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BioTIME 2.0: Expanding and Improving a Database of Biodiversity Time Series

Global Ecology and Biogeography 2025年第5期34卷

作者： Maria Dornelas Laura H. Antão Amanda E. Bates Viviana Brambilla Jonathan M. Chase Cher F. Y. Chow Ada Fontrodona-Eslava Anne E. Magurran Inês S. Martins Faye Moyes Alban Sagouis Samuel Adu-Acheampong Daniel Acquah-Lamptey Dušan Adam Penelope A. Ajani Aitor Albaina Pablo Almaraz Jeongseop An Roger Sigismund Anderson Madelaine Jean Robertson Anderson Alexsander Z. Antunes Ivan Arismendi Linda Armbrecht Pedro Aros-Mardones Sreejith Kalpuzha Ashtamoorthy Narayanan Ayyappan Gal Badihi Joseph J. Bailey Andrew H. Baird Mark Edward Baird Sreekumar Vadakkethil Balakrishnan José António L. Barão-Nóbrega Adi Barash Miguel Barbosa Jos Barlow Claus Bässler Matthieu Beaumont Natalie Beenaerts Tiago Octavio Begot Wallace Beiroz Ricardo Beldade David M. Bell Alecia Bellgrove Jonathan Belmaker Lisandro Benedetti-Cecchi Cassandra E. Benkwitt Pamela Medina-van Berkum Brandon T. Bestelmeyer Matthew G. Betts Maxwell Kelvin Billah Anne D. Bjorkman Magdalena Błażewicz Christopher P. Bloch Shane A. Blowes Antonio Bode Juliano A. Bogoni Thomas Bolger Timothy C. Bonebrake Erik Bonsdorff Roberta Bottarin Luke N. Brokensha Rob W. Brooker Andrew J. Brooks Helge Bruelheide Thiago Almeida Bueno Claire Laguionie Mariana Lopes Campagnoli James Cant Erica Pellegrini Caramaschi Alexandre Caron Tadhg Carroll Tancredi Caruso Juan Carvajal-Quintero Giuseppe Castaldelli Edward Castañeda-Moya Pedro V. Castilho Sonia Zanini Cechin Shahar Chaikin Uchangi Manjunatha Chandrashekara Tory J. Chase Chaolun Allen Chen Jorge José Cherem Sei-Woong Choi Erica M. Christensen Alexander V. Christianini Jackson Wing Four Chu Peter Coad Carl Van Colen Lise Comte Elisabeth J. Cooper J. Hans C. Cornelissen Eddy Cosson Unai Cotano Luc Crevecoeur Shannan Kyle Crow Graeme S. Cumming Vanessa S. Daga Gabriella Damasceno Gergana N. Daskalova Claire H. Davies Robert A. Davis Frank P. Day Sussy De-La-Zerda Amy Elizabeth Deacon Indradatta de Castro-Arrazola Steven Degraer Kharran Deonarinesingh Juan C. Diaz-Ricaurte Christopher R. Dickman Tara Dirilgen Ciaran John Dolan J. Emmet Centre for Biological Diversity School of Biology University of St Andrews Fife UK MARE—Centro de Ciências do Mar e do Ambiente Faculdade de Ciências Universidade de Lisboa Lisboa Portugal Research Centre for Ecological Change Organismal and Evolutionary Biology Research Programme Faculty of Biological and Environmental Sciences University of Helsinki Helsinki Finland Department of Biology University of Victoria Victoria British Columbia Canada German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig Leipzig Germany Department of Computer Science Martin Luther University Halle-Wittenberg Halle (Saale) Germany Leverhulme Centre for Anthropocene Biodiversity Department of Biology University of York York UK Department of Animal Biology and Conservation Science University of Ghana Accra Ghana Philipps Universität Marburg Marburg Germany Department of Forest Ecology Silva Tarouca Research Institute Brno Czech Republic School of Life Sciences University of Technology Sydney Sydney New South Wales Australia Zoology and Animal Cell Biology Department University of the Basque Country (UPV/EHU) Leioa Spain Grupo de Oceanografía de Ecosistemas Instituto de Ciencias Marinas de Andalucía CSIC Campus Universitario de Puerto Real Puerto Real Spain National Institute of Ecology Seocheon Republic of Korea African Regional Postgraduate Programme in Insect Science University of Ghana Accra Ghana Département de Biologie Université de Sherbrooke Sherbrooke Quebec Canada Núcleo de Conservação da Biodiversidade Instituto de Pesquisas Ambientais Secretaria de Meio Ambiente Infraestrutura e Logística do Estado de São Paulo São Paulo Brazil Department of Fisheries Wildlife and Conservation Sciences Oregon State University Corvallis Oregon USA Institute for Marine and Antarctic Studies Ecology & Biodiversity Centre University of Tasmania Hobart Tasmania Australia Graduate Program in Oceanography Department of Oceanography Faculty of Natural Scienc

