Search results for " Monte Carlo"

showing 10 items of 400 documents

Ab initio modelling of the Y, O, and Ti solute interaction in fcc-Fe matrix

2018

Abstract Strengthening of the ODS steels by Y2O3 precipitates permits to increase their operation temperature and radiation resistance, which is important in construction materials for future fusion and advanced fission reactors. Both size and spatial distribution of oxide particles significantly affect mechanical properties and radiation resistance of ODS steels. Addition of the Ti species (present also as a natural impurity atoms in iron lattice) in the particles of Y2O3 powder before their mechanical alloying leads to the formation of YTiO3, Y2TiO5, and Y2Ti2O7 nanoparticles in ODS steels. Modelling of these nanoparticle formation needs detailed knowledge of the energetic interactions be…

010302 applied physicsNuclear and High Energy PhysicsMaterials scienceFissionAb initioOxideNanoparticleThermodynamics02 engineering and technology021001 nanoscience & nanotechnology01 natural sciences7. Clean energyIonchemistry.chemical_compoundchemistryImpurity0103 physical sciencesKinetic Monte Carlo0210 nano-technologyInstrumentationRadiation resistanceNuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms
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GIGJ: a crustal gravity model of the Guangdong Province for predicting the geoneutrino signal at the JUNO experiment

2019

Gravimetric methods are expected to play a decisive role in geophysical modeling of the regional crustal structure applied to geoneutrino studies. GIGJ (GOCE Inversion for Geoneutrinos at JUNO) is a 3D numerical model constituted by ~46 x 10$^{3}$ voxels of 50 x 50 x 0.1 km, built by inverting gravimetric data over the 6{\deg} x 4{\deg} area centered at the Jiangmen Underground Neutrino Observatory (JUNO) experiment, currently under construction in the Guangdong Province (China). The a-priori modeling is based on the adoption of deep seismic sounding profiles, receiver functions, teleseismic P-wave velocity models and Moho depth maps, according to their own accuracy and spatial resolution. …

010504 meteorology & atmospheric sciencesGeoneutrinogeophysical uncertaintieInverse transform samplingFOS: Physical sciences01 natural sciencesBayesian methodUpper middle and lower crustStandard deviationNOSouth China BlockmiddlePhysics - GeophysicsMonte Carlo stochastic optimizationGOCE data gravimetric inversionGeophysical uncertaintiesGeochemistry and PetrologyEarth and Planetary Sciences (miscellaneous)Bayesian method; geophysical uncertainties; GOCE data gravimetric inversion; Monte Carlo stochastic optimization; South China Block; upper middle and lower crustImage resolution0105 earth and related environmental sciencesSubdivisionJiangmen Underground Neutrino Observatoryupper and middle and lower crustbusiness.industrySettore FIS/01 - Fisica SperimentaleCrustupperGeodesy[PHYS.PHYS.PHYS-GEN-PH]Physics [physics]/Physics [physics]/General Physics [physics.gen-ph]Geophysics (physics.geo-ph)and lower crustDepth soundingGeophysics13. Climate actionSpace and Planetary SciencebusinessGeologyBayesian method geophysical uncertainties GOCE data gravimetric inversion Monte Carlo stochastic optimization South China Blockupper and middle and lower crust
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Accounting for preferential sampling in species distribution models

2019

D. C., A. L. Q. and F. M. would like to thank the Ministerio de Educación y Ciencia (Spain) for financial support (jointly financed by the European Regional Development Fund) via Research Grants MTM2013‐42323‐P and MTM2016‐77501‐P, and ACOMP/2015/202 from Generalitat Valenciana (Spain). Species distribution models (SDMs) are now being widely used in ecology for management and conservation purposes across terrestrial, freshwater, and marine realms. The increasing interest in SDMs has drawn the attention of ecologists to spatial models and, in particular, to geostatistical models, which are used to associate observations of species occurrence or abundance with environmental covariates in a fi…

