Search results for "Monte Carlo."

showing 10 items of 1571 documents

A study of Type B uncertainties associated with the photoelectric effect in low-energy Monte Carlo simulations

2021

The goal of this manuscript is to estimate Type B uncertainties in absorbed-dose calculations arising from the different implementations in current state-of-the-art Monte Carlo codes of low-energy photon cross-sections (<200 keV). Monte Carlo simulations are carried out using three codes widely used in the low-energy domain: PENELOPE-2018, EGSnrc, and MCNP. Mass energy-absorption coefficients for water, air, graphite, and their respective ratios; absorbed dose; and photon-fluence spectra are considered. Benchmark simulations using similar cross-sections have been performed. The differences observed between these quantities when different cross-sections are considered are taken to be a go…

photoelectric effectPhotonbrachytherapyMonte Carlo methodFOS: Physical sciencesSpectral lineMonte Carlo simulations030218 nuclear medicine & medical imaging03 medical and health sciences0302 clinical medicineDosimetryRadiology Nuclear Medicine and imagingRadiometryPhysicsPhotonsRadiological and Ultrasound TechnologyPhantoms ImagingUncertaintyEstimatorRadiusPhotoelectric effectPhysics - Medical Physics3. Good healthComputational physics030220 oncology & carcinogenesisAbsorbed doseMedical Physics (physics.med-ph)low energy physicsMonte Carlo Method
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Alder pollen in Finland ripens after a short exposure to warm days in early spring, showing biennial variation in the onset of pollen ripening

2017

We developed a temperature sum model to predict the daily pollen release of alder, based on pollen data collected with pollen traps at seven locations in Finland over the years 2000–2014. We estimated the model parameters by minimizing the sum of squared errors (SSE) of the model, with weights that put more weight on binary recognition of daily presence or absence of pollen. The model results suggest that alder pollen ripens after a couple of warm days in February, while the whole pollen release period typically takes up to 4 weeks. We tested the model residuals against air humidity, precipitation and wind speed, but adding these meteorological features did not improve the model prediction …

pollen seasonMonte Carlo -menetelmätlepätkukintaotorhinolaryngologic diseasesfood and beveragesmodelingMonte Carlo resamplingennusteetAlnusleppäsiitepöly
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Probabilistic Evaluation of the Adaptation Time for Structures under Seismic Loads

2016

Abstract In this paper, a probabilistic approach for the evaluation of the adaptation time for elastic perfectly plastic frames is proposed. The considered load history acting on the structure is defined as a suitable combination of quasi-statical loads and seismic actions. The proposed approach utilizes the Monte Carlo method in order to generate a suitable large number of seismic acceleration histories and for each one the related load combination is defined. Furthermore, for each load combination the related adaptation time is determined, if any, as the optimal one for which the structure is able to shakedown under the unamplified applied actions. A known generalized Ceradini's theorem i…

probabilistic approachEngineeringMonte Carlo methodStructure (category theory)seismic loading020101 civil engineeringProbability density function02 engineering and technology0201 civil engineeringAccelerationEngineering (all)0203 mechanical engineeringAdaptation (computer science)Engineering(all)adaptation time; Dynamic shakedown; probabilistic approach; seismic loading; Engineering (all)Dynamic shakedownbusiness.industrySeismic loadingProbabilistic logicGeneral MedicineStructural engineeringShakedown020303 mechanical engineering & transportsadaptation timebusinessSettore ICAR/08 - Scienza Delle CostruzioniAlgorithm
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The efficiency of the incentives for the public buildings’ energy retrofit. The case of the Italian Regions of the “Objective Convergence”

2017

Gli obiettivi di riduzione del consumo energetico e delle emissioni di gas serra, enunciati in molte Direttive europee, hanno posto in risalto la necessità di promuovere interventi di miglioramento dell’efficienza energetica del patrimonio edilizio esistente. Le amministrazioni locali, in particolare, dovrebbero assumersi la responsabilità di diffondere la cultura ambientale anche effettuando interventi di retrofit negli edifici pubblici di loro proprietà, sulla base di diagnosi energetiche degli edifici, di proposte progettuali e di verifiche del raggiungimento della convenienza finanziaria, che può essere agevolata dall’erogazione di incentivi. L’Unione Province d’Italia (UPI) nel 2013, n…

retrofit energetico incentivi valutazione finanziaria edifici pubblici analisi del rischio metodo Monte Carloenergy retrofit incentives cash flow analysis public buildings risk analysis Monte Carlo methodSettore ICAR/22 - Estimo
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Identifying territories using presence-only citizen science data : An application to the Finnish wolf population

