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Bulletin of the American Meteorological Society 2024年第8期105卷 S277-S330页

作者： Druckenmiller, M.L. Thoman, R.L. Moon, T.A. National Snow and Ice Data Center Cooperative Institute for Research in Environmental Sciences University of Colorado Boulder Boulder Colorado International Arctic Research Center University of Alaska Fairbanks Fairbanks Alaska National Snow and Ice Data Center Cooperative Institute for Research in Environmental Sciences University of Colorado Boulder Colorado Section for Glaciers Ice and Snow Norwegian Water Resources and Energy Directorate Oslo Norway School of Informatics Computing and Cyber Systems Northern Arizona University Flagstaff Arizona Biospherical Instruments Inc. San Diego California Geophysical Institute University of Alaska Fairbanks Fairbanks Alaska Plant Soils and Climate Department Utah State University Logan Utah Norwegian Institute for Nature Research Trondheim Norway FRAM – High North Research Centre for Climate and the Environment Tromsø Norway Geological Survey of Denmark and Greenland (GEUS) Copenhagen Denmark NOAA/NWS Alaska Region Anchorage Alaska Alaska Native Tribal Health Consortium Anchorage Alaska Geological Survey of Canada Natural Resources Canada Ottawa Canada NOAA Chemical Sciences Laboratory Boulder Colorado Arctic Geophysics Department University Centre in Svalbard Longyearbyen Norway Geology Department University Centre in Svalbard Longyearben Norway Centre National de Recherches Météorologiques Météo-France/CNRS Toulouse France Climate Research Division Environment and Climate Change Canada Toronto Canada Norwegian Polar Institute Fram Centre Tromsø Norway Danish Meteorological Institute Copenhagen Denmark Department of Physics University of Toronto Toronto Canada Department of Environmental Sciences University of Virginia Charlottesville Virginia Department of Geographical Sciences University of Maryland College Park Maryland University of Liège Liège Belgium Environment and Climate Change Canada Toronto Canada Northern Rocky Mountain Science Center U.S. Geological Survey Bozeman Montana Arctic C

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Spatial-Temporal pattern analysis and prediction of air quality in Taiwan

Spatial-Temporal pattern analysis and prediction of air qual...

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IEEE International Conference on Ubi-Media computing

作者： Ping-Wei Soh Kai-Hsiang Chen Jen-Wei Huang Hone-Jay Chu Institute of Computer and Communication Engineering National Cheng Kung University Tainan Taiwan PhD Program for Multimedia Systems and Intelligent Computing National Cheng Kung University and Academia Sinica Tainan Taiwan Department of Electrical Engineering National Cheng Kung University Tainan Taiwan Department of Geomatics National Cheng Kung University Tainan Taiwan

This study explores the spatial-temporal patterns of particulate matter (PM) in Taiwan. Probability map of PM and daily patterns are discussed in this study. Data mining provides more detailed spatial-temporal information for PM variations and trends. The proposed model will show that data mining provides a relatively high goodness of fit and sufficient space-time explanatory power, particularly air pollution frequency and affect areas. In the proposed model, a method using Dynamic Time Warping is proposed to analyse temporal similarity between stations. The proposed model can eliminate global effect on a single station through the performance of multiple stations. The proposed model will further be used for prediction of PM2.5. The prediction results will discuss the spatial-temporal relations between stations. This study will investigate the distribution of PM and its cyclicality.

关键词： Predictive models Time series analysis Atmospheric modeling Air pollution Conferences Data mining

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The evolutionary history of 2,658 cancers (vol 578, pg 122, 2020)

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NATURE 2023年第7948期614卷 E42-E42页