0106 biological sciencesComputer scienceQH301 BiologySpecies distributionPoint processesStochastic partial differential equation01 natural scienceshttp://aims.fao.org/aos/agrovoc/c_6774EspèceAbundance (ecology)StatisticsPesqueríasQAOriginal Researchhttp://aims.fao.org/aos/agrovoc/c_241990303 health sciencesEcologyU10 - Informatique mathématiques et statistiquesSampling (statistics)Integrated nested Laplace approximationstochastic partial differential equationVariable (computer science)symbolsÉchantillonnageSpecies Distribution Models (SDMs)Modèle mathématiqueBayesian probabilityNDASDistribution des populations010603 evolutionary biologyQH30103 medical and health sciencessymbols.namesakeCovariateQA MathematicsSDG 14 - Life Below WaterCentro Oceanográfico de Murciaspecies distribution modelsRelative species abundanceEcology Evolution Behavior and Systematicspoint processes030304 developmental biologyNature and Landscape Conservationhttp://aims.fao.org/aos/agrovoc/c_6113http://aims.fao.org/aos/agrovoc/c_7280Markov chain Monte Carlointegrated nested Laplace approximationU30 - Méthodes de rechercheBayesian modelling
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Hierarchical log Gaussian Cox process for regeneration in uneven-aged forests

2021

We propose a hierarchical log Gaussian Cox process (LGCP) for point patterns, where a set of points x affects another set of points y but not vice versa. We use the model to investigate the effect of large trees to the locations of seedlings. In the model, every point in x has a parametric influence kernel or signal, which together form an influence field. Conditionally on the parameters, the influence field acts as a spatial covariate in the intensity of the model, and the intensity itself is a non-linear function of the parameters. Points outside the observation window may affect the influence field inside the window. We propose an edge correction to account for this missing data. The par…

0106 biological sciencesStatistics and ProbabilityFOS: Computer and information sciences62F15 (Primary) 62M30 60G55 (Secondary)MCMCGaussianBayesian inferenceMarkovin ketjutStatistics - Applications010603 evolutionary biology01 natural sciencesCox processMethodology (stat.ME)010104 statistics & probabilitysymbols.namesakeregeneraatio (biologia)Applied mathematicsApplications (stat.AP)0101 mathematicsLaplace approximationStatistics - MethodologyGeneral Environmental ScienceParametric statisticsMathematicsspatial random effectsbayesilainen menetelmäMarkov chain Monte CarloFunction (mathematics)15. Life on landMissing dataMonte Carlo -menetelmätcompetition kernelLaplace's methodKernel (statistics)symbolstree regenerationpuustometsänhoitomatemaattiset mallitStatistics Probability and Uncertainty
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Reducing waste and ecological impacts through a sustainable and efficient management of perishable food based on the Monte Carlo simulation

2019

Abstract In today’s competitive global market it is mandatory to improve warehousing operations integrating economic, environmental and social aspects. The recent advancement in monitoring technologies can greatly improve the performance of the food supply chain reducing product loss. In particular, in the perishable food supply chain, initially inventory operations are critical because they manage the material flows in very variable conditions. The deterioration level of the products as well as the market demand are the main factors that can influence warehouse strategy. This research aims to consider the application of sustainability principles in the context of warehouse storage, evaluat…

0106 biological sciencesTraceabilityComputer scienceSupply chainGeneral Decision SciencesContext (language use)010501 environmental sciences010603 evolutionary biology01 natural sciencesWarehouse managementSupply and demandSettore ING-IND/17 - Impianti Industriali MeccaniciMonte Carlo simulationEcology Evolution Behavior and Systematics0105 earth and related environmental sciencesEcologyEcological impacts Food waste reduction Monte Carlo simulation Shelf life model Sustainability Warehouse managementEnvironmental economicsEcological impactWarehouseProduct (business)SustainabilitySustainabilityShelf life modelCarbon footprintEcological impacts; Food waste reduction; Monte Carlo simulation; Shelf life model; Sustainability; Warehouse managementFood waste reductionEcological Indicators
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Intermittent targeted therapies and stochastic evolution in patients affected by chronic myeloid leukemia

