检索结果-内蒙古大学图书馆

arXiv 2021年

作者： Lukyanenko, Stanislav Jang, Won-Dong Wei, Donglai Struyven, Robbert Kim, Yoon Leahy, Brian Yang, Helen Rush, Alexander Ben-Yosef, Dalit Needleman, Daniel Pfister, Hanspeter Department of Informatics Technical University of Munich Germany School of Engineering and Applied Sciences Harvard University United States Department of Molecular and Cellular Biology Harvard University United States Graduate Program in Biophysics Harvard University United States University College London United Kingdom MIT United States Cornell University United States Center for Computational Biology Flatiron Institute United States Lis Maternity Hospital Tel-Aviv Sourasky Medical Center Israel Cell and Developmental Biology Tel-Aviv University Israel

The developmental process of embryos follows a monotonic order. An embryo can progressively cleave from one cell to multiple cells and finally transform to morula and blastocyst. For time-lapse videos of embryos, most existing developmental stage classification methods conduct per-frame predictions using an image frame at each time step. However, classification using only images suffers from overlapping between cells and imbalance between stages. Temporal information can be valuable in addressing this problem by capturing movements between neighboring frames. In this work, we propose a two-stream model for developmental stage classification. Unlike previous methods, our two-stream model accepts both temporal and image information. We develop a linear-chain conditional random field (CRF) on top of neural network features extracted from the temporal and image streams to make use of both modalities. The linear-chain CRF formulation enables tractable training of global sequential models over multiple frames while also making it possible to inject monotonic development order constraints into the learning process explicitly. We demonstrate our algorithm on two time-lapse embryo video datasets: i) mouse and ii) human embryo datasets. Our method achieves 98.1% and 80.6% for mouse and human embryo stage classification, respectively. Our approach will enable more profound clinical and biological studies and suggests a new direction for developmental stage classification by utilizing temporal information. Copyright © 2021, The Authors. All rights reserved.

关键词： Classification (of information)

来源：评论

学校读者我要写书评

暂无评论

Retraction Note: A new EEG software that supports emotion recognition by using an autonomous approach

引用

Neural Computing and Applications 2024年第28期36卷 17759-17759页

作者： Munoz, Roberto Olivares, Rodrigo Taramasco, Carla Villarroel, Rodolfo Soto, Ricardo Alonso-Sánchez, María Francisca Merino, Erick de Albuquerque, Victor Hugo C. Escuela de Ingeniería Civil Informática Universidad de Valparaíso Valparaíso Chile Centro de Investigación y Desarrollo en Ingeniería en Salud Universidad de Valparaíso Valparaíso Chile Pontificia Universidad Católica de Valparaíso Valparaíso Chile Escuela de Fonoaudiología Facultad de Salud Universidad Santo Tomás Viña del Mar Chile Graduate Program in Applied Informatics Laboratory of Bioinformatics University of Fortaleza Fortaleza Brazil

来源：评论

学校读者我要写书评

暂无评论

Microbiota analysis in the hemodialysis population - Focusing on Enterobacteriaceae

引用

Journal of microbiology, immunology, and infection = Wei mian yu gan ran za zhi 2023年第2期56卷 311-323页

作者： Hon-Yen Wu Yi-Tsung Lin Wan-Chuan Tsai Yen-Ling Chiu Mei-Ju Ko Ju-Yeh Yang Mei-Fen Pai Hsin-Sui Hsu Shih-Ping Hsu Yu-Sen Peng Chun-Hsing Liao Department of Internal Medicine Far Eastern Memorial Hospital New Taipei City Taiwan School of Medicine College of Medicine National Yang Ming Chiao Tung University Taipei City Taiwan Institute of Epidemiology and Preventive Medicine College of Public Health National Taiwan University Taipei City Taiwan Department of Internal Medicine National Taiwan University Hospital and College of Medicine Taipei City Taiwan. Division of Infectious Diseases Department of Medicine Taipei Veterans General Hospital Taipei City Taiwan Institute of Emergency and Critical Care Medicine National Yang Ming Chiao Tung University Taipei City Taiwan. Center for General Education Lee-Ming Institute of Technology New Taipei City Taiwan. Graduate Program in Biomedical Informatics Yuan Ze University Taoyuan City Taiwan Graduate Institute of Medicine Yuan Ze University Taoyuan City Taiwan. Department of Dermatology Taipei City Hospital Taipei City Taiwan Department of Dermatology National Taiwan University Hospital and College of Medicine Taipei City Taiwan. Department of Internal Medicine Far Eastern Memorial Hospital New Taipei City Taiwan. School of Life Science National Taiwan Normal University Taipei City Taiwan. Department of Applied Cosmetology Lee-Ming Institute of Technology New Taipei City Taiwan Department of Healthcare Administration Asia Eastern University of Science and Technology New Taipei City Taiwan. School of Medicine College of Medicine National Yang Ming Chiao Tung University Taipei City Taiwan. Electronic address: liaoch@femh.org.tw.

