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SPT clusters with DES and HST weak lensing. II. Cosmological constraints from the abundance of massive halos

Physical Review D 2024年第8期110卷 083510页

作者： S. Bocquet S. Grandis L. E. Bleem M. Klein J. J. Mohr T. Schrabback T. M. C. Abbott P. A. R. Ade M. Aguena A. Alarcon S. Allam S. W. Allen O. Alves A. Amon A. J. Anderson J. Annis B. Ansarinejad J. E. Austermann S. Avila D. Bacon M. Bayliss J. A. Beall K. Bechtol M. R. Becker A. N. Bender B. A. Benson G. M. Bernstein S. Bhargava F. Bianchini M. Brodwin D. Brooks L. Bryant A. Campos R. E. A. Canning J. E. Carlstrom A. Carnero Rosell M. Carrasco Kind J. Carretero F. J. Castander R. Cawthon C. L. Chang C. Chang P. Chaubal R. Chen H. C. Chiang A. Choi T-L. Chou R. Citron C. Corbett Moran J. Cordero M. Costanzi T. M. Crawford A. T. Crites L. N. da Costa M. E. S. Pereira C. Davis T. M. Davis J. DeRose S. Desai T. de Haan H. T. Diehl M. A. Dobbs S. Dodelson C. Doux A. Drlica-Wagner K. Eckert J. Elvin-Poole S. Everett W. Everett I. Ferrero A. Ferté A. M. Flores J. Frieman J. Gallicchio J. García-Bellido M. Gatti E. M. George G. Giannini M. D. Gladders D. Gruen R. A. Gruendl N. Gupta G. Gutierrez N. W. Halverson I. Harrison W. G. Hartley K. Herner S. R. Hinton G. P. Holder D. L. Hollowood W. L. Holzapfel K. Honscheid J. D. Hrubes N. Huang J. Hubmayr E. M. Huff D. Huterer K. D. Irwin D. J. James M. Jarvis G. Khullar K. Kim L. Knox R. Kraft E. Krause K. Kuehn N. Kuropatkin F. Kéruzoré O. Lahav A. T. Lee P.-F. Leget D. Li H. Lin A. Lowitz N. MacCrann G. Mahler A. Mantz J. L. Marshall J. McCullough M. McDonald J. J. McMahon J. Mena-Fernández F. Menanteau S. S. Meyer R. Miquel J. Montgomery J. Myles T. Natoli A. Navarro-Alsina J. P. Nibarger G. I. Noble V. Novosad R. L. C. Ogando Y. Omori S. Padin S. Pandey P. Paschos S. Patil A. Pieres A. A. Plazas Malagón A. Porredon J. Prat C. Pryke M. Raveri C. L. Reichardt J. Roberson R. P. Rollins C. Romero A. Roodman J. E. Ruhl E. S. Rykoff B. R. Saliwanchik L. Salvati C. Sánchez E. Sanchez D. Sanchez Cid A. Saro K. K. Schaffer L. F. Secco I. Sevilla-Noarbe K. Sharon E. Sheldon T. Shin C. Sievers G. Smecher M. Smith T. Somboonpanyakul M. Sommer B. Stalder A. A. Stark J. Stephen V. Straz University Observatory Faculty of Physics Universität Innsbruck Institut für Astro- und Teilchenphysik Technikerstraße 25/8 6020 Innsbruck Austria High-Energy Physics Division Argonne National Laboratory 9700 South Cass Avenue Lemont Illinois 60439 USA Kavli Institute for Cosmological Physics University of Chicago 5640 South Ellis Avenue Chicago Illinois 60637 USA Max Planck Institute for Extraterrestrial Physics Gießenbachstraße 1 85748 Garching Germany Argelander-Institut für Astronomie Auf dem Hügel 71 53121 Bonn Germany Cerro Tololo Inter-American Observatory NSF’s National Optical-Infrared Astronomy Research Laboratory Casilla 603 La Serena Chile School of Physics and Astronomy Cardiff University Cardiff CF24 3YB United Kingdom Laboratório Interinstitucional de e-Astronomia—LIneA Rua Gal. José Cristino 77 Rio de Janeiro Rio de Janeiro—20921-400 Brazil Fermi National Accelerator Laboratory P. O. Box 500 Batavia Illinois 60510 USA Kavli Institute for Particle Astrophysics and Cosmology Stanford University 452 Lomita Mall Stanford California 94305 USA Department of Physics Stanford University 382 Via Pueblo Mall Stanford California 94305 USA SLAC National Accelerator Laboratory 2575 Sand Hill Road Menlo Park California 94025 USA Department of Physics University of Michigan Ann Arbor Michigan 48109 USA Institute of Astronomy University of Cambridge Madingley Road Cambridge CB3 0HA United Kingdom Kavli Institute for Cosmology University of Cambridge Madingley Road Cambridge CB3 0HA United Kingdom School of Physics University of Melbourne Parkville Victoria 3010 Australia NIST Quantum Devices Group 325 Broadway Mailcode 817.03 Boulder Colorado 80305 USA Department of Physics University of Colorado Boulder Colorado 80309 USA Institut de Física d’Altes Energies (IFAE) The Barcelona Institute of Science and Technology Campus UAB 08193 Bellaterra (Barcelona) Spain Institute of Cosmology and Gravitation University of Portsmouth Portsmouth