2016

Front line therapy for the treatment of patients affected by chronic myeloid leukemia (CML) is based on the administration of tyrosine kinase inhibitors, namely imatinib or, more recently, axitinib. Although imatinib is highly effective and represents an example of a successful molecular targeted therapy, the appearance of resistance is observed in a proportion of patients, especially those in advanced stages. In this work, we investigate the appearance of resistance in patients affected by CML, by modeling the evolutionary dynamics of cancerous cell populations in a simulated patient treated by an intermittent targeted therapy. We simulate, with the Monte Carlo method, the stochastic evolu…

0301 basic medicineOncologyDrugStatistics and Probabilitymedicine.medical_specialtymedicine.medical_treatmentmedia_common.quotation_subjectTargeted therapy03 medical and health sciencesClassical Monte Carlo simulations; computational biology; models for evolution (theory); mutational and evolutionary processes (theory); Statistical and Nonlinear Physics; Statistics and Probability; Statistics Probability and Uncertainty0302 clinical medicinecomputational biologyInternal medicinemedicineClassical Monte Carlo simulationmutational and evolutionary processes (theory)media_commonbusiness.industryMyeloid leukemiaStatistical and Nonlinear PhysicsImatinibSettore FIS/07 - Fisica Applicata(Beni Culturali Ambientali Biol.e Medicin)Axitinib030104 developmental biology030220 oncology & carcinogenesisCancer cellToxicityStatistics Probability and Uncertaintybusinessmodels for evolution (theory)Tyrosine kinasemedicine.drugStatistical and Nonlinear Physic
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On the stability of some controlled Markov chains and its applications to stochastic approximation with Markovian dynamic

2015

We develop a practical approach to establish the stability, that is, the recurrence in a given set, of a large class of controlled Markov chains. These processes arise in various areas of applied science and encompass important numerical methods. We show in particular how individual Lyapunov functions and associated drift conditions for the parametrized family of Markov transition probabilities and the parameter update can be combined to form Lyapunov functions for the joint process, leading to the proof of the desired stability property. Of particular interest is the fact that the approach applies even in situations where the two components of the process present a time-scale separation, w…

65C05FOS: Computer and information sciencesStatistics and ProbabilityLyapunov functionStability (learning theory)Markov processContext (language use)Mathematics - Statistics Theorycontrolled Markov chainsStatistics Theory (math.ST)Stochastic approximation01 natural sciencesMethodology (stat.ME)010104 statistics & probabilitysymbols.namesake60J05stochastic approximationFOS: MathematicsComputational statisticsApplied mathematics60J220101 mathematicsStatistics - MethodologyMathematicsSequenceMarkov chain010102 general mathematicsStability Markov chainssymbolsStatistics Probability and Uncertaintyadaptive Markov chain Monte Carlo
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Measurement of the Lund jet plane using charged particles in 13 TeV proton-proton collisions with the ATLAS detector

2020

The prevalence of hadronic jets at the LHC requires that a deep understanding of jet formation and structure is achieved in order to reach the highest levels of experimental and theoretical precision. There have been many measurements of jet substructure at the LHC and previous colliders, but the targeted observables mix physical effects from various origins. Based on a recent proposal to factorize physical effects, this Letter presents a double-differential cross-section measurement of the Lund jet plane using 139  fb−1 of √s=13  TeV proton-proton collision data collected with the ATLAS detector using jets with transverse momentum above 675 GeV. The measurement uses charged particles to ac…