BACKGROUND:Infection is a recognized risk factor for mortality among hemodialysis (HD) population, including infection caused by Enterobacteriaceae. We aimed to investigate Enterobacteriaceae in gut microbiota among HD patients and to analyze associations between microbiota and clinical parameters. METHODS:This prospective study of microbiota analysis in HD patients was conducted in April-May 2019. A control group without recent antibiotic use or hospitalization was used for comparison. Stool samples underwent 16S rRNA sequencing, using Greengenes 16S rRNA database for microbiota analysis. RESULTS:Among 96 hemodialysis (HD) patients, mean age was 61.9 ± 0.8 years and mean duration of HD was 6.5 ± 0.7 years. No significant differences were found in alpha diversity between HD and control groups (HD group 949.5, controls 898; p = 0.16) although significant between-group differences were found in beta diversity (p < 0.001). At phylum level, HD group had a higher abundance of Firmicutes and Proteobacteria, but lower abundance of Bacteriodetes. At genus level, Escherichia-Shigella complex increased among HD patients who had hospitalization with 1 year (median 0.024 vs 0.004, p = 0.054) and Klebsiella was associated with emergency room visit within 1 year among HD patients (p = 0.002). CONCLUSIONS:Alpha diversity in HD patients is not lower than that in healthy controls but significant between-group differences are found in microbiota composition according to beta diversity, due to decreased Bacteriodetes and increased Firmicutes and Proteobacteria. Deeper microbiota analyses for Enterobacteriaceae are necessary. Whether change in dietary components can help to decrease mortality among dialysis population warrants further research.

关键词： Chronic Enterobacteriaceae Gastrointestinal microbiome Hemodialysis Kidney failure Microbiota Prognosis

来源：评论

学校读者我要写书评

暂无评论

Training of Physical Neural Networks

arXiv

引用

arXiv 2024年

作者： Momeni, Ali Rahmani, Babak Scellier, Benjamin Wright, Logan G. McMahon, Peter L. Wanjura, Clara C. Li, Yuhang Skalli, Anas Berloff, Natalia G. Onodera, Tatsuhiro Oguz, Ilker Morichetti, Francesco del Hougne, Philipp Le Gallo, Manuel Sebastian, Abu Mirhoseini, Azalia Zhang, Cheng Marković, Danijela Brunner, Daniel Moser, Christophe Gigan, Sylvain Marquardt, Florian Ozcan, Aydogan Grollier, Julie Liu, Andrea J. Psaltis, Demetri Alù, Andrea Fleury, Romain Lausanne Switzerland Microsoft Research 198 Cambridge Science Park CambridgeCB4 0AB United Kingdom Rain AI San Francisco United States Department of Applied Physics Yale University CT United States School of Applied and Engineering Physics Cornell University IthacaNY14853 United States Max Planck Institute for the Science of Light Staudtstraße 2 Erlangen91058 Germany Department of Electrical and Computer Engineering University of California Los AngelesCA90095 United States FEMTO-ST Institute Optics Department CNRS University Bourgogne Franche-Comté Besançon25030 Cedex France Department of Applied Mathematics and Theoretical Physics University of Cambridge Cambridge United Kingdom NTT Physics and Informatics Laboratories NTT Research Inc. Sunnyvale United States Lausanne Switzerland Dipartimento di Elettronica Informazione e Bioingegneria Politecnico di Milano Milan Italy Univ Rennes CNRS IETR UMR 6164 RennesF-35000 France IBM Research Europe– Zurich Rüschlikon8803 Switzerland Department of Computer Science Stanford University United States Google DeepMind 1600 Amphitheatre Parkway Mountain ViewCA94043 United States Unité Mixte de Physique CNRS/Thales CNRS Thales Université Paris-Saclay Palaiseau France Laboratoire Kastler Brossel Sorbonne Université École Normale Supérieure Collège de France CNRS UMR 8552 Paris France Laboratoire Albert Fert CNRS Thales UniversitéParis-Saclay Palaiseau91767 France Department of Physics and Astronomy University of Pennsylvania PhiladelphiaPA19104 United States Lausanne Switzerland Photonics Initiative Advanced Science Research Center City University of New York New YorkNY10031 United States Physics Program Graduate Center City University of New York New YorkNY10016 United States

