Astuti, Ani Budi; Iriawan, Nur; Irhamah, Kuswanto, Heri
2017-12-01
In the Bayesian mixture modeling requires stages the identification number of the most appropriate mixture components thus obtained mixture models fit the data through data driven concept. Reversible Jump Markov Chain Monte Carlo (RJMCMC) is a combination of the reversible jump (RJ) concept and the Markov Chain Monte Carlo (MCMC) concept used by some researchers to solve the problem of identifying the number of mixture components which are not known with certainty number. In its application, RJMCMC using the concept of the birth/death and the split-merge with six types of movement, that are w updating, θ updating, z updating, hyperparameter β updating, split-merge for components and birth/death from blank components. The development of the RJMCMC algorithm needs to be done according to the observed case. The purpose of this study is to know the performance of RJMCMC algorithm development in identifying the number of mixture components which are not known with certainty number in the Bayesian mixture modeling for microarray data in Indonesia. The results of this study represent that the concept RJMCMC algorithm development able to properly identify the number of mixture components in the Bayesian normal mixture model wherein the component mixture in the case of microarray data in Indonesia is not known for certain number.
Modeling the cathode region of noble gas mixture discharges using Monte Carlo simulation
International Nuclear Information System (INIS)
Donko, Z.; Janossy, M.
1992-10-01
A model of the cathode dark space of DC glow discharges was developed in order to study the effects caused by mixing small amounts (≤2%) of other noble gases (Ne, Ar, Kr and Xe) to He. The motion of charged particles was described by Monte Carlo simulation. Several discharge parameters (electron and ion energy distribution functions, electron and ion current densities, reduced ionization coefficients, and current density-voltage characteristics) were obtained. Small amounts of admixtures were found to modify significantly the discharge parameters. Current density-voltage characteristics obtained from the model showed good agreement with experimental data. (author) 40 refs.; 14 figs
Li, Jun
2013-09-01
We present a single-particle Lennard-Jones (L-J) model for CO2 and N2. Simplified L-J models for other small polyatomic molecules can be obtained following the methodology described herein. The phase-coexistence diagrams of single-component systems computed using the proposed single-particle models for CO2 and N2 agree well with experimental data over a wide range of temperatures. These diagrams are computed using the Markov Chain Monte Carlo method based on the Gibbs-NVT ensemble. This good agreement validates the proposed simplified models. That is, with properly selected parameters, the single-particle models have similar accuracy in predicting gas-phase properties as more complex, state-of-the-art molecular models. To further test these single-particle models, three binary mixtures of CH4, CO2 and N2 are studied using a Gibbs-NPT ensemble. These results are compared against experimental data over a wide range of pressures. The single-particle model has similar accuracy in the gas phase as traditional models although its deviation in the liquid phase is greater. Since the single-particle model reduces the particle number and avoids the time-consuming Ewald summation used to evaluate Coulomb interactions, the proposed model improves the computational efficiency significantly, particularly in the case of high liquid density where the acceptance rate of the particle-swap trial move increases. We compare, at constant temperature and pressure, the Gibbs-NPT and Gibbs-NVT ensembles to analyze their performance differences and results consistency. As theoretically predicted, the agreement between the simulations implies that Gibbs-NVT can be used to validate Gibbs-NPT predictions when experimental data is not available. © 2013 Elsevier Inc.
International Nuclear Information System (INIS)
Peterson, L.E.; Cucinotta, F.A.
1999-01-01
Estimating uncertainty in lifetime cancer risk for human exposure to space radiation is a unique challenge. Conventional risk assessment with low-linear-energy-transfer (LET)-based risk from Japanese atomic bomb survivor studies may be inappropriate for relativistic protons and nuclei in space due to track structure effects. This paper develops a Monte Carlo mixture model (MCMM) for transferring additive, National Institutes of Health multiplicative, and multiplicative excess cancer incidence risks based on Japanese atomic bomb survivor data to determine excess incidence risk for various US astronaut exposure profiles. The MCMM serves as an anchor point for future risk projection methods involving biophysical models of DNA damage from space radiation. Lifetime incidence risks of radiation-induced cancer for the MCMM based on low-LET Japanese data for nonleukemia (all cancers except leukemia) were 2.77 (90% confidence limit, 0.75-11.34) for males exposed to 1 Sv at age 45 and 2.20 (90% confidence limit, 0.59-10.12) for males exposed at age 55. For females, mixture model risks for nonleukemia exposed separately to 1 Sv at ages of 45 and 55 were 2.98 (90% confidence limit, 0.90-11.70) and 2.44 (90% confidence limit, 0.70-10.30), respectively. Risks for high-LET 200 MeV protons (LET=0.45 keV/μm), 1 MeV α-particles (LET=100 keV/μm), and 600 MeV iron particles (LET=180 keV/μm) were scored on a per particle basis by determining the particle fluence required for an average of one particle per cell nucleus of area 100 μm 2 . Lifetime risk per proton was 2.68x10 -2 % (90% confidence limit, 0.79x10 -3 %-0.514x10 -2 %). For α-particles, lifetime risk was 14.2% (90% confidence limit, 2.5%-31.2%). Conversely, lifetime risk per iron particle was 23.7% (90% confidence limit, 4.5%-53.0%). Uncertainty in the DDREF for high-LET particles may be less than that for low-LET radiation because typically there is very little dose-rate dependence. Probability density functions for
Monte Carlo simulations of interacting particle mixtures in ratchet potentials
International Nuclear Information System (INIS)
Fendrik, A J; Romanelli, L
2012-01-01
There are different models of devices for achieving a separation of mixtures of particles by using the ratchet effect. On the other hand, it has been proposed that one could also control the separation by means of appropriate interactions. Through Monte Carlo simulations, we show that inclusion of simple interactions leads to a decrease of the ratchet effect and therefore also a separation of the mixtures.
Directory of Open Access Journals (Sweden)
B. Hribar-Lee
2013-01-01
Full Text Available Very recently the effect of equisized charged hard sphere solutes in a mixture with core-softened fluid model on the structural and thermodynamic anomalies of the system has been explored in detail by using Monte Carlo simulations and integral equations theory (J. Chem. Phys., Vol. 137, 244502 (2012. Our objective of the present short work is to complement this study by considering univalent ions of unequal diameters in a mixture with the same soft-core fluid model. Specifically, we are interested in the analysis of changes of the temperature of maximum density (TMD lines with ion concentration for three model salt solutes, namely sodium chloride, potassium chloride and rubidium chloride models. We resort to Monte Carlo simulations for this purpose. Our discussion also involves the dependences of the pair contribution to excess entropy and of constant volume heat capacity on the temperature of maximum density line. Some examples of the microscopic structure of mixtures in question in terms of pair distributions functions are given in addition.
Dai, Yunyun
2013-01-01
Mixtures of item response theory (IRT) models have been proposed as a technique to explore response patterns in test data related to cognitive strategies, instructional sensitivity, and differential item functioning (DIF). Estimation proves challenging due to difficulties in identification and questions of effect size needed to recover underlying…
Li, Jun; Calo, Victor M.
2013-01-01
models although its deviation in the liquid phase is greater. Since the single-particle model reduces the particle number and avoids the time-consuming Ewald summation used to evaluate Coulomb interactions, the proposed model improves the computational
Nonparametric Mixture of Regression Models.
Huang, Mian; Li, Runze; Wang, Shaoli
2013-07-01
Motivated by an analysis of US house price index data, we propose nonparametric finite mixture of regression models. We study the identifiability issue of the proposed models, and develop an estimation procedure by employing kernel regression. We further systematically study the sampling properties of the proposed estimators, and establish their asymptotic normality. A modified EM algorithm is proposed to carry out the estimation procedure. We show that our algorithm preserves the ascent property of the EM algorithm in an asymptotic sense. Monte Carlo simulations are conducted to examine the finite sample performance of the proposed estimation procedure. An empirical analysis of the US house price index data is illustrated for the proposed methodology.
Prevalence Incidence Mixture Models
The R package and webtool fits Prevalence Incidence Mixture models to left-censored and irregularly interval-censored time to event data that is commonly found in screening cohorts assembled from electronic health records. Absolute and relative risk can be estimated for simple random sampling, and stratified sampling (the two approaches of superpopulation and a finite population are supported for target populations). Non-parametric (absolute risks only), semi-parametric, weakly-parametric (using B-splines), and some fully parametric (such as the logistic-Weibull) models are supported.
Binary gas mixture adsorption-induced deformation of microporous carbons by Monte Carlo simulation.
Cornette, Valeria; de Oliveira, J C Alexandre; Yelpo, Víctor; Azevedo, Diana; López, Raúl H
2018-07-15
Considering the thermodynamic grand potential for more than one adsorbate in an isothermal system, we generalize the model of adsorption-induced deformation of microporous carbons developed by Kowalczyk et al. [1]. We report a comprehensive study of the effects of adsorption-induced deformation of carbonaceous amorphous porous materials due to adsorption of carbon dioxide, methane and their mixtures. The adsorption process is simulated by using the Grand Canonical Monte Carlo (GCMC) method and the calculations are then used to analyze experimental isotherms for the pure gases and mixtures with different molar fraction in the gas phase. The pore size distribution determined from an experimental isotherm is used for predicting the adsorption-induced deformation of both pure gases and their mixtures. The volumetric strain (ε) predictions from the GCMC method are compared against relevant experiments with good agreement found in the cases of pure gases. Copyright © 2018 Elsevier Inc. All rights reserved.
Shell model Monte Carlo methods
International Nuclear Information System (INIS)
Koonin, S.E.; Dean, D.J.; Langanke, K.
1997-01-01
We review quantum Monte Carlo methods for dealing with large shell model problems. These methods reduce the imaginary-time many-body evolution operator to a coherent superposition of one-body evolutions in fluctuating one-body fields; the resultant path integral is evaluated stochastically. We first discuss the motivation, formalism, and implementation of such Shell Model Monte Carlo (SMMC) methods. There then follows a sampler of results and insights obtained from a number of applications. These include the ground state and thermal properties of pf-shell nuclei, the thermal and rotational behavior of rare-earth and γ-soft nuclei, and the calculation of double beta-decay matrix elements. Finally, prospects for further progress in such calculations are discussed. (orig.)
Shell model Monte Carlo methods
International Nuclear Information System (INIS)
Koonin, S.E.
1996-01-01
We review quantum Monte Carlo methods for dealing with large shell model problems. These methods reduce the imaginary-time many-body evolution operator to a coherent superposition of one-body evolutions in fluctuating one-body fields; resultant path integral is evaluated stochastically. We first discuss the motivation, formalism, and implementation of such Shell Model Monte Carlo methods. There then follows a sampler of results and insights obtained from a number of applications. These include the ground state and thermal properties of pf-shell nuclei, thermal behavior of γ-soft nuclei, and calculation of double beta-decay matrix elements. Finally, prospects for further progress in such calculations are discussed. 87 refs
Communication: Modeling electrolyte mixtures with concentration dependent dielectric permittivity
Chen, Hsieh; Panagiotopoulos, Athanassios Z.
2018-01-01
We report a new implicit-solvent simulation model for electrolyte mixtures based on the concept of concentration dependent dielectric permittivity. A combining rule is found to predict the dielectric permittivity of electrolyte mixtures based on the experimentally measured dielectric permittivity for pure electrolytes as well as the mole fractions of the electrolytes in mixtures. Using grand canonical Monte Carlo simulations, we demonstrate that this approach allows us to accurately reproduce the mean ionic activity coefficients of NaCl in NaCl-CaCl2 mixtures at ionic strengths up to I = 3M. These results are important for thermodynamic studies of geologically relevant brines and physiological fluids.
Modelling of an homogeneous equilibrium mixture model
International Nuclear Information System (INIS)
Bernard-Champmartin, A.; Poujade, O.; Mathiaud, J.; Mathiaud, J.; Ghidaglia, J.M.
2014-01-01
We present here a model for two phase flows which is simpler than the 6-equations models (with two densities, two velocities, two temperatures) but more accurate than the standard mixture models with 4 equations (with two densities, one velocity and one temperature). We are interested in the case when the two-phases have been interacting long enough for the drag force to be small but still not negligible. The so-called Homogeneous Equilibrium Mixture Model (HEM) that we present is dealing with both mixture and relative quantities, allowing in particular to follow both a mixture velocity and a relative velocity. This relative velocity is not tracked by a conservation law but by a closure law (drift relation), whose expression is related to the drag force terms of the two-phase flow. After the derivation of the model, a stability analysis and numerical experiments are presented. (authors)
International Nuclear Information System (INIS)
Erpenbeck, J.J.
1989-01-01
The thermal transport properties of mixtures can be formulated in a number of ways, depending on the choice of driving forces for the transport of heat and matter, without violating the Onsager conditions. Here we treat transport in mixtures based on the driving forces -del ln T and -T del(μ/sub a//T), with T the temperature and μ/sub a/ the specific chemical potential, to obtain the Green-Kubo expressions and the Enskog theory for the corresponding transport coefficients which seem most amenable to molecular-dynamics evaluation. The transport properties of a hard-sphere mixture (mass ratio of 0.1, diameter ratio of 1.0, at a volume of three times close-packed volume), calculated by a Monte Carlo, molecular-dynamics method based on the Green-Kubo formulas, are compared with the predictions of the Enskog theory. The long-time behavior of the Green-Kubo time-correlation functions for shear viscosity, thermal conductivity, thermal diffusion, and mutual diffusion are found to be in good agreement with the predictions of mode-coupling theory. Except for viscosity, the contribution of the long-time tails to the transport coefficients is found to be significant. We obtain values, relative to Enskog, of 1.016 +- 0.007 for shear viscosity, 1.218 +- 0.009 for thermal conductivity, 1.267 +- 0.026 for thermal diffusion, and 1.117 +- 0.008 for mutual diffusion
Consistency of the MLE under mixture models
Chen, Jiahua
2016-01-01
The large-sample properties of likelihood-based statistical inference under mixture models have received much attention from statisticians. Although the consistency of the nonparametric MLE is regarded as a standard conclusion, many researchers ignore the precise conditions required on the mixture model. An incorrect claim of consistency can lead to false conclusions even if the mixture model under investigation seems well behaved. Under a finite normal mixture model, for instance, the consis...
The Monte Carlo dynamics of a binary Lennard-Jones glass-forming mixture
International Nuclear Information System (INIS)
Berthier, L; Kob, W
2007-01-01
We use a standard Monte Carlo algorithm to study the slow dynamics of a binary Lennard-Jones glass-forming mixture at low temperature. We find that the Monte Carlo approach is by far the most efficient way to simulate a stochastic dynamics since the relaxation is about 10 times faster than in Brownian dynamics and about 30 times faster than in stochastic dynamics. Moreover, the average dynamical behaviour of the system is in quantitative agreement with that obtained using Newtonian dynamics, apart from at very short times where thermal vibrations are suppressed. We show, however, that dynamic fluctuations quantified by four-point dynamic susceptibilities do retain a dependence on the microscopic dynamics, as recently predicted theoretically
Monte Carlo simulation of Markov unreliability models
International Nuclear Information System (INIS)
Lewis, E.E.; Boehm, F.
1984-01-01
A Monte Carlo method is formulated for the evaluation of the unrealibility of complex systems with known component failure and repair rates. The formulation is in terms of a Markov process allowing dependences between components to be modeled and computational efficiencies to be achieved in the Monte Carlo simulation. Two variance reduction techniques, forced transition and failure biasing, are employed to increase computational efficiency of the random walk procedure. For an example problem these result in improved computational efficiency by more than three orders of magnitudes over analog Monte Carlo. The method is generalized to treat problems with distributed failure and repair rate data, and a batching technique is introduced and shown to result in substantial increases in computational efficiency for an example problem. A method for separating the variance due to the data uncertainty from that due to the finite number of random walks is presented. (orig.)
Shell model the Monte Carlo way
International Nuclear Information System (INIS)
Ormand, W.E.
1995-01-01
The formalism for the auxiliary-field Monte Carlo approach to the nuclear shell model is presented. The method is based on a linearization of the two-body part of the Hamiltonian in an imaginary-time propagator using the Hubbard-Stratonovich transformation. The foundation of the method, as applied to the nuclear many-body problem, is discussed. Topics presented in detail include: (1) the density-density formulation of the method, (2) computation of the overlaps, (3) the sign of the Monte Carlo weight function, (4) techniques for performing Monte Carlo sampling, and (5) the reconstruction of response functions from an imaginary-time auto-correlation function using MaxEnt techniques. Results obtained using schematic interactions, which have no sign problem, are presented to demonstrate the feasibility of the method, while an extrapolation method for realistic Hamiltonians is presented. In addition, applications at finite temperature are outlined
Shell model the Monte Carlo way
Energy Technology Data Exchange (ETDEWEB)
Ormand, W.E.
1995-03-01
The formalism for the auxiliary-field Monte Carlo approach to the nuclear shell model is presented. The method is based on a linearization of the two-body part of the Hamiltonian in an imaginary-time propagator using the Hubbard-Stratonovich transformation. The foundation of the method, as applied to the nuclear many-body problem, is discussed. Topics presented in detail include: (1) the density-density formulation of the method, (2) computation of the overlaps, (3) the sign of the Monte Carlo weight function, (4) techniques for performing Monte Carlo sampling, and (5) the reconstruction of response functions from an imaginary-time auto-correlation function using MaxEnt techniques. Results obtained using schematic interactions, which have no sign problem, are presented to demonstrate the feasibility of the method, while an extrapolation method for realistic Hamiltonians is presented. In addition, applications at finite temperature are outlined.
Monte Carlo models: Quo vadimus?
Energy Technology Data Exchange (ETDEWEB)
Wang, Xin-Nian
2001-01-01
Coherence, multiple scattering and the interplay between soft and hard processes are discussed. These physics phenomena are essential for understanding the nuclear dependences of rapidity density and p{sub T} spectra in high-energy heavy-ion collisions. The RHIC data have shown the onset of hard processes and indications of high p{sub T} spectra suppression due to parton energy loss. Within the pQCD parton model, the combination of azimuthal anisotropy ({nu}{sub 2}) and hadron spectra suppression at large p{sub T} can help one to determine the initial gluon density in heavy-ion collisions at RHIC.
Monte Carlo models: Quo vadimus?
International Nuclear Information System (INIS)
Wang, Xin-Nian
2001-01-01
Coherence, multiple scattering and the interplay between soft and hard processes are discussed. These physics phenomena are essential for understanding the nuclear dependences of rapidity density and p T spectra in high-energy heavy-ion collisions. The RHIC data have shown the onset of hard processes and indications of high p T spectra suppression due to parton energy loss. Within the pQCD parton model, the combination of azimuthal anisotropy (ν 2 ) and hadron spectra suppression at large p T can help one to determine the initial gluon density in heavy-ion collisions at RHIC
Mixture Modeling: Applications in Educational Psychology
Harring, Jeffrey R.; Hodis, Flaviu A.
2016-01-01
Model-based clustering methods, commonly referred to as finite mixture modeling, have been applied to a wide variety of cross-sectional and longitudinal data to account for heterogeneity in population characteristics. In this article, we elucidate 2 such approaches: growth mixture modeling and latent profile analysis. Both techniques are…
[Study of Determination of Oil Mixture Components Content Based on Quasi-Monte Carlo Method].
Wang, Yu-tian; Xu, Jing; Liu, Xiao-fei; Chen, Meng-han; Wang, Shi-tao
2015-05-01
Gasoline, kerosene, diesel is processed by crude oil with different distillation range. The boiling range of gasoline is 35 ~205 °C. The boiling range of kerosene is 140~250 °C. And the boiling range of diesel is 180~370 °C. At the same time, the carbon chain length of differentmineral oil is different. The carbon chain-length of gasoline is within the scope of C7 to C11. The carbon chain length of kerosene is within the scope of C12 to C15. And the carbon chain length of diesel is within the scope of C15 to C18. The recognition and quantitative measurement of three kinds of mineral oil is based on different fluorescence spectrum formed in their different carbon number distribution characteristics. Mineral oil pollution occurs frequently, so monitoring mineral oil content in the ocean is very important. A new method of components content determination of spectra overlapping mineral oil mixture is proposed, with calculation of characteristic peak power integrationof three-dimensional fluorescence spectrum by using Quasi-Monte Carlo Method, combined with optimal algorithm solving optimum number of characteristic peak and range of integral region, solving nonlinear equations by using BFGS(a rank to two update method named after its inventor surname first letter, Boyden, Fletcher, Goldfarb and Shanno) method. Peak power accumulation of determined points in selected area is sensitive to small changes of fluorescence spectral line, so the measurement of small changes of component content is sensitive. At the same time, compared with the single point measurement, measurement sensitivity is improved by the decrease influence of random error due to the selection of points. Three-dimensional fluorescence spectra and fluorescence contour spectra of single mineral oil and the mixture are measured by taking kerosene, diesel and gasoline as research objects, with a single mineral oil regarded whole, not considered each mineral oil components. Six characteristic peaks are
On modeling of structured multiphase mixtures
International Nuclear Information System (INIS)
Dobran, F.
1987-01-01
The usual modeling of multiphase mixtures involves a set of conservation and balance equations of mass, momentum, energy and entropy (the basic set) constructed by an averaging procedure or postulated. The averaged models are constructed by averaging, over space or time segments, the local macroscopic field equations of each phase, whereas the postulated models are usually motivated by the single phase multicomponent mixture models. In both situations, the resulting equations yield superimposed continua models and are closed by the constitutive equations which place restrictions on the possible material response during the motion and phase change. In modeling the structured multiphase mixtures, the modeling of intrinsic motion of grains or particles is accomplished by adjoining to the basic set of field equations the additional balance equations, thereby placing restrictions on the motion of phases only within the imposed extrinsic and intrinsic sources. The use of the additional balance equations has been primarily advocated in the postulatory theories of multiphase mixtures and are usually derived through very special assumptions of the material deformation. Nevertheless, the resulting mixture models can predict a wide variety of complex phenomena such as the Mohr-Coulomb yield criterion in granular media, Rayleigh bubble equation, wave dispersion and dilatancy. Fundamental to the construction of structured models of multiphase mixtures are the problems pertaining to the existence and number of additional balance equations to model the structural characteristics of a mixture. Utilizing a volume averaging procedure it is possible not only to derive the basic set of field equation discussed above, but also a very general set of additional balance equations for modeling of structural properties of the mixture
Model structure selection in convolutive mixtures
DEFF Research Database (Denmark)
Dyrholm, Mads; Makeig, S.; Hansen, Lars Kai
2006-01-01
The CICAAR algorithm (convolutive independent component analysis with an auto-regressive inverse model) allows separation of white (i.i.d) source signals from convolutive mixtures. We introduce a source color model as a simple extension to the CICAAR which allows for a more parsimonious represent......The CICAAR algorithm (convolutive independent component analysis with an auto-regressive inverse model) allows separation of white (i.i.d) source signals from convolutive mixtures. We introduce a source color model as a simple extension to the CICAAR which allows for a more parsimonious...... representation in many practical mixtures. The new filter-CICAAR allows Bayesian model selection and can help answer questions like: ’Are we actually dealing with a convolutive mixture?’. We try to answer this question for EEG data....
Directory of Open Access Journals (Sweden)
Silva-Aguilar Martín
2011-01-01
Full Text Available Metals are ubiquitous pollutants present as mixtures. In particular, mixture of arsenic-cadmium-lead is among the leading toxic agents detected in the environment. These metals have carcinogenic and cell-transforming potential. In this study, we used a two step cell transformation model, to determine the role of oxidative stress in transformation induced by a mixture of arsenic-cadmium-lead. Oxidative damage and antioxidant response were determined. Metal mixture treatment induces the increase of damage markers and the antioxidant response. Loss of cell viability and increased transforming potential were observed during the promotion phase. This finding correlated significantly with generation of reactive oxygen species. Cotreatment with N-acetyl-cysteine induces effect on the transforming capacity; while a diminution was found in initiation, in promotion phase a total block of the transforming capacity was observed. Our results suggest that oxidative stress generated by metal mixture plays an important role only in promotion phase promoting transforming capacity.
Probabilistic mixture-based image modelling
Czech Academy of Sciences Publication Activity Database
Haindl, Michal; Havlíček, Vojtěch; Grim, Jiří
2011-01-01
Roč. 47, č. 3 (2011), s. 482-500 ISSN 0023-5954 R&D Projects: GA MŠk 1M0572; GA ČR GA102/08/0593 Grant - others:CESNET(CZ) 387/2010; GA MŠk(CZ) 2C06019; GA ČR(CZ) GA103/11/0335 Institutional research plan: CEZ:AV0Z10750506 Keywords : BTF texture modelling * discrete distribution mixtures * Bernoulli mixture * Gaussian mixture * multi-spectral texture modelling Subject RIV: BD - Theory of Information Impact factor: 0.454, year: 2011 http://library.utia.cas.cz/separaty/2011/RO/haindl-0360244.pdf
Radiation Modeling with Direct Simulation Monte Carlo
Carlson, Ann B.; Hassan, H. A.
1991-01-01
Improvements in the modeling of radiation in low density shock waves with direct simulation Monte Carlo (DSMC) are the subject of this study. A new scheme to determine the relaxation collision numbers for excitation of electronic states is proposed. This scheme attempts to move the DSMC programs toward a more detailed modeling of the physics and more reliance on available rate data. The new method is compared with the current modeling technique and both techniques are compared with available experimental data. The differences in the results are evaluated. The test case is based on experimental measurements from the AVCO-Everett Research Laboratory electric arc-driven shock tube of a normal shock wave in air at 10 km/s and .1 Torr. The new method agrees with the available data as well as the results from the earlier scheme and is more easily extrapolated to di erent ow conditions.
Monte Carlo methods and models in finance and insurance
Korn, Ralf; Kroisandt, Gerald
2010-01-01
Offering a unique balance between applications and calculations, Monte Carlo Methods and Models in Finance and Insurance incorporates the application background of finance and insurance with the theory and applications of Monte Carlo methods. It presents recent methods and algorithms, including the multilevel Monte Carlo method, the statistical Romberg method, and the Heath-Platen estimator, as well as recent financial and actuarial models, such as the Cheyette and dynamic mortality models. The authors separately discuss Monte Carlo techniques, stochastic process basics, and the theoretical background and intuition behind financial and actuarial mathematics, before bringing the topics together to apply the Monte Carlo methods to areas of finance and insurance. This allows for the easy identification of standard Monte Carlo tools and for a detailed focus on the main principles of financial and insurance mathematics. The book describes high-level Monte Carlo methods for standard simulation and the simulation of...
Modeling text with generalizable Gaussian mixtures
DEFF Research Database (Denmark)
Hansen, Lars Kai; Sigurdsson, Sigurdur; Kolenda, Thomas
2000-01-01
We apply and discuss generalizable Gaussian mixture (GGM) models for text mining. The model automatically adapts model complexity for a given text representation. We show that the generalizability of these models depends on the dimensionality of the representation and the sample size. We discuss...
Monte Carlo modelling of TRIGA research reactor
El Bakkari, B.; Nacir, B.; El Bardouni, T.; El Younoussi, C.; Merroun, O.; Htet, A.; Boulaich, Y.; Zoubair, M.; Boukhal, H.; Chakir, M.
2010-10-01
The Moroccan 2 MW TRIGA MARK II research reactor at Centre des Etudes Nucléaires de la Maâmora (CENM) achieved initial criticality on May 2, 2007. The reactor is designed to effectively implement the various fields of basic nuclear research, manpower training, and production of radioisotopes for their use in agriculture, industry, and medicine. This study deals with the neutronic analysis of the 2-MW TRIGA MARK II research reactor at CENM and validation of the results by comparisons with the experimental, operational, and available final safety analysis report (FSAR) values. The study was prepared in collaboration between the Laboratory of Radiation and Nuclear Systems (ERSN-LMR) from Faculty of Sciences of Tetuan (Morocco) and CENM. The 3-D continuous energy Monte Carlo code MCNP (version 5) was used to develop a versatile and accurate full model of the TRIGA core. The model represents in detailed all components of the core with literally no physical approximation. Continuous energy cross-section data from the more recent nuclear data evaluations (ENDF/B-VI.8, ENDF/B-VII.0, JEFF-3.1, and JENDL-3.3) as well as S( α, β) thermal neutron scattering functions distributed with the MCNP code were used. The cross-section libraries were generated by using the NJOY99 system updated to its more recent patch file "up259". The consistency and accuracy of both the Monte Carlo simulation and neutron transport physics were established by benchmarking the TRIGA experiments. Core excess reactivity, total and integral control rods worth as well as power peaking factors were used in the validation process. Results of calculations are analysed and discussed.
Infinite von Mises-Fisher Mixture Modeling of Whole Brain fMRI Data
DEFF Research Database (Denmark)
Røge, Rasmus; Madsen, Kristoffer Hougaard; Schmidt, Mikkel Nørgaard
2017-01-01
spherical manifold are rarely analyzed, in part due to the computational challenges imposed by directional statistics. In this letter, we discuss a Bayesian von Mises-Fisher (vMF) mixture model for data on the unit hypersphere and present an efficient inference procedure based on collapsed Markov chain...... Monte Carlo sampling. Comparing the vMF and gaussian mixture models on synthetic data, we demonstrate that the vMF model has a slight advantage inferring the true underlying clustering when compared to gaussian-based models on data generated from both a mixture of vMFs and a mixture of gaussians......Cluster analysis of functional magnetic resonance imaging (fMRI) data is often performed using gaussian mixture models, but when the time series are standardized such that the data reside on a hypersphere, this modeling assumption is questionable. The consequences of ignoring the underlying...
Fast Bayesian Inference in Dirichlet Process Mixture Models.
Wang, Lianming; Dunson, David B
2011-01-01
There has been increasing interest in applying Bayesian nonparametric methods in large samples and high dimensions. As Markov chain Monte Carlo (MCMC) algorithms are often infeasible, there is a pressing need for much faster algorithms. This article proposes a fast approach for inference in Dirichlet process mixture (DPM) models. Viewing the partitioning of subjects into clusters as a model selection problem, we propose a sequential greedy search algorithm for selecting the partition. Then, when conjugate priors are chosen, the resulting posterior conditionally on the selected partition is available in closed form. This approach allows testing of parametric models versus nonparametric alternatives based on Bayes factors. We evaluate the approach using simulation studies and compare it with four other fast nonparametric methods in the literature. We apply the proposed approach to three datasets including one from a large epidemiologic study. Matlab codes for the simulation and data analyses using the proposed approach are available online in the supplemental materials.
Mazzola, Guglielmo; Helled, Ravit; Sorella, Sandro
2018-01-01
Understanding planetary interiors is directly linked to our ability of simulating exotic quantum mechanical systems such as hydrogen (H) and hydrogen-helium (H-He) mixtures at high pressures and temperatures. Equation of state (EOS) tables based on density functional theory are commonly used by planetary scientists, although this method allows only for a qualitative description of the phase diagram. Here we report quantum Monte Carlo (QMC) molecular dynamics simulations of pure H and H-He mixture. We calculate the first QMC EOS at 6000 K for a H-He mixture of a protosolar composition, and show the crucial influence of He on the H metallization pressure. Our results can be used to calibrate other EOS calculations and are very timely given the accurate determination of Jupiter's gravitational field from the NASA Juno mission and the effort to determine its structure.
Optimal designs for linear mixture models
Mendieta, E.J.; Linssen, H.N.; Doornbos, R.
1975-01-01
In a recent paper Snee and Marquardt [8] considered designs for linear mixture models, where the components are subject to individual lower and/or upper bounds. When the number of components is large their algorithm XVERT yields designs far too extensive for practical purposes. The purpose of this
Optimal designs for linear mixture models
Mendieta, E.J.; Linssen, H.N.; Doornbos, R.
1975-01-01
In a recent paper Snee and Marquardt (1974) considered designs for linear mixture models, where the components are subject to individual lower and/or upper bounds. When the number of components is large their algorithm XVERT yields designs far too extensive for practical purposes. The purpose of
Direct Importance Estimation with Gaussian Mixture Models
Yamada, Makoto; Sugiyama, Masashi
The ratio of two probability densities is called the importance and its estimation has gathered a great deal of attention these days since the importance can be used for various data processing purposes. In this paper, we propose a new importance estimation method using Gaussian mixture models (GMMs). Our method is an extention of the Kullback-Leibler importance estimation procedure (KLIEP), an importance estimation method using linear or kernel models. An advantage of GMMs is that covariance matrices can also be learned through an expectation-maximization procedure, so the proposed method — which we call the Gaussian mixture KLIEP (GM-KLIEP) — is expected to work well when the true importance function has high correlation. Through experiments, we show the validity of the proposed approach.
Text document classification based on mixture models
Czech Academy of Sciences Publication Activity Database
Novovičová, Jana; Malík, Antonín
2004-01-01
Roč. 40, č. 3 (2004), s. 293-304 ISSN 0023-5954 R&D Projects: GA AV ČR IAA2075302; GA ČR GA102/03/0049; GA AV ČR KSK1019101 Institutional research plan: CEZ:AV0Z1075907 Keywords : text classification * text categorization * multinomial mixture model Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 0.224, year: 2004
Longitudinal functional principal component modelling via Stochastic Approximation Monte Carlo
Martinez, Josue G.; Liang, Faming; Zhou, Lan; Carroll, Raymond J.
2010-01-01
model averaging using a Bayesian formulation. A relatively straightforward reversible jump Markov Chain Monte Carlo formulation has poor mixing properties and in simulated data often becomes trapped at the wrong number of principal components. In order
forecasting with nonlinear time series model: a monte-carlo
African Journals Online (AJOL)
PUBLICATIONS1
Carlo method of forecasting using a special nonlinear time series model, called logistic smooth transition ... We illustrate this new method using some simulation ..... in MATLAB 7.5.0. ... process (DGP) using the logistic smooth transi-.
Energy Technology Data Exchange (ETDEWEB)
Swaminathan-Gopalan, Krishnan; Stephani, Kelly A., E-mail: ksteph@illinois.edu [Department of Mechanical Science and Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois 61801 (United States)
2016-02-15
A systematic approach for calibrating the direct simulation Monte Carlo (DSMC) collision model parameters to achieve consistency in the transport processes is presented. The DSMC collision cross section model parameters are calibrated for high temperature atmospheric conditions by matching the collision integrals from DSMC against ab initio based collision integrals that are currently employed in the Langley Aerothermodynamic Upwind Relaxation Algorithm (LAURA) and Data Parallel Line Relaxation (DPLR) high temperature computational fluid dynamics solvers. The DSMC parameter values are computed for the widely used Variable Hard Sphere (VHS) and the Variable Soft Sphere (VSS) models using the collision-specific pairing approach. The recommended best-fit VHS/VSS parameter values are provided over a temperature range of 1000-20 000 K for a thirteen-species ionized air mixture. Use of the VSS model is necessary to achieve consistency in transport processes of ionized gases. The agreement of the VSS model transport properties with the transport properties as determined by the ab initio collision integral fits was found to be within 6% in the entire temperature range, regardless of the composition of the mixture. The recommended model parameter values can be readily applied to any gas mixture involving binary collisional interactions between the chemical species presented for the specified temperature range.
A turbulence model in mixtures. First part: Statistical description of mixture
International Nuclear Information System (INIS)
Besnard, D.
1987-03-01
Classical theory of mixtures gives a model for molecular mixtures. This kind of model is based on a small gradient approximation for concentration, temperature, and pression. We present here a mixture model, allowing for large gradients in the flow. We also show that, with a local balance assumption between material diffusion and flow gradients evolution, we obtain a model similar to those mentioned above [fr
Gaussian Mixture Model of Heart Rate Variability
Costa, Tommaso; Boccignone, Giuseppe; Ferraro, Mario
2012-01-01
Heart rate variability (HRV) is an important measure of sympathetic and parasympathetic functions of the autonomic nervous system and a key indicator of cardiovascular condition. This paper proposes a novel method to investigate HRV, namely by modelling it as a linear combination of Gaussians. Results show that three Gaussians are enough to describe the stationary statistics of heart variability and to provide a straightforward interpretation of the HRV power spectrum. Comparisons have been made also with synthetic data generated from different physiologically based models showing the plausibility of the Gaussian mixture parameters. PMID:22666386
Studies of Monte Carlo Modelling of Jets at ATLAS
Kar, Deepak; The ATLAS collaboration
2017-01-01
The predictions of different Monte Carlo generators for QCD jet production, both in multijets and for jets produced in association with other objects, are presented. Recent improvements in showering Monte Carlos provide new tools for assessing systematic uncertainties associated with these jets. Studies of the dependence of physical observables on the choice of shower tune parameters and new prescriptions for assessing systematic uncertainties associated with the choice of shower model and tune are presented.
A Mixture Innovation Heterogeneous Autoregressive Model for Structural Breaks and Long Memory
DEFF Research Database (Denmark)
Nonejad, Nima
We propose a flexible model to describe nonlinearities and long-range dependence in time series dynamics. Our model is an extension of the heterogeneous autoregressive model. Structural breaks occur through mixture distributions in state innovations of linear Gaussian state space models. Monte...... Carlo simulations evaluate the properties of the estimation procedures. Results show that the proposed model is viable and flexible for purposes of forecasting volatility. Model uncertainty is accounted for by employing Bayesian model averaging. Bayesian model averaging provides very competitive...... forecasts compared to any single model specification. It provides further improvements when we average over nonlinear specifications....
Monte Carlo modelling for neutron guide losses
International Nuclear Information System (INIS)
Cser, L.; Rosta, L.; Toeroek, Gy.
1989-09-01
In modern research reactors, neutron guides are commonly used for beam conducting. The neutron guide is a well polished or equivalently smooth glass tube covered inside by sputtered or evaporated film of natural Ni or 58 Ni isotope where the neutrons are totally reflected. A Monte Carlo calculation was carried out to establish the real efficiency and the spectral as well as spatial distribution of the neutron beam at the end of a glass mirror guide. The losses caused by mechanical inaccuracy and mirror quality were considered and the effects due to the geometrical arrangement were analyzed. (author) 2 refs.; 2 figs
Poisson Mixture Regression Models for Heart Disease Prediction.
Mufudza, Chipo; Erol, Hamza
2016-01-01
Early heart disease control can be achieved by high disease prediction and diagnosis efficiency. This paper focuses on the use of model based clustering techniques to predict and diagnose heart disease via Poisson mixture regression models. Analysis and application of Poisson mixture regression models is here addressed under two different classes: standard and concomitant variable mixture regression models. Results show that a two-component concomitant variable Poisson mixture regression model predicts heart disease better than both the standard Poisson mixture regression model and the ordinary general linear Poisson regression model due to its low Bayesian Information Criteria value. Furthermore, a Zero Inflated Poisson Mixture Regression model turned out to be the best model for heart prediction over all models as it both clusters individuals into high or low risk category and predicts rate to heart disease componentwise given clusters available. It is deduced that heart disease prediction can be effectively done by identifying the major risks componentwise using Poisson mixture regression model.
A smooth mixture of Tobits model for healthcare expenditure.
Keane, Michael; Stavrunova, Olena
2011-09-01
This paper develops a smooth mixture of Tobits (SMTobit) model for healthcare expenditure. The model is a generalization of the smoothly mixing regressions framework of Geweke and Keane (J Econometrics 2007; 138: 257-290) to the case of a Tobit-type limited dependent variable. A Markov chain Monte Carlo algorithm with data augmentation is developed to obtain the posterior distribution of model parameters. The model is applied to the US Medicare Current Beneficiary Survey data on total medical expenditure. The results suggest that the model can capture the overall shape of the expenditure distribution very well, and also provide a good fit to a number of characteristics of the conditional (on covariates) distribution of expenditure, such as the conditional mean, variance and probability of extreme outcomes, as well as the 50th, 90th, and 95th, percentiles. We find that healthier individuals face an expenditure distribution with lower mean, variance and probability of extreme outcomes, compared with their counterparts in a worse state of health. Males have an expenditure distribution with higher mean, variance and probability of an extreme outcome, compared with their female counterparts. The results also suggest that heart and cardiovascular diseases affect the expenditure of males more than that of females. Copyright © 2011 John Wiley & Sons, Ltd.
Gámez, Francisco; Acemel, Rafael D.; Cuetos, Alejandro
2013-10-01
Parsons-Lee approach is formulated for the isotropic-nematic transition in a binary mixture of oblate hard spherocylinders and hard spheres. Results for the phase coexistence and for the equation of state in both phases for fluids with different relative size and composition ranges are presented. The predicted behaviour is in agreement with Monte Carlo simulations in a qualitative fashion. The study serves to provide a rational view of how to control key aspects of the behaviour of these binary nematogenic colloidal systems. This behaviour can be tuned with an appropriate choice of the relative size and molar fractions of the depleting particles. In general, the mixture of discotic and spherical particles is stable against demixing up to very high packing fractions. We explore in detail the narrow geometrical range where demixing is predicted to be possible in the isotropic phase. The influence of molecular crowding effects on the stability of the mixture when spherical molecules are added to a system of discotic colloids is also studied.
Aspects of perturbative QCD in Monte Carlo shower models
International Nuclear Information System (INIS)
Gottschalk, T.D.
1986-01-01
The perturbative QCD content of Monte Carlo models for high energy hadron-hadron scattering is examined. Particular attention is given to the recently developed backwards evolution formalism for initial state parton showers, and the merging of parton shower evolution with hard scattering cross sections. Shower estimates of K-factors are discussed, and a simple scheme is presented for incorporating 2 → QCD cross sections into shower model calculations without double counting. Additional issues in the development of hard scattering Monte Carlo models are summarized. 69 references, 20 figures
Automatic modeling for the Monte Carlo transport code Geant4
International Nuclear Information System (INIS)
Nie Fanzhi; Hu Liqin; Wang Guozhong; Wang Dianxi; Wu Yican; Wang Dong; Long Pengcheng; FDS Team
2015-01-01
Geant4 is a widely used Monte Carlo transport simulation package. Its geometry models could be described in Geometry Description Markup Language (GDML), but it is time-consuming and error-prone to describe the geometry models manually. This study implemented the conversion between computer-aided design (CAD) geometry models and GDML models. This method has been Studied based on Multi-Physics Coupling Analysis Modeling Program (MCAM). The tests, including FDS-Ⅱ model, demonstrated its accuracy and feasibility. (authors)
Modeling the flow of activated H2 + CH4 mixture by deposition of diamond nanostructures
Directory of Open Access Journals (Sweden)
Plotnikov Mikhail
2017-01-01
Full Text Available Algorithm of the direct simulation Monte Carlo method for the flow of hydrogen and methane mixture in a cylindrical channel is developed. Heterogeneous reactions on tungsten channel surfaces are included into the model. Their effects on flows are analyzed. A one-dimensional approach based on the solution of equilibrium chemical kinetics equations is used to analyze gas-phase methane decomposition. The obtained results may be useful for optimization of gas-dynamic sources of activated gas diamond synthesis.
The phase behavior of a hard sphere chain model of a binary n-alkane mixture
International Nuclear Information System (INIS)
Malanoski, A. P.; Monson, P. A.
2000-01-01
Monte Carlo computer simulations have been used to study the solid and fluid phase properties as well as phase equilibrium in a flexible, united atom, hard sphere chain model of n-heptane/n-octane mixtures. We describe a methodology for calculating the chemical potentials for the components in the mixture based on a technique used previously for atomic mixtures. The mixture was found to conform accurately to ideal solution behavior in the fluid phase. However, much greater nonidealities were seen in the solid phase. Phase equilibrium calculations indicate a phase diagram with solid-fluid phase equilibrium and a eutectic point. The components are only miscible in the solid phase for dilute solutions of the shorter chains in the longer chains. (c) 2000 American Institute of Physics
Monte Carlo simulation models of breeding-population advancement.
J.N. King; G.R. Johnson
1993-01-01
Five generations of population improvement were modeled using Monte Carlo simulations. The model was designed to address questions that are important to the development of an advanced generation breeding population. Specifically we addressed the effects on both gain and effective population size of different mating schemes when creating a recombinant population for...
Mixture of Regression Models with Single-Index
Xiang, Sijia; Yao, Weixin
2016-01-01
In this article, we propose a class of semiparametric mixture regression models with single-index. We argue that many recently proposed semiparametric/nonparametric mixture regression models can be considered special cases of the proposed model. However, unlike existing semiparametric mixture regression models, the new pro- posed model can easily incorporate multivariate predictors into the nonparametric components. Backfitting estimates and the corresponding algorithms have been proposed for...
Maximum likelihood estimation of finite mixture model for economic data
Phoong, Seuk-Yen; Ismail, Mohd Tahir
2014-06-01
Finite mixture model is a mixture model with finite-dimension. This models are provides a natural representation of heterogeneity in a finite number of latent classes. In addition, finite mixture models also known as latent class models or unsupervised learning models. Recently, maximum likelihood estimation fitted finite mixture models has greatly drawn statistician's attention. The main reason is because maximum likelihood estimation is a powerful statistical method which provides consistent findings as the sample sizes increases to infinity. Thus, the application of maximum likelihood estimation is used to fit finite mixture model in the present paper in order to explore the relationship between nonlinear economic data. In this paper, a two-component normal mixture model is fitted by maximum likelihood estimation in order to investigate the relationship among stock market price and rubber price for sampled countries. Results described that there is a negative effect among rubber price and stock market price for Malaysia, Thailand, Philippines and Indonesia.
The structure of liquid water by polarized neutron diffraction and reverse Monte Carlo modelling.
Temleitner, László; Pusztai, László; Schweika, Werner
2007-08-22
The coherent static structure factor of water has been investigated by polarized neutron diffraction. Polarization analysis allows us to separate the huge incoherent scattering background from hydrogen and to obtain high quality data of the coherent scattering from four different mixtures of liquid H(2)O and D(2)O. The information obtained by the variation of the scattering contrast confines the configurational space of water and is used by the reverse Monte Carlo technique to model the total structure factors. Structural characteristics have been calculated directly from the resulting sets of particle coordinates. Consistency with existing partial pair correlation functions, derived without the application of polarized neutrons, was checked by incorporating them into our reverse Monte Carlo calculations. We also performed Monte Carlo simulations of a hard sphere system, which provides an accurate estimate of the information content of the measured data. It is shown that the present combination of polarized neutron scattering and reverse Monte Carlo structural modelling is a promising approach towards a detailed understanding of the microscopic structure of water.
Towards a Revised Monte Carlo Neutral Particle Surface Interaction Model
International Nuclear Information System (INIS)
Stotler, D.P.
2005-01-01
The components of the neutral- and plasma-surface interaction model used in the Monte Carlo neutral transport code DEGAS 2 are reviewed. The idealized surfaces and processes handled by that model are inadequate for accurately simulating neutral transport behavior in present day and future fusion devices. We identify some of the physical processes missing from the model, such as mixed materials and implanted hydrogen, and make some suggestions for improving the model
Nonparametric Mixture Models for Supervised Image Parcellation.
Sabuncu, Mert R; Yeo, B T Thomas; Van Leemput, Koen; Fischl, Bruce; Golland, Polina
2009-09-01
We present a nonparametric, probabilistic mixture model for the supervised parcellation of images. The proposed model yields segmentation algorithms conceptually similar to the recently developed label fusion methods, which register a new image with each training image separately. Segmentation is achieved via the fusion of transferred manual labels. We show that in our framework various settings of a model parameter yield algorithms that use image intensity information differently in determining the weight of a training subject during fusion. One particular setting computes a single, global weight per training subject, whereas another setting uses locally varying weights when fusing the training data. The proposed nonparametric parcellation approach capitalizes on recently developed fast and robust pairwise image alignment tools. The use of multiple registrations allows the algorithm to be robust to occasional registration failures. We report experiments on 39 volumetric brain MRI scans with expert manual labels for the white matter, cerebral cortex, ventricles and subcortical structures. The results demonstrate that the proposed nonparametric segmentation framework yields significantly better segmentation than state-of-the-art algorithms.
Stochastic radiative transfer model for mixture of discontinuous vegetation canopies
International Nuclear Information System (INIS)
Shabanov, Nikolay V.; Huang, D.; Knjazikhin, Y.; Dickinson, R.E.; Myneni, Ranga B.
2007-01-01
Modeling of the radiation regime of a mixture of vegetation species is a fundamental problem of the Earth's land remote sensing and climate applications. The major existing approaches, including the linear mixture model and the turbid medium (TM) mixture radiative transfer model, provide only an approximate solution to this problem. In this study, we developed the stochastic mixture radiative transfer (SMRT) model, a mathematically exact tool to evaluate radiation regime in a natural canopy with spatially varying optical properties, that is, canopy, which exhibits a structured mixture of vegetation species and gaps. The model solves for the radiation quantities, direct input to the remote sensing/climate applications: mean radiation fluxes over whole mixture and over individual species. The canopy structure is parameterized in the SMRT model in terms of two stochastic moments: the probability of finding species and the conditional pair-correlation of species. The second moment is responsible for the 3D radiation effects, namely, radiation streaming through gaps without interaction with vegetation and variation of the radiation fluxes between different species. We performed analytical and numerical analysis of the radiation effects, simulated with the SMRT model for the three cases of canopy structure: (a) non-ordered mixture of species and gaps (TM); (b) ordered mixture of species without gaps; and (c) ordered mixture of species with gaps. The analysis indicates that the variation of radiation fluxes between different species is proportional to the variation of species optical properties (leaf albedo, density of foliage, etc.) Gaps introduce significant disturbance to the radiation regime in the canopy as their optical properties constitute major contrast to those of any vegetation species. The SMRT model resolves deficiencies of the major existing mixture models: ignorance of species radiation coupling via multiple scattering of photons (the linear mixture model
Calibration and Monte Carlo modelling of neutron long counters
Tagziria, H
2000-01-01
The Monte Carlo technique has become a very powerful tool in radiation transport as full advantage is taken of enhanced cross-section data, more powerful computers and statistical techniques, together with better characterisation of neutron and photon source spectra. At the National Physical Laboratory, calculations using the Monte Carlo radiation transport code MCNP-4B have been combined with accurate measurements to characterise two long counters routinely used to standardise monoenergetic neutron fields. New and more accurate response function curves have been produced for both long counters. A novel approach using Monte Carlo methods has been developed, validated and used to model the response function of the counters and determine more accurately their effective centres, which have always been difficult to establish experimentally. Calculations and measurements agree well, especially for the De Pangher long counter for which details of the design and constructional material are well known. The sensitivit...
Profit Forecast Model Using Monte Carlo Simulation in Excel
Directory of Open Access Journals (Sweden)
Petru BALOGH
2014-01-01
Full Text Available Profit forecast is very important for any company. The purpose of this study is to provide a method to estimate the profit and the probability of obtaining the expected profit. Monte Carlo methods are stochastic techniques–meaning they are based on the use of random numbers and probability statistics to investigate problems. Monte Carlo simulation furnishes the decision-maker with a range of possible outcomes and the probabilities they will occur for any choice of action. Our example of Monte Carlo simulation in Excel will be a simplified profit forecast model. Each step of the analysis will be described in detail. The input data for the case presented: the number of leads per month, the percentage of leads that result in sales, , the cost of a single lead, the profit per sale and fixed cost, allow obtaining profit and associated probabilities of achieving.
Monte Carlo investigation of the one-dimensional Potts model
International Nuclear Information System (INIS)
Karma, A.S.; Nolan, M.J.
1983-01-01
Monte Carlo results are presented for a variety of one-dimensional dynamical q-state Potts models. Our calculations confirm the expected universal value z = 2 for the dynamic scaling exponent. Our results also indicate that an increase in q at fixed correlation length drives the dynamics into the scaling regime
A novel Monte Carlo approach to hybrid local volatility models
A.W. van der Stoep (Anton); L.A. Grzelak (Lech Aleksander); C.W. Oosterlee (Cornelis)
2017-01-01
textabstractWe present in a Monte Carlo simulation framework, a novel approach for the evaluation of hybrid local volatility [Risk, 1994, 7, 18–20], [Int. J. Theor. Appl. Finance, 1998, 1, 61–110] models. In particular, we consider the stochastic local volatility model—see e.g. Lipton et al. [Quant.
Yet another Monte Carlo study of the Schwinger model
International Nuclear Information System (INIS)
Sogo, K.; Kimura, N.
1986-01-01
Some methodological improvements are introduced in the quantum Monte Carlo simulation of the 1 + 1 dimensional quantum electrodynamics (the Schwinger model). Properties at finite temperatures are investigated, concentrating on the existence of the chirality transition and of the deconfinement transition. (author)
Reservoir Modeling Combining Geostatistics with Markov Chain Monte Carlo Inversion
DEFF Research Database (Denmark)
Zunino, Andrea; Lange, Katrine; Melnikova, Yulia
2014-01-01
We present a study on the inversion of seismic reflection data generated from a synthetic reservoir model. Our aim is to invert directly for rock facies and porosity of the target reservoir zone. We solve this inverse problem using a Markov chain Monte Carlo (McMC) method to handle the nonlinear...
Yet another Monte Carlo study of the Schwinger model
International Nuclear Information System (INIS)
Sogo, K.; Kimura, N.
1986-03-01
Some methodological improvements are introduced in the quantum Monte Carlo simulation of the 1 + 1 dimensional quantum electrodynamics (the Schwinger model). Properties at finite temperatures are investigated, concentrating on the existence of the chirality transition and of the deconfinement transition. (author)
mixtools: An R Package for Analyzing Mixture Models
Directory of Open Access Journals (Sweden)
Tatiana Benaglia
2009-10-01
Full Text Available The mixtools package for R provides a set of functions for analyzing a variety of finite mixture models. These functions include both traditional methods, such as EM algorithms for univariate and multivariate normal mixtures, and newer methods that reflect some recent research in finite mixture models. In the latter category, mixtools provides algorithms for estimating parameters in a wide range of different mixture-of-regression contexts, in multinomial mixtures such as those arising from discretizing continuous multivariate data, in nonparametric situations where the multivariate component densities are completely unspecified, and in semiparametric situations such as a univariate location mixture of symmetric but otherwise unspecified densities. Many of the algorithms of the mixtools package are EM algorithms or are based on EM-like ideas, so this article includes an overview of EM algorithms for finite mixture models.
Evaluating Mixture Modeling for Clustering: Recommendations and Cautions
Steinley, Douglas; Brusco, Michael J.
2011-01-01
This article provides a large-scale investigation into several of the properties of mixture-model clustering techniques (also referred to as latent class cluster analysis, latent profile analysis, model-based clustering, probabilistic clustering, Bayesian classification, unsupervised learning, and finite mixture models; see Vermunt & Magdison,…
A Monte Carlo simulation model for stationary non-Gaussian processes
DEFF Research Database (Denmark)
Grigoriu, M.; Ditlevsen, Ove Dalager; Arwade, S. R.
2003-01-01
includes translation processes and is useful for both Monte Carlo simulation and analytical studies. As for translation processes, the mixture of translation processes can have a wide range of marginal distributions and correlation functions. Moreover, these processes can match a broader range of second...... athe proposed Monte Carlo algorithm and compare features of translation processes and mixture of translation processes. Keywords: Monte Carlo simulation, non-Gaussian processes, sampling theorem, stochastic processes, translation processes......A class of stationary non-Gaussian processes, referred to as the class of mixtures of translation processes, is defined by their finite dimensional distributions consisting of mixtures of finite dimensional distributions of translation processes. The class of mixtures of translation processes...
Automatic modeling for the monte carlo transport TRIPOLI code
International Nuclear Information System (INIS)
Zhang Junjun; Zeng Qin; Wu Yican; Wang Guozhong; FDS Team
2010-01-01
TRIPOLI, developed by CEA, France, is Monte Carlo particle transport simulation code. It has been widely applied to nuclear physics, shielding design, evaluation of nuclear safety. However, it is time-consuming and error-prone to manually describe the TRIPOLI input file. This paper implemented bi-directional conversion between CAD model and TRIPOLI model. Its feasibility and efficiency have been demonstrated by several benchmarking examples. (authors)
Monte Carlo Euler approximations of HJM term structure financial models
Björk, Tomas
2012-11-22
We present Monte Carlo-Euler methods for a weak approximation problem related to the Heath-Jarrow-Morton (HJM) term structure model, based on Itô stochastic differential equations in infinite dimensional spaces, and prove strong and weak error convergence estimates. The weak error estimates are based on stochastic flows and discrete dual backward problems, and they can be used to identify different error contributions arising from time and maturity discretization as well as the classical statistical error due to finite sampling. Explicit formulas for efficient computation of sharp error approximation are included. Due to the structure of the HJM models considered here, the computational effort devoted to the error estimates is low compared to the work to compute Monte Carlo solutions to the HJM model. Numerical examples with known exact solution are included in order to show the behavior of the estimates. © 2012 Springer Science+Business Media Dordrecht.
Monte Carlo Euler approximations of HJM term structure financial models
Bjö rk, Tomas; Szepessy, Anders; Tempone, Raul; Zouraris, Georgios E.
2012-01-01
We present Monte Carlo-Euler methods for a weak approximation problem related to the Heath-Jarrow-Morton (HJM) term structure model, based on Itô stochastic differential equations in infinite dimensional spaces, and prove strong and weak error convergence estimates. The weak error estimates are based on stochastic flows and discrete dual backward problems, and they can be used to identify different error contributions arising from time and maturity discretization as well as the classical statistical error due to finite sampling. Explicit formulas for efficient computation of sharp error approximation are included. Due to the structure of the HJM models considered here, the computational effort devoted to the error estimates is low compared to the work to compute Monte Carlo solutions to the HJM model. Numerical examples with known exact solution are included in order to show the behavior of the estimates. © 2012 Springer Science+Business Media Dordrecht.
Modeling the effects of binary mixtures on survival in time.