作者： Gerstung, Moritz Jolly, Clemency Leshchiner, Ignaty Dentro, Stefan C. Gonzalez, Santiago Rosebrock, Daniel Mitchell, Thomas J. Rubanova, Yulia Anur, Pavana Yu, Kaixian Tarabichi, Maxime Deshwar, Amit Wintersinger, Jeff Kleinheinz, Kortine Vazquez-Garcia, Ignacio Haase, Kerstin Jerman, Lara Sengupta, Subhajit Macintyre, Geoff Malikic, Salem Donmez, Nilgun Livitz, Dimitri G. Cmero, Marek Demeulemeester, Jonas Schumacher, Steven Fan, Yu Yao, Xiaotong Lee, Juhee Schlesner, Matthias Boutros, Paul C. Bowtell, David D. Zhu, Hongtu Getz, Gad Imielinski, Marcin Beroukhim, Rameen Sahinalp, S. Cenk Ji, Yuan Peifer, Martin Markowetz, Florian Mustonen, Ville Yuan, Ke Wang, Wenyi Morris, Quaid D. Spellman, Paul T. Wedge, David C. Van Loo, Peter European Molecular Biology Laboratory European Bioinformatics Institute (EMBL-EBI) Cambridge UK European Molecular Biology Laboratory Genome Biology Unit Heidelberg Germany Wellcome Sanger Institute Cambridge UK Genome Biology Unit European Molecular Biology Laboratory (EMBL) Heidelberg Germany University of Ljubljana Ljubljana Slovenia The Francis Crick Institute London UK Big Data Institute University of Oxford Oxford UK Wellcome Sanger Institute Wellcome Genome Campus Hinxton UK Big Data Institute Li Ka Shing Centre University of Oxford Oxford UK University of Cambridge Cambridge UK Cambridge University Hospitals NHS Foundation Trust Cambridge UK Department of Applied Mathematics and Theoretical Physics Centre for Mathematical Sciences University of Cambridge Cambridge UK Department of Epidemiology and Biostatistics Memorial Sloan Kettering Cancer Center New York NY USA Department of Statistics Columbia University New York NY USA Department of Haematology University of Cambridge Cambridge UK University of Leuven Leuven Belgium Broad Institute of MIT and Harvard Cambridge MA USA Center for Cancer Research Massachusetts General Hospital Charlestown MA USA Department of Pathology Massachusetts General Hospital Boston MA USA Harvard Medical School Boston MA USA Center for Cancer Research Massachusetts General Hospital Boston MA USA Dana-Farber Cancer Institute Boston MA USA Department of Medical Oncology Dana-Farber Cancer Institute Boston MA USA Department of Cancer Biology Dana-Farber Cancer Institute Boston MA USA Oxford NIHR Biomedical Research Centre Oxford UK Oxford NIHR Biomedical Research Centre University of Oxford Oxford UK University of Toronto Toronto Ontario Canada Vector Institute Toronto Ontario Canada Vector Institute Toronto ON Canada Department of Computer Science University of Toronto Toronto ON Canada Ontario Institute for Cancer Research Toronto Ontario Canada University of California Los Angeles

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Mobile application to support dyslexia diagnostic and reading practice 13

Mobile application to support dyslexia diagnostic and readin...

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13th IEEE/ACS International Conference of Computer systems and Applications, AICCSA 2016

作者： Martins, Valeria Farinazzo Lima, Thiago Sampaio, Paulo N. M. De Paiva, Marcelo Laboratório de Usabilidade Faculdade de Computação e Informática Universidade Presbiteriana Mackenzie São Paulo Brazil Graduate Program in Computing and Systems Universidade Salvador - UNIFACS Salvador Brazil Guimarães Open University of Brazil-UNIFESP Faccamp's Master Program São Paulo Brazil

ISBN: (纸本)9781509043200

The treatment of the Dyslexia Disorder requires patients to be disciplined and have a lot of reading practice so that they can reach a level of fluency and accuracy similar to people not diagnosed with this disorder. Due to cost or availability of health professionals, patients are not able, in general, to follow the treatment accordingly keeping a constant attendance. Besides, nowadays there are not many available tools to support health professionals towards dyslexia diagnosis. This paper presents an application developed for mobile devices, through which dyslexic users can practice their reading skills, turning the treatment accessible as well as helping health professionals as an auxiliary tool to diagnose their patients. This tool can be applied to support dyslexia diagnosis and to help people already diagnosed with this disorder training reading. To validate the use of this tool, the application was tested with some students of a private school in the city of São Paulo (Brazil). The results regarding usability aspects and the tool accuracy are also discussed. © 2016 IEEE.

关键词： Diagnosis

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Publisher Correction: Large-scale metabolic interaction network of the mouse and human gut microbiota

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Scientific data 2020年第1期7卷 272页

作者： Roktaek Lim Josephine Jill T Cabatbat Thomas L P Martin Haneul Kim Seunghyeon Kim Jaeyun Sung Cheol-Min Ghim Pan-Jun Kim Department of Biology Hong Kong Baptist University Kowloon Hong Kong. School of Life Sciences Ulsan National Institute of Science and Technology Ulsan 44919 Republic of Korea. Analytics computing and complex Systems Laboratory Asian Institute of Management Makati 1229 Metro Manila Philippines. Department of Physics Ulsan National Institute of Science and Technology Ulsan 44919 Republic of Korea. Department of Physics Pohang University of Science and Technology Pohang Gyeongbuk 37673 Republic of Korea. Samsung SDS Seoul 05510 Republic of Korea. Microbiome Program Center for Individualized Medicine Mayo Clinic Rochester MN 55905 USA. Division of Surgical Research Department of Surgery Mayo Clinic Rochester MN 55905 USA. Division of Rheumatology Department of Internal Medicine Mayo Clinic Rochester MN 55905 USA. Department of Molecular Pharmacology and Experimental Therapeutics Mayo Clinic Rochester MN 55905 USA. Department of Biology Hong Kong Baptist University Kowloon Hong Kong. panjunkim@hkbu.edu.hk. Center for Quantitative Systems Biology Hong Kong Baptist University Kowloon Hong Kong. panjunkim@hkbu.edu.hk. Institute of computational and Theoretical Studies Hong Kong Baptist University Kowloon Hong Kong. panjunkim@hkbu.edu.hk. Abdus Salam International Centre for Theoretical Physics 34151 Trieste Italy. panjunkim@hkbu.edu.hk.