We present cosmological constraints from the abundance of galaxy clusters selected via the thermal Sunyaev-Zel’dovich (SZ) effect in South Pole Telescope (SPT) data with a simultaneous mass calibration using weak gravitational lensing data from the Dark Energy Survey (DES) and the Hubble Space Telescope (HST). The cluster sample is constructed from the combined SPT-SZ, SPTpol ECS, and SPTpol 500d surveys, and comprises 1,005 confirmed clusters in the redshift range 0.25–1.78 over a total sky area of 5200 deg2. We use DES Year 3 weak-lensing data for 688 clusters with redshifts z<0.95 and HST weak-lensing data for 39 clusters with 0.6

关键词： Cosmological parameters Dark energy Large scale structure of the Universe Galaxy clusters

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Author Correction: Federated learning enables big data for rare cancer boundary detection

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Nature communications 2023年第1期14卷 436页

作者： Sarthak Pati Ujjwal Baid Brandon Edwards Micah Sheller Shih-Han Wang G Anthony Reina Patrick Foley Alexey Gruzdev Deepthi Karkada Christos Davatzikos Chiharu Sako Satyam Ghodasara Michel Bilello Suyash Mohan Philipp Vollmuth Gianluca Brugnara Chandrakanth J Preetha Felix Sahm Klaus Maier-Hein Maximilian Zenk Martin Bendszus Wolfgang Wick Evan Calabrese Jeffrey Rudie Javier Villanueva-Meyer Soonmee Cha Madhura Ingalhalikar Manali Jadhav Umang Pandey Jitender Saini John Garrett Matthew Larson Robert Jeraj Stuart Currie Russell Frood Kavi Fatania Raymond Y Huang Ken Chang Carmen Balaña Jaume Capellades Josep Puig Johannes Trenkler Josef Pichler Georg Necker Andreas Haunschmidt Stephan Meckel Gaurav Shukla Spencer Liem Gregory S Alexander Joseph Lombardo Joshua D Palmer Adam E Flanders Adam P Dicker Haris I Sair Craig K Jones Archana Venkataraman Meirui Jiang Tiffany Y So Cheng Chen Pheng Ann Heng Qi Dou Michal Kozubek Filip Lux Jan Michálek Petr Matula Miloš Keřkovský Tereza Kopřivová Marek Dostál Václav Vybíhal Michael A Vogelbaum J Ross Mitchell Joaquim Farinhas Joseph A Maldjian Chandan Ganesh Bangalore Yogananda Marco C Pinho Divya Reddy James Holcomb Benjamin C Wagner Benjamin M Ellingson Timothy F Cloughesy Catalina Raymond Talia Oughourlian Akifumi Hagiwara Chencai Wang Minh-Son To Sargam Bhardwaj Chee Chong Marc Agzarian Alexandre Xavier Falcão Samuel B Martins Bernardo C A Teixeira Flávia Sprenger David Menotti Diego R Lucio Pamela LaMontagne Daniel Marcus Benedikt Wiestler Florian Kofler Ivan Ezhov Marie Metz Rajan Jain Matthew Lee Yvonne W Lui Richard McKinley Johannes Slotboom Piotr Radojewski Raphael Meier Roland Wiest Derrick Murcia Eric Fu Rourke Haas John Thompson David Ryan Ormond Chaitra Badve Andrew E Sloan Vachan Vadmal Kristin Waite Rivka R Colen Linmin Pei Murat Ak Ashok Srinivasan J Rajiv Bapuraj Arvind Rao Nicholas Wang Ota Yoshiaki Toshio Moritani Sevcan Turk Joonsang Lee Snehal Prabhudesai Fanny Morón Jacob Mandel Konstantinos Kamnitsas Ben Glocker Luke V M Dixon Matthew Williams Peter Zamp Center for Biomedical Image Computing and Analytics (CBICA) University of Pennsylvania Philadelphia PA USA. Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. Department of Informatics Technical University of Munich Munich Bavaria Germany. Intel Corporation Santa Clara CA USA. Department of Neuroradiology Heidelberg University Hospital Heidelberg Germany. Clinical Cooperation Unit Neuropathology German Cancer Consortium (DKTK) within the German Cancer Research Center (DKFZ) Heidelberg Germany. Department of Neuropathology Heidelberg University Hospital Heidelberg Germany. Division of Medical Image Computing German Cancer Research Center Heidelberg Germany. Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Heidelberg Germany. Neurology Clinic Heidelberg University Hospital Heidelberg Germany. Department of Radiology & Biomedical Imaging University of California San Francisco San Francisco CA USA. Symbiosis Center for Medical Image Analysis Symbiosis International University Pune Maharashtra India. Department of Neuroimaging and Interventional Radiology National Institute of Mental Health and Neurosciences Bangalore Karnataka India. Department of Radiology School of Medicine and Public Health University of Wisconsin Madison WI USA. Department of Medical Physics School of Medicine and Public Health University of Wisconsin Madison WI USA. Leeds Teaching Hospitals Trust Department of Radiology Leeds UK. Department of Radiology Brigham and Women's Hospital Harvard Medical School Boston MA USA. Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital Charlestown MA USA. Catalan Institute of Oncology Badalona Spain. Consorci MAR Parc de Salut de Barcelona Catalonia Spain. Department of Radiology (IDI