Baas, J.; van Houte, B.P.P.; van Gestel, C.A.M.; Kooijman, S.A.L.M.
2007-01-01
In general, effects of mixtures are difficult to describe, and most of the models in use are descriptive in nature and lack a strong mechanistic basis. The aim of this experiment was to develop a process-based model for the interpretation of mixture toxicity measurements, with effects of binary
Poisson Mixture Regression Models for Heart Disease Prediction
Erol, Hamza
2016-01-01
Early heart disease control can be achieved by high disease prediction and diagnosis efficiency. This paper focuses on the use of model based clustering techniques to predict and diagnose heart disease via Poisson mixture regression models. Analysis and application of Poisson mixture regression models is here addressed under two different classes: standard and concomitant variable mixture regression models. Results show that a two-component concomitant variable Poisson mixture regression model predicts heart disease better than both the standard Poisson mixture regression model and the ordinary general linear Poisson regression model due to its low Bayesian Information Criteria value. Furthermore, a Zero Inflated Poisson Mixture Regression model turned out to be the best model for heart prediction over all models as it both clusters individuals into high or low risk category and predicts rate to heart disease componentwise given clusters available. It is deduced that heart disease prediction can be effectively done by identifying the major risks componentwise using Poisson mixture regression model. PMID:27999611
Monte Carlo Modelling of Mammograms : Development and Validation
Energy Technology Data Exchange (ETDEWEB)
Spyrou, G; Panayiotakis, G [Univercity of Patras, School of Medicine, Medical Physics Department, 265 00 Patras (Greece); Bakas, A [Technological Educational Institution of Athens, Department of Radiography, 122 10 Athens (Greece); Tzanakos, G [University of Athens, Department of Physics, Divission of Nuclear and Particle Physics, 157 71 Athens (Greece)
1999-12-31
A software package using Monte Carlo methods has been developed for the simulation of x-ray mammography. A simplified geometry of the mammographic apparatus has been considered along with the software phantom of compressed breast. This phantom may contain inhomogeneities of various compositions and sizes at any point. Using this model one can produce simulated mammograms. Results that demonstrate the validity of this simulation are presented. (authors) 16 refs, 4 figs
GPU based Monte Carlo for PET image reconstruction: detector modeling
International Nuclear Information System (INIS)
Légrády; Cserkaszky, Á; Lantos, J.; Patay, G.; Bükki, T.
2011-01-01
Monte Carlo (MC) calculations and Graphical Processing Units (GPUs) are almost like the dedicated hardware designed for the specific task given the similarities between visible light transport and neutral particle trajectories. A GPU based MC gamma transport code has been developed for Positron Emission Tomography iterative image reconstruction calculating the projection from unknowns to data at each iteration step taking into account the full physics of the system. This paper describes the simplified scintillation detector modeling and its effect on convergence. (author)
Monte Carlo Modelling of Mammograms : Development and Validation
International Nuclear Information System (INIS)
Spyrou, G.; Panayiotakis, G.; Bakas, A.; Tzanakos, G.
1998-01-01
A software package using Monte Carlo methods has been developed for the simulation of x-ray mammography. A simplified geometry of the mammographic apparatus has been considered along with the software phantom of compressed breast. This phantom may contain inhomogeneities of various compositions and sizes at any point. Using this model one can produce simulated mammograms. Results that demonstrate the validity of this simulation are presented. (authors)
Modeling of Multicomponent Mixture Separation Processes Using Hollow fiber Membrane
Energy Technology Data Exchange (ETDEWEB)
Kim, Sin-Ah; Kim, Jin-Kuk; Lee, Young Moo; Yeo, Yeong-Koo [Hanyang University, Seoul (Korea, Republic of)
2015-02-15
So far, most of research activities on modeling of membrane separation processes have been focused on binary feed mixture. But, in actual separation operations, binary feed is hard to find and most separation processes involve multicomponent feed mixture. In this work models for membrane separation processes treating multicomponent feed mixture are developed. Various model types are investigated and validity of proposed models are analysed based on experimental data obtained using hollowfiber membranes. The proposed separation models show quick convergence and exhibit good tracking performance.
Modelling interactions in grass-clover mixtures
Nassiri Mahallati, M.
1998-01-01
The study described in this thesis focuses on a quantitative understanding of the complex interactions in binary mixtures of perennial ryegrass (Lolium perenne L.) and white clover (Trifolium repens L.) under cutting. The first part of the study describes the dynamics of growth, production
Thermodynamic modeling of CO2 mixtures
DEFF Research Database (Denmark)
Bjørner, Martin Gamel
Knowledge of the thermodynamic properties and phase equilibria of mixtures containing carbon dioxide (CO2) is important in several industrial processes such as enhanced oil recovery, carbon capture and storage, and supercritical extractions, where CO2 is used as a solvent. Despite this importance...
MOLECULAR SIMULATION OF THE VAPOR-LIQUID EQUILIBRIUM OF N2-NC5 MIXTURE BY MONTE CARLO SIMULATIONS
Directory of Open Access Journals (Sweden)
Florianne Castillo-Borja
2013-12-01
Full Text Available ABSTRACT This study used Monte Carlo simulations in the Gibbs ensemble to describe the liquid-vapor phase equilibrium of nitrogen-n-pentane system for three isotherms. The study analyzed a wide range of pressures ranging up to 25 MPa. The system was modeled using the intermolecular potential Galassi-Tildesley for nitrogen and SKS for n-pentane. Results were compared against experimental data. Far from the critical point region, analyzed models reproduce favorably shape of the curve of phase equilibrium and in the vicinity of the critical point, results tend to move away from the experimental behavior. Critical points were determined (pressure, density and composition for the three isotherms using an extrapolation method based on scaling laws, with satisfactory results. Calculated coexistence curves are adequate even if the models analyzed do not contain optimized binary interaction parameters .
Quantum Monte Carlo Simulation of Frustrated Kondo Lattice Models
Sato, Toshihiro; Assaad, Fakher F.; Grover, Tarun
2018-03-01
The absence of the negative sign problem in quantum Monte Carlo simulations of spin and fermion systems has different origins. World-line based algorithms for spins require positivity of matrix elements whereas auxiliary field approaches for fermions depend on symmetries such as particle-hole symmetry. For negative-sign-free spin and fermionic systems, we show that one can formulate a negative-sign-free auxiliary field quantum Monte Carlo algorithm that allows Kondo coupling of fermions with the spins. Using this general approach, we study a half-filled Kondo lattice model on the honeycomb lattice with geometric frustration. In addition to the conventional Kondo insulator and antiferromagnetically ordered phases, we find a partial Kondo screened state where spins are selectively screened so as to alleviate frustration, and the lattice rotation symmetry is broken nematically.
Monte Carlo Numerical Models for Nuclear Logging Applications
Directory of Open Access Journals (Sweden)
Fusheng Li
2012-06-01
Full Text Available Nuclear logging is one of most important logging services provided by many oil service companies. The main parameters of interest are formation porosity, bulk density, and natural radiation. Other services are also provided from using complex nuclear logging tools, such as formation lithology/mineralogy, etc. Some parameters can be measured by using neutron logging tools and some can only be measured by using a gamma ray tool. To understand the response of nuclear logging tools, the neutron transport/diffusion theory and photon diffusion theory are needed. Unfortunately, for most cases there are no analytical answers if complex tool geometry is involved. For many years, Monte Carlo numerical models have been used by nuclear scientists in the well logging industry to address these challenges. The models have been widely employed in the optimization of nuclear logging tool design, and the development of interpretation methods for nuclear logs. They have also been used to predict the response of nuclear logging systems for forward simulation problems. In this case, the system parameters including geometry, materials and nuclear sources, etc., are pre-defined and the transportation and interactions of nuclear particles (such as neutrons, photons and/or electrons in the regions of interest are simulated according to detailed nuclear physics theory and their nuclear cross-section data (probability of interacting. Then the deposited energies of particles entering the detectors are recorded and tallied and the tool responses to such a scenario are generated. A general-purpose code named Monte Carlo N– Particle (MCNP has been the industry-standard for some time. In this paper, we briefly introduce the fundamental principles of Monte Carlo numerical modeling and review the physics of MCNP. Some of the latest developments of Monte Carlo Models are also reviewed. A variety of examples are presented to illustrate the uses of Monte Carlo numerical models
A Dirichlet process mixture model for brain MRI tissue classification.
Ferreira da Silva, Adelino R
2007-04-01
Accurate classification of magnetic resonance images according to tissue type or region of interest has become a critical requirement in diagnosis, treatment planning, and cognitive neuroscience. Several authors have shown that finite mixture models give excellent results in the automated segmentation of MR images of the human normal brain. However, performance and robustness of finite mixture models deteriorate when the models have to deal with a variety of anatomical structures. In this paper, we propose a nonparametric Bayesian model for tissue classification of MR images of the brain. The model, known as Dirichlet process mixture model, uses Dirichlet process priors to overcome the limitations of current parametric finite mixture models. To validate the accuracy and robustness of our method we present the results of experiments carried out on simulated MR brain scans, as well as on real MR image data. The results are compared with similar results from other well-known MRI segmentation methods.
Uncertainties in ozone concentrations predicted with a Lagrangian photochemical air quality model have been estimated using Bayesian Monte Carlo (BMC) analysis. Bayesian Monte Carlo analysis provides a means of combining subjective "prior" uncertainty estimates developed ...
Monte Carlo modeling of human tooth optical coherence tomography imaging
International Nuclear Information System (INIS)
Shi, Boya; Meng, Zhuo; Wang, Longzhi; Liu, Tiegen
2013-01-01
We present a Monte Carlo model for optical coherence tomography (OCT) imaging of human tooth. The model is implemented by combining the simulation of a Gaussian beam with simulation for photon propagation in a two-layer human tooth model with non-parallel surfaces through a Monte Carlo method. The geometry and the optical parameters of the human tooth model are chosen on the basis of the experimental OCT images. The results show that the simulated OCT images are qualitatively consistent with the experimental ones. Using the model, we demonstrate the following: firstly, two types of photons contribute to the information of morphological features and noise in the OCT image of a human tooth, respectively. Secondly, the critical imaging depth of the tooth model is obtained, and it is found to decrease significantly with increasing mineral loss, simulated as different enamel scattering coefficients. Finally, the best focus position is located below and close to the dental surface by analysis of the effect of focus positions on the OCT signal and critical imaging depth. We anticipate that this modeling will become a powerful and accurate tool for a preliminary numerical study of the OCT technique on diseases of dental hard tissue in human teeth. (paper)
Manual hierarchical clustering of regional geochemical data using a Bayesian finite mixture model
International Nuclear Information System (INIS)
Ellefsen, Karl J.; Smith, David B.
2016-01-01
Interpretation of regional scale, multivariate geochemical data is aided by a statistical technique called “clustering.” We investigate a particular clustering procedure by applying it to geochemical data collected in the State of Colorado, United States of America. The clustering procedure partitions the field samples for the entire survey area into two clusters. The field samples in each cluster are partitioned again to create two subclusters, and so on. This manual procedure generates a hierarchy of clusters, and the different levels of the hierarchy show geochemical and geological processes occurring at different spatial scales. Although there are many different clustering methods, we use Bayesian finite mixture modeling with two probability distributions, which yields two clusters. The model parameters are estimated with Hamiltonian Monte Carlo sampling of the posterior probability density function, which usually has multiple modes. Each mode has its own set of model parameters; each set is checked to ensure that it is consistent both with the data and with independent geologic knowledge. The set of model parameters that is most consistent with the independent geologic knowledge is selected for detailed interpretation and partitioning of the field samples. - Highlights: • We evaluate a clustering procedure by applying it to geochemical data. • The procedure generates a hierarchy of clusters. • Different levels of the hierarchy show geochemical processes at different spatial scales. • The clustering method is Bayesian finite mixture modeling. • Model parameters are estimated with Hamiltonian Monte Carlo sampling.
Skin fluorescence model based on the Monte Carlo technique
Churmakov, Dmitry Y.; Meglinski, Igor V.; Piletsky, Sergey A.; Greenhalgh, Douglas A.
2003-10-01
The novel Monte Carlo technique of simulation of spatial fluorescence distribution within the human skin is presented. The computational model of skin takes into account spatial distribution of fluorophores following the collagen fibers packing, whereas in epidermis and stratum corneum the distribution of fluorophores assumed to be homogeneous. The results of simulation suggest that distribution of auto-fluorescence is significantly suppressed in the NIR spectral region, while fluorescence of sensor layer embedded in epidermis is localized at the adjusted depth. The model is also able to simulate the skin fluorescence spectra.
Conditional Monte Carlo randomization tests for regression models.
Parhat, Parwen; Rosenberger, William F; Diao, Guoqing
2014-08-15
We discuss the computation of randomization tests for clinical trials of two treatments when the primary outcome is based on a regression model. We begin by revisiting the seminal paper of Gail, Tan, and Piantadosi (1988), and then describe a method based on Monte Carlo generation of randomization sequences. The tests based on this Monte Carlo procedure are design based, in that they incorporate the particular randomization procedure used. We discuss permuted block designs, complete randomization, and biased coin designs. We also use a new technique by Plamadeala and Rosenberger (2012) for simple computation of conditional randomization tests. Like Gail, Tan, and Piantadosi, we focus on residuals from generalized linear models and martingale residuals from survival models. Such techniques do not apply to longitudinal data analysis, and we introduce a method for computation of randomization tests based on the predicted rate of change from a generalized linear mixed model when outcomes are longitudinal. We show, by simulation, that these randomization tests preserve the size and power well under model misspecification. Copyright © 2014 John Wiley & Sons, Ltd.
Schifferstein, H.N.J.
1996-01-01
The Equiratio Mixture Model predicts the psychophysical function for an equiratio mixture type on the basis of the psychophysical functions for the unmixed components. The model reliably estimates the sweetness of mixtures of sugars and sugar-alchohols, but is unable to predict intensity for
Ion swarm data for electrical discharge modeling in air and flue gas mixtures
International Nuclear Information System (INIS)
Nelson, D.; Benhenni, M.; Eichwald, O.; Yousfi, M.
2003-01-01
The first step of this work is the determination of the elastic and inelastic ion-molecule collision cross sections for the main ions (N 2 + , O 2 + , CO 2 + , H 2 O + and O - ) usually present either in the air or flue gas discharges. The obtained cross section sets, given for ion kinetic energies not exceeding 100 eV, correspond to the interactions of each ion with its parent molecule (symmetric case) or nonparent molecule (asymmetric case). Then by using these different cross section sets, it is possible to obtain the ion swarm data for the different gas mixtures involving N 2 , CO 2 , H 2 O and O 2 molecules whatever their relative proportions. These ion swarm data are obtained from an optimized Monte Carlo method well adapted for the ion transport in gas mixtures. This also allows us to clearly show that the classical linear approximations usually applied for the ion swarm data in mixtures such as Blanc's law are far to be valid. Then, the ion swarm data are given in three cases of gas mixtures: a dry air (80% N 2 , 20% O 2 ), a ternary gas mixture (82% N 2 , 12% CO 2 , 6% O 2 ) and a typical flue gas (76% N 2 , 12% CO 2 , 6% O 2 , 6% H 2 O). From these reliable ion swarm data, electrical discharge modeling for a wire to plane electrode configuration has been carried out in these three mixtures at the atmospheric pressure for different applied voltages. Under the same discharge conditions, large discrepancies in the streamer formation and propagation have been observed in these three mixture cases. They are due to the deviations existing not only between the different effective electron-molecule ionization rates but also between the ion transport properties mainly because of the presence of a highly polar molecule such as H 2 O. This emphasizes the necessity to properly consider the ion transport in the discharge modeling
Monte Carlo technique for very large ising models
Kalle, C.; Winkelmann, V.
1982-08-01
Rebbi's multispin coding technique is improved and applied to the kinetic Ising model with size 600*600*600. We give the central part of our computer program (for a CDC Cyber 76), which will be helpful also in a simulation of smaller systems, and describe the other tricks necessary to go to large lattices. The magnetization M at T=1.4* T c is found to decay asymptotically as exp(-t/2.90) if t is measured in Monte Carlo steps per spin, and M( t = 0) = 1 initially.
Novel extrapolation method in the Monte Carlo shell model
International Nuclear Information System (INIS)
Shimizu, Noritaka; Abe, Takashi; Utsuno, Yutaka; Mizusaki, Takahiro; Otsuka, Takaharu; Honma, Michio
2010-01-01
We propose an extrapolation method utilizing energy variance in the Monte Carlo shell model to estimate the energy eigenvalue and observables accurately. We derive a formula for the energy variance with deformed Slater determinants, which enables us to calculate the energy variance efficiently. The feasibility of the method is demonstrated for the full pf-shell calculation of 56 Ni, and the applicability of the method to a system beyond the current limit of exact diagonalization is shown for the pf+g 9/2 -shell calculation of 64 Ge.
Shell-model Monte Carlo studies of nuclei
International Nuclear Information System (INIS)
Dean, D.J.
1997-01-01
The pair content and structure of nuclei near N = Z are described in the frwnework of shell-model Monte Carlo (SMMC) calculations. Results include the enhancement of J=0 T=1 proton-neutron pairing at N=Z nuclei, and the maxked difference of thermal properties between even-even and odd-odd N=Z nuclei. Additionally, a study of the rotational properties of the T=1 (ground state), and T=0 band mixing seen in 74 Rb is presented
Beta Regression Finite Mixture Models of Polarization and Priming
Smithson, Michael; Merkle, Edgar C.; Verkuilen, Jay
2011-01-01
This paper describes the application of finite-mixture general linear models based on the beta distribution to modeling response styles, polarization, anchoring, and priming effects in probability judgments. These models, in turn, enhance our capacity for explicitly testing models and theories regarding the aforementioned phenomena. The mixture…
New models for predicting thermophysical properties of ionic liquid mixtures.
Huang, Ying; Zhang, Xiangping; Zhao, Yongsheng; Zeng, Shaojuan; Dong, Haifeng; Zhang, Suojiang
2015-10-28
Potential applications of ILs require the knowledge of the physicochemical properties of ionic liquid (IL) mixtures. In this work, a series of semi-empirical models were developed to predict the density, surface tension, heat capacity and thermal conductivity of IL mixtures. Each semi-empirical model only contains one new characteristic parameter, which can be determined using one experimental data point. In addition, as another effective tool, artificial neural network (ANN) models were also established. The two kinds of models were verified by a total of 2304 experimental data points for binary mixtures of ILs and molecular compounds. The overall average absolute deviations (AARDs) of both the semi-empirical and ANN models are less than 2%. Compared to previously reported models, these new semi-empirical models require fewer adjustable parameters and can be applied in a wider range of applications.
A BAYESIAN NONPARAMETRIC MIXTURE MODEL FOR SELECTING GENES AND GENE SUBNETWORKS.
Zhao, Yize; Kang, Jian; Yu, Tianwei
2014-06-01
It is very challenging to select informative features from tens of thousands of measured features in high-throughput data analysis. Recently, several parametric/regression models have been developed utilizing the gene network information to select genes or pathways strongly associated with a clinical/biological outcome. Alternatively, in this paper, we propose a nonparametric Bayesian model for gene selection incorporating network information. In addition to identifying genes that have a strong association with a clinical outcome, our model can select genes with particular expressional behavior, in which case the regression models are not directly applicable. We show that our proposed model is equivalent to an infinity mixture model for which we develop a posterior computation algorithm based on Markov chain Monte Carlo (MCMC) methods. We also propose two fast computing algorithms that approximate the posterior simulation with good accuracy but relatively low computational cost. We illustrate our methods on simulation studies and the analysis of Spellman yeast cell cycle microarray data.
Monte Carlo modeling of the Fastscan whole body counter response
International Nuclear Information System (INIS)
Graham, H.R.; Waller, E.J.
2015-01-01
Monte Carlo N-Particle (MCNP) was used to make a model of the Fastscan for the purpose of calibration. Two models were made one for the Pickering Nuclear Site, and one for the Darlington Nuclear Site. Once these models were benchmarked and found to be in good agreement, simulations were run to study the effect different sized phantoms had on the detected response, and the shielding effect of torso fat was not negligible. Simulations into the nature of a source being positioned externally on the anterior or posterior of a person were also conducted to determine a ratio that could be used to determine if a source is externally or internally placed. (author)
DEFF Research Database (Denmark)
Tsivintzelis, Ioannis; Kontogeorgis, Georgios; Michelsen, Michael Locht
2011-01-01
In Part I of this series of articles, the study of H2S mixtures has been presented with CPA. In this study the phase behavior of CO2 containing mixtures is modeled. Binary mixtures with water, alcohols, glycols and hydrocarbons are investigated. Both phase equilibria (vapor–liquid and liquid–liqu...
Modeling granular phosphor screens by Monte Carlo methods
International Nuclear Information System (INIS)
Liaparinos, Panagiotis F.; Kandarakis, Ioannis S.; Cavouras, Dionisis A.; Delis, Harry B.; Panayiotakis, George S.
2006-01-01
The intrinsic phosphor properties are of significant importance for the performance of phosphor screens used in medical imaging systems. In previous analytical-theoretical and Monte Carlo studies on granular phosphor materials, values of optical properties, and light interaction cross sections were found by fitting to experimental data. These values were then employed for the assessment of phosphor screen imaging performance. However, it was found that, depending on the experimental technique and fitting methodology, the optical parameters of a specific phosphor material varied within a wide range of values, i.e., variations of light scattering with respect to light absorption coefficients were often observed for the same phosphor material. In this study, x-ray and light transport within granular phosphor materials was studied by developing a computational model using Monte Carlo methods. The model was based on the intrinsic physical characteristics of the phosphor. Input values required to feed the model can be easily obtained from tabulated data. The complex refractive index was introduced and microscopic probabilities for light interactions were produced, using Mie scattering theory. Model validation was carried out by comparing model results on x-ray and light parameters (x-ray absorption, statistical fluctuations in the x-ray to light conversion process, number of emitted light photons, output light spatial distribution) with previous published experimental data on Gd 2 O 2 S:Tb phosphor material (Kodak Min-R screen). Results showed the dependence of the modulation transfer function (MTF) on phosphor grain size and material packing density. It was predicted that granular Gd 2 O 2 S:Tb screens of high packing density and small grain size may exhibit considerably better resolution and light emission properties than the conventional Gd 2 O 2 S:Tb screens, under similar conditions (x-ray incident energy, screen thickness)
Preliminary validation of a Monte Carlo model for IMRT fields
International Nuclear Information System (INIS)
Wright, Tracy; Lye, Jessica; Mohammadi, Mohammad
2011-01-01
Full text: A Monte Carlo model of an Elekta linac, validated for medium to large (10-30 cm) symmetric fields, has been investigated for small, irregular and asymmetric fields suitable for IMRT treatments. The model has been validated with field segments using radiochromic film in solid water. The modelled positions of the multileaf collimator (MLC) leaves have been validated using EBT film, In the model, electrons with a narrow energy spectrum are incident on the target and all components of the linac head are included. The MLC is modelled using the EGSnrc MLCE component module. For the validation, a number of single complex IMRT segments with dimensions approximately 1-8 cm were delivered to film in solid water (see Fig, I), The same segments were modelled using EGSnrc by adjusting the MLC leaf positions in the model validated for 10 cm symmetric fields. Dose distributions along the centre of each MLC leaf as determined by both methods were compared. A picket fence test was also performed to confirm the MLC leaf positions. 95% of the points in the modelled dose distribution along the leaf axis agree with the film measurement to within 1%/1 mm for dose difference and distance to agreement. Areas of most deviation occur in the penumbra region. A system has been developed to calculate the MLC leaf positions in the model for any planned field size.
Estimating Model Probabilities using Thermodynamic Markov Chain Monte Carlo Methods
Ye, M.; Liu, P.; Beerli, P.; Lu, D.; Hill, M. C.
2014-12-01
Markov chain Monte Carlo (MCMC) methods are widely used to evaluate model probability for quantifying model uncertainty. In a general procedure, MCMC simulations are first conducted for each individual model, and MCMC parameter samples are then used to approximate marginal likelihood of the model by calculating the geometric mean of the joint likelihood of the model and its parameters. It has been found the method of evaluating geometric mean suffers from the numerical problem of low convergence rate. A simple test case shows that even millions of MCMC samples are insufficient to yield accurate estimation of the marginal likelihood. To resolve this problem, a thermodynamic method is used to have multiple MCMC runs with different values of a heating coefficient between zero and one. When the heating coefficient is zero, the MCMC run is equivalent to a random walk MC in the prior parameter space; when the heating coefficient is one, the MCMC run is the conventional one. For a simple case with analytical form of the marginal likelihood, the thermodynamic method yields more accurate estimate than the method of using geometric mean. This is also demonstrated for a case of groundwater modeling with consideration of four alternative models postulated based on different conceptualization of a confining layer. This groundwater example shows that model probabilities estimated using the thermodynamic method are more reasonable than those obtained using the geometric method. The thermodynamic method is general, and can be used for a wide range of environmental problem for model uncertainty quantification.
Longitudinal functional principal component modelling via Stochastic Approximation Monte Carlo
Martinez, Josue G.
2010-06-01
The authors consider the analysis of hierarchical longitudinal functional data based upon a functional principal components approach. In contrast to standard frequentist approaches to selecting the number of principal components, the authors do model averaging using a Bayesian formulation. A relatively straightforward reversible jump Markov Chain Monte Carlo formulation has poor mixing properties and in simulated data often becomes trapped at the wrong number of principal components. In order to overcome this, the authors show how to apply Stochastic Approximation Monte Carlo (SAMC) to this problem, a method that has the potential to explore the entire space and does not become trapped in local extrema. The combination of reversible jump methods and SAMC in hierarchical longitudinal functional data is simplified by a polar coordinate representation of the principal components. The approach is easy to implement and does well in simulated data in determining the distribution of the number of principal components, and in terms of its frequentist estimation properties. Empirical applications are also presented.
Modeling Dynamic Objects in Monte Carlo Particle Transport Calculations
International Nuclear Information System (INIS)
Yegin, G.
2008-01-01
In this study, the Multi-Geometry geometry modeling technique was improved in order to handle moving objects in a Monte Carlo particle transport calculation. In the Multi-Geometry technique, the geometry is a superposition of objects not surfaces. By using this feature, we developed a new algorithm which allows a user to make enable or disable geometry elements during particle transport. A disabled object can be ignored at a certain stage of a calculation and switching among identical copies of the same object located adjacent poins during a particle simulation corresponds to the movement of that object in space. We called this powerfull feature as Dynamic Multi-Geometry technique (DMG) which is used for the first time in Brachy Dose Monte Carlo code to simulate HDR brachytherapy treatment systems. Our results showed that having disabled objects in a geometry does not effect calculated dose values. This technique is also suitable to be used in other areas such as IMRT treatment planning systems
Monte Carlo modelling of Schottky diode for rectenna simulation
Bernuchon, E.; Aniel, F.; Zerounian, N.; Grimault-Jacquin, A. S.
2017-09-01
Before designing a detector circuit, the electrical parameters extraction of the Schottky diode is a critical step. This article is based on a Monte-Carlo (MC) solver of the Boltzmann Transport Equation (BTE) including different transport mechanisms at the metal-semiconductor contact such as image force effect or tunneling. The weight of tunneling and thermionic current is quantified according to different degrees of tunneling modelling. The I-V characteristic highlights the dependence of the ideality factor and the current saturation with bias. Harmonic Balance (HB) simulation on a rectifier circuit within Advanced Design System (ADS) software shows that considering non-linear ideality factor and saturation current for the electrical model of the Schottky diode does not seem essential. Indeed, bias independent values extracted in forward regime on I-V curve are sufficient. However, the non-linear series resistance extracted from a small signal analysis (SSA) strongly influences the conversion efficiency at low input powers.
Shell model Monte Carlo investigation of rare earth nuclei
International Nuclear Information System (INIS)
White, J. A.; Koonin, S. E.; Dean, D. J.
2000-01-01
We utilize the shell model Monte Carlo method to study the structure of rare earth nuclei. This work demonstrates the first systematic full oscillator shell with intruder calculations in such heavy nuclei. Exact solutions of a pairing plus quadrupole Hamiltonian are compared with the static path approximation in several dysprosium isotopes from A=152 to 162, including the odd mass A=153. Some comparisons are also made with Hartree-Fock-Bogoliubov results from Baranger and Kumar. Basic properties of these nuclei at various temperatures and spin are explored. These include energy, deformation, moments of inertia, pairing channel strengths, band crossing, and evolution of shell model occupation numbers. Exact level densities are also calculated and, in the case of 162 Dy, compared with experimental data. (c) 2000 The American Physical Society
Estimating animal abundance with N-mixture models using the R-INLA package for R
Meehan, Timothy D.
2017-05-03
Successful management of wildlife populations requires accurate estimates of abundance. Abundance estimates can be confounded by imperfect detection during wildlife surveys. N-mixture models enable quantification of detection probability and often produce abundance estimates that are less biased. The purpose of this study was to demonstrate the use of the R-INLA package to analyze N-mixture models and to compare performance of R-INLA to two other common approaches -- JAGS (via the runjags package), which uses Markov chain Monte Carlo and allows Bayesian inference, and unmarked, which uses Maximum Likelihood and allows frequentist inference. We show that R-INLA is an attractive option for analyzing N-mixture models when (1) familiar model syntax and data format (relative to other R packages) are desired, (2) survey level covariates of detection are not essential, (3) fast computing times are necessary (R-INLA is 10 times faster than unmarked, 300 times faster than JAGS), and (4) Bayesian inference is preferred.
Monte Carlo model of diagnostic X-ray dosimetry
International Nuclear Information System (INIS)
Khrutchinsky, Arkady; Kutsen, Semion; Gatskevich, George
2008-01-01
Full text: A Monte Carlo simulation of absorbed dose distribution in patient's tissues is often used in a dosimetry assessment of X-ray examinations. The results of such simulations in Belarus are presented in the report based on an anthropomorphic tissue-equivalent Rando-like physical phantom. The phantom corresponds to an adult 173 cm high and of 73 kg and consists of a torso and a head made of tissue-equivalent plastics which model soft (muscular), bone, and lung tissues. It consists of 39 layers (each 25 mm thick), including 10 head and neck ones, 16 chest and 13 pelvis ones. A tomographic model of the phantom has been developed from its CT-scan images with a voxel size of 0.88 x 0.88 x 4 mm 3 . A necessary pixelization in Mathematics-based in-house program was carried out for the phantom to be used in the radiation transport code MCNP-4b. The final voxel size of 14.2 x 14.2 x 8 mm 3 was used for the reasonable computer consuming calculations of absorbed dose in tissues and organs in various diagnostic X-ray examinations. MCNP point detectors allocated through body slices obtained as a result of the pixelization were used to calculate the absorbed dose. X-ray spectra generated by the empirical TASMIP model were verified on the X-ray units MEVASIM and SIREGRAPH CF. Absorbed dose distributions in the phantom volume were determined by the corresponding Monte Carlo simulations with a set of point detectors. Doses in organs of the adult phantom computed from the absorbed dose distributions by another Mathematics-based in-house program were estimated for 22 standard organs for various standard X-ray examinations. The results of Monte Carlo simulations were compared with the results of direct measurements of the absorbed dose in the phantom on the X-ray unit SIREGRAPH CF with the calibrated thermo-luminescent dosimeter DTU-01. The measurements were carried out in specified locations of different layers in heart, lungs, liver, pancreas, and stomach at high voltage of
Detecting Housing Submarkets using Unsupervised Learning of Finite Mixture Models
DEFF Research Database (Denmark)
Ntantamis, Christos
association between prices that can be attributed, among others, to unobserved neighborhood effects. In this paper, a model of spatial association for housing markets is introduced. Spatial association is treated in the context of spatial heterogeneity, which is explicitly modeled in both a global and a local....... The identified mixtures are considered as the different spatial housing submarkets. The main advantage of the approach is that submarkets are recovered by the housing prices data compared to submarkets imposed by administrative or geographical criteria. The Finite Mixture Model is estimated using the Figueiredo...
Bayesian Plackett-Luce Mixture Models for Partially Ranked Data.
Mollica, Cristina; Tardella, Luca
2017-06-01
The elicitation of an ordinal judgment on multiple alternatives is often required in many psychological and behavioral experiments to investigate preference/choice orientation of a specific population. The Plackett-Luce model is one of the most popular and frequently applied parametric distributions to analyze rankings of a finite set of items. The present work introduces a Bayesian finite mixture of Plackett-Luce models to account for unobserved sample heterogeneity of partially ranked data. We describe an efficient way to incorporate the latent group structure in the data augmentation approach and the derivation of existing maximum likelihood procedures as special instances of the proposed Bayesian method. Inference can be conducted with the combination of the Expectation-Maximization algorithm for maximum a posteriori estimation and the Gibbs sampling iterative procedure. We additionally investigate several Bayesian criteria for selecting the optimal mixture configuration and describe diagnostic tools for assessing the fitness of ranking distributions conditionally and unconditionally on the number of ranked items. The utility of the novel Bayesian parametric Plackett-Luce mixture for characterizing sample heterogeneity is illustrated with several applications to simulated and real preference ranked data. We compare our method with the frequentist approach and a Bayesian nonparametric mixture model both assuming the Plackett-Luce model as a mixture component. Our analysis on real datasets reveals the importance of an accurate diagnostic check for an appropriate in-depth understanding of the heterogenous nature of the partial ranking data.
Kim, Minjung; Lamont, Andrea E; Jaki, Thomas; Feaster, Daniel; Howe, George; Van Horn, M Lee
2016-06-01
Regression mixture models are a novel approach to modeling the heterogeneous effects of predictors on an outcome. In the model-building process, often residual variances are disregarded and simplifying assumptions are made without thorough examination of the consequences. In this simulation study, we investigated the impact of an equality constraint on the residual variances across latent classes. We examined the consequences of constraining the residual variances on class enumeration (finding the true number of latent classes) and on the parameter estimates, under a number of different simulation conditions meant to reflect the types of heterogeneity likely to exist in applied analyses. The results showed that bias in class enumeration increased as the difference in residual variances between the classes increased. Also, an inappropriate equality constraint on the residual variances greatly impacted on the estimated class sizes and showed the potential to greatly affect the parameter estimates in each class. These results suggest that it is important to make assumptions about residual variances with care and to carefully report what assumptions are made.
Supervised Gaussian mixture model based remote sensing image ...
African Journals Online (AJOL)
Using the supervised classification technique, both simulated and empirical satellite remote sensing data are used to train and test the Gaussian mixture model algorithm. For the purpose of validating the experiment, the resulting classified satellite image is compared with the ground truth data. For the simulated modelling, ...
Investigating Individual Differences in Toddler Search with Mixture Models
Berthier, Neil E.; Boucher, Kelsea; Weisner, Nina
2015-01-01
Children's performance on cognitive tasks is often described in categorical terms in that a child is described as either passing or failing a test, or knowing or not knowing some concept. We used binomial mixture models to determine whether individual children could be classified as passing or failing two search tasks, the DeLoache model room…
Modelling a gamma irradiation process using the Monte Carlo method
Energy Technology Data Exchange (ETDEWEB)
Soares, Gabriela A.; Pereira, Marcio T., E-mail: gas@cdtn.br, E-mail: mtp@cdtn.br [Centro de Desenvolvimento da Tecnologia Nuclear (CDTN/CNEN-MG), Belo Horizonte, MG (Brazil)
2011-07-01
In gamma irradiation service it is of great importance the evaluation of absorbed dose in order to guarantee the service quality. When physical structure and human resources are not available for performing dosimetry in each product irradiated, the appliance of mathematic models may be a solution. Through this, the prediction of the delivered dose in a specific product, irradiated in a specific position and during a certain period of time becomes possible, if validated with dosimetry tests. At the gamma irradiation facility of CDTN, equipped with a Cobalt-60 source, the Monte Carlo method was applied to perform simulations of products irradiations and the results were compared with Fricke dosimeters irradiated under the same conditions of the simulations. The first obtained results showed applicability of this method, with a linear relation between simulation and experimental results. (author)
Monte Carlo Modeling of Crystal Channeling at High Energies
Schoofs, Philippe; Cerutti, Francesco
Charged particles entering a crystal close to some preferred direction can be trapped in the electromagnetic potential well existing between consecutive planes or strings of atoms. This channeling effect can be used to extract beam particles if the crystal is bent beforehand. Crystal channeling is becoming a reliable and efficient technique for collimating beams and removing halo particles. At CERN, the installation of silicon crystals in the LHC is under scrutiny by the UA9 collaboration with the goal of investigating if they are a viable option for the collimation system upgrade. This thesis describes a new Monte Carlo model of planar channeling which has been developed from scratch in order to be implemented in the FLUKA code simulating particle transport and interactions. Crystal channels are described through the concept of continuous potential taking into account thermal motion of the lattice atoms and using Moliere screening function. The energy of the particle transverse motion determines whether or n...
Modelling a gamma irradiation process using the Monte Carlo method
International Nuclear Information System (INIS)
Soares, Gabriela A.; Pereira, Marcio T.
2011-01-01
In gamma irradiation service it is of great importance the evaluation of absorbed dose in order to guarantee the service quality. When physical structure and human resources are not available for performing dosimetry in each product irradiated, the appliance of mathematic models may be a solution. Through this, the prediction of the delivered dose in a specific product, irradiated in a specific position and during a certain period of time becomes possible, if validated with dosimetry tests. At the gamma irradiation facility of CDTN, equipped with a Cobalt-60 source, the Monte Carlo method was applied to perform simulations of products irradiations and the results were compared with Fricke dosimeters irradiated under the same conditions of the simulations. The first obtained results showed applicability of this method, with a linear relation between simulation and experimental results. (author)
A Monte Carlo Simulation Framework for Testing Cosmological Models
Directory of Open Access Journals (Sweden)
Heymann Y.
2014-10-01
Full Text Available We tested alternative cosmologies using Monte Carlo simulations based on the sam- pling method of the zCosmos galactic survey. The survey encompasses a collection of observable galaxies with respective redshifts that have been obtained for a given spec- troscopic area of the sky. Using a cosmological model, we can convert the redshifts into light-travel times and, by slicing the survey into small redshift buckets, compute a curve of galactic density over time. Because foreground galaxies obstruct the images of more distant galaxies, we simulated the theoretical galactic density curve using an average galactic radius. By comparing the galactic density curves of the simulations with that of the survey, we could assess the cosmologies. We applied the test to the expanding-universe cosmology of de Sitter and to a dichotomous cosmology.
Ensemble bayesian model averaging using markov chain Monte Carlo sampling
Energy Technology Data Exchange (ETDEWEB)
Vrugt, Jasper A [Los Alamos National Laboratory; Diks, Cees G H [NON LANL; Clark, Martyn P [NON LANL
2008-01-01
Bayesian model averaging (BMA) has recently been proposed as a statistical method to calibrate forecast ensembles from numerical weather models. Successful implementation of BMA however, requires accurate estimates of the weights and variances of the individual competing models in the ensemble. In their seminal paper (Raftery etal. Mon Weather Rev 133: 1155-1174, 2(05)) has recommended the Expectation-Maximization (EM) algorithm for BMA model training, even though global convergence of this algorithm cannot be guaranteed. In this paper, we compare the performance of the EM algorithm and the recently developed Differential Evolution Adaptive Metropolis (DREAM) Markov Chain Monte Carlo (MCMC) algorithm for estimating the BMA weights and variances. Simulation experiments using 48-hour ensemble data of surface temperature and multi-model stream-flow forecasts show that both methods produce similar results, and that their performance is unaffected by the length of the training data set. However, MCMC simulation with DREAM is capable of efficiently handling a wide variety of BMA predictive distributions, and provides useful information about the uncertainty associated with the estimated BMA weights and variances.
Identifying Clusters with Mixture Models that Include Radial Velocity Observations
Czarnatowicz, Alexis; Ybarra, Jason E.
2018-01-01
The study of stellar clusters plays an integral role in the study of star formation. We present a cluster mixture model that considers radial velocity data in addition to spatial data. Maximum likelihood estimation through the Expectation-Maximization (EM) algorithm is used for parameter estimation. Our mixture model analysis can be used to distinguish adjacent or overlapping clusters, and estimate properties for each cluster.Work supported by awards from the Virginia Foundation for Independent Colleges (VFIC) Undergraduate Science Research Fellowship and The Research Experience @Bridgewater (TREB).
Bayesian Non-Parametric Mixtures of GARCH(1,1 Models
Directory of Open Access Journals (Sweden)
John W. Lau
2012-01-01
Full Text Available Traditional GARCH models describe volatility levels that evolve smoothly over time, generated by a single GARCH regime. However, nonstationary time series data may exhibit abrupt changes in volatility, suggesting changes in the underlying GARCH regimes. Further, the number and times of regime changes are not always obvious. This article outlines a nonparametric mixture of GARCH models that is able to estimate the number and time of volatility regime changes by mixing over the Poisson-Kingman process. The process is a generalisation of the Dirichlet process typically used in nonparametric models for time-dependent data provides a richer clustering structure, and its application to time series data is novel. Inference is Bayesian, and a Markov chain Monte Carlo algorithm to explore the posterior distribution is described. The methodology is illustrated on the Standard and Poor's 500 financial index.
Monte Carlo simulations of lattice models for single polymer systems
Hsu, Hsiao-Ping
2014-10-01
Single linear polymer chains in dilute solutions under good solvent conditions are studied by Monte Carlo simulations with the pruned-enriched Rosenbluth method up to the chain length N ˜ O(10^4). Based on the standard simple cubic lattice model (SCLM) with fixed bond length and the bond fluctuation model (BFM) with bond lengths in a range between 2 and sqrt{10}, we investigate the conformations of polymer chains described by self-avoiding walks on the simple cubic lattice, and by random walks and non-reversible random walks in the absence of excluded volume interactions. In addition to flexible chains, we also extend our study to semiflexible chains for different stiffness controlled by a bending potential. The persistence lengths of chains extracted from the orientational correlations are estimated for all cases. We show that chains based on the BFM are more flexible than those based on the SCLM for a fixed bending energy. The microscopic differences between these two lattice models are discussed and the theoretical predictions of scaling laws given in the literature are checked and verified. Our simulations clarify that a different mapping ratio between the coarse-grained models and the atomistically realistic description of polymers is required in a coarse-graining approach due to the different crossovers to the asymptotic behavior.
Monte Carlo simulations of lattice models for single polymer systems
International Nuclear Information System (INIS)
Hsu, Hsiao-Ping
2014-01-01
Single linear polymer chains in dilute solutions under good solvent conditions are studied by Monte Carlo simulations with the pruned-enriched Rosenbluth method up to the chain length N∼O(10 4 ). Based on the standard simple cubic lattice model (SCLM) with fixed bond length and the bond fluctuation model (BFM) with bond lengths in a range between 2 and √(10), we investigate the conformations of polymer chains described by self-avoiding walks on the simple cubic lattice, and by random walks and non-reversible random walks in the absence of excluded volume interactions. In addition to flexible chains, we also extend our study to semiflexible chains for different stiffness controlled by a bending potential. The persistence lengths of chains extracted from the orientational correlations are estimated for all cases. We show that chains based on the BFM are more flexible than those based on the SCLM for a fixed bending energy. The microscopic differences between these two lattice models are discussed and the theoretical predictions of scaling laws given in the literature are checked and verified. Our simulations clarify that a different mapping ratio between the coarse-grained models and the atomistically realistic description of polymers is required in a coarse-graining approach due to the different crossovers to the asymptotic behavior
A Bayesian Approach to Model Selection in Hierarchical Mixtures-of-Experts Architectures.
Tanner, Martin A.; Peng, Fengchun; Jacobs, Robert A.
1997-03-01
There does not exist a statistical model that shows good performance on all tasks. Consequently, the model selection problem is unavoidable; investigators must decide which model is best at summarizing the data for each task of interest. This article presents an approach to the model selection problem in hierarchical mixtures-of-experts architectures. These architectures combine aspects of generalized linear models with those of finite mixture models in order to perform tasks via a recursive "divide-and-conquer" strategy. Markov chain Monte Carlo methodology is used to estimate the distribution of the architectures' parameters. One part of our approach to model selection attempts to estimate the worth of each component of an architecture so that relatively unused components can be pruned from the architecture's structure. A second part of this approach uses a Bayesian hypothesis testing procedure in order to differentiate inputs that carry useful information from nuisance inputs. Simulation results suggest that the approach presented here adheres to the dictum of Occam's razor; simple architectures that are adequate for summarizing the data are favored over more complex structures. Copyright 1997 Elsevier Science Ltd. All Rights Reserved.
Models for the computation of opacity of mixtures
International Nuclear Information System (INIS)
Klapisch, Marcel; Busquet, Michel
2013-01-01
We compare four models for the partial densities of the components of mixtures. These models yield different opacities as shown on polystyrene, acrylic and polyimide in local thermodynamical equilibrium (LTE). Two of these models, the ‘whole volume partial pressure’ model (M1) and its modification (M2) are not thermodynamically consistent (TC). The other two models are TC and minimize free energy. M3, the ‘partial volume equal pressure’ model, uses equality of chemical potential. M4 uses commonality of free electron density. The latter two give essentially identical results in LTE, but M4’s convergence is slower. M4 is easily generalized to non-LTE conditions. Non-LTE effects are shown by the variation of the Planck mean opacity of the mixtures with temperature and density. (paper)
Copula Based Factorization in Bayesian Multivariate Infinite Mixture Models
Martin Burda; Artem Prokhorov
2012-01-01
Bayesian nonparametric models based on infinite mixtures of density kernels have been recently gaining in popularity due to their flexibility and feasibility of implementation even in complicated modeling scenarios. In economics, they have been particularly useful in estimating nonparametric distributions of latent variables. However, these models have been rarely applied in more than one dimension. Indeed, the multivariate case suffers from the curse of dimensionality, with a rapidly increas...
Monte Carlo Modeling the UCN τ Magneto-Gravitational Trap
Holley, A. T.; UCNτ Collaboration
2016-09-01
The current uncertainty in our knowledge of the free neutron lifetime is dominated by the nearly 4 σ discrepancy between complementary ``beam'' and ``bottle'' measurement techniques. An incomplete assessment of systematic effects is the most likely explanation for this difference and must be addressed in order to realize the potential of both approaches. The UCN τ collaboration has constructed a large-volume magneto-gravitational trap that eliminates the material interactions which complicated the interpretation of previous bottle experiments. This is accomplished using permanent NdFeB magnets in a bowl-shaped Halbach array to confine polarized UCN from the sides and below and the earth's gravitational field to trap them from above. New in situ detectors that count surviving UCN provide a means of empirically assessing residual systematic effects. The interpretation of that data, and its implication for experimental configurations with enhanced precision, can be bolstered by Monte Carlo models of the current experiment which provide the capability for stable tracking of trapped UCN and detailed modeling of their polarization. Work to develop such models and their comparison with data acquired during our first extensive set of systematics studies will be discussed.
Monte Carlo modelling of large scale NORM sources using MCNP.
Wallace, J D
2013-12-01
The representative Monte Carlo modelling of large scale planar sources (for comparison to external environmental radiation fields) is undertaken using substantial diameter and thin profile planar cylindrical sources. The relative impact of source extent, soil thickness and sky-shine are investigated to guide decisions relating to representative geometries. In addition, the impact of source to detector distance on the nature of the detector response, for a range of source sizes, has been investigated. These investigations, using an MCNP based model, indicate a soil cylinder of greater than 20 m diameter and of no less than 50 cm depth/height, combined with a 20 m deep sky section above the soil cylinder, are needed to representatively model the semi-infinite plane of uniformly distributed NORM sources. Initial investigation of the effect of detector placement indicate that smaller source sizes may be used to achieve a representative response at shorter source to detector distances. Crown Copyright © 2013. Published by Elsevier Ltd. All rights reserved.
The R Package bgmm : Mixture Modeling with Uncertain Knowledge
Directory of Open Access Journals (Sweden)
Przemys law Biecek
2012-04-01
Full Text Available Classical supervised learning enjoys the luxury of accessing the true known labels for the observations in a modeled dataset. Real life, however, poses an abundance of problems, where the labels are only partially defined, i.e., are uncertain and given only for a subsetof observations. Such partial labels can occur regardless of the knowledge source. For example, an experimental assessment of labels may have limited capacity and is prone to measurement errors. Also expert knowledge is often restricted to a specialized area and is thus unlikely to provide trustworthy labels for all observations in the dataset. Partially supervised mixture modeling is able to process such sparse and imprecise input. Here, we present an R package calledbgmm, which implements two partially supervised mixture modeling methods: soft-label and belief-based modeling. For completeness, we equipped the package also with the functionality of unsupervised, semi- and fully supervised mixture modeling. On real data we present the usage of bgmm for basic model-fitting in all modeling variants. The package can be applied also to selection of the best-fitting from a set of models with different component numbers or constraints on their structures. This functionality is presented on an artificial dataset, which can be simulated in bgmm from a distribution defined by a given model.
Option Pricing with Asymmetric Heteroskedastic Normal Mixture Models
DEFF Research Database (Denmark)
Rombouts, Jeroen V. K; Stentoft, Lars
2015-01-01
We propose an asymmetric GARCH in mean mixture model and provide a feasible method for option pricing within this general framework by deriving the appropriate risk neutral dynamics. We forecast the out-of-sample prices of a large sample of options on the S&P 500 index from January 2006 to December...
Application of association models to mixtures containing alkanolamines
DEFF Research Database (Denmark)
Avlund, Ane Søgaard; Eriksen, Daniel Kunisch; Kontogeorgis, Georgios
2011-01-01
Two association models,the CPA and sPC-SAFT equations of state, are applied to binarymixtures containing alkanolamines and hydrocarbons or water. CPA is applied to mixtures of MEA and DEA, while sPC-SAFT is applied to MEA–n-heptane liquid–liquid equilibria and MEA–water vapor–liquid equilibria. T...
The Semiparametric Normal Variance-Mean Mixture Model
DEFF Research Database (Denmark)
Korsholm, Lars
1997-01-01
We discuss the normal vairance-mean mixture model from a semi-parametric point of view, i.e. we let the mixing distribution belong to a non parametric family. The main results are consistency of the non parametric maximum likelihood estimat or in this case, and construction of an asymptotically...... normal and efficient estimator....
Evaluation of Distance Measures Between Gaussian Mixture Models of MFCCs
DEFF Research Database (Denmark)
Jensen, Jesper Højvang; Ellis, Dan P. W.; Christensen, Mads Græsbøll
2007-01-01
In music similarity and in the related task of genre classification, a distance measure between Gaussian mixture models is frequently needed. We present a comparison of the Kullback-Leibler distance, the earth movers distance and the normalized L2 distance for this application. Although...
Parameter Estimation and Model Selection for Mixtures of Truncated Exponentials
DEFF Research Database (Denmark)
Langseth, Helge; Nielsen, Thomas Dyhre; Rumí, Rafael
2010-01-01
Bayesian networks with mixtures of truncated exponentials (MTEs) support efficient inference algorithms and provide a flexible way of modeling hybrid domains (domains containing both discrete and continuous variables). On the other hand, estimating an MTE from data has turned out to be a difficul...
Detecting Math Anxiety with a Mixture Partial Credit Model
Ölmez, Ibrahim Burak; Cohen, Allan S.
2017-01-01
The purpose of this study was to investigate a new methodology for detection of differences in middle grades students' math anxiety. A mixture partial credit model analysis revealed two distinct latent classes based on homogeneities in response patterns within each latent class. Students in Class 1 had less anxiety about apprehension of math…
Mixture models with entropy regularization for community detection in networks
Chang, Zhenhai; Yin, Xianjun; Jia, Caiyan; Wang, Xiaoyang
2018-04-01
Community detection is a key exploratory tool in network analysis and has received much attention in recent years. NMM (Newman's mixture model) is one of the best models for exploring a range of network structures including community structure, bipartite and core-periphery structures, etc. However, NMM needs to know the number of communities in advance. Therefore, in this study, we have proposed an entropy regularized mixture model (called EMM), which is capable of inferring the number of communities and identifying network structure contained in a network, simultaneously. In the model, by minimizing the entropy of mixing coefficients of NMM using EM (expectation-maximization) solution, the small clusters contained little information can be discarded step by step. The empirical study on both synthetic networks and real networks has shown that the proposed model EMM is superior to the state-of-the-art methods.
Gilthorpe, M S; Dahly, D L; Tu, Y K; Kubzansky, L D; Goodman, E
2014-06-01
Lifecourse trajectories of clinical or anthropological attributes are useful for identifying how our early-life experiences influence later-life morbidity and mortality. Researchers often use growth mixture models (GMMs) to estimate such phenomena. It is common to place constrains on the random part of the GMM to improve parsimony or to aid convergence, but this can lead to an autoregressive structure that distorts the nature of the mixtures and subsequent model interpretation. This is especially true if changes in the outcome within individuals are gradual compared with the magnitude of differences between individuals. This is not widely appreciated, nor is its impact well understood. Using repeat measures of body mass index (BMI) for 1528 US adolescents, we estimated GMMs that required variance-covariance constraints to attain convergence. We contrasted constrained models with and without an autocorrelation structure to assess the impact this had on the ideal number of latent classes, their size and composition. We also contrasted model options using simulations. When the GMM variance-covariance structure was constrained, a within-class autocorrelation structure emerged. When not modelled explicitly, this led to poorer model fit and models that differed substantially in the ideal number of latent classes, as well as class size and composition. Failure to carefully consider the random structure of data within a GMM framework may lead to erroneous model inferences, especially for outcomes with greater within-person than between-person homogeneity, such as BMI. It is crucial to reflect on the underlying data generation processes when building such models.
Kadoura, Ahmad
2011-06-06
Lennard‐Jones (L‐J) and Buckingham exponential‐6 (exp‐6) potential models were used to produce isotherms for methane at temperatures below and above critical one. Molecular simulation approach, particularly Monte Carlo simulations, were employed to create these isotherms working with both canonical and Gibbs ensembles. Experiments in canonical ensemble with each model were conducted to estimate pressures at a range of temperatures above methane critical temperature. Results were collected and compared to experimental data existing in literature; both models showed an elegant agreement with the experimental data. In parallel, experiments below critical temperature were run in Gibbs ensemble using L‐J model only. Upon comparing results with experimental ones, a good fit was obtained with small deviations. The work was further developed by adding some statistical studies in order to achieve better understanding and interpretation to the estimated quantities by the simulation. Methane phase diagrams were successfully reproduced by an efficient molecular simulation technique with different potential models. This relatively simple demonstration shows how powerful molecular simulation methods could be, hence further applications on more complicated systems are considered. Prediction of phase behavior of elemental sulfur in sour natural gases has been an interesting and challenging field in oil and gas industry. Determination of elemental sulfur solubility conditions helps avoiding all kinds of problems caused by its dissolution in gas production and transportation processes. For this purpose, further enhancement to the methods used is to be considered in order to successfully simulate elemental sulfur phase behavior in sour natural gases mixtures.
Image based Monte Carlo modeling for computational phantom
International Nuclear Information System (INIS)
Cheng, M.; Wang, W.; Zhao, K.; Fan, Y.; Long, P.; Wu, Y.
2013-01-01
Full text of the publication follows. The evaluation on the effects of ionizing radiation and the risk of radiation exposure on human body has been becoming one of the most important issues for radiation protection and radiotherapy fields, which is helpful to avoid unnecessary radiation and decrease harm to human body. In order to accurately evaluate the dose on human body, it is necessary to construct more realistic computational phantom. However, manual description and verification of the models for Monte Carlo (MC) simulation are very tedious, error-prone and time-consuming. In addition, it is difficult to locate and fix the geometry error, and difficult to describe material information and assign it to cells. MCAM (CAD/Image-based Automatic Modeling Program for Neutronics and Radiation Transport Simulation) was developed as an interface program to achieve both CAD- and image-based automatic modeling. The advanced version (Version 6) of MCAM can achieve automatic conversion from CT/segmented sectioned images to computational phantoms such as MCNP models. Imaged-based automatic modeling program(MCAM6.0) has been tested by several medical images and sectioned images. And it has been applied in the construction of Rad-HUMAN. Following manual segmentation and 3D reconstruction, a whole-body computational phantom of Chinese adult female called Rad-HUMAN was created by using MCAM6.0 from sectioned images of a Chinese visible human dataset. Rad-HUMAN contains 46 organs/tissues, which faithfully represented the average anatomical characteristics of the Chinese female. The dose conversion coefficients (Dt/Ka) from kerma free-in-air to absorbed dose of Rad-HUMAN were calculated. Rad-HUMAN can be applied to predict and evaluate dose distributions in the Treatment Plan System (TPS), as well as radiation exposure for human body in radiation protection. (authors)
Improved Denoising via Poisson Mixture Modeling of Image Sensor Noise.
Zhang, Jiachao; Hirakawa, Keigo
2017-04-01
This paper describes a study aimed at comparing the real image sensor noise distribution to the models of noise often assumed in image denoising designs. A quantile analysis in pixel, wavelet transform, and variance stabilization domains reveal that the tails of Poisson, signal-dependent Gaussian, and Poisson-Gaussian models are too short to capture real sensor noise behavior. A new Poisson mixture noise model is proposed to correct the mismatch of tail behavior. Based on the fact that noise model mismatch results in image denoising that undersmoothes real sensor data, we propose a mixture of Poisson denoising method to remove the denoising artifacts without affecting image details, such as edge and textures. Experiments with real sensor data verify that denoising for real image sensor data is indeed improved by this new technique.
Mesoscopic kinetic Monte Carlo modeling of organic photovoltaic device characteristics
Kimber, Robin G. E.; Wright, Edward N.; O'Kane, Simon E. J.; Walker, Alison B.; Blakesley, James C.
