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Causes of blindness and vision impairment in 2020 and trends over 30 years, and prevalence of avoidable blindness in relation to VISION 2020: the Right to Sight: an analysis for the Global Burden of Disease Study (vol 2, pg 144, 2021)

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LANCET GLOBAL HEALTH 2021年第4期9卷 E408-E408页

作者： Steinmetz, Jaimie D. Vision and Eye Research Institute Anglia Ruskin University Cambridge United Kingdom Biomedical Informatics Seoul National University Hospital Seoul South Korea 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 Institute of Health Services Research Yonsei University Seoul South Korea Department of Ophthalmology University of Washington Seattle WA United States Department of Mathematics Imperial College London London United Kingdom WHO Collaborating Centre for Public Health Education and Training Imperial College London London United Kingdom Department of Primary Care and Public Health Imperial College London London United Kingdom School of Population and Global Health University of Melbourne Carlton VIC Australia School of Optometry and Vision Science UNSW Sydney NSW Australia Brien Holden Vision Institute Sydney Australia Department of Ophthalmology University of Adelaide Adelaide SA Australia Department of Parasitology and Mycology Jahrom University of Medical Sciences Jahrom Iran Department of Clinical Sciences University of Sharjah Sharjah United Arab Emirates Ophthalmic Research Center Shahid Beheshti University of Medical Sciences Tehran Iran Department of Ophthalmology Shahid Beheshti University of Medical Sciences Tehran Iran Research Institute for Endocrine Sciences Shahid Beheshti University of Medical Sciences Tehran Iran Ophthalmic Epidemiology Research Center Shahid Beheshti University of Medical Sciences Tehran Iran Health Services Management Department Qazvin University of Medical Sciences Qazvin Iran Department of Systems Populations and Leadership University of Michigan Ann Arbor MI United States Department of Medicine Sofia Ismail Memorial Medical Centre Bogura Bangladesh The Fred Hollows Foundation Sydney NSW Australia Berhan Public Health & Eye Care Consultancy Addis A

Hospital El Salvador: a novel paradigm of intensive care in response to COVID-19 in central America. Lancet Glob Health 2021;9: e241?42?In this Comment, the conflict of interest statement should have included the following: ?By virtue of their roles within a public hospital or the Ministry of Health, MB, LC, WH, and XS are government employees. The findings and conclusions in the Comment are only those of the authors.? This correction has been made as of Feb 26, 2021.

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Evaluation of approaches for power estimation in a computing cluster

Evaluation of approaches for power estimation in a computing...

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International Green Computing Conference

作者： Hao Zhu Paola Grosso Xiangke Liao Cees de Laat School of Computer National University of Defense Technology Changsha China System and Network Engineering research group University of Amsterdam Amsterdam The Netherlands

ISBN: (纸本)9781479961788

Data centers as a cost-effective infrastructure for hosting Cloud and Grid applications incur tremendous energy cost and C02 emissions in terms of power distribution and cooling. One of the effective approaches for saving energy in a cluster environment is workload consolidation. However, it is challenging to address this schedule problem as it requires the understanding of various cost factors. One of the important factors is the estimation of power consumption. Power models used in most of workload schedule solutions are a linear function of resource features, but we analysed the measurement data from our cluster and found the resource loads, in particular I/O load, had no convincing linear-correlation with power consumption. Based on measurement data sets from our cluster, we propose multiple non-linear machine learning approaches to estimate power consumption of an entire node using OS-reported resource features. We evaluate the accuracy, portability and usability of the linear and non-linear approaches. Our work shows the multiple-variable linear regression approach is more precise than the CPU only linear approach. The neural network approaches have a slight advantage - its mean root mean square error is at most 15% less than that of the multiple-variable linear approach. But the neural network models have worse portability when the models generated on a node are applied on its homogeneous nodes. Gaussian Mixture Model has the highest accuracy on Hadoop nodes but requires the longest training time.

关键词： Power demand Estimation Biological system modeling Neurons Computational modeling Training Google

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A framework for sentiment analysis on schema-based research content via lexica analysis

A framework for sentiment analysis on schema-based research ...

