Search results for "computer"

showing 10 items of 30657 documents

Genetic variability in Peregrine falcon populations of the Western Palaearctic region

2018

We analyzed variation in ten polymorphic microsatellites and a portion of cytochrome b mitochondrial DNA in 4 populations of the Peregrine falcon (Falco peregrinus). living in northern and southern Italy. Spain and Czech Republic to assess species diversity in the poorly investigated Western Palearctic region. The Spanish population lives in the contact zone between F. peregrinus peregrinus and F. p. brookei. both the northern Italian and the Czech populations live within the range of F. p. peregrinus and the southern Italian is within the F. p. brookei. We added to our cytochrome b sequence dataset comprising 81 samples. previously published mitochondrial DNA sequences (n = 31) of English …

0106 biological sciencesEcologySettore BIO/05 - ZoologiaZoologyWestern Palaearcticmitochondrial dnaBiology010603 evolutionary biology01 natural sciencesmicrosatellites010605 ornithologygenetic structuringGenetic structuring Falco peregrinus brookei microsatellites mitochondrial DNA Peregrine Falconperegrine falconAnimal Science and ZoologyPeregrine falcon mtDNA microsatellites genetic structuring genetic diversityGenetic variabilityfalco peregrinus brookeiFalconcomputerEcology Evolution Behavior and SystematicsQH540-549.5computer.programming_language
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Discard ban: A simulation-based approach combining hierarchical Bayesian and food web spatial models

2020

12 pages, 6 figures, 6 tables, 2 appendixes, supplementary data https://doi.org/10.1016/j.marpol.2019.103703

0106 biological sciencesEconomics and EconometricsComputer scienceFishingSede Central IEOContext (language use)Management Monitoring Policy and LawAquatic ScienceBayesian inference01 natural sciencesEnvironmental datamedia_common.cataloged_instanceEcoSimSpatial ecologyPesquerías14. Life underwaterEuropean unionGeneral Environmental Sciencemedia_commonEcospacebusiness.industry010604 marine biology & hydrobiologyEnvironmental resource management04 agricultural and veterinary sciencesFood web modelDiscardsDiscards13. Climate actionBayesian modelLanding obligationMediterranean sea040102 fisheries0401 agriculture forestry and fisheriesFisheries managementbusinessLaw
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Parameter identification and state estimation of a microalgae dynamical model in sulphur deprived conditions: Global sensitivity analysis, optimizati…

2014

International audience; In this article, a dynamic model describing the growth of the green microalgae Chlamydomonas reinhardtii , under light attenuation and sulphur‐deprived conditions leading to hydrogen production in a photobioreactor is presented. The strong interactions between biological and physical phenomena require complex mathematical expressions with an important number of parameters. This article presents a global identification procedure in three steps using data from batch experiments. First, it includes the application of a sensitivity function analysis, which allows one to determine the parameters having the greatest influence on model outputs. Secondly, the most influentia…

0106 biological sciencesEngineeringObserver (quantum physics)business.industryGeneral Chemical Engineering05 social sciencesExperimental dataPhotobioreactorFunction (mathematics)01 natural sciences7. Clean energy[SPI]Engineering Sciences [physics]Extended Kalman filterSoftware010608 biotechnology0502 economics and business[INFO]Computer Science [cs]Stage (hydrology)Gas composition050207 economicsBiological systembusinessSimulationThe Canadian Journal of Chemical Engineering
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Using foresight exercise to design adaptation policy to climate change: The case of the French wine industry

2018

Foresight studies are regularly conducted at sectoral or geographical scales, in order to help policy makers and economic actors to define their strategy of adaptation to climate change (CC). Some studies are rather “quick exercises”, in which a panel of experts is consulted to define the expected impacts of CC and to identify adaptation leviers for future policy. In other cases, a true foresight methodology is developed, leading to the building of scenarios based on : i) a systemic and participatory approach, ii) the definition of key variables, iii) the choice of assumptions and the coherent relations between these assumptions, the narrative description of scenarios. This participatory di…