2022

Citizens, community groups and local institutions participate in voluntary biological monitoring of population status and trends by providing species data e.g. for regulations and conservation. Sophisticated statistical methods are required to unlock the potential of such data in the assessment of wildlife populations. We develop a statistical modelling framework for identifying territories based on presence-only citizen science data. The framework can be used to jointly estimate the number of active animal territories and their locations in time. Our approach is based on a data generating model which consists of a dynamic submodel for the appearance/removal of territories and an observatio…

reviiritEcological Modelingbayesilainen menetelmäcitizen science datasusipaikkatietoanalyysisequential Monte CarloeläinkannatBayesian statisticsterritory identificationMonte Carlo -menetelmätpopulaatiotkansalaishavainnotkansalaistiedepresence-only dataspatio-temporal model
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Charged jet cross sections and properties in proton-proton collisions at $\sqrt{s}=7$ TeV

2015

The differential charged jet cross sections, jet fragmentation distributions, and jet shapes are measured in minimum bias proton-proton collisions at centre-of-mass energy $\sqrt{s}=7$ TeV using the ALICE detector at the LHC. Jets are reconstructed from charged particle momenta in the mid-rapidity region using the sequential recombination $k_{\rm T}$ and anti-$k_{\rm T}$ as well as the SISCone jet finding algorithms with several resolution parameters in the range $R=0.2$ to $0.6$. Differential jet production cross sections measured with the three jet finders are in agreement in the transverse momentum ($p_{\rm T}$) interval $20<p_{\rm T}^{\rm jet,ch}<100$ GeV/$c$. They are also consistent w…

shapes:Kjerne- og elementærpartikkelfysikk: 431 [VDP]parton distributionsMonte Carlo methodP(P)OVER-BAR COLLISIONSALICE Charged jet proton-proton 7 TeVATLAS DETECTOR01 natural sciencesSpectral lineHigh Energy Physics - Experimentdifferential charged jet cross sectionENERGYHigh Energy Physics - Experiment (hep-ex)ALICEFragmentation (mass spectrometry)Nuclear and High Energy Physics differential charged jet cross sectionfragmentation[PHYS.HEXP]Physics [physics]/High Energy Physics - Experiment [hep-ex]Nuclear Experiment (nucl-ex)ROOT-S(NN)=2.76 TEVNuclear ExperimentNuclear Experimentroot-s(nn)=2.76 tevatlas detectorPhysicsLarge Hadron Collidercross sectionPhysicsDetectorCharged particle3. Good health:Mathematics and natural scienses: 400::Physics: 430::Nuclear and elementary particle physics: 431 [VDP]charged jetsPRIRODNE ZNANOSTI. Fizika.:Nuclear and elementary particle physics: 431 [VDP]SHAPESTransverse momentumHADRON-COLLISIONSFRAGMENTATIONpp collisionsenergyParticle physicsNuclear and High Energy PhysicsAstrophysics::High Energy Astrophysical PhenomenaCharged jetVDP::Matematikk og naturvitenskap: 400::Fysikk: 430::Kjerne- og elementærpartikkelfysikk: 431FOS: Physical sciences[PHYS.NEXP]Physics [physics]/Nuclear Experiment [nucl-ex]114 Physical sciencestransverse-momentumNuclear physicsMinimum bias(P)OVER-BAR-P COLLISIONS P(P)OVER-BAR COLLISIONS PP COLLISIONS PARTON DISTRIBUTIONS TRANSVERSE-MOMENTUM SHAPES ALGORITHM ENERGY0103 physical sciences7 TeVNuclear Physics - Experimentproton-protonALGORITHM010306 general physics(p)over-bar-p collisionsPP COLLISIONSta114(P)OVER-BAR-P COLLISIONSVDP::Mathematics and natural scienses: 400::Physics: 430::Nuclear and elementary particle physics: 431010308 nuclear & particles physics:Matematikk og naturvitenskap: 400::Fysikk: 430::Kjerne- og elementærpartikkelfysikk: 431 [VDP]TRANSVERSE-MOMENTUMNATURAL SCIENCES. Physics.(p)over-bar-p collisions ; parton distributions ; transverse-momentum ; root-s(nn)=2.76 tev ; hadron-collisions ; atlas detector ; pp collisions ; fragmentation ; shapes ; energy ; charged jet ; cross section ; proton-proton ; 7 TeVhadron-collisionsPARTON DISTRIBUTIONSALICE; Charged jet; proton-proton; 7 TeVproton-proton collisionsHigh Energy Physics::Experimentcharged jet
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A comprehensive guide to the physics and usage of PYTHIA 8.3