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Computation of Resistance and Potential of Grounding Grids in Any Geometry

Computation of Resistance and Potential of Grounding Grids i...

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IEEE International Conference on Environment and Electrical Engineering

作者： Thyago G. Pires José W. L. Nerys Carlos L. B. Silva Diogo N. Oliveira Antonio M. Silva Filho Wesley P. Calixto Aylton J. Alves School of Electric and Computing Engineering. Federal University of Goias. Goiania Brazil Graduate Program in Electronic and Automation Systems Engineering. University of Brasilia. Brasilia Brazil Experimental & Technologic Research and Study Group. Federal Institute of Education Science and Technology. Goiania Brazil

ISBN: (纸本)9781509023219

This paper deals with the computation of ground resistance, surface voltage, touch voltage and step voltage, to mesh with horizontal wires arranged in different angles. The computer program implemented used in the mathematical modeling is based on the method proposed by Heppe, which allows obtaining the grounding parameters for homogeneous soil and soil stratified in two layers. The results obtained with the proposed method will be compared with other methods in literature. Also will be presented the results of a grounding grid using wires at various angles.

关键词： ground grid leakage current mesh voltage multilayer soil surface potential

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DiSCUS: A simulation platform for conjugation computing 1

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14th International Conference on Unconventional Computation and Natural Computation, UCNC 2015

作者： Goñi-Moreno, Angel Amos, Martyn Systems Biology Program Centro Nacional de Biotecnología Madrid Spain Informatics Research Centre School of Computing Mathematics and Digital Technology Manchester Metropolitan University Manchester United Kingdom

ISBN: (数字)9783319218199

ISBN: (纸本)9783319218182

In bacterial populations, cells are able to cooperate in order to yield complex collective functionalities. Interest in population-level cellular behaviour is increasing, due to both our expanding knowledge of the underlying biological principles, and the growing range of possible applications for engineered microbial consortia. The ability of cells to interact through small signalling molecules (a mechanism known as quorum sensing) is the basis for the majority of existing engineered systems. However, horizontal gene transfer (or conjugation) offers the possibility of cells exchanging messages (using DNA) that are much more information-rich. The potential of engineering this conjugation mechanism to suit specific goals will guide future developments in this area. Motivated by a lack of computational models for examining the specific dynamics of conjugation, we present a simulation framework for its further study. (This paper was first presented at the Spatial computing Workshop of the 13th International Conference on Autonomous Agents and Multiagent systems (AAMAS), Paris, France, May 5–9 2014. There were no published proceedings). © Springer International Publishing Switzerland 2015

关键词： Autonomous agents

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The power of fair information practices – A control agency approach 26

The power of fair information practices – A control agency ...