Motivation Here, we make available a second version of the BioTIME database, which compiles records of abundance estimates for species in sample events of ecological assemblages through time. The updated version expands version 1.0 of the database by doubling the number of studies and includes substantial additional curation to the taxonomic accuracy of the records, as well as the metadata. Moreover, we now provide an R package (BioTIMEr) to facilitate use of the database. Main Types of Variables Included The database is composed of one main data table containing the abundance records and 11 metadata tables. The data are organised in a hierarchy of scales where 11,989,233 records are nested in 1,603,067 sample events, from 553,253 sampling locations, which are nested in 708 studies. A study is defined as a sampling methodology applied to an assemblage for a minimum of 2 years. Spatial Location and Grain Sampling locations in BioTIME are distributed across the planet, including marine, terrestrial and freshwater realms. Spatial grain size and extent vary across studies depending on sampling methodology. We recommend gridding of sampling locations into areas of consistent size. Time Period and Grain The earliest time series in BioTIME start in 1874, and the most recent records are from 2023. Temporal grain and duration vary across studies. We recommend doing sample-level rarefaction to ensure consistent sampling effort through time before calculating any diversity metric. Major Taxa and Level of Measurement The database includes any eukaryotic taxa, with a combined total of 56,400 taxa. Software Format csv and. SQL.

关键词： assemblage biodiversity community database ecoinformatics species taxa time

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Retraction: Design of Decision Support System Determination of Inventory Inventory Using Single Exponential Smoothing Forecasting Method (J. Phys.: Conf. Ser. 1114 012082)

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Journal of Physics: Conference Series 2018年第1期1114卷

作者： Muhammad Iqbal Dahlan Abdullah H Hartono Cut Ita Erliana S Sriadhi Nurmawati M Adhi Prasnowo Abdurrozzaq Hasibuan Akbar Iskandar Kundharu Saddhono Abdussakir Hastuti Musa Muhammad Ikhsan Setiawan Youmeni Sagala Rumiris Siahaan Eka Mayastika Sinaga Anggraini Syahputri Feby Milanie Henry Aspan Muammar Khaddafi Department of Computer System Universitas Pembangunan Panca Budi Medan Indonesia Department of Informatics Universitas Malikussaleh Aceh Indonesia Department of Computer Science STMIK IBBI Medan Indonesia Department of Industrial Engineering Universitas Malikussaleh Aceh Indonesia Department of Electrical Engineering Universitas Negeri Medan Indonesia Faculty of Industry Technique University of 45 Surabaya Indonesia Department of Industrial Engineering Universitas Maarif Hasyim Latif Sidoarjo Indonesia Faculty of Engineering Universitas Islam Sumatera UtaraMedan-Indonesia Informatics Engineering STMIK AKBA Makassar Indonesia Graduate Program Universitas Sebelas Maret Surakarta Indonesia Department of Mathematics Education State Islamic University of Malang Malang Indonesia Department of Mathematics Universitas Muhammadiyah Parepare Indonesia Department of Civil Engineering Narotama University Surabaya Indonesia STIE Bina Karya Tebing Tinggi Indonesia Universitas Pembangunan Panca Budi Medan Indonesia Department of Accounting Universitas Malikussaleh Aceh Indonesia