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International Symposium on Telecommunications, IST

作者： Marjan Goodarzi Maryam Tayefeh Mahmoudi Ramin Zamani System and Network Security Assessment Group ICT Research Institute Tehran Iran Multi Media Group Alzahra University Tehran Iran Department of Computer Engineering & Information Technology Islamic Azad University Tehran Iran

Sentiment Analysis is one of the significant issues in the area of natural language processing, computational linguistics and text mining. It has also become a potential research area in bibliographic search and opinion mining, which is our main focus in this paper. Sentiment analysis of citations on schema-based research contents, such as scientific articles and reports, may not only makes an appropriate understanding of the issue, but also enhances the level of reliability of citations in those contents. Taking the above points into account, in this paper, a framework for sentiment analysis based on lexica analysis is proposed to determine the polarity of author's opinion about citations in content. This framework consists of three major steps: subjectivity detection, polarity classification and strength determination of opinion. In each step, some considerations are considered to improve the capability of proposed framework. We have applied SentiWordNet, AFINN and Bingliu lexica sets for this purpose. The schema-based research contents that have been considered for this experiment is PubMed, which consists of open source medical articles of US digital library of medicine. Experimental results reveal the superiority of SentiWordNet lexica set compared to the other existing sets in sentiment analysis of PubMed articles.

关键词： Context Sentiment analysis Dictionaries Abstracts Libraries Syntactics Pragmatics

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Global, regional, and national sex-specific burden and control of the HIV epidemic, 1990–2019, for 204 countries and territories: the Global Burden of Diseases Study 2019

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The Lancet HIV 2021年第10期8卷 e633-e651页

作者： Jahagirdar, Deepa Walters, Magdalene K. Novotney, Amanda Brewer, Edmond D. Frank, Tahvi D. Carter, Austin Biehl, Molly H. Abbastabar, Hedayat Abhilash, E.S. Abu-Gharbieh, Eman Abu-Raddad, Laith Jamal Adekanmbi, Victor Adeyinka, Daniel Adedayo Adnani, Qorinah Estiningtyas Sakilah Afzal, Saira Aghababaei, Soodabeh Ahinkorah, Bright Opoku Ahmad, Sajjad Ahmadi, Keivan Ahmadi, Sepideh Ahmadpour, Ehsan Ahmed, Muktar Beshir Rashid, Tarik Ahmed Salih, Yusra Ahmed Aklilu, Addis Akram, Tayyaba Akunna, Chisom Joyqueenet Al Hamad, Hanadi Alahdab, Fares Alanezi, Fahad Mashhour Aleksandrova, Ekaterina A. Alene, Kefyalew Addis Ali, Liaqat Alipour, Vahid Almustanyir, Sami Alvis-Guzman, Nelson Ameyaw, Edward Kwabena Amu, Hubert Andrei, Catalina Liliana Andrei, Tudorel Anvari, Davood Arabloo, Jalal Aremu, Olatunde Arulappan, Judie Atnafu, Desta Debalkie Quintanilla, Beatriz Paulina Ayala Ayza, Muluken Altaye Azari, Samad Darshan, B.B. Banach, Maciej Bärnighausen, Till Winfried Barra, Fabio Barrow, Amadou Basu, Sanjay Bazargan-Hejazi, Shahrzad Belay, Habtamu Gebrehana Berheto, Tezera Moshago Bezabhe, Woldesellassie Mequanint Bezabih, Yihienew Mequanint Bhagavathula, Akshaya Srikanth Bhardwaj, Nikha Bhardwaj, Pankaj Bhattacharyya, Krittika Bibi, Sadia Bijani, Ali Bisignano, Catherine Bolarinwa, Obasanjo Afolabi Boloor, Archith Boltaev, Azizbek A. Briko, Nikolay Ivanovich Buonsenso, Danilo Burkart, Katrin Butt, Zahid A. Cao, Chao Charan, Jaykaran Chatterjee, Souranshu Chattu, Soosanna Kumary Chattu, Vijay Kumar Choudhari, Sonali Gajanan Chu, Dinh-Toi Couto, Rosa A.S. Cowden, Richard G. Dachew, Berihun Assefa Dadras, Omid Dagnew, Amare Belachew Dahlawi, Saad M.A. Dai, Xiaochen Dandona, Lalit Dandona, Rakhi das Neves, José Degenhardt, Louisa Demeke, Feleke Mekonnen Desta, Abebaw Alemayehu Deuba, Keshab Dhamnetiya, Deepak Dhungana, Govinda Prasad Dianatinasab, Mostafa Diaz, Daniel Djalalinia, Shirin Doan, Linh Phuong Dorostkar, Fariba Edinur, Hisham Atan Effiong, Andem Eftekharzadeh, Sahar El Sayed Zaki, Maysaa Elayedath, Rajesh Elhadi, 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 Allergy and Infectious Diseases University of Washington Seattle WA United States Department of Global Health University of Washington Seattle WA United States School of Social Work University of Washington Seattle WA United States Department of Medicine University of Washington Seattle WA United States Vagelos College of Physicians and Surgeones Columbia University Medical Center New York NY United States Division of Data Analytics and Delivery for Impact WHO Geneva Switzerland Advanced Diagnostic and Interventional Radiology Research Centre Tehran University of Medical Sciences Tehran Iran Multiple Sclerosis Research Centre Tehran University of Medical Sciences Tehran Iran School of Public Health Tehran University of Medical Sciences Tehran Iran Department of Epidemiology and Biostatistics Tehran University of Medical Sciences Tehran Iran Paediatric Chronic Kidney Disease Research Centre Tehran University of Medical Sciences Tehran Iran Department of Psychiatry Tehran University of Medical Sciences Tehran Iran Metabolomics and Genomics Research Centre Tehran University of Medical Sciences Tehran Iran Sina Trauma and Surgery Research Centre Tehran University of Medical Sciences Tehran Iran Research Centre for Immunodeficiencies Tehran University of Medical Sciences Tehran Iran Non-Communicable Diseases Research Centre Tehran University of Medical Sciences Tehran Iran Department of Botany Sree Narayana Guru College Chelannur Kozhikode India Clinical Sciences Department University of Sharjah Sharjah United Arab Emirates Mass Communication Department University of Sharjah Sharjah United Arab Emirates Department of Health-Care Policy and Research Weill Cornell Medical College in Qatar Doha Qatar Department of Population Medicine Ca