2012-12-01
Measured mobility and current-voltage characteristics of single layer and photovoltaic (PV) devices composed of poly{9,9-dioctylfluorene-co-bis[N,N'-(4-butylphenyl)]bis(N,N'-phenyl-1,4-phenylene)diamine} (PFB) and poly(9,9-dioctylfluorene-co-benzothiadiazole) (F8BT) have been reproduced by a mesoscopic model employing the kinetic Monte Carlo (KMC) approach. Our aim is to show how to avoid the uncertainties common in electrical transport models arising from the need to fit a large number of parameters when little information is available, for example, a single current-voltage curve. Here, simulation parameters are derived from a series of measurements using a self-consistent “building-blocks” approach, starting from data on the simplest systems. We found that site energies show disorder and that correlations in the site energies and a distribution of deep traps must be included in order to reproduce measured charge mobility-field curves at low charge densities in bulk PFB and F8BT. The parameter set from the mobility-field curves reproduces the unipolar current in single layers of PFB and F8BT and allows us to deduce charge injection barriers. Finally, by combining these disorder descriptions and injection barriers with an optical model, the external quantum efficiency and current densities of blend and bilayer organic PV devices can be successfully reproduced across a voltage range encompassing reverse and forward bias, with the recombination rate the only parameter to be fitted, found to be 1×107 s-1. These findings demonstrate an approach that removes some of the arbitrariness present in transport models of organic devices, which validates the KMC as an accurate description of organic optoelectronic systems, and provides information on the microscopic origins of the device behavior.
Phylogenetic mixtures and linear invariants for equal input models.
Casanellas, Marta; Steel, Mike
2017-04-01
The reconstruction of phylogenetic trees from molecular sequence data relies on modelling site substitutions by a Markov process, or a mixture of such processes. In general, allowing mixed processes can result in different tree topologies becoming indistinguishable from the data, even for infinitely long sequences. However, when the underlying Markov process supports linear phylogenetic invariants, then provided these are sufficiently informative, the identifiability of the tree topology can be restored. In this paper, we investigate a class of processes that support linear invariants once the stationary distribution is fixed, the 'equal input model'. This model generalizes the 'Felsenstein 1981' model (and thereby the Jukes-Cantor model) from four states to an arbitrary number of states (finite or infinite), and it can also be described by a 'random cluster' process. We describe the structure and dimension of the vector spaces of phylogenetic mixtures and of linear invariants for any fixed phylogenetic tree (and for all trees-the so called 'model invariants'), on any number n of leaves. We also provide a precise description of the space of mixtures and linear invariants for the special case of [Formula: see text] leaves. By combining techniques from discrete random processes and (multi-) linear algebra, our results build on a classic result that was first established by James Lake (Mol Biol Evol 4:167-191, 1987).
Investigation of a Monte Carlo model for chemical reactions
International Nuclear Information System (INIS)
Hamm, R.N.; Turner, J.E.; Stabin, M.G.
1998-01-01
Monte Carlo computer simulations are in use at a number of laboratories for calculating time-dependent yields, which can be compared with experiments in the radiolysis of water. We report here on calculations to investigate the validity and consistency of the procedures used for simulating chemical reactions in our code, RADLYS. Model calculations were performed of the rate constants themselves. The rates thus determined showed an expected rapid decline over the first few hundred ps and a very gradual decline thereafter out to the termination of the calculations at 4.5 ns. Results are reported for different initial concentrations and numbers of reactive species. Generally, the calculated rate constants are smallest when the initial concentrations of the reactants are largest. It is found that inhomogeneities that quickly develop in the initial random spatial distribution of reactants persist in time as a result of subsequent chemical reactions, and thus conditions may poorly approximate those assumed from diffusion theory. We also investigated the reaction of a single species of one type placed among a large number of randomly distributed species of another type with which it could react. The distribution of survival times of the single species was calculated by using three different combinations of the diffusion constants for the two species, as is sometimes discussed in diffusion theory. The three methods gave virtually identical results. (orig.)
Monte Carlo Modeling Electronuclear Processes in Cascade Subcritical Reactor
Bznuni, S A; Zhamkochyan, V M; Polyanskii, A A; Sosnin, A N; Khudaverdian, A G
2000-01-01
Accelerator driven subcritical cascade reactor composed of the main thermal neutron reactor constructed analogous to the core of the VVER-1000 reactor and a booster-reactor, which is constructed similar to the core of the BN-350 fast breeder reactor, is taken as a model example. It is shown by means of Monte Carlo calculations that such system is a safe energy source (k_{eff}=0.94-0.98) and it is capable of transmuting produced radioactive wastes (neutron flux density in the thermal zone is PHI^{max} (r,z)=10^{14} n/(cm^{-2} s^{-1}), neutron flux in the fast zone is respectively equal PHI^{max} (r,z)=2.25 cdot 10^{15} n/(cm^{-2} s^{-1}) if the beam current of the proton accelerator is k_{eff}=0.98 and I=5.3 mA). Suggested configuration of the "cascade" reactor system essentially reduces the requirements on the proton accelerator current.
Monte Carlo modeling of ion chamber performance using MCNP.
Wallace, J D
2012-12-01
Ion Chambers have a generally flat energy response with some deviations at very low (2 MeV) energies. Some improvements in the low energy response can be achieved through use of high atomic number gases, such as argon and xenon, and higher chamber pressures. This work looks at the energy response of high pressure xenon-filled ion chambers using the MCNP Monte Carlo package to develop geometric models of a commercially available high pressure ion chamber (HPIC). The use of the F6 tally as an estimator of the energy deposited in a region of interest per unit mass, and the underlying assumptions associated with its use are described. The effect of gas composition, chamber gas pressure, chamber wall thickness, and chamber holder wall thicknesses on energy response are investigated and reported. The predicted energy response curve for the HPIC was found to be similar to that reported by other investigators. These investigations indicate that improvements to flatten the overall energy response of the HPIC down to 70 keV could be achieved through use of 3 mm-thick stainless steel walls for the ion chamber.
A nonparametric mixture model for cure rate estimation.
Peng, Y; Dear, K B
2000-03-01
Nonparametric methods have attracted less attention than their parametric counterparts for cure rate analysis. In this paper, we study a general nonparametric mixture model. The proportional hazards assumption is employed in modeling the effect of covariates on the failure time of patients who are not cured. The EM algorithm, the marginal likelihood approach, and multiple imputations are employed to estimate parameters of interest in the model. This model extends models and improves estimation methods proposed by other researchers. It also extends Cox's proportional hazards regression model by allowing a proportion of event-free patients and investigating covariate effects on that proportion. The model and its estimation method are investigated by simulations. An application to breast cancer data, including comparisons with previous analyses using a parametric model and an existing nonparametric model by other researchers, confirms the conclusions from the parametric model but not those from the existing nonparametric model.
Modeling adsorption of binary and ternary mixtures on microporous media
DEFF Research Database (Denmark)
Monsalvo, Matias Alfonso; Shapiro, Alexander
2007-01-01
it possible using the same equation of state to describe the thermodynamic properties of the segregated and the bulk phases. For comparison, we also used the ideal adsorbed solution theory (IAST) to describe adsorption equilibria. The main advantage of these two models is their capabilities to predict......The goal of this work is to analyze the adsorption of binary and ternary mixtures on the basis of the multicomponent potential theory of adsorption (MPTA). In the MPTA, the adsorbate is considered as a segregated mixture in the external potential field emitted by the solid adsorbent. This makes...... multicomponent adsorption equilibria on the basis of single-component adsorption data. We compare the MPTA and IAST models to a large set of experimental data, obtaining reasonable good agreement with experimental data and high degree of predictability. Some limitations of both models are also discussed....
Hydrogenic ionization model for mixtures in non-LTE plasmas
International Nuclear Information System (INIS)
Djaoui, A.
1999-01-01
The Hydrogenic Ionization Model for Mixtures (HIMM) is a non-Local Thermodynamic Equilibrium (non-LTE), time-dependent ionization model for laser-produced plasmas containing mixtures of elements (species). In this version, both collisional and radiative rates are taken into account. An ionization distribution for each species which is consistent with the ambient electron density is obtained by use of an iterative procedure in a single calculation for all species. Energy levels for each shell having a given principal quantum number and for each ion stage of each species in the mixture are calculated using screening constants. Steady-state non-LTE as well as LTE solutions are also provided. The non-LTE rate equations converge to the LTE solution at sufficiently high densities or as the radiation temperature approaches the electron temperature. The model is particularly useful at low temperatures where convergence problems are usually encountered in our previous models. We apply our model to typical situation in x-ray laser research, laser-produced plasmas and inertial confinement fusion. Our results compare well with previously published results for a selenium plasma. (author)
On an efficient multiple time step Monte Carlo simulation of the SABR model
Leitao Rodriguez, A.; Grzelak, L.A.; Oosterlee, C.W.
2017-01-01
In this paper, we will present a multiple time step Monte Carlo simulation technique for pricing options under the Stochastic Alpha Beta Rho model. The proposed method is an extension of the one time step Monte Carlo method that we proposed in an accompanying paper Leitao et al. [Appl. Math.
A Monte Carlo reflectance model for soil surfaces with three-dimensional structure
Cooper, K. D.; Smith, J. A.
1985-01-01
A Monte Carlo soil reflectance model has been developed to study the effect of macroscopic surface irregularities larger than the wavelength of incident flux. The model treats incoherent multiple scattering from Lambertian facets distributed on a periodic surface. Resulting bidirectional reflectance distribution functions are non-Lambertian and compare well with experimental trends reported in the literature. Examples showing the coupling of the Monte Carlo soil model to an adding bidirectional canopy of reflectance model are also given.
Color Texture Segmentation by Decomposition of Gaussian Mixture Model
Czech Academy of Sciences Publication Activity Database
Grim, Jiří; Somol, Petr; Haindl, Michal; Pudil, Pavel
2006-01-01
Roč. 19, č. 4225 (2006), s. 287-296 ISSN 0302-9743. [Iberoamerican Congress on Pattern Recognition. CIARP 2006 /11./. Cancun, 14.11.2006-17.11.2006] R&D Projects: GA AV ČR 1ET400750407; GA MŠk 1M0572; GA MŠk 2C06019 EU Projects: European Commission(XE) 507752 - MUSCLE Institutional research plan: CEZ:AV0Z10750506 Keywords : texture segmentation * gaussian mixture model * EM algorithm Subject RIV: IN - Informatics, Computer Science Impact factor: 0.402, year: 2005 http://library.utia.cas.cz/separaty/historie/grim-color texture segmentation by decomposition of gaussian mixture model.pdf
XDGMM: eXtreme Deconvolution Gaussian Mixture Modeling
Holoien, Thomas W.-S.; Marshall, Philip J.; Wechsler, Risa H.
2017-08-01
XDGMM uses Gaussian mixtures to do density estimation of noisy, heterogenous, and incomplete data using extreme deconvolution (XD) algorithms which is compatible with the scikit-learn machine learning methods. It implements both the astroML and Bovy et al. (2011) algorithms, and extends the BaseEstimator class from scikit-learn so that cross-validation methods work. It allows the user to produce a conditioned model if values of some parameters are known.
Option Pricing with Asymmetric Heteroskedastic Normal Mixture Models
DEFF Research Database (Denmark)
Rombouts, Jeroen V.K.; Stentoft, Lars
This paper uses asymmetric heteroskedastic normal mixture models to fit return data and to price options. The models can be estimated straightforwardly by maximum likelihood, have high statistical fit when used on S&P 500 index return data, and allow for substantial negative skewness and time...... varying higher order moments of the risk neutral distribution. When forecasting out-of-sample a large set of index options between 1996 and 2009, substantial improvements are found compared to several benchmark models in terms of dollar losses and the ability to explain the smirk in implied volatilities...
Variable selection for mixture and promotion time cure rate models.
Masud, Abdullah; Tu, Wanzhu; Yu, Zhangsheng
2016-11-16
Failure-time data with cured patients are common in clinical studies. Data from these studies are typically analyzed with cure rate models. Variable selection methods have not been well developed for cure rate models. In this research, we propose two least absolute shrinkage and selection operators based methods, for variable selection in mixture and promotion time cure models with parametric or nonparametric baseline hazards. We conduct an extensive simulation study to assess the operating characteristics of the proposed methods. We illustrate the use of the methods using data from a study of childhood wheezing. © The Author(s) 2016.
Sky-Radiance Models for Monte Carlo Radiative Transfer Applications
Santos, I.; Dalimonte, D.; Santos, J. P.
2012-04-01
Photon-tracing can be initialized through sky-radiance (Lsky) distribution models when executing Monte Carlo simulations for ocean color studies. To be effective, the Lsky model should: 1) properly represent sky-radiance features of interest; 2) require low computing time; and 3) depend on a limited number of input parameters. The present study verifies the satisfiability of these prerequisite by comparing results from different Lsky formulations. Specifically, two Lsky models were considered as reference cases because of their different approach among solutions presented in the literature. The first model, developed by the Harrisson and Coombes (HC), is based on a parametric expression where the sun geometry is the unique input. The HC model is one of the sky-radiance analytical distribution applied in state-of-art simulations for ocean optics. The coefficients of the HC model were set upon broad-band field measurements and the result is a model that requires a few implementation steps. The second model, implemented by Zibordi and Voss (ZV), is based on physical expressions that accounts for the optical thickness of permanent gases, aerosol, ozone and water vapour at specific wavelengths. Inter-comparisons between normalized ^LskyZV and ^LskyHC (i.e., with unitary scalar irradiance) are discussed by means of individual polar maps and percent difference between sky-radiance distributions. Sky-radiance cross-sections are presented as well. Considered cases include different sun zenith values and wavelengths (i.e., λ=413, 490 and 665 nm, corresponding to selected center-bands of the MEdium Resolution Imaging Spectrometer MERIS). Results have shown a significant convergence between ^LskyHC and ^LskyZV at 665 nm. Differences between models increase with the sun zenith and mostly with wavelength. For Instance, relative differences up to 50% between ^ L skyHC and ^ LskyZV can be observed in the antisolar region for λ=665 nm and θ*=45°. The effects of these
Utility of Monte Carlo Modelling for Holdup Measurements.
Energy Technology Data Exchange (ETDEWEB)
Belian, Anthony P.; Russo, P. A. (Phyllis A.); Weier, Dennis R. (Dennis Ray),
2005-01-01
Non-destructive assay (NDA) measurements performed to locate and quantify holdup in the Oak Ridge K25 enrichment cascade used neutron totals counting and low-resolution gamma-ray spectroscopy. This facility housed the gaseous diffusion process for enrichment of uranium, in the form of UF{sub 6} gas, from {approx} 20% to 93%. Inventory of {sup 235}U inventory in K-25 is all holdup. These buildings have been slated for decontaminatino and decommissioning. The NDA measurements establish the inventory quantities and will be used to assure criticality safety and meet criteria for waste analysis and transportation. The tendency to err on the side of conservatism for the sake of criticality safety in specifying total NDA uncertainty argues, in the interests of safety and costs, for obtaining the best possible value of uncertainty at the conservative confidence level for each item of process equipment. Variable deposit distribution is a complex systematic effect (i.e., determined by multiple independent variables) on the portable NDA results for very large and bulk converters that contributes greatly to total uncertainty for holdup in converters measured by gamma or neutron NDA methods. Because the magnitudes of complex systematic effects are difficult to estimate, computational tools are important for evaluating those that are large. Motivated by very large discrepancies between gamma and neutron measurements of high-mass converters with gamma results tending to dominate, the Monte Carlo code MCNP has been used to determine the systematic effects of deposit distribution on gamma and neutron results for {sup 235}U holdup mass in converters. This paper details the numerical methodology used to evaluate large systematic effects unique to each measurement type, validates the methodology by comparison with measurements, and discusses how modeling tools can supplement the calibration of instruments used for holdup measurements by providing realistic values at well
KONVERGENSI ESTIMATOR DALAM MODEL MIXTURE BERBASIS MISSING DATA
Directory of Open Access Journals (Sweden)
N Dwidayati
2014-06-01
Full Text Available Abstrak __________________________________________________________________________________________ Model mixture dapat mengestimasi proporsi pasien yang sembuh (cured dan fungsi survival pasien tak sembuh (uncured. Pada kajian ini, model mixture dikembangkan untuk analisis cure rate berbasis missing data. Ada beberapa metode yang dapat digunakan untuk analisis missing data. Salah satu metode yang dapat digunakan adalah Algoritma EM, Metode ini didasarkan pada 2 (dua langkah, yaitu: (1 Expectation Step dan (2 Maximization Step. Algoritma EM merupakan pendekatan iterasi untuk mempelajari model dari data dengan nilai hilang melalui 4 (empat langkah, yaitu(1 pilih himpunan inisial dari parameter untuk sebuah model, (2 tentukan nilai ekspektasi untuk data hilang, (3 buat induksi parameter model baru dari gabungan nilai ekspekstasi dan data asli, dan (4 jika parameter tidak converged, ulangi langkah 2 menggunakan model baru. Berdasar kajian yang dilakukan dapat ditunjukkan bahwa pada algoritma EM, log-likelihood untuk missing data mengalami kenaikan setelah dilakukan setiap iterasi dari algoritmanya. Dengan demikian berdasar algoritma EM, barisan likelihood konvergen jika likelihood terbatas ke bawah. Abstract __________________________________________________________________________________________ Model mixture can estimate proportion of recovering patient and function of patient survival do not recover. At this study, model mixture developed to analyse cure rate bases on missing data. There are some method which applicable to analyse missing data. One of method which can be applied is Algoritma EM, This method based on 2 ( two step, that is: ( 1 Expectation Step and ( 2 Maximization Step. EM Algorithm is approach of iteration to study model from data with value loses through 4 ( four step, yaitu(1 select;chooses initial gathering from parameter for a model, ( 2 determines expectation value for data to lose, ( 3 induce newfangled parameter
Bayesian mixture models for source separation in MEG
International Nuclear Information System (INIS)
Calvetti, Daniela; Homa, Laura; Somersalo, Erkki
2011-01-01
This paper discusses the problem of imaging electromagnetic brain activity from measurements of the induced magnetic field outside the head. This imaging modality, magnetoencephalography (MEG), is known to be severely ill posed, and in order to obtain useful estimates for the activity map, complementary information needs to be used to regularize the problem. In this paper, a particular emphasis is on finding non-superficial focal sources that induce a magnetic field that may be confused with noise due to external sources and with distributed brain noise. The data are assumed to come from a mixture of a focal source and a spatially distributed possibly virtual source; hence, to differentiate between those two components, the problem is solved within a Bayesian framework, with a mixture model prior encoding the information that different sources may be concurrently active. The mixture model prior combines one density that favors strongly focal sources and another that favors spatially distributed sources, interpreted as clutter in the source estimation. Furthermore, to address the challenge of localizing deep focal sources, a novel depth sounding algorithm is suggested, and it is shown with simulated data that the method is able to distinguish between a signal arising from a deep focal source and a clutter signal. (paper)
Determining of migraine prognosis using latent growth mixture models.
Tasdelen, Bahar; Ozge, Aynur; Kaleagasi, Hakan; Erdogan, Semra; Mengi, Tufan
2011-04-01
This paper presents a retrospective study to classify patients into subtypes of the treatment according to baseline and longitudinally observed values considering heterogenity in migraine prognosis. In the classical prospective clinical studies, participants are classified with respect to baseline status and followed within a certain time period. However, latent growth mixture model is the most suitable method, which considers the population heterogenity and is not affected drop-outs if they are missing at random. Hence, we planned this comprehensive study to identify prognostic factors in migraine. The study data have been based on a 10-year computer-based follow-up data of Mersin University Headache Outpatient Department. The developmental trajectories within subgroups were described for the severity, frequency, and duration of headache separately and the probabilities of each subgroup were estimated by using latent growth mixture models. SAS PROC TRAJ procedures, semiparametric and group-based mixture modeling approach, were applied to define the developmental trajectories. While the three-group model for the severity (mild, moderate, severe) and frequency (low, medium, high) of headache appeared to be appropriate, the four-group model for the duration (low, medium, high, extremely high) was more suitable. The severity of headache increased in the patients with nausea, vomiting, photophobia and phonophobia. The frequency of headache was especially related with increasing age and unilateral pain. Nausea and photophobia were also related with headache duration. Nausea, vomiting and photophobia were the most significant factors to identify developmental trajectories. The remission time was not the same for the severity, frequency, and duration of headache.
Spatially adaptive mixture modeling for analysis of FMRI time series.
Vincent, Thomas; Risser, Laurent; Ciuciu, Philippe
2010-04-01
Within-subject analysis in fMRI essentially addresses two problems, the detection of brain regions eliciting evoked activity and the estimation of the underlying dynamics. In Makni et aL, 2005 and Makni et aL, 2008, a detection-estimation framework has been proposed to tackle these problems jointly, since they are connected to one another. In the Bayesian formalism, detection is achieved by modeling activating and nonactivating voxels through independent mixture models (IMM) within each region while hemodynamic response estimation is performed at a regional scale in a nonparametric way. Instead of IMMs, in this paper we take advantage of spatial mixture models (SMM) for their nonlinear spatial regularizing properties. The proposed method is unsupervised and spatially adaptive in the sense that the amount of spatial correlation is automatically tuned from the data and this setting automatically varies across brain regions. In addition, the level of regularization is specific to each experimental condition since both the signal-to-noise ratio and the activation pattern may vary across stimulus types in a given brain region. These aspects require the precise estimation of multiple partition functions of underlying Ising fields. This is addressed efficiently using first path sampling for a small subset of fields and then using a recently developed fast extrapolation technique for the large remaining set. Simulation results emphasize that detection relying on supervised SMM outperforms its IMM counterpart and that unsupervised spatial mixture models achieve similar results without any hand-tuning of the correlation parameter. On real datasets, the gain is illustrated in a localizer fMRI experiment: brain activations appear more spatially resolved using SMM in comparison with classical general linear model (GLM)-based approaches, while estimating a specific parcel-based HRF shape. Our approach therefore validates the treatment of unsmoothed fMRI data without fixed GLM
Monte Carlo study of superconductivity in the three-band Emery model
International Nuclear Information System (INIS)
Frick, M.; Pattnaik, P.C.; Morgenstern, I.; Newns, D.M.; von der Linden, W.
1990-01-01
We have examined the three-band Hubbard model for the copper oxide planes in high-temperature superconductors using the projector quantum Monte Carlo method. We find no evidence for s-wave superconductivity
Escada, J; Rachinhas, P J B M; Lopes, J A M; Santos, F P; Távora, L M N; Conde, C A N; Stauffer, A D
2007-01-01
Monte Carlo simulation is used to investigate photoelectron backscattering effects in the emission from a CsI photocathode into CH4 and Ar-CH4 mixtures for incident monochromatic photons with energies Eph in the range 6.8 eV to 9.8 eV (182 nm to 127 nm), and photons from a continuous VUV Hg(Ar) lamp with a spectral distribution peaked at Eph = 6.7 eV (185 nm), considering reduced applied electric fields E/N in the 0.1 Td to 40 Td range. The addition of CH4 to a noble gas efficiently increases electron transmission and drift velocity, due to vibrational excitation of the molecules at low electron energies. Results are presented for the photoelectron transmission efficiencies f, where f is the fraction of the number of photoelectrons emitted from CsI which are transmitted through the gas as compared to vacuum. The dependence of f on Eph, E/N, and mixture composition is analyzed and explained in terms of electron scattering in the different gas media, and results are compared with available measurements. Electro...
Directory of Open Access Journals (Sweden)
Hea-Jung Kim
2017-06-01
Full Text Available This paper develops Bayesian inference in reliability of a class of scale mixtures of log-normal failure time (SMLNFT models with stochastic (or uncertain constraint in their reliability measures. The class is comprehensive and includes existing failure time (FT models (such as log-normal, log-Cauchy, and log-logistic FT models as well as new models that are robust in terms of heavy-tailed FT observations. Since classical frequency approaches to reliability analysis based on the SMLNFT model with stochastic constraint are intractable, the Bayesian method is pursued utilizing a Markov chain Monte Carlo (MCMC sampling based approach. This paper introduces a two-stage maximum entropy (MaxEnt prior, which elicits a priori uncertain constraint and develops Bayesian hierarchical SMLNFT model by using the prior. The paper also proposes an MCMC method for Bayesian inference in the SMLNFT model reliability and calls attention to properties of the MaxEnt prior that are useful for method development. Finally, two data sets are used to illustrate how the proposed methodology works.
DEFF Research Database (Denmark)
Tsivintzelis, Ioannis; Kontogeorgis, Georgios; Michelsen, Michael Locht
2010-01-01
(water, methanol, and glycols) are modeled assuming presence or not of cross-association interactions. Such interactions are accounted for using either a combining rule or a cross-solvation energy obtained from spectroscopic data. Using the parameters obtained from the binary systems, one ternary......The Cubic-Plus-Association (CPA) equation of state is applied to a large variety of mixtures containing H2S, which are of interest in the oil and gas industry. Binary H2S mixtures with alkanes, CO2, water, methanol, and glycols are first considered. The interactions of H2S with polar compounds...... and three quaternary mixtures are considered. It is shown that overall excellent correlation for binary, mixtures and satisfactory prediction results for multicomponent systems are obtained. There are significant differences between the various modeling approaches and the best results are obtained when...
Effective dielectric mixture model for characterization of diesel contaminated soil
International Nuclear Information System (INIS)
Al-Mattarneh, H.M.A.
2007-01-01
Human exposure to contaminated soil by diesel isomers can have serious health consequences like neurological diseases or cancer. The potential of dielectric measuring techniques for electromagnetic characterization of contaminated soils was investigated in this paper. The purpose of the research was to develop an empirical dielectric mixture model for soil hydrocarbon contamination application. The paper described the basic theory and elaborated in dielectric mixture theory. The analytical and empirical models were explained in simple algebraic formulas. The experimental study was then described with reference to materials, properties and experimental results. The results of the analytical models were also mathematically explained. The proposed semi-empirical model was also presented. According to the result of the electromagnetic properties of dry soil contaminated with diesel, the diesel presence had no significant effect on the electromagnetic properties of dry soil. It was concluded that diesel had no contribution to the soil electrical conductivity, which confirmed the nonconductive character of diesel. The results of diesel-contaminated soil at saturation condition indicated that both dielectric constant and loss factors of soil were decreased with increasing diesel content. 15 refs., 2 tabs., 9 figs
Simplest Validation of the HIJING Monte Carlo Model
Uzhinsky, V.V.
2003-01-01
Fulfillment of the energy-momentum conservation law, as well as the charge, baryon and lepton number conservation is checked for the HIJING Monte Carlo program in $pp$-interactions at $\\sqrt{s}=$ 200, 5500, and 14000 GeV. It is shown that the energy is conserved quite well. The transverse momentum is not conserved, the deviation from zero is at the level of 1--2 GeV/c, and it is connected with the hard jet production. The deviation is absent for soft interactions. Charge, baryon and lepton numbers are conserved. Azimuthal symmetry of the Monte Carlo events is studied, too. It is shown that there is a small signature of a "flow". The situation with the symmetry gets worse for nucleus-nucleus interactions.
Directory of Open Access Journals (Sweden)
T. A. Mikhailova
2016-01-01
Full Text Available In the paper the algorithm of modeling of continuous low-temperature free-radical butadiene-styrene copolymerization process in emulsion based on the Monte-Carlo method is offered. This process is the cornerstone of industrial production butadiene – styrene synthetic rubber which is the most widespread large-capacity rubber of general purpose. Imitation of growth of each macromolecule of the formed copolymer and tracking of the processes happening to it is the basis of algorithm of modeling. Modeling is carried out taking into account residence-time distribution of particles in system that gives the chance to research the process proceeding in the battery of consistently connected polymerization reactors. At the same time each polymerization reactor represents the continuous stirred tank reactor. Since the process is continuous, it is considered continuous addition of portions to the reaction mixture in the first reactor of battery. The constructed model allows to research molecular-weight and viscous characteristics of the formed copolymerization product, to predict the mass content of butadiene and styrene in copolymer, to carry out calculation of molecular-weight distribution of the received product at any moment of conducting process. According to the results of computational experiments analyzed the influence of mode of the process of the regulator introduced during the maintaining on change of characteristics of the formed butadiene-styrene copolymer. As the considered process takes place with participation of monomers of two types, besides listed the model allows to research compositional heterogeneity of the received product that is to carry out calculation of composite distribution and distribution of macromolecules for the size and structure. On the basis of the proposed algorithm created the software tool that allows you to keep track of changes in the characteristics of the resulting product in the dynamics.
Experiments with Mixtures Designs, Models, and the Analysis of Mixture Data
Cornell, John A
2011-01-01
The most comprehensive, single-volume guide to conducting experiments with mixtures"If one is involved, or heavily interested, in experiments on mixtures of ingredients, one must obtain this book. It is, as was the first edition, the definitive work."-Short Book Reviews (Publication of the International Statistical Institute)"The text contains many examples with worked solutions and with its extensive coverage of the subject matter will prove invaluable to those in the industrial and educational sectors whose work involves the design and analysis of mixture experiments."-Journal of the Royal S
On population size estimators in the Poisson mixture model.
Mao, Chang Xuan; Yang, Nan; Zhong, Jinhua
2013-09-01
Estimating population sizes via capture-recapture experiments has enormous applications. The Poisson mixture model can be adopted for those applications with a single list in which individuals appear one or more times. We compare several nonparametric estimators, including the Chao estimator, the Zelterman estimator, two jackknife estimators and the bootstrap estimator. The target parameter of the Chao estimator is a lower bound of the population size. Those of the other four estimators are not lower bounds, and they may produce lower confidence limits for the population size with poor coverage probabilities. A simulation study is reported and two examples are investigated. © 2013, The International Biometric Society.
Application of MCAM in generating Monte Carlo model for ITER port limiter
International Nuclear Information System (INIS)
Lu Lei; Li Ying; Ding Aiping; Zeng Qin; Huang Chenyu; Wu Yican
2007-01-01
On the basis of the pre-processing and conversion functions supplied by MCAM (Monte-Carlo Particle Transport Calculated Automatic Modeling System), this paper performed the generation of ITER Port Limiter MC (Monte-Carlo) calculation model from the CAD engineering model. The result was validated by using reverse function of MCAM and MCNP PLOT 2D cross-section drawing program. the successful application of MCAM to ITER Port Limiter demonstrates that MCAM is capable of dramatically increasing the efficiency and accuracy to generate MC calculation models from CAD engineering models with complex geometry comparing with the traditional manual modeling method. (authors)
A mixture model for robust registration in Kinect sensor
Peng, Li; Zhou, Huabing; Zhu, Shengguo
2018-03-01
The Microsoft Kinect sensor has been widely used in many applications, but it suffers from the drawback of low registration precision between color image and depth image. In this paper, we present a robust method to improve the registration precision by a mixture model that can handle multiply images with the nonparametric model. We impose non-parametric geometrical constraints on the correspondence, as a prior distribution, in a reproducing kernel Hilbert space (RKHS).The estimation is performed by the EM algorithm which by also estimating the variance of the prior model is able to obtain good estimates. We illustrate the proposed method on the public available dataset. The experimental results show that our approach outperforms the baseline methods.
Pushing the limits of Monte Carlo simulations for the three-dimensional Ising model
Ferrenberg, Alan M.; Xu, Jiahao; Landau, David P.
2018-04-01
While the three-dimensional Ising model has defied analytic solution, various numerical methods like Monte Carlo, Monte Carlo renormalization group, and series expansion have provided precise information about the phase transition. Using Monte Carlo simulation that employs the Wolff cluster flipping algorithm with both 32-bit and 53-bit random number generators and data analysis with histogram reweighting and quadruple precision arithmetic, we have investigated the critical behavior of the simple cubic Ising Model, with lattice sizes ranging from 163 to 10243. By analyzing data with cross correlations between various thermodynamic quantities obtained from the same data pool, e.g., logarithmic derivatives of magnetization and derivatives of magnetization cumulants, we have obtained the critical inverse temperature Kc=0.221 654 626 (5 ) and the critical exponent of the correlation length ν =0.629 912 (86 ) with precision that exceeds all previous Monte Carlo estimates.
DEFF Research Database (Denmark)
Fraser, Diane P.; Zuckermann, Martin J.; Mouritsen, Ole G.
1991-01-01
A two-dimensional Monte Carlo simulation method based on the NpT ensemble and the Voronoi tesselation, which was previously developed for single-species hard-disk systems, is extended, along with a version of scaled-particle theory, to many-component mixtures. These systems are unusual in the sense...... and internal degrees of freedom leads to a rich phase structure that includes solid-liquid transitions (governed by the translational variables) as well as transitions involving changes in average disk size (governed by the internal variables). The relationship between these two types of transitions is studied...... by the method in the case of a binary mixture, and results are presented for varying disk-size ratios and degeneracies. The results are also compared with the predictions of the extended scaled-particle theory. Applications of the model are discussed in relation to lipid monolayers spread on air...
Microbial comparative pan-genomics using binomial mixture models
Directory of Open Access Journals (Sweden)
Ussery David W
2009-08-01
Full Text Available Abstract Background The size of the core- and pan-genome of bacterial species is a topic of increasing interest due to the growing number of sequenced prokaryote genomes, many from the same species. Attempts to estimate these quantities have been made, using regression methods or mixture models. We extend the latter approach by using statistical ideas developed for capture-recapture problems in ecology and epidemiology. Results We estimate core- and pan-genome sizes for 16 different bacterial species. The results reveal a complex dependency structure for most species, manifested as heterogeneous detection probabilities. Estimated pan-genome sizes range from small (around 2600 gene families in Buchnera aphidicola to large (around 43000 gene families in Escherichia coli. Results for Echerichia coli show that as more data become available, a larger diversity is estimated, indicating an extensive pool of rarely occurring genes in the population. Conclusion Analyzing pan-genomics data with binomial mixture models is a way to handle dependencies between genomes, which we find is always present. A bottleneck in the estimation procedure is the annotation of rarely occurring genes.
New Flexible Models and Design Construction Algorithms for Mixtures and Binary Dependent Variables
A. Ruseckaite (Aiste)
2017-01-01
markdownabstractThis thesis discusses new mixture(-amount) models, choice models and the optimal design of experiments. Two chapters of the thesis relate to the so-called mixture, which is a product or service whose ingredients’ proportions sum to one. The thesis begins by introducing mixture
Tractography segmentation using a hierarchical Dirichlet processes mixture model.
Wang, Xiaogang; Grimson, W Eric L; Westin, Carl-Fredrik
2011-01-01
In this paper, we propose a new nonparametric Bayesian framework to cluster white matter fiber tracts into bundles using a hierarchical Dirichlet processes mixture (HDPM) model. The number of clusters is automatically learned driven by data with a Dirichlet process (DP) prior instead of being manually specified. After the models of bundles have been learned from training data without supervision, they can be used as priors to cluster/classify fibers of new subjects for comparison across subjects. When clustering fibers of new subjects, new clusters can be created for structures not observed in the training data. Our approach does not require computing pairwise distances between fibers and can cluster a huge set of fibers across multiple subjects. We present results on several data sets, the largest of which has more than 120,000 fibers. Copyright © 2010 Elsevier Inc. All rights reserved.
Improving system modeling accuracy with Monte Carlo codes
International Nuclear Information System (INIS)
Johnson, A.S.
1996-01-01
The use of computer codes based on Monte Carlo methods to perform criticality calculations has become common-place. Although results frequently published in the literature report calculated k eff values to four decimal places, people who use the codes in their everyday work say that they only believe the first two decimal places of any result. The lack of confidence in the computed k eff values may be due to the tendency of the reported standard deviation to underestimate errors associated with the Monte Carlo process. The standard deviation as reported by the codes is the standard deviation of the mean of the k eff values for individual generations in the computer simulation, not the standard deviation of the computed k eff value compared with the physical system. A more subtle problem with the standard deviation of the mean as reported by the codes is that all the k eff values from the separate generations are not statistically independent since the k eff of a given generation is a function of k eff of the previous generation, which is ultimately based on the starting source. To produce a standard deviation that is more representative of the physical system, statistically independent values of k eff are needed
Clustering disaggregated load profiles using a Dirichlet process mixture model
International Nuclear Information System (INIS)
Granell, Ramon; Axon, Colin J.; Wallom, David C.H.
2015-01-01
Highlights: • We show that the Dirichlet process mixture model is scaleable. • Our model does not require the number of clusters as an input. • Our model creates clusters only by the features of the demand profiles. • We have used both residential and commercial data sets. - Abstract: The increasing availability of substantial quantities of power-use data in both the residential and commercial sectors raises the possibility of mining the data to the advantage of both consumers and network operations. We present a Bayesian non-parametric model to cluster load profiles from households and business premises. Evaluators show that our model performs as well as other popular clustering methods, but unlike most other methods it does not require the number of clusters to be predetermined by the user. We used the so-called ‘Chinese restaurant process’ method to solve the model, making use of the Dirichlet-multinomial distribution. The number of clusters grew logarithmically with the quantity of data, making the technique suitable for scaling to large data sets. We were able to show that the model could distinguish features such as the nationality, household size, and type of dwelling between the cluster memberships
Bayesian nonparametric meta-analysis using Polya tree mixture models.
Branscum, Adam J; Hanson, Timothy E
2008-09-01
Summary. A common goal in meta-analysis is estimation of a single effect measure using data from several studies that are each designed to address the same scientific inquiry. Because studies are typically conducted in geographically disperse locations, recent developments in the statistical analysis of meta-analytic data involve the use of random effects models that account for study-to-study variability attributable to differences in environments, demographics, genetics, and other sources that lead to heterogeneity in populations. Stemming from asymptotic theory, study-specific summary statistics are modeled according to normal distributions with means representing latent true effect measures. A parametric approach subsequently models these latent measures using a normal distribution, which is strictly a convenient modeling assumption absent of theoretical justification. To eliminate the influence of overly restrictive parametric models on inferences, we consider a broader class of random effects distributions. We develop a novel hierarchical Bayesian nonparametric Polya tree mixture (PTM) model. We present methodology for testing the PTM versus a normal random effects model. These methods provide researchers a straightforward approach for conducting a sensitivity analysis of the normality assumption for random effects. An application involving meta-analysis of epidemiologic studies designed to characterize the association between alcohol consumption and breast cancer is presented, which together with results from simulated data highlight the performance of PTMs in the presence of nonnormality of effect measures in the source population.
Semiparametric Mixtures of Regressions with Single-index for Model Based Clustering
Xiang, Sijia; Yao, Weixin
2017-01-01
In this article, we propose two classes of semiparametric mixture regression models with single-index for model based clustering. Unlike many semiparametric/nonparametric mixture regression models that can only be applied to low dimensional predictors, the new semiparametric models can easily incorporate high dimensional predictors into the nonparametric components. The proposed models are very general, and many of the recently proposed semiparametric/nonparametric mixture regression models a...
Energy Technology Data Exchange (ETDEWEB)
Thienpont, Benedicte; Barata, Carlos [Department of Environmental Chemistry, Institute of Environmental Assessment and Water Research (IDAEA, CSIC), Jordi Girona, 18-26, 08034 Barcelona (Spain); Raldúa, Demetrio, E-mail: drpqam@cid.csic.es [Department of Environmental Chemistry, Institute of Environmental Assessment and Water Research (IDAEA, CSIC), Jordi Girona, 18-26, 08034 Barcelona (Spain); Maladies Rares: Génétique et Métabolisme (MRGM), University of Bordeaux, EA 4576, F-33400 Talence (France)
2013-06-01
Maternal thyroxine (T4) plays an essential role in fetal brain development, and even mild and transitory deficits in free-T4 in pregnant women can produce irreversible neurological effects in their offspring. Women of childbearing age are daily exposed to mixtures of chemicals disrupting the thyroid gland function (TGFDs) through the diet, drinking water, air and pharmaceuticals, which has raised the highest concern for the potential additive or synergic effects on the development of mild hypothyroxinemia during early pregnancy. Recently we demonstrated that zebrafish eleutheroembryos provide a suitable alternative model for screening chemicals impairing the thyroid hormone synthesis. The present study used the intrafollicular T4-content (IT4C) of zebrafish eleutheroembryos as integrative endpoint for testing the hypotheses that the effect of mixtures of TGFDs with a similar mode of action [inhibition of thyroid peroxidase (TPO)] was well predicted by a concentration addition concept (CA) model, whereas the response addition concept (RA) model predicted better the effect of dissimilarly acting binary mixtures of TGFDs [TPO-inhibitors and sodium-iodide symporter (NIS)-inhibitors]. However, CA model provided better prediction of joint effects than RA in five out of the six tested mixtures. The exception being the mixture MMI (TPO-inhibitor)-KClO{sub 4} (NIS-inhibitor) dosed at a fixed ratio of EC{sub 10} that provided similar CA and RA predictions and hence it was difficult to get any conclusive result. There results support the phenomenological similarity criterion stating that the concept of concentration addition could be extended to mixture constituents having common apical endpoints or common adverse outcomes. - Highlights: • Potential synergic or additive effect of mixtures of chemicals on thyroid function. • Zebrafish as alternative model for testing the effect of mixtures of goitrogens. • Concentration addition seems to predict better the effect of
International Nuclear Information System (INIS)
Thienpont, Benedicte; Barata, Carlos; Raldúa, Demetrio
2013-01-01
Maternal thyroxine (T4) plays an essential role in fetal brain development, and even mild and transitory deficits in free-T4 in pregnant women can produce irreversible neurological effects in their offspring. Women of childbearing age are daily exposed to mixtures of chemicals disrupting the thyroid gland function (TGFDs) through the diet, drinking water, air and pharmaceuticals, which has raised the highest concern for the potential additive or synergic effects on the development of mild hypothyroxinemia during early pregnancy. Recently we demonstrated that zebrafish eleutheroembryos provide a suitable alternative model for screening chemicals impairing the thyroid hormone synthesis. The present study used the intrafollicular T4-content (IT4C) of zebrafish eleutheroembryos as integrative endpoint for testing the hypotheses that the effect of mixtures of TGFDs with a similar mode of action [inhibition of thyroid peroxidase (TPO)] was well predicted by a concentration addition concept (CA) model, whereas the response addition concept (RA) model predicted better the effect of dissimilarly acting binary mixtures of TGFDs [TPO-inhibitors and sodium-iodide symporter (NIS)-inhibitors]. However, CA model provided better prediction of joint effects than RA in five out of the six tested mixtures. The exception being the mixture MMI (TPO-inhibitor)-KClO 4 (NIS-inhibitor) dosed at a fixed ratio of EC 10 that provided similar CA and RA predictions and hence it was difficult to get any conclusive result. There results support the phenomenological similarity criterion stating that the concept of concentration addition could be extended to mixture constituents having common apical endpoints or common adverse outcomes. - Highlights: • Potential synergic or additive effect of mixtures of chemicals on thyroid function. • Zebrafish as alternative model for testing the effect of mixtures of goitrogens. • Concentration addition seems to predict better the effect of mixtures of
Modeling dynamic functional connectivity using a wishart mixture model
DEFF Research Database (Denmark)
Nielsen, Søren Føns Vind; Madsen, Kristoffer Hougaard; Schmidt, Mikkel Nørgaard
2017-01-01
framework provides model selection by quantifying models generalization to new data. We use this to quantify the number of states within a prespecified window length. We further propose a heuristic procedure for choosing the window length based on contrasting for each window length the predictive...... together whereas short windows are more unstable and influenced by noise and we find that our heuristic correctly identifies an adequate level of complexity. On single subject resting state fMRI data we find that dynamic models generally outperform static models and using the proposed heuristic points...
Multiple Response Regression for Gaussian Mixture Models with Known Labels.
Lee, Wonyul; Du, Ying; Sun, Wei; Hayes, D Neil; Liu, Yufeng
2012-12-01
Multiple response regression is a useful regression technique to model multiple response variables using the same set of predictor variables. Most existing methods for multiple response regression are designed for modeling homogeneous data. In many applications, however, one may have heterogeneous data where the samples are divided into multiple groups. Our motivating example is a cancer dataset where the samples belong to multiple cancer subtypes. In this paper, we consider modeling the data coming from a mixture of several Gaussian distributions with known group labels. A naive approach is to split the data into several groups according to the labels and model each group separately. Although it is simple, this approach ignores potential common structures across different groups. We propose new penalized methods to model all groups jointly in which the common and unique structures can be identified. The proposed methods estimate the regression coefficient matrix, as well as the conditional inverse covariance matrix of response variables. Asymptotic properties of the proposed methods are explored. Through numerical examples, we demonstrate that both estimation and prediction can be improved by modeling all groups jointly using the proposed methods. An application to a glioblastoma cancer dataset reveals some interesting common and unique gene relationships across different cancer subtypes.
Exploring uncertainty in glacier mass balance modelling with Monte Carlo simulation
Machguth, H.; Purves, R.S.; Oerlemans, J.; Hoelzle, M.; Paul, F.
2008-01-01
By means of Monte Carlo simulations we calculated uncertainty in modelled cumulative mass balance over 400 days at one particular point on the tongue of Morteratsch Glacier, Switzerland, using a glacier energy balance model of intermediate complexity. Before uncertainty assessment, the model was
Microscopic imaging through turbid media Monte Carlo modeling and applications
Gu, Min; Deng, Xiaoyuan
2015-01-01
This book provides a systematic introduction to the principles of microscopic imaging through tissue-like turbid media in terms of Monte-Carlo simulation. It describes various gating mechanisms based on the physical differences between the unscattered and scattered photons and method for microscopic image reconstruction, using the concept of the effective point spread function. Imaging an object embedded in a turbid medium is a challenging problem in physics as well as in biophotonics. A turbid medium surrounding an object under inspection causes multiple scattering, which degrades the contrast, resolution and signal-to-noise ratio. Biological tissues are typically turbid media. Microscopic imaging through a tissue-like turbid medium can provide higher resolution than transillumination imaging in which no objective is used. This book serves as a valuable reference for engineers and scientists working on microscopy of tissue turbid media.
Modeling of columnar and equiaxed solidification of binary mixtures
International Nuclear Information System (INIS)
Roux, P.
2005-12-01
This work deals with the modelling of dendritic solidification in binary mixtures. Large scale phenomena are represented by volume averaging of the local conservation equations. This method allows to rigorously derive the partial differential equations of averaged fields and the closure problems associated to the deviations. Such problems can be resolved numerically on periodic cells, representative of dendritic structures, in order to give a precise evaluation of macroscopic transfer coefficients (Drag coefficients, exchange coefficients, diffusion-dispersion tensors...). The method had already been applied for a model of columnar dendritic mushy zone and it is extended to the case of equiaxed dendritic solidification, where solid grains can move. The two-phase flow is modelled with an Eulerian-Eulerian approach and the novelty is to account for the dispersion of solid velocity through the kinetic agitation of the particles. A coupling of the two models is proposed thanks to an original adaptation of the columnar model, allowing for undercooling calculation: a solid-liquid interfacial area density is introduced and calculated. At last, direct numerical simulations of crystal growth are proposed with a diffuse interface method for a representation of local phenomena. (author)
Implementation of a Monte Carlo based inverse planning model for clinical IMRT with MCNP code
International Nuclear Information System (INIS)
He, Tongming Tony
2003-01-01
Inaccurate dose calculations and limitations of optimization algorithms in inverse planning introduce systematic and convergence errors to treatment plans. This work was to implement a Monte Carlo based inverse planning model for clinical IMRT aiming to minimize the aforementioned errors. The strategy was to precalculate the dose matrices of beamlets in a Monte Carlo based method followed by the optimization of beamlet intensities. The MCNP 4B (Monte Carlo N-Particle version 4B) code was modified to implement selective particle transport and dose tallying in voxels and efficient estimation of statistical uncertainties. The resulting performance gain was over eleven thousand times. Due to concurrent calculation of multiple beamlets of individual ports, hundreds of beamlets in an IMRT plan could be calculated within a practical length of time. A finite-sized point source model provided a simple and accurate modeling of treatment beams. The dose matrix calculations were validated through measurements in phantoms. Agreements were better than 1.5% or 0.2 cm. The beamlet intensities were optimized using a parallel platform based optimization algorithm that was capable of escape from local minima and preventing premature convergence. The Monte Carlo based inverse planning model was applied to clinical cases. The feasibility and capability of Monte Carlo based inverse planning for clinical IMRT was demonstrated. Systematic errors in treatment plans of a commercial inverse planning system were assessed in comparison with the Monte Carlo based calculations. Discrepancies in tumor doses and critical structure doses were up to 12% and 17%, respectively. The clinical importance of Monte Carlo based inverse planning for IMRT was demonstrated
Microbial comparative pan-genomics using binomial mixture models
DEFF Research Database (Denmark)
Ussery, David; Snipen, L; Almøy, T
2009-01-01
The size of the core- and pan-genome of bacterial species is a topic of increasing interest due to the growing number of sequenced prokaryote genomes, many from the same species. Attempts to estimate these quantities have been made, using regression methods or mixture models. We extend the latter...... approach by using statistical ideas developed for capture-recapture problems in ecology and epidemiology. RESULTS: We estimate core- and pan-genome sizes for 16 different bacterial species. The results reveal a complex dependency structure for most species, manifested as heterogeneous detection...... probabilities. Estimated pan-genome sizes range from small (around 2600 gene families) in Buchnera aphidicola to large (around 43000 gene families) in Escherichia coli. Results for Echerichia coli show that as more data become available, a larger diversity is estimated, indicating an extensive pool of rarely...
Importance estimation in Monte Carlo modelling of neutron and photon transport
International Nuclear Information System (INIS)
Mickael, M.W.
1992-01-01
The estimation of neutron and photon importance in a three-dimensional geometry is achieved using a coupled Monte Carlo and diffusion theory calculation. The parameters required for the solution of the multigroup adjoint diffusion equation are estimated from an analog Monte Carlo simulation of the system under investigation. The solution of the adjoint diffusion equation is then used as an estimate of the particle importance in the actual simulation. This approach provides an automated and efficient variance reduction method for Monte Carlo simulations. The technique has been successfully applied to Monte Carlo simulation of neutron and coupled neutron-photon transport in the nuclear well-logging field. The results show that the importance maps obtained in a few minutes of computer time using this technique are in good agreement with Monte Carlo generated importance maps that require prohibitive computing times. The application of this method to Monte Carlo modelling of the response of neutron porosity and pulsed neutron instruments has resulted in major reductions in computation time. (Author)
Modelling of electron contamination in clinical photon beams for Monte Carlo dose calculation
International Nuclear Information System (INIS)
Yang, J; Li, J S; Qin, L; Xiong, W; Ma, C-M
2004-01-01
The purpose of this work is to model electron contamination in clinical photon beams and to commission the source model using measured data for Monte Carlo treatment planning. In this work, a planar source is used to represent the contaminant electrons at a plane above the upper jaws. The source size depends on the dimensions of the field size at the isocentre. The energy spectra of the contaminant electrons are predetermined using Monte Carlo simulations for photon beams from different clinical accelerators. A 'random creep' method is employed to derive the weight of the electron contamination source by matching Monte Carlo calculated monoenergetic photon and electron percent depth-dose (PDD) curves with measured PDD curves. We have integrated this electron contamination source into a previously developed multiple source model and validated the model for photon beams from Siemens PRIMUS accelerators. The EGS4 based Monte Carlo user code BEAM and MCSIM were used for linac head simulation and dose calculation. The Monte Carlo calculated dose distributions were compared with measured data. Our results showed good agreement (less than 2% or 2 mm) for 6, 10 and 18 MV photon beams
Automatic modeling for the Monte Carlo transport code Geant4 in MCAM
International Nuclear Information System (INIS)
Nie Fanzhi; Hu Liqin; Wang Guozhong; Wang Dianxi; Wu Yican; Wang Dong; Long Pengcheng; FDS Team
2014-01-01
Geant4 is a widely used Monte Carlo transport simulation package. Its geometry models could be described in geometry description markup language (GDML), but it is time-consuming and error-prone to describe the geometry models manually. This study implemented the conversion between computer-aided design (CAD) geometry models and GDML models. The conversion program was integrated into Multi-Physics Coupling Analysis Modeling Program (MCAM). The tests, including FDS-Ⅱ model, demonstrated its accuracy and feasibility. (authors)
R and D on automatic modeling methods for Monte Carlo codes FLUKA
International Nuclear Information System (INIS)
Wang Dianxi; Hu Liqin; Wang Guozhong; Zhao Zijia; Nie Fanzhi; Wu Yican; Long Pengcheng
2013-01-01
FLUKA is a fully integrated particle physics Monte Carlo simulation package. It is necessary to create the geometry models before calculation. However, it is time- consuming and error-prone to describe the geometry models manually. This study developed an automatic modeling method which could automatically convert computer-aided design (CAD) geometry models into FLUKA models. The conversion program was integrated into CAD/image-based automatic modeling program for nuclear and radiation transport simulation (MCAM). Its correctness has been demonstrated. (authors)
CAD-based Monte Carlo automatic modeling method based on primitive solid
International Nuclear Information System (INIS)
Wang, Dong; Song, Jing; Yu, Shengpeng; Long, Pengcheng; Wang, Yongliang
2016-01-01
Highlights: • We develop a method which bi-convert between CAD model and primitive solid. • This method was improved from convert method between CAD model and half space. • This method was test by ITER model and validated the correctness and efficiency. • This method was integrated in SuperMC which could model for SuperMC and Geant4. - Abstract: Monte Carlo method has been widely used in nuclear design and analysis, where geometries are described with primitive solids. However, it is time consuming and error prone to describe a primitive solid geometry, especially for a complicated model. To reuse the abundant existed CAD models and conveniently model with CAD modeling tools, an automatic modeling method for accurate prompt modeling between CAD model and primitive solid is needed. An automatic modeling method for Monte Carlo geometry described by primitive solid was developed which could bi-convert between CAD model and Monte Carlo geometry represented by primitive solids. While converting from CAD model to primitive solid model, the CAD model was decomposed into several convex solid sets, and then corresponding primitive solids were generated and exported. While converting from primitive solid model to the CAD model, the basic primitive solids were created and related operation was done. This method was integrated in the SuperMC and was benchmarked with ITER benchmark model. The correctness and efficiency of this method were demonstrated.
Directory of Open Access Journals (Sweden)
Oprisiu Ioana
2013-01-01
Full Text Available Abstract The Online Chemical Modeling Environment (OCHEM, http://ochem.eu is a web-based platform that provides tools for automation of typical steps necessary to create a predictive QSAR/QSPR model. The platform consists of two major subsystems: a database of experimental measurements and a modeling framework. So far, OCHEM has been limited to the processing of individual compounds. In this work, we extended OCHEM with a new ability to store and model properties of binary non-additive mixtures. The developed system is publicly accessible, meaning that any user on the Web can store new data for binary mixtures and develop models to predict their non-additive properties. The database already contains almost 10,000 data points for the density, bubble point, and azeotropic behavior of binary mixtures. For these data, we developed models for both qualitative (azeotrope/zeotrope and quantitative endpoints (density and bubble points using different learning methods and specially developed descriptors for mixtures. The prediction performance of the models was similar to or more accurate than results reported in previous studies. Thus, we have developed and made publicly available a powerful system for modeling mixtures of chemical compounds on the Web.
Flexible Mixture-Amount Models for Business and Industry Using Gaussian Processes
A. Ruseckaite (Aiste); D. Fok (Dennis); P.P. Goos (Peter)
2016-01-01
markdownabstractMany products and services can be described as mixtures of ingredients whose proportions sum to one. Specialized models have been developed for linking the mixture proportions to outcome variables, such as preference, quality and liking. In many scenarios, only the mixture
NRMC - A GPU code for N-Reverse Monte Carlo modeling of fluids in confined media
Sánchez-Gil, Vicente; Noya, Eva G.; Lomba, Enrique
2017-08-01
NRMC is a parallel code for performing N-Reverse Monte Carlo modeling of fluids in confined media [V. Sánchez-Gil, E.G. Noya, E. Lomba, J. Chem. Phys. 140 (2014) 024504]. This method is an extension of the usual Reverse Monte Carlo method to obtain structural models of confined fluids compatible with experimental diffraction patterns, specifically designed to overcome the problem of slow diffusion that can appear under conditions of tight confinement. Most of the computational time in N-Reverse Monte Carlo modeling is spent in the evaluation of the structure factor for each trial configuration, a calculation that can be easily parallelized. Implementation of the structure factor evaluation in NVIDIA® CUDA so that the code can be run on GPUs leads to a speed up of up to two orders of magnitude.
Induced polarization of clay-sand mixtures: experiments and modeling
International Nuclear Information System (INIS)
Okay, G.; Leroy, P.; Tournassat, C.; Ghorbani, A.; Jougnot, D.; Cosenza, P.; Camerlynck, C.; Cabrera, J.; Florsch, N.; Revil, A.