Physical neural networks (PNNs) are a class of neural-like networks that leverage the properties of physical systems to perform computation. While PNNs are so far a niche research area with small-scale laboratory demonstrations, they are arguably one of the most underappreciated important opportunities in modern artificial intelligence (AI). Could we train AI models 1000x larger than current ones? Could we do this and also have them perform inference locally and privately on edge devices, such as smartphones or sensors? Research over the past few years has shown that the answer to all these questions is likely "textityes, with enough research": PNNs could one day radically change what is possible and practical for AI systems. To do this will however require rethinking both how AI models work, and how they are trained – primarily by considering the problems through the constraints of the underlying hardware physics. To train PNNs at large scale, many methods including backpropagation-based and backpropagation-free approaches are now being explored. These methods have various trade-offs, and so far no method has been shown to scale to the same scale and performance as the backpropagation algorithm widely used in deep learning today. However, this is rapidly changing, and a diverse ecosystem of training techniques provides clues for how PNNs may one day be utilized to create both more efficient realizations of current-scale AI models, and to enable unprecedented-scale models. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Stroke Lesion Detection Using Convolutional Neural Networks

Stroke Lesion Detection Using Convolutional Neural Networks

引用

2018 International Joint Conference on Neural Networks, IJCNN 2018

作者： Pereira, Danillo Roberto Filho, Pedro P. Rebouças De Rosa, Gustavo Henrique Papa, João Paulo De Albuquerque, Victor Hugo C. Department of Computing São Paulo State University Bauru SP Brazil Federal Institute of Education Science and Technology of Ceará CE Brazil Graduate Program in Applied Informatics University of Fortaleza Fortaleza CE Brazil

ISBN: (纸本)9781509060146

Stroke is an injury that affects the brain tissue, mainly caused by changes in the blood supply to a particular region of the brain. As consequence, some specific functions related to that affected region can be reduced, decreasing the quality of life of the patient. In this work, we deal with the problem of stroke detection in Computed Tomography (CT) images using Convolutional Neural Networks (CNN) optimized by Particle Swarm optimization (PSO). We considered two different kinds of strokes, ischemic and hemorrhagic, as well as making available a public dataset to foster the research related to stroke detection in the human brain. The dataset comprises three different types of images for each case, i.e., the original CT image, one with the segmented cranium and an additional one with the radiological density's map. The results evidenced that CNN's are suitable to deal with stroke detection, obtaining promising results. © 2018 IEEE.

关键词： Convolution

来源：评论

学校读者我要写书评

暂无评论

Performance of multimodal prediction models for intracerebral hemorrhage outcomes using real-world data

引用

International Journal of Medical informatics 2025年 202卷 105989-105989页

作者： Matsumoto, Koutarou Suzuki, Masahiro Ishihara, Kazuaki Tokunaga, Koki Matsuda, Katsuhiko Chen, Jenhui Yamashiro, Shigeo Soejima, Hidehisa Nakashima, Naoki Kamouchi, Masahiro Department of Health Care Administration and Management of Medical Sciences Kyushu University Fukuoka Japan Graduate Degree Program of Applied Data Sciences Sophia University Tokyo Japan Department of Computer Science and Information Engineering Chang Gung University Taoyuan Taiwan Department of Pharmacy Saiseikai Kumamoto Hospital Kumamoto Japan Department of Radiology Saiseikai Kumamoto Hospital Kumamoto Japan Division of Neurosurgery Saiseikai Kumamoto Hospital Kumamoto Japan Institute for Medical Information Research and Analysis Saiseikai Kumamoto Hospital Kumamoto Japan Medical Information Center Kyushu University Hospital Fukuoka Japan Department of Medical Informatics of Medical Sciences Kyushu University Fukuoka Japan Center for Cohort Studies of Medical Sciences Kyushu University Fukuoka Japan

