jha, Ravi Prakash Jonas, Jost B Jozwiak, Jacek Jerzy Kasaeian, Amir Kaydi, Neda Khader, Yousef Saleh Khafaie, Morteza Abdullatif Khan, Ejaz Ahmad Khan, Junaid Khan, Md Nuruzzaman Khatab, Khaled Khater, Amir M Kim, Yun Jin Kimokoti, Ruth W Kisa, Adnan Kivimäki, Mika Knibbs, Luke D Kosen, Soewarta Koul, Parvaiz A Koyanagi, Ai Defo, Barthelemy Kuate Kugbey, Nuworza Lauriola, Paolo Lee, Paul H Leili, Mostafa Lewycka, Sonia Li, Shanshan Lim, Lee-Ling Li Institute for Health Metrics and Evaluation University of Washington Seattle WA United States Department of Health Metrics Sciences School of Medicine University of Washington Seattle WA United States Division of Plastic and Reconstructive Surgery University of Washington Seattle WA United States Research Insights and Innovation Seattle WA United States Health Effects Institute Boston MA United States School of Public Health Boston University Boston MA United States Department of Juridical and Economic Studies La Sapienza University Rome Italy Department of Population Medicine Cardiff University Cardiff United Kingdom Sport Science Department University of Extremadura Badajoz Spain School of Public Health Imperial College London London United Kingdom Department of Primary Care and Public Health Imperial College London London United Kingdom Mayo Evidence-based Practice Center Mayo Clinic Foundation for Medical Education and Research Rochester MN United States John T. Milliken Department of Internal Medicine Washington University in St. Louis St. Louis MO United States Clinical Epidemiology Center US Department of Veterans Affairs (VA) St Louis MO United States Health Management and Economics Research Center Iran University of Medical Sciences Tehran Iran Department of Health Economics Iran University of Medical Sciences Tehran Iran Preventive Medicine and Public Health Research Center Iran University of Medical Sciences Tehran Iran Pars Advanced and Minimally Invasive Medical Manners Research Center Iran University of Medical Sciences Tehran Iran Department of Community and Family Medicine Iran University of Medical Sciences Tehran Iran Research Group in Hospital Management and Health Policies Universidad de la Costa (University of the Coast) Barranquilla Colombia Research Group in Health Economics University of Cartagena Cartagena Colombia Department of Biomedical Science University of Cape Coast Cape Coast Ghana Cardiology Department Ca

Background: Experimental and epidemiological studies indicate an association between exposure to particulate matter (PM) air pollution and increased risk of type 2 diabetes. In view of the high and increasing prevalence of diabetes, we aimed to quantify the burden of type 2 diabetes attributable to PM_2·5 originating from ambient and household air pollution. Methods: We systematically compiled all relevant cohort and case-control studies assessing the effect of exposure to household and ambient fine particulate matter (PM_2·5) air pollution on type 2 diabetes incidence and mortality. We derived an exposure–response curve from the extracted relative risk estimates using the MR-BRT (meta-regression—Bayesian, regularised, trimmed) tool. The estimated curve was linked to ambient and household PM_2·5 exposures from the Global Burden of Diseases, Injuries, and Risk Factors Study 2019, and estimates of the attributable burden (population attributable fractions and rates per 100 000 population of deaths and disability-adjusted life-years) for 204 countries from 1990 to 2019 were calculated. We also assessed the role of changes in exposure, population size, age, and type 2 diabetes incidence in the observed trend in PM_2·5-attributable type 2 diabetes burden. All estimates are presented with 95% uncertainty intervals. Findings: In 2019, approximately a fifth of the global burden of type 2 diabetes was attributable to PM_2·5 exposure, with an estimated 3·78 (95% uncertainty interval 2·68–4·83) deaths per 100 000 population and 167 (117–223) disability-adjusted life-years (DALYs) per 100 000 population. Approximately 13·4% (9·49–17·5) of deaths and 13·6% (9·73–17·9) of DALYs due to type 2 diabetes were contributed by ambient PM2·5, and 6·50% (4·22–9·53) of deaths and 5·92% (3·81–8·64) of DALYs by household air pollution. High burdens, in terms of numbers as well as rates, were estimated in Asia, sub-Saharan Africa, and South Am

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Performance Evaluation of Hypervisors for HPC Applications

Performance Evaluation of Hypervisors for HPC Applications

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IEEE International Conference on systems, Man and Cybernetics

作者： David Beserra Felipe Oliveira Jean Araujo Felipe Fernandes Alberto Araújo Patrícia Endo Paulo Maciel Edward David Moreno Graduate Program in Computer Science University of Sergipe Aracaju Brazil Academic Unit of Garanhuns Federal Rural University of Pernambuco Garanhuns Brazil Informatics Center Federal University of Pernambuco Recife Brazil Graduate in Systems and Computing Military Institute of Engineering Rio de Janeiro Brazil University of Pernambuco Caruaru Brazil

ISBN: (纸本)9781479986989

High Performance computing (HPC) aggregates computing power in order to solve large and complex problems in different knowledge areas. Nowadays, HPC users can utilize virtualized infrastructures as a low-cost alternative to deploy their applications. However, virtualization brings some challenges for HPC, specially in regard to overhead caused by hyper visors. In this work, our main goal is to analyze the performance of two hyper visors (KVM and Virtual Box) under HPC activities, considering full virtualization, and Para virtualization approaches. We used the HPC Challenge Benchmark (HPCC) to evaluate processor, RAM, inter-process communication and network communication performance. Our results show KVM in Para virtualization mode has a similar performance of a native cluster.