2012-01-01
were performed with a cylindrical four-electrode sample-holder (cylinder made of PVC with 30 cm in length and 19 cm in diameter) associated with a SIP-Fuchs II impedance meter and non-polarizing Cu/CuSO 4 electrodes. These electrodes were installed at 10 cm from the base of the sample holder and regularly spaced (each 90 degree). The results illustrate the strong impact of the Cationic Exchange Capacity (CEC) of the clay minerals upon the complex conductivity. The amplitude of the in-phase conductivity of the kaolinite-clay samples is strongly dependent to saturating fluid salinity for all volumetric clay fractions, whereas the in-phase conductivity of the smectite-clay samples is quite independent on the salinity, except at the low clay content (5% and 1% of clay in volume). This is due to the strong and constant surface conductivity of smectite associated with its very high CEC. The quadrature conductivity increases steadily with the CEC and the clay content. We observe that the dependence on frequency of the quadrature conductivity of sand-kaolinite mixtures is more important than for sand-bentonite mixtures. For both types of clay, the quadrature conductivity seems to be fairly independent on the pore fluid salinity except at very low clay contents (1% in volume of kaolinite-clay). This is due to the constant surface site density of Na counter-ions in the Stern layer of clay materials. At the lowest clay content (1%), the magnitude of the quadrature conductivity increases with the salinity, as expected for silica sands. In this case, the surface site density of Na counter-ions in the Stern layer increases with salinity. The experimental data show good agreement with predicted values given by our Spectral Induced Polarization (SIP) model. This complex conductivity model considers the electrochemical polarization of the Stern layer coating the clay particles and the Maxwell-Wagner polarization. We use the differential effective medium theory to calculate the complex
Monte Carlo simulation of diblock copolymer microphases by means of a 'fast' off-lattice model
DEFF Research Database (Denmark)
Besold, Gerhard; Hassager, O.; Mouritsen, Ole G.
1999-01-01
We present a mesoscopic off-lattice model for the simulation of diblock copolymer melts by Monte Carlo techniques. A single copolymer molecule is modeled as a discrete Edwards chain consisting of two blocks with vertices of type A and B, respectively. The volume interaction is formulated in terms...
Modelling of the RA-1 reactor using a Monte Carlo code
International Nuclear Information System (INIS)
Quinteiro, Guillermo F.; Calabrese, Carlos R.
2000-01-01
It was carried out for the first time, a model of the Argentine RA-1 reactor using the MCNP Monte Carlo code. This model was validated using data for experimental neutron and gamma measurements at different energy ranges and locations. In addition, the resulting fluxes were compared with the data obtained using a 3D diffusion code. (author)
Modelling the effect of mixture components on permeation through skin.
Ghafourian, T; Samaras, E G; Brooks, J D; Riviere, J E
2010-10-15
A vehicle influences the concentration of penetrant within the membrane, affecting its diffusivity in the skin and rate of transport. Despite the huge amount of effort made for the understanding and modelling of the skin absorption of chemicals, a reliable estimation of the skin penetration potential from formulations remains a challenging objective. In this investigation, quantitative structure-activity relationship (QSAR) was employed to relate the skin permeation of compounds to the chemical properties of the mixture ingredients and the molecular structures of the penetrants. The skin permeability dataset consisted of permeability coefficients of 12 different penetrants each blended in 24 different solvent mixtures measured from finite-dose diffusion cell studies using porcine skin. Stepwise regression analysis resulted in a QSAR employing two penetrant descriptors and one solvent property. The penetrant descriptors were octanol/water partition coefficient, logP and the ninth order path molecular connectivity index, and the solvent property was the difference between boiling and melting points. The negative relationship between skin permeability coefficient and logP was attributed to the fact that most of the drugs in this particular dataset are extremely lipophilic in comparison with the compounds in the common skin permeability datasets used in QSAR. The findings show that compounds formulated in vehicles with small boiling and melting point gaps will be expected to have higher permeation through skin. The QSAR was validated internally, using a leave-many-out procedure, giving a mean absolute error of 0.396. The chemical space of the dataset was compared with that of the known skin permeability datasets and gaps were identified for future skin permeability measurements. Copyright 2010 Elsevier B.V. All rights reserved.
Toxicological risk assessment of complex mixtures through the Wtox model
Directory of Open Access Journals (Sweden)
William Gerson Matias
2015-01-01
Full Text Available Mathematical models are important tools for environmental management and risk assessment. Predictions about the toxicity of chemical mixtures must be enhanced due to the complexity of eects that can be caused to the living species. In this work, the environmental risk was accessed addressing the need to study the relationship between the organism and xenobiotics. Therefore, ve toxicological endpoints were applied through the WTox Model, and with this methodology we obtained the risk classication of potentially toxic substances. Acute and chronic toxicity, citotoxicity and genotoxicity were observed in the organisms Daphnia magna, Vibrio scheri and Oreochromis niloticus. A case study was conducted with solid wastes from textile, metal-mechanic and pulp and paper industries. The results have shown that several industrial wastes induced mortality, reproductive eects, micronucleus formation and increases in the rate of lipid peroxidation and DNA methylation of the organisms tested. These results, analyzed together through the WTox Model, allowed the classication of the environmental risk of industrial wastes. The evaluation showed that the toxicological environmental risk of the samples analyzed can be classied as signicant or critical.
Parameter uncertainty and model predictions: a review of Monte Carlo results
International Nuclear Information System (INIS)
Gardner, R.H.; O'Neill, R.V.
1979-01-01
Studies of parameter variability by Monte Carlo analysis are reviewed using repeated simulations of the model with randomly selected parameter values. At the beginning of each simulation, parameter values are chosen from specific frequency distributions. This process is continued for a number of iterations sufficient to converge on an estimate of the frequency distribution of the output variables. The purpose was to explore the general properties of error propagaton in models. Testing the implicit assumptions of analytical methods and pointing out counter-intuitive results produced by the Monte Carlo approach are additional points covered
Monte Carlo Simulations of Compressible Ising Models: Do We Understand Them?
Landau, D. P.; Dünweg, B.; Laradji, M.; Tavazza, F.; Adler, J.; Cannavaccioulo, L.; Zhu, X.
Extensive Monte Carlo simulations have begun to shed light on our understanding of phase transitions and universality classes for compressible Ising models. A comprehensive analysis of a Landau-Ginsburg-Wilson hamiltonian for systems with elastic degrees of freedom resulted in the prediction that there should be four distinct cases that would have different behavior, depending upon symmetries and thermodynamic constraints. We shall provide an account of the results of careful Monte Carlo simulations for a simple compressible Ising model that can be suitably modified so as to replicate all four cases.
Empirical Analysis of Stochastic Volatility Model by Hybrid Monte Carlo Algorithm
International Nuclear Information System (INIS)
Takaishi, Tetsuya
2013-01-01
The stochastic volatility model is one of volatility models which infer latent volatility of asset returns. The Bayesian inference of the stochastic volatility (SV) model is performed by the hybrid Monte Carlo (HMC) algorithm which is superior to other Markov Chain Monte Carlo methods in sampling volatility variables. We perform the HMC simulations of the SV model for two liquid stock returns traded on the Tokyo Stock Exchange and measure the volatilities of those stock returns. Then we calculate the accuracy of the volatility measurement using the realized volatility as a proxy of the true volatility and compare the SV model with the GARCH model which is one of other volatility models. Using the accuracy calculated with the realized volatility we find that empirically the SV model performs better than the GARCH model.
Polymer mixtures in confined geometries: Model systems to explore ...
Indian Academy of Sciences (India)
to mean field behavior for very long chains, the critical behavior of mixtures confined into thin film geometry falls in the 2d Ising class irrespective of chain length. ..... AB interface does not approach the wall; (b) corresponds to a temperature .... Very recently, these theoretical studies have been extended to polymer mixtures.
A study of finite mixture model: Bayesian approach on financial time series data
Phoong, Seuk-Yen; Ismail, Mohd Tahir
2014-07-01
Recently, statistician have emphasized on the fitting finite mixture model by using Bayesian method. Finite mixture model is a mixture of distributions in modeling a statistical distribution meanwhile Bayesian method is a statistical method that use to fit the mixture model. Bayesian method is being used widely because it has asymptotic properties which provide remarkable result. In addition, Bayesian method also shows consistency characteristic which means the parameter estimates are close to the predictive distributions. In the present paper, the number of components for mixture model is studied by using Bayesian Information Criterion. Identify the number of component is important because it may lead to an invalid result. Later, the Bayesian method is utilized to fit the k-component mixture model in order to explore the relationship between rubber price and stock market price for Malaysia, Thailand, Philippines and Indonesia. Lastly, the results showed that there is a negative effect among rubber price and stock market price for all selected countries.
International Nuclear Information System (INIS)
Stern, R.E.; Song, J.; Work, D.B.
2017-01-01
The two-terminal reliability problem in system reliability analysis is known to be computationally intractable for large infrastructure graphs. Monte Carlo techniques can estimate the probability of a disconnection between two points in a network by selecting a representative sample of network component failure realizations and determining the source-terminal connectivity of each realization. To reduce the runtime required for the Monte Carlo approximation, this article proposes an approximate framework in which the connectivity check of each sample is estimated using a machine-learning-based classifier. The framework is implemented using both a support vector machine (SVM) and a logistic regression based surrogate model. Numerical experiments are performed on the California gas distribution network using the epicenter and magnitude of the 1989 Loma Prieta earthquake as well as randomly-generated earthquakes. It is shown that the SVM and logistic regression surrogate models are able to predict network connectivity with accuracies of 99% for both methods, and are 1–2 orders of magnitude faster than using a Monte Carlo method with an exact connectivity check. - Highlights: • Surrogate models of network connectivity are developed by machine-learning algorithms. • Developed surrogate models can reduce the runtime required for Monte Carlo simulations. • Support vector machine and logistic regressions are employed to develop surrogate models. • Numerical example of California gas distribution network demonstrate the proposed approach. • The developed models have accuracies 99%, and are 1–2 orders of magnitude faster than MCS.
Energy Technology Data Exchange (ETDEWEB)
Quinteiro, Guillermo F; Calabrese, Carlos R [Comision Nacional de Energia Atomica, General San Martin (Argentina). Dept. de Reactores y Centrales Nucleares
2000-07-01
It was carried out for the first time, a model of the Argentine RA-1 reactor using the MCNP Monte Carlo code. This model was validated using data for experimental neutron and gamma measurements at different energy ranges and locations. In addition, the resulting fluxes were compared with the data obtained using a 3D diffusion code. (author)
Nonparametric Identification and Estimation of Finite Mixture Models of Dynamic Discrete Choices
Hiroyuki Kasahara; Katsumi Shimotsu
2006-01-01
In dynamic discrete choice analysis, controlling for unobserved heterogeneity is an important issue, and finite mixture models provide flexible ways to account for unobserved heterogeneity. This paper studies nonparametric identifiability of type probabilities and type-specific component distributions in finite mixture models of dynamic discrete choices. We derive sufficient conditions for nonparametric identification for various finite mixture models of dynamic discrete choices used in appli...
Wright, Aidan G C; Hallquist, Michael N
2014-01-01
Studying personality and its pathology as it changes, develops, or remains stable over time offers exciting insight into the nature of individual differences. Researchers interested in examining personal characteristics over time have a number of time-honored analytic approaches at their disposal. In recent years there have also been considerable advances in person-oriented analytic approaches, particularly longitudinal mixture models. In this methodological primer we focus on mixture modeling approaches to the study of normative and individual change in the form of growth mixture models and ipsative change in the form of latent transition analysis. We describe the conceptual underpinnings of each of these models, outline approaches for their implementation, and provide accessible examples for researchers studying personality and its assessment.
International Nuclear Information System (INIS)
Davis, J. E.; Eddy, M. J.; Sutton, T. M.; Altomari, T. J.
2007-01-01
Solid modeling computer software systems provide for the design of three-dimensional solid models used in the design and analysis of physical components. The current state-of-the-art in solid modeling representation uses a boundary representation format in which geometry and topology are used to form three-dimensional boundaries of the solid. The geometry representation used in these systems is cubic B-spline curves and surfaces - a network of cubic B-spline functions in three-dimensional Cartesian coordinate space. Many Monte Carlo codes, however, use a geometry representation in which geometry units are specified by intersections and unions of half-spaces. This paper describes an algorithm for converting from a boundary representation to a half-space representation. (authors)
A Monte Carlo Investigation of the Box-Cox Model and a Nonlinear Least Squares Alternative.
Showalter, Mark H
1994-01-01
This paper reports a Monte Carlo study of the Box-Cox model and a nonlinear least squares alternative. Key results include the following: the transformation parameter in the Box-Cox model appears to be inconsistently estimated in the presence of conditional heteroskedasticity; the constant term in both the Box-Cox and the nonlinear least squares models is poorly estimated in small samples; conditional mean forecasts tend to underestimate their true value in the Box-Cox model when the transfor...
Markov chain Monte Carlo methods in directed graphical models
DEFF Research Database (Denmark)
Højbjerre, Malene
Directed graphical models present data possessing a complex dependence structure, and MCMC methods are computer-intensive simulation techniques to approximate high-dimensional intractable integrals, which emerge in such models with incomplete data. MCMC computations in directed graphical models h...
Modelling of spark to ignition transition in gas mixtures
Energy Technology Data Exchange (ETDEWEB)
Akram, M.
1996-10-01
This thesis pertains to the models for studying sparking in chemically inert gases. The processes taking place in a spark to flame transition can be segregated into physical and chemical processes, and this study is focused on physical processes. The plasma is regarded as a single-substance material. One and two-dimensional models are developed. The transfer of electrical energy into thermal energy of the gas and its redistribution in space and time along with the evolution of a plasma kernel is studied in the time domain ranging from 10 ns to 40 micros. In the case of ultra-fast sparks, the propagation of the shock and its reflection from a rigid wall is presented. The influence of electrode shape and the gap size on the flow structure development is found to be a dominating factor. It is observed that the flow structure that has developed in the early stage more or less prevails at later stages and strongly influences the shape and evolution of the hot kernel. The electrode geometry and configuration are responsible for the development of the flow structure. The strength of the vortices generated in the flow field is influenced by the power input to the gap and their location of emergence is dictated by the electrode shape and configuration. The heat transfer after 2 micros in the case of ultra-fast sparks is dominated by convection and diffusion. The strong mixing produced by hydrodynamic effects and the electrode geometry give the indication that the magnetic pinch effect might be negligible. Finally, a model for a multicomponent gas mixture is presented. The chemical kinetics mechanism for dissociation and ionization is introduced. 56 refs
Identifiability in N-mixture models: a large-scale screening test with bird data.
Kéry, Marc
2018-02-01
Binomial N-mixture models have proven very useful in ecology, conservation, and monitoring: they allow estimation and modeling of abundance separately from detection probability using simple counts. Recently, doubts about parameter identifiability have been voiced. I conducted a large-scale screening test with 137 bird data sets from 2,037 sites. I found virtually no identifiability problems for Poisson and zero-inflated Poisson (ZIP) binomial N-mixture models, but negative-binomial (NB) models had problems in 25% of all data sets. The corresponding multinomial N-mixture models had no problems. Parameter estimates under Poisson and ZIP binomial and multinomial N-mixture models were extremely similar. Identifiability problems became a little more frequent with smaller sample sizes (267 and 50 sites), but were unaffected by whether the models did or did not include covariates. Hence, binomial N-mixture model parameters with Poisson and ZIP mixtures typically appeared identifiable. In contrast, NB mixtures were often unidentifiable, which is worrying since these were often selected by Akaike's information criterion. Identifiability of binomial N-mixture models should always be checked. If problems are found, simpler models, integrated models that combine different observation models or the use of external information via informative priors or penalized likelihoods, may help. © 2017 by the Ecological Society of America.
Nonlinear Structured Growth Mixture Models in M"plus" and OpenMx
Grimm, Kevin J.; Ram, Nilam; Estabrook, Ryne
2010-01-01
Growth mixture models (GMMs; B. O. Muthen & Muthen, 2000; B. O. Muthen & Shedden, 1999) are a combination of latent curve models (LCMs) and finite mixture models to examine the existence of latent classes that follow distinct developmental patterns. GMMs are often fit with linear, latent basis, multiphase, or polynomial change models…
Quasi-Monte Carlo methods: applications to modeling of light transport in tissue
Schafer, Steven A.
1996-05-01
Monte Carlo modeling of light propagation can accurately predict the distribution of light in scattering materials. A drawback of Monte Carlo methods is that they converge inversely with the square root of the number of iterations. Theoretical considerations suggest that convergence which scales inversely with the first power of the number of iterations is possible. We have previously shown that one can obtain at least a portion of that improvement by using van der Corput sequences in place of a conventional pseudo-random number generator. Here, we present our further analysis, and show that quasi-Monte Carlo methods do have limited applicability to light scattering problems. We also discuss potential improvements which may increase the applicability.
Model unspecific search in CMS. Treatment of insufficient Monte Carlo statistics
Energy Technology Data Exchange (ETDEWEB)
Lieb, Jonas; Albert, Andreas; Duchardt, Deborah; Hebbeker, Thomas; Knutzen, Simon; Meyer, Arnd; Pook, Tobias; Roemer, Jonas [III. Physikalisches Institut A, RWTH Aachen University (Germany)
2016-07-01
In 2015, the CMS detector recorded proton-proton collisions at an unprecedented center of mass energy of √(s)=13 TeV. The Model Unspecific Search in CMS (MUSiC) offers an analysis approach of these data which is complementary to dedicated analyses: By taking all produced final states into consideration, MUSiC is sensitive to indicators of new physics appearing in final states that are usually not investigated. In a two step process, MUSiC first classifies events according to their physics content and then searches kinematic distributions for the most significant deviations between Monte Carlo simulations and observed data. Such a general approach introduces its own set of challenges. One of them is the treatment of situations with insufficient Monte Carlo statistics. Complementing introductory presentations on the MUSiC event selection and classification, this talk will present a method of dealing with the issue of low Monte Carlo statistics.
A Monte Carlo modeling alternative for the API Gamma Ray Calibration Facility
International Nuclear Information System (INIS)
Galford, J.E.
2017-01-01
The gamma ray pit at the API Calibration Facility, located on the University of Houston campus, defines the API unit for natural gamma ray logs used throughout the petroleum logging industry. Future use of the facility is uncertain. An alternative method is proposed to preserve the gamma ray API unit definition as an industry standard by using Monte Carlo modeling to obtain accurate counting rate-to-API unit conversion factors for gross-counting and spectral gamma ray tool designs. - Highlights: • A Monte Carlo alternative is proposed to replace empirical calibration procedures. • The proposed Monte Carlo alternative preserves the original API unit definition. • MCNP source and materials descriptions are provided for the API gamma ray pit. • Simulated results are presented for several wireline logging tool designs. • The proposed method can be adapted for use with logging-while-drilling tools.
Confronting uncertainty in model-based geostatistics using Markov Chain Monte Carlo simulation
Minasny, B.; Vrugt, J.A.; McBratney, A.B.
2011-01-01
This paper demonstrates for the first time the use of Markov Chain Monte Carlo (MCMC) simulation for parameter inference in model-based soil geostatistics. We implemented the recently developed DiffeRential Evolution Adaptive Metropolis (DREAM) algorithm to jointly summarize the posterior
Monte Carlo study of the phase diagram for the two-dimensional Z(4) model
International Nuclear Information System (INIS)
Carneiro, G.M.; Pol, M.E.; Zagury, N.
1982-05-01
The phase diagram of the two-dimensional Z(4) model on a square lattice is determined using a Monte Carlo method. The results of this simulation confirm the general features of the phase diagram predicted theoretically for the ferromagnetic case, and show the existence of a new phase with perpendicular order. (Author) [pt
International Nuclear Information System (INIS)
Ranft, J.; Schiller, A.
1984-01-01
Lattice versions with restricted suppersymmetry of simple (1+1)-dimensional supersymmetric models are numerically studied using a local hamiltonian Monte Carlo method. The pattern of supersymmetry breaking closely follows the expectations of Bartels and Bronzan obtain in an alternative lattice formulation. (orig.)
DEFF Research Database (Denmark)
Tycho, Andreas; Jørgensen, Thomas Martini; Andersen, Peter E.
2002-01-01
A Monte Carlo (MC) method for modeling optical coherence tomography (OCT) measurements of a diffusely reflecting discontinuity emb edded in a scattering medium is presented. For the first time to the authors' knowledge it is shown analytically that the applicability of an MC approach to this opti...
Vrugt, J.A.; Braak, ter C.J.F.; Clark, M.P.; Hyman, J.M.; Robinson, B.A.
2008-01-01
There is increasing consensus in the hydrologic literature that an appropriate framework for streamflow forecasting and simulation should include explicit recognition of forcing and parameter and model structural error. This paper presents a novel Markov chain Monte Carlo (MCMC) sampler, entitled
SKIRT: The design of a suite of input models for Monte Carlo radiative transfer simulations
Baes, M.; Camps, P.
2015-09-01
The Monte Carlo method is the most popular technique to perform radiative transfer simulations in a general 3D geometry. The algorithms behind and acceleration techniques for Monte Carlo radiative transfer are discussed extensively in the literature, and many different Monte Carlo codes are publicly available. On the contrary, the design of a suite of components that can be used for the distribution of sources and sinks in radiative transfer codes has received very little attention. The availability of such models, with different degrees of complexity, has many benefits. For example, they can serve as toy models to test new physical ingredients, or as parameterised models for inverse radiative transfer fitting. For 3D Monte Carlo codes, this requires algorithms to efficiently generate random positions from 3D density distributions. We describe the design of a flexible suite of components for the Monte Carlo radiative transfer code SKIRT. The design is based on a combination of basic building blocks (which can be either analytical toy models or numerical models defined on grids or a set of particles) and the extensive use of decorators that combine and alter these building blocks to more complex structures. For a number of decorators, e.g. those that add spiral structure or clumpiness, we provide a detailed description of the algorithms that can be used to generate random positions. Advantages of this decorator-based design include code transparency, the avoidance of code duplication, and an increase in code maintainability. Moreover, since decorators can be chained without problems, very complex models can easily be constructed out of simple building blocks. Finally, based on a number of test simulations, we demonstrate that our design using customised random position generators is superior to a simpler design based on a generic black-box random position generator.
A measurement-based generalized source model for Monte Carlo dose simulations of CT scans.
Ming, Xin; Feng, Yuanming; Liu, Ransheng; Yang, Chengwen; Zhou, Li; Zhai, Hezheng; Deng, Jun
2017-03-07
The goal of this study is to develop a generalized source model for accurate Monte Carlo dose simulations of CT scans based solely on the measurement data without a priori knowledge of scanner specifications. The proposed generalized source model consists of an extended circular source located at x-ray target level with its energy spectrum, source distribution and fluence distribution derived from a set of measurement data conveniently available in the clinic. Specifically, the central axis percent depth dose (PDD) curves measured in water and the cone output factors measured in air were used to derive the energy spectrum and the source distribution respectively with a Levenberg-Marquardt algorithm. The in-air film measurement of fan-beam dose profiles at fixed gantry was back-projected to generate the fluence distribution of the source model. A benchmarked Monte Carlo user code was used to simulate the dose distributions in water with the developed source model as beam input. The feasibility and accuracy of the proposed source model was tested on a GE LightSpeed and a Philips Brilliance Big Bore multi-detector CT (MDCT) scanners available in our clinic. In general, the Monte Carlo simulations of the PDDs in water and dose profiles along lateral and longitudinal directions agreed with the measurements within 4%/1 mm for both CT scanners. The absolute dose comparison using two CTDI phantoms (16 cm and 32 cm in diameters) indicated a better than 5% agreement between the Monte Carlo-simulated and the ion chamber-measured doses at a variety of locations for the two scanners. Overall, this study demonstrated that a generalized source model can be constructed based only on a set of measurement data and used for accurate Monte Carlo dose simulations of patients' CT scans, which would facilitate patient-specific CT organ dose estimation and cancer risk management in the diagnostic and therapeutic radiology.
A measurement-based generalized source model for Monte Carlo dose simulations of CT scans
Ming, Xin; Feng, Yuanming; Liu, Ransheng; Yang, Chengwen; Zhou, Li; Zhai, Hezheng; Deng, Jun
2017-03-01
The goal of this study is to develop a generalized source model for accurate Monte Carlo dose simulations of CT scans based solely on the measurement data without a priori knowledge of scanner specifications. The proposed generalized source model consists of an extended circular source located at x-ray target level with its energy spectrum, source distribution and fluence distribution derived from a set of measurement data conveniently available in the clinic. Specifically, the central axis percent depth dose (PDD) curves measured in water and the cone output factors measured in air were used to derive the energy spectrum and the source distribution respectively with a Levenberg-Marquardt algorithm. The in-air film measurement of fan-beam dose profiles at fixed gantry was back-projected to generate the fluence distribution of the source model. A benchmarked Monte Carlo user code was used to simulate the dose distributions in water with the developed source model as beam input. The feasibility and accuracy of the proposed source model was tested on a GE LightSpeed and a Philips Brilliance Big Bore multi-detector CT (MDCT) scanners available in our clinic. In general, the Monte Carlo simulations of the PDDs in water and dose profiles along lateral and longitudinal directions agreed with the measurements within 4%/1 mm for both CT scanners. The absolute dose comparison using two CTDI phantoms (16 cm and 32 cm in diameters) indicated a better than 5% agreement between the Monte Carlo-simulated and the ion chamber-measured doses at a variety of locations for the two scanners. Overall, this study demonstrated that a generalized source model can be constructed based only on a set of measurement data and used for accurate Monte Carlo dose simulations of patients’ CT scans, which would facilitate patient-specific CT organ dose estimation and cancer risk management in the diagnostic and therapeutic radiology.
Modeling abundance using N-mixture models: the importance of considering ecological mechanisms.
Joseph, Liana N; Elkin, Ché; Martin, Tara G; Possinghami, Hugh P
2009-04-01
Predicting abundance across a species' distribution is useful for studies of ecology and biodiversity management. Modeling of survey data in relation to environmental variables can be a powerful method for extrapolating abundances across a species' distribution and, consequently, calculating total abundances and ultimately trends. Research in this area has demonstrated that models of abundance are often unstable and produce spurious estimates, and until recently our ability to remove detection error limited the development of accurate models. The N-mixture model accounts for detection and abundance simultaneously and has been a significant advance in abundance modeling. Case studies that have tested these new models have demonstrated success for some species, but doubt remains over the appropriateness of standard N-mixture models for many species. Here we develop the N-mixture model to accommodate zero-inflated data, a common occurrence in ecology, by employing zero-inflated count models. To our knowledge, this is the first application of this method to modeling count data. We use four variants of the N-mixture model (Poisson, zero-inflated Poisson, negative binomial, and zero-inflated negative binomial) to model abundance, occupancy (zero-inflated models only) and detection probability of six birds in South Australia. We assess models by their statistical fit and the ecological realism of the parameter estimates. Specifically, we assess the statistical fit with AIC and assess the ecological realism by comparing the parameter estimates with expected values derived from literature, ecological theory, and expert opinion. We demonstrate that, despite being frequently ranked the "best model" according to AIC, the negative binomial variants of the N-mixture often produce ecologically unrealistic parameter estimates. The zero-inflated Poisson variant is preferable to the negative binomial variants of the N-mixture, as it models an ecological mechanism rather than a
Real time tracking by LOPF algorithm with mixture model
Meng, Bo; Zhu, Ming; Han, Guangliang; Wu, Zhiguo
2007-11-01
A new particle filter-the Local Optimum Particle Filter (LOPF) algorithm is presented for tracking object accurately and steadily in visual sequences in real time which is a challenge task in computer vision field. In order to using the particles efficiently, we first use Sobel algorithm to extract the profile of the object. Then, we employ a new Local Optimum algorithm to auto-initialize some certain number of particles from these edge points as centre of the particles. The main advantage we do this in stead of selecting particles randomly in conventional particle filter is that we can pay more attentions on these more important optimum candidates and reduce the unnecessary calculation on those negligible ones, in addition we can overcome the conventional degeneracy phenomenon in a way and decrease the computational costs. Otherwise, the threshold is a key factor that affecting the results very much. So here we adapt an adaptive threshold choosing method to get the optimal Sobel result. The dissimilarities between the target model and the target candidates are expressed by a metric derived from the Bhattacharyya coefficient. Here, we use both the counter cue to select the particles and the color cur to describe the targets as the mixture target model. The effectiveness of our scheme is demonstrated by real visual tracking experiments. Results from simulations and experiments with real video data show the improved performance of the proposed algorithm when compared with that of the standard particle filter. The superior performance is evident when the target encountering the occlusion in real video where the standard particle filter usually fails.
forecasting with nonlinear time series model: a monte-carlo
African Journals Online (AJOL)
PUBLICATIONS1
erated recursively up to any step greater than one. For nonlinear time series model, point forecast for step one can be done easily like in the linear case but forecast for a step greater than or equal to ..... London. Franses, P. H. (1998). Time series models for business and Economic forecasting, Cam- bridge University press.
Akashi, Haruaki; Sasaki, K.; Yoshinaga, T.
2011-10-01
Recently, plasma-assisted combustion has been focused on for achieving more efficient combustion way of fossil fuels, reducing pollutants and so on. Shinohara et al has reported that the flame length of methane and air premixed burner shortened by irradiating microwave power without increase of gas temperature. This suggests that electrons heated by microwave electric field assist the combustion. They also measured emission from 2nd Positive Band System (2nd PBS) of nitrogen during the irradiation. To clarify this mechanism, electron behavior under microwave power should be examined. To obtain electron transport parameters, electron Monte Carlo simulations in methane and air mixture gas have been done. A simple model has been developed to simulate inside the flame. To make this model simple, some assumptions are made. The electrons diffuse from the combustion plasma region. And the electrons quickly reach their equilibrium state. And it is found that the simulated emission from 2nd PBS agrees with the experimental result. Recently, plasma-assisted combustion has been focused on for achieving more efficient combustion way of fossil fuels, reducing pollutants and so on. Shinohara et al has reported that the flame length of methane and air premixed burner shortened by irradiating microwave power without increase of gas temperature. This suggests that electrons heated by microwave electric field assist the combustion. They also measured emission from 2nd Positive Band System (2nd PBS) of nitrogen during the irradiation. To clarify this mechanism, electron behavior under microwave power should be examined. To obtain electron transport parameters, electron Monte Carlo simulations in methane and air mixture gas have been done. A simple model has been developed to simulate inside the flame. To make this model simple, some assumptions are made. The electrons diffuse from the combustion plasma region. And the electrons quickly reach their equilibrium state. And it is found
A Note on the Use of Mixture Models for Individual Prediction.
Cole, Veronica T; Bauer, Daniel J
Mixture models capture heterogeneity in data by decomposing the population into latent subgroups, each of which is governed by its own subgroup-specific set of parameters. Despite the flexibility and widespread use of these models, most applications have focused solely on making inferences for whole or sub-populations, rather than individual cases. The current article presents a general framework for computing marginal and conditional predicted values for individuals using mixture model results. These predicted values can be used to characterize covariate effects, examine the fit of the model for specific individuals, or forecast future observations from previous ones. Two empirical examples are provided to demonstrate the usefulness of individual predicted values in applications of mixture models. The first example examines the relative timing of initiation of substance use using a multiple event process survival mixture model whereas the second example evaluates changes in depressive symptoms over adolescence using a growth mixture model.
Monte Carlo sensitivity analysis of an Eulerian large-scale air pollution model
International Nuclear Information System (INIS)
Dimov, I.; Georgieva, R.; Ostromsky, Tz.
2012-01-01
Variance-based approaches for global sensitivity analysis have been applied and analyzed to study the sensitivity of air pollutant concentrations according to variations of rates of chemical reactions. The Unified Danish Eulerian Model has been used as a mathematical model simulating a remote transport of air pollutants. Various Monte Carlo algorithms for numerical integration have been applied to compute Sobol's global sensitivity indices. A newly developed Monte Carlo algorithm based on Sobol's quasi-random points MCA-MSS has been applied for numerical integration. It has been compared with some existing approaches, namely Sobol's ΛΠ τ sequences, an adaptive Monte Carlo algorithm, the plain Monte Carlo algorithm, as well as, eFAST and Sobol's sensitivity approaches both implemented in SIMLAB software. The analysis and numerical results show advantages of MCA-MSS for relatively small sensitivity indices in terms of accuracy and efficiency. Practical guidelines on the estimation of Sobol's global sensitivity indices in the presence of computational difficulties have been provided. - Highlights: ► Variance-based global sensitivity analysis is performed for the air pollution model UNI-DEM. ► The main effect of input parameters dominates over higher-order interactions. ► Ozone concentrations are influenced mostly by variability of three chemical reactions rates. ► The newly developed MCA-MSS for multidimensional integration is compared with other approaches. ► More precise approaches like MCA-MSS should be applied when the needed accuracy has not been achieved.
Perturbation analysis for Monte Carlo continuous cross section models
International Nuclear Information System (INIS)
Kennedy, Chris B.; Abdel-Khalik, Hany S.
2011-01-01
Sensitivity analysis, including both its forward and adjoint applications, collectively referred to hereinafter as Perturbation Analysis (PA), is an essential tool to complete Uncertainty Quantification (UQ) and Data Assimilation (DA). PA-assisted UQ and DA have traditionally been carried out for reactor analysis problems using deterministic as opposed to stochastic models for radiation transport. This is because PA requires many model executions to quantify how variations in input data, primarily cross sections, affect variations in model's responses, e.g. detectors readings, flux distribution, multiplication factor, etc. Although stochastic models are often sought for their higher accuracy, their repeated execution is at best computationally expensive and in reality intractable for typical reactor analysis problems involving many input data and output responses. Deterministic methods however achieve computational efficiency needed to carry out the PA analysis by reducing problem dimensionality via various spatial and energy homogenization assumptions. This however introduces modeling error components into the PA results which propagate to the following UQ and DA analyses. The introduced errors are problem specific and therefore are expected to limit the applicability of UQ and DA analyses to reactor systems that satisfy the introduced assumptions. This manuscript introduces a new method to complete PA employing a continuous cross section stochastic model and performed in a computationally efficient manner. If successful, the modeling error components introduced by deterministic methods could be eliminated, thereby allowing for wider applicability of DA and UQ results. Two MCNP models demonstrate the application of the new method - a Critical Pu Sphere (Jezebel), a Pu Fast Metal Array (Russian BR-1). The PA is completed for reaction rate densities, reaction rate ratios, and the multiplication factor. (author)
Background based Gaussian mixture model lesion segmentation in PET
Energy Technology Data Exchange (ETDEWEB)
Soffientini, Chiara Dolores, E-mail: chiaradolores.soffientini@polimi.it; Baselli, Giuseppe [DEIB, Department of Electronics, Information, and Bioengineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, Milan 20133 (Italy); De Bernardi, Elisabetta [Department of Medicine and Surgery, Tecnomed Foundation, University of Milano—Bicocca, Monza 20900 (Italy); Zito, Felicia; Castellani, Massimo [Nuclear Medicine Department, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, via Francesco Sforza 35, Milan 20122 (Italy)
2016-05-15
Purpose: Quantitative {sup 18}F-fluorodeoxyglucose positron emission tomography is limited by the uncertainty in lesion delineation due to poor SNR, low resolution, and partial volume effects, subsequently impacting oncological assessment, treatment planning, and follow-up. The present work develops and validates a segmentation algorithm based on statistical clustering. The introduction of constraints based on background features and contiguity priors is expected to improve robustness vs clinical image characteristics such as lesion dimension, noise, and contrast level. Methods: An eight-class Gaussian mixture model (GMM) clustering algorithm was modified by constraining the mean and variance parameters of four background classes according to the previous analysis of a lesion-free background volume of interest (background modeling). Hence, expectation maximization operated only on the four classes dedicated to lesion detection. To favor the segmentation of connected objects, a further variant was introduced by inserting priors relevant to the classification of neighbors. The algorithm was applied to simulated datasets and acquired phantom data. Feasibility and robustness toward initialization were assessed on a clinical dataset manually contoured by two expert clinicians. Comparisons were performed with respect to a standard eight-class GMM algorithm and to four different state-of-the-art methods in terms of volume error (VE), Dice index, classification error (CE), and Hausdorff distance (HD). Results: The proposed GMM segmentation with background modeling outperformed standard GMM and all the other tested methods. Medians of accuracy indexes were VE <3%, Dice >0.88, CE <0.25, and HD <1.2 in simulations; VE <23%, Dice >0.74, CE <0.43, and HD <1.77 in phantom data. Robustness toward image statistic changes (±15%) was shown by the low index changes: <26% for VE, <17% for Dice, and <15% for CE. Finally, robustness toward the user-dependent volume initialization was
Absorbed dose in fibrotic microenvironment models employing Monte Carlo simulation
International Nuclear Information System (INIS)
Zambrano Ramírez, O.D.; Rojas Calderón, E.L.; Azorín Vega, E.P.; Ferro Flores, G.; Martínez Caballero, E.
2015-01-01
The presence or absence of fibrosis and yet more, the multimeric and multivalent nature of the radiopharmaceutical have recently been reported to have an effect on the radiation absorbed dose in tumor microenvironment models. Fibroblast and myofibroblast cells produce the extracellular matrix by the secretion of proteins which provide structural and biochemical support to cells. The reactive and reparative mechanisms triggered during the inflammatory process causes the production and deposition of extracellular matrix proteins, the abnormal excessive growth of the connective tissue leads to fibrosis. In this work, microenvironment (either not fibrotic or fibrotic) models composed of seven spheres representing cancer cells of 10 μm in diameter each with a 5 μm diameter inner sphere (cell nucleus) were created in two distinct radiation transport codes (PENELOPE and MCNP). The purpose of creating these models was to determine the radiation absorbed dose in the nucleus of cancer cells, based on previously reported radiopharmaceutical retain (by HeLa cells) percentages of the 177 Lu-Tyr 3 -octreotate (monomeric) and 177 Lu-Tyr 3 -octreotate-AuNP (multimeric) radiopharmaceuticals. A comparison in the results between the PENELOPE and MCNP was done. We found a good agreement in the results of the codes. The percent difference between the increase percentages of the absorbed dose in the not fibrotic model with respect to the fibrotic model of the codes PENELOPE and MCNP was found to be under 1% for both radiopharmaceuticals. (authors)
Edla, Shwetha; Kovvali, Narayan; Papandreou-Suppappola, Antonia
2012-01-01
Constructing statistical models of electrocardiogram (ECG) signals, whose parameters can be used for automated disease classification, is of great importance in precluding manual annotation and providing prompt diagnosis of cardiac diseases. ECG signals consist of several segments with different morphologies (namely the P wave, QRS complex and the T wave) in a single heart beat, which can vary across individuals and diseases. Also, existing statistical ECG models exhibit a reliance upon obtaining a priori information from the ECG data by using preprocessing algorithms to initialize the filter parameters, or to define the user-specified model parameters. In this paper, we propose an ECG modeling technique using the sequential Markov chain Monte Carlo (SMCMC) filter that can perform simultaneous model selection, by adaptively choosing from different representations depending upon the nature of the data. Our results demonstrate the ability of the algorithm to track various types of ECG morphologies, including intermittently occurring ECG beats. In addition, we use the estimated model parameters as the feature set to classify between ECG signals with normal sinus rhythm and four different types of arrhythmia.
Automatic categorization of web pages and user clustering with mixtures of hidden Markov models
Ypma, A.; Heskes, T.M.; Zaiane, O.R.; Srivastav, J.
2003-01-01
We propose mixtures of hidden Markov models for modelling clickstreams of web surfers. Hence, the page categorization is learned from the data without the need for a (possibly cumbersome) manual categorization. We provide an EM algorithm for training a mixture of HMMs and show that additional static
de Jong, Martijn G.; Steenkamp, Jan-Benedict E. M.
2010-01-01
We present a class of finite mixture multilevel multidimensional ordinal IRT models for large scale cross-cultural research. Our model is proposed for confirmatory research settings. Our prior for item parameters is a mixture distribution to accommodate situations where different groups of countries have different measurement operations, while…
Genetic Analysis of Somatic Cell Score in Danish Holsteins Using a Liability-Normal Mixture Model
DEFF Research Database (Denmark)
Madsen, P; Shariati, M M; Ødegård, J
2008-01-01
Mixture models are appealing for identifying hidden structures affecting somatic cell score (SCS) data, such as unrecorded cases of subclinical mastitis. Thus, liability-normal mixture (LNM) models were used for genetic analysis of SCS data, with the aim of predicting breeding values for such cas...
Combinatorial bounds on the α-divergence of univariate mixture models
Nielsen, Frank
2017-06-20
We derive lower- and upper-bounds of α-divergence between univariate mixture models with components in the exponential family. Three pairs of bounds are presented in order with increasing quality and increasing computational cost. They are verified empirically through simulated Gaussian mixture models. The presented methodology generalizes to other divergence families relying on Hellinger-type integrals.
Adaptable three-dimensional Monte Carlo modeling of imaged blood vessels in skin
Pfefer, T. Joshua; Barton, Jennifer K.; Chan, Eric K.; Ducros, Mathieu G.; Sorg, Brian S.; Milner, Thomas E.; Nelson, J. Stuart; Welch, Ashley J.
1997-06-01
In order to reach a higher level of accuracy in simulation of port wine stain treatment, we propose to discard the typical layered geometry and cylindrical blood vessel assumptions made in optical models and use imaging techniques to define actual tissue geometry. Two main additions to the typical 3D, weighted photon, variable step size Monte Carlo routine were necessary to achieve this goal. First, optical low coherence reflectometry (OLCR) images of rat skin were used to specify a 3D material array, with each entry assigned a label to represent the type of tissue in that particular voxel. Second, the Monte Carlo algorithm was altered so that when a photon crosses into a new voxel, the remaining path length is recalculated using the new optical properties, as specified by the material array. The model has shown good agreement with data from the literature. Monte Carlo simulations using OLCR images of asymmetrically curved blood vessels show various effects such as shading, scattering-induced peaks at vessel surfaces, and directionality-induced gradients in energy deposition. In conclusion, this augmentation of the Monte Carlo method can accurately simulate light transport for a wide variety of nonhomogeneous tissue geometries.
ODE constrained mixture modelling: a method for unraveling subpopulation structures and dynamics.
Directory of Open Access Journals (Sweden)
Jan Hasenauer
2014-07-01
Full Text Available Functional cell-to-cell variability is ubiquitous in multicellular organisms as well as bacterial populations. Even genetically identical cells of the same cell type can respond differently to identical stimuli. Methods have been developed to analyse heterogeneous populations, e.g., mixture models and stochastic population models. The available methods are, however, either incapable of simultaneously analysing different experimental conditions or are computationally demanding and difficult to apply. Furthermore, they do not account for biological information available in the literature. To overcome disadvantages of existing methods, we combine mixture models and ordinary differential equation (ODE models. The ODE models provide a mechanistic description of the underlying processes while mixture models provide an easy way to capture variability. In a simulation study, we show that the class of ODE constrained mixture models can unravel the subpopulation structure and determine the sources of cell-to-cell variability. In addition, the method provides reliable estimates for kinetic rates and subpopulation characteristics. We use ODE constrained mixture modelling to study NGF-induced Erk1/2 phosphorylation in primary sensory neurones, a process relevant in inflammatory and neuropathic pain. We propose a mechanistic pathway model for this process and reconstructed static and dynamical subpopulation characteristics across experimental conditions. We validate the model predictions experimentally, which verifies the capabilities of ODE constrained mixture models. These results illustrate that ODE constrained mixture models can reveal novel mechanistic insights and possess a high sensitivity.
ODE constrained mixture modelling: a method for unraveling subpopulation structures and dynamics.
Hasenauer, Jan; Hasenauer, Christine; Hucho, Tim; Theis, Fabian J
2014-07-01
Functional cell-to-cell variability is ubiquitous in multicellular organisms as well as bacterial populations. Even genetically identical cells of the same cell type can respond differently to identical stimuli. Methods have been developed to analyse heterogeneous populations, e.g., mixture models and stochastic population models. The available methods are, however, either incapable of simultaneously analysing different experimental conditions or are computationally demanding and difficult to apply. Furthermore, they do not account for biological information available in the literature. To overcome disadvantages of existing methods, we combine mixture models and ordinary differential equation (ODE) models. The ODE models provide a mechanistic description of the underlying processes while mixture models provide an easy way to capture variability. In a simulation study, we show that the class of ODE constrained mixture models can unravel the subpopulation structure and determine the sources of cell-to-cell variability. In addition, the method provides reliable estimates for kinetic rates and subpopulation characteristics. We use ODE constrained mixture modelling to study NGF-induced Erk1/2 phosphorylation in primary sensory neurones, a process relevant in inflammatory and neuropathic pain. We propose a mechanistic pathway model for this process and reconstructed static and dynamical subpopulation characteristics across experimental conditions. We validate the model predictions experimentally, which verifies the capabilities of ODE constrained mixture models. These results illustrate that ODE constrained mixture models can reveal novel mechanistic insights and possess a high sensitivity.
Modelling the IRSN's radio-photo-luminescent dosimeter using the MCPNX Monte Carlo code
International Nuclear Information System (INIS)
Hocine, N.; Donadille, L.; Huet, Ch.; Itie, Ch.
2010-01-01
The authors report the modelling of the new radio-photo-luminescent (RPL) dosimeter of the IRSN using the MCPNX Monte Carlo code. The Hp(10) and Hp(0, 07) dose equivalents are computed for different irradiation configurations involving photonic beams (gamma and X) defined according to the ISO 4037-1 standard. Results are compared to experimental measurements performed on the RPL dosimeter. The agreement is good and the model is thus validated
Modelling of phase equilibria of glycol ethers mixtures using an association model
DEFF Research Database (Denmark)
Garrido, Nuno M.; Folas, Georgios; Kontogeorgis, Georgios
2008-01-01
Vapor-liquid and liquid-liquid equilibria of glycol ethers (surfactant) mixtures with hydrocarbons, polar compounds and water are calculated using an association model, the Cubic-Plus-Association Equation of State. Parameters are estimated for several non-ionic surfactants of the polyoxyethylene ...
Zhu, Xiaoshu
2013-01-01
The current study introduced a general modeling framework, multilevel mixture IRT (MMIRT) which detects and describes characteristics of population heterogeneity, while accommodating the hierarchical data structure. In addition to introducing both continuous and discrete approaches to MMIRT, the main focus of the current study was to distinguish…
Depaoli, Sarah; van de Schoot, Rens; van Loey, Nancy; Sijbrandij, Marit
2015-01-01
BACKGROUND: After traumatic events, such as disaster, war trauma, and injuries including burns (which is the focus here), the risk to develop posttraumatic stress disorder (PTSD) is approximately 10% (Breslau & Davis, 1992). Latent Growth Mixture Modeling can be used to classify individuals into
Monte Carlo based toy model for fission process
International Nuclear Information System (INIS)
Kurniadi, R.; Waris, A.; Viridi, S.
2014-01-01
There are many models and calculation techniques to obtain visible image of fission yield process. In particular, fission yield can be calculated by using two calculations approach, namely macroscopic approach and microscopic approach. This work proposes another calculation approach in which the nucleus is treated as a toy model. Hence, the fission process does not represent real fission process in nature completely. The toy model is formed by Gaussian distribution of random number that randomizes distance like the distance between particle and central point. The scission process is started by smashing compound nucleus central point into two parts that are left central and right central points. These three points have different Gaussian distribution parameters such as mean (μ CN , μ L , μ R ), and standard deviation (σ CN , σ L , σ R ). By overlaying of three distributions, the number of particles (N L , N R ) that are trapped by central points can be obtained. This process is iterated until (N L , N R ) become constant numbers. Smashing process is repeated by changing σ L and σ R , randomly
Monte Carlo Analysis of Reservoir Models Using Seismic Data and Geostatistical Models
Zunino, A.; Mosegaard, K.; Lange, K.; Melnikova, Y.; Hansen, T. M.
2013-12-01
We present a study on the analysis of petroleum reservoir models consistent with seismic data and geostatistical constraints performed on a synthetic reservoir model. Our aim is to invert directly for structure and rock bulk properties of the target reservoir zone. To infer the rock facies, porosity and oil saturation seismology alone is not sufficient but a rock physics model must be taken into account, which links the unknown properties to the elastic parameters. We then combine a rock physics model with a simple convolutional approach for seismic waves to invert the "measured" seismograms. To solve this inverse problem, we employ a Markov chain Monte Carlo (MCMC) method, because it offers the possibility to handle non-linearity, complex and multi-step forward models and provides realistic estimates of uncertainties. However, for large data sets the MCMC method may be impractical because of a very high computational demand. To face this challenge one strategy is to feed the algorithm with realistic models, hence relying on proper prior information. To address this problem, we utilize an algorithm drawn from geostatistics to generate geologically plausible models which represent samples of the prior distribution. The geostatistical algorithm learns the multiple-point statistics from prototype models (in the form of training images), then generates thousands of different models which are accepted or rejected by a Metropolis sampler. To further reduce the computation time we parallelize the software and run it on multi-core machines. The solution of the inverse problem is then represented by a collection of reservoir models in terms of facies, porosity and oil saturation, which constitute samples of the posterior distribution. We are finally able to produce probability maps of the properties we are interested in by performing statistical analysis on the collection of solutions.
Gassmann Modeling of Acoustic Properties of Sand-clay Mixtures
Gurevich, B.; Carcione, J. M.
The feasibility of modeling elastic properties of a fluid-saturated sand-clay mixture rock is analyzed by assuming that the rock is composed of macroscopic regions of sand and clay. The elastic properties of such a composite rock are computed using two alternative schemes.The first scheme, which we call the composite Gassmann (CG) scheme, uses Gassmann equations to compute elastic moduli of the saturated sand and clay from their respective dry moduli. The effective elastic moduli of the fluid-saturated composite rock are then computed by applying one of the mixing laws commonly used to estimate elastic properties of composite materials.In the second scheme which we call the Berryman-Milton scheme, the elastic moduli of the dry composite rock matrix are computed from the moduli of dry sand and clay matrices using the same composite mixing law used in the first scheme. Next, the saturated composite rock moduli are computed using the equations of Brown and Korringa, which, together with the expressions for the coefficients derived by Berryman and Milton, provide an extension of Gassmann equations to rocks with a heterogeneous solid matrix.For both schemes, the moduli of the dry homogeneous sand and clay matrices are assumed to obey the Krief's velocity-porosity relationship. As a mixing law we use the self-consistent coherent potential approximation proposed by Berryman.The calculated dependence of compressional and shear velocities on porosity and clay content for a given set of parameters using the two schemes depends on the distribution of total porosity between the sand and clay regions. If the distribution of total porosity between sand and clay is relatively uniform, the predictions of the two schemes in the porosity range up to 0.3 are very similar to each other. For higher porosities and medium-to-large clay content the elastic moduli predicted by CG scheme are significantly higher than those predicted by the BM scheme.This difference is explained by the fact
Monte Carlo simulations of a model for opinion formation
Bordogna, C. M.; Albano, E. V.
2007-04-01
A model for opinion formation based on the Theory of Social Impact is presented and studied by means of numerical simulations. Individuals with two states of opinion are impacted due to social interactions with: i) members of the society, ii) a strong leader with a well-defined opinion and iii) the mass media that could either support or compete with the leader. Due to that competition, the average opinion of the social group exhibits phase-transition like behaviour between different states of opinion.
Strain in the mesoscale kinetic Monte Carlo model for sintering
DEFF Research Database (Denmark)
Bjørk, Rasmus; Frandsen, Henrik Lund; Tikare, V.
2014-01-01
anisotropic strains for homogeneous powder compacts with aspect ratios different from unity. It is shown that the line direction biases shrinkage strains in proportion the compact dimension aspect ratios. A new algorithm that corrects this bias in strains is proposed; the direction for collapsing the column...... densification by vacancy annihilation removes an isolated pore site at a grain boundary and collapses a column of sites extending from the vacancy to the surface of sintering compact, through the center of mass of the nearest grain. Using this algorithm, the existing published kMC models are shown to produce...
Self-organising mixture autoregressive model for non-stationary time series modelling.
Ni, He; Yin, Hujun
2008-12-01
Modelling non-stationary time series has been a difficult task for both parametric and nonparametric methods. One promising solution is to combine the flexibility of nonparametric models with the simplicity of parametric models. In this paper, the self-organising mixture autoregressive (SOMAR) network is adopted as a such mixture model. It breaks time series into underlying segments and at the same time fits local linear regressive models to the clusters of segments. In such a way, a global non-stationary time series is represented by a dynamic set of local linear regressive models. Neural gas is used for a more flexible structure of the mixture model. Furthermore, a new similarity measure has been introduced in the self-organising network to better quantify the similarity of time series segments. The network can be used naturally in modelling and forecasting non-stationary time series. Experiments on artificial, benchmark time series (e.g. Mackey-Glass) and real-world data (e.g. numbers of sunspots and Forex rates) are presented and the results show that the proposed SOMAR network is effective and superior to other similar approaches.
International Nuclear Information System (INIS)
Allam, Kh. A.
2017-01-01
In this work, a new methodology is developed based on Monte Carlo simulation for tunnels and mines external dose calculation. Tunnels external dose evaluation model of a cylindrical shape of finite thickness with an entrance and with or without exit. A photon transportation model was applied for exposure dose calculations. A new software based on Monte Carlo solution was designed and programmed using Delphi programming language. The variation of external dose due to radioactive nuclei in a mine tunnel and the corresponding experimental data lies in the range 7.3 19.9%. The variation of specific external dose rate with position in, tunnel building material density and composition were studied. The given new model has more flexible for real external dose in any cylindrical tunnel structure calculations. (authors)
Setup of HDRK-Man voxel model in Geant4 Monte Carlo code
Energy Technology Data Exchange (ETDEWEB)
Jeong, Jong Hwi; Cho, Sung Koo; Kim, Chan Hyeong [Hanyang Univ., Seoul (Korea, Republic of); Choi, Sang Hyoun [Inha Univ., Incheon (Korea, Republic of); Cho, Kun Woo [Korea Institute of Nuclear Safety, Daejeon (Korea, Republic of)
2008-10-15
Many different voxel models, developed using tomographic images of human body, are used in various fields including both ionizing and non-ionizing radiation fields. Recently a high-quality voxel model/ named HDRK-Man, was constructed at Hanyang University and used to calculate the dose conversion coefficients (DCC) values for external photon and neutron beams using the MCNPX Monte Carlo code. The objective of the present study is to set up the HDRK-Man model in Geant4 in order to use it in more advanced calculations such as 4-D Monte Carlo simulations and space dosimetry studies involving very high energy particles. To that end, the HDRK-Man was ported to Geant4 and used to calculate the DCC values for external photon beams. The calculated values were then compared with the results of the MCNPX code. In addition, a computational Linux cluster was built to improve the computing speed in Geant4.
Randomly dispersed particle fuel model in the PSG Monte Carlo neutron transport code
International Nuclear Information System (INIS)
Leppaenen, J.
2007-01-01
High-temperature gas-cooled reactor fuels are composed of thousands of microscopic fuel particles, randomly dispersed in a graphite matrix. The modelling of such geometry is complicated, especially using continuous-energy Monte Carlo codes, which are unable to apply any deterministic corrections in the calculation. This paper presents the geometry routine developed for modelling randomly dispersed particle fuels using the PSG Monte Carlo reactor physics code. The model is based on the delta-tracking method, and it takes into account the spatial self-shielding effects and the random dispersion of the fuel particles. The calculation routine is validated by comparing the results to reference MCNP4C calculations using uranium and plutonium based fuels. (authors)
Diffusion models for mixtures using a stiff dissipative hyperbolic formalism
Boudin , Laurent; Grec , Bérénice; Pavan , Vincent
2018-01-01
In this article, we are interested in a system of uid equations for mixtures with a sti relaxation term of Maxwell-Stefan diusion type. We use the formalism developed by Chen, Levermore, Liu in [4] to obtain a limit system of Fick type where the species velocities tend to align to a bulk velocity when the relaxation parameter remains small.
Flat-histogram methods in quantum Monte Carlo simulations: Application to the t-J model
International Nuclear Information System (INIS)
Diamantis, Nikolaos G.; Manousakis, Efstratios
2016-01-01
We discuss that flat-histogram techniques can be appropriately applied in the sampling of quantum Monte Carlo simulation in order to improve the statistical quality of the results at long imaginary time or low excitation energy. Typical imaginary-time correlation functions calculated in quantum Monte Carlo are subject to exponentially growing errors as the range of imaginary time grows and this smears the information on the low energy excitations. We show that we can extract the low energy physics by modifying the Monte Carlo sampling technique to one in which configurations which contribute to making the histogram of certain quantities flat are promoted. We apply the diagrammatic Monte Carlo (diag-MC) method to the motion of a single hole in the t-J model and we show that the implementation of flat-histogram techniques allows us to calculate the Green's function in a wide range of imaginary-time. In addition, we show that applying the flat-histogram technique alleviates the “sign”-problem associated with the simulation of the single-hole Green's function at long imaginary time. (paper)
DEFF Research Database (Denmark)
Nathan, R.P.; Thomas, P.J.; Jain, M.
2003-01-01
and identify the likely size of these effects on D-e distributions. The study employs the MCNP 4C Monte Carlo electron/photon transport model, supported by an experimental validation of the code in several case studies. We find good agreement between the experimental measurements and the Monte Carlo...
A vectorized Monte Carlo code for modeling photon transport in SPECT
International Nuclear Information System (INIS)
Smith, M.F.; Floyd, C.E. Jr.; Jaszczak, R.J.