This paper was retracted by IOP Publishing on 12 December 2018. This paper was published due to a technical error and was not intended to be included in this journal. Retraction published: 8 February 2019

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Retraction: Effect of Types and Gap on Spark Plugs on Exhaust Emissions on the 110CC Double Wheel Vehicles and Its Dissemination Using Website (J. Phys.: Conf. Ser. 1114 012097)

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Journal of Physics: Conference Series 2018年第1期1114卷

作者： Tinus Ginting Hotler Manurung Astro Julida Harahap Erianto Ongko H Hartono Dahlan Abdullah Cut Ita Erliana Kundharu Saddhono S Sriadhi Supriyono Akbar Iskandar A A Saleh I Ketut Sudarsana Department of Mechanical Engineering Akademi Teknologi Industri Immanuel Indonesia Department of Informatics Akademi Teknologi Industri Immanuel Indonesia Department of Computer Science STMIK IBBI Medan Indonesia Department of Informatics Universitas Malikussaleh Aceh Indonesia Department of Industrial Engineering Universitas Malikussaleh Aceh Indonesia Graduate Program Universitas Sebelas Maret Surakarta Indonesia Department of Electrical Engineering Universitas Negeri Medan Indonesia Department of Geography Education Universitas Prof Dr Hazairin SH Bengkulu Indonesia Department of Informatics STMIK AKBA Makassar Indonesia Statistics Financial & Social Sciences Research Group Indonesia Department of Religious Education Institut Hindu Dharma Negeri Denpasar Denpasar Indonesia

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Effect of Types and Gap on Spark Plugs on Exhaust Emissions on the 110CC Double Wheel Vehicles and Its Dissemination Using Website

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Journal of Physics: Conference Series 2018年第1期1114卷

作者： Ginting, Tinus Manurung, Hotler Astro Julida Harahap Ongko, Erianto Hartono, H Dahlan Abdullah Cut Ita Erliana Saddhono, Kundharu Sriadhi, S Supriyono Iskandar, Akbar Saleh, A A Sudarsana, I Ketut Department of Mechanical Engineering Akademi Teknologi Industri Immanuel Indonesia Department of Informatics Akademi Teknologi Industri Immanuel Indonesia Department of Computer Science STMIK IBBI Medan Indonesia Department of Informatics Universitas Malikussaleh Aceh Indonesia Department of Industrial Engineering Universitas Malikussaleh Aceh Indonesia Graduate Program Universitas Sebelas Maret Surakarta Indonesia Department of Electrical Engineering Universitas Negeri Medan Indonesia Department of Geography Education Universitas Prof Dr Hazairin SH Bengkulu Indonesia Department of Informatics STMIK AKBA Makassar Indonesia Statistics Financial & Social Sciences Research Group Indonesia Department of Religious Education Institut Hindu Dharma Negeri Denpasar Denpasar Indonesia

Based on the formulation of the problem to be discussed by the writer is to know: the influence of 0.4 mm spark gap variation, and standard spark plug type, to CO and HC, the influence of 0.5 mm spark plug gap variation, and variation of platinum spark plug on CO and HC, influence of spark gap variation 0,6mm, and variation of iridium spark plug type to CO and HC emission Method used to obtain data needed to support the writer that is research method with experiment, literature study method or literature study The result of this research concludes that there is spark gap influence 0.4 mm, with variations of standard plug, platinum and iridium spark plugs against CO and HC emissions, where the lowest CO value is 0.27% and HC 98 ppm, 0.5 mm spark plug with lowest CO 0.15% and HC 46 ppm, 0.6 mm spark plug gap the lowest CO value of 0.19% and HC 24 ppm. The results of this research will be disseminated using the website