0106 biological sciencesEnvironmental Engineering[SDE.MCG]Environmental Sciences/Global Changeslcsh:QR1-502[SDV.SA.AGRO]Life Sciences [q-bio]/Agricultural sciences/AgronomyClimate changeadaptationfilière vitivinicole01 natural sciencesIndustrial and Manufacturing Engineeringlcsh:Microbiologylcsh:Physiology0404 agricultural biotechnologystratégie d'adaptationlcsh:Zoologylcsh:QL1-991Adaptation (computer science)Milieux et Changements globauxsud de la france2. Zero hungerlcsh:QP1-981Welfare economicsétude prospectivegestion de l'irrigation04 agricultural and veterinary sciences15. Life on landviticulture040401 food scienceFutures studies13. Climate actionOrder (business)[SDU.STU.CL]Sciences of the Universe [physics]/Earth Sciences/Climatologyadaptation au changement climatique010606 plant biology & botany
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Simple learning rules to cope with changing environments

2008

10 pages; International audience; We consider an agent that must choose repeatedly among several actions. Each action has a certain probability of giving the agent an energy reward, and costs may be associated with switching between actions. The agent does not know which action has the highest reward probability, and the probabilities change randomly over time. We study two learning rules that have been widely used to model decision-making processes in animals-one deterministic and the other stochastic. In particular, we examine the influence of the rules' 'learning rate' on the agent's energy gain. We compare the performance of each rule with the best performance attainable when the agent …

0106 biological sciencesError-driven learningExploitComputer scienceEnergy (esotericism)Biomedical EngineeringBiophysicsBioengineeringanimal behavior010603 evolutionary biology01 natural sciencesBiochemistryMulti-armed banditModels Biologicaldecision makingBiomaterials03 medical and health sciences[ INFO.INFO-BI ] Computer Science [cs]/Bioinformatics [q-bio.QM][ SDV.EE.IEO ] Life Sciences [q-bio]/Ecology environment/SymbiosisAnimalsLearningComputer Simulation[ SDV.BIBS ] Life Sciences [q-bio]/Quantitative Methods [q-bio.QM]multi-armed banditEcosystem030304 developmental biologySimple (philosophy)0303 health sciences[ SDE.BE ] Environmental Sciences/Biodiversity and Ecologybusiness.industrydynamic environmentslearning rulesdecision-making[SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM]Unlimited periodRange (mathematics)Action (philosophy)Artificial intelligence[SDE.BE]Environmental Sciences/Biodiversity and Ecology[INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM]businessBiotechnologyResearch Article[SDV.EE.IEO]Life Sciences [q-bio]/Ecology environment/Symbiosis
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Thompson Sampling Based Active Learning in Probabilistic Programs with Application to Travel Time Estimation

2019

The pertinent problem of Traveling Time Estimation (TTE) is to estimate the travel time, given a start location and a destination, solely based on the coordinates of the points under consideration. This is typically solved by fitting a function based on a sequence of observations. However, it can be expensive or slow to obtain labeled data or measurements to calibrate the estimation function. Active Learning tries to alleviate this problem by actively selecting samples that minimize the total number of samples needed to do accurate inference. Probabilistic Programming Languages (PPL) give us the opportunities to apply powerful Bayesian inference to model problems that involve uncertainties.…

0106 biological sciencesEstimation0303 health sciencesSequenceActive learning (machine learning)business.industryComputer scienceProbabilistic logicInferenceFunction (mathematics)Bayesian inferenceMachine learningcomputer.software_genre010603 evolutionary biology01 natural sciences03 medical and health sciencesArtificial intelligencebusinesscomputerThompson sampling030304 developmental biology
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Macrophyte assessment in European lakes: Diverse approaches but convergent views of ‘good’ ecological status

2018

Graphical abstract

0106 biological sciencesEvolution/dk/atira/pure/thematic/inbo_th_00006/dk/atira/pure/policy/kaderrichtlijn_water_krw_General Decision SciencesZannichellia palustrisSpecies and biotopes010501 environmental sciences01 natural sciencesArticle/dk/atira/pure/thematic/inbo_th_00044Water Framework DirectiveAbundance (ecology)Restoration ecologyEcology Evolution Behavior and SystematicsComputingMethodologies_COMPUTERGRAPHICS0105 earth and related environmental sciencesB003-ecologyEcologybiologyEcologyEcological status010604 marine biology & hydrobiologyPhosphorusEcological assessmentNutrientsVegetationEutrophication15. Life on landbiology.organism_classificationBehaviour and SystematicsMacrophytemacrophytes (aquatic plants)PolicyGeographyWater Framework DirectiveIndicator species13. Climate action/dk/atira/pure/discipline/B000/B003articlesSpecies richness/dk/atira/pure/taxonomic/macrofytenAquatic macrophytesSpecies richnessEcological Indicators
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A Methodology to Derive Global Maps of Leaf Traits Using Remote Sensing and Climate Data