Background: We aimed to develop and validate multimodal models integrating computed tomography (CT) images, text and tabular clinical data to predict poor functional outcomes and in-hospital mortality in patients with intracerebral hemorrhage (ICH). These models were designed to assist non-specialists in emergency settings with limited access to stroke specialists. Methods: A retrospective analysis of 527 patients with ICH admitted to a Japanese tertiary hospital between April 2019 and February 2022 was conducted. Deep learning techniques were used to extract features from three-dimensional CT images and unstructured data, which were then combined with tabular data to develop an L1-regularized logistic regression model to predict poor functional outcomes (modified Rankin scale score 3–6) and in-hospital mortality. The model's performance was evaluated by assessing discrimination metrics, calibration plots, and decision curve analysis (DCA) using temporal validation data. Results: The multimodal model utilizing both imaging and text data, such as medical interviews, exhibited the highest performance in predicting poor functional outcomes. In contrast, the model that combined imaging with tabular data, including physiological and laboratory results, demonstrated the best predictive performance for in-hospital mortality. These models exhibited high discriminative performance, with areas under the receiver operating curve (AUROCs) of 0.86 (95% CI: 0.79–0.92) and 0.91 (95% CI: 0.84–0.96) for poor functional outcomes and in-hospital mortality, respectively. Calibration was satisfactory for predicting poor functional outcomes, but requires refinement for mortality prediction. The models performed similar to or better than conventional risk scores, and DCA curves supported their clinical utility. Conclusion: Multimodal prediction models have the potential to aid non-specialists in making informed decisions regarding ICH cases in emergency departments as part of clinical dec

关键词： Logistic regression

来源：评论

学校读者我要写书评

暂无评论

Cross-calibration and combined analysis of the CTA-LST prototype and the MAGIC telescopes 37

Cross-calibration and combined analysis of the CTA-LST proto...