1993-01-01
A vectorized Monte Carlo computer code has been developed for modeling photon transport in single photon emission computed tomography (SPECT). The code models photon transport in a uniform attenuating region and photon detection by a gamma camera. It is adapted from a history-based Monte Carlo code in which photon history data are stored in scalar variables and photon histories are computed sequentially. The vectorized code is written in FORTRAN77 and uses an event-based algorithm in which photon history data are stored in arrays and photon history computations are performed within DO loops. The indices of the DO loops range over the number of photon histories, and these loops may take advantage of the vector processing unit of our Stellar GS1000 computer for pipelined computations. Without the use of the vector processor the event-based code is faster than the history-based code because of numerical optimization performed during conversion to the event-based algorithm. When only the detection of unscattered photons is modeled, the event-based code executes 5.1 times faster with the use of the vector processor than without; when the detection of scattered and unscattered photons is modeled the speed increase is a factor of 2.9. Vectorization is a valuable way to increase the performance of Monte Carlo code for modeling photon transport in SPECT
International Nuclear Information System (INIS)
Taillade, Frédéric; Dumont, Eric; Belin, Etienne
2008-01-01
We propose an analytical model for backscattered luminance in fog and derive an expression for the visibility signal-to-noise ratio as a function of meteorological visibility distance. The model uses single scattering processes. It is based on the Mie theory and the geometry of the optical device (emitter and receiver). In particular, we present an overlap function and take the phase function of fog into account. The results of the backscattered luminance obtained with our analytical model are compared to simulations made using the Monte Carlo method based on multiple scattering processes. An excellent agreement is found in that the discrepancy between the results is smaller than the Monte Carlo standard uncertainties. If we take no account of the geometry of the optical device, the results of the model-estimated backscattered luminance differ from the simulations by a factor 20. We also conclude that the signal-to-noise ratio computed with the Monte Carlo method and our analytical model is in good agreement with experimental results since the mean difference between the calculations and experimental measurements is smaller than the experimental uncertainty
Monte Carlo modeling of ion beam induced secondary electrons
Energy Technology Data Exchange (ETDEWEB)
Huh, U., E-mail: uhuh@vols.utk.edu [Biochemistry & Cellular & Molecular Biology, University of Tennessee, Knoxville, TN 37996-0840 (United States); Cho, W. [Electrical and Computer Engineering, University of Tennessee, Knoxville, TN 37996-2100 (United States); Joy, D.C. [Biochemistry & Cellular & Molecular Biology, University of Tennessee, Knoxville, TN 37996-0840 (United States); Center for Nanophase Materials Science, Oak Ridge National Laboratory, Oak Ridge, TN 37831 (United States)
2016-09-15
Ion induced secondary electrons (iSE) can produce high-resolution images ranging from a few eV to 100 keV over a wide range of materials. The interpretation of such images requires knowledge of the secondary electron yields (iSE δ) for each of the elements and materials present and as a function of the incident beam energy. Experimental data for helium ions are currently limited to 40 elements and six compounds while other ions are not well represented. To overcome this limitation, we propose a simple procedure based on the comprehensive work of Berger et al. Here we show that between the energy range of 10–100 keV the Berger et al. data for elements and compounds can be accurately represented by a single universal curve. The agreement between the limited experimental data that is available and the predictive model is good, and has been found to provide reliable yield data for a wide range of elements and compounds. - Highlights: • The Universal ASTAR Yield Curve was derived from data recently published by NIST. • IONiSE incorporated with the Curve will predict iSE yield for elements and compounds. • This approach can also handle other ion beams by changing basic scattering profile.
Sound speed models for a noncondensible gas-steam-water mixture
International Nuclear Information System (INIS)
Ransom, V.H.; Trapp, J.A.
1984-01-01
An analytical expression is derived for the homogeneous equilibrium speed of sound in a mixture of noncondensible gas, steam, and water. The expression is based on the Gibbs free energy interphase equilibrium condition for a Gibbs-Dalton mixture in contact with a pure liquid phase. Several simplified models are discussed including the homogeneous frozen model. These idealized models can be used as a reference for data comparison and also serve as a basis for empirically corrected nonhomogeneous and nonequilibrium models
A sequential Monte Carlo model of the combined GB gas and electricity network
International Nuclear Information System (INIS)
Chaudry, Modassar; Wu, Jianzhong; Jenkins, Nick
2013-01-01
A Monte Carlo model of the combined GB gas and electricity network was developed to determine the reliability of the energy infrastructure. The model integrates the gas and electricity network into a single sequential Monte Carlo simulation. The model minimises the combined costs of the gas and electricity network, these include gas supplies, gas storage operation and electricity generation. The Monte Carlo model calculates reliability indices such as loss of load probability and expected energy unserved for the combined gas and electricity network. The intention of this tool is to facilitate reliability analysis of integrated energy systems. Applications of this tool are demonstrated through a case study that quantifies the impact on the reliability of the GB gas and electricity network given uncertainties such as wind variability, gas supply availability and outages to energy infrastructure assets. Analysis is performed over a typical midwinter week on a hypothesised GB gas and electricity network in 2020 that meets European renewable energy targets. The efficacy of doubling GB gas storage capacity on the reliability of the energy system is assessed. The results highlight the value of greater gas storage facilities in enhancing the reliability of the GB energy system given various energy uncertainties. -- Highlights: •A Monte Carlo model of the combined GB gas and electricity network was developed. •Reliability indices are calculated for the combined GB gas and electricity system. •The efficacy of doubling GB gas storage capacity on reliability of the energy system is assessed. •Integrated reliability indices could be used to assess the impact of investment in energy assets
International Nuclear Information System (INIS)
Peterson, S W; Polf, J; Archambault, L; Beddar, S; Bues, M; Ciangaru, G; Smith, A
2009-01-01
The purpose of this study is to validate the accuracy of a Monte Carlo calculation model of a proton magnetic beam scanning delivery nozzle developed using the Geant4 toolkit. The Monte Carlo model was used to produce depth dose and lateral profiles, which were compared to data measured in the clinical scanning treatment nozzle at several energies. Comparisons were also made between measured and simulated off-axis profiles to test the accuracy of the model's magnetic steering. Comparison of the 80% distal dose fall-off values for the measured and simulated depth dose profiles agreed to within 1 mm for the beam energies evaluated. Agreement of the full width at half maximum values for the measured and simulated lateral fluence profiles was within 1.3 mm for all energies. The position of measured and simulated spot positions for the magnetically steered beams agreed to within 0.7 mm of each other. Based on these results, we found that the Geant4 Monte Carlo model of the beam scanning nozzle has the ability to accurately predict depth dose profiles, lateral profiles perpendicular to the beam axis and magnetic steering of a proton beam during beam scanning proton therapy.
Model-based experimental design for assessing effects of mixtures of chemicals
Energy Technology Data Exchange (ETDEWEB)
Baas, Jan, E-mail: jan.baas@falw.vu.n [Vrije Universiteit of Amsterdam, Dept of Theoretical Biology, De Boelelaan 1085, 1081 HV Amsterdam (Netherlands); Stefanowicz, Anna M., E-mail: anna.stefanowicz@uj.edu.p [Institute of Environmental Sciences, Jagiellonian University, Gronostajowa 7, 30-387 Krakow (Poland); Klimek, Beata, E-mail: beata.klimek@uj.edu.p [Institute of Environmental Sciences, Jagiellonian University, Gronostajowa 7, 30-387 Krakow (Poland); Laskowski, Ryszard, E-mail: ryszard.laskowski@uj.edu.p [Institute of Environmental Sciences, Jagiellonian University, Gronostajowa 7, 30-387 Krakow (Poland); Kooijman, Sebastiaan A.L.M., E-mail: bas@bio.vu.n [Vrije Universiteit of Amsterdam, Dept of Theoretical Biology, De Boelelaan 1085, 1081 HV Amsterdam (Netherlands)
2010-01-15
We exposed flour beetles (Tribolium castaneum) to a mixture of four poly aromatic hydrocarbons (PAHs). The experimental setup was chosen such that the emphasis was on assessing partial effects. We interpreted the effects of the mixture by a process-based model, with a threshold concentration for effects on survival. The behavior of the threshold concentration was one of the key features of this research. We showed that the threshold concentration is shared by toxicants with the same mode of action, which gives a mechanistic explanation for the observation that toxic effects in mixtures may occur in concentration ranges where the individual components do not show effects. Our approach gives reliable predictions of partial effects on survival and allows for a reduction of experimental effort in assessing effects of mixtures, extrapolations to other mixtures, other points in time, or in a wider perspective to other organisms. - We show a mechanistic approach to assess effects of mixtures in low concentrations.
Model-based experimental design for assessing effects of mixtures of chemicals
International Nuclear Information System (INIS)
Baas, Jan; Stefanowicz, Anna M.; Klimek, Beata; Laskowski, Ryszard; Kooijman, Sebastiaan A.L.M.
2010-01-01
We exposed flour beetles (Tribolium castaneum) to a mixture of four poly aromatic hydrocarbons (PAHs). The experimental setup was chosen such that the emphasis was on assessing partial effects. We interpreted the effects of the mixture by a process-based model, with a threshold concentration for effects on survival. The behavior of the threshold concentration was one of the key features of this research. We showed that the threshold concentration is shared by toxicants with the same mode of action, which gives a mechanistic explanation for the observation that toxic effects in mixtures may occur in concentration ranges where the individual components do not show effects. Our approach gives reliable predictions of partial effects on survival and allows for a reduction of experimental effort in assessing effects of mixtures, extrapolations to other mixtures, other points in time, or in a wider perspective to other organisms. - We show a mechanistic approach to assess effects of mixtures in low concentrations.
On Two Mixture-Based Clustering Approaches Used in Modeling an Insurance Portfolio
Directory of Open Access Journals (Sweden)
Tatjana Miljkovic
2018-05-01
Full Text Available We review two complementary mixture-based clustering approaches for modeling unobserved heterogeneity in an insurance portfolio: the generalized linear mixed cluster-weighted model (CWM and mixture-based clustering for an ordered stereotype model (OSM. The latter is for modeling of ordinal variables, and the former is for modeling losses as a function of mixed-type of covariates. The article extends the idea of mixture modeling to a multivariate classification for the purpose of testing unobserved heterogeneity in an insurance portfolio. The application of both methods is illustrated on a well-known French automobile portfolio, in which the model fitting is performed using the expectation-maximization (EM algorithm. Our findings show that these mixture-based clustering methods can be used to further test unobserved heterogeneity in an insurance portfolio and as such may be considered in insurance pricing, underwriting, and risk management.
Monte Carlo evaluation of path integral for the nuclear shell model
International Nuclear Information System (INIS)
Lang, G.H.
1993-01-01
The authors present a path-integral formulation of the nuclear shell model using auxillary fields; the path-integral is evaluated by Monte Carlo methods. The method scales favorably with valence-nucleon number and shell-model basis: full-basis calculations are demonstrated up to the rare-earth region, which cannot be treated by other methods. Observables are calculated for the ground state and in a thermal ensemble. Dynamical correlations are obtained, from which strength functions are extracted through the Maximum Entropy method. Examples in the s-d shell, where exact diagonalization can be carried out, compared well with exact results. The open-quotes sign problemclose quotes generic to quantum Monte Carlo calculations is found to be absent in the attractive pairing-plus-multipole interactions. The formulation is general for interacting fermion systems and is well suited for parallel computation. The authors have implemented it on the Intel Touchstone Delta System, achieving better than 99% parallelization
MONTE CARLO ANALYSES OF THE YALINA THERMAL FACILITY WITH SERPENT STEREOLITHOGRAPHY GEOMETRY MODEL
Energy Technology Data Exchange (ETDEWEB)
Talamo, A.; Gohar, Y.
2015-01-01
This paper analyzes the YALINA Thermal subcritical assembly of Belarus using two different Monte Carlo transport programs, SERPENT and MCNP. The MCNP model is based on combinatorial geometry and universes hierarchy, while the SERPENT model is based on Stereolithography geometry. The latter consists of unstructured triangulated surfaces defined by the normal and vertices. This geometry format is used by 3D printers and it has been created by: the CUBIT software, MATLAB scripts, and C coding. All the Monte Carlo simulations have been performed using the ENDF/B-VII.0 nuclear data library. Both MCNP and SERPENT share the same geometry specifications, which describe the facility details without using any material homogenization. Three different configurations have been studied with different number of fuel rods. The three fuel configurations use 216, 245, or 280 fuel rods, respectively. The numerical simulations show that the agreement between SERPENT and MCNP results is within few tens of pcms.
Use of Monte Carlo modeling approach for evaluating risk and environmental compliance
International Nuclear Information System (INIS)
Higley, K.A.; Strenge, D.L.
1988-09-01
Evaluating compliance with environmental regulations, specifically those regulations that pertain to human exposure, can be a difficult task. Historically, maximum individual or worst-case exposures have been calculated as a basis for evaluating risk or compliance with such regulations. However, these calculations may significantly overestimate exposure and may not provide a clear understanding of the uncertainty in the analysis. The use of Monte Carlo modeling techniques can provide a better understanding of the potential range of exposures and the likelihood of high (worst-case) exposures. This paper compares the results of standard exposure estimation techniques with the Monte Carlo modeling approach. The authors discuss the potential application of this approach for demonstrating regulatory compliance, along with the strengths and weaknesses of the approach. Suggestions on implementing this method as a routine tool in exposure and risk analyses are also presented. 16 refs., 5 tabs
Implementation of a Monte Carlo method to model photon conversion for solar cells
International Nuclear Information System (INIS)
Canizo, C. del; Tobias, I.; Perez-Bedmar, J.; Pan, A.C.; Luque, A.
2008-01-01
A physical model describing different photon conversion mechanisms is presented in the context of photovoltaic applications. To solve the resulting system of equations, a Monte Carlo ray-tracing model is implemented, which takes into account the coupling of the photon transport phenomena to the non-linear rate equations describing luminescence. It also separates the generation of rays from the two very different sources of photons involved (the sun and the luminescence centers). The Monte Carlo simulator presented in this paper is proposed as a tool to help in the evaluation of candidate materials for up- and down-conversion. Some application examples are presented, exploring the range of values that the most relevant parameters describing the converter should have in order to give significant gain in photocurrent
Testing Lorentz Invariance Emergence in the Ising Model using Monte Carlo simulations
Dias Astros, Maria Isabel
2017-01-01
In the context of the Lorentz invariance as an emergent phenomenon at low energy scales to study quantum gravity a system composed by two 3D interacting Ising models (one with an anisotropy in one direction) was proposed. Two Monte Carlo simulations were run: one for the 2D Ising model and one for the target model. In both cases the observables (energy, magnetization, heat capacity and magnetic susceptibility) were computed for different lattice sizes and a Binder cumulant introduced in order to estimate the critical temperature of the systems. Moreover, the correlation function was calculated for the 2D Ising model.
Monte Carlo study of radiation-induced demagnetization using the two-dimensional Ising model
International Nuclear Information System (INIS)
Samin, Adib; Cao, Lei
2015-01-01
A simple radiation-damage model based on the Ising model for magnets is proposed to study the effects of radiation on the magnetism of permanent magnets. The model is studied in two dimensions using a Monte Carlo simulation, and it accounts for the radiation through the introduction of a localized heat pulse. The model exhibits qualitative agreement with experimental results, and it clearly elucidates the role that the coercivity and the radiation particle’s energy play in the process. A more quantitative agreement with experiment will entail accounting for the long-range dipole–dipole interactions and the crystalline anisotropy.
Monte Carlo study of radiation-induced demagnetization using the two-dimensional Ising model
Energy Technology Data Exchange (ETDEWEB)
Samin, Adib; Cao, Lei
2015-10-01
A simple radiation-damage model based on the Ising model for magnets is proposed to study the effects of radiation on the magnetism of permanent magnets. The model is studied in two dimensions using a Monte Carlo simulation, and it accounts for the radiation through the introduction of a localized heat pulse. The model exhibits qualitative agreement with experimental results, and it clearly elucidates the role that the coercivity and the radiation particle’s energy play in the process. A more quantitative agreement with experiment will entail accounting for the long-range dipole–dipole interactions and the crystalline anisotropy.
Particle Markov Chain Monte Carlo Techniques of Unobserved Component Time Series Models Using Ox
DEFF Research Database (Denmark)
Nonejad, Nima
This paper details Particle Markov chain Monte Carlo techniques for analysis of unobserved component time series models using several economic data sets. PMCMC combines the particle filter with the Metropolis-Hastings algorithm. Overall PMCMC provides a very compelling, computationally fast...... and efficient framework for estimation. These advantages are used to for instance estimate stochastic volatility models with leverage effect or with Student-t distributed errors. We also model changing time series characteristics of the US inflation rate by considering a heteroskedastic ARFIMA model where...
Monte Carlo modeling of neutron imaging at the SINQ spallation source
International Nuclear Information System (INIS)
Lebenhaft, J.R.; Lehmann, E.H.; Pitcher, E.J.; McKinney, G.W.
2003-01-01
Modeling of the Swiss Spallation Neutron Source (SINQ) has been used to demonstrate the neutron radiography capability of the newly released MPI-version of the MCNPX Monte Carlo code. A detailed MCNPX model was developed of SINQ and its associated neutron transmission radiography (NEUTRA) facility. Preliminary validation of the model was performed by comparing the calculated and measured neutron fluxes in the NEUTRA beam line, and a simulated radiography image was generated for a sample consisting of steel tubes containing different materials. This paper describes the SINQ facility, provides details of the MCNPX model, and presents preliminary results of the neutron imaging. (authors)
State-to-state models of vibrational relaxation in Direct Simulation Monte Carlo (DSMC)
Oblapenko, G. P.; Kashkovsky, A. V.; Bondar, Ye A.
2017-02-01
In the present work, the application of state-to-state models of vibrational energy exchanges to the Direct Simulation Monte Carlo (DSMC) is considered. A state-to-state model for VT transitions of vibrational energy in nitrogen and oxygen, based on the application of the inverse Laplace transform to results of quasiclassical trajectory calculations (QCT) of vibrational energy transitions, along with the Forced Harmonic Oscillator (FHO) state-to-state model is implemented in DSMC code and applied to flows around blunt bodies. Comparisons are made with the widely used Larsen-Borgnakke model and the in uence of multi-quantum VT transitions is assessed.
Su, Peiran; Eri, Qitai; Wang, Qiang
2014-04-10
Optical roughness was introduced into the bidirectional reflectance distribution function (BRDF) model to simulate the reflectance characteristics of thermal radiation. The optical roughness BRDF model stemmed from the influence of surface roughness and wavelength on the ray reflectance calculation. This model was adopted to simulate real metal emissivity. The reverse Monte Carlo method was used to display the distribution of reflectance rays. The numerical simulations showed that the optical roughness BRDF model can calculate the wavelength effect on emissivity and simulate the real metal emissivity variance with incidence angles.
Model-based experimental design for assessing effects of mixtures of chemicals
Baas, J.; Stefanowicz, A.M.; Klimek, B.; Laskowski, R.; Kooijman, S.A.L.M.
2010-01-01
We exposed flour beetles (Tribolium castaneum) to a mixture of four poly aromatic hydrocarbons (PAHs). The experimental setup was chosen such that the emphasis was on assessing partial effects. We interpreted the effects of the mixture by a process-based model, with a threshold concentration for
A general mixture model for mapping quantitative trait loci by using molecular markers
Jansen, R.C.
1992-01-01
In a segregating population a quantitative trait may be considered to follow a mixture of (normal) distributions, the mixing proportions being based on Mendelian segregation rules. A general and flexible mixture model is proposed for mapping quantitative trait loci (QTLs) by using molecular markers.
Topological excitations and Monte-Carlo simulation of the Abelian-Higgs model
International Nuclear Information System (INIS)
Ranft, J.
1981-01-01
The phase structure and topological excitations, in particular the magnetic monopole current density, are investigated in a Monte-Carlo simulation of the lattice version of the four-dimensional Abelian-Higgs model. The monopole current density is found to be large in the confinement phase and rapidly decreasing in the Coulomb and Higgs phases. This result supports the view that confinement is neglected with the condensation of monopole-antimonopole pairs
Computer simulation of stochastic processes through model-sampling (Monte Carlo) techniques.
Sheppard, C W.
1969-03-01
A simple Monte Carlo simulation program is outlined which can be used for the investigation of random-walk problems, for example in diffusion, or the movement of tracers in the blood circulation. The results given by the simulation are compared with those predicted by well-established theory, and it is shown how the model can be expanded to deal with drift, and with reflexion from or adsorption at a boundary.
Extrapolation method in the Monte Carlo Shell Model and its applications
International Nuclear Information System (INIS)
Shimizu, Noritaka; Abe, Takashi; Utsuno, Yutaka; Mizusaki, Takahiro; Otsuka, Takaharu; Honma, Michio
2011-01-01
We demonstrate how the energy-variance extrapolation method works using the sequence of the approximated wave functions obtained by the Monte Carlo Shell Model (MCSM), taking 56 Ni with pf-shell as an example. The extrapolation method is shown to work well even in the case that the MCSM shows slow convergence, such as 72 Ge with f5pg9-shell. The structure of 72 Se is also studied including the discussion of the shape-coexistence phenomenon.
International Nuclear Information System (INIS)
Pérez-Andújar, Angélica; Zhang, Rui; Newhauser, Wayne
2013-01-01
Purpose: Stray neutron radiation is of concern after radiation therapy, especially in children, because of the high risk it might carry for secondary cancers. Several previous studies predicted the stray neutron exposure from proton therapy, mostly using Monte Carlo simulations. Promising attempts to develop analytical models have also been reported, but these were limited to only a few proton beam energies. The purpose of this study was to develop an analytical model to predict leakage neutron equivalent dose from passively scattered proton beams in the 100-250-MeV interval.Methods: To develop and validate the analytical model, the authors used values of equivalent dose per therapeutic absorbed dose (H/D) predicted with Monte Carlo simulations. The authors also characterized the behavior of the mean neutron radiation-weighting factor, w R , as a function of depth in a water phantom and distance from the beam central axis.Results: The simulated and analytical predictions agreed well. On average, the percentage difference between the analytical model and the Monte Carlo simulations was 10% for the energies and positions studied. The authors found that w R was highest at the shallowest depth and decreased with depth until around 10 cm, where it started to increase slowly with depth. This was consistent among all energies.Conclusion: Simple analytical methods are promising alternatives to complex and slow Monte Carlo simulations to predict H/D values. The authors' results also provide improved understanding of the behavior of w R which strongly depends on depth, but is nearly independent of lateral distance from the beam central axis
Direct Monte Carlo simulation of nanoscale mixed gas bearings
Directory of Open Access Journals (Sweden)
Kyaw Sett Myo
2015-06-01
Full Text Available The conception of sealed hard drives with helium gas mixture has been recently suggested over the current hard drives for achieving higher reliability and less position error. Therefore, it is important to understand the effects of different helium gas mixtures on the slider bearing characteristics in the head–disk interface. In this article, the helium/air and helium/argon gas mixtures are applied as the working fluids and their effects on the bearing characteristics are studied using the direct simulation Monte Carlo method. Based on direct simulation Monte Carlo simulations, the physical properties of these gas mixtures such as mean free path and dynamic viscosity are achieved and compared with those obtained from theoretical models. It is observed that both results are comparable. Using these gas mixture properties, the bearing pressure distributions are calculated under different fractions of helium with conventional molecular gas lubrication models. The outcomes reveal that the molecular gas lubrication results could have relatively good agreement with those of direct simulation Monte Carlo simulations, especially for pure air, helium, or argon gas cases. For gas mixtures, the bearing pressures predicted by molecular gas lubrication model are slightly larger than those from direct simulation Monte Carlo simulation.
Systematic vacuum study of the ITER model cryopump by test particle Monte Carlo simulation
Energy Technology Data Exchange (ETDEWEB)
Luo, Xueli; Haas, Horst; Day, Christian [Institute for Technical Physics, Karlsruhe Institute of Technology, P.O. Box 3640, 76021 Karlsruhe (Germany)
2011-07-01
The primary pumping systems on the ITER torus are based on eight tailor-made cryogenic pumps because not any standard commercial vacuum pump can meet the ITER working criteria. This kind of cryopump can provide high pumping speed, especially for light gases, by the cryosorption on activated charcoal at 4.5 K. In this paper we will present the systematic Monte Carlo simulation results of the model pump in a reduced scale by ProVac3D, a new Test Particle Monte Carlo simulation program developed by KIT. The simulation model has included the most important mechanical structures such as sixteen cryogenic panels working at 4.5 K, the 80 K radiation shield envelope with baffles, the pump housing, inlet valve and the TIMO (Test facility for the ITER Model Pump) test facility. Three typical gas species, i.e., deuterium, protium and helium are simulated. The pumping characteristics have been obtained. The result is in good agreement with the experiment data up to the gas throughput of 1000 sccm, which marks the limit for free molecular flow. This means that ProVac3D is a useful tool in the design of the prototype cryopump of ITER. Meanwhile, the capture factors at different critical positions are calculated. They can be used as the important input parameters for a follow-up Direct Simulation Monte Carlo (DSMC) simulation for higher gas throughput.
Energy Technology Data Exchange (ETDEWEB)
Murata, Isao [Osaka Univ., Suita (Japan); Mori, Takamasa; Nakagawa, Masayuki; Itakura, Hirofumi
1996-03-01
The method to calculate neutronics parameters of a core composed of randomly distributed spherical fuels has been developed based on a statistical geometry model with a continuous energy Monte Carlo method. This method was implemented in a general purpose Monte Carlo code MCNP, and a new code MCNP-CFP had been developed. This paper describes the model and method how to use it and the validation results. In the Monte Carlo calculation, the location of a spherical fuel is sampled probabilistically along the particle flight path from the spatial probability distribution of spherical fuels, called nearest neighbor distribution (NND). This sampling method was validated through the following two comparisons: (1) Calculations of inventory of coated fuel particles (CFPs) in a fuel compact by both track length estimator and direct evaluation method, and (2) Criticality calculations for ordered packed geometries. This method was also confined by applying to an analysis of the critical assembly experiment at VHTRC. The method established in the present study is quite unique so as to a probabilistic model of the geometry with a great number of spherical fuels distributed randomly. Realizing the speed-up by vector or parallel computations in future, it is expected to be widely used in calculation of a nuclear reactor core, especially HTGR cores. (author).
Svensson, Andreas; Schön, Thomas B.; Lindsten, Fredrik
2018-05-01
Probabilistic (or Bayesian) modeling and learning offers interesting possibilities for systematic representation of uncertainty using probability theory. However, probabilistic learning often leads to computationally challenging problems. Some problems of this type that were previously intractable can now be solved on standard personal computers thanks to recent advances in Monte Carlo methods. In particular, for learning of unknown parameters in nonlinear state-space models, methods based on the particle filter (a Monte Carlo method) have proven very useful. A notoriously challenging problem, however, still occurs when the observations in the state-space model are highly informative, i.e. when there is very little or no measurement noise present, relative to the amount of process noise. The particle filter will then struggle in estimating one of the basic components for probabilistic learning, namely the likelihood p (data | parameters). To this end we suggest an algorithm which initially assumes that there is substantial amount of artificial measurement noise present. The variance of this noise is sequentially decreased in an adaptive fashion such that we, in the end, recover the original problem or possibly a very close approximation of it. The main component in our algorithm is a sequential Monte Carlo (SMC) sampler, which gives our proposed method a clear resemblance to the SMC2 method. Another natural link is also made to the ideas underlying the approximate Bayesian computation (ABC). We illustrate it with numerical examples, and in particular show promising results for a challenging Wiener-Hammerstein benchmark problem.
Metal Mixture Modeling Evaluation project: 2. Comparison of four modeling approaches
Farley, Kevin J.; Meyer, Joe; Balistrieri, Laurie S.; DeSchamphelaere, Karl; Iwasaki, Yuichi; Janssen, Colin; Kamo, Masashi; Lofts, Steve; Mebane, Christopher A.; Naito, Wataru; Ryan, Adam C.; Santore, Robert C.; Tipping, Edward
2015-01-01
As part of the Metal Mixture Modeling Evaluation (MMME) project, models were developed by the National Institute of Advanced Industrial Science and Technology (Japan), the U.S. Geological Survey (USA), HDR⎪HydroQual, Inc. (USA), and the Centre for Ecology and Hydrology (UK) to address the effects of metal mixtures on biological responses of aquatic organisms. A comparison of the 4 models, as they were presented at the MMME Workshop in Brussels, Belgium (May 2012), is provided herein. Overall, the models were found to be similar in structure (free ion activities computed by WHAM; specific or non-specific binding of metals/cations in or on the organism; specification of metal potency factors and/or toxicity response functions to relate metal accumulation to biological response). Major differences in modeling approaches are attributed to various modeling assumptions (e.g., single versus multiple types of binding site on the organism) and specific calibration strategies that affected the selection of model parameters. The models provided a reasonable description of additive (or nearly additive) toxicity for a number of individual toxicity test results. Less-than-additive toxicity was more difficult to describe with the available models. Because of limitations in the available datasets and the strong inter-relationships among the model parameters (log KM values, potency factors, toxicity response parameters), further evaluation of specific model assumptions and calibration strategies is needed.
Park, Yoon Soo; Lee, Young-Sun; Xing, Kuan
2016-01-01
This study investigates the impact of item parameter drift (IPD) on parameter and ability estimation when the underlying measurement model fits a mixture distribution, thereby violating the item invariance property of unidimensional item response theory (IRT) models. An empirical study was conducted to demonstrate the occurrence of both IPD and an underlying mixture distribution using real-world data. Twenty-one trended anchor items from the 1999, 2003, and 2007 administrations of Trends in International Mathematics and Science Study (TIMSS) were analyzed using unidimensional and mixture IRT models. TIMSS treats trended anchor items as invariant over testing administrations and uses pre-calibrated item parameters based on unidimensional IRT. However, empirical results showed evidence of two latent subgroups with IPD. Results also showed changes in the distribution of examinee ability between latent classes over the three administrations. A simulation study was conducted to examine the impact of IPD on the estimation of ability and item parameters, when data have underlying mixture distributions. Simulations used data generated from a mixture IRT model and estimated using unidimensional IRT. Results showed that data reflecting IPD using mixture IRT model led to IPD in the unidimensional IRT model. Changes in the distribution of examinee ability also affected item parameters. Moreover, drift with respect to item discrimination and distribution of examinee ability affected estimates of examinee ability. These findings demonstrate the need to caution and evaluate IPD using a mixture IRT framework to understand its effects on item parameters and examinee ability.
Directory of Open Access Journals (Sweden)
Yoon Soo ePark
2016-02-01
Full Text Available This study investigates the impact of item parameter drift (IPD on parameter and ability estimation when the underlying measurement model fits a mixture distribution, thereby violating the item invariance property of unidimensional item response theory (IRT models. An empirical study was conducted to demonstrate the occurrence of both IPD and an underlying mixture distribution using real-world data. Twenty-one trended anchor items from the 1999, 2003, and 2007 administrations of Trends in International Mathematics and Science Study (TIMSS were analyzed using unidimensional and mixture IRT models. TIMSS treats trended anchor items as invariant over testing administrations and uses pre-calibrated item parameters based on unidimensional IRT. However, empirical results showed evidence of two latent subgroups with IPD. Results showed changes in the distribution of examinee ability between latent classes over the three administrations. A simulation study was conducted to examine the impact of IPD on the estimation of ability and item parameters, when data have underlying mixture distributions. Simulations used data generated from a mixture IRT model and estimated using unidimensional IRT. Results showed that data reflecting IPD using mixture IRT model led to IPD in the unidimensional IRT model. Changes in the distribution of examinee ability also affected item parameters. Moreover, drift with respect to item discrimination and distribution of examinee ability affected estimates of examinee ability. These findings demonstrate the need to caution and evaluate IPD using a mixture IRT framework to understand its effect on item parameters and examinee ability.
Directory of Open Access Journals (Sweden)
Orlov Alexey
2016-01-01
Full Text Available This article presents results of development of the mathematical model of nonstationary separation processes occurring in gas centrifuge cascades for separation of multicomponent isotope mixtures. This model was used for the calculation parameters of gas centrifuge cascade for separation of germanium isotopes. Comparison of obtained values with results of other authors revealed that developed mathematical model is adequate to describe nonstationary separation processes in gas centrifuge cascades for separation of multicomponent isotope mixtures.
Monte Carlo tests of the Rasch model based on scalability coefficients
DEFF Research Database (Denmark)
Christensen, Karl Bang; Kreiner, Svend
2010-01-01
that summarizes the number of Guttman errors in the data matrix. These coefficients are shown to yield efficient tests of the Rasch model using p-values computed using Markov chain Monte Carlo methods. The power of the tests of unequal item discrimination, and their ability to distinguish between local dependence......For item responses fitting the Rasch model, the assumptions underlying the Mokken model of double monotonicity are met. This makes non-parametric item response theory a natural starting-point for Rasch item analysis. This paper studies scalability coefficients based on Loevinger's H coefficient...
Monte Carlo tools for Beyond the Standard Model Physics , April 14-16
DEFF Research Database (Denmark)
Badger...[], Simon; Christensen, Christian Holm; Dalsgaard, Hans Hjersing
2011-01-01
This workshop aims to gather together theorists and experimentalists interested in developing and using Monte Carlo tools for Beyond the Standard Model Physics in an attempt to be prepared for the analysis of data focusing on the Large Hadron Collider. Since a large number of excellent tools....... To identify promising models (or processes) for which the tools have not yet been constructed and start filling up these gaps. To propose ways to streamline the process of going from models to events, i.e. to make the process more user-friendly so that more people can get involved and perform serious collider...
Influence of high power ultrasound on rheological and foaming properties of model ice-cream mixtures
Directory of Open Access Journals (Sweden)
Verica Batur
2010-03-01
Full Text Available This paper presents research of the high power ultrasound effect on rheological and foaming properties of ice cream model mixtures. Ice cream model mixtures are prepared according to specific recipes, and afterward undergone through different homogenization techniques: mechanical mixing, ultrasound treatment and combination of mechanical and ultrasound treatment. Specific diameter (12.7 mm of ultrasound probe tip has been used for ultrasound treatment that lasted 5 minutes at 100 percent amplitude. Rheological parameters have been determined using rotational rheometer and expressed as flow index, consistency coefficient and apparent viscosity. From the results it can be concluded that all model mixtures have non-newtonian, dilatant type behavior. The highest viscosities have been observed for model mixtures that were homogenizes with mechanical mixing, and significantly lower values of viscosity have been observed for ultrasound treated ones. Foaming properties are expressed as percentage of increase in foam volume, foam stability index and minimal viscosity. It has been determined that ice cream model mixtures treated only with ultrasound had minimal increase in foam volume, while the highest increase in foam volume has been observed for ice cream mixture that has been treated in combination with mechanical and ultrasound treatment. Also, ice cream mixtures having higher amount of proteins in composition had shown higher foam stability. It has been determined that optimal treatment time is 10 minutes.
International Nuclear Information System (INIS)
Mayeed, M.S.; Kato, T.
2014-01-01
Applying the reptation algorithm to a simplified perfluoro polyether Z off-lattice polymer model an NVT Monte Carlo simulation has been performed. Bulk condition has been simulated first to compare the average radius of gyration with the bulk experimental results. Then the model is tested for its ability to describe dynamics. After this, it is applied to observe the replenishment of nano scale ultrathin liquid films on solid flat carbon surfaces. The replenishment rate for trenches of different widths (8, 12, and 16 nms for several molecular weights) between two films of perfluoro polyether Z from the Monte Carlo simulation is compared to that obtained solving the diffusion equation using the experimental diffusion coefficients of Ma et al. (1999), with room condition in both cases. Replenishment per Monte Carlo cycle seems to be a constant multiple of replenishment per second at least up to 2 nm replenished film thickness of the trenches over the carbon surface. Considerable good agreement has been achieved here between the experimental results and the dynamics of molecules using reptation moves in the ultrathin liquid films on solid surfaces.
Mountris, K. A.; Bert, J.; Noailly, J.; Rodriguez Aguilera, A.; Valeri, A.; Pradier, O.; Schick, U.; Promayon, E.; Gonzalez Ballester, M. A.; Troccaz, J.; Visvikis, D.
2017-03-01
Prostate volume changes due to edema occurrence during transperineal permanent brachytherapy should be taken under consideration to ensure optimal dose delivery. Available edema models, based on prostate volume observations, face several limitations. Therefore, patient-specific models need to be developed to accurately account for the impact of edema. In this study we present a biomechanical model developed to reproduce edema resolution patterns documented in the literature. Using the biphasic mixture theory and finite element analysis, the proposed model takes into consideration the mechanical properties of the pubic area tissues in the evolution of prostate edema. The model’s computed deformations are incorporated in a Monte Carlo simulation to investigate their effect on post-operative dosimetry. The comparison of Day1 and Day30 dosimetry results demonstrates the capability of the proposed model for patient-specific dosimetry improvements, considering the edema dynamics. The proposed model shows excellent ability to reproduce previously described edema resolution patterns and was validated based on previous findings. According to our results, for a prostate volume increase of 10-20% the Day30 urethra D10 dose metric is higher by 4.2%-10.5% compared to the Day1 value. The introduction of the edema dynamics in Day30 dosimetry shows a significant global dose overestimation identified on the conventional static Day30 dosimetry. In conclusion, the proposed edema biomechanical model can improve the treatment planning of transperineal permanent brachytherapy accounting for post-implant dose alterations during the planning procedure.
International Nuclear Information System (INIS)
Ibrahim, Ahmad M.; Wilson, Paul P.H.; Sawan, Mohamed E.; Mosher, Scott W.; Peplow, Douglas E.; Wagner, John C.; Evans, Thomas M.; Grove, Robert E.
2015-01-01
The CADIS and FW-CADIS hybrid Monte Carlo/deterministic techniques dramatically increase the efficiency of neutronics modeling, but their use in the accurate design analysis of very large and geometrically complex nuclear systems has been limited by the large number of processors and memory requirements for their preliminary deterministic calculations and final Monte Carlo calculation. Three mesh adaptivity algorithms were developed to reduce the memory requirements of CADIS and FW-CADIS without sacrificing their efficiency improvement. First, a macromaterial approach enhances the fidelity of the deterministic models without changing the mesh. Second, a deterministic mesh refinement algorithm generates meshes that capture as much geometric detail as possible without exceeding a specified maximum number of mesh elements. Finally, a weight window coarsening algorithm decouples the weight window mesh and energy bins from the mesh and energy group structure of the deterministic calculations in order to remove the memory constraint of the weight window map from the deterministic mesh resolution. The three algorithms were used to enhance an FW-CADIS calculation of the prompt dose rate throughout the ITER experimental facility. Using these algorithms resulted in a 23.3% increase in the number of mesh tally elements in which the dose rates were calculated in a 10-day Monte Carlo calculation and, additionally, increased the efficiency of the Monte Carlo simulation by a factor of at least 3.4. The three algorithms enabled this difficult calculation to be accurately solved using an FW-CADIS simulation on a regular computer cluster, eliminating the need for a world-class super computer
Leung, Roger; Cheung, Howard; Gang, Hong; Ye, Wenjing
2010-01-01
Squeeze-film damping on microresonators is a significant damping source even when the surrounding gas is highly rarefied. This article presents a general modeling approach based on Monte Carlo (MC) simulations for the prediction of squeeze
Model uncertainty estimation and risk assessment is essential to environmental management and informed decision making on pollution mitigation strategies. In this study, we apply a probabilistic methodology, which combines Bayesian Monte Carlo simulation and Maximum Likelihood e...
Modeling dose-rate on/over the surface of cylindrical radio-models using Monte Carlo methods
International Nuclear Information System (INIS)
Xiao Xuefu; Ma Guoxue; Wen Fuping; Wang Zhongqi; Wang Chaohui; Zhang Jiyun; Huang Qingbo; Zhang Jiaqiu; Wang Xinxing; Wang Jun
2004-01-01
Objective: To determine the dose-rates on/over the surface of 10 cylindrical radio-models, which belong to the Metrology Station of Radio-Geological Survey of CNNC. Methods: The dose-rates on/over the surface of 10 cylindrical radio-models were modeled using the famous Monte Carlo code-MCNP. The dose-rates on/over the surface of 10 cylindrical radio-models were measured by a high gas pressurized ionization chamber dose-rate meter, respectively. The values of dose-rate modeled using MCNP code were compared with those obtained by authors in the present experimental measurement, and with those obtained by other workers previously. Some factors causing the discrepancy between the data obtained by authors using MCNP code and the data obtained using other methods are discussed in this paper. Results: The data of dose-rates on/over the surface of 10 cylindrical radio-models, obtained using MCNP code, were in good agreement with those obtained by other workers using the theoretical method. They were within the discrepancy of ±5% in general, and the maximum discrepancy was less than 10%. Conclusions: As if each factor needed for the Monte Carlo code is correct, the dose-rates on/over the surface of cylindrical radio-models modeled using the Monte Carlo code are correct with an uncertainty of 3%
Monte Carlo Studies of Phase Separation in Compressible 2-dim Ising Models
Mitchell, S. J.; Landau, D. P.
2006-03-01
Using high resolution Monte Carlo simulations, we study time-dependent domain growth in compressible 2-dim ferromagnetic (s=1/2) Ising models with continuous spin positions and spin-exchange moves [1]. Spins interact with slightly modified Lennard-Jones potentials, and we consider a model with no lattice mismatch and one with 4% mismatch. For comparison, we repeat calculations for the rigid Ising model [2]. For all models, large systems (512^2) and long times (10^ 6 MCS) are examined over multiple runs, and the growth exponent is measured in the asymptotic scaling regime. For the rigid model and the compressible model with no lattice mismatch, the growth exponent is consistent with the theoretically expected value of 1/3 [1] for Model B type growth. However, we find that non-zero lattice mismatch has a significant and unexpected effect on the growth behavior.Supported by the NSF.[1] D.P. Landau and K. Binder, A Guide to Monte Carlo Simulations in Statistical Physics, second ed. (Cambridge University Press, New York, 2005).[2] J. Amar, F. Sullivan, and R.D. Mountain, Phys. Rev. B 37, 196 (1988).
Combinatorial bounds on the α-divergence of univariate mixture models
Nielsen, Frank; Sun, Ke
2017-01-01
We derive lower- and upper-bounds of α-divergence between univariate mixture models with components in the exponential family. Three pairs of bounds are presented in order with increasing quality and increasing computational cost. They are verified
Statistical imitation system using relational interest points and Gaussian mixture models
CSIR Research Space (South Africa)
Claassens, J
2009-11-01
Full Text Available The author proposes an imitation system that uses relational interest points (RIPs) and Gaussian mixture models (GMMs) to characterize a behaviour. The system's structure is inspired by the Robot Programming by Demonstration (RDP) paradigm...
Reynolds, Gavin K; Campbell, Jacqueline I; Roberts, Ron J
2017-10-05
A new model to predict the compressibility and compactability of mixtures of pharmaceutical powders has been developed. The key aspect of the model is consideration of the volumetric occupancy of each powder under an applied compaction pressure and the respective contribution it then makes to the mixture properties. The compressibility and compactability of three pharmaceutical powders: microcrystalline cellulose, mannitol and anhydrous dicalcium phosphate have been characterised. Binary and ternary mixtures of these excipients have been tested and used to demonstrate the predictive capability of the model. Furthermore, the model is shown to be uniquely able to capture a broad range of mixture behaviours, including neutral, negative and positive deviations, illustrating its utility for formulation design. Copyright © 2017 Elsevier B.V. All rights reserved.
Modeling Hydrodynamic State of Oil and Gas Condensate Mixture in a Pipeline
Directory of Open Access Journals (Sweden)
Dudin Sergey
2016-01-01
Based on the developed model a calculation method was obtained which is used to analyze hydrodynamic state and composition of hydrocarbon mixture in each ith section of the pipeline when temperature-pressure and hydraulic conditions change.
A predictive model of natural gas mixture combustion in internal combustion engines
Directory of Open Access Journals (Sweden)
Henry Espinoza
2007-05-01
Full Text Available This study shows the development of a predictive natural gas mixture combustion model for conventional com-bustion (ignition engines. The model was based on resolving two areas; one having unburned combustion mixture and another having combustion products. Energy and matter conservation equations were solved for each crankshaft turn angle for each area. Nonlinear differential equations for each phase’s energy (considering compression, combustion and expansion were solved by applying the fourth-order Runge-Kutta method. The model also enabled studying different natural gas components’ composition and evaluating combustion in the presence of dry and humid air. Validation results are shown with experimental data, demonstrating the software’s precision and accuracy in the results so produced. The results showed cylinder pressure, unburned and burned mixture temperature, burned mass fraction and combustion reaction heat for the engine being modelled using a natural gas mixture.
Mixture estimation with state-space components and Markov model of switching
Czech Academy of Sciences Publication Activity Database
Nagy, Ivan; Suzdaleva, Evgenia
2013-01-01
Roč. 37, č. 24 (2013), s. 9970-9984 ISSN 0307-904X R&D Projects: GA TA ČR TA01030123 Institutional support: RVO:67985556 Keywords : probabilistic dynamic mixtures, * probability density function * state-space models * recursive mixture estimation * Bayesian dynamic decision making under uncertainty * Kerridge inaccuracy Subject RIV: BC - Control Systems Theory Impact factor: 2.158, year: 2013 http://library.utia.cas.cz/separaty/2013/AS/nagy-mixture estimation with state-space components and markov model of switching.pdf
Estimation of value at risk and conditional value at risk using normal mixture distributions model
Kamaruzzaman, Zetty Ain; Isa, Zaidi
2013-04-01
Normal mixture distributions model has been successfully applied in financial time series analysis. In this paper, we estimate the return distribution, value at risk (VaR) and conditional value at risk (CVaR) for monthly and weekly rates of returns for FTSE Bursa Malaysia Kuala Lumpur Composite Index (FBMKLCI) from July 1990 until July 2010 using the two component univariate normal mixture distributions model. First, we present the application of normal mixture distributions model in empirical finance where we fit our real data. Second, we present the application of normal mixture distributions model in risk analysis where we apply the normal mixture distributions model to evaluate the value at risk (VaR) and conditional value at risk (CVaR) with model validation for both risk measures. The empirical results provide evidence that using the two components normal mixture distributions model can fit the data well and can perform better in estimating value at risk (VaR) and conditional value at risk (CVaR) where it can capture the stylized facts of non-normality and leptokurtosis in returns distribution.
History and future perspectives of the Monte Carlo shell model -from Alphleet to K computer-
International Nuclear Information System (INIS)
Shimizu, Noritaka; Otsuka, Takaharu; Utsuno, Yutaka; Mizusaki, Takahiro; Honma, Michio; Abe, Takashi
2013-01-01
We report a history of the developments of the Monte Carlo shell model (MCSM). The MCSM was proposed in order to perform large-scale shell-model calculations which direct diagonalization method cannot reach. Since 1999 PC clusters were introduced for parallel computation of the MCSM. Since 2011 we participated the High Performance Computing Infrastructure Strategic Program and developed a new MCSM code for current massively parallel computers such as K computer. We discuss future perspectives concerning a new framework and parallel computation of the MCSM by incorporating conjugate gradient method and energy-variance extrapolation
Monte Carlo modelling of the Belgian materials testing reactor BR2: present status
International Nuclear Information System (INIS)
Verboomen, B.; Aoust, Th.; Raedt, Ch. de; Beeckmans de West-Meerbeeck, A.
2001-01-01
A very detailed 3-D MCNP-4B model of the BR2 reactor was developed to perform all neutron and gamma calculations needed for the design of new experimental irradiation rigs. The Monte Carlo model of BR2 includes the nearly exact geometrical representation of fuel elements (now with their axially varying burn-up), of partially inserted control and regulating rods, of experimental devices and of radioisotope production rigs. The multiple level-geometry possibilities of MCNP-4B are fully exploited to obtain sufficiently flexible tools to cope with the very changing core loading. (orig.)
Quantum Monte Carlo simulation for S=1 Heisenberg model with uniaxial anisotropy
International Nuclear Information System (INIS)
Tsukamoto, Mitsuaki; Batista, Cristian; Kawashima, Naoki
2007-01-01
We perform quantum Monte Carlo simulations for S=1 Heisenberg model with an uniaxial anisotropy. The system exhibits a phase transition as we vary the anisotropy and a long range order appears at a finite temperature when the exchange interaction J is comparable to the uniaxial anisotropy D. We investigate quantum critical phenomena of this model and obtain the line of the phase transition which approaches a power-law with logarithmic corrections at low temperature. We derive the form of logarithmic corrections analytically and compare it to our simulation results
Monte Carlo simulations of the NJL model near the nonzero temperature phase transition
International Nuclear Information System (INIS)
Strouthos, Costas; Christofi, Stavros
2005-01-01
We present results from numerical simulations of the Nambu-Jona-Lasinio model with an SU(2)xSU(2) chiral symmetry and N c = 4,8, and 16 quark colors at nonzero temperature. We performed the simulations by utilizing the hybrid Monte Carlo and hybrid Molecular Dynamics algorithms. We show that the model undergoes a second order phase transition. The critical exponents measured are consistent with the classical 3d O(4) universality class and hence in accordance with the dimensional reduction scenario. We also show that the Ginzburg region is suppressed by a factor of 1/N c in accordance with previous analytical predictions. (author)
Modeling of the YALINA booster facility by the Monte Carlo code MONK
International Nuclear Information System (INIS)
Talamo, A.; Gohar, Y.; Kondev, F.; Kiyavitskaya, H.; Serafimovich, I.; Bournos, V.; Fokov, Y.; Routkovskaya, C.
2007-01-01
The YALINA-Booster facility has been modeled according to the benchmark specifications defined for the IAEA activity without any geometrical homogenization using the Monte Carlo codes MONK and MCNP/MCNPX/MCB. The MONK model perfectly matches the MCNP one. The computational analyses have been extended through the MCB code, which is an extension of the MCNP code with burnup capability because of its additional feature for analyzing source driven multiplying assemblies. The main neutronics arameters of the YALINA-Booster facility were calculated using these computer codes with different nuclear data libraries based on ENDF/B-VI-0, -6, JEF-2.2, and JEF-3.1.
Bayesian estimation of realized stochastic volatility model by Hybrid Monte Carlo algorithm
International Nuclear Information System (INIS)
Takaishi, Tetsuya
2014-01-01
The hybrid Monte Carlo algorithm (HMCA) is applied for Bayesian parameter estimation of the realized stochastic volatility (RSV) model. Using the 2nd order minimum norm integrator (2MNI) for the molecular dynamics (MD) simulation in the HMCA, we find that the 2MNI is more efficient than the conventional leapfrog integrator. We also find that the autocorrelation time of the volatility variables sampled by the HMCA is very short. Thus it is concluded that the HMCA with the 2MNI is an efficient algorithm for parameter estimations of the RSV model
Design and evaluation of a Monte Carlo based model of an orthovoltage treatment system
International Nuclear Information System (INIS)
Penchev, Petar; Maeder, Ulf; Fiebich, Martin; Zink, Klemens; University Hospital Marburg
2015-01-01
The aim of this study was to develop a flexible framework of an orthovoltage treatment system capable of calculating and visualizing dose distributions in different phantoms and CT datasets. The framework provides a complete set of various filters, applicators and X-ray energies and therefore can be adapted to varying studies or be used for educational purposes. A dedicated user friendly graphical interface was developed allowing for easy setup of the simulation parameters and visualization of the results. For the Monte Carlo simulations the EGSnrc Monte Carlo code package was used. Building the geometry was accomplished with the help of the EGSnrc C++ class library. The deposited dose was calculated according to the KERMA approximation using the track-length estimator. The validation against measurements showed a good agreement within 4-5% deviation, down to depths of 20% of the depth dose maximum. Furthermore, to show its capabilities, the validated model was used to calculate the dose distribution on two CT datasets. Typical Monte Carlo calculation time for these simulations was about 10 minutes achieving an average statistical uncertainty of 2% on a standard PC. However, this calculation time depends strongly on the used CT dataset, tube potential, filter material/thickness and applicator size.
A quantitative trait locus mixture model that avoids spurious LOD score peaks.
Feenstra, Bjarke; Skovgaard, Ib M
2004-06-01
In standard interval mapping of quantitative trait loci (QTL), the QTL effect is described by a normal mixture model. At any given location in the genome, the evidence of a putative QTL is measured by the likelihood ratio of the mixture model compared to a single normal distribution (the LOD score). This approach can occasionally produce spurious LOD score peaks in regions of low genotype information (e.g., widely spaced markers), especially if the phenotype distribution deviates markedly from a normal distribution. Such peaks are not indicative of a QTL effect; rather, they are caused by the fact that a mixture of normals always produces a better fit than a single normal distribution. In this study, a mixture model for QTL mapping that avoids the problems of such spurious LOD score peaks is presented.
A Dirichlet process mixture of generalized Dirichlet distributions for proportional data modeling.
Bouguila, Nizar; Ziou, Djemel
2010-01-01
In this paper, we propose a clustering algorithm based on both Dirichlet processes and generalized Dirichlet distribution which has been shown to be very flexible for proportional data modeling. Our approach can be viewed as an extension of the finite generalized Dirichlet mixture model to the infinite case. The extension is based on nonparametric Bayesian analysis. This clustering algorithm does not require the specification of the number of mixture components to be given in advance and estimates it in a principled manner. Our approach is Bayesian and relies on the estimation of the posterior distribution of clusterings using Gibbs sampler. Through some applications involving real-data classification and image databases categorization using visual words, we show that clustering via infinite mixture models offers a more powerful and robust performance than classic finite mixtures.
International Nuclear Information System (INIS)
Maevskii, K. K.; Kinelovskii, S. A.
2015-01-01
The numerical results of modeling of shock wave loading of mixtures with the SiO 2 component are presented. The TEC (thermodynamic equilibrium component) model is employed to describe the behavior of solid and porous multicomponent mixtures and alloys under shock wave loading. State equations of a Mie–Grüneisen type are used to describe the behavior of condensed phases, taking into account the temperature dependence of the Grüneisen coefficient, gas in pores is one of the components of the environment. The model is based on the assumption that all components of the mixture under shock-wave loading are in thermodynamic equilibrium. The calculation results are compared with the experimental data derived by various authors. The behavior of the mixture containing components with a phase transition under high dynamic loads is described
Latent Transition Analysis with a Mixture Item Response Theory Measurement Model
Cho, Sun-Joo; Cohen, Allan S.; Kim, Seock-Ho; Bottge, Brian
2010-01-01
A latent transition analysis (LTA) model was described with a mixture Rasch model (MRM) as the measurement model. Unlike the LTA, which was developed with a latent class measurement model, the LTA-MRM permits within-class variability on the latent variable, making it more useful for measuring treatment effects within latent classes. A simulation…
Comparison of nonstationary generalized logistic models based on Monte Carlo simulation
Directory of Open Access Journals (Sweden)
S. Kim
2015-06-01
Full Text Available Recently, the evidences of climate change have been observed in hydrologic data such as rainfall and flow data. The time-dependent characteristics of statistics in hydrologic data are widely defined as nonstationarity. Therefore, various nonstationary GEV and generalized Pareto models have been suggested for frequency analysis of nonstationary annual maximum and POT (peak-over-threshold data, respectively. However, the alternative models are required for nonstatinoary frequency analysis because of analyzing the complex characteristics of nonstationary data based on climate change. This study proposed the nonstationary generalized logistic model including time-dependent parameters. The parameters of proposed model are estimated using the method of maximum likelihood based on the Newton-Raphson method. In addition, the proposed model is compared by Monte Carlo simulation to investigate the characteristics of models and applicability.
MONTE CARLO SIMULATION MODEL OF ENERGETIC PROTON TRANSPORT THROUGH SELF-GENERATED ALFVEN WAVES
Energy Technology Data Exchange (ETDEWEB)
Afanasiev, A.; Vainio, R., E-mail: alexandr.afanasiev@helsinki.fi [Department of Physics, University of Helsinki (Finland)
2013-08-15
A new Monte Carlo simulation model for the transport of energetic protons through self-generated Alfven waves is presented. The key point of the model is that, unlike the previous ones, it employs the full form (i.e., includes the dependence on the pitch-angle cosine) of the resonance condition governing the scattering of particles off Alfven waves-the process that approximates the wave-particle interactions in the framework of quasilinear theory. This allows us to model the wave-particle interactions in weak turbulence more adequately, in particular, to implement anisotropic particle scattering instead of isotropic scattering, which the previous Monte Carlo models were based on. The developed model is applied to study the transport of flare-accelerated protons in an open magnetic flux tube. Simulation results for the transport of monoenergetic protons through the spectrum of Alfven waves reveal that the anisotropic scattering leads to spatially more distributed wave growth than isotropic scattering. This result can have important implications for diffusive shock acceleration, e.g., affect the scattering mean free path of the accelerated particles in and the size of the foreshock region.
Zhu, Gaofeng; Li, Xin; Ma, Jinzhu; Wang, Yunquan; Liu, Shaomin; Huang, Chunlin; Zhang, Kun; Hu, Xiaoli
2018-04-01
Sequential Monte Carlo (SMC) samplers have become increasing popular for estimating the posterior parameter distribution with the non-linear dependency structures and multiple modes often present in hydrological models. However, the explorative capabilities and efficiency of the sampler depends strongly on the efficiency in the move step of SMC sampler. In this paper we presented a new SMC sampler entitled the Particle Evolution Metropolis Sequential Monte Carlo (PEM-SMC) algorithm, which is well suited to handle unknown static parameters of hydrologic model. The PEM-SMC sampler is inspired by the works of Liang and Wong (2001) and operates by incorporating the strengths of the genetic algorithm, differential evolution algorithm and Metropolis-Hasting algorithm into the framework of SMC. We also prove that the sampler admits the target distribution to be a stationary distribution. Two case studies including a multi-dimensional bimodal normal distribution and a conceptual rainfall-runoff hydrologic model by only considering parameter uncertainty and simultaneously considering parameter and input uncertainty show that PEM-SMC sampler is generally superior to other popular SMC algorithms in handling the high dimensional problems. The study also indicated that it may be important to account for model structural uncertainty by using multiplier different hydrological models in the SMC framework in future study.