Background: The sustainable development goals (SDGs) aim to end HIV/AIDS as a public health threat by 2030. Understanding the current state of the HIV epidemic and its change over time is essential to this effort. This study assesses the current sex-specific HIV burden in 204 countries and territories and measures progress in the control of the epidemic. Methods: To estimate age-specific and sex-specific trends in 48 of 204 countries, we extended the Estimation and Projection Package Age-Sex Model to also implement the spectrum paediatric model. We used this model in cases where age and sex specific HIV-seroprevalence surveys and antenatal care-clinic sentinel surveillance data were available. For the remaining 156 of 204 locations, we developed a cohort-incidence bias adjustment to derive incidence as a function of cause-of-death data from vital registration systems. The incidence was input to a custom Spectrum model. To assess progress, we measured the percentage change in incident cases and deaths between 2010 and 2019 (threshold >75% decline), the ratio of incident cases to number of people living with HIV (incidence-to-prevalence ratio threshold <0·03), and the ratio of incident cases to deaths (incidence-to-mortality ratio threshold <1·0). Findings: In 2019, there were 36·8 million (95% uncertainty interval [UI] 35·1–38·9) people living with HIV worldwide. There were 0·84 males (95% UI 0·78–0·91) per female living with HIV in 2019, 0·99 male infections (0·91–1·10) for every female infection, and 1·02 male deaths (0·95–1·10) per female death. Global progress in incident cases and deaths between 2010 and 2019 was driven by sub-Saharan Africa (with a 28·52% decrease in incident cases, 95% UI 19·58–35·43, and a 39·66% decrease in deaths, 36·49–42·36). Elsewhere, the incidence remained stable or increased, whereas deaths generally decreased. In 2019, the global incidence-to-prevalence ratio was 0·05 (95% UI 0·05–0·06) and the global incidence-to-mortality ratio was