2022

This manual describes the PYTHIA 8.3 event generator, the most recent version of an evolving physics tool used to answer fundamental questions in particle physics. The program is most often used to generate high-energy-physics collision "events", i.e. sets of particles produced in association with the collision of two incoming high-energy particles, but has several uses beyond that. The guiding philosophy is to produce and reproduce properties of experimentally obtained collisions as accurately as possible. The program includes a wide ranges of reactions within and beyond the Standard Model, and extending to heavy ion physics. Emphasis is put on phenomena where strong interactions play a ma…

showers [parton]numeeriset menetelmätnew physicskäsikirjatFOS: Physical scienceshiukkasfysiikkamanualprogrammingheavy ionHigh Energy Physics - ExperimentMonte Carlo -menetelmätHigh Energy Physics - PhenomenologyHigh Energy Physics - Experiment (hep-ex)High Energy Physics - Phenomenology (hep-ph)PYTHIAquantum chromodynamicsalgoritmitinterfacekvanttiväridynamiikkaohjelmointinumerical calculationsMonte Carlo
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SNP and SML estimation of univariate and bivariate binary–choice models

2008

We discuss the semi-nonparametric approach of Gallant and Nychka (1987, Econometrica 55: 363–390), the semiparametric maximum likelihood approach of Klein and Spady (1993, Econometrica 61: 387–421), and a set of new Stata commands for semiparametric estimation of three binary-choice models. The first is a univariate model, while the second and the third are bivariate models without and with sample selection, respectively. The proposed estimators are root-n consistent and asymptotically normal for the model parameters of interest under weak assumptions on the distribution of the underlying error terms. Our Monte Carlo simulations suggest that the efficiency losses of the semi-nonparametric a…

st0000 snp snp2 snp2s sml sml2s binary-choice models seminonparametric approach SNP estimation semiparametric maximum likelihood SML estimation Monte Carlo simulationSettore SECS-P/05 - EconometriaSettore SECS-P/01 - Economia Politica
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Can the adaptive Metropolis algorithm collapse without the covariance lower bound?

2011

The Adaptive Metropolis (AM) algorithm is based on the symmetric random-walk Metropolis algorithm. The proposal distribution has the following time-dependent covariance matrix at step $n+1$ \[ S_n = Cov(X_1,...,X_n) + \epsilon I, \] that is, the sample covariance matrix of the history of the chain plus a (small) constant $\epsilon>0$ multiple of the identity matrix $I$. The lower bound on the eigenvalues of $S_n$ induced by the factor $\epsilon I$ is theoretically convenient, but practically cumbersome, as a good value for the parameter $\epsilon$ may not always be easy to choose. This article considers variants of the AM algorithm that do not explicitly bound the eigenvalues of $S_n$ away …

stabiiliusMetropolis-algoritmiAdaptive Markov chain Monte Carlostochastic approximationstokastinen approksimaatiostabilityadaptiivinen Markov chain Monte CarloMetropolis algorithm
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Una variante del problema dell'ago di Buffon e verifica con il metodo Monte Carlo

2009

statistica Monte CarloSettore ING-IND/19 - Impianti Nucleari
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