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26th Australasian Conference on Information systems, ACIS 2015

作者： Libaque-Saenz, Christian Fernando Chang, Younghoon Wong, Siew Fan Lee, Hwansoo School of Engineering Universidad del Pacífico Lima Peru Department of Computing and Information Systems Sunway University Selangor Malaysia IT Law Program Graduate School Dankook University Yongin Korea Republic of

ISBN: (纸本)9780646953373

Most companies’ new business practices are based on customer data. These practices have raised privacy concerns because of the associated risks. Privacy laws require companies to gain customer consent before using their information, which stands as the biggest roadblock to monetise this asset. Privacy literature suggests that reducing privacy concerns and building trust may increase individuals’ intention to authorise the use of personal information. Fair information practices (FIPs) are potential means to achieve this goal. However, there is lack of empirical evidence on the mechanisms through which the FIPs affect privacy concerns and trust. This research argues that FIPs load individuals with control, which has been found to influence privacy concerns and trust level. We will use an experimental design methodology to conduct the study. The results are expected to have both theoretical and managerial implications. © 2015 Libaque-Saenz et al.

关键词： Data privacy

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NWChem: Past, present, and future

arXiv

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arXiv 2020年

作者： Aprà, E. Bylaska, E.J. de Jong, W.A. Govind, N. Kowalski, K. Straatsma, T.P. Valiev, M. van Dam, H.J.J. Alexeev, Y. Anchell, J. Anisimov, V. Aquino, F.W. Atta-Fynn, R. Autschbach, J. Bauman, N.P. Becca, J.C. Bernholdt, D.E. Bhaskaran-Nair, K. Bogatko, S. Borowski, P. Boschen, J. Brabec, J. Bruner, A. Cauët, E. Chen, Y. Chuev, G.N. Cramer, C.J. Daily, J. Deegan, M.J.O. Dunning, T.H. Dupuis, M. Dyall, K.G. Fann, G.I. Fischer, S.A. Fonari, A. Früchtl, H. Gagliardi, L. Garza, J. Gawande, N. Ghosh, S. Glaesemann, K. Götz, A.W. Hammond, J. Helms, V. Hermes, E.D. Hirao, K. Hirata, S. Jacquelin, M. Jensen, L. Johnson, B.G. Jónsson, H. Kendall, R.A. Klemm, M. Kobayashi, R. Konkov, V. Krishnamoorthy, S. Krishnan, M. Lin, Z. Lins, R.D. Littlefield, R.J. Logsdail, A.J. Lopata, K. Ma, W. Marenich, A.V. Martin del Campo, J. Mejia-Rodriguez, D. Moore, J.E. Mullin, J.M. Nakajima, T. Nascimento, D.R. Nichols, J.A. Nichols, P.J. Nieplocha, J. Otero-de-la-Roza, A. Palmer, B. Panyala, A. Pirojsirikul, T. Peng, B. Peverati, R. Pittner, J. Pollack, L. Richard, R.M. Sadayappan, P. Schatz, G.C. Shelton, W.A. Silverstein, D.W. Smith, D.M.A. Soares, T.A. Song, D. Swart, M. Taylor, H.L. Thomas, G.S. Tipparaju, V. Truhlar, D.G. Tsemekhman, K. van Voorhis, T. Vázquez-Mayagoitia, Á. Verma, P. Villa, O. Vishnu, A. Vogiatzis, K.D. Wang, D. Weare, J.H. Williamson, M.J. Windus, T.L. Woliński, K. Wong, A.T. Wu, Q. Yang, C. Yu, Q. Zacharias, M. Zhang, Z. Zhao, Y. Harrison, R.J. Pacific Northwest National Laboratory RichlandWA99352 United States Lawrence Berkeley National Laboratory BerkeleyCA94720 United States National Center for Computational Sciences Oak Ridge National Laboratory Oak RidgeTN37831 United States Brookhaven National Laboratory UptonNY11973 United States Argonne Leadership Computing Facility Argonne National Laboratory ArgonneIL60439 United States Intel Corporation Santa ClaraCA95054 United States QSimulate CambridgeMA02139 United States Department of Physics University of Texas at Arlington ArlingtonTX76019 United States Department of Chemistry University at Buffalo State University of New York BuffaloNY14260 United States Department of Chemistry Pennsylvania State University University ParkPA16802 United States Oak Ridge National Laboratory Oak RidgeTN37831 United States Washington University St. LouisMO63130 United States 4G Clinical WellesleyMA02481 United States Faculty of Chemistry Maria Curie-Sklodowska University in Lublin Lublin20-031 Poland Department of Chemistry Iowa State University AmesIA50011 United States J. Heyrovský Institute of Physical Chemistry Academy of Sciences of the Czech Republic Prague 818223 Czech Republic Department of Chemistry and Physics University of Tennessee at Martin MartinTN38238 United States Université libre de Bruxelles BrusselsB-1050 Belgium Facebook Menlo ParkCA94025 United States Institute of Theoretical and Experimental Biophysics Russian Academy of Science Pushchino Moscow142290 Russia Department of Chemistry Chemical Theory Center Supercomputing Institute University of Minnesota MinneapolisMN55455 United States SKAO Jodrell Bank Observatory MacclesfieldSK11 9DL United Kingdom Department of Chemistry University of Washington SeattleWA98195 United States Dirac Solutions PortlandOR97229 United States Chemistry Division U. S. Naval Research Laboratory WashingtonDC20375 United States School of Chemistry and Biochemis