Monte Carlo Modelling of Single-Crystal Diffuse Scattering from Intermetallics
Directory of Open Access Journals (Sweden)
Darren J. Goossens
2016-02-01
Full Text Available Single-crystal diffuse scattering (SCDS reveals detailed structural insights into materials. In particular, it is sensitive to two-body correlations, whereas traditional Bragg peak-based methods are sensitive to single-body correlations. This means that diffuse scattering is sensitive to ordering that persists for just a few unit cells: nanoscale order, sometimes referred to as “local structure”, which is often crucial for understanding a material and its function. Metals and alloys were early candidates for SCDS studies because of the availability of large single crystals. While great progress has been made in areas like ab initio modelling and molecular dynamics, a place remains for Monte Carlo modelling of model crystals because of its ability to model very large systems; important when correlations are relatively long (though still finite in range. This paper briefly outlines, and gives examples of, some Monte Carlo methods appropriate for the modelling of SCDS from metallic compounds, and considers data collection as well as analysis. Even if the interest in the material is driven primarily by magnetism or transport behaviour, an understanding of the local structure can underpin such studies and give an indication of nanoscale inhomogeneity.
The Physical Models and Statistical Procedures Used in the RACER Monte Carlo Code
International Nuclear Information System (INIS)
Sutton, T.M.; Brown, F.B.; Bischoff, F.G.; MacMillan, D.B.; Ellis, C.L.; Ward, J.T.; Ballinger, C.T.; Kelly, D.J.; Schindler, L.
1999-01-01
This report describes the MCV (Monte Carlo - Vectorized)Monte Carlo neutron transport code [Brown, 1982, 1983; Brown and Mendelson, 1984a]. MCV is a module in the RACER system of codes that is used for Monte Carlo reactor physics analysis. The MCV module contains all of the neutron transport and statistical analysis functions of the system, while other modules perform various input-related functions such as geometry description, material assignment, output edit specification, etc. MCV is very closely related to the 05R neutron Monte Carlo code [Irving et al., 1965] developed at Oak Ridge National Laboratory. 05R evolved into the 05RR module of the STEMB system, which was the forerunner of the RACER system. Much of the overall logic and physics treatment of 05RR has been retained and, indeed, the original verification of MCV was achieved through comparison with STEMB results. MCV has been designed to be very computationally efficient [Brown, 1981, Brown and Martin, 1984b; Brown, 1986]. It was originally programmed to make use of vector-computing architectures such as those of the CDC Cyber- 205 and Cray X-MP. MCV was the first full-scale production Monte Carlo code to effectively utilize vector-processing capabilities. Subsequently, MCV was modified to utilize both distributed-memory [Sutton and Brown, 1994] and shared memory parallelism. The code has been compiled and run on platforms ranging from 32-bit UNIX workstations to clusters of 64-bit vector-parallel supercomputers. The computational efficiency of the code allows the analyst to perform calculations using many more neutron histories than is practical with most other Monte Carlo codes, thereby yielding results with smaller statistical uncertainties. MCV also utilizes variance reduction techniques such as survival biasing, splitting, and rouletting to permit additional reduction in uncertainties. While a general-purpose neutron Monte Carlo code, MCV is optimized for reactor physics calculations. It has the
DEFF Research Database (Denmark)
Feng, Huan; Pettinari, Matteo; Stang, Henrik
2016-01-01
modulus. Three different approaches have been used and compared for calibrating the Burger's contact model. Values of the dynamic modulus and phase angle of asphalt mixtures were predicted by conducting DE simulation under dynamic strain control loading. The excellent agreement between the predicted......In this paper the viscoelastic behavior of asphalt mixture was investigated by employing a three-dimensional discrete element method. Combined with Burger's model, three contact models were used for the construction of constitutive asphalt mixture model with viscoelastic properties...
Directory of Open Access Journals (Sweden)
Mansoor Ahmed Siddiqui
2017-06-01
Full Text Available This research work is aimed at optimizing the availability of a framework comprising of two units linked together in series configuration utilizing Markov Model and Monte Carlo (MC Simulation techniques. In this article, effort has been made to develop a maintenance model that incorporates three distinct states for each unit, while taking into account their different levels of deterioration. Calculations are carried out using the proposed model for two distinct cases of corrective repair, namely perfect and imperfect repairs, with as well as without opportunistic maintenance. Initially, results are accomplished using an analytical technique i.e., Markov Model. Validation of the results achieved is later carried out with the help of MC Simulation. In addition, MC Simulation based codes also work well for the frameworks that follow non-exponential failure and repair rates, and thus overcome the limitations offered by the Markov Model.
Monte Carlo based statistical power analysis for mediation models: methods and software.
Zhang, Zhiyong
2014-12-01
The existing literature on statistical power analysis for mediation models often assumes data normality and is based on a less powerful Sobel test instead of the more powerful bootstrap test. This study proposes to estimate statistical power to detect mediation effects on the basis of the bootstrap method through Monte Carlo simulation. Nonnormal data with excessive skewness and kurtosis are allowed in the proposed method. A free R package called bmem is developed to conduct the power analysis discussed in this study. Four examples, including a simple mediation model, a multiple-mediator model with a latent mediator, a multiple-group mediation model, and a longitudinal mediation model, are provided to illustrate the proposed method.
New-generation Monte Carlo shell model for the K computer era
International Nuclear Information System (INIS)
Shimizu, Noritaka; Abe, Takashi; Yoshida, Tooru; Otsuka, Takaharu; Tsunoda, Yusuke; Utsuno, Yutaka; Mizusaki, Takahiro; Honma, Michio
2012-01-01
We present a newly enhanced version of the Monte Carlo shell-model (MCSM) method by incorporating the conjugate gradient method and energy-variance extrapolation. This new method enables us to perform large-scale shell-model calculations that the direct diagonalization method cannot reach. This new-generation framework of the MCSM provides us with a powerful tool to perform very advanced large-scale shell-model calculations on current massively parallel computers such as the K computer. We discuss the validity of this method in ab initio calculations of light nuclei, and propose a new method to describe the intrinsic wave function in terms of the shell-model picture. We also apply this new MCSM to the study of neutron-rich Cr and Ni isotopes using conventional shell-model calculations with an inert 40 Ca core and discuss how the magicity of N = 28, 40, 50 remains or is broken. (author)
Noh, Seong Jin; Tachikawa, Yasuto; Shiiba, Michiharu; Kim, Sunmin
Applications of data assimilation techniques have been widely used to improve upon the predictability of hydrologic modeling. Among various data assimilation techniques, sequential Monte Carlo (SMC) filters, known as "particle filters" provide the capability to handle non-linear and non-Gaussian state-space models. This paper proposes a dual state-parameter updating scheme (DUS) based on SMC methods to estimate both state and parameter variables of a hydrologic model. We introduce a kernel smoothing method for the robust estimation of uncertain model parameters in the DUS. The applicability of the dual updating scheme is illustrated using the implementation of the storage function model on a middle-sized Japanese catchment. We also compare performance results of DUS combined with various SMC methods, such as SIR, ASIR and RPF.
Directory of Open Access Journals (Sweden)
Abdenaceur Boudlal
2010-01-01
Full Text Available This article investigates a new method of motion estimation based on block matching criterion through the modeling of image blocks by a mixture of two and three Gaussian distributions. Mixture parameters (weights, means vectors, and covariance matrices are estimated by the Expectation Maximization algorithm (EM which maximizes the log-likelihood criterion. The similarity between a block in the current image and the more resembling one in a search window on the reference image is measured by the minimization of Extended Mahalanobis distance between the clusters of mixture. Performed experiments on sequences of real images have given good results, and PSNR reached 3 dB.
Thermodiffusion in Multicomponent Mixtures Thermodynamic, Algebraic, and Neuro-Computing Models
Srinivasan, Seshasai
2013-01-01
Thermodiffusion in Multicomponent Mixtures presents the computational approaches that are employed in the study of thermodiffusion in various types of mixtures, namely, hydrocarbons, polymers, water-alcohol, molten metals, and so forth. We present a detailed formalism of these methods that are based on non-equilibrium thermodynamics or algebraic correlations or principles of the artificial neural network. The book will serve as single complete reference to understand the theoretical derivations of thermodiffusion models and its application to different types of multi-component mixtures. An exhaustive discussion of these is used to give a complete perspective of the principles and the key factors that govern the thermodiffusion process.
A Monte Carlo model for 3D grain evolution during welding
Rodgers, Theron M.; Mitchell, John A.; Tikare, Veena
2017-09-01
Welding is one of the most wide-spread processes used in metal joining. However, there are currently no open-source software implementations for the simulation of microstructural evolution during a weld pass. Here we describe a Potts Monte Carlo based model implemented in the SPPARKS kinetic Monte Carlo computational framework. The model simulates melting, solidification and solid-state microstructural evolution of material in the fusion and heat-affected zones of a weld. The model does not simulate thermal behavior, but rather utilizes user input parameters to specify weld pool and heat-affect zone properties. Weld pool shapes are specified by Bézier curves, which allow for the specification of a wide range of pool shapes. Pool shapes can range from narrow and deep to wide and shallow representing different fluid flow conditions within the pool. Surrounding temperature gradients are calculated with the aide of a closest point projection algorithm. The model also allows simulation of pulsed power welding through time-dependent variation of the weld pool size. Example simulation results and comparisons with laboratory weld observations demonstrate microstructural variation with weld speed, pool shape, and pulsed-power.
Monte Carlo simulation as a tool to predict blasting fragmentation based on the Kuz Ram model
Morin, Mario A.; Ficarazzo, Francesco
2006-04-01
Rock fragmentation is considered the most important aspect of production blasting because of its direct effects on the costs of drilling and blasting and on the economics of the subsequent operations of loading, hauling and crushing. Over the past three decades, significant progress has been made in the development of new technologies for blasting applications. These technologies include increasingly sophisticated computer models for blast design and blast performance prediction. Rock fragmentation depends on many variables such as rock mass properties, site geology, in situ fracturing and blasting parameters and as such has no complete theoretical solution for its prediction. However, empirical models for the estimation of size distribution of rock fragments have been developed. In this study, a blast fragmentation Monte Carlo-based simulator, based on the Kuz-Ram fragmentation model, has been developed to predict the entire fragmentation size distribution, taking into account intact and joints rock properties, the type and properties of explosives and the drilling pattern. Results produced by this simulator were quite favorable when compared with real fragmentation data obtained from a blast quarry. It is anticipated that the use of Monte Carlo simulation will increase our understanding of the effects of rock mass and explosive properties on the rock fragmentation by blasting, as well as increase our confidence in these empirical models. This understanding will translate into improvements in blasting operations, its corresponding costs and the overall economics of open pit mines and rock quarries.
Fission yield calculation using toy model based on Monte Carlo simulation
International Nuclear Information System (INIS)
Jubaidah; Kurniadi, Rizal
2015-01-01
Toy model is a new approximation in predicting fission yield distribution. Toy model assumes nucleus as an elastic toy consist of marbles. The number of marbles represents the number of nucleons, A. This toy nucleus is able to imitate the real nucleus properties. In this research, the toy nucleons are only influenced by central force. A heavy toy nucleus induced by a toy nucleon will be split into two fragments. These two fission fragments are called fission yield. In this research, energy entanglement is neglected. Fission process in toy model is illustrated by two Gaussian curves intersecting each other. There are five Gaussian parameters used in this research. They are scission point of the two curves (R c ), mean of left curve (μ L ) and mean of right curve (μ R ), deviation of left curve (σ L ) and deviation of right curve (σ R ). The fission yields distribution is analyses based on Monte Carlo simulation. The result shows that variation in σ or µ can significanly move the average frequency of asymmetry fission yields. This also varies the range of fission yields distribution probability. In addition, variation in iteration coefficient only change the frequency of fission yields. Monte Carlo simulation for fission yield calculation using toy model successfully indicates the same tendency with experiment results, where average of light fission yield is in the range of 90
Fission yield calculation using toy model based on Monte Carlo simulation
Energy Technology Data Exchange (ETDEWEB)
Jubaidah, E-mail: jubaidah@student.itb.ac.id [Nuclear Physics and Biophysics Division, Department of Physics, Bandung Institute of Technology. Jl. Ganesa No. 10 Bandung – West Java, Indonesia 40132 (Indonesia); Physics Department, Faculty of Mathematics and Natural Science – State University of Medan. Jl. Willem Iskandar Pasar V Medan Estate – North Sumatera, Indonesia 20221 (Indonesia); Kurniadi, Rizal, E-mail: rijalk@fi.itb.ac.id [Nuclear Physics and Biophysics Division, Department of Physics, Bandung Institute of Technology. Jl. Ganesa No. 10 Bandung – West Java, Indonesia 40132 (Indonesia)
2015-09-30
Toy model is a new approximation in predicting fission yield distribution. Toy model assumes nucleus as an elastic toy consist of marbles. The number of marbles represents the number of nucleons, A. This toy nucleus is able to imitate the real nucleus properties. In this research, the toy nucleons are only influenced by central force. A heavy toy nucleus induced by a toy nucleon will be split into two fragments. These two fission fragments are called fission yield. In this research, energy entanglement is neglected. Fission process in toy model is illustrated by two Gaussian curves intersecting each other. There are five Gaussian parameters used in this research. They are scission point of the two curves (R{sub c}), mean of left curve (μ{sub L}) and mean of right curve (μ{sub R}), deviation of left curve (σ{sub L}) and deviation of right curve (σ{sub R}). The fission yields distribution is analyses based on Monte Carlo simulation. The result shows that variation in σ or µ can significanly move the average frequency of asymmetry fission yields. This also varies the range of fission yields distribution probability. In addition, variation in iteration coefficient only change the frequency of fission yields. Monte Carlo simulation for fission yield calculation using toy model successfully indicates the same tendency with experiment results, where average of light fission yield is in the range of 90
Effects of Test Conditions on APA Rutting and Prediction Modeling for Asphalt Mixtures
Directory of Open Access Journals (Sweden)
Hui Wang
2017-01-01
Full Text Available APA rutting tests were conducted for six kinds of asphalt mixtures under air-dry and immersing conditions. The influences of test conditions, including load, temperature, air voids, and moisture, on APA rutting depth were analyzed by using grey correlation method, and the APA rutting depth prediction model was established. Results show that the modified asphalt mixtures have bigger rutting depth ratios of air-dry to immersing conditions, indicating that the modified asphalt mixtures have better antirutting properties and water stability than the matrix asphalt mixtures. The grey correlation degrees of temperature, load, air void, and immersing conditions on APA rutting depth decrease successively, which means that temperature is the most significant influencing factor. The proposed indoor APA rutting prediction model has good prediction accuracy, and the correlation coefficient between the predicted and the measured rutting depths is 96.3%.
Study of the Internal Mechanical response of an asphalt mixture by 3-D Discrete Element Modeling
DEFF Research Database (Denmark)
Feng, Huan; Pettinari, Matteo; Hofko, Bernhard
2015-01-01
and the reliability of which have been validated. The dynamic modulus of asphalt mixtures were predicted by conducting Discrete Element simulation under dynamic strain control loading. In order to reduce the calculation time, a method based on frequency–temperature superposition principle has been implemented......In this paper the viscoelastic behavior of asphalt mixture was investigated by employing a three-dimensional Discrete Element Method (DEM). The cylinder model was filled with cubic array of spheres with a specified radius, and was considered as a whole mixture with uniform contact properties....... The ball density effect on the internal stress distribution of the asphalt mixture model has been studied when using this method. Furthermore, the internal stresses under dynamic loading have been studied. The agreement between the predicted and the laboratory test results of the complex modulus shows...
Studies on top-quark Monte Carlo modelling for Top2016
The ATLAS collaboration
2016-01-01
This note summarises recent studies on Monte Carlo simulation setups of top-quark pair production used by the ATLAS experiment and presents a new method to deal with interference effects for the $Wt$ single-top-quark production which is compared against previous techniques. The main focus for the top-quark pair production is on the improvement of the modelling of the Powheg generator interfaced to the Pythia8 and Herwig7 shower generators. The studies are done using unfolded data at centre-of-mass energies of 7, 8, and 13 TeV.
A study of potential energy curves from the model space quantum Monte Carlo method
Energy Technology Data Exchange (ETDEWEB)
Ohtsuka, Yuhki; Ten-no, Seiichiro, E-mail: tenno@cs.kobe-u.ac.jp [Department of Computational Sciences, Graduate School of System Informatics, Kobe University, Nada-ku, Kobe 657-8501 (Japan)
2015-12-07
We report on the first application of the model space quantum Monte Carlo (MSQMC) to potential energy curves (PECs) for the excited states of C{sub 2}, N{sub 2}, and O{sub 2} to validate the applicability of the method. A parallel MSQMC code is implemented with the initiator approximation to enable efficient sampling. The PECs of MSQMC for various excited and ionized states are compared with those from the Rydberg-Klein-Rees and full configuration interaction methods. The results indicate the usefulness of MSQMC for precise PECs in a wide range obviating problems concerning quasi-degeneracy.
A Monte Carlo modeling alternative for the API Gamma Ray Calibration Facility.
Galford, J E
2017-04-01
The gamma ray pit at the API Calibration Facility, located on the University of Houston campus, defines the API unit for natural gamma ray logs used throughout the petroleum logging industry. Future use of the facility is uncertain. An alternative method is proposed to preserve the gamma ray API unit definition as an industry standard by using Monte Carlo modeling to obtain accurate counting rate-to-API unit conversion factors for gross-counting and spectral gamma ray tool designs. Copyright © 2017 Elsevier Ltd. All rights reserved.
Excess Properties of Aqueous Mixtures of Methanol: Simple Models Versus Experiment
Czech Academy of Sciences Publication Activity Database
Vlček, Lukáš; Nezbeda, Ivo
roč. 131-132, - (2007), s. 158-162 ISSN 0167-7322. [International Conference on Solution Chemistry /29./. Portorož, 21.08.2005-25.08.2005] R&D Projects: GA AV ČR(CZ) IAA4072303; GA AV ČR(CZ) 1ET400720409 Institutional research plan: CEZ:AV0Z40720504 Keywords : aqueous mixtures * primitive models * water-alcohol mixtures Subject RIV: CF - Physical ; Theoretical Chemistry Impact factor: 0.982, year: 2007
Directory of Open Access Journals (Sweden)
F. C. PEIXOTO
1999-09-01
Full Text Available Fragmentation kinetics is employed to model a continuous reactive mixture of alkanes under catalytic cracking conditions. Standard moment analysis techniques are employed, and a dynamic system for the time evolution of moments of the mixture's dimensionless concentration distribution function (DCDF is found. The time behavior of the DCDF is recovered with successive estimations of scaled gamma distributions using the moments time data.
Modelling of scintillator based flat-panel detectors with Monte-Carlo simulations
International Nuclear Information System (INIS)
Reims, N; Sukowski, F; Uhlmann, N
2011-01-01
Scintillator based flat panel detectors are state of the art in the field of industrial X-ray imaging applications. Choosing the proper system and setup parameters for the vast range of different applications can be a time consuming task, especially when developing new detector systems. Since the system behaviour cannot always be foreseen easily, Monte-Carlo (MC) simulations are keys to gain further knowledge of system components and their behaviour for different imaging conditions. In this work we used two Monte-Carlo based models to examine an indirect converting flat panel detector, specifically the Hamamatsu C9312SK. We focused on the signal generation in the scintillation layer and its influence on the spatial resolution of the whole system. The models differ significantly in their level of complexity. The first model gives a global description of the detector based on different parameters characterizing the spatial resolution. With relatively small effort a simulation model can be developed which equates the real detector regarding signal transfer. The second model allows a more detailed insight of the system. It is based on the well established cascade theory, i.e. describing the detector as a cascade of elemental gain and scattering stages, which represent the built in components and their signal transfer behaviour. In comparison to the first model the influence of single components especially the important light spread behaviour in the scintillator can be analysed in a more differentiated way. Although the implementation of the second model is more time consuming both models have in common that a relatively small amount of system manufacturer parameters are needed. The results of both models were in good agreement with the measured parameters of the real system.
Piecewise Linear-Linear Latent Growth Mixture Models with Unknown Knots
Kohli, Nidhi; Harring, Jeffrey R.; Hancock, Gregory R.
2013-01-01
Latent growth curve models with piecewise functions are flexible and useful analytic models for investigating individual behaviors that exhibit distinct phases of development in observed variables. As an extension of this framework, this study considers a piecewise linear-linear latent growth mixture model (LGMM) for describing segmented change of…
A globally accurate theory for a class of binary mixture models
Dickman, Adriana G.; Stell, G.
The self-consistent Ornstein-Zernike approximation results for the 3D Ising model are used to obtain phase diagrams for binary mixtures described by decorated models, yielding the plait point, binodals, and closed-loop coexistence curves for the models proposed by Widom, Clark, Neece, and Wheeler. The results are in good agreement with series expansions and experiments.
Nguyen, Ngoc Minh; Corff, Sylvain Le; Moulines, Éric
2017-12-01
This paper focuses on sequential Monte Carlo approximations of smoothing distributions in conditionally linear and Gaussian state spaces. To reduce Monte Carlo variance of smoothers, it is typical in these models to use Rao-Blackwellization: particle approximation is used to sample sequences of hidden regimes while the Gaussian states are explicitly integrated conditional on the sequence of regimes and observations, using variants of the Kalman filter/smoother. The first successful attempt to use Rao-Blackwellization for smoothing extends the Bryson-Frazier smoother for Gaussian linear state space models using the generalized two-filter formula together with Kalman filters/smoothers. More recently, a forward-backward decomposition of smoothing distributions mimicking the Rauch-Tung-Striebel smoother for the regimes combined with backward Kalman updates has been introduced. This paper investigates the benefit of introducing additional rejuvenation steps in all these algorithms to sample at each time instant new regimes conditional on the forward and backward particles. This defines particle-based approximations of the smoothing distributions whose support is not restricted to the set of particles sampled in the forward or backward filter. These procedures are applied to commodity markets which are described using a two-factor model based on the spot price and a convenience yield for crude oil data.
Iterative optimisation of Monte Carlo detector models using measurements and simulations
Energy Technology Data Exchange (ETDEWEB)
Marzocchi, O., E-mail: olaf@marzocchi.net [European Patent Office, Rijswijk (Netherlands); Leone, D., E-mail: debora.leone@kit.edu [Institute for Nuclear Waste Disposal, Karlsruhe Institute of Technology, Karlsruhe (Germany)
2015-04-11
This work proposes a new technique to optimise the Monte Carlo models of radiation detectors, offering the advantage of a significantly lower user effort and therefore an improved work efficiency compared to the prior techniques. The method consists of four steps, two of which are iterative and suitable for automation using scripting languages. The four steps consist in the acquisition in the laboratory of measurement data to be used as reference; the modification of a previously available detector model; the simulation of a tentative model of the detector to obtain the coefficients of a set of linear equations; the solution of the system of equations and the update of the detector model. Steps three and four can be repeated for more accurate results. This method avoids the “try and fail” approach typical of the prior techniques.
Multi-chain Markov chain Monte Carlo methods for computationally expensive models
Huang, M.; Ray, J.; Ren, H.; Hou, Z.; Bao, J.
2017-12-01
Markov chain Monte Carlo (MCMC) methods are used to infer model parameters from observational data. The parameters are inferred as probability densities, thus capturing estimation error due to sparsity of the data, and the shortcomings of the model. Multiple communicating chains executing the MCMC method have the potential to explore the parameter space better, and conceivably accelerate the convergence to the final distribution. We present results from tests conducted with the multi-chain method to show how the acceleration occurs i.e., for loose convergence tolerances, the multiple chains do not make much of a difference. The ensemble of chains also seems to have the ability to accelerate the convergence of a few chains that might start from suboptimal starting points. Finally, we show the performance of the chains in the estimation of O(10) parameters using computationally expensive forward models such as the Community Land Model, where the sampling burden is distributed over multiple chains.
Three-dimensional Monte Carlo model of pulsed-laser treatment of cutaneous vascular lesions
Milanič, Matija; Majaron, Boris
2011-12-01
We present a three-dimensional Monte Carlo model of optical transport in skin with a novel approach to treatment of side boundaries of the volume of interest. This represents an effective way to overcome the inherent limitations of ``escape'' and ``mirror'' boundary conditions and enables high-resolution modeling of skin inclusions with complex geometries and arbitrary irradiation patterns. The optical model correctly reproduces measured values of diffuse reflectance for normal skin. When coupled with a sophisticated model of thermal transport and tissue coagulation kinetics, it also reproduces realistic values of radiant exposure thresholds for epidermal injury and for photocoagulation of port wine stain blood vessels in various skin phototypes, with or without application of cryogen spray cooling.
Collectivity in heavy nuclei in the shell model Monte Carlo approach
International Nuclear Information System (INIS)
Özen, C.; Alhassid, Y.; Nakada, H.
2014-01-01
The microscopic description of collectivity in heavy nuclei in the framework of the configuration-interaction shell model has been a major challenge. The size of the model space required for the description of heavy nuclei prohibits the use of conventional diagonalization methods. We have overcome this difficulty by using the shell model Monte Carlo (SMMC) method, which can treat model spaces that are many orders of magnitude larger than those that can be treated by conventional methods. We identify a thermal observable that can distinguish between vibrational and rotational collectivity and use it to describe the crossover from vibrational to rotational collectivity in families of even-even rare-earth isotopes. We calculate the state densities in these nuclei and find them to be in close agreement with experimental data. We also calculate the collective enhancement factors of the corresponding level densities and find that their decay with excitation energy is correlated with the pairing and shape phase transitions. (author)
International Nuclear Information System (INIS)
Courtine, Fabien
2007-03-01
The thesis proceeded in the context of dating by thermoluminescence. This method requires laboratory measurements of the natural radioactivity. For that purpose, we have been using a germanium spectrometer. To refine the calibration of this one, we modelled it by using a Monte-Carlo computer code: Geant4. We developed a geometrical model which takes into account the presence of inactive zones and zones of poor charge-collection within the germanium crystal. The parameters of the model were adjusted by comparison with experimental results obtained with a source of 137 Cs. It appeared that the form of the inactive zones is less simple than is presented in the specialized literature. This model was widened to the case of a more complex source, with cascade effect and angular correlations between photons: the 60 Co. Lastly, applied to extended sources, it gave correct results and allowed us to validate the simulation of matrix effect. (author)
Structure-reactivity modeling using mixture-based representation of chemical reactions.
Polishchuk, Pavel; Madzhidov, Timur; Gimadiev, Timur; Bodrov, Andrey; Nugmanov, Ramil; Varnek, Alexandre
2017-09-01
We describe a novel approach of reaction representation as a combination of two mixtures: a mixture of reactants and a mixture of products. In turn, each mixture can be encoded using an earlier reported approach involving simplex descriptors (SiRMS). The feature vector representing these two mixtures results from either concatenated product and reactant descriptors or the difference between descriptors of products and reactants. This reaction representation doesn't need an explicit labeling of a reaction center. The rigorous "product-out" cross-validation (CV) strategy has been suggested. Unlike the naïve "reaction-out" CV approach based on a random selection of items, the proposed one provides with more realistic estimation of prediction accuracy for reactions resulting in novel products. The new methodology has been applied to model rate constants of E2 reactions. It has been demonstrated that the use of the fragment control domain applicability approach significantly increases prediction accuracy of the models. The models obtained with new "mixture" approach performed better than those required either explicit (Condensed Graph of Reaction) or implicit (reaction fingerprints) reaction center labeling.
Karagiannis, Georgios; Lin, Guang
2017-08-01
For many real systems, several computer models may exist with different physics and predictive abilities. To achieve more accurate simulations/predictions, it is desirable for these models to be properly combined and calibrated. We propose the Bayesian calibration of computer model mixture method which relies on the idea of representing the real system output as a mixture of the available computer model outputs with unknown input dependent weight functions. The method builds a fully Bayesian predictive model as an emulator for the real system output by combining, weighting, and calibrating the available models in the Bayesian framework. Moreover, it fits a mixture of calibrated computer models that can be used by the domain scientist as a mean to combine the available computer models, in a flexible and principled manner, and perform reliable simulations. It can address realistic cases where one model may be more accurate than the others at different input values because the mixture weights, indicating the contribution of each model, are functions of the input. Inference on the calibration parameters can consider multiple computer models associated with different physics. The method does not require knowledge of the fidelity order of the models. We provide a technique able to mitigate the computational overhead due to the consideration of multiple computer models that is suitable to the mixture model framework. We implement the proposed method in a real-world application involving the Weather Research and Forecasting large-scale climate model.
Li, Shu; The ATLAS collaboration
2017-01-01
We present the Monte Carlo(MC) setup used by ATLAS to model multi-boson processes in √s = 13 TeV proton-proton collisions. The baseline Monte Carlo generators are compared with each other in key kinematic distributions of the processes under study. Sample normalization and systematic uncertainties are discussed.
Clinical Management and Burden of Prostate Cancer: A Markov Monte Carlo Model
Sanyal, Chiranjeev; Aprikian, Armen; Cury, Fabio; Chevalier, Simone; Dragomir, Alice
2014-01-01
Background Prostate cancer (PCa) is the most common non-skin cancer among men in developed countries. Several novel treatments have been adopted by healthcare systems to manage PCa. Most of the observational studies and randomized trials on PCa have concurrently evaluated fewer treatments over short follow-up. Further, preceding decision analytic models on PCa management have not evaluated various contemporary management options. Therefore, a contemporary decision analytic model was necessary to address limitations to the literature by synthesizing the evidence on novel treatments thereby forecasting short and long-term clinical outcomes. Objectives To develop and validate a Markov Monte Carlo model for the contemporary clinical management of PCa, and to assess the clinical burden of the disease from diagnosis to end-of-life. Methods A Markov Monte Carlo model was developed to simulate the management of PCa in men 65 years and older from diagnosis to end-of-life. Health states modeled were: risk at diagnosis, active surveillance, active treatment, PCa recurrence, PCa recurrence free, metastatic castrate resistant prostate cancer, overall and PCa death. Treatment trajectories were based on state transition probabilities derived from the literature. Validation and sensitivity analyses assessed the accuracy and robustness of model predicted outcomes. Results Validation indicated model predicted rates were comparable to observed rates in the published literature. The simulated distribution of clinical outcomes for the base case was consistent with sensitivity analyses. Predicted rate of clinical outcomes and mortality varied across risk groups. Life expectancy and health adjusted life expectancy predicted for the simulated cohort was 20.9 years (95%CI 20.5–21.3) and 18.2 years (95% CI 17.9–18.5), respectively. Conclusion Study findings indicated contemporary management strategies improved survival and quality of life in patients with PCa. This model could be used
The First 24 Years of Reverse Monte Carlo Modelling, Budapest, Hungary, 20-22 September 2012
Keen, David A.; Pusztai, László
2013-11-01
This special issue contains a collection of papers reflecting the content of the fifth workshop on reverse Monte Carlo (RMC) methods, held in a hotel on the banks of the Danube in the Budapest suburbs in the autumn of 2012. Over fifty participants gathered to hear talks and discuss a broad range of science based on the RMC technique in very convivial surroundings. Reverse Monte Carlo modelling is a method for producing three-dimensional disordered structural models in quantitative agreement with experimental data. The method was developed in the late 1980s and has since achieved wide acceptance within the scientific community [1], producing an average of over 90 papers and 1200 citations per year over the last five years. It is particularly suitable for the study of the structures of liquid and amorphous materials, as well as the structural analysis of disordered crystalline systems. The principal experimental data that are modelled are obtained from total x-ray or neutron scattering experiments, using the reciprocal space structure factor and/or the real space pair distribution function (PDF). Additional data might be included from extended x-ray absorption fine structure spectroscopy (EXAFS), Bragg peak intensities or indeed any measured data that can be calculated from a three-dimensional atomistic model. It is this use of total scattering (diffuse and Bragg), rather than just the Bragg peak intensities more commonly used for crystalline structure analysis, which enables RMC modelling to probe the often important deviations from the average crystal structure, to probe the structures of poorly crystalline or nanocrystalline materials, and the local structures of non-crystalline materials where only diffuse scattering is observed. This flexibility across various condensed matter structure-types has made the RMC method very attractive in a wide range of disciplines, as borne out in the contents of this special issue. It is however important to point out that since
Monitoring and modeling of ultrasonic wave propagation in crystallizing mixtures
Marshall, T.; Challis, R. E.; Tebbutt, J. S.
2002-05-01
The utility of ultrasonic compression wave techniques for monitoring crystallization processes is investigated in a study of the seeded crystallization of copper II sulfate pentahydrate from aqueous solution. Simple models are applied to predict crystal yield, crystal size distribution and the changing nature of the continuous phase. A scattering model is used to predict the ultrasonic attenuation as crystallization proceeds. Experiments confirm that modeled attenuation is in agreement with measured results.
Molenaar, Dylan; de Boeck, Paul
2018-06-01
In item response theory modeling of responses and response times, it is commonly assumed that the item responses have the same characteristics across the response times. However, heterogeneity might arise in the data if subjects resort to different response processes when solving the test items. These differences may be within-subject effects, that is, a subject might use a certain process on some of the items and a different process with different item characteristics on the other items. If the probability of using one process over the other process depends on the subject's response time, within-subject heterogeneity of the item characteristics across the response times arises. In this paper, the method of response mixture modeling is presented to account for such heterogeneity. Contrary to traditional mixture modeling where the full response vectors are classified, response mixture modeling involves classification of the individual elements in the response vector. In a simulation study, the response mixture model is shown to be viable in terms of parameter recovery. In addition, the response mixture model is applied to a real dataset to illustrate its use in investigating within-subject heterogeneity in the item characteristics across response times.
Latent Partially Ordered Classification Models and Normal Mixtures
Tatsuoka, Curtis; Varadi, Ferenc; Jaeger, Judith
2013-01-01
Latent partially ordered sets (posets) can be employed in modeling cognitive functioning, such as in the analysis of neuropsychological (NP) and educational test data. Posets are cognitively diagnostic in the sense that classification states in these models are associated with detailed profiles of cognitive functioning. These profiles allow for…
Energy Technology Data Exchange (ETDEWEB)
Leoevey, H.; Roemisch, W. [Humboldt-Univ., Berlin (Germany)
2015-07-01
We discuss progress in quasi Monte Carlo methods for numerical calculation integrals or expected values and justify why these methods are more efficient than the classic Monte Carlo methods. Quasi Monte Carlo methods are found to be particularly efficient if the integrands have a low effective dimension. That's why We also discuss the concept of effective dimension and prove on the example of a stochastic Optimization model of the energy industry that such models can posses a low effective dimension. Modern quasi Monte Carlo methods are therefore for such models very promising. [German] Wir diskutieren Fortschritte bei Quasi-Monte Carlo Methoden zur numerischen Berechnung von Integralen bzw. Erwartungswerten und begruenden warum diese Methoden effizienter sind als die klassischen Monte Carlo Methoden. Quasi-Monte Carlo Methoden erweisen sich als besonders effizient, falls die Integranden eine geringe effektive Dimension besitzen. Deshalb diskutieren wir auch den Begriff effektive Dimension und weisen am Beispiel eines stochastischen Optimierungsmodell aus der Energiewirtschaft nach, dass solche Modelle eine niedrige effektive Dimension besitzen koennen. Moderne Quasi-Monte Carlo Methoden sind deshalb fuer solche Modelle sehr erfolgversprechend.
Parameter sensitivity and uncertainty of the forest carbon flux model FORUG : a Monte Carlo analysis
Energy Technology Data Exchange (ETDEWEB)
Verbeeck, H.; Samson, R.; Lemeur, R. [Ghent Univ., Ghent (Belgium). Laboratory of Plant Ecology; Verdonck, F. [Ghent Univ., Ghent (Belgium). Dept. of Applied Mathematics, Biometrics and Process Control
2006-06-15
The FORUG model is a multi-layer process-based model that simulates carbon dioxide (CO{sub 2}) and water exchange between forest stands and the atmosphere. The main model outputs are net ecosystem exchange (NEE), total ecosystem respiration (TER), gross primary production (GPP) and evapotranspiration. This study used a sensitivity analysis to identify the parameters contributing to NEE uncertainty in the FORUG model. The aim was to determine if it is necessary to estimate the uncertainty of all parameters of a model to determine overall output uncertainty. Data used in the study were the meteorological and flux data of beech trees in Hesse. The Monte Carlo method was used to rank sensitivity and uncertainty parameters in combination with a multiple linear regression. Simulations were run in which parameters were assigned probability distributions and the effect of variance in the parameters on the output distribution was assessed. The uncertainty of the output for NEE was estimated. Based on the arbitrary uncertainty of 10 key parameters, a standard deviation of 0.88 Mg C per year per NEE was found, which was equal to 24 per cent of the mean value of NEE. The sensitivity analysis showed that the overall output uncertainty of the FORUG model could be determined by accounting for only a few key parameters, which were identified as corresponding to critical parameters in the literature. It was concluded that the 10 most important parameters determined more than 90 per cent of the output uncertainty. High ranking parameters included soil respiration; photosynthesis; and crown architecture. It was concluded that the Monte Carlo technique is a useful tool for ranking the uncertainty of parameters of process-based forest flux models. 48 refs., 2 tabs., 2 figs.
Safaei, Farinaz; Castorena, Cassie; Kim, Y. Richard
2016-08-01
Fatigue cracking is a major form of distress in asphalt pavements. Asphalt binder is the weakest asphalt concrete constituent and, thus, plays a critical role in determining the fatigue resistance of pavements. Therefore, the ability to characterize and model the inherent fatigue performance of an asphalt binder is a necessary first step to design mixtures and pavements that are not susceptible to premature fatigue failure. The simplified viscoelastic continuum damage (S-VECD) model has been used successfully by researchers to predict the damage evolution in asphalt mixtures for various traffic and climatic conditions using limited uniaxial test data. In this study, the S-VECD model, developed for asphalt mixtures, is adapted for asphalt binders tested under cyclic torsion in a dynamic shear rheometer. Derivation of the model framework is presented. The model is verified by producing damage characteristic curves that are both temperature- and loading history-independent based on time sweep tests, given that the effects of plasticity and adhesion loss on the material behavior are minimal. The applicability of the S-VECD model to the accelerated loading that is inherent of the linear amplitude sweep test is demonstrated, which reveals reasonable performance predictions, but with some loss in accuracy compared to time sweep tests due to the confounding effects of nonlinearity imposed by the high strain amplitudes included in the test. The asphalt binder S-VECD model is validated through comparisons to asphalt mixture S-VECD model results derived from cyclic direct tension tests and Accelerated Loading Facility performance tests. The results demonstrate good agreement between the asphalt binder and mixture test results and pavement performance, indicating that the developed model framework is able to capture the asphalt binder's contribution to mixture fatigue and pavement fatigue cracking performance.
Fast Monte Carlo-simulator with full collimator and detector response modelling for SPECT
International Nuclear Information System (INIS)
Sohlberg, A.O.; Kajaste, M.T.
2012-01-01
Monte Carlo (MC)-simulations have proved to be a valuable tool in studying single photon emission computed tomography (SPECT)-reconstruction algorithms. Despite their popularity, the use of Monte Carlo-simulations is still often limited by their large computation demand. This is especially true in situations where full collimator and detector modelling with septal penetration, scatter and X-ray fluorescence needs to be included. This paper presents a rapid and simple MC-simulator, which can effectively reduce the computation times. The simulator was built on the convolution-based forced detection principle, which can markedly lower the number of simulated photons. Full collimator and detector response look-up tables are pre-simulated and then later used in the actual MC-simulations to model the system response. The developed simulator was validated by comparing it against 123 I point source measurements made with a clinical gamma camera system and against 99m Tc software phantom simulations made with the SIMIND MC-package. The results showed good agreement between the new simulator, measurements and the SIMIND-package. The new simulator provided near noise-free projection data in approximately 1.5 min per projection with 99m Tc, which was less than one-tenth of SIMIND's time. The developed MC-simulator can markedly decrease the simulation time without sacrificing image quality. (author)
Kinetic Monte-Carlo modeling of hydrogen retention and re-emission from Tore Supra deposits
International Nuclear Information System (INIS)
Rai, A.; Schneider, R.; Warrier, M.; Roubin, P.; Martin, C.; Richou, M.
2009-01-01
A multi-scale model has been developed to study the reactive-diffusive transport of hydrogen in porous graphite [A. Rai, R. Schneider, M. Warrier, J. Nucl. Mater. (submitted for publication). http://dx.doi.org/10.1016/j.jnucmat.2007.08.013.]. The deposits found on the leading edge of the neutralizer of Tore Supra are multi-scale in nature, consisting of micropores with typical size lower than 2 nm (∼11%), mesopores (∼5%) and macropores with a typical size more than 50 nm [C. Martin, M. Richou, W. Sakaily, B. Pegourie, C. Brosset, P. Roubin, J. Nucl. Mater. 363-365 (2007) 1251]. Kinetic Monte-Carlo (KMC) has been used to study the hydrogen transport at meso-scales. Recombination rate and the diffusion coefficient calculated at the meso-scale was used as an input to scale up and analyze the hydrogen transport at macro-scale. A combination of KMC and MCD (Monte-Carlo diffusion) method was used at macro-scales. Flux dependence of hydrogen recycling has been studied. The retention and re-emission analysis of the model has been extended to study the chemical erosion process based on the Kueppers-Hopf cycle [M. Wittmann, J. Kueppers, J. Nucl. Mater. 227 (1996) 186].
Energy Technology Data Exchange (ETDEWEB)
Zakova, Jitka [Department of Nuclear and Reactor Physics, Royal Institute of Technology, KTH, Roslagstullsbacken 21, S-10691 Stockholm (Sweden)], E-mail: jitka.zakova@neutron.kth.se; Talamo, Alberto [Nuclear Engineering Division, Argonne National Laboratory, ANL, 9700 South Cass Avenue, Argonne, IL 60439 (United States)], E-mail: alby@anl.gov
2008-05-15
Modeling of prismatic high temperature reactors requires a high precision description due to the triple heterogeneity of the core and also to the random distribution of fuel particles inside the fuel pins. On the latter issue, even with the most advanced Monte Carlo techniques, some approximation often arises while assessing the criticality level: first, a regular lattice of TRISO particles inside the fuel pins and, second, the cutting of TRISO particles by the fuel boundaries. We utilized two of the most accurate Monte Codes: MONK and MCNP, which are both used for licensing nuclear power plants in United Kingdom and in the USA, respectively, to evaluate the influence of the two previous approximations on estimating the criticality level of the Gas Turbine Modular Helium Reactor. The two codes exactly shared the same geometry and nuclear data library, ENDF/B, and only modeled different lattices of TRISO particles inside the fuel pins. More precisely, we investigated the difference between a regular lattice that cuts TRISO particles and a random lattice that axially repeats a region containing over 3000 non-cut particles. We have found that both Monte Carlo codes provide similar excesses of reactivity, provided that they share the same approximations.
Study on Quantification for Multi-unit Seismic PSA Model using Monte Carlo Sampling
International Nuclear Information System (INIS)
Oh, Kyemin; Han, Sang Hoon; Jang, Seung-cheol; Park, Jin Hee; Lim, Ho-Gon; Yang, Joon Eon; Heo, Gyunyoung
2015-01-01
In existing PSA, frequency for accident sequences occurred in single-unit has been estimated. While multi-unit PSA has to consider various combinations because accident sequence in each units can be different. However, it is difficult to quantify all of combination between inter-units using traditional method such as Minimal Cut Upper Bound (MCUB). For this reason, we used Monte Carlo sampling as a method to quantify multi-unit PSA model. In this paper, Monte Carlo method was used to quantify multi-unit PSA model. The advantage of this method is to consider all of combinations by the increase of number of unit and to calculate nearly exact value compared to other method. However, it is difficult to get detailed information such as minimal cut sets and accident sequence. To solve partially this problem, FTeMC was modified. In multi-unit PSA, quantification for both internal and external multi-unit accidents is the significant issue. Although our result above mentioned was one of the case studies to check application of method suggested in this paper, it is expected that this method can be used in practical assessment for multi-unit risk
Pfefer, T Joshua; Wang, Quanzeng; Drezek, Rebekah A
2011-11-01
Computational approaches for simulation of light-tissue interactions have provided extensive insight into biophotonic procedures for diagnosis and therapy. However, few studies have addressed simulation of time-resolved fluorescence (TRF) in tissue and none have combined Monte Carlo simulations with standard TRF processing algorithms to elucidate approaches for cancer detection in layered biological tissue. In this study, we investigate how illumination-collection parameters (e.g., collection angle and source-detector separation) influence the ability to measure fluorophore lifetime and tissue layer thickness. Decay curves are simulated with a Monte Carlo TRF light propagation model. Multi-exponential iterative deconvolution is used to determine lifetimes and fractional signal contributions. The ability to detect changes in mucosal thickness is optimized by probes that selectively interrogate regions superficial to the mucosal-submucosal boundary. Optimal accuracy in simultaneous determination of lifetimes in both layers is achieved when each layer contributes 40-60% of the signal. These results indicate that depth-selective approaches to TRF have the potential to enhance disease detection in layered biological tissue and that modeling can play an important role in probe design optimization. Published by Elsevier Ireland Ltd.
Nonlinear Monte Carlo model of superdiffusive shock acceleration with magnetic field amplification
Bykov, Andrei M.; Ellison, Donald C.; Osipov, Sergei M.
2017-03-01
Fast collisionless shocks in cosmic plasmas convert their kinetic energy flow into the hot downstream thermal plasma with a substantial fraction of energy going into a broad spectrum of superthermal charged particles and magnetic fluctuations. The superthermal particles can penetrate into the shock upstream region producing an extended shock precursor. The cold upstream plasma flow is decelerated by the force provided by the superthermal particle pressure gradient. In high Mach number collisionless shocks, efficient particle acceleration is likely coupled with turbulent magnetic field amplification (MFA) generated by the anisotropic distribution of accelerated particles. This anisotropy is determined by fast particle transport, making the problem strongly nonlinear and multiscale. Here, we present a nonlinear Monte Carlo model of collisionless shock structure with superdiffusive propagation of high-energy Fermi accelerated particles coupled to particle acceleration and MFA, which affords a consistent description of strong shocks. A distinctive feature of the Monte Carlo technique is that it includes the full angular anisotropy of the particle distribution at all precursor positions. The model reveals that the superdiffusive transport of energetic particles (i.e., Lévy-walk propagation) generates a strong quadruple anisotropy in the precursor particle distribution. The resultant pressure anisotropy of the high-energy particles produces a nonresonant mirror-type instability that amplifies compressible wave modes with wavelengths longer than the gyroradii of the highest-energy protons produced by the shock.
Analysis of real-time mixture cytotoxicity data following repeated exposure using BK/TD models
International Nuclear Information System (INIS)
Teng, S.; Tebby, C.; Barcellini-Couget, S.; De Sousa, G.; Brochot, C.; Rahmani, R.; Pery, A.R.R.
2016-01-01
Cosmetic products generally consist of multiple ingredients. Thus, cosmetic risk assessment has to deal with mixture toxicity on a long-term scale which means it has to be assessed in the context of repeated exposure. Given that animal testing has been banned for cosmetics risk assessment, in vitro assays allowing long-term repeated exposure and adapted for in vitro – in vivo extrapolation need to be developed. However, most in vitro tests only assess short-term effects and consider static endpoints which hinder extrapolation to realistic human exposure scenarios where concentration in target organs is varies over time. Thanks to impedance metrics, real-time cell viability monitoring for repeated exposure has become possible. We recently constructed biokinetic/toxicodynamic models (BK/TD) to analyze such data (Teng et al., 2015) for three hepatotoxic cosmetic ingredients: coumarin, isoeugenol and benzophenone-2. In the present study, we aim to apply these models to analyze the dynamics of mixture impedance data using the concepts of concentration addition and independent action. Metabolic interactions between the mixture components were investigated, characterized and implemented in the models, as they impacted the actual cellular exposure. Indeed, cellular metabolism following mixture exposure induced a quick disappearance of the compounds from the exposure system. We showed that isoeugenol substantially decreased the metabolism of benzophenone-2, reducing the disappearance of this compound and enhancing its in vitro toxicity. Apart from this metabolic interaction, no mixtures showed any interaction, and all binary mixtures were successfully modeled by at least one model based on exposure to the individual compounds. - Highlights: • We could predict cell response over repeated exposure to mixtures of cosmetics. • Compounds acted independently on the cells. • Metabolic interactions impacted exposure concentrations to the compounds.
Analysis of real-time mixture cytotoxicity data following repeated exposure using BK/TD models
Energy Technology Data Exchange (ETDEWEB)
Teng, S.; Tebby, C. [Models for Toxicology and Ecotoxicology Unit, INERIS, Parc Technologique Alata, BP 2, 60550 Verneuil-en-Halatte (France); Barcellini-Couget, S. [ODESIA Neosciences, Sophia Antipolis, 400 route des chappes, 06903 Sophia Antipolis (France); De Sousa, G. [INRA, ToxAlim, 400 route des Chappes, BP, 167 06903 Sophia Antipolis, Cedex (France); Brochot, C. [Models for Toxicology and Ecotoxicology Unit, INERIS, Parc Technologique Alata, BP 2, 60550 Verneuil-en-Halatte (France); Rahmani, R. [INRA, ToxAlim, 400 route des Chappes, BP, 167 06903 Sophia Antipolis, Cedex (France); Pery, A.R.R., E-mail: alexandre.pery@agroparistech.fr [AgroParisTech, UMR 1402 INRA-AgroParisTech Ecosys, 78850 Thiverval Grignon (France); INRA, UMR 1402 INRA-AgroParisTech Ecosys, 78850 Thiverval Grignon (France)
2016-08-15
Cosmetic products generally consist of multiple ingredients. Thus, cosmetic risk assessment has to deal with mixture toxicity on a long-term scale which means it has to be assessed in the context of repeated exposure. Given that animal testing has been banned for cosmetics risk assessment, in vitro assays allowing long-term repeated exposure and adapted for in vitro – in vivo extrapolation need to be developed. However, most in vitro tests only assess short-term effects and consider static endpoints which hinder extrapolation to realistic human exposure scenarios where concentration in target organs is varies over time. Thanks to impedance metrics, real-time cell viability monitoring for repeated exposure has become possible. We recently constructed biokinetic/toxicodynamic models (BK/TD) to analyze such data (Teng et al., 2015) for three hepatotoxic cosmetic ingredients: coumarin, isoeugenol and benzophenone-2. In the present study, we aim to apply these models to analyze the dynamics of mixture impedance data using the concepts of concentration addition and independent action. Metabolic interactions between the mixture components were investigated, characterized and implemented in the models, as they impacted the actual cellular exposure. Indeed, cellular metabolism following mixture exposure induced a quick disappearance of the compounds from the exposure system. We showed that isoeugenol substantially decreased the metabolism of benzophenone-2, reducing the disappearance of this compound and enhancing its in vitro toxicity. Apart from this metabolic interaction, no mixtures showed any interaction, and all binary mixtures were successfully modeled by at least one model based on exposure to the individual compounds. - Highlights: • We could predict cell response over repeated exposure to mixtures of cosmetics. • Compounds acted independently on the cells. • Metabolic interactions impacted exposure concentrations to the compounds.
Modelling time course gene expression data with finite mixtures of linear additive models.
Grün, Bettina; Scharl, Theresa; Leisch, Friedrich
2012-01-15
A model class of finite mixtures of linear additive models is presented. The component-specific parameters in the regression models are estimated using regularized likelihood methods. The advantages of the regularization are that (i) the pre-specified maximum degrees of freedom for the splines is less crucial than for unregularized estimation and that (ii) for each component individually a suitable degree of freedom is selected in an automatic way. The performance is evaluated in a simulation study with artificial data as well as on a yeast cell cycle dataset of gene expression levels over time. The latest release version of the R package flexmix is available from CRAN (http://cran.r-project.org/).
Robust non-rigid point set registration using student's-t mixture model.
Directory of Open Access Journals (Sweden)
Zhiyong Zhou
Full Text Available The Student's-t mixture model, which is heavily tailed and more robust than the Gaussian mixture model, has recently received great attention on image processing. In this paper, we propose a robust non-rigid point set registration algorithm using the Student's-t mixture model. Specifically, first, we consider the alignment of two point sets as a probability density estimation problem and treat one point set as Student's-t mixture model centroids. Then, we fit the Student's-t mixture model centroids to the other point set which is treated as data. Finally, we get the closed-form solutions of registration parameters, leading to a computationally efficient registration algorithm. The proposed algorithm is especially effective for addressing the non-rigid point set registration problem when significant amounts of noise and outliers are present. Moreover, less registration parameters have to be set manually for our algorithm compared to the popular coherent points drift (CPD algorithm. We have compared our algorithm with other state-of-the-art registration algorithms on both 2D and 3D data with noise and outliers, where our non-rigid registration algorithm showed accurate results and outperformed the other algorithms.
Introduction to the special section on mixture modeling in personality assessment.
Wright, Aidan G C; Hallquist, Michael N
2014-01-01
Latent variable models offer a conceptual and statistical framework for evaluating the underlying structure of psychological constructs, including personality and psychopathology. Complex structures that combine or compare categorical and dimensional latent variables can be accommodated using mixture modeling approaches, which provide a powerful framework for testing nuanced theories about psychological structure. This special series includes introductory primers on cross-sectional and longitudinal mixture modeling, in addition to empirical examples applying these techniques to real-world data collected in clinical settings. This group of articles is designed to introduce personality assessment scientists and practitioners to a general latent variable framework that we hope will stimulate new research and application of mixture models to the assessment of personality and its pathology.
A BGK model for reactive mixtures of polyatomic gases with continuous internal energy
Bisi, M.; Monaco, R.; Soares, A. J.
2018-03-01
In this paper we derive a BGK relaxation model for a mixture of polyatomic gases with a continuous structure of internal energies. The emphasis of the paper is on the case of a quaternary mixture undergoing a reversible chemical reaction of bimolecular type. For such a mixture we prove an H -theorem and characterize the equilibrium solutions with the related mass action law of chemical kinetics. Further, a Chapman-Enskog asymptotic analysis is performed in view of computing the first-order non-equilibrium corrections to the distribution functions and investigating the transport properties of the reactive mixture. The chemical reaction rate is explicitly derived at the first order and the balance equations for the constituent number densities are derived at the Euler level.
Transport appraisal and Monte Carlo simulation by use of the CBA-DK model
DEFF Research Database (Denmark)
Salling, Kim Bang; Leleur, Steen
2011-01-01
calculation, where risk analysis is carried out using Monte Carlo simulation. Special emphasis has been placed on the separation between inherent randomness in the modeling system and lack of knowledge. These two concepts have been defined in terms of variability (ontological uncertainty) and uncertainty......This paper presents the Danish CBA-DK software model for assessment of transport infrastructure projects. The assessment model is based on both a deterministic calculation following the cost-benefit analysis (CBA) methodology in a Danish manual from the Ministry of Transport and on a stochastic...... (epistemic uncertainty). After a short introduction to deterministic calculation resulting in some evaluation criteria a more comprehensive evaluation of the stochastic calculation is made. Especially, the risk analysis part of CBA-DK, with considerations about which probability distributions should be used...
Kinetic Monte Carlo modeling of chemical reactions coupled with heat transfer.
Castonguay, Thomas C; Wang, Feng
2008-03-28
In this paper, we describe two types of effective events for describing heat transfer in a kinetic Monte Carlo (KMC) simulation that may involve stochastic chemical reactions. Simulations employing these events are referred to as KMC-TBT and KMC-PHE. In KMC-TBT, heat transfer is modeled as the stochastic transfer of "thermal bits" between adjacent grid points. In KMC-PHE, heat transfer is modeled by integrating the Poisson heat equation for a short time. Either approach is capable of capturing the time dependent system behavior exactly. Both KMC-PHE and KMC-TBT are validated by simulating pure heat transfer in a rod and a square and modeling a heated desorption problem where exact numerical results are available. KMC-PHE is much faster than KMC-TBT and is used to study the endothermic desorption of a lattice gas. Interesting findings from this study are reported.
Dynamic Value at Risk: A Comparative Study Between Heteroscedastic Models and Monte Carlo Simulation
Directory of Open Access Journals (Sweden)
José Lamartine Távora Junior
2006-12-01
Full Text Available The objective of this paper was to analyze the risk management of a portfolio composed by Petrobras PN, Telemar PN and Vale do Rio Doce PNA stocks. It was verified if the modeling of Value-at-Risk (VaR through the place Monte Carlo simulation with volatility of GARCH family is supported by hypothesis of efficient market. The results have shown that the statistic evaluation in inferior to dynamics, evidencing that the dynamic analysis supplies support to the hypothesis of efficient market of the Brazilian share holding market, in opposition of some empirical evidences. Also, it was verified that the GARCH models of volatility is enough to accommodate the variations of the shareholding Brazilian market, since the model is capable to accommodate the great dynamic of the Brazilian market.
Abbas, Ismail; Rovira, Joan; Casanovas, Josep
2007-05-01
The patient recruitment process of clinical trials is an essential element which needs to be designed properly. In this paper we describe different simulation models under continuous and discrete time assumptions for the design of recruitment in clinical trials. The results of hypothetical examples of clinical trial recruitments are presented. The recruitment time is calculated and the number of recruited patients is quantified for a given time and probability of recruitment. The expected delay and the effective recruitment durations are estimated using both continuous and discrete time modeling. The proposed type of Monte Carlo simulation Markov models will enable optimization of the recruitment process and the estimation and the calibration of its parameters to aid the proposed clinical trials. A continuous time simulation may minimize the duration of the recruitment and, consequently, the total duration of the trial.
Monte Carlo impurity transport modeling in the DIII-D transport
International Nuclear Information System (INIS)
Evans, T.E.; Finkenthal, D.F.