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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Optimization of grounding grid's multidesign geometry 16

Optimization of grounding grid's multidesign geometry

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16th International Conference on Environment and Electrical Engineering, EEEIC 2016

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

ISBN: (纸本)9781509023196

This paper deals with the optimization of grounding grids geometry. The goal is to reduce the amount of material to deploy a grounding grid, while keeping the system security. The touch voltages, step voltages, GPR and ground resistance are parameters of grounding grids and are related to safety aspect. Also discussed is a genetic algorithm used for optimizing ground grids, which can assume any form in a limited space of the project. The simulation is based in images method. The results are presented and discussed. © 2016 IEEE.

关键词： Ground penetrating radar systems

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Computation of resistance and potential of grounding grids in any geometry 16

Computation of resistance and potential of grounding grids i...

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16th International Conference on Environment and Electrical Engineering, EEEIC 2016

作者： Pires, Thyago G. Nerys, Jose W. L. Silva, Carlos L. B. Oliveira, Diogo N. Filho, Antonio M. Silva Calixto, Wesley P. Alves, Aylton J. 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 and Technologic Research and Study Group Federal Institute of Education Science and Technology Goiania Brazil

ISBN: (纸本)9781509023196

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. © 2016 IEEE.

关键词： Mesh generation

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Ontology for concepts's integration of corporate sustainability index (ISE) and G4 guidelines of global reporting initiative 14

Ontology for concepts's integration of corporate sustainabil...

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14th International Conference WWW/Internet 2015

作者： Reis, Tâmara Batista Da Silva, Paulo Caetano Program in Systems and Computing Salvador University Brazil

ISBN: (纸本)9789898533449

The sustainable development for the economic and global-environmental balance has required organizations to manage, control and report sustainability data. To do so, a diversity of indices was proposed, which has caused low interoperability among information systems and also difficulty the exchange of information and experiences among organizations which adopt different methodologies for sustainability data representation and manipulation. This work presents domain ontology for the representation of a common knowledge base between the Corporate Sustainability Index (ISE), one of the most used sustainability index in Brazil, and G4 Guidelines of the Global Reporting Initiative, adopted internationally. The development of the existing semantic structure between these two indexes can provide the availability of information in a fast and consistent way, independent of methodologies or computational difficulties faced by stakeholders using heterogeneous software. © 2015.

关键词： Sustainable development

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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 2023年第6期20卷 824-835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

BigNeuron is an open community bench-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is representative of the data obtained in many neuroscience laboratories interested in neuron tracing. Here, we report generated gold standard manual annotations for a subset of the available imaging datasets and quantified tracing quality for 35 automatic tracing algorithms. The goal of generating such a hand-curated diverse dataset is to advance the development of tracing algorithms and enable generalizable benchmarking. Together with image quality features, we pooled the data in an interactive web application that enables users and developers to perform principal component analysis, t-distributed stochastic neighbor embedding, correlation and clustering, visualization of imaging and tracing data, and benchmarking of automatic tracing algorithms in user-defined data subsets. The image quality metrics explain most of the variance in the data, followed by neuromorphological features related to neuron size. We observed that diverse algorithms can provide complementary information to obtain accurate results and developed a method to iteratively combine methods and generate consensus reconstructions. The consensus trees obtained provide estimates of the neuron structure ground truth that typically outperform single algorithms in noisy datasets. However, specific algorithms may outperform the consensus tree strategy in specific imaging conditions. Finally, to aid users in predicting the most accurate automatic tracing results without manual annotations for comparison, we used support vector machine regression to predict reconstruction quality given an image volume and a set of automatic tracings.

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A model for the construction of an inter-domain ontology: Corporate Sustainability Index and the G4 Guidelines of the Global Reporting Initiative

A model for the construction of an inter-domain ontology: Co...

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Brazilian Seminar on Ontologies, ONTOBRAS 2015

作者： Reis, Tâmara Batista Da Silva, Paulo Caetano Master Program in Systems and Computing Salvador University Brazil

Ontologies are tools for knowledge representation that can help solve the diversity of representation of concepts that have similar meaning. Therefore, this paper proposes a method for building an ontology for the representation of a common knowledge base among the Corporate Sustainability Index, adopted in Brazil, with the G4 Guidelines of the Global Reporting Initiative, an international standard.

关键词： Ontology

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