引用

37th International Cosmic Ray Conference, ICRC 2021

作者： Ohtani, Y. Berti, A. Depaoli, D. Di Pierro, F. Green, D. Heckmann, L. Hütten, M. Inada, T. López-Coto, R. Medina, E. Moralejo, A. Morcuende, D. Pirola, G. Strzys, M. Suda, Y. Vovk, I. Abe, H. Aguasca, A. Agudo, I. Antonelli, L.A. Aramo, C. Armstrong, T. Artero, M. Asano, K. Ashkar, H. Aubert, P. Baktash, A. Bamba, A. Baquero Larriva, A. Baroncelli, L. Barres de Almeida, U. Barrio, J.A. Batkovic, I. Becerra González, J. Bernardos, M.I. Biederbeck, N. Bigongiari, C. Blanch, O. Bonnoli, G. Bordas, P. Bose, D. Bulgarelli, A. Burelli, I. Buscemi, M. Cardillo, M. Caroff, S. Carosi, A. Cassol, F. Cerruti, M. Chai, Y. Cheng, K. Chikawa, M. Chytka, L. Contreras, J.L. Cortina, J. Costantini, H. Dalchenko, M. De Angelis, A. de Bony de Lavergne, M. Deleglise, G. Delgado, C. Delgado Mengual, J. della Volpe, D. Di Venere, L. Díaz, C. Dominik, R.M. Dominis Prester, D. Donini, A. Dorner, D. Doro, M. Elsässer, D. Emery, G. Escudero, J. Fiasson, A. Foffano, L. Fonseca, M.V. Freixas Coromina, L. Fukami, S. Fukazawa, Y. Garcia, E. Garcia López, R. Giglietto, N. Giordano, F. Gliwny, P. Godinovic, N. Grespan, P. Gunji, S. Hackfeld, J. Hadasch, D. Hahn, A. Hassan, T. Hayashi, K. Heller, M. Herrera Llorente, J. Hirotani, K. Hoffmann, D. Horns, D. Houles, J. Hrabovsky, M. Hrupec, D. Hui, D. Inome, Y. Iori, M. Ishio, K. Iwamura, Y. Jacquemont, M. Jimenez Martinez, I. Jouvin, L. Jurysek, J. Kagaya, M. Karas, V. Katagiri, H. Kataoka, J. Kerszberg, D. Kobayashi, Y. Kong, A. Kubo, H. Kushida, J. Lamanna, G. Lamastra, A. Le Flour, T. Longo, F. López-Moya, M. López-Oramas, A. Luque-Escamilla, P.L. Majumdar, P. Makariev, M. Mandat, D. Manganaro, M. Mannheim, K. Mariotti, M. Marquez, P. Marsella, G. Martí, J. Martinez, O. Martínez, G. Martínez, M. Marusevec, P. Mas, A. Maurin, G. Mazin, D. Mestre Guillen, E. Micanovic, S. Miceli, D. Miener, T. Miranda, J.M. Miranda, L.D.M. Mirzoyan, R. Mizuno, T. Molina, E. Montaruli, T. Monteiro, I. Moretti, E. ICRR The University of Tokyo 1-5-1 Kashiwa-no-ha Chiba Kashiwa City Japan Max Planck Institute for Physics Föhringer Ring 6 München80805 Germany INFN Sezione di Torino Via Pietro Giuria 1 Torino10125 Italy INFN Sezione di Padova Via Francesco Marzolo 8 Padova35131 Italy IFAE Edifici Cn Campus Universitat Autònoma de Barcelona Barcelona08193 Spain Universidad Complutense de Madrid Av. Séneca 2 Madrid28040 Spain Physics Program Graduate School of Advanced Science and Engineering Hiroshima University Hiroshima739-8526 Japan Departament de Física Quàntica i Astrofísica Institut de Ciències del Cosmos Universitat de Barcelona IEEC-UB Spain Instituto de Astrofísica de Andalucía-CSIC Spain INFN Sezione di Napoli Italy Aix Marseille Univ CNRS IN2P3 CPPM France IRFU CEA Université Paris-Saclay France LAPP Univ. Grenoble Alpes Univ. Savoie Mont Blanc CNRS-IN2P3 Annecy France Universität Hamburg Institut für Experimentalphysik Germany Graduate School of Science University of Tokyo Japan INAF - Osservatorio di Astrofisica e Scienza dello Spazio di Bologna Italy INFN Sezione di Padova Università degli Studi di Padova Italy Instituto de Astrofísica de Canarias Departamento de Astrofísica Universidad de La Laguna Tenerife La LagunaE-38200 Spain Department of Physics TU Dortmund University Germany INFN Sezione di Trieste Università degli Studi di Udine Italy INFN Sezione di Catania Italy University of Geneva Département de Physique Nucléaire et Corpusculaire Italy Palacky University Olomouc Faculty of Science Czech Republic Port d'Informació Científica Spain Dipartimento di Fisica Universitá degli Studi di Torino Italy University of Rijeka Department of Physics Croatia Institute for Theoretical Physics and Astrophysics Universität Würzburg Germany Physics Program Graduate School of Advanced Science and Engineering Hiroshima University Japan INFN Sezione di Bari Politecnico di Bari Italy Faculty of Physics and Applied Informatics University of

The Cherenkov Telescope Array (CTA) will be the next generation gamma-ray observatory, which will consist of three kinds of telescopes of different sizes. Among those, the Large Size Telescope (LST) will be the most sensitive in the low energy range starting from 20 GeV. The prototype LST (LST-1) proposed for CTA was inaugurated in October 2018 in the northern hemisphere site, La Palma (Spain), and is currently in its commissioning phase. MAGIC is a system of two gamma-ray Cherenkov telescopes of the current generation, located approximately 100 m away from LST-1, that have been operating in stereoscopic mode since 2009. Since LST-1 and MAGIC can observe the same air shower events, we can compare the brightness of showers, estimated energies of gamma rays, and other parameters event by event, which can be used to cross-calibrate the telescopes. Ultimately, by performing combined analyses of the events triggering the three telescopes, we can reconstruct the shower geometry more accurately, leading to better energy and angular resolutions, and a better discrimination of the background showers initiated by cosmic rays. For that purpose, as part of the commissioning of LST-1, we performed joint observations of established gamma-ray sources with LST-1 and MAGIC. Also, we have developed Monte Carlo simulations for such joint observations and an analysis pipeline which finds event coincidence in the offline analysis based on their timestamps. In this work, we present the first detection of an astronomical source, the Crab Nebula, with combined observation of LST-1 and MAGIC. Moreover, we show results of the inter-telescope cross-calibration obtained using Crab Nebula data taken during joint observations with LST-1 and MAGIC. © Copyright owned by the author(s) under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0)

关键词： Telescopes

来源：评论

学校读者我要写书评

暂无评论

μ-brkga: A parallel biased random-key genetic algorithm applied to nesting problems 47

μ-brkga: A parallel biased random-key genetic algorithm app...