Specialized computational chemistry packages have permanently reshaped the landscape of chemical and materials science by providing tools to support and guide experimental efforts and for the prediction of atomistic and electronic properties. In this regard, electronic structure packages have played a special role by using first-principle-driven methodologies to model complex chemical and materials processes. Over the last few decades, the rapid development of computing technologies and the tremendous increase in computational power have offered a unique chance to study complex transformations using sophisticated and predictive many-body techniques that describe correlated behavior of electrons in molecular and condensed phase systems at different levels of theory. In enabling these simulations, novel parallel algorithms have been able to take advantage of computational resources to address the polynomial scaling of electronic structure methods. In this paper, we briefly review the NWChem computational chemistry suite, including its history, design principles, parallel tools, current capabilities, outreach and outlook. Copyright © 2020, The Authors. All rights reserved.

关键词： Computational chemistry

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Author Correction: BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

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Nature methods 2024年第10期21卷 1959页

作者： Linus Manubens-Gil Zhi Zhou Hanbo Chen Arvind Ramanathan Xiaoxiao Liu Yufeng Liu Alessandro Bria Todd Gillette Zongcai Ruan Jian Yang Miroslav Radojević Ting Zhao Li Cheng Lei Qu Siqi Liu Kristofer E Bouchard Lin Gu Weidong Cai Shuiwang Ji Badrinath Roysam Ching-Wei Wang Hongchuan Yu Amos Sironi Daniel Maxim Iascone Jie Zhou Erhan Bas Eduardo Conde-Sousa Paulo Aguiar Xiang Li Yujie Li Sumit Nanda Yuan Wang Leila Muresan Pascal Fua Bing Ye Hai-Yan He Jochen F Staiger Manuel Peter Daniel N Cox Michel Simonneau Marcel Oberlaender Gregory Jefferis Kei Ito Paloma Gonzalez-Bellido Jinhyun Kim Edwin Rubel Hollis T Cline Hongkui Zeng Aljoscha Nern Ann-Shyn Chiang Jianhua Yao Jane Roskams Rick Livesey Janine Stevens Tianming Liu Chinh Dang Yike Guo Ning Zhong Georgia Tourassi Sean Hill Michael Hawrylycz Christof Koch Erik Meijering Giorgio A Ascoli Hanchuan Peng Institute for Brain and Intelligence Southeast University Nanjing China. Microsoft Corporation Redmond WA USA. Tencent AI Lab Bellevue WA USA. Computing Environment and Life Sciences Directorate Argonne National Laboratory Lemont IL USA. Kaya Medical Seattle WA USA. University of Cassino and Southern Lazio Cassino Italy. Center for Neural Informatics Structures and Plasticity Krasnow Institute for Advanced Study George Mason University Fairfax VA USA. Faculty of Information Technology Beijing University of Technology Beijing China. Beijing International Collaboration Base on Brain Informatics and Wisdom Services Beijing China. Nuctech Netherlands Rotterdam the Netherlands. Janelia Research Campus Howard Hughes Medical Institute Ashburn VA USA. Department of Electrical and Computer Engineering University of Alberta Edmonton Alberta Canada. Ministry of Education Key Laboratory of Intelligent Computation and Signal Processing Anhui University Hefei China. Paige AI New York NY USA. Scientific Data Division and Biological Systems and Engineering Division Lawrence Berkeley National Lab Berkeley CA USA. Helen Wills Neuroscience Institute and Redwood Center for Theoretical Neuroscience UC Berkeley Berkeley CA USA. RIKEN AIP Tokyo Japan. Research Center for Advanced Science and Technology (RCAST) The University of Tokyo Tokyo Japan. School of Computer Science University of Sydney Sydney New South Wales Australia. Texas A&M University College Station TX USA. Cullen College of Engineering University of Houston Houston TX USA. Graduate Institute of Biomedical Engineering National Taiwan University of Science and Technology Taipei Taiwan. National Centre for Computer Animation Bournemouth University Poole UK. PROPHESEE Paris France. Department of Neuroscience Columbia University New York NY USA. Mortimer B. Zuckerman Mind Brain Behavior Institute Columbia University New York NY USA. Department of Computer Science Northern Illinois Universit

