Search results for "machine"

showing 10 items of 2592 documents

Zeolite-silver-zinc nanoparticles : biocompatibility and their effect on the compressive strength of mineral trioxide aggregate

2016

Background This study was carried out to evaluate the biocompatibility of zeolite-silver-zinc (Ze-Ag-Zn) nanoparticles and their effect on the compressive strength of Mineral Trioxide Aggregate (MTA). Material and Methods Biocompatibility was evaluated by an MTT assay on the pulmonary adenocarcinoma cells with 0.05, 0.1, 0.25, 0.5, 1 and 5 mg/mL concentrations of Ze-Ag-Zn. For compressive strength test, four groups containing 15 stainless-steel cylinders with an internal diameter of 4 and a height of 6 mm were prepared and MTA (groups 1 and 2) or MTA + 2% Ze-Ag-Zn (groups 3 and 4) were placed in the cylinders. The compressive strength was evaluated using a universal testing machine 4 days a…

0106 biological sciencesMineral trioxide aggregateUniversal testing machineBiocompatibilityChemistryResearchPulmonary adenocarcinomaMineralogy030206 dentistryZinc nanoparticles:CIENCIAS MÉDICAS [UNESCO]01 natural sciences03 medical and health sciences0302 clinical medicineCompressive strength010608 biotechnologyBiomaterials and Bioengineering in DentistryUNESCO::CIENCIAS MÉDICASMTT assayZeoliteGeneral DentistryNuclear chemistry
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Discrimination of common defects in loquat fruit cv. ‘Algerie’ using hyperspectral imaging and machine learning techniques

2021

Abstract Loquat (Eriobotrya japonica L.) is an important fruit for the economy of some regions of Spain that is very susceptible to mechanical damage and physiological disorders. These problems depreciate its value and prevent it from being exported. Visible (VIS) and near infrared (NIR) hyperspectral imaging was used to discriminate between external and internal common defects of loquat cv. ‘Algerie’. Two classifiers, random forest (RF) and extreme gradient boost (XGBoost), and different spectral pre-processing techniques were evaluated in terms of their capacity to distinguish between sound and defective features according to three approaches. In the first approach the fruit pixels were c…

0106 biological sciencesN01 Agricultural engineeringEriobotryaHorticulture01 natural sciences040501 horticultureNon-destructiveClassification rateH20 Plant diseasesArtificial visionMathematicsPixelbiologybusiness.industryHyperspectral imagingPattern recognition04 agricultural and veterinary sciencesClassificationbiology.organism_classificationQualityRandom forestEriobotrya japonicaMultivariate analysisN20 Agricultural machinery and equipmentArtificial intelligence0405 other agricultural sciencesbusinessAgronomy and Crop Science010606 plant biology & botanyFood SciencePostharvest Biology and Technology
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Automatic measurement of wood fiber orientation and knot detection using an optical system based on heating conduction.

2015

In this paper, a new approach to computing the deviation of wood grain is proposed. To do this, the thermal conduction properties of timber are used (higher conduction in the fiber direction). Exciting the surface of the wood with a laser and capturing the thermal conduction using a thermal camera, an ellipse can be observed. Using a method similar to the tracheid effect, it is possible to extract information from this ellipse, such as the slope of grain and the presence of knots. With this method it is therefore possible to extend the mechanical model (assessing the mechanical properties of timber) to take certain singularities into account. Using this approach, the slope of grain can be e…

0106 biological sciencesOptical fiberMaterials scienceSoftwoodThermal imaging[ SPI.MECA ] Engineering Sciences [physics]/Mechanics [physics.med-ph]Machine VisionLaserEllipse01 natural sciencesgrain anglelaw.inventionOpticsKnot (unit)law010608 biotechnologyThermalHardwoodOptique / photonique [Sciences de l'ingénieur]040101 forestryMécanique [Sciences de l'ingénieur]business.industry04 agricultural and veterinary sciences[SPI.MECA]Engineering Sciences [physics]/Mechanics [physics.med-ph]Thermal conductionWoodAtomic and Molecular Physics and Optics[SPI.OPTI]Engineering Sciences [physics]/Optics / Photonic0401 agriculture forestry and fisheries[ SPI.OPTI ] Engineering Sciences [physics]/Optics / PhotonicWood grainbusinessOptics express
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Gene flow relates to evolutionary divergence among populations at the range margin

2020

Background Morphological differentiation between populations resulting from local adaptations to environmental conditions is likely to be more pronounced in populations with increasing genetic isolation. In a previous study a positive clinal variation in body size was observed in isolated Roesel’s bush-cricket, Metrioptera roeselii, populations, but were absent from populations within a continuous distribution at the same latitudinal range. This observational study inferred that there was a phenotypic effect of gene flow on climate-induced selection in this species. Methods To disentangle genetic versus environmental drivers of population differences in morphology, we measured the size of …