引用

47th International Conference on Computers and Industrial Engineering: How Digital Platforms and Industrial Engineering are Transforming Industry and Services, CIE 2017

作者： Amaro, Bonfim Pinheiro, Plácido Rogério Graduate Program in Applied Informatics University of Fortaleza Fortaleza Brazil

The Nesting Problems are a special class of cutting and packing (C&P) problems in which a set of items with arbitrary geometry must be positioned in a surface with a non-fixed length, aiming a minimization of the require area for allocation of all given demand. It is commonly applied in industries that operate in various types of manufacturers and exporters activities (e.g., shipbuilding, clothes, and glass). In this work, we propose a parallel biased random-key genetic algorithm with multiple populations (μ-BRKGA) to the Nesting Problems using a collision-free region (CFR) concept as position method, intending at a more efficient and fastest layout compaction to get "good" solutions. The problem layout of the proposed algorithm, a solution, is represented for placement order in the surface and the corresponding orientation. As the way of to evaluate our (μ-BRKGA) algorithm, computational tests using benchmark instances were applied, discussed and compared with different existing literature methods.

关键词： Genetic algorithms

来源：评论

学校读者我要写书评

暂无评论

Generation of Terahertz Transients from Co2Fe0.4Mn0.6Si Heusler alloy/Heavy-Metal Bilayers

Generation of Terahertz Transients from Co2Fe0.4Mn0.6Si Heus...

引用

International Conference on Infrared and Millimeter Waves

作者： S. Heidtfeld R. Adam T. Kubota K. Takanashi S. Suga D. Cao C. Greb F. Wang C. Schmitz-Antoniak D.E. Bürgler G. Chen I. Komissarov R. Sobolewski C. M. Schneider Research Centre Jülich Peter Grünberg Institute Jülich Germany University Duisburg-Essen Duisburg Germany Institute for Materials Research Tohoku University Sendai Japan Center for Spintronics Research Network Tohoku University Sendai Japan Center for Science and Innovation in Spintronics (Core Research Cluster) Tohoku University Sendai Japan Institute of Scientific and Industrial Research Osaka University Ibaraki Japan College of Physics National Demonstration Center for Applied Physics Education Qindao University Qingdao China Materials Science Graduate Program University of Rochester Rochester New York USA University of Rochester Rochester New York USA Belarusian State University of Informatics and Radioelectronics Minsk Belarus University of California Davis Davis California USA

We generated electro magnetic transients with a frequency content up to the terahertz regime from Co 2 Fe 0.4 Mn 0.6 Si Heusler alloy/heavy-metal bilayers by excitation with fs-laser pulses. We ascribe the generation process to the inverse spin Hall effect. We compared our results with ferromagnetic resonance measurements to investigate the efficiency of interfacial spin currents. We observed that the efficiency of the THz radiation can be described by the spin-orbit coupling, nonetheless the interface properties play an important role.

关键词： Ferromagnetic resonance Couplings Current measurement Hall effect Transient analysis

来源：评论

学校读者我要写书评

暂无评论

Task assignment to distributed teams based on a qualitative multi-criteria approach 12

Task assignment to distributed teams based on a qualitative ...

引用

12th Iberian Conference on Information Systems and Technologies, CISTI 2017

作者： Filho, Marum Simao Pinheiro, Placido Rogerio Albuquerque, Adriano Bessa Graduate Program in Applied Informatics University of Fortaleza / 7 de Setembro University Center Fortaleza Brazil Graduate Program in Applied Informatics University of Fortaleza Fortaleza Brazil

ISBN: (纸本)9789899843479

The high growth of companies in recent years has caused the transformation of local markets into global ones. The software industry is not stranger to this transformation and has been looking for effective ways to work in a distributed scenario. A critical activity in distributed projects concerns the assignment of tasks to remote teams. This is a multicriteria decision-making problem and can be supported by Verbal Decision Analysis approaches. This paper describes an approach based on Verbal Decision Analysis methods to aid decision on factors that influence the assignment of tasks to distrubuted teams. The approach is based on the ORCLASS and ZAPROS III-i methods. The first results showed that factors such as 'low turnover rate' and 'team maturity' are among those considered more important, and the factors 'personal trust', 'low labor cost', 'cultural similarities' and 'time zone' were considered less relevant in DSD context. © 2017 AISTI.

关键词： Decision making

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：