1998-04-01
A description of the carbon transport and sputtering physics contained in the Monte Carlo Impurity (MCI) transport code is given. Examples of statistically significant carbon transport pathways are examined using MCI's unique tracking visualizer and a mechanism for enhanced carbon accumulation on the high field side of the divertor chamber is discussed. Comparisons between carbon emissions calculated with MCI and those measured in the DIII-D tokamak are described. Good qualitative agreement is found between 2D carbon emission patterns calculated with MCI and experimentally measured carbon patterns. While uncertainties in the sputtering physics, atomic data, and transport models have made quantitative comparisons with experiments more difficult, recent results using a physics based model for physical and chemical sputtering has yielded simulations with about 50% of the total carbon radiation measured in the divertor. These results and plans for future improvement in the physics models and atomic data are discussed
DEFF Research Database (Denmark)
Blasone, Roberta-Serena; Madsen, Henrik; Rosbjerg, Dan
2008-01-01
uncertainty estimation (GLUE) procedure based on Markov chain Monte Carlo sampling is applied in order to improve the performance of the methodology in estimating parameters and posterior output distributions. The description of the spatial variations of the hydrological processes is accounted for by defining......In recent years, there has been an increase in the application of distributed, physically-based and integrated hydrological models. Many questions regarding how to properly calibrate and validate distributed models and assess the uncertainty of the estimated parameters and the spatially......-site validation must complement the usual time validation. In this study, we develop, through an application, a comprehensive framework for multi-criteria calibration and uncertainty assessment of distributed physically-based, integrated hydrological models. A revised version of the generalized likelihood...
Monte Carlo modelling of germanium crystals that are tilted and have rounded front edges
International Nuclear Information System (INIS)
Gasparro, Joel; Hult, Mikael; Johnston, Peter N.; Tagziria, Hamid
2008-01-01
Gamma-ray detection efficiencies and cascade summing effects in germanium detectors are often calculated using Monte Carlo codes based on a computer model of the detection system. Such a model can never fully replicate reality and it is important to understand how various parameters affect the results. This work concentrates on quantifying two issues, namely (i) the effect of having a Ge-crystal that is tilted inside the cryostat and (ii) the effect of having a model of a Ge-crystal with rounded edges (bulletization). The effect of the tilting is very small (in the order of per mille) when the tilting angles are within a realistic range. The effect of the rounded edges is, however, relatively large (5-10% or higher) particularly for gamma-ray energies below 100 keV
Monte Carlo modelling of germanium crystals that are tilted and have rounded front edges
Energy Technology Data Exchange (ETDEWEB)
Gasparro, Joel [EC-JRC-IRMM, Institute for Reference Materials and Measurements, Retieseweg 111, B-2440 Geel (Belgium); Hult, Mikael [EC-JRC-IRMM, Institute for Reference Materials and Measurements, Retieseweg 111, B-2440 Geel (Belgium)], E-mail: mikael.hult@ec.europa.eu; Johnston, Peter N. [Applied Physics, Royal Melbourne Institute of Technology, GPO Box 2476V, Melbourne 3001 (Australia); Tagziria, Hamid [EC-JRC-IPSC, Institute for the Protection and the Security of the Citizen, Via E. Fermi 1, I-21020 Ispra (Vatican City State, Holy See,) (Italy)
2008-09-01
Gamma-ray detection efficiencies and cascade summing effects in germanium detectors are often calculated using Monte Carlo codes based on a computer model of the detection system. Such a model can never fully replicate reality and it is important to understand how various parameters affect the results. This work concentrates on quantifying two issues, namely (i) the effect of having a Ge-crystal that is tilted inside the cryostat and (ii) the effect of having a model of a Ge-crystal with rounded edges (bulletization). The effect of the tilting is very small (in the order of per mille) when the tilting angles are within a realistic range. The effect of the rounded edges is, however, relatively large (5-10% or higher) particularly for gamma-ray energies below 100 keV.
Monte Carlo modeling of fiber-scintillator flow-cell radiation detector geometry
International Nuclear Information System (INIS)
Rucker, T.L.; Ross, H.H.; Tennessee Univ., Knoxville; Schweitzer, G.K.
1988-01-01
A Monte Carlo computer calculation is described which models the geometric efficiency of a fiber-scintillator flow-cell radiation detector designed to detect radiolabeled compounds in liquid chromatography eluates. By using special mathematical techniques, an efficiency prediction with a precision of 1% is obtained after generating only 1000 random events. Good agreement is seen between predicted and experimental efficiency except for very low energy beta emission where the geometric limitation on efficiency is overcome by pulse height limitations which the model does not consider. The modeling results show that in the test system, the detection efficiency for low energy beta emitters is limited primarily by light generation and collection rather than geometry. (orig.)
Modelling phase separation in Fe-Cr system using different atomistic kinetic Monte Carlo techniques
International Nuclear Information System (INIS)
Castin, N.; Bonny, G.; Terentyev, D.; Lavrentiev, M.Yu.; Nguyen-Manh, D.
2011-01-01
Atomistic kinetic Monte Carlo (AKMC) simulations were performed to study α-α' phase separation in Fe-Cr alloys. Two different energy models and two approaches to estimate the local vacancy migration barriers were used. The energy models considered are a two-band model Fe-Cr potential and a cluster expansion, both fitted to ab initio data. The classical Kang-Weinberg decomposition, based on the total energy change of the system, and an Artificial Neural Network (ANN), employed as a regression tool were used to predict the local vacancy migration barriers 'on the fly'. The results are compared with experimental thermal annealing data and differences between the applied AKMC approaches are discussed. The ability of the ANN regression method to accurately predict migration barriers not present in the training list is also addressed by performing cross-check calculations using the nudged elastic band method.
Martin-Bragado, I.; Castrillo, P.; Jaraiz, M.; Pinacho, R.; Rubio, J. E.; Barbolla, J.; Moroz, V.
2005-09-01
Atomistic process simulation is expected to play an important role for the development of next generations of integrated circuits. This work describes an approach for modeling electric charge effects in a three-dimensional atomistic kinetic Monte Carlo process simulator. The proposed model has been applied to the diffusion of electrically active boron and arsenic atoms in silicon. Several key aspects of the underlying physical mechanisms are discussed: (i) the use of the local Debye length to smooth out the atomistic point-charge distribution, (ii) algorithms to correctly update the charge state in a physically accurate and computationally efficient way, and (iii) an efficient implementation of the drift of charged particles in an electric field. High-concentration effects such as band-gap narrowing and degenerate statistics are also taken into account. The efficiency, accuracy, and relevance of the model are discussed.
Directory of Open Access Journals (Sweden)
Jacek Namieśnik
2013-04-01
Full Text Available The paper presents the potential of an electronic nose technique in the field of fast diagnostics of patients suspected of Chronic Obstructive Pulmonary Disease (COPD. The investigations were performed using a simple electronic nose prototype equipped with a set of six semiconductor sensors manufactured by FIGARO Co. They were aimed at verification of a possibility of differentiation between model reference mixtures with potential COPD markers (N,N-dimethylformamide and N,N-dimethylacetamide. These mixtures contained volatile organic compounds (VOCs such as acetone, isoprene, carbon disulphide, propan-2-ol, formamide, benzene, toluene, acetonitrile, acetic acid, dimethyl ether, dimethyl sulphide, acrolein, furan, propanol and pyridine, recognized as the components of exhaled air. The model reference mixtures were prepared at three concentration levels—10 ppb, 25 ppb, 50 ppb v/v—of each component, except for the COPD markers. Concentration of the COPD markers in the mixtures was from 0 ppb to 100 ppb v/v. Interpretation of the obtained data employed principal component analysis (PCA. The investigations revealed the usefulness of the electronic device only in the case when the concentration of the COPD markers was twice as high as the concentration of the remaining components of the mixture and for a limited number of basic mixture components.
A numerical model for boiling heat transfer coefficient of zeotropic mixtures
Barraza Vicencio, Rodrigo; Caviedes Aedo, Eduardo
2017-12-01
Zeotropic mixtures never have the same liquid and vapor composition in the liquid-vapor equilibrium. Also, the bubble and the dew point are separated; this gap is called glide temperature (Tglide). Those characteristics have made these mixtures suitable for cryogenics Joule-Thomson (JT) refrigeration cycles. Zeotropic mixtures as working fluid in JT cycles improve their performance in an order of magnitude. Optimization of JT cycles have earned substantial importance for cryogenics applications (e.g, gas liquefaction, cryosurgery probes, cooling of infrared sensors, cryopreservation, and biomedical samples). Heat exchangers design on those cycles is a critical point; consequently, heat transfer coefficient and pressure drop of two-phase zeotropic mixtures are relevant. In this work, it will be applied a methodology in order to calculate the local convective heat transfer coefficients based on the law of the wall approach for turbulent flows. The flow and heat transfer characteristics of zeotropic mixtures in a heated horizontal tube are investigated numerically. The temperature profile and heat transfer coefficient for zeotropic mixtures of different bulk compositions are analysed. The numerical model has been developed and locally applied in a fully developed, constant temperature wall, and two-phase annular flow in a duct. Numerical results have been obtained using this model taking into account continuity, momentum, and energy equations. Local heat transfer coefficient results are compared with available experimental data published by Barraza et al. (2016), and they have shown good agreement.
A Bayesian approach to the analysis of quantal bioassay studies using nonparametric mixture models.
Fronczyk, Kassandra; Kottas, Athanasios
2014-03-01
We develop a Bayesian nonparametric mixture modeling framework for quantal bioassay settings. The approach is built upon modeling dose-dependent response distributions. We adopt a structured nonparametric prior mixture model, which induces a monotonicity restriction for the dose-response curve. Particular emphasis is placed on the key risk assessment goal of calibration for the dose level that corresponds to a specified response. The proposed methodology yields flexible inference for the dose-response relationship as well as for other inferential objectives, as illustrated with two data sets from the literature. © 2013, The International Biometric Society.
A Monte Carlo-based model for simulation of digital chest tomo-synthesis
International Nuclear Information System (INIS)
Ullman, G.; Dance, D. R.; Sandborg, M.; Carlsson, G. A.; Svalkvist, A.; Baath, M.
2010-01-01
The aim of this work was to calculate synthetic digital chest tomo-synthesis projections using a computer simulation model based on the Monte Carlo method. An anthropomorphic chest phantom was scanned in a computed tomography scanner, segmented and included in the computer model to allow for simulation of realistic high-resolution X-ray images. The input parameters to the model were adapted to correspond to the VolumeRAD chest tomo-synthesis system from GE Healthcare. Sixty tomo-synthesis projections were calculated with projection angles ranging from + 15 to -15 deg. The images from primary photons were calculated using an analytical model of the anti-scatter grid and a pre-calculated detector response function. The contributions from scattered photons were calculated using an in-house Monte Carlo-based model employing a number of variance reduction techniques such as the collision density estimator. Tomographic section images were reconstructed by transferring the simulated projections into the VolumeRAD system. The reconstruction was performed for three types of images using: (i) noise-free primary projections, (ii) primary projections including contributions from scattered photons and (iii) projections as in (ii) with added correlated noise. The simulated section images were compared with corresponding section images from projections taken with the real, anthropomorphic phantom from which the digital voxel phantom was originally created. The present article describes a work in progress aiming towards developing a model intended for optimisation of chest tomo-synthesis, allowing for simulation of both existing and future chest tomo-synthesis systems. (authors)
Using a Monte Carlo model to predict dosimetric properties of small radiotherapy photon fields
International Nuclear Information System (INIS)
Scott, Alison J. D.; Nahum, Alan E.; Fenwick, John D.
2008-01-01
Accurate characterization of small-field dosimetry requires measurements to be made with precisely aligned specialized detectors and is thus time consuming and error prone. This work explores measurement differences between detectors by using a Monte Carlo model matched to large-field data to predict properties of smaller fields. Measurements made with a variety of detectors have been compared with calculated results to assess their validity and explore reasons for differences. Unshielded diodes are expected to produce some of the most useful data, as their small sensitive cross sections give good resolution whilst their energy dependence is shown to vary little with depth in a 15 MV linac beam. Their response is shown to be constant with field size over the range 1-10 cm, with a correction of 3% needed for a field size of 0.5 cm. BEAMnrc has been used to create a 15 MV beam model, matched to dosimetric data for square fields larger than 3 cm, and producing small-field profiles and percentage depth doses (PDDs) that agree well with unshielded diode data for field sizes down to 0.5 cm. For fields sizes of 1.5 cm and above, little detector-to-detector variation exists in measured output factors, however for a 0.5 cm field a relative spread of 18% is seen between output factors measured with different detectors--values measured with the diamond and pinpoint detectors lying below that of the unshielded diode, with the shielded diode value being higher. Relative to the corrected unshielded diode measurement, the Monte Carlo modeled output factor is 4.5% low, a discrepancy that is probably due to the focal spot fluence profile and source occlusion modeling. The large-field Monte Carlo model can, therefore, currently be used to predict small-field profiles and PDDs measured with an unshielded diode. However, determination of output factors for the smallest fields requires a more detailed model of focal spot fluence and source occlusion.
Full modelling of the MOSAIC animal PET system based on the GATE Monte Carlo simulation code
International Nuclear Information System (INIS)
Merheb, C; Petegnief, Y; Talbot, J N
2007-01-01
Positron emission tomography (PET) systems dedicated to animal imaging are now widely used for biological studies. The scanner performance strongly depends on the design and the characteristics of the system. Many parameters must be optimized like the dimensions and type of crystals, geometry and field-of-view (FOV), sampling, electronics, lightguide, shielding, etc. Monte Carlo modelling is a powerful tool to study the effect of each of these parameters on the basis of realistic simulated data. Performance assessment in terms of spatial resolution, count rates, scatter fraction and sensitivity is an important prerequisite before the model can be used instead of real data for a reliable description of the system response function or for optimization of reconstruction algorithms. The aim of this study is to model the performance of the Philips Mosaic(TM) animal PET system using a comprehensive PET simulation code in order to understand and describe the origin of important factors that influence image quality. We use GATE, a Monte Carlo simulation toolkit for a realistic description of the ring PET model, the detectors, shielding, cap, electronic processing and dead times. We incorporate new features to adjust signal processing to the Anger logic underlying the Mosaic(TM) system. Special attention was paid to dead time and energy spectra descriptions. Sorting of simulated events in a list mode format similar to the system outputs was developed to compare experimental and simulated sensitivity and scatter fractions for different energy thresholds using various models of phantoms describing rat and mouse geometries. Count rates were compared for both cylindrical homogeneous phantoms. Simulated spatial resolution was fitted to experimental data for 18 F point sources at different locations within the FOV with an analytical blurring function for electronic processing effects. Simulated and measured sensitivities differed by less than 3%, while scatter fractions agreed
Konishi, C.
2014-12-01
Gravel-sand-clay mixture model is proposed particularly for unconsolidated sediments to predict permeability and velocity from volume fractions of the three components (i.e. gravel, sand, and clay). A well-known sand-clay mixture model or bimodal mixture model treats clay contents as volume fraction of the small particle and the rest of the volume is considered as that of the large particle. This simple approach has been commonly accepted and has validated by many studies before. However, a collection of laboratory measurements of permeability and grain size distribution for unconsolidated samples show an impact of presence of another large particle; i.e. only a few percent of gravel particles increases the permeability of the sample significantly. This observation cannot be explained by the bimodal mixture model and it suggests the necessity of considering the gravel-sand-clay mixture model. In the proposed model, I consider the three volume fractions of each component instead of using only the clay contents. Sand becomes either larger or smaller particles in the three component mixture model, whereas it is always the large particle in the bimodal mixture model. The total porosity of the two cases, one is the case that the sand is smaller particle and the other is the case that the sand is larger particle, can be modeled independently from sand volume fraction by the same fashion in the bimodal model. However, the two cases can co-exist in one sample; thus, the total porosity of the mixed sample is calculated by weighted average of the two cases by the volume fractions of gravel and clay. The effective porosity is distinguished from the total porosity assuming that the porosity associated with clay is zero effective porosity. In addition, effective grain size can be computed from the volume fractions and representative grain sizes for each component. Using the effective porosity and the effective grain size, the permeability is predicted by Kozeny-Carman equation
A general mixture model and its application to coastal sandbar migration simulation
Liang, Lixin; Yu, Xiping
2017-04-01
A mixture model for general description of sediment laden flows is developed and then applied to coastal sandbar migration simulation. Firstly the mixture model is derived based on the Eulerian-Eulerian approach of the complete two-phase flow theory. The basic equations of the model include the mass and momentum conservation equations for the water-sediment mixture and the continuity equation for sediment concentration. The turbulent motion of the mixture is formulated for the fluid and the particles respectively. A modified k-ɛ model is used to describe the fluid turbulence while an algebraic model is adopted for the particles. A general formulation for the relative velocity between the two phases in sediment laden flows, which is derived by manipulating the momentum equations of the enhanced two-phase flow model, is incorporated into the mixture model. A finite difference method based on SMAC scheme is utilized for numerical solutions. The model is validated by suspended sediment motion in steady open channel flows, both in equilibrium and non-equilibrium state, and in oscillatory flows as well. The computed sediment concentrations, horizontal velocity and turbulence kinetic energy of the mixture are all shown to be in good agreement with experimental data. The mixture model is then applied to the study of sediment suspension and sandbar migration in surf zones under a vertical 2D framework. The VOF method for the description of water-air free surface and topography reaction model is coupled. The bed load transport rate and suspended load entrainment rate are all decided by the sea bed shear stress, which is obtained from the boundary layer resolved mixture model. The simulation results indicated that, under small amplitude regular waves, erosion occurred on the sandbar slope against the wave propagation direction, while deposition dominated on the slope towards wave propagation, indicating an onshore migration tendency. The computation results also shows that
Convex-based void filling method for CAD-based Monte Carlo geometry modeling
International Nuclear Information System (INIS)
Yu, Shengpeng; Cheng, Mengyun; Song, Jing; Long, Pengcheng; Hu, Liqin
2015-01-01
Highlights: • We present a new void filling method named CVF for CAD based MC geometry modeling. • We describe convex based void description based and quality-based space subdivision. • The results showed improvements provided by CVF for both modeling and MC calculation efficiency. - Abstract: CAD based automatic geometry modeling tools have been widely applied to generate Monte Carlo (MC) calculation geometry for complex systems according to CAD models. Automatic void filling is one of the main functions in the CAD based MC geometry modeling tools, because the void space between parts in CAD models is traditionally not modeled while MC codes such as MCNP need all the problem space to be described. A dedicated void filling method, named Convex-based Void Filling (CVF), is proposed in this study for efficient void filling and concise void descriptions. The method subdivides all the problem space into disjointed regions using Quality based Subdivision (QS) and describes the void space in each region with complementary descriptions of the convex volumes intersecting with that region. It has been implemented in SuperMC/MCAM, the Multiple-Physics Coupling Analysis Modeling Program, and tested on International Thermonuclear Experimental Reactor (ITER) Alite model. The results showed that the new method reduced both automatic modeling time and MC calculation time
Gao, Yongfei; Feng, Jianfeng; Kang, Lili; Xu, Xin; Zhu, Lin
2018-01-01
The joint toxicity of chemical mixtures has emerged as a popular topic, particularly on the additive and potential synergistic actions of environmental mixtures. We investigated the 24h toxicity of Cu-Zn, Cu-Cd, and Cu-Pb and 96h toxicity of Cd-Pb binary mixtures on the survival of zebrafish larvae. Joint toxicity was predicted and compared using the concentration addition (CA) and independent action (IA) models with different assumptions in the toxic action mode in toxicodynamic processes through single and binary metal mixture tests. Results showed that the CA and IA models presented varying predictive abilities for different metal combinations. For the Cu-Cd and Cd-Pb mixtures, the CA model simulated the observed survival rates better than the IA model. By contrast, the IA model simulated the observed survival rates better than the CA model for the Cu-Zn and Cu-Pb mixtures. These findings revealed that the toxic action mode may depend on the combinations and concentrations of tested metal mixtures. Statistical analysis of the antagonistic or synergistic interactions indicated that synergistic interactions were observed for the Cu-Cd and Cu-Pb mixtures, non-interactions were observed for the Cd-Pb mixtures, and slight antagonistic interactions for the Cu-Zn mixtures. These results illustrated that the CA and IA models are consistent in specifying the interaction patterns of binary metal mixtures. Copyright © 2017 Elsevier B.V. All rights reserved.
Modeling of nanoscale liquid mixture transport by density functional hydrodynamics
Dinariev, Oleg Yu.; Evseev, Nikolay V.
2017-06-01
Modeling of multiphase compositional hydrodynamics at nanoscale is performed by means of density functional hydrodynamics (DFH). DFH is the method based on density functional theory and continuum mechanics. This method has been developed by the authors over 20 years and used for modeling in various multiphase hydrodynamic applications. In this paper, DFH was further extended to encompass phenomena inherent in liquids at nanoscale. The new DFH extension is based on the introduction of external potentials for chemical components. These potentials are localized in the vicinity of solid surfaces and take account of the van der Waals forces. A set of numerical examples, including disjoining pressure, film precursors, anomalous rheology, liquid in contact with heterogeneous surface, capillary condensation, and forward and reverse osmosis, is presented to demonstrate modeling capabilities.
A Frank mixture copula family for modeling higher-order correlations of neural spike counts
International Nuclear Information System (INIS)
Onken, Arno; Obermayer, Klaus
2009-01-01
In order to evaluate the importance of higher-order correlations in neural spike count codes, flexible statistical models of dependent multivariate spike counts are required. Copula families, parametric multivariate distributions that represent dependencies, can be applied to construct such models. We introduce the Frank mixture family as a new copula family that has separate parameters for all pairwise and higher-order correlations. In contrast to the Farlie-Gumbel-Morgenstern copula family that shares this property, the Frank mixture copula can model strong correlations. We apply spike count models based on the Frank mixture copula to data generated by a network of leaky integrate-and-fire neurons and compare the goodness of fit to distributions based on the Farlie-Gumbel-Morgenstern family. Finally, we evaluate the importance of using proper single neuron spike count distributions on the Shannon information. We find notable deviations in the entropy that increase with decreasing firing rates. Moreover, we find that the Frank mixture family increases the log likelihood of the fit significantly compared to the Farlie-Gumbel-Morgenstern family. This shows that the Frank mixture copula is a useful tool to assess the importance of higher-order correlations in spike count codes.
A nonlinear isobologram model with Box-Cox transformation to both sides for chemical mixtures.
Chen, D G; Pounds, J G
1998-12-01
The linear logistical isobologram is a commonly used and powerful graphical and statistical tool for analyzing the combined effects of simple chemical mixtures. In this paper a nonlinear isobologram model is proposed to analyze the joint action of chemical mixtures for quantitative dose-response relationships. This nonlinear isobologram model incorporates two additional new parameters, Ymin and Ymax, to facilitate analysis of response data that are not constrained between 0 and 1, where parameters Ymin and Ymax represent the minimal and the maximal observed toxic response. This nonlinear isobologram model for binary mixtures can be expressed as [formula: see text] In addition, a Box-Cox transformation to both sides is introduced to improve the goodness of fit and to provide a more robust model for achieving homogeneity and normality of the residuals. Finally, a confidence band is proposed for selected isobols, e.g., the median effective dose, to facilitate graphical and statistical analysis of the isobologram. The versatility of this approach is demonstrated using published data describing the toxicity of the binary mixtures of citrinin and ochratoxin as well as a new experimental data from our laboratory for mixtures of mercury and cadmium.
Mixtures of skewed Kalman filters
Kim, Hyoungmoon
2014-01-01
Normal state-space models are prevalent, but to increase the applicability of the Kalman filter, we propose mixtures of skewed, and extended skewed, Kalman filters. To do so, the closed skew-normal distribution is extended to a scale mixture class of closed skew-normal distributions. Some basic properties are derived and a class of closed skew. t distributions is obtained. Our suggested family of distributions is skewed and has heavy tails too, so it is appropriate for robust analysis. Our proposed special sequential Monte Carlo methods use a random mixture of the closed skew-normal distributions to approximate a target distribution. Hence it is possible to handle skewed and heavy tailed data simultaneously. These methods are illustrated with numerical experiments. © 2013 Elsevier Inc.
Modeling diffusion coefficients in binary mixtures of polar and non-polar compounds
DEFF Research Database (Denmark)
Medvedev, Oleg; Shapiro, Alexander
2005-01-01
The theory of transport coefficients in liquids, developed previously, is tested on a description of the diffusion coefficients in binary polar/non-polar mixtures, by applying advanced thermodynamic models. Comparison to a large set of experimental data shows good performance of the model. Only f...
Johnson, David L.; Jansen, Ritsert C.; Arendonk, Johan A.M. van
1999-01-01
A mixture model approach is employed for the mapping of quantitative trait loci (QTL) for the situation where individuals, in an outbred population, are selectively genotyped. Maximum likelihood estimation of model parameters is obtained from an Expectation-Maximization (EM) algorithm facilitated by
Growth Kinetics and Modeling of Direct Oxynitride Growth with NO-O2 Gas Mixtures
Energy Technology Data Exchange (ETDEWEB)
Everist, Sarah; Nelson, Jerry; Sharangpani, Rahul; Smith, Paul Martin; Tay, Sing-Pin; Thakur, Randhir
1999-05-03
We have modeled growth kinetics of oxynitrides grown in NO-O_{2} gas mixtures from first principles using modified Deal-Grove equations. Retardation of oxygen diffusion through the nitrided dielectric was assumed to be the dominant growth-limiting step. The model was validated against experimentally obtained curves with good agreement. Excellent uniformity, which exceeded expected walues, was observed.
DEFF Research Database (Denmark)
Kogelman, Lisette; Trabzuni, Daniah; Bonder, Marc Jan
effects of the interactions between tissues and probes using BLUP (Best Linear Unbiased Prediction) linear models correcting for gender, which were subsequently used in a finite mixture model to detect DE genes in each tissue. This approach evades the multiple-testing problem and is able to detect...
Smoothed particle hydrodynamics model for phase separating fluid mixtures. I. General equations
Thieulot, C; Janssen, LPBM; Espanol, P
We present a thermodynamically consistent discrete fluid particle model for the simulation of a recently proposed set of hydrodynamic equations for a phase separating van der Waals fluid mixture [P. Espanol and C.A.P. Thieulot, J. Chem. Phys. 118, 9109 (2003)]. The discrete model is formulated by
Duarte, João C.; Schellart, Wouter P.; Cruden, Alexander R.
2014-01-01
Paraffins have been widely used in analogue modelling of geological processes. Petrolatum and paraffin oil are commonly used to lubricate model boundaries and to simulate weak layers. In this paper, we present rheological tests of petrolatum, paraffin oil and several homogeneous mixtures of the two.
Maximum likelihood pixel labeling using a spatially variant finite mixture model
International Nuclear Information System (INIS)
Gopal, S.S.; Hebert, T.J.
1996-01-01
We propose a spatially-variant mixture model for pixel labeling. Based on this spatially-variant mixture model we derive an expectation maximization algorithm for maximum likelihood estimation of the pixel labels. While most algorithms using mixture models entail the subsequent use of a Bayes classifier for pixel labeling, the proposed algorithm yields maximum likelihood estimates of the labels themselves and results in unambiguous pixel labels. The proposed algorithm is fast, robust, easy to implement, flexible in that it can be applied to any arbitrary image data where the number of classes is known and, most importantly, obviates the need for an explicit labeling rule. The algorithm is evaluated both quantitatively and qualitatively on simulated data and on clinical magnetic resonance images of the human brain
Modelling of phase equilibria for associating mixtures using an equation of state
International Nuclear Information System (INIS)
Ferreira, Olga; Brignole, Esteban A.; Macedo, Eugenia A.
2004-01-01
In the present work, the group contribution with association equation of state (GCA-EoS) is extended to represent phase equilibria in mixtures containing acids, esters, and ketones, with water, alcohols, and any number of inert components. Association effects are represented by a group-contribution approach. Self- and cross-association between the associating groups present in these mixtures are considered. The GCA-EoS model is compared to the group-contribution method MHV2, which does not take into account explicitly association effects. The results obtained with the GCA-EoS model are, in general, more accurate when compared to the ones achieved by the MHV2 equation with less number of parameters. Model predictions are presented for binary self- and cross-associating mixtures
International Nuclear Information System (INIS)
Zazula, J.M.
1988-01-01
The self-learning Monte Carlo technique has been implemented to the commonly used general purpose neutron transport code MORSE, in order to enhance sampling of the particle histories that contribute to a detector response. The parameters of all the biasing techniques available in MORSE, i.e. of splitting, Russian roulette, source and collision outgoing energy importance sampling, path length transformation and additional biasing of the source angular distribution are optimized. The learning process is iteratively performed after each batch of particles, by retrieving the data concerning the subset of histories that passed the detector region and energy range in the previous batches. This procedure has been tested on two sample problems in nuclear geophysics, where an unoptimized Monte Carlo calculation is particularly inefficient. The results are encouraging, although the presented method does not directly minimize the variance and the convergence of our algorithm is restricted by the statistics of successful histories from previous random walk. Further applications for modeling of the nuclear logging measurements seem to be promising. 11 refs., 2 figs., 3 tabs. (author)
A virtual source method for Monte Carlo simulation of Gamma Knife Model C
Energy Technology Data Exchange (ETDEWEB)
Kim, Tae Hoon; Kim, Yong Kyun [Hanyang University, Seoul (Korea, Republic of); Chung, Hyun Tai [Seoul National University College of Medicine, Seoul (Korea, Republic of)
2016-05-15
The Monte Carlo simulation method has been used for dosimetry of radiation treatment. Monte Carlo simulation is the method that determines paths and dosimetry of particles using random number. Recently, owing to the ability of fast processing of the computers, it is possible to treat a patient more precisely. However, it is necessary to increase the simulation time to improve the efficiency of accuracy uncertainty. When generating the particles from the cobalt source in a simulation, there are many particles cut off. So it takes time to simulate more accurately. For the efficiency, we generated the virtual source that has the phase space distribution which acquired a single gamma knife channel. We performed the simulation using the virtual sources on the 201 channel and compared the measurement with the simulation using virtual sources and real sources. A virtual source file was generated to reduce the simulation time of a Gamma Knife Model C. Simulations with a virtual source executed about 50 times faster than the original source code and there was no statistically significant difference in simulated results.
A virtual source method for Monte Carlo simulation of Gamma Knife Model C
International Nuclear Information System (INIS)
Kim, Tae Hoon; Kim, Yong Kyun; Chung, Hyun Tai
2016-01-01
The Monte Carlo simulation method has been used for dosimetry of radiation treatment. Monte Carlo simulation is the method that determines paths and dosimetry of particles using random number. Recently, owing to the ability of fast processing of the computers, it is possible to treat a patient more precisely. However, it is necessary to increase the simulation time to improve the efficiency of accuracy uncertainty. When generating the particles from the cobalt source in a simulation, there are many particles cut off. So it takes time to simulate more accurately. For the efficiency, we generated the virtual source that has the phase space distribution which acquired a single gamma knife channel. We performed the simulation using the virtual sources on the 201 channel and compared the measurement with the simulation using virtual sources and real sources. A virtual source file was generated to reduce the simulation time of a Gamma Knife Model C. Simulations with a virtual source executed about 50 times faster than the original source code and there was no statistically significant difference in simulated results
Monte Carlo climate change forecasts with a global coupled ocean-atmosphere model
International Nuclear Information System (INIS)
Cubasch, U.; Santer, B.D.; Hegerl, G.; Hoeck, H.; Maier-Reimer, E.; Mikolajwicz, U.; Stoessel, A.; Voss, R.
1992-01-01
The Monte Carlo approach, which has increasingly been used during the last decade in the field of extended range weather forecasting, has been applied for climate change experiments. Four integrations with a global coupled ocean-atmosphere model have been started from different initial conditions, but with the same greenhouse gas forcing according to the IPCC scenario A. All experiments have been run for a period of 50 years. The results indicate that the time evolution of the global mean warming depends strongly on the initial state of the climate system. It can vary between 6 and 31 years. The Monte Carlo approach delivers information about both the mean response and the statistical significance of the response. While the individual members of the ensemble show a considerable variation in the climate change pattern of temperature after 50 years, the ensemble mean climate change pattern closely resembles the pattern obtained in a 100 year integration and is, at least over most of the land areas, statistically significant. The ensemble averaged sea-level change due to thermal expansion is significant in the global mean and locally over wide regions of the Pacific. The hydrological cycle is also significantly enhanced in the global mean, but locally the changes in precipitation and soil moisture are masked by the variability of the experiments. (orig.)
HTCC - a heat transfer model for gas-steam mixtures
International Nuclear Information System (INIS)
Papadimitriou, P.
1983-01-01
The mathematical model HTCC (Heat Transfer Coefficient in Containment) has been developed for RALOC after a loss-of-coolant accident in order to determine the local heat transfer coefficients for transfer between the containment atmosphere and the walls of the reactor building. The model considers the current values of room and wall temperature, the concentration of steam and non-condensible gases, geometry data and those of fluid dynamics together with thermodynamic parameters and from these determines the heat transfer mechanisms due to convection, radiation and condensation. The HTCC is implemented in the RALOC program. Comparative analyses of computed temperature profiles, for HEDL Standard problems A and B on hydrogen distribution, and of computed temperature profiles determined during the heat-up phase in the CSE-A5 experiment show a good agreement with experimental data. (orig.) [de
A three-dimensional self-learning kinetic Monte Carlo model: application to Ag(111)
International Nuclear Information System (INIS)
Latz, Andreas; Brendel, Lothar; Wolf, Dietrich E
2012-01-01
The reliability of kinetic Monte Carlo (KMC) simulations depends on accurate transition rates. The self-learning KMC method (Trushin et al 2005 Phys. Rev. B 72 115401) combines the accuracy of rates calculated from a realistic potential with the efficiency of a rate catalog, using a pattern recognition scheme. This work expands the original two-dimensional method to three dimensions. The concomitant huge increase in the number of rate calculations on the fly needed can be avoided by setting up an initial database, containing exact activation energies calculated for processes gathered from a simpler KMC model. To provide two representative examples, the model is applied to the diffusion of Ag monolayer islands on Ag(111), and the homoepitaxial growth of Ag on Ag(111) at low temperatures.
Monte Carlo method for critical systems in infinite volume: The planar Ising model.
Herdeiro, Victor; Doyon, Benjamin
2016-10-01
In this paper we propose a Monte Carlo method for generating finite-domain marginals of critical distributions of statistical models in infinite volume. The algorithm corrects the problem of the long-range effects of boundaries associated to generating critical distributions on finite lattices. It uses the advantage of scale invariance combined with ideas of the renormalization group in order to construct a type of "holographic" boundary condition that encodes the presence of an infinite volume beyond it. We check the quality of the distribution obtained in the case of the planar Ising model by comparing various observables with their infinite-plane prediction. We accurately reproduce planar two-, three-, and four-point of spin and energy operators. We also define a lattice stress-energy tensor, and numerically obtain the associated conformal Ward identities and the Ising central charge.
Development of numerical models for Monte Carlo simulations of Th-Pb fuel assembly
Directory of Open Access Journals (Sweden)
Oettingen Mikołaj
2017-01-01
Full Text Available The thorium-uranium fuel cycle is a promising alternative against uranium-plutonium fuel cycle, but it demands many advanced research before starting its industrial application in commercial nuclear reactors. The paper presents the development of the thorium-lead (Th-Pb fuel assembly numerical models for the integral irradiation experiments. The Th-Pb assembly consists of a hexagonal array of ThO2 fuel rods and metallic Pb rods. The design of the assembly allows different combinations of rods for various types of irradiations and experimental measurements. The numerical model of the Th-Pb assembly was designed for the numerical simulations with the continuous energy Monte Carlo Burnup code (MCB implemented on the supercomputer Prometheus of the Academic Computer Centre Cyfronet AGH.
Hybrid transport and diffusion modeling using electron thermal transport Monte Carlo SNB in DRACO
Chenhall, Jeffrey; Moses, Gregory
2017-10-01
The iSNB (implicit Schurtz Nicolai Busquet) multigroup diffusion electron thermal transport method is adapted into an Electron Thermal Transport Monte Carlo (ETTMC) transport method to better model angular and long mean free path non-local effects. Previously, the ETTMC model had been implemented in the 2D DRACO multiphysics code and found to produce consistent results with the iSNB method. Current work is focused on a hybridization of the computationally slower but higher fidelity ETTMC transport method with the computationally faster iSNB diffusion method in order to maximize computational efficiency. Furthermore, effects on the energy distribution of the heat flux divergence are studied. Work to date on the hybrid method will be presented. This work was supported by Sandia National Laboratories and the Univ. of Rochester Laboratory for Laser Energetics.
Measurement and Monte Carlo modeling of the spatial response of scintillation screens
Energy Technology Data Exchange (ETDEWEB)
Pistrui-Maximean, S.A. [CNDRI (NDT using Ionizing Radiation) Laboratory, INSA-Lyon, 69621 Villeurbanne (France)], E-mail: spistrui@gmail.com; Letang, J.M. [CNDRI (NDT using Ionizing Radiation) Laboratory, INSA-Lyon, 69621 Villeurbanne (France)], E-mail: jean-michel.letang@insa-lyon.fr; Freud, N. [CNDRI (NDT using Ionizing Radiation) Laboratory, INSA-Lyon, 69621 Villeurbanne (France); Koch, A. [Thales Electron Devices, 38430 Moirans (France); Walenta, A.H. [Detectors and Electronics Department, FB Physik, Siegen University, 57068 Siegen (Germany); Montarou, G. [Corpuscular Physics Laboratory, Blaise Pascal University, 63177 Aubiere (France); Babot, D. [CNDRI (NDT using Ionizing Radiation) Laboratory, INSA-Lyon, 69621 Villeurbanne (France)
2007-11-01
In this article, we propose a detailed protocol to carry out measurements of the spatial response of scintillation screens and to assess the agreement with simulated results. The experimental measurements have been carried out using a practical implementation of the slit method. A Monte Carlo simulation model of scintillator screens, implemented with the toolkit Geant4, has been used to study the influence of the acquisition setup parameters and to compare with the experimental results. An algorithm of global stochastic optimization based on a localized random search method has been implemented to adjust the optical parameters (optical scattering and absorption coefficients). The algorithm has been tested for different X-ray tube voltages (40, 70 and 100 kV). A satisfactory convergence between the results simulated with the optimized model and the experimental measurements is obtained.
International Nuclear Information System (INIS)
Gelß, Patrick; Matera, Sebastian; Schütte, Christof
2016-01-01
In multiscale modeling of heterogeneous catalytic processes, one crucial point is the solution of a Markovian master equation describing the stochastic reaction kinetics. Usually, this is too high-dimensional to be solved with standard numerical techniques and one has to rely on sampling approaches based on the kinetic Monte Carlo method. In this study we break the curse of dimensionality for the direct solution of the Markovian master equation by exploiting the Tensor Train Format for this purpose. The performance of the approach is demonstrated on a first principles based, reduced model for the CO oxidation on the RuO 2 (110) surface. We investigate the complexity for increasing system size and for various reaction conditions. The advantage over the stochastic simulation approach is illustrated by a problem with increased stiffness.
Monte Carlo simulation of a statistical mechanical model of multiple protein sequence alignment.
Kinjo, Akira R
2017-01-01
A grand canonical Monte Carlo (MC) algorithm is presented for studying the lattice gas model (LGM) of multiple protein sequence alignment, which coherently combines long-range interactions and variable-length insertions. MC simulations are used for both parameter optimization of the model and production runs to explore the sequence subspace around a given protein family. In this Note, I describe the details of the MC algorithm as well as some preliminary results of MC simulations with various temperatures and chemical potentials, and compare them with the mean-field approximation. The existence of a two-state transition in the sequence space is suggested for the SH3 domain family, and inappropriateness of the mean-field approximation for the LGM is demonstrated.
Gelß, Patrick; Matera, Sebastian; Schütte, Christof
2016-06-01
In multiscale modeling of heterogeneous catalytic processes, one crucial point is the solution of a Markovian master equation describing the stochastic reaction kinetics. Usually, this is too high-dimensional to be solved with standard numerical techniques and one has to rely on sampling approaches based on the kinetic Monte Carlo method. In this study we break the curse of dimensionality for the direct solution of the Markovian master equation by exploiting the Tensor Train Format for this purpose. The performance of the approach is demonstrated on a first principles based, reduced model for the CO oxidation on the RuO2(110) surface. We investigate the complexity for increasing system size and for various reaction conditions. The advantage over the stochastic simulation approach is illustrated by a problem with increased stiffness.
Energy Technology Data Exchange (ETDEWEB)
Gelß, Patrick, E-mail: p.gelss@fu-berlin.de; Matera, Sebastian, E-mail: matera@math.fu-berlin.de; Schütte, Christof, E-mail: schuette@mi.fu-berlin.de
2016-06-01
In multiscale modeling of heterogeneous catalytic processes, one crucial point is the solution of a Markovian master equation describing the stochastic reaction kinetics. Usually, this is too high-dimensional to be solved with standard numerical techniques and one has to rely on sampling approaches based on the kinetic Monte Carlo method. In this study we break the curse of dimensionality for the direct solution of the Markovian master equation by exploiting the Tensor Train Format for this purpose. The performance of the approach is demonstrated on a first principles based, reduced model for the CO oxidation on the RuO{sub 2}(110) surface. We investigate the complexity for increasing system size and for various reaction conditions. The advantage over the stochastic simulation approach is illustrated by a problem with increased stiffness.
The SGHWR version of the Monte Carlo code W-MONTE. Part 1. The theoretical model
International Nuclear Information System (INIS)
Allen, F.R.
1976-03-01
W-MONTE provides a multi-group model of neutron transport in the exact geometry of a reactor lattice using Monte Carlo methods. It is currently restricted to uniform axial properties. Material data is normally obtained from a preliminary WIMS lattice calculation in the transport group structure. The SGHWR version has been required for analysis of zero energy experiments and special aspects of power reactor lattices, such as the unmoderated lattice region above the moderator when drained to dump height. Neutron transport is modelled for a uniform infinite lattice, simultaneously treating the cases of no leakage, radial leakage or axial leakage only, and the combined effects of radial and axial leakage. Multigroup neutron balance edits are incorporated for the separate effects of radial and axial leakage to facilitate the analysis of leakage and to provide effective diffusion theory parameters for core representation in reactor cores. (author)
Huang, Guanghui; Wan, Jianping; Chen, Hui
2013-02-01
Nonlinear stochastic differential equation models with unobservable state variables are now widely used in analysis of PK/PD data. Unobservable state variables are usually estimated with extended Kalman filter (EKF), and the unknown pharmacokinetic parameters are usually estimated by maximum likelihood estimator. However, EKF is inadequate for nonlinear PK/PD models, and MLE is known to be biased downwards. A density-based Monte Carlo filter (DMF) is proposed to estimate the unobservable state variables, and a simulation-based M estimator is proposed to estimate the unknown parameters in this paper, where a genetic algorithm is designed to search the optimal values of pharmacokinetic parameters. The performances of EKF and DMF are compared through simulations for discrete time and continuous time systems respectively, and it is found that the results based on DMF are more accurate than those given by EKF with respect to mean absolute error. Copyright © 2012 Elsevier Ltd. All rights reserved.
Factoring variations in natural images with deep Gaussian mixture models
van den Oord, Aäron; Schrauwen, Benjamin
2014-01-01
Generative models can be seen as the swiss army knives of machine learning, as many problems can be written probabilistically in terms of the distribution of the data, including prediction, reconstruction, imputation and simulation. One of the most promising directions for unsupervised learning may lie in Deep Learning methods, given their success in supervised learning. However, one of the cur- rent problems with deep unsupervised learning methods, is that they often are harder to scale. As ...
Nonlinear Structured Growth Mixture Models in Mplus and OpenMx
Grimm, Kevin J.; Ram, Nilam; Estabrook, Ryne
2014-01-01
Growth mixture models (GMMs; Muthén & Muthén, 2000; Muthén & Shedden, 1999) are a combination of latent curve models (LCMs) and finite mixture models to examine the existence of latent classes that follow distinct developmental patterns. GMMs are often fit with linear, latent basis, multiphase, or polynomial change models because of their common use, flexibility in modeling many types of change patterns, the availability of statistical programs to fit such models, and the ease of programming. In this paper, we present additional ways of modeling nonlinear change patterns with GMMs. Specifically, we show how LCMs that follow specific nonlinear functions can be extended to examine the presence of multiple latent classes using the Mplus and OpenMx computer programs. These models are fit to longitudinal reading data from the Early Childhood Longitudinal Study-Kindergarten Cohort to illustrate their use. PMID:25419006
Dynamic classification of fetal heart rates by hierarchical Dirichlet process mixture models.
Directory of Open Access Journals (Sweden)
Kezi Yu
Full Text Available In this paper, we propose an application of non-parametric Bayesian (NPB models for classification of fetal heart rate (FHR recordings. More specifically, we propose models that are used to differentiate between FHR recordings that are from fetuses with or without adverse outcomes. In our work, we rely on models based on hierarchical Dirichlet processes (HDP and the Chinese restaurant process with finite capacity (CRFC. Two mixture models were inferred from real recordings, one that represents healthy and another, non-healthy fetuses. The models were then used to classify new recordings and provide the probability of the fetus being healthy. First, we compared the classification performance of the HDP models with that of support vector machines on real data and concluded that the HDP models achieved better performance. Then we demonstrated the use of mixture models based on CRFC for dynamic classification of the performance of (FHR recordings in a real-time setting.
International Nuclear Information System (INIS)
Stute, Simon
2010-01-01
Positron Emission Tomography (PET) is a medical imaging technique that plays a major role in oncology, especially using "1"8F-Fluoro-Deoxyglucose. However, PET images suffer from a modest spatial resolution and from high noise. As a result, there is still no consensus on how tumor metabolically active volume and tumor uptake should be characterized. In the meantime, research groups keep producing new methods for such characterizations that need to be assessed. A Monte Carlo simulation based method has been developed to produce simulated PET images of patients suffering from cancer, indistinguishable from clinical images, and for which all parameters are known. The method uses high resolution PET images from patient acquisitions, from which the physiological heterogeneous activity distribution can be modeled. It was shown that the performance of quantification methods on such highly realistic simulated images are significantly lower and more variable than using simple phantom studies. Fourteen different quantification methods were also compared in realistic conditions using a group of such simulated patients. In addition, the proposed method was extended to simulate serial PET scans in the context of patient monitoring, including a modeling of the tumor changes, as well as the variability over time of non-tumoral physiological activity distribution. Monte Carlo simulations were also used to study the detection probability inside the crystals of the tomograph. A model of the crystal response was derived and included in the system matrix involved in tomographic reconstruction. The resulting reconstruction method was compared with other sophisticated methods for modeling the detector response in the image space, proposed in the literature. We demonstrated the superiority of the proposed method over equivalent approaches on simulated data, and illustrated its robustness on clinical data. For a same noise level, it is possible to reconstruct PET images offering a
Evaluation of thermodynamic properties of fluid mixtures by PC-SAFT model
International Nuclear Information System (INIS)
Almasi, Mohammad
2014-01-01
Experimental and calculated partial molar volumes (V ¯ m,1 ) of MIK with (♦) 2-PrOH, (♢) 2-BuOH, (●) 2-PenOH at T = 298.15 K. (—) PC-SAFT model. - Highlights: • Densities and viscosities of the mixtures (MIK + 2-alkanols) were measured. • PC-SAFT model was applied to correlate the volumetric properties of binary mixtures. • Agreement between experimental data and calculated values by PC-SAFT model is good. - Abstract: Densities and viscosities of binary mixtures of methyl isobutyl ketone (MIK) with polar solvents namely, 2-propanol, 2-butanol and 2-pentanol, were measured at 7 temperatures (293.15–323.15 K) over the entire range of composition. Using the experimental data, excess molar volumes V m E , isobaric thermal expansivity α p , partial molar volumes V ¯ m,i and viscosity deviations Δη, have been calculated due to their importance in the study of specific molecular interactions. The observed negative and positive values of deviation/excess parameters were explained on the basis of the intermolecular interactions occur in these mixtures. The Perturbed Chain Statistical Association Fluid Theory (PC-SAFT) has been used to correlate the volumetric behavior of the mixtures
Evaluation of thermodynamic properties of fluid mixtures by PC-SAFT model
Energy Technology Data Exchange (ETDEWEB)
Almasi, Mohammad, E-mail: m.almasi@khouzestan.srbiau.ac.ir
2014-09-10
Experimental and calculated partial molar volumes (V{sup ¯}{sub m,1}) of MIK with (♦) 2-PrOH, (♢) 2-BuOH, (●) 2-PenOH at T = 298.15 K. (—) PC-SAFT model. - Highlights: • Densities and viscosities of the mixtures (MIK + 2-alkanols) were measured. • PC-SAFT model was applied to correlate the volumetric properties of binary mixtures. • Agreement between experimental data and calculated values by PC-SAFT model is good. - Abstract: Densities and viscosities of binary mixtures of methyl isobutyl ketone (MIK) with polar solvents namely, 2-propanol, 2-butanol and 2-pentanol, were measured at 7 temperatures (293.15–323.15 K) over the entire range of composition. Using the experimental data, excess molar volumes V{sub m}{sup E}, isobaric thermal expansivity α{sub p}, partial molar volumes V{sup ¯}{sub m,i} and viscosity deviations Δη, have been calculated due to their importance in the study of specific molecular interactions. The observed negative and positive values of deviation/excess parameters were explained on the basis of the intermolecular interactions occur in these mixtures. The Perturbed Chain Statistical Association Fluid Theory (PC-SAFT) has been used to correlate the volumetric behavior of the mixtures.
Directory of Open Access Journals (Sweden)
Niels Hadrup
Full Text Available Humans are concomitantly exposed to numerous chemicals. An infinite number of combinations and doses thereof can be imagined. For toxicological risk assessment the mathematical prediction of mixture effects, using knowledge on single chemicals, is therefore desirable. We investigated pros and cons of the concentration addition (CA, independent action (IA and generalized concentration addition (GCA models. First we measured effects of single chemicals and mixtures thereof on steroid synthesis in H295R cells. Then single chemical data were applied to the models; predictions of mixture effects were calculated and compared to the experimental mixture data. Mixture 1 contained environmental chemicals adjusted in ratio according to human exposure levels. Mixture 2 was a potency adjusted mixture containing five pesticides. Prediction of testosterone effects coincided with the experimental Mixture 1 data. In contrast, antagonism was observed for effects of Mixture 2 on this hormone. The mixtures contained chemicals exerting only limited maximal effects. This hampered prediction by the CA and IA models, whereas the GCA model could be used to predict a full dose response curve. Regarding effects on progesterone and estradiol, some chemicals were having stimulatory effects whereas others had inhibitory effects. The three models were not applicable in this situation and no predictions could be performed. Finally, the expected contributions of single chemicals to the mixture effects were calculated. Prochloraz was the predominant but not sole driver of the mixtures, suggesting that one chemical alone was not responsible for the mixture effects. In conclusion, the GCA model seemed to be superior to the CA and IA models for the prediction of testosterone effects. A situation with chemicals exerting opposing effects, for which the models could not be applied, was identified. In addition, the data indicate that in non-potency adjusted mixtures the effects cannot
International Nuclear Information System (INIS)
Caon, M.
2010-01-01
Full text: Medical imaging provides two-dimensional pictures of the human internal anatomy from which may be constructed a three-dimensional model of organs and tissues suitable for calculation of dose from radiation. Diagnostic CT provides the greatest exposure to radiation per examination and the frequency of CT examination is high. Esti mates of dose from diagnostic radiography are still determined from data derived from geometric models (rather than anatomical models), models scaled from adult bodies (rather than bodies of children) and CT scanner hardware that is no longer used. The aim of anatomical modelling is to produce a mathematical representation of internal anatomy that has organs of realistic size, shape and positioning. The organs and tissues are represented by a great many cuboidal volumes (voxels). The conversion of medical images to voxels is called segmentation and on completion every pixel in an image is assigned to a tissue or organ. Segmentation is time consuming. An image processing pack age is used to identify organ boundaries in each image. Thirty to forty tomographic voxel models of anatomy have been reported in the literature. Each model is of an individual, or a composite from several individuals. Images of children are particularly scarce. So there remains a need for more paediatric anatomical models. I am working on segmenting ''William'' who is 368 PET-CT images from head to toe of a seven year old boy. William will be used for Monte Carlo dose calculations of dose from CT examination using a simulated modern CT scanner.
Lu, Dan; Ricciuto, Daniel; Walker, Anthony; Safta, Cosmin; Munger, William
2017-09-01
Calibration of terrestrial ecosystem models is important but challenging. Bayesian inference implemented by Markov chain Monte Carlo (MCMC) sampling provides a comprehensive framework to estimate model parameters and associated uncertainties using their posterior distributions. The effectiveness and efficiency of the method strongly depend on the MCMC algorithm used. In this work, a differential evolution adaptive Metropolis (DREAM) algorithm is used to estimate posterior distributions of 21 parameters for the data assimilation linked ecosystem carbon (DALEC) model using 14 years of daily net ecosystem exchange data collected at the Harvard Forest Environmental Measurement Site eddy-flux tower. The calibration of DREAM results in a better model fit and predictive performance compared to the popular adaptive Metropolis (AM) scheme. Moreover, DREAM indicates that two parameters controlling autumn phenology have multiple modes in their posterior distributions while AM only identifies one mode. The application suggests that DREAM is very suitable to calibrate complex terrestrial ecosystem models, where the uncertain parameter size is usually large and existence of local optima is always a concern. In addition, this effort justifies the assumptions of the error model used in Bayesian calibration according to the residual analysis. The result indicates that a heteroscedastic, correlated, Gaussian error model is appropriate for the problem, and the consequent constructed likelihood function can alleviate the underestimation of parameter uncertainty that is usually caused by using uncorrelated error models.
Application of Parameter Estimation for Diffusions and Mixture Models
DEFF Research Database (Denmark)
Nolsøe, Kim
The first part of this thesis proposes a method to determine the preferred number of structures, their proportions and the corresponding geometrical shapes of an m-membered ring molecule. This is obtained by formulating a statistical model for the data and constructing an algorithm which samples...... with the posterior score function. From an application point of view this methology is easy to apply, since the optimal estimating function G(;Xt1 ; : : : ;Xtn ) is equal to the classical optimal estimating function, plus a correction term which takes into account the prior information. The methology is particularly...
Szulczyński, Bartosz; Gębicki, Jacek; Namieśnik, Jacek
2018-01-01
The paper presents the possibility of application of fuzzy logic to determine the odour intensity of model, ternary gas mixtures (α-pinene, toluene and triethylamine) using electronic nose prototype. The results obtained using fuzzy logic algorithms were compared with the values obtained using multiple linear regression (MLR) model and sensory analysis. As the results of the studies, it was found the electronic nose prototype along with the fuzzy logic pattern recognition system can be successfully used to estimate the odour intensity of tested gas mixtures. The correctness of the results obtained using fuzzy logic was equal to 68%.
A Mixture Model of Consumers' Intended Purchase Decisions for Genetically Modified Foods
Kristine M. Grimsrud; Robert P. Berrens; Ron C. Mittelhammer
2006-01-01
A finite probability mixture model is used to analyze the existence of multiple market segments for a pre-market good. The approach has at least two principal benefits. First, the model is capable of identifying likely market segments and their differentiating characteristics. Second, the model can be used to estimate the discount different consumer groups require to purchase the good. The model is illustrated using stated preference survey data collected on consumer responses to the potentia...
Analysis of real-time mixture cytotoxicity data following repeated exposure using BK/TD models.
Teng, S; Tebby, C; Barcellini-Couget, S; De Sousa, G; Brochot, C; Rahmani, R; Pery, A R R
2016-08-15
Cosmetic products generally consist of multiple ingredients. Thus, cosmetic risk assessment has to deal with mixture toxicity on a long-term scale which means it has to be assessed in the context of repeated exposure. Given that animal testing has been banned for cosmetics risk assessment, in vitro assays allowing long-term repeated exposure and adapted for in vitro - in vivo extrapolation need to be developed. However, most in vitro tests only assess short-term effects and consider static endpoints which hinder extrapolation to realistic human exposure scenarios where concentration in target organs is varies over time. Thanks to impedance metrics, real-time cell viability monitoring for repeated exposure has become possible. We recently constructed biokinetic/toxicodynamic models (BK/TD) to analyze such data (Teng et al., 2015) for three hepatotoxic cosmetic ingredients: coumarin, isoeugenol and benzophenone-2. In the present study, we aim to apply these models to analyze the dynamics of mixture impedance data using the concepts of concentration addition and independent action. Metabolic interactions between the mixture components were investigated, characterized and implemented in the models, as they impacted the actual cellular exposure. Indeed, cellular metabolism following mixture exposure induced a quick disappearance of the compounds from the exposure system. We showed that isoeugenol substantially decreased the metabolism of benzophenone-2, reducing the disappearance of this compound and enhancing its in vitro toxicity. Apart from this metabolic interaction, no mixtures showed any interaction, and all binary mixtures were successfully modeled by at least one model based on exposure to the individual compounds. Copyright © 2016 Elsevier Inc. All rights reserved.