0106 biological sciencesRange (biology)Climatelcsh:MedicineBody sizeBiology010603 evolutionary biology01 natural sciencesGeneral Biochemistry Genetics and Molecular BiologyGene flowEvolutionsbiologi03 medical and health sciencesAdaptive divergenceMargin (machine learning)GeneticsGenetikGenetic isolation030304 developmental biologyEvolutionary Biology0303 health sciencesEcologyMorphological differentiationGeneral Neurosciencelcsh:RVDP::Matematikk og Naturvitenskap: 400Body sizeGeneral MedicineEvolutionary StudiesEvolutionary biologyOrthopteraEvolutionary divergenceGeneral Agricultural and Biological SciencesEntomologyZoologyGenetic isolatePeerJ
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Monitoring internet trade to inform species conservation actions

2017

Specimens, parts and products of threatened species are now commonly traded on the internet. This could threaten the survival of some wild populations if inadequately regulated. We outline two methods to monitor internet sales of threatened species in order to assess potential threats and inform conservation actions. Our first method combines systematic monitoring of online offers of plants for sale over the internet with consultation by experts experienced in identifying plants collected from the wild based on images of the specimens, species identity and details of the trade. Our second method utilises a computational model, trained using Bayesian techniques to records that have been clas…

0106 biological sciencesSettore BIO/07 - EcologiaEcologybusiness.industry010604 marine biology & hydrobiologyInternet privacyfood and beverages010603 evolutionary biology01 natural scienceslcsh:QK1-989Geographylcsh:Botanylcsh:ZoologySettore BIO/03 - Botanica Ambientale E ApplicataThe InternetAdenia Commiphora Operculicarya Uncarina Machine learning Infer.NET Naive Bayes classifierlcsh:QL1-991businessNature and Landscape Conservation
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Ecophysiological Modeling of Grapevine Water Stress in Burgundy Terroirs by a Machine-Learning Approach

2016

13 pages; International audience; In a climate change scenario, successful modeling of the relationships between plant-soil-meteorology is crucial for a sustainable agricultural production, especially for perennial crops. Grapevines (Vitis vinifera L. cv Chardonnay) located in eight experimental plots (Burgundy, France) along a hillslope were monitored weekly for 3 years for leaf water potentials, both at predawn (Ψpd) and at midday (Ψstem). The water stress experienced by grapevine was modeled as a function of meteorological data (minimum and maximum temperature, rainfall) and soil characteristics (soil texture, gravel content, slope) by a gradient boosting machine. Model performance was a…

0106 biological sciences[ SDV.BV ] Life Sciences [q-bio]/Vegetal BiologySoil texture[SDV.SA.AGRO]Life Sciences [q-bio]/Agricultural sciences/Agronomy[ SDV.SA.SDS ] Life Sciences [q-bio]/Agricultural sciences/Soil studyContext (language use)Plant Science[SDV.SA.SDS]Life Sciences [q-bio]/Agricultural sciences/Soil studylcsh:Plant culture01 natural sciencesVineyardwater stressWater balancewater balance[ SDV.SA.AGRO ] Life Sciences [q-bio]/Agricultural sciences/Agronomygradient boosting machine (GBM)Climate change scenarioBotany[SDV.BV]Life Sciences [q-bio]/Vegetal Biologylcsh:SB1-1110Original ResearchTerroir2. Zero hungerHydrologymachine-learninggrapevine (Vitis vinifera L.)temperature04 agricultural and veterinary sciences15. Life on landcarbon isotope discrimination δ13Cplant-soil water relationships040103 agronomy & agriculture0401 agriculture forestry and fisheriesEnvironmental scienceGradient boostingScale (map)carbon isotope discrimination d13Ccarbon isotopic discrimination (δ13C)010606 plant biology & botanyFrontiers in Plant Science
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Partitioning net carbon dioxide fluxes into photosynthesis and respiration using neural networks

2020

Abstract The eddy covariance (EC) technique is used to measure the net ecosystem exchange (NEE) of CO2 between ecosystems and the atmosphere, offering a unique opportunity to study ecosystem responses to climate change. NEE is the difference between the total CO2 release due to all respiration processes (RECO), and the gross carbon uptake by photosynthesis (GPP). These two gross CO2 fluxes are derived from EC measurements by applying partitioning methods that rely on physiologically based functional relationships with a limited number of environmental drivers. However, the partitioning methods applied in the global FLUXNET network of EC observations do not account for the multiple co‐acting…

0106 biological sciencesecosystem respiration010504 meteorology & atmospheric sciencesnet ecosystem exchangeneural networkEddy covarianceClimate changeAtmospheric sciencesPhotosynthesis01 natural sciences7. Clean energyCarbon CycleAtmosphereFlux (metallurgy)FluxNetRespirationeddy covarianceEnvironmental ChemistryEcosystemPrimary Research ArticlePhotosynthesisEcosystem0105 earth and related environmental sciencesGeneral Environmental ScienceGlobal and Planetary ChangeEcologycarbon dioxide fluxes partitioningRespirationgross primary production (GPP)Carbon DioxideBiological Sciences15. Life on landgross primary productionmachine learning13. Climate action[SDE]Environmental SciencesEnvironmental scienceNeural Networks ComputerSeasonsecosystem respiration (RECO)Environmental Sciences010606 plant biology & botanyGlobal Change Biology
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Which traits allow weed species to persist in grass margin strips ?