:Kjerne- og elementærpartikkelfysikk: 431 [VDP]Protonshowers [parton]13000 GeV-cmsPhysics::Instrumentation and DetectorsHadronGeneral Physics and Astronomyjet: transverse momentumPhysical Effects01 natural sciencestransverse momentum [jet]High Energy Physics - ExperimentSubatomär fysikHigh Energy Physics - Experiment (hep-ex)Charged ParticlesSubatomic PhysicsComputingMilieux_COMPUTERSANDEDUCATIONscattering [p p][PHYS.HEXP]Physics [physics]/High Energy Physics - Experiment [hep-ex]Parton showerNuclear ExperimentGeneralLiterature_REFERENCE(e.g.dictionariesencyclopediasglossaries)PhysicsSettore FIS/01Jet (fluid)Large Hadron ColliderDouble Differential Cross SectionsDetectorhadronic [jet]Monte Carlo [numerical calculations]ATLASTransverse Momentacharged particleCharged particlemedicine.anatomical_structureCERN LHC Coll:Nuclear and elementary particle physics: 431 [VDP]colliding beams [p p]numerical calculations: Monte CarloParticle Physics - Experimentp p: scatteringCiências Naturais::Ciências Físicas530 Physicsformation [jet]Astrophysics::High Energy Astrophysical Phenomena:Ciências Físicas [Ciências Naturais]FOS: Physical sciencesMeasurements ofLHC ATLAS High Energy Physicsjet: formation530GeneralLiterature_MISCELLANEOUSMonte Carlo Modelparton: showersNuclear physicsdifferential cross section: measuredAtlas (anatomy)Fragmentationmeasured [differential cross section]0103 physical sciencesmedicineddc:530High Energy Physicsstructure010306 general physicsATLAS CollaborationScience & Technology010308 nuclear & particles physicsComputerSystemsOrganization_COMPUTER-COMMUNICATIONNETWORKSFísicajet: hadronic530 Physikangular resolutionProton Proton CollisionsElementary Particles and FieldsHigh Energy Physics::ExperimentDetector EffectsHadron-hadron collisionsp p: colliding beamsMathematicsofComputing_DISCRETEMATHEMATICSacceptanceexperimental results
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ON THE CALCULATION OF THE HEAT CAPACITY IN PATH INTEGRAL MONTE CARLO SIMULATIONS

1992

In Path Integral Monte Carlo simulations the systems partition function is mapped to an equivalent classical one at the expense of a temperature-dependent Hamiltonian with an additional imaginary time dimension. As a consequence the standard relation linking the heat capacity Cv to the energy fluctuations, <E2>−<E>2, which is useful in standard classical problems with temperature-independent Hamiltonian, becomes invalid. Instead, it gets replaced by the general relation [Formula: see text] for the intensive heat capacity estimator; β being the inverse temperature and the subscript P indicates the P-fold discretization in the imaginary time direction. This heatcapacity estimator…

Absolute magnitudeDiscretizationGeneral Physics and AstronomyEstimatorStatistical and Nonlinear PhysicsHeat capacityImaginary timeComputer Science Applicationssymbols.namesakeComputational Theory and MathematicsQuantum mechanicssymbolsStatistical physicsHamiltonian (quantum mechanics)QuantumMathematical PhysicsPath integral Monte CarloMathematicsInternational Journal of Modern Physics C
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Effect of Stiffness on the Micellization Behavior of Model H4T4 Surfactant Chains

2006

The micellization behavior of a series of model surfactants, all with four head and tail groups (H4T4) but with different degrees of chain stiffness, was studied using grand canonical Monte Carlo simulations on a cubic lattice. The critical micelle concentration, micellar size, and thermodynamics of micellization were examined. In all cases investigated, the critical micelle concentration was found to increase with increasing temperature as observed for nonionic surfactants in apolar or slightly polar solvents. At a fixed reduced temperature and increasing chain stiffness, in agreement with previous observations, it was found that the critical micelle concentration decreased and the average…

Aggregation numberChemistryCrystal lattices Hydrophobicity Micelles Molecular structure Monte Carlo methods SolventsThermodynamics of micellizationMonte Carlo methodtechnology industry and agricultureThermodynamicsSurfaces and InterfacesCondensed Matter PhysicsMicelleSurface-Active AgentsReduced propertiesPulmonary surfactantCritical micelle concentrationElectrochemistryThermodynamicsOrganic chemistryPolarGeneral Materials ScienceMonte Carlo MethodMicellesSpectroscopySettore CHIM/02 - Chimica FisicaLangmuir
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