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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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A fast and simple graph kernel for RDF

A fast and simple graph kernel for RDF

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International Workshop on Data Mining on Linked Data, with Linked Data Mining Challenge, DMoLD 2013 - Collocated with the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECMLPKDD 2013

作者： De Vries, Gerben Klaas Dirk De Rooij, Steven System and Network Engineering Group Informatics Institute University of Amsterdam Netherlands Knowledge Representation and Reasoning Group Department of Computer Science VU University Amsterdam Netherlands

In this paper we study a graph kernel for RDF based on constructing a tree for each instance and counting the number of paths in that tree. In our experiments this kernel shows comparable classification performance to the previously introduced intersection subtree kernel, but is significantly faster in terms of computation time. Prediction performance is worse than the state-of-the-art Weisfeiler Lehman RDF kernel, but our kernel is a factor 10 faster to compute. Thus, we consider this kernel a very suitable baseline for learning from RDF data. Furthermore, we extend this kernel to handle RDF literals as bag-of-words feature vectors, which increases performance in two of the four experiments.

关键词： Forestry

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Author Correction: New genetic signals for lung function highlight pathways and chronic obstructive pulmonary disease associations across multiple ancestries

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Nature genetics 2019年第6期51卷 1067页

作者： Nick Shrine Anna L Guyatt A Mesut Erzurumluoglu Victoria E Jackson Brian D Hobbs Carl A Melbourne Chiara Batini Katherine A Fawcett Kijoung Song Phuwanat Sakornsakolpat Xingnan Li Ruth Boxall Nicola F Reeve Ma'en Obeidat Jing Hua Zhao Matthias Wielscher Stefan Weiss Katherine A Kentistou James P Cook Benjamin B Sun Jian Zhou Jennie Hui Stefan Karrasch Medea Imboden Sarah E Harris Jonathan Marten Stefan Enroth Shona M Kerr Ida Surakka Veronique Vitart Terho Lehtimäki Richard J Allen Per S Bakke Terri H Beaty Eugene R Bleecker Yohan Bossé Corry-Anke Brandsma Zhengming Chen James D Crapo John Danesh Dawn L DeMeo Frank Dudbridge Ralf Ewert Christian Gieger Amund Gulsvik Anna L Hansell Ke Hao Joshua D Hoffman John E Hokanson Georg Homuth Peter K Joshi Philippe Joubert Claudia Langenberg Xuan Li Liming Li Kuang Lin Lars Lind Nicholas Locantore Jian'an Luan Anubha Mahajan Joseph C Maranville Alison Murray David C Nickle Richard Packer Margaret M Parker Megan L Paynton David J Porteous Dmitry Prokopenko Dandi Qiao Rajesh Rawal Heiko Runz Ian Sayers Don D Sin Blair H Smith María Soler Artigas David Sparrow Ruth Tal-Singer Paul R H J Timmers Maarten Van den Berge John C Whittaker Prescott G Woodruff Laura M Yerges-Armstrong Olga G Troyanskaya Olli T Raitakari Mika Kähönen Ozren Polašek Ulf Gyllensten Igor Rudan Ian J Deary Nicole M Probst-Hensch Holger Schulz Alan L James James F Wilson Beate Stubbe Eleftheria Zeggini Marjo-Riitta Jarvelin Nick Wareham Edwin K Silverman Caroline Hayward Andrew P Morris Adam S Butterworth Robert A Scott Robin G Walters Deborah A Meyers Michael H Cho David P Strachan Ian P Hall Martin D Tobin Louise V Wain Department of Health Sciences University of Leicester Leicester UK. Population Health and Immunity Division The Walter and Eliza Hall Institute of Medical Research Parkville Victoria Australia. Department of Medical Biology University of Melbourne Parkville Victoria Australia. Channing Division of Network Medicine Brigham and Women's Hospital Boston MA USA. Division of Pulmonary and Critical Care Medicine Brigham and Women's Hospital Boston MA USA. Target Sciences GlaxoSmithKline Collegeville PA USA. Department of Medicine Faculty of Medicine Siriraj Hospital Mahidol University Bangkok Thailand. Division of Genetics Genomics and Precision Medicine Department of Medicine University of Arizona Tucson AZ USA. Nuffield Department of Population Health University of Oxford Oxford UK. Medical Research Council Population Health Research Unit University of Oxford Oxford UK. The University of British Columbia Centre for Heart Lung Innovation St Paul's Hospital Vancouver British Columbia Canada. Cardiovascular Epidemiology Unit Department of Public Health and Primary Care University of Cambridge Cambridge UK. Department of Epidemiology and Biostatistics MRC-PHE Centre for Environment & Health School of Public HealthImperial College London London UK. Interfaculty Institute for Genetics and Functional Genomics Department of Functional Genomics University Medicine Greifswald Greifswald Germany. Centre for Global Health Research Usher Institute for Population Health Sciences and Informatics University of Edinburgh Edinburgh UK. Centre for Cardiovascular Sciences Queen's Medical Research Institute University of Edinburgh Edinburgh UK. Department of Biostatistics University of Liverpool Liverpool UK. MRC/BHF Cardiovascular Epidemiology Unit Department of Public Health and Primary Care University of Cambridge Cambridge UK. Flatiron Institute Simons Foundation New York NY USA. Busselton Population Medical Research Institute Sir Charles Gairdner Hospital Ne