2017

EASPEGESTADINRA; Sown-grass margin strips, historically established to limit pesticide drift and soil erosion, are now also promoted for enhancing floral diversity and associated ecosystem services. To better understand weed community assembly in grass margin strips, we performed floral surveys in 75 sown-grass margin strips in two regions in France and characterized each species using information from trait databases. We hypothesized that traits of dominant species would differ between newly sown-grass margin strips and older strips. Weed species were separated into functional groups based on their traits using multiple correspondence analysis and hierarchical ascendant classification. Fun…

0106 biological sciencesfunctional group[SDV]Life Sciences [q-bio]Plant ScienceBiology010603 evolutionary biology01 natural sciencesMonocotyledonEcosystem servicesagri-environmental schemesMargin (machine learning)field marginRuderal species2. Zero hunger[ SDV ] Life Sciences [q-bio]traitEcologyfungiDicotyledonfood and beverages04 agricultural and veterinary sciences15. Life on landbiology.organism_classificationfield edgeDisturbance (ecology)040103 agronomy & agricultureTrait0401 agriculture forestry and fisheriescommunity assemblyWeedAgronomy and Crop Science
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Broker and Federation Based Cloud Networking Architecture for IaaS and NaaS QoS Guarantee

2016

International audience; Today, the Cloud networking aspect is a critical factor for adopting the Cloud computing approach. The main drawback of Cloud networking consists in the lack of Quality of Service (QoS) guarantee and management in conformance with a corresponding Service Level Agreement (SLA). This paper presents a framework for resource allocation according to an end-to-end SLA established between a Cloud Service User (CSU) and several Cloud Service Providers (CSPs) in a Cloud networking environment. We focus on QoS parameters for Network as a Service (NaaS) and Infrastructure as a Service (IaaS) services. In addition, we propose algorithms for the best CSPs selection to allocate Vi…

020203 distributed computing[SPI] Engineering Sciences [physics]business.industryComputer scienceQuality of service020207 software engineeringCloud computing02 engineering and technologyMobile QoScomputer.software_genre[SPI.TRON] Engineering Sciences [physics]/Electronics[SPI.TRON]Engineering Sciences [physics]/Electronics[ SPI.TRON ] Engineering Sciences [physics]/Electronics[SPI]Engineering Sciences [physics]Service-level agreementNetwork as a serviceVirtual machineCloud testing0202 electrical engineering electronic engineering information engineering[ SPI ] Engineering Sciences [physics]Resource allocationbusinesscomputerSimulationComputer network
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Adaptive Feedforward Control of a Pressure Compensated Differential Cylinder

2020

This paper presents the design, simulation and experimental verification of adaptive feedforward motion control for a hydraulic differential cylinder. The proposed solution is implemented on a hydraulic loader crane. Based on common adaptation methods, a typical electro-hydraulic motion control system has been extended with a novel adaptive feedforward controller that has two separate feedforward states, i.e, one for each direction of motion. Simulations show convergence of the feedforward states, as well as 23% reduction in root mean square (RMS) cylinder position error compared to a fixed gain feedforward controller. The experiments show an even more pronounced advantage of the proposed c…

0209 industrial biotechnologyAdaptive controlFluid PowerComputer sciencemotion controlComputer Science::Neural and Evolutionary Computationhydraulicsdifferential cylinder02 engineering and technologyAdaptiv reguleringadaptive controllcsh:TechnologyRoot mean squarelcsh:Chemistry020901 industrial engineering & automationControl theoryConvergence (routing)feedforwardCylinderGeneral Materials ScienceVDP::Andre maskinfag: 579Instrumentationlcsh:QH301-705.5Fluid Flow and Transfer Processeslcsh:TProcess Chemistry and TechnologyGeneral EngineeringFeed forwardVDP::Other machinery sciences: 579021001 nanoscience & nanotechnologyMotion controllcsh:QC1-999BevegelsesstyringComputer Science Applicationslcsh:Biology (General)lcsh:QD1-999lcsh:TA1-2040Hydraulikk0210 nano-technologyReduction (mathematics)lcsh:Engineering (General). Civil engineering (General)lcsh:PhysicsApplied Sciences
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