关键词： Virtualization Virtual machine monitors Bandwidth Benchmark testing Random access memory Performance evaluation Kernel

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26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 Antwerp, Belgium. 15-20 July 2017 Abstracts

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BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 95-176页

作者： [Anonymous] Department of Neuroscience Yale University New Haven CT 06520 USA Department Physiology & Pharmacology SUNY Downstate Brooklyn NY 11203 USA NYU School of Engineering 6 MetroTech Center Brooklyn NY 11201 USA Departament de Matemàtica Aplicada Universitat Politècnica de Catalunya Barcelona 08028 Spain Institut de Neurobiologie de la Méditerrannée (INMED) INSERM UMR901 Aix-Marseille Univ Marseille France Center of Neural Science New York University New York NY USA Aix-Marseille Univ INSERM INS Inst Neurosci Syst Marseille France Laboratoire de Physique Théorique et Modélisation CNRS UMR 8089 Université de Cergy-Pontoise 95300 Cergy-Pontoise Cedex France Department of Mathematics and Computer Science ENSAT Abdelmalek Essaadi’s University Tangier Morocco Laboratory of Natural Computation Department of Information and Electrical Engineering and Applied Mathematics University of Salerno 84084 Fisciano SA Italy Department of Medicine University of Salerno 84083 Lancusi SA Italy Dipartimento di Fisica Università degli Studi Aldo Moro Bari and INFN Sezione Di Bari Italy Data Analysis Department Ghent University Ghent Belgium Coma Science Group University of Liège Liège Belgium Cruces Hospital and Ikerbasque Research Center Bilbao Spain BIOtech Department of Industrial Engineering University of Trento and IRCS-PAT FBK 38010 Trento Italy Department of Data Analysis Ghent University Ghent 9000 Belgium The Wellcome Trust Centre for Neuroimaging University College London London WC1N 3BG UK Department of Electronic Engineering NED University of Engineering and Technology Karachi Pakistan Blue Brain Project École Polytechnique Fédérale de Lausanne Lausanne Switzerland Departement of Mathematics Swansea University Swansea Wales UK Laboratory for Topology and Neuroscience at the Brain Mind Institute École polytechnique fédérale de Lausanne Lausanne Switzerland Institute of Mathematics University of Aberdeen Aberdeen Scotland UK Department of Integrativ

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Developing and testing a design process for sustainable indicators

Developing and testing a design process for sustainable indi...

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IIE Annual Conference and Expo 2014

作者： De Lima, Edson Pinheiro Machado, Carla G. Kluska, Rafael A. Hundzinski, Leandro N. Rocha, Letícia M. Da Silva, Elias H.D. Ribeiro Da Costa, Sergio E. Gouvea Industrial and Systems Engineering Graduate Program Pontifical Catholic University of Parana Brazil Federal University of Technology Parana Curitiba Brazil

ISBN: (纸本)9780983762430

In the last few decades the competitive context development trajectory is leading companies to increase their contribution for sustainable development. Stakeholders' accountability demands are being reflected in present multidisciplinary regulatory framework that is formed by standards, guidelines and guidance reports, and allows markets and society to have evidences from sustainable behavior, which is certified or formally agreed or signatory by companies. Operations strategy performance information could be addressed to a set of evolutionary performance indicators, which could represent sustainability present performance, and also could orient companies in reaching higher performance levels according to a preset maturity trajectory. The objective of this paper is to develop a process for generating an integrated set of performance measures for managing sustainability at value chain or operations system level. An applied research was conducted based on action research approach, operationalized by Cambridge process or engineering approach, and managed in research process management cycle. Performance measures are formally and detailed defined as metadata. Process application not only allows a better understanding on how sustainability indicators could be integrated and related to a maturity model, but also created an information system that could be used for auditing, assessment and reporting sustainability performance.

关键词： Sustainable development

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Multiobjective Cuckoo Search Applied to Radial Basis Function Neural Networks Training for System Identification

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IFAC Proceedings Volumes 2014年第3期47卷 2539-2544页

作者： Helon Vicente Hultmann Ayala Leandro dos Santos Coelho Industrial and Systems Engineering Graduate Program (PPGEPS) PUCPR Curitiba Brazil Electrical Engineering Graduate Program (PPGEE) UFPR Curitiba Brazil

In the present work we introduce a system identification framework where no a priori information on the system to be identified is available. Focusing in the specific case of radial basis functions neural networks models, we insert the choice of the model complexity and its inputs in the optimization procedure together with the model parameters, aiming at accuracy, model validity and regularization in a multiobjective approach. The multicriteria problem is solved by means of the multiobjective cuckoo search, which is based on an archiving technique and the crowding distance metric. Simulation results are promising when the methodology is applied to identify a robot arm given solely input and output data.

关键词： System Identification Multiobjective optimization Cuckoo Search Multiobjective Cuckoo Search Neural Networks Radial Basis Functions Robotics

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