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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25th Annual Computational Neuroscience Meeting CNS-2016, Seogwipo City, South Korea, July 2-7, 2016 Abstracts

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BMC NEUROSCIENCE 2016年第1期17卷 1-112页

作者： [Anonymous] Computational Neurobiology Laboratory The Salk Institute for Biological Studies San Diego USA UNIC CNRS Gif sur Yvette France The European Institute for Theoretical Neuroscience (EITN) Paris France ATR Computational Neuroscience Laboratories Kyoto Japan Krembil Research Institute University Health Network Toronto Canada Department of Physiology University of Toronto Toronto Canada Department of Medicine (Neurology) University of Toronto Toronto Canada Department of Physics University of New Hampshire Durham USA Department of Neurophysiology Nencki Institute of Experimental Biology Warsaw Poland Department of Theory Wigner Research Centre for Physics of the Hungarian Academy of Sciences Budapest Hungary Department of Mathematical Sciences KAIST Daejoen Republic of Korea Department of Mathematics University of Houston Houston USA Department of Biochemistry & Cell Biology and Institute of Biosciences and Bioengineering Rice University Houston USA Department of Biology and Biochemistry University of Houston Houston USA Grupo de Neurocomputación Biológica Dpto. de Ingeniería Informática Escuela Politécnica Superior Universidad Autónoma de Madrid Madrid Spain Department of Biological Sciences University of Southern California Los Angeles USA Center for Neuroscience Korea Institute of Science and Technology Seoul South Korea Department of Neurology Albert Einstein College of Medicine Bronx USA Center for Neuroscience KIST Seoul South Korea Department of Neuroscience University of Science and Technology Daejon South Korea Systems Neuroscience Group QIMR Berghofer Medical Research Institute Herston Australia Department of Psychology Yonsei University Seoul South Korea Department of Psychiatry Kyung Hee University Hospital at Gangdong Seoul South Korea Department of Psychiatry Veterans Administration Boston Healthcare System and Harvard Medical School Brockton USA Department of Electrical and Electronic Engineering The University of Melbourne Parkvil