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Corrigendum to “Improved Combinatorial Benders Decomposition for a Scheduling Problem with Unrelated Parallel Machines”

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Journal of Applied Mathematics 2017年第1期2017卷

作者： Francisco Regis Abreu Gomes Geraldo Robson Mateus Graduate Program in Production Engineering Federal University of Minas Gerais and Federal Institute of Education Science and Technology of Ceará Belo Horizonte MG Brazilufmg.br Computer Science Department Federal University of Minas Gerais Belo Horizonte MG Brazilufmg.br

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Corrigendum to "A Gene Module-Based eQTL Analysis Prioritizing Disease Genes and Pathways in Kidney Cancer"

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Computational and structural biotechnology journal 2018年 16卷 196页

作者： M Q Yang D Li W Yang Y Zhang J Liu W Tong Joint Bioinformatics Graduate Program Department of Information Science George W. Donaghey College of Engineering and Information Technology University of Arkansas at Little Rock USA. University of Arkansas for Medical Sciences 2801 S. University Ave Little Rock AR 72204 USA. School of Computer Science Carnegie Mellon University 5000 Forbes Ave Pittsburgh PA 15213 USA. Department of Statistics Harvard University Cambridge MA 02138 USA. Divisions of Bioinformatics and Biostatistics National Center for Toxicological Research US Food and Drug Administration 3900 NCTR Road Jefferson AR 72079 USA.

[This corrects the article DOI: 10.1016/***.2017.09.003.].

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Unsupervised learning of depth during coordinated head/eye movements

Unsupervised learning of depth during coordinated head/eye m...

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IEEE/RSJ International Conference on Intelligent Robots and Systems

作者： Marco Antonelli Michele Rucci Bertram Shi Department of Electronic and Computer Engineering at the Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong Department of Psychological and Brain Sciences and the Graduate Program in Neuroscience Boston University MA 02215 USA

ISBN: (纸本)9781509037636

Autonomous robots and humans need to create a coherent 3D representation of their peripersonal space in order to interact with nearby objects. Recent studies in visual neuroscience suggest that the small coordinated head/eye movements that humans continually perform during fixation provides useful depth information. In this work, we mimic such a behavior on a humanoid robot and propose a computational model that extracts depth information without requiring the kinematic model of the robot. First, we show that, during fixational head/eye movements, proprioceptive cues and optic flow lie on a low dimensional subspace that is a function of the depth of the target. Then, we use the generative adaptive subspace self-organizing map (GASSOM) to learn these depth-dependent subspaces. The depth of the target is eventually decoded using a winner-take-all strategy. The proposed model is validated on a simulated model of the iCub robot.

关键词： Cameras Robot vision systems Optical imaging Robot kinematics Integrated optics Three-dimensional displays

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Parallel Solution of Diagonally Dominant Banded Triangular Toeplitz Systems Using Taylor Polynomials

Parallel Solution of Diagonally Dominant Banded Triangular T...

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IEEE International Conference on High Performance Computing and Communications

作者： Michael Kapralos Aron Wolinetz Brian J. Murphy Department of Electrical and Computer Engineering University of Connecticut Storrs CT 06269 The Graduate Center of the City University of New York Ph.D. Program in Computer Science NY NY 10016 Lehman College of the City University of New York Bronx NY 10468

ISBN: (纸本)9781509042982

We present a new numerical and inherently parallel algorithm for solving banded (strictly) diagonally dominant triangular and banded Toeplitz (BDDTT) systems of linear equations. A Taylor polynomial, derived from the matrix analog of the Taylor Series for evaluating (1-x) ~(-1)where |χ| < 1, is applied, in conjunction with other techniques, to compute an approximate solution to a BDDTT-type system. This method requires O(n log_2n) arithmetic operations (ops) and O(log_2n) parallel steps (steps). Two FFTs and an IFFT of order 2n are the main contributors to the workload. This matches the FFT count for a triangular Toeplitz-matrix-vector multiplication. Clearly then, our method improves on the many algebraic FFT based processes that first determine the triangular Toeplitz matrix that is the inverse of a BDDTT matrix in order to then solve the associated BDDTT system.