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Connections among university faculty engaged in the first two years of engineering and their impact on faculty attitudes and practice 123

Connections among university faculty engaged in the first tw...

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123rd ASEE Annual Conference and Exposition

作者： Middleton, James A. Krause, Stephen J. Judson, Eugene Culbertson, Robert J. Ross, Lydia Hjelmstad, Keith D. Park, Yong-Seok Collofello, James Smith, Bethany B. Department of Mechanical and Aerospace Engineering United States Center for Research on Education in Science Mathematics Engineering and Technology Arizona State University United States Materials Science Program Fulton School of Engineering Arizona State University United States Arizona State University Mary Lou Fulton Teachers College United States Department of Physics United States Arizona State University Department of Civil Engineering School of Sustainable Engineering and the Built Environment United States Arizona State University Department of Computer Science and Engineering School of Computing Informatics and Decision Systems Engineering Ira A. Fulton Schools of Engineering United States

This research paper examined the connectedness of STEM faculty to others both within and across academic departments who might be potential resources for diffusion of Learner-centered practices, and the impact of participants' social networks on their Learner-centered beliefs and practices. The research question under investigation was: To what extent is the degree of social connectedness among faculty within and among STEM departments related to the degree of implementation of Learner-centered practices? Participants were recruited from Physics, Chemistry, Mathematics departments, and from departments in the College of Engineering. The sample began with 21 randomly-selected faculty from each department engaged in the STEM instruction of first and second year engineering students. The second level consisted of the colleagues identified by the first level as people they utilize as resources for improving their instruction. The connectedness of faculty in the network was examined using network analysis, focusing on both number of connections, and depth of connections. Attitude and Observation Protocol measures were used as outcomes. Results show that faculty classified as Learner-Centered had deeper and more extensive social networks. Results are discussed in terms of the need for intra- and inter-departmental faculty professional development experiences that both introduce and encourage faculty to trial new tools and techniques, but that also run for the longterm, supporting collaborative organizations of faculty working together to transform early engineering experiences. © American Society for Engineering Education, 2016.

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Risk Management Lifecycle Implementation Services in SOA

Risk Management Lifecycle Implementation Services in SOA

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International Conference on Information Technology: New Generations (ITNG)

作者： Erasmo Leite Monteiro Paulo Caetano da Silva Master Program in Systems and Computing Salvador University - UNIFACS Salvador Brazil Master Program in Syst. & Comput. Universidade Salvador Salvador BA BR

A structured environment of IT risk management can influence an improving the flexibility and adaptability of business processes and information technology, including information systems in a secure manner. As with all other technologies, the adoption of SOA involves risk. These risks often manifest themselves during an implementation of the SOA solution and arise mainly due to insufficient detail in the SOA project. In this paper, we present the main ideas about the impact of IT risks on the life cycle of the implementation of service-oriented architecture and how we could minimize these impacts by using a model of managing IT risks.