A1 Functional advantages of cell-type heterogeneity in neural circuits Tatyana O. Sharpee A2 Mesoscopic modeling of propagating waves in visual cortex Alain Destexhe A3 Dynamics and biomarkers of mental disorders Mitsuo Kawato F1 Precise recruitment of spiking output at theta frequencies requires dendritic h-channels in multi-compartment models of oriens-lacunosum/moleculare hippocampal interneurons Vladislav Sekulić, Frances K. Skinner F2 Kernel methods in reconstruction of current sources from extracellular potentials for single cells and the whole brains Daniel K. Wójcik, Chaitanya Chintaluri, Dorottya Cserpán, Zoltán Somogyvári F3 The synchronized periods depend on intracellular transcriptional repression mechanisms in circadian clocks. Jae Kyoung Kim, Zachary P. Kilpatrick, Matthew R. Bennett, Kresimir Josić O1 Assessing irregularity and coordination of spiking-bursting rhythms in central pattern generators Irene Elices, David Arroyo, Rafael Levi, Francisco B. Rodriguez, Pablo Varona O2 Regulation of top-down processing by cortically-projecting parvalbumin positive neurons in basal forebrain Eunjin Hwang, Bowon Kim, Hio-Been Han, Tae Kim, James T. McKenna, Ritchie E. Brown, Robert W. McCarley, Jee Hyun Choi O3 Modeling auditory stream segregation, build-up and bistability James Rankin, Pamela Osborn Popp, John Rinzel O4 Strong competition between tonotopic neural ensembles explains pitch-related dynamics of auditory cortex evoked fields Alejandro Tabas, André Rupp, Emili Balaguer-Ballester O5 A simple model of retinal response to multi-electrode stimulation Matias I. Maturana, David B. Grayden, Shaun L. Cloherty, Tatiana Kameneva, Michael R. Ibbotson, Hamish Meffin O6 Noise correlations in V4 area correlate with behavioral performance in visual discrimination task Veronika Koren, Timm Lochmann, Valentin Dragoi, Klaus Obermayer O7 Input-location dependent gain modulation in cerebellar nucleus neurons Maria Psarrou, Maria Schilstra, Neil Davey, Benjamin Torben-Ni

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EKB: Semantic Information system for Energy-Aware Monitoring in Distributed Infrastructures

EKB: Semantic Information System for Energy-Aware Monitoring...

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International Conference on Cloud and Green Computing (CGC)

作者： Hao Zhu Karel Van Der Veldt Paola Grosso Xiangke Liao Cees De Laat System and Network Engineering research group University of Amsterdam Amsterdam The Netherlands School of Computer National University of Defense Technology Changsha China

Most monitoring and discovery systems deployed in computer infrastructures do not take dynamic energy characteristics into consideration, and do not provide the mechanisms to exchange information across systems and administrative domains. Our energy-aware semantic information system, called Energy Knowledge Base (EKB), aggregates metadata and data from a large number of distributed information sources. The EKB supports therefore management, scheduling and management of computing and data transport tasks across domains. In computing infrastructures, the available information can differ quite a lot, changes frequently, has differing sources, but there is no well-defined data model. Our system leverages the Energy Description Language (EDL) to model dynamic energy-related attributes of resources from different layers, the EDL metadata provides data interoperability across providers. In this article we present the EKB architecture, show the usability.

关键词： Semantics Monitoring Information systems Ontologies Measurement Educational institutions Data models

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Survivable smart grid communication: Smart-meters meshes to the rescue

Survivable smart grid communication: Smart-meters meshes to ...

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2012 International Conference on Computing, networking and Communications, ICNC'12

作者： Athreya, Arjun P. Tague, Patrick Wireless Network and System Security Group Electrical and Computer Engineering Carnegie Mellon University United States

ISBN: (纸本)9781467300094

Smart grids are critical cyber-physical infrastructures in the world now. Since these infrastructures are prone to large scale outages due to disasters or faults, a resilient and survivable communication architecture is desired. In this work, we propose a resilient and survivable hierarchical communication architecture for the smart grid that mirrors the hierarchy of the existing power grid. Post-disaster resilience in grid communication is achieved through the grid flattening process. This process involves smart-meters and other disaster surviving elements of higher system levels of the grid forming a wireless mesh network. The flattened network of grid elements with one-hop communication links help in reliable and timely relaying of grid's health information to working regions of the grid. This allows for swift action by control engineers of the utility provider and emergency services with real-time data. We propose analytical models to study the performance of the flattened architecture as a function of outage area, smart-meter density and smart-meter's neighborhood size. The results from the analytical model will be compared with simulation results from OPNET. © 2012 IEEE.

关键词： Smart meters

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