关键词： Banded Triangular Toeplitz Diagonally Dominant Linear System FFT

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Dilute-nitride GaNP planar and core/shell microwire solar cells

Dilute-nitride GaNP planar and core/shell microwire solar ce...

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Indium Phosphide and Related Materials Conference

作者： Supanee Sukrittanon Ren Liu Janet L Pan Katherine L Jungjohann Shadi A Dayeh Charles W Tu Graduate Program of Materials Science and Engineering University of California San Diego United States of America Department of Electrical and Computer Engineering University of California San Diego United States of America Center for Integrated Nanotechnologies Sandia National Laboratories United States of America

ISBN: (纸本)9781509019656

Dilute nitride GaNP is potentially useful as the top junction in dual-junction solar cells on Si because it has a tunable direct-bandgap and small lattice mismatch with Si. Investigations on GaP are the first step toward this goal. Our best GaNP solar cell on GaP ([N] = 1.8%, Eg = 2.05 eV) achieves an efficiency of 7.9%, which is 3 times higher than the most efficient GaP solar cell to date and higher than other solar cells with similar direct bandgap (InGaP, GaAsP). We also report GaP/GaNP core/shell microwire solar cells utilizing top-down reactiveion etching to create the GaP cores and molecular beam epitaxy to create the GaNP shells. Systematic studies of cell performance over a series of microwire lengths, array periods, and sidewall morphologies, examined by transmission electron microscopy, were conducted to shed light on performance-limiting factors and to optimize the cell efficiency.

关键词： Photovoltaic cells Silicon Compounds computers Laboratories Junctions Lattices

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A Hybrid Deep Learning Forecasting Model Using GPU Disaggregated Function Evaluations Applied for Household Electricity Demand Forecasting

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Energy Procedia 2016年 103卷 280-285页

作者： Vitor N. Coelho Igor M. Coelho Eyder Rios Alexandre S.T. Filho Agnaldo J.R. Reis Bruno N. Coelho Alysson Alves Guilherme G. Netto Marcone J.F. Souza Frederico G. Guimarães Graduate Program in Electrical Engineering Universidade Federal de Minas Gerais Belo Horizonte Brazil Instituto de Pesquisa e Desenvolvimento de Tecnologias Ouro Preto Brazil Department of Computer Science State University of Rio de Janeiro Rio de Janeiro Brazil Institute of Computer Science Fluminense Federal University Niteroi Brazil Department of Control Engineering and Automation Universidade Federal de Ouro Preto Ouro Preto Brazil Department of Computer Science Universidade Federal de Ouro Preto Ouro Preto Brazil Department of Electrical Engineering Universidade Federal de Minas Gerais BeloHorizonte Brazil

As the new generation of smart sensors is evolving towards high sampling acquisitions systems, the amount of information to be handled by learning algorithms has been increasing. The Graphics Processing Unit (GPU) architectures provide a greener alternative with low energy consumption for mining big-data, harnessing the power of thousands of processing cores in a single chip, opening a widely range of possible applications. Here, we design a novel evolutionary computing GPU parallel function evaluation mechanism, in which different parts of time series are evaluated by different processing threads. By applying a metaheuristics fuzzy model in a low-frequency data for household electricity demand forecasting, results suggested that the proposed GPU learning strategy is scalable as the number of training rounds increases.

关键词： Microgrid Household Electricity Demand Deep Learning Graphics Processing Unit Parallel forecasting model Big Time-series Data

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