2018

This paper introduces a modular processing chain to derive global high-resolution maps of leaf traits. In particular, we present global maps at 500 m resolution of specific leaf area, leaf dry matter content, leaf nitrogen and phosphorus content per dry mass, and leaf nitrogen/phosphorus ratio. The processing chain exploits machine learning techniques along with optical remote sensing data (MODIS/Landsat) and climate data for gap filling and up-scaling of in-situ measured leaf traits. The chain first uses random forests regression with surrogates to fill gaps in the database (> 45% of missing entries) and maximizes the global representativeness of the trait dataset. Plant species are then a…

0106 biological sciencesFOS: Computer and information sciences010504 meteorology & atmospheric sciencesSpecific leaf areaClimateBos- en LandschapsecologieSoil ScienceFOS: Physical sciencesApplied Physics (physics.app-ph)010603 evolutionary biology01 natural sciencesStatistics - ApplicationsGoodness of fitAbundance (ecology)Machine learningForest and Landscape EcologyApplications (stat.AP)Computers in Earth SciencesPlant ecologyVegetatie0105 earth and related environmental sciencesRemote sensingMathematics2. Zero hungerPlant traitsVegetationData stream miningClimate; Landsat; Machine learning; MODIS; Plant ecology; Plant traits; Random forests; Remote sensing; Soil Science; Geology; Computers in Earth SciencesGlobal MapRegression analysisGeologyPhysics - Applied Physics15. Life on landRandom forestsRemote sensingPE&RCRandom forestMODISTraitVegetatie Bos- en LandschapsecologieVegetation Forest and Landscape EcologyLandsat
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Temperate Fish Detection and Classification: a Deep Learning based Approach

2021

A wide range of applications in marine ecology extensively uses underwater cameras. Still, to efficiently process the vast amount of data generated, we need to develop tools that can automatically detect and recognize species captured on film. Classifying fish species from videos and images in natural environments can be challenging because of noise and variation in illumination and the surrounding habitat. In this paper, we propose a two-step deep learning approach for the detection and classification of temperate fishes without pre-filtering. The first step is to detect each single fish in an image, independent of species and sex. For this purpose, we employ the You Only Look Once (YOLO) …

0106 biological sciencesFOS: Computer and information sciencesComputer Science - Machine LearningComputer scienceComputer Vision and Pattern Recognition (cs.CV)Computer Science - Computer Vision and Pattern Recognition010603 evolutionary biology01 natural sciencesConvolutional neural networkVDP::Matematikk og Naturvitenskap: 400::Informasjons- og kommunikasjonsvitenskap: 420Machine Learning (cs.LG)Artificial IntelligenceClassifier (linguistics)FOS: Electrical engineering electronic engineering information engineeringbusiness.industry010604 marine biology & hydrobiologyDeep learningImage and Video Processing (eess.IV)Process (computing)Pattern recognitionElectrical Engineering and Systems Science - Image and Video ProcessingObject detectionA priori and a posterioriNoise (video)Artificial intelligenceTransfer of learningbusiness
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Developing an orientation and cutting point determination algorithm for a trout fish processing system using machine vision

2019

Abstract Fish processing in small and medium fish supplying centers requires an intelligent system to operate on different sizes. Therefore, an image processing algorithm was developed to extract the proper head and belly cutting points according to the trout dimensions. The algorithm detects the fish orientation and location of pectoral, anal, pelvic, and caudal fins. In this study, each of the trout images was divided into slices along its length in order to segment the fins and extract cutting points. The channel ‘B’ of RGB color space was considered in both initial segmentation and fin detection stages among the examined channels of RGB, HSV, and L*a*b* color spaces. The back-belly and …

0106 biological sciencesFinbiologyOrientation (computer vision)ForestryImage processing04 agricultural and veterinary sciencesHSL and HSVHorticultureColor spacebiology.organism_classification01 natural sciencesComputer Science ApplicationsRGB color spaceTrout040103 agronomy & agriculture0401 agriculture forestry and fisheriesRGB color modelAgronomy and Crop ScienceAlgorithm010606 plant biology & botanyMathematicsComputers and Electronics in Agriculture
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