关键词： Service-oriented architecture Risk management Security Organizations Semiconductor optical amplifiers Information technology

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Forecasting: theory and practice

arXiv

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arXiv 2020年

作者： Petropoulos, Fotios Apiletti, Daniele Assimakopoulos, Vassilios Babai, Mohamed Zied Barrow, Devon K. Taieb, Souhaib Ben Bergmeir, Christoph Bessa, Ricardo J. Bijak, Jakub Boylan, John E. Browell, Jethro Carnevale, Claudio Castle, Jennifer L. Cirillo, Pasquale Clements, Michael P. Cordeiro, Clara Oliveira, Fernando Luiz Cyrino de Baets, Shari Dokumentov, Alexander Ellison, Joanne Fiszeder, Piotr Franses, Philip Hans Frazier, David T. Gilliland, Michael Gönül, M. Sinan Goodwin, Paul Grossi, Luigi Grushka-Cockayne, Yael Guidolin, Mariangela Guidolin, Massimo Gunter, Ulrich Guo, Xiaojia Guseo, Renato Harvey, Nigel Hendry, David F. Hollyman, Ross Januschowski, Tim Jeon, Jooyoung Jose, Victor Richmond R. Kang, Yanfei Koehler, Anne B. Kolassa, Stephan Kourentzes, Nikolaos Leva, Sonia Li, Feng Litsiou, Konstantia Makridakis, Spyros Martin, Gael M. Martinez, Andrew B. Meeran, Sheik Modis, Theodore Nikolopoulos, Konstantinos Önkal, Dilek Paccagnini, Alessia Panagiotelis, Anastasios Panapakidis, Ioannis Pavía, Jose M. Pedio, Manuela Pedregal, Diego J. Pinson, Pierre Ramos, Patrícia Rapach, David E. Reade, J. James Rostami-Tabar, Bahman Rubaszek, Michal Sermpinis, Georgios Shang, Han Lin Spiliotis, Evangelos Syntetos, Aris A. Talagala, Priyanga Dilini Talagala, Thiyanga S. Tashman, Len Thomakos, Dimitrios Thorarinsdottir, Thordis Todini, Ezio Arenas, Juan Ramón Trapero Wang, Xiaoqian Winkler, Robert L. Yusupova, Alisa Ziel, Florian School of Management University of Bath United Kingdom Politecnico di Torino Turin Italy Forecasting and Strategy Unit School of Electrical and Computer Engineering National Technical University of Athens Greece Kedge Business School France Department of Management Birmingham Business School University of Birmingham United Kingdom Belgium Faculty of Information Technology Monash University Melbourne Australia INESC TEC – Institute for Systems and Computer Engineering Technology and Science Porto Portugal Department of Social Statistics and Demography University of Southampton United Kingdom Centre for Marketing Analytics and Forecasting Lancaster University Management School Lancaster University United Kingdom School of Mathematics and Statistics University of Glasgow United Kingdom Department of Mechanical and Industrial Engineering University of Brescia Italy Magdalen College University of Oxford United Kingdom ZHAW School of Management and Law Zurich University of Applied Sciences Switzerland ICMA Centre Henley Business School University of Reading United Kingdom Faculdade de Ciências e Tecnologia Universidade do Algarve Portugal CEAUL Faculdade de Ciências Universidade de Lisboa Portugal Brazil Department of Business Informatics and Operations Management Faculty of Economics and Business Administration Universiteit Gent Belgium Let’s Forecast Australia Faculty of Economic Sciences and Management Nicolaus Copernicus University in Torun Poland Econometric Institute Erasmus School of Economics Rotterdam Netherlands Department of Econometrics and Business Statistics Monash University Melbourne Australia SAS United States Newcastle Business School Northumbria University Newcastle upon Tyne United Kingdom Department of Statistical Sciences University of Padua Italy Darden School of Business University of Virginia United States Finance Department Bocconi University Baffi-CAREFIN Centre Milan Italy Department of Tourism and Service Management MODUL Un

Forecasting has always been at the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The large number of forecasting applications calls for a diverse set of forecasting methods to tackle real-life challenges. This article provides a non-systematic review of the theory and the practice of forecasting. We provide an overview of a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts. We do not claim that this review is an exhaustive list of methods and applications. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of forecasting theory and practice. Given its encyclopedic nature, the intended mode of reading is non-linear. We offer cross-references to allow the readers to navigate through the various topics. We complement the theoretical concepts and applications covered by large lists of free or open-source software implementations and publicly-available databases. © 2020, CC BY.

关键词： Forecasting

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