Implementation of 3D models in the Monte Carlo code MCNP
International Nuclear Information System (INIS)
Lopes, Vivaldo; Millian, Felix M.; Guevara, Maria Victoria M.; Garcia, Fermin; Sena, Isaac; Menezes, Hugo
2009-01-01
On the area of numerical dosimetry Applied to medical physics, the scientific community focuses on the elaboration of new hybrids models based on 3D models. But different steps of the process of simulation with 3D models needed improvement and optimization in order to expedite the calculations and accuracy using this methodology. This project was developed with the aim of optimize the process of introduction of 3D models within the simulation code of radiation transport by Monte Carlo (MCNP). The fast implementation of these models on the simulation code allows the estimation of the dose deposited on the patient organs on a more personalized way, increasing the accuracy with this on the estimates and reducing the risks to health, caused by ionizing radiations. The introduction o these models within the MCNP was made through a input file, that was constructed through a sequence of images, bi-dimensional in the 3D model, generated using the program '3DSMAX', imported by the program 'TOMO M C' and thus, introduced as INPUT FILE of the MCNP code. (author)
Hong-Ghi Min
2011-01-01
Using Monte Carlo simulation of the Portfolio-balance model of the exchange rates, we report finite sample properties of the GMM estimator for testing over-identifying restrictions in the simultaneous equations model. F-form of Sargans statistic performs better than its chi-squared form while Hansens GMM statistic has the smallest bias.
Kieftenbeld, Vincent; Natesan, Prathiba
2012-01-01
Markov chain Monte Carlo (MCMC) methods enable a fully Bayesian approach to parameter estimation of item response models. In this simulation study, the authors compared the recovery of graded response model parameters using marginal maximum likelihood (MML) and Gibbs sampling (MCMC) under various latent trait distributions, test lengths, and…
Okada, Kensuke; Vandekerckhove, Joachim; Lee, Michael D
2018-02-01
People often interact with environments that can provide only a finite number of items as resources. Eventually a book contains no more chapters, there are no more albums available from a band, and every Pokémon has been caught. When interacting with these sorts of environments, people either actively choose to quit collecting new items, or they are forced to quit when the items are exhausted. Modeling the distribution of how many items people collect before they quit involves untangling these two possibilities, We propose that censored geometric models are a useful basic technique for modeling the quitting distribution, and, show how, by implementing these models in a hierarchical and latent-mixture framework through Bayesian methods, they can be extended to capture the additional features of specific situations. We demonstrate this approach by developing and testing a series of models in two case studies involving real-world data. One case study deals with people choosing jokes from a recommender system, and the other deals with people completing items in a personality survey.
International Nuclear Information System (INIS)
Paul P.H. Wilson
2005-01-01
The development of Monte Carlo techniques for isotopic inventory analysis has been explored in order to facilitate the modeling of systems with flowing streams of material through varying neutron irradiation environments. This represents a novel application of Monte Carlo methods to a field that has traditionally relied on deterministic solutions to systems of first-order differential equations. The Monte Carlo techniques were based largely on the known modeling techniques of Monte Carlo radiation transport, but with important differences, particularly in the area of variance reduction and efficiency measurement. The software that was developed to implement and test these methods now provides a basis for validating approximate modeling techniques that are available to deterministic methodologies. The Monte Carlo methods have been shown to be effective in reproducing the solutions of simple problems that are possible using both stochastic and deterministic methods. The Monte Carlo methods are also effective for tracking flows of materials through complex systems including the ability to model removal of individual elements or isotopes in the system. Computational performance is best for flows that have characteristic times that are large fractions of the system lifetime. As the characteristic times become short, leading to thousands or millions of passes through the system, the computational performance drops significantly. Further research is underway to determine modeling techniques to improve performance within this range of problems. This report describes the technical development of Monte Carlo techniques for isotopic inventory analysis. The primary motivation for this solution methodology is the ability to model systems of flowing material being exposed to varying and stochastically varying radiation environments. The methodology was developed in three stages: analog methods which model each atom with true reaction probabilities (Section 2), non-analog methods
Energy Technology Data Exchange (ETDEWEB)
Karanam, Aditya; Sharma, Pavan K.; Ganju, Sunil; Singh, Ram Kumar [Bhabha Atomic Research Centre (BARC), Mumbai (India). Reactor Safety Div.
2016-12-15
During postulated accident sequences in nuclear reactors, hydrogen may get released from the core and form a flammable mixture in the surrounding containment structure. Ignition of such mixtures and the subsequent pressure rise are an imminent threat for safe and sustainable operation of nuclear reactors. Methods for evaluating post ignition characteristics are important for determining the design safety margins in such scenarios. This study presents two thermo-chemical models for determining the post ignition state. The first model is based on internal energy balance while the second model uses the concept of element potentials to minimize the free energy of the system with internal energy imposed as a constraint. Predictions from both the models have been compared against published data over a wide range of mixture compositions. Important differences in the regions close to flammability limits and for stoichiometric mixtures have been identified and explained. The equilibrium model has been validated for varied temperatures and pressures representative of initial conditions that may be present in the containment during accidents. Special emphasis has been given to the understanding of the role of dissociation and its effect on equilibrium pressure, temperature and species concentrations.
Semiparametric accelerated failure time cure rate mixture models with competing risks.
Choi, Sangbum; Zhu, Liang; Huang, Xuelin
2018-01-15
Modern medical treatments have substantially improved survival rates for many chronic diseases and have generated considerable interest in developing cure fraction models for survival data with a non-ignorable cured proportion. Statistical analysis of such data may be further complicated by competing risks that involve multiple types of endpoints. Regression analysis of competing risks is typically undertaken via a proportional hazards model adapted on cause-specific hazard or subdistribution hazard. In this article, we propose an alternative approach that treats competing events as distinct outcomes in a mixture. We consider semiparametric accelerated failure time models for the cause-conditional survival function that are combined through a multinomial logistic model within the cure-mixture modeling framework. The cure-mixture approach to competing risks provides a means to determine the overall effect of a treatment and insights into how this treatment modifies the components of the mixture in the presence of a cure fraction. The regression and nonparametric parameters are estimated by a nonparametric kernel-based maximum likelihood estimation method. Variance estimation is achieved through resampling methods for the kernel-smoothed likelihood function. Simulation studies show that the procedures work well in practical settings. Application to a sarcoma study demonstrates the use of the proposed method for competing risk data with a cure fraction. Copyright © 2017 John Wiley & Sons, Ltd.
Son, Heesook; Friedmann, Erika; Thomas, Sue A
2012-01-01
Longitudinal studies are used in nursing research to examine changes over time in health indicators. Traditional approaches to longitudinal analysis of means, such as analysis of variance with repeated measures, are limited to analyzing complete cases. This limitation can lead to biased results due to withdrawal or data omission bias or to imputation of missing data, which can lead to bias toward the null if data are not missing completely at random. Pattern mixture models are useful to evaluate the informativeness of missing data and to adjust linear mixed model (LMM) analyses if missing data are informative. The aim of this study was to provide an example of statistical procedures for applying a pattern mixture model to evaluate the informativeness of missing data and conduct analyses of data with informative missingness in longitudinal studies using SPSS. The data set from the Patients' and Families' Psychological Response to Home Automated External Defibrillator Trial was used as an example to examine informativeness of missing data with pattern mixture models and to use a missing data pattern in analysis of longitudinal data. Prevention of withdrawal bias, omitted data bias, and bias toward the null in longitudinal LMMs requires the assessment of the informativeness of the occurrence of missing data. Missing data patterns can be incorporated as fixed effects into LMMs to evaluate the contribution of the presence of informative missingness to and control for the effects of missingness on outcomes. Pattern mixture models are a useful method to address the presence and effect of informative missingness in longitudinal studies.
International Nuclear Information System (INIS)
Karanam, Aditya; Sharma, Pavan K.; Ganju, Sunil; Singh, Ram Kumar
2016-01-01
During postulated accident sequences in nuclear reactors, hydrogen may get released from the core and form a flammable mixture in the surrounding containment structure. Ignition of such mixtures and the subsequent pressure rise are an imminent threat for safe and sustainable operation of nuclear reactors. Methods for evaluating post ignition characteristics are important for determining the design safety margins in such scenarios. This study presents two thermo-chemical models for determining the post ignition state. The first model is based on internal energy balance while the second model uses the concept of element potentials to minimize the free energy of the system with internal energy imposed as a constraint. Predictions from both the models have been compared against published data over a wide range of mixture compositions. Important differences in the regions close to flammability limits and for stoichiometric mixtures have been identified and explained. The equilibrium model has been validated for varied temperatures and pressures representative of initial conditions that may be present in the containment during accidents. Special emphasis has been given to the understanding of the role of dissociation and its effect on equilibrium pressure, temperature and species concentrations.
DEFF Research Database (Denmark)
Tsivintzelis, Ioannis; Kontogeorgis, Georgios M.
2015-01-01
of CPA for ternary and multicomponent CO2 mixtures containing alcohols (methanol, ethanol or propanol) water and hydrocarbons. This work belongs to a series of studies aiming to arrive in a single "engineering approach" for applying CPA to acid gas mixtures, without introducing significant changes...... to the model. In this direction, CPA results were obtained using various approaches, i.e. different association schemes for pure CO2 (assuming that it is a non-associating compound, or that it is a self-associating fluid with two, three or four association sites) and different possibilities for modelling...... mixtures of CO2 with water and alcohols (only use of one interaction parameter kij or assuming cross-association interactions and obtaining the relevant parameters either via a combining rule or using an experimental value for the cross-association energy). It is concluded that CPA is a powerful model...
ICF target 2D modeling using Monte Carlo SNB electron thermal transport in DRACO
Chenhall, Jeffrey; Cao, Duc; Moses, Gregory
2016-10-01
The iSNB (implicit Schurtz Nicolai Busquet multigroup diffusion electron thermal transport method is adapted into a Monte Carlo (MC) transport method to better model angular and long mean free path non-local effects. The MC model was first implemented in the 1D LILAC code to verify consistency with the iSNB model. Implementation of the MC SNB model in the 2D DRACO code enables higher fidelity non-local thermal transport modeling in 2D implosions such as polar drive experiments on NIF. The final step is to optimize the MC model by hybridizing it with a MC version of the iSNB diffusion method. The hybrid method will combine the efficiency of a diffusion method in intermediate mean free path regions with the accuracy of a transport method in long mean free path regions allowing for improved computational efficiency while maintaining accuracy. Work to date on the method will be presented. This work was supported by Sandia National Laboratories and the Univ. of Rochester Laboratory for Laser Energetics.
Dynamic connectivity algorithms for Monte Carlo simulations of the random-cluster model
International Nuclear Information System (INIS)
Elçi, Eren Metin; Weigel, Martin
2014-01-01
We review Sweeny's algorithm for Monte Carlo simulations of the random cluster model. Straightforward implementations suffer from the problem of computational critical slowing down, where the computational effort per edge operation scales with a power of the system size. By using a tailored dynamic connectivity algorithm we are able to perform all operations with a poly-logarithmic computational effort. This approach is shown to be efficient in keeping online connectivity information and is of use for a number of applications also beyond cluster-update simulations, for instance in monitoring droplet shape transitions. As the handling of the relevant data structures is non-trivial, we provide a Python module with a full implementation for future reference.
3D Monte Carlo model with direct photon flux recording for optimal optogenetic light delivery
Shin, Younghoon; Kim, Dongmok; Lee, Jihoon; Kwon, Hyuk-Sang
2017-02-01
Configuring the light power emitted from the optical fiber is an essential first step in planning in-vivo optogenetic experiments. However, diffusion theory, which was adopted for optogenetic research, precluded accurate estimates of light intensity in the semi-diffusive region where the primary locus of the stimulation is located. We present a 3D Monte Carlo model that provides an accurate and direct solution for light distribution in this region. Our method directly records the photon trajectory in the separate volumetric grid planes for the near-source recording efficiency gain, and it incorporates a 3D brain mesh to support both homogeneous and heterogeneous brain tissue. We investigated the light emitted from optical fibers in brain tissue in 3D, and we applied the results to design optimal light delivery parameters for precise optogenetic manipulation by considering the fiber output power, wavelength, fiber-to-target distance, and the area of neural tissue activation.
Constrained-path quantum Monte Carlo approach for non-yrast states within the shell model
Energy Technology Data Exchange (ETDEWEB)
Bonnard, J. [INFN, Sezione di Padova, Padova (Italy); LPC Caen, ENSICAEN, Universite de Caen, CNRS/IN2P3, Caen (France); Juillet, O. [LPC Caen, ENSICAEN, Universite de Caen, CNRS/IN2P3, Caen (France)
2016-04-15
The present paper intends to present an extension of the constrained-path quantum Monte Carlo approach allowing to reconstruct non-yrast states in order to reach the complete spectroscopy of nuclei within the interacting shell model. As in the yrast case studied in a previous work, the formalism involves a variational symmetry-restored wave function assuming two central roles. First, it guides the underlying Brownian motion to improve the efficiency of the sampling. Second, it constrains the stochastic paths according to the phaseless approximation to control sign or phase problems that usually plague fermionic QMC simulations. Proof-of-principle results in the sd valence space are reported. They prove the ability of the scheme to offer remarkably accurate binding energies for both even- and odd-mass nuclei irrespective of the considered interaction. (orig.)
CERN. Geneva
2012-01-01
We present our effort for the creation of a new software library of geometrical primitives, which are used for solid modelling in Monte Carlo detector simulations. We plan to replace and unify current geometrical primitive classes in the CERN software projects Geant4 and ROOT with this library. Each solid is represented by a C++ class with methods suited for measuring distances of particles from the surface of a solid and for determination as to whether the particles are located inside, outside or on the surface of the solid. We use numerical tolerance for determining whether the particles are located on the surface. The class methods also contain basic support for visualization. We use dedicated test suites for validation of the shape codes. These include also special performance and numerical value comparison tests for help with analysis of possible candidates of class methods as well as to verify that our new implementation proposals were designed and implemented properly. Currently, bridge classes are u...
Monte Carlo based geometrical model for efficiency calculation of an n-type HPGe detector
Energy Technology Data Exchange (ETDEWEB)
Padilla Cabal, Fatima, E-mail: fpadilla@instec.c [Instituto Superior de Tecnologias y Ciencias Aplicadas, ' Quinta de los Molinos' Ave. Salvador Allende, esq. Luaces, Plaza de la Revolucion, Ciudad de la Habana, CP 10400 (Cuba); Lopez-Pino, Neivy; Luis Bernal-Castillo, Jose; Martinez-Palenzuela, Yisel; Aguilar-Mena, Jimmy; D' Alessandro, Katia; Arbelo, Yuniesky; Corrales, Yasser; Diaz, Oscar [Instituto Superior de Tecnologias y Ciencias Aplicadas, ' Quinta de los Molinos' Ave. Salvador Allende, esq. Luaces, Plaza de la Revolucion, Ciudad de la Habana, CP 10400 (Cuba)
2010-12-15
A procedure to optimize the geometrical model of an n-type detector is described. Sixteen lines from seven point sources ({sup 241}Am, {sup 133}Ba, {sup 22}Na, {sup 60}Co, {sup 57}Co, {sup 137}Cs and {sup 152}Eu) placed at three different source-to-detector distances (10, 20 and 30 cm) were used to calibrate a low-background gamma spectrometer between 26 and 1408 keV. Direct Monte Carlo techniques using the MCNPX 2.6 and GEANT 4 9.2 codes, and a semi-empirical procedure were performed to obtain theoretical efficiency curves. Since discrepancies were found between experimental and calculated data using the manufacturer parameters of the detector, a detail study of the crystal dimensions and the geometrical configuration is carried out. The relative deviation with experimental data decreases from a mean value of 18-4%, after the parameters were optimized.
Monte Carlo evidence for the gluon-chain model of QCD string formation
International Nuclear Information System (INIS)
Greensite, J.; San Francisco State Univ., CA
1988-08-01
The Monte Carlo method is used to calculate the overlaps string vertical stroken gluons>, where Ψ string [A] is the Yang-Mills wavefunctional due to a static quark-antiquark pair, and vertical stroken gluons > are orthogonal trial states containing n=0, 1, or 2 gluon operators multiplying the true ground state. The calculation is carried out for SU(2) lattice gauge theory in Coulomb gauge, in D=4 dimensions. It is found that the string state is dominated, at small qanti q separations, by the vacuum ('no-gluon') state, at larger separations by the 1-gluon state, and, at the largest separations attempted, the 2-gluon state begins to dominate. This behavior is in qualitative agreement with the gluon-chain model, which is a large-N colors motivated theory of QCD string formation. (orig.)
Calibration of lung counter using a CT model of Torso phantom and Monte Carlo method
International Nuclear Information System (INIS)
Zhang Binquan; Ma Jizeng; Yang Duanjie; Liu Liye; Cheng Jianping
2006-01-01
Tomography image of a Torso phantom was obtained from CT-Scan. The Torso phantom represents the trunk of an adult man that is 170 cm high and weight of 65 kg. After these images were segmented, cropped, and resized, a 3-dimension voxel phantom was created. The voxel phantom includes more than 2 million voxels, which size was 2.73 mm x 2.73 mm x 3 mm. This model could be used for the calibration of lung counter with Monte Carlo method. On the assumption that radioactive material was homogeneously distributed throughout the lung, counting efficiencies of a HPGe detector in different positions were calculated as Adipose Mass fraction (AMF) was different in the soft tissue in chest. The results showed that counting efficiencies of the lung counter changed up to 67% for 17.5 keV γ ray and 20% for 25 keV γ ray when AMF changed from 0 to 40%. (authors)
Orlov Alexey; Ushakov Anton; Sovach Victor
2016-01-01
This article presents results of development of the mathematical model of nonstationary separation processes occurring in gas centrifuge cascades for separation of multicomponent isotope mixtures. This model was used for the calculation parameters of gas centrifuge cascade for separation of germanium isotopes. Comparison of obtained values with results of other authors revealed that developed mathematical model is adequate to describe nonstationary separation processes in gas centrifuge casca...
Orlov, Aleksey Alekseevich; Ushakov, Anton; Sovach, Victor
2017-01-01
The article presents results of development of a mathematical model of nonstationary hydraulic processes in gas centrifuge cascade for separation of multicomponent isotope mixtures. This model was used for the calculation parameters of gas centrifuge cascade for separation of silicon isotopes. Comparison of obtained values with results of other authors revealed that developed mathematical model is adequate to describe nonstationary hydraulic processes in gas centrifuge cascades for separation...
Energy Technology Data Exchange (ETDEWEB)
Yoon, Jihyung; Jung, Jae Won, E-mail: jungj@ecu.edu [Department of Physics, East Carolina University, Greenville, North Carolina 27858 (United States); Kim, Jong Oh [Department of Radiation Oncology, University of Pittsburgh Cancer Institute, Pittsburgh, Pennsylvania 15232 (United States); Yeo, Inhwan [Department of Radiation Medicine, Loma Linda University Medical Center, Loma Linda, California 92354 (United States)
2016-05-15
Purpose: To develop and evaluate a fast Monte Carlo (MC) dose calculation model of electronic portal imaging device (EPID) based on its effective atomic number modeling in the XVMC code. Methods: A previously developed EPID model, based on the XVMC code by density scaling of EPID structures, was modified by additionally considering effective atomic number (Z{sub eff}) of each structure and adopting a phase space file from the EGSnrc code. The model was tested under various homogeneous and heterogeneous phantoms and field sizes by comparing the calculations in the model with measurements in EPID. In order to better evaluate the model, the performance of the XVMC code was separately tested by comparing calculated dose to water with ion chamber (IC) array measurement in the plane of EPID. Results: In the EPID plane, calculated dose to water by the code showed agreement with IC measurements within 1.8%. The difference was averaged across the in-field regions of the acquired profiles for all field sizes and phantoms. The maximum point difference was 2.8%, affected by proximity of the maximum points to penumbra and MC noise. The EPID model showed agreement with measured EPID images within 1.3%. The maximum point difference was 1.9%. The difference dropped from the higher value of the code by employing the calibration that is dependent on field sizes and thicknesses for the conversion of calculated images to measured images. Thanks to the Z{sub eff} correction, the EPID model showed a linear trend of the calibration factors unlike those of the density-only-scaled model. The phase space file from the EGSnrc code sharpened penumbra profiles significantly, improving agreement of calculated profiles with measured profiles. Conclusions: Demonstrating high accuracy, the EPID model with the associated calibration system may be used for in vivo dosimetry of radiation therapy. Through this study, a MC model of EPID has been developed, and their performance has been rigorously
Faught, Austin M; Davidson, Scott E; Fontenot, Jonas; Kry, Stephen F; Etzel, Carol; Ibbott, Geoffrey S; Followill, David S
2017-09-01
The Imaging and Radiation Oncology Core Houston (IROC-H) (formerly the Radiological Physics Center) has reported varying levels of agreement in their anthropomorphic phantom audits. There is reason to believe one source of error in this observed disagreement is the accuracy of the dose calculation algorithms and heterogeneity corrections used. To audit this component of the radiotherapy treatment process, an independent dose calculation tool is needed. Monte Carlo multiple source models for Elekta 6 MV and 10 MV therapeutic x-ray beams were commissioned based on measurement of central axis depth dose data for a 10 × 10 cm 2 field size and dose profiles for a 40 × 40 cm 2 field size. The models were validated against open field measurements consisting of depth dose data and dose profiles for field sizes ranging from 3 × 3 cm 2 to 30 × 30 cm 2 . The models were then benchmarked against measurements in IROC-H's anthropomorphic head and neck and lung phantoms. Validation results showed 97.9% and 96.8% of depth dose data passed a ±2% Van Dyk criterion for 6 MV and 10 MV models respectively. Dose profile comparisons showed an average agreement using a ±2%/2 mm criterion of 98.0% and 99.0% for 6 MV and 10 MV models respectively. Phantom plan comparisons were evaluated using ±3%/2 mm gamma criterion, and averaged passing rates between Monte Carlo and measurements were 87.4% and 89.9% for 6 MV and 10 MV models respectively. Accurate multiple source models for Elekta 6 MV and 10 MV x-ray beams have been developed for inclusion in an independent dose calculation tool for use in clinical trial audits. © 2017 American Association of Physicists in Medicine.
DEFF Research Database (Denmark)
Tsivintzelis, Ioannis; Ali, Shahid; Kontogeorgis, Georgios
2015-01-01
The thermodynamic properties of pure gaseous, liquid or supercritical CO2 and CO2 mixtures with hydrocarbons and other compounds such as water, alcohols, and glycols are very important in many processes in the oil and gas industry. Design of such processes requires use of accurate thermodynamic...... models, capable of predicting the complex phase behavior of multicomponent mixtures as well as their volumetric properties. In this direction, over the last several years, the cubic-plus-association (CPA) thermodynamic model has been successfully used for describing volumetric properties and phase...
O'Donnell, Katherine M; Thompson, Frank R; Semlitsch, Raymond D
2015-01-01
Detectability of individual animals is highly variable and nearly always binomial mixture models to account for multiple sources of variation in detectability. The state process of the hierarchical model describes ecological mechanisms that generate spatial and temporal patterns in abundance, while the observation model accounts for the imperfect nature of counting individuals due to temporary emigration and false absences. We illustrate our model's potential advantages, including the allowance of temporary emigration between sampling periods, with a case study of southern red-backed salamanders Plethodon serratus. We fit our model and a standard binomial mixture model to counts of terrestrial salamanders surveyed at 40 sites during 3-5 surveys each spring and fall 2010-2012. Our models generated similar parameter estimates to standard binomial mixture models. Aspect was the best predictor of salamander abundance in our case study; abundance increased as aspect became more northeasterly. Increased time-since-rainfall strongly decreased salamander surface activity (i.e. availability for sampling), while higher amounts of woody cover objects and rocks increased conditional detection probability (i.e. probability of capture, given an animal is exposed to sampling). By explicitly accounting for both components of detectability, we increased congruence between our statistical modeling and our ecological understanding of the system. We stress the importance of choosing survey locations and protocols that maximize species availability and conditional detection probability to increase population parameter estimate reliability.
Shiyko, Mariya P.; Li, Yuelin; Rindskopf, David
2012-01-01
Intensive longitudinal data (ILD) have become increasingly common in the social and behavioral sciences; count variables, such as the number of daily smoked cigarettes, are frequently used outcomes in many ILD studies. We demonstrate a generalized extension of growth mixture modeling (GMM) to Poisson-distributed ILD for identifying qualitatively…
Soot modeling of counterflow diffusion flames of ethylene-based binary mixture fuels
Wang, Yu; Raj, Abhijeet Dhayal; Chung, Suk-Ho
2015-01-01
of ethylene and its binary mixtures with methane, ethane and propane based on the method of moments. The soot model has 36 soot nucleation reactions from 8 PAH molecules including pyrene and larger PAHs. Soot surface growth reactions were based on a modified
Densities of Pure Ionic Liquids and Mixtures: Modeling and Data Analysis
DEFF Research Database (Denmark)
Abildskov, Jens; O’Connell, John P.
2015-01-01
Our two-parameter corresponding states model for liquid densities and compressibilities has been extended to more pure ionic liquids and to their mixtures with one or two solvents. A total of 19 new group contributions (5 new cations and 14 new anions) have been obtained for predicting pressure...
Estimating Lion Abundance using N-mixture Models for Social Species.
Belant, Jerrold L; Bled, Florent; Wilton, Clay M; Fyumagwa, Robert; Mwampeta, Stanslaus B; Beyer, Dean E
2016-10-27
Declining populations of large carnivores worldwide, and the complexities of managing human-carnivore conflicts, require accurate population estimates of large carnivores to promote their long-term persistence through well-informed management We used N-mixture models to estimate lion (Panthera leo) abundance from call-in and track surveys in southeastern Serengeti National Park, Tanzania. Because of potential habituation to broadcasted calls and social behavior, we developed a hierarchical observation process within the N-mixture model conditioning lion detectability on their group response to call-ins and individual detection probabilities. We estimated 270 lions (95% credible interval = 170-551) using call-ins but were unable to estimate lion abundance from track data. We found a weak negative relationship between predicted track density and predicted lion abundance from the call-in surveys. Luminosity was negatively correlated with individual detection probability during call-in surveys. Lion abundance and track density were influenced by landcover, but direction of the corresponding effects were undetermined. N-mixture models allowed us to incorporate multiple parameters (e.g., landcover, luminosity, observer effect) influencing lion abundance and probability of detection directly into abundance estimates. We suggest that N-mixture models employing a hierarchical observation process can be used to estimate abundance of other social, herding, and grouping species.
The Support Reduction Algorithm for Computing Non-Parametric Function Estimates in Mixture Models
GROENEBOOM, PIET; JONGBLOED, GEURT; WELLNER, JON A.
2008-01-01
In this paper, we study an algorithm (which we call the support reduction algorithm) that can be used to compute non-parametric M-estimators in mixture models. The algorithm is compared with natural competitors in the context of convex regression and the ‘Aspect problem’ in quantum physics.
Modelling and simulation of an energy transport phenomenon in a solid-fluid mixture
International Nuclear Information System (INIS)
Costa, M.L.M.; Sampaio, R.; Gama, R.M.S. da.
1989-08-01
In the present work a model for a local description of the energy transfer phenomenon in a binary (solid-fluid) saturated mixture is proposed. The heat transfer in a saturated flow (through a porous medium) between two parallel plates is simulated by using the Finite Volumes Method. (author) [pt
Zhang, Danhui; Orrill, Chandra; Campbell, Todd
2015-01-01
The purpose of this study was to investigate whether mixture Rasch models followed by qualitative item-by-item analysis of selected Programme for International Student Assessment (PISA) mathematics and science items offered insight into knowledge students invoke in mathematics and science separately and combined. The researchers administered an…
Market segment derivation and profiling via a finite mixture model framework
Wedel, M; Desarbo, WS
The Marketing literature has shown how difficult it is to profile market segments derived with finite mixture models. especially using traditional descriptor variables (e.g., demographics). Such profiling is critical for the proper implementation of segmentation strategy. we propose a new finite
Unified description of pf-shell nuclei by the Monte Carlo shell model calculations
Energy Technology Data Exchange (ETDEWEB)
Mizusaki, Takahiro; Otsuka, Takaharu [Tokyo Univ. (Japan). Dept. of Physics; Honma, Michio
1998-03-01
The attempts to solve shell model by new methods are briefed. The shell model calculation by quantum Monte Carlo diagonalization which was proposed by the authors is a more practical method, and it became to be known that it can solve the problem with sufficiently good accuracy. As to the treatment of angular momentum, in the method of the authors, deformed Slater determinant is used as the basis, therefore, for making angular momentum into the peculiar state, projected operator is used. The space determined dynamically is treated mainly stochastically, and the energy of the multibody by the basis formed as the result is evaluated and selectively adopted. The symmetry is discussed, and the method of decomposing shell model space into dynamically determined space and the product of spin and isospin spaces was devised. The calculation processes are shown with the example of {sup 50}Mn nuclei. The calculation of the level structure of {sup 48}Cr with known exact energy can be done with the accuracy of peculiar absolute energy value within 200 keV. {sup 56}Ni nuclei are the self-conjugate nuclei of Z=N=28. The results of the shell model calculation of {sup 56}Ni nucleus structure by using the interactions of nuclear models are reported. (K.I.)
Finite mixture models for sub-pixel coastal land cover classification
CSIR Research Space (South Africa)
Ritchie, Michaela C
2017-05-01
Full Text Available Models for Sub- pixel Coastal Land Cover Classification M. Ritchie Dr. M. Lück-Vogel Dr. P. Debba Dr. V. Goodall ISRSE - 37 Tshwane, South Africa 10 May 2017 2Study Area Africa South Africa FALSE BAY 3Strand Gordon’s Bay Study Area WorldView-2 Image.../Urban 1 10 10 Herbaceous Vegetation 1 5 5 Shadow 1 8 8 Sparse Vegetation 1 3 3 Water 1 10 10 Woody Vegetation 1 5 5 11 Maximum Likelihood Classification (MLC) 12 Gaussian Mixture Discriminant Analysis (GMDA) 13 A B C t-distribution Mixture Discriminant...
An Empirical Bayes Mixture Model for Effect Size Distributions in Genome-Wide Association Studies
DEFF Research Database (Denmark)
Thompson, Wesley K.; Wang, Yunpeng; Schork, Andrew J.
2015-01-01
-wide association study (GWAS) test statistics. Test statistics corresponding to null associations are modeled as random draws from a normal distribution with zero mean; test statistics corresponding to non-null associations are also modeled as normal with zero mean, but with larger variance. The model is fit via...... analytically and in simulations. We apply this approach to meta-analysis test statistics from two large GWAS, one for Crohn’s disease (CD) and the other for schizophrenia (SZ). A scale mixture of two normals distribution provides an excellent fit to the SZ nonparametric replication effect size estimates. While...... minimizing discrepancies between the parametric mixture model and resampling-based nonparametric estimates of replication effect sizes and variances. We describe in detail the implications of this model for estimation of the non-null proportion, the probability of replication in de novo samples, the local...
Modelling of associating mixtures for applications in the oil & gas and chemical industries
DEFF Research Database (Denmark)
Kontogeorgis, Georgios; Folas, Georgios; Muro Sunè, Nuria
2007-01-01
Thermodynamic properties and phase equilibria of associating mixtures cannot often be satisfactorily modelled using conventional models such as cubic equations of state. CPA (cubic-plus-association) is an equation of state (EoS), which combines the SRK EoS with the association term of SAFT. For non......-alcohol (glycol)-alkanes and certain acid and amine-containing mixtures. Recent results include glycol-aromatic hydrocarbons including multiphase, multicomponent equilibria and gas hydrate calculations in combination with the van der Waals-Platteeuw model. This article will outline some new applications...... thermodynamic models especially those combining cubic EoS with local composition activity coefficient models are included. (C) 2007 Elsevier B.V. All rights reserved....
Validation of a mixture-averaged thermal diffusion model for premixed lean hydrogen flames
Schlup, Jason; Blanquart, Guillaume
2018-03-01
The mixture-averaged thermal diffusion model originally proposed by Chapman and Cowling is validated using multiple flame configurations. Simulations using detailed hydrogen chemistry are done on one-, two-, and three-dimensional flames. The analysis spans flat and stretched, steady and unsteady, and laminar and turbulent flames. Quantitative and qualitative results using the thermal diffusion model compare very well with the more complex multicomponent diffusion model. Comparisons are made using flame speeds, surface areas, species profiles, and chemical source terms. Once validated, this model is applied to three-dimensional laminar and turbulent flames. For these cases, thermal diffusion causes an increase in the propagation speed of the flames as well as increased product chemical source terms in regions of high positive curvature. The results illustrate the necessity for including thermal diffusion, and the accuracy and computational efficiency of the mixture-averaged thermal diffusion model.
Dynamic mean field theory for lattice gas models of fluid mixtures confined in mesoporous materials.
Edison, J R; Monson, P A
2013-11-12
We present the extension of dynamic mean field theory (DMFT) for fluids in porous materials (Monson, P. A. J. Chem. Phys. 2008, 128, 084701) to the case of mixtures. The theory can be used to describe the relaxation processes in the approach to equilibrium or metastable equilibrium states for fluids in pores after a change in the bulk pressure or composition. It is especially useful for studying systems where there are capillary condensation or evaporation transitions. Nucleation processes associated with these transitions are emergent features of the theory and can be visualized via the time dependence of the density distribution and composition distribution in the system. For mixtures an important component of the dynamics is relaxation of the composition distribution in the system, especially in the neighborhood of vapor-liquid interfaces. We consider two different types of mixtures, modeling hydrocarbon adsorption in carbon-like slit pores. We first present results on bulk phase equilibria of the mixtures and then the equilibrium (stable/metastable) behavior of these mixtures in a finite slit pore and an inkbottle pore. We then use DMFT to describe the evolution of the density and composition in the pore in the approach to equilibrium after changing the state of the bulk fluid via composition or pressure changes.
Measurement and modelling of hydrogen bonding in 1-alkanol plus n-alkane binary mixtures
DEFF Research Database (Denmark)
von Solms, Nicolas; Jensen, Lars; Kofod, Jonas L.
2007-01-01
Two equations of state (simplified PC-SAFT and CPA) are used to predict the monomer fraction of 1-alkanols in binary mixtures with n-alkanes. It is found that the choice of parameters and association schemes significantly affects the ability of a model to predict hydrogen bonding in mixtures, eve...... studies, which is clarified in the present work. New hydrogen bonding data based on infrared spectroscopy are reported for seven binary mixtures of alcohols and alkanes. (C) 2007 Elsevier B.V. All rights reserved....... though pure-component liquid densities and vapour pressures are predicted equally accurately for the associating compound. As was the case in the study of pure components, there exists some confusion in the literature about the correct interpretation and comparison of experimental data and theoretical...
Maximum likelihood estimation of semiparametric mixture component models for competing risks data.
Choi, Sangbum; Huang, Xuelin
2014-09-01
In the analysis of competing risks data, the cumulative incidence function is a useful quantity to characterize the crude risk of failure from a specific event type. In this article, we consider an efficient semiparametric analysis of mixture component models on cumulative incidence functions. Under the proposed mixture model, latency survival regressions given the event type are performed through a class of semiparametric models that encompasses the proportional hazards model and the proportional odds model, allowing for time-dependent covariates. The marginal proportions of the occurrences of cause-specific events are assessed by a multinomial logistic model. Our mixture modeling approach is advantageous in that it makes a joint estimation of model parameters associated with all competing risks under consideration, satisfying the constraint that the cumulative probability of failing from any cause adds up to one given any covariates. We develop a novel maximum likelihood scheme based on semiparametric regression analysis that facilitates efficient and reliable estimation. Statistical inferences can be conveniently made from the inverse of the observed information matrix. We establish the consistency and asymptotic normality of the proposed estimators. We validate small sample properties with simulations and demonstrate the methodology with a data set from a study of follicular lymphoma. © 2014, The International Biometric Society.
Modeling a secular trend by Monte Carlo simulation of height biased migration in a spatial network.
Groth, Detlef
2017-04-01
Background: In a recent Monte Carlo simulation, the clustering of body height of Swiss military conscripts within a spatial network with characteristic features of the natural Swiss geography was investigated. In this study I examined the effect of migration of tall individuals into network hubs on the dynamics of body height within the whole spatial network. The aim of this study was to simulate height trends. Material and methods: Three networks were used for modeling, a regular rectangular fishing net like network, a real world example based on the geographic map of Switzerland, and a random network. All networks contained between 144 and 148 districts and between 265-307 road connections. Around 100,000 agents were initially released with average height of 170 cm, and height standard deviation of 6.5 cm. The simulation was started with the a priori assumption that height variation within a district is limited and also depends on height of neighboring districts (community effect on height). In addition to a neighborhood influence factor, which simulates a community effect, body height dependent migration of conscripts between adjacent districts in each Monte Carlo simulation was used to re-calculate next generation body heights. In order to determine the direction of migration for taller individuals, various centrality measures for the evaluation of district importance within the spatial network were applied. Taller individuals were favored to migrate more into network hubs, backward migration using the same number of individuals was random, not biased towards body height. Network hubs were defined by the importance of a district within the spatial network. The importance of a district was evaluated by various centrality measures. In the null model there were no road connections, height information could not be delivered between the districts. Results: Due to the favored migration of tall individuals into network hubs, average body height of the hubs, and later
Finite mixture models for the computation of isotope ratios in mixed isotopic samples
Koffler, Daniel; Laaha, Gregor; Leisch, Friedrich; Kappel, Stefanie; Prohaska, Thomas
2013-04-01
Finite mixture models have been used for more than 100 years, but have seen a real boost in popularity over the last two decades due to the tremendous increase in available computing power. The areas of application of mixture models range from biology and medicine to physics, economics and marketing. These models can be applied to data where observations originate from various groups and where group affiliations are not known, as is the case for multiple isotope ratios present in mixed isotopic samples. Recently, the potential of finite mixture models for the computation of 235U/238U isotope ratios from transient signals measured in individual (sub-)µm-sized particles by laser ablation - multi-collector - inductively coupled plasma mass spectrometry (LA-MC-ICPMS) was demonstrated by Kappel et al. [1]. The particles, which were deposited on the same substrate, were certified with respect to their isotopic compositions. Here, we focus on the statistical model and its application to isotope data in ecogeochemistry. Commonly applied evaluation approaches for mixed isotopic samples are time-consuming and are dependent on the judgement of the analyst. Thus, isotopic compositions may be overlooked due to the presence of more dominant constituents. Evaluation using finite mixture models can be accomplished unsupervised and automatically. The models try to fit several linear models (regression lines) to subgroups of data taking the respective slope as estimation for the isotope ratio. The finite mixture models are parameterised by: • The number of different ratios. • Number of points belonging to each ratio-group. • The ratios (i.e. slopes) of each group. Fitting of the parameters is done by maximising the log-likelihood function using an iterative expectation-maximisation (EM) algorithm. In each iteration step, groups of size smaller than a control parameter are dropped; thereby the number of different ratios is determined. The analyst only influences some control
Directory of Open Access Journals (Sweden)
Bastien Boussau
2009-06-01
Full Text Available Homologous recombination is a pervasive biological process that affects sequences in all living organisms and viruses. In the presence of recombination, the evolutionary history of an alignment of homologous sequences cannot be properly depicted by a single bifurcating tree: some sites have evolved along a specific phylogenetic tree, others have followed another path. Methods available to analyse recombination in sequences usually involve an analysis of the alignment through sliding-windows, or are particularly demanding in computational resources, and are often limited to nucleotide sequences. In this article, we propose and implement a Mixture Model on trees and a phylogenetic Hidden Markov Model to reveal recombination breakpoints while searching for the various evolutionary histories that are present in an alignment known to have undergone homologous recombination. These models are sufficiently efficient to be applied to dozens of sequences on a single desktop computer, and can handle equivalently nucleotide or protein sequences. We estimate their accuracy on simulated sequences and test them on real data.
Directory of Open Access Journals (Sweden)
Bastien Boussau
2009-01-01
Full Text Available Homologous recombination is a pervasive biological process that affects sequences in all living organisms and viruses. In the presence of recombination, the evolutionary history of an alignment of homologous sequences cannot be properly depicted by a single bifurcating tree: some sites have evolved along a specific phylogenetic tree, others have followed another path. Methods available to analyse recombination in sequences usually involve an analysis of the alignment through sliding-windows, or are particularly demanding in computational resources, and are often limited to nucleotide sequences. In this article, we propose and implement a Mixture Model on trees and a phylogenetic Hidden Markov Model to reveal recombination breakpoints while searching for the various evolutionary histories that are present in an alignment known to have undergone homologous recombination. These models are sufficiently efficient to be applied to dozens of sequences on a single desktop computer, and can handle equivalently nucleotide or protein sequences. We estimate their accuracy on simulated sequences and test them on real data.
Development of a randomized 3D cell model for Monte Carlo microdosimetry simulations
Energy Technology Data Exchange (ETDEWEB)
Douglass, Michael; Bezak, Eva; Penfold, Scott [School of Chemistry and Physics, University of Adelaide, North Terrace, Adelaide 5005, South Australia (Australia) and Department of Medical Physics, Royal Adelaide Hospital, North Terrace, Adelaide 5000, South Australia (Australia)
2012-06-15
Purpose: The objective of the current work was to develop an algorithm for growing a macroscopic tumor volume from individual randomized quasi-realistic cells. The major physical and chemical components of the cell need to be modeled. It is intended to import the tumor volume into GEANT4 (and potentially other Monte Carlo packages) to simulate ionization events within the cell regions. Methods: A MATLAB Copyright-Sign code was developed to produce a tumor coordinate system consisting of individual ellipsoidal cells randomized in their spatial coordinates, sizes, and rotations. An eigenvalue method using a mathematical equation to represent individual cells was used to detect overlapping cells. GEANT4 code was then developed to import the coordinate system into GEANT4 and populate it with individual cells of varying sizes and composed of the membrane, cytoplasm, reticulum, nucleus, and nucleolus. Each region is composed of chemically realistic materials. Results: The in-house developed MATLAB Copyright-Sign code was able to grow semi-realistic cell distributions ({approx}2 Multiplication-Sign 10{sup 8} cells in 1 cm{sup 3}) in under 36 h. The cell distribution can be used in any number of Monte Carlo particle tracking toolkits including GEANT4, which has been demonstrated in this work. Conclusions: Using the cell distribution and GEANT4, the authors were able to simulate ionization events in the individual cell components resulting from 80 keV gamma radiation (the code is applicable to other particles and a wide range of energies). This virtual microdosimetry tool will allow for a more complete picture of cell damage to be developed.
A Monte Carlo risk assessment model for acrylamide formation in French fries.
Cummins, Enda; Butler, Francis; Gormley, Ronan; Brunton, Nigel
2009-10-01
The objective of this study is to estimate the likely human exposure to the group 2a carcinogen, acrylamide, from French fries by Irish consumers by developing a quantitative risk assessment model using Monte Carlo simulation techniques. Various stages in the French-fry-making process were modeled from initial potato harvest, storage, and processing procedures. The model was developed in Microsoft Excel with the @Risk add-on package. The model was run for 10,000 iterations using Latin hypercube sampling. The simulated mean acrylamide level in French fries was calculated to be 317 microg/kg. It was found that females are exposed to smaller levels of acrylamide than males (mean exposure of 0.20 microg/kg bw/day and 0.27 microg/kg bw/day, respectively). Although the carcinogenic potency of acrylamide is not well known, the simulated probability of exceeding the average chronic human dietary intake of 1 microg/kg bw/day (as suggested by WHO) was 0.054 and 0.029 for males and females, respectively. A sensitivity analysis highlighted the importance of the selection of appropriate cultivars with known low reducing sugar levels for French fry production. Strict control of cooking conditions (correlation coefficient of 0.42 and 0.35 for frying time and temperature, respectively) and blanching procedures (correlation coefficient -0.25) were also found to be important in ensuring minimal acrylamide formation.
Parsons, Neal; Levin, Deborah A; van Duin, Adri C T; Zhu, Tong
2014-12-21
The Direct Simulation Monte Carlo (DSMC) method typically used for simulating hypersonic Earth re-entry flows requires accurate total collision cross sections and reaction probabilities. However, total cross sections are often determined from extrapolations of relatively low-temperature viscosity data, so their reliability is unknown for the high temperatures observed in hypersonic flows. Existing DSMC reaction models accurately reproduce experimental equilibrium reaction rates, but the applicability of these rates to the strong thermal nonequilibrium observed in hypersonic shocks is unknown. For hypersonic flows, these modeling issues are particularly relevant for nitrogen, the dominant species of air. To rectify this deficiency, the Molecular Dynamics/Quasi-Classical Trajectories (MD/QCT) method is used to accurately compute collision and reaction cross sections for the N2(Σg+1)-N2(Σg+1) collision pair for conditions expected in hypersonic shocks using a new potential energy surface developed using a ReaxFF fit to recent advanced ab initio calculations. The MD/QCT-computed reaction probabilities were found to exhibit better physical behavior and predict less dissociation than the baseline total collision energy reaction model for strong nonequilibrium conditions expected in a shock. The MD/QCT reaction model compared well with computed equilibrium reaction rates and shock-tube data. In addition, the MD/QCT-computed total cross sections were found to agree well with established variable hard sphere total cross sections.
International Nuclear Information System (INIS)
Parsons, Neal; Levin, Deborah A.; Duin, Adri C. T. van; Zhu, Tong
2014-01-01
The Direct Simulation Monte Carlo (DSMC) method typically used for simulating hypersonic Earth re-entry flows requires accurate total collision cross sections and reaction probabilities. However, total cross sections are often determined from extrapolations of relatively low-temperature viscosity data, so their reliability is unknown for the high temperatures observed in hypersonic flows. Existing DSMC reaction models accurately reproduce experimental equilibrium reaction rates, but the applicability of these rates to the strong thermal nonequilibrium observed in hypersonic shocks is unknown. For hypersonic flows, these modeling issues are particularly relevant for nitrogen, the dominant species of air. To rectify this deficiency, the Molecular Dynamics/Quasi-Classical Trajectories (MD/QCT) method is used to accurately compute collision and reaction cross sections for the N 2 ( 1 Σ g + )-N 2 ( 1 Σ g + ) collision pair for conditions expected in hypersonic shocks using a new potential energy surface developed using a ReaxFF fit to recent advanced ab initio calculations. The MD/QCT-computed reaction probabilities were found to exhibit better physical behavior and predict less dissociation than the baseline total collision energy reaction model for strong nonequilibrium conditions expected in a shock. The MD/QCT reaction model compared well with computed equilibrium reaction rates and shock-tube data. In addition, the MD/QCT-computed total cross sections were found to agree well with established variable hard sphere total cross sections
Monte Carlo modeling of a conventional X-ray computed tomography scanner for gel dosimetry purposes.
Hayati, Homa; Mesbahi, Asghar; Nazarpoor, Mahmood
2016-01-01
Our purpose in the current study was to model an X-ray CT scanner with the Monte Carlo (MC) method for gel dosimetry. In this study, a conventional CT scanner with one array detector was modeled with use of the MCNPX MC code. The MC calculated photon fluence in detector arrays was used for image reconstruction of a simple water phantom as well as polyacrylamide polymer gel (PAG) used for radiation therapy. Image reconstruction was performed with the filtered back-projection method with a Hann filter and the Spline interpolation method. Using MC results, we obtained the dose-response curve for images of irradiated gel at different absorbed doses. A spatial resolution of about 2 mm was found for our simulated MC model. The MC-based CT images of the PAG gel showed a reliable increase in the CT number with increasing absorbed dose for the studied gel. Also, our results showed that the current MC model of a CT scanner can be used for further studies on the parameters that influence the usability and reliability of results, such as the photon energy spectra and exposure techniques in X-ray CT gel dosimetry.
Assessing the convergence of LHS Monte Carlo simulations of wastewater treatment models.
Benedetti, Lorenzo; Claeys, Filip; Nopens, Ingmar; Vanrolleghem, Peter A
2011-01-01
Monte Carlo (MC) simulation appears to be the only currently adopted tool to estimate global sensitivities and uncertainties in wastewater treatment modelling. Such models are highly complex, dynamic and non-linear, requiring long computation times, especially in the scope of MC simulation, due to the large number of simulations usually required. However, no stopping rule to decide on the number of simulations required to achieve a given confidence in the MC simulation results has been adopted so far in the field. In this work, a pragmatic method is proposed to minimize the computation time by using a combination of several criteria. It makes no use of prior knowledge about the model, is very simple, intuitive and can be automated: all convenient features in engineering applications. A case study is used to show an application of the method, and the results indicate that the required number of simulations strongly depends on the model output(s) selected, and on the type and desired accuracy of the analysis conducted. Hence, no prior indication is available regarding the necessary number of MC simulations, but the proposed method is capable of dealing with these variations and stopping the calculations after convergence is reached.
3D Monte Carlo model of optical transport in laser-irradiated cutaneous vascular malformations
Majaron, Boris; Milanič, Matija; Jia, Wangcun; Nelson, J. S.
2010-11-01
We have developed a three-dimensional Monte Carlo (MC) model of optical transport in skin and applied it to analysis of port wine stain treatment with sequential laser irradiation and intermittent cryogen spray cooling. Our MC model extends the approaches of the popular multi-layer model by Wang et al.1 to three dimensions, thus allowing treatment of skin inclusions with more complex geometries and arbitrary irradiation patterns. To overcome the obvious drawbacks of either "escape" or "mirror" boundary conditions at the lateral boundaries of the finely discretized volume of interest (VOI), photons exiting the VOI are propagated in laterally infinite tissue layers with appropriate optical properties, until they loose all their energy, escape into the air, or return to the VOI, but the energy deposition outside of the VOI is not computed and recorded. After discussing the selection of tissue parameters, we apply the model to analysis of blood photocoagulation and collateral thermal damage in treatment of port wine stain (PWS) lesions with sequential laser irradiation and intermittent cryogen spray cooling.
EURADOS intercomparison exercise on Monte Carlo modelling of a medical linear accelerator.
Caccia, Barbara; Le Roy, Maïwenn; Blideanu, Valentin; Andenna, Claudio; Arun, Chairmadurai; Czarnecki, Damian; El Bardouni, Tarek; Gschwind, Régine; Huot, Nicolas; Martin, Eric; Zink, Klemens; Zoubair, Mariam; Price, Robert; de Carlan, Loïc
2017-01-01
In radiotherapy, Monte Carlo (MC) methods are considered a gold standard to calculate accurate dose distributions, particularly in heterogeneous tissues. EURADOS organized an international comparison with six participants applying different MC models to a real medical linear accelerator and to one homogeneous and four heterogeneous dosimetric phantoms. The aim of this exercise was to identify, by comparison of different MC models with a complete experimental dataset, critical aspects useful for MC users to build and calibrate a simulation and perform a dosimetric analysis. Results show on average a good agreement between simulated and experimental data. However, some significant differences have been observed especially in presence of heterogeneities. Moreover, the results are critically dependent on the different choices of the initial electron source parameters. This intercomparison allowed the participants to identify some critical issues in MC modelling of a medical linear accelerator. Therefore, the complete experimental dataset assembled for this intercomparison will be available to all the MC users, thus providing them an opportunity to build and calibrate a model for a real medical linear accelerator.
Kinetic Monte Carlo Potts Model for Simulating a High Burnup Structure in UO2
International Nuclear Information System (INIS)
Oh, Jae-Yong; Koo, Yang-Hyun; Lee, Byung-Ho
2008-01-01
A Potts model, based on the kinetic Monte Carlo method, was originally developed for magnetic domain evolutions, but it was also proposed as a model for a grain growth in polycrystals due to similarities between Potts domain structures and grain structures. It has modeled various microstructural phenomena such as grain growths, a recrystallization, a sintering, and so on. A high burnup structure (HBS) is observed in the periphery of a high burnup UO 2 fuel. Although its formation mechanism is not clearly understood yet, its characteristics are well recognized: The HBS microstructure consists of very small grains and large bubbles instead of original as-sintered grains. A threshold burnup for the HBS is observed at a local burnup 60-80 Gwd/tM, and the threshold temperature is 1000-1200 .deg. C. Concerning a energy stability, the HBS can be created if the system energy of the HBS is lower than that of the original structure in an irradiated UO 2 . In this paper, a Potts model was implemented for simulating the HBS by calculating system energies, and the simulation results were compared with the HBS characteristics mentioned above
Direct Simulation Monte Carlo Application of the Three Dimensional Forced Harmonic Oscillator Model
2017-12-07
NUMBER (Include area code) 07 December 2017 Journal Article 24 February 2017 - 31 December 2017 Direct Simulation Monte Carlo Application of the...is proposed. The implementation employs precalculated lookup tables for transition probabilities and is suitable for the direct simulation Monte Carlo...method. It takes into account the microscopic reversibility between the excitation and deexcitation processes , and it satisfies the detailed balance
Derwent, Richard G.; Parrish, David D.; Galbally, Ian E.; Stevenson, David S.; Doherty, Ruth M.; Naik, Vaishali; Young, Paul J.
2018-05-01
Recognising that global tropospheric ozone models have many uncertain input parameters, an attempt has been made to employ Monte Carlo sampling to quantify the uncertainties in model output that arise from global tropospheric ozone precursor emissions and from ozone production and destruction in a global Lagrangian chemistry-transport model. Ninety eight quasi-randomly Monte Carlo sampled model runs were completed and the uncertainties were quantified in tropospheric burdens and lifetimes of ozone, carbon monoxide and methane, together with the surface distribution and seasonal cycle in ozone. The results have shown a satisfactory degree of convergence and provide a first estimate of the likely uncertainties in tropospheric ozone model outputs. There are likely to be diminishing returns in carrying out many more Monte Carlo runs in order to refine further these outputs. Uncertainties due to model formulation were separately addressed using the results from 14 Atmospheric Chemistry Coupled Climate Model Intercomparison Project (ACCMIP) chemistry-climate models. The 95% confidence ranges surrounding the ACCMIP model burdens and lifetimes for ozone, carbon monoxide and methane were somewhat smaller than for the Monte Carlo estimates. This reflected the situation where the ACCMIP models used harmonised emissions data and differed only in their meteorological data and model formulations whereas a conscious effort was made to describe the uncertainties in the ozone precursor emissions and in the kinetic and photochemical data in the Monte Carlo runs. Attention was focussed on the model predictions of the ozone seasonal cycles at three marine boundary layer stations: Mace Head, Ireland, Trinidad Head, California and Cape Grim, Tasmania. Despite comprehensively addressing the uncertainties due to global emissions and ozone sources and sinks, none of the Monte Carlo runs were able to generate seasonal cycles that matched the observations at all three MBL stations. Although
Deposition behaviour of model biofuel ash in mixtures with quartz sand. Part 1: Experimental data
Energy Technology Data Exchange (ETDEWEB)
Mischa Theis; Christian Mueller; Bengt-Johan Skrifvars; Mikko Hupa; Honghi Tran [Aabo Akademi Process Chemistry Centre, Aabo (Finland). Combustion and Materials Chemistry
2006-10-15
Model biofuel ash of well-defined size and melting properties was fed into an entrained flow reactor (EFR) to simulate the deposition behaviour of commercially applied biofuel mixtures in large-scale boilers. The aim was to obtain consistent experimental data that can be used for validation of computational fluid dynamics (CFD)-based deposition models. The results showed that while up to 80 wt% of the feed was lost to the EFR wall, the composition of the model ash particles collected at the reactor exit did not change. When model ashes were fed into the reactor individually, the ash particles were found to be sticky when they contained more than 15 wt% molten phase. When model ashes were fed in mixtures with silica sand, it was found that only a small amount of sand particles was captured in the deposits; the majority rebounded upon impact. The presence of sand in the feed mixture reduced the deposit buildup by more than could be expected from linear interpolation between the model ash and the sand. The results suggested that sand addition to model ash may prevent deposit buildup through erosion. 22 refs., 6 figs., 3 tabs.
Directory of Open Access Journals (Sweden)
Shanshan Wang
2017-12-01
Full Text Available In cities’ policy-making, it is a hot issue to grasp the determinants of carbon dioxide emission in Chinese cities. And the common method is to use the STIRPAT model, where its coefficients represent the influence intensity of each determinants of carbon emission. However, less work discusses estimation accuracy, especially in the framework of non-normal distribution and heterogeneity among cities’ emission. To improve the estimation accuracy, this paper employs a new method to estimate the STIRPAT model. The method uses a mixture of Asymmetric Laplace distributions (ALDs to approximate the true distribution of the error term. Meantime, a designed two-layer EM algorithm is used to obtain estimators. We test the robustness via the comparison results of five different models. We find that the ALDs Mixture Model is more reliable the others. Further, a significant Kuznets curve relationship is identified in China.
Arendt, Carli A.; Aciego, Sarah M.; Hetland, Eric A.
2015-05-01
The implementation of isotopic tracers as constraints on source contributions has become increasingly relevant to understanding Earth surface processes. Interpretation of these isotopic tracers has become more accessible with the development of Bayesian Monte Carlo (BMC) mixing models, which allow uncertainty in mixing end-members and provide methodology for systems with multicomponent mixing. This study presents an open source multiple isotope BMC mixing model that is applicable to Earth surface environments with sources exhibiting distinct end-member isotopic signatures. Our model is first applied to new δ18O and δD measurements from the Athabasca Glacier, which showed expected seasonal melt evolution trends and vigorously assessed the statistical relevance of the resulting fraction estimations. To highlight the broad applicability of our model to a variety of Earth surface environments and relevant isotopic systems, we expand our model to two additional case studies: deriving melt sources from δ18O, δD, and 222Rn measurements of Greenland Ice Sheet bulk water samples and assessing nutrient sources from ɛNd and 87Sr/86Sr measurements of Hawaiian soil cores. The model produces results for the Greenland Ice Sheet and Hawaiian soil data sets that are consistent with the originally published fractional contribution estimates. The advantage of this method is that it quantifies the error induced by variability in the end-member compositions, unrealized by the models previously applied to the above case studies. Results from all three case studies demonstrate the broad applicability of this statistical BMC isotopic mixing model for estimating source contribution fractions in a variety of Earth surface systems.